diff --git a/.flake8 b/.flake8 new file mode 100644 index 00000000..f2c0e908 --- /dev/null +++ b/.flake8 @@ -0,0 +1,4 @@ +[flake8] +ignore = E203, W503 +max-line-length = 88 +max-complexity = 60 \ No newline at end of file diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml new file mode 100644 index 00000000..5e9b9b8d --- /dev/null +++ b/.github/workflows/codeql.yml @@ -0,0 +1,82 @@ +# For most projects, this workflow file will not need changing; you simply need +# to commit it to your repository. +# +# You may wish to alter this file to override the set of languages analyzed, +# or to provide custom queries or build logic. +# +# ******** NOTE ******** +# We have attempted to detect the languages in your repository. Please check +# the `language` matrix defined below to confirm you have the correct set of +# supported CodeQL languages. +# +name: "CodeQL" + +on: + push: + branches: [ "master" ] + pull_request: + # The branches below must be a subset of the branches above + branches: [ "master" ] + schedule: + - cron: '40 21 * * 1' + +jobs: + analyze: + name: Analyze + # Runner size impacts CodeQL analysis time. To learn more, please see: + # - https://gh.io/recommended-hardware-resources-for-running-codeql + # - https://gh.io/supported-runners-and-hardware-resources + # - https://gh.io/using-larger-runners + # Consider using larger runners for possible analysis time improvements. + runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }} + timeout-minutes: ${{ (matrix.language == 'swift' && 120) || 360 }} + permissions: + actions: read + contents: read + security-events: write + + strategy: + fail-fast: false + matrix: + language: [ 'python' ] + # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python', 'ruby', 'swift' ] + # Use only 'java' to analyze code written in Java, Kotlin or both + # Use only 'javascript' to analyze code written in JavaScript, TypeScript or both + # Learn more about CodeQL language support at https://aka.ms/codeql-docs/language-support + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + # Initializes the CodeQL tools for scanning. + - name: Initialize CodeQL + uses: github/codeql-action/init@v3 + with: + languages: ${{ matrix.language }} + # If you wish to specify custom queries, you can do so here or in a config file. + # By default, queries listed here will override any specified in a config file. + # Prefix the list here with "+" to use these queries and those in the config file. + + # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs + # queries: security-extended,security-and-quality + + + # Autobuild attempts to build any compiled languages (C/C++, C#, Go, Java, or Swift). + # If this step fails, then you should remove it and run the build manually (see below) + - name: Autobuild + uses: github/codeql-action/autobuild@v3 + + # ℹī¸ Command-line programs to run using the OS shell. + # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun + + # If the Autobuild fails above, remove it and uncomment the following three lines. + # modify them (or add more) to build your code if your project, please refer to the EXAMPLE below for guidance. + + # - run: | + # echo "Run, Build Application using script" + # ./location_of_script_within_repo/buildscript.sh + + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@v3 + with: + category: "/language:${{matrix.language}}" diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml new file mode 100644 index 00000000..31c8b5d5 --- /dev/null +++ b/.github/workflows/publish-to-pypi.yml @@ -0,0 +1,30 @@ +name: Upload Python Package + +on: + release: + types: [published] + +jobs: + deploy: + runs-on: ubuntu-latest + strategy: + max-parallel: 4 + matrix: + python-version: [ '3.10' ] + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install build wheel twine + - name: Build and publish + env: + TWINE_USERNAME: __token__ + TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }} + run: | + python -m build . + twine upload dist/* diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml new file mode 100644 index 00000000..8f24a9d1 --- /dev/null +++ b/.github/workflows/pylint.yml @@ -0,0 +1,25 @@ +name: pylint + +on: [push] + +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: [ '3.10' ] + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install -r docs/requirements.txt + python -m pip install -r requirements.txt + pip install pylint + - name: pylint + run: | + pylint --rcfile=pyproject.toml $(git ls-files '*.py') diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 00000000..b29a9389 --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,59 @@ +name: CI + +on: [push] + +jobs: + build: + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + python-version: [ '3.9', '3.10', '3.11', '3.12'] + os: [ macos-latest, windows-2019, ubuntu-latest ] + + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install flake8 pytest + pip install -r requirements.txt + pip install keyrings.alt + - name: Lint with flake8 + run: | + flake8 $(git ls-files '*.py') + - name: Test with pytest + run: | + pip install pytest ddt pytest-cov sphinx pandoc + pip install -r docs/requirements.txt + pip install keyrings.alt + pytest + - name: Test with pytest (coverage + long tests) + if: matrix.python-version == '3.11' && matrix.os == 'ubuntu-latest' + run: | + sudo apt update && sudo apt install -y texlive pandoc + pip install pytest ddt pytest-cov sphinx pandoc + pip install -r docs/requirements.txt + pip install keyrings.alt + pytest --cov=./ --cov-report=xml + - name: Check that release process is not broken + if: matrix.python-version == '3.12' && matrix.os == 'ubuntu-latest' + env: + TWINE_USERNAME: __token__ + TWINE_PASSWORD: ${{ secrets.TESTPYPI_API_TOKEN }} + run: | + pip install build wheel twine + python -m build . + twine check dist/* + twine upload --repository testpypi --skip-existing dist/* + - name: Upload coverage reports to Codecov + if: matrix.python-version == '3.11' && matrix.os == 'ubuntu-latest' + uses: codecov/codecov-action@v3 + with: + token: ${{ secrets.CODECOV_TOKEN }} + file: ./coverage.xml + flags: unittests diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..9482dc4f --- /dev/null +++ b/.gitignore @@ -0,0 +1,162 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +.idea/ +.virtual_documents/ \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..de260ca5 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,55 @@ +repos: + # The following code can be uncommented to automatically keep all pre-commit rev's updated + - repo: local + # First a local check if new "rev" exists... + hooks: + - id: pre-commit-autoupdate + name: Check for new rev with pre-commit autoupdate + entry: "pre-commit autoupdate" + language: system + pass_filenames: false + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: "v4.5.0" + hooks: + - id: check-yaml + - id: check-symlinks + - id: check-merge-conflict + - id: requirements-txt-fixer + - id: trailing-whitespace + - repo: https://github.com/pycqa/isort + rev: 5.13.2 + hooks: + - id: isort + name: isort (python) + - repo: https://github.com/psf/black-pre-commit-mirror + rev: 24.2.0 + hooks: + - id: black + language_version: python3.11 + - repo: https://github.com/nbQA-dev/nbQA + rev: 1.8.4 + hooks: + - id: nbqa-black + name: nbqa-black + description: "Run 'black' on a Jupyter Notebook" + entry: nbqa black + language: python + require_serial: true + types: [ jupyter ] + additional_dependencies: [ black ] + - repo: https://github.com/pycqa/flake8 + rev: "7.0.0" + hooks: + - id: flake8 + - repo: https://github.com/PyCQA/pylint + rev: v3.1.0 + hooks: + - id: pylint + args: + - --rcfile + - pyproject.toml + - --disable + - import-error + - --output-format + - colorized + diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100755 index 00000000..35126459 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,23 @@ +# .readthedocs.yaml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +version: 2 + +build: + os: ubuntu-22.04 + tools: + python: "3.10" + +sphinx: + configuration: docs/conf.py + +formats: + - epub + - pdf + +# Optionally declare the Python requirements required to build your docs +python: + install: + - requirements: docs/requirements.txt + - requirements: requirements.txt \ No newline at end of file diff --git a/.readthedocs.yml b/.readthedocs.yml deleted file mode 100644 index d7aeddb0..00000000 --- a/.readthedocs.yml +++ /dev/null @@ -1,8 +0,0 @@ -build: - image: latest - -python: - version: 3.8 - -conda: - file: docs/environment.yml \ No newline at end of file diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst index 005429dc..11101659 100644 --- a/CONTRIBUTING.rst +++ b/CONTRIBUTING.rst @@ -23,28 +23,8 @@ older python versions. Coding style ------------ -Stick as much as possible to -`PEP8 `__ for general -guidelines in term of coding conventions and to -`PEP257 `__ for typical -docstring conventions. You can also have a look to `Python -anti-pattern `__. - -Main guidelines from PEP8 -------------------------- -PEP8 coding conventions are: - -- Use 4 spaces per indentation level. -- Limit all lines to a maximum of 100 characters. -- Separate top-level function and class definitions with two blank - lines. -- Make sure that all variables are used. -- Imports should be grouped in the following order: - - - Standard library imports. - - Related third party imports. - - Local application/library specific imports. - - A blank line between each group of imports. +The pyrfu package uses the `Black code style `__ . Use Linters ------------ @@ -65,7 +45,7 @@ or `there `__. Also, a lot of features can also be provided natively or by installing plugins with your IDE (PyCharm, Spyder, Eclipse, etc.). -To be accepted to ``pyrfu`` every new code as to get a pylint score higher than 9/10. +To be accepted to ``pyrfu`` every new code as to get a pylint score of 10/10. Documentation ------------- diff --git a/README.rst b/README.rst index dc8a1991..78b1ca08 100644 --- a/README.rst +++ b/README.rst @@ -1,234 +1,160 @@ - -.. |Logo| image:: docs/source/_static/logo-pyrfu.png - :target: https://pypi.org/project/pyrfu/ - -.. |License| image:: https://img.shields.io/pypi/l/pyrfu - :target: https://opensource.org/licenses/MIT - -.. |Python| image:: https://img.shields.io/pypi/pyversions/pyrfu.svg?logo=python - :target: https://pypi.org/project/pyrfu/ - -.. |PyPi| image:: https://img.shields.io/pypi/v/pyrfu.svg?logo=pypi - :target: https://pypi.org/project/pyrfu/ - -.. |Format| image:: https://img.shields.io/pypi/format/pyrfu?color=blue&logo=pypi - :target: https://pypi.org/project/pyrfu/ - -.. |Wheel| image:: https://img.shields.io/pypi/wheel/pyrfu?logo=pypi&color=blue - :target: https://pypi.org/project/pyrfu/ - -.. |Status| image:: https://img.shields.io/pypi/status/pyrfu?logo=pypi&color=blue - :target: https://pypi.org/project/pyrfu/ - -.. |Downloads| image:: https://img.shields.io/pypi/dm/pyrfu?logo=pypi&color=blue - :target: https://pypi.org/project/pyrfu/ - -.. |ScrutinizerBuild| image:: https://img.shields.io/scrutinizer/build/g/louis-richard/irfu-python?logo=scrutinizer-ci - :target: https://scrutinizer-ci.com/g/louis-richard/irfu-python/ - -.. |ScrutinizerQuality| image:: https://img.shields.io/scrutinizer/quality/g/louis-richard/irfu-python?logo=scrutinizer-ci - :target: https://scrutinizer-ci.com/g/louis-richard/irfu-python/ - -.. |Issues| image:: https://img.shields.io/github/issues/louis-richard/irfu-python?logo=github&color=9cf - :target: https://github.com/louis-richard/irfu-python/issues - -.. |Commits| image:: https://img.shields.io/github/last-commit/louis-richard/irfu-python?logo=github&color=9cf - :target: https://github.com/louis-richard/irfu-python/commits/master - -.. |Readthedocs| image:: https://img.shields.io/readthedocs/pyrfu?logo=read-the-docs&color=blueviolet - :target: https://pyrfu.readthedocs.io/en/latest/ - -.. |Gitter| image:: https://img.shields.io/gitter/room/louis-richard/pyrfu?logo=gitter&color=orange - :target: https://gitter.im/pyrfu - -.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg - :target: https://github.com/psf/black - - -|Logo| - -pyRFU -===== -.. start-marker-intro-do-not-remove - -|License| |Python| |PyPi| |Format| |Wheel| |Status| |Downloads| |ScrutinizerBuild| -|ScrutinizerQuality| |Commits| |Issues| |Readthedocs| |Gitter| |Black| - -The Python package ``pyrfu`` is a software based on the IRFU-MATLAB library to work with space data, particularly the -Magnetospheric MultiScale (MMS) mission. - -It is distributed under the open-source MIT license. - -.. end-marker-intro-do-not-remove - -Full documentation can be found `here `_ - - -Installation ------------- -.. start-marker-install-do-not-remove - -The package ``pyrfu`` has been tested only for Mac OS. - -Using PyPi -********** - -``pyrfu`` is available for pip. - -.. code-block:: bash - - pip install pyrfu - - -From sources -************ - -The sources are located on **GitHub**: - - https://github.com/louis-richard/irfu-python - -1) Clone the GitHub repo: - -Open a terminal and write the below command to clone in your PC the ``pyrfu`` repo: - -.. code-block:: bash - - git clone https://github.com/louis-richard/irfu-python.git - cd irfu-python - - -2) Create a virtual env - -pyrfu needs a significant number of dependencies. The easiest -way to get everything installed is to use a virtual environment. - -- Anaconda - -You can create a virtual environment and install all the dependencies using conda_ -with the following commands: - -.. code-block:: bash - - pip install conda - conda create -n pyrfu - source activate pyrfu - -.. _conda: http://conda.io/ - - -- Virtual Env - -Virtualenv_ can also be used: - -.. code-block:: bash - - pip install virtualenv - virtualenv pyrfu - source pyrfu/bin/activate - -.. _virtualenv: https://virtualenv.pypa.io/en/latest/# - - -3) Install the package via the commands: - -.. code-block:: bash - - python setup.py install - - -5) (Optional) Generate the docs: install the extra dependencies of doc and run -the `setup.py` file: - -.. code-block:: bash - - pip install pyrfu - python setup.py build_sphinx - -Once installed, the doc can be generated with: - -.. code-block:: bash - - cd doc - make html - - -Dependencies -************ - -The required dependencies are: - -- `cdflib `_ >=0.4.7 -- `geopack `_ >=1.0.9 -- `matplotlib `_ >=3.5.2 -- `numba `_ >=0.54.1 -- `numpy `_ >=1.20.3 -- `pandas `_ >=1.3.4 -- `python-datetutil `_ >=2.8.2 -- `requests `_ >=2.26.0 -- `scipy `_ >=1.7.3 -- `Sphinx `_ >=4.3.0 -- `tqdm `_ >=4.62.3 -- `xarray `_ >=0.20.1 - - -Testing dependencies are: - -- `pytest `_ >= 2.8 - -Extra testing dependencies: - -- `coverage `_ >= 4.4 -- `pylint `_ >= 1.6.0 - -.. end-marker-install-do-not-remove - -Usage ------ -To import generic space plasma physics functions - -.. code:: python - - from pyrfu import pyrf - - -To import functions specific to Magnetospheric Multi-Scale (MMS) - -.. code:: python - - from pyrfu import mms - - -To import functions specific to Solar Orbiter (SolO) - -.. code:: python - - from pyrfu import solo - - -To import plotting functions - -.. code:: python - - from pyrfu import plot - - -Configuration -------------- -Default configuration settings for MMS data (i.e data path) are stored in pyrfu/mms/config.json and can be changed at anytime using mms.db_init(local_path_dir). - -Credits -------- -This software was developed by Louis RICHARD (louisr@irfu.se) based on the IRFU-MATLAB library. - -Acknowledgement ---------------- -Please use the following to acknowledge use of pyrfu in your publications: -Data analysis was performed using the pyrfu analysis package available at https://github.com/louis-richard/irfu-python - -Additional Information ----------------------- -MMS Science Data Center: https://lasp.colorado.edu/mms/sdc/public/ - -MMS Datasets: https://lasp.colorado.edu/mms/sdc/public/datasets/ - -MMS - Goddard Space Flight Center: http://mms.gsfc.nasa.gov/ +.. -*- mode: rst -*- + +pyRFU +===== + +.. start-marker-intro-do-not-remove + +.. |License| image:: https://img.shields.io/pypi/l/pyrfu +.. _License: https://opensource.org/licenses/MIT + +.. |Python| image:: https://img.shields.io/pypi/pyversions/pyrfu.svg?logo=python +.. _Python: https://pypi.org/project/pyrfu/ + +.. |PyPi| image:: https://img.shields.io/pypi/v/pyrfu.svg?logo=pypi +.. _PyPi: https://pypi.org/project/pyrfu/ + +.. |Format| image:: https://img.shields.io/pypi/format/pyrfu?color=blue&logo=pypi +.. _Format: https://pypi.org/project/pyrfu/ + +.. |Wheel| image:: https://img.shields.io/pypi/wheel/pyrfu?logo=pypi&color=blue +.. _Wheel: https://pypi.org/project/pyrfu/ + +.. |Status| image:: https://img.shields.io/pypi/status/pyrfu?logo=pypi&color=blue +.. _Status: https://pypi.org/project/pyrfu/ + +.. |Downloads| image:: https://img.shields.io/pypi/dm/pyrfu?logo=pypi&color=blue +.. _Downloads: https://pypi.org/project/pyrfu/ + +.. |CI| image:: https://github.com/louis-richard/irfu-python/actions/workflows/tests.yml/badge.svg +.. _CI: https://github.com/louis-richard/irfu-python/actions/workflows/tests.yml + +.. |PyLintB| image:: https://github.com/louis-richard/irfu-python/actions/workflows/pylint.yml/badge.svg +.. _PyLintB: https://github.com/louis-richard/irfu-python/actions/workflows/pylint.yml + +.. |CodeQL| image:: https://github.com/louis-richard/irfu-python/actions/workflows/codeql.yml/badge.svg +.. _CodeQL: https://github.com/louis-richard/irfu-python/actions/workflows/codeql.yml + +.. |CodeCov| image:: https://codecov.io/gh/louis-richard/irfu-python/coverage.svg?branch=main +.. _CodeCov: https://codecov.io/gh/louis-richard/irfu-python/branch/main + +.. |Issues| image:: https://img.shields.io/github/issues/louis-richard/irfu-python?logo=github&color=9cf +.. _Issues: https://github.com/louis-richard/irfu-python/issues + +.. |Commits| image:: https://img.shields.io/github/last-commit/louis-richard/irfu-python?logo=github&color=9cf +.. _Commits: https://github.com/louis-richard/irfu-python/commits/master + +.. |Readthedocs| image:: https://img.shields.io/readthedocs/pyrfu?logo=read-the-docs&color=blueviolet +.. _Readthedocs: https://pyrfu.readthedocs.io/en/latest/ + +.. |Matrix| image:: https://matrix.to/img/matrix-badge.svg +.. _Matrix: https://matrix.to/#/#pyrfu:matrix.org + +.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg +.. _Black: https://github.com/psf/black + +.. |Doi| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.10678695.svg +.. _Doi: https://doi.org/10.5281/zenodo.10678695 + +|License|_ |Python|_ |PyPi|_ |Format|_ |Wheel|_ |Status|_ |Downloads|_ |CI|_ +|PyLintB|_ |CodeQL|_ |CodeCov|_ |Issues|_ |Commits|_ |Readthedocs|_ |Matrix|_ +|Black|_ |Doi|_ + +The Python package ``pyrfu`` is a software based on the IRFU-MATLAB library to work with space data, particularly the Magnetospheric MultiScale (MMS) mission. + +It is distributed under the open-source MIT license. + +Quickstart +========== + +Installing pyrfu with pip (`more details here `_): + +.. code-block:: console + + $ python -m pip install pyrfu + # or + $ python -m pip install --user pyrfu + +Import `pyrfu.mms `_ package with routines specific to work with the +Magnetospheric Multiscale mission (MMS) + +.. code:: python + + from pyrfu import mms + +Setup path to MMS data + +.. code:: python + + mms.db_init("/Volumes/mms") + + +Load magnetic field and ion bulk velocity data + +.. code:: python + + tint = ["2019-09-14T07:54:00.000", "2019-09-14T08:11:00.000"] + b_gsm = mms.get_data("b_gsm_fgm_srvy_l2", tint, 1) + v_gse_i = mms.get_data("vi_gse_fpi_fast_l2", tint, 1) + + +Import `pyrfu.pyrf `_ package with generic routines + +.. code:: python + + from pyrfu import pyrf + +Transform ion bulk velocity to geocentric solar magnetospheric (GSM) coordinates + +.. code:: python + + v_gsm_i = pyrf.cotrans(v_gse_i, "gse>gsm") + +Import `pyrfu.plot `_ package with plotting routines + +.. code:: python + + from pyrfu import plot + + +Plot time series of magnetic field and ion bulk velocity + +.. code:: python + + import matplotlib.pyplot as plt + + f, axs = plt.subplots(2, sharex="all") + plot.plot_line(axs[0], b_gsm) + axs[0].set_ylabel("$B~[\\mathrm{nT}]$") + axs[0].legend(["$B_{x}$", "$B_{y}$", "$B_{z}$"], ncol=4) + + plot.plot_line(axs[1], v_gsm_i) + axs[1].set_ylabel("$V_i~[\\mathrm{km}~\\mathrm{s}^{-1}]$") + axs[1].legend(["$V_{ix}$", "$V_{iy}$", "$V_{iz}$"], ncol=4) + +.. end-marker-intro-do-not-remove + +Documentation +============= +Full documentation can be found on `pyrfu.readthedocs.io `_ + +Examples +======== +A list of examples is available `here `_ + +Credits +======= +This software was developed by Louis RICHARD (louisr@irfu.se) based on the IRFU-MATLAB library. + +Acknowledgement +=============== +Please use the following to acknowledge use of pyrfu in your publications: +Data analysis was performed using the pyrfu analysis package available at https://github.com/louis-richard/irfu-python + +Additional Information +====================== +MMS Science Data Center: https://lasp.colorado.edu/mms/sdc/public/ + +MMS Datasets: https://lasp.colorado.edu/mms/sdc/public/datasets/ + +MMS - Goddard Space Flight Center: http://mms.gsfc.nasa.gov/ diff --git a/docs/Makefile b/docs/Makefile index dcab548f..578212cf 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -4,8 +4,9 @@ # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS = -SPHINXBUILD = sphinx-build -SOURCEDIR = source +SPHINXBUILD = python -m sphinx +SPHINXPROJ = pyrfu +SOURCEDIR = . BUILDDIR = _build # Put it first so that "make" without argument is like "make help". @@ -17,4 +18,4 @@ help: # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(ALLSPHINXOPTS) $(O) + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/_static/example_dispersion_one_fluid_nb_thumbnail.png b/docs/_static/example_dispersion_one_fluid_nb_thumbnail.png new file mode 100644 index 00000000..9112613f Binary files /dev/null and b/docs/_static/example_dispersion_one_fluid_nb_thumbnail.png differ diff --git a/docs/_static/example_mms_b_e_j_nb_thumbnail.png b/docs/_static/example_mms_b_e_j_nb_thumbnail.png new file mode 100644 index 00000000..5fce0121 Binary files /dev/null and b/docs/_static/example_mms_b_e_j_nb_thumbnail.png differ diff --git a/docs/_static/example_mms_ebfields_nb_thumbnail.png b/docs/_static/example_mms_ebfields_nb_thumbnail.png new file mode 100644 index 00000000..eac4407e Binary files /dev/null and b/docs/_static/example_mms_ebfields_nb_thumbnail.png differ diff --git a/docs/_static/example_mms_edr_signatures_nb_thumbnail.png b/docs/_static/example_mms_edr_signatures_nb_thumbnail.png new file mode 100644 index 00000000..15e21007 Binary files /dev/null and b/docs/_static/example_mms_edr_signatures_nb_thumbnail.png differ diff --git a/docs/_static/example_mms_eis_nb_thumbnail.png b/docs/_static/example_mms_eis_nb_thumbnail.png new file mode 100644 index 00000000..adc9a084 Binary files /dev/null and b/docs/_static/example_mms_eis_nb_thumbnail.png differ diff --git a/docs/source/_static/logo-pyrfu.png b/docs/_static/logo-pyrfu.png similarity index 100% rename from docs/source/_static/logo-pyrfu.png rename to docs/_static/logo-pyrfu.png diff --git a/docs/source/_static/logo-pyrfu.svg b/docs/_static/logo-pyrfu.svg similarity index 100% rename from docs/source/_static/logo-pyrfu.svg rename to docs/_static/logo-pyrfu.svg diff --git a/docs/_static/quick-overview_nb_thumbnail.png b/docs/_static/quick-overview_nb_thumbnail.png new file mode 100644 index 00000000..15318310 Binary files /dev/null and b/docs/_static/quick-overview_nb_thumbnail.png differ diff --git a/docs/source/conf.py b/docs/conf.py similarity index 57% rename from docs/source/conf.py rename to docs/conf.py index 2534c035..7dc61e05 100644 --- a/docs/source/conf.py +++ b/docs/conf.py @@ -1,264 +1,240 @@ -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - - -import os -import sys -import shutil -from datetime import datetime -import sphinx_rtd_theme - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -# import os -# import sys -# sys.path.insert(0, "/Users/louisr/Documents/PhD/irfu-python/pyrfu") - -examples_source = os.path.abspath( - os.path.join(os.path.dirname(__file__), "..", "..", "examples") -) -examples_dest = os.path.abspath(os.path.join(os.path.dirname(__file__), "examples")) - -import sphinx.apidoc -def setup(app): - sphinx.apidoc.main(['-f', #Overwrite existing files - '-T', #Create table of contents - '-e', #Give modules their own pages - #'-E', #user docstring headers - #'-M', #Modules first - '-o', #Output the files to: - './_autogen/', #Output Directory - './../../pyrfu', #Main Module directory - ] - ) - -if os.path.exists(examples_dest): - shutil.rmtree(examples_dest) -os.mkdir(examples_dest) - -for root, dirs, files in os.walk(examples_source): - for dr in dirs: - os.mkdir(os.path.join(root.replace(examples_source, examples_dest), dr)) - for fil in files: - if os.path.splitext(fil)[1] in [".ipynb", ".md", ".rst"]: - source_filename = os.path.join(root, fil) - dest_filename = source_filename.replace(examples_source, examples_dest) - shutil.copyfile(source_filename, dest_filename) - -sys.path.insert(0, os.path.abspath("../..")) - -# -- Project information ----------------------------------------------------- - -project = "pyrfu" -author = "Louis Richard" -copyright = f"2019–{datetime.utcnow().year}, {author}" - - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named "sphinx.ext.*") or your custom -# ones. -extensions = [ - "sphinx.ext.autodoc", - "sphinx.ext.doctest", - "sphinx.ext.intersphinx", - "sphinx.ext.coverage", - "sphinx.ext.mathjax", - "sphinx.ext.viewcode", - "sphinx.ext.napoleon", - "sphinxcontrib.apidoc", - "sphinx.ext.todo", - "nbsphinx" - # "sphinx_gallery.gen_gallery" -] - -# sphinx_gallery_conf = { -# "examples_dirs": "../examples/gallery/", # path to your example scripts -# "gallery_dirs": "auto_examples", # path where to save gallery generated examples -# } - - -# Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# -source_suffix = [".rst", ".md"] - -# The master toctree document. -master_doc = "index" - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = "en" - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = [ - "_build", - "Thumbs.db", - ".DS_Store", - "**.ipynb_checkpoints", - "examples/**/README.md", -] - -autodoc_mock_imports = ["numba", "sfs"] - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -# html_theme = "alabaster" -html_theme = "pydata_sphinx_theme" -# html_theme = "sphinx_rtd_theme" - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. - - -# Add any paths that contain custom themes here, relative to this directory. -# html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] - - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# -# html_theme_options = {} - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] - -# Custom sidebar templates, must be a dictionary that maps document names -# to template names. -# -# The default sidebars (for documents that don"t match any pattern) are -# defined by theme itself. Builtin themes are using these templates by -# default: ``["localtoc.html", "relations.html", "sourcelink.html", -# "searchbox.html"]``. -# -# html_sidebars = {} - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -# -html_logo = "_static/logo-pyrfu.png" - -# The name of an image file (relative to this directory) to use as a favicon of -# the docs. This file should be a Windows icon file (.ico) being 16x16 or -# 32x32 pixels large. - - -# -- Options for HTMLHelp output --------------------------------------------- - -# Output file base name for HTML help builder. -htmlhelp_basename = "pyrfudoc" - - -# -- Options for LaTeX output ------------------------------------------------ - -latex_elements = { - # The paper size ("letterpaper" or "a4paper"). - # - # "papersize": "letterpaper", - # The font size ("10pt", "11pt" or "12pt"). - # - # "pointsize": "10pt", - # Additional stuff for the LaTeX preamble. - # - # "preamble": "", - # Latex figure (float) alignment - # - # "figure_align": "htbp", -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - (master_doc, "pyrfu.tex", "pyrfu Documentation", "Louis Richard", "manual"), -] - - -# -- Options for manual page output ------------------------------------------ - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [(master_doc, "pyrfu", "pyrfu Documentation", [author], 1)] - - -# -- Options for Texinfo output ---------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ( - master_doc, - "pyrfu", - "pyrfu Documentation", - author, - "pyrfu", - "One line description of project.", - "Miscellaneous", - ), -] - - -# -- Extension configuration ------------------------------------------------- - -# -- Options for todo extension ---------------------------------------------- - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = True - - -# -- Options for Epub output ------------------------------------------------- - -# Bibliographic Dublin Core info. -epub_title = project - -# The unique identifier of the text. This can be a ISBN number -# or the project homepage. -# -# epub_identifier = "" - -# A unique identification for the text. -# -# epub_uid = "" - -# A list of files that should not be packed into the epub file. -epub_exclude_files = ["search.html"] - - -# -- Extension configuration ------------------------------------------------- - -# -- Options for intersphinx extension --------------------------------------- - -# Example configuration for intersphinx: refer to the Python standard library. -intersphinx_mapping = { - "python": ("https://docs.python.org/3/", None), - "xarray": ("http://xarray.pydata.org/en/stable/", None), -} - -apidoc_module_dir = "../../pyrfu" -apidoc_output_dir = "_generated" -apidoc_excluded_paths = ["tests", "*/tests/", "*/*/tests/", "*/*/*/tests"] -apidoc_separate_modules = True +# Built-in imports +import os +import sys + +# 3rd party imports +import pydata_sphinx_theme + +# -- Path setup -------------------------------------------------------------- +src_path = os.path.abspath("..") +sys.path.insert(0, src_path) +os.environ["PYTHONPATH"] = src_path + +# -- General configuration --------------------------------------------------- +autosummary_generate = True + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named "sphinx.ext.*") or your custom +# ones. + +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.autosectionlabel", + "sphinx.ext.autosummary", + "sphinx.ext.doctest", + "sphinx.ext.intersphinx", + "sphinx.ext.coverage", + "sphinx.ext.mathjax", + "sphinx.ext.viewcode", + "sphinx.ext.napoleon", + "sphinx_gallery.load_style", + "sphinx_codeautolink", + "sphinxcontrib.apidoc", + "sphinx.ext.todo", + "nbsphinx", + "sphinx_copybutton", + "numpydoc", +] + + +apidoc_module_dir = "../pyrfu" +apidoc_output_dir = "dev" +apidoc_excluded_paths = ["tests", "*/tests/", "*/*/tests/", "*/*/*/tests"] +apidoc_separate_modules = True +apidoc_module_first = True + +# autosectionlabel_prefix_document = True +codeautolink_custom_blocks = { + "python3": None, + "pycon3": "sphinx_codeautolink.clean_pycon", +} + +nbsphinx_execute_arguments = [ + "--InlineBackend.figure_formats={'svg', 'pdf'}", + "--InlineBackend.rc=figure.dpi=96", +] + +nbsphinx_execute = "never" + +nbsphinx_thumbnails = {} + +mathjax3_config = { + "tex": {"tags": "ams", "useLabelIds": True}, +} + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +source_suffix = ".rst" + +# The master toctree document. +master_doc = "index" + +# General information about the project. +project = "pyrfu" +author = "Louis Richard" + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = "en" + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = [ + "_build", + "Thumbs.db", + ".DS_Store", + "**.ipynb_checkpoints", + "examples/**/README.md", +] + +numpydoc_show_class_members = False + +# The name of the Pygments (syntax highlighting) style to use. +# pygments_style = 'sphinx' + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = True + +# -- Options for HTML output ------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +# html_theme = 'sphinx_rtd_theme' +html_theme = pydata_sphinx_theme.__name__ + +# Theme options are theme-specific and customize the look and feel of a +# theme further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["_static"] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don"t match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``["localtoc.html", "relations.html", "sourcelink.html", +# "searchbox.html"]``. +# +# html_sidebars = {} + +# The name of an image file (relative to this directory) to place at the top +# of the sidebar. +# +html_logo = "_static/logo-pyrfu.png" + +# The name of an image file (relative to this directory) to use as a favicon of +# the docs. This file should be a Windows icon file (.ico) being 16x16 or +# 32x32 pixels large. + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = "pyrfudoc" + + +# -- Options for LaTeX output ------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + ( + master_doc, + "pyrfu.tex", + "Python Space Physics (RymdFysik) Utilities Documentation", + "Louis Richard", + "manual", + ), +] + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + ( + master_doc, + "pyrfu", + "Python Space Physics (RymdFysik) Utilities Documentation", + [author], + 1, + ) +] + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + ( + master_doc, + "pyrfu", + "Python Space Physics (RymdFysik) Utilities Documentation", + author, + "pyrfu", + "Python routines to work with space data, particularly with MMS data. " + "Also some general plasma routines.", + "Miscellaneous", + ), +] + +# -- Options for Epub output ------------------------------------------------- + +# Bibliographic Dublin Core info. +epub_title = project + +# The unique identifier of the text. This can be a ISBN number +# or the project homepage. +# +# epub_identifier = "" + +# A unique identification for the text. +# +# epub_uid = "" + +# A list of files that should not be packed into the epub file. +epub_exclude_files = ["search.html"] + + +# -- Options for intersphinx extension --------------------------------------- + +# Example configuration for intersphinx: refer to the Python standard library. +intersphinx_mapping = { + "python": ("https://docs.python.org/3/", None), + "xarray": ("http://xarray.pydata.org/en/stable/", None), + "scipy": ("https://docs.scipy.org/doc/scipy/", None), + "matplotlib": ("https://matplotlib.org/stable/", None), +} + +autodoc_mock_imports = ["numba", "sfs"] diff --git a/docs/source/contributing.rst b/docs/contributing.rst similarity index 76% rename from docs/source/contributing.rst rename to docs/contributing.rst index 21f5bd2b..4b566910 100644 --- a/docs/source/contributing.rst +++ b/docs/contributing.rst @@ -5,5 +5,5 @@ All contributions are welcome. For detailed information about the code style ple read the following instructions. All the code must have a adequate number of tests that assures it's credibility. Feel free to make ``pull`` requests. -.. include:: ./../../CONTRIBUTING.rst - :start-after: start-marker-style-do-not-remove +.. include:: ./../CONTRIBUTING.rst + :start-after: start-marker-style-do-not-remove \ No newline at end of file diff --git a/docs/dev/index.rst b/docs/dev/index.rst new file mode 100644 index 00000000..1b3e296a --- /dev/null +++ b/docs/dev/index.rst @@ -0,0 +1,12 @@ +Pyrfu developer documentation +============================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + +Indices and tables +------------------ +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` \ No newline at end of file diff --git a/docs/dev/modules.rst b/docs/dev/modules.rst new file mode 100644 index 00000000..b4a6619c --- /dev/null +++ b/docs/dev/modules.rst @@ -0,0 +1,7 @@ +pyrfu +===== + +.. toctree:: + :maxdepth: 4 + + pyrfu diff --git a/docs/environment.yml b/docs/environment.yml deleted file mode 100644 index b8330ff5..00000000 --- a/docs/environment.yml +++ /dev/null @@ -1,18 +0,0 @@ -name: pyrfu_ci -channels: -- conda-forge -- defaults -dependencies: -- cdflib -- ipykernel -- ipython -- matplotlib -- nbsphinx -- numpy -- pandas -- pydata-sphinx-theme -- scipy -- sphinx -- sphinxcontrib-apidoc -- sphinx_rtd_theme -- xarray \ No newline at end of file diff --git a/docs/examples/00_overview/.ipynb_checkpoints/quick-overview-checkpoint.ipynb b/docs/examples/00_overview/.ipynb_checkpoints/quick-overview-checkpoint.ipynb new file mode 100644 index 00000000..60c12b16 --- /dev/null +++ b/docs/examples/00_overview/.ipynb_checkpoints/quick-overview-checkpoint.ipynb @@ -0,0 +1,615 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MMS in pyRFU\n", + "Louis RICHARD (louis.richard@irfu.se)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting Started\n", + "To get up and running with Python, virtual environments and pyRFU, see: \\\n", + "https://pyrfu.readthedocs.io/en/latest/getting_started.html#installation\n", + "\n", + "Python 3.7 or later is required; we recommend installing Anaconda to get\n", + "everything up and running." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Virtual environments\n", + "It's best to setup and use virtual environments when using Python - these allow you to avoid common dependency problems when you install multiple packages\\\n", + "`python -m venv pyrfu-tutorial`\\\n", + "Then, to run the virtual environment, on Mac and Linux :\\\n", + "`source pyrfu-tutorial/bin/activate`\\\n", + "To exit the current virtual environment, type `deactivate`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Install pyRFU\n", + "`pip install pyrfu`\n", + "### Upgrade pyRFU\n", + "`pip install pyrfu --upgrade`\n", + "### Local data directory\n", + "We use environment variables to set the local data directories:\\\n", + "data_path (root data directory for all missions in pyRFU) e.g., if you set data_path=\"/Volumes/mms\", your data will be stored in /Volumes/mms\n", + "\n", + "The load routines supported include:\n", + "- Fluxgate Magnetometer (FGM)\n", + "- Search-coil Magnetometer (SCM)\n", + "- Electric field Double Probe (EDP)\n", + "- Fast Plasma Investigation (FPI)\n", + "- Hot Plasma Composition Analyzer (HPCA)\n", + "- Energetic Ion Spectrometer (EIS)\n", + "- Fly's Eye Energetic Particle Sensor (FEEPS)\n", + "- Ephemeris and Coordinates (MEC)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import MMS routines" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "from pyrfu import mms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define time interval" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "tint = [\"2019-09-14T07:54:00.000\", \"2019-09-14T08:11:00.000\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup the MMS data path\n", + "The MMS data path can be setup using mms.db_init" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "mms.db_init(\"/Volumes/mms\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data can also be downloaded directly from the MMS science data center using:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function download_data in module pyrfu.mms.download_data:\n", + "\n", + "download_data(var_str, tint, mms_id, login: str = '', password: str = '', data_path: str = '')\n", + " Downloads files containing field `var_str` over the time interval\n", + " `tint` for the spacecraft `mms_id`. The files are saved to `data_path`.\n", + " \n", + " Parameters\n", + " ----------\n", + " var_str : str\n", + " Input key of variable.\n", + " tint : list of str\n", + " Time interval.\n", + " mms_id : str or int\n", + " Index of the target spacecraft.\n", + " login : str, Optional\n", + " Login to LASP MMS SITL. Default downloads from\n", + " https://lasp.colorado.edu/mms/sdc/public/\n", + " password : str, Optional\n", + " Password to LASP MMS SITL. Default downloads from\n", + " https://lasp.colorado.edu/mms/sdc/public/\n", + " data_path : str, Optional\n", + " Path of MMS data. If None use `pyrfu/mms/config.json`\n", + "\n" + ] + } + ], + "source": [ + "help(mms.download_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function download_ancillary in module pyrfu.mms.download_ancillary:\n", + "\n", + "download_ancillary(product, tint, mms_id, login: str = '', password: str = '', data_path: str = '')\n", + " Downloads files containing field `var_str` over the time interval\n", + " `tint` for the spacecraft `mms_id`. The files are saved to `data_path`.\n", + " \n", + " Parameters\n", + " ----------\n", + " product : {\"predatt\", \"predeph\", \"defatt\", \"defeph\"}\n", + " Ancillary type.\n", + " tint : list of str\n", + " Time interval\n", + " mms_id : str or int\n", + " Spacecraft index\n", + " login : str, Optional\n", + " Login to LASP MMS SITL. Default downloads from\n", + " https://lasp.colorado.edu/mms/sdc/public/\n", + " password : str, Optional\n", + " Password to LASP MMS SITL. Default downloads from\n", + " https://lasp.colorado.edu/mms/sdc/public/\n", + " data_path : str, Optional\n", + " Path of MMS data. If None use `pyrfu/mms/config.json`\n", + "\n" + ] + } + ], + "source": [ + "help(mms.download_ancillary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data\n", + "Keywords to access data can be found in the help of mms.get_data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function get_data in module pyrfu.mms.get_data:\n", + "\n", + "get_data(var_str, tint, mms_id, verbose: bool = True, data_path: str = '')\n", + " Load a variable. var_str must be in var (see below)\n", + " \n", + " Parameters\n", + " ----------\n", + " var_str : str\n", + " Key of the target variable (use mms.get_data() to see keys.).\n", + " tint : list of str\n", + " Time interval.\n", + " mms_id : str or int\n", + " Index of the target spacecraft.\n", + " verbose : bool, Optional\n", + " Set to True to follow the loading. Default is True.\n", + " data_path : str, Optional\n", + " Path of MMS data. If None use `pyrfu/mms/config.json`\n", + " \n", + " Returns\n", + " -------\n", + " out : xarray.DataArray or xarray.Dataset\n", + " Time series of the target variable of measured by the target\n", + " spacecraft over the selected time interval.\n", + " \n", + " See also\n", + " --------\n", + " pyrfu.mms.get_ts : Read time series.\n", + " pyrfu.mms.get_dist : Read velocity distribution function.\n", + " \n", + " Examples\n", + " --------\n", + " >>> from pyrfu import mms\n", + " \n", + " Define time interval\n", + " \n", + " >>> tint_brst = [\"2019-09-14T07:54:00.000\", \"2019-09-14T08:11:00.000\"]\n", + " \n", + " Index of MMS spacecraft\n", + " \n", + " >>> ic = 1\n", + " \n", + " Load magnetic field from FGM\n", + " \n", + " >>> b_xyz = mms.get_data(\"B_gse_fgm_brst_l2\", tint_brst, ic)\n", + "\n" + ] + } + ], + "source": [ + "help(mms.get_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load magnetic field from (FGM)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:56:32] INFO: Loading mms1_fgm_b_gse_srvy_l2...\n" + ] + } + ], + "source": [ + "b_xyz = mms.get_data(\"b_gse_fgm_srvy_l2\", tint, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load ions and electrons bulk velocity, number density and DEF (FPI)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:56:32] INFO: Loading mms1_dis_numberdensity_fast...\n", + "[09-Jun-23 11:56:33] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[09-Jun-23 11:56:33] INFO: Loading mms1_des_numberdensity_fast...\n", + "[09-Jun-23 11:56:33] INFO: Loading mms1_dis_bulkv_gse_fast...\n", + "[09-Jun-23 11:56:33] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[09-Jun-23 11:56:33] INFO: Loading mms1_des_bulkv_gse_fast...\n", + "[09-Jun-23 11:56:33] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[09-Jun-23 11:56:33] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[09-Jun-23 11:56:33] INFO: Loading mms1_dis_energyspectr_omni_fast...\n", + "[09-Jun-23 11:56:33] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[09-Jun-23 11:56:33] INFO: Loading mms1_des_energyspectr_omni_fast...\n" + ] + } + ], + "source": [ + "n_i, n_e = [mms.get_data(f\"n{s}_fpi_fast_l2\", tint, 1) for s in [\"i\", \"e\"]]\n", + "# n_i, n_e = [mms.get_data(\"n{}_fpi_fast_l2\".format(s), tint, 1) for s in [\"i\", \"e\"]]\n", + "v_xyz_i, v_xyz_e = [mms.get_data(f\"v{s}_gse_fpi_fast_l2\", tint, 1) for s in [\"i\", \"e\"]]\n", + "def_omni_i, def_omni_e = [\n", + " mms.get_data(f\"def{s}_fpi_fast_l2\", tint, 1) for s in [\"i\", \"e\"]\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load electric field (EDP)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:56:33] INFO: Loading mms1_edp_dce_gse_fast_l2...\n" + ] + } + ], + "source": [ + "e_xyz = mms.get_data(\"e_gse_edp_fast_l2\", tint, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot overview" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pyrfu.plot import plot_line, plot_spectr" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'DEF\\n[kev/(cm$^2$ s sr keV)]')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "legend_options = dict(frameon=True, loc=\"upper right\")\n", + "\n", + "f, axs = plt.subplots(7, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "# magnetic field\n", + "plot_line(axs[0], b_xyz)\n", + "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], ncol=3, **legend_options)\n", + "axs[0].set_ylabel(\"$B$ [nT]\")\n", + "\n", + "# electric field\n", + "plot_line(axs[1], e_xyz)\n", + "axs[1].legend([\"$E_x$\", \"$E_y$\", \"$E_z$\"], ncol=3, **legend_options)\n", + "axs[1].set_ylabel(\"$E$ [mV.m$^{-1}$]\")\n", + "\n", + "# number density\n", + "plot_line(axs[2], n_i, color=\"tab:red\")\n", + "plot_line(axs[2], n_e, color=\"tab:blue\")\n", + "axs[2].legend([\"$Ions$\", \"$Electrons$\"], ncol=2, **legend_options)\n", + "axs[2].set_ylabel(\"$n$ [cm$^{-3}$]\")\n", + "\n", + "# Ion bulk velocity\n", + "plot_line(axs[3], v_xyz_i)\n", + "axs[3].legend([\"$V_{i,x}$\", \"$V_{i,y}$\", \"$V_{i,z}$\"], ncol=3, **legend_options)\n", + "axs[3].set_ylabel(\"$V_i$ [km.s$^{-1}$]\")\n", + "\n", + "# Ion DEF\n", + "axs[4], caxs4 = plot_spectr(\n", + " axs[4], def_omni_i, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\"\n", + ")\n", + "axs[4].set_ylabel(\"$E_i$ [eV]\")\n", + "caxs4.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", + "\n", + "# Electron bulk velocity\n", + "plot_line(axs[5], v_xyz_e)\n", + "axs[5].legend([\"$V_{e,x}$\", \"$V_{e,y}$\", \"$V_{e,z}$\"], ncol=3, **legend_options)\n", + "axs[5].set_ylabel(\"$V_e$ [km.s$^{-1}$]\")\n", + "\n", + "# Electron DEF\n", + "axs[6], caxs6 = plot_spectr(\n", + " axs[6], def_omni_e, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\"\n", + ")\n", + "axs[6].set_ylabel(\"$E_e$ [eV]\")\n", + "caxs6.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data for all spacecraft" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spacecaft position (MEC)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:56:36] INFO: Loading mms1_mec_r_gse...\n", + "[09-Jun-23 11:56:36] INFO: Loading mms2_mec_r_gse...\n", + "[09-Jun-23 11:56:36] INFO: Loading mms3_mec_r_gse...\n", + "[09-Jun-23 11:56:36] INFO: Loading mms4_mec_r_gse...\n" + ] + } + ], + "source": [ + "r_mms = [mms.get_data(\"r_gse_mec_srvy_l2\", tint, i) for i in range(1, 5)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Magnetic field (FGM)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:56:36] INFO: Loading mms1_fgm_b_gse_srvy_l2...\n", + "[09-Jun-23 11:56:36] INFO: Loading mms2_fgm_b_gse_srvy_l2...\n", + "[09-Jun-23 11:56:36] INFO: Loading mms3_fgm_b_gse_srvy_l2...\n", + "[09-Jun-23 11:56:37] INFO: Loading mms4_fgm_b_gse_srvy_l2...\n" + ] + } + ], + "source": [ + "b_mms = [mms.get_data(\"b_gse_fgm_srvy_l2\", tint, i) for i in range(1, 5)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(3, sharex=\"all\", figsize=(9, 6))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "labels = [\"MMS{:d}\".format(i + 1) for i in range(4)]\n", + "\n", + "for ax, j, c in zip(axs, [0, 1, 2], [\"x\", \"y\", \"z\"]):\n", + " for i, b in enumerate(b_mms):\n", + " plot_line(ax, b[:, j])\n", + "\n", + " ax.set_ylabel(\"$B_{}$ [nT]\".format(c))\n", + "\n", + "f.legend(labels, loc=\"upper center\", borderaxespad=0.1, ncol=4, frameon=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/00_overview/index.rst b/docs/examples/00_overview/index.rst new file mode 100644 index 00000000..c56888f1 --- /dev/null +++ b/docs/examples/00_overview/index.rst @@ -0,0 +1,7 @@ +Overview examples gallery +========================= + +.. nbgallery:: + :glob: + + ./quick-overview \ No newline at end of file diff --git a/docs/examples/00_overview/quick-overview.ipynb b/docs/examples/00_overview/quick-overview.ipynb new file mode 100644 index 00000000..fed0286f --- /dev/null +++ b/docs/examples/00_overview/quick-overview.ipynb @@ -0,0 +1,1100 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quickstart\n", + "Louis RICHARD (louis.richard@irfu.se)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting Started\n", + "To get up and running with Python, virtual environments and pyRFU, see: \\\n", + "https://pyrfu.readthedocs.io/en/latest/getting_started.html#installation\n", + "\n", + "Python 3.7 or later is required; we recommend installing Anaconda to get\n", + "everything up and running." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Virtual environments\n", + "It's best to setup and use virtual environments when using Python - these allow you to avoid common dependency problems when you install multiple packages\\\n", + "`python -m venv pyrfu-tutorial`\\\n", + "Then, to run the virtual environment, on Mac and Linux :\\\n", + "`source pyrfu-tutorial/bin/activate`\\\n", + "To exit the current virtual environment, type `deactivate`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Install pyRFU\n", + "`pip install pyrfu`\n", + "### Upgrade pyRFU\n", + "`pip install pyrfu --upgrade`\n", + "### Local data directory\n", + "We use environment variables to set the local data directories:\\\n", + "data_path (root data directory for all missions in pyRFU) e.g., if you set data_path=\"/Volumes/mms\", your data will be stored in /Volumes/mms\n", + "\n", + "The load routines supported include:\n", + "- Fluxgate Magnetometer (FGM)\n", + "- Search-coil Magnetometer (SCM)\n", + "- Electric field Double Probe (EDP)\n", + "- Fast Plasma Investigation (FPI)\n", + "- Hot Plasma Composition Analyzer (HPCA)\n", + "- Energetic Ion Spectrometer (EIS)\n", + "- Fly's Eye Energetic Particle Sensor (FEEPS)\n", + "- Ephemeris and Coordinates (MEC)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import MMS routines" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "from pyrfu import mms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define time interval" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "tint = [\"2019-09-14T07:54:00.000\", \"2019-09-14T08:11:00.000\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup the MMS data path\n", + "The MMS data path can be setup using mms.db_init" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "mms.db_init(\"/Volumes/mms\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data can also be downloaded directly from the MMS science data center using:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function download_data in module pyrfu.mms.download_data:\n", + "\n", + "download_data(var_str, tint, mms_id, login: str = '', password: str = '', data_path: str = '')\n", + " Downloads files containing field `var_str` over the time interval\n", + " `tint` for the spacecraft `mms_id`. The files are saved to `data_path`.\n", + " \n", + " Parameters\n", + " ----------\n", + " var_str : str\n", + " Input key of variable.\n", + " tint : list of str\n", + " Time interval.\n", + " mms_id : str or int\n", + " Index of the target spacecraft.\n", + " login : str, Optional\n", + " Login to LASP MMS SITL. Default downloads from\n", + " https://lasp.colorado.edu/mms/sdc/public/\n", + " password : str, Optional\n", + " Password to LASP MMS SITL. Default downloads from\n", + " https://lasp.colorado.edu/mms/sdc/public/\n", + " data_path : str, Optional\n", + " Path of MMS data. If None use `pyrfu/mms/config.json`\n", + "\n" + ] + } + ], + "source": [ + "help(mms.download_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function download_ancillary in module pyrfu.mms.download_ancillary:\n", + "\n", + "download_ancillary(product, tint, mms_id, login: str = '', password: str = '', data_path: str = '')\n", + " Downloads files containing field `var_str` over the time interval\n", + " `tint` for the spacecraft `mms_id`. The files are saved to `data_path`.\n", + " \n", + " Parameters\n", + " ----------\n", + " product : {\"predatt\", \"predeph\", \"defatt\", \"defeph\"}\n", + " Ancillary type.\n", + " tint : list of str\n", + " Time interval\n", + " mms_id : str or int\n", + " Spacecraft index\n", + " login : str, Optional\n", + " Login to LASP MMS SITL. Default downloads from\n", + " https://lasp.colorado.edu/mms/sdc/public/\n", + " password : str, Optional\n", + " Password to LASP MMS SITL. Default downloads from\n", + " https://lasp.colorado.edu/mms/sdc/public/\n", + " data_path : str, Optional\n", + " Path of MMS data. If None use `pyrfu/mms/config.json`\n", + "\n" + ] + } + ], + "source": [ + "help(mms.download_ancillary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data\n", + "Keywords to access data can be found in the help of mms.get_data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function get_data in module pyrfu.mms.get_data:\n", + "\n", + "get_data(var_str, tint, mms_id, verbose: bool = True, data_path: str = '')\n", + " Load a variable. var_str must be in var (see below)\n", + " \n", + " Parameters\n", + " ----------\n", + " var_str : str\n", + " Key of the target variable (use mms.get_data() to see keys.).\n", + " tint : list of str\n", + " Time interval.\n", + " mms_id : str or int\n", + " Index of the target spacecraft.\n", + " verbose : bool, Optional\n", + " Set to True to follow the loading. Default is True.\n", + " data_path : str, Optional\n", + " Path of MMS data. If None use `pyrfu/mms/config.json`\n", + " \n", + " Returns\n", + " -------\n", + " out : xarray.DataArray or xarray.Dataset\n", + " Time series of the target variable of measured by the target\n", + " spacecraft over the selected time interval.\n", + " \n", + " See also\n", + " --------\n", + " pyrfu.mms.get_ts : Read time series.\n", + " pyrfu.mms.get_dist : Read velocity distribution function.\n", + " \n", + " Examples\n", + " --------\n", + " >>> from pyrfu import mms\n", + " \n", + " Define time interval\n", + " \n", + " >>> tint_brst = [\"2019-09-14T07:54:00.000\", \"2019-09-14T08:11:00.000\"]\n", + " \n", + " Index of MMS spacecraft\n", + " \n", + " >>> ic = 1\n", + " \n", + " Load magnetic field from FGM\n", + " \n", + " >>> b_xyz = mms.get_data(\"B_gse_fgm_brst_l2\", tint_brst, ic)\n", + "\n" + ] + } + ], + "source": [ + "help(mms.get_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load magnetic field from (FGM)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[21-Jul-23 09:26:54] INFO: Loading mms1_fgm_b_gse_srvy_l2...\n" + ] + } + ], + "source": [ + "b_xyz = mms.get_data(\"b_gse_fgm_srvy_l2\", tint, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time series are xarray.DataArray objects" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (time: 16320, represent_vec_tot: 3)>\n",
+       "array([[-2.1031418 , -1.6935391 ,  2.835863  ],\n",
+       "       [-2.1007674 , -1.7059438 ,  2.8462613 ],\n",
+       "       [-2.1185403 , -1.6850468 ,  2.8634877 ],\n",
+       "       ...,\n",
+       "       [-0.7177708 ,  1.81181   ,  5.15961   ],\n",
+       "       [-0.75993973,  1.8259526 ,  5.165373  ],\n",
+       "       [-0.7561481 ,  1.8053148 ,  5.204192  ]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * time               (time) datetime64[ns] 2019-09-14T07:54:00.006946627 .....\n",
+       "  * represent_vec_tot  (represent_vec_tot) <U1 'x' 'y' 'z'\n",
+       "Attributes: (12/17)\n",
+       "    CATDESC:            Magnetic field vector in Geocentric Solar Ecliptic (G...\n",
+       "    COORDINATE_SYSTEM:  GSE\n",
+       "    DEPEND_0:           Epoch\n",
+       "    DISPLAY_TYPE:       time_series\n",
+       "    FIELDNAM:           Magnetic field vector in GSE plus Btotal (8 or 16 S/s)\n",
+       "    FILLVAL:            -1e+31\n",
+       "    ...                 ...\n",
+       "    SI_CONVERSION:      1.0e-9>T\n",
+       "    TENSOR_ORDER:       1\n",
+       "    UNITS:              nT\n",
+       "    VALIDMAX:           [17000. 17000. 17000. 17000.]\n",
+       "    VALIDMIN:           [-17000. -17000. -17000.      0.]\n",
+       "    VAR_TYPE:           data
" + ], + "text/plain": [ + "\n", + "array([[-2.1031418 , -1.6935391 , 2.835863 ],\n", + " [-2.1007674 , -1.7059438 , 2.8462613 ],\n", + " [-2.1185403 , -1.6850468 , 2.8634877 ],\n", + " ...,\n", + " [-0.7177708 , 1.81181 , 5.15961 ],\n", + " [-0.75993973, 1.8259526 , 5.165373 ],\n", + " [-0.7561481 , 1.8053148 , 5.204192 ]], dtype=float32)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2019-09-14T07:54:00.006946627 .....\n", + " * represent_vec_tot (represent_vec_tot) T\n", + " TENSOR_ORDER: 1\n", + " UNITS: nT\n", + " VALIDMAX: [17000. 17000. 17000. 17000.]\n", + " VALIDMIN: [-17000. -17000. -17000. 0.]\n", + " VAR_TYPE: data" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b_xyz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load ions and electrons bulk velocity, number density and DEF (FPI)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[21-Jul-23 09:26:55] INFO: Loading mms1_dis_numberdensity_fast...\n", + "[21-Jul-23 09:26:55] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[21-Jul-23 09:26:55] INFO: Loading mms1_des_numberdensity_fast...\n", + "[21-Jul-23 09:26:55] INFO: Loading mms1_dis_bulkv_gse_fast...\n", + "[21-Jul-23 09:26:55] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[21-Jul-23 09:26:55] INFO: Loading mms1_des_bulkv_gse_fast...\n", + "[21-Jul-23 09:26:55] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[21-Jul-23 09:26:55] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[21-Jul-23 09:26:55] INFO: Loading mms1_dis_energyspectr_omni_fast...\n", + "[21-Jul-23 09:26:55] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[21-Jul-23 09:26:55] INFO: Loading mms1_des_energyspectr_omni_fast...\n" + ] + } + ], + "source": [ + "n_i, n_e = [mms.get_data(f\"n{s}_fpi_fast_l2\", tint, 1) for s in [\"i\", \"e\"]]\n", + "# n_i, n_e = [mms.get_data(\"n{}_fpi_fast_l2\".format(s), tint, 1) for s in [\"i\", \"e\"]]\n", + "v_xyz_i, v_xyz_e = [mms.get_data(f\"v{s}_gse_fpi_fast_l2\", tint, 1) for s in [\"i\", \"e\"]]\n", + "def_omni_i, def_omni_e = [\n", + " mms.get_data(f\"def{s}_fpi_fast_l2\", tint, 1) for s in [\"i\", \"e\"]\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load electric field (EDP)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[21-Jul-23 09:26:55] INFO: Loading mms1_edp_dce_gse_fast_l2...\n" + ] + } + ], + "source": [ + "e_xyz = mms.get_data(\"e_gse_edp_fast_l2\", tint, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot overview" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import matplotlib and define backend" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import pyrfu plotting routines" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from pyrfu.plot import plot_line, plot_spectr" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "legend_options = dict(frameon=True, loc=\"upper right\")\n", + "\n", + "f, axs = plt.subplots(7, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.88, bottom=0.05, top=0.97)\n", + "\n", + "# magnetic field\n", + "plot_line(axs[0], b_xyz)\n", + "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], ncol=3, **legend_options)\n", + "axs[0].set_ylabel(\"$B$ [nT]\")\n", + "\n", + "# electric field\n", + "plot_line(axs[1], e_xyz)\n", + "axs[1].legend([\"$E_x$\", \"$E_y$\", \"$E_z$\"], ncol=3, **legend_options)\n", + "axs[1].set_ylabel(\"$E$ [mV.m$^{-1}$]\")\n", + "\n", + "# number density\n", + "plot_line(axs[2], n_i, color=\"tab:red\")\n", + "plot_line(axs[2], n_e, color=\"tab:blue\")\n", + "axs[2].legend([\"$Ions$\", \"$Electrons$\"], ncol=2, **legend_options)\n", + "axs[2].set_ylabel(\"$n$ [cm$^{-3}$]\")\n", + "\n", + "# Ion bulk velocity\n", + "plot_line(axs[3], v_xyz_i)\n", + "axs[3].legend([\"$V_{i,x}$\", \"$V_{i,y}$\", \"$V_{i,z}$\"], ncol=3, **legend_options)\n", + "axs[3].set_ylabel(\"$V_i$ [km.s$^{-1}$]\")\n", + "\n", + "# Ion DEF\n", + "axs[4], caxs4 = plot_spectr(\n", + " axs[4], def_omni_i, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\"\n", + ")\n", + "axs[4].set_ylabel(\"$E_i$ [eV]\")\n", + "caxs4.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", + "\n", + "# Electron bulk velocity\n", + "plot_line(axs[5], v_xyz_e)\n", + "axs[5].legend([\"$V_{e,x}$\", \"$V_{e,y}$\", \"$V_{e,z}$\"], ncol=3, **legend_options)\n", + "axs[5].set_ylabel(\"$V_e$ [km.s$^{-1}$]\")\n", + "\n", + "# Electron DEF\n", + "axs[6], caxs6 = plot_spectr(\n", + " axs[6], def_omni_e, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\"\n", + ")\n", + "axs[6].set_ylabel(\"$E_e$ [eV]\")\n", + "caxs6.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", + "\n", + "f.align_ylabels(axs)\n", + "f.savefig(\"../../_static/quick-overview_nb_thumbnail.png\", dpi=100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data for all spacecraft" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spacecaft position (MEC)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[21-Jul-23 09:27:00] INFO: Loading mms1_mec_r_gse...\n", + "[21-Jul-23 09:27:00] INFO: Loading mms2_mec_r_gse...\n", + "[21-Jul-23 09:27:01] INFO: Loading mms3_mec_r_gse...\n", + "[21-Jul-23 09:27:01] INFO: Loading mms4_mec_r_gse...\n" + ] + } + ], + "source": [ + "r_mms = [mms.get_data(\"r_gse_mec_srvy_l2\", tint, i) for i in range(1, 5)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Magnetic field (FGM)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[21-Jul-23 09:27:01] INFO: Loading mms1_fgm_b_gse_srvy_l2...\n", + "[21-Jul-23 09:27:01] INFO: Loading mms2_fgm_b_gse_srvy_l2...\n", + "[21-Jul-23 09:27:01] INFO: Loading mms3_fgm_b_gse_srvy_l2...\n", + "[21-Jul-23 09:27:02] INFO: Loading mms4_fgm_b_gse_srvy_l2...\n" + ] + } + ], + "source": [ + "b_mms = [mms.get_data(\"b_gse_fgm_srvy_l2\", tint, i) for i in range(1, 5)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(3, sharex=\"all\", figsize=(9, 6))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "labels = [\"MMS{:d}\".format(i + 1) for i in range(4)]\n", + "\n", + "for ax, j, c in zip(axs, [0, 1, 2], [\"x\", \"y\", \"z\"]):\n", + " for i, b in enumerate(b_mms):\n", + " plot_line(ax, b[:, j])\n", + "\n", + " ax.set_ylabel(\"$B_{}$ [nT]\".format(c))\n", + "\n", + "f.legend(labels, loc=\"upper center\", borderaxespad=0.1, ncol=4, frameon=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs/examples/01_mms/example_mms_b_e_j.ipynb b/docs/examples/01_mms/example_mms_b_e_j.ipynb new file mode 100644 index 00000000..beeaa598 --- /dev/null +++ b/docs/examples/01_mms/example_mms_b_e_j.ipynb @@ -0,0 +1,386 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# B, E, J\n", + "author: Louis Richard\\\n", + "Plots of B, J, E, JxB electric field, and J.E. Calculates J using Curlometer method. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pyrfu import mms, pyrf\n", + "from pyrfu.plot import plot_line" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define time interval and data path" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "tint = [\"2019-09-14T07:54:00.000\", \"2019-09-14T08:11:00.000\"]\n", + "mms.db_init(\"/Volumes/mms\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load background magnetic field (FGM)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21-Apr-23 17:43:57: Loading mms1_fgm_b_dmpa_srvy_l2...\n", + "21-Apr-23 17:43:58: Loading mms2_fgm_b_dmpa_srvy_l2...\n", + "21-Apr-23 17:43:58: Loading mms3_fgm_b_dmpa_srvy_l2...\n", + "21-Apr-23 17:43:58: Loading mms4_fgm_b_dmpa_srvy_l2...\n" + ] + } + ], + "source": [ + "b_mms = [mms.get_data(\"b_dmpa_fgm_srvy_l2\", tint, i) for i in range(1, 5)]\n", + "b_mms = [pyrf.resample(b_xyz, b_mms[0]) for b_xyz in b_mms]\n", + "b_dmpa = pyrf.avg_4sc(b_mms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load electric field (EDP)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21-Apr-23 17:43:58: Loading mms1_edp_dce_dsl_fast_l2pre...\n", + "21-Apr-23 17:43:58: Loading mms2_edp_dce_dsl_fast_l2pre...\n", + "21-Apr-23 17:43:58: Loading mms3_edp_dce_dsl_fast_l2pre...\n", + "21-Apr-23 17:43:58: Loading mms4_edp_dce_dsl_fast_l2pre...\n" + ] + } + ], + "source": [ + "e_mms = [mms.get_data(\"e2d_dsl_edp_fast_l2pre\", tint, i) for i in range(1, 5)]\n", + "e_mms = [pyrf.resample(e_xyz, e_mms[0]) for e_xyz in e_mms]\n", + "e_dsl = pyrf.avg_4sc(e_mms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load ion number density (FPI)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21-Apr-23 17:43:58: Loading mms1_dis_numberdensity_fast...\n", + "21-Apr-23 17:43:58: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/mms/get_ts.py:60: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "21-Apr-23 17:43:58: Loading mms2_dis_numberdensity_fast...\n", + "21-Apr-23 17:43:58: Loading mms3_dis_numberdensity_fast...\n", + "21-Apr-23 17:43:58: Loading mms4_dis_numberdensity_fast...\n" + ] + } + ], + "source": [ + "n_i_mms = [mms.get_data(\"ni_fpi_fast_l2\", tint, i) for i in range(1, 5)]\n", + "n_i_mms = [pyrf.resample(n_i, n_i_mms[0]) for n_i in n_i_mms]\n", + "\n", + "n_i = pyrf.avg_4sc(n_i_mms)\n", + "n_i = pyrf.resample(n_i, b_mms[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load spacecraft position (MEC)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21-Apr-23 17:43:59: Loading mms1_mec_r_gse...\n", + "21-Apr-23 17:43:59: Loading mms2_mec_r_gse...\n", + "21-Apr-23 17:43:59: Loading mms3_mec_r_gse...\n", + "21-Apr-23 17:43:59: Loading mms4_mec_r_gse...\n" + ] + } + ], + "source": [ + "r_mms = [mms.get_data(\"r_gse_mec_srvy_l2\", tint, i) for i in range(1, 5)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute current density, Hall electric field and E.J" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Compute current density using curlometer" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "j_dmpa, div_b, _, jxb_dmpa, _, _ = pyrf.c_4_j(r_mms, b_mms)\n", + "j_dmpa.data *= 1e9 # to nA m^{-2}\n", + "\n", + "# Error estimate |\\nabla \\dot B| / |\\nabla \\times B|\n", + "div_over_curl = div_b.copy()\n", + "div_over_curl.data = np.abs(div_over_curl.data) / pyrf.norm(j_dmpa).data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Transform current density into field-aligned coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "j_fac = pyrf.convert_fac(j_dmpa, b_dmpa, [1, 0, 0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Hall electric field" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "jxb_dmpa.data /= n_i.data[:, None]\n", + "jxb_dmpa.data /= 1.6e-19 * 1000 # Convert to (mV/m)\n", + "jxb_dmpa.data[np.abs(jxb_dmpa.data) > 100.0] = np.nan # Remove some questionable fields" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute E.J" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "j_dmpa = pyrf.resample(j_dmpa, e_dsl)\n", + "e_dot_j = pyrf.dot(e_dsl, j_dmpa) / 1000 # J (nA/m^2), E (mV/m), E.J (nW/m^3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot figure" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "legend_options = dict(ncol=4, loc=\"upper right\", frameon=False, handlelength=1.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18153.329166666666, 18153.34097222222)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21-Apr-23 17:43:59: Substituting symbol \\perp from STIXGeneral\n", + "21-Apr-23 17:43:59: Substituting symbol \\perp from STIXGeneral\n", + "21-Apr-23 17:43:59: Substituting symbol \\perp from STIXGeneral\n", + "21-Apr-23 17:44:00: Substituting symbol \\perp from STIXGeneral\n", + "21-Apr-23 17:44:01: Substituting symbol \\perp from STIXGeneral\n", + "21-Apr-23 17:44:01: Substituting symbol \\perp from STIXGeneral\n", + "21-Apr-23 17:44:01: Substituting symbol \\perp from STIXGeneral\n", + "21-Apr-23 17:44:01: Substituting symbol \\perp from STIXGeneral\n" + ] + }, + { + "data": { + "image/png": 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jI5lw+hkzZmD27NkwMjKqMZWAhHbt2kEgEODrr7/GkiVLUFRUhOXLl8PIyEhhduva0NPTw/fff48VK1aAz+ejR48eCAoKwq5du+pdV018//33+Oeff+Dj44Pvv/8eLi4uKCoqQkREBO7cuaNUIKvr/b5w4QL+/vtvDBw4EPb29igqKsKff/4JPp+Ptm3bNui1sPy7sAINC8snwsnJCbNmzUJAQABOnDiBzMxM6OjowMPDA8ePH8fQoUMb9Hzr1q1Dt27d8Ndff2HmzJnIzc2FgYEBvL29cerUKbi7u0vbdeLECSxYsAADBw6Es7Mz1q1bVydzVW3Y2NggODgYfn5+mD9/PjIyMmBoaAg3NzdMmDCh3vW5u7vDz88Pf//9N3bs2AGxWIzY2FiFeXQAYPv27dDR0cEff/yB8vJydOnSBYcPH5YxwQBAnz59EBgYiBUrVmDKlCkoKSmBmZkZvL29MWLEiHq10czMDDY2Nli3bh3evn0LDQ0NNGvWDBcuXICXl5fS41atWoWpU6di+PDhKCkpkUvMKEmI+OWXX9YpYaGxsTFOnz6NH374AUOHDoWFhQVmz56N7OxsLFmypF7XJMHPzw9EhJ07d2Lz5s1o06YNzp8/Xy9tZm3o6uri/v37WLp0KdasWYOkpCTo6enBxcWlVkGuLve7UaNG0NTUxLJly5CSkgI+n49WrVrh2rVrsLKyarDrYPn34RARfepGsLCwsLDUjx07dmD69Ol48+YNnJycPnVzWFg+OaxAw8LCwvIZ8fLlS0RHR2P69OlS7RoLCwsr0LCwsLB8Vvj6+uL+/fto164dDh8+/K9GhrGwfE6wAg0LCwsLCwvLZw/rFPyJEIvFSE5OBp/PV5rHg4WFhYWFhYXJjl5QUAALCwulCU5ZgeYTkZycLM3g+jEQCAQQCoXSXBQsLBLYscFSE+z4YFHGpxgbiYmJSqPPWIHmE8Hn8wEwN0cgEPzr5ysoKMCSJUvwxx9//OvnYvm8YMcGS02w44NFGR9zbOTn58Pa2lr67VQEK9B8IiRmJoFA8FEEGoBJ2f6xzsXyecGODZaaYMcHizI+9tioyUWDXZyShYWFhYWF5bOHFWj+H1FTmnWW/9+wY4OlJtjxwaKM/9LYYMO2PxH5+fnQ1dVFXl7eR1HX5efnAwCrNmaRgx0bLDXBjg8WZXzMsVGXbyaroWFhYWFhYWH57GEFGhYWFhYWFpbPHlagYWFhYWFhYfnsYQUaFhYWFhYWls8eVqBhYWFhYWFh+exhBRoWFhYWFhaWzx5WoGFhYWFhYWH57GEFGhYWFhYWFpbPHlagYWFhYWFhYfnsYQUaFhYWFhYWls8eVqBhYWFhYWFh+exhBRoWFhaWj8jOnTvB4XCkf1wuFyYmJhg0aBBiY2M/dfM+e9j+/feR9LGuri50dXX/M32s+snOzMLCwvL/kGfPnkFbWxsBAQEAgIqKCjx58gRz585FZmYm7ty584lb+HnD9u+/j6SPz549CwDg8Xj/iT5mBRoWFhaWj0hISAiaNWsGb29v6TYfHx9cv34dN2/e/IQt+9+A7V/lVFRU4IsvvsCIESMwceJEcDic96pH0setWrUCwKy2/V/oY9bkxMLCwvKRICKEhYWhefPmcvuSk5Ph4uLyCVr1vwPbvzWzdu1aXL16FT/++CP++OOP96rjv9zHrIaGhYXlX0UsFuPixYvo16/fp27KJycyMhKFhYVwc3ODUCgEESEpKQnr1q3Dq1evcPHixU/dxM8atn+VU1JSguXLlyMwMBCFhYWYOHEifvzxx3praRT1cVxc3H+ijzlERJ/s7P+Pyc/Ph66uLvLy8iAQCD7K+QB8lHOx/DvcunULM2fOxKNHj8Dn8xus3n97bISEhKBFixbw8PDAs2fP/pVz1AQRUFDw/sfz+cB7aublOHbsGEaMGCG33dzcHMePH0f79u0b5kQNSG3jg4hQUP4BHQyAz+O/t/mjKp9j/9aJBhjEDx4+xIABA5CWloaysjLo6uoiPDwcTk5O9arqU/VxXb6ZrIaGheVfJCQkBGpqanB1df3guk6cOIFXr15h165d+O677z68cR8JsVgMgOmLT0FBAaCr+/7H5+UBDSXrhYSEgMPh4O7du+DxeBCLxYiLi8OCBQswatQohIeHN6iw+jEoKC+A7uoP6GAAeT/nQaD+4Z38ufWvRJ9QqzDXAIP4yZMn8PT0BIfDgYaGBlq1aoXAwMB6CzRV+7i8vBxEhIyMjP9EH7MCDQvLvwQRoXXr1qioqEBJSQk0NDQ+qL7o6Gg0adIEAQEB/wmBpqCgAOrq6uDxeHL7cnJykJGRAWdnZxQXFwMAWrdu/bGbCIDRsOTlfdjxDcWzZ8/g6OiIdu3aSbe1bt0aqqqqGDJkCPz9/TF06NCGO+FHgM/jI+/n+nfw3LlzsX37dsTGxoLPa5hO/tz6d9asWYiJiandTNMAg/jy5cvo3LmzdFPLli0xefJkTJo0qV5VVe3jqtq7/0Ifs07B9eDo0aPw8fGBQCAAh8OBUCiU2R8WFoaOHTtCW1sbFhYW8PPzA2vR+/9LSEgINDQ0YGpqivv3739wfdHR0Rg+fDiuXbuGrKysBmjh+5ObmwuBQIBx48Yp3D9hwgSpc2BJSQkAwNLS8qO1ryocDqNhed+/+lpC5s6di379+qGiokJuX0hIiEJnSomwK+mrr776Cj/88AMAIC0tDc2aNcOlS5fqeeUfBw6HA4G6oF5/fB4fV85eAcqA5NjkBjE3AXXr3+zsbGhra6O8vFy6f/r06VizZk2DtKE+bN68uW739QMHcUFhIQICAjBo0CBpldOmTQMApKSk1KvNdR3DEydOhIeHBzw8PGBvbw8NDQ2UlZXV61z1hRVo6oG+vj5mzpyJDRs2yO0rKChAz5490b59e2RmZsLf3x87d+5UWJbl/wenT59G79690aZNG4SFhX1wfampqejfvz/Ky8thZGTUAC18f6KiogAw9nRFSF6SRCTV0EhedABQVlb2Pynsh4WFYdOmTfD395fLxZGWlobU1FSFH4MTJ06Ay+VKNQtLly7FgQMH8PTpU/Tu3RtLlixBnz59Pso1fAzCwsKQlZUFV1dXpKWlNUidde1fAwMDODg44OnTpwCAO3fu4PHjx1IB8n+RK1euwMnJCc7OztJtTZs2Rbt27XD+/Pk611OfMbxnzx6EhITg/Pnz0NPTw4kTJ6Curv7hF1MDrEBTD3r27IlRo0bBwcFBbt+pU6cgEomwbNkyaGpqolmzZvjpp5+wefPmT9BSlv8CFy5cwIABA9CsWTM8f/78g+oqKSlBQUEBrK2tMWHChAZq4fsTFxdX437JjLu4uFgqyJSWlgJghBwzMzOsXbv2X23jp+Ds2bPo168fxo4diytXrsjskzhEq6qq4uHDh3j48CHOnDmDcePGYc+ePZg3bx4cHR0BACYmJvjuu+/Qrl07/Pjjjxg8eHCt5y4sLERERETDX9S/wP379+Ht7Q1ra+sGE2jq07+dOnWS+oDMmDED27Ztg6rqx/XAkPiWAYBIJPpXz+Xv748vvvhCbnu/fv3g7+9f53qq93FQUBAuXLigsI8B4PXr1+jTpw82btyo8PwNDSvQNBCSSI6qD0WrVq0QExMjtTOy/P+hvLwcYWFhaN++Pdzc3BAeHv5B9aWnp4PL5cLAwECaPyI3N7cBWvp+xMbGQkdHR6mzs+RlnZubK6ehSU5ORm5uLh4+fPhxGvuB3L9/v87p3G/duoXOnTvD19cX9+7dk9kncYpesGAB2rZti3bt2mHixIlISUnBiRMnsHLlSmnZ7OxsHD9+HPr6+rC2tq71vHl5eXB1dUWTJk3w/fffIycnp+4X+Al48OABvL29oa+v32DjuD7927FjR9y7dw8rV65E586d0aZNmwZpQ32oajZuKKFOGSEhIfDy8pLb3rJly3o561fv4+7du2PmzJkK+/jp06cYMmQI9u3bh44dO37oJdQJ1im4gcjPz4eenp7MNn19fek+RWFmAoEABR8SilcPPtZ5WBiioqJgaGgIXV1dmJiYICcn570F22vXruH06dOwtbVFUVEReDwe7O3tERoaihYtWnxwW99nbCQnJ6Np06YoKyuTu64rV64gLi4OAoEAqampKCgokI7//Px8REREQCAQID8//z8v7JeXl6N3797S35GRkUqdu8ViMV68eIFVq1ahrKwMb9++lbm+mTNnYubMmdLfqampMDQ0hJqaGoB34dF5eXkYOHAgpk+fDh0dHcyaNQu3bt2q0c9kxYoVaNy4Mfr374/du3ejsLCwwTRg7zM+RCIRVFRUlO4PDQ3F4MGDkZ2djby8vAYZB9X7tzpVz9GiRQtMmzYNISEhuHPnTo3nf/ToETZu3IjDhw9/cBurEhsbC3Nzc2hqaiIyMhI6OjoNWr8EoVCIhIQEODk5yV2ng4MDMjMzkZSUVKfIpOp9LBkbkmMl9d+7dw8//vgj9u/fr/C870NdxiGroWkgBAKB3ExDMktSFjMvFAqxZMkSzJ8/H/Pnz5euPcLy+RMdHQ17e3vpom3p6envrVaeN28ezp8/D2NjY+k2Ozu7Ws0+9aGoqAiZmZl1Lp+ZmQlzc3OF1zR16lSpQ2xpaSlKS0uhp6cnNTm9ffsWAPNB/68TFBQk87umtO5JSUkoLy+Hg4MDHB0dkZubi+zsbIVlhUIhWrVqBQcHB7Rr1w4nT54EwNyHYcOGYfTo0Rg9ejT69+8PdXV1HDlyROl5MzMzsXfvXixZsgTm5uYAgFevXtX3UhuMP//8E40bN8b169cV7s/OzkZcXBw8PT3B5/NRWFj4kVvImPTU1NSwfPnyWvMvnTx5Enfu3GnwSWFmZiaMjY1hZmb2rz4LkveEvb293D4jIyMYGxs3uKly3LhxyMvLw8SJE9GhQwd06NBBqqmtDwEBAdLv45IlS2otz2poGggPDw8cOnQIQqFQanYKDg6Gg4OD0gemuLgYf/zxx0dNdscm1vs4RERESO+9k5MTcnNzUV5eDlNT03rXJdGC6OjoSO+fQCBo0KSMy5Ytw/bt2+vsqJuUlARHR0c8ffpUrg1VZ2McDgelpaXg8XjIycmBQCBASkoK7OzsEB0d/Z8fjyEhIejduzeOHj2KhQsX4vDhwwqTigFAYmIiTExMpA7bfD4fb9++hZ2dnVzZixcvori4GEKhEOHh4bh06RImTpwIgUAgZ4qrzTR39OhRuLi4wNvbG5qamvj1118RExPT4H1bl/ouX76M1atXY/bs2Rg7diz8/f3RoUMHmTL37t2DqakpbGxsIBAI8Pbt2399HBARfvrpJ7Rr1w6DBw/Gtm3b4OvrizFjxtR6bEJCAvLz81FSUtKgkXrZ2dkQCAQQCATS/9eFO3fu4Mcff8TKlSvRtWvXWsvHxMTA2tpaajGojq2tLeLj4+tUlzKqt70+k6OaGDx4sNR/LD8/Hzt27KixPKuhqQcikQilpaXScL+ysjKUlpZCLBZj8ODBUFFRweLFi1FSUoIXL17gjz/+wNdff/2JW83yKXj69KnUZq2hoQF9fX0kJye/V126lQm1qqYJMDIyarCXBgC8fPkSAOo8i0pLS4OlpaXC0GQJpqamKCkpQUlJCQwMDKQ+NDk5OXBwcEBRUZFc6oP/GlFRUdLIkGnTpuHcuXNKfQ4iIiLQuHFj6W8bGxupNqo6jx8/xtixY6XCjqLZc1159eoVPD09AQDu7u548+YNMjMzP3oUWXJyMsaOHYvt27djxYoVGDdunFTzVJVnz55JTaW6urofxex48uRJrF27FqNHj0ajRo1w/PjxWj+OEiTarvT09AZtU1xcHOzs7OqlpSorK8PEiRPx5s0bLFu2rE7HvHr1Ck2aNFG639nZGW/evKlTXf91WIGmHhw4cACampro2bMnAEBHRweampoIDAwEn8+Hv78/AgMDYWhoiG7dumHSpEn4/vvvP3GrWT4FkqycEszNzeud70FCUVERACC/MB9fX/wa2SXZMDQ0bNBcNNra2tJ/a3u5lpeXIyoqCk2bNq1RoDExMUFJSQmKi4thYGAgNTnl5eXBxsYGAP7zPjTR0dHSqA1JhNnu3bsVln39+rWMQGNubq7UlBAVFQVHR0cYGBgA+DAft9evX8ssCGhoaIiKioqPbsrZvn072rVrJ9V6eHp6KozukwRQAO80jf8m5eXlmDdvHnbs2IG+ffti5MiRuH79upzPo7JjExISYGRkVKPjbl5eHrp37y5noqyJN2/ewMHBATo6OnW+VytXroSurq7U90fybqiJ6uOjOqxA8/+UL7/8EkQk9+fr6wsAaN68Oe7cuYPi4mKkpqbCz8+vwRJGsXw+ZGRkIDExUcZh18LC4r0FGokvFhkRtgRvwe5nu2FkZFRvgWb37t2YOnWqwn1Vk4zNnz9faR2HDh2Curo6ysvL4eLiIqdhISKoqKjg2ctnyBXmorS0VKqhqSrQWFhYQEVF5V//mH0oCQkJUuELYFTg1cOxJVTX0JiZmSm95/Hx8bCzs8OFCxcwfPjwDxJoIiIiZD5Yenp64HK5HzX5okgkwu7du6XJ2gDAxcVFmq+oKhEREWjatCmQlATdjyDQbNmyBVpaWpg4cSImTpyIvXv31tmfLSEhASoqKvD09KxRQ7N//34EBARg48aNtdaZmZmJpk2b4sCBA+jSpUudBZr09HQsXboUq1atgo2NDQwNDeuUDkIq0BABI0YAp06BiJBXyvQ7K9CwsLAoRZIaXE9PD3j4EMjJgbm5ORITE+tdl0gkQl7lOixNJjWBuY45rkRdgaGhYb1MTsXFxZg8eTJ27typ0BRRta4tW7YorWf79u0AgNmzZyOpKAnFZbImqpKSEohEIhx5cwSJxYkoKCpAcXGxVGsguR5dXV3pQnP/VcRiMd6+fSsTNt2lSxdER0cjKSlJrvyrV69kBAtTU1Ols/qUlBRYWlrC3NwcHTp0eG+BprS0FHFxcTLnlYT3N6RJsjauXbsGkUgkExHm4OCAxMREGWGZiBATEwNHHg+wsgL/5s1/VZNUVlaGNWvWYOXKlVBRUUGvXr1QUVGh1GG5OvHx8bCxsYG5uXmNGpp//vkHQ4YMkQvVV8SaNWukZqz27dvXWaDZt28feDwefH19weFw4O7ujtDQ0BqPISJERkYyZtO9e4Fjx4AhQ7Bx+0TordGD2xY32DnYISoq6l/PhfMxYAUaFpYG5smTJ4z/jFgMDBoE+PmhSZMm0syk9UHywXd2dkZWeRZGuo3E9djrCMoJqtcM/M8//0SjRo0AMBqk6mRnZ+PKlSvYsGEDevXqJd1eXFwsYxbi8XhYvXo11q9fj52hO1FRzpicHjx4gBUrVkjLamhrAGpAcnay1OQEMB/gvLw86Oro/OcFmoyMDFRUVMg4guro6MDKykpO85CamipdogAA8svywdXmKuzrpKQkJCYmSgUlPp//3gJNVFQUNDU1YWFhIbO9oU2StbFnzx5MmDBBJg+Xubk5iEhGs5Geno6ioiLYX70KANC5f/9fFWhWrVoFQ0ND9O3bFwCTEG706NE4fvx4nY5/9uwZGjVqJI1UVERJSQkeP36MX3/9FfHx8bVGLN29exe//fYb7t27B1VV1ToLNPfv38eqVauka6e5u7vXmoE8KysLeXl5cNTTAyZNAs6fx9PubhDv24eFHRciPCMce+MZjdX7TLj+a7ACDQtLA/P06VPGf+bJEyArC9izB46Wlgo/brWRm5sLVVVVaGtrI60oDZ1sOwEAtkVsU6glUAQRYf/+/ViyZAmMjY2RkJAgs18oFCI/Px+urq6ws7OTqXfq1KlSp2SA+SC5ubmBQwTr6FRAzGiR2rVrhwULFiA/Px8aGhrIKs8CVIHsgmypyQlgXv65mZnQHTcOumpq/2mBJjExEUZGRtDU1JTZ7uDggJiYGJltYWFhcHR0ZCKb8t/CdoMtFj5aiNR02Y8bEWH06NEYOXKkdJXjDwldfvPmDVxcXMDlyr7K38ck+b6IxWIEBARg4MCBMttVVVVhbGws84GPiYmBubk5tB4/Br7+GjpRUbUKc3///fd7ZdreuXMnfv/9dxw+fFimf7y9veuUTI6IsHfvXowaNapGbVtQUBCMjIzg4eGBpk2b1hiVJhKJEBYWhr59+0qXCKjr/Q8PD5dJZNm8efNaNTTR0dEwMTEB/+efAQAv2zjgV5MXmB5niKW+S3Bi2AmcjzoPBweH/wmzEyvQsLA0MFKB5vx5iAcNQraxDtSfh73XLDwnJwf6+vrgcDhIL0qHmY4ZcublgGvIzP7rUmdYWBgSEhLQv39/WFtbywk0EqHC0NAQFhYWMtFYkrIS58P09HSYmJgAN29i4doHACCz4JwkiWRuegK4KkBuAZMpWBIyWlpairzMTOgB0BUKP2m249qoqkWpioODg1zm4OjoaEYDlpKCuBG9MErbG6ampkhKkRU6g4KCEBoaKmPWq66hEYlE2L59O9avXy+THl8Ryhw+62uS/BBevnyJsrIyGSd4CdVzrCQmJsLG2hoIDQUGDgQ/JwfFxcU1mjumT58u45tTF+7evYvvv/8ely9fllt3qHnz5njx4kWtEXb37t1DcnIyhgwZUqOG5u7du+jQoQM4HA5atWqlUBMrEolQUlKCKVOmoLi4WGZNJR0dnVqf45KSEkRHRzO+R5VINDQ1jRGpU3tkJLBxI448PwKD3oOgnVsERESgp1NPxObGwtLekhVoWFgagocPH+Lnn3+WsbV/ruTk5CAmJgaenp6g8+dxsTEXWyxTUHL3ynvNwnNycqCnpwcxiWEWnY5G245DT10XLRxaQFNHU05ToIjDhw9j4MCB0NbWhr6+vpxjcE5ODjQ0NCDkCmFhYYH09HRp9JLkQ3Pt2jXk5uYiLS0NpqamKD5/ChK9RWlpqTR7bn5+Pvh8PnZNu4Ce8UxkVklJCbS1taGuro6SkhLkFRZCF4CoOPuTamgqKipqFKhev34t8+GRoEhDEx8fD1tbWxSuWoIOV8Kx7KEWmtk3Q3aWbGK9GzduoFu3bjJZYasLNEuWLMHq1auxdu1arF+/vsZriIyMlJoSq/IxNTSBgYFo166dNONxVaoLNCkpKbCQXHunTtBSY0xUitIFJCYmSjUz9Q1B//PPPzFjxgyFKfcl/aXIYVlCdnY2fHx80L59e2hra9eoobl79y7at28PAHB1dZWmQKjK9OnToaWlhb1792LIkCEyprm6RHqFh4dDV1cXVlZW0m1NmjSR+lApIz4+HnZWVkBYGMR9+2Djo40Y6DEK8PUFrlyBDk8HLcxaQN1YHa9fv66xDRKKiopw/vz5/+TisqxAw/LJICL88MMPaNu2LdasWYMff/zxUzdJyp49e2Bvb1/vDJ7Pnj2Dra0tDIuLIXoeivFlR5Hc0hlGEdHvLdDoa2qieNlizHwkhsGKtcCdO7DVs4WBhUGdBJr79+9LUw1EREQgOztbJtwzMTERWgZaEKwWwNTUFFwuVxqdEx8fL70uSTSfsbExRE+CUcqY8lFSUiKNYCooKIBqfhbUKwiOhUBhcSGKi4uhqakJDQ0NFBUVobC8HKVagEpx7icTaE6dOgVdXV3Y29srneG+fPlSZkYsQalAY24O7NmD3eObwfD0FXjbe6KitEKmrx8+fAhvb2+ZY6vO0HNycrB27VocO3YMR44cweLFi2tcl0mZQPMxNTQPHjyQftCrU12gSU5OhrlQCHh7owRCFPIYLUn1Z0MoFKJ9+/ZS7UpdP57FxcXYuHEjLly4gOnTpysso6KiAjc3txrNNbdu3QLARLYCyh28xWIx7t+/L00e2LRpU4XrtlU9V/UlFPT19Wtde+vhw4fw8PCQiZrl8Xho0qRJjdeRmpoKMyLAxAQvdUpQUF6AgY0HAj17ApV+TK0tW6NEp0T6rNeEWCzGoEGD0L9/f6xYsaLW8h8bVqBh+WScOHECBw4cQFBQEG7cuIFNmzb9Z1YL/uOPPxAXF4d9+/bV6zhJ/pmKs6dx15qwYeRejBy2BKa5pTKmmbqSk5MD/dxc6CxejnFhAFq2BDp1Qu/QEmgaadbpJRQbGytdIV6iupeoz2fNmoVnz56Bq8u8CohDsLCwkEanpKSk4KeffsKTJ0+kL84NwRvAi4hEqI0qVAAUFL/TLuTn58MsIxcAoAmguIRZbVtLSwuamprS876yAkxEQEZ2/f2KauK3336TrhGkjKCgIIwbNw4bNmxAYWGh0mUCwsPD6yXQWL5NRJx2Bbx/OwyoqqKz2BTgyDphP3nyBK1bt5Y5VuJDQUTYvXs3vLy80KpVK/j4+KBJkyY4c+aM0muJioqS+uJUpa5OwQ8fPoSpqSkmTpz43jPuFy9eyJl1JFQXaBISEmCVlwe0a4fQtFAUaAGqqhw5k8vNmzchEomkwl9d2pabm4shQ4Zg+fLl+Pvvv2VWfa6Oq6trjctDhIaGYsKECRgyZAgARoiXZPuuSnp6unRhUABwdHREXFycTHvDw8MRHByMsLAwCIVCqVOvBAMDA8TGxioVrCMiIjB37lxMmDBBbl+TJk1qNBWlpaXBLC8P8PHBluCt6GLfBWoqakCnTsD9+4BIhNaWrZHCSamTT96WLVsQGRmJe/fuYdWqVQq1UZ+Sf0WgiY+Px7lz57Bs2TIMHz783zgFy2dOamoqvv32W6xbtw4tW7aEr68vhg8fjjlz5nzqpiEnJwcvX76En59fjWv3KEKSIbjw1FHccNXCePfxcHHzhUgNKKvUYtS3LZplzAxfQwjg3DkAwJSVl8ERcGrNPlxaWoqkpCRpJtqFCxfC2NhY+sHftGkT9h3Yh8xyZjYflBwEc0tzJCQkICUlBRwOB/369UNQUBB6DeoF8IDz5/+AemYOYhz1ocYF8grfaVny8vIgSYKuCWbGXFxcLBVoUlNTwQWQYK8FYyGQkdNwAs2NGzewcuVKvH37FnPnzlVabtGiRfjuu+8wbdo0uLu7KxSiRSIRXr58CTc3N7l99vb2SEtLk9G8xMfHwzzoIQ54qaKJiSvg7g7nxCJwtDhSIa64uBhv376V83nh8/kQi8UoLi7GoUOHMGnSJOm+gQMH4uLFiwqvo7CwEKmpqQoFmrqYnMRiMSZOnIhhw4bhypUrdc6cWxWRSPQur4wCqubiKSkpwc2bN9EqLQ1o3x7BycHI1gTUVDhyGhp/f3/07dtXOm6VCTSJiYnIzs6WRuelpKTg6dOnGDt2bI3tdnFxqdHEEhwcDA8PD+lvyXIW1bVeycnJMDQ0lJpczc3NpZF8Evbv348hQ4agWbNmChfslOQuUiRglZaWolevXujcubPCa6ptTbe0tDSYJicjsZktdj7dCb9OfsyOZs2YKMwXL9DasjVixbFKs1pLiIqKws8//4zdu3ejXbt2GDJkCE6dOlXjMR+bBhNovv76a/j4+MDR0RGTJk3Cd999B1NT03o7c7G8o6ys7H8iN4AiZsyYgS5dukizinI4HKxbtw4BAQENuuji+/DkyRPY29ujf//+uH//fq2OmVV5+vQpPF1cwL/7GJk9GEdBEx1TpBhwUCEU1nsWnJubC5UC5sN0q4UBYG4OhIWhQoMHkVZFrbOq+Ph4qKurw8zMDADTz9XDpfPz84FK94f2u9sjqCwIL168QFFREbS0tNCsWTOkp6fjZsxNwBvYeIwJzRaZGIPHYRx/JWRlZckINKWlzNIHEpNTanIydAFkNrKEmRDIzG44s8imTZswc+ZMHD16FIcPH8aTJ0/kymRmZiIgIABfffUVAMDJyQmRkZFy5SQzZkXCgrGxMbS1taXjVKLJcgmPwJPW1oxZoFkzmMRngLQI0W+jATARPtra2oxTdRUkKxUnJCTg2bNn+OKLL6T72rRpozTcPyoqCgKBQPqxrUpdTE4BAQHIzs7G2rVrcejQIXz//fc15lpRRFxcHEQikVJtiIGBgVR49vf3h0BHB11TU4FWrRCcHAyuoRF4KvImp4sXL6Jnz57SDNaK3oOhoaHSBHPr169H06ZNcefOHYWO3ACAS5eASif3xo0bK9UGFxYW4saNGzLpC3g8HvT19eUcg5OTk2VC5vl8PrS0tGQSKgYEBGDo0KGK21RZd+vWrRVGci1atAgmJiY4f/48Iwzl5ADt2gHt2wOvX8POzk7OQb0qqampMIuOxhH9txjTfAx8bH2YHaqqTB03bsDJwAmaBoz2VJkfo0gkwsSJEzFp0iR07twZANCvXz8EBgYqPfenoMEEmvv376N9+/aIjIzE9evX4eLigmnTpqFbt24NdYr/V1RUVEBTUxM//PDDp25Kg3P79m0EBARg/fr14BQUAOvWAYMHw1JXF23btv3k618FBwejZcuWcHNzQ1lZWZ38VABGMIiMjIRnRgbizbXQpA3zYeJwONBUUweAejs+5+TkwKICaD8J2Div0sGxaVNwwYFqaVatGprY2FjY2dmBy+EAM2cCu3ZJnRAfPGCilITlQqhyAEMVPtQrAF1nXaxcuRIJCQnQ1NR858BaDEAVKFIl3BzdDloCQ6hzgMSkd/kr0lJSZASa8oISWQ1NfDx0AVQ0bgRjEZCTU7MWIS8vD3369MGGDRtqLJeYmIiLFy9i+vTpcHBwwK+//oqhQ4fKOf0+evQIjRo1kn70nJycFDqHhoeHo3HjxjLOmxI4HI7MsgaJiYlQ4XKhYWsKFcdKAcjWFrykVKjyVfE6ntEESMxD1bOH83g88Hg8BAUFwczMTEZAcXd3R2xsrMIlIh49eoQWLVoozEZeFw3Nli1bMG3aNKirq6NLly7w8PDA2bNnazymOi9fvoSLi4vCfgJkHZ6fP38Ob0dHcKysQHp6uJ94H3wzG2hwSMbklJaWhtevX6NLly7SehVd/4EDBzBo0CCEhoYiPj4eu3fvlgqHcpw7B/TtC7RpA2Rno3Hjxnj9+rXCyYpksc+qWZ8BKIx0qi7QSMaGRKCRaPqqansU0bx5c4U5ZU6cOIGlS5cyYedEwOjRQFQUYGUFTJkCe3v7GgWatORkGKuq4Je0wxjffLzszjZtgDlzwBWK0MqlFbgqXKVamvnz5yMjIwOrVq2SbvP19ZX65H0M6rIWXoMJNE+fPoWTkxN69+6N27dvsyn/P5CzZ8+CiLBx40b8/vvvKCsr+0/n7KgrIpEIs2fPxq+//grTigrAywvYvRu4dQtwd8dvixfj6tWrUifTT4FEoFFTU0OTJk1qTV4lISQkBBYWFjDwv4xDTiXo7thduu9BH2ZxuICAAPz11191bktKejKsKoBEAWCiVTmzV1EBLCygVV6AxLfvhAmxWIytW7fi+fPn2Lx5M8RiMWJjYxm1fUAAsHUrMjb/Bl1dXWzcuFGaBwMARkdykbmwAG/XAyatjAEAV65cgaamJlRVVcHj8aAt0kZTY2e0jhfhpAcPOrrG0AQQn/DOjyclMR76ALIDLkCDA5QXMAtQSjQ0aW/fQsABdOwbQwCgoNIZsri4GGPGjJEzA2zatAlXr17FggULahz/O3bsQK9evWBrawuAeQHb29tj8+bNMuWioqJknGitrKwUvihfvHghk/OjOsbGxlINSHx8PKz5fCQ4G8NWlzk/bG2B+HhoG2gjNpH54CQmJsoso1AVPp+Px48fy31EjY2NYW5urnAM3rlzRzpbrk5tPjQVFRUICAiQcQkYNGgQTp8+rfQYRbx69UquzVWpmmMlLi4OtioqgKsrnqY8RUphCqxtm0ODQzICy7179+Dm5gZ9fX0MGDAAAOTuvUgkwpEjRzB9+nSl/jtShEJg3jxgyxbA0xOYPRuOjo4oLy9X+AE/dOgQJk6cKLddkUATGxsrpxGqamaLiYlRqumriqLoqMePHyM2NhZt27ZlNly/Dly5Aty5g/3TvCG+fw+N9PURExOjMAS9rKwMOfn5UGlsC011bfja+coWmDMH0NQE/vkHLcxbQMdIR6F2/NKlS9i6dSvOnTsn1ZgBzNh0cXGRToz+bdYsX15rmQYTaDgcDqZMmYKTJ0/i6tWriIiIqDEsjkUxpaWluH37NrZs2SJ1iPv9998xceJE6OnpYf78+f/JcLm6MmXKFBQVFeH7fv2Ajh0Z57SwMCAoCMjJQeutW2FgYIDg4OBP1kaJQAMAzZo1q3NSr+DgYHi6u4Pj748XHRqhsdG7F72uPRP+O2PGDHzzzTdK6ygoKJCZNaYnv4UBgFQdgMt597iqmJnDWV0bSUlJ0vFw/vx5zJw5E82bN8e3336LyMhIxMTEwMbOBuWHDyDTyQI5CW+gri0foskXMuc0KgaE6QkYM3YMnj59KvUN0NLSQllhGVqJTVCowcUB4RNo6xpDiwM8uvdImiE3OSkJ2lxAt3MvqPHUUJJfIj1eU1MTqSkp0FbhwNTAGuo8LorymJn5qVOncPjwYTnflxMnTuDgwYNwdnbGhQsXFPZZRUUFduzYgRkzZki3cTgcTJo0Sc7GX3WxSUD5oqHh4eEK/WckVF3tPD4+HnZqaogyVoGNbqXAUinQ6BnrITGZETpTUlJgbm6usD4+n4+goCCpcPA46TE67+uM7cHb4e7urjAR3NOnT6XjtDqGhoYoLi6WrnBenaCgIGhqasoIbQMHDsT169frtOChhOoLciq6Lon2JT4+HnZCIQptzdFyR0uMaz4OWsYW0OYAGbnvfKnu378vFbZ79eqF6Oho5Obmyrz3Hj16hLKyMnTV1ARGjgS8vQFluVwuXgSKioApU4AdO4CzZ8ELDYWDg4Oc2Sk1NRV37txRaCJSJNA8fvyYuQfh4UClWayq9k4i8CnTYElQFB3Vq1cveHp6vktseeUKCieMRo/H32LK/XkIMSEU3jsDLperUBBJS0sDl8NBuglhUotJ8koGXV1g+XJgzx446DtA1UBVLsiguLgYX3/9NdauXaswhUHbtm3rtNzDh3Lp0iUcP3as1nIN7hSso6ODFStW4ObNm/Dz85NK2Cy1k5ycDB8fH/j6+qKwsBD79+3D2LFjkZGRgWPHjuGbb77BqlWrsGLFCunDfe3aNZw/f77e58rKysKIESOUrhzckDx8+BBBQUE4ffo0Dh8+jOvXrkF9wgSgTx/g778BLhdwdARevgTnxAl00tKqtzNuQ5GRkYH4+Hh4mpkBd+/CxcVFoY+FIgIDA9HB0BBp+jy4dxkts8/Olvk4Smarkjwv58+fl5pTiAh6enqYN2+e9LicjAyoanBRoQroaei9q9DcHK15xigtKZXObm/dugV3d3dpkbi4OMTFxeFh/n3kHjuAXxsnwyofEKoJ5V7M6tx3LzujnHJYOVjh6dOn0iy52traKCsog1laHt64miK/vABauobQBgevw1+jZcuWUFFRQWpaKtTVOVDhqkBNQw0lhUxkl5aWFqOhycyEpioXJtomUNNWR0kh88G9dOkS+vXrh9u3b0vHdmFhIZ4/fw4fHx906dIFd+7cUdjv169fh5qaGhOa/vIlE71BhD59+iAsLEwmpXt1gUbZApK1aWiMjIyk0Uvx8fGwrahAqH6ZrECTlgZTQ0OkpTB+KampqTUKNJI0+09TnqLr/q5wN3XHz9d/hqaVppyGpqioCBEREQqT2QGQZmZWpqW5efMmfH19ZTLoOjk5wczMDI8fP1Z63dVRlqun6nVJxmdcXBxsk5IQQIxP0fIuy8EzNAEfQHr2u/F47949Ge2hnp4ehEKhTK6amzdvonObNlDt1AkoLgbS0oBt2xQ3YudO0KRJCM95g3RdVWDMGGD7doV+NKdOnULbtm1llruQUF2gEYlECAoKQpvycsDNjVn4EbJCckJCglRrWBMuLi6IjY2VvhdKSkqQn58vK5D7++OUdQGuxVxD9KxolPh4I+HiITg5OSl0cE5JSYExj4f7aino79Jf8YlHjwZu34ZzhQAiXZGcQLN//37o6upi8uTJCg9v167dv6qhISL8/vvvGDZkCP6sQ/kGE2hiYmLw448/4vvvv0dAQABsbW1x8ODBGlfuZWEgIpw5cwatW7dG06ZNUVRUhMePHqHRhAnYUulz0aNHD2zatAm3bt3Chg0bYGVlheXLl6NHjx4YUfkg1Yddu3bh/Pnz+PHHH+vl9Fpfrly5grZt26J169YYPHgw1qxeDZvNmxnntt9/Z4QZCWZmwLVr6BAXh9sBAdKH+2Py5MkTODk5Qbv/F4CPDyx43DppGsViMe7cuQOfhAQcaSxEd6ceMvsNTKxRNb4hNTUVIpEI/fv3x/fff4/i4mKEh4dDLBZj3759UifIgrx8qOhq4N6ke/ixXZU8PVZWcBfqQE1dTeoYHBYWhqlTp6JZs2ZwcHBAamoqEpITgIRX0NISQHfy19AQAQ9iA+TuuZoGD7C3B3R14SrSx8mEkygqKpIKNFpaWhCWCGGckY/ylswq4tq6xtAGkJaSBgMDA/B4PGRk50JDk5mNqmuqo6S4AioqKlBTU2M0NFlZUFcFTLRNoCnQQUkJI/A8fvwYkydPRn5+vvRj8PLlSxgaGsLS0lJpBlaAiW7q0aMHuDt3MibMXr2AtWthYGCA9u3by0QJKdLQZGRkyDidVlRU4PXr17VqaCTCQlxsLGwLCnBHIx0O+kx4PExNAR4PTtp6yMrIkt5ziXN2dfh8PioqKmBhYYF9u2fjV5ux2NBrA/w6+eEF54WcljAkJASmpqZKBSRVVVXo6ekpFWhu3bolzStUlfbt2+Pu3btKr7s6yjIVSxAIBCgsLIRQKERifDxsX77ELtXnuP3lbRhoGoCjrw8zcJGUyozhwsJCBAcHyyTE09XVBZfLlXFyvnnzJnxVVJhJ0blzjNl66VIkhd7F8fDjKCgrwE9Xf8IPfw+F8MolzDQNRssdLWGx1gIb3UtBR46gsZOTnEBz6dIl9O+v+ONfXaCJiIiASCSC2z//AD/+CDp3Dk8fnZERkt++fatQOKqOtbU1VFVVpf4wYWFh0NfXf2eiTE4GvXyJ39Wf4MKoC7DWtUZjn0HQikmAg5ODwvDp1NRUmHGAEO18tLVqq/jEZmZM6PayXSjSLEJs3Dt/HCLC5s2b8d1338ktrSHB29sbUVFRNa5E/iH8On8+1q1ciSsqKhjq51dr+QYTaEaMGAEjIyO4urpi1apV2Lt3LwDGS78mYmJi6vT3qSNf/i2uXr2KQYMGYfr06ZgzZw727t0LLS0t4MYN4OFD8I8dQ2rnzjjXtSvg4oJOp08j+PFjGBsbY9WqVfDy8kJJSUm91MQAozVZ9MsvqKioqHU9kPpSUlKCgoICfPPNNxgyZAh2796Na9euITAwEN+FhwOHDjG24MqPZURmBP589CdCU0OBbt3gZW+P64GB4PF4H9289uTJE7S0tUVJRjLOOQNFt47USUMTHh6OstJSeN67i0OeqvA0l505C4wsoQ5IE2ilpqYiMjIS6urqEAgEiIiIwL1799CpUydwuVyphqqgqAQ8Qz7aWbeDoZbhuwo9PODxMhs8fTUkJyeDiBAWFoaWLVsiLCwMrVq1QlpaGpIzk9G7SB86Q0fjt4GbkWXKh5WCdDjaFULmw9C2LcwKgKhiRohTV2ecmSWOwbqFpTBu7MUco2sEHQLKSspgYGDAZAIur4CWQAsAwNPSREWFmBnPALPGU2EhVNTEMNU2haahLkrLhCgoKEB0dDS8vb1hZ2cn/chUTRrn4eGB58+fK/QVuH79Oro4OQHffQf4+zPRLEuWABkZ8PX1larERSIRYmNj5TQ0YrFY5oUcFRUFFRUVaciwIqr6qMS9fg07AM80c+FlzvQNuFzA0hKNNQQoyGRMISkpKTUKNACge3Av1i66i7lT9gC//YbhrsMRpRaFFy9eyAih9+7dq/XdWtUsVpWysjLcu3dPof9N27Zt8ejRI7ntERERWLp0qYyWJDs7G5mZmbUKNADTp+VCIfSnfYlLZvloaVFpKtPTgxWHi7fJjC+LJErJzs5OWoeKigqMjY2lEVg5OTkIDAxEr5QUQGIB6NwZGDMG6WMGYPjx4XDf5o6o68fw7e+3EdzWDoVm+oidHYsXM1/gtyJ/FPHV4cLlymg2ysrKcPPmTWkSyupUF2gePXoELw8PqD54AHz3HeIaGeHPVYPwtPCpjEBTNbuvMrhcLuzt7REdzWivJAvcSs1E584hr0VTpKlXoIcjM1ky9GiLxjkq0LfXx7Nnz+TqTElOhll5ObRc3KDN05bbLyUgAPwrN2HJESEi8p2AFxkZicjISAwbNkzpoXp6emjSpAlu375d6zXWl19++QW7Nm3CLTU1+Pz9N1CDqV5Cgwk0Wlpa+PnnnzFlyhRcunQJR48erdNxTk5OaNSoUY1/Tk5ONap/P2fmzZsHfX19hIeHY86cOeBcuQJ07w5MmwaMGgUAMH36FKpz5zKzkY0bYbdgAUKePkVRURGCJkyAJo8ntz5PbcQ/fQrnRYvg27YtVq9eXWPZhIQETJkyBf369UO3bt1q9Ta3traGQCDAo8BAhAYEYGLv3ujWpQt8btwAjhwBgoOBRo1QUlGCngd7wn2bOzY93oQvjnyBwvJCeFQJl3yfFao/hODgYHgIK3DOUQididPR9tELZGdn1+rJf//+fbS2tESWmx2M3dtClStrM1fXNYR6ld9paWl4/PgxvLy80KhRI8TGxiIyMhJubm4YMmQITp8+DSJCUVkF1E0N5E84fDh003KhoSpEUlIS0tLSkJmZKX1OJC/f7LxseOaoMKGeAAyMbDBFQT4xQYWIsambm8OqSAWozP1VXaAR5BfD1rWyLgNL6FTKmxKBBgD4+ozNX12bEWQkWh7Jv8QjmGibQMvYCGJi7rGhoSFMTEzQuHFj6WwzMjISTo6OQHo6nJycwK32AQKYj2pISAg679uHou++xuCUDRif8TfEHTsCq1fD29tbulhgUlIShEKhjAmAx+PB0NBQZkxLEuopm5UCssJCXGwsjIwEaGXbDppqVRayNDaGi6Y+SnJLQES1mpwAwPrKNYz53Rvc24HAqlUwf/wSzs7OKC2TTXF/8+ZNpQ7B705vrHBB1MePH4PP56NJkyZy+1q3bo3Hjx/LTCTy8vIwceJELF68GH5VZslv3ryBqampzOKl1ZGMm+fPn8Ocx0OcrTaaGjeFlhozNqCnB1sxkJrC+JzcuXNHoeaoaoK+c+fOwa1JEziGhDAZbysJnTMGjlHZyLvRGn9G2OPUpgzYDZ8G7yvPcWDQAZjpmKGxUWP4+S7BbUcVNE5Lk9HQ3Lt3D7q6ulJ/sOqYmJjI9OejR4/QxsICsLFBqq4KLgrS8KN2d1xIviAVaGJjY+tkcgIgE7H07NkztGjBaEKRmgr8/jvOtxJgpNtIJjEeADg5wSJXhDKdfIXZiVNjYmAhJhg2bVXzibt2Bfr2RW+Rnow2+sqVK+jUqZOMI7Ai2rdvj3OV+bFqIiIiok4T79LSUuzbtw9/rluHG0IhXPz9GdNYHWgwgSYrKwt37txBSkoK1NXV62wu0NLSqpOGRtE6If8LPHv2DHv27IGRnh7jdT50KGBiwjiw7drFLCqWlwcMGwasXw8EBgKXLzORLjo64MyaBevy8nov/Z6UmgpLAEMFAhw7dkxpvpv4+Hh06tQJiYmJ8PT0hEAggKWlJXr27Klw/RWAmW0M6d0bNyMi4NSuHZM7RUUF8PMDTp4ELC1BRJh9ZTYKygqQPCcZr795DWuBNRbdXATtypeUCoAlP/yA58+ff7R1noKDg+EW+QpBLUzR+du1cM8DBLo60pmTMoKCgtA6Lw+XOpiijaX8zFlTz0gq0FhZWSEtLQ0PHz5EmzZtYG9vj5iYGERHR8PJyQndu3dHYGAg4yBMgJa1go+glhbKuvrCoqIciW8TERsbC3Nzc+kHRCLQlBeVwzYlm4nuAKDyzzEo+vxoAICeHuDggGlPOZjZYSYASLOaSurlFxRBYNMIufNyYWRkA76Y+fAZGBhIHR/1TZloLM3Kj3RVDQ0AQB0w1DKETqW2IiQkRBrO3LRpU2mCsaioKDR6/BgwNYXK6tUKHbRv3rwJF3NzmBUWYpRzGIorinE34S7Ojm0JbN2K1lZWiIqKQlZWFqKjo2FjYyOXqdXCwkLGj6Y2/xngnYZGKBTibWYmygwJ/Zz7yRYyMkJjNT6ogpCdnY309HSlGhrJTDytkS7athsBtG4NfPstsHYtfB19ITAXSAW9iooK3L17970FGon/jKJIVA8PD+Tk5Mj4Uhw9ehRmZma4c+cOduzYIY1ArM3cBDDaFW1tbTwPC4OtSITp8X/BxbDKMXp6sKsAMtKZdlZ1yK9KVTPOqVOnMMi1KdLM+Oh99ysUlRchoygD3977FXsXD4Qg+i2+2HYDnOvXgRUrgMrxJ6G/S3+cNs6CQ0Q4kpOTpT4+/v7+6Nmzp9IIXWNjE5msx48ePUIbIlD79vjqwlfguDWDa5oYHD0O4uLjIBaLERkZWaOPUVWqCjRhYWFMqHdaGuDrC3GbNvjJ7g2GNa2iLTEzQ4UGD8LSaISHh8vl8kmJjIS+ugoszeSXxpCjY0cMzOMhOz1b6sR969atOqVdmTBhAs6cOSO3pEN1mjRpgmXLltVYJi4uDq6urvjyyy9xXF0drrduARLBrg40mEAzcOBArFmzBi1btoSOjg6Cg4MxZ86cWiW3/v37w9bWtsY/Ozs7mWRT/3M8ecLY/q9dA0JCGJPML78wJhlHR8Y8U2nCg48PkJTECDvz5gEbN8ISQFIdFxYDGNtodnk5DPr3x5gXL6Ctra0wS+Uvv/yCdu3aoW3btrh06RKWLFmCU6dO4eXLl7h+/Tq2KXHC43A4+NnMDDoDBgCJicDt20x7nz5l/BsAzL02F+den8PRoUdhqGUILoeLzX02Y/uT7Uj1dEEpgFQA52/fRvPmzdGnVy8MGDCgxnDunJycDxJ8UlNTkZSUBO/UDJgOGgeOtjaSnM1hrMWt1ewU9OABWmVkYIdVOlpbtpbbr6XBh0Rn4+joiLS0NISHh8Pd3h72lcmxoqKi4OjoiKZNmyIyMhJZWVngAtCuon6vikaP3mhcCkTFRyEhIUEmJFiy9oyoRAj9CiEgCRtt2hSaqsxjv2TJEgBM5IA6AOjoAC4uUEvLQFtd5iVYXUPDF4sBY2PoaugCmpoyGhqJgKtryoR9a1fO3KtraFR1eFDlqkLD3AI8DuMrI1HLN23a9J2GJiwMjWJjcXTzVyhdthiupiZyzrFnz55Fv9JSXB/VBiE5r3B06FEs7bwUP6QfgLB7VxgeOwYnJyc8fvxYzn9Ggrm5uZyGpjaBRqKhSUxMBIcIsXp5GNVsVPVCsBGqAmpASGgIxGIxTE1NFdYn6bs7Zrn4wrnyXTdzJnDjBvoKHQAzSM0Kd+7cgYaGRo0+PgAj0CjybahJu6OhoYHmzZvLOAafP38ew4cPR/v27cHn86XO2XURaADG7PQ8KAh2IhGizNSwe0CVQAQ9PdiWCZGXkQehUIigoCC0aiWvUZA42hIR7t69C5u3objqBGQVZ6HxX41ht9EOhlqGmDhnPxAdDaSnA20V+42Y6piiuKU7jJ+/gKGhoVTrJxFoFHH0KODr64C3b9+irKwMRUVFeP78OdoEB+NJKys8SnqEcSNXghMRAY8mHigrK8OrV6+QkZGhcK2tqjx+DOTmvlvFXSQS4cWLF2jm5gYMHgx4eGDjjBZIK81EW+sq18ThQGhnA9X0KNjY2Mj5PqUmJkKNrwJ7feWmUyk+Pmgfkw+eDk/6rqspiq4qdnZ2OHbsGCZPnizThtOnT2Pq1KnIzMyULs+gbGKYnZ2NHTt2YPW8ebBJTUUOgD4HDjDRa/WgwQSa5cuX48KFC0hKSkJsbCyOHz8OMzOzWk1PtUl1Eg4ePNgQzfzvsXYtE7o8dCgTulx98HM4jFpVMrsFmBnHpEnAwoXArFkwVldHZj2Wfi8uLkYFEfRnzoRKcjLaVGbYrM7q1auRnJyMdevWyaTsbtKkiTQvgSLy8/MhuHWLuSYrKyY8e9IkqaS94eEG7AnZg3uT7r2LCgHgae6JPo36YHHIeqjPnQuj4GBIXMqv37yJc+fOKcz+CjBOuY0aNYK7u/t7OxM/efIEzsbGCLXjYkDrcQAAatkSpuKSGgWa4uJihEdEoIVrUzwujVIo0GiqakLSKolA8zoiAs6zZsHh1SupJtLR0RH29vYoLy/Hy5cvwecA6raKX0iqrb3hUcxBbEKsjEAjFgMzZ5ogIiIBJAI0rCwA9XcGLw0t5v9MJAxjdlDR1GDGWqW93OU640ch0bpI1M7aACBJXqalBcnc18DAQBoibG/H5AXh6+szdVeOHYmGhqdb6WNjagEtLiNASHJ5yAg00dGwad4UozK3YXtrLqwjQmQEmoqKCpw/fRr9hRUYqn0Rx4Ydg56GHka6jYS9vj2+NQlG2YG9aNOmDR49eqRUoKmuoXn16pXSVP4SJBqauLg4mKupQKtJc1jwLWQLGRmBX1gOCICb925CX1//nZaqGtJcLaY8OOpXttHMDBg9Gr5nQpCrn4ugJ0EAgK1bt2Ly5Mk1msQAeRMJwPiJPHz4UKFZR4LE7AQwJpPw8HD07t0bHA4HPj4+Up+k+gg0YWFhMNFRR+9mgyFQF7zbqacHSwCl+aUICAiAhoaGwsgtyT2KiYlBYWEhfEMi0PjLn3Bv0j381ecvbOmzBaeGnwJfnc+8K42Na2yTo3cflKpx0NjKCq9fv0ZqaiqeP3+O7t27Kyx/4AAAWEBNTR0xMTF4+vQpTPX0YFVYiN+MIvCV11cQuLcGkpLgY+QOvjEf//zzD8zNzaFf+RwooqCAyW83d+47Dc2bN28gFovhvHs3hBEvMbU/B3NuzMXiTovlTdnOTWGTJYRnO08EBATI7EtOSUGpQAx7vToINF5e0CgXQUdPFa9fv0ZWVhbi4+Pfmb1qoXfv3li4cCGmTp2Ko0ePYvr06Rg1ahT27t0rzVcDQOnq9rOmT8e0adOw/dgx/GhsDL3IyHf+UfWgwQSarl274qeffsLhw4eRmZmJ7t27Y+7cuXUSWCSOY/8vefgQ2L+fEU6UvOxqw1hfHxk1ZIskIhnnwJzKl9zp0oc4a1OM1hoq0tDX6qmsly9frlBN7uXlhZCQEDmn3fLycpSWlkJABPTrJ3ecf5Q/vvf/HkeHHoWjgfzHZXGnxdgfth9Jv84CvLyw/O1bjK5y/gwlWXuvXLkCsVgMIsKWLVuU9kVNBAcHw01cjkctjOBqwszQjT07wLGiAi9evVB63LNnz2DE40Ho7YxGBo1gqiM/C+ep8CDRHTk4OODNmzdIS0+HCwDD21fx9OlTFBcXw9raGmpqajA2NkZYWBiMAGjaKpnhOTnBqYKQnJiIxMREqVCQkgIIheZITGSEXL6LrMpbk88IFOrqGkClSKKmVWXs/fwzjCvzx0g0XhINjTbACD4AoKkJSV5WXV1dqfZM14qJ9OEbME7MEmdWiYZG3YB53jUsrCEAI9BINDRNmjRBeno63rx5g5ziYpS5maGddTvwv/0J3aMTcf/ePaljcODt29AsL0fxAHd0adwb3lbMbE6Vq4qrY69Cs99glGWkoLWpKYKCguSS6kmwsLCQamiEQiEiIyOV5lZJSACysxkNTVFRESJevYKdSASjNgo0HkZGUMnKhpqBGh48eqBUOwMAo0eNgiWHgxIHa1mTx+zZ0PnnNMxMdRESFoLnz5/jwoULdVpSRpHJKSgoCAKBoEZBpHXr1ggKYoSnq1evolWrVtDT0wMAdOjQQSrQRERE1FmgiUlJgZquKjrbVesnHR2YVF7v1q1bMWjQIIXrHUm0aKGhobDja6JYwIPX6B+gpqKG/i79McFjQr2SuXaw64iHtqpozOMhIiICV69ehZeXFwwNDRWWf/EC0NPjwtCwEd68eYNHDx+ijVAI+ulHXE28hT6N+gBGRoCREToWG0OoJsSyZcvQq1cvVFQAx44BilICSeaRZWXvBJoHDx6gZaNGUNmxA4PHqiGdW4zIbyPh5+sndzzXyQltSgxh7mEOf39/AExQhlgsxqv0dGSZCWGnZ1d7h/B4KPFyhwlK8fr1azx79gx2dnY1CmPVmTNnDnx8fLBkyRIkJSXh9u3buH37Nn799dcq/fgCISEh0rxCGRkZ+GbGDFw9exYS3VOHDRveaZTrSYMJNCkpKXj8+DGePHmCWbNmwc7ODm3btsXMmTNrPfZzThT3wRw/zqgVPwBjAwNkKFmD5cmTJ2jWrBm8vLykEnxOXBwEAPYmncdZJxEcIx7i5MmTEAqF6NSpk3S2qqOjg4EDByqs19XVFYWFhXJ5CyRRPPrffiu1Xe98uhPfXPoGfz76E0OPD8Xm3pvRzUGxbdbNxA09HXtiWzBjzuJYWkK18kPoDiBbQYIxAPjrr78wa9YsLFy4EFu3bn2vMRX88CHaZudB3LevdJtOMy94l6sg7KXybMGPHz1CKwCnLfLRy6mXwjIcDkcq0FhaWuLOnTvQ56niYGugcVEFMjIyoKOjA3V1Pi5dAtTVLRASHARTAgT2Sj4aenrQ01RFVko6kpKSpEIB4ztqBiIxOAC0XWVNE9oCRgzhctXBLFAAiPWqTCrs7GCYydzHoqJSFBa+84Phtq+iAubxpGY0HZ13x6uaMAIo35CZJYvEjH+WNEmfMePkrGVuCz1iZm0SYUxXVxeWlpY4c/o0zLhc+Fvlor9zf3j5DEe+Axeq5eXSvBcn169Hfw4HcxrHYlKLdws6AoAKVwV+PVfhqhMHFvEvERoaqnRl6qoCjcSHwU6BmY+ISS9jYQHpiz747l04igj2HRWE+hoZAZmZ4Jvw8fzpc6X+MwAwbcgQvCWCinM1QcrdHRwnJ8xQsUVibCI2btyIcePGSVdPrwkTExO5tZkCAwPRsWPHGj/+rVu3RnBwMIRCIa5du4b2hoYo7tgOa8fYo8yiDA8ePEBhYSFev34Nd3d3EAF//AE0b87EMlSPIJZMWPMEZWhlWc2cxOWCqyuAqibj7KvofZOfDxgaMian52FhcM8vQMSPX4KrxpMrW1faWbfDabsSOGWm49WrVzWam0Qi4O1bxlquru6MyMhIPDpzBq2JEDG6JyrEFWhhXqnJcHeHRypAA5j3T9++g8DjMSlq/lSQSOX+fWZ+EBvLCDQ5OTlMqguhEE8GtMLbplY4M+IMnAyUfODd3OCRrgKhrRCvXr2SRsn26tUL5SIRspzUYaQlv9aXIjQ7d0ezCjFCX4YiJCSk1uUaqqOhoYG///4br169woULF9CmTRu0a9cOy6tk+E1NTUWLFi2gr68PLpcLExMTBO7cicuOjjg0ciROANBVEjZfFxpMoHn+/DmGDh0q9Z2Jj4/HiRMn0LfKx0EZ7DIJH4axsTEyFEThlJeXY9KkSejatSs2bNiAmTNnIj4+HjkJCdDncPAw8xl+WX4bYyuVN7Nnz0aHDh3w999/o6ysDIWFhUoldHV1dTg5Ocn53mRnZ0OLw4F65Uw4IjMCU89PxaXIS5h9ZTbmtpuLr1vXvFbT0KZDcTnqsvT3ht698QCAi44OshUsxhgTE4OAgABYeWjjmf4zpKam1is5mIQnjx7BQsBFS58qeX3c3OBdKEJCTLxSISno5k20rKjAKtzB7DazldYvMTmZmpqiqKgIViREq5FzINZnHkM+n4+OHYH+/YHERAs8ffIMBgAMrZTb4HXM9JGXW4zk5GRpBA2jxGI+ngRAzUH2eB0DPQCASKSOSndglBlUmZlaWUEzlZnZBz0pQZs2AF+XEYI0+lV5njkciFSZ2fTly1XW0KkUTnj6TJ0SjYpEQyNxBuaZW8JALDnkXfp4V1dXnDxyBC5E2KbyDP1c+qGZaTMcbKeNQSTCKj8/nP3rL+y9fBmDpo9DtDgLXe27yvWNQF2Akp5d4fzkEZKSkqTOx9WpmggtIiICzs7OMpldJW5ZleuooqwMSE9n8rw8vH8XAk2guWO76tUyzv3p6TA0M0RGSkaNAg0iI5GnrwVLCwWaofHjMTGsBCrqKti1axdat5Y3aSrC2toaiYmJ2Lx5M7p164asrCxcu3YNnTp1kikXFgacP8+sEAAwCzdyOBw8f/4c1/390TEgADsbFeDrk4k4f/EXcFW4OHz4MAQCAaysrLB+PWM5/7EyTZKnJ5O898IF4O5dICODEWhyDURoaixvyuMaGEotot4KfCYcHYGlSxmT09Mb1+FKYriO/LZOfaAMvjofka0d0Sqe0fpdvXpVqUCTnc2YcXv2BEpLXREUFITrjx7Bd/x4PMt5ieamzd+Zglq0gEVUGsiccO/lPRQXM1rq775jVl6QzMeOHGE8DIKDgS+/BJ4/B3R09GBoaIjjx4+jZWwsFpm+xIKOC2r+Pnp7wy46E69yw+Du7i6NRrt27RoqABQ2tavz91XVtwu6FnDxMoKZANRXoKmJwMBAPFCQ32iVmRlCtm+H1927sD90CEPy8mRzk9UXamByc3Pphx9+oC+++IKeP39ep2N4PB4tWbKkxr//NfLy8ggA5eXlfXBdp0eNIg99fbnty5cvp+bNm1NZWRnl5eXRiBEj6JdffqEzK1ZQczVVavpXUyIieutsRr9Nb0lisZi2b99OWlpaBOZbSCUlJUrPO3DgQFq7dq3MtuvXr5Mdl0sUFERERHOvzqUh/wwhsVhM+aX5dbqetMI0gh9o4Y2FzIbSUqKkJJrh6Ei/dOkiV/7HH3+kgS5ORAAddgM169aUvvnmG5kymZmZdOPGDRKJRHT16lXy8/OjO3fuSPcnJSURlwNa3VaVSipkrznZzIAAUEpKCr169YpcXV3pyy+/JKFQSEREjUxM6C8bAc24MKPG6+JW9unYsU8IAA3jgOjNG3roqkcASFfXkpo1IyorI9LUnEIAaLh6zY9oeBdPAkDa2tq0efNNGj2aqH17Ig8Pkt5DOnNG5piIgd0IAC1depi43BISCAR0/It+7wqEhJBYj2kTuG0JIJr53SYCQIV/b5Wpa5UGjwDQ1q3id+dLSiIiItGePQSA7B3t6cEDol49dxEAWrZ5KnNwbi71qTwmISFBWuf8+fMJAE0yMiCT301ILBYTEdGgIwPpZidXaguQLUBrfX1pVeBK6nuor9L+CXp0moQckLGREQGgoqIi+TJBQWRgYEBisZjWrFlDQ4cOle7bs4cIIAoMZP49coTIy4vo0CEiJycnAkA/2WsrPvmjR0SmptRxdkcCQN99953SdtLRo/TSUZe2Bm2V35eWRmJVVVLhM3314MEDmd0iEfNXnbi4OAJAGhoaZGJiQr169SI1NTVKTk6WlrlyhUhbm8jCgqhXLyJJ93Tq1IkmTpxIBmpqdHvqQPL604tEK5ZTaCMBNW7TmDw8PKhz585UUkIkEBDdvPnuvPHxRNOmMf3F/I0hADR6gpXia/f0JD09FQIgvddEzGN/5IikjljicrlkxdemBc7qMuXelzEnx1CCtaF03JaXlyssFx7O9FFCAhGXe4cAkAGHQ8LcXJpydgpNOTvlXeF9+4h8fKj1jtZ0KOwQTZ1K9NNPzP0ZN465ljZtqvYNM7bMzYmuXiXy8fEhAPTczIQ0l2nIvYvkEImowkCPuk1Vp4ePH9KKFStILBbTlp9+ou3aGtTnUJ+6d0hhIQVzQRqaPHJ1daXTp08rOh2FhREVFjK/8/Ly6v4NW7qU/gToibMz5QCU1Lx5nZuWWpBKflf8av1mNpiGprCwEI8fP8apU6dAREhISMDOnTvrdKxIJMK1a9eU/lV3dmKRxd7RETEFBTIahOjoaKxcuRLbtm2ThqkOGzYMhw8fRlZyMnRUuWhlwah/hX37wCOQMal06tRJJhxbmRMjwDhwhoaGYtasWXBzc0NiYiJCnjxBc7EYMDdHTkkOtgZvxZy2c8DhcBiHvTpgos2E/S4LXMZck7o6YGEBA319ZFfLfFpWVobdO3bg65hYLPbzxWCxC7zyonDg8AGpZqCkpAReXl7o0qULjIyMMHbsWISHh6NHjx7SPDcP7txBU3CQ+4UPNFRlr1n1i14w4gHhL8OxcOFCNG/eHPfu3cOvv/6KvLw8RKanI8a+GAs7LqzxuiRp0Q4eZHwpLAiAnR1ybRjTTGmpCr79FuDxAGNjRtuirlNzugJ1F2foqHJQVFSEixfNEBQE3LvHzJClVFs8T8+UqXvRogqoq2vgS2sOHE2qlHF0BEfivMdhsvBt2dUJ3woATYGeTF1cHtO+rKwqs8BKrR630u+CQDh+HLjpz4SDGtjaMeUEAkiyUlTNzyLRQFg5m6GF2bvVpH3tO2PNFCvcT01FXF4e5ty8iZMRpzC4iXKTrWer/kjW5WJQB8bRVKtaCK9YDDg7N0Z2djYyMjLw8uVLqUNwSgozszYzY/za7e2Zfu3UiQnck/hb6DVWkmfEwgJIT4e9FeOsXaOGJi0NyVoixQ6cJiaAjw+mt9bGtovbZLQYREwaERUVZhWRqkgy1JaWluLGjRu4cuUKXF1dpX398iXjt//338CrV0BhITBoEFBaytyDPXv2YKBIhF2uJRjhOgLcadPhGlsEdb0SqUni+nXmdldV+tjYANu3M5E7P/8MAIzziImnEgdTExOoiZlPkeReEzGW+FHSwDFziMVivC0oglYHtwbR6nuae+JpMz0YqqvDxsZGaWqQ9HTGx9jaGrC3b4NuuiZY3bcvbmUFY+eznbLjr1EjICoKXuZeCEoKQnAwE4HP5TKukjt3Ao8eAfv2VWmHJ6P9uXHj3fjM6+qKzg5d5N5FcnC54A4djmFhYmjZaGH+/PngcDiY4eSEzs6m7xZLrQva2hA4mqGstBzh4eFyGpq3b4EePQAPD6bNf//NrDahIEmxYq5exbcAPAMDoScWw0JBMsDqvEh/gf5H+sNynSWuxVyrtXyDCTS6urqYO3cuysvLMXr0aDx+/Fi6Rk1taGlp4c6dO0r/qjuq/pchIixevBgWFhbQ1tZGx44d8eKFcofShsDB1RUFQqE0RwIR4auvvsL48ePfrdQKRlgpKCjAhXv3oMHjoJkJk0DKfNZ8dHpTjvgTO+FcaSqaPHkyLl26VON5XV1dsX//fly+fBmampqYOnUqHty8CU8OBzA1xT/h/8DNxA3trBWo42shZ14ODDUNsePpDuk2J2tr+EdHY/HixThx4gSKi4tx88YN6JSWIqydCrpPXAb1TVvw10suioWF8L/KOMn5+/tDJBIhISEB//zzD+Lj43Hs2DHMmTMHs2bNAhHhzuHD8OQB7QZ/J9cW/Ykz0FwIXAo4h/Pnz8PPzw/nzp3D2rVrce7MGVhzANuho2DOV5w0TR5GYHPR1QbU1FBkz/i+lJWpSD8M1tZMxIymfs1JrbSd3aBRKcgGBlpi1y7A0FAaHc9QLVOpurHkw6qKklIxWmsQynlVstNUOgADgIZWZVphdVVM43HAFchmsRltqIdOvoZ4+RLgqTOrb0syQEujocAst6MJZsVkI5tK/w8OBzkqzIdJVVUVmzYBixYx68PwuVyotDaUybjcybYT7ibeg9DYEBAIEJ8bj5DUEAxwUR4NweVwEdvEDGP0SSaHiISffgKcnXVgY2ODFy9eSAUaIsYfpE8fZqFygHmRA8zH+/Dhd35Dpm28FJ+80gm4mT4zLmpMsJaWhreaFVJhvjqc/v2x8nEFTmW/W9tHJAK++opxWJ01C/j+e0bQkiAxm/F4PGkYuiQSjgiYPp35Gz0aEAiYNRzz85l0WJLQ6cmdO+FifjCzcryREYo7tUPrPCarb9++fXHqFCN4KJIvdHWBVasAPQ3Gpm3t0V7xtbu4QKVC1pwryb/5Ljnsuyg9y4GDFNdTTzzNPXHMOh9Benp4rCA7siTiPTaWEdIAYED7PFwuyMHkPzdi7Omx6GjbEb0b9X53kJMTkJKCToZeuBP3AM+fy0YeT5gAnDkDjBsHvH7NBBVqawNdujCLac8cPx7tOBwcbC5CdwfFEVfV4Q4YgP5RXDxOfPhu47NniLDSqJ9AA6CsrQc0K9d1qzpejx9nfKRsbICsLGDGDEZA27OHMcUqSHf0jkePmLj3x4+ZkHpTU2bAKDAtiUmM3NJc3E+8j2HHh6HVjlZw0HdA/HfxuDSm5u8RgIYzOf3666/Ut29fsrOzI09PT5o4cSJt3LiRbt++XeuxfD6/oZrxyfntt9/IysqKwsLCqLi4mH7++WeysLCggoICmXINaXKilBTyBGj/X38REdGBAwfI3NyccnJyZM6Xl5dHM2bMIADUzUiNrkReke4/1cuW0X+uW0d5eXl1UukmJycTALp//z5lZ2dTq1atCACdNzYmIqIvDn9Ba+6uee/LOh5+nOAHuht/l4iIKs6cocXa2jRy0CCyt7eniRMn0vBOnegrnip5r20ibbOoaxfqasujDr07EBHRkCFDaN68eXL15+fnk6GhIV29epUaC3RoUlMelQsVqJ3FYpqqoUJGetrUuHFj6WZvb29ysbWhL7igpJwE+eOqgUrVdocOROPamVN0M3fmOtdPowkaqqSldZQk3f7ll2cZM0V7hxrrLL12hfQ4TL16ekRVb1vjZu1pOgeyG4mo5M/1tMYKBI10gnounW0moMBv1stWPGsWTXbhUYtexygggAhWDyhWoMLox6uQ4+VGkweaEEDUuumX9Fib927n48cEgCztrAkgMsccAkA3Ym5Ii7jwuASAHj8mUlUl4nKJgk/EEqmqUsf1zenYi2PSsiKxiPRX69Ojt4+IiGjd/XXUbX+3GvuHiMh/9hf0zN2MiIgqKoiGDydq0YKxxGlrM8O+bdtRNHfuXNLR0aHQ0FA6eZLIxIQoM5OpIz6eSPKo5uQQ6esT6eow4z3g/hHlJzc1pZv//E5aDlp06FA0jRtHlJEhX0w0eTIt6wiKz41XXE9sLBFA7Vc6Sa+jZ08ie3siiWX/zz+JHByYfRKmTJlCW7ZsISKiu3fvUlKlOfDqVSJDQ6JqryV68YJIQ4MoOjyLjmhp0e39y8ljg4f0PSXev5/CzdXoQvgFqqhg6qhiuVVIU8OuBIBOvzqtuMC2bWTFY0xORESvXxPx+USnTxOdPFnVPMOM85CUkJpPWEdySnKItwAk1tRk7EpVePKEOWfnzkQ/DXhN8ye8ZbZP2UJPtNrT1airZPq7qfz7QiwmEggo1H8/6S4zo5Yt69aWpCRm7Bf9/heJvb3J+Ddjup9wv24HFxVRga4mrV9WxfTaujUtnOpER57XMDYV8Gb3H7RBwKWVK1cSEZFQSDR1KpGBAdHx4/Llc3PzqF+/PPrlFyUVisXvbmDr1rWef+SJkQQ/kPoydfryzJcUnv7uvtTlm9ngPjREROnp6eTv709r1qyhUaNG1VpeR0fn32jGJ8HOzo42bNgg/V1RUUFGRka0f/9+mXINKtAQ0Spzc7I3NqZFixYRADpebfRJBJq7d+8SAOpqA0otSJXuD4wLpG5T1UmspUV04gTRw4fMnyLjfBVEIhHR2bNENjYUtWgR9XFwoMIhQ6ikooQ0l2vSi7QXH3Rd40+PpwXXFzA/xGKiIUOIHBwool8/UlVRIW0ul1b1tKa196v48gQG0nNNNQJAgYGBxOPxKO7xY+mHPSODsfmfOEE0dqwfuTg7kwpAK1cNlW9AJXO87AkATZkyl4YMIerWjahVK8bXY5qlEh+KakheyA8fEv3Wlk/32w8gIqLT/htJyAH5ti2Vll23jhEGloz1rbnSjAzqChCgSqtWye76ZtRwitXXlTtEfPgw3bMCQTeOwE+iG84Cujprn2yhX36h3V4C6jXjOsXHE8HpMmWqq1DRvWcyxbK7+9A33flkZ0f0y6jx9NJQ793OhARaBNDIbxcTQOSNU/S1Oig0NVRaZIWnBX3rbkctWxItXkw0fz7RDpffSNSjO6ktVaPIrEiZ8404PoJ+CfiFRGIReW73VOxzUo3bJ9dTvgaXSCSiPXuIHB2J1q5l3rG2tkR//UXU1mw3ASB1Ho/y84Vkb0/099/K68zPJ7LlDSQAFJMdo7yghwdF7V5LRr8ZkaMjkZUVUY8ecjImlfXpSV/3BhWWFSqtqtTBlgZ9qUVERAsXEjVtyrRDQkUFkZ0d0bFjRDwekbExMY4fX31FdO+eTF19+hAtWqT4PP37Ex3sd5TI2Zkmnv6Sfjr307v3VH4+lfFUaM22cXTjBpGpKfPRq4mdnX6nP8y5Mvddhtu3aZ4WjwysDSg+nhEk589ndj17xtyniAiicSqe1NUStfuV1APHjY6U7eVKtGmTdFtKCpGPD1GrVkSd9Z4SAVSib04kFlO5dwf6hrOZ+u0bQT9d/UlxpV5elLl/O8EP9MuCagJPcjLzXlVA48ZEOQ6elLlxNakuVaXi8uI6X8ebMb3pbFsD5kduLpGaGrX6xYgeJD6o+cBq5CfHkQigjBhGkPjhByJnZ6LERMXl8/Ly6NatPNLSIgpVdHvv3SPS0mJu4tSpSs8rFotp8c3FpLFcg7YHb1c4of6oAo1QKKTVq1dTnz59aMiQIbRt2zYqLS2t/UAiGefMz5nc3FypxqIq3bt3p++//15m24cINNdjrtPrzNcy2+K/+YbUOBxq2tSbJk+eITcgJAKNqLycZmpqUP/hBjL7xWIxmfxuQtELvpb1WLt0qebGnD/PDNgxY4j09Jhjrl2j6zHXyXKt5Qc7720L2kZe273obR4zQyKhkOjAAaLJkykNoERLczKer0axObEyxxWOHEI2lZqL8ZXX8uq7rdSrF6MJsLQkatmSyMgog9EiqIDCUpS8cIlo18Z5jKOq1jyaPp1o+3YiS8urBIB+G1mL0FGJWmV7RCKiY66qdMRnDhER3Yy+ToVqHPp10LvZyN27bwkAbVv6ba31BppqUC/TcZSdLbt9xeD2dM/CXv6AK1folSGHBk97RQvWv6ZHdgI69a2s4zAtX07/uOnR0F/PkVhM9OUfR6mUy6HXF2UFjJwRA2hBRzV6+pToz/ED6K6Z2bud5eUk4oBmzvmbRo0i2jXgOD0xw7t7SUSHv/GlZ41syN6eqLiYKC2NKIjbiu7NWUz8lXwSiWUF6hsxN4i/kk8zLswgm/U2NQoAEmIzIqlQDVT27Al16UIkmW8EBRG9esUIAk+0O1AkQE/696dffiHy9q5VlqcLQ3+mXbagipq+6H36UN7alQQ/kL5xKaWnMw6gp07JalKKPZvTmBFqNT4vpQP70c9dQcfPFJOW1jvNTFUWLWI+QJLHt+yLwZTvbEu5GqDg20eJiCg6mhF43r6VP56I0bj4q/ah0kVLyfg3Y/IP95d5TyX0akt/9zKhb74hmj69xi4iIqKgHt/SlpZQHhSQnk4E0NBNo6lPH6Lx42V3JyQQUUUFZakJqM9ki9pPWA+GHhtKD0Z3YjrLy4tKbZzoMq8fzfZ+SPm5IkrrMpIIlVqcCxeIVFWpT4cIUvVTp1cZrxRXOmIEla9YRvAD/bkrXXbfwIHMuRITicrLpcETREQrhz+jUlUtOvVwL3ls86jXdSRcPUG56qDywnyiI0dI5OZG8JOdtNaVFxZq9HLjAjp1ikhXlygyUnlZyXdlwQIid3fmkmT46ivGQ/zuXTJAJjk7M895dWZcmEFW66xqnADX5Zspm3bwA5g/fz7y8vIwb948ZGdnw9/fH97e3rhx40atyXk6dOgAgEnAdeDAATx+/Fi6noSE/fv3N1RT/zUka4JIklBJ0NfXl+6rikAgkLtOpXWX5iOlMAUaqhoYtJ+xId+acEuanE5z4nSkHDyKUW/9oJZkjtOnC9C1K2OqzM4GVFULGDv3uXOYZ6yLdcNGyLWph1UP7HczxNe3biA87Bo8F/zF2Dzz85k1pRwcGO9DCbGxTHr2bduYrI6vXzPLNrRsiUfBW9HBtEOdr08ZbYzbYG7KXDRd3xQJ3yUwzoD9+wP9+0Nj0SL8GbQRnUvewoBrIHs9G7dg19s3CI+KRx97e0Rou0N3tx96jW6BtaFNpG4lOQHP8XSyAQ51tIKtlp3C+wQArb+Yjp5BW7Hm8l6otXcDp38/eIU8xrqjAtiM/UXpcVVZ0M4V3CxN5ORlwbRcC9fFTsjPzwefo4swGz4cVJ8jP59pmL1xEWwEAhg796q17hc+zeF3/yFUVPKRnw/G01UohEF5AaIFNnCrfrymJtRU+Jiqdwo2ZcUoUeMjS6wqex5NTahyBeDzc1FQkA9vr3SU6fCRUgSYVSmXr28KvVRN8AW50OGUIlnHSKaeHHMD5EYkoVmHfBhGJiLNQAAroZq0TKazEwyPPMe8pTmoqFCBRmocHAQxGJyshzaN26CwQHZ9Gi9DL8z1mouTr05hZYudEJaIkF9ac//oqxnjrosezM6exrNnTti8mRnSkiV2iqOS4aAWjpFm53EkcALOPYzCrnMmqLY0jhz2nDA8s7bBg/tFcHdXUsjVFeLotxAY66L/iBioq1tizhwmVJfHY/KQ3LkD9MopgtDYosbnRdzKG26nb2OeXwxWrrSGjQ1zHVXp1g3YsIF5HFNjS1H48C5+/NYR3jeLMbLfNOTfdsH2Iw4YPJhxcVI0tJqZpkOo9RAbeOOhIdJAY35jmXaVDBuJNksW4veYXPzxO1dhHVXRFsUi1tgQVEbIL1NQWF0dxeamSL2WjIzofKxfL9suXV0g/0ogio21EUQtcfJkPrp0kX0VvS8t9Fpgc+/naLr4NcL/DMCFg3no0zIeS1/0AM0eBI3gS8i/dQv46y9mfb0+faDj8RAWpS1hwbNQ/Gw6OQEJbyEwNoY6Pwn5+ZX+P1lZjOevQABs3gxs3cpsf/0a0NJC37K9OGM2CiGZEWhj2KZO7xQJ/FZdEGcqgNrBbbA6cRVZ/bvDWCUNGiKNetUDAEHtXeB55QYWxP+EP/5gfNKVVSEZF7NmMSv3TJgA/PYbcPo0cPpoKY7HXYLavl0obdQMQgGQmpqP8+cBAwNmnG7ckYmzr8/gXNg5BIwLgIWGkj6tcq6aaDCn4Lt372Lbtm3o2LEjBg4ciK1bt2L27NlYuLDmyI+qzJw5E3PmzEFGRgZUVFRk/j4HJAmkqqd3zsnJUZgNWSgUYsmSJZg/fz7mz5+vNJrr4puL8PrbC90OdMO3l79FN/tu6GLXBX0P90V2CZN/Zt42E/xjbokTGI6l931xYOI1NLIXwtcXcHdnBtpmvwwU//ArfvXiouSlfH6g9lbtcSfhLrwfTcGA7L9wuqMx8OYN4y3YpQswdiwj2ADMg9mxI+Kb9oLoC8Ypk5xdEDV+CcDlIjQtFB6mHu/XkVVw0HfAlj5M5t/XmW+QkFKKxETGx2zoyifY9GInRjeTX4k1v1wD6wV3sch5KTp0KcGorI1402kqJt6dAquHJ5gvyebN0Js5Cvc7q+BW3G+oaTmshzcMEWxui5fLZ4Dz04/AoEGwvfI38gZo4sjOWlazraRMzxGdkYe8sjzY5gGhGUzOEX1NfbwyAhzTK53f37yBTqtWeA5AyK3doTqgWVs4FaUzYQgAk0e9SxfoF+QjQaQgskZPD7plQLtta2C1chO0ywl5wmrOx1paUCsB1LSYiLfSPGac5Ylky5XyDKFXCmjxSyEoLUQmV1tGEMjU0YBKair69gUM1TOQp86RidxIMnSCCMDg1Eop49gxiDt1QYgoAg6a8inwAaADbxay193CzP5tMHQos7RZTXA4HCQ6mSD75gM0aQJUBv+84/JllLXywIOvxiBYYImd/c9BwULUcmi+icJbU2tcvFhDIVdXcJ4/B6fUAO5tGc/J0QOLsbrJPjjYibFrF/D99wSV7DyU6+vWUBHAtbWFTZ4qMoszMH684jLNmjHf3V9/Bfo7vkCxigYucqPRfhvTyLStW7F/P+PwrAzO2TPIcmyNzc9D0dmuC1S4su9fwx4DYZEPaJcFKVsuiVEQrVsHDBkC/eRIxGlq1yj4pBtboqkoDYGBTFSZlLIypp6DB/GiszMs9a0xcybjkNoQ+VjdTN3wPOslEivMMPD4WNis+RrNLv/GfG1Pn2Yi1Tw8GO/d4mJg1ChwzENRFu+p/Px2dsxkr1QPPEEus+3KlXde5fPnvxNmAMDfHygrg83T09iRPwLBCeHSTOV1hctVQaC3GcwW/gbExSG8b2tYCazeKxoso70nTIMioKlShiFD6naMujqzXF9yMuDqytyyifzjiC02RSivFdavZ8qpqQFff804s98JTUS77V3x+/3fsbrralgILOTqDQgIkH4fmbXnaqFG3VM96Ny5s8Ltbdq0qXMdhoaG9ObNm4Zq0ifBzs6ONm7cKP1dUVFBxsbG7+1Dk5CbQPADLb21lDY+3EiD/xlM8bnxVFhWSI03N6YpZ6dQVl4x4UcTsp+rTScGN6ETrhwigMp0jenJ8NWUvXYX3Rr7B0XaeNE15x7Em29KQ4fJq8ljsmMIfiD4gcJSw2jCgCqmp82biSZOZLz1Ro6UbtdDNvn5EaXl5lKLH38mLFKhf66/Ipv1NnQ95voH9+eKFYyJoMu+LsRfasy0r/WfZDlkA3EXqRN3XG/6e4esbSAlhcjVlah3b6J7D8pJZb4+9Zxyj8qLK4h++41JJOLgQNSzJ+39fSx13tuZvvtOsW+DhE6diLxWjGVy4zx8SLR0Ka0//TP13TeYNDSU2I+rMajPd0QAvXl1jwggfWRTURFRhaiC3KeDytU0iLKyiL5jyv3cxZLW1MGnuuNPmyjCWIfo3DlmQ+W9ue+oQZOMfpDxsyAiKkrMkjEr5gkE9MuEaurzf/6h+4b6NHM/Y5/548gsIoCOHJLt61ffrqezzqD06Od0s7szrbHvINMXpxtb0y7fXkREFDNjGu1sxpPmsCAiGrRsJ232smXaYmzM6LgDAkjzxyb03dazcteamcn4baxbx/iPTJjAOPjWZh7atKgXxejpk5+fgp0DBlDg1/0Yn4fRpkSenjVXRkRUVkYVqlyasGgE2dsrHzeUkkJiVVVqPLoRnXp6mtm2ZQsRQFd+DyMPDyId5BMB1H2DV83nDAqiTA0V6jPnTM3lKgkcs438jS1p3D9fERHR+olN6am9M/XqVcNBYjFR8+Yk3LWXuLMak9+Jowpzjdy31aClXjXk1dmwgbHrqqoSAeQxykmZ6wgREV33HkqbW1XxRUtMZJzdrl+XjtN56/vSisAVdOIEUy2PRzR2LOPI/L5Icl4NHZtLEyZU2VFayjyHVa+70qbbYWdHUvHaTdHRSiq9e5dE5haEGW50OOgiU5fkeXv4kLE16uoStW3LmGR++okZE66upKUlJPxkTPcS7impXDlTTk+ifUuHEsXH09agrdTrYE03WjmbH2yhBG0Nuvp9Le4GpDgPTdTFCCpat43EVlZ0uN8hEgiYS582jej000BCs4PUyVdMrTf2IZUhEygujnl4xGJpCis5YmOJIiJq/2Y2mIYmPT0dQUFBcqshSxa1qws8Hg/29nVYSOs/zMyZM/HHH3/gxYsXKCkpweLFi6GmpoZBg5SHGhIRLry5gGHHh+GbS9/g54CfMensJPx+73fYbLBBY6PGWNhpIWa1mYWTw0/CRtcG2jxt7BmwBydencDvRx4BOuk4/M0NDDn5Es4BIeAvUUfusjnwzLoG/ctH0KL4Pkwm9cPlFbbwNRuEmGh5rZe9vj0ujb6EZ9OfoZlpMwR5WyPPy5WJpf36a2D3biZOT0UFF0YfhpZWKkzWdcGyewtgu6QTIsqvQ7XCEMturURiXiK8zJWEsyohMRHo3ZvJDMrhMH+//srkAmmp2wcF4gw4lA2G89i/wPPZiHNjTuJg70v4YQ4XGzcCFRWMtWXsWGaWcO4c0M5bDaNbfgH34WehpqnKxOkGBwPR0cg5fQSzhOewxHcJFi1iFj1XFKmelsZkPB3ZthMCYgKY1eQWLsTJnLsY4NoLEycCa9bUfn0hYELiDWb/jDhDFYgE+oiIYNYeemlggmQ7Z2DHDuDAAfyz6Suc8fGGgkXQ5ciLaYRXpmC0TlVWs20bXYogtY5ITJQtny2S1wSkl+vJ/C7X1kfbrBy0TGTyE3FzcpGrro78QtlXRpbIGAYlgLFjM/hee4NSbQGqLisWqmGEpsWM5ki9NAd5PB4qU/8AAGJf6eG3AcZMOt4NG4CNG1HQvhVKdF6h4JV81tgFC5i8Ht99x5hMtm1jNPmHDtXcRxrenWCfm4Pe7aupCYiAu3fhb1GMOd5z8JdNGigiggk1rYnwcFTwVGDW1gEpKVCu3TMzQ4jnJLw6HAnfYZUZsisX7G3JC4NnyC6cxQAUctUh1qtZQ0NW1jAsFcHePqHmtlXiJg5DmEU5kgMrY/ibdIX92xh8O1X5ivXw9weSkvCsgy24+gl4+k9vhcUCLWzQp+Cq4joOHmS0EEePSpd1SdM3rnEsv9ZuhWZpxaDTp5nkM9bWgI8PU1clD3ULYMm3xKBBjIlj/Hgmf0779pLs2PXHRNsELnrNcDriFBYtqrJDXR1Yv54xD0nQ14eYxAhNfwY3Q0+ZEHkZXF3BSUuFRZYApSrpTHpgQ0Pm5dSmDaCqysSE373LLNj78CGwciWweDHWn3gEqFTAy7z2Va6r42bWHMedSrF8vw38NsTBhm9X7zoAICXcBWeceeiaUfPC0nKUlgJffQXHIR7QOrYXnD59MOr0CCxbxryTN20Cdkf8DgwZi2Y/zUKyKAwDNdeif38Onj5lcvTY2zNDMDSUCRU/fRqYN4/ZXst6sQx1ldpqY8GCBdS3b1+ytrampk2b0qhRo2jNmjXk6upa5zqWLFlC69ata6gmfRLEYjEtXLiQTE1NSVNTk3x8fCgsLEyunERDk5ObQ3OvziXDNYY0+/JsGnNyDH11/iuadWmWVFuy+s5qhecSiUVk8rsJ4TtbwqgvZPbNvDCTOu3pJHUyzMvLo5zcHDL/w5y2+l8jvcpglFmzmNDLfVWCXDp1YjKI9jnQn9bdl70fublETZowEne/WdcJfiC337pQ218XUHJ6Cf2w/TzBD+S8yVmuvdevM5Es48czUUZ37zITsb/+YqIzJJOY9evf/X///sr/CxIIsxzo9x2xsn0gItq2jahRI0ZxNH06kZOTbPTH+dfnyXqdNYnEIhkn0wXXF1DXfV2lv//6i3GoLCuTbff27Uz23dSCVFJZokLJ+cn0OvM1qS1Vo/jceKmTZUwNwS5ERPo+R2htf1sigBaNs6bevYn++IPZJ5g4ivaO7sNcbIsW9PWFmdRnww/Utm3NdYrFRPp28fTVF4xWjubOJerWjcrn/0zhRiAn9zS6fFn2mJAQktHQZOgJaMCAXJkyb88z0R1FfA0mxASgPJ46dasWJX1iymWK0udK61rabjitX8/sy88n6taqH7Pv/n1KHtiNlnY0pIUL3x1v1Oo6Wa1pJFPnvYR7pLvMjKord5OSmJDi6s6wBw4QWVsT1ZDUmv66cJve6nCp4ka1NBKVccqu6xvR5cjLZL3OmuJmjiVycZFNf1udv/+m502MaP2D9dSuHTNOldHFu4iOt20kTYtAenpMtN7cuRQJRyKAwjVMqc/+AUrrEIuJPD1EVMHh0LJl04kuXpRm0JbxLq6Kjw9NHaRNLfs9ISIijvkjemLKpfIVKxWXF4kYddcff9CGBxuo264vSF2d6MkT2Vl4cjKRcedpVKShxsRWV23k/PlM6mB/f2bb3buUY21MdvO/oBk1JNKe0/bSuzHZqBHzUPv6yoxTpz+dKCA6QO7YadOIvvhCQaV1pNHElWQ3b1CdykZkRJD6MnWa+0s5ffml8nL5jVvSpPbetOrOKka73bOn4oKSMC4rKyKRiLY82EkY34VSUup/HTdibpD+MkvS1RMThg4n12mrZbShdaG0lKhxm0Ry+QYkVldXHt5UiVRDc/MmUbt2jPY7Kkpped+9vtLv2plXZ6iwkGjOHKYLOBzmHa6qyqRTcHR8F4l44wbRw4f/gbDtkSNH1vm4Dh06EI/HI0dHR/Lx8ZH5+19DItD02NGD7DbY0ZtMeVNbVnGW4rwoVVh0YxHBD9S/mm0ivzSfDNcYkn+Uv/R8AS8DyHCNIeXml5OGBtHMmcyAMTdnBlNoqDTdBQFE/N5M+KAkkkQkYsZs8+ZMSvj1D9ZT/yP9Zc4b8jqT4Afqe/ALOnyYCYDq2ZN5R0nqbdqUyMyMsV4BjGb6m2+YIAKJl3xGBpNeWyhkhAXJscqeFUmopa+vvFd+ubCcLNZakNFvRqS2VI067elEsy7NIu0V2tIcN0TMd6FpU+b9U5Xu3d8JHj67fei7y99Rsy3NaM6VOdIyI0cSff11jbeKNJqfJ+d1zejkmVXUeY8vbdjAmLmIiHR6raThvw1gLvLECep7qC/NPbGZ9PVrMGdUXjc4YtJarkW5A3szxx88SLHZMaTix6Vu3YVy4cc3bhClaqpJOzXe1JC6dKlmUrhWyAgxBtrScoVcNao+BdowNkjmo7Oq72T6tjIwKySEiN/7e2bfihWU0rkVLfzCUiqo5OQQwTSEdFfpydS588lOaretC6mqymr8V64kheYSyXe4JvPcwhXZdLwJqHjxr7I71qwhcd++pLFcg15lvKIvDn9Bfwb+wUjemprKX+gTJ9K+vlZ0IPQAzZrFTAwUkZVFpKJCNPXE9/Swe1OmL377jQkT7t1bKtDEaupTj+1jlLb/9m3m0AR1vmwUIhSEw6alEYnFJNbToxbTQHyztMpxIqSek3VIqK0lE10j5ehR5mEsLqZxp8bR4puLafJkosmTZQWaUaOImk7YQktneRDp6LwLCV+yhMjGhujlS5lqVwSuoK6bx5KFhXLTYFfnOCKAEv7ZIbujooLo6lUSP3xIGss1KCIjQu7Y9HTmHVFTNI4yCguJVJ1uktlvSpZlqMbhsMPU6u9WdPkykwNIGS97zqadTd3pzGQf5h4tWKC4oFBI1KEDE/ZGzFIx/JFf1ShLKyM1q4iwkEebDr0ht80eZN39FFXJIFInhg0j8mopIv5KPuV80a3Wl1peXh7l/fMPI4H88gvJ2berYb3Omm7H3aYyoeys8f595pETChmzcmkp896rulJJXdw0PsjkFBgYiOvXr4OqeUcZGxujR48emDt3Lo4cOVLn+rp164b58+dj3Lhx6Nq1q8zf/yoGWgYInhqMRobyCxAaaBpATaXm1PeLO/lB7cVETO8wXGY7X52PhR0XYur5qSiuYBw7d4fsxuhmo6HLV8PQoUBgIHDzJuPINWIEcOoUo+6TUBDwLYgIV6MZ1fKOHYwD5oMHTJRGaFoomps0lzlvIytmJWV1sREmTGD86Pz9GV/iQ4eYN3B4OJNWPi+P8fl7+5ZRR/btyziNAcxCxdraTCRDWRljTiovZ8xRijAze3c91dcfVFNRw7a+29Deuj3uTrqLUW6jIBQLcXToUbS3eZe9VFUVWLYMWLECKGGytSMuDrh1691SAss6L8Pe0L3wMPPA6m6rpcfOm8dY5CTZRasjFAKl+ToooyLEmfBgoGWIHj2YNldUAKVJznip8Za52CFDEJsbi1ZO9sjJATIzFdcJMNpsRwcOrHWtEew3jcnGOWYMUovSYKJjCjtbFRkTEMBEvSXy3y0BQCpcVC6SLiUxWxvRfH0kOhm/K8djzJRi8btykSWyWYi1jPhSq1dSEqChrYugdraAujq4eQUgIy0EBTH3/uVLwFjLDHlluSgVvjODvMp8BQ+rxnBwYPpewvHj1ZZ0qITLBX7/ndHaV78OCXcD9HHXkY/yq+8WPUVuLrB7Nwp7dUGpsBTWAmt4mHrgafYLwM+PCRVaulS+MpEIuHoVd2wBYy1jtGzJWDEVERzM+Ig6mppiw/TmzAD58Ucm7erly3AC01kqXDEyk3QUVwJmTPbrB5BkhemUFMb826wZcPLk/7F31uFx1Pkff60l2Wx2454maZJ66q6UCqXFKQ5FD+e4g1/hkOOAwh1W7I7jcCtuRUuBUqrUqSVpm8bds7tJdjdr8/vjm93Yxuql83qePElmx2d2vu/5aOtFqaoSlVh//hkaG8mJ0WCtjeD//g+mT1OhmT2TDdfNFr0cnnxSfBkbGuCHH0RZ3scfB62W7WXbGRc3jnvvha+/bg28LioSm3vgpkG8lWIUKS0LFogv7/PPi46UHSKqjTYjg5JCsFh8e/KsVvglO4kz7owhf+LA9h+q1TB3LnXD07A5bcQbOkZ0i7YECxa08071ml9/hQTVaCosJVQ2VvY4/66KXYyOGc3UqeJcdHTneshUj2ROeSnnvbleuND++lffM6pUIs2tJSQhuy6bBO0getEVoBM/rwhEVzcF5YBV5BoPsGjBIL7+uvfL794tLt8PK5SMiR3DqmtniIfab791vZAn+HvpUvEFbFMdvCPNzmZKzCWkhqbip2rfKX3yZPGVU6mEd87fX4QcdOhU0jM9CLYuue+++ySFQiEpFAppxowZUnV1tbRw4UJp1KhR0tNPP32oqz1l8KjN/PzDK6y3fr14A/Rlbne73dK418ZJD/7yoPTt7m+lqMejvJVIHY72b0tvvSVezqKihFV8wwbxYnHL8rulG7+5Udq6VbywtnVfjHl1jPRZZufykYo/D5IueWClNHKk+L+nwlsnEm63eNv39Ny87jpJuuKK3i17/vmSdNFFvi0qNTWSROx2KfLpKOnBXx6UbvrmJsnlEi82a9ZIEtG7Jf2/9JLb7Zbcbrek+6dOyqzKlOLju6/E+tBDIjBy9ruzpTd2vOGd/tW+r6RRr4ySnn++szn+1Vclaebls6Vv/nWtJIFUEh0hJSa2vw+fe06S/jV5jmQKDhA3Bkh2P42kULSvXzJnlqudteB/f3nIa8V57TVJGnDNM9KPc1Mk6W9/k2rTEqQH7h4lDR4sir+9+aYkzTzdKakeVbWrI7TggwXSf7b8R7rjjtYXxLw8YYqure36XMyeLeI4O1JbK5a98NFxklOjbn3tW7RIkubNk3YUbpbCnhJ1mT7e+7E07rWW8q6bN4uSwB3NCuvWSVJEhBT5RJi0o2yHlJkpvhu+PD9PPCHeet/6/S1p1rttGqvW14tztmCBJK1cKZ155VUSZ/yfz+PKz5ckjUaSKiokae9r/5RuWBTSWq/Gbhc+Y09F9o8/FuudM0cypw+Q+r/QX7rxRkny9xf32TMbnxFW1bVrhR9VqxXzR0RIUksyg9lmlhSPKKQys2hiee21JumSS0yS2y08muefL9yvPILU0NwgKlQ+8IDwRfngxm9ulP7+y9+lG24Q7qGOrFoldmHcq+N9Pk8kSZJ2le+Sgp8I9vmZJIkmlunpXX7cJbffLn4G/HuA9H329z3Of9YHZ0n/3vxvSZJE4b333/c9341jRanh7LiAPu3P0P8OlW597nvptNP6tJgkSZJ06aWSNOehpVLok6GSeolaOphvk9Tq1mrXPfHnP0vS9deLvxf/uFi66ZubhLk6IkKUb/aBaeVKyZSY2N6U4oOVB1dKT294WvJ/zL9TbaneclQtNO+99x7Lly+nurqalJQUzjjjDCoqKjjttNN48skn+aCnKL0W4uI6p2r5wtOD5I9GWhqcfbawYjQ19Tw/iJFjxQqRBTh9ukjL9tVDUqFQ8L+z/scbv7/BouWLeHL2kyQGi/OoVrdvpTF3rngLq6oSpWWmthgunBnn8mnm51z353L+9rfWHkFOt5PMqkxGRI+gI3Ff7ufTf83jyivF/ydJ1j0g3goee0y8vG7ZInrK9LbywGuviV46n33W+bOSEgjyD6LR3kCtpZYwbRhKpTjvS5YAdWk02BuobKqk3lZPk6OJfoZ+DBoE+/d3vc01a8TLdr/gfpSYS7zTKxoriNZFM3KkePNqS10dFMTGsHeasArqrXaMxvaWl6IiIFiHwWSDcePISTKwZsl1xMZCYWHrfOWVSnYn6XEEihswZZgIovzyS3E/hRt01AYpoboaTUMTToOOSy8V1rodO2DEcBVRuigqGlv7LO2v2c+QiCGccUarxfDdd8W9FxbW9bn4739FYOE337Sf/u67MG4chIwYjjlMJ1a6fLkwPbzxBkWWcu/3YljUMLKqs3BLbtGBz24XB7NtW+sKv/wS9znnUNNcT2RgJIMGie+Sr+u0c6eI+4wOim5vAQgJEdaRb76BefMIP88fHDqfPXE++EA0BYyOhgHX/R8fDLKxv6ZlYxpNa8fMW25pNWGtWkXJ2IHE6mN59VVhETvtNJiRNIP1hetxT58mglKNRhFVXVEhIm2BnRU7idPHeXuTPfywOPzzzhPn+P77RTBtuDacfdX7YOFCYUJq02C0LUabkZCAEO68E5Yt62zV2LZNNMmMM8RSZPId8FzaUEqCIcHnZyDujf37hUW1t0iSMEydeSaMjx/PttJtPS6TUZXhTan2NCn1td6vskUE66sjHeJe6gUut4ucuhyuPHMgGzaIZITe0tAgbqVHzruBmckzeWL2E6Ql+zNyJN2XFWjDihVeQxET4iewrWybSAa54QZxA65dS6dc9TfeEBaoHkwpZ35wJveuupf+of1RKo5YLlInDnnNRqOR8847j/DwcJ555hl27drF119/zQsvvMBHH33EK6+80qv11NfX8/bbb/PWW291+2MymQ51V09odu4U1ufrrhNda6+9VgS9e2p5lJaKEgge0XPhhZCUJB4A/fuLh0GX0fbAuLhxFP61kE03bGLh0K6LCiQkiIZjeXnC3OfhjYdOw/T7bGpH/IO//a11+qbiTRj8DaSFpXVa1z/+Ifob3nBDH0/GCcKCBaKh3KRJQtwNHty75SIjheX13ntbXVYe8vIgMToIq9NKtaWa8EDRqfmOO0RJHxyB9DP0I7s2m7z6PEIDQtH76xk0qOsMmu3bhShYsAASDYntBoPKpkpi9bGMHCkGkLq61uVqayHQT4vVYaUpyJ+MCcKP1/YrVlgI7ogQ8c/cudz46BjKz5xKcnL7QaOyEm7/23Q23XEeAEHRQtAsXChKcUQG66gOBKqr8Wuw4A42cM01YiB59VUh6GL1sV5BY3VYya/PZ3DEYE4/XezHtm1CLN58c/fnf9Ag4YW5/PJWUWk2w7PPCi/PwIhBfHFOqviyXXWVMKcnJFBsKqafQXQbHxg+ELvLTqGxUIiF6dPFF7ClAzhOJyxfjnnBLCQkIgIjUKnEy0Xb7C0Pv/8udFG0LprKpg4j1NChXrXvVDQSFRLExo2d19HW1eav9mda4jSRbefhn/8UxSa3bxcHu0dkpu2cPZTIwEgUitbv9OiY0dhddjKrMsUEPz+hEtu8dXjcTR7CwkQzzsRE+OkncSoUCgXDooaRWZ3Z/UWhVdCMGAGnny7W1fEcjRsHU/tNZW2h74dZibmkW0ETEiIu1Xff9bg7XnJyxIvG6afDxPiJbCntPrOtobmBQlMh6VHpgHiR8PXsrauD6oYATF98x3/GuihvKO/V/hSaCnFLbiYOSmbiRNHEsre8/ba4naaODeHLS79k8ZTFgPAE/txzk2pyc8VLzMyZ4v/x8ePZW7UXq8MqOoxed51QO4MHizc8q1U8mNasEZ8B5mYz5350LikvprD4p8UUm4opMZdw78/3ereTGtpFzMAR4pAFTdtidxEREej1esLDxUN69uzZHPQUYOuB6OholixZwmOPPdbtT2wX6v9kJylJuB5LS8WbtL+/eGNIShIpqQkJ8Pnn4ovjeUN9/HExKC1fLh4EQV273gHxEPRVtKgjL78sRFJbgoJgxeInaOz/MSvyWzv9fn3ga84aeJZPtX3TTeKNobu36RMZhUI8TPLyRGxGX7jyShH/8/LL7ad/+SXMnCIuVLG5mDCtODmzZol5t24Vg2l2bTbrCtcxKUGkLA8e3LWg+e9/hWiMj4fE4ESKzK2CpqKxghhdDGFhIgO2ZYwDRMxUcKAWq9PKHe9fzs+Lz0Olai96srNBG9XiD7/2WqwOK1q1tp2gkSTxgh+sDaJJLd7cNGEh3nWsXQsxYToqtW4oL8ff0ozbYKB/f5Ge73KJB2hMUIz3ob+/Zj8GfwNx+jiCgsRgM2GCGKwWLOj5/F90kcgWvvFG8XJ52mmiwPX558OA8AG8PNYtagE8/zyeqmFFpiKvhcZP5ceAsAGtA/UlbWLT1q0TKtffn7LJ6eg0OrQa0Vl8zBghLttiNotB02OhqbXU4nQ7fe53o72RISlBvPuuMAp5WLNG3IfnnNM6bV7qPH7MbRPsFhwsrE3bt4tW2cOHg93O/oQAIgIj2m1Ho9Iwpd8U1hWu6/IcdhQ0IAbLl16CKW3qPA6NGCosND1gtBkJ1Ypq8Z4X/baUlAixNLv/bNYUrMHldnVaR0+CBoTufOkleqzw7OGHH8T9odMJQbO1dGuneNC2HKw7SEhAiLcj+vTp4vqWd9ArBQUiDiT4wrOICksg35jfeWU+yK7NJjU0FbVSzcKFIlapN+Tmwl/+Av/3f50/GzfOt9DuyObN4j71jCVJwUmEBISwq2KXeCA+/LC4UH//u1CkQ4aIt5FLLoHYWOwuO39Z+RfqrHU8NOMh9lbtZdKbkzj7w7P5NPNTFo1YBECkLrLrnTgCHLKgcTgcrFu3zms5aStwVCoV1o6vqF1QUFBAfn5+jz/7u7O7/wFQKMQ98uqrYpD45z+FxUSvFzFZW7aIQfaNN0T9Ba326O/TqlVCtc+fkMZ757/HFV9cwV9++Avz3p/Hs5ue5aIhFx39nThOKJVC3PnocN8tKpUodfO//7W6cCRJWGEuPEc8LQqNhV5Bo1CI6zx+vBA0B2sP8nPez5yRegZAlxYaq1U88DwVYxOD21toKhoriA6KBsQY/OuvYrrdLh5wEcFaLA4LddY6tBotISGtAbWZmeLHMmsEV744A3Q6bE4bAeoAUlPx1hOxWoXBIjQwCHeTGEUUSUnefSgshIQYHeVap3jyA1JLW5A33hCWl6AgiAuK87rLMqtFlVRPhdOPPhJj9Qcf9P5anHOOEIh79sC0afDLL+K6DAwfSHbdQaTFi9uVyi02F3sFDUB6VDoZVRnin4ULhSICMfoNHAiffEK1w9Tu4Tx2bOfA4F27hNiMihIuGgmJ6iYfPiWEoLnovCD27RP33TffCIH0pz8Ji2dwcOu8Z6adya8Fv9LsbO76JGg0VDdVExnYeQCZkTSDdUVdC5rfin9jQvyErtfdwpDIIeyr6Z2gCQkIAYQI2LhRiFkPFRUiqH9UzChAJBt0pMRcQry+c0BwW268UaznwQd73CVAFO/1uNBHxYyiwd5Abn1ul/MXGAvoH9L6xhcSIqzr69d3mK9ABIKDqHSeV9+7IjkHag4wMFwERV94ofjO+nJBduS228TL4+WXd/5szBjhimv7suKLb78V3xUPCoWC8XHjhdvJQ2AgLFok3naWLhV++UceoaG5gdPfPZ3fy3/n3fPf5brR17HyypVcNfwqdlfuZu21a3n3/HcBsLvsHE0OWdAkJydz+umnExYWRv/+/bFYLDz33HNs2rQJu93erdKV6ZlbbhECZvVqEQE+dKjwYR9LZs8WbjCAC4ZcwDvnv8P28u2kR6bz/RXfc9bAzu0TZIRl1pPdBcL1Ul0NUyap0Kq1VDZVEq4N77TcwPCB7K3ay9qCtcxNmQsIQZOb2/6tHcTgGRQkBlKAtLA08urzvA+MisYKYoJEDfmFC4UwcDjEOL5/P0SFafnf9v/xzYFvCFQHEhIi3jQlSVilRo6E5H5aSlrqitlddvxUfsyeLdwOLpcQ3gAhOh1bpiVz5VWB+PsFUlnZWuU9OS5QCJqWmdVBwuoTESHeHgGGRg71WkQyqzIZFtla9j0sTHhT1H3sOjdwoBho/vOf1sy51NBUmhxN7eJ1QFhoPC4ngJHRI1sHVYNBiLGbbhIK/8MPYdQoqi3txcLpp4sXj9DQ1qw0T/wMCMtPaEAoVU2+0+CaHE3ERwSRmQmPPioGp9NPF9/9ltAWL8MihxEaEMqGog3dnoMaa43PN+IZSTNYV7jO5zO6wFhAaUMpU/tN7fRZR4ZGDiWrOqvH+doKmhEjhNDfu1d8JknivouNBZVSxbTEaawt6OzH6SmGBsQ98sILIhuzo9WkI1arEAzzW+oG+qv9GRUzii0lXbudCowF9A9tb8KeMaOzoMnPbxU0/UP6917Q1B5gUPggQCwfHi7OS9vYto44ncJw+Pnnvj+PjxdWte5aITY3CwHd4jny0knQeFCphCn0iivA35/nNj2Hn8qPLX/a4u0tqFAoeHLOk9TeW0tSSBIKhYL7p93PvVPu7by+I8ghC5p9+/ZRX1/Pjz/+yE033cT8+fN59tlnmTp1KgaDAYvFciT385TkvPNaH/onApelX8bG6zfy7LxnWTCgF/b/UxQ/P7j+evj3v8UDe/16cR21WrwPdo+Fpi2jYkbxQ84PBPkFMTRSBBUmJooB+cEHRWyOh507xduXp1VLUkgSgZpA7wDTVtBccIFYx003tT74khNaTXxajZbgYCEcXngBMjLgvvsgQB3gTaf2CBpPsPjatSLmJjAQDAFBVAe4+XKwG3+1P1FRrcGFqYmBFAY6vNsK9OtcOXxE9AivgMiszvQe+5FGq9GSGJxIdm12u+kdLTQjokewp7KNj06hEKbTNuUjqpuq24mFxEQR5xMbK2YFUd6g7fc3ShfVOY6mhUZ7I0F+QahUwipTWys04LJl4n5qi0KhYF7qPFbmrOz2eKubqju5nEAEfNZZ68ipy+n02ZqCNUyIn4DOx3XqyJCIIeTV54k4iy6QJKmdoFGrxQDrEQEmkygw64koOC3pNJ9xNKXmUuL0PbvNR40Sl+k//+l+vnXrhOWsbXzcjMQZ/JL/S5fLFBgLSA5Objdt5EhRfqDdfAWtrvuU0JReu5wO1B5gcETrDq1fL5I9VnZzmTdsENa7007rep4zz4S77ur68507hdttWIf2Uacln8bKnJU0NHfdFNLusvP5vs955LRH2vVoA3Gftn3O/Wv2vxgZM7LrHTkCHFa4scFgYM6cOdx///0sX76c0tJSioqK+OCDD1i8ePGR2kcZmZOOm28WLpVnnhEPz+nTxfQgP+F28gQFt2V6opjp9P6ne10uSiWkpwsL7zPPiAFg3jzhM/dYQQCUCiVT+k1hTcEamp3NlDa0DgB+fmJg3LpVZNLt2QPB2tasBH+Vv/eN9u67xQNuwoT2gsbhduCn8kOtFgGq990n3k4tFnFMDfYGr1sKRNxgSQmE6LSU+LXWmAnUdM6GSI9Kp8BYQKO9UbicIod1mudIMSBsAAfrWuP7nG4nZQ1l9AtuY6GJGcmBmgPtauN0pNrSWSzceKOIh/vPf8S5/vbb9lbVTplObWi0N7YTEQEB3bvY5qXN46e8LloPtNlHXy6nAHUAE+Mn+oyjWVOwhplJM7tdr4c4fRx6f30ngdgWi8OCw+3wChoQ940nULWiQhyrp8PAaUmnsb5ofafMoBpLjTd2pSf+8hdhpenunfqHH4R1pm3vxnMHnct32d/5jOGBzsIXRJZqdofDPxyX06CIQd7/BwwQLzLXXQd//rOIS+zIV1+JF5Hu7pWrrhK/u6pntXmzcEt37GN5evLpDI4YzNLflna57s3FmwlQBTA9aXrXO3AMOeL5UwkJCSxcuJAnn3yy55llZP6g9OsnMi48BffmzBHTXZJ4WPqy0KiUKir+r4L3zm9vH25bTG7QIOHygVZ3k4cFaQt45rdnCPhnAHaXvd3Dd8wYEbcC4iHsCWYFcEpOrruutWih2y3eMLVqrbcoo91l9xZ5POMMIdY8D0idRofRZgSEOALxcIyPFwLG4rDAI49QHB/kU9B4UoA3FW8irz7vqL7FeQKvPZQ1lAG0e/vvZ+iHzk/XmhrtgxpLjU+xcN55IgB54kTxu63o9Jnp1EKjvRGdpvd97yYnTCajKsN7fbrcxy6CMGf1n8UPOT90mr6mYA0zk2f2ah8UCgVDIoZ063by3BcG/9aeSNdcIwRNVlaru8kzmI6JHUOzs7k1hglh5am11vq0Nvli9mwR9tTdENQ2fsbDlH5TkJDYWOwj1QxxPjvuw8CBQri3DUTOz2+10PQP6U9+fc8WmobmBkobSr0uJw/33ivuo3ff7Rxy8NJL8OKLQtB0R1KSsOL4KkoJQtBMnNh5ukKh4Kk5T/HspmcpNftuab+mYA2nJ59+VFOx+8KJsRcyMn9AJk0SQmb06FaTcKNdPPk6Vsr0EB0Ujb/av920O+8UVpVrrhFp0unp4oHc8UF21sCzvAM00MkEPHmycGVotUKseGh2NnPrrd64XcLDxQCj99d799fhcnj3edYssQ+eIEKDv8E7UHfcd61Gi0ty4fj7A1z/xCSfgkahUDAxYSIvbXuJBENCr9/ED4WB4QPbWWiKTEXE6eNQK1uDdBQKRfvAYB90Zf1QKER24vr18Oab7T+L1kV3HUNjb/Ja73pDgiGBcG04uys6B9ACuCU3tZZan/sIcPHQi/n+4Pft3Ame+Jkp/ab4XMYXQyOHdhsYbLQZ0fvp253fhAQRzP7kkyK7M75NrK9GpWFq4tR2cTTmZjNOt9OnVdMXCoXIHHz+eZFc0ZHcXJE51rEAvUqpYuGQhXyS8YnP9dZZ6zq9iMTECHejp5iuJHW20JQ2lHbrlgOR4RQaENpJMKlUwoVZXCyCqV94QWRMut2tsW7z5nW7akDM+8svna08ktRqofHFlH5TuDz9cuYum9sp9gzg14Jfey2AjwWyoJGROYr8/LPIKvLEQUxLnNangQtE/Mvw4fDOOyITad068RDrWLAwJTSFvbfu5ZerfyH7Dt9uAE8qfUpoinfa6NjR3r8lqTWzQu+n9w54dpcdjVJYaLRaEdS5bp2If0gOSfam73YUUR4BY3FYsDgsPgUNwPy0+Xxz4Btm9Z/V8wk5DAaEDWhnoekYEOwhPbJ7QVPVVNVtCuq0aZ2LXUYH+bbQOFwOHG5Hl+fGFwqFgrFxY9lRvsPn5/XWelySq0urxrCoYaSGpvJddmvhlm8OfMOkhEm9ip/x0BsLTVt3k4d774VPPhHCzzP4e+gYR1NtqcZP5Yfer+uy+h0ZO1akvD/9dOcYlGeeETFevqr0X55+OZ9mfeozG6fOWudTVM2a1VJLCmG1tFiEVQRESYJg/2AO1HbVjl3giZ9RdPT7tBAcLGKP7rpLBPk/9JAICN68uTXovTtuvFGc502b2k/fuVO85Eye3PWyr5z9CmPjxnLGsjPaCeBCYyF59XlMS5zW9cLHGFnQyMgcQ14/53X23d5zqmtXhIS0Zp75Ij0qnVn9Z/nsDdaWiQkTWXHFCqSHpXaBiNBq/g/yC6LJ0YRbcnuDgjvO5+8vxFGTQ5S57jiPxxJkdVqxOCztLENtuWrEVVw89GIeOe2Rbvf7cEmPSudg7UFqLbUA5NXneTMz2jIsali3gqZt7ZreEhsU286C5sHjNuqLkAAYGzuW7WW+m0jVWGrQqrXdrvPSYZfySaawRrjcLv695d/cOu7WPu1Dbyw0nho0bUlNFbE0r73mW9B4srB2lu9k5jszWwoE+h7su2LsWOGSWbSopfI1wgr5zju+W3QBTE+ajk6jY8XBFe2mS5LkrfDdkdmzhfUDRDxNTIwIsoXeWftA1F9qGz/ji1deEaUa/v53Eav12GO+K8R3xWmnidihtnz0kXBp6bq59VRKFW+f9zZRuiiu/+Z6b3bce7vfY0byDAwBhq4XPsbIgkZG5hgSpg3rMf30WKBUKJk/YH638+j9xSusyWai2dXcLu6mLW0f8h196RqVBrVSjcVhweqwdmmFCAkI4dOLP+2UFnukSQpJYlLCJN7a+RYAOXU5pIV2rnY9JKLrGituyU2hsZDkkOQ+bTs5JNlnPIVH0HQl9rpiXNy4Li001ZbqHouYXZp+KT/k/EB5QzmPrn0Up9vJxUMv7tM+DI0cSnZtdpf1Rbqy0IDI/AVRkqIt4+PH02hvZF/NPl7d8SqlDaW9jp/pyLXXitpvc+YIV9Pf/y6KXw7qQjsoFUquHXUt7+x6p930JkcTDrfDp6CZM0dYOioqhOvJU1Taw7DIYeyt3Nvtfu4o38HomNHdzjNokKhP88gjwgXdMc26JxYtEi4rR0vSockkKgxfc03Py6qVaj6+6GO2lW7jiQ1P4HQ7eXXHq1w94uq+7cRRRhY0MjIyPtH76VGg8BYb81U7B+jRhaZVizYLVqe1S1F0LFk8ZTHPb34eq8MqBI2P9h1DI4eSX5/vM/ahvKEcp9vp01XVHSmhKRSaCjtl0VgcFjRKjTfoureMixtHVnWWz330FcDakYHhA7l02KWMenUUr2x/he+v+L7P+5AckoxOo+vSAlFrrSU0wLdJ8bLLRHmDM85oP91P5cfkfpNZW7DWG8N1qIIGRNbZGWeIYPh160TR2+64dtS1rDi4ol1GWq2lFgUKn+IsJkb0vvvySxGw70kA8DA1cSo/5PzQZW02SZJYcXAF4+PG9+p4VCrhgu6jwYqpU0Xa/HPPif/ffluIpI6xRF0RERjBV5d9xRMbnmDusrmolKqj7iLuK7KgkZGR8YlKqSJSF8mv+b8Sp4/rUoz05ArwZDp52iccb84ZeA5Ruiie3fRsl4ImTh9HkF+Qz5TkAmMBcfq4TgHQPdEvuB+SJLVrIgri7b+v7iaAeH08AeoAn3VOuqpB05HXz3mdlxe8zJY/bfE2XewL3liesi4sRU3VXQZ5BweLwOlwHzrZE0fjCUQ9lPPjQakUGUEVFcIl1FOf4+SQZEbFjGrXL6vOWkeoNrTLbJ4FC0RW44YNnQXNwiELyavP67LvlSebbkzsmN4f1CEQECDqRy1ZIso4fPWVsFb1RRiNihnFm+e+ybrCdbx/wfuolCdW52FZ0MjIyHRJbFAsn+/7/LAC/7yC5gSx0CgUCl4/53Ue+vUhKpsqfQ7kCoWCIZG+A14LjAUkhSR1mt4TaqWatLC0TgNbd8HS3aFQKEgJTSG3rnO5/q7Syjvir/Zn4dCFh+XqGxs71ndFWVqCp3uxHx2ZmzKXL/Z94S10p1Ic/sAZHd1z3zsPE+MntotP8pXh1JZ580R8SlhYZ3eWzk/HiOgRXPLZJT5jnupt9cTr4/sskA+FKVNEoPTVVwtx01PKty8uGXYJzoecJ0ztmbbIgkZGRqZLYoJi2Fq69bC65Go1Wm+W04lgoQERp7Hjph28ee6bXbrMugp4zTfm9zl+pu12t5ZubTetyd7Upxo0bUkNTfXZf8hX4b+jxYykGawpWOPzs97E8vhicr/JLBqxiAsGi5LTx9oSMDp2NC9secEraGuttV26XEGkTz/+uCim6KvI3bDIYeyr2cf418dTbCpu95nJZiI4ILjzQkeJ224TvQFXrBAp9IdCXwO0jxWyoJGRkekSTzO+uyfffcjrCNQEYm4245bcJ4SFxsOY2DFcP/r6Lj/vKjA4oyrjkKsZT4yfyJbS9v2CDtVCA6KH18Hag52md1Un52gwI2kGefV5FBoLO31W1VR1yHWF3jrvLb689EsAFBzbAfT05NMBeGHzCwBdZjh5UChEVd8xXXiNHpv1GMsvXU6wfzB3/9T+u2RqNhHsf+wEjUIhApfnd58TcFIiCxoZGZku+efsf7L9xu2H9bavVWuptdZ6/z5Z6KrGyq6KXYyMPrRqxhPiJ7C1dGu70v6HI2i6siL1Jij4SGHwNzAxYaLPPkhHSliNjR3b80xHkP6h/fns4s9YV7gOt+SmqqnK273+UIgJiuH8wefzxSVf8HnW5zjdTu9nx9pC80dGFjQyMjJdEqYNY2xcz4PJnJQ53DPlHp+fBWoCqbPWAZxQFpqeGBo5lIO1B9sNPnn1eeTV5zG5XzeVyLphdMxoAjWBvLe7tb3FoQYFgxBdvoJNOzbPPNrM6T+nXRCth54KEPYG030m7p16dLs0+2J+2nyqLdX8VvybEDS6Qxc0HmYmz0Sr1rYLNj/WFpo/MrKgkZGROWx+XvQzT8992udnWo2WWkstSoXSW234ZCA5JBmlQtku6PaTjE84I/WMbt0P3aFRaXjxzBe56dubMDebgcOz0AyOGExVU5W3Z5KHY2mhAZidMptVeavaiT+7y47RZjxsC43B33BcYjZ0fjouHnoxH+79kCrLoQU3d0SlVDEiegSvbn+V/2wRLcFNNlnQHClkQSMjI3NUCdQEUmutRavWnrDBhL5QKVUMDB/Yrknlx5kfc1l6F13+esmFQy4kOCCYneU7ASFoDjUoOFQbSpQuigM17UvrH8sYGoCp/aai89OxfN9y77QCYwH+Kn9igmKO2X4caRYMWMD6ovXUWnrfHLMnLh12Kf/e+m/uXHkntZZaYaGRXU5HBFnQ9JLdu3czf/58YmJiUCgUrFrV2bxaX1/PlVdeSXBwMCEhIVx55ZUYjcZjv7MyMicQgWrhcjqZ3E0eUkJTKDAWAJBVnUV2bTbnDTqv+4V6wYjoEeTU5bCrYhfL9iw7ZAsNwKDwQe1Elyej7FhaaFRKFXeMv4MXt7zonXag5gADwgeccLVK+kJySDLFpmJqrd0HBfeFP0/8s7fFx895P/eYQSXTe2RB00v8/Py48MIL+e6777qc56qrrqKyspLc3FxycnKorKzkmt7UlZaR+QOj1Wi9FpqTjf4h/cmpE23IP8n4hLMHnu1tCXE4DAgbwMG6gyz8dCEZVRl9bljalsERg9s1P6yx1KBAccQG4N5y/ejr2Vmxky0lIovrQO0BBoV335/oRCc5JBlTs4mcupxed/vuCbVSzcMzH+by9MvJr88/5vFOf2TUPc8iAzBkyBCGDBnS5eeFhYWsWLGCXbt2EREh3oyeffZZRo0aRVFREYk9laeUkfmD4gkKPhktNJP7TebxdY/zr/X/Ysm6JXx+8edHZL1pYWlsLtnsbYPQl07SHRkZPZLPsj7z/l/eUE6YNuyYW0ZCtaH83+T/45bvb2HTDZvYWLzxkNPbTxQM/gYiAyOptlQfcStKP0M/ikxFh5XaLtMe2UJzhNi1axf+/v6MHNmazjly5Ej8/PzYtWvX8dsxGZnjjFYtgoJPRgvN7P6z2Vu1lwdXPwjA2QPPPiLrHRUzii/2fUGhSdRu8dWFu7csHLqQjcUbKTYV88PBH5j05iRi9bFHZD/7yoPTH0SBgqlvTWVD0QZuHnvzcdmPI4mnq/qRtnglBidSbC4+5vFOf2ROeQvNtddey7vvvtvl56eddhpr1qzpcT1ms5ng4M6BXSEhIZjNZp/LGAwGGhoaer2vh8Ox2o7MycfRvjcCXAE4rA5Cgrv+LpyoaNAQQggKhYL9d+yn2dJMM82Hvd5x4eOIVEXS7GpmVvIszk0+95DPTSCBXNj/Qh776TFCtaEYMDDMMOyIneu+3h+vzH2Ft35/ixdnvkiwIviku+YdSQ1M5SAHUTlUR/RYojXRVNVV0dzUTKA78KQ8T8dyXOnNtk55QfPSSy+xdOnSLj/XaHqXZmowGDCZTJ2mG41GDAaDz2WcTiePPvoofn5+AMyaNYs5HTubycic5ASoA9r9PtnY8icRE3Ik91+pUJL95+wumx32lUdmPsKcZa3PjuPp6hkcMZinz/Cdwn8ycuGQC6mx1KBWHtnhMjYo1hv7dCwDuE8mVq1axerVqwGw2+09zn/KC5qgoCCCetuxrBtGjRpFc3Mze/bsYcSIEQDs2bMHu93OqFGjfC5jsVhYunRpl4LnaHAstyVzcnG07g29QY8ZMyqt6qS8/06GfTYYDHx21We8t/s9LhxyITOTZ+Kn8jvi2zgVuXTspVw69tIjvt5UZSpmzChQEBMec1KVNOjI0bo3LrzwQi688EJAeEFef/31buc/5QVNb5EkiebmVlOzw+HAZrOhVqtRq9UkJSWxYMECFi9ezIcffgjA4sWLOeecc+SAYJlTGk/szMkYQ3MyMaXfFKb0m3K8d0Oml3jiZiSkk1rMnEjIQcG9pLCwEK1Wi1YrHsoLFixAq9Xy+OOPe+dZtmwZERERpKamkpqaSmRkJO+9915Xq5SROSXw1Fg50hYDGZmTmZO5Ps+Jimyh6SXJyclIktTtPGFhYV7rjIyMjMAjaD7K+IgPF8rfDxkZmaODLGhkZGSOKh7LzMDwgcd5T2RkTixWLVqFzWk73rvxh0EWNDIyMkeVgeEDCdeGs+/2fcd7V2RkTihmp8w+3rvwh0IWNDIyMkeVWH0sNffWHO/dkJGR+YMjBwXLyMjIyMjInPTIgkZGRkZGRkbmpEcWNDIyMjIyMjInPbKgkZGRkZGRkTnpkQWNjIyMjIyMzEmPLGhkZGRkZGRkTnpkQSMjIyMjIyNz0nNKCxpJknj44YeJi4tDp9MxY8YMMjIyelzObDaTnJyMQqHA6XR6pz/33HOMHTuW4OBgoqKiOPvss8nMzDyahyAjIyMjIyPDKS5oli5dyltvvcWPP/5ITU0NU6dOZd68eTQ2Nna73F//+lcGDRrUabrNZuOFF16goqKCwsJCBg8ezJw5c7BarUfrEPrEqlWrjvcuyJygyPeGTHfI94dMV5xI98YpLWhefvllFi9ezPDhw9FqtTz22GPY7XaWL1/e5TLffvste/fu5Z577un02QMPPMD06dO9Xbn//ve/U1FRwf79+4/mYfSa1atXH+9dkDlBke8Nme6Q7w+ZrjiR7o1TVtCYTCYKCgqYMGGCd5parWb06NHs3LnT5zK1tbXccccdvP3226jVPXeN+Omnn9DpdAwcKDflk5GRkZGROZqcsr2czGYzACEhIe2mh4aGej/ryK233sqNN95Ieno6a9as6Xb9e/bs4ZZbbuGFF15Ap9N1+tzlcgFQUlKCwWDo+wH0kcbGRhobGykpKTnq25I5uZDvDZnukO8Pma44lveGZ1z2jJ2+OGUFjUdEGI3GdtPr6+uJj4/vNP/HH39Mbm4uH374YY/r3rp1K2eddRZLlizhT3/6k895cnJyCAwMZPTo0d5parW6V5afQyEoKAin09mtO03m1ES+N2S6Q74/ZLriaN8bTqezXeINiLFz/PjxPuc/ZQVNcHAwycnJbNu2jcmTJwPi5O3atYtFixZ1mn/lypXs37+fmJgYABwOBwAxMTE8++yzXHPNNQD88ssvLFy4kBdeeIFrr722y+2npaVhsVgoLi4+JhaahoYGHn30UZYuXXrUtyVzciHfGzLdId8fMl1xLO8Ns9lMv379SEtL63KeU1bQANx2220sXbqUWbNmkZqayuOPP45Go+GCCy7oNO/zzz/P448/7v1/06ZNXHLJJezYsYOIiAgAli9fzjXXXMNbb73FRRdd1O22VSoVICxFx0LQAPj5+R2zbcmcXMj3hkx3yPeHTFcc63vDM3b64pQNCgZYvHgx1157LXPmzCE8PJz169ezcuVKgoKCKCoqIigoiPXr1wMitiYhIcH7ExkZCUB8fLw3Rubuu++mqamJa6+9lqCgIO/PBx98cNyOUUZGRkZG5lTglLbQKBQKlixZwpIlSzp9lpiY2G09mpkzZyJJUrtp+fn5R3wfjySzZs063rsgc4Ii3xsy3SHfHzJdcSLdGwqp46gsc0wwm80EBwdjMpmOibnOEyEum41lOiLfGzLdId8fMl1xLO+N3oyZp7TLSUZGRkZGRuaPgSxoZGRkZGRkZE56ZEEjIyMjIyMjc9IjCxoZGRkZGRmZkx5Z0MjIyMjIyMic9MiCRkZGRkZGRuakRxY0MjIyMjIyMic9sqCRkZGRkZGROemRBY2MjIyMjIzMSY8saGQOm3feeQeFQoFCoSA7O7vT52vXrvV+vmrVKgAeeeQRFAoFWq0Wk8nUaZl3333Xu0xOTo53enNzM88//zwjR45Er9djMBgYPHgw11xzDQcPHvTOl5GRwc0338zYsWPx8/NDoVAchSP/43IiXtPXX3+dBQsWePunpaen88wzz2C324/CGfjjcajXNDg4mOjo6KNyTd9//32mTp1KZGQk/v7+JCcnc8MNN1BUVHQUzsAfixPhO3rLLbeQm5vrc/8cDgfDhw9HoVDwxhtvHKGj7h5Z0MgcMfR6PcuWLes0/d1330Wv1/tcRqPR8Pnnn/d6mcsvv5yHHnqIhQsX8sUXX/DJJ59w0003kZ2dTVZWlne+HTt2sGLFChITExk3btxhHNWpzYl0TZcsWUJMTAwvvvgi3333HZdeeikPPfQQV1555WEc4anHiXRNa2trmT17Nm+88QY//fQTDzzwAD/99BNTp06loaHhMI7y1OF4Xs+cnBz279/vcxtLly6lpqamj0dzmEgyxwWTySQBkslkOmbbO1rbevvttyVAuuaaa6Tk5GTJ7XZ7P7NYLJJer5euvfZaCZB+/vlnSZIk6eGHH/Yuc9ppp7VbX1FRkaRQKLzLHDx4UJIkScrNzZUA6YUXXvC5Hy6Xy+ffDz74oCTf6l3j6944Ea9pVVVVp88fffRRCZByc3MP6dhPJQ7nml5xxRVH5Zr6YuXKlRIgff7554dxtH98ToTvqMlkkurr6ztNz83NlQIDA6UPPvhAAqTXX3/9sI+3N2OmbKGROWIsWrSIwsJCNmzY4J22fPly3G43Cxcu9LnM1Vdfzbp16ygsLPROW7ZsGUlJScyYMaPdvHV1dQDExMT4XJdSqfT5t8yhcyJd08jIyE6fjx8/HoDS0tJeHpHMoVzTyy677KhcU1+Eh4cDoFarez4YmRPqO+rh1ltv5bLLLmPKlCl9Pp7DQX7qyxwxPF+GtubP9957jwsuuICgoCCfy0yfPp3k5GQ++OAD77Rly5Zx1VVXdYp7GTx4MAaDgfvuu4/333+fysrKo3MgMl5O9Gu6du1alEolAwcO7NNypzKHck2nTJlyVK+py+WiubmZPXv2cPfddzN06FDmzZt3iEd4anGifUc/+OADtm/fzlNPPXUYR3VoyILmJEWSwGw+vB9JOvL7dfXVV/PZZ59hs9koLy9n1apVXH311V3Or1AouOqqq7xfxq1bt7J//36fywQFBfH+++/T2NjIokWLiImJITU1lTvuuKNLP+7JhCRJmJvNh/UjHYWLeqJe0z179vDiiy9y/fXXEx0dfXgHebQ4Qb+oJ9o1jY6OJiAggJEjR2K1Wlm1ahUBAQFH5mCPMJIkYXY6D/nnj/Qd7RiMXF9fz913381TTz1FRETEkT3IXiDb9E5SGhogOLgvSxg6TTGZwNB58mFx8cUXc8cdd/Dtt99SWFhITEwMs2fPZt26dV0uc/XVV/PYY4+xbds23nvvPSZNmsSAAQPYuHFjp3nPOeccCgoK+Omnn/j111/ZuHEjL7/8Mm+++Sbffvstc+bMObIHdAxpsDcQ/GSfLmonTPeZMPgf2Yt6Il7T8vJyzjvvPFJTU3nuueeO6PEeUfr+Re3MUfiinmjX9JdffsFisbBv3z6eeOIJ5s6dy4YNGwgJCTmix30kaHC5CG7j3ukrpmnTMBxhd9rxvJ4ff/wx5513HgD33HMPqamp3HDDDUf0+HqLLGhOUvR68ZzrLWazGQBDmwdjFwHwh4Ver+f8889n2bJlFBQUcOWVV/boM09LS2Py5Mm8+eabfP755zz22GPdzq/T6bjgggu44IILANi8eTNz5szhvvvuY/v27UfsWI41ej89pvv6cFG7WMeR5kS7prW1tcydOxdJkvjxxx+7zOQ4IejrF7WrdRxhTrRrOnLkSAAmT57MzJkzSUtL45VXXuG+++47jKM8OuhVKkzTph3W8kcaz/V8/D+PY640H9Pr+cgjj3DeeeexZcsW3n77bVavXu1NCfeMO1arFaPRSHBw8FEtoSELmpMUheLQXtqOtEXGF1dffTVnnXUWbrebjz76qNfL3H777ajVai677LI+bW/SpEmcccYZrFy58lB294RBoVAccevKkeJEuaZms5l58+ZRW1vL+vXriY+P79N6jzmH+kU9Bpwo17QjKSkphIWFtauDciKhUCiOuIXlSHDlVVdy9tlngwRz5lxNWVnPyxzu9Tz99NP55ZdfANi3bx9ut5uZM2d2mvfOO+/kzjvvpL6+/qha3U68q3IMkSSJRx55hNdffx2TycTYsWN5+eWXSU9P9zn/ueeey44dO2hoaECn03HmmWeydOlSb1Q+QElJCX/7299YuXIldrudxMREPvroI0aMGHGsDuu4M3fuXC655BJCQkIYNmxYr5a59NJL+fHHHxkxYgShoaE+52loaECpVKLT6dpNd7lcHDx4kNjY2MPedxnfnAjX1GKxcNZZZ5Gfn8+aNWtIS0s79AOSOSGuqS8yMzOpra0lNTW1dwdyjJAkCaPTSahGc7x3xSeu/i4UwxToDDr+9a9hjBgBLcaULjnc65mXl+eNXzvzzDP59ddf281TUVHB5ZdfzuLFiznrrLO6DFI+UpzSgmbp0qW89dZb/Pjjj6SlpbFkyRLmzZvHgQMHfJ74xx57jIEDB6LVaqmvr+fWW2/lpptu4osvvgBEetu0adNYuHAh2dnZ3reM4MP1oZ9kqFSqXr/xeQgNDWX58uXdznPgwAHOPPNMLr/8cmbOnElUVBTl5eW88cYbZGRk8PLLL3vntVgsrFixAsAbiOgpJJWcnCwX2+sjJ8I1XbhwIRs3buTFF1+kqamJzZs3ez9LTU31mdYt0zUnwjWdNm0aF1xwAYMHDyYgIIA9e/bw7LPPkpCQwI033nhIx3W0WGs0cnN2NgcmTjzeu+KT9/a+xx1P38FLW19izbNlGAxxPS5zuNczKyuLZ599FhBp3R1TuwsKCgAYNGiQT8vNkeaUFjQvv/wyixcvZvjw4YAQLG+88QbLly9n0aJFneb3+Hk9KJVKDhw44P3/+eefJyIiwnuBAQYMGHCU9v7UIy0tjTvvvJNVq1bx+eefU1NTQ1BQEKNGjeKzzz7joosu8s5bVVXFxRdf3G55z//XXHMN77zzzrHcdZku6Ms19bgq7rzzzk7refvtt7n22muP1W7LdENfrunEiRN55513KCwsxO12k5iYyOWXX84999xzXLJkuqPK4SDbasXocBBygllpai21fJv9LRm3ZvDR5l+xDd5CYWEP5ple0t31fPfddzn//POPyHaOBArpaOSQnQSYTCZCQkL47bffmDx5snf6GWecQXp6epeZE/fffz8vvfQSjY2NaLVa3n33Xe9AOWnSJBITE3E4HKxdu5bIyEiuuuoqHnjgAVQdAsHMZjPBwcGYTKZ2gbpHC19BwTIyIN8bMt0j3x+CN8vL+dOBA/w6ciQzu3DPHC9e2voSH2d8zOqrNhC86EYmjQxj1zNPUV9/dLd7LO+N3oyZp6yFxnMhOgYohYaGej/zxRNPPMETTzxBTk4O77zzTruCXjU1NWzdupX33nuPTz/9lKysLM4++2z8/Pz429/+1mldBoPhmPUrkfuiyHSFfG/IdId8fwhMJhOG5mZ2VFYy5ihkKh0O67PXc27yuXz5pZnopnEowr7A7TZTUnJ0Y9KP5b3Rm22dsoLGo/CMRmO76fX19b3KnEhLS+Pcc89l3rx5FBcXo9FoMBgMjB8/nquuugoQLqrbbruNL7/80qegcTqdPProo/j5+QEwa9ask7qOioyMjMwflUaXC4CMpqbjvCedqWyqJF4fz6oP4cyRY1he+w90eielpeoTNcmuV6xatYrVq1cDYLfbe5z/lBU0wcHBJCcns23bNq/Lyel0smvXLp/xM75wOBxUVlZiMpmIiIhgzJgxZGRk9HofLBYLS5cuPaam3FPdbCzTNfK9IdMdp/r90RgQgMFgYLvbfcKdi3xrPolRiZSVGTh77Fg+MtmI6l9EVdUojkUM89E6HxdeeCEXXnghILwqr7/+erfzn9KtD2677TaWLl1KRkYGVquVhx9+GI1G4y0c1Jbs7Gy+/PJLzGZRXv7AgQPcc889jB8/3hu8duutt/L777/z8ccf43K5yMzM5JVXXuGSSy451ocmIyMjI+ODOoeD8ubmPi/X4HIxIziYfU1N2FqsNScCkiRR3lBOrD6WmhqIilQyIX4C2gGbaEkyOmU4pQXN4sWLufbaa5kzZw7h4eGsX7+elStXEhQURFFREUFBQaxfvx4QN81zzz1HYmIier2eefPmMXz4cL755hvv+saOHcsXX3zBP//5TwwGA+eeey633nord9111/E6RBkZGRmZNjxfUsKD+fl9Xq7B6WSYTodBre6V28lqhX/8A9zuQ9nL3lNrrcXhdhAbJARNRASkR6ajjDpAm2bapwSnrMsJRMXHJUuWsGTJkk6fJSYm0tjY6P1/0KBB7dqzd8U555zDOeecc0T3U0ZGRuaUoboajmJNoTqHg8pexGN0pMHlQq9SMTooiJ2NjYzrwc2ybBk89hj89a8QFnaIO9sLyhrKMPgb0PnpqKkRpy6yLhJlUCaFu4/edk9ETmkLjYyMjIzMCcRHH0FcHPz++1HbhNHppNrh6PNyHQVNd7jd4Kn80SHv5IhT3lBOnD6O5mbRCzUiAiIDI3EFVMsuJxkZGRkZmWPOa6/BTTdBSAgUFx/26h57DHzFkJqcTmoOUdAY1GpG9ULQrFghepKGhBwDQdNY7nU3gbAGReoisamqTzmXkyxoZGRkZGSOP//3f/DJJzBmjHA7HSZbt8LevZ2nm1yuQxI0dVYnH72lYrzBwK7GRuxdBMc0NDdw3cqF3H6Hm8jIoy9oyhrKiNPHUVMDoaGg0QgLTYOrmupqsFiO7vZPJGRBIyMjIyNzfLFYoLERxo+HqCioqjrsVZaXQ11d5+kmp5MGl4vmPkbr1je7WP2dioFaLUEqFb93UehtU3YONZFfct6ismNjoWkobxcQDMJCU2urJkArnXRWGpsNDrV/wTEPCs7Ly+vVfEqlkuTk5KO7MzIyMjIyx5/aWvE7NFREtR4BQVNRIbRRR0xOJwA1Dgfx/v69Xp8FF85yNZKkYIrBwEazmUk+Gg/nlFcAUOfOJSQkoU+C5tZbYfFi6G2j8R0NDeRaTMyNT6G6sI2gCYzE7rIzILWBwkIDQ4b0fh+ONzNmwDPPwGmn9X3ZYy5o0tLSUCgU3c4jSRJarZamE7Aio4yMjMzJzk91dVjdbs47URpA1tYKMaNWCxXiy1fUB9xuqKz0baExtgiaaru914JGkiSalS5oUGE0wtTgYDaaTPxfv36d5i2qrwCU5NbnEhJyWq/7KZWUwCsZT6B49XpetjwGN98MLY2Tu+If+flkKuK5Wt/eQhMSEIJaqSYmpYbCwt4Xvdvf1MQao5FbelEt/2hRWnroIVTHXNAEBgaSmZnZ7TySJDFq1Khjs0MyMjIypxjf1NTQ6HKdOIKm7Wh8BFxONTXgdLYafjy4JYkGlwutUumNo6muFvMNHtz1+ppcLlAAFhVZRZXsq/+M9ZoJSJLU6QU921ILU5azu25nn1xOK350wOkPs+lHCbb+V+zURx91u4zZ5aLerSQ2KJb9Na3Z7gqFgojACMITq8nPT+ndDgC/Go38r6zsuAoao5FDbqp5zAXNueeeS1JSUo/znX322cdgb2RkZGROPeqcTjFInyjU1kJ4uPg7MvKwg4LLy8XvjhaaBpcLCUgJCPCmbi9dClu2wJo1Xa+vwXOuLCre3P0/vql6lbqx7/H3Tf/jn1NuazdvruQEjYGfLXrODum9oPlqbQ6kOjjX9iWmlFEEf/mlMNskJHS5jMnppFHh7w0KbqtPIwMjie5fze6fe7d9gCqHg4pDqNFzpHA4RDjVoQqaYx4U/OGHH/Zqvvfff/8o74mMzOHxU10d1+zbd7x3Q0amz9Q5HNS3uF5OCI6whaa8HIKCxMDYNvbX5HSiAPprtV4Lzbp1sGMHdKfvzC4XCrsSJInvSt/mrXNeYWiAiqcyvsfhap8xVemnRtlQQbb/EALCnb0SNC4XrD+QiRIlF1Vm8G7Y3TB/Prz0UrfL1TvsSJpQYvWxVFe3FzQRgRFEJlWzbVvvg2yr7HZqHA4cR7u8cReYTOL3SSNoPBiNRpoPoZ+GjMyJwkGrlX2nUk6kzB+GWqfzxBI0bS00UVHCQnOoqS4IQTN0qFiFZ5AEIWj0KhXRGg01DgcWC2zfLtoUHDjQ9foanE6wqIiYsJpmZzMLBixgflQiGNLJqctpN69ZqyeyoBqFpZCM5LJeCZodO8AZmsl1gVMZUNPMo9nTMV53l6jN000saUWDE4VfJEF+QZ0tNLpIAiKqMZnodYG9aocDqeX3IWOzHfKinnN1wguaDz74gPj4eObPn88bb7zByJEjGTFiBJ999tmx2gUZmSOK6RiY7X0FNcqcOuzc2b3l4FCpczioP5xB60jTLuc4UgTAHEa+c0UFpKWBn1/7OBqT00mIWk2ERkO1w8HmzRATA5Mmibo1gBAQOe1FSo3FhdSkQjPhTYa7r0aj0jBdoyEgeBQZVRnt5rXpokhq6Iez8H1+jSim3tSzMPvxR4gYmsk1BwLYMFSHYVIGa9wzxEE89ZTPZSRJwqlxI/mFIUmSt+2Bh8jASIzN1YwYAdu29eq0UdXibjpkt1NVlbiORUWHtLjnkh/qpT9mgubFF19k3759vPHGG9x///3s2LGDnTt38uKLLx6rXZCROaIcbUHjdkNyMiddHQmZI8eZZ8Jvvx359dadiBYaj6DR6UCr7dLt9PfVf+f38u5bI5SXQ2ysqJrb9qXA6HQS3CJoahwO1q2D6dNhwoQ2g/6zz8I117RbX1GNC5qhKuwrxuXNgf79GTZlCnZ1BHsq93jnq7bbkQJCGeZMJdSag1HloMbes3D88UdwhWcy4rdccueMxT/tN3LzFKLU8dKlwozUgdI6FygVoPLD6HT6jKGptlQzfnwfBE2LyD1kQZOXJwThf/5zSIufNBYanU6HwWAgPj6eoUOHEhERQWBgIP59qAMgI3MiYXI6sRxFX3NFhejN0jFTQ+bUwOUSnpcjUDS3/XolCWPLvdtVtduONDidbDWb+7yt+noYOBB6jC6oqWl1OSkUXcbROFwOnt/8PCsOruh2dV0JGpPLRbBaTaSfH9UOB+vXi7on48e3sdB8+mknC01pvQu1soFo1WDS9+ZAv37027IFh0ZDfnZrHN1vxmqwlpKiiyMtJBE/yU291LOgydxvp9p5AH1hOWFT59AUvpHcXGDkSHjgASGwOpzELRktL1MOJyVWe6cYmkhdq6DxHlsPVDscBKtUhy5oioogOFi4yrooPNgdJ00Mjd1ux93y5fniiy+8050n0luCjEwfMLlcR9VC47HayuWYTk3q6kQMyJEWtMY2z9zeWmm+r63luv37+7ytsjI4eFD8dEsn84LvTKftZduxOCzsqtjV7erKy4UrqZOgcToJVqmI0GiosFnZmJXvFTS7d4N9ZyZkZwsx1WZALjc5USnNpASOJLbid5g2Df+EBGKM9TQWte7nhrpKaMgjLjSMiY5oomtqsCl6Htgb/Q4S7vBDabEyYvSZVKq2cjDPgSRJcN99Qt1+9VW7ZXZmO1E4ndBgY/2+ZhyONi6nRx5h2se/Ud0kBE1PQc8ATrebWoeD4TrdIXUjB6CoCNfsuTBoELz9dp8XNxqFge6EFzTff/89SqXYXETLjWu321m6dOmx2gUZmSOKueUt130YwYvdIQuaUxvPeO5pOnikqHM48FcoCFAqex1HU2SzkWeziQG2L9tqERNZWVBr6UaZtQ0Khi4tNGsK1hCuDWd35e5ut9ulhabF5RSp0VDYZMY9/zaGDBGVeYOCoPrlT+Hcc8FgEO6TFqoaXCiUdQwMG0pK/Q7RbwroZ7OhbFBhcYjkgN8bGlAZKwgJUXD98gJia2qJC8imu9Nst4MzLJPJyhTQ6RiQPIYAtZashp2M2bGDlysrqRiUgOVA+1idvXlOlC4bflaJ9Zl21Gqx2wCsXEny6t+psdQwdKhwX/ekR2udTiQgv/SXQ7bQWPYV8vJ3SdhuuxteeKFdillvsq2MRuFm9yVoenPvHTNBExIS0mman58f48ePP1a7ICNzRPGUULcdJbeTp1pmD419Zf6geATNkbbQ1DmdhGk0hKrV7aw13VHU3IzN7aa8jwOdZ2BanvUNsc/GYrKZfM/oy0LjQ9CsLVzLzWNvJrcul0a77y+GJLUKmvDwroOCrQoNUsImJNwoFDB+nIT220/h0kuFwmkjaGqsLtyKKsaED2RAcwaMHUtODrgK/dCr49hXLdxO+20OFNUNJNTuZvj6A2jtjcSH5LXLtOpIUxMQmcl4RTzExaFUqhgbPZnymbk0OJ3ck5vL4+FN5O9c0265zPI6XK56Iu2R7Cy2ExEB+TYrFU1NsGsX+r3Z2OqqUKuF/uopjqbIYkbhslBdt7fP19l7LPuLOGhPJGvwhSK1qqUgkNkMEye2O6U+MRqhf39xTjqKwCt7USLjuKVtL1y48HhtWkbmiGBqseEeLbfTsbDQlJUdkbY5MkeB7gTNunV0O0h2R63DQZhaTaha3WuXU1FL/EZ2UyMFxoJeb6uuDtBVstz1JxxuB+WN5Z1nstnETd5W0HhSt9vgcDnYULSBy9IvI1IXyd5K3+0RGhpEGnZ3QcHGQg0olTj9nF4xcl6/39HVFsGCBTjS0vibzcYNLWaNOpsdu7uCGXYlDehxxCezdi3U5Qfj9otmb1UGjU4n5S4VihIHg969n/KrLqBBZSYipLLbrJ2mJiAqkxGuUKS4OADGps1CmhnBk8FDeCAuhDcvupecfJPXwuFyQUHganQKBSnaCPKMzUREwF9zcnh+zx7w98edmMDonCZsThtjx4qMue54M+NLNC4LLls1hZa+x0sBKIqLKCKRrBw/cQ1LSwFhoeuN+9QjaKC9labe4eDHXpgqj5ugKSsrO16blpE5IngsNCezoHn8cXjllaO3fplDx/P89vUcv+MO+O67Q1tvncPhtdB8sO8b7vzhzh6XKbLZCFKp+DRvPRNen0Czs3c1xGprJZQX/An/slkkhyRT0VjhaybxOyysdZoPl9OO8h0EqAMYFjWMkdEju3Q7lZdDQICITfUVFGxQqbnnDhW4HQQExrJ723dw6aXc/N4UPg67jR1uN6ddfjnvhYTwQ8vCtVI9SredwZXl/M4YamoV7N4N/WLiqAuJ4OsffmdPUxNayU78QTBsXYXq73+nimoCQ0wY67t2lzQ1gSI6k9RmLUP/8hf6b97MJ+qxKCp/w1BsILRyF/4NpWSl9OfVV8UyubkSrtSNDGtUMNqaj8NgJyJSYrPZTEFNDYwdi2L2HE7Ph+qmamJiuhcTjfZGPjzwPfGaUEIcekpsh1ZfS1vdImiygPh4r6DJaPGW9VSGwmSC6Ghx/doKmuWVlQzvRbrncRM0PTWolJE50fEKmqPkcioqEl/soyloamoO/U1f5uhSXS2uv6+BqK7u0F1RdU6nsNBoNOwzlrC+aH238zc5ndQ6nZwWHMwuUxXVlmq+2v9Vr7aVZ8xDSvkRy2f/I0YXS3mDDwtNbS2EhIjGlB58BAWvKVjDacmnoVQoGRk90hsYvG2byAj04HE3KRS+Y2hy96jI2ucGh4mpKQsIefN9qKpixbc7uO7pi5i5axcTgE1vv0253U6Ty0WjykioUo9mz06y/MdQVQW7dsGYIZEUxMZSs3ELOxsbCXHUMKJSwjVwCLFJ6dRrLTSE+GPN7rrbYp2pGSn0IDZJT57BwMsDBnBXv0SkgufZdrCIb3ZtgAY79iA9i+928+OP8NGmtSiC3CSU1TBq67cQbkebYqPa4aDAboexY1HOms2cIhXVlmoMhu6/51/u+xJDUBJNOVE0Hoih1nUIcYGNjehsdSgSE9m3j0MSNEajuBVCQ9sLmk/37eOCHTt63IXjJmhkZE5mPE3uACxH0UIzcODRjaGpqxP+bZkTj+pqkSziy0JTX3/owcJtLTRl1gYyqzKxu7qOmShubiZQqWSCwUCe1cqwyGG89vtrvdpWUWMOIfRH7QhFr4j1aaGp3l+DFBbefqIPC82PuT8yM2kmACNjWi00994LH3wAXHUVvPyyV9CAEDQdY2gKs9Sce0UFOIyMS5pNWEYeXH45S9RbkRTb+S1mCi/o9ST9/js6pZI8qxWbXxOx/uHw++/khY2lslJkRU1J01IcE82w5iw215rRNBWTXmdBOXwYCoWCaL2ewvAo1Ns3d3mO9tccQOHSsj0whEkWC/PDw7k7KYVI2wA2lfzG9qoNBDtTqAyL4H9PZnLhhfDSb28QIU0mpLKS5M3r8Ytswp5mRqtUUqjRwLhxMHMmw8tc1JflYTB0/z0vNZcSoE6mLleDOjOOZtR9fq7VFxZiUQRy2oXhPVtoJAnOOadTCWNfgqbW4eAXpZILUlN73IdTWtBIksTDDz9MXFwcOp2OGTNmkJGR0eX85557LvHx8RgMBmJjY7nuuuuo7fCa9PnnnzN48GC0Wi1Dhgzhyy+/PNqHIdMFmzcfvUq7jS1N7sLV6qPicrJaxYA1dOjRtdDIgubEpboahgzpbImx20UDv0MWNE4n4RoNIWoVNXYbDreDrOqsLucvam4myd+f/gH+1Lg1PDfvOTYUbeBgbU+52FDRnEuUOpXBg0FljekkaCQJ7lpUy9b8CGbOhLVrWz7oIGje2/0emVWZXDLsEgBGxYxib+Ve3JKb8nKorJDg559h9WqvoDE5nQSGuzpZaBrK1WhjCvFz24gNHcSQIgvZ/YP5vbEBLD+xYVcOpKaiKCwkTaslu8mKW9vMgKAI2LOHyrgxbN0qvqOnDwmgIUDLwAYta2pKcZoOMMJiRJk+DIDUoAjKwkMIyuhG0NRlEmAeytqwME5r47kYHDiF3dZvqFXsZ2z4YPJjIkiPW8sXy13UhK4gVdcfg9lM7JAhENaMOd7M2aGhVBoMWMeMgdhYimO0qDZs7FHQVFuqqTaGcrV7BWt2vAeSq0+p20U2GwlVVWT5D+C88xXk5oIzKq6doBk0qM3zOCdH+Ew7pKIbjcJV2FbQfJmRwdjsbBIvvbTH/TgkQfPpp5+2bNx4KIsDvUvBOtosXbqUt956ix9//JGamhqmTp3KvHnzaOzilfixxx4jJycHs9lMVlYWVquVm266yfv5li1buOqqq/jnP/+J2Wzm8ccf58orr2S7jyqPMkefO+/s9H05Ynia3EX7+R0Vl1NxMfj7ixRGWdCcmlRXw+DB4sHeVjN7HvS9FTR2uyh+6+kmXdcSFKxyWXCqtEyMn9htXZcim43EgAACHHW4/KOZmTyTCwZfwOu/v97jtmtcecQGpDJ0KDjqYzoFBRuNoLfXMHhqBGeeCWedBbfdBo6QSKHkXC4O1Bzg9hW3894F7xEdFA3AoPBBONwOcutyKS8Hd06eEEBbtlBRIQTNX3Ny+C6wuFMMjbFEjSKkCL3CRXNtIwEuBRdk/gOVYTBRFfF8euA96NcPgFS3m93VNtC6GOvUQEAAjsRUfv5ZiM3IQDXBbjfhrqGUAk31uxhuK4P0dACGhcRTFxxE2MGuBU2OKROddRhrExOZqdd7p0+Mm0pJ8KdQO5iZg8IojY6iZt92wobtIDhYYlCsimCNhtiLL8bur6I6qY5zm5oIaG6mqCW4+MCwaIK27upR0BwsrSK5zsKw2ixGNxxA0VxHnqX3vuhcqxWLQsGvQ8YzdapwlVaohYWmpgYqK2HatDaCxqNcf/yx3XpMplYLTaXRxZvl5fyzvJzL6uuFyO2BQxI0hpZk97PPPpsRI0Zw6aWX8vTTT/PLL7/0eh2bNm06lE0fUV5++WUWL17M8OHD0Wq1PPbYY9jtdpYvX+5z/pEjR6LVar3/K5VKDrTpaPbKK68wf/58Fi5ciEajYeHChZx55pn873//O+rHItOZmpojX8PDg6fJnV6l6pOFprqXbz1FRZCYKGpjHE2XU339IRX0lDkGeCw0ktQ+nsAzKPTm3s7IEN6H+++Hjz8W02pb0rabbbXotDFMjJ/IzvKuU2CKmpvpFxBAXX0W+IfjRMVNY2/i7V1v9xgcbFTmkqRPYehQaCjr7HKqqoJodS2GlHDuuw/27IGvv4bVeyPB7UaqrWXR8kXcMvYWZsSd6c3y0ag0pEel8/jaJzErCgnP/RXrkAFIZWXYckqIjYWMpiYa/Jrbddw2Op1UF6pxBBYRqlZSXV5OSf9w9tttoPJnpnIBW23LcKuUkJREWlMTe+sbwV9FelUTjBlDZLSS336DUaPEOhM1GvKH9EdyO2io301aczEMExaaMREpWLWBRJb+3mW55IKmTHSa0VQFBTHJ4ysD5g2dAko3AZXTGZPoT1V4JNaD+/gp9yfmps2hsakRg16PYcECAm02CtxWJm/dSmJjI4Ut22qKj8KvrLJHQZO0OgttoIKYv/6FhvD+GBrMbKvO7fbatqWkZXs7JiRz5odzSEs3kmcTgubLDDMJ/ST6928jaNasgYsuEr+tVu962rqcno5by9KiAu7/7DNub6n70xOHJGjOPPNMADZs2MCuXbv4xz/+QUxMDN9+++2hrO64YDKZKCgoYMKECd5parWa0aNHs7Ob/Lb7778fvV5PWFgYX331FQ8//LD3s127drVbH8D48eO7XZ/M0eOoChqXC4NajU6l6rWveX9TEwN7WYO8qEi8JOp0h2ehufyLy/lw74c+P3M4xEPuRA8K/ve/T03RVV0tRG3HwOC+WGj+9S9RPf+551rDFTwWmgZLOf4B4YyKGUXBwa1dlvMtstlI9Pcnt3I7aslJvs3G6cmnExIQ0mNwsMU/l7RwYaGpzu/scqqslPjvstG816IOUlJEK4LtmVoICmLtts8oNBWyeNwSEs8y8t8Vrer+tbNfo9xYB3cOIMTvRl4OOUhV/yhCDmwhPkFiv8VCk8qB2y3uc5ck0ehyUVuookFVSLSfPyVGIwETp3LZ5H8wKkjP+YPnYXc382v+r5CaSmpVFfssdaAKJP5gEUyYQFSU6J3pETRJwcF8PW0acTUmBtRJuBUqb+7x5KghoFRRbQjrsp13cXMmQRGJTNy3j8D4eO/0ycMSwJhEun468f5+1AQFIxUV81PuT5yRcgZmm43g0FAU/v7EKpVENDSQ8pe/kAwUtHS8Vsf1Q1NVQ3Bw94Kmf0kFJRHRRMXHY5swg9iqRvbUl3R7bdsfQzNKt5vfx2g4UHsA16RnyDTGs9/t4mZ+R39FdmuAtiQJC80tt4jg7/UiKN3lEvsYEgIhoRIVQXauqFjPze+/j2batF7th7rnWVqprKzktddeQ5IkRowYwYQJE4iLi2PYsGEMGzaMq6++ui+r4+DBg9x5551s3bqVhg5PLPuhll7uJeaWq9ux4F9oaKj3M1888cQTPPHEE+Tk5PDOO+8wcODAduvsy/oMBkOn4z5aHKvtnCjY7SLLob7+6LhUqo1GYux2QpRKzA0NmHW6Hpc5aDTibmqior6eQJWq23mLi8Uzsc7QiN2hw2w+tKzAjOIMBuoGYk7qfBJqa0VlUbe74YR2Oz3xhIgl6vCucNzZUbaTp9Yt5dPLPjji65Yk8ULfHGAnMdGPkpLWQNfKSnHdrNae722zWcRexsWJ5cxmsDU2orPZaKgpwKAZxKCgeKasOIjpp7tQfNhB/K5ZQ1VjI7GjRvFj8V76J0xlf3U1/cLC+NPQP/H2lreZnzi/i2OQCNBVkWqIJTnKjLtchdlsbvc8zC13YA9Vc69hEOTmckFkJKNHi1L9ppQUvlr9JncvuJtnn3TguPF3HlQ6+GSDjv8OHMiAoAHcm/guWx6sYqz9MoruGsGW3zYTt2cLqsgZKC0WbCo1oaFmioogJN6BobkZlBbM1nKmKEfwvl6PauY5xAWPJ8TtZkCKlaCPruTZNc+S3j+ByJwDVA1XYNDoUGdkYF60iPAqMwaDcAeazdDf7WZdUjxzNuUxonEh2THlJLe8hUSgw2Axs2XAKObs3Cl8yG2wOW00u6vQRGqYuT8bs2dUbyFp9bdcdFU8OpsNncOBze5HRnEGU+ZM4f2M7WjDwjCbzfQPCiIoOJiG3Fz619VRWFuLOSgIXVI6knUNSqWZgAAhkn21Twy3uXEGaNA3N6NcMIXB5SXkVSi6HQuhdVzJrSpjYuZOchMH8dGg97jkk8v5bf/5mCdOwmAqo2qqFYU5nKYmP8wZhSIILD0dFiwQbqdJkzAaxX2tVII9ugSD041u9QrMU6eC292rMaxPFpqFCxeyfPlydu7cyV133UVCQgLx8fGcf/75fVmNl0WLFqHT6Xj99df59ttv2/0cbTxus45xQPX19d7PuiMtLY1zzz2XefPm4WgpaWgwGPq0PqfTyaOPPsoDDzzAAw88wKpVq/p+IDI+8bzFHq2gYLPTiV6tRqtUYu2lhaamRaTX9qLcfGkpxMZL/CduL3UB1h7n90Wzs5liUzFVTb4r53luVbdb1DY7EXE6xbPvWAiu2lrxslhc3K5ie5cs+3Uzm8rXcbA2p+eZ+4jZDM7xNVxYuQP1pLp2LidPrY76+p5LyU87+DZjNv2Xb4MLKBxbjCSB0eFAalRTUVuIW6llYMRAkmscuHfv6rRC97L3KdNoiHM6yajKIDlA6y2yd8mwS6jO3EbZz75d9FWNNeBnZVBsP/Tf/hvrEjsxZQ4sttb7v9DkRGN38mZVFffm5pJtsTB8uHA91cWGElBYyjTDIt5e3gxhDmZ8PAa92cz3a9dCURFVVdBfp2Wg8yCGyXNZF95IWsNOmsPEd6bO6SAkRNzrDS6XiHsLVlHeWMb4oBASqqv5YeBA9jY1MSIwUOiNbbehdAbxYvnXVK/7CJe/AVQadFlZMHKkt/7f0KHid3yLQpiYsZ0plelUhra+5CqVSlROGxkxUT4tNLl1ufhJOmpjFEz00bvqnpuSOO8cNQEqFSGShFIZTGJwIv2C+9EA6KNFTFFSQACTDAaIiKBfYKDXBRSTOoIQs4MmPytE2Lp0X4dZnFgD/AlTqwmYPoFB1RWUN1lh3z7oRcjE3vpSZu/YhUvjT0jIEIYFTWdFv2v4Yfx4knbtJdhWx/dhReKZs3mzMG9ptXgjwa1WHF99zyh2odO6KQ89ALZqylfv4v6mJh544AEeffTRHvejTxaaXbt2UVlZia7lbbS6upotW7YcctBrVlYWGzZsQK3u024cEYKDg0lOTmbbtm1MnjwZEAJj165dLFq0qFfrcDgcVFZWYjKZiIiIYNSoUWzrUF96+/btjB492ufyFouFpUuX9kpAHSmO5baOJ4WFYlAoL2/T3+QIYm1sJECvx9/PD6u/f6/Oa43RiNnfH0tAQI/z5+fDoCnNGAP8qXZoD+m6ZVVnYcJEmaPM5/I2m3BnNDaKAfxEvDc8QctNTYd+HR9a/RBTE6dyZtqZ3c731FPw4ovivFx3HbzeQ8zr5tIMzBozT+1cQ2OamhUjRhzaDvrgxbwyzHeWsiA2lq1TGjCbk73HbzYLi8vBg+Jttk0caSemFK1gfPZP/OXcwdQPT6bJoqdYrebTt8PYFZqBKuVKIkIjGFtmJ7S8lNWfVLOmJI0lSwCTidJ166n90w2EbV1LuaOcC2ISKVAoMZjNGC6+gm1b6ml+9Sa0jVcLk2gbNpVk0GAOZPi/7mGNuZaqGdO4NyeOm+6y8vX74SgUUGR1E2Bt5myDgalxcfxgs/HnyTHs3w+bk/JZ6Erm8cfjGH9NDVn+oWRtiORG/Qq29++P4YorSJ/4J84JO5/63DASUxfwffAVLDHXsDccNA4dJUolIQEGrFZwBipxanTMUGcR8tNuRg6QuPzXX3lnzBh2NjXxZGwsEXo9ccEGbon7lNRL/03C3Uu4WRUIGolYt5ugwYMZ2KQgJgaSksRxJttsmCsrOW3PZvqZdrAl9Yp23yWnWs3esEAMGRmdbuK8gjzU7mRKwgMY7nR2+g7efnvr38F6PWopkBkpYzBoNFTodEQOHIjBYODlUaNQKRSoFAqSw8P5wWbDYDAwaORU1LVm7ijPwbxEjcU5jv6G9tZhi8NCmEWB2d+f5PBwNBERBJuasLuUGK6/HnJz4a9/9Wna2bQJXnoJMm5zcG9OIYnqYHZJEvdMf5hzVixgw7BhnPb3EpSxX/Jz5F8IRIVhwwaYNEmcizPPFOn2AwfiFxrDNw2VBA8JoPHBu7BZorm11ETgq7cTc/G1mM1mXu/hS9knC01qamq77KTIyEjOPvtsHnnkkb6sxkt6ejrl5T4KLR0jbrvtNpYuXUpGRgZWq5WHH34YjUbDBRdc0Gne7OxsvvzyS8xmM5IkceDAAe655x7Gjx/vbbZ58803s2LFCpYvX47D4WD58uX88MMP3HLLLcf60E55uquyetiYzZgqKghuiaEps9SzOn810H1HW0/Dt+peWGiKikAbL+ZvUnRfnl6SJK77+jp2V7SvnHqgRrwR+qzOihALUVEiTudEdTl54nsONaHynV3v8Pj6x1mZs7LHefPz4YEHhJDpqecMQKFtD8qsS/jBWMj2I+jSdUkSjxtzCHtxGC8NGEBdWh2FJnEvSJJEfb3wXKhUPd/fUc3FlN73Iln+fqgH17OrwIEEZO+206zIwSKB0+EgqdpOY6iO8m+28sknLQsvX86WAVMIqrPB99+QHpXOgEAdeTYbLFsGAQGsW7cMf6td9NDoQEZZLoPz41B/8wV7W5oQ10cNxH/lh94q2GVWO9FGI4SHc35EBF/V1BASAkmD6/jFv5hB5WpWrICB5zYwMVhPTZ6JsZs3s2PSJMjLIy5rFYszr2WH3xQsdcFYE9Owq5TkVRYzJTiYGoeD0DCJujoREOzvUHOt+Wke+cHGgAtu4Krqan5raMDudjOs5UV96FDIzIQhl9xGoFuDrtyNQpLQjRwJCgXjxsHeNl0X+gcEoEZBbWEScVW7aeg3rN15CFX4sT9G3VqMpQ2Z1ZnopDFISgUxPSj2OJ2OyvAIbohdAAcPYtbpCG7J/PFTKlG1CMrkgIDWGJqIKJrVCtY3WlAEOXmhpqjTeqsbKvFTBaN3KtAolaBQ4KeNJtquxx0YCHo95jW/09Eb+fHHcPXVEBVeAFIgiVXVTIsK5zeTiflj05kXtYFRB0s4L1BHQd0vzNKGYRpbgfTrrzRNGc/q/NW4gw3w3nvw449sfns/E5Kr4U9/IqqylrAiFYkmWJPQuwaq0EdBc++99/LEE0/0ZZFuee2117jtttv49NNPWbduXbufY8HixYu59tprmTNnDuHh4axfv56VK1cSFBREUVERQUFBrG8JWJIkieeee47ExET0ej3z5s1j+PDhfPPNN971TZo0iWXLlnkDh++//37ef/99uQHncaC2Vlg0j4agsb/xGtXfLEengApTAe9mfMJN397ETz/Baad1vZynrkNVD/FhkiTcHspIMZ9V1b2g+SjjI97Z9Q4rDq5oN/1A7QHi9HFdCpr6epFNoNd3FjQl5hIyqzK73W5HHEchff1wBE1GVQZ3rLiDcwae06v+Q9n1NvzSmjo1NPRFk82OJXAflyc8QKMujHqn47BKUezY0ertybdacUsSCdUh9NdqiakNZr22ErfkJv1/6exuWEVYmGi82N39bW+WSJCKqJs3E3NgIAHOZn6orEejUJBZlAONYgA05uWhVCj5ZZSBiuaDZCdWCtfExx/zdeJZSJUBhG/azYTI0QwMDGRvYyPSd9/BlVcydPRc8kPAuqdz4kN2VR7p1QYUAweyR6HAoFKxfPoIljY9R3GeGKSqHHZia6shPJyY5gL2NDVRbLMRO/UXKqL6o8zKY/Zs2C+ZmR5l4LTg3aTXNVLgcFAbE8Pjc9ZiC4rk98h5VFRAov84dkfEcKCujmnBwTgkieBoF7W1IjNRZVMxvHYtF12jhZdfJvIvf+Gc8HBGBgWJwRyRoJSVBajV5E25iqFVxQQ5HCjaPMf9/FqPc5xezy9DRvODXcQSWVPT252HGE0Q+bEuyM+nqjyXV7a/wiWfXcLPuT+TWZ2JmmGENtrwi4np9h6JCwigPCWVkVYDzgMHaNJqMWg0neZL8venzG7H7naDQsH2YQMxSQpiXkpnWXMx+zpkGRgL9mM0hBKibLXApA0aQnlEFIXPPwlTprDr5Y1cf32ra3r3brjhBnjnqQoeXnM21sBgts94ntnxYWwwmbDjgqn1XG4s5/bTldicNk4PchE0K4cyex1R269i9u49vJS1Eq64QsTQmBToQjRIDz5IQXQci7ZupmxADGtqe64Q7KFPguZPf/oTS5cuZd68ebz++uvs3r0b92E8xDIzM/n111+57LLLmDlzpvfn9NNPP+R19gWFQsGSJUuoqKjAYrGwbt06hg8fDkBiYiKNjY1Mnz4dgEGDBrFhwwaMRiONjY0UFBTw6quvEtPhJrz44ovZv38/NpuN/fv3H/cmnDZb7+IB/mjU1Igqu/X1Ig7jSLJ303IqLWbe3P5vvsr6hOGxE8irz+PXDZaOhS/b0RsLjclmoqlJBHw2Bwk/uE3d9QE02Zu49+d7GRs7lp0V7QeVA7UHSFacRnlDhc/Btq5OVFL1lQHxv23/49G1PfusPayur2dcL0qT+6KkmwAej5A5FEHzt1V/47bxt/GnMX8i35jf4/z7xhfybXR+rwTN91sOgMufx+8cDgGjcEpgPcQvmtMpAp5b+iCSZbEQbQ8kKlw8nsfVxLAruoJ1BevIqs4i2/UzYWGil2N3gqYuz0gQTRQnh5Hi78/0vXvYbCkmRKmmQZNNsD0FlVtBfU4O7gFpfB1SyTdzIuDiYrLWViP98gvfh47GUhaJVelmfqWBacHBVNjtZJeWwoIFRAZGkh2tpm5H5/YJecZc0uvVMGgQe5uauDQqivx+A9FLTTSu+x0Au6OKmPo6fvOrYt47kxmotvF1TQ3u5J8p50yCGiq47sJ6tjU0MN6gZ27ULhz6AfQPCOD3hgb2meL4eslutg5aREUFhNvH8N0wPfudTibq9agAbayDujohaPxMdgKcFirTk1Fccw2cdRaPJifzjzbBulOmiDhVmw1WRF7DhKLf0Tc2dhmVrlQomBFt4FfD+WzRTEWZnNju85SgcJpCVFTp4Jx/pPHe7vdIDE5k4acL+eHgD+BKJdrUIPyI3RDn50dZ//7w7LM0tGTOBvtILoj190elUHjjaFZPmUJKbQFR1cHMssfwVFF7K401dz8HosOJ8GsVNJNuv5Sq0DDeL/WDqVNRbNxIczP89pv4/LvvYP58mMpv7OwfgRo3t74+j2khwZhdLgLXr+fn+nrOt9lQVVQyJnYM8cVrweDkmutHcMul61EGj+Dp8tYHjydlu8BWwa6UfpyfvRbb1ElsLN7Y7XlpS58Ezbp163jhhRdISEjgpZdeYty4cej1eqb1MqWqI/fccw//+te/MBqNOBwO78/RznA6lZg3D6ZP7zIj8w9LTY2oTAlHPjDYcfAAbr8g7hl/K/dPuYv+YYMw+BvYuP8ANTVdB2pW2O30DwjoUtCYbCZin40lp6wGhQJMKvE9aNY4u1znUxufIikkidfcZ5GT1z6W7UDNAco3zcDhtmO0GTst6xE0viw0xeZi6qy9P3HrTSb2NjX1uWqyyekkZcsW6ro6J4dooXG6nawvXM+Vw68kOSSZAmNBtxYUm03CMqSeGo21R6EAsPL3PQTbhqOPcwlFCFQ3H1rwtqdOSm5L2Y+spibCGwKJjBT/T3VF0qCzsTTja+L18ZSpNxAa2rOgacwqwkgw2SqJYXo9Q0qb2BdixGlrwG/ms6RHDUHZpKa+qAjN0HQsU05n47jBKJIt2N94i+aRE6nrpyLNX8fP/RVM3FFJoErFaRYLK88/H+Li2LBBQVZgOOYdnS00RY25DGtoxjVoEFkWC9fExGDVhLJ+4EA0W4QAUitKCcGfW375K0MihuCoWsPXNTUUqn8i47dzqVDEMDxtH2aXi5E6HRPUO9nnP4qxej07GhpEVeA4BTGxCoorXQQYx/LO5AaKQ0MZsns34RoN4f0d7N8PO6oPENiwj4zkcGIjkr37OTwoiLPCW1svzJkjsonfew++L0wnrkkSgqYHS7syqR+THBsIDmkfS5RiMED+IgJHTubXsf/mtxt+Y+kZS8m6PYu7Jt2F5E4lrq62Z0Hj70/Z1KnQ1ITprbdQShI6H4JGpVCQ6O/vdTutHzWO1IPbMBggpSGEfZb2jSebc3PYHxNDv4BWs1NUkA6Fy843u8upHzqVQbUbmT1LYrXwrPPjj3DGGWDb8hurkrTE+/mhUCgI12gonTyZ8smTyRg/ntTQUCgtZXzsOIY//Dwj9myh7LR7ea26kUcjJcoIYmWtuIk9RfXWlGzHpgtnR9FUQq66lcyqTOqt9fSGPgma8ePHc+utt/Lmm2+ye/duGhoa+Pnnn7m0FyWJfWEymbjzzjsxGAyoVKp2PzJHhoMHhWl6wQLRxO1kwel2s2DPnkNuK1BTAwkJojDdkXQ7WR1WgsvqMGsDSXSpiQsMpcntZljUMDKqMmluFlk5vqh0OEjX6boUNPtq9mF1WtlfVkJoKFQ4WuImdM6uanLxUcZHvLUnhTG3LmH45nzMza3KJLs2G0vOONRSoE+3k0fQ+Cq6VWIu6ZOg2dHQgIQYjPtChd2OQ5KoaXNO3i4v97qvPIKmvnfPMy+7K3ajUqpIj0onOSSZBqsZ847fuiy6s7HACrE2Slw2wsIkbLauryPAtuI9pOhGsKOhgRCLPzhtvLLr/b7tZAsewe2J28myWNDV6LyCJj5MRVCBjp9qKnjxzBcxBm5HF2zrUdDYDhZToelHpsXCMJ2OZE0STYFqmixlpNou5t/nPYmjXk1VURkMHoxt3E1MzMpEpXRh2P022857HPWAJlINFr4Y7CDmsxWQkcGZO3bww4wZALz8MmSpk2jYdLCTJbiiOZeBTfXkDBuGJElM1OsJliz8OHEo4fs2AKDQmrAHNONwO/hp0U8U5HzAr8Z66qVG7AenUx89mIzqYkbodASoVKQ27uI3yyjGBgWxo7GR8nL4IiyPr6/6jUcmr2dHbDQ1oUpC7HYiX3yRcI2GQeOd/LBrJ//b+R6xxnLWpDiYltj1S7hSKfpDPfMMbN8OI/sPZ3Zubvtu4D5ISBC/O1TuIClYQ0i8H59njKPqp9bgrARDAk/NfQqLRkFCZUVrXn4XxPv7UwawciXmxx/HoFR22eQ5KSCAQpsNh9vNpuQ0Bu5aiyHYRZBJy0Fre+HtyC0kJzqeFH2Ad5pCoSDQ2cheYx2f548lRGHitrkHWb1aPCs2bRIvy65tWziY0o9kra7dsjH+/gzR6YRIKy1l4X4FcRmF7JU2sM8/hLmhodw7ZDp+pZ/xl+ws3JLktdD8UnmQALeNOxteRZF+BmlhaWwq2UROL5IJD6uXU0BAAFOmTOHPf/7zIS0/f/58Nm7svTlJpm84naL2xEsvCfPgyXSqaxwOfqir85pN+7x8jRByERGtTXv//Gf4/PPD268NhevpX48IyDMa0SmVNLlcpAQNo16diULh213hcLupcTgYrtN1WS14X/U+APJrKggPh3K7nSClCoKcXRbXu+r7YlLf/x6mT2dkU5C3hH2tpZZaay2GLBVRjVE9CpqOMa19tdDsaGhAr1Syp4+CxhNP5LHQ2N1urj9wgIyW9fhyOR20WHD24N5ZV7iOqf2molKqMLz1AfVPKwgeP00UtfHB16X1+OfrMbtcSHonCkX3YiG/aQ8Tk0ewraGBUYEGlE3+/Of3t32nyDc3w9KlXZruOgmapiY0ZYHe9ODwcHBW5RMUPoELh1yIsjmUGr/tPQoaZ14RNdpEspqaGBoYSNqY01G5XESYEzk/8l7GDIxG69RQUSy6YO4lkWlbVpBekMdHQy/me2kajvBmIoIPsjN0EIq77oJzzmH+hx+yNiyM0hoXy5dD//np9DdW8NxzrdtusjdhdleS1FjG3sREhup0qJVKklTNbBmeSP/SDWSUZ+IMs7LFvZ3/LvgvCYYEzk2agKphH9HDFjNhdCAhk4awrbGR8Xo92O2ElWfyfdloxgTp2W5uoCahni9cpVxROITZn40hb0AdAQnXEOcnoVixgnCXC3V8FaYF5zAkbDYDKhu5/PoveWD6A12fOOCyy0Qtq8ZGmHPXtfz3+uu7nR+83RI6CZpofz+SRtjpf3Y6OV9ldIqftgc0klhW1GpS7oI4Pz/KmptBpcJ00UUY2gbydCBVq+X9yko+qaoiABh9MAdFeB7aGi31Tme70hGqonJKImNJ0bXPYorWOHFFW3j4iQCqksYzQ7WRrVtFJef+g12U+ZWjzSmgKjGZfgEB+CQ+HvLymPL0x/x1noRu/0zGN0fwVEoKfio/ztE5KbdZ+La21tvHaWt9BYkaCX9/8SIzLGoY2/KyWbCg29MDHOfmlFFRUZxzzjnceOON/OMf/2j3I3P4VFYKU3ZsrKjA6StzY+9eKOl9QchjRlXLF643NVt8UVsrxEzbh/5HNX/j/Z2fdL9gD2ze/hUBTglTRAQ1W6r5z7Oi9UGQZRiB/TMJC/M9yHisMj1ZaABK6isIC4Py5maG6AIhyOmzfoTdZeemjc2Y3nwZzjiDUZZgbwn7A7UHiA6M5dPm67h6h+9Mp/r6VkHT1nAhSRIl5hJqrT0EkrRQ0dxMeXMzF23cyN4+9mnwXOf6lkAnz+/iFiFrMomHnEfQuCSJyb//zg89+BHXF61nRpKwIvDpp7w7P5bd913nM9MEYIO1noT8CCI0GgrtVkJDu46jMRqhKWgPC8aOYHtDAzOi9LjrdAyLmcz9q+7vvEBmJtxzT6tPqQNtBY1bkthnseDOa7XQRESAxfEtimDRwVldNo0854aeXWMlxdTr+5HZ1MQwnQ77ADMDCnOJKrJ6KvMTFaimyWpm14ABlLrthOcUkV6Qx/9iL2FdcRN6hwZ34F4cJcNxP/QwTJzIIKORaH9/Hv/OyJgxkDZ3IpHNNv7zcI03Xq3AWEBUkxZDcx17Q0MZ3pJBNNhfRW5CNH6uJv7yjzGYDXpuPO8hZvWfBcCNY27Evu9JKgzj+c/PZoJnD2V1UBDj9HrIykIRqOX3+mQSbHoKmm1wzwEeTkpmui4U+14DoR+mYYueikaqg7PPJqKkhGX7lxPnnI6zOpnUihLCz+w5SUOjEZds/Hjw16lbc7S7oStBE6nRUO1wcNrt6YxQZnRy/ysD64iyOfAq2C6I8/enyuHA7nZjcjoJ7qbcySPJyST4+3PN/v3Mbm5mgE2LIySTZqOaaI2Gg23Mj0Hl1VRHRNKvQ1p2gp8KbaJo/qmbO5WIAxtJToaHXmzGtGQX0/bvJ79/P5yhcZ2W9RIfD7W1qNKHo7/+FoYY7+CGgnQGBAYCcP7ABQTWruHN8nKMRqgI+ZoiB0wIjfE2qIzXJfG/D4uYM6fb0yPOZc+zHD327t3LyJEjycnJYf369d6fDRs2HM/d+sNQWirScjUaSE31LWgeegjefvvY71tPVPWhCJ0vamraCxq3G+qCV7PB/exh7VfejlVYokIx6fU4MivZulaFyebGXjYURVQmERG+B8IKu51wtZo4f38haFZ2TiP2CJoyc4ugsdsZEhiI0uDbQlNXXUxsIxgmTBdl2o0Kb2DwgZoDjGvsxyh2069G3a2FRq9vb6GptdZic9potDdid/Ucz7ajsZGBNhtTNm1iTx8FjSfzq65lJPRc76IW/7/JJMYSj6DZ0dBArdNJVjf+IKdTYl3hOqYnioB+CgupTU8lO85fiIuO87vdZAXUk24LIyUggDybrcvrCPDLphowlDFj0HC2mc3MjNWjsqqZGHk5H2d+jFvqYD3yBGF6ukN2oK2gKdq4Ebsk0XRQ6xU0+hA7buVX1Et+VDQ348yfyr6mngWNpryI4v5pmF0uBgUGkmH7ibDcPAYe2OfpnUh/vQtjkIa/qFSMqorht9S/8HtUGY2pLrZWNZGm1lHq2Iu7YjhFxQp47z0UmzczPzycL4rruOEG6J80krJgJYOlfd7TW2QqYmRVJA1BMex1Or2CZqzBQKMums3R/izdkUBFWDgzhrUG285JmcPsmIHcFhXM1fv2MWLYMDSNjZwXEQG7dqEYOZL+KUpKMjXEKgJQO1T8tV88MTGi5pTp4xgmuK1Ulq5EmjGDsPJy9tQVsKj/vVS7TLiqQ/A3dDH4duD220UD797iETQtIVVePIJGGjqUKFcFFbvafxfdBith/j0XWYrWaFAgniVmlwtDN6EZ8f7+vDtkCPsnTOA5lYqEJhUWXRZmMwwIDGzndgqtNmIODSaxg5UlVavDHaJh7lwIPWsKbN7MxLOaKbx/B6OCdMwwmXh3/mzcfuEkdCVo4uJg/HgUr7zCy2f/j5jgsHYxjfMHzKcmbxk/1NZQ5K7ge8XNDE2az9jgCEJDReuOb5Yl4tAV8vzzPZ6i4ytofv31V58/qz2RRzKHRWmpEMggBE1ubmerd3n5idnL51AtNM1uNzsaGqg2ur0up5oacZySvpjagG3srdzb84p8UN1UjSsvB3XqQExaLYH5pWBTUt3ooipjGBa/fEIiLV0Kmhg/PyI1GqpsNuED3LevXWzM/pr9DIscRpWlgtAwiYoeBI15/y4a/EATEwcpKURXNrYKmtoDnLVXPPDi6t1dCprQ0M5ZTsWmYnQaMQD1Jhhve0MD40pLGXHwIHsaGvqUvtzR5eS53h4LjdEoaq54BM2PLU/DjqmnbTlz0T4abBbGxo0VhYGKi/FPG8QncVGs1+s7dfvc1tAATgWj9UGkaLXkWa3dpkRvyd9LgC0JiyKAMrudcXo9OtQ0mWO91ZnbUVgofns6DHegrg5i536MPTefrMWLGWCxUFuh9AqaKjLBBoMCAllX04Arbxo7azcSFu7uNhtLW1NM/pBEUgICCFSp+LXwJwJ+cvLfZU8zaIAQXQP8zDx12eU0uBQMWZPKsP43oY5WwrBSXIlNTAjXkVG9h3jNcCFW/PwgnxxvYwABAABJREFUMZGBxjBqkuu55BIYED6AjHA3s5N+x9OqrNhczJByPaYYkeE0PCgIgGmhUbgCovl5QAAp2ZXY/TREt3GdqJQqVl29iqcHj2GITsedUVFsvOUWIt1u2LkTRo9m9mzhOr6sqT8pHw9BrVQSEyMEocOu4Puxp9FY/CXZ/QIJyc9FFxjLDWeNojFGidXav+sT1gGFAloMCb2iK0ET7eeHQ5IoDQggJ2oy2p++bve5LRSiwqN7XL9aqSS6xe3Uk4XGw4DAQOJiYgg32Wnw34/ZDAO1WrI9gkaSMFhcNOsCOllZ0g3h2PyC+OknYMAAyM8naFYtivIAPp80iBvXr+f7UWNpVgZ1baHRaGDrVkhLA2jt59RCmDaMF2beD8ZdrJn2MPGhZ5HrFha5228X49bcCUn0Sy/02bKhI8dV0Cxbtoxdu3a1m7Zz504++ODI90Y5FSkraxU0BYpfaKSMqhyz6ErYQkXFoRctO5p4LTR9yLm+PTubkA0bGLdjBxVpNe0sNPuy7RBUSUDZbN7c+Wa75QqMBZQ1dC4M5iE3V8Qf/VrwK5PsUWgGDMKk0RBSWsiC2SoaHC6ytsaiUwXjF7ef4jonn1e1j6WotNuJbhE0TYDVzw/js28SFQU5OaKnS159HrP6z6K2uYLAGCd2SWKIToeiixga24FMiiP9xJM3JYWAGiMFpZmYbCY2Fm9k+q4qtgadTrzZRnkfgoJLzCWkhKag0+h6FUezo6GBsZmZDCsooM7l8qan94aOLiePpaaojcspKUn8druFoJkTGtopU6Mte0zrSWAyfio/8SVwuwlJS2edLok3L7igNT+6hV/q69EfDCUlWUFKQAD5Nlu3qds59QcJcQ3k98ZGBgUGolerCdGoKa2TGBA+gKzqrPYLFBaK4iZr1viMoymvM1M+9XLOU/+bjJGjGLpzJ46SSk9/Qw6Yf0dRMZrhSgPra8xQMQqH245Fl9WthcZgLKIkJYphOh1N9ibWF63HpP8TfkonAfuE8E0w12BoaOb2snSKDqpITVHw7+l/xaXXoR5bSVqQgzprHelRQ9t56/Z+qkfqZ0HSOgnThpEX6w8ji3nRmcMVWVkUmopIq1JRmzKUXKvVa6EZFhIPtkrWGO6lMjQUtUPyaWnwUypZnp7OnUOHotJqRSPDzz+HUaO4+Wb48EOIyYgmBSGUYmLE/REeDhHBgVw45ELeZRcx5ZUk6gcSnuTEEuSPw//IVXPuyODBMHy4qL7dFoNazfywMF4tLyd/7MWk/P6Z9zO7y01DiD8x/ZJ7tY14Pz+Km5uFhaa3FfZjYwk0W7FJucJCo9Wy+oCFv/8dMJmoDw5H6ZSI6FDTZkxoHC6/SJFo0K8fWCxY+1VzyeAQ9DqJC95/n7rAYKrw71rQdCA0tHPW6R0T7uD18RegiFlI+dDruKdfP6aFhHD77aKp6q2XJ1HaVNir9R9XQfPoo492quMSGxvbroO1zKFTWtqaCfj4bw+hm/YumisuEvmIiGdrRcXJZ6Gpf+Jh3Mb2loM6h4NXy8pYN2oUF4dH0RxmbSdotmeXgluNY/WDLNuzjGZnq2nkvlX38dDqh7rcl9deE53ud5VlMsKix5KaikuhIL4qjz9dqcLt5yYvV8HQyGEQlclW6rirQ7yEx0ITptGgkCSqx4zB75P3sJrtLFkCB2sPEqgJZFzcOEyuCjTRzQSpVCT4++PW+Y6hceccpDK6JbsgIgIpKIj0Rh1DXx5KgLGJIQUFbJ9wO/2sjRTXdzBzu7tO2y42F9MvuB9h2rDeC5qNG9GFh5PqcLC3D4HBVXY7aoWis4Wmg8vJ7YYSo5PNZjN/iY9nv8XSyRL039JS9pibqAn6lUhLi7upoADi40mKSMOkCGK7pwwsLc0fm2FXYyPOTD1JSaLqa561+9TtksZ8ItT9yWhq8g7UUVo1ZQ1OhkYO9Qoap1MU1HXmFYko08pKyMujyeXiH/n53sDmQlMBAFdJX7P1tLNIbFbzfOAD3jf+neW/ozWNJs1uYEujmaBADWemncnPppe6FjQuFyFNpZT10zFUp2Nd4Tri9fEMiBnEvujTEa/dcNdnn/Gfl3awZ40/eXkt3a5j0/F3NuNMdvLi6r+SFpbG0IGBXneSzQZfveVPBH7sarkxd06awaPXzaWszsW3tbXsaWoipdrBxkljSAoIILbFChMcEMysiARK+p9DQUQ8AU3qLjN1ACHW77lH3KQPPQRXXsnYsaKa73/+05oYFBYGanVrptEV6VfwZt7nSE4TekLIKy0lzGRCNWxi19s6TOLiRA8qXyzu14+XS0upXXAhgyvXejMVCs12QCJ22OBebWNuWBhvV1QIC01vs4GjopCUSjQNuZhMwmqTY7Pyww9AYSH74qMItKo6XYcRhkjQGMiqKxTnPziYzY0mFo0OhuxstGYzauMWQNG1y6kDHS00HuIPJuJyBHF2YAwPdohXSgxOpMZSg8XRTdphC8dV0FRVVXUSNDExMVRWVh6nPTqJKSjo9PbnsdBUNlZSbC5GSvoFXdY2PPlv9fUikr+toGmyN2F1HFo9jSNJld2OAqhstnHfqvtotIsHp62kgNAHlvDukoXtBrSf6+sZqtMx3mAgWgqAGFu74mN7C4vRueJw5cwkWBPGNwdaKzxnVGXwa8GvXe7L/v1C+P28tZCEGgfmlBQABjUcZPgAJfi7iY2XGBU3jGZDJiWStZMQq2ix0KgUCsKtVqqvuQazS8fL87/j008kNq/awOCIwcQGxdJIOYTbifXzI0Stxq110tjU+c1enV9IXVyo+EehQJGSwg3Bp3PbuNtYGXIHBSGjkCZPIcZppqa2vQWqoUGIhK4sNAn6hF4JmnUZzZQ12xmRtR/rjHmMqKvrUxxNlcNBakAAtQ4Hd628iwqbhVg/P6+FxmgUL4cKBfxQUc/AwEBmh4bS4HJRnp/vrRppdbn4W24u7+UUIA36iojKllIShYWQlER8cBJ2v3D2RUbS1NIk8B//gGuvhb1NTZh260hORricerDQVDkKiNP29wbbAsQa1FRbnAyNaBU0O3aI0vDbV5VyecQY6qdMhzVreDg/n8cKC73HWGbJZ2ANDLYWsTs0mirtAs63fuSts7CzYifxyjHoigxkOMyERrh5Zu4zrCxbRrXfFt/JUxUVqCUn5aFO9hetYvHPi5mXOo+LL1Lgf84ZIjjk4EH0y5cTcNmN/PKLCPVJSRFpt1MjhAvkX5Nu5qk5TzFqlKg9UlkJX30l6rRMDhe1YAAOjpjO/K2bMT40iFn6UDJcelLqTfyansR54eHtBsvTI+JxDXFwZcJnBDh74dN56CGhXm65xVui95ZbaNeBXKkUDTs9InB2ymwAmgOcNDohPyuLFKORh58L9rWFo87pISEkBQSwfZSaXepx4iQC+fllRBqN6Memd7+CFu5OSGCd0cgv9fW9t9CoVLgjI9AZKzE2NjNAq6VOayV3nw1HVha7k6IIcXQWJKEaDUqXld/ry2i0N5Kb2o8cl4spBgMcOIB7QBq2ih8IUSkJ91Gx2BceQSNJ8OCDIunwjTfggrOVvGSawLJJqZ2EVWRgJFq1lhJzz9krx1XQxMbGkp2d3W5adnY2US39KWS65t7cXIwOh+jgunAh9O8PbYKpz927l0xMxMXBltItxOnjCHMcZH9sOOUtr3UVLS/tbQXNPT/fw4OrHzzk/XK5eq6y2huqHA6SAwJYW7aTpzY+xao80Ym84PM3AAhfs5X/++n/vKLmh9pa5rfUiQi1BaBOsKHRtAqanKoSIvwTiIhQMDn8XNYUrAFEptCB2gPkG/P5oHg/q+rqRCfENiP8gQNwxx2wu6CQiAozpqQkAiQFca5KwhXCvfL4My6GRg7FHJBJlcqK1e1u14W7ssVCAxBpNFKWPIDXHNdxZcmTZIRO4/pFtzNFkURMUAxWdQXuEDF/iFqNpJaot3ROUw4oLOOgKpqSEpGZREoK14fM5MEZD6L69jtW688ndHA0doUav4r2gqa+XvQCMhg6p217LDThgeE9CppvshrRl7upJYx/5f/G8KKiPlloKu12BgcGUtBUywtbXiDLWMyooCBKm5txut2YTMJMHRICPxvrmBcWhlalIlmhYN+VV8IXXwDwQ10dTW43Kwo3wcH52EuHiA0UFIggnIBYkFyEu5zsarn/c3Ph4y9d5FisKAt1xMVBSkAARTYbzvD9XWc5kU9ysLDQDGsJskgMU1PvbLHQ1AhBk5cnevBJA9x8PDiQCWffxsvLMnm5rIwQtdpb+KzKUcAUUzrnP/IPCvyt7N/an8wF98Cdd+JyOthduZupKWMo2qADSYF2aBMpoSncM2ox/xd3IcadPqpmFhdTrY6hSF1FTtl67pxwJ0/OeZILL4RR98wVz4rHH4dLLmH8hf3IzBT3g8dFPSIoiOSAABalL2R8/HhmzoRZs0SV+tdfh+uvx1vcDqAofiiztu9gVEwFQ4zhlGuSSbbVsCbCXwT0tmGsXk9TfAM1agUh7q5Tj7vj0ktFrErb0i0xMa0WGrVSzd+m/o1+/QdQ63aTV1xMf0nq5A46VigUCu7p14/PFcW877oY18fC7VSWm0t4bSN+Ib0L1on08+OO+Hi2NjT0KobGgzIunoQmNXWuQtI0Gpz+Lg4ExKG54gr2JEYSIwX53Gedy0yGuYanNjzFOynBxJkqCcApLI2JsfjbStk2dlz3VrY2eATNzp2iEez27fD008IKfvt1vq11CoWCxOBEioyd+1B1Os5e7cVR4uKLL+bqq69m586dNDU1sXPnTq699louueSS47lbJzxNLhfPFBezs7ERZs8W3+xRo0R3PcTgtsZopDiwgfh42FS8ien9pjOjPo1b//pXZs+dS73DgacvaNsYmoyqDNYW+g5e7A1ffAFz5x76sXmostuJkJooaKzl7IFn83OuSDdo/O57NiZEMT9XzbIdb7OucB1uSWJlXZ1X0AQ1BqCIFYOFR9CUmItJ0PejXz+Ico5le7moqptdm42/yp/JCZP5R0Ehf87ORpo6VRStQYQb5ebC3XdDgKGAwGojprg4/F0ampUBBLYUlThroZtZoWNIPfgzVXqhFNtaaTwuJywWIqur+c0Sz/fx1/HK1LGorzmbDIOB6bkqYoJicKnNNOkaifXz85qVa6ydY4mCS2vYVJfA+x9ITN+5k2UzZohR1GKBlSv51H4+/ZKUVPrHEVxbh9MtKg5LUmtAsELROW27xFxCP4NwOfWUul1mdRDf2Ej5IDeZCdmkZ2R0W4um3lrPjLdn4HSL46lyOBii01HcZAQgt6GGETodEiLLy1M9NCQEttiNzA0NZfdvyxm8Yzv7Bw8WT0bg06oqdEolB5oLYcN93tpDHgtNqRNUzZUMdTfjqadcWgqhIy1gE649tRoS/P2RJDcvKmfxQ/TpfL3/606urSZNPimRSexrbGTYbbfB6tWkRKtpVrlIDBQWGkmSyMuDIUkWqgPVqJqKYWgAdz9wJtcHJTA6KIjCFkFTpyzk/aufpzREwcyvR7H3ex2Gx/8GpaVUvvIskiRx5vhBbN2koF9zEMq0Jti7l4fv/5Knt5Sx5+0nO5/ooiLyVUk0a/TcM+ZKbh53M8EBLdaJ1FShXN57DxYvJiZGtAnxNLwEmBMayrltqucqFGLQKS0Vsc1XX423uJ3d7aZYFUb/8mwuSdqCansozdokvps8GadKyfQOUbJj9HrM+v9n76yjo7j+Nv5Z32ySjbsb7gR3py016u5G5VehrpTqW+peWqCu1IACLUVLcXfi7rax9Xn/mNVkIziUfc7JyezMnZk7d+7c+9yvNkNMMyHSoyM0vr7w1VdwwQXOfVFREO+SdeDBYQ8ybMi5VCqV5NTXixF7TyEuDQuj1mrilx7nI127CnJzKS0txrfa0jJRebt4KC4OX6m08yonQBIVRVdDEA2STDQ33khERQ3psetY8NV6fukbQ4zcM6EKlhjZVVfO25vepnT4eaTl7eP8b8+HrCyqogKI8o9CfQT1CA4WF1Pz54ukdOFCOHxYJMrtIT4g/vSX0Dz11FOkpqYycOBAtFot6enpJCcne+PQ2FCnryPhrQTq9O5GLjk2C/VcnU4cYV59VVQq2wLKVJpM1Fss1Kj1xMTAxqKNDIgawLimCA7GxSE1mZi+bx+FpVbUavfJ7HDVYXaV7nKoeI4UW7aIid2OMsCvA2VGI/vylhIZ1I3bBtzGX9l/UV4mEL1xP68EXYleL+cuIZ3VuavZ1dBAk9XKcNvAqaxRYw7RYxUER2C9CmMhqeEiodHUprOrdBcmi4m95XvpGd6T9KRzyDErKK2tZW1EhMMjJSvLppuPsxAYEMw999xHhVaLrFlOrTYeZUEBMqDJYqH3X7v46nc1DSEiCXA1aC4zmYhQKqnZuZHwhgb+KdQQfpMvD15xBWMuGsvc8b3purOGEE0IWGXUKuuIUiqRS6XIjTKqjC0IjdlMULmOQ9Ykvt+tY3N9Pff37k1lSQmGFSu46YknWJkYRWws1PomElcnemndfjs8/7yT0ICoHtfrnbbiBXUFxGpjCVZ3rHKq0JsIa6xmrbaY7CApqdu3kdnc3Kan097yvazLX0dWdRZGq5Vas5muPj5UmgzE+MdQ2KwjQqkk2qZ2sgfbCggSKBH0dNdoMF59BfrGLH4cnEjzji00Wywsrqqip7UQpTKRNM0QJ6GxSWgymprwt9QRqTKyLTQUGhspKoKLHmjEmuVLUqI4o8ilUpTmGiZGP8OAff1Y9PKNDJo7iH8LxCQ2TaYmzOpywkLCsJjNpAoCXHwxsd/MRRZohqouNBgbKK4vJicH+gYXkB8ZAcZSGvfdxy2/L+WS9VWOSK4AKlUZSpOJ+or5bPgsgOBgSOmtgTlzCJr1ChOV3RgxTMbu3eBfq8Ea2wgXXIB06lSGvPJ/rPPgWi/kF7A1tCsIFkZH9nA/KJGIXnZTpkDfvgCMHi2qm+w4LySEt9PS3E7z9xc1JXbblYH+/hxqamJNbS0+Minl5DBGvZEdm+sILz3Ivffcz7khIcil7tNMhFIpSmYGVxOh7JyqwhOmTXMnMG+8Abff7l4mtFcv9Eole9Vqkuz6qFMEhVTKUK0W02gf8ifeApdfTkVdFbLaztmf2BGmVPJ1jx6c60I4O0RUFJfuNbPmwK1Y9+yjujAG1ehgtpUZQBlDnNpzHaIVUjZU5jA0dih7U3pybbXoHKE/tI/iMDXR/u2na2gJe5yur78WpXydRUJAAgW6gg7LnVJCo1ar+eqrr6ioqGDTpk2UlZXx1Vdf4ePjcyqrddpgU9Em8uvyya5xDyCTbRsIcyoqQKUSxRCxsQ5Ck2kjPMaQZsIjzWwp2sKA6AGkGZXUBASw7OGHqTQa+cVSTJcuTkJTp6+jrLGMAHUAmwo3HVWdd+0SDS3zO5YOtotSox61sRSr3I+xiWPJqc3htmlLCTIYeXDuLSwxT2H8Ljnrc9aw9KefmKBWo7QNnNIKFYJCoMxoxDZ3YfIpoEdsLHFxoC9ORSlTsr9iP3vL99IrrBfWkBEk5+/h5n/+4aMXXxTTXRcWcvCguHotaypBrh3Ah9Mv4ub9GVjrZRjD45Dk5+MrE4PrsWoVquZmUIcgtTRT6eLtU2o00thYwL2vT0BtaWZvkRHJsCpuj4ri/tgYPrz7aQIP5SNBCo0RVMkaibIZ2qlMcmpNLQiNrYGzmlLZFVPKdSFRjJRIeGjgQK6qrWVJ+iCs92UQHGmhISiBlFofShtK2bAB3n9fdGO3R3K3L6Dr6pxB9TprFFxtMhNWV0BDagLZTekk5+TSYLE4vJZaIqNaVI/sr9jvCDCYqFLQKEgZKXuQSpOJYIWCOLWa7AY9BoNYP584A1YE1KY6+haYmDD+QvIiYqnf8g9/VFURrpCy58CnyKUpDBokklhBwCGhyWhuJkJqQiWtYGv37lj3HaAo+FtywzeRiK9jMjdajBgb8oiLGMwdO0p55kAqYxPHcvcfdwOQXZ0LRg3K7X/QtaICxQ8/wP79BC5fjiagkYJsH5KDktlXsY/sbOjmk0dmfDTd/EKY2mUMQ9d+Q/AP80lQqciz2dBEKZpIrCinQF5AY7OZ0aNFzsFll7F7YBw/PLOH2LnPEh9ppGaXBkVAIdTVIbzwArt79WZ/UOuAbMbsAnYnRCIxVJAQ4GEinzPHLWz2//4HM2e2+6oB0ZPnrrvE7SiVikilkneLihgTGMTmKCuJNatZuj6fCZs3UxYZyMVhnifdnnJ/iG8mVnN0EhpPSEsTvZxcERgfj8RqZXtqKsl2t7FTiOEBAUh761g88S0QBGp1pQgNR27Xc2FoKMlHMk8OGoS/oOCNxL7s/WwT1rIAQvs1c7iwAkEVRoqfZ11cko8PgiqCx0Y9wzZ/f8bu3svAqIEYDx8gKwii/Y6c0BgMog3W8OGdPy8hMIF83WmucrIjJCSE9PR0QjuIlHi2YWPhRkB0K3ZFtquEJjZWHP1cCE1WczNSQBLTTIllL1KJlLTgNHRGPcqmOmJLSrhEoyFDWk/XruLq3GgUpTOhmlAmp0x2rEiPBIIAO3eKBnotTKOOCI0WC3pBwtiwJKpMJvyV/gyKGkKC8Vk2JisZO7g3+nHnELsilxveXcsynY5zNjkJWF2FDJ8mJXl6vWPSVoQUkBomSmgKC6QMiBrA1uKtIqEJ78Uus5ablq7ion5p/NzYSPmIEbB+PYcOiYN4Xm0ePj5x3Pj7RtTVPpgqlEgS48FOaMxmWL2arNhYZHoBSXMh/5SILg96i4Vas5mG+nwG1/nT0FxJbepi9vlXc15ICBMUtVgFM7UY0WcVQX0k1RgcniFqi5xtkp/cRa5ZWRQFKzBbImBcOV0yI3knKoqf+vUjUxD4pVCNvFnO++WFGCLiSahVU1BbyqFD4nuaO9dJaFQqMVxEbS1UNlVisBhECY0LodFbLG4EzdHWFjMxlSX0HXcl+oaBIBMIBIf0oSUOV4kdY1/FPsqNRoLlcoqq9oJcy/dPXIseOSqrgXiViow6ccIPCAB5jJ4Ak4rCXWtAAmO7D8IQlkZYlZ7/O7gVKtYwJW4QeqWcwYPF/lxfZxUJTWIiGc3NdPf1p6piAwdjYqhY8C3vNj9MbnE2d03x5RWb1mZ7yXYUpkp2KYzcNfdKbr38Cu6Pms6esj00m5rZV5RLYn40eQe30TM21mF4EjhgAHKfJjIyoGdYT/ZX7Cc7GxKleWREhtDFP4j3zn2PeelNJG/5igSZjDy9HotFIFCtIVFvQAKgLcCWJgkkEh6/KozlH82Eb79lVvDb5K71oVFbC5ddRjGg91VTEBpJS5iz8shM8kMrNHi2b9BoxERnNvTqRacisbbEQH9/FldVMTYwiIZ+PQjJ3Mkzr+Twv2Xr0AJT2sh/NDjQH4AE7fEjNJ4gk0oJ1utpUqtJOpKgMicIw7Ra6uN0ZBaq4aefyI9NpNEU3/GJx4o77uCbTx5kfi9/dmf4EKrzpTqmjqyKYvAJomuQZwnNhMguSEKGc1GBQBiQvHs3E+PHoSmuYJ+/nhhtzBFVwy4VvukmjkjNdtqqnKI7yChqR3z8SXjJpwHe2fROm8c2FG4AIK/O3Qc/W68nTKEg12h0mvXHxTkJjV5PF/whSs/Gwo0MjhmMrLGJHIkEqa4JnZ+Gvg0NFPs00M3mLVhXJ042XUK6MCJuxBGlbLejtBQqmsz0u7oOmyNJhyiuL2bQ3EFYrE4dVbnRCIKVy5NHiFFTLRbSgycxSb+V4sHdkUgkTHljCsk1h+hfLGdj796M++QTcRZDFGkG6tXk6kXD4MBAIEC0C4mLE4Uv6dHpbCvZxt7yvcSE9GKDrp6LNm3kUFwzowICWHDZZfDPPxw8aCM0dXlYfSLopfRF+Xhf9M92w7ebSGg0UimNGRlgMnFo4lQCi/TE+WhZkisaaZebTEiBgtJcEg77oqgZgiQmhEJjMxOCgliTtwa5vpEdXYbTtHQNNERRLVgdhMbHKmWv5lsmfzmZqiabTUtWFtlBEugVRIhRzeHF/iSkpLDqwQdZOWsW5QEjSF6aysv5+ZR3SSSuRsL2jCLUanj8cVi3zkloJBJRnVBXB2/kZaHq+iAaqYoQRYCD0MwrLeW6FvFbAPQUEV6rY9K596Bt7kNesIIEk6lNQpNRnUGoJpT9FfsdsXl2FKwBqRyswUiVwVTUZRKnUpHTaEClEuN6SCIN+DerqNj+D6URvnTz96fEYuW+hx5gZ0MzTXk/8MrohzFrzPToa0Uuh+oDZWKfiIvjcFMTY8JT2Z77F5FGI/v2bCdWKMWoiiJMWi4GsXvrLQJvvJOuShnFhjrGLD3E4ZQUYrYcJEQTws7SnewtymHaIRX7Bw2ip4sKI+C882hWS8g4KBqH7y3bT0EBRBjyyAkOpF9QLBqFBkO36VSoZCRs20aeXk9+RQ0yTQRpvv4kBCYQlJLFBNFBB0EQ2FG6g7jJl8G773JZ9iv45xopDtFgvuoq9jQ0IBEEcmISaTS62y1JsrMoTNYQ1Xm70aPCQD8/BGBUQAChg8cimM0MP/wrvQoKKB0+HP82DFdHR4lkqkfkiSU0ACEyGVJBaBUJ91RgiFZLvUbP/jIDJCRwuPsgrJb2k1IeL3QJS4KgHPbsgV7FERT7NJAbWwIKBT3CPBOam+J7cmjIEP7p359NkZFICgs5V90LrFZ2qqqPWEIjl4uqpptuOrK6JwQktA5Y6QEnndDU1NQwf/585s2b1+5f3ekYHOUEYPaa2RysFCcKQRBYZnOtsApWNhZuZETcCPJqWxCa5mbGBwaSC9Rp4xAE+L/wcMptbZbV3ExafRCCysrq4l0Mix0GBw6Qk5aGT7OCQn8l/crKqAtsIibRilIprs4PVR2ia0hXhscNZ0PhBjeS0Rns2gXBdxWQc8XBThOapRlL2Vq81S2w3b9lB5CY6jg/eQxSRFuUNMlERueBerKYoSyqTxjvdP2Am/93BQqpldSmJocrZGUlhFnUDi+SkHADJmU5sdpYB6EZGDWQdfnryK7JJlcey5CyMqoHJ7OuZAPTw8JY0bUrrF/vIDS5tblU+4eRPiSZkgIpcr2CgH5JsGMHvjIZTbt2wahR5PbtRVxRJV18E9hRmUV1czUlRiNhCgV7cvPpWdPM9OmxCJo4kqjDVyZjVe4qAo0KNsUNgTWrURojqbPKHIRGaTUikQXQLbQb076dht6sR8jM5FCgCekoGZf5RbF8mQRBqWJwQwOhY8dSUCKnuymQCYGBLB6RQFyNnDd3P0nSsB3cdBP4+LgnD9ZqxT6wRlePIeJc8l9/nfGv/kBmUTVPPy2qMTNbZOkFEJRlIOgJDowiXOjLQa2BBJ3O4ZLcEhlVGZzf5XxRQmMyEa5QsD7nT1FspDUjyP3JrdxDvM3byK4Os4bq8dGpad6/i7r4CEIUCsIUCr4fN4HZq7/kq/NeJ0EdAhbwjRNVjY+UZ/DeDTegVyjINxiYFtePyqZKeoQH8eYjL3LJFSEUhUdi+Pl90att1izCtx/isy9XUXD7begWdic/MBDDmjUMih7E5qLNZFTkMLBSYF9cHL18nRmGA8eMQa9S4r/rb3qE9WBn0X7kcqB0L+XBoaSHiLE1Lkq7nHl9BOJ//JECg4GdeVk0BMTQMyGOlOAUXvow27HIyK3NpcHYQM+wnjB5Msa0niwsvQYBCbmDBrGnsZF+Jl+KwiPYkrvN5aUIqAqyKI9Tk6Zx1vFEIN3fHz+ZjH5+fgxKGMa+eDX9Fywjs188Pu0kTxwcIEpo0kJPPKEJDQsj1kUlfSqhlctJsPpyQKrDbLVSKTUQYDrxbQCQHJSExEZoUiMV3BEWg2VUOjRCXIhn4imRSEjTaOjj50dsQgIYjQwuECgIgPWlm4nWHhmhAfjsM9HF/kiQEJhAka6ow3In/Q1HRETw/PPPM3v27Hb/ojpIpf5fwYXdLmTutrkAHGpq4pw9e6g2mThUeQiD2cAFXS8gty7XLRhRtl7P+KAgipRKPvgrnhVrLTxpNPJ7t26g15PZ3Ez83gJ86+VsrSkmPTod9u0jJyWFJFkAOb5mEvPykBilmKIbCQhwSmjSgrvQM7w3FquFfRWt8960h227rDSNLaHOV8+Bw+1nQ7ZjedZyAHJqc5z78jfhL7GgUfgQJJdTZTIRUppKgAEGjrnKUa7qsjsp9pmAn74AyW23iW4YiG7j0TI1K0sOkFebh39MITIUlOGLT7SR4mLoF5HO3vK9BPkEsbqmiYsXL8Z83dVsKdrCEK2WLWo1lt27KT6oo2tXKCrOojwomC6D+nLuuWIkb+lll0BlJb7V1TQePAjjx5MdH0fP0hzCCSUsIIWvdn9FkcFArEpFcUU2SY06whNs0sfqTVgFK2vy1hAvD2FbcFd8Nq1GrYjFKJE7MtjKhSbkkji+ueQbSupLWJmzEnPmYfaGKbGmGrl/UCi1tWLcEy66CG68kcJCUQt5bUQEa+PURNY30b/pQQ4NG8easl+5+x6B3r2d78Hu6ZRpsKK2NjGnuZnAnFIKKqtZtEjMrZSn12NZu1YMIGGDTGnA6CsadsapenEoyEBMabFHCY1VsJJZnclF3S7iUOUhSg16gmQSthZvRm2RQagBQSrncOlW4lQqis0GR6I/faAeZY0aeVY21jQxjPpLSUn8vWMHj+gimZg8kaICCdQq4e+vuFC9nK1SIy9On86BpiZ8pFJSfAMYEDWAnpYClmuaUQdfT7C+iR6f/wDvvYfQqxeT79DQd80+5ILAVsNU1AJk79/P4OhBbC7eTG5dLr3rGzjo7++IQQMQYLN36l/6Ld2D+7CvaiexKToM2YfQacNI9BHLXjVkEp/3sRL76y+YBYGDB3eRHxlDl7Q0kgOTyal1BmTcXrKdXuG9UMlVIJGgeu1FJlhXE1/XzMHmZvY0NjIlPoyY8nL+3LbL2dClpcgMTejCfOkXcIQzxxFianAw6/r1Qy6VMiRmCKvCmwgvrKF8aPsReaNUKr7v0YO0k2AvGaJQHJm9yQnGIF8tJcF1vF1YhL9RSVRTa5fpE4GkoCQEnyp2HawnPh6uCjOD0hdZpQ+d4noaDYSEoPznX6qig2gwNhyxUfDRIsY/BoGOU6qcdEKTm5tLTk5Oh38HPYi3/4u4se+NLNi1AL1ZT6FtVXuoqYkNhRtIj04nLTiNnOo8YmLEAKdWQSC7WVSLyKwCe4ISWZ7RiBlY268fFBWR1dzMxT8+Re/8fRirzHQJToNdu8iJjGRkRCw5QY1Y8nKQZvtRFdTgRmj+UfbDZ916jEO+56Ws/e3WHcSFtd3+8w9dJT4yKVIJHKj2rHJwhcVqYUX2CgLVgW6Gz+tL9xGjFvXdIQoFVSYT+oOlNCoCSIx3zsLDhkEVSegqN2G98QZYtw7j/kz274dkXxVryg7ywtoXmHpFIZGaGO7KyOQ3ihAE0OhT0Kq0dI/oz8q6Oi6oqqLIfAkHKw/iV2elWRA4PCCdrrUb6dIFjIeLUZpMRMTEcM89cN11iDYIr7+O7+HDNGZnw7hxZAUFMbhwD8omGUlh/ViwcwGFBgMxKhWqwgwMMiVhNrVrTta3bC7ajNFipF9ADAf9IlCX5pLoo0VpaXTEmZAIdciIQi1XMzR2KDtLd2LNzGBHt17IdSq6aNXcfTdcdhkUPPwOjB5Nfr5IaM4JCaFMaSU/IZTwLbdzpfZ9blt0G9t6TmDUxc5vTKuFsjoLFVYlF5at59MhQ6iqMmBWVnHoEOTpDZgEgeLffxetii0W0QVcLYNI0RIzMtiX0qAIonIyHYSmrk70qgJRvWiwGJiYLBprHK6voKGhgAfzY1Dr5RDTDILA7qL1xKlUlAlOCU2TnwFJhYqg/Ap8e/YHIH5vNDl7kylcupvSUjFqgapBQdnPP/Fq8RUUaKQgk/FUTg6pPj5IJBJGxo/EULaCgIKdNPQ9l96BgQw52IDl1Vco+t9N7PVtRPjzL/j8c4LDFETjS4afH2ObI9lStIXShmyUGhlIJKS4TJJqmQw1MECzEd3hXsTI+iEb9h5N1XWYFGpHJNXYKCVlFdPZ1TueqOZmynIPkx8eTmpgICnBKWTXOr+D7SXb6R/Z33mPSaP4Jf1FkoPCOWQjNOkhfqTm57Oz2MW9PiuL+oBoLL7BjAh3cV06AZBLpfTzF6UtiYGJ7EsSJ2fjuNHtnQbA5eHhSI/EkOIoEapQkHQaqJvsmBQdgDm9imdzczkvMw1/n5MzDYf4hCA1+VHclENCAuwpWo8yezM+VUcgxYuLg9WrkaSIi4ojVTkdLRQyBVH+HQs5Tr0M7izHiPgRhGpC+fnAzw5Cc7CpiQ0FGxgaO5RA/3hyanLR6+G778TYHEZBINnHh9iqarIjY9hQXY9WJmNdv37UFxRQbjLRN2s3wXoZVx8IpevQ8xCWLCFbq2V6r1gKfJVUHjiI+aAfheoGAgOhtlbgUNVhtpk0fNO9O+fJS/i12RdLB4kGv/8e+vQRPYl2xhdzoTSaRKWaUlmzx/xDrthaLEYEuaDrBQ5CU6uvJbuhhq5acWVpJzTGrALqtbFu5w8dCk1hCoS6feyXVMKYMWx8/k98faFXSjlGRTBf7/ma6H67SQhNYEd9PbuaGoiMhKJC0TDYP2IcCVVVpJ03jVefjkLWFE3vCTsIrvJnxbCpnKv9Bz8/kJYbia2vQyqRMGYMPPqorRKXX45GoaBJrYa+fcmUyehanIdPWT1qn3B2le0ir7mRWJWKgMpCKgOi6e7ry4KuXYmSCzy/5nlGxo+kV5iGOn8r+d2m0Ic6lEZnLiirtQoQDeb7R/Znd9F2FJnZZKcOJjBfnPFfeQUmT4Zx40QX3F9/Fb0IfGUyJmpD+Gr0ZEo2F3Bz+jVk3ptJr/BejJo/it1louGyvz8cNjaiFAzcvHkf4zIzmT9xNCqpDrNgIqdJJCi5hw6JLGX3burrrBh9fAjs1gUQPReq/bqScPiww4Nn61Z49llobhbVTfEB8WgUGrqGdiWroYbqwh28+mkeCcWHSEo4iNIop6AuD4WphnqpGb8QC/sr9lOr0mMqN5NYZiRiwCiKi+HCC2FdbW+CCvdw681WcnIgurGRktBQPh/+GDKLmVeysvijuhpVhUiQR8SN4J/8f2jMepuEChgXGc229GgOhgjc2vw9g2IGoRyQDuPHi3mBTBoyRoxgwIEaMqoz8C87wLauXenh64usxWQcoFBg9JPw708lDGh4mqLo16mTqPARzI6orhIJJDVdxtMD6kjIzaXcAgqzmSilkuSgZLKqnRKaHaU7GBA1wO0eF295gvSkMPY1NnLAlnohvqyaEpmLyiAri0PRiaDQMjj45LkqSyQS9KOG8tFACOozuOMTThKuj4jg5hYR6U8lxoZqIb6ZYeZQwouD8D2xWkEHJBIJan0SBIqEZm3+WrofNtLjt+6dv0hcHBw6RESfYcT4x+CnOjnSJYA4bcd9+awmNIIg8OyzzxIdHY2vry+jR49mr2sGNheUl5dzww03kJSUhJ+fH4mJiTz++OMY2rAV+O2335BIJFx77bXt1kEikXD7gNv5ZNsn7oSmcAM9oodzTlYDOpkPqHR8/z1kNTUTq1KhkkpJKC6mLiWEDEk9N0ZGUhAczOqqKrSCQJMxnMakVH4Zloz03feo2rGDBmBghA9l8jjqD2UjyfbjsFWU0ORWFdOkjKRRgIvDwni7/zkYLEa+LjjQqs2sglOdtGcPHDgAVz/eSFNSHQ90jaSbnwZ1WnOHnk5/Zv3JxKQJ9BciHSqn3WW78fWNJUEjBsEKUSioMpuRFBbQHObeoa1+RojSE29UsLtsN8b04VT89i8vvghFlVuQqqNIjxnEq+tfxS+oDwZBYHt9vcOO5omRT6DxSWfa2rXs7nElOTkwtc8g7nlpC8IBf96TDuEq4+cIBgOKZg1xnkSeEgm+AwbQeOWVmCUS8gwGfGt80OYU0ijI8Ff6c0BXSYRCRoSuhqaQJORSKTdERTE5ZTJLM5cyNmEsfcJVEGFgZcRVBKoaEJqcBnBGcxlIRPeAfpH9qN67BUEQqI7sS0RZoL0afPihaGx38cVieCK7t8yV0WEsHD2aAF0+vXuL+XTeOecd7h9yP+M/H8/ByoMEBECW0IjaUEyfjTkM1WlZ27svCXWQ2LeaKquJLkol2SYTlqFDYPVqarccosbfn8SBAwExekCNbCBdMnMdEhpbrEdyckSD4LRgMbZJz7Ce5DfVoc/agTE+hmZjKIMjV6FqlDKtyzTmb3kLuVWKPmUvvT/sQ4W8AVNlJvE68O89kDlzYOpUeO33rmhkBso25fL++9C1Np/iwYP5c+LNxFY2cXX37ihrVGz5xYdHH4WhMcPZV7EPvayYh2oG80xCAtHfLubPN+6mT2Rfnhr1lKPdQ0MhsEFDZu/e+K3+l6TAJHqU6Zl7wcVc4cEIIFAupyw2lfxl+xEyJ9OrOY7ikDAiW8RaGRg0ia2RvoQ215MXFkdgdT1SiYSUoBSyarIcMXy2l2xvRWgAumk0/FFVhcQmJUprNlMd7OIhmpnJxvgYJKYmgjoZkv54oWe30dx1PsQHJnRc+CRhbFAQI+26y9MAqT4+dNkex5DtKTQ0uDmbnXD4mUXD4IQEWJu3lpGxo4kP73xgPLsTSmz/MWTdl9VB4eOLZ8d0nOPxrCY0c+bMYd68eSxfvpzKykpGjBjBlClTaPCQi6ahoYGuXbuyYsUKdDodK1asYMmSJTzqWKo7UVlZyf3338+IESM6VY8b+t3AhsIN7K0rx18mY3d9Lfsr9iMP6IlBEJCFTWTYOXmUlMCqQ3qS1WpoaCCluJCwcb5UhdYzKiCQAVVVLABSGxvZZulPoKqU8thkOO88cqxWIhQK/ORymgO6oSkvJbjaj10NDWgDBLLqDhMYM5WJQcGopFLitTGkmnJ5NXOXW10/2voR1/x8jeN3Xh7ccqvAkpQMVKui6B2vJE3jQ0CP5g4Ng9ceWMpL8/O59/LXKS8S2c+O4j0IlniCbRFEQ2w2NKryAqwx7oRmU3092noffGoSOVh5kJ9LhjFE2MDFF8OevL+wShVcN/BeiuqLsPp3YZhWS7HRyMOGewid+zITkieyXg/nS6XM+y2ESy+FYfGDKLRu4f9u0tI8IRx1kA8N8z5CpggnKdCzC6pvQACNvXuTbxBdbwUhlpCcbKrMZnqF9yK7uR6NtZHEGilCdBfHeVNSpgAwNnEsib5qCDPwZf35lIZp0RYfdkxszcYiLFJx1OsX2Q/t4XxK0mIw+SeRUOuMYSGViuYtDzzgHo/j/NAQcuMiSOhe4GYI/OToJ7mo20Us2LmAoCA4aGhAVnOY8G0HWbRmMAeSEularyRyYCVyq4QhdXUc6JHG3PgKWLOGyrVrMCkU9IkSSUpYGJQ3DiextJQKk4kmi8VBaDIzRQmNndD0COtBXrOOXjozym49ya9P5mBCMn61TTw/7nk+2fYxEXUq9nf/mrTw/pikUkIr19KollEuieDjjxGzBSuVSHr25J2LVrJrF/Qs3ktJly4YovRYDGnIJkxCNqsXH5wTzcKF8OVHEaQFp6Gq6UdynC8SiYTEpP48cNEr/N+k/2NK6hRH+4SEgE+1Dxnx8bB8ORP8+hBvTGJLtxRu8rDiD5TLaerRBd/cfWzcIGGG5SJ2JIWT5OseobZHVyXpBV9wSJ7L5u7d0TSKE0pyUDI6g040JK8vobyxnD4RrW1Rumo0lJlM9NBokEkkDAsOpiwigbzqai7bt4+3fXzYGhuMTxueZicSQ2KHIJPIiPI7O2wgjwYSiYSbTSkc3KiksZGTJqEBCBSSkMRtwKjJJbc2l+dvG87nnx/BBeyefcnJom3XScSI+I7n07Oa0HzwwQfMnDmT3r174+Pjw+zZszEajfzyyy+tyiYnJ/PEE0+QkpKCVColNTWVm2++mVWrWic1vOOOO/jf//5Hampqp+oRqgllevfpbKnKYWxgIFtqy5iQPIFMkxx/mQxCxhPRJY8LL4Q/9zST7OODJbeAmNIqFL0FrPGNvLX0AcJyNvN7YAAxBUVsF/qjUeRgUogrt+zmZoce2T9hABGN9cSZ1DRarcijDTSWbEIWMITzXGa8h5P7sJ8QKgzOLKe7y3azNGMpec2NLKqsJDdfwHJOCUG9mrnZnIxEIqanlyW0TWgOVR7iy3Xv8+azG4jVSTBHhBK2Uwy49m/mHpr04ZgqxJWlXeXkX1uAIqkFodHp6IGWhrxubMw8yMwfhxBjzMFaWsy63L8JlkGP2PFE+0ejU8YwMSiINLkctWYHw1a+wNYNGzCYzQweO4lvvhETFQ6OGcyW4i0MC/SnzL8B9StPoHpuNpXBkaTGehZ5+kqlNFosHGhqIkmtpi6qFzE5B6gymegd3psykwUMlSRW+yBNSnScNyllElf0vIKB0QNFGwu5wKYiJXtTezBub4Ej7YBOn4tVJtprRPhFMLzWj1/6pSC1mIgVOo6toZXL6bKzma0zFJS0mOSGxQ4jpzaH66+HbGk9qQf3UBGWRm15KpUBWhIsEWjSavFpUJOclcXmxHC+CS5CWLuW2r1irKJEP7GPhYVBc3E6FnMtPoiGxG6EpjqDtBCnhMYi82dMoy+W5DT05QpyYqIIrq6hX2Q/zutyHjU1v1AfGsAH0xciMTcQ7/cRlXEhvPmWqPazCYbgqacY+uujvHDuvyQVZVISHk59cCPyEg15eWDa588t09Q89RT8/DOMSxyHkD2OjiJIhISAosyHDIChQ7l6D+R2m8aY/HrCPXjwBMrlNKQlMSZsP4WFMLlRoHnQKOLV7gapXbtC7e4RJPXsjVkux9cqkiN/lT99Ivrw9Z6v2V6yna6hXfFTtl6+d7XZ7tgzfY/uloBFJmfqjj3sb2zkyYEDWTasL3GKE+yz7QFjEsbw65W/IpMewar/LER6uqiObWw8uRKaXpbrkcXsot8nvekf2Z9gXy1HFJ7HniwrJeWE1O9YcdYSmrq6OnJzcxk82Knrlcvl9O/fnx22/DAd4c8//6R///5u+7766ivKy8u57777jqg+tw+4nYKmJsauX0eVoOLeIQ+ypb6eB2JjsfjHYowp5sorYVeFniS1msKNhfiXmthkrUaqh+35ixgvV2OWyUk4uJ+mtH7U1e6lSeojrpT1epJsA2HX9GFIBYGe/uV012gwxDVw6d9fUKOO5hyXpf2t3Sag1Bfz1J6/HftyanOoM9Qx6/AOLti7l823buWH4Cy+7t+VD14TB9A0Hx/0IU0eCc2Okh30+agPBZ+9jVISjXrNP1jHj6fXwRqaTc3sKdsLygBqc2wSGoWCCqOJkOYCNF3dCcVGnY7x0VqKdnZj9d6D3P5IIJIePchd+i0WwUKaxp8DzXr+uPoPahSRDPb3Z0B2NtsvupiXZU/z25dfM3XnTlZYp+DrK6po0qPTya3NRWttwFcmY/e0aZiUMg7GR5PYIieNHRqZjCarle/KyzkvJISGpN6k5O6k1myme1gvdKgwNRcTVy1D3dUpiteqtHx36XfIpXJUUinqJiXNWj25kaHctKmYrYWbqWmuQW8ox6p0Tk5Dan35pUsMmuoqggI7Z1QZv3ociRUl9N+wgQ0uIRFSg1PJqckhJkZA3aOBibty+K76PN59RU50UxM+ykQk0Q1IylTEb9vKnlB/tkZasRoNaA9uRaFvcoS2Dw2FuvwESgKkRBmayTMYyM0Vx8CMTIGDtUUOCU330O6gDGRohZHasDQkDXJ0Pkqia0oQBHhuzHM01f2BPGgIdRIfIqUy0vTlNMYl8N57YgJmBy65BMmUKTz573nERkdRYrVS7duENVfDnj0igVAqxUj/W7bAzF5vY1j6giMRY1sIDQVrgQ8FBgPNN9/M4NU5rBw2kYvNQR7LB8rl1MbE0F8tGtOH5mymLDnNYRBsR5cuYsLTO3tfDECi4LQNe2XCK8xaM4uVOSvdDILd7qNQEKFQ0Ns2EyoTE+ian0+OXs/fffvy8RvvUJbSnTFxsR7PP5FQyBRM6zLtpN/3TMOAAaJ0Ozf35Epokn0GMGz7PuZdMI8Xxr9w5BdITRXzgNkMwU83nHwKf5pAZ8umHNhCtxoUFOQ41h5mz57Njh072LJli2NfUVERjz76KKtXr0baCT84rVZLvS1b7YDgAQRaqhn00dsEvDSbSP+B7C84wMzgYN7NL6YqxMDQoTpU+dVUbo4ke2sO/kYFsuZmQmvKSLNezaWxvXneYKBHXh4lo9OoKM8kLEDPuuJi9lZWMlyrRafTMb5/GlmRWlKDdhOpiuGLrhsIOu9CBhYV4W8woDMY+PNPMbfP2CAj/xQXOdqkpKqEeHU820uLeDm2Dy9/KOXuB/0YKpc7ykSbzVgkdeQW1KLTubfD3wf/5vyE87l2mZ53msfwYHkTvqPGMWTLH+wr3EdlYy5ahUDlfj06nY5Ag4Hqyno0ITom94zk9sOHuToykiWVlewqK+PFXhF8ZoxHH1jC3ffUoCseSeHav5g4fiLj/f35KCODyfv2UZacTPfaWvqtXMn2a69FcWU//h27mlfy85jzdjM339xMQwNIkdInsA8bsjYwUh7BhspKcu48l4rwUCJNJo99Q6PXc7i+nrV1dSzu3ZsDaXFE/ZWDv8GAf1AqfrUW6kpzCEaNpGtEm/0rttFKee9SsBqJNJqoeuhZ9s95nUjBhyZZE7W1OqRSiKlTkh3XnYiSerRaHZ3ortx7L3RbauGXv//mCouFn3v1ootGQ5QyivqGeg5WlCIzN3PewVrWDB3KsGE6+i1rwOyfAD46fHf5E33wINaLphDZNJ6MwVVUN8nQNjU6nsfXFwx6qOwWS1xlKdmVlZRVS4ieWcDC6CoMmieJVEaj0+nQSMPQmvIIyysls28CsQojdQYDXerzKcivJS4ojsk75rKtax3LCwsZpAoiuTaCzaqBTJ6so2dP3J/7lVdgyxYC+/alrq6OJqsV/5wodu/W0b+/WNbXFwYPhu++guAAUCoN7bZdWBg0/CMQYTSyd/Ro9vz+O7G11QxI7OfxHQYZjdRptfiZ8ujRvY7mw/sp9/Ojf4t+ExUlBgtsPiygNRhIlVgdx4eHD2do2FDmbZzH46Meb7Ov3BcUxFiFQjweGsobn8zl6iGRlPr14PyN/5C0K4zrL3upU2NZR6h3TcXuxXGBTAa9e4sR1VUqOvUNHw/ExoqpB6bEiarVI+4f3buLmdpt553MvtGZe521EhqtLfNqrWuqacTAf9oOsrI+/fTTfPLJJ6xevZrYWOcq6JZbbuGhhx4irUVSt7ZgNpuZNWsWTzzxBI8+/jiNew8SU1VFnLGZf+p01JnNdPfxxbrXQKFfIPsM9aiSmvn5QzXlO0sIlYiyQqNhM365lxMUGck5GzeSWJhNyrBQ8mpziVHJuXz/fpotFsbYyFtqVCjFfjI0yjUYsj+lx9ZP2da1G+dv2eqo2zffiHNEml8I1TYbYKtgpUhXxKXdL6PAoiTJ4I/0zyhmprksdRsaiD58GLkEso01tHSS2lq8lZGa7vjv/ZdFnE9eHkiGDqVPqcC/Gf9gkCvBDwr3ihKaILmcSr2JvekxNMqkvJyfz+0HD/JAZiYfde1KVz8N33wYh0xmpbihEAYMwGfnfkbEj+Dy8HDq9XpezssjprqasMmT6aNUskcmI+zSWuoMoeyrv4nmZrjzTmcd+0X2Y2fpTgb6+/N3TQ3fxjVQpwkgto1AYRqplJW1tXTTaOjm64siMRaNvhk1YFJHg7mRw/l7iDTo0XZvWywQJlVCr1qCrEoOvf4IwftzCXn0OaJVAeBrprlZAL2ewNI6asK6oCqRdHqhNGIEhNx/Pbd+8gk3ymRce+AAP5eX812NCUXIcDZVFRKKgZ6ljcyYlw5AmkJBrV8kDSorsaUlyKV6zMogGnP6szPRh9ywEGQusReDgkQSbI6IIriyhNwmA5XDCylJzMW69w+QqVD5iOqVEqMRP6kU7eHD5EmSCFKKKoqghnpqt2WBxcIdG+aT2qji98pKEv18SMnsxqp9w/BgtiaGg/77byIuvJAqs5lmrOgO+zhSV9gxdqzoLRgRQYexN3r2hL17JSSq1RwWBF6/+hqu/XkVwYmBHssHyGTotFrkjTrWv70VGhooseU7coVaLRpur/pBBSZIUjlVUhKJhKdGPYUVK73De7e8hQP3xMaSZtcVhIczJCODGMka3nhiGzVyJZMjAx0SHC9OT/TpA1YrR6byOUbcdBO8cBSCGQckEveInCcYK1as4IknnuCJJ55g1qxZHZY/ayU0AQEBJCYmsmXLFoYNGwaIBGPnzp1cd911Hs8RBIF77rmH5cuXs27dOhITE92OL1++nC1btvDSSy8BOIyLly1bRlFREaoWouempibmzJmDVqsls7aW+Vu2IJs7h+4GK183NJAQEoJFH0TdPwHozgvi4pwcpiZEUxcUSf3qAgaeH0WjSkmApZzsf8eifbGE78aOZWmKBFWX3VTmVbJiQDqBSjUBtrQAdrJWqVKizH2VfP0FvL5OQ+3c5Ywv+xbJCy8g+Gv5918oL4fJjUmUqZrx8/ejuL6Yams156bfwBuHylHVagkI0Lr376efhq+/xufn7yk8/xHM5h8dBqqCILCmdA2z9bHUaLpzuKQbRUUw7JJe/NKnOw/KemIZMQ+NXsmO1SH4+cmIslgokpbww/hR3JSczPToaG4/dIjP09MdeWLGj4WIfREUGApIGzaY3ncX4Jc6njCtlv8tW8bdF17IZUFBaG+7jaGTJnGgoQF9eCX6b7rwyq/BbNwI4eHORxiZOpL5O+fz27jHSd20CYPJikWlJjUsrJWrLoBfQwM1KhVXJyWh1WqJToC6plCUSiX5Cl+apI1kHl6Kql6PKi21zZk0IiCYDf1LSNMEMXbQFfS49B4Of7aFc3oO4cXuKvT4EVWQyeGoSHQaf4x5zQT31dIB/3ZCq4Xbb2fWBRfQ+MEHfCiXkwY0JFzCC7UmEhuqafKH6OREALqFhfFjSCSFSiXTqvewTZaBTqlGVzKY989dhCG+J2aZr6NP+ftDUxOoQuMJKc5jTaMF3RU1kPl/1K8+h7CJGmpVPvTSaik1GgnTaAiorSXL0INA31p0KhWGen8Um/eitWQzPfNjtm+N48VzRhDf1MRg3WYWXzyPwYPbeGCtFrXVik6lIk6hoiA/iH//hcsvx9FGU6eKRH34cDpsN3uSy96qEGZVVCAPCWfvkrH87yctnrhtQEAAhY2NaENC4IfvIDmZDJmM5JAQtC2Y5223wZgxELhqHF3+T+lWl2HaYay6fRX9IvshlXRyzRkUxHiplrLqX6hQ6bhgwOQOF2ZHiuN9vbMd/fqJWaeDgjrui6c7TlTfmD59OtOnTwdEadLcuXPbLX/WSmgAZsyYwZw5c9i7dy/Nzc08++yzKBQKLr744lZlzWYz1157LatXr/ZIZgAKCgrYtWsXO3fuZOfOnVxwwQWce+657Ny5sxWZaYnCDRuIqakh4Z4n6ZGTy/aGBgb7+5OdDeHGBEJ23cVvcVC1/QFmvVxGqrqQ6EFxdK9Zyo0xA8nKlNCsjUCQydjlH83ysvnEB8TTWxvoiDjrir7vvs99u1T8Nvxduu/7l1/9r0cSHg77RYPGykox58ahZUkIyhAK6grIrskmVhuLThGJQl/M+sMbSHD1ziwqEqP1mkwEVeZCpMDm/WWOw3l1eVQ2VZL852aWBVyJVApZWZCr13PTo09x7tL3UC/4iN/kwzE1yMjOFm1oKqUm/h4wgGtjY+mi0bC6f/9WSe+6hXbjYOVBVquKMSildP/kZ3jmGW757TdiFAqGBAfD448TlJ5OslpNg9XCowMiePNNaGEGxYXdLmRz0WZMhkquChAwJt5CglrtkcyAGOtFLZVypY0VpaXBfnMXgvUGdjU2EoCJsOoGSn3C2hULdA1QgZ+FVLWGEE0I8akDmdfXwsXrxbQQpfVm2LOHHePGI23Kw1QdShtmPW3jhReQLFvG659+yqaLL+aradP46p3ZjJcUcvHaxZT0dmYk7hEdTUZ0LLVWOZNK/mRRnJxAg5rQ0B78azzIjmgTgbhKF0S7E2lYfxJz89hkqkVRlM+NyekoNz1JrMzfkY4iV68n0WCApCRyixTEBYpG4HprFPLtm+G11/gg4HGmLf8ZgIT332frpCe4/532A8UppVLCFAp6+PkikUB2Nm4RkYcPF4lXR/YzIKoC+vQBVYUPGc3N3OXbnUUBN3kkM2CzoTGboUcP+P57GocNo9ZsbmVDA6JBc48eULRX6XHBOyBqQOfJDED37ty3w0p65K9khpoZkzCm8+d6cUpgN2o/mTY0/3Wc1YRm5syZ3HjjjUycOJGQkBDWrVvHsmXL8PPzIz8/Hz8/P9atWwfA+vXr+eabb8jKyiItLQ0/Pz/Hnx2xsbFufxqNBo1G46aWaonNm8X/hVu3EiuTQf/+dLMZJQ+yEZrU0AQq6zKZ+vUUsmuyWVT8MWOSCzCnScjY9w4PjLyWoCD4dZGM+oBYCkIH8uP+H0kNbtvLKnXcJUinXwJXX42qoZK/LWPFlLt797J5syhuv/deWPG1H8h82F5xiJyaHJKCkthWX0+izMi/ZX+6E5pXXoHzzoOrrybhwF7wieXPA86s3f8W/Mt5su7I/t3A/PpLGToUDuSbuXjfPsZU5PC/n39Hn9OTHj3EquzYIRIaswyG780ktp1on3ZC80fWMpbdMALJ9u2wdCmqDz5g7YAB3OGSSmNcYCAPxcXxzKMyZsxofa1w33BGxo/klwO/EFq9Bh8sJLZz7zEBAXzatSuBtpgf0dGQq0gjoKaeXQ0NRCrlJNRCtV/7CVd7R4r36BUokoRJyZP4qEczfTdkIGmUklllhL172d63L1MikjFnjT5yQgOi/mnNGpG17tzJ4O3FJOau5bzlf9M4qK+jWPeYGAoiItBJNMRU5LIp92l6hqiJ6uqPnyQMs8pKmMxd8hAaCpLA4fTNKkRmlSApepPzu04jORkCmtSOgHs5zc0k1dRAWhr5+ZBkyyVjUCUR9883kJnJy5IniJpwLRHV1XSrrWX0kkdp51NyIEqppLuvhqAgkby49lGFAiZNcnqfdoT0dJDs13JRaChdaoLdJHktEWAnND17Qn09+UOGoJZKCWkjFsxdd4n/j4sE/9NPiTFpeHC1DktSAr5K7yx5umOALcSQl9AcP5zVhEYikfD8889TWlpKU1MTa9eupbdtORcfH09DQwOjRo0CYMyYMQiCgF6vp6Ghwe2vLSxYsICvvvqq3Tqcdx4060wU5uURGxYGffvSbZuYaG6QVktWFnSNC+epUU+x9batvHvOuyz96wPIzubjmhVc0esKYgNi+PhjuPVWuNb3V+qG3kCjqdHhUdImZs+GTZuoHzSBinq1OBDv28eWLaK4vW9f6BYnR2I2MeebDGo/XMxbH+awbetWzolMYbP1Y9QJYqRZCgvh00/h2WepO28io3ZkovAfyfeaGh7OysJstbI5ex13/K3mnAWfszVaxciLDPw0egcxSiX3hEoZVgC+td2JihKlJjt34pgMLtrafhCnbqHdOFB5gCUZS/C7/xFYvFhMbjR1Ksk+Pvi5ZP39tFs3Hu0gm/v07tP5+eDPrM5ezq1+DVzVTja1WLWaa1yOSyRQH5lGYHkVBQYDyRp/EuqgKaj999ElQFzJ9wtxEprDodDQPZUe+Tq+3FcLe/awLS6OC2O6UV/tc3SExhVJSZQM7kGfRZvokl2HdOQox6EwlYrQ+noEqYyQLmN5fMJMugao8U8zENg0CGSBxPi4VyAsDCrkXThv2z4Gr9mMYM5mdMJoUlNBUa12l9AUFzsITVqU+J6tod1RN9dSfeUMypv8CHnqHvJ+/51er74qspFOoIevL4P8/QkLE4lxS8Ha+++LGcc7g/R0qPkjlF969eLAfkm7RChQLqfOYhFFL8D27t3p6yvGuvGEK64QbWk6Iy3qEBERKFet4ctLu6CYftlxuKAXJxoBAaLKydZdvDgOOKsJzemAkBDY/eMhCgMDiY2OhtBQulss3KiDnhoN2dmQkixh9vjZdA/rzuSokSz4Qseu8wfxWvXvzBw2E4BLLoE//oB19f24bNRYgI4JTXIyvPYajTffS10dCD2chMbuzT77eQmqJj1Xr/uVC79bRmNKPNuUSq5URRFX8DC/qqZT01wD8+eLS9/evVkZb2HM3n1EN0qpsZSxpKqKh7Oz6f3hMu6862GM8T3h6f28M2gTHPTn1169iBo4CrMUJklUSCSifnnnTlBJpdwyfw/jy1pnenZFt9BubC7aTJGuiHGJ447tpQAXd7uYtXlr2VS0iQe7DueWI0yWKqSkEVJWCsCA4HhSq7WYo9qP3RBvkwLZjT2Hxw0nVhuLcN21XL/tLzKMuxHWr2e7ry/9ff3Q6Th2QgPUX3c556wtpUEhENZ/pNux7rW1RFVWknTT+Tz/PCSp1Uii9AiFg5EQQaTGXf8SGgoFxgjMMgk1lS/TXTEFpUxJSgqYC52EJkevJykzEyFVJDQ9YkXC6ReVxPqU6/kl7j5GjQK/IAWqBQtEst1JfNujB1dHRBAW5q5usiMyUqxnZ5CeLvJiiwXmzoX2An+HKBSUGo0I/ftDeDhb/P0Z1I5tgUYjxsU5bkahCgVXf7+fy65/5Thd0IsTjauvpk0VphdHDi+hOcUYNQoO/F1MYWysQ6XSEN0L/wt3885bUpHQuMyD0rvvISA0hqHd/mFc4jh6RzhH7DFjREPeCycHMzJ+pMew6a1w//0oz5uEyQTGtJ4I+/axdasooQE4r18R6UX55CUF0uvSPmx5eAbVWi19P/4Y6bqnSdZ257ZFt8EPP8BVYibs1UXrscar+O3D7+j27zP8kpnBl4cO8eJ1s+kRFcFtJX3o/9oQPovuReOsrlhMUrqGdSc7JpFJshJAJDT2cEAX/fwvmrj2l7FdQ7pitBgZmzj2uIjb4wLiGBA1gKTAJBKOIoy7pm8aURViuvvBoYn0LByAJLH964QrFHzerZsjA7FKrqLggQKCbrqLK5b/TGEXNX++9x0NEglJgh+CcHwITejYc9kXDv/Gtg5Z38NgIL6iQgzighi2vca/iep1lyE09iDKz11qEhYGFVVSGsIDiWysYXTk+eJ5qdCQKRIaq1UQJTR79pCnSsNohG5JYi6yLkFqXuzyOT//E8455xzbc6Wni7mtjgU9eojJVz/9FEpLRQPjttDX15d6s5nDqamQm8uW+noGneR4HTKprE2JkBde/NfhJTSnGGPGQOGWEgrDwx3Gg39X9uOC+J08+6y4Oky220EeOgTffovfz0vw9w/h0RGt/VftUvl1N61jTGLnDAPtk2JNdE8kxcVoDDX06oWYSvumm4iQCHw4OoTGwDzKTXH0kMvx+fhjmvKqeXPsZ+RuWIol4zBMEwNqrc5bjfHKy+m1ZQkrvrAQ9/wzPLdtGdWaar4fMpyMQxJ6Jii4MjUYlVJCbq4YkKvM/1IGCCKL6dsXqqogPh4CGwpbBdVr9QzqAKL8ojg37dxOPXNnMCN9Brf0v+Wozo0cHE+QToybEKNSEdKQj6pL+4RGIpFwfWRk6wzEwcEkrFmDpkrLTG0ivXx90deLn+7xmC/DfMOZPUXNh5MC8VG4R7UdK5MxuqZGdItGVINmWBtoLk8FSzSxge6OkmFhommOJSaauDq4qKfISlJTIXejmqwGPaEpJnQWC4nbtvHBn2lcf70YLXVkYCBRkWJ/WLVK9Eg6Frz5Jlx55bFdQ6EQyfVjj4lG8j4+bZdVy2SMCAhgZU0NZpWKHQ0NpJ+mAci88OK/CC+hOcUYNQqas4tFlZNKRVYW/Jzdl9Hanfzwg2gw5ghr89VXMG0aAcndKXqwqNOEpSMoleJAXSsJoikwigtS9onEaNky2LaNmGHDaZD6gLaIAzUBDAwPx5g+grtNb9K/Szhv6IaxtrsGq58v1c3V7CnbQ/cr7qYyt5HQi87nvlcm8Zh8Pgv79kQrl3PwoBgbRCoVyVqWzTxmk2kgKTViLBx/f8jPFx95QFgB0UM6tuKcd+E8ru97/XFpExBzbD060lPQk47RtYcMq06UuMUqFESYCvDreQwJ+5KTGaUIZW9wJQP9/amrE109ZcchwrxEIqG+RwqVfVsbkV951VX8n0uQnmS1mgC5nPhxDaA1E6N1JzShoaKrsyaxF3EbL6F/1zBAVP2odWoEqUCvK3SoG8FXpuC9RQnMnOk8PyJCTHYaGnr62Bakp4sJxl1jFbWF8UFBrKytZV9TEzKJhK4nM8iIF16c5fASmlOMhARICiylTKMhVqXi7bchamo/lIf2cs7YZioqxDgFCII4u9ti5Chlx1fxGhAgDtq5mp5MCtshytlnzoRnniElJByVJgapVcX+ZoF0f38O3/gS90veRv3Hz4zcVMrCXlIeWv4Ql/xwCX0i+hDuG054OCjKRvDZro+5dcCtTEieAIiCpq5dxfumpop5fgD+rE4npGg32GLmRETA6MF6NDVFHaprAKamTiVQHXhc2+VokZYGDbUBqKxWNAUVyDET2r+TrjVt4J6BYkCfFIsftbXHR91kR1JQEgkBHtpYrXZzw5FIJAz29ydweD0SrZlgRWuVU14ebClKIrU6VOy7iJ5feZlSolVKel1bS1RRJT8qrmHKuTJHXwDRvgVE6czpojmZOlXM89WZ9DXjAwNZVVPDJp2OgX5+bbr6e+GFF8cfXkJzGiA0pR6pVaBwj5L58+Hqp1NEprNkiXMF/u+/IuM4VsOCNhAQIOYVmVtxEZf886DINIxGuOsuYlQqFJpogqRJ5KsaGOjnx35NOs+mfgPXXos0K4sJ977JipwVTE6ezLJrlwHihJRkPI9+/pN5ecLLgMjLXKO3pqSIEpraWlhfliKKivbudVZs40Zxlkw4BunGKYCvL2jK/bnoQB7GV95kN30Iizk2Ejo53h91sS/qw4HU1R1fQjMucRyTUyZ3quxgrRZLFx2Cr4lgubuEJjoadu+GbZXxXNAvvxUpSVSr2dhUTb+yQ7xafiMPP+x+3O4sdqzqpuOJ886DefM6V3aQvz9GQWBeSUm7BsFeeOHF8cdZGyn4dEJNkozAPDNj75bwzDMwaLBEdKf46iu49FKx0JdfihaJHQToO1oEBsLrr4PPsLuR/Hq1qG6yZfWLUSqxKoKIDunDHqWJvn5+fL4arOMugFGfwMGDXDz4ei4e3Frd0zO8JyNZjo9tIV9UJGaYtSciT0mBpUvFmBwTJkiQCAPFNLT2IA0rV8L48afPcv0I4KOK4/OHH8AqVfBklw0sOcblg1Qi4ZqVg6iIgPCex5fQXN7z8k5H+xzi78874aLBc1ALQjNypEhQk/fHweMFrc5NVKv5tqyM8UY9T2zuR/og9+MajRia3WaDfMZBLpUyOiCAJdXVPNjZYDdeeOHFcYGX0JwGOBAfRFylgs/WOaNHcs018OKLUF0tijV++AF+//2E1SEgAP78U3QjJSjI4bEEolFrs9SHob1vYE+mL401Mr76SjTcJL0dP1acEhg73npLzKdjj1OXkiLeNzRUdNOWvGHzk7Vj5UrRGvMMhKVHbyQZBi7wWcZDb7cd5PBIMGAALFokJpk7noTmSDBYq6UaExJA24LQSCQ2I3Z5H1GXuH+/mzFMolqNIJGQnJZG+iDPJPXJJ09g5U8CxgcFsaS6+qR7OHnhxdkOr8rpVEMQ2BoTzYyJAU4yA6IIY8AA+P57uP560R1qxIgTVo2AAEhKggsuaH0sUqlEAhjCk1Dk+PPEE6LKKD294+u6EpodO+CDD+C995zHu3cXJ0F7wkAG2iQ0AA0NsGmTKKE5A6EcO5xwSwm+E4YeNxXKgAEi3zveKqcjQbBCQZqPD4FyeWuPLDvi4+H22+Ghh5z7LBYSbZG3E09gXz7VODc4mP5+fu1Gl/bCCy+OP7wSmlMMa00NW9PSeCM6uvXBa6+Fhx8WMydu335C1S4TJohRSz15zSikUiKUShZXVRHXnMSnn4pxOTqDvn3hwQfh7rtFc5gHH3T3XklMFL1iHJNzejrs2SP6/m7bJooiPOTNOhPQqxfoVYG88cbxu2afPqI7+759p47QAAz292ejTtd+oWefFa2jFy8WO9bs2SSGhsKDD5Jkt/79D6Kbry/bO8P2vfDCi+MKr4TmFCOjuBizTEbPsLDWB6+4QnT7+PFHRxyQE4U77nDTMrVCjEpFtdlMf19/AgI6H99j2DBRa2QwiLFGPKkT3CZmu5jo2mvhr7/OWOkMiKq1zEyXOELHARqNKNVaterUEpqhWm0rD6dWCA6GWbPE93nLLTBlCskLFiAFErzSCy+88OI4wyuhOcXYVllJ//JyFJ6yMIeFOX2aTzFilEp2AHdN8uWchCML1z50qPjXKUgk8NlnoqRm5UoxpcIZComETiVTPFIMGCDaiJ9KQnNjZCSjOkOy77pLDEIzfDgoFKQAewcNQnM8Auh44YUXXrjAK6E5xdje3MzgiopTXY0OEaNS0dPXlwmjZNxydMFzO4+AAFi4UGQDEyac4JudebDbWp1goV278JPL6euSab5NyGSi/ZeLNKe7N72wF154cQLgldCcYmyTSJjZ2Hiqq9Ehhmi1jszXJwV9+ojWxGegu/aJht2j/VRKaLzwwgsvTjd4Cc0pxm4fHwadAZP29afCiPMMaJdTgX79xP9eQuOFF1544YRX5XSKEazXk+KNKOrFEcDfX/RI69LlVNfECy+88OL0gZfQnGIcfPNNJJ5ctr3woh38/LNL0lIvvPDCCy+8hOZUQ1ZaClFRp7oaXnjhhRdeeHFGw0toTjVKSryExgsvvPDCCy+OEWc1oREEgWeffZbo6Gh8fX0ZPXo0e10zPbfA6NGjUalUSCQSFAoFU6ZMYefOnY7jFouF2267DbVajUQiQS6XM23aNARBaLsSer2YotgLL7zwwgsvvDhqnNWEZs6cOcybN4/ly5dTWVnJiBEjmDJlCg0NDR7LT506lQULFnD33XczaNAgJk+ezJQpU7BYLAC88cYbfPbZZ1x77bU0NTUxd+5clixZwl133dV2JdRqOElGwStWrDgp9/HizIO3b3jRHrz9w4u2cDr1jbOa0HzwwQfMnDmT3r174+Pjw+zZszEajfzyyy8eyz/xxBNcddVVhIaGAiCTySgvL6e6uhqAv/76C4VCwUcffYSPjw833XQTsbGx/Pjjj21X4ocfTpp78sqVK0/Kfbw48+DtG160B2//8KItnE5946wlNHV1deTm5jJ48GDHPrlcTv/+/dmxY0eb5y1ZsoRXXnmFDRs28OCDD/LAAw8QZsvDFBkZiUKhYM+ePVitVv7++29qa2uprq5G11YivzFjjutzeeGFF1544cXZiLM2sJ6dYAS2iB8fFBTUNvkAzjvvPB577DGWLl3KlVdeSaxLsh6JREJ0dDTp6elIJBKkUimPPfYYs2fPRqfToXVRLVksFvz8/Dh8+DB+nQkhf4xoamqiqamJgwcPnvB7eXFmwds3vGgP3v7hRVs4mX3DbgpiN/HwhLOW0NjJRW1trdv+mpoaYmJiOjxfoVDwv//9j6CgILp06ULfvn3ZunUrpaWlHDp0iJSUFPbs2cPUqVPd7mdHZmYmVquVESNGOPbJ5XLk8hPzSvz8/DCbzSxcuPCEXN+LMxfevuFFe/D2Dy/awonuG2azGbPZ7LYvMzOTQYMGeSx/1hKagIAAEhMT2bJlC8OGDQPExtu5cyfXXXddp65htVoxmUxkZGTQt29f6urqEASBxMREJBIJffr0oWvXrtTW1rYiNKmpqTQ1NVFQUNDq2IlAfX09s2bNYs6cOSf8Xl6cWfD2DS/ag7d/eNEWTmbf0Ol0xMXFkZqa2maZs5bQAMyYMYM5c+Ywfvx4UlJSeOGFF1AoFFx88cUey7/xxhtccsklmM1mjEYjd9xxB0ql0iFlmTp1Kl988QX3338/r732Gn/99Rfr1q1j3Lhxra4lk8kAUXJzMggNgFKpPGn38uLMgrdveNEevP3Di7ZwsvuGfe70hLOa0MycOZP6+nomTpyITqcjPT2dZcuW4efnR35+Pj169GDp0qWMGjUKgDfffJOHHnrIcf6WLVsAiLIFxnvrrbdoaGhg7ty5vP/++0ilUgYPHsyiRYtO/sN54YUXXnjhxVmEs5rQSCQSnn/+eZ5//vlWx+Lj41vFoykoKGj3en5+fnz33XfHtY7HE+PHjz/VVfDiNIW3b3jRHrz9w4u2cDr1DYnQbhhbL04UdDodAQEB1NXVnRRxnd1zyys29qIlvH3Di/bg7R9etIWT2Tc6M2eetXFovPDCCy+88MKL/w68hMYLL7zwwgsvvDjj4SU0XnjhhRdeeOHFGQ8vofHCCy+88MILL854eAmNF1544YUXXnhxxsNLaLzwwgvPyM+H7793/LzhBjE5vBdeeOHF6QgvofHCi7MRt98O8+e3X+buu+HKKx0/v/gCXn75BNfLCy+88OIo4SU0XnhxNmLuXHj66VNdCy+88MKL4wYvofHCi7MVHcXUlEhOTj288MILL44DvITGCy/OVtgIzQebP2BV7qqOyw99i4agf09wpbzwwgsvjg5ndS4nL7w4q2EjNC+vf5lo/2gu7HOh+/GWEpqpD1BY2w/YcVKq54UXXnhxJPBKaLzwwosjgDf126nAe++9h0QiYd++fae6Kv8JeNvz+ON0aFMvofHCi7MVgoA9N63HHLVeG5rTBjt27ECj0dCtW7dTXZX/BLztefxxOrSpl9B44cXZCquVjYUbj/AkL8k5FdixYwd9+vRBJpOd6qr8J+Btz+OP06FNvYTGCy/OYhgsBgBKGko6VV4fuPME1sYLTzCZTOzbt4/+/fuf6qr8J+Btz+OP06VNvYTGi+MOySwJmdWZp7oaXnQEF5UTQHlj+SmsjBdtYd++fRiNRgYMGHCqq/KfgLc9jz9Olzb1ejl5cUKQVZ1FanDqqa6GF+1BEBBcjHwtVov78bPQhkYQoL7+6M/39z/+zbZjh+hVdqoni6OBIAjUG4+hQQF/pT+S49ioZ3J7AsfeSeG4d9TTpU29hMaL4wr7iv94DkD/GXz3HUybBn5+p7omIqqq3CQ0QksPprPwHdbXQ0DA0Z9fVwda7fGrD4iThUKhoFevXsf3wicB9cZ6Al45hgYF6h6rQ6s6fo16JrcncOydFI57Rz1d2tRLaI4A3333He+//z67du2ivr4ek8mEXO5sQolEglqtdjOK2rBhA7179z4V1T0lsAjiKl/iNR5tjauuEpMhPfbYqa6JA1bB6nG7LfiVTTqR1Tnl8PcXx/pjOf94Y8eOHfTs2ROlUnn8L36C4a/0p+6xY2hQ2zWOJ87k9gSOvZPar3Eccbq0qZfQHAGCgoKYMWMGzc3N3HLLLR7LLFq0iIkTJ57kmp0+aKW28OK0hqtU5p/8f7iylzMZpScJjdSqOhnVOmWQSI6/hOVYIAgCu3bt4rLLLnPbf+edd+Lr68vrr79OWVkZEydO5NVXX+Xcc889RTX1DIlEclylK8cKT+1ZXV1NXFwcNTU1jgn5jjvuIDk5mUcfffRUVbVtnGadtK0+etNNNzlUUXV1dZSUlFBXV4dKdeLGEK9R8BFgypQpXHXVVSQnJ5/qqpy2MFvNgFfl1CZOs3Zxlcr8lfVXJ85oe8jwfcmX7Jrs41ArL+zIysqivr7e6T3y+efQ2Mjzzz/Pl19+yfbt2znnnHOYNWvWaUdmTke0ak8gODiY5ORktm/fDsC6devYvHkzDz300Kmq5hmF7OzsVm0KMH/+fHbu3MmiRYsIDAzkp59+OqFkBryE5rjj2muvJSQkhAEDBjB37txTXZ2TDjuhkUm88R3OBLja0FhpoXLyRL6EtoeMJlMTm4s2H6+qeYEHY8sbb4RPPyU8PJz777+f4cOHM3PmTKZPn37qKnkGoS3j1TFjxvDPP/9gNBq56667+Oijj9zMCbxoG7t37wY8GwQfOnSIc889l7fffptp06ad8Lp439hxxIoVKxg+fDgymYwVK1ZwzTXXYDabueuuuzyW12q11B+rtXoncbLuU9tcixYtxiYjOp3upNzzjIFdTHw6tIutLvpGPX6IRspSg9T9nSkUYjnbPi1a/CWqNt+rFi2GRoP3vR9HrF69GqVSSWpqqtiuWi1YLFTn5vLdd98RGBhIcHDwCW3zEz52rFsHI0eeFOllq/a0IT09nYULF1JVVcWIESPo3r27tx93AvX19fzzzz8e23Tnzp3ceeedfPTRR/Tr1++Y27Mz/dBLaI4jJkyY4Ng+99xz+d///seXX37ZJqExm83MmjXLobcdP378GW9/YzcK9uLMwMefCBAhbo9LHHfM12vlKWXf7/V+O2IYDAYWL17MpEmTUKvVjv11jY1ccsklzJgxAz8/Px5//HFWr159ZrZtQwNcfTWsWgWpJzbMQ1vtCTBixAjuv/9+9u7dy7p1605oPf5LMBgMLFu2rFWbrl+/npkzZ/Lll1+SlpZ21NdfsWIFK1euBMBoNHZY/qQTmuzszunYpVIpiYmJJ7YyJxhSqdRzjhwbmpqamDNnDtqTaOB1ou/VIGlAhw6Nn+akPtcZAZ0OpNLTw6DPtlpavlKO9qoGAIICg9zfmdkslrPt06FDF/U91dZXSAxMbH1JdMjUMo/vPfCVQC7oegFfXPzF8X+W/xjKy8vZvn07H374IWVlZfz666+ONm3U6bjys8+45ZFHuPPOOwH48MMP+fXXX7nhhhtOaL1OyPdstYp9TKk8Yd9Fe+1ph7+/P0qlkjfeeIPY2NgTUo//Euxt+u6771JRUcHs2bPd2vT6669HrVY7nGckEgnr169Ho9Ec0X2mT5/uUKfqdLoOzThOOqFJTU3tcCUhCAI+Pj40NjaepFp1DhaLBZPJ5GCKBoMBs9mMUqlk586dCIJA7969kUqlrFy5krfeeovnnnvu1Fb6JMNuQ+NFGzgdVtFuJNslsF4npWu7SneJhMZohEWL4JJLHMfacv2uM9SxPGv50dT2rMNbb73Fxx9/zIABA1i3bp2bsaUvsP6uu2DGDMe+9evXn4JaHieYTCf8Fu21px0ff/wxY8aM4eKLLz7h9fkvwN6mffr0YenSpa3atKKi4pTU66QTGo1G02F6cUEQ6Nev38mp0BHgyy+/5KabbnL89rMFSFu1ahX19fU88sgjFBQUIJfLSUhI4KWXXnKsos4W2AlNZ2KanJU4HQiN1eXdSJzbrVzuf/rJ4+n2/E8sWwaXXgo9eoDtm27vvXtjE3lGQwNUVEBSkvj7pZde4qWXXmr7BOt/6NuyqxFO4HfRXnsePHiQ6dOnExUVxcKFC09YHf5rsLfp6WZndNIJzQUXXEBCQkKH5U6GRfSR4sYbb+TGG29s8/j5559/8ipzmsI+KXptaVrgdJqE7HWZOhXyOieh2V6y3bFtMNsIjX11vX+/89JeInvEuPFGWLiwheCsPZxOfelYYe9DllMzXnTr1o39Lv3XizMbJ91t+5tvvulUua+++uoE18SLEwGvhKYNmG2quNNBQmOfPOTy9iU0NuTW5jLwk4GtD7g+i14PdCChOR2e/TREUdERnnCKJv8TAruE5iSonrz478Mbh8aL4wovoWkD5uNsW/T333D4cLtFqpur6fJul9aG6W4rfJc4NG28M4dExnGG7RzX69pcKr3v/QTC/t7+S4TGTmS8hMaL44CTRmg2b97MkCFDGD58OEuXLnXs9xph/bdwIgmNXn8GS9vtA7bMQ8BBo1FMXHkkmDgRJrWfV+lw1WEyqjNoNLUwrrdPiBIJSI7cKNgjbCttj+/93nsZm+O1oTlm2N/bf4nQ2CU0ubnHnkH6BGLzCYwXaTB4+dzxwkkjNA899BDz5s1jwYIFvPnmm8ybNw+A2trak1UFL2w4VHmIGUtmdFzwKGCfFE8EofHxgUceOe6XPTmwj1hSD5/c77+LiStd8Mhfj1Crr23/mh2wO3u05pYSFn7+2eWHC6FpQ+XUUlXkkPi47s/IQGpt472/9x5PrRUzLw/9dKhXinO0sBOZThvbnAGwfxdXXQWnabRjqxWGDIGdO4/wxIcfhqamDoslJ8Po0UdVNS9a4KQRGplMRs+ePenSpQtLlixh+fLlvPnmm169+nFAYSFs3Nj58h9s+YAPt354Qupil9CcqCSVJ3KldELRHqHxQExe+/c1Fh1a1P41OyI0UpHQGC0tAlK5jsyXOZNRtiWh8Vnzb/v1ABg3jgc3iNdYntnaPVsqQIOxgU1Fm84s1/66uuOvLmwDBw92UOAMltC0OR64Bkvz4P1qMBvIr8s/QbXqHOyfmc1MrHMQBJgzB/7t+NspLj6y8fuEoLQUOhkj7nTGSSM0ZrOZhgYxgJdCoeDbb79l27ZtbD5jZ6jTB3FxMGxY58u3Fc31eOBEqJwEQWDutrkgsZ65i1M7oVm8uMOidgmIwz26o2u2gWWZywCIfiPa/YBCIf5vsZhoa9KRFxS3Xw8b4usgszqTqV9Ppbq52u2Y1OW9nVEZ2QMD4YknTsqtRozooICdyHSkd+0gLMbJRJOpCcksCfLZbTjUuhKakpJWh59YOIP4wATRt/0UoT3+aBWsmCwevkM7CZ40Cd55p+0LV1d7PnayMWQIpKSc6locM04aoXnnnXfcAuVJpVK+/PJLPvvss5NVBS9saDCeuMHhRBAanUHH7Ytvh9ADp4WT0FHBTj6WLWt9rAVLM1nFsharhcdWPNZ2wscOJAdPrnzS8wG7lKiFPY/bO3NZjsqam92r2wYh7lMG725+F3CRCu3dC4DSZVI441z69+w5oZcXBOC2QVRL2jfy7jSh6dULtm07LnU7VujNHYg1OiDllaVZ4sYpjHfSnqbv9kW3o3xB2fqA63N9+63nC7/8MoSEQL/5cOkVx17RY4EHMnkm4qQRmgEDBhAREeG2TyKRcMUVp/hFnoWYv3P+Cbu2ffU9/RIr27d3UPhIIbWc+YQGWg/O9gnKNmI2m0QCYbaaeXX9q7yw9gWPlzQ2H6UqJCQEgLLGcrfdbkTj8ccdm4tbqL7aSuex3GWBl1WdJZbr3RsAX5eFuMcV7emME9zpqquBmK3QvZ3Abo2NYCeW7Ykp7cdOoUTDFa3st1qig/w8jic9haLZ9vjjP/n/eD7g+r23tfA4dEj8P+I16PXD0VXueOGMHVjdccrcti9xCZfuxcnFRd0uOmHXdthHSKwcr3hVjolW8h8hNJ9/7n5McHeDtq9q/y0U9e92icjguYMp0jmDllgNR0louncHYH2+e8h8N1VQVZVjc3vZrk5d1uwymoycP1JMdXDvvQDU+DiPrcs/w5L/HY9Ot2SJ6M7SAg8uf5CMWpuKSNrO+/Tzg5gYcbtdHYg7OT5h+PhjqKzssFgr+62W6EBC014uvJOF9gRj9cY2PLNcjYG3bm3/BlZ3dVyHbXaE0Bl05NXmtV+oE4kfzwQcM6HJy8vj999/Z/bs2Vx++eWdPq+4uHN6eS86hgk5CeR2urxGISYIOxGDhSuhOV6Xd0y0xyKhsVqxLvQcyv+kwHXgbjkh2RvKNmLaCc03e8QglH9m/QnAluItrM1b6zhNLbirglzR0oal3FUaY7uftMX7uW/ZfXyw5QPxh8sKX2jR5m2pE40tPNIrGivgr78AGJMH3/4o7r/wuwvbrPdpiWMlNE1NMG0atMjwDPDmxjfhglvFH7JOTirtiQxaEBqz1dyx2udocOed0F56BhtcJ2eP/aaDidR+zn1/3HvKjMmPyrnsSPIQWp0fzoaCDaheUHG46nDbquYjxAXfXkDi24nH5VrHBRUVbgsmEG2tvlm3vqPQWh3iqAjN3XffzahRo0hJSeHmm2/m/vvvJyIigttvv73T1/B6N3UO33zTsbGgHAsD6bzO3D4wNJvbnhCPFo5BR2o5bjFjnCTpGAjNpk1IL72MZfs78Bw6UXAVO1utXL3wag5WHnT8dv3f8r0YLUZ2lYpSks5+N7f8fovbbze7qdJSoDWhAfh6z9fihouBoKXFLduygTG3GE16fvq7m+vOlS1tVffuhaFDPV7rtEIHbZ5bm4tkVjtlOlq8+doS+Y15gXqDhxX/88+7/25HQqM32CZSW1+67MfL8H3Jt/37Hy064fbjSkI8EquWhKYFa5Dafv+2/xeRIJ8CtEdoYrUeMnNLJLBqVedvIDgJTUmDaMsyaO4ghnw65EiqSWZ1JqtzV7faf6jqUOcvcsRhq48C4eGQmuq268W1L3LNypF07Xpslz4qQvPvv/8yYsQIMjIy+Pvvv+natSu33347EydOPLbaeNEKn37ageefbeCS0XlDS61STPNut9U4njheEpq82jyaTE1u10w37SHA5C7mnr9jPp9u/7TdawmCQNltVwNQuWXN0VfqWOAiobFaBb7d+y3vb37fvgOALQWbAM8Dvz0mTavgdEVFHnX0Nc014sZa0UNHZ3Cx27EtPDwRGofULj4egAo/CYoWxLQtL6WW1xvwnrtErBW/7d0bNm06feOqrF4t/u+A0HR5twvQRqb5nTshLc3525Pxpcypiqoz1LkfM5vh2Wfd97WjpnnxS9ui0tamvx781SkZ2bLliFy+O4yDtG5dh77mruQ3rzaPr3Z/xT1/3OMs0PJZWqyCJBbxt9wKFuup6Sf2Kl1wQetjraTcdlVTi8X9ujwXNavdFsr+3bpIaIJ9ggHn9/r7od/dv912MPnLyYz7fFyr/Y3GNqRF9gdzVR2WlXXqXkeM+noxGKi9vWpr3eaf3Lpc29axveOjIjTbt28nNTWVc845hzVr1nilLScQHTVtbbm4wrHgIQJtB7AThuOJTUXipNwRocmvy0cyS0Jlk2c9/KhZiTw6XyQh9kFxS/ltPHbgBrdyN/9+M7ctuq3dOjUY6onYlwtA884trY5XNVUhmSUh+e3kti+yeDEsbx1fpdNwGbgP5dUCsPCAzQjU1lAHy0QRhieiKZeKevZWHkaxsU43bBcoZLZ9JnF13v/j/q3KeCI0Dtg7nkyOosUc6LRpcu+cMtv1PloEt3swG2ipunLgjz/aqcgpQnExXHeduN2GqDGnJoerF17t8ErzaADbv0W7R0e3LmNROTZbEVZPhKEdQpNWYQts6Yl0Dh6M8P33Yn871P6qPas6i6BXgzyTNHt77N0r2mO186G7kt8eH/Tgul+u4/0t7zsLtJTQtCDnEvuCzQp1ulNDaOwc0FMM2Fbfo4dox01yGL3AFjmvpgY0GpFErFxpu4hz7JZK3KfkC7+7kNfWv4bFakH1gsq5ULFj40Yxyviff7apCrbb+bgdN5lEL8fCQpg507n/7789XuNYsLd8L5adO+Dvv/lz3QLHfs1LGocq3K5ep/e3lDaUHvW9jorQSCQSbr31VhYuXMiff/7JwYMHyczMPKJrnA7GXmcMfMuRzJJ4NBZ7+lF3QrP48GKmf99+xE17fJMTQWgW7Fwgbqjq2iU0CW+JGdfvX3a/x+P5b8G7d/wGgoD//71NqG2RobEcufvmO587oyJ/U7O21fHQ10IByKnNafsi558vZqduB+vXw9h5E7lz8Z0AlDWUcceiO8SDLpPQ/IOi19LEhPPEHbaGspjFMnqznugWj2lfNOze1QniajazInuFuD322TaLSQWIq4VAT93AVidTSmIrCc29S+8VPa9aEhpbuTu2wcsr2q+iIAjiig0g3xk4rbOr0ROOSZPEwR5orKv0GOrg6z1f8+1ep0vukowlR3SLpRm2FDAutjN6Y4vG9kBeGhpc7KMyM93sERK1omTNajazuWgzw2JtAapss/L2gs3wxRfQrVubnlCCIDhsqTyqiVq48fNkG+EB6ISLfsvna0FopBaxH8qO93TR1AQTJrTtDXboELz4IuAu1Cq3maI1m5o9Syo9XM9t4WA//uabmKpEFZrMRafryQPwhXUv8PmuzzFajGRUZ7gfHDZMjLI8ZYrHx5i3Y55j+4JvL3DayNnbubzc3Unhyy89XscNHozbATKqMthZurPV/t4f9mbZAVHVP+BCcWzcGiUea6VGvOQaol4XD24o2EBZw5FJjI7JKNjPz48XX3yRVatW8dxzz3HhhZ039tuwYcOx3PrswhAxMJMnS3Vzo9i57ITmvc3v8cvBX9q9nN1G40TY0Dwx6CGE50A7/j6PhGZ32W5kzzsn5Q7j1UilhLz8FhWv2XccuTTwyTu/dmx/045nLMBn2zuIi9SObnzkSFhT8Dcfb/sYySwJka9H8sn2T8SDLgO33ubUIFjEdqgoFxtqwiMfgNnM+oy/KXoDXspN4e2pbwPOdnrrDRn7yj0ETnMNpqZQcNEB27asbUPKWJ1IHBd+DzILLPgFZGaXBIjTp1MTrm0loQF4etXTraIeu046niYg110rc1ZCWJj4w7YKtwpWAl4J4H9L/4dkloQNBS3GiCMxtDwC1Bvq3fthUhKuLnq+/2xi9sfXtDqv5QLj7U1vd+p+BrMBi9XC57tsE4nU2Tf+2Noi5s1jj7U6/4/9vzvvnZYGkyc76+QjxkSxSmHIp0PYUCi2oWATLyS+/y3ceKNYuI3V+KGqQ7yx8Q0Ah+2WG1oSmpdf9ngd6EQQxU5KaORWMJs7GCuOBMOGiRKSXbsgLw9eecV5zGqF2bPhqaccP+3Q60XCp3lJw22Lbmu9MPcgobF/C7X6WucioKgIg43pnJsvqhlnLJlBVk2Wx+rabeLcJHhtqPsEQaD768MYf26dmy3dkowl3P3H3eKPtqJOdxRz6Y8/PBq3A4xZMMajJBicJDy0VnzfdgcCvVnvcV6TzJIwfN5wIl+P5HCVaClcUFfQft04SkKTnZ3NzJkzeeCBB1ixYgUJCQl89dVXPHGSImqeTbBYAKuMS/fBU3+1Htzqi8QVrZ3QtBRZesJP+0XbhmOV0Dz5pDgWuGL2UrGOEQ2tJfWCIPDoikfdJg+7x1VnIbShg2vXKNMFUR2E57h10a3U6mvd7QdcDdjGj/coYm/XOl8Q4LzzHD/tA5xgFd/V3j3ijrhNB6GwkA+XiyvDx7/IIS1YtL+o04uDXreqXJp//r71PXr1cv9pd2r6zOma3VIl0su2OIpshNy34YZdcOFa24lmM8hkbCjb1kpC43isuXPdfsusnrft7saCBEofEsXJBotBDHENcI9oUxH2mkhw3tksEvhtJU5D9wvPt4KfH8L9D4jpCI4TCnWFaF/R8tzq58QdEomYKLEFAg+3HnRbqgZbqZxcRfkuse39nldz66JbUcpsAdlcJDT3bXL2k8OHgT//bHVftRmu/MmZsoLCQv7M+pP7l91PU7A/ACuK3V3yTZXiSjekwMXb7aKLWl0boPv73R3bI+ePbF3ATkRd0YY4ti0JTXG9aChtaXYnNEsPuBvtS212MzIr/JW7/Ng8nWwSNwB27xb/jxwpSmIef9xFzSqDr52LIMecP/w1Rn7bkz8yRBXp/J3zW6ucWpI9cHw/Gws3ihI1AIMBPxuPVavFd/Ph1g/dJCqe4Gbe0b2727Gnf61h9weilPNgw0ZW7fRMjgAX+x0PH3dbovXqardxrCXsBs2SWRL2lu91bAPklLurOE02QtNgbKDn/yXSrQLu3QgPuXdbALq+1xWL1UKvD3q1PtgCR0VorrjiCkJDQ+nZsycvv/wyCxYsAGDIkCOzys7IyOCcc84hJCQEpVLp9ucF7NgBa8ZJICiTH3+E+x75uVWZoGqx09qNgjuyZ3IlE60IzbZtnUqmZsdLL8Enn7jvU9u+E4u09XchfV7Ke/cuQ3jOue+7vR6yTLejq5IcqdFYiyB2v3TrOM5D0KtBboM6We4Dg9UWW6VOX+doz65dAUkbM3+Fu1hVZWujrw/b8mm59vfPPsPPXr1bbmFQzCAArlooJq/cnvcQ6XfN9nyfG27gQIUompltFyQZ/Lkl9WkAftz/Y4dGWdcsKxLVLTodBiyYZHiU0ABIWqRxaFNCY7MbEYAIPzG4ZpA6yD1qcllZ63QJLuT8r8Wi6kPy9lsQGMjkLyfTHoR//wWJpF3V9jub3mFPmbgi/Sv7r3YlQI+/55RUGC1GmkxNqOXuK1UHQbHjbZvE5s47xdDy34h2AtftgrWZf6OU2uycNK3D3xsMtOnxoTW0UG+ZzTy18ine3vQ2ErP4smob3d1i9Qf2tvlsRwSX9hTGuRigvvGGR/XYrj2eO0/8m6JqTPaMu7pq7mb3HHM7C0VSKxPg4bW3OW0t7KhpYVPSBiSzJCKB3r691fdIC2LeEg5Ck/4RBfr9DjIG7qYTDzwA44br20whYLQYwd5mLpIQpcppR+iwQWwD7QUqvGhrA73LRUNiAOI8sAN7ne0P5UnFJJV6Hic+dTpfbP7Duf3SupcoKcvilm04xLBr89ay5fePkNqGxM057nUZY1sfjP18LO/9AQfeh3eWwZy/PD9bm6kzWla9U6VaQKPR8Nhjj3Hrrbfyxx9/8N13HialTuC6667D19eXuXPnsmjRIre/swlf7vqSUfNHue0LfCWQAb9LWPQ1TPQTP+IRBSDY9ZfTpkFVFc9U/088xnoQhA4lNK4JD1sRmvR08au04fnn4dZb23eMUKncf/vYxjS5FebVXN+qfEKt++9Gk4dJpA0dLbgrnBYfXkyvMuhqG5/cAsWZTDTffjMEBLidb5Hgppe99udrgdaSrfYM06Tvv8+a3DUEvhro9FQC8PWs7xVcVm3Zw69B1aI9Ja6r2BdeoI/9Mt27E6oJJUQZSNecNgJ4ueLvv6nRtxjgrXJuTRHdfv/Kdo4W7w52FpG78LCoaiOsWAGvv45FCiYpbhKarhkftbrtntHdAHdbAdfty8aLA7YgEReEyUHJom2GfZUMEBnJwBp3ad3df9yN0WLko49Ag3tfLdjUxsiXnw979iCxxToo/vlzcXC2D9CXXOIQ1X8+73+c+825AGTXZNPwbuciaIf+Xyi+L/kSqA50299KGmFbBRvvu5uw18JovER0k5n3O2TNLOD8b9sOpT1jBrxMa4kswNg8+CjpPuczVVez+JGdPLkGx8eam+FO5LQXe47I/u6mdx3berOehftb62TdRP0ufXnNi7dRbed0M2d6TOvx2yIz1MW12m8RLGRVt5YgbM351y0ZpZ0Y2/voP/n/sPjwYrYVbxPFw8HBrWKaVDZVutlyDJo7iDE287jmB+4R3Ybbwvvvt9rlEGIEi8kbHapCYEfpDkBUJ731FkRR4pEMDCgGq+viykVFnB/QqnibOOfrc1j698ceY2rZv7lQjWgTyLn3ifutIDwHUTpQm2yGzHYJzZG4aT/6qGPzo4+djhgfbv0Q3WUX8OkiuNPmDHDforsZdOFd3GfjZ2oPgjWtzTzL5ziGFzoqQlNVVcW6desoKSlBpVJh6iDaY1vYv38/3333HdOnT2fKlCluf2cLqpqquP7X6/kn/x+3D9nuvjktA55e5WSnhy+1MfwlS6j6fDH5MaLR32O8Cn/+iUzSwmhUrxc/sBUr2FCwgYu+v8hxyEFoGhqc7qQubqXPPguffSYuLtusv+tYsmQJmbbxUWGBTfrW7L/ap9Wu1mjH0l6QSERjUomEHx89nz0fwkHbGDRy/kin6umHH/CZ6z5BmTU+BDdD/Fvi6rCgrsARd+WFca3TC2wq3ERVoWc7o4deGgvA4arDvGufE8bMpkc5XNBCtW1pcuq5kv/9mlmrXepkBqnF/Yv+0RaAzi4xuPOvWra0t4i0264VFbnr2AWgKcyxcHZVo8Un9XNsu6qHFtmlAk1NIJdjksGMLRBvOzWwvnUW1M8en8LcAe7XkQpQpwL27GGTwWls/c47IJPInH3PRWy+9e0mx33sOFx1mLvugp9xN3Q/8L4Yd8PxqAIMHAgkJECfPo79MZfe5DypshJ+/hmGDSNn52q2fQIGW4iXWp2Rnx63jcZXXonkOXHT6u/X6nntXiMz/5rptn9z0WbO//Z8PtxikzLYRCz1CVFUNlWyv8I9dPaF3+9sdW07vp6nF79pwNqtR6vjN107x+13eK2JF1bBmmzRc2ZVrbPDqFsMzzdfAE+MF7cXv3cfW4pEzz+fF3249MdLAQhoFidBpRmGzxvuPPkOm5H7d9+xU1JGiCvnauE59X/r/48VoReCxbPE/e+c1vZom+ciSs1cvJtc/2sUGs7/9nwmfjmRwzm29/WPM/1Anb6OsNfC6P9xf6yCleL6YrYWbWW1jYP4rO3AdvOee1rtarmgW1/QWvIR9GoQAN9wjVOt5IJtn8BFg6/zeEtlOwvGlqg31tPt0juRXnpZ6zo0iY3UUkpvl7Deth2aX4QV2SsQ7IPCr786C8a2iKnjSnZavNt5vzu31Ubouk7s2x8uEftNkI2s9LYtznw9UIQ6m9lSw3FUyBwVobnooot49dVXSU9Px8/Pj61bt/Lggw/y+++/d3yyC3r16kXJfyQp1tHClJrsYKop76Qwe81sMqpES3Zfm6BidKFzwuu6eANmk3hgxcxFDNzlnLRrSnIY9sced+tLHxuDuOkmbvzaGclZLpU7J5U+fRxqgaKC1moTT3nu7Orod943OK5jes+ZVbal3YVdNRPuQaNlf147vp32VetC9noLJgfh+fzX1selVkQCd+21rY7J5n5KuItAKH6quMqJrYN7fca0Kj/0no95NU5kK2aFO1F83+ZpvCJrJff98T+QWKAuno2fwm8tBJaSl1+hLfz4Iyj09fzczcNBG6HRtiGwuuRy2D2yC6WjnIZ4Uz5wTj5KC9AUijwvi/MOwdQUp5dWsNxpF6RyeVcKX61j24gFk1QcjPLegkv2wUf/3MI9m+DPL5znGMwGLBJ3NZNUsKnWVCpHwD2VRZS0Z1RnMO2baeLOZHdX+by33J+x94e9AYHRtE6XkPZumqNfmUywfXsH6ki77UdtLUn9xYWB0vbsRlktNyLOeitedBpSSutdjK48RLUNah7IS2Nm86BVZP2LDy9mxh8z0Jv1FMUPxfDU887UFZ8ObnW+J6SF16HHyfwvOeg5l5cn3PieONnaCcBXC8VJzBVf9HUaZS7/SqyXa0oNgGibQNCu/qzV10JODnwlfptV0ybwwPIH3M7h4Ycdm59s+4RHVzyKSVbXKrS/HS+9CAfpysM+jzj2aUwga2gS7VhKSx19yv7fbnhdq69l+g8i+TIF+PP93u95c8ObzPrdOR7evuh2Yt6IIc1dgHNkeO01evSUkER2h0Xlchfbrquv5ruenbvF0MLW+5LnrGVULig8SC9yAtu/3oLHznFshzTC/TbzLftCatmMKez5zd35Yfz1wOYWkYljY2HOHHE87dZ6gLKr7h/yECdtqW0Iv3mn+N+ey21NAqxOcC9raLEGr/s8CessCWENohRHYxTb4d1ORHc4KkLzwgsvsHjxYoqKisjJyeHHH38kMjLyiFVPn3zyCTNmzOCHH35g7dq1bn9nKgRB4NlnnyU6OhpfX19Gjx7N3r1t668jG0WmKjwHg3PNPLP6Ga7/VVTVdGnjQxz+YToAVwju4uE5X97Fo19mE2Efg1tkhzz0PnSxqWsnp0x2Epoc5wq6OKu5lQ2LJ6P2e+5BnMSfVuP7ki/f7vkWYaszxot91fHLAdHj6qf9P7nZzgjP4fi9dWwX3vniYVauFFXiVyH2o8yg1vftUtO2jvnOLWB5vs3DSMLC6FUhfuRGIzD1QVQmKHgT/AaNYN1N7pOmpud8/g9RzBrxgIWgR+FycQxliG38P1i9H4a+A8/KYeLj+Ns+3Gl1TvGs7EsnQXt4Eix0Mc9ZuBAk1VVUaqBB20J8ZSM0VTZNTHATfB3uNIxb1AWG9YwnWvccjTZzDFeS5/fL+4DAsOtSWfwtDiIM8MVqz3LuIJ1tKeXnx3cHF/KYy2L0+l3Qr3or7y6FSS7ju8FiwCqBJ9dBgk3jJRNAbcGN0IDTa1Vju83f8imYx4x2q8OCXyDc1Xg71mlQa5ecAPjr4dU/vkEigVe+WU+YvIN8NZ3E2xvFSXPSdXA4b4fzwKBBrcrWKw/zeHEyrz+/ybEwAVF9E/PX5+z+8bCbp9CE6+FwcPv371rh3g//4Fy33zvdc/y6wW7obScA13hwWrHIYHWi+77YN52rc+E52G/z7v3Utkbt/WFvN/IZOseDYTA4VBl3LL7Duc8qI/uuMl6f/Lpb0by+t2FGTsSAEMc+rRGmDrQtvN56i3gbR5C3Xmc5Vv0Xzp3AlQuv5ME/H+TNA06S9dkOcdJO8GRD3qWLu7G/bxvRlB8RydZ2Bng+7oKg8Xc7f3z9NVe1FqJ4xAurWpvfZTWMZu0CML4gvo/RueL+4CYYn9v+9W7ZAam2uaPyNXilhcD7jT8h9f7n3PbtDwP/BV1EL05Xmz4Xkgrwr4sQZ0X2CvRmPXd60Jymu8gpPp72seNdya2woYUG0tCC72pzcpAIAuVzxLmx8SWxHa7fTYc4KkIzYcIEHn74Yb755hsqKyuZNGkSjzzyCN98803HJ7tg3759rFq1iiuvvJKxY8c6/sa5GpudYZgzZw7z5s1j+fLlVFZWMmLECKZMmUJDJ7Lf9i2FiVlwIEMcwM/NaF0mM9oHyR7PBOka2wsvtY8bAwe2KnPoPfH/9qI9vP7tFirS3WOrDKpfiV2H0oddhFFu/6YdMFlM/LZEDzKnHPHqn69GWem037h0v/ghTv9hOosOLeKtNzzr8Fd8Dlfthar5c5gwtZHewc7lSsvcQACy1vFmRQjwvAeP6n4u46p90BpaCLf+LuqA9S6r15EJ7nZM97lwp2oN1PpAZjuTkau6ZNGbr3osozK7vFdBwsKFAjv/rqRSA/MenOBe2EZohsWL9iCTs6Ap1vnuTTJoilqBIIX3bIv/i13UXQG1gfjgVJk9/IdzsD/kG+ixfkMybeUbGrC0GB0aa1urPkAkNPayPVpGp1epHB4NLo/Etba+es5vd7C/hQjqhl3wk0vy4T5GdxF+rURUA92yA57aegM8J0FYMJJyc5LH+nUErR43qebiDNHYeUUK7NC5iNp37+bnA+6G+WZZPVwjunRPcjEJsXvjDDr0FVcudHokNcuhS2sbYDfIte7SACPuhmp1nr1m3SBr4zOxY1sMfGPjxmEuQ9MlLSICXHxQdOlvD1e7agOnT4d9+4hytcUXZFx2bjjhvq1tVzQ0YfZpg0y8+ipf25rb0/PYV/1tSTDtuPhA632lT7woumzbjXNjY6lpqKQ+UMOYG1uXD6QOGtsgcTb4dfu63ePt4SLbdyuxwv0bIAt3yeWaBeL/iR0LipiUDRnvtl9G00LyI0hs6VHGjm3XhrHMD8psr+viL87jyb/ajnMF0BAXQXZNtuNdqc3u43pII9yws/26HgmOitCUlJSwefNmtm3bxn333UdiYiLDhg1jxowZHZ/sgocffpiXXnqJ2tpaTCaT4894Bmf+/OCDD5g5cya9e/fGx8eH2bNnYzQa+eWX9mPDANy9Bf76Et5aBv/3p8jcW2K/XzOb2oj036ON5LdRD7XeV1puILf+AGHbPES//Z9oaLyLfvzGhTQ3CWIAJluI7PO/PR+e8nEL2d4SD7uIIS/47gL+bcMbcYJNOCQRIOI+Pwpx0vdGJW2KbcfeAMn3geGPRRAWhjALwjyos3ZFwofpMONcxNgTwOJv4btdn5LiQQL2iYvWtGUOI4A/Xi9h3hhRLRPc4n4tJ/PEGjFwnSsyQqDUT7RRkFsFkJoJRSQ0O3pHuhfeIUoH5HJxxaQyOz2kalW4WUh/3bt1XV+v/ogxOFM9uBrmNY15vfUJLWCWwhWXOn8bBM8GUN/s+cbRVhftdrc5EZRKNwnNEptzzlQbRzGh5NVB59CzxdAxvND5cB/Lbgag913i76+EGwB4czlYsZJYA8+6ZLT4so+7JKcjrFoA4239cD43uh27cuGV7HWZxy754RL3k12IkCvRMO0Xiecl/OQWDbulQfgLHszFXjA7VUyTWQ6KJiTPQY3t+p76ZUt8axPeHgppu8zjtriG5XPg0Dvw3hL46cfW5TwRArd79XH5sWgR9OpFztuQZn/siD3s2OE5RIOPpAGdrOPQDZ4kNA/azGFc370nzGgRtfrKSyAq9zIxYq8t3IHVaODTnfPQ3t/E2kTI17a+DjIjyWvWgABJ1dDwuPsiNdupcbenS+P6i9qvm/271RqAtU8wMl/s1ykeVFw+cp9W6hlX7G7BFz2Nb22h0vYKzvn6nDbL6IL9uOlC6GYzMwrQw4/72xdi+BWUcVvqFTxhM3NSm91zvn39MwQfx9ypR0Vo9uzZw6WXXuqwncnLy+Onn37ivHZ81D2hrq6O++67D61Wi0wmc/s7E1FXV0dubi6DBzt15XK5nP79+7Njx452zhTR0zYhRjS6EwJXaEye97fEzBecYvxSf/djU7L2QMZ5XF7XOg0AQFNcFwoPi6NACFX83xwpRERAD3GFblm7VpRGnHc3l+6DETZJ/99J8OKoFhcTwKcT/FRlcZEs2SARoNil7tu1oY7tNUmQEwwDzk1q7YZpwxPjAQnMmAYfDsbN+2DxNzgMmF1x23Yof1BcQtulMRF228+tdxDpF8mAi0RJx4xt7uKaG3e6XyvnbTFwnSsqNeIHXfsq/N9fgFxPKBVUamDBnhZMdccO5kpu4+8DosrCInVOiIEtuOSeFlwI4OLmdbyjdorXUlwcoJpbZ0toBakAP7iEfhDaCWp4v02adfte90HerJC1Slo5IGoAVS7c6Bvrc+wPh5Rhdzr2yawCzBI7TqlWZGL5ATBkRRMP8Kbb9XJaxLO73iYx6H9Ha/38gNtB8bRoGPvIY6IEc0Ap2EP7PEprydqSLh4e+KV6eCeDB5932kfVuQhS8r4SR/CNuCff3NHiPT25DobZfAFeXCGSil5NYn++fmRX/ureALeL9bTaw6TYSNSj7aTO8zUBZhWrEiFHHdCK4K2+YbVjEgNRanS35+GAWatB49LfuqW+0arMoBbZR1QWOPwejgCPVitY9BqkVugf0c9RTkMzFdIgprQ2eXODp0CN02ySzu6VENTkvhgBUfLW28VZ0W678Yt7+BYAiqpyeWSF81sZdbOHOihMZO8JY9phkbz4Pv4M0UpPnUOMzdhWvV1h759yK7DxAY9SaTteGv6RG2keb3MitZOmz1poxTyNb23BFhKLZZk2TzVXswON2FEC7mugzgdqbXWYtRq62UjrHr9g7vZ3qtldkZLkrJiPyZ2QT7FJNf9kUucr2w4659zdAjKZjHvvvZfrr7+e2bNn88EHH/Dyyy8fMaE555xzWL9+PSM6Sid9hkBnc8sLDAx02x8UFOQ45gqtVsu50+GPFuFlRpSDztMKARhc1fYxVzzz2i50WrGgFuj+gB8fLIZxGTDhtwUU37SQoHytx2ut0A9g4qAJ6LRaIilDh62QwQBffMHC7xWAgrgHF/HZG+KxzVEwqAoyotzrF26CCw91XOe7d7cu0+wLRo1z/1djjKSucT4T7xyiXmt01s8F466HzFBbuff2cecDteh0Osxd0pCXljG0su06PfmQDK11En6STfwZH4rer1K8TnV/dDodt82/h7+1b/LwFjM/9dFSbDNHGV/U/nOefxWYJaCRiOWiDaANK8CnvgmdVosWuOh66LluGo+eHwoLFnAFP/BMEOi0Wp7eKNpc2e/R8lbb4yG11n1fBPnobMlIX1rnPNesdm7X+7X25AG44SA8da7LOb5qdIbWD6il7ed+e/1bqGTOfjaStTw6dBbfL7mXwVn+aNE5nqSy8C50WueKr4ZQEtAyvsR2fRUs+tlEaqrJ8c77VLvf+/KBF6P96la2LOnDsO+SGHy3mYhGeG61aF+SFQ0aYOEAyLpvGboPxJghEkCnAktQFtoWLSuXe2hztQXM4Tzr/5KjLneuTeRSH7gxLxe19TV0Wi0yLG7XG5I6FJ12o9v1I0wwSXEL9+wWbT7s9/ptYgkXJX7JytxiQEu9v5i2y6ISy3w0EgZWwtQ21BDaL1chixrH/PixaFeNQad9zvEM/YL68fNVS9G951kVnEkyqTYpwbIefjy22VmvkmufQ4uWC6wLePCmRMZ+PpbDMZ77wJQCWNkd0OpY+LWUmrlaIJuDo4cxKX0fJi1sbFpN/hAt746EG9qwk1BLQbvlDhj0MV8uhKBm9/t98QeMzoeZwJOr4brd4GuTSOq0sM9/CBcOTCD80h9Qy0EN/PabTgwLo9USgPv3pJS1fh5f5KAxceN+W3/+9FP8IgvQ1tbAjD6O8g/zfyjR0Ss4HYX8cLtjQpOfeB+lxRetXEmg1fOYDLBrTRAjS8XjOyKhwV88d20X8b9V1bm5oSXeHOL+7AGzAlh2zTJ62gNYWiw011Wi/Vy03VTIFOi0Jq45DAPq/aj3g5EN+wAJX2sf5TyW8CF3t7oPQKgVzJYwhsyo4C8X34+vuYZPuYVPEZN6Ps6LvIx7jKJ6aBVXrCWOSkLT0NDA5s2b+fnnnxEEgfz8fD799NOOT2yB8PBwzj//fG677TaeeeYZt78zEVobgahtkcWspqbGccwVZrOZLT9O40G9lSf0elZ4yJrsKCtTctMFrfd360DLd7HdsUniNNS6g7n8Mx/KPc9jKMzlng8A2ILKtcRg27VXtMjv2L+UVit0gAd4ne14DpNth6QmkQabJGHOMKf++EAIkDEFzBrqCHQ7Z8RNkHKvSGYABoePBaOWj16NF3dc3tpSL44CuuA0Pln+fTU0hfLkWhha667Hi4uDw7lOo7knv3c+Q3vxJH7rAjujRLWBv03Kdk4WcNswwqh0SCxWJcN70rvhJqe7sWq/uFCI7CDy/yfp7R/v6SLIqlO1Xa4tBFuOPLJ0ni4fwVXlxPkIZjUxDWbU6HHLwV2X6NYnpIguoQ5InHGPLkU0snHJdcfA2+DBJ16H8j4MGgR/XPk3ucGwKc4p3bBDq9K2DoQHmG+y2ZT9+6Bjn59HCaOEYbi7715amsudObmorU6VRR3uneL5ca2t1mUCBMjcLX2/talaV+audOyzi+ZdF/GPTIa32gqrYFGhtNhscHY4Pbd4o4DHH5cwLH64GHOoBaawlBl8yPuIOr5zMuFSm6RlqUvMuN9/9CUl2LljTXzrKnR3+XwkFqd4odvaffg3g1KA5kGiZ9LB0JZnOyHPmALrnoB5qxmbB31bDFGjbVIujQHu3O4kM3bcWv8GrPg/yivHOvZdP/9pcmtzPd7Po6REZmasfjOTnD4UbCmN43b9N26qx++0I+COfhyuPsxOD5JTT/eR1omN115MlhuXvM/DNjXbBT7z3KSP902FH/S3ez6xA7wxvPW+bcXbyMyEuDfjuO6Lp+nyuXNwyf6fk0HHOsJjSRHDnspYjDhRNaa7G/vbEbNvJPUtPr2hbGA55zgiwfdENOaaZR7ME3o9T+j1zNJ3QjclHAWkUqkwZswY4aOPPhK2bt0q6PX6o7mMMHbsWI9/48aNO6rrnQ5ITEwU3n77bcdvk8kkhIWFCV988YVbubq6OgEQoE5QoheU6AVBFPR5/Pvw1Trh+glRrfbzHIJJ0vZ5PCf+aZ/TCul3a92OXXZp6/Ivj2j7Wp6u3XLf8LCPWu3bHe7c/o3zBQGEG5kn9GBvu9ffpk4UfJ5AmHqNeK9ed4n77zwPgZEvOYraN5YyReDKCx3PzHMIT32y1llOEAShqqr1cyAIYHXfF5DXqg1RNDqKvM29juNhklK3c7uzr9U9Qh8Wr6F8yn1/vzsQjBKpkHyfy31iNgpFRYKwkcGCAML3PuM9t39QltuzenofAgh6lK32qZ50btdptUKdVuv5Hi7X/DWoZ6f7gf3v852fCzyHMGcYQk6AuO/COzYJTw/3FQqi0gUGv+uo+znnCMKYMYJwGx+3fR/n6251XKevF1audO5KThYc116V4P4ud5XuEiyW1tdwtGVwhtDvvcECzyF83Us8ZpC6HPdwf491Pv9WgecQUt5OET7a8pEgCILQuHu7kBHkXu7i/5vj9rvHDNp8tz9PTXCr66Rrxf2XXoawNcrl3n4lwk/dEe6PvUz44w9B+KYXwicDcNzGAZf7TmaZAILw4YfONnbtHzzrUqde3wiCIAhLDv8/e+cdJzXRBuAnu9fL3nH0joL0coBgoXcBkaoiNkRFgU+xomIBBMGCgigCoiIqgl0UBaRLFVQ60nvvdwdXdy/fH0l2k012b++4CvP8fge72UkySSYz77xtfpMj34iUGaE7zn1e9xRZvv5m4/v+WFflf8eLSpnw4cgZMz6Vqz6B3PMuDMeTQf6QwfJAzP2L/u/9pj6eg/bRccR0Xx+4pZ7c8T7jtojh5mN0v9v62D/RXT4RFmpuQ+rf0E7mfe7sg1zyOeSF1yvf/xcfL4Msz7quhbvM9NA75QHhb1pfT8XVcrUn1P7nOWRu/EhGcsnRcevl0PDDAbVNX/VlJPIzP42Vafip+bcBtxrajNY2fB3eauO7tiHy9d1bGLZN4TEZZDn16Rfk+XSSb2KtLIMcTJrnXJIkA3JCQoLP8TdHGpqXXnqJqKgo3nzzTQYOHMigQYOYNGlStsOtly1bZvm3dOnSrHcupAwePJjx48ezbds2UlJSGDFiBMHBwfTs2dPnPumEmiIZAG7q6/HyGvSCA9s6Y9KnAxGKu7nsw7VhVCv1wzhFdbjb2cDwu9VswJ2lNgAcFgLzhbO3UDfIGFpdT51NjeZluqMYujfQhPP4CRkCpPRIUkJggbKcEUfUWe+yKsBqj73WhgsHCXRmAfw8A77wZJEdM9DLqcfLHPg4Wqp1ie/xOHzOTvAK7QLI8Dgd6M1cp2XjNOw/avMQRi9oLaGg98xv4zQIljONGpPjTWjSBL5AMZLflWJ8H05GKuYrd8Kyz/y/dw/whWmbldZMT2IIlFJ9h+5BMQM51WzKp/CTadWLB39+EAAZT3jmoh/h9TWXqXDib+iiavx+mc7vv8Py5fAJjzCaVwzHmfuo9WzPzeHDRIdG0aqVZ9N+nSmmbx+4WVVS/PngGuqXrk96OlRF8U4ecIfiW+PmfDXOrlLe2YohimftvOpQ4jLUPQWb0XvC+qGxorme3Xs2j92ohNyF1KnHLY8Yi/Ve5nnGY1rADj+3ePdLjxOquz3aYqff18aQgqpk+iXCnJBml+jcGfr1gYEWWl49i+jA9u2gcwM0ou9rtivmqi43dOHS8EtkvJZBieehz50wdZLRDhbHOYL/MyZcmao6iGuz9ZQQkB54kH3F4afaXucChvAR03jcsG08xoiHJ71SqZhIrAC/GNdr+aLTVv6oBkef9tQv2UuDkGGDpj6S6tZkJ2VSFSejuoOMv91b714+uElZckXPd3XhTJTnmB9s2gTI9DvgCdl/NO1bPkt5gUZYJAI7cotxEZi/B4FsI+l8E9JSjHHRCWpftQWLyAGAnz43bXpv83Do/rC57PEbldiQEn7UaToSiTZtC5GdnJ8717DtKSYCEPTOm3RmARtowqN8TAbB1GAnNfmPk7L/KDO4wjw0Bw4cYMGCBfTt25fU1FSmTjWnRb/WeO655+jfvz/t27enePHirFy5kgULFhDlw09BT1PbSsbalBd0LTezvlwQM+I9v89J8diXpOsWcn3aeR6sPcjdsHfFGR/nSC36Pc0BU/+GH2dxUacCH6+uezdKNwiM1n32xWQ1HcffH5t/OyuXIcVpkUAGeAdFSJCQ2U5dLmBdTkPySsiVEGznvp6wqwTo7RgyNhzlVQEjtRjsbw9fLoT3Dhv2f+89DCtEX88+ZoQ8TjG1GnfiSSfeF8VLtLlm+Zlv9DzdgDknicby5ZCiS4wGHqc7X361hlBc2cbx4xCEtf7563owrwbMnBarbAi/4P7tlVrxbsFP4xLmtucdku1NzHClwwU4rQowTtXYkUCMKdJmgfXyNQY0FXmklQlnvydkPSraxmsY16uqMPI9ykaVBTxZ42uiC79RBVWb93UlKIkzTkUrpieAcYNvISMDvv8e9lMViUxmNAKnWr/Yi8pLcPSw0v4qBCkCTa+dcOYdmPEz1MeT4KWGzlzpi5R9nvYSZAvirFekcoi6BtO45z7lVa/ofY3j6vNY+EcIh1/weLuurAxtHoQX4icgn/Z4cZ9Ov4GueyC9kvUoL0lK4KKETDjJxHIBGRu1ayt+QwDN8GThvf5JGBDxMwDt/75oeAe16zoXCdc98hzXFTOG0L/PUHYmGFNEaOgPY5Ns7gVMA+E9nsmyjPfkgn8fMRc6fz2lI8px7OnjpL+SQXiQ5/3tdo+SKPTRf62PXxNPaP92L0H0tVavUbtMXZ/maL1z/ifo6iV7xJWNljlwJPeEJqugt2SUidhlfITHb1YmHWQGEIyzZBwlS0LQWU+imTM167Npk3XxG/mb+mxmFv3c28LlNC7q+v5MJCrdoHSAWjxQJnY+4VFAYjc12EVNy37MmxwJNC6Xi7feeouuXbsyaNAgDhw4wNChQ7Odh+ZqRJIkXn/9dU6ePElycjJ//vkn9er5kIxRUiFobMhszsuZ77CcVnRgEdiMA1pqZqzny6G29Ls7hHe6jHL7B9R8MpOakbp1V375GKarDojJpSEthgN4OhotzHlkG7jpEajwNIbIB2+aPwRVhsIn6vt1w3nPb535nTBSOEMpMiy0TUcpT5I6U2indthpKI14DoqjT+nOfbi3l2cfCRnm6Dym5RBmNQAkw/sOwJEjXsFO+zpConGm8qw6mWsRn4REJge4nsOH/a+Uvbqy8v9PLxtnhr/QnRuw2HHlSlq1gkFP+17jYRfmyAinRV+iaWi8SQ6GJuWa8EDfKJ6rMQ32egaKN/7bSKVnMEQRJRPB67xqPEgAob8aa1HC3TNV+7aMZIreGONDgaKF6sqSR5N4OkFx+nCF66S4pPLujx291p2Ub7yRxuUac/xZZVHA2rWVFRN2UZMGbOLQ+O8g2jwTBGDd06ZN8+fDU0/B/e5M9BI/3OVJUln3X9WvRF0Fu9w2Y8I+fdIwgN3UIAT/yVDUVDU+SVZzJL3UXBGeqsRWMZWpNxiaPgJ7d9spHVWa488c16rP8uvgrZ5PgSvMtF/IlrtM2zS05MmphJNALO+qkYba+7WGZqwc/xdUr8728cl8+nx3El9M5Ma6ygjtnV5ry+NbeL3N6yTplh678XgJ7iOwHC2SJLkXMA0EKy3vRZtn4F5CW37DGKwiW6m0d/QhPV2ifExZQoKCeKP5JK5/EqQRsFUVUrT+8gKxtGQFX2CxlIHXoSODI9k6aCuVHB4Hozid8levsX3YW/Dywe+S8oIcm/8d/5WACxte8lt+IcpSQmkW/bKbkTK8nkUI7Tsn3Vpqly6eKKPDPTRo4Gnja3QRunuozlbqG56/993vwu/s0eVcmzFDWfjYm2fJOtVEjgSa4cOHc+DAAV544QXuu+8+Nm3axM0338yFAFc+FXgoW9Yz0CpItGE5l4kCmxNJNu+zMrYCZAYhy8qAoW8guy5XYBb9aCktgX8fZWDXm+jf3/P7//jQIC1rrK8Ax2KUxF/efFsbet6tDO6HiuHOSuupsUxGu85uASUDc0xwZZRBIT1dWYT4nXc8v11S1ZKpfz3P1/U9OUS2UB929oSJBxSzijojGdjI6Pw2erQy2yxRQokuz4pVm6IAiVOnlPIlSkCZMp5rsaL77Z5rukE1ge3lBmOhceOgeXMAbr7J+jgcb8SW1kOtf/v2W6oke8xeF31osCoOf5P1jyqz7hc7DLRcK6elbgmjNEIZwSjrcwaAFq7t/b+eDB89iZaNWsa8avfyDsr9i5UqUqm85xq0Rarblt4OO3cibTDHE3+m9v1baEDlZ/sYfnMvJgiw9hmls96mDuqfKL3tRx95inz3HfSq1YtXW77KZ3d8xqsvqy/BQUVTE5rsW1jRnJgz8NR/hPQqW0vBwzrzjrZUyJYtnuwB+lDqMG1tnTJlaFm5JXP7zuXvR40JVM5HwIYKcPaUUj/vxTHBuv0WT8h6ZquhBbbo4xNu7VMOliwhPFiRkqNDo2nbVvmtWzfj/vVK1yM8OByHA9qgmNE2fGxOkLUTH0uJe5GZheStH6Q3oZjUYzM93vPtWcIZVcOoLXkGUCPCyxM25jCndOb2Zx66jgNxgOSVybZJEya+doGVtORdL3OXFZEhinB1OtazBtoF3WTDX5i23nx6gCruz70lxSE+9WIdav8PMjY9TiCMYkQWJXzc63lTYPxxuGzsXKtwAABbqHKDtDiGW8zLvRkYhtL536+awjWBS6N/f8+kV89y2vo/MDkUaFatWsXUqVNp2bIlPXr0YMqUKQwdOpRXX301650FBoKDleUy9C42HTsqa9M4Ys2vs4RM8dWKFiYjA0KDQplVD/4tAxxS/EXuYxYrZeXhf/CBIvHu3AkHDyozrjE6/4T9YTWZ1vwP9/dki/wkP9aCn3W5G/Z8ct5URr/Qbrraucu6pbgzVXNFcDDUqaMszgvQmmU8ySQG8RGSswl/3KtkGot5ET5ooK43crEKHG4BYUpv27GqcQr/is6fIKvlxPRKRP2iu4YZwT41OUKJEu5IGEmS2LpVWetz926PN5vblly+PLzoWakvKNNjV0kM1gkcp+tRqoWxM3dnWt17G/vfNK+i683pKE+r8LZkajNrvU/OwqUhZEsl40Wm2k1oGppXvcxBALKPBQg1Sl82J35s31Ax26wZtJBDOiVIsNoGn5xa273Aozc+Ug8BisCgF1gAt78HR829bR9VHnq9zes81PAhj4boSDMYKXPSe0l5HVXO/sPy5cZtr8uvU38wfNYIZbHQtR4t0datWBKpZWdt0oQV/VdQv3R9GpdrjDxCRh5hFFJSkpV3afM/Zi2gVYr+OzC+FE895fNyaKTuXru2p2lbpQXTNDvLl5u1pU8+qfy/l2r44h5mW273PtZJyWjDGc0r3IQ+7N3TrhvEK5+TVNNEezy+dGfOKMuM/KW69/3QZbUi6P6uJmuJOWJcVuxAO3d/augTN2xwt48tGH0SbRbBuZHBikDz+HfK9a6obKiy3ySJ+jb8Pp5JUGqmqo308865Bca0NPqrFqVd6rZPYxRp4aTOArV9u7qY9hKvhb8A/n4cLpU1JRA+imLODWqlTOLatTM/PyvOoTSsr7hfFcAlt4lTw6cPVxbkSKAJtXjB+/fvz99//21RWuAPzeY/Qic8f/utYsO2YeOv8uZ91Px2pKUpduuHe0Djx4FZxtW79uzxLMsRGenpmPT+Hdef+4eB7TqQ8GIC6a+kmxKuPdjLzje65Gp88Yfip6Kyhlt45x2lvrPVPkrT0Eheb8C8ecZj79wJK2hNChFMZRC7dkuUdSgdWGIYOG0hVsvnsOzgMkCZiV++bPytaVNFC3TihPXL5Uv1378/rFgBU6agrFmzZQvs3Uvtkp50/3XresKG3Zw5A0uXgpdDvN6Xw5GhcxrJtCNHGG3ZyzQroGxDkpR6gOrzY8Gu8x5TV2ioEkrujT4hYXQx63RTn8VbH98bTSOjaQv/w5yZTJb9298f3GyxUe3Mo0KMUpkm0BQrhk+0gbezj8Sm/byVkLvugFnzLMtmRYbJMcdDXHHJtEixHhkZDjf3fPdqk1Hd+/BjTQhOuai8rD7ONae2TmWi+pYZZsIXFZPGYD6iXkudxspup8mqiYZjvfMOVKliPP433yjmI02LER6uJAc/cgRL4uM9n/XrFh4/7l45hROUtd4ZeOAl5aZNaWq83owM5d1lymaYsYIgm+6GnTxJmY9Hsx7FZKlpZBg7FsaORVIdOaK5RKWKMkvwZB4sUULp/7SBUutDOa6GI5+3cAJzKQ3RWyPdrBlUUi1I+1UTfsr6rd4uRYCSs0VBaew/ezkHy5KXUBoZCRMnuuuo9ZlGzZuN5s3hs6nKS77uT7MGLrJLa+WCQ0KggXKfNB+Us6oZeKBOu1a7tiLY108YDgdbKu3p34dxpNYmOVlZkzTES35yEUTKfwcpEe/7BbAS4H/91bxtxgzc5k6NxESI8eF75IscCTSnT59mw4YNpHrFhUf6WuArQC5dusTkyZOpW7du1oWvMurrgia022izSUxp6lFNHzni6RAbNoR77vE6SLqxYVezmCBt3QqHdOpLLQukI9RBsD3YHTGhcT7U5R54IhLjYb+S0bGBquUcxBT3efr2VQZjb5NTRxby++/QxbjGHjVqwH//adeqmH/qlPSsdSDLZg0EwL4LigZFktzVNxAc7DEhbdoEL/k3MbuP1bIlPK5pb+vVg5gYNj620TRDNtGmjWnVaKmWp+c6XVUXBSXbsIUYB38tpb3WI2qCp74D0eecCAsy+kmYBp1NDxg7Vx/Tpod7wNIqnu8jbK9YltM0NCm2YC4Qy3YCXEY4QEKDjFKiJtBIfmav5VVB39ezjYnxMj1lBsGewBN/6leY14+ppSv8xH18aSjr3UYrVTJ+J0MZtBYuVAZ8PZelSKpegE6r11uvAqtyVx87FZNUVV6m3fxIJyoqLhdBbPvzOzJD1WOlpysjsI6gIMVMazj+Xeb1GSUJv8Lah+q6cLt3w8CBsGoV3Kqz5Lj85G198hUH7NjB2G7GESsjQzVjnaoPh1oSFaF7iMHBPKpmJK7EIdqqJi1eekn52+tZ80szid14o/Vtdbeto2om5+gT5kKqD5XLQlbX1vNtziq+bvwu4U0CG7P+WfqbIjAcvQmm/0Wq00ta2rPHvfQMeNrWXLobis2ZAw/dE0vGqxncVD+OY7oIrK1boeKvHynqZFBUcsnJJOFAQibFrrzPi+tEGsyeN96o+nR+sRg+2MMvj3xCwrjthIf7ngiGR1lPZMaPV9za9EN5FEmkXcpA71K6aBFs2AD33QfPePl3R0dDACsGGciRQNOzZ09GjRpF9erVqVOnDv369ePtt9/mlN4ImQ22bt3KoEGDKFeuHC+++CIdOuROGuSihCQpMyJZ9kQY9KplDPXWdzj//msWaPQz+scew5K6df2rBZ++xehEqVeJXhq/kf37FadKezHFGe8cxamu83EtV84s0Nw9sja33WY9QGmzO21iKnkVsuqMRrTKyhbsoUEDqFzZvH348IAPkTPq13ff6DiHzvYs25E0ieUNRb2r2eiHD1dugpY2/Xedwk2/BpCV7wSz5jHn9vnKZ23p3rLqDFm1D1yyMCfqnXu1LA7eywVoGpobnOeYQ1+szFe+UgcAsLejnx/NAlogAg0ot7eF91IbOiTVeXzcOP/HsaJRI6Wdt29vFGgykkszi/sUs6RqYtRMM6llKpP66hts81o79oMPlQPcdpuq1tfY2xEyg2igdZt+BBpJguaxqtopM4isVlJJ2HQQfvzRp8anZRZR8IGgV0pOn648C73p8MwZDAki9dgjQqFWLY66jH6XiYlGYTLhhnjPF7Vj/OUXGP15JS54OwRXVbQsF4jlppvg/HlFyDrrY407A2UtQqODLHynVHO0zaZc2gnKEfmqMhL/dLdu9E2JhVFOUlJwRwBJyKzM6AKfr4BP1sGxpvxbztO43hwne95ZFe05HeB6bLjc5jYtT2uQTbkn5cp5+rm6ddUKagOJJEF4uLvf3xSuFAySlBdttrf1LzMYXCEm/ygwjh3+hN1nn/Uk9T16FG66CS4TRUhEkKFJtm+vCFK+0NapfuYZGBWAG2COBJrRo0czb948Dh8+zPLly+mvep36i+bxJiMjg6+//prmzZsTHx/PkiVLcLlcbNy4kQkTJuSkWkUezS6tMf2O6dxWzRPB4iuQQ+NpnSzirb4zsWeP2V4DXF/MqGnQZ1iVJGWwve02KH1DPL9Wh6NppTzqW5R3yKXlMn3xRRg7lodfK5/l4GT6/XQdZPP7DUCD0g3MG/2grXWqH0xeftm6bK5zxx0EDXuRJQ+oqxBm2kmJU9ULzz9P2pqVbvmgRnXlQ7lyvg+3txg8dfNT5h/2dOX2mmpbuVhF3bYHZs6EChVYsQLOWWiztFWMF9LBFIarUbKkUq9WF/ZQoqR1GSsZOVmboKcbG+7GJz6lnG425kug8WPpyRZPPunpNFNS4Pnn1XpsNOaq8ea22xTnY71A45LVyl1/vVtSstuVjj7sxEHCXh9uek9jSl+0PkFKcbDpIkuS/WditmnvlWyhoVH5UQ0KtJUtbXTMA0pElMARqoyElSp5FlW2MgEEwjCLVE16ihcHGjc2bPuOPtaFVfRtf8YMWP/qHM8GdSLQrRs8+KAyATQtDN2zJ6+V+5T27RWTZWioWfOk4XIp7SFsymF4zyLJjKqhYZ7OmUWniZ00SfE90cx0PWr2IO0VpUL1tv0Ksp2ICEWb7ovHenvCwaz6SE0oByU9xbmqirnNaiy4/36jRsQXc4+PQWr4qft7r17G32++2SNIWKG9n77Mkd6UL69Y5V94Qbme7C7VKMvKeObP90sjoC5j/PjxrFixgiR9LJ5KyZIl6dixI8OGDWO2SdSzZvjw4VSsWJEBAwZQsWJFFi9ezK5du3A4HJZLBFzLvNvxXSZ3mWzQ3Pjju+8UG3SW1r9q1SztNd7+DL7ylXxx1xziFq0yGVaVOqpvZkiIogrOSprBWGTa7dOUBHko5uT2qin8qZueAnBHWwSKZnoY41nEmPDsHSLnzJ0Lffu6Z1LIdtJiSytvaXAwobd4/Cvu6Wu82U2bAg0acAZPEquGj2PIkaFHm+D//PRrrOp1VGkEDyih3y1bQnCY+XlrGpoa8RFoz+2B24xmgNFjPA/njh4WDWLDIPM2YL4WBJZubIwNJw3gfDGPmSnYZlQd2WyK8/bNxnUdc0xEBKxfr5gzwsIUl4v16xU/EE0b5ovYWMiwea7fJYcYwlIDoW5pi9UQQdGkaYMmmGOgvYtrAk1mkGdW+88jsGyk8rvuHbISBnf9bxcHhh5wfw8JUaw0t9+exQX4wEtWMddXwqB1upXV9GWOf49uHbfcAnZ9m/W6qJIlzX4d/PgjHxzr5Y5E9IfNplRvyY8V+fbrMA4fhl27dAO1ljYjPZqmj8CxAcZlU6KiMEzmwNOWp75ZJesKoJizvH10/PHEExhCnPWMHu3b6dxAejRsHMDjN7wJGwaZBIw1ayxXxHDz++/w5Ze+f7ciIgLeVNdx9aOIvGICEmimTJlCmzZtKFasGDVq1ODee+9lwoQJ/Pnnn1y2mOVnxZtvvklmZia//PILs2fPpk2bNiZTg0ChdsnaDG6SxYJNOvr08fiP5IRQeyihr0C8arLKlOCVaj+xpqNxSlgysiTNKpkXFTUIXYG4vFsUHdh4IBxvgiwrMxGt4xzZeiSfdMv+mmHasYOCFHV2WlpAMlau4hFobD5vi13XYZ88qWqR1q7lpb4H3dk+L4V6cruY9lc7pu7dgmlWz+xNXuYOsyE8SBX2qmyaS6oadv9tOWOenKY3eW5W8I5N5hM3meLf5JQZZEoHcOZ5z6Bm9e7fc0/2Z3L+kCRP2wwKwtLZ3IroaGjZvAs3P6ykLvh0dmxAgtZXPT0r7zUs62OKfqypUaDJAreGRp8A7dfpsEIxwbp0ZkkrgSYuPI64cKOZpmoACRH9ceCA9XZNC6b3ol+TeQsu2W7IMtu7Vm98cf31YLfrLiQ4GyN/Nrj1VrjzTsW5vnp1vSlFfVGdoWyoAIdGPpXlsSRJQh4hU7WkH3uMF1Ev+04XoXFYzRH6wAPWvpE5YchNj8FvH5naiiT51462b6/4vOSUuDgYYr125RUTkECzb98+zp07x8KFC3n44YdxOp1MnjyZ1q1bExMTQ506dXj++efZ40t09OLTTz+lSpUqdO7cmcaNG/Pxxx9z6dIlIdQUAlpVaUV6EJxUFTWSDPXi07PMLaARiBbJCu9B/uab4RGvhJ4xYTE83MgiHXcWPPQQTJ6sfG7UyGJWlw+4BRorh04VSeebUrq0ei/Dw/nih0i3Y648Qja9Jz/8oEZnZYFt6lTSLicaHAHtOp/LD3iCjvfBm7cbPW0b6Cx8toMHDCH6GlbPfW0F4LIyeNm8rjk6NJqf7/6Z5OHZX/AyvzkQFcFfFZXUBS2bBQckDN9b3yg86vxVFUY5lXBur+SZ/pBkj4bG8nddvXJTGPSHLzO424dPE0J697acRfjr8+12CNZfSE47lyvFqWaxlQK/qTnRQpQ3z0HcVKyo9JH+Iv8CISpKCcbo2VPxuZHl/J/cgRIJp+U8yk0CtlIXK1aMdu3aMWzYML755hv27t3L+fPn+eOPP+jfvz/btm2jcePGAa3D9NBDD7F+/XrWrVtH/fr1efrppylbtiznzp1j586s04gL8o5y0eX4ts+37oRPdhk6V/MRG2uB4eUIUEPz2GNu/1g3a9d6FvbOhqLHkpgYGBy4kitP0JucvK/n7jpKjhRfnfurr0IxLvo8dq9euugsf9hs2MOM5qognUCTSjirKlXi/ja+lm9WjtFJlwfrAzUMNtgiVcvn8UBCZZBkj0CjS8vcvWb3bJsPCwSbR/XhCcPNHlWrKmYuUE1psh2QKF4ycIGmRg2PD403mpCpta3c8j/KipgYxXSnj+OQZZ3mR8so+MMP3rtmic3mJdAUFC6lcdtteSvQmFIN5AFJSUq6DM3XqqCQJI9jc25yRc0+NjaWtm3b8vzzzzN//nxmzZrFi7rkYlnRpEkTZsyYwbFjxxg1ahRVqlShTZs23Hab9ZofAt80r9Q860LZIEN9dxfe8xvRoVl4I+swhMoGKIlMnerfwfBKBZrCgF5DY7hHZD1IvvwyOG/Oneern2VGSSVMSxi023KIctF+vJK9Rsonu8DNFW7GV+K+uvWUE7gzXgfi3FDY0Dnuhtr9pI/3YvH9i3mjrUdSb9JEactr13rKyJKLHncHdrybm1hraFq0wC1kamanPLLOmAgKUpyr/1Bzc5o0uVmM7N/v0CWSPOgJvdLkILu9EGjtncozd7/DAZBdLXDz5vknhF7N5Oot7NatW8BmJz2xsbE888wz7Nq1i/nz5xNhlVxE4BfJx4CSE8KCwjyp7L1H3ywoXlznfJtLkshVJdDINpPmPMTmv/ez2SBo7cpcuRF6LVDn2OcMJicIoCPWel2d7vvVlq8qCeSAFLvRATg0RAJnmMnkVKQI8fgJapmjA6Hd9e0Y3sJ/foCqeyeQelwVVrPQ/bs1BKpAo4X06nfTliwoKOu9KRw8O5LVihG8846SPkUzvxTUIF+jBrDoHVj5Uo5MTgHff6fSnlauzGYFBZbkulFyt7+V/izYv38///zzjyGC6vacut1fw8hZOJVlh67Vu/LrA/Phjc7ZFmhsNtWZ9cQQsxNMDrmaBJo7+9hNCQZzasbIKbdUuIW1R9fy6l3dsI/yaFT/+MPoL2OJJo3pPFCblm+KrD6kTK9OX5IkWDwOGwE4+RRSuoWPIz34ZiKq/pstgSYQIi7XwrXyNaBjlqOaNqBOmWznhmBlgc7y5Y3OwPrP+Y3TaSGAaE42AUom2pIoGjYbbCkFMdHlsEgnlWc8/DAsWNCF5s268Pr2TUD2TE5WjB2rREX16KGYkUePBlLiIDrw1cUF/sl1gaakdzIVP0ydOpX//e9/xMXFGbIMS5LEgAEDcrtqVzVyLo76NslGhxu8dNjZRUsjmgtcTQJNnVp2k4bGO2w5r1l7VLF5lIyKo8x5SFRt2QHls9QGph9/5KEPlHh6m2RzRzlFOhPdRWVJve60GB67HcongsVyN4WeXz6pBwSeYys7vPACbAkLgoVkmUtAG1Dr1AqihTq6165tTLCZje4317F0d7n7biUsJ5CwMotQObsdGj0Gj9ceQO71KFnz/PPKX+/euE1O2dHQaHTurCRoBCVfTe3aSvbbTp1UgUbK3oRR4J8CchtXGDduHN9++y29vDP7CLJNrkeIaccrBIbdgQOzTNFR6NEEGqtZXn5raDRyZKbUpMt27fh8lec4slUEiwzRIcoM/WM1b0pRFGjyisGDlQGvc6Q9MIFGHVD1bWj7dmOZTp3UtZAKCzabX2Hm+mLXs/+Cmt3Qab5+m03J1eK0F4zqKSICSMvmgkIqVaooqxisWwcXLnisbz166Aqteok7n8lmYiOBTwp0tEpISBDCTC5wU/mbeLhh9sOZsyQ62rMKYAFSowZMm1bQtbgy/DkU5rYZIztkZLcHiIszbbJJ1geZ2WMmn3X/LAe1ujZwuyFpqo0sHGg1QSYr59QryUOV39Qorq4KPe1vZX0jL7T51I2lcjfoIVBq1MC9qnV2zfoHDigC5v33K98to87XPcW3d357ZZUUuClQgaZr166s0JYWFuSYdY+so398/9w/cGKi9VLOgmyjDUJWpsH8NjlpSJLkiT4KfCfTJptkM3T2n01QhOuuNbpSwVHBvfSEwIh7gNP81ALU0BRUe8kL7q5zNzVL1IQTjena1aJt2YCRMu0qdTHvnA906gSlS16Z74wmr1ottJuTdcYEvilQk1PJkiXp0aMHvXv3ppzXAjavv/56AdVKIMh93AKNxSwvv01OTzZ9kknrJwG+gq09dKraybhBJ5CNaTOGV5a9gk2y8UjDR4APYe1a0vbOMeySXyHERYnBg0FdAs+z2FiAPjTZCR8u7DwY/yAPxj+I9D9r67YmDBSU5btJE9i5U6LYWzn3U9QWjfT2b9qxI/ey/goUCvTN2Lx5M/Hx8ezbt4996iqmkAf+IAJBAeNPQ5PfA5QmVMWGxXry0HTqZFl2wX1eaYF19S8VWQpQ3teBTR4DPoTq1Um6FMfCqtApJme+B9cCWuZqQFlkCgLX0BSQz1VeYyW0aENBQebX09ZOKxFRIouS1rRqZZ0OoZaPJb4EOadABZply5YV5OmzxebNm3nxxRfZuHEjp06dYtGiRbTXVk1Uad26NWvWrCFE13rffvttBhd0mlpBgaMJLZmyOaqhoGbcBt+dOnUMvzUs05CNJzf63V+beBh8aCSJzLAQbrsf5IJKVV/U0DQ0WaiyrkYNjR5/WpiCjE0IDQpFHpHzUMv4eItVwQV5QsGHsBQRQkJC6NWrF/PmzfNbbtiwYVy6dMn9J4QZAfg3OeUkHDTXqVLF8NWnFsBCw+Qt0ORmksdrAs07OKvEelehD41G377w5JO+fy8EwZaCIkC+i/rlypXj+PHjWZarVKkSh7UlRgsBtWrVopbQEQpyiD+Tk9+lBvIAKy2RMZY0e3gLNJVjLVKgfbRV+X9Ejk9z9dKiBRw5kmUxTSN2NWpoZs+23l4YTE6CokO+vxkXLlxgxowZWTpYJeTFUpz5wJQpU5g8eTKlS5emR48evPLKK0RZubcLrim0Qd9KmOhbty83VfCzIGQuYynQeGkHnr75aeZsm2MuZ/Heemtk7q5zN+2ua2csdLpugaXjLxJUqJBlEa3PvBoFGl/k92KbgqJNvr8ZpUuXDiiCqWzZsvlQG+jfvz8zZ870+XurVq1Yvnx5QMcaO3YsNWvWJDY2lq1bt9K/f38OHDjAN998Y1ne4XAYlnzIS/LrPALfOHCQmZpJYmKi6bcS9hKW2/MCKU3CgUM5348/knTmjLIMr44ulbrQpVIXT520pXFDQ5VwfsCV4sKBg0tJl5BSUpQySUkgSYQSargebfd8usSrksTERBw4SL2cSmJm/t3Iguw7kpOVtpOcLKLlCiP52TYCOVe+CzQHDx7M71P65cMPP2T8+PE+fw/Oxlt06623uj83aNCACRMm0L59e1JSUgi3iGBwOp2MGjXK7UTctm1bk6Ox4OqiMETwGTQ07dqZhBm/WGloCsE1XQtoTtxXow9NVogmdm2yePFili5dCkB6AAmtrh3dpQ+ioqLyzCRkU/WkvsxrycnJjB8/Hoc2fc0H8vNcAiOJJEJowT+D4sWKk0iiqR5+66WpVlJT3eqWoPAgz3HCw5Uy0dEedYzF7qL55Zw6jjr888Q/lIvLX58rjYJot2FhStuJifGscykofORV2+jVq5d7NYHExESmT5/ut7ywTAaILMukpqaSmpoKQEZGBqmpqTidTgBOnTrFggULuHz5MrIss337dp555hnuuOMOIiIiCrLqgkJEbi4imlNea/UaO4fszNdzTpoE34oM71dMtbhrKxNb5crKn+hCBYEgBJoAOXToEOHh4W7TUZcuXQgPD2fMmDEApKam8tprr1GuXDmio6Pp3r07bdu29eufIxAUBCH2EGqUqJGznXUCmcHUFBmp/O8jHOWJJ+DOO3N2SsG1i8MBBw+KKCdBYFzzJqdAqVKlit/ZdeXKlVm/fn0+1kggKAB8vQPXXQd79lgvWCMQCAT5gNDQCASC3EEsTCMQCAoQIdAIBILACQsr6BoIBAKBJUKgEQjykaIc4nzxtjbwww8FXQ2BQCCwRPjQCAT5SJHN8rpyJbFNmiiJ9QQCgaAQUkR7V4GgaNKsYrOCrkLOaN68oGsgEAgEfhECjUCQT8gjCj4HTW4iVtUWCASFCeFDIxAIcsRt1W6jW/VuBV0NgUAgAIRAIxAIckjZ6LL8cs8vBV0NgUAgAIRAIxAIBAKB4CpACDQCgUAgEAiKPEKgEQgEAoFAUOQRAo1AIBAIBIIijxBoBAKBQCAQFHmEQCMQCAQCgaDIIwQagUAgEAgERR4h0AgEAoFAICjyCIFGIBAIBAJBkUcINAKBQCAQCIo8QqARCAQCgUBQ5BECTYDMnTuX0qVLY7fbkSSJoKAg4uPj2bRpk7vMhQsX6NKlC0FBQUiSREREBC+++CKyfHWtsiwQCAQCQWFDCDQBcuONN/Ldd99x5swZMjMzWbhwIdu3b6ddu3a4XC4A+vbty5IlSxgyZAiHDx+mfv36vP/++0ycOLFgK6+yePHigq6CoJAi2obAH6J9CHxRmNqGEGgCpHz58rRs2ZK4uDhkWUaSJGRZ5vz585w/f55Dhw7xxx9/EBkZybvvvkvFihWZNm0aqamphUagWbp0aUFXQVBIEW1D4A/RPgS+KExtI6igK1CUSEhIoFy5ciQnJ7u3PfHEE5QsWZK5c+dit9tp0qQJQUHKbW3QoAFBQUEcPnyYxMREHA5HQVVdIBAIBIKrmmteoOnfvz8zZ870+XurVq1Yvnw5ADExMVy+fJnU1FTmzJnDd999R7NmzQBITEwkODiY2NhYw/4Oh4Pz58+bBBqXy0VUVBS7d+8mKioq16/Lm+TkZJKTk9m5c2een0tQtBBtQ+AP0T4EvsjPtnHp0iUAt4uHFZJ8jXusXrp0idTUVJ+/BwcHExMTY/lb586dWb58OevWrePgwYP07t2bdu3asXDhQsP+TqeThIQEg0CzYcMGWrdujdPpdG8LCgpya3dym6ioKJxOp99rFVybiLYh8IdoHwJf5HXbcDqdhjEyPT2d9evX06RJE8vy17yGJioqKscakvT0dJxOJ3v27KFJkya4XC42bNiA0+kkKCiILVu24HQ6qVSpksncVK1aNZKTkzly5Ei+mKKSkpIYNWoU48ePz/NzCYoWom0I/CHah8AX+dk2EhMTqVixItWqVfNZ5poXaALliy++YNeuXQwZMoRixYrx4YcfsnTpUiIjI2nWrBlly5alY8eOLF++nOeff55nnnmGxx9/nLCwMIYOHWo6nt1uBxSTVH751oSEhAg/HoElom0I/CHah8AX+d02tLHTChHlFCD79u1jwoQJlC9fnoiICF5++WUaN27M8uXLKVu2LACzZ8+mbdu2fPDBB1SqVImNGzcydOhQnn766QKuvUAgEAgEVzdCQxMgo0aNYtSoUX7LxMXFMX/+/HyqUfZp27ZtQVdBUEgRbUPgD9E+BL4oTG3jmncKLigSExOJiYkxOQvn5fkAoTYWmAi0bRw7BsWKQUREftRKUFgQfYfAF/nZNgIZM4XJSSAQBESFCvDAAwVdC4FAILBGCDQCgSAw7uvEBudnBV0LgUAgsET40AgEgsCo9gdnEk4BAwq6JgKBQGBCaGgEgmuQlIwUnJnOrAt6IUuZeVAbgUAguHKEQCMQXINEjI3g3h/vDXyHp5+mWDIgQggEAkEhRQg0AsE1ytojawMvPHEid+wCkPKqOgKBQHBFCIFGILhGkbOpbpEASQg0AoGgkCIEGoFAEBA2YW4SCASFGCHQCAQCS37b/RvPLnzW/V0SAo1AICjECIFGIBBY8vLSl3lv3Xvu75LuX4FAIChsCIFGILhGyWrVE7vNuKqt0NAIBILCjBBoBIJrlKycgm2SsXuQAGShofHFhx9+iCRJbN++vaCrck0i7n/BUhjuvxBoBAKBJSaBRmho/LJx40YiIiKoWbNmQVflmkTc/4KlMNx/IdAIBNcoWZmcLDU0Ap9s3LiR+vXrY7fbsy4syHXE/S9YCsP9FwKNQHCNk+5Kx5XpMm231tAIscaKjIwMtm/fTsOGDQu6Ktck4v4XLIXl/guBRiC4RtF8aGp+WJPH5z1u+t0ueTkF50utiibbt28nPT2dRo0a5el5fvzvxxytwXW1k1/3X2BNYbn/YrVtgeAaZ//4DN6/aRncZ9wuSUYRprD40MgyJCXlfP/oaJByWTrbuHEjQJ536L2/7c2Pd/1Iz1o98/Q8/pBlmaT0K3gAQHRItKl9XQn5df8LBVf6AkCuvwSF5f4LgUYguEbR+9AM/SsNTp2C0qXd27yXObAVEpNTUhLExOR8/4QEcDhyrz6gdOjBwcHUrVvXZ5k9e/YQERFB+fLlLX8fPXo0X375JXv37uXHH3+kR48eluUKWkOTlJ5EzJtX8ACAhBcTcITm3kO40vt/4cIF7r//fnbv3k14eDilS5fmo48+olq1arlWx1zjSl8AyPWXwOr+9+/fn/79+9O6dWvL73mBEGiywZw5c5g8eTKbN28mKSmJjIwMgoI8t1CSJMLCwgxOUWvXrqVevXoFUd0CI92VTog9pKCrIQiEpUs9n9PSDD9ZOQVLhSBsOzpa6Y+vZP/cZuPGjdSpU4eQEN/tfuHChVSoUMGnQNOhQwfuvfdeBgwY4Pdc2V2DK7eJDokm4cUreADqMXKTK73/kiTx1FNP0b59ewAmTZrEI488wvLly3O1nrnClb4A2jFykUDuf34gBJpsUKxYMQYPHkxKSgoPP/ywZZlff/3V/VJcq4SOCWXjYxuJLxNf0FUR+EFGhnbtPDM1LxV0YQ3blqTc17BcCbIss3nzZu68884rOs7NN9+cSzXKWyRJylXtypWSG/c/NjbW0G/feuutvPfee372KEAK2QuQW+0/NxACTTbo1KkTQOGU2gsZRxOPCoGmkGMK2/b6bvKhyesKFVH27dtHUlJSvkV4ZBVuf62RF/d/4sSJdO/ePdeOdzWzf//+fG3//hBRTrnMfffdR/HixWnUqBHTp08v6OrkO1pn6+1/ISgCuIyh29bPUDxXb/LbIbKgTU6Fjdy+/6NGjWL//v2MGzcuV453tbNlyxag4B2CQWhocpXFixdz6623YrfbWbx4Mffeey9Op5NBgwZZlnc4HCRdqbd6gOTXeVyZLhw4cKY4SUxMzJdzCrKPAwcRmREkOhwkRUUpG5OSQPfMQl2hOHAoz9HhYNRaWFPOJp6rF8uXLyckJIRq1ar5vTepqakkJydnef9cLpdluUw5EwcO0i6n5eszyK++I6fk5v1/5513mD9/Pj/99BNOp+jDsiIpKYlVq1ZZ3v+MjAwuX77s3ub9PSfnygoh0OQi7dq1c3/u0qULQ4cO5csvv/Qp0DidTkaNGuV2pGrbtm2R97/RIjByMyRTkE84jdEzVhqasasO5VdtigRpaWnMmzePDh06EBYWlqfnypQz8/T4RZHcvP9vvvkmf/zxBz/99BMxVxpFdI2QlpbGggUL8qz9L168mKVq4EJ6enqW5fNdoNm/f39A5Ww2G1WqVMnbyuQxNpvNr707OTmZ8ePH48hHB6+8Ptfl9Mskkkh4ZHi+XpcgeySSSLAUjEM3W3KEhhqcDZ3BThJJVJ6jWm5DzSja5eS5aue5StrE6dOn+ffff5kyZQqnTp3i559/tmzvv/32G6NHjwbg1KlThISEMGnSJAA++ugjg5p+5MiRfPLJJ5w5c4b//vuPYcOGsW7dOipUqAAo0YOJJBIUHlQg71Zhep9z+/5v376dcePGUbVqVbp16wZAUFAQf//9t2KKHTIEJk2CAo7iKSxo9/+DDz7gzJkzjB492nT/g4ODiYyMdG/3/h4IvXr1olevXgAkJiZm6caR7wJNtWrVspy9y7JMeHg4ly9fzqdaBYbL5SIjI8MtKaalpeF0OgkJCWHTpk3Isky9evWw2WwsXbqUiRMnMnLkyIKtdD6TkZkBmCNkBEUALx+acynnTEUSQ3LYZdSqpYSFnz2bs/0LGRMnTmTatGk0atSIlStX+nSI7Nq1K127dg3omCNHjvTbX2jLUwgfmty//3Xq1PE9+bx4EaZNg6efhho1rqDWVw/a/a9fvz7z588vFA7BUAACTURERJbLi8uyTHx8fP5UKBt8+eWXPPTQQ+7vUarvwbJly0hKSmLYsGEcOXKEoKAgKleuzNixY3n8cXNK+asZzeSUFwLNsGHQrRu0aJHrh74mMQ2MXgLN+mPrTftIOY2wOX48Z/sVUsaOHcvYsWNz5VivvALjxpluvwnN5CSinHL3/guyj3b/C5uPUb4LNHfccQeVK1fOstztt9+eD7XJHlqmQ19oqsprmbwUaN55B/75B5YsyfVDFygXLkD37vDnn/l7XtPAmNWICogop9xn4ULIDMA9xi3QCA1N/qL5lolVvLNFjx49DG4j3t/zgny3C3z99dcBlfvqq6/yuCaCvEA4BWefv/+GlSsLuhaYnIKtsAeoHUhMK1wzt6sBl6yanISGJn/JUMzoeSXQvPce1KmTJ4cuUK4JgUaQ+wwYAOvWFXQtFAp6nRlB4GRlcrIixJX1YHrw4kFi3owh3aX4mononNxBu4/ifuYzmkCTR4wZAzt25OkprhnyTaBZv349N910E7feeivz5893b+/Zs+BWjb1amDFDscEXBjSBRnS6ecu5c6all66c226Db77xWyT+9GVTRmFvLqRcADxOrPbXhareH4EqM4XJqYDQBJpA7II54MKFPDnsNUm+CTTPPvssn332GZ9//jkTJkzgs88+A+DixYv5VYWrmuwMbnN3zkUalTcmIU2g0QYzQdbkxDpXogT4WE4sYEymi0uXYPJkv/vcfuACLFpk2p6SkRKQKUQaJQlhN4e4o5yEySl/yWOBRpB75JtAY7fbqVOnDtWrV+e3335j4cKFTJgwQfha5BIBuD+4WXZwWZ7VI8OlvPy5PWglpSlZIotyc0l3pXM22XfYcnb7y507r7BCVgRyg8+Zw7k/aB3B0Vtqk+pM5XDCYaJTfe+umaIEwIULVEveElDRa1FDk+ZMo+vXXQvWlK0ldLN4Qedsm8OgedaJUwX5T74JNE6nk0uXLgFKgp3Zs2fzzz//sH69OTRUkH2yE/mz7mjeOdzkhcnpcvplHG86IPpYkRZonlrwFCXfKenz91yfeH/yCTz7rO/zWQ2MtgC6BIuO/Yn1UPGvnYS/Ec5LH/Qg8U3fuxclP6uLqRfztr79+/P19gYBFS2qYdt/HvozxxrhI4lH+H3P75xPOZ/LtcoGfjQ0ry17jan/TM3RYc9cPsP8LT8Si7A55Rb5JtBMmjTJkCjPZrPx5Zdf8umnn+ZXFQQq9UrVy7Nj54VA4x5Q4j8v0gLN7nO7Lbdr45PVONXrm16MXjHacr8sNTrPPquEUPggJiUwgeb3Pb8bvh++aF7+QH+kksnaNpmUjBRT2aIk0BR7qxgvLHoBTp5UtFfZUYUGQkICANezL8uiWpRTVu/WL7t+KVRmvX+O/5NlmXRXOnN3zjVtt0k2em+HzIJ0NPEj0FxJeornFj2H3Ls3F4jL8TGuJk6dUl6zKyHfBJpGjRpRunRpwzZJkrj77rvzqwoCFbstm06aLlfAnv55IdBoHTkhl4q0QBNkU9I+7Tm3x/J3KwHlp50/8dry1yxn5VkmYrMpNyvt/n6Wv0uZFgKNxQ3ee36v4fuB8+blS/R7SbrDbji+wVR22+ltlvUprEirV8ONNypfAlhPJlsEKW1iIk9lWVR7p7ISCLvP6c6S/YUnWVN4cLj7c3JGMieSTpjKfLPtG3p808O03S7Z+f47iHzvg7yson+uQKA5lnjMUqgHxSeq/qkrrl2RY9iiYZam9ypV4LrrruzYBRa23bt374I69TWPpr7V/F18sXevOr4FBQW8hkmeCDSag/GRWwOyiBRWNEFywd4Fhu2aDOHPkvDXsb9M27KyPMiqiTf0q9m+Spg3WQg03sJUmM3TFhLTEpFGSYSrY+wXP3rK/fTfT7y5ymx7ajGjaKV67vjnMTh2TPkSUPLBbBDkyW26dq3/oto75Rbw/VCY/GzCgxSBJlPOpP/P/Sn3XjlTGV9CmuZjuX2rM/dNslmxaRN8/jlyutJPpiZnX6CpMKEC/ef29/174V7IPE94Z807lqb31FTl70oosOHh+FWWCr0oUSaqDJD1TO+GG7J/bHeUUwCdbqC4jyXJOdLQpLvSkUZJdPqqU4H6H2gaGgBmzzbN+LyrNnPTTPfnvef3snj/Yr/lvbE7dc9ANW3ouZxmsVaahcToPTjGbt/HrGn/4+DFg6aZ1v06/9Y95/fQo2YP/5UsxGhtxRC4kEcCjSxl0npxOKlO3z26Jtj7e281oacwraWmaWhu/fRWa/+99HRavPYJNos5kHbNq1Y6yfeA2AcfhIceIjNNEWj27jTf90CCWrw1nNnZN1/Yt8/S0b8oUmCtvtA8zCLOggVZl/FG6xBXHs799LTa4pRXqqF5d827rDmyBjCGgOek2by/7n0A/tj3R4FG2ITaQ5UPLhf06wd/GbUu3hpt/czuf7//jw5fdjD87hZokpKyNoXste5UA8FbCKwx+w/ufXwy171/HSF2s+YuRk0h4E8DWBQyCbt9h/SNzsr0un59lg0zKS2J1YdXm38IDgZADkojPTOVhFSz4Knh1tD4SYmgvdt2SdEGnk85z9IDS5X9M7Mnj+WW8K9paP469hdHEo+YCxw4QLV5ayhmYZlx9yO2zPzX0Kgn1AQa2Wm+eYEIjv+e+DfgU2a4Mvh558+Wv0mjJI4mHg34WAFTrRp06pT7x/VDmyptDN9XH14Njza54uMWHjFekCNGjoRbWEP5coG/7VqHqCVAy03SnMpodqUCzXOLnuOJ+U8A4HLPSM0amo82fMQn/37i91jDFg9zfz51OYdG6z/+gDfeyNm+KqFBikBjy1CvJ1OLWsHwv/LZ+DwT0owDXXV20SHhe+WLwwG6RVMtefFFQHHQfPDnBwEIsnpEFoJRds0Xc+co/2dkZpDhyqCN2eUm7308NHvpFYyCmtAlZyXQrFql/K+ZpSwYtmgYzWc0N/+gmnK1Wvqb6GnPQa+huZBygcvpHk2b91pqxd8uTrsv2gHQrBk0berz8AZOJJ3A9rrN/T5fCQbNpBXqe/CChbznXu5Bysz/NDCaQKOanCpPesZUJCeaMM2HzKqPnLd7Hj2/6enT72bHmRykFD55UtG8Wmhp3fzzjxIVCfDRR5DLwTrnks9R4u0S7n7NO3XI9zu+h/J/X/F5hEBTBMhwZTBqWj/LF+Cvv2ANzWjvDFxVo3UwpaNK+y6UkoKMRDTZm0mnOJUXMTd8aMJTXXDhApWKVaHzbsDmNAk0Q34fwqO/PhrwMb3NNgC/7f5NeaH8MWSIsizyFaBpaOwu9d7oe+j6X5GmGyx9CRGbT24GYBwvMeHonR4BZEsWuUwWK9c95e8pfLH5CwDGLLUoZ6F69jdT9/ecM1wZZGRmMGRXtOm3nAwER4/C1q0BFj59Wvk/OTnb59HQzD/JLp0ZSPeM0pxprDi4Akqq/gB+VhddckAR4D5c/6GyoUwZ+PFHt4YmGEUQCeRe6825cW/HuQUW8GjFrBz/162DfwNUFpxLUdqBPxNYoGRpflbfg6oWkdnuZIKSXGB57WRVQ+PYYpa4stuOT106Rb0p9Th56SR/7PvD9Hux8GKAzmdu0yZFMFc7PsP57rorMM1rly6KcLZjB2zz45D/qNqPDhkCjzwSyOUEzM6zOzmXcs6twQeo8F4F9+eJf01UPkhXZtItMIGmqOVSKEhmrJ3CiMdnk7TZHDGiEZ55KeDjaUKHfmZn4vPPAbgBXUTOgAGez7NmwS23mHbbckoZWH0OdC+/HJDdKMQJq4ZuhjglpLHnTsDmyr7JafNmHLo++YY4s2PQ7bNvp9/sO+G8n1wXasex/4KFuiFAbl2+n3u26DQ0bh8NoNf9LDzgCY9OTsnkpiNQOgkaH4PZ30HiWFi/fBaXLkEJVN+VKVNM5zmccJjZs63fL/1sufYZiwLZ1ND4F2jSyXBlYJPMg2uwPdjnfr6oWBHq1w+w8DJ1Bqhdz6lTsGZNwOc6ffk0H/39EQB2vYZBd3+m/TON1jNbwwMPKBtiY30e7+QlJR5V0zpy6hT07u32oemUoWh5AvGP8S6z+dRm92e9hkaW4frgW30ezx/awHkp3bpfkUZJAWvZrNqIwWymSiqZkrmsx3/OlevuSwAcOgTbt5s2/7b7N3ZcUNIsaE7BAHu8AhSzK9Boz+f9de8bhEUJ5brDgsIA+GHHD7y68EVo2NCwf9jx03DzzcqX776DrBZ7XrgQNm5UPrdrB/XUlB2yrAjT+eQ7k+ZSNH0p/27g1sPKtmNJx9zjhZsSu64o51COBJp169axadOmHJ8UYG1WLv0CADb8d4J1a5QXa9nO+abfu3RWBxtX4NMXt0CT4UOgOXUKBg8GIIR0MsKilO0zZnicdqZPh3XrSPRS4Lyx8g21Oj56n7Fjlf+bNVNGch+z2rQxxu+P/oulhsYvJ05AfDwJukAbXz406WOA4sUBxRyy4uAKy3JVJ1X1ebq9e2H60G04l/5pma9kwIRlfP0jbFirdpAHDhh+7/dLD7eQeeiwzLpP4eS7cN8W6LsdotOhwn/H+Ge9ixrsUnY6qtjUzxeP4EiC4p9QeWJl+r0zw1zBQ8b8MaFWj2jXLpPA6c9nw59Ac92WI2RkZhCsE2iqxFYB4Lsd3/ncDzCZOrI9mP2hzn6nTYPff1c0Is2aZbnbhZQLLNq3iA/++oC/jysq8FKRpTwFdM81zZlmCBQ7EOJbG/RwQ2WdClMOKK+JnXZOE7t2UfHBJ5Uq+BF69AKNywX7D+RMCpDUQHx/7b39l+35bONnWR7r/AVzGzFcw2ZFIJMlc1vbvdejoQkkBVC6K93cXlNSFI1d06bGRe8WLlRihevWZcYvr7s3/7b7N2ZunklKptJXyCkewWPSJEVrVXliZVYeWhmQQKMJKeCZHJxJPmPoixqg3APtvny44UM+/eMtw3HmfAdxf21V1PJ6O7Usw9SpJpNnSgrIXboYN2g4ncpfiRLGyl72M8m9An7Y8QMAMU2bs1rXZIJtwfy882dqnoGIdGBIHYq/rfTD0ijJoMUJhBwJNMOGDaNUqVKGbX///TdLspOuVhAQvaaXY/kRJRNl1EefgBYddv48yDJ/rVZV1XqBZtUqvxmKNPusTw3Nt9+6P9rIJCVK96w7d1YWjlJfqPHjjbs+FK/4ctz3033+L0ydLV/+MusOUaNB6n7DWKt3oLTUnPTta9pklRfFQJkytP+yvTLz1hg1ylDEl1/BDTfAo5PqEdSuFUfaN6XzrM7uJRv0LPxFfVb9+wPGMe1YktIp1a3r2ZiiU2b8taQSp8d+QmlUk4r6AHbsX0/3Od0BqH4WRjgsFnqaOtWgGW1sTgdiSWRIpOX2KhdAOurbbyT84iUyXBkG84c2AHy+6XNAyclT7l1zGG/YG2HM2z3P/V2Lbq7DNv6QOmZdaS1a66WX4Isvsi6vEvd2HB2/6siYlR6J+p+TGz0FdPdPRqaWTsv11T9Zt+Wtp71sZqrGZ3uQIjh8vNhsEgVg1ixiFihaJ+8BW/9MtQFRwq4syxWjTIeXrXBC/AwID2z2qwmq2szaFw//8jDvrnnXb5mHBnjqW/cUVLyIwfTA/fcD4LLQ0Nzd149Ak5YGQ4cansmDPz9Ir7caGl+qbt2gdGnYsAGGD/dsv+0298d+vUa4sxnfPvt2vtvxHS61rwkd72kLh3ZcZsHeBRxOOEzLz1u6Bb8vN3/p8/r1zvEPzVX6x5OXTpLuSidNfTUG8rFyD3TPtoaX8uTu7fDd/l8AOP2h2kfLsmK6HDQIKhgH/4iYZJLtFhOOLVuQT/gYH6KifF7HlVCvtFGQf0ZVll5IvUDPb3ry32SYpJuv155cG1D6w+NJypjnS1uoJ0cCjSzLlCtn7IRq167N66+/7mMPa/bs2UPnzp0pXrw4ISEhhr9rCWem0xT1cTn9Mt/+9jZHJsBbS5VG3n7lMZgwQSlQvDjMmsUD/dS8Ly7dC9yiBdxzj8/zaRqa5AzjjLJfP/julc3w5JPubSU4S6Z3MwkLQ9PCBntZDmZsstAM+CHyk5lZF1LZtOdVql9cjyzL/LrrV2LfiiUqDSLT1JlkaqpH+Dh50lL789KSl/yf5JTHadgtjIwcaSgy+LfBWda14oqNDBm1gKQSDmWDroMNSQs1lNX7BgxbNEx5Lq96yry0yvP7ruIT2LDE2rlvozrwvrQSRq6AJdE1OKHvn1JTabhmP5N+t9zdJ76ilQ68D5Vvuc3yN4BMl5OMzAx3xI2y0Wh+euqtvkQdPOH2DdLjHeIbTSLbqEdHFvHT9h98V1iSYPlyz/csVhDPCoPz9A+e80pI7PjI89Pi/37n03+tnSn177dBIC5bFoBpET0B+P2k4pjpdCqXcfgwyqKh73qEBk1oOXTxEHYXyC6n4bcSl2HpIhdPPQXs6gZA206XoccAaDma+3+6n2cWmh1c9RxLOgYy1D7ttxigOPD/d+Y/y99W/3iKOmkH3d+3ToG/PoHS40ubtKWZktnfxuMUbOHTunq1ojLRmYrnbJvD3OFbWTH+SS6mXlS0P94T7fR0RbjRYaWtzFQFGvsxT2TW2qXJhmep+bo88PMD5gN4XQN4fPi0CdjPNZXtQaoPlfZsa56BNkYFLgAR6qtY6kl1spaSAn36WJ/4fzVItfLHbtAAqXIln/XNC7wF1XdVBWqzz5rxsJpI+mHdvOG/s0p7KnUJLl5fnsy9e6gxtnyW58mRQJOZmWnygYmIiMi2X8z9999PZGQk06dP59dffzX8XStkypmUHl+amDdjDANH1Lgoxv76AgB36hzbD5zVOYHt2kVkqPICfHz5Xrf5AVA6dB/2Uc12621ymj0bVr9h9D7/gCdwZRpNEAB79ipNx64bn7wda/t86/Wi+bIXOZ0Br8zY+cBkfvjvB+6YcwcAJ8fDvklQ4wyK+nTkSEWV/IP1gHddrDkV5S2Hrc9l0NKo2F3w2SbzTNxLiQPA7Xug3CV1Bl3PM0MJjlZMP1/XVb7LMsgjFY3H3F1zOXfZd/TZS+vSGMr7pu27SpjLXpd+ludu0s3aEhPp+cV6nghk+bQjnk7cMJv2wnbZt5kl0+VUNDQ6geb8OTuPNX7M/f23d/5l94cQPy2eHSf3GgatN1a+YUiHP4oR7s8hPc2duCwH4J6127z8RFKSIgtrfi7eGAa7115zf6wQbRwUYlOh4p2PwJ13mo6RmO4ZBA2RKhUrKv/b1JMEpxpOc+gQOB96xODc7JJd/HviX6q8X4V9k2DxZy7OrliALMtkZGZw5h2o/LE6sXCpgnGIOru9ZSJfbfmKCesm8Pi8xy2vF6DTV524Yxds/8hnEQO1P6ptucRBs95l2Lb/OQAigiMAKJ6sTKZ++u8nQ1mXzax9klDbnpRJwxuVfFLuTMOqJvn4dnNum1bDPlQ0f/Hxpt/k10b4DPd67o/ndOc207e3k2px1YhKU/zbbt9leRgTpy6d4mzyWaRMuH8TNFj2H45UsKlD5qN8wuOPQ0qa0p8v/xxes7DG7z7pFeXk5RfW6Std+HXMUbcGKCf89ccMRWhUcWY6kUZJSKMk02TYint/vNdtBvdnJr1H56f8/CqlL5RHQsJYODUeap8F2w3VOTYh6zrnSKDp3LkzL7zwgmFbZmYmGQGmx9fYsWMHc+bMoVevXnTq1Mnwd61Q7M1iihOUDB2+7MC6o+vcpiCrsNrDp/cYHB7lJJ0aTuscNb75BlmWmbJhiqFBaSYnrbHpuQ7jtCCYDBLOmxtjnTPLAfhAl5F85majtuWH/37whIan+VFdBwejTCezps3hL7jzO8+AEZkBpS/Dzsl47L+33Qb/+59p3xGtRtC6Smv39+uvh7NnYcg/1qOgVf6IMK9bob3gI/982a3e9ybVmWpwPAxpqKiX+6kvsmYurHxR+T5n3XLL4wDUPw3lMSelrJSgqPIB0tRZ2fVp58hIj2PAHWo9LpwBq+UOrBjjUbNnOHOWu0d2ucjIzCBI52fw+F8XmdptmmX5OtNuIDYWt4oZoMc3Pfj4sxQkMnmaie7tXfdgSOrncilWpllYL/Og0faNGkxcN9GwzeGA8HAYU28IF8fB4aeMzzHEYvZ+5Aj0u8/YGObOgY77ge/NEXMJqQl80k3RvjT6uJHnh33KGk42ydh3aq4eLVtC0Pc6DZOsDA6aM2XlBGhxGEq07szMzTPd73npjf94dgAIMkcrTfvH+jkcPKj8H6k+9kajK/oPIFBZfnC57x8PNWdGd0XICsmEIX9B3x+MJmGXTkNz+fghOHaMIJt2X2QIU/qSF6bfxRs/PMWC4sr3dt/ezuEE5ZnF6txEnpCthRbpLeuVUyskwLtrPZqwmyysqSViMkh3pZM0TvFv+3U2hGVAsWT4c49RE9R5Vmf35zLvlqH5sJJkvg5f/Ayzf4CENz0CDSjuXt26q5oaHyNzN28BatUqw1ctckpTLshXkO7tpk4DoHlzdx8XPNqjjl95yJjD7MQJ2STkfL31a374T5lYevuGLavi+VxBZ5x4W2dxdeSg28mRQDN8+HAuXrxIvXr1GD16NNOmTaNXr1506NAh65111K1blxMnAjTkX8XYMkEeBUc2ruCWT2/h5aUvK9stxp7TJcKgbVvl8zufMuYzL/8DvaF5yBBsr9sY/Ptgrnv/Oncj10xOH2740LDfhwxhKJMMhyvLSa7ngEeI8kLvqnPT9gSi1X7z1sOw+hPFJ+FE0gkIC7Pc340uWmftEuXFuBxk/TYO9x0d65cKjgrujs/pVHxy9+6FkAivsGLdfddZ3wCPQLP9tFdkRMux8HRly/P+9J9nPYDNpSH4tCfS6tZPm7kTdmnPe9ifvmfOvuiwH/Z8AF9t+YoUnZo5I8PBDHX8DPvpV0qf9J9r3T2j+/hjwtSx5HJq9iYqGq5MTUPjqdC4RVmsPmdPo/x7RtXyY6/8Rwjm3q3kOyU5m3wWp1MJ9qjEIfrha5kHhQ774emFTwNmk+uHB38kJg0qRJfnwgseLZlJoHn9dSpVAuy+74u3tjoxLZHoULWd6X+aPBkAmxTYopeONEWTpPli6Fl2cJlboGmXpgo0Wh2vt/bN8Y4oycjwrKejmSum/go3Tr/RUygpidg0Gy80e4Fy0Z7+R4sKs6JYrJ3IYI8v1ofzITwdd/JM8DgFX0q/RFClKlChAjZZdcyVZCiuhBh98ewqHn7wfW4bq2hoyiZB3Tcrk/77PKJ186a3XrV27DcwbRqfVFD8Nd73irlYY+GP+sVnGaZQ9pQ34PzbsO+u9obt3kuclLVwAentbamzuUCWSM0oZlndO6zXt3UT7FQco7/a8hUAVi40uYE75LxiRc58t4xyPT8gcmyk+526mHoRUN612Vtns2j1lyTrAj3aHITeahe6xmsefiXkSKCx2Wx8/PHHfP755zidTv777z/69euXbR+ajz/+mMGDB/Ptt9/y559/Gv6KKrIsM2LECMqVK0dkZCQtW7Zkm5/Y/4Q34XlVq7dvkqJqS1ukvAhWAk37XzwDaSnZwqS0c6fh63ndZERzxEzJSDEnuzp+nCH40S8nJSkmLK/otJascIegvPLGSkYtV7Z33wm3qhYwq7VbTDidvPOO8vHO9konezLaWpvwxlKISoOq2Yw4LBNVhjPJihentnjv+PGwN9b4GsijIE4d6/QaKIDxLZW38vlFzwd+3rs9OR3qnYK/f/TEfq49uobzyYoJqlmp5oQ4gdDAQ/ABthRTVPmhLrj/p/t5SpeAOIOsw6MPOTyf9eYVTXgbtzZ777WbzEzVh8bczZSONOZAcs+sX44wH6f295YCTaWLilAT/EJ5PnhgPRvRhbjefLPBuV1D80eavXU2kWMj3Y6gj+MRqPf/l05sWCyjWo/i0FOHOOrwOsgIxfRV1uXbweToSePIk+5Kd0e7BFtofGySk5dXwHPLIvj5Z892LZxX47UVviPEvtj8BfWmeEVR2dSH2M1aSH5psdGnbNYsz+cMVbhtcEp1yDx+nLQ77gSHgwvjMhncZDDHnvGoMfylNBi87Ti31e9l2NboBDT/xBN5Jskw6LdBzN05190Og0ptAmD4he+gx4PusmV0CqNp8yDxTQjp2o1gHwP4ZV+vwcCB7A1W/Jh67VQ0LbZM6LDX03/pCSGduztbO1Y/tEn9IMuWCX8yAhltbU44fiOuNGuBJivSx8D2CqFun54yWSvWcsR7697zTEKfGQqdhwJQ78NGnE85z86znnGo34/9eGkl7vXeNL5Xm7HVOJdTrigPTePGjRkwYABt27Zl165d3HXXXdnaf/v27Sxbtoy+ffvSunVr91+bNm2y3rmQMn78eD777DMWLlzI2bNnadasGZ06deLSJd+D1JtePmtT3tvF9LmwzsK/sNjFLBJd1TN2aMXU4ndvhQUzXyMlI4UUZ4rHBPXVV0rStfVGx4opvRYZvu8+HKbkhNlv7LRW0FoJQVHDA59eB0xfR5w6QMWmgGTRybzSBp58ZKhh27BkiYY3phOGUumxLWBEa+vLHLcY9gawAO8zHeF8GBQPL05sWKyiqj96lAu7FcHmhx/gYloiP4YZT3RgIspMOtio0ShhV7xs5++drzcvA9DioHUd2u70PDOrFy5V9a94fo1kCFe/GGpRWMd76grNex2+tV+XbaFwtrppe4xONX/AR9+paSakHHY4z0zZRIYrgyCLq7ZJNkOUaYx2i6wW9GkxjtGYkxoemqj83+PYcdZzE3F4tCq2dav5I83Tg072yqre70ejaWoKHkfv7z9WBqzXWr1GpZhK7ImzuDjggQsWqW1VXl3gyU7NkiVEJqYSZAti2YPLCLG4RJvkZMwyeGdFMr0f3kEYKcRygfoY83Q8q5tPWL1X3kQ6lHsQ5FJm7wBbB23llRbK/fz434/dZQ8dUhNOj1SEvHbqqx6qnad8eUJ/9ZjTtElRg9IN3Nu6z+nOX0f/Ys+5PQZf7A57zmFPMwql3XZjEEAkFGFNHyEZ1F53H8MuWl7jDTr54knzGq4ATLoJ7o16x7DtmFRe8beu5DFNnX8bzk9xUMnLAXmv+o4ERxyl7o1ealsdn3wCrFgBjRv7LOMXmxMyg6gu5zzfVUNVCTrUT2aUY+Zclz6RR8IZNYJcM5sC7tQeJXXv3f7EXRR/uzi3fGrMUZbmNX/WqHsKHjTHA+SYHAk0Q4YMoUWLFlStWpUBAwbw1FNPUaZMGQYOHJit4zz//POMHTuWixcvkpGR4f5Lz2pdmkLMRx99xHPPPUe9evUIDw9n9OjRpKen89NPP2W9s45HNhq/77Bw+gyUiKBIxi+Cb36A6NERnLmg+ijIKCGTHTqYHBkH/2hUn9674AWkvr3c5i4T83X62sul3bN7m6z4O3iTfLwFyz7xhBbPrqu8OKv+jWEvN5AYAp81gnR1lvibVz68/2URfa2xrgI4MoI5+9g+T3RJxYpUb16KlxgLD99KhCuT8yGh9MMzPXWkQ/9NUKe60d7erownUVlzr0z2ldVOcGYDskVK+kXlnGuMdumsZi5vSoqWyKUrKI80lrn5/Dn4eh7e7PESBg97ayGAUK8Z1cDbofO9cKMuMXMmUCzM92zSmZlBtR1ms3KKM8UQZdrgX/M6LkEu5XpKXoKhKBVedD1unyBQBumfLIKY5KB0Ou3rR0YJRRp5srO5jOcgRqH1hUnlOXsWJSdP//4+n8PxUOtwdoCfd/3i+dK+Pa/OOkKQLYgQe4jpvoLR5NT8jjqkEMEF4ngP39FI9b1W8RixzPj8z1CCS1/NRB4JGaOV2bs8EuqWqsvI1vqCZ2DjRt70ci3Ra/qOvGc+vybQbHp8k3vbv+t/ofnHN9PgvepUGuAZ1B1p5oteXdHoJ1hTdYkqo3scw3Qyo223v4eoMNRCoBl6G7zaBpY+ZtSq3ibPJzoanF5qnZgziXQ7bgxf/rscnKQ0ITW/xx7mWzU86tEjpB9QVDt1dM8n2KlokrLE5jRFAuYIGSYu9P2zLx8dX5RIgb9qT2RAwwGUiMj+YLRCZ40/Hu6JYt5qzgtq4Dtb9pQkORJo1qxZQ7NmzdizZw9LliyhRo0aPProo7Rv3z7rnXUkJCTw5JNP4nA4sNvthr+iSEJCAgcPHqSpzoM+KCiIhg0bsnHjRj97Zs2bzbMu44tdl992f3bqOra7zAkyAehb3RzyvanhRqj1E42GZH0dndNWuAeB+qfgPd2LtVFZ6JvkPX1IIdy9XfN0j5SVqbom0S9V7fn39YLrhkLD/r4HESvORkCQKwNiY6kWV83w21heplL0WiIyIDmlHLO9HErv2Qrbto81bLs8/H1IVH08QhNAhj6TxoIM5VXntqGnF/E0FiOAF1qf+f7+uw3bF6u+1fYsBJoUm6JHz7TSaqhcsoXC+Ru4FGR8p0rq3Edk4NGmdU37amp/zZNpXnVYcAMc0wk/mZL/tXpm9vqSYqfNfjv2RKP/ytyVRgl1x4fKIAxGgbjjAzAjzGO6+NgiILLMs0D/1gBcLK8sS5Dpp6e796abTdvWrUNZ6mLmTJ8CzRtHlPj3Ya3Mo+j66TBvnkzSecWHpePmSwTZgigVWcrg56GhF2jq6CxZbTH7rtU7CQ/9Cw+l1TRsH+nlMlIC3wOv3WanU1Ul+GJFk1LQqBFfbXjHrZ0JBP1zv6uOMvAcmQB7JkH1c3BLssfs0vCEeQmVX+YYzW+aE+4JXVobfcqC54+sJEzOvilmXQVw2SHRS+O5DVWbbU9nQ02j/1v3f4wa9Qw7OKVgQqIPWK+BpnKESoQMUPLqlNKZezZNhTpWmbm9kVyQmcXaVwEw0BxwZsDfNQD85Shj2tb0rqeQ/vyThfctDCicX888nWvbovIxpt/f42nD95/oAcA9mbNMZf2Rozv377//8umnn9K5c2eGDx+e45WzO3fuzOrVq2kWQAbPokCimjY31isFerFixdy/6XE4HOi7+gd6KB7wViysDc1Lw+/+fR7dPNsB3lWtRo7JL5FkkTCpfBokWszMgxr+huNkIokoP86sDxHqeLivfl9OL3MQ5icB6Rye5OWqDm4/DHNVxZR2ntb1nuNI8njSiMJGpvsc3kgSOICdFSBmpLLtfDg4LzQk0RG4cBjtCiJRVbsXsxXDITsM17z1M5jRAJLDS+EITzTU5+aznnq3qvIqKw6OZs2aBBxHpsOdfYluNo6+/zh4zfkmtglzGF71LRId+5AvNyXZsc/ntWk4Q5VrlDLSSXR4yjY5B44LlbkceYgR8gscqnSETw+ZU5wHBaWQ6HSQHhlJosN61J19fTyOo4kc5XrKYb0wZ0Z4FIflW5nZdJuyxIRKbKZaP5T7EGFTvofZPPclXYJoOZpER/YWMSydDC7HWcM9ckz5FwYpHszl0z3neH+Z57MDYMFnJDoUJ9deB8xtOCUaHNH/AQ7qdzlByOHtOLiFSS2g/2b4t8PXtF7UDwkJGZlyMUdJdDh4vykMVS2v996byJGBVcDhICjYfI5dVCcGmcQwB9M21uQ2R2tuxDOAl3FCk0cO8Ee5V6mlPltXiotSxUtROsNhOp4tWiYxVdn45irr91Ljo8VKtBscN7SbQEk+mUhYGEzrMI36++rT8IJyvmN7xtDzqIO/KwCypw5WfQdAclIyQRnKEPJxh49ZsG0BiQ6Ik2HMav/XoFE7wVjOge/9KoRfZPhqsn3NJ4/dg6PCbAhR+pIj78EOauHQ1qkLczHu/l58/pZ1DiGAVzrD1oOJfHjgL2ZFm5+fFRERYThU83mF9Kzvh4NEqLoG7KGma/yJ7rS5C2K/VdIYdO4H83VdQuNH4Z/pnu8j1nrOd4yylMejJV1bHq6/AInqsP1YV5j2G6znRj7lYXbe8hsp6cVZ/49FjrBPPyX6zVf5cp5yD7S24X1pQU51KQs7hrYEcDqmmKm/2EpdQ18wiI8IIoNIkjkdVpGwtESSZBlTanovcqShkSSJRx55hB9++IE//viDnTt3sjeQRbK8KFWqFN26dePRRx/ltddeM/wVRRxqI7x48aJh+4ULF9y/6XE6nXROrc/w1FTapdbjb6k0r/hwH8r4ahFbywZel59qZV3GSvUNsPfs7RCpGGIvhMErXoo3TSGwl+t9HtvKAe5UBPDXUA5QhbXcQiq+fT9CfMyKE6/fSG+vNB/XsY8VtGDKDeW57klo8Bhsl6rxUIUBuM4aNQ9xFi5I4YmxBm2RFYfCo9kWE8E8boejigbuycwp7lwRZarHwIMPktmiFYCf1Y88hGXA1F/gUYvZVGhqFWwyLLB14I+Imyz3nz5fXbU32PfZMoNDWLkSEjHPigy4QnF6zUssfWi+WGjQdmTacrbQ5CMboeotQwzbOqX4SPtvQqIi1iHy870y9SeHwsWKSqxrBdUkWLVzP47c/C2HnjrMjUehtDoZ119XNXbDx4p/idVstoyXcChZPfGWb1Brny7bsarR6GuhGW0jBZ5lvYL/Pj1LatRQEss6Qs190k+qH3W5n8aYfvNGr6GRJIk7guu4v7f0kdvJdIxs+Gc9sMUjcGo0Mwd7cYAq/KM6iFfkCBdXm8O056OZr2SouoAlqyyc0XVcVLuHahdhhPrOuw4eoZvDdyTVjcdyEGLU4GtKxHmcX+oNgvjH4DnGc/oGJRrLCWzzUqCc9vKJidIF4O1F0Uynoph65t8AUTqvjt9rQMXgnfTmJ37ndvZHR3AiJI4DI80Czfy5qRQPK0W1i14/LBsJyz1+bgcmwSE1XVa4tweJbO4vGqi+YukVr+MRPiaFSO7ja5zOxdxRtiPDe/dmVGoAC6XKOeCnn34yfD906JB87733ynfccUe2jtO6dWvLvzZt2uSkWoWCKlWqyO+//777e0ZGhlyyZEn5iy++MJRLSEiQARkSZG1BjjKj6smMcK/OYfhDSpN5qIVp+8LrkTMki/Ijjd8THA45weEwbBsWNdT6XK1GyrxmkysPRS77jHIs/d87tyDPrY58ouWdsgzyZ/HKfnUGeY6xpIrFcb022cmwPL/+Gnz9reUmWQbZhtOzy80TTOUqPK07pizLm7YsMp1nd1At+TnelkGWG7BRrsNWU5muIz6U18fFyD/QU+7IAvlsuI96N24sgyw/xKd+ry2rv3LP3CE7JeTKtp0ytb+1LPPWqrdkWZblS8GSz+MEdX5eTkuT5X+J91nm53oO2dF+tPxVPeP2mx5W7uH1TyrfSw9xyCDLjhc9ZS4FI5d9p9wVXav+b/aK6bKjyg/+24T2OL1+OxmJXM6ivTISmeGR8kfRtxvKpx0+afhuew15dh3l80u84d5+NtL3/dXa9UCmmu/9q8bv64+ul+W0tBzdlxbBv7k/J4Tk/P4upIP76+efy/IXm74wlRn9Wgf5CSZ6zqfrO15oh/yfraosg3zsZIqnQxs+3Oc5j5Yo5fO3mx/2fE4IQf68Qfaup9Rz5m0/0kN+kBnuZwOyXOG2r2WeLykzEjn0ZWQpKFH5rfQmpX10GSy7PjI/Q327895WubIs31Jqr9/6bdvwu7zp1qoBXUsJ6Zgc+wLytpK6bcH7ZcpslEGW+wVPk2WQGwatMtWHkcgd77M+7mQGyTLIO6gpyyAP6mLe9+6Ph3s29bxPpsUYGfUd20w9r/aeaWobs0K7yhU4JCO5ZMpsNBz70duN5xtfs4rcgYWGbWU5Jn/MI3LmkqU+b5Fi4EdOSEjwOf7mSEMzaZInV8nEiROpVKkSX331FcP162QEwLJlyyz/li5dmpNqFQoGDx7M+PHj2bZtGykpKYwYMYLg4GB69uyZ5b4nj9tBgpVWWanlYPjH7HRdYthIk21Y49Gwt6x/0A7pNGtIet0FlPsHbJkcKgYnLNSkz1dvQ/d+8Mv/OtD2ARjQA6SR8F9JT5m2B/2eGgBXziyeAPTja9qziEzs1KunJoBd95Sp3NEY4/f6UebF9m5w/kcyygxtM/Fsx+xPUq7ufpqcT6AXP/E+QymeYiqi8M8/yDK8/bbZDPtjTYvyPjge/Qt2GcpXCPNpU/980+cMmjeIQbf79iuQXUEEB8N4nvNZ5rqLgDOMc15KqrgUxUGz3kntWpT/XbpLi8yAc+euKFiS+SU8Db5vq0dJONjbZ1lHqMM6qfSrr1L6ksy4B2ZSPf1u8+8hl9nrqmPcVMk4zc20wf2qe85YXnZvL35Zdn++4EOp+DGPmbZtn2z8HmQLUiJgfHA4wsfBy5Zl5QOj3V8dt91hXS4AOrKImijJT/r3h0/uam0q88rri7j/RSU5yF80JQmP39oHN0G3IUoywNvK6JKojDX6mumRvMLkqrOLEigOJfG6tESpQdmPeEmxeDV68jMzeZBozaQE7Pz+HnhHcfxICwb5FbVjq7Jc+b/sRjJqmN97PZcdxhfk0CE4dFp5Zpex1vDUWb2bBmv2BXAlcEYuz/LPPb42z/AuZzOqwMl4AKQM5RzbnGYHeoA/qkJzfLcvp9rfuixe1x9P6iLA7BmQqfjnScjEs4lReKwmdsz+Bv3SfuMIlUG2EXdSl1jmtw+5/4Ax8rZEqpMDGDO2n6YUA5mO1LYNvX28/m3IWot5ZT0R8MsvHm/+m26yVo1fSzz33HP079+f9u3bU7x4cVauXMmCBQuI8mGHVlNaKKweBru70jJtk4UKW4L/PELR3aVfIIwUGj02AtmmjDD7Y3XF93bi09Tn6Osn0djbqR6BZ3ZdRSj5qTZQw+Np+WrdLzw7pEXBxv6wV1m/57EdA1mmszpl2mBRsDFcT+NWPOEKg72WQjqHj7hYPxzgepag2MK2bFEWhpRlYLRvtWSXLiBZrVEAtL/Dv8p5+vb3eKeaMiDW1Fa69kO0wyzQzKqf5W5utDGgUpVgKGE+32utlfVOpv4zlaOqj9AeqpnKya4gJAm+5l6f56qSADjDTB3d71+rDpqy4kAlq92Fa5zRodeVfAXpSIElcVUCKnfrAFj78Fr38gY2fceqZpp+oMED7Bg9i6SXkuB3Y5LISbZBWZ7joSaP+vxtbHMo7VsudJOu5v6p7pWupFa7uz2LZloQnCmT8ZBFpOjhw1BRl94/kGWnLTgQq/z/H7UBmZtYx4rdVrMnKJd+EIAWrCSeTbRDcciTZNyCb5CWIycLH0rJK+19AjGcQ4mUeUuXHaKUvln9+y/PYQyztiL1V7NZZBJPABKX8NhhIiPVdbEMFXNB56eUz7KNLdHN2L/b972NTDTPYo5Tnkb843vC4CddhxUNdJbMCTwNSGhDrE293xkEw/jjNFRl6M/i1R0kWE1Ln8fWBBpnWqzpt4zMDM9qELYMcHnDoRwAAQAASURBVHkikWRsjMTTbx7BI7DUsXilauERdDdumE6L7cYFWR88eBQXniCFyhw0TG6//964hqjGv9xo3uhFjgSa06dPs27dOpKS/GcdvRaRJInXX3+dkydPkpyczJ9//kk9r9wweipX1n3Zdo8SYnuqAQDPtvISgpzhhLwC9tfg27OvExmnzA60pZbaNdJFJ30/Gxkb36CkF5+F5zcrP51+uqVxbKcaQroyKwuy2Tj+zHFWD1gN45Jg7gzYa7E0xbS/YcWrjHSOs7zOtSjhzjNnwnvvGZYK4jsUp5jVMRapOX0ge8t7Knd0DYVxniQSszoupgGbAFg436VUAKhh28IhPJ15m0iPcb5ePZCLmbUec1v4CAuzwHvcKvUc2VpXJS5EmUFWr2mDaGMO9q/qwehWnu/JQcpMdD3mVO+yK0AtmCT7DOW0Z6idm6rQ1XdGAKHezjfZ5ESwD8dwZENiubWVoHZJxY+galWlo63FDrbc/YaSI0mrr81OVEgU3cs+AZ94MtE6M2KzrMukzpMst3/cCF5uDxm622mVSRbgP4wObNrAHPbfHvATCVo2NY3gyuYklNpzaazJOmWNznRfcL/PY2osii/Je7q5hoyNdVhPPgDKv/csAP/tDWHdv2GM+rwKcu9epATDhQhFU1WTXVmvnQU4My4odVAnIDbdM/WZ3r5hQ961EBLW2xvQAk/i1Yzt5mvfww2mbaCsDDOx00TPhhG6h1lpNeHhULW6nY74iXdW+ZQB7s8bacRZfIQym1bUDJyBAyUefRTatfP+ReKtNyLdmtLh6u/Xfa1IhKl4VPZpb02kbj0teeRUAEIWWWvTnn0WZYmM2j8qQo0PyqKo1W6s9B6XvNaRPkY5VtHC/T0es8otmXCC8Rz/CBVNS2t5p8QIlBwJND179mTMmDHUqlWLtWvX0qdPH958800WL7ZOry3wTY8esGmT9W/T20bwhX5WL9vICILMtFg+/zSEf1RnUm1sl5bqtA+pyoDcrh2U4xivMpqOLOCGeiN4o6pRkyaN9DpxmsPdoIPsdspGl+XWirfitpqlewlaI2U40RiWvc4auRX+eOABCA31rHRfh21MU9X1XZP+Zkwpjzq9xIFBSvIcLUxapV4p3wLi3Lkw8EEHvHmBe8q9Qr9b2nEcZZCYiidb6u7MeiS95BG+YqVEt7V2yxaQdFnz9qmyjcUanT6xpRlnc2eiML383qySbiapqdI7PVVL6TCHvRKC9zJ5smTcpA14czCujQNGgWadvYHleZ0AVZYZTEl67KuU/B2yujDMlQo0B/FI8ftqlOLSIw2YVX6YZVlZlkCWcbmcbHnck2ROlUvZSS3qzbY2df/8M/Rsohu006MMZggDlSsjj5Dd2Xy9aWqxrk/H+6HSU6DPyHCCMryGMbPy+exoINU8T2O7OpBGKu+mlkbevXJ6slFDZmUCeFd32dPL3MiC9vUDy1TrRUwMlCwJzduFIX3/A3PvVbS3xVIVrd8rjDbtk3DEeI+1NaHu4yueYgKnKG3axxtfa9XGuFIMA6bVEpLF/YSrD2oyiDFtLByeEyqirdyzVhX0ZFVam1bnBVPxlzBO3KZZmBwB/2vYZcG0aYpfeoSqPJ7NPXRAWXKgZ9cId1oHLVeX5FRUZw8xQzl1uSqEDhtK4zuUzjaiqtIPxmAtZNVpvZ2+c1Qv63rmqMoXva75+OE7yVxtzDRdThdN5YvHMK4hJmPjjz+UdfU0GjYkR+RIoBk9ejTz5s3j6NGjHD58mIEDB2Kz2fjss89yVotrGLsdGjSAL780bv/wQwgNzzS8ruPHA+/vZe+QIzz4IFSpomzPVO0TwZh7gblz4duV5XjimVA6PFmHvVtHwideq9OOcioz2ZEyX/X8CjnsAgRpUydPE/nxR+jYEbhg9kPZvdu31uQxdWbwiley19WrYQd13NFOzsxIpDGjmXAzlHgeyuwcSY8aveD9fTDuIhxQVEvhwf4jkj78EE4fjuXrR5XONl317n+UTwzl6o7W+Vp4ZxWrVQvGj+ev8lBtqLLJcti+/36jWrmk6kh0552MYKShqF6g0dT/eloELyJ60hsAFFumaBYii4WYfGi8BY/NZeAl+2vM43bOvOSV/8blyfnuy2dpfQVg2es+BbbidnUAUleEzvTqNkokex7895VKGX7bWMksxXXAY2fYW6MEjRuGcO8PvUzlHtdl67fb7NQr7RFk9Z2fPy3Bjz9C7Sg1LYRsN5ghDBQv7vsgKHlVNGoOgeYPweVQ+HPEAeLjPYlh0wnhF7ob9o3GrMk+S3Gm84hpO+HhsHQpT88+6N50Llk5uSa4ZlwwHs9KoJmmS1Q7sEk8e2pV8CuQ34D1IkHhXq9anZJGP6TROt8KAE6cILZiNEtoyzfBXWn9oCeiJoVw3ucpt+nSJ5995tMlp4Zaz+W04l016eAfdKAtS3AGK/1IEB6z0fr1yvpUGiH2EAY09GhX3Ew86F6XTjPNNJL/QULmvbAnTMXPYGznVkuMbAqvSFqy2YRVjy3spIb1BWr88INpk5NgFqNIXVWvC+LVW14EPGtuaf3vjSjRgqHHDwIQOfJ5OHTIvRyfVXsBqDulLnO2zVG+pJvfk9fHGq/xInHw92BTOX/8UxYavHM/B7iOb7iLpig5nGJijK9gmTKKLNjK//zYREACzfjx41mxYoWlialkyZJ07NiRYcOG8fXXZqlOEBh368bWS5dgiBrRqvlSZGbCLbcAF6pStaJRQ6KFX5/2eslkWbEdN28OTz+t/GlE6TrZY0ftcPQWatSAJQeWIJfyzIQlr6yVc+bAjh2wZoCqylfNTzdYaHn/C1Kc7P5U7br9vBZCvlVNuruTmrRhKf2HRDH8kXo8cxuci4Tg9FJs2AC4QiEthqo1lZlp+WhFY3PhAiyx8BMLDvbIFeAnZNluh85q+Kb3SuUAzz7LzTqXiu73v2Euc8MNyk3WehPVKcpWqgSvM8JQ9LJubA9xwUqbMaFbp06SsgQ40HWlmrkqNBRWGWeIjcsbTQVOO7yZOQKQKNnXSz+tE4Z8xQDIAM4wEta/ZPl7mRLKfrJd65yNI+PurzzOCb+UN86+D0WZpVz9DH1HtVgigiPAwv+ua1fL6gDWj8sX259dxcH+HmE/iAze8TZnVNL5kjxmnm2nBAMTD8Cb59lVElarSqYKDmX2+/ffIDdsyIeYV3k/V86c0iKBGGQrEXn/fmjThvDoYix5QGnc2kKDmkBzZoGimtWSj+kH8L0hSsNP0gcKVP6TiOBIvwLNuWI3KDk+PvEI/VvaPkWkVx7LKrFVfB5j9+LD7LyoOFq3Zwl9nXM5H457qYc0nSnk5ZetjqCQHF6cV1+1/m1zmGKbaMNynkPJwNdR/oOlcluCeimCZDAZrFG7p7p1lZVZ9JSJMjqDV9/8DfpQ4nRCOEAV9qtpKXbvUKS6fzRL35dfGiZvd98NJ096bu7MBspSCSH2S0xZahZctlGPxmSR+a6XUcD/n1ezstnAnqlUwntZgd/pYtwQFKS077JlmUF/3uMZmrOSiyF+hv/vPOm3N26EDRsgpKrxpUvx4Qjtj+vL1KNXL8gghL58wwYLM7lGSIjlwvV+CUigmTJlCm3atKFYsWLUqFGDe++9lwkTJvDnn39y+XLurX516dIlJk+eTN26/r3Nr0b0L53WidgkmztLqSQpAoCVFmRhVTgaDRcphoMEarHDLSz4OkenXlHw1luwYwflVLN93bpwJPGIYR/Jq4kUK6YoL8pGq2/3r9MMi+kZztdIMXEE4WTPHmU/b5TF1iWW04YPP8SUpFG/3s++NEWaH95CMTHExvpeiUGP9yLwT+hXFP/9d9/6bSD9lXTuqKGYwYa1Vk0b77yjDDy7d8OLLxp3UI3BkgT7vIIbNA2Nyx7Et3WgR/Aszj2smFrCX4boKJv74Vfer6oEgoKoUcmoPahRwjhLBkC2sXIl5t5bFWiio/E7M+7RXWJiuiKAnTDKy3Rsopo4QpP41SIzr6EaXufIzDTb4pN0CbSW3Vic8CCzxm0ZrQn2s65mo0bK//r1gvxRubKnXbkI4kW8NHJ6n6l3lYHyX91il5kScLGKYsrd7PHbMORi+fdfqk01L1q64u4PTdvsuIgg2bRdvxZb2+uUxj1mpWIi0QSacpzg8v+G8QBfcJEYfsMj+T2T/om77BstoMx99eGnL3k2/g1+2TaLw+36m05Zq1wC58+jNJKHH3YLd/W/NF+LJEmUj7KWJmu0r2h8x2W7wS9Lr8UYNAg4bZ1utuM9vk109aYP9fmbtnL5Cy8Hc8stsHKlWcMExj5m/r3z+fdLY3r9TOxczwH3RKhMnKKtGNhN6Wfp0QOA5cuV8oMHQ2mdHN+/Jzx6B4S6Mjm232NyevcWiK2r1DFZFzk2TzcZTAmJpCTm+3Lbbebr6HDnC4xpoQRjPNjgQffYENvMx/gZFMQAZpBKOKtpzlDbu7wZ2d+y6JH/ynLsmLLucHw83HgjpuVxQOdorQsOAnj31u8phscrXluLLSY0ipAsTO96SmRzlYWABJp9+/Zx7tw5Fi5cyMMPP4zT6WTy5Mm0bt2amJgY6tSpw/PPP8+ePRaL9gTA1q1bGTRoEOXKlePFF1+kg/cIdA0gSZ6Ie43KMZUDWon0nj5Q8RlFTZeEg53UslpoGPDYY+fMAYYNc0sZ588r61TGl4431iurJuIKcZu+vKnWsx4P8wnbLMKgNcqokyVfZgO17zAQYs/GG2FxjPU05Xl9X+3HZhFsD2Z279n81u83ZYMsw3PPwXXXKdoZ/agry9DEE1KpKls4F6TcdE2gmTNuDs/cBucpQfEXFLNDuh0iwiX3A5I0bagkUamCUUgJaRpvrOQ/yjHi43ELRDUqfEbH+wCHsq7MmjVgldBKo2FDiXR1Bv1ZqDFu8pbPn3F/ziqpd6aX1sHXwpcav+7+lRWH1FDTrUo0RNrns+nD934FGlAWgM/merjcfLNWT0XzOKPPPJg4ESZM8BTShMrh93Gsm6KtMSSh/EkX+efFY4+ZJx3eqQMA/qCj5QriVDWbczX0GpbEy4r5rBgX+ZyHWI/S7jStz3vtPuOVdlBl/wY41pTikbGcOdCPbc/MMB133GQvp+ypionYlw159d17kRxHLH/TEx8PfRp4/Lo0YVeWoXx5jGpUleQKN7Aaa4/QhM+nYevXl99/9/hQGSheXHnA6sAbiGNpTGiMSQvlTWiQ0hD/LQcbN/wCasRqq1bKtbTUAovqexwe0+wQnhpKWZ1PyW87p5Gw3bzqed8+cINq1dryzJecxXxfyuiUSpp7X0RUMV5VFbKf9/jc/bhqtTJq6n3x8MKnOD5gBlWLGdvcvwP/pUIFKFfOK7jBop+UJJQIDy916uPjqvDXrmIgy6ykOV/wgFIeGf3KRrt3m1zCTHzwAXzxRYDCzZUkkbtw4YK8ZMkS+e2335Zvu+02OTo6Wl6yZElA+6anp8uzZs2SmzVrJttsNvmGG26QIyIi5D179lxJlYoMWmI9f0mCzieflxc2ilHkHD9oycNkWZbvukuWH37Y+nwJCQny11/7P9zXW742JCT7YOGvluVcmS75rVVvyalpLsP2u++WPZmQ3nrL/TEz00/9kWWbzfP94IWDMo4jcny8cj3aMSpNqCQzEvl88nnfB7Ng6lRZvpNv3AcKIVV2ubLeLzc49sc2+ac/fpQZiRz8inL++ZP+knmqkowtQ5ZlWe46taXMSOTHh6QqO+mzScmy3KKFLE8s1dqzLTVVZiSyfZRd3nBsgwyZMshyhnI4uQ5b5Sbd1yvPsMPz7rqsQEnM+HhXY8aq32s75NX/HnAn0urIAuvEX8/jzgvnK/tV31saGb5r16z/A1nePXeR3PQRpY01nNrQc8MOHHCfY8WK3H0Wp0/L8qVLsnzwoHL89HQ/hUGWZ82SM50uWQb5TLgxMeSS/UvkyhMq+99fu96Rxmu3kyFLuOTH+ch8D71elNErRhveR63coUHjDLut50ZZBvnvkb/KMsin956Xf9n5i5yUpFz3kSNKud9/l2W5a1f3jjNnyuZ34fJl5ffLl2VZ9vQden780XONZyhu2RxkWZZn/jRSlkE+dX0Zw3ar+ySDvIpbjcdQP/xdFvncxROGXbdtk2U/3adf1h9V3o9NJzbJsizLR4/6bNJybKyxj/XJgQPy3ucfkRmJ3Gig+UAtWGF5bdqx3x6T5vfw2n7Hjnm2PTX/KXntkbWyLMtypUrK7ykpsmx9s83Px71drcPCvQv9X6PXAQxtQ9v+/femXQYOVD80bSqfOuWzen5ZtCjrMfOK8tDExsbStm1bnn/+eebPn8+sWbN40VsFb8Hw4cOpWLEiAwYMoGLFiixevJhdu3bhcDgslwi4VikWXozgCOv8Nb745huDGdxE48bWaliNqnFGaf3666ybiE2yMazZMEK97LAvvgh1UfMOqBL9gAFZpqow/F45tjIkVkCSFOlcm7RuH7ydr3t9TbHwLKb9Xpw/bwyl7T8w1F86kFylXIc69OighIdlBEHjRgNxXteU+Z0OkXBB0bxkRilTxPBw60olJ8NTp5d5NqhmpWB7MDeWuxHNp0WzNm2nLrZTas6GG35376bN3r19KWQJbLoHYJnGXyUkBF8Td/c5LtoV58yO9ynXfF01Y8SXLEOZTjcpzsjA+I7jPT9WqeLWzJQxujpcMSVLKsqXypWVOvjVAO3dC/fcg2RXnsk/XtHUba9ry8GnDvre/7rrfP7kIggZG1NRkngYwr+9XpQuN3j5Q6hMmWJ8CJuIBzzPOCg0hG41uhEVpVx3mBq85XTiMa+tXs0DD1ikxomIUG5QhG8fiZ49cXc0zVnls1xQqNLZyCG+0wck6hSuj+gc9w8fhos3KFqPGx8De6ix46pTB3I6XDQp34SVD62kfmnl+OXLK2HLY8Yovnl6Ll4Epv3NS82tfczcVKnCqSeUKCGrFA3OrBKJBmiL0Ws4Jtw2gZsrKGpH7b3UnnVOiA7x4TSvMUaNEDMkUFNp314xW3plxpNlJWILAEnKcf28Q7utyNVuvVu3bgGZnd58800yMzP55ZdfmD17Nm3atMnxApdXO38M7cZNFsEQOaV6df8qPmWA9JDddXrCw42Jl1wu/wKWhq/HX6qUx34cFRLFPfXMK4FnxaVLupV10b1c+cgHnT8A4N/o8siyck1aZ6yFCkeEqzdh5EjDvmvXKtFqbtQRKFhdbXvKFMX1QePZZ2GUumpyiRiPPr1kccVXyFugyZRAskkcPKh89w7L1pD8CDLuY2Hj1zglJfIW1a/g4F5zDxYd6uk4ZS8JSZLg8mWlrRYYVasaGuWmQx6vTCtHdBM7dnD9k7qkZ8DfNLYsmupnnNMGXG8+whhdMpiPOL0nwS3Q2EOMz1AbRDIyUJzPQFnU6UpQ+/pdFhE72rgXHKIIIUHOTM6cMeeZe2xiO2oPgVVql7FTN/EoXRoyS3j6ktweI5pXam445vjxirOy19rCCicaM7ad72zIGjGhMQCkbzSbliri30xn4YtuYMUKZXLoy/RieI1KlFACCrJJ0/JZSA0vv6ycyKuPAmDePNyhYr6QZRyOwHwfc0Kuz1N377YO/9Pz6aefUqVKFTp37kzjxo35+OOPuXTpkhBqLBjV833mTsg6tt9bEMkp3gKM92CTFXa7biYiSdhsWWtn1KImNHeUbFbBhC7nWoFxfTHVoUa2mXyQNYEmLFS9CVoSBjVdZnAw3HEHcOoUHD/uvlmaQ+rjjxuFxvHjoZOa+3BUzwfd22vXttbQ7I0Dm6RoLm5jPkswZfIKmExsZAb4Ht9X/z4AziafNf3mRzmQ7zhI4CXn++7v5cv7KawRFsYz937A4lfu4cjgRF6xvcq7VczOweDJI2KF3ulYYxXNTJF7ToIpVc1BpporyB5q3E8TaNLTUYIB1q/PMlQ9S0aNIv3bn7FKaNBXdZ0JVv3diu89TokSmPxVEsqX4FiMta+V3W48dHb7ooJA0x7f1dbswKOlj9CYyFAG8REsehMO30p0FsqRli3h00+NGhqf7NxpzF4aIHZbNrJ/ehMamvWLq0pjn3+uaN9zm1wXaEpaOHp589BDD7F+/XrWrVtH/fr1efrppylbtiznzp1j586duV2lIk2IPcQUZmiFXbqChuiHTDl7K8YGBfme4fvDO5HS5cvuoIUrxhB58YQ5p0R+4H4+FgJNaJAyk4rQTE7a6OOtgi5VypAl1mqw0yOPkBncRDeTnz0b18o/DQLNyzVu5PVWEKSaVhZyW9Z5QvydE4nMhMoBlY0MVka326pZhHAUIpJwuO/J8eOBKzb+1/R/fN37a0rFRPNG5uv0n3ozx46pJgwdGVm8LvKIwAdyrW0FhRoPqpkjDx5EGXR0zus5JjSUkDu7u50qNGTZYya2h/q3L0QEKwPgoK5Q3SI0WW/+1Gv1CivaCuZrNpofqrdA8zQTmcog3unxAny2OqCJX8AUL27pdD14MD5D4vOcHTtg1ixASbvgHYqeG+STJ4E1TZo0YcaMGRw7doxRo0ZRpUoV2rRpw21WMWoCv1yRZO2H7Ao03hqaQLhwAbzXI42I8HTCVzox69JFl1yrdesrO1gOcT8fKw2N6nNSurR6v3wJNF4E27MIA/KmfHlszZobBJpz1f7GZQe7LXd6UxmJ42nK8gTnwwGn72vQNFMxYTE+yxQ2shtGCh5fHZdLiRyJ0V3ujvIdmF4tsCU/HHZlgGquWxdN4091NYCj1dvyBJMIDTM/z88+y5tBRI8psigL9WhsWCygJCnco7u3rVsr3Yf+KrJr/s5NAtKK4BHQzpwJLNIIlBRMx4/npFbZZ/JkGDXKnFKiUdlG7rrnGbVq+bDn5R4FKtBoxMbG8swzz7Br1y7mz59PRGHSNxcBGpZpyMMNH866YA7IiUCTXQ1NbKxZFa0nB6ZgE0FBQEqKKWFVfqHX0HiHuWuaFnfiwUOHlP+zEGiy0tBYIUmSQaCRdNtBGfA0/wcTR3yv/aORic29cnlGENiDjVlJd+msHFb5ZwojmqN9t25ZOBL7QJKUZ2uVG6ra/j+4/4V3AzrOfaWVxWRX0pwbb4TNmz2/tVBXA0iXQvkQay3kQw+5I47zhO++U4QmPVkJ3d7a5+efhzNncGe1tU7PnffUrm387rJOrmvCJtk4/dxpGlQwJ4q8hPXNv+EG0/JceYrkyd/pZtVDqzj13CnrHYoQ2e8Rc5n9+/fzzz//GLIQ33777QVYo6LHv4/9m6vHC7GHkO5ScmTkRKDJ7V7o9ddVH5Ir5Urc/68QTUPz1ps2d5p8DW3m6VZoaeE9WYyeOTUz6gWanSW0Oigb3XZtr2UqAKpUzlpVJst2w9IILtkzEuyOg5pP4DYilI7Kek2fwoCW6+bhK5gzqJp2EyEh4IjLWmKXR8j89BPAAMJKx7Jmjad56J2nc7gQd67Qp495m5V/lB7N7AjAb5N5e73x94LyqpwxA7ZvVzS7WTnrelMysiSTpjnhZ+P2R79oyaw2irll1ChPoFBuR/PlhKyWkykqFKhAM3XqVP73v/8RFxdHpG6KLkkSAwZYrLchyBdm9ZrFnd8pyamyq+Y1JKrNJaNwdDS0aZMrhyowNOFD81Ux/OZtLqxvHdmiZ9itw7jJYhYYCM3U7J4L+s9heZW+OAC7vzj2vXuhWjXK+nGG3bDme5rc2kdxCvah+JUSKrP/SU/4+ZAmQ7ilQtZan6sdSdXEXW7fEn853iQJWrGcWb/Xcgszd9xhTOBaKnBLR76Q5kzD9hqsfni15dreBi1jotn05owrmAtq2lT504YhRZgMnLhSynXJISFI6crk8L77Pf1hVgkqc0JMDBw9mvvHLUoUqEAzbtw4vv32W3oVkBlAYE2f2n3Y+8Reqn1QLdsCTaC25msNTWixup8mTYumSfKzJMNbHd7KcV0aq0FznW4Phm3KZ5s9AOEz07eGxlla8e+oXcdGuygbLFZMSjcUvwFtZTDJFcR1xTz5WYLtwTkWyq4GNEHEHqQINDZn1trQP2lFsE6wNITzo6QDyC9/jECwSTZkG8SXtV4+2S3QbLsL9nQ2/X78pQ9oftbGgOpZB5vkBfv3K/93756z/aVff2X3s1M5te2Me43wvArW+uMPxVx3LVOgPjQJCQlCmCmkaAn2SkZmryMxCDQiDN+NJrRYCTSmbZpAc+BAntQlWZ3dSzpHYG+f4GMR1fwfxCsPhaZlKFNWokYt5Xq29T/pWcQU/wn7rkW0BV01/6Xw5b4T1OnxN2mQpPz1x8gKzazoy6ThFmj+fQQyzSZWOTKKnWs/5bn4N02/5Qfaas856spkGTp2ZOVTP9KSlblaLyvKlYMGDfL8NIWaAhVounbtyooVKwqyCgI/HHn6iDsLZaAIDY01fjU03iYnzRn444/zpC7ntLElxZPB1zvKyRYXa9pP1hfRZQr9ed3nSKq3gyxJRDmUa4wKdhAZEsnffyvlhEBjRDPPJqYlApB8i/9Qam1QLUrvWOdqnTn6tG87iFugybQ2FmiW0IK6Zu+1XnNC//5GB24Nlwv++uvKjy/wUKAmp5IlS9KjRw969+5NOW3JZ5XXX3+9gGol0KjgCCycVI/BFaOOxarQ1yiaIBOQyUkbuTp2zJO6vH9XJfqMOmzQfdu96lW2TnHwGod8ZQru2OhOtp1WbVeSRHSMciwtPFlzgraRPQfzq5m+feF+ddFurU2EVL3Bzx4eipJAI0kS5R2+na/cAo1sfVFaf5JfS5V489RTULPmlR3Dbrd2i7PZAkvnLwicAhVoNm/eTHx8PPv27WOfLjBeZAwuuoSGKivhZvZJwRZRcFFFhQ1/JifLHELHjuVZ+MPEe7+EUa1AlpnQ8A9GbOxj9qGZPduUQ0T20rBcqFGZYrsOISG5r0uWJELClM/e4fZCQ+Nh9mzPZy2S0JZmsfq2BUVJoMkKd1h3ZpCluaSgNTTR0Uana0HhpkAFmmXLlmVdqJCwefNmXnzxRTZu3MipU6dYtGgR7du3N5Rp3bo1a9asIUSXP+Ttt99m8ODB3oe7apEkeOABACHM6NGEFskiENUy/NpLY5mbNK7omRbaJaULMCXW0xYw9MOC78bR78d+pEo243XVq2dZXgg01mgCjeTH6VrP1STQrD6sJgkspCYnQdGiUCTWKwqEhITQq1cv5s2b57fcsGHDuHTpkvvvWhJmBL7JllNwXqPT37vUQTQ0xM+IoQo3KSHW9bRJNmySjc8bwP6apZU0rxahHEKgsUaWZRJDQMoiQEK7pbnh11FYSHOlKR8yg/w63haUyUlQtMj3ZuLtK+OLSpUq5XFNsketWrV49NFHufHG3FkEUnBt4dbQWPTaebVshe/KaFmLZaqFN4FdXf3PgOPikEZCSpixkGaC0gSah3pCarjvZIBCoLEmU84kZji6VNHWaNlqryaBxr3WmA8NjZa5QGhoBIGQ76/GhQsXmDFjRpYrpyYkJORTjXKXKVOmMHnyZEqXLk2PHj145ZVXiMrLfOOCIkFh1dB0ahPNuL1TcxSWqr3DkiQF5PcmnIKt8ec0qyfQ9PtFCW09L18CjYYQaASBkO8CTenSpQOKYCqbT8kU+vfvz8yZM33+3qpVK5YvXx7QscaOHUvNmjWJjY1l69at9O/fnwMHDvDNN99Ylnc4HIYlH/KS/DqPwJrUy6k4cOBKcZGYmGj4TUqTcOAwbc9THA4lr3tKIt27J2HZPA6rKYUTE3HgIMQZYqhjRkoGDhwkJSaRcikFBw7CM8Otr8Ph4BKR+XuNRYS6MXU5PuR4lvcmMlJ5bPl9C/Oy70i7nIYDB4SnERaWaLq2y5eVa05Ozv/rFmRNfo4rgZwr3wWagwcP5vcp/fLhhx8yfvx4n78HZ2M1ult1q881aNCACRMm0L59e1JSUggPNyeWcjqdjBo1yu1E3LZtW5OjseDqQNPQeEcKQQGuIpxVylL9stB+0Gtngm05WL1RQGSIv0UPFFq0gLVr86Ey+YhmbrUHSe4kdnq0Jip8aK5NFi9ezNKlSwFIT886CvAqssbmjKioqDwzCdnUt9CXeS05OZnx48fjcDjy5PxW5Oe5BB6SbckkkogtzGZ6BvZwO4kk5u+zSUxUnDF05/R3/kQSSbWnGsrYwzz1Dk8JJ5FEOtXpZH2cxEQuESna3xXivQp0fpIXzy7NnkYiiexaH0H14ubjh4crTTUmxtBUBYWMvHqve/Xq5V5NIDExkenTp/stL+TeAJFlmdTUVFJTUwHIyMggNTUVp7q87alTp1iwYAGXL19GlmW2b9/OM888wx133EFERERBVl1QCHBraPJqIZd8wFu7pP9eu2RtRrUeRaeqnXzuH+soutcuyBs0DY2v90LLZXQ1OUIL8g4h0ATIoUOHCA8Pd5uOunTpQnh4OGPGjAEgNTWV1157jXLlyhEdHU337t1p27atX/8cwbWD1nFrOUf0dLi+Q46yMuc33jl09NcSZAvitVav+XUOjigjptgCI1Z5mfRUrQobNyr+QwJBVgi5N0CqVKnid3ZduXJl1q9fn481EhQlNA2NlUBTp1Qdjjx9JL+rlG1ql7wCe8fmzVCiRO5VRnBVoPkOlYmyzootSRAfn48VEhRphEAjEOQDbtV6YcrFkg09/tnnzxIdGm3Yli3zmdViNoJrnhB7CPKIQvROCIo0QqARCPIBfxqaAuGXX+C22wIuXjyiuGlboRLOBALBNY8QaASCfMCfD02B0K1bQddAIBAIchXhFCwQ5ANXQ5STQCAQFGaEQCMQ5ANa8rxCo6ERCASCqwwh0AgE+YAWziwEGoFAIMgbhEAjEOQjwpFWIBAI8gYh0AgE+YjQ0AgEAkHeIAQagSAfuZoEGuHgLBAIChMibFsgyEeuJiGgd+3eHLh4oKCrIRAIBIDQ0AgE+crVpKGJDYtlTNsxBV0NgUAgAIRAIxDkK1eTQCMQCASFCSHQFALmzJlDixYtcDgcSJKE0+nMs3OdOnWKfv36Ubp0aWJjY7nllltYsWJFnp1PYEREOQkEAkHeIASaQkCxYsUYPHgwEydOzPNzDR48mCNHjrBt2zbOnTtHnz59uP322zl//nyen1sgNDQCgUCQVwiBphDQqVMn7rnnHq6//nrL3//66y9at25N8eLFqVy5Mq+++mqOtTh79+6lT58+lCxZErvdzmOPPcalS5fYs2fPlVyCIECEQCMQCAR5gxBoCjm7du2iXbt2PP7445w6dYo///yTX375hbfeeitHx3vhhRf4+eefOXHiBBkZGUyePJmqVatSv379XK65wIqrKcpJIBAIChNCoCnkTJ48mW7dutG3b1+CgoKoXLkyw4YNY8aMGe4yVapUQZIkn38jR450l23WrBlhYWGUK1eO8PBw3n33XWbOnEl4eHgBXJ1AIBAIBLmDyENTyNmzZw/Lli0jNjbWvS0zM5PMTI/pYuPGjbhcLp/HiIiIwOl0kpmZSdu2bWnZsiXnzp3D4XDw22+/0aVLF1asWEF8fHweXongh7t+oP317Qu6GgKBQHBVIgSaQk6ZMmXo168fn332mc8yxYoVy/I4iYmJXLx4kf379/P9998TFxcHQPfu3alatSoLFy4UAk0e06tWr4KugkAgEFy1CJNTAKxdu5b69etjt9uRJImgoCAaNmzI999/byh34cIF7rzzToKDg5EkiZCQEO68804uXrzo9/gul4vU1FTS09MBSEtLIzU1lczMTAYPHsz333/Pd999R3p6Oi6Xi71797JgwYJsX0dcXBy1atVi8uTJJCYmkpmZybx589i+fTuNGzfO9vEEAoFAICgsCIEmC9auXUvnzp15+OGH2bVrF+np6axatYqJEydy//33s3nzZnfZ++67j2XLlnHrrbeyc+dObr75ZpYvX86DDz7o9xxffvkl4eHhdOrUCYCoqCjCw8P5888/adKkCYsWLWL69OmUL1+e4sWL06dPHw4dOpTta1m8eDFz587l7NmzVKtWjdjYWF544QU++OAD2rcXppBrmcWLFxd0FQSFGNE+BL4oVG1DFvilefPm8jPPPGPY5nK55JUrV8qhoaHyd999J8uyLB88eFAGZEDetGmTLMuyvGnTJve2Q4cOGY6RkJAgA3JCQkK+XEdCQoI8ZMiQfDmXoGgh2obAH6J9CHyRn20jkDFTaGj8kJyczJo1a7Db7TRt2pS4uDjsdjvBwcG0aNGCJk2a0LVrVwA2bdpEUFAQoaGhNGjQAIAGDRoQEhJCUFAQmzZtKsArEQgEAoHg6uaadwru378/M2fO9Ftm5syZzJs3j4YNG/LLL7/Qt29fRo8e7faTAcXpNjw83BT+HBsbS2pqKomJiYbtLpeLqKgodu/eTVRUVO5elAXJyckkJyezc+fOPD+XoGgh2obAH6J9CHyRn23j0qVLAH4jeiVZvrYzfV26dInU1FTL3xITE6latSrDhg0zJLLr1KkT8fHxbN26lc6dO/PEE08wd+5c+vTpg91uNxwvNDSUzMxMfvjhB+644w739g0bNtC6dWtDxt+goCCCgvJGxoyKisLpdPq8VsG1i2gbAn+I9iHwRV63DafTaRgj09PTWb9+PU2aNLEsf81raKKionxqSEqUKEHVqlWRJMny94yMDHbt2gVAfHy8++Zv2bKF+vXrs2XLFnfkkndIdLVq1UhOTubIkSM4HI7cuyAfJCUlMWrUKMaPH5/n5xIULUTbEPhDtA+BL/KzbSQmJlKxYkWqVavms8w1L9BkxRNPPMG4ceOIjIzkzjvvZOfOne4kdEuXLuWpp54CoHLlynTp0oX169czdOhQpk6dypNPPkmJEiW45ZZbqFSpkuG4drsdAIfDkS8CDUBISEi+nUtQtBBtQ+AP0T4EvsjvtqGNnVYIp+AsGDp0KEOHDmX8+PHUrl2bXr16ERwczJ9//smkSZO4++67WblyJaCEX7ds2ZJVq1ZRs2ZN1qxZQ+vWrfniiy8K+CoEAoFAILi6ERqaAHjppZd46aWXLH8bMmSI+3NcXBw//PBDflUr27Rt27agqyAopIi2IfCHaB8CXxSmtnHNOwUXFImJicTExJCQkJAv6jotykqojQXeiLYh8IdoHwJf5GfbCGTMFCYngUAgEAgERR4h0AgEAoFAICjyCIFGIBAIBAJBkUcINAKBQCAQCIo8QqARCAQCgUBQ5BECjUAgEAgEgiKPEGgEAoFAIBAUeYRAIxAIBAKBoMgjBBqBQCAQCARFHiHQCAQCgUAgKPIIgUYgEAgEAkGRRwg0AoFAIBAIijxCoBEIBAKBQFDkEQKNQCAQCASCIo8QaAQCgUAgEBR5hEAjEAgEAoGgyCMEGoFAIBAIBEUeIdAIBAKBQCAo8giBJkB69uyJJEksXrzYZ5kqVaoQFhZGVFSU+2/evHn5WEuBQCAQCK5Nggq6AkWBL774guTk5IDKfvjhhzzyyCN5XCOBQCAQCAR6hECTBUePHuWVV15h1apVVK5cuaCrIxAIBAKBwAJhcvKDLMsMGDCAV155hUqVKgW0z/Dhw4mLi6Nu3bq8/fbbZGRk5HEtBQKBQCAovEgSHDyY9+cRGho/TJkyBVmWGThwYEDlZ86cSaNGjQgPD2fdunXcd999nDt3jrfeesuyvMPhICkpKTer7JP8Oo+g6CHahsAfon0IfBFo23A4YNcuiIvL23MJgcYH+/btY/To0axbty7gfVq1auX+3Lx5c0aOHMnw4cN9CjROp5NRo0YREhICQNu2bWnfvv2VVVwgEAgEgquAxYsXs3TpUgDS09OzLC8EGh+sXLmSc+fO0bhxY8P23r17c/fdd/Pxxx9neQybzYYsyz5/T05OZvz48Tgcjiuub6Dk57kERQvRNgT+EO1D4Ius2kZiIoSFKZqa7NCrVy969eqlHiOR6dOn+y0vBBof3HXXXSZtScWKFZk2bRodO3Y0ld+zZw8nT56kSZMmhISEsH79ekaOHMk999yTX1UWCAQCgeCaRQg0PoiIiCAiIsK0vUSJEsTFxbFy5Uo6d+7Mjh07qFSpEhcuXOB///sf+/fvR5IkypcvzyOPPMLzzz9fALUXCAQCgaDw4MdYkWsIgSYb6M1HLVq04NKlS+7vTZs2ZfPmzQVRLYFAIBAIrnlE2LZAIBAIBIIijxBoBAKBQCAQFHmEQCMQCAQCgaDIIwQagUAgEAgEeUp+OAULgUYgEAgEAkGRRwg0AoFAIBAI8hRJyvtzCIFGIBAIBAJBkUcINAKBQCAQCIo8QqARCASCQsLZs7BvX0HXQiAomhSIQGO32wvitAKBQFCoadsWqlUr6FoIBLnPVRvl5G8FaoFAILhWOXGioGsgEBRdCkSgkfLD3VkgEAgEAsE1Q4EuTtmmTRufwo0sy0iSxNKlS/O5VgKBQFAwCOW1QJBzClSgWbZsWUGeXiAQCAQCwVWCiHISCASCQoLQ0AiuVq5ap2CNTz75hIYNGzJ48GASExOZNGkSc+bMKcgqCQQCgUAgKILkmslJ84fRfF+0SCb9Nm9/mAkTJjBnzhzWrl1LkyZN6NKlC+vWrWP79u2MHj06t6omEAgEAoHgKifXBJqc+MOULFmSevXqUa9ePaZOncqECRMAaN26dW5VSyAQCAQCQQFTpNZy+vTTT2nUqBFDhgwJ2Hx09uxZ1q1bR1JSErGxse7tQUEF6qssEAgEAoGgiJFrksOECROYPXt2tsxHPXr0YMyYMWzatIlz587Ru3dvbrzxRk6ePJlb1RIIBAKBQFDAFCmn4BIlSlCvXj0GDhxIZGQkEyZM4Ouvv2blypU+9xkzZgzz5s3j6NGjHD58mMceewy73U69evVyq1pXxKhRo6hatSoxMTGUKFGCTp06sWnTJr/7XLhwgXvvvZeYmBhiY2O59957uXjxYr7UVyAQFG1ElJNAkHNyTaC5UvNRyZIl6dixI8OGDWP27Nm5Va0rom/fvvz9998kJCRw/PhxOnbsSKdOnXC5XD73ue+++zh16hT79u1j7969nDp1igcffDAfay0QCAQCwbVHrpmcevbsedWZj2rUqOH+LMsydrud06dPc/78eUqWLGkqf+jQIX7//Xc2bdpEiRIlAHj33XeJj4/n8OHDVKpUKd/qLhAIBALBtUSuCTR6P5kzZ86wceNGNm3alKX5KDExkY0bN9KqVSsAkpKS2LhxIy1btsytql0Rv/32G/feey8JCQlIksTTTz9tKcwAbNq0idDQUBo0aODe1qBBA0JCQti0aZMQaAQCgUAgyCPyJJxIMx917Ngxy7LBwcGMGTMGWZZp2rQpPXr0YMSIEXlRrRzRtWtXLl68yPnz55k5cyYVKlTwWTYxMZGYmBjT9tjYWBITE03bHQ4HSUlJuVpfX+TXeQRFD9E2Cg9RUeBygUV3UWCI9iHwRaBtw+GA1NQra9eBnKvA46PDw8P5/vvvueOOO5BlmRdeeKHQaGf0xMXFMXToUIoVK0b16tUNWhgNh8NBQkKCafvFixdxOBym7U6nk1GjRhESEgJA27Ztad++fe5XXiAQCK6A1FRYsQJuv72gayK4lli8eLE7IW96enqW5QtcoAGIjo4mLi6O/fv3+9WAFDSZmZlkZGSwZ88eS4EmPj6etLQ0tmzZQv369QHYsmUL6enpxMfHm8onJyczfvx4S2Enr8jPcwmKFqJtFDxJScostrA9iq+/hmHDHCIKS2BJVn1HYiKEhma/Xffq1YtevXqpx0hk+vTpfsvn+VpObdu29fu7LMvcf//99OzZk4ULFzJo0CB27dqV19UKiPfff59Tp04Bil/Q4MGDCQkJoVmzZpblK1euTJcuXXjuuec4e/YsZ8+e5bnnnqNbt27Cf0YgEBRZMjMLugYCQdbkmkBz4sQJy+3e6zd5c+bMGTp16sQDDzxAmTJlmDNnDps3b86tal0RixYton79+kRGRlK/fn1OnjzJ4sWLKVu2LACHDx8mKirKkGvnyy+/pESJElStWpWqVatSsmRJvvjii4K6BIFAILhi8iNtveDqJj/aUK6ZnLp27YrD4eCxxx6jT58+BAcHB7RfqVKleOCBB9zfK1WqVGi0GfPmzfP7e6VKlbh06ZJhW1xcHF9//XVeVksgEAjyFSHQCIoCuaahkWWZadOmsWHDBurXr88LL7zA/v37c+vwAoFAICgghEAj+D97Zx0exfHG8e/F3XAJDsWtSJHiUtzaYqWlBfrDCgWKlCIBalC0FA1aXIpDsWLBNbhrsACRi8vl3t8fc3t7e7dnySWXhPk8zz23uzM78+7u7My777wzk1Fy1NIHCoUCH3zwAWbPnq2df6ZHjx4YO3as0XOMdVNxOBwOh8PhWEOmOAVfu3YNR48eRWpqKmrUqGE0Xrt27dCkSRNs2LABqampmSEKh8Ph5Biy6ygibqHJevbtAxYssLcUOQubdjktWrQItWvXxvTp09GzZ09cuXIFPXr0MHkO76bicDgcDkdK9+7A0KH2liLjZKWSbjOFJjAwEO/evcOuXbuwdetWiyeIs7abisPhcDhZC7fQcHICNhvltGvXrgydb2k3FYfD4eRWeJcTJ7eSFWXbZgpN06ZNoTBS6okICoVCdk6aRYsWYcWKFShevDgGDhyI5cuX20okDofDyVFkpzWcOJychs0UmqNHj1p9TtGiRbXdVMJkdRwOh8PJXnALDScnYNe1nHbv3m3P7DkcDodjAVyh4eQE7KrQpLebisPhcDgcTs4hRy19YA2k8Q5KTzcVh8Ph6FK1KjBsGNC/v70l4XA4xshRMwVbg5ov3crhcGzE9evAmjX2loLD4dgbuyg0HA6HY0u4j0fmkl2Hk3M4unCFhsPh5Hi4QpO5TJ5sbwk4OZUcOVMwh8PhcDgcjr3gCg2Hw8nxcAsNh5O9ybVOwRwOh2NLuELD4XC4QsPhcHI8XKHh5DZ4mbYertBw3kvS0oB9++wtBYfD4XBsBVdoTLBx40Z8/PHH8PHxgUKhgEqlMhm/SZMmcHFxgZeXl/a3cOHCLJKWYw3btwPt2tlbCo6tyG1fs/fu2VsCDsc8T54AgYH2lkLErksfZHf8/f0xePBgJCYmol+/fhadM2bMGPz888+ZLBkno6Sk2FsCji3JbQoNX3WbkxO4ccPeEkjhCo0JWrduDQA4duyYfQXh2Bw+URiHw+FkjOxWj/IuJxuzaNEi+Pv7o3z58hg3bhzi4uLsLRKHk+vJbRYaDicnsHGj+ThZqfRwC40N+fXXX1G+fHn4+fnh+vXr6Nu3Lx4/foxNmzbJxvfx8UFsbGyWyJZV+aSX1FTg4kWgXr2syU+lAnx8uGkfyP5lwxw+PoCra+54lj4+7D8xMftcT2xsLLy82HZ2kel9wMuLKerZ+Z6HhrKyYUrGtDRWrpOTM3YtltRTXKGxIfXr19duV6tWDXPmzEGLFi2QmJgId3d3g/gqlQpTpkyBi4sLAKBZs2Zo0aJFlsmbndi6FRgzBggLy5r8spuplMPRJbtanOLjAU9Pe0vByS5k9jrThw8fxpEjRwAAKRY4PnKFJhNxcGA9emSk9UxISMDMmTPhI3yWZQFZmZc1pKQw7T2rxHN0zNr8cgLZtWyYIyYG2L0b+OUXYPp0e0uTMYQvWHf37FU24+KAmBgfXLsGaFwLOZlMXBwQG5u9yoE+gkeFqbojLY2Va1dX66+la9eu6Nq1KwAgJiYGwcHBJuNzHxoTpKWlISkpSasZJicnIykpCWoZtTQ8PBz79+9HfHw8iAg3b97EyJEj0bFjR3h4eGS16DkOQeerUMG+cnByLjNm2FuC3I+rq70l4ORU+NIHdmbNmjVwd3fXjnby8vKCu7s7Tpw4gWfPnsHLywshISEAgKSkJEyaNAmFCxeGt7c3OnXqhGbNmmH16tX2vIQcx5079paAw+FwODkR3uVkgr59+6Jv375Gw3VHMBUvXhznz5/PAqk4toD70HCyM9nVh4bDyc5wC00OpmZNQNO9mOPhCgaHYxtmzgQ0fpQ2J7u9pwkJQHi4vaXgZBe4QpODuXKFTeHPsZ7sVjFz7AcREBycvcpERiw0o0cDgwbZThZdlMrMSTe99OwJFCxobyk42QWu0ORSUlKAESMyf1idwO7dwJs3WZOXLclOjRgn45hZbk2W8HDg22+Bx49tL4+9yKxy/dVXmZNuerl7194ScLITXKHJpdy9C8ydyyboygo6dgSGD0//+VmtWAj5cYUmd5ERBZ6XBfNkt0ne+DPL/mTlM+IKTS7FHi96TqxccqLMHNuimS4qV5WF3HQtpnhfrpNjGVyhyaVY8qLHxgKnTmVtntkFQdas6pLjZA3peZ6Cv0p2Kr8ZHeWUna6Fkz74SDfr4QqNnVm6NHPTN1XBT5wINGxou7yuX7ddWlmFqYq/TRtg2bKsk4WTcdLTkOdGheZ9ITs9M1uTm68ts+AKjZ0ZPdr6cx4/BgoXNh3HEgtEcrLhsWvXrJdHWDPs4UPjcR48yJpK+vhx69aDMlVp7N8PzJ+fcZk4WYe1jYBSCbx9m75zM5OslEWhAGbPzrr8bEl2emZ8GjLT8JmC3wMcHa0/5+uvgVevLIublmY8zElvWsWwMKBaNSAqSjymUrEKz9RcD2PHsv+UFOOjDkyNRujXL+POhteuAfv2AU2aAG3bmo8vvFwzZ5qOZyslbMMGYNQo26T15k3OHFGWGehXkvfuWXe+nx8wdCjbzk5WkaxuqE+ezNr8bIU19yk+HsiXT/5DLqPcvw/UrcvysBUWLC7N0YMrNHZGX6kAmFUlKYk1WnKF+vhxy9M3ZaFxdpbuC0NedV944djLl8bT0X2JGzWSjyM0FhMnGoatWAFoVpBINy1aAO3ase0bN8zHFyrCCRNMx7NVIzdqlGVfwY8fA2fPmo5TogRQrJhNxMrx6Ddo6WmYX79m/+n5uMgsMqrQWHu+QsHe9SdPMpZvViNcp86k7UYJDwfevcucuXSEOjM7WYzeR7hCY2fkFJoff2Sr7RYoYLiybXS0YXw5x15Lupz0FRpBFl2rjiX+BboNgTmnzJ9/lj+eUcXBlCVKjv/+syyerRQaS51VmzQB6tUzHScxMXO+MnMi+vfVmgZFiCso7Q7ZqDb88MOMnW9tw7p3LzB+PFCyZMbytRcBAebjCO9yZgwEeN8VmUePssc9yEav8PuJ3Fehbl+s/mKNkycbxt+yxXj6BQoYD9NXpgRZdCcnEwqpKYUhIsIwvj7mGouMNibWKjQbNlgW78oV62WRw9KXPSnJNvnldi5fBp4/t03jlJLC/nXLcVYQEwOYWCouS0lOBv74g23rdjlnd4TJEFNTzccV6pjMVGjMfQDduWObOkXw+7KGiAjr/AutoXRpYP168/G2bGHvbmbBFZpsiG7jrN8Qyo0kkntBdc8jYl06334rjSMoNMLX/oULhvkLYaYqgVu3jMsrYO5Ft1ShiYpiaembmLP78GtL5cvIdbx9y+6NpQ3z2bPApk3pz88abN1Ifvgh0KyZbSw0QmNozp/KElJTLXcODQkBVq/OeJ62ZupU6b6g8GV3xo0zHW6r+YaWLDHsIrd0os4KFdgafNbw+LG0vgsPB/Lnl3+n0tKYj6WcBbdhQ/mualtZe8359anVwOefA5072yY/ObhCY2f0X4AXL6Qvi34X0yefGKZhTqGZNg3o1Qv4919pHMEic/Ei+xcKmu7Xzt697F9Xybl/X+rn8eCBfL7WoPvCmnKsE2R88UJ63BoLjbV96BmtAN++tVxRscQXwBiCQjpypGXx27cHevRIf37WEBAAbN5s/XlhYUxJkxvVFx7OHMF1UavZgoWWIDxXW3bfLVrEnEMtwdruzJgYIDLSsrgZKbNz50r39d8t/Xoks0hMtO693rjRdLitupwGDjRcAsLamcc9PYHp0y2LW6oU8M034r5QRyxaZBg3NhZYtUp+xOnTp4bHEhIANzfTPpKWYuzaheOCvJnZNcUVGjuj/3B/+810fLkuKpWKvRy6FaTuSyvXTQWIXyz6FauuQtO7N/vX/Ur76CPjfh5yXw1xccDRo+K+bgMvXL+uEuflZZjGzZvAmTPAiRNsX79rRq4Ru3RJ/uXp00e6v2OHYRxdTFWq334rKn3GyJ/f8oYoI11OLi7s/++/LYtvqmIpXTr96+SMGMHKlOBsK2BqWL8xhOctN6ovNRXo1s0wb09Py9IWrv/dO+vlMobQ2FgyJ5P+excaKt2/fVu6X6cOkCdPukXDoEHAmjXWn3f4sHTfklGEAPswSc/aWgIeHsCQIZbHl2uwAfYBlpZmWx8afYuyUD9aer0JCebrDV103yVB/p9+AipWZOVYKMO63WrPn0vTkFOgBbnlfDOtRfe+HjkiLrsjvGdCfcIVmlxMaqp0vaUFCwzjXL8uFgK5rpkLFwxfDmONsK41Rbfw646mkjMx6za01ja6338v/RrJn1+c38XSwl2tGlC/vrhfvbr5c2rVYorUtm3S47t3G8pnCmOVVFwcW6X5l1/My2IJGR3pZa0PiKmK/dEjyx2n9RHut36Fmh6++MJ4mKmyY6xxM4U13W/GrDrCO9WunfmGU2hgdu1i//p+ERUrittqtWUKpuCfIDdaafFi+VGGpoiPBwYPtu4cAS8v4NNP03euwJIl1sWXUyTLlZNaM2zRoOor5+PHs39LfHnSg+4ADt26/fZt4J9/2HB0QGxLEhOBwEBRzthYw4++2bPZauWAZdZCc0qP7n1t3ly09AkWd+GfKzS5mORk9iUSE2O84axaFThwgHUNyc1cK/jICKjVxgtf2bLitmBN+fhjqaOWnBzGlBhjE/ElJ4uVkZx1YvlyUVY59F8wuXimKvjvvmP///xj+BWvjzmz9oAB8sevXmX/Z85Y3n0wfjzg7y8fJtedaA263UeWVBrCPTX2DMylMXeuaJFYutRw1JylFVdysthdZg2m0j9yJH3nW/KFrVIxM/2pU8bvXViY+TlJhDLTqZP5PKtWNR6mVovvvO7oKCL2cZKWJn6wWFpON21i3YSff25ZfGPs3Jmx8wFmadWHSN7BVXfKBqUS+Osvtv3ihfi8V6ywnbO/gGBBzCx/I13LvL4yLSjvCgVQsCDbFuproYu+QgXDNEeNYpOHAsbfpbg45iN58yart3TLu37dL4QJ8gnKlfARLdcOhIfbVsHhCk02wdcXGDbMePjr16zfVtcBV0ChYOcLzJ/Ppu03xZMnUifIJk3Ebbm+XXd3cVu3v9hYhfXpp6yvGQC2bzcMF/psTRVm3ZdHv7EEgPLl2b+cQiJUZHIT9rm6SvflLAm6cq1dayinQmHZshH6Devu3aKyqZ+mJb4fljoU3rwpfzw+XrxfQv7GuiGMPZvERGbGHzEC2LqVHdu3L/3dCwEBrDvFnFOn/jWZ8n0x5q9z545Y9uSUEf1RhXII+TZsKH6FKpWskdRV/K2tqE01hsaeJ8AUotKlxTIvEBrKynr58oCPj3Wy7NrFPngyY0SKWg1MmsTeIUsUgFq1gD//ZM7WBQuywQwODvIOrroWbD8/8cMmOlp01p42TbRMWAOR8a5EoSwFBlre5StXb61dK62LBQSF5r//xJFdAj/8YBh/zx72Lyh4uj6Hs2ZZVjajogBvb6BSJaByZXZMtzehWjVpfOHeC8rdr7+yf6HuFs7Vve6CBcU6xBzBwRZEIo5dUCqVBIAAJbHiRVSnDmm39X+9ehkP+/BDIk9Ptk1ENGSIYRwfHyX5+Ci1cXbvNp6ebqno0IHtHzwoyC3G2bVL/tx798Tt0FDTeYSHGw///HNRDldX+ThqtenrEH4XL4pplSxpGP7qFQs7dIjo/HnD8JkzxfNTUuTz+PFHoilTiF6/JrpwgSg42Lg8TZuy/9GjiSIiWLq64amphmVm+3b5Z0REdPeuNCwkhCgpyTANgGjECLbt5cX2x41TklKpNIg3d67h+UREgwaJ+fz0EzvWsqUoU7FibPvcObaflsb2hwyRT09X7s6dibp0Yc91zx7j5dKSZx4eTlSrlniOUFYKFCA6c4bou+8Mz/nf/+Rl1OX5c0OZevZk246O4vE5c0yns2GDNA398lK5shhm7Lnrhk2ZIo1n7N2LjRXPjYmRj9Oxo7itW3cYk0GXGzdYfWEs7m+/iWGVKhGpVPLp6Mv05Zfmn/mWLexc3ToIIPL3J2rdWtyvWNH0Ncgxf77x51ChgjTs9m32M3dN+tSvL//M27QhWr3asnKv/9Mvr/rlFCBav95QFrl2RHin160zLBsODixMty4iIvr3X/Pv8fPn4rGEBPn7z9pKGNRVkjhGQzhERKRWq2nSpElUqFAhcnFxIR8fH/L09CQAlKrX6ly9epXq1atHjo6OpFAoyNXVlXr27ElRUVEG6copNOn9NWkircSEhgogypvXsOAlJhJNmGA6TYEaNTImm6nfqVNE06cbD3d3Zy/OixfG45Qvb1leR46I1yQXPnMm0dOnlt2ToUNtfy/0FbMbN6Tl5a+/jMtDRLRihWF4nTpi+PXr4vEGDdgxQQmeOFFJ4eFKEuqJt2/Z8Vmz5N+JFi0M5dDdLlKEbY8fz/ajo9n+l18apnX7tnX3iYgoLs7weGSk4THdSvv6ddPKte6venWiP/4gOnqUKaf6fPKJoUweHqbLjMCjR0ROTqy86cZbvFj+GQJEV6+aTtPYdZj7mDB1ru5PqDvmzhWPVa4sXzaSkw3P37ZNDG/Y0DD83TvDdPSVM0t/X3zBzu/b13zcxEQxP6GKPnmSvYtyFC8ufw+dnc3fZ7VaXiERwp4+ZWVNOC53HzP7t3ixKG9EhPF4wgeKbtnQDa9Xz3xeRETz5kmPzZxJFBgo7g8dyj7KkpKEY1yhyTAzZsygokWL0rVr12jnzp3UsWNH8vPzI32FJiYmhgoWLEilSpWiJk2a0PHjx6lQoUJUtmxZ6tixo0G6tlRoChc2XXCEgufh8Q8BRIsWMWuCsXMKFWIa+/79Wf9SDRyYOekKFhqVSjx28qR1adSvb1ipZdavTBmxrGzZIh9HaADPnCH6+2+27edn+PyFMqD706+U6tRhZUO3wfnlF3auWs2sQwJly8qXMf1tYV9QFAVF6uef2X6rVra5V7duGb9OW/yIiEaNIurfn+jECevO3bePWUEENm6Uj1elinXyEEmtKHK/b74xHubvT9Snj+FxOeukj4+SunT5h4ik78zDh6IsCQnGLbaCzBcuyIeFh7NyGx3N4slZzjLr17GjaHnRVUzS0pgsc+YwZadSJfnzf//dsmdlLDw1Neuu1ZJfRAT7ODCmoANEEydKy4bQrljza9YsPfJxhSbDlChRgubq2N5TU1PJx8eH9BWaVatWUZ48eQgAhYaGEhHR3LlzqWjRogSAnj59KklXUGgGDDBUaLp2tUzLlTMJ6v7evmV5HT5M1KCBklxchhjEadHCNhaHPXukjaTc759/TIcL3T7m8vrsM6Jhw4yHd+okmm51f/qWHiKidu1M51W1qunwr76yvNvLkt/Ro+J2gQLGuwSM/UJCpPvPnhEFBZk+x8dHvmwI1yds37ol/9Xfpo30nuqG/for0bJl4v6TJ/Iy6H+t6f4ePGDpGAvX5e1bqcyZ9cuXz7r4+fOzZ1m7tvm4T58StW9vPHzWLNtfz4sXRFeuyL9/DRooaYhOf2H6GiPjvzlz2L8xZc+Sn7m6xdpfu3ZiV8kff6Q/nbg4orFjjYdba6G0x+/cOVZ+5cKGDlVS48ZDDLrjMufHFZoMER0dTQDo9OnTkuMffvgh6Ss033//PVWvXp1cXV21x06dOkUAyNnZmXbu3ClJQ1BolEqlgQk7JYXFUavl/Wr692f/8+YZ963p2pUM8vvf/6SNlm4XwMuXrFtD6Lc39xMsAd98I+3zNBb/wAF2PXfvin45+j8Bcw2w8PWka5atV4+l/+iReO/evmVmZbk0dN8JJyf5OG/esPDUVPn++yJFmAxCfj/9JJ9OcrLUB8iYRa1uXZZWQIDp6zf29eTnlz7lypRCkxW/Bw/YdT94QLR8uTRs8GDxOe3YYXhucDDJMn68fF5Pn7LnOmYMU/SDg4l27hTLw88/s3zatjUts0DFioZhjRqJjXR6fsL7CBBNmmTduZ06yR8XuhGdnOTj6KLffff0qVShMfWe6z4XU2Xxzh32AWMqDbku4CFDWHfUnTusi+ToUVEmfWuJQiF2K+3cyeLHxkp9l6z9NWvGusr1j69cybrPq1XL2LtgaRc/EVFYGJGPj2GYqe4i/Z8pK/zQoYbvlW54aiprV4SysWCBGGbKOqj7mzzZ8OPUWO9B27bmFRoFE5IjR1hYGIoVK4Zbt26hgs64t6ZNm+LYsWNITU2Fk2b9gH79+uHGjRt48uQJwsPDAQC3b99GxYoVkSdPHsydOxdf6EyqERUVhWLFiuHo0aPwkptJTgMRG9n0zz9sVEWFCkChQsxT3MGBjSxp0IDFPX6cjXBq357F0x2imZCQgL/++gs//DAGH33EjslN0U4knel06lTpcOLUVODZMzaqQo5Hj8Thw7NmsZEf/fuzoem6qNVsKHrdumyUUd680tlgz59noxTS0tjEXsJInB49DGfCTUszvVLyli3iOjUAcO6c4fBV4S1Qq42nlZbGJvoaNUo6/F2X16/ZfCJlyrD4Hh7sOb18KQ6h3LuXze2xbBnL79w5NupDkIuITf5375407UWL2Cgn3Wd++DDQrx+b7HDUKHYsJITJd+CAdF6jbdvYaJxz59icJOXKsTw8PROQnPwXVKoxANgK5HfuiKMPpk5l8mWE/PnZwofnzomyFCrEtvXvNxFb42jqVKB4ccO01GrxnsrNICwQGsrmKxLmcHFxMV5ujbFpEyvHAlOnsgVjdctPcjIb7TR8OBvOrUtaGpsfqH178VjjxuxddXVl9/jnn8X7IvdOJiYy+T08xHl5OnZkZaBCBTbZXkwMe1/i4oCWLdnoupcvgaJF2QhF/fJ++zYbwXLpkvywcbWaTfIZEAB89RWrO8aMGSOJQ8TmMtm0iU3aV6kSGzau+zwjIqQjLqdOZSOBKlVi+198IS3nefMCY8eyewSwZygs2XLmjPkV0WNj2XsyaBC7PnPExLBndvs2Kx8VKrBRnv/8Yxh3xgxxBNLVq+ze/PILu56ffhLjnTrF0lu6VHr+ypXsOdy6xZ5Ry5bS8NOn2QiuhAQmizA9wL594mgjX1821Fmu3F+7xoayt2vH9iMjWVxfX1YXtGnDys2hQ2wy0c8/F0evnj7NruWLL1gdr1JJZycWiI9n9VaPHmydQKFd0S8b+hAx+UqWZHktX87akVOnWFhsLKtzHj8WJ3PVJy4uDrVr10ZkZCT8jcx9wRUaEyiVSvj5+eH06dOopzM1bq1atXDp0iWJQjNixAgcO3YMt2/fRpJm7Obp06fRoEEDODs7Y+vWrejYsaM2jQsXLqBJkyZQ6Yx1dXJy0qZna7y8vKBSqbSycTgCvGxwTMHLB8cYmV02VCqVpI1MSUnB+fPnUbt2bdn4mdN65hJ8fX1RokQJXLhwQavQqFQqPNCdbldD9erVsWbNGiQnJ+PatWuoWrUqLl68iKJFi+L58+eorje1bZkyZZCQkICwsDD4WDtJRDqIjY3FlClTMNMWK/BxchW8bHBMwcsHxxhZWTZiYmIQGBiIMmXKGI3DFRozDB48GDNnzkSzZs1QokQJTJs2DY4au2dycjJUKhVcXFzQtWtXjBs3DqVLl8aIESMQFBSEGTNmwNPTEx06dEAxvVmghDR8fHyyRKEBABcXlyzLi5Oz4GWDYwpePjjGyOqy4Wii35ErNGb44YcfEBsbixYtWiAyMhKpOot1CL4v8+bNw7Bhw3DgwAH873//w/Hjx9G4cWM4OzujUaNGWLhwob3E53A4HA7nvYArNGZQKBSYOnUqpk6dajZu1apVcebMmSyQKn00a9bM3iJwsim8bHBMwcsHxxjZqWxwp2A7ERMTA19fXyiVyiwx18VoFjXiZmOOPrxscEzBywfHGFlZNixpM/nilBwOh8PhcHI8XKHhcDgcDoeT4+EKDYfD4XA4nBwPV2g4HA6Hw+HkeLhCw+FwOBwOJ8fDFRoOh8PhcDg5Hq7QcDgcDofDyfFwhYbD4XA4HE6Ohys0HA4n85k8GVi1yt5ScDicXAxf+oDD4WQ+U6cCbm5A3772loTD4eRSuIWGw+FwOBxOjocrNBwOh2MHli1bBoVCIfvz9PREWlqavUXM0fD7m7ksW7YMvr6+8PX1zTb3l3c5cTgcjh24cuUKPD09cfjwYYMwd3d3ODo62kGq3AO/v5mLcH937twJT09PSZi97i9XaDgcTtZAZG8JshWhoaGoVKkSPvroI3uLkivh9zdzCQ0NRfny5VG7du1ssxI773LicDicLIaIcO3aNVStWtXeouRK+P3NXIT7W6lSJXuLIoFbaDgcDieLuX//PuLi4lCpUiWoVCpJmEKh4N0hGYTf38xFuL8VKlSASqWS3GN73l8FEbcD24OYmBj4+vpCqVRmibkuJiYGALKNaZCTfciSsqFQAK6uQFJS5uVhBiIgNjZjaXh7s0vJKJs3b0b37t1lw6pXr44rV65kPBMbYWn5ICLEpqT/Bnu7eENhi5uLnHV/LSajBdhWhRf2ub+WtJncQsPhcLIGG1Wm6SU2FvD1zVgaSiVgC70vNDQUCoUCJ0+ehIuLiyTM398/4xnYgdiUWPj+nv4brBynhI+rbZTq3Hh/M1yAbVV4Id7fAwcOwNnZGV5eXtowe95frtDYmKCgIEybNg3u7u7aYx06dMCGDRvsKBWHw/H2ZnV6RtOwBVeuXEGpUqVQv3592ySYDfB28YZyXPpvsLeLjW4ucuf9zXABtlXhhXh/69atCyD7WP65QpMJ1KtXDydPnrS3GBwORweFwmYfqBkmNDTUotE3AwcOhKenJ2bNmoXw8HC0aNEC06dPR9u2bbNASutQKBQ2s7BkFEvub2RkJAIDAxEVFaW14vzvf/9DqVKlMHbs2KwQ0zqyUQG2tPx+/fXX2u4npVKJV69eQalUwtXVNVPk4qOcOBxOlsDd9Rjh4eF4/fq1RSNwpk6dijVr1uDy5cto06YNpkyZki2VmeyEpfc3ICAApUqVwuXLlwEAISEhOH/+PEaNGpUVYuZYrCm/K1euRGhoKHbv3g0/Pz9s3bo105QZgFtoMoUrV64gX7588PDwQIMGDfDLL7+gZMmS9haLw7ErqamAi/louR7hi9XJyQlnz541CK9atSo8PDwAAPnz58f333+P+vXrY8WKFejatWuWypoTseb+Nm7cGCdPnkTNmjUxaNAgLF++HE5OvFk0he79vXDhAgBIJtbTvb8AcPfuXXz66adYsGABGjVqlKmy8SdnYz799FN8/fXXKFasGF6+fIkxY8agRYsWuHr1qsRxCmD9jrEZHXZhIVmVDyfnYWnZaN0a6NgRGDIkHZn4+CAJLnDTjJh5nxEa2UmTJmHSpEmSMIVCgSdPnsDPzw8A6xbZuHEj/Pz8EBAQoB1xlJXktLrDmvtbq1Yt/PPPP4iIiECDBg1QoUIFu9zjnITu/dVH//6GhoZi4MCBWLx4MapXr56he2tJOeTDtjOZ5ORk+Pr6YteuXWjVqpX2eExMDAoVKoTevXtr+2+bNWuGFi1aZIocQmHwtqFjGCd3YGnZ+C5wO+ICK2Dl6fLWZxIYyBSasIfpEfG9RKlUonPnzhgwYAC8vLwwa9YsHDt2zGZDmy0lN9cd4eHhqF27Nvz9/RESEpJtnFtzCqbKxqlTp/DDDz/g77//RtmyZdOV/uHDh3HkyBEAQEpKCoKDg00O2+YKTSaTkpICX19f7NixA61bt9Ye5/PQcLILFpcNhQL3XCujXNJ1k9HevAEKFABUKkA7v5ZCgSS4wo3sNw9NuhEW2cvCycLi4+PRqlUr9O7dG4MHDwYANGjQAN9++y2++uqrLJMDyN11BxGhQIECWLJkCbp06WJvcXIcpspGvnz54Obmhjx58gBg1ptTp05JuqOszSvbz0OTlpaWq2Zt3Lx5M5o1a4a8efMiPDwco0ePRoECBXLX8EHOe4vCgu+fJ0/Yv1rNdIBLl4APM1eszKVBAyA+HrhuWpGzJZ6enjh16pTkmP4+J+MsWbIEjRs35spMJvD27dssz9Puo5zatGljbxFsytq1a1GhQgV4eHigZs2aSE1NxeHDh3OluZaTS7h0CXj+3ObJCj0jBw6wf4J9J9ZLN+fOATdu2FsKjg25c+cOKlasiC1btiA4ONje4nBsRJZZaOQciIgIDx/mrj71Xbt22VsEDsc6OncGSpQArl41G1UB63uoBaNOes7N1Vy7BkybBmzZYtt0nzwBChViS01wZClfvjxu3bplbzE4NibLLDSLFi1CmTJlULp0ae2vTJky6e5P43A4NiQlxeZJCorMunU2Tzp3sGIFsHWr7dMtWRL48Ufbp8vhZHOyzEJToUIFNG/eHEWKFJEcPyDYozkcjt1ITCTYyuUzUZUAfNYXRJsAKHD7to0Szm3Ex2de2i9eZF7aHE42JcssNMePHzdQZgBg/fr1WSUCh8MxguVLxJjvNgq5ex2otAWx8WkZkilXc/48sGxZ5qVv54VAORx7kGUKTVbPncDh5DjCw4F79+yStVpNSEiwTVoTJ6kBAPPmSY87I9U2GWQhn32WSQmvWWO7tEJCgMWLbZceh5NDsdsop27dutkraw4ne9K8OfDBB3bL/vffzccRPks8PU0YGBTMihMTy/7HYDoAwBHqDEqY9WSGiwsAoHp126X15ZfAoEHAq1fiMf4ByXkPsZtC8/LlS3tlzeFkTyIi7Ja1AjCw0Dx4ACxaJBNRE3fxqfWotriaTGJMcblzhyk00zHOtsLmBmzpWCRM/LNvn3hs40bbpc/h5BDsptDwLigORw9nZ9nDL16wyWrd3QGZtfZshv6ceWPHAoMHixPlAkDRlEdsCmAAj7z/xrXwa4YJaRSaAwf4MG2jlC5t+zTj45GcbPtkORlkxAige3d7S/FeYPeJ9Tic3IxVRpewMPb/7p32EBFQtChbSiApCTh0yEaC7d4NrFql3VWADBQaQZFZvVo85krJwKxZAICoKMNk37wBtI7DCrKr1Slb8/PPtk8zLk7QNTnZiblzgc2bM5zMhQts9m2OcbhCw+FkEhcuAHnzpuNEHYUmVeNHK+gFtnLcRceOwNdfSw7pKzQ7d0rz1vL6tXCGQbJpaQAcdEw6mTk0OSdjK1PKmTPi9k8/GTxDTu6hTh1g2zZ7S5G9sZtCw9fE5OR2hKVMrC7qOn08qXoDgx49yphMWjQrvAsURDhKvjgpOSb0iiQmGklDYXhhRNB2OQEEdUxcxuTMTmTHbvIvvxS3XVygePvGfrJkEhZMYP3eYPRdtJDY2HTURzkIuyk0Z3S/LDicXIiD5u2yuhtA5wR9hcYGlmuGzMzA5R//K9mvWZP96w6eAYC3By+zjdKG/V8rVgC6XU6Jb3ORQmMLFApAoYAq1UZ9B7p9ECkpcAkab5t0bcW9e8Ds2ek+/fFjNiBMs6hzrkB4nxISDKc2MEdGdOpbtwAfH2DlyvSnkd3hXU4cTiYhKDQWryrQrJnBCfoKjVnOnEl/R7vep1tSEvvXH0l+K6ao0SQmToTEQpPwNpd1Odno8zYuxjYKTVKiNB3nv5fbJF2bMWkSMGpUuk8XXoXc4hv09ClQuDCztOzYAXz/veXn5sXbdCs0//0HVKrEtnPzzN12VWju37+PNm3aIE+ePHBxcZH8OJycjlD5WLz+qlDj6NRamzYSbqM8fBFt+ty0NLbYYf36gL+/2axuoQIAoEoV8Zg6Tdo4Cv46+m14bNkPDdL79Vfg/n3NjkahqVSZkBiVZFYWuxIRYdSOn5YGOENPGzXTshYvDphdvNnFxWYLdUa8sv0aXOmidWugWDHD43otcEwMYM2MHcLpuUWhEV5xANiwwfLziIC3yI/CV/81H1mPatWAFi0AdyTgD/yAmTOtTiLHYFOF5q+//rIqfp8+feDp6Yng4GDs3r1b8uNwcjqChaZvX/HY2rWi5cMAYaFWHbPM98PSUB538QHuAr5P4TY5QP7c8eNZzQVYZJ+PgxcA4MYN8VhailShiY8j+MFwKNOxo9J4NWoAP/2k07OgUWjKlQMSYw1bounTs8nEtgoF89ru3NkwrFEj7Fr8Eg1wSnp8/nyjrevt28CzZ8C335rJNyUFEciTLpH1KYJsMp/XwYNslJ6+cqin0NSrB8isgGMU4fTsOhw9JcWKDxaIPvJqNbBnj+XnqVXsnSp+wbqZHl1c2HcOADTAKfyAWQAIMTG505HGpgrNrl27rIp/69YtbNy4EV27dkXr1q0lP04OJjbWaNDNNzezUBD7kZoKtGrFtq9cEY/36SMdBi1B6CrSUeiL5GE1OUEBFD2HJEUUQp6GGJ4rDEkCRMUGho8iPp61PQRD2zWppZXchLtfIAoBcIqXLvTkiDQQFGitsciEhrJ/7TQ6GoVGlaZGUpy08V+/Hhg3jk1sa1dGjhS3Dx6UhkVGAiEh8Bj6teFyDaNGGZ0vqGJFy7NfgCHijlqdezw1v/vOZPCtW+y/Y0fLklOpgBY4hJQ7tvKGty0rxtzBujKTjEcw0t/cs6cFiW/apJ3KQRXP6gHFOzbSIDop2iL5dLus1Zrm3inwOHznOODAgwMoNkfGqpaDSZdCs3TpUpw/fx5JRj81LaNy5cp4pe9xyMn5+PjITphy6eUlVF5UGSp1LrEfmyAqytCVRRi8ZPS1EU4oX157qH3EKgBAD2xE1+gLcFEBIc9kFJq7d8VtjS8OEXsUJ3UGL40axXoHhMqtC3TGgaZJF5NsF80WjvV5Ie10F2b+3b9OKoL241yj0NzN+wcS9Rao/O03Q9GzFIWC2d/nzDEeh02mg8Y4joNI38eVyaoxf37t/QcAODoCv/ySrnyyHfqrfOtZaIZo9Ljdu9l2ZKTp5FJTgUNoBd8+HUzG27TJegdbW1BrbxAmYZrxCEZMS2Y7IRQKoEcP0HfDAAAXT7F0Ht2Ix5kzgP90fxx9fNRiOf0RiX5g/lWOnsyqt/HmRoTFhFmcRk4gXQpNWFgYpk6dinLlyqFSpUro1asXZsyYgfDwcKvSWbp0KQYPHozNmzfjxIkTkh8HSElLyXHD24Wv/LAzzw3CktPYSxmZaKYWywZ8+CHQpk36z9+2IhqfYgsGeUxB0ZrM5ivMY2fU0VdQaBzE13IBhgIARmIO/nkxE8k/Az8d+Qndlo6Unjt2rLit+SoUho3rLl/g7c3+0+DI5IS4plqeAMLYsYbdQf4vbsAcrkhC+SjNyEXNcO57BX9Gsp6F5ob5pDKf//4TtytVAgYOlIZrWtlo+JlNassW7cAlAMAOdEINXDbZUKuLBhr60KRn1OfatabDs6ru0CnQqU/1FBodGTZu1FkbK0iBhVEdkMdMz5uQtIeL8ZXbw8KAHj2sc7C1FbGOzF/NaPdRmnG5BYSVK+S4d5fdv/MnmIbcHEcwYwYLex5jWMfqQgSMwGxcQk2MxGz0AnPacUxxBwCcDY0GAHTd1NV4AjpzYuUE0qXQTJs2DXv27MGzZ89w7Ngx9NU4CVS0xuYK4ObNmzh69Ch69OiBJk2aaH9NmzZNj1i5DtefXTFwD6tsl19ejsTUDE5CkAUkv2N9HK9jPAzCHBSsuJ278QYKhXx9e/X1VRAR/nv0H55EPwEAvIp9hUUX9BcVyjzmzQMUly/i5v5nVp/78iWgVAJ5Vs3EFnyOhQlBeNumGr7c/iWOPWUNqVEHR0GhsWCyiW2v9CwMATq+Nfv3AxCddNevF4NMtXHOjmrMmAEMYx+FiHVlswLGOPjJxr+vk+Ub5Meof+qzHYVomkqMk2pvHh5s1uNswcKFQK9eBv1yL64zbaQgzH+gff65dL8TduFL/K1tdHSZM5vd/FcxXrZxCu7Tx3S4NY3R5ctWZR0ZCfz1F/Op3rlJNEfdu2189FbPnkB47Dug5Wh24IM9krIih1ZXUhtXDDQTV0t4/Bg4fx5oPKcvFFMyb/4gUjG5unZWIjrW0BrT7yPWxX4ZNdgBj3eAm9QvrWRJvduvowS9fMnKSVqCmPaO+DEWybZkCTAc81ATV1AIYk+I0+dMgbmDHQCA7Xe2IyVNpmvsiy+AfPmyyVeIZaRLoZk/fz6io6MBAPny5UOpUqUwbNgwbLDGbRvA6NGj8euvvyI6OhqpqanaX4rF41xzLwcfsn79S68u4VXsK/Tf3R9bbv5jF1lcprlYXCkkvtN4vcn4GQgvzaXrbG4SYbXmgw8P4s67O7j19haqL6mONuvaoMWaFvh086cAgN9O/obB+wanW/7z56V+LObY9P1pXERtPENxq/MqUgTw8wMc7ordNM5qYM21NVjr2AKAcQtNQhyr3B/fSUKcielbSsgsObB6hY6WpPFSlFufUFBoPGE4nNozWvrF99yXfaC0OzISWLfOIH5ZHSuEDwSlgEC3u2HtP4BHChAbL3VQThijQKFvvzX7ZZ4eUtNSTXdn6iuK/fuzEWF6azgoH0dCCR/TmclohkMxHwDwPebJdn+MG8XKf+gDT0OFZt8+KBRmuqp0SGvTznwkS4fvr13LTJLbt1sWH8Do0cxdJm9e4Ps+ouKUX18J1JiuHj/W7I/JBzTQGWbj9Rqm0L4rRiwdN29Ku5oGDAAmTABKlQLq1gVOxDCHtTGHLFMCdPOVW9pDH0pl5e2+lx/8x9Q1lO8us4Q+QinWCz8mHzCwukE8yVBqnYxjNc67j++KbaJz3T8AAF/u+BJrrxm30q0fdALFwT7K+mGF9rjyd2C9nm9xmpzCGKLp2tYdCgkATZtmaEKc9esNe+Lu3TN0ZUsP6VJo/v77b/j5+QEAJk+ejLp166JcuXI4a+XKeUqlEsOGDYOPjw8cHR0lv/eJ40+OIzZZ+pXYei3ru7/06hIKzy4Mz2Rg39n7SE2zdmIS67nz7g5OPTuFD/76AG/i3yBVzfJUTFGYzT8xhoU7krRhCX0diiH7WAf6mdtPALDRIIcfHUbrta1RYUEFVFrIxjQeeHgAABCTzBpDR0XGykPdumzacEtISwM+h3T2OsUUBS69vGT2XF1F5Q3yabfd9NpYOQPMiRPAiuWsASo5exi8W83G78vuGkYE8Hge8ECvwXx0NxVr8IV2f/FitgZUhQpspIPQ/j53OAWMLIIaCDVIV6WKAkofgNopDn/+CdxyY8Oz/WPD2NeaBbh4MKWo93VgzCkgOdGwhQ51DEZComUWiuikaPmvRz1exb6Cy88ucJ7mjIMPD4JIRufQ1E/Pzr8Gnj9nSneePAZrOyydHomLqGU6Q7VaO5gsKopVyPMxTAwPMqzwfwBryBPhLmuhSXJUaJ1mtSQkGJr03ryB4/59MIvMh6HQPUbfj2C+Wj/9JFp6uhrpepBhhdg+whdKRMMXAJAP7yQ9egJaheZeWwBA/UCNNW+U6SFPwjulkFFoFAo2S4EoyFMs23kTv8yOwFCH2fi4r/gMgv/7Az6KGPz4o3w+GzdKuw5HjGBGz3fv2DFjRtO0ZCZgcSWAgtIpjYmYAz3A/oVBAvAztPzqltWER6KSVz5gNxRTFLh5SOzKG6XTO7njzg55wQAsxkCjYT31jC4kZzEcN05ewGPHjKZrCb17M58ngdWr2VxXthgLlC6FxseHfb0QEVasWIH79+8jJCQEEyZMsCqdNm3a4NSpU+Yj5iCICJMnT0bhwoXh6emJRo0a4YYJk11KWgqarG4Cn999cD/iPhqsaIA/Tv2hDa/xEvBMBuJ+Ax5enopvdn2D6our43XcaySmJiJJlT7H7J9/NpwBVqDCggpouLIh7kXcw9ZbUlV+xIERJtNNimGV6OU3O/Ek+gkUUxRYfHExaiypgRtv2H046NMTyMdq7pZrWhpN634k6zNxdMi4gqtSsb52cz7oFy+yL2x9zj43r6y/fAnUw2l4IRaDsER7vG+oNJ6DzFvXuDHgAJ0v6tajMOP2ADyF/CiE0lGAOjFJq/T5e6mQAA+cc28CAKg4qBE+OL4UxeqdQzXfFXjdfTjcaq/DvadfYspF+aG+DxVHgT6fwPXryvhz+AM8f2a+/1+ffZ61tduTjwNvU+Rn8Up0eSqpI8ePl7dc+U/3h/v4YlBMURgd2TF6NFB4dmHtfuu1rZHng7vwKyIuA7BrFxDXjDmWFq9TQBw77O8v8UxNSQECEIn4fCVNX2hqKl68YF1ovr5A2XyGssWliGY2tRr4Bax+TIKb9FlrcE0D6n6iY0qcNg3w9NRaO6dP18xxY+m4Zz2FRndgl2LeXOZI/uuv0nMOHzabrPDcvvsO2I7OCEUNvNUo8FdQHS1aMAVPl+Zt4piSV24fmsWsxKlvpPW+QqHxxb56Feq69dCnD7tncl1Od+8yv3cXl7d4GOuPKtCMSx5RAhhSGRibF/PVo3BilZh+1HQgBr743U3esqA76ujhQ2b5AcQ5c+R8e1UqID5CtE5VeFxNEu7gAPwE5uwtKDayBDyQ+Eqe2PAcCWB+LuUiWf0/s1g/bfhIHYXmn9v/sAci89wqwvQMeiNOQzupt5oMy+PN0FSQYFxwcGAPSXewh7+/tM/1/n3gq69M5ilcpq5CY8serXQpNK6urggLC8PJkydRoUIFBAQEoHjx4lBbOUNp/vz50aFDBwwYMACTJk2S/HIqM2fOxIoVK3DgwAG8e/cODRo0QOvWrRFnpA8h34x8cNO8tOX+KofTYacx5vAYuKiA/HHA5aVMmQEAn2Rg7bW1uBp+FYVmFYLHrx5w/8Vdq3QEXwqWH9Krh0oFTLz8JcovLYJBewbhbfxbDN03FKMOjMLT6KeSuIJVpXul7gCABRcWAGAvwMcrPzZw8E3W+Eyce7ceJeexRmHQXukYXddU4HPnFUDgabOyAqKFJlklP2Kg2+ZuWHZ5mdl0ihVjs3R6e7OhxrqVmEIBKD7Yg5/HyNuZ1aQGEeFMmNR5MyaGnRsbC2zYGonTaICJ9aXdFQk6vW8uSEaZu3u1+7oVmX4jF+UTgjxOxh3/wlrWhu/v7Ms4j08qVHDChsROAIBGCMGHF5fgQLGPcP5tPxTa8ieS23+BHQcfYZIRn3sFgCW7gPiFT/EAZfGBwrIh9rpDwJu/lXY5qFKkWsoHb4Ey3mXhFHBL2966uLDRT8LIj4TUBHy88mO0+ZY1emoPluahh/JLjc9cIn69bunGRm1F9S6PmP8VwPbbO6EmNe7eBbx0utl27NBseHlJFtB0dSUE4J3UJ0mXP//UXJgKz58zK9jZ52cMJjMs/xbw/s1bW2YnjxU/PBQgoz40rVrWZAXqzBk2y66GK8/OY1ySAt/uHKS12EzFRMm5H+EM7nSfLB7Qq3Pc3eUvSULLltoRYPvu78PJZycNomgGgWHSJKAz2HQB75AX0fDFcrCGV392aYxn3ug+ScDAfPkBACtedEP9Z2LPWN8C+4Dq1eFw/iwi1+7F4cOiTuYSG4ELLy5AMUWB8h/fxNGEP5FnaH7kpWhcQzXAVQk59jpuwOd6Deayy8tkB1t4egJwjUGZtQrcqMLGlQuWmX/15rNTVNkIZ1cVvAP3a4/dWi210FTFVbQFO9FAgS10Cai1COjeFRhWFjcTj2iDPpnbBh4QTUK1XwK1nonD1vPpL1B77hx7bjrXpHt9D4zMszn7IOCZwtqZBdt+FEcRgBW/vcEvcNJXr2tTa2YCEB0tdWbv0AH4+2/DjEJCtLIJ93PfPlHcqpXS0Ak7UMHzGZKTmYtAekmXQvPbb7+hWbNmaNu2LQbpTChhrNE2xvXr11GtWjU8ePAAISEh2t/Jk4YvUU5h4cKF+OGHH1ClShW4u7tj2rRpSElJwXYT/dOJvwAP5wJeyYBvIhCyHEj+GditceZUurJ/TyOW98+2fAYA+HbPt/h0y6cm5UtJS0G98ZVA29egwLuXWHxpMfLPzI8FFxZg9tnZKDGvhME5w+oMw8ZPNyKocRAAICIhAs9jnuPks5NoulrqwB0TwxQCVxlXBhcV86sYeBHY9HIWSnRtYFJWAFCpVZhxmnlYuv3ipj3+Ou41vt//PZ5EP8G229swYPcAKKYoJD4UBx8ehKL0YWBwZUkXQFwcmwxu4yZWySiVxMJ7dcD2EGlDlnbpIgZeAIrtCcHXO79G/RX18e1ucea0LVuA8g2+gs9sBdY9ZI4hY/T0tLs6/iLJcEPfre2Zs4RCgYEdHRB96TRC0BAf1VHjNsQh226pgJdKjeU15O9NwXOiwpGcoEIqnHEILRED1ngEvr0MChLjF1ECO8rDKLEuwLc6zomfkEzfgR5OZow4zqnSCu7OAuDshPtIfdQOSReuA46OqJJ6CQqotd0BV15dwclnJ3HOvyGq6VjUPt/6ObbdZgrL9tvboZiiwIbrG4FRmqUYpqThsypdoFCLGmTXzZ3hONUR9x22SOTo0kWz4eYm+fzu9pkDhmIRmhY6g7Q0sSyNxe9sw9OT/atUePECeN3+IwQP1e33YEw6zv6H7x+Ozz4DgmdGa8MckQYnyPv67BUcuIXxzRpqFGf+GR8VZEPQzqEOTjSbIolzDh9hsYdohiHBYp6SAqxciTQ14fOhNwy6w6IXrpeax0aORGpaKtqtb4ePV34sHk9IABQKFCioQJEiQL4FYjq1OhTECc8P4Oh3H66N5NeT8khh/huf/dgOcHLC18H/4NQKIFWlhqL0PuyD2HjuRXu0bi32bjimJKHOMk2/8ZDKCKw3HL+IOgAo2Q/jTwB4Wx6dHogKbtuJPbFJx8hMQcCA3QPgMFVs+oTp0zaGXAB+ZB8I7/LsBvo2wa4na4F8t9DrC839efcOf4/tCHzaE9+2dUaLx5C8n4opCoQ8DQERcBXVtccdnWMAvydixP/VAtoPBiqwdiFBzerNlGTWyv/YHHiuGZHoH6avHQL/rRK3j99lzidzT4re0SG37mu3N+vMTqyPAwHhM4GxPf8C8ufH/HPz8TjyKYbXP48x+AMJTj5M8zA6eRZYXaZWs48DORo1YubFx49x+vEluLXpDwD4af9viEiIwN6vt2AHuuBWfHHsdPscn9VlH9U/jCL07m7lFB+UAWJiYrTbt2/fpr59+2YkuRxPdHQ0AaDTp09Ljrds2ZJGjBghOaZUKgkARbVrJ3T3G/1dKSBupziAtt/eTggCIQjk+hPIeQJo1IFRhCBQ5UEsniqwKFF0NNHDh0SRkaR8+pRGbB9OCALdC2BxenaFNh0EgRwmsbQQBLpWK5AIoERnBdGdO0SbN1NSnJIwGYTJoF13dmlleuMBGvd7S7rwyxCqNYAdC64BKjRSmv6REqB4J+m15RkN2lAJpJgkjav78/oR1L4n2z487zrRtm10oVcTcpgEKjDKMP6jQb0oYcVSKjKCyZr/B3Y8H8K1+c76CHShEOjpv8dJMYndtzLfiXKNaGX4HLx+ZOkUHgl62rAqUUAARbnmJwKowmD5cwig9k4btLIJx4K/YPfvWn7Q8i6ltMcv+xSnJ74gx4mgoiPYsV8bGi8b0a7i9jr0pOJ4bLY86f+UPj6k9PGx+jwC6Kem1p8j/P6p6SnZL49b7JrcHQzi6j9jB53y4jgRVGpOKfLy0jml1CFC9ZUG950A7ebTp0R06RLbqVlT+s5920ly3veYzbZjY7XHKvbPR2eLiHHu+4P+K8G2D5Vk+a6tkr57k9a+o8Gxf0vrlE/MovMX0gyuyxOxRtN84wHKOxrkP4btN8IxbTARaePdyiue4zdWvnwMalWCvuloXP6fMZ5eDJ+u3VeMdzUa9+KDUPr0M/mwZeir3S41DOQ9jt3XbeXl43uU3Enxb+JM3lvdckRE9FPfZ5Qfr8hlAsh3LCjBCVRrAOizT0ETm7BzkhUORJcvE337rUF6M/7sQe8O7CMCqF1Pds+2bUqhG6jIyrMrSOkC2lIB1LYXq7N8x4I+7svOn9gE9HffoURHjlBcg5ZEALmPB732ZOGn8ZHsdThNZP8rqrP/CU1BdO8eUUiItl7o0AM0ubHxe+E3Vu/eTAaVHwL6GMeJADoT2ILu3SNKSiJSRSpZvB07tPGVPj5Uf0R+adlduZKuPTxDaU8eU+zOLZKwzz7VebY1WN1syfvw4qcFpAQIACmVSqNtsIKVY+tIS0vDzJkzceLECbi7u6Nly5bo27cvXF1drU0qVxEWFoZixYrh1q1bqFChgvZ49+7d4e3tjWXLxG6RmJgYBAYG4hYANbzhi1iZFDXpegOBOsFJF64jwVuBgPKVLZYtVqM9e+tY0baVroLhna4DBOSNB64stTg5i4hxAX7+cTrOTqkLf7zDThhaj4Z9Avy5H+jSwQ/heaMRllIRWLcfqLwBaMXmVhlxGhh5FrhSAKhhZCTtRyXuI7ptWdxZmHG5UxRA/07A3zsynhYALMfXCCrRBJP8v8KAK9KwxTWBSA9gvAmj5Kx6UkdAYxwvBvQP7Iv7p1ZZJZ9c2chu9OkChBYAHAkIXWIkUlgY/vgDWPRnMh6hDADgQ1xAx1ITMPnRAW20lIdhKF1ac8q+60DbtgZJNf0SeJAX6H8RiHMBkg/Pw58YzrxbSxr61/zWAFioGeQSplkCInAEEDYn3ZfMePYMNX8uhst67+YCDMLMUcF4PIt9wb5xBz702w5EF0fzxFCswjdGk+zRFVi5E7iy6QG6f8rq7DNngD6LAnFUpsfA0vIR5wx4pWPMwoOaZVHm8n3zETUcLQE0fWJ9PgIlhwEqJ3FfeF7pYuhQYOxYpCQlwKXsB5hXBxiu02XSoSdQ4S0ww7xrkoTAkcD1BYCfTi/7H/WA0VZOV1RpEPDtJSZT+cHA8l1AA51e7O7dgE06g2f/qg0MvSDuX8zjji4RokNU/vzAgQNA3hqBAFjZWFEdGH4y43XH3LrA9+eMh8cCKBoTA6VSqfXj1cdJ9qgZxo8fD6VSibFjxyIyMhIHDhzARx99hCNHjsDfgoXxcivCTRaGtAtERUWhiIwjn0qlwhSVCi5Iwlr0Qn+nMExyumAQz0/P79etdhW4GcQSqTWAnfPrYaBcSn5c8n6DPZWBLreBRjrlruvD6zhw3QtLDhkWxuX4GkGYgvo4hTkYgcI1CrGxz5UrS7y4kh/cw/2lv6HyjJW4lReo+A6IdAMCkgCfFGDGlLEGaevyp6YLevvuaADALHyCXXiIYTfOopte37euMjOxKTDtKJAIN7gjCQFP7uKsnjIT7gE8VFVGUkoAmoE5jkyvD/xdDaj3HLhUCFi3jcn8wjEARdKYP1CEB3C8uEmxAQBfdwSi3YDtm+XDA0cCu9YDqa+dgSfN8LHMEkv5VHlxMt87KF2A2gOA1o+A+Xr99YWM67pagmsAU5sCwCp8UB24u8D8OSGBwMdmJgq9UAioredI3bsLsG67+fQBIMEJmPVnP0wcLL8KdBqAtr3ZKBG/JGB3OTYcfJdmyHnS1btwq/YB1liSX7duGP399xj9YSrwFTt0CbWR8kis5lZVA/rqrH2b5ugMweV8fFNgTQ2gb+UBeHCDrTC5TBjsFPUKV54swpOSDtC9ZYO+KopFrm3x3ejvsXCRdB6u2pqej1n1gEd+wIJ/gWc+QLEYoNtnwD/SXjBE+RSDf4x0BEyxzitAjcT90S3YPYpP3AvojCTcXR5AU9aP9h+AM3t8Ue8e8ytp+QVwJz/wvwvAhBBgo2Zy6O6nywCaHqp6jW4D3wG38wAVIoCBbYHFMgOpQgsA1cOBaFexsb1crwRGlnsCTY8rGj5h3cobKwO/3asAvwd6zqmzZgGffILkXl/B9epF7Ha+jxEAltQE/qfp8jxVFIgr1RKX0w7hR71xI/rKTOBII0rJyZPAo0ds5roqVbT9jA3CgONGfL7VU6di1oo8cHjyEHmqzUbhGGBMK6DWS2DmQWBYG+CNBxB/8BRCrogO+y5uHriZF7ivNx1BaAHgdl6gSjjwe0Ng9GngpTfwIID5rvxXCiAF+6AsHQVcKwAU0Lh0nWhZDh33iMrE9vKmFZo1VYDQgqz+WqVZCSXGHXDSuO/EuwE9PpfeKw89BXToBeCpD1BcU1d5qQgYGagNfwOgxhrAEfex5fhNlG/3Bb4JlaaxvTzQ4iHwQyvguQ+w18xsLg/9gBZfMiVzYS0gMTEQATFuiAy8D9VDFRR3VPggAnjkA+Cq6bRg1HZjgvr16xscW7lyJQ0ZMiQ9yeUqSpQoQfPmzdPup6amUr58+ejvv/+WxBO6nJQak9qePcy6VhvniAAqhzv0xNvRrCnuW6fZ2m23nzSm1K+aiGbVbz8kBIF8gnzIx0dJ//5L1H7EHurZVT69ofhTNquEBFH2VVdWEYJAYcowIiLavJlo+nQiNJlMcEqg+YHVzcq9oFwvi0yNsibjySB07EcAkQNUsnHq9tNcP4h8fMigy+Ly1RRxX5FGAJH6+++p4degksMMuykIoBJ4pN19Gv1Ue75iEuhZQFUigM7tDaaYpBiDrg6A6GQBT4M015Xxp1hn0RTeS/Nc4JhMuxRtKLlOA6Ly5c3fE73r04Y9fEjb6pUyeo5gNjbW5XS+sPHzLPkpXWTip6Rot93H68jdcjT7bz6OSuIhfYNlrMDppTka08kd8XQPZWg9etDoYUmGeffpIyvP+sosL9+P/yYoVJQ/3yEigDwGFycMqE0IAj17944QBLoRfoPmnpkrvbeVNknT1EGVpqJum7oZ5LmkTjA9eHefpszsSPW/Af2vHchlgqFsT8q3or/O/UV5R7P9/h0Mn6fQ5aJ/fNgn0uNbKrDjn/n8Qai0iVBzKQ0eotbGf+0p3717E+W15bU1/tWWjx1VfOhUURZn+sYTROfPa9NKU6cZ7S7+pLfmOX84i0ipJBo4kEitJiKimG2HDMpVa018lwmG1+j0XaDBPYvRlK+NIYsNn7dKJa2c08Quumr/E9NPcAKVHQp6G/+WPUcVUdu2mqhy1wU1HTpkWPefDAT1rd7Q5Htp6U+LcI937qAiI0y/awPbSe/Z9QIuhCDWrbRidEvZ7tezTlUM0nnknJ8Uk0BfdQKVG2paznMlpHVHMTwhuEVpdtUE9wgigJLgQgRQ2dLzCJNZV1jLL0AIPEn4yc2y+zLOfJcTjIaYoGnTprLH69atm57kchUzZsygwMBAun79OiUkJND48eOpcOHCFBsbK4knKDSAUlIvTp7Mysa1a0SPfQwVmr1lQOPxMxGY8hEQQJQGBXt5Km7RNuL6L+OIbePIx0epDRsyhKgddhukPwxzCSD67z+iGjXEoC1bWB12/TqRtzdRTIz8e3X5MtE1n/pEAM3HECKAFuNbbWMiRFTt2E0PN56XT0S3QgBR6e9A+XT6WuESI4mW+lU/7U5nbCOAaNu+KAqPYFpYRAQRRhYmBIGSUpMoNS2ViIjevSN69oxo+XKiAwfY/U9KTdJW0B7jWYMrpH3mYAwNHMh2//c/IlReTwgChTw5ySrrvXu1z7H9+vZWNfxC5VdxMNsOCSEKDNTU/W3amD+39ycGDZ3az5eIiJ5MmG0Q/++qlik0Se4esnJu2ztLNv4bD+l+lKu0Ej1atAa7QTppCT+1Wk3lykmTJCKioCAigPwQSX2xgk6dkomjL8uBA0RXrhgc11UShJ/LBJ13xuslJSaK7+PxJ8eNK4vazPXYsEES5/WibdogIQ1njUJTp78Y759VMXTu+Tn5Bk4TJzY5ljpv7EwIAp0NO6s9Xny4NP4aje9OYxwlgGj7do0A7doRDRpEEQkRNGzfMHoQ8UBy3lE0JgLo3TuW9O3bRJGRSvIJ8pE2tqdPy94Dfbnb9mJxXEruJX0SwqWVCIJApFbT7jUTZa+93/BXdKloR6JFi4iI6HXsa6lMx46ZfzY64e16ittvr52TRLt7V6/oVNrE8sp3Q9DHTKYt/CITIulx1GPpfcl303ijXWUtBe8/JaZZrJj2Wu7dOMG2f/xRNq+V3zclNPyVoGCK238Ni2rTHbl/pHivFAqz9UlKClGnDZ2059x5e4dS01LpTdwbibyNv2V1R0+sI39EaJP4SOL2o6ZfMY49YxCde3KVCvxRgKJikunRI6KIhAh6FPmIEAQqOruo/H3xeJN5Ck2lSpXo/PnzlKj75hNRs2bNLD5/wYIFBo18bkCtVtPEiROpQIEC5O7uTh9//DFdu3bNIJ6g0BQurKQrV8TjcXFEgjFHV6FpU3gSLajFrAGtWrHDe/YQ3b9P5IpEKoQXhKKnZRWaeksbkVKppK++UkrK7bNnZFCQg7xnGtQFZsq+9le7tvSEhjhB8fGschg3jqjKUM31/PuvQeI9sN4gwTx4K7kOAqhbraYEGH580datRFu3Gq1sUHYP9Vv/o8XPMSElgRAEmrmDfaXSzJlExBQMXTFvhj2XPf/o46NUZFYRozcr/spdCvX3kxzrtKETFZtTzDCxGzdM3viqA9kzn/DfBPFe7dunecBEdOECa6SGjiJHjSNh7f6gf2+yClJfoREcWwkg9ceNDPITGpAwD/bVNQBLtGHBNQzl022UPv+MPaD69aPJcYIDoe5c2nJzC114cYGI2HMVTg0NNf581GqiW7eI3r7VCzh/nmj+fHE/IkLSwCEIFDA9wKDCXLyY6OpVFk23DKnValnl4hoq01RMMC6gzvWnHjmuPbz77m7CWH+tQ2fdfmI8QR8ef3g8wf+hNr9LLy8xr0y5+lJzrss0FzpxQk3o0ZEQxBwuCaAlI27Ty5fGxSRiZb1ecANSq9XshurdeKVSST4+0YRKm0iVpnnxnj9neT+Xln/JvfqmgVZpWL77rEG+aWnS+xQeFy6bjhD+6LH0pY9IiCAEgU4/0wzCePnS8AHqkVitjvx79OSJJN6DB2JQ3rzi9jffmLiRemmeqRqgDardQrToSqLq3i+xmIqoVMyiSUT04gWLIPzr/WLjIrW73/WOoNjYCPr1xK/UY2sPmnpsKiEI9Fz5nCg42HxlTkT/3PqHEAR6FftKItJf5/4ijAgkOMfT0P+9IeVvv8ne85gYdvjVKyJ1cgr1/yTM8Pp0b18QqOqiqvQ69jWdf36eiIi+3vE1RSRE0PPnRHv2aHo1bK3QTJgwgdq1a0eBgYFUsWJF6tmzJ02fPp0qVapk0fljx46l/Pnzk7e3Nw0aNIiuX7+eHjFyNNouJxMPR1ehgUOq9GtEB4Bo6FD24QK3KCol9DB07EcIAkUnRpNSqaSICKbQlCnDChsR0bN7iazdG8c0aJozRzZ9Uz/9wW1HC/UkAuhK8AXJ8WuhB9kJute8ZAlRcDCtWEHUDIeJAGqAEPJAnMFLX3YoqMhAUzVKJgEQ/fWXZBdgVhqzdOgguVkHfrmgGV5D9GPB5azBc2LPWTB5G6CvRckoDMJjQxDrKjFgxQqKfRNFGFmYPvTcTT9v/Ud7MfoKzXSPvmL669fTHUjNJh6/eEhuxKfYrN3+8NvJsvIJIzb695feQ2MV3K1bFtxbS1m0iCl1RPQi5gVruInITWN1KNh5jsnT9RWa0v/zIkBtsnKWXOCNGwZBNUZ+zxQax2PaeIcPi3EOHyaj77tsPsJu/muEINDBD4syJeBylOnzLUCpVFJYmJKOHTMf9/eQ3wlBoBcR0QQQdSjXnwigWzfTZOOnKuQLQr+drO5q21kcsRQRIT1X+Oh4F//OugtyctKmGVrjK6Lvvzf4Qnr8WBTr4UNxOzLSRLo6zzzm0VtJmmlpRJ+saUNbb26VRhXKVb+PCCBascKM7EeOGOQl/NRqNanV8vpcYmoinXomWn7O/PGnwflfdgalLl8t2wboo1YTvX7NyoapNswaEAQqObek0XBL2kyLFJq9ew3NhQJv3ryhAwcO0PTp06lHjx6WJEdERCkpKbRu3Tpq1KgRKRQKatCgAa1fv55SBG00l2PJw3nqUkDysk88MpGarpLv7tNF+Hi6dDmNNlzfQGq12rKC9++/UmcZDU+eEB09avgORURIdROBS8FsKOz1jTINqwnKlSMqhQcEEPXrRzR3ruZLSacxGbh7oFVp2gSAaMEC7a5SSfTTTxaeq6eM7Nkcrw1aslhNAe4JNG/3RCox3MyruGEDUdeuBg/hFfLT1bD7Fl+KWs2+OLWWDRmF5t8lT8U8VCpW/AA6Vpwdex37WrwvAB0eso0IoM0VQXWGLDCQMf8f+Slc0xU1cqTkVNNKQRYQEUGUmmo6jm75E0zpkyaZPifNP0C8wPh4SVhoKJE6lZmi6uKMNt7Jk9I0UtNSKSk1yYxwegoNiNCrrfZ4SrJxa4WlpLfRAog6OrHut/BwE5EAOjLuT8lhtVpNCSkJTCcQrsVWTUOS6HN124iiJVgK796VGkRM0r+/0Weuj4FC06mvde/Cy5f0/GESOU4E9e+Zn1kmreBtZLLsh4e12FKh6b+zP805M8dkXjZRaLy9vSX727Udsrbhxo0bNHToUPLz86MCBQrQ+PHjbZp+dsSSh/PMsbjNan1bFLwvvmCiREVpzMVGuH80jAigO3sfWJV+cDDRhx+yPEJCxOO6DcqQvXZwPK9enfXtpRedSuPgQWmQWk30IOKBfFeTPidOSCugHwqkXyYd2XQVmuOf/imVmYhcXYm64B9ymgiqObOswXVFrtlDFdo2JgSBenz7nEJRVRv2yKcyxSbHan1rgoLYqYKCrOcrny0RuwTUtHu3Zeeo3zInlO7YIB9B46D6EURflIsX0yOcjEIDw+MZIb11R+3aRJ2xzaQyUrvQJRpWtSaFhBhXvL7A3zQQC63O3yQAxXbsZVHUN28svJ2pqUQNGrCIeu4YMtkTQFSu8SVWtmoGy/YomsP/h3r05LUps5EFQmQThcaSvMy1mQ5mBkEJI6Ek+998841kv63MPA7WUKlSJcyfPx/Hjx+Hl5cXfv/99wyll3vIvGXv08OaNWzxRj8/+fWIBHw/KIi/0QdJAYWNR5Khf39x1k5jUxo5OaRrpoGMceUKUKZMuk9P/orNLFwG9w2mn1cogNIBpfH0+6cyZ2Y+c0pI161q1ETzYHWWMG7SBNiOrij96hdM7yIz0Y9CgeKu24Gzw+HvWARvkF8bpHTODy8XLyg0VYgwfUTevOy/dm3kGNLSFGjf3rK4irx5oABhE3oYicDe7bc6i5jaYhqvWpph5h6IR2G8MB05kzl8WJzy39lZPs4bl5r489oluLsbr+vWog8WY5DR8HRBBK+dhivIyyEsWRRmZooDODmJS2CYWWC5alXg6lWgpFtNYP5d4Mo3RifaNUXkH6dRvEA6p0oRFiUVmGOfOsiWWKTQKPSWCtdXcDKyVIFarcaOHTvwySefoGbNmggICMDy5fJzVrxv7PYyUhnaEVOKjEBAfid8hb/hHmDJ4jFSPDzYf4EC8uGujjlv8kbXVUuwe6caD1HGsvV0jKGz2ODVAoBC5Zlh2YY9GAaqpDNB40KNwvLNN9p1WebPZ4fuLhuPFqVaGKShcFCge0d/YP9cuLgAlcqL69a8S2QyCjWIry/7d9NMpOTnl+FLyHTcXrSG89taFpV9XU6fZj9ZFAogKQkPISrKbqYmlzKG8H2twUmj7yfCA4duWPdBYWt8fID/0Bwr8LXROE81bWh2npPVyYktpli0qAWR9+xh/8Y0OA1XrzKlxsEBQEQ5wDLbgm357DPtZi1cAJTyC+HmJNL1uauv4KSHly9fIjg4GMuWLUNkZCS6d++Os2fPopbwicHBzIDfoFbGYigsmCUtG+HoyBZrTM8Xh5+fpH6W8G/vf9Eg0Pz6T9kRJ2f2zmRIoXESX9dv0tbAZ1sjYE7G5HJ0BBR5AthOzZriynBeXtovOHMNrUKhNTjA1RUo1KwCcIcttOPsxU4WLDRRUey/ZEm20HP+/PqpZT/ezdlv8BFnCfXqmYng6or79wGU1e5mGJ0igkom1vDJKoI3+8HBYYXZeC4uxsNOnjQdnq1Ys8bQ8mGCadMMF77MMjp0AFq0AA4fRoOvymJsO/OnZHcsUmhSU1Nx/PhxVK1a1SYzAXfp0gV79+5FyZIlMXLkSHz99dfwywmfallMcDDQscV0LMW3uGZvYawkPcqMOT4p84ntE80iBAt0hhQaYQXohw9xuXQp5MljOrrFLFjAVtr98EPZYKGhNWahcDl7Aoqabdi2C6D45mtgIVPClSkahUYTV+i5c3SEdjHK7A5bjzJzun91ezLTZaHJ5ugYAUyiVhsPa5CTvmHat7f8oiEq9GfPZpI85pg5E6heHRMmOyKfkdmTcxIWKTQlSpRAs2bNAABFihRBQkICZs2ahY8++ggfGqkETaFQKLB37160bNnS6nPfJ5o3B6rU9cS5c1XtLQongwgf+BlSaKpUAZ4/B4oUwbFjNrRu+PmZ7PsRFJr//U8+3PHRfThoDKtv3kCi+VSuzBQBwUJT2L69INkaWyg0PXowi0ZOQWMggMzKMDkTPz9gs5G1UGQQ6gWLurMyA03XWL681lsgsyMWKTS3b99GTEwMzp8/jwsXLuD8+fOYPXs2Xr16BWdnZ6SlpVmV6bZt29Il7PtI6dLAORMLdr0PXPnfFSSrks1HzMZEsmWiMu4zoqn5GzfOYDpW4Klx1Zk6VT48ceofcLjFtocOBaAWFZpSZZlpqsWXQKFoF/yaC60QtsIWXU5DhmieQQ5BaMi9ve0rh70QutLs1qVWoQKwY0eueQAW+9D4+PigRYsWaNFCdAp8/vw5zp07hwsXDBdUtIT79+9j2LBhOH/+PGJjpSvwpeg4QL7PzJ8PDBhgbynsS/WC1e0tQoZJSGD/ObFbwcnJuF8TAMDfX2uUKVMGgKoUUKIEWxRw5UpgxQpcKgIgvwNm58DrzyrM+JFaRe/etksrM5kyBahTx95S2I+CBYHr14F8+czHzRQUCqBTJztlbnsyNAa2aNGiKFq0KLp165au8/v06YOiRYsiODgYnp4ZH7GRGwkIYMNmOTmbnj0z2N2UjVE4KLROwQoF2Nfe48dsp2NHAECdqFk4f6gY3Ix0W3FEx+qMsmlTzvE7KVYMGGTjEdk5jcqVzcfhWIYdJvUQuXXrFk6ePAknJ7uKweFkOh4eQK9e9pYic1A4OmgtNJJGeft25ggGoB5G4vytnGmhyml8/rm9JeBw7IMdBr+LVK5cGa9evbKnCBwOJ4MoHBTyCk3nztq++WSNC1RutVJxOBz7Y1fTyNKlSzF48GD06dMHBQsWlIQ1atTITlJxOBxrcHAyYqHRQZgBmltoOBxOZmFXhebmzZs4evQo9u7dKzmuUCisHjnF4XDsg64PjbG5al6+ZP+8d5nD4WQWdu1yGj16NH799VdER0cjNTVV++MjnDicnIPC0UE7Csra5QE4HA7HVtj1e0mpVGLYsGH2FIHD4WQQBydRobHVSJ33ilGjgIgIe0vB4eR47Po91aZNG5w6dcqeInA4nHRyutdfAFiXk6mp6zlmmDmTzdfD4XAyhF0tNPnz50eHDh3QrVs3FCpUSBI21di0pBwOJ3ugMccoHB3Mjl7y8QFiYrJAJg6H895iV4Xm+vXrqFatGh48eIAHDx5oj9tiNW8Oh5O5aP1mHBVo2xY4fdp8XA6Hw8ks7KrQHD161J7Z25xjx46hadOmklmP/fz88Pz5cztKxeFkDoKOIoxyqlfPeFzeJcXhcDIbPogyE4iOjuazH3PeGywxqHKFhsPhZDZZ7hRcuHBhi+IVK1YskyXhcDgZwopuJN7lxOFwMpssNyNERUVh5cqVIDM1nFKpzCKJbE/JkiWRkpKCypUrY9KkSWjcuLG9ReJwbA7Bcl+3xo2BM2cyURgOh/Pek+UKTYECBSwawaQ/6sme9O3bF6tXrzYa3rhxYxw7dgzly5dHaGgoKlWqhMTERCxZsgStW7fG2bNnUb16dYPzfHx8EBsbm4mSi2RVPpycR3rLRpJTGmIsHL60fj2QlsZHOuVEeN3BMUZWlg1L8spyhebJkydZnWWG+euvvzBz5kyj4c7OzgCAggULatek8vb2xg8//IA9e/Zg8+bNsgqNSqXClClT4OLiAgBo1qwZWrRoYfsL4HAyAWu6kZyc+LIHHA7HOg4fPowjR44AgEUrCPAqxgK8vLzg5eWVrnMdHByMdq8lJCRg5syZ8PHxyYh4VpGVeXFyFtaWDTeVI3xiYtgkM5xcD687OMbIrLLRtWtXdO3aFQAQExOD4OBgk/H5yis25MCBA3j8+DHUajUSEhIwb948nDp1Ct26dbO3aByOzbHGh4bD4XAyG26hsSHnz5/HgAEDEBERAXd3d1SpUgX//vsvatWqZW/ROBwOh8PJ1XCFxoZMnDgREydOtLcYHA6Hw+G8d/AuJw6Hky743DIcDic7wRUaDoeTLrg+w+FwshNcoeFwOBwOh5Pj4QoNh8NJF3yUE4fDyU5whYbD4aQP3ufE4XCyEVyh4XA46eJu9e6YhCn2FoPD4XAAcIWGw+GkkxQPP0zDJHuLweFwOAC4QsPhcDgcDicXwBUaDofD4XA4OR6u0HA4HA6Hw8nxcIWGw+FwOBxOjocrNBwOh8PhcHI8XKHhcDgcDoeT4+EKDYfD4XA4nBwPV2g4HA6Hw+HkeLhCw+FwOBwOJ8fDFRoOh8PhcDg5Hq7QcDicdOHtbW8JOBwOR8TJ3gJwOJycyVdfAVWr2lsKDofDYXALjYVcvXoVbdq0QcGCBaFQKHD48GGDOFFRUejduzc8PT3h5OQEZ2dn+Pr6on79+lCr1XaQmsPJPBwdgVq17C0Fh8PhMLhCYyEuLi7o2rUr9uzZYzTOF198gXv37sHJyQnz5s1Dw4YN0ahRI8yZMwcKhSILpZVHTgnjcABeNjim4eWDY4zsVDa4QmMhFSpUwIABA1DLyCfp06dPsW/fPqSlpaF///4YMmQI5s6diz179qBQoULZQqE5cuSIvUXgZFN42eCYgpcPjjGyU9ngCo2NCA0NhYuLC65evQpHR0fUqVMHzZo1g0KhwMKFC+0tHofD4XA4uZr33im4b9++WL16tdHwxo0b49ixY2bTiYmJgbe3NyIiIrB69Wrs2bMHNWrUQJ48eTBz5kx06tQJ9erV08ZPS0uDl5cX7t27By8vL1tcikkSEhKQkJCAO3fuZHpenJwFLxscU/DywTFGVpaNuLg4AKztNIaCiCjTJcnGxMXFISkpyWi44Niri0KhwKFDh9CiRQvtsZ07d+Lzzz9HSkoKxowZg+nTpwMAXF1dUbFiRbRq1Up7DAAuXLiAJk2aQKVSaY85OTnBySlzdEwvLy+oVCqT18p5P+Flg2MKXj44xsjssqFSqSRtZEpKCs6fP4/atWvLxn/vLTReXl42sZBUr14dKSkpCAwM1PrLXLt2DSkpKfDx8TGIX6ZMGSQkJCAsLEw23NbExsZiypQpmDlzZqbnxclZ8LLBMQUvHxxjZGXZiImJQWBgIMqUKWM0znuv0FgKESE5OVm7n5qaiqSkJK1VpXjx4mjbti0ePnyIFStWoHXr1vj1119Ru3ZtnDt3DjNmzJCk5+joCADw8fHJEoUGYCO1siovTs6Clw2OKXj54Bgjq8uG0HbKwZ2CLeTp06dwd3eHu7s7AKBt27Zwd3fHzz//rI2zZs0a1KxZEzExMWjevDmOHTuG5ORkbNq0CXXr1rWX6BwOh8Ph5Hq4hcZCSpQoAXPuRgEBAVi/fn0WSWQ9zZo1s7cInGwKLxscU/DywTFGdiob771TsL2IiYmBr68vlEpllpjrYmJiAICbjTkG8LLBMQUvHxxjZGXZsKTN5F1OHA6Hw+FwcjxcoeFwOBwOh5Pj4QoNh8PJfK5fBx4+tLcUHA4nF8OdgjkcTuZTtSrg6grwydk4HE4mwS00HA4na9CZx4nD4XBsDVdoOBwOh8Ph5Hi4QsPhcDgcDifHwxUaDofD4XA4OR6u0HA4HA6Hw8nxcIWGw+FwOBxOjocrNBwOh8PhcHI8XKHhcDgcDoeT4+EKDYfD4XA4nBwPV2g4HA6Hw+HkeLhCw+FwLCIkBHjyxN5ScDgcjjxcoeFwOBbxSaN4tP9EZW8xOBwORxau0HA4HIuIhxcmvBpsbzE4HA5HFq7QcDgci6mSfDH9JysUthOEw+Fw9OAKjQ2ZMmUKSpcuDV9fX+TNmxetW7dGaGiovcXicDgcDifXwxUaG9KjRw9cvHgRSqUSL1++RKtWrdC6dWukpaXZWzQOxyYQuJWFw+FkT7hCY0M++OAD+Pv7AwCICI6Ojnjz5g0iIyPtLBmHYxsUIHuLwOFwOLI4ZeRktVqNQ4cOwcnJCc2bN7eVTDmavXv3onfv3lAqlVAoFBgxYgTy5ctnb7E4HJuQIfsM96HhcDiZSIYUml69eiF//vw4deoUzp07h4ULF2LYsGG2ki1H0q5dO0RHRyMyMhKrV69G0aJFjcb18fFBbGxslsiVVflwch4Wlw0fHyS4uCImJsb6THx8AAcHID3ncuwKrzs4xsjKsmFJXhlSaMLDw7Fx40Y0bdoUTk5O2LNnj1mF5tGjRxal7eDggBIlSmREPLsSEBCA4cOHw9/fH+XKlUO1atUM4qhUKkyZMgUuLi4AgGbNmqFFixZZLSqHYzHpsbE8fZiK4gAonedzOJz3k8OHD+PIkSMAgJSUFLPxM6TQODs7Iy4uDgqNKdmSDMuUKaONbwwigru7O+Lj4zMint1Rq9VITU3F/fv3ZRWahIQEzJw5Ez4+PlkmU1bmxclZmC0bMTHwcEuyqAyFhQGBgWz7bbNuqBITAxUc4cTLX46F1x0cY2RW2ejatSu6du0KAIiJiUFwcLDJ+BlSaH788Uc0b94cSqUSK1euhIODeR9jDw8P3Lx502QcIkL16tUzIppdmDdvHnr06IECBQrg7du3+Omnn+Di4oIGDRrYWzQOJ8t48AAoWxZISgJcXYEKyjP2FonD4bwHZEihadq0KTZt2oSNGzfi2bNnWLdundlzOnbsiOLFi5uN1759+4yIZhcOHTqEX3/9FXFxcfDx8UHt2rVx+PBhFCpUyN6icTg2QUHmRzkJXd3CbAWF6BUAwAl8+gIOR8vdu0D58oAF7xTHMjKk0ABAiRIlMG7cOIvjr1+/3qJ4a9euTa9IdmPPnj32FoHDsYi7dwFfX6BgQWDXLqBIEaBpUwtO5E4wHI5tOH/e3hLkOqxSaIoVK4ZatWqhRo0aqFmzJmrWrJlh60NaWhocHR0zlAaHw7GO8uWB0qVZ99CQIUBAAPD4sfnzrJlYj4/StgMpKYhaswf+/braWxKOOfgLYnOsmljv+++/x969e7Fq1Sr06tULRYsWRaFChdCuXTtMnDgR27dvx9OnT60SoE2bNlbF53A4tiE8XNy29JvCkon1FAoARc5zS7opFArg2DHbp7txI/z7d+Mf/zkBrtDYHKssNOvXr8fOnTvxySefAAB27tyJoUOHomDBgjh//jyWLVuGN2/eyE71P2nSJINjRISHDx+mU3QOh5MRhNf0H3TB+bd1sGzZb+jfXwzfvRv46y/gwAGdkyxQUoI3hQED6iIi8R08PPLYVOZcRdOmtvef0KQXEWHbZO1GdDTg7w8kJgJubvaWxqYknboIN4A9rDz8PbEFVllo7t69q1VmAKBTp05YsWIFVCoVDhw4gFevXiFc97NPh0WLFqFMmTIoXbq09lemTBl4eHhk7Ao4HE66UKnYfx1cRG+sxxm9wUjBwcDBg9anu3BpEgAgJiaXmGg6dABGjLC3FJbhxL5Rc4117BVzKEdion3lyATcFs1lG5cv21WO3IRVFpoSJUrg6NGjaKrjPdisWTP07NlTu583b17ZcytUqIDmzZujSJEikuMHJJ9/HA4nq9Bt9AiGjaBcN5RF7aQDM/3Mm+OEpX+mV7rsQYECQPgbjbP/3LnZXlMgB8fc6bdtr/uuVDJT5RdfZF4e3IfUZlhlofn999/Rs2dPbN68GWq1GgCwf/9+i+afOX78uIEyA1g+6onD4dgWzSsMQN7ZV66eNedDk5QEwIGZftRpOX/t2zdv9A4IZq1silrBHppCnUuGyGssTkhNtU/+c+YAffqIlqLMYPLkzEv7PcOqGqddu3b4888/MXz4cHh6eqJo0aLo1KkTRo4cafZcc7MDczicrCU9Co0qlSk0u3cTnj5TG4Q/ewZAwRpTNeWSRlUXW3d9jBgBjBqVMQvErVtaB1O1qzsAQJFqftb2HIFQCJOT7ZO/4Ljdp0/m5XHyZOalrccfp/7Aw8jc67dq9SfU559/jqdPn2L37t2YMWMGLl26ZNU8NALdunWz+hwO530kKor1digUOvX63r0a7cF2WNLlpFYD+/YBHReOQImVYoTYWHa+uzu0XU7H1L/YVL5sQVKSbdObOxeYPRtYvDj9ady9q90UFFMHGCqbmc6tW6yLxpYInuv2soxp1tnDf//ZLk0fH+DiRdulZwVjDo/BLyG58L3UkC6bsIuLC1q0aIEePXrg9evX+C8dD/vly5fpyZrDea8gYnPEjB/P9jdv1gS0b2/Tr0aCwkChEXqSQ0N1jkGN588BlDgqievjA0yfrjlHY6F5nJQLnR0tWK8uXVy/nvE0EhKgJo2lTWakaaZTqRLQujWzOtlqHT57KzTffGP7NGNjgdq1bZ+uhVx4eQFFZxe1W/6ZSYY6uXv16oW9e/dizJgxUKlU+PNPyz0AeRcUh2MeoT4XejoiI3UCbegomQ8RiHwrbQQFC43u94oD1EbbyqtXNd1YGgsNSh6VjWcvdwibULQo8OSJ7dNNb304ejSwYAHbrlFD7DpU28FCAwDnzjGrk60GewjXYQ8FDQBsMQo3ORk4dIhtZwOn8htvbuBF7At7i5EpZEihCQ8Px59//gkfHx84OTnxqf85HBsTHS1u70E7VNwyRTxg48qx9aNFkn1BodHNRtuVUfCawfkODpr2R2G68XFxyRkjVY3eXisnD7WI9DacM2eKGue9e0hTM4VGQVms0OhbUGzkUkAqO1toOnXKeBrBwUCrVsCpU8DHH2c8PY5RMqTQODs7Iy4uTmttSckscyyHk0MZeWAUfgv5Ld3n9+0rbrfDPjS+Mkdco0Do37cRAcnSkRxyPjQOUEsaepVabGgcHICLd14BZcSvc31DgbCfExSarVsBN8g4AWe0npPTlFxdM5amBq/P2MzrlJbFCk1UVKYk++o5U2gi32Tv0WUmEWY5bNiQKTWcTCNDCs2PP/6I5s2b4+XLl1i5cqVFw7cFKBuY3jiZRPXq0nn1zXDk8RFceXUl8+SxE6dOAXPOzsb4I+PTncbevQDcooBCTANITiLg3j0WaIuvVp3hqA6Uhqarm6Lvjr4A5C00jkiT7JeYW0I83wH4dE0/oPE07bG4OGl2Eyaw/wEDMi56ZhMdDVTCTcOA7t3Tn+iyZUD9+obHbaTQaMnqLpr8+Q2PydTxZ8+yHilLESxNqUlZfD179wIdOwINGmQ8rbdvM54GxyIypNA0bdoUGzduRN++ffHs2TOsW7fO4nPP6E9Lysk9XL2K1FOWLybT/O/mqLusbiYKlPUkJdlwqZ6O/YH/fQgAIDWJjZ+VQ4gLziyIrbe2avfr4BxQuLB2P9HtJo49OYbVV1cDEN06dNul0ngEpyRRSxH64j0RhzyqcMA5QZKnMlraqP32GwFBCqBg9ldg3d2B6RhrGJARa8SAAaxV18fJqjlOzaJWZaGFxtiInX/+MTg0dKh1ky4L8+lQahZbaH78kU2od+oU4OkJ9OqV/rQqVDAf599/05++hSTdDwMFifurQlfhUdSjTM83K7FKoQkPD8e0adMwdepU7NixAy9evEDJkiUxbtw4TJ48OcMrb3NyAZovwwvnrbPAkWVz0OYYKpZLwZ7HNnJ8Dzyt3fRBrNigWuldGx4fjs+2fAZhgGFevJOEv/OX+sCp1cAXWAPnxBjJ8dq7JxrEi4M3Zq8vCJQ4LgmLfRop2YezRgkbWNMq2e2BuzvQHEesPu/Qw0N4HffaupNsbKH5cF4mzpuij7ERO599ZnBIdzqZt/HmLReqZM2cRilZrNDojjpzc8uYJ3tgoPk4bduajaKYosCJpyfSLcbdA5quak1V+/XOr1H6z9LpTi87YpVC061bN2zfvh1XrlzBiBEjEBgYiCJFiqBLly7pyvz+/fto06YN8uTJAxcXF8mPkzNJS9T4F1g5aiO3dUF+EhaMM8sh+SJKN96vpWsOdO3K/q3w5UhWsZbEF8VQpKahQy8AOOg9AiJgDb5EtZMLJMffJUnPNzU1S/yLaMn+0O9zzpo8CQnm4+iSmgo0Wdkcrda2Qq9/ZL7oV6wwfrKRJWNMYqIrI99VG86bYo0Ma9ZI92NjJbs3brD/7bd3Iv9MmW4qgE3PrKk/0lKZpSktxY6TNLq5Ge3eTUiwoHfd0p4LC0amHX18zLK0dHj0iFmLhYkuFXrvuTqrHcgzEasUmtDQUISEhGD79u14/PgxwsPDsWTJElSrVi1dmffp0weenp4IDg7G7t27JT+O/Tn08BDSrJxCPUmp+QSzVqHRs9AkqZKszjs74auQ75aoFBiD06dlg+RRqFHrBVA8WibM2FfjpUsGteyQfUMAAEo8Awax91V/GQN9habNVebMnEYOkm6nOyS1Wkga/vttJGGJL6UWmifJofIyZyVXr7LyeeECcEW+6ystDTj9n3XKV+duKTj+jN2bo09khqz362f8ZGuHWcfFyfutZDW65axbN8M1j6ZMkeyW6DEXCFJg8JLVxtMUJgqMitIqMlluodHFy8vou9azJ1CwoJnztZNHmcHEEunCB1/Q8cmISY4xGk+OTz/VLOyusZ4LNbOz5pZardCkptpv5mYzWKXQlC5dWvIlnS9fPrRv3x5BQUHpyvzWrVvYuHEjunbtitatW0t+HCn2MGC0WtsK225vs+qchChW0J1cWNG6Fn4NEQmGL2pSEmtTduxhn/f6L5X7L+7ouPi79IidYeLiwCaPywAl8ET2+M3nvri+MMTidFyqL8SFYKBspEygMYWmVi2xz19TcJZfWW42r1J6OliX88yZ2dWFkPpQnJVY5QAc+Bvw1ygywjMHADhKK7qEcOkX/J5XS8zKkelUr87+69QBasp3fU2ZAixaLQ6lPoKm0gh6L+TvvwP7kiakXyYLX/Bz5zS9us2apT8vXTI6oZ+uL+RvmtF8ukPYdCbYyze1LJ6UZw40r/23y6enVgONGrHtgABQAlMq05LtqNB4eBh915xuhKI9THyAW3N/ly41GnTkkdiV6/u7r+VpAgARHKHCD6PYPXQgwCMFSPkZyB8H6z8c27UD/P2lM25mE6xSaMaMGYPffkv/EFR9KleujFeZuehXDoOIDLpeXr4Ebt9mI0h05yTJKAsWmDapC3KkpFk3RDU+Shq/2uJqyPtHXiSmil+7u3drpsgH0OWSu/b4vvv7JOcevc+cJ59GP0XjaT9m2VQU3t6s2/vSpfSd//w58D/SUSCER6q5p56RYRalk5QEuOS7CgDodksmgql+/SNHmGOogwNq+hk6/ikKXkQvF+l0+25G7m/VM4vhUra4dl/lALR6BHymkSl6qc4XaMAD6TW8FVd3TIUT8LaicZmzAgsr4VXLxJtxDVXQHEcQFUmA8PGmc+8TE4EftwQDdTOwtLiFFpqPPgI2rE1j1qWMkpgIVK0K/PBDupOgEKacj8cvuKsuyw7WqCFG0HzJp6UB7+iB/ul49FjvuvUsPAGbWRm1tMtp+e4bCFq/F7HJsai2uJrYWEdHW/5VqG9dNtHltPhZG+xGRyimKDBk7xCD8Oj6bWTOMoKJMnDikTjIosIbABMnGo2rT8/QMVDBGXm6MVkUBLhqLsc3CVZZfJKTwSYJTEyUPucsQHdAgzGsUmj69++PmTNnonXr1ggODsbVq1e1q26nh6VLl2Lw4MHYvHkzTpw4Ifm9bxARHKY6wGGqA1RqFVQqYNMmoEjZd6hSMQ3zMRT+ZW9AMUWRYX+T48eBoeOfY84y48qkoMg4KKwbCJcYranAklLx3yOxH//OuzsAmD/rL8JSIl2llVe79e2gJrWOEkWITopG761f4YT6d6TTEJhuatWy/hxV509RNTASBxTNtcec1MCb+DfaSlGdZtnzi4gAYg8uAwAMlFOuzAiofM78F1yU+ktGA0sK10avlL2SY8lGBtr4RctPJLdkD5AvDqg680vtMQefZxh0HiiocZ1Ii4jA5XyttOHu1XeZlDnTWWLeQkQEjH01XLt/HI0BaLoWhmuO6zgOrVsHoOO3gJPUOrXnnuhknZwMPEFxTEYQAOAT/IuxlcX7v/LCckChgLpiJZNyAcAXfeUf1HJYMU2/UilO5jdrlrjOhZUoND4zv2E8Nm3SCdilec4ahebdO/kyX7qaTh2UnAxs2CAJ9z/GLDlyXU7NVjfD1zu/lhzrf7kKptxvD5/ffXAt/BqcpjnBYYoDsyjopW1AfDwQYmg9vf/IwejHQ6qLaIFaeHGhQbhfnOkZeR+ilLgjWS2W2DOaPBkIC8PU0+Jou59CAPz8s8l0BYoqnmM0ZgIAJmqMPLpdywoAg/cNtiit5GQg1i0dvl4aHmj02W3bDHvXEhMNdfSUFFZlXnhxAXff3UW/nSa6bDVYVYpPnDiBuXPnomjRovjrr79Qq1YteHt7o2HDhtYko+XmzZs4evQoevTogSZNmmh/TZs2NX9yNoWIMHnyZBQuXBienp5o1KgRbgiecEZITARuPxAXdXOe5oz21b9G9x4KUEI+bM9THkOxALMD2KyV2+8YMddaSGQkgJGBmBBV2GichFRmvjFQaCIj2SJ0RkiKZOdtXpeqHf4LADWX1sSnP/6LgADC5XOJgGsMUNXQWa7frn5w/VkY8aHAgN0DcOrFcYDYx2Rmc/EigArbgObpmDtm61Y47fwHkciD1iQqc85pwNyzc5Eaw6xUjw/e04bdfafxF3j3DvHxwBEd9xQTXeqMnTul+0+eADprpEU9YWVqCz5DUz0jjZPMd8jhUtL9EyXly4duhfh4njSsxitg4T7gB8FPSBmJFE9/dh7USHxeVht3/fX12u1mzdjyVEY5fBh4985EBA2Jiaa/xOUWgXwgtRxMnw4Mgdg4jcIsABofbDc3dlDHh+DP+fLWgw4bOmi3t898iBJ4ipX4GoXwEgfwCZbfqKMNf3SPdd043Na8W+HhzFKg08glJwMKnUUnT+BjPIM4gqY/liECAaIAJu7DgyN6C5sSsVFJVnws3b0knWRo8mSdnQ6aa/fxAQCsPqdXVgEUUQLFy4lWrbSS4ogb0hsYUnD9bIPzjz45ilWhq6BoGoS6i5qI75IezirNNfXuDWWsiTLk5SV2dwGoCmYdPRVeBndvGSpUd97dQZJLrMFx8SLEezkYC2SjxMBb3ClTRtx2cQH8/ICpU6Ga+xcAoJ7mkfU21oulUkkUbeWdV3iuUz6qar5rFBD9aBRkwvJBBBw8iMheQwCFApXcHiAv9CqlwEDzXQfv3uHKJTUqlE2FSsVcrfR94IsXZz3A166xauz1azbwz9kjHnWW1UH5BeVN56HBKoWmdu3aGDRoEJYvX46rV68iNjYWhw4dQvd0TjQ1evRo/Prrr4iOjkZqaqr2l5NnHJ45cyZWrFiBAwcO4N27d2jQoAFat26NOP0ZxjTU87sNdw8FKpbzx6RjQBlNedl/c5U2TocIVuGOuPcIJSOBbpu7odS8Uth5x7CSsISE8FhMPgoo1MCKKysQlSh1nth6aysarGATSo05PAZhSk0XSWAgkCcPW4TOyLwMjjdYJRD+9i3WXJOOeLgW1xbhHg5IgQfwo3w/8KrQVQayOKsAmgL4/7fM2ksFAKiTUjCiVgicnU3PNxYZCdRu9gLo3g3032/iujiWIjNMFQCc1cDfV//G+eNMoZmMqVCrgZ6rRqL8gvJsREy+fDjr1Ry/Nj+AY8eAO2FvEP7SAjO75oJuD5oHlCwJFCmiDYp4xBSaQDzHkb8BPBfn+vk61DAptc7ltl3XFo/8fGSz1FVoPPU+XH007XzJKMBBDTgoI6H29gOgcUIusx8f+bJRkYIF78x/CXhx9C727gVOnhTTWr02BYopCtawt2wJ5MsHHBWdbeNS9N4phYJZHTTWBn1LZspPooPqfK8fxYA5c7SbSUlsChJdUnVnTBaGV2smUyECrj9n2mLDp8AHRgYe+T1l74VL6WJ4DTa9RQTEWr2QfrsoTOi2S7RoxcWJytX13r+hMU6gOJ5hBzrhLsoB+uXVyAKRI/rFoExXma+DrVutmg/lVK1hBsdatmQNltYKu3gxkpOBsUGGTmC3FwBPLs0AwBR5x1fMmpH44AUcUqTWLt9Q0Yfk1LNTyDupshjYZArOvzlutNHrrvM9ebtCPuCm4WSJ8et2GBy7jqpwRwJ2owOU7wwtNBUWVECapgVt/lAmY01Pw0Z0R5iOYqFLTPGr4o6gLAOSLi6n2TMw4CJwegWwW+cbcPnl5VLfw759JUtoHG0cJJunAwHlNO2MIwHlkr3kl/NwcABat0bABqbcP0BZwziAvMPh48fAqlVMy82XD1U/ckcqXBDzQlPQy++AolM/XHxyB+u2v9UOlqtWjVVjhQoR0Okb4Ccv+TyNQRawZcsWio+PtySqVfj4+Ng8TXtTokQJmjt3rnY/NTWV8ubNS3///bcknlKpJACkZHWiyd/V/OL2kRIg/zGg4a1B+X8AAUTFihmXJzaWqHep03RzyG+kPHGC6NYt2tv5dyKA2vUEIYj9jj85TtdeXyMi0h4TfpN3jKDkYkUN5FKr1SwDlUqbX7x3fm14vJM0Hf3zr+cTw8oOBf38MQzyRhBoRCsW/3/t5Ivr+vUsyfh4iSha9n+90fC+DhsmiaNWE6HUIUIQqOJgMV6qb4Ak0fv3iVJSpOmrVGTy+eUdDXKYBOr34RXtsRUriJwnyN+bCgOmEIJAo744YbZs0KJFTAiZsFVD9kr2FR/9IZuf0seHlD4+NLy19L5vrJTXfP56vwQncXtEK9Cust0ppOE47bGSw6R5JCeLsgvJHD9O1GPcQRpZvgHV/8YwjzE/pNGsRW+1aXw39I1BnDR1mjY8TBlmcI8AohY4KB7T0Lo10QRM1R5v+DUIbYdIo+nsAGpCEKjAKDHtpTV1nquGgwW/oE0efem336SiChtrqogH1/72vTTSkiVERPT4sRg/L97IPoIwFJEemKq5lsuXif78k77sHG36GS5fTkREqalEY8YQHTpEVKzsc5q4bQ573zWsXaMWy/LsKNmkhI2DB9Ta+1E932o613c+JRw4ILmPKxYkEAEUuWYPCUGGCRIdvH5Jto6Q/Caz54EgUKGRRq5zzx7x/U1INggvgFeaTTV1KDGcLisqa+MnJhLhwyWEINCtvDrnjJLWTzE9B2jKmpqckUyfYZNBPh8O0Nn/4ANKSlRTuEsReZmNvG+xu/9hGQYGimFbthg9x2ccq28JoGLfS++vwM21ly3KX+njQ2/+/Y8+/ZTIB9GUuGId0fXrRuOfbz+FfF0f04ZKIN+x4jMbG9CPCKDZ+J6q4zKhwj+Gz3UcCAAplUoyBoyG6NCgQQPy8vKizp0705o1a0wmaA3du3enkydP2iSt7EB0dDQBoNOnT0uOt2zZkkaMGCE5Zo1CY+73hdMS1iIfPEjUv7/Rgqf08ZEc2/4Be+GFAvNRP9C/9/YRgkBuP4GafSmviAg/v7HS/ZOBhnGKjGBpFDZSsawd3ZUQBAquwfZXVgMtqWk8z2P3/6OGRR/QcnxNK32HycZx+wm0//5+IiJKS0oxee/UaqKICKIa5SbTl52NxPvzT6IJE2j6r2H0s/v/iAA6492M6JdfKPR8kiTuXx6fGZyvX6lurghq3usQEUBTG8nf4686ga7kdbasUvl6uMGxOGfQ/zp0lRzzcn9Mikkg73HyZWNUSxBq/6UtD7vKZaxc3swL2vJBHjrV8XfJ8cf/G6bNI3ih+HwGtwW5TADtdWpqNu08o9n5dfuZjic0bHc2HtIe64qtBBA5QcxbrVaTSkUUiKfaYyd6NmByftFamyQRacPDw8no+6GYJFVoHjqVo7Vlg2jPHmlUYWNnOVCyg4ly+sEHNHbtYu2+sUt+jsKSAxFupu9PbZwzOJavwUjyc3sgKR/9PvWhqgNBd2+dJEpIoANoycrZ4OGUmpYqm7z2nVh5nbZ/IF8/EEAUEUEhXWYRAeSGBO3hEZiljfPg8x/p+atkg0aOAOrUXdxPVVhYPn/4QTzH0YUIoP1oSYU9LtMPi+cQPv6Z0PBXwgc7qE0v0LV87FmmpRFVmtqFun4OeuBvmG6aOo2I2EfPSdSnX8t/QGg6geU19AOa2kis6wiiYiH8Lv68K0PvnCW/bX7VtB9tJYfphK1YQUREUe9UJs8P6TpW+3yFumOpiTrb1C/cw3wcbRtlgUKjYO+neV69eoXt27dj27ZtOHv2LD7++GN069YNnTt3Rt70TAoFYNiwYVi7di26detmMMvw1KlT05WmPQkLC0OxYsVw69YtVNCZ7rp79+7w9vbGsmVil0lMTAwCAwNxC8DlYkDjZ4bpPfMBilk35YBRYr2Y6c5bpuvriS8Q5wJU1pj9Pu4LHP4bcFUDnboDOzcZnGIVV51LoVqq8Sm2H/sBr11dUS88c+c2mFkP+KcCMPo00PWO7dNvWXQy6kx5iV/6BUuOf/QNcFZnTrX9pYBPdG5Hse+BZ3Mznr/us0pwAqY1AgZcBlp8CTz6E/ipXQjcYz7GBD2/R6FszI8bikWtHwCVWJ/6PxuAcC+gw33T+b52zgNyiUAhmR6OoyUAt9o/ot4W6ejIwJHsP8zQNUKWld7NsSl2NAZhATqZGCY7yut7zIqbq93/sjNwrIR4f2Pv3sTx464Y9K0r4JSAMNUHRtMq/R2Q4gwgLj+wlHllh4VBO/NrtCvgZ6TI1hoAhHsDYSPCmI9BlSpYNO5H9BvYH6UbnWUjorZsQRiKWXYDNMQ7AdNVQViJftizB2j/+RugYChQ+hBwdAqCnb7AJ0mid+WREkCzJ/JpBY4EsPEfdPKfhZTkfFj6QL4L21TdITxHAMDfB4F3FQC3aGBwFYufLZ0/j6mdJmPyq38RCE339qDKgLvSaBrfftgDZRs2wOh56Z/aIXAk63YXykaJ4UCazIKsDZ8AG7YBkV98i4C1xodWA0BovTJwmjEd/y4ujb7rmuPbzyNwsahhvLDZwNnCwJhWwIlV0rBwD6DFV8D1Rem5Ksto9WEnHLy0Ex/3BUJ08tevi7oMzoO0yAhcKQQUi2bHnvkDWHAdF/x7wjPuCQD5sgEAB0oBP7YALpu+bQCAyY2BKceNh8cCKBoTA6VSCR8f+e5wixUaXSIiIrBz505s27YNR48eRZ06ddCtWzd8/vnnyG/FZE/GnH8VCgWO6HpH5hCUSiX8/Pxw+vRp1KtXT3u8VatWqFy5MmbPFt/OmJgYFCpUCKpKKsARaF+uPdwOFcLkhL0oSJGo9w2w4R+ghMZXuPR3wN35gKkVXwY1Loo9NZ9ru9IrhAMH1wHNvgTiPL2gVgDhbnFw3LQRT170sOia3ro5IF+S2E+7vTxrpP7cL8YphkeY5jYcZ/L44r8ua+GVAlyxoABnhHOFgekNgQtFAS8XLzS7GocFMt3/i75sigWeR5HoBKzqvh7j9gfhWcI9+CWKFcabYoUxpRVhgc6or18bAOP1Fsb9rg0wX90aOCCuJr29Rj3Mr3IG9wMg8UhzTwbuafwAm3wFHNP4R0e4AXmMzKy7ac1UdO8zSXIs6cFduJUx3ugKVBoExLiLCoJaI068E1B+GDv+plx9HHc8jc9uA8kOTGEFxAZrYdwg/OXXEfjmY206g9oCi/YZZCfhUkGgUBxQWKZOu1QQON2kHr7baN3abdcmDMWc/LexchjzswkcCWB2GPBlMyDgPlo9BJbr6DUdewCJTmykln4DIXC4BPB1V7Y9rsE4/H7qd6MNZrMvgfu632krjgHRpREWBoz6+WPMWvLEpPxNa/REoRIbsPrn63CsXhMOqakIHAGpm8ujpgjbcdRkOnIMc5uIpNY98W9pwyHwTirg+7PAcJ2l1D7vBlwowuoPFwJq9wdey7QHcvei79wmaLzuIj69bdholR8MxLsZnmMqPTm+6AKs3c4av/6d05eGOWbWA34wUQRn1QPKTl2AukXrooBXAdx+8g6ttrNhyfVWTcXmSOl7eSdoIcoHyY8OSlUAzppWtcogINpdL0KaI4afT8PZgvlBqR/inz3SiivRESg3PH3X/mcdYF0VYMYhIKQ4+yCZL1MvdskfhO1vgrCtvPzHnVBvmKL6K2D9bkNl97NPgdCCQJIL4PnuY8SvX4aRXqMwIpqN+pvSCNjWMB+2tD4Jcn2AFpvbyaZfLArwvqFCzRDmT5QCIDglxaRCA6O2GwtRKpW0du1a6tatG82cOTOjyeV4SpQoQfPmzdPup6amUr58+Yz60GCc2D0yaZJoaTv88D965Cea3VymOhMmi/t/DL4jMXe/dnc0KZdSqZSa6lJTKfGjJmbNfQleeYgA2ojPqSUOEEA0aBBpw0990NcgL6FL44cegQbpYbJxnxr9X9tezPfEq/8H2vhHCvgb+LAIxKfE043VM5npd8hQ1g0nQ2qqZmP/fmZDlmF16GraWoHlGe0K8voR9CxMxkGHiNRqtYEp3O0n8ToeuubRbj/PW9Do9Z65edDwOJHR+DX7FqTRLUAlhrMukxSVYfdaZLGidODBAdnzO/aAxGwcDdYlqftsWvaoQTEupp/TlgqgZz7yYbfzgFp+Yfr8EeN+MzwucP++6OvlE6a9v1GJUUREFKYMM7j3B0uBLhQyzMdpoqG/xdy6MnlqiEuOI9dprmJ8qGnzZpLcn6f3kphTxV9/SfIq7xBqWPZ18nWa4iz7DtT4FvRlZ9D5wqCun4O8f/Wm+UenS+Lo+r7J/cY2l6bZ/Y+6hCDQQz+2vzp0Nf1zy9BHoXkf+Wcg1B0nvxxIJwNBn33K4vv97keJqYlG5Xjj4KdNa3edrgbXKvyiXNn/rnLiua7TXCkhJcEw/rZtZusNuR+CmP+hsfCk+BjJs0/ReZUauOp1A+nWK4cPE718aTJfgx/U9Omn7FIqfFnM4JyXL1IIQaDxzUA/NQXFJMUQBQUZzeNuAOvGUkwClZtfTuvrJOT3S0PDc5rln2PyfiWrkin4UrBE7olHJtLRx0clx4qN0XFl2L+f6NUrmfqRKCxMTP7CiwsUHheuDT/17JTBPRp9cDQ9iXqiqYxXE61bR8rKlQmwgQ8NEdHx48fp8OHDEscwjiEzZsygwMBAun79OiUkJND48eOpcOHCFBsbK4mn9aEx8XCe6jQS9yPuE4JA+fMcIw/EUVoaK7CC38dDLzeTchkoNKR5LzXpp12/SSWHgS42rygeg0K2sn/9mmg+hhAB9GjFEYO8FJNA+0uDosJiJS9J6e/0nCUvG3c869eBxTt1OZL6bRhLDlMciAA6XT2vyeu0FWq1mlwn+Wvl7dDBdPzQV6GSF9JlgukK9t98XxC9kTqz3nx2mahUKfFYSAhL3ExlWeib77Ry7FkbRZO+fGxY+cqc7+V3lQiG/lVdIDoU/jJjEuVzijR5LfPqGFdoXrm6U+3+pu/FgUMqIqVSPHbunOTe6ld2C84vMLj/C84vkMQJf5tKacdDdGrRCxSZEEk9tvbQxmmwvAGt3XubHpdoQnTliuxzTUhJkOZf+oBEEZFUh+PHG73GCZUqklqtpoSUBEpRpcgq9QPaG1dS/MeAxrRg8ep9Ix6vtbSWWYVGIC78OVG42JCo1WqqsbgGjT44hqYe/ZmOHiVWXi5ckCj6cnVHalqqdvtZ9DOqsrCKKEP5bUREFIIGWhmCx5yVytS9u+x9ehHzQvoAdMIS3P3ZsS1biNq00R5Pef6ajQioWJHIxUWappsbRZ/8jzW+34M+/xSSD0P9uk0u6zo4o91Z719LPvKAAQbX0l6jeO69t5c+/PEHwpfNCR5vJFlWb/Op5Jw+WE1ERDFJMfTLiV8oWZUsPCyjZauoxk9RrVZrfXiIiLx+9SIEgT7pbXjOX56GCua37UH/uXxI//5r9JYQEZH3r97aZ+0T5EPr1x0nioszfRIRvXtHtGmT8XAhTWM6hiVtpkUKzbhx40ihUJBCoaBGjRrR27dvqVu3blS9enWaMWOGJUm8N6jVapo4cSIVKFCA3N3d6eOPP6Zr164ZxLPk4YR5OUheOlWaimbPFuukZ9HPaOjuwWZfTCE/c87cscmx7IU4c0Za2KdNM4gb/jie2pS+S4mJhulceHGB+u/sz3YmTyY6doyIiIb/O1yq0BAZNtIgQoHLhKEf0Jfb+hIRaV/UFn1An89pYPIabMl/124yeV2jLYovXNuRR0fol/+CTDbiNH++zons2LPIJ0QJCeyLT4fYvMUNzh/2idiAtelzS0YYvTIhI4MCabIKje4veN8vVMQzyuS1vPAChfu6y4YlwYXKfAda/CHbP1+gvUGcy5c1MrZuLVuO99/fL2mw5dAd1ZRneh4xIDmZ6O1bSdx119YRgiCp/E0hp2AU9z1CBaD3NTp9utF7NH26NOrPx382UGhW9/2JaiyuQXNWPJXNs31t5og7fn4XSaW//MRebZySHddLFJrYgmUsukZTWFJ3EBElpiZK7mkA3mnl2LAmlShJ40D/7h0REcUlJdDKaqaVCxXEOvD54dvSQLlzkpOJbt9mJpaBA7UKvf69FM49s/uN0eu5e5eN8rqz7qI2/hE0MX4D9OuyINA3O74hIqLvvhODvhO/P6hZ8SvagF5YS0cOmyiTN28a1AHHioOcJ4CarW5mED01LVVUEm7cMPkOC07sgHmDhTJJSV02diEEgQZPPkuRkbYZJGTq/SayoUJTuHBh2rFjB71794769u1LNWrUoAYNGtDw4cMpICCA1q5da7307zkWWWicihh90QXyzshrNo6Qn1Wj0yIixHQ7d7b8PDPcenOLdYHoIlS+Ry/QkiXGLwdB8i9uZqFWE9WvTzR0qGXxN93YRAhiX5nHHx6RrTjOV/6Qbe/cKZ6oCYtOlFecDlcbIUljxwc6XXcfLqa7d2VO0r+JDRsayLJzJ2kVmlUN5bvCjtw9QAsXkmyYtrFu2IGifLyMhucZLZq9LxbuQHTqlDZsRCvQgwcaGePjJRYEyeUEiV+7pkAQaO1V29ZHslYT7+dUpIhexDeGw8cJoOuoJDudAECSeIdG7jOeZ9fe1NxhH3tux5ZI0jn0kE03cD/iPlVp/IDGNRSV08eH7mf4+q2uO4RrgGjJ/e8/w3C1Wk1/1bZcoXlz/nE6pGe8i39HNRbXoEF7BhGCQPk+a0i+nncoMtL8uY92XNXK8LbvD0bj3Tspdk191jeAVlxewbqBiejePflLbFfqlqgAwWgPOOPuXW1c37GQdt9bwMjd38mWT0H5QhBo1CiLktKS3rIhh/NUZ3Kd5moyL5soNB4eHtrtt2/fkkKhoHcaLfvAgQPUsGFDS2XmaLDk4fRteJ9uO5Y0qawIXxtHC5cwm5/VBU8o8LVrW3eetQBETk4G2RpECwI1XdU0c2WxFUZMxGv+t5DVbjpf2Pc/bU4EkCpN3kfn0IdjiQBK0pjTl+jMc2K0aABEOvMhUbLhXBtCPKWPD+1u3ka+siOmY3w4QPTB0P8taL6CkrzzyJ8P0Kzj0+mxL9u+FNhJK97QD7aRi1OEvgFFlvC4cLt1d8sqNKbuu95vAJYYjdoXK7TxDgeFaMMeRDxg+XzandC3EcEpkZZ+e4EIoHdRUgueWq2mc89ZN12dOkTj8Ks2TTkLqrWkt9FavpyoMq7RQgyk69fl48ysp3OvNJZcXXQVmug7hv4Z6eHgg4N09VY8PX1qWfxn+0XLiGrUGJNxr25j0wOsWLfZIAwg0u/Q6FJeVGguXTIjiEah2dyDde/FJSYxq9wflSy7kNRUswpNSIj5ZHSxpULzIuaFYZejXl7m2kwH037MDEdHcSxb3rx54e3tjTx58gAAmjdvjvv3zYzr1KNy5cpYuHCh0dlzOYxFh8pgVo+bKCoMZZQhrwcbivGohrPtBbhwAWjeHDh40PZp66OzINzff0uXANClecnm8gHZDYWCVRUaCvuxKXDzOOYHypaVXK9LITau09FBZswoAKc0Njb4eZdRAKQz1+pkIYVIXHcIYFOpm6DuI+PD6l1dgUsRvdG3M7DrowCD8Kf568JBswhgspOHQfjIRmO0o/XOVhmgPb74YRekqALg62tSNABAfs/8UOgvGphFOL3RWzNr/xzjkXVGNwpshPyIwkKFgFUQ1yJS+IojN0r5l8JvzX9D48g1wKrj+KiWG76aXwt49Ah5/KRTXCgUCtQpIi6jEIwBCEZ/VMVV7cTG9uCrr4AbqILBWAR/f/k4KZqyHPL5CKBxY4PwBcX/0G47uNqmjmtZuiWqVvBAMQtHzDt7iPk6/vG7ybiVOzdD0Rkl0a1ba9lw/YG9Ya7icgdGFn4X0ZT/Br+swerOq+Hp5gqaTHj0g+mldbSYWq9r33zg+ERUqWJZUplBYe/CKOxtfDkei7BEc3Jzc6Pjx49TdDQzifv7+0vCrZ3xd+zYsZQ/f37y9vamQYMG0XVj6nsuxhJtk4h9WMs4jmvZeH0j+YwDjT84zmx+ttKkbU7jxtKO5dyEjjm5PxbTqiVJhnHOnjVphRPSuNePTVDX9EvrTM366chZaIz50BBpZkYFs1TUm1eV7egMyXNziKJUd9bl9DD/R5Lzn6OwJO8fh7LRJCNHMr9Od3frLsEeBBR9S/hgJ7vnLrEEEG02/AAX0bn+ejhFBQrIR3vyRHOLNXGPrngkG2/RIrLY0lK3rpi9OUd2S8lI3SHIYsxn9FJBFuHvmftkwyMjxURS31jQP5QJvDn/mAigFDiZjWstNWuScZO0PjExLF56zW5GrMbL8bXFIuiTle2KzSw0JUqUQNOmTREQEICSJUsiISEBs2fPxpkzZ5CSksJeSSv4/fff8fz5cyxevBg3b95E1apV0bBhQ2zYsAGpRlY1fV9xcdGs8muE7pW74+rYx5jcbIrxSNmdY8eAP/+0txSZzjL8Dw7uMp/MdeuaMLWIpDmwL8Vb+dIpABEUIHyHP7EdnaVhLVoYPU33K79K8TqsQA4TJ6lQkzMUmkUUz1WWrogb5VdSsu/pzyxFV6+yZYOMTSeRrUjIC9ztAKzbg63rvbBlC9C1q4n4v7FJBOvjFM6gPjZulI9WvLh039FXft2agQOly/xYQrdukmWg7IawTJaHoeEOABAYw/6dPeULgq5lx8lbf0KXrEHhzGb/2vbdMZunbdUi597erJ6wtjAI2MnCmZVYdDtv376NqKgoHDhwAN9++y3atGmDWbNmoUGDBvDx8UFCQoLVGTs7O6NXr144fvw4rl+/jho1amDw4MEIDAzETz/9ZHV67zMl/ErAxdF0lwIne+BkamZEI1B5tuhemgM7WSW8tclWLtwGthj1X/gOXbFdGqBv7m8udu0J9WCfsDgs6LAIePWKLVKqQU3OuDTrGADgcg2pQpPoKJXR1YspZf9pFiMPD7f6ErIcpmsq8PJ4O3TtCnz6KeAo3zvIGDcOIMIZ1AcANGliPKruYpyOft7GI1pIZc2ajVuNLKCc1Qh6urG21DWJ9Tfm9y9nPrH0NuQZRCizzTOhtzs76Bhr0MfeItgMi/VDHx8ftGjRAj/++CO2b9+OFy9e4NmzZ1i3bh1++OGHDAlRqVIlzJ8/H8ePH4eXlxd+/910PyWHk2M4fRpHZ1/R7ppsCI2g0PhlCH35wgq/iLd8Vm4BZ2NuCPqfijIWIye1J5wcDDWyNm0dEVu+NhQgKBykNXSSg6dk/9gJlk9OesU1xicUKmT7BkhYVBsAXHwy3mAvXMhWjc8ufPMNMH++8XAfNXOucvQybn1Zjm8wAdNsLZrFePuzMp/OFX5M4mX9N4nNcJwE3L53FfsSmqJzZ6BSJfvJYiusMXgZULRoUXTr1i1DCoharcaOHTvwySefoGbNmggICMDy5cszIhaHk32oVw9nEqtrd9NjoUFh5ihXtgqr9K8MusqOp6NxFfSWJ080B4QaVbM+kZZevYBy0q9mY4354qUO2nSrVgVCmk3WhiU5SBuqmzfZ/+efWym4HREUmszGFg68Li4w6oBrD3x9gaFDjYeH+jLLoIO3p9E4/bEcv2CCrUWzHOGlzQRzyurVNk/SYtQKIE/hgnB3B7ZvB25Y6FucncmQQpMRXr58iSlTpqB48eLo3bs3ChcujLNnz+L8+fPo27evvcTicGyO7iiedCk0o9joJkUD1oVRIh8bGaFIh0YjDL7S+m+8fQvcu8eGX8TEiBH79QPu3pWcu2IFZHFwVGgVmnbtgARP0cmnZfg62TQEPerYMasvIcux0kUw3dhzRJK9mFD/CNyQCGeXbND3YgzBrJoJCo3+d0RWkjQhCfk9rbfyZmfSU71mmC5dumDv3r0oWbIkRo4cia+//hp+fn72EIXDyXQq6qwhmJ4uJ/j7i63qu3fGPSzTg5sbkJLCtr1N+3AY+9JWKETLj0IBKMi4SaOoZuVhT80HecmSRqNmG9Kr0Fy7ZqKLT4b3UaFxdHZAMtysuk9ZTiYqNFlOz55AWhqweTNcnXJfgbOLQqNQKLB37160bNnSHtlzOFlKkybAokXAoEHptNDoouOMm5X1q6lG3cFBT6GBYeRUB8BZLfp1engw32JTI/iyC+lVaKyd0+N9VGiEkVimFJorV+ysS2Ril1OWs349q4w2b7a3JJmCVdXr06dPUVxvrOHr16+RJ08eOFuhYm/bts2abDmcHI1CIXbxpMtCYzThTOgL2brV7CR8BmLo1PMODpDVAKLUeZEf7yRJ5wRlBmCjsG/dyvx83keFZto0YOJE0wpN9epZJo48wkur8zGRo7FpJZS9sNiH5sKFC6hduzZu6bzZb9++RdOmTbE5ndre/fv30aZNG+TJkwcuLi6SH4eTmxAsGLY0rSsywwWuWzegQwfr5NBRaBwdAU8PUaF558y0lg9xGRVwK0c22sOGAYsXZ34+OfHeZJRCmkmPs3WXk4MDEBUFlCljPm5OwKrJb3IWFltoateujUmTJqFly5Y4efIk8uTJg08++QQtW7ZE796905V5nz59ULRoUQQHB8PT07iXO4eT0xE+ijLc5aSDwrJ5MTONZw17odjJ9ZL60ckJ+KguAVuAhgjBM5eKeAbgOZj3I/9WMSSibF3kuX/uvVRoBD/0bK3QAEBu8vHs3TvXvohWVa9Dhw5FdHQ0mjdvjsKFC6NSpUr4MwMzvN66dQsnT56Eky1reQ4nGyI0+jZVaGBf03GiZk0hfQuNQ3IiAOAUGqJKKek572OjbRZNF10u7gkwyv797D+rhsZzALi7A19+aW8pMgWrP/HGjh0LAAgNDcXMmTMzlHnlypXx6tWrDKXB4eQEhArbprq7nS0091sPxUAsgoODOPDKwQHM+3nNGgBsHU5d3sdG2xwOucDXNL3k14wa5ivecGyBVdWrWq1G7969ERgYiKZNm6Jjx47477//0t1dtHTpUgwePBh9+vRBQT0PwUaNGqUrTQ4nO3LqFPtXqWyXpqeHfbWDhPwlsAQD8YcCqFEDCA3VWGv8/YEvvsCy5MyZLj638f/2zjyuiur//6+LCLJ4WU1FBQoLyxBN8Zdf1xBDccklP6Zo8VHLQisryqXSj2sbVn4+ipqlubaZmmlqqSimuSahZiokYu4ocEGEy3J+f4xz78y9M3df4f18POZxZ845c8575p455zVnzhIY6KCJblyQV18F1q6tO91TCOdilqB5/vnnkZ+fjz179sDPzw8jRozAoEGDsH37dos68p4+fRqZmZnYtm2byF2hUKCmpsbs+AjCVeE7P9ryU3xEuGu82vOfnGJjxe7jxumHJfRxjX/ROXTq5LiJC4m6j1mjnI4fP46dO3fC398fCoUC69evh7e3NzZYuBLam2++ifnz56O4uBhVVVWaTc1P9EUQdYRx44Bjx4DoaNvFqXDyvBh8RWTKoAnqJmcAqtHrNxs2AD//7Gwr6gRmjXI6duyYqAOvp6cntmzZYnGn3pKSErzyyisWnUsQ7kSDBkDHjraN09krrBtbSVmIp6dtP7fVKUaOBG7dcrYVhLMYNszZFtQZzOpVKCVcrBmh1K9fPxzgOxfUAX744Qe0a9cOQUFBCAwMxGOPPWZx6xVBGGJfyj58/6/vnWqDOS009AXZAK+/Dvz9t7OtIAi3x6kNwffddx8GDhyIYcOGoTnfyeAes2fPdpJVltOpUyds374dYfdWR96/fz8SExPx4IMPIla3gwFBWEGPCNfpNG9KC82UKXprXRIEQdgUpwqakydPIjY2Frm5ucjNzdW4O7tvgKW0aNFCs19bWwsPDw8wxnD+/HkSNESdxZTHdc4c+9tBEET9xiRBk56ejri4ODz22GNobGRFXnPIzMy0WVyuQklJCSIiInDnzh1UV1ejW7du6N+/v7PNIgibY84nJ4IgCHtjkqBZsmQJ3nrrLXh4eCAqKgqdOnVCp06d0LFjR3Ts2LHOL1uQkpKCVatWyfr37NkTe/fuBQAEBASguLgYFRUV2LZtG86dOyc7pF2pVKK0tNQeJuvhqHQI98PSvFFVBSiVQGkpiZq6DJUdhByOzBumpGWSoMnLy0NRURF+//13HD9+HMePH8fixYvx999/w8PDA9HR0UhKSsILL7yAB3WnBtUhLCwMV65cMZpmeHg4CgoKTDHP7ixatMjgrMhSK403atQIw4YNQ1JSEvz9/fHyyy/rhamursasWbM0gic+Ph4JCQm2M5wgHICbfiEmCMLF2bVrF/bs2QMAJk3nomDM8kkQiouLNSJnz549OHDgADZv3oz4+HjZc3x8fJCRkQFjyb722msoKSmx1DSXoU+fPoiOjsaiRYtE7iqVCgEBASgpKYFSqbS7Hap7q8A5Ii3CvbA0b6xbB4weTdOo1HWo7CDkcGTeMKXOtKpTcGBgIOLj4xEfH48333wTP/74I6ZOnYojR47IntO0aVOTRjDpjnpyB1avXo3HH38cUVFRUKvVWLVqFfbs2YPJkyc72zSCsDkkZAiCcCVsOspp4MCBeNbIKp75+fm2TNKlyMvLw8yZM3Hjxg00atQIDz30ENavX0+dgok6CQkagiBcCZsP2z537pyto3QbZs2ahVmzZjnbDIIgCIKod9h8bEKTJk1sHSVBEARBEIRBaLAlQRAWQZ+cCIJwJUjQEARBEATh9pCgIQjCIqiFhiAIV4IEDUEQFkGChiAIV4IEDUEQBEEQbg8JGoIgLIJaaAiCcCVI0BAEQRAE4faQoCEIgiAIwu0hQUMQhEXQJyeCIFwJEjQEQVgECRqCIFwJEjQEQVhEUhIwdKizrSAIguAgQUMQhEU0bw58/72zrSAIguAgQUMQBEEQhNtDgoYgCIIgCLeHBA1BEARBEG4PCRqCIAiCINweEjQEQRAEQbg9JGgIgiAIgnB76rWgYYxh5syZCAsLg5+fH3r06IFTp07Jhi8qKkJycjICAgIQGBiI5ORkFBcXi8L8888/SE5Ohp+fHxQKBZo0aYKcnBw7XwlBEARB1G/qtaBJT0/HihUrsHPnThQWFqJr165ITExEWVmZZPjRo0fj+vXryMvLQ25uLq5fv47nnntO43/79m1069YNXl5eaNKkCWJiYjB8+HA0a9bMUZdkkF27djnbBMJFobxBGILyByGHK+WNei1oMjIykJaWhpiYGPj4+GDOnDlQq9XYtGmTXtiLFy/ip59+woIFCxAaGorQ0FAsWLAAW7ZsQUFBAQDgk08+QUhICP788098+umnCA4ORnBwMO677z5HX5oke/bscbYJhItCeYMwBOUPQg5Xyhv1VtCUlJQgPz8fnTt31rh5enqiQ4cOOHHihF747OxseHt7IzY2VuMWGxsLLy8vZGdnAwB++eUXVFZW4sqVKxg7diyOHDmC/fv3o6amxu7XQxAEQRD1GU9nG+AsVCoVACAwMFDkHhQUpPHjSUlJwapVqwAACoVCL64pU6Zg0KBBuHz5Mv755x9kZGRg/Pjx6NKlC06cOIH09HRMmTJFdE5NTQ38/f1x7tw5+Pv72/DKpCkvL0d5eTn++usvu6dFuBeUNwhDUP4g5HBk3uC7ghhqIFAwVj/XzC0pKUFgYCAOHjyILl26aNyffPJJPProo/j44481bmVlZdi0aROef/55/PPPP6J4WrRogS+//BJPP/00lEolWrZsifPnzwMAevXqBYVCgfLychw+fFh03tGjR9GrVy9UV1dr3Dw9PeHpaR+N6e/vj+rqalRUVNglfsJ9obxBGILyByGHvfNGdXW1qI5Uq9U4cuQI4uLiJMPX2xaagIAAREZG4ujRoxpBU11djezsbIwZM0YU1t/fHz169NB8TmrXrh0AICcnB2q1Gl27dsXly5dRUVGB/Px8hIaGAuBEk0KhkBQprVu3Rnl5OS5dugSlUmnnqwVKS0sxa9YspKen2z0twr2gvEEYgvIHIYcj84ZKpUKrVq3QunVr2TD1VtAAQGpqKtLT0xEfH4+oqCjMnTsXDRs2xJAhQ/TCRkREICkpCWlpaVi/fj0AIC0tDQMHDkR4eDhqamqwbds2PPXUU5g1axYGDBiAp556Cnl5eXj99df14mvQoAEAQKlUOkTQAICXl5fD0iLcC8obhCEofxByODpv8HWnFPW2UzDACZKUlBQkJCQgJCQE+/fvx44dO+Dv74+CggL4+/tj//79mvBr1qxBaGgooqKiEBUVhSZNmmD16tUAuJuclJSEjRs3YunSpXjkkUdw9uxZxMXFYebMmc66RIIgCIKoF9TbPjTORqVSISAgACUlJQ5RtyqVCrt27cLQoUPtnhbhXlDeIAxB+YOQw5F5w5Q6kwSNk3CGoAFAzcaEHpQ3CENQ/iDkcGTeMKXOrNefnAiCIAiCqBuQoCEIgiAIwu0hQUMQBEEQhNtDgoYgCIIgCLeHBA1BEARBEG4PCRqCIAiCINweEjQEQRAEQbg9JGgIoh5SUwMI1nwjCIJwe0jQEEQ9pFcvIDra2VYQBEHYjnq9OCVB1Fd+/dXZFhAEQdgWaqEhCMLuHDgA5OY62wqCIOoy1EJDEITd6dYNaN4cuHLF2ZYQBFFXoRYagiAcQm2tsy0gCKIuQ4KGIAiCIAi3hwQNQRAOQaFwtgUEQdRlSNAQBEEQBOH2kKAhCMIhUAsNQRD2hAQNQRAOgQQNQRD2pF4LGsYYZs6cibCwMPj5+aFHjx44deqUbPiioiIkJycjICAAgYGBSE5ORnFxscZ/9erV6Nq1K4KDgxESEoJevXrhwIEDDrgSgrA9d+8Cf/zhbCsIgiBMo14LmvT0dKxYsQI7d+5EYWEhunbtisTERJSVlUmGHz16NK5fv468vDzk5ubi+vXreO655zT+paWlmDFjBi5evIhr165h8ODB6Nu3L/755x9HXRJB2Ix33wXat7ddfNRCQxCEPanXgiYjIwNpaWmIiYmBj48P5syZA7VajU2bNumFvXjxIn766ScsWLAAoaGhCA0NxYIFC7BlyxYUFBQAACZOnIjExEQ0btwYDRs2xOTJk9GgQQMcPXrU0ZdGEFZTVORsCwiCIEyn3gqakpIS5Ofno3Pnzho3T09PdOjQASdOnNALn52dDW9vb8TGxmrcYmNj4eXlhezsbMk0Dh8+jLKyMtE5BOEuMGbb+KiFhiAIe+LwpQ/+/vtvk8J5eHggMjLSbnaoVCoAQGBgoMg9KChI46cbPiAgQM89MDBQMvylS5cwYsQITJ06FQ888ICkDUqlEqWlpRZYbz6OSodwD5RK7lelks8bDRpw4SSyt0Xp+fnZJi7CsVDZQcjhyLxhSloOFzStW7eGwsirGmMMPj4+uHPnjt3sUN4r0YWdegGu42+LFi0kw5eUlOi5FxcXa+Liyc3NRZ8+ffCvf/0Lc+fOlbWhuroas2bNgpeXFwAgPj4eCQkJ5l4KQRAEQdQ5du3ahT179gAA1Gq10fAOFzS+vr44ffq0wTCMMbS3ZW9ECQICAhAZGYmjR4+iS5cuADiBkZ2djTFjxuiFb9++PSorK5GTk4N27doBAHJycqBWq0W25uTkIDExEampqXj33XcN2lBeXo709HQ9QWRPHJkW4brwLSXC7KCbN2pquHC2yDIqFRAYaJu4COdAZQchh73yxtChQzF06FAA3FeS5cuXGwzvcEEzaNAgREREGA03YMAAu9uSmpqK9PR0xMfHIyoqCnPnzkXDhg0xZMgQvbARERFISkpCWloa1q9fDwBIS0vDwIEDER4eDgA4ePAgBgwYgBkzZmDy5Ml2t58gbMH+/UBcHIkNgiDcG4d3CubFgDHWrl1rZ0s4QZKSkoKEhASEhIRg//792LFjB/z9/VFQUAB/f3/s379fE37NmjUIDQ1FVFQUoqKi0KRJE6xevVrj//bbb6O4uBjvvPMO/P39Ndv8+fPtfi0EYSmjRgFLl9o/nXuDAQmCIOyCw1toeIqLi+Hj4wNvb29nmQCFQoHZs2dj9uzZen7h4eF689EEBwcbFGSZmZk2t5EgHEFVlbMtcF3u3uVGaDVq5GxLCIIwhMNaaNatW4cWLVqgX79++PzzzxEbG4t27drhu+++c5QJBOGSVFfTjLyuzEMPAR07OtsKgiCM4bAWmoULF+LMmTMoLS1F+/btcebMGfj6+uLJJ5/E8OHDHWUGQbgcn30GTJxo+3lfzOHmTeel7erQRN8E4R44rIXGz88PSqUSLVq0wCOPPILQ0FD4+vo69ZMTQbgCOjMHOIWvvjLsf/GicwUXQRCEMRwmaNRqNWprawEA33//vca9urraUSYQBGEhkZHAqlXOtoIgCEIehwmabdu2wcODSy40NBQAJ3LS09MdZQJBuCSuuiSAbovM9evOsYMwHcYAWjqOqK84TNDoLjEAAF5eXoiLi3OUCQThkthL0DDGxa272sjevZbF56rCi9By/DggWJ6OIOoVTlucctiwYc5KmiBcCnsJhWPHuN/CQrH7xo32SY9wPnfvOtsCgnAeThM0V65ccVbSBFEv4D89eOg85brHRN2BOm67F5cvA2fPOtuKuoPTJtYztkAlQRDW4Xnv6dat5CwVNPTIEoRtadmS+yUhahvoXY0gnAwvFNRqwJYLzPPx6g4kNFXQfPml7WwhHAtVkER9hAQNQTgZvvIZMgTw97d9/JWV4mNHttAUFVHLjjMgQUPUR5wmaBg9cQQh4qefuN+8PNvEFxnJ/fr6it0d2YeGhhAbhzHbCRA+ns2bbRMf4RhoOjbb4DRB89tvvzkracLOTJjAzSxLmIZuC8auXbaJt0ED7le3suTdHQEtqWAcDw/gnXdsGyc9f+7F8uXOtqBuQJ+cCJvz2WfAmjXOtsJ98bRRV/2aGvEvjymCRqovj1B4mdrX59Ah08K5EowB33/v2M82WVnAgAG2G3b9+uu2iYdwDJmZ+s8pYT5OFTTnz59Hv379EBISAi8vL9FGiCkvd7YF5mFtZXD7tn7fj7qK7r2SEzQ//2zeqtx8M7Zuc7YpgmbyZHm/mhqur49wwr5Jk4C1a/XD6rYU8J/VXJkbN4CnnwZOnHBcmleuANu2AadPW3b+2bPc/+xqX/JVKuPiKjfXMba4Mt99R53wbYFTBc2YMWPg5+eH5cuX48cffxRthBg/P+4tzl2wtmANCQH+/W/t8Z07rldY2ws5wZGYCPTqZXo8/Buf7lv/f/5j/FzdyfiE3FuSDbduad0WLwbGjOH2N2zQtuY8+KD43P79gaoq4+k7glGjgJEj9d3Vau73ueccZ4tKxf0a6z81cSJQUqLv3qYN8MknwEcf2d42a/jlF84uOf74Qz+P1FdKS8X75ry8EBwmCZr09HTs27cPpcI7bgP+/PNPfP311xg6dCgSExNFG6GP7hT27og5b71//qnd9/cHunUD8vNtbpJdMeXTjG4fGkOfnPiVuflK1xC8oOnbVz6MXJ6SElW8ncaE5e+/a/fj4/X9v/7a8PmO4quvpG3h/zPd/j/2XMuKH91m7FNtRgawZ4+039Wr4hYwd1gCwZX7WFVUODY94XM1bx7Qvr31cfIvH/UFkwTNkiVL8MQTTyAoKAjR0dFITk7GJ598gqysLNyxYuKMRx99FFevXrX4/PqGO/WEl6r0jhwBHnvM9DgUCq51ga90Dx60fedJWyMUbDk5hodhV1RwlRDPoEHcLy8mli+X/s///BPw9tYe5+dzyxwIhQQg/U2eF0Q8UVHa/c8+A9avBz74wPBnKWOF5Jkz3O+dO9Jh+Tf2sjIuTSmGDNG2WtgDQ6KML9LKy4EfftC6N2sGHD5sH3t4od6hg/GwcuWAbkucK40wmzdP2p232RVbX318uDLIFpPaN29ufPoC4fN6+zb3a819+esv7jmOjQUeecTyeNwJkwRNXl4ebt26hZ07d2LcuHGorq7G4sWL0atXLwQEBKBt27Z48803cf78ebMS/+yzz5Camopvv/0WWVlZoq2+c+sWEBDANTHzBZMpmXvsWE44OBupioxv4BM+uEVFhj9BTJ4srnRdseDjYYwTbHylZ6wgfP11ICxMe00tWmj9du8GXngB4AcDCvtQ6bZS3X8/EBcHdOwodh83TnxcVgYEBcnbM2ECkJwMTJ3KdYrVZfduLm1jgkZY4UqJKv7/zsjg0pTy37xZuxaVPTh1St6vrIz7LS0FBg8W+xUWAteu2c0sREeLj3/9Fbh0idvn77uu2OT/K6H4cjXkXkT4T2Su1vosLGdatOA+nVmDbp5RKPQFjtRz4+EB6PbAuHkT2L7deJp8OZSTw71kNG4MPPmkdNjTp03vwG+raSXsgcl9aIKCgtC7d2+89dZb+Oabb5Cbm4vbt2/j559/RkpKCk6dOoWOHTtij1x7qASnT59GZmYmnnnmGfTq1UuzPfHEExZdTF0iN5d7Q83I0DYdG5s/hDFg5Uqgd2/gwgXpMHl53INkiTAYPZo7t7JS28fi7l3pzx9SFRnfqiDsAxAcLO4ro4tu5W2poOErKXvCF8qmPvC6jZN8gVZVBSQkcPs9euiH7d/ftPiFrTG7dpnXsVz4//EtJT/9BAwdalzQbN3K/TImHZYXE1OmcL+6YXg77dERPiODq1yEhfeNG+Iwq1bJn19ayr1tW9P6wefprl31KzWh4CwsBLp3B8LDuWO+k7xQ0Bw6xHVgBvTzkzuwfz/362rrGem2gpnzqXzFCsN90OTgy7bKSi4OHl2h8dprQFKS8fgWLBAfl5XJC7OYGKBLF7Hbr79y+VPYUnr2LNC6tWWjF3lhbleYDdmyZQuLi4szOXyrVq3YwoULWUlJCauurhZtjqC2tpbNmDGDNW/enPn6+rLu3buzkydPyoa/ffs2GzVqFFMqlSwgIICNGjWKFRUVicJ89913LDo6mjVq1Ii1adOGff/995JxlZSUMADsxo0S9t13jP3xh9hfO92WeNu3j/O/cYMxtdr4OYWF2vS4NDn3r782505Jx8+73X+/frg33uBsPHyYsWXLGKupYey33zi/l1/Wj1MqrY4dGevTRz/dmhrz7N6xQzoNWxMVpbXx9m3j6YaHc/5NmnC/KSnc75df6t/nc+ek739enr4bj274996TziNKZQlTKksk/XS39u0ZU6m4/YQE/bSE+6WljD36qHQ8f/yh3T97Vmz31auc+zffGL7fFy4Y/Uv0ABibNo2xzZu16U+bZvi+SW0rV5qftjD+b7+Vj5unZ0+t26ZNjBUVcfubN2vD8HnIWFw8tbWm23nkCGMFBdqywxgTJzJ26ZI4rQ0b5G1hTGzrl19q3e/cYez0adNtNYRazZVHcuXGb78xVlUlduPzuKF7KUVlpeFnMSJCm791t6++4sI+95zY/d13peOS4s4d7X8slyek7oMwzvJyxioqGGvZUut+4QIX9yuviOMyJW/s3s3YgQNceHPyny58nWkoPZsX84GBgSaHVSqVtk7eLD788EPWsmVLlpOTw8rLy9nUqVNZWFgYKy0tlQyflJTEevfuzW7evMlu3rzJevfuzQYNGqTxP3ToEPP29mYbNmxgarWabdiwgTVq1IgdPXpULy7+z1m0SFuRvPIKY8eOMXb5snxmnDqVsX79uP3Jk7XxFRQYLtRKSkrYhQvatJKSzL9fuvFWVWn3b93iwjz/vLQNW7aIK9S7d/ULjYQExrKzxWlFR+vHlZ/PhTl3jrGXXuLcOneWt3v5cq29cpw5w22mUFvLxXf9uthdWLns38/Yzp2GH+KJE8XXlZzM/b79ttYtJkZ8P4RbbS1j990nX+gOHy720y2M+M1cQVNcrJ8ef3zmjLCwk49n/37t/l9/ie1evVq6IBfC39unn9a6vfQSJxR0qapibMECbZqDBzM2ZIg2/enTxeFNuQ8AY2PHytsnx0cfGY+Xp00bsfu1a/phIiMNxyXMe7Nna899+WXGPvhA3s6zZ7mw4eGmCxqAsfnzuf127bhjoaC5fVsbdvVq7n+Qu3b+2eDjXb2aEyZ8OWOIf/5h7Mkntcdz50o/H0K7hwzRHldVMZaaKm8bY/ovkzwVFdrwV66I0+C3uDjp/2r9ev2wUmlLuVVXc2Uq77dvn3ye4NsLamsZ++EH/Tjvu4+xDh2450L33KlTxcfCvHH4sL5dZWXi8MIyeOJErgy9eVP/GZTCKYLmxo0bJocdMWIE+/XXX21tgslERkayTz/9VHNcVVXFQkND2erVq/XC5ufnMwAsm69xGWPZ2dkMALt48SJjjLGUlBQ2ePBg0XmDBw9mYyVKPv7PAUyrSKS21q218f34o3y4n3/m0pOqtP74Q/tmtH27tgCcP1+bOauqGBs/Xj/eGzfEx61aWXYdUg+vJWFWr+bsv3iRe1AvX+behpKSOP+ffpLOB//9rzYOlUrff98+7lw5O27c4NL9v/8T+333nbYAWb6csZkzxfG+8444/IgR0oWG3P2orpZ2HzeO+500ybT7bY6gadWKq5j445QUriWFP46J0e7v2SMdR3Q0Y7/+KnaTK/xzc7UFsJA5c7Rh/vUv8Tl8qyTP//5n+JqEj+fJk+bl1atXGXvzTcbWrhWneeuWdOVrary6rXQAl6/5fb6F9f77DcdTUaF9I+/eXT8/Mca1/pw6xQlQvq7gRSXAWFGRttKqreX+/8OHubh5ePE1Z444/ubN9fOyofug619Tw/2OHCkdbs4crkLkad6csYAAbZg//xSfJxTPQvd+/bTuQlEulSbfWipsjeIRigpTrle4rVolH5YvO/iXM/6/TU6Wb/GR23hRkZ2t76dWa/fT043HtXFjCSssLBHZzb/wSW28AF+2jPsVtmpPmcL9vv+++J7W1HCi9Px5Jwgac3j55ZdZUFAQGz9+PHv33XdFm70pLi5mANjBgwdF7n369GGvvfaaXvjNmzczb29vPXcvLy/2wz2Z2759ezaff0W5x7x581iHDh30zrOFoBE+pMbCCCutp5+WD5eYKC7MGJMWM7qZ0ZabnHiwdgsL464nNZUTDQ88wFhmpjjMqFFcRcyYfguH1Ma/lRjaKioYUyi4fb7w5d+WhdvQodLnyxUQwhYyazZzBA3ACQY5P74yARjLyjLPjjt3xGJJuH3+ufbZ6drVeFyvvKIVsB98YDw8Y4zt3WvdfRSi66dWM3bwoGXx+vhwv199JXYvLrbsGRTaJyWKdVtF5fLHe+9xcRmqwHS37GzGVqwwbJvuWz3A2KBB4uO1axn7+29uPyODO0+3VWLBAsP/k9AtJYVzk/rUJDyvpkYrkEeM0MZVW8u1sOi+5MnlB7nN0DOtG8+oUVo7zPn/Kyv1Xyj4rXFj8fFTT3HiulkzrZvwhUupLGHNmllfhwm3nj3lniXjgsYmk6z3798f27ZtM/u8kydPIjY2Frm5ucgVTBepcMDyvKp7PZ0CAwNF7kFBQRo/3fABAQF67oGBgZrwKpXK5PgAQKlUArB+bp/OnQGlUnvs6cl1vGzTRuvm78+l8+yzXKdTYXghv/3Gbbx/SQnw7bfS4W/e5EYA2Hh6IjzzjLx9Uuzda9qEc2VlXAc34Yy2Tz0lTmvrVm7r1o3rFGfMjj59jIdZsYIbYQCIR2zpnrdrl3Rct25Ju1+/bt59koPPG6aiUsmny5jW79w58+xr3pz7lTrn9de5jpCHDwMnTxqP98svxTOvSoXfvZvrQA9wIwrlwpkK/5j/61/68YSGWh7/6dNA27bciDDh+YmJ3DNobpzC/yU4WP/8q1fFbnL54733uMkHPTxMt+H997nO5XLh33oL2LJF33/vXs6tQQOuw3pqKueuVHKTOk6dqj3mmTVLOp3YWG6SUqHfn39y/9+JE/K2ZWQA06Zp09m+nSt7c3OBhx/mytzp08Xnl5QAS5aYfn+uXJEPm5OjX1bxdpiTB0pKuM7YcucI3WtruSkUhgzRulVVcfcC0OYNQ+nHxpo3SWBuLmcjX65r4y41OpWDTWYKDue74ZtJZmam5GbOSClLUd67S8U6E3MUFRVp/HTDl0hM0VlcXKwJr1QqTY4PAKqrq9G37yyMGjUdY8ZMh6+vdlVCoRgBuLlBjNG1Kzf3wIUL3MzC//uf2P/gQW4+COEMr8Zo1058nJ0tHmJZWsr1fPfx4R5ooVjgC5n//lc+/oULTbdFikuXOJEgHOL74IOcLbt364fv00c6Ht1p+3/9VT/MDz9w6R04wBXMUkiNfuHvg7nwhYDcJHrZ2cbj+PBDy9I2hKkTfpmynpDcyIcjR7SjoHjatNHO3tuqFXD8OCduXnqJm/14/HitKDKFhx7SdxsyBEhJ4WYRNpf4eG4uIVuuu/vNN9yzLAU/34+5jB1rWrhz57iNX7Ud0J8KQPifmIKxpS+WLTM8Wkvq+TJl9IywSsnP119m4K+/uBce3Xu6ZIl2nxczQs6e5QQWP3Jv/nyx/3//ywk/U0lJ4X6lhrF37256PIa4c8f0xUulyp6GDY3f8+ef58KcOaMd9SjFiBH6bqWl3Mi+Z58FwsJ2oaJi+r1tlnGDZdtuBGzbts2UYG5HZGQkW7hwoea4qqqKNWnSxGAfmj8Ew5H++OMPBoj70AwR9i5jjA0ZMsRgHxph81laGtcH4Z9/xL3lAa6zML9/4ADX9FlezljTplr38nL9a7x7l7Hz5xm7dk37HZzvRc+PIjGnOZBHyo0xbmQC7370qPY7O99JUHfLzub6AQQGmm5Daan0tW7dyliXLmI3Y3EJOypOm2Y4rHA0Tm0t17EyLc3wqAFj24oVjHl4SPvducP9Ll1qefMtY9KdL4WbuZ+cbLVNmMDZx3/S5D9FCDusP/ig4XyoS1UV93lq/nzpz3oA98zwHbrl4hX2JZDajI0kEW6m9mUSps8fX7tmXn6S25Ytk/805OmpvVdSeVmqU7ApaQYFafc7d7ZNnrH0XlRXMzZ6tL77gAHafb4jtHATjoYT/ve6n5ZsvQmv09RPep6e3G95Ofer2xeP33QHEphiixQHDzKWkMB9curShQsr1QeRMe2Ah9JSbby8rBB+4vT1lbbhs88YO3vWRn1oGjduLDretGmTKae5PB9++CFr1aoVO3nyJCsvL2fTp083OsqpT58+mlFOffr0YQMHDtT4//bbb8zb25tt3LiRqdVqtnHjRtaoUSN25MgRvbhM6rEt+EN5oaBbkXt7azOCIYSFEp/5+Apa6kE2lrmFD4UQYX+e48fFfkqlOK7MTK2f3Lfjr79m7IknuOG/69ZJd8Qzxtix+vFevaofTqrgED6A//xjepqm3Ete7PHDtQGu4nnuOW6UBt+/QVgxyG2rVkn3PWCMse+/lz6HzzvOEjRC1Gru/s+eLe5syiPsuG1u8SNMc9IkaT/dkWjGKhFz/ufz582/J5akI7edOycdDz8sXCjqpZASNHyHd0ObsD9V377m282XU/z24YeW3wvGxKPz+G3ZMq7jOcD1y9H137bNsv9+4EDrnw2pfX5bv17fjR89JZW/LU2/TRvz84YU169rpxvg0zh/nju+fVs7oCIjQ9qesjIbjnLy9/cXHQcFBYmO+wm7iLsRtbW17N1332VNmzZlPj4+rHv37iwnJ4cxxtjFixeZn58fy8rK0oS/desWGzlyJFMqlUypVErOQ/Ptt9+y6Oho5u3tzaKjo9mGDRsk0zZX0NTWci0zuvDDpPkOr3IIM15srH7GZ4zrlCocZslvr7zCjSThe+EzxnVuBbjOekKEhfexY2K/DRu4Dpr//rd0+lev6ndAZoy7dmvmL2BMXxTIDeH+4gv5gkWqVUgO3SHZulujRuLwuoWlrruh7Z13tOF37eLcVq/WDm9njNvXPY8fHeQKgsYYtbWMzZsnHg1lDjNmcGmOHy925zvaSqFr74kT+rY//LDx67x5U/v2bGgTzud05Qr3vMjZYul95kfJCOeCMoZUpbVnj/ERVk88od23pJVRdyoKfoDpuXPyHax/+026w6vc/1pcrO+Wm6t9YeOnBxA+Mzy6c0AJN0Odi41tr7zCxf/oo9xcXMbyIr+VlGhb9KzJN6+/rj23e3fz84Yx+HSEtvIvEPzw7Zs3tS+iwrTs0kKjO9eMrj9hHHMFjRxHj3L+TZsaT49Pi1fBpqRrKH1AO+8EDz/yAOCGi0tRW8sNp5TzM+W6zUU4ekY4f48uwlEf/NuJGTMRaNAVRrobP4kWj1wlY0oBZCq6Tc386AhjgsaUytiSzZE0asSlyU9MaQq69hYVcZON8UOTeYxVXrduMfbWW8bvx+7dptnyxx/aES66m3B+HYCxV1+15G6Jkaq0hPMISW0tWoiHMH/9tfn5gx+yzW+nThn+f4TlDe82YYJ0y6puHtT9v3r00P4nQj/d+a7kRJ2wZdec7YsvtHFXVWlfvHTD/fWX/ugmKZ55xvS0t2wRn3vzJvcfGsISQcO33Ol+CBk8mJvMkae0lBNuwrTsImh0W2hI0JiPrQSNUEAYS084l4Sukhci/K4MiCepEpKbq9/SIZwrQ6Irkknw5+/fb9n5UsjN4GkofWu4fl2+4FizRjrNKVPkbTG0mYNwwjC+sNMVNAqF+A142DDLCmepjZ8V2dr7ay6Fhea1SjDG2O+/i23XaYwVYeiai4u1M6XqbhMmaPcNiS1eVPItBEeOSMcnnJMkL8+865VDqtLiZ/2W2woKuHD8TN9btoj95SaMvHJF3IIs9JOa+JJ/znT7/vDnyM0OPH26eCoA3eeJFzS6Q8GFQ7V1zxOG5fu/mbN9/LF8y7Fu2Npa7csswM0PJcXJk/pz+NiiHOGxRNDw0yOYuyCAKXWmSaOcqqqqsG/fPhQVFZkS3ChjxozB2rVraaVtGxAcbP45CgXQtKm8/2efATt2cKtBZ2Zy+1JERXFDxIXw68ysWcOt/WQpr77KDZ22FV5e3BBz3VEI9oIfpiuF1DpXAODra346mZnmhReuB6Y7O8Izz3C/jHH3i0d3xJ01vPyy7eIyh5AQw6PtpNBd+Zox+bC6fsJZLPz8tOux6TJpknZf91kS8t//cmv4fPMNdxwXJx2uUSPt/gMPyMdnLXJ5mKdVK/ExP1KLt++VV7R+np7coqgAN0otO1s7wk84Qkbq/tx3H3fvdde5y87mRj7KrX83b554xBb//0VEiMPppvnuu9LxAVxa/PQMCoV2KgBTee01+Tzw9ddiWxUKoFMnbhTShx8CcutCP/oosH49sHq1tD8/Enb8ePNstYYePbi1oHQXWbUFJgmayMhIxMfHIzQ0FOHh4SgvL8eCBQtw4MABVFRUmJ3onTt38Morr6Bly5aIiYnBG2+8gZ07d+Iuv5Y8YTLmPjSm0Lw5N8eFtzc3D4A50wLxYaUW3jMHe1R8//wjPfRSl59+Mm01W0MYWkhUrmKUG55rCFPm3xEiFCrC1b11/YT/3Ysvmm2WLAUF3K/OdE0ui+4ihabCLx74zDNcJSVXUQkX5jQkaF56Cfj4Y+PpOmAKLwDahTJ5hLYJRRpvDy/W+TzGC57r17ltyRLxM8dPF+Hjo3UzpwKMjdVffd4YjOkvhtuwofi4bVtp/y++4BZ35G1UKLjFYYVTR3TqZJ49QoRD53XTf/NN7cK/cowZI+3+7LPc78CBFptmNgoF8P/+n33iNknQnDlzBkVFRdi5cydeeukl9OvXDx9//DG6d++OgIAAlJu5LO7GjRtRWFiIAwcOYMSIETh27BgGDRqE4OBg9O7dGx+YMulKPUD4tlWfYEw8AZ2j6dcP6NvX+nj4aYtMmY8FMG1yrLIyYPhwy23iK5aiIn0xJCe0WrQQv1FbAy9o3AVr3iI9PbXzisghbOkwN62vvhIfW1NhmouurSdPaveFLWG8gOF/GzTg8nBiInd8331cK3PjxtLPnFCgGRJ89sJYmi1bcr9jx3LXxs/7xNsdH68Na6h1xxi2aM2oqhIfN27MlTmMcfMn1QVMnlhPqVQiISEB06ZNw6ZNm3D58mUUFBRg/fr1SEtLMz9hDw88/vjjeOedd7Bv3z7cvn0bn376Kf766y9Mnz7d7PjqInl5+m/Rrg7/xmmohaK+wBcW6elid6m36MWLgZEjjcfp58fN3Gwp/BtvYKC+HUJBI5xQS6GwfgJEHr6CiI62TXyuxGuviY+rqrQVNwDMmaN/jvCem1th858IefbsMfxZzJbwn1T5oj8oSOsnzFd8fvPyAt55B1i0yLyWSGE5Yo9PFMbQbaHRhRc0PPwcs1K29u9vuR22EHO6ZfKAAdbH6WpYdZtatmyJli1bYtiwYRadX15ejr179+KXX37BL7/8goKCAnTv3h0JCQnWmFVnCAvjBM3ly8bD8lO4Oxv+U0JIiFPNcCkUCm4mYr5PkZSg4adyN8STT1pvi6F+OsLmdN1PCrbC0xO4dq1utj6+9hrwySdcvwUp3nkH+Pe/uVmoeTFi6icnU3DU5yaAyyvnzgGtW3PidMwY6U9ifHnQtq20oDOGsBJ2ZAtNdDS3PIJQmEi9Z0+cyC0jwMP/n8Lz+vbl+iFaI8j4cz//3PI4dPPH4sWWx+WqOOU9eu7cuejVqxeaNm2K9957D4GBgVi6dClu376Nbdu24TXdVx3CIL16cVNNuwL+/txbor+/sy1xLYR9nSyteKzt1wMAQ4dq3xSFdgQHi5caKCyUj8OUvhxy8B3S7dH3y94Ya/1o1YrrdP777/JhWrQQT/duS0Hj6GfuwQe5/3P8ePmWDD6PWZrnhec5soVm0SLgxg2uxaVZM85t6FD9cCNGiPMF/x/YWlzevs39WjIIhEfXJnfpx2YOTvgqCcyYMQMxMTFYvXo1kpKS4G2sRxNhEHNHuhCOR1hZWVrY2eIzXni49NoqSqW+wJFD2HnYXHS/47sDzZtz6wsJP6vIYUqncyG2EjTWVHS2wMODE8q6I8OsrdidJWi8vIAmTbh9fjFPU56/bt24RSSF2OIzIL9EoC1bTh3ZouconNJCs3v3bvTv3x/z589HSEgIEhIS8P777+OYcIVBAgD1RakrCAsPSwoSqWGXloyKEiIsaHUL3SeeAPbt495SddF9Gzfnetz5rdAeFYCwL5E1guaFF8THli5caQ1bt1r2WckQwnvurLKQt8EUQaVQADExYreePa23QTh6ipDHKVnkiSeewPz583H06FFcvHgREyZMwN+CjxjMAAAaWElEQVR//43hw4cjJCQEw60ZxlHHkBuuR7gXup1szeXhh01zMwdh64Bwn6dHD+1bqhBdQSMcpmsMU1o5XA17drQV3g9rBI2pK6C7M1J51BHw/7+lgmraNOvzED99wPXr1sVT13H6+39ISAgGDx6MUaNGYeTIkWjUqBE2btzobLNchs8/N32pd8J1ERZolggaqU81pgzzNoShFhpD6Fa8xkaCCHnkEdPDugqOqkitETTCfjl1CakRU46Gz9/OHOhw//3cL/VNNIzTBM2pU6fwySefICkpCUFBQejTpw92796Nf//738ikTiEa/Py0QwEJ90VYKZojaPh5LXQFze+/a2eNtRRhBWFNpW1I0Ag7GgPcBHHuhqOGQttiFI+jbHUUwmfFWV0tGzXiWkiaN3dO+oD2M5Y5Lw/1Ead0Cm7evDlu3LiBVq1aoU+fPli5ciV69+6NYGf3bCMIO2FpRfPmm8ClS/qTpul2vrSE3r25KciffFJrX+/exqfM52cX5TFUET/+uPjYHfsAuJOg4XGV+2zLTsHOxBlz4Ajh70NdE6y2ximC5u2330afPn0QXRdn1yIICYRzCZlbSJu7BpGpCKcg5wvKXbuMn/fgg+K1YwxVxIMHW2yey2DvT07jxnFT5zu70rQH1lbAriJonA0JGtNwiqCZJOhF+Pfff+P48eMoLS0VhRk7dqyjzSIIuyFsKnbFQtqcgrJFC7GgqevN4PauRPjOprYQNEoltzSGqwwmWLbMuvNd8Vlxd7p25da1q4s4RdDwLF26FJMmTUJwcDD8BGNQFQoFCRqiTjFuHLdqtbmLSToKczo88n1v2rfnVjWW6+PlyBV87Ym9W2j49ZwsHUUj/O8aNLBuaQzCMfTvL16R3ZE8+CA3W3VdxKmC5r333sO3336LoVJTMBJEHcLT0zbzUdiD/fvNGzH19tvcGjZRUZygkVt2wxb9fFwBRzXz18VPTl26AL/9Zvn5dbWFZtIkywSNLfJiXb2ngJOHbZeUlJCYIeodrlagREaaN9Osry/w2WfahfmEn5yE+xERNjHP6dhL0PCT4fEtXJa20LhafhISFWXd+a58bdZg6dp7tsiLdbkfjlMFTf/+/bFv3z5nmkAQDuWZZ4D4eGdbYR18aw5fMDZsqF3o7s8/uUkEi4qsW13Yldi5E9iwwfbx8gKGHw5Ms4LrU1cFjbMmCazrOPURatKkCQYPHozx48djxowZos3ebNiwAW3atIGPjw8efvhho5P5McYwc+ZMhIWFwc/PDz169MCpU6c0/ocPH8bAgQPRrFkzKJVKxMTEYOXKlfa+DMLN+Oor91+JXPetW6EA7ruP22/dmhM47rzEgS5dugDDhtk+XlsJmLpa6QN179patOB+LRU0dbl1xRY4tQ/NH3/8gfbt2yMvLw95eXkad4Wdc/Hhw4cxevRorFu3DoMGDcKWLVuQnJyM/fv3o5PuhB/3SE9Px4oVK7Bz5060bt0as2fPRmJiIs6ePQt/f3/cunULw4YNwxdffIEmTZpg7969eOqppxAUFITBdWHsKkEYgO/YSpgOL2jqciVlq3loDh2y3hZX4KefgNhY5wqauiYShThV0DhrRuClS5eiX79+GHbvtWvYsGFYu3YtlixZgi+++ELynIyMDKSlpSHm3pSNc+bMweeff45NmzZhzJgxSEpKEoV/4oknEB8fj8zMTBI0RJ1GoSBBYwn14ROTrSrPxo1tE4+z8fXlfhUKyzpMW3s/N28G/u//rIvDlakHj5Q+2dnZ6Ny5s8gtLi4OJ06ckAxfUlKC/Px80Tmenp7o0KGD7DkqlQqHDx9Gh7oy1IMgDECCxnxI0Jh+fl1pVWjdmlu2xNcXmDzZ/POtzTNPPSW94GxdweEtNGFhYbhy5YrRcOHh4SgoKDAr7pSUFKxatUrWv2fPnti7dy9UKhUCdT7yBwUFQaVSSZ7Hu5t6jlqtxogRI9CmTRuMHj1a1h6lUqk3oaC9cFQ6hPthat7gOwPzWb66mnMrLQUqK7l9qUdIOCRc5hGrdyiVXOWkUnFiUO7emRKPn59976s1ZUeDBpZfGwBUVHDnV1TUnbwTFcVdi1pt3rMxbx7Qp49r3QdH1iumpOVwQVNUVISVK1eCGfkYWFJSYnbcixYtQnp6uqx/w3tjSpVKJYqLi/XsUspMxsG7S53Tgu/ldY/y8nIMHToUarUaP/74IzwNzAtfXV2NWbNmwcvLCwAQHx+PhIQE2fAE4YooFNrVgAnTsVUfGlduvbCVba58jZbC/++NGpk2bYJggv16w65du7Bnzx4AXEOBMRwuaJo2bYrZs2cbDdfcgqVN/f394W/C+urt27fH0aNHRW7Hjh2T/TwUEBCAyMhIHD16FF26dAHAiZHs7GyMGTNGE66oqAj9+/dHcHAwNm/ejEaNGhm0o7y8HOnp6bJCyh44Mi3CvTCWN/g3Qz6Ypyfn1rgxN69GcbF0xSN8o6Tsx6FSaVu4GjTgji25NyoVN3OzI+6rJWVHTY3l1wZoW5/8/Ope3uGfn/x8Tty68/XZq14ZOnSoZq46lUqF5cuXGwzvcEGTn5/v6CT1mDBhAnr16oVNmzZhwIAB2Lp1K7Zv346srCzZc1JTU5Geno74+HhERUVh7ty5aNiwIYYMGQIAuHbtGp588kk8/PDDWLt2raY1iCDqOnWtn4OjoFFOpp9fl/sbBQU524K6g1NHOTmLxx9/HGvWrMG0adMwcuRIREZGYu3atYiLi9OEadu2LZKTkzF9+nQAQFpaGkpLS5GQkACVSoVOnTphx44dmhahZcuW4eTJk8jLy0OQIId2794d27dvd+wFEoQDqMsVsSMQLF9HyEBimTCHeiloAGD48OEYPny4rP/p06dFxwqFArNnz5b9XDZz5kzMnDnTpjYShDtAlY1l8Gs39e1r3VBaV77/trKtLrbQ0AuB7amD2YQgCML14Svp++8HDhywPB4D4w6cDg3blocEje0hQUMQhFXUxcrGEdiq1aFvX9vEYw+aNbPu/Lrch+aRR5xtQd3DhbU9QRBE3aRXL+De4A2rKCsDvL2tj8dezJgBPPus9fHURdHcoQO10tgaEjQEQZjE++/TiAxbYatVX1y9Y7GXF/DQQ5afz1f4dbGFhrA9JGgIgjCJKVOk3evi2zPhGvCChvIYYQqkewmCsAhqLifsDQkawhxI0BAEYRVU2RD2hj45EaZA2YQgCIJwSUgsE+ZAgoYgCKswtdKhYaqEpZCwIUyBBA1BEHanb1/g8GFnW0G4KyRoCFOgUU4EQdiVZcuAPn2Ae8ueEYTZkKAhTIEEDUEQVmGssnnhBcfYQRBE/YY+OREEYRE0bJtwFNRCQ5gCCRqCIKyCKhvC3lAeI0yBBA1BEATh0pCgIUyBBA1BEFZBlQ1BEK4ACRqCIAjCJSGxTJgDCRqCIAjCJaGO54Q5kKAhCMIq6C2aIAhXoN4Kmg0bNqBNmzbw8fHBww8/jI0bNxoMzxjDzJkzERYWBj8/P/To0QOnTp2SDHv8+HE0bNgQ3bp1s4fpBOES0NszQRCuRL0UNIcPH8bo0aMxb948qFQqzJ07F8nJyTh27JjsOenp6VixYgV27tyJwsJCdO3aFYmJiSgrKxOFq6ioQEpKCnr27GnvyzCbXbt2OdsEwkWxJm9QC03dx1llB+Ut18eV6pV6KWiWLl2Kfv36YdiwYWjYsCGGDRuGvn37YsmSJbLnZGRkIC0tDTExMfDx8cGcOXOgVquxadMmUbi3334bvXv3dsnWmT179jjbBMJFobxBGILyByGHK+WNeilosrOz0blzZ5FbXFwcTpw4IRm+pKQE+fn5onM8PT3RoUMH0TlZWVnYunUr5s+fbx/DCcIFobdogiBcgTq1llNKSgpWrVol69+zZ0/s3bsXKpUKgYGBIr+goCCoVCrJ83h3Q+eUlZVh7NixWLFiBXx9fY3ayhiDUqnE1atXUVpaajS8tdy5cwfV1dW4fPmy3dMi3AtL80ZZGaBUApcvk6ipyziz7Lh5k8tjV64Ad+44PHnCCI7MG3z3Dmag816dEjSLFi1Cenq6rH/Dhg0BAEqlEsXFxSK/oqIiKJVKyfN4d6lzWrRoAQBIS0tDUlISevToYZKtpaWlUKlUaNOmjUnhbcWyZcscmh7hPliaN1q1srEhhEvizLKjbVunJU2YgCPzRmlpKQICAiT96pSg8ff3h7+/v9Fw7du3x9GjR0Vux44dQ4cOHSTDBwQEIDIyEkePHkWXLl0AANXV1cjOzsaYMWMAADt27EBxcTHWr18PACgvL0dVVRVCQ0Nx6NAhtG7dWhRnWFgYLl26hMaNG0NBr7cEQRAEIQtjDKWlpQgLC5MNU6cEjalMmDABvXr1wqZNmzBgwABs3boV27dvR1ZWluw5qampSE9PR3x8PKKiojB37lw0bNgQQ4YMAQAcOnQI1dXVmvAff/wxfv31V2zcuBHNmjXTi8/DwwMtW7a0/cURBEEQRB1ErmWGp14Kmscffxxr1qzBtGnTMHLkSERGRmLt2rWIi4vThGnbti2Sk5Mxffp0ANwnpdLSUiQkJEClUqFTp07YsWOHpkVIV7QolUp4eXmRaCEIgiAIB6BghnrYEARBEARBuAH1ctg2QRAEQRB1CxI0boChZRfWrVun6QzNb56enoiNjTUYp0KhgI+Pj+i8kydPavz/85//oEGDBiL/kSNH2vU6CcswtizHunXrEBMTA6VSiRYtWmDy5MmorKw0GOfevXvx2GOPwdfXF/fff7/epJOVlZWYOHEiQkND0bhxYwwYMACXLl2yy/URluOMvJGSkoKGDRuKyo4pU6bY5foI67B1/rh8+TKeeuopREREQKFQ4PPPP9cLY9eygxEuz4cffshatmzJcnJyWHl5OZs6dSoLCwtjpaWlemHVajVr2rQp++ijjwzGCYD98ssvsv4zZ85kXbt2tdp2wv4Yyh/Z2dlMoVCwr7/+mtXU1LD8/HzWpk0bNm3aNNn48vPzma+vL1u0aBGrrKxke/fuZUqlkm3cuFETJjU1lcXExLD8/HxWUlLCxowZw9q3b89qamocccmEiTgjbzz33HMsOTnZEZdHWImt88eVK1fYokWL2K+//spatmzJli9frhfGnmUHCRo3IDIykn366aea46qqKhYaGspWr16tF/arr75iPj4+7NatWwbjJEFTdzCUPzZu3MgCAwNF4d944w3Wt29f2fj+85//sPbt24vcJk+ezOLj4xljjN29e5f5+PiwzZs3a/xv3rzJPD09WVZWli0uibARjs4bjJGgcSdsnT+ERERE6Akae5cd9MnJxTF12QWejIwMjBgxAsHBwRq39evX681yDACjR49GSEgIHnvsMSxfvlzP/8SJE2jSpAkiIiIwatQoXLhwwTYXRdgMY/kjMTERDz74INatW4eamhrk5eXhxx9/xNChQzXh33//fbRr105zbGxpkLNnz+Lu3buiMKGhobj//vtllw8hHI8z8gbP1q1bERISgqioKLz44ou4efOmna6SsBR75A9j2LvsIEHj4piy7ALPqVOnsH//fqSmporcR40apTfL8a5du3DhwgVcvXoVc+fOxVtvvSX6Fv7000/jzz//xI0bN3Dw4EEoFAokJCTorS5OOBdj+cPX1xfjx4/HpEmT4O3tjdatW+Pxxx/H2LFjNWGnTp2KnJwcUZyG8ps5eZJwHs7IGwDw8ssv46+//kJhYSF27tyJvLw8DBo0yOCU9YTjsUf+sDZNayFB4+IYWnZBd6mGjIwMdOrUSTSfjhy9e/eGj48PvLy8kJSUhFdffRVr1qzR+D/66KOajl0tWrTAihUrcPnyZRw8eND6iyJshrH8sWrVKkyZMgU//PAD1Go1rly5glu3biE5OdlgnIbymzl5knAezsgbANCxY0c0a9YMCoUCrVu3xvLly3Ho0CGcP3/eZtdGWI898oe1aVoLCRoXR7jsAg+/7IJwqYbS0lKsXbtWr3XGVDw8PAy+QSkUCigUCnrLcjGM5Y9jx46hR48e6NGjBzw8PNC8eXO88MIL+OGHH2TjNLY0SHR0NHx8fERhCgsLkZ+fL7t8COF4nJE3pPDw4KoZKjtcC3vkD2PYveywuhcOYXc+/PBD1qpVK3by5ElWXl7Opk+frjfKafHixSw4OJiVl5cbje/48ePs2LFjrLKyklVVVbGdO3eyoKAgtnDhQk2Yb775ht28eZMxxti1a9fYmDFjWEREBFOpVLa/QMIqDOWPb775hgUGBrIDBw6w2tpaduPGDTZw4ECDHb7z8/OZj48Py8jIYJWVlSwrK4sFBASw77//XhMmNTWVxcbGsosXLzKVSsWeffZZFhsbS6OcXAxH5427d++y7777jhUXFzPGGLtw4QJLTExkHTt2pLzhgtg6fzDG5YG7d++y8PBwtmTJEnb37l2mVqs1/vYsO0jQuAG1tbXs3XffZU2bNmU+Pj6se/fuLCcnRxTm0UcfZa+//rrk+WvXrmV+fn6a4y1btrA2bdowPz8/FhAQwNq1a8eWLFkiOmfgwIEsNDSU+fj4sLCwMPbMM8+w8+fP2/7iCKsxlj8+/fRTFh0dzRo3bszuu+8+9vTTT7P8/HyN/7x589gjjzwiijMzM5O1b9+eNWrUiEVERLDFixeL/CsqKlhqaioLDg5mfn5+LCkpiRUUFNj3QgmzcXTeuHPnDuvWrRsLCgpivr6+LDw8nL3wwgvs6tWr9r9YwmzskT8A6G3PPfecxt+eZQctfUAQBEEQhNtDfWgIgiAIgnB7SNAQBEEQBOH2kKAhCIIgCMLtIUFDEARBEITbQ4KGIAiCIAi3hwQNQRAEQRBuDwkagiAIgrAxU6dORUxMDJRKJZo3b46RI0fi0qVLojAFBQUYMGAAGjdujNDQUEyaNAlqtVrjv3v3bvTu3RshISFQKBTIzc3VS+e3335D9+7dERgYiKZNm+KNN95AVVWVQdsuXLiAkSNHIiwsDP7+/ggLC0NSUhKuXr1qm4uX4euvv0b37t2hVCqhUChQXV0tG/b48eNo2LAhunXrZnL8JGgIgiAIwsYoFAp8+eWXKCwsxJkzZ6BQKDBw4ECNf21tLQYMGIDg4GBcvnwZx48fR1ZWFt58801NGD8/Pzz77LNYvXq1ZBoFBQVITExEcnIyCgsLcfDgQWzfvh1TpkwxaFtSUhIaN26MU6dOoaysDCdOnMCIESOgUChsc/EyBAUFITU1FZ9++qnBcBUVFUhJSUHPnj3NS8Am0/MRBEEQBCHLiRMnGAB2+/Ztxhhje/fuZZ6enpolZhhjbPPmzczX15fdvXtXdO6FCxcYAL3Z2jMyMlh0dLTIbfny5czPz49VVFRI2lFYWMgAsOPHjxu099ChQ6xnz54sODiYhYeHs3feeYdVVVVp/AGwBQsWsE6dOjE/Pz8WFxfHjh49avxGMG62aQCi+IS8/vrr7NVXX2UzZ840utSCEGqhIQiCIAg78/PPPyMiIgJBQUEAgOzsbDzwwAMIDQ3VhImLi0N5eTnOnTtnUpyMW75I5FZbW4s7d+7IxhESEoKYmBhMmDABK1euRE5ODmpra0Vhzp49i969e+PFF1/E9evXkZWVhS1btuCDDz4QhcvIyMCqVatw69YtJCUloV+/figpKTHJdjmysrKwdetWzJ8/3+xzSdAQBEEQhB3ZtWsXZs2ahaVLl2rcVCoVAgMDReF4saNSqUyKNzExEQUFBVi0aBHUajXOnTun+ZxjKI7MzEz069cPS5YsQefOnREaGoq0tDRUVlYCABYvXoyBAwfimWeegaenJyIiIvDWW29h5cqVonheffVVPPLII/D29saMGTPg6emJLVu2mGS7FGVlZRg7diyWL18OX19fs88nQUMQBEEQdmLr1q14+umnsXbtWvTt21fjrlQqUVxcLApbVFSk8TOFqKgobN26FV999RWaN2+OoUOHYvz48QCA0NBQFBQUwN/fX7OtW7cOANdKM3v2bBw5cgQlJSVYsWIFli9fjvfeew8AcP78eWzatAmBgYGa7aWXXsK1a9dE6d9///2afQ8PD0RERODSpUuy6RojLS0NSUlJ6NGjh0nhdfG06CyCIAiCIAyybt06pKam4ttvv0ViYqLIr3379rhw4QJu3bqFkJAQAMCxY8fg6+uLhx56yOQ0evfujd69e2uOFy5ciFatWuGhhx6CQqFAWVmZwfO9vb0xePBgJCQk4PfffwcANGvWDKNGjcKKFSsMnpufn6/Zr62tRUFBAVq2bInw8HCj6UqxY8cOFBcXY/369QCA8vJyVFVVITQ0FIcOHULr1q0Nnk8tNARBEARhYxYtWoRJkyZh69atemIGALp37442bdrgjTfeQGlpKQoKCjBjxgyMGzcOjRo1AsCJhIqKCs2nILVajYqKCtTU1GjiOXLkCCorK6FWq/Hjjz9i7ty5+Oijj2RHLBUVFWHq1KnIyclBZWUlampqsHv3bmRmZmpaRlJTU7FhwwZ89913UKvVqKmpQW5uLnbs2CGKa+HChThz5gzUajXmzZsHtVqNQYMGyd6TmpoaVFRUaIamV1ZWoqKiQtOH59ChQzh16hSys7ORnZ2NF198ER06dEB2djYiIyON33STuw8TBEEQBGESAJinpyfz8/MTbVlZWZow+fn5LCkpifn5+bHg4GA2ceJE0egkfjSQ7rZy5UpNmIEDB7KAgADm6+vLOnXqxDZv3mzQrrKyMjZu3Dj20EMPMX9/fxYQEMDatm3L3n//fVZbW6sJd+jQIdanTx8WGhrKAgICWGxsLFu6dKno+hYsWMA6duzI/Pz8WKdOndjhw4cNpr1y5UrJ68nMzJQMb+4oJ8U9wwiCIAiCIExCoVDgl19+QUJCgrNN0UCfnAiCIAiCcHtI0BAEQRAE4fbQKCeCIAiCIMzCFXurUAsNQRAEQRBuDwkagiAIgiDcHhI0BEEQBEG4PSRoCIIgCIJwe0jQEARBEATh9pCgIQiCIAjC7SFBQxAEQRCE2/P/AcGge0EoWlkjAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(8, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.18, right=0.82, bottom=0.05, top=0.95)\n", + "plot_line(axs[0], b_dmpa)\n", + "plot_line(axs[0], pyrf.norm(b_dmpa), color=\"k\")\n", + "axs[0].set_ylim([-16, 19])\n", + "axs[0].set_ylabel(\"$B_{DMPA}$\" + \"\\n\" + \"[nT]\")\n", + "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\"], **legend_options)\n", + "\n", + "\n", + "labels = []\n", + "for i, n in enumerate(n_i_mms):\n", + " plot_line(axs[1], n)\n", + " labels.append(\"MMS{:d}\".format(i + 1))\n", + "\n", + "axs[1].set_ylabel(\"$n_i$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", + "axs[1].legend(labels, **legend_options)\n", + "\n", + "plot_line(axs[2], j_dmpa)\n", + "axs[2].set_ylabel(\"$J_{DMPA}$\" + \"\\n\" + \"[nA m$^{-2}]$\")\n", + "axs[2].legend([\"$J_{x}$\", \"$J_{y}$\", \"$J_{z}$\"], **legend_options)\n", + "\n", + "plot_line(axs[3], j_fac)\n", + "axs[3].set_ylabel(\"$J_{FAC}$\" + \"\\n\" + \"[nA m$^{-2}]$\")\n", + "axs[3].legend([\"$J_{\\\\perp 1}$\", \"$J_{\\\\perp 2}$\", \"$J_{||}$\"], **legend_options)\n", + "\n", + "plot_line(axs[4], div_over_curl)\n", + "axs[4].set_ylabel(\"$\\\\frac{|\\\\nabla . B|}{|\\\\nabla \\\\times B|}$\")\n", + "\n", + "plot_line(axs[5], e_dsl)\n", + "axs[5].set_ylabel(\"$E_{DSL}$\" + \"\\n\" + \"[mV m$^{-1}$\")\n", + "axs[5].legend([\"$E_{x}$\", \"$E_{y}$\", \"$E_{z}$\"], **legend_options)\n", + "\n", + "plot_line(axs[6], jxb_dmpa)\n", + "axs[6].set_ylabel(\"$J \\\\times B/n_{e} q_{e}$\" + \"\\n\" + \"[mV m$^{-1}$]\")\n", + "\n", + "plot_line(axs[7], e_dot_j)\n", + "axs[7].set_ylabel(\"$E . J$\" + \"\\n\" + \"[nW m$^{-3}$]\")\n", + "axs[7].set_ylim([-0.04, 0.04])\n", + "axs[0].set_title(\"MMS - Current density and fields\")\n", + "\n", + "f.align_ylabels(axs)\n", + "axs[-1].set_xlim(tint)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/01_mms/example_mms_ebfields.ipynb b/docs/examples/01_mms/example_mms_ebfields.ipynb new file mode 100644 index 00000000..95ad1da8 --- /dev/null +++ b/docs/examples/01_mms/example_mms_ebfields.ipynb @@ -0,0 +1,448 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# EB Fields\n", + "author: Louis Richard\\\n", + "Plots E and B time series and of burst mode electric field in GSE coordinates and field-aligned coordinates. Plots spectrograms of paralleland perpendicular electric fields and fluctuating magnetic field." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import time\n", + "\n", + "import numpy as np\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from scipy import constants\n", + "\n", + "from pyrfu import mms, pyrf\n", + "from pyrfu.plot import plot_line, plot_spectr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define time interval, data path and spacecraft index" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mms.db_init(\"/Volumes/mms\")\n", + "\n", + "tint = [\"2015-10-30T05:15:20.000\", \"2015-10-30T05:16:20.000\"]\n", + "ic = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load FGM data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:07:45] INFO: Loading mms1_fgm_b_gse_brst_l2...\n" + ] + } + ], + "source": [ + "b_xyz = mms.get_data(\"b_gse_fgm_brst_l2\", tint, ic)\n", + "b_mag = pyrf.norm(b_xyz)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load EDP data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:07:45] INFO: Loading mms1_edp_dce_gse_brst_l2...\n" + ] + } + ], + "source": [ + "e_xyz = mms.get_data(\"e_gse_edp_brst_l2\", tint, ic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load SCM data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:07:47] INFO: Loading mms1_scm_acb_gse_scb_brst_l2...\n" + ] + } + ], + "source": [ + "b_scm = mms.get_data(\"b_gse_scm_brst_l2\", tint, ic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load FPI data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:07:49] INFO: Loading mms1_des_numberdensity_brst...\n", + "[08-Jun-23 17:07:49] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:07:50] INFO: Loading mms1_dis_numberdensity_brst...\n", + "[08-Jun-23 17:07:50] INFO: Loading mms1_dis_temptensor_gse_brst...\n", + "[08-Jun-23 17:07:50] INFO: Loading mms1_des_temptensor_gse_brst...\n", + "[08-Jun-23 17:07:50] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n" + ] + } + ], + "source": [ + "n_e = mms.get_data(\"ne_fpi_brst_l2\", tint, ic)\n", + "n_i = mms.get_data(\"ni_fpi_brst_l2\", tint, ic)\n", + "\n", + "t_gse_i = mms.get_data(\"ti_gse_fpi_brst_l2\", tint, ic)\n", + "t_gse_e = mms.get_data(\"te_gse_fpi_brst_l2\", tint, ic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute low and high frequency electric field and magnetic field fluctuations in FAC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rotate E and B into field-aligned coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "e_fac = pyrf.convert_fac(e_xyz, b_xyz, [1, 0, 0])\n", + "b_fac = pyrf.convert_fac(b_scm, b_xyz, [1, 0, 0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Bandpass filter E and B waveforms" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "fmin, fmax = [0.5, 1000] # Hz\n", + "e_fac_hf = pyrf.filt(e_fac, fmin, 0, 3)\n", + "e_fac_lf = pyrf.filt(e_fac, 0, fmin, 3)\n", + "b_fac_hf = pyrf.filt(b_fac, fmin, 0, 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wavelet transform of the electric field and the magnetic field fluctuations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute wavelet transforms" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "nf = 100\n", + "e_cwt = pyrf.wavelet(e_fac, f=[fmin, fmax], nf=nf)\n", + "b_cwt = pyrf.wavelet(b_fac, f=[fmin, fmax], nf=nf)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Compress wavelet transform" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "e_cwt_t, e_cwt_perp1, e_cwt_perp2, e_cwt_para = pyrf.compress_cwt(e_cwt, 100)\n", + "b_cwt_t, b_cwt_perp1, b_cwt_perp2, b_cwt_para = pyrf.compress_cwt(b_cwt, 100)\n", + "\n", + "options = dict(coords=[e_cwt_t, e_cwt.frequency], dims=[\"time\", \"frequency\"])\n", + "e_perp_cwt = xr.DataArray(e_cwt_perp1 + e_cwt_perp2, **options)\n", + "e_para_cwt = xr.DataArray(e_cwt_para, **options)\n", + "\n", + "options = dict(coords=[b_cwt_t, b_cwt.frequency], dims=[\"time\", \"frequency\"])\n", + "b_tota_cwt = xr.DataArray(b_cwt_perp1 + b_cwt_perp2 + b_cwt_para, **options)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute characteristic frequencies" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:08:34] INFO: Using averages in resample\n", + "[08-Jun-23 17:08:34] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "t_fac_i = mms.rotate_tensor(t_gse_i, \"fac\", b_xyz, \"pp\")\n", + "t_i = pyrf.trace(t_fac_i) / 3\n", + "\n", + "t_fac_e = mms.rotate_tensor(t_gse_e, \"fac\", b_xyz, \"pp\")\n", + "t_e = pyrf.trace(t_fac_e) / 3\n", + "\n", + "pparams = pyrf.plasma_calc(b_xyz, t_i, t_e, n_i, n_e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot figure" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "legend_options = dict(ncol=4, handlelength=1.5, loc=\"upper right\", frameon=False)\n", + "spectr_options = dict(yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:09:10] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:10] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:10] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:10] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:11] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:11] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:11] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:11] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:11] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:11] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:13] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:13] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:13] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:13] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:14] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:14] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:14] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:14] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:14] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 17:09:14] INFO: Substituting symbol \\perp from STIXGeneral\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(7, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "plot_line(axs[0], b_xyz)\n", + "plot_line(axs[0], b_mag, color=\"k\")\n", + "axs[0].set_ylabel(\"$B$ [nT]\")\n", + "labels = [\"$B_x$\", \"$B_y$\", \"$B_z$\", \"$|B|$\"]\n", + "axs[0].legend(labels, **legend_options)\n", + "\n", + "plot_line(axs[1], e_fac_lf)\n", + "axs[1].set_ylabel(\"$E$ [mV m$^{-1}$]\")\n", + "labels = [\"$E_{\\\\perp 1}$\", \"$E_{\\\\perp 2}$\", \"$E_{||}$\"]\n", + "axs[1].legend(labels, **legend_options)\n", + "\n", + "plot_line(axs[2], e_fac_hf)\n", + "axs[2].set_ylabel(\"$E$ [mV m$^{-1}$]\")\n", + "labels = [\"$E_{\\\\perp 1}$\", \"$E_{\\\\perp 2}$\", \"$E_{||}$\"]\n", + "axs[2].legend(labels, **legend_options)\n", + "\n", + "axs[3], caxs3 = plot_spectr(axs[3], e_perp_cwt, **spectr_options)\n", + "axs[3].set_ylabel(\"$f$ [Hz]\")\n", + "caxs3.set_ylabel(\"$E_{\\\\perp}^2$\" + \"\\n\" + \"[mV$^{2}$ m$^{-2}$ Hz$^{-1}$]\")\n", + "plot_line(axs[3], pparams.f_lh, color=\"k\")\n", + "plot_line(axs[3], pparams.f_ce, color=\"tab:blue\")\n", + "plot_line(axs[3], pparams.f_pp, color=\"tab:red\")\n", + "labels = [\"$f_{lh}$\", \"$f_{ce}$\", \"$f_{pp}$\"]\n", + "axs[3].legend(labels, **legend_options)\n", + "axs[3].set_yticks([1e0, 1e1, 1e2, 1e3])\n", + "\n", + "axs[4], caxs4 = plot_spectr(axs[4], e_para_cwt, **spectr_options)\n", + "axs[4].set_ylabel(\"$f$ [Hz]\")\n", + "caxs4.set_ylabel(\"$E_{||}^2$\" + \"\\n\" + \"[mV$^{2}$ m$^{-2}$ Hz$^{-1}$]\")\n", + "plot_line(axs[4], pparams.f_lh, color=\"k\")\n", + "plot_line(axs[4], pparams.f_ce, color=\"tab:blue\")\n", + "plot_line(axs[4], pparams.f_pp, color=\"tab:red\")\n", + "labels = [\"$f_{lh}$\", \"$f_{ce}$\", \"$f_{pp}$\"]\n", + "axs[4].legend(labels, **legend_options)\n", + "axs[4].set_yticks([1e0, 1e1, 1e2, 1e3])\n", + "\n", + "plot_line(axs[5], b_fac_hf)\n", + "axs[5].set_ylabel(\"$\\\\delta B$ [nT]\")\n", + "labels = [\"$B_{\\\\perp 1}$\", \"$B_{\\\\perp 2}$\", \"$B_{||}$\"]\n", + "axs[5].legend(labels, **legend_options)\n", + "\n", + "axs[6], caxs6 = plot_spectr(axs[6], b_tota_cwt, **spectr_options)\n", + "axs[6].set_ylabel(\"$f$ [Hz]\")\n", + "caxs6.set_ylabel(\"$B^2$\" + \"\\n\" + \"[nT$^{2}$ Hz$^{-1}$]\")\n", + "plot_line(axs[6], pparams.f_lh, color=\"k\")\n", + "plot_line(axs[6], pparams.f_ce, color=\"tab:blue\")\n", + "plot_line(axs[6], pparams.f_pp, color=\"tab:red\")\n", + "labels = [\"$f_{lh}$\", \"$f_{ce}$\", \"$f_{pp}$\"]\n", + "axs[6].legend(labels, **legend_options)\n", + "axs[6].set_yticks([1e0, 1e1, 1e2, 1e3])\n", + "\n", + "f.align_ylabels(axs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/01_mms/example_mms_edr_signatures.ipynb b/docs/examples/01_mms/example_mms_edr_signatures.ipynb new file mode 100644 index 00000000..6d5a96da --- /dev/null +++ b/docs/examples/01_mms/example_mms_edr_signatures.ipynb @@ -0,0 +1,568 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# EDR Signatures\n", + "Author: Louis Richard\\\n", + "A routine to compute various parameters used to identify electron diffusion regions for the four MMS spacecraft. \n", + "\n", + "Quantities calculated so far are:\n", + "- sqrt(Q) : Based on Swisdak, GRL ,2016. Values around 0.1 indicate electron agyrotropies. Computed based on the off-diagonal terms in the pressure tensor for Pe_perp1 = Pe_perp2.\n", + "\n", + "- Dng: Based on Aunai et al., 2013; Computed based on the off-diagonal terms in the pressure tensor for Pe_perp1 = Pe_perp2. Similar to sqrt(Q) but with different normalization. Calculated but not plotted. \n", + "\n", + "- AG^(1/3): Based on Che et al., POP, 2018. Constructed from determinant of field-aligned rotation of the electron pressure tensor (Pe_perp1 = Pe_perp2). \n", + "\n", + "- A phi_e/2 = abs(Perp1-Perp2)/(Perp1+Perp2): This is a measure of electron agyrotropy. Values of O(1) are expected for EDRs. We transform the pressure tensor into field-aligned coordinates such that the difference in Pe_perp1 and Pe_perp2 is maximal. This corresponds to P23 being zero. (Note that this definition of agyrotropy neglects the off-diagonal pressure terms P12 and P13, therefore it doesn't capture all agyrotropies.)\n", + "\n", + "- A n_e = T_parallel/T_perp: Values much larger than 1 are expected. Large T_parallel/T_perp are a feature of the ion diffusion region. For MP reconnection ion diffusion regions have A n_e ~ 3 based on MMS observations. Scudder says A n_e ~ 7 at IDR-EDR boundary, but this is extremely large for MP reconnection.\n", + "\n", + "- Mperp e: electron Mach number: bulk velocity divided by the electron thermal speed perpendicular to B. Values of O(1) are expected in EDRs (Scudder et al., 2012, 2015). \n", + "\n", + "- J.E': J.E > 0 is expected in the electron diffusion region, corresponding to dissipation of field energy. J is calculated on each spacecraft using the particle moments (Zenitani et al., 2011, PRL). \n", + "\n", + "- epsilon_e: Energy gain per cyclotron period. Values of O(1) are expected in EDRs (Scudder et al., 2012, 2015). \n", + "- delta_e: Relative strength of the electric and magnetic force in the bulk electron rest frame. N. B. Very sensitive to electron moments and electric field. Check version of these quantities (Scudder et al., 2012, 2015). \n", + " \n", + "Notes: \n", + "kappa_e (not yet included) is taken to be the largest value of epsilon_e and delta_e at any given point. Requires electron distributions with version number v2.0.0 or higher. Calculations of agyrotropy measures (1)--(3) become unreliable at low densities n_e <~ 2 cm^-3, when the raw particle counts are low. Agyrotropies are removed for n_e < 1 cm^-3" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pyrfu import mms, pyrf\n", + "from pyrfu.plot import make_labels, pl_tx\n", + "from scipy import constants" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Time interval selection and data path" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "tint = [\"2015-12-14T01:17:38.000\", \"2015-12-14T01:17:41.000\"]\n", + "mms.db_init(\"/Volumes/mms\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load fields" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[07-Jun-23 16:17:04] INFO: Loading mms1_fgm_b_dmpa_srvy_l2...\n", + "[07-Jun-23 16:17:04] INFO: Loading mms2_fgm_b_dmpa_srvy_l2...\n", + "[07-Jun-23 16:17:05] INFO: Loading mms3_fgm_b_dmpa_srvy_l2...\n", + "[07-Jun-23 16:17:05] INFO: Loading mms4_fgm_b_dmpa_srvy_l2...\n", + "[07-Jun-23 16:17:06] INFO: Loading mms1_edp_dce_dsl_brst_l2...\n", + "[07-Jun-23 16:17:06] INFO: Loading mms2_edp_dce_dsl_brst_l2...\n", + "[07-Jun-23 16:17:06] INFO: Loading mms3_edp_dce_dsl_brst_l2...\n", + "[07-Jun-23 16:17:07] INFO: Loading mms4_edp_dce_dsl_brst_l2...\n" + ] + } + ], + "source": [ + "b_mms = [mms.get_data(\"b_dmpa_fgm_srvy_l2\", tint, i) for i in range(1, 5)]\n", + "e_mms = [mms.get_data(\"e_dsl_edp_brst_l2\", tint, i) for i in range(1, 5)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load particles moments" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[07-Jun-23 16:17:07] INFO: Loading mms1_des_numberdensity_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:07] INFO: Loading mms2_des_numberdensity_brst...\n", + "[07-Jun-23 16:17:07] INFO: Loading mms3_des_numberdensity_brst...\n", + "[07-Jun-23 16:17:07] INFO: Loading mms4_des_numberdensity_brst...\n", + "[07-Jun-23 16:17:07] INFO: Loading mms1_des_bulkv_dbcs_brst...\n", + "[07-Jun-23 16:17:07] INFO: Loading mms2_des_bulkv_dbcs_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:07] INFO: Loading mms3_des_bulkv_dbcs_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:07] INFO: Loading mms4_des_bulkv_dbcs_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:07] INFO: Loading mms1_dis_bulkv_dbcs_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:07] INFO: Loading mms2_dis_bulkv_dbcs_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:07] INFO: Loading mms3_dis_bulkv_dbcs_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:07] INFO: Loading mms4_dis_bulkv_dbcs_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:07] INFO: Loading mms1_des_temptensor_dbcs_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:07] INFO: Loading mms2_des_temptensor_dbcs_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:07] INFO: Loading mms3_des_temptensor_dbcs_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:07] INFO: Loading mms4_des_temptensor_dbcs_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:07] INFO: Loading mms1_des_prestensor_dbcs_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:07] INFO: Loading mms2_des_prestensor_dbcs_brst...\n", + "[07-Jun-23 16:17:07] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:08] INFO: Loading mms3_des_prestensor_dbcs_brst...\n", + "[07-Jun-23 16:17:08] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[07-Jun-23 16:17:08] INFO: Loading mms4_des_prestensor_dbcs_brst...\n", + "[07-Jun-23 16:17:08] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n" + ] + } + ], + "source": [ + "n_mms_e = [mms.get_data(\"ne_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", + "v_mms_e = [mms.get_data(\"ve_dbcs_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", + "v_mms_i = [mms.get_data(\"vi_dbcs_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", + "t_mms_e = [mms.get_data(\"te_dbcs_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", + "p_mms_e = [mms.get_data(\"pe_dbcs_fpi_brst_l2\", tint, i) for i in range(1, 5)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resample to DES sampling frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[07-Jun-23 16:17:08] INFO: Using averages in resample\n", + "[07-Jun-23 16:17:08] INFO: Using averages in resample\n", + "[07-Jun-23 16:17:08] INFO: Using averages in resample\n", + "[07-Jun-23 16:17:08] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "e_mms = [pyrf.resample(e_xyz, n_e) for e_xyz, n_e in zip(e_mms, n_mms_e)]\n", + "b_mms = [pyrf.resample(b_xyz, n_e) for b_xyz, n_e in zip(b_mms, n_mms_e)]\n", + "v_mms_i = [pyrf.resample(v_xyz_i, n_e) for v_xyz_i, n_e in zip(v_mms_i, n_mms_e)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rotate pressure and temperature tensors" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "p_mms_e_pp = [\n", + " mms.rotate_tensor(p_xyz, \"fac\", b_xyz, \"pp\") for p_xyz, b_xyz in zip(p_mms_e, b_mms)\n", + "]\n", + "p_mms_e_qq = [\n", + " mms.rotate_tensor(p_xyz, \"fac\", b_xyz, \"qq\") for p_xyz, b_xyz in zip(p_mms_e, b_mms)\n", + "]\n", + "t_mms_e_fac = [\n", + " mms.rotate_tensor(t_xyz, \"fac\", b_xyz) for t_xyz, b_xyz in zip(t_mms_e, b_mms)\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute tests for EDR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Q and Dng from Pepp" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "sqrtq_mms = [pyrf.calc_sqrtq(p_pp) for p_pp in p_mms_e_pp]\n", + "dng_mms = [pyrf.calc_dng(p_pp) for p_pp in p_mms_e_pp]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute agyrotropy measure AG1/3" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "ag_mms = [pyrf.calc_ag(p_pp) for p_pp in p_mms_e_pp]\n", + "ag_cr_mms = [ag ** (1 / 3) for ag in ag_mms]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute agyrotropy Aphi from Peqq" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "agyro_mms = [pyrf.calc_agyro(p_qq) for p_qq in p_mms_e_qq]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simple fix to remove spurious points" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "for sqrtq, dng, agyro, ag_cr in zip(sqrtq_mms, dng_mms, agyro_mms, ag_cr_mms):\n", + " for coeff in [sqrtq, dng, agyro, ag_cr]:\n", + " coeff_data = coeff.data.copy()\n", + " for ii in range(len(coeff_data) - 1):\n", + " if coeff[ii] > 2 * coeff[ii - 1] and coeff[ii] > 2 * coeff[ii + 1]:\n", + " coeff_data[ii] = np.nan\n", + "\n", + " coeff.data = coeff_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove all points corresponding to densities below 1cm^-3" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "for n_e, sqrtq, dng, agyro, ag_cr in zip(\n", + " n_mms_e, sqrtq_mms, dng_mms, agyro_mms, ag_cr_mms\n", + "):\n", + " sqrtq.data[n_e.data < 1] = np.nan\n", + " dng.data[n_e.data < 1] = np.nan\n", + " agyro.data[n_e.data < 1] = np.nan\n", + " ag_cr.data[n_e.data < 1] = np.nan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute temperature ratio An" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "t_rat_mms = [p_pp[:, 0, 0] / p_pp[:, 1, 1] for p_pp in p_mms_e_pp]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute electron Mach number" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "qe, me = [constants.elementary_charge, constants.electron_mass]\n", + "v_mms_e_mag = [pyrf.norm(v_xyz_e) for v_xyz_e in v_mms_e]\n", + "v_mms_e_per = [\n", + " np.sqrt((t_fac_e[:, 1, 1] + t_fac_e[:, 2, 2]) * qe / me) for t_fac_e in t_mms_e_fac\n", + "]\n", + "m_mms_e = [\n", + " 1e3 * v_e_mag / v_e_perp for v_e_mag, v_e_perp in zip(v_mms_e_mag, v_mms_e_per)\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute current density and J.E" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Current density in nA m^-2\n", + "j_mms_moms = [\n", + " 1e18 * qe * n_e * (v_xyz_i - v_xyz_e)\n", + " for n_e, v_xyz_i, v_xyz_e in zip(n_mms_e, v_mms_i, v_mms_e)\n", + "]\n", + "vexb_mms = [\n", + " e_xyz + 1e-3 * pyrf.cross(v_xyz_e, b_xyz)\n", + " for e_xyz, v_xyz_e, b_xyz in zip(e_mms, v_mms_e, b_mms)\n", + "]\n", + "# J (nA/m^2), E (mV/m), E.J (nW/m^3)\n", + "edotj_mms = [\n", + " 1e-3 * pyrf.dot(vexb_xyz, j_xyz) for vexb_xyz, j_xyz in zip(vexb_mms, j_mms_moms)\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate epsilon and delta parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "w_mms_ce = [1e-9 * qe * pyrf.norm(b_xyz) / me for b_xyz in b_mms]\n", + "edotve_mms = [pyrf.dot(e_xyz, v_xyz_e) for e_xyz, v_xyz_e in zip(e_mms, v_mms_e)]\n", + "eps_mms_e = [\n", + " np.abs(6 * np.pi * edotve_xyz / (w_ce * pyrf.trace(t_fac_e)))\n", + " for edotve_xyz, w_ce, t_fac_e in zip(edotve_mms, w_mms_ce, t_mms_e_fac)\n", + "]\n", + "delta_mms_e = [\n", + " 1e-3 * pyrf.norm(vexb_xyz) / (v_xyz_e_per * pyrf.norm(b_xyz) * 1e-9)\n", + " for vexb_xyz, v_xyz_e_per, b_xyz in zip(vexb_mms, v_mms_e_per, b_mms)\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot figure" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "legend_options = dict(ncol=4, frameon=True, loc=\"upper right\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[07-Jun-23 16:17:11] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[07-Jun-23 16:17:11] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[07-Jun-23 16:17:11] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[07-Jun-23 16:17:11] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[07-Jun-23 16:17:13] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[07-Jun-23 16:17:13] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[07-Jun-23 16:17:13] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[07-Jun-23 16:17:13] INFO: Substituting symbol \\perp from STIXGeneral\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(9, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.18, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "pl_tx(axs[0], b_mms, 2)\n", + "axs[0].set_ylabel(\"$B_{z}$ [nT]\")\n", + "labels = [\"MMS{:d}\".format(ic) for ic in range(1, 5)]\n", + "f.legend(\n", + " labels=labels,\n", + " loc=\"upper center\",\n", + " borderaxespad=0.1,\n", + " ncol=4,\n", + " frameon=False,\n", + ")\n", + "\n", + "pl_tx(axs[1], sqrtq_mms, 0)\n", + "axs[1].set_ylabel(\"$\\sqrt{Q}$\")\n", + "\n", + "pl_tx(axs[2], ag_cr_mms, 0)\n", + "axs[2].set_ylabel(\"$AG^{1/3}$\")\n", + "\n", + "pl_tx(axs[3], agyro_mms, 0)\n", + "axs[3].set_ylabel(\"$A\\Phi_e / 2$\")\n", + "\n", + "pl_tx(axs[4], t_rat_mms, 0)\n", + "axs[4].set_ylabel(\"$T_{e||}/T_{e \\perp}$\")\n", + "\n", + "pl_tx(axs[5], m_mms_e, 0)\n", + "axs[5].set_ylabel(\"$M_{e \\perp}$\")\n", + "\n", + "pl_tx(axs[6], edotj_mms, 0)\n", + "axs[6].set_ylabel(\"$E'.J$ [nW m$^{-3}$]\")\n", + "\n", + "pl_tx(axs[7], eps_mms_e, 0)\n", + "axs[7].set_ylabel(\"$\\epsilon_{e}$\")\n", + "\n", + "pl_tx(axs[8], delta_mms_e, 0)\n", + "axs[8].set_ylabel(\"$\\delta_{e}$\")\n", + "\n", + "make_labels(axs, [0.025, 0.83])\n", + "\n", + "\n", + "axs[-1].set_xlim(pyrf.iso86012datetime64(np.array(tint)))\n", + "f.align_ylabels(axs)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/01_mms/example_mms_eis.ipynb b/docs/examples/01_mms/example_mms_eis.ipynb new file mode 100644 index 00000000..3476308e --- /dev/null +++ b/docs/examples/01_mms/example_mms_eis.ipynb @@ -0,0 +1,704 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Energetic Ion Spectrometer (EIS)\n", + "author: Louis Richard" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pyrfu import mms, pyrf\n", + "from pyrfu.plot import plot_line, plot_spectr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define path to data, time interval and spacecraft index" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mms.db_init(\"/Volumes/mms\")\n", + "tint_long = [\"2017-07-23T16:10:00\", \"2017-07-23T18:10:00\"]\n", + "mms_id = 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Magnetic field in GSE coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:34:30] INFO: Loading mms2_fgm_b_gse_srvy_l2...\n" + ] + } + ], + "source": [ + "# Single spacecraft\n", + "b_gse = mms.get_data(\"b_gse_fgm_srvy_l2\", tint_long, mms_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Single spacecraft H$^+$ (PHxTOF and ExTOF), He$^{n+}$ (ExTOF), O$^{n+}$ (ExTOF) and electrons differential particle fluxes for all 6 telescopes." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:34:30] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_spin...\n", + "[08-Jun-23 17:34:30] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_sector...\n", + "[08-Jun-23 17:34:30] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_proton_t0_energy_dminus...\n", + "[08-Jun-23 17:34:30] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_proton_t0_energy_dplus...\n", + "[08-Jun-23 17:34:30] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_proton_P4_flux_t0...\n", + "[08-Jun-23 17:34:30] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:30] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_look_t0...\n", + "[08-Jun-23 17:34:30] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_proton_P4_flux_t1...\n", + "[08-Jun-23 17:34:31] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_look_t1...\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_proton_P4_flux_t2...\n", + "[08-Jun-23 17:34:31] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_look_t2...\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_proton_P4_flux_t3...\n", + "[08-Jun-23 17:34:31] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_look_t3...\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_proton_P4_flux_t4...\n", + "[08-Jun-23 17:34:31] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_look_t4...\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_proton_P4_flux_t5...\n", + "[08-Jun-23 17:34:31] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_phxtof_look_t5...\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_extof_spin...\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_extof_sector...\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_extof_proton_t0_energy_dminus...\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_extof_proton_t0_energy_dplus...\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_extof_proton_P4_flux_t0...\n", + "[08-Jun-23 17:34:31] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t0...\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_extof_proton_P4_flux_t1...\n", + "[08-Jun-23 17:34:31] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:31] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t1...\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_proton_P4_flux_t2...\n", + "[08-Jun-23 17:34:32] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t2...\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_proton_P4_flux_t3...\n", + "[08-Jun-23 17:34:32] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t3...\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_proton_P4_flux_t4...\n", + "[08-Jun-23 17:34:32] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t4...\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_proton_P4_flux_t5...\n", + "[08-Jun-23 17:34:32] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t5...\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_spin...\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_sector...\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_oxygen_t0_energy_dminus...\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_oxygen_t0_energy_dplus...\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_oxygen_P4_flux_t0...\n", + "[08-Jun-23 17:34:32] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t0...\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_oxygen_P4_flux_t1...\n", + "[08-Jun-23 17:34:32] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:32] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t1...\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_oxygen_P4_flux_t2...\n", + "[08-Jun-23 17:34:33] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t2...\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_oxygen_P4_flux_t3...\n", + "[08-Jun-23 17:34:33] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t3...\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_oxygen_P4_flux_t4...\n", + "[08-Jun-23 17:34:33] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t4...\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_oxygen_P4_flux_t5...\n", + "[08-Jun-23 17:34:33] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t5...\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_spin...\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_sector...\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_helium_t0_energy_dminus...\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_helium_t0_energy_dplus...\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_helium_P4_flux_t0...\n", + "[08-Jun-23 17:34:33] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t0...\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_helium_P4_flux_t1...\n", + "[08-Jun-23 17:34:33] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t1...\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_helium_P4_flux_t2...\n", + "[08-Jun-23 17:34:33] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:33] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t2...\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_extof_helium_P4_flux_t3...\n", + "[08-Jun-23 17:34:34] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t3...\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_extof_helium_P4_flux_t4...\n", + "[08-Jun-23 17:34:34] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t4...\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_extof_helium_P4_flux_t5...\n", + "[08-Jun-23 17:34:34] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_extof_look_t5...\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_spin...\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_sector...\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_electron_t0_energy_dminus...\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_electron_t0_energy_dplus...\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_electron_P4_flux_t0...\n", + "[08-Jun-23 17:34:34] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_look_t0...\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_electron_P4_flux_t1...\n", + "[08-Jun-23 17:34:34] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_look_t1...\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_electron_P4_flux_t2...\n", + "[08-Jun-23 17:34:34] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_look_t2...\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_electron_P4_flux_t3...\n", + "[08-Jun-23 17:34:34] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_look_t3...\n", + "[08-Jun-23 17:34:34] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_electron_P4_flux_t4...\n", + "[08-Jun-23 17:34:35] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:35] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_look_t4...\n", + "[08-Jun-23 17:34:35] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_electron_P4_flux_t5...\n", + "[08-Jun-23 17:34:35] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_eis_allt.py:147: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " outdict[scope_key] = outdict[scope_key].rename(\n", + "\n", + "[08-Jun-23 17:34:35] INFO: Loading mms2_epd_eis_srvy_l2_electronenergy_look_t5...\n" + ] + } + ], + "source": [ + "# Proton\n", + "dpf_phxtof_allt_proton = mms.get_eis_allt(\n", + " \"flux_phxtof_proton_srvy_l2\", tint_long, mms_id\n", + ")\n", + "dpf_extof_allt_proton = mms.get_eis_allt(\"flux_extof_proton_srvy_l2\", tint_long, mms_id)\n", + "# Oxygen\n", + "dpf_extof_allt_oxygen = mms.get_eis_allt(\"flux_extof_oxygen_srvy_l2\", tint_long, mms_id)\n", + "# Helium\n", + "dpf_extof_allt_helium = mms.get_eis_allt(\"flux_extof_alpha_srvy_l2\", tint_long, mms_id)\n", + "# Electron\n", + "dpf_een_allt_electron = mms.get_eis_allt(\n", + " \"flux_electronenergy_electron_srvy_l2\", tint_long, mms_id\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Post-processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute H$^+$ combined PHxTOF and ExTOF differential particle flux for all 6 telescopes" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "dpf_eis_allt_proton = mms.eis_combine_proton_spec(\n", + " dpf_phxtof_allt_proton, dpf_extof_allt_proton\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spin average H+, He𝑛+ (ExTOF) and O𝑛+ (ExTOF) differential particle fluxes for all 6 telescopes" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Helium\n", + "dpf_eis_allt_proton_spin = mms.eis_spin_avg(dpf_eis_allt_proton)\n", + "\n", + "# Helium\n", + "dpf_extof_allt_helium_spin = mms.eis_spin_avg(dpf_extof_allt_helium)\n", + "\n", + "# Oxygen\n", + "dpf_extof_allt_oxygen_spin = mms.eis_spin_avg(dpf_extof_allt_oxygen)\n", + "\n", + "# Electron\n", + "dpf_een_allt_electron_spin = mms.eis_spin_avg(dpf_een_allt_electron)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute H$^+$, He$^{n+}$ (ExTOF) and O$^{n+}$ (ExTOF) omni-directional differential particle fluxes with an without spin averaging" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Helium\n", + "dpf_eis_omni_proton = mms.eis_omni(dpf_eis_allt_proton)\n", + "dpf_eis_omni_proton_spin = mms.eis_omni(dpf_eis_allt_proton_spin)\n", + "\n", + "# Helium\n", + "dpf_extof_omni_helium = mms.eis_omni(dpf_extof_allt_helium)\n", + "dpf_extof_omni_helium_spin = mms.eis_omni(dpf_extof_allt_helium_spin)\n", + "\n", + "# Oxygen\n", + "dpf_extof_omni_oxygen = mms.eis_omni(dpf_extof_allt_oxygen)\n", + "dpf_extof_omni_oxygen_spin = mms.eis_omni(dpf_extof_allt_oxygen_spin)\n", + "\n", + "# Electron\n", + "dpf_een_omni_electron = mms.eis_omni(dpf_een_allt_electron)\n", + "dpf_een_omni_electron_spin = mms.eis_omni(dpf_een_allt_electron_spin)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply H$^+$ omni-directional differential particle flux cross calibration correction" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "dpf_eis_omni_proton_corr = mms.eis_proton_correction(dpf_eis_omni_proton)\n", + "dpf_eis_omni_proton_spin_corr = mms.eis_proton_correction(dpf_eis_omni_proton_spin)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot in a MMS SDC Quicklook fashion" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(5, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "plot_line(axs[0], b_gse)\n", + "plot_line(axs[0], pyrf.norm(b_gse), color=\"k\")\n", + "axs[0].legend(\n", + " [\"$B_x$\", \"$B_y$\", \"$B_z$\", \"$|B|$\"],\n", + " frameon=False,\n", + " handlelength=1.5,\n", + " loc=\"upper right\",\n", + " ncol=4,\n", + ")\n", + "axs[0].set_ylabel(\"$B$ [nT]\")\n", + "axs[0].set_ylim([-20, 20])\n", + "\n", + "# Electron spin averaged\n", + "axs[1], caxs1 = plot_spectr(\n", + " axs[1], dpf_een_omni_electron_spin, yscale=\"log\", cscale=\"log\"\n", + ")\n", + "caxs1.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[1].set_ylabel(\"$E_{e}$ [keV]\")\n", + "\n", + "# Proton spin averaged\n", + "axs[2], caxs2 = plot_spectr(\n", + " axs[2], dpf_eis_omni_proton_spin_corr, yscale=\"log\", cscale=\"log\"\n", + ")\n", + "caxs2.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[2].set_ylabel(\"$E_{H^{+}}$ [keV]\")\n", + "\n", + "# Helium spin averaged\n", + "axs[3], caxs3 = plot_spectr(\n", + " axs[3], dpf_extof_omni_helium_spin, yscale=\"log\", cscale=\"log\"\n", + ")\n", + "caxs3.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[3].set_ylabel(\"$E_{He^{n+}}$ [keV]\")\n", + "\n", + "# Oxygen spin averaged\n", + "axs[4], caxs4 = plot_spectr(\n", + " axs[4], dpf_extof_omni_oxygen_spin, yscale=\"log\", cscale=\"log\"\n", + ")\n", + "caxs4.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[4].set_ylabel(\"$E_{O^{n+}}$ [keV]\")\n", + "\n", + "f.align_ylabels(axs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute H$^{+}$ (PHxTOF and ExTOF), He$^{n+}$, O$^{n+}$ and electron pitch angle distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:34:38] INFO: Using averages in resample\n", + "[08-Jun-23 17:34:38] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/eis_pad.py:124: RuntimeWarning: Mean of empty slice\n", + " pa_flux[i, j, :] = np.nanmean(flux_file[i, ind, :], axis=0)\n", + "\n", + "[08-Jun-23 17:34:39] INFO: Using averages in resample\n", + "[08-Jun-23 17:34:39] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/eis_pad.py:124: RuntimeWarning: Mean of empty slice\n", + " pa_flux[i, j, :] = np.nanmean(flux_file[i, ind, :], axis=0)\n", + "\n", + "[08-Jun-23 17:34:40] INFO: Using averages in resample\n", + "[08-Jun-23 17:34:40] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/eis_pad.py:124: RuntimeWarning: Mean of empty slice\n", + " pa_flux[i, j, :] = np.nanmean(flux_file[i, ind, :], axis=0)\n", + "\n", + "[08-Jun-23 17:34:41] INFO: Using averages in resample\n", + "[08-Jun-23 17:34:41] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/eis_pad.py:124: RuntimeWarning: Mean of empty slice\n", + " pa_flux[i, j, :] = np.nanmean(flux_file[i, ind, :], axis=0)\n", + "\n", + "[08-Jun-23 17:34:42] INFO: Using averages in resample\n", + "[08-Jun-23 17:34:42] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/eis_pad.py:124: RuntimeWarning: Mean of empty slice\n", + " pa_flux[i, j, :] = np.nanmean(flux_file[i, ind, :], axis=0)\n", + "\n" + ] + } + ], + "source": [ + "# Low energy proton\n", + "dpf_phxtof_pad_proton = mms.eis_pad(dpf_phxtof_allt_proton, vec=b_gse, energy=[10, 50])\n", + "\n", + "# High energy proton\n", + "dpf_extof_pad_proton = mms.eis_pad(dpf_extof_allt_proton, vec=b_gse)\n", + "\n", + "# Helium\n", + "dpf_extof_pad_helium = mms.eis_pad(dpf_extof_allt_helium, vec=b_gse)\n", + "\n", + "# Oxygen\n", + "dpf_extof_pad_oxygen = mms.eis_pad(dpf_extof_allt_oxygen, vec=b_gse)\n", + "\n", + "# Electron\n", + "dpf_een_pad_electron = mms.eis_pad(dpf_een_allt_electron, vec=b_gse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spin average H$^+$, He$^{n+}$ and O$^{n+}$ pitch angle distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:34:44] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/eis_pad_spinavg.py:48: RuntimeWarning: Mean of empty slice\n", + " spin_sum_flux[i, :, :] = np.nanmean(inp.data[idx_, :, :], axis=0)\n", + "\n" + ] + } + ], + "source": [ + "# Low energy proton\n", + "dpf_phxtof_pad_proton_spin = mms.eis_pad_spinavg(\n", + " dpf_phxtof_pad_proton, dpf_phxtof_allt_proton.spin\n", + ")\n", + "\n", + "# High energy proton\n", + "dpf_extof_pad_proton_spin = mms.eis_pad_spinavg(\n", + " dpf_extof_pad_proton, dpf_extof_allt_proton.spin\n", + ")\n", + "\n", + "# Helium\n", + "dpf_extof_pad_helium_spin = mms.eis_pad_spinavg(\n", + " dpf_extof_pad_helium, dpf_extof_allt_helium.spin\n", + ")\n", + "\n", + "# Oxygen\n", + "dpf_extof_pad_oxygen_spin = mms.eis_pad_spinavg(\n", + " dpf_extof_pad_oxygen, dpf_extof_allt_oxygen.spin\n", + ")\n", + "\n", + "# Electron\n", + "dpf_een_pad_electron_spin = mms.eis_pad_spinavg(\n", + " dpf_een_pad_electron, dpf_een_allt_electron.spin\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(6, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "\n", + "plot_line(axs[0], b_gse)\n", + "plot_line(axs[0], pyrf.norm(b_gse), color=\"k\")\n", + "axs[0].legend(\n", + " [\"$B_x$\", \"$B_y$\", \"$B_z$\", \"$|B|$\"],\n", + " frameon=False,\n", + " handlelength=1.5,\n", + " loc=\"upper right\",\n", + " ncol=4,\n", + ")\n", + "axs[0].set_ylabel(\"$B$ [nT]\")\n", + "axs[0].set_ylim([-20, 20])\n", + "\n", + "axs[1], caxs1 = plot_spectr(\n", + " axs[1], dpf_een_pad_electron_spin.mean(dim=\"energy\", skipna=True), cscale=\"log\"\n", + ")\n", + "caxs1.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[1].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", + "e_min, e_max = list(dpf_een_pad_electron.energy.data[[0, -1]])\n", + "axs[1].text(\n", + " 0.02,\n", + " 0.1,\n", + " f\"{e_min:3.1f} keV < $E_{{e}}$ < {e_max:3.1f} keV\",\n", + " bbox=dict(fc=(1, 1, 1)),\n", + " transform=axs[1].transAxes,\n", + ")\n", + "\n", + "axs[2], caxs2 = plot_spectr(\n", + " axs[2], dpf_extof_pad_proton_spin.mean(dim=\"energy\", skipna=True), cscale=\"log\"\n", + ")\n", + "caxs2.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[2].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", + "e_min, e_max = list(dpf_extof_pad_proton.energy.data[[0, -1]])\n", + "axs[2].text(\n", + " 0.02,\n", + " 0.1,\n", + " f\"{e_min:3.1f} keV < $E_{{H^+}}$ < {e_max:3.1f} keV\",\n", + " bbox=dict(fc=(1, 1, 1)),\n", + " transform=axs[2].transAxes,\n", + ")\n", + "\n", + "axs[3], caxs3 = plot_spectr(\n", + " axs[3], dpf_phxtof_pad_proton_spin.mean(dim=\"energy\", skipna=True), cscale=\"log\"\n", + ")\n", + "caxs3.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[3].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", + "e_min, e_max = list(dpf_phxtof_pad_proton.energy.data[[0, -1]])\n", + "axs[3].text(\n", + " 0.02,\n", + " 0.1,\n", + " f\"{e_min:3.1f} keV < $E_{{H^+}}$ < {e_max:3.1f} keV\",\n", + " bbox=dict(fc=(1, 1, 1)),\n", + " transform=axs[3].transAxes,\n", + ")\n", + "\n", + "axs[4], caxs4 = plot_spectr(\n", + " axs[4], dpf_extof_pad_helium_spin.mean(dim=\"energy\", skipna=True), cscale=\"log\"\n", + ")\n", + "caxs4.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[4].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", + "e_min, e_max = list(dpf_extof_pad_helium_spin.energy.data[[0, -1]])\n", + "axs[4].text(\n", + " 0.02,\n", + " 0.1,\n", + " f\"{e_min:3.1f} keV < $E_{{He^{{n+}}}}$ < {e_max:3.1f} keV\",\n", + " bbox=dict(fc=(1, 1, 1)),\n", + " transform=axs[4].transAxes,\n", + ")\n", + "\n", + "axs[5], caxs5 = plot_spectr(\n", + " axs[5], dpf_extof_pad_oxygen_spin.mean(dim=\"energy\", skipna=True), cscale=\"log\"\n", + ")\n", + "caxs5.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[5].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", + "e_min, e_max = list(dpf_extof_pad_oxygen_spin.energy.data[[0, -1]])\n", + "axs[5].text(\n", + " 0.02,\n", + " 0.1,\n", + " f\"{e_min:3.1f} keV < $E_{{O^{{n+}}}}$ < {e_max:3.1f} keV\",\n", + " bbox=dict(fc=(1, 1, 1)),\n", + " transform=axs[5].transAxes,\n", + ")\n", + "\n", + "\n", + "f.align_ylabels(axs)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/01_mms/example_mms_electron_psd.ipynb b/docs/examples/01_mms/example_mms_electron_psd.ipynb new file mode 100644 index 00000000..756660d3 --- /dev/null +++ b/docs/examples/01_mms/example_mms_electron_psd.ipynb @@ -0,0 +1,244 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Electron PSD\n", + "author: Louis Richard\n", + "\n", + "Script to plot electron PSD around pitch angles 0, 90, and 180 deg and PSD versus pitch angle L1b brst data " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pyrfu import mms, pyrf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define time interval and spacecraft index" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mms.db_init(\"/Volumes/mms\")\n", + "tint_r = [\"2015-10-30T05:15:20.000\", \"2015-10-30T05:16:20.000\"]\n", + "mms_id = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 18:07:05] INFO: Loading mms1_des_dist_brst...\n", + "[08-Jun-23 18:07:05] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_dist.py:68: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 18:07:08] INFO: Loading mms1_fgm_b_dmpa_brst_l2...\n", + "[08-Jun-23 18:07:08] INFO: Loading mms1_edp_scpot_brst_l2...\n" + ] + } + ], + "source": [ + "vdf_e = mms.get_data(\"pde_fpi_brst_l2\", tint_r, mms_id)\n", + "b_xyz = mms.get_data(\"b_dmpa_fgm_brst_l2\", tint_r, mms_id)\n", + "sc_pot = mms.get_data(\"v_edp_brst_l2\", tint_r, mms_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert PSD units to s^3/km^6" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "vdf_e.data.data *= 1e36" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resample spacecraft potential to VDF sampling" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 18:07:09] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "sc_pot = pyrf.resample(sc_pot, vdf_e.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Produce a single PAD at a selected time" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 18:07:09] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "t_pad = \"2015-10-30T05:15:45.731587000\"\n", + "\n", + "pad_vdf_e = mms.get_pitch_angle_dist(vdf_e, b_xyz, tint=tint_r)\n", + "idx_pad = np.argmin(\n", + " np.abs(pad_vdf_e.time.data.astype(int) - np.datetime64(t_pad).astype(int))\n", + ")\n", + "pad_vdf_e = pad_vdf_e.isel(time=idx_pad)\n", + "\n", + "idx = np.argmin(abs(sc_pot.time.data.astype(int) - np.datetime64(t_pad).astype(int)))\n", + "energy_pad = pad_vdf_e.energy.data - sc_pot.data[idx, ...]\n", + "thetas_pad = pad_vdf_e.theta.data\n", + "pad_data_e = pad_vdf_e.data.data\n", + "\n", + "pad_data_e[pad_data_e == 0.0] = np.nan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, '05:15:45.731 UT')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(1, 2, figsize=(9, 6))\n", + "f.subplots_adjust(hspace=0, wspace=0.5, left=0.1, right=0.95, bottom=0.1, top=0.94)\n", + "\n", + "\n", + "for i, color, ang in zip([0, 6, -1], [\"k\", \"tab:red\", \"tab:blue\"], [0, 90, 180]):\n", + " axs[0].loglog(energy_pad, pad_data_e[:, i], color=color, label=f\"{ang} deg\")\n", + "\n", + "axs[0].legend(loc=\"upper right\", frameon=True)\n", + "axs[0].set_xlim([5, 3e4])\n", + "axs[0].set_xlabel(\"$E$ [eV]\")\n", + "axs[0].set_ylim([10**2, 10 ** np.ceil(np.nanmax(np.nanmax(np.log10(pad_data_e))))])\n", + "axs[0].set_ylabel(\"$f_e$ [s$^3$ km$^{-6}$]\")\n", + "axs[0].set_title(\"{} UT\".format(t_pad[11:23]))\n", + "\n", + "colors = pl.cm.jet(np.linspace(0, 1, len(energy_pad)))\n", + "\n", + "for (i, energy), color in zip(enumerate(energy_pad), colors):\n", + " axs[1].semilogy(thetas_pad, pad_data_e[i, :], color=color)\n", + "\n", + "axs[1].set_xlim([0, 180])\n", + "axs[1].set_xticks([0, 45, 90, 135, 180])\n", + "axs[1].set_xlabel(\"$\\\\theta$ [deg.]\")\n", + "axs[1].set_ylim([10**2, 10 ** np.ceil(np.nanmax(np.nanmax(np.log10(pad_data_e))))])\n", + "axs[1].set_ylabel(\"$f_e$ [s$^3$ km$^{-6}$]\")\n", + "axs[1].set_title(\"{} UT\".format(t_pad[11:23]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/01_mms/example_mms_feeps.ipynb b/docs/examples/01_mms/example_mms_feeps.ipynb new file mode 100644 index 00000000..4bdafbb6 --- /dev/null +++ b/docs/examples/01_mms/example_mms_feeps.ipynb @@ -0,0 +1,630 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fly’s Eye Energetic Particle Spectrometer (FEEPS)\n", + "author: Louis Richard\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pyrfu import mms\n", + "from pyrfu.plot import plot_line, plot_spectr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define data path, time interval and spacecraft index" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mms.db_init(\"/Volumes/mms\")\n", + "tint_long = [\"2017-07-23T16:10:00\", \"2017-07-23T18:10:00\"]\n", + "mms_id = 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Magnetic field in GSM" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 14:04:38] INFO: Loading mms2_fgm_b_bcs_srvy_l2...\n", + "[09-Jun-23 14:04:39] INFO: Loading mms2_fgm_b_gsm_srvy_l2...\n" + ] + } + ], + "source": [ + "b_bcs = mms.get_data(\"b_bcs_fgm_srvy_l2\", tint_long, mms_id)\n", + "b_gsm = mms.get_data(\"b_gsm_fgm_srvy_l2\", tint_long, mms_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Electron and ion differential particle flux for all FEEPS sensors" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 14:04:39] INFO: Loading mms2_epd_feeps_srvy_l2_electron_spinsectnum...\n", + "[09-Jun-23 14:04:39] INFO: Loading mms2_epd_feeps_srvy_l2_electron_pitch_angle...\n", + "[09-Jun-23 14:04:40] INFO: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_3...\n", + "[09-Jun-23 14:04:40] INFO: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_4...\n", + "[09-Jun-23 14:04:40] INFO: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_5...\n", + "[09-Jun-23 14:04:40] INFO: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_11...\n", + "[09-Jun-23 14:04:40] INFO: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_12...\n", + "[09-Jun-23 14:04:40] INFO: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_3...\n", + "[09-Jun-23 14:04:40] INFO: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_4...\n", + "[09-Jun-23 14:04:40] INFO: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_5...\n", + "[09-Jun-23 14:04:40] INFO: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_11...\n", + "[09-Jun-23 14:04:40] INFO: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_12...\n", + "[09-Jun-23 14:04:41] INFO: Loading mms2_epd_feeps_srvy_l2_ion_spinsectnum...\n", + "[09-Jun-23 14:04:41] INFO: Loading mms2_epd_feeps_srvy_l2_ion_pitch_angle...\n", + "[09-Jun-23 14:04:41] INFO: Loading mms2_epd_feeps_srvy_l2_ion_top_count_rate_sensorid_6...\n", + "[09-Jun-23 14:04:41] INFO: Loading mms2_epd_feeps_srvy_l2_ion_top_count_rate_sensorid_7...\n", + "[09-Jun-23 14:04:41] INFO: Loading mms2_epd_feeps_srvy_l2_ion_top_count_rate_sensorid_8...\n", + "[09-Jun-23 14:04:41] INFO: Loading mms2_epd_feeps_srvy_l2_ion_bottom_count_rate_sensorid_6...\n", + "[09-Jun-23 14:04:41] INFO: Loading mms2_epd_feeps_srvy_l2_ion_bottom_count_rate_sensorid_7...\n", + "[09-Jun-23 14:04:41] INFO: Loading mms2_epd_feeps_srvy_l2_ion_bottom_count_rate_sensorid_8...\n" + ] + } + ], + "source": [ + "# Electron\n", + "dpf_feeps_alle_e = mms.get_feeps_alleyes(\"cpse_srvy_l2\", tint_long, mms_id)\n", + "\n", + "# Ion\n", + "dpf_feeps_alle_i = mms.get_feeps_alleyes(\"cpsi_srvy_l2\", tint_long, mms_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Post-processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Correct energy table" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 14:04:41] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/feeps_correct_energies.py:53: UserWarning: rename 'time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " out_dict[sensors_eye] = out_dict[sensors_eye].rename(\n", + "\n" + ] + } + ], + "source": [ + "# Electron\n", + "dpf_feeps_alle_e = mms.feeps_correct_energies(dpf_feeps_alle_e)\n", + "\n", + "# Ion\n", + "dpf_feeps_alle_i = mms.feeps_correct_energies(dpf_feeps_alle_i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply flat field correction to electron and ion differential particle flux spectra" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Electron\n", + "dpf_feeps_alle_e = mms.feeps_flat_field_corrections(dpf_feeps_alle_e)\n", + "\n", + "# Ion\n", + "dpf_feeps_alle_i = mms.feeps_flat_field_corrections(dpf_feeps_alle_i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove bad data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Electron\n", + "dpf_feeps_alle_e = mms.feeps_remove_bad_data(dpf_feeps_alle_e)\n", + "\n", + "# Ion\n", + "dpf_feeps_alle_i = mms.feeps_remove_bad_data(dpf_feeps_alle_i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Split the last integral channel from the electron and ion differential particle flux spectra" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Electron\n", + "dpf_feeps_alle_e_clean, dpf_feeps_alle_e_500kev = mms.feeps_split_integral_ch(\n", + " dpf_feeps_alle_e\n", + ")\n", + "\n", + "# Ion\n", + "dpf_feeps_alle_i_clean, dpf_feeps_alle_i_500kev = mms.feeps_split_integral_ch(\n", + " dpf_feeps_alle_i\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove sunlight contamination" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Electron\n", + "dpf_feeps_alle_e_clean_sun_removed = mms.feeps_remove_sun(dpf_feeps_alle_e_clean)\n", + "\n", + "# Ion\n", + "dpf_feeps_alle_i_clean_sun_removed = mms.feeps_remove_sun(dpf_feeps_alle_i_clean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute the electron and ion omni-directional flux for all 24 sensors" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Electron\n", + "dpf_feeps_omni_e = mms.feeps_omni(dpf_feeps_alle_e_clean_sun_removed)\n", + "\n", + "# Ion\n", + "dpf_feeps_omni_i = mms.feeps_omni(dpf_feeps_alle_i_clean_sun_removed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creates sector-spectrograms with FEEPS data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 14:04:41] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/feeps_sector_spec.py:54: RuntimeWarning: Mean of empty slice\n", + " sector_spec[i, spin_sect] = np.nanmean(\n", + "\n" + ] + } + ], + "source": [ + "# Electron\n", + "dpf_feeps_alle_ss_e_clean_sun_removed = mms.feeps_sector_spec(\n", + " dpf_feeps_alle_e_clean_sun_removed\n", + ")\n", + "\n", + "# Ion\n", + "dpf_feeps_alle_ss_i_clean_sun_removed = mms.feeps_sector_spec(\n", + " dpf_feeps_alle_i_clean_sun_removed\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute the electron and ion pitch angle distribution for energies below and above 100 keV" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 14:04:42] INFO: Using averages in resample\n", + "[09-Jun-23 14:04:43] INFO: Using averages in resample\n", + "[09-Jun-23 14:04:45] INFO: Using averages in resample\n", + "[09-Jun-23 14:04:46] INFO: Using averages in resample\n", + "[09-Jun-23 14:04:47] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "dpf_feeps_pad_i_070_100 = mms.feeps_pad(\n", + " dpf_feeps_alle_i_clean_sun_removed, b_bcs, energy=[70, 100]\n", + ")\n", + "\n", + "# Electron\n", + "dpf_feeps_pad_e_050_100 = mms.feeps_pad(\n", + " dpf_feeps_alle_e_clean_sun_removed, b_bcs, energy=[50, 100]\n", + ")\n", + "dpf_feeps_pad_e_100_200 = mms.feeps_pad(\n", + " dpf_feeps_alle_e_clean_sun_removed, b_bcs, energy=[100, 200]\n", + ")\n", + "\n", + "# Ion\n", + "dpf_feeps_pad_i_070_100 = mms.feeps_pad(\n", + " dpf_feeps_alle_i_clean_sun_removed, b_bcs, energy=[70, 100]\n", + ")\n", + "dpf_feeps_pad_i_100_200 = mms.feeps_pad(\n", + " dpf_feeps_alle_i_clean_sun_removed, b_bcs, energy=[100, 200]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot in a MMS SDC Quicklook fashion" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0.98, 'MMS 2')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(7, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "plot_line(axs[0], b_gsm)\n", + "axs[0].legend(\n", + " [\"$B_x$\", \"$B_y$\", \"$B_z$\"],\n", + " loc=\"upper right\",\n", + " frameon=False,\n", + " bbox_to_anchor=(1.0, 1.0),\n", + ")\n", + "axs[0].set_ylabel(\"$B$ [nT]\")\n", + "\n", + "axs[1], caxs1 = plot_spectr(axs[1], dpf_feeps_omni_e, yscale=\"log\", cscale=\"log\")\n", + "caxs1.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[1].set_ylabel(\"$E_e$ [keV]\")\n", + "\n", + "axs[2], caxs2 = plot_spectr(axs[2], dpf_feeps_pad_e_050_100, cscale=\"log\")\n", + "caxs2.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[2].set_ylim([0, 180])\n", + "axs[2].set_yticks([45, 90, 135])\n", + "axs[2].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", + "\n", + "axs[3], caxs3 = plot_spectr(axs[3], dpf_feeps_pad_e_100_200, cscale=\"log\")\n", + "caxs3.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[3].set_ylim([0, 180])\n", + "axs[3].set_yticks([45, 90, 135])\n", + "axs[3].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", + "\n", + "axs[4], caxs4 = plot_spectr(axs[4], dpf_feeps_omni_i[:, 1:], yscale=\"log\", cscale=\"log\")\n", + "caxs4.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[4].set_ylabel(\"$E_i$ [keV]\")\n", + "\n", + "axs[5], caxs5 = plot_spectr(\n", + " axs[5], dpf_feeps_pad_i_070_100, cscale=\"log\", clim=[3e-1, 1e2]\n", + ")\n", + "caxs5.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[5].set_ylim([0, 180])\n", + "axs[5].set_yticks([45, 90, 135])\n", + "axs[5].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", + "\n", + "axs[6], caxs6 = plot_spectr(axs[6], dpf_feeps_pad_i_100_200, cscale=\"log\")\n", + "caxs6.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[6].set_ylim([0, 180])\n", + "axs[6].set_yticks([45, 90, 135])\n", + "axs[6].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", + "\n", + "f.align_ylabels(axs)\n", + "f.suptitle(f\"MMS {mms_id:d}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spin average omni-directional differential particle flux and pitch angle distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Electron\n", + "dpf_feeps_omni_e_spin = mms.feeps_spin_avg(\n", + " dpf_feeps_omni_e, dpf_feeps_alle_e.spinsectnum\n", + ")\n", + "\n", + "# Ion\n", + "dpf_feeps_omni_i_spin = mms.feeps_spin_avg(\n", + " dpf_feeps_omni_i, dpf_feeps_alle_i.spinsectnum\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Electron\n", + "dpf_feeps_pad_e_050_100_spin = mms.feeps_pad_spinavg(\n", + " dpf_feeps_pad_e_050_100, dpf_feeps_alle_e.spinsectnum\n", + ")\n", + "dpf_feeps_pad_e_100_200_spin = mms.feeps_pad_spinavg(\n", + " dpf_feeps_pad_e_100_200, dpf_feeps_alle_e.spinsectnum\n", + ")\n", + "\n", + "# Ion\n", + "dpf_feeps_pad_i_070_100_spin = mms.feeps_pad_spinavg(\n", + " dpf_feeps_pad_i_070_100, dpf_feeps_alle_i.spinsectnum\n", + ")\n", + "dpf_feeps_pad_i_100_200_spin = mms.feeps_pad_spinavg(\n", + " dpf_feeps_pad_i_100_200, dpf_feeps_alle_i.spinsectnum\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0.98, 'MMS 2 spin averaged')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(7, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "plot_line(axs[0], b_gsm)\n", + "axs[0].legend(\n", + " [\"$B_x$\", \"$B_y$\", \"$B_z$\"],\n", + " loc=\"upper right\",\n", + " frameon=False,\n", + " bbox_to_anchor=(1.0, 1.0),\n", + ")\n", + "axs[0].set_ylabel(\"$B$ [nT]\")\n", + "\n", + "axs[1], caxs1 = plot_spectr(axs[1], dpf_feeps_omni_e_spin, yscale=\"log\", cscale=\"log\")\n", + "caxs1.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[1].set_ylabel(\"$E_e$ [keV]\")\n", + "\n", + "axs[2], caxs2 = plot_spectr(axs[2], dpf_feeps_pad_e_050_100_spin, cscale=\"log\")\n", + "caxs2.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[2].set_ylim([0, 180])\n", + "axs[2].set_yticks([45, 90, 135])\n", + "axs[2].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", + "axs[2].text(\n", + " 0.02,\n", + " 0.1,\n", + " \"50 keV < $E_e$ < 100 keV\",\n", + " bbox=dict(fc=(1, 1, 1)),\n", + " transform=axs[2].transAxes,\n", + ")\n", + "\n", + "axs[3], caxs3 = plot_spectr(axs[3], dpf_feeps_pad_e_100_200_spin, cscale=\"log\")\n", + "caxs3.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[3].set_ylim([0, 180])\n", + "axs[3].set_yticks([45, 90, 135])\n", + "axs[3].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", + "axs[3].text(\n", + " 0.02,\n", + " 0.1,\n", + " \"100 keV < $E_e$ < 200 keV\",\n", + " bbox=dict(fc=(1, 1, 1)),\n", + " transform=axs[3].transAxes,\n", + ")\n", + "\n", + "axs[4], caxs4 = plot_spectr(\n", + " axs[4], dpf_feeps_omni_i_spin[:, 1:], yscale=\"log\", cscale=\"log\"\n", + ")\n", + "caxs4.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[4].set_ylabel(\"$E_i$ [keV]\")\n", + "\n", + "axs[5], caxs5 = plot_spectr(\n", + " axs[5], dpf_feeps_pad_i_070_100_spin, cscale=\"log\", clim=[3e-1, 1e2]\n", + ")\n", + "caxs5.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[5].set_ylim([0, 180])\n", + "axs[5].set_yticks([45, 90, 135])\n", + "axs[5].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", + "axs[5].text(\n", + " 0.02,\n", + " 0.1,\n", + " \"70 keV < $E_e$ < 100 keV\",\n", + " bbox=dict(fc=(1, 1, 1)),\n", + " transform=axs[5].transAxes,\n", + ")\n", + "\n", + "axs[6], caxs6 = plot_spectr(axs[6], dpf_feeps_pad_i_100_200_spin, cscale=\"log\")\n", + "caxs6.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", + "axs[6].set_ylim([0, 180])\n", + "axs[6].set_yticks([45, 90, 135])\n", + "axs[6].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", + "axs[6].text(\n", + " 0.02,\n", + " 0.1,\n", + " \"100 keV < $E_i$ < 200 keV\",\n", + " bbox=dict(fc=(1, 1, 1)),\n", + " transform=axs[6].transAxes,\n", + ")\n", + "\n", + "f.align_ylabels(axs)\n", + "f.suptitle(f\"MMS {mms_id:d} spin averaged\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/01_mms/example_mms_hpca.ipynb b/docs/examples/01_mms/example_mms_hpca.ipynb new file mode 100644 index 00000000..c8198e17 --- /dev/null +++ b/docs/examples/01_mms/example_mms_hpca.ipynb @@ -0,0 +1,298 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hot Plasma Composition Analyzer\n", + "author: Louis Richard\\\n", + "HPCA summary plot" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pyrfu import mms\n", + "from pyrfu.plot import plot_line, plot_spectr, make_labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define spacecraft index, time interval and species" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "ic = 1\n", + "mms.db_init(\"/Volumes/mms\")\n", + "tint = [\"2017-07-23T16:54:00.000\", \"2017-07-23T17:00:00.000\"]\n", + "species = {\n", + " \"hplus\": \"$H^+$\",\n", + " \"heplus\": \"$He^+$\",\n", + " \"heplusplus\": \"$He^{2+}$\",\n", + " \"oplus\": \"$O^+$\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load HPCA moments" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:39:51] INFO: Loading mms1_hpca_hplus_number_density...\n", + "[08-Jun-23 17:39:51] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:39:51] INFO: Loading mms1_hpca_heplus_number_density...\n", + "[08-Jun-23 17:39:51] INFO: Loading mms1_hpca_heplusplus_number_density...\n", + "[08-Jun-23 17:39:51] INFO: Loading mms1_hpca_oplus_number_density...\n", + "[08-Jun-23 17:39:51] INFO: Loading mms1_hpca_hplus_ion_bulk_velocity...\n", + "[08-Jun-23 17:39:51] INFO: Loading mms1_hpca_heplus_ion_bulk_velocity...\n", + "[08-Jun-23 17:39:51] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:39:51] INFO: Loading mms1_hpca_heplusplus_ion_bulk_velocity...\n", + "[08-Jun-23 17:39:51] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:39:51] INFO: Loading mms1_hpca_oplus_ion_bulk_velocity...\n", + "[08-Jun-23 17:39:51] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n" + ] + } + ], + "source": [ + "n_hpca = [mms.get_data(f\"n{s}_hpca_srvy_l2\", tint, ic) for s in species.keys()]\n", + "v_xyz_hpca = [mms.get_data(f\"v{s}_dbcs_hpca_srvy_l2\", tint, ic) for s in species.keys()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load HPCA ion" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:39:51] INFO: Loading mms1_hpca_hplus_flux...\n", + "[08-Jun-23 17:39:51] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:39:53] INFO: Loading mms1_hpca_heplus_flux...\n", + "[08-Jun-23 17:39:55] INFO: Loading mms1_hpca_heplusplus_flux...\n", + "[08-Jun-23 17:39:55] INFO: Loading mms1_hpca_oplus_flux...\n" + ] + } + ], + "source": [ + "flux_hpca = [mms.get_data(f\"dpf{s}_hpca_srvy_l2\", tint, ic) for s in species.keys()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load magnetic field" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:39:55] INFO: Loading mms1_fgm_b_gse_srvy_l2...\n" + ] + } + ], + "source": [ + "b_xyz = mms.get_data(\"b_gse_fgm_srvy_l2\", tint, ic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute ion fluxes" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:39:55] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_66155/699958699.py:7: RuntimeWarning: Mean of empty slice\n", + " np.nanmean(flux.data, axis=1), coords=coords, dims=dims, attrs=flux.attrs\n", + "\n" + ] + } + ], + "source": [ + "def calc_hpca_flux(flux):\n", + " flux.data[flux.data <= 0] = np.nan\n", + " coords = [flux.time.data, flux.ccomp.data]\n", + " dims = [\"time\", \"energy\"]\n", + "\n", + " out = xr.DataArray(\n", + " np.nanmean(flux.data, axis=1), coords=coords, dims=dims, attrs=flux.attrs\n", + " )\n", + " return out\n", + "\n", + "\n", + "flux_hpca = [calc_hpca_flux(flux) for flux in flux_hpca]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "legend_options = dict(\n", + " ncol=1, frameon=False, loc=\"upper left\", bbox_to_anchor=(1.0, 1.0)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([,\n", + " , ,\n", + " , ,\n", + " ], dtype=object)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(6, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "\n", + "plot_line(axs[0], b_xyz)\n", + "axs[0].set_ylabel(\"$B_{GSE}$\" + \"\\n\" + \"[nT]\")\n", + "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\"], **legend_options)\n", + "\n", + "labels = []\n", + "for n, s in zip(n_hpca, species.keys()):\n", + " plot_line(axs[1], n, linestyle=\"-\", marker=\"o\")\n", + " labels.append(species[s])\n", + "\n", + "axs[1].set_yscale(\"log\")\n", + "axs[1].set_ylabel(\"$n$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", + "axs[1].legend(labels, **legend_options)\n", + "\n", + "caxs = [None] * len(species)\n", + "for s, ax, cax, flux in zip(species.keys(), axs[2:], caxs, flux_hpca):\n", + " ax, cax = plot_spectr(ax, flux, yscale=\"log\", cscale=\"log\", cmap=\"viridis\")\n", + " ax.set_ylabel(\"$E$\" + \"\\n\" + \"[eV]\")\n", + " cax.set_ylabel(\"flux\" + \"\\n\" + \"[1/cc s sr eV]\")\n", + " ax.text(0.05, 0.1, species[s], transform=ax.transAxes)\n", + "\n", + "f.align_ylabels(axs)\n", + "make_labels(axs, [0.02, 0.85])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/01_mms/example_mms_ipshocks.ipynb b/docs/examples/01_mms/example_mms_ipshocks.ipynb new file mode 100644 index 00000000..e7240587 --- /dev/null +++ b/docs/examples/01_mms/example_mms_ipshocks.ipynb @@ -0,0 +1,382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2f9d878d", + "metadata": {}, + "source": [ + "# Interplanetary Shock Parameters and Transformation to Normal Incidence Frame\n", + "author: Louis Richard\n", + "\n", + "Example to shows how to convert shock related plasma data to the normal incidence frame. The same procedure can be used at the bow shock." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f11bde2d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from pyrfu import mms, pyrf\n", + "from pyrfu.plot import plot_spectr, plot_line\n", + "\n", + "mms.db_init(\"/Volumes/mms\")" + ] + }, + { + "cell_type": "markdown", + "id": "3fefa468", + "metadata": {}, + "source": [ + "## Non-exhaustive list of IP shock events observed by MMS (burst mode)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "87789d5f", + "metadata": {}, + "outputs": [], + "source": [ + "ipshock_events = [\n", + " {\n", + " \"tint\": [\"2017-10-24T08:25:45.000000\", \"2017-10-24T08:27:45.000000\"],\n", + " \"tintu\": [\"2017-10-24T08:26:36.000000\", \"2017-10-24T08:26:42.000000\"],\n", + " \"tintd\": [\"2017-10-24T08:26:54.000000\", \"2017-10-24T08:27:04.000000\"],\n", + " },\n", + " {\n", + " \"tint\": [\"2018-01-08T06:40:45.000000\", \"2018-01-08T06:41:30.000000\"],\n", + " \"tintu\": [\"2018-01-08T06:40:45.000000\", \"2018-01-08T06:41:00.000000\"],\n", + " \"tintd\": [\"2018-01-08T06:41:20.000000\", \"2018-01-08T06:41:30.000000\"],\n", + " },\n", + " {\n", + " \"tint\": [\"2022-02-01T22:17:00.000000\", \"2022-02-01T22:20:00.000000\"],\n", + " \"tintu\": [\"2022-02-01T22:18:08.000000\", \"2022-02-01T22:18:26.000000\"],\n", + " \"tintd\": [\"2022-02-01T22:18:43.000000\", \"2022-02-01T22:18:56.000000\"],\n", + " },\n", + " {\n", + " \"tint\": [\"2022-02-11T10:23:20.000000\", \"2022-02-11T10:25:10.000000\"],\n", + " \"tintu\": [\"2022-02-11T10:24:05.000000\", \"2022-02-11T10:24:07.000000\"],\n", + " \"tintd\": [\"2022-02-11T10:24:16.000000\", \"2022-02-11T10:24:22.000000\"],\n", + " },\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "790fa197", + "metadata": {}, + "source": [ + "## Select IP shock event" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5043d7e6", + "metadata": {}, + "outputs": [], + "source": [ + "event_num = 0\n", + "reference_frame = \"nif\"\n", + "mms_id = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8ca360c2", + "metadata": {}, + "outputs": [], + "source": [ + "tint = ipshock_events[event_num][\"tint\"]\n", + "tintu = ipshock_events[event_num][\"tintu\"]\n", + "tintd = ipshock_events[event_num][\"tintd\"]" + ] + }, + { + "cell_type": "markdown", + "id": "ca3b8fe4", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b4a39bbc", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[14-Jun-23 11:14:26] INFO: Loading mms2_mec_r_gse...\n", + "[14-Jun-23 11:14:26] INFO: Loading mms2_fgm_b_gse_brst_l2...\n", + "[14-Jun-23 11:14:26] INFO: Loading mms2_dis_numberdensity_brst...\n", + "[14-Jun-23 11:14:26] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[14-Jun-23 11:14:26] INFO: Loading mms2_dis_bulkv_gse_brst...\n", + "[14-Jun-23 11:14:26] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[14-Jun-23 11:14:26] INFO: Loading mms2_des_numberdensity_brst...\n", + "[14-Jun-23 11:14:26] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[14-Jun-23 11:14:26] INFO: Loading mms2_dis_dist_brst...\n", + "[14-Jun-23 11:14:26] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_dist.py:68: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[14-Jun-23 11:14:27] INFO: Loading mms2_dis_disterr_brst...\n" + ] + } + ], + "source": [ + "# Spacecraft location\n", + "r_gse = mms.get_data(\"r_gse_mec_srvy_l2\", tint, mms_id)\n", + "\n", + "# Magnetic field\n", + "b_gse = mms.get_data(\"b_gse_fgm_brst_l2\", tint, mms_id)\n", + "\n", + "# Ion number density and bulk velocity\n", + "n_i = mms.get_data(\"ni_fpi_brst_l2\", tint, mms_id)\n", + "v_gse_i = mms.get_data(\"vi_gse_fpi_brst_l2\", tint, mms_id)\n", + "\n", + "# Electron number density\n", + "n_e = mms.get_data(\"ne_fpi_brst_l2\", tint, mms_id)\n", + "\n", + "vdf_i = mms.get_data(\"pdi_fpi_brst_l2\", tint, mms_id)\n", + "vdf_err_i = mms.get_data(\"pderri_fpi_brst_l2\", tint, mms_id)\n", + "vdf_i.data.data[vdf_i.data.data < 1.1 * vdf_err_i.data.data] = 0.0" + ] + }, + { + "cell_type": "markdown", + "id": "a4ec7931", + "metadata": {}, + "source": [ + "### Compute the shock normal and shock parameters (if normal incidence frame NIF selected)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "002c6816", + "metadata": {}, + "outputs": [], + "source": [ + "if reference_frame.lower() == \"nif\":\n", + " plp = {}\n", + " plp[\"b_u\"] = np.mean(pyrf.time_clip(b_gse, tintu).data, axis=0)\n", + " plp[\"n_u\"] = np.mean(pyrf.time_clip(n_i, tintu).data, axis=0)\n", + " plp[\"v_u\"] = np.mean(pyrf.time_clip(v_gse_i, tintu).data, axis=0)\n", + " plp[\"b_d\"] = np.mean(pyrf.time_clip(b_gse, tintd).data, axis=0)\n", + " plp[\"n_d\"] = np.mean(pyrf.time_clip(n_i, tintd).data, axis=0)\n", + " plp[\"v_d\"] = np.mean(pyrf.time_clip(v_gse_i, tintd).data, axis=0)\n", + " plp[\"r_xyz\"] = np.mean(r_gse.data, axis=0)\n", + "\n", + " # get normal and shock speed\n", + " nst = pyrf.shock_normal(plp)\n", + "\n", + " # let's use mixed mode 3 as normal\n", + " nvec = nst[\"n\"][\"mx_3\"]\n", + "\n", + " # and the SB for shock speed\n", + " v_sh = nst[\"v_sh\"][\"sb\"][\"mx_3\"]\n", + "\n", + " # then add info to plp\n", + " plp[\"nvec\"] = nvec\n", + " plp[\"v_sh\"] = v_sh\n", + " plp[\"ref_sys\"] = reference_frame.lower()\n", + "\n", + " # set coordinate system\n", + " t2vec = np.cross(nvec, plp[\"b_u\"])\n", + " t2vec /= np.linalg.norm(t2vec)\n", + "\n", + " t1vec = np.cross(t2vec, nvec)\n", + " t1vec /= np.linalg.norm(t1vec)\n", + "\n", + " t1vec = np.tile(t1vec, (len(vdf_i.time), 1))\n", + " t2vec = np.tile(t2vec, (len(vdf_i.time), 1))\n", + " nvec = np.tile(nvec, (len(vdf_i.time), 1))\n", + "\n", + " # rotation matrix to shock-aligned coordinate system\n", + " r_mat = np.transpose(np.stack([nvec, t1vec, t2vec]), [1, 2, 0])\n", + " r_mat = pyrf.ts_tensor_xyz(vdf_i.time.data, r_mat)\n", + "\n", + " # Get more shock parameters\n", + " shp = pyrf.shock_parameters(plp)\n", + "\n", + " # velocity of the NI frame in the SC frame\n", + " v_nif = shp[\"v_nif_u\"]\n", + "elif reference_frame.lower() == \"s/c\":\n", + " r_mat = np.tile(np.eye(3), (len(vdf_i.time), 1, 1))\n", + " r_mat = pyrf.ts_tensor_xyz(vdf_i.time.data, r_mat)\n", + " v_nif = np.zeros(3)\n", + " nvec = r_mat.data[:, :, 0]" + ] + }, + { + "cell_type": "markdown", + "id": "4f58bb7d", + "metadata": {}, + "source": [ + "## Reduce ion VDF along the normal (if NIF selected) or x GSE" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "462b271f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████| 800/800 [00:02<00:00, 287.96it/s]\n" + ] + } + ], + "source": [ + "# Number of Monte Carlo iterations per bin. Decrease to improve performance, increase to improve plot.\n", + "n_mc = 2e2\n", + "\n", + "\n", + "if reference_frame.lower() == \"nif\":\n", + " # Define velocity grid\n", + " v_lim = [0.0, 1000.0] # km/s\n", + " v_1d = np.linspace(v_lim[0], v_lim[1], 100) * 1e3\n", + "\n", + " # Reduce ion disctribution\n", + " f1d = mms.reduce(vdf_i, projection_dim=\"1d\", xyz=r_mat, n_mc=n_mc, vg=v_1d)\n", + " f1d = f1d.assign_coords(vx=f1d.vx.data - np.dot(v_nif, nvec[0, :]) / 1e3)\n", + "\n", + "elif reference_frame.lower() == \"s/c\":\n", + " # Define velocity grid\n", + " v_lim = [-1000.0, 0.0] # km/s\n", + " v_1d = np.linspace(v_lim[0], v_lim[1], 100) * 1e3\n", + "\n", + " # Reduce ion disctribution\n", + " f1d = mms.reduce(vdf_i, projection_dim=\"1d\", xyz=r_mat, n_mc=n_mc, vg=v_1d)\n", + " f1d = f1d.assign_coords(vx=f1d.vx.data - np.dot(v_nif, nvec[0, :]) / 1e3)" + ] + }, + { + "cell_type": "markdown", + "id": "8569a7ef", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "615b205b", + "metadata": {}, + "outputs": [], + "source": [ + "legend_options = dict(\n", + " handlelength=1, ncol=1, frameon=False, loc=\"upper left\", bbox_to_anchor=(1.0, 1.0)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a262afeb", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0.98, 'MMS 2 - IP shock in the NIF frame')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(5, sharex=\"all\", figsize=(6, 8))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "plot_line(axs[0], pyrf.norm(b_gse), color=\"k\")\n", + "axs[0].set_ylabel(\"$|\\\\mathbf{B}|~[\\\\mathrm{nT}]$\")\n", + "\n", + "plot_line(axs[1], pyrf.new_xyz(b_gse, r_mat.data[0, ...]))\n", + "axs[1].axhline(0.0, color=\"k\", linestyle=\"--\")\n", + "axs[1].legend([\"$B_{n}$\", \"$B_{t1}$\", \"$B_{t2}$\"], **legend_options)\n", + "axs[1].set_ylabel(\"$\\\\mathbf{B}~[\\\\mathrm{nT}]$\")\n", + "\n", + "plot_line(axs[2], n_i, label=\"$n_i$\")\n", + "plot_line(axs[2], n_e, label=\"$n_e$\")\n", + "axs[2].legend(**legend_options)\n", + "axs[2].set_ylabel(\"$n~[\\\\mathrm{cm}^{-3}]$\")\n", + "\n", + "plot_line(axs[3], pyrf.new_xyz(v_gse_i - v_nif / 1e3, r_mat.data[0, ...]))\n", + "axs[3].legend([\"$V_{n}$\", \"$V_{t2}$\", \"$V_{t2}$\"], **legend_options)\n", + "axs[3].set_ylabel(\"$\\\\mathbf{V}_i~[\\\\mathrm{km} \\\\mathrm{s}^{-1}]$\")\n", + "\n", + "axs[4], caxs4 = plot_spectr(axs[4], f1d, cscale=\"log\", cmap=\"Spectral_r\")\n", + "axs[4].set_ylabel(\"$v_n~[\\\\mathrm{km} \\\\mathrm{s}^{-1}]$\")\n", + "caxs4.set_ylabel(\"$F_i~[\\\\mathrm{s}~\\\\mathrm{m}^{-4}]$\")\n", + "\n", + "f.align_ylabels(axs)\n", + "f.suptitle(f\"MMS {mms_id} - IP shock in the {reference_frame.upper()} frame\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/01_mms/example_mms_ohmslaw.ipynb b/docs/examples/01_mms/example_mms_ohmslaw.ipynb new file mode 100644 index 00000000..7d4913b1 --- /dev/null +++ b/docs/examples/01_mms/example_mms_ohmslaw.ipynb @@ -0,0 +1,520 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ohm's Law\n", + "Author: Louis Richard\\\n", + "Compute the terms in the generalized Ohm's law equation: Ion convection, Hall, and electron pressure divergence terms. Hall and pressure terms are computed using four-spacecraft methods. The observed electric fields and convection terms are averaged over the four spacecraft. Terms computed in GSE coordinates." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from scipy import constants\n", + "from pyrfu import mms, pyrf\n", + "from pyrfu.plot import plot_line" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define time interval and data path" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mms.db_init(\"/Volumes/mms\")\n", + "tint = [\"2015-10-30T05:15:40.000\", \"2015-10-30T05:15:55.000\"]\n", + "tint_long = pyrf.extend_tint(tint, [-60, 60])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load all data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load FPI data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:43:03] INFO: Loading mms1_des_numberdensity_brst...\n", + "[08-Jun-23 17:43:03] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:43:04] INFO: Loading mms2_des_numberdensity_brst...\n", + "[08-Jun-23 17:43:04] INFO: Loading mms3_des_numberdensity_brst...\n", + "[08-Jun-23 17:43:04] INFO: Loading mms4_des_numberdensity_brst...\n", + "[08-Jun-23 17:43:04] INFO: Loading mms1_des_bulkv_gse_brst...\n", + "[08-Jun-23 17:43:04] INFO: Loading mms2_des_bulkv_gse_brst...\n", + "[08-Jun-23 17:43:04] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:43:04] INFO: Loading mms3_des_bulkv_gse_brst...\n", + "[08-Jun-23 17:43:04] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:43:04] INFO: Loading mms4_des_bulkv_gse_brst...\n", + "[08-Jun-23 17:43:04] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:43:04] INFO: Loading mms1_des_prestensor_gse_brst...\n", + "[08-Jun-23 17:43:04] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:43:04] INFO: Loading mms2_des_prestensor_gse_brst...\n", + "[08-Jun-23 17:43:04] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:43:04] INFO: Loading mms3_des_prestensor_gse_brst...\n", + "[08-Jun-23 17:43:04] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:43:04] INFO: Loading mms4_des_prestensor_gse_brst...\n", + "[08-Jun-23 17:43:04] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:43:04] INFO: Loading mms1_dis_numberdensity_brst...\n", + "[08-Jun-23 17:43:04] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:43:04] INFO: Loading mms2_dis_numberdensity_brst...\n", + "[08-Jun-23 17:43:05] INFO: Loading mms3_dis_numberdensity_brst...\n", + "[08-Jun-23 17:43:05] INFO: Loading mms4_dis_numberdensity_brst...\n", + "[08-Jun-23 17:43:05] INFO: Loading mms1_dis_bulkv_gse_brst...\n", + "[08-Jun-23 17:43:05] INFO: Loading mms2_dis_bulkv_gse_brst...\n", + "[08-Jun-23 17:43:05] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:43:05] INFO: Loading mms3_dis_bulkv_gse_brst...\n", + "[08-Jun-23 17:43:05] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 17:43:05] INFO: Loading mms4_dis_bulkv_gse_brst...\n", + "[08-Jun-23 17:43:05] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n" + ] + } + ], + "source": [ + "n_mms_e = [mms.get_data(\"ne_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", + "v_mms_e = [mms.get_data(\"ve_gse_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", + "p_mms_e = [mms.get_data(\"pe_gse_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", + "\n", + "n_mms_i = [mms.get_data(\"ni_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", + "v_mms_i = [mms.get_data(\"vi_gse_fpi_brst_l2\", tint, i) for i in range(1, 5)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load FGM data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:43:05] INFO: Loading mms1_fgm_b_gse_brst_l2...\n", + "[08-Jun-23 17:43:05] INFO: Loading mms2_fgm_b_gse_brst_l2...\n", + "[08-Jun-23 17:43:05] INFO: Loading mms3_fgm_b_gse_brst_l2...\n", + "[08-Jun-23 17:43:05] INFO: Loading mms4_fgm_b_gse_brst_l2...\n" + ] + } + ], + "source": [ + "b_mms = [mms.get_data(\"b_gse_fgm_brst_l2\", tint, i) for i in range(1, 5)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load spacecraft position" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:43:05] INFO: Loading mms1_mec_r_gse...\n", + "[08-Jun-23 17:43:06] INFO: Loading mms2_mec_r_gse...\n", + "[08-Jun-23 17:43:06] INFO: Loading mms3_mec_r_gse...\n", + "[08-Jun-23 17:43:06] INFO: Loading mms4_mec_r_gse...\n" + ] + } + ], + "source": [ + "r_mms = [mms.get_data(\"r_gse_mec_srvy_l2\", tint_long, i) for i in range(1, 5)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load electric field" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:43:06] INFO: Loading mms1_edp_dce_gse_brst_l2...\n", + "[08-Jun-23 17:43:07] INFO: Loading mms2_edp_dce_gse_brst_l2...\n", + "[08-Jun-23 17:43:08] INFO: Loading mms3_edp_dce_gse_brst_l2...\n", + "[08-Jun-23 17:43:09] INFO: Loading mms4_edp_dce_gse_brst_l2...\n" + ] + } + ], + "source": [ + "e_mms = [mms.get_data(\"e_gse_edp_brst_l2\", tint, i) for i in range(1, 5)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resample and compute the 4 s/c averages" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 17:43:10] INFO: Using averages in resample\n", + "[08-Jun-23 17:43:10] INFO: Using averages in resample\n", + "[08-Jun-23 17:43:10] INFO: Using averages in resample\n", + "[08-Jun-23 17:43:10] INFO: Using averages in resample\n", + "[08-Jun-23 17:43:10] INFO: Using averages in resample\n", + "[08-Jun-23 17:43:10] INFO: Using averages in resample\n", + "[08-Jun-23 17:43:10] INFO: Using averages in resample\n", + "[08-Jun-23 17:43:10] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "n_mms_e = [pyrf.resample(n_e, n_mms_e[0]) for n_e in n_mms_e]\n", + "v_mms_e = [pyrf.resample(v_xyz_e, n_mms_e[0]) for v_xyz_e in v_mms_e]\n", + "p_mms_e = [pyrf.resample(p_xyz_e, n_mms_e[0]) for p_xyz_e in p_mms_e]\n", + "n_mms_i = [pyrf.resample(n_i, n_mms_e[0]) for n_i in n_mms_i]\n", + "v_mms_i = [pyrf.resample(v_xyz_i, n_mms_e[0]) for v_xyz_i in v_mms_i]\n", + "r_mms = [pyrf.resample(r_xyz, n_mms_e[0]) for r_xyz in r_mms]\n", + "b_mms = [pyrf.resample(b_xyz, n_mms_e[0]) for b_xyz in b_mms]\n", + "e_mms = [pyrf.resample(e_xyz, n_mms_e[0]) for e_xyz in e_mms]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "n_e = pyrf.avg_4sc(n_mms_e)\n", + "b_xyz = pyrf.avg_4sc(b_mms)\n", + "e_xyz = pyrf.avg_4sc(e_mms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute convection terms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute ion convection term (MMS average)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "evxb_mms_i = [\n", + " 1e-3 * pyrf.cross(v_xyz_i, b_xyz) for v_xyz_i, b_xyz in zip(v_mms_i, b_mms)\n", + "]\n", + "evxb_xyz_i = pyrf.avg_4sc(evxb_mms_i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute electron convection term (MMS average)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "evxb_mms_e = [\n", + " 1e-3 * pyrf.cross(v_xyz_e, b_xyz) for v_xyz_e, b_xyz in zip(v_mms_e, b_mms)\n", + "]\n", + "evxb_xyz_e = pyrf.avg_4sc(evxb_mms_e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute pressure divergence term" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "p_mms_xx = [1e-9 * p_xyz[:, 0, 0] for p_xyz in p_mms_e]\n", + "p_mms_yy = [1e-9 * p_xyz[:, 1, 1] for p_xyz in p_mms_e]\n", + "p_mms_zz = [1e-9 * p_xyz[:, 2, 2] for p_xyz in p_mms_e]\n", + "p_mms_xy = [1e-9 * p_xyz[:, 0, 1] for p_xyz in p_mms_e]\n", + "p_mms_xz = [1e-9 * p_xyz[:, 0, 2] for p_xyz in p_mms_e]\n", + "p_mms_yz = [1e-9 * p_xyz[:, 1, 2] for p_xyz in p_mms_e]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "ep_xyz_xx = pyrf.c_4_grad(r_mms, p_mms_xx, \"grad\")\n", + "ep_xyz_yy = pyrf.c_4_grad(r_mms, p_mms_yy, \"grad\")\n", + "ep_xyz_zz = pyrf.c_4_grad(r_mms, p_mms_zz, \"grad\")\n", + "ep_xyz_xy = pyrf.c_4_grad(r_mms, p_mms_xy, \"grad\")\n", + "ep_xyz_xz = pyrf.c_4_grad(r_mms, p_mms_xz, \"grad\")\n", + "ep_xyz_yz = pyrf.c_4_grad(r_mms, p_mms_yz, \"grad\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "ep_x = -(ep_xyz_xx[:, 0] + ep_xyz_xy[:, 1] + ep_xyz_xz[:, 2]) / (\n", + " n_e * 1e6 * constants.elementary_charge\n", + ")\n", + "ep_y = -(ep_xyz_xy[:, 0] + ep_xyz_yy[:, 1] + ep_xyz_yz[:, 2]) / (\n", + " n_e * 1e6 * constants.elementary_charge\n", + ")\n", + "ep_z = -(ep_xyz_xz[:, 0] + ep_xyz_yz[:, 1] + ep_xyz_zz[:, 2]) / (\n", + " n_e * 1e6 * constants.elementary_charge\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "ep_xyz = pyrf.ts_vec_xyz(\n", + " ep_xyz_xx.time.data, np.vstack([ep_x.data, ep_y.data, ep_z.data]).T\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Hall term and current density using curlometer" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "j_xyz, div_b, b_xyz, jxb_xyz, div_t_shear, div_pb = pyrf.c_4_j(r_mms, b_mms)\n", + "jxb_xyz.data /= n_e.data[:, None] * constants.elementary_charge * 1e6\n", + "jxb_xyz.data *= 1e3\n", + "j_xyz.data *= 1e9" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "e_lhs = pyrf.ts_vec_xyz(e_xyz.time.data, e_xyz.data - evxb_xyz_i.data)\n", + "e_rhs = pyrf.ts_vec_xyz(e_xyz.time.data, jxb_xyz.data + ep_xyz.data);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot figure" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "legend_options = dict(\n", + " ncol=1, loc=\"upper left\", frameon=False, bbox_to_anchor=(1.0, 1.0)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'MMS - 4 Spacecraft average')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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yZVq3bk2vXr1o3Lgx7u7u3Lt3L61ZZeoUBC+rQIECT0w1cfPmTUDVuGRUM/i41q1b4+XlRYcOHShdujRxcXHs3buX7777jjp16lCvXr10258/f54333yTnj17cuXKFcaPH0/jxo3TkpDmzZtjbm5Ov379GDVqFPfu3WPixIkUKVIk3XOgT58+zJs3j169evHRRx9Rq1YtoqKi2LZtG8OHD6dMmTJMmTIFX19fGjZsyHvvvYe3tzdhYWGcO3eO69evM3/+fACmT59Oo0aNqFOnDqNGjaJw4cJcv36dU6dOMWvWrKdef1bjzUy5cuXYv38/mzZtokCBAri5ueHt7Z3pZ/3VV18xbdo0fH192bVrF6tXr85w21KlSlGrVi3GjRvH3bt3n2hq3LhxY3r16kXXrl0ZOXIkvr6+mJqacvPmTbZs2cJXX32VboTOl4ln8uTJtGzZkk6dOvG///2PkJAQJk2aRIECBdI1mx4xYgQrVqygQYMGjBgxgtKlSxMTE8OlS5fYv38/GzZseManKYQQQuiJoUeheVWljgr4+MhuAwYM0N588029HD8kJEQDtJMnT6ZbX7VqVe3LL7/UyzlE3pLRSJ3/9axRQLN67LCwMG3y5MlagwYNtAIFCmjm5uaas7Oz1rhxY23VqlX6u6gMPM8ooMuXL9e6deumFS9eXLOxsdGsra21smXLah999JEWGRmZtl3q57JmzRqtf//+mpOTk2Zvb6/16tVLe/DgQbpjrlixQitdurRmZWWllStXTlu2bJnWv39/rWjRoum2i4qK0j788EOtSJEimoWFhVagQAGtS5cuWlBQUNo2AQEB2qBBgzRPT8+0bZo3b64tXrw43bFOnjyptWvXTnNyctKsra210qVLp3tONGrUSKtXr16Gn0FW481oFNCLFy9q9evX12xsbDTgifcfFxsbqw0ePFhzc3PT7O3ttddee027fv26BmgTJ058YvvZs2drwBMjgqZKSUnRZs6cqVWqVEmzsrLSHB0dtUqVKmmjR4/WwsPDNU17+r3wPPEsXbpUK1WqlGZpaamVK1dOW7t2rValShWtY8eO6bZ7+PChNnz4cM3b21uzsLDQ8ufPr9WvX1+bMWNGpp+LEEIIoW8mmpaFERDyuMOHDzN+/HiOHTuGmZkZ5cqV48CBAxkOipGSkkJSUhL79u2jVatWREdHY2ZmhqWlJSdOnKBZs2b89ttvvP7665iZmXHjxg2uXr2aNv/X8yhXrhx169blu+++w97eni1bttClSxc2b95M8+bN9XHpQogs2LNnD02aNGH79u3yb09w584dSpQowfjx49PmRRRCCCFyCxkF9BkOHz5MmzZtGDBgAEFBQYSEhDBjxoxMJ4NevHgxNjY2tGrVCgB7e3tsbGzYt28fNWvWZPv27cybN49ChQrh6upK165dn9rn72k2bNhASEgIJUqUwNnZmbFjxzJr1iwpgAohRA6Ji4tjyJAhrFmzhr1797JgwQJatGiBra0tb731lqHDE0IIIZ4gNYDP0KBBA3x9fdONBCeEEI+TGsC8KzExkR49enDkyBFCQ0Oxs7OjQYMGTJs2Ld0IsUIIIURuIQngU8TGxuLg4MCoUaPYs2cP165dw9vbm48//pguXboYOjwhhBBCCCGEeC6SAD7FnTt38PLywt3dnU2bNlG1alX+/PNPevbsyd69e9OGdgc1KmdgYCAODg6ZNg8VQgghhHiVaJpGVFQUnp6eGY6NIITIfWQaiKdwcHAAYMCAAdSsWROAzp0706RJE9avX58uAQwMDKR06dIkJyenrTM3N8fcXD5i8eqzt7cnOTmZ+Ph4Q4ciRI6T+1/kNcnJyenKO4mJiQQEBGRpflAhhOFJdvIUTk5O+Pj4ZKlGz8HBgdjYWAICAp6YPFiIV11UVBSTJ09m+vTphg5FiBwn97/IyyIjI/Hy8kr70lwIkftJAvgM77//Pl988QU9e/akUqVKbNq0ib179zJlypR026Umie3atcPW1hYrKyusrKywsbHB1tY2bbGyssLS0hILC4unvlpZWeHg4ICjoyOOjo7Y2tpibW2ddlwrKytpaipyFUtLS/nyQ+RZcv+LvE7KJEIYD0kAn+GDDz4gNjaW9u3bEx4eTsmSJVmxYgW1atXKcPvRo0djbm5OQkICCQkJxMXFERsbm7bExMQQHh5OUlISiYmJmb4mJCQQHR1NZGQkERERxMXFPXEuBwcHnJycnmtxdHRM97M0URVCCCGEECLvkEFg9CQyMhInJyciIiKy5VtgTdPSEsP4+Hji4uKIiooiIiIiS0tqIpm66HQ6AOzs7NIlhZkli09bZ2VlpffrNTRN09L69GS0pP4dUpekpCSSk5NJSUlJe33856y8JiYmEh0dTWxsLKamppiZmQFqgKFnLRYWFuTLly9tSa0x1ul0aV8e5MuXD1dX1ycWffz9IiMj2bFjB507d37pYwlhbOT+F3lZdpd/hBD6JwmgnhjTA1DTNGJiYjJMDJ9nXWJiIgBWVlZZThYdHR2xsLDAxMQEMzMzLC0tMTc3R9O0py6Px56amKUmYQkJCWkd0h9fHk+Ys5LE/Xf57z+N1Ca41tbW6RYrKyssLCwwNzfHzMws7fXxn7PynqWlJQ4ODtja2qLT6UhOTsbExARTU9O0xczMLN3vqUtiYiKhoaGEhoby8OFD4uLiSEhIwNTUFBsbGwAePnxISEhI2naRkZGAarqWemwLCwsKFiyIl5cXXl5eFC5cmIIFC+Lp6UmBAgUoWLAghQoVeqKpT+qxcvu9L0R2kPtf5GXGVP4RQijS/i8PMjExwd7eHnt7ewoVKvTCx0lISMhSonjv3j0uXbqU9ntycnJaIpeUlERSUhImJibPXFJjt7CwSOsHaW1tjaWlZVoi+d/FxsaGfPnypUvW/pvA/TeZy2i9paXlK9e/ISkpiYcPHxIVFYVOpyMlJYXExETu3bvH7du3CQgI4ObNmxw+fJh79+5x//59QkJCcHR0pFq1alSvXp0KFSrg7e1N8eLF5T9+IYQQQggjIDWAeiLfgIm8ICEhgfPnz3PixAlOnDjBxYsXuX79OpGRkZiYmODh4UGFChXSllKlSuHh4YGbm5v0NxWvLKkBFHmZlH+EMD6SAOqJPADFS4uLg7AwiIiA1H+WKSmQnKyWpKRHP/93nbk5uLo+WqytwcICEhLUcZycIBtrMAMCAggPD+fu3bucO3eO8+fPc+7cOfz9/YmIiABU4djFxQVnZ2eKFi1K6dKlKVy4MO3bt6dYsWLZFpsQ2U0SQJGXSflHCOMjCaCeyAMwD9A0iI2F6GiIilKv//05JkZtExenXh9fUt+LjYX4eJWgxcTAw4cq8UudRNrWFkxN1fnMzZ9cLCye/D0pCUJD1RIb+2Ts5ubg5gYeHuDurl49PFRiaGEBlpbq1doaihSBEiXUq4VFlj6apxWAExISePDgAWFhYTx8+JDw8HCuX7/O1atX8ff3Z8+ePZQrV44yZcpQp04dOnToIAmhMCqSAIq8TMo/QhgfSQD1JO0B2Lkzji4uqhCfWlC3sEi//HeduTnY2anCuJMTODo+erWxydaamzxB01SiFRGR9SUyUr0+nuTFxDyqmbO0BHt7tTg4PPrZzk79zWxs1M+2to+Wx3+3slJJm50d5MsHLi7q1dk5y0lXpuLjVc1fUpI6j6bBgwdqCQ6GoKBHS1SU2i4xUS1xcXD7Nly/DjodFCoEnp4qISxZEooVg8qVoVw5dd/+62UKwGFhYfz9999cv36dXbt2sWfPHqpUqcJbb71F/fr1sba2pnjx4q9cH0zx6pAEUORlkgAKYXwkAdSTtAfg5Mk4pqSoWpikpEdN9DL6+fElJuZR0hER8ag2yNxcJYO2tirpsLJ69JqadDyefPx3XUZLanJpDH2ydDqVgD1P8vbfRC4yUjWlhPSJ9rMWR8dHyd3jSZ69vfobvMpSUiAgQCWDd+7A1atquX4dTp9Wf5dq1VQy6ONDZNWq4OGBY5Ei6jN+CeHh4Sxbtow//viDEydOkJSUhLe3N3369KFPnz74+Pjo6SKF0A9JAEVeJgmgEMZHEkA90fsDMDExfUIYG6vWJSSo1/j4R80RM1tiYp5cFxWlCu+gkkoHh0c1U3Z2Ty7OzpA/v0qIUqXu93hNlU73ZHKb1d8TEjKufUv9OfUWdXB4vuQto3XGkPTmdikpcPkyHD0K58/DpUtEnjsHDx/iGBmp7pcyZaBsWfD2Vp978eKqFtHJSX154eCgmrlmQXx8PJs2bWLx4sX89ddfODk54ebmRu3atbG1taVTp040a9ZMagiFwUgCKPIySQCFMD6SAOqJ0TwAU/uxpSZYqYliZkt4uGo6GBGhmqKm7h8V9ahWDdR7/23e+qyfU1+trDJO2B5fHBzg34nRRe4T+W8tq2NKiqolvHRJLbduqXvo2jW1PilJ7ZAvH9Spo2oQu3SBqlWz1NQ5NDSUmzdvcufOHU6cOEFYWBgrVqzA29ub3377jYoVK2bvhQqRAUkARV5mNOUfIUQaSQD1RB6AIi/LUgFYp3tUi33pkqpB/Ocf2LABChRQSWCzZtC1qxrJNItiYmL4/PPP+emnn1i6dClt27Z92csR4rlIAijyMin/CGF8JAHUE3kAirzspQrAsbGwYwecOwcbN8KJE9C2LQwYAPXqPWqe/AyLFi1i2LBhNG/enBkzZuDl5fX8sQjxAiQBFHmZlH+EMD5Z64QjhBDZxdYWOnSAjz+Gw4dV7WDFijBmjJqywt5e1Qz+8cejAX1S5zd8TL9+/bh8+TJ2dnaUKVOGzz77DF1qf1chhBBCCAFIAiiEyG2KF4fPPoMrV1Rf04AAaNMGPv9cDUpkY6OSxsKFoXRplRj+25DBw8ODhQsXsmvXLn7//Xfee+89SQKFEEJki19//RUTE5O0xdTUFHd3dzp16sSNGzcMHZ4QmZIhEYUQuVfqtBsffqiWe/dUk1FLS5UYXr0KI0bAn3/Cr7+qbYFatWqxa9cumjVrRosWLZgzZw6lS5c28MUIIYR4lfj5+WFnZ8eOHTsASEpK4sSJE4wZM4aQkBD2799v4AiFyJj0AdQTaQMv8jKD9oEKCoLu3dW0Elu2qBrCf0VFRTF27Fjmz59Pjx49mDJlCkWLFs35GMUrTfoAirwsL5d/6tWrh06n4/Dhw+nWt2/fnt27dxMdHW2gyIR4OmkCKoQwbh4eKvFLSoImTeCxZjcODg7MmTOHCxcuoNPpqF69unwjK4QQ4qVpmsaZM2eoVKnSE+8FBgZKqxORq0kCKIQwfnZ2sH07VKumppNYsSKtXyBA8eLFWbx4MZ9//jlt27Zl165dBgxWCCGEsfP39yc6OpoKFSqQnJxMUlISN2/eZNiwYVy8eJHp06cbOkQhMiV9AIUQrwYbG5gzR40Y+s47sHQpzJ4NRYqkbfLOO+9gbW1Nhw4dmD17NgMGDDBcvEIIIdLRNDX2V3ZxcAATE/0c69SpUwAMGzaMYcOGpa0vWLAg27dvp169evo5kRDZQBJAIcSrpUsXaNQIRo+GsmWhc2fo3RuaNwdzc/r370/hwoXp1q0bkZGR6f7jFkIIYThRUeDklH3Hj4gAfXVTPHXqFCYmJhw4cABLS0t0Oh03b95kwoQJ9OrVi/Pnz+Pg4KCfkwmhZ5IACiFePW5usGABDB8OS5bAoEHg7a2ahhYuTLNmzdi6dSstWrQgMDCQqVOnYm4uj0MhhDAkBweVpGXn8fXFz88PHx8f6tatm7bO19cXc3NzunTpwrZt2+jatav+TiiEHkkfQCHEq6tyZfjmG/D3h5IloWFDuHsXUP9RHzlyhA0bNjBq1CgDByqEEMLERNXQZdeir+afoGoAMxoAxtraGoC4uDgABg8enPZ/TFBQEBUrVmTLli36C0SIFyAJoBDi1Wdrq2oEmzZVI4Vevw5A2bJl2bhxIwsWLGD58uUGDlIIIYQxCAoK4v79+xkmgKtXr8bU1DStZnDKlCksXryYkydP0qZNGyZPnkzbtm1zOmQh0pE2T0KIvMHEBObOhZEjoV492LcPSpakRIkSLFu2jF69erF+/Xo6dOhAr169MNHnV8VCCCFeGX5+fgCYm5tz5MgRAO7fv8+aNWtYsmQJH330ET4+PgC4u7szfPhw6taty/z58+ncubPB4hYildQACiHyDlNTmDED+vZVo4XeugXAa6+9xvHjxylZsiSjR4+mT58+6HQ6AwcrhBAiN0odAXTChAnUqVOHunXr8uabb3Lv3j1Wr17NtGnT0rZ9+PAhq1atwsXFBS8vLwNFLER6Jpr22GRZ4oVFRkbi5OREREQEjvoaYkoIIxEZGQlgPPe+psH776sJ5N98EwYOhEKFAAgODsbX15ePPvqId955x8CBCmNgdPe/EHok5Z/MRURE0KJFC4YOHYqDgwPTpk3j2LFj0sJEGJzUAAoh8h4TE/jhB/jgAzh4EBo0SKsNdHd35+eff2bs2LEEBwcbOFAhhBDGKCYmhrZt2zJgwAD69+9P586dsbKyYtGiRYYOTQipAdQX+QZM5GVGXQOi06nawM2bYfduKFYMgHbt2lGuXDm+/vprAwcocjujvv+FeElS/hHC+EgNoBAibzM1hdmzoWNH8PWFTZsAmDhxIj/++KPUAgohhBDilSIJoBBCmJjAzJlq6dkT5syhZs2aNGzYkNmzZxs6OiGEEEIIvZEmoHoiTSBEXvZKNYE7fBhatIBNm9iZkkKPHj24ffs2tra2ho5M5FKv1P0vxHOS8o8QxkdqAIUQ4nF16sC0aTBwIE3r1MHLy4tvvvnG0FEJIYQQQuiFTAQvhBD/9d57MH8+JgsXsmDBAho2bIiFhQVDhgzBxcXF0NEJIYQQQrwwqQEUQoj/MjWFMWNg+nSqVKzIxo0b+fPPP6lVqxaxsbGGjk4IIYQQ4oVJAiiEEBnp1g1SUmDtWho1asTBgwdxc3Nj/Pjxho5MCCGEEOKFSQIohBAZsbCAkSPhq69A0zAzM2PBggXMnz+fhQsXIuNnCSGEEMIYSQIohBCZGTQIbtyA1asBKF26NGvXruWdd97B2dmZ48ePGzhAIYQQQojnIwmgEEJkxs4OfvwRBg5U8wOeO0ezZs0IDQ1l0KBBfPLJJ4aOUAghhBDiuUgCKIQQT9OzJ5w/D/nyQYMGcPUqdnZ2fPzxxxw4cIBt27YZOkIhhBBCiCyTBFAIIZ6lSBGYMwfefBM6dYKYGNzc3Pjxxx/p0qULO3bsMHSEQgghctivv/6KiYlJ2mJqaoq7uzudOnXixo0bhg5PiEzJPIBCCJFVX30FJ09Cr17QtSv9evTAxMSELl26cODAASpWrGjoCIUQQuQQPz8/7Ozs0r4ETEpK4sSJE4wZM4aQkBD2799v4AiFyJgkgEIIkVUWFrByJQweDOPHQ0AAfceP58qVK/Tr14+TJ09iYmJi6CiFEELkgFOnTlGxYkVq166dtq5Bgwbs3LmT3bt3GzAyIZ5OmoAKIcTzcHeHtWvhjz/gyy8hMJCPP/6YoKAg1q9fb+johBBC5ABN0zhz5gyVKlV64r3AwEBKly5tgKiEyBpJAIUQ4kU0aABdukD79tgkJjJu3Dg+++wzmR9QCCHyAH9/f6Kjo6lQoQLJyckkJSVx8+ZNhg0bxsWLF5k+fbqhQxQiU5IACiHEi5o7Fzw8oHt3Bvbvz9WrVzl69KihoxJCCJHNTp06BcCwYcOwsLDA0tKSYsWKsXr1arZv306TJk0MG6AQTyF9AIUQ4kVZWsKyZVCzJvazZtG3b19+/vlnatWqZejIhBDC6GiaRlRiVLYd38HSQW/9tE+dOoWJiQkHDhzA0tISnU7HzZs3mTBhAr169eL8+fM4ODjo5VxC6JskgEII8TKcnGDhQmjRgiF//YVvq1Z0796dNm3aGDoyIYQwKlGJUTh96ZRtx48YF4GjlaNejuXn54ePjw9169ZNW+fr64u5uTldunRh27ZtdO3aVS/nEkLfJAEUQoiXVacO1KtHha1bWbBgAV27duXEiROUKVPG0JEJIYTRcLB0IGJcRLYeX19OnTqVLvlLZW1tDUBcXBwPHz7Ey8uLsLAwLC0tAXjnnXcoXrw4Y8eO1VssQjwvSQCFEEIfJk6E1q3pcfMmO954g88++wwzMzNq1arF0KFDDR2dEELkeiYmJnqroctOQUFB3L9/P8MRQFevXo2pqSl169YlX758FC9enJMnT1K7dm3279/P0aNH+fHHHw0QtRCPSAIohBD6ULcu+PrCzJmMGzeOUqVK4ePjw9q1aylTpgzNmjUzdIRCCCH0wM/PDwBzc3OOHDkCwP3791mzZg1Llizho48+wsfHB4BGjRpx4MABqlWrxpAhQ/jtt98wN5fitzAsuQOFEEJfJk2CNm3wqVWLlStXUqdOHVasWMG4ceM4duyYoaMTQgihB6kjgE6YMIEJEyZgYmKCk5MT1atXZ/Xq1XTp0iVt24YNG7Js2TKio6Np0qSJDBImcgUTTSat0ovIyEicnJyIiIjA0TH3N18QQp8iIyMB5N4HWL4cBg2CvXuhenWioqPx9PRk9+7d1KhRw9DRiWwg97/Iy6T883T379+nTJkyuLq64ufnJ5+RyBWkBlAIIfSpZ084cQI++gjCw3Fo04Y+ffowffp0li1bprchyIUQQuR+Hh4eWFpaMn36dEn+RK4hE8E/xeTJk/Hx8cHJyQk3NzdatWqVVu0vhBCZGj0aDh4EKyuYPp1x7dpx6NAhxowZY+jIhBBC5KBffvmFRo0a0alTJ0OHIkQaSQCfomfPnhw/fpyIiAgCAwNp2bIlrVq1IiUlxdChCSFyM3d3OHoUtm+HkSMp+s037N69m9mzZ3Pu3DlDRyeEECKbXbp0iXLlyrFq1SrmzZtn6HCESEcSwKcoXbo0Li4uAGiahpmZGcHBwTx8+NDAkQkhcr0KFcDGBj78EPz88Ll3j/79+/Pll18aOjIhhBDZrEyZMly4cIGdO3fi7Oxs6HCESEcSwGfYvHkzzs7OWFtbM3LkSEaMGEH+/PkNHZYQwlg4O8OwYfD++4zp35+1a9eyZcsWQ0clhBBCiDxKEsBneO211wgPDyc0NJRvv/2WOnXqGDokIYSxGT8eKlem+Btv8PvcuXTv3p0JEyYQHR1t6MiEEEIIkcfIKKBZlC9fPj744ANcXFwoVaoUlStXfmIbR0dHoqKiDBCdEIYl930WzJwJrVvTOjSUtWvX8umnn2JmZsaoUaMMHZl4SXL/i7xM7n8hjI8kgM9Bp9ORlJSEv79/hglgcnIykydPxtLSEoCmTZvSvHnznA5TCJEbmZrCiBEwYQK1//qLkSNHMmnSJEaMGIGpqTTGEEIYjx07drBr1y4AEhMTDRyNEOJ5yUTwT/H999/Ts2dPPDw8ePDgAePHj2flypVcvHiRggULpttWJkIVeZlMhJ1FOh0MGACHDpE0dy6FevZk+fLlNG3a1NCRiZcg97/ISFBQEBs2bODs2bMkJCSQnJxMUlISycnJdOjQgV69ehk6RL2Q8o8Qxke+dn6K7du3U6lSJezs7KhUqRL3799nx44dTyR/QgiRJaam8PvvMGgQFh070t/Xl59++snQUQkh9Cg8PJwRI0bg7e3NkiVLsLS0xN3dnSJFilCmTBnKlSvH+++/T7t27diyZQtJSUkcP36cDz74gJEjR3LmzBlDX4IQ4hUnTUCfYtOmTYYOQQjxqjE1hY8+Amtrhq5YQWk/P27evIm3t7ehIxNCPKfY2FhWrlzJH3/8wYMHD7CwsODMmTM0aNCAf/75h0qVKmW431tvvcWPP/7I22+/TXBwMFZWVvTs2RNN06hTpw6jR49m8ODBFChQIIevSAiRF0gTUD2RJhAiL5MmcC8gKAi8vOjeogWOBQsyb948TExMDB2VeAFy/+c9UVFRjBs3jvnz51O0aFGGDBmCt7c3cXFxVKlShdKlS2fp33NycjLXrl2jePHiWFhYAHDgwAGmTp3K/v37GTVqFKNHj8bBwSG7L+mFSflHCOMjCaCeyANQ5GVSAH5B7dtztVgxGqxaxaBBg5g6daqhIxIvQO7/vCUxMZEGDRpgY2PDDz/8QMWKFbPly5sjR47w4YcfcvHiRRo2bIiVlRVxcXGUKlWK6tWr8+DBAwIDAylbtiyVK1emfPnymJvnfMMuKf8IYXykCagQQhjKkCGUGDSIPX//Td3GjSlWrBiDBg0ydFRCPNWNGzdYtWoVR44cwc/Pj4SEBBo2bMiHH35IjRo1DB1etpswYQKJiYns3bsXa2vrbDtP7dq12b9/PwcPHuTIkSNomoaVlRXHjx/n+++/x9XVlYL/th44ffo0JiYmfPrpp4wcOVJaEwghnkoSQCGEMJTWrcHBgdKnT7NmzRpee+01QkJCGDNmjBTgRK40efJkpk2bRqtWrahfvz5Dhw7FysqK9evX07BhQ/r168fnn3+Oq6uroUPNFuHh4cyaNYsjR45ka/KXysTEhPr161O/fv2nbpeSksL+/fvp1asXN27c4Pvvv8fMzCzb4xNCGCcZBVQIIQzF1BRGj4Zx42hcogT79u3jxx9/ZMiQIeh0OkNHJ0Q6M2bM4Mcff+TkyZP8+eefjBkzhmbNmlG/fn2mT5/O2bNnCQgIoGLFihw+fNjQ4WaL5cuXU7ly5QznAjYkMzMzGjduzOHDh9m1axddunQhNjbW0GEJIXIpSQCFEMKQ3noLWrWCdu2oXqoUBw8eZPfu3fTr14/k5OS0zXQ6HSdPnuTgwYOsX7+e7du3p/U9EyK7XbhwgfHjx7Nx40bKly+f4TY+Pj5s2rSJcePG0bx5c+bNm5fDUWa/+fPn8+abbxo6jEx5e3tz8OBBwsLCqFGjBqNGjeKNN95g/PjxnDt3ztDhvXJ+/fVXTExM0hZTU1Pc3d3p1KkTN27cMHR4QmRKBoHRE+kELfIyGQTjJSUlQdu2YGYGa9cSFBVFixYtqFy5MrNmzWLhwoX8+OOPBAUF4ejoiJubGxERESQmJrJw4UKaNWtm6CvI0171+//+/fu0aNGC1q1b880332Rpnz179tCtWze6dOnCjBkzsLGxyeYos9/Vq1cpX748QUFBODs7Gzqcp4qPj2fZsmUcPXoUT09PLl++zJo1a2jWrBkTJ06kZs2aejtXXi7/DB06lIULF7Jjxw4AkpKSOHHiBGPGjKFWrVrs37/fwBEKkTFJAPUkLz8AhXjVC8A5IiIC2reHyEhYt477NjbUqVOHu3fvUr16dd577z26du2KlZUVAJqmMW/ePEaNGkW/fv34+OOPKVSokIEvIm96le//8PBwfH198fX1ZcGCBWlTFWTF7du36d69O8HBwUyePJmePXs+1/65zRdffMGhQ4fYuHGjoUN5Iffv32fmzJn88MMPDB48mA8++ICiRYu+9HHzcvmnXr166HS6J5o8t2/fnt27dxMdHW2gyIR4ulydAF6/fj1L25mamhp8EuW8/AAU4lUuAOeopCQYNQqWLoXlywkoU4aQkBCqVq2a6S5Xrlxh3Lhx/P333yxdupTXX389BwMW8Ore/zqdji5dupCYmMjGjRsxNX3+XiMpKSksXLiQL774Ajs7O5YuXZquCemff/7J/PnzcXV1pV+/fjRo0OCFzpMTqlWrxvDhw+nXr5+hQ3kp586dY8KECWzatIlq1arRvn17KlasSNGiRSlSpAj58uV7rkGo8mr5R9M0HB0deeONN/jll1/SvVe9enUATpw4YYjQhHimXD0KaIkSJZ75ENI0DRsbG2JiYnIoKiGEyCYWFvDDD1CjBnTqhNdPP+HVt+9TdylVqhRr165l3bp1vPHGG6xbt46WLVvmUMDiVZWSksKQIUM4e/Ys//zzzwsnZWZmZgwcOJA+ffowadIkfH19GTduHLVq1WLJkiVs2bKF4cOHExQUROfOnXFycqJv377069cPHx8fPV/Vi/Pz8+PChQt06NDB0KG8tAoVKrB+/XoePHjAli1b2Lx5M2vWrOH27duEhYVRuHBhOnXqRMmSJSlUqBBJSUlERUURGRmJi4sLzZs3x8vLy9CXYXD+/v5ER0dToUIFkpOT0TSNu3fv8t1333Hx4kU2b95s6BCFyFSurgG0t7fn/PnzT91G0zSqVKlCeHh4zgSVibz6DZgQ8OrWgBjU9u3QqRMsWQIFCsD587B+PVSsCE2bQqNGKmF8zO+//86HH36In5+fFNBy0Kt0/1+5coUtW7awYMECEhIS2L59u17vpf379zNt2jQuXrxIo0aNmDp1atrxExMT2bx5MwsXLmTr1q306NGD77//Plf0t2vZsiWVK1fOch9IYxUVFcX+/fvZunUrN2/e5O7du1haWuLo6IijoyN3797l6NGjlCpVikaNGpGcnMzevXvx9/fXT/lH0yAqSj8XkxEHB9DTFDsrV66kR48eT6wvWLAgq1atol69eno5jxDZIVcngG+88QZ//PHHM7fr06cPS5YsyYGIMicJoMjLXqUCcK7y3XeqRjAxETw9oXNnuHIFduxQU0h8/71KEh/z1ltvceHCBfbs2YOlpaWBAs9bjPX+j4mJYe/evSQmJrJ+/XrOnz/P2bNnad68Oa1ateJ///tfWp/TnHbz5k3efvtt4uPj2bZtG7a2tgaJA2D37t106dKFa9eu4eLiYrA4covw8HB2797NwYMHsbS0pEKFCvTu3Vs/5Z/ISHBy0k+gGYmIAD39O/3444/58ssvOXDgAJaWluh0Om7evMmECROIj4/n/PnzODg46OVcQuhbrk4AAQ4ePGgU36JIAijyMmMtAButlBRYtgwGD4a5c+GNN9LeiouLo06dOjRp0oQZM2YYMMi8w5D3f2xsLObm5llO9oOCgpg1axb79u3j/PnzODk5YWVlRdu2bfH19aVhw4YULFgwm6POmtjYWFq2bEnhwoVZtmzZc/VL06fXX3+d8uXLM23aNIOcP7fTa/nHiGoA27Rpw9WrV/H390+3fu3atXTp0oVVq1bRtWtXvZxLCH3L1X0AQf0Dk7muhBDiMWZm0KcP5M8P3brBjRvw8cdgYoKNjQ2rV6+mRo0a1KlTh+7duxs6WqFne/bs4bfffmPfvn3cvn0bS0tLOnTowIABA6hatSpnz57l0KFDnDt3jjt37pA/f37c3d2JjIxky5YtNGnShLfeegsfHx/q1q1rsMTqWWxtbVm7di01atSgVatWtGnThq5du+Zo8+YbN26wdetWZs+enWPnzNNMTPRWQ5fdTp06Rd26dZ9Yb21tDagv4wDefPNN/Pz8AIiIiODevXtEREQYrHZdCDCCBDCXV1A+oWtX1XrBwgLMzR8tVlaQOg1STEzGS3y8aumVkKBeU1LSL6lzQltbq+NZWT36ObPXzNZZWYGlpVrs7SFfPnB1fbQ4OurtSzIhRHZp1Qr274d27cDfH+bPB1NTSpQowZIlS+jRowcuLi60aNHC0JEKPQgMDGTt2rWMHTuWYcOG8dtvv1G5cmUePHjAkiVLePfdd7l9+zZeXl40bdqUunXr4uXlRUhICEFBQdjY2PDpp59SqVIlQ19Klrm7u7N//35Wr17NX3/9xfjx4xk+fDjdunWjcOHCuLm56S2BTUlJ4datWxQrViztmF9//TUdO3aUPrUinaCgIO7fv5/hv6XVq1djamqalhwuWLAAgICAADp06MCsWbMk+RMGl+ubgDo6OhpFDWBqE4jvvotAp3NMS9iSk9XI7vHx8O+XQdjZZbzY2DxKzFITSFNT9Wpmpl5BJYjx8elfM1r3tNekpEfJZlQUhIaq5eFDlWyamz+ZFObPD+7uUKgQFCkCPj5qkW5GQpqAGti9e1C7NowbB0OGpK3+/fffGT16NP7+/rliII1XVU7c/xcuXMDX15dSpUoxbdo0WrduneF2CQkJWFpa5tpavZd14sQJpkyZwq5du4iOjsbLy4uSJUvSpEkTPvroI8zMzJ77mDqdjjlz5vDpp58SHh5O+fLlmT9/Pg4ODlStWpVTp05RunTpbLiaV0Ne7AKzdetW2rRpw9SpU2nWrBmg5llcs2YNS5Ys4aOPPkrXZPjy5ct07dqVH3/8kYYNGxoqbCHS5PoE0Nzc/Jn/WHbt2pVD0WTuVXkAaprqg52aEKYuISFqCQ6GO3fg1i24fl0lkcWLQ+nSUKZM+lc3N6lFzCskAcwFduxQA8KcOQPFigGqBUXLli2pVKkS3377rYEDfHVl9/2flJREtWrV6NChA59//nm2nMMYRUdHc+TIEW7fvs0333yDo6MjPXv2JCgoiNu3b1OgQAHKli1LmzZtKFy4cIbH0DSNwYMHs23bNn7++Wfq16/P7NmzmTp1KqampgwaNEj60j7Dq1L+eR5ffvklH330UdrvJiYmODk5Ub16dYYMGUKXLl3S3jt58iT9+vVj0aJFVKtWzRDhCvGEXJ8AWllZMWbMmKdu89lnn+VQNJnLiw9AnQ4CAuDyZbh0Kf3r3buqGWmxYipBLFFCLSVLqsXTU9VuileDJIC5xODB6UcJRU36XLNmTS5evIi3t7dh43tFZff9v2rVKsaNG8elS5ew+M/UH0KJjo5m7ty57Ny5Ey8vL4oUKcK9e/c4e/Ysp06dYvLkyXTs2JEiRYoQERHBrl27qFSpErNmzWLt2rUcOnQoXTPPI0eOYG5uTvXq1V/Z2lR9yYvln6zat28fQ4cOZfXq1VKLLHKVXJ8AGlsTUHkAKlFRqobw5k24dg2uXlWLvz/cvq2auvr4qGQwNTFMfZXk0PhIAphLREWpeQInTIC33kpb3a9fP8zNzZk/f74Bg3t1Zff937x5c1q0aMHYsWOz5fivuo0bN/L1119z6NAhrK2tiY2NpWTJkly/fp2SJUuyceNGSpQoYegwjZaUfzKXP39+rK2tcXV1BVRN4cGDBw06rYkQIAmg3sgDMOsSEtSghakJYerrf5NDHx9Vg+jtrV7LlFG1iea5fuiivEcSwFxk/Xp4+21VE/jvnGXXrl2jYsWK7NixI8NR68TLyc77/+rVq5QvX56AgADc3d31fvy8JDY2lhs3buDu7k7+/Pm5f/8+jo6OUhh/SVL+EcL45PoE0MHBgajsnBNGT+QBqB+PJ4fXrqmfU2sR/f1VH8XChcHDI/1SoMCT6+ztpQ9iTpEEMBfRNHjtNTU605o1agQpYObMmXz77becPn2afPnyGTjIV0t23v/Tpk3jyJEj/Pnnn3o/thD6IOUfIYxPrk8AjYU8ALNfSopqVhoQAEFBTy7376vX4GA1yqmNTcaJoacneHmpV2dn1VfR0VGNvCpejCSAuczDh1C/PrRoAd9/D6jBLl577TUKFizIb7/9ZuAAXy3Zef9Xr16dYcOG0b9/f70fWwh9kPKPEMbHqBLA69evc+LEiSdqBAcOHGigiB6RB2DuoWkQFpZ5ghgYqJLIwECIiFCD2YBKGF1dn16zmPq7i4vULj4uNyWA/v5w9KhK6tu3N3Q0BnTjBtSoATNnQt++ANy6dYsKFSqwdetW6tWrZ9j4siAiIoJPPvkEDw8PBg0axLVr15gxYwaWlpYsXbo01wzOkV33/40bNyhdujRBQUG4/NucV4jcRso/Qhgfo0kAf/75Z9577z3y5cuHnZ1d2noTExOuX79uwMgUeQAaJ02D2Fg19UVEhJry4vFkMaMEMjZW9UN0c1MJo6enmhuxcGH1WqiQWufpqeZNfIFpqYyOvgrAYWEqgYuOVvNOOjmp+TPj4lSfUAeHjPc7dgyWL4dNm1ST4erV4fx5+O036Nr1pUIybps3Q+/e6kPNnx+A0aNHc/fuXf744w8DB/d0N2/epG3bthQsWBArKysOHTpEUlISw4cP56effmLdunU0atTI0GEC2ZcAfvvtt/z9999s27ZNr8cVQp+k/COE8TGa4TS++OILVq5cSefOnQ0diniFmJiAnZ1aChbM2j7R0SoRDA2FBw9UTeLdu2p+xOPH1c/37ql5E83MHjU7fTwptLdXA9qUKqUSRyenvDfy6fnzsGiRqq2LjFSJ28OHKk9xcFCfbVSUqpm1slI/V62qurcNGqSa8UZHw/DhKvnr0QO++gqaN1ef78qV8M47qhIso9kPNC0P1OK+9ho0aqRGBf3lFwAGDBhAjRo1CA8Pz7WTw588eZLXXnuNLl268P3332NmZsbWrVuxs7OjQYMGaJrGt99+m2sSwOyyZs0aBgwYYOgwhBBCvGKMpgbQ2dmZ8PBwQ4eRKfkGTPxXQoKqNQwMTL8EBT2aJsPfX/1saqpqE11cVMJjbf1osbF5tDz+e1Z/jolRceh0j5q7mpmpWkwLC/X635+TkiA+XtW8xcdn/HOhQlC5MiQnq/tf06BuXUf+O6Cepql+mZcvP5orct8+OHdO1c41aaL6YhYpoqYBcXJ6tK9O9ygxDghQ+y1fDtu2Qe3aKukuWhR+/129/tf778POnWq/xETVHe7uXZV8f/cdtG0Lv/6qmou+svz9oUIF9eH/mwnXrFmTgQMHMmTIEMPGloG//vqLHj168OmnnzJq1KgMm3kGBgZSrFgxzp49S6lSpQwQZXrZUQN49+5dihYtSmBgoIz+KXI1Kf8IYXyMJgHs3bs3//vf/3LtN77yABQvIrUJakiIWh4+VInjf5Ot1N9f5Gdra1XzaGb2qMZLp1NJXnKyWv77s4VF+mTy8WQ09eerV1VOYWkJzs6RJCfDvXuOVKqkmmuamT1K+sLDVY1d6dJqOo8aNVTllJvbi31ut2+ruc5tbVUSmdnUIMnJ0KcP7N+vEu1mzVSN6/HjMH48TJoEvXrBqFEvFofR6NVLfdizZgGwcuVKhg0bxqVLl3B2duby5cvMnDmT5ORk+vbtS8OGDXM8xJSUFL7//ns++eQTfvvtN3r27MnZoLOsPL+SHhV6UMG9Qrrt+/Xrh52dHT/99FOOx/pf2ZEAzp49m9WrV7Nnzx69HVOI7CDlHyGMj9EkgMOHD2fhwoV06dIFT0/PdO9NmTLFQFE9Ig9AkZel1gAGBDhy9qyq3UxKUsle6dKqqetjXXdzlKbB0qVqAJ/mzdO/t3IlfPSRmjLvle6reeoU1K2rLrRwYTRNo3Xr1iQmJlKiRAmWLFlC7969cXJyYu7cubRr146vvvqKIkWK5Eh4K1asYMqUKSQnJzN//nxcSrnw7uZ3+efuPzQq2oiDAQdZ0mkJr5d5/bFLOkXdunU5d+4cxYsXz5E4M6PvBFDTNCpXrsz777/P22+/rZdjCpFdpPwjhPExmgSwSZMmGa43MTFh165dORzNk+QBKPKy3DQK6PNISlJNR3/+GTp0yHibx5uhGrW+fVX18sqVAAQFBTF79mwCAwMZM2YMpUuXBlTzynHjxrFy5Ur69u3Le++9h729PUFBQZQvXx6nx9voAsnJyRw9epSQkBAcHBzw8PDAx8cHKyurLIU1d+5cxowZw3fffUfv3r3ZdG0Tb254k8E1BjO23lhcbV1Ze3Etfdf1pYl3EzwdPPmsyWd42HswYsQIVq9ezaRJk+jcubPBRsrU9/2/b98+OnbsyJ07d2SScpHrSflHCONjNAlgbicPQJGXGWsCCPDNN7B6NRw5AuvXw5gxqllr1aqq6eidO/Dxx/DZZ4aO9CXdv6+qZFeuhJYtn7n5xYsX+fLLL1m+fDkpKSm4ubnx8OFDKleujJmZGfHx8eTLl49z584B4OnpSVRUFEFBQXh5efHXX3/hndHoO49ZsmQJQ4YM4a+//qJU1VJM3D2RP879wcKOC+lYpmO6bQ8FHOLo3aPsv72f0/dPc+X9K5hgwqxZs1i0aBHx8fHs2rXLIP3l9Hn/63Q62rRpQ6VKlfjmm29e+nhCZDcp/whhfCQB1BN5AIq8zJgTwOhoKFYMOnaEZctgxgw1UuvRo2oudScnaNVK1RK+8YaRjxw6e7YaCefsWdWR8znduHGDY8eOodPpsLa2JjQ0lDJlylC7dm3M/m1Dm5KSwvDhw1m2bBk//PAD9evXf6IpqaZpzJ49m48++oj169dzyfES43eNp7F3Y75t+S0l8pXINIaklCQKzyjMH53/oFnxZoCqhezduzfBwcHs2rUrx+cH1Nf9r9PpmDp1KvPnz+fkyZPky5dPH+EJkS0i4iOY7zef3Zd3s/HNjVL+EcKI5OoE0NPTk8DAwGduV6RIEW7fvp0DEWVOEkCRlxlzAgiwcSP8+Se0aQMZzTSzbh0MHqxGPd24UU1LYZRSUtQIPAMHqiFSs9HSpUuZOHEit27donPnznz11Vd4e3sTFxfH+++/z6ZNm1i3bh1HTY7y2b7PWNVtFU2KZdzU/79GbRtFcGwwizstTlsXGRlJ6dKlmTlzJj169Miuy8qQPu7/+/fv07p1ax48eMCGDRuoUaOGvsITQi/87vmx88ZOzj84z4UHFzgXfI6qBarSsnBLJraaKOUfIYxIrk4AbWxsmDNnDs8KccSIEURERORQVBmTBFDkZcaeAGZFdDQ0bar6DM6ereZ3NEqLFqkJE8+dy5HqzMDAQD7++GNWrlxJqVKluHr1KuXLl2fNmjXsfbiXIZuHsKPfDnwL+Wb5mGeDzlLr11rcHXkXF5tH/f4WLlzIp59+ypUrV7LcB1EfXvb+j4uLo2HDhpQqVYr58+fnaOxCPE2yLpnfT/3OnGNzuBJ6hZY+LangXoFy+ctRyaMS5fKXk/KPEEYoVyeA3t7eWWrKY2VlxaVLl3IgoszJA1DkZXkhAQTVje7tt9Xcgm+/DVOmqJaUCxfCoUOq3+AHHxg6ymeIi1PzgmzaBPXq6f3wqf+l/PfZff78ea5du4a3tzcVK1Zk7cW19Fvfj3U91tHS59l9Ev+r/vz6dC7bmZF1RqatS0lJoVKlSgwdOpR333335S7kObzs/f/+++9z/Phx9uzZI8mfMKjYpFg2XdnEoYBD3Iq4xZmgM5iZmDGm3hh6lO+Bg5XDE/tI+UcI45OrE0BjIg9AkZfllQQwlZ+fGizm+nU136Kzs5qPcNYs+OUX6Nbt0bb37qnRRnNoRoWs+fBDOHlSZbIvWQuo03RsvLyRFedXcP7BeW6E3aCQYyE+afgJPcr3wMz0yfk1lp1dxtsb32Z51+W0K9Xuhc678vxKxu0Yh//7/unOsWbNGt577z1OnTqFRw5V077M/b9u3Tr69++Pn58fPj4++g5NiCxbeX4lI7aNwMnKiebFm1PcpTjFnIvRpmQbLM0sM91Pyj9CGB9JAPVEHoAiL8trCSCo7nRz56r5BV9/XU0VsXo1vPUW/PUX1KmjEsW2bdUcg0eOqEnoc4XISKhQQfUD/OAD+OcfOHBArWvTBszNs3SYA7cPMHLbSAKjAhlQZQA1PWtSzKUYR+8eZeq+qSSmJFLYsTCtS7SmR/ke2FnaMe/EPL7/53vW9lj7QjV/qZJSkqjySxVK5CvB76//ntYUVNM0+vbty/Xr19m6dWuO3JMvev/v2rWLDh06sGTJEjp27JgNkQnxdLcjbnPw9kFWXljJvlv7mN1mNj0r9HyugZSk/COE8ZEEUE/kASjysryYAGZm9mwYOxYaNIB9+2DiRPD3hx074MsvoUePXDKS6N690Ls3BAerKsz69dXQp3Xrwh9/ZJoEJuuSmX10Nj8f/5k7kXcYV38cI+uMxNYi/Xx1iSmJHLx9kLtRd1l/aT2b/TeTmJJIE+8mfNn8S2p4vvwgJw/jHtJ3XV/239rPz+1+5o2KbwCqT13qPHoLFy7M9gFVnvf+T0pKYvHixQwbNow5c+bQr1+/7AxPCDRNY+OVjZy6f4qklCRC40I5cucIZ4PPUsmjEq19WjOk5hAKOz7/t1RS/hHC+EgCqCfyABR5mSSA6Z05A9u2qen2KldWtYWLFsGECVCoEHz0Edy6pXKs2rWhWrWXm2z++nU1hcWlS1CrlqqR9PLKwo7x8XD1KpQrpwJ48ECNdOPpCUuWQP78aZtqmsbOGzsZuW0kSbokpjSeQgufFjhbO2cpxrikOMxMzZ7alOxFbby8kZ5revJX779oWLQhoKaG+Oyzz/jmm29o1qwZw4YNo3nz5pnWbNy8eZPTp09jYmJCmTJl8PHxSZva4lmyev+npKQwa9YsvvvuOywsLJg5cybt27d/jisV4vkdu3uMkX+PxD/Un9YlWmNpZomTlRNVClShbcm26QZSehFS/hHC+EgCqCfyABR5mSSAWRMbCz/8oGoJy5VTCeDBg2Brq/oNTp4MLlkoiwUGwhdfqFrFwEA1rkvbtlCpkmrJuX8/dO8Oc+aoeQyzIkWXwp+X/+TK9WN0nL6RYmcCMPt2Bro+vVl9cQ1T9k3hQcwDRtcdzYg6I7IlkXsZM4/M5JcTv3BuyLl0fQIDAwP56aef+OWXX/Dw8GDMmDG0bduWlJQUDh48yM6dO/n777+5ceMG5cqVA+Dy5cuYmJhQtmxZKlSoQMWKFalcuTKlSpXC/N+aUVNTUzw8PDAzM8vS/R8cHMwbb7xBQEAAX375JR06dMhyginE8wqLC2PfrX2sOL+CDZc3MLL2SMbUG5PhIC4vS8o/Qhgfo0gA9+3bR1JSEk2bNs3xCX6zSh6AIi+TBPDFJSWpJPDrr+H4cTXf4NixYGf3aJvQUNWM1MEBFixQiV3btvDmm2pwmYIFwc3t0faBgWqqvxMnVG3jBx+ofoiZOR54nL7r+hKXFEcj70bEJcbiuHkHU9aFc8PNnO/budGi/xT6Ve6HlXnuHKUyMSWRkrNK8nXzr+lR4cl5AOPj41mwYAE//vgj58+fB6Bs2bI0a9aM5s2b06RJk7T7NyUlhWvXrnH27Nm05fTp01y7di3dMe3t7fH19aVOnTrUqlWLVq1aYWmZPjHWNI3169fz3nvvUb9+fX799VccHPRfCBfiQcwDfj7+M+sureN00GlKuZaiRfEWjK47Gi+nrDQJeDFS/hHC+OT6BPCjjz7iq6++AqBBgwasWbOGwYMHc+3aNd544w1Gjx5t4AgVeQCKvEwSwJenafD33/DZZxAWBqtWQdmyMH68mrbP1RUePlTNOz/6SM3n/jQ6ncbUlX8xc90e7LSCNGtqwmkWkqRLYnCNwQytORQTExNWnFvBwD8H8knDTxhVZxQWZhZqf03H1ZsnsZ35I4V+X4tJtWrw229QvHgOfBov5pfjv/Dt4W85M+QM1ubWmW4XFhaGhYUF9vb2z3X81P8uNU0jJSWFCxcucOTIEY4dO8b+/fsJDg7mtddeo2jRooSGhnL79m38/PzQ6XRMmzaNgQMH5tovMYVxOxd8jhaLW1DBvQJvVnmTJt5NKOhQMEfOLeUfIYxPrk8ACxUqxJw5c6hfvz4ffvghp0+fxtbWlho1arB48WJ++OEHevfubegw5QEo8jRJAPUnOVkNHPPDD1CsGISHw9atqsloSsrTa/KiEqKY7zef2cdmcz3sOvls8tGuREeOnw/F/3YkxaJ68+7bdnzhNxyffD54Oniy+cpmVndfTesSrTM/cHg4fPwxrFunOjdWqqTvy9aLZF0ytX6tRbuS7ZjcZHKOnTcyMhKdTsfly5fZsmUL9+7dw9XVlSJFilC+fHl8fX2xts48IRXiZRwPPE6rJa34oNYHfNLwkxz/kkHKP0IYn1yfANrZ2RETEwNASEgI7u7uPHjwAFdXV/7++28+++wz9u/fb+Ao5QEo8jZJAPXvwAE18Xzjxumbd2bm4O2DdFjegZL5SjK89nBqF66Nh50HNhY2AERFwbhxatL6mb88JLL474TEhvBmlTcp6Vry2SfQNNXx8Ouv1Yg27dvnkuFM0zt57yQNFjRgU69NNCnWJEfOKfe/MISwuDCm7pvKLyd+4bMmnzGizgiDxCHlHyGMT65PAB0dHdP+cwXSHjKg+mkUKlSI+/fvGyq8NPIAFHmZFIAN40HMA07cO8G+W/v44Z8fmN5yOoNrDH7qPlu2qKkoJk2CESNeYPTRhQvVRPIVKqgOid7eLxp+tpnvN58P//6QzW9spo5XnWw/n9z/IqcdvXuUzis6U8G9ApMaT6J24doGi0XKP0IYn6zN9mtASUlJ7Nu3j8qVK+Pk5JRu1DQzMzPi4uIMGJ0QQuS8M0Fn+OLAF6y5sIbiLsUp41aGXf134VvI95n7tm0L27fDG2+omR769FEjkBYpkvk+CQlw44aqBCzWoz/WnTvD6NGqKeiUKVxv+x57Dphz8KAagMbRESpWVLNInD6tBq+pVUv1aXRxUc1cQ0IgXz61fXw8lCoF7u5qpNQHD9SUFleuqO369FH7ZtXAqgOJS4qjxeIWjKk3hlF1RmFnaffsHYUwAocCDtF2aVs+afgJI+uMlH6lQojnlutrAMuWLcuVK1cAKFKkCPfu3WPatGnUqVOH6tWr4+bmlq6G0FDkGzCRl0kNSM44cucIXxz4gr+v/c2gqoMYXns4JfKVeKFjRUfDihVq2b0batZUU0eUKKESsIsXVRJ28aKaZ9DSUtUWahpUqQIFCkDFkN0MOjaY0AQ7vqm2nKItSlGsmBq19Nw51YS1ShWIiVFTU9y6pX42MVHTU4SHq+PY2Kj3dDoVm4UFlCypkkIrK/jzTxg6VM2r+OABWFtD587PvsbDAYcZsW0EAZEBfN70c/pW6ptuigh9kftf5ARN01h0ehFDtwzNUm1/TpHyjxDGJ9cngKAeLkePHuXYsWMcPXqUo0ePcu/ePSwtLUlOTiY5OdnQIcoDUORpUgDOXvei7tF7bW+OBR5jcPXBjKwzUq8j/D14AGvXquXePTXiaNmyUKaMWsqWVRPYm5iohPDUKQgOVr87WCbQ8/wEbJfMgz/+UFWMT5GQoF6trNIPahMVpZJSOzu1PD7YzfHjanTUixdVbP7+qvnq+PHPvjZN01h5fiXjdo7D2dqZaU2n0dKnpV4TQbn/RXYLig5i8ObBHAo4xKKOi2hVopWhQ0oj5R8hjI9RJIAZuXPnDv/88w/Hjh3jyy+/NHQ48gAUeZoUgLOPTtPRcnFLXGxcmNtuLi42WZgp3hD++AP+9z947z345JP0Exnq2dmz0KgRzJwJ/fplbZ/45Hhm/TOL6YenY4IJnct2plu5bjQs2vClk0G5/0V2WnV+FUM2D6F58ebMbjsbN9ssjAqVg6T8I4TxMdoEMLeRB6DIy6QAnD1SdCm8/9f7bLu2Db93/HC0yuWf79mz8M47qiPfjh1ZG770BW3fDh07wvTpKu982vQYj0vRpbDv1j5Wnl/J2ktrMcGELmW70K18NxoUafBCyaDc/+JlaZrGn5f/5I9zf6BpGr6FfCnjVobFZxaz8/pOfnrtJ7qV72boMDMk5R8hjI8kgHoiD0CRl0kBWH+Sdcmsv7SeLf5b2HljJ3YWdmzpvQVvZ29Dh5Y1SUlqhJkrV2DnzmxNAnfsgLffVv0Khw6FsWNV/8CsStYls+/WPladX8Wai2swNTGlc9nOvF76dRp7N8bK3CpLx5H7X7yM4Jhg3tn0DocDDjO4xmBszG3Ye2svtyJuUatQLb5o9gUe9h6GDjNTUv4RwvhIAqgn8gAUeZkUgPXjUMAh+q7rC0C3ct1oWqwpDYs2xNrcyCYRT0qC3r1Vp70tW8DLK9tOlZwMe/fCmDFqMJkdO54vCUw7zr/J4JoLa9h4ZSNh8WG09GlJ+1Lt6VimI87WzpnuK/e/eB7RidHMPTEXv/t+JKYksvP6TpoWa8qc1+bkuuadWSHlHyGMjySAeiIPQJGXSQH45e26sYv2y9rzedPPed/3/WwZrTJHJSWp/oB//gkbNoDvs6eoeBlxcdC4MRQtCosXq0FmXpSmaZwOOs3GyxtZf3k9t8JvMbnxZAZVG5RhMi73v8gKTdNYe3EtH2z9AE8HTzqU7oCFqQW+hXxpUqyJocN7YVL+EcL4SAKoJ/IAFHmZFIBfzv3o+1T+uTKfN/2ct6q9Zehw9EfTYMYM+PRTWL0aWrfO1tMFBUH79mBuDr//rqaReFmaprHxykY+2f0JtyNu09i7MT4uPng5euHl5EXtwrWx1+wBuf9F5o4HHmfktpFcDr3MNy2+oW+lvq/M/H1S/hHC+EgC+AyapjFp0iTmzZtHREQE1atXZ86cOVSoUCHddvIAFHmZJIAvZ/zO8ZwKOsWmXptemUJhOitWwKBBsGhR1ibwewlxcaov4G+/wccfq/nqLS1f/riapnEs8Bh7b6q+WXci73Ar4hbngs/R0bsjo+qMonaJ2i9/Ij1LTElk7829bPbfzKn7p7Ayt1LJq6MXjlaOxCXHUb1gdQo6FKRc/nKYm5obOmSjFJcUh42FTbp1mqax68Yuph+ezt6bexlRewTj6o/DwcrBQFFmDyn/CGF8JAF8hm+++YYffviBLVu2UKJECaZMmcKiRYu4fPky9vb2advJA1DkZZIAvrjElES8ZnixuNNiWvq0NHQ42WfDBujbF95/H6ZMyfqwnS/on3/UADGaBvPmQe1sys3uRN5hxp4ZLDmzhF41ejGh4QSD9+MKjw/nL/+/2HB5A39d/Qt7S3val2qPbyFfknXJBEQEEBAZQGRCJJZmlpy4d4L70fextbClR/kedC7bGVcbV7Ze3cqum7u4HXGbel71eM/3PSp5VDLoteUmQdFB/PDPD/xx7g9uht+kgH0ByuUvRxPvJlTyqMScY3M4Hnicd2u+y9CaQ3P1QC4vQ8o/QhgfSQCfoVixYgwfPpwPPvgAgOTkZAoWLMh3331H375907aTB6DIyyQBfHErzq1gwu4JXH7vMqYmpoYOJ3tduACdOqnZ5VeseLHRWp5DUpKaJmLqVHjzTZg2DbLjFo2MjOTqw6uMPzieA7cPMMx3GKPqjiKfTT79n+wpTt47yeS9k9niv4Vy+cvRoVQHXi/zOtULVn9mzXLq9BhLzy5ls/9mIuIjaFa8GS2Lt6SIUxH+uvoXC08vpGS+kthb2hMaF4qlmSUF7QtiYWaBm60bldwr0bBoQ6oWrPpcNYmJKYmExIbgYOlgNLVje2/upfvq7tTwrMGQGkOoVrAaN8JucDb4LDuu7+BSyCV8C/kyo9UMnKydDB1utpLyjxDGRxLAp4iIiMDZ2ZlDhw5Rp06dtPUtW7akQoUKfPfdd2nr5AEo8jJJAF9c26VtaVCkAR81+MjQoeSM8HBo00ZlYuvWga1ttp/S3x8GD4bLl+G771QOamGhv+M/fv8fDjjMxD0TOXLnCG9WeZPu5bvjW8gXCzM9nvAxybpkdt3Yxc/Hf2br1a1ptU3FXIq98DE1TSNZl/xEzPej7/PPnX+ITYrFzdaNxJREAqMCSdYl8yD2AccDj7P/9n6SdcnU86pHY+/GtPJphZO1EwnJCVibW+Nm60ZQTBBrLqwhOCaY+zH32XxlMxEJEQBUKVCFoTWH0r9y/2z7zF7W1qtb6bqyK9+2/Jb/Vf/fq9ls+zlI+UcI4yMJ4FMEBARQpEgRLly4QNmyZdPW9+jRAwcHB3799de0dZGRkXh5eXHhwgUcHIzjG0wh9CUqKgogR+792MRYNvlvYsf1HZiZmjG81nBKu5XO9vNmhwcxD/Cd58uBgQco5FjI0OHknOho6Nfv0WgtOZAEahqsWqVqBBMS4PXXoUsXqFQJXrb8ntH9fyLwBEvPLmXH9R0kpiRSz6sebUq2oUXxFi9cI5SQnMDdqLvcjrjNnYg7HL93nJ03dmJhakGXsl0YVG0QBewLvNzFvKQUXQqXQy5z5O4RDt4+yP7b+0lMScTSzJKE5AR06DAzMaNpsaYUdiyMi7ULDYs2pHKByoTFhbHj+g5+Pv4zKVoK3cp1o0PpDi+VzOrbtqvbeO+v95jeYjqvl3nd0OHkClFRURQuXFgSQCGMiCSAT/G8NYAFCxakd+/eWP474kDTpk1p3rx5jsctRE7LjgQwPjmeaw+vERYXRnh8OGHxYRwMOMjOGzsp6lSUTmU6ERwTzB/n/mBglYG85/ue0TQfSzXvxDz+vvY3q7qvMnQoOS82FgYMUBP5LVwIOfTFmU4HR47AmjWweTOUKAHvvKMqJc1fcPyTp93/Ok3HheAL7Lqxiy1Xt3Ap5BJl3MpQyb0SFT0qUsmjEmXcymQ66Xx4fDhH7hxh3aV17Li2A4DCjoUp4lyEsq5laVWiFVULVs21zYc1TUurIdM0jYiECEwweWoSnKxLZvOVzWy4vIE9N/dQtUBVupXvRhHHItyPuU9gZCCRCZEMqDIAT0fPnLoU1l9cz5gdY/ihzQ+0LpG9I9rmdjt27GDXrl0AJCYmpg2UJwmgEMZBEsBnKFasGCNGjGDYsGGA6gPo6enJt99+K30AhfiXvpqAJuuSmXdiHovOLOLkvZPYmNuQ3y4/LtYuuNi44OvpS/fy3angXiGtUHki8ASj/h7FhQcX6FquK/aW9rhYu/B29bcNPhjH09wMv0nVX6ry++u/592ahNhY1R4zIkJlY66uOXr66GiYP1/NVJGSouau9/WFqlXB0zPro4c+z/1/O+I2R+8e5XjgcU7cO8GJwBPEJcdRv0h9mng3oYhTESITIjkUcIiDAQe5GX6TEvlK0KN8D3pV6EXZ/GVzbbKXHR7GPeTXk7+y/tJ67kXfo7BjYbwcvYhNiuXA7QNs77udqgWrZsu5UwfMuR99n1UXVjHv5DxWd1tNqxKtsuV8xkrKP0IYH0kAn+Gbb75h1qxZbNmyBR8fH6ZOncrvv/8uo4DmMgnJCYTEhhAaF6peY9VreHw4Gs++xR0sHXCydsLZ2vmJxd7SPk8VuF7EyyaAOk3HX/5/MXbHWJJ0SYytN5aGRRvi4+KTpf41mqax2X8z+27tIzYpliuhVzgUcIgx9cYwqs4o7CztXiiu7BIYFUjbpW2pXbg2P7f72dDhGFZ8PPTpA4cPq2kimjXL8RCSk2HLFtUl8eRJOH9eJYTOzqqJaM+eahaLzBLCl7n/NU3jcuhldl7fyb7b+7gXdQ97S3uqFaxGY+/GVC1QFVfbnE2MjcWUvVP4/dTvnPjfCVxsXPR67CN3jvDmhjfxD/XHw96Diu4Vmdl6JmXcyuj1PK8CKf8IYXwkAXwGTdOYOHEic+fOJTIykho1avDjjz9SsWLFdNvJA1B/4pPj0yVxqUld2rq4R++lvh+dGA2As7UzrjauuNm64WbrhrO18zOTNw2N6MRowuPD0y0R8RFoaJiamOJk5YSLjQtOVipJdLJ2wsnq3+XfxDH15/9uY2lmiZmpGWYmZpiamKb7+fGmURoaOk2HpqlXSzNLoxlc4GUKwIcDDvPmhjcJiw9jXL1xDPUdiqXZy0/cdijgECO3jeRO5B1G1RnFwKoDc8VofLtv7Kbvur40L96cue3n6uVajZ6mwdy5MGoUfPQRjB9v0HCSkyEkBO7fV9NJ/PCD+v2116BiRTWIaZkyUKSIms1CBkEyDJ2mo/OKzlwMucgv7X6hUdFGgJqaI79dfqzNMx5l9k7kHfbe3MuFBxeIS47D1caVEvlK4O3sjamJKUvOLOFXv1/5tOGnjKwzMtcORpNbSPlHCOMjCaCeyAPwEZ2mIyYxhpikGGISY4hOjE5LsMLiVX+u0NjQR7V1/0nwYpJiAHCxdsHVViVzjyd16X62ffRzPpt8ep3EWKfpiEqISov58cQwPD6ciISI9D//9/f4CBJSEp56DhP+TQAzqaW0NrfGyswKSzNLLM0ssTK3wtrcGnc7dzwdPCloXzD9q4N6tbe0z/B42eVFC8Dz/ebz/l/vM7nxZIbVGqb3ZEjTNNZcXMOMIzM4ff80/6v+P7qW64p/qD8P4x7ybs13M+17pW9RCVF8vPNjFpxawNctvmZIjSFGk+DnmHPnoFEjNW/DkCGGjiZNSgrs2wfbtsGlS2q5elWNJFq7NnTqFEnnzlC4cN5+9htCii6Fbw9/y7T903Cwckj7AtHUxJQanjXwcfHB1sIWWws10NCFBxfYe2sv1QpWo7JHZews7AiKCeJa2DVuhd8iPjmelj4tmdR4EuXylzPw1RkHKf8IYXwkAdQTY3oApnbED40NJSoxirikOOKS44hPjk/7+fHX2KTYtCQuOik67ef45HgSkhOIT45Pl+zFJcelncvC1AI7S7u0flmpzSpdbV2fmtTpO5kzlITkBBJTEtFpOlK0FFJ0KWk/6zQdKboUTExMMMEEUxPTtFpBE0xISEkgLimOxJTEtCV1XXBMMIFRgdyLvpf+NeoeCSkJFHIoRHn38pTPX54K7hUon7885fKXy7ZBUp43AUxKSWLcjnH85vcbG3puoJF3o2yJ63HH7h5jyr4pHLlzhLJuZQmJDaGUaylWd1+d7ffa6fun6bC8A8Wci/Frh18pka9Etp7PqB08CK1awZIl0LGjoaPJVGIiXLsGu3bBypWRXL4MHTo4MmSI6kMoXtzVq/D333Dvnhqwx8oK3n0X2rbNfJ+ohChO3juJtbk1lQtU5kHMA/be2ktgVCCxSbHEJsWSokuhiFMRupfvTkGHgjl3Qa8onQ7u3IG4uEjKlDGO8o8QQpEEUE+eJwGMT44nOjGaqISoR4lVYjRRier3mMQYElJU4pCQnEBCSgKxSbEqwUpS7yfpkkhKSUr3mqxLfmJdUsq/6/+zTkPD3NQcB0sHbCxssDG3yfzV3AYHKwfsLe2xs7DDwcoBOws7bCxssDKzwsrcKu09e0t77CztsLOww87STpq35TBN0wiNC+Xig4ucf3Ce88HnOf/gPOeCz/Eg9gGFHQtT1q0sZdzKUMq1FCXzlaSUaymKOhd9qX6Oz5MAHg44zKA/B2FpZsmqbqso6Vryhc/7Mh7GPaTBgga0L9WeL5t/mW3niU2Kpfrc6nQs3ZHPm30u/UmzYt066NsXTp1Sw3TmcpGRkZw9C7//7sgff6hmol99pSozxfP5/nsYOxYaNAAvL6hRQ40RNHUqLFsGHToYOsK8IT4ejh6FAwdUjbe/P8TEqJFyLSzUFyBXrqjtdLpIQBJAIYyJJIB6kpoAfrDuA0ysTUjSJZGQnEBUYhSRCZGExoUSHBNMcEwwsUmxAJiamOJgqRIre0v7tCTL1sIWa3Nr1eTPzAorMytsLWzTatJsLWyxMLXAwswi01dzU/NM37M0s8TZ2hkHSwdpgpaHPIh5wMWQi1wKucTFBxfxf+jP1YdXuRZ2DUszS8q6laW8e3nKuZVLqz3MamKY1QRwzYU19F/fn08bfcqI2iMM3rfmUsglas6ryYquK2hb8inVCy9x/CGbh5CYksjeAXtfiVrtHPP+++Dnp9pemubupPnx+z8iAn7+GT77TOWwX32l5rwXz/bLLyr5274datZM/97KlfDWW6rJrY8P3L4N48apRFG8GJ0ObtxQgx6dP69aYN+8qfq+3rkD+fKpz7dCBShZEpycVP/YpCTV97V0afX9TFRUJK6ukgAKYUwkAdST1ATw7VVvY+9gn9Zny8HSAQcrB9xs3chvmx93O3dcbV1xsHTA2txaEjBhcEkpSfg/9Od88HkuPLigag4fnOdK6BUcrRxp7N2Ypt5NaVqsKWXcymR4zz4rAYyIj+CjnR+x9OxSFndaTIfSuedr/CVnljB863D83vHDy8nrpY+XrEvG3NScPy//SZ+1fXizyptMbjIZZ2vnlw82L4mJUSXPjz+Gt982dDRPldH9f/26SljCw1UzUWdnw8RmLBYuhPfeg61boV69jLe5dw+WL1evlpZq+o7Fi6Fz56ydIzXhsbSEwoUhL/z3q2kQFqbux7NnVaX69esQGAgXL6qErnRpKF9eLT4+ULAgFC0KxYpl7TMypi4wQghFEkA9kQegeNUkpiRy6v4pdl7fya6buzhw+wDO1s74uPiQ3y4/5dzKUatwLRytHDFLNMPO0g73fO7kt81Psi6Z2xG3ORN0hr+u/sWmK5uoUqAKc9vPpbhLcUNf2hMGrB9AaFwoG3ttfOK9+OR4BqwfgKOVI3Pbz830GH73/Bi8eTB+9/xoULQBR+8eZX6H+XQr3y07Q3+1/fknDByo2prly2foaDKV2RcgiYmPpjnctg3snnc2kuholbEULw62tq9sxrJkCQweDBs3QpMmWd9v3ToYMAB274bKleHhQ7C3B2vr9B9VQoIayfXbbyE0VA3q4+YG9evD8OHQsKG+r8hwUlLST2ly/TpERakpNsuXhypVVG1egQJQrpz62eIlG2JI+UcI4yMJoJ7IA1C86hKSEzgWeIyAiACCYoI4ce8Ep++fJiYpBi1eIyYxhuCU4LTtbcxtKOVailY+rXit1Gs0KNIg19Z4h8SGUHJWSZZ3WZ5ukudkXTLt/mhHeHw4l0MvZzpp+5XQKzRY0IBBVQfRqUwnNl7ZSP/K/fHJ55OTl/Hq0TQ1IEz16vDFF4aOJlNPqwGPi4M2bcDGRiU45llpBZySotqRTpqkksD4eJXZ/PCDynhy6b+j55WQAKNHq+kfV66Eli2f/xjTpqnmtpaW8O+fATMz1ey2SBH1vcHp01CokGqO27y5uq2OH1dJ+fTp8N138M47+r22nBYYCPPnq9lUdDp44w2oU0fV6Hl7Z28zZCn/CGF8JAHUE3kAirwstQBsa29LcEwwlmaWuNq45tqELyNzjs3hq4NfcfJ/J3G1dUXTNN7b8h77bu/j0MBDbLqyibc2vsUPrX9gULVBgJoqZNnZZQzdMpShNYcytelUo7pmo3D0KDRtqobc9PAwdDQZelYT6MhIVRhv2xa++SaTgyQnw+HDsGkTrF+vksCZM9Xkg6GhajjMgQPVcJiTJmXHZeSoGzegRw91matWqUrOF3XnDgQHq6aMsbGqxisiQvUTfPgQSpWCunUzzpv37YMuXdTf5sMP1QA+xiAwEPbuVfHv3asGamnZUtWktmuXxS8a9ETKP0IYH0kA9UQegCIvexUmwtY0ja6ruvIg5gEzWs3g60Nfc/TuUXb120Uxl2IA7Ly+k+6ruzOwykAqelTk28Pf8jDuITNazaBrua4GvoJXWI8eanSKLVtUW7ZcJiv3v78/+PrCTz9Bz56okTS+/lpVf92/r6ptbGxUJtKunUr8bGzSH+TCBWjcGIYNgwkTsu+CsklKihpsZONGVfP2xhuqWaZ1xvO155ibN1VOvXKlqmyeNw/KlDFsTP915Yrqs3f9OqxYAceOqeacjRqpJqwNGhjun4aUf4QwPpIA6ok8AEVe9iokgKDmEnt3y7ssPbOUTmU7MaftHDzs09c6nQ8+z5DNQ4hLjqNPxT68U+MdrM0NXIJ91SUkQLd/+1L++adhY8lAVu//LVtgRLc7bB64hhLbflQT3H3+uRptQ9NUJy0zs6ef7OxZ1VGuXz/V/tHQ2VMWBQSoxPfMGVUbOno0tGhh6KjSCwtTH+kvv8Bvvz265Qzpn39g8mQ1kFDp0qo5Z/Pm0KcPuLgYOjpFyj9CGB9JAPVEHoAiL3tVEsBUMYkx2Fk+74gdIluFhKgRK5YvV/0CX4SmqbaCQUFQtuzLj37xr3T3v6ap9nlnzqjOZ6mvAQFgbo4WHs5+k4bYf/AW1b7srjqvPa/Ll1UCmJCgEuIiRfRyHdll40bVdbFLFzXP338rNnOb9etVvAMHqn6DerpNskSnU806161TI6LevQtDh8KoUeDunnNxPA8p/whhfCQB1BN5AIq87FVLAEUuNWuW6hd3+rQaFCUzd++qjmUnTqgELCJCrQ8PVz/b2am5BUePhpEjX2B4zvQiIyPh6lUcly6FpUvhwQOVrFaqpIanrFRJVd0kJUHJkqz524H+/VV3v8aNX/CkSUlq3oQNG1TGUrv2S11DdtA0NVffTz+pMW3eeMPQEWXd1asqYXVwUE1DPT2z93yaBnv2qOkvg4LUudu0Ud1fHRyy99wvS8o/QhgfSQD1RB6AIi+TBFDkiJQUaNZMDQbz+efq9zNnVNPJcuXUNgsXwv/+pzpH1aunki83N/Weg4NKzGxs4MABlQBeuaK2Hzo085q0uDg1+Vx4uDqGg4OqkTx+HPbvJ/LECbh7F8eWLVW1UZ06atqGp5g1S9WGnTv3Eq04NU0d6OOPVSLYrNkLHih7TJkCv/4KO3aogViMTWysujVOnVJTKrxIZW1WnDihcvkLF+CTT1QXz+w6V3aQ8o8QxkcSQD2RB6DIyyQBFDkmIECVyrdtU23zypZVQyBaWKjELCpKNYvMyoRymgb796taxc2bVXVc4cJq6Mjg4EdLZKQaVtHJSR0/MVHVGlapAvXrE+nrCzVr4ujlleXLSEmBmjWhY0f49NMX/CxSLVqkEthNm1TimwssWqQSmQMHoEIFQ0fz4pKS1N+pUyeYOFG/xz5/Xv3t//oLxoxRzTxze21fRqT8I4TxkQRQT+QBKPIySQBFjktKUkmZiYlK0O7cUcmZh4dqbvm8rl9Xo7QEB6vhFN3dHy0eHqoW0dT00bkf6xj2ovf/sWMqX9u/X40++VIWLFAZ186darhRA9q0SQ3cumGDGrDE2J08qSaNP3ZMVTa/rJAQ9Z3DjBnw9ttqMvoXuWVzCyn/CGF8cnCmGCGEEEJPHh+Zw9HxURPQF1W8uGqH97znfgk1a6rWm336gJ/fSw7o+eabqonq66+rTKVwYb3E+Lw2b1bJ38KFr0byB1Ctmuqb99ZbcOhQxvMJZtXBg6p/X6VKamTPWrX0F6cQQmSVqaEDEEIIIfKqceNUd8HPP9fDwYYPh9at1WgrKSl6OODz8fODXr1UZWTXV2xazIkTVXfRAwdebH9NU9NLtGyppnDctk2SPyGE4UgCKIQQQhiIubmac+7bb9V4Ni/FxARmz1ajkL50x8LnExGharbGjYPu3XP01DnC1hb69oW5c59/3/h41dTzk09Uf7/33nu5WkQhhHhZkgAKIYQQBlSlCnzwgWpiGBPzkgezs4M1ax7NvZADNE0lOKVKqQTwVfX227B6tZowPqsCA1U/z7NnVV/Chg2zLz4hhMgqSQCFEEIIA/v0UzU7ReXKcOPGSx6sXDk1EsuHH8LatXqJ72nmzlVNIxctejROzquofHmoWFHl11lx6pSanrFsWTW5u4G6ZQohxBNe4Ue1EEIIYRxsbNSgIA0awODBqlbtpdStC3/8Af36wd69eokxIzt3wsiR6lTu7tl2mlzjjTdg2bKM30tIgPXr1TatW6s/wf/+p/pEWlnlaJhCCPFUkgAKIYQQuYCZmZoa4NQpNYXCS+vQQc038PrrsH27Hg6Y3qlT0Lmzam3auLHeD58r9eihavPu3FG/axoEBala0EKFVDJcrJia3/HAATXgi/T3E0LkNjINhBBCCJFLODur/oC//qqSiJf21ltgaakO9sUXaj4DPWQkN29Cmzbw0UeqkjGvKFhQDXLz+utQsqSa9iI6GkqXhvnzoX17SfiEELmfJIBCCCFELtKrF0yapCYMd3PTwwH79VMjtHTqpKaHGDHiqZsfOgS7d6v+iG3bPtmv7+5dlfx17gxjx+ohPiOzYIFqpmturmr5SpZUo4QKIYSxkCagQgghRC5SrBjUqAGrVunxoLVrq9FLJkyAc+eeeDslRY1w2batmqvu9GmV5FSurHYLCYHjx9W4MmXKqP5tP/yQN2u7LC1Vbd/cuerzkeRPCGFsJAEUQgghcpmuXeHPP/V80Lp11SR0/xllZvlyNcLlyJFqEJrLl2HlSrh2Dd55R1UY5s8PzZur2r+//1ZzF5qZ6Tk+IYQQOcJE0156rDEBREZG4uTkREREBI6OjoYOR4gcFRkZCSD3vsiTsuP+v3BB1QI+fAjW1no7rOqwVqYMTJ1KSt8BfPEFfP+96h7Yr5+q3cpsN1vbV3uaB/FipPwjhPGRPoBCCCFELlO2LLi6wv790KKFHg9sb4/2088kd+3B0HfN+dOxDzt3QqVKz9xNCCHEK0K+yxNCCCFyGRMTaNUKtm3T73GTkuDdLe14w3Y9c0yGcvf9L6hUURoCCSFEXiIJoBBCCJELtW2r+gHqq6PGsWNQvz4cPgzfnm6B+cG9mP04C4YOVaPACCGEyBMkARRCCCFyodat1aArZ8++3HEiI+Htt6FRI3XMQ4egSBGgShU4cgT27lVzOvzbl1EIIcSrTRJAIYQQIheytYXXXnu56SAuXoRq1eDGDTh/HiZP/s+0BUWKqMns4uOhRAlYv/5lwxZCCJHLSQIohBBC5FLdusGKFc/fDFTTYOlSqFcPevRQUzcUK5bJxi4usHWrmtivb1/48kvQ6V46diGEELmTjAIqhBBC5FLt2sH//qeabdar9+zt4+NV4jdjBoSHw88/Q/fuWTiRiQn07AklS6odtm1T80M8a3hQYxAWBjt2gL8/3L+vMuE6dWDLFvXzgAFPzmgfHQ3JyeDsbIiIcz9Ng3/+gYMHQaZ+EMLoSA2gEEIIkUvZ2EDv3mri9We5fBkqV4bvvlOTt1+9msXk73HVq4Ofn5qEsE4dWLToheI2iIcPYcMGOH4cgoJU09Zvvkmb95Bz58DcHHbuVLPa//MPTJwIHTqohC/V7dtQqpSqGR092nDXkxvFxcGvv6obrU0b2LcP5s41dFRCiOckE8HriUyEKvIymQhe5GXZff+fOqVq/86eheLFM97m779VU8933oHPPwczMz2ceMcONTjMggXQpYseDphNNA2+/RY+/RSKFoWQELV4eECtWiqD7tYtfS2fpqnfw8PVNQYHwxdfgJMTvPUWNGsG778PNWvC7t3g62uwyzOowEA1fOyxY6oGdedOKFgQPvhAfa42NlL+EcIISRNQIYQQIherUkWN4vnaa2oUz65dHzUHTUmB2bPh449Vc8++ffV44ubNVQ1gv36qqWS1ano8uB7ExcG0aWqUnNhYlbDWravei41V1af/bdqZKnW9s/Oj/o9Dh6pEcPx49YGamcG4cfDee6q2MLNjvUo0TTWN/e03OHpUJYDlyqlEuHp1GDwYGjfOG5+FEK8wqQHUE/kGTORlUgMo8rKcuP+Tk2HSJJWfLF2qyuMWFmpkT1NTlQPVqZNNJ//uO1U7dviwGinUkGJjVVK6dy/s2aNGMf34Y2jSRD990XQ69YE+fr4SJVSW3bnzyx8/t9I0+OsvdZPduqWS3kaNVNJvb//UXaX8I4TxkQRQT+QBKPIySQBFXpbT97+/vxoUJilJNQlt2FB1bctWb78NUVGwfHk2nygT8fEqy50yBRwcoFMnNat9w4Z6au/6FD//DNOnq7a4z0iGjM7Vq6q2b+NG9e3CmDEwZAjY2WX5EFL+EcL4SAKoJ/IAFHmZJIAiL8sT93/qwCh+flC2bM6e+7ffYOxYyJ9f9T17++3sT/oel5KimsN6e6v+kK+C8+fhs8/UvI+vv65qN1977YUSXCn/CGF8ZBRQIYQQQjxdkSIwcKAaXjQnvzfevRuGDYOFC+HCBdUHLSeTP1DnW7IEVq6EEydy9tz6lpSkRjatXl2NcnrlipposkePV692UwiRKUkAhRBCCPFs06apqRR+/vnFj3H7NkyeDKNGwaVLT9/27Fk1+uj336vaKUMOPFKokKp5/Oorw8XwsrZuVSMKbdsGp0/DTz+pxF4IkedIAiiEEEKIZ3N2VjVhY8aogWGepyYwMRF+/FFNLO/np/qbVa2qjvdfDx6oPneNGsHIkWpahtxg5Ej480+VPBkDTYMbN9R8iP36qVq+IUPUPImlSxs6OiGEAck0EEIIIYTImsaNVbPMdu1UcjFzZuZNMpOT1cTsv/+uRuz08oJly9QE4gD9+6vBXFJHF717F86cUcevVQv++EPNe5FbFCkCH34Ib76ppoWwsDB0RJk7dQr+9z/16uysJrs/fVr1YxRC5HlSAyiEEEKIrKtRA44ceTRRfEhI+vejolSzzRIlVFPP2rXh4EHVpDM1+QM1sMo//0BkpJrWISkJ2raFa9dUrVVuSv5SffKJqlkbMEANDpMb7dmjak/btIHQUFXb+uuvkvwJIdLIKKB6IqNgibwsT4yCKEQm8uz9HxamasP27FEjSXp4qDnk/v4bSpZUg4106pQDc1TksKAgaNpU9adbtCjnB6V5mgMHVOL3/fdq0J4cIOUfIYyP1AAKIYQQ4vm5uKhpBDZvVs074+KgcmXYskXV7HXr9uolf6AS3V27VPPKRo1g7dqcHRk1MwcPqsFyvv02x5I/IYRxkhpAPZFvwERelmdrQIRA7v88KywM5s1T/SC9vNQchZUrQ7lyOTdiaUoKHDqkBs3Ztk0lf0OH5sy5/yXlHyGMj9QACiGEEEI8LxcXNSKqvz90766miPD1VXPs7dql33PFxqpBd/75Rw1EU6ECuLmpGDp0gLJl4fr1HE/+hBDG6RVsmyGEEEIIkUPs7NRgN6NGqWawc+aoPpF9+8KMGWBl9WLH1TTVvPSbb+DYMVWrmC+fanb6+eeq1tHMDMqXfzWb2gohso08MYQQQggh9MHGRiWCXbqoPpC+vjBrFtSvD6YZNLq6cwcuXFDv29qqddHRsHGjaloaEAAffwyrVqnJ6DM6hhBCPCdJAIUQQggh9MnbW43I+c030L69Su7atlWjh9atq+ZGXLRIJXgFCkBEhJrwvkgR1Y/P2VnVIL73nqphFEIIPZKvkoQQQggh9M3KCiZMgAcPYOlS1Xzz++/VFBkbNqimog8eqCRwxw41n+LOnfDZZ2rOxLFjJfkTQmQLo00Aly9fToMGDXB0dMTExITk5OR07585c4aGDRtiZ2eHp6cnkyZN4vEBTzVNY+LEiXh6emJnZ0fDhg05d+5c2vsbNmygZMmSmJubY2JigoWFBd27d0cGTRVCCCFElllaqpq/b76Bo0dVbd+JE2q+PhcX1bevVi1YvBg2bVJzK+bUKKJCiDzJaBNAFxcX3n33XWbOnPnEe1FRUbRq1Yp69eoREhLCtm3b+PXXX9NtO336dObPn8+2bdsICQmhXr16tGrViujoaADKlClDZGQkH374ITExMcydO5dVq1YxevToHLpCIYzLjh07DB2CEAYj97/IMju73DV5vBAizzHaBLBVq1b06tWL4sWLP/He2rVrSUlJ4bPPPsPGxoaKFSsyevRoZs+enbbNnDlz+PDDD6lYsSI2NjZ89tlnJCYmsm7dOgCOHDmCpmlMnToVa2trSpYsiZmZGUuWLMmxaxTCmOzS97DnQhgRuf+FEEIYC6NNAJ/m1KlTVK1aFfPHhkWuWbMm169fJzIykoiICG7evImvr2/a++bm5lStWhU/P7+0Y1SsWBE3NzesrKxo0KAB5cuXJygoKG3SXyGEEEIIIYQwJq/kKKCRkZE4OzunW+fi4gLAoEGDWL16NQB169Z9Yt+zZ8/y3XffERkZiZubG+Hh4cTHx7N582YOHDjAmTNniIyMxNHRMd1+KSkp2Nvbc+XKFezt7bPnwoTIpWJjY4mNjeXSpUuGDkWIHCf3v8jLUrvOpKSkGDgSIURWvZIJoKOjI3fu3Em3LiwsDIDvv/+er776Ch8fH7Zs2ULNmjXTtunWrRvlypV74hjW1tZ06dKF6dOnp733X1evXkWn01GvXr20debm5ulqIYV4Vdnb25OcnMyaNWsMHYoQOU7uf5HXJCcnPzH43tWrV9OVqYQQudcrmZ1UqVKFpUuXkpycnJaAHT9+nOLFi+Pp6QmAt7c3/v7+tGnTBlAPs/PnzzNw4MBMj/Hw4UMcHR0zTABLlChBbGwsAQEBGb4vxKssKiqKyZMnp31JkpccvXMUO0s7yruXN3QowkDy8v0vRGRkJF5eXpQoUcLQoQghsshoE8CUlBSSkpJITEwEICEhgeTkZCwtLencuTPjxo1j4sSJTJgwgWvXrjF9+nSGDx+etv+7777L9OnTadq0KT4+PkydOhULCws6deoEQHx8PACffPIJo0ePZsaMGVy5coVBgwZlGI/ZvyN6ZZYgCvGqs7S0zDP3fkxiDHaWan6u2Wdm4+PiQ50SdTLeOCICkpPB1TUHIxQ5LS/d/0JkxExGNhXCaBjtIDCLFy/GxsaGVq1aAaoJjo2NDfv27cPBwYFt27axb98+XF1dad68OQMHDmTEiBFp+3/44YcMGDCA5s2b4+rqyv79+9m6dWta/73AwEAsLCz4+uuvcXV15auvvqJLly7MmzfPINcrhMgd7kbexWO6B+Hx4QBcCrlEcGxw5jt89RW8/37OBCeEEEII8QwmmsxsrheRkZE4OTkREREh3wKLPCcyMpIdO3bQuXNnQ4eS7X4/9TtvbniTfQP2UbtwbWyn2dLEuwl/9/074x3efhu2bIE7d2Ry51dUXrr/hfgvKf8IYXyMtgZQCJG7NG/e3NAh5Igd19WE3+eCz3Et7BrJumSCYoIy3yEsDAID4fr19Ov37YPw8OwLVOSovHL/CyGEMH6SAAohRBZpmsaO6zuo4VmDc8HnuBRyCTMTM4Kin5IApiZ5+/c/Wrd2LTRpAkuXZmu8QgghhBD/JQmgEEJk0cWQi0QlRvF2tbc5G3yWSyGXqOFZgwexD9Bpuox3Cg+H0qVh7171u6bBO+8QVcSDiIt+ORa7EEIIIQRIAiiEEFl2Lvgc5fOXp3rB6pwLPsfFkIs0LNoQnaYjNDY0453CwuC11+Cff9TvoaEQEsLiivDgzJGcC14IIYQQAkkAhRAiy66EXqGUaynK5i9LeHw4q86vommxpjhYOmTeDzA8HJo1g0uXIDoa/P3Bw4N/3OKxuh2Yo/ELIYQQQhjtPIBCCJHT/B/6UzJfSWwtbPmsyWc0KNqAhkUb4m7nTnBMBlNB6HQqASxXDjw8SDl5ArNbt9FKlsDP9jCu91FNQmV0UCGEEELkEKkBFEKILEqtAQQY33A8DYs2BMDD3iPjgWCio1US6OJCRMVSTJnxOrorl4kv5sVVJx22CTq0oKcMICOEEEIIoWeSAAohxDPcCr/FldAr+If6U9K15BPve9h5ZNwENCxM1e45OHDbxw2f6xGEnjlCeOH8mNrZEegAIWf/yYErEEIIIYRQJAEUQohn+Pbwt3RY1oHQuFBK5nsyAcy0CWh4ODg7g6kpl4raUTcAks6fJbigI15OXtzNb839M4eyPX4hhBBCiFSSAAohxDPcibzD5dDLeNh54GTt9MT7HnaZNAENC1MJILC3hAWalSWe14K57W5FAfsCRBVyI/rSmWyOXgghhBDiEUkAhRDiGe5E3qGSRyXK5S+X4fsF7AtwL/rek2+k1gACV+Pvsv/r9zjmCbst7lDAvgAUKEDyvbvZF7gQQgghxH9IAiiEEM9wJ/IOs9vMZlW3VRm+X8ixEIFRakoHnaaj+PfFVY1geDi4uABwM/wmheq0ZMjE6iy/sZECdgWwcPfEJDST+QOFEEIIIbKBJIBCCPEUSSlJ3I++j7ezN662rhlu4+ngmZYAhsSGcCP8BtfCrqU1AdU0jVsRt/B29qaeVz3uRd+jgH0BbDy9sAyLysnLEUIIoWezZ8/GxMSE8+fPGzoUIbJEEkAhhHiK+9H3AdXMMzOeDp48iH1AYkoi96JUU9DAqMC0GsDgmGDik+Mp4lSE+kXqpx3PqZAPtpGx2X4NQgghso+fnx+2traUKVPG0KEIkSUyEbwQQjzFnUjVX8/CzCLTbdzt3DE1MeVe1L20voCBUYFpNYA3w2/iYeeBjYUN9YrUA1QC6OqVn/ioFOKS4rCxsMmR6xFCCKFffn5+VKpUCTMzM0OHIkSWSA2gEEI8xd2ouxRyLPTUbcxNzSlgX4DAqMC0GsC7kXfTagBvht+kqHNRQNUW9qvcj4oeFXH2KoFbHARE3M7uyxBCCJENkpKSOH/+PFWrVjV0KEJkmSSAQgjxFHci71DYsfAzt0vtB5hWAxgdCMHB4ObG1YdXKZGvRNq2CzsuxNPBE1N3DyxTIPDu5WyLXwghRPY5f/48iYmJVKtWzdChCJFl0gRUCCGe4m7kXQo7ZC0BvBt1l3tR93C1cVVNQG/cgWLFuPLwEKXylXpyJ0dHks1MCLl1ESp3yIbohRDCeGgaRGXjuFgODmBiot9j+vn5AUgCKIyKJIBCCPEUAZEBVClQ5ZnbFXIolFYDWN2zOgFht+DmTShWDP99/rTyafXkTiYmRDtaE3H3mt7jFkIIYxMVBU5O2Xf8iAhwdNTvMf38/LCwsKBChQr6PbAQ2UgSQCGEyISmaRy+c5iBVQc+c1tPB0+uhF7hXvQ9GhdtzK1zByEpCYoW5UroFUq5ZlADCMQ7O3Dr2gkexj0kn00+fV+CEEIYDQcHlaRl5/H1zc/Pj/Lly2Npaan/gwuRTaQPoBBCZOJG+A3uRd2jnle9Z27r6eBJeNAtRv58hhpulcgfHEN8wfxcirxOaFwoJfOVzHA/e09vzELDaLKwib7DF0IIo2JiomrosmvRd/NPTdM4ffr0E80/Bw8ezKhRowAICgqiYsWKbNmyRb8nF+IlSAIohBD/0jSN+OT4tN9339hN7cK1sbO0e+a+Fd0rEnbhBF2ORVNnyxlKRVpw1OIBHZd3xN3OHSfrjNs12XsWZUypgZwLPpc2mbwQQojc79q1a0RFRT0xAuiUKVNYvHgxJ0+epE2bNkyePJm2bdsaKEohniQJoBBC/Gvl+ZXU+rVW2u+7bu6iiXfWauZqFqpJfYfyJJuA+4y51A2zI8nbC/+H/pnW/gGQPz82EdFUK1iN3Td2v+wlCCGEyCGZDQDj7u7O8OHDqVu3Lh9++CGdO3c2RHhCZEoSQCGE+NfPJ37mTNAZbv87L9++W/to7N04y/sP8enJFS9bTEqV5s2DsTRqPIBu5bpR1q3sE9vu3AmXLgFubvDgAY2LNmbPzT36uRAhhBDZ7tChQ1haWj6RAD58+JBVq1bh4uKCl5eXgaITInOSAAohBHDt4TUOBRyiXP5ybL+2ncCoQAKjAqlZqGaWj1FEc6RcqbqYfPklpgmJmJcoybz28/im5TdPbDtmDNSoAf5RBeDePZoUa8LOGzvRaTp9XpYQQohskJCQwLp162jTpg3W1tZp6yMiImjdujXDhw/nxx9/ZMSIEWiaZsBIhXiSJIBCCAFsuLyB5sWb06N8D/6+/jfH7h6jXP5y2FvaZ/0gYWHg4gL168MXX0CjRjhYOeBs7fzEpkFBULIk7LruDTdu0Ni7MRoa3x3+Tm/XJIQQQr+Cg4PZunUr3bt35/79+0ycODHtvZiYGNq2bcuAAQPo378/nTt3xsrKikWLFhkwYiGeJNNACCEEcD/6PsWdi9PKpxUzjsygoH1BfD19n+8gqQkgwLhxmW6m06kEsE0b8L9XDG7exNbchhVdV9BgQQP6Ve6Hu537S1yNEEKI7DBz5kx++eUXqlWrxv79+9MNAGNnZ8fBgwfTbf/f34XIDaQGUAghgJDYENxs3fAt5EsZtzLMPjob30IvkAA6O2dps+RkqFgRLsZ5Q3w83L+PbyFfnKycCIgIeKFrECLHJCcbOgIhDGLatGmEhoayfft2atbMehcBIXITSQCFEIJHCaCJiQlfN/+aFC2FWoVrPXvHx4WHP6oBfIr798HeHooWhcCH1lCwINy4AYCbrRshsSGQkAApKS9wJUJks3XrwMMDduwwdCRCCCFegCSAQgjBowQQoEHRBpwefJoqBao830EebwL6FEFBUKAA5M8PDx4AxYqlJYCutq6ExoXCwIFQrRpcvPicVyJENvv9d6hSBTp0gGvXDB2NEEKI5yQJoBBCkD4BBKjkUen5D5LFBPD+fVWBkpoAao8lgGk1gFevQkwMTJr0/HEIkV3CwmDrVvjlF+jdG8aONXREQgghnpMkgEIIwZMJ4AsJD89SH8D79x/VACYmQkLBx2oAbVxVAnjvHvToAadOvVxMQujDxo1Qrx5UqACVKkGJEv9n777jmyq7OID/ku69F90tGwotHYyy91SGArLFAYoDxIULFUVfBUFlKCDKVlAZsvfe0MHuhJZC995tct8/TpMmbdqmbbrP108+0uTm5sloes895zkPsGQJLWj5zjs8J5AxxpoQDgAZYy1esbQYaflptQ8Aq1ECamcHmJkBOjpAhqVyBjA1uyQAHDECCA+nTCBjDSUnB5g+HXj2WWDjRmDrVrre3h64ehXYtQvYt69hx8gYY0xtHAAyxlq81LxUADT/rsYEoVoloPb2gEhEWcB4Jz/gyhUgNRXWhtbIT4yjjIqvL2BtDdy6VfNxNSMXLgDPPw/8/XdDj6SF+fNP6lj03nvAsGFAu3alt7VpAwwfTp9fxhhjTQIHgIyxFi85NxnGusbQ19av+U5yc4GiIrVKQGUZQIACwEdmXYCAAODXX2FlYAXR06e0HwMDarbBZaAAgNOn6fLOOw09khZEEIA1a4C5c+mMhSoBAcC1a/U7LsYYYzXGASBjrMXT2Pw/oFpzAAGFTqDvvAOsWgVrAyvoxicDrVrRBt7eQEhI7cbWTCQkAP7+QGpqQ4+kBdm7F3j8mEpAK+LvD9y4AUil9TcuxhhjNcYBIGOsxdNIAJiWBpiYANraVW4q6wIKKASA/foBT57Arlgf+klptDYgQKvFcwkoAAoAO3SgKWkFBQ09mhagqAhYtAhYvJgWrqxIp07UzSgsrP7GxhhjrMaabQD4+eefQ0tLC8bGxvLLCy+8IL89NDQUffv2hZGREVq1aoXPP/8cgiDIbxcEAYsXL0arVq1gZGSEvn374vbt2w3xVBhjdUxjAaAa8/8KC4HERMDJiX62saGfYWoKmJjANq0AJilZEGQZQA8PIDoagiBg34N9uJfUctcFlAWAAGcB68WyZYBYDLzySuXb6egAPj7UEIYxxlij12wDQADo2bMnsrOz5ZcdO3YAALKysjBs2DAEBgYiOTkZR44cwYYNG7By5Ur5fZctW4aNGzfiyJEjSE5ORmBgIIYNG4bs7OwGejaMsbpSn0tAxMUBWlrKJaAJCSU3OjvDKjkXNpkSFNmWjMfdHXjyBNFP72HcX+Pg86sPLsVeqt1YmyhZ4GxszAFgnYuKAr76CvjtNwrwqjJgAHD0aN2PizHGWK016wCwIv/++y8kEgmWLFkCAwMDeHl54b333sOqVavk26xZswbvvvsuvLy8YGBggCVLlqCwsBC7d+9uwJEzxupCcm4yrA1qGQBmZNC6DlV4/Jim92lp0c+9e9O62nl5AJydYZSQCscsINvahDawtwf09HDv5hF0c+iGiZ0m4r+w/9QeVnp+On649AMScxJr8KQaF1nzHEtLDgDr3A8/AGPHAj16qLf9M88ABw5Q2ShjLcyqVasgEolw586dhh4KY2pp1gFgUFAQbGxs4OrqiilTpiC6ZJ2t4OBg+Pj4QFthro6/vz+ioqKQmZmJjIwMPHz4EAEBAfLbtbW14ePjg6CgoHp/HoyxuhWbGQsHE4fa7UTNADA2trT8E6Cpf3Z2tJQanJ0hjnsC5xxtpFkY0gZiMeDmhrjQC/Bv5Y9hnsNwNFL9TMvnpz/HN+e/QbtV7ZBVkFXNJ9V4FBVR0GdrC1hZASkpDT2iZiw1Ffj9d2DhQvXv4+8P6OnRWh2MtTBBQUEwNDRE+/btG3oojKml6m4FTdRzzz2HF198ES4uLnjy5Anef/99DB48GCEhIcjMzIR5mVIti5K5O5mZmfK5gKq2yczMrPAxTU1NkZXVdA+wGKuppv65j3gSgWltp1X6+12l9HSKTKrYR0wMVXUqbjZnDnXaHzPUCVox0XDMM8IJgyzYyDZq2xYZEeHw7j8UPW17Yt7TeYiOj65y3cLI1Ehsv74dh6YewsRdE3Hz4U34OPjU/Dk2oPh4miapr09J0cTEKl/qetPUP//lbNtGmb/WrYHMTOTn0+tepbFjgT17gG7d6niArDFpdp//GggKCkKXLl2gJSvtYKyRa7YBYOfOneX/dnR0xMaNG2FmZoaLFy/C1NQUjx8/Vto+LS0NAAVxsgAwXdbWXWEbR0fHCh+zuLgYX3zxBXR1dQEAAwcOxODBgzXxdBhjdSS/KB/R6dFob13LM7dZWdQFtApPn5au8CDzwgvA9u3A0VsOGBH8B3S09fDeo/Wwju2Dns49IXVygjjiArradYW1oTU62XbC4YjDmNplKgRBgESQQFtc/uv8xys/YkKHCWhr1RZtLNsgPDW8yQaASUk0xVJHh3rtlPl6Zpp08SLN6QN9rHv1ApYvB4YOreJ+Y8fSeoGffAKU/B1kzdPx48dx8uRJAEBhYWEDj6ZhFRUV4c6dO3jppZcaeiiMqa3ZBoBliUQiiEQiCIIAb29vbNu2DcXFxfIy0OvXr8PDwwOmpqYAADc3N1y7dg09e/YEQMFdcHAwpleyFlJubi6WLVsm3wdjLU1T/OyHPwmHWF+Mtq3aQlTRQtfqyMigyKSK1+DRIzq2VtzM1JR6bXwc4I5JeQ+BoUOxdMwLGL93PL4b/B2GmBTCNT4Dvu6+0BZr480+b2Lh0YVIlaZi5ZWVMNE1QdCcIJjpUwmqIAi4k3QHf4b/ibvz7sLU1BTu9u4Izw5vku8RQNk+IyN6rUxNqelqY3sqTfW1VSIIwIkTwBtvAKam+OMPCr6/+AIYN6507qpKQ4fSYvHnztHGrNkaP348xo8fD4Aqp9avX9/AI2o4d+7cQWFhIbpx5ps1Ic12DuDOnTuRnJwMAEhISMDLL78MOzs79OrVC+PHj4eWlhYWL16MvLw83L59G8uWLcO8efPk93/99dexbNky3L59G3l5eVi8eDF0dHQwjv+oMdashCaEwsvOq3bBH0ABoBoBQGws4Oxc/vrOnYFPfqUbMrsEYpb3LByddhRLzi7B5w//QF+JszzLN6nTJLiYueDXG79ix4QdaGPVBu8dew8AUCQpwqDNg+C11gsver8IDwsPAEAH6w64l9w0l5D47NRnuBhzGba29DM3galD9+4B2dmAvz8KC4Gff6by5IwM6vFSKbEYmDULWLeuPkbKmiFBEJBZkFlnF8XlvjRF1huCA0DWlDTbDODWrVsxb9485OTkwMLCAn379sXx48dhUlKideTIEcybNw9WVlYwNTXF3LlzsWDBAvn93333XWRlZWHw4MHIzMyEn58fDh8+DOPKFsNljDU5txJvwcvWq/Y7qkYXUFUBIAD4j6PuME/desEUQKBLIILmBCGh7X+wfeVT+XYikQgHphyAWCSGub45Wlu2htdaL0zuPBn7HuxDcm4yUt5PgYV+6bqEHWw6YNW1VWUfstG7GHsRS84uwUhtU9jZUUdKKytec7zOnD4N9OqFIpEuJk+iquYpU4Dr1ykx+MwzVdx/7lzgu++Aa9eoMQxj1ZBVmAWzb6v+Hq2pjA8zYKqn2Ux9UFAQdHR0lKYeMdbYNdsAcN++fZXe3qVLF5w7d67C20UiEb788kt8+eWXmh4aY6yRkApSnI85j5e7vVz7nakRABYUKC8CX46xMb62/xl+Tr3RruQqBxMHOPiMAJLmAsXFQEnZuqWBpfxubuZuWDpwKUZtHwVrQ2ucnHFS6XaAMoBRaVHIL86HvrY6HT0ahw+PfwhjXWMkZiXC046us7TkLqAatX8/RdX+/sCaNRBeex1vvglERgKnTtF0vt69aV34KtnZUfnovHnAggXAxIlV1I1qTkJ2AtbdWIdP+n5S+4w+axAmuibI+DCjTvevaUFBQejUqZO8/wNjTUGzDQAZY6wqP135CYk5iZjYaWLtd6ZGAJiQQFOkZKWMqhxt+wZcyna3tLWluVlJSYCD6uUq5gXMQ35xPqZ1maZySQsnUycY6hgiJD4E3Z26V/VsGgWJVIIrcVcwqdMknD+XCHt7up5LQDUoOxsYMwYwN0eRpR1y0wswde/LuB4KXL1KrzVAAeC0aWr2Olq0CMjPBz7+GNiwAdi5kwLMOrbq6ip8de4rDPUc2mQ+40yZSCTSeIauLgmCgJCQEDz//PPy61JTU+Hs7Iy0tDR5UDhnzhx4eHjggw8+aKihMqak2c4BZIyxymTkZ+Djkx9j87jNMNc3r/0OMzOrDACTkug4uLKEiLW1iuyWnh4dicfHV3g/sUiM9wLfq3A9Q5FIhJd9XsYHxz+ok3kwdSEuKw7F0mL4t/JHRnGiPPa1suIAUGOioymie/gQh7J6Y6ndT+gzSBfXrwMuLqWbOTnR5coVNfZpbg78+CMQEkK/E/36UfejOlQoKcT6m+vhbe/dJEudWdMUGRmJrKws+PiUdle2tLSEh4cHbt68CQA4d+4crl69ioXVWVeTsTrGASBjrEX68/af6GTTCX1d+2pmh2o0gUlKAmxsKt+NtTVQ0r9KmZ0dpRBr4csBXyIiNQKbQzbXaj8aIQgUTUgkFW4SmRoJFzMXOJk6IQelASCXgGpQdDTg7o6kQjOMS9mABSfH4IMPVJcp9+9P0/sy1K3QMzGh7F+/fkCnTsBff2ly5Er+e/AfjHWNsW38Nuy6swsRqRF19liMyVTUAKZfv344f/48CgsL8dprr+GXX36Rd51nrDHgAJAx1uIUFBdg/c31eMlHQ+s2SaVqZwCrCgCtrCoIAO3tK80AqsNEzwQ/jfgJ7x57Fym5FURQ9ZUdvHyZFhtv25YyRjt3ltskMi0Sb4bow6HYAIW6CfL1E83NgdxcoKiofobarJUEgEeOAN7ekJfZqvLDD1TCPG5cNT4m2trA6tW00OXLLwMPHmhi1OUcCD+A8R3Go6NNR8z1m4tp/05DsbS4Th6LMZmLFy9CV1e3XADYt29fXLhwAUuXLsWAAQPQvTuXJLPGhQNAxliLsvbaWlj8zwLp+emY3HmyZnaanU1HxBoIAFWWgAIaCQABYFz7cfB18MWqqxWUyc2eDXz9da0fp0q7dwOTJgHffgvMnw98+mm5bKDOgcN45/f7sA96DMEgEfb2FHUYGdHtOTl1P8xmryQAPHQIGDmy8k0tLYFdu4C7d4G//67m4zzzDDWG6d6d5gZqMHoXBAFHIo9gmOcwAMA3g75BVmEWvj5bD59j1mIVFBRg9+7dGDFiBPT1lRtr9e3bF6dOncKWLVvwdX18nzJWTRwAMsZajOUXl2Px6cXYP2U/wt8Mly+cXmuymrgqumM0dAYQoLmAfVz6IDw1XPUAt22jAPDu3Yp3cuoUUJsOyYJAAeDkycDzz5cGBIolgllZGPXjQRQa6EL8MBfQLoSOMXXHMTSkTXJzaz4EViI6GkVO7jhwABg9uurNTU0pZn/33Rq8/t98Q4sJHjgAdOxIi19W0o1bXXeS7iA9Px29XXoDAAx0DLBt/Db878L/cOPJjVrvnzFFiYmJOHz4MCZOnIj4+HgsXry43DZ2dnbQ1dXFsmXLYKrG+rCM1TcOABljzZqs4cnVuKv47PRn2D9lPwa6D9Rsm/iMDAr+qmh3X+sMYC3nAMq4mLkgJiOm/A3btwM9ewIvvgh8/33FO/j6a2DxYmDTppoN4N49IC4OGDqUftbRAd56C/j119JtPvkED6208GR4IIToZKBYD8l5iQCoqlBXlzOAGhEdjWvJ7rC2BgIC1LvLjBn0cazsI6KSSAQEBgIXL9LnZ+pUijrv3Kn2sBUdizyGfq79oKetJ7/O294bbwS8gS/P8lJOTLNWrlyJqVOnIjc3F+fOnVNqACPz66+/ol+/fhg3blwDjJCxqnEAyBhr1vzW++Hco3P49fqveLXbqwhwVPMotzrUmP8H1DIDaGenkQwgADibOSM2M1b5SqkUWL8emDULGDaM1gBQFBoK9OkDvP8+cPYszev68MOazRkMDQW6dClN5QEUDFy+TMHh//4HYcMGzBkN6Lu1hiTmCXSLbJGYkyjf3MiIM4C1JghAdDR2XnPH1KkUn6lDLAZ++okSeoMHUwV0tRga0poSixbRwvGzZ1faDKgqd5LuwNfBt9z1C3oswNHIo7ibVEk2m7FqWrp0KVJSUnDs2DH4+/sr3Xb//n107NgRu3btwvr16xtohIxVjQNAxlizVSgpREh8CM48OoOb8TflJWIap0YHUKB6XUDLxVUaKgEFAGdTZ8RlxkEqSEuv3LsXSEsDXngB8PUF7t8vTbFdvkyZm65dgXXrKHP30ktAejptV12xsYCrq/J1NjbAqFH0GFu2IHXfX7hpmg3z1p0hfvIYhlI7JOSUZkCNjDgDWGspKUB2NjafdcOUKdW7a/fuwMOHQEQEcOFCLcbw+ee0poe3N/DHHzXaRVRaFNwt3Mtd72DigCmdp+CnKz/VYoAa9u23ddYIhzW89u3b4+7duzhx4gTMzc0bejiMVUjtnrRRUVFqbScWi+Hm5lbT8TDGmMZEpkZCIkhwPuY8bifeRjeHblXfqSbUWAQeUD8ALCqirIrSlEINBoBOpk4okhYhITuhdN3Ar74CPvgA0NcHWrWigQYH0zyt0aPpwHXePLro69PahL16AadPAx06VG8AMTGAs3P567//HnjlFWDYMFyNPII2lm2g7+oJ/eQ4mGq5KGUADQ05AKy1W7eQa+0MG0tjtGtX/bvb29MC8ZcvU9K4RgwMKIL87z/6bAUGAm3aVGsXUWlR8LDwUHnbK76vYPjW4VgxbAUMdAxqOEgNSUig+a4xMcCaNQ07FsZYi6Z2ANi6desq58wIggADAwPk8F9lxlgj8CDlAXTEOjgWdQymeqZwM3ermwfSYABoZkZTCZOTVQSA6elAXh4dNNeCnrYe7IzsEJMRQwHgo0e0aPeLL9IGIhFlAW/coJJMNzc6OAeUg70BA6ghzGuvVW8AsbHAwIHlr/f0pAuAoPgg+Dj4AI6OMM6Mg6W+H5eAatqFC7hj1gsjRtR8Fz16UE+XWrG1pYxySAjwzjsUDKqpSFKE2MzYCgPA7o7dYW9sjz339+AFrxdqOdBa2rWLvgD+/BNYsYJOojDGWANQuwTU0NAQUVFRVV50dHTqcryMseZEIgG2bAGK62a9rvvJ9zHUkxqNdHPoptnGL4oyMystAS0sBCIjKU6sKgAUiWgeYLlGMLa2FBGGhdV+vCgzD/DsWcDPTzni9PWlZh2bNgEzZ6reyYABlAGs7vytmBjAxaXSTYLig+Bj7wM4OcG4MA3uuraIzSidt8gloLUnXLyI/Sm9qlz+oTI9elAGUCqtetsqffABcPgwNQhSU0xGDLREWnA0cVR5u0gkwsyuM7Hj9g4NDLCWduwAPvuMFrI8eLChR8MYa8HUDgCfeeYZuLq6Vnpxc3PDaHX6SDPGGEAHezNmAG++WScLkD9IeQBfB190sO6AbvZ1VP4JVBkAbttGPU8AKvGsio2NioafIhHtJDS05uNU4GzqXBpQnTkD9O2rvMHYscCxY8CVKzQvUJXu3akd56FD1XvwikpAFQQ9LQkArayQDz300DdHdHq0/HZDQ84A1opUCumFSzie26vcW18dXbsC+fnVOy8hCALCU1QsQ+LoSCcVtm1Te19RaVFwM3eDlrjiDrzDWw/H6YenG3Zh+Ph4ipSfe45+t44cabixMMZaPLUDwO3bt6u13datW2s8GMZYC/P775Rd2rULOH5c47u/n3wf7azb4aM+H2Fql6ka379cVlalawA+eUKVm+bmtOJBVTw9gXAVx8caDwAVM4D9+ilv0K0bdfm4dKniqFVbG3j1VWDNGmTkZ+B24m3kF+dX/sC5uZTedHHBk6wnKjfJyM9AZFokfBx8kJ0jwhO0Qk8jfUSllc5F5wxgxf4I/gOvH3gd4SnhKJYW42D4QflyKHL370MoKERx564os4Z1tejoAF5e1ftY7n2wF21XtVW9Rt+MGdUKAKPTo1U2gFHU1b4rtMXaDbsm4OHDlGW3taWJk7XqnMMYY7XDXUAZYw0jJYXm+nz8sTyI0CRBEPAg+QHaW7fHFK8p8Lb31uj+lVQRACYl0RSnHWpWobVvX0GjwK5daZ6UBsjXAkxIoFaOgYHlNzIxoVLQSmROn4Sio4fRZZE5fNf5YtjWYSgoLqj4DrGxgL4+DqVdg8sKFyTlJCnd/DjzMQZsGoBezr1ga2SLBw+AeG0ndBBEiMmIQZGkCIDmm8Csvroahl8bwm2lGy7GXtTcjutZeEo45h2ch4ScBHRb1w3Dtg7DqO2j8M+9f5Q3DArCE5uu6OBV+2kbHh5AdHTV2wH0e/n1ua/RxrINZu+bDe9fvPHj5R9LN+jXD7h9m+qm1RCVFgUPc9Xz/2TEIjEGuA/AyeiT6g1SUwShtLz90CHIa20DA2ntw7S0+hlHcDDw3Xd0JooxxqBGAHj16lV0794dvXr1wiGFMh9e3JIxVisHDlBGq00bCgAPHAAeP9bY7mMyYpBZkIn21u01ts8KVREAJiZSVm/4cPV2165dBQGgJjOAsjmA9+7RfLwatCwvlhZj1tVFuN/aHCEe3yP1/VQUFBfgnSPvVHynmBgIzs745PSnkApSnH54Wunm1VdXw87YDidn0MH6/ftAkYkVLPPpQF6WtdRkE5hjkcfw/vH38c/Ef/BOz3cwZMsQ3E68rZmd17P3j7+PWV1n4Z+J/+CPZ/+AvrY+vhv8Hd49+i7yivJKN4yIQIS4LTp3rv1jengAajYKx8XYi4hMjcTZF8/C2tAaEzpMwNfnvsbW0JLqIUdHao6i5g7vJt2Fp6VnldsNdBuIE9En1BukJkiltKZmmzbAv/8CR4+WBoB2dkDr1pRdr2sPH9JEzXXrgK+/rvvHY4w1CVUGgAsXLsTGjRvxxx9/YMWKFdi4cSMAID09va7Hxhhrzo4cKY2I3NxobbkNGzS2+ytxV9DFrgsMdQyr3ri21AgAbW3V3127dhUsr9e5M2Xsyk0QrD75HMCwMKBtW6XboqKAW7cqv//xqOPw/sUbD1IewHPSXJifvQIjXSPsajUfcX+tR1hKGFBQAEyYQN1LZWJikG5jgtiMWLzq+6rSQbkgCNh1dxde7fYq9LSpQ+L9+4C2pSnEmVlwM3eTl4FqqgQ0Pjse03ZPw6oRqzCizQi81f0tvO73Ol4/8Hr5skkV4jLjcPPpTbW2rWuZBZk4GH4Qb/d4GwAwoeMEHJhyAAt7LYSJngm23VIorYyIQEhOa3h51f5xqxMABscHo4dTD9gb2+PEjBP4tN+nmOc/DwfDS5qiiMUUNFUxqfBB8gOk56fjaORRjGozqsrH7ePaB1firkAirfmC89WyYgVw/jyVtL77LjBmDODri9TUkunOgYHAuXN1P47//Q8YPx7YuZPWWUxNrfvHZIw1elUGgFpaWujUqRPatm2LAwcO4MiRI1ixYkXdddNjjDV/Uik1GBk6tPS6114D1q+nRfA04GrcVXR37K6RfVVJwwFg+/bA06fUW0aJiQkFa9ev12ycCpzNnBGfHQ/Jg/tK665JJNSnYvBgGrcq62+sx4SdE/CSz0sImhMEgxHPInvfCZw9WQzn97/CmtNGmLXrNcyZ/A3w77949OMXpXe+fBnR7ubo7tQdo9uOxonoE9iyhapQg+ODkZCTgOGtS1Ol9+4B+vZmQGYmPCw85AGgpkpA5+yfg0HugzDLe5b8us/6fUZLC/zkgbXX1kIqqG5xGRIfAt91vpj892TM2T9Hrcery0Bxf9h+tLNqh7ZWygG9WCTGWwFv4eerP8sfXxIWgaspres9AxiRGoE2lsrr/PVy7qVcdltFAHj58WW0X90ez/75LDradEQHm6rXoexk0wmCIOBe8j31BlpbBw5QV9MvvqAXZ/NmQCxGr17A55+DAsIdO6rfQTc+nr5vVFmzBti/v/TnpCSaZ/3RRzSn19eXxsEYa/GqDACLi4uRnZ0NANDR0cGOHTtw48YNXL16tc4HxxhrpkJCqHVgjx6l1w0fTh0lqrEGWFmCIMjniF2Ju4IAx4DajlQ9aswBrE4AaGVFF5XHwH37UtfOity+XXGGcMgQbP4kDEFBgIOxA8QiMfLv3VLKAP7+OyXsAgOBN96g6wQBePll6tUjCAJ+uPwDVo9cjQU9F0BXSxd/P/RDUaGAxPFzISQlwSG5AMbXxDDO+hrZBjqQrPoZiVkli9ifOoXLrQ3Q3qo9+rr2xcP0h3j78ygMeXM3xv81ARM6TFBasPvePcDY0QzIyICHhQciUyMBaKYE9GjkUZx9dBY/Dv9R6aSmiZ4J7r5+F8uGLMP/LvwPCw4vKHffnMIcjPtrHOb6zcWpmadwJOIIMvIzKnwsQRCw6uoqWH1nhQ03NZfpVvTPvX8wocMElbdN7TIVsRmxuBh7EVFRQN6tCMQbt0arVrV/XA8PWkpSndVcItIi0NqytdJ13Z26IzYzFnGZJcs/tG1bYQAokUrwxsE3MKHDBJyPOY8pXlPUGqNWWjrGowOuxtXDsYsgADdvlps/++QJPa3vvwfC2j9DJ7sOH1Z/v9u2UXDcowcFe199RWvLAPQ7v3AhMHly6WTjf/+lwE8W5U+YUL3HY4w1W1UGgD/99JPSwu5isRhbtmzBb7/9VqcDY4w1Y3v2AEOGKLfE1NICpk8H/v67xrtdcnYJJv8zGUWSItx4cgPdnRo+AyiVqrcAfFkVlYGGOfSDcOq06jtt2UKdBl1c6OBPUUICcPw47v1xBVu2AFpiLTiaOkIUFqaUAfzzT2Ds2+cx6O0/sXcvJS/OnKG3Ze5cYMirJ5GYnYTnOj4HgIKw9z7UwvU5G+CdfwkHuyxCwYAR6LspEEOvjkRI309gVihGyObvKUp4+BAHHbLQwaYDTPVMMdTlWaS1/wGxPq+ia+47WDdmnXwsGRn0Gti3NZUHgFHptc8A5hblYtedXZi5ZyY+7/c5bIzKvzkGOgaY0HECjk4/inU31+FeknLm6NNTn8LBxAGf9fsMjqaO8LDwqHSO2ZW4K/joxEf4vP/neOfIOzgWeaxmg6+AVJDiZPRJjGqruhzSUMcQgzwG4ULsBfz4RTqM85IxZK4nNFHM4+REq5TExla9bXhKONpYKWcATfVM4WXrhUuPS+bEVRIA3k68jbCUMGwdvxWnZp7Ca36vVf2ge/YArVph3dehuHWvkpMnmhIVRb8YZdKr585RH6cxY4DtO7WBV16huXnquHaNtt++nb47t2yheYWtWwOLF9P6ggMGAKtWUWAoCHTG5vnnS/cxcCANQrHBTloaXV/ZSSXGWLNTZQDYrVs32NnZKV0nEokwadKkOhsUY6wZk0ppcfEZM8rfNmIEHdRUtywKlBlYf3M99tzfg00hm2Cka1Q/DWCASgPAtDR6OtXJAALUDFExFk5NpSrZQUv6Qbh5s3wZWHIytRr97z86IPzjD+Xbb1ALfMvE+zh9mq5yNXaC3qM4pQzgrXv5+KtgBt45NxOucxZg6Y8JWLGCgr9Ra+fihOMQOMfNh742rR2weDHQqhUwaM0EFN68g2nX5+OVc9Pxps5a9JVewD8pAxA8xh82f+wCTp0CAgIQlBMhf2/66b8FBKyGs4krYv+dB10tXflYLlyg7JKZc/kS0JpmAE9EnYDN9zZ45+g7+Hrg13ir+1uVbt/Wqi1e9nkZL+17CRn5GSgoLsDyi8vxR/Af2PjMRohF9Ge0n2s/HImoeG23LSFbMKnTJLzV/S0s6r0I35z/pvqDr0R4Sjjyi/PR1a5rhdt0sumEO4l38OBgJApMLdFmlmaWXtHWBlxdqy4DlUgliEqLKpcBBIBA50Cce1QyJ66SADAsJQztrdtDX1sffV37wkjXqOoBbtgALF6MjC7t0GFbPay/d+MGBX96ekpXnz8P9OkDdOpU0jX12WeB06er/r6LjgamTAE+/ZSix5UrqYHMmTOUFQwNpRM+H34ITJxIzbT27KHbn3uudD+dOwPGxrS2J0Ap2+eeo/3Pns3rqjDWglR7GYgJE1SXlzDGmFpOnaJF8WQd8RR1704BYg3muJ2MPokiSRH6u/XHnP1z8HGfj+UH53UuK6vCheCTkihbZaTGcaqiefOoWuvBA1oi0dmZ/j3hbWfE67oqz/UBgN27qdxryBBg9Gjg5ElqwiJz7RoEsRgeRfcREkIBpU+hJWUK3NwA0Mocia6rYWlkihuv3oBl23v4zaADTt2MwfSXM/FX2O/YMzwI99d9goQEOuBftYqmborFQMeOFL8/+9uzMJ00AvoFGfgtxA+FL85Bx6BYYNEi5D8zEk+ynqCdVTsAQFFEHzhkj8T/hi1BaIgIDx+WDvns2ZL16c1KS0Br0wTmfvJ9jPtrHNaMXIOY+TGY7TNbrfns3w7+Fub65nBd6Qrz/5njxys/4uDUg2hn3U6+TT/3fjgSeUTlHL9CSSH+vPMnpnedDgB41fdVXH58GcHxwdV7ApW4EncF3Ry6QUer4mUdOtp0xPWYu7DPjkCykzFm75uNQol6yy1UxcOD5nFWJjYzFlJBClcz13K3jWwzEnsf7IUgCMh2awU8fYq81PKTUB+kPCg3x1EuKwt4+20gIKC0o3BuLnDiBDBuHKQfLcLEkwkoKqx8rcojR4CrV0uatdSEivJPgJJvffoA7u4lAaCXFwV/d+9WvK+wMMDfn0rk339f+TaRiOZR795NXzR9+9KXzeTJ1PhlyhT64lDcfuDA0jVXly6lutSQEKo558quGlu1ahVEIhHu3LnT0ENhTC3VPjp6wuvIMMZqY8sWOjBRtSK6lhYd0CgsOVMZQRAQmxGLvKI8fHn2S8zoOgNvBryJjjYd8br/6xoeeAUKCynQqiADWN0GMDKOjsALLwDjxgGTJlGgdfIkneRfVPQlpC+/AnkqD6Byr4kT6d9eXhQ0KXYZvH4dmf6D0FnrHtq2LSlHS9NHsr0JpXBAS5Pp+PyF93svRGfbzrj8xmG82GM8ui2ZiaDcfWhr1RbP9uiKfv2AX3+lt3LoUMpoyPj7A889L4Lol1+A02egZWwAA8Nn8W1vIOO9t3Br2lDYGNrAytAKABASIsJbVgcwsdsIDBigXLkqDwBNqQTU3dwd6fnpSMtLq1EJ6MKjCzGz60zM9J5ZrUZmRrpG2Dt5Lw5NPYTgOcF4NP8Rejj1UNqmh2MPxGfHIzw1vNz9D4UfgomuCXq79AYAWBlaYYrXFPx2U3MH3FfjriKgVeVzXjvZdEJ4+l2MdgvFXYsiZBdm40KMZhYk79KF4p7KhKeEw93CXWWQOthjMNLy03Dj6Q0czwhChh4QdbN8SW1YSljFAeCiRcDly4C9PaWsBYGCHUdHoH172A6fAL1i4GlQxd03CwooMde/P7B1a+XPp0LXr5cLAAsKqLNu9+4KAaC2NgWrFS0HkZ9Pv/wzZwI//0zfj+pYvJjOxJStAgDoO2LDBvoy+fZbKik1NaWg8Ug9ZEebqaCgIBgaGqJ9+3qqOmGslqodAHL3T8ZYjRUWAnv30kFNRUaMUCsAfJz5GO4/usNlpQtsvrdBkaQIi/stxtj2YxEyN0SplLBOyUoxKwkAqzv/T2b1amD+fGok+OKLdJ29PfB04FRc6/sulYIBdPb/1KnSci+RiDIGBw7Qz4IAXL+OB/7T4CGNQP/eRTh/Hmj/tBDhjqXLZNy8nYNim5vo59ZPft1PI1ciNS8Fc/bPwdh2YwFQU8Hvv6fM3/TpFQxeXx+iwF7w8gKi75thzws+2DOwFUITbyllzkJCAG9v+vfEiVTRlplJS0PeuEGlsDCjElAzfTNYGVghKi2q2iWgx6OO40LMBSzuv1j9OynQ0dJBT+eeaGfdTuXfQQMdA/Rx7aOyDHRL6BZM6zJNKSM9vsN4/Bf2n8a6gl6Ju1LlnNc2Vm1QLC1Ej8KD2GmXhEDnwNLlF2qpe/fSysKKRKSWbwAjo6ethzFtx+Dvu39jf/gBRFkAT0PLB6cVBoDXr1MHo23bKPC5epW+a7ZupYhOJIKWji4i7XWRfr3iAPDmTfpVnj+fEofVJpHQC9Gzp9LVkZGAvj7Nl3RzA+LiShL0vXoBFy+W348g0C+Bnh7wTTXLhVu1okoAVcdrzz5LXyLDhwNffgn4+ND1Q4ZQyWihioxwI1jipLELCgpCly5doKVukM5YA6un+ijGGAMdUZmY0NFiRYYNo4O5pKRKd/XB8Q/Qw6kHshZlYd8L+3B42mH5fKB6K/0EKAAUi6n0SoWaZgAB2uWrr5Z245Tp1QvYqzWeshsFBXQWv1cvav4iM3EidXQpLqZOKmlpuGQ3DoJIjN6tonH7NuASnYYgu9IlDk6FXYGx0AouZqX7MdY1xrHpx9DbpTemdpkKgIKyl16iDNzo0ZU/By8vynzM85+HL858gW/Of4MXOr8AgIYWEQF0KOniP2kSlblOmUKB4dmzJdWpJRlAAPIy0OqUgEqkErx79F183OdjWBtaq3enGhjmOQxHIpUDwLS8NOwP24/pXZQj5YHuA5GUm6SRBeezC7MREh9S8bInqanA668j63wYTJ54wOFhCII7W2N+j/k4GKGZALBHD3qfK3tP7iTdQUfrjhXePttnNtZcW4N/7/2LFHtTZN8PVbpdEISKS0D/9z/K+rVuDVhaUlOUN9+kUukFpV1cn7hZoSgkqMIxXLxIv0qBgTVcp/12yftZpgHMgwfUa0kspvhMRweIiQE92KlT5ef0bttGWbrduwFdDZ7MEovpxNGMGcA775Re37kzfTeXfdKRkTTwF16gGnFWTlFREe7cuQMfWTDNWBPAASBjrP78+Se1Iq+sksDenlrlHT1a4SbXn1zH3vt7sXzochjrGmOg+0CY65trfrzqyMqixgoqntOuXdTkr6YBYEUCAoB/wrwAc3OKkjZuLE0RygweTCVmR44Af/0FjBqF8HgTJFu1g7fePdy+DdhGPMUJ02QUFNNcwZD08+ho3Lvc49kZ2+HItCNKTXW+/56yJfr6lY9VFgDO8p4Fc31zWBpYYo4vrZknm1Hg6Ej/NzGhIPDgQeCXXxTOE5iZ0esskcgDQEND9TOA225tQ0ZBBt7s/qZ6d6ihYZ7DcOrhKeQXl84x235rO7rYdVHKegKAvrY+hnoOxb4H+2r9uEcijsDT0hNZse7Yv58aD8nl5FB0duECjEb1w5JrQJilgFED5mCIxxCEpYThUfqjWo/ByQmws5P3GlLpVuItdLateOHBge4D8WHvD6El1oJ154ByXWVS8lKQnp9ebh1BPHoE7NsHvKXQ0OeVVyjKevttGlyJjDau0Ltb8RqDFy5Q8NejBxAeTr2VquXiRbpzmUxQWBh19gUoBnN1Bc13HTSImt7061caPALUzOnVVwEHh2oOQA19+lAZqFjhEFAkoizgjz9S6SlAv3MDBlC2MC2NvrsVG9akpVGA2MIzhHfu3EFhYSG6devW0ENhTG3VDgDrchFbxlgzFhEB7NwJzFFjwewqykB/uPQDXvJ5CY6mjhocYA1V0gF0xQqaCvTBB5p9SH9/ICxchILBI+kANzJSudsfQAegM2ZQZmT7dmDyZERHA9luXnDPDEHy4zzoREQh0tkYtxJvQSoF4sTn0d+jfACoio4OzWWqSufOdFyrJdbCwakHcWDKAWiJ6eA4NpaOb0umIAKgaVxr1pRWpgGgABAAsrOrnQEUBAHfnv8Wn/b9VN65tK50tu0Md3N3/BH8BwCgWFqMHy7/gLe7v61y++c6PIctoVtq/Xd1z4M9GNNmLPr3p+ZB3bsrxE4HDgBaWpDeCMIvbv/DnLuxsBs7DZ/3/xxm+mYIdA7EoQj15txWRiSqvAxUEATcSrgFLzuvSvfzUZ+PEP12NMw7doPR40Sl1+ZB8gM4GDvARK/M79uWLfSd4arQXEZPj6LRr79W2lTi1RHWEap7GQhCaQBoZUWJr8uXqSryvffoRIWqCkklshRiGWFhSs12S+cB6unRezRoEP1iv/ACpQZPnaLr6tM331Bt6jPPUDOu776j1/Tnn6klcVwcLQi6ciX9glpaUsZV1lRGEwSBasDr6lIHx7BBQZRR5gCQNSXVDgAv1agmgjHW4n36KTB1KqDOJPmxY+kMeJkUT2peKg6EHcA/9/6p82yO2ioIAAWBKi9ff730zL+m2NhQaeSNPgsoZXb4MGUhy1q4EDAwAOLjgVGj8PAhIOnaDYb3b6K/1W0UG5vDrr0vbjy5gQfhxSi2v4SJ3ftodKydO9PDJycD9sb2SmvuPX6s3KQQoOPJuXPL7ESWYVVYC9DQkA7Gq1p8/FjUMaTkpai9YHhtiEQifNznY3xz/hsUSgqx/dZ2SKQSTOqses7rhI4TkJKXgpPRJ2v8mEWSIuwP2w+X3LHQ06NzAYMHU/Oghw+BiG93IS7weUydLsbPeS8j9XIkLJetkt9/ZJuRGpsHOHIkJZZUBUlPsp4gPT8dHW0qLgGVMdY1hr1XLzgnFyE+O15+fWhCqOoMYng4dcAty8JCOcsFwLBbd9gmZqs8exAZCaSnl/ZvCQyk2GbdOppOmJEB3LtX5k7x8cCxY9TZODaWKhcCA8vt+8ED5e8BeQAIUInn99/TmZLcXHohc3MpIKxPjo5Uph8VRSeUfvgBWL6cfveMjSnLWliI/F078FW3HHyzeyGKX3pRs2sIZmXRCZ+6upQttdWAoKAg6OjooHPnirPbjDU2XALKGKt7R4/SWe7PP69wk5AQhb/NssXMFRbCe5j+EH7r/DD3wFzM8Z1TYTOJeldBAJicTBVSmg7+ZAICgNMJHajjX+8KsnaWlpRJDQ+HYGCIhw8Bg96+wI0bGG5zA4kOXeHbyg83nt7A7ouh0BJrwduxk+p91ZCZGR1QBwRQJaqi2NjyAaBKYjG9xhkZaGPZBveS7smX1bgVF45iqeooMK8oD5+e+hSv+b1W59k/mYmdJsLa0Bqd13TGawdew/Khy6Et1la5rb62Pl72eRlrr6+tcH9FkiJci7tW4e3XnlyDtlgbD07649lnKZsq6w3UrV0OWgUfxJhNz0MioXXoHLo5lGZUQQHgiegTSmWrNfXiixTL/Pxz+dtuJd5Ca8vWMNRRPVe2LP12HeGeDtxLLG2rf+PpDfg6lF9eAbGxyvNfK+Hg6Y00A5GKSI6yf76+pWXNr7xCKyP8+CPwySdUmR4SonCHI0foF3zGDCrH7tyZIu/Bg8vtW7EEFCgTAMp4elI595MnVKapybl/6jI2poqBggJqyRug0Fm2Qwdg2zZ889Vw7O9pid1JZ/Gd9hVIzp3V3OOX/J7X2aWCao3aCAoKQqdOnaDbEO8XYzVUowAwPDwcI0aMgJWVFXR1dZUujDGmJDSUjqS+/VZpLo6iL76gE/iBgSXLd4lE1GVk3TpAEFAsLcYzO57B8NbDETM/Bj+N+Kl+n0NlKggA79+np6sqMacJ3bpV3XYfAAVPdnZITqakgs0Qb+DxY0xMWo2rNqPh28oXN57ewNEH5+AoDayTBjoXLwJLltBC9orBgdoBICDvBOrXyg/x2fFILHwITB2Bbn+0xaqrq8ptnluUi0l/T4KWSAvvB75ffn91REushYuzL+LD3h/izKwzmNCx8rVzn+v4HI5HHYdUkKq8/b+w/zB4y+AKb78Uewm9nHph7x4x5hcvA9atg640H3/9BeyafQgGbZ1xPb8zdu5UPZ2sk00nuJu7Y0vIlmo/17K0tan3iqrl5NQp/1Ti6godKRD/oPRDfuPpDfi2UhEAxsSo/UFyt/TAXWsB+bfKN4KRlX/K9OxJAWFqKvVUKhcArlpFDWaePKG1/P77D1i7tlzWMS2Nelq1UZi66OamIgAEqPZ0yxaqOW0oAQF0wm7YsHI3SQUpNoVswqLei3By5kmcd9eCSOlFqSWRiJo+1dVFw53sBUFASEhIufLPF198Ed7e3vD29oa7uzv09fVRoLguK2MNrEZ/6adPnw4jIyOsX78e//33n9KFMcYAUG3ekiXUEGH6dBV1fSQsjKae3LhBQc3o0SWVnzNmUCnSihX49fqvKJAUYOXwlY1vKZpKAsC6XBLKxwcIDlZ/+4cPqXTUqJUZ0KYNbNMfYEXydPRw6oHbibdxvfh3+NuqN/+vunR1qfp340aqdJP1kahWAFjSCdRI1wjdHLrh5+s/AM6X8Ln/aqy5tkYpQMorykPf3/siLT8N+6fsVzvrpCl62nqY7TMbfq38qty2i10XFEgKEJaiujHJ+ZjzyCzIRGRqJHr+1rNc19BLjy/BXbcnhOQUtPvjQzrR8v33aN8eGJS6C6Lnn4dYq+LfGZFIhA8CP8B3F7+DRCqpcDt19etHn/3U1NLrsgqysDl0M3o69az4jmXp6iLVxgQ5dyhQyy/Ox+3E2+UzgFIpnTVSMwNoZWCFSFttZAVfLXdb2QAQoBhv40bKCioFgAUF1KVT1tTK05MWrVTx/RQZSb975ual16nMAMqMGkULtjcijx5RE9BT0aeQW5SLkW1GwljXGJ9NXYdo84YeXcOJjIxEVlZWuQ6gv//+O4KDg/Hff//B3Nwcf//9N/T09BpolIyVV6MA8O7du/jzzz8xfvx4DBs2TOnCGGMAaKXw33+nLpVffVXurLjM+vU05c/bmxJ+xsZAYG8Bv/xrBGHPXgiffYpzaz7AymEr629tv+qoIAAsO+dH03x86MCyZHWEKj18WLKkAgD4+UE6agyuPbRBdpwLFgd+ixyTEIzxLj93SZNGj6bzArIGr7GxFSaFyzMzkz/Z3i69sfb6Wug+fAbDbV9CRkEGjkeVNqJYfmk5RCIRTsw4AUsDSw0/C83S0dKBr4MvLj++rPL28zHnAQBbQ7fi8uPL+OHSD4iLoylsgiDg0uNLKIjoifltD0LUtSul0/fupbMo+/cDzz9f5Rgmd56MYmkxNodsrvXzsbWleZyXLgk4HnUc6fnpmLBzAhyMHSpshlOR9NZO0LpLpZqhCaEw1TOFm7mb8kZJSRSMqflBEolESHSxguTeXaXrU1OpKrRs/5bOnWnpPKA0ABQEUD2trOyzCpGRgIeH8nXu7jR0dZcyaUjZ2UD//pQQ/PHyz3jJ5yXoaOkAAHo49cDVZ1VkZVuIyhrAPHjwACNHjsSPP/6I0VWtl8NYPatRANi5c2c8ffpU02NhjDUXgkDR3Cef0Hy+ChQWAps2UbdzgDJFe/YVw2jSq3g92gbd/vwPK1/pio07CzBC6lHhfhpUA2UAbWyoZ4O61VdKAeB330Hn19UYNYrm5UkvzIfXzROY1rduMoAy2to0T0xWIljtDGBmJgAKAIulxbB8OgE5mXqY330+Pjj+AYqlxUjMScS357/FT8N/apwnDFTo4dRDZQCYU5iDoPggDPEYgjXX16CNZRtsurkDTm2TMGUKEJMRi4TsBDw46YdnsZe6N44YQanh5cspKPKquuxSR0sHy4cuxwfHP0BaXlqV21cmPT8d7frfwPdXlmLolqGwX2aP/OJ8/D3xb3nQoC5Jx/Ywj3gMgJZ+8XXwLV8BEBNDvwwGBmrvN9vDCXrhyktMXLpEgWtlS7Z4eVEW7OlT0Py/4cNVZvzu3lUO7FQFgNbWgJFRyVIQjdxnn9FHqcAoEgfDDmFewDyl2yevPNFAI2t4Fy9ehK6ubrkA8ObNm5gwYQI2bdqEvn37NtDoGKtYjQLAdevW4fXXX8fOnTtx9uxZpQtjjOHaNapvmqS6+6HMkSO02Hn//qXX/Rj8BVKNL+DXEX8gOvcWPtZJRcrYKTSBrDEuQ9NAASBAWcCgMlOZDh+mrOrevcrrwSkFgE5OgL09ZsygOOHbb0X44c2B0KogS6tJU6fS9KKUFCAhoZpzAEsygP1c+6GPSx+0yhuK9HTgnZ7vIK8oD2uurcHF2IvwsPBAT+dqlBs2sB5OPXAlrvz6CVfjrsLe2B5j249Fcm4ynnV5CULEUPRePhMXrxShz/TTEJ5649bJIniEH6ZUlbU1lV0vXgwsW6b2nKdx7cehg00HbArZVKPnUCwtxuqrq+H5kyeOOvXFOSzFtVeu4Z+J/+DwtMMw1TOt9j71ffzh9CgdgiDgZPRJ9HPtV36jajSAkSlq2xqmsYlAUZH8OlXln2UZGVGQGBICmtiq4sD+8GEqZVec6xoVRRWiikSiKspAG4ni4tLSbY+Ja2H0eCycTJWzrY2uLL+eFBQUYPfu3RgxYgT0FRZEPXv2LGbOnIl//vmHl4ZgjZbq1mRVuHPnDk6dOoUDBw4oXS8SiSCR1H4OAWOsaQp6GgR3C3fEvz0NIf6G6F6UBDcYVbj99u207JUs7kjJTcHKKytxdNpR9HTuiSk+4zF/PuD7byZuS9ojZMBS9Nj3EUxMG9EBR1yccgQLqkiLjq7bElCAymYvXqSlAAGqSpswgQ5kIyOBjh2pLwVAAeCoUcr3f+YZmsZ0+3b9LTnWsSMdRL/2GvW7sLNT844KAaCVoRXOvngWQ7ZTkKunrYf3A9/HxqCNGOQ+CD4OPlXsrHEJcAzArYRbyCvKg4FOaSbr+pPrCHAMQDcHOohMuT4AowrmIhb9MODLz3DzcShmuj2HWaH/g8igB30gAODDD6kN7Zgxao9BJBKhj0ufcnMMASA6LRpu5m6VHujP3jsbV+KuYPPYzbDJHIaho3Lg+6VZhdurwzqgPywTJEjMTsCJ6BP4IFDFgprVaAAjY9SmIyRaImhFRsrP0ly8CEybVvV9u3YFQoMkGBEcXLpeRAmplKY7BwbSMn4ffkjXR0aqDi4rbATTiFy6RJUZ3r6FuHBmEzJP/oXUVGow3FIlJibi5s2bWLt2LeLj47F48WKl2ydMmAB9fX1MKjkBKhKJcOHCBRga1u9cZMYqU6PTve+99x6WLl2K9PR0FBUVyS+FVa6Qyhhr7ARBQFRaFDYGbcTKyyvx85Wf8daht5CQnQCA5iSdfni6XOv9QkkhhmwZgs/e84NtSDhOTOsF33W++O7CdwjcGAiH5Q5KnQazsylLNfq5dCTlJCEmIwbzDs5DoHOgPHtjZETZrEt3THHv233wuvQrDgQurb8XQx1lV3gGrXlvYEAlmnVp5kya5nXhAlVHzphB614fPUrrlx05QnEAUCYDqCAgAJg9W+PN8So1aRKwaxewejWtV68WW1tKGSqwsKB12wAqC73+5DquxF2Bt523Jodb55xNnWFhYIHQhFCl6+8l30NH647wsffBWwHzcWxzN7w60wzrx6zHgeSfEKNzDIsDB6Jv8E8Qfftt6R1HjwZmzar2ODpYd8C9ZOXlEQ6EHUDrn1tjx+0dAIB9D/Zh+63tStsUSgqx+/5u/DnhT4xqOwqtPbSRkWgmq9itMaMuvtAvBg4cXwOxSCwPhJXUIAPoYuGGaAcDef10URFw9WrVGUCAAsA9UQGQSIrLpfgfPKDSz2XL6GSM7JBIVQYQaBoZwEOHaO7fgfD/YKpvgnZ6/XH+fEOPqmGtXLkSU6dORW5uLs6dO1euAUxSUhJiY2MRHByM4OBgBAUFcfDHGp0aZQAzMjLw1ltvaXosjDENEkrKJUUiEQRBwJOsJ4hIjYCJngm0RFow1TOFvrY+knKTkJiTCAA4GH4Qu+/vRlxmHLo7dYeFvgVyinJgqGOIzms7w7+VP87HnIehjiEcTBywZMASPMl6gqzMLNgb28NEzwQvHUnEual9sO7Ff9E7ZDN+vPIjZnadCSsDK7y6/1VYGVphZJuR+HHnDUhen4Le+8OA/TTmEa1HYO2o8muieXoCngv9kKTzP7Sdvww3b36MnBxa/q5Bq48kEjq9r9jfHVT+2a5dhX1vNKZ1a+r5MXky0KULzTOSfTW7u9M60rt2UQPWigLAhvDqqxS8VVEhrMzFBbiiXCZpbl5a5trGsg1M9ExwLOqY6kxRIyYSidDNoRtuPr2J7k7d5dffS76HwR6Doaeth9HaK/BXAU3x09b2xxCPIYjPjofr4UsUxVcy11ZdHWw64F7SPQiCAJFIhJPRJzH5n8kY03YMvr/4PdLz0/HWobfgaemJKV5T5Pe7GHsRxrrG8Lb3BkDvrbExdY5UYwpixXR1EWdviGN7fsCg0SOhJVZxtiAmBujevfz1lXAxc8ElFzHanT8PTJqEoCDq8qlOxr6jVwHu3gzGI1dreGgrH0JduEBvhbc3nby6do3eltjY8nMAAfod1eTMmezCbOhp6VV7rqWisJQw7Lm/B8M8h6GrfVccPAh88AGw+tpqvNztZTzqK8bZs1Q90FItXboUS5c2shORjFVTjQLAESNG4MKFCwhU53RZEycIAj7//HOsX78eGRkZ8PX1xZo1a9BZjc5fjNUFqSDF9SfXoSPWgVgkRmxmLNLz0yGRSnDtyTVEp0ejoLgAwfHBSM9Ph5m+GSRSCbIKs+Bs6ozswmxIBAmyCrIgESQw1jWGvbE9CiWFGOw+GKtGrMIA9wFKrfMFQcCJ6BO4/Pgyfhz+I1zNXbHo+CK8ffhteFp4Iik1CXFZcfjI8wV0ebgWXc7TAu4zus7AjK4z5PvRFmtj0t+TsGLYCiwN/wa9zF7Av+8ulD+WnnblbbJtnu8Pq/nTYO2Xjlxdc4waBezcWY0skqbFxlLdV5nIShYA1od33qHmKmvWACdOKAedsozgjRtUQenuXj9jqoqtLTBvXtXbKXFxoYN9BYoZQJFIhF7OvbDvwT50te+qkXHWJ18HX9x8WrrmnSAIuJd0Dx2sOwCghrrTp9N7DQC/P/s7MgsygaHPl3ZRqqV2Vu2Qlp+GhJwEJOcmY8yOMVg7ai2e6/gcXFa4YMnZJdg7eS/G/jUWj9IfwdXcFQBwKPwQhnkOk5eIikT0K1HrABCAfc8hWGAigl6fj1VvEBOjVqdTRc6mzljbKgczz56FCMD16xS4qXPCRt/lLrrFS3HeOhfuJYGyzMWL1EVUJKKq8NOnqT+Nri7QqlX5fbm7A5tr2Xg1qyALJnomKJIUofuG7sgsyMSGMRswrHX1u7LnFObgmR3PwNLAEl+c+QI/tgtFbKwnrLwv4fqe6/h74t84HEvfKd9/38An3xhjtVKjANDW1hZjxozBhAkT4FBmZdkvv/xSIwNrLJYtW4aNGzfiyJEjaN26Nb788ksMGzYMDx48gLE6KzxnZ1OJmFhME14kEprHoqdX2sM9N5dmjVd3snBxMdXQJSZSKUq7dtTL2tOzWh3RWN3JKcyBjpYOUnJTkJafhpTcFNx4egOOJo7wsPCAm7kb8ovzEZMRg+zCbFgbWqNAUoDtt7YjMi0SEqkELmYuEIvEuJd8D5kFmXia9RQFkgKIIIIAAQ7GDrAytIJUkKK7Y3eMajMKelp6WDJgCeyM7ZCRnwGRSIR2Vu2U5hdJBSkkUolaZ4tFIhEGewzGYI/B8uuWD1uO5cOWAwCi4qPwwbEPMOuODkTDhlXYSm9S50nIK87Dqsu/IO9BH2xY9iUsDKqRJnNwgMSjDf6bcBbt3nsGHTsC586Vm4JXf8LC6ChOR/k1fPCg7hvAyIjFwPz5dCnrlVfoK2jbNuDMmSb+teDiQgG3IMiPPC0saAk4mUDnQITEhzT6pR9U6ebQDUvPlWYV4rPjkVmQiXbW7ZCeDuzeTYG8jIWuKSyOnqUyxgmVLzavLiNdI7iaueJe0j38F/Yfnu/4vPwEzokZJ9DKpBVsjGzQw6kHjkUdwxSvKRi6ZSiuP7mOTWOVm8e4ulIAqEpKCpUsy05I3LtHc0FVzSvT9/ZDwN27QEVBfQ1KQJ1MnXDKWQLsvAWkpuL2bUu1A9UEhMD3kQn+9MtD95QwtLMuPdNz4QI1VQKAAQOAf/4BOnSgAgFVwWVNSkBDQ4GffgKMTaSQDHof64J/xuqRqxGXGQcRRHjJ5yUsPr242gGgIAh449AbsDGywemZpzF9x5t46+hr+O7LQ/j++meY5z8PlgaWGDcOWLiQ5ha35CwgY01djQLAW7duoWvXroiIiEBERIT8+ubYCWrNmjV499134VXy12HJkiXYsGEDdu/ejenTp1d8x6wsYMECYMsWqgUB6FS1WEwdA3NzqX7L0pKCwfnz6Y/YqFF0CQykg0pBoEk8jx9Ts4nISPqDf+sW/dvcnP7CfPklEB8PwcgIooICYMgQYOJEunDtOQAgNS8VeUV5KJAUID47HlkFWTDXN0dmQSasDa3hbuGOR+mPEJIQgseZjxEcHwxjXWNYGliiSFIEbbE2zPXNYWFgARNdEwgQIAgCJIIET7OeIj0/HQAQnR6NwxGHoaOlQ2foAYgggqGOIQx0DNDdsTsSchIQlRaF1DxaLdnB2AHGusZIyUtBkaQIkzpNwqg2o6At1kZUWhQkUgle9nkZZvpmsDSwRHfH7rUq8wEAsUgMsZZmahStDa3xW4+lMH2vD6WiKjHLexbST8+CbjzgWYOVHXSGDEBg0WnA+hlMnEiNZBosAAwPL1f+CVAGsDEs+yQW08HawoUNPRINcHam783UVDqZBuUSUIAyzrKMWVPTzaEbbifeRkFxAfS09XAv+R5czV1hqGOIzX9SiW/Hjgp3+PJLYO1aYOlSioQ1pINNB9xOvI0dt3dg89jS9JRiVnWIxxAcijiE3KJc5Bbl4tyL58rNz3N1Vb3EwcmT1HBIR4cCRJGISrlnzSoNnpR4eVGaX5XCQiA+vtpNYPS09aBlb488Tz3kHduHXTgEO+tUfCccxZ77e/D9xe+x4ZkN6GjTsdx9QxNCMCpbijtFPnj9l2048Qmd9Jb9ee5Z0ny2f3/Kzjs4AEOHqh6HmxudA05LU+8tLCigRq/9+gFHEzYj4txO/DxhLT4//RkKJAXY9fwueNl6Yem5pYhKi4KHhfpfsD9d+QlHIo7g2ivXcPaMFv55fSn03/LD79rdkZKSjJ3P0XtgYAC89x7w+uvAihX01vD65ow1PTUKAE+dOqXpcTRKGRkZePjwIQICAuTXaWtrw8fHB0FBQRUGgCmR6RCPfQZaeto4/3UQrmR1RHExYGwgQWtPAUNHasPUtEz5RFYWdWw4eJAm9GRm0l+ElBT61rewgODkhCIHV2R7dsWVzsMR3dkdh+J9kJisjUxTKRIzCpCaow9/i0i8GfMPhnzwA4zf/RyP5i6F1uSJsHeix63reUk5hTlIyk1CSHwI7iffR6GkEMm5ybgcdxkFxQUY6D4QPvY0aVpfW58OdpLu4Wn2U1x+fBlJuUmQSCWQCBJ5lkoiSCCR0s82RjZoZ9UOba3aopVJK+QW5WJ46+FwMnVCbEYsBAjILcrFofBDuPT4EnKLcvEw/SHS8ulIUVusDTsjO5jomSA9Px0muiZIyk1Cen46jHSM4NvKF3ZGdvB18EVecR5S81Khq6WLYmkxItIikPYkDVmFWRRAicQQQQR7Y3t51sHb3huf9fsMYpEY9sb2kEglMNUzhZFu+W6YmQWZ0NPSq7L0saHcv08NDEaOVGPjN9+kEw/jxuHuXVorS1Vnybw84H//qzJOrFivXsAvvwAApkyhQOvnn1UfhKSlUZfRTz9Vr8FDtYWHl2sAIwj1WwLaYhgb0wmzmBh5AKhYAgoA9sb2GNNO/c6XjYm7uTvsjO1wMvokRrQZUa7888UXFTYuKKDgb9s2+p3TID8HP3x66lMY6BhgoPtAldtM7jwZKy+vxOGIw9g8djP8Hf3LbePqSqWVZV26RHM/U1LoeV26ROdEjx6tYECdO9MvVFFRuUw74uKo/tvevprPEujp1BPXXG8hcvNHyLDsgWJpMN44+Aa23tqKtlZt8ev1X/HjiB/L3e/O45uwycrF2zMWY+KtifhorzVG+nTDjd290a+f/KOJ9u3pBMWOHRU/NzMz+gxHR6sXAH60+hoMzNvjt99MMGTrFmQdWIDj389GD2E2kpIA5/GAlSEwvPVw7Li1Ax/3LV82+8MP9J2+ahUQEh+Cn6/+jAkdJuCjkx/h2PRjcDR1xMyvgY8XmmPGW8cx+e/J2DFhBywMSgf42mtUirxrFwXtH32kzivOGGtMahQAthSZJS3MzM3Nla63sLCQ36bI1NQUz83ugVVnk3HP2A1v20yH8bVLcLC7gmLdVEiytJF8phNefr0rtHQFmBjowcREQF6uGJaWgI7OUOjpDcXYT76CRfEFhISdRZR2AtKtRHgqeoqU7AwUJoqApCTY256Enb4bzNrtRWcTC2jrFgP6afBp1RVZaQa48aAvjj+aiA5Xj+LZNUuh8/Mn2Iqh2G4wEYKbLwSIEBAADB5Mf1MzM4G27YuRZXAX8dlPcDH2ojxgEkEEXbEuzPTNkFOUA1sjW+hp6cFY1xg6Yh1EpkXiTtIdALQW1M2nN1EkLYKHuQc623WWb/u61+vQ09LD0aij2Bq7FQCQL8lHXlEeWlu2hq2RLeZ6zYWDsYM8uNISa9H/RVry65JykhCZFomotChcT74OLbEW1l5Yi9yiXFgZWEEEEbTEWujn2g+zO86GkY4RHE0c4W7hDmmBIYqKRDAzFZc7lsjIz4Ceth70tfWhMSWrokjyJcjMV90Sr6Dkv8bkyhU6ODt+nE4YLFlSecOOrPBw4M4dOprLysJXX1HJ4dmzNAdGpriY9uXuDgwciJp1CXR1pQO/zEx06kTLnp06RXGhorw8Cv7i4oD336e15zR+8uPhQ4pyFZ7I06f0OA4ONXx+rGJt21KL1ZKWigYGQH5+w7/OWVlZGtnP5NaTsfPmTgTaBeJCxAV0NfPBTz9lIjqaCkPkz3P3bvqA+ftr/Mm/7fM2/Kz9YKBtgJzsHJXb2OvY48SkE9h+azsGthqo8u9hq1bUtLXsTXfuUCdNFxdqWNSqFbBvHy2pFxamIpazsqLoKDi4/FmVsDDKwOeoHmdlvg78Gpv+7Is+T3RgcOkXzJ/yG364+i3WjFwDa0NrzNk/B5/2+BS6Wrry+0gFKXLCIpBhYY5hwwMxOngFfju7EXsif4X9/kuYMEH5+Q4eTBnPLl0qfps6dKCn0bq16tuzsoCvvgJs2kZjQ9pIDHhpIqIS5iP4URB2vLUWK7/ORO/ewLFjVHI6dy7wnMdz+OnqT3ij6xtKlVn5+cCPP1JZ+ODpNzHn2CQEOAVg8vbJ6JI/ByH7O6OwQyZu3QJ++w2wEFvi6ESKXsu+x7Nm0fN64QVgyhTNfP4ZY/WHA8BKmJrSwrXpiqeYAaSlpcFRRX/34uJiuByIxvx2jgizT4Vdp62w6GgBiVQCWwNLFEoKEeu4EuidCgmALGgjHcXwMe+LNvp9kFqQiNCMs/gs5QFQrA9bgz6wFXkh97EBrAU7DGprCffBMUgpiIdIS4KH6aEw0TVBcu49iEViWOhbYEvYKhQWFyJBKwEZjhnYMw5Yo2WEbo8lGBG6A3vu70DCUzEueppgb64Ftp/Uh0QvDYJOFoQgbUAE6Be3gnGGPyy02sPWjqZz6ZrnI0uaAQtjMzzOfIwiaREy8jNQLC2Gk6kTnm33LMQiOrr+bsh3cDd3r7AkeGRbddJJquXkAMb5HdDVoy/MzKgiLDERmGktRUKiFKnJ2rC3p+PDhw+BrHtAYjbwMBd480868yljbEyVQzo6dMBuZmaGvLzS5KulJR17WFqWXqys6GJvX72qq8JCOuOdkkJ/fMViKhdKSqIslURCXegMDOj/urr0XDMzqUQoI4OCJ1NT6jkikdA4unShuSays861IQgU+G3aRActM2dSU5EnT6iZiItLaXlTOdeuAZ06AaamyM+n+3t6At98Q2ecAapkfuklOqBZvboWDQTc3elFS0+HyNwc/v40N0oxACwuBubMoff2+HFqY75tGzXR0KgnT2hRdQUPHlCMqq/B8wisRKtW9JqXMDNr+OBPk55t9yye//t5PEx/iKORRzFTOIlf/6KEt5kZqPz/wAE6O/PVV3VSzqGrpYsBbgOq3M7T0hOf9vu0wtudnJTnZ8rcu0czEwID6bvr/fepFNLbm75n9PSAv/5SmLkgFlPgpyqt/vRpjddacTR1xPixi+C4ZD1cHPXxWsBL6GjXGsNaD4NUkMJI1whnH51Vmvd8J/EOHDKKIXJyBsRibFw4GeMnj0KoSSc8jXiKDSOUeyKMHk1zG3V1yz56KWfncr2NlOzcCZw8JSBN9CG6uA3ExfRdeOtwFAIcA9Cziz16/kXbGRrS9+7cucBQz6F47/h7uJ98Hx1sSkui//6bzhtYWgn4+PBSzOk+B+/2ehd/X7mI96d3wxcoLRuv6u/b8ePHcfLkSVhaAkOH8hJgjDU1HABWwszMDG5ubrh27Rp6lhz5FhcXIzg4WGX5Z25uLn74+2+YVjIhXyKVIDEnETpaOkjPT4cgCNh1dxeC4q/BUs8cSz3fQ1/XvrAxspEHVDWVVZCFtPw0ZOTT4sk2RjbQzi+C9r4dGHX4FGb/eQYiAcj27oisyTPxZHBf6BT0wr07WtDSoj/eoaFA0G6qdMvLoyDAxYX+qLWypemNGRnAyQw6Gd2xI+DpRSU9NVWyegHy8uhg+vZtSixdvEhTHwEKgoyMKEjS0aGATHa5d4/G3rYt/REzMaFylbfeoknr+vp00JiURHNQiopofykpdJuVFQUrycm0TWwsEBRE/05KouszM+mAxdOTnn9OTmlgpq9PMUpKCmWf4uKAjKQCOOAp3E1TYWWYB+eCCPQ2ugkXYy1ILayQb2SFdC0rpMIS8VqOiNJpB2MTEczN6bVu356eQ0YGVTyJRHR2/ddfqRynVy86kBg4kA4Uk5Op7XivXlU3/khIoKBvwwYa84wZtCaWbFqNlxdl7WbMoNdfZX+Xy5cBf3+Ympri1CkKrrdvp8/DSy9RQDR0KH1GDh6sZTMSU1N6kZ8+BVxc4O9PQd7HCtVOu3ZRwiAkhF6Pn3+m975nz+r3WqpUZCQdvZacLAIoQeXurnQV0xQHB/rlLnlxW7VS+rHBmdZyIIEmgWhl3QoD/hyA7u49sXVhF2zaBAwfDjrbNXQoTQLbvbt8yruRadeOTrjp6paeDMnNpb8p/v5UGfD336XbP/88lUqmpdGU+B07FE4StW9P3zFl/+4+eUJfkDV83QOGTYHw4rvoMEILtla2eN6qtJtoe8f2eJT3SOk9PR96HgPQCma21vLH/PdPU/ita43nvrwJD48XlPY/aVLVS53IAkBVT0EioVLNGR9fwA+JF7H/rRhsDd2K6PRovBHwhtLYhg2j70BdXfocDu4wGLujd6O7Z+kSGRv/KMacV7TwWOcUjoTdwDv9dsHUwBRX/xmO0cPp5c3Pp2xzVcaPH4/x48ejqAiYNCkTu3evr/pOjLFGgwPAKrz++utYtmwZBg4cCE9PT3z11VfQ0dHBuHHjVN+hivkYWmItOJjQWUJrQ2sAwEd96qaA3kTPBCZ6JoCZwpXGgOXs94HZ71OEFREB82PHYL7+Dzgv+gZ4/nl0e/NNwMenXIomI4MyLU+fUtCQkEB/0K2t6QD/wgUKSGJjKTOlr0+7cHamP0opKRQYWFlRYPfoEQU12tq0rZMTnX28dIkeQyKhg/dOnYAePYBPPgF6BkjgmHEXGfeeIDVDC5Y2WjAx14JYW0w7at0agpU1BKHyk+NGRoCDnRRdbBMp7ZWfA8iC1kwAugDMc4HkCKCDFUWVrXTpQEQsRs7TTEQ80kHBkxQI1jYo7OQB6OpBPycFouQkCHej4JQSDG0jfZiJwqGLxxC0tCDStQDEukBHd8DXFxBJgZT7penB1FQ6om3fnlJYY8ZUeYY7LIwWHY6MpLKd3FwKfO/fp4B17Fiac+fqWvqWxsbSvJPNm+nSpw/1lBg3TvVculdeoYOz+fMpsFMiCPTm//wzMjKAb7+lgzlXV2DRIjoAEomoUnLDBg0lLdq2pSfevTt69qQAVaE5JFavBt54oyRrAnrsefOoX4biQSdAY7K2ptepUoJAvzOy1ERBAb1fZToh37lDn1lWB1xclFphmpvT25Cf3zwyriKRCOdePIfVV1cj99ZQPLKmA3sAVE9tZ1cmMmq87OzouyQmpnSa7O3b9N2kakmEefPokphIJ522bQOmTSu58bXXKGX4ySfKv28xMdVuAFN2kDm6luhjfQ+A8jqKtoa2SMhJULruaNRR/K/YAXAr/U62sQEm+PVFbtFZAMoBoDrc3Smpq8qBA/T5DjFcgVe6vQIzfTPMC1C9fkqHDvTaXrxIJwJndZ2FZ/58BveT76OrXVecjbiBG0NOI8xpARJy4yAKnYEn0WbQcqZedRcu0EnN6tLRoYT07t3Vvy9jrOFoJADMzs7Gpk2bsHbtWty+fVsTu2w03n33XWRlZWHw4MHIzMyEn58fDh8+rN4SEI2dgQH9pfXyoo6lwcHA+vUUDZiaUtQ1Zgw1pTE0hJkZ/WGpSlgY7aqoiErx4uKoBNLbmzJlycnUVKRDBwryiorouPrxY/r5lVeA1m7FMCjKhJ1hFkTXr1FK6ucr8oM/MycnmEmlpWk32Y4SEiCys4PIxoYOFDw86C+snh4NJCKCLrGxFNEKAqWpVPUf19WlFJ+s02NRER2ESaUwMjVFV6EI6GgBJD8Ezv1FT9LKiqKJvi6Az+t0nZsb0KkTRNbW6i1Yl50N/PknRSbz5lG0am9Pk0SeeYY6nyic+W3btlwfEgD01CIiaKFwd3fKhLq5UQB29y4dhPXqRQdlqu6vSCSiM9EdOwKffUYX+RrIJ08C+fkQunXD2LF0UP5pSWXYe+/R4+jrUyCosYo1WQAIOleRk0M/tmtHAdiVKzQfRtGECbSItkSi/DYsWUInM959l6rqKvTFF9T7/Pp1ekHi4+kJKU5yBL22qprfMA1o1w7YulX+o2x6dlpauTi8yTLUMcR7ge/huRUUAMljvePH6QRjEwj+APrVcHGhE32y75fQUDo5WNlTsLUF1q2jOWbjxpU00fbxoTToa6/RWS5ZzXtsLE0orCmRCBH6neGtdQtlA0A7YzvEZJTWZuYW5eJ8zHm0yxoBdHVT2raPSx98fLKCdQqr4OZW8VIQP/4ITH8tHsvD9yH8zfBK9yMS0ffb/v30d3qI5xBcf+U6/gv7D3eS7kDvaX90fToWv9/6EFJBil5m+7FnD/3etG1bs+BP8bEZY02MUAuhoaHC3LlzBRMTE8HY2FiYP39+bXbXpGVkZAgAhIyMjIYeSu3l5AjC2bOC8O23guDlJQj6+oIweLAg/O9/gnDjhiAUFGj+MQsLBeHQIUGYPZseU1dXEABBEIsFoWtXQXj1VUHYsEEQQkMFobi44v0kJgrC5cuCsG+fIPzyiyC8/74gTJwoCGPHCsJrrwnC8uV0W3CwIMTECEJ2tuafiyZlZgrCrVuCcOwYjd3fXxCMjQXhjTcE4fp1QZBK1dpNVpYghITQU//7b0GIjy+zgUSi1n5CQgShfXtBaN1aEP77T6DH795dyFi6VNizJ0OwsqIh17lvv6X3tcTQoYIwaZIgFBUJwrx5gjBzZvm7FBUJgpmZIFy7Rj/HxgpCejp9zM6dEwRDQ0E4fbqCx4uLEwQjI0HQ0yvdwcWLgtCqldJmUqkgmJoKQlBQbZ8gUyk6WhC0tQUhP19+lZGRINy923BDEgT6/tf0d7+joyCcOqVwRZcugvDXXxp9jLo2eLAgrF9f+vP8+YLw1ltV308qFQQnJ0E4eVLhysePBWHkSEGwtKSdSiSC4OpKfzdq4TfDeULcpAXlrl99dbUwatso+c+XYi8Jtt/bCkLv3oKwdavSthn5GYLh14ZC8NPgaj9+VJQg6OiU/1N065YgGBgIwsbLfwtea7zU2teBA/SSqPqz4OUlCFu3SoVOqzsJjssdhd82SoQOHQTBz08QVqyo9rCVNKvjH8ZaiGpnAIuKirBr1y6sWbMGly5dgqenJyQSCUJCQtC6ojZWrGkxNKQsYJ8+NEM/LIxajB07RimSvDyqrTM2ptO1bdtSGY62NmUT/f1L1zqUSinTKLuIxZSeuXQJuHmT0n6CQJPLjIyoO8CSJVRDZ29fWh+qLhubchmZJs3EhNqgd+5MLeXeeYeyoatX00JTDg7A1KnUscXNrcLdGBvTmfcuXUquSEkBNh+gCXlnz1JnlhkzgK+/Lk2rqNClC53F37SJMnpH3zuOXlFRWFf0IrYupoxfbeZ/qq1tWyqFK7FlCzWUmDaNntLx4+Xvoq1N2xw/TmVVffpQmZmDA61DtnAhzRXs10/F461bR2k9W1tg40bAz4/qlMuknYKCKBvJS0DUEVdX+h65f1+e+bGwUF4LsDl4/Jg+Xn6ypFRaGv3iDai6OUtjUnYx+Lt3gfHjq76fbG3A8+cVnrKjI9VEHjhAVSmPH9Pfolq8JpmZwI3cDpiWUr4G087ITqkE9HbibXS27Qw8DKMnpsBUzxSTOk3C+pvrsWrkqmqNwc2NqiTOn1co9wVl8kaOBO5kXEJPp4q6bykbNIg+Kjdv0gwDmUePaBbDqFEiWCUvw5OsJ3i+tRinT9HHasqUag2ZMdYMVKsg66OPPoKzszNmz54NZ2dnHD9+HA8ePICpqWmtJ7+zRkokoqPZN94A9u6lRbciIigY3LyZ6ubat6e/OnFxNNmsbVsKwlxdqQTT3p4CRl1dmjDwzDPAuXN0v5deAl5+mSaYxcRQy8hnn6VyR2Pj5jGxR9MCAigCS0iggO3GDXrNBw+m9yghoeL7FhTQZI127YCffqL34M8/6f2MjKSjhnXr6MCqAjo69JZt2QLEf/0bjjnNxpZ/DPHcc7QUYL1o3Zrqpko6BtnaUiVqSAi9FP7llyUDQFVkv/8OfP453XX5cjpnAdA5h7i4Ch4vJobK0KZMoZ71ADWgUJjMlJpKZaYff1zLJjesYiIRnYUICZFfVXYx+MasqEi97a5coc+jfKbBo0f0RJvYya2yi8HfvUul/+qQBYDljBpFZ2u++IK+iGqxCnlYGPDUpB10ox6Uu83WyBaJOYnyn+8k3kEX8/b0JaHiZNsr3V7BltAtyCyoXlta2fzokyeVr4+IoNfq0uNL6OWsXsMfPT3qPPrXX8rXHzhAr6e5Oa0RONtnNkxM6E94cHAFjb0YY81atTKA3377LaytrbFv3z4MHTq0rsbEGjOxmP6qlzkDqqSggDJ/suBNNskvL49uc3JSby4cq5yhIXVbef55CvrWr6cMYXQ0pQ769KEAz8uLjiS3bqXMoZERBdozZijv7+BBmne4ejWwbBmwYgVNKqlg4t74/qkoEvbAKygEv56m+Ejeur2uubrS6fv0dHm/cjs7aoCQm1vxnJSXXgIOHaLLa6/RWtoLF9JtZVYYUJaYSK+puzvN/ZNKy2UAT5ygl/nzzzX2LJkqXbtS2qKErS01O9KY5GSaY1byITp9mv6pMjOshuvXge+/p89cVhZ9ZPbvr7wb7ZUrNAVb7vHjcsuNNAWurnRuCaBf18ePaR6xOgIDgQ8/pGm3UVGUHWvfvuTGhQupEmLu3FqN78EDQGjbDgh6SH+bFIJJO2M7JGQnQBAEiEQi3E66jdmm/amUQMWE0x5OPeBj74Nvzn2DbwZ/U61xDBxIX7eKIiKAnr0LcePxDfR8Vr0MIEDfcRMnUnwsOxF14AAFhowxJlOtDOBvv/0GNzc3jBgxAr6+vli3bh2ys7MrXO+NtVB6evSXRySii64uZQDt7emIgIM/zbOzow55kZF0ADt3LjWg+esvauTj70+nu48cocClbPAHUKD36qtUP/T663Qk8dxzpetyKMrNBV5/HeIAf6w50Q4+PnX/FJWYmlLgp5heAF1VWdNUbW16Sa5coSYTQGkG0MGBXhpVTxeJiRRp2NlRF5mUlHIZwMRESgzw12EdK5MBtLen961aTp2i+rorV5Sv37qVsmweHhBSUvHTT5RwGjmSEuXV9fnntMC5szM9ZFwctdp/+WVqkFXZ8AIDFa5owgGgrAT0/n16aa2t1buvlxdVG8ybR+elvv5a4UYTEzphVZsOoKAA0KKzI52sjIhQus3WyBYFkgJkFdIi53cS78Arx5g626j4GyYSifDDsB+w8spK3E++X61xDBhA5ePx8aXXhYcDxTY3YKxrjDaWbaq1L3v70m7Nt27R5+nZZ6s1JMZYM1etAPDFF1/E1atXcfnyZXTp0gULFiyAg4MDUlJScP9+9b7wGGN1xNISmD2bJrQdP06RSWYm8O+/1PazqghFS4vWe7h7l86yryozpyUjg8pNHz+G1j871eoMWyfc3MoFgOrQ06OOd76+tIuAALrewYGS1SkpKu4kCwANDOjgMyGhXAZQtgmrY2UygPb2ygfOVbp5k6I6a2taV+/qVbr+0SOqYd65E4KJCX4ecxTff08ZrF9+Ke1sq66ff6ZfnevXKaHu60vnC778kuaJbt6s+n6PHlFZnlLGpokGgG5uFPQWF1ev/BOgr6E7dyj7t3IlBTEqT87UwoMHQNv2Yqobf6BcBmqmZwZdLV0kZCcgNS8VT7OfwiNLq9K51t0cuuHt7m9j7J9jkZ6fXu72lNwUTP57MoLjg5Wub9WKPopr19LPOTl0fim46G+MajuqWifZRSLgo4/oo/zSS1Qg8u67VDXPGGMyNWrK7u/vj99//x1xcXH44osv4ObmhgEDBmD48OGaHh9jrLZEIjqVXl2WlpQR+ewzqr9KS6O5mgEBlGo7frxhe+9XFgDm5NAcx8LCCu+upUXVsrKDUiMjSiyWKwMVBOXozs6OAsAyGcCEBLqJ1bHOnanmsyTqc3CoZgD43Xe01szmzRSNjRpFi1d2706Z8eefxz23kbALOoygIDpnMm4cTQMtkySq0N9/00H4gQPlSx719Ojg/LffVN/3n38oiyNb6QAARVFNMACUZePj4mi5GXXLP2UcHChr36sXveXqvv7qunev5Pe/XbtyAaBIJJLPA7yTeAeOJo4wjEusNAAEgK8Hfg1HU0csObNEfp1UkOKX67/Ab70fDkUcwh/Bf5S73zvvAGvW0EyJqCjA2FSCfVF/Ykrn6ndomTKF1vWzsKBy0OqevGCMNX+1WpXL3Nwc77zzDh48eIBDhw7BsN4mADHG6kX//hQl6evTWogTJ1JN1t69Dd+gx81NucWgop9/Bt5+m8Yrkai9y1atypcTBp3PoaOysgFgXFy5ElDOANYDY2Nan7MkC1itEtCoKGqCtGAB/fz22zTR7MYNYOlSYOVKPHwIfHp+GMboH4W1lSB/yL59aR5fVR4+pPLi7duB7ulHqGFTmc/glCn0kPfuKd9XKqX7PfdcmZ020QygtjYlbP/9l0poFbtcVoeBAc2JPHWq/G1795Z/HdVRXExV8R07QmUACFAn0MScRDxIeYD21u3pza0iANQSa+F/g/+HX278Im8i8+7Rd/G/C//D4n6LsWXcFux9sBdCmXTm4MEU8G7ZQoGuffczKJYWY5BHzRYV9fGhzPOXX9bs/B9jrHnT1LLMGDp0KP79919N7Y4x1liYm1NK45tvaGLKW28prALfgMq2GJTJyqIjnx07qP5OnaP2Eg4OyhnA5GRg9uhESMTapctj2NlRPVtqqlJdFQeA9UhhHmC1SkB/+IFatZYcxMfHA8uEhTj26i5g9mycPCVC796AywuBMCjKVJprOGKEeh+lN96gVQrG6B+jNQ8+/ZTm4Uql8m0sLWlOlsJKJgAoaZ2SArzwQpmdNtEAEKCuuO+9Rxn2Z56p+X4GDqRGS4oOHaKXeOXK6u9P1kTY3R2UBrx7t9w2tka2SMhJQFhKGNpataXvm8oaoJXwa+WH/m79MWzrMAzYNADbb23H8enHMct7FoZ4DEFiTiJuJ95Wuo9IRFnAFSsoFs3xWolZXWdBW9wIvmsZY81OjQPAqKgo7Nq1Cxs3bpRffv/9d02OjTHWWOjoUNlcY2pDX1EJ6KFDFMlNmgS8+GLFtXYqlM0AfvYZYFqQiHQdm9JuqHZ2tIyJvT3VjJbgALAeyRakRDUCwKQkWsPxvfcAAPn5VPX5338URHTsCIwdC3zwAfDDKl2IxoxR6vwyeDC97ZUllK9eBcQnjmFt2CCK8FavptrHe/fKzaUdMaI0oJFIgMWLqWx0y5Yya2kKAhAbW3l3o0Zs3DhaguDTTytsKKyWkSOBw4dLq7olEmqmM3Fi+cBQHffu0dQ/bW3QBM3Q0HIl47JOoEoBYBUZQJkdE3Zgju8cjGw9EndevwNPS08AgIGOAYZ5DsPeB3vL3eeFF6ix8SergpFsehwLei6o/hNjjDE11OjU0i+//II33ngDlpaWMDIykl8vEonw4osvamxwjDFWoYoCwPv3qf5JJKIAsH171RP0BIFSEj/+SOtVonwGcN8+4PtxiYj/xxbyKVl2dhRIdO+utDsOAOtR1640WQ70nmVl0bRPhT9H5X37LS2NUtKyds0aysSdOkWlgLdv03psJauKULvOOXOoNFQsRseOlMS7f5/W6FPl8ge7sVMyAzpDPwB27yydyLdpE0UwY8dSF0nQPL9XXqGx799P5ykuXED5jrqZmfTkmmgGUCSi17i23XF9fakU98wZYMgQWiOwsJDeR1tbqgZXIzknd++ewpxET08qab99W2l9jg7WHXAx9iLCUsIwp8vsCtcAVMVUzxRz/VQvU/Fsu2ex+tpqfNL3E6Xr9fSA/QekePPquwhwexX2xvbqPyHGGKuGGp2P++abb7Bz504kJiYiOjpafomKitL0+BhjTDVXV+pImp6ufH1YGJ3aByiw8/VVXbsXHk5H3gpZHsUMoERCmaVerRMRV2SL1NSSjezsaM2wdu3k9ysooKFwAFhPunShI/iiIlhYUIK60ixgcDBFCj/8AICqd5cupaUFxGI6R/DccwrBH0BtGQsL5eklbW2KDWRNQ8tKu5+AGadfRPJ3v9OSLIpdXPr2pXaM77wjv8rVlWLBc+eoL82iRSqCP4CCDiMjWkqnidLE0ihiMVXS7ttHP//1V+l7FhBQ/SygUldSsZjW+bx+XWmb/m79cebRGUSmRaJzTD4tdKow77emRrUdhZtPb+JJVvmFRy8Xr8WT/Ah8OeDLWj8OY4xVpEYBYEZGBsaPH6/psTDGmPrMzelSthGMYgAIUKrlzJny9z91iiKH3bvlV7VqRdOtAMroSSSAo24ScgxtS6eDyTKJCo+RlEQHuequccZqydWVDtofPoRYTG9JRQHg2YmrUNSjN6QffSxP3S1aRAncESMqeQxtbWrX+cEH8kX7AgKAa9dUb373w814YNMHTvPLdnAp8e231BZUYa7ZwIHAa69RhWeFxTNRUfR8eYFJPPccna+JjQV27aIqb4DKc2ULzgN0QmbCBIq7L1xQvS+lDCBAAWCZN7ebQzcUS4shFaRwXLWJ3iwNrGNrbWiNXs698N+D/5SuT8xJxEcnP8KGZzbAVM+0gnszxljt1SgAHDVqFM6oOqBijLH6VLYRjCCUDwD79QNOny5/31OngLlzaQXm2FgApUvM5edTKai1NaCdmggtexsEB5fcT0UAmJhICZ/G0BunRdDSorK98HAAFXcCPT5nF3x2fYQpZgfx7NVPUFBADUO2b6dGsVXGVO+9B+TmyufvBQSozgCmJxSg3Y1tMPnozYr3ZWdHJaB//CG/6t136XL+PCWXVJKvVcAGDaIF4n19KVvaty9dP2IEzQ8sidOxZAk1efHzo46s+fnK+xEEFS+rv3+5AFBbrI0+Ln0wJdEe4lOngYULNfZcRrYZiSORR5Su++zUZxjoPhCDPQZr7HEYY0yVGh2u2NjYYOzYsZgwYQJalSmH+PJLLltgjNWTsvMAk5KoFlNx1eNevSjAi4mRz7+CIFBQuGsX9eM/eRKYORPt2tG8sIsXgezskmqvxETouXQuTdxUEABy+Wc9a9NGHgA6OJQGgIJAMdaeHXn449gcPPr8d6x/uy+GDClNGp84IZ/2WTk9PVqd+9lngeeeQ0CAE0JC6CNmZiyhFeIjIpDy3x3oGlqh01tVHLi/+CIwcybVn2pro107pUpi1TgAlBOJaMru1Kk0DVfWVMbfn06+XLpEcypXrKB/d+xIv+YrV9JqHzKPH1Ncr3ieCH5+NAcwL4/WnSgxRdoJ4387Bqz6VaMLfQY6B+LHKz9CEASIRCLkF+djU8gmXHn5isYegzHGKlKjDGBISAi8vb0RGRmJc+fOyS/nz5/X9PgYY6xiZQPAsDDqlmhsXHqdiQmlDM6eLb0uLo6CxYAAOgIvmb8sElGW4cQJ2sTREUByMvRaWSEuruS+zs6UVvD0lO+OA8AGoBAAenqWLuN29iwt8/ei6T8waWuPzp+Nh7k5JXzPn6eK4YCAajzOgAHUynLMGLjf/g9dvAT8s6MQGD0aWLkSUZFS7MvoB509/1Td5nLQIIpUDh9W//E5AFTi5UVZesWeOFpalAX86CMqC/39d5omqq1NsfYPP1BcJ3PvHn1m9PQUduziQmd/5Kl+MiVUgN5zk4HZszX6PHxb+SI5NxkxGTEAgIuxF2FlYAUvWy+NPg5jjKlSowzgKVWrsTLGWH1zc1MO7MqWf8p06ULtG2UePqT0np4epYIUFoEeNIiSPkOGlASAoekw9rbA41slG+jq0hGmAg4AG0CbNrTCOCh5I1tl4eZNKg0cm7QemPOyvM7T2JjOA9TImjW0pMPs2fjXxhdPPhIDbglIOXYTvXqY4JvvMmGvToympUUZwN9/pwCyKiprFZkq06fToud//UUNV2WGDaMM8R9/0BQ+oIKXVCQqbQTTs6f8anFwCE0+1DBDHUN0teuKS48vwdXcFcejjmOwx2CIeK4nY6weaGwheMYYq3dlM4DBwap79JfdTrFnvIeHPAMIUEOJ69eBW7dKSkDT0mDmZlGaAVQhIYEDwHqnkAH09aW3vriYpnT2bf2E0n3Tp2vmsYyMgPffB+7dg80zvRCc4YGN4/7D2Okm6NWL1hFU26xZ1H02ObnqbePjaRmIKutE2ZAh1FFVMfgDKK5btAj45pvSuYBKHUAVlZ0HKAh0RkFle9ba6+HUA5cfXwYAeQDIGGP1Qe0AsOxcv4q4yObYMMZYXSvbBObSJaWz93LVCAAdHanM7MCBkgxgWhosPS2QmqpcRqaIM4ANoE0beh8LC+ULet+9SwFgf+EUrdlgY6PZx7S2huG3n8Hj4Cp8vMYRrq7A5s3VbNDZujUFFPv3V73tvXv0Oa2wQwxTx8SJVN25di39XK4DqIy/v/JSELGxNOHTq27KMgOdA3Hq4Skk5iTi5tObGOQ+qE4ehzHGylK7BDQtLQ2///47BEGodLuMjIxaD4oxxtTi5gakpVGWREeH0kA1CQDj46krRMmB9ujRtCvHVgKQng4Ld3Po6FBnUIWpf3KJidRrhtUjR0cqx42OhrhdO/j4UMv/e/eA9h1P0hoLdWTYMGokIlsRIDOzmjvo2ZMCjVmzKt/u1q2KV51nahOLqTPonDm0skeFGUA/PyoVz8qiucM3b9KGCk1hNGlU21F4ce+LWHJmCXo594KDiUOdPA5jjJWldgBoZ2enVodPBwf+AmOM1RNZW8f792nRbisrCvbKcnen6K2ggOb9PXpEGSKAskRGRtQ3vuRge8wY4KuvACeLHKC4GGIrC/kagRUFgJwBrGdiMdXoPn0KtGsHPz9g2TLA1BQwunoSmP1LnT58rZaD8/WlOYVVuXqVslKs1kaMAIqK6PdaLKYlX8qxt6dLaCgQGEjpZNn3RB0w1TPFqLajsOraKqwZuabOHocxxspSOwB8qHj2nDHGGgORCBg1Cvj7bwrkevZUXY9nb08ZwpgYKh2MiSnNAIpEpWWgJQGgnx/w9ttAW5s02sbMDI6OtNj0woWUSZgyheJGgAJADXaIZ+qyt5evAL9oEZWB2uY9guiXx0Dv3g08uEr4+ZVOWqxs8cirVzU3j7GF09amxeGXLAHeeYeSxyp5e1PgFxhI8wGHD6/Tcb3Q+QXsvb8Xz3XUfKMZxhirCDeBYYw1bVOnAtu2Ab/+WvHBmlhcOl9QEJRLQAFK60VGKm2+ciVgUJBOKSUtLTg6UqdJsZgWEXdwoKYTgsAZwAajEADa2ADffQe8O+oeBfmy6LwxUpy0WJGUFCAigjOAGjRxIiCVAi+/XMlG3t4UnAsCBeDdu9fpmMa1H4cLsy/AxkjD81UZY6wSHAAyxpq2wYOp/NPDA3jllYq3c3OjMs+UFJrvp9iwqlUreSChJC0NsLAAQFPOMjJorbGQEODrr2nNsYgIengOABuAvX3pCvAykZFqrvLegMRiagRz40bF21y7Rg1jrKzqb1zN3IABNK2v0qaqPj4UAEZF0eROb+86HZOWWAvdneo2yGSMsbI4AGSMNW06OrSw9l9/Vb4Qt7s7HdRFRQHW1soZInNzID29/H3S0+k20MLTxsbUAEQkAt54gxI5P/5I0wpNTDT4nJh6FDKAclFRqidqNjZeXpVnAK9ereaK9awqIpEaKzp4ewO3b1NHIW/vMqvFM8ZY81CjheAZY6xRUWeF74AAYN06iuL69FG+zdxcqQRUTiEDOHgwVe3JGgKKRED//rQMgK1tNZcCYJrh4EDr/SmKjKRUT2Pn7g5cuVLx7UFBjXseY3Pl6UkTBJctA/r1a+jRMMZYnahWBnDZsmU4c+YMsrKy6mo8jDFWN559luq/NmwAxo5Vvs3CgoK9shQCwK5dqTGMIj8/qirl8s8G0pQzgO7u9OGpSGgo0KVL/Y2HEbGYqgn09al1KGOMNUPVygCuXbsW77//PsRiMTw9PeHn5wc/Pz/4+vrC19cXRo150j1jrGWztqbM0MmT1DlUkRoloKrIEo8cADaQsnMABYECwMY+BxAovzalosxMeh4q1ypgdW7ECA7+GGPNWrUCwMjISKSlpeHmzZu4ceMGbty4gdWrVyMqKgpisRjt2rXDyJEj8eqrr6JNmzZ1NWbGGKuZGTPozH7ZxhoVBYAKGUBVHByoOQwvAdFA7O2B5GRa4E1HB0hIAHJyKLvW2Lm7U0Mi2aLjim7doufGZxYYY4zVgWrPAbSwsMCgQYMwaNAg+XXp6enyoPDkyZPw9fXFnj17MHDgQI0OljHGamXaNLqUVVkAWMXJLD8/Pk5vMLa2lPVLSqJOrlFR9H/ZRM3GzMKCAr+HD6khjKKQEC7/ZIwxVmc00gXU3NwcAwcOxHvvvYdDhw5h27Zt+PDDDzWxa8YYq3uyAFAQlK9PT680AwjQ2nNv2qSpEgABAABJREFUvFFXA2OV0tWlbK5sHuCjR1Ra2RSIRBXPAwwN5fJPxhhjdaZOloEYM2YMwsPD62LXjDGmeebmtJhffr7y9VWUgAK0FISzc90NjVXBwaE0AExIoNLJpqKieYBnz9b5AuSMMcZarjpbBzAsLKyuds0YY5ola/RStgw0NbXSJjCsEVBsBJOY2LTqcVVlAMPDaSmLoUMbZkyMMcaavToLAG1sbOpq14wxplna2rQ+oGIAKJE0nSUFWjLFpSCaWgDYvj01fFG0bx8wcGD5xjCMMcaYhtRZAMgYY02KubnyWoDR0RQEtm7dYENiamjKAWBgIHD5MlBcXHrdf/8BzzzTcGNijDHW7HEAyBhjQPlOoHfvAu3aUXaQNV5NOQDs1ImWrwgOpp+lUuDaNaBPnwYdFmOMseaNA0DGGAPKB4B37tABOmvcHBya7hxAsZiygOfO0c+PHlEzorZtG3ZcjDHGmjUOABljDFAdAHbs2FCjYepqyhlAAOjduzQAvH2bgj9d3YYdE2OMsWatWQaAp0+fhkgkgrGxsfzi5OSktE1aWhqmTp0KMzMzmJubY+rUqUgv0wHw77//Rvv27WFgYIAOHTrg33//rcdnwRirVxYW5UtAOQPY+MkCwJwcujS1AHDQIODkSaCoiE46dO7c0CNijDHWzDXLAFAmPT0d2dnZyM7OxuPHj5VumzZtGhISEhAZGYmIiAgkJCRg5syZ8tuvXLmCadOm4euvv0ZmZia++uorTJ06FdevX6/vp8EYqw+KGcCiIuDePQ4AmwJ7ewr8oqMBLa0q121sdHx9AUNDWvuPy44ZY4zVg2YdAFbk0aNHOHjwIJYvXw5ra2tYW1tj+fLl2LdvH2JiYgAAv/zyC0aMGIEJEyZAR0cHEyZMwPDhw7F27doGHj1jrE4oBoDXrwNGRjwXqymwsKCSydBQwMaG5tU1JWIxMGYMLf9w+zYHgIwxxupcE/tLWT3u7u6ws7PDoEGDcObMGfn1wcHB0NPTQ9euXeXXde3aFbq6uggu6cYWHByMgIAApf35+/sjKCioXsbOGKtnzs5U9gkAp04B/fo1vWCiJRKJKAsYEkIBYFP07LPA779TAMgloIwxxupYk+pvPmvWLGzatKnC2/v164fTp0+jffv2CA4ORqdOnZCXl4dff/0Vw4YNw+XLl+Ht7Y3MzEyYmZmVu7+5uTkyMzMBAJmZmTA3N1e63cLCQn67KqampsjKyqrZk2OsCWsWn/tBg4APP6Qg8MIFYOhQoJLfd9aIuLkB9+8DTk4N8p7V+vPfsyewZg1gZkbBLH/uWBPSLL7/GWthmlQAuGrVKixbtqzC23V0dAAA9vb2sLe3BwCYmJjg3Xffxf79+7Fz5054e3vD1NQUGRkZ5e6fnp4OU1NTABTMlW0Kk5aWJr9dleLiYnzxxRfQLengNnDgQAwePLhaz5Ex1kCsrWn9tY0bqQT0888bekRMXTY2pUF7U6SlxYu/sybl+PHjOHnyJACgsLCwgUfDGKuuJhUAyjp61oRYLIYgCAAAb29vFBQUIDQ0FF26dAEAhIaGorCwEN7e3vJtrl27prSP69evw8fHp8LHyM3NxbJlyyoNEhlrzpr8Z3/KFOCFF4CRIwE/PyovZI3fW28BffsCo0cDDfgZbPKff8bUNH78eIwfPx4AVUytX7++gUfEGKsOkSCLipqRI0eOoG3btnB1dUV+fj7Wr1+P999/HxcuXICfnx8AYNSoUSgqKsL27dsBAFOmTIG+vj727dsHALh8+TL69++PHTt2YPTo0di/fz+mTJmCs2fPwt/fv9xjyspKMzIy+CCAtTiy0ugm/9kXBCA/HzAwaOiRsCak2Xz+GasBPv5hrOlplh0Orl69in79+sHExAQuLi7Ys2cPDh06JA/+AGDLli2wtraGp6cnPD09YWNjg82bN8tv79GjB7Zs2YJFixbBxMQEixYtwtatW1UGf4yxZkIk4uCPMcYYY81as8wANgQ+A8ZaMs6AsJaMP/+sJePjH8aanmaZAWSMMcYYY4wxVh4HgIwxxhhjjDHWQnAAyBhjjDHGGGMtBAeAjDHGGGOMMdZCcADIGGOMMcYYYy0EB4CMMcYYY4wx1kJwAMgYY4wxxhhjLQQHgIwxxhhjjDHWQnAAyBhjjDHGGGMtBAeAjDHGGGOMMdZCcADIGGOMMcYYYy0EB4CMMcYYY4wx1kJwAMgYY4wxxhhjLQQHgIwxxhhjjDHWQnAAyBhjjDHGGGMtBAeAjDHGGGOMMdZCcADIGGOMMcYYYy0EB4CMMcYYY4wx1kJwAMgYY4wxxhhjLQQHgIwxxhhjjDHWQnAAyBhjjDHGGGMtBAeAjDHGGGOMMdZCcADIGGOMMcYYYy0EB4CMMcYYY4wx1kJwAMgYY4wxxhhjLQQHgIwxxhhjjDHWQnAAyBhjjDHGGGMtBAeAjDHGGGOMMdZCcADIGGOMMcYYYy0EB4CMMcYYY4wx1kJwAMgYY4wxxhhjLQQHgIwxxhhjjDHWQnAAyBhjjDHGGGMtBAeAjDHGGGOMMdZCcADIGGOMMcYYYy0EB4CMMcYYY4wx1kI0yQAwJCQEI0aMgL29PUQiEY4fP15um7S0NEydOhVmZmYwNzfH1KlTkZ6errTNggULYGhoCJFIBJFIhF27dslvu3LlCoYPHw4DAwOIRCJoaWmhR48e5fbBGGOMMcYYY01FkwwAdXV1MX78eOzfv7/CbaZNm4aEhARERkYiIiICCQkJmDlzpvz2K1euYPXq1ZgzZw7WrVsHAJg+fTquX78OAEhJScHTp0/h7++PxMRE/PPPP7h+/TqGDh1at0+OMcYYY4wxxupIkwwAO3TogFdeeQV+fn4qb3/06BEOHjyI5cuXw9raGtbW1li+fDn27duHmJgYAMAvv/yCUaNGYcWKFWjTpg0AYNiwYVi7di0AoFOnTggNDcXPP/8MGxsbjB07Fn369MG1a9fk+2CMlVKViWespeDPP2OMsaaiSQaAVQkODoaenh66du0qv65r167Q1dVFcHCwfJuAgACl+/n5+SEoKEjlPjIzM3H//n1oaWnJ98EYK3Xy5MmGHgJjDYY//4wxxpqKRhUAzpo1Sz4fT9Wlf//+au0nMzMTZmZm5a43NzdHZmamfBtzc3Ol2y0sLJRul+2jsLAQkyZNQvv27WFpaSnfhjHGGGOMMcaaEu2GHoCiVatWYdmyZRXerqOjo9Z+TE1NkZGRUe769PR0mJqayrcp29AlLS1N6faMjAzk5uZi/PjxKCwsxH///QcrKyv5NookEgmMjY0RFhYGY2NjtcbJWHORm5uL3Nxc3L9/v6GHwli9488/a8mys7MB0HEQY6xpaFQBoLGxsUaCJ29vbxQUFCA0NBRdunQBAISGhqKwsBDe3t7yba5du6Z0vxs3bsDHx0dpH7169YKTkxP27NmDsLAwpX0oioiIgFQqRWBgoPw6bW1taGs3qpeYsTphbGyM4uJi/PPPPw09FMbqHX/+WUtTXFyM4uJipesiIiLg7+/fQCNijFWHSBAEoaEHUV2CIKCgoAAAYGBggIMHD2LAgAFKAdeoUaNQVFSE7du3AwCmTJkCfX197Nu3DwBw+fJl9OvXD5s3b4aJiQlGjRoFPT09HD9+HL169UJiYiLatm0LQ0NDBAUFQUdHp9w+FKWlpcHS0hKxsbEqM4SMNWdZWVn44osvKs3gt1QLFwIZGcCGDQ09ElZX+PPPWrLMzEw4OzsjNTUVFhYWDT0cxpgammR66tGjR3B3d5f/PHLkSADA4sWL8fnnnwMAtmzZgjfeeAOenp4AgNGjR2P16tXy+/To0QMvv/wyJk+eLL+uoKAAffr0walTp3DmzBlkZWUhNzcXrVq1AkAZvX79+qkck5aWFgAqHeUAkLVEurq6/NlXQSoFiosBfmmaN/78s5ZOdhzEGGv8mmQA6ObmhqoSl5aWlvLsX0VWr16tFBQq6t+/PxYvXlzjMTLGGADk59OFMcYYY6wxaFRdQBljTdfAgQMbegiNUkEBkJfX0KNgdY0//4wxxpoKDgAZYxoxePDghh5Co8QZwJaBP/+MMcaaCg4AGWOsDEEAZs4ESrqb1woHgIwxxhhrTDgAZIyxMoqKgM2bgYiI2u+LS0AZY4wx1phwAMgYY2XIArbExNrvizOAjDHGGGtMOABkjLEyZAFbQoJm9sUZQMYYY4w1FhwAMsZYGbKATVMBIGcAGWOMMdZYcADIGGvx3n8fWLWq9GdNloDyHEDGGGOMNSZNciF4xhjTpLt3AR2d0p81nQEsKgIkEkBLq/b7Y4wxxhirDc4AMsZavKdPgeLi0p81HQAClAlkjDHGGGtoHAAyxlq8+HjK0sloMgCUBX5cBsoYY4yxxoADQMZYiyaVUqCnKgCs7RxAqRQoLKR/cyMYxhhjjDUGHAAyxlq0lBSan1c2ALSxoQBQKq35vhXLPjkDyBhjzcuGDRsgEolUXoyMjCCRSBp6iIypxE1gGGMtWnw8/b/sHEA3NyApCUhLA6ysSm8TBGDFCiAmBvjiC8DMrOJ9y7J+YjFnABljrLkJCgqCkZERjh8/Xu42AwMDaHHnL9ZIcQDIGGvRZAFg2QygtTVgaEhZQMUAMDUVWLiQbnv+eSAwsOJ9yzKA5uacAWSMseYmODgYnTp1Qo8ePRp6KIxVC5eAMsZaNFUBYH4+YGAA2NmVbwQjyxQ6OgJZWZXvOz8fEIkAExPOADLGWHMiCAJCQ0PRpUuXhh4KY9XGGUDGWItWUQbQwAAwNQUyM5W3lwWAFhblbysrPx/Q16d9cQDIGGPNR3h4OLKzs9GpUycUK84hACASibj8kzVqHAAyxlq0+HhaoF1VAGhkBOTmKm9fXExz+szNqw4ACwooANTX5xJQxhiriiBUXVlRGyYmVJWhCcHBwQCABQsWYMGCBUq3eXt7IygoSDMPxFgd4ACQMdaixcdTOWfZJjAGBjTPLydHefviYkBbmw4k1MkA6ulxBpAxxtSRlVV5Y63aysigyg5NCA4Ohkgkwvnz56Grq6t0m4WFhWYehLE6wgEgY6zFSk8HgoMBF5fyGUAjo4ozgNradBChzhxAzgAyxph6TEwoSKvL/WtKUFAQPDw80KtXL83tlLF6wk1gGGMtSmws8NJL9O+BAyn4e/75iktAK8oAqpofWJasBJQzgIwxVjWRiL5b6+qiqfJPgDKAXl5eVW43d+5cLFy4EACQkJAALy8vHDx4UHMDYawGOABkjLUo4eHAv//SXJPbt4Gff6YlH8oGgPr6VAJaWQZQ3RJQzgAyxljzkZCQgPj4eLU6gH755ZfYsmULbt68iREjRuCLL77AyJEj62GUjFWMS0AZYy1Kbi6VGOXkUNBnYUEBnao5gJVlAE1MgAcPKn8sxRJQzgAyxljzIGvwoq2tjcuXL5e7vUuXLjA0NAQA2NraYv78+ejVqxc2btyI8ePH1+tYGVOFA0DGWIuSl0fZv5gY+tnMDNDRUV0CamgIpKUp37+oqPpzALkElDHGmg9ZB9DPPvsMn332mdJtIpEIqamp8gAwNTUVu3btgoWFBZydnet7qIypxCWgjLEWRVbSGR0NGBtTMFc2AJQtBF+dOYCCUH5bXgaCMcaanw8//BCCIKi8SKVSmJubAwAyMjIwfPhwzJ8/H6tXr8aCBQsgCELDDp4xcADIGGthZIHYw4e0lh9QeQZQ3TmAJ04AgwYpb8vLQDDGWMuUk5ODkSNHYtasWZg5cybGjx8PPT09bN68uaGHxhiXgDLGWhZZQPfwIc3/Ayigq04XUB0dmgOoWAL6+DGQmKi8reIcwLpsbc4YY6xxMTIywoULF5SuK/szYw2FM4CMsRZFsQRUMQNY0ULw6mYAU1OB7GzlbXkZCMYYY4w1NhwAMsZaFFkJaNkAsKbrAMqmc6gKAHkheMYYY4w1NhwAMsZaFMUS0NrMATQxoX/LMnupqXQ/iaR0W8V1ADkDyBhjjLHGgANAxliLIsvEpaaWzgGsaQYQKJ0HmJpK/1cMGBWXgeAMIGOMMcYaAw4AGWMtimKAJssAKi4ELwgUrOnrV54B1NMDdHVL5wHKAkDFMlDFOYAcADLGGGOsMeAAkDHWouTlAVZW9G9VJaAFBfT/qjKAgHIjGFUBoKwE1NCQA0DGGGOMNQ4cADLGWpTcXKBVK/q3qgBQFqjJ5gDm5QFSaen9FQNAE5OqA0BZBrBsJpExxhhjrCFwAMgYa1Hy8koDQMU5gBIJlX/KmrXIMoCy+8iUzQCWnQOoqgRUVSkpY4wxxlhD4ACQMdaiqMoAygK6oiIK9rS16WJoSNcrloGWDQAzMui6jAzan6oMYJrwEOkOu+vyaTHGGGOMqYUDQMZYi6KYAVQsAQUokMvKotJOgObvicXK2TvFANDcHEhPpwsAuLjQ/V1cgKSk0jmANzOOIcf727p9YowxxhhjauAAUA2XLl3CwIEDYWJiAnNzc/Tq1QtSxUlBjLEmIzcXcHCgf5cNAIuKKJiTXS8SlW8EoxgAWljQ9qmplOmzsQFiY+mSklKaASwSZUOqnyJfNJ4xxhhjrKFwAFiFS5cuYcSIEZg1axYSEhKQnJyMFStWQCQSNfTQGGM1kJcHODnRvxXnAAKlAaCZWen2Wu0P4W7iffnPZQPAtDQKAC0tAWNjICqKbsvPL50DWCzKAQxS5B1GGWOMMcYaCgeAVXj//ffx0ksvYcaMGTA0NIS2tja6d+/OASBjTVRuLuDmBpw/XxroKQaAsrl8MgVdf8aRmH/lP5ctAS0bAEZH0235+aUloIXIBgzSkZVTXNdPjzHGWD3ZsGEDRCKRyouRkREkEklDD5ExlbQbegCNWW5uLi5evIiePXsiICAAkZGRcHNzw0cffYQJEyY09PAYYzWQl0fNXbp2Lb1OS4v+X1ysXAIKACK9HCTlJsh/LpsBDA1VDgCvX6fbZAGgvj5QkEU1pE/T0mBjZVN3T44xxli9CQoKgpGREY4fP17uNgMDA2jJ/rgw1shwAFiJ1NRUSKVSbNq0Cfv374ePjw/27duHyZMn48yZM+jZs6fS9qampsiS9YRnrAVpKp97qRTQ1aX/y9bvk5HN50tLo4XiZbeb6EmQlZuCzJIriospq5eZSQFfbi41fLGzo59TUkq7g2pr09ISBTn5MIUpop/Gws1Wr36fNKtzTeXzz1hdaMmf/+DgYHTq1Ak9evRo6KEwVi0cAFbCpKQV4KxZs+Dv7w8AGD9+PAYMGIA9e/aUCwCLi4vxxRdfQFdXFwAwcOBADB48uH4HzRirkGwOnoFB+dt0dCi4y8ws7QIKAIJ2LtKLkuU/FxeXZgzNzCjQS0qiBjBGRkBhId0mmwOopwfkFFEGMCk7tS6eFmOM1avjx4/j5MmTAIBC2ZdeCyMIAkJDQzF58uSGHgpj1cYBYCXMzMzg6emp9ny/3NxcLFu2DKampnU8MsYap8b+2S8spADPxqZ8EJifT0Fgejrg7ExZPADI1EqGqDhP/tyKi2lpCFNTwNYWePoUePIE8PCgfcoyh1IpkJxMmcUMZCATmUgtymn0rxGrOX5vWUsxfvx4jB8/HgCQmZmJ9evXN/CI6l94eDiys7PRqVMnFBcrz+8WiURc/skaNW4CU4U333wTf/zxB4KDgyGVSrFv3z6cOXNG/sXHGGs68vLo//r65W/T1lY9B1CqlYNMScVzANPTKQBs1YpKQBUfS9YFNLuQVodPzknR6PNhjLHmRBAEZBZk1tlF0OBaPMHBwQCABQsWQEdHR+ni5+enscdhrC5wBrAKb7/9NnJzczFmzBikp6ejTZs2+Ouvv9C9e/eGHhpjrJpycylLpyqpr6NTfh1AAJBo5aAQBSiUFEJXS7dcAJiRQev+tWpF8/9kMjLo//r6QE5hDkQSXSTncgDIGGMVySrMgtm3ZlVvWEMZH2bAVE8zmfrg4GCIRCKcP39ePvVHxkK2xhBjjRQHgGpYtGgRFi1a1NDDYIzVUm4udQBVRVUAKJFKIBHRxMHEnEQ4mTqhuLg0gyjbLiKCAkDZHEMjI9oPUJoB1M13QWoeB4CMMVYRE10TZHyYUaf715SgoCB4eHigV69eGtsnY/WFA0DGWIuRl6e6AQygHADK1geUNW8RC7pIyE6QB4CyDKCRUWnpqIMDNYMBaA6hLACUNYExKGyL9AIOABljrCIikUhjGbq6FhwcXGX3z9TUVDg7OyMtLU2eJZwzZw48PDzwwQcf1McwGVOJ5wAyxloMdTKAigvB5xRSAGhU5IaEHJoHqBgAikS0rZZWaRdQAHBxKS0B1dWlDKBxsSsyijgAZIyxpi4hIQHx8fHo0qVLpdtZWlrCw8MDN2/eBACcO3cOV69excKFC+tjmIxViDOAjLEWo7IMoLZ2+RLQnKIcaEEHegVOSMguHwACNA9QX586g8qawLi4UDZQX5+CxJzCHDgILsgsjqyz58YYY6x+BAUFAQC0tbVx+fLlcrd36dIFhiVnG/v164fz58+jW7dueO211/Dbb79BW5sPv1nD4k8gY6zFqCoDmJ4OSCTKGUA9kRG08uxUZgABCgBl27u6AtOm0XXh4VT+WSgpRJG0COZwRaQkGYwxxpo2WQfQzz77DJ999pnSbSKRCKmpqfIAsG/fvtixYweys7MxYMAAbiLIGgUuAWWMtRiyLqCq6OjQun1A6RqAOUU50NcygijXrsIMoLk5NYABaO7gli2U+UtPL+0ACgCWWi7IEXgheMYYa+o+/PBDCIKg8iKVSmGu0Eq6b9++OHXqFLZs2YKvv/664QbNmIJ6zwBGRUWptZ1YLIabm1vdDoYx1qKkpgKWlqpv09Ghsk0TE5rTB1DwZqBthMI8Q+QWUXSoKgNYdp+KAaBsDUArnVYoQN11t2OMMdb42NnZQVdXF8uWLYOpadNocMOav3oPAFu3bg2RqkW4FAiCAAMDA+Tk5NTTqBhjLUFyMmBtrfo2bW26XXENwJyiHBhqGyGnQAdF0iIA5QPAAQPKB4AGBhQA2tuX7EPHEOb6FigW5cnXE2SMMdb8/frrr+jXrx/GjRvX0ENhTK7eA0BDQ0PcuXOn0m0EQYC3t3f9DIgx1mIkJZWWa5YlywCaKaxBnFOYAyNdI8TlVxwAzplTfl/6+kBmJs0JzC7MhpGOEcz1zQEpkJGfARsjG809KcYYY43O/fv3MX78eDg4OOCff/5p6OEwpqTeA8BnnnkGrq6uVW43evToehgNY6wlSU4GKuraraMDxMcrZwhzinJgrGuEwjwdFElUB4Cq6OsDglA6B9BY1xhmWvoQZ+giPT+dA0DGGGvm2rdvj7t37zb0MBhTqd6bwGzfvl2t7bZu3VrHI2GMtTRJSbRenyo6OsDTp4CVVel1OYU5MNE3QnGBDookxQDUDwBl/88uzIaRrhEMDQFtiTkyCngeIGOMMcYaToN3AZVIJA09BMZYC7D84nL8n73zDm+qeuP4J7sj3RNo2XtPZYsMARFBcO+Jgnv/3ChucW8BEQcqiqACAorI3nvv2QLdu808vz/eJm1pC0WRqpzP8+Rpm9zce5Le3Jzv+b5jR9zzleYA+gTg8Q5gaGAweC0UuaruAPoqjdpsPhfRTmAgmFxhZBVl/fUXo9FoNBqNRvMnqXYBOHDgwOoegkajOQs4lHOIXOPBSh1As1naRJQRgM58wgKDwWOhyHlqIaC+n74cwKAgMDrDyS7SDqBGo9FoNJrq44zlAB7fKBOk2MuePXvO1BA0Gs1ZTJHLidNbdEIHEMo7gCG2YMzGEgfQ5To1AejLAQwMBINDO4AajUaj0WiqlzMmAD/88ENef/11lFJl7g8KCjpTQ9BoNGcxuQVOMJ+iAHTmExEaQYDFguMUQkArywFURToHUKPRaDQaTfVyxgRgs2bN6NOnD7Vq1Spz/5w5c87UEDQazX+clSvh8GEYNqz8Y7kFTkw2h1+cHU9lDmCwNZgAqwWH+8/nAPpCQFWhdgA1Go1Go9FUL2dMAC5YsKDCBvBVrQqq0Wg0ycng9UJCQsWPf/89bNxYsQDMK3RiDSyqdN8+UVdOAFqCCbBYcJ6CACztAOY6cgm1hRIcDO48nQOo0fzXKSyUxR9jtVdZ0Gg0moo5Y5enisSfRqPRnApvvgkvvlj54wcPyq0i8oocmAMqF4CVhYAGW4MJtFlwnkIfwL4z6kFQqjSEd+aUEoDaAdRo/qtkZsKNN0JEBIwfX92j0Wg0msqptvWp4cOHV9ehNRrNv5S8POnlVxkHD8KhQ9KE/XgKHBICWhk+AVimD6AvfNNW9UbwHq+Hw3n7ITBDBKBDBKDdDt6CcDILq9kB/PVX2L+/eseg0fzHKCqCc8+V69OIEfDzz9U9Io1Go6mcMxYCejzJycnVdWiNRvMvpbDw5AIwLw+ysyE8vOT+zZshNd2JwXpiB9BqBbu95L4CV4EUcAmw4PJWTQD6tsPswGYrKwBxhJFRkHXS1/m38sgjEBQEixbpGDWN5i+QnCzCr359WLhQfv/5Z9iyBTp3hvXrIT0d+vSp7pFqNBpNWart21+HhGo0mlOloADS0uT3/Hz5uXQpeDzSniE5GQyG8mGgjz4KeUVOAkNPLACjo+X5/uO5CgiyBBFks+AuJQB9bmFF+JxCzEVlHMDAQKCovAOYnS2u5RlBKdi7FzZtgokTz9BBNZr/JvfdBw8/LL/PmgUXXihrKi1bygJU9+7wxBMVP9fpFMFYEbm5stil0Wg0fxd6+Vej0fxrKCwUAbhyJbRtK3qmXz+YN0+qfxqN0KRJeUGVnQ01E51YTpADaDZTrkVEkbuIAHMAwYFVdwCdHmfxDh1lisAYDBBoCCP7uBzAcePgnnuq+Ab8VTIzIScHnn0WPvzwDB1Uo/nvkZoK06eLywclAhBkEemSS6BhQ9i6teKQ9DFjJGT0eKH3+usQH1+5cNT8sxg/fjwGg6HCW3BwMB6Pp7qHqNFUiBaAGo2mWth0bBNdJnQ5pef4BODOnbBvH2RkiCu4bJm4fgkJULdueQGYkwPK6MDhOXEO4PECsNBVSKA5kOBACx51aiGg1sCSENAaWw/B6NHYDXZGzjkqlmUxaWkiXs8I+/ZBZCTcdJPExW7bdoYOrNH8t5g0CZo1E0N9zRo4cAB69y55/K23YPlyiVSoyOFfvBh27xYX0Ud+PoweDVdfLc/V/PNZt24dwcHBLFu2rNxt6dKlmEym6h6iRlMh1SYAj28Ir9Fozi4OZh8kKSep3P25uZCSIr8rpXh7+du4vW5ABKDHIzk2Hk/J6vvSpSIAa9eWmy8ENDu75KfX4KTIXbkDGB0N9eqVvc/nAMbHmk/ZAbQGOfwhoFE7DsInn9DJdYQn5hWh0tIYNQrmzxdT7k+lRHs8ZYRkldi3T15keDhcfDF88cWfOLBGc/aybp3k+z33HLz0EtSsKb/36VM2f9hsljYwPhewNF6viMavv4bvvpOfAD/+KNev++6Ta5s2j/75rF+/nhYtWtC5c+dytzZt2lT38DSaSqk2Abhs2bLqOrRGo/kHkOvM9Qu70nzwATz4oPzu9Di5b859fqFYUCD3+4TfqlUy0Vq+XApb1q4NiYmy4u7rF3jkiDiAHk4sAG+7DT7+uORvpRSF7kICLYG0blE2B/CEDmBxDqAlsAirzUuuM5dAh4IjRxiYvxiA/NQkVqyQVLzMTDh69E9M9h58EJ5++tSes2+fVKwAGDwYFiw4xYNWQl7e6dmPRvMPJjcXOnWCXr3ggQck5LNNG/jpp4p7jwK0aCELVqXZsUNylgcOhM8+gzvukGiGzz+Ha6+Fpk1lu+3b/85Xo/mrKKXYuHEjrVu3ru6haDSnjA4B1Wg01UKeMw+PKq960tJEFIEUYYESV82XL1NaAHbuDPkDr+LDX/6gfn0wxG1mvvUBCgtFl5QWgA63o9LoA4MBSkfr+I4ZYA6gdUsLyuAiKanqDqAlwAFWEUaBLi8Al6V/Kdukp5CbKxUCMzNFrJ6oummFbNp06rXmfQ4gQOvWso+/Go1x+DDExcEvv1T9OS4XbNz4146r0ZxhVq6EWrVkIWr0aLmvbVvJPb744oqfU5EAXL0a2rWTsPOLL4ZzzhEBuXy59BE0mWS/ixfDhg1/3+vR/DV27dpFXl4eLVq0wO12l7np3D/NP51qFYC7du1i4MCBREVFYbVay9w0Gs1/jB9+ECVWTK6jYgcwO1tW2qFEAPpy93wC8OhRmXStXCmuX2TDXbTunsQDD0Be4DbSQn/zb2v54G1ivEdx40ShSto0nIRCt+wg0BxIeIgFTC5Wrap6DmD9xg6iasrrtRbJfXFOcTLdGWnk5ZUVu6ccBrpnjwi4I0eq/pzSArBpU5nJHjhwigcuhdsNr74qbSUeekj+rgrvvQcdO0rs7ksvSTIUwNq1kgA1cqQug/gPY9UqyMr66/vJyZGiTf9Gli6Frl0ltNNHnz5w+eUQG1vxc1q0kHRbkAWfDh3g3nvFSfTx3HNixr/3HtSoIfd16AB33SXi0Pfx0PyzWF+8Enn//fdjsVjK3Dp27Fi9g9NoTkK19QEEuO6660hISGDcuHEEBwdX51A0Gs3fzahRMHasxDghIaAeb/lV0pycCgSgu0QAhoWJSGzRQvRPzZoQV8vBVV3chIeD0eLEa3D4w0Xrff8q5xPLr15x5orcRVhNJ19k8oWLBpgDsBgtYPSwYqXC6zVUyQG84WYHNevkYLfaMaYXQmAgFBZyNBi86Wnk5pYXgO3bH7ez33+XF3vJJccdxClxrrVqwW+/wXXXnfT1AGUFoM0mJVM3bZLKOadCerqItB9+EOt06VL5vz75JLz88omf6/HAu+/KDLh7d0mSev55mDFDSh82bCjuYJMm4HDApZdKacTSs27NGeeqe7dwef86vPiM/eQbI8byb7+JQCrdbnLiRKmAmZpatuVKdbJlC/z6a9mCLBWxZKmHlV3rcSh7CYlhiYC8vhP1+evaVT6emZlw2WVQp45cCrt3L9mmSxcJC23UqOS+4cPlPSwshNtvhxdekGiHlBQIDpZbRSxcKO9rjx5Ve+3/OJQq+QL4OwgJOW0n3vr16zEYDCxevLiccREREXFajqHR/F1UqwDcunUrixcvxnyi2ZRGo/n34/HIjG/FihIBeAIH0JdSlu+SZn8+B7CgQLRKdraUUPcJQIfb4d+X0eLAY3D6BaA1L4Mmlr3MKN5HkbuIUFvoSYdc6CrEZDBhMVmwmKTx34pVbsBSpRxAh9vhbwFBQQH06kXGgo1sij9G48yMMg5gXFwlDuCPP4r1crwAPHBAbMjrrpOZa2UCcP58Sar86itpOrZrlzQp8+ELAx08+KTvh5+MDCl3mJgojl1+voi5n36SWW2dOiIOKyI3VxxDh0PG/fPPEvv2wQdwxRUyxtmz5bXNmgUREXDDDZJsNWhQ1ceo+Us4neKy164tfxcVKfacM5iJ6+7jRarWs2TPHrjgAhg/Hm65peT+n3+W9YMdO0py3aqbyZPltLzsMllTKY1SclrabLBk637yOh9iZ/pOvwA8GbVrS7XQZ56RAjKHDpUtFuOjceOyf/fqJbf0dCna26sXLFki+7FaZe2lIsaOlaCAVauqNLx/Hrm5ssr3d5GdDaEnv/5XhXXr1lG/fn26du16Wvan0ZxJqjUEtGXLlhw5lfAljUbz7yQ9XRLdVq7033V8ERilFEqpCkNAnR6nfzW8dm3JkfG5ZTVrikD07ctgdqKKBWAgBVg9RTQx7/U7cz438WT4KoAC4gACGftTuYt3qxQCWuQu8jeBJz8fevVi9FU7yQ4w40zNAEQTZ2WJm1nhpTAjQ2Zyx6+I790rTl6PHmXe03JMnAjTpknM2fLlIs5Kz3BbtRIBWO7FF5UJ18XhgAEDJCnpuutEhU+bJgKyS3ErjyZN5L6HHpIkJx+5uTKDvftumfHPnSuCNChIRJ/FIrFu9epJNYzQUAgKwjt8GF/GHMHZqrk4l5ozxscfS4Suz52evXoHROwjxbq8XEXLyti8WVyqhx+W0xhk7r1wITRoIKfSP4WFC8Vgfv/98o+98oqsP6xYAd5IaZtyOOfU+rYMGiSm9/XXVyz+TkRUlKyt9OsnYaJLlsjff/xRflul5GO+enVJnvS/jpAQOVH+rltIyGkb6vr162nVqtVJt7vpppto27Ytbdu2pV69egQEBOBwVO17SKP5u6hWAfjJJ58watQopkyZwsKFC8vc/nNkZ8skSqM5Gzl2TMJu1q+Xz8HRo8Rt3lemCMzArwYye/fscgLQ6hbR5nKJhkxMlEbJPnfieAfQYHaijCIAo0gHoD57Cc1xEpvHCSuBlsZXARTwO4B1kpfyDM9WKQTU4XGQ48ghxBoiAjA4GFtEEDk2K+60LEB0jVIiACt0ANPTJa9u0aKyG+zZI7PoDh3ESvH1uyiNxyMu2mefwaefSsuHbt3KbtO2rTRR9EqRGtxusWzq15eZZ4MGIvbuvlvCPC+6SATpxIki3I6ne3d47DGxfDweKfYyaJCUaHU6JTx0+XKxM0pjMsks/KWX/Hc9Me8JRs4cycTsP8jYulaG53WT59QVR/9u1q4V0fb44/L3d+tnYXKFE9BwOd9/X7V9bN4MQ4bIZ3XpUrlv8mRxuq68snIBmJ9/+orTVoXCQllDefttEYDHt8b84gsRW48+Cq37iPo9VQF40UXyc8SIPz/Orl3lY+dwSKjquHFyv1JipislH7OsLEmj9T3+r8NgkEWgv+t2msI/jx07xtGjR6tUAXTixImsX7+en3/+mfDwcL7//ntsNttpGYdG82ep1tjLLVu2MH/+fGbOnFnmfoPB8N+roHTTTbLyNGlSdY9Eo/nL+Jy5IEtQ1Z5w7JiIifR0ye/6/HNu+2Epz40ocQCP5B1hf9Z+srNlEuj1giMrnfRXwPnzvRR++wcQT2KitHfwGVk1a4ro8jlvyuQAk5Ps7BIBWNuzl1d/hQILFD1UNQFYkQMY7T5MKDm4TAqoeCJROgTU7wAWFEBwMMHBkGu14s3IAiTU1WSSSfGcORXsLCNDGrffeKM4citWSN35PXtEpMXFlYRinn9+2eeuWCGzwiuvlFDSL7+Ejz4qu03fviL6ZsyQ3/v0kbjUd94Ri3X3btnP009L5Y4JE2DoUBlTZTz8sIjOCRNE9KWmyvgCA0/4flM8IUrOTeb5hc/z9eavWX3bahZuvYT0LauIBF5b8hqLDi5i1jWzTrwvzV9iwwbJORszRlIwl6TMol3hA6wJfYYffzvG00/HnXQfmzfLqepySY7dwYPwv/+JoLLZxPQ9HqXg5pvFSE5LgylTJBfu70ynWrFC1jpuukk+VhdeKCIwIEDGvXevCNkff4SLbtiKOdnMoZwKOrufgK5dZX2jRYsTb/f60te5oe0NRAdFV7iPxx8X079DB3EUQaK6L7hAxrxunazp3H23GPavvSZGe2WcrKCVpnLWrVsHgNlsZvny5eUeb926NUGl3vwdO3Zw6aWX8v7779OzZ88zNk6NpjKq1QF8+OGHefHFF8nKysLlcvlvTqezOod1+nG7ZfI0ebIs0Wk0/3Ke/eNZxiwYU/UnHDsmVkC3bvI5+PprEpLzqJsJXiXuU74zn4zCDL+ZlZ8PQas3kBEIBqcTNWcuAFddJSXY69YV8VcuBNTkBJOTtDSIJIN8gohzHqL/bogtNPjzCU9GoauQQHNZB7AmyVhxYT7BPo53AP0hoEFB2O2QbbVhyMrxG2jh4SJmK3QAMzJkRtysmYRHXnKJVIT4+WcR1CCxeqVDLn3Mni2zQLNZngtlK0+AJBPdfbe8oVdcIdtu3iyz/vr1ZWb51FOQlCR5f199JYlSJ8JmEzvlvvtkxj927AnFn1KKP/b/QVJOEnfMuIPENxPZk7mHNSPW0CS6CbEtzsV04CBKKaYsn8DhjYv954zm9OMTbMOHF9dsGnWYgyziotrX0TC8KetSV5RrWfLoo9LWYPr0kvs2b5YI4xYtpBH6F1/IaTFkiEQN79tXNuz5119FMC5bJp+HTz6R3pw+ofN3sH+/iNxevcQYev55EaHz58vj330ngvDppyXy+Zh3K10Tu56yA1iVoixrj6zloV8f4sftP1b4eMeOsljUtatc+3xTiblyWWTLFjHXO3eW/OiEBE7o1s6fLx/x/9p060zhqwD69NNP06VLlzK3rl27lpnHrl27luHDhzNp0iQt/jT/GKpVAGZnZ3PPPfcQGhqKyWQqc/tPsW6dfAMMGybfaIsXy7ecRvMvJSk3iayirKo/4ehRcateeEHirEJCWNPYzoDd+IVbgauAtIIMfwGY3FyIWLWJBXUhrXld1LZtmM2iewYMEOG0bOtBAgPLhoB6DOIAFuw5QhTpbKU5HqOFmnkQ4zBXOQS0tANoMsg1qa5FVJq5sPIqdZXmABY7gDmWAIw5udSqJdURIyIgJkYcDz8DBog1kZ4u8VwLFshs/IEHZOMhQ+QGlQvApUtLZp29e8tstnnz8tvdfrsUhomIkG0qCk2qWfPEb9bxDBokY3rxRZlBlyLXkcuMnTN4efHLPDjnQbpM6MLQb4ZS5606/Lb3N/bdu485186hfoQ0rG/caQBRR3NYdmgpD317kLe+z2dX+q5TG4+mymzfLusC9evLqVH3svdRuwbSu31dutU9l7gOK8q41UeOSJHWCy6Q09PlElGxY4ecVs2bw5o1cvOdjmFhIg6XLCnZz0svSZ7b6rUuLrlERFdCgtQHmjVL1h2uvlrWIjZuFKE2bhx8842krFbGhAmyj9LtLhctEmHZpo2IqbfflvsNBgnXnDFD/v7lF+nT1749TJqk2Ja2jQvqX1AlB9CrvBzMPljl6+RrS1/DbrXzx4E/Knw8OBjq3zKagPbfU7cuHDmiyMl3MnduybrNkiUiAA0GmWpMmFDxsZSSdZ1Dh/RU5M/yv//9z5+3fvzN6/USHh4OwMKFC7nhhhuYOnUq7cuVedZoqo9qFYADBw5kSelvgP8C+/ZJwsCtt5Zk0M+fL0uMviY/Q4fKN93pCtI/cEA7i5ozSnphepWdNKDEAWzdWsILn36a35sFMHAX/lYQBa4CjuWk+5+Slwcxa7ezqDak14vDtGNrGSMpozCDhu80JN+Zj0d5/ALQrZw0Tfdyy8sNiSWFNKJJDUnEbYCoIsOfygE0GAxYjBbqmMWyMOblVPq80sVmyoSAFjuAWeYgLLl5hIVJ6FlEhPwsIwDXrJE4vMxMeRBklnfXXWJTvPZaSTuH88+XGfKaNSXP93olV+/ccyl+AeLqVZT/Eh4On38ut/j4Kr03VaJ5c7jnHlIL0sgozKDv532p93Y9Yl6L4YE5D7D+6HqcHic3tr2Rg/cfZO3ta1l882Jqh9Uus5vGHfsT5oB3x17OVevctMowsTKp4sI3ixdLKJ/mz7N+PTTrkMaigwvIKspkZ+gnTLzjXrp3h/bx7bE3XM9PP5VsP22auFIvvCCn6OTJIkYCAiRP19cIPTy8xLQGMaO/X7aSfGc+6ekiygy9xjD0x/MYOFBy8954Q07/m2+WFglZWSIKO3USATlmjAiZq66Sz0/pAjXbt0sI6T33yHbnnCMC9aefZH3lu+8kNXbCBFlT8eETgJmZsobRt6/cvzllMw63gz71+1TJAbxjxh3UeasOl313Ysfc5XFx7y/38vOOn3mr/1v8sf8PlFJkFWWRXlByPdxwdAOHa7/KO/tGcPeiyzBeM4TG7zdg3pr9XHGFnPvr1pWk1154oTiCFbXmfHXiNtZZ3+GSYYpffjnpSymrnjWnxPDhw8nKyuKKK66gbdu2tGvXjgJfiWqNphqp1ujv2NhYBg8ezPDhw6nh635azHPPPVdNo/qLdOok34Jt28rvY8ZI2NTNN8u3zOefy3aLFsHAgbKtb8ailCyfHtdP5oQUFsrSa/36VO1KrtH8ddIL0okIOIXEnGPHpL8b+MMRF699mGvmlTiAOYUFbNmb4XfF8tId1Nu8n4VdYHDtaKzfzi4jAPOcebi8Lv8Ku28/DreDhhlgdRXQOXAD6YVRHIvysD5mD62y4MCfcABBwkBrqmMAGHIrF4D+HECPg1xnruTzlHIAs01B2PLTCIkvEYDR0aIRCwsh0OyS2ezmzXJNOFG+HUg83ejRcj1ZskRmytu3y7WkChXq/k5+2vETQ74ZgsVoYUDDAXx68afUCKlB0+jy9f9bx1VcTMEUFk5+aCAffJuH98oriJn8DRt3LYY20vpi+vbpTFg3gZva3sT6ucP48ktxdE5TpfezgrQ0OQdBRI865236fP4S7Wu0p0tCF27o2QuDAdrVaEdO4Mv8/LPUHQoMLMnTM5slR+2FF8RwHjpUHO5GjeSxrl3Lrj+07ZLBbbs689tbkdwXMZ/G3Q18sPElvMpL5AWbGHJvBhdd1JP+/Q0EBkrNIY9Hggkc3gJ6jxvKt1d9QuPYurRtK2LTYBCzPDRUQlJDQsQ4Hz5cvh7ffx/efFPWXm+9teL3wtd64ZlnpLCtL9d4zMIx3NLuFhpGNiSjMIMCV0GlOdCHsg8xacMkZlw1g2FThlHgKsBmsmEylkQ3uTwuFIrvtnzHTzt/Ys2INSSGJTJq1iiGfjuUmTtncm7CuSy6aRHfbP6GSRsmcUu7W3i8x+M8Nf8p7CYXNfMGsfGygVzedxNxlzXnwQsb0WWKdJ2/qc0tmM1Ps327whG5lvY12mMwGPjkiwwe2zQI6/lH8UYcZfb4F/2tOwtdhShU2dfl8VDUuSPGJ57EOnT4qZ9cZzmpx8dLazT/EKrVAdy0aRNt2rRh9+7dLFq0yH9b/E+qD32qrFsnBQ8WL5bV+ocflqXH45ele/SQPJ777pPfFy+WfJlu3aq+2rZ0aUkZ9d9+k2+t3Fy45pqSRmqlOd2reAsWwNSpp3efJ2PixJIGSF99JTFGFb1Wzd+KKz0VU05xGGRursymTpRMcuyYhICW4qDNQXQBeLxumQwZXSRnZviLtYV8M468KDs7ouFYnShsSXsJDygRb752DtkOSRr0CS+nx0ndLNmmE6tIJ4rJw1/m+T5mwgo8VW4DUToHEKQQTJwrRf7IqYIDeHwOYHCwOIAGO4EFBYSEyKQ7MrKkyEV6OtLpGchfvUw+25V1fC7Ngw9KoZh+/SQRa8kSCQ09AxUevMrLuDXjmLB2Arf/fDuPz3ucvZl7WXZoGddPu57Jwyaz7JZlTL18KufXO79C8Xcygpu0JCImEfO4CRSF20la/TtKKVLzU7l66tUYDUZeX/Y669eL41FRiXxNeZSSNgexsSWVN+fNg8zo2QxrNgyX18WkoZMwFCu31nGtSSlKpkn7FO64Q1y97dvFXAb56nG7pY3EyHsKuW7adbgppHHj8gVorfWXQ0YDQo4O4oVfPsHUZzQjOoxgWLNhXPrDxfwY0Ysd2RsIDS0pOGsyiSD75chEDph+5at9YwmLcPPzTA8//gjPPQf9+8Pll8uaa0qKtKSMjZV2kr//Ll8bpfsSKqVIzk32u20BARKO+u678nEC2HRsEzN2zuDJnk8SFRhFgDmAPRl7/NELxzN26VgGNRrEhY0uJN4ez7sr3iX+9XhWJa3iyu+vZN2RdYxZOIbzPjuPcWvHcWenO2kS3YQgSxDn1TmPPGcey25ZxsqklXy46kNGzRyFx+vhkW6PUCOkBuMvHk/no5NIHv8eAfYCVnnH0tGxi0GFXsYPHs8nF33Ce6veIWz4/7j6lwF0HNeRn3f+zMKFMOrHBzmnXgvWj1zLnKw32XRsM8nJ8j4M/nowTd5rwrsr3mXWrll4lZc5z99EwOr1rB4zssrRExqN5p9PtTqA832Z1v8lEhNLlp7vu09ulXH++ZIoMX68rN57vVKya8aMkzdmXrNGYlNuvlmWNYcPl4lfdLTE4HToIPsJD5ey7HPmyFLtH3/IjLOoSELEfLk/pXG55JuvSROJwfFNQNPTZXZwwQViVQwbJjWp168Xp7My1q4Vofrzz3+t829enryfLpe4HsuXSzzQ3XdLKbQmTeT19u0r7+c/laIiqZrw4otVm9z/A3lgajLhkQpuQs6jOXPEsTq+vL+P4wSgUooDlnysXsjNzsIw7ELqd4J8VyoxIXnUtaZQ75P/MX7MRZD/LRmRgXgCgmlm2gmIU+QLQT3eAXR6nDSRu2hUtImvGQoxAWQarNgLCyhyVi385ngHMAAzEUXpZBBB5AkEYOkcQI/XQ4jF7g8BDQ6GDEIIKiwkJARMgXmERJgwmwOJiJCPWJzzMBbAvHmrWIRVLVv+yisSwnn33ZKYdf/9VXven0Qpxfa07Xyy5hOmbptKvYh6tI5tzc70nTR7vxlGg5Gx/cZyVaur/vrBxo4ViycoCHOzlsQe3sU7K94hqyiL7rW7M+HiCdR8vSZRu/fSunV95syR3K1/O0opitxF/lDk082XX0r+3pVXSmRxo0aw9UAKhqK1LL1wJrHBsWW2D7WF0iCiAV2Hr2fcoxfwzTfyNeA7RS0Wuax9/z3kRS7iy1lfclnzy/j444sxxG5lR5qJJtFNANhZuJRzanSjhft6vo+6jO3eXKafu539WfuZsXMGnWp24qcdP9E2vi0A21K3sS1tGxc3uZg3lr/Bw10f5r2V7zFt+zSyi7IZ3Ws0Dz34EI0aicNXURCR3S55hADZRdmE2kJ5dsGzPLvgWVrFtmLjyI2ArN3m50sqK8Anaz7hypZXUitU7MCE0ATafdyOJ3s+yeheozmad5Slh5YypMkQXl78Mp9v/JyFNy7EYDDQt15fHv/9cWqH1abrp11xe900jGzIggMLWJW0CoPBwLeXfusf44yrZ2AxWjAYDPSs05MH5j7A6PNG81iPx8q8lrp1Ye5cM5fHjWTyvMcYo+BS6tKogajWby/9lmuyniE4uyf3dW/OC7PHsemtuhhv+oZvrt9G3fC6XN/men64+GUmzXgad5Nv2ZqynWvrPcpPO35mU8pGbMrEH++lsPe2S+k4cSrnv9uRV4Z/RPfa3fEqLzM/eZi5e+bQ8MJK7FSNRvOPRRcAPglKKUaPHs24cePIzs6mQ4cOfPDBB7Rs2fL0HCA+Hp58UnIDrVYpmf788zB4cEmJ5lmzpFnzdddJMYYjR+Qb+4kn5AYisD7/XEK+6tWDRx4RsdW2rQjNoCB5bNgwaUb0wAOS5HDLLZIhDzL7fOMNmaynpoogbd9e7tu7V/ZZo4aE8JlMkmTw5JPicHbsWFKUAmSpNTpajnnvvRILdNFF8OGHknn/2Wci3q6+WhI3Fi6UrPWKCgB5PPDqqzK+hg2lnNzChdJTzOsVMXj//bL0nJMjy9EffQTXXitL3JMny1jef18qCVx/fdX+N4WF8prefVdEst1+4praVdlferrkhL7zjrxn110nSTQm08lnrB6P/B8aNfrzYzgNuL1ueuxykpZY7ACuWiU/V6wQAfjjj/J+la52dpwALHAVkGlTuIygDh/GvGw5vaNh4PYDpDleJttSi9RGXdncPBpWgcPjJKdWM5q5tuITgL7V6OyibP+4QIRh3SxwY8KsPKQTRUSwg4KiAIyqAG9WZpVeZ+kcQICaBSaMystuUxPOqYoD6HZQ4Cog3Bgk/7tiBzDDG0Kg00lYsJvtsY9TEBQDPEVUlJweB5NX0gCw5RZAYt0qjRWQWfgDD0jS06xZ8tn/G3l6/tO8uvRV6obX5fcbfqdhZEP/Y0fzjpJdlO2f7P9lSp1L5uYteCCwEY1+ewSnx8nca+cSHRTN+bX7MzdmMq/d9iRPPQVpeZmsPLKMbWvmEFSjNkPbX02NkBqyaDV9uoTNd+xIvquApNwkLEYLiWGJmI1mvMrLwgML6ZzQucwiwJnmpcUvMWfXbzxb/3fOO0/5nbhdu6RqbKtWJ48QPp5ly+SjGhgoQSeffSb5eHXryulTr8+vRMS3Kyf+fLSr0Y7EuPVs2nRBhet5V14pt0d+/Q2jwci07dOYOORi+n1xL2uPrOX363+nTXwblh5ays39ruDW9ucx+00bXeM6UT+iPvUj6pPycArfbfmOt1a8xdPnPQ3AhHUTeGv5W/Rv2J8gSxAv9H6BQHMgTaKbEGIN4ZofruG29rfRra+boJbrMAS1B8q/ObmOXB797VHGrx3P2AvG8uHqD/nh8h+45odr2JKyhRaxLTAY5L0Buc58tekrfrqqJPFx/ODxrD+6npeXvEznhM5c8f0VmI1m7p19LwYM/HHDH7SKk/DrCxpcwJStU1h2yzLGrx2PyWDixx0/sv7oeiYOmciRvCPE2UuujVZTSQrIZc0vY8nBJdzW4bZyr6NuXfn5zOBbefPNT4B9NCjc7H+8T/0+PFXjfNI/+5mbbqvPW0s7EHnzJm4+9y7qhsuTH+72MOPXNuPJpO9p52pK4qx3eGfxUMzWu7nkqkxGJjxMnbA/MH7wNd4VHRh9oDYDvhxAa3tfeqWm8NQry+lvtnDjoWcqPFc0Gs0/Fy0AT8LYsWP59NNPmTNnDg0bNuS5556jf//+7NixA7vdfvoO5ItJyc2VfJ5Vq6g/vBM//wxtnnxShM1PP8nkumtXmRD5vqFAQkxHj5bCEZ9+Kt/qzz4riRBPPSVCyGyWb/gHH5THOnaE886TJVuvVxI37MVuxaRJ8txXX5XlUK9XXJ4ePSS8TCkpN2axSLzPVVdJjE3TpjLGNWvkOd27S3Ol7dvhrbckRig1VbZLSpLb009L3E1goOzD1wPSJwbfeUcSN7KyJGO/efOy1QxTUkoSWEBK5d94o4wrJka6/Ho8csxnnhHRaTbLJPDOO2WM/ftLIsyBA/KeXH65LI//+qsI0z17pIDJtGny2kwmef3r1kmJ/oBKJolKSdjq2rUllQVq1pTxT5okY7nvPvm/b9smYnjnTjmm0SjPnz1bmmjNmyfH37+/JDGlGsjetp662WCyFTtpq1eLuFtZXJjjk0+kvNyGDSJKcnMhJYXkIC8DP2rDkz2epEedHmCAtCCwbpHKDd0PQu+DDrYGryfNlUVqfCsKXCLWHB4HuVH1SDxy0D8OXyhnRQ5gnWxYa2vNOY51FAZGUSPYiavAittkwJBZNQF4vANYK89AbkgEWUVRJwwBLZ0DmOPIIcJTPKErzgFM9YQBEGPNpmXHLKKC5Dz3FYIx71qLPRji8ikpAHMqmM1/q/21O2M3H6z6gHFrx7F2xFpaxJZvbhZvjyfefhoLypSmSRNiF6yg7oI0el2YSu+5v0HPLXS0XsP89s9y6aVP8NCHc6n1xiXcsS+MVyensqFxGK2uGsOLbR/imie/xZyWAZmZbKkbzIOds1lW24BbeYgIjODSZpey6OAitqVt4/IWlzN52GQyCjN4c/GHfLriO7JcKUR4GxIdHMnQ2rdxYcOLCAxUHDmiWLRIRFlUnINzOljweozs3i3Cft8+uVz4Qn8bN4aWbZxs2uJh0fwA7JG5bF4TSmAgFPa9neZ1o/hw5SdkFWVx/uMbsY8YzMWpSziwKcH/kSsqErerXTtZVzp0SIz4WrVk3a9tW/m9oEDWBxMS5BLctatcnv/3P1njKnAVMHlyEJdfDg0e/oUBDQdU+va3i2/H2qOreOwk1ex/2/sbIzuO5OvNX5NdlM3CAwu5pd0t9PysJ6/0fYUVSSt4e8DbmIwm3rvwPRpElFSICTAHMKjxIG7+6WYOZh+kdlhtNqVsolOtTmxO2cyimxZhMVl49vxn/c9pFdeKC768gDXJa6gZUpPk3GTu6HgHL/V5iRBbCCDXii4TulDDXoNX+r7Cg3MfpIa9Bhc3uZhBjQcxZcsUno19tszrmLJlCtFB0XRLLIlhPa/ueXSv3Z03l7/JxV9fzPsXvs/lLS5n/Nrx3NTuJiIDS4TnZS0uo0tiF+Lt8TzZ80l2pe/i8d8fJzIwkmtbX+sX9RVxU9ub6FCjQ4V9AZs3lxpPzetGM67lYxD7JMYtm+Q7o3ifPVhEi7XDMLQIokmLEdz5Rlvu7Hqzfx8NIxsyq3cyD1+UzRzrIKLShuPp3JUtr8ykzwAr48N/w/iaVPcxvvoq/S69lIm9nuTy3EI+2v0h3sf+R2BuEa+nJPE1U058Qmg0mn8UWgCehA8++ICHHnqIVsXFFMaMGcP48eOZNm0a11133ek/YEgIXHstrnc+5NChTiyekkybjRtFQLVuLSGfTZqIwCv9xREZCaNGSTxP377i9PkoHeI5fnzZ47VpI8Jxzx4Jsfrhh5KEC5Dw0cceK/OlUq6X2JVXypg+/ljEygUXSIm1xYvFHRo/XsJin35axGhengjNoUNlBjJ3roi4Rx6R/Y0eLQLro4/keU89JY5GmzYVV3aIPu7L8cILZYl83DgRJ6tXi3Ppdsus69prRURt3izCct8+eT+jo6FOHRGPzZuLYB07VgTywIHyeuLjRezl58vrnDZNBOBTT8lz3G55vwMDRVzu3y/u19VXi7O7f79UcFyxQv6fY8bIc849V44dESFi1eOR51x5pYjHFi1kYt+zpziZL754orOo6qxeLYJ8wICy59O2bVIDvnnzcs3Dnb/O5lAo1EhzyNhXrxbB+tFHMqvcv1+E7MyZ4vo+/TR0786wFfezNXUrB7MPkuvIxWaykRrkoHaxALxsCwS5oYFxI6GmfJIiriPfJfXmHW4HecFxxBYXYYFSDqCjYgfwY9O5nMM6vOGRBAQ7sWbayA+2YsjMqtJbUyYH8NtveX5qFvmRieQdCz1pDqDVZPVXAQ3zFn+eAgOx2yHfFYLTaCTKlMVuHDg8IqSjo0Uo2PZvY3UtA4N2VqEAzBkmqyiLvp/3pUPNDsy5dk6F4u9vp2lTCt78DGtUCD+Ns/FB4QMQH0vUDQtRIYeZtPkTsvo/SNMZr/DmxkcwfvYF5zz/POv/CCN93GvMDSrghuut9IjtzLNL3fz23QZMAy9k3wufMe6P+SzZ+jMxyY8SMqUGP11xC/bVDXHajmBL7UKtI4/Tt1kiBTnr2ZScwfP51zBmk40QqxucYdQovIR3lqwlN2IJzFeQH4MlrSNRqUOw1txBSq0JNHRcRlZaKplJaeSv2gAmD+H2BHJdh3m8207y08N5Y98X/LzPhEpvRGRMLPGP3czWrIPsjHyLUbeM5bvv5JI2bpx8TJ1H0/k1ZBgZbe6hRf/h7Nkjl9Ndu6QW2cGD8tWyZ498Tbz5ZsnbmZKfQoN3GrDqtlWs39CYbtPnMKDhtErf/j71+jB26VjcXjdmo0whjuQeITY4ljVH1qCUokFkAzYc28DMq2fy046fGDFjBImhiXww6AP61e/Hy0tepnVca5rHyELesGbDZKFs/Ci57t15J5GBkZxf93y+2/IdD3Z9kE3HNjHlsil0TeyK0VC+fMFzvZ7j7RVvs3nUZppGN2VH2g6un349//vtf7w/6H1AQjkjAyP59bpfMRqMTN8xnb71+mIymri8+eU89OtD3NL+Fn8l2pT8FB6a+xBvD3i7nFAzGU080eMJtqZu9Tt0D3Z9sNy4jAZjmcq2DSMbEm+Pp2PNjicUfwA2s41OtTpV+NjFKeO5qO1a4AOJDBkwQBYtDx+WVBSg6dapfGa6lbYNXby0p4hLupUP1eybWMSsvN78FHAxDaYvp+f462l9SyeWhsSxz9iApldeKRv274+aOYuLevVnRaebiUqykHvd4wTvXkzwiBEnfB0ajeYfiNJUSlZWlgLU0qVLy9zfr18/df/995e5Lzs7WwEqOzv7rx94wwblsQUoOznqjRbjlerSRe6/4QalQKmVKyt+XkaGUt98c2rHWrNGqebNlapbV55/JklKUmr2bPnd41Fq5EilzjlHqbFjlXr3XaVCQpQKCFDqrbdO3zGnTlXq+uuVmjRJqeTkire5+26ljEal2rVTqqhIqWPHlHK7ldq0SamffpKxrlmjVOfOSi1eLONt2VKp8HCloqOVslrl9yuuUOqzz5RKTS27/5wc+fnmm0rZbEo995xSR48qNXeuHGfePHnd4eFKDRqkVOlz6vff5X3p0UOpPn2Umj5dqdxcpebPLzkvHA6lXnpJqY8/luempyv14YdKvfaaUgcPyjYZGTK+4GAZ8+DB8pq6dVPq/vuVCgtTasgQpeLilNqxQ+73epVSSqUO6q1e7I5yGlFq1SqlQGXv3auyY2OV2rJF9nnllUpddJFSu3fLa9m6VdUYW0M1ebeJeu6P59Ta5LUq6pUo9Xs9g8ob1E/lx8QoBWphIkqByreEqnG3LlcXTb5IBT4fqO6ZdY9aetELan6ta/xvxcydMxWjUS8velkxGnXbT7cppZS6ZMIFSoE6L2K8UqD2Tlml5uz8TTV6p5FKqhWqvnr9piqdKvf9cp+6f3bx5/yGG9SmRJtaeecINafuCKWeeKLS5z0z/xkV+Uqkav9xexX1SpTasvAHpQIDlVJyOhl7jVGpATb12d2r1dBvhqprpspruv56ORW+6ROnvuufqPIDTErdJGPNc+Sprzd9XaVxn068Xq/anb5buTwuNW/vPNXj0x5q4JcDlcfrOeNj8XP4sHJjVG+OyVX3d1qkCuwxSvXpo6a1eUa1evq64nPiFbWi6XXqC9P1avJkpfL3HFHzYy9Tiy29VP+eBap/f6XMZqVq1VLqpotS1P7Apmqy4SrVrV2+GjhQqY8unqm8RpNKbXqOemTMt+ryuzeoxx5TyuVSSn31lVImk1ItWqjUhbPVysMr1Yb9G9W3q75X10+7Xn206iO1O323mr9uv/p53TI1ev5o1XtSb3Xx1xer6dumq1t/vFW9tOgl9fn6z9WivSvV8v1r1bebv1UXf32xGjVjlPpp+0+q9tgG6qOpW9T8TVvVfb/cpxiNemTuI8r+ol1lFmaWeTu8XqWcV9+gVMeOcm349Vf/YwcPyiXo55/l0pKRodTLi15RV04errbuW6WUUuq1Ja8pRqNeXPiiWpW0SoW9FKZcHlelb7/b41bRr0arxQcWK6WU2nh0owp+IVhd+NWFKuylMDXwy4Fq6tapqsX7LZRSSv2w9QfFaNSdM+888f+1VSulbr1VrolHjiillPp07aeq/cftVVp+mmI05V57OTwepbZv9/+5LXWbCng+QG1J2aKcjkLV5MWa6oetP/gfd7gdylt8XXN5XGrETyNU9KvRKqNAvgvv/eVeNfSbof5tThc3T79ZvbP8nT+/gylTlAoKku+a/Hy5lr/0klKNGys1a5Zs4/EoVauWeqXPHNXGvFk5TAHynevD5ZLvihYtVP61I5TT4S25//33VVrfK1RccK5auLDkKatXK/Wu5X7ltdvVDW3WqU8+UUrl56tsi+X0zX80Gs0ZQTuAJyCneJXf19DTR0REhP+x0oSGhpKbW3mDaID1R9czaf0k3hzwZuUb1a1LQb3mXHLsZ2q5XmHduT1okJMjxR3q1RO3rSIHwmQSp+oE7kQ5GjaUlhQOhzz/VJ77V7HbJX/Pd0xfLWof/fqJ83n++advXH37ljR2gor3+8QT4u517y7vi8/xq11bbnl58r75uiG3aiUOmI+0NCmM07u3hHFWdJycHHFzL76Yo+YijN5cYs89V47TsaO4okqVlPHPyaHIVcT8kBQGvvWWuHXp6bL8b7VKbmJOjjz3yBH5X5rNkr9pNou9FBoqhUKuv14c1SZNZJxWq4Shnn++hL4ePCjPGzxYigQ99ZTkoO7aBQcPYly1ia+uCmXIITMJX3wBbduSa7WKozlvnhz75pslnPeHH6BPH6hVC6PTSEJoAgV5BaRmphJnieNItJvC/YdIbdIUl9XDD60s1HEGEF6Yyf7gBNyFbhJsCbgKXaTYo4i3Zfo/e7k5uYQSSnZONqGEoooUOTk5RB9zcjQylG0Rdcj0RhDVys7GgiTCCCMtxo3KKqjw83s8jgIHEQHFn/Vjx5jSuzbtbh1An6Bl5OTlVXpOOgucxJnjMDgMqCKFMd9NTkyMf/uGNa0kh4UTZE5DFSlcLpeMOxrSMxTeHAfh53Zmz65C6kRHQ04OP2z7gXtn30uCNYHW8a1Zf3Q9dcPrEh4QXun43V43SiksJkul2yTlJPHpuk+5pvU1/sbrpfls3Wc89cdTWIwW7FY7V7a8klGdRpGXW32Vd/ONIeSGNqB/rSXsCl7OmvoX0HpwJ8Je+53LEx7m/JgIbg/tjzH1VTI/XsAN9+fwgCGINu3GM3EiTAl2AS5SUsQ4Wb7cxuxzf2b4r7czKONciGoLc+eS+8nHWGfN4onVkyVc251OwQvvidM9ZQps3oz1mtto8uGH5LZpQ7canRnQuJ9/nDH1ASLoSXPub19SkOf8mueXe03NIhpQL7AegyYPYt2BdVzaaAhX9UoAoED148fAH3m448OYJ01h+bq5dG0xQJz2pCTpx/DHrxTOmyeW4LffSl420nD9kkuK37d8+Wj+tOlHrv8jk90v92XCmFv5dd+vDKo9iNlbZ+MudDMgcQAFeSculDSo9iC+X/c925K28drS1xjZaiRrjqyhX61+LD+0nEX2RZwXfx45OTn0qdWHJ895kgsbXVj5527HDomUeO45iQb58EN48EH61erHIzMe4ds139LE3gSj00iO8wSf3e++kzSHTz6BgQOpaa3JvW3vZczDPXhltoc5BgOhl7YoM44iSipbvnbea2xL2saUtVO4qtVVrN63mjs63HHS7/VT5c3z5fu/KtehciglkT6vvy5RKr//LgmhgwfL/33FCim5unIlGI20ub8DL6yysLPLtdT29SNetUoqeN9+u5wUb7+EuyiXQt9bce21WK69lic/93LppTk89piEDj/xBDS4+UlyH7iDZtPi+eWXHK64AnLPP1++szQazb+GahGALVu2ZNSoUVx//fWnN4/uNBNaHG6YlZVV5v7MzExqVZCD5Xa7efbZZ7EW9/Hr3bs3fUuLDWBX+i5+3PEjL/V96YTFBQ4068+onE+pn3KM12uE8ARI8Y8KKvv5Qt984TinjNl8RsrFnzI1asjtTBMYWLaAyakSHV1WZBajlEKh8Hg93PjjjXxy0ScER0fz2pwHCbYG82CXB/lj/x8MaTpEwl2P4+vNX/PcgufYc++ekhCo7t1lAti5s0ygvv1WwlSHDAGbTUKC8vLki95ohI0bJSTz9tslb9QXgjRhgiQodehQNhT0nHMkrBckLPTVV9lydV+ORMzhcLiJhG++EbEH0uV53jypxNqpk4QSf/KJv+9fgauAqMAoCt2F5DvzsVvsZAcZsexIJiuhHg/3TiQ5RnHFgRAMmQFkOEModBUSZgvD6XGSZYmhiaukW7ovBzDHIZMo3+cgLC2PpBAoMhuZ+b9FXFyzJs7dm7CarTjsAZhzKhYvB7IOkF6YTvsa7f37939Gk5PJaBIgFT7tdskVrQS3x43daifbkY1buQl2GcsUD6pTM4B8i4lQQy4OjwOPkpzXyEjYvCeTyDwvsXVakGRfRJ3ixafFBxcTbAnmnZXvMP7i8dw/+34uaXoJ93S+p9JxvL38bQ5mH+TtgW9Xus3EdROZsWsGn677lOaxzXlv4HvUi5AG89tSt/HSkpf48pIvibPH0TCy4Z+/xpxG1q8HZ0A7zk9ZjyVjMT8wjFZtmlM/9w0CWrSiRcvWEm59wQV0uSyRuZ2k/lLjxmVrTMXGyq1zZ4BYGDlFmsVt2iT51s2aySJU376S47x7t8RRTp8uj/XtK4sqI0fK59C3gFXc8qNSiookBtO3wFJMk+gmfNFtLE9teU+uAcV0r9OdxTctxury8tDPGWyxTIK3+suizZo1soDzwQeSWzxokISwv/yyfP6cTtmmSxc5tKuIbanbuCStNbYDycw6vJd2hz38796xdJnQhR1pO3h7QOXni49+iefz1uR7WNq0KY83vpUhdfpj7PkkLq+b5h80Z9r2aTzbqySX7uFuD5fdQW6uiL1rrpFERd9CUXCwLKaNGgWXXUZo7doMbzacZxc8S5fELiXPz86W9770tcrrFXF+8cVSCGnFCoiM5PFzHsT54A8cun0AdXelYHrscbneVRJ+OaTJEKbvmM6wZsPYlrqN1vEV96c843g8klYQEyPJnkOHipBbskQW7RITJblz2jQRwV98AcOH07m7heuvh5AHx8KML6TwGMj3xBNPyPdBJd//118v2Qe33y79HRs3hiefMUNwPMHBvzF79u889BCkGOIqfL5Go/kHUx2246OPPqpiY2NVSEiIGjlypNq0aVN1DKNK1K1bV7399tv+v10ul4qJiVGff/55me2qGgL61rK3FKPxh89UxsSRK5QC9VWLYNXmheHK4XaoPEdehds++uuj6pG5j1TxFf278Hq9atOx6js/fKFAp4NvNn2j+n3eT+3P3K8YjVp0YJFSSqmuE7qqS765RH2/5XsV+Uqkf/vSYVhuj1s1eLuBYjQqKSep3L59HDum1HvvKVVQUPHjy5Yp1a+fRGpWiVdflXDQwYOVuucepUwm9e7sMarOm3XUZ10ClQLlXb1azd48W2W/9pqEe7ZvL8+97DIJWd62TTndTsVo1IifRqhbfrxFfb3pa9V1Qlf1Uh+bUqB+HTRUBd/TRTV5rbNaOuQitadRf3XNNUq1/rC16jahm7rq+6vUu9evVNmBsf6hTVw3UTEadfXUqxWjUVd9f5VSSqmnb2mgFtRGUXe+mjxZtv1287eq8/jOanXvZuqnG7pU+FJfWvSSuvCrC/1/X/7d5eq1Ja9JjF1goLr8uVZqyuYpEpJ8ySWVvmX3zLpHdRnfRdnG2BSjUY6ZPyvVrJn/8Vvem6AWxIar1fd/qbpN6Ka6f9pdKaXURx8p1W3YBrUjxqjSpk1W3W9COXduV16vVyW+kag+W/eZMj9nVltTtirDaIPq9VmvE/7r+n/RXwU+H6hyHbkVPu7yuFTca3Fq7u65Kr0gXd018y5V76166utNX6unf39a2V+0qxcWvnDCY1QHX3+t1Pu1X1aqVSvlsQWoNuH7VdKeQuXErAq37pWTPzJSqT/+OD0H3LBBqTfeUOqXXySO8ngyMlR2u3Yq+6KLJCwbJET7hRckbLw0eXkSOh4Xp1TTpkoVFpY8lpQkoaWLK/lu2LhRucxG5bCZlXrwQdnHxo1K/fhjyTbFYX9qzhz5+7PPZDyPPaaUUmrpwaUq4YVo5bXZlKpTR85LUOqHH9SYBWPU3N1zq/SWeD6fpLwWi8QsWywy7qefVurYMTXp4jpqTA/U/sz9le9gxgylQkMljHHYMAkbX7Kk5PG775aw+pwcVbR0kRrwQVf1/ILny76mevVKwutdLqWefVap+Hh5T9u3V+qLL+Sxt99WqkUL+d+lpytVo4ZSEyZUOrSknCRlfs6spm2bpiJfiTzt4Z9/ioMH5Vpcu7aE7N9ZHE77+edK2e1yjc7KUmrnTgkL3btXfu7YUX5fv/6q1LRpSi1YICGjuRVfH0pTVKTUunUl2QtKyWXxnHPkNI+LO40pMBqN5oxQLY3gX375ZQ4fPsxHH33Eli1baN26Nd27d+frr7/G5XJVx5AqZdSoUYwdO5bNmzdTWFjIM888g8Vi4RJfXM0p4qtYuPTQ0hNut5qObGo0jJd7mdlfsIXnFz7P7TNuL7PNXbPuYkfaDhYfXMz+7P1/ajz/dDYc20Dbj9r6KyueSXIduSS8mcDujN2nZX/fbf2O7WnbScpNAiQcGGBn+k4O5xzmYPZBMgozSC9IJ6soi8bvNua7Ld8BMG/fPApcBcQExXAw+2C5fWcWZvL6/Ak0bOLif/+Tin/Hs2SJLLIfOiT1FqrETTfBlCmoTufg+uRTVPsOJJsLqBlSk73hQP36LIrK4/LvL6egfqK4G3XqAJDevh+5UXWhSRPyXfkARAdFU+AqINeRS4g1hEy7WDLHAoKwGoJoUDOSPbd0ZsXQl8jNFdcwIjACp8dJmikOe1Gav0psZW0gbPkOcmyAyek33nyFWZyhwdhy8it8qU6Pk6ScJP/f/iIwGRlQWEhGVLAcI/TERWBcXhd2qx2Hx4HFaMFa5CzjADZrFECu1UCwEgcw31n83kRDauER4vMgtE5jFteBlDg7uzN2cyz/GJe1uIwONTrw6tJXCbIEsfTQUv9zK2LDsQ0YDAY+Xv0xc3bPKff47N2zsZgs9K7Xm8jASN4e+DbXtLqGN5a9wcaUjfx45Y883uPxSvdfXWRkwKGa58KmTbjvf4QNWXX4Y3kA262tCdi0SooPRUf/NRe/NK1bS+TFgAEVt6mJiBC3pW1bKXq0fbv0ZV23Thz088+Hxx+Xz9Lw4eLM79kjbuLIkSVFn2bMkJ++tjzHs307GY0SmTisvhRZGjdOQsRLV3w1GuUY38l1o3Dy58ztXYfsN18mffY0tq+cxS1Z9TBEREgv0oMHpRDVLbfwZIuR9GvQr+JjH4dx/QYMcXFSVXnyZLFl33gD6tSha5KR+1YaqL2p/HXKz8qVEqWwUfru8d134l75eOMNcah69cLWoxez0gZIH7z168UFmzFDnM/ly2X7J5+UaIfp0yVkf9AgOQ+2bxeX66235H8XGSlVsu+7T4p//fyzvJc+ioqoeayAixpfxLOTR9A5tv1JC7VUCbdbnDmlKn7c4xH30uks/9jatZJ68OSTcuHevl2qc4O8zltukfcxLExSE2JixF2+4AKx7I6nb19xD3v2lH1VIQrLZpPTOySk5D6DQQoNPfmkmM8ajebfRbUIQACLxcLVV1/NggUL2LRpE+3atWPUqFEkJibyhK+33T+Ahx56iBtvvJG+ffsSFRXFokWLmD179p8OXc12ZGM1WVlyaAkgIaEer6fcdoeTjcy/ayrbYgrJNu1i6rapbEvb5n/c6XHyyZpPmLJlCuuOriMlP+XPvcB/OOuPrsejPBzNO3rGj/37vt8pcBWwM32n/z63101GYUaVnr8/a7//d6fHyZw9c0jOTeZQ9iEANhzdQGZhJmkFaRzOOcyBbAkr3JG+g/tm30d6YTofrv4QgHVH1tGjTg/qR9T3C8DU/FRefMXJSy/B2yve5uGFI/De1IVHxm7hvfdkjvnddxIldPAgXHW1lxdflMKmU6ZUPhcpzb7caMYduIDceq2xFOVxrGUfMgozqBlSk0ltwTP5W77e/I2MJ6G4YmVxg6pZMTdwYeB8MBj8QiUmOEYEoDOXEFsImXYJPUoOCCDAGEx0UDR7Qtw4mrcrEYABETg8Do6pWIzKKzmWlISA+qqA+hqwBxY4yQ4ATE4CA0vef6vJijvUji23sMLX6nA7OJxz2P+3vw3EoUMQHo47uDgE9CQC0Olx+svOh9pCy4UE1ksMJD8AIkw5RB/NxZ4m+4qKAk/hLkILvVjq1ic6KJpFBxdx9Q9XM6DhAIIsQfSt35cvN35Jvwb9qGGvweKDi8sd/9vN37I1dStH845y37n38dCvD3HR1xeRnJtcZrv3Vr7Hre1uxWQUUWM0GBnTewwrb1vJtCum0bte70pfY3WSng6pDTrDs89iHf04detKoeHD8R1h6VI56S+7rNIQv7+FxER4+GERFk2aSO/T776Tc6d/f8nRio2VSfp338n5MG2arMqEh0uc3ddfi0icOlUq8x7Ptm14mzbmxXOKUDNnoi66qOKxXHopTJtGftJ+zPPmM3N4K368oDbOu0Zy1TUv89Qba6TVzYgREtb95JMicj/9tOqvd8MGyQ0+dEiO17Kl5KItX45n9i/svm04Bl9V54pYuVLEcYMG8noHDiz7uNksF6moKLjySgyzZ2P0KgkPvf9+ET4dOhTHAztl7B9/LNWUQR7/5RcRx3fcUTYkf8AAqQR90UWSIDlmTMmYIiOhcWMmTvWy6JVU7l9QgSD7M8yaJZW5P/wQjh6VUPm0tLKPjxwpIfPH8/77EoOZny955VFRJQtKkZEibmsXVxk1GCRs2eOp2irfX/yMDB0qi4ovvPCXdqPRaKqBahOApWnRogXvvvsuCxYswG638/LxxUCqEYPBwHPPPcfRo0cpKChg4cKF/pYQf4asoizOq3MeSw8tRSnF+ZPOZ/7++eW2O3wYYms4ceMEg5etqVvZk7EHVTxr35a6DZfXxSdrP6HAVfCfFYAbj8kKsc81OxMUFsrC8i+7fwHKCrnvtnzHwK8GVvLMEn7Y9gP13q7HhqMbAFh4YCF2qx2P8rD2yFosRgsbjm1gV8YuzEYzR/OOsidzDwDLDy/niw1fEDt7LosOLGZf5j52ZeyiUWQjaofV5kCWCMXLvrmG0XPe4NnnXby//BPOPfAd7cP6MSalHZsvttPkol947TUxIJoM/I2MG+pwxc3H6NdP9Itv8fxE/PQTvPQSHAqXc35tZB/SC9OpGVKTgzYHXe9vw3dbv8OAgaPBSiazxQ7g0Qwry4/Wxe0WIWcz2QixhlDgKiDPmYfdaierWAAmWa0EmINoFduK9cfW+zWWXwC6HeS6AigKCJNcR6TdA5TvAxhU4CS7AgfQZrLhCg8hoBIB6PQ4SS9Mp9Alj/sbwR86BImJWIwWcaKr4ACGWEsJwIKCMgIwyBKIIVYRF5jLbTOPcOU8eT1RUVAzfzPZYQEQEUG8PZ5bfrqFdvHt+PbSbwHoV78fbq+bc2qewwUNLmDmrplljj1t2zSunHol1027jvoR9XnqvKfYcdcO+tTrw6T1k5ixcwa5jlw2p2xmwYEFjOw08sQnwD+Q9HQIjQ2Q3CWbjdGjYcEC2NvtenHFZswQAfhPICqqpNv6K69Ijm5MjDxWq5Z8CH/7TSbwf/wBDz0kBTwqEmPbthHUqgMHsw9yxfdX8ObyN3F6nGxP2152u27dwGrlyMDu7E4M5rXbp1J39FsEJB3j+d4mjrz8pAhUk8nfLoA77xRx4vWe/DUpJQKwTZuy/Ug7dYI2bWgS3YS2oz+SHLyUCr6XlCoRgCciIkIKi7z8suzr6aelF6xvgbhtWxGAM2fK5+u888qOJSZG3NCK5hOvvSa53nffLRe5/Hyxs+67D7ZuJXz3IQ7360zP3/dU7T05GZMmiSN3773yng0fLu2VfLzzDvTqJWLU16c0O1tyI7/+WnIiq5qn/9Zb8n4d3x7pb8Bkkla53bqdfFuNRvPPotoFoNfrZfr06QwYMID27dsTGRnJhAkTqntYfxtZRVn0rNOT1IJUdmXsIik3qdzKPEhxt4i44spjqc1pHN6CbEc2e4+I+7Th2AZq2GtwOOcwdqud1PwKVoyrSE6ORIL8E9lwTARURe9RjiOHj1aX9KjLd+bjVSf/st6Wuo1Hf32UPRl7Knz82+/c9Lp+CbN2/kKjyEbsy9znf2xH+g7WHlnrDz/04fa6WXxwMZuObeJI7hFumH4DMUEx/jDP2btnM7jxYOKC41iRtIIedXqwKWUTW1O30qFGBwwGAysOryA6KJrxa8ej0poQnHUuNQv78+2Wb9mZvrNYANbhu7kH6d8flu/YS2DH77j40alkpQWw5YchvDHoJfbft59bWt5D1wdfZ+VKWLepiOgbRhIW7uXOX0bx5spXOP+ybXz7bdnXnS1GGp99Jo4hSKvEgwdhe1FdHuNF5uR151D2IeqF10PhZWXyMlxOI63iWpFakCZVRKWqBikpEvmUnAz5rnyCLEEEWYLKhIBmhUiFykNWC0GWIM6pdQ4rk1aSmCh1VgpcBYQHhOPwOCgshIKQOJlxTJxYaQhoYIHL7wAeHwLqCQ0lMLfs/86HT1D6FhvKOICJiVhMlqo7gNbjHMBSIaAB5gCyrQpycwnNcxGeIy5DdDTUd+wirbYIhHh7PDXsNXhn4Dv+YjSdEzpjt9o5N+Fcrm19LZM3TfY7oUsPLeWmH2/i3nPvZe2RtbSJa0OAOYDGUY25ud3NjF4wmiHfDOGCLy/gumnXcX3r64kNjq30dfyTyMgAX3ZARkbZ9og33CBN0C98oZu4PYmJ4mj9G4iMFNfqs8/EUWvWTJygjz8uLzy2bSOkTSdCrCF8t/U7Jq6fyNvL36bdx+38C00AGI1kXDKQgtRkTN98i9VkpUfbIbw19WGu/Gw1CQ896y8K42foUHGNEhPFjToRycmiwksVsClHVJS4mouLHerCQunvOmOGhCbk5VVY6KpCEhJkX6++KqG2AcWFmdq2lTDbDz8U59RYajpjNMLOnbJ6VVHYbnCw9C994w0R31dcIcV/Hn0UmjaFtWtpOm0h1iKXOJsVcf/9IraKi2P5QtPLkZ4ur/u992DLFnH+5s0TYXfokCwQLF4sjme3biJeX3kF2rWT0OGRI0/tfA4Lq7hfrkaj0ZSi2gRgcnIyzz77LHXq1OGaa66hZs2aLF++nJUrV3LjjTdW17D+drId2SSEJlA3vC5Tt04F4FjesTLbuFxicoTF5GE0GIlIHUTvsBGEGGPo1H8POTkSPnhJ00uoGVKTfvX7kVaQVmEoaVV4/vmyfePfWv6Wv7JidbJli2LRzg2EEF8mN8vH/H3zufuXu/0T4AsnX8in604exvTZ+s/4ctOXdBrXqZyQA3hv0xgcl/UnN9/Dda2vY3/2fg5mHySrKIv9Wftxe92sP7oepRRvL3+bjMIM7p7xIAO+HMDQb4fy5cYvObfWuVza/FI2p2wGxAE8r855JIYlsjp5Nb3q9MKAgSlbptA8pjk17DVILUilT70+bEvbhtp3Pi+8ANnr+rD44GJ2ZeyicVRjXGm1Wb//IEOHKjzBh8kJXssS+4Oc63wMe5CJ9u2hZkhNXhh8LyuOLmJX+i6mJb9DTFgIS25ewqqkVczdO5dZiR35Ytkc/xxz6VKZs911l8ylHn/c9z+Qec2SpQbGmh9jxQYrO9J30DJGJm/9Lz1CUVo8sUFxpBaklgnD8i3+798vQi7YGlwiAJ0iANPDrXhsVg5aTARbg+lQowNH844SFJ9EWoYbp8fpzwHMygJneJxMJB97DKdT/nfHO4DBhR4KAqxgcvh1l8PtwGqyYrDbZVJXAU6PCDHfuebPATxVB9AjOYBQcQhooDmQLKsHcnIIzXcTlifjjoqCJq5DZCdI+f+Wzlt4ssXnZaoF28w21t++nvPrnk+P2j0IDwjn550/88uuX+j/ZX9e7vsyb/Z/kzZxbWgb39b/vCFNhnBHhzvYduc26kfU56JGF/HWgLcqfQ3/NK69VkwbkPl0VFTZx3v2lA45vPYaLFt2ZsM/Twc1a0pVTINBcuOKikrazICIwR07MDRvTpv4Njxz3jPsTN/J2GVj6VCjA5d/f7n//F18cDHt689lwoS7aHzuhYBEsjx74au0jK1EtFks4urdcIO0FzgRGzZIReoTVToF+acsXCi/Dx4sOW5PPQU//igC1GaryjsjPPKIhDQWLy4BIgClj4c4mMdTlXPAYBB3LT9fnMSwsJLHLBZxCG+9VW6+8FIfixZJ7OOIEXJNuuKKio/xzTfSnqdRI8nJi4iQ8NXGjaF5c4nR/+MPcSy//16OuWqV/Ny69eT/D41Go/kTVIsAvOSSS6hbty6TJ0/mgQceICkpiU8//ZSOHTtWx3DOKFlFWYQHhNM8pjlTt4kALB2+uenYJrqM74bBAAFhuditds5zvUJC0j0YsxqQZ9nDSy/B+mPraRPfhse6P8adne5Eoaqcm1aaoiKJNtq2TRwXt9fNQ3MfOmmRmtNFVlEW7654t8LHvpl5BJc5g9wNF7A9ubwA3JSyCbfXzba0bTjcDlYcXsGsXRWvXi8/vNz//vy27zee7/kagYYwft7xM90+7eYXavuz9rMuYCydtv5Ogx8P0yymGXNX7mPIZzfwxrI32Je1D6PByKqkVXy2/jPum3Mfw56ZzEd/TMc07RuSUwp5bPYYzNuuoUVMSzalbCLPmcfaI2vpUacHCaEJ5LvySQxL5L5zH+CX3b9Qw9aIhFCZ9PerL0UYIrPPp29fcO7pyh/7FnI07yiNohqxY0VtQhMPMPz6VNw46F67O5GBkcx/42Y2by5ZBI+zx3F5i8sZMWMELy9+mVf6vkLd8LocvP8g866fx4u9XyS706MsXiwhxU8/LalK48bJPPT776Xt3+bNsoC+YIFEKG3YlUJWURbeIxISeuHwdJTDjis7WgRgKUoLwHynOIDB1mDyXfn+HMCsYBtLF39NOk7sNnm8ZWxLduStICJGQjF9IaCHD4MhPk7cg+xsonYeon0y5DrFKfcJQHuhm4LAgApzAC2h4VKUpQJ8DqAvD/CEDmB+fqUr/i6vq2wOYG5umUILgZZAMi1uyM0lrMBDVJ4Xj9eD1QrNCtPISWgEwK5pV3Jgcddy+28Q2QCDwYDBYGBUp1FcPfVqhk8ZzvjB47mj4x0YDAZmXj2T+zrf53+OzWzj7YFv0ziqMV8N+4oxvcdIeOu/hORkqdzv9ZZ3AMtgNMoE+9+MxSKK95tvSu6bPFnESYMGzL12Ls+c9wx96/fF7XUz97q5GDDwxYYv8CovN0y/gds63cFbJ2j/USHh4bICtHChhKD4cDjg6qvlgwziEJ4sfBNEAC5aJGJm+XJx3HbtkjDHe+89tbFdc428J6WJiRF38L77yq8InAp33y0Wcmlx6eOJJ0TY7d4t4aa7iwuCKSUOo8EgPQw3bpRwzYpCXidNEmF9PC+8ICttv/5aIi6NRnlvvv9eHEZjtQdpaTSa/yjVcnUxGAzMnDmTHTt2cP/995drtP5fJqsoizBbGC1iWrDmyBoAjuWXOIBbUrewPnUlcTU8FHpEAN54o7SNyjnQgMtv38ubbynWJW+gbXxb7jrnLvrU74PdaudYXkqFtQNOxPffy/doz56SM38o+xAe5WFX+q7T+KorprAQvlw2hwfmPoDT42TWLJnc+Vi4fRNRhobEWxuy6UB5AegTbRuObmDDsQ24vC7m7ZvnFwI+lFIM/now3T7txroj61h/ZD2PXt6HI/MuY8RPI1l6aCmfr/iZ7ak7OP+z3njX3sTE589hwwZwHK1HjmUnG7OWsP7oevZn7adnnZ5M3vgNt0+/j3b2C1noeBdLxFE+f7Y33QJux2B2suHrYWTvasXmlM0sO7SMhNAEaofVJjFUcm5qhtRkxctj4KdxFK25jFqhtbCZbPSs0xMDBhqYzsNmgwvbt6XI5SI8IJwd66JYNrsOzsCDHMo+RGRgJJ9e/CnTr5iOxWwqNyn+cNCHuL1u2tVoR9/6ZfsSjjxnBNbIo9w/4RumTZM52pdfSu2Jx5/w0GDUA1zyzFfk58vcZP364joK0duJNCXy5XiZZGc700mMs3Nga2y5MOSUFJmz+h1AS3CZENCUwyHs22tid6GdQncBoQFi151T8xxWHF5B3UbSjDoiUIrAHD4MO+yH2NemDgwcyKVvzmbNJxBVXAizRAB6cQQGVhgCGhwZV6kDWHPXUYZtLRGA/hzA1FSIiytxAH2l8PKkn6DH6ykTeuz0OLFb7VjdEGoNkZUVX5EGJAQ00+yGnBzCChTRBfL+ADTOzSU3Xlya3FypF3Ei7u98P9vu3MbGkRu5omWJA1ErtJaIz/8I6eli9ixcWLED+J9j2DDJTXO5RP3ec484aBYLgZZADAYDj3Z7lNcveJ0gSxBP9nySFxa9wE87fiLPmcfD3R7+c9Ura9aUlZ7S8eGvvy7hihMmiNiZMKFs/lpl9OwpIQQ33ihhBQkJIqaio8tWLv0r/PRT1cbyZzEYJBzzjz+gRw/5kgQJ0cnNlaqj334r1TdLh7z62LZNQksvv7z8vgcOlLFrkafRaKqBarny/PDDD/TrV7Vy0/81souyCQ8Ip0VMCwCaRTcrIwCTcpLwKDex9Y+Q58wjxBrCxRdLOkDNwAZY4/dw/sAMMh3pNItu5n9eTFAMP81LpUOHUxvPjz/K4uqFF8rC7t7MvQDsyji5APQqr3/SvWKFRORcf7185/nYu1eOUZoCVwFur5uxY+HZcWtwe92s2ruTiy+WRVhf/tmWI3toEN6IZrVqsT8tmRf/eM1fkMXhgHVJm6gTVoeNxzay4vAK+tbvi8VoYWXSyjLH25a2jXxnPj1r96TLhC6YM1ty7y1xXNLkUrKc6Rj39+G17+ZzwTujaB08gJob3qFFC0nHmPBGXbDl4lUeFuxaw8Gsw7TgcpYnL4UlD7PuxbdRkTvpUac7Qy4MYtrDDzD7upk8cGcYP37SkqTcJKZum0qPOj0A/E5fXFAtFvxhYOS5t7J3dUOCXAlEGGvTKKoR95q30bCm5IG9OMaKOtwJZ3Jjhg410LVFbXLdmWxJ3UJiaCKNohrRILJBhf8fu9XO/BvmM+OqGeUmg4GWQN4e+BbrEm9j+Kej+GSch6AQB4N/OI+2H7clNe5rtiTeQ/3mmTRrJs5LvXpw2cgdFBxsysH9ZkwGE2kFaTSuayd5Vwyrt5Vd/U5JkXSW/fvL5wDmOfPIS7ejPGbGjvVQ5C4gtFitdajZgfXH1pPYIB8jZuxWO4VOBwUF8GnvIF68rg4MGEDinjRcRmiQCXetsxCfJGGZoUVenPZgvwCctH4SybnJ2Ew27OFxBDgqdu5arDnEG3MgKUuqtPodwKwsCA8vcQB9oW/FAvCeX+7h1SWv+vfj8riwW4LZ+CF02pEr5ebr1y95782BpFvcqNxcIgoVMQXy/nhdThpmusmJ6gRUTQAaDAYaRDagYWTDE2/4Lyc9XQogTpsmi0T/eQF4zjlyns2fLxUgBw6UPL1S9KzTkxvb3gjAlS2vpG54XYZPGc5NbW/CarL++WMPGiSWP0hi8PPP4+8tc8stcmve/OT7qVFDhFOTJtKYHCREd9asivPy/gzt2uG3+f9uBg4sEYC7domg7dpVqmyBCMRFi0q2379fihFdd524qxqNRvMPolqXnnbt2sXAgQOJiorCarWWuf0XUUr5Q0BbxIoAPK/OeaTkp/Dhqg95f+X7/gIUoYkH/WFyBoMswN5/QwN2Z+zmijv2Qn4MylHSlCc2OJZZC1I4dMg/Lz0p21K3M39ZNuefL9Wwf/0VVu8RAVi69UFFbDi6gdYftuaOGXewd68UOBs6VCLdOnSQwna+Nlg33VbIuyve8z/3su8u44NVH/Lll5BmERf063mbadlS6gI8/7zkyad799IqsR6dmtQi2b2FJ+Y/xphp31DoKuSltzLYmbGTq1tdzZqkDaxIWkHXhK70a9CPH7eXVZwLDyykS2IXPrroI3qpZ6l1+C7+9z/44IlOtFw/m0+GvYWl0QIOsZilL4zm4sHysRgwAObPjMToCiFg72XkqGQMGJl0301Yf5jO9/c8wRdvN6RJZDN/6GaILYQ+9fswYgTs3hxBuLEWkzZMolHhdUyaBDWCiqvu5UjjpGHDxH3bviqRlJ11SEqCgoNNfJ0UaNwYBjQ7j8SAlhw8CL/8EEntsNpM3TaVxLDEk/6PzUZzpaF+t3W5kp8HbCW+y3xmWm5i4vqJpOSn8GCXB9l81wZqqE4Yz3tZcqsQEyu66XZuHdqEBQskrDC9MJ2YMDsPjYxl79E0fvtNtlVKBOA551SeA5ifFUJoiInMHDdZBflEFAurVrGt2HRsE1F1jmLzRGEz2Sh0OgkNhSW2QyyyHYMrruDju7uwNtFMgwx4dIGHgUtT8CovoUXgCQnxh4A+/cfTTNs+DavJSmh0TYIdqsJ8WVORkzrZYF+7CSiVA+gTgD4H0GSSSWe+WI+rj6xmR/oO/36cHiex+9Nokg6N9xULQN+biIjvXCuoo0eweSDMAQX5WWRsWYMyQI5NBGBe3skF4NlAYaHc+vWTSLvMzBOEgP5XMBplNe2iiyR08p13Tri52Whm9rWzebH3i9x77imGVx5Pu3bSew5kZS8+Hp59VqrZOhwlPeiqQufOEhrpO/+L2yz8KxkwQAS50ynhn8e/juMF4KhRkvtXWV9HjUajqUaqWFf47+G6664jISGBcePGEXyyhPL/APmufDzKQ1hAGLHBsQRZguhdrzc/7viRmbtmYjQY/ZP1wPgD5Dqc/mIS0dHQvWkTXlm3ncBOe7EV1GP5chFeAFEBsfy2NRWjEeau28awHs3KHPvzDZ+z/shGzskay7BhsPrYUvpO6oer0dN06vQoVqsIuG/m7qVBYoOTOoD3zbmPBpEN+GbzN8SseZs+fYIZPVoeS0iQ4mter8xh3p37K/fMvpsb296AyWhi3t55kJ1I8hGFMWENCda2/L55M5cPFUHUubNEDwXX2kfzGt1oXq8mrx45BsrAHwfncc8ve/gs6ycwBNAmcDAv7fqAqHAr1w2/jj71+zBo8iBqhdbinWUfUvj9h7S/dSE96/bEYDCQNOVRXnxC5vBxcQY2TeuPUoondkTQIup87nozliFD5HUMGACPPWagfnBrdi+/AaKXUifRwvQ/AvjmmyEMHiwRQp0zfiLeHl/m/QkLkx7DfUZ8yscv1+e95xqSmQlXPpoArgCS94ZTp46EVyYnQ/bkm6lT52LGjBHB1L59yb6m3fcUbq+bQCmYyXl1zuObzd9wa/tbT/EMLM+g7rVZ0+Z3en/em8mbJvPZ0M+4trXk2rx/3QPcOedm6tR4CTBSuzbs+GMHAxtKGwybSQRgvfB6dGoQQ9CmFN54Q0JFc3JkntSpk6Qy+XIAgyxBFLmLyC7KJi8thMAQM/fc6+Gx6QVEhYoD2CK2BUfyjpAROZvA3R2xmW0UuRzUquNgR+ZezEYz3tAQ5vVIIHb5JtofySMhy0unbTk4PU7CisAbGoI5wInRKK67r/9mRFQCgW5Iz08nKqRsBUxLoeQAtv1jO96nvOUdwCKLv9cgdjvk5aF27ODg4S3+qp8gOYC1F0v7kvq70mQ1o5QADDAHkGsDw6GSnoOOo0lkrN/AsUgTWZlyLczNLal8eTaTni4/u3WT9DGv9yxwAEFWwm68UXJOq/CCrSYrj3Z/9K8ft21b6UOUkiICsHNnabo+Y4acx6Uq2p5VNGsm+ZnbtokAbNSo7OM9e0qeYny89FacN08Wf6ravkGj0WjOINV6Zdq6dSuLFy/GfJZcIH3l6kNtoZiNZpIeSCKjMIOU/BQ2HttIkCWIqKAojMqCMeIgeU57mYll85jmpBaksiJpBfG2+ixbViIAnZkxBMekkNDlEMN/b87uFrtpENmA5xY8x/i14zmSdwS7MZo3n3yN0Fsvo6D2j8QYmmBosgef4frMM9Dimb1c02EAX+380J83dTwer5cVB9fw9QUL2ZOxh4nTp/HeiJIE/REjpJCI1SqLv19lzSEFCS9Nzk3G4XEwa/lurrp+D1MshVi2X8ku7zIuvlmqXXfuLFEzsU/to174tXSqI72m2npGsNE4iS83LsWb2gGL0chPn7QBc3OGNLyEfg36YTQY6VCjAw/MeYDu1rtZcN4gkvc5YO2vTMuUyJ1Bg8q+HoPBwIt9XuScWufQspQmaNNGir+9PGIR3SYZSHO3oX5UAW3alK1gXln4Xdeu8NwNFzD6HhF5RiN8/2ZncE1ldq6BevUknaxpU0hJieSniZF07CjRQr6IKZCJXen/Q886Pfli4xf+fMK/So2QGsy/YT7vrHiHK1te6b9/UPNe3DwrH0fUKiyWc4mPhx1pO7jv3PuAYgewIJ1Wsa2IDoqmyJjGvN8V27cbMJmkyF/nzpICl5KV788BBCl8VDM1BFuUiYaN3bRqX0CT+vJYeEA4iaGJLCuciGPvCCxGKw6Pg8gGu7GZbBS5i0jKScLhcZASF8LFS/MosBpodrCA/NRjhDoguEY419/kxKu8/oq2VpOVwAgJrc1IO1ROAJqLXByqF0mHLRlkFWWhUEQFRpYIwNRiBxD8AtA5agSfpufzwN2H/PtxepzUXLiOBQ0tdFm9R/7JpSyrQHMgOTYwuN1kBoABcKUcwbF5LYejwvyCJy9PDq3Uv6+o5ekkLU3qurRqJSaUxXLyApT/CYzG6nHLwsIkp23dOine0r+/3H98FcyzDaNRLvzr1skXSffuZR+vVUvc2mXLpCrp8OGSU6nRaDT/QKo1BLRly5YcOXKkOofwt7AqaRV136rLnN1zytyfVZRFiDUEs1EEb3hAOHHBcXiUh0M5h9iTuYcDWQcIzmmPK/iAPwTUR4gthNphtZm5ayZNYkUA+nBlxxJVO4WoJtLQb9r26QB8ufFL7j33Xpbdsows91FGPLqfnISpjHLvJnzb/QTU2OvfR9OmEF5/L4eX9MRqsvr73zkckvvv8Xq4YdqNDBm1kkKnkxkTW9An+gZSG79Cp14l+V/R0VJM7oknZOLmSJiLQRnZm7mXaZvmQFpTElrvZvDtq2kY2oqDK9oT0mAzrdt4eGnRS6Rf2pExv3yMI2gv9SPqExkYwcCGA3m40zMYiiIIdzXnsvw/GJj1M998EYRx4lLqJD2M0SCn8weDPmD6ldMx//Ymz0UeYKhxAvb0HgwbJmFkISVvqZ+b291crkS6wSBVMaOiDFx6KfRr3J3WsafWX+zOO2XSOnSozKO2b7WQWHQh06eXpIV17ixV0lu2FPfs2DF/L/UK6VmnJ0CVQkCrSrw9nhf7vOg/N0EE08VNLma7cSq//w4Gg+JwzmFqh0lBkwBzAGkFaditdmKCYnArNx27Z/r7P8fGymJ4+/awcZsUgflpqoi8bEc26UdCCLCaJRzTUoDdWjKrbxnbkhTHYawp57BkgQ2X14EtYTtNo5uSGJbI3sy9FLmLyKgZQeMM2NQghAMxVgyzZmIETGERRMc6yXXkolD+1+NTDlnp5YsKWR0u0hslUivDTVpeCjaTjWC3UaxMXwjocQ6gKy2Fvnuh9/z9KCXHMRU6iFq3gwm9QrEWOMQ1KaXgTEYTRQHyPmcEQqbdjOfYUdi+gyMxNUhLE5crL0+q9J6g40Sl5ObKsP8L+Iq+hIfLfDoq6uwWxGcEXxjo8uVa+JXG976sXVtxHmS7dhL6+c47sqKq0Wg0/1CqVQB+8sknjBo1iilTprBw4cIyt38zM3fNJMgSxOCvB5NekO6vEOjL/ytNsDWYYEswMUExGA1GknKTMBzuSr75ILmOXOwWe5ntm8c0Z2f6Ts5pVJ8VK0p6BbuzYzGEpBJQaycGZeKZr6cz9dfD7M3cy20dbqNJaHtw26jZ7XdibAm893wdMvY0wB1Sthm6N3Qvi39qSG17A7anbcfrlSIxzZvDtf9bxecbJzHP9Aj1glrz62wLrkX3UDewNb0nd+FQdokLMnYsJAz8iuFThpNnOkBo6gXszdzLjK2/Ui9tFEl5B5m//3cuaNaVXUtakWc6SLtP2vLZhs/oXudcJu0aS7Yjm3oR9TAYDMy6Zha9OtbAs+IOnPP+xzVXG+lxTgherxSWW7265DU0jW5Kz/iLWLgQrrkkmmnP3MDkL82MGSOVtf8Mzz8PX9/1KG8OePOUnhcUJEVwXnlFKql37y4O5MGDJVGBY8dKlVeABx+U8NRSRSPL0SiyEXXD69IkqsmfezGnwFUtr+LzjZNo2TGL9MJ0HB4HtULFkfWFgIZYQwi2BhNoDiSkxlFSUuDYMUVMrJycQ4bA1t0SAvrpOBsGJZed1MMhBNrMuL1uf5EYH61ipc3EQ1d34r13bHhwQtQOmkY3pUFEA/Zk7sHhdpCbII7e4YRQlje0Yf5FFl1MYdI7MNshrrvRYMRmtoHZjMNsIDe9/MKTxeHG2bg+AW44tnsD0UHRGLLl+f4iMMc5gConhz11QmhwtORYdQ/l4goLYVWzMLwmY5kCMD4cwdIDLSMQckIseNNSCdp3iKyaDUhPl0UDH5s2wZIlVf+f5ebKPPS/knpUuupnixZnSfhnddO+vTRYz8uTkFCN0K4dfPWVfMh8xV8q4u67xbLWaDSafyjVKgC3bNnC/PnzufLKK+nVq5f/dv6JLqz/Aubvn899ne+jYWRDFh9cTNcJXfll1y/SAiIgrNz2scGxtIlv4w8lLNjRhQzPAakCaitrV/mqh/ZsVZ+iopKKm66UeuRat+MO34Fh62UURC7j7k8/pUONDnzwZigx0UZsRXXY4ZxH8/gGPPwwfPp6fQ7nHfRPavdl7iPLmcEFneoTltGb6Tum89570gLg119hQ/4sTB47RbGL6NO8A8eOwafjbHw25Av61utLr0m9WHxQymBP3TqVO2beQd2wutzV5nHy9rTmj12rOOLexi3nXkGAOYApW6bQo3YP6kTFs/zW5dx37n0sv2U5z57/LHsz9xIZGFmmlH2NGhC1+WnYNpz+/eX7t1YtKZK3Zk3Z93TyZHE0fXNvg0HSMnr3/ov/3D/BueeKo3fZZVIjwBc+6htbZGRJkbjzzxe39URpNgaDgR137aBTrU5/67gB+jfoT9v4tjz5+5MczjlMeEC4Py/VZraR48jx/x1mC8Melc369GXcubUFqefeAYj7eSC5AKsxmLRUA0avvLjUJDuBASbcXjcFroIyArBtfFuaRDXh/jsiKcixogxe8oI2+wWgzwEsrC0hVsl1I9lc04x16UpyrWCzBYkALMom1BZKk6gm/jBah81Ebmb56io2h5uA2JokhUDK5hXEBMdIDKbdDmYzZqO5xAEMDoa8PMx5BbgTahLjMvvbRzQ+VEBe84a0rXMOzvp1yuT/+fDai53IIAM5oQGQkkLsoQw8dVuTni7zS5Dz++mn4bbbTvx/Ukp6RislrvPevRKl9l+gtABs3vwsKADzT+D662HkSBg//tQatv/Xad9eytBeeaV+XzQazb+aahWADz/8MC+++CJZWVm4XC7/zfkvjl0qdBWy/PByzq97Ps2Dz2Pi+omsSFrBzvSdZDuyyzmAIE27W8a0pHFUY8JtEbiPNeFoYXEVUGvFArBxTH2GDYMXX5T7i3Z145h3K0mG5Xj3nUdHw+0cafoMyUt788ILMHcudG9Zh9/3/U7DyIa8/DL071ILk8HEgewDANw/535uansTo24OZ/+0m5myZQpvf5jL6DEuEtvsIqjNLMZeOAaz0UznxI706iVz2+7djHx40YeMaD+C/l/2Z+6euTzy2yO8f+H7vN7/dd4aOppzGtVn5u4fMWc34s4bY2kQ2YBsRzbdancDoH2N9tzS/hYiAiOIDoqme+3u1I8o65wYDJIjeNllkl/YoYMUTOnQAY4cKWkfkZ8vOYhPP33a/q2nldbFUaQVGEMANKi4q0MZ/lKZ91PAYDDwat9XGbd2HPuz9vvbWIA4gIBfANqtdgIjclnqeRu3B/LDVgHFkVKWfJx5QaSmgnKI0PMWhhAUYMajPCUVN4u5tPmlzL1uLoGB8OVncpx041YaRjakfkR9cQA9Doxx8aQHQnKDWLbEmzAfOUpOgAGryep3AMNsYYzoMIKONTsC4Ai0UpBZvmGz1eHBFBzCoSgzeTs2ER0U7c//A8qFgP627gcCC10ENWhKnDvQLwCbHC6ksGVjvh7+NQGDhlTYYNoaFILHbCI70ER+aACBew8RnusiuHln0tJEAAYESEGl+fNh+/YTV/ddtEjcsb59pQLvo49K//r/AqUF4AUXSJs6zd9MzZrSo+6666p7JP8smjeXojw33ljdI9FoNJq/RLVWX8nOzuaee+6pziGcdpYfXk50UDSe1IZMff08uPQqAJJzk7GYLBUKwAENBtAlsQvz9s5jS/IelLceOc5cdmfs9gs+H63iWmE1WUkITeDVV6Uw2aJFcGxvDLWDG7MtZxXBRS/zw6238fraCIpc13DhV1KgrH52Xebt/9XvNJqMJupF1GNv5l5S8lP4Y/8f7Lp7F5EBYB3RFldOE47WfYvfAw9y3XvjMWDgl2t+ITIwkoENB9L4cQlVMxjAgJFHuz+KwWDgqqlXEWAO4KqWV/nHffe19Vk2u4i+TbsRHi6FUwpcBdQMqThJ/tpW17Lx2MZy97/7ruQY+jCbxaC57DKpmD53Lnz6KSQmSg7+P5HWrSU3ruG/pHVby9iWGA1GFuxfUEYABpgDgBIBGGILISA0h/ysbOJyB7DD/iEerweT0YTNXoAzL5j0dBGAlmAb4VEWLCZxAB0eh4RoFmMxWfy5hm1b2WAGJDl2UD+iPkopZu2ehcPtIDwwgmZ3wkVNE9nsWg9AbrEAdHgcZBdlExYQxn2d7/Pv2x1oozArrdzrtDk9GIKDORYThHffHqK7nFtWAJYKAXUHBbJ2/Y/0Beq36UX2ykWsKRaAzQ47KbqyuArvmxWHDIfYQnAE2cgJ9mAIDyJ+2UaO2KFRI3EA8/LkvK5RQ7a328WJP77uhI9vvpFentnZ0hs7KamkZdm/nbS0ks/8hRfKTaOpFiwWqZBaUSK5RqPR/IuoVgE4cOBAlixZQrdu3apzGKeVZ/54hiFNhrB2rYEmAT3ZAcSY65Gcl4zdaiciIKL8c3pJsniBq4DVWzMIrR1CfnQTlh9e7i/J76NDjQ6sGbEGk9FErVqSnzdlihQOuSqhBwd27GDzgsYkRpp4K+F5uLjkuXXCpLJIg4gSi6l+RH32ZOxhe9p2Lmt+mYS9IdU7L3/oHdJ6D2fmbtg8cjM5jhxigmO4vs31AMT0KP/67zrnLt5e8TZ3droTi8niv//cxg1gNlx2jsxgW8a0JDowuvwOirmtQ8Uxby1aVHg3X34pFbiHD4fNmyXv7p9aKMJuF8fy34LJaKJpdFPm7p1L14Su/vt9gq20A2gx5VDgzcaY2h7VyMOB7APUj6iPNTifjGNBKAVmFYTJE0KtWtK/zOP1VFpxFkrcziJ3EfXC65FdlE1aQZoIwIBwUu0QbAkm2+qlqE4tckkp5wCWxhMUiCM7vdxxApxeCA4hMz6MwENH5fysxAHMMDmpm28BnJCQQITDIA6gx0OzIy4OtT5xo+wQawiFgWZy7UYMsWEEph7kyX4mHqlZk8xMOWxIiCwUtGolOaFr1lQsAF0u+O47mDpVFnpAChaWdgCVgjlzpBDRP/VzURnp6RLOrdH8I9DiT6PR/AeoVgEYGxvL4MGDGT58ODV8S93FPPfcc9U0qr/GttRtzLhpBm89B33OqUmjoDGsXxVEcuJMLEYLdcPrltn++++hSxfJ9RnadChrvhpKUmtw1+zE9rTt5UJADQZDmWqV554LTz0lhUMuaNKDH/dOpnZExa6a79il2xY0jGjIiqQVzNs3j08u+sR//znnwL4F3UnN30y+K496EeXzmCoiyBLE+tvXExlYNlEnMTSRiIAIzqtzHgBPnfeUvzjO6cBigQkTpO9c1646TOx00yKmBV9t+orLm1/uv6+iEFATOTgN2WQdiSKhdWMW7F/AeyvfwxSYz9FDwQQFgdUWRFZ2COeeC0eMJhweB17l9e/veCxGWUgIsgQRGxxLTHAMqfmp2Mw2v6MebA3G5XGR37QF+QfSSwRgsQNYGhUchCs3q9xxApxenMF2cmtFU2fTIVkM2Z9VxgF0e90AHCGXho5gsFshMpKQQq+EUh84gNUD3saNyu2/NHarnXybgbxgK1sGtmZiG0WKoYCxMSaUEvFmt0vIY4cO8vfxea4+Fi+W87+0OKxdW1KV8vMlXXHzZhg4UJzBU61M73BI/uzTT1fP3Ld0CKhGo9FoNJq/TrUKwE2bNtGmTRt2797N7t27/fcb/m1L1KW4q+3jJIQmsGaNOFIvXfYkcZ/PwXzuONxet18AgazK33qriL8XXhDxsnGjFAIx1ezEFxu/8E+uK+PccyUiJSEBLmk2BKMRfzuE46kTXuwARpY4gKM6jaLLhC4A9Knfp8z2BgPE2mOAmFN6D3wuYmksJgtHHjzid43+jhw2u10mw2dJW8kzSvMYcbR8FUChYgcQYw5uUw7pSWF0i2jG478/ztG8owQH1CRpezAxMWCPDcaW7+C9J+CKH8wUuKTkZWXnhMFgwGayUTe8LgaDgZigGDIKMwgLCCsRgJZg3F432S0akpW+/oQOoMFux5ObXe44gU4vXnsojsSa1Mtcx7agaMjMLOsAFoeAHvZk0jHXIPlAYWGEFim+3/o9r0ZciQoUkXsiQmwhZAYZyQmzERAUwqKMdYxoP4KgIBFsu3eL2LrkEtn+p59ksQikLURoSW0k1q+XBRtjqY99VJTkEB46JO7Z3Lly/58RgFOnSqVat7vSiNa/FS0ANRqNRqM5vVTrVHn+/PnVefi/hfevvZ/kqdIr9o03ZKLWtn5NNuQn4zLklhFfx45Jzs6wYXDffZIbtmkT3HMPBBdXeDy+CujxNGkix0hIgLCAMH94ZkW0jW/LEz2eKFNZs1lMM2ZePZNtadv+9sIipXO8/i70RPHvwZeLeqIcQLvVjosclC2bgoww2iY059eV32MxWsgnmUN7gmgZAzE1gghzuDCbwWQwnVQAgpw7vqJA0UHRKFSZqrrBVhGAu28awis1lnFrsQDMKsoqJwCN9lA8ucnljhHogqLgELx1apOQAzG2SMjaVzYHsDgEdK87hX6ZhZAYJy0icvIY0GAIkxa+zVBriWtZGSHWEB67viZpdiO9i6ufXtT4IkDyV7dtK+u2de4s96WlSR2KL78UdxCk+ufxodEGg7iABw+KAJxT3JI0KUkWmiri+IbzHo9s//77cMcd8PHHcvznnquwsGkZiopgz56ScRUWQmDgiZ/z3XeS81jayVRKwqWjK48W12g0Go1Gc4pUaxXQ/yILF8KGDTLh8U1+EsNrUqhySMpNKpN/t2OHTPY+/RTmzYPZs6WqZevWItYsRksZsVYRRqNM6BISTrgZIBP053s/X+7+brW7cWv7W0/lZWrOMnwO4ImqgIZYQygiC2y5BBhD6VK3HY2jGjO8uVTjKcoVBzDIEuQPbTYbzRS6CmV/J1ggsJqs1AsX1RFoCSTYIm0UjncAC83gCAksEwJ6fOElU2gopoLCMvd5vR6CXGAJCSMgoR5mBTWKzGVyAAPMARS6CvEqL7scR7DmFfodQNxuHmw7ig27FpFjO7nDHWINYXNADgQEEGwJJsAc4HfgfQLQXsr8j42FRo2kv3RqKowbV/LYli0V96ROTBQHsKBArkuNG4ugq4j334eICLl2LVsGK1ZIBEOdOuIwvvii7CMzU4pDnowXXoCWLaVgy8KFksv48MPw2WfyGkaPlnzFO++U0NT335fOA4MHw2uvwZgxEr66YIH81L3INRqNRqM5fZxxB7BmzZokJ5dffT+e2rVrc9BX1/9fRNOmMGOGiDlr8RywdmwkRmUlwGom3h7v33bHDnHwQEr/+9oaxMQABLDwpoW0jW970mMOGybhWRrN30X9iPrc3PbmMgsYPgEYbA2myFWE3WonOUMURkJ0GEObDmFAw/58tPojvtn8DbiCiIkBZQnyh3mbjCUO4IlcM5vJ5heAIC5gfnZ+mRxAhaLIXYTVZC0TAuoLffYTFExAsqfMXa7CfGwKzPZQIrzSWiIu2yMCsLgCSUxQDKkFqRzJPUK6qbhVjU8AAi0tCRhy8sgOgNqmkziAthDSC9OpE16H2OBYBjQc4O+DmJgorR/OOafsc3r0gLfekl7UP/0kblxUlAjAioojJSaKAzh1qgi588+vWACuWQP/+58sJL3+uoSLZmRIqOj+/eIKRkRAx45ShbdpU9i3r3IXMDcX3ntPCkl9/TWcd544iN99J9fG2rXB65VWatu3y+t8+GF46CHJZf7xR3l84kQRvrfdduK+mBqNRqPRaE6NMy4AMzMzmThxIkqpE26XnV0+R+ffQv36MGpUyd814g0EZNSgQVxomfzGHTvKVre75Zaypds7J5TvH1YRpY+l0fwdmIwmJgyZUOY+m9nmF1tFiAA8lH0IlIHa8XYMBgOBlkB//z2c4gDmWYIwG+XSYzaaKXAXYDFaTpj7G2AOKNMXMiY4hgPZBwiyBGExWvyOYL4rH5vZhs1sqzQHUNntBDjKCkBHbiY2wBoaQbQ7muQQqJXlFAEYIZV74+3xHM07SnJuMqaQUKA4Gc9igeBg7IVu6hsjybFlVMkBLHAVYDPZuK3DbdzU7ib/Y4mJsqBjPy79t0cPKXQ0YgRMmgQ//wz9+knLiIqqZLZuDZ98ImLqkUcgOVlyC4/n559h0CARYJ06yULUhg3yvONqc1GvnlTaffRR+Pbb8hVFvV5xCJs2haFDYcgQuP12EZ8+jBXEndxzjzR4NxjEHfR64cMPRZDeeecJ30qNRqPRaDSnyBkXgHFxcVWq8Hl8VdB/M/HxYDpckwaRccyfL5OaGTNEAA4YULLdLbfITaP5NxBgDihTpMhutXMo5xBmTyiJCSWz/HY12hFiCSW3KIKYGAgMivFX0zQZTOQ5806aH/rlsC9pF9/O/3dMUIx/DAHmAIKtxQLQmV/WAaygCqghOJggR9kKtO68HACs9jBiHDEkh0CzjLwyIaDx9ngyCjM4kH0AW3gUkON3/wgLg+xsGppjybFlnDwHsDi312a2YTQYywjG2rWLtzku/dfX4qFvX8kV3rJFXLoGDaTgy/Hcfbc4iRs2SD/vL7+UkMrjmTFDcpA7doSrrhLBFhdX+djfektE4pNPSvTBp59KcZgFCyQ/8MgRmDWruD+oAfr0qXxfPo7P3TUaRfhp8afRaDQazennjAvA/fv3n+lDVjvx8aByatIgoi6//SYuX2amCMB77y3Z7l9c/FRzFmIz2coIwBBbCDmOHIJVberWLdnObrWTdP8xwp4MIDoa7j/vKX8EgNkoVUBP5pgd74ZHB0X7x/DMec/4W6P4XLUTVQHFbiewtAOoFM7cLArNYLUE0CSqCdvrt8B8NEVss9hYQFxHAwbWH11PcEQcsK+kHGd4OGRlUdsQwcYq5AD63reKWl8kJvqHWYZ69UT4NWwoOX8zZ4pQa9my3C4AqYb7/feiYa1WqTZ8fAhocrLk+PkWoiZPPuGwAXk7fv5ZQjhfflkKtCgl+X1PPgkjR/pNU41Go9FoNP9AdMH8M0B8PHgXPMHI10IZ9bqEN02dKnk0FRVv0Gj+DdjMtnIOIEDtuDDuP64YbUigiL+YmLLiqKoC8HhigkSMmY1mHuz6oD+PMN9V4gA6PA4KXAXlHUC7nSBHqRD0Xr2w9uhEgQUCjSZCbCF06nixWGzJyf4EO7PRTExwDOuOrqNdZHEur08AFjuANb3BLLFJyOyJ8BXBqcj59AnAinru+cRe8+ZSLMVmkz6ilWG1+vVrhQJwwQLJKTzVKptt24qzmJIiPQYvvFBCSB9//NT2o9FoNBqN5syjBeAZIC4OCva0I94Gq1dLns0DD0goV61aJ3++RvNPpJwDWCxqouxh/sjI0oweXV6s+NpAnLIADI4hwBzgzxv05RQeHwKa58wr3wYiJJQgp0IpheHwYVi4EEuIjWxrKQu+Rg346CNRY6XsrHh7POuOrKNXjWFyx3EOYJw3AHdI8EnH7w8BPQUHsDTNm8OBA9IT8OGHT3o4QK41ubly84nLNWsk9PPPYLPJWBMSJB9w8OA/tx+NRqPRaDRnFt0G4gwQGSnhWMuWyeTr/vvl56OPVvfINJo/T2UOYGWtS0aN8lW4LcHXBqIiIXQiooOiy7hnPgGY7cgm2BKM1WQlozCDPGceMcFlD2oMDsHuBK/ySjlNwLx3P4WWUgKwZk2J027Vqsxz44LjOJJ3hIiYYpV2XA5gSJHi0QtfOOn4fWK5IuEbHCyasyIH0EdsrFxXCgokH68qhIdLL77SLuCaNVV/fmUYDHD55Sfv86fRaDQajeafQbUIwFmzZlXHYasNo1FcwBkzpDLfkCFS4a5Xr+oemUbz5+lZpyfXty6J9fQJwHI5dyfA1wbiz4SAlhaNRoMRo8FIVlEWdqtdKpO6i7jnnHvKtF4BcQDtTqSp+/TpUKcO1n0HKbKWuhzWrCk/W7cu81zfvqJj68odxzmA5ORgjTh5POWJHECQnLxGjSp/vsEAzZpJNIGtitrZYJAKxTt3yt9eL6xdC+3bV+35Go1Go9Fo/htUiwC88sory/w9ffr06hjGGSU+Xkq39+kjPa3uuEMXfdH8u2kd15ob2t7g//vPCMA/mwPYNLop7WuUVS5mo9kvABNDE7mx7Y282OfFcs81RkcTWQgujwuWL4err8ZU5MBRWgD6qhAf5wD6BGBcRKLY+sflAJKdTYXxr8dxohxAkGIsbdqceB9dukD//ic9VBl8uXsAe/dCUVHlRWQ0Go1Go9H8N6kWAXh8D8Cbb765zN8XXnjhmRzOGSG+2IR45JHqHYdG83fh6wt4fNGVE+HLATxZG4jjaRLdhFnXlI0k8AnAEFsIYQFhTBwykUBL+bhEU3xNwh3gSTkmTfSKO647bKUKt9SoIdZ9JQ5gjdCakqRX2gHMyJCkvNCKQ2BLczIHsCq89ppU3TwV2raVqp8gDd9btZJCMRqNRqPRaM4eqkUAHt/w+XhBuHjx4jM5nDPCBRdIr6zj+11pNP8lQm2hZ8QBrGxfPgfwhNvFxuMxgNqwXtRPcZVPh7VUTSybTeIjix/z4ROA8fZ4+OADKaEJ0qNh374qC0Bf4/pTFb5/FZ8AfPZZ+N//JB9Zo9FoNBrN2cU/ogro8YLwv8g991T3CDSav59QW+ipOYBGEy6v67QJwMyizJMKQIs1gGPBELRxo1jzCQkAOG3HtW6oIAYz3h5PZGAkAeYA6Zruo2lT2LYNCgurFAJqMpoIsgT9JQfwz9CmjYR+vvYazJsH5557Rg+v0Wg0Go3mH0C1OIAul4sFCxaQmZlZHYfXaDR/EyHWkFN2AOGvhUKW3ldVHECjwcixYDBt3CICMDAQR2gwrgDLSY/RvXZ3pl4+tfwDjRtLOKnbXSUHEOS9Oh3C91SIiZF2EO3aafGn0Wg0Gs3ZSrUIwLp169K7d2+io6OpXbs2BQUFvP766yxZsoSioqLqGJJGozkNjDl/DH3r963y9iaDuG6nQwhZjBa8yntSAQiQEmLAsmmLPzm3IDYCl+3kAtBqstKrbq/yDwQGQt268ntVBaAt5IyHgALccguMGXPGD6vRaDQajeYfQrWEgG7bto2cnBxWrlzJqlWrWLlyJW+88QZHjhzBYrHg8XiqY1gajeYvMrjJqXUD9zmApysEFKiiADRi3bQHup8HQH5MGK6A/L82gKZN4ehRsJxcSII4gGc6BBQk/0+j0Wg0Gs3ZS7XlAIaGhtK3b1/69i1xCw4fPsyKFStYtWpVdQ1Lo9GcQUxGcQBPhxN2KgIwLcSEwe30O4CpDWuSYUz+awNo2lQKx1SROuF1yvUo1Gg0Go1Go/m7+UcUgfGRkJBAQkICw4cPr+6haDSaM4DfATSeWQcwLdQMlAjABaMuZNHBRYz8KwNo2rTK4Z8A066Y9leOptFoNBqNRvOn+EcJQI1Gc3ZRXSGgGaHFYZrFAtDpcf71MQwcKEVgNBqNRqPRaP7BVEsRGI1Go4GSIjCnIwTUYhJRVyUBGFYs9ooFoMPt+Ov5eImJMGrUX9uHRqPRaDQazd+MFoAn4PDhw1xzzTVERUUREhJCixYt2LhxY3UPS6P5z/B3OIBBlqCTbnu8ADwtDqBGo9FoNBrNvwAtACshIyOD7t27Ex8fz86dO8nJyWH69OnEx+uiDRrN6cJXBOZ0CcBgSzBGw8kva2lRgeTXrQU1agDg8JwGB1Cj0Wg0Go3mX4DOAayEN998k+joaF5//XX/fY0aNarGEWk0/z1OtwNYlfBPAEeQlUW/vsuAgABAO4AajUaj0WjOHrQDWAm//vor9evX55JLLiEyMpImTZowZswY3aNQozmN+HMAT4P7dioC0GK04PK4/H873I5qacqu0Wg0Go1Gc6Y56xzAG2+8kUmTJlX6+Hnnnccff/xBWloaK1eu5PPPP2fKlCls3bqViy66CKvVyqOPPlrhc0NDQ8nNzf27hq7R/GP5s+e9t8hLKKGYXWZycnL+0hiCvEHEmGOqtJ8QQijIK/Bv6yxwYrPY/vIYNGcn+rqvOZvR579G8+/jrBOA7733HmPHjq30cYtFKgmGhobSqVMnrr32WgDatGnDqFGj+OGHHyoVgG63m2effRarVULJevfuXabRvUajKYsvB9BitPzlfVmMFoItwVU7rsGE21vSsiG9MJ32Ndr/5TFoNBrN2cBvv/3G77//DoDT6azm0Wg0mlPlrBOAdrsdu/3kYWLt27dn8+bNp7TvgoICxo4dS+gpNIPWaP5LnOq5H2QPIoccgkKC/vLnxmv1YjFbqrQfr9ULNkj3pFM3vC6HHIeoGV1Tf3Y1fwl9/mjOFoYNG8awYcMAyMnJYdy4cdU8Io1GcyroHMBKGDlyJGvXruWbb77B4/GwZcsWPvroIy6//PLqHppG85/BlwN4povAmI1m3F43XSZ0YXXyao7mHSXOHveXx6DRaDQajUbzT0cLwEro0KEDU6dO5YUXXiA0NJSLL76YkSNHcv/991f30DSa/wzVVQXUYrLg8rrIdeayJ3MPKfkpxAVrAajRaDQajea/z1kXAnoqDB48mMGDB1f3MDSa/yw+AXjaqoBaqu4AujwuitxFbDi6AbfXrR1AjUaj0Wg0ZwXaAdRoNNXG6WwEbzFZTqkNRKG7EK/ysip5FcGW4Co/V6PRaDQajebfjHYANRpNtXE6Q0AHNhxIdFB0lba1mCzkOqR0+erk1dr902g0Go1Gc9agBaBGo6k2/I3gT0MT9itbXlnlbc1GM3nOPACyHdk0j2n+l4+v0Wg0Go1G829Ah4BqNJpq43Q6gKeCxWjxC0BAO4AajUaj0WjOGrQA1Gg01cbpzAE8FcxGM7nOXP/fugKoRqPRaDSaswUtADUaTbVxOquAngo+BzDEGkKAOYB4e/wZPb5Go9FoNBpNdaFzADUaTbVxOhvBnwoWk4VcZy6BlkDibfHaAdRoNBqNRnPWoB1AjUZTbVRXDqCvCEyAOYBb2t1C99rdz+jxNRqNRqPRaKoL7QBqNJpqo7pyAC1GaQMRYA7g0e6PntFjazQajUaj0VQn2gHUaDTVhj8H8DS0gTjV4/ocQI1Go9FoNJqzCS0ANRpNtVGdOYBaAGo0Go1Gozkb0QJQo9FUG3H2OD4a9BGB5sAzelyLUYrAaAGo0Wg0Go3mbEPnAGo0mmrDaDBye8fbz/hxzUYzbq9bC0CNRqPRaDRnHdoB1Gg0Zx0WkwVAC0CNRqPRaDRnHVoAajSasw5f8RktADUajUaj0ZxtaAGo0WjOOixGcQBtpjNbfVSj0Wg0Go2mutECUKPRnHVoB1Cj0Wg0Gs3ZihaAGo3mrEPnAGo0Go1Gozlb0QJQo9GcdfhCQLUA1Gg0Go1Gc7ahBaBGoznr0CGgGo1Go9Fozla0ANRoNGcdOgRUo9FoNBrN2YoWgBqN5qxDO4AajUaj0WjOVrQA1Gg0Zx06B1Cj0Wg0Gs3ZihaAGo3mrEOHgGo0Go1Gozlb0QJQo9GcdfhCQHUjeI1Go9FoNGcbWgBqNJqzDh0CqtFoNBqN5mxFC0CNRnPWoYvAaDQajUajOVvRAlCj0Zx16BxAjUaj0Wg0ZytaAGo0mrMOHQKq0Wg0Go3mbEULQI1Gc9ahQ0A1Go1Go9GcrWgBqNFozjp0CKhGo9FoNJqzFS0ANRrNWYd2ADUajUaj0ZytaAGo0WjOOnQOoEaj0Wg0mrMVLQA1Gs1Zh78RvFk3gtdoNBqNRnN2oQWgRqM56wgwB2A0GAk0B1b3UDQajUaj0WjOKFoAajSas46IwAg2j9xMsDW4uoei0Wg0Go1Gc0bRAlCj0ZyVNItpVt1D0Gg0Gs3/2Tvv6KiKvw8/25Pd9Ep6IIQQeu8oTSmKBUGxgL0rYm/87Iq+9gKKYu8igigq0nsnoQQIBNJ7zybZvvv+MSQhpNIM6Dzn7Ely79y5czd3785nvk0ikfzj/CcF4O7duxk/fjw+Pj4oFAoMBgMKhQK73Q5AQUEBN954I9HR0Wi1WtRqNVqtlsjISL766qs2Hr1EIpFIJBKJRCKRnBr/SQGo1WqZNGkSL774IgB33313vf2VlZXExcURExPDBRdcwLJly4iLi2PcuHEMHDiwLYYskZzzrFixoq2HIJG0GfL+l0gkEsn5wn9SAMbHx3P77bdz//33AxASElJvf4cOHRg4cCCbNm3iu+++Y/To0dx6661s3bqVuLi4thiyRHLOs2rVqrYegkTSZsj7XyKRSCTnC+q2HsC5yvLly2nfvj2vvfYa3333HWVlZYSFhVFUVERAQEBbD08ikUgkEolEIpFITpp/lQC86aab+PLLL5vcf+GFF7JmzZpW9VVUVMSBAwcYNWoUt912G/PmzSM0NJRp06bx559/NmjvcDjw8PDg0KFDeHh4nOolSCTnJdXV1VRXV3Pw4MG2HopE8o8j73/Jf5nKykpAzIMkEsn5wb9KAH7wwQe88cYbTe7XaDSt7svLywuFQoGHhwdffPEF69evp6SkhKFDh1JdXY1er6/XPiUlBafTydChQ2u3qdVq1Op/1VsskTSKh4cHdrudhQsXtvVQJJJ/HHn/S/5r2O322sR5NaSkpNC/f/82GpFEIjkZ/lXqxMPD44xZ33r37o3L5eKnn35i/fr1REdHs3nzZhQKBS6Xq0H7jh07Ul1dTWZmJl5eXmdkDBLJ+YLRaOT5559vdgFGIvm3Iu9/yX+ZiooKIiIi6NixY1sPRSKRtJJ/lQBsLS6XC4vFUuuuYDabAaiqqkKn06FUKvn9999Rq9WMHj2adu3aUVxczHPPPceECRMwGBoWj1apVICwHEoBKPkvotVq5b0v+c8i73/Jf52aeZBEIjn3+U8KwPT0dNq3b1/796xZswDw8fEB4O233+ann35Co9Hw2WefMX/+fBQKBSqVivz8/LYYskQikUgkEolEIpGcNv9JARgdHd2oG+fxzJw5858ZjETyL2HUqFFtPQSJpM2Q979EIpFIzhf+k3UAJRLJmWfMmDFtPYTTIiE3gWmLpvHyupcb7Pt2z7d8tOOjNhiV5HzhfL//JRKJRPLfQQpAiUQiAX5N/pVVqav4aGdDobcqdRULD8gMjxKJRCKRSM5/pACUSCQSoLi6mOGRw8kx5mBz2Orty6nMYW/+3jYamUQikUgkEsmZQwpAiUQiAYpNxXQP6o4CBdnG7Hr7cow55Fflk12Rzfr09W00QolEIpFIJJLTRwpAiUQiQQjAIEMQYV5hZJRn1NuXa8xFgYLHVjzGBV9cwLwd89polBKJRCKRSCSnhxSAEolEgnAB9df7E+UdVU8AWh1WCqsL6RPSh+/2fse0HtO49497KTWVtuFoJRKJRCKRSE4NKQAlEokEKKouwt/dn0jvyHoCMK8yDwUKRrcfjUap4a2xbxFoCORA0YE2HK1EIpFIJBLJqfGfrAMokUgkJ1JsKiZAH0CkdyTpZem123ONuQR7BHN558vRqDQE6APoEtiF/YX7GRIxpA1HLJFIJBKJRHLySAEokUj+81gdViqtlfjrhQUwIS+hdl+OMYdQz1CGRAypFXzxAfEcKJQWQIlEIpFIJOcf0gVUIpH8q/lmzzeUmEqabVNcXQyAn7tfgxjA3MpcQjxC6rWPD4iXLqASiUQikUjOS6QAlEgk/2oeX/E4GzI2NNum2FSMp9YTrUpLlE8UaWVpOF1OoM4CeDw1LqASiUQikUgk5xtSAEokkn8tLpeLgqqCVlkA/fX+AMT6xWJ32jlaehRoXADGB8aTXp5OlbWK59Y8x6e7Pj07FyCRSCSSc5b58+ejUCgafRkMBhwOR1sPUSJpFBkDKJFI/rWUmkuxO+21Lp5NUWwqxt9dCECNSkP3oO7syt1FjG8MW7O3MjZmbL32wYZgAvQBbMnawoc7PmRip4nc2ufWs3YdEolEIjn3SEhIwGAwsGLFigb73N3dUalUbTAqiaRlpACUSCT/WvIr8wFaZQEM0AfU/t0npA+7cnfRwbcDmeWZTIybWK+9QqHgpp43ccfvd1BQVUBmReaZH7xEIpFIzmkSExPp2rUrgwYNauuhSCQnhXQBlUgk/1oKqgqAVghAU50LKNQJwC8Sv+Dqrlej1+gbHDNj4AwyyjNqRaJEIpFI/ju4XC727NlDjx492nooEslJIy2AEonkX0t+lbAAFpuKMdlMVNmq6ln6aqgpAl9Dn5A+PPz3w2zK3MTqG1c32neEdwTvjXuPIEMQN/16Ey6XC4VCcXYuRCKRSCTnFIcPH6ayspKuXbtit9vr7VMoFNL9U3JOIy2AEonkX8vxLqDPrXmOwNcDuXrB1bUZPmtILUutl+ile1B31Eo1741/j/5h/Zvs/+7+dzOmwxgqrZWUW8rPzkVIJBLJfwSXCyoqzt7L5TpzY01MTATgwQcfRKPR1Hv169fvzJ1IIjkLSAugRCL511JQVUCgPpASUwkppSk8PvRxftj3A3O2zeH+gfcDogj88iPLmTV8Vu1x7hp3Ch4pQKfWtXgObzdvPLWeZJZn4uPm02B/SQnY7RAUdMYuSyKRSP6VGI3g7X32+i8vBy+vM9NXYmIiCoWCDRs2oNVq6+3z9fU9MyeRSM4SUgBKJJJ/LflV+cQHxpNWloZKqeK6btdxUYeLuPLHK7mtz238mvwrChR4u3nTq12vese2RvzVEOEdQWZFJt2DuzfY9+abUFYGc+ac5sVIJBLJvxxPTyHSzmb/Z4qEhAQ6dOjAkCFDzlynEsk/hBSAEonkX0t+VT7xAfHsyt2F2W4m0juSfqH9CPEMYfaG2by47kVUChV39L3jtOL3IrwimkwEU15+dic0EolE8m9BoThzFrqzTWJiosz+KTlvkQJQIpH8aymoKmBU9CgqrZVUWiuJ9I5EoVBwXbfreG7tc9zS6xY8dZ7c1Oum0zpPhFdEk6Ugqquhquq0updIJBLJOUR+fj55eXkyA6jkvEUKQIlE8q8lvzKfzgGdAdCpdAQaAgG4rvt1/N+m/+PxYY/Tyb/TaZ8nwjuCI6VHGt1XXQ2Vlad9ColEIpGcIyQkJACgVqvZsmVLg/09evRAr29YPkgiOVeQAlAikfxrya/KJ8I7AoPGQKhnKEqFSHwc6x9LwSMFGLSGM3KeMM8w1qWva3SftABKJBLJv4uaDKDPPPMMzzzzTL19CoWCkpISKQAl5zSyDIREIvlXUmoqpdpWTZhnGP56fyK9I+vtb078uVzQsycUFbXuXKGeoeRW5ja6r6pKCkCJRCL5N/HEE0/gcrkafTmdTnx8fNp6iBJJs0gBKJFI/jUsP7KceTvmAaK2n6+bL95u3vi5+zUQgM1hMsGePZCc3Lr2oZ6h5BhzGt0nLYASiUQikUjOJaQAlEgk5yV2p513t7xbr6j7r8m/smD/AgBSS1Pp4NsBAD93P6K8o1rdd03MXkZG69qHeIZQZi6j2lbdYJ+MAZRIJBKJRHIuIQWgRCI5L9lXsI+Zy2by95G/a7cdLDpYm43zaOlR2vu2B+CWXrcwMW5ik30lJ0PmcUk8T1YABugDUCvV5BobuoFKF1CJRCKRSCTnElIASiSS85LEvEQA5u2cV7stuTiZjPIMXC4XqWWpdPARFsDre1xPn5A+Tfb1zDMwf37d3zWCLT29dWNRKpSEeIQ0GgdY4wLqcrWuL4lEIpFIJJKziRSAEonkvGR33m4mdprI0kNLyTXmUmWtIqsiC7PdTLGpuJ4FsCXS0sBiqfv7ZC2A0HQcYHW1EH9mc+v7kkgkEolEIjlbyDIQEonkvCQxP5FpPaZRai5lSfISBoQNwMfNBwUKMsszSS1Lpb3PPycAQzxDmhSAIKyA7u6t708ikUgkEonkbCAtgBKJ5LzD5XKRmJdIr3a9mNhpIr8f/p3k4mTi/OOI9I4krSyNtLK02iQwzVFdDQUFYLXWbausBJ2u9S6gAKEewgLodDmZtmgaRosRmw1stro+JRKJRCKRSNoaKQAlEsl5R0Z5BkaLkS6BXbi006WsOLqCXbm7iAuII8I7gs1Zm7E5bK0q/VAj8k60AHbuDBUVUF5ev73LBW+8AQ8/LPbXUFML8GjpUb7Z8w3Jxcm11j+FQiaCkUgkEolEcm4gBaBEIjnvSMxLJD4wHje1G/EB8YR5hvHm5je5uMPFRHhF8FnCZwyOGIxOrWuxr7Q08fNEC2BYGHh6NnQDLS6GRx+FefNg9+667TUuoAm5CQBkVWTVCkB/fykAJRKJRCKRnBvIGECJRHLesTt/N73a9QJAoVCw7IZleGg9CPYIJrMik2JTMbf3ub1VfTUlAD09ITwcsrOhe/e6fTXtOnSAkpK67R39OrKvYB87cnYAkF2RTZUaNBrw9pYCUCKRSCQSybmBFIASieS8IzEvkaERQ2v/jvGLqf090jsST60nU7pMaVVfaWmgVNZ3Aa2qAg8PIdyOd/MEEdOnUEBgoLAG1jAkYghOl5PPEz9Hp9KRbcym2g30etGXjAGUSCQSiURyLiBdQCUSyXlHYl4iPdv1bHTfxE4T+fP6PzFoDa3qKy0NoqMbWgANBvDyalwAajTCrfN4C6BaqWZS50kUVhcysv1IIQCrhQA0GKQFUCKRSCQSybmBFIAtkJ+fz3XXXUdwcDA+Pj4MHjyYtWvXtvWwJJL/JJnlmSTkJpBalkrP4MYFoKfOk6GRQxvdByLr58KF4vfnn4clS2DYsIYC0MOjcQFotQoB6OdXXwACXN31apQKJeM7jq+NATQYxOtI2SGmLGidVVIikUgk5w8ffPABCoWCpKSks9L//PnzUSgUtS+lUklQUBBXXnklqampDdrn5uaiVCrZsGHDWRmP5PxHuoC2wD333ENBQQH79u3Dz8+Pd955h0svvZT09HT8/PzaengSyX+K63+5ni1ZWwjzDCPQEHhKfWzdCnfcAZMmwauvwh9/QE6OSOpSQ2UlREU1bQHUaoUFsKio/r5R7Uex4/YdFFQViBjAqjoL4Lryr/k7/2dKTaX4uvue0tglEolEcu6RkJCAXq+nc+fOZ61/g8HAihUrALDZbOzcuZPHHnuMoqIi1q9fX6/9r7/+SmBgIEOGDDkr45Gc/0gLYAukpKQwefJkAgMDUalU3HnnnVRWVnL48OG2HppEcs6SWZ7Jnvw9Z7TP9enr2Z2/m1kXzOKarteccj9Go7DclZeD2Qy9eglB11oLYI0LaGMWQIVCQe+Q3oR5hdV3AfVwscu8AICEvIRTHrtEIpFIzj0SEhLo0aMHKpWq0f25ubls2rSp0X3V1dX8+eefzfafmJhI9+7dGTRoEIMGDWL48OHMnDmTsWPHkpDQ8Dtl8eLFTJw4EaVSTvMljSPvjBZ4/PHHWbx4Mbm5udhsNubMmUNMTAw9evRo66FJJOcsH+/8mJl/zTyjfb637T3u6XcPz1z4DG+OffOU+6lJxrJ/P6jV4OMjir43JQBPrAPYnAtoDWGeYVRaK/ktfw7msL8xeSdQ5srg4piL2ZW765THLpFIJJJzC5vNRlJSEr17926yzTvvvMPYsWMbuGSaTCYmTpzIzTffTGUTmcJcLhd79uxpdN6Zk5NDXFxcvW0VFRWsXr2aK664ot52u93Oyy+/TPv27XFzc2PgwIHs3r0bvV7Pa6+91qpr7datG7fffjtff/01vXv3xt3dnS5durB69epWHS85d5AuoC0wdOhQvv76a0JDQ1GpVPj5+bFo0SLc3d3bemgSyTlLXmUeW7O3YnPY0Kg0p92fy+ViXfo6Zg6cedp9GY3i5759EBQkMnpqtQ0LwTdnAaxxAT0+C+iWLfDyy/Dbb+Dj5oNeo+eH0gehi5YkpYKelhkMj/SQAlAikUj+RSQlJWG1WunTp0+TbV5++WWOHj3K+PHj+euvvxg6dGit+Nu9ezerVq3Cw8Oj0WMPHz5MZWUl3bp1w26343K5yM7O5q233uLAgQMsXbq0Xvs//vgDrVbLmDFj6m2//vrrWbFiBc8++yzdu3fnjz/+YMKECZhMpmbHXoPZbObgwYNUVVWRnZ3N//73PzQaDY8++ijTp08nMzOzFe+W5FxBCsBmcDqdjBo1igsuuIDi4mK8vLxYunQpEyZMYO3atfTq1ateey8vL4w1s0uJ5D/Eifd9UWkRapuazUc219brOx0yyjKwVduINcRScaIiO0nKy4Ww278fIiOFwHO5hDWwpmunU4g8g0G4iR5/SqNRHK/X19+3ezccOVL3d3fv7gSWXgLpowkMN6ItHUxXr5UsTFhIRUUFNocNtVKNQqE4reuRtD3yuS/5L3Mm73+Xy4XRevY+T55azzP+zK1xwWxORKnVar7//numTp3K+PHjWbRoEbNnzyYxMZGVK1c261WWmJgIwIwZM5gxY0bt9pCQEJYvX87QofWTni1evJixY8fi5uZWu+3LL79kwYIFbN26lf79+wMwcuRI1q1bR05OTqsE4N69e3E4HPTt25eff/65dntmZib33nsvJpNJGkfOI6QAbIbS0lKOHj3Kzz//XJvw5fLLLycmJoZly5Y1EIB2u53nn38erVYLwKhRoxqswEgk5xNOlxOlovWe4i6XC4DC6kI0Sg3bs7e3TgCaTLBxI6SkiMrrFRVCkanV4O5OdnstPfzicdO4tdxXC1RXi5/JycKKBw0tgFVV4O4urIAnzm3sdtHexwfKyuq2FxbWF4oLr17Ix3M8yFUr6KCHpCroFtSNo2VHqTBXcNtvtzG9x3Qujbv0tK9JIpFI/klWrFjBqlWrALAe7z9/mhitRrxf9T5j/Z1I+RPleOm8zmifCQkJaDQaunXr1mw7tVrNDz/8wNVXX82YMWPw9fVl1apV9OzZeEbrGhITE1EoFGzYsAGtVovT6SQtLY1Zs2Zx7bXXkpSUhKenJyD+F3/++Sdz5syp18err77KVVddVSv+aujYsSN5eXn413wZtnCdAC+88EK97UVFRXh5eUnxd54hBWAz+Pv7Ex8fz5w5c3jrrbfw8PDgjz/+ICkpib59+zZoX11dzRtvvIGX15l9uEgkbcHdv9+NyW7iiyu+aPUxV/16FQ8OepA0UxpDOg5hU+EmHvZ6uPHGxcXw+++weDEsWwYhIdC3rzDLxcSAwyH8LUtLGfDCJyy12DCsuw26dhVtevWCLl1EAN9JYDQKoZaQAGPGCGuel5cQczUf3YICCAgQQygsrNsOwmXUboeICMjPF6fX6cTvOTlCNCqV4IUXpmqxz9NTWB5jQ70I9A1kY8FGVmSvoF/7flzndd1JjV9y7iKf/ZL/CpMmTWLSpEmAiDn75JNPzki/nlpPyp8ob7nhafR/pklISKBr1661i//NYbfbqaysRKlUYrPZqK5ZkWyh/5iYmHoZPQcMGIBareaqq65i2bJlTJ48GYBVq1ZRXV3NpZfWLSympaVx8OBBnn322QZ9Z2RktMr6B0KIhoeH06VLlwbjk3kxzj+kAGyBX3/9lUcffZSOHTtiNpuJiIjg/fffl5Y9yTmPy+U6ZVeXBUkL+GrPVwDMvWQueo2+sRNAZiakp4OvL0REkFGewY6cHeRX5TMpfhKzVs0SbYuLhY9kYmLdz6Qk6N0brrgCXnxRCLsmxjuh5w6edRvLuCw3OHwYVq0S/djtMHcuTJ/e6mursejl54sYQGg6C+iJ7p9QlwTG91glh5ISoV3z8sRbYjSC97EF7Koq0Y/BUJd8ZmT0SGZvmI3D5eBQ8aFWj1sikUj+7SgUijNuoTubuFwudu/ezZQpLdd4NZvNXHHFFezatYstW7Ywe/Zsxo0bx7Jlyxg8eHCTxyUmJjZazqHGxdNkMtVuW7x4MRdeeCE+Pj6127KysgAIqvnCO0ZBQQG7du3iiSeeqN1mNBp5+umn2bFjB6WlpQwZMoR58+ahVqtJSEho1PiRkJDA5Zdf3uL1S84tZBbQFoiNjWXx4sUUFBRQUVFBUlISd9xxR1sPSyJpkSkLpvD0yqdr/86vzOfS7y7lSMmRZo9beXQlN/96M99N+o5Qz1Dmbp/LS+teEjurqmDBApgyRainDh3guuvgwguhRw/en5tG5JxvuXmbnSnf7ebDedk4wkMhIADT9GtxrF0N0dHw3HNCOG7fDk8/Dd261RN/meWZfJn4JQAOp4PdhXuJGn8tPPUUfP45rF0LpaXw44/w0ENw6611vp0tYDQKwQcQeKyUoE5X5wJqtwvh11ISGK1WtKnJBJqXJ34e7xZaUwbCw+M4Adh+JLvzdxOgD5ACUCKRSM5jjhw5gtFobDYDKIDFYuHKK69k+/btrFixgv79+/Pjjz8yZswYxo0bx5YtWxo9Lj8/n7y8vEYtbD///DNKpbJWHLpcLpYsWdIg+2dAQAAAhw7V/755+umnqa6urmcBvPbaaxk9ejSbNm1i//79FBQUsHjxYpxOJ3v37m1gLSwtLSU9Pb3F65ece0gLoETyL8LpcvJ5wudoVVqWHVnG0sNLmdZzGrF+sdyw6AaSCpK44scrWDl9JUGGoAbH51XmMWXBFOaM/4DLDX0orehF8jOPEl8ITo8/UO7cBe3bw9VXC+HVq5cIlisshP37Wf7aaAYfOMrVFhWGgDL2dg/G//L72ehr5LGds/lp8nSmdG15pfSNTW/weeLnXNf9OtLK0nC4HMT6x9ZvpFDAJZcIX86pU2HgQPj6azGmZqisFJeQnNy4BbCqSvz08BBi0GgUSWFqyinV1AGE+plAc3PFz7IyUUQehAA0GIQLaI3lcUT0CACm95jORzs/Ouk4S4lEIpGcG7QmAQwIsbVt2zZWrFhRmz9Co9Hw448/cs011zBx4kRSU1MbZAKt6V+tVteKxLy8PBYuXMg333zDk08+SUxMDABbt24lNze3gQCMjY2lc+fOPP/883h5eREQEMCXX37JunXr6o197dq1rF+/nqysrFp30YqKClwuF4cOHaKqqqqBBbC11y8595ACUCI5D2nUvdPl4oN5t5K05icic6rZpuyKW0EJyvd6YXSo+VhlJazPSP7WpPPC8kium/w8QwZfjb26kvI92/HPKibprw/ZmKsm/rWHoLyc6zt2ICOyL98F7iHvissI/eJL6NixoaumToe5ezxvDHTAQOjk34nk+z4nefGNbDZtYtWeVQyPHM6+gn0tCkCTzcRXe77CbDezLXsbBVUFxAfEo1Y28biKiIA1a+DZZ2HIEBHY98wz0K9fo82NRhFieLwA1OmE2HM66yx1BoP42+USovBYjH2tCyjUrwXYmAWwqkpYAD096/oN9QzlwUEPMmPgDN7e8jY5xhzCvcKbfU8kEolEcu6xadMmtFptqwTgjTfeSPfu3ettrxGBO3bsaLQMRE0G0FmzZjFr1iwUCgXe3t61mTivuuqq2raLFy+mb9++hIfX/z5RqVQsXLiQu+66i9tvv53g4GDuvPNOQkJC+PbbbwkJCQFgx44d3HLLLbz99tsNxvHDDz8ADYVeQkICOp2uQVyg5NxHCkCJ5DzD5XLR75N+vDjyRSbETiB79wbMC74neOFf3Jh6FE3PPjjjOqHv1gtnRDgvJH3I1tK9fDZyHtr8ai49eJA+21fD3U/irHoKm9JFjr8CU48B7HJm0e+BN6DfUOjSBY27OzHAj3O70WdMd0JjY5scl9FSly4zxEN8ofQP7c/9f97PjAEziPCOYFPmphav75cDvxDiEULPdj35+8jfqJQqugd3b/4gjQZeeQXuvx/eeku4pN57L7z0Up2/Z804jVDzHVzjAlrTxGoV+93dQaWqE30VFXW/17iAgkgUU1goxF1VlchNU1oq9OdTT9W5gB5vAQR4a+xbAET7RHO4+LAUgBKJRHKeYbFYWLRoEePHj69XcqExfH198a0JHD8BjUbTZAzgE088US9GrzkWL17MDTfc0Oi+Ll261Fr8arjooovqCbrw8HC++OILjEYjnp6eWK1WkpOT6d69O1OnTmXq1KkN+n344Yd5+OEmEr1Jzmn+cQF49OjRVrVTKpVER0ef3cFIJOcTNhts2UJmwhoGLttF5RfXYi9tR+DhQ6zpoOSLgSG4vfM0T499qfYQJfCscypl5jL89XVpnkOBmX/NZHPmJm7udTMr01bxy4FfeHr403iPnNHg1DF+MRwtbf6za7Qa0al0+Lr70s6jHQADwwaiVqp5eMjDHCg8wMc7P27xMrfnbOfimIvpGdyTj3d9TLhXOP1CGrfmNSAkBF5/HW65RcQmrl8v4gQjI2ubVFYKCyDUdwEFIQALC4WwAyECDQYhAMPC6trUWACDgkT7vDyxrUMHyMgQOW2uv77OBbSmnITLVd942sm/E4eKDzGy/cjWXZ9EIpFI2pSa5CkffvgheXl5jWbXbAsOHjx4Uu0TEhK4++67a/+eMmUKGzdupFevXnh6eqJWq3nssccaWC0l/w7+cQHYsWPHFjMTulwu3N3dqaoJxpFI/svs2ycyXf70E6jVuEL03OIKZG8wTIo6hPbB0XTrNIwPt33AkQsfbXC4SqmqJ/5qeGfcO7W/X9LpUiqtlTw8uPGVvA4+HThSKpLHPLXyKQaGDeTyzvWzflVYKvB28ybWL7ZWAPYL7ceh+w4R6R2JWqkmpSSFalt141lFj5FSksL4juMZHzueh/5+iN15u7m5180tvk31iI+HzZvhgQdEptE5c+Caa0ChwGiETp2ElS84WDSvqSRRIwBrLIPQMBHM8RbAoCBRMiIvD9q1E7UB9+4V+6qq6lsAnU5R7lB/3KV39OvI4ZLDJ3dtEolEImkz3nnnHebNm0efPn1Yv379eZkAJT09neLi4noWQKVSyXvvvcd7773XhiOT/FP84wJQr9eTlJTUbBuXy9WgyLpE8l/C6XLy21/vccFnK/H9fQXccAP88gsMG8b1X1zAjT1vZFrPaVxcXUw7j3YoFUoeHPQg3m6nVkA3wjuCP6//s8n9HXw7sPzocgC+3/c9vyb/ysS4ifWSl1SYK/DWeXNpp0uJ9okGRErv9r7tAeEW6uPmw/7C/fQLbdqil1KSQke/jrTzaMeSqUuYsmAKvdudwhesmxvMmwejR8OMGbBkCXz6KUajO1FRkJUlrHNQZ9GzWFonAI+3AO7ZU18A7tsn9lVV1Y8BBGEFPF4ARvtEszV768lfm0QikUjahFdeeYVXXnmlrYdxWkRFReFyudp6GJI25B8XgJdddhlRNSnymuH4IpYSyb+FvMo8Xlz7Iu9PeL/xzI8WC6xdy5r/u5uxa4+yoLsKv1/fZfSoW/kt+Tc2LHuQnbk7+XHyj7ip3QjzCqs99FTFX2vo4NuBo6VHKTOXkVaWRoA+gN8P/c5lcZfVtjFajfi4+fDY0Mca7UOhUNA9uDt78/c2KQAdTgdHS48S4yd8NIdHDSf34dxTrmcIiIylI0bAFVfgGjcOKn/H09MTP7+6JiqVeLXGAniiC2iNBTA4uKEArHEB1enEMZWVdVZHgCjvKH5K+unUr00ikUgkEonkJPnHBeB3333XqnbffPPNWR6JRPLPs3D/QubumMvIqAuZ7D0YjhyBlBShGhISYOdOHN5eHA7JpfvmtVSxn3d3zWfT2hy+2vMVwyKHsf327fWE3z9BTQxgQm4CEV4RXN/9ehYfXFxPANa4gDZHZ//OtbXvDhYd5JLvLiHYEMyf1/+Jt5s3mRWZOF1OorzrFolOS/zVEBQEK1bguOQyNjAMn6Q5EDmsXpOaWoCn4gKaliZKP/j41GX7PN4FFBomggFhAUwrSzv965NIJBKJRCJpJW1efMrhcLT1ECSSs0dqKsyfLwqVDx7M1eMfwfySgit6XoMrOhpuvhl++AGH3Ypx6iTYvJk/Vn7EuzfHE9jvAq7rfh0Hig7w1pa3+HbSt3x/1fd0C+r2j19GtE80DpeDr/d8Ta92vege3J0DRQfqtamwVODj5tNsP7H+sbUxbztyduCl8wLgg20fAML9M9onGo1Kc+YvQq+n8POl/MTVeE4ZJ9xqa6q/U1cLsDEBWF5e9/eJLqAFBXD0qEgA4+NT1+54F1CoSwRzPNE+0RRWF1Jta10Re4lEIpFIJJLTpc0F4Pjx49t6CBLJmSc3F+64A7p0gW++IVfvYMMVfbj2KheHVi1g4nNxXDJ/JFXJ+3Au/5srh2YRbXyBjd4VbM3ZxsDwgQB46byY2nUqvdv1Znjk8Da7HDe1G7f0uoXPEz+nd7vexAfEc6DwAC6Xi1xjLvf9cR+l5lK8dc1bAGP9YmstgEdKjtAjuAcvjHyBt7a8RYWlgiMlR+jo1/GsXYfRquNN3dMokpPh0CHxPzoWB6HTNS4AfXzqC0Crtb4FsLBQGHFPFIClpeBwNG8BDNAH4K52J70s/Yxfq0QikUgkEklj/GMuoM8880yDbS6XiyNHjvxTQ5BIzj6VlfDGG+I1bhzLF71J92GTuPbnqWzI2EBIfAjdhk3ixwEXceEXF/LJrk8w2UzsL9zP40Mf59LvLyXGN4Zbe99a2+V749/D4rCcGVfI0+DJ4U/yacKn9GrXi7iAOCosFeRX5fPYisdYkryEC6IuoFNIp2b76OTfiZSSFJwuJ0fLjhLjG8Po9qPp1a4X/1v1Pxwux1kVgJWVxxKyhIXBr7/CgAEwcya8/TZarbJRF1Bv7/rF3W02kV8GhAB0OmH/flFaouZx5u0t+oG6RDPHF4OvQaFQEO0TTXp5OvGB8WfhiiUSiUQikUjq848JwA8//JA333yzQdYhvb7pdPASyXmD3Q6ffQbPPgsdOpC18HPyu3dg3PwB9Mn8nL35e0m8KxGz3YxCocBL58UTQ5/g0eWPUmwqZsW0FQyOGMyGjA38dug35l06r7Zrg9aAAUMbXpwg0juSNTetoW9IX3RqHVE+UXy1+ysWHVhEpGckCbkJDGg/oNk+Ovh2wOKwkF2RzZGSI1zU4SIUCgUfXvIhPT/qiUapYcm1S87aNRiNwhUTEDUD160TWUI1GrTaN5q0AKYfZ6A7PgmMXi/6q6yE9u2huFhs79wZiorE7+7u4mdjFkCAKJ8oGQcokUgkEonkH+MfE4Dx8fGMHj2asLD6ySuWLVv2Tw1BIjmzmEywZQusXg1ffSX8AufM4eDweOLndkG/U8+9/e/lx6QfmdxlcoPYvUnxk3jo74e4PO5yBkcMBuDNi9+kylZFj+AebXFFLTIkYkjt7/EB8byw9gVu7X0rZRVlLD64uMUkMDq1jkjvSA6XHBbZPn1Fts9O/p346/q/aO/bnkjvyGb7OB2MxrqSDIBQbX//DYMHM03dEYvlLoqKGgrA3bvr/j4+CQwIK2CNEKxxAY2LE0JSqwX1sadsYzGAANHe0dIFVCKRSCQSyT/GPyYA165d26gLW2uzgkok/yguF5jNIvir5pWVJYK9UlIgKQm2bxdK4YIL4K234LLLQK1ma+KX9A3py7397+Xa7tdyZ987CdAHNDiFRqVh+bTlhHnWLYrE+seycvrKf/JKT5n4gHj+TPmTewfcy087RSmDlmIAQYi93Xm7ya3MpYNvh9rtF0ZfeNbGWkMDAQjQsSP88guPjRjH2q29sVoHtugCqjkuR01QkHiBcAO9+25xjk2b6tw/oWkLYHvf9mzM3Hi6lyaRSCQSiUTSKv4xAdjW8UsSSQMyM4UFb/t2Ie4KC0VKx8JC4ctntYp2bm5CBYSFiRl+x44iechXX4nMHyfc29tztjM8cjg3974ZgK5BXZscQpfALmft8s42wyKHkVuZSyf/TnQKELF/LWUBBVEK4tu932LQGAgyBJ3lUdanrEz8KxswfDgfhb/Mna9fSi/1ary966y1jSWBOVEA1ohKT0+YOxdeeEHcRsd7uDclACd3mcz/Vv+PjPKMs2r9lEgkEolEIoE2qANYw1VXXcXChQvb6vRthsPswJRswqOnR8uNJWcWlwv27IFffoGFC+HgQejRQyQC6dFDWPOOmXMs3h68l/wVA+LHcEHsGJwuJ0qFslULGdtztvPAwAf+gQtqW66Mv5Ir468EhFUPWleM/qHBD/H1nq+J8Yv5xxeGsrOFjm+MRVEziQ2tZO3WoSg+/j9RokOrbdQCeLwLaJcudRbAGgwGIQD9/eu2eXrWF5I1dPDtwMROE3lv63u8cfEbp3xtEolEIpFIJK2hzcpA5OTktNWp25SCbwvYOWAnxsRGTAGSs4PTCT//DD17wtChcOAAzJoFJSWwaxd89BE88YSo1TdxIgwcyFJXMq8mz+fyX6bw7tZ3GfzpYB74q2VRZ3VYScxLpF9ov3/gws4dIr0jcVO54e/u32LbKJ8ofpryE3f0ueMfGFl9srIgPLzxfVotfBYyiyejf4A33xSV3V94gQBHfpN1AAFmz4YHH6zfl8Egbq/WWAABZgycweeJn2Nz2E7twiQSiURyznP11VcTFRXV5P6UlBS0Wi133XXXWR/L/PnzUSgUtS+lUklQUBBXXnklqampZ/38kralzQTgf80ltHhpMeVbyqlMrETto2b/1P04TI62Hta/G4dDCL9evUSq/zvvFC6eP/0EU6eKCt9NsPDAQu7ocweLrlnEw38/jEKh4IvEL/gs4TOsDmuD9guSFjDiixH0+7gf7mr3s1rK4FxErVSzYvqKVhepH9NhDPcOuPcsj6ohzVkAdTpITYXUzuOFdfjTT2HTJrpeEsW24g647rwL7PZ6dQCboib27/gYwKaSwIBwp3VXu7Pi6IqTvyiJRCKRnBd07dqVzMxMKk+sCXSMJ598End3d1544YWzPpaEhAQMBgObN29m8+bNrF27lqeeeoqlS5cyffr0s35+SdvSZi6g/zVyPslB5a7CkmWhw+wO5MzLIf2FdDrM7tDywZLW4XKJ2L1Dh2DZMlGWAeqsezXF21rAYrfw+6HfWTl9Jf1C+7F82nJ6tevFmrQ1PLjsQWZvmM3fN/xNe9/2ACTmJXLTrzcxe/RsAvWBuKndUCrabG2lzYjyiTrnF3aaE4BarRCAffsCSiVMmAATJlCxI4U7+qexbONDqCZPRmH5AY2m+XupRvi11gKoVCi5uuvV/Jj0I+Njx5/8hUkkEonknKdbt264XC4OHDhA//796+3bsmULP//8M7NnzyboxLiCRsjNzSU1NZUhQ4Y02FddXc3atWsZP77p75PExES6d+/OoEGDarcNHz6clStXsnr16pO4Ksn5yH9vltpGWHOslK4qpXJPJZ59PYn7NI6sd7KoOljV1kM7P3G5RPKWl1+G6dNh0CARcBUYCJMni7z9c+eKGf2997Za/KWUpDD+2/EEGYLoG9IXgFHtR+Hn7sek+EmkPpDKhI4TiJ8TT6f3O/H3kb+56qereGLoE8wYOINru19bGxcnOfdoSQCWlzfc79GrIysZQ/4PayA/n1f2XIK7vXkX7pMVgADXdruWhQcWsi59XcsXIpFIJJLzjm7dhJfM/v37G+x79NFHiYqKYubMma3q65133mHs2LFs2LCh3naTycTEiRO5+eabm7Q0ulwu9uzZQ48eDUtO5eTkEBcX16oxSM5f2kwAnlgQ/t+OJceCrcCG0+REH6/Ho5sHQdcHkfFKxmn3bU43YzpqOgOjPMdxOkXWzkceEfXbxowRQq9jR+HiuWIFVFRATg4sXizi+dR1Rm6n3YnLKe47l8tFQVUBAD/v/5lSUykAH2z7AL1Gz6ZbNjVqzVIqlLwz7h0S70rkpl43MfabsfRu15unL3j6rF++5PQwGsXt0VQMoE4nfoaG1t+uVgvxVqrwg+XLsbtUjHl+GPzvf8La3AiNuYB6eoqC8U3RP6w/r1/0OmO/GUt2RXYrr0oikUgk5wsdO3ZEp9ORlJRUb/vixYvZsGEDs2fPxq2VC9Yvv/wy48aNY/z48WzcKEoJ1Yi/3bt38/fff+Ph0XjCwcOHD1NZWUm3bt2w2+3YbDbS0tKYMWMGBw4c4I03ZEKyfztt5gK6efPmtjr1P47L4cKaZ8U9zh2VuwqlVujuqCej2NZ1G1H/i0Ifq2+hl6ZJvjMZpU5J91+7n6khnzs4nbB5s4jl+/lnMYO+4gqYM0cIwGOzdluZDY2PptmuDk4/iNdAL8IfCOetzW/x5MonuW/Afby95W36h/ZnxfQVrEtfx+NDHyfQENhkPwqFgs4BnXlq+FMMCh/EoPBB/0mXz3ORnTuFle+yyxruy84WhmBf38aPrYnra8xCWJsJtKsH90f9xhf95+BzeJvIHjtsGFx+uTjpseD+piyAFRXNj/+ufnfx/rb3SchLIMzrhIHs3w+rV4v0omVldTUtuncXr65dRaChRCKRSM5JVCoVnTt3rmcBtNvtPPHEEwwcOJCpU6e2ui+1Ws3333/P1KlTGT9+PIsWLWL27NkkJiaycuXKRq17NSQmJgIwY8YMZsyYUbs9JCSE5cuXM3To0JO/OMl5hYwBPNu4XFh3pIADwu8LwZJnr93lHuNOu5vakTIzhZDbQlBoFARc2rBgeHNUp1RTurwUpbsSp8WJUvfPCZGcj3NwVDqIeCjizHZcWQkbN8LSpaJcg9kMV14Jn3wCo0Y1yMBRtqGM3WN20/XnrlTtqUIbrCXk1pAG3VZsrsBpdpIRnkHqu6k8MOsB3tj8Bj9N/okPtn/AY8sfY3f+boZHDW/1UEe1H3XalytpPWvWiCycF13U+P5vvhFG4aYEYFhYg7KNtTQnAI+vBVhl15E55SG6jkPUkly0SFicH3pIlBT58EMMBvHFe7wA9PJqRgAWF8P338OePXyeUIb3T09Az+Uia62HB3zwgbj44cOFCdPHR9SgLCkRxz31lBCGMTFwww1w113Qrl0TJ4OSZSVo22llORqJ5AyTlQV//w1jxzbtbi5pBpereV/508XTs+kvgZNkxYoVXNTUl9FxXHjhhaxZs6b2727durFp06bavz/55BOSk5PZuHFjPc+jiooKLr30UtasWYNS2fjcTq1W88MPP3D11VczZswYfH19WbVqFT179mx2TImJiSgUCjZs2IBWq8XpdJKWlsasWbO49tprSUpKwrOmwK3kX0mbCsDDhw8zY8YMtm3bhvGED7zV2jDT4vlA2stphPc24OfYIlwSV67EmuuBmtmE3R8lJmh5I4T16tJL6fByB7bGbRXixOqk09xOtJveToierCzIyKjLJx8fD9HRtQ8ve4WdtGfSCJwSSPn6cvK/zcdpcRJ295n91jHuMpL+Sjpdf+yKQlX3cCpaXIS9wn56AtBmg/R02LpVvLZsEaUZIiPFLP+LL3BdeCEpj6XTflh71Nr6t6w138q+y/fh0cuDjFczqEysxGuAVwMBaCuzYU4zYzKZWJO3hktyL2H8xeN5bOhjBBoCCfUMZdjnw+jo15FQzxN8AM8wTruTQ7cfIvbDWFRuqrN6rn8bv/wiDF9NfecePAjJyY3va64EBDTtAgpCb9XUAqxXBzAiAmbMEK/SUnj9dRg0iHZ3P04A93DtxufhklTw8aFjTgl/GI04Zw5A6estBtS1K1gs8MYb4vM9dCjFg3qQUllEHMDzz4tnweTJ8PXX9QsLnkh+vvj8zJsHr74KU6aIcZ2QaODok0fJ+L8MvAZ50Wdjn6b7k0gkrcblEvnG3ntPLPYkJMD777f1qM5DjEbh2XC2KC9vNgP4yTBkyBAOHDjQYju9vr6HV9euXfnuu++orq7G6XTy/PPPM2XKlAbJXLy8vFi3ruWYcLvdTmVlJUqlEpvNRnV1dYvHJCQkEBMTU++cAwYMQK1Wc9VVV7Fs2TImT57cYj+S85c2FYDTpk0jPDycTz75BMPxwTLnMWXvraPMHEOvzgvwurwzfPoploou6J7Phj+zhHli9Wp46SW48UY0QUH08uuKxl9DeXkkR28bRfCj96IoyBezzPBwMemrqoLDh8XsdNQoGDmS3W/HotBp6fxlZzJfzyT5VjHz9ezniVf/M/OAAyhaUkTRwiIy/i8DbZCW4OnBKNQKjDuNOIwOnHYnSnX91amydWWoDCo8+x5bQXI6ITFR+OglJIjfU1KgqEgEWfXuLRK5zJwpXOoiI2v7cpTbyX43m8CrAvEZ7lPvPGnPp+E93JtOczuxOXIz7u3dqdhcgcPsqCeuMrZkUG4ox7PAk8GVg/HQCMtHjavn0MihDIkYQmf/zvX6P5NWVZfThdPixJJlIe+LPCIej8DQ+d9x3/9TGI3CktcUBw+KEFCjUSz01rBzpzDSNbcir9WKWzGwEe/f44vBn1gHsBZfX3jlFbjqKvR33kM+z5NdOBhuvlZYtft5M3+NO/2qduBWlgohIbBkiTjx/PnCbKlQUJm0gLc2vsZ1d7zb7HtR/EcxKMF/3DFRGBwsXFEvv1yo4DlzxLOif3/Rf4cOOO1Osj/MpscfPUianETFtgq8Bpy5Z8WZpjq5Gn3cqbvHSyRnk5wcMJnEuu769eJjtn27WLO98kqRn6y5BStJI3h6Uq/w6tno/wyh1+vp3Llzyw1P4PhMoL/++itlZWW89tprDdo988wzAM2WhDCbzVxxxRXs2rWLLVu2MHv2bMaNG8eyZcsYPHhwk8clJiY2mj20Jv7QZPoP5JX4j9OmAnD//v1s2LABtfrf44ka85Qfdmt7kn99mv6vipV368c5aEN1YvYZFibSy7/+Ohw9Cnl5eJSUQHEx/lYlB+71o/q9RRguiBYTuuPN/lVVwjVy1Spc771PVcIL9I2ajf7tLoR2vBjtw51wuHSkv5xO98XNxwMennmYsPvC0HcUkytroZWcj3KI/l90g7YVmyvwu8SP1KdSUborUeqVeA/zxlZsQ6lTUn2gGo/u9V3Jst7NQqVXEH9zPvzyC65fFqGoqhST0V69RGbOTp3EJDg4uIkZtcCSbQHEZLBicwWGrgZ8RvqQ+VYmeZ/n0TehL7pQHdHPReM72pekSUkYtxrxudCnto+vfviKmLgYQu2hVO2rwl5lx2FyULykmMCrA1EoFHx/1fdolHXjKFlWwr5J++i5sifeg05/RbJoURFZ72YR+aQQt5YsixSAJ0llpRCA1dXCwH7ZZfDCC3DffSK+Lz1d6KlDh46VczjG008LD8lnn226b61W3I6Nedoc7wLaYh3Avn1xbdpCvO4Qd93bngfvE411Lvj6EXju6ZuIjq5rvmcPpKXBZceM612DunKg6ABOl7PZ2NIai3e/Pf1wj3avvzMuTpgiXnxRXHzv3vDddxh9hqHUKPG9yJd2t7Qj+/1svL4+NwVg3jd5HJx2kJ4re+I7qonATYmkjXC54JprhIf2n3+KqkM33ADduol9Pj4weLD4+1i4VT327xdG+5Ej628vKRGLVb16QZ//ooFeoThjFrpzlZpMoCtXruStt97i/vvvp3379g3a7dy5kzvvvLPJfiwWC1deeSXbt29n5cqV9OrVix9//JFrrrmmVgQeX+Khhvz8fPLy8hqNEfz5559RKpWNikPJv4s2VV7dunUjNzeXiIgzHEPWhnjcfwl6l57UlzdiLbCiDdJizbWiC9U1bNyhg3gdQwX4LNxNSa4fhpCGMWwYDHDxxXDxxdhLbDj9N6J77RHYsgrP757Dc+9eLHFD2HzwRWzf/EaFW0+yPy0jeFowwdcF13ZjK7WR/X42nv08awWgcYeRtGfS8L/UH4VSgaGbAYVKgcvpomJrBb1W9SL+q3jyv84n+71slG5KDF0MqH3UGLeU4qHLEYL26FFIScG8rD/OahssfwzT6OvZq/6G3ocHoQkSE9WyDWUo7Aq8w1sWVrUC8GA1JUtLcO/kjjHBSMH3BXRb3K1WREXPigbAZ4QPZWvKagVgclEyln0Weo/sjbZciyZQQ9maMozbjeyfup9BQwbhFuFGpHed1dGcYWbfVfvw6O1B5v9l4v3L6QvA6uRqjDuNVCcL9wxr9vnp5nyqZL6ZSfANwWiDW6ii3gyVlWLVfcUKMdkqLITnnhPCrX9/MW+IjxcGsOMFYFGR0EGNxQbWoNM1bSE80QW0mfUKADRaBamaONyOm8coFPUtiTX88gusXVs3tli/WGwOG+ll6bW1JmtYnbqaQ8WHuDr2aiq2VeA91JvUp1Pp8m2Xxgfi7S3iB4cPh6uvpqTdU/j2H4lCqSDkthB2DdxF7NxY1J7nziJcdUo1WW9mkf9NPr4X+5L+SroUgJJzjmXLhLVPoxGibcECqAnrUihg9mzhtPPMMw09EkA4Af30E/z4I1x1ldjmdApBmJMjPrK//PLPXpPknyE6OhqDwcCzzz6Lh4cHs2bNarTdzp076Xv8F9kJPP3002zbto0VK1bQq1cvADQaTa0InDhxIqmpqQ0ygSYkJAAifnDLli0A5OXlsXDhQr755huefPJJYmJizsCVSs5l2jR14ccff8w999zDTz/9xLp16+q9zlcUSgUaXw36OD0VW0TGB0uOBW1I6ya9/uP9KfmzpMV2liwLKm8V6msmwttvC9fS/Hx0L85E51FN5VOfcHjKBuwbdpLzuLAa2sssZL6TSdnaMnCCJd1S2581T4iR5FuT2dFrB9lzhZ9d9cFqXFYXhh4GNMoq2vlvpyqhlIy7N+JZugXPPQsw3vGWyIb44IOwdCkuqw2TM5hqIrEnZ1B6wcNUZyrInpcPQMHPBeweuZuUB1Na9Z7UCMCdy3ZSlVxFyfIS8r/OJ2pWFH5j/Rq0D5gUQPacbEypwoXhox0f0be0L8H9g4l8KpLYd2PRBmsp3yBMOtbchkIs76s8fEf60uX7LhQvLab6cMs+9S1hTjfjrHZSukyUnLBkWUi+I5n87/JPu+/zgbQX0yjf1LJrT3GxsOQ1htEoXrt2iZ/bt4vV9t9+E+6fnTuLV00cYE0/xcXNh8+BEI/HW+aOJzRUrG1A6wQgiPWaEz3bvb3rLImzZomw14ICOHKkro1GpaFHcA9+P/R7vWMT8xKZ8N0EPtj+AXfPvhuVl4qIRyMwbm9FwoRrroGjRykxdcF3xavw5Zd4dPdAH6encGFhy8efRUpXl1K4qBBHtYOKrRXsGrgLh8lB11+60uX7LlRsrqByXzP1MySSf5A1a0R+sltuEaG2Go2IXOjaFY7Pu3H11WLRKSxMPKeOx+mE5cvh8cfhxhtFrjMQHuFFRSKv1OrV4HD8U1cl+SdRKBR06dIFs9nMc889h3cjMY9ZWVkoFArCmolbePrpp1mzZg29e/eut71GBC5ZsqTRMhA1GUBnzZrF4MGDGTJkCDfffDO5ubn8/PPPvPLKK6d3gZLzgjZd9k1KSmL16tUsXbq03naFQoHjPH/yeQ3xomJzBQGXBWDNseLRo3XZ9ryGeJH+UhOz3+OwZFnQhZ9gVTxWBN3w+R7KB72H+Zk0uj2Zz45ZPtim3k6BaRBHKm9HH2IGhQ7zxhT4YTPo9Vj3hmLorqdydyWhd4eS9nwawdOCKf8tDc8wI8oJ42DNGtRxcXTrNZTUtIvwu9gHRXgXjn7jhuvQGyhUIubOXmLDMWcjaj81lYnVlK4uxWeED1nvZBF2TxgpM1KIeTuGIw8eIf/bfPK+zKPHsh4N6u5tydrCV7u/Ysq+KZQbyglKCsLobQQNKDIV+E9sfEYfNCWIsrVl7LluD9dccw2FpYVcnnk5nv08cW8vLJDakDoBaMmx1Dve5XKR/1U+7V9uj1uEG/6X+FPwQ0E991inzUnOhzmE3h2KUtO6dRRLhjhP6cpS1D5qLNkWSv4qoXRFKYFTAlvVT41V+XzDVmbDUe7AmmfFWmAFF01aAj/8EPbtgx9+aLivpo7e2rXi599/CxesFStEOcj4eOH9mJgoJk+xsSLUtDUC8I47xGSsMUaPFnlaHI5WuIAew2ConwUU6ruSLl0qxpSfL/LBWCx1iWheGf0KUxZMYVL8pNpyEF8kfsGdhjuZfdts/u+G/2NryFY+TPuQW1Nu5cklT/LipS+iVjb9SLe7+2PM98X3u5lwy0Tw9iZ4Wl9yP86l3eV6FBvWYS+sJm1zLCUb7PiN86Pj2x1bvtDToHhpMUnXJKEN1pLxagamFBPRz0QT/kBdth5DFwOmZBMe3WTGUknbkpcn8rf5+4v8Sg88IATh11/DV181fszgwaKS0ajjEkYnJIjnyHPPif3XXCPC3p99VpS5rQnd2rWrQQ4nyb+Ebdu2Nbu/JesfgK+vL75N1DXSaDRNxgA+8cQTPPHEE60bqORfS5taAB999FFeeeUVysrKsNlsta/zNQPo8XgN9qJ8czkuh4vKxErcY91bPgjQheuwFdlwWpzNtrNkNiIAj2HoZiD/q3x0kTo8nroGj15elLy5gbyw2/FoV051rhu+bvsxr0sWcUKzZmF97xt8933J0IgZxB6+Dw9dFhnxL5PzxEYCWQuXXCICq/buxXfLR/TJu4qgT6fh98QIrEVOjDuras9vTjWj9lfjc4EPFdsqKFtdRvSL0Xj09CBxRCJKvZKwe8LwGeXDgekHKF1eStW+uuMzyzP5/dDvXPnjlfxy4Bd+X/c7jkEOFC4FIUNDWBm7EtVYFWqPpie7UU9FUb2tmhBC2H7BdtQeatw61BVX1YXoaq1R1pz691vB9wXYCm21AjPgygCKFhXVa5P7SS4pD6RQvq6cwsWFtW6dzWHOMKMJ0uCyufAe7k31gWosmRacVicF3xe0eHzF9gq2xTf/pXGuUmNttuZZSX8xnQPTm86cVlAgBFtj1AjAmjKif/8NI0aIigdz5ojbtGNHIfqMRmGtO3pU/N6SAHRzazr5XL9+4ueOHa23AHp5NXT7Ot4FtLRUuHrl5wsr5vFWz+7runN9u+t54vEnODL/CBa7hXXL13HFg1dg2Wrh8qLLCR8dTnx8PPjAng17uHHxjbhcribHU76hHLf2brhdM0rMVqdPJ+SP+7DvSeVAwFscmraDLXd4UvXpaiLtX5AzJ4PK1WnYim1N9um0OsmZl0Pi6ESOPHYEc7q5xfelZEUJCcMT2NhuI/uv20+nOZ3ov68/blFuhM8Mryf+QDwTLVni/rGV2Kg6UNVYt5KzTEnJmbFIuVyir/ORpUuFIMvPF9Y9hUI8f4KChMWvMWoEIIDdLlxDZ80Si0oaDVx6qRCTQ4aI58Pdd4NKJfqdOVMkkKmUBvD/HK0RgBLJ6dCmArC8vJwZM2bg5eWFSqWq9zrf8R7sjXG7kfzv83E5XfiM8mnVcdogLagaWqVqcDldVO2vwpJlwS3CrdE2hu4GTCmm2gycgZMDSXnoKFUZSronjCN4WjAhn0/BHN5XBC3s2YN10u1oH78DzWfvoJgymZixR8kqvBCzXzwhibPFUmcj/nEqdxVBVweR81EOpqPC5dJ01IR7e3e8h3mT+X+ZOCodeA3wotO8TphSTITdGyZikG4NwXuIN37j/GrdXh9a9hAx78Xw2PLHmN5jOgl3JjBQPZB+E/qhdFMSNDgI9eNq3rzizWbfR12ojiq/KiZbJ+N9yBvPfp71LIzaEC2OcjGbOf69Lv6zmEN3HqLzV51rs4j6X+JP1d6q2smt3Wgn7bk09J315H+Tz8FpB9l3xT7KN5ZTubfxb2qXy4Ulw4L/BKFCfEb4UL6xHE2QhtC7QkVGxxYwbjNiL7FjL7e32LYtsZXZsFfUH2PNe2fNtWJOM1P6dylVSY1P5IuKhDhqDKNRWNUsFhE+u327yMD399/CijZ5Mvj5CZFVU1mmpt5uSwKwOVQqsfK/bJmYxLXGAvjzz/VX/aG+BbBGABYc0/41LqYuh4uUGSk87XqaIfuHsOylZUS/G82otFGggMN3H8a4zciVT1zJrAtn4dfbj7ei3mJjxkbe2/pevfM5nU5KSkuwOqyUrS3DZ4SP2HHVVbB7N+pLR9J9Zj7O0ePhhuvptnoIPfKn0+7hHoQEbWfHqCNsDFjP4Y5v47zpdnj9dVx/L8e55wAlP6Wyvcd2st7Lwv9Sf8zpZrZ13sbhBw4LK++xaylcXEjSlCSKlxaTMy+HfZfvI+CKALou6EqX77oQPD0YlbuKrj91bTQJ1fECMPP1TFJmts51XHJmGTsW3nqr9e1dLpHM5ETR+OmnIsGJs/k1zn+UlBRRZrYlfvutYRzxbbfBypV11vsTGT1auHOWl4vKLF9/LRabbrutrs1zz4lYwSVLxD6ASZNE3reqKrj2WpFoxuUSCaOKiho7k2DLFvGSnN+88MILPP/88209DMm/mDZ1AR0/fjwbN25k6NChbTmMs4K+ix6/cX4cvPEgkU9GNiiT0BQKlQJdiA5LtqXWXTHzLSGiomZFkfdVHsm3JuM33q/JUg81GTlrBGDk45F49vPEaXaia6cj/qt4qg5UYcmw4HK5UCgUWPOtaC8NgVE9YdQoPO+AMN8U3KLcUBmaF+Ttbm1H4gWJ5H2Zx4D9AzCnmnHr4EbYA2FoQ7XYS+0otUr0sXq6bOuCb5xwWQi6OojAKYHkfJRD4U+FeNzvwZztc9h5x066B9dlMe1g6YA+So/vxb74jPbhsW6PEfNeDNuyt9EnpA8L9y9kStcp9TImulwu9obvZWDeQIzJRjz71zfF1MRk6rvo61kAS/4qod0t7QiYGFC7TeOnwWeEDxmvZRA7J5bMNzLRx+mJfDKSvZfsxbO/J0o3JQnDE/AZ5UPPZT3J/z6f4OuDa0WnvdSOo9KB/0R/8r/Nx2uwFy6bC0MXA579PMn/suU4QGOCUDSWLAtq73MnacfxuFwu9k7YS9X+Kjp91IngqSL5kDntmADMs2LOMKOL0JH1fhZxH8U16KM5AVhZKZLHJibC+PHC6tehg7D61eDlVRcrCJCUJNxEWyPammP0aPjuO/F7ayyAXRrJy1JjAbTbRVH47GxhTYiIqIsDrNpXhaPSgSXFwkDbQCryKvh6wtf4/eqH92NiUSX6uehaV2BDdwMkw3ePfseYr8awLWcb/UP7U2WtovC9Qi5ZdAkJ8QkMdQwl5qnjAvvbt4eZM3EHup040Lvuov0NdoKWp6I6uJODc+PYtzGCrqULOPCciqJqDSoqae/5M6GRCSh/84feval8eTipy9zZ0iEXv3F+VO6qxGl1EnhVIElTktAEaOj2azf8xjSM3W0KbZiWqt1isaDkr/PUdHSeceSIcLO+5Rbxd3m5cEfMzRVWqdbc/0lJohTCQw/Bm8et1334IWRmCmv6gAFnZfgnzXvviVxJffrUTx4FwjKfni68AJYvF8lbjsfDQ2T6bIru3cWz4NprxXMrKUlUjDkenQ7+97/626ZNE6+CAmEhvPpqIaiff74uIbhbI2vATz8NqakiJvp0n3kSieTfS5vOIoOCgpg4cSJXXXUVISdkvWyu7sn5gEKhoPPnnUlWJxN658kVFteGaWszRFryLKTOSkUTqKF8QznVh6pR6pSU/FlC4JWNFCwD9J31oAKPPkIIKlQK/C6uP+Fyi3TDaXJiK7KhDdRizbOibVf/26Ljm62L//Ee5M0Flgs4cMMBsudm47Q4cWvvhlKtJPhaIQBMNhP9P+lPUmESf1z3B+Njx4uxKRT4T/An5YEUfl/0O3O/nounwxPXM65a8WTJtqAN09L9VyEKvfHmvgH3cc/SexgWOYx3t75L16CudAuq+xY+VHyIhLAExu0bR0VOBe1frJ9NsUYAeg/3rpcMp3p/NYFTGr6vsXNj2T16N+YMM2Vryui5oicevTxQeaoImxFG4FWBlK4o5chDR6jaV8XBaQfx6O6BR0/xPzBnmFH7qPG/1J9ui7rhFiW+ufVd9Xj29cSUYsJebqcqqYqy1WWEzwxvILwrdwnroiXLgumoCZ+RPs26wbYFJX+WYDpsInxGOHmf5dUJwHQz2hBxn5nTzUQ8HEHhgsaTjzQlAB0OUf4hLk5MUIcMEQLwxGRlXl5CXNUIwH37ICCgYX8nS2xsXXKZ1kyAG6MmCUyNG+jRo2Ks48bVWQDLNwsToemQiepD1WCB3qm92btpL12+7YLfxX54Da5b/PHo4UHel3kMiRjCltu2sCBpAWvT1+Jt8Wba8mlEfhBJ+i/pWFZa8Lqg9enV1R5qvK+MBWLpdY+dvRP3snXnAyj8FAxI7IlWZ0KdEyD8dfPyYMcOPL56ju5791Le6UrKDHfQ7q3e+F3ij1KjJGpWFCq9qsUFpRPRheso+aMES66FysRKtKFyVnu2+eQTeO018Zm7/XZhUYqIEHUyf/5ZiJmW2LhRCKOPPxbt+/UT/Rw6BBMnwq+/Ni4AXS7xDGisHufZwOkU2TaHDYO77oJVq+q7bs+cKcTwffeJZ0/Xrid/jttvhzvvhI8+aij+WiIoSMRD33EHfPmlcNrp3FmUHK2ZJhUWiverokLUIwwOFjUJ77nn5McqkUj+G7SpC+jevXvp2bMnKSkprF+/vva1YcOGthzWGUPtpabrj12bdNVsCl2YrjbzZdZbWfiO9qX/nv7oInXownREPh4JTtBFNO5zotQp6fpz12ZTp6sMKjQBmjrXvPyGAvBkUCgUhN0bRt4XeZQuK8U9pn7M46/Jv2Jz2ri5180sO7Ks3j63KDfCHw4ndHooIcoQcj7K4egTR3G5XDhtTmwFNnRh9a/12QufJcYvho92fEScfxwbMurfM78f+h33ge6ULy/HXmbHe1j94C5diOjPZ7hPPRfQqqQqDF0b1ubTx+rps6UPHj09CJ8Zjvcgb1RuKgYcHEDw9cKFTd9ZjznDjClFuMLmfy+sejXun7pIHUqtEv9L/EUCFBUYuhrQBmrRReg4+tRR9ozdQ/73+ey9bG+9OFCn1UnVvirc2rthzjCz/9r95H2a16r/zelgSjWR83FOq9tnvplJxCMR+Fzog+lIXSFZc5oZr0FeVB+qxlHuwO9iP0yHTbicDWPWioqEQDrRRazqmMdoXJwQY7Gx4u8TBaCnJ5jNdSIyKen03D9riIoS1jo49ZX1GhfQmrFlZoq6gwMG1AnAis0VGLobqNhagb3Yjvdwb5LvSMZnuA9uEW74jvJF5V4nonxG+FCxuQJroZUewT14cdSLLLpmEc8kP4N/X3863tWR6/+4njufuZMtzlPzDVN7q+m5oiftprejyw9d0MfqUUf6w6BBIvDy1luFaScxEUpL8b5jKFF/TSPghbEo33sbCgvRBmpPWvxBnQtoyV8laNtpsRXYmo11lJw+a9YI0TJzpnCv3rgRLrhACIp58xo/5sR/yYYNwgI4YoQQJV9/LVyiH31UWLYWL27Yh9Uqztuxo7D2f/JJ0/HAZ4pt28Sz5bffhDgbPlw8P0CUcfjjD2Esf/FF8TohV1mruO46EfdXY1E9FS67TLyHPXqI97AmEVZ6usg0mp4ukmHFxMC774qxVrcQmn6+xmJKJJLTp00F4OrVqxt9rVq1qi2H1eYcLwBL/iyh3a3tUHur6Ty/M3029iHgSmHOaCoJDEDgFYEodc3/e3VROizpFhwmB45yx2kJQBCJb0JuDSHkjhCCbwiut+/L3V9yY88buST2Elal1v//ulwu3h/+PgvHLaTnbz3ptbYX+d/mc+SRI1QmVIKSBmNz17jz/VXfkz4znandptYKwHk75vHV7q9YeGAhQ8cPpffm3gw6Oght4AnHx7rj1t4Nfec6F1BbqQ1rrhV91xNSN9a8X6E6OrzcgQ4vdai3rcZS6RbphsvqonxjOWp/NQU/FOByujhw/QEO3nSw1uoHwiqr76SvdU317OtJztwcYt6Koc+WPlhzreR+llvbviqpCqVeifcF3pRvKMdZ5ST301xcLhfWAmuryiucCiV/lXDkkSM4ba0L2KnaW4XPCB/cYtxE2Ytjx5nThQB0lDtQearw6OOBy+7CnNEwaUhRkZhM1ljwaqhJhDB9upjcxMXBwIFicnY8NTWEs0Ulk1ZlAG0NERF1k7/TsQCWlQkBWJMhNCBATHiPF4DtbmqHJdOCJlCD3wQ/bEU2On3UqdE+3Tu44zXYi/xv69yILXkWst7NosPsDigUCtw17kwYOIFv9nxzagMHlFolHV7pgPfQFmpienmJkjBHj4qMFkuXihSHN98sTC1HjpxUAJguXDwPS/4oIfiGYFx2F/ayczsO9nymslK4Zz79tBBgV10FX3whLGTTpomEJinHhWG6XELYqdX13SM3boShQ+uSoLz+unCzfOYZYfFOSRHWwBrmzBGLN1u2iLIrjzwirF5PPSX2Hz0qXBuPx+lsKDxPpKqqcSFkt8PcuTB1qnCv9PYWt6rZXFd/74MPRFzxZ5+JW3nChFa/jfXw8BDPrFN9boBwQXd3F9bTrl3FwpbLBd98IxJTrV8vxn/JJUJ4h4eL8TfF/PnimdaUu71EIvl306YCUNI4xwtAS5YFt+j6FkRDdwOdPu6Ee6fWZRZtCkO8gYrtFVjzraACjf9pfDshrIAd3+pI1JNRqNxVOJwOduXu4sPtH7Ly6Eqm9ZjGiOgRJBUmUVBVULuKvy59HV/u/ZLHvn6MsI5h6Dvp6b22NwXfFbB79G6in4lutESCUqEk2COYoRFD2ZCxgeLqYh5Z/gj3LL2HbdnbuCL+CrwHeaPUNjzW0MXAwMMD0YZqa7OuViVVoQ3VovE5tfdBqVOiDdFSuqqUdtPb4axyUriwkMJfCgmeHkzQ1KB67fvv7Y9XP6FWPPt74tbejXY3tUPtoSb62WgyZmfgtIqJctWeKjx6eOAW6UbJshLcOrhhTjVj3Gkk74s8EkcmUrGt4pTG3RzmVDMOo6jP1hL2Sju2QhtuMW64RbihUCpqS1+Y08x4DRTXqovUodQocYtxo/pg/ZlZdTWYjhkOT5yYGI0i5iUmRkzEvLzEZPHE5At6vbCq5RxnuDwTAlCrFQXn4dQncsdbAKOixIQuKEhM1nJyhKXXdNhEwKQAUIqFitA7Qun5d0/0cY0vTAC0u7kdeZ/m1VpUU2elClfRgXUun9f3uJ6FBxby5+E/yas8+9ZjDAZhzlm1SigKg0H4rMXHizczKEj8PnSoMG/cfDM8/LDwbZs/XygIhwNdmA6X1UXRb0WiXIq7UjyzGiErSxTWlpw6mzYJYRAVJSxXv/wiPnNjxwo3w0svFYKohhUrhJv1smXiX5eQIO7ltDRhIB40SOw7cKCu4Lmnp0iq9Ouv4m+bDV5+Wbg47tkjxNa8eaI8wtdfi9ukc2cRSzdxohCjL70ksv9GRorxOByinxpMJmFh799feAu8+qpIpJKRIdxRg4JE7N9rr9UJJY1G3LIffyz6+v57kaylZ08hFk/F+tcS69PXM2XBFNLLmi//5O4uSv7ecot4L4qLRXzgV1+JOOi1a0USmcsuE+N87jlhCWxMIB88KD5qXl7y8yKR/Fc5twKIJMCxGMDfrdgr7djL7A1cSBUKBaG3n1xcYWME3xDMwZsO4n+pP9ogLQrVmf12e3bNs7y95W06B3RmwZQFRHhHANAjuAedP+iM0WpkWOQwlAol9/S7h/a+daYc9xh3eq7oSdFvRcLltRkGhQ8isyKT63+5nkHhgwjzDCOjPIMgQ1CzxylUCpFIQymSk1QnVTfq/nkyuEW5UbGlgrB7wgi7P4zkW5IxdDUQ+05so+evIey+MIKuDaoVuoGTA0l7MY2UB1OI/SCW6uRq9J31okxIvg2fa3xwWVyUry3HnGFG205L4shE/Mb7EflEZK2wbI4aAX5i/cXjMR01gRJK/y7FZ5hPs/1ZMiyovFRo/DUoFArcot0wHTFRtb8Kl82FR28PVB4q3CKPxT/G6TElm2CcOP7uu4WbmUIhVuNLS+snnq2sbFhWoTEUCtEuO1u4dJWWnhkBCHVuoMpTXDqriQEsLRVjs9lEvE5oqLB8VmZYQSGsXm7t3dB30oskRBf4NNtv0JQg0l9KJ+3ZNLRhWop+KaLvrvrZLPqH9ufCqAt5cNmDHCk9whsXvcEDgx44tQs5Wbp2rZtlW61CqZWUiFdxsXjV/L5/vxCNu3eDRoPqmmtQ+05CoVLi2c8TTZAGW4ENOtd1vyNnByklKexbPI63Z/twySXC6iJpGZdLvOVduojPzooVwm2zhosuEq8a7rtPCJlLs6EAAQAASURBVLknnhDW7NdfF/FtY8YIq90tt8CFFwqLlbe3cG+urISRI+uXWbniCmFZfPRRITLd3YUlTqmE66+Hn34SAua668Qtc/HFQlQuXSrWEn76SWQlNRhE/c7nnxei77ffRCbggQPF5+y660TCqMWLRXuzWVj95s6F3r0bLuZMny6sjs89J9YpLrzwLL3xwMqjK7nsh8u4IOoC+n3SjxXTVtCzXc8m2995Z93v0dHw+efiefTBB0Ko+vkJcQzCKltUJAzuxyfJ+uEHIXIffFA8d774QsQ+SiSS/xZSALaAy+Xiueee45NPPqG8vJy+ffsyd+5cujWR9svmaLpmVmupsQBaMi0o3ZWo/dS8s+UdtmRt4furvm92wn4y+I7xRaFVkPVO1mm7f56I0+Xky91fsmDKAibE1vebeW/ce5SYSugW1I2H/36YP1P+5NtJ3zbow9DV0CpB5qnz5M2L32RP/h4eGfIInfw7UW1ruS4fHMu6Gq6jOrkaY4IRQ7fTFIDRQgC6x7gTeFUgGa9lNLD8NYbaS43aq+7jqFAp6P57dxIvTMSjpwfVh6rxHuxd6/Zr6GbAXmbHlGrCkmEh4tEIfC7wIf/rfBIvSKTHXz2aFQ22Yhv7rtqHvpOegCsCyHg1g15re1GxpUJkNT2Wtdacasb/Un9K/i6h/Qvtm+wPhJune4x77f3pHuOOcbuRrHeziJ0Ti9pLjTZEiy5SXIO+s75e/cRVq0SWQT8/YSkrLXJS+EsxAVcGoFAoqKxs/aTey0sIwNhYEeNzJgVgQsKpH3+8C6ivr3CbCw4W1gilEvL2W9EEalCqlRjiDSKhUytQGVR0+6UbCcMTULorif8uHvfo+h4CCoWCJdcuAWDpoaXcsuQW7up3Fzp1067kZwWtVpgsOnRovp3dLm6KuXPRGY9i6B+IwulAG6StLTMBkFqayuivRtPOox25Za9Tbd7KL7+omT79LF/Hv4CyMrjhBiGqvvpKCK8ffxTumE0xYoTQ8yNGwN69QjjWJBt5+mlYuBDef1+Eg4L4zPbqBZdfXr+fiRPFok/79iJ+7cMP6xZW/P1FDCHUL7vQo4d4gciMWUP79kLoZGSIRSRvb7jpJnjySeFirVCI6ywsFELwlltEaZfGCAwU47/rLiFoT3WxpyXyK/O5/pfreXfcu9zW5zZmr5/NRV9fxAcTPuCq+KtQKZuPl+3SRVg1r75aiG2LRbir1ozX3V2I73XrhAC024XV9K23hAi85BLxHJo5U1hn4+PPznVKJJJzE+kC2gJvvPEGn332GcuWLaOoqIihQ4cyduxYKpuozLo+ff0pnedAVRX7j2W5qBWAGaLYu8VhYfaG2Sw+uJhfk3895Ws5EYVKQfSz0VTtrcL34pNMTdYCa9LWYHVYuTjm4gb7hkcN5/LOlxPjF8OCKQvYe/de2nm0O63zzRw0k88u/4wugV1QK9V46Vqf7dD/Un8KFxRStKiotvj7qaKLEpNptxg3NP4aeq3tRdi9YafUl3u0O6F3hVK6vBRTsgn3Tu61iX8M3Qy4t3fHnGrGnGHGLcINjx4exLweQ9SsKI48cqReogxrvpUDN9UVXz901yGUGiX53+Zz8JaDlK8vp3xdOQlDEyj4voC8r/IoW1eG+aiZkNtCMG43Yq9sPu7KnGaul/zHLcaNjFcz8OjpQfD1IiZU205bzwJ4vAtoUZFIPhEQIASgcUsFSVclkf2+COYzGk9OAObk1CWKOZMC8HRSq9e4gJaUCAEYFiYEoEol3NkKkuuSMXX6uBOh97be0u/Rw4OhxUMZmjcU/3HNX/D42PH4uvnyU9JPp34xZxu1Wph9Fi3Cb6wvwUc+hM6d0VjysOUIP2G7087D7z/Mg44H2TI5gUqLib4zXufLL9t47GcQl0tkdzye6mpxD51KLb2CArEosmePqDVntwsXygceEC6YVVXC3bMpFApRzDwgQLgS7t5d5xqt0wkBOX++KH9Qw49L7HSaWsqW8nIcx55L7dqJ2MDPPhNJZI+3bp0svXoJq+SDD4pMvS+8IFw7AwPru20GBgrrV0ulhu+4Qwinxx+H1amrmb9r/kmPaU/+Hj7Z+QmfJ3zOtuxt9Z7HhVWFjP92PKM7jObW3rcC8OTwJ3ll9Cs8tvwx4j6IY0HSgmb779pVPEumTxfv/4UX0mDR44IL6pLFTJkCCxaIWMFLLhHbfH3rLLEg4hQHDxbv0cKFjZ/X4RALdTU47U6qU1q34CqRSM4dpAWwBebOncsjjzxC92PfZi+++CLz589n0aJFTJs2rUH7x1Y8RkRQBH1DhftVpd2Oh7r5t9nscDBx714CtVo29+kjkoW44L3byugfaOTepfcSoA/gtTGvcduS23BTu1FmLmNKlykUm4rx1nmTW5nLvoJ9XNrp0pO6vpBbQgi5JaTlhifBb8m/MXPZTKb3mI5a2fy1a1QaOvk3ntziZLE7nahPYbk26OogEkclog3Rtuhq1xJuUW4o1IpaS11rXDGbw2ugF9lzsrEV2dDH6dEECn8lQzcDSq0Sc6oZa5611qoGED4znOy52eR9mUfITeJ/W7aujPwv8+nwagd07XRUH6ym/ez2eF/gTfHSYgzdDBy65xAKtYKM2RmYjprwGuglMqgO90YbosW43YjvyPoLBQ6zg6o9VdD5WKKXmLrrdY9xr61fWWMVjHg4Avc499prqNxdicvhwuFS1Gak69ZNrF5bkqtw6+DG0SeOUrSoiOpr4vH0bJ21ytNTJIwYOVKIxjNRBgKEADydRA7HWwD9/ERSjZpkMCEhUHbESsQxAViTqfZkaG29UaVCyYyBM3h367vc0OOGM+ZVcFZQKLD+OBqXYgz8/DPa+xMxP7uSW7+axqEJi7n/o6m0q2rHnsP5+O76nP3XjESTdgULFsQzZcqpn7a4WPxv3I+taeTliYlyVpaYOAcFif+ht7eITS0tFVavDh3EgkO3bsLdMDNTCLi8PJH0xN1dLARERIgFiowM2L5d9FNTu23aNGFh27xZZI/cuBHuvVcsgmzfDvsOOXGpnHQKV/PssyJGb+dOYUnLzhaufT17ilix3r3FtXz7rYg1nTFDuFTa7SI+b+FC4UZ54ICwjN1zT8v3+LBhIjtoY3TrVlcXL81k4pm0NBYUFhKk0VDpcOACrgkK4p2OHenf/+Se14VWK+vLy+ng5obV5aKfpyfKE+7d6Ogz49I4bJhYyJz4/UQUCgV6jZ7rul/X7DEOp4MlyUv4ft/3/H7od4ZFDsPmtJGQm0A7j3ZcHnc5l3S6hLt+v4vuwd354vIv6n32butzGzf2vJHv9n7Hnb/fyfqM9bw99u1GrYE9eoj/e00Z5TVrGo7nggvgzjtcJL+Ry/IlQRzOUnNCxS1uukmE3wYGihjD//s/YU294QZhFTyxnunbb9e5yN53UQX7rthHWVFZi++nRCI5t2gTAditWzfuuecepk+fjsc5HKhRXl5OWloaA44rVqRWq+nduzcJCQmNCkDPuJkM2b2fnIDu5NlsDNi5k2uCgrhGm0e4RyBdg7ricLnYZTTioVKx02jkj5IS9CoVeysreT0jgxWlpTzaxY0O+7LZ6L+aUnMpD4x4nY6hQ3hyWDGX/3A5WpWWvVVVfLv1VSbFTaTYVMzvh34n9+FctKq2q5PlcDq47bfbeGzIY9zV/z7K7Xa8jwlgk8OB+7Gl15WlpRjtdq44Q8Weyu12Bu3axZUBAbzSkmvZCXgP80YbrCVoatApx0GaHQ5UCgX6eD36eH2rJ+It4dnPE2uOFYVKIWorapT03twbfUc9LpsLU4oJl81Va1UDUOlVxM2LY/91+9H4afC+wFtkUwUqd1aiu0RYmHVhOgIuDSDyyUiy38/myENHaP9Se9Jnp+PZx5PydeWofdVofDR4D/amYnMFviN9yf0sF4/eHnj29iT16VSy3s0ibmMclnQL7lfUWQC9h3gTNDUI7wvqAn8CLq9TYp59PUEh6t5lfFGIJ9EY0eDvfyyxy9EqAq4IIHxmOHsn7MW5owwPj/rZZZvCy0tYOjw9xYQ8IuI0/xHHOF0B6OMjXLVyc8Xkv1+/un2hoVCVeXrlWPIsFpaXltJZr6efp2ezwm56z+k8tfIpNmdtZkjEkFM+59nE5XLxv9RU/i8zEx+1mk8mTKDbvjLSvt/A+7v78hF+BBX+TMRjwSQsNjKm10Ai+t/DYs00bnngN9LTQ7jvPiHQdu4UCUWysoSQuugikUm2Wzch3lauhK1JNnJsFsp2GziQJN670FBxD+3fLxKKdO8uxNzu3eK48nIhqNzchNjaskVY55KSxL0SHS3EXUCAOJ/FIoTg8uVwKMWFp7+TMcNU5OcLq47RKFz5ysqEwLz1VmHRmjtXjOPx5+28ELyLVJsJpdGHR/8IwPR0IP07aenYUUza09KES+XevULoWSzCsnP0qJjsv/RSw4Qm//d/4rzHx902xdaKCn4oKGCkjw+d9XoidTocgF6prL3n1pSWcunevUwJCmJH3750NRhwuFzsMBq57/Bhxu3Zw5UBAWSYzfT38mKEjw8Wp5Ov8/P5s7iYm0NC0CoUZFss/FFSQpXDQarZTKBGQ57Vigvo6eHBpIAA9CoV6WYzGWYzYTodw7y9Gefnh+Y0/DddLhcz/5rJiyNfJD4wnsk/Taa9T3sGRwxutP227G3c/OvNVForub779STdk1Qb126ymfj90O8sOriIMV+NYVqPaXw88eNGhZ1GpeHGXjdyYfSFTPh2AtcuvJYfJv+AUlH/WqZOFQZyhQKMdjvbjUaOmkx4qFSM9PUlWKtl2DCILCkj99FD3BHtJCQkvMH5LrpILEp89ZVYDBg5Umw3GkWc56+/inOYs8w4vbS8+aaS55+Hb541MvTl3XR4IZrYG+PhH6rbKJFIzgwKVxsUVHriiSf4/PPPMZlM3HDDDdxzzz1NxtS1JZmZmURGRrJ//37ij3OQv+aaa/D09GT+/Dq3kIqKCiIiIvD55msqTYcJ8OuGRq2lp85FUrWJtKJ9eJszGdj5erKsdtLMZixOJ90MBjq6u3NnaCif5+XxfUEB1wYF4fFqAWP+gkUjK/B9oTPfFORjdTrppNdTYLUSo6xgo1mHe+lmFKnzsQSNB+8e3BwWxayuI1EpFJQdS4lWI8AUCgWlNhuf5+bya3ExY3x9eTgigkWFhWwsL2duXBwp1dW0d3fn5oMHuTYoiPHH+c6ZHA6m7t/PmzExdNTr2VZezo7KSroZDHiqVLhcLpZmJ/Hj5mdIuPEP7j18hMSqKr7s3Jm/Skp4PyuLz+LjCdVquWzvXhwuFx/HxTHS1xeb09nol3VKdTW/FhURbzAwwNOTgBP87w5WVfFGZiY5Fgs6pZKD1dVMDAgg32ql2Gbjp65d0R8TnWaHA6VCgbaR85RvKcc9xr1BuYjGcLlcFFqtBB2XfvLWAwewA5/HxeGyuOrVaTtdEkYngA16r+tdb7vD7GBb7DYUbgoGHhrYYLKf8WYGBT8UYOhiACeUrS8j/IFwQu8OZVvsNvom9EUbIK7XdMRE4phE+mzugznVjD5ez56xe1D7qen5Z09yPs6hfFM5nT7sxI7eO/Aa4kXEzAj2XbUP34t8Kc0qxV5gp9ub3fAZ6tPqazv8wGGqD1ZTvb+aN3WdOWzw5aKLhIvWiDX76PNQIMHXBHP4gcMkFbvxhz6Cjz9uud877xT1u/73P2FNcXM7Mxn8jEYRLzV16qkd73RCnz7i9/vvF5P7Gp54AvrtPkq/4Uqin4pudZ8Wp5PvCwqYk51NntVKd72eVLOZAK2Wy/39mRIURJRb47VIX1jzAjmVOXx06UendkFnmU9ycvgoJ4cfunRhX1UVTx89yr3LtViXJVAwbguTfwrCVTKIWN7lAI/gHJdDn7ev5JF1s/j78Grcj05BnzuW3h168fcaBaMvcxAU6cDhtJCz24hngZldUS4yBprBV2RDVjsVuKNCq1HQTWegnckApVpi9e5c2E9NusWCl0qFv0ZDgdWKQaXCUlXFEZMJlV6PAnC6XISq3XBXK3FTK/FSq0muribRaKSTXs+eqipyLBa2G40U2mx4KpUEa7VEuLlxbVAQw938KCp3Uqk3U6mw465U0tVg4Ov8fBYUFOCj0fBS+/asLC1laXExuyorCdfpKLPZGO7jwyX+/hw1mdhRUYnVBAaNkuFBXgz39qaje12cbr7FwtycHFJMJvw1GswOBwqFggqHA7PDwQgfH+4MDcXtOJ/J9WVl3HrwIBf5+bGvqooss5maiMxoNzeGeXtT7XDwd0kJz0ZHMzW44aJNhd3OB1lZJJtMtNNq2VRRQZrZjALo4+HBxX5+/FZUhF6lIkCjYbC3NwFqNd5qNcN9fACostuZl5PD/upqLC4XgRoNUTod2VYr68rKcLhcTG/XjokBAUTpdI0uhpSYStBr9LipG34+tmRt4bYlt7H99u24a9z5POFz3t7yNnMnzGVY1LB6bZelLOO+P+9j5oAHub3fbc0uwlrsllbH3ZaaSrn4x6n0iL8dP/9elNvttNPp6OTuTlJ1NctKSqiw27G4XIRotcS4u1Nms5FntfJoRARmlwvDDbk4MyDIoKDy7zDWVlRQabcTqNWiUSrRAB4qNT4aNUVWK2vLy8k0mxlg8qDzDNg00IepRzIJTXaQEqvk3WEhDLmsirE3l7F2pIYtN6vIKC2l9MorKS8vx8vr9LxeJBLJP0ObWABfffVVXnzxRRYsWMC8efPo0aMHQ4YM4d5772Xy5MloTmeJ/QxS8yArKyurt720tJSwsIZxXXa7nYG/LCKlZC9HzWaID6Ek+CAajQfBPV6lzBJBSdFOpnS8iOuDgtAdt1oK8GhEBFcGBDDI25uf/GxAKSVVIeQVWHmpfXt6e3qyuawMg1rN57m5DChdzgH/oZg9u6JEQT9VKV+XWvh751ZuDI3kncxMjMeCRALUauL0ehIqK+nr6cnM8HC+zMvjkj17yLKIVP2vpKXxYW4ufT08SKqq5EBFCfnZGfh6RTM2YgDLS0vZVVnJi+npfNipE/elpBCu0/F9QQEVNjM2J7jZi6no/CwT9iZRarczzNubi/fsobfBwC0hITySkoLN5eK2kBA66fXcdegQt4WEMCc7m1h3d6Lc3Lg6KAiL08mXeXkkVlYywseHv0pKSDaZeKNDB64+NqHYWVHBdQcOMDUoiF4eHlwfHEyKycRfJSVEenqyqqyMB1NSGO7tTaHNxme5ubiA+8PDuTNUxFbZj604f6DOJr5QT0ylOz5qNQ9GRLC0qIjn09P5qnNnfi8uJtNi4cbgYFaVlTEnO5uv4uMZ7uNDUmUl68rLMahUzM/L4+rAQHxoWgA6XS7SzWai3dxQKBSU22woFQrUCgU7jUb6eXqytryc9m5udNLr8eztia2oLrmQ2eFAq1SiclOhCdKg8lQ1OrlJucUTz/F6yi45jMqgwvdiXyr3VGLLs4EaNH51nzP3GHf6busritK3E5MT3zG+2IrFeT36epD1fhbFfxaj9lRTtroMc6qZsHvCCL0rlENvHsJR7cCjx8lZ9H0v8qXolyJQQQd3M+p4YfUAF+7F1eg7Cf9It2g3lAfNGLo0210tHh5gwIaHh6bWhe9M4Ol56uIPRIKGkSPh55/rZ0QEEQvoLLGiDWzdBGq30chneXksLykhQKvlxehohnh746VWY3Y4WF1WxqLCQsbu3s28uDguPDZxPp5bet/CiC9HkFyUTFxA3Klf2ClSZbezv7qaUK2WUJ0Ok9PJbqORo2Yz68rLWV1ayo9duxKr1xOr1zPQ05Pnl69ngK4Tqy/th7rETniCAvP7n6Aal8sht81seOlVyjv3o3PocLI6BBCRDG5ZiUzq78Ja5sTnsIqBq8Dio6AqSs3U720o1mpRq3SodUoCJweSddSIooMbe+NcHGhnIcejgl35Zr5faqNHsRa3PAdVTge5vTUcCnfibjbT2WDAYTLhdLmwO20cNVsABVYXVDocRLu50cPDg1VlZfT08KCPpyc3BAcTr9eTba5mc0EyGwoPc1+xL9iqsKsNoNTipQQTapROE3qlij7KYsLKDvFOeh4eWg8eih5HR4/eHPF14KFS8VtxMR/n5OCv0TDG3xcFQnCtKC3llfR0YtzdUSsUFNtsVDudDPL0ZJyfHyU2G24qFU6ngypzMb46DxaXlvJzURGX+fszzMuLQK2W+w4f5vn27bk2OBinywmIvhTA+vJyDlRVEaTRMC8ujgsauecAvNRqnjrB1FgjPnXHFujuaeQ79ngMajUPRTaeIdrhcrGytJTP8/J4KzOTvp6ezO7Qgdhj/tZ55mpuWDaL5JxN6A1hDA4fSKjOna0WLf7enejl6c2GXW8xtetU3DXiATK9103k48VNf8/i4oEvkutQU2DMRGnJJ7vkED2GzOdtm45f9+5njK8vl/j708VgqH02W51OtEplo+LP5XKxqby8VsjtP7agsLe6msK451hemkQ/xy7GRA4i3+bgj5IS/NVq5sbGEqjV4qNS4X9scdTlcvFFXh4LCguJKFVy9RELD87W8NILNhYuTafTCF9CVWBVKLEDJqeTfJuJ8ko7fmo1UwMDidXrSZ95BIPNTP8/y9gbrOO9AbHcceQId6UX4bdExx7DHr5ad5BRCgV+BU5WN/vfkkgk5xptYgE8kaSkJD766CO++eYbdDodt956Ky+//HJbDwuA9u3b8+CDDzLjWMoxu91OaGgob775Zj0X0IqKCry9vSkvL6fYUcwza57hjj53MDRyKEqFEovTyYb0dVy38BqyH8puMTbuhhnzue39jvw2pDuBE/154onG26VUV7MmL5k4HUR7BHDzkttZa/UkMv4+ZrXvxJUBARgdDg6bTByqrmaQlxe9juXStzudPJ+ejq9aTY7FwptZWTwfHc3PhYWU7XuJvNAbcGq8cCh1eLhM9PQOpofeje+LSmin1eKjVrOxd2/+PPwHkxdMxtfNl9zKXL6d+idVnj3o4+lJHw8PSu12/DQanC4X9x4+zHBvb647JuLezszk2bQ0vomPx+J0kmIy8VpGBhqlkmejosTq7THrxfKSEq7ct49xfn5olUo2lJfzUHg4M5vw78swm7n70CFcgKdKxQ3BwXioVExJSuLO0FBG+PjwyJEjWJxOXmrfnk0VFZTZ7SwpKqKPpyfbjUZ6GAxoFAo2lJdzQ3AwPxUWogQejIjgzcxM5sTG8nFuLr09PBjv58cDKSmkmEz09vDgpnbtyLBYuMhXTMJ2GI3kW638VlxMqtlMV4OBRyMieCEtDU+1mjh3d34uLESjVBKk0VBos3F3aCi6/Rb25lbQflQAA728uOXgQZQKBXeFhnLd9HJsegWVP0UzwtuHqYsz2ZFn4pGRvjxVlIwL+HWGFvUeM5VfR+G8L4vXNV14QXmIkXnClSnPYiGpuppgjYb3s7O5LCCAET4+rD5ayNFSEz07+TLc3YutsVuxFlhp/2J7SleUYsmw0C+xH0qdkopjWSqOX/11uYRr3Il1+o7HbrST8mAKqRkKdu1W0vvzWAICYP2vVvq+solhxmGoPdTkf5vPxseyWXVln2aLG9fw1M0mBn6xE9P3w05LsJ0NfvpJ1DdbskRkQqxh/nzQPbKLiz8MI/jahlYTs8PBUbOZzno9n+flMePwYe4IDeXaoCD6N+Pu+W1+PnckJ/NxXBzXBwezo6KCDu7u+B1baLt36b3kVubyyzW/1B5TUFXAM6ufYUnyEqJ9ovlk4id0Dera5DU5XU5WHl2JxWFhWOQwfNx8mn0P9lVWcltyMjuMRgI0GopsNvp6epJuNqNVKums19PTw4OZ4eGEHXcDZZRncPWMa3lo0Ss4D3dGeUMWS+0ulj1v5Y1rbGycZaC6r52B5RWEfmzG5zcDWo9y9KrDaCqKUVh1uBQm3LwXYw0uo6RTOKaO/TEEjCYqfhiuHAWFCwvRd9ZTtK8Iyw4LDrUDJ060Ri0WnYWS4BKMQUb0Cj0he0JAp8ASZ8bY28hh3WFUm1Uc8DxARrsM0n3TKQkqpX27DgyLGMLg8MEEGgLJM+ZRuKWQlIIUNnptJKUqBQ+tBxdGX8jgiAvJdIvFX61Al7GHbUe2csSjhICIK4lddYT2h8KwdbXBhaD5Q0P0/Gj8KvxQ3qbE+6A3PiN98LvYj7I1ZRh3GtGF6wi5LQSPHh5UORz8UFCAEgjX6bC5XIz386u9dxxOB2O/GcuWrC0oFApmj/4/3MMuYXV5JStLS8mz2ZgWHMwNmiwe+vtBjpYe5Z7+99A1sCuT4ifh7ebdxH/81Ln/j/tZeGAhozuM5uNLP64VZK3FaLfzdGoq83JyiDjmqppmNqO1FqPUBaBXujDZLZjR0NlVQEFxIsXaMDRecUwNCmF8QBDldjvvZmeTY7FQ6bCjLNmComw3AyIvwK4LQaXxYmRQe64MDCTFZGJxURG/FRXRTqtlgJcXKSYTu4xGJgcG0tVg4BJ/f/ocV9PmncxMnktLI0ynI81sJtbdnYFeXnRwd+f6oCCKy5KZtmgaldZKqmxVVFormRQ/qcn3w2K38M6Wd0j+MZnLf7ucqt+qCP60PartVm6fehuppalc3vlyHhr0EEMihqBQKDBnmnHZXbhFu1H4UyHJtyfTd18/jOsrsA/wZ0+Kmq5BZjLG7sBR4aDnyp5c8T8fQkJg6dIKjEZvaQFsgS+++AK7vflkaiDmnaNHjz7p/ufPn8/tt99e+7dOp6Njx448+uij3Hjjja3uJzc3l7CwMNatW8ewYcNaPkByXnJOCMAa9uzZw6RJk0hNTcXhcLT1cAB4/fXXef/99/njjz+IiYnhpZde4osvviA5Oble/OLxArCpB6DD6SDsrTC+u+o7RrUf1ex5wx6dwJvf3EfhzSNZkeReWzC3NTyx4gkS8hJYdsOyVh9TaLXybX4+D4SHsyd/D0M+G8Jfd6VSjZoOrhJ6rfmWav8LUW6bxgfjP8Dl148xvr500uu57PvL6BvSl2cufIZDxYfo5N/ppJJK1KyM1lBss6ECfBqxBH+Vl0eq2YzRbsfqcvFux44nncAiwWjk/sOH2VVZyf+iong4IqLe+VeVlvJWZibvx8Zid7mI27aN64KC+KZLF4x2O2V2OxFubvxYUMCtBw8yMSCAD2Nja8ebZ7Hwe3ExX+Tl0d7dnSVFRWiVSi7y9cVPrWa0ry+jfH35Nj+fZ9PSuNTfnzSzmQNVVWzr25cci4X+Xl6kmc1cmJCAv0bDc9HRfJiTw6rSUr7r0oUObm48dOQI4x+rpFLnZM7jSq6xRTG/MId2qf7k9c/hwYhwYq3ebPogiUv+hLtf17BgqpUfrtEzaK2T4i2hrC8v56+SEvw1GvKtVqYEBrKitJQSu51wnY7e7p6sLitlUmAg6x4KYOkIC7F3B+EwOsAFxnZK1paVcfExkX78vb90qSjuvGmTWJH/saCACrudxUVFeKvVvNKhA/5qNT4aDT9cl41peRE3F4oaWF8/WIrXRwe53CREasXWCjaO3Mu6B4Yye3bL/+M3bimn3+cJVP12IZdcem4lOCktFQkXHliVjVu4hRvbtaOTXs8ff0DVFVsYsyyuXrIdu9PJG8cmiA7EYoabUskXnTtzsTCXtsjykhImJyXRxWBgW0UFXQwGVvXsSaBWS64xl54f9eS67tfhrnYnqTCJ5UeXc3HMxTw46EH+PvI37219j7v73U2UTxR6jZ6BYQPpGtSVQ8WH+HD7hyw5tASTzYSnzpNyczn9w/qzKXMTGpWWmWO/5KBNzbaKcvq7K3it2wguTjrMhT4+PBYRQYSbGxV2O1/n5xOq1XJFQECTn+n7/riPhPWVvPTqTQwrGsrOQbt4PKsjA+7zJ/zTJK583JPIxyPJmZ9D2nNpdP25K96DjhMlVVWUHd5L0u4VVBzZj9veAwQdyKDD0RIULsgO0FEQbCBXayVHbULl1w6Dohdu3oHourmhDw+hykNLmZuCVEUZmUX5GFOMeGd60/VIVzrkdEA/Qk9QWRC2QzaqD1XjNDmxR9rJ6ZhDsmcy4QfCCSkKQW/So9AqUFer0fbWEtA9AFOKiaoDVWiDtCh1Cir3VILLRdAFTixFCqrSlfiO8aFsYzXWXBEvGvZ/YSw2Lsb3eV/ShqYxInMEmjwNhh4GfEf5UnWgioLvCmh3czucFieadho8L/DEb6Afau+6hcikgiRmb5jNztydbL99O5syN/HAXw+Qa8zlopiL+CvlLyrtNjr6RJJjzOb/xvwf3YO7M3f7XPYX7ketVPPK6Ffw0HpQZa0itzKXKO8oDhYdpJN/J4ZEDDlp8bYxYyPjvh3HomsW8dya51ApVfx1/V8n3Q9Aqc3GdqORffl7mPXbVDZPX0aXoO6oFApsLhdGu702xMDpcrLdWMkPBQVsKi9HqVBwe0gI04KDUQBvbPo/RkSPYFD4oCbPZ3I4WFZSwoHqakJ1OnoaDPxYWEia2cySoiK81GqGenkRp9fzblYWK3v1YmAz4snutLMgaQHtPNrhr/fnrt/vosRUwpCIIRwqPoSnzpNHhzxK/9D+XP7D5RSbivnf3v9hSjLxwQ0fkJWZxcevfczul3dz3a3XMWfbHD7Z9QnRHtEMOjSISfMnobKpsPpa0Rq1LJ6xmMXtFgMwoeMEbutzG/1C+1G6vJSiX4voNKcT+/eLZEM33VTB/PlSALbEZZddxm+//dZiu3nz5nHHHXecdP/33nsvn332GatXC3tsaWkpL774Ips3b2bVqlWMrAnwbIGPPvqIZ599ltzcXJRnqw6KpM1pcwHodDpZsmQJH330EStWrKBPnz7cc8893HTTTW05rFpcLhfPPvssH3/8MRUVFfTr1485c+bUZgWtoTUCEMQEZk3aGnzcfHh51MtszNzIHX3vIEBflxxje/Z2Bs4bzusxuxnSKY7LLhMJLVqrc4qri4l5L4aXRr3EfQPuO+lrnrVqFodLDvPj5B9rt+3M3cM9Gz/i8uBw3tj0Bld2vpIbetxA54DORL0TRcqMFCK9my/Yfq7hdLkaZJADUUw4IQGGHMuL8XFODmP9/BqNozo+sU1TGO121ApFo+2sTicahYJqp5NSm43wY+f44w9480147EUbwwcq0R+LscyxWmstIrkFTiY8cBB7lQrdoyZ2OsoYvzOej68JptMAK8pKDeZqBfqexUR1LUCdEsv75bs4qrSQqlaw6QNfhoYZmB4cTJSbG2V2O74aDVank4pjv3/8kYLXvzHR8410FhcUMVjtR99OGnQKBbokXz4uz6Qksoy9XbqwoqyMnkFBXODtjUKh4M034elX7Ly4K5cfC/Mpsdvp6O7OIC8vkqqq+KWoiGCNhozBg3n/thLCl+6n9GFfhsQHcOAPO+XflnBHhRCE1iIrmwI3seV/w3jihZY91+fdXkLc/D0o/h7OhReduXjMM8WzfxTzjud+LvL1ZWNFBT906cIPB0q5bGQ6yxYG8ODojiwuKuLP4mK2Go0EajR80bkzfT09WVdWxiAvLzxbyC58IvlWK5/l5jLR35/n09PJs1pZ3bMnaqWS7XlJTF7/GRaffnR2U/F0+05cFN6r9tht2dt4c/ObmGwmyhwOtlRaUfv2xqz2Jc7gRWf/GDr6xuCmVKEu34PaeICQ0JG8m1vIHrMDvXE/7TUuDunisGkD6OGmYseAoSeVpKPEVELE2xFcWbqeqz+10+fZUI48eoTfbx3CGx9peGdIJiP0JXT+ujPbOm+jy/dd8B/fuvofLpuNrN3rydyxCmV6Ov4mBdEubzQVlSKjS2lp/Vf1sbT3CgWoVFQEBICnJ14qlchAdOzl8vTC6vClstSPisJAqo0+eMdUY/AqxkufjrIgE1OOknJjFNXmQPSGUvSGImxFdlzZeRiUaThDo8kv749GUUmI+wo0WQdxxnbG1nkg6vhIVPEdIDaW0vAAXkv+lPe3f8Cg8EGM7zieWL9Yugd3Jzg/mNSnUjEGGVm3fR1RqVEEVgUSfHUwSdck8VbeW+zJ38Ok+Em8MOIFYvxixPvicrE1eyu/HvyVSzpdQqxfLNuytxFoCKwnfqwOKzP+nMG69HVU26rRqrQEewSTWppK54DOJBcnU1hVyIsjX+TRoY/WHmdMNOLRwwOFsuFz2OVyccEXFzAqehTPj3yeals1F319ESEeISyYsuCUMtfuXbWX9bevRztHy23jbjvp488UpTYbydXVfJmfz4GqKt7u2JHex1kEW0N1VTW/Lf+NRI9E4gPi0T+k56leT5Hqm8qI6BEsvmYxadPT8OjpQdSTUQAceeIIphQT3X7uRnVyNXuu3IPpiAmnzsn++/dTMLgARYKCEnUJMSNi6BvaF7vTzg/7fuD7fd/T3qc903tO54YeN9SWbtqZ4OCzow8yd/L7UgC2wJIlS7j88sv5+++/ueiiixrsv/fee/nyyy/Jzc3F8yTvB4ChQ4diNpvZuXNn7bYDBw7QpUsXHnnkEV5//fVW9TNu3DjCw8Pr5bmQ/PtoMwGYk5PDJ598wvz58ykpKeGaa67hnnvuod/xafHOI1orAPcV7OONTW8QZAji9U2vo1VpmTthLrf2EdkgNmduZvRXo1GsfZa1rzxO9+5iLpGUJOKP1qwR7mMtsSVrCxO+ncDrF71e27fNYeNQ8aEGrlxJSULw3HCDqHk0bdE03hn3DpO7TG607093fcregr3M3zWfMK8wugd15+erf27dG3US2O0iNXlMTF2q/H+Cjz4SqdIPHWpdRrwzjdUqYtj69xd14n46rlRbZSVceqnI7gcinmzwYHj2p3JMkzLYNaEbPbsr2LhRpHbv0UO0TU0V7oZH7j9Ezoc5HOoRxvoesXz9tejHYhE1wCZPFtd+yy0wapRIoPLdd/D553DjQxai/p+9O4+Lqnr/AP65s7HvAqKgoLjv+75ramlq5laZppVl5bf6ZWkuqZW2qJll5ZJlqWlpaqVpLoiCuIMbKCIugOz7MMBsz++PIyMju4KD8rxfL17FvXfOnDsOM/e5z3POWXoVT3VXwQjgp4hU5GfI0dDBCp7uWYjJz4fG2hpOCgXclErciCEka3Xo4G2FyfU8MNXLyywI1hqNaHLyJJY2bIhPfo/FshmZUDsBh/sAeY4SnPa6YF5cC9gq5PghNha+Da5j/1xfNH9Fgan3zmV+j02vJqHu2nA4HOqBDv0sO6ZYYzAgz2hEtsGAk1lZyDUa8U5UFJb7++NFT0+8Hx2Ntbdvo5XOFh8PyMaGIHf8qktGfWtrvFPXB12cHNDewQHySlyqQWMwoPOZM+jk6Ig+Tk5499o1NLG1xXMeHgjMyMDetDRMrl0bo9zd0dnBAatv38aq27fhrlTiUk4O/KxVaKbUobVjLaSREnoiGImQYzDgz5QUGIiglCSM8/DAwvo+qG0t/oBzDXrMClyMned/xpU3w4udfKM4e/YAgYYvcDLtX3jtC8CIq5dRNzoFDp0c0GZvG/zzD9DQTYukfiFw6ecCmY0MLf+swonF8vNFIHhnPYWs1FQgJweOWq1Y86HgJzNTHGswADqdWGQvJ0f8gbu4iMXwXF1FvbTRePfHzU2s4dC4cdFF61JTRVr90iXg6lXxExUlppZ1coKuQX1c9rbBYR899tZW4wCi0cC1ITQ6DeKz4zGr5yyMbDoS7696H532dkKfC31wa/0tDMwfCOMlI2q/WBv2bUofy5sXm4fUXamoM71OuQIxIsKxmGN4YuMT2D9xP7r7dEfmMbHuaJMfm5iWItIatNgQtgFn4s+ggUsDrDyxEuFvhJvWdU3VpKLND20wv898vNrhVeiNeihkokRcE6mB5wueSPs3DZ4veJqNcQaAgycPIv2JdNjY26BOozpoe6BtibM+E1H1XhoFQNwPcYiaEYXmW5pD4arAuX7n4L7YHbav2aK+c33IJBlOtjiJBp83QK1h4gZzbnQuTjY7ia43u+LKy1egcleh3ux6sPG3KTYIL0ytVWNHxA78cv4XHLsRhDfdn4TP9TQoL0dCo87Fu4fSOQAsg8FgQL169dC7d2/89ttvZvvy8vLg5eWFZ555Bj/++GOF2yYiODo6YvTo0fi5YGFHAHFxcfD29sb06dOxatWqMtvJysqCu7s7tm/fjmHDxLJier0en3/+OdatW4f4+Hi0adMGa9asQbdu3fDRRx/hgw8+KFcfW7ZsiW7duqF3795Yvnw5Ll++DD8/P6xatarc2UlWicgCRo4cSUqlkho3bkzLly+n9PR0S3SjUmVmZhIAyszMLPdj0jRpNPO/mfTyrpdN257b/hxN3/UuAUQFL0u3bkQ//0z0xhtEANGuXeVr/8C1A2T3qR2djD1Jx24do9bftybZQhldTb1qOsZoJOralcjenujszStk9bEVfRz4MekMujLb33pxK30V8lW5jr0fmzeL8/XwIMrJqdy2jUairKzi9z31FJGTE9GkSeL3774junmz5Lb+/pto/Hii118nWr+e6OuvifbuLV8/YmOJ8vPNt128SGRnR3TlCpFKRZSUdHff8uVEHToQnTlDdPAgkVottv/xh3j+siRsTKAABNDZ92+SSkV065bYPns2kUJBZGtLVLcuUb164vVp0ED8G4wcSeTtLX6MRtFvudJIu/YYSNk1lRz37qUt0dGUZzDQjqQk2pyQQF1mJhH6JNKOncYS+/NR9HVyPHKEHLeH0AFFIAUggEKHhNG2Z0/Ti88fJufAo7QqNpYQEEBrfAOp/4LD5Hz0KH1640ax7d3KzaXTWVm049XbFIAAijiSW/aLUokC0tJoyLlzlKrVktFopBmRkaQ6fJgQEEDygABqe+oU+R8/TpsTEoo8Vn05h/bhMF27ZqRz2dk07iUdffdd1fX1mkZDT4SFkXtQEP2ZlERG491/p+OZmfT6lStUKyiIlIcPU+fTp2lbUhL9Eh9PZ0r6w7kjRaulC9nZpDMYit1vMBqo7Q9t6YugL8rd127diNzfGUzfnviW+vUj+n2K+PeNWxtndlz4xHAKkAJIfUld7rYrQ2ZmZoU++6tEdjZRaCjR1q1EM2cSde9OpFSSwb0WJXVuSTEvjKDcr5cRRUYSEVF6bjoFXA+ga/OuUaBtIB3zPkZne5+lC6MulPlUkf+LpAAEUOz3sWbbNdc1dOX1K3Tx2YuUdabo+2Rp8FJq/E1jytPm0enOpym0XygF1QqipO1JlJSURL3W96Im3zShYZuHERaA/rr8l3n70Ro6sOAA9Xq+F3Ve0ZmsPraiPp/2oQC7ADrmc4wCZAF0ss1JOuJwhMInhZM2TUsGo4EWH1lM89vMp3/6/0O6bB0d8zlGSduTivSPSHxGBnkEFXlvVTfhL4ZTaL9QCrQLpBPNTlBQrSCzfztDnoEC5AGUe8P8MzBsUBidanuKjjgdofzk/HubNafTiQ/7f/4hWrCAaOxYovbtSW9rQzqlnG7716bLQztT0ovPVfj6p6aaM2cOWVlZUWpqqtn2jRs3EgA6duzYfbV75coVAkDLli0z275p0yYCQOvXry9XO7/99hvZ29tTbu7d983YsWPJ1dWVvv76azp06BC99957VKdOHQJA//33X7nazc3NJblcTr6+vjR06FDavn07/fXXX9SkSRPy9vYu/4mySmORAHDUqFHlftM8Ku4nACQi2h6+nVp914qIiJJzksnqYyv6/VAE1ap195gVK4h8fcXF+axZRC4uRPHxRdsyGEzf7SafHf2MrD62IttPbenTI5/ShG0T6JW/XjHt//NPIg/vbGrY6SqN+upjGvHbiAr1vzBdBeNAtdq8v9u3i++aAhMnisCkYUOiv/4q+vj7FRJC1Ly5CHqvXBEBV3AwUV6eCDStrcX3nVIp9imVREuWiPMr+ExUq4nefFMEXjY2RPPnE02bRtSnD9HTT4u2y3qLP/ecCK6WLzff/vvvRB07iv/v14/o3XeJMjNF4F+3rvg3u1+a6xoKQAAlbEqgZ54hGjOGaOdO8d46e5Zo9WqitDSivn3FPkkiatlSvCZz5oiA9PvvRXDcv79oc8pUIw15M5YyMszf+x07inYXLy6+L2fPEnm1zaXaR45R1+eyaW/dk3Sm2xk60fQEhfYPpVd8btOoPdcIAQH0yfUbtKjNUVr2vzA6n51NTkeO0KZigqhRFy6QTWAgbXhFXKDeDK7kOwelOJ2VRbaBgdTm5El68tw5WnzjBrkHBdFFtZoydDpK02pLfXz6kXT6XX6MgoLE7337iveYJWn0egrLzjYLDivD4euHye5TO/ovqnzfA/XqGwnvu9KPe09Q06ZEe9drKNAmkPKTzC9gNVEailvz8C/cq0UAWByNRnzgrV9P9N57RE88If6IO3cmWrmSKD6ejHojxayIofykfNJc09Bh5WHKi88zNZF9Lpui50aTLkN8wOtz9XTU9Sjd+OQGBdoGUuT/IsmgM5DRYKTTHU/ThZEXKHJGJB1xOkKGfHETIGl7EuVE5pDOoKOW37Wkb7/6loLcgyg3K5euvnuVjvkdo687fk3DNw+nHG0OGfQGCv0jlIwGI2muaej8sPN0vNFxCrQJpND+oRTkH0QHrQ9SyIwQ+tv/b/qo40d0O+a2KejMPJVJZ3udpYAXAmjG1Bn0edvPKcA+gPLixHnd/OwmhfYNLfJy5d7MpcOKw3RjyQ0KtAuk9MD0qv33eQDHGx+nlN0plLovlYLrBlPCpgQKqhVk+lvNCs2iI45HivztZp3Johuf3qCs0GJu5ERHE331FdGQIUTu7uILQJKImjQRX1hLlhBt20Z04QJRoc+z+73+KY7RaKRMna7Kfir7s6yioqOjSZIkWrlypdn2fv36UYsWLUp83MGDB0kul9OGDRuK3b9161YCQP/++y/pdDpKS0uj7du3k4eHBzVt2tQsoCvNuHHjaPTo0abff/75Z5IkiU6ePGl2XOfOnQkApaSklKvdkydPEgCztomIVq1aRQBIo9GUqx1WeSwSAD6O7vcDMDYzlmQLZZSVl0UrQlZQr/W9aNMmcce7gMEggooBA8Tv48aJG3H3WreOyM1NZGgKS8lJofhsETFeSrpE1p9Y06HoQ0RENGiwjvwXDSTrBU5k9X4D2nhuU4X6X9C/JUtEoOTvT/TZZyJQMhhEQDFzZvGPe+stkWkyGMT3jlxO5OhIFBgozsHTk+jwYaJ33iF6+WUR+GRkiMdevy4Cl4qKjCRydSX69FOiGTPE62VjI/47YADR0qVEfn7i+Z94gqhtWxGkDRhA9L//icDuzTeJpk4VwRhA9NFHRZ/nm2/Ea1HwdtBqzbOYGg2RTCayuk88IbKIb74pnnfBAqIXXxTHRUSIgN/VlahxY6IXXhCv1/0yGo103P84ZZ7KpIQEombNxPXgH3+YH3f1qgj6WrUSgS0gAvR160QG8qmnRABHJIL2Bg0y6dlnM00ZSSLx7/fUU0TPP198X9atE89d39dIfn5Ef32dTVlnsijQOpBCGobQBwNTad58I21MSKBb8QaaiQi6MjuaiIj+TUkh28BAmn3tmukL/ZpGQ6rDh2ledDRNnXyYAhBAScdKz1ZVpneuXqWpERGUqtVS/9BQan7iBO2/5y6v0WikhM0JZDQUvQhJ/D2RNtidpt9/F7+3aUM0bNhD6LiFbDq/iaw/saYXd7xI5xPOU1ZeFu2J3EOBNwLNjjMaiZQe10iar6SXXs4jJyeisDAiXWbVVB7cj2obABYnNVV8MPfqJS7umzcnmj5dfMCmp1PYoDC6sURk2FN2p1CgTSAddT5KiVsTiYgoYXMCnWh6goxGI+VczqEQvxCKWxtHcWvj6Fj9Y6TX6MloNFJwnWBKO5BGmmgRVIb4hlB+Qj4F3QyiDzt8SPP6zCPZQhm1+b4NdV/Snf61/pci50dS7HexFP5COAUggC49d4mOuhyly69eptT/Uin7XLbpNDKCMyi0fyhdm3ONntv0HPmu8KUVISvov6j/aNKOSdRpdifap9hH/zn8RxFzIijtYJrpsdoULQXaBNLNL26SLuvu++j2T7fpTNczRER09f+uUsTkiIfxL1Juib8nUu6NXNKmaCkAAaRNEUGY0WgkQ76BAq0DSR0uPoRjVsZQ2KCw4hsyGkVZy99/E33yibigaNxYlIEMGiTuOp84QRQTI+6MlqEyA8BMnY4QEFBlP5kVvVNdiv379xOAMn/69Olj9riBAwdS27ZtTb8XBIVfffVVmc/1008/Fbt/9uzZRZ5XqVTS2LFjKb64jEEx8vPzydHRkX799VfTtqZNm9Kzzz5b5NjnnnuO6tWrV652iYhWr15NAOjSpUtm2xcuXEiOjo7lbodVHosGgJGRkTRkyBBydXUlpVJp9vOoeZAPQO/l3nQo+hB1XdeVVp9eTQsX3g0ACuj1dwOIhASRWblQqFLHaBTf4wBRCdVxRCQCrfVn15Ptp7bU/JtWhLcakf9Xzei5rZMJc63o27UV7//mzUReXiJY276dqEULorffJnr/fREkyeV3y1kLJCSIwMvOTgR8s2cTPfMM0ZdfiqDk9GkRbOXnEx06JAIFgEwlcc8+K25QZmcTTZ58NzNXWHY20bx55qWeo0YRvfaa+P/8fKJly4ji4sSxAwaITOvWrXfPCxD/FlZWIjhdu5Zo8GDx+l+9KgI0vb7oc+v1RE8+KW6c3rxJ9NJLIhgKDRU330+fFmWmEREi0GrbVgSEP/0kvos/++xuW6dPi5uulXXj0qi/21ByMtH588Uft369uA745hvxOtzzuW3m+vVM6t07k2bNEr/n5YnHfPMNUbt2xT9m1iwR2L/4ojg2OFhkFwIQQAEIoFWzcujpp8WxR44QzbS/SpEz7qaML6rV5HPsGH0dE0MXsrNpQGgojbt4kYiINj53TlxAHkws9+vyIIxGIzUICaG/kpNLPS4/IZ8CEEApu4veNY1ZGUPrap+nFSvE7/Xqiezr4+xKyhV69a9XyepjK8ICkM9yH3Ja4kRP//Y0RaVGERFRSgoRWmwhvyUdqGFD8V5JfDj/rOX2SAWAhSUni8BvxgzxwSuXU2LjVyjEZTdpfj1AR50CKWFTAl1544rpb+/i2IsU/VG0qYnELYkUVCuIAu0Czd7Xl1+5TJH/i6SLYy9SxOQICp8YTsd8jlHCbwkU6BRI2zZto8vJl2nLhS208dxGivopisKeCKOwwWEUNjCM0o+kU4hfCMV8E1PmaegMOtp0fhP1+akPuX3uRu/sfYf2Xt1LcVviKPt8drGPSdqRRCdbn6TLr142bQt/MZyufXiNiIgygjLoqNtRMuge4I5bJcpPzqfDqsN0uuNpSvoziY43Ol7kmNB+oRTztXi9zg87Tze/vDN2ITdXZII//FCUqTg7iy/m5s3F+IUlS4h27y76RV1ONTUDmJOTQxEREWX+3LxnDElBtu7MGXGzYe7cuWRlZVXubFpxhgwZQt7e3nTq1Ck6ffo0Xbp0qcJZtX///ZcUCoVpWNb169cJAP32229Fju3ZsyeNHDmy3G2//vrrxZZ6jhw5knr27FmhfrLKYdEAsEuXLjR69Gjavn077d271+znUfMgH4BTd02l7j92J+UiJaXkpNALLxAtWlT6Y55+WnxmF9i/XwREjRuXnBn7919x8dSvH1Ho9Wia8e1f5P/kP5Sck0x5ujz65o9QcnQsPYAkEqWQ69eL4MVoFJmKwmOVLl4UQY2NjQgaWrQgU1aDSFzwKxTiHKZPv1tt8t9/IlPWtCmRg4PIdBKJbUOHinFoEyaIss2CgOytt8Q57d9ftJ8//CBucHfsKDJtO3aIQLK08XyFvxs0GpGJjY4WY9/atBH7jUZRJlkWvV6ca/v2or8qlRiWA4iAt2dP0Va9eiIQ3rJFnFO9epVb8vqgDh8W1wr3jlUsLDMzk4KDM8namigqSvwolSJIVqnERfy9xowh+uIL8e/7+ed3xzMGewVTAALo0L96qltXZDx//JFovt91Cp8YbtbGkfR0Uhw+TFaHD9O0y5cp8U4nL06+TAEIoLfXiTSlvorLfi6q1WQTGEia4u4GFJJzJYcCEFDsnflrH16jdc0v0/vvi98dHMRNEAtXLD0UmXmZlKpJJaPRSEnqJHr1r1fJYbEDnYo7RRcuECmHvUsv/v4aAeK9+CBZ8KrwyAaA94qJIcOa9RRktYeOK36jCLxP5OhICT3m0Sn/A2TI1dIRpyOUeeLuuRoNRro45iIl/m4elSfvSqbDysMUXCeY8mLzyGg0UtwPcRTSMIROND9Rrgvxqi7Xyz6fbSolNhqNdMz7GKXuF1l7o95IQR5BpjLQygwE0wPTSRNVsYvzm1/epDM9ztCpDqcoAAFmgWuB2+tu08nWJ8mQnE5HrA9R9vg54otLLhdlJBMmiA/T06eLv2t6nyozAKwJ8vPzqVatWjR9+nQyGAzk7e1N48ePf6A2a9euXa6ALCsri9566y3q1q0bNW3alKZMmUK6O1nRadOm0YCCUjMiOnr0KAGggwcPmrWRmJhItra2tKjQhWpp7RIRde3alUaMGFGkP/Xr16cZM2ZU9HRZJbBoAOjg4GD2BnmUPcgHYJomjXxX+NKTm54kIjEpSzE3XMysXk3Uo8fd3xctEqV2EyeKEsKgoKKVGwMGiHLMQYNEBq1ZM1HyWNjUqeK4whdYFy6I49LTRfDUrp0IaMaNI9qzRwRv995oWrBATIZCJIaevPSS+P+ICPHYEydE/86cIfLxEZm6gueMihIZn3v7f/CgCMSmThVZv4IxdLa2ZLpoLqxDB9GHefOIpkwRwWAxlQzl8tlnIgtXUdnZYiKVyZPF6yWXiwDX3f3upC1vvCF+iET/APEaVBc6nbh5UJqCC+C+fUVpZ0CAyKYSEfXuffd9NmeOGF9JJN5HxY1nPNP9DAW5B5FGI8Z/rlhB9MEHRF/2iKXzw4qmK89lZxcp67k47iIFIID6LT5MxzMzqc/Zs/TK5ctkNBopV6+nX+PjaXuhiU90BgMZyrjYzNXr6UYJF03zoqNpZOGUfAkyT2TSEYcjdNjqcJELwIgpEbShXzRNnCheczE1ZPHBc1n++afoeOBHzbJjy8j9C3f6aVcUKf/Pnzae20i+vkR16li6Z0U9NgHgHVEzo+ioy1HKv5VFdOIE5U6bRwE4QCkNJ1CQcwAZ9WUHQ/ocPV167lKR97nRaKw2WTUiorAnwujanGuUfSGbDqsOkz7n7k2cy69epvAXwyn+l3gKaRhChrwH73duTC4dcThCUe+V70O+YIxmcJ1gStySSIY8A+Un5hctI9frSff3ATqi3EfXFNMoSL6LjG+8Ke6+3rhRpXeSOACsuHfffZecnZ3pzz//JAB04MCB+24rISGBANDChQvLPPapp56inXeyBEajkYYNG0Z//PEHGY1G8vLyom+++cZ0bEREBAGg77//3qyNl19+mQDQPwVf5qW0S0RkMBjIzs6uSP/S0tJKLWtlVcuiAWC3bt3oVsE0hI+4B/0AvJlxk26ki9SbmxvRqVOlHx8bK4KJgovDiROJFi4UJY3Nm4tgZ/p0sS8oSEzaYWMjjo+JEZmmCRPMxnHfOQ8RZG3eLH4/dkwc26aNyORYWYkxYWFh4v9btRLZm9IcPCjGg+XkiJK/yZMr9NKY5OSIzKFSSRQeTrRpkyib/PLLomWGwcEiq1a4/DMwsOzsZlVIShIBckQE0bffioAUIFq1SuzX6++Wkd68KUpAy0gkVTsFF8CTJong/5dfxBAjInEzw99fBPgdOoj3otEoMlzFlZ9eev4Sneog/gCOHBH/js2bE61/PoHO9DhTrv6cGypKQL/6+jz5hoSQ29GjVCc4mBZdv07vXr1K9Y8do9rBwTTt8mUaeWfimFcvF72jXtj/IiOp8fHjpM83/8fR6PXkHhRE++4Z71ec1P9S6XiT43Si2QlK2WMe2Z178hz9PjGWBgy4U/Z45+bG7NnFj/m9V8ENk19/FX//s2eX/ZjqbsrOKWS7yJns/68t6Qw6mjBBvIeqm8ctANSr9aaxZETigi64bjCFuOymcOV88Qc9ebKoib9+vWLBRV6e+CA+f17cIbtflZQGzjyZSYE2gXSq3Sm68sYVs/ZzT4iJbo46H6Wjzkcp7ocHn2Do4piLdNT5KJ0fXkLt/T1ur79NwXWDKeabGPPAWacTA7F//VWMuahTh8jTk662W0dBLofvln8+BBwAVlx4eDgBIHd3d2rQoMEDZbv//fdfAkB/lVE6dPjwYXJ0dKQ2bdqYfvz8/Oj333+nkJAQAkAxMXdLrvV6PTVt2pRq165NmzZton379tFzzz1H3t7eBIBu375dZrtEdwPJwgEjkZjYBgCdO3fuvs+d3b+KrSRcydasWYPp06dj4sSJqF27ttm+3r17W6hXlYMIOHgQ6N0bUKnKPr5gEfX0dLHMU6NGpR9fty7QsycwaxawZo1Ys27oUMDTEwgPF+u5bd4M3L4N7N8PvPIK8NdfYnkpQCwb5eYG3LsOs6Mj8NFH4mf0aOD114EPPgBmzxaP0WqB5s3F+sft2gE3bgBvlrHWfJ8+YjmrPn1EP0+eLPv1KI6tLdCxI+DlBTRrBvj5ieW0unYVfbx5E6hfX6yVN3kyMGeOWDuxgKXeUu7u4r9Nm4qfAwfE761aif8WXuarXj1g69aH27/K5OMDxMSIc6on3tJ45hlg0iTg2jUgNla8v5OTgexsoEGDom3YNLCBMdcIAOjVC1i5Urx/vcYpoA/Vl6sf+ixx3JO2LpirTcd8X18McXXFgHPnoDYYENqhA+SShKHnz+NJNzf83aoVnr6zuGKWwYDNzZpBkiTcysuDvVwOPRHWxsejXqQRxyadQq/LdxfB3piYCE+VCoNcXMruV6YeCicF5PZyaBO0yE/Ih0wpg9JNCW2CFk4dVLh9WnwOKJXi/bJ8ufg80WhKXg/z1Cnx/h4xAti9G3j6aeDy5XK9VNXayqErEXwuAfXT50MhU2DAAPHZw6qW3E4Ou2Z2pt8lSUKjrxtBc1UDz1FdgajOYi3CDRuA114Tb0x/f/EF5OAAKBRARoZYC1GSxJs3OxtISRFvbplMPCYnRyxk2qgR4OQk1kZ0dwdq1RLrI2q1wJUrwMWL4nF5eUBcnPhiS0sTXwJt24qf2rWB3FzxXFqt6Iezs/jjiY0Vj8vLE+sy5ueL4y5fhqONDbz930BiTDM0mKgSX1J79wJffgnr2Fj42U5GRu1h8HivPa5/cgteL3vh0rhL8HzeE+6j3Cv0uubH5SNlZwoarWqEmKUxZvvyYvNwefJlNPq2EeyaitfeoDHgxoIbaPhFQ3g+5wkkJIg/9n/+AXbsEOfRsqX4Mlm/HhgwAP4KBRo+AusY1nTNmjVDz549ERQUhLfffvuB/r3CwsIAAO3atSv1uNOnT2PKlCn46quviuybNWsWOnToAG9vb9M2uVyO7du347XXXsMrr7wCT09PTJs2DV5eXti0aRO87qzHW1q7hfvXvn17s+2hoaGwsrJC8+bNy3uqrBJZbCF4ANi6dSumTp0KjUZjtl2SJBgMBgv16v7cuxD82bNAhw7iAu7QIRG0lMfJk2Kh76Skso+NjRUB0bffAtOmAfv2ie/gHj1E8JmVBfzyC9C5s7ggLC+dTnynWFuL79zISPH/9woJEd+hAwaUr68jRwLLlolA8H6dOQN4eIhAo7BXXwX+/FMsmr5pExAdLQKte9dQrg5ycsQC7gcOiID7cZCVlQUA2LLFEdu3i2uxBg3EjQRA3AD46ivx3hbHAe++K67J7qVN0cKQbYCNn41p29WrgHtyJsKfvYTut7uX2Z9TrU8hJzwHjVY2Qt5LrqhvZQWFTIZIjQZRubl4suBOSCHLY2KwIyUFlzUaLG/YEH+mpOCf1FToiWAlSRjg4oLWe7To96UGT6T2xs28PDjK5Whx6hSWNWyICZ6eZfbr9rrbSP4jGcpaSti1sIMmUgOlqxL+y/1xrO4xWH/eAv3fdML+/eK16tUL2LlTXAtv3Ag88UTx7Y4ZI9Yaz88H5s8X19nvvy/WCn/UvfWW+Pz58ktxLV8dr2sL3v81chFsjUbc3bl6VbzxsrMBvV4EX/b24h/M1lb8v5ub+DJ0dxdBYFyc+OKKiwMyM8UXTnKyaCctTdwF8fcXX0ju7uKNUKeOuAPq6iruSoaFAaGh4jG2toCNjbjrqlaLIFSSxAdSvXpin5WV+LG2FoFnbi4o4DCMB4Mgv3pBBInduok33tNPA4GBwCefwBhyEiHYDv/n0xCxwQdKVzk6hbWFysehjBforpuLbyLzaCYafdcIJxufRC9NL8iUMuTH5SO0Zyh0qTr4vO8D3w/rIeNQKi6Nvwxbr3y07b4N0r69wK1bQJMmwMCB4o++R49q8SV37/UPK5/09HRkZmaidu3asC7uIquSbd26FZ988gmOHTsGBwcHaLVaXLlyBa1atULTpk3xwgsvYO7cuWW2M2jQIKhUKuzevbvMdln1ZdEM4MyZM7F48WJMnjwZdnZ2ZT/gEXLwIPDkk+J7Zu5c4Mcfiz/u2jXA21scBwDHj4vAsTy8vYHp04EffhDflQU3UQsu+mrXBj75pOJ9VyqBoCDgm2/E92BJn0vdupW/TW9v4PTpivflXiW9NmvWiEzgU0+J7/jz56vF92Kx7OzuPwta3RVkALOygCFD7m738xM3DORycbP+009LznKraqmAWubbGjUCNEYl9BnlzwCqPFQwaAxoaHM3kGxsa4vGJaTR3vXxwbs+PlgWE4MXL1/G025uuNW1K6xlMsTm58Pfxgb7N1+GPl+NZy5exI6UFDjJ5Wjv4IDxHh7l6pch0wCFkwIqLxW08VrkRedBm6AFGQnaRC18m6uQmSmuh11cxLXvk0+Ka98DB4oPAKOjgb//FjdqCrKuUVHiR68XyZjUVHHT4dgxcR1eEUTiuQcMKFox8DDEx4vrXKB6Bn81nq2tyEDdz8Wetzcwder9P7evrwiGHpD05JMwfV3ce5fhiSeAJ56ALDoatSZdQNRmVzjLz0OZloaL9c6jtXIuFO624o5Xw4bij7ZjR/EF6eRkakaXpkP82ng0+LwBrO01gGRE3hufQqVPxPk/+8HZLgaOrteQ+Glr+H46A7F5s+GJeDR0OQjJeiiwerVokwOsx4aLiwtcylE5UlnGjBmD4OBgtG3bFg4ODlAoFHj//ffRqlUrXK5AyUhoaChef/31crXLqi+LBoCZmZmYMWOGJbtQ6S5fFkHKgQMiGBk+XJRMvvsu0KKF+bFEQPfu4nvj9dfFBdrRo+Kuf3kNHSqyLB4eZt81D8zdHVi0qPLaeximTBE3hZ2dxXUFe/i8vUUAmJgosn4FfH3Fe7t2bWDcOFFh9fXXFWtb4ayAMdcIY74RMqvSIxFDpgHWftamUtKKeKNOHbgpFHjB0xOKOxGPi1IJAGiULEe8XkJjGxtEdemCg+npeMLFpdzlO/oMUQJq5WWFrONZyLuZBwDQpeoAA+DRVAUrK+DCBfE+njNHBHF79wJffCHaSEwUf+8FT/nvv6L8syD4A8TrTSRKtP39gd9+E21evSpKtyti1y5g1Cjgww9F4P6w3b5d/goKxh5YSX/LDRrAfa4z4oech9tXo1BnshsujQlHyImdqNNXQoMnbkCKvibGYPz0ExKj/QAnZzg43MaN7GeQlesHe1k0ar3QH5IuF7aqjdDcJNyM7waluwKN37OH1tgPkW+6IO/QGaQOTkbH4BaQOnz/cM+fPbZkMhlWrlyJlStX3ncbN2/eRGpqqlk5Z2W0yx4+C9zPvWvo0KEIDg62ZBcqXZcuolrl6FFxx9zPT5Q8Hj1a9Nhbt8Sd+fbtgaVLxYVxRQPADh3EcInCF9s12ciRQN++lu5FzeXjIyqvCjLSBXx9RdbT2xuYOVNkyFu2rFjbChdxvyrpjyRc++BaiccRkcgAeqpg1FQ8ALSWyzHZy8sU/BWWfzMPsnzCkgYN0NDGBq/WqQPfQhnGsugz9VA4K6CqrUJeTB7y4/KRfysfuddyIXeSQ2ErR6NG4rVycRHDmFxcgEGDxDCoy5dFQPfLL3fbDAkRN5IKUyjE63/livi94PioqOL7lZsLbNsmyr8LREQAn30mSknffRf47jvR5v79ZZ/nlSvixlR4uAhEf/tNDF8yGsWPWi3KudPTRcWCXg+88YYIMA0GUVUIiP8WzmwyZknO/Z3h2NUR7s+4Q+5si1b7OqD1f22RcECOBBoELFwIbN4MiorCjQYLcVnzJi5gMdC2LepPs0GLfd0gu3QOyMyEzfA2uJn+FFJj66PZ4SGQTXsZ1q8/A9smdgj/MA+2jW1h16FW2Z1i7CGqX78+iAijRo2ydFfYA7JoBtDDwwPDhw/H6NGjTYNJCyx61NJPdyQnAwsWiIkYCsa1+vmJO/H3On1aVM2sWiVK5urXFxc8nTqV//lkMmDw4LslpIxZkpOTKDG0tzevVPLzE0NrHiQzK1PJILOVIXlbMnKjctHw84Zm+7PPZEPuIIdVXSvACKhqixLQypR3Q2TsSE+QlBWvR9Rn6mHTyAYqLxVyzuUAAOROcqT/lw5VbTFbVOPGwJEjIugrUKuWuDE0ZYoI0hYuBJ57TpRrh4QAEycWfa4mTUQg5uUlSqKHDCk+AExMFBVr8fHAihUiwEtIEIFXwQ2tzz8X2cg1a4CXXxaBXUlV+0ajmPQnP1+M22vdWgR51tYiMFWr71bZqdXiHGrVEpWEeXki6DQaxdwiRqMIeLt2Lf65GHuYZEoZ2ofczXxIMglOXZ3QdENThI8Jh1N3J9g2toU6TI38RAN8ZtZD1vEsNN3bGjKl+Q0lh3YOyD6VjdZ7W4vPrDua/tIUscti4TrU9aGdF2Os5rFoAHjhwgW0adMGUVFRiCp0ZfIoz16lUolJWZYtu1tJ4usrxqgD4sInL0+MUzt9Wlx4AeJiecYMceFX0bHAy5aJCyXGLE2SRBawYObTAr6+4r8PWpqrcFEg61gWjLlG0D0z3d1YdANWPlaoP6c+AEDpqYQ+TYwZ1KXqILOTQW59/wNDyUDIv5UPAKIMVVnxAgp9xt0MoDHPCKt6VrCqa4WY5THwelncBGvSRExodO/QkGeeEfNSfPKJmHzxzz9F2fj16yJQu1ebNmLimE2bgP/7PzH+srgA8KefRND57bdiXgkvL5Gl9fISMwEXcHUV2cC//xZjj9evLzrONi9PlIomJYmMZUyMGEu8fbuYq0OlEhNFEonAMD1dfBb+/LMY62hjc/dm2aefihtpwcGWGXvIWHm5DXGD1yteCJ8QjjYH2yDx10TUGlELDT5tUORzqoDP+z7wmekDmcr8ze3Y0RHNf+NZERljVcuiAWBAQIAln75KFc7I1a8vZvIDRJnVwoViPM6pU+KCq8C8eWKGyIoqx+SDjD003t7iPV9YQQBYt+6Dta10USLnovgj0afpoXRTmvZpIjTQZ+hhyDJAZi2DwkmB/FgRsEW8EAG3YW6o+8b9dyA/Ph+kE5MmUz4BFZxMBbi7DITKS2T7rH2tYeNvg6yQLNR9XfStSRNxrLOz+WNHjhTLvrzwgpjw8OhRcbOoWbOixwIiWLtyRWT/5s0TM+QGBpofYzQC69YBS5aICQ9fekm0X1IZukwm2hk4UFQedOsmgrRdu0Sg99NPolxz1y6R0WvSRASWQPH/9q53khyF5hNAwXw6u3aJCSULL+XCWHXVYEkDXHr2Ek40OAEyEFrvbQ2g5Bva93MDiTHGKotFA8Cawtf37l3tf/4R5VAnT4oxMAUTOwCiPKoyJ3JhzBKGDCm6TIenpwhWKiMDCACSQkLejTxTAGjMNyI3Ohe6FB30mXrIHeWQ28ph1IhMYfaZbNi3vY+IrZC8G3mm2TuN2vtLuRfMAqpwVkCykmBd3xoOHR2gS9HBpqEYS1gQAN6bAfT2Fpk1W1tREvnllyLjWtKyKjY2Yi1Qg0Fk6vz9i2YAg4LE7PsjRoi2Vq8u+xzq1hWPW7pUVDF4e4txzA4OIivZvXvlzcDLwR97VMisZGi5qyVS/kqBYxdHWHnxuAzGWPX10APAOnXq4Pbt22UeV69ePdy6desh9Kjq+fqKMTW5uWJ8TcOGIvPn7S3KoBh7nLz7btFtkiTe8/esA1thCmcF5I5y2DS0Qd7NPDh0EBFC7rVcyJQy6DP00FzWQOGogMxWBmOuEdoELXTJOmgTtff9vESElB0psG1iC22CFsb8+wsAC0pAJUmCqrYK1vWtUff1uqgzrY7pmIIJnYqbHbxgBYuuXYFz58QMmevXl/6cBcGYv784Pifn7vi9fftE6aVKVbHzcHMTWUNAtDd9uij9LAheGauJJJkE95EVWxyeMcYs4aEHgOnp6fjpp59Q1vrzmZmZD6lHVa8g+7Fnj7jb/u23YmzNli0i68dYTVB45sr7pXBRwLaZLazqWCH3Wi7if4qH50RPaCI0sG1mC4PagMTNiVDVUUFmI4NBY0DOeVEyqk24/wDw9urbSNqShNb/tsbZLmdFCeh9KCgBBQBrH2tYNxADfiXZ3TIxV1cxKUppy0PVqyeCMLVajAMsD3d3kVGLihLjAwGxXM0bb9zXqZjY2YkxiYwxxhh7NDz08MPT07NcM3zeOyvoo0ySxJioL78USxSMGiXu3nP2j7GKUdZSwk5hB4WjAvFr45F7NVdk9gyAbVNbGPOMSNmRghY7WgBGwKgxQn1ODZmN7IEygJpwDTyf84R9a3tIVtJ9lYASkShPdRIpuWa/NYOylrLYY5ctA3r2LLktSRJZQCur8s8ALEli2Zjjx0UAmJEhSjgHDKjgiTDGGGPskfbQA8Abxa2HUAP4+gKHD4sp2yWJgz/G7ke9WfUAI5C0JQm5V3PhNtwNNxfdhLWvNTwmiNlD8m7modaIWkjblwZDrgHqc2o493WG+rz6vp/XkGMwjTeUqWT3VQJqUBsAoyhjBQBr75Kn+33xxbLbW7Kk4qWbffuKz6GpU0XpaOPGDz4xD2OMMcYeLVyA+JA895yYYa9dO0v3hLFHl8pdRDxW9UXay/8rf+Rey8W196/BqYcTHLs4ou70upAkCXIbuSkD6PWyF9IPpJc4JXtZDGoD5PYicyezkoG0FS8B1WeKJSkUDpXzsdusWcUf07cv8MMPwPPPA8eOiYlcGGOMMVazcAD4kJTnjj5jrHycezmj4VcNYdPQBjYNbeD6xN1Fk+V2dwI1Wxn06Xrob+nhOsgV13TXoE/XQ+lafNllaQoHgJJKuq8MoD5DD7mDHJLccuucdukiSj/37RNLRPASMowxxljNwwvRMMYeOUo3JXze9in1GLmtXARd9nLYNreFzFZW6kQwmcGZiHonCrk3covsuzcDeF8loJkGU/mnpVhbiwXlly7l4I8xxhirqTgAZIw9lmQ24uPNvrW9admF0iaCyTicgbjv4xA+JrzIPoPaAJmdaK+8JaBXpl1BTkSO6ffCM4Ba0qZNwMsvW7oXjDFW84wdOxb169cvcX9UVBRUKhVee+21h9grYd26dZAkyfQjk8ng4eGBUaNG4fr16w+9P6xqVasA8OzZs5buAmPsMSGzvRMAthELwKs8Sw8ADRoD7FrYIS8mr+i+CpaAkoGQ+Esici4UCgDvrAHIGGOsZmrRogViYmKgVhc/Kdns2bNhY2NTrtnyK1toaCjs7OwQEhKCkJAQBAYG4sMPP8Tu3bvxIo9jeuxUqwDwvffeQ0hICADAYDDgm2++sXCPGGOPKrmtCNjsWotVz1WeKugSdSUeb8w1wtrXGrpkHchonuGraAlo3o08GPOM0KXefb7CS0AwxhireVq2bAkiQkRERJF9x48fx7Zt2zB79mx4eHhUqN34+HgcO3as2H0ajQb//vtvmW2EhYWhVatW6Nq1K7p27YpevXrh7bffxuDBgxEaGlqh/rDqr1oFgDt27MCiRYuwdOlSdO7cGYmJiZbuEmPsEWUqAb2TAVR6KksdA2jQGGBd3xowAro080CxorOA5oSLzJ8+TW/aps+oHiWgjDHGLKNly5YAgPDwokMNZs6cifr16+Ptt9+ucLsrVqzA4MGDERQUZLY9NzcXw4cPx0svvVRi1hEQ69SeP38erYtZo+z27dto0qRJhfvEqrdqdTVy69YtGAwGrFixAkuXLsX48eMt3SXG2CNKppShybomsG8nAkBrX2uow0r+AjRqjLD2sYbMTgZdkg6qWmLJCSKqcAmoJlwDAEUygFwCyhhjNZe/vz+srKxw6dIls+07d+5EUFAQNm/eDGvrkteILcmnn36K6OhoDB06FHv37kWPHj1Mwd+5c+dw6NAh2Nvbl/j4q1evQq1Wo2XLltDr9SAixMXFYfny5YiIiMDu3bsr3CdWvVWrq5Hp06dj+fLlaNKkCZ599lm4ublh0KBBlu4WY+wR5TXVy/T/di3tkLix5KoCY64RMhsZVB4q6JLvBm7GfCNgRIkZQH2mHpJCMi0/AYgMoMxaZhYAGjINULhUq49cxhirVogIhmxDlbUvd5Df11qwlfb8cjmaNm1qlgHU6/WYNWsWunTpct+JD4VCgd9++w3jx4/H0KFDsWPHDixZsgRhYWE4ePBgsZm9wsLCwgAAM2bMwIwZM0zbvby8sH//fvTo0eO++sWqL4tcjezZswdPPvlkke1Hjx41/f+OHTswduxYDgAZY5XCrqUdcq/kwqg1QqYqWv1u0Bggs5VB6aGENuluqahBLS5GShoDeG3mNchsZGj0dSPTNk24Bg5dHIqUgFr7VvzOLmOM1RSGbAOCnILKPvA+9czsCYVj5Vz6HjhwoFzXqH369MHhw4dNv7ds2dJsvN7atWtx5coVBAcHP1BwqlAosGXLFowdOxYDBw6Ei4sLDh06hDZt2pT52LCwMEiShKCgIKhUKhiNRty4cQNz587FhAkTcOnSJTg4ONx331j1Y5EAcPz48cjKyjL9vnPnTowcOdLsGDs7O/z5558PuWeMsceVdT1ryKxl0FzRwL5V0VIYY64Rclu5yAAmFcrcqQ2AXAR+QNES0Py4fGgiNPBf4Q9JkkBEyInIgff/vJERkGE6jktAGWOsdHIHOXpm9qzS9itL9+7di53M5V62trZmv7do0QKbN2+GRqOB0WjEwoULMWbMGHTv3v2B+6TX66FWqyGTyaDT6aDRaMr1uNDQUDRs2NCsD507d4ZCocDo0aOxb98+PPvssw/cP1Z9WORqhMh8AoUpU6aYBYBPPvkk9uzZAysrq4fcM8bY40qSSbBtYYucCznFBoAGjQEyGxmU7kUzgHK7u2VD95aA6pJ0yLueB81lDeya2cGoMcKYY4R9a3skb0s2HafP4FlAGWOsNJIkVVqGrqrZ2tqiadOmFX5c4ZlAd+3ahYyMDHz++edFjsvOzsacOXNw+vRppKeno3v37li9ejUUiuJfn7y8PIwcORJnz57F8ePHsWTJEgwZMgT79u1Dt27dSu1TWFhYsQFowXjE3NzcCp8nq94sMgvovSnuewPCe2cxYoyxymDfyh45F3OK3WfUlJwBLCj/BIqWgGoTtVC4KZC6OxXAnYlfJMC6gbV5CWg1WQieMcaY5RTMBHrw4EEsX74cb731Fvz8/IocN2HCBAwYMADHjh1DeHg4kpKSsHPnzmLbzM/Px6hRo3Dq1CkcOHAAnTp1wtatWzFw4EAMGTIEx48fL7E/iYmJSEhIKHac4LZt2yCTySolO8mql2pxNWLJAbmMsZrDtoUtMg5lFLuvYBIYpYcSmsi7ZTP3BoCFS0CJCNokLWpPqo3MoEzgPbGEhMJFAaW7Ero0saagJJO4BJQxxhh8fX1hZ2eHjz76CPb29pg7d26RYwIDA3H06FHExsbio48+AgBkZWUVSZgUmDNnDk6ePIkDBw6gbdu2AAClUomtW7di3LhxGD58OK5fv17sTKAFa/wpFApToJiQkIDt27dj48aNmD17Nho2bFgZp86qEYtcjeh0OgQGBqJ169ZwcXGxRBcYYzWQVV2rEtcCLJgE5t5ZQIvLAOo1IrNnyDaA8gmOXRwR920cAECfqofSVQmlmxIwAvosPZTOSl4HkDHGGCRJQvPmzXHq1CksXboUTk5ORY45ffo0pkyZgq+++qpcbc6ZMweTJk1Cq1atzLYXBIGnT58ucRmIghlA586di7lz50KSJDg5OaFDhw7Ytm0bRo8eXbETZI8Ei1yN+Pr6on///gCAunXrQqPRYNmyZejatSs6dOhgiS4xxmoApZuyyCLvBQomgSl2DGAJGUBtkhaSSoJdKzvkx+QDuJMBdFNAbi+HpJCgT9VDbi+HMcfIGUDGGGM4efJkqfu9vb3x888/Izs7Gw4ODtBqtbhy5UqRAK+Ai4tLiQkVpVJZ6hjAWbNmYdasWeXvPHssWORqJCIiAllZWTh58iROnTqFkydPYvny5YiPj4dSqYTBUHVrwDDGai6FqwL6VJG9yzqZBcfOjqZ9pklgPJTQJZYxBlArAkBdkg4qDxWs61tDl6KDQWOAPk1kACVJgsJNAV2qzrT+H2cAGWOMlWXMmDEIDg5G27Zt4eDgAIVCgffff7/EAJCxirLY1YijoyMGDhyIgQMHmrbFxsbixIkTOHXqlKW6ZWIwGDB//nxs3LgRaWlpqFOnDt59911MmzbN0l1jjN0npZsoxcyLycPZLmfRM6snFA4KkIFA+QS5rRySUoI+Sw8igiRJMOYYiwSAlC/GYWiTtFB6KKF0V0KykpAfmw9dqk6UfwJQuoqMozJDKZaSsLXIvFuMMcYeITKZDCtXrsTKlSst3RX2mKpWt6O9vb3h7e1dLeqNv/vuO6xZswaHDh1Cq1atEBgYiCFDhsDPzw9PPPGEpbvHGLsPBYGZJlxM8mLINkDhoIAxT2T0ZDYySEoJMAKkI0gqqdQS0IIMoCRJsPaxRt6tPFEC6qowPZ8+VW+aAIYnvGKMMcaYpfHt6BJERUWhZ8+epnR7nz590KJFC9NsSYyxR4/MRgbJSkJOuFgKwpAtys0NGvFfma0MMhuZ2bbSSkALMoAAYOVjhfyYfNMkMIAoOdWl6XgCGMYYY4xVGxwAluDVV19FZGQkQkNDYTQacfDgQVy7dg1Dhw61dNcYY/dJkiQo3ZTIuSQCQH22GA9ozDUCkgjuCgJAo0YEeQULwRcoXAKqSxQZQACwqmeF/Fv5pklgAEDhqIAh28BrADLGGGOs2qhxVySTJ0/Ghg0bStzfp08fHD582FTq2bFjR0iSBJlMhhUrVhS7UGYBR0dHZGdnV0W3GavWHqX3vd5bj9Rrqch3zEdGagaQBWhSNNC6a03nke+Wj4yUDNjY2yA7Nxs23jbIysoCAORIOchBDrKyspCRlgH71vbIysqCwceAtNtpyFXnwtbBFllZWch3zIc+Rw9dqg46D52pDfZ4eZTe/4xVNn7/M/boqXEB4LfffoulS5eWuF+pFKVbb7zxBs6fP48rV66gYcOGuHDhAkaOHAmZTIbXXnut2Mfq9XosXLgQKpXICPTv399skhvGmOUpnBWmDKBRLbJ8xlwj5DZ3s3xyG7nICkKUgpplAFUyGHV3xgAm66B0F58ZqroqqEPV0GfooXQR22S2MhhzjDCoDZDZc8EFY+zxcODAARw6dAgAoNUWv7YqY6z6qnEBoL29fYmLYRZWsAinv78/AKB169YYOXIkdu3aVWIAqNFosHTpUjg6Oha7n7HH3aPw3ne0d4Q2Tlyw2Ght4OjoCALB1mhr6r+t0Ra2JH5Xparg6Oxo2qdz0ME601r8fgFwX+gOR0dHGJsbkfxRMhRGBZy9nOHo6AgHOwfkx+TDWmMNByuHR+L1YfeP/31ZTfHMM8/gmWeeAQBkZWVh7dq1Fu4RY6wi+JZ0CXr16oXNmzfj5s2bAMTahbt27eKF6hl7xBVM0AKYTwJTeIkGuW2hDGCWAQrHu/fKJJUEo9YIXZoO2gQtbJvZAgAcuzsCRkCXcncZCLm9HAa1AYZsA+QOd7OIjDHGGGOWwgFgCb744gt06dIFPXv2hL29PYYMGYIRI0Zg3rx5lu4aY+wBmCZocVbAoBYBoDHXCLltoTJPG5lpEph7J3CRWclgzDci51IOVHVVUDrfKfdUyFDrmVqi7TvLQBQOABUONa7ggjHGGGPVEF+RlMDe3h7ffvstvv32W0t3hTFWiQqyczb+NuYZQJu798NktjLTMhD6LD3kjkVnAc25lAO7FnZmbXuM9UDCTwmmjGFBAKjP1sPG3aZKz4sxxhhjrDw4A8gYq1EKSkBt/G3uZgA15hlAua387jIQJZSAai5pigSAzv2d0eFUB0gyseA7l4AyxhhjrLrhDCBjrEZRuCkAOWDtaw1dug4XR10sMQNo1BlFeeg9GUBjvhE54TnwfM7TrG1JkmDf5u4kUxwAMsYYY6y64QwgY6xGsaprBev61pA7ymHINkB9Xo30g+lFJ4HRGGHIEhlCszGAKhmMeUaoQ9Wwa2lXpP3CeAwgY4wxxqobDgAZYzWKQ0cHdDzbEXIHEQBqE7SAAeaTwNzJAOqz9IAcZtlByUoCDICVtxUcOjmU+lyFxwByBpAxxhhj1QEHgIyxGkWSJCicFFA4KKCN15rG+hUO8gqWgSgY/ydJkmmfzEocV292PdNYv5JwCShjjDHGqhuuSWKM1Uhyezk0VzWQVBKU7soiy0AUZAALj/8DxCyi/l/7w32Me7meA0ZAl6zjAJAxxhhj1QJnABljNZLcQQ5DpgGq2io493KG3KnoLKD3rgEIAJJMgvcMb8gUZX98yu1Em8ZcI48BZIwxxli1wFckjLEaSW4vgjOVlwqNvm8EmbLoLKD3LgFRUTJrmbjNZgRnABljjDFWLXAGkDFWIxUEZKraKiidlaZsHVAoA1hMCWhFSJJkCjQ5AGSMMcZYdcABIGOsRjJlAGuriuwzZQAzHywDaHoe+Z1sIGOMMcaYhXEJKGOsRirIyFl5WRXdd2cWUH2W3mxs4H09j70cCo35TKKMMcYYY5bCASBjrEYqmJSl2Aygjcy0EHxlZACNucYHaoMxxhhjrLJwTRJjrEaSVBIkhQSVV8kloA86BhAQAWBBuSljjDHGmKVxBpAxViNJkgRlLSWsfEooAS1YBqIyMoB5nAFkjDHGWPXAASBjrMbqcLZDsWMAzZaBcKqEADCfA0DGGGOMVQ8cADLGaqzigj+g8paBAEQASFp6oDYYY4wxxioLB4CMMXaPggxgXnQerOoWHySWl9xeDtJxAMgYY4yx6oEDQMYYu4fcVg4YAZmVDA4dHR6oLbsWdtBn6SupZ4wxxhhjD4YDQMYYu4fMRkyQ7DbCDZLswdbvq/NqncroEmOMMcZYpeBlIBhj7B5yWzHuz/0Zdwv3hDHGGGOscnEAyBhj95DkElrubAmXAS6W7gpjjDHGWKXiElDGGCtGrRG1LN0FxhhjjLFKxxlAxhhjjDHGGKshOABkjDHGGGOMsRqCA0DGGGOMMcYYqyE4AGSMMcYYY4yxGoIDQMYYY4wxxhirITgAZIwxxhhjjLEaggNAxhhjjDHGGKshOABkjDHGGGOMsRqCA0DGGGOMMcYYqyE4AGSMMcYYY4yxGoIDQMYYY4wxxhirITgAZIwxxhhjjLEaggNAxhhjjDHGGKshOABkjDHGGGOMsRqCA0DGGGOMMcYYqyFqbAB47tw5tG3bFiqVCpIkQZIk6PV6s2Py8/MxbNgw0zFyuRyDBg2yUI8ZY4wxxhhj7MHU2ABQpVKhb9+++Oijj0o8pn379jh48CA2bNgArVaLqKgovP766w+xl4w9Og4cOGDpLjBmMfz+Z4wx9qiosQFgs2bNsGLFCsyZM6fY/X/99RfCw8OxZs0aTJgwAUqlEn5+fnjmmWceck8ZezQcOnTI0l1gzGL4/c8YY+xRUWMDwLJs3LgRALBu3TooFArI5XLUrVsXhw8ftmzHGGOMMcYYY+w+KSzdgco2efJkbNiwocT9ffr0KVcQl5SUBAC4evUqIiIioFQq0bdvXwwePBhJSUlwcnIyO95gMMDe3h6RkZGwt7d/oHNg7FGj0Wig0Whw+fJlS3eFsYeO3/+sJlOr1QDEdRBj7NEgERFZuhOVSa1WIy8vr8T9SqWySPAmSRIAQKfTQaEQMfGIESPw119/4c8//8SoUaMAAEFBQejVq5fZtgKnTp1C3759zSaSUSgUpvYYe5zZ29tDr9eX+rfH2OOK3/+sptHr9WbXO1qtFidPnkSnTp0s2CvGWHk9dtGJvb19pWTg+vfvj7/++ssUHJbF398fGo0GMTExcHR0fODnZ+xRkp2djYULF2Lp0qWW7gpjDx2//1lNlpWVBR8fH/j7+1u6K4yxcnrsAsDyIiJoNBrodDrTtszMTFhZWcHW1hYvv/wyZs+ejTfeeAPt2rWDUqnEiy++CCsrKwwYMKBIe3K5HADg6OjIASCrkVQqFb/3WY3F739W0xVcBzHGqr8aOwnMzZs3YW9vDxcXF9O2WrVqwcHBAUeOHIGdnR2OHDkCAPD19YW3tze0Wi0OHjzIX/KMMcYYY4yxR1KNzQD6+vqirOGPHTt2RFxc3EPqEWOPtv79+1u6C4xZDL//GWOMPSpqbAaQMVa5Bg4caOkuMGYx/P5njDH2qOAAkDHGGGOMMcZqCA4AGWOMMcYYY6yG4ACQMcYYY4wxxmoIDgAZY4wxxhhjrIbgAJAxxhhjjDHGaggOABljjDHGGGOshuAAkDHGGGOMMcZqCA4AGWOMMcYYY6yG4ACQMcYYY4wxxmoIDgAZY4wxxhhjrIbgAJAxxhhjjLEKWrduHSRJKvbHzs4OBoOhUp9v7NixqF+/fon7o6KioFKp8Nprr1Xq8xbn3nOXyWTw8PDAqFGjcP369Sp/fvZgFJbuAGOMVRek1cKQlQVFrVqW7gpjjLFqLjQ0FHZ2djhw4ECRfTY2NpDL5ZX6fC1atMC2bdugVqthb29fZP/s2bNhY2ODRYsWVerzFufec9fpdDhz5gzef/99pKSk4OjRo1XeB3b/OABkjDEAurg4xLz5FgCgwY4/zfZpY2NBWh2sGvhZomuMMcaqobCwMLRo0QJdu3a97zZ8fX0xefJkLFiwoMxjW7ZsCSJCREQEOnXqZLbv+PHj2LZtG5YsWQIPD48y24qPj8f169fRvXv3Ivs0Gg0CAwMxdOjQEh8fFhaGVq1amZ17r169cPDgQQQEBJT5/MyyuASUMcYAZOzcCWN2NvS3bxfZl/bzBiR9/rkFesUYY6w6IiKcP38erVu3fmjP2bJlSwBAeHh4kX0zZ85E/fr18fbbb5errRUrVmDw4MEICgoy256bm4vhw4fjpZdeglqtLvaxpZ377du30aRJk3L1gVkOZwAZYwyAITUNNu3bIeuvv2HUaiFTqUz79ElJyDl5ssh2xhhjNdPVq1ehVqvRokUL6PV6s32SJFV6+ScA+Pv7w8rKCpcuXTLbvnPnTgQFBWHz5s2wtrYuV1uffvopoqOjMXToUOzduxc9evQwBX/nzp3DoUOHii0zBe6ee8uWLaHX60FEiIuLw/LlyxEREYHdu3c/8LmyqsUZQMYYA6BPT4NVo0aATAZDSor5vqQkUG4ucs+csVDvGGOMVSdhYWEAgHfeeQdKpdLsp2PHjsU+hoig1+vNfgDAaDSabStp8hi5XI6mTZuaZQD1ej1mzZqFLl26YPz48eXuv0KhwG+//YbBgwdj6NChOHjwIIYPH46wsDAcPHiw1MxmwbnPmDEDSqUSKpUKfn5+2LZtG/bv349+/fqVux/MMjgDyBhjEBlARS13yF1doU9JgbJOHdM+fVISlPXrIftQAOy6dbNgLxlj7PFFRMjOzq6y9h0cHCBJUqW0FRYWBkmSEBQUBNU9lSEuLi7FPiYwMLDY4Ojjjz/Gxx9/bPq9T58+OHz4cLFttGzZEseOHTP9vnbtWly5cgXBwcFm55aVlYVhw4bh8OHDkMmKz/coFAps2bIFY8eOxcCBA+Hi4oJDhw6hTZs2JZ43UPTcjUYjbty4gblz52LChAm4dOkSHBwcSm2DWRYHgIwxBkCflgqFmysU7u7QJyebthMRdMnJ8PpoPhIXL4E2Ohreq76FrJxlNowxxsonOzsbTk5OVdZ+ZmYmHB0dK6Wt0NBQNGjQoNhJVErSoUMHnDp1ymzb008/jWHDhuHVV181bSsteGrRogU2b94MjUYDo9GIhQsXYsyYMUX64ejoiCNHjpTZJ71eD7VaDZlMBp1OB41GU+ZjQkND0bBhQ7Pn7Ny5MxQKBUaPHo19+/bh2WefLbMdZjkcADLGGEQGUO7qBkWtWtAn3y0BNWRkADodHIcOhcMTTyDmlVeRsOhj1Fn8qeU6yxhjjyEHBwdkZmZWafuVJSwsrMKzfzo4OBQpD1WpVKhTp06JZaP3KjwT6K5du5CRkYHPi5mkbP78+QBQ6pIQeXl5GDlyJM6ePYvjx49jyZIlGDJkCPbt24dupVS7hIWFFRv4Fow/zM3NLde5MMvhAJAxVuORwQBDRkaxGUB9UjJk9vaQ2dkBAOp+vQJR/QfA/e3/QVmOqbYZY4yVjyRJlZahq0qJiYlISEh4qDOAFiiYCfTgwYNYvnw53nrrLfj5FV2i6MyZM5g2bVqJ7eTn52PUqFE4deoUDh48iLZt22Lr1q0YN26cKQgsLsAt7dy3bdsGmUxWoawoswwOABljNZ4hIwMggty1uAAwCYpCgZ6ydm2o6tVD/pVIDgAZY6wGCg0NBSDG0B0/frzI/tatW8PW1rZKntvX1xd2dnb46KOPYG9vj7lz5xZ73JkzZ9ChQ4cS25kzZw5OnjyJAwcOoG3btgAApVJpCgKHDx+O69evF5kJtLhzT0hIwPbt27Fx40bMnj0bDRs2rIQzZVWJA0DG2GPtxrjx8JzzIWxKuVOrT00VWT4rKyhq1UL+1at3990TAAKAVZMmyL9yGfa9elZZvxljjFVPBbNgzp8/31RqWUCSJKSlpVVZAChJEpo3b45Tp05h6dKlxY6ZjI2NhSRJqFu3bontzJkzB5MmTUKrVq3MthcEgadPny52GYiCc587dy7mzp0LSZLg5OSEDh06YNu2bRg9evSDnSB7KDgAZIw9togIeZGR0N68VWoAaEhLg9zVFQCKZgCTk6DwcDc73qpJY+RdiayaTjPGGKvWZs2ahVmzZlVKWzdu3KjwY06ePFnq/rKyf4CYqbSk2UqVSmWJYwAr89yZ5fA6gIyxx5YxOxuUmwtDelqpx+lTU6EwBYC1oC+0DqA+KalIqad1kybIv3IFAJDwyadIXbeuknvOGGOM3Z/yBICsZuMMIGPssaVLSAAA6FNLDwANqWmQu7kBAJReXtAnJcGYmwuZjQ3yr1+Hw4CBZsdbNWmC/OhokFaLnOMhYgF5xhhjrBoobeZPxgDOADLGHmP6xCQAosSz1OPS0+5mAL28oHBzQ+75C9DFxUFz+gwcBpkHgMo6dSCztobm7Flor0VDe/1GlfSfMcYYY6yycQaQMfbY0ifeyQCmpyH30iUY1Tmw69K5mOOSoPAUZZ6SJMG2Ywfknj2DnOMhsO/dG0pPT7PjJZkM9n37ImnpMkgKBbQ3boCMRkgyvqfGGGOMseqNr1YYY48tXWIiJCsrGFLTkL55M2KmTUP2wYPIi7w7gQsRQXPiBGzbtTNts2nfAdkBh5GxZStcxo8vtm2n4cOQd/Ei7Pr0Bmm10CcmVvn5MMYYY4w9KA4AGWOPLX1CIqyaNIEhLQ26WzGwbt4c8XPn4cb4CTBqNAAA7fUb0Ccnw7ZTJ9PjbDt2QN7587Bp2xZ2PXsU27Zd9+6Qu7jAtkNHKL29ob1+/aGcE2OMMcbYg+AAkDH22Mjavx8pa9aaftclJcK6WTPo09OhjYmBx/+9i0bHgqGqWwfZhwKQFx6O7P/2wbZTJ8hsbEyPs2rUCE6jRqH2/HmQJKnY55KUSnh/+w2cRo6Ayrc+8jkAZIwxxtgjgANAxthjI/f0aeSEHDP9rk9MgnWzZjBmZUGfkACljw8kSYLjU08hecUKXB8zFskrvoZ9715m7UhyOeosWQyll1epz2fboQMULi6w8vOD9sbNKjknxhhjjLHKxAEgY+yxobsdD31SoUXcExJg3bQJAECysYHCXSzo7jhsGPRpafBe9S38du2E87hxD/S8Kj8/LgFljDHG2COBZwFljD2SSKtF8qrv4D7jLUhyOQCx7p8+6c7SD2o1DBkZUNavD5mjI5S1a5vKOVXe3mhyPASSSlUpfVH5+kJ740altMUYY4wxVpU4A8gYeyTlhYcjdfVq5IVHmLbpEuJhzM6GUaOBNioKcjc3KFxcoHBxgbKej9njKyv4A0QGUBcXB2N+fqW1yRhjjDFWFTgAZIw9knIvXAQA5BwPASAygoaUVACAPjkZ+VFRsPL3BwDI3dyg8qlXZX1ReHhAsrGB9iaPA2SMMcZY9cYBIGPskZR38QLk7rWgOX4CAKBLSgLkcihq14Y+KQn5V6/CqlEjAIBNu7aw7dC+yvoiSRJUvvWLLQM9ceIEAgMDq+y5GWOMMcYqggPAMiQmJuK5556Dp6cnnJ2d0a1bN76YY6wayD1/AW6TJkFz5oxYiD0+HgoPdyhr14YuKQn5V+9mAD1nzoTDwIFV2h8rXz9or98AGY2InzcPxpwcAMDvv/+OX3/9tUqfmzHGGGOsvDgALMP06dMRExODixcvIjU1Fc8++yyGDRuGtLQ0S3eNsRrLkJ0N7Y0bcBo5EnIHB2hOn4YuIQHK2l5QeHhAn5R8JwPo/9D6pPL1hfb6deRfuYKMP7Yh//oNAIBGo4FarX5o/WCMMfZwffvtt5AkCZcuXaqS9tetWwdJkkw/MpkMHh4eGDVqFK4XMwN1fHw8ZDIZgoKCqqQ/D8vPP/+MdevWlflz8ODB+2r/3tfV2toaLVu2xIYNGyrUzqP4evMsoGWIiorClClT4H5n+vhp06bhvffew9WrV9GlSxcL946xminvwgUovbygqFULDoMGImvff1DWrQtl7dqQu7oi/+pV6JOTTRnAh0Hl5wd1YCBy7pSk6pMSAbSARqNBzp1sIGOMscdPaGgobG1t0bRp0ypr387ODgcOHAAA6HQ6nDlzBu+//z5SUlJw9OhRs+N37doFd3d3dO/evUr687D8+eef+Pvvv8s8bvXq1RgwYECF2w8NDYW1tTUCAgIAAOnp6fj4448xefJk1KtXD/369StXO4/i680ZwDJ88MEH2LlzJ+Lj46HT6bBq1So0bNgQrVu3tnTXGKuxck6dgm2nTgAAh8FDkL1/P/IuXIDCqzYUHh7I+vtvWLduDbmT00Prk13PHsi/fh3pW34DAOgTE0Vfc3I4AGSMscdYaGgoWrduDfmdJYnuFR8fj2PHjhW7T6PR4N9//y21/bCwMLRq1Qpdu3ZF165d0atXL7z99tsYPHgwQkNDixy/c+dODB8+HDLZo32Z//LLLwMA/vvvPxBRkZ/p06fDzs4OEyZMuK/2w8LC0Lx5c9PrOnToUPz4448AgD179pS7nUfx9eYMYBl69OiBX3/9FXXq1IFcLoerqyt27NgBGxubIsc6OjoiOzvbAr1kzLIe1vs+JyQExnwtMs6GwnHoEGRlZYEaN4LGxgZ5V6/Ca9qryA0PR45CAdd330FWVtZD6RcAQC6H8tnRyPhjG6zat0d6fDzkWVnQ6/UwGAxIvnABKd99h7qff/7w+sQeCv7cZzVZTX//63Q6XLp0CVOnTi3xmBUrVuC7777Dv//+i549e5q25+bmYvjw4bh06RKioqJgb29f5LFEhPPnz+O5554rsu/27dto0qSJ2basrCwEBARg+/btZtv1ej0+//xzrFu3DvHx8WjTpg3WrFmDbt264aOPPsIHH3xQ5rm2bNkS3bp1Q+/evbF8+XJcvnwZfn5+WLVqVbmzZRXx1FNPoU6dOli/fj0GDRpkti8vLw+bN2/GuHHj4ODgUOG2C17X0aNHm213dHQEIALz8nhUX28OAEthNBrRv39/9O7dG6mpqXB0dMTu3bvx5JNPIjAwEG3btjU7Xq/XY+HChVDdWV+sf//+GFjFE08wVpOkbf4Nuhs3YMjNhefsWQAASS5HnSVLoKjtCYWzM2Q2NvB4fyasmzd/6P1zHjcOhqwsKGt7QZeQAEB8iWg0GqgDAqA5fgKGzMyHmplkjLHKduDAARw6dAgAoNVqLdwby7p06RK0Wi3aty95pulPP/0U0dHRGDp0KPbu3YsePXqYgr9z587h0KFDxQZ/AHD16lWo1Wq0bNkSer0eRIS4uDgsX74cERER2L17t9nxe/bsgUqlKnL9+fzzz+PAgQP46KOP0KpVK+zZswdPPvkkcnNzS+17gby8PFy+fBk5OTmIi4vDvHnzoFQqMXPmTLz44ouIiYkpx6tVMXK5HC+99BKWLl2KtLQ0uLq6mvZt374dGRkZpixhRRW8rvdW9BVM9NixY8dytfPIvt7ESpSSkkIA6OzZs2bb27VrR5999pnZtszMTAJAmZmZD7OLjFULmZmZZu/99G3bKGHx4kp9Dl1aGoU3b0HXhj9NkX37kdForNT2K1Pa77/TzZdeIiKijh07Uv369Sn62TEU3qQpqUOOW7h3rLLd+/5nrCap6dc/69evJwB05syZUo/T6XQ0evRocnBwoAMHDtCAAQPIzc2NwsLCSn3c1q1bCUCRHy8vLwoKCipy/Lhx42j06NFm237++WeSJIlOnjxptr1z584EgFJSUso8z5MnTxKAIm2vWrWKAJBGoymzjfsRHR1NkiTRypUrzbb369ePWrRoUeLjDh48SHK5nDZs2FDs/oLX9d9//yWdTkdpaWm0fft28vDwoKZNm1Jubm65+veovt6PTrGqBbi5uaFZs2ZYtWoVsrKyYDQa8c8//+DSpUvo0KGDpbvHWJUirRaG+5y9Mu/KFWQfPFSp/ckJCoJVk8aou3wZPGe+B0mSKrX9yqT09IQuMQlGrRa5ajVUGg3ywsNh06YN8i5HABAL2RsyMy3cU8YYqz6ICAa1usp+iKjS+xwaGgqlUomWLVuWepxCocCWLVswcOBADBw4EGfPnsWBAwfQpk2bUh8XFhYGSZIQHByMU6dO4cSJE9i6dSvs7e0xYcIEsxJcrVaLf//9FyNHjjRr47PPPsPo0aPR6c7Y+QL+/v6oV68e3NzcynWeALBo0SKz7SkpKXB0dCx2aBQgssWFZ9os6adv377FPt7Pzw8DBgzA+vXrTduuX7+Ow4cPl5r9MxqNMBgMMBqNxe4PCwsDAAwdOhRKpRKurq4YP348+vbti4CAAFhbW5fYdoHq+HqXF5eAlmHXrl2YOXMm/P39kZeXBx8fH3zzzTdc2smqPX1KCiRra8hLKCspy+3ZH8KYkwOfH76v8GMNKSnQxcZCl5QEpYfHfT3/vdQBh+HQty+s/P0f6uye90Ph6Ql9YiJi33wTXxqMSLe1g03btrDr2hX5EZcBAPEfzobz+PFwff55C/eWMcaqB2NODiI7dir7wPvU+PSp+/5OLEloaChatGhhGv5TGr1eD7VaDZlMBp1OV65xZqGhoWjYsKHZDJOdO3eGQqHA6NGjsW/fPjz77LMAgEOHDkGj0WDYsGGmY2/cuIHLly/jo48+KtL2rVu3ylWOCIiAydvbG83vGV5RMAFOSbp3746IiIgy27e1tS1x3yuvvIJx48bh7NmzaN++PdavXw+VSoWJEyeW+JiBAweWGvCHhobC29sbO3bsgCRJsLGxgZ+fX4UCq+r4epcXB4BlaNSoEXbu3GnpbjBWYbff/wDKej7wWrDAtI2MRkjlmKUq9/x5ZO/fD9y5G1vRL0x9copo52wolEMGV+ixxSGdDuqgINSbPOmB23oYFB4eMKrVyDkWgv15ecjJycGzK1ciP/Qssvfvh1GrRX70dehu3bJ0VxljrNqQ2dmh8elTVdp+ZSIinDt3DmPGjCnz2Ly8PIwcORJnz57F8ePHsWTJEgwZMgT79u1Dt27dSnxcWFhYscsLFGSocnNzTdt27tyJPn36wNnZ2bQtNjYWAOBxz83YpKQknD17FrNmzTJty87Oxpw5c3D69Gmkp6eje/fuWL16NRQKBUJDQ4utfgsNDcWIESNK7H9lLI8xcuRI1KpVCz/++CPatm2Ln3/+GaNGjSpXJq0kYWFh6Nq1a5lj/Up7Tarj611eXALK2GPIkJ2NnJMnkfXPbhjv3GHMj76Oq336QHvjRqmP1SUm4fb7H8Dt5Zeh8vWF+s6A6PJIWLwYuRcuQJ+SAqWPD3KLmZ76fmhCQyEplbAuo8SmupA7O0NSqWDbsSNWp6ZgTVoq8lVKWDVthvxr15B38RJgMEB746alu8oYY9WGJEmQ29tX2U9lDx24du0asrOz0a5du1KPy8/Px6hRo3Dq1CkcOHAAnTp1wtatWzFw4EAMGTIEx48fL/ZxiYmJSEhIKDbjs23bNshkMlNwSET466+/ipQj1qpVCwAQGRlptn3OnDnQaDRmGakJEyZgwIABOHbsGMLDw5GUlISdO3fCaDTiwoULRbJX6enpuHnzZpnn/6BUKhVefPFFbN68Gbt27UJsbOx9T/4C3H1dy9Pvkl6TR/315gwgY48RQ2Ymbr//AawaN4KVnx8AIGvff3AaOQIJCxfCqM5B+pat8JxV/PTDusRE3HpxEmw6dECtN6aDyIikZcuQuWMn6q1bW+bz5wQfg9KrDvQpKXAeOwaaU6dN+zJ37YJ9//6Q38d0zerDgbDv06dc2cvKYjQa73tNH0mSoKxTB45PPQXNhp8BiPUA7evWgbJOHaT9/DOgVEJ7kwNAxhh7VBWM0yqrrG/OnDk4efIkDhw4YJpBXqlUYuvWrRg3bhyGDx+O69evF5kJtKB9hUJhChITEhKwfft2bNy4EbNnz0bDhg0BACdOnEB8fHyRgKRRo0Zo2rQpFi5cCEdHR9SqVQsbNmzAkSNHzPoeGBiIo0ePIjY21lS+mJWVBSJCZGQkcnJyimSkynv+leHll1/G8uXLMW3aNDRo0AD9+/e/77YK+l1WIFXaa/Kov94PPQCMjo4u13EymQy+vr5V2xnGHlFpv26E45DBULi7m7YREW6//wHyo6KgDgyE22vToKjljrQNGyCzsYY2Ohp1v1qO2x/MQq3XXzMtRUAGAwxpaZC7uuLWlKmw6dABXp98DEkmg8u4cZBkcqSsWgWDOgdy+9LLZ4xZWciPugpjdjYcn3gCaRt+gTE3F5qzZ3H7g1mos2wpnJ56qsLnmxMUhFrTp1f4cfeLiFC7dm2cO3cOXl5e99WGz5rV0Lm4mH7PycmBJElw6N8PaRt+gV2vXsg5fhyk10NS8L04xhh71Bw7dgwqlapcAeCkSZPQqlUrs+0FQeDp06eLXQaiYKKSuXPnYu7cuZAkCU5OTujQoQO2bdtmtobdzp070aFDB3h7e5u1IZfLsX37drz22mt45ZVX4OnpiWnTpsHLywubNm0yfcedPn0aU6ZMwVdffVWkH1u2bAFQNPAIDQ2FlZVVkXFqVaFZs2bo2bMngoKC8Pbbbz9QNrfgdS0rACztNZk1a9aj/Xo/0Byi90GSJJLJZKX+SJJEtra2D7trD6SmT4PMHh5dcjKFN21GiV9+SUREuRERlPHPP6S5cJEud+hI+owMSv35Z9LGxZEhN5cie/Wmy+07UNqWrWQ0GunmK69QROs2lL5jBxny8ylmxv/ocoeOlPHXXxTZqzcZdTqz5zMajXS5fQfKvXy5xD4VTIMf0bYdRQ0ZSuHNmpNRr6fInr1IfewYRT0xmK507kJJX39drnPUZ2RQzp0ptY0GA0W0ak150dH3+YpVnFqtJgB0+PDhB2onOTmZAJCVlRWdO3dOtH38BIU3aUppmzdTeIuWlH/rVmV0mVkQLwPBarKaev2Tl5dH9evXpxEjRli6K0RE1KRJE/r444/LffzAgQPpySefNP2+ZcsWatmyJWVlZRERUX5+Pp0/f77S+/kg0tLS6Pr16+VeouFBlfaaPOqv90O/7Wxra4tLly6VegwRFVlknTEmqAMDIXd0RMYf21DrzTeR9OVS5EdGwnXyJNh27Ai5kxNcJ92dLKXWm28g7Zdf4PzMKEiShHpr1kAdGIjYd95F+i+/AnI5rBo1QvzceXCd8lKRbJQkSVB6e0MXEwPrJk1K7BdptaDcXGivX4fcvRYkuRw27dsjaflXIJ0OrlOnIu/ixXKdY/yCBdCcOIlGQUehT0oC6XRQ1a17fy/YfcjJyQEgppru06fPfbej0WggSRLc3NxMbdp2aA+Fpyds2rWDytsb2pu3oPLxqZR+M8YYq1oFk3l8//33SEhIKHa2R0u4fPlyhY4PDQ3F66+/bvp9zJgxCA4ORtu2beHg4ACFQoH333+/SNbSklxcXOBSqLKmqpX2mjzqr/dDDwCffvpp1K9fv8zjCk+pylhNlB99HSofb0hKpdn27IAAuL40GVm79yD+ww+hOXUKklKJtE2b4Prii0XacRk7Fs7PPGMW2Nn36QP3GW9Bn5wM9xkzkHvuHG69NAXOhcpJClP6eEN7Z2arkhgKTWetqCVKU23bt0Pivn3weO//YNWoETJ37SrzvNXBwcgJPgbS65F/9SqMWVlQeNWGVI4ptiuL+s76hzdKmTCHiMosQcnJyYGtrS3s7e1NbUoKBfwPHYQkl0NZvx60N28APXtUVtcZY4xVoRUrVmD16tVo3749jh49WuUToFSFmzdvIjU11azEUCaTYeXKlVi5cqUFe1a9VNZrUh1f74ceAG7evLlcx23cuLGKe8KYZRiysiB3dCz1mLj330fWX3/D7ZVX4PF/7959bEYGco6FwP2tt+A4ZAhipr8Bx+HDYNRokP3vXth16VJse8WNMXObPNn0/3adO6PR0SNQuLoW+3hVXW/oYuNEHzIzIbO1LRKYGtVqQKmEBEBxZxYs206dINnYwGn0aBhzNNDevAnSaksN5nKOBsFp2DBoY2OQExICuaMTVD71Sjy+KpQWAGq1WvTt2xehoaE4d+4cGjduXGI7Go0Gtra2sLOzM2UAAUCSywEAKp960MWUHlgzxh4t8fHxcHNzK9e6cOzRs3jxYixevNjS3Xgg9evXL3WNPFa5quPrbfFlIAwGg6W7wNhDozlzBld79Ya2lPXf9CkpyPrrb3h//x3Sfv0V+dHRyLsSicQvv0TC4sWw7dABVo0bQ+Xriwa7dsLro4/g0L8/5E5OsCqlRLMsJQV/AEQJ6J0MYNw77yLl+x+KHGPMVkPu6Ailt7cpALRu3hz+hw5C4eICZR0vSEplqecOANqYGKjq14Nd127QhByHNuYWlD7epT6mshUEa8UFgBkZGQgJCYGbm1upGcKCduzs7IoEgAWUtT2hT0qsjC4zxiwsIyMDr732Gnx8fPD5559bujuMMVYiiweAQ4cOtXQXGHto1EePggwGJC5eUuLdoLyICCjr1YNDv35wHjMGCR9/jKTPP0PWnn+Rvf8Aan8031R6KCmVkFQqOA4divqbNlbZMglK77rQxcWCiJAXHo70rVth1GrNjjHmiAXjVfXqQVHr7uKsijv1+pJMBqsGDZAfda3U59LdugWlTz3Yde+GnFOnkH/5ClTeD3eMXGkZQJ1OBwDw8fFBampqqe0UZADt7e2LDQAVnp7QJXAAyNijLicnB+3atcP169exbNkyrF69GocPH8Yff/xh6a4xxlgRD60EdP78+UW2ERGuXSv9YpCxx0lOSAg8/u//kLpuHTK2bYPLmDGmfaTVQp+ejrzwCFjfmeLXfcZbuDb0SVBeHvwP7Afk8mLX0ZMUClj5+1dZv1Xe3tDGxkGflAxDZiaUPj7I/vdfOI0YYTrGkK2GzNERbi9PhezOEhP3svJviPxrUQAGQ5+cjPTff4f7G2+Y9hMRtLGxUNXzgaphQ1j5+UEdEACn4VU7JvjQoUOmqbUBEQDWqlULsbGx0Ov1UBQqodXpdJDJZHB3dy8zACycASwIKgtTeHpCn8gBIGOPooSEBOTl5cHX1xe7du2Cra0t9u7dC6PRiGXLlmHAgAFo2rQpxhT6nGeMsergoWUAv//+e/j7+6Nhw4amH39/f9ja2j6sLjBmMeqjQUhe+Q3yLl6C49AhqLtsKRIXL4H2xg2oAwMRM/0NRA0YiOinR0Bz9gysmzUDAMgdHFD3i89Re+ECyJ2d72sR9cqg9PYG5edDHXgYyno+cBo+HDnHQsyOMWpyIHdwgG2nTrAuYVycqqE/tHdu+mjOnEHKN99CWyjLZkhJAeXmQuntDUmS4PriRPH8VTxL5nfffYedO3eafs/JyUHjxo0hSRJi75n8RqvVQqlUws3NrdwZwJJLQGtDl5QEMhor5TwYYw/PxIkTMf3O+qQbN27ExIkTIUkS5HI5li1bhqVLlyIiIgJpaWlFHvvtt99i7NixpoqCAomJidi6dSsiIyMfyjkwxmqmh5YBbNasGQYMGIC690zlvm/fvofVBcYeKiJC4iefwmnkSKSs/gH54RFQ1a8PZe3aUNauDfs+fZCxcyfUBw/CtlNnuEwYj+SvVyIn8AhcX5hoaseue3cLnoUgs7GBXdcuSP1hNaxbtICqgR9ygoLMjjFmq8sMUK38GyLrn38AiLF+AJCxcyc83n7btE3h6QmZtTUAwGHoUDgdOwarhg0r+YzMxcbGokGDBqbf1Wo1nJyc4O3tjRs3bsDX19e0T6fTQaVSlTsALG0MoMLDA9DpYEhLM42bZIxVfyEhITh8+DBsbGwQFxeHAwcO4Icf7o6NLsj6ff/99zh+/DiefPJJs8f/9NNPiIqKwpQpU7BhwwbIZDLo9Xr07t0baWlpGDBggGkxaMYYq2wPLQMYGBhYJPgDyj8rKGOPCqNGg4RFHyNrzx6kb9qEhEWLkBt2Dn67dqLej+tMxzk+ORTpm3+DNu42PN77P9j36gW3KS8BAKybN7NU90vk+NQw6G7fNk1Ak3/jBojIlL0y5KghdywjAGzYENrr10F6PXS3YmDVpAkyd+yEPjkZAKC9Zb4mnkylQp3PP4esiisFYmNjkZ+fb/pdrVbD3t4ebm5uRe7e63S6cmcACy8DUVwAKLO2htzZmccBMvYISU5OxmuvvYb33nsPCoUCM2bMQO/evVGvXtHZirt3745jx46ZbYuNjcW5c+cQEhKCkJAQvPfeewCA3377DXq9Htu2bcOhQ4dg5MoAxlgVeWgBYFnrZTH2uNCcDUX65s24/X/vwXXSi8i/cgW2HTpA5eMDpZeX6Tj7Pn0AgwEOAweYAhyHwYPhvepbKNzcSmreYhyeGARJpYJV48aw8vWFMTMT2QcOILJbd2QfOgRjthoyh9KXt1B6ewOSBG1MDLSxMXCZMAG2HTsievjTSP3xR+SeOVvl5Z730uv1iI+Ph7bQpDY5OTmwt7eHk5MTMjMzzY4vKQAMDAxE7969zY4tqwQUABS1a0OfmFCJZ8QYqwo6nQ7+/v7w9vZGs2bNsGjRIvTu3Rt//vknXnvttWIf0717dwQHBwMQnzUrVqzAnDlz0KNHDzRv3hz//fcffvzxR/zyyy+YP38+5syZg+7duyM3NxcXLlx4mKfHGKtBHvo6gAVGjx6N7du3W+rpGasyuefPwWHwYNh26QznkSOhqO0FK/+iJYwyKyt4zHwPNq1bm7ZJcjkcBgx4mN0tN7mDA+r9uA7WrVtDZmUFhYcH0n5cD1X9+kj45FPYduwAeZs2pbYhyeVQNWgA7bVr0MXEQuXrC5fx45B9+DDSflyPvMhIeLzz9sM5oTsSEhJgNBqLZADt7OyKDQBLGgMYGRlZZNbQsiaBAQClpyd0PBEMY9XeyZMnkZmZaVr/UyaToV+/fjh+/DhGFJoQq7DBgwfjzTffRFxcHKZOnYqbN2/CysoKb731FgCgQYMG+OKLLzBp0iSMHz8ekyZNglwuR+/evbF06VJ06NABb98pkWeMscpisQDw9u3blnpqxspEREjfuAlZ//yDuitXIj/qKhTu7iVObpJ3JRKSTIJVo0bIO3cedj26w/W55wAAbi9NLvF5XMaPr4ruVxnbTp1M/6/y84PmxAnU/Wo5sn9Yjdywc5D17FVmG1YNGyIv4jJ08fFQ1RPZPoe+feHQt29VdbtUBZO83JsBdHJyKjEDWNwYwMTERKSnp5sdW9YyEMCdmUC5BJSxam///v0YOHAgmjZtato2depUDBw4EEqlstjH1K9fH3379sXIkSORmpqKc+fOweGesdKvvPIKXFxcMGrUKMjlcgDAsGHDMG/ePPz1119o1KgRnnrqqao7McZYjWOxdQC5JJRVZ3nnziH5669hyMqCOiAAiZ98ivhZs0ucrTHp889wa+rL0KenI/fcObOs3uNK5esLSBJsu3aFdfPmoPz8MscAAoBdt65I/+03QCaDwtOz6jtahoIAsLgxgKWVgLq6uhYJANVqNfR6vWlb4UlgSsoAKmp7QhcXV5mnxFi1Vt3GthERsrKyyjzuv//+w6BBg8y22dvbo0WLFqU+burUqTh9+jQ+++yzIsEfAMhkMowdO9YsiHzttdeQmJiIpUuX4pVXXsH3339f7V43xtijy+ILwTNWHeWEhMCuRw84jRyJ9E2boIuLgz41FZm7/ipyrEGdg5xTp6H09sbN51+AIScHVs2q3yQulc2qYUNYt2oFhYsLrO9cAMnsyw4AHZ9+GpJSCVWdOpDu3O22pJg7s5EWzgCWtwQ0KyvLNI17QoIYx5eRkWE6tmASGAcHhxIDQPtevaAOCIChHBegjD0q9Ho9Fi9eXOR9v2vXLnSvBjMbF7ZmzRo0a9bM7G/3XmlpaTh58mSRALA8Ro0ahV9++aVC6wEWLCcxdepULFq0CHPnzsWhQ4cwb948fP/99xXuA2OMFcYBIGPFyAk5DrtuXWHfuxfyr16FXbduqD1/HhIWLULM69MR+9Zbpgv2nJBjUHl7o966tXAePRpuk16EzMrKwmdQ9ZzHPAvvb1YCgCkALE8GUKZSodYb02HdpnpkSWNjY2FjY2OWASxrEpiCElAApllCE++M4yt8EVm4BDQ7O7vY57dp1QpWzZoh4/ffK/O0GLOos2fPYs6cOXj++edhMBhM25cuXYoTJ04gIiLCgr0zt3PnTmRlZWHGjBlFsmz//fcfZs+ejdWrV6NHjx7wuY9JqlQqlWmNwIqSyWR4+eWXMWrUKPz9999YtWoV3nvvPdONK8YYux8WCwCJyFJPzViJsvbuxa2XX0FuaCjsunaFVdOmUHh4wH7gADgMGADfzZtg06YNjDkaxL39NshggPrwYdj36QOZrS3cpk6Bx50pvR93MhsbKO+UcFr51ofC3b3cJZ0uY8ei7hdfVGX3yq1gDcCSSkDvLQ0rKAG1traGra2tqQy0IABMT0/H9evXAdydBKa0DCAgxomm/boRVCgLyVh18tJLL1Vo2aagoCD07t0bx48fR2BgIAAxicqZM2fQt29f7Nixo9jHHT9+HNOmTRPrqCYmVvm1Qk5ODgICArBjxw4cOXIEY8eONQtYlyxZgs8++wyLFi3CBx98UKV9Kc3QoUOxZs0aWFtb4+mnn8Znn31W7HF79uzBnj17HnLvGGOPGosFgCEhIZZ6asbMpP3yKzRnzwIAsg8eQl5EBBS1a0NZvz4kSUL9X3+B88iRAADrZs1Q67VpqLtyJbS3YpCyejWydu+B09PDLXgGlifJ5fD943ezNfweBbm5uTh16hSaNWtW7hLQggAQgNlEMAkJCZAkCUlJSWjSpAlu3bplygA6ODiUmAEEAPt+/SBZWyFr794qOEvGHoxer8fvv/+ON998E0lJSUX2GwwGREVFmcqhASA4OBhPPfUU+vTpg+PHj2P16tXo3bs3PvjgA0yePLlIAJiamoply5Zh8ODB2LBhA3bs2AE/Pz9s2LChSs/t4MGD8Pb2xoABA3D69GkcPXoUAQEBAIDr168jODgYGzZswODBgzF06NAq7UtpBgwYAJ1OhxEjRmDYsGEIDQ0FIF77zz77zPTaz5s3D9OnTzcbi8wYY/fiElBWo2lOn0bSV18h5uVXkPn3P8gNDUWdz5bAb8cOU7mOqn59SPfM8Ca3t4PH/72LlJXfwKF/P1g3b26J7lcrj+LETvPnz4e7uzvGjh1b7hLQgjGAwN0AMC8vD1lZWfD19UV4eDh0Oh0yMjKKZABLmsRBksvhOmkSUtf/BOILN2ZhOp3OlNEGgEuXLpmWJli4cKHZsUajEZ06dUKjRo0wfvx4EBGICEFBQejRowe6du2K48ePY8WKFVi/fj0++ugjDBs2DGFhYWZljK+99hq2bt2KtWvX4pVXXsHEiRPh7OyM+fPn48qVK1i/fj02b95slhFMTk5GZGRkqcHOt99+i2+//dbscWq1GtHR0Vi6dCkmTZqEqVOnQpIk1KpVC+PHj8emTZsAAL/++iuGDBmCF198ETt37rToZ5yzszNeffVVvPTSS2jWrBkiIiJARNi/fz9mz56Ns2fPIjo6GhcuXIDBYMCff/5psb4yxqo/iy0DAQBXr17FjBkzcPLkySJ3x7VcCsUqmUGdA0mlhEylQta+/5C6bh2MGg3cpkyB3NEBqWvXQhcXB5s2bSC3tyuzPYchQ+B68SJcJjz3EHrPqsLatWuxZ88epKenF5sBBFDiGEAAcHd3R3JyMhITEyGTyeDv749Lly4BEEFk4TGABduKmwUQAJxHjULGb78h4eNPUHvBR49kQM0eDz/88APmz5+P4OBgNG/eHMePH0enTp2waNEidO3aFQsWLIC7uzsAICAgALGxsYiJiUHnzp2xfv16dOvWDZmZmejYsSMkScLcuXNhMBjw9NNPAxA3Tnr16oWdO3firbfewu3bt/HXX38hIiICDRo0QLNmzbBq1SocOHAA7733Hlq1aoW2bdsiLi4OX3zxBZydnfHUU09h0aJFyM/PR+fOnfHCCy9Ar9fjjTfeABFh/vz5iIiIwJEjR2BtbY21a9eie/fuGDp0KCZMmACNRoM+ffpg8+bNZpm9559/HgMHDsSqVauwYcMGfPnllxb5NyjOd999B0B8jmRkZCApKQnr1q0DIMprc3NzMWjQIAwaNAjLli3DmDFj+HOEMVYsiwaAEydOhLe3N9auXWu62GKsKhARbk2ZAkNqKmzatkV2QADcXp6K/CuRcJ08CZSXh8TPv4CVf0PIHR3L1aYkSfCcObOKe84qGxEhLS0NDg4OyMzMhJ+fHzQaTbEZQLlcXmoJqKenJ5KSkpCQkAB3d3e4ubkVCQALMoCACCxLCgBlNjbwWb0a14Y+CddJL8KqQYOqOH3GyhQYGAgPDw8MGjQI+/btw4kTJ9ClSxe0bt0affr0wcqVK/Hxxx8DEDNovvTSS/D29sb69esxfvx4tG/fHs8//zysrKzQrl076HQ6DBw40HQjBABGjhyJnTt3YsyYMZg7dy4GDhyIBnfe861atUJcXBy8vLxw9OhRAGIylJSUFOzduxfx8fFYtWoV1q5dixEjRmD69On4+eefER8fj1OnTqFu3brYsmULnn/+eXzyySfw8PDAgQMH8Msvv2DEiBFYu3YtpkyZApmsaBFUp06d4Ofnh/HjxyMjI6Narr9nZ2eHevXqITAwEH///TcmTpyIkydP4uLFi/jf//6H0aNHY8GCBQgODkbPnj0t3V3GWDVk0QAwPDwcQUFBUCgs2g1WA+QcOQJdbCzc3/4f9AkJ8P3tN1g3KbSou4MD7Lp3h9KrtuU6yR6K06dPY+zYsTh27BgAkY2wsrIyBYBGo9EUAKpUKuTm5poFfYX/38PDA4mJiUhMTISnpydcXFwQHh4OQASABctAKBQKWFtbIzs7G15eXiX2TVmnDlR+ftBGR3MAyCyCiBAcHIytW7ciICAAXbt2hUwmw8aNGwEAc+fOxZAhQzBy5EjMnj0bx44dw7lz5wAAQ4YMwVNPPYV9+/bh9zuz2trY2KBz58545plnzJ5n5MiReOedd+Dj44Pu3bvj66+/Nttf8HdSOEirVasWXnjhBQDAzEI333766ScAYkmXN998E3/88QcCAgLQpUsX0zHjxo3D2LFjERUVhUaNGpV4/pIk4eeff0aXLl3w6quvwqqazujcrFkzzJs3Dz179sSECRMwbtw4GI1GjB49Gg4ODpg2bRqWLVvGASBjrFgWjbxatmyJ+Pj4+5pWmbGKSPn+B7hNnQKXsWNLPMbr40WQ+GbEYy8lJQUxMTFITk6Go6MjVCoVVCqVqQQ0NzcXRAQ7OzvY2toCEGWgtWrVAmA+BtDT0xNnzpxBYmIiateuDWdnZ2g0GgDmJaAASl0KojCVry/yr19H2QtqMFb5oqOjkZqaik6dOqF3794YPnw4goKCMHDgQABAjx49MGDAAHTp0gUTJ05EdHQ0PDw8TI9fs2YNEhISTH8vALB3794iVT716tXDf//9h2bNmqFOnTqV0ncfHx/s2rULer2+2BvLkiSVGvwVaNeuHf755x+0atWqUvpVFZo2bYp9+/ZhwYIF6NSpE7KzszFlyhRThcHkyZPRvn17aLVaU8l6YadPn8bHH3+MP//8E/JqsB4rY+zhsujV7po1azB9+nRMnDgRtWubZ1569+5toV6x6ig/Ohramzdh160bZNbWFXps7qVLyIuMhM/aNaUepywlM8MeH2q1GgaDAZGRkaa1/ApnAHNycgDAlAFUKpVmAWDhMYCenp5ITExEbGws6tSpAxcXF9PzZGRkQKvVmi58y1oKooDKzxfaGzcq7XwZq4jg4GB07NgRNjY2AID27dujffv2Zsd89dVX6NmzJ959990iZZR2dnZo2LCh2baSyp4HDBhQiT2/qzKqip544olK6EnVadasGVxcXDBq1ChYW1tj8ODBmD59uml/06ZNYWdnh7Nnz6Jr165mj42NjcWzzz6LmJgYHDlyBP369XvY3WeMWZhFA8BLly4hICAAu3fvNtsuSZLZOjysZjLm5SEvPAKk1SJm2jTI7O3hOHgwas+fV6F2MrZsgdOwYZCXcBHCapaCLFx4eLgpqCucAVSr1ZDL5VCpVJAkqchMoMWNAbx+/Tr8/f3NAsDk5GQAMGUAy1oKooCVry80J08Vu093+zYUnp6Q7tyx16enQ30oAMacHDg+9SQUdwJaxu7XgQMH0KdPn1KP8fPzw3s1ZL3T6mrcuHFo3bo1rO/cEN17zxIykiShe/fuCA4ONgWAer0eixYtwpdffmkao/nbb79xAMhYDWTRZSBmzpyJxYsXIyMjAzqdzvTDM4AyAEhc8hluPvccYl55BZ6zZ8N300Zk/PkncsPCyvV4XVwcrvbpi8ydu+Dy3ISq7Sx7ZBRk4S5dumQKAAtnANPT0+Hq6mqaPe/eALBwCWjBGMDr16/Dz88Pzs7OAIA6deoUCQArUgKqvbOQfGHaW7cQNWAgIrv3QMLixUheuRLRQ4Yi/fetyD5wANeeGIy8iIj7eUlYDZaRkYH3338fKSkp0Gq1+PvvvzFq1ChLd4uVwdnZGd26dSv1mB49eiA4OBiAGCfZokULbNu2DcHBwVi3bh2ef/55bNu2ja+5GKuBLJoBzMzMxIwZMyzZBVbNGDUaSCoVsvfvR9bff8Pvz+3QxsbCYdAgsU7Ta9MQP/8j+G3fVmRtvnulb9kK6+bNUWfJYsjvXJgzVjgAbNeuHQCRAdTpdCAipKSkmI1fKi4DWLgENC0tDZGRkfDz8zMFkY0aNUJycjKUSqUpWCxvBlDl6wtDWhoMmZmQOzmZtmft+Rd2vXuh1iuvIGPbdhgzM+G1ZDEc+vcHAKT88ANipr0G2y5dYN+7N+x69oDcwYHHtbISpaWloW/fvoiLi0NoaCjefvtt2Nvbo2PHjpbuGqsEPXr0wPLly6FWq/Hmm2/i66+/xvPPP28q7+3WrRscHR2xb98+DB8+3MK9ZYw9TBbNAA4dOtR0d4o93shoRNKy5dCnppZ63M2JLyKyW3fEz5uPOl9+AevmzeH4xBOmbIzblCkAGZH2y68AAH1qKq6PHQeDOsesHaNWi4zt2+E68QUO/piZgiAsMjLSLAMIiOxeSkqKaWwgUHwAWDgDCABJSUlmGcDGjRsjKSnJlP0Dyj8GUO7kBLmrK/IiLpttz9qzB07DhsG2Y0fU+WwJ6nz+uSn4AwC3adPg/vbbUNWrh+SVK3G1W3dcbtkK158dA31KSrlfH1YzEBFeeukl1KtXD1FRUUhOTsaIESMwatSoYpdHYI+ezp07Qy6X47XXXoOPjw+mTp1qCv4AUSY6YcIEbN68GQAQEhKCbdu2QafTWarLjLGHxKK3hj08PDB8+HCMHj26yNToixYtslCvWFXIC49A6tq1yLt8GT6rf4BUzAVG/tWryI+Kgs/q1VDVr1fspCySSgX3d95BwqKP4TrpRaT9+ivyzp9H3vlzsOve3XRczpEjkNnZwfaewe+MFQRhOp3ObBIYAMjPzy9XBrBgYhelUglXV1fk5OTAy8vLNBlDnTp1EBwcbDbzYXlLQAHAZcIExL3zDrxXrYJt+3bIOXkS2ps3Yd+vf4mPkSQJzs+I0r1ab74Bys2FISsLSUuX4dZLU+C3c4dp7CCruWJjY7F+/Xps3boVOTk5OHv2LFxcXHDmzBns378fbdq0sXQXWSVRqVT4v//7P7z33nuYN29esYvCP/fcc+jatSs+/fRTLF68GB4eHnjnnXcwf/58vPLKKxboNWPsYbDobb4LFy6gTZs2iIqKwtGjR00/QUFBluwWqwLqwMOw69ED+ZGRUB86VOwxmf/shn3/frDr2qXUGTnt+/QBJAnpW7ciffNvUNavB01oKABxV5uMRqgDA+EwYECxgSar2bKzs02zBBaeBAa4mwEsLQAsPAYQEDeyfH19IZPJ4ObmZpryvrgMYHkDQPe33kStt97ErZdfRsKnixE343/wnPUB5PZ2ZT8YIhiU2dpCWbs26ny2BIYcNbL3HyjXY9njKzs7G/369cOxY8cwb948XLx4Ea6urgAAuVyOIUOGlLpOJXv0TJs2De3bt8fzzz9f7P5WrVrhqaeeQlBQEP766y9ERUVhxYoVmDFjBiKKGVOs1+tx69atqu42Y6yKWTQDGBAQYMmnZ1WMiACIi1H14UC4PPcctNHXkPXff7Dp0AHQ66FwdwcAaM6GImPbNnh9XHbmV5LL4TJ2DBI/+RTOz46GVaPGUB89ivzoaMS98y5s2rSBOvAI6ny2pErPjz2a1Go1fH19ERUVVSQAzM/PR2pqqlkA6OjoWOIYQECMAyxcVgWIbF9qairq1q1r2ubg4IDUMkqgC3N97jlY+foi++AhuL/7TqlrWJZGUijgNmkSUtevh8PgJ4rNArDHl06nw+7du7FgwQKo1WrUq1cPu3fv5rXfagh7e3ucOXOm1GN+//13s99Hjx6NAwcOYMGCBdi6davZvsWLF+PTTz/F2rVr8eKLL1Z6fxljDwfPDvCIICLAaHykSrgytm5Fxo4dcH/jDeSFh8O+dy/oGvjh1iuvQhcTC11CAny3bAFAuDV5Mtz/NwP25ZyO2m3qVDgOfxoq77rIvXgJyV9/jZsTX4TDgAHI3LULkMthwxMZsGKo1Wo0bNgQUVFRphJQuVwOuVxuygAWXsfMyckJCQkJpt8LjwEERADods/yC3Z2diCiIhnAGxVc38+ue3ez0ub75TT6WSR9tQLaqChYlWMh7EdVQkICTp8+jWHDhlm6K8XSaDSQyWSmqfvvV2RkJM6cOQMfHx/06NEDRqMRQUFBCA4Oxt9//43Y2Fjo9Xro9XpkZ2fDyckJ8+fPh5OTE4YMGcLBHyvThx9+iLZt2+L555/Hd999BycnJ6Snp2P58uWYO3cuXn75ZTzxxBNF1nBmjD0aOAB8RKT9+CNyz1+A98qvLd2VclMHHIbu5i3EvD4dnh/OhsLNDXIXF8isrKBLTIBthw64PXMmHJ54AjZt28Jt6tRyty2pVFB5i+yKdZPGICI4DhiA2gsXQOnjDW30dcgKZWkYK5CdnY22bdsCgFmmr2ApiOJKQK9cuWL6/d4AcMqUKUUWui4Y+3e/YwArm9zeDnZduyL78OHHOgDcuHEjFi5ciPj4eNjb21dq23q9Hv/99x/OnTsHg8EANzc31K5dG46OjjAYDIiNjcWxY8dQt25dyGQyeHl5IT4+HjExMcjOzkZ4eDguXrwIAHBxcUGHDh3Qt29fZGZm4ujRowCAqKgoODs746233sL06dMhSRLWr1+PgwcPYsGCBYiKisKWLVuwfft2tGvXDpcvX4aTkxO0Wi30ej169eqFqVOnonnz5lAoFFAoFHBwcEDDhg15YhdWIT4+Prh48SImT56Mrl27YvHixVizZg06deqEefPmITAwEL/++itmzpxp6a4yxu4DB4BlICIsWLAAa9euRWZmJjp06IDvvvsOLVu2LPZ4bUwM0KJFpfcjc9cu5F+NgvbGDah8fSu9/cpGBgM0Z8/CZ/VqyJ2dYdXADwAgyWTweH8mlHXrwqpRI0T16w/trVsVCv7uJSmV8Pt9K1T164ulIl55xVR+yti9CjKAQNEA8H7GAA4ePLjIcxQEfvczC2hVse/XD5m7dsGhf38oa9eGzK584wkfBfqUFORfi8a5o0ehVqvxxx9/4KWXXrqvtogIkZGR2LVrFyRJgpOTE6ysrPDTTz/h2rVr6NGjB6ysrBASEoL4+Hio1WoolUo4OzujZ8+eiImJAREhKCgIHh4eaNiwIezt7TFu3Dh069YNMpkMMTExOH78OAIDAyGXy/HGG29AJpPB398fMTEx+OCDDzBr1ixIkgQPDw/07NkTzZo1g6+vL5555hmcP38ejRo1glqtRkhICGQyGXr37m32vmSPBqNWi9S1a6E5fgKec+fCukljS3fJxMvLC3v27MGSJUvw/vvvo23btli9ejUAcePrk08+wXvvvcfft4w9gjgALMPSpUuxfv167Nu3D/7+/li0aBEGDx6MK1euFHuHOWbaa8hr375SM3X50dehvXET9v36IXX9T6i9cEGRcTxkNIL0+mqT9cqPjASMRti0blVkHTKnQusNuYwfh9R1P8Jh0MAHej4rf3+z33mcEyuJWq1G8+bNMWbMGLNAT6VSlXsWUFUZf2clBYCWygACgH3fPkhYuBDRT4+AfZ8+8F717WPxd6K7fRs3xk8AGY2YmJgIw8iRWLNmDSZPnlzu88vIyMAPP/yA7du3IyIiAlqtFoMHD4aNjQ2ys7ORn5+PFi1aYPfu3WZZ3QJZWVkAxHjR8vD09ETHjh3x5ptvFtnXuXNnjBw5EleuXAERoWnTppDL5Vi2bBnc3d3Nzsne3h6DBg0q13Oy6inl21XIPngQth07Iubll+H9w/ewqYKbyPdLLpdj7ty5mDt3rtn2UaNG4X//+x92796NX3/91UK9Y4zdLw4Ay/Ddd9/hvffeQ6tWrQAAH3/8MdatW4cdO3Zg4sSJRY53GNAf6h07YczLg8zaGkR03xdZRISUb75FzrFjsOvRA+5vv41bU6ZAe+MGnEaOhNOIp01jAjN+/wNZe/ag/i8b7v9kK4kxPx/Z+w/ApkP7Mhehdp06FUpvbyh5HAF7SLKzs1GrVq0iEx+UVgJa0jqAJaluJaAAoPT0hPvbb8OmVUvcnjMHGdu2wWXMGIv1pzIYMjNx69VXYd+vH/InvYjEfv2wyNcXA0ND8dFHH2HhwoWmz9+4uDhs2rQJFy9eRExMDBo0aIC0tDTk5eX9f3t3HhdV9T5w/DMwbA4MOwiCoLiLuOG+lWG4luKaWrnvZYvaZq65ZNnX0sTKLE3LLJfcK0VNMzX3TFNUBEQQkF1AtvP7g5ifBCoqOijP+/Xy9Yp7z5z73NsA83DOeQ779u2jYcOGvPLKK9SvX5/q1asbtgYxBlNTU+rUqVPoWMGek+Lxcf3gIRJWrMD7+++xqFGda25uhA8YiEW1alg/+QROI0agKaOjulZWVkyZMoUXXnhBphcL8Qgyynetr68vixcvNup0qJJITk7m0qVLNG3a1HBMq9XSsGFDjv277cB/Ob/2GqZOjqQfPkJOfDznn2xPfHAwWZcukZeVdVfXT9m6lcTvvsOqcSMcRwzHsmYNfLZtRdeqFfGLFxP16mvEzJhB+pEjpP76K+l//lkmNnyOGDSYhFWrsOve/Y5ttfb22Pfr9+CDEuJfaWlpRdbsQf4IYHx8PNnZ2Xc1BbQ4BbMDytIIIIDTiOHoWrTAcehQUrZsNWos9yv98GEiho/AvLIXFd+dzIFDh1jh4EDu/j/Y8vbbBAcHU6VKFZYtW8bmzZtp0qQJO3fuxMfHh+eff56KFSvStm1bunXrxo4dO/jtt98YMGAAvr6+Rk3+SiLr0iVSQ3aRk5Bg7FAee5mnT5Py6693bKdycoj/7HPCBw/m0sCBhD7xJOcDOhA5ZixX3nyL2I8/JvemzzyJP/xA5KhRVHw3f9qnRqPBadRIqm5Yj+OQwaRu305Yr96kluFq6SNHjqRevXosWbLE2KEIIe6SUUYAu3btyvTp03nzzTcZOHAgY8aMueWaOmMqmNZjZ2dX6Li9vb3h3H9pNBqsW7Xi+t69JG/YgLmXF0kbNhC36FMsqlXDc/GnmN1UGv5WVHY2V2fNpuKUd9F36mQ4bqrX4zRyBHa9exE9+V0y/zlLxomT3Dh3DjM3N1J3hmDf997KxZeGzLNnyfznH6rv3VviPcuEeFhyc3PJyMgodvq2hYUFUVFRaLXaQlP5bG1tC32/3+sIoJ2dXaFE0pisW7cmdu775KWnY3JTkvqouLJkCTEff8IPyckM+nk7Gq2WgwcPUqNlS9y6d+fKG2/y97wP2JuTzStTp6LRaJg0aRLjx49/5Ke9ZkdHc+m5/pjq9eTEx+My4XXs+vV76PeVl56OxsrqgV9X5eSQumMHZpUqYfXvTJyHJTVkF1ETJqAxMSH9zz9xee01TG5RwfXq+/O4vncvDoMGYVLBCq1rRVR2NlkXL5KbmkL6wUNc2roNp3HjuP7HH6SFhOC5ZAm6Zk0L9WPu7Y25tzfWTz1F0pofuDJhIi5vTLrnbWAeJHNzc/bs2XPLz0NCiLLLKAng3LlzmTlzJj/88AOfffYZfn5+tGzZkrFjx9KrV68ys5C94ENgUlJSoeOJiYmF9ve6uX1qaio0acqVqVMxtbfH8/PP8ve6y8nh6gcfcH7mTCrNm3fHa18/cIAMGxtUixbF/3DVarGdO4fctDQu9e6Dqbc3Vl27EP3LL5gEPn3fG6DHf7GUCv6NqdCwYYna56alkZeeTuL3azAJfJrrebkgvxTKDWOPbJVUSkoKer0epVSR7yu9Xs+VK1fw8vIqdD8FW0QkJCSg1WoxNTVFo9Hc9kNPXl4eer0eKysrQ7uCfq5du2b8n3H29mRVrkzM3r1Yt2hh3FjuUtKPa4n+ejlzLC1Icq2O5969uHl58ddff9G3b19o1gy7Dz/g6qeL8b50iU0+PljW80Pn6kpCaChaV9dST1pufr/kZWWRHR2dX/X4NpVIlVJcP3CAzBMnyE1PJyvsEnmpqVjV90PXujXZ4eHkJCdj4VMNM08PtI6OJP/0EylbtlAh4CmcJ04k/fBhwmfPJv70GZzHjcXkP/tR3ovMixdJ2bgJU70NDoMGFfu7JOXnn7k6ew6mej2OY0aja9EC7X/+UAqQHRdH7AcfcOPsOdBocBg4ELtePUsci1KKqNdfJzvyMnlpabjNnlXi30n3Ky8ri/B57+MwaRJWfvW4Oms2sb174zZnDhaVKxdqm7RxEwm//oLHkiWYursDkPvvOa1fPbSAef/+JG/4ifDPPsOienUcly8n163ibX+OaJ99BjtvLy69/Q4xO3biOHQo2oqumFhZ3TIRvZPrBw5w4+JF7IKC7rmP/3pUfv4LIf6f0dYAmpmZ0b9/f/r378/ff//NkiVLGDNmDK+++ipDhw5l1qxZxgrNwNbWFm9vb/78809a/PshKScnh+PHjxe7/i8nJ4fp06djptWSW6smT/foQdWCdRtmZjgMGkT4Cy+Sm5qKaTFT0G6Wums31k88ccdEztTaGod/N2O1Dggg+aeNXJk4Efe5c+957YDKzSV53Tqu79+Px4L/ofLy0Nrbo/LyiJ0/H6fRowt9sMk4fpzLr70Oufm/8jw+XnBP1xXiQUtPTwcotpCHubk5MTEx2NvbFzpe8IegtLQ07OzsyM7ORnuHta0FUz9v3iDe1tbW0M9/r2EMVk38ST/05yOVAKaGhHBt2TJ+9KiE77/FUQ4dOsRzzz3HqVOnDL83Kvj74/XVMvIyM8kKCyP98BFSNm8h9sP5mFhbY+ZRCZWVDRoNGlNTNFotWkdHzCpVIi89HXOPSlz/809MrW3Q2FijsrPRaDQopciNjcPU3g7LmjXR6Kwx9/AgS2tKxrHjpBw5TMaJk0D+LA60WsxcXbFq2ABTvS2gsPStR/qRI2QcPUJuQgK6J57AxMoKfZfOmFSoQPrhw8TOfR+tqytmHh6kHzhIdmQkeRkZVPBvjNPo0Vi3aWO4T8/gYGJmvkfEi4OwHzIYfYcO97xfbHZcHFcmTKCCvz/pf/5JdlwcrhMnFvo9lJeRwbXPPsflrTcxsbLi2uJgYue+j12/vjiNHGlom3n2LNFvv4OVvz/uc+eQfTWWq3PmoGvdqsRrvtP/PEzW+fN4fbea1J07ufLmW1i3a4vrW2/dVxKvcnK48tbbZEdEoNHbUKFBQ8yreJO0fj2WNWqg79CBjL9OobGyQt8xEI2JCR6LFnJt6VKixr2E2+xZpPz8CxmHDoGFOTlXY3GfOwfzf5O/4mhMTbHrGYRdz6C7itWqfn28Vq0kafVqLo9/GXUjC425OVYNG2Ddvj3mlSqRcfwEWVeisGnfngr+/uQmJ5O8fj2pv+7AzNMDu569qPDv93v0tOmYVaxIyoafcBg+HOu2bTD5z5RnlZeXv+/wbX7O7dixg5CQECB/WrwQ4tGiUWWofu/JkycJCgoiLCyM3NzcO7/gIfjggw9YuHAhW7duxcfHh/fee4+vv/66SBXQlJQUw1qh21WCu9i9B7rmzcmKiMD1zTdIWrsO+wH9Mfs3UczLzCRx5Uril3xG5S+XYlW//l3Fm5t2nbBnnsHlzTfQP/30Pd3zjdBQwvr0xdTejtz4a2jMzHB/fy5aV1cu9e6D+/tzsX32WUP7y6++illFN1wmvE5eWhqm/37QFeXH3VZBNJazZ8/SsGFDQyJ4syeeeAITExNMTEzYsWOH4bhSCjMzM0JDQ6lSpQq+vr7MmzePzp073/Za5ubmfPLJJ4waNcrQj4WFBWfOnCm00fz69es5duwYM2bMKKW7LJm0334jespUqoXsvO8ZAw9DTmIiFzt3wemdd6gyoD87d+4kMTGRESNGsHXrVho1akRqauptk/O8zExunL9AdkQ4GktLUAqVnYPKzSE78jLZUVForCzJOn8BXauWqKys/KmOZuYolQd5CjM3N3Li4sg49RcqPYPMc+e4Dlg1bIBrq9boWrfC3MuL3ORk8tLSuBEaSsbxE+QmJ6Oys7l+4A90TZpi/VR7dC1a3PGPgZCfsOTEx98ycVJ5eST/tJG4hZ+ga9oMt1nv3VUSeCM0lLTffydh+Qp0rVriNnMmObGxRLzwIlZN/HGbOhWNmRlKKa7OnEnm36fxWv2dIQm7ERZG5MhRmNraYtOhA9lXokjZuAmnMaNxGDrU0O7KW2+jbtyg0kfzb32vublkR0Vh5unJpX79sHkqAKcRwwHIjo0lrGdPXN98E9suXUp8f/91bdlXJK1dS8UpU8hNTiJh+Qqyzp/H+ZXx3Dh/geRNm9BYmOP+3ntYt2tX+LVff03s+/OwqFkT1zffIC8zE62dHVb/7i36IOWmpaExNSU7JobUX34ldccOsiMjqdC0KWZubiStW0deWhoohWXdujiNHsWN0FASvl4O5mbkXU/HbeoU9N26kbzhJ+KDg/PfVy4uWNSoTk5sHFmXLpGbmorG3JwKTZrkf09YWqB1cMSuV0/0HTsWievKz79QqWPgHT//CCHKDqNXAc3Ly2Pjxo0sWbKEHTt20KhRoyLlho1pwoQJpKamEhAQQEpKCv7+/mzfvv2eNxm26RBA/MJFWNarx4WOnSAvDzM3N+z79QUgZes2Er79FqcxY7D087vr/k2tddj17UvS6tX3nABmnDiBla8vLhMnkJeRSU5cHFfeeBO7fn1BqyXl118NCWBOQgJpO3ZSZeNPaExNJfkTZVpqauotv3fNzc25ePEizZs3L3S8YC+4gvV7JdkGAvJHGW8uAlPQT8GU8oJiMocOHWL37t0PPQHUtWiByswk4+hRKvj7P9Rr34uEL7/E0q8ef+ttqFChAo0bNyYtLY2IiAg2btxIgwYN7jgya2JpiZVvXax8S6/MvsrJITkxERMLi0IffrX29mBvj7mnJzbt29/XNTRa7W1HzTQmJtj16I6uVUsiXniR8AEDsaxTh5y4WCxq1kLfqSPmVasC+TM2si5exLJOHcy9vIieMjV/G4KGDXCdOAGbwEA0Gg1mrq5UXrGcyBEjCQsKQuXkotGakpOUhPd3qwuNwFlUqULVDetJXLOGzJN/5S9/+HJpkemazi+/xIWOncg8exbLmjWLPsu8PKLffofkLVuw79uXnKuxOAzobzhv5uKC6xtvEjNtOmkhu7Bu/yQ2AQFFRrBuJzsqivhFi/D8bAkVmjQBwCYgAJWRYdgb03XyO7ccYXQcNAgrPz8sqlXD9CEnOwUzbyyqVMFi5AicRo4odN7ppXHkJSdjamdnuBebgAAchg4l/dCfWNSobvhjs11QD2x7dCfr4kWyo2O48c8ZTB0csaxVE1NbW3JTUkg/dAgzLy/IySHr8mWi336H6/v/oIJ/Y2wCAshLTyf90CGuvPnWQ30OQoj7Z7QE8MqVK3zxxRcsXbqUhIQE+vbty4EDB/AvYx9ENBoNM2bMKLUPZ/bPPYeFjw82Tz9N6s6dZBw/Tsbx44YEMO2337AL6onjkHvbxBjArldP4j/9lLCevXCZNKnIIvPbyU27np8ANqiP1b8JqFKKhK+/JuHr5Ti88AKJ331HbkoKSWvXkbBsGbqWLbGoUuWe4xXiYblVBVDILwITGRlJ92Kq1/43ASzJGj5ra+siU01vLgTTvXt3hg4dytWrVwkPD7/LO7l/GjMzbAIDSd6y5ZFIANP278dp2DD2Xb6Mj48PGo0GGxsb2rZty9SpUxk2bJhR4tJotXeVgDxIZi4uVFn7IwkrV5ETG4tVw0ZkHDtG2GefgZlZ/pQ+jQbLmjW5OnsOln71UDey8Nm+DTNX16L9ubri/cMakteuRevqSl5GBla+vph7FF0Db1KhAo6DBt0+Pjc37Pr0IW7Bx3gGLy50TilFzMyZZBw/jtOI4cQvDqbSxx8bEpkC+i6d0To5kX7oIPGLPuXqnLm4z5ltmBZ7O0opYt6bhU3HjobkD/ITaM1N17nT9NIKjRrd8VrGYGptXey6UxNzc6xbtypyXKPRYOHjg4WPT5HzZpUqYVm7dqFjFfybkLxhA9e+WMqVSW/kX9PRkYpTp0CvXqV4J0KIB80oCWCPHj3YsmULVapU4bXXXmPw4MFFKm0+rrQODoYpFPoOHdCYmRE7930g/y/J1/fvv6/kD0Dr6EiVdWtJ3ryZ6LfeouqmjZjodKTt3UvCN9/g8b//FfmlCpB9NZbzTz2FRqOh0v8+MhzXaDTYDxxA9Jtv4TDoRdKPHuFcs+aYV65MxZkzSvSLV4iyIC0t7bYjgFlZWVQsZqTl5gSwJNtAQH614P/+XLt5BDAiIoJz584RExNDdHQ0mZmZWJZSUYaSsusZRPigwdgFBWFVr55h3drdrq+6ERpK6s6dYGKKbZfOmFWqhFKKnLg4NCYmJKxaRer2n9F36ojTSy/ddf+5qanc+OcsVo39iVm1EtebkpVt27axevXqQtv1lGcmOt1/RoYGk3fjBlnh4ajsbCyqVcPEwoJrX35J0o9r8fp2Vf5o5a36MzfH/rnnSi0+p9GjuNilK8lbtqBr2RJTGxs0Wi1xH39M2q7deH+7Cq2bG9bt2hU7C0aj0aBr3gxd82Y4vfQSSWt+IGrCRKpuWI+Zm9str6uys7k6930yT52iysafSu1+ypOC0XOlFDmxsfkjjRYWUgVUiEeQURJAjUbDli1b6NChgzEuX6ZY1a9P1qVL5CQmknnyJBpTUyxLYUsMi2rVcH75ZTKOHiOsb1/MPTy5fugQZq6uxC1chOubbxjapvz6K2m7d2Pdth3mnp7YP9cPXcuWhfqz7dwZE50u/y/C331H9pVotM5OZeYv30KURGpq6m1HAIE7JoAlHQHctm1bkb5uHgFMTEzk8uXLXL16FchPCGvUqFHymykFVn5+OI8bR+TIUdg++yxJP/6IqY0NVg0bkhMfT25yMg4vvohNhw6GbV1yEhNRGRnkZWSQsm07Kdu3kX05Cus2rcm7nk7S2h9xfullEr7+msxTpwDQtWmD48gRxH30P7JjY6n4zjt3VbEy49gxzCpVwszVhatXrxZ6rpaWlgy6w8hTeWdiYYHlf95bjkOH4jBkyEPfPkLr4EDFqVOJeuWV/AOmpljUrEFOdAxeq1Zi9m8hlZKsf9doNNj37UPm339z/unA/KmJTz5J9tWrWFSvjtbensx/zpJ18QLXDxzEVK/H67vvbpvwijsrmCIshHh0GSUBXLdunTEuWyZp7e0x9/IiYvAQsi5exPmVV0qtIIPGxATPz5aQsm07uQnXcH37LVR2NmG9epMTF4fzq69g7uFB4jcryTh+PH/Rd9OmhqqihfoyN0f/b8KuMTEpdgqQEGVdUlLSLYsUFKzrK0kCWJI1gMVtFWNnZ2cYAUxMTCQqKoqYmBgAwsPDH3oCCOAweBBm7u4kb96E+9w5aLRaMv85i1WjhmhMtSQs+5Lot94CjQYzDw+yY2IgNxeNVot1u7Y4jxmDdbt2mOh0qLw8ol55ldh583AYMpjKXy1D3biB1skJAF3TpkRNmMj5p5/GLqgnTmPHYFKCZ5l++IhhmurVq1cLFdER985YeyLqOwZSYd9eTKysyLl2LX9dWeNGWPy7TvFuVZw+Dcfhw0j9+WfSftubXxDl+zXkpqbkr5erXZuKgR2xbtf2niukCiHE48SoRWBCQ0N5+eWXOXToUJF9ZMpTWWHHUaPIiYvD9tlnSv2vaiZWVtgF9Sh0rOr6dcQvXUpY9x44jR5N+rFjaO3sSF63norTppXq9YUoS6Kjo3G7xTSx0h4BLE7BFNDMzEwyMjK4fPkysbGx+Pj4cOnSpXvq835pNBr0HQPRdww0HLu58qF9v75kx8aisrLICruEubcXWldXyM0tMoqnMTGh0oL/gVL//0H7phFXM3d3vL5ZQfrBg8TO/4i0PXuwCQjAvv9zaB0cyE1OJjc1rdAfmJRSpO3ejcO/U+NjYmJo+Z8ZCuLRU/BHAXOdDvP/7Kt3tzQaDeaenjgOG4ajkdaCCiHEo8SoCeDzzz+Ph4cHX3zxRbH7cpUXdj26P9TrmXt74/7ee6S2b0/02+9g3bo1Zm5uJH777V1vOyHEoyQmJua+E8CSrgEsTsEU0MTERABOnz5NTk4OzZo1M1oCWBIFlQPNPTzu2PZOMxg0pqboWrbEq3Fjkn78kbRdu0lctQr7/v1JWr+OnKux6Fq3wnHQIHQtWpBx+DCply5xPCODICgyBVQIIYQQd8eoCeDp06fZt2/fHUt3iwfDpn17rLZtBSDz1N+kbN+OeRVv4wYlxAMUHR2N3y22VzE3N0er1eLg4FDkXJUqVdi+fTtwfyOAdnZ2XLhwgYSEBAAyMjKwtramTp06nDlz5p76fFSZWFjgMGAA9v37k7Z7N0lrfsC+Tx9sg4JIWv09l195Fcs6tclLu86vKo/3Ro+mSevWxMTEFCoCI4QQQoi7Y9Tdf319fYmOjjZmCOWe1t4erb09utatqLppo9HWhAjxMNxpCqirqysmxYxg9e3bl927dxMREYFSqkRrAItTMAU0MTERNzc3TExMcHV1xdvbu0yPAD5IGo0GmyefxDN4MU6jRmHm4oLzyy9R7eft6Jo1x6J6dVYkJuLu7s6cOXOIi4uTEUAhhBDiPhh16O3zzz9nzJgxPP/880V+obdt29ZIUZVPGo0GraOjscMQ4oG63RRQc3PzW56rVKkS7du3Z9myZQClMgXU5d9pla6urnh4eHD58uV76vNxZWpnh9OokeTl5XHmg3m8/9przJs3j9zcXBkBFEIIIe6DURPAv//+m127drFly5ZCxzUaDbm5uUaKSgjxOMrLy+Pq1au3HQG83chSnz59+Pjjj4F7TwALRgATEhKwt7fH3NycihUr4u7uTnR0NEopGYX/j4SEBHJycujduzevv/46tra2D32/RCGEEOJxYtQpoBMnTmT27NkkJSWRnZ1t+FeeKoAKIR6O+Ph4cnJybpnkBQUFMXHixFu+vnbt2pw7dw7gnqeAFmwDkZiYiIODA5UqVcLV1RU3NzeysrIMawPF/4uJiUGv1+Pp6UndunVl9E8IIYS4T0ZNAJOTk3n55ZfR6/WYmpoW+idg6tSpXLt2zdhhCPFYiI6OxtbWFqtbbEBeo0aN20499/HxITMzEyidKaD29vZ069aNgIAArK2tsbGxkTXRxbi56Eu7du1k/Z8QQghxn4yaAHbq1Inff//dmCGUWRkZGcyYMYPVq1cbOxQhHgu3W/9XEs7OzlhbWwPcc+Xigu0krl27hoODA0OGDCEoKAgANzc3rly5cs/xPa5iYmIMSd/QoUMZPny4kSMSQgghHm1GXQPo4uJCt27d6NmzZ5EPZjNmzDBSVGXDhQsXAFizZg1jx459oNdKSEjg0qVLNGrU6IFeRwhjio6Ovq/RI41GQ9WqVTl9+vQ9r9NzdHTML2py5gwBAQGFzhWsAxSF3bzvX6NGjeTnlBBCCHGfjJoA/vXXX9SvX5/z589z/vx5w3EpggDnzp3D09OT/fv3c+XKFdzd3R/YtV577TV+/vlnIiIi7nlqmxBl3e22gCgpHx+fQj+r7paNjQ3+/v7s3r2b3r17FzonI4DFu3kEUAghhBD3z6gJ4K5du4x5+TItNDSUFi1aEBUVxdatWxk2bNgDuc6FCxdYvXo1dnZ2bNq0yTAdTYjHycGDB5k/fz6zZs26r358fHzu+48kXbp04fDhw0U2nJcRwOLFxMRQq1YtY4chhBBCPDaMugZQ3FpoaCg1atSgdevW/PHHHw/sOh9//DF9+vThpZdeYvHixSilHti1hHjQ9uzZw/Lly4scnzlzJkOHDmXkyJH31X9pJYAA9vb2hY7LCGDxZARQCCGEKF0PPQEs6VTGypUrP+BIyrZz585RvXp1WrRowYEDBx7INXJzc1mzZg2DBg1ixIgRnDx5kq+//vqBXEuIh2Ht2rUsXbq0yPG4uDiaNGly3/1Xq1YNCwuL++qjcePG+Pr64u3tXei4jAAWTxJAIYQQonQ99CmgiYmJfPXVV3ccaUpOTn5IEZVNoaGhVK9eHR8fH86cOUNSUhKZmZn8+uuvPP/888W+5saNGxw8ePCWpeyvX7/ORx99RPv27WnVqhV79uwB8kurm5qasnLlSoKCgmjWrBl16tS5q3h37NjBJ598QkBAAMOGDaNChQp3d8NClILQ0FD+/vvvIhuqJyQkFJlyeS+eeOIJ1q9ff199mJiY8NdffxU5Xt5HAHNycjA1NS30/+38+fOEhobi5+dnxMiEEEKIx8tDTwBdXV1LVOHzfos1PMpSU1OJiYmhRo0aODo6UqVKFQ4ePMgvv/zCRx99xJUrV3jjjTeKvO7LL79k0qRJpKSkYGJSdHB31apVfPrpp8ybN4/g4GA2bNhAr169DPsuPv3004wfP57evXvz559/ljiJ++WXXwgKCmLcuHGsWLGCBQsWsHnzZurUqUNycjLJycnlfkRXPByhoaEkJiYWqhwJpZcAarXaUhlJLE7BCOB/k9fHUUZGBqdPn8bV1ZXTp0/TqFEjOnXqhF6vp3///ly8eJGePXvy6aef0qdPHzw9PY0dshBCCPH4UKJUJCcnK0AlJyffd19HjhxR9vb2hq8HDRqkgoKClIODg/rf//6nbGxs1Lp16wq9Ji8vT/n5+SlA/fPPP8X227hxY7VkyRK1ceNGZWJioqpWrarCw8MLtcnOzlb+/v5qxowZSimlkpKS1CuvvKICAwNVZGSkUkqp9evXq4CAALV27VqVl5enWrRooebOnauUUio3N1eNHDlSde7cWS1cuFCZmZmpypUrq7y8vCLxZGZmFrn+zdLT01VISEixrxVlS3Jycqm89+/HjRs3lImJidLpdGrnzp0qLy9PzZw5U6WnpyuNRnPb91pZkJKSogB17do1pZRSO3bsUGfPnjVyVKVj7dq1avPmzWro0KGqWbNmysnJSVlbWytAVaxYUZmbm6u2bduqfv36KT8/P9WnTx+l0+mUqampOnXqlLHDv6Oy8P4XwlhK8/OPEOLhkASwlJTmD8DVq1erpk2bGr6Ojo5Wnp6eqmbNmiovL0+tXbtW2djYqNjYWEObAwcOKL1er+rVq6dWrVqlsrOzDedu3LihFi5cqHQ6nUpJSVFKKbVu3ToVHR1d7PX37dundDqdioiIUM8++6xq3bq1GjhwoHJ2dladOnVSjo6Oqlu3bqpdu3bq119/VQ4ODoZ+lVIqNjZW6XQ6ZWVlpbZv367Mzc2L/SA7fvx4VbVqVZWXl6dycnJUr1691KZNmwzng4ODFaDatm2rcnNz7/2BigfuYX0Anj17tpo8eXKx5/755x9laWmpOnXqpBYuXKgyMzMVoPbs2aMAlZaW9sDju1/W1taGhKdTp07q3XffNXJE9yY3N1f9/vvvatu2bWrZsmVKr9crLy8v1bt3b7V69Wq1a9culZeXp9LT01Vubq7asmWLSkhIKNRHXl6eSk1NNdId3B1JAEV5JgmgEI8eo24DIYpXUAG0QMWKFdm1axcpKSloNBqCgoL44IMP2LBhA8OHDwfgt99+o0OHDri4uLB7925effVVpk6dyujRo+nfvz8nT55k5cqV2NjYANCjR49bXr9Vq1Y8//zzNGjQAI1Gwz///IOjoyO//PILISEhBAcHY2FhgaenJ2PHjmXSpEmGfgGcnZ2ZP38+VlZWBAYG0rJlS3bu3Gm4pytXrhAREcHnn38OwJ9//kl0dDQhISFs27aN77//ni5durBx40amT5/O/PnzOXbsGI0bNy71Zy0eLREREcTGxhY69tNPPxEYGEhoaCjVqlXD19eXv//+m9TUVABOnTqFubn5I7EutWAdYN26dUlKSiIsLMzYIZGRkcHbb7/NO++8g5OTU4leU7Dlhp2dHeHh4WzatImuXbsWaWdlZQVA586di5zTaDRYW1vfX/BCCCGEKEISwDKooALozXx8fAp93bNnT9auXWtIAM+fP0+NGjWoWrUqI0eOxMfHh0mTJrFixQqio6M5cuRIiT+8ASxevJg6depQrVo1w+sCAwMJDAw0tGnfvj3//PMP48ePL/L6m8vtP/XUU+zcuZPRo0eTl5dHhw4dOHPmDD179sTa2pply5Zx6tQpJk2ahE6n480336RNmzbs3LmTBQsWcOLECbZv3y4JoCAtLY2oqCiUUsTGxuLs7EyfPn0IDg4mKSmJ6tWrU6dOHb766ivS0tKA/ATQwcHhkVhXd3Ml0MTExDKRAC5evJhPPvmEw4cPM3z4cLp27Xrb9ZSZmZnMnz+fVatW0blzZ+Lj43F2dn6IEQshhBDidmQfwDKooALo7fTs2ZOQkBASExOB/ASwWrVqNGrUiLy8PObMmcOBAwcYMmQIO3bsuKvkD/L/+v7SSy/RqVOnW7ZZsGABGzZswNLS8rZ9BQQEEBISQlpaGuvWrSMlJYXk5GS+/fZbnn/+eT777DMSEhIYMWIEw4cPJykpiWeeeQZvb29q1KhBx44d2bp1Kz/88AMpKSl3dR/i8ZKamkpUVBR79+6lSZMmxMTEkJWVxVdffcXvv/9OjRo1cHd3JzY2ttAIoKOjo5EjLxk3NzdDApiUlMTFixeNFktiYiJLlixhzpw5/PDDD3h6ejJ79mwGDBhwyyrOSinmzJmDs7MznTt3RqPRSPInhBBClDFlagTw6NGjNGrUyNhhGN25c+cKTQEtTpUqVWjVqhVjxoxh1apVXLhwAR8fH/z8/Pjggw/o3r07pqam+Pr6PrA4a9euXaJ2zZo1o27duowfP549e/YUmjLavn17EhISCm2K/f3337Nu3TratWsHQMeOHRkxYgT79+9n6dKlDB06lPj4eEJDQ2nRokXp35gos9LS0oiOjubUqVNERkby119/YW9vz8GDB7G3t2fBggVERUWRnJxcaATwQX4flCZ3d3fDVhCJiYncuHGDjIwMw1TJklq8eDGNGjWiefPmJX6Nuqn6aHZ2Nj169CAlJYWXX36ZHj16EBQURFJSEnXr1mXhwoW8/PLLhV4XHR3NxIkT2bFjB5s3b34kRlyFEEKI8qhMjQBOmDCBP/74A8jfpHzhwoVGjujhS0hIICEh4Y4jgJCfKB06dIhPP/2UiIgIqlWrhlarZcKECYatHcoCjUbDokWLWL58OYGBgYwePbrQ+ZuTP4CWLVvy4Ycf0q1bNwA8PT3ZtGkTY8aM4cCBAwC88sor9OvXD5VfyOjh3IgwurS0NHJzc9m/fz+Qv/9knTp1+PDDD9m0aROenp7o9XpSUlIMI4CJiYmlsgXEw1AwApiRkcGNGzcAOHz4ML///nuJ+zh48CDjxo1j9erVhmOZmZk8//zzhj5vlpCQwJNPPomNjQ2HDx9m4MCB2NjYkJCQwN69e5kyZYohmbOzs2PNmjVMmTKFsWPH8vrrr+Pu7s6ePXvw9/cnMzOTw4cP4+/vf59PQgghhBAPSplKANevX8+MGTP48MMPadq0KVevXjV2SPfsxo0bTJ48mbi4uLt63dmzZ3FxcUGv19+xrYuLC+PHj2f+/PlYWlqW6b0T69evT1RUFIsWLUKrvfuB565du9KhQwcOHDjA6dOn+fHHH4mJiWHPnj04Ozvf9XO+nRs3bnD48OFS60/cncOHD7Np06ZizxWM6u3evRvITwC9vLx4+eWXadq0KQB6vZ7r16+TnJxseN2jkgAWjAAmJSUBUL16dYYPH87gwYNL9Prr168zZMgQ6tSpw+nTpw3H9+zZw8qVKzl37lyR1yxatIicnBxefPFF+vXrx6ZNm9i3bx9//PEHOp2uSPtWrVrx22+/cf36dUJDQ+nWrRtPPPEErVq14ocffsDDw+Pebl4IIYQQD0WZSgAjIiLIzc1lwYIFTJw4kffee8/YId2zXbt2MXv2bJo3b37b5OTPP/80fNgD2L9/P82aNSvxdZ599lnCw8Px8fEpdvP3ssTV1fW+poU1a9aMv//+m/HjxzNkyBBatWrF0KFDuXbtGrt27SrSPi8v754Sua+//ppWrVoRGRl5z7GKe7dhwwaWLVtW7LmCBDAqKgp7e3uOHz+Ot7d3oTa2trZAfrVZc3NzgEduDWBiYiI6nY4aNWpw9uxZQkNDi1Q/vdmZM2dwcXHhySefxNHRkUWLFhVKALdt2wZQJAFMT09n4cKFTJkyhVmzZpGUlMRbb72Fv79/sclfAT8/P77++ms2btxIcHAwCxYs4IsvvpBpn0IIIcQjoExlDGPGjGHWrFmcPn2aZcuW8euvvxo7pLu2bt068vLy2LZtG8OHD6dGjRrMmzev2LZKKQIDA6lfvz7jxo3jt99+Y8+ePbRt27bE1/Py8qJ+/fpFqoQ+jtzc3KhcuTKHDh1i+vTpdOjQgYsXL1K7dm1CQkKKtF+3bh1NmjRh48aNhmNKKRYsWEDlypWpVatWoeS7wKZNmzA3N2fWrFkA7Ny5s9Bokniw4uPjDcWN/is1NRV3d3cAOnToAOR/D9xMp9Oh0Wi4cuUK1apVAx69EcDExETs7OyoUqUKjRo1ok6dOoZpr8VZsGABjRs3xtfXl++++4569eoZ1kICbN++HQcHB86ePVvodZs2bcLFxYWAgADs7Ow4ffo0kyZNuquYTU1NGT9+vCHxFkIIIUTZZpQEcOvWrcUeL6jsp9frWb9+PQsWLHi4gZWC1157jXbt2rFlyxY6d+7Me++9x+LFi4udzhoWFkZaWhrvvPMOiYmJDB48mL179xqKn5TUuHHj6NKlS2ndQpnWv39/5s+fj7OzM926dcPPz4/Zs2ezc+fOQu0KqhE+9dRTDB8+nA0bNqCUYvbs2XzwwQcsXryYypUrM2rUKCZNmsSUKVOIiIggPT2dnTt3smLFCr766isiIiLo0aMHK1euNNIdlz+3SwDT0tKoWbMmJiYmPP300wBFRgBNTEywsbEhKirKsJb2UUkA3dzcyMzM5NKlS9jb2/PKK6+wfPlyWrduzb59+4p9zbVr1/jmm294//33WbZsGZUqVcLR0RFXV1dOnz7NhQsXuHDhAoMGDSoyAhgSEkLHjh0NI3cuLi5lfiaBEEIIIe6PUX7T9+vXr9DXGzZsKNJGp9Oxbt26hxRR6Tlz5gw5OTlERETQvn17GjduTP369Q1TsG52+PBh6tWrx4gRI1i+fDl5eXnk5OTQsGHDu7rmsGHDDPsBPu5mz57NsGHDAPD19eXEiRO0b9+esLAwjh8/bmi3YsUKIiIi+Omnn3j77bcZPXo048eP5/3332fNmjV07dqV4OBg9u/fT0xMDIcPHyYgIIA5c+ZQqVIlunfvTrNmzRg8eDCpqamGTerfeOMNIiMjSU9PZ+rUqVy9epWBAwfyxBNPFCq6Ie7drRLArKwssrKyqFmzJl5eXtSpUwcomgBC/jrAqKgoXF1dsbW1fWSmgNrY2GBra8vx48ext7fHx8cHX19fWrVqdctCMFu3bqVu3br4+fkVOl63bl1Onz5NcHAwzzzzDP7+/kVGAENCQmjfvv0Dux8hhBBClD1G2Qbiv1UbhwwZQvfu3Q1fd+7cma1bt2JhYfGQI7t/VlZW/PTTT+zbt8+w1UHNmjW5dOlSkbZHjhwxbG6u1Wp599132blz5z0VSSnP9Ho9kydPpl27dmzcuJGMjAzGjBnD+vXr0el0jB8/nqeeeoqmTZvSunVrWrVqBYCPjw8RERFA/nrB0aNH88svvzB//nw0Gg0vvvgiw4YN46mnnmLXrl2cPXsWCwsLPvvsM/z9/Tl48CAfffQRVatWZeTIkYwcOZJjx47x6quvUrFiRWM+kkfarRLA69evA9C0aVPi4uKoXbs2NWvWpHLlykXa6vV6rly5QuPGjWnfvj01a9Z84HGXBo1GQ40aNTh48CB2dnaG40888QRDhw4lOTmZzz77jBdeeMHwHjt06JDhPX2zOnXq8P3333Pw4EG2bduGhYVFoRHAyMhIwsLCaNOmzQO/LyGEEEKUIcoIbGxsCn1tZ2d32/OPguTkZAWo5OTkIuemTZumXnzxRaWUUqmpqerPP/9USinVvn17tWTJkocZ5mPtiy++ULa2tsra2lqtXr26yPm9e/eqsLCwEveXlJSk3N3d1bFjx5Rer1eOjo4qPT1dffXVV6pRo0bq8uXLas6cOSoiIkIppdSxY8dUx44dlbW1tQoODi6t23okJCcnF/vevxeurq4KUFlZWYWOh4eHK41Go3Jzc+/YR4sWLZROp1NTp04tlZgepoEDByorKyv1wgsvFDpet25dNX36dAWol156yXC8adOmatWqVUX6CQ8PV127dlUdOnRQSimVkpKiABUXF6eUUurjjz9WTZs2fYB3Un6U5vtfiEfN7T7/CCHKpjIx1PS4V46rUqWKoUrl8uXLeffdd4mMjOTo0aO8//77Ro7u8TFs2DAyMzOxtramb9++Rc63bt36rvqztbUlIiICU1NTOnToQO3atbGysmLQoEEMGjQIgDfffNPQvkGDBmzbto2QkBC6du1K8+bNadCgwf3cUrmjlCI+Ph6ApKQknJ2dDefS0tLQ6XQlWqNWsBWEtbX1A4v1QalZsyYZGRlF9sfs0qUL7733Hg0bNmTp0qW89dZbODg4cPz48WIrB1euXLnQdho2NjZ4eHhw4sQJtFotb7311iM5zV4IIYQQ98coawCzs7PZs2fPLQs9PG68vb0JCwsD8tfcJCYm0r9/fxwcHCRBKGXjxo0zJGelwdTUFIBVq1Yxbdq0Er2mffv2DBo0iI8++uier/vyyy/Tr18/QkND77mPR83u3bu5ePEiubm5aDSaIj8f0tLSSpzQFeyjWTAN+1FSMF315imgkD81Pjs7mxkzZtCyZUt++OEHTpw4gbW1NVWrVi1R3x06dGDbtm1MnjyZKVOmEBgYWNrhCyGEEKKMM0oC6O3tTfv27XFycqJy5cqkp6czf/58fv/9dzIzM40R0gPl7e3N5cuXuXHjBrt37+aZZ55h48aNTJkyRdb7PSIsLCwMyWBJvPrqq6xZs6bYjbcLzJs3j8aNG7N9+/ZCx69du8bChQuJiIjgyy+/vOeYHzWvvPIKixYtwsLCAmdn5yJbdNxLAviojgACRUYAW7VqxfTp0wkMDKRt27YcOHCAP/74g6ZNm5Z4FkW3bt1YtWoVBw8eLPHm8kIIIYR4vBglATxz5gyJiYn8/PPPjB49mk6dOvHRRx/Rpk0bbG1tSU9PN0ZYD4y7uzsmJiZs3bqV7OxsFi9ezKhRoxgwYICxQxMPSPXq1Rk3bhy9e/cmIyODK1eucP78ecP57OxsJk+ejJ2dHd9//z15eXlkZGQA8Pfff+Pp6cnEiRNZu3ZtkaJJN/vtt98K7Q8XFRVFSEgIeXl5xbbPzs5m5cqVpKSklNKdlp64uDhCQkJwcnLC3t6+yAhgampqiUf0HuURwOrVq6PRaIokgFqtlilTpmBmZkbz5s05ePAg27dvN+yHWBIBAQEkJCQQEBCAi4tLaYcuhBBCiEeA0TZ80uv1BAQE8NZbb7F+/XqioqKIiIjg22+/ZcKECcYKyyA3N5d33nkHLy8vbGxsqFmzJp999tk99aXVavH09GTBggW0bduWSpUqERwcLKN/j7k5c+ZgaWnJzJkz6dOnD/379+fUqVN07dqVs2fPYm5uzhtvvMHOnTuZMWMGzZo1Izs7m7/++gtfX18CAwO5cuUKp06dKtTv5s2bmTdvHt999x1dunTh2WefJSYmhpMnT+Lj40OXLl345ptvisQTHx9Ps2bNePHFF/nwww8f1mMoEaUUcXFxnDx58pYJ4N2MABZsSv4ojgBaWVnh7e19260rmjZtysWLF9m5c+dd7QFqY2PD888/z+jRo0sjVCGEEEI8gspUBuLh4YGHhwc9e/Y0digsXryYzz//nJCQEOrVq8eePXvo2LEjVapUMWxAfTe8vb357bffbrmZs3j8mJmZsWTJEpo2bYqjoyNpaWm88MILHDt2jKZNm+Lr60ubNm2IjY3lgw8+wMrKio8//pgLFy7g6+tLhQoV6Nq1K59//jkLFy4E8tfJPffcczz99NN8/PHHTJo0iTNnzjBw4ECcnZ0ZMmQIzZs3Z+rUqfj4+HD9+nVq1aqFo6MjvXr1okqVKrz//vv07t2b119/3ZAolURsbOwDGzVKSUkhOzsbACcnJ8zNzcvtGkDI39vvduv67OzsqFWrFtnZ2dSoUeOu+l66dOn9hieEEEKIR1iZSgDLkvPnz9O6dWvq1asHQLt27ahbty7Hjh27pwQwMDCQLl260Lx589IOVZRhDRs2ZObMmdSqVYvNmzezfPlyatasyZIlS+jatStWVla0atWK2NhYPvnkE5599lm8vLwMo+DTp0+nYcOGNG7c2JBQTpw4kSlTphiukZqaStu2bQkJCeHcuXNUqVKFBQsW0KdPH2xtbQkLC8PGxgY/Pz9WrFhBhQoVqFevHosXL+att94y9KOUKrKW7OjRo3z//fdMnjwZT09P/v77b6pVq1aie09NTeXMmTM0bdr0jm3j4uLQarUopXBycsLU1LRUEsBHcQQQoFatWnds06ZNG/R6/WNfRVkIIYQQpUsSwFsYMWIEffr04dixY9SvX59du3Zx4cIFOnXqdE/9vfHGG6UcoXhUFGwVUb9+fdq1a8fZs2eZNWsWfn5+ALzzzjuYmJjwxBNP0KxZM3bs2IGvry+QnwhMnDiRadOm4eDgQFRUFJs3by7Uv42NDVu3bmXPnj2G5Ozo0aOG8+fPn+fChQs8/fTThmRh8uTJDBw4kJdffhmdTse+ffvo3bs3c+fOZeHChbzxxhv07t2bH3/8kS+//JKePXuSlZXF7t27S5wArl+/npkzZ5aokmlcXBwuLi7Y2tri5OQEUK5HAEti4cKFkvwJIYQQ4q6VuwRw0KBBLF++/Jbn27Vrx+7duw1TPf39/dFoNJiYmLBgwQLDh/bi6PV6UlNTH0TY4jHg6OjIs88+y549e9Dr9VSvXp2UlBT8/f2B/GmQb7/9NidPnsTDw8NQqGXChAmGEcG8vDw0Gk2RIi46nY7OnTsXW9zFxcUFFxeXQu/N5s2bU6tWLd5//3369+/PkCFDaN++Pa+88gqNGzdm8uTJtGnThkOHDpGdnc2uXbvQ6/Xs27ePPn36APnTUUNCQpgxY0ax7/vw8HDi4uKIjY3F0tLyts8mKioKT09PatWqhZubG5mZmcTGxha6n7S0NPR6fYkK2FhYWKDX61FKlcmCN6Xpcayc/KiRn/uiPJP3vxCPnnKXAC5atOi2BTDMzMwAGDt2LCdPnuTs2bP4+Pjw119/0b17d0xMTBg1alSxr83JyWH69OmYm5sD+fvBBQQElP5NiEeav78/NWvWpE6dOkXONW7cmKNHj2JlZVXsa0uyCXpJaDQa5s6dS//+/Vm6dClBQUHMmjWL2NhYnJ2d6dGjBwsWLOD48eNYWlqyZcsWvLy8OHDggGGqaHBwMPv376ddu3bFTvOMiopCKUVYWBi1a9e+bTzXrl3D0dGRWbNmYWZmxooVK4qMHF6/fr1IZcxbKRj50+l0JXwiQgghSmrHjh2EhIQAkJWVZeRohBB3q9wlgNbW1iWaRnb48GGGDBlimO7m5+dH9+7d+emnn26ZAKanp/Phhx8app8JURy9Xs+hQ4due/5haNWqFdu2beP06dP06tUL+P/qmfPmzaNZs2Y4OzvTtGlTNm/ezHvvvce0adOIiIhAr9ezY8cO3nnnHV5//XUOHDiApaVlodgvXbpESkoK4eHhNGvW7LaxJCYmYmtrS6VKlYD80dK4uLhC/aWkpODp6Vmi5+Pp6YlGo8HBweGun4sQ90p+9ovyIigoiKCgICD/Z/MXX3xh5IiEEHfDaNtAlHVt2rTh22+/JTw8HMjfu/Cnn36icePGRo5MiNJTp04dQ/J3syZNmjBo0CA6dOhA3bp1gfyRy/Hjx9O6dWu6detGhw4dePfdd3F0dOTTTz8t0kdERAQuLi6cOXPmjnHExcUZ1v4BODs7ExMTU6hNSkpKidcAVqtWrUTXFUIIIYQob8rdCGBJzZs3jzfffJPWrVuTmJiIo6MjPXv25N133zV2aEI8FEuXLiUrK4sffvgBgNq1axMYGEi3bt24ePEiHTt2xNTUlODgYDp37szZs2f59NNPcXR0RClFREQEPXr0uGUiFhcXR3BwMJMnTyY+Pr5QcRlfX1/OnTtHZmamYf1gREQEnp6eJY7fzc3tPu5eCCGEEOLxJCOAt2Btbc2iRYuIjIwkLS2N8PBwPvroIywsLIwdmhAPhYmJCZaWljRu3BhnZ2c8PDyA/EJJgwcPNiRY/v7+7Nq1i/T0dIYPH45SisTERK5fv05gYOAtE8CQkBCmTp1KcHAwcXFxODs7G855eXmh0+k4ffo0kL9Fxfnz50tcgVQIIYQQQhRPEkAhxG3VqVOH8PDw2xag8fT05JNPPuH333/n119/JSIiAjs7O1q3bs0///xDQkJCkdecO3eO2rVr88Ybb/DXX38VSgA1Gg0NGjTg+PHjAFy9epXr16/fdnN0IYQQQghxZ5IACiHu6FZVSW/m4OBAy5YtOXPmDBEREVSuXBlvb2/8/f357rvvgPy1tAX7+4WGhtKvXz+GDx/O5cuXCyWAkL9v4okTJwC4cOEClSpVokKFCqV8Z0IIIYQQ5YskgEKIUuPp6UlERIQhAQQYMmQIy5YtA2DcuHF88sknQP4IYI0aNZg9ezaDBw82FJspcPMIoEz/FEIIIYQoHZIACiFKjaenJ5GRkVy8eJEqVaoA0KdPH06cOEFkZCSRkZH8/vvvQP4IYPXq1bGysmLZsmU4OjoW6qtRo0YcOXKEuLg4SQCFEEIIIUqJJIBCiFJTkACeO3eOmjVrAvmbsnt4eHDx4kUuX77MH3/8wdWrV0lISKB69eq37MvPz4/AwEBGjRrFuXPnJAEUQgghhCgFkgAKIUpNQQJ49uxZatSoYTju7e3N0aNHycjIIC8vjx9//JGKFSveduNsjUZDcHAwR48eZc2aNZIACiGEEEKUAtkHUAhRajw9Pbly5QomJiZFEsB9+/Zha2tLixYtmDdvHrVq1bpjfy4uLpw+fZpvv/2Wp59++kGGLoQQQghRLsgIoBCi1Li7u6PRaDAzMyu0aXtBAujp6cmYMWMICgri888/L1GfVlZWDB069LajhUIIIYQQomRkBFAIUWq0Wi1ubm44ODgU2jfQy8uL2NhYGjVqRLdu3ejWrZsRoxRCCCGEKL8kARRClCpPT08qVapU6Ji3t7fhnBBCCCGEMB5JAIUQpcrb2xsfH58ixwA8PDyMEJEQQgghhCggCaAQolT973//w9LSstAxDw8PTExMJAEUQgghhDAySQCFEKWqYsWKRY6ZmZlRtWrVIiODQgghhBDi4ZIEUAjxUPzxxx84OjoaOwwhhBBCiHJNEkAhxEPh5ORk7BCEEEIIIco92QdQCCGEEEIIIcoJSQCFEEIIIYQQopyQBFAIIYQQQgghyglJAIUQQgghhBCinJAEUAghhBBCCCHKCUkAhRBCCCGEEKKckARQCCGEEEIIIcoJSQCFEEIIIYQQopyQBFAIIYQQQgghyglJAIUQQgghhBCinJAEUAghhBBCCCHKCUkAhRBCCCGEEKKckARQCCGEEEIIIcoJSQCFEEIIIYQQopyQBFAIIYQQQgghyglJAIUQQgghhBCinJAEUAghhBBCCCHKCUkAhRBCCCGEEKKckARQCCGEEEIIIcoJSQCFEEIIIYQQopyQBFAIIYQQQgghyglJAIUQQgghhBCinCi3CeCJEyfo1KkTFStWRKPRsGPHjmLbff3119SrVw+dToeLiwsvv/zyQ45UCCGEEEIIIUpHuU0Azc3NCQoKYvPmzbdsM3/+fKZPn87ixYtJTk4mLCyMQYMGPbwghXiE3OqPKEKUB/L+F0II8agotwlg7dq1GT58OP7+/sWeT0lJYerUqXzyySe0adMGrVaLTqejUaNGDzlSIR4NISEhxg5BCKOR978QQohHRblNAO9k//79XL9+nXPnzlG9enVcXFwIDAzkxIkTxg5NCCGEEEIIIe6J1tgBlLZBgwaxfPnyW55v164du3fvvmM/8fHxAGzYsIHdu3fj4ODAtGnT6NixI//88w+2traF2iul0Ov1REdHk5qael/3IMSj5vr16+Tk5BAVFWXsUIR46OT9L8qztLQ0IP9zkBDi0aBRj9l3bFpaGpmZmbc8b2ZmViR502g0/PrrrwQEBBiObdy4kWeffZatW7fSqVMnAHJzc7GxsWHt2rWGYwUuX76Mp6dnKd6JEEIIIcSjITIyEg8PD2OHIYQogcduBNDa2hpra+v77qdhw4ZAfnJYEu7u7kRGRmJjY1Pi1wghhBBCPMqUUqSmpuLu7m7sUIQQJfTYJYAlpZTixo0bhq+zs7PJzMxEq9Wi1Wrx9PSke/fuzJ49m4YNG2JnZ8f06dOxt7enVatWRfozMTGRv3wJIYQQotz578wqIUTZVm6LwISHh2NlZYWVlRUAnTt3xsrKivfee8/QZvny5VSrVo1atWrh7u7O0aNH+fnnn9Hr9cYKWwghhBBCCCHu2WO3BlAIIYQQQgghRPHK7QigEEIIIYQQQpQ3kgAKIe5Jjx490Gg07NixA4BLly6h0WjQ6XSGYkzW1tYkJycbOVIhSse0adMwNTUt9P5+7rnnDOdPnjxJ27Zt0el0uLu7M23aNCmNL4QQoswpt0VghBD3bsWKFaSnpxd77sSJE1SrVu0hRyTEw9GiRQv27dtX5HhqaiqBgYEMGjSIn3/+mfPnz9OpUydsbW159dVXjRCpEEIIUTwZARRC3JXLly8zefJkvvjiC2OHIkSZsW7dOnJzc5k5cyZWVlbUq1ePiRMnsmjRImOHJoQQQhQiCaAQosSUUgwZMoTJkydTuXLlYtu0a9cOJycnWrZsyfr16x9yhEI8WMeOHcPZ2RkvLy/69+9PWFgYAMePH6dhw4Zotf8/saZJkyZcvHiRlJQUY4UrhBBCFCEJoBCixIKDg1FKMWLEiCLnnJyc2L9/P2FhYURGRjJ69Gj69evH1q1bjRCpEKWvV69enD59mtjYWPbv349GoyEgIIC0tDRSUlKws7Mr1N7e3h5AEkAhhBBliqwBFEKUyIULF5g5cyYHDhwo9ry1tTUtWrQwfP3888+zc+dOVq5cSefOnR9WmEI8ML6+vob/rlSpEsuWLcPW1pb9+/ej1+u5fPlyofaJiYkAsnesEEKIMkVGAIUQJbJ3716uXbtG48aNcXJywsnJCYCePXsWOyIIYGJiIlUQxWNLo9Gg0WhQStGgQQOOHTtGTk6O4fzhw4epWrWqJIBCCCHKFEkAhRAl0qdPHy5evMjx48cN/wA+++wz5s6dy969ezlz5gy5ublkZWXx3Xff8e233xYqky/Eo2zNmjXEx8cDcPXqVYYNG4arqystW7YkKCgIU1NTpk6dSkZGBqdOneLDDz9k7NixRo5aCCGEKEymgAohSqRChQpUqFChyHEnJyccHBw4e/YsgwYNIiYmBgsLC2rUqME333zDM888Y4RohSh9K1euZOzYsVy/fh17e3vatm3Ljh07sLGxAeDnn39m7NixODo6otfrGTVqlGwBIYQQoszRKJmfJYQQQgghhBDlgkwBFUIIIYQQQohyQhJAIYQQQgghhCgnJAEUQgghhBBCiHJCEkAhhBBCCCGEKCckARRCCCGEEEKIckISQCGEEEIIIYQoJyQBFEIIIUSpePPNN6lXrx56vR43Nzeee+45IiMjC7WJiIiga9eu2NjY4OTkxLhx48jKyjKc37lzJ0899RSOjo5oNBrOnz9f5Dre3t5YWlpibW1t+Ld58+Y7xhcTE8PIkSPx8PDAysoKDw8PRo0axdWrV0t8j4MGDWLgwIF3bPfTTz/h5+eHvb09dnZ2NGrUiB9//LFQm8TERAYMGICtrS12dnYMGDCApKSk2/Z78uRJ2rZti06nw93dnWnTpnHzjl7Tpk3D1NS00LN57rnnbtvn6tWradOmDXq9Ho1GQ05OTpE2Go0GKyurQv3+9ddfd3wOQoiyRxJAIYQQQpQKjUbD119/TXx8PGfOnEGj0dCtWzfD+by8PLp27YqDgwNRUVEcOXKE3377jYkTJxra6HQ6XnjhBVasWHHbay1atIi0tDTDv65du962fUxMDE2bNiUyMpKdO3eSlpZGSEgIERERNGvW7K6SwJLw9/dn27ZtJCQkkJiYyIIFC3j++ec5ceKEoc3AgQO5evUqFy5c4Pz581y9epUXX3zxln2mpqYSGBhIq1atiI+P5+eff2bp0qUsWLCgULsWLVoUejbffffdbWO1t7dnzJgxRfr5r02bNhXqt169end8DkKIMkgJIYQQQjwAx44dU4BKSEhQSim1e/dupdVqVVxcnKHNhg0bVIUKFVRGRkah14aFhSlAhYaGFunXy8tLffHFF3cVy/Dhw1XVqlXVjRs3Ch3PzMxUVatWVSNHjjQci4yMVP3791eVKlVSNjY2qn79+urIkSNq1qxZSqvVKq1Wq3Q6ndLpdCo8PPyO187NzVV79+5VFhYW6ocfflBKKXXp0iUFqOPHjxvaHT9+XAG37PPrr79Wzs7OKjs723BswYIFqmrVqoavp06dqlq1alWyh/Ifu3btUkCh/gsA6tdff72nfoUQZYuMAAohhBDigfjll1/w8vLC3t4egOPHj1O1alWcnJwMbZo0aUJ6ejrnzp27q77ffvttHBwc8PX1Zd68eWRnZ9+2/ebNm3nuuecwNzcvdNzCwoJ+/foZppBmZGTQvn17zM3NOX78OElJSXz77bc4Ojry9ttvM2DAAPr27WsYBatcufItr5mcnIydnR0WFha0adOGJk2a0KVLF8OzsLCwoH79+ob29evXN1y3OMePH6dhw4ZotVrDsSZNmnDx4kVSUlIMx44dO4azszNeXl7079+fsLCwQv34+fkxd+7c2z6v4gwcOBBHR0caNWrEF198cdevF0KUDdo7NxFCCCGEuDs7duxg+vTprF271nAsJSUFOzu7Qu0KksObE5g7Wb58OY0aNcLKyooDBw4wcOBArl27xvvvv3/L18TFxVGpUqViz3l4eBAbGwvAli1buHbtGkuWLMHCwgKAOnXqlDi2m9na2pKUlERmZiZbtmzh3LlzhgQ0JSUFW1vbIq+xs7O75bO40/PT6/X06tWLwYMHU7lyZa5cucKkSZMICAjgxIkTWFtbA/nrCO/Wjh07aNmyJaampuzYsYMBAwaQk5PD6NGj77ovIYRxyQigEEIIIUrV5s2b6dWrFytXrqRjx46G43q9vkiRk8TERMO5kmrXrh02NjZotVpat27NtGnT+OabbwDYu3dvoUIle/fuBcDZ2ZmoqKhi+7t8+TIuLi4AhIWF4e3tbUj+SqJu3bqG640aNarIeUtLS3r27MnevXtZvHix4X6Tk5OLtE1KSrrlsyjJ8/P19cXLywuNRkOlSpVYtmwZUVFR7N+/v8T3U5ynnnoKKysrzM3N6dy5M+PHjzc8cyHEo0USQCGEEEKUmlWrVjFgwAC+//57evToUehcgwYNCAsL49q1a4Zjhw8fpkKFCtSoUeOer2liYmKohNmmTZtChUratGkDQJcuXfj+++8LVRwFyMrK4vvvvzdMzfT29ubSpUtF2t18rf/6+++/DddbsmTJLePMzs7m7NmzQP6zuHHjRqHRuJMnT5KVlUWDBg2KfX2DBg04duxYoSqdhw8fpmrVqrdMGjUaDRqNplCl0NJw8zMXQjxaJAEUQgghRKlYtGgR48aNY/PmzQQGBhY536ZNG2rVqsXrr79OamoqERERTJkyhaFDh2JpaQnkVwrNzMzkxo0bQH6ClpmZSW5uLgChoaHs3buXzMxM8vLyOHDgANOmTbvjVgfTp08nIyODnj17cu7cOfLy8ggNDaVnz55kZWUxffp0ALp27WqoihkfH49SitOnTxMeHg5AxYoVuXDhgiGeW1mxYgXnzp0jNzeXjIwMlixZQkhICJ06dQLAy8uLzp07M2HCBOLj44mPj2fChAl069btluu/HUdQAAACFElEQVQKg4KCMDU1ZerUqWRkZHDq1Ck+/PBDxo4da2izZs0a4uPjAbh69SrDhg3D1dWVli1b3jLW3NxcMjMzDUnvjRs3DM8X4OjRoxw5coSsrCxycnL45ZdfWLBgwR2fuRCijDJuDRohhBBCPC6AQhUyC/799ttvhjaXLl1SnTt3VjqdTjk4OKixY8eqzMxMw/mCSpT//ffVV18ppZQ6ePCg8vPzU9bW1srGxkbVqlVLzZo1S2VlZd0xvqioKDVs2DDl5uamLCwslJubmxo+fLiKjo4u1C48PFz17dtXVaxYUdnY2KgGDRqoo0ePKqXyq5M2b95c2dnZKVtb21tW7JwyZYry9vZWFSpUUA4ODqp58+Zq9erVhdpcu3ZNPffcc0qv1yu9Xq/69++vEhMTb3sPJ06cUK1bt1ZWVlbK1dVVTZ06VeXl5RnOd+vWTTk5OSkrKyvl7u6u+vXrV6SSap06ddSsWbMMX3/11VfFPvNdu3YppZTauHGjqlWrltLpdMrW1lb5+fmp4ODg28YphCi7NErJ+L0QQgghhBBClAcyBVQIIYQQQgghyglJAIUQQgghhBCinJAEUAghhBBCCCHKCUkAhRBCCCGEEKKckARQCCGEEEIIIcoJSQCFEEIIIYQQopyQBFAIIYQQQgghyglJAIUQQgghhBCinJAEUAghhBBCCCHKCUkAhRBCCCGEEKKc+D+F1FODKCGd/wAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(5, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.9, bottom=0.05, top=0.95)\n", + "\n", + "plot_line(axs[0], b_xyz)\n", + "plot_line(axs[0], pyrf.norm(b_xyz), color=\"k\")\n", + "axs[0].set_ylabel(\"$B$ [nT]\")\n", + "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\"], **legend_options)\n", + "\n", + "plot_line(axs[1], j_xyz)\n", + "axs[1].set_ylabel(\"$J$ [nA m$^{-2}$]\")\n", + "axs[1].legend([\"$J_{x}$\", \"$J_{y}$\", \"$J_{z}$\"], **legend_options)\n", + "\n", + "plot_line(axs[2], e_xyz)\n", + "axs[2].set_ylabel(\"$E$ [mV m$^{-1}$]\")\n", + "axs[2].legend([\"$E_{x}$\", \"$E_{y}$\", \"$E_{z}$\"], **legend_options)\n", + "\n", + "plot_line(axs[3], e_xyz[:, 0])\n", + "plot_line(axs[3], jxb_xyz[:, 0])\n", + "plot_line(axs[3], evxb_xyz_i[:, 0])\n", + "plot_line(axs[3], ep_xyz[:, 0])\n", + "plot_line(axs[3], evxb_xyz_e[:, 0])\n", + "axs[3].set_ylabel(\"$E_x$ [mV m$^{-1}$]\")\n", + "labels = [\n", + " \"$E$\",\n", + " \"$J \\\\times B/q_{e}n$\",\n", + " \"$-V_{i} \\\\times B$\",\n", + " \"$-\\\\nabla \\\\cdot P_{e}/q_{e}n$\",\n", + " \"$-V_{e} \\\\times B$\",\n", + "]\n", + "axs[3].legend(labels, **legend_options)\n", + "\n", + "plot_line(axs[4], e_lhs[:, 0], color=\"k\")\n", + "plot_line(axs[4], e_rhs[:, 0], color=\"tab:red\")\n", + "axs[4].set_ylabel(\"$E_x$ [mV m$^{-1}$]\")\n", + "labels = [\"$E+V_{i} \\\\times B$\", \"$J \\\\times B/q_{e}n - \\\\nabla \\cdot P_{e}/q_{e}n$\"]\n", + "axs[4].legend(labels, **legend_options)\n", + "\n", + "axs[0].set_title(\"MMS - 4 Spacecraft average\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/01_mms/example_mms_particle_deflux.ipynb b/docs/examples/01_mms/example_mms_particle_deflux.ipynb new file mode 100644 index 00000000..e0c7662e --- /dev/null +++ b/docs/examples/01_mms/example_mms_particle_deflux.ipynb @@ -0,0 +1,444 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Particle Differential Energy Fluxes\n", + "author: Louis Richard\\\n", + "Load brst particle distributions and convert to differential energy fluxes. Plots electron and ion fluxes and electron anisotropies." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pyrfu import mms, pyrf\n", + "from pyrfu.plot import plot_line, plot_spectr, make_labels\n", + "from scipy import constants" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define data path, spacecraft index and time interval" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mms.db_init(\"/Volumes/mms\")\n", + "ic = 3 # Spacecraft number\n", + "tint = [\"2015-10-30T05:15:20.000\", \"2015-10-30T05:16:20.000\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Particle distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 18:27:47] INFO: Loading mms3_dis_dist_brst...\n", + "[08-Jun-23 18:27:47] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_dist.py:68: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 18:27:49] INFO: Loading mms3_des_dist_brst...\n" + ] + } + ], + "source": [ + "vdf_i, vdf_e = [mms.get_data(f\"pd{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Particle moments" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 18:27:57] INFO: Loading mms3_dis_numberdensity_brst...\n", + "[08-Jun-23 18:27:57] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 18:27:57] INFO: Loading mms3_des_numberdensity_brst...\n", + "[08-Jun-23 18:27:57] INFO: Loading mms3_dis_bulkv_gse_brst...\n", + "[08-Jun-23 18:27:57] INFO: Loading mms3_des_bulkv_gse_brst...\n", + "[08-Jun-23 18:27:57] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 18:27:57] INFO: Loading mms3_dis_temptensor_gse_brst...\n", + "[08-Jun-23 18:27:57] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[08-Jun-23 18:27:57] INFO: Loading mms3_des_temptensor_gse_brst...\n", + "[08-Jun-23 18:27:57] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n" + ] + } + ], + "source": [ + "n_i, n_e = [mms.get_data(f\"n{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]\n", + "v_xyz_i, v_xyz_e = [mms.get_data(f\"v{s}_gse_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]\n", + "t_xyz_i, t_xyz_e = [mms.get_data(f\"t{s}_gse_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Other variables" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 18:27:57] INFO: Loading mms3_fgm_b_dmpa_brst_l2...\n", + "[08-Jun-23 18:27:57] INFO: Loading mms3_fgm_b_gse_brst_l2...\n", + "[08-Jun-23 18:27:57] INFO: Loading mms3_edp_dce_gse_brst_l2...\n", + "[08-Jun-23 18:27:58] INFO: Loading mms3_edp_scpot_brst_l2...\n", + "[08-Jun-23 18:27:58] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "b_xyz, b_gse = [mms.get_data(f\"b_{cs}_fgm_brst_l2\", tint, ic) for cs in [\"dmpa\", \"gse\"]]\n", + "e_xyz = mms.get_data(\"e_gse_edp_brst_l2\", tint, ic)\n", + "scpot = mms.get_data(\"v_edp_brst_l2\", tint, ic)\n", + "scpot = pyrf.resample(scpot, n_e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute moments " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "v_av = (\n", + " 0.5\n", + " * constants.proton_mass\n", + " * (1e3 * pyrf.norm(v_xyz_i)) ** 2\n", + " / constants.electron_volt\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute parallel and perpendicular electron and ion temperatures" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 18:27:58] INFO: Using averages in resample\n", + "[08-Jun-23 18:27:58] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "t_fac_i, t_fac_e = [\n", + " mms.rotate_tensor(t_xyz, \"fac\", b_xyz, \"pp\") for t_xyz in [t_xyz_i, t_xyz_e]\n", + "]\n", + "\n", + "t_para_i, t_para_e = [t_fac[:, 0, 0] for t_fac in [t_fac_i, t_fac_e]]\n", + "t_perp_i, t_perp_e = [t_fac[:, 1, 1] for t_fac in [t_fac_i, t_fac_e]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Differential Energy Fluxes" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def_omni_i, def_omni_e = [mms.vdf_omni(mms.psd2def(vdf)) for vdf in [vdf_i, vdf_e]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Pitch-Angle Distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 18:28:00] INFO: User defined number of pitch angles.\n", + "[08-Jun-23 18:28:00] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "def_pad_e = mms.psd2def(mms.get_pitch_angle_dist(vdf_e, b_xyz, tint, angles=13))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute parallel/anti-parallel and parallel+anti-parallel/perpandicular" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 18:28:09] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_34368/3690123158.py:5: RuntimeWarning: divide by zero encountered in divide\n", + " psd_parapar = def_pad_e.data.data[:, :, 0] / def_pad_e.data.data[:, :, -1]\n", + "\n", + "[08-Jun-23 18:28:09] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_34368/3690123158.py:5: RuntimeWarning: invalid value encountered in divide\n", + " psd_parapar = def_pad_e.data.data[:, :, 0] / def_pad_e.data.data[:, :, -1]\n", + "\n", + "[08-Jun-23 18:28:09] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_34368/3690123158.py:6: RuntimeWarning: divide by zero encountered in divide\n", + " psd_parperp = (def_pad_e.data.data[:, :, 0] + def_pad_e.data.data[:, :, -1]) / (\n", + "\n", + "[08-Jun-23 18:28:09] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_34368/3690123158.py:6: RuntimeWarning: invalid value encountered in divide\n", + " psd_parperp = (def_pad_e.data.data[:, :, 0] + def_pad_e.data.data[:, :, -1]) / (\n", + "\n" + ] + } + ], + "source": [ + "def calc_parapar_parperp(pad):\n", + " coords = [pad.time.data, pad.energy.data[0, :]]\n", + " dims = [\"time\", \"energy\"]\n", + "\n", + " psd_parapar = def_pad_e.data.data[:, :, 0] / def_pad_e.data.data[:, :, -1]\n", + " psd_parperp = (def_pad_e.data.data[:, :, 0] + def_pad_e.data.data[:, :, -1]) / (\n", + " 2 * def_pad_e.data.data[:, :, 7]\n", + " )\n", + "\n", + " psd_parapar = xr.DataArray(psd_parapar, coords=coords, dims=dims)\n", + " psd_parperp = xr.DataArray(psd_parperp, coords=coords, dims=dims)\n", + "\n", + " return psd_parapar, psd_parperp\n", + "\n", + "\n", + "vdf_parapar_e, vdf_parperp_e = calc_parapar_parperp(def_pad_e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "legend_options = dict(\n", + " frameon=False, loc=\"upper left\", ncol=1, bbox_to_anchor=(1.0, 1.0)\n", + ")\n", + "e_lim_i = [min(def_omni_i.energy.data), max(def_omni_i.energy.data)]\n", + "e_lim_e = [min(def_omni_e.energy.data), max(def_omni_e.energy.data)]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[08-Jun-23 18:28:10] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 18:28:10] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 18:28:12] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 18:28:12] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 18:28:14] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 18:28:14] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 18:28:14] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[08-Jun-23 18:28:14] INFO: Substituting symbol \\perp from STIXGeneral\n" + ] + }, + { + "data": { + "image/png": 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oiOjy+XQ9W1ugTh06RsE4enr0QqTgW4OQwt8gcjkVxSIR9XlODn2P8vLo+6aj82EsU1P6DVRSNLqoU6lIz8//8Dc/nx5LfDxw9y5w/z4QHg4kJ38Q2dradF8FF1ja2tSOgv+1tQv/L5MpbObzePRiRCKh2xdcaDk50ed2dkDduoCLC/3L5RYyWSgUIj4+HhkZGYp0GC6XCwsTE1jJ5TDOyoJOcjI9D6KiPpyTEgl9ZGXRc+DjrydLS8DZGbK6dZFgY4MIXV3ckMux7qefAADNmjXDuHHjUL9+ffTp06dQvnl1gokQ1cD8qhqYX1WHMkUzn09/ylQFj0d/TpXBkiVLsH79egQFBYHL5UIulyM6OhpLly6FSCRCeHg4jIyMlLMzJcNEcxVQKdGsagihn4ICsVIQXY2MBCIigHfvaORbJKICz8iIRshtbam4Sk6mgjAlhYo3LpcKso//GhvTT/GrV3RsiYSKMysrKsY9PKgQMzenj4LoqKUlFa0GBnS/JU1mIwT8tDRARwfGqvy2qGlIJPQbtCDanpND3zOxmD6k0g/i9OP/NTWp//X0wDc3BwwNYaytTccqOE9iYuh4CQlAbCx9LpHQi5pGjej7XRBlNzCg50hGBn3/C9YnhIpuJ6cPwtvenkbuuVx6jpiY0HPA1JRekEgk9JwsENnv3gGPHwMhIRA6OGC+nh7SzczA09PDk9BQ9OzZEwcPHoSOjo6a34yiMBGiGphfVQPzq+r4XCPNvXv3xtu3bxEREVHo9b/++gtDhw7FyZMnMWzYMOXsTMmwnObPFQ7nQ9SzcePi15HLaQQ4MZFGINPTaYRXS+uDqLW2piKnQJB9HBEtiCA3avQhkq3MTx6HQ6O/jMJoa9MLHAuLyo/x/oeyzNCCXE7Pieho4OVLul1B/nduLo1Ku7jQCyEnJ3oHw97+Qx53RbC0BFq3LvxacDD0xo3Dno+6Z0a5uKDv48fo27cvfvnlF9SrV6/i+2IwGIwaREHGX00gJCQE7dq1K/J6QYqdUCgEAMyYMQMGBgbYvHkzUlJS0K1bN2zYsKHYeTxVBRPNjJLR0KBCxdJS3ZYwqisaGh9SN9SRh+bjA4SF0f9lMoDHg8uSJThx9CjGaGjA1dUV+/btw5QpU6reNgaDwWAUIiUlBcnJyfDy8iqy7NSpU9DQ0FAI6lWrVsHT0xNffPEFpkyZgpUrV6pVMANMNDMYjNqCpiZN89mzB54iES4fOgR7AFOnTsXAgQNhZWWlbgsZDAbjsyY4OBgALYd6//59AEBycjJOnz6NI0eOYPHixXB1dQUAWFtb4+uvv0a7du1w4MABDBkyRG12F8DaaDMYjNrH/v2wW7oUGQDqAYp29AwGg8FQHyEhIQCApUuXom3btmjXrh2+/PJLJCUl4dSpU1i7dq1i3czMTJw8eRJmZmZwdHRUk8WFYZFmBoNR+9DUBFavhnmrVug/YAAmT54MMzMzDB48WN2WMRgMxmfLokWLsGjRojLX4/F46NWrF77++msYGRlh/vz5ePTokdqrIrFIM4PBqL30748lnToBAE6ePKlmYxgMBoNRFrm5uejTpw8mTpyICRMmYMiQIdDR0cHhw4fVbRqLNDMYjNqNdffumBkYiN3Hj+PAgQM1pgkKg8FgfI4YGBjg7t27hV7773N1wSLNDAajdrNgAb5/Xyh/86ZNajaGwWAwGDUVJpoZDEbtRlcXdb/5Br0BLF22DKyfE4PBYDAqAxPN5WDw4MHgcDi4fv264rWAgAA0a9YM+vr6cHFxwe7du9VoIYPBKJUVK9DB3BwAFOWMGAwGg8GoCEw0l8Hhw4eRl5dX6LWYmBj07dsXkydPRnZ2Ng4ePIhFixbhzJkzarKSwWCUCoeDeQMGAACioqLUbAyDwWAwaiJMNJdCfHw8li5din379hV6/eDBg3B3d8fs2bPB5XLRqVMnTJo0CTt27FCTpQwGoywMfH1xBYCHug1hMBgMRo2EieYSIIRg0qRJWLp0KZycnAotCwkJQatWrQq91rJlS0WnGwaDUQ2ZOBEGAF4DiIuLU7c1DAaDwahhsJJzJbB7924QQjBt2rQiy/h8Ptzd3Qu9ZmZmBj6fX+J4hoaGyM3NLXUdRsURCATqNqFWUlv9Kt25E8aLF8PT01Ntwrm2+lbdML+qBuZX1ZGbm6tuExgVhInmYnj37h1Wr16t6Iv+X4yNjZGdnV3otaysLBgbG5c4pkgkwrp162BgYAAA6Nq1K7p166Y0mxkMRtnYfnTXSCwWg8vlqtEaBoPxuXH9+nXcvHkTABPNNREmmovhzp07yMjIQPPmzQu9PnToUIwcORLe3t44e/ZsoWWPHz+Gj49PiWNKpVKsWbMG9vb2KrH5c6e0CxZG5altfjVu0AAZfD60AaSmpqJRo0bqs6WW+ba6wPxaPgghuHHjBhISEtC/f3+Yv68uUxLMr8phyJAhGDJkCAAgISEBBw8eVK9BjArBRHMxjBgxokgU2NHREXv37kWPHj0gEAiwYcMG7N69G5MnT8aDBw9w4MABHDhwQE0WMxiMcmFtDS0AbgCSEhPVKpoZDHWQkZEBbW1t/Pzzz1i+fDkAwN7eHjt27IC1tTXkcjnat28PDodT7PZv3rzB9evXMXLkSAgEAhgbG8PMzKzE9RmM2oTSJwLGxMTgn3/+werVqzFixAhlD18l6Ovrw8HBodADACwtLWFubo66devi4sWL+OWXX2BiYoJx48Zh7dq1iqtHBoNRTTE2Bk6fhg2ApLAwdVvDqMXcuXOnWk4O9/Pzg4mJCVavXo0hQ4ZAIpHAzc0N48ePx8CBA9GxY8cid1ILyMvLw8CBA7Fw4UJYWlrCxcUFFhYWGDlyZJHSrAxGbUQpkebZs2cjNDQUiYmJcHZ2RlRUFBYtWoS2bdsqY/hqwX+7iHXu3LlafiEyGIwyGDIEttraSGYVNBgq4uDBg/jyyy8BADKZDBoa1aNQ1blz5xATE4NHjx7By8tLkdN/48YNAICmpib+97//YfDgwejduze4XC7y8/PRvXt32NjYYPbs2bCzs0NcXBwuXryIYcOGIS4uDqNHj8aIESNw/vx5dR4eowbx66+/YurUqYrnHA4HlpaWaN++PbZs2QIXFxc1WlcyShHN//77L3r27Im1a9dCQ0MDvXv3LrbqBIPBYFQHnDQ1ER0ZqW4zGLWQhw8f4ssvv0SDBg2Qn58Pc3NznDt3Di1btoSurq7a7IqPj8f06dPx008/oUWLFoWWaWpqKv5fuHAh3rx5A01NTTRt2hQSiQQnTpyARCLB7NmzMXXqVFhYWGDcuHEAAHd3d1y5cgVWVlbYtGkTFi5cWKXHxaiZBAcHw8DAQNFpWSKR4MmTJ/juu++Qnp6OO3fuqNnC4lGKaH769Cn279+P3r17Y8mSJSy3icFgVGsaiETYwaJiDCWTn5+PSZMmYc2aNfjf//6HkJAQ+Pj4wNfXF/r6+mjcuDE6dOgAc3NzNGzYENbW1ujQoYPKfjNfvXqFzZs3IyEhAc+fP0f//v0xYcKEUrcxMzPDyZMnFc/5fD6+/vrrUicCWlpa4p9//sHYsWPRvn37WnWXmaEaQkJC0KRJE7Rp00bxWseOHXHjxg3cunVLjZaVjlLuGXE4HEyZMgWnT5/G1atX8erVK7x9+1YZQzMYDIbSGeTujjCZjLXUZiiVH374AVwuF9999x0AwNvbG1KpFGKxGNevX8ekSZOQmpqKp0+f4ptvvsHAgQPh6+uLhQsXwsHBAVOmTMGzZ88+2Y7IyEh06NABDRs2hEQigZ+fH9atW4c9e/YUEuhRWVFYdnMZNv+7+ZP32b9/f6xbtw5du3bFsmXLIBKJPnlMRu2EEILQ0FB4eXkVWZaYmAgPj2rct5WogOjoaPLFF1+QAQMGqGL4GgePxyPGxsYkPj5e3abUOng8HuHxeOo2o9ZR6/168SLx0NIiE8ePr/Jd13rfqgl1+/XWrVtET0+PBAcHl3sbPp9PFi1aRHr16kVOnDhBhg4dSvT19cmaNWsqbUdYWBgxNzcnM2fOJFFRUSWul8hPJIZrDUnzvc2J9ipt0m5/OzLz/EzyxekvCF/EV6xXUb/euHGDNGvWjBgZGZHvvvuOPHz4kLx48YLIZLJKH1NtJT4+ngD47L4PXr9+TQCQbdu2EYlEQsRiMYmKiiJz5swhenp65ObNm+o2sUQ4hPxnhlsliIyMxK5duyCTydC3b19FubYHDx6gdevWnzp8jYfP58PR0REvXrxgdZqVTEGHRVZDVLnUer/KZBipp4cTEgkuXryI3r17V9mua71vlURwcDB2796NuLg4pKSkAAC0tbVhZmaGhg0bwt3dHSYmJmjQoAHc3d0hl8sBqMevfD4fDRo0wPLlyzFjxoxPGuv58+fo0KEDDhw4gKFDh1Z4+zZt2qBz585Yv359iesI8gVou78t4vhxSPs2DU+TniIgOgBJgiScfnkay3yXYXqL6QAqd76KxWLcvHkTP/74I27dugVdXV00aNAAPXr0gL+/P/T09Cp8XLWRhIQEODg4gMfjfVbfBydOnMDIkSOLvG5ra4uTJ0+iffv2arCqfCglp3nkyJEYOnQoLC0tsW7dOsTHx2PixInlEsyR5ZyMo6GhAWdn50+0lMFgMABoauJ/EglOABgxdCgErFxWtSEwMBDz58/Hq1ev8MUXX2DgwIGwtrZGTk4O9PX1kZGRgbCwMJw/fx5ZWVl4+fIlsrOz4e7ujqZNm2Lw4MFo2rQp6tatq+jAqmr8/f3h4eGB6dOnf/JYTZo0weHDhzFmzBhYWVnB19e30PKUlBSYmZkV283yp59+wuPHj3H16tUSx59zcQ4OPTuExtaNET4rHFxNLto4tEEbB5pbam1gjQsRF9DIqhF0tXThauAKLY2KSQUul4tevXqhZ8+ekEgkkMlk2LNnD1atWoX09HS4ubnByMgIX331VYXGZZQMIQQCsepanhtxjZSWex8SEgIOh4OgoCBwuVzI5XJER0dj6dKlGD16NMLDw2FkZKSUfSkdZYSrfX19Ff+LRCLSs2fP8oe6ORyioaFR6oPD4RB9fX1lmKoWWHqG6lD3LdnayufgV/GIEWQuQACQ0aNHV9l+K+NbqVRKwsPDydy5c4lAICCE0Fv75d1fREREodfEYjERiURk+fLlpGnTpuTcuXMVskcV3Llzh4wZM4ZwuVyyevXqUlMLPkYul5OUlBRy5coV4u/vT7y9vYm+vj4BQKysrIirqysZOXIk2bBhA3n58iWRy+VKtTsyMpLo6uqSsLAwpY67detW4u7uTqRSKSGEEKFQSObOnUvw/py1srIi8+fPJ/n5+YQQQk6fPk0AkJ9++qnEMfkiPoE/yNZ7W0m+NL/YdZ4mPiXwBzFbb0aM1xkTr61e5MbLG4QQQtJz04nPHh8y6tSoSh1TXFwcMTQ0VBxD06ZNyaZNmyo1Vm1AmekZPBGPwB8qe/BEyvs96NWrF3FzcyvyesE5fPLkSaXtS9koRTQ3btyY3L59myQmJhJCCOnatWu5tzUwMCDR0dGlPqKiooiJiYkyTFULTDSrjs9B3KmDz8KvQiGRDxlCurz/AU9PT6+S3VbGt/Pnz1cIDQCke/fuBAA5cuRImdt27NiRACBTp04lw4YNIxs2bCAAiLGxsWI8c3Pzyh6OUli3bh0xNjYmX331FXnz5k2lxvjYr3K5nKSmppIHDx6QK1eukNWrV5MBAwYQLpdL6tSpQ+zt7cmCBQtIaGgoefjwYSEhzefziUQiKfd+ly1bRgYNGlQpm0tDLBaTunXrkm+++Ya8e/eOeHh4ECsrKxIaGkoCAwPJ+vXrCQCyevVqkpqaSnR1dclff/1V6pg7HuwgLX5pUeaFw52YOyRfmk+kMilZeXklqb+xPrkbe5f8dO8nUmdjHQJ/kPTcyn1eEhISyL179xTnnqGhIUlLS6vUWFVJeno6uX//vlLHVKZolsvlhCfiqeyhzItNGxsbMmTIkCKvX7hwgQAghw8fJhkZGURfX19xUUgIIdOmTSPr169Xmh2VQSmi+X//+x/p27cvsbOzIwYGBsTY2JjMnz+fnD17tsxtyxvh+eKLLz7VTLXBRLPq+CzEnRr4bPwqlxPSpg3xBMjpU6eqZJcV9W1eXh4BQL744guSnp5OBg8eXEg437t3r8xtv/rqK9KtWzdFlK9jx47k3LlzJD4+nkRGRhIApF27dqWKl+joaJKRkVGhYyWEkKioKPLLL7+QPXv2kG+//ZaMHj2aHD9+nPzwww9k/fr1ZPXq1YTL5ZJHjx5VeOyPKY9fc3JyyOXLl8n58+fJiBEjiJaWFtHR0SFjxowhp06dIkOGDCHa2trEycmJrF27luTl5ZU6nkQiIXZ2diqL1F++fJlYWVkRAGTgwIGKqHMBQUFBRF9fnzg5OZHu3buXOpZcLicNdjQgh0IOVciGrOwsMv3kdGL1oxWBP8ieR3uIzx4fcjT0aIWP52NOnz5N/v33X9KlSxeyZcuWTxpL1eTm5pIGDRoQACQ6Olpp436OEwGTk5MJAOLv719k2Zdffkk0NDTI27dvCSGEeHp6Kr7fbt++Tby9vSt0QasKlF49IzU1lVy5coVs2LChSm95VmeYaFYdn424q2I+K78KheQrDQ0ya8yYKtldRX1bEH0RCoWK1+RyOZHJZKR79+5k2bJlJCoqikyaNKlIJCw8PJwYGBgookRyuZxs3ryZpKSkFFrvxx9/JM7OzmTs2LHk8uXLJCMjg9y7d4+IxWKydetWcuvWLQKAtG/fvlw2BwcHEx8fH9K7d2/C5XJJx44dSY8ePUjHjh2JhoYGAUAGDBhAunXrRgYOHEiOHTtWbn+URGXO2fz8fJKUlER69+5NPDw8yHfffUdevnxJfvvtN+Lp6Uns7e3JmDFjyO7du4v4LCMjg3z11Vekbt26RcQsIYS8SX9DHsQ/+KRjIoSKBQAkISGh2OU3btwgS5cuVQiNkrj27hqx+tGKiCSiCu2/wK9yuVxRVWPhlYXEcK0hied9+m/asmXLyIQJEz55HFWRlpZGevXqRTp06EC8vLzI8ePHlTb25yiaL126RACQNWvWkHv37pF79+6RM2fOkLFjxxIAZPHixYp1Z8+eTTZu3Ejy8/NJ48aNlR7prwxKEc1du3YlCxcuJEePHiUvXryocBjfyMhIGWZUW5hoVh2flbirQj43v97w9CQWhoYkJydH5fsqy7dyuZzI5XLy6NEjsm3bNtK3b18yd+7cYtc9ePAgadCggSLqbGZmRt69e6dYfubMGdKkSZNy2XX37l3i4OBQKA3k48f06dOJhoYGSU5OLnb7ly9fklWrVpE+ffoQTU1NYmJiQjgcDjl0qHBkUyaTqeR2vLLPWbFYTC5cuEBWrFhBWrRoQTQ1NUnXrl2Jn58fmThxItHT0yPt2rUrMZe5z9E+BP4gsy/MJnni0iPWZVHZcm08EY8M+mMQGXh8IHH92ZWsDFhZ8TGK8evzlOfEeqM1+e7qd5Wy62NOnTpF6tevX+Ul6U6dOlXm3Y24uDji5uZGunXrRrKzs8n69esJh8MpMkegAIFAQHJzc0scTyaTEYFAQORyORGJRAqh+Dl9165bt67Q9wqHwyGmpqbEz8+PnPrP3b4///yTDBo0iKxYsYJ89dVXarK4MEoRzQ0bNiS+vr7km2++Id26dSNOTk6kTZs2ZObMmeXa3tDQUBlmVFuYaFYdn5u4qyo+N7/Kly8nPqamZNu2bSQ5OZksX7682OihMijOt3K5nPB4PCISiUibNm2IpaUlAUCMjIxIvXr1yOvXr4sdi8/nk/79+5MmTZqQmJgYMmLECPLddx+EzDfffEOmTZtWYfvOnTtHMjMzydq1a0lUVBSJjIwkhBDSqlUrcvDgwSLbHDlyhOjr6xNfX18ybtw4snLlSpX5rzS7VXnOvnjxgvj5+ZFvv/2WTJ48mdy4caPEdZdcX0LgD7Lr4S5iu8mWtN/fXqkTqcpDvjSfdD/cnfgd8iMbgjaQVQGriFgqrvA4Jfn1wpsLxH6zPZHKPu19FgqFxMLCggQGBn7SOBXh2rVrBACpV69eievcuHGD6Ovrk759+xY6l2fMmEEMDAzIyJEjyYoVK0hsbCwhhKYhmZubEwDEy8uLDBo0iIwdO5aMGjWKXLx4kVy+fJl4eXkRAMTR0ZH4+fmRli1bfnaiuSIkJSURExMTUq9evWrjI6WIZqlUSrZt20Z8fX3JxYsXCSH0tsP58+fLtT2LNDMqy+cm7qqKz86vT56Qv3R1C0VAPD09K/SZzcrKIu7u7mXmwRbn2127din2a29vTw4cOEBmzJhBJBJJhSJwFy5cIHXq1CFubm4EAOnSpQvZvXt3ubcvC39/fzJixIhCr+3du5cYGxuTK1euKG0/lUHd56wgX0AeJzwmK26tIPAHGXZiGL1jkPCIOG91Ju32tyNCibDsgZTEgisLiPce708W6yX5VSKTEKefnMiuh7s+aXxCCJk5c2aFL+4qg1wuJ8eOHSMASMOGDQkA8s033xRZTygUEicnJ7Jz584iy2QyGQkKCiIdOnQgZmZmpF69emTChAkEAJkwYQJ58OABOXnyJPnhhx+Io6Oj4u6Nvr4+mT9/PgkNDSWrVq0i8+bNI//++y8TzaUgl8uJlZVVmRNcqxKl5jRnZ2eTBQsWkH79+pHnz5+Xezsul0tWrlxZ6qMmw0Sz6lD3D2Vt5bPzq1xO5Pb25Mv3aQ4ASI8ePUijRo3KnW527tw5AqDM3NLifDt+/HhFCkRSUtInHIac+Pr6FhL/AQEBlR7vvzx8+JCYmJgQsZhGLJOTk4mRkVGpUdeqQp3n7NPEp4TjzyH6P+iTRjsbkd+Cfyu0PE+cR1rva02a7W1W6aoTFeH86/NEd40ueZ5S/t/hkijNr5ciLhH4g3Q+2Jkcf36cpOeml1jKrjSCgoKImZlZoUoJqmDr1q2Ey+WSdevWEUII0X1/ofzixYtC661evZp4e3srzvPikMvlRCqVkjlz5hAA5NChQ8XeXZFKpUQmkxX7PfI55jRXhN27d5Nhw4ap24xCKKUjYE5ODl68eIHw8HCEhYXh+vXr6NKlC7Zu3Vqu7bW0tNC2bdsSl3M4HNy+fftTzVQbrCOg6mDd1VTDZ+nXZcuAR49w/4svYJ+SAus5c2BhYYF///0XXl5eZW6+ePFirF+/Ho8ePUKLFi1KXK843/bs2ROWlpY4dOgQtLQ+redUQEAAhgwZgnHjxmHbtm1IT0+HhYXFJ41ZgFwuh42NDU6dOgVfX19Mnz4dqampOHPmjFLG/xTSs9KRmZeJVFkq/o37Fy/SXuBV+ivE8+Ohp60HS31LtHVoi95uvSGSimBtYI1W9q0AoFDThujsaNyIvIEMYQa61+sOT2tPaGtql7hfOZGj9a+t0c2lG5b6LoUBt/iGKoJ8Acb8NQa54lxcH38dGhwN5TrgPXMvzcWvT3/FgYEHMMpz1CePV9Z3wT+v/8EvT37BhYgLAIA6BnWwustqTG0+tdz7kMvlqFevHvz9/TFx4sRPtrk4zpw5g7Fjx+L69esKvcHn8zF+/Hg4Ojpi+/btAICsrCy4uLjg77//RufOncsclxCC27dvw9fXt8LNPz7XjoBl8erVKwwZMgS2trY4ffo0TE1N1W2SAqWIZk1NTXTs2BGjR49GixYt4OnpCR0dnXJvb2xsrPhg1kaYaFYdn6W4KwGZDIiKArS0ADs7oKBhWGYmEBpKH3FxgL4+oKkJmJoCbm6ASAQYGgJNmgDW1nTZZ+nX5GTA1vbD85MnMfT4cTRu3BirVq0qdVOhUIimTZsiIiICV69eRffu3Utctzjfuru7Y9euXejWrdunHcNHJCYm4uzZs5g5c6bSxgRoB1gvLy94e3tj5MiRCAkJgZubm1L3URGOhB7Bj3d/RHxaPGREBl0DXbR3bA+vOl7wsPBAXdO6EEqESMtLw4WICwiIDoCprikS+AnganIhEAuwotMKDG4wGCfCT2DD3Q1oZNUIprqmuB1zG2KZGMs7Lccy32XFiqJTL05h3uV5eDvnLfS0S28PnSPOgdduL3zd5mvMbT1X6b6YcX4G9j7Zi6h5UXA2dVbKmOX9LhDkC5Cel45nKc8w8e+J2NtvL0Z6Fm2VXBJHjx7Fd999h6ioqGK7HX4KOTk5cHNzw8aNGzFu3LhCy8LCwtCyZUs8f/4cbm5uWLp0Ke7du4cbN24o1YbiYKK55qEU0bx06VKEhIQgPDwc5ubmaNq0Kby9veHt7V2kBWhxMNHMqCyfm7jLzwcSEgCJBMjOBp4+BZ49A0JCgOfP6etyOUAIFcX5+UBuLuDoCHh5Ac7OVCTL5UBKChAdDejpATwe8PYt3YeFBeDlxUebNoCtrTE0Nek+BQIqxCUSup6GBmBgANjbf3g0aEBfq7FcvAj07at4enTWLPz86BEePnxY4iY8Hg+NGjVCYmIi6jasix/+9wO++OKLEtf/7zkrk8mgp6eHly9fwtXVVUkHojp27tyJ77//HoQQHDx4EMOHD1eLHck5yZh5YSbuxt7Fj91/RAvzFrA1soWFWfmi6vnSfNyKvoWUnBQcfHYQgdGBaOPQBuv81qGTcycAgFQuRUhyCPof7w8nEyes6LQCfer3UYwhJ3K02tcK47zGYV6beeXab2B0IPoe64t/J/8Lrzpl38EoL9feXUOPIz0wr/U8bO21VWnjVuY79sKbCxh2chgODzqM4Y3Ld37IZDJ4eXlhzpw5mDFjRqVsLQ4+n4/Zs2cjMjISQUFBxV74zJgxA1FRUfjpp5/QunVrXLlyBe3atVOaDSXBRHPNQymi+WPS0tIQHByMkJAQhISE4NixY2VuY2RkBIFAdT3T1Q0TzaqjtormrCwqgoODgSdPqKCNiQESE2kkmMulEePmzQFvb6BpU/rX3Z2K2YQEICODrmdnRwV0WeTl0W2Sk4GQED4ePQJSU40hkdAArIkJIBYDOjpUlMvlQE4O3VdiIhAbS4V8ixbAuHFAly5Ao0bUnhqFTEaN3rEDcb//DpenT5GVlQUjI6NiV9+9ezc2/bQJhtMMEXogFGsmrsH/vvtficP/95yNjY2Fi4sLRCIRtLVLTgOoLuTn52PZsmXo1KkT+n50gVFVCCVC/PP6H3x16Sv0cO2BrT23wsrA6pO/C8QyMbQ1tIsVVW8y3sA/wB/Hw44DAIY3Gg6BWIB4fjwE+QKEzwovMS2jOFbcWoE/w//EgykPYKJrUil7PyY6Oxo+e33wU8+fMNF74ieP9zGV9eupF6cw4/wMvPrqFSz1Lcu1ze7du3H48GHcu3evzHVjYmKwcuVKPH/+HL6+vvD39y/0Gc3OzkarVq0QERGBDh064I8//ijx9zcrKwt16tSBRCJBz549cfny5fId5CeS8OoVHBo2ZKK5BqEU0SyTybBp0ybcvn0benp66N69OyZOnFjuFI2goCB06NDhU82otjDRrDpqmmiWSIC7d4H792kUmMejQrPgb1YWFaApKTRy6+NDHw0bAnXr0kixjY3qhWhl/EoIFdCXLwNHjwIPHwJmZsDQoYCfH9CxI31eY4iMBDw8wJFKAQArVqyAVCrFqlWroPHRG/DFjC9wPPg4Wk1thcf7HmNc83H4bddvJQ77X98GBgZiwoQJiI6OVt2x1ALyJHnY/3Q/lgcshxHXCGv91mKs11jF8qr4LniW/AzbH27H9cjr+NL7S1joW6BP/T6oZ1avQuNI5VIM/nMw4nhxCJoUBANtA8Tx43Ax4iJ2PtqJ40OPw9Pas1xjyYkcI06OgI6WDo4OOVqZwyqVyvqVEIKhJ4YiOjsaf4/6G04mTmVuk5mZCVtbWzx//hzu7u4lrpebm4vmzZvDw8MDbdq0wcGDB+Hm5oYLFy4o1lm1ahWOHTuGRYsWYcKECWXmGz979gwCgQA+Pj4wqIrbZQkJSGjZEg5JSUw01yCUIpq///578Hg8jBkzBpmZmbhy5Qru37+PmzdvwqwCv5JyuRy///47Hj58WCTyfPjw4U81U20w0VyU/HwgPZ1GQD/l+6m6iWZCaF7xgwfArVs0SpyTQ4OXMTE0OmtpCXTuTCPFJibUBwUPExMaGXZzo2kS6kIZfhWLqQ/++gsIDAQiIugFQIcONBrdsiVQv341jkQTAjg4oKGWFl7FxhZa1KhRI3Tp0gUnTpxAWloavMd7I/hQMCy+sIDpU1O8e/muxGH/69vffvsNv//+O27evKm6Y6kEhBBEZUdVWBAqG5lcht9CfsPSm0thqW+JzT02o4drjyIiqLp9F5SFVC5Fj997QE9bD7aGtjgQfACmuqawM7KDq7krzo46W65x9j7ei5WBK/Fw6kM4GDso3c5P8atYJsbkfyYjUZCI6+Oul2ui3IgRI+Dg4IAtW7YUWSaVSiEQCPDjjz/i9u3buH37NjQ1NZGUlAQHBwfs3r0bkyZNwqlTpzB69GhcuXIFPXr0KHY/wUnBiOfHo79H/wofl1LYtg0J58/D4do1JpprEJ82Tfs9QUFBuHv3ruL5oEGDcPDgQSxbtgw7duwo9zizZs3CyZMn4efnVzVXelXMkyd0olVxd2D5fODePeDqVRqhi42luadSKc1FnTSJRuz09avebmUQHQ2cOwdcukTTDhISqCYBqIhs1oxGVk1MaDTSxIROTjMyog9zc6BVK6CCk5MrBCE0/zcjA0hKopPm4uKAtDQa5XVzo7nBzs40ReFj0tKAQ4eAXbuA+HiaKtGhA7BkCT0OTU06BpdLx6i2QlGJcLlAz570AVCf3rxJo+w7d9LUEx0dmlrSujUV0e3a0fPgU97nlBSa4+3kBHh40Ai+oyONgHfsWIGBOBygZ0/c0NWF/e7dAICuXbvi5s2bePHiBV68eKFYtVGDRgAAh1YOePXXK4SHh6Nx48bl2k1ERES1yWV+nPgYd2LugJfPw4WIC3ic+BjaGtq4MOYCuruWPLlRVbxMe4mxZ8YiW5SNnX12YkjDIRWuUFBd0dLQwp/D/kSLfS0Qz49H5LxI1DWpi9TcVLhuc8XFiIvoVq8bEvgJsDawxrOUZ7j67irieHEY5TkKjxIfQYOjgR/u/IDTI06rRDB/KlxNLn7u9TOa7mmKn+7/hG/aflPmNlOmTMGkSZOwefPmIu/15s2bsWjRIgBAcHAwNDU1AQC2trbYs2cPFi1ahNu3b+PoURpx9/PzK3YfIqkIfof9kCXKQsCEAEUOe5Vy5gzQqxdw7VrV75tRaZQSaS74Ifkvbdq0wf3798s9jqWlJe7du4f69et/qknVioJIs5nZC4hE9li8mFYqCAmhE7mePgVev6ZirHNnoFMnwMWFTtDS0AACAoD9+6kY69ULGDiQCugKFChRC7m5wOnTwL59VCh16kTnWLVoAdSrB9SpQ9MS3ryhlR2Sk+nzzEx6ESEQ0EdODhXd33wDfFzEQCoF4uP5yMoCcnKMkZkJ6OpSgW1gQC8wjIyoeEtIoAI4KYnuLzycpkHweHQcsZhGvoVCqpXq1KFCy8mJRnxjYoB37+h7IJfT96lNG7qv69eBK1eo4Js9G+jfn9pRk6mKqJ1EAoSF0c/B/fvA48f0fwsLKqCbNKGRaDMz+h5KpfTzUPAeSST0fUlIAMaMAdq3pxee7drRz0Z+PjB4MP1tAoABA4B//gFu366AeD56FPj5Z7TmcKBvpI/hK4ajnlk99D7Zmy6PBnAY2HltJ2Z1m4Uev/dA6v5UjOgxAkuWLAEAJCUl4ejRo5g8eTLMzMwK+TYjIwOtW7fGokWLMGXKFCV6t/xk5GUgLDUME89ORHR2NMx0zaCjpYPVXVajvnl9HAk9gtuxt3F/8n2Y6VVNfo1MLsOqwFVYG7QWM1vMxMbuG6GjVfoXXk2LNBcgk8vA4XAKlaD7/dnvmHZ+GkRSEQDA0dgRcfw4AIC3jTdCkkMAALaGtljYbmG5xGhlUYZfg2KD4HfYD63sW+HEsBOwNbItcV2xWAxLS0sEBgbCx8en0LLRo0eDx+NhyJAhxX5enj17Bm9vbwwYMABnzxYfqY/IiID7Dne0tm+NpnWa4penv2Bvv72Y1nxapY+vwsTFAa6uSLh3Dw4tWrBIcw1CKaLZ09MTv/32G5o0aQLdj9SCn59fhcq22NnZITY29pPrlFY3CkRzWNgLhITYw9+fCjQfHxphbdaMTuiyLfl7BIQAjx7RaNmRIzSyOXQo0L070LYtFXfVBbkc2LsXWLyYCs/Jk4Hx46nArCxhYVQUTZhAKzS8ewccPw7k5vLfT4ozhrk5FVTZ2VSw5+Z+iGbr6lJbbGxoxLhJExrRNDWlJdq4XBrxtramr5UUCZbL6aS8CxdoxDwjg75/48bRC4HagroEiEBQuBpIZCS9iJJK6fskFtOLSU1N+pBI6J0ZgF5opqQA69YBc+bQNJmCAO706fScBIAtW4D588tp0Lt3QMOGEGWkwe/PPvg34V/Fot5uvXHp7SVABgTPCoa3jTdGnx4NeYgcz08/x/PnzyEUCtGyZUskJyejdevWWLx4MXJzc9G+fXssWbIEu3btwuDBg3Hq1KlCedJVybRz07Dv6T70d++PmS1monf93pATuULEFeTfXnl7BQ+nPoS3jbfSbTgQfAAB0QHgcDiw1LPEs5RniMiMwJ6+e9C7fu9yjVFTRXNJZIuy8TzlOVJzU/HttW+xr/8+eFh6wN7IHtmibIhlYtQxrKNyO5Tl19TcVEw9NxU54hxcGXsFWhol/84PHToUPj4+WLp0aaHXmzVrhqVLl2LIkCElbhsYGIjGjRvD0rL4yYfjz4xHck4yzo46C64mF+uC1uHnBz8jYk4ETHVNK3VsFeb774GICCRs386qZ9QwlCKaly1bhuDgYISGhsLIyEhRcu7w4cMICwsr9zirVq2CkZER5pf7F61moOycZkJoZO74cRqFfvWKpi7o69MIXMFDQ6Pw/5qaVER4eVHxyOVSoe7iUnzKSEWRy2nUdeVKGsXdvZvemlfW3dSHD4FffqFjOzoCffoAbdrwoasLmJgU/cIhhIpokYhGK2vJXd0qoSYJEB4POHyYXshs3EgviAp4+JBWBdHVpReXAGBlRQV5nVL0BiEEHA4Hb9Jfo65rc3QckYtHDkB7x/Yw0TXBhTF0wpEgX4BDzw5hdsvZ4HA48A/wx+vk1whZGQINDQ3k5eXB3d0dx48fR8eOHREZGQkTExMMGjQIhw4dwp07d9CsWTO1CWZ+Ph9N9zTFyMYjsaLTihLrDBNCMPXcVAilQqVONsuX5mP2xdk4/fI0vm79NTQ4GkjKSYKBtgGW+i6tUGWJmnTO1iSU6VeeiAfP3Z7Y1msbBjccXOJ6Bw4cwI4dO/Dw4UNoaWnhzZs3CAoKwvTp0/HixYtK340+9vwYZl+cjZDpIahrWhcAPbd7HumJ+ub1sbPvzkqNWyFycugP2D//IKFePSaaaxgqLTkXHByM48ePl3vbjh074uHDh3B0dISdnV2hZawjYMlER9OJVjLZhxq9BX8//l8ioakJz5/TSHV+Pk2J0NKiQkIupyK6RQuaf2tnR59LJFSkP3tGBYiFBV1fW5t+/tPTafT17VsaBfzqK5pKYWio9EMtAvuhVA21za8yGc2jjomhkWYA+PNPYMSI/6wnl2HIiSH45/U/2N13N2ZemInkjUCdXEBnlTaES0XggFNiXu29uHvofbQ3nn/5HHdu3IGOjg4GDhwILS0tSCQSSCQSBAUFYfjw4Zg2bRo2btyo4iMvnVGnRuFt5lsETgwss2Tay7SX8N7rjazvs6Cv/emTK5IESRh1ehSSBEm4Pv56uaorlEZtO2erC8r265rba7D53mZkfpdZ4ueIx+PB19cXnTt3xqBBg9C1a1fFMplMVqmLTP8Af2y9vxUHBh7AkIaFI9VPk56i428dwVvEKzUCrhR27QIOHAAePUJCYiITzTWMSovm27dvQyKRoGvXrkqbmLFy5coSl61YsUIp+1AH1bl6hkxGRXR2No1Ex8bSCYsFtXcTE+l6bdrQFBIDA3q7PC2N3jI3MKBpF25uNP/Uw0M5Uevywn4oVUNt9euFC0C/fh+ev3lD79Q0bAi4uhJ8c+UbbH2wFQDgalofrTVmY4kx0Hji18g30ofOvgPAyJK7nBFC0PrX1viiyRclNrvg8/ng8/mwtbVVTGRSBxcjLqLvsb6ImBMBN/OyO/oRQuC01QkHBx6EX70PE6zCUsNw/Plx5Eny0MiqEb70+VIhPFYGrMS2h9vQw7UHbAxssKjDItQxrAORVIS2+9vCxdQFO/vsLDXHtbzU1nNW3SjbryKpCHo/6OHtnLdwNS95AmxISEihnGYHBwcsXrwYs2bNqvA+X6e/hvdeb9yffB9NbZoWWS6Ty2Cy3gT3Jt9DkzpNihlBScjlNL9w+XJg7FjW3KQGUqlLqsWLF2PDhg0AaHT49OnTmDFjBt69e4cxY8bg22+/rZQxNVkY11Q0NWmlh48ppZkZg1GjaduW5sYbGNBKNR9KwRI0290Gz9OCcXDgQVhmDMKWtSY4dhO47QDE4WvoCPKA9esLieYnT2guvEBAc+EDAzn4us3X8A/wx5zWcwpN7voYY2NjtQrmbFE2vrr4Fbb23FouwQwAHA4HvnV9cSf2jkI0P0t+Bt+Dvujv3h86mjrYcHcDLr29hMODD2PFrRXYcn8LNvfYjNTcVNyOuQ2bzTaY32Y+AqIDoMHRwPGhx8uc4MeoXehq6aKVfSvcjLoJOyO7ElOCvL29sXv3bpiYmKBPnz4wMalcE5hccS6mnpuKyT6TixXMAKCpoYkmdZrgeepz1YnmlBRg7lx6e/a/t7gYNYZKiebDhw/jzJkz6NChAxYuXIgePXpAX18fnTp1wvr162FnZ1dqG9mPsbOzQ2JBOLMUnJycEPufWqkMBoNREczNgaAg+n+HDrTRDACgyTE8jY7AiOQUuOaYoWM3Kq7PnaPVUN6hHqzczaAXFYPAhnNgc2IbXr7iFPvb9yZyKLJFX2P/0/2Y2nzqJ9ucKcxEel463C1KbvZQXgghkMqlGHN6DBpYNsDc1nMrtL2vky/+DP8TAJCel46hJ4ZiQdsFWN5pueK1fsf6wWidEYx1jPHnsD8xojF1Ej+fj4Y7GyIlNwVedbwwr/U8Jpg/U/rW74tp56dhf/B+3J9ScoWtT22nTQjBmL/GAADW+q0tdd0m1k0QmhKKMU3GfNI+i+XNG6BrV5rLeP48nVDEqJFUSjRnZ2dj4MCBAICNGzfC2toaaWlpsLCwQJ8+fbB69epyi+asrCz89ttvKCtLhMfjVcZUBoPBKJatW2kJx7/OyNHnzkz0JD+BF2KGjh1p++/bt+kE2s2bgWYLnkL4Rg/9cQ6necPQ3GsiRI2aY+BA4OxZKq69vWlZuycPaGe2EadGICguCFt7bq10qba03DRM/mcyzr05hyENh+DokKPQ1apcPUNCCBrsbIA3GW/Q2KoxgiYFVTi1zreuL2ZcmIGlN5fi7OuzaGbbDEt9P1Q4sNS3xInhJzDy1Eis81uHzs6dFcuMdYyR8E1CpWxn1C5GuszBgaDziOPFqWwfMrkM4/8ejwfxD/By9ksY65Se/uBVxwsXIy4q35D4eJq32KULnVShxjtM1Ylff/0VU6d+CCpwOBxYWlqiffv22LJlC1xcXNRoXclUKqfZ2NhYkecEACYmJgpRK5PJYG9vj+Tk5HKN5ezsXK4vbh0dHbx69aqipqoUQgj8/f2xb98+8Hg8NG/eHLt27YKnZ+H2p9U5p7mmw/IYVcPn5NdX6a/gs9cH/EV85PC10bQp8PPPtMZzAXI5bTDUvDkw7cYIcDIzwL1+CeByIRDQeuAAvftaMOkwnh+P6een413mOzyZ9kQx0a68vt3872YsvLYQhlxD5IhzAADDGg3Der/1peaCFkdAdADW3lmLG1E38Eu/X9DLrRfsjSv+XUQIwbqgdQhPC0cT6yZY0HYBtDWrcBJDKXxO52xVoiy/EkL7DcTE0Go3sfxoaMyrD+GyXHA1yxd5jYuj23foUPa62x9sx7aH23Bt3DU4mzqXuf7jxMfwO+yHtG/Tym1PmWzdSutb9u9Pi8T/h885p3n27Nk4dOgQrl+/DgCQSCR48uQJvvvuO7Ru3Rp37txRs4UlQCqBrq4uCQwMJNnZ2YQQQszMzAotNzY2rsywNY4ff/yRODg4kNDQUJKXl0cWLVpE7OzsiEAgKLQej8cjxsbGJD4+Xk2W1l54PB7h8XjqNqPW8Tn5ddfDXaTtr23Lv8HLl4S4uRHy009FFt29S4i+PiF//kmfS2VS0vVQV+Kzx4fE8eIIIWX7ViQRkSF/DiHaq7TJ5YjLJFuYTcRSMUngJxCXrS4E/iDpuenlNvdg8EECfxD4Q2FDbeRzOmerEmX4NSODkAEDCKlTh5ARIwg5dIiQl69kBEv0yYmbL8rcPjGRkFGjCupBEQIdHoFhMrG0pB9DgYAQmVxGPHd5Ks51+IOcf32+3DbK5XJiscGCPIx/+AlH+p60NEK2byeEyyVk3jxCYmKKXS0+Pp4A+CzP23bt2pE2bdoUeb1fv37EwMBADRaVj0oVB3V2dkaXLl1gbm4OFxcX5OXlYcuWLbh37x7EYnGZqRa1hV27dmHhwoVo0qQJ9PT0sHr1aojFYpwpaEHGYDCqNRKZBGvurMGcVnPKv1GDBsBPPwE//EAn93xEu3a0fvqXXwJ37tAJRpe+uARnU2csv7W8XPaMOj0Kcbw4xHwdg55uPWGiawJtTW3YGdnhzZw36OHaA05bneCwxQEnwk+UOp6cyLH4xmLs678PcfPjqmWrZYb6yc5Wzbj5+bRcqacnrZMfFkZLPY4fDzTw0IC1lgd++OUlzpyhzbq2bv3QkCo2lvYh6N6dlj/94w8A9g8Ar9+BxSbAQhuk4yXmz6e9BzZc34uw1MJ9IfrU71OibVFRtEpUfj59zuFw4GHpgYjMiMof8PnztCOTlRWwYgXw66/0oD7qPvaZyKNSIYQgNDQUXl5eRZYlJibCw8NDDVaVj0rlNL98+RJ8Ph8PHz7Eo0eP8PDhQ2zevBlJSUngcrmQSqXKtrPawePxEB0djVatWile09LSgo+PD4KDgzFu3Dg1WsdgfL4QQiAncmhqlJ07GJkViYy8DMVktXLTty/QowdNYg4IoAXK3zNgAM2DHjaMtgZ3dORind86NNndBFK5FMgHlvouLfZ27MwLMxGZFYlbE27BXK9oC00tDS3sH7Afa++shb2RPeZdnofebr1hpGNUrJmv0l8hW5SNCU0nVJs0Ckb1IiGBNr1asIDW1o+Lo3PWHB1pZ84//qBFY1q3puu/fQsMHw5MmQLMnv1hHIGANth68YJeMJ4+Ddy8SecFbNhAx/9vJqZvg4Y4deslhgwBOnWimQy//UaXhYbSv2a2POC7eoB+ZqFtOzp1BH/1aMxqNg+3d4zD/66tQt3Qs+jfuQ6+GtwKzs4c8HiAiUnh/aanA99+Cxw8+OE1b29aNcqjvgdepr2snCPXrQOWLKH/Dx9Orw7e73jLFurLgk6n7drRAhrx8ZXbVU0nIiICOTk58PT0hFQqBSEECQkJ2LJlC16+fIkLFy6o28QSqXQVb2NjY3Tr1g3dunVTvBYfH48HDx7g0aNHSjGuOlOQ52VqalrodTMzs0L53gUYGhoiNze32GWMyiMQCNRtQq2kJvt1VcAq/BH+Bzb32FxmC+bw+HB4mngiNye34jvaupX+OK5fTxXBR4weTX8gJ08GTp4EbLm2mO8zH48TH8Na2xqDDw/GMO9hmNVyFvS09XAi/AROvTiFsNQw3Bx/E1oSLfAlxX9XGMMY6zuuByEEgW8C0WFPBwxvOBzTmk+DrnbhSYKBrwPha+MLYa4QQggrfowqhBDldumsyDkrlQIREbS+/H/ryhNCq4IZFX8dUuvYuJGWYjxxgr4f7drRmyiRkYCzM2BuLsCYMdQv06fT8zknB9i2jZ7nv/5KRaHw/emlp0dz/7t3p7n9xsZUjBf39nhZNEDmgAh8NZ8PPz+ar3z7Ng3U1qlDxeysi7Nw/o0UAL3IHOM5BvPbzodEJsHyW8vx7aWv0bnrWdjEGCHrXkccvqeJw+s+7MzeHli9mjbp+vtvIDycXhDcv09tXbKEHv+2bUCfZY3xin+/4r/TYjFtWrJvH/3gf/89IBBALqedcdevB5o1o43EvL1pxF0uB9zcKvG9UxKEFO9kZWFkpLQPbEhICABg7ty5mDv3QwUfW1tbXLt2De3bt1fKflSBUlvfODg4wMHBAUOHDlXmsNWSgihR9n/ua2VlZRU72U8kEmHdunUwMKCTgbp27VrogoPBYHw6yTnJ+D30d0xuNhnTz08HV5OL2xNvw87Yrtj1o7Ki4GJayVnaOjr0x3HGDKomPmqByeHQ/gVdugBHjtBbz9+2+R+4XHrBfSf2DnY+24m9T/aiiXUTvEx/iWnNpmG573LYGNmUa/ccDgf/a70Ocy/OxcZ7G7Hx3kYcHnwYr9JewUzPDKM8RyEwJhBtHNpU7viUQF4ejTw2akS7j1pY0Ojj2bP09vuKFbS+dePGVFBUBWFhND0gI4OK5n796ATOt2/pHYL794GsLNrVdM6cqrOrskgktF28pye11aD0xo6FCAuj6USXLgH16lEhV3C8ycmAjQ2NzAYE0NO7oNjB69f0RkujRrSM4969tEGQSERFankbXNU3r4/LBpfh975XTt26wMc3ae/F3cPNqJs4OfwkWtu3Ro44p9Bdld8G/Ya7sXdx6e0lzG/7NZrN18SRI/Q8u34dWLaMNjSaMwfIzQU6d6YR5RkzaAQcAI4do38NDYFdfzYGev0CQkjFKstcvQqpoQn8nw2HVZ0RuDuVvg937tD0jx07gPcFx3D9+nXcvHkTAJCbq0TRLBDQsLqq4PHoFZASCAkJAYfDQVBQELhcLuRyOaKjo7F06VKMHj0a4eHhMKquV63qTKiu6Tg7O5Off/5Z8VwikRArKyty+PDhQuuxiYCqg03+UQ2q9KtIIiKJ/ESSwE8gqTmpRCwVK23sZTeXkf7H+hNCCInMjCQ2m2zIN5e/KXH9mednkoVXFlZ+h3I5IW3bEvLjj8UuDgggRE+PEA0NQmxsCDl6lJDnz6lv5XI5CUkKIUuuLyH34u5Vavdz5tCJUfvv/0mG/DmEwB/Ea7cX0V6lTQYcH0BM1pmQiIyICo0pkRDy+++E7NpF5zK9fVsp08hffxGipUXt09Sk/3M49Pno0YQMHEiImRkhJiaE2NkR8uxZ5fZTQFnn7L17hCxYQPe1ciUheXmErFtHiL09IQ4O1L7ZswkJCiLkwQNC3N2pbbNnE7JnDyEy2afZV1kuX6aT51xcCOnUiZD9++n77udHSOPGhLi60smnBZPk7t4t37hSKSEtWxKydGnp633s1zdv6PtKCCHZ2YTcukWIUFjpQyNhKWFE/wd9IpMX79wOBzqQdXfWVX4H70lKIiQ8vPR1JBJCGjbJI5r+2uRtRsVO+twWvuQHqy3E0ZGQFi3o+T11KiHHj9NJiiWh1ImAcjkhPJ7qHnL5p9v4nl69ehE3N7cir58+fZoAICdPnlTavpRNNb+Grt7MmjULmzZtQteuXeHq6oo1a9ZAW1sbgz+uVcVgfKYIJULE8GIQnhqOsNQwhKfRv28y3kBGZIr1DLQNUNe0LuyN7FHXpC4s9C2gr60PDjgw1TVFQ6uGaGnXEia6pUdR5ESO30N/x089fwIAuJi54NCgQ5j8z2R0d+2ObvW6Kdo7FxCRGYERjT6hOxeHQxMk584Fvv66SIitUyeamymRALduAf7+NPp68yZgbMxBU5umJXYpK4uUFHprXFMTED4egVOzhuNI6BHoRQ/FrD8u4LxsDE6P+KvcHf/S04E1a2gOq0xGc1hFIhqlGz8e2L6d9mhYtIimNujoAE2a0EPW0wNcXGiHxNBQOnErPJzmso4YQSOhHTvSZjIuLkDTjw5ZJqPpAN26AYGBNGKpTG7epLfer16lZQTnzgW++46+dYsWAdOm0YhgmzY0JaCA58/pvK6TJ4FVq2h0MiyMRsVVhVRKy7FFRQGpqdSGFy9o7vD06bQxz7ZtdOLboEGAmRmNmA4bRo/zf/+jviwrJVQopD7IyPiQhlse6tenD4AGNTt3ruyRvh/Poj7ypfmI48WhrmndQsveZr7Fw4SHODPy0yfW29jQR2loaQF7duih89FmWH3oHg7OL19Zx8gXIjg8voeUfr/h5R8Vi/QrFQ5HaZFgVRMSEoJ27doVeV1Xl6aXCd/n+nz55ZcIDg4GQOeRJSUlgcfjQUdHfU2RmGj+BBYuXAiBQIBu3bqBz+ejRYsWuHz5Mgw/uk3LYNQE5ESOjLwMiKQiGHINIZfJIZPLEB4XjidJTxCcFIwcSQ40OBoQ5AvAz+dDRmTQ4GiAAw5kRAZCCPJl+cgV5yJDmIH0vHToaumikVUjNLZqjBZ2LTDReyI8rT1hY2gDTY4mJHIJ3ma+RSwvFvH8eMTyYpGRl4GU3BQQQpCSm4Kf7v+EWF4serr1xOyWs+Hn4ldsJ7ljz48hV5xbaMZ8Z+fOiOfHo/fR3tjVZxdmtpxZaJuIjAjUt6j/ac4bMIAK5z/+KHxv+T1u7zVrw4bArFlU/AwcCFy7Bjh8QjGLn3+mXQvHj6c5k7Nnc9CCOw4txgA9egzH4wPD8EqXAzQqfvv8fCoW790DgoOpYG3ShOanDhz44cc/JoYeVtu2dJLYpEnAwoV02YsXVPQKBFRQe3jQ4wsNpbfsC0rWFxznoEFF7dDUpKksQiFNlXjwgN5e37uXXmx89VXFUylTU2lZ3DNn6HHNnUtTTu2KydIxN/9w6/xjuFxgyBD6SE2lfh4z5kMurLJ59Ij6LjcX8PWl7dmnTKE+sbam63TtSn1VHD17UhHdujVNtRCLqZh2d6fvme5H6e6LFwM3btBTVhXHUl64mly4mrsiNCW0iGg+/Oww+tTvA0t9yyqzx9cXGPKmLY6c+xfZgWMxahS98LW1pcvv36cpPN2707zu0FAg5NAr/MvVx09/u6Acc48/e1JSUpCcnFxs5YxTp05BQ0NDIah/ez8rNC4uDgMGDMD27dvVKpiBSjY3YVQM1txEdbCGBhUjR5yDZ8nP8Dz1OcJSw5Cck4yw1DBEZkVCIpco1jN+P+lGR18HzWyboYVdC0VHLWMdYxhxjaDB0QABASEEmhqa4IADbU1t6Gvrw0LPAk4mTrAysIIGp1KVLQsRkx2DA8EHsO/pPmSJsuBu4Y7pzadjevPp0NTQRHhqODod7IR9/fdhcMPCd3o6HOiAu3F3YaVvhZGNR2J5p+WwMrCCSCqC/g/6iJsfV6lGH4XYtYuGYl+WPfOex+NjzhxAU9NYUSmgMnToQHNM+/ShEdK0NBqFfPGCRkcDAqiej46mwvBjoqOpQIiLo4VA3Nzo88GDixeoIhGNyrq60sizKpDLgVGjaHTV1ZVGreVyOnFqwgQaUa1fnwqVP/6gKZZNm9ILBxsbQCjkIygIuH7dGM+fAy1aUME7fDiNbn8qYjEVUB07Aj/++OnjfczBgzSa/P33NAL8scCtCITQHHptbSr6r1+nF0ZaWvQcCQkBfHzoa0+e0IucslD1d6zZBjNki7IhXipWVHiREzlct7nip54/YVCDQSrZb0mcDD+JlbfWou2zYFy9SqPxixfTi5KxY+l5aGVFy/RxucAen71o/uooncFYQT7H5iaXL19G7969sWbNGvi9T2ZPTk7G6dOnceTIESxevBhr135oef769WsMGzYMO3fuhK+vr7rMVsBEcxXARHNh5ESOixEX8Tr9NXj5PBpdFGYgJjsGEZkR0NbQhrWBNVzMXNDNpRumNp9abPktgInmj3mX+Q6Hnx3G0+SnEElFsNS3hIWeBURSEZJykhCeGo4YXgzqGNSBVx0veFp7wt7IHg0sG8DD0gN2RnbQ1dJFniQPyRnJkBM56tvWr3CrZVVCCMGr9Fd4lPgIqwJXwauOF3q79cbMCzMxq+Us/Nzr52LtlRM5NFfRMNDKzivRvV53TDs/DS/SXkC6TPrpx5iTQ5Xpq1c0zFcKfD4foaFA797GSEp6P39QLKa/wOUkJoZGEF+8oALTw4NGiR88oNUQRo2i6zVsCGzaRIUx3u+maVPg3TsaxfzhBxqdrC5IpbTawPPnNCWCw6HXIxcuULHy+jU97jFjqFB++JBGaFNSAH19Plq2BLp2NUanTnRCmrK5f59OgIuPV96d8IgImm5x7hxNUVE2QiHwyy/0wsfWll6A/PQTzSYqD6r+jn2V/gqt9rXCtObTsKnHJgBAYHQghp4YisQFicrrzldOEvgJcNrqhKzvs2DENcaGDcBff9H3ydmZpuwUOrcGDKBXsN99V/F9fYaief369Vi8eLHiOYfDgYmJCZo3b46ZM2cWKiTx9OlTjB8/HocPH0azZs3UYW4RmGiuAgpE851Hd9CkfhMAQFJOEp6nPEdKbgrEMjF0NHWgo6UDLQ0tyOQymOqawsnECY4mjtDX1lfzESiHbFE2ToafxNYHW8ET8dDWsS0MuYZwNnGGhb4FHIwd4GHhAalcirS8NLxMe4nTL0/jceJjLGi7AEt9lxapvVtbRDMhBKm5qXiX9Q7vMt/Rv+//T81NVUSB65nVQyPLRqhjWAeCfAF0tHRgoG2AwJhA3Iq+hf7u/dGpbifoaeshPS8dmcJM6GrpwtrAGo2tGqORVSNYG1iXKRJrgl8z8jIw+M/BeJz4GGOajMGvA34tdf0FVxZgy/0t0NXSxdRmU7H94XYAAFmhpK/Azp1pKGrKlFJX4/P5IATo3NkYffoAa7reBKebH1UzHxePLYWVK2lKxd9/0+f37tFAt6UlFc0FdzDHjKHCedky+nzaNPqjf+XKh4YTtYWqOGcJobnPI0YUqTJYKfLzaaS8Y0cqZKuC/Hx6fVbe974q/BqWGoZme5shcl4kHIwdMOnsJBhoG2B7n+0q22dpNNzZECs7ryy7fnt6Os09Cg6uVDL+5yiay8vt27cxe/ZsnDp1qlo1O2GiuQooEM2YBYgMRNDkaEIkFcHV3BX2RvbganKRL8tHvjQfUrkUmhqayBJmIY4fB5FUBAs9C7R2aI1uLt3Q3bU7Gls1rlbRv7J4nf4aa+6swYnwE2hapymmNpuK8U3HF5uXWhxBsUGYdHYSern1KhJJ/PgLPUecg5jsGEVZonpm9aCrVbn7nIQQyIisyMSxipAtysaLtBdIzklGam4qUnJSkCHMQCwvFgmCBHDAUbzvCYIE5IhzYGtoC1dzV7iavX+Yu8LG0AbaGtogIHiX+Q4v0l4gJTcFprqmEEqEyJXkwquOF8Z5jfv0NIP31ATRDFTsfSKE4Ozrs1gRsAKOxo64EHEBfev3xfkx55VjzFdf0QTRDRsAPp/WUiuGAt8GBRlj/HjgsVk3OBtm0HvnsbG0iGwp/PsvTcnYt4+mHpTGpk108t2ZM3RyWb16NGrbp+RGaTWWqjpnz5+n9bffvStUZbDCEAJMnEjvFgQFfbjQqW5UlV+b7mmK0JRQTGs2Db88/QXPpofAKyiCduBs3LhKr/B+vPsjLkRcQODEwNJX3LSJnhABAZXaDxPNJWNlZQVdXV1YWFgAoBHpu3fvQl9fvUFEJpqrgALR/DT0KWSGMsiJHI7GjjDglj7NlhCC9Lx0RGVHISg2CNcjryMwJhD93ftje+/tsDKwqqIjqBwRGRFYdXsVToafxDivcVjQbgEaWDao1FiRWZHofLAznE2d0ad+HzgaOyIgOgCpWangiXhIkiThbeZb6GnpwVjHGLx8HoQSIVzMXOBh4UEflh5oaNkQhlxDENCucUKJEHraerAzskNGXgaOhx3Hndg7igivi5kL2ju2x4K2C+Bo4ghBvgAyIoONoQ0SBYnIyMtAjjgH/Hw+XqW/wov0F3iR9gKv018jV5ILW0Nb2BnZwULfgv7Vs4C9kT0cTRzBAQdcTS50tHRga2iLemb1yjwnqoqaIporSkhyCPwO+8FS3xKbum9CP/d+yrsA/eknmtfYsSMNQzZuTP9++WWh1fh8PvDPPzB2dMSdfzXReokfuClxNISZk0NzDUqwSSCgaQHDh9P6smXVEL5wgebJ3r5NJ+E5OdGc19pIVZ2zhNAUDbmcplRU9jd81Spa/eTBgw8TzaojVeXXd5nv0OdYH7zJeAN/zzlYceN9rk4BZmY0aX/sWJXaAdC7WPZb7BE8PRgNrUqJIHfpQm87zJxZ8jqlwERzzYOJ5ipAmTnNKTkpmHJuCm7H3Maqzqswu9XsT4qGqorzb85jxMkRGNNkDP7X8X9wMfv0mTgZeRk4EnoEQXFBSBQkoqVdS7gbuMNE1wRW5lZoaNkQDsYO4HA4tC2nIAGv01/jVforvM748DdPkqeo+qCrpQuhVIi03DToa+ujd/3e6O/eH65mrnAwdkBEZgSOPz+Oo8+PIl+WD02OJjgcDqRyKbQ1tGGuZw4TXRPoaemhoVVDNLJshIZWDdHAsgHsjexhpleNEkYrQG0VzSk5KbDZTGtPxc2Pg4PxJ5Sv+C/nztHOCZqaNAwsENC+wPPn0y4e7+EfPgzMnw9jgQCEw8FqyWKMj/SHMzeRJhyvX09DmcWwejVNrbhzp3yBt5AQOsEvN5eKvISE4itI1Aaq8pzNyQF696bpMH/9VfEg6I0bNBX2/n2ai16dqUq/yomcTixevoJGcLdsoTM+hwz5aCV5lUSdx/41FuZ65tjWe1vxK6Sn0w9TRATtylIJmGiugVRhTejPFlU0N7n27hpx3+5OGuxoQNbfWU9epb0i0VnRJIGfQGKzY0lMdoyiaUR6bjo5HHKYHAs9RrKF2UqzoSTOvT5HDH4wIKdfnFb5vpTVhEMsFROJTFLicrlcTgT5AiKWiolcLidJgiSSk5/zyfutrtTWpjEyuYzAHwT+KvjqCw+n3SV69/7w2vPnhOjoEPLiBSHffUdIhw6E5+xMePv3E5KYSEh6OmnZkjZBIIQQcugQIU2bFju8VEqIrS0hFy+W36TUVGqSmRlt3lCbqepzNiODkLp1acOU8vR98PcnpHNn2qSEy6XNY2oCVf5dIJXSbjP//PPhtSdPaFcXDoeQKmoSdjf2LoE/yLxL84pfYcsWQjp0+KR9KLW5CaNKqH4hSka56FavG0JnhOLUi1PY82QPVgSsgFgmBgEBBxxwOBzIiRz62vrIk+TBx8YHADD9/HTMbzMfPrY+EMvEqGNQBx2cOigm2MmJvMIlwuREjneZ73A37i7+evkXbkTdwKFBhzCk4ZCyN64mFJQ6KgkOhwND7ocERhvD8rU6ZlQvNDgaaG7bHM1sVTATu6CuWf/+H17z9KSJqz4+NKF48GBa/mHQIEXLWy8vWi1i1CjQ8OOUKUBkZJEqHHfu0OoS3buX3yTL9yVu586t/u2gaxrm5sDly7QMnUBAbw60alV4HZGIViF8+pQ2tqlbl94tGDBAtR2PazRXr9IC3b16fXitWTP6WL2aFgQv7Y7t/ft0Ut4nOridYzs8nPIQHX7rgP91/F/hdEiplKaKrFv3Sftg1DxYekYVUFUl58j7SVEFpOamIluUDVtDW0WawO2Y29j470Yk8BPA1eQiOjsaBlwDtLZvjcisSDxOfIy6pnXRzLYZfGx80MCyAZxMnGBtYA0zXTOIpCIkChIRx49DaEoozr4+i+CkYHA4HLSyb4Wuzl0xvcV02BlVzT3g2ppGoG6YXyvJmjW04O7HddxEIuDiRap2jYyK+Nbfn5aQU9Rs7tWLrvuf8gxTp9KqBzt3VsykoCDa8EK79OvCGo+6ztmHD2lGze3bVBw7OX1Y9s03H6pinDhR9sTN6kiV+7VfP6Bly0IpTQp696ZXKL16AUuXFl3+9i0t5t27N/3MKYFBfwyCnMjxz+h/Prx4/Dhtv/jmzSddjbL0jJoHE81VQHWu0yyRSXDm1RmEpoSivnl9tHFogxheDIKTgvE0+SneZb5DdHY0MoQZim1MdEzgaOKIuiZ1MbzRcLRzbAcnE6dyV8NQJkzcqQbmV9XxX9/+8gvNi718+f0K+/bR4sRPnypyN0Ui2pzi2rWi0UwGRd3n7Fdf0ffwyRMa5IyMpHNBHzygqa8FUf+aRpX6VSCgjgoP/9BK82POnAFGj6Z18x48KPxhCAujIXyBgD7y88uf/ywU0jeuQ4cii9Jy02C/xR6vv3pN5+bIZPT20OzZtMXnJ8BEc82DieYqoDqL5vIiJ3LwRDzoaOlUq7rR6v6hrK0wv6qO//r23DkatAoNfb9CXh5Qvz6Ec7/Hvy3mYvdu4NIl+nIVzYGqkajsnJVKC0cThUJaUzshgQooe3vgyBHIdfTQuzdtunLoEK08GBRE39+aTJV+F5w8SW+9hIeXvt6aNTTaGxxMb7+IxTTEP3gwLVT+xx/01syOHTQ9yqCUqkQ8Hr2z8+gR8Pgx0Lx5kVX6H++Ptg5tsaTjEmrjggU0ql2BhkTFwURzzePT+9syPgs0OBow0zOrVoKZwagN2NkBiYkfvaCvD+G+IxAvWoYB3XIRHk4F84oV1UgwZ2bSvNPazI0btHeyjg6tgOLvD7RtSxOTnz+nUcbhw4Fnz4DvvoMGh2D/fuD0abraokU1Mx1DrZw9CwwcWPZ6339PL2RGjqQ50AsWAEZGNHfJ0JDOC7h8mYb/DQ3p/IA9e4qOExxMc2h0dWnif3EpHwAGuA/ApbeXaB31efNo979PFMyMmgmbGsJgMBhqxN4eyMigKRi673vxbHnaGdO19JErNYSw71L8MMgfy5drlj5QVREcTCdlNWlC80YdlFi2T12IxTTpm8OhhZhnzAAOHAAWL6aC7Ntv6a2APXvoOv37fyiuPHAgbesnFsNh9278848GQkPpRU6PHuo9rBpFZiZNvwgKKntdbW0azv/6a1qOrn59+lzjozhgz540Gs3l0jsCU6cCWVn0aobDobdtCloz37hByz06OdHJhgUtNAuGcuuJmRdmIv/bb5Dd1gvxg9ugaDy6YhBCkCRI+sRRGFUNE80MBoOhRqytadAsMZEGxFJTgQ0/ctB729+wPL8KepvXYM0iKaBRTWbqnz0LDB0KWFgALVrQFoX/qfRR7REKacHrR4/o8y1baAfHnj1pq7+4OOD16w/H1b07rctrVUxDKTc3mg/buTMwcya67tiBrl1r+axLVfDnn1S4+viUb31vb9qJryBnqbjbMKNGffjf3p6et9u3A0lJNC8ZoBdFXbrQ7a9do+/1l18Wuhh0MnFCcwM3CM6dxrBRQNCvVxA6IxRN6lS+yPaPd3/E2gtrK709Qz2w9AwGg8FQIxoaNEXjl1+oLps7l+qvZjNb05Z+t27RyYEVnX6SklK4o1pFycykt64L5Y4ACAykwmLPHhrlmziRCpfqRm4ucPMmFfmLFtFJYt260TblNjYfju3lSzoT89AhKqyGD6e5rR9fCHA4xQvmAhwc6L5u3SpS9aRG8PfftJlIUBCNzqqDU6cKi9zyoqFRvryldu1o3cahQ6k4Dw2ldQI3bfqwfbt2tMPfwIG0pE0BERG4vSkD8Q5G2LnqMdZ2XYvmvzTHrke7Km4vgAtvLmD17dU4MqSWtuesxbCJgFVAbZgIWF1hE9ZUA/Or6ijOt35+VHMBNHh78iTg7Px+oVBI8zUjIwvXMyuJ6GgaTdPXpxOmKtIG8NdfadRVLAaSk6nwNDamQnDZMppcbW5Oc3rd3WlrvCZN6MQ4NSddK/waE0MngB05QqPhFha0jEXr1oCeHhXMGRm0HbOmklNeIiPpG7hz54cqDzwevZ2gSgpKdDg6ln+b2Fh60XD8OL1gIAR470MsW0Yvmnx8wB82DOBwVPtdkJFB35d378p3jiuD3FyaD/XfcyA/n4r3v/+mEwu9vWnaSLt29Lx6f47/8/ofjDk9Bqu6rEI9s3oY1GBQuXYrlAjh/LMzNnXfhK6WXdlEwBoGS89gMBgMNePmRkXz77/TQFehOUZ6ekCDBjSXuDyCYuVK4ODBD8/v3y/chrgkQkJoGa1OnYC+falonjeP5vOuXUtFGZ9PG0fUr0+3MTSkUdp+/YCoKGDv3g+J2VVJXh6NVB4/Dty9C4wZQ6Om5b3Vryzq1aNv4rBhwD//APfu0Yjld98BP/ygmg4zly4BffrQmoS//krfu5IuXuRy+l4aGNBIO0AvyB4/ptsnJNAJnr6+9L02MwN+/JGeA1OmKN/2As6f/5BTXFWUVFFDR4eK5OBgevFz9iz9TKxdW8ivAzwGoKlNUyy4Su8svJz9Eg0sGxQZTiwT41bULdyMuol+7v1wI+oGbAxtMNZrLBL/exeHUf1RVyvCzwlVtNFmUGpru2d1w/yqOorzbUpK4a7BRRg7lpDmzcse/OFDQjQ0aO9sgJBRowiZMKHs7d68oe27//e/4pdfv/5hzGvXii5PTCSkdWtC7OwIWbOGHhAh5esv/SnI5bQftbk54bVoQXibNtH+1urm2DFC3NwImTWLkJ9/JsTVlZAvv6ycP549I+SrrwjJzS26TColpH59QnbsIGTgQEK0tAixtiYkM5MuT0wkZONGQvbuJeTmTUKmTqXvoY8PId9/T8ju3YTk5RUdNz2dPlJSCG/OHMKztibkjz8ISUsj5N69ih9DWfTpQ8jatcofV8WIJCIilAjJwOMDidWPVuTCmwuEEEKkMim5+OYi6XigI/HY7kHMN5iTPkf7EO1V2gT+IA/iHxBCPu822gDIb7/9pni+YsUKAqDQQ1tbm9ja2pL+/fuT27dvlzlGVcBEcxXARLPqYOJONTC/qo5K+TYkhIqdyMiiy44cIWTAAEJu3SKkSxdCFi4kRCYjJDubiqKGDUsfWyolxNCQkBYtCBEKS15v40ZCvv665OX5+YQcOkRI3bqEcDiETJ9OiIEBIcuW0X0UR1wcITt3EnL6dOk2FodAQMiwYYQ4OhJy4QLhZWdX33M2MZEQBwdCBg36cEFRHq5c+XCx0rt3UdH911+E2NsTIhbT57GxhHTsSMjgwYScOkWIri4hfn6EdOtGiK0tIR06EFKM+CgNHo9HeAcOUDFeYMvTpxUao1RSUwnR1i7+3K4hCPIFZOLfEwn8QTy2exDNlZqkzsY6pP+x/mT6uekkW5hNCCEkS5hF0nLTFNsx0fyb4nlxovnjh5aWFnn06FGpY1SJ3VW6t88UJppVBxN3qoH5VXVU2re9ehGyYUPh1+7coYLD05OKmaZNP0QZCaECDaDi6+lTKroeP/4gsgihIsrKqmRhW1HkckK2b6f7rVfvgyAfNYpGE7/8kpDGjQlp146u4+lJ1ynvj19eHo0uOzkR0rUrjX6SGnDOvnlDSL9+1Cfl+S2Qy6nfOnYkJDycEBsbQnx9CXnyhC47eJD6b+PGwtulpFDfAITs2fPJZiv8mphIiJcXIe3b08iwsjh8mJCWLZU3nhr5++Xf5Ncnv5I36W+IRCYpc30mmn9TPP9YNK9YsYIQQkhWVhbp2bOn4vUFCxaUOkZVwHKaGQwGoyYwahStVLFkCc0pvnyZ5s2OHQts3UprzXbtSns4F2BtTXNRe/akz52c6ASw77+n+b7XrgH79wOTJilvUhyHQ5tKfPUVfZ6dTTu0paXRXOs3b6j93bvTkiGNG1M7hgyh+bMTJ9L6ux8ndhNCS8Rt20YrINSrB6xfT5tbaNSQIlD169P82IkTac71tWulN8gICAAiIoD4eJo7fuUKzast6FjH4dAGKwV+LsDamuaXi0R0MqiysLWljVzi46n/Y2OL5iCLRMDChXSSna0tneHasWPh4xQIqF23blFbw8OrPvdcRQxsUI7GLIxyY2pqioEDB+LKlSsAAJFIpGaL2ERABoPBqBmMHg28eEFLpJ0796G8xokTtMLF4MHFb7d+PS211r8/FWLXrtEez7q6VOR06wb8/LPq7DY1pc1CAFqVgZCiE9W6d6ci7OpV2nnvjz9ow5DOnanNly5RsTVzJrBuHa2xW23aI1YADQ1g1y5aF7h7dyqEi5s4GRJCawUvWUIFM0CP+Y8/aKm/169Lr5ahoaFcwfwxDg50omDjxnTiYEHVB7mcXthFRdGSbXFx9KJGJqOVKLy8aPWJ5ctpU5GnT6lYtrOj5yCD8R94PB7OfdSHfmB5ukWqGCaaGQwGoybA5VKxC9ASXe7uVEj371/6dhYWVLwAtMpC7960GLSLS/ECVtWUtD8zM2pnp07A4cNAWBgV2Z070zJ43bqpTghWJYaGNMratSs93qlTaX1ob28aRZ85k0aYly+nUdv/YmpKy+epk6VLqfA/c4aWGwQ+VAt59Yq+lwA9v3bsAHr1oqXcLCxoPcWC/uLBwfR93rJFPcdRiyCEQCAQqGx8IyMjcKrou2LlypVYuXJlode+/fZbdO/evUr2XxpMNDMYDEZNw8KCCufKwOFQwVzwf3XDxoaWaKvNGBpSkbl4MU2xSE+nqQrp6cCqVbTuYHVuT965M00V+eUXYPx4Wp5w8GBg8+YPghmg59ecOTSFKD39Q6nC//2PpnDs30+j1u7uajmM2oRAIIDJx6lZSkbdtaQ3btwINzc3TJs2TW02AEw0MxgMBoNR9djYAL/9Rv/Py6PdH5s3rzktyefMoU10li2jtbrHjftQ+/m/mJkVFtNr1nz4W1peN6PcGBkZgcfjqXT8qmLFihXw9/dHfn4+/vrrL4wZMwYAsHjxYkyZMgUaapzHwEQzg8FgMBjqRF//Q8pCTcHQEDh2jKZpTJ9OG4FUlJIajDAqDEfVXRvVgI6ODkaPHo25c+ciPT0dmZmZSE1NhY2NjdpsYqKZwWAwGAxGxencmbZc19ZWtyWMWkhBpDk9PR0AFdFmH9+xUAM1pFYPg8FgMBiMagcTzAwls3LlSnA4HOjq6ipSMwBg5syZ0NHRUaNlLNLMYDAYDAaDwahmcDgcGBkZoVGjRhg7dixmzpypbpOYaGYwGAwGg8FgqA9/f3/4+/ur24wy+WzTM/744w907NgRxsbG4HA4kEqlhZbn5+djyZIlMDc3B4fDgYmJCQ4fPqxYHhMTA3d3d2hoaIDD4cDOzg7BwcFVfRgMBoPBYDAYjCrgsxXNZmZmmDVrFrZu3Vrs8uHDhyMwMBAGBgbw9PTEpEmT0Pp9QXm5XI42bdogISEBd+/exatXryCVStGxY0fk5OSUuM+AgAAVHAnj+vXr6jahVsL8qjqYb1UD86tqYH5lMCifrWju2bMnRo8ejXrF1MS8ceMGrl27BqFQiO3bt8PCwgIGBgbw8PAAANy5cwfJyclYsmQJ2rZtCw8PD+zZswe5ubk4ceJEifsMDAxU2fF8zty8eVPdJtRKmF9VB/OtamB+VQ3MrwwGheU0F8O1a9dgZGSEvLw8zJw5E1lZWcjKysLXX38NS0tL3Lt3DwDQtWtXxTZt2rQBANy6dQuTJk1Si90MBoPBYDAY1R1CSLUYo6J8VqJ54sSJOHToULHLtLW10alTJwQEBOD169dIS0vDgAED8PPPP6N79+5ITk7GuHHjcOnSJUXNQFNTU8X2BbUDs7Kyiowtk8mgq6uLvLw8vHr1SvkH9hmTl5fH/KoCmF9VB/OtamB+VQ3Mr6ojNTUVANUIjJoBh6hDqquJnJwciESiQq/dvXsXgwYNQlJSEvT09KCvrw9bW1tkZmYiJycH+vr66Ny5M5ydnXH48GHk5ORg27ZtWLx4Mf7991+0bdsWAJCYmAh7e3uMHTsWv//+e6F9PHr0CL169YJQKFR8OLS0tKCl9Vlds6gEQ0NDSKXSIu8r49NgflUdzLeqgflVNTC/KhepVKooPKCtrY3c3Fw8fPgQLVu2VLNljPLwWak2Q0NDGBoaFnrNxMQEAGBpaQktLS1ER0cjIyMDAODo6AgOhwMej4d79+6BEAJCiEIoBwQEKP5/8OABAKBLly5F9uvm5gapVIoRI0Zg27ZtKju+zxGBQICVK1di06ZN6jalVsH8qjqYb1UD86tqYH5VHUlJSWjQoAHc3NzUbQqjnHxWovljZDIZJBIJxGIxAFpiTiqVwt7eHq9fv0bnzp3Rp08fLFq0CKNGjUJaWhrc3d1hYGCAjh07wsbGBj/88AO6du0KMzMzzJgxAwYGBhgxYkSRfWlqagIAuFxuresNXx1gflUNNdGveZI86Gvrq9uMMqmJvq0JML+qBuZX1SAQCAB80AiM6s9nWz3j999/h56eHnr27AmARqH19PRw9+5duLu74+bNm4iJiYG3tzeeP38OMzMz/PnnnwAADQ0N3Lt3D7a2torqGZqamggMDCwSyWYwGKojU5iJpnuaIiA6AFffXYXLzy7IEZdc9pHBYDAYjMry2UaaJ06ciIkTJ5a4vEGDBrh27VqJy52dnREREVGhfXbq1KlC6zPKx8dVTBjKo7r7lRCC8WfG403GGxwNPQoNjgZSc1NxKOQQZrearW7zSqW6+7amwvyqGphfGQzKZzURUF3w+Xw4OjrixYsXsLe3V7c5tQo+nw8A7NahkqkJfr0VdQsjTo3AL/1+wYwLM6CloYXu9bojKDYIp0echoelB3S1dNVtZhFqgm9rIsyvqoH5VXUkJCTAwcEBPB6P+beG8NmmZzAYjJrNkdAjGNV4FPp79IdULoUgX4CdfXbC0cQRvgd9YbPJBj/c/kHdZjIYDAajlvDZpmcwGIyai0gqwqmXp3B17FVoaWhhgMcA5EnyYMA1wK0Jt0AIwZ3YO+h5pCcGeAxAkzpN1G0yg8FgMGo4LNJcjbl79y6aNm0KuVwOf39/dOjQocR1c3NzUbduXbx586YKLWQw1MPZV2dhpW+FVvatAABbemzBzj47Fcs5HA586/pievPpWHJzibrMZDAYDEYtgonmasycOXOwcuVKaGiU/TYZGBjgm2++wYIFC6rAMgZDfQglQiy+sRjftvsWHA4HAGCmZwZLfcsi6/6v4//wMOEh5l+eD5mcdd1iMBgMRuVhormacuvWLSQnJ6N///7l3mb8+PG4fv06izYzai0yuQyLri+ClYEVpjSbUub6VgZWuDf5Hs68OoMjoUeqwEIGg8Fg1FaYaK6mnD59Gn5+fkWKni9evBjW1tawsbHBt99+C4lEolhmZmaGli1b4syZM1VtLoOhcqRyKXoc6YELERfw28DfoKlRvoYA9czq4UvvL3H53WUVW8hgMBiM2gwTzdWUx48fw9PTs9BrDx48gJaWFuLi4hAQEIDTp0/jxx9/LLSOl5cXHj16VJWmMhgq5XnKc7xKf4VLEZfwOv01QmaEoJFVowqN0a1eN9yIvAE5kavISgaDwWDUdphorqZkZmbCxMSk0Gvm5ubw9/eHjo4OGjRogG+//RYHDhwotI6JiQkyMzOr0lQGQ6UsvbUU/Y71w7aH2zCl2RQYcivedbOVfSsIpUKEpoSqwEIGg8FgfA4w0VxNMTc3B4/HK/Sao6NjoXQNFxcXxMXFFVqHx+PB3Ny8SmxkMKqC4KRg8PJ5uBl1E5N9JldqDG1NbXR27ozrkdeVbB2DwWAwPheYaK6mNG/eHOHh4YVei4uLg0z2oQJAdHQ0HBwcCq3z/PlztGjRokpsZDBUTUZeBuL4cbgw5gK29twKRxPHSo81uMFg7Hm8B3mSPLzJeIOorCiWrsFgMBiMcsOam1RThgwZgvHjx0Mmkymiy5mZmVi1ahWWLFmC6OhobNy4EV9++aVim+zsbDx8+BB79+5Vl9kMhlIJTg6Gs6kzWtm3UtRkriwTmk7A/uD98P3NF2GpYZDKpbA1ssUUnylY0G4BDj87jMeJj5EtykZqbioiMiPgbuGOyT6TMaHpBEV5OwaDwWB8nrBIczXFz88P1tbWOHfunOK11q1bQywWw8HBAb6+vhg0aBAWLVqkWH748GH4+fmhQYMG6jCZwVA6wUnB8LHxUcpYmhqaODDgAOyN7fF0+lPkLsnFnr57cPHtRVhvtMa2B9vgZOKETnU7YUaLGfh75N/4oskXWH5rOVrsa4ElN5YgR5yjFFuqG4QQEELwIu0FEgWJLALPYDBUxogRI1C3bt0Sl799+xZcLhczZsyoQqvKB4cQQtRtRG2Hz+fD0dERL168gL29fbm3CwoKwuzZsxEcHFxmg5Pc3Fw0btwYV65cgYeHx6eaXGPg8/kAAGNjYzVbUrtQp1/jeHGIyIxAZ+fOGPvXWDS0bIhlnZapbH9SuRQB0QHo4NQBulq6RZbniHNw/Plx7H2yFy3tWmJ3v92ftD91+bbgOF1MXcDhcHA09ChMdU2x7+k+xPJiYcg1RIYwA/nSfOho6cC3ri/29tuLHHEOHIwdYKprWqX2VhT2XaAamF9VR0JCAhwcHMDj8T4r/65cuRIrV64En8+HoWHRid3Dhw/H1atXERERAWtrazVYWDIsPaMa06FDBzx79qxc6xoYGCA6Olq1BjEYVcCcS3NwMeIiLPQtIMgXYEyTMSrdn5aGFrrV61bickOuIaY2n4pu9brBa48Xnqc+h46WDo4NOYY6hnVUaltFkBM50nLT8CDhAYJig5AnyUNLu5aw1LfE3bi7OPTsEGRyGbJEWdDgaKB7ve4QSoWY0mwKOjh1QJYwCx3rdoScyBGdHY3N/26Gy88u0NPSg0QuQQPLBmhi3QS7+u6q9gKawWBUXzw9PUEIwcuXL9GyZctCy+7fv49Tp05h3bp11U4wAyzSXCVUNtLMKBsWBVEN6vJrREYEPHd74vVXrxHLi0U8Px5DGw6FjpZOldpREpciLiE6Oxp3Yu/gXvw9TGs2DZObTYa1Qfm/3JXtW4lMgnVB67Dj4Q6k5aXBzdwNfi5+MNA2wN24uxCIBWhi3QQTmk5AD9ceSBQkIleSiwaWZadxxfPjYWtoiwRBAkJTQrHn8R7E8+OxzHcZHE0ckSfJg1QuRXPb5jDTM1PK8VQW9l2gGphfVcfnGml+/fo1GjRogIMHD2LChAmFlnXs2BFxcXF49eoVdHWL3vlTNyzSzGAwqg1b72/FiMYj4GzqDGdTZ3WbU4Te9XsDAKa3mI7fn/2Oo8+PYtO9Tdg/YD8GNRhUpbYQQpArycXUc1MRlhqG3wf/Dt+6vtDT1it1u4pUIHEwptV5nEyc4GTihJ6uPeEf4A//QH9k5GVAX1sfciJHDC8GrexbYULTCZjefDqbNMlgMErEzc0NOjo6RSqE/f333wgKCsKxY8eqpWAGWKS5SmCRZtXBoiCqQR1+fZ3+Gt57vfFo6iN4WnuWvUE14c+wPzHpn0m4MOYCOjt3LnN9Zfg2JScFfY71wdOkp2ho2RABEwMqFO1WNvH8eFx9dxX+Af7wsPRAY6vGmOQzCV51vKrMBvZdoBqYX1WHMiPNhAACgZIMKwYjI0CZ18Le3t5wcHDA+fPnAQBSqRSenp4wNTXFvXv3qu2FN4s0MxgMtUMIwddXvsYUnyk1SjADwEjPkcgR52DoiaEInREKe2PVXhi/THuJAX8MQGv71rgy9gpMdU2hpaHer3IHYwdM8pmEAR4DsOvRLsTyYtHm1zb4vv33+Lb9t9DS0AJXk6tWGxmM2oxAAPynibBS4fEAZV43eXp64t9//1U837dvH16/fo27d+9WW8EMsJJzpZKSkoIxY8agTp06MDU1Rdu2bREYGKhYHhAQgGbNmkFfXx8uLi7YvfvTZtUzGJ8rm/7dhGfJz7Cyy0p1m1IpJjebjP7u/THx7ESVlWu7HXMb089NR5v9bTCi0QgcHnwYlvqWahfMH2Opb4nlnZbj1wG/InBiIC69vQTDtYbQWaODLoe64GbUTXWbyGDUSoyMqLBV1cPISLn2Nm7cGNHR0cjLy0NOTg5WrlyJ4cOHo127dsrdkZJhorkUZs2ahbi4OISFhSEjIwPDhg1Dv379kJmZiZiYGPTt2xeTJ09GdnY2Dh48iEWLFuHMmTPqNpvBqBFIZBJ88dcXcNvmhtW3V+P8mPMw16u5LeC39d6GqKwozDg/AzK5rOwNyolEJsGks5PQ71g/6Gvr459R/+AHvx+gwaneX98t7Vvi38n/4t3cd4iaF4Werj0x8I+BWHJjCVhWIIOhXDgcGglW1UPZwd+PK2j8+OOPyM7OxoYNGwqtM2PGDCxYsAAADWI2adIEFy9eVK4hFYTlNJdC06ZNMWnSJMybNw8AkJOTAyMjI9y/fx+XL1/G33//jeDgYMX68+fPR2hoKG7cuFFoHJbTrDpYvp1qqAq/Tjo7CU+SnmBT901wMnGCh2XNry8ex4tD99+7o45hHWzqvgkt7T+UU5ITOS5FXMKGmxtQ17QuFnZeiKY2TRXL+fl8PE95jjqGdfDz/Z9xK/oWDLmGsNS3RBw/DhfHXFR56oeqeZ3+Gj2O9MAgj0HY0nMLNDU0lTY2+y5QDcyvquNzrZ4BAFFRUahXrx42bNiAVatWYebMmdi4cWOhdVJTU+Hp6YnLly9jypQpWLp0KYYMGaImiynV575eNeT777/Hvn37MGLECFhaWmLnzp1wdXWFl5cX1q9fj1atCrf1bdmyJQ4dOqQmaxmMmkNERgSOPj+KyLmRNV4IfoyjiSMeTHmAH+/+iM6HOmNFpxUY6zUWf4T9ga33t0IoFeL7Ft8jnh+PdgfaYWTjkXiS9ASNrRrjdsxt5IhzwMvnYUTjEVjrtxaRWZG4EXUDV8derVY1oSuLh6UHAiYEoNfRXnhy8Al86/qivnl9jG86HgCKFdFSuRTBScF4lvIMGXkZqGtaF81sm8HN3E0RbRfLxHiX+Q4aHA3wsnjwsfGBkY6S7yczGAyl4ezsDAMDA6xYsQKGhoZYunRpkXWsra3x9ddfo127djhw4IDaBTPARHOptG/fHr///jvs7OygqakJc3NznDlzBnp6euDz+XB3dy+0vpmZmeKq/L8YGhoiNze3xOWMyiFQ5XThzxhV+/XY42MYUHcAjGBU6z4THHDwfcvv0cOxBxZeWYgfrv8ADwsPbO60GV1cuiA/Lx8AMMBjAE6Gn8Q3Pt/gTcYb+LX1w7BGw5AjzlEIvs62nTGp0SRAjlrjJwtNCwSOCsTORzuRnp2OLc+3YNXVVcgSZaG/R3+Y6ZrhadJTOJs6Q0ZkuB93HzIig6e1J8z1zHEh7AJepr0EAJjqmsLd0h2v019DIpQABNDU04S+tj4WtF0AN3M31DWty5qxfALV8Ts2OjsaB4IPQCaXISU3BTpaOujq3BV1TeuiiXUTxPBicPz5cQQnB8OQa4jW9q0xpOGQaneBnpubq24T1AaHw0GjRo3w6NEjbNq0CSbFzGLMzMzEyZMnYWZmBkfH8pfKVCVMNJeAXC5H165d4evri4yMDBgbG+PChQvo06cPAgMDYWxsjOzs7ELbZGVllXiLRSQSYd26dTAwMAAAdO3aFd26ldyFjMGorRBC8Perv7Gw3UJ1m6JSfGx8cGPCDeSKc6GrpauIouaDiuYWdi3Qwq5Fke0+hwipPlcf37b/FgAgkorwNPEpzPTMsP/pfuSIczCt2TTE8+OhqaGJiU0nopV9q0JRaIlMgjh+HDLyMvAy7SWmNZuGZhbNoMHRgIGhAX4L/g0Hnx1ETHYMskRZGNloJJZ2WsrEcw0mkZ+IiMwIXHl3BSdfnERvt96wMbBBK/tWyBZl4/Czw4jhxUAoFUIml6G/e3+M9RqLXHEubkTdwOZ7m9HKvhWGNByCdg7t4GjiqJYqDdevX8fNm3RC7OcsmgHg4cOHJS7j8Xjo1asXvv76axgZGWH+/Pl49OiR2itrsJzmEsjIyIClpSWePn0KHx8fxevNmjXDyJEjIRKJcPbsWTx9+lSx7JtvvsGzZ89YTnMVwvLtVIMq/Concux4uANnX5/Fw4SHSFmYAn1tfaWNX1Ng56xqKMmvUVlRmH1xNgJjAtHKvhXeZb6DuZ45pHIpDLmGGNNkDCb5TIIh1xASmQTZomxYGVip4xCqJeo6XyUyCaKzo/Ek6Qn+DP8TF95cgJOJEzrV7YSvWn0FH1ufItsQQvAs5RnMdM1Q17RuoWXJOck49vwYjocdR0hyCLxtvHF0yFG4W7gXGaeq+JxzmksjNzcXPXr0wBdffIFZs2YBoHf+p02bVqSDYFXDRHMpNGrUCO3atcOWLVtgaGiIixcvYujQobhw4QLq16+Phg0bYvPmzZg8eTIePHiA/v37F5t3w0Sz6mACRDWowq8jTo7A06SnWNhuIdo6tC00Ce5zgp2zqqEsv4Ykh+BB/AN4WHqAJ+JBW1MbiYJE/PLkF0RmRcLOyA7vst5BKBGil1svzGo5C53qdoJALEBkViTaObZTWcUSQgjeZr5FeFo44nhxCEsNg5meGcZ5jUNj68Yq2Wd5Ucf5GhAdgNGnRyMjLwOe1p7o5dYLX7X6CnZGdkoZP0+Sh6U3l2J/8H6cHXW2XE2JVAETzTWPKk3PiIyMLNd6GhoacHZ2Vq0x5eDs2bP49ttv4ebmBpFIBEdHR2zfvl2RVnHx4kXMnz8f33zzDerUqYO1a9dWi0R1BqO6kSXMwumXpxHzdYyiNTODUZV423jD28a7yOuTfSbjTuwd8PP5cDF1gamuKfYH78f089ORKEiEBkcDhlxDtHVoi18H/KrU8zdJkIS/X/2NHY924G3mWzS0bAhHE0c0smyEBEECWv3aCuv81mF2y9lKrTRSXUnOScaym8tw9PlR/NTzJ0xpNkUlx62vrY8tPbegiXUT9D3WF196f4m5reeqNerMqBlUaaRZQ0OjzHwUQgj09PRqVa4PizSrDha1Uw0f+/XfuH/Ryr7VJzXROP3iNFYErEDYrDBlmVhjYeesalC2X+VEDn4+H9oa2pDKpZhzaQ7+evkXhjceDlczV3R16Yq2Dm0rnWN5J+YOeh7pCR9bH0zynoQvvL6ArpZuoXWCYoMw5vQYiGVijGg8AlOaTanS1uRA1ZyvhBAcCD6AhdcWws/FD+u7rYebuZvK9vcxwUnB2HJ/C06Gn0Qbhzbo4NQBSzouqZLUMRZprnlUqWg2NDREeHh4qesQQuDt7V1kkl1Nholm1cEEiGoo8OvTzKfocqgLdvfdjRktZlR6vBnnZ0BXSxdbe21VkoU1F3bOqoaq8Ou9uHu4EHEBEZkRuPbuGvzq+WFP3z2w0LcAAGTkZSBDmIEsYRayRdno4dqjiKjOFefi6rurmHZ+Gn7o+gOmNZ9W6j5lchkCogNw7Pkx/BH+B5b5LsOcVnNgwDVQ2XF+jKr9mi3KxvCTw/Eq/RV2992Nfu79VLKfskgUJOLc63M4+OwgdDR1MNpzNDKEGTDTNUMbhzbg5/Nha2SL+ub18TbzLW5E3UC+NB/WBtYw1jFGpjAT7RzbwdXctdz7ZKK55lGlonnMmDE4duxYmeuNHTsWR44cqQKLqgYmmosnNTcV1gbWiudyIq9wziATIKqBz+dDIpOg8x+d4WDsgKdJTzG4wWC8TH+JMyPPlKtzn1AihK6WLjgcDty2ueHnXj+jr3vfKrC+esPOWdVQ1X5Ny03DuDPjcOXdFTiZOEFXSxdvM99CW0MbRjpGkBM5RjYeiSUdl8BYxxjaGtp4kvQEX/z1BXQ0dTC12VRFBZHy8jDhIaadm4bIrEicHnEa3V27q+joPqBKv77JeINxZ8bBSt8Kx4Yeg7GO+j8TueJcTDs/DWm5abA2sEZqbiruxd+Dma4ZUnNTYaZnhkxhJto7toeprilSc1PBy+fBkGuIp0lPYW9kD4lcgvS8dJjpmqG9U3uMbDwSzqbOCE4KRqYwE04mTujr3heCdAETzTUMNhGwCmCiuShPEp+g9a+tETw9GO+y3mHJjSUQiAW4N/lehXIGmQApm8PPDiNLmIV5beaVexs+n4+joUexNWQrns98jp5HekIql8JU1xSJgkTcnXS3yK3kjxFJRfDZ64Muzl0wvNFw9DraCxnfZcCQa6iMQ6rRsHNWNajLr6m5qXiZ9hJCqRDNbJspAgEx2THod7wfwlI/pCRxNblY02UNFrZbWOm0DkIIdj7aiR/v/ojwWeEqL1GoCr/G8eIw6Z9JuB1zG6M8R+GXfr9AR0tHaeOritTcVISnhqO1Q+ti0zeSc5LxKv0VuJpcmOuZI1OYifNvzuNCxAXEZMfAx9YHtoa2CEsNQ6IgEU9GPYFzXWcmmmsQahXNMpkMmpq1f3JDdRfN/Hw+HsQ/QGPrxkqbnVwWKwNW4oc7P6CZbTOEp4VjU/dNuJ9wH2GpYehRrweMdYxRz6weNDga6FO/D/S09Yq3nQmQUskWZaPez/WgqaGJhG8SwNXklmu7tMw0dPytI1b2XImRniMhlUuhydGEVC5Fy30tMdZrbIl1lqVyKVbcWoF/3vyDeH48pHIptvTYgqnNpyrz0Gos7JxVDdXVrzK5DDniHIhlYpjrmStlYhshBH6H/WCmZ4Yjg4+U+P2oDJThV5lchgRBAvY83oMdD3dAIBZgkvckbO65+bOtnZ2ckwwZT8YizTUMtYrmHj164OrVq+rafZVRHUUzP58PnoiHpJwk9DzSE3IiR0/Xnjgx/ESV7L/VvlYY5TkKqwJXYWqzqdjYYyOEEiHmXZ4HQgiyRFmI58cjW5SNLFEWdvbZiWGNhhU9jmr6Q1ldWHJjCe7H30dkViR+7vUzern1KjaiIydySOVSEEIw7fw0RCVHQSgR4sFXD4qkzFyPvI5hJ4bh0KBDaGXfCrZGtoplOx7uwLzL86CjqYM7X95BhjADIckh+K79dyo/1poCO2dVw+fm19TcVAz5cwgkcgmujL2iMvFZWb8+THiI35/9jlh+LG5F3UKuJBcdnDrgh660Qyarhc1ymmsiVSKaly9fXuQ1QgiOHTuGd+/eqXr3aqe6iWZCCPoc64PLby9DV0sX6/3Wo697XzTe1RixX8eijmEdle4/JScFdlvskLQgCXIih5W+VYnRF0II/s/efYc1fXVxAP8m7L0REAQVNyLujVvcWtfrrlqt1lWt2mptRZy1VavWuvfee+HCgXuAyhJxAqLshA1J7vvHLVFkawbjfJ6HR5P8xs0lJCf3d+65h4IOYczJMRjuOhwzWsxAZbPK8sdfRL3A96e+x7S209Q2gURVnsU+g6OpY4FpEZ8Kiw+D6zpXXB91HSdCTmBvwF68S3qHvX33wlTXFDuf7MT67uuho6mDH07/gOPPjsPF2gXiDDGGVx+OlpVaor5T7gUEAGDq+ak4HnIcrhVccXLwSQDAseBjGH5sOE4POY1m9s2K3M7yprwFd6pSHvs1Q5KBfgf7ITE9EccHHYelvqXCz1Hcfs2UZmLBtQVYfns5RrqNRFWzqmhXuR3qWteFloaWwttXmlHQXPqopE7zunXrsHz5cnwen+vrl7/VwFSNMYZhx4ahpkVN/N7mdwDAgcAD8IvyQ8jEECSmJ6KpfVMAQOtKrbHh4QbMajWryJfxiyswOhCeVz3R0LZhjkmA+REIBBhYZyDqWtfFvGvzUOvfWhjbYCyWdloKPU09TL8wHTImw+AjgzG9+XTMbTNXaQsQqEtaVho67+4M37e++Nvjb0xtNrXQfRLSEjDx7ESMdBuJRnaNYG1gjeDYYAyqMwhTzk+BVCaFUCBEv4P90MKhBfYF7MP8dvNxOvQ0Tg46CT1ZwZd7V3ZZidH1R6PFlhbIlGbCL8oPw48Nx+6+u9W2UAAh5Y2Opg4ODTiEoUeHosaaGvix6Y8Y5joMVcyqqKU9EeII9NnfB1myLNwZc0fl5fEIUTqmAq1bt2YRERG57h88eLAqTq92IpGIGRsb59kHiiaVSXPc/ufuP8x+hT0zX2rOvjvxHRt8eDDTWaDDDgcezrXvmdAzTMNLg+kt1GM+r3wU3rYMSQZzWOHAhh8dzkJiQr7oGEHRQazxxsZsyJEhbPal2azGshrszYc37Mn7J8x5tTP7/uT3Cm616jx+/5jdfHsz1/07/HewmmtqMk8fT9Zjb49Cj7PTfycTeglZww0NWXxqfI7HpDIpa721Nftm/zcsNiWWDT48mDXf3JydDDmZYzuRSMREIlGB55HJZMx2mS079ewUs11my/6+/XfhT5IUqW9J8ZX3fj0Teob12teL6S/SZ1sfbVXYcYv6XnAs+BizW27HxpwYw9Kz0hV2/rIsIiKCASjXr9vSRiXpGYyxL54pXBZ8TXpG9q+nKP2XIclA/Q31sazzMnSr1g37nu7DmFNjcG7oORhoGWDjw40w1TXF2IZj8y0cnyXNwha/LZhzZQ4ejH2QIxXia23z24Y/bv6BoAlBXzUZ5n3yezTc2BAyJsOZfmfgbO4MY2NjvEx4iVr/1kLY5DA4mDgorN2q0mJLC8iYDHfG3AEATDs/DXpaerj25hoG1h6INk5t4L7NHeHTwrHryS6MdBuZqxpFuiQdzqudsazzMgxyGZTnedIl6dAUaha4WElRL8mOPD4S58POo7JZZdwafatc/50XVXlMI1AF6lfu4ouLGHRkEGa1nFXsknZ5Kahfs6RZmHNlDg4GHkSGNANebb0wtsFYeh8oIkrPKH1Ukp5Bf0BfzuuaF8JF4djSe0uh2+55ugehcaFYcH0BxBlijDs9DkcGHoG7ozsAYIPdhkKPoaWhhfGNxuPyq8vY/GgzFnVY9NXPAeAB/R83/8AvLX/56tnjNoY2uD7yOrQ0tGAqMJXfX8WsCrpX644199ZgaaelRT5ecmYyDgYexIh6I/INJAv64pf92IfkDxhwaAAODjgIG0ObHNt4XfXC+RfnIRQI8Sz2GX5s+iMMtQ2hp6WH8Y3G4+G7h3j84TEyJBmISorCi4QX2PRoE4x1jJGQnoDTg0/DRNcE2hra+N/h/+Hq66v4+87fuDbyWo4Sfevur4OVgRUG1hmY7/NVZK6xR1UP7Hi8Awf6H6C/c0JKgE5VO+Hi8IvosLMD3ojeYEG7BTDTM1PoOVbcXoFjIccggABJmUnY0GMD3B3dlVrFg5QdAwcOxN27d/HmzZs8Hw8LC0Pt2rUxevRorF+/XsWtK5hakj/79eunjtOWSvsD9mN/4H4kZyYXuJ1UJsWfN//ECo8VCI4JxsjjI7G33150ce7yRef9puY3OBd27ov2/RxjDOPPjIexjjGGuQ5TyDGrmldFJZNKue6f2mwqNjzcgAhxBNbeX4tVd1YBALzDvHEk6AhiUmJy7TPt/DR8f+p7dNjZAR12dsCfN//M8fh2/+3osof3452IO7gVfgu+b32x5MYSdNvTDeZ/mmPS2UlYenMpbry9geW3lsv3lTEZFt9YjLUP1mJ8w/EY13Acdn2zCyeencC+gH2YfmE6EtMTsfreaoxwHYFm9s2wL2AffjjzA35z/w1XR17Fjj47YKZnBqFAiHaV28H7hTeuj7qOyqaVse/pPsSnxWPjw4148uEJ5l6di786/aWyvO7eNXtjT989aOPURiXnI4QUroFtA9z57g5C40JRd11d3A6/Xaz9o1OisfzWcjTd3BR9D/TFouuLcDv8NkLjQjHn8hwsvL4QfWv2RdOKTXH126vwcPaggJkUWZ06dRAeHo7k5LzjmtmzZ0NPTw/z589XccsKp5KR5s+9e/dOHactdUJiQ/A68TUqm1XGkaAjsDG0QbvK7aCtoQ0Zk+FQ4CH0q90PjDGMPDESAoEA4xuNhwACpGSlfFU1ic5VO2P4seF4n/w+16jpp4JigvAi/gV61uiJm29v4q3oLVKyUiCAAKPrj8bLhJeY5j0ND6Me4v7Y+0qbYJjN3dEd/Wv3R9vtbfEh5QNkTAb/D/44HnIclUwq4X3ye2zosQEeVT2w5t4a+L33w9nnZ/F4/GNs8dsCAy0DzLs6D43tGmOz32as6boGJ5+dxIUXF3Dq2SkMOjIIhtqGEAqEaFWpFTyqemBas2kYenQoEtITsKHHBvzk/RN0NHUQGBOIZ7HPkJyZjLNDzqKhXUN5O7tW6woAaLm1JSaenYjjIcfxePxjHA0+ihkXZqBDlQ74qflP0NbQRnWL6vL9RruNRi3LWmhSsQn61eqH/YH7kZqVigXXF4CBYXrz6ehYpaNS+/hT+lr6GFJ3iMrORwgpmhqWNeA9zBsr76xEm+1tUNW8KiY1ngRNoSZOPz8Na31rXHtzDWZ6ZnAydcLdiLuw1LeElEkREhuClg4tMb7heCADuPrmKnrv7424tDi0dWqLC8MvoJFdI3U/RVJKubi4gDGG4OBgNG7cOMdjd+7cweHDh7FkyRJYWxdeLEDV1BI002XcojkRcgIdq3REW6e2GHd6HDKkGfiz45+Y2XImToScwKAjg7AxYyMCogPw9MNTXP32KrQ1tDG56eSvPrelviUa2TXChRcXMKLeiDy3ORR4CKNOjIKMyTCx8USsfbAW9W3qQ0dTB08+PEFqViqW316O9pXbw2+cX5GqZSjCmm5rMOTIEIyuPxoR4gj8cukXXBlxBQ1sG2DH4x0Yd3ockjKSUMuqFnpV74VJTSahjnUdrPBYAQCISo5C+53tYahtiBb2LeDz2gdNKzZF/0P90b92f+zpuyfXOY/97xiuvr6K7xt+j9sRt/Es7hnaOrZF/1p8n/xWu5rQaAKGHRuGPzv+CWdzZwypOwTPYp9hVddVeX7B6Fqtqzzg7l69Oyafm4zQuFAcHHAQGgINdKvWTYE9SQgpzQQCAaY1n4bR9UfjXNg5eF3zAmMMo+uPRkJaApZ3Xo74tHhEiCPwQ6MfIEoXQUtDC3Ws6sjns4jFYvSr3Q/GxsaQyqQKWZyFlG8uLi4AgKCgoFxB88yZM+Ho6IipU6eqoWVFoI7Zh82bN1fHadVGXj3j1at8t3kR/4L96fsn84/yZ4zx2cj119dnGx9sZLEpsWyezzx2JOgIM1liwqKTo5nbejfmscuDmf5hygwWGbDncc8V3m5PH0/WYEMDFiHKXfUjNTOVWf1pxfY93ccOBhxkGl4a7FDgIfnjx4KPMcwD672vN5PJZApvW7aizOxOy0rLdfv66+tMIpXkuX18ajw7FnyM/XP3H1bhrwrM9A9TFvAhgFVcXpG9SXyjsLYzxlh6Vjr7996/LEua9UX7u65zZRWXV/zi/fNT3isRKBP1rXJQvyoH9avylNfqGRKJhOno6LCZM2fmuP/YsWMMANu7d6+aWlY4tawI2KJFC9y6dUvVp1Wb7OoZgbVqwX7nTqD6x8vte57swa9XfkWkOBIu1i5Ik6TBf5w/Tjw7gekXpiN0UigMtA3k239z4BucCT0DC30LvJjyAgMODUCv6r0wrtE4hbc7OTMZk89NxvGQ45jXZh4mNZmEcHE4lt9aDlsjWxwIPAD/cf4QCARIzkzOVcnhUOAhdK7aGSa6JgpvWzZlzpiPTomG3XI79K7ZG0cGHimRVWA2PtwIGZNhfKPxCj0uVSJQHupb5aB+VQ7qV+Upz9Uz3NzcYG9vj9OnTwMAJBIJXFxcYGpqitu3b5e4z9psaknPUEOcXiK8rWoB+86dIb57HaOv/YTQuFBEiCOwrvs6dK/eHXqaemi1rRW67+2OZ3HPsKj9ohwBMwAcGnAIrxJeQUdTB/pa+jgz5IzS2muobYhtvbdhUJ1BGHtqLHQ0dfA87jl2PtkJcYYYu7/ZLX9hfx4wA8CAOgOU1jZVsDawxv9c/oeuzjwdoiT+EX/f8Ht1N4EQQoiaMcaQlJmktOMbaRsp9DPQxcUlx+Dppk2b8OzZM9y8ebNEftZmU8tIc3mTPdJcy7MWfL2NcSvmITaNcEG/LtPQyK5RjpJh4aJwbPPfBn0tffzU/KcSs7rdrse74HXNC6IMEQ72PwgZk6GtU1u157fRKIhyUL8qD/WtclC/Kgf1q/IocqRZnCGGyR/Ku6ormiWCsY7iXgNLlizBnDlzkJycDJlMBmdnZ7i7u+PgwYMKO4cyqGWkOS9hYWHQ0NBA5cqKW0yjpIlKjkKbNjFYtQ/YOfMWBHvTgAkTgBEjACEPjh1MHDC3zVw1tzS3QS6DMOfKHJjqmqKtU9sS/U2QEEIIKU+MtI0gmiVS6vEV6dMKGidOnEBiYiKWLv24vkJ8fDwcHByQkJAAbW0+KX7cuHGoUqUKfvnlF4W2pTjUFjSPHj0ao0ePRqtWrbB//34MHToUAoEAu3btwuDBg9XVLKWa0HgCfOJ8UOvhEQjEacCxY4CnJ7BzJ3D6NKCvr+4m5ktLQwv/dvsXMiajgJkQQggpQQQCgUJHgpUtu4LG5cuXsWLFCkyePDnHoKm5uTmqVKmCR48eoVmzZrhx4wbu3buHf//9V11NBqDG9AxbW1u8ePEC+vr6aNq0KWbMmAFjY2PMnDkTT548UUeTlKbAZbRTUoBu3QBjYx5Ea2oCjAFhYUC1auppcClClw6Vg/pVeahvlYP6VTmoX5WnPE8EZIzByMgIUqkUhoaGCAsLg4lJzvSSSZMmwcnJCVOmTEGDBg2wZcsWNG3aVE0t5oqdMJudb5KYmPhVJ05NTYW+vj6SkpIQGhqKfv36wcPDA2/fvv2q46oSYwyenp6ws7ODgYEB3N3dERAQULyDGBgAJ04AL14Ac/9Ly9i7l1fYCAtTfKMJIYQQQtRIIBCgdu3aSE9Px7x583IFzADg7u6OmzdvYvHixWjXrp3aA2bgC9Izsr8N9ejRA2KxGLVq1ULDhg3RsGFDdOjQocjHsbKyQnBwMAICAtCsWTMIhUKkpKSUqkv/y5Ytw9atW+Ht7Q1nZ2fMnz8fHh4eePbsGQwNc1eTyJepKXD0KNCkCZCZCezYATg4AHv28PQNQgghhJAy5N69ewU+7u7uju+//x5PnjyBn5+filpVsEJHmr///nuIRB+Ty7t06QIA8PX1hb+/P+bOnQsbGxucOnWqWCeeOnUqGjVqhJEjR2LChAkAgOvXr6NOnTrFOo46rV27FjNmzEDdunWhp6eHBQsWIDMzE8eOHSv+wWrWBLy9gchIoF8/YPlyHjRTcRNCCCGElDMVKlSAtrY2li1bVmLSVwrNaW7QoAHevXuHv//+W+ET9MLCwqCpqQknJycAQGhoKDIzM+UJ4iWZSCSCqakpbt26hebNm8vv79y5M1xcXLBixQr5fQXmNOcnPR2oUAG4eJGPQJM8Ub6dclC/Kg/1rXJQvyoH9avylOec5qJYv349Ll++jEOHDqm7KXKFpmc8ePAA//zzD8aPH48dO3Zg3bp1CisL5+zsnON29U9Wyivpst9ITE1Nc9xvZmYmf+xThoaGSElJyfOxfP3vfzy/uUYNIDYWsLL6miaXSUlJyivmXp5RvyoP9a1yUL8qB/Wr8qSkpKi7CSVSSEgI+vbtC1tbWxw5ckTdzcmh0KBZKBTixx9/xIABAzBt2jS4uLjgt99+w8yZM6GpWWLKPKtc9rfCzydEJiQk5DmanJ6ejiVLlsDAgK/w1759e3Ts2LHgk3zzDTBxIlC5MrBkCfDgAa+yQQghhJBS59KlS7hy5QoACprzU7NmTQQFBam7GXkqdsm5ixcvYtKkSdDS0sLGjRvRokULZbWtxKtcuTKmTZuGKVOmAOBrp9vZ2WH58uUYPny4fLsvSs8AAKkUcHQE3r8HzM2B+fOB8eP5YzIZEBUFVKwI/PUXEB4OrF6tyKdXKtClQ+WgflUe6lvloH5VDupX5aH0jNKn2CXnOnXqhKdPn2LgwIHo3Lkzxo0bp4x2lQoTJkzAsmXLEBAQgLS0NHh6ekJLSwvffPONYk6goYE3ncciqkU/YOFCYO1aoGtXHihbWwP29sDo0cCcOXyBlKysgo/HGB+tfvaM/z8ujq9GWLs2cP++YtpMCCGEEFIGFStoZozh2bNnOHHiBDIzM+Hs7IzNmzcrq20l3owZMzBy5Eh07NgRFhYWuHHjBs6fP1+8cnN5iInh6czR0UCnG3PRO20/MGgQ8OYNYGYGHDzIVxB8+hS4do2XpdPWBm7f5iXrNm7MWXkjPBwQiYADB4DWrYF69YBly4AFC4DXrwF3d2DcOODIEeDnn/koNiGEEEIIkSs0PeOff/7BkydP8PjxYwQGBiItLQ3W1tZwc3ODm5sb6tevj//973+qam+pVNT0jHfv+FonXl7AP//wUs0ZGTw748MHwFIzETAxAT6tZS2TAUIh8O23PHC+c4endSQmAvXr85HoMWN4LnRKCrBuHT9whw5837t3+UIqdevyKN3UFBg8GPjjD2V3i0LQpUPloH5VHupb5aB+VQ7qV+Wh9IzSp9CZfKtXr4abmxv69OmDefPmoX79+rC1tf3qEz9//hxTpkzBvXv3cs3OzczM/Orjl0YDBvBB4dhY4Px5Hu8uWgSsWsUrzw0ebJp7J+F/Fwu6deOj0f37A/v2AWIxT9v43/+ApUv58tyvX/PHBQJgxgweJLu68v0vXAC0tHhg3bgx0KUL0Latip45IYQQQkjJVmjQ/Pz5c6WcePjw4bC3t8emTZvkFSXKs+hoPuj7++98dLlDB76KtpYWEBLC1z3JLpO9Zw9w+TKwZQv/t2ZNwL57d7B5XpgV/zP+90QTDRqY81HlZcv48PXnvLxy3v60jOCcOcC0aTz/WUNDeU+aEEIIIaSUUFvNuKCgIPj6+pbrsnWfOncOaNAg56rZWlr83549gY4dgaQkwMiIpx5rawNOTnwkun59wNfXEAec52L1GGD3YeDePT5fEAYGWLqUpzsPHswHngv144/Ahg18iPunn5TwbAkhhBBCSpdiV89QFBcXF0RFRanr9CVGaCgwYQKwaxfQo0fe27RsyVOVGzcGbGx4qsbSpTzAnjSJB9M9e/L4dutWoH17nn0B8GB5/nzAzQ0YNYpXqSuUri5P8Zg7lw9xE/IZWt2dEEJIeVPsOs2KEhAQgNmzZ2P48OGwsbHJ8Zi7u7s6mqQ02RMBu3ULQvv2FTF2LODry0eIv/uOV3t79Qp49IgHt0UhlQLbtwPDhwNv3/Kg29wcmDIFeP6cz+sLC+OB+MCBPOOib18+B3DVqiI2fM8evrjKnTs8B6QEokkqylFYv3bvzueeDhyoylaVDfSaVQ7qV+WgflUemghY+qgtNyIwMBA+Pj44c+ZMjvsFAgGkUqmaWqVcVarwVOGgIGDlSj5y/OQJH23W0yveKtkaGjzgBgBn55wpytWr88pyLVvykensUed584BmzfiI8/PnQGoqD3wYy1mQQ27oUCAwkEfevr78YKTce/cOOHuWl/vu25fPMSWEEELKOrWlZ8ycOROLFy9GYmIisrKy5D9luXLGhAnA338Da9bwvGQDA14ho1Kl4gXMRfHjj3ytkyNHAB0dfp+rK/Drr3x9lFGjgB9+4MGPoyOv1pGnhQuBFi14zoefH5CezgtJk3Lr7FmgSRP+RWvjRnW3hhBCCFENtaVnmJiYQCQSqePUKvd5nebYWMDSko/wMvaxapyiSSS5RwFlMmDqVH55/dgxPt+veXOe6xwYCOjr59yeMUAUL4Xp4p+B9euBtDR+Z40avMTHjBk5K2+oGF06/DIxMcDmzbzE4bJluX/vefXr8+c8YL50iX+PcnPj6RnDhgErVvCrJaRw9JpVDupX5aB+VR5Kzyh91DbS3LVrV9y8eVNdp1crS0v+r0CgvIAZyPuyuVAIrF4NeHjwyYSbNgE3bgB2dsDIkTxwnjaNL6hy5AhQtSpga6+BlxOX88j67VsgPp7v/PYtMHYsP/D798DixTw5m5Ror1/zoPfiRf6zfXvB2795w78n/f03MH06X4yyRw9+xeLpU8DfH2jViq/KTgghhBRk4MCBcHR0zPfxsLAwaGtrY/z48SpsVdGoLRvR2toaPXv2RL9+/XItljJ//nw1tap8MTHhiwUCfNS5Vy8+gdDNDTh6lAdBa9cCPj7AzJnAkSPGfGVBAOjdG2jThidqT5jAIy9LSx5F7dunrqdECiGT8bKD7dvzMt779/MiKUOH8i9URkY5t4+L4znze/bw18Tx4/xig4sLf9zJia/k3qsXrzG+dq2qnxEhhJDSpE6dOjh8+DCSk5NhaGiY6/HZs2dDT0+vRMaCahtpfvr0KerVq4ewsDDcuHFD/uPr66uuJpVr1tbA1atAcDBf0+Tbb/lI84gRfEXtS5f45MXbt3lKR1YWMHupKR71+J1HUmfO8CHrY8f4DLFieviQL1b48iWvIpKQwO/PyFDks1S8N294IFpaHDwIREbyUWOhkK9CmZXFK684O/PAeOfOj2nrZ8/yf8eP58+zSxe+z6cTR7W1+bLv27bxUedPHTrEv4h16cKDbUIIIeWbi4sLGGMIDg7O9didO3dw+PBhzJ49G9bW1mpoXSGYmgQGBuZ5/7lz51TcEuUTiUTM2NiYRUREqLspX8zXl7GKFRkzMGDMyYmxSpX4j76ejF08l/Vxw+++Y6x9e8ZSU4t8bLGYsapVGWvWjDGhkDEtLcZcXRlbs4YxPT3GXr7Mf1+RSMREItFXPLMvl5rK27dqlVpOX2xZWYxVrszYtm057w8LY+zFC8bWrWPM0pIxFxfGqlYVsUuXRKx/f8Zmz2bMzo6x778v+PgLFzJmYsLYvn38dnQ0Y+bmjP39N2NubowtX66EJ1UKqfM1W5ZRvypHcfo1JoaxuXMZ69mTsQEDGJs2jb9/PHqk5EaWUhEREQxAuXvdhoSEMABs+/btuR5r1aoVc3R0ZGlpaWpoWeHUNtLcq1cvxH2WBHn79m0MHTpUTS0iBWnZki/nHRHBRxMHDwZu3gTWrRdgwGBNvHzJa0ezZcv58HDPnnwIE8CHD8Dhw/kf+/ffeQWPmzf5IHV8PGBry1NCatbkedeqxBhvc2Hu3eMjrnPm8BHZ6Gjlt+1rHDvGn9uwYTnvr1qVZ9mMH89HmJ88ASZP5lcZzp7lk/3OnuWL5BRkzhw+Sj1uHPDzz3w/d3c+8XTRImDJEr4QD+F/IlFRfLIuKRkSE/n7T2EVT58+5X8fDRvyybSZmcB/c+XKHZmM/83v3Qv89ht/L7l/n88Rb9iQ92V4OJ/z0KQJcOGCultMSgJnZ2fo6OggMDAwx/3Hjx+Hr68vlixZAl1dXTW1rhDqitbnz5/PWrRowTIyMhhjjD19+pRZWVmxQ4cOqatJSlMWRpoLMnkyH4nU12ds1y7GwoOT2FtzV5Y+cRpjjLFZsxjT1uYjjwcPMnbhAmMnTjDWtCljAQF8v4cPcx4zJYWPfl64wJi1NWP/vUxyiI5mbP/+rxtdkkoZ+/wL7bZtfMQ0JqbgfefPZ+x//2NszhzGHBz4CHxWVsH7qMOdO4ytXs1YkyaMrVhRtH1EIhH74w8Rq1OHMZmseOd79IhfNZg+/WMfymT8AsTAgbzPyyuZjLFVq0SsShURA/iVm549GbO356Py331X/P4m3JeMNMtkjD14wNju3YzZ2PCrXFpajDk6MjZmDGPDhzPWvz9/f5oyhV+F0dXlv6d16xizsuLvbfr6/MpYSfz7/1p59WtkJGMHDjDWqxe/4tikCWM9ejB27Vrex0hM5O89BgaM3bungkaXEgodaZbJGBOJlPej4DemevXqse7du8tvZ2VlsRo1arCmTZsyWQl+E1Rb0MwYY0OGDGFDhgxhL1++ZHZ2dmz9+vXqbI7SlPWgOSODsX/+4cFx/fqMTZzIWC3t50ysacqyOndlHWwCmIUF/xDS0+MBqYEBY61bM2ZkxIOp/EiljFWvzlifPjyI/tTs2YxZWopYeHjR3nACAxmLisp538SJjHXqlPO+Ll34h+CYMTz1YtEixh4/5o8NGsTY7dv8/x06MLZ2Lf9/Vhb/8DhyhLE9e/gHxKFDjN2/X6SmFS49Pddd27YxFhtb+K6DBjFmasp/ivrenP1BqcgANzqap4dMn87ff6OiGGve/GMflnVZWYyNHs1Y9eoidviwiKWn8y8YS5Ywdu4cDybs7Rkro2+DCvXoEf/SGhHB2M2b/N/s12x4OA/qJBLGMjP59jIZvy0S8S+RR4/yoNfdnTFjY8bq1mVs0yb+O3r+nH9Z//FHxn75hbFRo3hx0F69GDt+nLEPHz62IzaWsZAQxi5d4q9tR0f+uywLLl1izNmZMVdXERs9WsRWr2Zs2DDGGjViTFOT/zt2bNHeg7ItW8a/HC5YwP8WZszg6WHZv6fyRqFBs0iUXcVWOT8KTiEZOnQoq1y5svz22rVrGQB28+ZNhZ5H0dQaNKenp7PmzZszY2NjtnDhQnU2RanKetCcLSmJfwBpavIPsp6N3rHjpiPYNa0ObO9e/nc3YQJ/kw0P59u3asWYj0/Bxw0PZ2zIEJ7nLJEwNm8e/2Czt2esQgUR27Ah5x+zVMo/7MaMYeyvv/iHpEzGWL0aaWz44I9DQaGhjOno8ED+3j3+YRgdzUeazp7lz6NJE8b6dUlmTWzfsidP+HMYP55/UdDT4yPl2ZYu5SNP5uaM9e7NWOPG/PjZATd79Yp/s/j8E0IiKbgDfH358NY//8jvSknhI1yrV/Pn++pV3rvKZPwqgI8PH+0pKmXlh4aG8lH5Nm34FYTq1Rlr2VLhp1E5sZgHctlfMiSSj99zZDL+M3YsY7VrM/bsWf59e/EiY4aGjF2+rKKGlwJZWfz1++uvjLVowa9i6Onx1xDAvwwaGzM2c6aIjRghYtrajAkE/DFdXX41qHLlj5/91tb8y32XLoz9/jv/3RUmLq5o7dy4kQ8IHDtW+J91SSOTMbZhA2MjRvD32uzR8xMnRGzqVBHr2JHnK+/bx7+UfOk5Tp1izMODsalT+WCIQMDfJ1evVu5VlpgYHqx3787Yzz/zL0Y+Pox5evIBnD59+OfFzz/zL0izZjG2YwdjK1fyz6yUFMUH9+V5pHnx4sVMIBCwlJQUlpSUxCpUqMAGDBig0HMog0oXN5k7d26u+96/f4/Tp09jTHbtM5S9knOfL25Slv38M899PnmS5wj2ax2N88GVAD9/LPuLYdzfNWF++RAvELx+PV8PvAjS04F2Tq8wzvwQxobOhLkZX/v7p+lJ8PEBfpkohOcSXcQmaqJjR0C6/xAcBrXE/Ug7nDsHbJkXjiaz2uGysDN6hq/FokXANR8ZmjcHLDRFCD/pB5P3Ibhl1Qdajna4d4/n91qay4AePfDh4mN8UzME0cn66JmwE6OHpKHr4TGIiNaWV5KIj+eV+FatAho04Pf99BOvSHLuHIDZs3l96759gQMH+HMPCwNr2RKC+/f50pAAr/PWtSvQti1PBpw0ieeIHzjA9//uO1y4wGttd+nCzzljBq888vlk47AwoE4d/rsozuIjylzQ4O1bYPdunvPo4sLz2d9HSmF56yQk3XtDU/vjVAup9ONL5M4d3hWLFwOdOyu8WcUWEcFzte/c4fXNNTV51UVNTZ7HqavLy/t5e/Olx+3sePUZY+OC+3b7dmDiRGDrVr5/QSIjeTvq1QMGDQKqV+d98/Ytz1N3d1duLXhlSU3lpRAtLHgFy+ho/rzat+crnLq48KosERGAjQ1w+TJw9KgYpqbA2LHGsLPjFXhiYnhZxcaN+SJOurq8Uowy7d7Na91raPBVV3/4IfffpTIwBjx+zEvlN2kCFOej5v17PmfhxAk+J6FWLaBdO/56VvbiJllZgK8vn0NRqxYwaxZgb89fy4rw/j3/qFm1CmjalL9vPnjA56SkpQGtW/PXVVQU7z9ra6BaNf54RAT/+7l3j+euW1vz91uJhL+OevfmNe8jI/nfd3Q0n/Pz7Bn/HAwN5a9hV1f+PO3t+ZyR5GT+t12nTiQcHcvn4ianTp1Cr1698ODBA5w4cQJ//vkngoODUfmTxdJGjRoFPz8/AIBIJEJUVBREIhF0spc5VgdVRuht27Yt9Kddu3aqbJJKlJeRZsb4SNun38azshiTDhnGhzsBXmLB0pIP90yYUPC314gIPmxbpw5jgwezVENLlgx9FjjxX+Zn0Z5FVnBjr3adYa6mT1iE0J7FWNRgWwdfYE20/ZhMIOBDvZcvs/s1hrL3sGZ+1fqzNIEuG2d1hN016cgytfTkw08f9B2Z2LUFS9U1ZYH/m8eTsz09eW6DkxOLcGzBDmAAS3RyZS+EVVmQsA5LsKzKczAKeA6xsfwpaGvJ2FtNJ7aj3VaWaWPPXi0/wvbsYexgbU/GABbZbxLvvPBwPhTSuTNPEnR15deJZTLGTp7kB4uJYT//zEfddHT46KW5OR+5+dzmzXwUpbiUXolAKmXs/HnG0tJYw4aM3Rq9iTGA/Wh7gM2ezTd59IiP3O/Zw0eFDAx4Lqm+PmNnziivafnJzPz4qz59mo8IDxrEL/WHhfGrD+fP81HisDB+gWDsWJ6y8+rVxxHNovTt2bP8ea5dy9i7d3lvc+QIY2Zm/DXQpAm/XN65M08RaNeOMQsLPmqmSomJ/Hdz8SLvo7Nn+ZWely/z/jPJHoXP9vQpYzduMDZ4MGMNGvDRvz//zHtOw+dKUvWMrCz+3Dt35qPdY8cy5u+f87lmZTEWFJR/jr9UmnOEWyLhc0GGD+d/692789/zhAl8dNjenr8mXVwY09DgaWc7dvDfxdatjL1///FYiYn83NHR/MqZUMjfJ/K6YqWqfk1IYGzSJJ7mpqvLry4eOcJfT76+X3ZMf3/+HtK1K++HLxUczD+Ozp7lKSW//86rCRkZ8XTD7KsaAgG/eta7N//b27KFf+QNG8bTfDp0YOyHH3iKWpMmjL15Uz6rZzDG2MuXLxkAtnTpUmZgYMBmzJiR77Zv375lbm5u7NSpUypsYd7Utox2eVKeRprzFBjIC/l27cqL/LZrx4s9u7vz4aPFiz8OxaSlfRwS9fLiQxBTpgCPH0PqVBWh741Ra2YPsCZNgF69kbRhPVhSEoyGj4CwRnXgl18gs64AYe9efAgwIAAZ303Ar5fbY9yhTsgYOwl1fNcjdeLPMPxhOF+sxcDg4/DT6dN8qOjtWz7UYWgIjBsHcZIAWV17wnTORPwQOBmPHmvg7g/bofH7r3zkuHdvXrS5b99cw0qiY1eQ+iYGlr+Nw7jeH2C5bzV6C09hZuNrOBlSHSH1B6Ohz18QVnaEzutQvrrM5cuAmVmuroxp3gsJ8Qyjdffih5+NMHcuUPX9TRyuNhvVQk7hVqAJtLSAe9fT0dv8BoZs7YiatQRYsKB4vzKlji6JRLy0xuXLQNOm2FV5LrofGI7X9q1h+e4JTgj6oE6FWMwSzUbVrtVx9iyvUvLPP8Dw4bxiwcKFfARfFUt3JycDGzfy6iDW1kCFCkBQEK9LPWBA8Y9X1L49eZKP/gUG8n9/+omPXmZl8eoNBw7wdlWqxB/fvJlXncn2/DnQrBmvXjJnTtFGnBkDUlL4supCIa+OsHo1v4pStSrvbx8f3g4rKz4ifOsWXxRHJOL11rMvmOjq8v3j4oDYWP7nZGfH923ThldSePQIMDUFvvmGj8pdvcqPpa/P/3wrVFB8v6ra06e8xv2+fbz/Klfmtc3Dwni/OjryFTZr1uRvf8eO8X57+ZKPklpZAbVr8/4RCPjfQGoqP46BAT+Onh6vcNSmDR+J//CBvx6OH+ej7TY2/Hdjacmv2iUm8t9Pejoffd23jx8vL+ro18hIwNOTvz4yMviVm7Fj+VUcOzt+1eDOHf6abNDg4+s+Lg7w8+MLN508yftxwQLg118LONmLF/zFGBjI/x8Tw6/sjR4NODgU2M6YGP57qlePj/Db2vLXc9GfZ/ldRpsxBiMjI0ilUhgaGiIsLAwmJia5tnv27Bn69++Pf//9F+7u7mpo6WfUHLSXSBKJhP3666+sUqVKzNDQkFWvXj3XJMU3b96w7t27M0NDQ2ZhYcEmTpworwTyufI00lwoH5+PCXEREbyEBsCHRXbu5F/X+/blQ0729jwB7nO7d/MhEsaYKDqaiXx9Pw7hXL/Oh3dEIj60Fx+fY1eZOIklXf+6oqEi0ScjQG/e8FlEFSvyoV9dXZ7YePAgb+fJkzwBU1OTz3xhjCW9jWcyfX0+Hd/YmLG0NHax3SI23WwLe/s894S/hAQ+QnH3LmNuFaPZNc32LBTOLGHldjb7ZwkLrNKdMSMj9sSpJxtR/wn71W4rCxM6MykEbJbRGhZ0JUreX0V/jkocXVq4kP/e379nbMwYJqlSjflWHsaM9bNYanVXltikI7tffwzL0tFn7O+/2W9zZGzx4o+7S6V8VLVnT8b++OPjCO6ePYx161Z4vmVSEh8t8vPL/Vj25DGZjOcyNmvGf3V16vCJUZcuMXb4MB+l+1LF7ds7d/iErFq1eJv69eMXIN68KXzfR494DfS6dfkIV0QEH7nev5+PQoaE8PuOHWNs6FCeH5yd9zt4MO/PqlX5KJm7O2P16vFRzpkzGRs5kr+kt21j7N9/+cs9JCTvdqSkMLZ3L//T+OsvPkq6ahUfCTx6lI92/vEHv9CSPWmvuErSSHNeMjL43InDh3n/37jBK/ccPswrEHXrxvOs163jr+UrV/jb1507fMTy4sWvq84RGcmP5efH31MY4+9jhfV1SejXwEA+Ev7tt3yE1tCQ50XXrMlHdytU4H8TQiGvYjRpEp9v8vp1AQeVyfiLUU+Pz0afPp1f2tm1i79AtbQY++Yb/lmkJOW1TnO2xo0bMwBszZo1eT7+8OFDVqdOHfbw8/JaaqTSoNnW1rZI2zk4OCi5JQVbvXo1s7S0ZE+ePGGMMXb16lWmq6vLvL29GWOMSaVSVrduXTZ8+HAmEonY69evWd26ddmUKVPyPB4FzYWIj+fXs7S1+XXE77/n/3dwKPQdvSS8oTOZ7OM11suXefpJ9lR6gYDPnImLYyw5+eM+mzfzN+QtW+SH+OEHnoIwdSoPiNq355f4Fi/m6RcaGjxYjIuWsLtjN/FPhy5deF8FBDBJqzYsRaDPwozqscx/N7LAddeYLDtg79GjWE8pz36Njc070syvT8aN47O1Jk5kbPt2/omfkcGYrW2eX4YkEpbzWvXNmzzHII9yBMHBPGulZUv+MmnalKcq9OrFbwcE8MB2376cl8TfvuWBX82a/PvKhQsfHzt3jgemFSrwS94VKvCAsMAP3i/wJa/ZjAw+SatrV/6dsLByiJ8Si3lA+7//8T7S02OsYUP+HU9bmwfJVavyybN37vBjX7vGmJcX/9V9Wi2iJCsR7wVlUEns10//ppOSePrGwYM5U1DylZ7O86f69eNvFnfv5r1deDiPvrW1+R/Mf5//ilTeg+aCXLt2jbm4uLCQ/L6Jq4lK0zP09PSwdu1aFHbKadOmQSQSqahVuf344494+/Ytjh07Jr+vUaNGGDBgAH755Rdcu3YNHTt2RFRUFCwtLQEAJ06cwJAhQxAXF5erKHe5T88oCpmMz6KwseG3o6P5igHOzgXuViIvyWZm8mvQGRl8xkkxLik9fMgvJV68yOcB3rvHD7VvH7/c3q4dn1QCgPdRp05A/fp89hiA2BgGQyMB5C9BX1++okifPvwauZFRkdqRq1/T0niDnj/n1yPzugaZmsp/LC35tWFPT543EBzMr5VaWPDf5927fJZMUfIFpk3j+RH5rHAjk/GFc7KyePPs7HhWz8qVfLKOtjbQrRswfTq/jD1lCr/y+u+/fILY+PHAhAn8ku6jR3xfgF/SPXsWaNSoSN1VLOp8zV68yFNM6tXjt7MXV9HUVHlTFK5EvheUAWWqX1+/5pOro6L4LOodO/j7VUESEvgKLr//DowaBSxbBmhpfV07pFLg9m1EVq5cbtMzCmNlZQVdXV1YWFgAAAQCAW7evAl9fX31NkyVEbqjoyNzcnIq9KdGjRqqbFYuAQEBrHbt2uzRo0dMKpWyS5cuMVNTU/b4v9phK1euZNWrV8+xT2RkJAMg3+ZTNNKsPCVxFORryWT80m326GKrVgXMNczMzLOGcy61avGhmCLK0a8yGb9236wZHzn+9dePGz54wC9lhoXxGYlCIR+21Nbms+Kypabywrrff5//Cgh5uX6dj9wX83r9oUP88va7d3yynr4+T3FYuzZnX166xJf7XbIk56QrZZa+yvc1GxTEU1fu3i3fK8B8obL4XlASqKxfY2P5FalVq4o4ZFwMMhm/rFS9Or988iV/Xy9e8DynVq2+LGVDKuXvl//+y3NJatRgES9e0EhzKVOuJgKOHDkSO3bsyPfxNm3a4OrVq0hNTcWcOXOwevVqCAQCCIVCrFy5EhMmTAAALFiwAKdPn8bdu3fl+6alpUFfXx83btxAq1atchxXLBajVq1a8PHxgU32SCpRiKT/1mU2KuIIamnEeHW9r7N0KR/GrlGDzxjq3PnjSG9qKrBnDx8ZHzMGOHYMSQcPAomJMKpRg09IvHQJOHOGz9D53//4kKWJCR+t0dfnIziNGgHLl/PJNFWrFjqJpkikUl6bbt06PvvnC0kkfMT+q/tRAeSvWR0dPnKfvQ7z1Km81lVAAJ/Z1bkzX69efmmBFKQ8vBeog9L7NSCAXwJ6/pzXxzQ25rUZbWz4LD9zcz7LtGJFPuorFPKR3pgYPouxc+eC32ueP+d17J4/5yPFU6YUudRpLikp/D1u507+tzlhQs7Zt3l58oRfKbt2jb8R1a/PL4uNGIH3CQmoUaMGjTSXImXgolzRrVmzBsuWLcv3ca3/LrlMnDgRT548wbNnz1C1alU8ffoUffr0gVAoxPjx42FsbIzExMQc+yYkJADI/xJWeno6lixZAgMDAwBA+/bt0bFjRwU8K1LWKSTQ69ULOHqUf7j89huvELJ2LQ/O+vblH0RZWbxUgo0N8P33/EPq4UOeTrF1K7+ub20N9O/PA7z0dF4wd/16nh7Suzef6q+IYDmbhgbPp1izhhef/cIPO5WmH9y9y4Pg1q0/lii4fRs4fx64f5/nkAA8J0Qm478DDQ1g2DBeIiMrix/j3DmgXz9+Obh3bxU+gS8QH8+L2j58yPNaEhL4F7Tu3XlQo6nJn2tgIM+Tycri6UXm5rx4rZER/xJWuXLJ+GZDlC8tjb8PLV/Og8/hwz9WDEpI4Clc9+/z9Cx7e/76qlCBjyJkZvIco0uXeC5Vs2Y8fUJfn5dvEQh42ZIzZ3iZm2HDeKD73+fvFzMw4AXEhw7lJXyaNQOcnPjftLU1/ztIT+e33dx4wHz6NH9uW7YA9evj0tWruHLlCuDlhZSUlK/tRaJi5Wqkuajq1q2L0aNHY9q0afL7fvrpJwQHB+PcuXO4du0aOnXqhKioKHm+zcmTJzF48GDKaVaxMpVvpypiMR8tfv2a16jy9eUJvVIpcP060L49xGlpAPLp1+RkPvrbogUvHaitrdz2ikR8FLtdO54ffvw4Hy0qCeWHPpWWxkeyzp3j+dtv3vAgID6ef+h/8w3v2w8fAE1NGLu48OdVUG73uXP8d/X773wFm5ISUEqlPF8/IIBfffjrLx78OzsD337Lg4b793nOaEYGX4nE35//39WVjxTa2PDAOSKC593Hx/ORw0aN+Gts8mQ+8ljE50zvBcqhlH4NDORf1g0MgCVL+IojXyo+ns+f+Pdf/uXTzo4H1mIxf3+bO5e/XylDTAz/O4iM5HnSFhb8Ob14wb8ou7oC333HawbmoTyXnCutKGjOw4QJE3D//n0cPnwYjo6OCA4ORo8ePTB48GAsXLgQMpkMbm5uaNCgAf755x8kJCSgV69ecHd3x+rVq3Mdj4Jm5aEPyi8kkfD62EuW8HrJLVrkeLjQfv10qT5VCAriMyQjIvgoU+fOfAS2pEhP56Oq6el8RN/amheXjYnho2MVK8qDv2K/Zh894sfu25dfCVBlv38uKgr45Rc+egbwS826unwp0DZtcm8vkQA3b/LJoHXr8qsF+U2iSkriv9MPH/jra8cOPgLt4cFH7UJDeV8mJPDXQO3afATR2hqQSCAODgYyM2HcoEHJ+XJRBij0PTY2lqdK7N4NzJwJzJunuNezRMKD5XPn+DE7d/76CXtKRkFz6UNBcx6Sk5Mxa9YsnDhxAgkJCbCwsEC/fv2wZMkS+fKNb968wYQJE3Dt2jXo6Ohg8ODBWL58eZ7LO1LQrDwUNH+lzMw8R4pLdL9u2MBLZly8qO6WfDRrFr9UfPUqXxCnAF/Ut2/e8MWBDA35yFX2KheqWN1FJuOXuY8c4VVQunTh6TkNGyo3gE9O5l+WDhzgFVtq1+Yj0xYWPN3j9m3gxg2eDhQdDbGWFqChAeMaNfhoX3g4H8UXCvmxdHT4yN+QIXxFEX19PipZyO+rvFPYe8Hz5/w1XLs2n2NRq5YCWle6UdBc+lDQrAIUNCtPiQ7uSrES3a937vAc7Q8fSsaI4v37fJT13j2e412IL+7b5GQ+IfLsWT7iW706//JQUHpMWhq/fB0VxfONa9fmx3nxgo/CjRzJg8m8vH3L896PHOEToEaO5KO+eY0oq0t4OA/GLCwgrlQJSE2F8b59fPTb2ZkH/DIZH7FOTeWpSIcO8aXjUlJ4wPz777yPTp7kt3v25FcLpk/no/tZWcBnKXflSb6vV4mE595HR/PUqYKWwsuetNe7N89hLkq5yXKAgubSh4JmFaCgWXlKdHBXipXofk1J4UFQRMTHSXXq8vIlr0YyfTrPOS4ChfRtQgLQvj2f7NSqFZ+I6eDwceT3wQMeDF65wq8mGBvzkeHgYN53NWrwnHaBgE+QcnPj+wUG8pQdPz8eWPfsyasEdOtW4gPHL+rXI0eAFSv4BEQPD74W8oULfDT9zz/55X5NTZ4za28PdOxYvHWSy4Bc/ZqRwa/2LF/Ov5SZmfH84T/+AAYN4l9izMx4vx06xPPdxWJeU37/fgqYP0FBc+lTrqpnEELKAAMDXprt8WP1Bs0pKTzXeNAgHjSrkpkZH+EOD+d5wFWr8hFVBwc+chwRwReGWb2aPyYQ5B6Vz8oCFi3i+ewdO/J800ePgNGjeaBTqxYPJsuyfv34z6fmzuX/jhjBR6DDw3lAGBPDKyZculT4ghhl1cOH/EuUri7/stG7N/+iduAAMGcOTx3S0eHBNGP8y9nff/OybA0bUsBMSj0aaVYBGmlWnhI9IlqKlfh+HTiQ13CdNUt9bRgzho+qXblSrNxepfRteDj/NzSUB8NNm34s31UYf39eNUVfn1f4+K8iUGmj9NdsVhYvNebjw0syzphR9D4uxcRiMSCVwnjLFv6F4vff+cTPz1/zWVn86kT16vz/QP6pPwQAjTSXRjTSTAgpfdzceAqCuhw8yPNeHz9WbzWLbNm1sb+kRrab28f0DJI/LS0+ourjw1NYqlXj9YUnT+b1ycuqbdt4nXSZjH9BbNYs7+20tICaNfn/KVgmZRRdKyGElD49e/IJcSKR6s/96hUfadyyRbELuZCSTyDgueQXLgD79gG3bvFJcOp4HSpTSgrPP/7hB2DlSp6u8+xZ/gEzIeUEjTQTQkqfunV5pYr9+4Fx41R33g8f+ISxkSN5KgMpnwQCPrGtXTugRw/+b9++vBSfKkvYMcYXmFm/nucNN2/Oa1kHBPDUkcxM/uPkVPgCHxIJr58cGMj/rkxMeF774cN8VF2ly2oSUjLRXwEhpHT67js+2qvKoHn4cJ5LvWKF6s5JSq7sChFbt/J/Dx0CTp3iSzkrSlISz91/945PQMzM5F8YT5/mC8dkL8EeEABs3MgnLNaty0e/dXR4ScLQUKBtWz658fMKIFFRPEjevp2X5evShddRHjyYfzn4L1ecEEITAVWCJgIqT4mfsFZKlYp+FYv5IhVHj/KRPmW7cIFXynjx4qsmgJWKvi2F1N6vWVl8efejR4Fjx3KtsllsV68Cq1bxKik1avAR7fh4Hsg+esSPP3AgH1k2Mvq4n0yWu0pFTAxfAfTiRR5Mr1zJj3XlCl+WvlUrPlI+enSufGS192sZRhMBSx8aaSaElE7GxryCwW+/8UUrlLnQiUzGl4/+9ddyUTGBfAEtLb4YTO3afDR3yhRe37m4qzYyxvdbsYK/vseO5SlBRZ1wmldZNysrXvqNMf7vzz/zlI1WrT4uekMIKRRNBCSElF5TpvBLz7t2Kfc8+/bxkblJk5R7HlK6CQS8msa9e3wU180NuHy56PunpPDX2NatfIR53jy+sIyiKrQIBMBPP/GrJZcvA15eFDATUgwUNBNCSi8jI2DPHj7L/8YN5ZwjI4OPZi9YUOJXxSMlhIsLcPs2MH48T3vo3p0HqfnlB0ulvLRbtWp8Ncbr1/kkPEJIiUJBMyGkdOvcmQe048bx4EPRZs7kE7CGDlX8sUnZpaHBV2V8/pxPzBs2jE/A+/ZbXt87NZXnLU+ezEsXzp/Pc41v3gSqVFFz4wkheaGgmRBS+k2axIOQffsUe9xDh3gZroMHS8YiJqT0sbbmy3BHRfEAGgBatuTLwQ8axKth7NjB04wGDlRubj4h5KvQREBCSOmnrc0nTy1YwEeEFRF4yGR8yeBly4DKlb/+eIRUrcoD5I0bgdhYwM6OgmRCShEaaSaElA1DhgDR0TyXVBFOn+Y1cocNU8zxCMmmowNUrEgBMyGlDAXNhJCyQUeHX95WRCWNrCw+aj1lCh/FJoQQUu5R0EwIKTuGDwcOHADS07/uOLNn82NMnqyYdhFCCCn1KGj+Sg8fPkTLli3h7u6Oli1b4u7du+puEiHlV8uWfPW0fv2AtLQvO4a3N885PXIE0NdXbPsIIYSUWhQ0fyU7OzucO3cO169fx4YNGzCZRqYIUR+BADh/HkhM5MsOp6QUb3+xmK/AtmwZUL26UppICCGkdCp3QfPjx4/RtWtX2NjYQCAQ4NKlS3lut337dtStWxcGBgawtrbGlClTcjx++PBh1KxZE1WqVEHTpk1x9OhRaGtrQ5jXEqaEENUxMeGjxYwB7u58meDPMfbx/zIZT+nYvh1o3pzX1B07VmXNJYQQUjqUuwhPW1sbffv2xenTpwEAPj4+aN26NYyNjSEQCCCRSLB8+XJ4eXlh7dq1SExMxHfffYd9+/bBwMAA7u7u2Lt3L4YNG4ZFixZBLBZj4cKFGDJkCIYPH47ffvst33NfvXpVRc+yfMnviw/5OqW6Xw0NgbNn+UpsLVoA9va8lvPBg7yMXIUKwNq1vDrGoEHAjBk8JWPIEODECaVXNSjVfVuCUb8qB/UrIVy5C5pr1aqFsWPHolGjRgAAQ0NDTJgwAStXrgQAiMVieHp6YvXq1WjdujVWrlyJ3bt348qVK4iNjUXLli0xduxYdOrUCf369YOWlhb69OkDCwsLaGlpoUePHvme+9q1a6p4iuXOlStX1N2EMqnU96uuLl9lLTaW18ZNTQWWLweCgoBFi/hkv4oVgbg4vnTxrVvAnDmApvLL15f6vi2hqF+Vg/qVEK7cL27SuHFjdOzYUT4KfPv2baSkpCA0NBTVqlXDy5cvUa1aNchkMujp6WHBggX466+/oKenBwCQyWT49ttvUa9ePbx//16Nz4QQkicjI6BDB/7zqdatee5z06ZUL5cQQkihykzQPHLkSOzYsSPfx9u0aVOk9IjY2FgAwPHjx3Hy5EnUrl0bjRo1QpcuXRASEgITExNoa2sjLi4OAHDw4EEcPXoU9vb2CA8PR+/evXHixIkcx5RKpdDV1UVqaipCQkK+/EmSXFJTU6lflaDc9KupKfDsmUpPWW76VsWoX5WD+lV5oqOjAfAYgZQOAsY+nRFTeiUnJyO9gNqsWlpaMDExyXGfQCDAxYsX5SPN7dq1w9GjR9G3b1+cPXsWLi4uqFSpEp4+fYomTZrgyJEj6Nq1K8zMzODs7Iz79+/Lj7V48WIcPnwYjx49ynXu+/fvo0uXLkhLS5P/cWhqakJTBZeByzpDQ0NIJJICf/ek+KhflYf6VjmoX5WD+lWxJBIJJBIJAB6XpKSk4N69e2jcuLGaW0aKosxEbYaGhjA0NPzq47i5uQHgAbWxsTEAIDExMcc2+vr6SEhIyHHfgwcPUL9+/TyP6ezsDIlEgoEDB2L16tVf3UbyUVJSEry8vLBs2TJ1N6VMoX5VHupb5aB+VQ7qV+WJiopCzZo14ezsrO6mkCIqM0FzUTHGkJGRIb+dlZWF9PR0+Qiwg4MD+vTpg8WLF+PQoUNwdHSEl5cXzMzM0LJlS0gkEqSlpSE6OhrHjh1Djx49cPr0aXmt5rxoaGgA4JU7sgNxojjUr8pB/ao81LfKQf2qHNSvypGUlATgY4xASr5yVz3jzZs30NPTk0/k69atG/T09LB9+3YAQEZGBjZs2ICqVauiZs2aiI6Ohq+vL9asWQMtLS14enpCT08PW7duxezZs2FkZITZs2dj9+7dBV5ekUgkuH37Nry9vVXxNAkhhBBSQnl7e8PLy0vdzSDFVO5Gmp2cnPBpGvf27dsxatQo7N69GwDkKR4+Pj7Ytm0bGGPw9PTEDz/8ALFYjEaNGuH8+fOoW7cuhg8fXuTzampqYtasWfDw8FDsEyJo3769uptQJlG/Kg/1rXJQvyoH9avieXh4wMXFBZs2bVJ3U0gxlJmJgCWZWCyGg4MDgoKCULFiRXU3p0wRi8UAQJcOFYz6VXmob5WD+lU5qF+VJzIyEvb29hCJRNS/pUS5S89QF4lEAi8vL0rPIIQQQso5Ss8onWikWQVopFl5aBREOahflYf6VjmoX5WD+lV5aKS59KGR5hLs5s2bqFevHmQyWYHbRUREwN7eXr7gCiGEEEIIUSwKmlXkS9IzJk+eDC8vLwiFBf+a7O3tMWDAAMybN+8rW0kIIYQQZaP0jNKJ0jNU4EvSM3x8fDB06FCEh4cXqYZjUFAQmjRpgoiICJiamn5li0sPunSoHNSvykN9qxzUr8pB/ao8lJ5R+tBIcwl15MgRdOjQIUfAvGPHDri5ucHExAQVKlTAjz/+KH+sdu3aMDc3p4mGhBBCCCFKQEFzCfXgwQO4uLjIb2/evBkzZszAsmXLEBcXh7CwMPTt2zfHPq6urrh//76qm0oIIYQQUuZR0Kwixc1pjo+Ph4mJifz2ypUr8csvv6Bjx47Q1NSEkZER2rRpk2MfExMTxMfHK7TdhBBCCFEsymkuncrdioDqoqmpCU9PzyLnNJubm0MkEslvv3r1CjVq1ChwH5FIhJo1a35VOwkhhBCiXLQiYOlEI80lVMOGDREYGCi/7eTkhNDQ0AL3efr0KRo1aqTsphFCCCHkK0hlVIOhNKKguYTq27cvLl++DKlUCgD48ccfsXTpUly5cgVSqRRJSUm4du2afPvg4GDExsbCw8NDXU0mhBBCyh1RWhaKUohMJmOQyRhOHbyMpd95IiU8QgWtI4pE6RklVIcOHWBtbY1Tp06hT58++P7776GhoYGpU6fi9evX0NfXx+DBg+V5zZs2bcKoUaNgZmam5pYTQgghZRdjDCcfv8OjNwlIuHMPWqHB8KvZDJWr2qO6tQEcpMmwfOgLYWgIYtMkMI2JhGP8xwDZ+b+f949OqO05kC9DdZpVQCwWw9bWFkOHDkW/fv2KPBrs6+uLiRMnws/Pr8AFTiIjI9GsWTP4+fnB0tJSUc0uFaiGqHJQvyoP9a1yUL8qR3noV5mM4WpoNG6FxUFfWwPdXG1R0yb385XJGK49j8Gem6/Q9PBatHp574vOZzHXE/dkEhw/dx5b9u4pl3WaXVxc0Lx5c7i7u2PFihUICQlB5cqV8e+//6Jdu3bqbl6+KGhWgS9Z3IQUTXl4Q1cH6lflob5VDupX5VBHv2ZIpDj39D38wxMRk5QBoVAAZytDdKhljTp2xhAIBDm2l8oY/MMTcOpxFG49j4aBrhba1qgAQ11NtKhqgRoVjCAUCvI81+PwRMw//BDtruxHh+e+eNSyJ1ZYNkVlJ1u0dLaEtqYQVSwN4F7dCkvOBsFs5wZ0C74i399+/TroODsj9e5dJB49hqzwcJj27w/TgQOgaWkJWXoGhPp6EOQx8FVeFzdJT0+HoaEhHBwcUKtWLYwZMwZaWlqYOXMmUlJSEB4eru4m5ovSMwghhBBSItx5GYftf+7Aj1c3IbtelEyoASYQ4HTVVviuYW+4WGjB1NoSDRxNcedlPK4ERWH04xP4X+h1/O+/feKsHXC8x3hsOClDvIYe6tibYkp7Z7SuZgVtTSGypDL86xOG06du4x/vP+Tnb3DzFHbjFADgeb3WyNTSwWH7RpimaYX/vbwhD5grHz0C3dq15ftp29vDtF+/XM9Hw1A1YRZjDMkZEqUd31BHM9eXlS/19OlTSKVSNGzYEIcPH5bfHx4ejokTJyItLQ16enoKOZeiUdBMCCGkyMKik/Hn+RA8iRChgokulvarm+elbKIYstRUPNt1EOYD+qGCuVGOxxhjCH8Xj9CQN2jerBYMDEpmoFFUB+6/xbXV2/Hj/X057hfK+IT43qFX0Tv0qvz+GDNbOOnpYsK7Vx+3NTYGGINFdDi+2zoH331ynBVPv8dk61poXsUCL2NT4CCKwuqrfwMAnA4dhF7dusiKjMTbsd8j8+VLVHt8AwBQ58ElzPjvGNazfoHFyJGKfupfLTlDgrrzLijt+E/ndYaRrpZCjuXn5wcAmD9/fo77Y2NjYWxsLA+YxWIxevTogatXrxaYoqpKFDQTQgiBKC0Lj8MT4WihDwcz/TwvZ7+NS8WEJUex/NIKaGZl4FWbHuj3LgE9G1RCn/oVYaijiefRSWhexRIaQgGkMgYLQ21oafAPvPQsKZ5GivA6NgWS/0pu2ZnqoUVVC/k25COpjOHirMVwvHAE8X8vRcioydCysoDuri3Qi/p4CbsigLcAsGUParVsoK7mfjHGGP44Gwy9DSsxOfQGdF1c4HTwQI6UBsYYkry9EbdxE3RdXJB48CCsEqKABEDDwgK2ixbCqG1b+fayzEwk7N2LjOAQZLx8ifSnT/HT1Y34CUCqtR0yqtWG2c1LgKYmavj7QairCwDQqlgRVc+e4efMyoI0ORkpN28h5u+/AaEQZoMHq7JrisxQRxNP53VW6vEVxd/fH/b29qj9yUg9wINpV1dX+W1jY2Ncv35dYedVBMppVgHKaVYeymNUDupX5VF23zLG8DRShJeRcTB/EYx633jARE+LT3Z69gFvV/2LC/YNwaxtYKqvBZeKJki7cweGl8+gbvxrvDS2RYC5Ey7W6wxLYz20qW6F1tWsYGmog4UH7+P39ZNynXP/rI04E5GBtEwJnM118OR9ChgDNJgMGlpaqGlrBE2hAMFRSTDS0UA1M20IdXWhm56CkLgMZGpqYfOIxqhrb5LHMyoaVb1mMyUyvIlLRsjbeDw4eRmW9jaYOLZbvjmzAPA6NgX7775G0Iv3qK+fhTpu1VG7qg3szfQRm5wBEz2tXF8anr+JhsSDV0eSde0J4blTeR5b38MDRu3b48MvvwAAYitUQqOLZ6GnraGQ56uK1+uS3b74ZtH3/Dy9eqLin38WeV8ARUobYDIZ4rduRfSy5R/v1NJCjYcPINTWLn7DFaC85jQ3b94cFSpUwPHjx3Pc7+TkhN69e2PVqlUAgLlz5wLIPSKtThQ0q8CXVs8ghaPgTjmoX5VHmX3LGMP69SdRefsqOIqiAAB7Ww9FkzGDcXv7EQz32VbsY4a5tkRccgbWNBiAZdfWoEJMOKrdvgUNExOk3ruHt6NGA0X9GNHUBCR5512O6LMYnZo6w7WiCbQ0hEjNkqJtdSs4mOsj9EMSjvtF4mmkCEa6mmAMiEnKgI2JLiqa6cHeVA+a0gxEJ6VDqqELE31tVLEyQENHMxjrakGcnoXUDCn0tDQQkZgKY10tWBnpQEdTiAyJDKmZUqRmSmBhoJMr2JTJ+JcQn2fRCLt2B22uHIBL3Msc29xsOxBtvGagWoWc6RMSqQz7Pf9Bw8PrC+2a4Dkr0KWPO/QM9XHs3wOotYYvsWzQtSsc/voTAk1NpAUEgmVlQrd2bQh1dHK2Mz0dzxo0BGQyhDi6QP+vlWjmbA0jXU1kSmXQ1fqyIFrZ7wXn772Ezag+0JFmwX7dWhipoHKCJCEBMrEYQmNjaKqpTKu3tzeOHDmCTZs2laugWSaTwdjYGD///LM8KAaAhIQEmJubY9u2bRj5X/pL9+7dMW7cOPTq1UtNrc2NgmYVoJFm5aHgTjmoX5VHJBJBlJYFaOnDRF8LJnpflycoSUxExKsomNdyxqZtF9Bt1Qz5Y9pVqyLzxYsc21uMHYtkX19kBAfL79Nr2BCVtm2FUFsbWR+iIYmNQfi48ZDGxuY6X6Xt22DQrJn8NmMM7+d6Qnz+PDSMjWHQqhVSHz6AhoEhhAYGSLl1K9cxTHr3Rubr19Bv3gzpAYFI8fUFAKQZmeJUx28R7FAHGhoauPc2ERpCARgDfkoLQKPHV5BhYAyzZ0+QaW2DBMcaSMuUwN+pPpKFQlgLMmGdEINYXWM8EZjgeYYmNJ2cEBmfggyZAEaZqXCSihFkYAvtjHRUSo3FGwNL2CfHQIPJEO9YHZPaV4O2phB1K5rC51k0tt54ibrhTzE69AKsPryVPwfDdu1gPmI43v27HpIHvPTY9T7j0OfXcahgrIcMiRQ7v5+DVrf+q8WroQHLceOg37gREo8dg/jsuXy/QGT7fLJZUcQcOIhYT0/57aeWVZAl0ECsQzVU+3kaurnZF+t4X/teIJUxaAgFeBuXiv333+JhWDSsNaRocWQtwmo1RkZiEobePwzna9egVcH6i85RWpXHkeaQkBDUqlULp0+fRvfu3eX3X7lyBR06dMDjx4/lKRo2NjZ4+PBhiYqbKGhWAQqalYeCO+WgflW81EQx9t18gVeHj6LVM184xr+XP6azbjOqtGuZ535Z795BaGgIjTx+FxcuPYLDpKHy21KBEBpMhup3bkPD1BQsMxMhrvUAAHZL/4BRx44QGhjkOAZjrMDL21nv3wNSKd799hvMBg2GsYfi8yazIiPxasBASOPjv/gYqf9NHtJPS/uqtpx3HwQNmRQufldgmxKX4zH9xo1RaesWCLQ+ftFhjEF0/ASiZs+W33djwnxkhQSjrc9+6LdoAadNG/MsOSY/RmYmohYtRsLTQAiDAmD44zTYjx/7xdUK0kNC8KrPN3k/v4U7MK1/k7zbwRiOPHiLsF0HkZkogl7btkiIiYGGoyP6t6wNC0Nt2Jvp57lvQkom1l4Ng//tJ5DoGkCfSWAe8QJJqelIrlkP3S7vQvN3T/Pc127ZMpj06J7nY2VZeQya9+/fj8GDB+Pdu3ewtbWV3798+XLMmTMHycnJ0NTUREREBBo3boyoqCg1tjY3CppVgIJm5aHgTjmoXxVDJmN4HpWIl7sPwmnbSgB5B3fRJtaod+UCTA34Jfe0gEC87t9f/jhzro7ap0+ASSRIS0lD6KgxSEjOgEZsNKxSE6DXsiXSbt6EtrMznPbthYbRxzQBxhhYRoZ8olNJxqRSpN69i+gVf0OWnIzM168BAAIdHdj8/huMunSFQCiAUF8fTCYDy8wEk0ghE4sgEokg0NaBuWMlZL5+DYGmJlLu3kPa48eQxMZA28kJ2o6O0LSwhPjMaei61OVfIGQy6NauBUlcPGJWrULmy5ypF3r16qHCr7OhV69eoe0Xnz2LyJ+my28LraxQ/fo1hZXq+lLZk+gip05DmHklYN029KxnJ3/s5q1ApM37Dfrx0dCUZsE4PVm+7+ev1+cuzVHtn5WoUcEIMckZuPcqHllSGW5t2ofvfLYW3hgNDWhWsIb50KHIfP0aGqamsPrpJ7X3kTqUx6C5qE6cOIFNmzbh9OnT6m5KDhQ0qwAFzcpDwZ1yUL9+nQ8JKbg7/TdUu3U+x/2mo0aB1XOFQfPmMDHhk94yxUnwb+8Bo+SEPI8VX7kmzF+F5Hsux927oN+oEWTp6aUiMFYWRb1ms6KjIdDS+uJcV5aZicSjxyDQ0YF2JQfoN2z4Ve1RpMzwcLzo1BkXnZriYcNOqNW8Hh6GizB/1Zgc2zkdOghtBwdkhkcg/uULxO/eA82nH0eJU7R0kSXQwG07FyQ51UCbp5dQIe4dAMB20SJIYmKgXckBevXq8S96/v4w6tQJQv28R6nLKwqa8zd37lwIBAJ4eXmpuyk5UNCsAhQ0Kw8Fd8pB/frl/J5FQrd3R/lt84kTYeBaFwbu7hAIBHn2bcr9+3g7fIT8tsTaBpbHTsHG3AACgQD+HTygE8nzaQVuDVF58QJoVbQDpFIKRP5Dr9miifhxKpK8vXPdX833BoQGBhBoauZIP/m8XyOmTkPS+fO59rea+iMsx49XUqvLJgqaSx+q00wIIQoiy8qCZBDPJbVftw5G7doWaT+Dxo1RKyQ438frXToPlpmZq2ICIcVlv2olWGYmkq74IHLqVACAw6ZN0LS0LNr+K/8GYyv4DYkEkthYCHR11VaFghBVoqBZRSQSCby8vKjkHCFl2JPNe2CYlgS7CxdhVKl4VQoKIhAIIKCAmSiIQFsbxl08YFzAF7UC98/OP9bSgtYnk7lI0WWXnCOlCwXNKqKpqQlPT09KzyCkBMuUyBD0Ng7JWQwtq1sVe3LSm4vXIPH4H2opMGAmhJQ9Hh4ecHFxwaZNm9TdFFIMFDQTQso9xhi2nvGD8TIvuLwPhQ6AEAC2131ham1RpGM8/5AE09fPUOmHkcpsKiGEEDXJv3AkIYSUA4wxLDt8Hy1mDIXL+1AAgE4bviqZ99Dx8HuTf+1glpUF0ekzYJmZWHk+CJbpIlSs5aySdhNCCFEtGmkmhJRbjDGs2O6DHksnAgCqP3gADUO++EfStevAuHEIHDoct+u3gNvQvmjetCYgkyH8xFkk/TYbQpkUAPAOwKT/jqllbaWGZ0IIIUTZKGgmhJRLTCLB+Wme6HbxKADA6fBhecAMAEZt3GG7aBEEf/0FdnE3cHE3Pq2WnH2ZLqJ6fdiH+gEAHDZugEBbW0XPgBBCiCpR0EwIKZdunrgCp/8CZmefK3lWATDt1xem/fpCfO4c3v0+FyyZr5QmXbgcVWs6Qs+lDmqBr2QHobBcrmpGCCHlhUqD5pefLU+aH6FQCCcnJ+U2hhBSbkmTU2Ax50ckOzqj/v6dhdaYNe7aFcZdu+b7uEBDQ9FNJIQQUsKoNGh2dnYudCSGMQY9PT2kpKSoqFWEkPIm1u8JZBDAbvceaJrRSlyEEEIKp9KgWV9fH4GBgQVuwxiDm5ubahpECCmXgq/cRqxTXfS3ooCZEEJI0ag0aO7VqxccHR0L3a5Hjx4qaI1q0YqAhJQcYv/H0KxXX93NIISUU7QiYOmk0jrNe/fuLdJ2u3fvVnJLVC97RUAKmAlRL5mMQfDhPaq6VlN3Uwgh5ZSHhwc8PT3V3Qy1cXFxwdixY7Fr1y7Ur18fenp6qF27Nnx8fNTdtAKppXpGYmIi9PT0oKOjo47TE0LKsbD3ItiKo1GlXk11N4UQQsqd9PR0hISEICUlBZGRkfj999+hpaWFmTNnYsSIEQgPD1d3E/OlkpHmPXv2oGLFiujatSs2b96MevXqwdXVFYcOHVLF6QkhRO7ZjYcQagigX4uCZkIIUbWnT59CKpWiYcOGOHv2LPr27YuePXtiypQpiIiIQFpamrqbmC+VjDSvWrUKwcHBSEpKgpubG4KDg6Gvr4/OnTtjwIABqmgCIYQAALQO7kRsnUZUJo4QUmYwxiBTYtUxoYGBwurQ+/nxxaDmz5+f4/7Y2FgYGxtDT09PIedRBpUEzQYGBjA2NoaxsTFq164NS0tLAKD0DEKISmW8fAnHwHuIWLdP3U0hhBCFkaWkILRRY6Udv/qD+9AwNFTIsfz9/WFvb4/atWvnuN/Pzw+urq4KOYeyqCRozszMhEwmg1AozDFbVCKRqOL0hBACAEiKiEKkgSVcG1JqBiGk7BAaGKD6g/tKPb6i+Pn5oWHDhnne37t3b/ntpKQkzJkzBw8ePEBCQgJatGiBDRs2QFNTfYtZqySn+cyZMxAK+amyR5kzMzOxbNkyVZyeEEIAAC9evkOGngEqGOuquymEEKIwAoEAGoaGSvtRVGqGTCbD06dP0aBBgxz3JyQk4M2bN6hf/2Mp0MGDB6NDhw64desWgoKCEB0djePHjyukHV9KJeG6qalprvu0tbXRuLHyLiUQQsjnwp+/hVEhS2YTQghRjtDQUKSkpOQaac7Oc84Opq9du4YbN24gIiJCXppPLBaDMabaBn9GLWPc/fr1o6LehBCVYjIZzK97g3Xuru6mEEJIueTv7w8AuUaa/fz8oKOjI89zfvDgAUaPHo2///5b1U0skEoXN8n27t07dZxWKWQyGVq3bg1LS0v89ttv6m4OISQfifcewkAUh6ojh6i7KYQQUi4NGjQIjDHY2trmuH/69OlIT0+X5yvb29vj0qVLSEpKAsBTep8+fary9n5OLUGzonJjSgKhUIi9e/dSfjYhJdzLWw/x0roKHO0t1d0UQgghBRgwYADatWsHNzc3uLm5oUWLFggODlZ3s9STnlHWODg4qLsJhJBCJDwJgKSyc5n60k4IIWWRUCjE6tWrsXr1anU3JQe1jDSXJPv370fr1q1hbGwMgUCQqwweYwyenp6ws7ODgYEB3N3dERAQoKbWEkK+BGMMFv53oFurlrqbQgghpJRSS9Cs7tmPnzIzM8OECROwcuXKPB9ftmwZtm7dCm9vb8TGxqJly5bw8PBAcnKyahtKCPliqXfvQjc9BVaN3dTdFEIIIaVUsdMzPnz4gI0bN4IxBldXVzRu3BgVK1Ys1jFu375d3NMqjYeHBwDg6tWreT6+du1azJgxA3Xr1gUALFiwAJs3b8axY8cwfPhwVTWTEPIVUsMj8dSyKlrWrabuphBCCCmlih009+vXD6mpqXB0dMS2bdvw5s0b2NraonHjxmovOq1oIpEIr1+/RpMmTeT3aWpqon79+vDz85MHzYMHD8aTJ0+QmpqKO3fu4Ny5c9DS0lJXswkhn4l+FYFEA1PYmdCiJoQQQr5MsYNmf39/fPjwAQb/LakYExODu3fv4sGDB8U6zvPnzzFlyhTcu3dPXlIkW2ZmZnGbpRRisRhA7sVZzMzM5I8BwL59+wo9lqGhIVJSUnLsR77e568dohhlrV/fR0ZBamNdIp5XSWhDWUT9qhzUr8qTkpKi7iaQYip20Fy1atUcOclWVlbo0aMHevToUazjDB8+HPb29ti0aZM8AC9pjI2NAQCJiYk57k9ISCh2Skp6ejqWLFkif67t27dHx44dFdJOQkjBMqJjoFPRWd3NIISUc5cuXcKVK1cAUNBcGhU7aP7555+xZMkSLFq06KtOHBQUBF9fX3kh65LIxMQETk5OuH//Ppo3bw4AkEgk8Pf3L3Y+s0QiwcKFC4sdbJOiyf6CQxSrrPSr7rtIWLVoV6KeT0lqS1lC/aoc1K+K0bdvX/Tt2xcAEBkZie3bt6u3QaRYil09Y8yYMVi2bBk8PDywadMmPH78GDKZrNgndnFxQVRUVLH3UzSpVIr09HR5SkhGRgbS09Plz2nChAlYtmwZAgICkJaWBk9PT2hpaeGbb74p1nkkEgm8vLzg7e2t8OdACMlfQrwYZrFRqNXUVd1NIYQQAIC3tze8vLzU3QxSTMUe5r1+/ToePHiABw8eYM2aNQgKCoK2tjbq168PX1/fIh9n48aNmDBhAoYPHw4bG5scj7m7uxe3WV9s165dGDVqlPy2oaEhAMDHxwdt27bFjBkzkJSUhI4dO0IsFqNRo0Y4f/68fLui0tTUhKenJ400E6JiT5auQqqVA9wa1lF3UwghBACv3OXi4oJNmzapuymkGIodNDdu3BiNGzeW305PT8ejR4/w8OHDYh0nMDAQPj4+OHPmTI77BQIBpFJpcZv1xUaOHImRI0fm+7hAIMD8+fMxf/58lbWJEKI4GffvIrNrLwg0NNTdFEIIIaXYVy9uoqurixYtWmDy5MnF2m/mzJlYvHgxEhMTkZWVJf8pKZUzFI3SMwhRvcSEZNi8f41G3duquymEECJH6Rmlk4CpaXk+ExMTiEQidZxa5cRiMRwcHBAUFETpGQqWXcKPJqkoVlnp17snrwBzZ6GJ310IBAJ1NwdA2enbkob6VTmoX5UnMjIS9vb2EIlE1L+lhFqW0QaArl274ubNm+o6PSGkHIi/+wAfKtUoMQEzIYSQ0ktt9d6sra3Rs2dP9OvXD7a2tjkeo/xhQohCBDyBtA5VzSCEEPL11BY0P336FPXq1UNYWBjCwsLk95fVEaHsnOZ+/frBw8ND3c0hpMyTxMbC6dlDfPh2VOEbE0KICnl7e+PIkSPqbgYpJrUFzT4+Puo6tVpQyTlCVCvu2HEAQNWWDdXbEEII+QyVnCud1JbTvGvXLvj7++e4z8/PD3v27FFPgwghZUpMVBwuV20Oe2sTdTeFEEJIGaC2oNnLyyvXoia2trbw9PRUU4sIIWWJ2P8xBBUdymzKFyGEENVSW9AcHR2dK2i2sbHBhw8f1NQi5aI6zYSoTvK1azAL8oPdN73U3RRCCMmF6jSXTmoLmm1tbREaGprjvtDQUFhbW6upRcqVndNMkwAJUb6Ik2cRq2eCDu2ocgYhpOTx8PCgK+ulkNqC5gEDBmDEiBHw8/NDSkoK/Pz8MHLkSAwcOFBdTSKElBHxASF41GUojHW11N0UQgghZYTaqmf89ttveP36NRo2bCjPORw8eDDmzp2rriYRQsoI3ahwVG/mpu5mEEIIKUPUNtKsq6uL3bt3IyYmBnfv3sWHDx+we/du6OnpqatJSkU5zYSoRuLrcOhlpsG1vrO6m0IIIXminObSScAYY+puRFknFovh4OCAoKAgqtOsYGKxGABgbGys5paULaW5XwNatYFGbDRqBgeVyMoZpblvSzLqV+WgflWeyMhI2NvbQyQSUf+WEiodabazsyvSdpUqVVJySwghZZEsLQ0sSYyrfcaXyICZEEJI6aXSnOaEhARs27YNhQ1ui0QiFbWIEFJWpN6/j/AJExFnZAnjPr3V3RxCCCFljEqD5goVKmD+/PmFbmdra6uC1hBCygrx2bOI/Gk6AOCNYSW4O1upuUWEEELKGpUGza9fv1bl6Qgh5cS72w8AAAfq90LVwf1R2dJAzS0ihBBS1qitekZ5Q9UzCFGe0OA3uNCiL6pO+QHDejZSd3MIIaRAVD2jdKLqGSpA1TOUh2Z2K0dp6leZVIpndVyQMuUXNJowUt3NKVRp6tvShPpVOahflYeqZ5Q+NNJcimW+fo20gEDE79gBJpMp5JhpAYGIXb8e0cuXQ5qUBCaRIPmGr0KOTYgyvDl5HgBQrUNLNbeEEEJIWaa2FQHJ10kLCMTr/v3lt/UaNYJenTrFPs6HpX9CEheLin/+CcYYwidMhDT6AwAgbtNmCLr1Ajt7EvZbtyLrxQuk3LyJiiv/hlBXt9Bj+4cnIiNLCld7U+hpaxS7bYQURfrsGQAAkxrV1NwSQgghZRkFzaVM7MZNiP3nH7CsrBz3Z754UaSgOfn6dcTv3IVKmzch6/17xG/bBgCo8PPPSLlzF9LoD/irx3R0bOwM6aF9kPi/hKFFZWD0aPkxLv+6BB2XzyuwDu7j8ERMW3YC9ZPCMat2K/zV3xVuDqbQ1KCLG4QQQggpfShoLkWkySmIWbECAKDXsCEcd+9C6t27SLl1G0lXfGDSq1eB+6c+8kP49+MAAPE7d4FlZUGvQQNIYmORHvIM72bwEbsG3dpgSPtqwP/aAgDSP0TjlYcHknsORHiFyrDftAxLD3TDqywNtKthjUFNci5GkyGR4pcjT+D17gqsnt7DrXbNMWLrPWgIBNj0bSM0q2Kh4J4h5VViQCBkECBhw151N4UQQkgZR0FzCZHx6hXCvx+HKqdOQqCjA5aeDqGeXo5tEg8dgm7t2nDYsB4alpYQCAQwaNYMmtbWeNmtO9ICxkDPJf/R5tg1awAAdn/9hXczZ/L/L1uGpMuXkXzlMgQGhpjW5DtsdMs5WVG3gjVq+fsBABoxhhDfU9A6sAO6/xsLr1NBcKlogipWBhi57T7q2BkjPD4NnZ9cgHXoY+i4uKBn7FOMnPcjDtwPx9gdD3BwfHPUsqVJD+Tr+S36G+9qNMMQ93rqbgohhJAyjq6Vq0h+JeeiV/yN4Jq18LJrN2SFhyNiyhSE1KqNZ/UbINXPD7LUVPm2ydeuwaRPb2haWeVIjdCpUgUG7q0Rs2pVjmOnBQYievlyMJkM6c+eIeXWLVQ+eQLGPbrDpE8f6FSrhtd1muJHWS0k7N2HeLMKsGjgBgdz/Xyfh0AgQOW5c9At2Afj5w3BtEYW+G79dYzYcg9SGcPj8EREi9PQ480dWP80DRZjxyJuwwZEz/fC4CYOGNXSCT/u90N6llRBPUvKq9uPwmDx+A5q/TSBlswmhJQqVHKudKKScyqQV8k5lpmJ9OBgvP7fIPl2VlOnImblSmja2UIg1EBWRAT0GjSA457dQFYWQho0RNUzp6Ht6JjrHJLYWDxv1RoVV62CsUdnAMDb0d8h5dYtaFhYQKtiRbC0NFQ+eUIeYLyNS0XPNb5o6WyBTJ8rCDOogPWz+6CmTeGjwNEr/kbcxo3y2+tnbMBv/RvC0kQfcTt2Iu7ff1Htqg8gFOJZ/Qby7ez378dQ3yQ0r2KB2d1qfVmHfoLKISlHSe/XbT7P0OyHPgCAmsFBpSpoLul9W1pRvyoH9avyUMm50ofSM9Qk8udfkHT+PIw6dUTFlSsBqRQCbW0YNG8GXRcXgDGkBwfj7chRSA8IhEBTA0JtbWg5OOR5PE1LS5j064sPS/+AUedOyHz1CqkPHsDGywvxO3Yg/ckTVNq+PUdwMf90ELq62GBJ37r4WVsTfcz0ihQwA4D1T9OgW6sm4jZtRnpQEMYvG4fYZUCyiwvSAwJgOnAghAZ8VbaawUFI9rmKiAkTEDFoEP78dgz63K6NbxpUlJ9PIpUhPjUT1kaFV+VQphcxydDWEBY42v4lYpMzoKMphJGulkKPWx7tvB6GjKWLIHOohGqbNpSqgJkQQkjpRSPNKvD5SHPm27d40b0HHLdugZ6bGwRa+QdSkTN/hkBTE0J9fWS9fw+Hf9fku60sIwPPGjaC3R9/ICsiAmlPn8Lh3zWQZWYixdcXRu3bAwDOPInC/dfxOPIoAldntIWFoc5XPT+pSIQPf/6JrLfhkCYnQxIbg2pXruR6XmmBgRCfPoOEAwfg870nLsossHtMUxz3e4e/L4UiNjkDnWpVwIR2znBzMC3SuRU1ChKXnIFFZ4Jx5mkUhAIBZnWtiW9bOBVpX5mMITwhFUKBIFewLZMxzDkegEMPwtG6miW2jWryVe0sSlsAQCgseiC51fcV6jmYoKGjufy+kjq6FJ+Sic2DJqPn8+uw/WMJTPv0UXeTiq2k9m1pR/2qHNSvykMjzaUPjTSrQbKPDwyaNYN+48aFbmvctSsiJkwAAFRc+Xee2ySlZ0EiZTAz0IFx507yKhg283m+lFBbWx4wb77xEv9cCUNXFxusHlz/qwNmANAwMYHdokWFbqdXp468LF7HY+tws8dMtFt2FRkSGf7qXw+17Yyx/eYrDN10BzpaGqhtawzPnrVRrYJRrmNJpDI8jkhEwOv3AATQ1U+EUCBALVtjuFQ0AcC/HBx5FIHKlgaY2M4ZBjoauP8qAS9ikmFuoI0GjmaoaKqHOy/jMHHPIzRyMsPl6W0QJUrHsM130ayKBWrY5D73p1IzJfh26z34hydCQyjA9E41MKqlE6KTMhCdlIEHr+Nx43kMDo1vjhFb7uHOyzilVQ/xCYnG7KNPoSEUoKuLDepXMkMXFxtoFBBAB0SKsOhsMCwMtHHux9YKeT0APHjfdecNGjqayX8firD5+gs0jw+DnpsbjDp2VNhxCSGEkMLQSLMKZI80P7l6FY716yPy55+h7VAJVpMnFWn/+B07kHLnLhzWrZXflyGR4sjDSDyJSMTpJ1GoW9EE+75vBsYY3k2fAfHZs6h+7y40Pvn2GvJejD7/3sTBcc3ham+q6KdZZLLMTDxz5dUOHi7dhubN68Au5i00La2gYWSIZKEW3sSm4uTjSOy+8xZHJ7TIVW1j3K4HuP86Aa7W2hAKgCwNXUhlDH5vE+HZszY61KqA9suuYlRLJwRFiXHrRRwyJTJYGuqgtp0xYpIyEBQlRkVTPcQkZWBu95posWE+0h8/RsWVf2OFyBIvY5OxPY+RYcYYjj6KxFG/CMQlZ8JETwvbRzVBUJQY0w/6gwH4IE6HllCIlEwJDo5pDGf/G9htWheXQmJwbEILhacUhLwXo/eam5jfuw7MDXRw9Vk0roXGoIKxLn7pUhONncxynPPqs2jsvfsWb+NT0baGNd7EpeDOyzjUsDHCX/3rwURTAiD/0aX0LCnexqdCW0MIRwt9CAQCMMaw4mIo9t17C2sjXbwXp0MoAOZ0r4XQD8mY1rE6tDW/fO5xYmom/hj1O0a89UWN82ehYaK4YFyVaOROOahflYP6VXlopLn0oaBZBbKDZu82beDcoyfid+yA7YIFMGrf7ouO9zYuFd/tuA8NoQBtqluhfiUzTNz7CHd/7QBLQx1IxWKkBwXDoFnTHPv9fjwAaVlSLBug/vJcWR+i8WHhAiRdvQZ8tlCLlp0dKsz5FWAMGyUVcflZLI5PbCkPuC4FfcCMw49xeVJzpPlcgF69ejCvWhUCgQC3w2IxaZ8fZIyhWRULrBvWEAAfUTXR00JFQ00IhEJkhIZCFBqGUP0KMHsRCMNrF5Hm7y9vg+WRE/A4+gY/dqiWK01jzr776P7nZLz0XIksqwrobZIOzbevoF25MiRCDVxKNYCztSGcrQ0RHvQCkv/1BgAY9OqFfiadsKC3C7q42CisLxljGLH1HqpXMMLvPWrL70/LlOKfK8+x+84b9Khnh8Xf1EWUKA2XgqOx5GwwhjdzhOGjO+h07F+YjhyJiE59cTQoFpdDorG8T3W+kqOBIa6HxsC9uhUCIkXYdecNNIUCnH36HlIZg1TGYGmojUZO5ogSpSEiIQ1eveogIiEN/RraY+WlUJx9GgUdTQ10rWuD2V2/fPLn5o2n0HLFz6i0fRsMmjVTRNepBQUhykH9qhzUr8pDQXPpQ0GzCmQHzRd79IDRw0eAlhZqPHwAobZ2sY8lkzEM2ngHTpb6WPRNXWj9t8Je37U30b+hA4Y0rZTnfikZEjRdfBm7xzQtcr6wskkSEvDul1+QcvsOtCtVgtmQwRBoaUF08iTSHjwEAFh5emLoezsMa1YJPVzt8PuJAFwLjcHcplaoO/s7pPw3eqqflpbj2EFTF6CxKaDx6B6y3rxFVkw0tB0qIfXuXQj19XOU8stm2LYtHNavw7tf5yAjJAQJf/6LYTv90cPVFo2czFHTxgh3XsbBf8chTL65A9DUBKRS4LM/oRqP/ZF06RK0bO0gPn0aCXs/LrwR8usy/BNjAO+p7vLf3de68zIO43c/hM/EpkhZOA+6NWtBoKUF465doGllhQhxJjqsuIbVg+pj+kF/VLcxwjj3Kmh47Shi/8mZI69duTKuTvsL/94IQ117E0QkA6/jUtHT1RZ3X8WjRVVLmOproburLeo7mCJDIsOD1wl48CYeFZCB5k99UHHoIET/sRRGnTvBuEsXMMbwIiYFvdf4YsvIxkVOT5FIZdji+wo2JrqwjQ2H0aRRAEpftYzPURCiHNSvykH9qjwUNJc+FDSrwKcTAU3CwqBVsSJ0nJ2/6Fh77r7BWp8XuDDNHQY6H1PSN11/ievPY7Dru6a59gmOEmPhmSAkpUtwYmLLEh9wyDIyEL9jJ5IvX0bmmzcI9PwHG56lokMta9x5GY9ZLW1hMn4YWHo6LA8dQsqNG0j54w8AgKatLSRRUfke+9OA2fnaNURO/wlZb8NhPnoUTL/5BhomJpCKxXjZuw+MO3fC+2E/4OzTKDx6m4Dn0ckw19fG8qgLMAkNQMbz5wAA20WLYNi2DYQGBnjR2QMCXV1kvX0rP6fZkCEwGzYUL7t1BwD8NOYfdK3viCkdqimkv34/HgAmkWDYwlFg6el5buPbfyIWSSpjYruqmOlRE7LUVDxr0BAVZs+C2dChyIqIQMK+fUg8dhyGrVsj/oep8ItMgp6BIZpVsUD/9bdQt6IJ1rQyh0AohE7lyjmOn3T1KqJm/wppQkKO+7Xs7WE+fBj0GjbCzjhdHPOLxOnJrQpdTl0qY5h56DH8whNR58NzTDzFV8KsdtMXmhale0VJCkKUg/pVOahflYeC5tKHgmYVyKtOc7YP4nQc84vEmFaVCw0kkjMkaPOnD5b2c0XH2hVyPBaRkIr2y67h8vQ2OSo4fBCno+uqG+jjVhGT2jvD3KD4o9vqFD5uPJiZGfpotkQyNLCnsw0Mvx/ClxHfthVJ/wWJRvr6EGjyLxGytDTErFwFk77fQNvREUJdXsZOEh8PoZ4eZOnp0DA1LfDLQ9KVK4iYMBFGXbrAdp4nNExNAfBKIaFNm8FmnieSr9+AcdcuMOnZU75fZkQEEg8chEmf3tAwMYH4vDdM+/WFUE8PcVu2IGbVakgHDkW/jLrYOLwR3Ktb8f0ksi/K95XJGJr/cRlr2FMY7toIs+HDUWHWL2ASCRL27UPc5i2QxsYCAN7XbgSboAc59q/h7yfvH97+SLwdMQLaQ4fAdMAAGBkZ4e23I6H708/QfB+B6KlTP+4sFAIymfymSe/eMB81EnFbtsLQ3R1CA33E/LMGGcHBAABLT08MemOBWpUs4FHHBj1cbfP8HchkDL8ee4p7r+Kx7qQnZNHRsBjzHaymTy/xX/iKgoIQ5aB+VQ7qV+WhoLn0oaBZBQoKmheeDsKWm6/QroY11gypD33t/AuarLwUipthsTg4rnmewcPkfX4w0dPEwj51AfBc16Gb78LGRBcrBrop9DmpSvqzUISPHYuU5DSsHzALP+6YAwCwX/MPjDp2SB6PoQABAABJREFUVNobuiwjA8/quQHgwaDtksVIOn8eorNnkXzpMqp6n89zkZnCpD54gDejRiPUaw3mPE5DK2dLdHGxwbxTgfjZo2aO9BqZjOUqHRcQKeKpNlUskJopQXBUEr7f5It9PkthM88Txp065dieZWUh4+UrvOrdW36fQcuW0KtfH2ZDh0DTzCxXG5MuX0boDL7MuqFQCFlKCqChAYGGBvTc3JB6716O7Q3cW8N8xLcwaNZU/sVFfn6pFBlhYYia8xvSAwIAAIf+PIxTT6PQuXYF/N6jNoKixHgZk4I+bnb45chT3AyLhZ5Ahs1xl5F5+gRsFy2Cab++xe7rkoqCEOWgflUO6lfloaC59KGgWQXEYjFsbW0xdOhQ9OvXDx4eHgB4BYJmSy7jr/71sPnGS2RIZFg2wBVVrQxzBcUZEikaLbyETSMa5ZsTml1B4fxUd1S2NIDPs2jMPPQYV2e2g6FO6a0uKE1KQmjjJjDo1w8pR46gZmAABBoaAJT7hi5LTUXS5ct4N/NnaNrZQvKOp304X/WBls2XT+R7v3ARMl+9gu7y1fj7UihOPf2AnvVs4R34Abu/a4proTFITMvE4QcRqG1njHXDGuJlTDK233qNs0+jIIAAferb4bjfO6RLpJgpeAmPJ96ocupUviOxjLEij9IyxvCoAZ9AqZ+WBi0HB0jj4iBLTUWNx/4Q6vCydLKUFMgyMqBpbl7Q4eRSHz3CmyFDYTZkCCSTp2PiXj+Ex6ciNVMCbQ0hKlsZotHDi/gm9gm0EmIhi4mBxZjvYP1fCcWygoIQ5aB+VQ7qV+Xw9vbGkSNHsGnTJgqaSxEKmlUgv5Hmgw/Csen6S1yY5o4MiQy/Hw/Aicfv4OZgih2jmkBPW0O+rc+zaPx2LAC+v7QrMPhZej4Ee+68wW89amPfvbfwqGOD8W2qKvX5qcKb4SOQev8+zIYMgc3c3+X3K/sNXZaejsQDB5D2+AnSAgNQactWaNtXLHzHAkji4/G8RUv5bfu1/8KwXTsM3nQH917Fo10Na5gbaKNT7QrYdvM17r+Oh7amEH3qV8S4Zg6IjBVh5a13+LFDNVga6kBz+gSYduoAi5Ejv/LZfpTdr1rh4dB2dIJQVwcsKwtCPb2vOm7s+vWIWbkKmlZWsF+3Dg+EZqhw/xqyWrZB8NSZqBP2CABfUl6aEA/LSZOgYVRwrezShoIQ5aB+VQ7qV+WhkebSh4JmFcgraH74Jh7fbr2PP/rVRQ9XO/m2KRkSjNp2H8Z6Wlg3rIG8wsKsI0+gq6WBeb3qFHq+c0+jsPR8CERpWbjxS/tSPcqcLdXPD4mHD8PW0xOCT6qOlNY39ORr1xA+brz8tkBbG0Yr/0GaS304W38MEjMkUryKTUFVK0NoSiV41a8/Mp4/R60Qniec8fw5XvXtB+fr1/JMtfhSyuzX2E2bELN8RZ6PmfTuDbulfyj8nCVJaX3NlnTUr8pB/ao8FDSXPhQ0q8DnQbNMxtDijyv43r0KRreqnGt7UWoWBm+6gwrGOujlZgdjXS38cuQJ/hncAM2rFq1ygFTGIErLKnUT/4qrNL+hM8YgPnkS736ZJb/PoEULaNrZQiYSIz0oCPpNmkAqFiMjJARZkZHy7Yy7dYNxzx6I+GECtKtUQdWzZxTaNmX3a9qTJ2CZmcgIC0Oyz1VoV6kCTSsrWIwepZTzlSSl+TVbklG/Kgf1q/JQ0Fz6lP4hyFLIPyIRaVlSDG+e90QyE30t7Pu+GX4+/Bh77rxFbHIGNIQCNHYq+kiihlBQ5gPm0k4gEMCkd2/o1KwF3RrVIb5wAZHTfuK1n/8jOnZM/v9K27aCSSRIPHIU4rNnIT57lt+/eZPK2/619FxdAQD6jRrBbNAgNbeGEEIIKRwFzWpwKegD2tWwKnBxCxM9LWwY3kh+O0sqK7QkHSmddGtUBwAYd+4M48AAZF/8YZmZkLx/D4GWFrTsPqbwGLRoASxfBkl0NASamtC0slJLuwkhhJDyhIJmFXr4JgFHQlLhHfgeP3WqUax9FbV6HCn5sid6CnR08ixrl105RMvWVqXtIoQQQsozCppVyOtkIKxsbBGRkAb36pbqbg4hhBBCCCkiCppVqG8De/w6oAWikzJgpKul7uYQQgghhJAiomv+KjSiuSMEAgEqGOsWvjEhhBBCCCkxKGj+SgcOHECzZs3QvHlzHDhwoMBtP18SmRBCCCGElA6UnvEVRCIRvLy8cO/ePQgEAjRu3BhdunSBiYmJuptGCCGEEEIUiEaaC3H79m20b98eRkZGMDU1RYsWLSCTyQAA+/btQ0JCAipUqIBq1apBV1cXd+7cUXOLCSGEEEKIopXroHn//v1o3bo1jI2NIRAIIJFIcjx+69YttGvXDo8ePYJUKkXdunUxadIkCAQCJCUlYfbs2XByckJsbCy8vb3x/Plz7N69O9/zXb16VcnPqHy6dOmSuptQJlG/Kg/1rXJQvyoH9SshXLkOms3MzDBhwgSsXLkyz8eHDh0KLS0t3LhxA3FxcWjVqhVmzpyJlJQUHD16FIwxuLi4QE9PD3Xr1kXdunVx+fLlfM937do1JT2T8u3KlSvqbkKZRP2qPNS3ykH9qhzUr4Rw5Tqn2cPDA0DeI8Cpqal4/fo12rdvj++++w4vXryAo6MjkpKScOzYMfj7+6Nhw4a4desWUlNTAQBRUVGIioqCWCymdeQJIYQQQsqQch00F+T169cAAD8/P3h7e6N+/fo4efIk+vfvjzNnzsDAwACWlpYYM2YM2rdvD4FAgIkTJ2LmzJm5gmapVApdXV2kpqYiJCRETc+obEpNTaV+VQLqV+WhvlUO6lfloH5VnujoaAA8RiClg4AxxtTdCHW7evUq2rVrh6ysLGhq8u8RgYGBcHFxwXfffYfNmzfLt7WxsYG1tTU6dOiAoKAgeHt7yx+7desWWrZsCZFIlCNovn//Prp06YK0tDT5H4empqb8XOTLGRoaQiKRID09Xd1NKVOoX5WH+lY5qF+Vg/pVsSQSiXz+lJaWFlJSUnDv3j00btxYzS0jRUFRWz7s7e0BABkZGTnul0gk0NHRgZubG/bs2QOJRCIPfh88eIAqVarkSs1wdnaGRCLBwIEDsXr1atU8gXIiKSkJXl5eWLZsmbqbUqZQvyoP9a1yUL8qB/Wr8kRFRaFmzZpwdnZWd1NIEVHQnA8TExOYm5vjxIkT8Pf3h6urK06cOIG4uDhMmjQJffv2xaxZs+Dp6YnffvsNL168wLJlyzB16tRcx9LQ0AAAaGtrU66zElC/Kgf1q/JQ3yoH9atyUL8qR1JSEoCPMQIp+cp19QypVIr09HRkZmYC4KPK6enp8jrMs2bNglAolC9Y8sMPP8DMzAwzZsyAkZERvL29cf36dVhYWKBjx44YPXo0pk2bps6nRAghhBBClKBcjzTv2rULo0aNkt82NDQEAPj4+KBt27aYMWMGkpKSsHHjRkilUlSvXh3//vuvfDtXV1fcuHGjyOdr06aNYp8AAQC0b99e3U0ok6hflYf6VjmoX5WD+pUQjiYCqoBYLIaDgwOCgoJQsWJFdTenTBGLxQBAlw4VjPpVeahvlYP6VTmoX5UnMjIS9vb2uYoHkJKrXKdnEEIIIYQQUhQUNBNCCCGEEFIICppLsJs3b6JevXryiYmfa9WqFebNmwcASElJgaOjI0JDQ1XYQkIIIYSQ8oGC5hJs8uTJ8PLyglBY+K/JwMAAP/30E6ZPn66ClhFCCCGElC8UNJdQPj4+eP/+PXr27FnkfUaMGIFLly7RaDMhhBBCiIJR0FxCHTlyBB06dJAXPZdIJPj5559hY2MDKysrzJ49O9c+ZmZmaNy4MY4dO6bq5hJCCCGElGkUNJdQDx48gIuLi/z2n3/+iYMHD+LKlSuIiIiApqYm7t69m2s/V1dX3L9/X5VNJYQQQggp8yhoLqHi4+NhYmIiv71t2zZMnz4dtWvXho6ODubNmwczM7Nc+5mYmCA+Pl6VTSWEEEIIKfMoaC6hzM3NIRKJ5LcjIiJQuXJl+W0NDQ1UqlQp134ikQjm5uYqaSMhhBBCSHlBQXMJ1bBhQwQGBspv29vb4/Xr1/LbUqkU4eHhufZ7+vQpGjVqpIomEkIIIYSUGxQ0l1B9+/bF5cuXIZVKAQDffvstli9fjpCQEGRkZGD+/Pm50jASExNx79499OnTRw0tJoQQQggpuyhoLqE6dOgAa2trnDp1CgDwyy+/oG/fvmjTpg3s7e2RmZmJpk2b5thn586d6NChA2rWrKmOJhNCCCGEFGrz5s0QCAR5/hgYGMgHDEsaTXU3gOTvn3/+wcSJE9GrVy9oaWlh+fLlWL58eZ7bpqSkYMWKFfD29lZxKwkhhBBCis7Pzw8GBga4dOlSrsf09PTk5XZLGgqaS7BWrVrh8ePHRdrWwMAgR84zIYQQQkhJ5O/vjzp16qBZs2bqbkqxUHoGIYQQQghRCcYYnjx5AldXV3U3pdhopJkQQgghhKjE8+fPkZycjDp16kAikeR4TCAQlNjUDIBGmgkh5YivL1C9urpbQQghisUYIBYr74cxxbXV398fADBt2jRoaWnl+CnpJXNppJkQUm60bq3uFhBCiOIlJQGfLCKscCIRYGysmGP5+/tDIBDA19cX2traOR7La6XjkoSC5nx4eXlh586diI2NhZaWFho2bIilS5fCzc1Nvs2TJ08wadIkPHz4ECYmJvj+++/h6ekJgUCgvoYTQgrWeQZuh/dDc4fm6m4JIYQohJERD2yVeXxF8fPzQ5UqVdCiRQvFHVRFKD0jH4MGDcKDBw8gEonw7t07dO7cGR4eHvLagUlJSfDw8EDLli0RGxsLb29vbN68GStXrlRvwwkhBWuxHAtvLFR3KwghRGEEAj4SrKwfRY4F+vv7o27duoVuN378eEyfPh0A8OHDB9StWxdnz55VXEO+AAXN+ahRo4b8MgFjDBoaGoiOjpavwnf06FFIpVIsWLAAenp6qFu3LmbOnIk1a9aos9mEkDykpwNbt6q7FYQQUr59+PAB79+/L1LljPnz52PXrl149OgRunbtCi8vL3Tr1k0FrcwfpWcU4MyZMxg6dChEIhEEAgGmTZsGKysrAPybUv369aGp+bELGzdujJcvX0IsFsNYUck/hJCvVrcuEBam7lYQQkj55ufnBwDQ1NTEnTt3cj3u6uoKfX19AIC1tTWmTp2KFi1aYOvWrejbt69K25oXCpoL0L17dyQmJiI+Ph47duyAvb29/DGxWAxTU9Mc22ePTOcVNBsaGiIlJQVisVjp7S5PkpKS1N2EMqms9Wt09KeTWIyhlaWltr/Fsta3JQX1q3JQvypPSkqKupugctmVM+bOnYu5c+fmeEwgECA+Pl4eNMfHx+PQoUMwMzODg4ODqpuaJ5UGzS9fvizSdkKhEE5OTsptTDGYm5vjxx9/hJmZGapXr4569erB2NgYERERObZLSEgAgDxHmdPT07FkyRIYGBgAANq3b4+OHTsqv/GEkFxo1JkQog6XLl3ClStXAJTPoHnWrFmYNWtWoduJRCJ06dIFU6dOhZGREaZNm4b79++rvdCCSoNmZ2fnQp8wYwx6enol7sUkk8mQlZWF58+fo169enBzc8OePXsgkUjkKRoPHjxAlSpV8gyaJRIJFi5ciIoVK6q66eUCpcMoR1no18mTeZ3Rj8QIfJmFPXuM8cMP6mpV2ejbkoj6VTmoXxWjb9++8jSDyMhIbN++Xb0NKoFSUlLQrVs3jBw5Et9++y0AYPny5di5c6f8trqoNGjW19dHYGBggdswxnKUdVOXVatWYdCgQahQoQJiYmIwZ84caGtro2XLlgD4C3/WrFnw9PTEb7/9hhcvXmDZsmWYOnWqehtOSDnzIfkD7kXeQ88aPfN8PL+5uf8VwiGEEFKCGBgY4ObNmznu+/y2uqg0aO7VqxccHR0L3a5Hjx4qaE3BLl68iMWLFyM5ORnGxsZo3LgxLl26BFtbWwCAkZERvL29MXHiRFhYWMDY2Bjjx4/HtGnT1NxyQsqXqd5TsT9gP5hnPktWCaSAVTAgrghMrK3axhFCCCkzVBo07927t0jb7d69W8ktKdzp06cL3cbV1RU3btxQQWsIIflhha3v2ngd0G0ysOkOYPRefjetQUQIIaQ41FqnWUrXRwkhyqYXl/s+qyAwyFTfFkIIIaWWWoPmrl27qvP0hJAyoMizqb/7ZMlWs9e4n6H+K1qEEEJKD5WkZ3xeiw/gl1RfvHihitMTQsozwX/pG8KcI8vpEKmhMYQQQkorlQTN69atw/Lly3PlHmYXsCaEkC8lQGEjzXnnPAsFGopvDCGEkDJLJUFzrVq10KFDh1w1ir29vVVxekJIGVZQesatJ+8B57zfZzRAQTMhhJCiU0nQfO3atTw/2IpaTYMQQr5Ey2O2gH3ejwkFap3SQQghpJRRyaeGupc9JISUXYWnZ+S3HwXNhBBCik4tnxr9+vVTx2kJIWXQl34p9/amoJkQQkjRqeVT4927d+o4LSGkjLkbcRe7nxS9dJx5KqAt4f//EEVBMyGEkKJTy6cGpWsQQhTh7zt/y/8vzhDneOzO89Bc28f9CWw//t8NrTQltowQQkh+Nm/eDIFAkOePgYFBiV38TqXLaBNCiCJ9+gV8q99WTG02FQDwJvENmu+tkec+gwOA3a7A2R4/ABivglYSQgj5lJ+fHwwMDHDp0qVcj+np6UFDo2RWN6KgmRBSan06CTC7DrxUCqxeV/Ao8vgHwNnqwOLFwK+/KrWJhBBCPuPv7486deqgWbNm6m5KsaglPePzRU4IIaS4srKA91G5U71WrABWrMj5HmORAhinf7wt/W+3OXOU2UJCCCGfY4zhyZMncHV1VXdTik0tQfPt27fVcVpSij1+zEcQCcm2dy/g4/MxaBZmZgFCIY698wIm1c6xbexfwIONH2/LaFoFIYSoxfPnz5GcnIw6depAIpHk+CmpuczZaPo4KRXc3IAdO9TdCi4yEoiPV3crSHo6gIr35LerXfEHGEO0dF6e21f75Hcm/eSd780bpTSPEEJIHvz9/QEA06ZNg5aWVo6fRo0aqbdxhVBbTvPz588xZcoU3Lt3D0lJSTkey8zMVFOrSEkmEqm7BZy9PVC7NhAYqO6WEGhkyP/LsicFJlsDFtEAAE0pkLUg927Z6RnQTcDIkWbw8VFyOwkhRIkYY0jKTCp8wy9kpG2ksMpn/v7+EAgE8PX1hba2do7HzMzMFHIOZVFb0Dx8+HDY29tj06ZNMDAwUFczSCnBIAB+AjCtZOTDBwWpuwWEMQCmbz/e/u/9nL3oCjjyyxK6krz3lY80zzLH250l4zVFCCFfKikzCSZ/mCjt+KJZIhjrGCvkWH5+fqhSpQpatGihkOOpktqC5qCgIPj6+kJTkwp4lGW6C3Xxu/vvmOP+ccbVmntrUNOyJjpW6Vjs4126BHQs/m4qlZoKCASAnp66W1K2fTqfmM0DAro8AwC8uL4DVd2Al+aAhizvfTU/uf/lS8W1SSDgr9EOHRR3TEIIKYyRthFEs5R3OdZI20hhx/L39y+0akZ8fDwcHByQkJAgH40eN24cqlSpgl9++UVhbSkuteU0u7i4ICoqSl2nL19kMuDkSWD7dv7/L+DtDbx4Ufz9MqQZOBl6Msd9k89NRqddnb6oHZ0+2e3KFSAjI/9tlcoyGBHiiDwfqloVaNIk9/03396EwEv1M9AYY2WyYs3nT8nl/EP5/xv9t+ioMJ+n/b9AoHEEUCta8e26fl3xx/xaBw7wgH7//q87TmYmkJysmDYRUtpI8rlyVRIIBAIY6xgr7UdRqRkfPnzA+/fvC62cYW5ujipVquDRo0cAgBs3buDevXuYPn26QtrxpdQWNG/cuBETJkzAwYMHcf369Rw/ZZ6qZ4f6+AC9ewOjRgGhuVdJK4ouXYBGjYA9e4q2vVT6MT6Xygp5vikpwJMnAIDhx4ZD4CVA2LtAeT+9Tnwt31QDH9+1OnRQ0+RA/RhgUm0029wM6em5A/f374GAgNy7XXtzrdinWnd/Ha68upLv44wBW7bkDiA/tfruagjnF/6nLpVJIWO5v1QZLTHCmntritReVcp+zjN9cz+26yjQ/iVgL879WLZ7m4GgtYCBRnr+GxVD9uv9sxQ9pSjqdyCxmH+JGzSI3x48+OvO26YNYKS4Aaeya/16YNw44Lvv+DcVcQEvRHV78QLw8lLb6d+/B2Jj1Xb6YtHSAg4dUncrSjc/Pz8AgKamJu7cuZPrJzU1Vb5tmzZt4Ovri8zMTPzwww9Yv3692rMT1BY0BwYGwsfHB4MGDULbtm3lP+3atVNXk4qNMQZPT0/Y2dnBwMAA7u7uCMgrWvrU6dOApib/9EoqOGk/ZMMiHHG3+vqGfjqD7v37fDd79YqPRuW3SWIiMGxY4afLygKcnIC2bfltKcsZNB/fB4T8A2RJs/gdU6YA9eoBs2djV9/dYPMA54ouQNu2eJ/8HpVXVZbvOwy7cxxLkSPNqalAWNhnd6ak5C6V8bM1ACAhPQE1agBFqc3OGINEVvxhiglnJ+Db49/m+3h4ODBmDLBsWf7HOBV6qsBzZEmzkC5Jh/M/zuixt0eux5Mzk+XHSEvj5f9KggyWAgD4M/eCUtCWAZd3Ak/WF36cZKkelg755El9+23elwo+d/ky8N8scODjKFSaElbn/vAh50UiobBoKSC3/RPw0mBvjvvefkwDh1QKtG9f9GD6zh3+73haSLFgP/wAbNwIbN3KO7dOHXW3KG8yGeDsDMybB9y8yV8cixbxF8anL5RssbEKiXBTU4Ho/67yODryAZnSYuBAwPe/L+qPHn391ZvyJrtyxty5c9G8efMcPy1atMhRCMLd3R03b97E4sWL0a5dOzRt2lRNrf4EK6IzZ84UddMicfg/e+cdN0Vx//HPbLt+T6WLoGBFqsSuUYOCRk0QE1RE0Wh+EUvUEJNoFGxojC1FY5RYojHGXmIhMWKPQlQExYaKSufheZ7re9vm98fM7O49BR6Bh6PMm9e9uJvdnflO2Xvmdj/7mf796e9+9zuayWSo4zgVr62F66+/nu6www50wYIFtFgs0l/+8pe0b9++NJfLVeyXyWRoOp2mS5cupXTPPSllF4qCVxvy5TzFjGD7P/53b8cBnHcepbkcbSm1UNdzO95nzpz2ZbbltNMobW2ld93V+S5tQ/a8ztvlRz8K7TsDdOhtQzvNrHDoge0zD71OPh4VbUEBetFFlE6axD5eeWWGZjKZzoP5BvzkJx3UvbaWJZ5+ehD+DBbTk7sodHjynxQD57SvnlailFL64ouUfvghpSecVUvPPBZ09Fmg9J57KH377fYBtLS0SxJlhSkkInTu9LMopZR+9lnnfSbQf813+Pzzdttcz6WYAZqcmfTLuuH1G+jS1UtpZskSSl95hWIG6Ji/jmHlELb/jDkzOi9wHZxw4Rv0uXff8T97HqXZ7PqPc932Y+7n134a1G0jX1fjEup8ytun7fb+/Vm6ZVH6zjsskDVrKveZMYN+smIpxbif0gsvXHddMplvNmbnfjmfYgboDydn/DYDKEXfee3GhueFtoNSHHgd61dUnpfPPUfpgw/ycQPQk/A3umrVus9r217310jbON56q336G29QunAhpV98QSmIQ0HW8X3vdvKd1gnrbde1aynt6O/Lpv6bc9BBHY+zvn0p/f3v2T5r1gT7P/wwpQ880Gl2Cxfy3T2P0tdeW3fZy5dT+uWXXYszn6c0Hu841t69/feZf/yDZtLpiu3lpV9S+8MPKKWUFpcspp/89lc0P/ufXSuXUtq3v0mhFWmx2HHxI0d2OStKKRubX3/9zY7J5zv8SqSUsqH34ovB6U4ppeXyur9GrrqK0lKJ0h//mNJ//5vSN99kx7W2dh7D0qVLKYBN9jdsW2PFihW0pqaG7rzzzltMG3V50pxKpSo+P/744xtVcDqd3qjjtwQGDhxIb7nlFv+zbdu0sbGR/vWvf63Yr2LS3NHZRimljzzif07/EvTL9Lon1vTrr/1t6mXBft5LL7H3zz5L6bx5HZcXntTzv7DlRx+iJ58c7PLUU2zzv/5F6fvvU0rg0tdwAO2FFf5OXyxopmedRWnhH0+xby1OuKjxPwQd+oc9g/Jcd6MnOOGPE077hO5+3d708ccpXb2a0hUrQm00dSql118ffH7rrU5nBWceuIgCHmvq//2PfvbzM6kbj1WWzY/tMQ10t3OC9BMngK5eTemdb99JB94ykCXPAP33Z/9mfyv7eB3W48PlC1ieTU1B+qOPUrpsWUX/+hOj//s/Si+5JNh3n33oh6fOrMjW/9sbqueqeJtx9OGHlK5aRelHH9GPPnyNHjwF9MDTg316TAM99rS0/4cSM0AfPqSR9sPXtNcJh9CGn4MeescBFX3eIe++6799Zckr9KvWr+gOF7L8lrasohj8HB08dO16J2DTp1c23XvvsfQ/GSd32K4b8/qy+YuNOn5NDHTEiPbDbM0aSp98kr1vO7mzXZsuWfERpddc02H9T/nNA2zie5lOEVtL7/5bnqLP2/6PHOx3M/v/W7cGoez4ChvPR15EMR20F5bR1xd9TpFYxdJ7zafY82E6/IRdKQXo/fr3/GP33ZfSIaOy9MWXHL9v7LJLz8PvKPa+nU/C259Ha9eGmkIrsnKizRQ7vMEmyKAU5+7mx61cDkouB73lvo/oE+++TB9+g82y313xLv3vuKH+2PvPrEvpo7e8RXulP1vnOFnvpJkHVzr+OEqLRdZJra3tv2MXLaLenntSmkqxL78nnqB05Up2bnoepa+/TumMGewcmj+fHXP++ew7N1TON30tf/Yh+s7Z4+mPJyynjz+bpa9++Sq1HIttTq6g5cE7UQrQU3+g0w8/n0e9v/0tiPnDDzvOd8gQSsePp3THHdlFlg2IK5NOt5s0i9eto9un1V4Rp8+9/wT92bWHBWOUv54dzPbpfwHoTfuxv12YAYr9b2A/yNtkt99+QRU9j9JC0aE33uRSx/Foa75IjxhXpkccEey/cCGlS5YE+3se+60bxnNdOvOmpbShgf1d08DaePJklseIEetukuHDN/wrYqedKD3iCEpvvZXFIifN68bzPNqjRw/62GOPVTsUn/X8uQpIJpMVn+vq6io+H3XUUd+o4IkTJ9LX1vereQumtbWVAqBvvPFGRfoRRxxBL2xzqcmfNH/yySb/I7/Brw8/pPde8zX94JqH/TSA0p5YSV/CIfQU/JXuiCUUoPRizKQ21A7z2eXc0OcDDqBN58+gO2Mx1WDRw/FCt8T+F5zO/nj0+mmnX+geIR0e6wzejb3/97/bbZuL0dXvlw14TcFd/scISnRRzb4bnee6/lB25bViELuD8CtcQ/P9dqF3D6/cfuIE0BnfBr145GBq/Br09NSVdETyabo7FtGp+COduwf7C/Zj5fdUg0VV2PQH+Ac9FffQk3AfnYe9q97unb1m79w+7ZTxbHJw6vdB9/9JmqZ7LqHQs9RAkeq/6EdPP47ttyOWUPRcQKHYlMClNdpSiulBPpccDvr4buyOxdhJQfru54AeFPrhc+dIXuZ00E/rWBpmsM87XgD6+306j//KQzrfNvz/+IR3zwfosKEe1Qc9RftexPLd4axeNP6zBO31s8pjTj4e9DuTQR/ac/1t98geGz9m/3X1Gd3Wt62a0b69Dv1P1cfcpnqZauft2h3lzRkQvMd00LqLgwl29FLQ2K8Ive5A0GXJzvP43sRg8t73ItDdT9iL9pgG+l5P0Fv2Bb1jFOj+gy6j3zpzPePHAH1iN9DIpezHXMWE/zK93Q+AHtf3oP/8+J/07c8/6yRLj70UiyL9NUUkQ6GWK34cfPaZnDSviz/96U/0hBNOqHYYFRBKKe2KjCOdTiMbepihrq4OLS0tnW5fH+effz7uv/9+TJgwAX369KnYduWVV3Y5n2rx9ddfY8cdd8SiRYuwxx57+OkTJ05EKpXCrFmz/LRsNos99tgDcwD0/oaPntNUEiQnH1fvjFwyCQBIyUf6NymyXbuPjtr2T3sDZ7/d2RGbhqE/AV67C6jZRteOkmN2w3hkD+CwR/6HxSsWYfjO+2P27mehh1XEAV8/CgDIDB0KxXGQ+vBDfLr2UxTeeQsjzvhVRR6zcDrOxN3VCH+zses5wLQ3gBsPAIqRjvcZ2nMoFq5euN68BjcBc/4KrEwmsdvy5chkMkinN40H8rbARx99hOOPPx59+vTBo48+itra2mqH5LPBDwJurP3IwoULMXz4cCxevBivvvqq/3rttQ4ehd8CEQO8tbW1Ir2lpaXDwW+aJq7NZHCJaWJYfxMvrMe7hj79T4zu9TV2zH2IIzt4+O75nYGRP97g8Dc7J48Hhv8fMHYS8MvDg/Q/j6peTG/V1mPFE3/DOxhZkX77KGCvsyv3/TLeybdkG849Kni/x9TO9/vbXsCOFwCDzmNt8EjwuwtjJwGnfG8S9mjYuMe0j8FT6K+/j31+BOw+FTjuROB/vYFLDgNOxN/wKQZ1euyYycAhpwEXHQl8UgfcNaL9PmujwN/5803vGgMBANfvDzy4Z8d5LqnZqOpsVl7eERh6NjDwp+233aSc3e4hqfsuXogfD3w+SPj6a+TmLoI9eDd4oR/VndHdE2YAWHh71yfMtLYW71z7kP85j+4xHadjjsDKy/6IhWffBnuHgSj02wXla67H/8btDdoFm467G9iDQd8+DVgajeGSyMUAgDmNPfB0bdDu7zZEsWPtf7AjlqA/vm73Gor38BYZiVPxFwDAjliCMdoT+BZ5FXdHvo/fkIv8vBZswLPZRQ3ofxGw2znAhB8Ah51auf0LDER/fI0B+MJPe/L3X2Ik3sad+BHux8nt8nT4n+9HMR4AMBQLsLcWuE/1xxL0J1+gP77GQH0BZu8MDJ4YnJzn156LJdE0Bv3ma9QmemFIv8MQ1aM417oPE/EoLIs9FH5C7zewD+aipQUoLt0FXxZP8dttf7yOP+MsXIErO2zXtq8Nou2E6amngNtuYzYuU6awtB/8oN1h5rhKQ//W/nsBAM7FLe32/eq4Q9cbxie3Aj9+F/j4VuDrmzp+PfvLhZ1uE6+7r3ew/x0mLuFzAkl7dt99dyxatAj/+c9/tqgJMwB0+UpzLBbD888/j2HDhqGurg719fVoDrkKfNMrzdsCO+20Ey688EKcf/75AADHcdC3b1/ceOONmDx5sr9fNptF//79sQhAv2wWZEaQR6IM5K8FBlwAfFULqA8+BPejEwCEfpRckkAcRRRmsj/iX4pVJj8dh0F/+wMWYxf8CLNwF34EaCWokSZEz98R+Ws7jrv+6In4ZM4/sOOFwPlvAT0LwEVvBttP+AHwKJ8M9csAS29edzvELgVMHaAzgDf7Afstq9xu/Bqw27jEUN4GZAaw+nqgR7Fye/qXwC9fA644FNh3KfDKPR2X/fuD07j2IGBlJIuz5wIRF7hlf7btwjeAef2ATxqATATYpRl4vxegusD824FhZwOU/2x890/AiFVBTACgeEDhGqDHxUCez5l3yABfd9Aew38CfFYHFCKVdaMzgM9rgUEXsLT6InDUp8DfhrfPo7EAxG02DjqEAn1zwJo40DsP3H7LUbiw8cf4uGl8ZT4/B9Z2ZZHN69cAF3c8A0iD/fDLIjinRb1E3TYFMQsY8znw9O6d70M8YOhqYMhqIGkBd48EHLUynk3B5YcCV77E3keOPBvWv/8IUAW34Kf4KX4PADBQho2OPeV0WLDAB0rbr1XbZpYXqup/T6Z1na2A07cvwD3r+14E7NwCzO0HWFd3Hus1Y/riV08vxi6/iOOBR4F9+TlX9wsg4gArntoFb5Y+xf4dWYk3NDDryWQSeOYZ4PjjWfqUKcD114M29gAhzA3k5puBadOYqw6A4E0mA1ThytjHHwO79c7AM6LIWRHU1DDf6FQKuOSSLH7xC7S7aLFgATM6ueiiyrwUBRg1Cvjf/yrT99qrY8vIs88GbvqNjWhS89th7X8/QcMBuwFgdphDlPlYOOIdYO1uuEM/Eect/gwRJQ9KKHJuzw7rNBLv4D0MhwcVAJBAHi1mHHpEgWkCf/4zcMEFbN+D9nPwzPMq4gmCP/wBOOss1pV77x3k1xfLsBz9Om1DCoJFyVoMybe02/bd77IhESadZuM1m93w/h45EnjjDWDJJxYKJQWj99Pw1I2fYth//4wBK99iHaTrwEMPAd//PhDp2kWKDaFQABbMNbH/YdH2G8V5a5psIn7nnczX9Fe/ar/vRuIceTRu+N8u+FXz7+SV5q2ILk+a99hjD3zCPX779euH1atX45prrsF+++2HvffeGz179tzuJs2//e1v8Yc//AHPPvssBg0ahKuvvhr33HMPPv74YyT5rUIgmDS/9r/XMOyBdRt6Y0YH3aGWgcuiiFlAyVjPvpx//AOY+MYg0N+1X+6sqxOeKw+9EqlIChfOvrDDCUr6l0C/HPBRm3nXOidXv/sM+OmgiomlYPZfgeYY8Md9gNcHtM9zdRzoWWTHxC2gMBO4bGwav9+/cnK3oRy5mP2IOGbSuvdr2xZlFYhexj88dQfoO+wWAJkBHPAVsCoBfNYA4PefAufvstFxtuWgL4HXBgAt1wLfPxF4eaf1H9OOWz8AzglssTqaNCseQBH80NgQbj34CZzz6vcr0n445Ic4c+SZyJazqI3W4js7d+6lVrJLiGpRrC6sxp+/2xuXb0Jbd1L7BZDZEZiuAn9+G1gR3AY591zgj+u1qqagUIDx44HHHut0L3/SLP5IPvcccPTR+P5EoMfJZ+L3R/0e8ZlxTNhjAh798FGcuvPxuPfUx2ApgGo7uOm/N2HKiCnokeiBqc9MxZ/+9yfQi4vtlqC8+N8X4/ojf9tBmJXfG4QAV14JXHZZ+123Jtq1ayd88QXQvz9z/uwOVq1itmSdLTlQKgHnnQfMmsVWjxw+nC3UNGoUc3/bUMaMYcdfdx0r49FHmXviRRcxa8rZszcs33VNms85B7j8cuCJJ5gt9SWXsLHkumxc6fqG12eL5KOPgPvuY7Z8gvnz2S+XnXdmE+2TTmKdmkwyD9NEglnNqiq7dN+7N/D3vwOGgWXLlmGHHXaQk+atiC5PmgH2pTR37lzMmzcPc+fOxdy5c7FixQroug7XdeFsycvldAOUMp/mO+64A9lsFqNHj8att96KoUOHVuznX2letAgkHkO/3ze0z+xfvwXemIbPP2cex2H1i+sCF/7uJfwhdxgOjp+FIdYZmPHjvVGb1v0f5PPnswX/bryRnZuCYycU8LvX98bOq9gSw6d+H7hvBDCkxxC883/voPcNfdBiVvoQr/zZSpz06El4/pTnYagGyBUEuzQBvz7+VjT++Bwczb2Mo1dFUHaZUXLtH0toPZf90dZcYNVvgfd6A4dPYfuOj9+Ex1t/DVzXClxuBJPmHd4Azuxg/flrCsBOLwInH8vaegZw0gTgwVDT0hnAz45J4/WeN+HVO06FcTX7RUGn03Yr72nEgEP5vek/LgLO7VhD0CPSH2sevxhYeDLQ+BHwowPb7SNiH3ABsOBPwENDgB8fx9IW/2QFrv6/PsgbwP0PmIheE7qacZUJXMY/X1UGoq2IHvNLmHuEtIB3vQqccXCHsXUbhUbgt2uAGUGbtZs037gM+Flw9arlFy0YMfUGtAz6E6YffinmLpuLhxc9jJ3rdsbi5sDsenSfb+F/K+YBYP0iOPOpM/GXd//SLv2b8MzEE/Ddhx7doGPDFBFDHCWAUrS0APV7LABWsR+3//0vsOuuQH09u+p60UXApZdW/s0MQ7H+GWhnk7sPVn+AIT078fP97DNghx3aXYGjlMKjHlRF7fi4gw9mprKUdjpbNE22IItSNdf+TUNXJ82bix//mF2oFHT9L233ce+9gaJhXTz2GPNjv+IKYPnyLHbfvf2k+Z57gKOPBnrwiyarV7PzpMprT2xVyEnz1sc3mjR3xNKlS/HWW29h3rx5uO666zZVXNsUYtIMLEI2269icgIAmEHxxz+yX+3dwnXXBbeXFi9mS4RxHAfQr6mMJ/+rPBJGcH+/dmYjMvZaNP28Cb2va4R9FfDCqD4Y8/ZyNJea0XvU//DsH47EEa+zfPbtty/e/uotUADvnfu+PxHwPPbSryHBpBkU6LkQmDoM+M1a4Bf8B8UMCjR8ApzHbn3GLaDY5q74t78Axv5gBs45+EKk02l/okynUzT8pgHN/McAnU5x3N2n4umv7gMA/POgZhzzWj0iTg+c4b6FP0V29vOk02nwg6X/68CPDmrXnB1dJQeAJT9dgr6pvh1P3j89Cvjbs5X5UNb+8+YBB/DfDcuWAT17O9AUDaT3AmDVUMx66+848zl2+dtQorC8da9gJ8p9btJzGDd4HPoP/xRLj98VAHDkoCPxvR3PwOurnsMDi/hyilc4AFXxkwtacfurDwHH/h9m7nMLrpt7ObIfHQrs/hTObF6DWfXsr+OzJz+Lo3Y5ql25TcUmaIqGdCSNlfmVuHD2hfjBnj/ACXue0G7fVflVmLNkDk7c68R11mVd/O9XUzD6usolIX+GG3Ajpn2jfGb+uoif/jKGBB/yov+ffBI47rh1lP8/NmEYwO+KeB6wYubd6Hfe8UBN5wLuzTq5o5T9oh45cr27bu1saZNmIFgZdUu54kop+3F0442BXGXxYuCmm4CrrmKx9uzJZDC7sq8Mv12feSaNk7m0ulAA4vEqVGAbQ06atz42+jfhDjvsgB122AETJkzYFPFsHwhZxZB/AJ8f0f1XIBpCV7bVyitS/lWBd84AffIveOPrNyomzADQekmwAtSefYcBWIBiT3ZVuT5WD+vDIwEA10avxbjB47Brw65IzExgl/pdKq6cKQq/mjUzizRciOdQL5k6CDMBwGwz0Vi7K2urk49Bcdc2QjswGcKRRvDXaNW0VRC/AR/6wUMYc1/wIEgywer91lGULfb2GvCvMx/BIQN2wk1lB7vtpuD9hW1+zMQrV77yLvegXKng8kOBL2vbhYMBtQNAKcX3d/8+Xvg8WKZut4bd8PEV/6zYdxKXgGga0yOqKlt4kH1vsk45bM9heLsE/GifkzFxxHFYmV+JBjIY9TfrgMru6uz45sPoMf46vL2CPUk2bvA4AJVXb+uxC4S0dfYp7B7tVEzEA1fci+/t9j3sfYWKWAyYNq0WP/n8GIy4D/jJ/qfijqmnI2u8Auz+FG683kD0P+dip7qdOpwwA0BjvNF/3zfVF/844R8d7gcAvZK9NmrCDAC5HXu1S7sJP/tGk+bWIYNxyVWVsobBg4HGxnVPmIH2q5gpCtDv16d3uezNAiHbxYR5S0VV233lVhVC2l/xHjyYPVcHsG23396xTOSkkzZ+GXaJZGtns99I2WuvvTB16lSceuqpFbrf7YFzzgGuDT+c98FE/9ZWtxJu544KbN0RqWXsYbID+ncglQgxoGYAgAVY3q/9k+2/POiXFZ8tt5NH9a0UwguIXzM9jlM/pth9Rse7D3nvn/ggdxaw96x22+pj9f77nongQZu2utibj/4NCvOOxzAuKQ9PKqMRFV8uCfalFDjsMOClN74DuBqw/Fs4fK+hIISwq7hoM7kOQQjB4xMf9z8XLylCUzTUXKygVGJXni66CPhtSGpqGMESzGFefDF4nzSSGFzP/5Jd14oTL1iIBxP745xT++Ls4+dg2r+m4Y537sBxu7af6T3xBLDzTy5CXaNdkX7v9+/FCXuegHjoKtjwnfuCTqfIZrO47DLgN48RfALAUA384eg/dFrvarDiIPY05d0jgEkLgL8PBfANl/iuff/TdmkffFApj5JIthcIYdpkiUTSMZtdxXbMMcfgiiuuQN++fTF16lS839Fjytso55wDvB2yliqX2XMB3Y64f0wp/HvQIaZpX+K9h4/pUlb3fP8e9L0I+PcRO69zv+/u8l1csN8F69ynXGYP4QPAbrvxxEce8LevWcOueCxcCLzwM+Ze0CPSD/POYhrZR3/4KE4cuu6rlaTArkb2SvbEk789FtEOHpjuiMceAz79IIkd7raBv7yB/1zw564d2IaYHoOu6vje99htT0UBbrll427XOqUE/nbdvvj9uN/joon7IBVJ4c/H/hlf/PQL/N/o9n/xBg4E8K8b8d51v69IP3X4qYjrnd9jPeEE4Oab2exRV7aQ+8sdQAHELwVO/x77/OVGWtsZxpZzO10ikUgkWw6bfdJ83XXXYenSpbj99tvxwQcfYNiwYTjooIPw97//HbZtrz+DrZxRo5izxQEHbMaHbw44gInQOuG3vwV26qLjQn2sHivSgEPdde73z5P/ud5Js2FUulZNmwZcfeJJvnylsRH49FN29eM732a30HWDYnTf0Vg0dRGO3+N4KGQdDTjDA/3tii7Vqy11dWzC3lXpzE/37cDQN8QDD/iuYhuNqgKKQnDevudBU4KbRQNrB3bYHuKWbP/+37wscVW90wfNthBclbl6UAoMvJCl/aOTZ+okEolEItkQqvK8tK7rOPnkk/Hyyy9j4cKFGDlyJKZOnYr+/fvj0ksvrUZIm5Uf/hB4/fXNXOgmfmrjpSUvbdL8ADZ5v/TSYBHSjhCSjz16rH/BiFtvJbjvvo27z+5569+nb6ovbhl3yzr3IWTrdCc4ZMAh+MWBv6h2GJ3y5G7ArE4WyPmqBjh/3OaNRyKRSCTbLlX/Mz5kyBD84Q9/wMsvv4xkMikdOLYCThl2Cp468anNXu4Lk1/AS6e91OX9p04FTulgNcVvwsUXB2s/dMa6NM5bOwkjgevGbJnnJKUU3z8J+O+OnWwH8Nymt8WWSCQSyXZKVR0VPc/DU089hdtvvx0vvPACRo0ahV//+tfVDEnSBe4bf99GHX/eecxa7ZuyrkUvuosLLghW4xJ8fv7noKAY9PvOl6GWbBms2L6eNZZIJBJJN1KVSfPy5ctx5513YtasWWhubsbEiRPx5ptvYnRbDyfJNsnvf7/+fbZkdqqrFIATabWwRTF+9/EAmINJoftW45VIJBLJdsZml2eMHz8eAwcOxAMPPICLLroIy5Ytw1133SUnzBKJ5Buhq5UWF/0U5kf82MT2S1gvTQHndmwvLZFIJJLNzKxZs0AI6fCVSCTguus2G6gWm/1KMyEEzzzzDI444ojNXbREsknpmeiJ1YXV/oIhks3L8XtUis1f/clz/vv3RvXF34Ytr9jeUrmGiUQikUiqxLvvvotEIoEXXnih3bZYLAZ1S1oVKMRmnzQ/9lj7q0ASydbImz96E4ubF2PPHntWO5TtkrDdHgDs1CNYIXD428uw4IpK2YwrVTQSiUSyRTB//nwMGTIE++23X7VD+UZUzT3j008/xVFHHYWGhgYYhlHxkki2Bnaq2wlHDJJ3TLYIvjx4vbt4ctIskUgkVYdSigULFmCYWKJ3K6Jq7hmTJ0/GDjvsgDvvvBOJDlapk0gkki5z98vAXZVJSSOJvJX3P7tVN9iUSCQSyaeffop8Po8hQ4bAcZyKbYSQLVaaAVRx0rxo0SK89tpr0LSqut5JJJJtANNsfxl5vx32wwufM70cJfJKs0Qi2YahFMjlui//VIqt0rUJmD9/PgDgwgsvxIUXXlixbcSIEXj33Xc3STndQdVmrHvttRdWrFiB/huytq9EIpGEiKzDWm7K94BPGoDG4uaLRyKRSDYruRxQU9N9+WcyQDq9SbKaP38+CCF47bXX2kly6+rqNkkZ3UXVJs133HEHpk6dismTJ6N3794V2w455JAqRSWRSLYVKF+L/V7mRIejP6liMBKJRNKdpFJsYtud+W8i3n33Xey888444IADNlmem4uqTZo/+OADzJkzB88880xFOiFki/Xnk0gkWycqUfFWP/m9IpFItlEI2WRXgrub+fPnd8k14/TTT/elGplMBitWrEAmk0FkXbcWu5mqPRrz85//HDNnzkRrayts2/ZflmVVKySJRLKN8q1+38LaBPCLMcADe1U7GolEItk+WbVqFVauXNkl54y7774b8+fPx9NPP43a2lo88sgjVZ0wA1W80pzJZHD++edXq3iJRLKNQ0H99wph1weuP4h9Pvl94M4pw3BWNQKTSCSS7RRx5VjTNLz55pvttg8bNgzxeNz//PHHH+OEE07ArbfeukVId6t2pfmoo47C66+/Xq3i18mTTz6JYcOGoa6uDrW1tRg1ahQeeeSRin1aWlowadIk1NTUoLa2FpMmTUJra2t1ApZItmNOG35ah+kjeo3w3xMQPHTCQ8HnGcBz4wZ1c2QSiUQiCSOcMy6//HLsv//+Fa8DDjigQm3wzjvvYMKECbj33nu3iAkzUMUrzT179sSxxx6LCRMmoE+fPhXbrrzyyipFxRg9ejSee+459O3bFwDw6quvYuzYsdhll10wfPhwAMApp5yCcrmMzz77DABw4okn4rTTTsOTTz5ZtbglEknA9UdcjysOuwKpa1NQiIIfDPkBEPrtG74SLZFIJJLu55e//CV++ctfrne/V155Beeccw4effRR7Lbbbpshsq5RtUnzwoULMXz4cCxevBiLFy/208km8gHcGPr16+e/9zwPiqKAUopPP/0Uw4cPx5dffolnn30W8+fPR2NjIwDgxhtvxIgRI/DVV19hxx13rFboEomEoyoqkkYSQCDPCCPcNSQSiUSyZTFhwgREo1FMnDgRAJsbvv766xXSjWpQtUnznDlzqlV0l8hkMhgwYAAKhQIcx8FBBx2E7373uwDY7YVIJOJfdQaA4cOHwzAMzJ8/X06aJZItjI4mzRKJRCLZMlmzZk21Q+iQ7Wo5vilTpuDee+/tdPu3v/1tvPTSSwCAmpoatLa2wjRNPPPMM/jkk098E+5sNouaDkzEa2trkc1mO8w7mUyiUCh0ul2yYeS6cwWk7ZitpV3TSEO11HWeV/+31//hmF2OQTabRRqBJZNu61U5H7eWtt3akO3aPch27T4KhUK1Q5B8QzbrpLlv375Yvnz5evfbcccd8dVXX23y8v/4xz/ihhtu6HS7ruvt0qLRKCZMmICjjz4ayWQS5513HtLpNDIdmIi3trYi3YlPommauPbaa5FIJAAAhx9+OMaMGbOBNZFIJIJUZN2m+9cfcX2H6VLTLJFINjcvvPACXnzxRQBy0rw1slknzS0tLbj77rvXqyXsaEK6KUgmk0gmkxt0rG3b+PjjjwGwtdHL5TIWLFjgew0uWLAAlmVhxIgRHR7vOA6uvvrqCr20ZNPR2Y8Vycaxpbfrf876D/Zo3AMJI9Gl/bMIrizbul3V+m3pbbu1Itu1e5Dtumk4/vjjcfzxxwMAli1bhnvuuae6AUm+EZt10tyrV68uOWO0ddPY3Pz1r3/Ffvvth0GDBsGyLNx777148cUXccEFFwAABgwYgKOPPhrTpk3DAw88AACYNm0ajj32WKlnlkg2I6P7jt7gY8fsJO/0SCQSiaTrbNZJ85IlSzZncRvMZ599hunTp2P16tWIRqPYdddd8cADD/gPAgLAfffdh3PPPReDBjGv12OOOQa33nprtUKWSCTfgA8nUuy+e7WjkEgkEsnWxHb1IGBXueKKK3DFFVesc5/6+nr/KrNEItm6kG5zEolEIvmmSB8miUQikUgkEolkPchJs0QikUgkEolEsh7kpFkikUgkEolEIlkPctIskUi2O/r3r3YEEolEItnakJNmiUSy3bGBdu0SiUQi2Y6Rk2aJRCKRSCQSiWQ9yEmzRCKRSCQSiUSyHuSkWSKRSCQSiUQiWQ9y0iyRSCQSiUQikawHOWmWSCQSiUQikUjWg5w0SyQSiUQikUgk60FOmiUSiUQikUgkkvUgJ80SiUQikUgkEsl6kJNmiUQikUgkEolkPchJs0QikUgkEolEsh7kpFkikUgkEolEIlkPctIskUgkEolEIpGsB63aAUgkEolEIpFItl8+//zzLu2nKAoGDhzYvcGsAzlplkgkEolEIpFUjcGDB4MQss59KKWIxWIoFAqbKar2SHlGJyxduhSTJk1CQ0MDUqkUhgwZggULFvjbW1paMGnSJNTU1KC2thaTJk1Ca2tr9QKWSCTrZeroqRi/+/hqhyGRSCSSEPF4HJ9//vl6X7quVzVOOWnugObmZhx00EHo3bs3PvnkE2SzWTzxxBPo3bu3v88pp5yCVatW4bPPPsPixYuxatUqnHbaaevM96WXXurmyLdPXnjhhWqHsE2yLbbrrd+9FY9NfKzaYWyTbbslINu1e5DtKulujjvuOAwYMGCdr4EDB+KYY46papxy0twBN998MxobG3HjjTeioaEBhBDssssu6NmzJwDgyy+/xLPPPosbb7wRjY2N/r5PPfUUvvrqq07zffnllzdXFbYrXnzxxWqHsE0i27X7kG3bPch27R5ku0q6mwceeKBL+91///3dHMm6kZPmDvj3v/+NnXfeGePHj0d9fT122203XHXVVXBdFwAwf/58RCIRDB8+3D9m+PDhMAwD8+fPr1LUEolEIpFIJJLuYrt6EHDKlCm49957O93+7W9/Gy+99BKampowd+5c/PWvf8VDDz2ERYsW4ZhjjoFhGPjFL36BbDaLmpqadsfX1tYim822S3ddF9FoFMViER999NEmrdP2TrFYlO3aDch27T5k23YPsl27B9mu3cfq1asBwL8g1xbTNGFZVofbDMNANBrtttgkHUMopbTaQWwu8vk8TNPsdLuu66ipqcGoUaOg6zreeustf9u1116LJ554Am+99RaefPJJTJw4sV1ekUgEDz/8MI477riK9Hnz5mGfffbZtJWRSCQSiUSy1TN37lx861vfqkgzTRO9YzXIoONJc+/evfHFF19sUxPncrmM+++/H4899hjmzZuHlpYW1NXVYfTo0Rg/fjwmT55c9fpuV1eak8kkksnkevcbNWoU3n///U63jxgxAuVyGQsWLMCwYcMAAAsWLIBlWRgxYkS7/QcPHgwAOPPUQ3HDVSeDqAbgOaAOm3QTLQqoBqjZyg5QDBA9CppbBaj8SVFFAYmkAYV1GS00AXoURI8D1GNpjgWiahX5UisPkmIPMNLcSpBUb9CmL0AaBrB8rSKgaKB2kZejAbk1ILX9AM9hx7kOUGwBSffyy4GiAPm1rJxUD9BSK2AkQYw428fKA1YRJNEYxEsUQNXY/+F6l/nVeaKwePh2aAarQ2EtSJ89WT5rvwCJ1oKWmtkhkTR+9eu7MfOS7wGawdKitYBrATo7uaiZBTGSoLmVbHuikcUTqwU81nZweZsZSd5Wa0B67wra9AXbHk2zPD0H0OO87CRooYn1AW8nohms7ryfiGoAehQ0y8tuGAhaaGbpAGixmbWJogX5FJtBUj1BzTyPtx60dSlrSzf4AqXFZoC3N0o5kB6Dgu2ex/q7xyDQ7Bre3qxM/xgANL+ajSuxTYwHK49Lrn4UM6efBHgeiMrHHfVYvUtZkNo+LC27CiRWC9hm0PehtgTg9zGJ1bLPpVaQeD1oiaenGkEzy0Ei6WDfmr6gudVBn5Sa2Vi2in4daKEJJNkTtNDs54NSFkg2sO2tK+DUNkJbvYT1IYdE0/749ts8WuvHT4wkaHYFa1MAdO0S1paZFTy2PkAxA6gaqMnjre8PuvJjkIaBLNNiKxuDvK+hKKzvI0lcctm9rG35ueufw64DUtMLtPlL/7z1t0cS7L2ZY+1XP8D/DEVj9RHnjmMF3ycASF1/oJRj40PsY8T9/gbAYrXNoP8SjawcIx6MRRFbI2+X5i9B4vWAYwV1KLWC1O7AvhMAdnyxmcUAgGZWgUSTgOsEY5GPHX/8Khqoa7F+KrOy3XQD1Oxavz1pbiVI3Y6sDgCo5+CSKx/CtVeeCsrTSIyPbcfisWXZeaAZ/vcDykXAtYLvzWRPVgf+fQfqAY7N4ojwMe05fp4A2Dlsm/52amZBIkm2jzi3BOK4aJKN72iatQUAxGuAQgucJLuTqa79mp/3jt9nNLeSfT+roXwLLUE5tgkvWQsl2wQnVcfyaV0DUtcPKBeCeBU1+HvS/CWcut7QyxZgxPx6l4kLlaj49aV347rLT2b9YgSTFuLaQCkHL1nLui3XzNqAjwXb0KAXi0CiDihl2EF6FDTXBNT1DfIhBMi3wE2kgjQQeJRdgdU8AJQG4wRAyVAQowZKhLVnrGSx894qsbJ1BXrZgheNg2TYFV2iaPBS9VA8fq0wvxbFRAyxPGsXr6YHCAgUqP7fN8RSgFUCjaWDOtvl4Hz0HHZu8b4DwMowiyjxP98xJQEoChzPgmay+KBoyFoHon///v4cIYxlWcjAwi36gYi1maqV4OCCla/DsqyqTyI3Fffeey9+8YtfYMCAARg3bhzOOOMM1NTUIJPJYOHChfjLX/6Cyy67DNdffz1OPfXUqsW5XU2au8rZZ5+N/fffHw8++CB+8IMf4KOPPsLtt9+O888/HwAwYMAAHH300Zg2bZovXp82bRqOPfZY7Ljjju3yU1UVAGC4JtJ2M2AD0HX/5IajAMkagPDPBgHWrgDSyeAL1rIBKwfU1rPPms2OWfUlkOJf5Gubgd49AY/no6tAMQ/Yq3gkJlD4EqhLA+YylkQUwAVA+ZeyFgdSUYDkAI3/cVU9oGABKs/XM9lxEeGpmAPsAhChQAsvKx4FIgoQ5V9OpgPEU+wLRuHHZVcCyRTAJx2oqQVUNZjM5NeyuukUKC/ndXIBpQAYPF97DQziIR1TAWqztPIK1l51PfjnZkApAY74suLHa+XgS9jQgHye5Q8AxAHySwAi/jDmAM8GNBUoN/F9CvwPKY/FswBPYWeV+PJvXQVYAHReZ3ctUFoDGPzbNGoAcAGVAJT/USm2AorFxggA5DMsT6UAOPzHjaoBZh5o5HV0skDxS8Dh8acbAK8MZBYHaXqCjafcavikGwCzBVD4GHKagUgcqEnDiBhI67xNVR6/57E4lSLg8HYorGJtWeJ/kEXcmgoUWvl7g/8hzfH2dlk+SSNoF1pkPxIpr2PhSyASDdrXpQAygJcHO4kAWK2AXgPYzbxCERaLyceLagJuDqhNATbvf9Vg+fA/cFAIK8fgedoF1ic6ZWMJAKIEKC0FVD427dWAVwQUA1D4GLKbgJganCdamZ+L/GtWjQNuC6Dp0ImNlFIAqekD2rosmIy5LtD0IRsfCm/Pcp6dbyYfH54DoAi08tvlehRQomz88R83cPJAPA4UMkG8pRaQXoNBW75macQF3FYgyr9T1nwJ9NoRKPM+KpUA6oHEkqC87Yhmg0ZJcD4qJcBrAaJxNqkBAKICpa+DSam7FogAcHkfRfm5p3pBHZ1mPpHl8dMye+JGKbB9AaC0EogZbBsAxHV+DrN4SV0fGBqQIjnA8II+sDPsRz54frEIkF0G2HzsaQagK0CEjVsSUwAjCWrz8U09NjF3LTbZB0Cza0BSvUCLLfyYGlDYwXiJpwG7BTAMwBXfmw5IogG0zOInehTUcFmMbj5ou7gKmPwcjalsu87j9zJAKgYgDxT492Y0DegWqycAGFHAaQG0MghhZdHaBAhyoGX2/UzSPHbxoyWhs/NEcdi4AADHREqPAqUsIk6RtSsUEF0DXfsly6fnLqBKkZUHsHPIWgGS4j84iAdKciDEAGK8DvE4KImB8POGumUQIwVq2IDTyseMBZLuA1pkdSJGAtQqAOJ3gmsh5SkALSBd24+lqa2AWgblY4oYNaBOCXDKrE0BkHgtqJkB0SKs7BhBOpYIvt9UF9QqgBiJ4LtJpUCqnv24AtjfBo2VzcaPAjTUg5oZIMLajigWkIoiLX7seAVAMUC9EkgN/9sQSQJ5NhEXc4SOSOk64iSYqr3nrMXb7ppO999aefTRR/Gvf/3LvxAZZsKECZgxYwYWLFiAyy67rKqTZvkgYAfsvffeePTRR3HNNdcgnU7juOOOw9lnn40LL7zQ3+e+++5DY2MjBg0ahEGDBqFHjx7461//WsWoJRKJRCKRbEsYEaXi9a1ED5ya2rXaYW1ynnrqqQ4nzGGGDRuGJ598cjNF1DHySnMnHHvssTj22GM73V5fX99lixTBdw5ofwtGsvGMPXiXaoewTTL28KHVDmGbZeyhe1Q7hG2SsYcNqXYI2yRyvFYPTSPQxN1Zkeate+W8rZW99toLU6dOxamnntolKW01kFeaNyP/eWMxZr/ySbXD2OYYe/C296t7S0BOmruPsYfuWe0QtknGHr5XtUPYJpHjddMze/Y8XHLJJevdT9dIxWuB04S/FT/dDBFufo455hhcccUV6Nu3L6ZOnbrOZ8uqxXblnlEthEVd6xu/RDoZATwKmGUgyfVkphXofAGmBzYtQFNBc1yf1aMeaM4APbn+cGUTkE6CtmRAIlyfZdsgDbVsPwBorAOKJZYXwPSovRuBplam4QVACyWQRCykhU0CrTkgnQCyofXd49HgoTnLZrGL7fEoUDSBHvXAGq5bNDS/Dv4xtSn2v0cr8qVrW1kdUwkgXwzqWDTZ8flSZbx1aRYjANTXsGOikSA+TeUacPHwDI9F6IgVwvKJRVk/+PWjQbwiZkFtCnTFGta+eaFR9JhWUvSdobNyokbQnlGD5ZvNB+2bL3ItM4IyHbdyDChKEK+oX9QItJlmmZUl6izqLcrVVHaMoQdtZeiga1tAejUG+fbsAaxcDdSng7R4jP0fjl+QL7K2UpSgrUR8Dm/vZJyV2VDHdLEAoGksrTZ40If1rSgnwsZtbYr1u8jHsll5AEu3bBaTxm+SFUtAbS3QxB/aiUdZG0QjQTuJfMKIfhdlOy6Q5m1QLACRCGDbgMHzyedZW4t8DJ2VpRmAxfNSCGurBLejXLuaxSZ0jdRjeSRqmR4dAMrlyjFUNFm8bceDR4PP0STT2Iq+LpaAHr3ZA5BifDguEEsAa5sq6wmEzhONvRftrRCWT0tT0JZFkz1H0bQ66JOiyeoFBA+6qQaQ47pWTWVxhMe8ZQfjxXGBaAxobmHfMwD7LunRI3jGQDOAfA5I1/kP+okHFX39dynL9mttDfpEUVjewvpTfJfW8HiF7l5Tg3zWrGT1yfN4k/xBNvHAJPXYWFAIa3sAyLaw9hTjt7YWMEtBH2kGyy+dZuNJlGlaleNZnF/iOQCisPLEeZOqYX0ovgviCTbejEhQxx69gczayu/auh5s3IUeDoRtBhpmx2L5iLEq6mnmgzrmM6wtxYNryRqWhx4Nxm8NfzA33EeeU/m95FhsP/5gKBT2MDiJs4cUaX4N6wtVY89ziGNi6aAPxIOuYjvAtmfXAg1c02zmA02/OKacZ+0gzgs9GuQJsLgjSf9hU7gOyyNRz8YXAJLsAZpfA5Jkz4/QUouf5h9DFPYQqxhT1GPPA4hjii3BszriwdtyHrnUFP9ht7T4/uGIecPjO45BQqlcOrrg2Rj/1QsdHre1Y9s2Hn74Yfz5z3/Gq6++igMOOADnnHMOTjjhhKovoQ3IK82bhKamJtTV1WHWrFnVDkUikUgkEsk2gqYRaHqbl7ZtyjMAZv178skn4+WXX8bChQsxcuRITJ06Ff3798ell15a7fDkpHlTcMUVV+Dggw/e5Pm+/s5XGDn5bnje+m8G/G/Rcuw5cRYscXVVIpFIJBLJVk0kQjp8bQ8MGTIEf/jDH/Dyyy8jmUziuuuuq3ZIctK8sSxatAj5fB6jRo3a5Hmff+2zmH7mQVCU9Z8go/fsi90HNuAP9/13k8chkUgkEolk86O2ucr8v9Ia/HnNx9UOq9vxPA9PPPEExo0bh1GjRqG+vh5/+ctfqh3W9u2e8eCDD+LWW2/Fe++9h1wuB9u2oWlBk1BKMWPGDNx5553IZDLYe++9cdttt2GvvYKHTS699FL87ne/w1133bXe8mg2B+pagKYxM3euj6SFYuDdCICubcWct7/CyqYcjt1vANOKtuZAXRdkNdMMU9cFWpj/qPD9POv4UTj72ufw06N2g6oqIFzbTPkSnUTXQb9eARKL+TpGoutMYyg0c605pkfLF0HtkA50bcl/S3Sd6U95W9EMO4asaQ50dR5l+XiK35YkX2qv280WWH4AYNkVdQQAaBrTagu9rOOAfr0y0ASvaQbhHpe0xOuUSnC9INdwCs0m14BS22aLf5RMX4dLHD3Qmot4VTVou+YsSF1NoEMEQNc0gyTigBPW7zmgOZvpxEV7JuPwHx0QGnShM48agXZbmNQ7LtNK9ubaY6HXFvsBTFetkKDfRB3FXQlFYXXJFwPtvOexPgvreYUWWGgzFQVoagnarjXHjhH9piiguQLTlWdDWk3LDvK1HNByGWTF6qDsbIFpOYXOVeg7w+Ouvob9L/q2Ncf06GI8CK1v0QQts/YjDbXA6qagXbIFVgehG/U8dnzUYNpaIND0h/Xhhh5oTy2b18cC0qG+NfRA06woQHOWleOnkSA/kU+xEJSTL/JyVgaaT0MPyg/3g2gjgeMG5cTt4LkCgPXVqhUsD3GeFE0gWQ7GmYgzGgny8Wil1tiymZ5ZxK+pLL/Vq0J1488pNDcH5dSmWLsLvboYi+K7VDwjIWIxy2wfhQS6bIUAhVzoc5mN8bbPJjguYBSDNqlVgnYomkx/G9aeC12/0EqL86E1B0SD8QrPCT1n4LAYw7p5kZfQdjsu2y7aTuiqub6Ufr2MnecKqRwz0UjwjEE8GrRbhuuTDb3yGYJMa2VaNsvqZ5aDMbRmJf+OCB2Xa2F9538fECAWA5pDenUjEujQHTd4FiMbShP9BTA/bo8yn3QxXs0s01w38O+qbAvTU3M9MKtTFFjzdXCM+B4W+0TiQHZ1sC/ANMFh/bLQ+Iv6OA5Q4M9XiP1ss/K7zdCZplszgnon40xTLLTeZonpp4WnuGawcUiUYLGm1mVAJB5omPMZtsCXwHVYPppaqZcmCugq/sCeZQM1DaxdRB3NEhB6xKMzNJ1AU4O/mwc19MKI2no82vTl+g/eClm+fDnuvPNOzJo1C83NzZg4cSLefPNNjB49utqhAdjOrzTX1dVh6tSpuOWWWzrcfsMNN+Cuu+7C7Nmz0dTUhAMPPBBjx45Fnj8w8vzzz2PXXXftcEGTjeWxlz7Bd/bdGaoadNFfn30fI07+C2oPuwlDJ92NB1/4qOKYw781EKuaC/jfR6vaZieRSCQSiWQrQ1dJOwcNXd025Rnjx4/HwIED8cADD+Ciiy7CsmXLcNddd20xE2ZgO7/SPHbsWADASy+91OH22267DdOmTcPQocx666qrrsKsWbPw+OOPY/LkyXj77bfx1ltvYdy4cVi8eDGi0Sh23nlnHH744Rsd29sfrcL3jwxsfu55+j1Mv+NVPPqbCRi1e2+8/s4SHDPtMezQK42DhrLlSKMRDbv0r8f/PlqJfYf02egYJBKJRCKRVI+OHvzTyLY5aSaE4JlnnsERRxxR7VA6ZbueNK+LTCaDJUuWYJ999vHTNE3DyJEj8e6772Ly5Mm49NJL/ac5Z8yYgR122GGdE2ai60wiQSm77V/it/4jBmgmBxLht4wUBc05E2ld8W9H3Xzff/GrU/fD3rs0Aq6Dg0cNwA/H7I57/7kQB4/eCQBAy2XUJCNozpV92QbR9QorNiGF8GUH4taquGWWy4M01DHJSCLuH1dhb6ap7NYXv5VMCAFSCZZH2MJNWHkBTA6Ry4OkkqFlZPUKCQhRVSa14LezackEiUZANDUkvUiCIu/b7Al5Ay1bgUyjUGLv/dvMbYa54zCJiucBsZAkIiQdIbw+xOFX+g0ua/Cof5uZxKKBLZfIIx5FxddZlEkxfPlN0WTlCqstj7IlvS07sLITMYdvNRbNyrKSsYpb9tR1QaJGcAuUy2No2QIRfSDqFbYl8zyWl3/L3mN5ibKFTaAoNxoJ6ickENk8i9e3xYqCeB7rG5GvGENh+Y6hB7fETYvVNyzhMHSeLw3aIGyrB3BpQ6h/62uYdCgsc2hj+0dtG0RTAUNYoKHS9lFRWNxGmz4QtokiXsdheQp5gN+mbpAPl+f4x4t9RMyWXXn7XVgNtpNM0cp6Om4gd2grDxH5aGrQvgDr2zXNTFoj8hV2cKK988VQH4WkKb7MQwHygUQJisKlFkogO+hZzywxw7IgUQfRH6bF+kGMRcsJxoRfBxJYYgrC8hJRB1FvIQkT+wFB/4XzzRdZum8VqbExI8aV2LdWWPSF+ib8XeI4wXkh5C+8PiQRZ+0Ytlm07GBsA4GFYnMmKNOyAytAESsQlOtbQIb6xvOYpKq+JsjDsivPEyFVEO3tUWb5J85hIPiO92VLVpv+UADTrJT0lApsDAlrw2gkkIb4bcdlE6JPDC4dEzZqpUJQXzMbtG081sZSUw/q7VHWFq05QCsGdRTfaaLNPAewvcrxkArVuVjibcv7MRFnx5dDlo5eOZBfiHZwLGYXCQQWix4N7CWFxEeUqyiBJWRYBtYFVN2DqnmVacTrZO+tm8cee8x/n81m8c9//hNLly7FxRdfjFWrVoFSit69e1cxQjlp7pQs98CsFX6knLq6On9bmBkzZmzS8uvTUWQLgW7q06Wt+PkfX8Kv/vSKn+a4Hg4evkPFcZl8GfXpKCQSiUQikWzdKBqFqlU6aCnYtpfXmD9/PsaOHYuamhosX74cF198Md59913MmjULjzzySFVjk5PmThCG4a3iAQ9OS0sL+vXrt0F5XnLbyzB09mv1yH0GYOy+O3W676jdeuODz4OFCXrXJzD9zANx6lFsmViiquzhstAvcbPs4NOvm7H37tX9JSaRSCQSiaQ9s1/9BLNf/RTwPNip1evdX1EplLaT5m18TboLLrgA06dPx9SpU1FXxxbBOfDAA3HmmWdWOTI5ae6UmpoaDBw4EPPmzcP+++8PAHAcB/Pnz8fkyZM3KM+ZU7+NdCLSbrLbEeMP3RVTrnwGrutBVRX8dMIIXHPPf7HHwHrsvVtvOK6D9z5dDQUUo4cPAADMeecr9KyP41t7yEmzRCKRSCRbGmMP3hVjD94VsGzk+l+AW2+9dZ37q1rlQocAoG7bc2YsXLgQL774IgAu/wSQSqWQy+WqGRaA7XzS7LoubNuGZTEZRLlchuM4MAwDiqJg6tSpuOGGG3D44Ydj0KBBuPrqq6HrOsaPH79B5V1yx2s49tu7Yuyo/kw7FdIOkpCtF9FUjBmzF3re+jL+Oe8rfP/bu+L8k/dBY586TL3xRXy2tBmaqmCvnRpwxbnf8fVjf3nmA5z/w72hCQ0d17wKrS88j1mpxaNsWVgg0FRyjaKvMVSUCj0y09K10dEJCy+uw6OFUqDLthymFeTHkEQciPH3Ka7njceAFasDfbLnVWjoSCzKtNVif7GPplVq8eJRENMC+PLQZFUT0/cJXZ2wrRJ1TCcqba+AQC8q8hUWauKz0PfVtrHOCtubhZcJFu0otMhiH2FbFrac09RK+zihR/T1nmVWt7Uh3aLQRvNjiFgqOtxnisL6vsJyTg00mpoa6AZFXzoua8uKJY8jQSxiCe1sPsinvqbS5ktRmI4zrOkTCI2p0KaKPDyPLRlcKgX6TUOvtF0TFmWGBiL6S2h3hbZUWPxl2Jcr6VHPy/T8fAkhbZaPDulqRTkiT9Eu2Txbll7Y7CXjfBltLTTOnMqlzoXGNWzhFl4WXLRvGDE+QkuM05LJ6hG2BQw9U+Cfv/lSaCy6gdYYYD5Jhg7i0UA7X5vi57kT5CtiDBNeXr5o8hiFDZwenCeiL1c2se+DHGsrouusTUVsjsOerwhrsIWdnGgrrhElqUSlRV5o+XaayYH061WpExY6eKGvFudW+LwQS6CHtbvJWHCM2Cek84emAtm8b3UIz2PnVthGTVEAywxisezAahPg5zqCthPa97B+3XK4vj70PawolRrnaITV0bf2Cy1nz8v1LejENtMCVq0K2kF874j2Nq2Q5V7o2RXHDZ0XSvA9JdomX2J9J84TYRkZtr9TSGAXCQQ64rDGX+wX/n4IW+YJTbaQRvoWm16gjc4W2LMiFVp8/h0dCX0PtTSH9NV6YL0IAEopaHOFVuYT59p6x6nUs4u6GXqwFLvYT3yX+bZ9wQWz2fOX459/ugTrQ1EplDazZKULC55tzdTV1WHVqlXo0ycwNPjqq6+qrmcGtvNJ83333YfTTz/d/5xMshN/zpw5OPTQQzFt2jTkcjmMGTMG2WwWo0ePxvPPP+/v1938/pfjcO41z+C4g3cBATDp6KGYdNwIttGjwcN6Zhlvf7QSH3y2Bg9cccxmiU0ikUgkEkn3omiV8oyXVqzBv5avX9axNfPDH/4Qp512mn8VfunSpTjvvPMwadKkKke2nfs0T5kyhTlZtHkdeuihANgVqSuvvBIrV65EsVjEK6+84tvPbQgzz/sOxh4wuMv7HzRyR8y//4wurQi49+698dFjZyNibNe/gyQSiUQi2eIZe8iumDlz5nr30zQKTff8l6pRKMq2faV5+vTp6Nu3L3bbbTe0trZiwIAB0DQNv/jFL6od2vY9ad7cXPKH/2D2G4urHYZEIpFIJJIqMvuVT3DJJV2QZ/ArzeJFVIpt1KbZJxKJ4J577kFTUxPefPNNfPHFF3j00UcRiUTWf3A3Qyjdxh/D3ALIZrOoqalB6/vXIJ2KsiVT08lKPZmmhpb+5Z6yQrsIBP6ZQn8otFJCkwwEy4wWQ7o6oRUEAi1abapSDxfyF/U1cIYe6DfFMUIrVjSZjlXEH+Nar0w20H2JmIVWLJVkHpZCiwswXVkkwpZDBVjdatLBkrKi/mENnaGzthGatLD/rzih+DLZQV2izBtTbA/rZsMayppatmxtuM7hZVHLZZaH78VJWcyi/kInHtayCW1s+G5BW69XUb6oY1tfX7MMNPYEirlAt1hXw5aabetDG9a6CU2iWAZY+L+KthNLCofjdZzKJZrD/sXh/MM6YtE/wnvaslmb0JC20F9CWvg9G0H7ASzOxgbWT2K8irFZoWvk5YY1qkLPLfZxnGC7OKdac5U6USOkuQ17dYtjhL5WtIvw1ha6V6GFFPrncDuE2yWkwfV9pg0dtIUtcU8iEeZXXMe16mJsa2pwnlhOZR+kk5WackPjbekGZfG+9/3NRR3D57qoV9tzXyB001GjQu9NC0Xmcw7mDU8S8Urv3vDzEEClXzfAvMN13k/h77yQ9piWLRZzWF8d5X72qfCzFKGYfU/wkM+x8O4N7+efk9wP3raDeADfD933sq+rCc4Vcf4J3XNorNKy5T+05MehkEpfdIUES8BzD33quv6zIH7bhHXbqURFX9Ncgbknib5NxFkewqfettkxRbNSuxv2Lk/G23wXh7yzhae80OmGPJdpoQhSV1MxfqGQyu+UsJd6PBp8h7T9bgprxsXfw/Cy3/Fo5ZLj4XEV7vvwEvRhX2nhay9iEGltvdPFEuLis/CADmutw8+YOC7TwIs8xfda1Ai+UyKGr2n3Pzsu+7vJv49proD8XpehpqYGmUzGd+wSiHnDklMPQ7rNHeSs5WDgX+d0eNy2wKJFi7Dnnnu2S3/++ecxbty4KkQUIK80SyQSiUQikWyBiAcBxWvO8iZc/fan1Q6rWznuuOOwdu3airQ33nhDapq3Ny65/lnMfvmjaochkUgkEomkisx+64suyTNUvriJeB25Uz2uOGDQZoiwepx22mk47rjjfGez999/H9///vfx5z//ucqRyUnzZmXmxUdj7Ld3r3YYEolEIpFIqsjYfXfq0oOAiqFAiQSv/6xoxhVzP9sMEVaPyy67DAMHDsTpp5+OL774AmPHjsVVV12FE044odqhSU3z5kBok6aesj+OHbMHxh66B9PKCg2s0C+GtZyOW6nFUxQgngJam9nn2nr2Pp0GnGC5bSgh7ZNVZsdrXK+lGkApW+EV6euxhC6quTnQu/XgnojlIhCJAxl+uyRZA5j5IF+rzHRfYX218AgVmmCiMI2rqgWaYFVjvqbCud22gWgsiE1RWN00g8UOsDw8J8i3XGTHa0bQDtEkUGgN2lPEKxBxew5Qw+vYspyle06wH1GC2FxepqJUlm1EA+/Q2nrANoFcSHMdTzEtcjQW9IHnBG0g4jDLQDwRfG6rERXjI6zbDrevqjE9cIWXNq3UFwptaVinK7xXhX69vo7lI/qxqZWNBzE+zBL3ao2ysQIwHTrANN9AoGuMRkIexVqljjEeZTHneL8IfahCKrWCjhO0pe8NWwzS+Dni6zsJYWWFNIq0UKzU8kYjTFcY1k8CvlaWZnIgDbWgq5oqvMhJTapSm1symZ5U1EHE5Ht0l7hfLvcnj8WYJjRi+HrZcB1Y+3I/WLMc8nFn/ej7rYPpIf06gmujC0Vfu8z8zZOB9rQmxfSqiXjgNcx9cH09rfg+Cmu/wbS3lD9nQFJJ1tZhH2+gvce6qvr5idiCPnMCn962OmquuyUNdaCZLCsv3L7Cg1i0VfhZAKGTD495PoaEBlvocymlvv5YWHf6etRUwm+rtnX0/1w6DjtmbSs7pq4maD+AjRfRz6I9Pa9yvAgP72ik0vM3X6z0ixfjVeC4lc/EKArbLp4nEc8kOKHvGNNiY0CcJxHDH78AG8sV+mseb2X7svYnsWj7uop8CsXgORNRH8dhY6AkfNH5OR7yjye6DprLgzSw1d9oLl9xDIkwvbDQ/tNC0f+bEy67o/FCalIVWmPqusExuXyFZzRJxH2Nuz/mG+qY7zsfq/55KDTx5bLvKV5RTtkKzi1K/Tr6Ps0LVuKZxT1x6623rlPTvOKCMUhH2miayw763PLCNqtpBti6GYcddhg++OADXHzxxbj00kurHRKA7dyneXMz8+fj2IOAEolEIpFItlvG7rcTDjzj0vWuCEh0AqIHP2b+/dkaPPvJqu4Ob7Nz+eWXt0vba6+9sGTJEpTLZX/7lVdeublDq0BOmiUSiUQikUi2QIimVEyaj9y9F/YbWI8/v/1VFaPa9Lz66qsdpu+2227+NrIFeO3JSXM10KNMSqDz25nRJLutH+XdQT1AcyqXZY2lWXr4Vkw6DRjxSjmD57F9AcBrBqnrD1psCY5J1DJpQDacFg/kDw2NrJyiWSmjsE0mcxCoGpBiy1ajlGXH1KSBQnNlLHo0+Fwuss/iLi5RWPxCVpFsBJyQDZIeBcp5JjkJSypsk+0r9hGxuzwfqwjSdwhgsttr1C6xeP3lYfn+5eBWGRK1gBb1jyGJBlDqgqjsVjDNr2HHhW3UNIPFHxG38Ivsc23otKIe6+doOviMQEpC4nWgmRVAr97seIDVxw7ZBtomUNMIFFsry7KKQV1ch0lAxO3VfI71a1hiYpuVS/aCWxim64LjaLDcNACgb28mPyly+YaQWKgaUFvL0sySb6UGILCH0tQgr6LJLNvCtk1hmYWwJwxbrQkLKnHrmlvBUUr9JZhpLl9hSUiBylv4Qt4hlkEHgNVrg1v9gC9RokKukS+Ceh63MmRl05acvyw9AL5sdRHULAe3ohUlsCbj+VRYmxVNVk7YuiwZZ7fWa9PBMcL2StjMOS5QmwJtzgTtUCwFsVg2KK8bFbfsiyao4/ptJ+pDLbtyKXDLDo4JLxUc6hMqljQGvy0etlAsmoGkIBnIGWjIbpIK6y8hdeHyHVqyQBJCTmKy9+KY4gpmzVdsCuqZptxWjNcpVwKJhSzGwhaXYUmS44KGl5jmdoGizeA4oF7Bz4aWeD+F5UDJOLMqq00FbW5avkSClsvtt4ullkNyEjYGuNQineTLTlvBmPe8YAz48ZqVsia+nZb4d0gNt5fj45fmSiAAsyXkfUIzBW6vxvuR14eGbUQ1lR1bkwjGQ9i+j5+ztNYDxFisTQXngYhX0wLpSL4IartAKlZ5vgmbUNEuilJ5DhZLbNyJ9uXv/T7hVpN0bTY4/zyPta+/XLfpj2+a4f2fYtInKsZiNs/6QSxZXyyx86Q25edLM7lAagWAZlvY+SrawGDLxFOzHJxv6QQbd0IiY9msbpYdSG9WNgHtXdXaQXQVRK+UMRHX62TvrZc5c+ZUO4QuIR8E3Ixc8tvnMfuVj6sdhkQikUgkkioy+92lXXLPIFG1w5ekOsgrzZsRqWmWSCQSiUQyduQOOPDwmevVNENTgdCV5n99uBLPLFzezdFJOkNeaZZIJBKJRCLZAiG6UvEaO6wvrj5+eLXD2m6RV5o3J9EE050acSCqAKVWAACJ1YGWvgSp6wWA62cVhWl5Y4EWlsTrAn1yLM00rdQDSbPjQBRQqwAS57Y9tglazgWaVgBwLJaP+Cw0x0JXTBSW1iN0RVxobA2mjyNGAjSzrNLeTI+CRFOgQvMrbNWETjdey7TbetS3fyPJBtDcqqCOVpHpih1hM8TyI0YipGn2gGQPX/NLXQtEZ0s2izqRVArQDFDKl8GN14FahaDcRAOrb20/P42KegnNLVFAVAPUKfnH0FQPoPmrwObNtVjMou0iSaanrusParLlX4kWYX2icT2cVQCJ1oCSQlBOuhfTXftWdnnWTuJzNMn6LdmD1QPg2uOQxlq0j/jcoz/Lx+J2gQDTlAsdMsB0z6karq+PBvvEEux/0fei3QEgkgKcHCtP7KNw3XSJazWFZjFsQ+YvRcu1sG21j4bOLLBUtcJqjYaXPM/k/KV0aVjzGV4qXOhrw3pfNwsvU4bayNrBy5QBtQx3FWtLtTEOartQykx/6K4qQG204TYVoST5krdlF6Tswmtl8Sq1EXitZSgpA9RkxylFG26zCbUXGw9eUwlKfdQvR9shBXdVESRlgRZYfCovj2S5djNnQWmM+ccCgNdaBmkt+ceQBHtP+DMQXs5icRRtkJThH6P28OCuYfGr9VF4mTJIcwlekcWr9nDgLM1B7cU0rF5zCWovG14zP//iup+3l2FpSn0M3poi1L5ME+quKbFyVhWgNrLzzW0qQqmJwsuxdlBSNqjpgPC28/IWlPoYaMGGwjWsXrMJUnSAkFaT2gVAJaBF3laNLtxVBSj1Md6PJisnI6zM+MQioQd92ysBmrNA4kLz7UDtEYO7pgSS0IM2rw/FWxOB12xCEW2Zs6DUO6BFBwpyfvsq9TG4TewcUC3W90quFBxTE2FlJ8qhNMtvX6Vk8zHkBmXzcUUSTlAOT2P1ceE1lUDiut8upOjwfrKDcmyP9SfvEy9TrmzfFrOiT2jZZW3QWoZie34+RFdAy/x7NKLCKzpQi7Y/5onJYhdjEa4HktCDOtZE4GVYni5PU+ujcJuKUHn8YrzQog2lPqg3iZqhMcTOM6U2EmxPlFgd+D7U9thY5fkqKcNvS8r3IbbH+oSfAzRngeRsvxxxnohzGQCUOH/ORZxvRRuq48JdxfpejB21PgpnOR939Ta8ol0xhkhUY+dtgsdbCNn2rQMxWa5Ic7bd6522beOnP/0pbrrpJkSjW96d+W235bdALrn2Kcx+aVG1w5BIJBKJRFJF/vXBii5pmmEogKG2eW27Uzdd1/Hggw9ukRNmQE6aNyszf3Ucxh7ahcdlJRKJRCKRbLMcOaRPl1YEJIYKEgle//pwJX796HvdGtuiRYvw3e9+F4cffjiOOuqobi2rI8aOHYvZs2dv9nK7gpRnSCQSiUQikWyBtLWcG/etHXHAkN740+zuceKybRvnnnsuHn74YTQ0NHRLGeujsbERJ5xwAo477jjstNNOUEIrVcrFTbYjSKwWJB73l7oWGlxqZkAadmL6YyDwHqaer2slsRrQzIpAr+yUQeINTLub46sDUQ9QNF9Pi1gahKigrh1sVz2mi+YaWGIkQM1MpXcxiQKuAxKr8ctiPsfcc9JzmL62wJfVjiR9z2OiM70hLawFSfUK6mQVA90s/5/mVjF9Ma8jVSwQVQcNLWVNIineRjk/Xn9Ja76d2iXAtVheANPSFppBYrytsitYmWHdr8f11qIsRWHa3+JavosH0rgTSIlrAFUNBATUtXxtNzwFUA14PXZiu+SagURDkD8A6vAYXa7dVQ2mY+Zty3S/cYC6rG5iH6votx1J9WJezjTUvn7cYkls/lnooB2T1Te8BHmitnKfhmTQ50ILne7J9OLhpZQNHUixtkShNaRTFvuA9UmSe9S2toa2cQ1zNMI8acWxpgUYWqBXzpeYvjkWDTSUhDDvWeGxq6lMf50v+vnSvAUSVQP9Yd4CtT1A5drINUWQqMY0oVyr6SzPQ4nrcFu4Lt50mcayjmuI80xr6uVtkDg/V3MW1MY43AzXlhZtpuPtm4S7nOnilfoYvOaSrzX1MmUoOQtuUylUTgkkroGaXP+7qsD0pEnWR26mDLW1zP7nOku32fS1lgCgJA24mTK0XmwcOktzfhxCt+02FeFlyv4xNGfBzZShxDU/zWsusfeW55dNC45fRyVlwGsuMf0xrxMtOHBWF3wNqNtisjoUbXg526+T1icJZzXTd/oaYaHRbjaZdjpThlIT8dtKbYwF/ZgyWFpd1I/Hy9ksnmJIe94rAa+5FJSTt6EkdXh5Ea9dqWfPs/4QGlSWrwXSbPp1ZDr0IlTevl6zCSVv+fpugGvGi46vnaY5th094v4YE9ppUY6zqgitVxzOCqGlj8FtKvn/izQvU/Y12F5zCWrfpK8RFv2ohjTYQndMc4ZfR5qzmLbXpX7fEl1lnskQGmEXSjzQxVOT9b3mse8hd00RJKGHtPQ6G7e2W6E19nKWrw/3chbUmoi/3Suyc0m1g+cBvEy5YrzQog1qumw88H3cTJm1n9AN10SY5jkT4eO75I8NVYyhol1xXovxJPT+4TYPjzulJgJnBX+2JZdg+bSW4eW59rgmwvTUIX0y5fUCmE7ebWLnkqg3bNfvWyA4h8NlU7uLXsta6DmPcFo38eabbyKZTOKss87C2rVrMWXKFJx++undVl5HvP/++xg9ejSWL1+O5csDpxC5uIlEIpFIJBKJpGPUDibNaueT5gcffBC33nor3nvvPeRyOdi2DS20YBWlFDNmzMCdd96JTCaDvffeG7fddhv22msvAMCyZcswb948LFiwAPF4HIcccggOOOAA7Lbbbt1SvY7Ykhc6kZpmiUQikUgkki0RTev41Ql1dXWYOnUqbrnllg6333DDDbjrrrswe/ZsNDU14cADD8TYsWORz7Or7fX19dh3333Ro0cPJBIJHHrooViwYEF31KzLLF68GF988UVVYxDISfNm5JIr/4HZ/+leAb9EIpFIJJItm39/urpr7hmaEkg0/FfnU7exY8fipJNOws4779zh9ttuuw3Tpk3D0KFDEYvFcNVVV8GyLDz++OMAgP322w+ff/45TNOE53mYN28edtlllw2q44Zyxhln4LXXXgPArpzvtttu2GWXXfD3v/99s8bREYRSSte/m2RjyGazqKmpQablSaTTCcDMghZbfH0qwDXKQtMKMO/iYquvGybxOqCmN5BdzbZrBmgpwzTJYp90L6ZX5vpWEq1h/sWljH+M7yksNKeqwfyGXa6JpR57rxmB9lWPsn0EQm9dzgdpisb2Dwn2/Xz4exKvY9pcDqntx7yl82tYQjTJjuG+0kTRgVRPpj22Ay9WprnlWlO3zOpEPV/DDNcCpS6IxvW/VoF5uCZ7sO22yTTXeizQ+wodciSk8zXilZph22SxRpmvNEk0sLJ4/CRaw8oS3tIAi7/YCsq10iSSAlQj0CtzD2pf+w0wHXopw9oHABwLtNQCUj8AKLb69SaRlN++NLcmiFPko8fYOOM6eCgai0+0JfVYfUutgd7czLP210N2P4oW9LXw8XasoKxIEihlg/0di2mWowbTJANMrxyPAkVetvBR9oKvH2rbLF14NysK0y/HuQa+KQfoCrymEsB9S2nRBonrFbpit6kIRWhCM2VAIXBbyv5wLmY06FEP2dWs7WJpB2ZORayG1aeU0ZCoc1DMaIiLtJyKeI2DcoHdFo0kXFglFdGkAzPPxogRc1EuqIimmG7UNhUYMdffHkmw7UaMHQsA0STLM8aPKWY1xNOO/79IS9TaKGZYPrGUW7G90KohmnJh5oKyzZwKI+bB5PEmam0UWnXEUg7ya1nbpHrYKLRoftmlnIpEXbA9XuPALKj+sazsynYR70u59mki3niNUxFbMaMhmnBhFlTEUuyYckFl/RBqKzNXmRZNOihltYo6RlOuX0404cIqKYgkXJRyml9vMx+0VSmnsrxDaeWSgkjMQ7nEBoh4H4kxzalZUBFNuCiXlIq2Ch8TSwV9y7ZriNe7KDYHaaKfc816RZ+0bV8xrgDAzGsVY0y8D6clam2UcqxNRX1itR7sIoFRw+Irt3iIpAG3xM43NanCzbtQ01zTnLeh1kXgtpShJFksbtaBmlR9X28lybyhlZQBN8M1zREFbolC0ahfx0ialQ0ARhKw8ux/u8D20WJAOatAj3hBvG3azyqp0CMeHJvnE/NQLqj+MaUca8tySfHrXcxo/jkmxpBVYmNMjAcj5sIuVx4TS7ko5dgxRtyDVWRjyLX5eEh78Mrw28pudaDFENSxRoGbd6FEAG7rDy3G3msGq7NdVqDHKbwyoDBJM6w8gXrjI2xukMkgnebrFXDEvKHlX+chnYhUbiuUUXfkHzo8TvDSSy/hsMMOq5BnZDIZ1NbW4o033sD+++/v73vkkUdir732wk033QQAePTRR3HzzTfDcRwce+yxuPTSSzsso7vo06cPPvvsM8Tjcey7776YNm0a0uk0fv7zn1f9qrfUNEskEolEIpFsgRBVBVFVzH7zc8x+k0kULP5A5zclm2UXN2rDi1yBSTrENgCYMGECJkyYsGEBbwKKxSLi8ThyuRw++eQTTJgwAYqiYOLEiVWLSSDlGRtBS0sLDjjgAHz729/G6NGjcf/991c7JIlEIpFIJNsKKtMwjz1oV9w0bSzGHji48o7uN0BclW5tba1Ib2lp6fSKdTXo0aMHPvzwQzz//PPYb7/9oCgKCoWCdM/Y2qmpqcErr7wCTdOQyWSwxx574JRTTql2WBKJRCKRSLYF2ljOjT14V+w/cgBue/jtb5xVTU0NBg4ciHnz5vnyDMdxMH/+fEyePHmThbyxXHDBBRg9ejQApmkGgFdeeQVDhgypZlgA5KR5o1AUxTfdzufzGDp06LoPyK8FlBLz6TUSQLyWpZeyzL+Za02pUwbRo+z/mj5sH9sEXbYQpIbrXLn2l5YCzSottrBtXLdMnTLTnfr+ygp7X8gBNdzTuHVVpZ2NorFftrYZ6JytIlAqBR67XHOMEhdwpWqA1mYgEQdyXPuajAPZPBAP6bY9BygWgBjXGi//BEjXBRpbPcrL5XpVMw8IDW4s7bcDVAMQHtGKxjyjyznmNw0E2m3C6+WYgOf5vthEizGtt11i+YuyS1nAKft1JvHakIbYZv7UPQYF+mHbZFpv4TNtl/xYKc+H5JgGnUS557VVAMo5X19NzRyIqoGokSAfqwCSaPD7kxgJVudQXxIjAZpfw3TOAItDNYK2JApr71ialQkEGnPuM020CCsjVgui6ryeXFPO+57E65gmPlHP29IKxoDQPdsm06MLr2dFYdrkSDwYQ2KMiSsk8SjTPYMf47ggug6oKqjYl/s60wyPXyXwmkrMa9bkXr1NJUAhgadq2YVXtEG5X2qhSQFRKEqZiK9pLrRq0HQPLWvYVYtUbQS5VopULRvXhRyFWVBRzBJf62jmCcoFFXY5pGEtKrCKiq8bjibYe9tk+wjdpdiu51RYRQVGnP0v9rGKCopZ1g5WUUEpx/IoZthxVklFuaDCzLNjilmm7xTazUJLoOWMcK1moVVBst5FvpntU8qoKGYVRJMaMvzUKeVU5LNAkms181kg2aIhz+/SpnKsDQotGsw8a6toUkMxSxBrYceU8gTJnAqzoKJc4PFlNJQLCkyuIxXvo1y/bOY0lFMOr4PCh5ACq6T67R1LuTDzLE20QySh8n0C3WspF2hYrZQCk2uWRVq5oFS0lZlXfA2r6AOTa43DZQsNu6iP0EubuUpNrT8+Ui7KBaVCt13KejDzCvSo56cxfXfQLsWMBjMX1LuUYP2UahT9piGS0Pz4oymV63SDY6yiAqukoJwQ40WBVWLxxgpCT23ANp1AS19yUC7oiJWFBttApODAKhmIlYX+W4dRcuGU2XeDUfJglXRESg6sEkvTIx7MguZrkc2cBqvk+eVEClznX3T8c0fVKKySCo3rk50y62erpECPBHUInztGzPPTWH1UeC6BWVDhlEPnEs9H7GMWVJi5IM2IqXBsEoyZkgK7rPi6eD3qwbUIrJIKu8w1yzkPrq0gkhXPKhi+5h4AjIIH2zSgRz04FuFj1UUpE+jv7bICw3Jh5jT/HC0XVHTp2q54EJAz+/XFeHrOR53u7roubNuGZbHvwHK5DMdxYBgGFEXB1KlTccMNN+Dwww/HoEGDcPXVV0PXdYwfP74r0WwWzj33XIwbNw6apmHgwIEAgEGDBuH222+vbmDYzuUZDz74IA4++GCk02kQQuA4TsV2SimmT5+Ovn37IpFI4JBDDsH7779fsc+aNWtwyCGHYNiwYVXVAEkkEolEItnGMPTKl6YCaudTt/vuuw+xWAxjx44FACSTScRiMbzyyisAgGnTpmHKlCkYM2YMGhoa8Oqrr+L5559HMpncLNXpKoMHD/YnzACw6667+l7S1WS7njRvrJ8hwLQ3r7zyCj788ENcffXVaG5u3kzRSyQSiUQi2aYRi5vw19jD9sDMi4/udPcpU6aAUtrudeihhwJgq+pdeeWVWLlyJYrFIl555ZX13yWX+GzX8gzxS+yll17qcHvYzxAArrrqKsyaNQuPP/44Jk+ejHK5jEiEWcEkk0lEIhHEYrEO8wLAJAaxOLP1Mvj/QGC7Ju4few5bGpsolZILooRutVtMNuE6vuwArsNumQubtEIzYJWBKI8p18KWbbZsoGVNEFexBFj8Kns8yiy/NDWwB0snWVqeLwdcLoP0qAddw34gENMCLZkg+SKzDQNAANBcAYTnS0slkJoUK0fYjTkugBZ+mx5Aucxu34tbUfkikxMUcoEUoJQNLM8AIGqAtnxdYbUGogBGHNTiyy+Xi0CyHljzNYsFAOKJQDoA8CW1vcAizzZ5viHJAcDShIzGtdh7364ty5a8bvmaWc0BTNqgKEFZYnnt7Krgs7ANFMtzOyYo9XybP0o9IN8UlAcE+woruOZVQF0PoInLQQYOYfKXWCwoO1lfucR3PsvGSssK0DhfAltYyQmJj6qxOEKyD6LHuMwkZDkII2gr1WDyHy+wD/T/b0gH/WjoQXsXmS0gzDII73/qeYBHQcQdIEMHsT3QNcVgCeGIypa95qoar2zDsYkvkSgXVLgu8W+lAkDrWg+eR9G0ho3DVFZFMe+iZS07xip7yLSoMIseaur4bf4yRbmswrEDG7JS0UOqRkepKG4z68hlXcS4FVgh7yFV1lHIs9uxsZiKcpkiVtb8YyJFFaWSh5TN5Q4lD2peRbnsQeO2embRRTSnoJhnx0RjCsySi3iOS8MyNqJxBYW8jUSSpWVabCSyLA0AElkFuayFRFL14ynmFZRKHvIZxY83n2FpbDvbN5VncQNAJEuQy7pIJLk0hG8vFS3EWnibl20k8qGykwrMoo14XuX5svdmyUU0xutY8pBKs3qH21drYXUFgHhSgVl0YUS4DCTvIFmjIp9x+HYVxbyDeFKtOKaYd5EqBfEm8pr/PwCUih5iWS0oO6+hXKawSkZwTIn1c6yg+2lWqbLvSyUvdIyHWElDLuPCiAb9VGjTLoW8jVhGQakUbisHpZLoExuptIpclo/VtOrX2ywKmYrGxlqN7tcnWdJRLsGXoJSLBOWi4ksZhCxByBYci723TcU/V+yyAiOmwLG49VrcQ5nLkVwuQ9CjFMWMCiPG5SQ5lUtZAjmMXVZgmwTFLMsnnvZ8Gz9Rjqqz2OyI6AMmkSjk2LhLpNj7hJAk5SgSKRVmAb4sxSwAsSRFlv/JTCTZ+ZgI9VMiqcOxKSLxoF2MWFBOJKLBsSkcW/GlFrbpwXOJ/9l1COwy8evo2C5sU4EeSisXmCREWOYxOz2VS7xYWjGjdU2eoWrB9yeA2S9/hKdfWNSVIyXdwHZ9pXldZDIZLFmyBPvss4+fpmkaRo4ciXfffRcA8N577+GQQw7BYYcdhjFjxuCqq65a96RZIpFIJBKJpKuISTN/jT18L8z81XHVjmq7pepXmg855JAu7ReNRvGvf/2rm6MJ6Iqf4T777OPrhCQSiUQikUg2KYoa3D0GMPulRXj6XwurGFDHzJo1C2eeeab/mVK6RVjEbWqqPml+66231ruUJKXUX6lmc7EuP8N+/fptUJ6X/PoeGBENcGyMHfstjD2s+qJ2iUQikUgkm4dXVq/BK2vWwLEUpLq0jLYRyNgAjB0zAvvvuxtuu2fLumB30UUXVUyaGxoaNtkzXosXL4aqqthpp502SX4bQ9UnzYZhYPr06evd73e/+91miCagO/wMaTmHIw8djnFHH8SWfxbaUrvEbNNamOaWLVvtVBqY69FgCW2A6XQ1vpy10JLmM4CWr9A/QVMBMfF3XKZZtmz/GJorgMSigRY567BfiJ4OWuI6Vtdl+wvtsVkGzeZ9zTM1LaBYAk3GQddyv6rWHGjBBBJc7+t5oK05Fk+S63FNCyjHgVZ+TJo9vUtUNYitaLJ2iLA60KUrKvXUDbWgLVmQHvVAM7ecSyfYfjWpIJ98MYg/HgVd8zVIXU2g29ZUpsuO8XgtG0iXuO4aQDTC0qJGkA+ygEIAJRfUub7E23eln4aGOqCFxxY1WJl9+A+vFStBy0W2X29uL5htYVrkMhfqRiLMvk/EIrBN0CZeTtEElLWgOa55/+hdUNcFcdwgXssGXdsKwnX4tFwO2q/G9vsWPXsEZbgOYJaC5cUza0B1PbCXE8ekQrrtSJyNT0UJtOcGt1T0bek0IBFS9DkWkKgFlBYgxpYgJy3NQNEEFctqRyNQ4iZI3yTAl832tc0K0yQGOkOuQacEVlGB6xBfy1sqeTCLHhybHbN2tQ3HoVAUFpvnUdg2hVWmsJ1gmW+r7MHjXVDIEzgOhVX2YPN8rLKCcpmikBeaYJfpXLlW1kqqyGVcJJKKf0wkqqBsBrGUih4iUYXpeXXil1suU5hcl1kqsjxLxUCDXch7KJc9mMLKruShVArly9+XQvUu5Nl7Ea9V9lAqKX68Yl/HpoGmOUJQKnr+Z9GOpaIHU1h9lSnMohJohPl7UY7tUJRKrC3LJt+n5PE2Z59jMdaWmk4q4rHKnq+Dtsq0oo6iDXJZFy4fZmVTgVny/HIs3j/henserai3ycsW9SkV2XgplTzEYkEdrTL1tbJmifVZLB4cU8wz/bfox0Le89uPjQ8FhbyHaFyBJdqhyHTdepb45RTzrh+rWfT8thTtW8h7fn+LfjRLKsqmhxi3KXRsCiOvwCqz8zxZo8IyPV+bzurGNfdxoeP34Dma375Wib03yhos3v9GhNUx4muwHRTyBBa3rTMi7L2RJ7C4vR07dxxoGTG+HUTjTPcejYt8XEQiJFRv1lZifBfyHtPkh9q8kHcRiynIZYWenT2XYIbGq2OzZwiE9t8suvx5ADHuxDmtwuN/IyO2AtejcJ2QLj7J9OoAkEiqyLS6/rMGALgO3UXcf8bA8bX9e8R7Yo/annhlzSosQRdQtMorzS8uxNOz53flyM0KpXSdn78JZ5xxBs444wwcdNBBePDBBzFp0iQQQnDffffhpJNO2thQN4qqa5qXLVvWpf2+/PLLTV6267owTbPCz9A0Tf9kEX6G77//PkqlEqZPn75RfoYzLx2PsYdV35xbIpFIJBJJ9dgn0QMzZ85c/45KG03zESMx8/ITuz/Ab0hbKcbGSDOee+45jBo1CgBw880348EHH8QzzzyDa6+9dqNi3BRU/UpzOp3Gww8/jOOPPx6qqq5zv03Nfffdh9NPP93/LHwK58yZg0MPPRTTpk1DLpfDmDFjkM1mMXr06I3yM7zkmsdx7Fh2pVkikUgkEsn2ydzCGjzUBXkGUTQQRW+TZndXWBsMpRRffPGFf4XZ87yKzwCw8847dymvYrGIeDyOXC6HTz75BBMmTICiKJg4cWK3xP5NqPqkGWC+ghdccAHOOuss/PjHP0bfvn03W7lTpkzpdLvwM7zyyis3SXkzLx2PdEq6a0gkEolEsj2zT6IHfjxzJm699dZ179hWnvHCfDz97Lxuju6bUygUMHjwYP8zpdT/LB4KdF23s8Mr6NGjBz788EO8//772G+//aAoCgqFwhbxYOEmmTQ/9NBD+OEPf4jW1tZ2bhNdYdmyZbj77rtxxx13YObMmTj22GMxdepUfOc739kU4W0xiCvNY8eMBIqtoELcn88wT2GhCS03s2WJPQT+uKrFNa3cezhfBLRAYwyAbfc8ZlgJAOkE2y+sjc4XQcsWCNdnwfMC7TLA9KOmBSq0zyL/oukvo02zJkhch1dk8SrIg7oUyLfCK7JjFNuDl7NAcmLZZQVQCajpQK0PyoNlA8KHtzXHdLdR3i4eBW3JsHoJfS/X5aKppTLe1mxQz3KZ1VNovYsmqGUHmuf6GqA1y9JEHQ2d7Rct+WUTrnMGAKIooCtWgyTiQVoizrTgIg/LBvE8UKEBF31SMv0+EscT5yu//dGaY2nLl/NjnKCeAEiK39mwcoGHdbHE+kRjpzDN5YFCkfU3wPpPaM9FfPU1QFMLaDQStAvvW8qPg+eBOG6gcY/xcSDybckw/Xi+yPTxvK3gtQY685oUaKHI/s/keB0SoJkcSB/mXw1dZz7i4o+BojDvZs8D1jaxNF430oMt4U1LJlCbAjEtqDuwL0/V9mB/kYFax2IhK/KAGniqKgqFbRIAHpAXRRNYbW5qKQqB57ErIrZN4QpRLNefsqGl+AtxmSUKRWGaS4XnVSp6zAada03LZQrbdvyhUC5TWKYH26G+hjWRZBpLh2unPRdwHBeOTWHxIe96QLnswnN5fA6BY1PYNvsDJDSnrGzXr4OqsscRRJ3bpqkq027b3EvW81i9RTuI945D/WMcm+VTLgd//ISGNKwRdmzq52OVqZ8m4nRsAs+lKJWCP4Ku5/r7ODaF6wFqGb4+2XNZ+7q8QR2bQlHCVuoUnsfaWbSV51ZqsgFAKXONbzTQ+4ry2Oe27cr6gm0P+taxA82751E/jfURO0bol/3+L8PXs4v9yyGtvIhT3HRlOm+CsklD+XhwYtQfM44dvAcAy2T52U7lPkYkiM/1Ko/zNfw2hcm10WXTgxlX/JiYJtfjemihwQfX9vPnBbgWX+jvjQiBWfIQT6rt9OpifCgKQbns+tp9Ea9ZhL+PY9OK5wPYswMuPA/+swpWmSKXcf06hfvI12WXPb/u4XxF+4v8XS9YdM9zWboVEdp519e4s8+sjrlM8B1SyLtMb50Pa+BZ/SL8uDdaV3fpSnNbn+axY0dj//33xK13zl7/sZuRL774YpPldcEFF2D06NEA2MrNAPDKK69gyJDqy1s3yaRZSCeOOeYYZLNZ7LHHHth7772x9957d2niW1tbiwsvvBAXXnghXnzxRdx+++04+uijsdNOO+Hss8/GlClTUFNTsylCrSrySrNEIpFIJJKRRiMO78qV5jbuGSzN6XjfKjJgwIBNlte5556LcePGQdM0fyntQYMG4fbbb99kZWwoG/Qg4LPPPlvxedy4cQCA1157DfPnz8fll1+O3r174+mnn/7GeR9++OF46KGH8NVXX2Hy5Mm44YYbsMMOO2xImFscl1zzOGbP+aDaYUgkEolEIqki71pN67XbBRD4NFe8On/+q1qcf/75FZ/vvffeis/f+973vlF+gwcP9ifMALDrrrtir72qb9O7QZPmE0+sfHLziSeeCDJUFAwZMgSnnnoqbrnllg0O7IsvvsDHH3+MtWvXomfPnhucz5aEdM+QSCQSiUQy0mjsuntGR68tjHvuuafi84UXXljxec6cOZsxmu5jg1q+rf/eGWecge9///v+56OPPrrd1eiuUCgUcP/99+P222/HwoULMW7cODzyyCM46qijNiTMLQ6S7AmSijPtspEANbnGtkd/INcExLhDSLEVIApgFQNfW7PE/HozIU9jy2aaWeHfq6mBfhUAsgXmIyz0qprGtKqeB1rk2l3LZh7E/j4qaMEEMcuBfpbn763molBVgddUBIRu0HJBPQqiq36a28S01LQc3EYiEQ20aMPTg1/JxHRBbVZHElVBCzaU2pAHsIhB6LZNK6g3wPTApss01s2sTkpthIlQhY+07YFEQjrq1iy8piIUj4JyDTaJqvByNkicnxK2F5QJMP2zaYF6FLSF+zLni1ygyWKhLgUcl7Wf8Kfm7UhLvF88yjythRDTspme2rJB47xO2TxL4xptWigGum/hK51O+nn7sQCgee5b7DigJRtED/0uXt3M+iPcJ605eK1lKPW8DmUn0MYDoBrvc+GtXTRBv1zO4hZpnsf0zSIGrrem2Txojo+zGr5N6J4JAaKRkD485mu0KdevE10HtW0QoVVXFCAeAwwdRCF+e+qDCbxWdoweVUFNF0aRlaeqFFZJRUT1YJeZJj8WoyibHlJp1nbMS5fplAEgEmG+wpGI4msUNV2BY1OoEaGVJv7FHkURmmAWovgciTCNp6JyP1qTaXKL+c71wJpO4JUrv19FvoK2Wl4Avo5TaI89j/LjWNm2LXTF4XzE/6GMQIP6CA01gnhsm5cvHolwESon2E+0m3jP4grSRNmBrhUwbep7Ggttq9fmso6iBHpU8V4cI9qJxUeCtlID3bTnUnguoKikIp9ymYbaWOwfxCs0rmFtdNjDW6QL7+RwHcPHlM1Q23nB+0iUlem6TM9s+7ExPXNYK6vplVp6sW94XBX894Fu27bdijHTto+Ehl78z/rWq9D/AoDbEmjPIxHily/yMYvBZ+G/LcY5aw8XukYq2iHsJ87K5vVXxfgNtMwiFs9jY1WUr6psDIhzoFT0oKrM21r0banIxku4HEUlfnlC+x+ur9CCi3Sz5DGdOe9rMZaEpj4cpzivRVmOTeG5wp+6az7GlP8TzJ49D08//d8uHbs52ZQ+zVsyGzRpbvsEY9vGee21175RfgsWLMDtt9+Ov/3tb4hGozjjjDPw+OOPV1ya3xa4ZPp9OPbofTD28KHVDkUikUgkEkmVmO804ZUuyDM8uPAQ/Og4Yuwo7Lv/brjttie7M7xvzKb0ad6S2STX+De2cUaMGIEDDjgAt912G37wgx/AMIz1H7QVMvOKyUin44FLhkQikUgkku2OEVojxnXhQUDXc+B6drs0SXXYoEmzbdt4+eWXMWzYMNTV1W10EPPnz8ewYcM2Op+tBeqUQCIptjQ2AKLooJE4SJItX0w1g6WFl8M2LGY/19DIPrsOoLiAoQSSDG7RBWEpls2ze48inVuZwdB9+zgA7Da/+Ox5IDEDtGSBGFwe4DqgRQckyu3NTAdEIf4NI2p7IAkdsFxA3AoV0gIhvYho8PIWlLgOyi2diEJAVeJLCGjBBkkEVnZEISBRsNiEfMRxxT1wP15qe6B5i8UAJpMg8ODluPRC5zIRftuZ5iyQlAEvUwaJsnuEYl8qLPIMFSiJ5bIBwu/90pYciKij4zJ5B/9MTRckwr/MeJ94RSe438k6BSSmBzILRWF1S8ZZf4HX1wstj+44bHvRDGzeRJ+JdlEU1jaiLXl9qen6dYSmggDwMkL+oMDN2CC6Co/LOuBSkIjttxVMLtvgMgsS0ZjEBGC2dwAbX9kCaGsxqKauAM0FkLgexOvRQPZh6MDaVmaLCDBrujbLhNN8ETB0UL+tCLPsC+8Xj4J4nv9whgdAqYkgKsaYriLtFqGqFI6wVnMI6pzKr75EUkHTGtZ3iYSClrUOGnrq/i3vmjrNXwIbgL+kMlt2mKXZNoWqBLddozG2XHawzK8HI8rsuISkgFmBEb+rIxGCchkVyy3HYgpshyLCpSFi6WCxPRIJlmMW8cbizOLLiAS35zW9/VLPkUhwqzoSab89FmdSFX+55SJbglgcE4uzOiaSim9VZkSUdstdR2OkYullh7dLeHnucFuVy5S3RVBvIcUQx8RiSsXS1qWS5x8T88v2Ko6JJBVeBzVU78pyxPHi9rxYylvUK1zHyn4MtQuPTY8p8LifmbBfE31ilT1/qWexfLRlVvZtLK7w5ZjZeC3mXcT5cuypGtYnmRYHqbTqt3+Ux67pxLc2jMWDMQIwaYmukYo6imPCaZ4Xlr+wOoYlMTaXvwRSBmZLZ0SEBAFIpRUUCl67PhHyENYeHhQlWDJd19iYD9u+iX4C4C893jYfq+xV9KOQGoUlOiy/sFQoeC8s88KyKmZJR2E7JLQ/Dcl7xPHtLx6GZSa+Iq8cyIK6QtsrzSJtS6NYLOKQQw7xP+dyuYrPpVKpw+Pmzp2L8847D6qq4rLLLvPluOPHj8fjjz/evUFvABs0aR44cCAOP/xwAEC/fv1QLBZx4403Yr/99sPee+/9jfMLT5jfeOMN3HPPPVixYgWefvppvPPOOygWizjoILmKnkQikUgkku0Hj7rwqNsubUvjsssuq/jc1m64M/vhn/3sZ7jrrrug6zrOPfdcrFixAmeccQZaW1u7K9SNYoMmzR9++CGy2Szmzp2LefPmYe7cubjpppuwYsUK6Lre5VVf2vKPf/wDP/7xjzFx4kS8/PLLANiDCJdffjlefPHFDcpzS0Jomo88ZPD6d5ZIJBKJRLJNMt9uwktd0TRvJZPm6dOnAwA+//xz/Oc//8HatWvR0NCAww8/HIMGDer0OFVV/UVLnnnmGZxyyinIZDJbrCZ6gzXN6XQaY8aMwZgxY/y0pUuX4q233sK8eRu2xOPVV1+N5557DgcccAAefvhhAMDQoUPx/vvvb2iYWxRC00zLuWqHIpFIJBKJpEqM0BtxZBc0zVvLpBlgV5uvu+46UErR2NiINWvWQFEUXHzxxbjmmms6PMZxHOTzeSSTSei6jr///e849dRTMXfu3M0cfdcgdAvyBamtrfUvydfX16O5uRmUUjQ0NKC5ubm6wW0E2WwWNTU1yCy9hz0ImGoEykXAZTpSWlgLEqvzLeiIkQB1yoCZ9XXPUDQgu5otrw0Ey2uHl3EWWlghlnIcpje1fH8ntry0bQe6VIvrV8VnhTDtrOdV2sWppEJrDACUH0MUAi9nASqp2EaLNtPAijx0hWmcuVaM6ZuDz0Qh7D3XgTEbO4VpWWNiaW2uLS6INYYp01hHtUBbbHsgEdWvU9vPIi0MUQiL2QzqrKQqH0j1tdemWIeYx+rriF2//kJHTE1mxweLa2xTRqUu2NBZm5tWoFcGWB+KfosagR5YC5naC303wPTFYc2wS5ltXshyjkS0ij4FbwO//URb6UpwnO2BehRK0uB1dEASEdCsGezjUdYOId0gtT3WFnwfEtFYvKKOjst02kKfHK6b0NdbdkWdYOjcYtEJ7O5MC3AceE35ij7yhDbd9mB/mQU8CnMtt4Fq0VDKqbDLgd7X9ai/NLCiMt1hKq3C5f2ka0xPqXMtZyHvhvSSoS6xaYWtV9iOK2zdFbb76si+rnJ5aP6/GljBhXWkrPkqbbJE2eIYXeMa1JBOVNioieWjdY1UbBfHh/WfQXnB+7bxCdSQZlPTib9Usa+V1YhvladppOKrq21eHSHaoaN00U+ijr6FWyf7d1TPcHu3rbOwOwu0vB1rZ9v2NbMmC/YJt4Uoo62doCgr/JnZ5lXmGV6WWmjgvfXMrcLWge0tFKnfLyJWoLJf29bXt39bh143vES1yIMt4x1UupBnNnFhHbxVpv4zBeGx3dH4FPl2NnZDj8RUaIvFMWHrucBSju1jcXvC8JLnts2OCS9bH+5Xxwna0teIuxRHfvUImxtkMv7KygIxb1iy+g42b6jYVsTAnj/u8Lhq8eyzz+LEE0/ELbfcgpNPPhnRaBSmaeKBBx7AhRdeiAceeADf/e532x33zjvvoF+/fujVq5efRinFQw89hIkTJ27OKnSJLcohe+DAgXj33XcxcuRIP+3tt9/GzjvvXMWoJBKJRCKRSDY/lHoVV5Zf/PdCzH7m3SpG1DGzZs3Cb37zG5xxxhl+mrAQtm0bs2bN6nDSPGrUqHZphJAtcsIMbOCKgN3FRRddhPHjx+POO++E4zi4//77MWnSJPzsZz+rdmibhEuu+DtmvzC/2mFIJBKJRCKpIvPtri2jLdwzgpdXsdjJlsLbb7+NH/zgBx1umzBhAt5+++0u5/Xpp5/iqKOOQkNDAwzDqHhVmy3qSvOpp54Kz/Nw8803w3EcTJ8+HT/96U9x0kknVTu0TcLM6Se1u80ikUgkEolk+2JDNc2HjtkDe+8zEHf9ecsyR8hms2hsbOxwW2NjI7LZbJfzmjx5MnbYYQfceeedSCQSmyrETcIWNWkGgClTpmDKlCnVDqNbcBIpOIkENAogEgdsrvdM9gBsk3k3A6B2CSSaAvWcYI1512LHOFyrSbmPr0ICDahCAD0SLMedXQskagCriX22bFBKQWIx0Ba+hHf4WID5/kYjQLEU6JEdF9QN6VqLNqjtQamJ+J/VPsmKZZup6YLqarBMdVwHLdpQaiKBFjqqVmhu4etpQ59TMaZh9YV1TKPtl+O6UGojFRplElGZL3S27H9m/3PdbtmBEtcqdc1uoM9msWnMs5jX2fe6NsvtNMy07Abl2B5bDlz4U8d1oGiD1HLvbOEx7Qs8Vb48tBLoe5NxplH2+4b5mMJDSOccYWevOEZRQMuBrzQ8HrvjVmiEScwI2jKhgbYWQVKGr29WUswnWxFtBQdKIhr4NOsK8/Dmy1WLOnqZMpR4MB6UuMaX565hZRXNQJMs4ndcptcGuDaZb+uo3iLd89hS2m10/Eo6aF8Wn/AsB5S4BqgKlAyrQ6zGgWMTaAbrc6ukQFEBm48TPeLBLqswYp6/5G0k4cIoqDBiLMZITIWqUxSzxNezlkrM47UolsTWSMWy2pbCfHILec/X3AovZKGfjMUU3xNY6H2FnlP4Hpslj+mB9UAPXMi7Fb7HAPPDFd64tk0RiSq+N7NIE/69AHx/4LDPre97HPJuVhT4nsBhn2GhSRX+1b6nte/3HCxV7LmU63nh1yHsKy3eh32ChXbXb5ekUulxXA500mEtqREhUNp4RDPv5cp8YjHVPyast1YiQs8e6FiFJ7Doe9F+lR7HlX7buk5ga6wfAKBsBu0i2soqs3jNYuAHLfyngz5RKvT1Qk8rxpAoU/R3R30bHg+sz1gdwz7NRkSB51b6kOs6G9Nt+ync92H/as8L2lycB23bV9PYeAj7NGs6aeP3zNpFtL+mE6iK8Jf2u6BCL+16zHs8rNMHAs0365OO9dHhtLbL1isKq0O4/SN8rIr29bzALxtg7z0PiMVDWmmlUg/eGW0nzS/9+wP869kFXTp2c+KFhfgbsD3MokWL8Nprr0HTtrgpavUnzX379sXy5cvXu9+OO+6Ir776ajNEJJFIJBKJRFJ9bI/A9oIJtkNJ+Jn2LQbTNHH55Zd3ut2yrE63tWWvvfbCihUr0L9//00R2ial6pPmlpYW3H333VifiUcmk9lMEUkkEolEIpFUH8slKLvBpHm/w/bCXnsPwl/vfLmKUbVn//33x6uvvrrO7V3ljjvuwNSpUzF58mT07t27Ylt4lcFqUPVJc69evXDllVeud78+ffpshmgkEolEIpFItgwcj8AJXWl+7T/vY87z71Uxoo556aWXNlleH3zwAebMmYNnnnmmIp0QssGL520qqj5pXrJkSbVD2Gxcdum9OObY/XDUGG6xUmxl/6d6Mu2yyrqDAIBjgSQaAML1s63LgEgSKDM/WsRr2fHUAyJGkMaPBcD0zNQDhI9j3ALJ5gFN87W7vs6Zr75Da9NMkCU8ngHALINoKtOlAiC18eB4AMTzmEa1HPjnEhQBXQGJ8zrpKjyVgMR1lhcAKAo7VmhYzTIXjHGtbEIPPHlD2l04DhDn5WhlVh/NAdLcwzqbB5JxKMJYMx4FXZsNPILtAhCPgjguixtgGm5NYzHwY2DZgR8wwNpDUyFqTswyEI2A8HZBPAralAOScZB8kaUZOmursDYrGnoCOBph5UTjrF6ijsk4SE2Kx2YCmgZaKrUxGK3UCBPkA69jcf8uHgv06vEoSw/VkThO0AYAoKlQopafD1EUVocaEvSZWWZp4PWur4FiFINjuE5baVSD9gt7NIs6GjpIgm2nXg6koRY0VwjGIgASSwbtYugsTVVBhfY8FgNtY7xKknE2znk5al8HsD0YzUzTrNsunLIClWuCzZwKPerBLIQ1zQr0iAfK/1jpUQ9GzIOiCE0lBVEAVVN8bbSiqSCEQuWGt5EYUCxQX1dslRVoOoGmE983ORpTEIkEGstIREGpSJBMExQLQVokQhGJgX9WoaiVWllNJ1xvyrWwUQKzSHy9r2OzOKIxpUKLGYsriMY9vxxNc30Na5lrUw0z0NwaEeLrTVk7EEQiBImk6sdjRAmidqBF1nSCWEyBVgqOsUwKRQ2k/UaUIBbXfA1rLgskkiocm1ZoxoVfNou3si0VJdBgV+pNid9vQtsbiyvQdN5+WmU+tkN9z2oAUBXWrkaUtQXAPI2FvlcQjSt+fUSfqgqpaAfLDDTCmkaQqlH9eAAgFqP+GAnHJjyUjQivd8lDJBJoxjWdwIgEungjwsaV7x+tsPe+xp3XUWyPRBQU8i6MiOLr1ZmvMPX1y5pGfV9pod1VVAWqQiCMuPw+0YUGm8Wp6dRvX9F/YV9oxyGIRACPn2+GwjTLomxF9Sp8p41I4Hks+lb4OhshXbGmE2ihsSg02FrIz1zTqZ+vx59BaOuNHR4P0AmU8PjmY0ycTwB83XxY02xxzbiI9x1rDV7ognuGTQlsGsSy7+FDMWT0YPz9Ly+t99itlZ///OeYOXMmpkyZssU9CLhFWc5t61wz8wyMHfutaochkUgkEomkioyO9cDMmTPXu59DiX+12X+FJtHbIplMBueffz7S6TRUVa14VRs5aZZIJBKJRCLZAnH4g4Di9ep/PsDvr3qs2mF1K0cddRRef/31aofRIVWXZ2ztvP322zj//POhqipc18VNN92Efffdt8N9XerApQ6IaoDCg5eqBwBo4h6fWBo7XgvYJmixBaSGablJbT/AKvqW5iTZg92qjqRAi2t5AQ5IrIbdtgeAeB2oVQBK3B8xVQsYccBzAIsfU5sCWrK+tEFIDWhtyGNHLPUsZBTxGJNAiNvt0QiTB2gq2waw2/hAcGs9GoESLbNyeL5E19kDoKKcaAQkYoAWmLSB9KgHXdsK0lDrS0Ng6KDxKIjOYqGFYiBxEPFFIyCE+NZ2iEZAetSCpLh8Ix4FqUmxvGOsDtTQuSVaSCJh2YF8IJcHqUuDtmSDtJYMbxvef8k4iKaCJOKgPI2kEswKTuSraUx2ISQSyTiz+UsnAxs1hTA5g7+0uQIkYyCGBlg8H8dhfWdyKY5lM8mCOMbzQFJJFjePF4rCJBS8bEppIM3gZZOIAWroIBFuH1dm8pdw/NBUlneyHPQjPxYAaMkEqasBLRRB+JUBGo2wduF6NNHuQjpC6tKsX3gfAwCx7ECOI9pOUYCowbbxfYmh+TIVWrZY34bGlLg24Qn7Q4UghQyzAwRgxCzoCQKjlcsU0h5KrQoiMQ/lEutHPcLs5/QI28cqqSAKhRFToEdZmqprXM7BZR5RD3qEQNFY/IqmQtNdqJoCI8alASUmA3FsUY4LzWBpwvJK1Vx4HoFmcFtAVYFmeNCMILZyQUUk4SLCb7+rOkUspkHlEgTPZWVqBdVPi1ICQjzofAhoRlA2AES5RKUcstrTIwoUlULnUhwjBngOgRbxYHDLPkWj8BzFL8cuq7w+Kq9PIFsIL0NNFOofU1OvQFE9qBqBHnF5vrxsfvtd1Sl0m0BRuX1YhLC0SJBm2Aq0iAeN20QqGkVCYbGpXC5AFEBzFCgaz8cjft6svSk0XfXjBoQspdIOLxIDXIf4fWJQAkJoRbvEYkG7RGIKjBhrc01ndfQ8AlXzoGhKqOwgNtHWms7GHwAoilLRlpE49dtC01nZWsSDU1ah8b7VbQIj5iES5/UxPBgRNl4ivvUiUC6hgzoGY0SzWX0MO2hfgMDgfwY0nfV9uRhIThSVQNUoyiXi971AjHlmu0d8eQaTmgTWdszikNkwCjlJNKagmHcrLAiFfZ/IR9dJO7vGUqnSAlJIfMI2f5pOYLiBZWKpFNg5hq36fEs/jTLJUimQUAHMBrArS42HKbsKNDfYeeQhw7DryF3w6N1blk/zpqRnz5449thjMWHChHbPs3XlGbjuRE6aN5K+ffviueeeQzqdxvvvv48zzjgDc+fOrXZYEolEIpFItnJsz7fRr0jbllm4cCGGDx+OxYsXY/HixX66eN6lmshJ80YS/hVkGEbw8JlEIpFIJBLJRtDWPeN/Ly/Em/9+t4oRdT9z5sypdgidst3P8B588EEcfPDBSKfTIITAEbeCOZRSTJ8+HX379kUikcAhhxyC999/v10+tm3jnHPOwa9//evNFbpEIpFIJJJtmLaa5uEHD8Pkn/+w2mFtt2z3V5rr6uowdepUlEol/OhHP2q3/YYbbsBdd92F2bNnY/DgwbjyyisxduxYfPzxx0gmmUbWdV1MmjQJP/zhD3HMMcd0WpbO/4FSUAJoBaY1pnYJRIsAGrciK7Yy7XHdDoDLtZtWEbSwli25DTDrOc8Bza1iVnR8HyQagAT7TFu+BqnrDxCuK82vYVro7KrACszzmGZYWJelE0BrztcMAwBVbJBYFDSX99NIXU1gb6YogEKYrnVHvoLPsuXMgi2TY/s31ALZQqDhBYBejSAtmSCW1hzT7oqllw2daVg1NdArexQkGQ80uPEokEoCpVLQ0KYF1NWAGC3sc22K7S90xvEokIizvIVFHrfi87XT9WkWD4+FxKMsnobaQHOb5MK9bCFoO10H6tNsf942wraN9ZHN4xF68BjbFokAhh2kuY5vNwjHAhK1wJqVQI/6oJ6uE+QLgKxu9uOFWWYaa4UEaQAbYy77YUg0A8hlfP02q0MapLXV16YTjy/XLsZHKgkUioBCQJzAQo7Eo6G25JZ+nhe0n6EDDXUgNi9HWA0avO+LObZMvKoFaaUsX26e90kkyZaTJ0pgq6hqQD4H1NaycooFIF0X2Bp6nv8cgNqLjRGlnlkYkRQ735RVRSg1EcRWsX5U66NQ4iVA16HmWDkkqoPaHkiEtXdEt0AUwjTKSWHP54B68LWmqkbhWIqvRdYjFJrhwSoFGmHNULnelGtwYy5sU4ER92AVuQ4z6sF1iK+DtssKVI1WbNcjHoy4B8XXGnsoFxTo3IbMc7kW1qC+3tcuE387wPSomhHoivWIB9Wgft4A/DLDemrHZvnYhrCUo3BsIhw0oWoUepT6w1kzPBBFhR5xYZd5fjrTbUd4OcJF0HVYfqx9AztAUbZVUiraRcQjjrHLFEbMg6UJW0AKzyH+fiJf1wra1yop0KMUtsm10waFqokYAgs0zSC+Ftl1CIy4B9cSFoUUjkWgaBSuRf20clHx6+g6bEwQhfr94HCtsdAwqyqFpVD/M/XY8u+qFqTpEcJ12YE+mXosn4o+MTy/jq6t+GNN1FHRKFSVQhV6ao1C1RVoBqujbTL9vmMFOn7PYWPGsUT7cFs1LbTctUGhakGaY7HxK9qSadbZeRAraf6YicWUkFWcgmiC9Q3AtPRWiemuY9zWVOjmEyl+jNgeU3zdvl1mmu+yOHcigJYlIRtAF0aUQCt6SPDzuu1S95EIQbSN5V8iyZY7F8cU8i632uPjQSft7BqFznp9tF0RUKRty9x3330YOnQoRowY4ae9++67WLRoESZNmlS9wCCvNGPs2LE46aSTsPPOO3e4/bbbbsO0adMwdOhQxGIxXHXVVbAsC48//jgAtp76aaedhn333RdnnXXW5gxdIpFIJBLJNkzZBcp8VcDgVe2oupcrrrii3UqAffr0wfTp06sUUcB2P2leF5lMBkuWLME+++zjp2mahpEjR+Ldd5mm6KGHHsJjjz2Gp59+Goceeii+973vVStciUQikUgk2xAODR4GtD3g7ZcX4IEbH+r2cpuamlBXV4dZs2Z1e1ltWb16dbtJc+/evbFq1arNHktbtnt5xrrIZpl8opbf+hXU1dX520488USceOKJXcrvkkv/AoPfTj9i7N446sDBmy5YiUQikUgkWzRv5dbgrfwaqCpBXRdWBLQ8QA25Zex+4DAMGD4Y//nbC90YJbvae/DBB3drGZ3Rp08ffPLJJ9h11139tE8++QQ9e/asSjxh5KR5HaT58tOtra0V6S0tLejXr983zm/m9JORTsdh6SoM6P4y2kTVQT078O51TMAqgMTrAJ37CNtMj0lLXKfrOkzPaZuB5lOPgpoZELEctx4Diq2gZaYrRqyW5ROJo2L9V9cBuJcsTIsvW+0EGmBNZd64WqA1brcsMrhulfKzO50AYrFAWwowfasRCZZ1TjYyjS2vI1sjNh74ShtxoC4KRNMgqh7UsXUZUMd1r4VmkLr+oPg6yCcN1jaNvI6ROKAVgVQj+5xrAmJptr/Qg8fSgGoA+aag7HolWJpcj7I06oFE2PLWNL+GHW+sZvuoGhCLAdFkoE9PNQLZ1UFstgmke4JwnTmIAhpnmnbqcN/jWA1osSVoS6KAGAnQVJ5p1AHmv61qwVLsehQwoqwOABApstgj8aCOrgUSb/DzFWMqrHNm7WYEumKryNpGbLeKQI9a5vUtdMV6lPVZguutjSwrM2UFdegdZ/mIeLlfuN+WRpx5jpdagrbSo6y9Ew0QULvERKiKWNK9H6i+AtCCY0isBlTETz02LqNJKE7gEa0B/vhValpBElEoXBup1Eag9kqAxHU4X2XYMXEdNGf53s5epgwlZUBpNkGifNlsnZ+HYG3FNLfMPxkAytwjWdWpr2lWdQo96sEL+fsyjbAHPeTvSz0gwr6OUM4ybaoVC7x8IwmmYRX+uXrEQzQR+PtaRaaTFrrUtmms6Sq3Oxbx6/D/7J13nB5Vvf/fZ+rTny3ZbDaNhBRaCB1BLIBAEKkKgoSqYMHrvdwrPwsqXb0i14teQaWXbCgKKIoSAcGAIE0CBAgtCemb7U+fen5/nHmezZIEIiZZyrxfr+e1+8zMmfM9ZWbPznzO59S3abrS4OrVyP/XDDEjvXRdT23aIcE6/smGrbTGujmkRdYNojR1LbfSA1sNva+INOFinWXKZaSFVfVgWEpzXV8Ou/677g35CJsJVd91Xbluyob2ua6NtpKBapu6RjhKU/eVVjpeFY8blVs3Vfq6Rth3lB90GGlY7XTQyGdIh6vqoN4f/Ch9bR0fbN/RSGQDNE1vxCs0GvEHfqRpNofqpe6j7btD5QkD5dNc93u2kmrp+HreQaTr1us+zlaIpumqDyWG+pXnDC0T70WafN8d6r++p5Yjr/uMG1G91NPU28+rDWn73SoNfb3qH2GjfupLTNc15Xr9mJpGIh1g2kN9yEoqLb1T1hppDEusM4dAYKcDpVGPNON1DbyZqHugg25q6EZ0PRqqzZJJo1FGy9ZJZP1G21vJALOsN9o+kdaieLSGp7hhKU9xv67Zt0MsR13/h9gdHEIHhiXp+MEPuOKKK3grvBCMrWw59+KLL1Iqldh99923bEYb4bjjjuOUU07hF7/4BdOnT+eVV17hq1/9Kp/97MhPgIzlGW9BPp9n0qRJPPnkk41tvu+zYMECdtttt3/6fOeedxPz7vvH5gwxJiYmJiYm5j3Gw71rOXcTnjR7oXravO7nrQbNm8MR7Dvf+Q4XXnjhOyrX5uC73/0uU6dOZY899iCXy7Hnnnuy7bbbct55541YTHU+8IPmIAio1Wq4rnpq5jgOtVqNMFpR7KyzzuKyyy5j4cKFVKtVzj//fEzT5Jhjjvmn8/rBRacw6+CR+c8tJiYmJiYm5t3BR1tH84Mf/OBtj1tXz7zuZ2PUHcEuv/zyDe5f1xGsp6eH/fbbj1mzZlEqKXese++9l+nTpzNx4sR3UqzNQiKRYM6cOXR3d/P444/T1dXFnDlzSCaTIxZTnQ+8POPmm2/m9NNPb3yv28g9+OCD7L///pxzzjkUi0UOOuggCoUCe+65J/fee2/juH+Gc8+7iSM+tTcHHLbXZos/JiYmJiYm5r3Fw71rWbAJT5prgYBArL9tI8yaNQuAhx56aIP713UEA7j44ou55ppruOuuuzj55JN5+umnefzxxzn00EN57bXXSCQSbLvtthx44IGbWLLNR2trK62trW9/4FZESCnl2x8W869QKBTI5/MMLruGXC7SmLqVhg+v9KrKf7auEQXlx2wmlG4WEK2ToTqgPJZhSMcMNAxRDUudU9T9iJuH+f1Kp6jyqZuggvq9PKj0xACuozx7aw6MijprXz8056HusZtrVfEnIpFlZUDpikvdSs8L4FYQqVZkpTeKLaE0tcnmobgzLUrvG2lh5eBqRDIPWlSewEVWB1U56nUTaWHr5Ze+g8i2gVMZKpMeaXJrkTZamecO6WsDX+mpi2sbmmU5sBKRbUeWVbwiHWl/Gz7CAyrOcl8jfOkUEelWZC3SvWqm0pS3TkYOrlbbcu3KLzvS5crB1YhRk4d026kmFVtlQMUE+NLFKBdwU8pP2HIcFYfvDul9qwXcZBJLKp1doGvoxb5GvLI2iNBM1dfWbWs7peocCLItaoKJ7yILUbyjpkCtQC3y4U2EhqqvqP5luhnhlJUWu943k/nhvtIyBN0Y7ivuVlQs9X6bHaW05frQ/+1+IoXh1Ib6lRu1aT1NqgkG1yADT+n1QfW3devFrVAyQzK6ale8GrKwWumll7+stuVy0NPb8LiWg0VENo3sitq+OY8cLCCyGWRXpHFP2MjeAqI58kDvKSJSJuGgg4i00GFPFRlIgsjvWUuZBP01tEykg674iIRO2FdDy6vrLSy5aHkbWYs0lXmbYNBBSxkE/arcWsZCOgH6KFVmlaeJrKjrUcvbBH019JYEYcNX2iDsqza8qGUtQJgaYclF2Cpe6fhoKZOwfp7ody0TpXECtExUhrqWu+IhTB13MNKNpiShA3pGxyvUdaES39PQG37QyhN4Xd2rW9XRDDlMT13XUAMNHbVc54laXW+9bpq6NheUF3Vdq1zfVtcAD/lBK/3tulpjpSPXGtpo39MwUxKvEuledYlT1Rr6blC61nW13vXY6vFaGYn0JJqt4Ub29mZK4lfBzEXXbClo1JvZpNokKPhoNo28zZTEKWjr+DQrz+V6PPX6VZ7WKk0i0lNbyaCxzbCULr6u25c1Hy1vEw5G8yhSZiNvtxTpsnPglmi0ox/p1IN1NON1T+y653W9jup6YM/RGmnq/t/1/fW6rHuXr9smhj20TaXRIn2ySpOMNMaJjE+1oMqUzPnUSsZ6aZzy0LyC+hwDpzykv68Wh/TsdY/0YWkqSme+bn9xyvow7/I3zweolXUS68Srm1Ga5PB+l7/hFjU2GBxszKGqUx83fPOhq7HraxlEOKUKP9r/zA2mq/PQQw9xwAEH4HkehqHqaHBwkKamJh599FH23XffxrGHHHIIM2bM4Cc/+cmwc1xwwQWMHz+eM844Y4N5bE523XVXLr74Yg4//PANLpctpeTuu+/mggsuaLiXjQQf+CfNW5NzL7qNIz65O7MO22+kQ4mJiYmJiYkZIf7a1c3fN0XTHIAWrL/tnbApjmDrcsEFF7yzjN4BV1xxBf/+7//OV77yFT7xiU8wY8aMxj8UL7zwAg888ACjR4/m5z//+VaLaUPEg+atyA/OO149aY6JiYmJiYn5wPLx9jaO3BT3DAlaCEv+/hxLH38OgKD+1vefZHM7gm1O9ttvP55++mkefPBB7rzzTu688076+vpoaWlhjz324MYbbxwRicibiQfNW5H4SXNMTExMTEzMpj5pdkMQIYzbeybj9p7JG48/x+JHnn5Hea7rCFaXZ9QdwU4++eR3dM7NyW9+8xsOO+wwDjjggJEOZaPEg+atyA8uPYtcLj1MuwwoX2YrpTScoPyUS92IUZMRCeWNLPveUJrRIEpbF88ZVuP3un5UukpTiZlAVlYP5WNYSh8a+jSavn6eZKSLkgPKo7kpO5Surm2ua1Y1A9ItCEtpbiWR1jX0h86TbSfUBFp0fjeVVt7U2jpdLgzxM00Y0TaRbqWoVcgadY1WCqEZhHaSauSxa+k6prQaulcRuNSESyLTghso32HLcXBCsCOvZGnYiDBgXYFkgI+eGdXQ+3rNY7AwEQlV7gFRIGe14YVKV2qnmqiFJRKJDDWh2iBht1PTfBIJ9R96IH1EtpVABujN46M6E4hcB05UbLttimqDyDvZ0yShdHFsiR2q9neCMnrCIAxVO0o7haFpOATIUHluW6k0fljD0NV5dHSQIW7ku2rprUjDxpcugkgfpmsUnVXks+3qvDLAwQcDjFHbAFDz+0kmcoRBJMTUUwSGgRfZZBvSQ7OThDLAyHdEGy0IQ/o81ddajHZcPKzUFOpTJoRmKD11pHmv+IOkEhncyJfVwiSQLoadIYh8jnXdQOomgR5p9KWPnh+D8N3GtVIRDikrjSdU/6iZPpaWUtcLUGtqJZlpU/1ltKo7kWxGahoiqgeyKyGZQ7SOavQPkUpAOoWIXHQY3QLhG4h8dF34AbTk0RIFSCWjePvB0JV2HKVFFgl9SL886CCyFmHWGqYR1psThBVVZpHQG2lEIrouomNF5HOs9icIB2uN/VrWQmQttGLdO1tDmFojb+mFDU1zI++ii9aSIOxW143WkiDsqw3poCueOm/abOSt1QIIJJamtLBa1kKreCpeO8pbF+he2IhbL7poKQutXkbTJGF7iISBMehGdWViRvkB6IMuWuTJS71POz7C1DGj82gpQ2lzozRhxVP51HxEQm3TvBAtY6JF6w6LlInWV0XLJ7FLkZY7Y6OVPIRtNtIIXWBFGlyRMtArPsK20PtVufVRCYxBp6EPNx2/oT1X+5OEgw56ewrRp9pJz9uqLppUm+hlH63JRuuuNMqgpdR+ravSiDehVRt9oa451ysemGqbXvIadVHPWy95aJkk+jrllhUPraWu/Q/RmmzCKBbVr1TfqPchLWVimU7Dh1yvBYiEjozqAlDn1wR6GF3ntkkiFajrB9CKLiJlKf19PV7DQ89bGHX9fcpEG/AxkjR0z0YS7GSIFoVnVQPMtCDhDPUXbcDHzOnohtfYphteYw6BVVL5WEW3UX9WUvUxM+VF8RropocRTZHwKgLDDLGSAVZGNraZKYlVVXVppgVWacgT3UtrDd17XRtd91/Xzei7ITEsOUxPfdiY5k160uwEINeRY7ghBG8xEy0IAjzPG+YI5vs+lmWhaVrDEezAAw9kypQpXHLJJe/YEWxzc/nll3P66adz0EEHceyxx3LkkUeSzWbfPuFW5ANvORcTExMTExMT827E97Rhn47ddmX30z+30eNvvvlmkslkw0Ujk8mQTCaZP38+AOeccw6nnXYaBx10EK2trTz88MPv2BFsc/PII4/wyiuvcPDBB3P99dfT0dHBJz/5Sa655hp6enpGOjwgHjRvVc79znXMm/fUSIcRExMTExMTM4L8ZUXPJi1u4vvasM8bf3+OJ6++baPHn3baaUgp1/vsv//+AAghuOiii1izZg2VSoX58+c37OfeDXR0dHDWWWdx//3388Ybb3Dcccfx29/+lm222YYDDjiAn//856xdu3bE4ost57YCdeuYgSW/JJdLIgwbZDi0jLHQlJVZZDkmdFPZw4Vh43W2sLPgVtTyyjBko2YmhmQdiZyydYuWJm7IKep2XJFVmwzWmUQQ+sr+rC6rcCtKPmAmhtILTUk/wmhlobr9WfQ9zLbihlUsLYmGevUUEhBIH1Oo92uedJAyxAnLmJqKp+IPYmlJnEDJECw9iRNUaLXVUtH97ioSeoaKP9iQSST0DIZmoQuzUYRaUMIQFr5Ur6PyVjsDzmpyllqnvuj1YAiLjKms7fqdVZhagoSeoc9ZCUDGbEEXBk4kkUjpeTShU4tkCraeRsqQQa8LQ1hRmlYC6RFG785sPU3VVzOQLU2976uX1w9VbPUY160XQ1hIwsZ5QhlgaUncsNrIxw0qeNKhu6riaU+q+OrH2FqKhJGl5hcbsZT9flJGE26gylQNipiaTcVXFnk5sw1POnhhrVEmW08TyqARZ80voWsGVX+oz+StHAKtYQukCxNDWI1YAunhhQ4Zs6VRf3mzneXl18hbuUYaXRiIqI/pwsQPI/uraFtdbmPrdRnQkLymXr+1oEQtKFGIroGELnhl0GK3UWrCbcHtoy0xAV+6pGT0rlePJE2RLEj2vaGumbr9XTKH7H4dkWlDFiOLR6HBQDc0RRZ6hV5lzej7kIjOWyiD7w/Z1BkGslhFpCOJRFXZvcmqh8ir+MKeEtqoDFQiW71MCtlfQjRnkP2q7kTSRDo+Iq/qgVJFLUlfiCQ0CVulTyWQg+paEs1ZZH8RkVX1JItVRD5NuLaAlovkIgUHrSWNHIikAAmdsOihZSMrvlqAyFiE/TW01ije3goEIWFp6NW6LLpoLcmG3Z2WMpQMJFOXTfgIXQztz9vIiqdszur2fFmLsOI38g4HHETaRNaCYdu0liRBT6WRRpa9htyhIUEpeuiRDCHoq6G3pwgiuYM+Ktmw+WvYrdlGIx6gIWOo79cylpLWJHSkNyS9GVbGvhoibTTem2ttqSFbtx7Vj/X2lCpjU9QmhRoilyBYUxySmBTdYWkwNXXuSCIRFj0VRy1o2McFg07DqhBAb0kgyz5aWxK5rgVhxUNvj66lKLZh+wedhn1hvR2D/hp6s6rLutXhuhIetZy8OVQvKVO1U9QmQWSvKCP5ylAai6A7apO2FH5XGX1UqtG2et1GsS051I55u5GPiqOK1rJOGbMWQU8FPZIk1eslrPhDMqWii8hajTRak43fVW5Ilup5DrOFLLqqf9bTRL/X2yyI2kdW/IY0JOipoo9KEkTtqOrfHyajEaaGf/5v3tZy7rjbb8RMDRkIrHp6ASufeIpX75n3lpZz7zcKhQK///3vueuuu9h33335+te/PiJxxJrmmJiYmJiYmJh3Ib6vIfwhUcDoXXanedr2vHrPvBGMasvy29/+liOOOAJd1xvbcrkcs2fPZvbs2SMYWSzP2Kqce8mvmfeX50c6jJiYmJiYmJgR5L6lmybPcB1t2Gf548+y4Ia5WyHCkeOLX/wiEyZM4Fvf+havvfbaSIczjHjQvBX5wXePY9aB7x7tUExMTExMTMzW5+BJo/jBD37wtsd5njbs07LTHkw79pStEOHIsXLlSv73f/+Xf/zjH2y//fbsv//+zJkzB8dxRjq0WJ6xNRGGpfTMZkItD11fWrk6CKkmtQ9AhkpDbGeGdMROtGy1H+nwDFvpnjVjyGou9BBmvrEsNU5JndspRgFoSrNsWEN2cVItrdz4LjRlIQaIVBSfW1Ya52g7MiS0k2hSaVor/oDSxZoW1UBpep2wgq2lqEr1XQhByeujq2oyOau0u6vKDqMSVdZGy+JOSOtIQnqd5QCsqfiMTYMT1FhVUXmPTZVoTYzlqW615PGUnINAwxM1Cq7SEwZyBRo6y0vqPDnLwtAtXuxXNmSTs8340qXXWY6hWY32KfsDmJHHUdHrQQiNrkhrOjEj6HV6yFlZnutV2jZbX01bwicTWWKtqXSjCcnY1PghTXBQQkrZ0P8awqLs9+NHtnVLiiGtdokVZYsW24/iDUgaLm5kL6gJHT90sfQkoxJK22ZoFiWvr3HeQPrU/CLVoNhIowuT5/tWMTUX2dIJg1AGDW130evF1Gz80EVE1ntrq2vJmHZDP6wJHUNYdEUSy4kZpVUvuBWa7SF98qrKCsKoP4xOtrCqWqLgrqEl0uSvqb5KQh/SoQ+6PST0ZEO/vKrs0JrwSRlKq13P2wtr9NRWRGUUpAx7mA5eoOEGVbKRbt/QLCZmCjy6RsWyT3ueWlCi6PUj7DEA+EGJlNFEv7MMgFH5DgJdG7LmkwFa0zjKVEnXlwE3E0gzgYhikzmULro8AOmmRrnUst+RnVU6BakiIhnNKQgChK6D5yGSSqupWaZatjuy0hLpJFimSltf/jxhIfwAkY2W8M6kELaNNFQsIpmEfKStjpYGF9kMaALRGi1bn6tAGKKNNxvxaIkiJGxEfYleTaAlyojWqDzlCiRstJYQYUZ2bJoGho6o662zSaXtTiXQmqM/aJaJKFYbemoxWEGkbbTCkEVeWHLRcnZDF4qpoTlBQ28drC4pS7R19LHCNtBaEo00wtSUTjfSr9c1rmFfFS3aprUkEEkTEV2jWt5GpE1lB1jfljUb2ltQy4cLW2/oXEUugTBLaKNShPWlzZsTiJ4K2ujo2mpT7SbrZRydh5oDmoZe16KnEmilCkTXo2jKqv2G3timF0qQy6CbfSqNEVkWRpZuWkuIsPWGNrden1rWamiytayFzPiItAnRNnShyp6L+mK9nZJWIx89Y4FhoOsi6os2WHqjTeq/i4QxZH9oaiq/SKerjUqpfOqxjEoidNFYJr6eRiQMqNsYZi2MqG3qaFkLTB29PTWsjHVdsd6SIEjoaun4ikqnZayoHoxGW7+5D8kghUhahAPVRhph62gt0fW4uoRImcqKrr3er2poTTayHF3X2Ujjrolh5VlX5ywShjpP1McaaRL60HnSZmSu+dYEwXB5Rn3b+xnTNDn++OM5/vjjWbp0Kddddx3nnnsu//Ef/0Fvb++IxhYPmmNiYmJiYmJi3oX4ngbG0CC5d+HTdD/7+AhGtHVpaWlhzJgxtLa2jqhrRp33978rMTExMTExMTHvUXxfDLOcy2+/F5OOPH2kw9rizJ8/n1NOOYWOjg5+8YtfcMopp7By5cqRDit+0rw1OffCWznik3sw65MfGulQYmJiYmJiYkaI+17u4t5N8Wl+05Pmxrb3MdOmTaOrq4vjjjuO+++/v7Hk97uB2Kd5K1D3Wxwc+L1aRjtwlc+xV2scIwOnoStGt5SGuDLQ8GmWuokIA+RA9J+WbiD0SB8dbROt20Ct1PBc9jUwQpQHNCgf6MrAkDYZlFetUxrSQdc9n73a0LLTiRxYqYZON5A+gfQafr+B9EjoGSSy4afshy5OIMhZShfWW6vSmkjym8WCI7ZResjumkHGCOmN1pienNXwwhoFV+nAnutNctB4WF6ukTFULLYeUvJ0BqJjmqyAx7qS7DemwoCrzrO0YPHxsQ6BVNqxF/sTbN9U48V+pVvbrqlGT83AC0VDR5wyQrqqJk2W0t5lTJ1+J8QLRWO/JiRVX8MJ1Q3LEBJTk+QtdQmtquhoApYULD48JtI+RprgNRWloRuTSvJGySFnBtF5YU1VJ5TwUr/S3e3U4tBk+Zj1JaZ11V4ry5L2ZKSHQ8MNZeMYAFNL8GJk471jMzzdLdhtlE/F96P6NjE1yZhkfUnyJMtKLmljSG+4uGDjhYLtmlT8L/UnmZavYesqn6QRkrfaWVtd22jbql/m1cEEyaiNJmUDnuu12LNNb+iTvdDh9ULIKwOqLPu0V8hZgqofRufVeHnAZKdmQS3yZzY1k1rgowuVd69jMCXbTMnva/h0/3WVwcSsy9/WqPNOz4c02wG2Hi1xGwq6qwbT8g5adJ4m24i8sesaeA8pZUPfLmWIqSWoBSWa7bFRfQsMzSKQ0VLFIepa0YzGdSzdMsJMItcuVpXp+0qLGi0pTKkCmZTSuhrrPK8wdKULBsjkoa8HshmoT3pxPeXDXNce1xy1tH2hwDCamqAWic+TaSgXG0t6E2mRGSg29LMMFKGtBQaj8yRsde6673TfIIwZpeKO9NMUymDoyGKkKc+mkY7b0Nar8hjIYmnoPPVzuvWliy3koNJTN7ymc5mh+qnXVSqhYhwd3Zt6+qEpBwNRvKmkOne9PK6nNN31n6DOvyFP63WWOyeTUuWqt9Ob28cyoVBCtLUg+9U9TzTn1e/1Mka67kYZ21qQxTKifZSqR4Cm7PAyul4Uhxyq30oNNAG1aEnyMERWa0rjDsoDvF5XTdmhMlrmUJp62TMpiO479f0iG81dCQJEMoGsRn+DNE31V01T567XlSaG6qKeT6kyvNz1Ol83z/p+PxjKv17f9fT174auyp2wh+JNJVU89bxdT5W3J7rBjWpW8WRSQ/HmMmpbtKy9ysdS567XVaky1G/q8a7tU32kXsZUQtXlun0xlx7yUjf0YXUgB8sIU1da+kjjHg5U0XJ2QwMvUpEPdsokrETLfqdMyof//G19mne/+HfoiXRj++DLTzLw4mOsffTu961P89VXX83nPve5d8UqhW8mftIcExMTExMTE/MuJPRBrDNjMDtlL1LjdmTto3ePXFBbmDPPPHOkQ9go7+9n/DExMTExMTEx71F0L9zg5/1OoVBg7ty5XHrppQB0dXWxZs2aEY4qHjTHxMTExMTExLwr0YMQwx/+0YP396B5wYIFTJs2jQsuuICLLroIgGeeeYZ/+7d/G+HIYnnGVqUWlrFCScILlQezHlV/GCJCi2okRUtWK4SWjZbMIbXI57HuIdsyUX0v9SK9KkI3lP4Z1E/doiaVxiuhZXBlFSun/GnLfj9poSkdZj1vw4LApRwqf9+0MNU+zYBIs4qo65jVO6IBdzVZcxR+qDR0KSPPy4Nlts3qvDao4tyhOYcmyg2f4wHXBKq0JZIsKynt6Ev9FqYG05vUeVaUA5YU0rQmlA6uyQ54o+SzrGg3NKptSZ/nem0SkQRwepPDqESIE2gsK1qNdL9davPhdqVZbU/6LCvZLCnW/X9txqY85q9OslOz2jYp63Db6wmO3VZp3R5cZTEp6ze8hzNmSJPlUw00gmhbr6tz11KTL+6gdHgFV2mam+0hjfAtr1kcPrHCogGlswtkjRUli/aUOkd3JGH82xqLT01U7eYEGqsrFulI9/xsj8Xeo6t0V4e8nJ0AHlyV5qBxKs0Ta1O0JoKGbnfecoNxaZ9new0ykR9q2gx4dSBBKFUZlxQE3TWbrAmHTFBtMOeFJJ/bscbigtLr2XrIq4MJ2lNKh2dqEi9cy9KiTeerqu6yVpaZLZIJGRXb6ookaYR4oc9T3apTb9/kEkqT3khK+MDKFJ8YVyFjRv6zQqfqC1ZXPMq+ascdmtKsKJcaGveOlEfZH2D+qiRNUR3vNqrCspLFrq0q71cGDZxAMCalvld8QdIIWV0xGZVQ28qex/f/keBrO6u6a0v4lDwNW69E/TLBDs0DlD0NQ+sBIKFnKPv9ZEzlXe7gotk2ptTxTXUtGaEPyRxilLpGSeSQ/cuHrqO0r+YJmDV1/YOaT2ClIFP3vU1G3ukZEAPqmHzrsLkK+K5Ks+7cBKEhmsY1tslCl/pdH/JWVzGtc99JJNVchsgrG8OChDvkD53OqrxaRg15wWdWq/tVw9tZU361hgWlSDecSiBy6aHzlCowuh0GorkVYYhIJZTOtTkqU8JW+uW6HjedAsuAZKKhN5Wg/KIjfS+JdcoPSveay6jzZCJda7tqL+xIY5tJDulv6xrgXBpMc0jT7PqRrjXqrIau4siklLcyQDKpvM1zkdbUD4ZiB0ilEU1Nqq4jSbYYNRFpr1ZzSwCqA6r+y31Dc0oKa9X+3pWNuMVgoaHtFanxQ1rvum7YNCGXRnYp/1rR1qJiz6TAtobqdx0dtABIJBF1DXwYDumT6/Vr6KqO6vXc2qSOK5Qb5ZbdfcoLvK4rTiVUnvX+Uakp/+pqDZFXectEEWFbQ+2QSiD7C4h0ChmlE6aJrFYb3uS4njpn5BdOSx50Xf1MlofiNc2hvNMpFWehPNQfTFP1kWieB01N6jz1urQtpZfu6R/qD805laZnYCjewagM0PBV13KlRh/Qor6h1TXZqQS6WYSmLHq9ruo69rdB9yS6NjRI7l/8NL2vvr8t584++2zOP/98zjrrLJqbldf8fvvtxxlnnDHCkcVPmrcq533nJu6b94+RDiMmJiYmJiZmBJn39PJNWkbbdINhn9Hjd2Xah0/eChGOHM8//zxf/vKXARqTjLPZLMVicSTDAuInzVuVi75/CrlcCtz396uVmJiYmJiYmI0za48J7PfhH3DFFVe85XGGF2JobxozvA81zYsXL278nslkeOKJJxg9ejRBELB48WJWrVpFS0sLS5cuZdKkSSMWZzxo3op4YQ0v1LATrQjfgWpknSRDyI0hWV8yO5XCCx1Mzabsq1duaaOZYtBLPoxeVWaicwgNEUTpwhAPDw312scJKmoJ5nWWdE4nO/BF2JBaaKHETDYhAmWLFNpqieKkZlH01atpwTpLDAOhDCh6PRSjt2s9tSKgcd8KnfEZdTEX3CIDrs5AZCc34Oj0Ozr3rdT4z51V3jNbJavKJl0VdcyKssYebS7dVfX9169YHDlFo+ILcpGt2yNrbD4yxuG5XvU6bUnBRhOSlWWTVwoqxjUVg8Mn+izsU6/XTA16HRiTVOdotgMueybDdq0Bf16h8vrslIBV/SZ/Vis202RJ7lpqckCHSjMl59DrGPzy+SS2pcp4zCSftAF/71L5LC0KxqUl0/MBA9Gb3R2aApxAEK1Miy6UdOHbv2sH4P/N6iFthpwwpcKiyI6t39HJmCGlyItzcs7jD8uS6AJCmYjaVuPQCWXuW5GO4qUhWQDYpdVn0YBk0YBBwdOjvE2aLBpyh8k5hz8uTfGZqR5n/Un1q69+qMjyktGQUXxotMcvn01w4g6qzGkzZHHBRhM05A1JPeSBlSlShqqrX7yY4GNjJG8UYduc6iTP9KQIJYxLNULkud5UQ07S5wgGXJiYcemtqTZ5aJXLXqM9+h0Vb8HTMTWftBk2LPH+uCxNW0KVBWB6Hp7rM3i0S9VlQodVRY2Pjgsa57lvpcYJU91Gu22TCVhR1vnQ6FrUD3V2aRV0+TpOSV07XZUCbUmfsSn1pMPSk1T9MqOT2zSW9NZTCZywj6b6q3ZQsobINhKvpmQVoY8XdQgzkFSEQ4ro9bFhIcyEuifUl/AOXEg2D8koAlfJN4z68sYpkCFVIyQZ2d+JlglQGSBIDi2hjldTH80YiieRASPKO92C7F+OqJ8XlCTEqTQkHaJ1MgCyTy1JT6oJ3AoikUemqkPpKgNqH0C6hki3Io11pCK+q+Rk9XtgqknZ7dWPqRZUnpka5JW8TFimKnfdqtNOqdjsqFOFYSQxSQ6XO6RbIFO31cup2MwEDEbL8bZ2qNfq9bxrVSVNSbtD8TYZqi6ie7RI5JWMpp6PU4JkU8OyU6RbkV618XujnXKjCXJtUVNHEod0CzIqo6aZSN+BpraozGmkmRguzTEsVS91yU2rakuRiqQimgb5SIZXt3xralEyi3pdaQYimUcakYWbnVEykVSTKgsM2Z7W866VEPkOZKa7IQkU9XrLR20d+JDLDbV9Ya3q077buA5EGELbGOjvVsc0tym5j24gqlEfyuYRxUGol6lWVTIlLUqTa1W2ipoxJItJ5lS/qcsVExW1xH06ktQAZLKqrOu2k24MyaUGutR5x9pD5/FqiFw7sn7dGBbC0BuxCU2D5jwiYQ1JkvJZFVddTmJEUiPDQNaXutc3TZ6hBSG6P3yQHL4PNc1Tp05tPFWWUrLvvvuiaRphGDJt2jTCyKJwp512olwuj1icsTwjJiYmJiYmJuZdiOGFG/y830ilUixevJjFixezaNEiPvOZzzQGylJKDj30UBYtWoRpmm9zpi1LPGjeijz0l+dHOoT3Jc/Mf26kQ3hfsuhvcb1uKf487+mRDuF9yby/LBzpEN6XzHvwhZEO4QOL7gcb/LzfOPLII9lmm23YZptt8H2f3/zmN/T29vL444+zdOlS/vSnP7FkyRIOP/zwEY0zHjRvRf4a39C3CAvmx/+MbAkWPRoPmrcU9/05HjRvCeY9GN9jtwTxoHnkMN0A03nTx33/DZrnzp3b+P3II4+kt7eXlpYW9t57byZOnMijjz7K7NmzmTNnzghGGWuatyqmliBjtOKEZWwjjahr6MwEVAeoWup/GF2YeGENSYgbLSnshy55qx0iuzM3rGIHoUqbVRZGg24XlpZsaJgTepZADmmcWxMT8EKHQbeLnKk0c7WgSCFY24gxlAECQc8629JGEwWvGz8MonKY3LnE5pleFctXdqzxh2U2J00tc9lzSud1QIfJgEvDGk4XsKysMSUnmb9a6epeLwge/NMovnNaFwBjU5KlBYtadD/44s5VFhdMZk+b3Yjlie5O3igqqzqAsWmPSgCzJpzCX1eri+4j7ZL7VxrMbFF6WV1Cd03QGkk1n+mxGZ8PGZeC51erVz1LC5JSwSQ7XtXdo0ttDp7q0JZUGsafPJ/ms9t67Dgq4PCJqk2e6k6ytqjzow8dD8D/PNfJE98JMX+o81yf0ol+feZs7l46h+6aqisnSPJMr+D+Mw4DoPPVTtJmyKXPpjlonCr44qJgTFJvzPVYWTHImpKZLX5D57y4KLjl+VZO3kVp8y65awzfPWYNz/YN6eQmpiV7tLk806P62Yfba1z5YpJcZJHW+arFodsozfUJu1Sj+jXoSMILfRq9NcHLAyYzx3iN5cR/tSDNcds7dNcEX9j+VAAeWnUj0/I+j3apujxknGRNFabmAl7sV9uaLPjRr8dwzrHKnH6brMeqsknGrFsJhkzOOvxlZYaTp6v2PuuRuUC6YXf3tzUWB40Lufj6di45YzWg5tRe2jmaXfdXWsfdWiXLS4IxKdX2L/bofGfPIq8O2jyyRpX7qb+3853dXuPW15RWsy0h8ULoirT00/I+D6xMkbfCRnxP9RgcMj6gFqjzLhoI6EjpwBuNumlNNGNpScJIq+iHbqQzjWz1Eil0qeHpAi90qPgD5M12CBy8aDn0IChhJ7L40sWM8iLUCDPNjXkIZpBSOmhD6Wmr1DC1FJr0qZiqrjy3i1xyNE6gtLwpPY+nCwwjT8UfACBhN+GHLnakP3XDKlbTuIYuU0pJxR8gmc5T8dWch4zZoizjWrZRsSUyyg4PGrMeZLYN0TQOX6i6M2oVsFJDsyKSOWTfG4hcBzLSaYvmCUovHOlIZe8biGRe6bgjyz4Z+ggzCanmxnlwKkPaU6eEsDOIbPuQRtVIKh1vXVdsWMjAVfnVt+kG0qsh2qaofPqXq9jKkYVbMq+0s5o2pKfWLcToaUP68EybijUbWfMZNkII/NDFiP7MutLBTIyl5Kn7XVN6DAWvm2x6LFp0b/UzTY3jo0IjdGtIb13XguuVId2wq+pXDqprQqRbkeVepcOt64iNJDSNG9InD66B3BhEXZMtNLDSkU480gDbKbCzykYxkYXcaMiOVpr7qN0kkW6/oTPPICv9iHob2ZHNXKUXkVTbpG4gWicj68toWymlsbazyGjOAIY1VD5A5E2QIbIl6i+Ztoa+W1b6o2M6kAMrVfsDstiFyHcM09fLQpeKLaHmcNSSFgk709CqS91QOvJaUVk4QkPrLep9SobIdXTRNOfV+YU2pGmua8/rjBqn9N26MTQHqT7X4W0w/BBDDJdjSH/LyjMuueQS7rnnHgzD4JJLLuHjH//4Fs3vzZx66qkceeSRPPjgg1iWxcKFCzn66KP51a9+tVXj2BDxoHkrEATqhlgsllmxohs3rGBpFUQtmpxiJsCrUTOHBs2urGEIs/HHSgiNoqmh1QfN0sHyQzUZKJqkU/B6sUQCH/WHM6HVCKTfGDSbegI/dCh4fRSNaBAnK3hBrRGrpdcQCGpBqbEtpfsU/T6C6CZnaAb9ayxK/eocXU0OhS6bNckqpbXqXL1ICh7Y6wyaByuCmi4JI0lSoShwBiU9q5V/aygFbgBOdD9YW/bpLRmsSK5oxDLQ1UtPGarRwDpT8qmVa6xYsYJC5FPa60oK/YJeVw06LAGDJUFvRX0f9KBQUd/L3erG1ptxqfRWGehSN8JKj01fxiFbVhmV1hp0J30GenXW6FEZ+xKUuw1WrFjRiK1ckfSvEZSi++KKFSvoWd1HXzSxzjOg0C8aaXrX9JIs+RTXmvREM6QHCgIzAfX74mBVEJiSbiegHA2aB0qCSk+CntVqsOsM6PSs7mNwYOjlUW9K0lXx6O9TFd7lO5TWJuiNBnGlbo1ePUTXQEbjs4Ey2Am1z63U6FvTy4ALPVHdlbttevIufQ6syKgydHf1EUgY6FeN3VuEfge6yyF9FRVPYII7qNO7WrVRsujTWzFwokmVCSMkUXDpW+OwIrUiqvNeenSJSKr+PNBj0oWDM5igO+ozA4M67qCgtFadt9+TFEuCRDTps9Sns3plme6iRWGtis8d0Fi9sp9StzpvjxnSX4PuSv2fQugt63impBoNmge7Dbo0Fz/yye4uW4iER9oMG4PmWiJEx8DWVZsEodeY2AIghI4uNXw8SqUqK1f2UjQ0qkEBU1P/0QXSx9IqBNLFqP9d9F1Cu0IYDZqNEKo4JFGDxRoOhmYTyoBARoNm6VAwBLVADUKSuosvHXRhUY0G0rZeIwg9LGFHaVxMYQ0bNFeDAgndoRqdJ20EasJdNMkNuwpONCnHVdtkBkQYDA2anSqYycbgmoSDHOhHlGxkSbWjKCdABo3JjbK/D5Hw1EDEVP+AysE+hDE0YCPpglMFM/pv2ClTKlVYsbIX7Oie5jlgV9QENQDdVHlXklCL4tZ1ZLEf4XQP5VM0keVoMJb0NjhoJvSHBs0yHDZokrq13qDZky6GZlOIJneXDJ2i30vGkIj6oFmE6w2aqZXAiG6avqfK61Yhui7wVP3KQlSXaYks9yMKGrIcbTMSkR9xdCMq9EJBV5NKQcVeK6m6cuptW4FqEZIupWKVFSv7EAUDnOLQoLnUj0gLVc8Adg1ZGUSkgqF4AVntRyQifWptEFHNIPuiSYiWC4GLsFykWxqq33UQmqEGq159Ap9Q5zZMZDX6G1k0kIU+REaPYutDlEyoDELk2y6LfYhkANHfZCdhYvsMDZrdKsKqImtlRLSegJoM6YHvNNpEVovgR/XkViEZtdObB83lqDxuEooD6m91Y9DsUUyoe119jLAhasU+gshDvs66f7M3NwsWLOAvf/kLjz76KAMDAxx44IE89dRT6Js4cXFz8L3vfY9FixZx+umnc8kllzBr1iwuvvhijj322K0Ww0aRMVucJ554QuZyOZlKpSTqn/P4s5k+cb3G9fpe+8R1G9fre+kT1+uWrVtAPvHEE+uNG/r7+2Uea6NphRCyv79/s49XbrvtNvn1r3+98X3vvfeWL7744mbPZ0PUajV5zTXXyMMOO0y2tbVJIYQUQsjp06fLq666Slar1a0Sx1shpKw/Y4rZUvT39zNp0iT2PXRXbr/695wx77d8enuHw7ZRr/UPv/4+/nD6wQBc8vTtrCzoHDbZ54hJxw87z/88dztfn/lZAK584TaqgSCUsKIMP/3wZ/n2E7czIS15qV9D0yS+p3HFx4/l+pdv4/TtjueSp29nXEZy+ESXtuTJnPP329mrTXL8lOO5dtFtfGH74zeYV53P/+l37DahxtdmHM//PHc7WVPyxR2O58Knbuf8PYcf21Obw3O9One8ZrL2tza//t8jALj1tdtYWRGNcz/W1clT3QbjU3DMtsfzfwtv42szVKzf3UMd84NnbmfXFsm1P0tz2tfKvFJQ6b/75O2M030W3nQbH/7qicyedjz3r7iFp3p0vrXr8Hg6X72NSdmA/cacOKw8133yqPXKes7fb+eYST6PdhmkDclZO6l6+eWLt/HlHY/n20/czvS85MOja2zXfCp3Lb6NFRX42ozj+fyffsfgoMUOU4pcstfwGOos6L2ZXVuVOf1Jv/09H5leocmCE6YOb++Tfvt7jti5wvFTjue/F9w+rEz3r7iFg8Z/boPnv3fZrRw68YTG90MvvI+PHN7fqM91efN5AR5aNZc9skdwyJdPZf//OIEf7q323/TKbZwyfXiM//XY7fxk38/yl5W3kLcD9hh1Ele9dBuDruD/7TL8vPX0R337T/zuh59sbP/qX3/DFR8/trH/N6/fxrFThucDqq/s2z57ve3rtt/Dq+fy0Y4T1zvmzTFsLgruneSsT7/lMc/13czMlqHFCL41/xb8u5/msssuG3bcXYtv45ht31lsJ975B+Z++p1NkNlQH/hn+cvKWzhw3Ib741vx+6W3DbvPbazt65z0298z5+gjNrivWCxy4YUX8pn/t9uwfnL587dx9s7vvM3/+MZtNCf8Dfa9N4rXs0329E1qu2sX3cYJU5KkzSPfcSx1zpj3W66ZdfQ/leaJtZ3sPXp4GR7r6uSyB1v48I7F9e73db76p9sZ/P1c5lz528a2N99jNpV30sc3FPeGeKd98Ljb/sivjz/sn063Ib79xO2N++Uf37it8Td+Y6xevZrtt9+evr6+xsp3dQqFAvl8nsvN/Ui+SRRQxeds728MDg6Sy+WG7bv11lu54oorePbZZykWi3ieh2EMpZdScsEFF3D11VczODjIHnvswZVXXsmMGTMAeOmllzj11FOZP38+XV1d7LTTTtx777185CMfecf1sikcddRRPPDAA+TzeaZMmUJ7ezuVSoVHH32UmTNn8vrrrzMwMMCVV17JKaecskVjeUtGeND+gWBwcFDmcjl55plnyjmvzJFSSnn9ojny3//WKe954yb5jb93Siml/NWLc+T9K26QUkp5y6tz5NPd18ozHporf7fkZnniH34tb39tjvzkTXc3zrvfz+bJny+cIy97Vp3z3//WKQed26SUUp4y7zZ5yT/UeY++7S4ppZTnPNbZyGtdfvXiHHnvshullFLOeWWO/N/n5sj/fLRT/nn5jfK7T3TKX704R/5+6U0bLNtlz86R5zzWKX+0YE5jW/1c332iUx5x5h3rxS2llD9aMEfeufhm+XzvNfKeN26S3358/bj+9zl1zi/OV3Ff89Kc9Y95/Ab51a9+VUop5U+fV7H/bsnNGzzXE2uvk1JK+eDKG+SFT3fKy56dIy/5R+ew4296WcV14dMqnq88vH5c63LPGzfJ854aOmZD9Xv9IhX3fz6q9tXzO+exzvXKXc9XStlo1zon/en2Yd/rdX7h053yq48MP0+9vepl/tWLc+Snf33nsGPq8Ugp5XlPdcqfL1T94MGVN8jBwUH51a9+Vf7iBZXHTS+rn2c8NFd+6st3ykX9V8tzHuuUt7y6fpus21ceXHmD/O4TnfL1wavkd58YXt5T5t0mP//ALVJKKU+4+zfyi/OH9s15ZY784vxO+YsX5my0DdbtD2c8NLeR74Mrb2ict866bfzomuvlKfNuk/e8cdN6+7Ykp8xT1+bg4KA8+rSDpZSqXTZGvf9vjHXj/uRZd73lsT1VdU0+uPIGeefizVfedfvrP0u9T22Ip7uvlVKu3+cP+fffSSnlBvvdHS9c17gXSCnls73XNH7/zetvXZd1fr5ww8cd+PXfD8t3Y/fDt2Ov79837PsrA1dJKaV8fp1YpXzrfiGllB/99h/fNq96/3476n+T6vx5+Y3DvtfvBY+uuV5KKeXDq69v7Dv4uj9s8JwPrrxBvth39Sblv6nU70Xr8uZ6k1LdS57pubbxvR73utz+2vr3wzr1+269D0qp/pZtCnv86IHG7+Fr//22x69YsUICcnBwcL19g4ODEpDXpvaXt6QPGva5NrX/RtPde++9cu7cufLaa6+VgPQ8b9j+Sy+9VI4fP14+99xzslKpyG9961ty7NixslgsNo656qqr5Mc+9jH5uc99Tn70ox+Vixcv3qTy/yu0trbKPffcU+6///4b/ey1117yyCOP3OKxvBWxpjkmJiYmJiYm5l2IZWtYQhu2zZcaVDZ8/KxZswB46KGHNrj/yiuv5JxzzmHnnXcG4OKLL+aaa67hrrvu4uST1RuxM888kzPPPJOVK1fyhS98gcmTJ2+ewrwFPT09WzyPzUFsObeV8H2f5cuX89zDsY3X5ubAAw8c6RDel8T1uuXY/SM7jXQI70viPrtliOt18zNv3jwuvPDCtz3OMASG+aaPId423YYYHBxk6dKl7L333uuc32C33XbjmWeeaWz75Cc/yYEHHshXvvIVfvGLX7yjvN4JM2bM4Morr6RUKr39wSNEPGjeShiGwTXXXMPMj84c6VDedxx00EEjHcL7krhetxx7fHTGSIfwviTus1uGuF43P7NmzeL8889/2+NMQ2zw804oFJRzTlNT07Dtzc3NjX0Af/rTn/jLX/7C3XffvVWeMgOMHTuWww8/nAsvvJCxY8dy1llnsXDh+r7rEydO3CrxbIx40LwVuf313zc8hzsiSx4nEA1f4lDC6wWDVwevxtYlvTWD/caEtCV9fvjxPo6bMps/nnwElz/fCcBJn+hmbU3wYo/OfStu4qcfPpGc9Vl+/XonR071eOxF5QO529QKlz/fSTVQeazLNx+fyxd3mM0jawz2/9YfGZ0M0AR8aqLHweNPYddWyaIBweHbnMxVL3Vy7aJOLvrHkAn5uBTs2ip5dUDjf57r5Nevd/Jsn84vX+xkVVHj7qs+zWNrBX88+Qj+8MbN/PLFTv7j0bl8Y5fZ+KFAF5J+R2N6XnL30jk82X19o3xn76zq6lcfPZFQwiuDgh8umMtzfdcyb/lNPNZ1A60JuG7RXfz69U7WVAQHjauxoE/j3Cfm8tCqG/m3v83lI//3Z1ZXBHu1nU537Xr2H3sqOzeHuKGg1ZZoQnLuE3O54oVOTp4+m+f7Nc7b/UQ+8n9/5sqPnMh9K27i3/42l+8/M5erXurkriXKXP2HC+bih4JdW0LOfULVyYZWNx2VCPnCX25l+ybJwr5rOXLSSVz+fCcpE1oTkh8uGKrP3shJ6IoXOpmYhjsWq7q4e+kcjpzmcvfSOVz+fCc3v9LJLi0Bf3jjZpb06fx8vxM56ow7APjZwk68UHD+03PRhWrwj3dU6ehQFkn/81wn5z89F1ODo279HV96eC4X7nEiz/UJ7l9psP/YU+l89S5A+SvftWQON/1STTa5+uOf4w+/OIazLhnDlJzkmV7BvOU38cDKGwHoqd3I4duczB/euJnLn+9k/7GnomnwtzUpLt7rRNKRc9bB//V7fE/jqG19/uuxudxyxGfYrXWoc86eNptfffRECh6svs7kzL/eAsB/PDqX+atVXq8Xhv5wLP59tuFhvf/YU7n2wOGTlAbdoWP3bT+N1x/OcdhE9SryyEknAcoz+5uPz+XNzFt+07DvL/Vfs34jvwW/fl214Wt/yze2fWTMcQB8cYfZ3LfipvXS3PpaZ6P/b2j/unEDsI+yg/q3v6n4b36lc9ixT3WrdncCwTGTT2JTeHzt9W97zHm7rz/p8o7FncPS3vxKJ39cdvN6x9X9uN8cK8BXrxjPVS91suRv2WHb5/1UTaKr+2evy0HjP9P4/ZE1N9BVUZ3tj8tu5jPbvv1EsvOfnstXd1r/uM5XO3ngssP55YudnDBV7T98m5PX6xebwhPnDg0+/7jsZqblzwRgRssXhh33xR2G4lhbvW6988z/wdBE2se6bthgXvX+DXDDy+vXcZ3Z02Zz1UtD+w8eP3yS1fzVv+G6RXexb/tpAHxkjPp51Bl3cNieAxs85/5jT+WyZzbNh3jdvnHFC8PjrNfx/NU3cv0do9dLW/aHhjD1++jqvyX50pUTANW36nGvy3FTZlP5q7Ze/qC89QF2H/V5AM786y1cvNfwfr6gd/02ebL7enaY2cvCvmsBEFO+CQxdk+vyvSfX37YxdBMMU7DA7+Ga4iKuKS7ixtIrm5x+XeqTBgcGBoZt7+/vX29C4damv7+f7bbbjosvvpgTTjiBBx54gJkzZzJt2jS+9KUvcdVVV3HdddcxODg4onHGmubNQE9PD9OmTePHP/4xZ5xxxkiHExMTs5Wp1Wq4rrvBfZVihUKhgO+UKRQKw57oAJSL1fW21dNsbP+b8arqeLesflZLlWFpysUqtZJG2Qjf9lzD4kps2rFvjr0svUbaaqlCxZMbzffNsQL4tTLVUgXfSWwwXWUDdfLm8lZ9jUKhsMFjN4SzgTgAKqWN1+mm1uWG2NS4itUqCW/jx5WLVQrJtz7Phup4U/dXSlVqJX29/Z5boVaqbTRdvS++HevWw8bquFysEtTKG752on5WK9WvswqBbmywzdbFdyub1D82VI5SsUrBXH+bW9YoFaoUjKF9vrPh+8KmYhgCQxfsY45mn9xonix382i56x2dK5/PM2nSJJ588kn23XdfFZ/vs2DBgoaeeaRob2/noosuGrato6ODrq4urrnmGq655hqy2SwdHR0jFKEiHjRvBi688EI++tGPbvbzvvDkK/zk2zdzy4PqFc5ll13G//zP/9A32M/U2/PcdsuNhOksU084brPnHRMTs2nUajUmT57MmjVrNnrMaXwRgPyPNnbEl9fb8oUozcb2v5n8l9TPq1FPL780LP1b57VxvvpPHPt2ab+00SM3FOtT/61+5n+88VRvT72sm3Is/CiquzfzxSi+/1ovzn+mLjfEpsUF//Y2+8962zOctdH+oDj7bfZ/m6+st+1PN8O33yLdHL6w0X3DGaqH//cWdZz/wYbSDvWzi9Zpv/qxG78OIH/V+vlviJvY0MOwDbfJ7QB8rfE9N6qFy5efQCKReMs8NoZtC2x96E2Z6Qp0fePyjCAI8Dyv8U+84zj4vo9lWWiaxllnncVll13GgQceyJQpU7jkkkswTZNjjjnmHcW3uVi6dOlG9z333HN8+tOfZsmSJes9Jd/axIPmf5EXX3yRUqnE7rvvvtnPfcV5czj6a59G0zTWrOrnG9/4No8//jh/S7zC4Tsfzov5ZZx32P+j4xOfgOmb9iosJiZm8+K6LmvWrGH58uUj/oozJibm3UOhUGDChAm4rvuOB826qZ40N74bQ4tiboibb76Z008/vfE9k1FjgwcffJD999+fc845h2KxyEEHHUShUGDPPffk3nvvbRz3biEMQ+6++25++ctfcv/997P77rvz3e9+d6TD4gO9uMm/agIOcMwxx/DTn/6U6667jvHjx29QnlG/cB7+x8U8H7aSt0I0AaYmuWupQZMFe7WF/GGJwTm7lfjj8iQDz73IL8+5godf/BGdr2exFi/ivBMu5revXU9zSrCqbFDw4P7zf8e0adP4/ve/zx/euJk3ShrT8wEDjk57yqfgalzU2cGsg9cya4LLH5dZ7NUW8miXRpMNWVNy4NgabihYUbJIRmv3mpqk4mss7NeYkoW/dQlempvmT/93FD9b2ImlwZd3nM1L/dcw4Br8dqnFzBbJz+e2cdEX1tCe8nh90Ob5fo1PTnBoS3jc8nqGb+96YqS5hRnNAf2OzviMp16lujptSZ9FAwZjUyEPr9Ep+7B0TpKvnVvk/pUGS1cn+I99C/xllcX+HR61ko8bCg7f7vP8bGEnM1t8HlhpcvFeJ3L6/bdx/UHH87OFnegC5r+W5It7FllTMZg9bTYPrbqR+WvMhi7ze0/OpS0p+c38URz1kR50ARPSMD7j8kyPRcGDfUb7PLzG5KlXU9z12aO5Y3Enf18reOnGJFNOrpIzYW0NdmqWjEvB8rLSfdcCpeE88Bv38JlTB9i+yecT407lsK/+luQBkjuOPYbDvvpb/njF0Y1+c9eSOTTbAQ+sNPnEOI/rFyaZ2Oxz8V4ncvnznbTaMPf/MgxOTfHofxzMuU/M5Qd7D2nvLn++k4In2K9dLWXbnvT5n39k+Mw0lxktVUIp6HUM+mo6syYM6RhveLmTT3eoBSRyuZzSU086iTsWd/KZbWfzx2U3c9jEkznu5Nv49c3Hc9VLnYxJSsZnXHYf9Xl+uGAuT7yi6ueoW3/HrtPKTExLnusT7DtakrNCHuvS2alZMufxLF/cr8CgK8iYkjdKgmogmJqVrKzA0yst9pngsrYm+NgYjwdWmsxskTy4WvCVHWvs2vr5RtvVdYdXvKDK/e1d1fejzriDs84v80ZJH6YVrTN/9Y18rONU5LKf8NPB9oaWeFM4/+m5fH3a4eTz+Q0uNBATE/PBpb5AyeDgIA/13d2Yh3DUrb/jdyccxcqVKxk/fvwG7x31tH/d41AyutnY/ujAWuYPrOHXa994X95zVq1axdVXX80111xDX18fxx9/PGeddRZ77rnnSIcGfMAnAjY3N3PWWWdx+eWXb3D/ZZddxnXXXce8efPo6elhv/32Y9asWQ07lHvvvZfp06dvkdmcT8x7kg99bAd0XeOZ3z3Exaeqd5UnzPwyh04depV06KGHcuedd272/GNiYmJiYmJGFlMf7prx8VHt/OekHUc6rC3CMcccw6RJk5g7dy7/9V//xcqVK7nuuuveNQNm+IDLM/5VE/Cnn36axx9/nEMPPZTXXnuNRCLBtttuu1k8LRc/v5jDjtoVgN2O2p89d2jjwpN+wK3P/TJ60qyOmzlzJosWLXpX+xrGxMTExMTE/PO82Zf5kb61PNi7egQj2nIIIbjnnns4+OCDRzqUjfKBftL8VmyKCfh3vvMdHnroIe69915OOukkzj777LccMAsh2KnFwdQkz/TqPNFtMLNF0mRLrv9himsPPIGfPJfmmEkV3GKJsa02uoAvbF+ix1HnSBohN75k80S3YExS8tf+vwLwt8VX8vGxCXZq9mlPeXyovcyT3QaTsi73/NdyxqVlwyqo5An2bpNkTcmurT5FT2/Y6xw8/hRmtroEEsakPF4vCEIJ+3eEZA4MuPU1JXW4++kcK8u/YofmM/jlsymm5CR5K+Q/ZnejCYkTaOzYXGPPUQF3L7XprpkkdckdizvZqdnnYx0uSUOyS2sVW5e0JX0m5xxyVsBfF9scvs3JfHqyw+gk7HFGlaShVETf+dgAOStgpyZJk+2zbc4joYesrvyK46dU6a4a7Nse8FjXDcya5HPI9fcwKSP5+3KLr32oQHvSZ3Qy4Mnu69m+yWWHvKTz1U7mLb+JNRWBLuCgfXrZLh+yU3PAZ7adTXfVQBNwwpQympBMykjO+nChIVf48T4n8r0LB8iZcPFeJzYs8v68UrDfGJeWhM82WZ/H117P2Wf1U/ZhwDH43pNzOesbRcaPrXLWI3M58qvqP6FrF3Vy34qbGtZgx0+pkjRC/t+eRfYf6/Hq4NWcvfNsRicD+kanuPjYVfzhjZv5wd4n0vlqJ4+suYHHum5gTBJmjXdotgO6qwbn/6WFE7ZzsHXJywMJXuxPcOkjeXpqOsfc/lue67uWW1/rJG3AE2tvZ/7q33DfipsYn3F5aNWN/K1L8KWH5/JaQeOKFzo5/fsu5z89l0MnlPnJL1u4f6XNywPX0FeDcw/oB+A/PzbAoeNdXi8IxqUlD6zUWFHW+MgYn4IHp+9b5NJrR9GaCNm3vcrZO89mv3YPTcBOzQFjmz3WVARhCKEU/GDvEzlh6mwmZeGhVTYAD626kSk5yflPz+WPy27mzuvzdCSHVGejTvSYNeEUumvrT57Z7/x5fKzjVACeSTVx9s6zuXZRJ4fd/PvGMUuLV62Xrs6Fe5zIqvL69lMxMTEx63LXa0MSC+/hTVfF6mY47PPx9lGcM337LRHiiHPnnXc2BsyFQoG5c+dy6aWXAtDV1fWWk623Fh/oJ81vxaaagNe54IIL3vJ8mUyGSjkk1H2cKsiqumiCAKQPpuZRKBTQax7lYkAml6JnbZlyMUATkjAyc3bKPrrjIUSIW5YUu4sAWKZNseDhlHyqvo9phoRVn0rJp+RrBBVlv0TVw7fA0yCsQc32CTRJIVnALfsUCgVKtQCnLKh6AVrNwyuD0CWG6+FVfAIHLN+lXAwoBAV0xyOo+LgyxPM1nNCn6oYYRohb9qEaUi35hBUND3B8tb/ma1S8gGog8TVJKEETYLiqLmolH1nVQEKt5CNqUC35aELilX2qhg9egFMRlIrKX9or+7hmSM0L8CoCy3dxyz66o1Er+VSsAKeqylYKA7yyjy/BCQL0GgQViXTBLYegy8jGyyeoapSLAbWyj18ROGGA5w7ZaNVKPlS1xvew4qNF8TqeRiBA6iFuVSCrAk8DqiofUQvRQggqqt/5FR9HBBQKBZyST8X3qfkawghxalCRAQVRwCn7JKSDU/ZxI3stv+JTEz66AK8iqGk+vibxKjqm5+KUfTSh/Lr9UGC4Hn7Fx/RcqiUfr+ITAg4BbqD07VXfx3ElogaaD2EFEOBqIVRDysUAGxdZ9akUA0TNo1ryKdgqfqlLqApkCHpNEFQkjggIKhLPBxsHt+xTsgMKvmp3rwaOFyBqEqKxrlMOGvUrqx6hRqOO/MpQfVrCxa/4jWN1R/UnWfXWu3ZtnHXsrnwKlqpDy3eHLN9KAQW5cUuqSil4y2s/Jibmg02hUGjchwAsTd1fyuXy26bVDIluDA2y53ev5YHud2Y5915hwYIFzJo1i3w+z6pVq/jGN77BM888wzXXXMNvfvObEY0tHjRvhLcyAR83btw/fb5arcblP74LaRlUfY2mXWYy7UM7b/T47WZM5NWX3/4VzIpXV9A6cQyp9DubmRsTExMTExOzdbj//vtZ+PBNnHvu/E0bNOsSbZ1B8/4dbewxqonOZW9syTBHlLPPPpvzzz+fs846i+bmZgD222+/d8U6GPGgeSNsbhPwWq2GaQr2mTWTKfvsylM9qup1WyI88EKTXC5HkDBJZ3UOOWZPLvy3a0ikBKahoSXUil922iCwTWQixEqHLHx8ITscuBfJjEE2a2I7HskEpK0ALWmQyhhkUhp6ylD/CCRNjJTE1EHTIJFRThm5XA4rrY6pmjq2Z5C0BWHCxExLLF3iWyZmykfXwTUs0lmdXDpHYJvoKbBSIa6nYSchaYWkzABLGlDSSWZCtJKBmQbbVvvxNVIJSejrmOs8afYtVReJmoFImiBVnDJhksyoJ++mb5DMGKQ8iS11MlnQhI5ZNLDMkIQtMKWBa1hYUZ0lMgYpS2BrqmyZNJglAxmCnRAECRM9FSJ0gZUOsPWhetGFRjqrkxAGhjSwkwLH1Bv/XCVqBiTNxnctZRC6gmQmxHM1TA1sPcTSDIQvMNNATeUjEzphCHpK5WekDOy0IJfLYZcMUgmQvkbKCLF1g1QmVPsGDWrCxk4bhL7WSJvISHQBpjRIZEJMTdWXZ1rYaafxpFkLBb5lYqR8PNMimTEwXQNdgC11ZKBhpw2SCfBcHZkwCX3QUur8VjqEik46q+NgIZIGqawetVPYiN/WJSRNhC0JHA09FWKnBbrUMW1wsLHSBpmsTy6VI1E2MHUD2xLIhNEQkdXrBEAkTbSofeySahNCVZ+utDDq/R0IbNUuYp32qeNgN7YlXaNRh65hNbanhU4uu/EZ6qmy/k/fD2JiYj445HK5xn3o05/+NNc8oHHEEUnuuOOOt02rG+pT569d3dy/5v39pPn555/nL3/5C6BkrQDZbJZisTiSYQEfcE1zEATDVvJyHIdarUYYqtfSdRPwhQsXUq1WOf/889+xCbhhGHzma8dy8CE7AHDUNlX2a/dosuCjYzy+cVGBectv4os71tCE5BOf2I620XkuuuFlnEBjZquyDUsaki/OqHL0JI9y/yALH3meb579cUYnQtZUBpiWr9FVMelzDA6bUKXZ9nltUPCRMTX+uOxmjpnsUFCnYmou5G9dJhVf8FL/NRia5PG113P8hWNxAg1NwCHjJEkjxAsFR+5U5ZWC4La/juLQ3Qo825vioVU3cu7eBabkfHZsrlELYFrewdZDVkfL2O7VFmLrIdPzIa8UBK8OGjyx1mJixqE9lSBjBDzdrfSpTZbPabtUuOqlTtJGyJSs5IAOj6QRcsQ2HmkjoOzptCZ8Cq5OryPoSHk83Z0ilAGTci69NQ0vFKypwr8f2EfSCDlppyopI2TA1an6GjkzYEnRZIfmGgeOK1FwdZpsyfR8wH7tHv2ORmvC5/Cv3EWTHbD3aIerX8rw2qDJ0pKgNeFTC2B15Vfc/EonXRWT3UeFXPSPudz6Wicf63DYd7RkUb9Fd9VgYsahI+XhBIJDJ9Rosn3GpCQryhqzxgekDdijzeWbj8/lhX5B1deYv/pGlpcMTE3yUr/Fwr4EVV9jccHm7qVzmLfC4JwT1+KHgvaUT+ernQy4sF2TpLtqsEtrlYKrE0jBHm0VfvGpfrxQ4IUCQ1N5H7x9lT8sSrDntAqmkLxeFEzKubihIG2ENNkBu4/6PIFUS6tPyioLvSYLHu/WeeZXCX69OM11567k1OlVMmbApyZ6/Gm5zcryr+hIqc62Q5NkWUmw1+iQRQOCf/QaFD1oS/p874trGZv26HcEv369k2n5Gne+ZOMEgv3aQ2a2SKbkJM/16dz8Siedr3bSkZT8trOZh1bdSMXX+FhHhRlNSht/8pmD9Drwyxc7ufW1Tq498ATuWNxJ2lhfR7jtvkOyi3//6VgAvrD9bGbvM7R9Unb44gin/vl2FvZdy0OrbuTy5zsZm/78P30/eK8yMDCApmnccMMNIx3Ke55FixYhhHjbz6c+9amRDvV9x9au+wufup3rDzoegKte6iTc22DWrFmcf/75b5tW0+WwzwFjR/GtnadvlrjerTQ3N9PVNfwfg2XLljFmzJgRimiID/ST5q1pAu77PtdddgdHfXom4/batIVQfvg/J3PGV27m3FO3Z8/9tmfOK3MwDA9Qf/xv+dnvOOL0Q2gf2wL4/3RMMTExMf8MTz31FFJKPvShD410KO95xowZw2OPPdb4/tRTT/G1r32N73//+8MmlL8bBgrvN94NdT9v3rxNetKsGcPlGQDa+3x5jc9+9rOceuqpXHHFFQCsWLGCr33ta8yeveke+luKD/Sg+bTTTuO0007b6H4hBBdddNF666G/EwzD4PPnfIYdJ1ss3vicomHss990/v32H6JpFQjX3//lC08mlBAPmGNiYrYGTz75JLlcju23f3/O3t+aNDU1sc8++zS+P/nkkwAcffTR7Ljj+9OH993Cu6HuZ82axYwZM7j66qvf8jjDkBjm8AGA8T4fNJ9//vl86UtfYrvttgNgm2224eijj+ab3/zmCEf2AZdnbE3qT5offuCFkQ4lJiYmZpMJgoAf/ehHTJkyhe9+97sUCgXGjBnDhRdeONKhva9YsGABiUSiMVCI2XqMRN3Pmzdvk66h+pPm+uehrm5+8OwrWyHCkcO2bW644QZ6enr4+9//zpIlS7jjjjuwbXukQ4sHzVsLwzD46reOZof9dqUj7bGwL0HF19i+2WFV2WzoUm09JG+lKfs6vnQ5fXoZWw95qtvmox1lAgm2ribNhRImZl38UOCGkrKvkzTSzGx1oyfQUHB1OlJKo7pjc5Wuilq2e7vmGmNSHkdMrPLqoEHSCJmcdUgbIQd+doCqr7GkYLFdU41+R0MTkpOnKw/dAz7Uy07NPk4gaEkoC7iUIUmbgv3GVPjrqjStts/rBYN+R2N5WdBXM5icdThwrEPagN1HebzYn+TFfh9Dkxw8vkxHyiOhC9ZUdNIGrK6YTM27JI2QsqeRswJaE0neKJp4ofJUbrUlZcUJnBAAAQAASURBVF+jLelT9TX8UJC3VP1Mz4dqqeia0vX21gym50N2HVVBE5KJGYfVFZNHVmfQBIxLS9ZUdJxAY+dWh2d6bI7/zwpvFE3GpQWzJrjkLcnJ00pMzWX4xLgKv16cYacWh0BCnyM4bnKFKXmXVWUTN4Rtsj4rK7CsZLOgJ8V2zTVGJ0IyZsiAK2hPSgquxhnbV3hlwOLTkx327wiZ0VJlfNplbNrn7O+1MSHjM+DCjs01WhIBbUmfL+1QYXzG47WCzrKiBcDUXMA1i2x2HVVhSdFmjzYNWw/xQ6XRm5h1KXsarYmAsg/bZAI+t3OVj3e4DLgGB451abV9dCBhhBRcjW8+Ppeyp9NsK2u1e5dreCHsNSrguHOqjElCxtT58/IkFV+jJeFz5vZFCq5Od82g4gu2b3aZPdVV+U11SOqS0QkYn3bZrqlGUg/xpGBmpMP+5r6DhFJw5KSTSJsh2+Z8PjO5zDZZHy966PLVM/vJWQFlT6M92Up7yqfV9slbkjFJOHpShd+/bDPo3krSCFlbFXS+2slL/dfw6uDVfPPxuZy2c4Vjbv8tdy+dw/e+uBaAzlc7eb2oJp/8bGEnALe+pn7evXQOh09ROu3uqsHZO8/moVVzt8YtZMQ45ZRT+PGPf8w3vvEN8vk8J554IkcccQQXXHBBrG3ejDz77LPMmDEDXY8nlm5ttnTdP/ZMCx+7ch4ASR2+/om+Tdc0R5Zz9c/B27Ry3oembpE43y28+OKLALS0tLD33ns3Vl2+9957RzIs4AMuz9ia+L7Pry69i48ctjufOCR+9RYT80FFStiSk8CzWRDrr+Pyjrj++uu59dZbeeSRR5g6dSpf/vKXOeKIIzjuuOO48847ueuuu95S4jYSSCkpult2ln3WyjZm9W8OfN/nhRdeeFdoNv8l3kudO2Kk6n6TNc3RBMA3b3s/c+SRR/L444/T2tra2Pboo48ye/Zsent7RzCyeNC81TAMgy994xiybTkgXgwhJuaDSrEI+fyWO//gIOQ27pD3T3HllVdy2GGHse+++zJvnnpStttuu6HrOhMnTtwkn9mtTdEtkv/vLVjBwOC3BsnZm6mSgZdffplarcauu+663r763Jv9999/g9/fVbyXOnfEW9X9m3n11VdJpVLvaK2GN7Opmmb9TYubAOjh+3vQfOqpp3LkkUfy4IMPYlkWCxcu5Oijj+ZXv/rVSIcWD5pjYmJitibZrPrbvyXPvzlYtWoVTz31FD//+c8BeOaZZ8hkMkybNg0pJatWrXpXumhkrSyD39qCFRzlsTlZsGABALvssstmPe9W573Sudfhn6n7efPmMX78+M0yaN5UNEtDs4crabX3ubL2e9/7HosWLeL000/nkksuYdasWVx88cUce+yxIx1aPGjeWvi+z80/uZNDj5rJzh/dhd1GVelzVPXv2FzFCTX+vMLmtOkOFb9EWwIKrkaTbWDjcsQ2JSwtydiUwzM9KUIJTXaAKSReKFhcsNmh2UMg6KoKpuRSeKGDJnQMYbG6UmFF2WJs2qMtKXACjaQe0lMz2KPNJWPaeKHLKFvnlGkluqomBVfn5YEEeUtiaJI/LrsZP9T58Gife5ebnL1zkYqvLt7JWY+CC3lLZ0JGLdU8Ph1Q8nT2Hu1i6yGGpjyfP9pRJpSCiRkHgJKnkzRUXfihy96jqwCNcs1oqbKkaLGo30YTNXZsgdGJkH5HoHkaoxM6K0JB1jJ4cJXJtLxPxgzorhp8qL3M6orZWNDD0pPUgiKjEin6nQodKY+JGZeyrzExK1hdNpXXddVkjzaHZUWL8RmXUIb4ockurdWorgusqers1OxT8pQObrdRDl1Vk6VFgxZbsmebQ9YMOG26oKfm0ZFyWVay0PCZmHE5dnJAV9XE1CTP9CRJGZKCq7NHm3p692J/krakz48v7MHUJK2JACcQvDJgMToZsLiQZL8xZSakDVoSPlNzHkuKJn99Nscp04rYekjBdSi4Fu1Jj5cHBAOORZMdMH+1zQ5NIRVfacUBOlIuCV1Q8dUbUF1IclbIV3Ys8WJ/kvtX2uzd5tGRNNi9rcY/uhOUffh4R42bX01wynSXIARbDxBoVAONKTmH7prBsqJFayIgaSgPaU3A1LxHd81gRcliRkuVAcdguyaPPy9PMmuCgx+6zF99I7pQ3t63vm7yxR1qzGwNcAKN9qSHJiRaEwy4JdqTOl4o2KW1yh+Wpbh3eYozdyvz9y7BYRNP4TAli+O+FTcxKevw8Y6AT4w7lRd27OTISScx6z/uZtYlv2Z0MmD2tFP4n+c6+cdym488+Gce+dpsPvzT+9hrL40zt6/yTE+Ck6fP5twn5nJMu/dP3QuE2OwPy7YIixcvBmDsWOVf/Y9//INddtkFTdN46qmn6O7ufkee9VsaIcRmfQq8NXj22WcRQrz3B83vlc69Dluj7jse6+foH1T55YudTMv7XLcgTfjCpskzMHX1WZf3+ZNmgOuuu44DDjiAXXfdlW984xt86UtfGumQgHjQvNUwDINzzj2aUR1Jep2RjiYmJibmrUmn0wAsWbIEUIPmww47DIDzzjuP6dOnc9BBB41YfO8nFixYwKRJk9ZbsTJmyzNSdb+p8gxhCoQ5/MmyCDevrvvdwHnnnbfethkzZrB06VIcx2ns3xwWwP8K8aA5JiYmJmY9Zs6cydSpU7n00ktpb29n8eLFBEHAcccdx/z583nssccwjPhPyObg2WefZb/99hvpMD6QvNvrXhjasEHzfa+t5Z5F779ltB9++OENbt9uu+0a+zbn5Nt3SnzH24oYmkVCT/J8r87uo6p0pFxWlC1KaNi65LTpDikjTygDvLCGLhw0dGw9jROUWVnx8EOdyTn1qDprBvihIJSwQ7NH2ZNolktSD6kFJWw9jRtUEZpAE5L2pE8goRooazZbD9k251D1NQIZkDFDQhnQXTPprhrs0Vahq2pS9TWaLJ8X+5NMzjmsKFkcNE4tqNJi27xR8hmdTGHpPlKGdKRcqr5GSyIgbYboQskOXq8qqQVAIJUsoy2ZIJAVVlcE41ImujDpqup4ITTbAR9qr7Gwz6I96TM25dGWNOmuerihxNZB83RWVwN2aDcJpM+HRtcoejqPrLHZLh+wumLSlvDpqpo0WT5B6NFsN+MGVXKWBSjpyKuDNq2RldtzvSm2a6qxrGThhYLp+TyDbg+7jcpQCzxKnkYoAzpS0JEKWVrUGZOSPLHW5oCxqnw5K2BV2aQ9JchbPkkjJKEb7NoqqAU+XiioBhpNtk9fzaA1EfBEt8FurZJH12TYo63CxIyLJiTVQNnGddcMNEuy9+gKLw8k2KnFYWFfkpwVkLMChBA4geDWzxUZcAQ7NtcwtQQ7NusMOB47NGlctUjnc1NKVP0UY9MeTZaPE2iUfQ1Tk9h6mn6nypS8TygDBoCMqZa0/niHS9oI+dnDTRx4eJkDx5V5qT/B6ooZSTN8Hl6dYtdRFWxdMC4dogkLP5TsPdrjjZJawhtg33aXnBnghYIDx8GSosaUXI3nepMcOlHJYbqrSkpS9XU+3F5lt1EaK0pmtE3D0AwmZaE1kaHg9tGaUMu2rygHjEtBa8InZwUUXJ3P/f4OZs+s0FXVWFM1OHj8KZT967jh5U4+u22Fh1bdyLyfnspdS+ZwzORTuG/FTdz11zEcsE8Pu0/oBuCzB65lSVEwo+ULLC7MAWBiRuLI96e+UNd17r77bs4880xOP/10pJTceOONHHDAATz++OPstNNOIx3i+4I1a9awdu3a97404z3I1qr76687jj+u/T0AVV9jbKu7yWmFqSPWkWcIXQdt5AePm5sHH3xwpEPYJOJB81bC930u/f4dHH707rD9riMdTkxMTMzbssMOO/DII49w6aWXcsEFFzA4OBj7CG9mxowZg3yfr/D2bmUk635TLedEQkck1rnmLA1hvD//UX8vEA+atxKGYfCN73yGMWMz3Lt8pKOJiYmJ2XSeeuopdt1113jAHPOB45577uHiiy8GoKurC8uy+O///m9AWTLuvvvu7+i8m6ppxnjTREBDe18+aX6vEA+aY2JiYmLekqeeeorDDz98pMOIidnqfOpTn+JTn/rUiOUvzOGa5lkzx7LvtDZ++fDrIxbTB5n4Gf9WxNAsdM3kox1lHu1KsqJsMSXn0GorfXAt8LG0JG5YJWO2UvFVGk3o6MIklIKxqYCsGdBkKT1z2gzZodmjqyrIWRaGsGi2LVJGnoJbJKFncIIK7UmPkq/hBBqhFLQnPbImLOxLMiaVwQnU2sSGsNgmI9m3XeIEagnqcWmXJjsgaYSsKptUfEHSUFZ3Ra/G5KzN2mqRgltBEzqaUNZyA47O2JRHe9JjbMpjSt6hGmiUPJ2cJdgm246p2TTbzeTMACE0TM3G1CTj0x5ZM2DAgWl5B11IPCkIZcCopE7aSJAxdXTNJGOE6JpJLfBpS0omZz0OHFuLbObqem6P9lQCQ7MIZYBEIglpspNYukXSCKn4gqwZMDnn0OcYjM+oJbxrQYmSp9PvFFhd0dizrYalJUnoGUIZ0JZQy5/v0+7gRG2iCZiUdXECQULPYOsalp4kkErPnDJsvFCgAUkjxAkEn55UwdQk05ocUgaMTiZV/ECznWO7JsnoRMjSoo2tq/qfknOYmHFpsgJWljUMTVL2CjiBhqUlCaSHLgxaEhkMzeLTkyqq/VORXVtkxddkBWgC3LCKoanXlc/3JmlLeATSo9kOsHXJS/0JvvuJPl7qTwCQNkPGp110YVL0YK/RZUYlUhjCQhcmAo1ACl4vaFR9jfFpF1OTtCU8lhYtip7OGyWHcemQfsdg79EWbuDSXRXkrJC0EdKR9nh1MMHYVMC0vNLztyV9ts3qJPUctaCEoenRdWKwbTYTlQ/SRkjJ0/j3vYtoAr6w/Wx2bAp5ru9aSp5G2oDXCmpZ9kfW3MCtz6WYt/wmDh5/Cl85tBtTh6/PnM3PFnZyz/U5Dp+ortUZLVV+uGAuf3ouw02v2Fv3RjICLF68mJ/97GcjHUZMzHuS8566nR1bakzMhDiBxqcmbrpNZX3QXP/c93IX37v7+S0Y7cjieR5nnXUWtVptpEPZIPGgeSvh+z4/vOQ2HvjzsyMdSkxMTEzMe4Sjjz6aSZMmbfR7zHuTefPmceGFF779gZYGlt74HLL7eC753G5bPsARwjRNbr31VhKJxEiHskFiecZWwjAMvv3d42kfm6bgFkc6nJiYmJiY9wBHH330W36PeW+yyT7Nlo6whzTN8xas5A9Pv78nRs2aNYt58+Yxa9askQ5lPeJBc0xMTExMTEzMu5A3W84dutdEPrzTGH4x7+URjGrLMmrUKI499liOPPJIJk+ejKYNiSLixU0+YBTcIraucdQkn0D6GCJJS8IlHXikDJtqUCCpZ5GEjErkkYRYWhKBYELapez7ZE1BIENAw9IEmtCZmBl6leEFNUzNJmXYuGGVlJHH1GqYWhVdaEhCpacVIXuPDil5gyR05XErkejCIGnkCKRPe9Kn7KklkcdnlP/yjs0+TiDImgksPUnB7WNcup2C240bVsmakDKamZQdBMAJtIZO1o98epN6lpLXR1LPIoRGQlfbQxmwbc5HSsmqik570iOQgiZbLc2cNJooewOkzDyB9HGokTF1/NCl1R7FgNuNqZnkLKVv1oROOZKP1TW+mtDJmW1UgkF0YVB1y0zOanRVTWw9pOwbJI2QjpSGE/howiRj1rD1BAndZU1VZ3xaqvbTLAZcydhUQMVXS39nDJ9VFbUM+fZNNQbcASxNoAuDUAZkzQTLyz6vDNjs1OI09MQ5K6BDeuhCxR1In45UjmpQoBaUAKgFkr1HWzzVrfy1nUCwXZOkpxrQYvt0pFTajlQOU7OpBkUkEi+sEUq1XLahSSZmXHprBo93pfnwmBIFVydt6lhakrzl4HoJ9hotsQ3Vbu1Jj5wV0N7hsaxkMbO1Sm/NoNVWS6Y7QZlRiQwAgfRJ6BnKfj8ArbYkbQoKrmTA1Rt6421zDnlLJ5AeA47OxEyWalDA1EwmZjIsLamlwOv68oqvPMbHp90oHyI/c5OUnickoOT1YetpCq7eWMJ7Ytbl928kuWd+GxNPvpaxabWEvKNraAKm5Bw6rEksGFzJThNqfHxsM/OW30TeEtR8nXOfmMtOzXDOf/aTMUMW9F4H6EzPST59aDdLuz3eZv57TEzMB5hZ433uXZ7lkPHqvt0SzWPaJAxdfSLm/WM5f3hi2RaI8t3DwoUL2XPPPVm1ahWrVq1qbI8XN4mJiYmJiYmJidkw+vBB86y9J7HvTmO58p4XRzCoLcu7eaGTeNAcExMTExMTE/NuxDDUZ9i2cItl9/TTT/P1r38dgDAMeeyxx1i7di3Nzc1bLM+347XXXkPXdSZPnjxiMdSJ3TO2EnX3jL/e/8JIhxITExMTExMzgmyye4ahDUk0Gp8tN3TbY489eOihh3jooYc477zzmDVr1lYfMH/+85/nkUceAeDWW29lu+22Y9q0adxyyy1bNY4NEQ+atxKGYXDeeadyzKc+hqUlMTUbXZi4YRWBRtpIUPIcBBqGZjPodiEJMbUEppYgkD61oIoTKE2PrSfQhFSaWsdHyhApQ7ywRkLP4IculpbE1lPowsCXLoamRz7FYOuq6f3QpdluRxcGGbMFW0+hCZ2C203JC2hLSpptSVIPSeohbQmfiq+0oEJouEGVrNlM2e9H1wx0YZIwMiT0DEVPx9CU33MowdYk49PQYivNq6kpf1svrJEwMkp3S4AhLNxQYmnKi9jUJAOOjhACS0uRMVtwwgqGZmEKG196pI0mKsEgz/SkSBtNmJGGuup7ZE2l964FJXRh4oUOfc5KAAQazXY7ecsmkJCzBG0Jn7Epl6Lr05bwKHkVmu12Kr6DpScZlxYE0iOpZ0kbzZHHsCBrGcqH2tfYoUljSs5hVCJPzsqSMDKYWoKkkUXXTCZmLA6Z4KIJiakpH+nXCwl0ISNP7Cy2nsKXLqaWIGe2YQiLnJVCFyYzWjwmZR2m50OC0KfZlizsS2LraXWsZlENikrbrucBpelOGiF+KOitGbSnNHZorlFwdZptSUJXXs5CCAxhUfVLCCHIWW0YmqTk6awoW+TMAE3oVAONAddAExJLS6IJnYpfouQ5uGEVPwzww4DumoEfBuQtnVEJn/akJGkoDb2lJ0kbzeSsAFOzsfU0lp7E0CxGJ0KKnk5HyqPF9in5Gk2WT9LQyJg2oQzQhdnwNg+kT8rIkzaa2bddI29JbF1ia5Idm0L22qubnppBzgzoqproQvLxsT63vZ7B16DkaeRMiR+6pM2Q9pTPh9sDvBBmT5tNkx1Q8jT+79kUY5IBE7MuTigaOv2YmJiYDaEJyQFj1ZygkqdR8HQO2auL888//+0TW+awz7xnVvCd6/6+5YMGbrjhBk477bStkte6/OlPf2qstPi///u/3Hrrrdxzzz388Ic/3OqxvJlYnhETExOzGSgUCiMdQkxMzLuIzXFPELqOWGf5+kM/PJUP7zKBK+9csMHjb731Vq644gqeffZZisUinudhrCPvkFJywQUXcPXVVzM4OMgee+zBlVdeyYwZM9aL/a9//SvXXXfdv1yGf5ZKpUIqlaJYLPLKK6/wmc98Bk3TOP7447d6LG8mHjT/C/T39/OpT30K0zQpl8ucffbZnHTSSSMdVkxMzFbENHWa2lqYMGHCSIcSExPzLqNldAuG+S8MtfQNaJr1YKOHNzc3c9ZZZ1GtVvnCF76w3v7LLruM6667jnnz5jF16lQuuugiZs2axcsvv0wmk2kcd/vtt3PUUUdhWdY7j/0d0tbWxksvvcTChQvZZ5990DSNcrkcu2e818nn88yfPx/DMBgcHGSHHXaIB80xMR8wrITFd/90GdL32X2UR1/N4GNj88xfNcj8NRrT85KdWjy6yiajkj7ZoMryssUh207Ex+e213vRBdgaJA3JmLTH2orJY10aM1skRzf5PE/IK/0Wx05q4+n+VRRdjVEJnzUVk8VFjS9OzvFqbS3P9iZIGXDYNsezpHADth6yaCDJ6KTHon6byXmPoqvhh8rG8bVBJZPZvtllbcXk+X6Nr8/8LA+tmksolc1lv2vQlvCYnJ7IkvIyrn05xUHjfAYdncl5l+d7LWwNdmx2+O+/NXHTEUfSV7uFWlBlbc0krYcYmuSl/iRTcjUGPYNz7+jgG4ev5ZAJJ7Cg92aWFy2m52tYuuT1gs1B4z/H75bcxgsDgtVlwVGTfHpqGqMSIcvLGi0WvDAgOHe3zyL7OlltO9z7aoJPjPOx0z5Prk0zOedQ8XV0TfJ4l8mebT4rSwZ/fsPAqRnsM6lGiwV/7xacNr3GsqJN3g4Ym3JZUzUpexplT2N1FfZs83i9YDI+HbDfmBNZ0HszSwYtslao2tQIWFszqfoag47GrqOqLOhJUvTBCyFtwPKy4PPb+SwvB2SNgCe7k0zNe+zaOoZ73uhlfNZj0NEpuhqDHkzJqbzuWnwbgYQ+F7bNhmgClhQ1tm8KsPWQPdtO4ndLbqPkw5qqwAvh0AkOT3db/P37IE+y+bc9i6wpm5Q8wcSsT0/V4LBt1FO7Hz97O30O/HDvz/KXlbdw3ZMZEsmAE3aq4gYaOC45M2BcKzy+NsmHRldZ2JfECWBi1mdJ0SBrwIoK5C14tEswPg37jPZ5raBz+nbH01Obgxu4PLwmzeSsz8NrTMYkJdvmfJaVDPwQts35PL7WIG/B5GygZGpayNqaiaFJ0mbIvOU2OzeH3PEdwWk/Dpi3wmDp6gSztq8ya4LHirJkTdlkZUWw6ygvSm/gBhpLS4K8BePTAZqQhFLwyqDOuJTE0CQHjvscXZUbKLgCTUjWVC2aLZ81VZPumkbJg1E2jEoGVH2BrUuabZ+1VZPJWYc3Shb7t7Wx3FnBspJN0VU2rctrKeyEBsh3doN5k+VcY9tGqC8I8tBDD21w/5VXXsk555zDzjvvDMDFF1/MNddcw1133cXJJ5/cOO6GG27g8ssvf2cx/4ucffbZ7LnnnoB6cg4wf/58dtpppxGJZ12ElPIdtmTMuqxcuZLPf/7zzJs3b719hUKBCRMm8PdnL2XyhLEUvR6Kro+th5iaianZDe2qhk7SyBHKgFAqL0chlP646hcoemVShk0gPWw9TXe1RLNtkTFbKLjdpI0mdM2k4HajCR1NKO/dalDAD5WmKmsZ+KGr9KtoJPWs0qBKF4GGQOBLFyklkpAVZYuJmYCyJwkkdFVNxqY8MqZOQs8gCfFDF1+6aEJHoJEz26gGhYZHMICGutAD6ZMxWzC1BGW/H0Oo/2R9Gfnvhh5uWMULBUnDxA/dRlly5uiG13IQelRKAjeo0JRP4kmHIPSQSEzNZk2lRM4KorrTGJ8eR9HrIWXk6a510Z4cR8FbS1LP8VphkIkZCycoY2oJuqseTXaAEwhGJ0dHWvEsTlDG0CzcsIohLGpBiUB6WFqSleUaOSsglKAJCCW02E1RGw79h2xraWpBKdL9ugQypLtm0pEKkVLihpLRyQ5CGSBlSEiAFzrowlB9ROisqazGCTSSRkjSMPlHt84OzTVMTZIxW6hf1ikjz6DbpXTCwsIJK7hBtdF/vLCGH6o6aktMoOT3qb5XSVELyoxpGU3J74s03DlKXh9CCEwtwYBTJWsZFF2fnGWpfhy66JqJlCEJPcOAuwaBhq4ZSCkJpEfB1RmdTOJLlyaro+HnnNRz+KHDgNtNxswz6BZIGUq7XAt80kaCVwZDpuUFXlgjb7UTygBfug0Pbikltp7GCcoE0qPiKw9qJ9SiPELyluSFfouxKWXg7UnBzlLnO69bfGRMQLMdRD7mkhUlk1oA+42pEEpBn2OQNdVgJZSCAVf16b01yd98jTuXWMxskRy3bZIHVlYamvUZLVUAlpVsEq7Dg6sSnL33OJ7uUSt7lT3lub26otNiSxb0aTz9SprvfqKPvVrGs8ZZykOrUpga3Ltc45jJPruNqvD7NzKcNr2ZU/9Y5eNTavS7gv07XPodnb1G+6yuhOTMgD7HQBMwIa3mNzzXa2FGcwaWlXT2Gl1jddnkN4tN9mgL+dKYgGdCdf+p+BopI2TnlrEs6F3d0GU226oeLE3yx+Vq3sBHx3iMS7s815tkYtbFFFLp4K2Ax9dafGSMQ8nT+ftag6IrOGZyjd1HfR6ApcWrqPoaaVP1aQ2dPsdhWnoi33y6l5ktknFpnw+3t/G7pf0EErbJehRcjaQh6dDKvDpoM2NswAQ3wXKrxu/fSLNvu0PF18iYIUsKFt01QVKH0cmAH88bhWGGDC61uOW/lpLQBasqOmNTAU32GC59tsiuLSHdNcHtv8hyxr8XaUn4TMy4OIGGras5AmkzZGLmi9y9dA62HvJ4t8HHxng4gaCnpjN7YhNPFtYyIe1z9xspDhhbYWnRpjXhU/J02hIehiaZnJ3IooEVlH2Ngqtz++smPV1JztynSFtS/c14dSDBh8eUuHpRlqMnVRlwDF7sN9ix2WdV2SCQMODCNpmQZ/t0PrttmateSnPUJJcpOYfempp7EUZ/+ZcULJrtgI60x82vJEnocPB4jye7DbbNSkrFgNaET1uzRlvCY8BV8wJuW5xmek6iCUlrIuD2xSZTc5JlJcFOzZI92hyWFa2GR//uo2oUPUgZIfetyFDwYK82j2d7TcamJB1pj66Kwd6jPdZWVZqemoEXCiZlHTJmyF9XZbhvmc7BEwN2aa3y6mCCnBXwwEqT46ZUWV022b0toK8WcNmzab69Wynq4xZOIFhc1DhwbI3VFZPfLDH40g415q+2OW7bEiVPxwkFppBoQumQdQEPrkoxJeezaMBgl1aPR7tMZjQHpM0QXcDv37D48o4luqome42axLLyq2TNNEJoXPGCZJeWgLakz4Cj0+/o2LpkTMpn3/bTWLlyJePHj2dwcJBcLrfeuCGfzzPw92+TywxfUrpQqtG0zw83mK7OQw89xAEHHDBMnjE4OEhTUxOPPvoo++67b+PYQw45hBkzZvCTn/wEUG4VxxxzDM8///wGz701eO211zAMo7Fk/CuvvILruuvJSLY2H+iJgLfeeisf/ehHyeVyCCHw/eGG41JKzj//fMaOHUs6neZjH/sYCxcuHHZMd3c3H/vYx5g5cyaf+cxntmb4MTExMTExMe9n3jQRsPF5B9Q11k1NTcO2Nzc3D9NfT506dUQHzPUY6gNmgOnTp4/4gBk+4PKMzaH9aWtrY/78+axdu5Y999yTY489lpaWlq1dlJiYmJiYmJj3G9HiJvPmv8y8+a8A4Lob1zS/FfWn0gMDA8O29/f3M27cuH8pzA8KH+gnzbNmzeJzn/sc22677Qb3r6v9SSaTXHzxxbiuy1133QWA4ziNYzOZDLZtk0wmN5pfUsvhhTUEGhlTx9B0smYrvnSV/VxQRRcGTlCmFhQxtQQSiYaOLkw0oaMLiUA0Xr+3JpKEMoiWEzaQKMuskheQMvLkzDY0oQ+TZihbOyXNCKSHJx1sPU1KzxNID10zCaV6PWlqCVptnwEHkoZ6HZWz1GvZQTdACEHaaMbQLGU7ho5AvT73QiWXsLShOumpKalFQs+gCR1bS5Ey8g0ZSb0MAo2clUUXZkNuINBwgjIJPUMtKOFLFzeoYOtpdM0kb7YjhCpTv1NgVMJS+4SpbNH0JBmzFVtP02Q14YYVBBqhDOhIhXRXa40ly1sTGl4oCKUglMoGTy3xrJYmzxgtkR2gTcrIowuTnBVgaYKCp5PQDTKmjaUnGzEk9Cxe6OCFNWw9jUBEMeWZkDZwghA3lI1X04FU8gFLS0X2gSqNyitF0gixdQ1TS7DXaI0mq4mkkUNDx9KVBVzJ7yNtNhPKgED6Sj4hDBK6+qev7El0oZHQlbwhY7SQt9op+wMkjCy+dEnqWQD6nF50zSCpqxtvxlRSHFsPSRtNJKLjVJs2oQmDtNHMmqrqf0IIBBqmJqO2c5VVoTAwhDW0xLnVQspoIqGr/+k1oZO3cnihw7Y5H0tLktAzjXpM6XkMYZHQs+SsNgLpqX4sTFJGhrQZYmsh7UmPaqCRNLK0JX3GpJIEUrC0YHN/VeOQ8R5j0x62HtKRcjnnlvHkrJAdml2Kns4fliUZlfD4y6oEOUswOpmkLeErC7tUQF/N4FMTPbImLOwfxNAkOSskbSr5TdrMMTnrkbPh6MkOy0qLyZkB3VWDbXMOSUMyKauWqP/0pCoXHtwLQKgJEkaGg8b7TMk7XLxXiTEpDy8U/HWx6o/f/PAgU3IBE9OSZLTwQV8tYGbTJLqqJuPSIbs2TcCXLlU/ZGVZ2f/ZumRa3uenC9I4gcb3964yMRMimicwOWujCWhL+GybS7K4sAJbDxlw1BLlK0omQSRRmZKFGc0h/Y5Os93Mh9pVvSSNkIwZYmqSqbmQaxYlsPWQGc0hu48KKbg6rw5ezX0rblJ9R5cUXJ2eakCzPZaVZYtQ10mbMCallmz3pUtH2mObrEfSCJmYcfn7WgMpYT9bkjYS9KQCJtoT2CYTUvJ07lxiMaN5HG1Jn6VFQSp6XPT9I7v4v6NX853Tuih5Os/32WTNgHY3iRfWOGlqmUDCjs0enzyjyIfHlBib8piW3ZZRiZCpvqoDW9fA+xNHZH2a7IDjJlfQBPQ7Oju1ODzS381eudG8VjDZp71Gs22xY3OViRmdafkaTXaALpQt5sqyyWV/y2NqkhOmutx1pM7YtMfqssmEdIIdm6ssLdqctaOHHwpWlQ1mtHj8o8dgSt5FF3DI+AqDrsbaqpJDfG5qjSYrYHXFbEgV2pMe37tnNG1Jn35H5+9dCY6a5LL7qJDuqsGSoqAt6bNN1qXi6fihYHXFIqmHLCtZ7Dva44UBwaqKYHnJ4CPtStZw5DYeHxpdI20EXPv3LP2OxrR8jQdWpnh1MEHF1+hIBcy9r53uqsHkbEB7ymOv3GiSRkjF96kGGm1JybY5By9U+uHumkGzHXD4ZJ+jJ+d4fG2S7ZpqBBKm5yXjUibj0h5PrjXIW5L//pCSxzTZMC1fo+prjEvBTqGgPeXxlR1r9Ds6H+twSBkZWhMm49M2nhS0JHSabIO0KTh8mypNdsDYlOS1QZMDxjq0JAIGHJ3tm0y++v/Ze+94S4oy//9d1d0n3ntumhyYIQ1pyDlK0kEEFREUEURWN4xZcPerq6Kou+4u67r722VdMS7oYgIVUTCQFBEQGGCYIU3Oc/PJp0PV74/q7nPOvXdmYJiZS+jP63Ve954+XV1PPVXd5zlVn/o8h5TocHLM74SSP0Dakmg0db/MFQtK7NXpkrfNveFIOHpq9cWl0bZssGwWnXEIX7nmAhadfhDInQvdurq6mD9/Pg8//HB8zPd9lixZwpFHHrlT13yt4TUdNG8Po6OjrF69muOOOy4+Zts2Rx55JI899hgAjz/+OKeddhpnnHEGZ599Nl/4whe2GzQnSJAgQYIECRK8YIRBc/ySFmxHRSIIAur1Oq5r9gg1Gg3q9TpKmR/Tixcv5rrrrmPp0qXUajWuueYaHMfhggsu2CPNeaVj0ukZp5122gs6L5PJ8Otf/3o3W9PEC+H+HHfccdx3330v6HodHR1UKh6posYNfBSBmVG1XSq+jyUEvvZRltnIFmiPwG7gKTNzIISg5ntU/QBf+ng6wLM8tFb42se3PLzAx7XM7GSlHlDyXRwp8cKZ57qvEY5J6KFRZqaZAFd6pKSF1ppKYN7XgwCr4VL1fCpegK8EMmWuUQsUFTeg5mtKvodnNaj6HkortDbLRp7lUQ98MzMqBZ7ykWiqjYC0K7DTDbNJMHBpWDaNINwEqH3cwMdvaZ8tJVU/wBY+jvRQToOS5yEQVMsKT7ooLNLSpuwa39R9hbB9hJD42qfmBxR1g0bg4kioeB5CCNzAXLOuAqoNKLtmBiDApxGYdkd1edrDlS4N5aHsBoEOaAQeUpgNfBUvwJWCWkNR9kMvO244m+1hS0HF83GlhyUFVc/YJ4UmUD7VwPgusATCcXGVh0RhSYGvPCwJaIUloeL5VPwAz9K40vjJEgJFgCMkUmoC5eFrD9dycQMPS0Aj7BMhNKCpuAHKBkf4BLZJtiKEoFIO0LaH1gECQcX3qQcBgSVwpWdmrbXpU1cFOA0XIRRVz1xHCPOAdgOPWsWnHPZfoANqPggHPBWgbC9cPZFIEYSbUD1cq0HZNdcHsKWkHpiVDT+sXwqFbzfiVRezQRFqvksj8LGEGU8VP6DqaWw3oFYXlLRHrepTCnyqNR+3YiFthRKammc2clq2IqMauBWfuu/jS42qWVRLAarqUy4FOMKn4ga4gcBxNW7Fx3YCvAbU8XEbULeV2VDkKEraoxEEVCsKgUD64AamXMUJqJd9tAavDlXHJ9ACTwmKKTPeA+VTqwWUfUWtZsab7XqUioJa2adRB7+uqaV83KqgKgKKNKiXfcoosk4jfA4o/KpPtexTDxfLrIbErQSUSz5uRVAsNii5PrWqj5bmPq/WAlwNbsWiagV4FZ+a9HEDgVcTWOEMd6no0Qh8qr4Zz/Waj7IUbk0g64p62fjGDQQNL6CqAhoVn4oMUFpQCxQWUJQNGmWfYrGBrnk0Kj6BgFLapV42/YutCIRG13yq5YCSGxAUzfM1bbmm/1yFqEOxaHxBTeBVoOEE1L2AqhvgVnyqwqdRE1QJKGqfSiCp1M1ndeWjqoJKKaDqQlE0KDcCsnXj57IPWctFl33qgca2FfWGwKtBzfGpe5KiZex2UuZ8VwV44bMcoO5rSp5pp+16NMo+AVB0XGo1H7ciKRUF1VpA3YOyENSqAV7Voo6xr1b28aqSainAr/qIOlRKZtz74cZPtyaouwEVFZDyXRoVH68OgQd1x8etg9YCWYd62UfVA7yaoF42Y6XqBjRqJmjTNQgC8H1QAlQdGimfqu8TSI3juWashfa4vqIqTX9H9xeWou4FFIWxpSICag1BWSvcQMX3R83TNKoC15cUi83rNmoCvwrlYkC1bvxUTpv7s1oPSHlQ9xVexaxUFKVPTRr1jEbD3PMlbaG0jxSaWs0kUgJQmDFdqyncisT3BLWUuTfdGpSKMnwu+gTax7M8aoGp21UBSitqLgjAq/ogTH/UfU1RFqlUKjsOHqQF0m5/L7Y933njjTfy3ve+ty32ALj77rs5/fTTufrqqymVSpx99tkUi0WOOeYY7rjjjja5uZ3BN77xDd73vvfF77XWLwuJuF2NSQ+aH3zwQT71qU9t9xytdbyrc09hV3N/6vU6//xP3yedlSjtc8oZB3HqGQfvClMTJEiQIEGCBK8A/OGeZdx713K600++sKDZTplXBGlvl55xxRVXbDeLnxCCa6+9lmuvvfZFWL1jfPzjH28Lmvv6+hgaGtol137++eexLIu99957l1zvpWDSJec6OzsplUo7PK+np4fh4eHdYsNE0iwAe++9Nx/72Mf48Ic/DBjuz6xZs/jXf/3XNj3DHaFYLDJz5kzeecnrePPbjuXQU+bQlUqbWVDlxpJztkgZnq+Vxw1q5GyT/lgKi0ZQoRaUYu6nEDKcXXQYcUcopDpJyxyuqiGFFcuhpa08vnLxVN1wm0MuaYfTha9cXFVjWnafcFbSjeXm3KBGITWNgfqaMM2ypOr7ONLw1jwlmJWbh6tqVPxhOmyz+bEelOPU32VvMLZf6SDmSlvC8GIt6WAJG081Qqk6wx+u+MN4qoEj02g0I40SecfBEg55uwcpLNygikbTqNjU/TKz+maiURS9fgLlUQ9q5Gzzy1mjYx51hgz93nq6nOkxJ9ZImpXD2XwzFi1hM+o2yNqK7tQMLOFQC4rk7Z7Yr25QJdA+m6rFWELNVy6NQNCVylPyKszO7x2nNK/6I1jSIS3z+NplxN2E0gECaVKfh/73VIO83R33sRQWQhg+t+E1S+pBiUD74Ux8KpZ6c4MaHU6vmckUduxXMOnKG6pKoJpyc2AkDZUOKDhTUeGsV60saKgqXYUMtkwx3NgYc9AjDn7FHzYccCFJS5PeO/KNRqG0Sbc90tgcSwZqzJiNOMcddi+BNhKDABmrM5SOU1T8kTjVutJBLFEY8eG7nOmU/SFydhdKB7iqRtYqmBWIYDSWB6wHZXzlMiUzD6fRoOYQc8uzttlnUCi7bMmYadeSZ2QV+9I+I65N2ZPMybukLc30bB9CSLbWtjIl00c9KJOxOuI6t1QVUzKKjVWLvTosyl6DjGU4yUW3xIzsHAZGRnCDGluQHN43hWdHt8bPiqxlZrbSlmZ1Kc3MnIuvBPM6p8SSek8NVzmxdzoADanYUNnIc6NpFs2ayYb6Sh7pzzM163NQT5puawojwQCWsOmw+xClfrRbwe2ZzkB9PQ9tzXPyDJetNc3UrPkqmC5ngmUz6G9Bo+N+7KtblHIpVpdL9KXNbBvAhkqKTifAVYKDuvtoqCoCQaB91lca7Ffoo+IPU/E81pbTOFKTdwJq4Wxe0bXoy/gclelisxyK93l0OlPYUltBpzOF1aURLKGpBZL+ms3C3hqrSmn60j69GZiemseKwZVkZAfZTrOvY1pqLktH18bygHM7plD2hthcM7OMBSdgn/x80IqiGg1lNhWeapCzu7hvU4NCSrGgy6yC/X5TjgN66hxWmIOyLEbdLTSCKsMNwex8gYLVw7C/leFGledG0xzUUyclBQN1w/sveRb7FnI4MkPG6qDqj5K3e2B0I/WOThqqGkpAunhKMC07k+dHt+JrQVoq5nVOif0IMD8zl9X1dXQ4aQbr5lh/3Q5XWKARCGq+ZN9Cg4O69mZLYxWNQFHzJYVUEEt6Lh829oHZtzJUt2kEAimgL+OTdhsMNmwOm92B0gHPjPjs3enxfNGhGEouLh2WTMvAfl0eNV8yPefRl/ZJWSmeGRHMynl0p82+CYHkf5Y7nDDN4+CeOk8MZjmox8jATc349GU6yVidVPxh0jKHFBZVf5ShRgNfCdZXUpydgyfxmJoJZVnD1dPBus2sfBopLOp+OX5+WNKhW3WwWW1mTdlmS9Xh+Ol1ar7Rve5LT0ER0OmnGJKleA+JPbyZwbxgY9UnaykqvsWMbEBfZi5ba2vI2R3k7G4aQSX+nnNkmszIIKpvLj9dtZULp2V4xh9mSibFqNsgZ5vA9bu3Ps1jv/4zN3/33u1Lzq26nkJnO+2zWKrRvffi7UrO7WmMjeVeSsx25ZVXcuWVV3LKKadw8803c+mllyKE4MYbb+SSSy7ZVSbvFCad07xhw4YXdN6aNWt2ed17kvtj2zaf/uy7ef0bErJ9ggQJEiRI8FrGcacfykc++dYdnyjbOc133rOMT33xlt1u34vFWCrGS6Fm/OpXv+Koo44C4N/+7d+4+eabuf322/nHf/zHl2TjrsAupWcopfjNb36DbducddZZL6hMoVDgRz/6EW9729uwrG1nudkdv6b2JPfH932+eO1N8UxzggQJEiRIkOC1iYfueZLHfv3nHZ4npI2QTV3mc15/NCcdfxDXf/O3u9O8Fw2tNatWrYr3mCil2t4D21QqG4tqtUoul6NUKvHss89y4YUXIqXkHe94x26x/cVglwbN73rXu5g2bRr3338/Dz74INdff31MbdgerrjiCj760Y/y/ve/n7/8y79k1qxZu9Ks7da7p7g/0Uzz9Fk5Nlf7X/L1EiRIkCBBggSvTBx3+qGcdvQcbv7uvds/UdptGwHv/O0Sbvvlw9spMDmoVCrst99+8Xutdfw+2hQYBC9MX3rq1KksX76cpUuXcsIJJyClpFKpvCw2Fu5STvMZZ5zB3XffHf99wxve8IIUL0ZGRvj2t7/N17/+dVasWMH555/P4sWLX/Bs9csdEaf5HZecytvf/joOP7WXlJXCFimEMGmrbZEK0z9nYq3mjG10byOeadUfifmFESIN5h57GjVdjc+P9Hor/jBZu8CIuzk+X2sVakAbGoolHMND1QodqhVE9dSDUsx91lpjSTvWLc7ahVBb2TFppWUq5nZFnGalA8P/VTWj2WwZblanMwUwN5OvXRwclDB1e6qBrxo0VJWUzKIx6hRSmBTjEbfXEjbDoxUsYZPrMHzosj+EI9MIJL52Y1s1ii7diUqlkUGAsiyjhYxRk6j6o2gUjaAa6xh7qkHVH2Vqdn7sG0eatM6tfoq5vSGPU6NC7rlP2srHvDoaVTzH9J0j05S9oTgdt69d0jJHLSgZTpzViS1TBMoLFTYMNzmqt+IN01AVslaBelAmJbMoDF+8056CpxvG9zIX6ktnsEWKajAaano7pGQ2Tp8usSikphqtcOmEO7xLdBZSpKwcZW8QSzhtY8YSDkr7NFSVTmcKlpZUVSkcT7nYTyVvIEwLb7TDA+1R9obIWB1k7QKOzMTpywEEIubwm7rsWFc84n07MkNO5PFEgBNoUAoCF5wMeHX8TI5A+7FmeENV6UpNZ7ixkSleDrIFGsInbeXAd81GGw2DjXX0DpcZ6MnGae0LDYGXN1zrjOwA19xnpHNQK6JHNsDIEPX9Dw11kEtxqvFu2UuVGjnfwk+ZPQvlotlFX+hMowdWsC5rUjEXUiksYdOjCwyLIq6qGU1x0Q1Kocv9iI6p6MogItsFWuFlsoy6WwCYkt6LgcbaeJz0pmcjMM8XBKFWtkQ++xB6wfHIeoUt9JOSWQbqVaZmO7CETaB9ujZtQHTPRhemI1QAvotevxQxdT6qs4+i1x/3aV96Lusqz5O1HbpTM5vjfXQzqjDVPHfKw5QzRkM9GsMjjRJpS8ea4d01TTWfM/f2lhX40/fBHtmKsBwG04azK4SkV/bxXHU9U7MddFtT0IOrEOlOhrRJXdzTmUULszej6G7F14Z+F6VXN1rhaRpBlRmqF9I5fKvJVAy0T2p4CxuzHjNz+7J0eA3zOvKsKFaZ12HTl56LIkAW+41/EKB8EBI9tAaR72NYlhFC0uVMZ9TbghvUEEIwZaiCmHEQNMr46QzW8CZEuhMvl8cWKZ4eWcl+XXPwVYNstQ7ZAkVdwhI2edkJQuLjGz4+GdAKrBT97lp603PYXF2JFMRtjHTrp6bnUwuK9Ne30Jvui/c5CCFZUxpgatZhsO7Rl3Eoui6BhqGGzf5dDuWijy1T9HSZ/R+KAK01K0tlCk6AFJq0pRms2/japJ8/oGtGmJNAxD61hE3KMhzl/tpqUqE2vNGw74ifA/mRYRq9040GfLiHQgoLqQVVVaLqj8bjtBHUmTXSoDh1GoViFdE7Dw+jdmRXywT5AlZxANId+KkUJW8gHn9GRUgSKI/euoTRzZDrxu+bg711NXr6fjGf2lU1fOXSV7dYZ1eZnp1N2lOQyqGFQGiNHtmAsNNo5SGyPejasLlf+1dQnTKDfOCwVfeTtTtxZIZ7bvopt/x+Czd88/btcppHN3+fQiE35rMqXTPe9bLiNL8QCu28efNe0LX+8z//k7/7u78DTObm888/n1/96ld86Utf4g9/+MNLsvOlYpfONDuOQ7lcjn8NRFzhHaG7u5uPfexjfOxjH+Ouu+7ia1/7Gueeey577703f/M3f8MVV1xBV1fXrjR1j8O2bf7+s+9i/tyZbK6unGxzEiRIkCBBggSThEWnHcChZyzihm/evv0Tx6hn3PnrR7jtFw/uZutePF5oQPxC8MEPfpBzzjkH27bjVNr77rsvX/va13ZZHTuLXboR8JOf/CRnnXUWGzdu5Nvf/jZyJ7LWnHnmmfzwhz9k7dq1XHbZZVx33XXMmfPK5wD7vs+Xrv0+d965Yw5TggQJEiRIkODVizvve4bPX/u/Oz4x0mkOX4vOOZ5/+Ie/2P0GvkiMpeJ+97vfbXv/lre85UVdb7/99osDZoAFCxawcOHCnbZvV2GnguZf/vKXEx4/44wz+MEPfsAVV1zB2rVr+d73vrfThq1atYpnnnmGwcFBpk2bttPXebkgmmletOiYyTYlQYIECRIkSDCJWHTaAVzz2ct3fGJLwNz2epnhO9/5Ttv7j33sY23v77777j1oze7DTnGaC4VCnBUP4Kc//SlvfetbX7IxlUqFm266ia997Ws8+eSTnHPOOSxevJg3vvGNLwsC+M6iWCwyd+5clj15A7Pn9FEORmL+a6TPG2jP8IStguHHhdDSQjQqYNlUMFq2jswghDRczaBCzu4mre2m6HkLR1OjESqgTj3MTOfHHGkpLFxVo9OZgkbHesIRhNbUVDnW5Y25s2H5guwCKwV+HZQiSKWwfD/ml1YcHfOvpbCxhE09MGmlOkSHsTMdKpEErslypHxqwo150q36zRpNh8qYtnl1sGyKI3WwLApZjc500lCVmMcZ8cUBXFUjp9PQqEK2YOp2MqGTFWVVjHnFDVU1/aJcHJkhLXMxZxEpIfDBMpw6S0tc3aChKkisWFM54nt22L2mbVbKcGGlNP8Lyai3hZRs6jsb/rY2OtayAyU0UmlQPsp22vqn7A+F2RaN9rXRazb6pI4XGHkiIaFRNu1UCuwUFVXCDjmnEWdao/FUnQ7fNv2hFcXRGmhNoa8AgU9NV2OtZI3GVVXzf1iv43qQ6TD1eHXTTiFBK+rSjGelAzxVj7n8UlhkyJhxG97ecXY/v0GFmslwKDNx3REfGyDtY3waZsfS1UHzv1dHTD8gHiO+JdFo7OHNpi9Kw2CHWbXsFKRyxobSVvO/kIaL2CgheudBZQjthkkINqyEXNaUVwpGQl1SKWHuvohMp7lWugMaZRqOJF2toreuhHwXBC7FmgXKp5AJTH3TFyCKWyBTMP1lp0ApVL4LWR5GVwZhcCvkMpDKQLmEHi4i5s6GWQsN57g6YvzvmXsRJ4MeWmPa2L8ZCh2mrnoZ/AA6e2D6AhhYiUjl0YGHyPWg03lEeRC9djl0hDzKbMHcNyFfXHTPhs4pUB4ytgppeNbds9HrHw9T/YYSWfUyeB7kOhHdswkyOQLlxfsfsnYh1naX659CFKajPfOcw05BrQjZAiLdaTjAYd8LrXF1g5S20FufM2PWz4HXoNDbabi+gYueanbrCyTCraFTZu8BtRF0tsvoVpf7zfPAspvPpFQOt6uPtJWj7A3RYfeiNz2FyPehClMRQ+ubY8+yYaQf8mYPipi6L8p2kCWjUx909iIxGuWifyVs2Qi9vaa/lW/6TCuQNqJnrrmenUEPrDD+zvdBusNw56Pz872IdCe6uAkCHz33MPOsrY5ApkBD1/FVA1fV6NEFw7197k+I+UfEfRbdHyNqiJQ0/OJGUI33OmgUU+1ZFKsafJdCIW36I9cNyqeC2Q/hqQa2NPSBlMyitSLvW2a8SBsaZXRpC6J3nvF1dYTK1JnxPhpbppCYe0JLCzY/bcZc/7rwPtPmr51Czzo43qfSUFXSpPCFuYbe/Iy5B0oDZhyV+82zWkgoFhEHnkggwaqVcdNpnNIQIpWHTMH4ujQM2XzzuyGVg6FN0D0V6mX82QcarnRpCLpmmP0MQ2vMeKkVQ365bZ7vjfA+65qKyHRBR6/ZDBd4xu92Ct2/gg2r+5l78rXb12ke+TmFQn7MZxW6u9/8suI070ineWzc+ErFTs00j42zr7zyyrb355577ou63hNPPMHixYuZNWsWn/3sZznnnHN4/vnn+cUvfsG55577ig6YI/i+z+ev/V/uvPPlt+s1QYIECRIkSLDncOc9y/n8f9y5w/MUQdvrV3c+yCc/dcMesPDFYVfqNL+csVNz/GOdMTaIfrG7G4844ghOOukkrr/+ei666CJSqdSOC73CYNs213z28nimOUGCBAkSJEjw2sSi0w9i4fwpfOMH29/UFyifQHnx+7NffzjHHb8//339bbvbxAQTYJcQY17qL4olS5Zw2GGH7QpTXt6ol6CsyYbLdUII0JASWXwVKo2UthrJGumYZWKIl+7ymQ6z9AMEGGpH1ioYSaVGNVxua/nBoRV4NbAcnMIUlA5iOoAj06AhaxeM1I60sQBUEa0DU7+0cXIdOBgaQ87qIsBHYpkUzOUhszSrPISwsBrSLGm5VXR9lPzU/Q09JJS80lqTt3uMhNzQerPUWB02f+0UQlhov0Gqd3Yb1UEgYqoE5UGz9CUkeKBrJdAKLTMIr47V0RWmkDZSfE4ocSeEhNIQ2m8glG98UzdtBejong0aQ9Gw8gTaxw59qYfXIey0KQNmqbNRMss00iHVOQ1LOm2ScDGVolEO6yrH7aRuKCqdHVOQworTYUepsrN2AUoDyJDeQKYDWSuRCnzAUHXSGbO0aUknLmspUEKgawMIEdIPpG2WzqUDnk0u3xPLPGma0oOOTKNLGxFB5BsPLAtGq5DuIJXOxcuiQoh4STXqW9zRkBrgmzHQqMf0BzudCeX3MqRk1tBCQjk4KqPGpyHlRgiJ9muQypPJdsbL2pFfs1bBLHMj0eW1hm7hh+N/6xBk0uhK1SwLh/62M0aeTRe3QjqHfuo56C4gOvPmXNtGOA660UDMnm7k3Qa3gm2hhzaF9hmakd4yaOgPUoLrQb0BKQf8AGFbaD+AadNjKk/Kq6NTORgYRvcPge+jXQth22gMlUIELlpIKG6Bes1cGxC5TiN1uHKVqSvaWB0uWesVqxGBj47oECMbTN+lciAles0ahBBoz4PRkikfZjsVuTxsXAr1GjrngldHV4ZMG90GenAEhsO+IfSBH0Amba5TDI8pFT5nPHR5AL3RyN/heoh0qmVCZQgsGzlUN/dNKkdGSDSDpEOaAJu3ojdvjf0ZX19K9Ow5WKlc8xmZLeAoZUbFli3mfM9Cux56QCOyGag3zH0f+IZ6MmKyz2orpM9UBtFrV4dt8029rgc5c3+mtLl+fmgTuqvPlK0OI7warHo+7gvdcBGWBfUGulQJn2XS2Bb4yOF1hmJSL6M3bkGkU7B+I1pvMPVCs2/2Cf3pu8aWlIMeNRQMtg6ZJX7HQW/qR4e+EUIgMivNe60QXp20kKT8Bvnu2eh1j5lrK43evDymM+FkwLLp7p1njgUunanecAxNNfdBcRO65Bs6SyNl7KoNg5Dk+/YGKdtlUEN6lq5sMfeftA1lxPPQw/1g26AUHZ3TQY+ivZqRadu62tAgykMwVITUVtOnSsdjlpSDsJ7Hsmx04Jt7K9eNJSTarcKG9ebe8AP00LApn7Lj+0YPrUEC2krhVAx1Rvuuue82bUDX6ohs2YyD6oD5K8PYpnMK9tbV5jlip2BgVTish8DvN3a2jCHteea5kus0tpW2GMpURL2sFU0fjuxYYSyaYR577OWGarXKaaedFr8vlUpt72u12oTlHnroIT70oQ9hWRaf+cxneOMb3wjABRdcwK233rp7jd4J7FTQ7Hke9957L4cddhg9PT0v2YjWgPmPf/wj3/nOd9i0aRO33XYbjz76KNVqlVNOOeUl15MgQYIECRIkSPBKQbQ/Zuyxlxs+85nPtL0fm2djW3k3rrrqKr71rW/hOA4f/OAH2bRpE1deeSUjIyO7y9SXhJ0KmufPn8+ZZ54JwOzZs6lWq/zrv/4rJ5xwAkcfffROG/ODH/yAv/zLv+Qd73gH995rsuQopfjsZz/LXXfdtdPXfTnA930+/48/4MLzj+HsC86YbHMSJEiQIEGCBJOEO/+0ilvufX6H571SguZrrrkGgJUrV/K73/2OwcFB+vr6OPPMM9l33323Wc6yLA455BAAbr/9dt797nczOjr6suVE71TQvHz5corFIg899BAPP/wwDz30EF/5ylfYtGkTjuO84FSJY/HFL36RX/3qV5x00kn86Ec/AuDQQw9l6dKlO3W9lxNs2+aaT76D2TM6XoYLKwkSJEiQIEGCPYVFJ+zNwgWz+cZPH9vuea+UoBnMbPOXv/xltNZMmTKF/v5+pJT87d/+LV/60pcmLOP7PuVymY6ODhzH4f/+7/+4/PLLeeihh/aw9S8MO81pLhQKnH322Zx99tnxsfXr1/Pggw/y8MM7pxCxbt06TjrpJKDJk06lUvgR7+sVDt0oo12B1Qi5eZFcUb2MHfISdWmLkRByMk2JM981ckRaxcesiO8KOIEf8/VwwtSqoXwbYLivmQKWW4VUDidwQXrN1K+jm5pSOaGMUsT7s6NMRFoBdazAN1xNK4WuDhseaypnuFpCQn3UcHa1gvIA8W9FOxXzs4WQ6OqIqT+ytxLyOi0bq5yHbMEMzsAF5ZNyMlAbNfJbTsZwhaUNDc/wtmXKpAfPFsBvAJASEtwi2Cksrx5LEGnLjmXjYu5ruhO8OnYqZ7iXoUQbvosOXHQoq0dg+iKSa9JCIuwUlpMJpYYqhh8uJfjNOuNyEVI5pN8BeFhRXUqRE3mol5vt9OqGQxiEfGgrBeUyjjXPcFeVD14ol6UUUit0vWz4lC0c1ui9sFPYyodMwUiVESC8eigbNoSuG3/pcs1w8AIPqsNYMw7CalRBmoe1FfV35KPKoOnHaOxF4y+VwxbTsbUCHUrvWWB5dTPeh9cZO6Udc/cBdHUEK30ACCOZGPM8Ax+hlZElG9pk2ucH6P4h9MYhsAS64mFlM+Z4EEBPwfw/OAK2hff0ICJXRGQshBRgSUTGgigdt+tBtR5yeFOhfRJsC//JjeF9ZqEbPrihf72AlBSGs1kqN8sA+D7B0xtNXUDgGf5ngNncY0Uc3oi7GXOXB4yU41Mb0J4y/GfHQgehLJ8lsGwLYVmmnRFCnmuwdC0iY0Og0V4Qt1XXfSzbNhxMKUFtbRsrAP6StQjHAifkV4e2a6WxlGqvM7JXKdTKzeb/QEMQ8qczNlppJOFzPWpvWKcOAoQQqBUbTT8AOtAIS6DrQbOfUg6UDQcdu2T4pq6PemYtWmkUhoOq8dCWsVfatuEd++vRnofIZw3/FMNNV8+sRWQs419AhO3VQST3GPLXy+ua/FopCZ5ZF/pToD2FkAKtNAQay7LG87LtETMOlq6N+yHyjxkEpl7LceJDOgjHhG3uCfX8xvizyE4AVQ+QnofIGilEbQ8Ym1MOujaC3tRv+lqF/PKQnx/3XbSPIXrWW6nmfbxxNVQBz0cPeYa7nUmBbaPDe1ZE3xdKGflRgA3rzH0Ucpi1F/L/o3GXyYLbMJ8pZe63+nrDX99ixr2uuWa8e4EZi0qZ75DIftuCaAlfafS6Fp591DYpTD/Ylinb4s/o2YFtmbJ+gAq0qQtM2Y4cIpNulovKSGFs3dRv2ukHaC+IbYm40MJxmvKUKQfqIYfZ91HPbEDX2SEC7RFob9yxlxt++ctf8u///u/8z//8D+9617vIZDLU63W+//3v87GPfYyTTjqJN73pTePK/cd//AeVSoWODiM/K6Xkxhtv5Ic//OGebsILwi5VyJ4zZw5z5szhwgsv3Kny8+fP57HHHuPII4+Mjz3yyCPss88+u8rEBAkSJEiQIEGCVwS0Vm0zy3f95knuvH37s9OTgW984xv80z/9U5sEcSaT4corr8TzPL7xjW9MGDQfddRR444JIXjHO96xW+3dWezSNNovFR//+Me54IILuOGGG/B9n5tuuolLL72Uq666arJNe8nwfZ/P/8ut3HnXK59qkiBBggQJEiTYefz6yY1c+6NHdnjeWJ1m1aIm9HLCI488wkUXXTThZxdeeCGPPLLjtkZ47rnneOMb30hfXx+pVKrtNdl4WeVivPzyy1FK8W//9m/4vs8111zDRz7yES655JLJNu0lw7ZtrvnEBcye2TnZpiRIkCBBggQJJhFvOHQWC/efxTd/9/R2zxvLadY6QKO2U2JyUCwWmTJlyoSfTZky5UVlA7zsssuYM2cON9xwA/l8fscF9iBeVkEzwBVXXMEVV1wx2WbsHlRHYLhqdB7r5bZUwyaVsIRyKeTClQ0PKpOOOVR6DO8w5sn6LgwMmutlUk2+IBiumG0ZrdjAN/zYiKcH5thosck1i8raFjCEnjpGRzLi32oFQwPhsfBmiNKd+oHRLE3lmm2LOHJgrj8wGP4fchOVDjlfDXS2G9xKk2cX8l/RyqQ6DTVHqTegEbWj2tQ0jRDVGekr9/c3+WWRHSFXTac7Qn5wrVkmeo30x9w3pIRqU0sXPzD9GfF47VSTIyik0R2NuJCRpq/SYI+iwWiU6gB816R0jcrVik0N40j/0w+aWrL5kPccjYPWdo4OtvRXy4yEbRneuFZQG232RzSOyuX4PCqB6deOkONpPdfkOsoxj43ANWNBStPGyDeZFLhD6JiLbzftjFKLl8vN8Rj1i21B3TWcSTB87WisRlxqr274hAD1Bmr9MP76EroRoD1FujOFLpmxK2cU0JW6eW9J6ivL4WWNb2RaInKGS+p4Cl330VXPcFUjjq2nEI6k9kwRrQVCaFQg8F2JkOY6HVY/Qgqs6eGeBUvGPEf3qQHD8wU826Qxb7iGe58OubvjOM0Y7qr71ABeOM4ty1wjCAxnMi+F4QxHfEpPxXzK+uODyLCNrbef8gUZa8zO9EAbfm49QFiC2rLRNh8JScjf1aRbyrRya7GMrdH9HNkU8X3TbtDkj0sBnjI8YDf00bPDiMiuSB9XaXAkqaBlHEec0XBsN54w48AlhbAELm58jZQUhhsdlQ+vrwMNStNYshWRbuH4hj7XSpt+iTiuSsVlCDRuWKdpRxC3n0CRztmmHyJ+dCOIOd5RuagOxtiVhpjrHH9uCYQUuE8PNfm9YbvAjM2UJeK+EPm0SbkOkHJQK7ZAoBB5x4wzS8Q+l91piHjmkW9tq8nZXbfZ8G49Hy19UzaXMbz5iP+fCUdEC29ar9vUxu3VDd/0d93ss5ERr7jeaI77kAPuLeuHQKMa4V6mFh+k2rTKVdvzrbFki/HNWK54oMCSpuzYZ3/YXnfJlnDfgEZ2RNxugcgVTZDU+t0GYVmFv3wrWml0xTwvon4kHCspaHLzc07I71YQaBpLtuDqJod9WxgfNKtxCeVeDlCtsclOfN6KZcuW8Yc//AHbftmFqJMfNM+aNYuNGzfu8Ly99tqLtWvX7gGLEiRIkCBBggQJJh+eEniq+SP35LMWctix+/Kdr987iVaNR71e57Of/ew2P3fdHSdyibBw4UI2bdrE3Llzd4VpuxSTHjQPDw/z7W9/e4e/nEZHR7f7eYIECRIkSJAgwasJbiBoBM2g+fe/e4q7fvX4JFo0MU488UR+//vfb/fzF4qvf/3rLF68mMsuu4wZM2a0fdaaZXAyMOlB8/Tp07n22mt3eN7MmTP3gDUJEiRIkCBBggQvD/hK4LfMNJ94xkIOPXpfvveNeybPqAlwzz337LJrPfXUU9x9993cfvvtbceFEDudB2RXYdKD5tWrV0+2CXsEvu/z+S//nLedMp9z3u40tR6hXbN0uNjkhSpl9CUdB6210Zn0A8Mhcz3zSjlQraP7Bw0vOdKVHcMTEymnyQOULXxGpdH9Q03tyoiXFnLaRFRXxDtt4Uzr/qEm1xfa663WER25Zj221eSFuV6smdvK38T3wbYRo5ub14r4rCLUqx4pGf3S0E5dM9wxXTXcZJFyYu3gqH1xnaOldj53iz+EHWqrtvJrLcOj1v1DxgTLQjfc+HoRx02EHL+4rG019Ya3DILvI9Kh1me1bnRpLQtyeaNRGuqjarfa1I0eGGnXUy1W2rRCdW5Lk+fdqBpbPc9w38f2Z6QvCsYOz2teW0hoGK61Hh4NtYAddI2QfyfMsZQT+6M5dlTsPz082s4TBBg1PhaFYtM3UX3h+0iTlbHcNd834yeyP/K36zXH9cAw2lPoioe3togaNRxh1QB/1Siq6hnd3NEGquSiysY/xa3NHdhaC9L5ACEC7JQGawRdD9ANH+1pZNqMlaBm2jS8KY3yBdLWKF/guyKUTtdYdg1paZyBqrl4ixbv0JoUWguUglra8BjrDXMfTpX9KN98FvGlo9tJpmFofTrmNLdylLUSWNYgVlbEfErVIOZY96/JIm3dNjSVMpzm6ekmxzq6nvlrbBpcZzixdkrHXR2Vnyr7DXezhWsa6QyPrJXxdZTfzhXuc4YMtzbkj0cc0ohzOrJWYtna0OYthdah1q3wKchBtNJtesiRDcW1oJSgmgLQRtZeakDTRX8bz1Q4ssk1D3RYp2rb/iCtAK0FXXLA1BnyZGN7gaGVVuznwEshLR2+l/Rmtppx70gzPhtBPA7612RMv1oa3xNtjz8hNH1ya+g/3dY/WIKhNSnACrdP6JYtCRZ9wRajye1IZGcK2ZGK2+o9PwxKI9JWfA/okC8su9Kko+dwy/1MygEp8Z8fwPdstK/wlQspiXBKCMfCcr1YDzp+7ofX8J4Ln5lRf7kK1fDRVVNvOmOjPYUqtS/Z64ZPaXkVKYnHfOsjp0sZ3nKrRrYZSwHF5wOE8NvuodatHl2hbyN/RmMCYPRZH63MOHLSdYQ0943lKHKObPrFEuiqF5cdXVJBBcJs0/AFQprnQnQPTGHruO9ds+8i4Md/LHLHYD87gqcFnm5+Z99/11LuvePlN9O8K/GJT3yCf/iHf+CKK65INgK+VmHbNtf85euY1fnyTA2ZIEGCBAkSJNgzOG3aVA6cOY2bV6za7nm+bp9pPv70Qznk6P34wTfv3t0mThpGR0f58Ic/PNlmTIiXlU5zggQJEiRIkCBBAgM/3AgYvX7/u6f4jy/cMtlm7Va88Y1v5P77759sMyZEMtP8EvHII4/w4Q9/GMuyCIKAr3zlKxx//PETnquLVbAsKJab6TTbUn5KGCmaz1qWdHS4hqehfbm93oBcFqo19FDFSDnVnSb9wPWMRJElIGsoBNrz4vqEEOggQPePItK2qasc2hVJSWXSps4IUprUoFKadkRL8S0SUNoL0I0AWRhtplIN6R7RNfRgsSmbFFE3IuRzTUmh1rXliAYQpTL1A/BtUzZcpiOfay/Xeu2RFp3IsfJ9nR0wNNq0FZply1VDVYjOjeTuIhm4zo5mX0a0h4jWUq6afpDVpqwfoG0bUSyGaV6LTTmjqJ2VajuFp9XPgBgaao4D12vK2kkJI6WmHUo3JeuUhqjvWqkfUT0jpeZ4i6T8fCOtRFenGZcpuym71OrDcrV5PLpm1IfpdDslBoxPtDZ1RoiWeaNrjpTQjRYJu8jHvqHnBP01UBpVcmN5OQKzbK1KLrrqoRoKVXZRjWi5V1MrOeGSqrHFq4tQLVAjpIvvSlRgoxVYjqELeA2JVoLSgNU2vDzP9Es6LUkNOkhbYxU1Sgmk1ChlHrFDm2yCsA9rGdOedMX8tZ00KhAEflMWLqIL2I5maJNNo6HCcyUqABVey0mnkLYm8CRCaIJwqVhI2LxWYzsCKdtXt3xPk8qmDMWjZSk7aNlstHU9cX0RpGXKWlY6XvaOqAKRNN3o5ib1xW007+lAaaStEVJiO2acmDZLQCJks2xEPWhTqbSb+YajfhNSo5VgeKOhklRD2ovXaH5uOY22tgvph5mjjZ+GN2awU7qNAhDXaZmylu3F56vQR1vX2VjS0CsaDU06LZCWQAUaO+UiLYWQvqHweBKtzPgZ2CBjX6qg/a8lRQv9RrTZBLBlrRWfF/eJBM83/nIyCiF80vkGTl6AYyEcSXltgAoEluOiQgpB4IVf/VsCejqHzPPfC2JaiHAkOBa1ZaO4dgrfldi6gQwlDy1bk2qRv4u/qzxlZP9WlJqMjUAQ+ILAkwS+RPmCvtwQBJpguBH73PhKMLAmG/ajuceC1u8GPCzHPEPtlMKrN8fn1lXpuA/kGElFKUHatXHHIhu3rsrQaET3ssB2jJ+kpZkpB+PxZOwyfakUrHs6je9pfC+iVIn43ozoVtIy4zSiGyllo3yHFcsCRnM7XnluBBI7aLbzyNMOY8GR+/OTb9+1w7KvVEybNo3zzz+fCy+8cNx+theyB253IgmaXyJmzZrFr371KwqFAkuXLuXKK6/koYcemmyzEiRIkCBBggSvcHiqOSfUemx3Ip1Ox2oXr3/96/n7v//73VvhGDz55JMcfvjhPP/88zz//PPxcSEmn96aBM0vEa2/glKpFLJ1VixBggQJEiRIkGAnMVY9Izq2OzF16tRdqobxYnH33S9fvvZrPsK7+eabOfXUUykUCggh8FupCIDWmmuuuYZZs2aRz+c57bTTWLp06bjreJ7HBz7wAT796U/vKdMTJEiQIEGCBK9ijOU0P3j3k3zryz/arXUODw9z+umnc+655/L4469upY4Xi9f8THNPTw+LFy+mVqvxF3/xF+M+v+666/jWt77FnXfeyX777ce1117LokWLeOaZZ+joMFzWIAi49NJLufjiiznvvPO2XVm9jh4FOnJNGbdWLqoU6KFKnGoUS8bSOEIKRKrcLunleiZNtusZua2SQOR9I8cDJh1wmP5XFiqmTCQpl3LQ4f9qqI7oTEGgTfrgUIZHOBKrq9yU+4o4y6EkkRquN1OWWpH+kUmNq+sBcmDE2Jty2jmtUqJGGkauyrHA8tF13/wPyEKxKUHWyhMG9GgF4VjGTqVRjQAsUFFa2EJLWuYxvGVVbLTLZEWwBERcaWjaGrZZj1baU/F6KvSrSdksC8Vmimswfyuh7FixbGSRxvBKhSUMT3gcbzv8fxt88fj/fM5ww4OgyWuOZAuLIf8zkkoCaPimDbV6u19bUq7rkuH8ibQNbujz8Pp6OOR7V6rt9oS8ZV2qoRtBmxRU7ONKdbzMn++bcVB2m+c1/FgCSiuNzI3G5zUJkjrmNauQf6/C9NiC0M9ec0xqpfHKIXe5IWN+sgo0tZoZM3ZN4NgCJw1COASBiGWjIo6pVzdlS8UA3zNc4ciNAL4nyHRYbdJlWjd5qQNbvfjcRk6ilCYdSlflOwwvUimwHcPJjGA7gqFBL/xcY9uinYpfSIX1h3b6OuY8D/Sb66fToa1Rpm1f09mVQQV6XAZzMBzSwf4wBXkrfzbk32azqeZwtUSoJGj4n5s3emFduu2aSmmy2XR8XuQj1cKj3rjOjbmojtPOTbWdiO88VvVQsHmjhyWhGlLnixUv6n4sJxVyy0UoQ2euEYT9u3WzN66ueDuE47TVqZQg8Az/vX+z28ZflVLEfZfOOTHHOyoTcWD7Nzdif0gpYp9GfZ1Kp8LPicdBxOvdsrG9zqhepTSOnTbjV0I6H+CkleHoi4DBdRl8z7TPsjVOWuF7zfmy7HOlmNcd+SXium9dlaWaMnsAaq5EWoa/LqRmiho1PPWWlOARZ3rrqqzhgbfcAxFH2fM0qayLCgRuLdNsr2+4z2tWuKjAjNNoLEVw7EzMN4540BHWrqqP+zpqG7+2qUsIHbcvsm/VikZ8D9mOwJLE/SNkNryW+dytmX0NvqdZ+Uwt5kJbVjun2cgJp2MbWu8Hz1NsXOdS7WqXZZwIYzMCHnrq4ex7xAJuv/F3E55/880381//9V88/vjjlEolPM9rS0etteZzn/scN9xwA6Ojoxx99NFcf/31LFy4MD5n9erVTJ06lT//+c+84x3v4Omnn96hnbsSN954I4ceeihHHHFEfOyxxx5j2bJlXHrppXvUlrF4zc80L1q0iEsuuYR99tlnws+vv/56rr76ag499FCy2Sxf+MIXcF2XW2+9FQClFO95z3s4/vjjef/7378nTU+QIEEIjWaTaDAgXM7vfIxfiC3UCQia2zcTJEiQ4BWHRgCNMCtg87Xt86OJwK9+9asTft46ETgwMMDJJ5/MokWLKJfL8TlTp04F4JhjjiGbzdLfv2M96V2Jz3/+8+MyAc6cOZNrrrlmj9oxEV7zQfP2MDo6yurVqznuuOPiY7Ztc+SRR/LYY48B8MMf/pBbbrmF2267jdNPP523vOUtk2VuggSvWXzHXs9+nfcxr/Ne7k4NcbH1CH353/LhwvLJNi1BggQJdhq+bm4GjF7+duYCXupEYLlcjrPurVmzhtHRUfr6+nZ5u7aHrVu3jguaZ8yYwZYtW/aoHRPhNU/P2B6KRSNR1t3d3Xa8p6cn/uyd73wn73znO3d4rY6ODiq+RdEDURfgCVDh8rmiSc/wLHREq9aSaKJMCIFoSHOujugZlrmGZxEENmiB8K14h6n2QQcKISQylhCTpryW8f9BYCN8CwLQgW7SM6TEakjwJASyWa8QoCXKt5v0DN1Cz9AaHQhs1wItANlcnheGnhEENiAQwgINOsD8D8bWaNlNi+b/gPYshLZMpi2tKfsWrZOJshH6VYrQzmZZFbYRPWYXrhKIqBw0qRS+ABXWqVrpGQLtSwTmr7FXN+tSwrwAPMtk1RtLz1ACUTO2IFr+SkydbotU3gT0DFE35+uoLq/ZXu1FS36yrZxQAupj/BqE5ULfmi6yKLUs36LDcrYAN2xLtNRom/qjcStE+N4XsY9Fo1lHXDYwfaRaxitKNOkZWod+jSgrkS3h/eKDp8LMciielAEFCpzgd3NdaX9O6XkYgJ9ly1yUqXO4V8CzDX2imnZQgaauIxk3gWcJnBTgCAIpUKKdnuErQ8+oZxW+PZ6eYduCakpsk57RyDVpFW7WaVtir6YdfKlDibvx9IxGFnzHLOkHY+gZkcyaHy4d+3aTnuHmQ7+mogxl5q3n69gHrfZGCLTGDZNwtS5vR+dV005cLqZnCEO7aGRl7Jex9Ixq2onPm4ie0cg1M+QFYygTlbANQjQfQ6ZNgkZWGum3tGOGVwvLqpqyYmm6SDJMCFBhuxpZOa6uiJ4xtk6tBIEw9IxGjnH0jCBUgKymrDbpuECIOBukm48oF4ae0er7wBZxf7bSM7Rq9sm26BnVtIOTChOnOhLHDmkU6Hh8SUtgWRo7pQhE8/4uyRS21GggiKh9IX2hmnKoOQ4qSvpqRf2nSUuNlBohJFpokBoljZ8rKQclx9AzwrZ4lqZsgRICz2kZL0KgpKCRM2UmomdU046hZ4iQntEiLefm1XbpGdWQPjcRPcPNa4KoPrtJzxASqikTJsWSc8rQMwKp8ToC/ChrphwvOVfPOvGYbr0ffFvjd2qCDguG2C5cBZYaf2xnsKOJwMsuu4xly5bxV3/1V3R2duL7Pt/85jf3uMDBzJkzefbZZ1mwYEF87Nlnn2XatGl71I6JkATN20GhUABgZGSk7fjw8DCzZ89+Udeq1+t8+aYHyDkCkXM489DZnHX4HBPUaAWODb5JK6pbHhLCMkGKtgRWrYH2je6ySc+qwMKkKK14CNsESTp68HkKfIW2QNbqcV2Gj+vG56iii4ge0G4Qc0I1ILur4/VtHInwfFQp5Me2iqpqZYLvRoCquuAqRE7FKWWFI0FIdM03ttuq2d7oyVCrN4NsywLCXxG2FfOJdSMwwXk91AdNhetVtVCXdey3q2WF6WzDdo7R8bQ6XfBa6gmDe6Q0PhECHf28DxSq5htd7MhepSMSG7jhdaRElf34h4RIt/DXbAm1WtN/EaSAIEDXveaxkIetfWUCTEsYLnvkH88PiY+h/m3dN+f5uo1zrgGZc4nTVtfdpo8sC+2H/d5w0dhgS7T20b5C1hpg++1azACRDK6nwA21rMP00Sq8tlVvSZUbaUtH3yABhpc9FpY06bYhbJ9u+sZTxidKoz1FAPyfs4m/d/flPcFcsAP+LE8nXVdcJh/jGbvM4X4nLgrpWnGwqBX4vvmy9KSgXgdpWUbDWTe/mIXQ8TG3ERjuqmqXj/Z9jR/ywH1P4wc65PWa4KhRV9HQwZNR9GzOKY022y9CV7UGsbWqir/8Pbd5TYByKYjb0frlrALTLt/TJr1vy+1g6vRbdHTHT1vVa+bDVr3b6BqlYhCXiYLmyJ7RkfF9GZUbHfbjQCRC4Ku2tkTXtMZ8R2fzJuCxhCBoua9VAOWysdXLmb9Otem87LAVt0EFhqM6tuzYuiJ7qqNO005tfowE4T1Sq6o2HwBYtvlbGm2t01D4At9wvOv18eVa/TQ63NTV9luCRt83/mnyY9s54+mMbyTmLYFtC5y0xLaMdvT61Y34MZBKCTIZox0cjZdMh+FRB35TAzv6MbNuZYCfNyfaFT/ediElaGU46raj4x8FEX973cogDh6jr4bAb47bfEeKIBAEIY840NFYVYwOt4zNlh8lAIP9PtuK34qjQdsPitZyANlNos3v0Y8j39NUSsG4HyMRMplWXr+gXvNQyjxDojG7LYwMB9HjMMaS6laW1Ppp1AI8b8fBqKfAVuOP7QxeyETgcccdF6+kTxYuuugiLr/8cv77v/+bBQsW8Oyzz/KBD3yAiy++eFLtgiRo3i66urqYP38+Dz/8cKxZ6Ps+S5Ys4bLLLntR16rX69hCcd5hszjnDQeFGwGD5samlAQZEFg+umXqNAqasQRWKkCLMGhGmWDDEqACAuEhpERYOp7V1ObbE6TATjXr0iiEEz7QUPjCM/UAWvogmnekSUQwNmjWiJTAl83gsG1HkdZoEWDZAagA4QiEo5v1So0vPTP7aem4vZENVko1iUN2yxeLLdBhYgQd+GbziWVmLAtW0CwL7bOzYdnA8omnpccGzWkFIojPNRsWMZO1ttngpiM/CIWyfOPrqE6lmrZGfyUo2/hTK41ouduEoxHpaBa4daoc8HXczvjzsM+jjXYiHU1xSpBh/eEXemCFm0Et2ezLMDKQ6cjWcPoi3tAnUHZYp9AIBDiaghWgUaacLcZPeYTQToAOWjawhm2OfRv3g2oLmiP/jIMlkVFfRptfpQDf2IPU+MJDC8X14nmKFDlL5enEBR3QJbNoAg5WklXWIF/JD/Ptqet5dPlZBIEk7zpQU3GwGW36ybkiTnwSbTaKg2YlSNc8HL/9SzdCLtwQ5Hu6bUOelIJUxUTDrV+eqYqLCjSZlpwLUVCngtbzvLYv8dagOZu3UcHEQbNT9hFelOCEtiA5U2O7QbNT9kJ7WgKG8BqZDr3NoNkpu+OuFZcrNDe/RfC9ZlsiH00YNEe/EaVoS3ahgma5pr/ctnKtPh0bNKcq7jaD5mxLXpRAmf6MNos5ZXdc8ButPoytUykdb/R0yt42g2YpIVPTEwbNnqdxyl5b0Nz6WzPdqXEcEQfNKW02t0rLjL1o3KV9QUbLuN8A8uHv89agOdoQmK42g1Sn6sabPy0JeU+aoJmWoNkVKCVIV/3mjGvo99a+zrmmjsifQegj39M4ZTdu29igOd05PnFJBLvkbjdoju6zsUGz5+lxZScqF5XVVWV+tASmzu0hldXjgubj6Oa4TDf3jm7mUYa3Wx5MgGwpWH7/Ezx9/xMA+NEEz4vErpwI3J349Kc/zerVqzn66KPjlchLLrmEz372s5NsWcJpJggC6vU6rmsGf6PRoF6vo8JvlMWLF3PdddexdOlSarUa11xzDY7jcMEFF7yoemzb5rMXHs0bDp21y9uQIMFrGV+VK/m0vx8H6Y5xn+2jclzfuZZ/71xN0fJ54373c8Wxf5gEKxMkSJCgiYWij7dlDtjheRGPeb8TD+O8q9/N/icehhr/O/cFoXUiMEI0EXjkkUfu3EV3AzKZDDfddBP9/f08+OCDbNmyhZtuuolsNjvZpiUzzTfeeCPvfe974/eRjNzdd9/N6aefztVXX02pVOLss8+mWCxyzDHHcMcdd8TnvVD4vs+1P3mEtx01i3Nmdu3SNiRI8FrFH/Qg60Sd96i5E35+kt/d9v65TBkye8CwBAkSJNgOlupBHq3veKa5HoR7T0K4SjDB4lCMIAjwPK9tItD3/Tj5WjQReOaZZ7LvvvvyxS9+cacmAvcE+vr69vgmxB3hNR80X3HFFVxxxRXb/FwIwbXXXvuS853bts1n3nIEszKA66FrbnNpPNBQD/moVa9NS1irkFvbUOiGj64H6FALOb5zQm4ngTY0gLThjTa5nwJd8+LPCZpljZZzgPACo69c8drs1hMtP9Ux3OKKb2glMX1Dxdc2Ws1+kx+dslrqNbxUgUQT0i08w4cVjgx5s6H+sG01SXRKxRrWuhHRNMLNY5bhuNKanCZa0gdzPLIl0GZTXFubXLRnrimifkk5xo5oc2S4xqYjQpkX8rEjjeR6I9Q+9tvtjTfjhXxcS0BgIaJyUVtbtYyjvpTC9FnYb9F+wZhbHLW59S+EZYK2MQIYW6O/MvR1uHsq8q3I2OiGBh2g7bBPo/oize5WG6DZL55qH9etdcqWjYmtaF2/tKTpBxW0tFE1bW0rJ3h9cD8As1O55gZYZcV886NkHxfUp3FrZmtb0XQWPK+d8uD7EHhWcyMZZpOQDJ+SQmqyWRlymse3I9ooFLGimhuCdKjhG3JBQ/qOYwuzF7hFs7e5LNyytB1qADcpFq2UCYHvqTaKhJRmU1I6LWLNWWhZInZErNs7EaQlSGWai5Bj6Qsq0PG1AqXDz8cui7eXiXjgvqfalqqjsjLk30YYe0495CmP5ZLvCPWamnAjVus1vNAXzc2Apn21cBl+bFkVhP3ZwkqLec0ypHB4tI2ReMOoI8ARbddqbbc5r8lXbvV1Ki1byjWl7KNyjYbZmFdTIMtNm2otHG/fE9SqinRaxhrR+c0pAqVx6xFtxPSTUoa/3vB8pCVIV4O29mY2ybYNYpGWuFLa8NdbfNpk8Jk6BrYEcT/EHPzwr22Llv5pH6O+r0M2WjttA4y2d3T+RJ/XahF/Xrf1v1ITl20t1wrVQh1JpaXZdrMNOHYrFUS32XNsfioH5Gdwz9a1274A5mtLtpjgtXz1T4Q9NRG4q3HEEUfwhS98gfPOO2/CdNlaa37+85/zuc99blI516/5oHlPwfd9vnDrY1xw2AzeOLsw2eYkSPCKhq8Vn2qYzJy/t04d/yOoBTnMt9rX+w/lL6c+CcCyjlH2Kib3YYIECSYHj/uDPFwa2uF5XgCyZb4gUO0/4sZiT00E7mr813/9Fx/+8If5m7/5G8466ywWLlxIV1cXo6OjPPXUU/zud79j2rRp/Od//uek2pkEzXsItm3zmQuONDPNCRIk2Glorfng1gf5vreKD4l9OEb0MG6zaguO9gp8L7OJTtV83D3cNcDcjZ17wNoECRIkGI/D7T72zk/jd7U12z3P0+0zzXOPO4xph+zHoz/57W62cM/i5JNP5pFHHuHuu+/mlltu4ZZbbmFoaIje3l6OPvpovvvd73LmmWdOtplJ0LynkMw0bxuB1gitJ1ySSfDqhqcVFuIF7Uj2taIYeEx95v+wETydW8RsN7XDcu+rz+YdjRnUGpLPbD2AtZ7Hl/d7iu6RDK9bO/OlNyJBggQJXiRe6Eyzq9oFhtY8+AQr//DIbrRs8vDjH/+Yc889lzPOOGOyTdkmkqB5D8G2bT5z3hHMymM4nl5Tkgs3aBIHI95voNEpCZ5CZGiSmIKWchF/NTDc1xheME5SDUK+acTLjY5F12gRfoyOCSmaxy0Ra+MaCbyI1DiBhJslGJAuD1TW8b3aar495UQ6Axlfu1/VeUvjXpbURjjK6uHBwDw4fpg/gbcwO+S0asNvVhoI16NSTrPtgNKaAd3gGb/EPCk5JNVteK+21SZRNpYEKcb4Rge6KWsGhuNtiSY3udXvY8hk8bWU4TcLwvoi4ptlpO90oEPtIMNVj9fYYntb5NWkbPZLK6859LcGRKS1HHG4I/tVuNU6Gg9qgv6JzvVDrrHCyNoFoc/dwGw8saSpzws51z7tvOSYkKiaXO2x4y5630IsDSzBX6+6jzWNEr8rbgDg8o59OC89izdlZ/F7f4BvFp9n66jHveVN/PWUA3GV4ltDz8aX/cXcs5g7mkNHCSuk3Ry3gOxIoaWHdiS20nR7is4g4EPl+awu+3x39kpWTS1y7tDsWHbK90zSBkuCtFpkHyVYoTxgd180juW4JdJUNgj1n62Qw9y8hl9ocqXrYQKQTMgNz+Qkji3aEqa0cj3dhmzjs5pzDD8ym5NGamws71gZjmmg2jnJ0ftMTsY8ajWGKg7QWbDGSWVF18l1WLFUGLTLuHUWwgRFY2XcLEEmK2OprlYetzlfkM/LFn3jdq5vKt0sG9kcccCjOhtZ0yfplp9gqbSIr98qgRb5urNgTSi5F7XL8HLBdpr8WKU0ea/JdW5tq+GDt2sdt6K1jRMh19HOW47qi4+ptlspRuSDsecD5DuscVJ7sd8taDRUW3+2yqhnsxIrbyEkOEHTtqaEXCt3v73O6LxWtPLuodkPvhcFIwLf3zZJOGpnawKbpt1W21ge2+Z8hzUhH97cK9aYY01/ZLOt7RYTjpdWrnKbvV0Tt2XJlEFWHjPKeWvm87sl259pbgQm+VeE6cccRs9B+/HkLa+umWaAr371q7z3ve/l7LPP5u1vfztvfvOb6ex8ea0IJkHzJKOsPPJasO3H6PYxrFw+WX6c96u9mCUyTCe33fOV1nyrsoIrC/sjd9PM7hNqlOMad5sNg0Dvhh9zZce+FJXHZ/oO4weVNTyshnlvaj73+wNxuYsrf+Lwehe3TXkDs6wdS8v8U3U5/1oxmyiKlSKN6XtG+Fxrza/dzZydmY4lJngK72ForRn06+AHjHh19t7Obe1phRt45MWevfW11vzzlscpyBQfXGc27310xmHM0Rkayme9X+XicrsUXIc0ySX+WN7K3imzSeWcjtn869RjOCDdRTBa3ilbpvoZPrp+ASvT1RdcJjM0iFOp4LuCdKXE1r0PJj88SN+GFQROik37HsbLQcHT8lwy1VF0oBnVPYzdSJVg12BNV4k5xQ52/smd4NWKquPzxMwh/rj3Zjb31vjoY4ew30gBjebO+Rv456OfiJPP3nHQACzZ/vV8TyJbkqCsf/hx1j7w6pxp/sMf/sCmTZu49dZb+fa3v81f/dVfceqpp3LhhRfy1re+lSlTpky2iUnQvKfg+z5f+PkSLjh8Bm+cvk98vGfVD/jX7qP4cPdBO7zGSODSqc3X4Aq/xA3l5ykqj281VvEtVpFGsjJ1DuuCGkdavdwfDPCfjRW8xZnFrEoPh1LgvtoW/mb4YU7NzeAAZ9fTRLTWXO09yZvkDD7RfTAHyk5mDPyM75ZXEqD5cdUEuT/JnMB52VBMPZxWu8/r533VP3PO2l/z09lnsrQ2CkJQsFP829alHJTv5UviQIQQrAuqXFNZSoFmG9JbfsjfqUN575QDuHVkNb8oruPn887k5yPreaI+xG+G1rNKVTjM7uJ4u48vdRyGEwa9l6+7j7SWfG3GiQjNuC/Dn9fWc4TVzdfKz/NHt5/7vQEowUnOFI4OpjIcuHy45yCO7tg1N/XfDjzCkele3t4xD1tLqsonT3PWwlUBKWxWN4rst+T7bdoF3y+cyIXZuQgheM4vsbeVR2j4UW0d73n0ATTw/t4DODbbR5eVos9Kc0bB9MUxm3/F4U4P/9Z1yk7ZXdM+7xt+iB/X1nKAVWBVUKZ3S4ZNfjNAnZfq4I+HvI0ZqRx6qBKnRL+xtIJ761v5RPchLJAd2NM7W9Q9dKxustMipS2YX8/z1f2e5T0r92PfHW0IVIqZDz1oZvdD9K5fCYCXyuC4dVLVMmvndfPF46t8asUJvNhg9fGuIZ7vLvGOjfNZ1jnCIZWuF3yNX0/fSKeyOaW/j4MfviM+vi+wZPZxFHfRmEwAKztKPN49xD8c+QRzRvMcu2EK80Y7OO+5vXbJ9QM01ph+94RiQ75K3+jka9S+WlFxPPo76swffnGzmnXb5579N9Hppvj1AetxAsmf5hulninlDAMddd73+vbJgFnlHB9+7GAGnm7wk9w6ijuow/cl0m8GzTOOPJLeBQfw9M9+86JsfaVg5syZLF68mMWLFzM4OMjPfvYzbrnlFj7ykY9w3HHHceGFF3LxxRdPWkptobV+6d9ACbaLYrHI3LlzeeIz5zCrU7Cuw+fCdXexpNHUaFw15wJmk+asjb/hQFlgFhly0uI0aypOyuZ7jTX8W90sT5+ansrvG/1x2Y/lDmA/L8vn/OWUtEcdxcWpufzS3US3cCjhM6rbpeQOdro4NTudz+UOoWdIhdJxcG11GXksPq73AUtiTc9BoPn3+rN8YuQxPp4/kC93H4GQgmCgRlkG/EZtpSEUf/aHeF5X+LM/xFPdb6S3q8OkU3YkUggeaPQz286x0ivzOq+nSSGwREwp2OhVmT942zZ9efe0s9ns17hk6H76RIql0y6kP2jwncYyvlJ6esIyWWFR1wFpLD5TWMjv3a3cUd8EwMmpKYwoj6f80fj8gnQoKo8jsn28b+qBPDC0ke+VV8WfT5FpLkjN5ob6ynF1zXM6+O7813FqbjoAwZYKt9c3skB2sG+mwPKgyHK/yNu75mNNNQ/o20bWkkYwK9PBPtluvMCn9+FvArCP08FKz8yo5oXNOzvmc2dtI+v9Kkflp/JopX+cDQDzZI68tFnmj38kv7t3P24aer7t2D/POIbXeT0cP/gbJIL+eZcbX9gBuuHDjE4CASkFvytu4P1r7uO8rr14T98Cbh5ewb1DG8hJq21cAvTIFOd1z+Phaj+v65xFVfn8+/yT6UqZAEAPl9uzN0ZSiEoje/PmM7dl7MbUJEXQX2uhijTpKDrQyO50LHmoRhqoUK4xGPWoFS0CLfhQ5yNc/kjAwT0nEmgb3xXksg32/vUd1KZPY+TgA/C6CvQ+9gQda9YxfPxheFYGr6ODjudXMTJvH1Q+Q/bZp5nx7AoAfrkfzDzoDRS8Jtf63q6t3Nq9ES3grP6ZnFicS1F43DrzGQ4d7eHkei8nH30Hn75Hs289zxePrWBpKHV38JXnjmT+5h56BtYh0YzO2ItMeRQdKPxcnilbV/CXJ23gJ/tU2fDNaUwdLLPp8GPAFsz60x9R0uLZo85GSxkmjVSs7axwEB0ooD9d4/szV3NEsZc/9GylM7AZclwyNZszBmZw1OD4gDuTJ6ShGJ9HUntaCSrl5g+LwFY4WuIJxajjMjediVVOooyLJj+pxpGS0oget5wfId8p4jqF1PFfgGrF/K1lzMpEtiUFfVQuWhCK0pNH7yulbX/95fLtweuj3YNcerhZJTm+fwqB0Px5yqDp41+fQ4fvxHW2wg83qUoE96cHGXDqDGVcKtLjiZ5h/jCtXQ7xgT+9gV4/zZ+6Bphez/LrKZv4yt7LueGBkzhioI+7ZmzigelbOWS4m/PWzcXWknwBRmyXtG+TVXYbVaI0GrRRWsb+zYa0lomoC5Wyop417cq5Xhu1I6JgjKUrSEvEdW4LMX1jAqpJpRyMs3Fsubb6wmuUiqFsaxDZ0TxHBe1UidYMmTfPXsV1+z1F3Q44emsff//o4UwvZ1tSfk+8guQLxRVH3s8TU5rf43NLefYbLfA3Tx3I3EqeXIfgxnkrUWgOHe2h07dZUO4K26kYSgne9OfbGB0djbP1RSgWi3R1dXHRD7+Lk2uuIG98ZAkbHvozz91+54TlXq0oFovcdttt3HrrrZx44olcddVVk2JHEjTvAURB802fOoFDOtO8cfRennOLFKTD2ZmZ/KS6fZ1GB4GHZlF+FqsbZZ4JA6FVM97MOr/KCXYfarTBN/UaPlBv1y9c1/UmpskMd+aHeaoyRFZbbAiq/EtpeXzOAjp4ouP1rFFVDqjeCcD/chQX2XP4bdcoP6ys4cb66vj8+VaeH085lbO3/I4R2oPxHuFwS8dJnJSahuxMGVJkyjysWnnXarQxYdCM0izrqFPVPt1OmvuqW5mT6WSR6uWq1Q/wH8oEr+/N7cPXOo+hJFKIdSP0fPmXrL7+PO5yhkkLiw1+FUvDgnSB1+Vn0iFt1GAttuPR+iCfHH2cu9wtsU0f7DuI/xxczkGpLpa7zSAa4M25Oewj81zZs4CDrAL1Sp2HgiGmyAz/HDzLyfnp9Pt1nqmP8uPRVVzcvTfH56byX5uX8XRg+muB3cmzfim+5j/NPYH/3LKUde54msHbn4LV3fDnCTKbdtZhVgmemQL7Zbu496C3sNWvUXEbHJnpIf/4d+NzP5Dfnz97Q6zxK3yk8wDeN/8weu0Md46sJS0supXFVVse5p7K5rY6/m/qGzg9O53vlJfyyaHmmJpmZ9nq15gIhzvdXJKbz990HUBOy1h/WU4L9T83FWFGAZxQe9v10eUabC3D1A7QsQr1bgua7dueJLVsA+WF+zI4Osi8dSMoKdl80JGMFqYz79mHyA02KUM3Hp/j0ofrDJxxPP6UbvyG+QKNUg4Lqblu2lP8vGs1B1Vz/OrrVR48eh7pUoWtHTAgKlx1eo3+Dsj7NlXpM7dRoOKA5xd515PwV4+ArSULt4xXAFnRA58+t4f/+962kyD8YgH8+BDBd27VrD/hZGqFXqPXOwILHv0dvgj43YIc63oy/HC/EZSqU8ynmTfoMqtkOPJTqnDLQfDJP8CcIjw8CzpcyKssnaKXJ3vLvG39POq11exTFHTUA/xUhtKUmRRnzkH6Pp6TpRIGsHfP2MTfHvvncbbOr+b50aOnkfdSLCsM8/aj7zPjbckp7LuuG2sbfN/tBc2VikIgdlvQXJE+7zvsAZZ0DXNEsYdvLD2BYFRia4lG856T/sD8cgcfePZAut0UvfnmAu5zuSIfPejPrMiNv8d7GymG0i7pQOILzQmDU7l/6lZ6aoBlM+z4vPVpeNOz8OnXW2zJBbx5zRx+Pm9923XSgaQxJr39p58+jHO2zKZm+VRLioF8nQMHurFDjvYrKmiWmmy9TM/IJrxp06h2tSe7eClBc1X6HHfG7ZyzZjYHjXTz34c8TSA0Vz12CEcN9jG30tEWND/WPchXFixjQanAz2eto2Epbr/99eQaFrYwY6K9ndumbL3QoPmC79/YFjQDeNUqt77rsldt0PzTn/6U888/H2t7ItiThCRo3gOIgmYWQzFjAqj+/d9BDymzNC3h/upW/n10OQ/U+vlM+iD20Xnm2FkuqD3AEC6nO9P497knohoBZ276DXdMOZ35dhiMBBo12kA4kh+767m0/hB3F85g2KvzpoxJ221Nz6PrQbyzZ42ocf3oM1w/+ix1Av4ptZB/cJ/hLXImy1WJh2n/kv6HrsP5m9z+fLe8ko+WHo2Pn2VN46uZwylKnwPppCOVjner7DBoBsTTW9CHzkQ8sRE9twc60sgpZhbSbLITiP/v94h7zMzoly7p4kCrwPnHHouQktLGGvLXy+l+ZCX+f74d+adVcNq+JhDrSMG83rjOYHMJsXoY9u1DC4H2FSPSp1vZqGlZUgizYQ/w1wxSmdfFiFenZ9Cj8OwwuicL+04xmzhbgnxruAwHz4jruWHoGf465O2OxcIt8Og3JHM/rOjPw7wR+N/yASxTRVYUNHbd588L0tx59Qa0FHGiFZW1UV96E8/VRznoU/e0XVN3ptGffD0cOA0aHiPFKm4QsDaocGSmz/h9/Qj05JB7h/4Iv6V0w0dYgnVuhRvWL2P6j55iRQ98+0TzIK75RTwLLu3Zl+NXBfQdtQ9H5vpYEGQJcg4rG0XuGF3PIY0MZzS6oCtr6praATnT/3JqHooN5LtvRF12LLzjSFg9hPzgj9H5FKLSTKATfOx02H8KGoGc19sMmrWGu56D1+0XJ2N5UUFz1UWsGSb9/YcQLbvb/jgHTgpjkFpHF9nyKGuPOZxDXv84193l8N6HPX5/zExGDtqLY+vdXDj3IVZkynx15ZGcMjKNH0xbw7Vzn+JfVh/O2xrTmfXDJj1iWyiGX3KFYhEl4Ff7wayOvelMdfHEXllOXuXidnaSHhhi2pNPAHDbArj1QHjbcrjp2DzZSoWZJajMmMm/f9+smvzFBRYfabyerO8gpaZalIjKKJtK9/L68YsiO0Tdscl4/o5PbMFTs7r46QEBe/eXWTklRWfNZe9Slmlib57pHeT787bw7BTB64f34WdTVzDNy/LeB2scuRlWdUM118G3D63hp/M80Vtkei3DjFqWLfkam9N19q50sDlTo8dNcfLwNAIR8Ou+DbxzeZ45bi+HDhfoLA7T7aWZOdLAKXQTOGnW5xsEfp1U5178bE4/vl/m4DUj9BUD+uwFzB0soqRNZ7EfL5WlnskzMC3N9w5y+Zf9zQTD5587jIs3z0Mg2oLttXINz1hPcMJ6aFigO/t4ZnqKNZkya9Ml3r4MDh7NcfDGGq6Top7pIFc3PzxHemZSz+QRlkWpayqZxnr2fWrZdn28fHoWNe0Ehh1Bf+0+jtjkU8nYLD9oX9598jO42yBdHj7Qy5cfPhrhC3pUao8HzY/2DdKfqXPa5ulMyabNuWOCZqEUYssAdm0Ep7aa+UPjf6D7TppaLs9vF07h1OdK5Ctl0rWqKYvGtVO4lsRLdYD2SQUBo/kp0NWJQjE0ZSrTBvqp5bu49qiN3Du1n+/febppr+1x+Vm/Z2O+St6z+cXtr6cv5/BA71a0gH848EnW5isAHDHcwyceOpR9KoVxiUsi7Iqg+U3f/t6EQfPt7730VRs0T5s2Ddu2ufzyy3nf+97HfvvtN9kmxUiC5j2AYrHIzJkzeWMHVE5x+NQ5x3FSoxN68y0Z4kzGOeuDP8b7wGnoqZ1gCfzNowQdKTLSwprTiZZWu4IGtAXNLN+M/YcV+ItPa1PRGBs0R4Gsrvvss+FnrMc8nFY4b2CmSnNF8Ag/ZCMAz0x5E/tkC7F6hCsV93sDnFYqIGxpgrKoDY5RgnCuupXgi28ywdNEQfPyrVi/WoZ8ZB3BeYdg/eIp05TzF8K5B8JoHVGsI25fhnh847Z92xKA6N4cYqjJndWWQH/ybHADxG1PIZY3Z5XVvF70/F7UmxcaGspzW2FGJzyyHlwf+cvl4+pqhTphPuKZLaij98L67TOmvlP3Qb/hAJhZwG24bH3gWYIHVzPno+dS1D568yhTvnjXdq/7UqE7UgRvPwI9qxuxdgjr5kdRVx6P9fUH0Gkb/e6jkd98EH3UHJjfi3rLQkRHCrQmGKjj/PUPAOPXr5+R4eqfmaVjPbOA2FREv/VQ9Ov2RX7sp+h9+hArzfK0TlkIt7nFWx27F+oDp0J/CetbD7b5/kW154jZ0JmGVUOI9SOoL51rfqC8mKC5v4p4YCWpO5rByOixh5BeuYnL3plnwGpw411zcEpVGnvP4ofzBvnE3o9z/aqjeD5d4iuzngMgqyxqLVkGTh+Zxj3dxj93P3k6C+wsbn8/S/OC2aoXAeQ9yZTnn8MeLuKlszRynQxOnYYOFHkvoNbRjZPX2HaT5hC3XQlkrUFlqMxpix5gKAf/uvQYrlpoZnCPGO1hSdcwf/t4Hx9Zuy9vOv1Zzh6ewV+u3z8Omr+y3zLum7aZW/7wOmSg0Z6Hg6KnMUC5bzoShdcYpZHO0hHY+LkcCEGtbBQ5XBVQkXU6PZsne4rMrqS5bf/NfHv2CoqOx9QyvC7c/H/543D+s2wTWgjQ+gWzvRsWlDI2dRvW9Fqs603RW9XsvbVK1YHeqmJuC/uo9VlQsyH7AuL9igN5b/vnrC9A0D2bTjI41Qql6bMYlgVSbp18eZhZG57Bs20c32dLh2R6efyKQXnKdNAaDwe0pmdo28+0/v0PJnBSiCCgVuhBKMWM5UuoOzkct06u1k656p+yFylL0bXF/Ppbu/B4Nk/p45HuIRSaLX6Do4d6+cgJD7GmUGZaNcNPf3MWIhATBs0aHe/p2BVBc4BmTWeZS868F4Bz1s3mn549msCvMG/lcurd01mdGmX61i3su6XSdq1lU+CXCyT37Z3hnC2HsjG3htc/sZXXrW76+PHp4NqSDR2KQgMO7oe+GjjbSQDSiltO3Ju5xQX0jm5m6tBaHN9FE/BMn0fWDZhaEzwwW/OTg814+ecH5zFn0yZk4KGFxLcc0l4dz3LQQmIHLp6dpprpJEVAvjREYDkEto0WEst30UJyl9fgxs5evlca2m7Q/Iav/9+EQfOv//KSV23Q7Hket9xyC9/85je56667OOWUU3jf+97HRRddRDqdnlTbkqB5DyCaaV4GzC42H3jBv74Z8dBa0KDO2B82FbGvvRO1cCY6n0YMlJErBtquFXzqbKKUymK4ip7aAfN6UA+tRyiF/Z0HAVCn7INaOAsxUoWGj1g4A71wBroRIP60BhaawEN3ZFkzMEi66tNbDLBndYMUaN9Incne7DgZsSj4VUu3mI1as7sQG0fRtgXTOqDu4fy/29CHzUR9/HRE1oG7n0fv1Y1csgGe3op4YtML9p/6yOtM8O1r9PJNWD8wdAE9JU/JNU/mQrE4YVldyCCK9eb72V3oWV2QsRF/WIV4gcNfvfto5E27bsey3qsHsbY5m6/PPRjxy2XoQgZ99VmwVwGe60d+ycgKaSnglL0R95kpQ/WpNyCe3Gh+kDyyDrF6x3qf27XHluhDZiDDHyitAchLhbZk2+wugH7HUeiT90X8bjl6dheM1mHhDMT3H0V3ZWCvHuT3H534eikLzj+E4PxDmz/WxkD2ZNA1D/3MVuxrzOyv/7r98Q6bQ6Akvidxq5JHUqOcu9efOL7cy3eeOQ46fM7f+wEuG53LX4/Oo4HiN51b+YvpjzMzSPPbNSeRQvAX0x/nvvwgl4/M5e+2HEAvNul8gFaCesVCSo2K+bvg1iKpOkHFCbmvnoeQGietkbbGdhQqEAjZpBEoJaiXLO7p2UJeWxxX7KNhBdQIsLTgxrkreXf/PKboFPfm+1k853EefvZ0ctpmQ8Pn5CN+y/eePpFjyr3xNQFSWfOmta7oPUCjIsd91lpWa4FGU5UBG1I1Hs4PIZXg9E2z6HFtEKYdhAo9IghIdQqE5+EUS1iuhwgClG3R6OtDOzaNikBKjfYU2eEBOjdvMuMeAXmHrpA3Xp/ah7Js3K5OtG1TlznqhQLF7h5cqSk0fIYdlxHbZV13CbRmWt2iR2WYvrlE70gNb8ZM/HyOUh3uy6xmMC94Plfm59M3Mey4oOEvVnTziedns++SFdj1Ol4uh1NtV1zRQjC0zwJG9tsPpSVSaqy0Rw2XgVTAXiWJyOQRwozReGxEwafWMQUhPTSMmJpDZcYHBVoLGtXmtGzn5g0IpSjOmgtSks4pOlasIrdxM9J12XDqqXEU7NZCST4R0J9qcMFR9/DDB47k4GGHRkcXdkEyLDz+dp/HebhzkNNHp/HVVUfGZSu2g5AmaG6luqSy7VqFj+SHeDpXYlm2yMpU1XC+uwbjz/963X6cPjiDa+c+yP97qo8Dn97MsRthIGfoQRUH7jogzUcWCY4pTeVv1i1gU6rOb6Zu4jtzzHNvipfmrzftS4cnOHe1w+37+DyXq/Cb7s3sVyowy80xr5bnkGIXq/MVNqVrrMyWsRUEts9e9Qx7ba3zv/sVsas1fnFbF3ttaKfiaSEQWjPa3cVDcyyOXTGChSTXCLCilT8paXR1kxkZRmhNtdCDclKkKiVst4FUAbXObjNRYVlUps0gMzJMqlQkMzIS17W2t495Q4PbDZrP+u8fYGebQfPAk4+ydclDrPvd7a/aoLkVq1ev5lvf+hbf+c53qFQqDA4O7rjQbkQSNO8BREHz0r26mT09g9haQjz5woNG/69Owv6fP+42+/xFB2E9sKotuPQXHYRwfeRj61HnHoQVBjB6vynocw6ER9YjH1j9kuoNPnY6jNbRh0yHcgP5x9WQcZA/exJ97F7ocw82QeHhsw0/1rZRm0eg7kPGrEEW+z1Ew6WzQ4IUyBmd0F+BjAM9WfAVPLMVpneiPR+dccAys+N67Qjy9yug3IADpyO/bnysD5mBvuoMmNkFT29BF1KQb36RibueQ4/WERtGEU9thhPmIX61HH3wDJjTDXUPijV4fgBRdmMKgu7Kos7eHy44DFIWomfMTu1oQ5wUMNLkPutAG6qKlOjhKlRcxMGz21Uk1g/D+mHE//wRdd4hMFRF9JdQbz8CsWSD+XE1txvZkYbuHPxpNczoQPzjbxEbi+hpHaij5yI2jKLfcCBFV2J95yHy71qI3mcKcv9pMFxF/O4Z8AL0RUeCYxuy4DP96IaHnt2FWLIRsWoQdfw85C+egjldcMUJsKIf9p8Om0YRNz+K/vtF5ku9WDbtsC3wg1ghQ1gCOsIvCi+A4SqsH0F+sn2TqDpxPvrkfRB/Wo0692DEmiGoesjNo+jZXcjvPGzOO3A67ruOQ9d9glE3Dpq1FjxGiTfufz+2EvhSs7Deyc0bj2WqMsGtkPBcpsQskSJXMeNABYJn7AoHqBzKN8HS7gyahdTx8dbP03mF8kOeL5qjF9zNf68/gql+movmPcixxT7+feVRsR0RdkXQPBZKgRsFdi3XipDObX/qr1GVbX4bV1ZrRKDQttU2MxoFk9VUc0b0xdQ5FptSNbp8h96siNsphEZrgQg39enNZfx0hiCVBiFiu6XUsW8jaC3GB83huUD8v1KCTH4C0WzGBM0TIGqndF2m3/dHUJrynNmM7L8/br2Fr6E11814iBu+31z5qcyYzifOcbh59hbeNDyLJ4M13PKLTrRlM3NEM9jbx4BdYt8Ng2RdzdD0aWSVRXFqJ789KMuGnMdXZj/LkBPSrLSRfT9huIc5ZZsTN2c4dCTPgkYPXRvWUthitNl/dgC8561whDuVqzYcwCHVAgIRB/nQHO+eUGxJ1dlHjv9BEY3TRsVq466PHX+tQX7reZmBAVQpoNFRwM11jLt+a39K10XZdssPkvF90sq3HzsWTN2Gk+/WJP2W4LTf/my7QfPr/uNHbUEzgF+rcu+HL3pNBM3FYpGbbrqJG264geXLl1Ov13dcaDciCZr3AGL1jGvfyOxOiZySQz+6HvFcP3p+L+L5AcSvn0EfNgtd9VBnLABLoPvykLIRORsCjexwkJ/4OfqI2egpecSaYZiSh7SN8jXC80EI9L5TzEzqkg3oWd0AWD94BDHaMuM6pxuxfiR+r/adMm5We0dQMwuo4+cjGj768FkmqHlsPXpqB3LDqGlfZxqUNkHjUXPQ7zgCPb8XXWv5cmidyQ40sittAkfbbqr5h+/V5hGTfCOkfBQbFsIWdFq+KduXMwEYNANQMAFZsT5x4hZATu0w3NmaB51hrvMwSYqu1NsD1EA3aTVgNqyNJQWGSUfUcN3QaaouIpcywbIU4MidC5qjoLKv0G5TS0YCNVA2s68TbKqSUzqadbjt69LB1kpItZEUtQ0BFNKB8evMribxMKrXtsw1bMtIxyndnqgn8s+0Qks/ymZbX0jQHB6P2xiEryc3Ij/9y3HtGwu9YCr+R05HNwJU1UdXvXFBs1eXfKt3Nf89dSUZJHesO5Fu5cSBjpBg2SawjYJCFQiUEli2JvDFyyJoBvjw7Me5pdusFhxZ7ubmpSdjy6YdEV6RQfOYunZX0Bwhkw/GB80twW8rXi5BMwC1BlMee4LcVkMdKs6YjV2v49QM9UFpxX8d5fKt47P81cOKD/3R7C+pZVNkaybw3ZyH/rxRHlepArcdACOiyGfug7oNU1sm3O+dBw/v38mlj/nMHJx4k7AWIqacbD3gUB5cIFjXqTmx0kefn4KWPp8oaI4wdnYbXnrQDKbstjBR4Nu0dfcHzSd/5cfY2XzbZ36twv0ff/urOmi+7777+MY3vsFPfvIT9tlnH6688kouv/xy+vr6dlx4NyLRad5DiNJov+2oWZxz+r5wyAz0QdMNFeKQmajzFwKgRhrbvkjKQl33FiDM2teiPqH6ay0Z9ECkLVQYMAPoRQtQdQ/dMLN2YkbBBOZVH7W1bILQYsPMzgLaDZr0DG3EocVjGxDLt6BP3Qf27iUYMQ9Y4YQPjvl9BKfsa66rFCJlQdZBODLmvYltLKe/LCAF5HeclnmnEO7sT/ASIYTpp8Nn43/nUhNA1zwzg71+1NB4AHH4TPTGUfOjseIBEwcjEd47MJ/LNu1NOq+w7Jfp+HwB+NKmgzmw3kmnsrlo/XyS5CavPah0mi3HHYvwPDrWb8AZKqJsm9GZe2E36mRKI/RNO5Sneh7hw2+Ar53cyT//qY+zlwzidqQoT+3jj8fOYpqf4ecdW0iLDhZUO3nLUJZVp2g+cfCj/HrKJj6+Zh8+siTLycue47S1ZYTWVGZMp7TXXgRVTZBKoaWkXugxdJ3whwfA4X7A4cMtwesk+muy8IfBrfxmZGLJ0Fb4ngRbjj/2Ksb+++/Pli1buOiii/jtb3/LiSeeONkmxUhmmvcAopnmJ//pPGZ1SGR3i0j9mNk71bpbuHXGLtDInsz42cVwukWV3bbZxbEpsGV3tjljN6buoL82flYyvJbsMsthwrEYm5ZajTaa9TkS7am2zX6iO9fextaZ2HqjqfvTclx7AaIz276tWkoz4yglun/EXDuceS26FmhFpzQzu6Kr/Rc5UppZX9tGD5ficnHq58jWno7m+ZG90Yz1SKmZmhuafohQ6GjXGm7RT9KjFSOnB01fpcPfqt2d7TOvrdcfGjXvU465XlR/NKNc6GifUY/TaWv0cHOWOm5f1L/dne31tJSNZouFFBSVWd0o5LRJ5T6tq71t0QxwaL8eLsVpwuNxGI4F0dcyExLNNqccU2+5amzIpMysddTGVlujNke+inxfNDJe0Uy8bvkxJrry5np+gK566LpvZpobfryB0AslqryGRCtB4AmyBb9tmEYzRjJtJPSCsinje+EsutUMBMKkhYxVEIxmwSKU7RRKCTp8N56hFhIsS49Lza21oDpqI7c50zz+x0A001Ur2aHL22e9W8tNJPklZXMmtbW+6O/2Zt5a29kqDaeVCNupY33n1vMA6iV73GbIqD2t7Rw7i+jWTP9FM815343LZTr9Nj3p1lnAsfaO/Syqc6zMXTRLOBFa29l6LKp/ezOaUZ2tjyDdNgMrx/kuum5rn2jVPrv9QuqcaOUAzMpB68pIK1I51QyEtY7561G57SEVzoxHNgKxDdEK0LZsHYvIhkbFagvMWz8bW7b1uFkJalJ+xn5dRTPUE91/rbPiE7YzG4yb/W4tu0kLTvjZL7Y703zUF36GlWn/XgvqFR79zFtetTPNN9xwA5dccgkdHePpMpONZKY5QYIECRIkSLDzEBMHuQleOpQPwh9/7NWM97///ZNtwjaRBM0JEiRIkCBBggQvQ1iewmpJXjPy3J8ZeeaBSbRoz6BYLPKLX/yC9evX87d/+7ds2bIFrTUzZszYceHdiFc3MSZBggQJEiRIkOAVCitQ2H7zNWXvo5h/xhWTbdZuxZIlS9h///353Oc+x7XXXgvAY489xgc/+MFJtiyZad6jEFkH2WFBpMPZkpAh4m2Kzok3LAlLNMuNVSJQavyvnyibXMS/zaTbuaFKxfXK7lBGKeTexhv7Ao3ItyhJRGoJYd2ylWcqBSKt2xUgUo55RTa3nm+3cMH8IObIipQDuRbOd3S98HyRTZnrpJyQmyvNJhMnHMqtvo24u5mUUfCIytrmXGH7TV+knHaOMJhjrte8ZnTdMf4j07J5MPos5PuKegMc3WKzavokk0aEy5pam3NEmDZU57LjFTkiP7a0My4fjSUpEa7HOHJsZFsm3WK/avdtzo23jYlQ/1qklbE/9GGbb1t8J9I2ZFsIga0KG63jFhCOY8oKgQ77wvDW2x9HolXEXil0EDT7BWJevAg/j/4iJeQyMZ89UiuxMjaq6qErHlppoq2Zdt1HK7O5xilYhp8fK5aEfNRQ4pDA7DmQaR0qjVhNxY9OY2/KGrN2GihES1IUV5j/c9rUm+5p3kPjGJJKo8O12FYeb8xNnoDOqOO+98fxdI3SRwu/czsbc4U0yiDQzn0eq/DQWkcr93csXzrbGYzjdTbrYlw90XulmmWjc9vTYRsutB/uAUkHTb72RBxYY7Mp28phba0fWjnN45UYxAQ5oqOy26pz7LkT1Tu2bLu/7HHc6sjvmTbOd6ij3/L4aOXVSlu38azT2+GoS8vCD59JmSCIy+gxSh9juerSGs9tb21Tdhvfc0qBENY2VUvG+qd1vLTylCf6P9Pht22jafX/2P5tRToftHGvgXbe9A7KtqLNLgmZYMfzlpansWTT2OGVjzD43IM7LPdKxkc/+lGuueYaFi9eTE9PDwAnn3wy73vf+ybZsiRo3mPwfZ9r/+9h3nb8HBadumCyzUmQIEGCBAkSTBLu3tjP7Rt3nKjDcQNs0Qy+LV8hX64KVLsITz75JHfddRfQnBjq7OykVBq/yX1PI6Fn7CHYts1nLzmWRUfOmWxTEiRIkCBBggSTiDNmTeXqIw/e4Xm2p9pelq+QavcFzY888ggnn3wyp512GieffDIPPrjnZ7V7enrYsmVL27G1a9dOOp8ZkpnmPYuI4pDLtK8RSRnTHkTdbaEJtKwf2bZJ9tCakCJa7oeQDtBC94Aw8YTdPLf181CGDd9HhBJeItuyAzqiWHTkzLkRHcK2m8vzucz4JftW5EJqxxgdHyFEyxJyaJsfQEqGNA9Dd4jUEIXTpHjoSH4uSnwiQrui5auUE/8yjerTQWDqbPUXGNpAJJuWcpq2aW2uISWkBdr12uX2Yhm80N5stin5FsnZhXbrMZSDSDoPKU27wmsJpcBx2uXsoqQuEVrqF05Lf/oB2HaTqmFbJllAq5Z3iz+EEOA4TbpD1J5Mo0k5EY6RkbIVUWKZNioJxL6KfRlda2x9ltW+7h3RK6SAhtviz/ai7eMbRKsvfR+dSRu/Swk+zf9bZQpDCEDjI3MOKmMjAm20w0MKhlAaxwuQXelYPhGaVCXhyFDXPLz/WnXHQx/LznDcOi1L92H6cMerxZQCJxyrdkRXyaUQUiAcy+ivj0k57lRraCWQoX60Vk1KhIgS8UQzT5aIKTaOV29bygewHSNrJztTE9+zYVucULZNWnrckrbVlWrK+40ZY44/flt/xPaK/APjZQKFJbAbPkLotqECNO3dBjK+6RM39Gu6ZQxGkpmxrVEbQ7ujdrYi8lfso9ayYfm070401Nvb2Vo2rNNpeX5b2yo7Qb/oQJNq8a2hLDQNiPqk1afR9dMhvaeV5tDqY7unXUO+tU9SysMJqUUp1aS9KDVxuUjaMoM3jn7SNoYKE4QeSmMBasz4b6WhOHnRlGJtwUTlxsIpWG0UqCglNoFCBcGE0oRaiVBKMsqJINvuT+WPr7P1fotkKCMpz1bddN9VWNtXrANABiZQjjB99hH0Tl3Auid3nNxpZzBr1ix+9atfUSgUWLp0KVdeeSUPPfTQbqlrW7j44ot5z3vew3/9138BsH79ej70oQ/xrne9a4/aMRGSmeYECRIkSJAgQYKXIcbONA+tfITn/njjbqtv5syZsfZzKpVCjv0VuwdwzTXXMGvWLA444ABGRkaYN28etm3z//7f/9vjtoxFEjTvQdz7xPrJNuFVid89smayTXhV4nePrZ1sE161uGfNjjOBJXjxuGdt4tfdgcSvkwfLD9peM2cexsIj37nN82+++WZOPfVUCoUCQgj8Mas/Wus4KM3n85x22mksXbp03HU8z+MDH/gAn/70p3d5m3aEFStW8J3vfIeBgQH+9Kc/sWrVKn7yk59w991373FbxiIJmvcg7n1iw2Sb8KrEXY8mQfPuwF2PrZtsE161uGftwGSb8KpEEtztHty7LvHrZMFxA5xG8zW46lGefuh72zy/p6eHxYsX89WvfnXCz6+77jq+9a1vceeddzIwMMDJJ5/MokWLKJebqUyDIODSSy/l4osv5rzzztvVTdoh3vzmNzM4OEhvby/HHXcce+21F3/84x+59NJL97gtY5FwmvckQp6ssCzDzY3kzyIZsjE8zLHkPmFZ6BTtknOt50TvXS/kzkZybXbMSzVlaUqn2RZU66Z8q85TdNmoXFw25HgKgS5Xm3W38gWhKU2nlDk34tKOTYUsJbiNlna0SI953hjubgt/LPr17AdAk18Z82cjSTVCbrHnjZNYa/JlfSNvFvKJBTTTVttWkyvbikhWDqBTjesHkQ4l/sZylMfaH/ss4n239GGrjWNlBtvSboec6FbfxoTCFk2mFkm7yC/4vukbyyJmUUoJ0WZt22qWCyUHhWiOJRFKBeqIU98qNRf9jfozSr0d+7A1/XcLt79NAjAsY9PSH0HMx2/3lQbfM9zKWFKvZUyD4SW3pE8n4mFG3OaMFUrJNdPRY0X3bYsEHbSl7Tb+lG3XJdDQIk0WSVMJSSu1ERmlVbeEqcORbTJwQoappkN5uPg9IHOOaU87vbSlrG77G/M204Y/3WqzVtG5LdJhLVJwQmokxr7YnsiH4XVkMZLH0+M4oqZck+crpGh1A7YzEb8YhDL2bgtCunE9QoR/I187ssmBbeW8htzU6NyxsnLCAZEb49QxnNmJ0q3DmHItZbTSWPYEfmkpP1E7TT+BlMa3SpmU69H/xhiBiPw7ph/tqhfy4Il58a3p7mVHyMX3ArAkosVmO+ViS420wE41y8rI1hZ+sQhU7Fdpu/FXSCzvF/5vSZC59tBDx/xijZAmPbcKRNtYh1D6sXWfRgunXoSc9rF9Gb039o7Z4xFoUBJR9qH5BIz9JC2FyDhj6jQX14Fuk5I0NkT1hdcK9zeMy5eoNFJqxijZTQjbV9gt/PU50w5les++rHjuNxOev2jRIgDuueeeCT+//vrrufrqqzn00EMB+MIXvsA3vvENbr31Vi677DKUUrznPe/h+OOPn7TMfO95z3t485vfzN13300qlWLp0qW89a1v5X/+538mxZ5WJEHzHkAQBCilKNdc1g+UEYEMdXn1mOBHwFB54otIacoFqiWAarkVR8oQBYyeZ/5XymzmAqirZiAI7SKeI+XmsbCuGC7mGo7d/oASAj08ga3RJjzPA5wwkJXNzXXh00yPlpt1tQalUiLccMOZ7yHslgeW55k6Wzb6lX2HctVjfX8ZtEYouxk0t4ixat+DUXNOvPsi8o/vI+xie4Ab+UoKdH+5PXqIgrFoQ5zaxm2kFXqkDEFL0Bz9cFAKocKNblFdLX2iB8b0yZigWch0+2ahwG/6dqg8vi+joFlmmmWkhMA3fSMt49uwTWXfplwL/aoUwg+DZi8MUC27absfmDqFaI43qxk0i/A3WTPgbQYGbWNICnD9OGgWUSToRwGwbgu89WC5Od7BHNc63JCUAs9v/oAIAnTdA6XxR82PPd2INkKGAWUAlmOCubYA0jLjVlgCv1iLy+gAhGXKAdi5cNx6KvwybvaPX67H/1ekpuwGbAiPOaMtP4QiTFR2gg1i9oiFnmAz0tg6Y38rgVKQHtXNezL6rGWDp1dujCsbBXl2RsUBF2ACHdu0ISo3UdBsZ1TbZlIxJvXyRPZGQYidHvfRuHKtfo2CJsuhzc4YYdDoh+eO02KWwpRtRUv5sf5pDezs0ZZ2tZTRWhOUG9sNmidqZ9Qvftn8OGh9dEe3eSodmB9dLZtzI/8G5Xq8eTQOmls2Q9rplnE7Rrc7KNfHjddonFqtP5yitoblg5a+bPVttBl2bDvjDcWBplH224LmVqRT1jaD5kbZH1df6/t0xt9G0KxplJs64NDyA1doUo4/4b2pA2iUJw6aY3udbfzYU5p6WbM1DMCDIJj4PKBeGiKwmhNUWwaWs2nreDrFC8Ho6CirV6/muOOOi4/Zts2RRx7JY489xmWXXcYPf/hDbrnlFtavX89tt91GV1cXP/vZz3aqvp3FZz7zGZ5++mne+9738sUvfpFFixbxhS98gbe//e171I4JoRPsdjz00EO6UCjoXC6nMT9nk9cueiV+Tfz6Snslvk38+kp6JX7dvb4F9EMPPTQubhgeHtY5rG2WFULo4eHhbcYdd999twa053nxsbVr12pAL1u2rO3ciy++WP/FX/zFLot5dgXq9bo+8cQTdaFQ0F/84hcn25wYyUzzHsB+++0HwEUXXcR//Md/TLI1ry6USiU+//nPc9111022Ka8qJH7dfUh8u3uQ+HX3IPHr7sOmTZs48MAD4xihFVJKqgS8V8whNWb7mYvi23r9i1a2iFQxRkZG2o4PDw8ze/bsF2f8LsZnP/vZcccWLlzI6tWraTQa8efXhmm1JwtJ0LwHYIVL1alUKh60CXYdEr/uHiR+3X1IfLt7kPh19yDx6+5BlOHO2o5gc15apFv4JqtVled1dZvnbw9dXV3Mnz+fhx9+mBNPPBEw2YqXLFnCZZddtlPX3FX4/e9/P+HxAw44IP5sLKVrMpAEzQkSJEiQIEGCBC9DZC1BuiVYTAmBHQhD0pgAQRDgeR6uazjwjUYD3/djzeXFixdz3XXXceaZZ7LvvvvyxS9+EcdxuOCCC/ZEc7aJl4Oc3AtBIjm3h+D7PuvWrePOO++cbFNedTjzzDMn24RXJRK/7j4kvt09SPy6e5D4ddfjzjvv5POf//wOz7OFwGl5HWjleYPTt83zb7zxRrLZbKyi0dHRQTab5b777gPg6quv5oorruDss8+mr6+P3//+99xxxx10dHTsmoa9yiG01tv4vZJgV6FYLDJ37lyWLVs26byhVxuKxSJAsnS4i5H4dfch8e3uQeLX3YPEr7sPGzZsYM6cOYyOjo7zb7FYpKuri2uy+5BpoWc8G1RY7lf5UzA6YblXKmbNmsXGjRt3eN5ee+3F2rWTl3groWckSJAgQYIECRK8DJGShpIRYaHsYD87y58qo5No1a7H8PAw3/72t9nRPO7o6OS2OwmadwEGBgbYf//9+Zd/+Rfe9773TbY5CRIkSJAgQYJXARwp2oLm5X6FJ71t5HN4BWP69OkvSBlj5syZe8CabSMJmncBPv/5z3Pqqafu8uvef//9LF68mMcee2yH0jKVSoWDDz6Y3/zmNyxYsGCX25IgQYIECRIk2LPIWoJsS9B8lNXBQaksvx95dc00r169erJNeEFINgK+RCxbtoxyucxRRx21y6/9oQ99iM9//vMvSIsxn8/z8Y9/nKuuumqX25EgQYIECRIk2PNICUFKNl9P+xV+WhuYbLNes3hNB80333wzp556KoVCASEEvu+3fa615pprrmHWrFnk83lOO+00li5tT1/593//9y9oB+yLxd13383mzZs5//zzX3CZyy+/nN/+9rc8++yzu9yeBAkmE+7o+C+Jeq02CZYkSJAgwZ6DI9uD5mMynVzaMW2yzXrN4jUdNPf09LB48WK++tWvTvj5ddddx7e+9S3uvPNOBgYGOPnkk1m0aBHlsuET3XHHHSxYsIC99tprl9v2k5/8hLPOOisWPfc8j6uuuooZM2YwdepUPvnJT3LKKafwuc99rq09xx57LLfeeusutydBggQJEiRIsGeRGhM0R68Ek4PXdNC8aNEiLrnkEvbZZ58JP7/++uu5+uqrOfTQQ8lms3zhC1/Add04KH3kkUd48MEHOeecc7jpppv46le/yl133bVLbPvzn//MwoUL4/f/+I//yE9/+lPuvfde1q9fTzqd5sEHHxxX7rDDDuPhhx/eJTYkSJAgQYIECSYPjpRtrye9Cv9X6Z9ss16zeE0HzdvD6Ogoq1ev5rjjjouP2bbNkUceyWOPPQYYasY999zDHXfcwbvf/W4++tGPvmAR+EaltN3Ph4aG6Orqit//7//+L1dddRUHHHAA6XSaz3zmM/T29o4r19XVxdDQ0Auy4bWMDcOVyTYhwYtAqmvKuGOZbHYSLEmQIEGCPQcra2O3vI7r7uG905J8D5OFRD1jG4gE3bu7u9uO9/T0xJ+1opUmMRE6OjqoVCpx2Ua1TDrYth5hV1cXW7Zsic/fsGEDU6dObat71qxZNBqNtmP9/f10dnZOaOOrEaXS9n98bAuVUpWiFexia1492Fm/JtgxEt/uHiR+3T1I/Lr7UKnsePLGciSW1T6/aW0ndkiwe5EEzdtAlGVnZGSk7fjw8PBOZfWr1+v84z/+I/l8HoBTTz6JN77pvG2ef8QRR/D000/H72fOnNmWBUcpNWH2nGXLlnH22We/aPsSJEiQIEGCBLsXv/3tb2Ma5wsLmq14b1N8LOEITBqSoHkb6OrqYv78+Tz88MOceOKJAPi+z5IlS7jssste9PXq9TqO43D++eezaNEiGpUS6XznNs9/5zvfyeWXX04+n8eyLN7znvfwta99jfPPP5/58+fz5S9/maGhIdLpdFuA/8gjj/CNb3zjVZNa84Xixba3FFgUCvndZM2rB6+1cbQnkfh29yDx6+5B4tddg7e97W287W1v48477+QnP/nJDs+XKYlsCZofGh3hj6PDu9PEBNvBa/r3ShAE1Ot1XNcFoNFoUK/XUUoBsHjxYq677jqWLl1KrVbjmmuu4f9n773DLDnqe/23qsPJcyZsTlrtKidYSSiigNISjDFXNkjGxsL+2cbytTG2yLosSCIYi2Bs42vA5CDANlwQKOeAcpZW0uY8O7MTTj4dqur3R/U5s7PapGWDxPb7PP3MnO7q7urqqjp1uj/1+Xqex9vf/vaXfS7XdVmyZAmLFy8G2OmAGeD8889n2rRp/OIXvwDgIx/5CG95y1s466yzmD17Nq1Wi0WLFhHoiVm03/nOdzj//PM56qijXnb+9oSgNr73jlXdvzrs2X3pgDnl1c1gJdXlp6TsjHe84x0ccsghO9y+fPlyfN/nve99737MlWXx4sUsWbJkl+kcV1qJRrKcPqWf9+7kmlL2LQf1oPm73/0uuVyuO5AtFovkcjnuvvtuAK644gouu+wyLrjgAgYGBrjnnnu48cYbKRaLL/tccRzzyU9+kptuumm39/mXf/kXlixZgtYa3/f50pe+xObNmxkeHubqq69m1apVzJ5r7e4ajQZf+MIX+PznP/+y85aSkpKSkvLbxrHHHsu6deu6NrHb8pGPfIRcLrdb4Zv3NjfddNNuxXhwfGe7S8qBQRhjUkX5PqZarTJ37lyee+65PdJDg52Mceedd7J48WLCMGTJkiV861vf4u5HnuTYQ+fs5RzvHkFtnEypd+8cqzpKpuelbiC7ojPhMX11uHdJy3XfsbfKdrDSYEY5fWPSIa2z+4ZXc7n+93//N7//+7/PQw89xOte97pJ2x544AFOP/10PvOZz/DhD3/4gORvw4YNzJkzh0ql8pLyrVarlMtlbjv/LIruZCVtPY45/7Z7trtfyr7loH7SvD/ZkyfNW6O15uqrr2bKlCnMnj2bhx56iF/96leUy717N6MpKSkpKSm/BXRiHTz33HMv2faBD3yAQw45hL/7u7/bz7my7PaTZtexkwG3Xtz0SfOBIp0IuJ/oaJp39KR5V09ty+UyDz300D7K3Z6xs/yO1pr0l/K7f6w9eMr8SmHTeIOZvekTv5T9S/qUOSVl5xx22GFkMhmeffbZSet/9rOfce+99/KDH/yAbDZ7QPK2ePFijjvuOL72ta/tNJ3jWy1zh/s3b+GuTWlwkwNFOmjeT3SeNF988cVdDXVKSkpKSsqrCWNgX1s3l0og9kKkaMdxOOqooyY9aY7jmA9/+MOceuqpXHLJJb/5SfaQ3XXPsIPmiSfLZ82dzqIZ/fx45bp9ki+tNeeccw5Lly7lve99L9dcc80+Oc+rlXTQvJ/Y1ZPmlJSUlJSUVzq1GmwVrHafUKnA3pLqHnfccdx///3dz1/72td44YUXuO+++xDJyDwMQ6655hquv/56lFLU63UuueQSPvWpT3X3W7p0KUuWLOHZZ5/F932klLzvfe/j3e9+9x7la7efNCfyjEnrjN6jc+4OUkp+8IMfcNttt7F8+fJ9dp5XK+mgOSUlJSUlJWW3KJXsoHZfn2Nvceyxx/KDH/yAZrOJ1ppPfvKT/MEf/AFnnHFGN80HPvABqtUqDzzwAL7v0263WblyZXf7I488wtve9ja+/OUvc/HFFwOwbt06fvWrX+29jO4AJ+vh+N7kdft4NtrcuXP37QlexaQTAfcTW08EDCtbDnR2fiPG680dbts0br1jO3rmnaU9EKwZ2b710NZsm+dmq73T9PtCz1xvtvb6MV/p7C2v7pteGNorxzmYGK6+stppyisXIexT4H257A1pRofjjjsOYwxLly7lc5/7HOPj4/zjP/7jpDTXX389F154Ib7vA5DNZjnmmGMAG8/hXe96F9dcc013wAx2YPmXf/mXe5yv3Z0IKH0XZ5tF+jt+3nnddddx1lln0dPTgxCCOI4nbTfGsGTJEmbNmkWhUODss8/mmWee2ePrONhIB837iW2Dm6SkpKSkpKTsWzoOGrfddhtf+MIX+Ju/+RsOPfTQSWne+ta38p73vIc3velNfPGLX2RoaOKH90033US1Wt2jSMA7Y3eDm0jXRXrbLO6OB819fX1cfvnlfOlLX9ru9muvvZZvfOMb3HTTTWzZsoUzzzyTxYsX79DLOmUy6aA5JSUlJSUl5beS+fPnUygUWLJkCblcjiuvvPIlab70pS/xzDPP8Ja3vIX/+Z//4bjjjmNszIaqfvTRRznxxBNxdzJQ3ZdI30X63jbLjvOyePFiLr30UhYsWLDd7V/5yle44oorOP7448nlclx99dWEYchPf/rTfXUJv1Wkg+b9zAtDVfzyFNqtya/fd2TfFo7bX7zbpt9d1o9O/HrUKx/ZZfrmTz63yzS9xR1byW0rVdhZ2r1F9PiNO90eb3yh+385M7nKd+QkW9PJc2dbPrf/LYmK+dx+P+eBpmM7+Pzm6m90nMVHTtsb2dlt2s1XRjjrl9tHtJsNgoa1QZjas+/baUrKgUAIwTHHHEO73eYTn/gE5R3MYjz88MP53//7f3PrrbeyZcsW1qxZA9igLlrvu4l3u8Lx3O0ue0KlUmH16tWccsop3XWu67Jo0SIef/zx7rpLL72Uf/qnf+L73/8+F1xwAVEU/cbX8dtCOhFwP9HRNJ9y/hs58p3/60BnJyUlJSUl5aBgZzEOfvnLX3L22WdTKpUwxvD1r3+dww47jGOPPRaAt7zlLSxZsoR7772X17/+9QCsXLmShx566DeyrNtty7mMj5P1uXPZOu5cbm3mwljt0Tk70R17e3snre/r6+tuA/jhD3+4R8c/GEgHzfuJjqa57u3FacEpKSkpKSkpe8wvf/lL3ve+95HL5XAch+OPP55bb70Vz7OOFYcddhg/+9nP+PCHP0y1WiWKIgYGBrYr83g57H5wEzv57/xjD+X8Y60Wu9YO+fZDz+50v+3RCbk9Pj4+af3Y2Fhqh7ubpIPmlJSUlJSUlIOSr3zlK7tMc+6553Lvvffuh9y8FOk5yK3kGLc/v5qbn125kz12TLlcZv78+Tz88MOcfvrpgH0L/sQTT+z1iY6/raSa5v3MkdPsL71s7qWa1Q1jDZYPTw61ZLzcDtPvjFuX2TCbc/qLXL90MwBywcnAhIXa2tGXzpbN/8EHX9Z5tkW/eN+kz+1WC/XcnTtM3261dmkDt7X9mlrzJAArt9hyih6/EW/RG7ebtoM760iC2jj1ZoucCQFYley/M7u4bbfF67f/y76jJa00Xr7uPBpeu1vpdmTH1mrv3A5vWzaM7R397cs978vhqOkvL6rBnlxTR8u7N8jmXxnhrF9uH5HNF8gU0jdfKQee33Qew28zL3HPcB2E3PHQTSlFu90mDO13XRAEtNvtri778ssv59prr+WZZ56h1WqxZMkSPM/j7W9/+365nlc76aB5P7G1T3NKSkpKSkrKwcvu+zTb4Cad5cLXHMHHLz5vh+m/+93vksvluva2xWKRXC7H3XffDcAVV1zBZZddxgUXXMDAwAD33HMPN954I8Vice9c2G85qTxjP5GG0U5JSUlJSUmBl6FpztqJgB1ufXo5Nzz+4g7TX3bZZVx22WU73C6E4KqrruKqq6562XlOSZ80p6SkpKSkpKS8IpGuM0mecdGJR/HJSy860Nk6aEkHzfuZeP2zLBuyesqOrjIaWg3A7L4Ch021GkO16jGAruYwfvSX2z1e51gA0cM/7/6/oG/CW/i8Z77d1eOqdU+TWfUA968emXTzt+dXPF5vEm1e1dXTbh1qt5P+rhVbCLesZ6TWpN1qIY84s5um3myRzeVwjjl30nGf3lThuUGrYcvmchwyMPm1UOfaO2SqG7vncw55jb2+KbZcvEVvJGhUu6Gvd+RvnCn1Usznun7Yh06Z0HJ29M4dwtGNwEvDZ7tzjiWoV1g7Wp+ko+1oScuFHEOVHetrO9ryrfGmznvJurtWbEEvf2By/hMP423JZSd7SNe+84ntpuvoymf3vVR/+93H1m93n52x7Xl3l/WjdZ7eVNnh9q21xrujEdfLH+D6F7cQP/pLgvr2j7s9/+L9qeXdVRj2PWFr7/HfBqrf/PiBzsIes6O5Dim7z8udx3AwIVx/0nLrM6v4xE9uP9DZOmhJB80pKSkpKSkpKa9AhOdNWi563XFc9e63HuhsHbSkmuaUlJSUlJSUlFcirgfuhKb5lsee41cPPX0AM3Rwkz5p3k903DNuvvO+XSdOSUlJSUlJ+a1ld90zhOMh3InlolNew1XvuXg/5DBle6SD5v1Exz3jzX/0Fxw+rUQ4upF7NlqtozdtPsAkrWc8dSFq1WMEtXEAVGWku2242iTe+AK3Lx/m8Gmlrs+x97rfJd60DJjQ/AZ3fh/5O3+DO8eGBHXmHg/S4Yz5AzhSEG5ZT7R5FTN7CwS3fpNoaHXXM3N1JcSbfiijOsPzm6tM7cl389DxMJ7fm8WpDXHn6vFJPrFBvcJgI95uWRw/s8wxM3q6XtJgta4dPatz6Ind9cuHa3gzFk7yTF4/Wu9qogEyhR56izZvHe/pbfWtnTJqt1pEw2snbe/onQGiwRX4/bNQz9xGPmd1uze9MGSP2ahhHJ95/cWuNvipjZPPM628Y8/e3zl6+m7pdF87o0C8eS16xUOEW16e3rj07k9sd/22uvKt+eMT52x3fTg+uF0v7w7xhqWTrufhtWO7zN+c/iLHzywDVrve8cvukCmUuh7c1z0ztF098tbIw07jL089BPekt5AplrvrH1gz4WvdqZfrO3Xj1m/uMp+wfS30ntCpRx1+E4/rnz6zCbDe4690tr23MOGzvi0973n1zuTv9K0pe8725tQcSPaWD327uePrWrx4MUuWLNnlMYSf3e6ScmBI5RkpKSkpKSkpKa9ArJZ5Qp5x80NP8sv7Hj2AOTq4SZ80/waMjY1xxhlncM4553DyySfzve9970BnKSUlJSUlJeW3BdebtFx0xslcfXka8vpAkT5p/g0ol8vcfffduK5LpVLh6KOP5o/+6I8OdLZSUlJSUlJSfgvoaJq3XZdyYEifNP8GSClxXfu7o16vc/zxx+9yn45G1e+fxbn5LYSjG4mfvJllQzWOn1km3rSMtaN1HhqK2VQ+gkypl2hwBZnz3t09Rl9tDQjJOaH1B93a59ideXj3/+jhn5M59108uNHqqtTqJwAYn30Swa3fpBVr/Clz0IV+6xV92v/CmzYf3xEAvHZ2L89uqjKzt8BCJjTVMKHlPWSgiHPoiVx8/CyGq82uZjFTLHc9p7eHWvMkZ013WD5co9lqM6e/SKZQIhpcQbR5VTfdrKLtHP776Y3ddXP6ixwzw/p6qvXPddc3f/QZ5vUXWTtaZzSe/HuwU0bPbgnwps7D3PsjwOpwt/ZW9mYsBEBPW9hdt/jIabSbDTKFEltaatJxT5hVZls6ns9b+1p3yD74k1167PqOwJlzJMbN4E/Zvt64w7YewPevHmH8qx/d6T7AJJ9poKuFn5SP3hnM67fltj2fcHf20ZQLEzr2183rY+WWWld3uzXRgz/r6so7OIeeyJzY6sWj4bXd9R09/l+eegjuyMqXHCscH9ruNbXabcLRjTy7qcprn7X3d2tt8zTHllXmgvdsd/9t2Vqjv7cIqqN77HEN8PbjZr6s9Fv7j++MnWnX95StvdA7dHzWd5egUaN1/b/trSy9InlhqLrrRK9ydqRl77D1nJWtiTcs3RfZ2Snh6MbfqI1uTTa/4zkuu4udAOhvs6SD5gPFQT1ovu666zjrrLPo6elBCEEcT564ZoxhyZIlzJo1i0KhwNlnn80zzzwzKc3w8DBnn302J5xwAhdfnM5oTUlJSUlJSdk7iEx20nLzw09x5b9/90Bn66DloB409/X1cfnll/OlL31pu9uvvfZavvGNb3DTTTexZcsWzjzzTBYvXky9PvFUZurUqdx9990sXbqUa665htHR0e0eKyUlJSUlJSXl5bC13ZxwPd547pl8+orLD3S2Dl5MirnjjjsMYKIomrR+/vz55ktf+lL3cxRFZsqUKeY73/mOMcaYdrvd3dZoNMxhhx1mms3mS45fqVRMT0+PWb9+vd2vXjVrRmrGGGOqjaZZOlgxNz6/2RhjzMrhqjHGmOc3V4wxxjy0ZtSM15smfvaO7vGCkQ2Tjr8uOVaHcPMqs3m8vt1rbTRbpnXDf5joiZtMOLTGtGvjpl2vmhc3V82X7l3RPf/26BxTLX/QBOPDJhjdZIwxZku1YTaOTT7f+lH7edN43QT3/9d2j/fh6581SwcrJhheZ4Lx4e76Hz1pry9a/5wJN6/qrl8xXO2eZ/QrHzLGGDP0q/80lUqlm2a83tzuNbRr42bNSM2Em5Z313X+f2jNqD1WpbHDazfG3itjjFEv3GuMMd3r3xGtberC4+vHuvcuHFpjoo0vTtoer3p80uc7lw9P+hw9cdP2z/PLr5hlQzu+b7tDtP657v/LhqqmUqmYSqVi2tWxne+31TVse73b0ikvtfzB7rpw8yoTbl5lnt9cMd9/bJ1pNZum2WqZVsPe53bdXtf60bp5fP2YqTUmzhE/fevEcR76f8aYiXazYrhqlg1Vu+k/efPzk/aNHrl+p3nd2zSarW796ZTty6VTFtvyR999+CXr7lu1xbSrYybctNy061UTr33qZZ2rU/6vBpqtljFmz8v1lc6u2uC+Zn+U69b9/8vhkbVjL3ufznft3mBH37O7y/r16w2w3fKtVCoGMGOP32bU8gcnLWOP37bD/VL2LQf1k+adUalUWL16Naecckp3neu6LFq0iMcffxyAJ598krPPPps3vOENXHDBBVx99dXk9oEOMiUlJSUlJeXgw0h3u0vKgeGAl/zZZ5+9W+my2Sw333zzPs7NBNWqnZzR29s7aX1fX1932ymnnMLdd9+9W8crFos0Gg2q1SpBs04jFFRdTb3VplmPCJoB1WqWRr1O1Tc06zWqWWg1atT8CN0McJLzhvU6vjsxeaRRb1B1dfdzVG9QD7NkhXpJPlrtNiJQuK0AU2+gYwlC0myCbjdo1LNUfbPda6jXmmSFQjdaxJk66BjfqVKvtwiVoSonzteoNak6inqtiduO8KvbmewSNu11Og1wY3xhvSijVp1qtUrcaGJcjZetJtdZRzmSqlTUFDjVKvUwJqxNBFGoNds02vFLriFo1GhEDtWwgZfkJarb/1uNGtWqQ73WIsP2A7IA1FttTByhm21ktUpYq+M7O57E0263CaNoouwbNaqqhe9WieoNRBziblUuqtHq3mOAdqNOtTrhzxm3gknpu+lCZcsxs/37tjvEjWb32M16nVpojxUITWYnv63j+sR+217vtnTKSzdayK3uAUATj6jVoFpLrtdowlgRNOtklKFRa9JqR9TcEB3bc6it2kTUCvGq1W67adTrGAM17aLjCBM0qdVq3X3jVrjdstxXtNptlAETR9RqLw36sTt0ymJbnLjV7ZM6tBt1qr5ENhtoariN5qS6tSvarSZh/NL+45VIu90mCsM9LtdXOkG9ttM2uK/ZH+Ua1urd/v/lYPvul1c2QbNOtbp3Jvp1vhP3lEZjN4K6SMcu265LOSAc8EHzgw8+yEc/uvPZ/sYYvvCFL+ynHFl6eqw7w/j4+KT1Y2NjzJ49+2Ufr91u85nPfIZCoUAchZz0+jfwzre9ZW9kNSUlJSUlJeVVwK233srtt98O7N6g2bg+xt0quMmd93L9LXfss/wdaBzHQalX8A/2A60PKRaLu5Wut7d3n+VhZ5rmf/7nf+5+jqLITJ06tatp3l0qlYrJ5/Pmj/7kMvPL7321q6mKNjzfTROMbDAvbq6acGiNUS/ca+o/uNq0b/u2CUY3mfZt3zbGGNO+43umXRu3/9fGzbObKuZjv3quq48OB1d2txkzofWL1j9n1LJfm2ar1dXxtisjxhhj1Aqrh4w2vmii9c91tZ4dDeR/PrzGGGP1vCuGq+aHT6yfdOxfLR3sHi9e/YTZPF434eDKrhZ55XDVtCsjJnzgp91rbderXX1urdE0wcgG8+vVI+aRtWMmHFzZPXZHOxxuXmWarZaJ1j9n2pURM1Kd0B4/vWqDWfMvH+pqejv7dLht2VC3nNu1cROMbjLRumdM/OwdXe307tBotszqLbWuhu6HT6w3T20c724fqzXM+Nc/Numedsp1a8LBleapjePmtmVDk9av3lLr6nJ3RbTh+V3qryelf+R6077je6bVqJvGj//xJdvvW7XlJce/7ZnVplKpvEQvb4yZVP7bHme83jTtW76x3e1bkv3CzatMq1E38eonjDHGxEvvnqQNXDpYmaSpXTZUNf/11Iaunr2TX7XiYfOfD68x963aYhpNWz+e3VQxzVbLBPf/V1dj3a6OmV+vHjHv+OaD5vuPretq2I0xk/7/Tdhae789mj/9gr3Wp281m3/6L10t4rZzAXbE9u5bMD5sHrjg3Eka8e62kQ2mfcs3zC+eG+yu++v/enK7ed1dPfzK4apZvaX2Eq1995xbafyrW+nHd6TF3pvUf3D1LrW3L1fXvS/Z0TyPnbGjOQ3bTbsXNft7S9Pc6ZvjlY9OWr9sqDqpvmyP6reXdP8fqzVM9NgN3c9LByfnbeu+o33H94wxtp2N1XavzxyrNUwwvG630u7ovN96ZO0u0994443mz//8z3epaR554TH7/bzVMvLCY/tU03zdddeZU0891Zx22mnmuuuu2yfn2BFCiP16vpfLAX/SvGHDht1Kt2bNmr1+bqUUURQRhiEAQRAQxzG+7yOl5PLLL+faa6/lvPPOY+HChVxzzTV4nsfb3/72l30u13X5wIc/yjGlmNv2viVqSkpKSkpKyquExYsXc9xxx/G1r31tp+mMdF6iYTb7UJ5RqVT45Cc/yUMPPYQQgte97nW88Y1vpFx+aUyCfYEQYr+cZ0/Zq0IprTU33XQTt912227v09PTw09+8pNdPo7vyCX2Jt/97nfJ5XIsXrwYsLrjXC7X1SlfccUVXHbZZVxwwQUMDAxwzz33cOONN1IsFnd22O0SxzH/9NlPc/Od9+7Va0hJSUlJSUl5dXHTTTfxyU9+ctcJO5rmbZd9xIMPPsiZZ55JsVikUChw5pln8uCDD+6z873a2KuD5j/8wz/kl7/8JR/84AeJ45gvf/nLu7XfZZddxrx58/jEJz7Bxo0bd73DXuKyyy7DGPOS5dxzzwXsL56rrrqKwcFBms0md999925F/dsenSfNF537+r14BSkpKSkpKSmvNhYvXsySJUt2mc5IF+Nss+zEPeM3Ddo2MjLCwMBA93N/fz8jI5MjAh/M7NVB8+bNm/nyl79MT08Pruty/fXX79Z+GzZs4IorruBHP/oR8+fP5+KLL35ZT6tfDcRxzPs+uoSbf/kLzi8M8+8PrEb7BVo//zKNH16Ds3GpDV9tNMGLT5A9/nTcGfPw+2YgFl1E62dfJN60ikzRhtoWj9/AjILLlefMY15/keo3P079f/6DZquNaNfQL96H8+jPiTe+gFn/AlumHk9m4zMYNwOACJs0W23CGUcTDa5AezmWylmYOcewaksN7RcJRzfypyfPA6Cw6Wnmjz/L28fuBCCXzfLspirnT1PUmi1kq0I8ZQHTygU+9EADjOGqW16gFRt+uqqFd+rv0Wq3Caqj3Lepjfuai4g2r6KYz+H3z2LRgMPsHg+d70OGTeJHf4nJFLnh+c0YL0tm7aO4s48G6ZB3DMuG7Izuef1Fsu/4e369rsKyoRrxtMN5YahK87+vBeCMWXnip+7i1mXDyPoWNukiujgV55hzeXaTdRPYOtRyJ2xrJ7RtNLiCerPF6krITDPOSXN7Abg4fJTjZ5ZpN+1Ejqw0ZN/5AeK+eTy/uYpe+QjxpmWonhnd8OXquTvxph/K8TPLnHfY1O45R2pNWrHmX9rHds+p1j3NaM2G4f7xUxt5dN14N70760ic7bzB6pTJ1ly/dDPuSW/hu/nXc8PKKtGb/waA5wYTx4tWizPmD1Brtghq9hy/Gi9T8l3arSbfePSl8qnvPjX40pMDZ8wfICN0N0x10Kjx1MYKg5UG9WaLLa2YHz+1kWZhOvLxXxIPzAdA+DlCZRipNXl+c5VDCwa3shG9/AFa7Tbz19/HxcfPYmZvgejBn3HG/AHCyhbU8Hou61nHYX1ZlDFsyM5lVtFlsB4Rv/YtPD3c5tuPrmNpFbQxfHzxUVx45+eZXvS4ddkwAK8ptth4zV91w4Tf9MIQqz/0JzRbba57cuLa//m+l4bz3hpv2vzu/9sL/evOXsjf/79nUHOORxYmXnP2Ozt2G9mayoV/zcNrx7qfH147xmcfGqH03f9H5eb/IRpey3cfWw/Y8O1hto+njvg9Tp5l34qpZ27jXy8+gS/cswJVnNq9/uD273DY1FI3jHqrbUON177zCQD0i/d1z9n7k0/h/us/cNp0j/r3r+rW/aA2TrR5FX7fDPTyBwjvuY7csnt47t1vBSBTKNH8yee6xxmqNHh+8zZuH80GeuUj3bDf0eAKANZd+WeT0gDUv3/VpH1bP/8yhUuv3GUZhlMP32UasG0irGyZtG7NSL3bZnaEev6eHW7rXG+7Za9v/Jg3drfpFQ+hVzz0ktD21W9+fNJn9zUX2fQv3sfy4RrR5lU7voajz+v+32y1d5rvXVH/DfYfuvZ9AMRP3kw8bNuTc+iJgL3uVrvN3Kd/SiaodPcJ77mu+796xo4D3Hd8mJFak8/esYx8OM6vMq/tpqkFCrXuacLRjcSblpHNF/j3B1YT3PpNMue+i5Xv/0Nm9hboLeZ3mtdVV/yxPX57FJOzbXTtaL3bJgDuWjFRL+JHf0k0vLb7eeuQ2Wcd0jtxDdsJIX790s386t8/zZIPvn+neQJAuttfdsBvGrStv7+fsbGJvmZsbGzSIHpvsKfGDh/72Mf2aj72hL06aPY8j3q93tWkdLTCu6K3t5f3v//9LF26lBtvvBHHcXjzm9/MUUcdxT//8z9TqVR2fZBXOK7r8va/fD+LX3/KrhOnpKSkpKSk/NZy0Rkn8/G//+tdpjOOh3H8bRZvh+kXL17MpZdeyoIFC7a7/Stf+QpXXHEFxx9/PLlcjquvvpowDPnpT38KwGmnnca9995Ls9mk2Wxy3333ceqpp+7ZRe6A5cuX79F+Tz/99F7Nx56wVwfNH/nIRzj//PPZuHEj3/zmN5Hy5R/+vPPO48c//jFr167lj//4j7n22muZM2fO3szmASGOY376H1/kpnsfOtBZSUlJSUlJSTmA3Hz/I1z1hX/bdcK9qGnenaBt5XKZK6+8kvPOO4/zzz+fK6+8cq9NArzvvvv45Cc/ySOPPMJVV13FVVddteudXmHskXvGr371K9785je/ZP0b3vAGfvSjH3Hdddexdu1avv/97+9xxlatWsULL7zAyMgIM2fO3OPjvFLoPmme22DPQ1CkpKSkpKSkvNq56IyTOe6UM/nPH/xkp+m2FwFwTyMC7k7QNoBLL72USy+9dI/OsTPOPPNMzjzzTAYHB/n4xz++6x22wZgDP3raoyfNl1xyyaTPP/vZz7r/z58/nw9/+MMsWbLkZQ92G40G//Ef/8GiRYt4/etfz+joKP/1X/+1x4/yX2m8dUGZ+JCTWJudx//nPIXJlfEXvYHorX9Pbf7prB5vI4dWoGtjtJ/+NT+JDido1JBrnyJzwllkjjqZFe+7BHfm4ThzjqDU3kI2X2Cw0sCfPpPCJX9HqDQyamH8AuPHvYXh/BzUCYsZGF/O0MDR4Lio5+7EHV+PP7YGGTbA8VgW9XBktoVT2UgrNtxfL7E8tHrISqNFPGUB8rDT8OYdSbvV4v7VI7RjzSbTQyascf1wFmd8PbVmi8+dJNBP38mHX9fH3B6PNx3WT/zoL3l8sMHm2Oe10ws8sGaU9c4UhqtNogd/hogD+pwYZ/kDOI0RomPPJ5p6GOfNzfNcM0s06zjazQarGgJ3bC3z8prblw9TbbZpRIZz5veScwWyVWGh2kz9gr9itNakGkHtjD/i7KE7eLjdSzkj4enbCe/7MWctGCDcsp5mpLh12TCVRgu19AGGq002VAMA1rnT+PFzwyx44jqa2X7UuqfRKx9h3aFvIKiNIwOrI755dY1MsUw2l2NzPeTZ3OE0euZQVQ660E/zR59h+ZQTufKGpSwbqrFsqEZw5/cZrTXp3fI8QsB7T57NDc9v5nPPKm6oT6Me2SiPbzs0T8GXtFstfvzURtTqJxgPFOH4EJvGG1QaLX76zCYWbHmUkVqTkVqToF7hhaEqi6cEPLFhnN85cgpPbKigv2Nnax+26hYA1tQilg3VcKVAuVnCLev53WNmcFi4lsGm5r2nzOH5zVWeG6wSPfxz1LqnOXZaia8/tKar01RrnuTWZcP81X89iYgDwsoWRmpNNragN+swo1xgTSVi2n9/mnMPKZMLK7gz5iOiFvGTN7Nl4Ghm1lZQXnkvh488hrtlFUuZRjTzWEZbCtE7jUfXjdP+1b+jjz6bZquNbIxgjno9SAdXCoabMfVQ04gMs/yIRqRpx5rXzCjRjjVF32VawaXn+BPYXI84aiBH/OTNPNfKM/UvPkTl13fyg8fXc9ZT32LWBz5FPpflwgV93bb71/MDlg3VWDNSR61+gh88bvXD96wcYeX7/5CnNloJ2frROnq1fYWoVz7C733d1ienbxqfO8nWT448nbA6ylW3vIB8fiKiaEdDrZ67k02fsa9tO9re3qzDorGHiR7+OdHDP2eRt4WPzd7Egh6H4u9fjnr0Rv7XC99i6Nr3UVZV1A8/xSJ3iH7TIBzdSLTwDIJ6hb/JPovTGOGs6fZJ1QcrixiuNlEF2xZkHPDz5wbJn3gW+sX7kEeciV7xEJVGC/nuJcz8oz/DqWyEt3+AbL6Aeu5OZGsc1j5NeM91qPIsnIEZtA8/iyM/+UmizasIx4fInLKY6OGfs+GTf0nZiZlfclDP3MaqLTXCX/83z45E6MoWivmc1S4LSXDn9xlYdDTtZoPmTz5nz7f6CYrv+jjB7d8BoPHDa6i9uGJSP7u1hnTlFts+W+02uezuRYEzN/1f/PKUSesOGShyyB1fJrj1m7wwVJ103HB0I+HYIM5RZxGvfxb1/D3olY9M2n9Oyb5Ov3lVhXjDUsqP/4x6s8Xty4eRC08hLs/Ckbb+PL+5yvrROtm3/gVPbBhHrZuoTwDx1IXMuPmLeNMP7c5DAKt17ujpi/lcd30+99LrfmDNKNHgCqKh1d11Hc14vP7ZSWmL29m/9XM7yX9rfXn8+I3d/ztzSqZd8c9224YV+Ge+gw1jDYLbv8O6K/8MEzQZu/b9iBPOo/mTL7P6Q3/CyL9+gNETrBa++s2PE658lvaNX8UffJ7wXz/AmYf04/fP4i36WZqtNvHjN/KaskIVBqh5vWz6t88ydO37uPSpr6LGrG6/fNXXCUetwUC4Zf2kfI79+4cB2+amXPVVjpxaRBcG+P7SMR5dN86qMVtvBisNood/zjkLp3TL3D3pLYhwQoe+ckuNVVtsv/7koL2Pf3bd49zanola8yQ/fmpjt4wXzSiyetbpjOZnvaRst0Uj0AhuuuVWPvChD/GBD32I/7MbEwi3x86Ctu0Lh7Id8Xd/93d7tN+VV+567sK+Zo8GzduO9v/0T/900uftPYXeGU899RSXX345s2bN4uMf/zhvfOMbWb58Oddffz1vfvObX/G+fbtDHMdc8+nPcMuttx7orKSkpKSkpKQcQO65/Vau/cdP7zKdMgZlDOddcAGf/uw/ct75F7CnD1zL5TLz58/n4Ycf7q6L45gnnniCRYsW7dlB94Ajjzxyj/bb29rqPWGPnvFvO4jddhB9770vz4v4ta99LWeccQZf+cpX+IM/+AN8/+XHoH+l47ouV370I8yaNZMNrQOdm5SUlJSUlJQDxVnnXcCiE47j+9/+1k7TRcoQqYkx1jnnXcCik0/ha1/9j+2m359B214un/rUp7Y7fhRC8NGPfnSv77cvEGYPRCI9PT2T9C/9/f2Mjo7ucPuueOqppzjhhBNebjZeNVSrVebOnctfffV6rnzryXg6pKo96qEi1IYXtjQ5ekqBjbWA04fuIlj2NEOPPs8hH1xCu28+oTJsqscceve/Mfb8ama8849YP3URc+orWJVfwJyCRD55Ixx+KjJqEffOwdz2DcR5l+HUhpCtCq1pR+LFLRASd90T6P651t6tOUbQMws/akDURhjN83GZI3MBzzV8ji4LZHOMIW8qOVfgOYJQGSJtiJVhmh7HPHU7vO53UW4Wtz2OCBro0jREHICOQcXowgD+uscYn7mIu9ZUeNOCHkQcoLw8lUDR54M7uppoykJWjoUs7PXwVj2IGhvCnHARTmUDavkTuIccTTx1IWhF5OZoj25GRC1K/VNxqptoDRyGI23j8kZWYdysvc52lfHMFKSAIiHe0IuMTzsOzxHI//d5VpzzNxzDIHc3+zljdpFmbMi7gk2NmDlmDIymlptGoAybGxFH9GfRxrChHjE15zLUjDk0r2nLDLmoxvo4xxwvAKMZFQUcIVgx1ubYqTk2N2LmvXgDj825gJOyFa7b4PHOQ12CTJnc2GraffPJjq5kY/4QZqpRdK7M6qZgwZo7EbMOR/XOQT53B8/POYdZRQ9XCjJCc8faOm86anrXImmoETPTjONWBzHNCg8WX8vJfYYbN8Rc/8wg//LmQxkOHTbVI44YyCIFDDdjpuZdoqduRc17LT3lXrTj4bSr1JwiBVfgVAf59hqJMnDZcf2MxZJ+WjxZcTjyti+Sf+MfUynOphSNs0GXMIDSMLfk4g0uxWQK6MIAzron0VPmI9s1TKaAbFUwYQs9cAjO2DpM2CZavwL/iBOJe2cRZvvwwxraLyCbYyAlTm2YZdlDaYQabQxT8i59WYdCfRPr3WmsrQT05TzynsQRMOupn/LogrcwpyfDrNY6ooFDcZqjiDjk6yvh0uOm8YsXR3nD/F6muiGyOYbTHCOYdRy5bJZ4/bO4c44lHBvk7i0O58lVhLOOx6ltZpnuZ84N17L5LR9gvB2T9STGwOH9GfxNzyF0DEYzRh4cj558Fp0rs7QmWdiXwTcxzvh6ooFD8Tc9h3PoidYy0vHRz96Dc9SpsHkVIlcgnn08SIeWccgRgVY41U1sKc2nX1VQ+X6c1jgq14tT34LQMao4lRhJbugF1KYVuAMzGJu5iHJlFXe2pnHm3BL+ygdQM4/CPH6zPZ+Oifvn445bOYrO9xFnesgMvUC87DHcw09kpPcwep+7EdOoIkt9sOBEWPkYLDgRObyS+JCTkM0xjOsjojZi04vo6ijCz/LA3/0Tx958C4WH/wvnyNehVz9NPLiW7GteT9w7h4rfT//YMnS+D+Pn0b/+H0S2wD2zL+KsGR7imdtwZi6E5jiNOScSNOuUnroB95jTQEjU0gcwWjPyukuY4msyhRJbvvwP9Cx6HbpRZd2Jl5L54t8y7zPfJLj9O9SeeZIpf/t59MpHMM0Kws8ijziT4Pbv4M1ZiC5OwZ11JK2ffRH3jLejHrkBkS0gFl2EO7KaaMbRNL/7GQp/eAXay7G6pljgN6k4PZTDUXRhAG/oRdt/LX0Q/4hFIF1Mu46eugBURNAzC1eAUx/GqWxElaYTlGbg3Px/8Y86mU3TFpH59v+h7w/+P4xfYN1nPsqcj32OVm6A0VbM9Lv+g/zFV/DQG8/jlBtvJ7znOp7+3H/y2s98GDX3NTR+cC2lU86i9ezDlN79ia50RT1zG85x59P872vJX3zFpO8vmHi1H9z5fdyFJ+DMPZ74yZsRjoMZmIesDaFbDUZu+gV9Z55F7fGHefpb93LKB36XqN5AhzHlC3+P55ZczfSTj0A4kuIRR+IMzKD51EMEYzX63v1+xrPT6Ft1L6J3OqowQDPbT8/mZ1CFAZzGCBiNro0j+mYAYPwcGA1b1qGrozjzjobaFkzfLGo//zbF378cObIG4pDa/bfR+xefJl7/LC9+9MMc9YklrP2Pf2HWh/8RZ8OzxPMWEbo52rGhEiimF1xWV0KOHvw1z00/nbwnKWccir4k/P41FC96JzpTYjUDDDdDTpiW5841VU6dXeLX66u8ubdC3DuHTU2NIwXz+q3kcbzexHckq9at57gjD6dSqbxEHlGtVimXy6xYt4nSVttuv/UWbr7hV3zj61/d7n7f+ta3eM973vOSccgdd9zBueee2/Vp/upXv0q1WuXkk0/m3/7t3/Y4BsXLYWeRnQ855BAAHMd5SbC7He0nhGDevHl7L4O7wR49aY6iiLvuuosTTjiBvr6+Xe+wC7YeMN9///1861vfYtOmTfziF7/gscceo9ls8vrXp0FBUlJSUlJSUg4eOvKMDjpZdsRll13GZZddtsPtnaBt+8u54he/+AVvfavVqX/ve9/bodx22yfGe7rfvmaPBs3z58/nvPOsefrs2bNpNpt8/vOf57TTTuOkk07a48z86Ec/4i/+4i945zvfyV133QXY0Nwf//jHuf322/f4uK8E4jjmtu/8K6dl38Wbzj/nQGcnJSUlJSUl5QBxyy238D+JN/LOUNouHc5+wwUsOukUvvX1r+7D3O09Pv/5z/PWt76Vv/qrv+IjH/nIbjtg7Ol++5o9GjQvXbqUarXKQw89xMMPP8xDDz3EF77wBTZt2oTneS95tL67XHPNNdxwww2cccYZ/OQn1obl+OOPnxTi8dWK67qc/+7/zQUXngx694K+pKSkpKSkpPz2ceGFF3LYUUfznW99c6fptn3SfNftt3Lrjb/a19nba8yYMYP3vve93HfffWzZsoUTTjgB19310HNP99vX7JGmeUesX7+eBx98kIcffpjPfvazL3v/3t7erhVKRydtjGFgYGCSZvrVRkfTfM+Dj1CcMoOBnIvvCJzWOBtUgXLGoSAVLeNQGllG+/5fkDn3nYh2jWDGMbiB1ZQ5gy8QD65ly2t+j3JGkh1dCY4PKkS2a6AVxstB0EDNOJLQL+EKcMfWorMlnMYIcf98ZGMEZ3wD0cxjcerDGCFpFGeSD8dteE4hqck85foGor65OEEdlSliDEij8IaWocozaHo9dh8AIRFRG53vQ6ioG67bCElTCaQAVwpibXCkwBMQG8gNv4jOlRFRC1WehYgDRLtGu2cWvg6pKJe2MnhSMH3kWaJpR2DcDELHiLBBtd4EFVHO2nwbx6Pi91PwJG5YJ/AK5EeWE049vKvLdQTkhMKpDhKXZ2Gkg1vZBEbT7plFJqwxKgr4UtA7/BztmceR2bIM7eUxmQJxthd/ZAWbC/PJugJXCvLNYYyXBSHZEGVQBuZmIpSXx6sPYfw8NZnHTfTWWRMiHr8BjjsXd3gFwbyTcII6cvVj6HknYPwCLeNQHF/FUmcOC3szbKhFzMspIieDK6ARGzKO4KmhJqd4Q0QDhyKMwQiB0gYDVNqKaaLOC+0cc0seykDGERhgcyNmbk5TN9YOq1xZxWD+EEoZh2hsM+bZu+g7/jTqPXPJR1Wc+jCV3oX0Dj2Dro7SOOIcsiakbjyKUmGki/j1T3AXnABCWP1raRqjLcXMsaXEvbNwxzcSb16DOf4CzL0/Qp7yO1bTLF3E5hXIQo/Vr3o5nOpgUjfaVv8+OoiQDmLaPKgMWR1lq0Iw6zjuXlOlGSnm9GSZkneZLWu8EBRoR5q5ZZ9QGeqh5pA7vswvj/1TjppS4KictWZ01zwKhT7qU44g3xxmPDOFHhFS0R4D1ZWoXB+PNfKclKsynp3G4F/+Pkd89QeYX/83+qx3WR1/HFr96cggW468kKmr7oGpc237HFwBUiJ6pti2Va0Q9x9CXzyO6puDaFVsu4nbds7ByGp0rozOlXFqw5jRTQDoQ0/EHVqGLk2zuk6/AI6t9+gYtEaoJCy3dHDG1hHMs2/+vLF1ti237JwDkykwKkv0iQBhNKFXIDu+FtmuEffMwK3aUOlx/zziTA/SKKsjB5zWGGbLBkSuQO3uGyi+7mxMbB8EqJFBdKNK5sTzbACGxhimd6btl7R9mCI2voBuVnEGZhIdcjIyqCFXPEx8zHk4lY0YN4OzZRXxrGORzTGECtGrn8aZfYQth1yZIX8aMyrLUD3TEFFg6+bIFnSrTvHEi2j/+FoKiy9FqJj6lCNwBPhBBXXPj8kcdRI/bs3nf80VhNk+Vv/571OcM4U5l1zK+D230HvOYtTIIJnz3s3y//0O5r3rnQjXwyw8mfiWb5N93YWELz6Gd9Qp0K4hvAx6fBjdrGEWvQkRtXAqgxgvA4MrMbOPYvBfP82Mv7kSpz6MGp0IQ++UBzB+AZ3vRWx6EabMRbYqqNJ0TLZky2JsLQ+966849Tv/gtmyHtk7lQ3f/A+mnHYiWx54jIHjDyOqN8jMnofIFlj7o5/iFnLkp/Uy/YP/QvMnn0OHbaqrNjH99/+Q+/7kQ5z94P0Ed36fR678v7zmry6k+K6P0/rZF1lz/V0s/PPLMFrhn34xK953CT1vvIhg7UoG/9+vUZHmpM9egXvSWwB47t1vZcElb8bEEePPvoBfylNbN0RhRj+Z3hJfuPwH/Nk/nEN54Wx63nMVwe3fobX8eV74ya9Z8ObXUpg1Ddk3DTWyidbwGL1nXYjwPESuhHF8ommH461+GDU2hDPnSGiMoWYcgWyO2X7D8VGlaciohRjbgBoZxAQtRKEHPWY11v5hJ/Czi97PMYsXkJ/Zz8zF5yOPO5uVV/49h/3tXxOufBZ3+jx0o4pz5OsYLc6jLCOW1yWHe1VMroy3+QXWlY/i7jXjvP2oAXIjy0FrXsgcyoKyhwwb3LZJoQycPKtIEBscAY4U9D/239wyczGLB1o08tPQBn65bJSjphQwtWFOPe7InWqan1i5nlJp8rZarcprF8zZ7n6vNLTW3HPPPVx22WW8/vWv5+mnnyafz3PiiSfyr//6ry9J39E0v9z99hd7ddg+Z84c5syZw8UXX7xH+8+fP5/HH398kvXJo48+usNwkCkpKSkpKSkpv63obeQZnXWvFqSUnHPOOfziF7/guOOOA+wPgkcffXS76TvPcV/ufvuLvRpG+zfl7//+73n729/O1772NeI45nvf+x7vete7+Id/+IcDnbXfmDiO+afPfpq7b099mlNSUlJSUg5mfn3XbXzpc5/ZZbqOPKOz3HX7LVz7qU/s+wzuZToDX7BOLG94wxu2m05v84tgd/fbXxx4gchWvPvd70ZrzRe/+EXiOGbJkiW8733v2yfhHPc3ruvygQ9/lOKUGQc6KykpKSkpKSkHkNPPOZ/TTjyBH37nWztNt+1EwNPPuYDjTzyFH3zz6/s2gynb5RX1pBmsXcqTTz5Js9lkxYoV/O3f/u2BztJeoyRj+rMOxdHljLYUsl1jSt4l50mMkGRciSrPJPe6C9BeDhG1cMM6sj5M6JcQfg7x2guZUVmGH1QQcUSjZw4634duVK0uTsfo/rmIqI0f1ogMGMcHrdFe3moGV9jXGyKoo7MlhNHkoyoVt9fqHR2PjCN4gemMNGMAZBwQaYM7vp761CNBxeTDcUZkGZ0tWy/ZbI8NKxq1MdK1/r61IXsuIfBUQE5bD2GNwG+PYfw8Tm0IoWOc6iAiamPcDJmgglARA6MvMN0NmRIOY4ImIqjj1IeRtSHqbg8ibCKjNkKFyLCOiAPK8TjSKJyx9bhSoLNl/OFlaL9AKa5SaGxGtCoYITHSwRtdQ1yeic73kRtfizO2nj4ZUSCkOeNY3MYWtF+0WuqojVcfQhen0pd1UNrgOwKd7SHO9OBUNzGbCjMKLsJonKiJKk5F1jZTcAyxNtQChYha1F/7uzS9HjZOeQ2ALbPZx1gdZxyQdQS6OJUjeiTSKOaLMZzKBjwVIMMGpWicWqg5aiCH6plpNdHtCt7YOjwVUNj0tK1bjo8nBRtqMQLw4haVtiLrCDa2JeuqIUobVM9M8p4kXx/ESAdx7DkYN0PP5mdw6sPE/fPxHIHOllCHn05hbCVObYhSNI6sDeENvYjIFdDDa8EYW39rQ0wVDeKeGdS9XozjIfMl5LN34B15Ms7QCnvvoiay0IMqTsW4GYyQqOJUxPgmaFVRo4M4A7Ng5mHobBkKfTC0CuIAd3wDlSBmetFn1XiLF0daqHw/jVBRCWKMgZwrGawHVFdvYmYxgyMExvERUcvqVUc3URxaivGylONxnMoGIg3NgcMYlL0cOzWHKk6lHI5yzKevsZrhE84lM/gcAK3rv0609kWazzzCQDyG8DzCgYVoLwdT5yJzBUymgKkMYRw/ucdtRMuG30Y6OLVh3LH1Vt9c2YxQSdsrT0FksgR+iXjqwq6/utAxxvG77dutDhKXZyLbVYyQ6IL1anarg+hMAaddRQQNhNF27gLg1DbjVDeRHV2JGN9kvXBb1pfcNCvIVgV/fB1R8lUhjEaELXS7gWlUyR/7WkQmi27UMGEb4WdxBmZaLbOXtX3R6HpEHCBbFUTURDeryGyBeHCt9ZMPGsjyAO7ISqvPdn0o9OI0RmyZ+QWceUdjRjag1j0PRjOjuhyEQCRa6vCFRxHZPHLKXJzaZnJvey/q+Ycwo5vIBWNkX7gLEbWRPQM0HriFd+RW446tx2+OsOD3zqE0dxrhi4/Tc9KpxJtWIws9VP7zSjY+vJ613/8RwvWRqx4nGK0Qb1iOcD2oj/DcJz8DKqJ+5BswSuENLoXn78P4OasPH96Afv4BZv7BOxGDy8BohHRQY8NWz6y1rQebXkQ4DhhtQ5FXNuKMrsUZXw9CMvWYaUTP3g+uZ++N0qAV6+9fzpYnXqRw8tmMPf4kJmwz963nM3DsoQw/uZLn3v1Wln7nFjbc9QSP/Ns9NB+6jUV/fRF3LDqVTTfczJn33oM3eyG/Pu8corEx5l14Ko0nHyJe+yKbP/c31DdVGLnvATbe/wxRO6a5pUm48lmCO7/P05e+memnHIWqjNBYvQYdxpROOo381F6EI1HtkL/+9FtpDI5QXb2JFe+7hOYLz+GXS2T7sow8u4rsorMBaI/YdmDCNhQHGPn5D9HDa5FBDTVmv0PM6CZ0bRwZNDAbltmy3bLefn/EbUwUWu3zwEzU8AYG73rA7tdu8tYffoi5F5zEjPPPxhmYgRxdx/w/vJix239lvbhLvehmzYa5T5hRdAnyA0TCReX7mFZwuWR2hB/WiMuzCacfyRHxOmTYIPaLeI7kyIE86yohyhjKWYextqJ1yu9z+pwStdw02srw8MY688pZNjfC3Yrst+2T5rvvuJUvfvqTL2fokbIXOeCD5lmzdh17HdjvBtYpKSkpKSkpKQeSTkTAznLKWefxVx/8Pwc6WwctB1yeMTY2xje/+c1devBVKpWdbk9JSUlJSUlJ+W0iVJpgGxvfcNuZgSn7jQM+aJ4+ffpuRaaZOXPmfshNSkpKSkpKSsorg84T5m3XpRwYDvigefXq1Qc6C/uFOI65+rPXcvFFZ3PReefQn3MwcQZXCkJlyKKRQYumW6RQnoUw2mqUgzoijjBYzaszvhHjehgvh3E9pACnMogo9GCiANWTTDRUETJqkQFMpgA6RugYdIycexRRz0yrpQtbGDdDnO0lawxChSgnQy1QzOnxMMagZQ6ERMUGVZxKLm5gsiVkq0LOF4BBtqvowgDG8ayeEusVLPN95B2DDOsEfglfBfg6JJQ+rnRRpWlWVx2HkHjMOq0xgimHIzHIRHOtsz1I6WCyJbTjY4RAxhrTqmD8PKKy2epcARGHyKBGPDAfd3wDxrd6VC0chJcD6Vof6ThAxoH1w61vAUDWtxD3H0JbZjDGUBpZbvXahQFEq2J1pF4O4hAvGqKnOBWhIhr4FOI24dTDcauDhMrgCYn28zjNUVTPTGIkkdZUQ800X1NqbibqmclULwRlqGiPPh2DkGgvhwxqaL/AeARTmhutN2+2jGyNYzJFjJdnanUtqjSdlszieBl8FaCzSZ0rz6IUjSPCFrN7ZrFiLCBQhlBkyHuCwUZsPcI9KLsa7eQpxIHVl9e2YJwMQnhWZ6oUTmUjfnkWbFlHo+cQCj2JftbN4lQGwfVwpx+Cro1iMgVUaRru2FpM4r3dEzaIV9lARU7fVITRtl67WURotd/Gz3V1vagQpIOujCCyeXRlC2RLiKiFyRQQXhXcDEKFxCqDFIL+nMfMYobBekRfznZvGUfgO4KsK/EKWRb0ZXEkCBNa/XTYRuQK1l93bD0j/UfSW/IxypAJKkzJ9eKGdZzxDaje2eihtTiZAsbLEa96Bm9OC7dYRA1vIG4FmFwZoxT+6CqMl0NEgf2rQttOlQNYXbGMmqCUvefZHtuO1jyDM+swRFBLvJqHINdDpjliPZLdLPGm1dA/DxnU0ZkiSDsvwmmNYxwXoSJkq2LbpOuDdLvzBYgDAHp8J/FXD2wfUegDIVC5PtwowPTOQOfKuKNr8Tv9itHoTAG0QrcbVpccRRCHGK1wZx1K+PwjiLmHI+pbrFdzvowx2vZXYQsxMJNo9VK8OQsRcxZCbTMmjmw9S/o5aiOY/rkT64S0aVzP6ut759hr9pP7NnU24fKncI89HVEdwnU9zKxDMZH1sBe909DSxZt7OO6MeUQzj0000QHh2Dg6ilFBgFn7IsK3Dag5NMacM+ZTXjgbkbPXXL7w94jWr8BoTbRxNYf/8VvB8cjHdeLhDbiHHoecexToGJ0t4R/+WkwUILyM1Z+3a4hMFm/OQlRlBKd/Bro+jhregHfosXZOSRyhC/0wtAZZKKEL/cz7g99FZLKITA7TbgAQDg/hZV16Dp2JzBXoPfow9NgQuB6VFRtojrSQUjBwzCx0FDH31Fm4vf1svu9R1qytcsprD2fF+y7ByWY47rI34E2bRbBhLYXXnMKW22+hvnYzh7/zfGqBwu/J88Jdqzjy7cfi9E0lXLuMuBVTPOoYTByh1m2m94i5xGtfpHjIbKJqlcryDZTmTWf42WFmnXkCw0/UcLI+jY1D9MybQnagB1UZwbQbeIUc/vSZOAMziDe8SM9rFlnP9rAFfha0RhZKmEwWs/FFANyBGehWw9aRxjjC85E9AwjPw19wLP5TS4nGxwlHHsIvl3B7+7te4eHyp6g9/yLFuTOsjlorvJnzMY6PIwVChRR8DxG30X6euHc2Qiv7fSUc682uM6jeOYTSJ4o1c3oy5FxBvuQTKE2lrShnHBqRoR7Z/x0hmFnKkHEFj91wE9+55Ze7HDtExhDpiUHyw/fczgO337x7A4+Uvc4BHzQfLLiuy0c/9jEOKW4/fnpKSkpKSkrKwcGp55zHia85gZ98/9s7TRcrTbSVHOO1Z5zLESecxM+//419ncWU7ZAOmlNSUlJSUlJSXoFEevKT5s66lANDOmj+DXn00Uf527/9227oxy984QuceuqpO0yvc2WMm2GkGTPNcWlFmrxjiHDBc2mFmryXRWV78KIWCEncPw9trMzCOJ61xTHGWpoJYS2khCQemG9fA8cBImra0MRRG50pWis6v2BlCEKA49lX414eAEdHKOHa8L3GUPQl2oA21mYKFZF3PLTMI7RCxAGN3BTycR2dKVm5gHSRrTEaXi8GTd6ThFgJinEz+MZaaLXw0MqQ0TGyYUP6oiJMrmxDCmdKuK1RcHz7GtZoZLuC7ptjbcJ0jDCaHNDws6jiFFCutcuqDqHKMzBejrbwyfTMsLIUY5AY0Mpa7fkFIs/Fi1tgtA1zHIfo4hRruZUbIB/XUYUBG9Y2U8Srbca4WRsuGzBGExnwtaIgNEZ6OK1xMIaiblrrPS+PcbM0RJY8mpwrKfqGcadMPitxBIxpj15XUnZCVGk6Ig7s63q/gNAxvb6PcmfQ0A7FYBRdGACtcCobaPUvwEMTRoYeR4B0kK0Krd4y+dY6VGk67Z5Z1EPNlJyLEJB1BI4UlDMOOVcwkHWJDDhgpQ+OhyrNsK/3833o4tRuCGURtYiPOItiYwSTLYExeFtWEk1ZgLdlJdrPwbRDQYUTdoNJeGh0jDv/WGu7ZbQt9zi0x1YRsjGGnroAnS1hhAS/gMgEOFNzEAfoQr+VD/XMtMcvhWjpUi8fQmZ0nL6cS2/WJedKlDHMKHhMz7soY8PZLujLkuktUcxa+RCBBmPQ0xbi1LdgpIvJFOgLRzBCMlAsouilHWmK9oYja5sRhRJGx6AihOdjtMadMQ9VGcFrthBBDaYdis4UJqQPSYh3GUcY4SO0QvuubX+dsgDiFU/iTpuNcVxk2MIYjdqysStnAUCFCD+Lge5+CJnUc23DCxuNiSNEmPQDYc0mS9IZHeMGVURkLSCFjm29M8bKHrxM93wq32f7lNhKT4SKcMoD6EYNp28aTqnXWg02qqAV7uyFVopQHbWv1N0sImjYvmVkwySJgRobQk6ZY8ONSxfj2FfigJWSgC1nFSP7pqLGhq2tWKZgZVPSRYQtZK6A7JuGiEIoljDNcYTr2XKqJvUwF1h7PsdPOmOF9gtk58zFq4wQN9tIz0WW+oiHN+B4LsXZUykccRSm1UD0TrN1eNpsTKthpUP1cStXATInX2Bt9rIl3C2rrMRCyInw4SpMpHLKWgyWBzB+DlH2cTM5TKuGyBagOY4oDcCU2fb6ogATtm0dGxvCnX4IU157BCJXYMbrFqDCiHh4A7g+7tTZyGIvzZvuZ8GbF+F4HtmZ04mrFaS3An/BsQyMj/IH/3o4o8+uAqB34Wzc3n6cvmn4YRvdqCIdyYzTj7OyiHI/fq3GWZ+6GJnNE6xbhT99JmEjQpb6aD33GH2nn4kaG0I3qnjzjkCMDDL7zMXEg2s59doTEX6WKVrj9vSQOeQw8jPWIqSDbtTwZi/s2r6ZdgM5/3j02ufQU20kYFnqgzgEx0MAFHoQWHs6mStg6iOIXAldH8ebs7Bbt/pPPMHKg6Rj62HQtm3CcVCVEXqOO5bG8mXk52ShNAUzpQhG2+8rkUHEAdrPE8SajGvtSYNYk4+rto35BZSTwQFCDOWMY6uVgaInaStD0RUYoBULjAGD1ScXfcnMos/m+q7fPLdjjYz1S9alHBjSQfNvyKxZs7jhhhvo6enhmWee4U//9E956KGHDnS2UlJSUlJSUl7lxEoTb+OWse3nlP1HOmj+Ddna1cP3faQ84NbXKSkpKSkpKb8FxNvIM57+9V08cfctBzBHBzcH/Qjvuuuu46yzzqKnpwchBHEcT9pujGHJkiXMmjWLQqHA2WefzTPPPPOS40RRxF//9V9z5ZVX7q+sp6SkpKSkpPwWEyUTATvLUaecxdve+4EDkhetNWeddRZTpkw5aMc6B/2T5r6+Pi6//HJarRZ/9md/9pLt1157Ld/4xje46aabOOyww7jqqqtYvHgxL7zwAsViEQClFO9617t4xzvewe/8zu/s8Fxu1ESovNWKelMJsn3kpACtaMXG2tV4EtFsQ7bH6luDOrJVoZnJkHc8unE3E/sxRwU25HD/PGuFhtUCCh0TegU8x7e6R8cHqVF9c6ym1/FwwobVEQoJGjwnCbmsAjCa0M2RlQqMpiV8hBGoSFMQMSJs4uRzBH6JdmQog7VvyvaBMlQDRZhot5qRpuD6BBocx8UXgtFWTBEwfg50jMmVbehwIXGqmzBeNtFvW4st42aR7RqyMYIqTkHGAVsiF784DaSDyk0FZW2eRNBAZ0pkBMjGmNVhhjWUm0X4eYyXoxnb8NcyqGMcFxw/CavtImKXrA5wxjei870ACGNQ5dkYx8MdszZvoZsjVIZq7DHFiwmMJB+HNgyyjqln+vGNwRHSniuso5wiOVeSTbRuwhh6fAfZHu/q0bWXRSSa9cHAYWpe4AB5x2CyJUSrQpAfQPctoDC+lkZ5HuuqAQv7fLKOiy4MoLQhGliAkQ5a2ScVEvsreWM9ZmFBM9U0aYpeGrGhKCLQMCpL9PsSWR9OwqK3rBY1UyDM9uEKq39FStrCJ5ctEeXKVicrJ7oT4+WtXjOQCBWCm7X32fVseTbHrb4wX7b1T7ro8iyrqU5CyKMA18PURhI7NPsbXzbHuqG2jWfvwXHTixRciSMFOVegDWSERgYVa9NX34Kf6Sdz6FEYFSLcjNUwuxmrQ80UEtvD0J4nVyZUSR0RWCvFTMFuL0/D+AVMpohc8Bpr59g7C2/TMvz5R6GSfKKtVtm4GVsnVIQGTDvGoNHFXnuuqN3VD7uzDsWUZ4AKu1pZWR4A6VgruaABcWR11c0xTKZk9dKuvQaEtNribAnpZazdnJDWzlEIjJdFNNv2WrWyNnfNMXSmhGyM27kQXgbtF+3ch04bNRrj55K+Irb3o9VAlnoRhR57Pumge2chcxWM61mbt2zJ2nZlknZUnmYtDafOBtdH5uheW7f+OD7Cz3Y1pcLxQEo73wCs3WZy70TUthro4gCOLNj76eesnWFH553NWs22Cu01SWnDLns5hIrwDjsBE7YxUYTwPESuhHf0Kcj7r0dkslZPO3Wu1ZArB9wM9FiNuZx2iJ1n0a7Z8pGurZ9+oavRNj1Tbbl19OeZmXauiePauhk2EELYexUFCEAn/bZJLDK9+Uej++ciy9PRQOa0NyOCOtOnz0O4Hqo2jglaOAMzEa7H/He81Wrtp85G9M3Acz3mnRWg+udRyJWI1jzP1CSMt66NYaIQ4Xq4yfF6T34dzoC1GWzXGuRKfeSmTQcg69kyfs0/OEQnvJFi31TikUGcgRn4h52AKU9HLDyJqDgVtzwDEbeJy7Mpzji02z84AzOQxV5M0ERki3iFHsj1oDNFdGkaps9GAO7MZRFREyMksjGKKU2xoeiDOsQBIp9B+zmEn8NoDV4O4/r4+bKdY+H4ts4OrUGWp2DCFpnDTkBk8xT9LO602ajyTHSmhIgDfEcQaR/pCBwV4UgXGbVRbhbfkRDGqOJUGtoh54CMAwQefRmJEYJYGxwhKAm6/4vEBtSRghlFD09C3pMEiQ56Z0TG4G47EXB34m/vA6SU/OAHP+C2225j+fLlByQPB5qD/knz4sWLufTSS1mwYMF2t3/lK1/hiiuu4PjjjyeXy3H11VcThiE//elPAfvL60/+5E849dRT+fM///P9mfWUlJSUlJSU32LakaK1zdKO1K533EfMnTv3gJ37lcBBP2jeGZVKhdWrV3PKKad017muy6JFi3j88ccB+PGPf8z//M//8Itf/IJzzz2Xt73tbQcquykpKSkpKSm/RYSxIYz1NsuOnzTvLclpyvY56OUZO6NarQLQ29s7aX1fX1932yWXXMIll1yyy2MVi0UazSbVWh0RtWl4WdzIwRWA0TRiQewKlIG41UCZqrV5CpsIFVEPa/hRw9peBS1it44MG+hY4jZaKLeODifOJ8MmkadxVdC1pyOxphNhA0UV2a5PyDOESOyekleYxhC5MZ6xNlZtPIQQaG1QIkIEDUJlrZuC2CBMCyMdYickUoZGqIgdifElkTIoVxBqcARIAY22okod+7tNg/TRXoyIQ5xGE60zGC+2r+Uc2+idRhPtg9Y+QoU0Ipew3bCvW0Nr7SSSaGdK1EBIZLOBiSUirKNil47BTzs2eI4gaDYwjgMyeYWXHENrH7fRQmtrgRV7NWu55Xi4jSaKOpEbEylDEBt8LyYQPnGrgY4EQiuaUuE5AlcFxE6IF9ZpOgZlIHJtTlwBBnDadYxnrfGMa2+k0DGNQJBVDlLH3dfNnbLXBlS9SVPWaDUCaq5P6Ahk2KTlKEJpQEhCDY1QIYFQCpptRVVrRNyi5TnWWlDYaIx15eKaJo1GEx1LpKPQsYNQbaLIxUnkGSKs0w5dorjZzZfTaGHcCGSMkQ7GU7b+6giEC0ldApCtNiaOEcqx9Y/OfY0wbty1HRM6hmYAtDCuRugQ42ibJmxiQqiHWZqhAlcihW1HxkDsGBsxL5LIVoNm6KHbEaZWwziBjZDnxch2Hdlu2sibjoNxFCZ2aLsaX0KgDBERTr0JOrQRKiOJCUE2m9bO0TWIVoCINKpWRwZNtKdtfZQOWrkIFSPCBvVA2TovXCudiIOuPEM22xjHnqcjRxHNNsJVxH4dt2lts4gjjNfERBIdiYn2oTPIVgCNpu0nPNvGRNS2bdyNkW1bbh0rNNlqoU3Dnls0bZ2PwkTOoe19S+6fcWKcRhMRttEtW0+l0wblQtBGuw1ku2XvfRRikrqjI9k9l9AxcTvCabYhjkC2bLm7BuN44Ma4zTaKBlp5yLCBDJoYGds8umAig3GT/ixqIqKQWisA15ajCFtdGz4jXXuPYoGII5ACZSbam9dsY8LAyoUijdAuJjKEkUEIDSjcRtMeV9kyM45jJSDSxcgoqasRRkYIFSDiGBOJxMLRRl6csAd0k77EQccuMmzaz26MiEKECtC6CWhb51WMaLbRfmOiXcQRImyj2iHCMaggRkeaIKkrURBbeUYrQPi2XYooRHl13EbLbg8VaI1OrtNrhRgVdY/ntGxfWm+HGK2RzeTcrsZEhihQmFqVsNkmbkfWBrMZYNwmxtUok8WtNxGqTSzr3TIEMK0AKVqYMLB9QNQG7aEjgabW7RNkULdlEreS/ryNpomRkY0u2bHnjJLvuETGYNwYGbTQkQEZ2zrcCpBuCxO10e0QYRxUEOM2A1TNfn/a+xCijP2ekjomEi6eCtBJv+y06xhX0cIndm30wJbxiJM+PTb2e05s9X+QDHKlFDRDjStBeZJms8GuCLdjORfuxHJub0hOTzvttJfsN3PmzO4b9oOZdNC8E3p6egAYHx+ftH5sbIzZs2e/rGO1223+8QtfIl/sQUQtTj//TZx+1rmUM9YfOO9lCZUh1pDr7CQdZFhH5/ooZySyWkWVpqIBoawuMZI+MlsCoBpqSr6kHRuQOQphndAv4pmYULi4wg6mjZfDgNUzBnUiL4+nQ7a0DVNyWeLEn1krg+u6YDRRZPCTTqFlPDL5PrQGrQ2OgKbIIaVAYgcZALExxMp0O492nHg3K0PekxBbzR9CoB3fhhMXAlWcTktk8KTA9e0XfuQXEdkSxvFpx4aclHiO1QEaQMRWC6v9vP2x0dFwZkv2SzMpUqFjtLTevR5Wq2oHDyHGy1PXHkXfhm9Wpam2E/XyVg9pDG51M6owFaFjPBVQjz1KGYnBxRegCgMIHRM5GTwgUgZXOoTK4GSKEBuMMQhEMoi2utVstodQQ0bEBMqQFYrRUCCEvRfSaJRw8ds2tLIrINAGoUN8odHG4EphdZCOS1Yo2toh42DDmQPKANr6ibZwyWQzmNjYL4fmOMbz6UUSZXpAjOE0R6DUawcHXh4HTaAEQrh42V7QYFyflnbIE6N6rAZf9czo3gPjZjBOERHUwc13vwwR9t7LoI7OlRPNbAHRHLF6U+lZrauQ4BWsN3iid9aZgvUez5UTL3JDyXcoJd7ijkiuVQiMk0WGTXA8XEcgDn0NWto63RQZolBjRJG+rAYkztAK9PTDbfhzA63YkJcKLX1EpoDOzrRaSkAEdeLeuYg4sPruOVl0xrYpnS1ZP+/QQWd7aMWGrO8jvCyapm1TfhEHDUmdlUEdU+i1ddrJgTY4jVFU/zxkUEUohXGz4GQQboj2i2i/0NUe61wZne3BzfeghfUtN44VU9rBQCbx/y6CdKzXrZdDG23Dlif+wcbNoTM9E4P2OEh+SHtoN4Mj/CaqAADiNElEQVTIlhFOBqfPDhpxJCrbi8j1WB/6TNG2Q5UMJlWEcX3rg24MQiucGQusBrvT/qQzMSdDSFt+ft5qmf3E29hodL4/8bx2rT5eG0y2DFnQrv1Rq30QyZyIzqDKarpl4ptutd4isuGTdWEKur8AdAbcbZAOmeNOtXUyDgj7DsGpD6MLBYx0iJwMrkjqB6HVqyeDc6MyCF/b8zuJ9lpFyTyUHvsQI9HOG8eWqb1wjXCayMBqz9Eq0WgXMPk+YjeLF9Yxrk/TeORcAbNs3+qicRsjKNezP3CmLETGAVpIq9mXLpGGjA4IZxyN7JtrB+sqsuHOM0VUeaZ9cBA2caYG6ILVYkdBjBNUiXv6bTZdn5YSlA9tsamtmDLtCNxskajvEIyKaAsfIew8DMozu9fW7jsUJ+mIRe9cAuHi14es/3oc2rklWhMLFzfx9NfZXsh0fhy1MU6mWzbKzwMa4xcn9u9gDHGvLVeT9H1+tpR8dypM/zw7L2dgHipXJvTyYMBxMwhsH9J5ltvNc+fQXh4jXTySeSlC4kphf+CrGE+IpO/xEcl+WVcQG/BMzH2/voc77rgDMDTrux40R+qlg+ZoJ5ZzixcvBuDOO+/c7vatJacAV199NV//+tf56U9/yh//8R8D8MADD+wyXwcr6aB5J5TLZebPn8/DDz/M6aefDkAcxzzxxBPdyrW7tNttPNfjTW9+C28+7XiquWmEytCTkYjIRXl5QqWJNPQEAXGpB6kiHFVBF4roTAmvmUOViojQwXgZpCfwSiWypobJFolFgZLv4CYNrKQEgV/C7wyapcAJ7BOPONODxCB9bBod0gwcevIOUTJoVtqQ8yRCK4gg40pU8ks+60oibbqfwU5ykAJEqHFjjSMFRc9+8XqOQEaagjdxjHxsOxaEQDkZQqXJmRCMxhFZfEfgmRgRuYR+Cd/XGMfHEVkKUoFyMJ7tuHqQGD9vO6rYs4MLIewgwfFw3Jg412ODuEgPGWlyrsD3NSg7sc34BdAePcIOmkVo7w1ConMl+yRFBqiCDRZjHJ8wGTS7aIx07AS+5OkEQKgMBamQ2qHgCtzYEGv7o8GOze0v+7wrCDRkifGMQ04ogkCggVLWwdP2B1JGttH5Etrx8GNNKc4TlUrkgpBSKYuLtp23kHjGIeNKnEjjRNoG+RD2PmUcQdaVeLFGCihIOwEMIQkzJYJmHhFBqVjAZIt2QpN0aWuBEAJPCiJtyBLjaIeisl8pwjPEPT3IqG0HBnEIro/wjA2o03mCFGIn87gKnS/YQXOmiBQtwG5DOt3gEPbpNhg/j87a9tB50icigzJ24KyNwZECpY2dtBjZgQzYep41NVTJ/sh0lL0GY6BH2y9mp55BlezxY2N/ABalQkkP19OoXA+OfeCGCET3Wj3hkw8VOltC+iQDtBwilHafyAa2kUZ1nzrmSyVckgFB7CM9gwh0EsTIDiAcWqhiAenGmEwBKcNk0OWgivbHA9JOJhKhROV7yNRy6JxtCzpn65f0tB1sCJkEinDtQMPPI1yFLpWQtOxT8Vwp+THbGTT73bdPys3iehoROjiinQyaHVTOBuLR+VIyga+w1aA5tAPHOEAEInlbYtDZbQbNvg20ZITEoYHOF+1AWiukawfNnbptHM9OnNMaOoMl6WJcnx4fhIqTgbE7adBsB+fZZBKhCypGRhKdLXafBIvQBsRw4iwmk0PEkrBUwqHZDeIUOjZokxNpioT2GpNBM7F9G9E5v/Gy9g2e0bYfMXqrQbPfrZ8YjQjERJvQyk7CTN4GRm4OP7QTvaXxKLiiO/HMQ9v74XaCxNh+sFMPlfRse9U+2svitG39FSpE6Cxk7XcLOrbrYwddKtofMF6M48aUSrYuGTeDowQ9zSzVYokeL8ZVth8SKsLrDJqTvkYYY79zkPbNmhBIFREJlwwNdLHUfYuHVkRuzr7hBFu/jO1bZegi3aRMVGzvu9GYzFb7dzCmGxxHI1DGkPHipD7at3YytO1U54sEnn144QiBxGCEfVvV+b5w4vbEBO3kXsfYN1uOjgiFa4N3deqZEGg30+2HABsIy8S87Xd/l7f+7tu47eab+NnPdv3kNow14mU8ad4Zu5Kc7s645tJLL+Wpp56i2WzywAMPcMMNN+B53i73+23hoB80K6WIoogwtK9egiAgjuOu5/Lll1/Otddey3nnncfChQu55ppr8DyPt7/97S/rPK7rcuVHPsj0eQuhNrgvLiUlJSUlJSXlVcCFF17AcUcfyTe+9e2dpgtiDdsMkoM9HDTvjuR0V/zwhz/co3P/tnDQD5q/+93v8p73vKf7uaPpueOOOzj33HO54oorqNVqXHDBBVSrVU4++WRuvPHGbrrdJY5jrvnM5/jd//X7vPm04/fqNaSkpKSkpKS8erjlllt360lzO1LoSLHuiftZ/9T9AOgo2qNz7k3J6cGKMOYAGf4dRFSrVebOncuTD97HnHIGjKZSmtvV9vqOfcUGMFiPmV3ykBjaypCVBhnU0JkSTnXQeg63a4D1T1W5XtzqIDrXS0V7bGkpir6k6EmKqo72CzSU6Homh8pQNG20n0dpgxe3UF6eLS376irnSnKuwEXT1oKsI4gSqUYrNpR8+0pKG/sZrBdlO5FjOELgSKgEiljB1LxDpMGRUAs0vmN9dCMN/TmHILayAW3sr+f+nIvbHse4WdoyQ1a17MQrv4BT2YguDNhClQ6jkcQEDWJlmNZbBCFpxFbb60mBEzVpiCyFZJJb91WoVhjHo60FedW0r2pbFca9PjKuJGusFMIAftyyE84S3W3nlXVn8tu6lmRKzkEZq11zpCAjoRmbroSlo7ENlWG8rci4gh5fkhN2UpGSHmNtRU/G6tFLnqC51exoKbCyHRFS0R59wXBXg+pUN6ELAwyaIq1YM7fkd2U+rVjjSUHJlxhgcyNmSs6hGmqKnvU0bkYabWCKN/E6NDCSaGQdOttLOW8lG5Fw8cMaLa9kvYtVxFgskYArBQWSWahGJx64eZRr9bRuYwsmW+pOPIPOZMImGIPOlZFBnbg0DRlNTAC0ntltkK7VWkoX7eW699l3RFdn6Ar7GlZiiLbq0Tx0oku3XsNaOEijkFGLQZXFGKiHmql5K2XJBdb7eDSZuOY79jVzqAxlGaG9LPXQyoycuM1g6DKQcxhsxEzNu1ZylUwMaxgP3xHJXIVE6gRUazVcAX6+SNYRaKx23QnqyMYIYd8hSOxrYNkcs7KKpAw6vtmyOWZlHF6uq4mVUbvrI268HDrXC0JYmYQKJ/S8wk4WFTq2/sytMeJcP46O0I6HMAaNbeMTddB+NoCvgq5coyNp0Im0Qmk70TXWBm0gl/iRe8kEUiUnXuN2DuFoW17a8ZAqsq+4Ha/b3uyr9AbG8a1OOJFodF7Jd2RD9XodKaCnVCLWth9whZWHaDfTlQm0tSBHZHXIXo6mEoTa4EuBIwWdr0SRaMUzEsZDTa8HERO6+VqocaWg6Mtu/+0JrPbezXQnd3aux064lt373UFp05Ux2LkA4YS8RQiMdBkJbB+adQS+I6kEClfaumUSSZIjoOg7tu5EVuaE4xEKF09YeUAmqLBJFxECyolHcBYrVxiPoBXZPlhpQyVQOFIQN+sUfInJFMi5ktFWTNaVlHxJLdRkHFtunhS4of3O6WjoO21SRC2rc9eKCEk10BR9iUzKIVSm+x3lJHVNblVIne8XL9nB0REt4+BKO3m+8x0aJfNpPEfQiuz3TUeW1knnoWkqK0/r1EFtbL315IQ8A+jK7bTjddN17lVHZtL5PoFEv+94Ez7hRlt5R9K2ZByg3QwyDoikz/D6NSw48mgqlUp3QNuhWq1SLpe59Gt34ecnHtJteOp+1j16Ny/c+pPt7tfhzjvv5A1veANRFNm5SQmHHnoo73//+/nbv/1bwD7QmzVrFp///Odftuz0YCS1nNtPxHHMNZ/9HDffec+BzkpKSkpKSkrKAeTWW27hms98dpfpIqUI44ll6jGncvzv/9UO0yulaLfbkySn7XYbre2PuI7k9JlnnqHVarFkyZI9kpwerBz08oz9heu6XPnhD3afNKekpKSkpKQcnFxw4YW85ugjdkPTbNBbaZg3P/MAm57Y8cO3/SU5PVhJnzTvJ9InzSkpKSkpKSmw+0+atw1s0nfUKRz+tvfuMP1ll12GMeYly7nnngtY2dFVV13F4OAgzWaTu+++u2s/l7Jr0ifN+wnXdbnyQ1cwe6AH7fr4jqAdG+qh7mritAFXwljb6pKtphlI7G1M72yEVrjNMVRpGmA1V0ZaL9CMhqJvfXg9KTDGZSyEMPF0DJXV1xonk+iQDR4gsZpLAShjvaJjrBasGVsNlysFjjCJhVrMxqbVhzUjTcl3rNbREYy3FUJYbZwjBfVI044NPb7EkXT1ob4jqAVW49qKDZkksEuoNDJbZjzUFB0I3Ry+idFY308R1Lu6ZlcaIiDrGCIkrVDT41utY8v4+F4eRxtC4Vs7IKOtNk16bKxHlDMO2i9YbVqujKMFo60YTzrkPEPBFdbOy3G7ujUtHJyoSeDkQPj0+LqrMdQGPGlvYs6T3bKQWE2yJ6Ho27QS8HMejUgjtbW/8wSEAmqR7eSUsbrPQqJ7N2ToAZQ/vWs15U5ZaL23I4gVbKhFtGLNzKJH0ZO0YsNoW5F1rD5dG+jLOgw3Y3ozDmNtxZScY/WTOgbHp64yiEwvXhwQyRL1SNPrQ+iXINGeK+PgS2hGtm5VhY8UkPcl+Hlk2KQRabKuQBSnEMSanEgsyxpbrL1YpohoVaxmMFfueojHBjxhA0PoxE5K53q79k0dvXemPUaQ7bP1Q3o4YdNahTmZCT2ukOB4VkcqHcJY4zsO0mhMUr4aQyPStl7m+2lFmlas8KVACdBC0FaGnJeh3la0kqc+xnhUgpi8J1HGMNy0uvBCMYOjI4LEA9vWVZG0L6thdR1r3YfRRMa2/6LjYbzchA5e+GSKU7t9iNU3Z6w+NlPo2u517Lvwsgit0MXEX9fJJFaIrr1exyMy4GBtw9AORgh0rs+Wl/SoJH2PK+ka1Uphrfk6+mzjeETJ8xaB9Z7tlGWoTNfSsB5pWrHVNYPV7reTOQzKGDvvQIArXZykoLTjIYSkqQR5JwnmA8QZq9uMtIGkrvlSJcFFrNUjJFaZxg4MOnMuPOnhaIMjBO3EGtG4vtVFG00rlgRKE0uBm5jy5lzJSMueu5xx6PEdYmO6fVdHJ+s7outkEGlwPIn0cgg9YfMZd/5qg69jlHDBWL19hNXwgrViw82gPau1l24S9AeBJ1VXYx0qqyO2GlwIFWQcYYPw6E65yq5+HW0SG08HleulP+n7W7GmGVlddsETgKE/5+I7glais865klainZZbvSDVxmq6e7NO93vF0ZH11Daa0Mng65gIGxDJk05SRyQO1prUEVCP7LwLldhwutJavdlzTOiaO/UjVDa/wrH31GDbk0nalxTd+CbdfUSSX4H9XhKeJNa6+73YwXcSjTMTemaNQIrEdlU6YLD/AwYnsRm0n60NqYeRjo1dlFgJdqTZQis7ryb5zn7TG17Pa445apdPmtuhInYm5oOMLH2Q4afv2+k+KfuOdNCckpKSkpKSkvIKRKnJPs1aGcxW8RFS9i+pPGM/Eccx1/zjP3HzHXcd6KykpKSkpKSkHEBuufV2rvn0Z3aZTis9aek9/GTmv+n/2w85TNke6aB5P+G6Lh/76Ee56NyzAWhEGmUM9UgRKsNYWzHajgm1oRlpRlsxzUjTUNZyyDMxwhhGAtD5PmttI100Arws7eQVWX/WIWtCHB3RdnKsr4YMNewr+3Zs8B1JhOxa89RFFqHjxCrOyi3ayTalDaE2ZCSMthUaayVlpH3lVg9199W0AGJlKHoSpSHnScoZh/6sgzLWPk9pG6g2SuzrNDb0tzKG0ZYil0g02soQJLICpQ2hcKmGyesxKZGNETCaINb2vFgrvZxnI611IqRFya/xSBki4dJUgnqoqYeKgicpJOmNgYZ2Eosi2b12+2ozCV2MDSccKg2ORyvWyWJfidZD3bV6szZQ9m+nPAueoJRxyDiia/UUqokIiYEyjAWaaqAYacU0koiFjrCvHb36kA1vjmBjI0Zo1bVpajs5hIBmZKUDrhS0Ys1ISzHeVgw3YlQSEhrsK8pc8up3at6+Gq+IPCJqWzmMI/CjJpGTYbgZEyvDlpZitK0m5BiBmnTMHkeRSSQ3sTa0ZJaCZ+25mpEmKw0bA1sXdGGAushae7ueWSxrZdFuhtDNEWhbx+rGY1yWiJG0nRy1QNnXzwbCREqEkGTCGmESfVEYGzFSGys3ctFdSY4Q9vWoMuC2x601XiIn8KRACms71rFAdISNxtixIAuVvceVQFH0JPXQln/OlbRijUxeOXfKta6d7vGT5oQhCc+bSAgywlrgdSJkGukSFKZ28660sZZr0sFIh7gwBRwfIyTGzaKFY+unsJaC9rWvg/ZyRK4NSd2xZ0NIhIpwEqlFjEyicJqu1EcZY+3DhKASKAw2/5FOQq3HgZUJSZvnKLHSi4SLMrbO+4591d4pGz+RO0RbWctlXWGlL9r2R53ob8IYK7MQEs8RE/aOshNdU2OM6Z7DuBliJCKReniSrhwGkrwkFmFhchM6+TFCoKS1q/MdG7m06MvEcsxed8aR9GQcaqHuWo3lXNl9/a+xUqWO9KngyUTuY4/tx63uve28wu9ECnU71mkdKz9j60WcyJ+0MXRcJ5UxZFzZlR5oYy3VOteScSVhIukBK7eLHGv3JrSVjXXKUsYBXhIuvJD00RIr7+hzYrKqhVQRWVfSn3WsRZtjJROetP1i0Xe60gZhDBmRyP+E/T7SwsETSWRHkdhBdkKFJ9cjhJX9OCKR/iVSHaFVVy7odiQfZkLmpI093tZGuVlXdu0QhRDdtB1ZhhR2UcZ+L0khulIXIex3TiY5RueauuougZVbwFbt1LZHIehKUZoqiW6b3GstHLSbwTi+bVvSsyHLddw1Ajj3jW/ho//n/7ArVGy2u6QcGFJ5RkpKSkpKSkrKKxClNGIr/fX4socZW/rrA5ijg5v0SXNKSkpKSkpKyisQFSnirZbi/BOZ9YbLDnS2DlrSQfN+wobR/kduvuPuA52VlJSUlJSUlAPIrbfcwqc+9aldptNKo+Ntlm2cP1L2H+mgeT/hui7/54N/z4VvfBPojmUV9GddjLHhQwuuXXKuDZXa0f0Zx7PhZ43VuWI0KlMkItHQeTnrKoTVeylnIoBKb9Yl71lboL6sg6MCImUYbsVWexppQuFOsh9yutZGVjdlLdBsZXGkDas9Je/S40uCONHjMaEhLvqSnCuZlrVatf6sS9GXlDLWXinnWa1bNglR7Eur83WkDX3aVoZyxuohdaIv9aXA+HmMl6eW6SfGhoFWBuqB1ZrWQ201sLiJhjrxqMTaI8XaUAsVY4HV31ZDhdAx9VB19YE51+rdMq6kFWkiN4dysxg3w6jOkHUELeN0NYi9WYfxtuqWlTEGT9qyKPpyQletrSbO6iOhlHHISqsXzXmJfVeixSsmnyuB1V+vqoQ0clOoBFb/Xc44rKnbPGeJu3mfVfJxhOhaOBV9Wz/KWYdWbPf1HKshdKXo6iAdAeV4PNGqC3qScNG1QNGT3Bc/0WJvbkTWdjDR9UbaUAk065qwthpRDTWVtr0XY22V6DOhoez+lbaiFtl7UzU+o21FOSO7FojNyOrCwWpGDXR1xh3LKAkUCNHZsg2vnoSFjv0iRtjQydpYjbMw1kquFWlk1LKaXL8AKibriK4GOdKG/qzT1fOLpH6KpM73+LbOZhxrZZZ1ZdeezIYkZ0L7axQFV3Q1vrY9T1hnZV2Bq0OrScZqMjOOQBiNZ2JrqaUiq2FFIOMAGQc4UdNaXBmr1XZUYLXZxrbbjswxSCzsDBN6YBt+XBAqbXWm2P3jRH/cscQDa/Xly4n8QmKPJn2MSWy7hOg40hFrY7cn54QJHakUdENMK+kl+lOrLy36Tne9SfT6Hd2+K2148Uj63fvjO0mb14ZI23Sd8wlh60c7qTxS2PaWdayVo+1LEp1sck0dbbsjJizqXCnIJ+3Pk/ZaS77sWgVKbN31pKDHt5rfQEOgrVZ363as/TyJ2x5GOt2w3r6JbX8hJO1Y4yZlJ43VPWcTOzmZWBK6SfvLe/b684k9pTGmG8bbl7bfMiYJvy5sOGgtHKSwGm5pVFdbXPQlvrYR40oZZ6L/8nKEwrXWbsLq7jta6VxifSmFDTedc4XVwyfDCBvSeuI62tr2/21lkCqyWmZt63fWmQjT3tGpO9LqoYWOybh2vomSng2Fju2nvCTsvKMjPGG/G0RyTztpsoku30v02K4U5ITaKjy6rQ++M9GmpYrwVGDDYhvdvQYAqSJirPbeSSzxOvpmhMQTUJCJ7ZzRiDjADetWF200kTJoY9Be1obSDmoY6fCmC97ABz/8UXaFUgaldHcZW/Yw62/75i73S9k3pIPmlJSUlJSUlJRXINtOACzMO5Fpr/+TA52tg5Z0ImBKSkpKSkpKyisQHWuEo1+yLuXAkA6a9xMdn+bfe+vvcNE5Zx7o7KSkpKSkpKQcIG659Vb+62e/2GU6pTRspWFurHmc2sqH9mXWUnZCKs/YT1if5o9x0TlnYnJlfCko+dYrOO9ZDXA56yASH9Wsa3W1KvHY7OjkDKAzJUyi9Q1iTVN1fC6tvk4bQ1V7NmyrI+jNOtaHWUcgrN6xkGgLM66kHVtPVXcrj9K2st6gnrTnsOFuRTccqTbgOgJXQn/OYWreZVbOanz7En/Phko8hqXVLfd6VksHJNcJ/TmXnCfpzzkI7P7ljIM21ke0FlrPagNov4DQMTnX+tIWfetdmvMEAzmXKPG4ziRaxI5HciPSiSfshDfqUDOm15e0hU8p43S1jlLY64m19Qp1pMBRAYGGfm8iXC5JXkNlPUd7sw5BUm6t2FAJFI6Avoy9noIn8aMGec+Wfy1QRNiQ2mGivx5vq27Y2FZskrQ2jO/qSkigNBtqEXlPIrH6740tGGnGXf1sRwvuJbrlgicxBkp+4pmtra65E8a4IJX1zM71Yfw87Vh3fX0HZECUaF7XVELqoSabeO+OBYp6pHGS+1/OOPTnnK5HaltZ399OiN1WpAmTULnjbUUj0oy3FX1Jne/omR1Bt+w62uxI27onEy1ijy9BCIQKEcZqtbWf73rHGiEotYas/rdVIYv1P0fHVo8bNkDatlXwrCbSGHtf/cTT15MTXq4dT928J2lG1q+5o8XPuYLRtrLtS1gfcCMdq801NjSvMnTD1Hd0jsbxcVSAoyMkxvqwhw1aJvFAdqz2tB1rMIYWnvVsdrwJL3KtJkL1JmHXZRxgjOn6+cbI7vwGEQd2joC0OtSO37pJfK0NdPX1HY2oJ61uXqgo8Tu2dcBL+gKwfYiRTlcz2vVmTuq1K60GVwir++2EUNaJ/lUmymRHR9a7V0e2f1MBnolpbhWOvENHZyuS++20q4DVvMpEN+w7to8JEu90P2pgEs0/QJgpI4ymYNrdEPcZoQmUoeSJbt+ZTby4XZH4TSf7t2PrP660IWtCImWsF7g2ODqiGWk0Aj9uIaM2UkVdD+cssfXHl4LA2DYVJSUhdEwubgD2/gmtEElbMsbQjCf652yiyfcdq5XPOgLtZqwXt7H6el+HSBURGHssGTashtzNWJ10UpaR9Lv1Vhvrod8hl2h246360U6/BVYjH8S2n9XCQWO/v2zeJEp6OELQ0E63zrjCenZ35q106oDV7Rva2rbNKNHAd7TN0ihC4Vqv7U7dTTTgKtHEd+aSeIk/sxGSsqu7un2Z1EcpBJ4KQCtCJ9MNyw4k/YvpxknQjmdDa2/l/W2EQOiYNi4iatlycXPEfhGJQUYtm3c0whhk1CbMlJFxwBnnLebjV36MXaHiye4ZmVkn0H/KH+5yv5R9Q/qkOSUlJSUlJSXlFYiKNUam8oxXCumgOSUlJSUlJSXlFYiOQ5DuS9elHBDSQXNKSkpKSkpKyisQHUUgwpeuSzkgpJrm/YrBZIpgNEVVBzoaP6ufqwaagaxkat5qDXOu9TGuhpp6qKnFVpGlSbwpdUROtylIq4uth4qsauFiPYkzjtVNA2RckejA7OeiLyl4VpMpBUzNu0zNW31xR4McxJpGpMm4kqIvaSZi3kBZfV2/adCXTfRfWL9Vz8TWNxers1PGakJznrQ+tFLQm2iRi74kiDUl3cQY63VpsLpPz7Ga3f6cQznrUNJNnMYIImwy1lZ4aLY0Y6tbjq3OTmK9fSMDszKKctYhl2hwB/IukYbejNP1HBZRiyz2GJGx2uG8agJ0/XubkcZIl1gbRiOJEdYXtZyx2tNYG6bmrc91b0fLHWmC2Gpf65Hu6jFVpkgP7a5Wsh1bTWAj0mis7rijn421YdloC2WsPhrsXJCiJ1lbDWlEilLGoSfjJPmRDDdjlLF6z6kZq8vUxnpe51yrQ+74sSoNRanQjofvCGqRoe3krA+3sL+l224egJIvmVbwqEeKmUWPjCvp8R0W9mYoZxwyjq2/Rc/6xXbKty9r8zbaVlafr60mP1S2zpcytoyzrmBTPSJKysIR0IisLtd6Iice2kJZPa6xXuSiXbN1uz2GDJvkiJJrM8Q9M8BowvwAoXDpczWo2Prx+mWMX6A36xBpq9t15IQPbDljfZO79y3RdRusdj3vCYq+QzPStGLDnIIt31gbXDnhb66M9fX1HesTK4W9H0ZIhAox0iUULlGiudSZkvUJdgx+e8zq2l2BcTPkdBsR1K2PbRyAMRgvh/FyViMtbH47vsaQ+PgKq2UmOX5Hj+rpMJkrYf2JO21YaNsGM8JqeIWwXs8t4eMEdXJE1utZRxR96xssEi2zNlbH3PFOzrrS+iTHLUQcYJL5EB19ekdbjdF2zkbi5Yu2/t/azSDigKKqW10wBkdYLWmY6IY7PsgjFKxPvbS68rp2unp6Kew9if0iQawJlUZohacCjHRRXh4n0V03lfVDHgs0OaG6ZVDwJCNt3b230ijyrvUVzrsCETYoSIUwGj+oYISkKCI7j8TxaAmflnG6GnYjJGhFvjlMRtpytz7DAu14hF4BaRSeiYmRRIZumXW8h8H6Vwut8HTY1Rc7Qd36r0uBkQ4N44G28wSqkZ0T09GNg9X1drTRdm6M1bbnPatV70ibtbHl2NEFZ1xpy1gKmirxL5dWp96KrYY3iPWEb3Xix9zRPhshiJTpbpNR2+anXSEykDNhdy5Altj6d6MRyno0i8Sn2mNCqiCF1Zp36nk1nPCZtvWL7nV3/zoeoZOxGn2jMcbWA4S037U6JhKJzlol8w+07UuFMUTCJSOsp7+Rbvc+RMb2+UVfIrTNv3G8pDCsz/6EynrHaBWi44mlue5xxh/70S73S9k3pIPm/UQnIuAtt952oLOSkpKSkpKScgC59ZZb+PRuRARUYRsVtrqLN7CAwtFv2g85TNke6aB5P+G6Lld+5ENceMH5BzorKSkpKSkpKQeQCy68kI9+bHfcM8LtLvuKRx99lDPPPJOzzz6bM888kwcffHC3th0spIPm/YiRLm2ZSayjfGtlFNmQnUobHAkjbU0ttFZpBmvdVQ11NxxvPdS4QRVhDGOxRHl52rjdULCRm0MLpxvu13dE1wLO1yFG2rDI2kyEay1KZe2gkrDKZVfjJa/cVRIaOONKcp6VaeQTCce4KNgQrzq0VkkCtOPhqIBSIl9Q2lCMq4TKUFX29aRQobVoi630oyLyOFETnby6clSAh92WCWv4JqYu86AVOtvDFDdiLIQZRY+CZ+34aoFiSta+uh1rK5AO1cDKVAqelYHkXMGmetwN66v9PGOxtdyrBvYVX0XkbZhUY8NbVwJFLbZSkaIvrXVTrBlu2uP0Zp2ubMUY6PEl/VmHucWJslpdCVkxHrChFtGSWUJlGGrGBLHGd2wo8jgJe92fc6gEmmakOKSc7doFKm3lOtVQMyXn0pd1+f/Ze/NwOao6//9Va3ffvvtNbm5WQggQlkR2kF1AAiqjDurI6Cg6X2cU11GcceEngjjMjIi4MW6gI6iACyAICTuBQAiEhOx7bm7uvvVa1bWf3x+nu5Ob3CyEJJelXs9Tz9NdderUp04tfbrqfd6fki/jkOeHWpVDNCY1coFK1pPppxd3F+nI+1JKoat05Hw0FQY9Vb7K9iKGSgHrhxyyTojty/UKXkjJL1twmdJmrBTI8nWmSt4LSWrSvm+4FJB1pHwiAuoMeSwsf7t8pSJdqaSxDUJBnxWQ1uS+G6pCc0qnoSw5qVf9ql1gRSpROabDpYBioplIM4hSTah2BsqveA0RyNe/qoZOhCkChKoT1TQBctuWkPZR9YpXTSsuyvXLVNfSBrJGl58VRaE95yGETKEdCsG4lEZLSiNSNBpUn5aUTn1CI0mAqkhrPKf8CjdUDUqBfL3vhdLGSokCEvaQlDSoGlpxQMoBRIRtNtKChepZKKEHmoESuDJ1duCBiAgE+IpOqCWqFpQj5FBKWcpi1Mg6RITpW6iBS6QnpFVbWSallfdfdXI4FblGJM/xCGlpGZi1qKWctKlEl9Z8ynZrrhpNXjNmUCKpSmsuRERo1BDqyR3SJsv7iVKRj0RSIoWqYYYuSiBf02tukchMEybr5fVKWd4CNJpS3lC+bOU+lGUJCU0p2/1JS0UFqqmxnbKcwEfFURMooYcWODiBwBdQU5bZNCSk7Ez1LDR7GDN0Gae51Co+Zjnl+ZAj791K6OElm4g0Q1qW6UnZfoGLg45QdVKKvFYIfSm1sDMgIqJkPapnYYcKatluVPVsdEWmQw9VAyMoYRChKQqmkLaJRuShBi419oCUb6gmKSWU7Z2oLccupWaGplSPfSMlaZPmFkak/VYR6EhpghGUpFUg0rbTVKEQqCQjl5QhreIqKeQr0peUIS1LhaqBIuVNTiglVpU4NFWenwldJedK+UYlLXcQCVA1SkKubxDha4mqDKWaDl7R8LSElEyUpRYB8jcgEgIz8qRkKAoxFHncQyHXC42aqtWhrio4QTkVfVmymFBkSvpKrELVq9Z1XihQ/FJVvqZZQ1J2EXoYIiDjUZUKKeUU5gmvgBq46LkeaWkXRgRIy00UVaZPD/fugiF8j2inSfgHr9M8adIkHn74YRYsWMDPf/5zPv/5z+/TsrcK8UDAmJiYmJiYmJjXIVHooygjn2+K8OANBJw4cWL1s2maqKq6T8veKrz19ngMWfDMM2MdwpuSZ556cqxDeFPy7NNPjXUIb1qefDI+Zw8GcbseHBbE99gxIwr8Uafd8YUvfIGGhgYURSZochxnZH1RxHnnnYemycRSDQ0N3HvvvbvU4/s+n/3sZ7nmmmte1bI3O3Gn+RDyTNxpPig8E3fuDgoL43Y9aDz51FNjHcKbkrjTfHCI77FjR+CVRp12x4QJE/jYxz7GlVdeOeryyy67jIULF3LPPfcwNDTEscceywc+8AF6e3urZU4//XTa2trYtGkTN9xwA2eccQbvf//7AQjDkI985CN86EMf4j3vec8B3dc3AnGn+RASIfVboWoQ6kn8SFBvgI+KEbo0JGQqWieQ2rLeorRvS5dTnZYCqTmOEnVSZ6goqCIkoUobOCGkDk0LpA1d2lAxvQKKIu3UhGZKjWLZvqjgi6odjxq4hOW0vw46SWsATVWYmNapVcOq7ZbmFhFl66EGLaBYtmRzyrpckPqzrBNKbTEwrNYBUucq1O0azKQq6LcC6nRBTiTIOiF5NyLUEmULo0imDFd1qTFLpGWqU11q3UQ5PXFFR6tEUsua1BQygdQUa8p2neqAHTC5Tscsp2fuyHtV/XJDQkVFUKfLtNhGeb0aQ6XohdSaKhknJB9qZJyQRDllbcGVbVNJGW350iIu60uLpjCC5qROW9pgSq1OxglJ6VK7mDKkNrY5pdGYkNronqJPQlfIOQEDtk9XXlp8dRUctmRdUrpStheUaamn1Bm4gaCrIJ88rOy30VSpHS56ERPSOseOr2HA8mRcTsiUeoOSL3XMFau3GkNlXI1Uaw3YIV0FH72sQbTKGudJNSqNhrSICwVkHWm71JY2cENBZ8Ej74UM2AEZV+p3C17Ilqw7Qtdca0otdcqQ9opZHybWGqT07TpJTQGisKzT1kjrCrlAZZwu93O8KS2/tOIgqlvArptEqCdleu3AJVHoRfUdMh5VCz0l9KnXQjQnT8pQpR62rD1O6ZVJnlcV/a0SegyWAsYZARNqdMYnBH4k298OBPXFLpkK2hoiqUttdElo9Fk+SU3B1KTdlF+2zqpYYRHJVNoYSersPpmu16yRqXiF1PNXLPUACH2imiYUv4RI1I7QXvrl8y8SgiRSh1zyZVrjSspnhEAJfZlSW0ibMjVwpR0cVDWzYU0zDVERHxVP0aWVpBCYQQkvjAjTLQTltNEJVaawlvVHMg20AFSNAKnZFGp5HINC1YpORY51cCOpMa5YzAnNBFVDmGmZ6lpEUlPsSntOVYTy/qbI7TpqgoQqNbMJewhhJOUYjciWWlyvQKgaFMq2Y3lPpjtPZTvQFHl/8VWT4dCg1pT2ab6AoidTs4eqgeIWt2uUQw/FswgjQTHSaEmA6hZAiKrG2AoEWqFPppI209J6LQpkCnMUQqMGQwGRrJPHsHwcU4aKFvkkI1c2p6KQCqyqZhpFRfeKRJpBg+rLe6iqS020okhtuCdTb6ueLdveSOGFgoQ9BIpa1TprxUFQ5bHVnDx6oZ9ASA225tsoQlopBkhtciCkhtlRExS9CCuQmu+ESlU7rojyvTMKcUKpVTc1aS3q6Sm8UN4PUoFFqTwGxAll6vfKdeKgUxPa0kaxfG67gdRVB4IdrDcFkWZgEFEKour5LpBWlJpCNZW3pkqNekW/rKkKpptD9WxqdDm2wS9bIQbl7lBFx6wELsmwRKQZ1PrZ6u9LEAnCdAuATG8fSp17ULasKwXyuEZmGqGZRLXjQDPkvSEQNKd0UKSuXqlcP3sgCvxdNM17etL8zW9+kx//+Me87W1vG3X5I488wt/93d9x+eWX09zczJNPPokQgm9/+9tye1HEkUceyTe+8Q02b97MokWLWLRoEffeey9RFPHxj3+c008/nU996lN7jf3NSKxpPgSEYUgURViWRVdXF5qy3WMzocgfGzN0iYwkGTvAF4KkpmL5ESld3njyphyw5gURbkreEGw/olYXoMiBFaoCBUN2DlA1QtVAdwuEyYK8earSO9INpJ9sEMkOkCECEBGFyKDGUGUnqjREIeFKD9KyT6XlRTiKQ2CmsbyQohaQC3UsA4qBQqLcaVCikJwHThhRNDXpGa2rlG15q96UqgjptSLUtEqh3MmMBNhJDad8M7TKPtNuBCm/iGvWYihQ9GVHL5vPYxWL9HR1YWsBva5Kja5Kr1ldRQhByRcUEirDpQA7oWGXjUftMMIyNPxIUEpqciCHkD/0ihBknAhNhVIQYSc0il5EUlex/JCUrmKZKoVyXTkvJKGp6IqCF8njpqsKbnlQlaEp1BoKfXZAja6S80K8pI7tR9VBSn4oKIURkYCBrANpgzBCDtrLe/gJHa1okNRUsm6Il9SoMVT6rQCQXrIDGYcu0vRb0pM0qNEZKgUM5D0oGCQ0FSuhkXUCrPKPV1KX/soVz3Dbj3BKNpZVpKe7CwDL1OS5hvyTFwnot3ychEba1OgteOTdADcpB8/4CXlryXshbhhR6yQZsgNMTakOhnOSWvWHsZjQKLohZvmY6ar097WUJEld/qEpeBFF1aekmKTwEZopB1RpBiWjVvothy5KGEhP45RNNjTIm+p2v2JVQ/Gd6jWRFB4lDIJIDhpyAnmMo3JbaKHLgKcRmCH5UKeoRwy4CkbZLzdv9xIWBVqxH6cg/5CKsrd2VKPLH+7Ix0H6t5qaQr5QqN4LVM9GcYuEhRDVLcqBVKpGpCfQ8z2IhIXQdIhkh0EJA4RuSp/Ysr+w9L2WP/yGCCgJrfyHTVCb0OQgwfLgOiX0EZqB0AyUKJAdVj2FTiQ9oBXQnAKuKTtYCrKzawYlSmqSpCrk/SryEJop/aED6Xfso6KV28xXzWqnWQ19Is2oeu7qCnKAYrmtUpFDTiSwyn62QPXeVRmkJRTpVw3gRErVIzihguKXUHyHQqhhFYt0dXXTFyaI1BJBokDBDcmXfeZVRcGye/FtnaIXUmOoWF6EldTkADtNwfYiaky5L0axnygh41DKAzC9pI0XyoGPqmshdJOKGbElDAqWrF8VIXkfbD1CCTwCMy3vewjZAQ8DOShPCMJEGjX05XEWEZFZg+ZaREYS1bWIkkVUz5blfIdQT5Y71L78jEB1CkSJvPTE1uWAczeClJNBmDVERhLNtSBwQTPwzFpMr4gSuLg1HkYgn14qQpBXUlV/dKtYpFgs0tXVhVNuI+m7L7ADUf1c+QPgRPKBQoQiPb3LDykURSEV2BTVFG4gSBlSPpBQ5W+CHwlSgY3QTYQiPaLdIMIvDzS0PHmf8MLyn8ooLA+glL8bhiYfJFWuXZCHpdJJTys+kZ5AK+URmo4wUtiBwNCUqluyIQKccrcoGbnbj4WdBVXD1uUxTKqiGl8qcrCUJKYmfaTzPlhlO2aU7ee0r8iHSzWGiha6eKpJT29ftY+wO0I7C9pOXbUw2G35PdHR0UEQBFxyySXVeclkkqamJl588UUA7rnnHv7yl7/Q2dnJAw88QENDA/fff/9el71lEDEHncWLF4v6+npRU1MjkL9B8XSAprhd43Z9o01x28bt+kaa4nY9uG0LiMWLF+/Sb8hkMqIGbY/rZzKZ3fY7fvCDHwhAlEql6rxFixYJQDzwwAMjyk6dOlUcddRRB6zP82YmftJ8CJg5cyYAH/zgB/nRj340xtGA8/hvSV74sTHZ9oaBPEeOrz9g9RUKBa677jpuuummA1ZnTNyuB5O4bQ8Ob4R2Ddc+gzbrnLEO41XxRmjXNyo9PT3MmjWr2kfYEVVVsQn5hDIFcyclrUfEr0Xnq3avqLhfdHd3j5hvWRbjx49/ldG/NYk7zYcATZOen6ZpUl9/4DqMo+GUSiRTqT2WqX//5w5qDHvi5IOw/4eiXd+K7Kld/d5NGG1HHOKI3jzE5+ye8fvbMVqnv+r1Xvftetq7R3wd+slXafnc98YomH1nf9o13LYCbersgxTR6x/vmbswz/nwHssUCnLcQqWPMBppVSOxg+Vce2SzUdj7FdO0adPQdZ358+fzL//yLwA4jkMmk+GDH/zgftX5ViMeCBgTExMTExMT8zokpSkjJlNT0PcwgNDzPLLZLKWS1Kjn83my2SxBIHXQF198Mffffz/33nsvw8PDXHjhhSiKUh0IGLNn4k7zISIIArZt28b8+fPHOpQ3HRdccMFYh/CmJG7Xg0fctgeHuF0PDnG7Hnjmz5/Pddddt9dyuqJg7DDN0tJcbLTstvxVV11FU1MT3/jGNwBpQdfU1MRPfvITAB544AHOPPNMPvCBD9DS0sLKlSu55557aGtrOzA79iZHEaLsrRRz0Mjn80ydOpXVq1czefLkg7qtfZFnvJnI5/MAr+9Xsm9A9tausTxj/4nP2b2zP/KMN2K7vhHkGfvbrrE8Y+/yjK6uLqZMmUIul9ulffP5PA0NDVybmkFyB3nG+tBiTWCzKMyNul7MwSV+0vw6xSnt3rx8T7yVOsyj4WX7xzqEtwRxhznmYLI/euY3Iq/3DvNr4a3cYQb22mHe53pUMFWlOh1v1HJZcvdPmmMOLnGn+QAwODhIU1MTv/rVr8Y6lJiYmJiYmJg3CcYOHWZTVdgU2czzhsY6rLcscaf5AHDddddxzjkH3kZo4cKFvO1tbyOKIr797W9z9tln77asZVkcdthhrF+//oDHERMTExMTE3Po2Xkg4EmJWj6Uju3hxoq40/waWb16NcVikZNOOumA1/35z3+e6667bp+8GNPpNF/+8pf5yle+csDjiImJiYmJiTn0mMrIJ81rA4v7SoNjHdZblrd0p/muu+7inHPOob6+HkVRqpYsFYQQXHvttUyaNIl0Os25557LypUrR5T55je/uU8jYF8tzy9aRG9vL5dddtmI+XvSOn/sYx/jscce2+PTZi/Te8BifD1iNraOdQhvGPy+LWMdwusK1yqMdQgxB4lw7TNjHUJMzH6xszzjlGQdH6mNf+fGird0p7mpqYmrrrqKW265ZdTlN910E7fffjvz589ncHCQs846i7lz51IsFgGYN28eRx11FNOmTTvgsf35z3/mwgsv3MX0/Lvf/S4TJ06kubmZf/3Xfx3R0W9qauLUU0/l3nvvPeDxxMTExMTExBxazJ06zZUpZmx4S3ea586dyxVXXMGMGTNGXX7rrbdy9dVXM3v2bFKpFN/5znfwPK/aKV2yZAkvvPACl1xyCXfeeSe33HILTzzxxAGJ7aWXXuL4448fMe+FF16gJp1m69atLFq0iD/+8Y/ccccdI8rMmTOHF1988YDEEBMTExMTEzN2GKo6YlrhW/zBGhjrsN6yvKU7zXsil8vR3t7OaaedVp2n6zonnngiS5cuBaQ046mnnmLevHl89KMf5Utf+tI+m8C7xdwelw8MDtHQ0EDncLE6b+rUqXz961/HNE2OOuooLrzwQhYvXlyt757l3TQ0NDA8PDxivR0/m02HzsD8fxe1s6wru8cy3nA3vTlrr3W5+eFd5j3XLkcQe1kpOSnYJYKl8159oPvIuv589bNdcg7adg4FXqYXY8LhB6Su0Y7Nq6Vo75/F4u7YHxlSIl036vzSX3/0WsOJGWO0WQd+oHZMzKFAS+noO0ynNTbxidaDm+8hZvfoYx3A65WKoXtjY+OI+U1NTdVlO7K3FJS1tbVYllVd17UKJKLdv2Kpr6+nr68Pq1ggr0e4rsuECRNGbNs0TYaHh8nn87hWAb9kMzAwQF1dXXU9AKtoVT8fSkLHomRp5PO7/2/mFYsUVZW8Eu6xLrdQJLHT6epYRQqGjm/ZmPk8xZKDX3LRRzk+BwK7WCCflJ9LjkPgewdlO4cCr1DE1HbfToXCvut7Rzs2rxar5BAF/muqY0f2tn+vBseL8A/gOfVq2jZm34nb9eAQt+vBw7L2/sBIM1Q0beRvqBbGOenGirjTvBsqWXay2eyI+ZlMZr+y+jmOw4033kg6nQbgnLPO4NJ3/91uy8+eM4e1a9e+6u2sXr2aiy666FWvFxMTExMTE3Nweeyxx6oyzn3rNGu7jG3SYo3AmBF3mndDQ0MD06dP58UXX+Ttb387AEEQsGzZMv7pn/7pVdfnOA6GYXDZZZcxd+5c3GKORO3u01++//IP8LlP/wvJVA319fUkEgk0TRuRMtMwDBRFob6+HlcVeH6BJUuW8Ktf/Yp0bR319bUA5AO1+vlQoiXTpNJ1e0zz6QVFAq2O+vr0HutyCUjsVE8y7VNXZ+CFlnRA0Q1SqQT6QUorWuNs/zOlGyY1qeRB2c6hwAttzH1op31J0TrasXm1qLpBbc2By2a5r/u3LximSuognFNx+tuDQ9yuB4e4XQ8Mf//3f8/f//3fM3/+fP785z/vtbxqqqg7dJoX57I8l8sczBBj9sBb+v9KGIY4joPnydfsruviOA5RJKUMV111FTfddBMrV66kVCpx7bXXYhgG73//+1/1tnRd59prr+XIk88EIFHbsNuyPVmLd51/Fq2trSxd+CQdO2iSK2wckK/MvLJ7RqK2gcLLj/KO889n1qxZTGmWneTNgwUSurpPuuH9oXO4SE929Lo/c8Z0TpjcuMf1zeZJtDXsucMMkKhv3mXemdNlKlGzUeq062pS6CdeMqJMtH7hqPVtGdz+ytHvb69+zhZtwq2v7FJ+VU+e5A5/7w9Eh9nL7dlrM1jyt32ua+vQrufInjCb2l71OrtjtGOzIy927P0GX1uTYnP5mDy8tu81x7Szdv+16K5Tf/eF1xpOTMxBI3jlkbEO4XXLhz70IQ477LDdLt+4cSOmafLpT3+6Om9VT55w5eMHPba5c+dy7bXX7rWcpqtSolGe3j6umU/vYZ9iDi5v6U7zHXfcQSqVYu7cuYDUHadSKRYsWADA1VdfzZVXXslFF11ES0sLzzzzDPPmzaO29tU/tQ2CgOuuu44FTzy2z+v8+Mc/5tprr61mBHz22WdHLP/Nb37DL351OyBf89x88838143/+apji4mJiYmJebNx3HHHsW3btqpN7M58/etfJ5VKcf311x/iyGD+/Pn7lONBM7VRp5ix4S0tz7jyyiu58sord7tcURSuv/76A3JBVZ40u4l9f8V19tln88orr4z6pHln0uk07e3tOPbBeaIcExMTExPzRuL4449HCMGaNWs49dRTRyxbtGgRf/rTn7jxxhtpbT30yULmzp3L8ccfzy9/+cs9ltNSOro+squmBbspHHPQeUs/aT6U7M+T5piYmJiYmJj9o5LrYPXq1bss++pXv8phhx3Gl770pUMclWSfnzTrmhwMuOOkx0+ax4q403yIqDxpvvJDo+uhhwt29fOy3iKJukbcQhaAu1dIz9kl27JV3acoO8401tZU1yvaJZI1I/XBM8bVkXVCVGBD/0jrIMe2qn62o/na5qwSJWd0P+Jo4yIApjTX0mSO1AgDdAwX99nL2Mv273ZZtPmlfapjdwQTjh51/uHj6nixI0N/zsJonV6d31hbg3bY23bRaR+ZKHJYy3ZZzo46aH+gY0TZnLVvnsNmw7jdLusYLqKf/O59qgeoxrazTnogL8+rRVulpnfJtqwsN9i52/3ZH/bkx33qtCZ+tXhr9fvmwdEtrGaMkz7Jl86aMGJ+RRP92AZp6N8/ij6/Mm9H/XTFV9uxrRG669e6r68nVvUcHHvFmH0j+4tv7Nd6K6541wGLQX/bxQesrjcbM2fOJJFIsGrVqhHz77vvPp599lluvPFGksmR41OOm1iPdvyFBz22fdY0m+qI6YXMMLdu2HTQ44sZnbe0PONQUnnSfPnll1c11DExMTExMW8khICDbd1cVwfKAcgUrWkas2bNGvGkOQgCvva1r3H66afz4Q9/+LVvZD/ZV/cMzVTRjO1Pls+ZOoET25q5Z/O2gxJXFEWcd955rFmzhk9/+tPccMMNB2U7b1TiTvMhovKkeX88nmNiYmJiYl4PFArQsHvzpwNCLgcHyuHu+OOP57nnnqt+/+Uvf8m6detYuHAhSrln7nkeN9xwAw8++CBhGFIsFvnwhz/Md7/73ep6a9as4dprr2XVqlWYpomqqnzxi1/kYx/72H7Ftc+a5rI8Y8Q8cfCSlamqyu9//3sef/xxNm7ceNC280Yl7jTHxMTExMTE7BN1dbJTe7C3caA47rjj+P3vf49t20RRxHXXXccHP/hBzjzzzGqZr371q+TzeRYtWoRpmjiOw+bNm6vLX3rpJd773vfyox/9iMsvvxyAbdu28dBDDx24QHeDljTQTGPkvIMsrJ06derB3cAbmFjTfIioyDPmz58/qk7Xi7anxbxwnEwnnKhr5OlNg3z+NPl0+m9r+zBV+c/4yNaRdxXHtqrJIYYLNs4jt1WXHdlaR+sOXsgVrXGyJo0w5fwdfW2DTqn/akinSCVH9yNWZ55R/ZysSWP7AtcqVH2Rt2ZdalLJql654itdoeQ4VZ9gs1GOXO7KWLvorrfWH83q3l11m9GmxbvMG82Lekfd8PLuHGv7ttfVZ3m0NqTpHC5Wdcm/X9qJY1tMbEyzrCvL/y5qx8v2YzZPAqhqnXfUQW+KGkdssyH96pJ0VHTGFV27UyoxwdyeVvyJjQN7XH9H3Xllfyv7mXVkPWccJjW9J0+VsZrjpoyoQ7W2+xgX7X3TZO/Izn7cXRlrhN72/5223Ve0ol3emXDtM9XzZONAgaB7HSA10QAXHTkegNaGNA+u6cO1tpf1ymll65Pbn8gc3VpPx3BxF53/jsduZ9ziQe4N7AODP/rKLvO8Z+4atexxEw/M47iD5eP+ZqfxX/bd4tP6w/bX3LP/cPA7WwcLRZFPgQ/mdCCkGRV2dND4n//5H7LZLP/93/89osyDDz7IO9/5TkzTBCCZTHLssccCMp/DRz7yEW644YZqhxlkx/Jf//Vf9zuufR0IqJo62k6Tau7+eeddd93FOeecIxN+KQpBMNJqQwjBtddey6RJk0in05x77rmsXLlyv/fjrUbcaT5EVOQZsZ45JiYmJibm0FBx0Hj88ce5+eab+fznP8/hhx8+osxll13GJz7xCS699FJ+8IMf0N+/fXD6/Pnzyefz+5UJeE/s60BAVddRjZ0mffed5qamJq666ipuueWWUZffdNNN3H777cyfP5/BwUHOOuss5s6du1sv65iRxJ3mmJiYmJiYmDcl06dPJ51Oc+2115JKpbjmmmt2KXPLLbewcuVK3v3ud/OXv/yF448/nkxGOvEsWbKEk046aRev5EOFauqoprHTtPtY5s6dyxVXXMGMGTNGXX7rrbdy9dVXM3v2bFKpFN/5znfwPI977733YO3Cm4q403yI2TJYQJ1xyohUzcMFm34roGO4yIqeHL/Y4LNo6zDLu3O01Zl0WwE5q8T7jmurpsf+3tMbGS7Y/H5pJ0D1FfRfV/fSXFdD8uJ/xu+VtjQDeZugcxUbh22uf3QdOXf7q39EhFvMVW3SwpWPo085rrp4x1f1FdnGjvtSYZa7mS1FweCEEwg6V3HyxLLsoyy9mDl++yv5h9f2kUomSenb38F1DBdZuC3HM2XLMH+gg3uWdzMpEXJsm3wFXbEcA1CPOA2AGx9fX513x7KeXezfYLtt3ZxJDRxZ2syfV3QD8J5jpLXZlOZaMmYLvTmLGc01JGukNOOEyY3889FJzMbWqk3axMbtr/pX9eTZ0F/A1EZ/lzhaOu4dmb+uH7vkVCUTzXXSPnDFgDMizfpZraN7clYsCVV/uzyjIm+YNUG2WUJXqimknZI8ljtbrnUMF+moO7L6vSLzcUpSLvK/i7aXv3dlDyAt7Oavk09jssXtdokVJjelR0gHdnz9P1r68I0DBYYmnsTM8XW4VkGeL3sY7PKeYyaQSMtzaub4uup1cXRrPUHPhmq5ac17z965ome7JGNP6e33l52tF8PuNXssP+4L399lnnnOyFH+o9kaOra1TynLR2N8fvPeC73F2FHKdSBIX7FrZ+31Rrj2mbEO4YCjKArHHnssjuPw7W9/m4bdjGI88sgj+dznPsdjjz3G4OAgW7dKm8z6+nqi6OANvNsbmqGPOu0PuVyO9vZ2TjvttOo8Xdc58cQTWbp0aXXeFVdcwfe+9z1+97vfcdFFF+H7/mvejzcL8UDAQ0RF03zWRZdy+G68mmNiYmJiYmIOLIsX7zoGpsLf/vY3zj33XOrq6hBC8Ktf/YqZM2dy3HHy4dG73/1urr32Wp599lnOPvtsADZv3szixYtfk2XdPlvOJUy0pMlTG7bx1EZpM+cF4V7WGp18Xv4RbGxsHDG/qampugzgD3/4w37V/1Yg7jQfIiqaZu9VpNGOiYmJiYmJOXj87W9/44tf/CKpVApN05g9ezaPPfYYhiEdK2bOnMl9993H1772NfL5PL7v09LSMqrM49Wwz5Zz5cF/Fx53OBceJ7XYBcfj/xav2uN6o1Ff9vHLZrMj5mcymdgOdx+JO80xMTExMTExb0luvfXWvZY5//zzefbZZw9BNLuiGhrqDnKMJ9a288iq/ZNTNTQ0MH36dF588UXe/va3A/It+LJlyw74QMc3K7Gm+RDy8Lp+NFXhwTV9aIe9rTq/ua6GOZMamJzfSMEN+NzbD6cj53Bkc4JJtQYJTeU3y3qY+cytfO7Pyxkq2Hz1vJk019XwjydOqepYveFujh2fxnvmLrxsP0bbEQA0rHoYfcpxXNRY5FOnTiHnhvz8ha1sHSqidyzlqS6XhBKxti/PXzmW/13UXtXz2b7UchXs0gitM4CpqWzoL3DP8m606ScwQ8vjhAJ9ynHUpJI81z6Et/AeSo5T1dXC9jTJlh9RsEs8sXGA4VLApTObOGdaE24hy709Oh+aM4k71uTY0C+t7N42IU24+imAqm71y6dvT7n81fNmYoyfxj3Lu0fE+VR0GMMFmyXbsjwXTeWyaSbhthWA1PO+2JGhtSFNW0O6as3WkZM6VLOpjeXdOWaMq8N94rd4g51VffNxE+s5srWOiqLZsUfadq015VOBio1eRQO8rj/P957eyNyjW0fqy5HpzCsWaxUS9c276IA7hmWqdYB7N1u4+WHc/DBT0ir3ruwhZ5XYOlRk0pan2Wjp3LO8mxsXbMUb7h5huVa0S0wMBpm87uER9d+5tJNkSmqsT5m0XQP4/uMnAnCav57zp0jtcyWVe8WO74v3rhhR16Ktw7SVLQ+9bD9mwzjmr+sfoZc3VKVqi5hI18k04pOPYTR2tLJb3p2rtseDa/oAuH9wu+58dym7d+SoenkbPFh2czWpkbaNkZHeTcl9pyGdomiX2DhQqOrMkzXpXc4dGKlT3dn6sYI2dfZet7mv6eG94ZHXX+W47I1g6bzdLvNf/Os+1bGvbPjMB/ZapjIuYEe8wc4DGseO/PNdS0fV+x9KtFnnjMl2dxxnURkD4PdteU11OvN+sV/rjTZGYyzZxT1D11DU3XfdwjDEcRw8zwPAdV0cx6nqsq+66ipuuukmVq5cSalU4tprr8UwDN7//lg2ui/ET5oPEUEQcPet3yfx4Q/QdNzbxzqcmJiYmJiYmDFiXzXNqjkyuck733YUpx91GLc9s2zU8nfccQef+MQnqt9ra+Vg6CeffJLzzz+fq6++mkKhwEUXXUQ+n+eUU05h3rx51XIxeyZ+0nyI0HWdf7jqK5x3wUVjHUpMTExMTEzMGLKvPs1a0hwxPbmhgxse2L3LyZVXXokQYpfp/PPPB6SbyPXXX09vby+2bbNgwQJmz977m6YYSdxpjomJiYmJiYl5HaLq2gh5xsUnzeK6Ky4e67DessSd5kPIiRPSTDBDmlNSFTN/XT9DBZuO4SJbh4po00/gzOktfO7Py3l/a4lUMsmmjMvkpjSfPHEi2ruv4ieXz+HB9UMAdA4XWduXZ+b4OrxMLzm9kZnj6zDP+TBqUXoa9+YslBkn4fe34zdMpvnlPzNrQj0f3XwH42t0HtOOJeP4JGvStKR03n/8RP5pzgTufLkLb7ibJm+IcO0z1NWk8LL9rO7NV3WRbcXNHO51cLm6Br+/HTvZPMIX93StG/OsD6F6Nptsveqz7Pe3s2WwwOHj6tiS9bhg5njCCOpqUhzZWseaPMwal8bL9HLBjGYGbI/MxBN5uafIrwpSJ6xPPJJFW4ervroVraFrFegpbPfF/d9F7Zw9TmD7ESdPbaS11uD5Qbj43izz1/XjhYIaQ6vq51b35ik5DrYf8vQmqS+sNVVe7MiQuOBjmOOmMGNcHeHWV3hmszwOh5fTQj+8OV/1QobtKY4rKc9njZPa3+mpiK+eN5Ohgl31fXZsi39/YBVDSh1etp+gaw0D+e3aurs3yX2qeApPa66tfm5NmyTqm9lk6/RedxXvP34iychFU0A/+d280JXl745s4rq5szCbJ43QDz64QWrNvRMvq+rY3WKOj544hftX9wIwp1GmqI7WL6zqstWZZ3D7iqFqPfPX9ZNzQ25+ZhOfP3s61zwsvYhvf6mjqhP//dJOgrJ7zH0resg6YTUF+GEt8rwJuuR61z2yYYQPOMCyrixOqcR95bgAptUblByHhoRGv+Vilxwunz2punyq2L1vceWcWT4k08wa/VInv/Ij797tOq+Wnhs/u8u8Ymp89fPevLz3RG1Nit+81FnVme+OHXWqO/qlA4Tty/Z5ew3pFFuH9p41rJJyvkLFD31PlBwH/cRLqt931pUap/7dPka5d6JNiznyf/80Yt5o/u6j4Tywq052tGP8anAeuQ2A2z58ImbDuNdU14Gg738+f1Dq3dnnf0eM1unVe0FlDIAxQd7rd/Rdjzbtah03XBh5rlTGliQv+Re5fve6XbzpK2T+92u7zKvxsruN89USbVwEQNd1Mt32d2uO3FPxUVF0c8T02MotfPuPTxywGGNeHXGnOSYmJiYmJibmdYhiGCOmi089nus/dtlYh3VQ0bTRE3q9HogHAsbExMTExMTEvB7RDdDN6tdHX17NQ4tX7GGFNz5CiLEOYbfET5oPEUEQcPP/3Mijjz0+1qHExMTExMTEjCHz58/nuuuu22s5RTNQ9O3Txae9jes/cfkhiHDsUBRl74XGChFz0MnlcqK+vl48v2Kd8LvWigdW94qhvCVK839VLfPLF9qF17tZCCHEp/+4TPidq8WmgbwoWLa48Yn1wnnyzmrZTMESW4cKomQVhRBCvNKVFe2DBSGEEF7vZlH6263ithe3ivV9eVGyiuKpjQMi2LJUeH1bxKPr+4X/0oNi3to+UbJtcf+qHtGTLYqebFEs7cwIyy6JcNOLQgghhvKW+NqDq8RLHRlhl0pCCCH6skXxSldWrOrJiZ8tahdudkD0ZIvVfVjenRVCCNGTLQq/c3U1vpJti5XdOfF8+5BY2pkRD6zuFeGG58X6vry45dlN4jcvdYg1vTmxcMug+NqDq8TSzozcn/6t4pN/eFkIIcT6vnx1205uSHz8d0vEHxatE/817xUhhBAru3PV7bnZgWod7YMFcfcrXaI7UxTz1vYJp5AVa/ty4uO/WyJ+81LHiGNVsm0hhBBOPiO+9uAq8ej6fuFmB4QQQvidq8XSzoz47ZJt1fKVfV/flxeLtw5XP+9MJTbLLonrHlkr+nOW+OpfV1bX8V9+uLrdNb05UbKKwu9aK7JFGc/WoYJ4aE2vEEKIG59YL4QQIti8RPzu5W3VY1Ohci4IIcSPn5Pn1KPr+8XmAdnWQgjxwOre6raFEOK2F7cKIYT4y4puIYQQD72yWQwveUR0Z4riT8u7qvUM5i3xm5c6qt933L/K+Tga1z2yVvxpeZf42oOrqvFli7YoWUWxcMtgtZxll8SaXnmeBO3LhFOUbdk5vL3u7kxRPL6hv/r9q39dWf38wOpe8dk/vSJW9eTEt+at2W08FTYN7Hqsbn1+ixBCiNzt/99e199X3GfvFl6/bONcLidyudw+r3vLs5uE++zdQojt54mb6RN/WNZZLbO+Ly829I/clwWbBsWqnj1vx8kN7XMcB4odj+WBoHKfGa1dSw///IBu67WSKVhCCHntCiGEt/j+/a7LHeo6IDHtDvvem4UQ29t127f+32uqb2/XU+Xeu6+s6d33a6iC37m62vbVedu23z+CzUuE37W2et95tex8btsP/GS3ZcN1z4rOzk4BjHo/yOVyAhD9f/2ZcB7/vxFT/19/ttv13gyoqjrWIeyW+ElzTExMTExMTMzrEKllNqvTo0vX8K1f793f+fXON7/5zbEOYb+IO82vgUwmw5lnnsl5553HKaecwp133jnWIcXExMTExMS8WdCNEdPFZ57Cd65646e8XrHijanLjgcCvgYaGhpYsGABuq6Ty+U45phj+OhHPzrWYcXExMTExMS8CahomneeFzNGjLU+5M1CZ2enuPjii0ddVtE0d3Z2iuyvvilKti38rrXC690sgi1LRckqVrXHQ3lLFCyp9cwWbfFKV1ZYd/2n+OUL7eKzf3pF+F1rhZsdEO5Ql+jLyjL/89QG8f0FG0XBkpqwVT050TlcFMGWpaJ9sCCC9mVi4ZZBsW2oIEq2LRZuGRRez0YRrntWxj5cFH3ZonCHusTCLYNVna4QQvjd64XXt0X43VJHu6P+dChvibtf6RKvdEkdc0Wj6jz+f0IIIb7wl+XiPT9/TjzfPiT+tLxLdA4XqxrL/pwlFm8dFu6CP4i1fXLeSx1lPa9ti61DBRGselI8sLpXuNkB4Tx6uxBCiCv+b7H4wl+Wi5c6MkIIIQae+IPI5XLigdW9ou97XxBf/etK4S2+X/xpudT7/fi5zeJ3L28TBcsWtz6/RWwayIv/eWqDCLYsFUJIDXGw6knhDmwT2aItvvnQavHbJdvEhv68+MJflotH1/eLj/9uidg2VBCf/dMrwh3uEf7LD4sN/VJf/a15a8SCTbJN2gcL4lvz1ojFW4elXm24KDIFS/xsUbtY1ZMTNz6xXrhDXaJk2+K6R9aOOEfaBwsj6lnenRWbd9DbzlvbV9W8CyGq+uadNblObki4A9vEV/+6Uizvzoo/Le8Sj66X+t9fvtAufrtkm3CzA2LBpkHxzYekFnRtX064C/4gj3fnatGTLYpbnlgxQi+3s057VU9O9JXPk+fbh8S8tX3VuJxCVrzSlRXPtw+Jxzf0i58tahcru3MiU5DnS/tgQbzn588JIYTw+raI59uHROnhn4tfvtAudmbzQF48ur5fWHZJ/Nt9K8Tmgbyw7JLwejZWt/fj5zaL3y7ZJobylsgWbdGfs0TJtkfo6oUQItzwvPjdy9uEO7Btl+04hWz1c9CxXAghdtGK70jnt/9lxPeddZJ3jT9mt+sOrXtpj1pE667/3O2y0Sj97dZdt5G3ROdwUVj27vdBCLFH7Wb/zV+qfva71++iIfW75DnsZvqq83Zuh53Zkw61MpaiMoZACCHyll3V0np9W4QQQgz88Mujx/vgL6rtWrm+hRDCXzZ/l7Luc38atY4DqYGuxCuEEL9dsk1em+W22pseecfzcV+pnLf7y46a4o5vfnIXTfOOrP/05a+6/so1K4QQQfuyXZZX7nc77kdVw58dGKHhH43fvbzrdS2EvMfvK/nyb2jlfvhqeWrj9nN3x2O44h/fNWr5fdE0Dy34k/BffnjENLTgT294TfN73vOe3S6LNc2vU+666y7OOecc6uvrURSFIAhGLBdCcO211zJp0iTS6TTnnnsuK1euHFFmYGCAc889lzlz5nD55W/uEa0xMTExMTExhw4lkRwxPfLicq753zvGOqzXzDXXXDPWIewXb+lOc1NTE1dddRW33HLLqMtvuukmbr/9dubPn8/g4CBnnXUWc+fOpVjcnhVr/PjxLFiwgDVr1nDDDTcwPDx8iKKPiYmJiYmJeTOzo92cohtccv5Z/OfVV411WK+Z008/faxD2D/G+lH364Enn3xSAML3/RHzp0+fLm655Zbqd9/3xbhx48Rvf/tbIYQQjuNUl1mWJWbOnCnsUWxzKvKMeS+uFgXLFk4xL5zckPjLim5pq/XkncId7pHb6FwtNvTnhTvUJX62qF10Z4pyncf/T1h2SbjZAXH3K13i1ue3CDc7IB7f0C+lDFuWiqc2DoiSbQtv0b3C690sbnhsnegcLgqvb4twB7aJcMPzoj9nCb97vejOFMW2oYLwFt1bfeVdkXc8sLpX/Gl5l7h/VY/YNlQQBcsWmYIl3OEeYZdKwlt0rxjMW8LN9Ilbnt0kBvNlG6UtS8Wj6/vFQ2t6xeMb+sXavpx4auOAGMxbVXuz59ulxdWtz28RBcsWa3pzYjBvVa3qNvTnxeaBvPjT8i6xtk8u29CfF1/960rhZvqE37VWXPF/i6sShFc2d4llm7rENx9aLb41b40o2bbYNiQtze5f1VN9XRaseEzc+vwWsb4vL741b41wM31VS7pVPTnxyxfaxfq+vHCK+Wosq3pyYm1fTnRnpAzBHe4Rv3t5W1WqsqY3J259fov4/oKNVYnJ3a90SXnDs5vE4q3DVfnE/at6xPPtQ9VXdpsH8sLvXi8suyS2DhXEyu5c9VX6DY+tEz9b1C76c5a44bF14pcvtIuSVazKNyqs6pHrbBrIj7AW29GObetQobrsU3cvFUJIic0X/iJfgW4ayIs/LOusxrVwy6B4vn1I5HI58eMnV4rHN/RXbeh+9/K2qtXcj5/bLD77p1eqbfj9BRvF4xv6xW+XbBNX/N9iIYS0ufvDsk5x6/NbRH/Oqp4HXq+UzDy1cUBsGsiLvGWLBZsGxVMbB8TmAWnd9/0FG8XCLYPCHeoSv12yTbzUkRFLOzOiPyfPtUzBEu5Ql/jQr18QH/r1C+L+VT3ivb98vmqd5/VvFQs2DVbjC1Y8VrXv689Zwn/5YRGsWbDLtSrE7l/d78xDh83ZZd7mr3x0xPcdpRN+11qxdaggBrZuEI+v3FWKsjsqr7J3liVUXlfvSWJReTXtLvhD9fzZUbogxOiSip1fn28dKuxSZmcq0otNA/nqdT4aFVlYsGXpbo/BvrKjZV4ulxPDq56vfl/69yPlcsO3/sce69rREm1n+74KFdvABWecKb/vIMFwF/yheg8tWcWqpMxbfL/wFt272+2OJq/ZkR1j2VGOVbnvHmxerUXi7qj8xlWO/85s6M9XrRWF2G7TWWFolP31X364KhPb8b4nhKhK+HZmd9KX7kxReL2bhVPMV6VHo11bO8p9KhKW0e4Z9v0/3KvMY1/kGZmlj4tw4wsjpszSx9/w8ow98XqWZ8QDAXdDLpejvb2d0047rTpP13VOPPFEli5dyj/90z/xyiuvcPXVV6NpGq7r8p3vfIdUKjWGUcfExMTExMS8WRCqjlD1Xea9Ufnud7+7S/ISIQSKovCNb3xjjKLad8a85c8999x9KpdMJnnkkUcOcjTbyefzADQ2No6Y39TUVF122mmnsWDBgn2qr7a2FrdkUygU0EUAYYBnu+TVEoEboBSKmFqewLKxvQJ5rUjoBBQLCTBVdDcgKuTRPBu/5BE6PvkCuLbAMgPyVgknLJIvRGiOB0UL4QZYhQL5wEKoOrrtUCwU0GybYlAgEoK842EVCxQinUhAFPh4dhE/CBGailUUaL5GEAkCv0jkq2iOR7FQQIQWkWNRLBQwREBolXCDIkEYga5iqx6O5VMshJQsl3xew7GK5PM6oWNRKBSwiz7FUMe2fPJ5BbtYRFXAt4vYRUEx0LHdEMWzyReKqCUbPSiRL1oonk3JMhAC8GxQFPKFApYTktcjPLuIE+jk8yah7RI6FnbRBNcmXzAplQzyeRW7WCAsWdhFg3wEdjEqx1JAU0E1NPJqiFco4pdC8kUHPZ+X6zkWkR9iF5Pkk+CXihQLKpFjUbJ0rEgnbwoZCyZ5UyURKVjFInnfxtfyWE6I7UYUNIPA9xCuTagHFAvJ6ud8IYFjueTz20dN28UCBdXAsgPcQFA+LXHtIvl8AgCraGF7Efk8qH6JfD6PYxVle+bzWMUifskiXxAkIgXHKqIqCgVdI3RtXLuI5wTk83n8kkXo+uTzeULHQvVLlKwC+bzcX9dWCUqePD75PK5dxC/5hI5PsVBAVZDngW/hlwwcLcDSPJKhjmOViITA0jxKpYDIsXAsnbzqEpQEJUugqVDEJEFAwSoR+EX0oASAZxcxQoegZMlYixaOJdADlXxeJbRdrEIBJfQpFkpoJRdFaGiVRtsBz/ExR5m/S7maVPVeUMFStBHzHC/EK38PijZWUCCybFxb32Xd3RFaJbR8nmKkjIjLL++raxdJhKOnnXWLBRJCxXP88rVnVOvbuf7Rtlndr6JFXo/2GKddLJBPgVUs4tgB+fzoPy+R7aDm84RWCVxn1GOwr7iFIonyz1ihUCC0t9dXMswRbVwI2eO2CgFULiK7WCSf2LVN/aKFkcjjJBLV88xIynU8x8cvFIgCH6dko7gBiXwev+SBiDB2s+0dz5HR2DEWq1gkb8rPxWIJQwS7Xe9AUSgUDkg9Xvk3rnL8d8YuFsnvcO3ZxQI7FisWS/K3cweCkkuxUCCphCPue0D13rQzrlUgEe2aca5YsDE9i0gk0Io2+m6uraDkopcDc7wIP58f9Z7heBHqXu4llmXtdlkVVZPTzvPeoLzRHcbGvNP8wgsv7PXfhRCCm2+++RBFJKmvrwcgm82OmJ/JZJg8efKrrs9xHG77yc38rakORUS849xz4MgzDkSoMTExMTExMW8AHnvsMZ544glg3zrNQjcRuln9/shTz/Lgo08etPgOBg888ACXXXYZAHfeeedu02S/EZ40j7mmuba2dp/KNTY2HrQY9qRp/uEPf1j97vu+GD9+fFXTvK/kcjlRU1Mj/uGfrhQ///2fhTvUJVNBF7Ji61BBzFvbJ+5+pUsM5i1pKTewTbhDXWLzgNTXer2bhd8lUy/7navFH5Z1ioIlLbX6c5awSyXh5DMiU7DE+r68uPX5LWJ5d1Z0Z4rCzQ4Ir3+r8Pq2iL+s6BZLOzPi0fX94qWOjLj1+S2ic7gofvfyNrF5IC/W9uXEml6p4XWKefG1B1cJ+96bxbahgkwt+uSdwu9cLdyhLmGXSuKWZzdVNZN+93rhDveIDf1Sz7h5QOqQ71/VI5yctJyraGsfWtMrBvOW2DyQFzc8tk4s3jos8pa0mVvflxc/W9QuvjVvjXhq44D4+9sWiXDTi1LXW07F7RSywh3uEQ+s7hXrtvaIvr4+4WZl2aWdGfFSR0b421aKpzYOVG3w7n6lS7iZPvHNh2Qa1e5MUWwdKlQ1ryd84yEhhLRle8/Pn6vqeCspq4fylljVk6taq/Vli6JzuCi+v2CjCLYsFfev6hFBx3IxmLeq1mm/e3mb2DpUqGrrzv/B02LhFpnaeLBs13f3K13C69siFmwaFJmCXHdDf14s3DIoHlrTK7y+LaIvK+0In28fkvrjfEZsHSoIu1QS2771/8SGfqkDXrBJ2gUu3josfvzcZvFv960QK7tz4qWOjHDyGbFg06Doz1kjNJKPru+v6oofWtMrbnxivVjZnRN9vT2iv0daEH71ryvFo+ulddxH73ixqqus2PoJIcTPFrULr3+r8JfNr2rKNw3kq5ryyrn1tQdXiXlr+0S2aAt/20rxoV+/IDqHiyLc+IIQQmrdN/TnRftgoXoMKt8rmu/HN/SL5d1Z8dsl20RPtijsUqmavrZzuCjW9uXE+r682DpUELe9uFXqqPukhn7rUKGq6xdCauyd3JBw8hlRevjnVW36jnT/51VCCCF6//tzVXtFIUZqGwt3Xlf9XImzL1scYbNVWdZ127VicJNM3Wvf/8NR7beEGJmCt6Kd3DG+wp3XVe3p3EyfsO75byGEqOq+w00vVm3cBvOWePHSC8TFP322arnnv/xw1R6yQrhhuyZ4Rz3nYN6q2k0O/vjq6vydLeScQrZ6fCt0DhfF+r68eGB1rwg3viA2D+ya8ruiz3WK+Wqa9R8/t1k4hawIVj0phNhJP1zWxwoh7dvs+38oUz339FX34zcvdYzYRl+2WN33HW3IKvuzoz1hRZtcOcY7tvvOqckrOvaKtnbHNtwZN9M3QkNesOzqPUYIUbWV9Pq37mrx17laeD0bpQXnDvGPZjUXrHhsxPfKfa5iJ1o5n3Yk3PjCqPrqiqZ5T/tVoWLZJoTUalesGd2hLuE+e/eo5/q2oUI1lbX/8sPVa6Zyn/O71o6o113wB9GTLYrfLpHHsj8n783bhgpiQ3++ei+p7GfFOnJ9X16O+Sm38cru7baZ6/vywvrT90SwZam0qty2UrQPFoTfvX6E7t6+X/YHfrZI3uMr15wQ0oavcu37Lz24yzo7M2/ePPH//uF9e7ecW/ey8LvXj5iG1r18UDXNd911lzj99NPFGWecIe66667XXN95550nhBDi05/+tNi6datob28fdaoQa5r3QFdX1z6V27p16wHfdhiG+L6P53kAuK5LEASYpomqqlx11VXcdNNNXHDBBRxxxBHccMMNGIbB+9///le9LV3X+cJXv07j+DaguNfyMTExMTExMW9O5s6dy7F1Ib+6+749lhOqNoqm+eDJM3K5HNdddx2LFy9GURROPfVULrnkEhoaGva7zra2Nj796U+zcOFCBgcHmTNnDro+5t3P/eKAWs5FUcT8+fN5/PHH93md+vp6/vjHPxKG4V7LHWjuuOMOUqkUc+fOBaTuOJVKVXXKV199NVdeeSUXXXQRLS0tPPPMM8ybN4/a2tpXva0gCPjR927k2ScfO6D7EBMTExMTE/PGYv78+Vz/o1/uvWBF07zzdJB44YUXOOuss6itrSWdTnPWWWfxwgsvvKY6f//733PFFVdQLBb5wQ9+wCmnnMKZZ57J5z73uQMU9aHjgHaa//Ef/5G//e1v/Pu//7vsJP7oR/u03pVXXsm0adP49re/TXd394EMaa/bFULsMp1//vkAKIrC9ddfT29vL7Zts2DBAmbPnr1f26o8aT77HRcdwD2IiYmJiYmJeaMxd+5cvvWFT+21nFB1hLbTtAf3jNeatG1oaIiWlpbq9+bmZoaGhvZjD7ejqirnnXceDzzwAHfccQfLli1j3rx5rykhXHt7+2uKaX85oJ3mvr4+fvSjH1FfX4+u6zz44IP7tF5XVxdXX301d999N9OnT+fyyy9/VU+r3wgEQcDt136Jxx59hM6ojnEpDYRgghlyxpQ6agyNnBsijBRacZDe732TyXqJIxoTKL0bKDVOI22oRLXj+cC4PFtzPs1hjuc784SRABFR17mEAdvj/x0umFRr0JjU0La9gmpnyKdamViX4PDGBBcdOZ7xaZ33zRpPfULlg1MiGhIah9UZHMEQqwZstEwH/3zaVJ4//qMs2JpFbFuDPv04ulNTEWaaDcMu/3rSRJTQI2eVEJ1r0LpWMXN8HSf3LmBiWueGc8ZzabNFX2By+exJZB2fLYMFpjWk+L9lPZiaytsPa6IppdOR8+nMuzSnNAxNYWpTiiOaktz2odkIzeDSI8exdtjl5Z48w4HOimKSV7pzrBm0MIa3QBTwtYuOYnawlaNbEpSaZ/DrFzroyHsMuXD65Hqe7IPPvn0aL2uHs3HYYYIRMKs5QedwkXu+eDbPtQ/xj7Mn8IsPzeGY8Wl+8vwWvnxiA+v686QMlaPdLUypTzJUsGmMCqzst/jkiRNZnTichoROV/pwavG4crLNQN5mwPYYsAJqEyrLu3PcdPkcVEXhjiWdOIHgQ3MmMaU+wWaamdaQIBTwgWPHA3DyeJPTJ9fx5x6DBsXlzHSO//jTcjYNWty0uJ+tWZdUMsngv3yPpqTGU1uGOLUtyc0L2rl+3louP6aVT5w2lRlNJid66ykpJm4YUQoi5m0cZPNggfnr+nmpM8t/PbEJUwQ0JHW+9o4jmVmvkHEFT3fanJq2+OyZh3HP0i4MTeHS49qYnIz48v0reWBFL999fD03P7OJC2c0Y4yfxm3O0czbOMTy7hx1pnwasqonT0pXURT4hxMmMaHWJJ3rQPVK/Nd7jmFb3iXbejyuVeDjb2tjutfJhuESc49ownnof5k5vo5SEHHH8j7sksPzWzNMbzD5hyOSNBqgFQdpK26m5DhMHFrB4YbNjOFlTC5s4iNH13Oi3k+47HFMe4jxC35J89BalMBhy2CBWS1JujyDXk8nc8ZHOLq1nnX9ebxML+3/8XEAGv/t+/Tc+Fn0z/w3bWmd4YLNzc9sQn/bxQwXbEqOg/73X2W4YOMNd6O//FcAGhSXP/clAQhXP4X3zF1okY/y7qtY49XTOVxEvPNf0A572y73i2jTYrjonwEYLthoZ38Qt5Dl6NZ6gs5VBEvnkZx1MuZRJ+LlBhn44f9H6ZLP8fDaPoadALeYwx83g99l2wCoX/sY2/7nTu6fuRbVLdBz42cZnHEOygnyT3y49RUANt9yM24xh1vMkUjXEW1aTH/OIq0Jjh2XIty2gsbzL8Epldg4UKAlpZOzShR/dz0Aqp3hxLY0tcMb8Ya76claTMysYYbbTnNKxx8/k/E1OmlD/vS4xRxFu0RTz1LcQhZP0WnPOmzoL/DOI1ogCgknzgJk5yHoXkf2F9/AbGrD79sit1nKcXn3KQDYXkTQvQ515hl8/OSpuIVstU2b3AHQdDYPFkjUNQLwXPsQjZd9lLB9GWH9RAD8gQ4SxT6CpfOIpp8EwOF6kdKDPwVg5pZHCbrWEHSvwxvuZspXr6fkODTb3ZT++iOyE+ZUt1m67wd42X76/ufzALyc0wkb2gh6NtA5XGTlQIl/PyxHuG0FbiGLMeFweTxqxzNj86N87+mNZIs2YfsycvWH0WNOQB9qp9vVyPzv13hm8xBh5/qR5876hYSFLP6L8jz0+7Yw5c/fIVz9FG0NabZc/U+k3vdvBN3r6Lnxs9X11CNOwzj27SPq8nKD2Pf9kHDts6gzzyB32zWs6MlVl+d//S38vi3Yf/wfcrddgxmVpY5P/Jbkuz6Dm5XOG9uu+zKKmSRMtxC2L8O1Ctglh6JdwosEIlGHM+8XKIZBVNfKhn55jCb3vkRU00Qq0866/jwDeRv35PdS/OYneOcRTazrzzPsBBxRK2hVbTYNl/DbjsEb7mbbNf+M9o5/Ql30ZxzbotZUcUKB0rkagOMm1tOg+vx1dS+Hb1uA/o5/pL/xSJr9DFbTDCZ7PYjONYTjDmdtn3TA0M77RwA+vPKXuI/9mtw7PyvPg9wgyvW/JhSC3pyFCHzCra8QbnkZVI2gcxX+C/fJNh3sJOhaw51/uo/rfvEH9oqqjz7thteatK25uZlMJlMtn8lkRnSiXwvHH3989XN9fT3veMc79ruusRo0eEA7zYZhUCwWqyMjK1rhvdHY2Mi//du/sWbNGubNm4emabzrXe9i1qxZ/PCHPySXy+29ktc5uq7zrX/9KG8/78KxDiUmJiYmJiZmDDn/gou45j++utdyQjMQmrnTZOy2/Ny5c7niiiuYMWPGqMtvvfVWrr76ambPnk0qleI73/kOnudx7733AnDGGWfw7LPPYts2tm2zcOHC10X2vrvuuouf/vSn3Hrrrfz0pz9lw4YNYxLHAVVif/3rX+fCCy8kl8vx61//GlV99X3yCy64gAsuuIC+vj5+9atfcdNNN3HNNdccMK/IsSIIAq7/+Z3Meb/Jey6dO9bhxMTExMTExIwRTz3xGE89+Oe9FzyAPs37krStoaGBa665hgsuuABFUbjmmmte0yDAA8WHP/zhEd+fffbZMYljv540P/TQQ6POf8c73sHdd9/NlVdeSUdHB7/73e/2O7AtW7awbt06hoaGaG1t3e96Xi/ET5pjYmJiYmJi4FU8aS5nBNx52h/2JWkbwBVXXMGiRYt4/vnnueKKK/ZrW3viM5/5zGuu47zzzjsAkbx6FCHE6Kmk9kB9ff2IBr7vvvt43/ve95qDsSyLO++8k5/97GesWLGCSy65hKuuuopLL710t2bYbwTy+TxTp05l2YuLMMdPY5zmog+1M9B8NL1Fn/qExjSng1fEZI5bdQ9RIUvy5AtwJx0Pj92GccL5DNdNp0FxiRb8Afu8TzBYCjiiZxEL0idyztBC1HQdUclCa2pl+KE/0njWBYRHn40+1E6UrAdFIahvwxjaQrDiGfQ55yFUHTXbzZIvXcuJv72d8OVH5HzNpFAzgZwboioKGSdgtreZtcmZtKZ1mjsX4x5+BlroohYHUUSEUOT/L9W3GaqfQb0BSuASGjVEQrByoMSkOpOJ/gBKFOC/8CD2hf9KXWSjlHIEjZPR7GEU3yWsa8UOFZK6SigECa/AA9sCzpnWQAQ0U6LLTzCZHPmihUikUWubUe+8ntSV17I559OW1qmz+3Dq2ni5x+L0SWkyboSuKrQMrMSdPAdlwe8Izv5HtuZ8xtdotGxbxIqmkzmmHgYDg98v7+Fzp09BCOgu+kwzShS0WopeRJsZED39O/RT34WVnkAoIOOEuIGgI1didmuaUECb4dHhGGgqeKFgep2GUDUKbkiDFrCxIM/rhK4QRIIjh16mp+0UvEgwVWSYP5jg6JYajsgux287BktJsiXryUyFisLU+2+k9gNXEdaOJ+tFNBqQ9WF8diNRopbN6niSmooXCtpqdYpexJW/X8b/feQE7l87yMdnj+OR9gIXH94AIqKvJOi3fFp1j/ruZaSOPZuX+x02DZd436wWDAV6rID6hNTgX//IBj522lTG1ZgcXScI9SRa5MvzIvToTU5msBRwbKJIv9pIramiqQolP0JTFepL/WjFQcLacWjZLrzNqzBmnYYwEqjWMJmJJ5JxQjYNlzh9ci0pJWTJgMfJLRpK4NAVpjlseDnu2iWIkoU5cw7exuVo4yeDqqGm61j4mRs5/VtXYJx6KR1GG4dZm6GUp3fCSYzTXDIige1LXf+AHTCt3uRf/7ySr7xjJhPSBp15j8akzgxrA1vrjiSMoK1W58XuIm+bkJbnQ1LQbgkOqzOwA8HTW3O8fWo9jQt/i3L2P6BEAYpTIKxvo9QhB934E4+n7unbeHTmh5jdmmZKOMgT2TQXNBSIapoIjRrM4S2sUyczszZiTR5mNSfQhzbL+4EW0OdptKY09Gwn9G4iP/NcQgHNQ2vxJh6LnutBmCmCZCPrhx2OM7I4tRMII0HdwFo2pmcyPRXR6+lMqNGwA0HBi8g4AUe3JEklk7j5YUpqEicUpHSVmpXz+Wb/kdx4xAA9bafQqhQRmom2aTHKuMmEteMp6PU0ZjfRXzcDy4+YpuRAVQlrmkl0LiOsb0O1hrEnHEMYCbxQ0EiJ5wfhjFYdo28tqDrhQCfLJp7H7NV/ZPMJH2bmlkfpP3oubVueRj/53UTrF8rjPPMMuvv6Kbgh09fP49HJ7+KSukHCpmnM32px1tR6Wupq2NBf4MjWOvyBDrYpTcwYV0d/zqLeVBGKguo7KL5NTm8kpSvU9K4CERGlGlDtDIWJbyPlZtCHO3CnnECiYwmZiSfSkNtCX3o6SV2hMbuJoGkailOglJJaUFWBpDXAoNFCsx6gFgcZSrXROriKwXHHoasKdaV+3NoJ6ESofom7NzlcMbHEQruJQdvjsikq/aIWTVVoEQWEnkRoBqpb4L5tEe9edTt97/wiM8bVkbNKNKRTeMPdCKOGRF0jXRmLtKGQLvbgN0xGf/mvaNOOwX3uQWr+4euE7cvwW4/E6FuH98rT6G9/HyJZx8p//wx9n/0fLnJX4G1cjv6ezxHO+wXquz/LwH9+gak33IY/0IH9119Q8+5PoA5vg1Q9iogIa5qIVi9ErWtErWtC1I0DoPNH/8XEuRfSPef91JoazR3PE0w/Bb39JYqHn8mAHdCedTivwUIrDvKMmM7pE1MYAxvZkDyco6z1BE1TcBMNaH/7MaVLPkedO0wp1UJd3yqstuMII0HeiwgjwSTTJycS1D3zG9TzPoK2fiEPpU7mPYkO/PEzWW9pHBt0IBQVJfCJ0s1EyXpUa4hMqo0GLUDPdDBUP4Pmzc/QPuUsDFWhNa3TW/RJGSqaotBU3IaS74dEmuGWWTTltyCMGkQiDVFA0Wgk8eDNbDrv88xK+/SuXcZhZ7yTXC63i0NYPp+noaGBnt4+6uvrefTRR3nssUcBKX39xc9/Pup6FZ566ine8Y534Pt+1d4tl8vR2NjIc889x9vfvl2/fvHFF3P88ccfsiRyF1xwQTXBy2homkYYhiOSorxe2K8nzTv3sz/5yU+O+P6ud73rVdW3fPlyrrrqKiZNmsS3vvUtLrnkEjZu3MiDDz7Iu971rjd0h7lCEATccON/89TjseVcTExMTEzMW5lHHn+C62/52V7LhUIQCsEFF13Ef/7Xf3PBhRfx6h91ShoaGpg+fTovvvhidV4QBCxbtowTTzxx/yrdRxYsWMCCBQt4+umnyWaz1e974vvf/z5wYJ5MHyj26xn/zp3YnTvRr1ZrcsIJJ3DmmWdy66238sEPfhDTNPe+0hsMXde55uv/gTl+GuCOdTgxMTExMTExY8TFF17AnMnN3HbXnnXNfijww+19rPMuuIgTTzmNX/7i56OWP5RJ214NTz65PfV3JpPhqaeeAuDcc8/d7To7JkV5+eWXXxdJUQ6IPKO5uZnh4eHdLt8by5cvZ86cOXsv+AalKs946UUmTZyAlu0ERSVK1CEStailLO20MG3lvWhNrUTFLIqZREnWEA71oh53DsELD2CcdCFKGCDMGlRrCBH4kKxDCRwiKw8tkwnrJ6K88gjKUadL2UJxgGjLCpRZZ6J0r0VpnohSyuN3rEdraiU86iz0wc2EnevRphyFEgWEtdL2TGgGItWAWugnWrkAVA1j+rGE6RaU0EMtDuK1r0U95VKUTS8RHX026rpncdcvo+b0i3Emn4BuDYKqog934E1+G/rQZoLm6Rh968C1IN2IUsoTDHShTTkaJfSx244jObwZYdSAqqF4FigqTuM09AV3yNfdTgG1azWZcbNQ3QINmXZ6/3of4796E4O+TkpXqM9tIayfiFYcIGicgvrKPNRkGqV5IsG4GRD6aMUBopomWPEE2TmXoasKDdsW4x9+OlquGyUKUEs5okQaBrYRzTiFYVLUGiqmPQSqitCTKKGHMNPo25YRTD0BX9HRibBChVrhoJZyKIFD0DQNfd0zBLPOQ/FLqG6RTqWJqX4vUU0TQyLFOLefsL4NLd9LlGqAxfejnHwpXqKh+up2q5vgiOI6wtpxZBLjaVh6H4Nvex+bMw5nOisYnnIaDd4wqCqqnUG1s0QlC7W2kWzr8ZiaQiigrm8V2fHH0pDdRNS9icjKox19KoXMMGH9eOpra3GTTQBoioKKwI2gp+hzWIPJkB0w3gyrGjuzcxlB3za06ccRdW9CbWihOPUUUoGFameIUg0ono33+J2I934F9/Zv0XDOO1EaWhG6HBEerF6EWtdIdMK7UEsZFN+tnsuqUyDM9GMf+06cn/47DSecgN+7DdXQWX/3AnwnwEwbNB81CS1p4tsOk95zKd0PPszUj3+CoLcD4TkouoE+80SClumEepKcGzJgBzQmNX6ycCsnTGlgy7DNKVMamdqQZHyNjhtEOKFAVaDGUBmyQ0xNQVNhfI1OxgmZGAwS1o5nbcavSiF0VUH1bNAMiELsjjUIXad2wmGo1hBB0zRUJweqjjBSGH3rCFqm0x8mCQVMqNFQ/BIlNYnly+3XlWUualD+Ax6FoKh0uRqtNTqhgDASmJqC7Uc0292EaSkVyIgEKpAypGwHoM4dJqwdRxAJzNBFtYaI6lrJhxr1ikdJMUmqAidS6LMD0rpKY1LDDQV9VsDhjSZuEFE3uJ6woY0oUYcVKjiBwFCpbl8YKXwBqVwnQk8gzBoGohT1ie2xZJyQ5qRGrTtMVNOElu+l25jAZK+HoGESdij3qTGpkSj2MWiOp0UU6LCg3lRpSOo4Wgo3iGjyhqRMrK4Vo28d7uQ5FL2IesUDRSHSEyjltnNCQU1o891Fg3xrai/+4afL8y8MpMQN0IoD8n5gplDCgLBxMkQBBb2eWjy0fA9h4xQUz8JNNJDKdZJJT6YhyBIlpcQjSI/DyHUh9AR6rpugeRpCM1GiAGGk8BQdPxTUlfoppOR4Hk2VD6pSnjxP1OIAwkxLCUEpi52eQI09gOKXQEQEzdMxt72MP3kOev8GcC2ElSfMDWFMOQJ/0vEIzSQQUNP5Ms7UkzGK/YRLH0WffS7KUAeRVUCZfCT59vVYR5xJ65r56EeeSFg3AVY8gTZ1FiLXj79lFcL3Mc65HHV4G976pRiHH0dUzBIOdKHWt6DWNaJMPALFd/E3LEWbcz5sW4V1zEVoqkKy0Ivq5BGazkDtdHRVocnqImiYhOoWyGn19FsB42s0ak2NUhDRWwyYXKeTwkcJXGxdSldyTkhLjS6vOVUj1BIMlwISuoqmQMGLmKg7qNYQpcZpGApEKCT61xHWjUfL9xPWjkOYNXh6Cj8UrBoocXqjK6UWegLVyRElG9DyveRrJgDQOLQeYabkb7uRIms0MW5gBcJIESVqEYk0HUGaCWmd5KpHUVsPo6u3j8POmLtHecambT3U7bDsicce5ZGHH+L2X/1i1PV+85vf8IlPfGKXfsiTTz7J+eefX/Vp/sUvfkE+n+eUU07hpz/96X7noNgf9lWeEUURzzzzDFdeeSVnn302K1asoKamhpNOOomf/OQnhyzeHdmvLrvv+zz99NPMmTOHpqam1xzEjh3m5557jt/85jf09PTwwAMP8PLLL2PbNmefffZr3k5MTExMTExMzBuFijyjQlSedseVV17JlVdeudvllaRt119//QGL8dWyr89qK0lRHnzwQY477jhA/plYsmTJwQxvzzHtz0rTp0/nggsuYNy4cUybNg3btvn+97/PwoULcRxnv4O5++67ufTSSwF4+umnAZma+1vf+tZ+1/l6IQgCbvjPG3n0sTdX0paYmJiYmJiYV8f8p5/j+ltGl1jsSBiNnM59x0X8xzXfPvgBHkQefvjhfSq3ePFiTj/9dD71qU9V16mvr9/nbNMHg/160rxmzRry+TyLFy/mxRdfZPHixdx888309PRgGAZhGO5XMDfccAMPP/wwZ555Jn/84x8BmD179ogUj29UdF3nmm98nUkTJ0C2c6zDiYmJiYmJiRkj5p53JscffQS33fWXPZbb+Unz0088xmPzRrf9faOQTCb3qdxXvvIVbr/9dgzD4HOf+xw9PT188pOfJJvNHtwA98B+K6rr6+u56KKLuOiii6rzOjs7eeGFF0aMzHw1bNu2jTPPPBPYPtjQNM1dcqe/UdGsQbS8QAl8ma5TdVHXLkDMPI0jBpYjDj8eVA13xSIKHX1MeN8H0JpaoXsdAKpXwt+8AmPaUVKrNdAFsy9A3fKy1DT2taPnBxD1zVKTGAUoxWGC/k6SE7uJxk1FcQr43e2odU147WswgSjwYfYF+M/9GWPG8dIeR0RofRsQtc14LzyM9s5Pome2QeCihB5hwyRQVLS3TULZthK1qRWtfz1eZoDEzDk4S57EqB2Plu8FIGyYhLbyUZS2wzE7lyGiCOGWiDL9KKomNdxuEeeVhZjvnAZDXVgvPEX6vZ+SmlbdQHvkZ5jHnoboWAo1jfh921CGh1EnTEdEIW2f+hLK4BbaFAVUqRnXO5cjXAe9bwvKlKMQ/R14K57FmNyD8BycjctJnfV3REeeSoPiwpKHEUefhp7pIGicgjG0WWrMSwVobIXVTzNu6iyUwCesb0Uf7oAoRJhpio//idqL/wGikGSugyjdQp2IIIpQM51SR6t2ozS2ojz/R5Q5F6BltjGlCcJ1i9GOOoWWVANacQAUhfCVJ1BPuwxOeCdacYCEa6H6NgQ+01qmY9ceR2poI82Bizr1SMbpPm3OCkTDBBrXPII66QgIQ8LacYQbl6EdfRreK0+SnDSHRN/aqoa4NshDFCGOOAV1y8uE614k0mtgYBv6YTNRCysRjo0y+UiibWupmXESM7vXEZkn0GYPQUmF3s1Eh72N4sL5GE1N6JNmEA71gKpSu/UFFN0gqh2HPtxBlBskOfvteE/cTvqoWajpeoKu9egTphEVshB4CCuPmP8z7P4B6i69gqj9FZS6RkQUougmiWfuwDh8qtTCp2pYf9fjrHx2G4cd0cRDT3fwLstneEOGCbPHY9Y9TaFjAL99DWpTK32PPsn4t59IlGpgODRIEGFqChuGLGpNnb+fMxFNUfAjgaYqCAH9VkBbrY6uCbryPn4kGLQ9cm7A9MYUDQnBgBUwfnwbQSQ4oimBr7RhIFA8GyX0UOwMShQQJeuqyQmimiZcoWKkmtF8G6HqhHWtFNUaWhIqQoASODhailTkkkwYFAIFQwT4kY6imSh+CaEncCKFlpRC1glRFIVaU8UQAQ1ahNc4VabwdgukkykSXgERJQgVg5TwyJnN1Ic+ETqoGlHtOCLNoCEoggDNSGCHUKMr1JsaTSYokQeayfQGOXA7XewhbGhDCTxIQK3iU5NKoOd7pb2lnQGtiO5ZeM2HYxT7UZwCLfVplCjA0HVUa4jaZC1C1xFKHapfQiTSTBR5olQDeqaDOiNFfeiDC2FdKy1eDi/ZhFnKo/gOmUQL2aLPEcowfl0beS+kQdcYGn88mh/REOYp6vXUF7rwG6aQc6ElEeGFsKArYO7R4xGJcvKINc+iTj4KQp+gcTJ6rpewaQpqphNR3wqhh9a9hvQRZ0IApabpJOwhRLKO5KpHEdNPoGVwtTzmiorqWWipBqmLTjUQJdIoroUaDKEEPiLTS3LyLIy6CYhELSlDpehFGEp5TEEpJzW1rkWkqCiaTlg3gWTgIswaCH1EIo2e7STo20a0fhlMn0VYyBK97RJ5/uV7UUIfXzVJFHoRiVpp7dm3AaWlDcUp4M08C7NnNWKoS17Dw5shCsnc+xua3vsxaGlDJNJ465eROG0uQtMJVy9EOfpUgnwOs3ECimNXE3NEVgE9PwA1jVLf3LWWyLGpCW2E0FEznRB44Pu01E0gMtJEqQayPrQEHrkgpMZQ0FUFL4zIOCEtNRqmpkIIQjPR1fI4jYS09EzoKTRFSgFqDFXawhUHqTWShIlaAFLZDgbTUxjf8zJBbwd62zSCScehOnkIPXSzBlMEnDqxBhGZqNYQYe14vEQDbiCoTTVQF+QRpvzNJJJ9Fc2zaaxTCZqnUTAaqSlv+zAyKBmLaOZp0LUKJdx73yYQgiDa3mk+6/wLmXPyqfz2tl++1i7J6x5VVauyjL/97W989KMfJZfLjamj2gFNoz1lyhQuv/xy/uu//mu/1p8+fTpLly4dMW/JkiW7TQcZExMTExMTE/NmJYp2lWhEexI1v4kIw5BisQiAYRj84Q9/YMmSJSxevHjMYjqgnebXype//GXe//7388tf/pIgCLjzzjv5yEc+wle+8pWxDu01EwQB19/0Ix556pmxDiUmJiYmJiZmDJm/8EWu/9HenxZX5BmV6eknHuWm73774Af4OuBHP/oRlmVVv6uqyh133MFtt902ZjGNreHdTnzsYx8jiiJ+8IMfEAQB1157LV/84hcPShrHQ42u63zr6i8wqbUFvNJYhxMTExMTExMzRsw961Rmz57Dr+6+b4/lKk+XK7z9vIuYfdJp/P7Xvzq4AR5kbr75Zr785S/vscxJJ520yzxFUfiHf/iHgxXWXnlddZph73Ypb2i8EqqdwZ8wi9ahDYSNk1DbDodsN1EhS3TYYWilDOakw2iZdBjoJn77WswZx0EUEg50ok86nKiuleCVJzGmHUWg6oRDPagNLajpekT9eFQ7CyIiXLcYY9J09EmHE/R1oOgmfiGD2iD9Ws2ZcxCOTZgbgkX3odTUQyItfZvrJhA1TyVKt5A4bS6RW0QJfYSZQnUKoPTKlKPd61AapbZPaCaKqhIVMkSBjzrYTvHFp0kdfwpqFCF0E++VBRgTpxMMdKGYcjBA5qUXaTjhBMKBLvS2abDiCULfo/bUc7EevI3UsSeiTTmKxPFvp/D0g6RPeDtR2W9UbNlA2LMJp2sDyShCqWsEQNEN1MIQQW+HbJfAg/YsYaYffeJ0gr4OUDXM6bMIt61FSdag6SZRIonQEygdKzDtDKg6wUCXbOvujUSFLPqEEkHPZpS+diIzib91DebJF5GeewXuwr+SOP1SlCiAgWUoTW1EAx0oLZOk1zNI/eZhswhDH5Gohc0vox8xB8UpgKJK7WMph3rkidC9mrB1JoQhalQk7Fwnvaa7N5Kadgxh+yr0yTMRqo6ybB6BW0LJ9ONuWk1KNxCBL/W5nkPp0d+ROHI2xqaFROOmE61dhDrlCJSNiwk9BzWZJnJsmfJWSSAcG2/ti5gz5xAFPv5Lj6CddwVi7UK0CYchNi5GbWrF27wKYefRoxBz4hSCoV68dUtQm1qJckMoqobXtYnErJPx2tfidW3FnDARtbaR0vpVqKk0YW4Ie9UyktOm4/Z0kTrqePSGFqKSRdi5HgIfd9Vi6XPcN0jNxAkExSLFrgECy6FnSS8vZx1WLu0lranktxVY0WexbsDmIxfMpqa1nva/zGf6B96FlpQaXCVwICEvzZIfMbk+SabkY/shzSmDUyY1SK2nrpApBfQWIWUojE/r9BV9GpI642pM/CgiEjCtwUT1bCyRoNZQ8cJIesh6NqpnSV9izUAEGkrgSg2kopLyC3KZkZJewWYNNeW06qEAw8lDzXgUtwipBlRFq3r5ukBaT1RvMT1FX/pCK4LAkWmvNVWjRoFIM9BUnYQnz7OiMKgxVERk0OAVEVoKAAedlJcBPUGYqCWMBIYChl72CtYVhKqiBC6qpqAF0vNW6AkUryTTWtsZ0E30Uq6s+U9JrbOIiBJ1Ui+eqJXnuWdJLaiRAkNef6o1VD5GLlHtOBSngGb1ETRMRgk9iEKi2nH4qomeMtEBVVUQahJDVTi8VmHQH08D0KQFlAKFtKGSc0NUo546q48o1YAReYwzVRTfpS5RyxmT63BCQWi2EghINbUSNE9DLeXQcz2EmX7UhjapvxURSuAhGttQPQvFKWDUtSKSdShOAWXKMRC4RGXv3srxVouDhHWtoOnV81DxXXnPaGojTNajeha2VkMqcNEUA1MEWKGGnqyTbRy4oDQAoJYysn5VJ0q3oGe3EXasQa2pk+NGSvJJnbbqcbTmNuklLCJMr4ASegTjZmAGJcLcEFpTKyKRRnOLCCOBkkihqimElSGyC6SnTSHsWANA1LkJP58nURhENZNEqTT0b0Wvb4DCIFEhg1pTJ4+/5+B3bkJvaUM1k6gNLYjAJ1J19OGt8jesph4mHI7iFnH1NDWejZasRRhJtLL0V1MVwkgwJa3iRPJ89FQTPxSkVIWSF5HSFUxNIaFK/2UVgYlHKJKUUi2k/AJq4MqxBr5Ds59B0Q0UTSMqZGQOAmuYKN0MKShGGrUEDPsqerKVBnsYNT2OeruXsK5VHjcRVc9xoZuE9RNQnQJCUUknG1CLg/LcjgIUv4SW7UYxk0Tpmr12G3YeCLjwqcd5+pF9c594PbNx48axDmG/GHN5xqRJk/ap3LRp0w5yJDExMTExMTExrx8qGQEr02nnXMBn/v3/G+uw9puFCxdy3XXX8dJLL425X/T+MOZPmjOZDL/+9a/3anady+UOUUQxMTExMTExMWOPF0a4O9n4euEbdyTgWWedxVlnnUVvb+8bMgfHmHeaJ0yYsE//NCZOnHgIoomJiYmJiYmJeX1QecK887w3Ol/60pfGOoT9Ysw7ze3t7WMdwiEhCAK+c/NPeN/73sfcs+sAUDYsIooi1HQdakMLSu86hOsgogg1lSbo3oIxcTrhUC+JY0/D71iPs24ZNef8HVpLm1yeqCFI10MU4bevwZihIZJ1hD2b0OoasV98HHPGcQjfl37BdU3SM7NtGsLKA6C3tKGk0gjfQwz3IHQDJVkHqo7Ru4agfxt661QIfXCKiFQ9ailHWNMEbUdA4IBfQlEjlHQ9wspjTD4Cd80S9NpawqFehGOj1jZit7eTNpMIz0EEPopu0HjKqQjPQampJxzqIXG81CxHjkXy8KOkzq6/A2f9UsyWFsJCFq1pPEF/F1pLG2FumMSRs1GSNfgd6yEKUeuaQFWlbq5kQRSiGCbC93DXL8OYfARRfgjSdUQlC3PCVOlrauXRO1cT9HWgA0oqjTbrNIKtqyCKUHQD4ZWIrALmEbOJilmpDR/uQbQdSfLUdxINdaE0TZDb9KSeMMr0EdkF1Blvk5pCK1v2Y1bRxk0C1yIc6kVtKBEWMgBoTa1EJQt120pIpQl6O1ASScJCFmPKEQTtq9AnTsdZ8gTGtKOkhrCpFQKf1OwzQFXxN69CzQ2hT5iKsPPS2xvQU0NoU4/EWbaA3PottJx9DsFAF/r4yUSeQ1iUb3a08ZNx1y8jMetkovUW+sbFCJCabt0kqmlCnzCVyC4gPAe3qwOjsRG3p4ualokIwO9pR7gOzorn8YaGUQ0dJZnG2bQGEUq/buGWSLS1oegGRn09ipmUGvfxkwkHujCmHoWXWU6irY1S/0ZK/VlCPyB0XIyaFI4TcFJjkg47YFvJZ3N3gUEvYGrKwMvbFHtytBwzBberA9XUUWsb5TWogABShsq4GgM/FNQlNCbVGhT9CF1VR3ik+pGgtUanFESU/IjmlIblK9QrHkoUoJRyuMYENEVQpwsiNYFm+IgoQJouS52u0A2pifUdwroJqJ6Fp5qEApRSDowUqqJhRK7UdCogzBqEqpNQwAhKKHoKNxREioYW+eiqjqKAFwqEgIQOplAwy5amShQi9IT0dAepfQ59GY8mdd5q2Q8YIIgEBhG2Dw16RKQZBJHAjyAV+iPub6KsqxaagSIi0E3pRZxIo0RheRyEilBUMJKoCqhuESUKECKCMEBRZZ2qW9hebyIt203T5ZiJ0JNexaoOUYjp56RWWDNJEJB1VdpqFYqhhq6CE0QIjOqxbtZ8PDVBlG6W+63q2KFCWtUJIkFCV4EIJXAxIwvFTMrtGQl5rU45CpwCwnWqx1sJfQIjhRp6cjuhjyIiOYZBUVF8pzpWodpGoV/VOFfmR4k0ShjItgx9UpEDmiGPR+hj6jpEJsKk6vMNENY0ozl5ArMWo9ALUYTW1IpiJuWYhpZJRIVhaD1cpl8ub9cz0hiqjlroRxERSstERH2r1Iz7DoqIELXNiFwexUxIfTJUrx12uFYr8agN49AmTENJptHGT4bAlz7Ndl5q3RvHy/1zi9Kbv3xdaA0tCN9HCT2iZB2lICKlm5iaAmFU9UsvBRFpQ8UVUresRT6qouIjl4O8nhO6ilrKoqq63J6qoSiQDEvbz1UjCaqK6kjdfeX3QvFKoBvy/i3kuIBiAJoiZDyqiu7mGUy00uKXiBJ1aLlulMAjStZXx/0IRZX69sAFIwmhJ3X9yOM378ln+MsTz7M3fCHwd7gHvfjMEyx64pG9rvd65+ijjx7rEPaLMe80v1XQdZ1vfeFTtB1xDPj2WIcTExMTExMTM0bMPetUjj/2GG675697LBeEEf4OcowTzjyfo+aczF9/d/vBDvGg8N3vfneX5CRCCBRF4Rvf+MYYRbXvxJ3mmJiYmJiYmJjXIX408klzZd4blY9+9KNjHcJrIu40v0aWLFnCF77wBTRNIwxDbr75Zk4//fRRywozhZbrllIEMylfSZXTSYtCFlQVVA29pY3IcxBWHqV5IpRffaNqmNOOhFIeRTfl6/v8MEHnJsyZc6RlXOBJaUZDC8L3SM46GRH4iCgksvMoNfWoqTRRTlo6RSULY+qRMn1v/xq0pvEoDa0IIDLTKPl+9PGTpaQgVyDMDKC1AMlatHwvwkiBlUHUtoCIpG1Z4ON1rEfVDUoDw9S2tBFZecKhHupPP0dKL1xHNkoqLV+dmUn8rk3o4yfjrl6MWtuI3jqZMAqlTVzrVNRUWkohylITra6RKJtF5AYRCR1Rrketa5Sv/jQNUbLwuzaRPPY0AJRMP4lpRxHlhqTkpX0Nal0j7urFJI5/O0FfByLw0FomEtkFor4OFHMLSiot665vJujeIi2nQp8wM4DwHIxpR8nU5YVhALyNr2AeMRvh2qh1TVJqMGkGYfcGlFQaGlqJ+jtQ0vVSZnPYLLSWNlB11HSIqB9PNNSF2tBCVJbRAOgTDiPo2kjQ044+eSYELoljTpb7ayaJ7DwiDFEMgzA3JO3iHCn9UJJpKXfI9ONtWoExaTpKIsW4i+bib16FMf0YgqFeFDOJ274Bc8p0tLI9od+5CWPKTITnoNY2Eg50oTQkiTYtBd0g6FhP8uR3kIoigv5OjMZGgoEu+YpTN8AwUMwkySlpROBLyUYYkWgdJ1+JGqa0fUqkUOvkuoqq4nVvRUvX4m9bj5e3MFs8Go6YTLFrAADN0Ble300ooMnUGVdrckIo6LU8jqo1aUubKJpKw+GtmHU16DVJGo+ZidbShhIF1Bpq1dotqSk0pnRUFFKGiqIo1BkKg6WQhK5Sa8rX6EEkqDU0Sn5EQlNwAlB8GyUMUEs5Eqk2jLIvkSKEtFMTQuoDytIIJfBlengjgVAUhKpjKBAq8j6hRAEaAaGWQAMMEVRf44cCDM3AEAEe8rVzBRWFvBcQCoGu6fiRQI8UTKkMKUuGbPkqWk2iBCUU35HWh2EEuo4XRmiJOgwFIjRMTT7lUqIQTdEQIsJTdHTToLppIyltD30bgQpRIC0pvRJoBornEaaa0EoZQjOFpigITUfxSxAFUgoQBaDqUnqRMFF8uywlUEDVpZzFk6/XhS6lGkIzZWpoIwW2g65qKEAkwFDBj6TkREEeAhSZipkIWV8UoCqGlFBoCcJyZ0QY0pbNnzCLUE9iDm1CGDVSbqAbKIkkiu/Ksoq03yOKqrKLqKZJSrPCAKGbVUs4oajldNdlKYfvIPRyXeXlSuCCZkAEQtXRyqnUVUPKc5QoIEpJuzk0E7W8zcp5IDQdtSwvUNsORwBqKo0IHMJ0C4rvEiZqEZHA11OYqQilOACpehTXkpKCKJC/CfUTEbaH0HXUOe9A8SyUMCCqaULtXkvypPOrcjN12rHy3jlppkznPfEoVDtLVNMoZUCeTaTpoJlEyXqUWk9aMJopaJqE0EyEqsl7IFJ2EvqRTFHtUj2XI1E+poo8hr6ioyqCSEj5hAKEkUDVk/haAp2yHCUUaHoKnQjVL8nft2QDWq4b9ARqMo2IQmnXmmoC3cSNpCwjLEt3NAWUMCBMNZEIBETI9RWVqJyaW3UKEHqoIpJyGD0h5S9uAaEnpT3dYGdZArL3AX1OEKEG0S7z3kg88MADXHbZZQDceeedu02DHT9pfgswadIkHn74Yerr61m5ciWf/OQnxzTFY0xMTExMTMybgyCMCHZyy9j5++ud73//+1x22WV85jOf4etf//pe3dJez8Sd5tfIjq4epmmiqmNufR0TExMTExPzJiDYSZ6x4vmnWbbg0TGM6NXT1tbGpz/9aRYuXMjg4CBz5sxB19+Y3c+3fA/vrrvu4pxzzqG+vh5FUQiCYMRyIQTXXnstkyZNIp1Oc+6557Jy5cpd6vF9n89+9rNcc801hyr0mJiYmJiYmDcxfnkgYGWaddo5vPfTXx2TWKIo4pxzzmHcuHGvqq/z+9//niuuuIJiscgPfvADTjnlFM4880w+97nPHcRoDw5vzK7+AaSpqYmrrrqKUqnEP//zP++y/KabbuL2229n/vz5zJw5k+uvv565c+eybt06amulhikMQz7ykY/woQ99iPe85z2731gpD40ToLaFcKADQNp22fmyFrcJEmmwMqiA0tImNc6TZxJl+qrpSAHU+mai7IDU76bSUg8NCNdBnXYsijWMkqghKmalni0KZVndJHIstKZWCDzU1mmIcmzGtKMRThHsLDRNRnVyRI2TULPdKFEezKS0EUrWQeAgzDRRsg4l2y9TQKfqpfbXc9DHT0Yxk6R0A2fLeswJ8om88BxZppgFkHppzyGy8qjJGqlrrW3EmHKEtFtLplFSaYLuzaiNrQjXkfE3tCBCmbKbVC1aQ4u0zfMcotyQtNbzHNS6JsxymmZh59EnTEM4NlpTK8FAF1rLRKnDmzCNoKcdtbYRNV0nreoKWYzpx2y3dwo8opJsu6CvQ+p/E0mprc4NyfZNpMAwMafPgtBHSdYS5QbRWtqIilmZxrZUgFw/auN4CH30GXOkbR/gt6/GOGwW+C5i/GEI10JpaEUJHGkT6JXQJ81AlApEhWGpL85sQ7gl9JknQjEr9cyFbDkttoWiaijpepSGFqnZ9hyMw44hzAygT5gqYyrr7BVd2nOljjkRJVkjNcx1TbKMZkJxiLCQRUmkUFSNsCRtucxZJxMO9YJuoNY1oeiGTDmcSkv7MTMJuomiaQTdW1DNJImGFkTJQkmmy+UHqpeK1tKGKFkYbVOlPaGVp/7YWTJOTSXV0kAUhmTWbiXZmGL6yW3yWvQjNEOlPiPbU0/pqJqKUZOkZmILWlOrPDfrx8nU5Iq0rgKpg02V/U9NEWDoKqpbpNaspRRIzWRDQkNXFTRVkDJUBFCf0FBKHsJIEqUa0BQFRVFQRIjil6rpo1E0GEXLV9HaIiISuoZSCqqvL7XQJdITqL5TPQ+FEAhNanxVBblMRHiKhqkphEJg+yFpU8NUBYYqq1fCQNqkhT5CS5HUFAiQOufQA1UnjASmpkrttYhQAV1VpY9XOR5TMyj5ETWGKq3A9AQhSbTIJ0g1o1XcgcoaX1G1i/PlOaSoaJG0l6tY3QlFrS6TbSGk3lOX6wFS21ouSxQhEjWgKFIHXNbgGoqCroCmSEs9EERCpl+OhCBUTbSKNZlmypTGCZMo2VCVl+qqQqQZqIGL0AwsP8Iw09vTgUcBqBpCM0AIRCItt2/WSDu5ciyKqkMYQGUfVR1FM2S68XIZVB1Cj0jUVdMxy7YrHxMALSHbUkGeA4pKlKiTOnA9Ifcf0EpZqZcGQl3awCl+CWGmUVWVKNkg9dNeSd5rQoGqgKGohA2T0DMdCCNJmJbjGKL6Nkp+RNA4GQEUUrWoNVL7LxQFfYKP0zAZvTSMYtQgUg1Sy66ZoBn4ig61E/BCea2ogSvb0EhhB9vnhXUTZEr7sp7dURNo5SerSV2my9YUmR5bVeT570VghvL4KEidv47UMhu6KocPqElUAYFQpWth2Y5R1yAwa/EjQSLdgmrJcShq0wSEqhNVdOdAQoVAyHPCUGVKbpk+3SUtIqld9u3tWuXy8azo3dUgh0iWLWbDANUtjyVSNRTDBKxd7gc74wuBvvNAwDGSN6iqyu9//3sef/zxV5UGW1VVzjvvPB544AGOP/54APL5PEuWLBm1/OtZvvGWf9I8d+5crrjiCmbMmDHq8ltvvZWrr76a2bNnk0ql+M53voPnedx7772A/Of18Y9/nNNPP51PfepThzL0mJiYmJiYmDcxjh9S2mly/HDvKx4kpk6dut/rVjrMAPX19bzjHe8YtVy0DwMkx4q3fKd5T+RyOdrb2znttNOq83Rd58QTT2Tp0qUA3HPPPfzlL3/hgQce4Pzzz+e9733vWIUbExMTExMT8ybCCwReEO007f5J7IGSnMaMzltenrEn8nlp9dXY2DhiflNTU3XZhz/8YT784Q/vta7a2losxyNvOwhNEJXKlkO6IHJ8lFBF1RwIVLBdhOdJSYVVQgl9opJH6AaIIMSwXZRAISp5qIaD54XoJZfQDdB0Dywb1XZADYhKLoovCB0fRRNye66PZjvSPgn5qg7HRQk1hFu2UTJsiDyEFqLaDkpF/hFFEOoooSv3I1BRSi6K0CAywHGIHBmrIgIiP8IPwPRCCCJUR75qjTz5T1JzPITnE7khwo9QXXmBG7ZD5LgQBChCJ3R8RBSh6Z6M33ARUUjRC0FT0csxhI6P8AJUQ9arRDLLkwgChBdWt6cqHpHvowpXLg8DCHxUzUVVDEQQEDkequZWpS8EPpHjoeiCwA2r+6KGKiL0t5eNIhS9/CRAi+QxCBVE4KEqJYQj21iJdPk601dRQjnPdwMMq4RQdYSvyFepilwuAr98zkQIR+6/qpQIyvusWyVCxy9bF0aooUrk+yhq2eZMEwReCF6EXnLleSc8NEvWoSryHEIVlAIPhCZf0+uR3KYWgu3KbQSyDYPycdZKLsL1EFFE5AYooULkRyhaWM72pkKooqgRoS9AFShaiPCFPOZBQOQLFE/Gqjo+wg0QQQC+LKeUYw8CCEIQoYKt6oSqQsmUr8AjRaAaCk5C/qjopk5RqISoqIFAc0P0kodiyXb1Ctvt/GxfYJe3nw+lDED1bEq6wC6FaCponoahKVh+hO2FFIPyLdSxEHqA4tkUwwKGpuARoASetFUL/aq8omiV5QtCIAyfMNBRQ0++2lVUNMsi8itp/BSE5kobMlVDqDpuKPA0KedwIwVfeCAEJcXH8iMcy8cNQmxhoBgquq/haQpKOSOZ4tkIIyTypfyk8h1Vp6SFmJqCWraMAwhR0UVQlUE4yMyJoSEtuECqN9QoIFJ1KRcBKU0JfYThozo2QvPlvSzQEGaE4hVRfA9h+BAJhFG2lyvLMxTPBs2rWu0pvrxXVeISntgu5VAUiraDL1RMFZxAYGgKQSSzI6qqIrMqIl/Tq6FXlo94eJ6KroAXQVR+DR7osr2EqmMFCqpbzioa+VJGYbvy3ikEwlfkPqk6QvdQfYfIjFA9GwIXdHlPE6qG6lqEkYnq2VKu8f+39+5RdlVluvczL+uy965dl1RVrkBCUD8buQQEBSGIiEa5jG8g2BKQFi+oH9jDbps+h4G2CSh9uh14TI9W5LQcRVARW1tscSiCJJ2cg9iETog0jgYxQECTkEoqtWvvvfZaa873++Oda+0qcqmAkArJ/I2xR1LrOudcc6397jWf+bxkAZtCpAmENQDlMGIcEMLJWQLkKkXQbsJattcTWcLSL7Kwhm3qZGfc9RfpbPwMyzPyBJQCstOCzTVsCsh2E0aOIckJQgCZ5eeWbrZAMoehcfcdkCPJCXnSBAEI3Papu+hqvIlcjkEl4xBpAsrVBHmGRi743sgMle0Ja7vHLZYRuf7NtqEdmcJYgtGCM4paoOW+L4Tga2MJsGDLOgO2k0uV4JTTWpRWiASWXRHYdjAzhFwRSEhkFugIi6DZds9jlsxYo2FzVbaBIUw6rhpv8noiCJNAtVogZZ08Q0GYDkSWls8VcsdiqRY/54vvhGbS3W5PpLuxnEv3Yjn3ckhOTznllF32mzNnTjnCfijjg+a90NvbCwAYHR2dtHzHjh2YN2/eizpWkiT4+3/6FmpxBFIKb3vDkXj7qSdD9vRBxFXYxg4IHYK0Bqp9QLoVqn+4kBJChBGn1+7pBSX8pWuzDvu9RhX2ba73Q/b0gbKUvTFbo6C0A1nthaoPuOPErBW0lv1Gc07bTfUZrNUCADJA1gakgEhbEIHTayVN9tQNq+ytaSw/6AEIKdnjEoBQAWS1B5R2IKRCOGt2WWahAw68AciBmbAJP7Bk7wBMY4fT0XZgW+OwnTZrYq1hT8u0A9E/E0oq0PY/QIQR9MzDynoJKSHr/bx/uwk9PBeUJjBjOyDCmFOBA1B9g6xHDiLIag9s0oKwAUTfIOz4TtikxV+oUQUidClXkxZQG4CUCkJrqP5BqIFZyJ57EnJgGGbnCMzObdDD8wAI2KgK2WnB1GZANEe53eMeUGsnB09xD+s4oyprExFBmA70/KOBnFP0wqYQeQI7PgbR0wvh/F5tGPOXbM8MUNKAmrMQNLaNg6KoAhlXAamQj2yGqveDsg7k4DzQ6FaogZncHjqAHp6LfGQzAIKs9ULW+iF6hyDyBG2rgXYDolLna58mECHB5hlkVGF/bQDBnAXlDwUAoOYY9MBMkDXcZ3MXMLttKE2gD38NzOgIRFyFqPSwTt9ayMHZHJjEVdgdW7l/bHkGsm8IkBIiqnJQnuVQUYis2UJ9/mzknQwqVCBjoaoxbJajx/2gUXGAqL8OGWioGbOh+gYhe3phQ+7TwqUULl7cREqgYwgkNWSL05mTAuphd1BOU45qoKGEQKAENCynxZYaUAEiXQRoGhA5ZJ46DTKVmlSQBQUhIDSnzlZd32G+IRR7+EoNUmDfXiGRWj6MIUCbDFpFEBlrpxFFUAKoBQpSAKHioCMqI1sqj81/227aa0ckqdQQC2tAKuDA2LqUz9YiCgRyy7P6J77DihR/nVAQ8/4mAcmY+2xc5/oEsTuOAakYIs8AGYK0KvXAEJLbCyiDjDIFd9F+QnbbEy6gIovA/cDVivWvSnLd9QQpuSiCerKgIIYW3J4RcnSkRugk3MW1iDVAJnJezBkHqVEV5PyeSYfuB4/Txjs9LCndTf9dBGTE7Us65PtBKv5hH8aAMexNXfwQMJlLo83tILKk/AEvyDrf6hY/g6WAMMZ5WAeAqLCeWQZQja2gMIbIWlB5CtgUSc7e5FoAuea05nnvXIi8DQpijKcWpmOREyHpsPa/HnL/scQ+wQO9s7E9MeiP+qFioJ0TlBYIJGuHlfPH1q7/iZx1/x1DaOcWhiSqQQhLYN022H+ZDKGihfOelpAC6Is5yDWWoCX3a+TE/YK61zkuLrTr0wKAKn5kubIIMiAhEUiAIEFBCFIR/0CVmgshZXmfKiEhtZh0n3C/tNzmKgYJwXMWCp17qHl9mvA6AMKV8b4H/wP3r/4/gArRci9Q9kZmdg2as71Yzi1ZsgQAsGrVqt2unyg5BYDPfe5zuOWWW/DDH/4Ql112GQDgwQcfnLJchyo+aN4LfX19WLBgAR566CGceuqpAIA8z7F+/fqyc+0rSZIg0ArnnXU63nn222B//ySEDiCrMShXsFkIVatwMJpnoE4IWavwpBGjQSJFFmnIOICMQ4hKBJMFUNUYeaShqxFIER9Px6AggkQCUxxX8wNPRBXYjPeHVC7YtaAaB28ic28Lgkr3ranR7le0eytaq3AQa4wzto/4vEEF0BZEKazIQMLAdiREqGANP7iCOARZAxNKqDgAGRdoRwHyQEDHAUgYqEoEK3LISgShFIzh5bpW4Tdu7RAiispAvbcaQegAVhh+A2470NUYpAh5pCFCDVVxD+ZqjLwZQIQhB1nCcD0rMaxpc6BsLSAlZJUDAJIGqFUA0eFJax1u1yzSCKoxTCcE5YCuuiAhqkJqgumpQTQivpZSA3ATuioVfuBrF7QYA5ELUFCByMDrwgqEzGFNwuVwgZMNKxCmyeWRGRBVQBmfw3RCSHdt83GuMyni83dCUC5gOhpBNYIIuR10NYallLcREiIDyGoAKXprnOSBNHHyFOOS0hRf3kHI9bGG37gZ7p9kDUhZUAYIpbpBs8gh4xAmUhCRBqwAKeJg2f0t4gAmVFBxgDyQUJEGEW9vjULKL2SRSgIRIRecpIKshSYDSzlyN9FMk0DdzbcKIg1Vcde8VgXIIq/XASGREb9J6+QWyhB6ezSk5LdAMqojd28gtRSoSYNUaAQ5IdYCASxkYAEVQqQSSa3uJgMCMtVQslNOGpsYsNZ7egCp0Qk5kUgRNCuRwFbrEBlPjDJBFZIMICQ6LnAIlUBgQmQqQphJiFRCxHXIzCIPLJAZVGOFQArUewIIIoicA3ORaZAOeTKZySBlCopqgNSwgZvoBvCXv+tzxVtXkXdgwypEassELgWR5gBGup/6UucTAlwLkSq+d4OYA1LidqOox/046AbNVgWQWQKRB91jZByEFn+TkC5IcQGEkCCpUe/tRW4JSvCkSAAIJgXNLiB3PxpIhciJE8gk0PxjY8LotyVCGFqITENk0pVbgAJ+TthqjRNbCFm2F4ByIl+ZgEUFkEXnLRLdCMl1JMsTNVPA1OuuvSS3jY6gbAO22uPeHne4Di7oh8n5R6XJ+W+XJEak/ONEiQQUViZMPNRAvc5JciT3aSkEVJ5AZAqm2gt0DL9htQQJQAqBnjpPaFNSQGUW9apGonPUY8WTL3MqJ8ylxkJJfjNLrv1lyEljAlIQqUWkJWqBhJ3Q2FLwvrEbSSGpymuZWUJuCZH7MRjmGjasIrdusqfgHwEkRPkyp+gXAGDB/SGkHEYG5eowq/JEvjJoljzptOhjQsJC8D0IcHu6F0nC5BC5Lkc/oAKInK8NyEIEAta9YJCZgAgELnjn23DBGSfhnn9/FP9yz/2YijS3EC/iTfPemEpyui9xzdKlS7Fhwwa0Wi08+OCD+OlPf4ogCKbc72DhkA+ajTHIsgxpyg+4TqeDPM9Lz+Urr7wSN954I8466ywcddRR+PznP48gCHDBBRe8qPNorfHZT34M84YHQFNv7vF4PB6P5yBlyVvfgmOOfj3+93e+v9ftOjyss+uyl8C+SE6n4o477nhJ5z5YOOSD5ttvvx0f/OAHy78LTc/KlStx5pln4uqrr0aj0cDZZ5+NsbExnHTSSfjZz35Wbrev5HmO6//hf+E97zwT7zx79zNGPR6Px+PxHPzc828P7NOb5iQzsJnBpvUP4NkNDwAAbJa9pHO+nJLTQ5VDPmi+/PLLcfnll+9xvRAC119/Pa6//vo/6jxaa/zNxy7BvCNfyx6OTgNsG6MA2Jd24oQX0gEPh3bGYav9rO2qj7KPrg5gm+5XYe6GALMUIophqwOAzUufSNtslDpmETltcp4BWcp6YQAirMAGVSBr8fCSzcvhd1vpgxrbzMOIhddu1oYNIghhIZMGKIphq/0AeIKNSZpcL2sgqr2wO0eghufBjo/CjPyB9cdhzBKPThu21YDs6Yc+7CjIMIZpjkFUapDWQAQhbGMUslqHqPdz3aM65CCxt+d4C0IqUJay5hgA9Q5B6K0QUsKmCYK5C1w9ndQiTaAGZkI4XbGo9EKkbVDhrVmfwRKVPONh6zwDenj4DrUB9snNnwNlHei5R/J21kCEMXsK9w5DmBw2rkO2d0LUekGdFmty60MsZYjr5RAeyLLmk2qQrR18fSq9IBWCajMgoh5QewwCGWzPEESHvZFtwDpu5Blkrc4+0H2DrBmv9SOIe/jYo88DnSbEjDmQZCFrdd6/2YCs9gJSQ9Z6YeM6bHUAevszMLIOxP3IBmdBP/0w0DPIk2lmzAEa20BZBtE/k4ft04Tb3mSQtV4uf7sJWalBxG6YM+uAOm32sbYGamC4nNgIqUDNMYhaL/Lfb4Sq9wN5xrrruUdCSAUrFag1BtvYwXKSLEc82IdkZCekkqgMD0AoJwGqxRBKwiQpel+3ENAha5l7Z0BEFVBY4+FXspAp+6TaoA4pWPNYkwIia0N2GqCwhqoWyEhASx72FVkHoQaCoNDdunrYHLAGIeWwMoA0GQ/tBhXnqes0v0oBUDxUDtZdskcvj0FREDs9Kk8Us0RQbr1SEaQS7I+MAIFNgSwBrEGsWRiRGEKoNOqRZG9Zk7lhZwWirsShkGFQ6CQ4QkK6yXYAT7hj6RBPYhRk+flAVE4AFE6vaSwhNSwbKQ2PXf3Iab0hJwxbO29b0mFX0uC8mkm4MquA5SzkhNxRz2Tdd1HG4v9Ss/zEGgQAhLWQrgxw2nWejMUe82TdPlkboStXRUgQBc5X2sllbOo8nZOy3+TVAchOk+UdYZXrZw3LMDTLwKyKIMmwz6+QEES8HdCdGErExybL9SpkBUICgXvmOO09T5or+pDmZ3qW8IS0LOdnVliFSNu8fdIo25S/VzqA4mteCWT5trKVsVRCScXtMmEotC9S0JmCchp5JUXpu1zsb4j17aESCCmH6LShpIYwlu8JkwNKQ7Z3wlYs4rgPJpCoaInU8LkLDTTg/LWJJVesU9cQeQIlFSABEtpJI1LIXCFQIaxg+YRM204GNwHXh5SQgAwAQtcnXGr3PKiWmnmRd0ppBs83YZsxYTKWcARx2ZdJ6q4kxrW10CHXGTxXoZjAa6WCUKx/F0GEs885H0ef+KYp3zSn7k3zrGNOwaxjTsFzGx7ApodX73WfPfFySk4PVbzl3H4iz3Nc/5Vbcc+/PTDdRfF4PB6PxzON/Pz+Vfj8F/7nlNtlxiDNu5/ho9+MYy/6//a4vTEGSZJMkpwmSVJ6HxeS00cffRTtdhvLli17SZLTQ5VD/k3z/kJrjc9edTnmHflafsvg8Xg8Ho/nkOSdZ52JY47+E/zv27+z1+06OcFO0DBvefRB/GH9mj1uv78kp4cq/k3zfsK/afZ4PB6PxwPs+5vmFyY2GXj9m/Da//fje9z+8ssvZwnhCz5nnnkmgK7kdPPmzWi1Wli9enVpP+eZGv+meT+htcayT12F2Ue+DjZPICs12GajNKGnNIHQrCc2O55njXPWBpwhPsiyXrXdZA1wGAPSgtyEAEpaoDSBrPSyXnZ8O6jSC9U3yDrSPGXdsw5YTxrWQGRZO5mMQ1T6ujqtNAFV+iAyTqrAHqsd0Nh2yFodZC37Q9ucdYeV3rKelGdQfYNQfYPInn0SyFOogWEIxfZ2anAOa2B1CGqOccKSgZmgLAVlGaxMoAbnQIRcZhHGUMOHwe7cBvTPgo37+E29s2wSPTOAtAUMzu1qAttjrL01GdTAzK6W0llSCWdTZXdugxiY7QpuQYVO3OkLqdLn9N0KpFhfSlJDdppQc1/TteLSEXQYg5JmqZO2UY/TKVrAhqD+AZi4DtUc4TYHYMMaRN6B7DQhLdtHUViB7RkGANZe6hiisQ0iiGArfVzu2gzAGsisBdMzDLVzM/eTnn5uA8G6Ulvpg+g0IfuHue4AbFgpfynL3gAo9K4qgOg0gdogTH0mLMVQ488DQsAOL3S+wW2Isa0gY0BzXss6vlTC9gyz/tBkfM60zR6/7SZEtY819jpge8NKL2vETQpUa+xDXemDdLra4IjXwTbHoOe9BraxHUIqmMYoZK3O3tBSgZIm1GAvKGmiIp3WPs8gKjX2AU+T0tpNVusQlRr0rCP40kY1Z8kY83yAQksK9jOOlGDf5JTXwRrITgNhoct13riFlrbQ0UJqiCxhTW2nAeksqwCwV6/rf6x7jEsNa2FLViSrIERdizbn7askGwmTCljLTM69zpVPmIyfD1kLoa6g13lKswUclcfiTiVdEgnd9W125WL9csr2WxORqptQRCpIkyHWXYspSwQ4qzFjqfulUliwAaU9XOEBXdjEUVDtWns5i7FCgy1MCqsj1oaje52Ke590xG0mZNkGZcIQd47CKm/iOeF0pSi0vkVZpC615cKkEIaTsRQJO2zcW9rUgQi2sDJzOmcoCSsUlMlKSzMrFDTxNbJBzH1GSCCoQOSd0iebrOFjhBWUiU3cNRKW50egvAaunDoECTdPw6QQTkdLOuJjxSit/KC4fxb+0IIIsWSfYyXZNpH9+w0UJehTwp0nReo03aGbM1ANWNccSIGZFeE06DlgXB2c5R3g7PaKvhBUyr5Y0QGU6UDJEIL4WKz/7r7H4z6QlZp1blDTbcNiToBJoVwfKv2yXcKbUptcdGVQ2ZeL624rfWViIeGuNU1oaxKC9eiFJV3ocgcU5yFblqW8v3Ts7l/3twrdvRXy32Txjnedgzccf8KUb5qT1CBXXQu9kd/8Cs//+v/udR/PK4cPmj0ej8fj8XgOQIyZ7NNsDYGsN66dLrw8Yz/BlnP/hJ+vfGmzXj0ej8fj8Rwc3Hvf/fj8//jClNtZYyd9+l97Eha8+yP7oYSe3eHfNO8ntNb47F98HHPnzIZI27DGwLabPNQ8MBM2aUIPzoZtjrEdWGMUolIDggrbvjVH3IECiDyDTRO2WksTHpaOq7DjozyETpzNjtpjEAOzQTu3so1aMs72aO0mxGAN0AFnpOs02SbJZJw6OO7hZWGlO3Rr83J4XEjOGmjDCmdByxNQUOUUsJXecghW1vtB7SZbfg0Ms9WcDrrWbJaHK2VcY9lHmoCMYVuw1k7OFuiGUGWtDhvEPGymI6C9k4fLW9u4HlGdLbNMCsTzuHxxL8sGOk0epg2i7tBcnnD5FA/jk45ZxmAyHp5zdTdRHTJrwwYVtuNyQ+YiT2B6hiGzlsseqIH6EJC1y+FtAkD1WZDNEZZJkIUNeyCkLu2MZNJwtkdVkApKSYWwOUtoghhm6MgyrWuR2lnYHKY2CJCFGTiMl5sUMmnA1gZZ/mBzPo6OuD/oGMKdrxyy1xFQtEFY7bbb+Dja1WHUpGb5RGec61vYaVX6+ZwhAKlgdVTKeETaBvXNAvoljI6gGlvZCtHJGHh40qUn1wGKdM6F3ZYIQlBrJ2evTBPomc4/VCq+N8ZHy0ySwfA8HrJ1doKy1gvqcNZCs3OErebq/aV1oA2q/H+HyBKXhQ8IcpZDBYVUIKzx8KvNuZ9nbR6KNRn3+UIWICSQJSy7CSp83LTJMoDCas0N+5ZZ4grphpCcma0sT7trdeWG4YnQzSQIl3GvkCWooBziFzZnyy9nhSeFgCVAZBn3AdeGVtUg3XkgFfcLk7rjde3cBFnkMoCyWVcGUVyjCZm5lRQgS7yMUG5bZFMjqTmj2sRXNFaXVlyl7ECw9MQ6WzuWWnQlJBYCsrTE7KYjn1igIr00hITVEVsEuvIAnOJaujTaFgLKdEo7PpZLhF3ZmbsGKOQqUnFiKmtgdMwWaO5eLLIlQitYFcC6N4FKCpBQTv5CkzLRda8oJmUSnPT3RCu6CfcIy1q6wgVBlm1DC0kCwBZ9xf1f2hmyLEQ4e0SjYwQw3XTlE+szUdKDSadDIEV5vWXeKeUQwlp+/kkFIs58SCIuy8XXWZbHKlJiCwAWbMGXW4LSivtAIV9xkhMhin6YdyVGZJ0nnOyex2XQZMmGgsiySRUgqUAonv1RV1bk5JKT6l2co9zZstSmyOhYlsV2ZTVCOFlMUB6LSENMkH28453vwBuOOQZf/+Zt2BsmJ4icdlnmmR580OzxeDwej8dzAGKMZc9rx+gTD2HHb345jSU6tPHyDI/H4/F4PJ4DEJMZ5BM+PQtOxNy3XT7dxTpk8UHzfqJIo+0t5zwej8fjObS5975f4PP/4++m3M4aC5u/4DPhzbNn/+LlGfsJrTX+5i8+jnkzh1jbmWcQQQA1sICt4sKY9aVoQkQx6zKj2qT0siLkG0VEFdidI6wNzlO2lYtrkPUZnNZVh2xdBLDmqn8WWxpJBRFEoN5hIOuAZAQKKmx3pjSgQ1ibs9VTVGe7ozyByNlyiEafhxiYBfP8s5Azj4BM27CVkHWsmlN4q52bWcMY16HmvgY0upU1pXnWTRVOllNr14e5zi7Vs5gxh9OEN0fZqkxI1isLCYpqsNUByNYOtngCYGqDEM0WSAZdS7mgCgCguM4pZm3u9GVhaVll+gYh0jZrcm1hfcWaz0Ibal3600I7LNIW65rTZjdleMfpgxVbzMn2TtjaoNPPpqxDNikoqLAmzxrWLNsaRNLgdORxnc8f1coUw1TpA9kcNu6D7DTYsszZ09mozvrF6gD/P++461UD6UHWM0vNKXQBoNOEqbPlnMhTIOKU3bK1g7XK7Z2lVRTFfZCtHchnLEAuU4ToprLN6zORW0IUcrpYmYxxXwsiUFSH6DQgDPcdiutl6miRd2B6Z/PxCz1loQ80KV8vsgASwKWyFa7fy/ZOyOE6MD7C6dvdNVJBAOok3F+yNqcNz1OgZ5D7a8T6/uCwo/iak4WpDPC+7R0wzmKKtausV9VFWui809XZCgnYjK28RAKYHBT1sA7aafgpqgNpExT1gMjdR2DNaWHDR07HzZZVFQApX1Ond4yRd3WkzrKrsKyCkFB5ApBFpisITcaa3+J9h+UUxdalYgaAwDqrtrwDoSPWzBf1dTpscnZwpd2asyok4uUybfI+omurZ2VQ6qlLrbDULN4Haz8DmqCHVV39riw0phO1su5+IziNqTsO60ttV4vsdL0SNMFOjtgKDC69sdM4k3vm8XmMu4bch0v7Oqd7lWRgXKprkXe4z+ad8l4tLe6KvuvaD4r3nWhlZyGgXCrnjCQsoZtqfEJeaiEm/22FYjluoYst+t0ELAS0zUFSTdDs2kn/FrrliTrwUs/s0ntTWHPP0rC8BkIAokh7LgoNcuAScMnSblCYFMJZ8QFgvbjT+k68pqV2HnBCZdO1CJzYH0wGEMFYghSsj5YgSAFIJcs6kFSAdW0sFBT4WnIb2dKWrixDoaVXXVs5ggLpCBZOhy1UeV06FoiEBSBBQrg08bbsl1YobiOwRp7IlW+CntmqoNREk7NVLO/lsqxwmnhVlvXsJe/GG44+Gl+/9ZvYG8YQMCFIHvvdwxh74ld73cfzyuHfNHs8Ho/H4/EcgJicJn1qR5yImad/YLqLdcji3zR7PB6Px+PxHIDY3EIou8syz/Tgg+b9RJ7nuH7F/8J73vU2vOu0k6a7OB6Px+PxeKaJe++9F3f98F+m3M4YO0me0Xx6HRq/+/dXsmieveCD5v2E1hqf/e9/hXn9VVgdQEoN6bxbMbaNvYmFhOibyVrT1g7WEVYHQGEFZHP209UxRNqE7BuEnXE4e+DGdU6TXaR6dnrcwnNSdpow1QGIuA7hfH5tpa9Mjcz+vBYU12BNDmHC0g8UUsNWWUOs0iYoiCBnH8kenz1DnI4Y4NTCtUHkQwshOuNOz5kCM+bAVFiby9pFTkNNh78B1Bpln14AYmYFoj3Ges6oxn7H9ZmsZXMaZpFxymU1toW1tDpC3jtnQnrrBkzvHIjOOHvsSs0647iXfXVdu6jx50EqhJlxxCRtr+mpl57CFNWQR72sJwWAIGYfZQCQEjJpwNRnQaQtp5ss9Mp56Y8MQ6yVNhkorLCmWCogT1ijHVRYU1xo3wDWB2dt1iWbFLK9E9nA4VDtUdY1mxStaBAxpax3Jgsb96Gw7QxUAL39KdiwBxREyGb9P5CNrS5NdsP5DXdg4z6o8W0QZGEqfbw+78D2DEOPPovABOxFmjYhTA4pJKIJ+slCuwebQza2lpps0hF7OCecktzUBvnaGaeLD6usj8wS5AOHgwjQ7e2lLluk7dLP2LqU14jrZTsJHYDyFLJvEKY6ANVkfbfsjLNuMqjARj1AbRDU3gnoAFbHoKgHsjnCXrYAazfDmmtrxcd216DQIlJYhWjvBOmQr53TxdrqQNfPV+puml9rWPNs0q6PcqGRDyplqmeoyY/dQvNsgiqUcZrvImVv4WE9YVtjqUynLaXzfi3TULt+4/yjS/1sUbciLbzoprcWEmXdpNOcF97FuSUoIQAVQVmnp56op3XHVDIACp1v0Z+JYIO4TItdtHGhNy50nxNTZ+fEmmxSIfv32hzW+egqKVhXTd2UwiQkrEvJXF4GpzEN8g77ZhfeuIRSyw0AOUloAYg842uJCCaoQqfjXa9sHYEEe0MbGbD2NkuQqQi6uB+k6qaKlgoBESBYH58hgpKiqwF3/s9WKCipSp12oY/m8wm+fiYDFPtkp2EdAeVlH2Bf5eL8bt8J6Z4hJF8rm8OG1UkaXLiU3nB+2CXOx7hMO13cFyZjH3+yyGTIGmR3Pgh37dzcAWHYE1xkba5LEHDdJ5QTZGFVAEkWUghug6zDZQJr2iW4jeDSxZfezkVabLKlHr307C70zBP6DUGVumlJBhbKpdIWAFkEUnU1+RM8vXNw35AgEHhbAcn+5kWfKp7Z5OZEFLeajvj6OP96Wfh/m5TL7DZ8x9lvxxve8IapNc25Aclun4/mHodg6DXY+euf7nU/zyuDD5o9Ho/H4/F4DkBMbkHSyzMOFHzQ7PF4PB6Px3MAYnOXSfWFyzzTgg+aPR6Px+PxeA5AbJYBIt11mWda8EHzfsSGNVDEXrRWhazfDSrsP0sWNqqDdAjZabKesO8IqMbzoPoskHaaykoNMq2UurZs+DWQyRj7KgOAlOwrW2g00wSmZ4g9PFUAG9dZf+Y0rrY+q/RBLbV/YaXU/7J2DoCQML2zS51foZ+EyVnLqyPWlQKsc84s8sEFgMlLraiN6qz5tDlspR8UxBCdZqkBK7SZpSdxUIEa3wZU+iCyBEnPLAgAYS9AOoZsjgA6gmg+D8g+2NoghM3ZS9m1hemfB9EZ5/YJYrR1DZHbjtw1QJ6CdATVHIEZOAyytQOEGnRnzNWnwttZwzrXPGE/ZJPy30mDz21SIGmwnhqujJ1xmL65rNGNWfNMUR0wGdTYZti4jrwyg7XTUsHIACpwWk2hQH1zodqjaAW9qFAG0jGU02G2gzqUAMK0ibaoolcZyOYITM8wbFRnXR5sqTe2vXNYa54lrNHsnwvkKQRZyGQnbMB90/QMg8bHIbMEcP2VdASR7OSuALAWPKoB1sLWZ0J0GtwWnSZke9T5V2vIzjhIsY49Gzicu2iuuG3SFvclFcJW+rh9NGstYTLWAycNQAUw9Zmsiw9rELUOrPOOzWccwR7alT7Wrqct3q/TgO0Z4uug2CMZKmC9M1Bqkk2lH6ozzl7GQrIHs+ujrGuulJr10ku78Bp3GnMIiUxFCHqGYGQA7XyBqdBxFveRKzNMDgpi5JCsFRaC9Z0gt71xcw1qEGmCTFegBaAg4FSUrL8sHiyFv26pJ2ZNLZye2ugYujNW3tNWBZBpC5musK+yu+9k2oINq8gtIdDc/7RgT10jA6dn7mpgyzZ0/s0TvW2ldJ7nzjuYPZULNSf4+eM8kwURlynvQKuw9LHNhC7PD8XaWEiFnCTrhIm4TYXzmUYHBpI1prCwYbXsrwBKTTbgPKUFy1AzFUGrACQVVOFhHVT4/haSPatVCCs0NFm+1qbjdP0pP7eKdlcBMlfJUAgoKZAagnJtHDjttgSV7VlqsE2nbA/SkesvGYyOEXbGkEe93Xo4rbiRrHkmIWGcRhtgXbfM+Jki805X768CkAzY39rpnFnXzt7Fkzy2nYYZUkHaHCLvQAcV7n956vy/3TPLdFj3XN4bVdY6Q5XP/9xpf0sNu/Pe5mdlhInyauu8lEtfcAAE1e2DZKGsCxzds6K4Fwq/ZumupXTHyiGhybAGnQg5JIgIEoCSArl1Gneyrh3Zu5l1/XwMoysQxNsTFFTegQJKfTOIkKkIKoidRprc3CI3H8Ky5lxKXdZxKqxJgby7bfKH/0Ty3CP7tK/n5cf7NO8n8jzH5//+Rvx81ZrpLorH4/F4PJ5p5N5778UNN9ww5XYmTWDSdvkJBhei9ifv3g8l9OwOHzTvJ7TW+Mx/vxrvPHPxdBfF4/F4PB7PNPKOd7wDn/70p6fczuTpbj/TwcMPP4zTTjsNZ5xxBk477TT86leHXmZCL8/Yj5i4DsAAKuCUu1GN005HNZjqAKcCViFMLYaoDrD1zYz5UOPPw/QMQzq7Kltsa3nIxsa9bMklBEx1GKo5wml9s8TZthFLOOJeCGfrw9ZhYWm/VFiLkdKlpY5sjjgZAttXodIHpC22UItqpaSDdASKe6HHNsPWBiGdbRvgRomdLRoJCdnawbZweQIb9yGL+hDmbHsnO+OcAtzZfQGA6Z/LNmZxH0Kb87B1wCnCqdIH7BwFBVVQVOf03RDQpYSE6006BFxK3Ni0efguTyGclVhen8lSgSBmK7r6LC5fVIdMm8iEhgwDGHL2W0EVKk/QlFX0dLaznVyRRjasIJUhIttgSUttcHKZVcgpd8kiHZgPlSc8zOjsnrQbhlVZCzKooGUEaipEqCRSCiEkEFCOTlBDnLexJQ8xS0vUAwEYy9cLPLzJMo4cO1UvYi0QwCJFBWFhlyYksmodgelw3zAp17sygFymCDpNkOzlvuBs8AortSJdLjm5Q16ZAd0ZYxlFawfy2lBZHxvWQEEVMm3xeYMK4IZW1fjzoCCGrQyw3CaIIZojLM+pDnD/lPzb3tRnOXlDxnKYrAUKKlA7nmUphkt7buNeqLzDw84256HksAYKqywzsTlyAsKsXQ5Dd0iiQpbvGxXwed3+JCSUsxYs7OiENTA6htARjOS+lQpOYVykNycV8HWwWWkBSToGBVwfJUuBxaQ01wBLe2AyTnMvBDIihJZTfCuyLAPpsCyLU01T2Ye4Axi2+dMRVJ6wbaOQsBCwliAFSxyQW1jJzwDp+qgSAsX7lE5uoaV2/wpIAQQTh5adPIClFGy5Jgu7Spc6WViD1ErEcPdIUUdniVbYeWUyhISAFBLG9V+C4mHzCWP3AeXIrO5auUEiI8C4TZTpwKgIaW4RacltlY4jDWrQRKXkzAYxBBG0ADKSCF0KdXIWbcW9KhQ/JwPjbNEsW+mlhhC5NM7dVNsGyqVehiF0cotYCVgUdm4aHcup03MnPynrpCL+16UmD5w9oHV1t0QgAEZoBNaU7QiwxVoATm9tVQRlsvJ4VkeQTpIFIbuSDXeNSntSFSCDRODs40gFMIbQtgqxFtw/nV2ecGm0pUvrTiqEsQRDQCRZIlPYI5IMyjqQEOUy6aQS0rVfcUxFOeD2n3RvEEopCkkNkbXZptF27di4MWQpb5NkYIgg3P2WWYk0s6hoCSWB3FL57A6KVOYmA4JKKc3Q7j4V1sBCsqTJSkhn50eESZaHWjgLVic7LGVQUNDglN0ya6Oja9gXKEthX/B+k7LpCZrnzp2Ln/70p+jt7cWjjz6KD33oQ/j3fz+0PKN90OzxeDwej8dzAGJNxrr5CZCZnomAc+bMKf8fhiGkPPTECodejaeR1atXT3cRDkruX/1/p7sIByX3/5vX379SrFy5crqLcFCyyrfrK4Lvr9OHzbPdfvbEd7/7XSxevBi9vb0QQiDP80nriQjLli3D3LlzUavVcMYZZ+DRRx99UWXKsgxXXXUVPvOZz7ykOr2a8UHzfmTNGh+EvBLcv8YHza8EK9f8n+kuwkGLD0JeGVat8u36SuD76/SRp+3dfvbEwMAArrzySqxYsWK362+88UZ8/etfxz333INt27bhtNNOw5IlSzA+Pl5uc8opp+zyueCCCwAAxhhceuml+NM//VOcd955L2tdXw14ecZ+RJicdZrFUIuQyIePKlNAU+xSKAcVEMlS75f2Hw6djrNNmwogk52sy1MB26T1DKNZmwUlBUKblmmj+ZxscWQrfZDj29hCy7oUwWC9mGqOIOuZCWVY0yfTJgAg750NYQ0SGaNiE05PHcSwPcNlWmSZNgHDaVX5vKxFLe3rypTIPWypFtdd6le271JCcVpfqUFON0o6LlMUy/Yon6e9A6Y6AyprI5Uh6yutgYl72eLO6a4VWXTCOkLKIVK2QbNBBYIsmlahqggiSUrtKsIaVHu01OsWKZAhJOv7ojokBJRha6EiLaoNYsjc8jVLGmiFVVRsApm2EKUN5LUhzmZrTZke2eoIuSVEyXbYygAMEaBjTp2cd9BQPRCqBzV37SwEevKdIBVCJ6PQUiMNaiAhEeZtZLqCAQ0kVEGrY6GERjOzqAUSfTJBEyF6Og0E8QyETnMXqADjCGFFiD4zBhlU2ebKpRzOhGaNnnC6WiFhdMyWYkXaaJuzVruwHSzs0oIKX//aIFTG1m9pWEeQt6EaW2D6D+PUtlIBKmANfHUAsrkdKm07G0PJOmki1unbnLXMZNlOLu+whV/Who372F7QWcvBGti4F5muQDptKtsWPg+RNGB6hiBNBpE0IGtDrBfOO6U+20Q9nEba2WsVOnThNNRF+t7CvktlLeygCH0RW1g1c0I9HwOFNRjNcwfKlM9EnMo9qHL/cjpMYY1Lsx13NZtBBTLZySnsVQUqTyB1BKOi7j0K6lreTXzGuPT1nMK4UmpThcmQqgiBIBRS6iKVNcBy6NwCbdd/cgJya2EIUAAqWsISIbCcClgIIC1ExDKAhNMFF6nC4dIJE1vRRVLCgNNQq2QMFFTYAkxqp0EVCGyGDBoG3P+4fAodQ8gFEGlVpiQObI52rhBphU5uoaSAcsrhIlVxpCVUZ5w19TqCIdawWoQIlYACa2yJgACm7NuJFYi0my9iCZmMEOftsp9oAVgCKnkTpAKkKoJ2Fniy0wBV+strYWSIjgWUIGhY5JAIlYChACBCxwKBZIs4TQRCV+9uZcR6cENAWC/1zxmxbZtwfaiw9APYSk86uz7W40rWGquI05IDIEuADKGEgDbjSGUFWgHNnFANBCwBiixyKxFTisxygGbDGjJIGGMRS0JiBYTQCAggCGjBmmsSApKIU6JTDiMCBDZFKsPSVk6C+N4Na9203e6Zp1ydCu13Yec3yX7O2QKWWmJnQwenxwa62nYQoWMIEVivX9OAdf0/MwJQIQQAXezrvruMJVhX3tQQiASEAAJn55rJEEGWIJERpAr5u1sFbK0XVCDbO3iuBwhGBAABKUnElq0MY9tBug+J/WyeQZCYtGxv8owlS5YAAFatWrXb9TfddBOuvvpqHHvssQCAz33uc7jlllvwwx/+EJdddhkA4MEHH9x9WazFBz7wAbz5zW/GFVdcMXXhD0J80LwfIPekyLIUY2ON8gE30V9VdsZBQcY+sUHGX7LuoZiHFjptgqRyQXOjvEFFnsLYCG0RlkEziskuNgd0xkGKEJDNBihw/rNBzsFARFCtBjIbTwiaWwAAk/KDJbECmU34C0FnsKmYEDS32AcX4MlHRFy/4ocBWQ7cVcATTly9i2DLCtU9b7sBuEk3xRemTBqwmeRJH7mGShrohDw5Q6YtNDoWWZpibGzMfUlbpEJz0NwZZw/gICuD5lwRZMLtUEyClJ1xDpqLiTLuwdkJ2cvVuqAZmBA06wid3CJDBtEZRyvV3TYKDPIIZdBc1LEbNDdgM4WM+CHOQXOKccVPUAMO3E2QQyeNMuiB1EgDAw3rJphk7ItLHOwoATRzC6slhMzQRAibNrhsxMEVqQAtw1+MwjSQp7I8PwU8oYsAJK0m0jTD2NgYjE53CZqLALL0Bi78UU3m+nEbgghpYDhoHh+HkWNuEg/3A91owOYKstUo+woFOX+JdRrc/20OQxX2O80TDprFGE+u1BFPLix+ZHS4/TKdIRpvwGYCFGRQzQb72NqwDJpzE/IEPfcFmwgOpIoJUkV7FP0aUnWD5gl1bFAE4YLmVk6gvAEKDUwxuY5MGWjzhNAc4+0OsozbtgyanU+tss7HN2lw0BwaDuB1VPZDcoFm4cMM57dbTAQUeQrSnXIbcpPVOGh2t6CbqAjwpFEhOEhIcoIJOEDOXdAQKAElxB6D5sBF4YYIIeXdoNlNvi36jYVwQXOj7Gsatgyapen2P/au5mdAO7OQAoi0LINmYXO0SSHSsgyaO+ON8lkgwc8h1WlyG9ocCTSIuE6hEuVETKLujxthc7QRINLdwMkQkBrub4XHr4XgZ7LS5Y94QfxsMZkLfDtNjMucA3qBMmhWkgN1S3zsQHK7FP7DxTZcHoOWEQiVmBQ0azcJ0gpV1hXgY4gJQXMq2Y8YQDdodt9LSnAd0sBAC6CVE/JAlv0wExqBTdEcHy/bNQMH4WkZNIvy+ktQt2wTg2bn45y6QN26yZeyMw4bunYnyxMKJ9wDHQukE4Jm0lHpi17+2DP8fdd9fmTd55PNYVQEQ4TMEDpKQAp2C7fg/tUyAoHilz668DY3GUhHyN3k0lAJFzQThBBI3bM0kyEC00EiI0QS5Xdy6S/ebiDPdHk+AMgs8f7unh1vNl0fnGBS/QJspwmoF0z8e4ma5p07d+Kpp57Cm970pnKZ1honnHAC1q1bVwbNe+J73/se/uVf/gXPPvssfvzjH6Ovrw8/+tGPXlJZXq0I2tvV8rwsPPvsszj88MOnuxgej8fj8XgOMDZt2oTDDjts0rIkSXDkkUdi8+bNu91nYGAAv//97xHH8W7Xr1q1Cm9729uQZRm01uV5jjjiCDz22GP4kz/5k3Lb973vfajX67jllltephodvPg3zfuBuXPnYtOmTajX6xBCTL2Dx+PxeDyegxoiQqPRwNy5c3dZF8cxNm7ciDTdvb1cGIZ7DJj3RG8vZ5UcHR2dtHzHjh2YN2/eizrWoYoPmvcDUspdfkV6PB6Px+M5tOnr69vjujiOX3RgPNW5FixYgIceeginnnoqAM5WvH79+imlGR7Gu2d4PB6Px+PxHAQYY5AkSfmGutPpIEkSWMtzDa688krceOONePTRR9Fut7Fs2TIEQVC6Y3j2jn/T7PF4PB6Px3MQcPvtt+ODH/xg+XdPD2dtXblyJc4880xcffXVaDQaOPvsszE2NoaTTjoJP/vZz8rtPHvHTwT0eDwej8fj8XimwMszXkauueYaHHvssejt7cWcOXOwdOlSbNq0adI2zzzzDM477zzU63UMDQ3hE5/4xB6F/h7muuuuw1FHHYW+vj4MDQ1hyZIlWL9+/aRtNmzYgDPOOAO1Wg1z587F8uXL92rj49mVCy64AEII3HfffeWyVatW4cQTT0S1WsWRRx6Jr371q9NYwlcPy5cvh1IKPT095Wfp0qXlet9fd2WqTGVCCFQqlUlt+utf/3qPx2u323jve9+L1772tZBS7jF72S9/+UucddZZqNfr6O/vx1ve8pZyKPtQYqpMcr7PvjR8XHBw4YPmlxEhBG699VZs27YNv/nNbyCEwPnnn1+ut9bivPPOw4wZM/Dcc8/h4YcfxurVq/HXf/3X01jqA5+LL74Ya9euxc6dO/H73/8e73znO7FkyRIYwx6fjUYDS5YswWmnnYZt27bhnnvuwS233LLHjEieXbntttvQarUmLXv66adx7rnn4sMf/jBGR0dx66234pprrsEPf/jDaSrlq4tTTz0V4+Pj5eeOO+4A4PvrntiXTGU//vGPJ7VpkaBhdwgh8Ja3vAX/9E//NMmXdiK//OUv8e53vxuXX345tmzZgm3btuFLX/rSIelytLdMcr7PvnR8XHCQQZ5XjHXr1hEA2r59OxERrVq1irTW9Pzzz5fb3HXXXVStVqndbk9XMV9VJElCX/rSlwgAbd26lYiIbr31VhoeHqYsy8rtVqxYQQsXLpyuYr6q2LRpEx1++OH09NNPEwC69957iYho+fLltGjRoknb/sVf/AWdddZZ01HMVxXLli2j0047bbfrfH/dPQsWLKAVK1aUf2dZRkNDQ3TbbbcREU3qmy+Wt771rfTpT396l+Wnn346fepTn3ppBT5IWblyJQGY1D99n3358HHBqxv/pvkV5Oc//znmz5+PgYEBAMD69euxcOFCDA0NlducfPLJaLVaePzxx6ermK8KfvKTn6C/vx9xHONTn/oU/vIv/xLDw8MAuF1POOGE0sAd4Hb93e9+h7Gxsekq8qsCIsKHPvQhfOYzn8ERRxwxad369et3eUN38sknY926dfuziK9a1q1bh+HhYcyfPx+XXHIJNm7cCMD3190xVaaygve///0YHBzEiSeeiK997WuTjvGd73wH/f39+3zOVquFBx54AEopvOlNb8Lg4CDe+MY34gc/+MEfXZ+DDd9nXz58XPDqxgfNrxD33XcfrrvuOtx8883lsrGxsV0e6sWN4x88e+fcc8/F6OgoRkZG8MUvfrH0mAR8u/4xfPWrXwUR4aMf/egu6/bUrr5Np+aiiy7CY489hq1bt+KBBx6AEAJnn302xsfHfX/dDUW999bf7rvvPmzcuBF/+MMf8PnPfx7/7b/9t0ka+0suuWSXpA17Y/v27bDW4pvf/Ca+8pWvYMuWLfj0pz+NpUuX4pe//OUfXaeDCd9nXx58XPDqxwfNrwB33303LrroInzrW9/Cu971rnJ5b2/vbjPxFOs8UzNjxgx88pOfxEc+8hE88sgjAHy7vlSefPJJfO5zn9tj6tQ9tatv06k55phjMH/+fAghMG/ePHz961/Hc889hwceeMD3192wt0xlxbq3v/3tqFQqCMMQ55xzDj75yU/i9ttvf8nnrNfrAIDLL78cJ598MrTWeM973oO3ve1tuOuuu17ycQ9GfJ/94/FxwcGBD5pfZr797W/j0ksvxZ133rmLWfiiRYuwceNGjIyMlMvWrl2LarWK173udfu7qK9arLXIsgxPPPEEAG7XdevWTZrtvXbtWixcuNA/dPbCmjVrMDIygje+8Y0YGhoqhwcvvPBCfPSjH8WiRYvw0EMPTdpn7dq1OOGEE6ajuK9qhBAQQoCIfH/dDRMzlRUUmcr21N+klH+Ue0NfXx+OOuqoQ3LS34vF99k/Dh8XHERMr6T64OIf//Efqb+/n1avXr3b9cYYOvbYY+kDH/gAjY2N0dNPP03HH388/fmf//l+LumrixUrVtDmzZuJiGjr1q10xRVXUF9fH/3+978nIqKxsTGaPXs2XXvttdRqtejXv/41HX744fTFL35xOot9wNNsNmnTpk2TPgDojjvuoJGREXrqqaeoUqnQTTfdRJ1Oh1avXk19fX30gx/8YLqLfsBz5513lhN7Nm/eTJdddhnNnz+fxsbGfH/dA1/4whfo8MMPp1//+tfUarXo2muvpblz51Kj0aCHH36Y1q5dS51Oh7Iso3vuuYcGBgboH/7hH/Z6zCRJqN1u0+LFi+maa66hdrtNnU6nXL9ixQqaNWsWrVu3jowx9KMf/YiiKKIHH3zwla7uAUee59Rut+mee+4hADQ+Pk7tdpuMMb7P/hH4uODgwgfNLyMASGtNtVpt0mfizfLUU0/ROeecQ7VajWbMmEFXXXUVJUkyjaU+8Dn33HNp5syZVK1Wafbs2XT++efTQw89NGmbRx55hE4//XSqVCo0a9YsWrZsGVlrp6nEr17wAoeClStX0qJFiyiOY5o/fz595StfmcbSvXo4//zzaWhoiCqVCs2dO5cuvvhieuKJJ8r1vr/uirWW/uZv/oZmzZpFlUqFFi9eTBs2bCAion/913+l17/+9VSr1aivr4+OO+44+upXvzpp/29961tUq9UmLZs/fz4BmPR561vfOmmbv/3bv6XDDjuMenp66IQTTqC77rrrFa3ngco3vvGNXdoKAK1cuZKIfJ99qfi44ODCZwT0eDwej8fj8XimwGuaPR6Px+PxeDyeKfBBs8fj8Xg8Ho/HMwU+aPZ4PB6Px+PxeKbAB80ej8fj8Xg8Hs8U+KDZ4/F4PB6Px+OZAh80ezwej8fj8Xg8U+CDZo/H4/F4pplrrrkGxx57LHp7ezFnzhwsXboUmzZtmrTNM888g/POOw/1eh1DQ0P4xCc+gTRNy/W/+MUv8Pa3vx2Dg4MQQuC3v/3tLudZsGAB4jhGT09P+bn77runLN/mzZvxsY99DIcddhgqlQoOO+wwfPzjH8eWLVv2uY6XX3453v/+90+53Y9+9CMcd9xxGBgYQH9/P0488UR8//vfn7TNjh07cOmll6Kvrw/9/f249NJLd0lH/UI2bNiAM844A7VaDXPnzsXy5csnZZVcvnw5lFKT2mbp0qV7PeZ3v/tdLF68GL29vRBCTMqaWNDpdHDttddi/vz5qNVqmD9/Pm677bYp28Fz4OGDZo/H4/F4phkhBG699VZs27YNv/nNbyCEwPnnn1+ut9bivPPOw4wZM/Dcc8/h4YcfxurVq/HXf/3X5Ta1Wg1/9md/NmVA9uUvfxnj4+Pl57zzztvr9ps3b8ab3vQmbNq0Cb/4xS8wPj6O+++/H8888wze/OY3v6jAeV846aST8NOf/hTbt2/Hjh07sGLFClx22WV45JFHym3e//73Y8uWLXjyySfx29/+Flu2bMEHPvCBPR6z0WhgyZIlOO2007Bt2zbcc889uOWWW7BixYpJ25166qmT2uaOO+7Ya1kHBgZw5ZVX7nKcibz3ve/FQw89VLbdQw89hDe/+c371BaeA4xpTq7i8Xg8Ho/nBaxbt44A0Pbt24mIaNWqVaS1LtOzExHdddddVK1Wqd1uT9p348aNBGBSFsqC+fPn09e+9rUXVZYrrriCFi5cOCkFORGnKV+4cCF97GMfK5dt2rSJLrnkEpo3bx7V63U6/vjj6eGHH6YbbriBtNaTsuM9/fTTU57bGENr1qyhKIron//5n4mIM+gBoPXr15fbrV+/ngDs8Zi33norDQ8PU5Zl5bIVK1bQwoULy7+XLVtGp5122r41ygtYuXIlAZh0fCKi++67j+I4pi1btryk43oOLPybZo/H4/F4DjB+/vOfY/78+RgYGAAArF+/HgsXLsTQ0FC5zcknn4xWq4XHH3/8RR372muvxYwZM3DMMcfgC1/4ArIs2+v2d999N5YuXYowDCctj6IIF198cSnvaLfbOOussxCGIdavX4/R0VF85zvfweDgIK699lpceumleN/73le+xT3iiCP2eM6dO3eiv78fURRh8eLFOPnkk3HuueeWbRFFEY4//vhy++OPP7487+5Yv349TjjhBGity2Unn3wyfve732FsbKxctm7dOgwPD2P+/Pm45JJLsHHjxknHOe644/B3f/d3e22vidx777048sgj8fd///eYM2cODj/8cHzwgx/Etm3b9vkYngMHHzR7PB6Px3MAcd999+G6667DzTffXC4bGxtDf3//pO2KgHpi0DcV3/zmN/Hkk09i69atuPnmm3HTTTfhM5/5zF73ef755zFv3rzdrjvssMOwdetWAMBPfvITjIyM4Oabb8bQ0BCklDj66KMxf/78fS5fQV9fH0ZHR9FoNPD9738f55xzThm0j42Noa+vb5d9+vv799gW+9J+F110ER577DFs3boVDzzwAIQQOPvsszE+Pl7us2HDBlxzzTX7XI9CbtPpdPDb3/4Wa9euxbPPPovLLrtsn4/hOXDwQbPH4/F4PAcId999Ny666CJ861vfwrve9a5yeW9v7y4T3Xbs2FGu21fe+ta3ol6vQ2uN008/HcuXL8ftt98OAFizZs2kSXBr1qwBAAwPD+O5557b7fGeffZZzJw5EwCwceNGLFiwAFEU7XN53vCGN5Tn+/jHP77L+jiOceGFF2LNmjW46aabyvru3Llzl21HR0f32Bb70n7HHHMM5s+fDyEE5s2bh69//et47rnn8MADD+xzfXZ3XiEEvvCFL6BWq2HWrFm4/vrrcc8996DVar3k43qmBx80ezwej8dzAPDtb38bl156Ke68805ccMEFk9YtWrQIGzduxMjISLls7dq1qFareN3rXveSzymlLB0kFi9ePGkS3OLFiwEA5557Lu68885JTh0AkKYp7rzzzlI2sWDBAjz11FO7bDfxXC/kP//zP8vzTXyz/kKyLMN//dd/AeC26HQ62LBhQ7l+w4YNSNMUixYt2u3+ixYtwrp16ya5W6xduxYLFy7cY6AthIAQYpLDxovlxBNP3OOx/5jjeqYHHzR7PB6PxzPNfPnLX8YnPvEJ3H333ViyZMku6xcvXozXv/71+Ku/+is0Gg0888wz+OxnP4sPf/jDiOMYADtsJEmCTqcDgIPaJElgjAEAPPHEE1izZg2SJIG1Fg8++CCWL18+pa3addddh3a7jQsvvBCPP/44rLV44okncOGFFyJNU1x33XUAgPPOO690k9i2bRuICI899hiefvppAMDs2bPx5JNPluXZE7fddhsef/xxGGPQbrdx88034/7778e73/1uAMD8+fNxzjnn4Oqrr8a2bduwbds2XH311Tj//PP3qJN+z3veA6UUli1bhna7jUcffRQ33ngjrrrqqnKb733ve6XWeMuWLfjIRz6CWbNm4S1vecsey2qMQZIk5Q+FTqdTti8AXHDBBZg3bx6uvfZaJEmCkZERLF++HOeccw5qtdpe28FzADK98xA9Ho/H4/EAmOQsUXxWr15dbvPUU0/ROeecQ7VajWbMmEFXXXUVJUlSri8cHF74+cY3vkFERL/61a/ouOOOo56eHqrX6/T617+ebrjhBkrTdMryPffcc/SRj3yE5syZQ1EU0Zw5c+iKK66gP/zhD5O2e/rpp+l973sfzZ49m+r1Oi1atIj+4z/+g4jY1eOUU06h/v5+6uvr26PTxWc/+1lasGABVatVmjFjBp1yyin03e9+d9I2IyMjtHTpUurt7aXe3l665JJLaMeOHXutwyOPPEKnn346VSoVmjVrFi1btoysteX6888/n4aGhqhSqdDcuXPp4osv3sWB5Oijj6Ybbrih/Psb3/jGbtt85cqV5Ta/+c1v6Oyzz6ZarUZz5syhD3/4wzQyMrLXsnoOTASRHx/weDwej8fj8Xj2hpdneDwej8fj8Xg8U+CDZo/H4/F4PB6PZwp80OzxeDwej8fj8UyBD5o9Ho/H4/F4PJ4p8EGzx+PxeDwej8czBT5o9ng8Ho/H4/F4psAHzR6Px+PxeDwezxT4oNnj8Xg8Ho/H45kCHzR7PB6Px+PxeDxT4INmj8fj8Xg8Ho9nCv5/Ps9dFeDlsloAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(8, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "plot_line(axs[0], b_xyz)\n", + "plot_line(axs[0], pyrf.norm(b_xyz), color=\"k\")\n", + "axs[0].set_ylabel(\"$B_{DMPA}$\" + \"\\n\" + \"[nT]\")\n", + "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\", \"$|\\\\mathbf{B}|$\"], **legend_options)\n", + "\n", + "plot_line(axs[1], v_xyz_i)\n", + "axs[1].set_ylabel(\"$V_{i}$\" + \"\\n\" + \"[km s$^{-1}$]\")\n", + "axs[1].legend([\"$V_{x}$\", \"$V_{y}$\", \"$V_{z}$\"], **legend_options)\n", + "\n", + "plot_line(axs[2], n_i, color=\"tab:blue\")\n", + "plot_line(axs[2], n_e, color=\"tab:red\")\n", + "axs[2].set_yscale(\"log\")\n", + "axs[2].set_ylabel(\"$n$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", + "axs[2].legend([\"$n_i$\", \"$n_e$\"], **legend_options)\n", + "\n", + "plot_line(axs[3], e_xyz)\n", + "axs[3].set_ylabel(\"$E$\" + \"\\n\" + \"[mV m$^{-1}$]\")\n", + "axs[3].legend([\"$E_{x}$\", \"$E_{y}$\", \"$E_{z}$\"], **legend_options)\n", + "\n", + "\n", + "axs[4], caxs4 = plot_spectr(\n", + " axs[4], def_omni_i, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\"\n", + ")\n", + "axs[4].set_ylabel(\"$E_i$\" + \"\\n\" + \"[eV]\")\n", + "caxs4.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", + "axs[4].set_ylim(e_lim_i)\n", + "\n", + "axs[5], caxs5 = plot_spectr(\n", + " axs[5], def_omni_e, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\"\n", + ")\n", + "plot_line(axs[5], scpot)\n", + "plot_line(axs[5], t_para_e)\n", + "plot_line(axs[5], t_perp_e)\n", + "axs[5].set_ylabel(\"$E_e$\" + \"\\n\" + \"[eV]\")\n", + "caxs5.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", + "axs[5].legend([\"$\\phi$\", \"$T_{||}$\", \"$T_{\\perp}$\"], ncols=3)\n", + "axs[5].set_yscale(\"log\")\n", + "axs[5].set_ylim(e_lim_e)\n", + "\n", + "axs[6], caxs6 = plot_spectr(\n", + " axs[6], vdf_parapar_e, yscale=\"log\", cscale=\"log\", clim=[1e-2, 1e2], cmap=\"RdBu_r\"\n", + ")\n", + "plot_line(axs[6], scpot)\n", + "axs[6].set_ylabel(\"$E_e$\" + \"\\n\" + \"[eV]\")\n", + "caxs6.set_ylabel(\"$\\\\frac{f_{||+}}{f_{||-}}$\" + \"\\n\" + \" \")\n", + "axs[6].legend([\"$V_{SC}$\"], frameon=False)\n", + "axs[6].set_yscale(\"log\")\n", + "axs[6].set_ylim(e_lim_e)\n", + "\n", + "axs[7], caxs7 = plot_spectr(\n", + " axs[7], vdf_parperp_e, yscale=\"log\", cscale=\"log\", clim=[1e-2, 1e2], cmap=\"RdBu_r\"\n", + ")\n", + "plot_line(axs[7], scpot)\n", + "axs[7].set_ylabel(\"$E_e$\" + \"\\n\" + \"[eV]\")\n", + "caxs7.set_ylabel(\"$\\\\frac{f_{||+}+f_{||-}}{2 f_{\\perp}}$\" + \"\\n\" + \" \")\n", + "axs[7].legend([\"$V_{SC}$\"], frameon=False)\n", + "axs[7].set_yscale(\"log\")\n", + "axs[7].set_ylim(e_lim_e)\n", + "\n", + "make_labels(axs, [0.02, 0.85])\n", + "\n", + "f.align_ylabels(axs)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/01_mms/example_mms_particle_distributions.ipynb b/docs/examples/01_mms/example_mms_particle_distributions.ipynb new file mode 100644 index 00000000..2fd02abe --- /dev/null +++ b/docs/examples/01_mms/example_mms_particle_distributions.ipynb @@ -0,0 +1,726 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Particle Distributions\n", + "author: Louis Richard\\\n", + "Example showing how to you can work with particle distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import xarray as xr\n", + "import matplotlib.pylab as pl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pyrfu import mms, pyrf\n", + "from pyrfu.plot import plot_line, plot_spectr, plot_projection, make_labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define spacecraft index, time interval and data path" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mms_id = 3\n", + "tint = [\"2015-12-02T01:14:15.000\", \"2015-12-02T01:15:13.000\"]\n", + "mms.db_init(\"/Volumes/mms\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Velocity Distribution Functions (VDFs)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:15:02] INFO: Loading mms3_dis_dist_brst...\n", + "[09-Jun-23 11:15:02] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_dist.py:68: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[09-Jun-23 11:15:02] INFO: Loading mms3_des_dist_brst...\n" + ] + } + ], + "source": [ + "vdf_i = mms.get_data(\"pdi_fpi_brst_l2\", tint, mms_id)\n", + "vdf_e = mms.get_data(\"pde_fpi_brst_l2\", tint, mms_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load supporting information" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:15:02] INFO: Loading mms3_fgm_b_dmpa_brst_l2...\n", + "[09-Jun-23 11:15:02] INFO: Loading mms3_edp_dce_dsl_brst_l2...\n", + "[09-Jun-23 11:15:03] INFO: Loading mms3_edp_scpot_brst_l2...\n" + ] + } + ], + "source": [ + "b_xyz = mms.get_data(\"b_dmpa_fgm_brst_l2\", tint, mms_id)\n", + "e_xyz = mms.get_data(\"e_dsl_edp_brst_l2\", tint, mms_id)\n", + "sc_pot = mms.get_data(\"v_edp_brst_l2\", tint, mms_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Omnidirectional differential energy flux" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "vdf_e_omni = mms.vdf_omni(vdf_e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Construt pitchangle distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:15:04] INFO: User defined number of pitch angles.\n", + "[09-Jun-23 11:15:04] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "vdf_e_pad = mms.get_pitch_angle_dist(vdf_e, b_xyz, tint=tint, angles=24)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Limit energy range" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:15:22] INFO: Effective eint = [10.96, 191.15]\n" + ] + } + ], + "source": [ + "vdf_e_lowen = mms.vdf_elim(vdf_e, [0, 200])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Change units to differential energy flux" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "vdf_e_deflux = mms.psd2def(vdf_e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Change units to particle energy flux" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "vdf_e_dpflux = mms.psd2dpf(vdf_e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resample energy to 64 energy levels, reduces the time resolution" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "vdf_e_e64 = mms.vdf_to_e64(vdf_e)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:15:23] INFO: Effective eint = [20.40, 191.15]\n", + "[09-Jun-23 11:15:23] INFO: User defined number of pitch angles.\n", + "[09-Jun-23 11:15:23] INFO: Using averages in resample\n", + "[09-Jun-23 11:15:26] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_66159/946414841.py:5: RuntimeWarning: Mean of empty slice\n", + " np.nanmean(vdf_e_pa_lowen.data, axis=1),\n", + "\n", + "[09-Jun-23 11:15:26] INFO: Effective eint = [216.45, 1790.88]\n", + "[09-Jun-23 11:15:26] INFO: User defined number of pitch angles.\n", + "[09-Jun-23 11:15:26] INFO: Using averages in resample\n", + "[09-Jun-23 11:15:30] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_66159/946414841.py:14: RuntimeWarning: Mean of empty slice\n", + " np.nanmean(vdf_e_pa_miden.data, axis=1),\n", + "\n" + ] + } + ], + "source": [ + "vdf_e_pa_lowen = mms.get_pitch_angle_dist(\n", + " mms.vdf_elim(vdf_e_e64, [20, 200]), b_xyz, tint=tint, angles=18\n", + ")\n", + "vdf_e_pa_lowen_spectr = xr.DataArray(\n", + " np.nanmean(vdf_e_pa_lowen.data, axis=1),\n", + " coords=[vdf_e_pa_lowen.time.data, vdf_e_pa_lowen.theta.data[0, :]],\n", + " dims=[\"time\", \"theta\"],\n", + ")\n", + "\n", + "vdf_e_pa_miden = mms.get_pitch_angle_dist(\n", + " mms.vdf_elim(vdf_e_e64, [200, 2000]), b_xyz, tint=tint, angles=18\n", + ")\n", + "vdf_e_pa_miden_spectr = xr.DataArray(\n", + " np.nanmean(vdf_e_pa_miden.data, axis=1),\n", + " coords=[vdf_e_pa_miden.time.data, vdf_e_pa_miden.theta.data[0, :]],\n", + " dims=[\"time\", \"theta\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:15:31] INFO: Effective eint = [20.40, 191.15]\n", + "[09-Jun-23 11:15:31] INFO: User defined number of pitch angles.\n", + "[09-Jun-23 11:15:31] INFO: Using averages in resample\n", + "[09-Jun-23 11:15:35] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_66159/4257680054.py:24: RuntimeWarning: Mean of empty slice\n", + " 1e12 * np.nanmean(vdf_e_pa_lowen.data, axis=1),\n", + "\n", + "[09-Jun-23 11:15:35] INFO: Effective eint = [216.45, 1790.88]\n", + "[09-Jun-23 11:15:35] INFO: User defined number of pitch angles.\n", + "[09-Jun-23 11:15:35] INFO: Using averages in resample\n", + "[09-Jun-23 11:15:39] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_66159/4257680054.py:48: RuntimeWarning: Mean of empty slice\n", + " 1e12 * np.nanmean(vdf_e_pa_miden.data, axis=1),\n", + "\n", + "[09-Jun-23 11:15:39] INFO: User defined pitch angle limits.\n", + "[09-Jun-23 11:15:39] INFO: Using averages in resample\n", + "[09-Jun-23 11:15:42] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_66159/4257680054.py:72: RuntimeWarning: Mean of empty slice\n", + " 1e12 * np.nanmean(vdf_e_lowan.data, axis=2),\n", + "\n", + "[09-Jun-23 11:15:42] INFO: User defined pitch angle limits.\n", + "[09-Jun-23 11:15:43] INFO: Using averages in resample\n", + "[09-Jun-23 11:15:45] INFO: User defined pitch angle limits.\n", + "[09-Jun-23 11:15:46] INFO: Using averages in resample\n", + "[09-Jun-23 11:15:48] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_66159/4257680054.py:118: RuntimeWarning: Mean of empty slice\n", + " 1e12 * np.nanmean(vdf_e_higan.data, axis=2),\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(7, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "plot_line(axs[0], b_xyz)\n", + "plot_line(axs[0], pyrf.norm(b_xyz), color=\"k\")\n", + "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\", \"$|B|$\"], ncols=4, frameon=False)\n", + "axs[0].set_ylim([-30, 90])\n", + "axs[0].set_ylabel(\"$B$ [nT]\")\n", + "axs[0].set_title(f\"MMS{mms_id:d}\")\n", + "\n", + "\n", + "axs[1], caxs1 = plot_spectr(\n", + " axs[1],\n", + " 1e12 * mms.vdf_omni(vdf_e),\n", + " yscale=\"log\",\n", + " cscale=\"log\",\n", + " cmap=\"jet\",\n", + " clim=[1e-18, 1e-13],\n", + ")\n", + "axs[1].set_yticks(np.logspace(1, 4, 4))\n", + "caxs1.set_ylabel(\"$f_e~[\\\\mathrm{s}^{3}~\\\\mathrm{m}^{-6}]$\")\n", + "axs[1].set_ylabel(\"$E_e~[\\\\mathrm{eV}]$\")\n", + "\n", + "e_lim = [20, 200]\n", + "vdf_e_pa_lowen = mms.get_pitch_angle_dist(\n", + " mms.vdf_elim(vdf_e_e64, e_lim), b_xyz, tint=tint, angles=18\n", + ")\n", + "vdf_e_pa_lowen_spectr = xr.DataArray(\n", + " 1e12 * np.nanmean(vdf_e_pa_lowen.data, axis=1),\n", + " coords=[vdf_e_pa_lowen.time.data, vdf_e_pa_lowen.theta.data[0, :]],\n", + " dims=[\"time\", \"theta\"],\n", + ")\n", + "\n", + "axs[2] = plot_spectr(\n", + " axs[2],\n", + " vdf_e_pa_lowen_spectr,\n", + " cscale=\"log\",\n", + " cmap=\"jet\",\n", + " clim=[1e-18, 1e-13],\n", + " colorbar=\"none\",\n", + ")\n", + "axs[2].set_yticks([0, 45, 90, 135])\n", + "axs[2].set_ylabel(\"$\\\\theta~[\\\\mathrm{{deg.}}]$\")\n", + "\n", + "axs[2].text(\n", + " 0.03,\n", + " 0.1,\n", + " f\"${e_lim[0]:d}~\\\\mathrm{{eV}} < E_e < {e_lim[1]:d}~\\\\mathrm{{eV}}$\",\n", + " transform=axs[2].transAxes,\n", + " bbox=dict(boxstyle=\"square\", ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0)),\n", + ")\n", + "\n", + "\n", + "e_lim = [200, 2000]\n", + "vdf_e_pa_miden = mms.get_pitch_angle_dist(\n", + " mms.vdf_elim(vdf_e_e64, e_lim), b_xyz, tint=tint, angles=18\n", + ")\n", + "vdf_e_pa_miden_spectr = xr.DataArray(\n", + " 1e12 * np.nanmean(vdf_e_pa_miden.data, axis=1),\n", + " coords=[vdf_e_pa_miden.time.data, vdf_e_pa_miden.theta.data[0, :]],\n", + " dims=[\"time\", \"theta\"],\n", + ")\n", + "\n", + "axs[3] = plot_spectr(\n", + " axs[3],\n", + " vdf_e_pa_miden_spectr,\n", + " cscale=\"log\",\n", + " cmap=\"jet\",\n", + " clim=[1e-18, 1e-13],\n", + " colorbar=\"none\",\n", + ")\n", + "axs[3].set_yticks([0, 45, 90, 135])\n", + "axs[3].set_ylabel(\"$\\\\theta~[\\\\mathrm{{deg.}}]$\")\n", + "\n", + "axs[3].text(\n", + " 0.03,\n", + " 0.1,\n", + " f\"${e_lim[0]:d}~\\\\mathrm{{eV}} < E_e < {e_lim[1]:d}~\\\\mathrm{{eV}}$\",\n", + " transform=axs[3].transAxes,\n", + " bbox=dict(boxstyle=\"square\", ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0)),\n", + ")\n", + "\n", + "\n", + "pa_lim = [0, 15]\n", + "vdf_e_lowan = mms.get_pitch_angle_dist(vdf_e_e64, b_xyz, tint=tint, angles=pa_lim)\n", + "vdf_e_lowan_spectr = xr.DataArray(\n", + " 1e12 * np.nanmean(vdf_e_lowan.data, axis=2),\n", + " coords=[vdf_e_lowan.time.data, vdf_e_lowan.energy.data[0, :]],\n", + " dims=[\"time\", \"energy\"],\n", + ")\n", + "\n", + "axs[4] = plot_spectr(\n", + " axs[4],\n", + " vdf_e_lowan_spectr,\n", + " yscale=\"log\",\n", + " cscale=\"log\",\n", + " cmap=\"jet\",\n", + " clim=[1e-18, 1e-13],\n", + " colorbar=\"none\",\n", + ")\n", + "axs[4].set_ylabel(\"$E_e~[\\\\mathrm{eV}]$\")\n", + "\n", + "axs[4].text(\n", + " 0.03,\n", + " 0.1,\n", + " f\"${pa_lim[0]:d}~\\\\mathrm{{deg.}} < \\\\theta < {pa_lim[1]:d}~\\\\mathrm{{deg.}}$\",\n", + " transform=axs[4].transAxes,\n", + " bbox=dict(boxstyle=\"square\", ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0)),\n", + ")\n", + "\n", + "pa_lim = [75, 105]\n", + "vdf_e_midan = mms.get_pitch_angle_dist(vdf_e_e64, b_xyz, tint=tint, angles=pa_lim)\n", + "vdf_e_midan_spectr = xr.DataArray(\n", + " 1e12 * np.nanmean(vdf_e_midan.data, axis=2),\n", + " coords=[vdf_e_midan.time.data, vdf_e_midan.energy.data[0, :]],\n", + " dims=[\"time\", \"energy\"],\n", + ")\n", + "\n", + "axs[5] = plot_spectr(\n", + " axs[5],\n", + " vdf_e_midan_spectr,\n", + " yscale=\"log\",\n", + " cscale=\"log\",\n", + " cmap=\"jet\",\n", + " clim=[1e-18, 1e-13],\n", + " colorbar=\"none\",\n", + ")\n", + "axs[5].set_ylabel(\"$E_e~[\\\\mathrm{eV}]$\")\n", + "\n", + "axs[5].text(\n", + " 0.03,\n", + " 0.1,\n", + " f\"${pa_lim[0]:d}~\\\\mathrm{{deg.}} < \\\\theta < {pa_lim[1]:d}~\\\\mathrm{{deg.}}$\",\n", + " transform=axs[5].transAxes,\n", + " bbox=dict(boxstyle=\"square\", ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0)),\n", + ")\n", + "\n", + "pa_lim = [165, 180]\n", + "vdf_e_higan = mms.get_pitch_angle_dist(vdf_e_e64, b_xyz, tint=tint, angles=pa_lim)\n", + "vdf_e_higan_spectr = xr.DataArray(\n", + " 1e12 * np.nanmean(vdf_e_higan.data, axis=2),\n", + " coords=[vdf_e_higan.time.data, vdf_e_higan.energy.data[0, :]],\n", + " dims=[\"time\", \"energy\"],\n", + ")\n", + "\n", + "axs[6] = plot_spectr(\n", + " axs[6],\n", + " vdf_e_higan_spectr,\n", + " yscale=\"log\",\n", + " cscale=\"log\",\n", + " cmap=\"jet\",\n", + " clim=[1e-18, 1e-13],\n", + " colorbar=\"none\",\n", + ")\n", + "axs[6].set_ylabel(\"$E_e~[\\\\mathrm{eV}]$\")\n", + "\n", + "axs[6].text(\n", + " 0.03,\n", + " 0.1,\n", + " f\"${pa_lim[0]:d}~\\\\mathrm{{deg.}} < \\\\theta < {pa_lim[1]:d}~\\\\mathrm{{deg.}}$\",\n", + " transform=axs[6].transAxes,\n", + " bbox=dict(boxstyle=\"square\", ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0)),\n", + ")\n", + "\n", + "pos_axs6 = axs[6].get_position()\n", + "pos_cax1 = caxs1.get_position()\n", + "x0 = pos_cax1.x0\n", + "y0 = pos_axs6.y0\n", + "width = pos_cax1.width\n", + "height = pos_cax1.y0 + pos_cax1.height - y0\n", + "caxs1.set_position([x0, y0, width, height])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Project distribution onto the (E, ExB), (ExB, B), (B, E)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute pitchangle distribution with 17 angles" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:15:51] INFO: User defined number of pitch angles.\n", + "[09-Jun-23 11:15:51] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "vdf_e_pad = mms.get_pitch_angle_dist(vdf_e, b_xyz, tint, angles=17)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resample background magnetic field, electric field and ExB drift" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:16:00] INFO: Using averages in resample\n", + "[09-Jun-23 11:16:00] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "b_0 = pyrf.resample(b_xyz, vdf_e)\n", + "e_0 = pyrf.resample(e_xyz, vdf_e)\n", + "exb = pyrf.cross(e_0, b_0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "idx = 1339\n", + "x = e_0.data[idx, :]\n", + "y = exb.data[idx, :]\n", + "z = b_0.data[idx, :]\n", + "time = list(pyrf.datetime642iso8601(vdf_e.time.data[idx]))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:16:00] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/vdf_projection.py:47: RuntimeWarning: invalid value encountered in arccos\n", + " if abs(np.rad2deg(np.arccos(np.dot(vec, coord_sys[:, i])))) > 1.0:\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 0.98, '2015-12-02T01:14:55.191087000')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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TJ07E1KlTMXbsWHzwwQchLX/Xrl249NJLceGFF2LGjBkhLVsgcBphTwKBcwh7ch+PJElSa1+EoPPS0tKClpYWREREoLKyEkOHDkVxcXFIym5sbERubi7+/ve/IzU1NSRlCgRuIuxJIHAOYU/uIzyhbZCcnBw89thjrX0ZWLRoETIzM5GYmIivvvqK65isrCx06dIFPXr04No/LCwMERERAIDq6mqMGDHC9vVaZcOGDYiPj8ftt9+OqVOnYunSpartc+fORVxcHDweDwoLC0N2XQJnEfYUGoQ9dQ6EPYWGzmJPEa19AYK2yddff40333wTBQUFSE9Pt3Tsq6++ittuu417/5MnT2LOnDnYuXMn/vjHP1q9VNscO3YMmzZtQkFBAWJjYzFlyhRMnDgRgwcPBgAsW7YMhw4dQr9+/UJ2TYKOibAnYU8C5xD21HHsSYhQAZMvv/wSl1xyiWUDt0O3bt2wdu1alJaWYuzYsbj66quRkpLi2PknTJgQsC4jIwN33303xo8fj27dugGQW/gFBQU+IxcInELYk0DgHMKeOg4iHN/GqaiowB133IFevXohLS0NM2bMwN69e33bS0pKcMUVVyA5ORn9+/fHhx9+CI/Hg7y8PNtl9uvXD6+++io+/vhjxMfH48033wz6e7z66qsYMGAAEhISkJ6ejgULFgAA6uvrffvEx8cjOjoaMTExzHPk5OTgl7/8Ja699lokJiaiV69eWL58ObZv347zzjsPCQkJOPfcc1W/DyCHNbTLZ599hgkTJuDgwYOoq6tDS0sLNm3ahEGDBgX9XQVtF2FPfoQ9CYJF2JMfYU/2EJ7QNs4NN9yA6upqbN68GYmJiVi4cCGmTZuG3bt3Iz4+HvPmzUNsbCyKiooAwGc8wXDgwAEkJSXhX//6F6ZMmRL0+fbv34+HH34YP/30E4YPH47q6mr897//BQBs27YNDz74IMLDw1FfX4+nnnpK18gB4P3338eKFSuwbNkyvPLKK7j11luRk5ODZcuWITMzE3PnzsWvfvUrrFq1yvS6EhMTsWjRIkybNg1NTU2YNWsWsrOzg/6+graLsCc1wp4EwSDsSY2wJxtIgjbH1KlTpUcffVQqLi6WAEj5+fm+bQ0NDVJqaqq0bNky6ejRoxIAaefOnb7t27dvlwBIa9askSRJkpqbm6Xzzz9fSk1NlR599FFVOcuXL5fGjx8vTZgwQVq+fLlv/c6dO6WwsDCpqqrK8rX37dtXevvtt1XrDh48KHXp0kVavny5VFlZafmchKlTp0q33HKL73NFRYUEQProo4986z755BMpOTnZdhlaioqKJADS/v37HTunILQIe2Ij7ElgB2FPbIQ92UOE49swR48eBQAMGDDAty4yMhJ9+/bFkSNHcOzYMQBA3759fduzsrJU5wgLC8NHH32E559/XrW+srISf/jDH7B69WqsXr0af/jDH1BZWQkA2LRpE4YOHYr4+HhHvke/fv2wfPlyLF26FH369MG4ceOwbNkyW+fKyMjwvY+Li2Ouq6qqCu6CBR0SYU+BCHsS2EXYUyDCnqwjRGgbpnfv3gDk8AOhqakJR44cQZ8+fdCzZ08AwOHDh33b6ffa89Bs3LgRkyZNQnx8POLi4jBp0iRs3LgRgGzk48aNU+3/n//8BxdffDGmTp2Ka665Bk1NTZa+y+WXX46vvvoKp06dwkMPPYT58+dj3759ls4hEASDsCeBwDmEPQmcQIjQNkxGRgZmzpyJ3/zmNygpKUFtbS1++9vfIioqCpdeeil69eqFnJwcPPLII6ioqEBFRQV3/raysjJVAtyUlBSUlZUBCDTyoqIi/O53v8M//vEPfPfdd0hPT8c333zD/T327t2LlStXorq6GhEREUhKSgIAhIeHc59DIAgWYU8CgXMIexI4gRChbZz3338fWVlZGDNmDHr16oWdO3di9erVSEhIAAB89NFHkCQJffv2xejRozF79mwAQJcuXQzPm5KSgvLyct/n8vJypKamorGxEdu2bcPYsWN925YvX46KigrMnj0bOTk5+OGHH3wJfHloaGjAM888g549eyIxMRG/+c1v8N5776nCOAJBKBD2JBA4h7AnQdC0cp9UgcNs3bpVAiAVFxer1i9dulTV8buiokIaNmyYdObMGenMmTPSsGHDpIqKCuY5Fy1aJC1ZssT3ub6+Xrf8s846S4qPj5d69uwZ5DdpfebNmyclJiZK0dHR0oEDB1r7cgStgLAn5xD2JBD25BwdxZ7E3PHtnB07dqChoQHZ2dkoLi7GjTfeCEDuI0OYO3cuCgoKUFNTgwEDBuDf//43IiMjsWzZMrz88svweDz41a9+hblz5zLLKC4uxvz589HS0oLIyEhcdtlluP/++0Px9QSCkCLsSSBwDmFPAjOECG3nrFu3DjfffDOOHTuG+Ph4TJ06FS+//LJqRJ5AIOBD2JNA4BzCngRmCBEqEAgEAoFAIAg5YmCSQCAQCAQCgSDkCBEqEAgEAoFAIAg5QoQKBAKBQCAQCEKOEKECgUAgEAgEgpAjRKgJ27Ztw4wZM9CjRw94PB6sXr06YJ9NmzbhggsuQNeuXZGWloYrr7ySOT2Z02UfOXIE8fHxqiU6Ohrh4eE4deqUa+US3n33XYwYMQJxcXHo3r07fvWrX9kuk7fcnJwcREVFqb7z66+/HlS5vGUTvF4vsrKy4PF4LE8PZ6fc2bNn+xIpZ2Rk4Oabb/bNHtIeaS2bEvYk7AkQ9iTsyT6tZVMd2Z6ECDUhKioKV111Fb788kvm9paWFlx66aUYOXIkTpw4gaKiIkRGRurmNHOy7D59+qC6ulq1XHrppZgxYwbS0tJcKxcA/vznP+MPf/gDXn/9dVRWVqKoqAgLFiywXSZvuQDw8MMPq77zPffcE1S5VsoGgPvvvx+DBw8Oukzecp966ikUFhbC6/Vi165dqK2txR133OFI+a1Ba9mUsCc2wp6EPblRbkezJ96yAedtqkPbU2tmym9vAJC++eYb1brTp09LAKT8/Hzfui+++ELq0qWL62Vr+fnnn6WIiAhp5cqVrpZbWVkpxcXFSStWrHCsHJ5yJUmSpk6dqppZI5RlS5IkrVixQho7dqz0zTffSACkxsbGkJRLOH36tDR37lzp7LPPdqzc1qS1bErYk4ywJ2FPbpWrpSPZk17ZkuS+TXU0exKe0CDp2rUr7r33Xrz99tuoqalBRUUF3n33XVx11VUhv5a33noLffr0wSWXXOJqOevXr8eZM2ewb98+DBo0CN27d0dubi62bdvmarmEN954A127dsWQIUPwu9/9DtXV1SEpt6ysDPfddx+WLl1qaW5iJ3jkkUeQkJCAlJQUfP7551i0aFFIyw8lbcWmhD25i7Cn0CDsKbT2BLSOTbVXe+q0InTBggXweDy6S05ODve5fvGLX+Dbb7/1/RMOHjyIF154ISRlExobG7FkyRLcdddd8Hg8rpZL+vN8/vnnyMvLw+HDh5GdnY1LLrkElZWVrn7fxYsXY//+/SgrK8PHH3+MVatW4dZbb9Xd38my7777btx+++0YPny46b5O/4//+Mc/oqqqCvv378evf/1rnHXWWZaODwWtZVPCnoQ9CXsyRtiTvj05/Z2t2JSwJ3TecHxVVZV08uRJ3aWioiLgGDDc0fv27ZMiIyOl1157Taqrq5Oqq6ulxx9/XOrfv79UXV3tatk0H3/8sdSlSxfp1KlTrn/nf/7znxIAVVilqalJiomJYYZa3Pi+hDVr1kjh4eFSTU2Nq9952bJl0pgxY3zhjTVr1hiGO9z8zhs3bpTS09OlhoYG031DSWvZlLAnYU/CnmSEPVm3J7e+M8HIpoQ9SVJofbZtCDJqLVgKCgoQExPj63gcHR2NBx98EE899RR27NiB8ePHu1Y2zeuvv45rrrkGqampuvs4Ve7o0aMBQLdF61a5LMLCZGe+pDP7rFNlf/XVV9izZw969OgBQG7ZA0CPHj3w5z//GTfddJMr5bJobGxESUkJKisrg+rg7zStZVPCnpxD2JOwp85mT06WzcLIpoQ9deJwPC+SJKGurg51dXUA5B+4rq7Ol/pg7NixaGhowF//+lc0NTWhrq4OL774IuLj44MO8ZiVTdi1axe+++47R0a18pTbu3dvXHHFFVi8eDFKSkpQX1+Pxx9/HF27dsWkSZNcK7ekpARfffUVzpw5A0mSsHPnTvz617/G7NmzERsb6+p3fvHFF7F3717k5+cjPz8fS5YsAQBs2bIFV199tWvl7tu3D59++im8Xi8kScLevXvx0EMPYdy4cW2qwrRCa9mUsCdhT8KehD25WbZbNtWh7YnLX9qJKSoqkgAELIsWLfLts2rVKmnChAlScnKylJycLE2ePFnKy8sLSdmSJEn33XefNGbMmKDLs1JuZWWldPPNN0vJyclSSkqKlJubK23fvt3Vcg8dOiSNGzdOSkxMlOLi4qQBAwZIDz30kOT1eoMql6dsLWbhDqfK3bNnjzRp0iQpKSlJiouLk/r27Svdcccd0vHjx4MqtzVpLZsS9iTsSdiTsCc3y3bLpjqyPXkkSSfuIhAIBAKBQCAQuIQIxwsEAoFAIBAIQo4QoQKBQCAQCASCkCNEqEAgEAgEAoEg5AgRKhAIBAKBQCAIOUKECgQCgUAgEAhCTqdNVm+Huro6NDQ0tPZlCDooUVFR6NKlS2tfRsgQ9iRwE2FPAoFzuGVPQoRyUldXh5iYrgDqWvtSBB2Url27ori4uFNUnMKeBG4j7EkgcA637EmIUE7kFmYdgEcAJEP+6WIAdAEQSb2P0Wwj78n6SP9JuyhLmLI5mjqEvA8HULQQyF4sH0rvE6UcF66sJ+eI0DlXwPsmeKLrEd2lAeERLYjqUo+osAaEoxnRaEAkGnBq4V/QZ/HtiEQDwtGEaDQiEvWIQDOi0ODbjz7Gv5//fRTqEa4cE6Xsrz6e7NeAMDThrYXFeHBxouqYCDQjsrkB0fWNiKoDPC3Kv6QeQLPyWg+gSXltoN5r19cxjmkAFn4PLB6t7EOtRx31vl75TJXf2AzU1gN19fJutcrSpOxSq7w2at6TbWcAvFhejoaGhk5RaartKR6BtqO1I/JZu5/Gnmi7oO93rV3sXQics1j+HK7sHwW/jRF7CqeOJ/uRc3XRvm8CIpoRGV2PqC6NiOpSj/Aw/30ehmaUL3wRfRbfjnA0IxL1iEajco/Xq+yCHKPdj31Mi++93jFvLSzGA4tTVOVEowGRzQ0Ib2pCdL2ECNZ9rrUfs/UaO1u4Dlg8CmpbJO/roGtnjQ2yPTU1AXXNflti2Y52WxOACnRWe3oIcv1E6iViL/R72o5Y+ylo7YLc76Reou//A4o90XWRdh/6eD07Jcd0ARAuAV0aEBldj/CIZkR1aUBUlLr+KF/4InouvgtRaKDqpcD6RnXPU/uxj6Hf12vqPPn9mwuP49eLu6rKITYeVV+P6HoJ4U2Ah/xbtHVRM8d6HTtbuA5YnB24PqBOo+yssUG2pdp62Z5o+9HaTo1mWx3ctSchQi3DqiwjqffazxEAYhEgQj3UEkYt4coSoSzhAMKjgMhE+dAoZSGGq61ciYFzvZdFqCemAZ6IZoR1iUJYmGxIYahHOBrgiYpEeGIcIhCJcDQhAg2IRLgsCJW1UQhXDDEMUQhTBGUYouBBOMIRDQ+iIClGDOV9GKIgIRotiEKL8uxqUc4lITLKg/jEMERAUs7rQQQ8iGr2ILoeiIoCPM3w/yZNyvsI5X0E9T5c+W2bqN8ZkA2WEC6/RIUDiVGQ54VoUf4/LfBbSjP1P4L/fI0tQKRHvoQmahe6+DDIxk/+7eSUkrJf54TckCzb0doVsblYBIhQXnsiJhim2FSUsj0Ksg1pK1vaZkilrGtXsgj1dKmHp0uDYk/NCEODYk/NPnsKRzMiEIEIn2gMRyQiEI4mRCk2JdtSM6LgQbTvPRANj1KhSsrlNyu2I9tTODyIUtbJXy3cZ0/RPhuVZLts9iC8ySOL0Hr4b2D6PW1L5JlE2xJ5T6bqJrYBjT0RW2pRNkbAb0+S+v/W6JHtqUmxKdqWwiHbESnSo3wmNCn/vs4JuXGNbImuu1j7wdyeaJui7YmIS7qO0toL7RCJgYGNySLU06UeHsWuwqIaFHtqUtVPsvVEIALNvjqK2BWxJ9m2SF1C6iso7z2IhkTZWAvCEYYotCh21izXP0odFRkV5rOncKWOku3Sg6h6j1+EEjsiYpHUUc3Ub0hXEs3Ub+6BumLQ1lHElug6yoPASkexpyaPv45qhN+UG6E2ZfLvB/XZTXsSA5MEAoFAIBAIBCFHiND2QLfcVis6PndCq5Q7MTeuVcrN7dUqxQpCTXrr2FSns6ferVKsINR0Mns6r5XsCQBye7Za0a4gwvGWIb0l6GBqJPzxW+IXb4Y/ftUEvwM80n+YpDmsBX53fJOySAC6ngc0euXjiN+chLTCqP08ymsE41KaqUshl9XYBKmhHlJjA6SIFrQ01KMlrAEeNKMFDWhGA2LPG4Fm7xk0oQESmhCORnhQDwnN8EAOjUAJKXrQAEnp3wk0okV5L/neN6MZDWhW+qo1oVH524RwNKEJzWhEM8LQjFHndUG1l/R3a1FC+BIimyU5HO9Sn9DzugPeBuXf1Uj9XmRpphbq/9YoAbWSv1+NlT6h5Ct0Tuqgfgw1wm8Y5EbWvYHBZU/EFAF//ClFsSk6xAj47YrYUzh1TvKeXEqAicvheKmhHlJDI1oa6uEJk22pBQ0Amn32BDSjCfUIRyMkpfuLBw1oUewIaIBHsRkJ9YBiMy2oh+R7L9toOFrQjEafPYUrn5rQjHDF4mR7kq9E7v7SggbFnsKbZJtyo0/oeemAl9zsTQi0KRN7apLYfdfM+oR2XnsiHdYBf8VA6ihWpUBixOSBx2lP5PTEbog9kfBwOPx1GaCuLolpN2veN1PHNUHuE9rYINtTRDOkhga0RMm25FHqEmJPzWhAk1IRhKERYaj32ZInoI6qRzOalXpJ7uvZoqqjGpl1VISypQHNGHleDKq9jWhQbClKsacwSIiql+3JrT6h53UzqKMM7KmJqqOs9gl1056ECOUkKioKPXr0wIkTf3TupESBtCISdQlnWvNCBOjRoweioqJa+zJCgmv21AYgdYOwp9alc9rT/zp30mYA1c6dzi7EntqIeXda3LInjyRJkuNn7aCIPGwCNxF5DQUC5xD2JBA4h1v2JESoQCAQCAQCgSDkiIFJAoFAIBAIBIKQI0SoQCAQCAQCgSDkiIFJnLS0tKC4uBgJCQnweDzmBwgEFpAkCVVVVcjMzERYWMdvGwp7EriJsCeBwDnctCchQjkpLi5G794i6Z3AXY4ePYpevTp+slJhT4JQIOxJIHAON+xJiFBOEhISlHc/QZ7rmjdj7EIAi4EUiwUO4NjnWpPtKxYCsxcDBRbLpjkdxLEnFwLTF9s7tk8Q5QLAvxcCdxmU/YGFcxVx7vf9BurDWwDu5DqssnICvF4vevfuTd1nHRv/91wBIA7AN5xHfgvgIuX9g9YKHRlpvs+NBtuIPaVbK1ZFMINLX1HKb42yTwH4aiFwiU75qyycy+IctZWrgYULF2LxYvPvnpREUn7VA3ixE9sTb9X+KoD75LfDsq0VypOD7J8m456fXwg8uBjYEIT3Ntx8FyZLgqifAOAs+4cCAN5dCPzCoPzngjy/AffO4LMngpv1kxChnPhDHPEAEgB4AQ9Hi0CKAtISrRdYBGCQwfbrOc4REQXEKPNj2yRsyRm0lNicHeLVcOAuG7PO5gU5U20WgNVRQJzB70704VKTcx0Av5VcMB1Ys075EAm5MjBGkiapPneWUJr/e8YpyxUAvuI4MhyymlpovdBwAxF6G8fxxJ5irRdNGHrVVvTGUVvH7l1/FIN/k2fr2K/fmW3rOB9dIH//Ljo2dbny+i+T85Dk/hZIugR4YEIUEhONn6Mezx+gVdqd154AX+J5Q6IAxAMjxlgv1Cgqu5oz6U5UFJCQCMTY/z+F5Z5BbEKt5eOq86KAK2zUzQBwyN5h/sLhf57osQjAU0GWo8NrH0Th1Vetf3c37EmI0GCQfjYXosF4H/YjUIjyiE8towFs5d897OnWTbPda16h7/3PHw10t7CblVczMcrLBYqoXPOK6a5aASq4BOZCdDxsCVAA2NoIjNZUzDziU8sJAD34dx96lQXjc4nzb/F7mn9452JrB5+0sO+lyquZGLXIi8uBFzcA0nr2dlmACtRQMyDpkdodyLQhQAHZH6NNZs8rPrWcLwE/8AucsNxWngoinnrvZkL/x5VXF8SoZxAg7Xf+vFYRIjRYjIRoKoAGh+bUtSM+h/KV7ZrozLFY2TGgBSnAIUqzlNfxFn93lhg9YO0UKkbebNgNQghQPYyE6EIAXztTjB3xyWlPbonO1NzRjpyHFqSABVF6lgWbYolRi2F4Fcly2Z6JgUJUCFAjDIToiDFAlZUWhgF2xedkzjrKDdE5waG6OV7z2UyUku0jLJTvtBiNVeypDQhRkayeE6/Xi6SkJAC7IIfj1Uyc1AuvvwaMHgNIEmQBqsM5o4AP3gBGTgUaG00KzgFwge3L9qOpF60IT9vh+DR7M872yuQLVzIFaZatIgN5zKHz+MLzflgClNxflZWVpmHHjoDfnr4Fq9vCxIk78frrMzF69Fv4/e+nYtq0KZg8uTlgPwCIjQV27YrAxRc3Yb/ZA3V5JFAW9OUHeEKtCE+74fhgqEEM135MQeqETvmnA+egIEJUX4DWAfiTsCeFiROT8frrvTF69B65ftIJv/dMBzZ8AIy6GjhdaV5u34I9OHxwcFDX7kPjCbUiPG2F4/ekWT7GxyneQjjXWcVhz6iZEHWzfhKeUId45S8NWPRElKkABYAt24Dd+4Bf3ga88IbBjjnK6xoELUSD8XaGpQfRL9RFAryk6x0M3U8AsMF0L3MumKQSosIDyscrr1yLRYu+kO0Jkw33rakBXnihBX/+czhmz2YLVQCyAAVk+wxSiA64aieiYG+KxKPo3SpClIcAL+mzwUczAMjPsjxnTgXIHlH8KDygvLzySi8sWnTcUIACwLES4O9fA0/cDfzqT8bn7FuwR37tv9cRITrgxp0oKskK+jxtCq2X9IRD550LYJlD50LrekQ7dAK15cuXY/LkyUhMTITH40FTkzoeVFBQgClTpiAuLg6ZmZl44oknYMcxnJMTjR49wvFFMUwFKOHtD4D/uQPQTbmVY/kydAmb08r9Z0JE6oRjzpzoS+V1gjOnwwWT5OXq9i1AQ2dPiejRIxJf7L4OvP0/33uvBdOmeTBIbzDfck1IktNOWQy4Z6f9g1sBXi8oi3N/uzb4CyAetZzgT9WRCLk9NQ3gGoD09j+Am68AkgwGQhMB6vvcf6/l66IZ0H9XUMeHFF4vKIP4q4M4mEB8L3ODPxWNJ4gBl8HQoUVo165dcc899+Cll14K2FZVVYXc3FxMmjQJp06dwqpVq7BkyRLmvmbMmRODb7+tQ0sLgCxg6kRg3b+AU3uB0/uBbz8FRg1XH/Of74H0bsDYbM3JcsB+WK+xfFkIm3Om0whQQuqEY8GJ0S81nyfAGTGqiB7PLxw4VysROnvqim+/9cr2NNAvHhcvDkNJSQSOH4/Ac8+FIYKK41RUAJs2SbjySs0jbXlkoAC1yYB7dqoEaEMwaSfaAQ2Qs1Sc+9u1zohRQP/5ZhULA8PaKq1iT8rwhRtnA1v/BlSsA06sAV76rX//3QflUHzuxMBz9S3YEyBAg2FA/10qAdov/ZBj525rxJ8vC9D4q085I0YBWYg6IUaVKrM1hGiHFqG5ubmYO3cu+vfvH7Dt008/RXNzM5566inExMRgxIgReOihh/Dqq69aLmfs2EjsqPCHqxvTgAefADKGA32ygcIi4J/vAZFUXVhfD+w/CIyjxxrkmBTEKUQ7o/isbVF7e2yJUa0ApQlGiGq8bu1ViIbOnuKx4wTVXyslHOPHe9DUBPTu3YScnCbMmROGhx9WP74KCiSMG0f1KzMTn5zeUK347KzYEqN6/QpzgrgQIkCvXBTESVqfkNpTo9+ebr0VeP7XwIN/BlKnAAMvBT5drT6mYB8wTuM4MROfVryhWvHZGbElRgt11gcjRDXVZKiFaKftE5qfn4/Ro0cjgnKnjBs3DgcPHoTX67XU+TYlIxqVVGfj9WRUdKM88Oi3TwJ33AgMHgjs2O3fr7IKSElWPuTY/SZ+Wkt4XjFyOXN9FNgDk/5WfJ2bl6OCCNGyDbyTCxhgp5+ojtDx/AKQ/h7sBbUdHLWn9HhUVqnXna704IknWtDSAuzdC/zv/7bgwQfDsHhxi2+fykpg2DDlA6/306R/qJn4bECU7b6heszUyW/UTyc54Wu419HyAb8XlAURoj89O8X4JGYDW3JgvZ+o1gN65SLgs47XN9RRe+odj8rN/s/3Xw48+ynw7Ub5c3UNsHaL+pjKaiCFKoLX+2nWP7Q1hOfY2M3M9SVj2LNO7P6vM1koaIgXlLlNEaLVn5gMlNIToAQ7/UR1/DSeWECqsXgum3RaEer1epGcnKxa17VrV982biMf2wunvUAS1QF5xEDgmXuAMYOBhBjIYRAA3TX3WFICcLoC1gQoY5BSKMTn+SO/QZojw4qBazLZohUA1sOdfpOmYtTIC0pjRYgG0e+wveGYPQ0cj9OVgf3Rjh4HWvpHAoVyOomiIgnaWQqTkoDTp2E9/M4Qom57Pi/AGsTAmaf8vXhNd9v/Wp1VygLcYtSIHPALUb0QfAcUoo7Z04zxOF0FJFHerX49gL0/Qw7N/8w+LCke2HNIfm81/M4SolbEZ7/0Q5YHKF0d+wkOOZQaZegY/UwXu792XqASuMWoEVaEqENDKIKl04rQxMRE/Pyz2gLLy8t92/R5Fr68a4OuBtALW/YAZ1MRlb//Cfj3euDGRUBFFZBcDpQXAvRkA9HRwKCBwOYgOkS4KT4nputkhXaZLM0cmYfQz/QYbSjeCKYY5RWgBBKaNxKjHALUM3kVHhgnz3fY0OCsNy3U2Len1+Gzp4wFAIAtO4CzNYkOemfIg/haBspCNCvLA01xGHFNOP59HEAQXdbsiE8eb+iNeM/uJQXFzXhX9XkpFpgeY+QFZcEUoxzpfXzkKK95BvuY9QG9chHw2Q3wu4sMsiS0Axyxp7ELAABbCoGz+/r3OFQCnEUefzpCdMQgYNXUY+h7U1XgRgu45fm8Mf19V85rRvZ09UM//2vzflpGXlDm/iwxauYFpSGheSMxyiFAPdGr8MC97tdPnVaEZmdn48MPP0RTU5Mv5LF582b079/fxMh/C1+e0CS5l/ena4D3nlAqyRa5Fek9I4c0uiYCzz8eeJYL7gZKK4FNNhKip951DOUnk60faEBriU4z7IhSHnxi9LEgwvR6XlFeD2iPXLx4NBfS32Xvxmuv6Xu02jr27eke+PIaxo0HAHz6NfDec357AoCUJOD39wGL3wSyLo7EQw9JWLrUH4pP+kckzk0B7mRH3kxx2vPZWqLTDDuilBefGF1o0zOaA7YQ5R6ENFBZADlP6CZ719EGcMSeuin2tA547zd+e3r5n8DTNwJbDwLfbQdiBwFjEv0h+SH9gG6ZLfiOa3J4Nr/p/ww+x5W2j9fSWqLTDDuilBefGP2TTc+onleU1wManosX38yFVONu/dShByY1Nzejrq7Op+Lr6+tRV1eHlpYWXHXVVQgPD8eiRYtQW1uLHTt24Pnnn8e993L2rxrrnyXpP5uA0nJglpLO8JYngV9cBFR9B2x4R/aKqsgBbp8GvLzSX8nykvqxfAd17VZh7UANV6Z/jonp631LeyELRarFiheUxfDFQVZU2meOjRB8exmo5Ko9DRzve/ufH4HS08CsC/2bN24DoiKBn78H1n4EfJ7nwZ/+pBjP8kjcmAV8WwrsteG4GXBt8AL0WnyMG/Geb+GhNphJ6B3iZryrWqx6QVkEZVM5ms9WRsG3s4FKrtrTDMqetskOj1nKqre/Ah55F3jpDqD8b8D+t4ErZvsP/c0/T+PvqIQXFisnhavxCQDgCnxm63gAeCj9edyY/r5v4SEr6Andgyd7+gbVYtULyqLvk0GEdrQDlmyE4N0eqNShZ0x69913cfPNNwesX7NmDXJyclBQUIB7770XW7ZsQWJiIu666y4sWrQIHjpurqCaMWns0IDtk0YBrz0MjL5emTGJxXkAjgNj+gMf/Q8w8jdAg4Wp7IgAJdj1hl6Z/jkAoATdLR1ntU+o3sAkPYqRaWl/AKhGAo629DbfkUFGWLHv/Y6F42ydQ0UwyX4bvcA/2/aMSe7Y07fAwAsDtk86B3htETD6cgN7mgVgPBAbDuycAeR+B+yzKEKdEKA3QK4koy3e7wAs9QvVG5ikxylY96AUIwNfYLb5jjrQOUmDtim7de9nf0B7mDHJNXuawbCnYcBr9wCjf6lvT+euXAsgE5+hLy7FIZTb6NJABCjBjjeUnKME7IFDRljpF2rn/FZtfAAOYFWN/elBU2P9Ivbw74fYPg8A4PkgjpW8QJ079VOHFqFO4jPy0ZVAuM1/wnnK63Hrh2oFKMGKECXik8aKEG2rIpRgVYzSIpRgu+K8HMEZeTsQoU7is6f+lUCYze87S3kdb7gXEyfFJ43VSqotilCCHTGqTYxv255yALxp71AAwGePoK2LUCfx2dO0SiDS3veVRah15wQQKD5peIUo6xxWhWJbFKEEO2KUFqFAEEK0F4D77R0KwFUR2qHD8W2K86j3Gbp7MdEToABfWP7K9M+ZArS9QwtQAOgdxj8VIkuAAjZDiZcrr+4NRBZomUW932jt0GAF6A14nylA2zvFmgfTLKzALKzgPp41M5Mte8pRXu+yfqiPyx4J4uDOBxGgAJCOUkvHGglQgC8sb3aO9ggtQAEgN3YVcmNXcR+vFaCAzdA86Tn4kvVDQ0GnHZgUUs5jrMuAqUfUSHzy4qT4HIHtlrybVvqXrXFoTj8iRI28onoClDB88SZ+D87lms8PIjiPqMCcWYx1G8HlEXVCgDrJjXgfR8HnwU+1EIlYBfshQC2zsMLUK2o0NSgRolw2laP5fBeC84gKTKEFKCEdpVwe0WDFo9Piczw2Iga1XPtanfFsJWbauaQAcmNXmXpFWQKUQIQol1e0l+bzSwjOI+oCIhzPie1wPEuA0ugIUasCVBuWtyI+zR42F1BDVt0SoeFK/6PVuIj7GK0nVIueEDUToTSGFadWgNJYFaIiHM8HS4DS6AjRUIpPnpDdnXjL994NEUoq2DXapMIGaD2hLPTEKO/89Ib2lGNwoFUh2ugFvuyE9mQxHM8SoDR6dYMd8agNy/Oegydsno4S33u3RGi9sv+3mMa1v9YTykJPjBqJUBpDIaoVoDT3c53ej4vheOEJdRMzAQowPaLBeECd8nxewJ1B2lmm4VvfeyNBaiZAAbZX1IoABQy8okYCVNB6MDyiwQhQJz2ftPAMFRdQc/0aCVIeAQrAF56nxSivAAUM7CmH+xSCEMLyiLYV7yctPEPJRfDPcaonSHkEKABfeJ4Wo7wCFJC9okwhaiRAgTblERWeUE4se0J5BCiNIkTtCtCLwlaj2Wabgn7I8IhPXm+oHU+oHlpByiNCtTQh3PIxBFXFyStArXhDhSfUHDMvKI0iRO0K0FysQneLfeMItCeUR3i66QnVQytIeUWolo9xra3jVPaUw3mQFW+o8ISaYuYFpSF1RDACcjx+whacY/k42hPKIzzd9oSy0IpRXhGqZbON3wfQeETNBCjN/Zz7idHxrY8lEWpVgALA/RJSs6x56QgXhflbZnaEaG8cRQL4c9u0hgil+RxXcO9LE4NaVCHefEcDdmy0ONqXV4gKEWqMFQEK4NwX1qLM5rypufAPHrAjRGdjhaWcha0hQmk+xDzufQPLicZnNu0RAHZ8bdGeeIWoEKGGWBGggDz5QlEQ02KOx0++91aFaAaKcdxC5hQ3RKiRANUSzIQqsajBWky2ffzhv9oYPX8/xz5idHznoOyQ9RRFtAAFgHDwJx7tjaPoDf4R5W2FO/GW5dAmeTAloNp2uVU2vK9ixLwDWBSgBCuijUALUAAotZCuZjZWYLaF0eRthVysCvjePJBG5pX43OErMiCYEfMCANYFKMFqijACLUCtkIFiZMCeY6Y1GYvNGAvrU7fFKunapuB7py+pTSNEqNPY9IISrAhRrQAlmAnR9io+AXU4hleMalvGdoQoEaB9x9tIkSGEqH1sCNBzX/BXslaEqJ4QMxOi7VV8arEiRrVRDrtCdPh0GymchBC1jR0BSs8AZlWIsgToOdhieAxLfHZ0MRqryRdsR4hGowFn3VFg+bjWTt0kwvGc+MId4ZXAaB13dJAClMYoNK8nPrVoQ/NmwpM3JD8eG7nC57zh0ELffM/mGPUJegt3MtcbhWd4wvMsD+jhjRbDHmZh+bpOGj7ECQCJwECdwS02wvAsjO5FXuGlDc2bCU/ekHzXbbXyBD9mcCamKJrC37/zlMHvopfmyaybjZ3wvONh+c/WA5jU+ezp1UogJhF6XTfthOFZmIXmebyf2rC8mdDkDclPxveG9zWBt+vaLgTOkKiH0YQumzGWuV4rQLXwhOej0aD6vO+vI02PCeB+g22iT2jroxKhHuWfkE3t4KAAJbCEKK8ABfxGxuv1NBKh4zUZwZ0UobS3aqNBskfe0ZC0GOXpH2QkRI1C8I4J0WZ03j5sRIQSaDHqkAAlsO5HK2FoIkJ5vZ5GIrTrNs196aAIpcffFU0yFqQ8lTUtRnn7ebeqEP2sAEA1OrUIJVBi1CkBStATorzhdyJCrXg59YToZI3n0EkRSvcdLcQAw315ZhWkxaiZACUYCVGtACU4JkSHAGj2AgUiRVPbIx+yELUjQDkoO5SpEqJWBCgAZGMrymxM3UfQCs9QQJdpJEiNICH693Aj1/4JqGYKUVt9QI3QJrK3PjVzx6ewVhaiNvuBGpGKMpUQtdoPciw2IzOIsGCA8AwB/db587+ZCVI9yO9kZRrPK/F5UAOWuNAmsv/MRiiyo3M1gE/s9wM1oh8OBQhRK/0/Z+EL26PBgUDhGQoGUqPezQSpHiREvwvDuI+Zgu+ZQlRPgNrmJaiFaJDT1fPguCf04MGDXPuFhYUhKyvLyaJdhekJBYCnlVermZVMvKAEIkKtCtAR2A4AlkToRKznbpm55QnVw8qcwIQKJGOFhYqTFqK8AtSyNxQAnmWsE55QPy8r3tBD/Ocz84ISyD1pVYDOxErfe14hOvrkbnBrVpc8oUZsmjSc84QyFUjG95hi6RirQtSyNxQAclniU3hCCTNu+RSAtbrAzAtKQ4SoFQFK25AVIToY+9DMcYO75Qk1ogLJXPsRsnDI8ixMtBDlEaC2vKFAYJShPXlCBw4cCI/HY7iPJEmIiYnBmTNnnC6+9egJfiHKKUAB6+IT8AtQAEjFKa6Hz0Sst1xOKOmOEpRyzJxBIA+E2VjBLUTJgCUrORP7jt9jTYjyTzglyAKXEOUVoABwjo1Rq7QA5WX0yd2Wjwk1447vwKYMPiFK7Gky1loSomTAEq8YHT59kzUh+gP/rp0d3rrAigAFgNn4gmtWI4LdaMJg7LN1XKiYiPVYj4lc+5KuOjOx0pIQJQOWeKOEZ91RYF2IllvbPVgcHx0fGxuLgwcPmi6RkZFOF9369FQWIywIUDvQApSHiVivEqA1iHX6khyjO0rQnaNfqLZFamXkcrINC+QeMU8E6P2Wi+g8vKwZoJRlvLsVAUrD4ykB2AK02GCAxOiTu9UC1HrWtdDQX34Zd3wHxh3fYbir1p4mw9pvbvR7seAeMU8E6OM2vT2dAOIFJaTCeDYeqwKUwNtfnyVAx5qMlh+MfW1egA7DLgCB9SkPVhu5PHUgDfeI+XKEXIACLnhCZ8+ejb59+5rud9lllzlddOh5Wme9Fa+oAdf0538gGIlPVgs4WM9nFopM9+GZQ7ueY6CDdlpAYoRWPKNEiBp5RYkAHYZdlvrrmCK8n8GRpbweCvY06nv2FFJ1BxKYVQzFyAyoUIPyfu6G+X3CkwI4217xVryigF+ImnlFiQAdj5+wEefauzgtwvsZFESIBjNegEDXO+ko0fWImnk/x2JLQFheT3iGo9k0JD8a+abhcZ5JIuyOCyD1K8szyhqwSJ43Zl5RUvfNwgpLfbRN0YrPawF87NzpjXDcE/rRRx9x7ffBBx84XXTbguUVteAFdUqAauFpqRl5QwegEANQyF2eGQmosjRbEw3LM2r24NHzimo9oKRly4uuN1RPWNxv6fSdA60XVEuW+qMVL6heo4nlEbXqmQjwfmoxcgRuVRan2KMsRvRnr2Z5Rc3sycgrqvWAWukzqOsN1ROgwhsagNYLqkXrFbXqBWXVOyyPqNXwezCeT6dzYGcjH9nIN9zHqK6w6hk1evZo67pZFiJ8ut7QVvJ+0ohk9XbR84JqIUK0lQXobKyw7f0kwtNJ8amFiFE7gpSIUd6O4VohqheCD1qICg+o82TJL04IUAItRK0I0FSU2fN+boXz4lPLHvAJUgZEjPLaE0uI6oXggxKiwgPqOESIOiFACbQQtSJAL8VKW+KTCE83J2AhYtRMkOpBxChP7mDWM0gvBB+UEDUTn9dynzooXM8T2tzcjPBwzuGabZiA0fG8IhQArlReI8x/al4BOhZbuOeznUSJz1OcIZiBKEQM50h5PcySC0dxjO4rsTBtYgKqsEUnIbAePImArYbmD6+1MFDpJeW1s4+ON/OCUsTfJlecw2LNGwk83UYAuZKg06+YQVfCfU5yzC+/FeYj5Z0Ix3cx2W5hMO6pjHhstRjf/xjXce1nJTS/4/cWBio9RSrazj063swLSnMvXgfAn4GEx/GRhUNc4W4CLVx5Ji9JRwlXhhajhhTP9Q0weSZYmX8ekMVkPkZz78+bOcBKaH7fsxaiBiQk7+LoeNc9oTNmzHC7iNBjRYDSNBlnDbAKT4qGSRrvZ5pJx/SBKMRAFz2eVuFt4RIPqp3Rz2ZY8YjyJvMWBM+uGgf77YI/75+lwX9uezytsltZTDiVIacrG23B8+PGvW+l37cgOHi8dFbufV7PJO+gJrKvlf3dJguHuGdGI97MbM4HgtkgMjvwCHcVIfCGOuYJ/f3vfx+wTpIkfPTRRzhwgN/D0FZReUKfsdASuFJnPcMrasULSqPnEdUKUBqtR1RPeAbjDXXCExqlcQ/ptV5ZYXxeryiPNxQw94jSlfDxtf24zglA9oZ2Zk/oy/xCg3hBtbC8ola8oDR6HlGjCjjAG6pXzxh5Q0PhCdXeljozEhIRSmPkFaXv/c90H3pqzLyhtAAt/X0frnMCULyhndcTOuNe/rR+xAuqheUV5RWgWlGm98w2EpNab6jevmaiym1PqLa7gZ43mRVON/KI0gJUb/pcLWbeUPq32v0svzcWH6N95Al944038Oc//xlaTRsb23ZT/tjicYfO0+RRCVG7AhSQPaJaIWokQGmC8Xr2rjdp6Ro4RnhGZmoFKOBvXdMPEL1+pOdgM5cQ1ZuNQoveqHk3vZ8ddfIHp9lVM0wlRO0KUED2iGqFKLcHKBivZz2Mk9abPa2TTLaz2kXEK0qJUZYABWSvKEuIau//K/EZlxDVGzHvpvdT2BMfWTikElR2BSggP7O1go/Xmxms19MoimWW6qgCXQ23s/q7ku9P/3Z65WRjK1OIaj2guVjFJUT1Rsxb9n5quRYA33hzWzjmCZ0yZQqWLVuGnj3VQ8LnzZvHPWK+LeNraT5RCXThbAnwOAQipKAEKA0RojwCtDeOcqWf0HpCTYUnxdFo/Zbm4NOHVZ/XpwT2fWGJUC08gyfc9IgaCVBL3tD/ZXtCw8LCOuTkDz57epbfnvS8oDTDYncFJUBpiBDlqYAzvaWI2MBRqLbe8mo+ByNC6e1xjO0ct+OpC9kClIYWokb3vx2PqJEAteYNXQ+WJ7Sj29O0yvcRmcjn+NHzgtIcQlZQApSGCFEeYRmDWpRyjAfQCqxkVKg+G80AaJYrmK7rtkcH9qPkGXSlTS/IghaiRiF4Ox5RIwFqyRv6UTvwhH733XdM427LAvSJJ57AU089hZgY/40ya9YsLFu2LGTXcEX/j9GAaC7BZcZAHOAycOJNTECVqRAdfPowauLcT6Iw8bRaYG9O4ctZyPICayH9RK0OWtJjGHZZ6lweDLGxsdi5c6fhPpIkITs7OyTXY0RbsKcEVKEMaY70p0pFGVdFk+mVQ/FNE2AuRCMAnA760szR6ifOFKBpRdU41c9YiJJ+oryztpgxHj/hC8xy5FxmtCd7Alrfpm6r+T8AwMbY4HO8jmjejtJwc2EZg1oAQHeUmgpRXmdKsIyoV48sL4vmG+CbhjJTsUv6iFoZyGXELKzASlzqyLl8XAWAM+e9VRwToWaty7bKeeedhx9+cCH3B58TwAfxKOiJUTMvKKEE6YZC1Eoai+zTrTf1II9ArlXymfIIUR54w/J6Cc61ZEwp4veG3gPgy8DV7W3yB7fsiccLSkO6e+iJUd50Zayk9DREgHJhYVfHKYb57E3KrcojRHngDcuHc3V2Bbo/eYTfG/rwcOC5wNXtzZ4Ad2yKxwtKM75GTq2lJ0a5B+c0lxoKUSJAeeAt0w1S60+ZClHiheURojzwhuUjOMPvQ3+71Zo31CVcc3HNmTPHrVN3GK44a3nAugZEB4S4eAQoLS71Zq1gCVBWf8rs07tVAjT2TItuuXHeFtRGxzKXIYWHdRcj9qb09V0bb95QnkwBPCPnydy8LNJQ5hOg08Df+T8YxOQP/IyP3Riwrkz5r9HwCFA6n6VezkuWAG2awNixFGoBmmJQMCuMTqg0WIzcCaS8YpiniVJIK6o23H4KqVx5g6/EZ7rbwtHkE6BXGOznJMKe+JlW823AuvE1P/kEKYFHDCY3V/jed29mt8ZYArQ7o+WmHZFuVEck1FRTGajVy5Ciw7oLT7ez1PpTSK3naxybOS6SUcHV7SEXq3S3RaDZJ0CtTFXd2rgmQouLrc2S0Fps3boV3bp1Q9++fTFv3jwUFfH1JzPEoheUBRGiVgUoQStEeTygWvGpR5y3xbfYpaGLfzGDJUZrGbM6uSVEafFplYwpDtxPFM3NQXYydxk37MmqF5QFEaJWBShBK0S5PKBa8alHOLXY5Qy1mMESowyHvZ4Qpb06doQoLT6t0v3JI7aOa884bVNWvaAsiBC1KkAJWiHK4wHlTYeUXlPqW+wSd7TFt5jBEqOsvqh6dQjdj9WOEKXFp1WG/rb188e5JkLbQ3j+6quvxq5du1BaWor169fD4/Fg2rRpqK429gI4AcsLqmUgDpgOvOERl2b7ZKDYVHzGnQ5eeOpBC1LiBWVBxChLgBKcFKJm4jNU3lCatpx3tzXtieUF1TIWW0wzGfDM6GMqQONgLj67I3jhqQctSI28rkSMGvQY0QpRVliRV4iaic9QeUNp2nqjrrVsiuUF1TLl+E/oc9z4RmcJUC1mAjQDxabis8/J4IWnHryClIhRo8FQ2vpEO5AK4BeiZuKzvXhDHesT2h4ZPtzfW79nz5545513kJSUhPXr12P69Onsg9YvBCKi5FGtZ+XKC40DXlAtFUhm3qxm4pJHoMYrHsaSlCSkn65k7uMJ4fSTQ04exp5uxn22spvzkR+erbudCNFg+okGO1sUjW7f0J9WyQsANKrFs17e3bacc9eWPX2q2FMkgCG5wFC1PTnhBdVidyAgTwUXQeqPfoDuIP1QPnXrYZgqDYBpf1EiRPf0M+9LqcdeDLZ9rBbdvqEHVskLADSbN0YBuVH39ddfO3ZtTmPVpk4ufAURUREoQwrScrPRLVfd588JL6iWPsdLcSSDf2Y7wsDjPwMAjmXo95ckjZZ6ROk6GLqe5O9HGixxR1twprex7y7teLVumjPAL0SbDFqgI7Ad2zFCdzuPs4UX3b6h+1bJCwA0OVeelk4tQrV4PB54PJ6AXKcqrlsMxFIpCg5ZL4fHC6rNb6YVomYCc6yJty+e0Y9GK0RDKT4BIFoJJQ45KfcZZYnRzObjAGQhCsBQjBqhl0OUFp88eUanYTVWY5qta8C5ufLyBYB6L4DXfJs6Qt5dLnuavpg/5ZkOPF5QbQiLNRCQd17z5gggXOPQi2A5L7RCtLWetuQrssQoaRuR8LyOGK03Sd05AIU4wJhukRafg7EPe3GW4XmuwGf43G5LfkCuvBwH0OAFNvvtqT026liY2dTkxRcjOtHfx4l/rjc/PF7QaI2/gnhEaTHK4wUFgJ7HywKEKMtjrhWiWvEZfQaoN+pT7RDEI8oSo3Gn5W1px+WGm54YTTsqbz/RWz+5r54QJb/BTKzESpN5eGdjBVZYmNJTBXGybYY8mQpVPzmJa49Fl6ekd4S//e1vuPDCC5GWloaSkhI89NBDSE9Px8SJE/lPkqW8HoIrXlAaIkTdEKBarAjQtMJqdl80nYhRPeeAPK1XlAhQGiOvqNmoea3IdNL7qYXpDf1Cf/+hQ4fioosuCsi7u2qVfsf01sYReyL3TLw7XlAtxCtqJkBH1+SrPtNClClAtVh50vYH23b0bJI35ZPWK8oKwzO8okSA9qs5jKJYfW+oVog66f3UwvSGBj4efLTXRl2wNkWcGbswzBUvqBbiFTUToImlas8aLUR5+gtb8X7223YczExPevbEaatarygRoDQsrygRoADQ42ilJSHqpAeUhukNdX4GbCaOJatvj8yePRs//vgjzpw5g65du2LKlCl46qmnMHBgYIvel1z7nUq1J5SQRb03mGjBjheUhgglvakFjQRoKspMQ9Q91lYCg3Q2NkHfcC2IUBNnCBNvinloXU+Mmn1ns+k4efKL8npDjz+k0wGv3gu84k8GLElSu+hXTWPLnh5nJ6sPu9V/Q8Um6Fc4drygWojN6PVN0wpQQvR+6N/jhHjoz9XeBP1pNq2IUL3BSEYzKA0x2EaK02ksGglRAKY5Cs28oQC4vaGlt+ukbGrwAu/57am9TqbCa1PEnu6rfFTlCfWdh2rxGqW+s+MFDUDZ7h3Efu5qBaiPCOBEN+Npv3ocrzQUifVxil1qYYlQve+h1zvA6HvzeGANtLWREAWAfQYNOjNvKABub+juX+gYfaMX+GcbT1bPQ2FhIcLDw9Gvn4WZZFxkxQqXOu6WU++NZ/4KwEiA0hRiAAC1GDXzgALG3sEeaxUr249AIWpvMCs3RGQmnmY/oBJLG+Dtri8mw5uadAd5OJVHNBiOF5slaVTT3gQo4J491VT5E3UbCVIWZgKUplaZ3YQWo3oC1Ec89IUocYAMRaAQddmeTCkFu2ImGITfjTyiPNPx8oTlzbA6FWF7nEwFcMem6EwjbiZ6T9wvP8tpMaorQBV6nKzUFaI9jiv1UxMClEsowvBSCuBxaaIJI49oj6OV2GeQx54nLG8Ga8xJqHB1KpxbbrnFl2R3+fLlGDx4MAYNGhTSGVRanXL4RCmPF9QqRIyaCdBUalSe1qXfY22lX4BqaYK6wtQb5LCdsZxhLCZ1jzclKtDrqZSv9wALb5J3OKde/zcwCmOYCX+e0fSskfLHizN9S7CIvLsyNVUxPlHK4wU1g2U3RIwaCVCVt4XV7UtvXILWnvSm6WyGXNHSSxxjseMFJSPm9cZZKQI02iDLXr8ada5fOh/reAT/f2GNlA9Hs2+xSnts1IUCOv2dk15QGiJGDQUoJSp7nAw8iU+AaqiP4xSgxZAbgvSyn7HoeEElxcEhpciLCp7ySXkG9DhaGfBZu84urJHyyajwLa2JqyL03//+N8aMGQMAePHFF7F8+XL861//wh//+Ec3i22TDD9rEwoxwCcaWZiJIb0+i+Ox0fDBnGqQZkhXfJaCz1tzRFl4Icm1TfCJUc01aB9kRIAS7ApRJ5iG1Y4KT5r2knc3VDgRhjciDadwNLYXcxsz3Bev8953EPjsidM+fCTBWHBq0VagWiGq8YCaCVHWZAA8DMY+032uwGdBCU8jRKNOzaVF3yK6BIg2n/XZFon7G/Tva0Y8lhaiLAHaFM0pPrWNPjPqoN8wpPCJUV4BSuAQoizxOeWocZ/1mVhpehmzsaLNCE8aV0VoTU0NYmNjUVVVhX379mHOnDnIzc3FkSMdLOFwlrXdzcSoXVgPaz0BmuY18H6SSk0v9FAG6+JTCzXbi15/z8SiBqanJ7G0AYmlDQEClKAnRI1C8sF6Q3dhmGnfUgDAA/ZSDnQmTw7dH9RNeLqvaIUoU4AS+oAtQA8qr3oVVhOsi08tSZrFCiSpvk4IXk+IGvUNDdYbyituM962l7i9MzXqZhuNgGRAxKhWkNrxgpruY9AhsMfxSrYAVexIm6GCIPWGdfGppc6/SPqZlIB8k/Ow+oMbPUNMRKpdzsI+nMXR8Dvv72vcuQADXBWh3bp1w+7du/HVV19hwoQJCAsLw5kzZzpVhWoELUbtekHnI7BPExGiRh5QAGjKZqw0qsAsTPtnhcTiBiQWq72UiUXUZ4YmOZURj9po/ZGtWiHqRp9QIjy5xKcgpATjBdVmnyBC1FCApmheCQe1O1KQMLvT6AlRo8T1XWBYAWqFKBGgqbCfwYDlDbXrWbWKqIP4cMU7SnSl0b2vIyCbNA05WohG1cmL03jq2dliPKSdla9zoN56INDW9lPrdOzQjjeUV3y2Jq4OTLr//vsxdqw8snj5crk/5Nq1a3H22We7WWybY/j4TYbbu6PENxOJ3ekhtcSgFjWIZc7ekOD1D7xoygYi8pUPrIqLNyw/AtgzSu0ZGbJHM0e8yd3mE6IsZ+EZ+DxJdMqL2uhYxNSzBfo59ZuxPpo/PdAw7DIUlOdgM97HjdznE7jDBel5httnUf2f9GZI4vGC0jSFh6FpCBC3hzFrilbcpUCOIrAEaHdYS6lEo3UKmXk86e1G3hx6MDVrUKJCdDGwZyB/wvrx2IiNGG+4z2Dsw3ro2+hErDfcLggN0UTfTNLZwaoXn+zP6oPJKUAJRbF9Mfj0YfZGGm1ZFoNSKiGar9mYDyCbsd1osggXvJ4zsRKFjHy9hNuwBEtwm/MFB4GrntD77rsP27Ztw86dOzFr1iwAwIABA/Dmm2+6WWxoyXL2dKeQGjA1nhUvqJYazRSXtAAl1I8Fu0IjDwOWeMyEbNRk4aB8YEzAwjRQvbyLZ9jJf/U8otH1zk0xGo0GvilBMznEjY2QfGfJpOZ0KD4K9ZZmRzLLwXtmiOaRqeddZAlQUomyjkmCnLeTXsyoZCx6wlTvZ2WliNKpHOt1ujnb9YY6FUWwG5LvDPCE4qcU8U3SAABYpyxOofW56Dk8dAbRFSbqNIp2wx+5sxDBO9M9TLXoOk5Yjbp8aqGxk9/Xhjc0E8XIdCBUGeqQvKsiFAAGDhyIrKws3+ezzjpLNRWZgA1LjPISpRFLRIiyBCihnjclk50+Z0bo9S3VEaJphex8OFohaleAartF8IpPt/nxxx9b+xLaNbQYteoF1eITonoClHV6vf6gTofkS5SFhoQoWUKU00ujJ0DNYPUNbQtdWDpLo841aDEa7ABuIkRNBGiEpq3BEqCelfLiyHWRsrUCmNRNLCGq90zQCtHTmtcgcEp8thaui9DOjlko3owEVCECzYjQDDji8YLSdD1eiwhGJdRMVYA+Icp4GEiDYCo+h9x9WLUEpGfSXtM6ShQTMUpX4DaEaHR9i0qAXuD9wfiiGQzDLl3xeRuWWD6fWxQWFqKoqHN5gcxC8Wb0xlGUIB0ljFE4el7QjHrGAz4TbC+iVQFqBD1gqRJyKJ9ejCBiVNtHjrZDYs8GQrQ+Uy1AtamZCEbe0PHYGFT/6YlYb/kYM0SjzgJbDLblQ56aNtjHUB3YIlQjAIkQ1QpQzxlKfOpxRLNooghnLjSQQ0SMauskWojq9Qsn1EMWnbzC08AbSoSnHfHZluowQIjQVsfKA5YlRrVovaAB5zgDnxhtZlWC4QiolCQlNChpK76DkA2OLCZ0Laz1L+v0vbIqOIVosrcayV6z6WuMqUBX39IWEXl3zZnFyIenBxGjLEHKTRf4xShLgPZH4KhzUglpK91SqHMWmqFt5LHEJEson0Fgg1J7LI/QNaEMqb7FiLaSLqYzNuocpQjBC1J6VLtOCF47Kn7IUaVhNJWxs4WeT3GvtagXVv9v1qMiHOx+4TQHoT9I0Yo3lOPZMAVrLZyw9REiNBiyWqfYSViPQ8jCIc4L6Hk8cLBThFkDSqmUJL2+aXpTEfISj8B0NqyHTip0+/OkFVZziU8jb6gbwtOtfqEdPe9uqFIzsWhCOIqQhSKNTbG8oHFeRuVk1k+OVF56FY4TMyhpfz5SplaI6kU0ohEgPqMZdm7kDeUVnk5jp19oR2/UOd4f1Apl1GIGK1KgI9gkZeYgMhDJJ0C10I1Du+ilINQKUb1yUhAoPq1mGtAm0w8RoewXKkSoiwQbiu+HQ6b70GLUzAuqJVpjZNFG6WQI+dAXoJmQpygkCz07hV7FR8SongAlsIRoEH1+mhW/sh5G6a1aK5zRafLu6hBsKD5dd4ogNSwxyoW2XdFf85klQKthLEDp3J90ZaSnuYhXlFVR0v25WXo/iKkPzRpyrBlbrOBGSL6jN+ocwSgUz3u/8IpRLRrfgqSZunLI1wwBWglj8RlNLdoZlFjojVlIh1rosnwKVkQj69lg4FUe+J+fLZw8kLYUkndVhO7fvx8zZsxAamoqoqKiVEu7xQtnOjw7jJ4AZXlB6esnQlRPgHooI/B8zdjhZQDvKosBRwZ29y0BFeBWBP6mrO55tBBV9o/gNPQLvD/4hKeR+GzLiLy7oSUGtaiIVgsrpheUnu2QVEZaAcqiTvMKABPA5/ko0yx0OL0egZUqK38ibYdEUDAEsZ43tK13XzGjIzbqBqGQazaqVoE1NoAlZOmfXxGiWgHqg743STtH620shWwTZsGnSdSiHaj7N81nlj3R5ye2y3K+6HlDT8OZLg3tDFdr4xtuuAG9evXC22+/jbi4IJrZbZHzTOJnx81/Wjda93aILoNsLLQQpAzK8xnUD4vuAGNqZ5lPqfe0kLze//bIKH+8r8+7lHeKlO9VXvOhzr0GyELU4q30feJ5utsSUIUqJFg7oVM8UA/kKeqBnYVLRUfOuxt1l9dwe1OjUaJLGSv9QWnMUgwRIZpcX853wiTIwpD25NOV5WYA2gQhg8B+Gq+izkNv1/4cpNLcwSiTdKlhpXA6A8t9Pwtj9Wd7645SlNroTJqMClQg2fJxNBlvF+H4k8qX5UhaThp1O3bs6HCNut/hWdN9XMtQkGiynQhR3ud4NeDZDUhD/as8dI+nzxBoD1vBbgguA3CR8p4Wu3dp9nufek9siwjR2cprNAKFbT3szSRIfEUOKbIpWIu1mBLUOc77+xr8+PoF8odaAP8M/rpYuCpCd+3ahR9++AEREe3T8xQM2eM3qD43GWaLDoQnFG9Gz6PKnU3//EZeXCJEWUl5SbJ40iK9En4hug7AH5T3tFeIos95stg88qOmgjpKnROQK1xaj+RDJUT3TOmLIVvUYZiI/UATI8G2kfjkIRVlun3bzJL+npO5GVv+q5fZ2R733XcfLrnkEkRERPjSnnW4vLs6TEn/Hs2UDVmdS5w3FG9E3GnFC0rbk879DsAvRFmhvh3wC9ERmm37oT9inbCHen8n9Z5VMRZBX4iyBhQ2IaBmiN4N7DwnuKmGZ2MFVvhqcOtMxHr84735QV0DTUdu1PEwZZumP6hWPDoRijeCeEbpKsFAwBEh6mF1uW+GX4iyum4thXFjaxH1nv4dtII1E3IjcqzyWWtvrMaP1sEDyN7QIGXRwP/8jMILe5nvqMNtWIJPMCe4i3AAV9Xh8OHDcfz4cfTuredL7zxoR7VbFaUEW3MyGyWd1zxMJCUdi6cIgUl3i6AWjOsQ4FnxbgRKFEMcRFqilPH3eYsSA+Qt8ZhmK6/9oA5H5AN7fuVPybHnnEAhSnMo0b5hBgs9x/wW3elF7DNwoHo2jLPOOsvxMtoDzRr7sSpKzRhmNPLOyJ60FV0x/DZSCfUI+gjIfacJn4I91R/xHJF2rdbD85zyeqXySgQsXTlqhSg94JAhOlXFD2f32gpFFEH7f3aSztyoY0I3/s28mU5C6gEOB7pnB/z9MOugboztgLpRtQFsQUqcJ2YTONCP7z5Qd1XRClFaZHaH7sh+1fl5ByPSthskrBkUWxtXRehf//pX3HPPPbjhhhvQo0cP1bYpU4JzFbd3LkCe7jar85xzzw1L16uZMG7NpgC4EMB/NOvXApgC4EHN+tmAVy+iOp0qkyYT/oeE1vPTD34j5yBiP1B4jrH4jEY96hkuJruV6W1Ygq0B/QUErcFErNedc9wo3B7M3Oc4CHXlYNaPi1Xp7IYsRLU9Cb4DkMs+Tckq//v0CdSGdfBXnGcQ+HQvAn/ovQk4k21vyEAwIflQj6oXjTodiqCfds8opZCeeOUZqX4a6owpZhn3WOMGTkO+biICqTrGu1y5xJmM44gHsw+1LhX+MHkpAn+PzfB3ldFu0wrRJPClizJpEFphCtZis5VKtJVwVYTu3LkTa9aswb/+9S/Veo/Hg+ZmZz0XHQlaoPKmYbJK+ZAYAEDXo/58nRJLlBIhShsULUz3w/xhQXiXek8i2ZnwGzqpkMl6iiHHD2NPhsYbupZjvuAgSUVZ0H3VBK1LMip8DTtXZ78iz3sSumNVOtkI7OtMh/DiYBzi10IGFBIxuo46t7ZCo3Uh3R2A7BtEulReZmOF4XzyoRahAhvQQjOU1TirrDjIDS7awbIO6oFFR8E/mJgMKPqN8kp3pyECl3C+wTbA3CPqAAP/8zOKLsxwtxCXcVWEPvTQQ1i8eDEWLFjQ8QYmhYgsqm+oFUHq6w9qQnnvQDHq0Rrsd8rrUKi9ORoDe7EOuAbAv5XPg5QKsvuT8ut4UlHSIZJblenznlMGA+yGvx9OMQK9pwoDveYCtDeO4ijsdQVxMwwoaD3oKIOeIGWF4plZJvTog8C+bfThc5XX3VB7UkkFeBFkIfodfAL19Glgt2J7Bcpudyui0usF8DWQOJ06Dz37Gc+UmxwpWrPqi3AoWh0X5I0isCIQgg4A/ZgMpSClM0eUwS/4dlPbBwHa3mun64DTnwLpccB+5Z4nVdoYOqqwBH5HyXb4+22fBlTdmkkqQi35yquR7UXAfn7gDjTMxtUUTZWVlfjVr36FxMREhIeHqxaBdSZiPcLRrFq4MUtldAbwmLXahlDvK+ELoT9TCvyBo8VXQPfReRLq/qSnIRu6dpAGXfzxwxjoPcwlQK2SgCrVLyvo+PTGUcSiVrVwYzYC9jR0p6tlkq/eN4/DnvJK1V1gvF/D38VmP9R5QVnsML7GuKOMdFScdEcp6hHtWwSdADf7kRZBP2cnQTsoiOp/vY5jxPruDfLis5kl8I+I15m5T8VpyHacr7M9GDMogiw8ydKBcPXrzJgxA+vWrcOkSc4P0GhVLnFiehNnmFgTOAfyqVj7IS0P3XIcAvWoQUAO4VFpYNYp3k7SqBsqSaqxFoQCbdqTPyuvn3gCpzsshT90WAx1Px2KpmwgIp+9TY9o1KO8nYbX9+/fj1/96lf46aefUFVVpdrW0OBimNllzNIzhZLB2Gdv0JORyUXD3zdNaxyaQQcFyzRd7krkSEEK/N09JwHIU+wpYBAggT4JyWxBuIh6f1Dznie/qYYEVKEYbSQkmAU4kFhE4CTNYOekNcKoixcRhF0QKErJoFYlmrd/uzpglyVJvvcjqfW7FXuqBfDfVcAYbb9RumvNCgR6Q7+Dc7QdeYEZ93yKf79+latluCpCu3fvjlmzZmHOnDnIyFA/pJ588kk3i+7U9DxZpu4IXgJ1fzCNl6XrHh0P0HPw97m5klq/HQFiFAAWUQauZaQkAX1lQ//4CHCtwfX7rpE8uHREqJaBa3/GsSlqNXAW9iHdqw6lrkicwXfCILljzMv463//x7Hzdei8u22YaNTjVIZ/xETa2mrtDnycgb/BRY0XaLxUfo1U5r5OgaIhD+vbU44kYb9ScUbSGzYiMNE2KZt1y/SH/pzWULyhlDf1bBxAPeM8xbFtRIRaoKM26nhI28bbkT9E0CLVyHtPQ3sn6WNSlYV0B4NcnSTW6tvTUEnCf1n5YekBfvSI+BVgD9zqAuMctdEIjEqy7JL3N+gAuCpCt2/fjlGjRqGwsBCFhYW+9R0hGXB7RsoGkk/LwrMiJcb8gDLAN8sXlRrz37vlCMyjBuJTxWEJHyv/+49/AVybrawno4C/AwzGLDBpyjbdpcPQmfPutmm0IW2ORlO9MrVtNDW5Q8kKeWxQCWSRacYgScIhxZ4OHQRSlIZnIhGhX0MeWGgFliAdbfEc7QTRqGujaLtf8zTySOOOdrY0A4NGyN7QSRz2NEbZ55jHg2MrgZ7EAahNzWQlsGswrkEg42pttmbNGjdP7wiSJOGJJ57A22+/jcrKSpxzzjl4/fXXMXy4dkqTjknX/1BeUDqkMCFgVxkys4TiteExbpprqYrTxyr4W34sEfodfOGZCCAgdVMTp6e0vdMe8u52dnsCIHs2SEVFRJ1eepZHgcgkoJEaDDjSgk1lSRLeUOyJpHH3fgckPqN8YIX2voU6nDgC/hykgDOJyNsB7aFRJ+wJ6oT2e4x2BGr/Lr/GUOnNBlmso3pSUQYAcqVjNgNXOPw2HgF1Q5Rj9q7OjKsDk9oDzz//PN555x2sWrUKp06dwqRJk5Cbm4vq6jYWrgg130KujHQqpIK/Az1tnjpLkpCVCTlGMhvqCvFl+OfNPgggQ1kEvry7f/vb37B27VrV0lYQ9qTDRsgx9tMA3gSiNd6UY5Vyf7R0ixUmANytHPNhHZCYKC8+IiD3yWumyjfK89iJII26toywJwPIwLsdCGjknV4BnN4NoMC6PQGyiWz8FOyBe8XKsh/+OrKTNNzcwJUm4MGDBp2MFPr3t9ED3gVef/11PPjggxgxQh6W/dRTT2HJkiX47LPPcMMNN7Ty1bUxlJDHoVI5DO/IcJIl1PuXlVfz26dT0h7y7gp74id6KmQPzxkgayhwyGCSJl7eKAWmAcBvgEHk52aNFBS0i8lUhD3xc2il/z0xpWB7/29cBYy/T/lQEuTJBExcEaGDBvl7xktUy97j8UCSpDZTaVZWVuLQoUM499xzfesiIiIwevRobN26VRi5CVbChgH8Vnl91pFL6RS09by7wp6CIysIe7pbkvBv0dfeEm29USfsqfUYz+o2JnAFV0RobGwssrKycNddd2HmzJkIC2ubUX+vkmQvOTlZtb5r166+bQG8txD4r5LwelIucD57br0axBqWbWeu6wRUBaxrCg/8baPrNPn9NKPhPXR4gRqpV/9b//toehpBsr+SvumQ8jFL/1JNIYPGx9BeGlIXzKXWkakQ6dlcNN+H7tKVEBf4G5UkqkfMD9aZ5nQXhjHXr8dE5noApsnwk4af0N3W+PUa1LyxSf7QZD4al+TdbavYsqe/L0RDsfIPnDpdXhhUpCQz1/NQq2OLyajgOl6bjD0tUx0KPdNbbYNxmyn7I4Pq6X5hxM4Wyy//Vdw2Y7iuxpxBZCAE6edJJ2ggI3q1c2rTJqIdmauZvCKcMVAkgvE805unmvUcI5jl6I2/Wn+a1aZv/oPm1WvQuCWGy57aeqPOjj2tWLgZEVHh2NZSgwunh2Fark7da5YGyOjn0Mt9y1ulsQYa0fZhdG10V5OtyitDxTgRpfuv8trzffk18hfKCtpW6K4t9HXQNqPtE8ozhSnrN9AxjaggZoFrMBj1VbZqC8pW/RfY9QOXPdnFFRF6/PhxvP/++3jjjTfw/PPP44477sAtt9yC9PQQzAtngUSl81RFRYVqfXl5OXr21OnxmLMYuNXNrLyCzkLk9AuAnYrirvMCG14z3L+t5921ZU/nLAZ+b9xgEwh4iLj4QkRcfCEan0+T7WmTsT219UadHXu6evFIxCRG4srmQy5fnaCjk5p7DlJzz8HR9y4Far3At8b2ZBdXXJTx8fG4++67UVBQgPfffx87d+7EoEGDcN1116G4WNsEbz2SkpKQlZWFTZs2+dY1NTUhPz8fo0d30LwkwdBdXnKGyrkJ1wURrkhHSKaq7lCQvLu33347fv/736uWtoCwJ4vUycuYQbItHAvCnj71eFAL57ypnQHSqGurCHuyRlZ//xIsBR4PBgK4ymAGP4EzuJ6b4vzzz8fIkSPRr18/LF68GHfeeScyM9tO4qx77rkHzz//PC688EIMGDAATz/9NCIjI3HllVeaH9yRodsKJDQwCYDyzLY7Mh4AcBZV2ZJzkxHyv0BA+E8g0x7y7gp70oFOIN8EuRvMXADL5FXkQVzi8VgeIa+djaykGEhXUqiJHIX6tIfJVIQ9GTCUet0PoDeA/8irSJT8DY/Hlz3CLpF9AGhnUBI4hqsidNOmTXjjjTfw6aef4oILLsDKlStxwQUXuFmkZR588EFUVVVh2rRp8Hq9GDt2LL766ivEx8ebH9wRoJL7Rq+CP7/ZVDCnIvMqfTRTugCng8h/1nMQ0Kg3Pzbpk0aGOGaCv79RB6Y95N3t9PYEqDukeSGLziSwG1dK/7r0TFk82mUgDCILZIpc0tDLhEgpg/bRqBP2BLU9xcMw76ZXsaH5XeSUZXY5DSBHL/80aVAegd+JMgjAUfvldWZcEaFvv/023nrrLZSUlOC2227Dzp079fuEtTIejwdPPvlkm2n5ugIrRQtJg0TmNDMi2/82MRvwPii/z+oDeSpOg+kFAxipfsA3llIdvo0gnbK136Xt9O4QoJPYk0bAxZ1uUd+HPDZFGn8L4R+gRLZ5PJjB6b350OOBNmLo84IaoTe4JNNgWwejPTTqOoU9AerBOl2gtqdo6E/2QFAEYOJUebIGACBzAb7r8WCBBW/obroRUglgAUf5AHxjVGmHyRHwT+vbSXFFhN555504++yzccMNN6C5uRlvvfVWwD4d3qhakSO9uwes63PcrFZkcIZ6pebJTaSm7sRmAOcpRvujsaGXeDxIHwH5IRMHdUVdCfshD8XD5B0bZfME7rC54RxHztOe8u52BooyMtAv2CTntGc0W36ZkQ2UKLkOecLyZArc7QDm5wIlqzQ75EI9vzYvtMjW8+AK2jXe7Cgk5rs34tkQHkHHAz0yPRtAvvw2Ubl/F8QBXqUO88Z4DOeOJ/sQxmsjBURIjoX1Rhr9fdvu5Fythis/yZQpU+DxePDjjz8yt7elcIctPgBwfWtfhExZdFrwJ6E9Nys028gdchr+eaizASyHLwVMo5LmqVT5v/akKs8SZV06cddQqScihyrnpMUoaQEfhTodBwsOL2hDeNsSpnZoL3l3bfNEBPCEWc6Y0KCXVkibpimATOjfj/Q0s99S779WXiMg20UlkK4kPti/DthOPSenUf/3d5VBSCkA5lN9PtNzlZX0dRC762d8+b7vwIJK2SQZZ1BqdaqfN38eikZdiGFlsTObtcvInkZQr8uo9SRz2hAAe4ASag76WqgH/g2l7Gmdx4OhEf5Uf0O7A7VngNoyIIZOxUrsOA5yRO4I49p4HsNxmvdleju2Pt++d6nrZbgiQvPy8tw4baeHVRFWIQFZvsydQUB38qYNuwnyXfKdsvxBWU9yEE4CIknGIGXGo9p4diMj/SKoHyxE1HaH3AeVtDBJ3xovgHMQdHgwmmqKrsdEDMCB4E4YYtpL3t32BisnZTEykelEHw8qcoAj8Pe1vgiyEK2H3EeTNPpob2MpMGgQcFhp3JVADrtnaYq4dhL8eXS3w185Z0JuKJIKkRag9HXZJFzTXjiU0r7m1e3wjbq2xlGwhagVBumsJ4P7kgBcCn+u6SHwDaKN6SKH5o9RfUR3ezwB4ieRlFECxMTJQhSA7CShG2ikrrxKea0EW5Dykqr5nIRONTuTcA63QfSSOVchwTDRsxFHMrobh+TL4DcGatQuAPUApUWQjXCC5vjTQE+l1Vj7qXpTejb1gbRwaWFJ+rDFadafQ60HIA3VJNq3yQEMCFhXjmTEojb4k7tAe8m721bRE5WHkGW7AccMyWs9iUc074kQpcNzswF8Cr+3URGex/b76zptfTSbfmr3gyxEh8IfZchWXsPB9syQyrg/jKMJ2qT1Bmh/xwokm07Y0VqIRl2QhEo16A0M0kLfp0SITvX3jfa+IL8OVc63WyMYJ7HKuUbpU9oEv13nQzU+AoC/8dgHslg1qp+s/G7aR/sQdNhuMY5bH2/6pT59eO+wjk8qTqmWrQF3uguYRZu6U4teNpDhkCs/erDQbiBmqLKkAun0v3mSspCBSF8wzrnH+LKkOHkxDefAeii+BjEBy/aAYR+hp73k3W0rlCFVtawCe1YzPYoZcWnDxl8dtRixXVn0vEIRCLQnADnKMlBZVJwPwGj2RjMnZaaycPSGYIXijbygmSgOWEraQHbg48eP45577sFbb72FCy+8EB999BG6dOmCvn37+hYBRZNmcaJLhpFHPklZzDI4/KAs2m5b9MC8dCDxWXkhDO0uL1lJ8qJiAfwzmrEwE4Jx1GI2IMlqhoqkwGXPpPZ/rzrepikvL8fSpUtVYQ4WlZUdVNZzkgr96eesYtuj0x/+UfKAzxta3wfAzUD0UmrbldAXfmb5nsfqrJ+r+bxFeT0D+WFAXZvEGuFPRlRypuLQm36zHMmGx/Wmcm+YTdPpNm09725rURYQ0+IjGG+o6X3XB8AR4NR4fzqdtK+Vjmu9AexQVl6kvJKIQynQk4o0HNsAFSlUharqT52tKZ+MTyTXSY9AZjVCiYcoSP9AhYk9dacuuhSBgyjdhjTq7r77bvzwww948803MWjQIMycORMvvPCCsCeAq1FiCZ6QPK/nnYi3XABkMF4i/GqGCFw64vADgPHKe0bqQeagWDKDcL7ySrq10PZI6iVtkJFO3bQf/FiIPhCq4H++JPg6xrYfHBeh6enpXCPftcmBOxpmc8N/hPmYhw9Dci2ZXtlCmuKACJNwdj1VAdXTQpT0l1Hmj0c05Fah1mjo5/dLyuu7mn3uUhayXjtacoHyqhXJCh5tPrYugBQibUgL0s9wBdJNc/E4R3vIu+sWZg2Ff2EmLsVKx8oz6htK5pwv7xeDrkWaLhx1UIk9WoACwKnp8X4hOlxZuQNyxToTYH0FnyDV6xcHyBWs0ahjck2nEeiF0vZnO2JSloITfUFpQbqyFTKCd+pGnVnXplANQiMeQ43tAAjsoqX1HhIhSjeqjgBNlIMj4l1qWxflGACRRn4w2ouZDX0huRvsFIg0xJaKDPdyBFqQfo4rnRkv4jKOi9BDhw45fcp2QdLAE6rPxQ0ZyIyyn8ZlK7Ix2tcE82OlXygRnzQBQpQIPUVMRh8E6imDrr8ZiP6B2n88oLosqw7tuzSfh2ve79Bs7w/TNDNEgCZUyilHqpKcGRF/IDD4GUCJjifnaINzqrg95d11itRe6p6Q1S0JiA+z1x8aAFYhF7k+t4k9iPikYQpRwNd4SjtYjVOTTBKLD4e/gpqJwEabGbRYoG/HUuWztmIfCmPBSipNlgc1CLbCfKrJKOinDdroc2U5Q2dq1FWEJweumwL0WWuzEa3X39gKrHA1S4gCKB8V43vftVBjb1qvvsaT37QAiPgB/JC253eQQ/tEgBJ70rIb6sFFel5k4km1KkaHsFfvGW4eij8UMJzRzye4mvlMCzViYJJNtKKztWGllqlIjEeyl8M9r/Fm0kI0+m/KSuIcSIevJYlVyrFkdPwRqEMuxKvTHYGh9zoA1yFQdGq9NIoA9WwGJL2wvoaEygZUpMSY7whzD5sen+EKW8fZoTPk3dWKzlCiF5JnZaPgemhrvPdp6/xCNG1btWxD9Nf1Qq7EyiBXdLRDkA75k+4w++EPLabAX0lOQqDHZgLUjUZiqzwJwOlrqAfXyHqzUHywJMdWqMursV5eZ2jUsUSnZZrhrDdUqzY4c9DSAhQAygfG+ISolAJgPODZSO1wEIhQbLDpBiDit/CLRNrJTevv6dT7YmpfrT3ppY6iB/aa0Q+yPbWBHolae20NUSpEqFVKAEQBlXf1QNKbwQnRYEPy9A1jJbWMzxtKjIDkKaSIPgg5Eb2/gMDRv7lQ94/hRa8vnfbyNR5Qz2bIDy6qpWwUhqe7RLSFAUZaKl/twbVfh867OwxAF6DspZ5Ivf9YUKdyIiTfXTMWvZZjhLfPG0ruS0Y3krR11QDtECVClJ6SMBXmMy3pwWqgzdZ8nqT5HI3AkKxBGN5D9Qk/09vZMa12Q/G0KP35I/PIBdCxG3V7MRjR6ILdGIZ78HprX47awcHqCsJC8YZKyr7Jp2sDnArlA2OQfNrvEZWIENXYXcT7Nq4ZYOfMzTY5pgzW+nRq9yWZLhzgc93RxMbQonTLSu0Dwx2ECHURt0Lyeq0VlhDV9YZqW2EMIcooQJU64syQMFWoIO7JFt1DC4af5Xs/smiffwMJwdOXvQKBFSjgN1qdkA1BzwtKfjcebw1PKN4RIpVXg9CWyLsr42ZIXis+9ahAMtv+tPejTn9mFelQi9CDCAxR6jXYMjXviZgkAyFo+8kGGI8R/zFGicEBXY9p93r5NyuNNh/xzhOKN4I7FE/Mlj3nAIAO3qgLJUYheV4xpuMNlTRilSVEA44ZD3i0NkdH6gBjkUcG2LJUUTb1Xs9eeL8zy576aV5NxChPKN6IT3A1345k7gcDewoWIULbEXsx2Pc6DattnSOCGAAJ/6k2QhaixONB+toQw+5DHcMKPXCGy2kK+/UC+gED3/rZv3IqAh9MWgOvAySOwRNAoBeUiIgig/4yRpiF4p3sDypwl1Tlhj6AgRiAwoDtMajh84ZmxKDrcUZftYNAk8YJH0EqmCYYVzokRZoFjlwpH9Bnj8almq3ZUesBzYRsYxyjorVeUCJGD0XzTMvU+ohGnZ8jU7ob9wu1GpKn71eW2DLzhir3pWd/4POdFqJd11JeUGVcgace/gTymlzVAOQoAzWwqegV9cC6fusYDiPWoKMzCBTPLAGqHVRlBcWUpH6Ap4Nn4RNZeoOg8i6+cKoRH2G+6T57MdgnQM1g5TisSJTjgBHahwJLSLLSMPVHYNqWIKcaK+zXSxag5POdyns6xxspk2HgegKUty9oa8Mbiu9MeXfLXgq+X96/OMO6JIuoHfQ86eUZgfeeVoACQFM/BIo9B/QbEaAAcGQIQ70aVYjExrRuCYvzfEehwXBwEQsnRsXzhuI7E6/jntAVZqPB5IPce5r708MYkZ58ulYlQAHAs0MRoDRXwS9I7UILUHJ++hqNPJ9xmlfteWhMbF+ymKzBbiieJlSheCBEntD/+7//w6uvvorzzjsPf/rTn/Duu++ie/fuuO6660JRfKsSbEheT3yuxjRL3tAAAcqiDP5WLx1moVt8xKhKgbjPNOF3k+S8IzdSYXiN4Q3c+HOgtyYJ8lSEmhQyvB5QQL8vqFteUKcReXfVBBuSNxKewXpDgUCPKMlE0URVRhGV8FfYxAnFSotk9i81iIL32aKcWFMJSspnj1FlatA1R68vqNYLSoRoA4LPVOHkqPjMzEyuCR769OmDI0eCmYuxA2HkDTUSnnqD31jeUJ0GktYjWhMX5huUF7dRqX9K4bcjWjuRMQQEs/7WBmLvzIXyfR/3tabOI7e90eQpNj2iEmVSRIg64RXlDsWHiJCI0BdffBHLli3Djz/+iHHjxmHmzJnYsGEDdu7ciaeeeioUl9BuKPN1wiCf1bkpeaD7hpIRvlXdEtDvJEMMk7C8tm4m/X20FeEZsGd6YK2jjHrkln2MHWQGbvw5cCX98FCSfgPGAvRoSnfb05rSaP8HrYnIuxscw7BL9Tkf2chmdpDkI7PGb0M1sWyvOys0H3FGGRCotafu0K8cteKQc4YVnwDVILEEg543JwL2Q4kUUWhAsem0TaFDNOocQBt0CeY+IQqEesZrIUK0Jk7dADozPswvRAnrIAvRzQhg56/V0zWf/fUBrkskAjSAFM17IkT16sc4IKAqtxgBkTKBXSkDEO74bAKtR0hEaFpaGkaMGIERI0bgzTffxIsvvggAyMnJCUXxbR67osfIG8pKL1PULYMtRPWcQ3qtXwcqp4Ebf0bh+F7mApRg8JACZAEK+L+3kRg18oKS/wU9+MRK2hmn+4N21ry7waAVnlqMhKieN5QWn4TYmlpdIcpCd6KI7jC8t60QIECVRqNWgErdAY+RZ8iCjRv1BSUCtDf1BY8GOyVTEIhGXSCm/UIB41m0jDx9et5QlvIweMZrBSjhzPgwxH3BEKJmU2ZyohWgZ6aHyd5QVr9WWohqIcKUVA8mfiXJRJw2Uz9gexekIRGhp06dwoYNG3D22WcjOTnZX3hE+x8XxZOqiRWST6dG45p5Z46iN7c3tF4Jf9UjFWkMdakVomcSw3wDiuI2a4yZzFELqAdOsLyRPFOTaf7dA7doBKhRH5sy6HqCiAClqUICauBM/1AiSL/ALEfOx9sftDPCk6qJFZIf75vKC1iLyZiC7x25HvKwPxWbirSaQHtiCdGAAUoEup6nK7HdYN/bZkJQU2H3OVIaEFLUq8x8/e1YNqdTbvnAGETVW+wkyoAWpN9jiuG+PKF4K/1BO1uj7nXcYz9Vk3ZQDuf0yIaQed71MpwwhGj9CCCiWa6bmsLVojBuT4u/PiL3tIXuWgFMN94ct65FPyKRrixaIc3av7ey3qI57UoZELCOFqTP4reGjXCeUHwo+4MCIRKhV155JZ5++mnk5+ejrKwMc+bMwdixY3HiRNtK+B4K0nVSwdgNExJvaD2j79UpEyF6JlHTyhur9HvZ3MLOYQbop45g9afRtkaNDI4uTzsdKPUVPMXqjtosAQrAJ0C7owSlVOc5Hi+oEeT3PGVzrnKBs9Dik8ZMiPJ4Q5s1j0ceIcolQAG/x0RvpHAz2FMY0hjYk5EnRTXg4wjUXi4DAQoADdHRKiHK4wXVgwhQUmnuwjDD/QUhwGwKSj14vKGJmvUcQrRem1miucUnROP2aJwmRvO0JwJnF2nC71qxOgi6NhW3Tj/9YEDfbFpI6wlWsp7hKTbzgurxLH4LwG9HZhGhtkJIRCjd7/PkyZPYunUr8vPzMWJE20sg7hZ64pMXI28oS4AS9ISoVoCqto0NQ9x+HaNzIwsLyxtDhCijqwARomYClKAVonbQekHp31QI0tCjJz6dQitACUZCNNpON0KjAQ024RagBCJETQQoQStEnUKI0VaER3x2gX1vqFaAEgyEaL1OZCyiuQXRemIzGC+oDkwBWg9ZQOpVK0bdx7TClDhrTEyK5QU13L+diFGPZNZDWwAA8Hq9SEpKAuZXAlGBFqUXko+N8ntGBmOvaTlG3lCtCM2i3JJm4XpaNNH9HVmVSdxRyui0Dx2zPi80LE8oq/sK42Fzpp/S2mWI4aLhsoclnJEp2SgEn4cLEKOTddfMC8oTil9bMhkJXY0HRumG4uu8wKNJqKysRGKi3hO74+Czp2cqgS6B31cvJN89zO9SHIEC03LMwvJae6Onv+1uMJyWFqLhmns6oO8nbUNeg200LDvT/kw6ORclhqDwzXbE6v9t0IbSClCaZoSjRKcW5vWCGrEZ52A7RhruoxuKr/ECt3c+e7q/8neITgxUdXoh+XwqJcnsk1+bF2QmQjX3Pt0X2WPUSKMuuUlTlzRr2oPR9D2ssZMmnQZYBG82NlZDjPUYIGJXTziS78ATmqc4MTwJ6afZP5SRCCVeUCN4Gne6ofgaL/ALd+yp/XfKbKPQ4tMpaG9oliYubtZvlHhEtbO9mHo1yMOhDvoCVC9NB6slazIAg4hPPYgABeRKkBaiZgIUkKdh1BOieljpC1pVrgyMMhGjAmvQ4tMtYjX3RSm66wpR4hHVClCAmhaXBXl+e6FfodeDbyQ8xxSIHjNPaxACFJAjPHpC1AlIQ8NMjAqskW86B6UOFryhAYPhkgyEqOIR1QpQQG7kaYWoD2IDBvd5hN7U0iwPrJlN8Xha6e/AE5pXODFcrjBLUgKFqFUvaHui0yarz8vLg8fjQXx8vG/p1auX+YEmxEbV6gpQnoTzPA8HrQAlHIXxyGy96T4boqPREC27LVVeUBqDaTKd4Ey/MKYAPTPIv44WoIRmjuk8iAAlaHM+OpGSaW3JZNXnqvIEnyB1mv/7v//D6NGjcc8998Dr9eIvf/kLli9f7kpZvLhlT93DSnUFKI8wWYvJhtuJvWkFKKHUIBEiS4ASfJWpXoXtonPOs1t5ZVXMqTrvLaC1OW1XI6e8oDQjUMDl+bbDrbfe6nvPk0M0VLhhU/nI1q1jVnQzGZXDg3LfM9OBQRaierAEKIHYWrSeRzMFfPPS24F8F5YApaN9JA2TFo5kEESAEkpS+Cehd8oL2lqETIR6vV589913vs9VVVVYu3ZtqIrXpaKiAtXV1aiursbPPzPSBXFiJD6dRE+AEvSEaASacQhZOGQwMIcIUSbhkEf0kcUBjozvLi+s2V00sAQoYReGWTYy3uTjwY6Id0OMvvjii3jvvfeQnZ2NcePGoaioCCtWrMDjjz/uaDl2cMqejMSn0+gJUCOS68tRHx2G+mj9R6hRpWqaONsuEfLC7PtJ43A35mD7vPPihhg9eNA/4fj111/ve//QQw85Wo5dnLApI/FpGROHhJ4ANeJUv3hUJMb7ZvdjYdToA4BjvVNRkuhfHMXsOxnY+qmMeJwar/+9tAKUYEWItmdCJkIjIyPx9NNPIy8vDzU1NbjiiitCVbR7pPqX4w/xjdix6w09it44it5c03xqhWiEpu+knhCtRxRODEzCiYEcN38QgpSITx4imltQ319/O/17HmKMmtJ6QWlqEeuKF5RFVXkCKj9xJjUTybt7xx13IC4uDi+++CI++ugjfP+9M2mJWo1l/mX3uNFchwTjDc1SmmWsqW5ptN7Q5Ppy1Wc9IRpRCnmUe2DXZWeJoBYz6sCd9L5rYWCj2ijykI4SU0FvxwvKYgQK8PM7zkzV6fF4fO/pIRJbtmxx5PytxVaM9i2v4V6uY4Lxhp7onYQTvZNMxZPWG3qqn1qg6QnRiCOQB6xyDgB0RYxqaEoCmgyqslMZ/u9yanjg99IToISSlCS8l3KN7nYnvaBb3gltaiZCyERoTEwMPvnkEyxatAiXXHIJfv3rX2PKFPMHktv069cP6enpuOiii1SeWkOI+NTAK0StohWVVoSoVoAStEJUO8KeV4x6+0TBO4i9HMtIDVzGsx8KZdFqMRjR3OLLDacHS9DTQtRIgAJAjYk31Km8oADQ8q1S8/dQliAgeXerqqraXN5dW/ZExKcGXiFqhywcUn3mFaJaAUrQCtEIraeTV4x2gT8/r3ZJ11l4qIOtkc20EDXr+kImihgIvploguHf7ygTg9v8XjSlpaU+e6IFaVvBqk0R4amFV4hyofGGnuhtLZxMhKhWgBK0QjRCO5ZAI0aP9WbXKz2Plskj/1nLIMbCqVmbkuTFCFqA+tZRQtRMgALAKuQC4GuYBYNPgJ5QlhAS0j6hCQkJSElJQWVlpSP9xVgsWLAAHo9HdyGzNA0ZMgT5+fkoKipCYWEhZsyYgdzcXOTn5xsX4EC/EyveUL3wOo8Q1ROgBCJEjVI8cXtGHUJPfGpnrDH6DVkeUS1mApQXHi8okyDEKMm7O3ToUPz444+YM2cO/vjHP7qSd9d1e/ok+Gu04g0l3k8WZkJUT4ASiBANEKA0ofCM0uiJNAvCjeUR1aKdoW0gDgSIUae8oEyCEKN69lRa6k6fCTdtqgCjgr4+K95Q4v1kYSZE9QQogQjRAAFKY8Ez6gR64lPb9YYlQH3bGB5RFkSAErS24Wpf0BCK0ZClaJIkCddffz1yc3Mxffp0XHXVVVi6dCkGDzYXZFaorq5GXZ3+0ygyMlJODcMgJycHEydOxOLFiwO2+VLKZN8LhCuiLSsX6JcbsG/G/xr32wT40jWl6s6n6WcePjTdp59OpUvIgHFnfK2YTSuq9r339tEXsFXh7L6QWo8K/Zk1PSJNcWyGqYgn05gdADtcxytAedMymeHzgmopWCUvAOBtANa9xkyBcccdd+B///d/mfctnXd369atWLaM4VIMAtftqdu9QJhyDyXmAkmB9jR001bT6+TpJ3gj3jfdBwAyGfZA20hCvXHmA93BfQQjszbqb6cXRjc6n1nonWfAofLv16s8WVME0xRCHtnrlAj1eUG17FglLwAgNQCr2549Ae7YFLGn3vfORFiUHBFJzR2NtNwxAee4F6+ZXiNPuqYT3ficE6yUQ3tT+vres/JY06TtqDbcLmnajsUpfndmz6MG59YLHGkOabIwy2zEGWMBCvgbu90N+lFrBSjNWMjdRBxLy6QXhqftKaoB+BfbnoIlZPG7kydPIjc3FzfeeCMAYPny5diwYYPjIpSMIrRDWFgYTDX55MVAdPD/hL0YrCtESfLzU0jFYOwzPM9HmG8qRIuQpStES5DuS7GSDfPKHlC3YKOaG7iO0cIzql3Lmtgc033oeXQHoDBAiPIK0I9xrWn/NtteUMLIXHkB5Dxs69iVw+bNmzF48GC8+OKLmDt3rmpbt27dMH36dEyf7sDIVgau21OvxUB48Pa0HSN1hWgm5IYNmV3MjGJkqoSotpFWFZ2gK0TjiigB2vq9I4w5oyxGIUhKK6XtqA4QomYCFJC9omZZCgAHQo7Dc+UFAGq9wOq2Z0+AuzY1aPENiEgMPsqzott0XSG6rpv//zQAhabn0qYcogUooD+him+7cs+ZiVFC5mnepKD6WBGehL2JA4BE40F6dLSlFOlMIWokQAHZTtZjoun1BD0iXmtP/zJvvNghZOH47t27+wQoAPTp0wfXXKPf4dZtVq1ahaKiIrS0tKCmpgYvv/wy1q1bhzlz5gR9brt9Q08hNWD2nb04y/Q4ntA8a7pKbX6/fIxGvqYvkVlIvyE8SnfRg5Vk3og1sTmWBSiBfkhaEaBW9jdC1wtqgc2bN+ORRx7BXXfdhUsuuQRFReae9lDjpj3Z7RuaieM+AUpYjWlcx5LKQi9KUBUdKL5UAhSQJ2bQ3pJG9WMEdQxrOaKzmM0xr4WITzMYzjpaCPAIUMDfFYJHsJih6wW1QHuwJ8A9m7LbN3Rdt3NUAhTQjzZpIaF5rQAl8Mw6d2p4fEAjSOsF1ULC56ylPCOGvQzXz4/LYm/iAFmAmsDq7qOdyc9MgALwCVCr+a5ZtNZgJJpOmyf0p59+wtSpU5GQkIA+ffrg888/x7///W+MHTs2ZNdAwsos8anezz0hyoIlRkNFcaw/FROv+ATYApRgpfIjApRQg1imGA3aC2qBsLAw/M///A92796NpKQkDB8+HH/84x/R1GSSsySEtAV7In1DWeKThleImnVTYQlRJiwxGirOaN6zxCdLGBv0r+T1SAGBmQkGoJBpj24PvKBpD/YEtL5Nkb6hLPFJwytE9QQogXf6Y5YYbQ30xCdr8gaj/uZEiFoRoIQY1DDFaFvOC6olJH1Cb731Vvzf//0fADkZcGamSfOlDeLrw/bLSu5wPE/f0FSc4r4Go9A8yR86EetNz9MPh7hnORmLzXwXx6A+YN5OPw0Gg6Eya45zi0/AWIACwBbID+0oGHcd0ApQFiREH1RfUBbzvQD4pkX75ptvcN999yEyMhJ//etfMXGieWimreGzp9GV3OF4nr6huVjFfQ1GoXl6vmUzj0NCfVWgF1QPo0EUZuF7o2ONbrU4mHs+6frfZIAPSZlGNxhZ8ITgiYAJqi8oi6+9wMedz54urFzGHY7n6RvKMy6BYNTYp4WqWZ1n1keUJvW0cYNId6YlAFWJ+h7PBK/xYDwzzycdkjcb8AgAa5CjO5kMgScET/JfB9UXlHliL3CfO9N2hsQT2taTAbcGx3f1w/Fd/bBj1zjuY/Q8onQCe54bldcj2oAorMdE1eIWJA/qUfTGxthzuY/jFaCAsfDlEaCA7BnNKzZO/eQ2F198MbZv345rrrkG06dPx5133tmq19MWIHePFQ+AnkeUFqCA+cQG3B7RMgSG2N2inlrMpu4E/N5QTgEKGA8i5BGggCxceMP6biHsKRAyAcj3nP9HQN8jql1vlpuZ1yO6FdlYnXK+anGLzYnZqsUM4uThFaAAUIFk3X14694Y1HBl37HM750/JSEkIrSjJgM2Q69v6PFd6vXBCFHWDEpmN2w0GtCMcFuDg5wSpLToNJtulEUFkg2NFlALUAJLiPIKUAD4qXg8AKDFqIkNq15Q/l0lScLevXvxz3/+Ew0NDRg4cCCWLFnCf4K2hoWokV7f0N44qvocjBDVClCCkRDtcbzSn8vTKk4KUlp4WqULTL2lrEkjWEKUV4AC8HX7MRuAackLmse/a0ezp/8sm8m9r17fUK39BCNE9YSpkRCtQgKKkMXtLKFxUpRaEZ1aKpDMJQaJAKWP02KlriX2ZDbuoi30BSWEZPwmSQZ89tlnt8lkwJaoAQyizIZoxSfNjl3jMHzYJq7z7MVZGIx9hlN4rsdEZmg+WhOSJkJUe9MaeQ0BcM0041QuThoz4UlgCVBCA6J8oXk7ApTQ0hyBMLO55BzglVdeQUFBAbZt24adO3eitrYW3bt3R3Z2Ni655BI88sgjrl9DW0QrPml2YZiuoNRCRs2b7V+L2IDQfI/jmhg5EaLa0DlPhNEsz6XR09qO8AT4UjSBLUAJmTXHfaF5OwKUMBj7uPq/B4uwJzZGjbfvMRmTwTcj2wEMZGYm0VKGtIDQvNYrToSoNrvLVo4pSBNQbSggo42MxmbEmbd+AgIFKH0OEpq3I0AJ4Wi25WgKwDwTVFCEpE/o448/7su9VlZWhpkzZ2Ls2LH48MMPsWPHDreLdwRfH7ZbK4Gu1u7QjP8tMhSgNLxClLfvGy1EtQKUBRGjPCLUDCMRanZ+7fFGxq19kBkJUJooNHCLUK0A1UKLUXteUP0+oYMGDUJ2djZGjx7te83IMO6P19bx2dP8SiAqEdjOf+zQTVsNBSgNrxD9Ff7CXT4tRANEqBayOfisMcEnuacbz0biU1NvGQlQmuLYDG4RajbwkRajtrygDfp9Qju0Pb1ZCcQkYvqNK7iPvRevcUcPeIWoFejnt1nXDCJGeUWoEYYiFMZjGuTz+9O0GdVP9H4EPQFKk4wKbhFqZk+0GLXsBf0tgBYvcNqdPqGueEK1yYCfeuop3zY6GfCIESPcKN59zoB7/mXA2ANqhzKk+UbDm+UIJR5RHgEK2Mvf6SZWWpYAvwDdzLkfYC5AAZteUc4w/P79+62dtz0yAtxClFeA8jIeG7ER433vzSAeUVMBCvg9o06IUCfg9HwSeAWolX7cPNjyiubx7dYZ7Onr92ZzC1Er3Vd4PKLb4a/XR3AYNfGI8vQN5g3RmwlQp+Cpn6qQoBKiPAL0Y1wHgG8SDh5se0Vd9oICLvUJJcmAWbNNkGTADz/8sCuzUbQpDijLY/yHmPUP1fal4UnNZLX/Zg1iUY9o1eI0eiPVSYf4XRgWML+9HuQ3aS0BSmhpjkBLXvB5QQU6PCMvX389m/sQswpWKzqJGDXDbLCSit1Q99e0GzoPFt5yFY+rHQFqNjsbYO61IQzGPvy7+FK+ixBYZkvLOdjScg6eb3nQ0nFGfURpAcr6rIfZYCWaaiQgAs2qJVRUId63FIPfe04EthUBCvBNScxrT+Foxpa/tp2+oATXRGh7SAYcFEad+In4pHFAiOoZqpkQjUUtNmK8bzFCL4TuliilRaed3GaFSuZBXvGgFaBGXjUrAhQA8L3yu/A0OC0MRuo06NVXivikcUKI6nk9ee6l3t7jaIoLnDM6gN0669uCKNUjGtwxMpYH1EiIWsk//FLxA/KbSzh+oDzu03Yavn6PbSNEfNI4IUT1BCePEC3EgKDqAbdEKS06q2AvN+l2jMB2jOByBtEC1H+8vhC1Yk8//VWZNpc3YBcCLyjgcp/Q4uJiPPDAA/jyyy/x2GOP4aGHHkJERFufy46Nqk9olNInQlsBaYUni6f5y6T7h/K0FPVC87Fg5zxjVcJODiYqRXfTfcxG8enlTiPzUdOYPXyMPKDaEfq2BagWvUsKEKH8eUI7AgF9Qgna6J1GfGqZPp2/7xvdP5Qn7K63T28vOzVRBKthqidCaXgrBZ5HJ88+eqP4NbdwvcnUhWYheG3o1JYA1fKVjp3laT4b9AntiGj7hBK0IXmt+NTyYNjzlsoloXkeoakXmmc9ywnaPt3VHCF73hRFPOF/nqms9epX1m9Sb5gmMFCA0mhD87YEqBaj5wUtQl3sE+pqiqbMzEx8/PHH+Pzzz/Huu+8iOzsb69ebJ1NvN5BKh+X51MOGR5Q3VMHyiOoZCIAAz6gbo9mDRdvnhng+WTQZuCDNQvC0R9QxAQqwvaLCC6oPeW4zvJ8s7HhEeQQowB+aJwR4RnkEaFsgGsyMH9FH9A/h6QNKe0QdEaAA2yuax33qTgfxhrK8nyzseER5Q+68+9HY9Yy2JsTzyUJvbIaZAJXP6/eIOiJAAf30cCHyggIhyhPaoZMB84pPGgtC1EpfGYCvj6gWnjB9a2MkPmlYQpS3D6itAS9GApQQDr4QvUCGQ3zSWBGivAKUoLULPS8oDVeYvjUh46l0xCcNS4haGYTE00eUxlCAEi6p5wvRCwCYez+1WBWiVtCKM55nOtC2xWgN5JmXjMSnETwClMDTR5TGUIDStOLUwq7HxiVJwr59+1BQUKBKBvzWW2+5XbS70JmlrOlEfk4Ax0/II+szLuTvV/sR5mMePjT0gmopQyryNJ2mc1rZxUAPTIpnpLngwcogpLyWHFtlcBMO4HZ3i2jXkIlSvgFwsfOnH4EC3/SQY8E/UcZGjMd4bOQSoISIIiBg4pfWHiFPC+Mm2Mp3bHUUvKuN20vqgSXOD5rsMCiz0pat7onUd485fnqt4OJN37QdIzAC27kFKKEU6b551oHgppR2gjyoZ87jdWJEo8EXlrciQAGgFjH4CeNNp6C2TROAl9w5tR6ueEJfeeUV3H777Tj33HMRHx+PoUOH4pe//CU2b96MSy65BB999JEbxYYObWpT/unf/Zh5Q0+oPx7/j7U0T3Y8olrykONbQsUhZPkWuxBvqF0BmtqD8x/K4wWlIQK0dWcpbJtoBds31g4384Zq+1PxzFVO44iYSqWWUBFHLTYh3tBgBGgmik335/KC0hABapwTvXOyWv2xbEFPS4ebeUNZHj8rMyvZ8Rhq2YyxviVU5OEC3xIsdgQogSfPNrcXlOYl5bWb9UPt4srApA6dDPi8SiBCp2OuHY8oa6DSCcY6BV6P6PEj/oE2j/dZZLp/mYWakddDyjswiUdwWvGEklGIvJ5gPQ9o2QmDf6hdAUqj+kqddGDSLyuBaIPva9EjyhqoZJRrj9cjSofxeTyiEVYSgpRw7sc7MIlXcHLud2qIPCrYSjhUT7TrzbJmW4DSFFLvO+vApGs1A/00WPWIsgYqmQlIHo8ofX/kYI3p/rQH1AxWcngWvAOTeAUnryeUTJBiJcsMLUBp9DyiQQlQmpPKq4sDk0IyY1JHgEuEAsELUQMBSjATorQApdETo1YEqN5odRZmI98JPK06wFyIalNg8IhQsxA8U4g6IUBpqgAhQnWwEZanhShPsmczIWp5lLwVAcqR796HydzuPszbfjImIpSITwKvCDXzGmuFqCMClKYQQoTqYCcsTwtRXg+mkRDVuz+MxCivCLUS4udN5cRb55mJUO3sfLwiVE+AErRC1DEBSjiJ9js6vlMSTGieQ4ACcmheLzyvJ0AB4Kkjf8BTR/5g8eLaPusxkZmDrcbAePNacrj6gAaE5q0KUB5EeF4fi2F5wB+a551tZDPO0Q3PGw1kOpqYgaOJ7TjCoyNqTw2JDxCggPkUqLwDHOnQvGUByoMIz+tiNSwP+EPzVkLo32OypfA8AN0wtxUvaGuiTfNH+BjXMqeHNps2tBYxpgIUUDtxHBeggByad2vcC4QIbTtwClAarRA1EqA07VWManPE6YlPGpYQtToAibuPKAvegUj28iB3DmwIUTvT3WmFKO9I+nYvRhX0xCeNnhC12meWp48oE96BSJyzPXVG7AhRu304tUKU5z5xqs9la6MnPmn0hCiP+KThjSa2Rdpn5vi2zilYazlMA7BHeT/EWlHH/9MPGRcWcQtQGiJE7+nzF679rYTi3cTqNKQ0tkfA51mYDYkgRsI7h4UR80k5J7Ck4TYAwG1RSywVsxnnYCy2WE7lBMAnRPuBfxR9W8BMeGoZhl2q0LydQVsfYZ7lY8RIeOcoW8A/Yj48rBmrMQ0AME074okDMt+81fuECFEzD3xbw0x4aolGvSo0b1WAAkD+6xPkN1YV3UuWi3Ic0SeUE+4+oTQ8QnQaY51FIQoAGGgvyVdUfE3AuttS2BW3VRHqVJ9Q7ejHKBvzHMai1rYALVuu8RzwCFGrAlTyAtWdsA+bWZ9QLSZCNCknMKRgVYgCwL143fIxANDvOEOAlhoc0Bp9QlPUH0/1s+eG34VhjgjQ/GKOxNtWBWidF/hjJ7Qnkz6hWsyEaHhY4DPcjhAF9AfRGKGdrAQAJkJ/whs3+oQCxnXfWo231+60ofWIDk6A+i+Aj5csFNLsBfa5Y0/CE+omZh5RlgAFZK+oFSFaCKBQ+VdeEnzG2SWnb/O91xOkbuJ0yo1/F18KwF5YPUCAAvJUnCL5fJuDJUABYEnDbZaE6DDsxhrFC3MBx8hdU7Si0EiUukGK+S5W0Va8vLA8oNmZW/mEqCCksAQoAKzGNMtClJ5KU2/6Tl60kTAjUeoWdu9/PUqUfq9m/US1BAhQQM71aabsXrJUjKsIEeo2ekJUT4ASeIVooebzV/xilOUF1UIL0gdTrM0rbAU3cr0R8WkHpvikMRKiIgzvHjpheT0BSuANzw/TzLVpRYwyvaAsaFFqxRNqBReEJxBc5WsUgjcUoiIM7xp6YXk9AUqwEp7XzuVO+peaiVGWF5QFLUq7c+c7s47TwhPwi087MAUowUiIvmS7SFcQ4XhObIXjaWghaiZAteiJUa0AZWEgRnlEKKF3ivUpLWPAf/50i+4ho5C8kfjk8YaaClAarRC1K0BFON4alBA1E6BajISoVoRqMRKj3CKUYDA3OxMrQQ52Ok5dzELyRhXwAZPh6Fb6fwYIUbsCVITjLUELUTMBqsVIiGoFKAs9McorQglWp/UsLLE2Y9PEdH6Pq1lI3kh8mnlDDcVn4IWoeYn/UBUuhuPF6PhQQbSPVQEK+Act2eGrCL93lMKKAG0v/Lv4UlPvp2ECelgUoABUzxrhAQ0dyoh5qwIU8HtFtZgJUED2jK5hjNy1LEDbCWsx2dQDNMCgNWx1AFJ25lb/h2A8oH98yf6xnRAyYt6qAAX8XlG7sOZctypA2wslSDf1fhrlD7UkQAF1w/Ula4eq2Of8tK+EDitCt23bhhkzZqBHjx7weDxYvTqwtVZeXo758+cjKSkJycnJmD9/PioqKoxP/OO/7V9UMLaqFaI8XlAaHTHaXmmgDJVHfJpRtryndQFKsNcPvV3hmj0ttX9NdgQoYUnDbbpilAc9MdpeSSuqVn3mEZ9m2BoBD40QtcOi54I7PgS4Zk//sn9NdgQoYTWmBYhRHi8oDUuMtleaNCEyHvFphmUB6r+Y4Nj9c5AnMKbDitCoqChcddVV+PLLL3X3uf7661FSUoIDBw6gsLAQJSUluOmmm9y5oB2Qk9KbzRlvBBGiVgUoTQcTo3bEp9Ybalt8EubnAdflBXWKSvcamo7Q5uypDKi8qwcq7+oR1GmIEOXxgrLoaGLUrvjUekPtClAAyL9xAvC7jt0PtM3ZUy1Q+os+KP1Fn6BOQ4SoVQFK01GEKGBffGq9obYFKAB8BUD/Nmt1OkWfUI/Hg2+++QbTpvlbaocPH0ZWVhby8/MxatQoAHLrNDs7G4cPH0afPmpj9PW5wXIAscDkWfwXsIOxjjVnPC/Bashp6n95VLL5XLtu9wkF+PuFbqzxp4WprrA/3VBqj1POCFCahBzLp5C8/vurPfRhc9Se4isBTyJwp4ULKAtclfSmfa8oALwQ9Rvbx46G2nM3+jiHoLXaHxSw7tHg7Be6p19f33ur/eq0HMDA4AUojdXZj3xe0DoAizqvPV1h4QIYsxt3/7udG9QP7zzqLLafVovQmSkrTY+xc9+61Sf0hyP+RunQPsFlAohGffAClMaqA8vnBa0CMEykaHKS/Px8REdH+wwcAEaNGoWoqCjk5+cHGHkA33/BJ0RZAhSQPaJ2hCjt+AliIh+aBkrI8QjS1oAWno7wVTTK0BPoEsQ5tAIUAKrybAnR9k7Q9vQW+IQoQ4ACslfUrhAdFrUbS6CMnkfwKcm2Zgz1vecSpK0ALTyd4n3cGNTxAQIUkCtNXiHaDsLwvARtT5+DT4gyBCgge0XtCtHSjX1QCvn6zhm/ztY5aFaenul7zyNIWwNaeDrF7heUQXp26yitAAVkW+IVoi6H4QntToQuWLAA/+///T/d7VOnTkVeXp7pefyeTTXJycnwer0GR74HIFJ++/1/gcmL9HfVE6AEq0JUG3kkUWUrYnSaseO7LQlSx4UnAHzlUJiPJUAJVXl8QrRpFR64axV+/WugocF6ImcnaHV7ql8IkMkKXssF7s3V31dHgBLsCNFhUWqRaFWMar2gWtqSIOURntrZkHgIVnwCOgKUwCNEFz0HYC+AfcqK1umo3abs6ZNc4GoDe9IRoAQ7QrR0o1ocb9k4CQC/GNV6QbW0JUHKIzx3Hxlh2RvqE5/BwBKgBB4huvtnAHkAvlNWNAZ/TTq0OxH66quv4vnn9fNVRkZGcp0nMTERlZWBSfoqKipM3M03Aoj1f9TziJoJUIJdjyiNHTHKAS1I3co7qMWq8IxPruILyeuJzzpYb2kaCVBCVZ6pEJVqcgHIlYTX68Vrr71m8UKCp9XtKXqxHD4k6HlETQQoIRiPKI2TnlGCSpAeCY0gdcPjSdATn1k4hEPI4j6PoQAlGAlRnwd0sLIAsmH/yH0NTtHm7OlzsD2iJgKUEIxHlMaqGOWBFqRZKYccO68Rbng8Cbri02odZSRACUZC1OcBzVEWQA7H6zeugqHdidD4+HjEx9ubZo4mOzsb9fX1KCgowMiRIwEABQUFaGhoQHZ2trWTaYUorwAl8AhRnvEXZmLUxAuqR3yPMpTUWO9cXX0i1dL+O5SyHIXH82nFyHkEKKEqT1eISkbO9hDSJu1JK0Qt3hK8QlTrBWWxBLfpClEzL6geZ9XvxZls62NCmyNC87g284Y64fkkcAlQAkuItrEQfJu0p8+hFqKcApTAK0S1XlAWbojRhrpo7Cu2MRCq2lpU7IfKi4Eu1oeam3lDHfF8EngEKIElREMUgqfpsKPjJUlCXV0d6urqAACNjY2oq6tDU5N8E/Xt2xczZ87Egw8+iFOnTuHUqVN48MEHMWvWLPP+Niy+/0J+tSpACcGOnKdJA9+89R2Zr6KdC70Dsvi0IkAJVYHHtBUBaoWQ29NbyqvNNonZyHkeAUpYgtt8ntHOzPu4kVuAZuGQ4fb8GydYE6AEutJsYwLUCiG3p8+VV4sClGA2cp5HgNJs2TjJJ0gJZqH4jsbuF0bzC9A6k+1fwZoAJdCNulYQoEAHFqGHDx9GTEwMYmJiAAAzZ85ETEwMnn7a73J8//33kZaWhgEDBmDAgAHo1q0b3nvvPfuF2hWgNCwhajcLDS1GbXpB2wPxdN9Vu+LTyMjtiE+aKv/x7VGAAq1kTw44xYNN4URDi1G7XtD2wjDs8r23Ij55sCU+aYJJUddGaBV7silAaYJN4aSFJUY7IruP+AW2JfHJgx3xSWM1A4XDdIoUTU4QkKIpAGpC6K4ODKihw/NO1KPZ9v/NdkPkVsPxQZVVkeCM51Mblg9WgAIA/A9ZSWL3CWtPKZqcICCljJaqPP/7m3OCLo8Oz1vxgurxGu61fexZ9XttHWcnHF8cnmGrLEBOexOs+NT2DQ1agALA+39Q3sQY7NR+UjQ5gbk9UXOqXx1c0nRAncLJqhdUj6hB9lroDXU2n/sWw/EAbIXjCUP7bA9efGrrp2AFKAB8Qd4YeUJFiqY2Tnf1x/KNwQtR0k/UCQH6CYBPPP7PT/MLUsf7aDpM9btUv4Ng0i2xcFiAAoDH06grRAUKVXnqz0vzghaipJ+oEwJ0ItbhQyoX5nx8xH2sXQEaSj7Hla6c11kBCsiuPSMhKgCgFqAA8ElJ0EKU9BN1SoBiL9Cw1y9uoi5rpyEjFl/KMms3HPR+Ag4LUADoBWMh6g5ChAZNd/Zqp4Soc4NzqfPaE6RtBZXwdBoySMkFAUoQQtSAqjz2eoeEKN5xflS6XUEaCjKbj3N5Q90SnmSkvPMClCCEqCFaAUpwSIjiwaBOoUvDl21UkNZF8HlDv3RJWpH6yXEBSgi9EBUiNCh0BCghWCFa/gIwR3n/j1/bO8cnJtvbiSB1VXjS3Pqx8ibYkJVxPychRBlU5RlvD1aIxgM//kpOsXLeX9bYP48BbVmQanFLeKrKWHmdMydiClCCEKJM9AQoIVghWg/gGeX9o/ZPA5PgQJsVpFrcEp40byivQw33MocpQAmhFaJChNrGRIASyl8Guv6P9dOXv6D+PEf5bEWMbl0FkoeSC6cF6dpVwBQL5WuwLDzpVEs7VgHDLZTtE5/BQsTn1wCmG+4phChFVR7ffkufA3CudTGqyZpjR4xWrNqEmbn8kwo4LUhXr2rBtNzgxpIGIzxLVm1Deu4o8x3hhvg0y1gvhKgKMwFK+GQ5gAusi9F6zWe7YnTDKqAr/3PaUUH6/Spgsv36CYB14dkF/kGwe1YBQzjLf8N8F26+AAAzbRA6IdphR8e7C6cABQBskD2iVtAKUJo5L/gFqRn5q6yVS/OYRy1K7fC9vfKr300L3vO5g7PsWz/WEaCcD3EVtPfzG64jPB73ZqJoN1TlWdh5k/yy1Mox+vz4qwt8gtSMilWbbJfzIeapRKkd/vN1i+1jP8eVtgToePifXaWrCszLWXkdW4Da0aQq7yfPkHgHhn93BHgFKAB5VhzIXlEneAZ+QcrDBvt1VMOXiSpRapkfgqgfv4wI3vO5l6P8N8AWoHZ7Ffk8oDzfvZfNQqwhPKGW6WbvMCJEzcLzRgKUZs4L5l7R/QDG8J2OSSNQfVMQYvCMB9hgTchWB5vglDfxPJfnswT8YXn7aUY8nkYwJkfpHFR/DyDO3rG84XmO3OFuh+kBYCy24Ivo2baP3xe2G5+En+PgFTkHl+fzOsjJRXgwDL8b0ck9otWlADhmkGPBG57XekFZ8HpGyznOZUQhgEKbo+OPAfjBzoFByibaG2qEmfdzN/jD8obhdyPc94gKEcqJP5PVGYtHNqqPKf8PkDzWYH+eu1NhzmL59f372NtbGoDGIEIWwTrpGhuAGhvlB+vQkAA0NQC1jLLv+4fFk/H8vycA0JZVz1inT1KSnOu0s2RM83/PGotHauxp6b+A+ZP1d48HwB9Bx493ySJv3HNrA7Z1bziCeq+NmVkUaoI0qKaGFtR67Z2jjks5sGlU/kctDY1o9Kr/X//6+mrlHee9zvO/+PiPjJXN4H821gGQG7+dz56qDPcLpEF9zCdVwOUGkT6rt9ETyutDOtv1ntO82L+tgyvbQjXNpB5AcwNQxyj//yycZyeAs0z2YQ5iagB//ZQI4DgAd+xJ5Anl5Oeff0bv3r1b+zIEHZyjR4+iV6/QhEFaE2FPglAg7EkgcA437EmIUE5aWlpQXFyMhIQEeDxB9pUUCDRIkoSqqipkZmYiLKzjd9UW9iRwE2FPAoFzuGlPQoQKBAKBQCAQCEJOx28iCgQCgUAgEAjaHEKECgQCgUAgEAhCjhChAoFAIBAIBIKQI0SoC2zbtg0zZsxAjx494PF4sHr16oB9ysvLMX/+fCQlJSE5ORnz589HRUWFK9eTl5cHj8eD+Ph43+LWiFFJkrBo0SJkZmYiLi4OU6ZMwY4dO1wpS8sTTzyB8PBw1fecO3eua+UtX74ckydPRmJiIjweD5qa1HMKFxQUYMqUKYiLi0NmZiaeeOKJTpMyxkk6sz0BrWdTwp46JsKeOkcd1V7sSYhQF4iKisJVV12FL7/8Unef66+/HiUlJThw4AAKCwtRUlKCm266ydXrqqioQHV1Naqrq/Hzz+4koH3++efxzjvvYNWqVTh16hQmTZqE3NxcVFdXu1KelvPOO8/3Haurq7Fs2TLXyuratSvuuecevPTSSwHbqqqqkJubi0mTJuHUqVNYtWoVlixZwtxXYExntiegdW1K2FPHQ9hT56ij2o09SQJXASB98803qnWHDh2SAEj5+fm+dfn5+RIA6fDhw45fw5o1ayQAUmNjo+Pn1pKVlSW99NJLvs+NjY1SWlqa9N5777le9qJFi6RJkya5Xo4W1u/77rvvSt26dVOte+mll6T+/fuH/Po6Ep3NniSp9WxK2FPHR9hTx6+j2ro9CU9oK5Cfn4/o6GiMGjXKt27UqFGIiopCfn6+a+X269cP6enpuOiii/Ddd985fv7KykocOnQI5557rm9dREQERo8eja1btzpeHoutW7eiW7du6Nu3L+bNm4eioqKQlKslPz8fo0ePRkSEf1KycePG4eDBg/B6g5ghRBBAR7UnoPVtSthT50PYk7u0BZtqS/YkRKgFFixYAI/Ho7vk5ORwncfr9SIpKSlgfXJysqUbgPd6hgwZgvz8fBQVFaGwsBAzZsxAbm6u4w8Ucu3Jycmq9V27dg3JjX311Vdj165dKC0txfr16+HxeDBt2rSQhVlovF4v83cg2wTCnnhoTZsS9tS+EPZkjqijZNqSPYm54y3w6quv4vnnn9fdHhkZyXWexMREVFZWBqyvqKhAYmKi49fTo0cP9OjRAwCQkJCABx98EF9++SX+9re/ITs7m7s8M8i1azuwl5eXo2fPno6Vo8fw4cN973v27Il33nkHSUlJWL9+PaZPn+56+TSJiYkB/ZrKy8t92wTCnnhoTZsS9tS+EPZkjqijZNqSPQkRagEymi1YsrOzUV9fj4KCAowcORKAPFKtoaHBktEFcz1hYWGOj4RLSkpCVlYWNm3ahPPOOw8A0NTUhPz8fNxwww2OlsUDaXE7/T15yM7OxocffoimpiZfyGPz5s3o37+/qDQVhD2Z05ZsSthT20bYkzltyZ6A1rOpNmVPIe+F2gloaWmRamtrpdraWgmAtHLlSqm2tlbVCXjmzJnSxRdfLJ08eVI6efKkdPHFF0uzZs1y5Xq++uor6eDBg1Jzc7N05swZ6aWXXpKioqKkTZs2OV7Wc889J/Xu3Vvavn27VFNTIy1cuFDKzMyUqqqqHC9Ly8cffyydPHlSkiRJOnHihHTDDTdIffv2lbxeryvlNTU1SbW1tdKqVaskAFJ1dbVUW1srNTc3S16vV+rRo4e0cOFCqaamRtq+fbvUu3dv6c9//rMr19KR6cz2JEmtZ1PCnjomwp46Rx3VXuxJiFAXKCoqkgAELIsWLfLtU1ZWJs2dO1dKTEyUEhMTpXnz5knl5eWuXM+TTz4p9e7dW4qNjZVSU1OlnJwc6dtvv3WlrJaWFunxxx+X0tPTpZiYGGny5MlSQUGBK2VpmTVrlpSWlibFxMRImZmZ0nXXXSft37/ftfKWLl3K/D+vWbNGkiRJ2rZtm3T++edLMTExUnp6urRo0SKppaXFtevpqHRme5Kk1rMpYU8dE2FPnaOOai/25JEkke1XIBAIBAKBQBBaxOh4gUAgEAgEAkHIESJUIBAIBAKBQBByhAgVCAQCgUAgEIQcIUIFAoFAIBAIBCFHiFCBQCAQCAQCQcgRIlQgEAgEAoFAEHKECBUIBAKBQCAQhBwhQgUCgUAgEAgEIUeIUEGHoaWlBZMnT0ZaWhoee+yx1r4cgaDDI2xOIOBH2EsgEa19AQKBU4SFheGjjz7Ct99+i8LCwta+HIGgwyNsTiDgR9hLIMITKvCRlZWFLl26oEePHq19Kbbp3bu37ra5c+ciLi4OHo9HPAAEbQJhcwIBPx3ZXjqrrQgR2s4ZOHAgXn755YD1zc3NGDNmDG677TZL53v11Vdx4sQJ3+ecnBx4PB689dZbqv2qqqqQkJDQrgxm2bJl2LlzZ2tfhqCVcNpWnELYnKCj4obNdVR76ay2IsLx7Zzs7GwUFBQErH/11Vfx888/Y/Xq1UGXMWzYMLz11lu48847fevef/999O3bN+RGM2HChIB1GRkZ+Oyzz0J6HYL2RyhsxSmEzQk6AqGyOWEv7RfhCW3njB49Gtu2bVOtKy4uxuOPP44XXngBKSkpQZcxa9YslJSUYOPGjb51b7zxhsrgCTk5OfjlL3+Ja6+9FomJiejVqxeWL1+O7du347zzzkNCQgLOPfdc7N27V7e8V199FQMGDEBCQgLS09OxYMEC37YNGzYELMK4BTyEwlacQticoCMQKpsT9tKOkQTtmi+//FLq0qWL1NTU5Ft3zTXXSNOmTbN8rr59+0pvv/22at3UqVOlRx99VPr9738vLViwQJIkSfr++++lPn36SAcOHJAASPv371ftn5SUJH333XdSc3Oz9NJLL0mxsbHSzJkzpaKiIqm+vl666qqrpOnTpzOvYd++fVJMTIy0fft2SZIkqaqqSvruu++4v8N1110nDRs2TMrKypIuuugiqaGhQbW9qKgo4JoFnQMnbcUphM0JOjJO21xHt5fOaCvCE9rOGT16NOrq6rBv3z4AwDfffIMVK1bgjTfe8O3z2GOP4eyzz0ZOTg5ycnJw8803Wy7n9ttvxz/+8Q9UVFTgjTfewO23346wMPbtM2fOHEyZMgVhYWFYsGABampqcP311yMrKwtRUVGYN28efvrpJ+axERERkCQJO3fuhNfrRXx8PKZMmcJ9naRfTVFREVavXo3IyEjL31XQMQmVrZBzTJ06FcOGDcPbb79t63qFzQnaO1ZsTthL50SI0HZOZmYmunfvjoKCAtTX1+Pee+/Fo48+ioEDB/r22bp1K/76178iLy8PeXl5WLp0qeVyevXqhQsuuADPP/88/vnPf+LWW2/V3TcjI8P3Pi4ujrmuqqqKeWy/fv2wfPlyLF26FH369MG4ceOwbNkyy9crEGgJ1lZKSkrQ0tLi+1xTU4PKysqAcsg5vvvuOyxfvhyvvfaaresVNido71ixOWEvnRMxMKkDkJ2djW3btmHPnj2IiIjAww8/rNqen5+P4cOHB13O3XffjZkzZ2LOnDnIyMjAoUOHgj4ni8svvxyXX345mpqa8Omnn+K6667DOeecg7POOsuV8gSdh2Bs5auvvsKqVavw3nvv4cyZM5gzZw4effRRXHDBBbrn2LBhA3OgAi/C5gTtHSs2J+yl8yFEaAcgOzsb//rXv7B//36sWrUKUVFRvm2lpaUoKyvD5Zdf7lv3wQcfoFevXpbLyc3NxTfffIPBgwc7ct0s9u7diwMHDmDKlCmIj49HUlISACA8PNy1MgWdh2Bs5aabboIkSbj66qtx+vRpLFq0KECA0ueorKxEdXU1Nm3aZPt6hc0J2ju8NifspXMiwvEdgNGjR6OgoADz5s3D5MmTVdu2bt2KGTNm+MKLeXl5tgQoAHg8Hlx00UW2j+ehoaEBzzzzDHr27InExET85je/wXvvvYcBAwa4Vqag8xCsreTm5uLgwYNoaWnB+PHjA85Pn2Pr1q249NJL8e6779q+XmFzgvYOr80Je+mceCRJklr7IgTu8eyzz6KpqQmPPvqo6b6DBw9GcXExkpKS8PPPP4fg6kLL/Pnz8eWXX6K+vh67du1C//79W/uSBG0IM1s5fPgwrrvuOrz++us4duwYXnjhBXz22Wc+TwjrHHPnzsWsWbMwb9485jmFzQk6M8Je/HRWWxEitINz3XXXoaCgAN27dwcAnHPOOfjzn//cylclELQ9zGzl448/xsiRIzF06FAAQF5eHgA576D2HD169EBTUxMmT56Mp59+Gh6PJ2TfQyBoLwh7EQgRKhAIBAKBQCAIOaJPqEAgEAgEAoEg5AgRKhAIBAKBQCAIOUKECgQCgUAgEAhCjhChAoFAIBAIBIKQI0SoQCAQCAQCgSDkCBEqEAgEAoFAIAg5QoQKBAKBQCAQCEKOEKECgUAgEAgEgpAjRKhAIBAIBAKBIOQIESoQCAQCgUAgCDmdQoS2tLRg8uTJSEtLw2OPPeZbX15ejokTJ2Lq1KkYO3YsPvjgg1a8SoFAIGh7iOenQCBwi04zd/zRo0fx7bfforCwEE8//TQA+eHa0tKCiIgIVFZWYujQoSguLm7lKxUIBIK2hXh+CgQCN4ho7QsIFb179w5YFxYWhrAw2RlcXV2NESNGhPqyBAKBoM0jnp8CgcAN2kw4fvny5Zg8eTISExPh8XjQ1NSk2i5JEhYtWoTMzEzExcVhypQp2LFjR9Dlnjx5ElOmTMHIkSMxZ86coM8nEAgEoUY8PwUCQXukzXhCu3btinvuuQe1tbW49dZbA7Y///zzeOedd7Bq1SoMHDgQTz75JHJzc7F3717Ex8cDACZMmBBwXEZGBj777DPdcrt164a1a9eitLQUY8eOxdVXX42UlBTnvphAIBC4jHh+CgSC9kibEaG5ubkAgLy8POb2119/HQ8++KAv5PPUU0/9f/bOOzyKqovD79b0ngApdAKh96Y0RUEURBALKvYKFlQsgAo2LPjZELuCFWwUUSA2mhTpPZDQ04D0vnXu98dNIRAgZVtg3ue5z8zOzM69O7tz9zfn3nMOn3/+OQsXLmTcuHEAbNiwoUZ1ms1mvLy8APD398fLywsfH59afgIVFRUV96D2nyoqKvURjxGh5yIvL48jR47Qq1ev8m16vZ6uXbuybdu28k70XIwdO5adO3dSXFzMhg0bWLZsGTt27GDSpEnodDrMZjMvv/zyWTtRRVFIS0sjICAAjUbjsM+moqLifIQQFBQUEBUVVT6P8WJB7T9VVFTqgjP7z3ohQvPz8wEIDg6utD0kJKR83/mYN2/eGdt69erF6tWrq/X+tLS0Kifnq6io1B+Sk5OJiYlxdzNcitp/qqioOAJn9J/1QoQGBgYCkJubW2l7Tk4O0dHRLmlDQEAAIL+Esva4mylTpjBjxgyPOWdN31ud4893zLn213SfM65nXVC/X8d9v/n5+TRu3Lj8Pr6YUPtP9+Bp/YmzUT/vhYsz+896IUKDgoJo1qwZmzZtom/fvgDYbDa2b99eraEkR1A2hBQYGOgxneiIESMc3pa6nLOm763O8ec75lz7a7rPaDR6zHcL6vd7vv212XcxDgWr/ad78LT+xNmon/fCxxn9p8dMjrLb7ZhMJiwWCyAnvZtMJhRFAWD8+PG89dZb7N69m5KSEqZNm4bBYGDUqFEubeeUKVOIj493aZ1no8wZwVPOWdP3Vuf48x1zrv213ecpqN+vY77f+Ph4pkyZct621GfU/lNFRcUZOL3/FB7CnDlzBHBGWbFihRBCCEVRxPPPPy8aNmwofHx8RP/+/cXOnTtd1r68vDwBiLy8PJfVqeJali9f7u4mqDiJC/3+VftPz+Ni60/Uz3vh4sz796JJ21lX8vPzCQoKYsKECYwYMaJeWNJUVFTkk/ySJUuYPXs2eXl5F90Qmieg9p8qKvUTZ/efqgitJmWdqPonpqJS/1DvX/eiXn8VlfqLM+9fj5kTeqGwdu1aOnfuXD4X63T69evH9OnTASgqKqJp06YkJia6sIUqKioqnsn5+s9T2bx5M3FxceXzYFVUVOofqgitIeebWP/II4/w4osvViugq5+fH0888QRPPvmkI5uooqJyCheDY1J9wZH9Z2qqP0VFQTz//OuObKKKisopOLv/VIfjq0l1zNErVqzg1ltvJTk5GZ1OV+Ux/fr144orrii3hubk5BAVFcWOHTto3bq1s5qvonJRow4HuxdH9Z9l/PXXIUaOnE/37oWsWfMhDz30DS++eDkREX7OaL6KykWNOhxfT/jll18YPHhweQdqs9l4+umnadSoEREREUyePPmM94SEhNCzZ08WLlzo6uaqqKioeAyn958AX3/9NZ07dyYoKIj27dszf/58/vsvhdGjf+CTT4bzxx8vYDCUsGvXdlq1msUbb/xLSYnVjZ9CRUWlJqgi1IFs3ryZDh06lL9+8803+fHHH/nnn39ISUlBr9fz33//nfG+Tp06sWnTJlc2VUVFRcWjOL3/nDt3Ls899xxffPEFOTk5fPLJJ9x7731ceeWrvPLK5dx2Wye8vb1p3TqWm24KYeHCm/jhhz3Exc3mu+92oijqIJ/KxUNJiZWVK4/wyiurmTt3O9nZJe5uUrVQRWgNOdecpuzsbIKCgspfz5kzhyeffJJ27drh5eXF9OnTCQkJOeN9QUFBZGdnO63NKioXM+qcUM+hJv3n22+/zdSpU+nRowdarZaYmA4I0Z5WrY7x6KO9y48r6z8vv7w5mzffzyuvXMazz/5Nr16fsWrVEWd/JJV6ghCC7duPM2/eLvLzze5uTp0pKrLw558Hee65fxgwYA7BwW9wyy2/sHPnCWbP3kTDhm8xZMg3fPLJZk6cKKx1Pc7uP+tF2k5PYsaMGWedExEaGkpeXl7565SUFJo3b17+WqfT0aRJkzPel5eXR2hoqOMbq6KiwtChQ+nbty+zZ892d1MuemrSfyYlJfHkk0/yzDPPIAQUFlrQ6QSNGl1W6X2n9p9arYZx4zozZkw73n13AyNGzOPyy5vzxhtX0KZNuPM+mIpHUlJiZcWKIyxZsp/ffksiN9dEkyZB3H//b9x2W0fGj+9Jx44N3d3MapGfb+bff4+xevVRVq06yubNaURFBTBwYFPuuKMzX345kpYtQ8pTax45ksuCBQl8/fVOHn54GZde2pjrr2/L6NFtiY6u/rxOZ/efqgh1IN27d2fPnj3lr2NiYjhy5Ej5a7vdTnJy8hnv27VrF8OGDXNFE1VUVFQ8ktP7z0aNGvHiiy9y7bU3MmjQXDp2bMhXX12HVluRv9pkMpGUlESPHj0qncvHx8Dkyf25555uTJ++ki5dPuGee7oybdrAC955KT/fzF9/HcJo1BEXF06zZsHo9RfPoGd6egG//57EkiWJ/PXXISIifBkxojVffHEtAwc2xctLz+bNaXz00SZ69/6c7t2jGD++B6NHt8XLy3MkUWZmMevWJbNq1RFWrTrKtm3Had48mIEDm/Lgg90ZOPB6mjULPuv7mzUL5okn+vLEE31JTc1n4cJ9/PJLAo8/Hk/PntFcf31brr++Lc2bnzk660pU7/hqcl7vMIuFv1eu5Pa77uLYsWPodDpeeeUVvvjiC5YtW0bz5s2ZMWMGM2bMYOrUqeXe8bm5uURGRrJt2zbi4uJc+6FUVC4SVO9491Kd6//3339z++23l/ef7733HrNmfYCf3800bhzH/PmjSEjYg1arpXv37gAsW7aM+++/nyNHjpzToz4hIYNnnvmLVauOMmVKPx59tDc+PganfFZ3kJFRxK+/7mfhwn38+echWrYMQafTkpiYBUBsbChxceGVSps2YQQEeLm55XWnbJh9yZJElixJZNu2dHr3jmHEiNYMH96a9u0jyq2Dp5OTU8JXX+3gww83kZtr4t57u/HAA91p2jTYJW0vKrKQlJRNYmIWSUlZJCbK9cTELLKzS4iLC2fAgCYMHNiMgQOb1siCeTZOnixi8WIpSP/++zAdOzZgzJh2XH9927OOFjiz/1RFaDU5b9q5jz6CDz6gq9nMtLfe4rrrrsNqtfLss8/y7bffoigK9957L2vWrKkUoun999/njz/+4LfffnP9h1JRuQhQ03a6n+qm7ezatSvTpk3juuuuw2Kx07PnIxw6tBSdLhe9Xk+HDh146aWXGDBgAACjR4/mkksuYdKkSdVqxz//HGbSpD84cCCbRo38CQvzJTTUh9BQH8LCKi/lum/5tsBAr7OKGXeQnJzHwoX7WLhwH2vWHKVHjyhGjYpj1Ki2tG4dBoDdrnDkSC779mWeUrLYty+TzMxioqICSkVpWLk47dkzmuBgbzd/unOTm2ti7dpjLFmSyG+/JZKfb2bo0FYMHx7L1VfH1tjarSiCf/45zEcfbea33xIZMqQl48f3YOjQVpUs77XBYrFz6FDOKUJTis2kpCxSUwsIDPSideuw0hJKbKxcj40NJSjIud9DTk4Jv/66n19+SeCPPw7y3HMDeO65AZWOUdN2egjnfRKwWuHDD/n3ueeYoNGwbcMGtO3anfOcRUVFtG/fnvj4eNq0aeOklquoqKiWUPdS3ev/77//MmHCBDZv3sLtty9m//5MVqy4o8o/4y1btnDLLbewc+dOvLyqb9FTFMHOnSfIzCwmK6uY7OwSsrNLyMqqvJTrcr/dLtDpNISH+9K6dRht20rB1rZtBHFx4TRpElRnsVId9u/PZMGCBBYu3Me2bccZMKApo0fHMXJkHDExNftdZ2YWs39/ZXGakJDB0aN5DBjQlJEj23DttW3OOeTrCrKzS9i6NZ2tW9PZsiWdLVvSOHgwhxYtQrjmmlhGjGjNwIHNMBrPHVu2uqSk5PPZZ1v49NOt+PoaePDB7tx1V1fCw30rHWezKZw8WURaWkF5SU8vWy8s35aRUYSXl55WrULPEJqtW4cREeHrEQ83BQVmioutNGzof8Y+1RLqAVT7S8jKgunT4fPP4f77Ydo0UJ2OVFTciipC3UtNrr8QggkTlvL334dZs+YuGjRw7xxOIQQFBRaysoo5caKoXLglJMjlgQPZGI062rQJP0WcymVsbBje3rWfZyiEYNu24yxYkMCCBQkcOpTDkCEtGT26LSNGtCYszPf8J6khR47ksmTJfhYv3s+qVUdp2za8XJB27x7lVLGdlVXMli2VBefhw7k0axZM9+6RpSWKbt0izxCFjsZqtbN48X4+/HAT69Ylc/XVsVitSrnQPHGiCEURRET4EhUVQGRkAFFR/kRFBZzyuqK44iHFWagi1AM435dwNPcoiVmJXNnySrlh71548kn47z948UV48EEwXDhzkFRU6hOqCHUvNbn+zz//D3Pn7uDff+9y2dy8umCx2Dl4MLtclJYt9+3LpLjYSvPmwcTGhqHXa7HZlPMWu71ivajIitVqZ/jw1owaFcewYbH4+xtd9tlyc00sW5bEr78msnRpEv7+RkaMaM3IkW247LLmtRLYVqud9PRCUlPzSUnJJzExi61bj7NlSxpHj+bRokVIJcHZtWsjp4jtmpCQkMGiRfsIDPSqJDIbNfJ3mAXWk1FFqAdwvi9h8b7F3LrgVpbduoz+TftX7Fi2TIpRIeDtt0H1gldRcTmqCHUv1b3+77yznldfXcOaNXfRtm2EC1voeIQQpKYWkJCQwYED2QgBer22RsVo1NGxYwOP8Nq2WOysXn2UxYv38euviWRnlzB0aEuuvbYN11wTS1iYL0VFFlJTC8oFZmpqwWnLfI4flzErIyMDiI4OOEV0SsEZEuLj5k+qcjqqCPUAqjOx/tMtn/L0n0+z6s5VdG7UuWKH1QqffCKH5nv2lGL0PPNFVVRUHIPqmOR+qtN/zp27ncceW87ff99Ojx5RbmilSnURQrBjxwl+/VUO2+/YcZyAAC9yc00YjTpiYgKJjg44bRlITIwsjRr5X1Rho+ozqmOSh1DdJ4EZa2Ywa+Ms1t69lhYhLSrvzM6Gl16SgvSee+Tc0XA1gLKKirNRLaHu5XzXf9Gifdx66wJ++20sl13WvIozqHgyKSn5ZGYWEx0dQHi4ZzjaXKz88Uc6R44UEhRkICjISFCQgeBgY/lrX19djb8fZ/af7rfxXyD8+y/s2weT75lMRlEGV35zJWvvXksj/0YVB4WGwrvvwkMPwaRJEBsrBemNN7qt3SoqKiru5J9/DnPbbQv4/vvRqgCtp5RZOFXcR26uhUcf3cxvv6XSq1cYeXlW8vIs5OVZyc21UFxsB0Cv15SL09MF6qhRMYwc2dil7VZFqIOw2+Gpp+CmmzT8b+j/yCzJ5Kpvr2LlnSsJ9g6ufHCbNrBkCcyfLz3oBw+GsDC3tFtFRUXFnWi1Gj7+eDgjR6rJOlRUasPffx/nrrvW0759ELt3X0NU1JmOXFarQn6+FKa5uRUCtUyk5uVZ8fV1vSRUh+OryfnM0ULApZfC6NHSyGm1Wxn1wyjyzfnE3xaPj+Esk62HDYO2beU8URUVFaegDse7F/X6q6g4nuJiG5Mnb+fLLw8yc2ZXHngg1ilTIZx5/6ozgx2ERgPPPiu1pNkMBp2BH2/4EUUo3PzLzdgUW9VvfPNNmW3p0CHXNlhFRUWlHvPvvye58871bNmS5e6mqKi4nE2bsujWbRmbNmWxbdvVPPhg63o5F1cVoQ5k+HA57fPrr+VrX4MvS8Yu4XDOYe5bch9VGp07doSxY2HqVNc2VkVFRaWeYrMpPPDARo4eLWLAgD+54oq/+eOP9Kr7WBWVCwirVWHatJ0MHPgnd9zRnDVrrqRVqwB3N6vWqCK0hkyZMoX4+Pgq92m18Mwz0rhpl3OACfEJYflty1l5ZCVP/flU1Z3kSy/Br7/Cpk1ObLmKysVJfHw8U6ZMcXczVDh3/1kTPv30ADabQnz8ZRw7NoqBAxtw661r6dZtGfPmHcFmUxzQWhUVzyIhIY++feNZsOAY69YNYfLkDuh0zpVxzu4/1Tmh1aS6cyKsVun0/uablZ3eD2Qf4NIvL+WJPk/wTL9nznzj1Kmwdi2sWCHH9lVUVByGOifRvTjy+ufkmImNXcLcuX0YPjymfHtxsY25cw/x1lsJCCF44om23H13S/z8VP9blfqNogjef38/U6du5+GH2/DSS53w8nJdpiZ1Tmg9wmCQjkmvvSadlcpoFdqK+Nviee3f1/hi6xdnvvGZZ2DPHvj9d9c1VkVFRaWe8dJLu+nWLYRrromutN3XV8/48a1JTBzB6693Yc6cgzRtuojp03eSmWlyU2tVVOrG0aOFDB78N++/v5/lyy/njTe6ulSAOhtVhDqBu++G1FT444/K27s06sLimxfz2PLHWJiwsPLOwECZUenpp8F2FicmFRUVlYuYffvy+PjjJN5+u/tZnTD0ei033dSMLVuGMX/+paxbl0HTpot45JFNHD5c6OIWq6jUDiEEc+cepFOnpcTGBrBjx9X079/A3c1yOOpwfDWpqTn61Vfhzz9h5coz95Xlmf/tlt8Y1GxQxQ6rFdq3lwFH77vPYW1XUbnYUYfj3Yujrv/w4Sto2tSP2bN71eh927Zl8+abe1mwIJnRoxtz//2tCA/3wsdHj4+PDh8fHb6+ery8tPXSw1ilahRFnBEbs7DQhkYjH1Z0Os15SsUxRqOWiAj5m3E0Foudo0eLOHiwkAMHCjh4sJAtW7JITCzgiy/6nGH1dzVq7ngPoKZfQm4uNGkC8fHQt++Z++dsm8PE+ImsuGMF3SK7Vez45Rd4+GFISgJ/f8d9ABWVixhVhLoXR1z/+Pg0br55LUlJIwgP967VOQ4fLuTttxNYsCCZoiIbJSV2LJYKJyaNBry9dZWEadm6j48ePz8dYWFeRER4Ex7uRUSEF+HhXqXrcltwsBGtVhWy1eHQoQL27MnDbhfY7QJFqbyU61S5z2JRKonLqpYFBVaEkN9rUJCR4GADfn56hKD8/LIop70+s1itCkJAUJCBRo18iIz0oVEj79OWFdvDwrwqPdAUFlo5dKhCZB48WFAuOo8dK0av19C8uT8tW/rTsmUArVr5c8stzWr9W3ckqgj1AGrzJTz9NOzfD4sXV73/9X9f56PNH7H9ge2E+ITIjWVR76+6Cl54wUGtV1G5uFFFqHup6/W32RQ6d17Kffe1YuJEx2ZWstsVSkrslUpxse2U17bSbXaKimxkZZnJzDSTkWEiM7NsXb42mxV0Og1hYV5nFalnFu9a5fOuj9jtCuvXZ/Lbb6ksWZJKUlIBbdsGYjBo0WqlxVGrpXSpqbQ8fZvRqK2UdrIi/eTp24z4++vr/GCgKIKsLDPp6SUcP24qXZZU+bqgwIbBoKVhQ/mdp6eXcOKEiYAAfanADCgXmy1b+tOqVQDR0T5O93SvLaoI9QBq8yWkp0OLFrB5sxxlPx0hBCPmjcDX4MsPY36o6ITWrpUiNCkJGjU6840qKio1QhWh7qWu13/27P28//5+du26BqPRM50yhBAUFdlOEaaVRWrZemZmxfasLAuKIvD21p0hWsPDvQkNNaLRyNiQVqu0xlksSulrpdL201/7+OgIDDSU5wUPCjKc97UzHF7y863Ex6exZEkqS5emAXDNNVGMGBHDkCGRBAYaHF6nuykqsnHihBSkGRlmGjXyoWVLf8LDverlw4YqQj2A2n4JDzwAJSUVAexPJ6Mog04fd+Lly17m3m73VuwYPRoaNpTZlFRUVOqEKkLdS12uf3a2mdjYX/n660vcPjfO0SiKIDfXcoZorRCpZoQAo1GLwVBWNOXrZ9tuMGgpKbGXz4csyxGel2cp3Vb5tdkspyQYjVqionxo0UJa6Vq08C9dl8uQEK9qfa7DhwtZsiSFJUtSWbXqJLGxAYwYEc2IEdH06RPusRY/lapRRagHUPYlTJgwgREjRjB06NBqve/AAejQQQ7LN21a9TF/H/qbkfNHsum+TbSNaCs3JiZC586wbRvEOXb4SUXlYiI+Pp4lS5Ywe/ZsVYS6idr2nwATJ24mISGf5csvq5dWpPqA2WwvF6WpqSUcOiTnLB46VFi6XkhWlpngYGO5ID1dqKanl5QLz/378xk4sCEjRkQzfHg0LVvW34w+FzvO7j9VEVpN6vIkcPPNEBEBs2ad/ZjJf01m6YGl/Hfvf3jrSyciT5ggYz0tWlT7hquoqKiWUDdT2+u/b18eXbsuY/Pmq2jfPth5DVQ5L3l5Fg4floK0QpxKoXr0aBGBgQauvloOsw8dGklQkNHdTVZxEKol1AOoy5ewfTtccgkcOQINzhLmy2q30m9OP3pH9+b9Ye/LjSdPQqtWMoB9//51ar+KysWMKkLdS22v/zXXrKB5c38++KCnE1unUldsNgWNBnWY3c0IIdi5s5joaCPh4Y6ba6tmTKrndOkCgwbB+++f/RiDzsD3o7/nqx1fsWT/ErmxQQPpYv/UU5XTL6moqKhc4Cxfnsa6dZm8+GIndzdF5TzImJuqnHAX+fk2PvroBN267aZv3700b76d559PJjfX8xPfqL8aF/Hss/DBB5Cff/ZjWoa25KNrPuKuxXeRmp8qNz7xBCQnw88/u6ahKioqKm7GalV44oktTJ/ekbCw6jnDqKhcbGzeXMh99x0iKmobX36ZwYQJDTl5sht//BHH2rWFNG++nddeS6WoyO7upp4VVYS6iP79ZZimTz4593G3dLyF4a2HM27hOOyKHXx94aWXYPJksFhc01gVFRUVN/LJJ0koCowf3/qsx5w4YWXZslzUGWUqFxMFBXY+/fQk3bvv4rLLEtBqNaxe3Y5Nmzpw770N8PfX0bdvAP/805aff45l8eIcWrTYznvvHcdkUs5fgYtRRaiL0GikNfTtt8FkOvexs4bNIiU/hTfXvik33HkneHvDxx87vZ0qKioq7iQ728y0aTt5++1uGAxV/0WZTAojRuznuusS6d9/L5s2qTnhVS5stm0r4sEHDxMVtZVPPjnBAw80JC2tG5980pxu3fyqfM/gwUGsX9+ezz9vwZdfZhAbu4PPPjuJ1eo5YtTxSVBVzso110B4uIwZev/9Zz8uwCuAedfPY8DcAVzW/DL6xPSBN9+E22+XJTjYZW1WUVFRcSUvvriLnj3DGDYsqsr9QggeeOAwOp2GtLSuvPvucQYNSmD06BBmzGhM48bq8L2Ke8nPt7FsWR6LFuXw1195eHtradTIQKNGBiIjDTRqZDxlvawY8fGp/NBVVGRn/vwsPvnkJHv2lHDLLWGsWNGWHj2qn9Jbo9EwYkQI11wTzE8/ZfPCCym88UYaL74Yw803h6HTuTfsmeodX00c5R327bcwfTrs2wf68zwCvL3+bWZtnMX2B7YT5BUIgwdD797w2mu1rl9F5WJE9Y53L9W9/gkJeXTrtowtW4bRrl1Qlce8+246b76ZzubNHYiKkmGAjh0zM2VKMgsX5vDkk5E8/XQk/v6emVlJ5cIkLc3Cr7/msGhRDv/8k0+bNt5cd10Iw4YFIwQcP24lPd3C8ePW0nUrx4/L1ydOWLHbIShIVy5Og4P1/P13Hi1aePPAAw249dYwAgPrbje02QTffJPJiy+m4O+v4+WXY7juupBzxuB1av8pLgLsdrvo16+fCAsLE1OnTj1jf0ZGhggODhafffbZWc+Rl5cnAJGXl1entlitQjRrJsT8+ec/1q7YxVXfXiVu+ukmoSiKEJs3C+HjI8SxY3Vqg4rKxYaj7t+LEVf2n8OG/SMefnjjWff/+Weu8PXdKDZsKKhy/4YNBeKSS3aLyMgt4ssvTwq7XTlnfSoqtUVRFLFnT5GYMSNF9Oq1S+h0G0T//nvE//6XJg4cKKnRuWw2RZw4YRE7dhSJ5ctzxNy5J8Wbb6aKDRsK5H+/EzCZ7OKDD9JFo0ZbRI8eu8Ty5TlnrcuZ/edFMRyv1Wr5/vvv+fvvvzlw4MAZ+1988UX6uygOp14PkyZJY+aNN8q5omdDq9Eyd+RcOn/cmbnb53JX97vg+uuhWzeZTal9e1natZPLkBCXfAYVFZWLB1f1n8uWpbJhQybffHNJlfsPHjRx000HmD27Gb17Vz0c2bu3P//+246ffsrmmWeSmTXrOG+/3ZRBg1Trt0rdsdsFGzYUsnixtHgmJ5sZMiSIBx9syPDhwURE1C42p06noUEDAw0aGOjUydfBra4aLy8tEyY04q67Ipg9+wS33HKQJ55oxNSprk2Ne1GIUIDGjRtXuX3v3r0UFhbSrVs3l7Xl7rvhxRchPh6uuurcxzb0b8hX133FmJ/GcEnjS2jzxReweTPs2SPLzz/LZVoaNGp0pjBVxamKikodcXb/abUqPPnk1rOGZCostHPddYmMGxfOnXdGnPNcGo2GG28M49prQ3jvveNce+1+Bg8O4s03mxAb612ndqpcPOTn2zh0yMyhQ2YOHzazZ08Jv/+eg90Ow4cH8+abjbnyyiD8/Or3tA9fXx1PPRXF/fc3oKTE9Q5LHuMdP3/+fPr3709gYCAajQabrXKQVSEE06ZNIyoqCj8/PwYMGMDu3bvrXO/UqVN58cUX63yemuDjAxMnVn9q59BWQ3mw+4Pc/MvNmLVCpl+67z5491344w+Z2jM7WwrSm26S5tWy9dBQiIyEV1915kdSUVFxI/W9//z44ySEgIceOjMkk6IIbr/9IA0aGHjrrSbVPqe3t5ZnnokiKakLDRsa6NRpJ088cZScHM8P4F1biovt7N5dzK+/5rB4cTb//JPHpk2FJCSUkJJiJi/Pht1+YbiB2O2iTuG5bDbB4cMm/v47j88+O8nkyce46aYkevXaTXj4FoKCtjBoUAKvvprK+vUFNGyo58cfYzl+vBtz57bkuutC670ALSM/X/DEEya++8718UQ9xhIaEhLC+PHjKSkp4Z577jlj/1tvvcWXX35JfHw8rVq14qWXXmLo0KHs378ff385NNOnT58z3hcZGcnChQurrHP58uW0bt2aJk2q37E5ivHj4fXXYd06qSnPx6uDX2XFFyt49q9neeeqd848ICQELr1UllPJyYEtW+CWW8BgkBmYVFRULijqc/9ZFpLpu+8uqTIk0yuvpLJ9ezGbNrVHr6+5J2/DhgY+/rg5EyY05Mknj9Kq1Q4mTGhIcLAOjYbSojllvWKa1Onb9XoNQUE6goP1hIToCQ7WERKiJyhI5zIv4/x8GwcPmjlwwFRazBw8KNdTU634+Wlp0cILnU5DQYGdwkKFggI7xcUVVi5fXy3+/loCAnSnFS2tW/swYEAAffr4u825q6DATkqKheRkc+nSUr4sW8/Pl4LJaNRgMGgwGjUYjdpT1su2V95mt8ORI2aOHTOj0Who2tRIixZeNG/uTbdufowZE0qLFt60aOFFSIjHSCSnsWqVlTvuKCI2Vsv06T4ur99jrvDQoUMBWLlyZZX7P/zwQyZNmkTHjh0BePnll/n8889ZuHAh48aNA2DDhg01qnPLli38999/XHXVVRw4cABvb29atGjB5ZdfXvsPUk2Cg+GRR+COO+C776BXr3Mfb9QZmXf9PLp/2p0rWlzBNa2vqV5FISFwxRVy7P+yy2TF54oPpaKiUu+oz/3nZ58doHfvMIYNO3Mu2uLF2cycmc7ate0JC6tbLuyOHX2Jj49j2bI8vvkmE5tNWtKE4LRy9m02myAvz05urp2cHBu5uXZsNmmNCwzUlYvS05dBQTr0eg1arZz/p9XKdVk0Z1nKulNSLBw4YCoXnhkZNoKDdbRq5V1avOjfP6B8vWFDQ5Wezna7oLCwQpSWlVNf5+fb2bWrmK+/ziAlxUK3bn4MGBBA//4B9OsXUOfvoIzCQjv795vYt6+EgwdNlURmSoqFvDw7Pj5aGjc20rixkZgYuezRw4/Gjb2IiTESFqbHZhNYrQKLRSldynKubRXC05uYGGOtHmwuBEwmwdSpJXzyiYnXX/dl/HgvtFrXXwuPC9G0cuVKLrvsMqxWK/rSGEZ5eXkEBwezbt06+vbtW37skCFD6NChA2+//fZ5zzt27Fh27txJcXExLVu2ZNmyZRgMFTfU9OnTiYmJ4d57763y/c4IUWC3w8yZMiHSs8/ClCnnD9v0/a7vmbB0Ahvv3UhsWGzNKly7Vk5C/ewzuPnm2jdcRaWecbGEaKqP/aeiCPLyLISEVJ4LumdPMX377mHOnJZcf31ota+BKxFCUFSkkJtrIyfHftZlXp4Nm02gKJQWcZZl5W1CQFSUkVatvE4Rnd6EhjrffnTsmJk1awpYvbqANWvy2bfPRLt2PuWitH//AGJizh6TVQhBaqqlXGzu21exTEmxEBKio21bH1q18j5NbEqRGRKiO2fYIJXas2WLjdtvLyIgAL7+2p/Wrc9t8XZm/+kxltBzkV+acD34tCDtISEh5fvOx7x58865f/r06dU6z5QpUzAaZWy6oUOHllsgaoNOJ8Xn0KFw222wdCl88w3EnkNb3tLxFralb+Pa+dey4Z4NBHlXHUuvSi69VM4VHT0aAgJk9HwVlQuU+Ph44uPjAbBcxClvPb3/1Go1ZwjQ7GwbI0cm8thjjc4qQDMzFfbutTNggGOsc7VBo9Hg76/D319HTIzbmuEUmjTx4tZbvbj11nAAMjKs/PuvFKX/+99xbrvtIE2bSivsgAEBBAXpKgnN/ftLKCpSaN7ci7g4H+LivLnxxjDi4ryJi/MhPFyvikwXY7UKXnvNxOuvlzB1qg/PPON9Vkuwq/rPi8YSWlecbUkpKZHp4b/4Av73P+l3dLb7067YGT5vODqNjsU3L0anreG8nZ9/lqlAf/8dBg6sc9tVVDwd1RJaf/pPm01wzTX78fLSsGhR6yqHCEtKBIMG5bNpk5377vPi7bd98fNTBY0ryc+3sX59YamltICiInup2PQpF5qxsd54e3uM//NFzb59dm6/vZDiYvj6az+6dau+DdKZ/We9+HUEBQXRrFkzNm3aVL7NZrOxfft2unbt6saWOQ4fH+nsvmCBDN80ciScPFn1sTqtjnnXz+NA9gGm/jO15pWNGQPvvw/XXivDPamoqFyw1Lf+c/LkZJKTzXz7bcsqBagQgrvvLkKv17BvXxAJCXa6d89j69YL1+vdEwkM1DN0aDCvvtqY1avbsWVLR777rhXPPx/NDTeE0bGjrypAPQBFEbz/vonu3fMYONDA5s2BNRKgzsZjfiF2ux2TyVRu9jWbzZhMJhRFevSNHz+et956i927d1NSUsK0adMwGAyMGjXKpe2cMmVKuYnaGVx5JezaJUVphw6wZEnVxwV7B7P45sV8vPljvt/1fc0rKgtWetVVsHdv3RqtouLBxMfHM2XKFHc3w6lcKP3nd99l8vnnJ1m8uM1ZUxS+8oqJdetsLFwo57KtWBHAuHFe9O+fz8yZJSiKRw3uqai4jWPH7Fx5ZQHvvGNi2bIAZs70xdu7ZiMGTu8/HZ6DqZbMmTNHAGeUFStWCCFkiqznn39eNGzYUPj4+Ij+/fuLnTt3uqx9rk77pyhCfPONEEFBQtx3nxAFVWepE0sTlwq/V/3EptRNtato2jQhoqKEOHSotk1VUfF4LvS0nRdC/7l5c6Hw89soli3LOesxP/5oFgEBWWLHDusZ+zZssIqWLXPE5ZfniZQUuyOaraJSL1EURXz1lUkEBWWLe+8tFPn5dUv96cz+02NEqKdT9iVMmDBBLF++3GX1HjkixMCBQrRqJcT69VUfM3PtTBH9v2iRlp9W8woURYjHHhOiRQsh0mrxfhUVD2f58uViwoQJF7QI9XTO138eP24RMTFbxZtvpp71HJs3W4WfX5b49VfzWY/Jz1fEnXcWiNDQbLFgwdmPU1FxNlarIg4csIl//rGI3FzXPRSdOGEXo0bli4YNs8WSJXW/B5zdf3qcY5Kn4k7HBrsd3nkHpk2Teeefe07GnS9DCMHti27nQPYBVt6xEi/92cNmVImiwD33wKZNsGoVhIU59gO4mZUrZSgsX9+KeP5dulS+hioXNheLY5Kncq7r/+FmhWd/MhGitXPDlf40CdTQJBCaBELjQAj3gfR0hZ4985g40ZunnpIBtU+ehKwsaNv2zPp++MHMgw8WM2aMkXffVZ2WVJyDEILMTMH+/XYSExX277eXrts5cEBOhWnQQENGhmDoUAM33GDk2msNBAU5diZkRobC4sUWFi608tdfVq691sBHH/kRHu6YepzZf6oitJp4wp/Yzp1w660QFCQd3Bs1qthnspkYOHcg7SLa8eW1X9Y89IXNJtN8pqTAX3/JEE71nJUrYfp02L4dHn0UvL1lqNR168BikQkCykRp374yjr/KhYkn3L8XM+e6/s+8cYIf15u5d1IMx0u0HMuH5AI4lg9ZJeCtE4hchVCtYGgfHfoCDbvXwZZ/QJMBo6+A116BZs0q13nsmJ1x44o4flzhu+/86dHDc5wxPA2rVbB4sZXPPjNhsUCPHnp69NDRo4eeFi20F2UoJSEEJSVQWCgoKhLk5QkOHFDKRaYUnAq5uYKoKA1t2uho3VpXutTSpo2OZs206PUaEhPt/PyzhZ9+srB3r50hQwzceGPdBGlysp2FC60sWGDh339t9OihY/RoI6NGGYmNdWymK1WEegBlX8KECRMYMWJEneKD1oWSEhm+aeVKWLQIevSo2JdWkEaPT3vw9KVPM7HPxJqf3GyGESOkIF26VKq2esjKldLnats2ePxxeOyxygJTUSAhQQrSsnLoELRvXyFKL70Umjc/e5gslfpDfHw8S5YsYfbs2aoIdRPn6j/NZoXcXDsNG545NFFgEtxwXzHJBdBrqC9/bNRw0gKNWoMuDNJKILwAMv+Ahy+Dac/Kh/Qy7HbBG2+YeOWVEqZN82HSJG+XpdesCiEERUXScpWRIcqXmZmVX5ctc3MFl16q5+abjVx7rRF/f8e2PTnZzqefmvn8czM+Phruv9+L8HANmzfb2bzZxs6ddvz9NXTvrisVpnp69tTRuHH9EqZms2D3bjtbtthISLBTUABFReK0UnlbcbHMWAUye1VgoIaWLbWniE0trVvL9Zp8L0lJdn76qbIgLbOQBgefW5Du329nwQILCxZY2L7dzoABekaPNnLddUaio53jZ+7s/lMVodXEkywpQshYotOnw8cfy0D3ZfyX8h+Dvx7MgpsWMKTlkJqfvKgIhgyB8HBpbq3mmLXVCjt2QMOG0Lhxzat1BKtWyWtyNvF5Lk6cqCxKt26VsxI6dJDJA1q3rlg2a6YO5dc3POn+vRg51/V/aSN8nwrRvtA8EJoFQowvRPvAN7NMLJ4LBsWLsFANDz8Ed9wOZadIK4Bv9sDHmyAlD/R74ZG+8MoDUBoTH4CNG23cemshMTFavvnGn5gY1wWG2bDBxtNPF3P4sEJmpoLJJBOVhIdriIjQEhGhITxcLmWp2Obvr+HPP63Mn2/h4EE7I0YYuflmI8OGGWrs5VyGogji46189JGZ5cutDBtm4KGHvBgyxHBGSCyzWbBrlxSkZcJ09247oaGaStbSHj30REV5RrCdsjZv2WJjyxa53LXLjp+fhm7ddHTsqCMoSIOf3+mFs7728sIpojspqcJCumePnSuvrLCQBgdrEUKwbZsUngsXWjl4UIrW0aONjBhhICzMsddcUaTgPh3VEuoBeOKfWHy8zL55773w+uuyYwP4Zsc3PLr80dql9gTIzYVBgyA0FD7/HFq0OOOQ7GxYv75ieHvjRtnpFxVB//5SGI8ZU/Fn4UzqIj7PRkkJbNkiLaaJiZCUJMuBA3KObvPmFcK0rLRuLQW4zrEjISoOwBPv34uJc13/V+bD+39CtgJ2X9CFyGL1EQh/wEuD3g5hOmholOK0eRDEhkKbQBgYAT46WJ8KLy6Fv06Avghui4MZY6Chv6ynsFDw6KNFLFpk5eOPfbnhBqNTrXlWq+Dll0t46y0TU6f6MGiQnogILeHhGoKDNTXO0717t4358y3Mn28hI0MwapSBm282MniwAYPh/Oc6eVJhzhwzn3xipqREcN99Xtx7rxdNmtSswyopEezYUSZMpThNSLDTvLmWkSONjBxp4JJL9C7JyW4ynSk4d++W1ttu3XR0766ne3e59PRpBQcOVFhI9+yxM3CgnsREhawshWuuMTJ6tIFhw4wEBJz7M9jt8v85J6d0mSvXc3IgO+fc608+Di+/eOY5VRHqAXjqn1hSkgxs37gxzJ8PISFy+6Q/JrE0aSkb7t1AoFct2pufL3OKfv01YvqLJF39GOs26ssthfv2QatWctj6kkvkMi5OOgvMmyfTjyYkyLaNGyeNq462HjpDfJ4Pux2Sk+V1P1WcJibC4cNSiL/9Njz4oHPboVIzPPX+vVg41/V/PQ1mHYeWRrAehgOrIX+/wHbMyrV9tIQ11ZOYB6lWyBZQaACbDxAAmhDAB+L0cGc7uK4JRGhh4ucwLwls0TCgITzRH4a1AIMOfvrJwvjxRXTrpuO99/yIi3P8U2NCgp1x4wqxWODtd/w4ckTPgP7yQbWuCCHYssXOvHkWfvjBjNkMY8ZIC2n//vpK4lYIwb//2vjoIzMLFljo10/Pgw96M3Jk9YRrdSksFKxYYWXRIgtLllhRFBg+3MB11xkZMsSAr2/d6xJCkJiosH69jfXrbWzcKAVnYKCmXGiWic5mzTxbcJ6PAwfs/P67lVattAwefG6rt9UqDSYrV8PKVfDvWmkM8vKSdqSQEAgNkcuQ4Iptp66X7Y+MrNpwpIpQD8BT5oRWRX6+FHp79sDixXJuY11Sewohh9b//BNyfl3D3evvJVcJ5IPOn9Pgys7lwjMi4tzn2bMHvv0WvvsOTCZptR03Ts5jrU3/UFIihd6+ffDBB3LIfOJEWTzBqchqlcL4xhulI9S0aeqcUk9AnRPqfs7Vf/6yEd74A7Zlgr4pGBsL8r2AUECngULQFYK/CcIUiNZAEx1ECkg7ASuOQbo/ECHf42uHvkEwpgls+gm+3g5efcAnAO7oCHd1hCi9wrRpJXz2mZkJE7x54QUfAgPrfrMqimDWLDOTJxdz7UhfrDYvfluqoXMn2LETenSHO2+HG2+oPHe1LvWtXWtj3jxpQTMa4aabjFx/vZFt2+x8/LGZtDSFu+7y4v77vWjTxvnDNHa7YMMGG4sWWVm82EJyssKVV0pBOny4gQYNqjeEXFAg2LjRVi46N2ywUVQk6NFDT9++enr3ltMBmjat34KzplgsMtHhqjVSdK5dJ903Bg6Agf3lsnVrmfDGEahzQj0ET7ekKIq0Cr77rrRCjhwJuaZcen/em9Fxo3ntitfOe44TJ6RgnDtXOupceaW0cPbrYaLHspfRz3pHmhyff75GTkuKIsXZt9/CTz9BVJQcrr/ttsoerUJARgYcPCjrP32ZlibDLLVsCaNGyaZ4gvg8nT17ZCKqq6+GDz9Uh+c9AU+/fy90znX975gBy5Ogc2to11Tw89cmYmM0PP+kF/oADQXekKpAkkmWxBI4aAa7gN7+MLMJ9PaFhWvgyxWwJh8KGwCNAG8INYHPETixBWL7wmEv6NwAHuoK7ew2nn68mIQEO2+84cu4ccYaD5OXkZxs57ZxRezercc/yIeiIg333AUP3CdnNGVlwfwfYO7XsHsPjL4O7roTLhvkmD7CZhP884+N+fPNLFhgJS5Oy0MPeXPjjUZ8fNwj0oQQ7NunsGiRhcWLLWzebKdPHz0jR0pRWubFfbqVc/16G3v22ImJ0dK3r768dOmiw2i8eAQnSNG5aZO0dK5aLUWnr68Um4MGwKCB0K5d1XM5HYVqCfUA6suf2C+/wJ13wlNPyXiiSdn76f15bx7v8zgTek0g3De80vFms0wNOncu/PEH9OsHd9wB118P/v6nnXzHDhlPtLBQzhXt16/G7SspgV9/lYI0Ph769JEOQIcOyVJYKIcEWrSQYvP0ZYMG9cO6mJwMQ4fKKQrff19vAw1cMNSX+/dC5VzXf9lGWLwO7Irgr7+slJhgyBADaDQoQj7Enr60CyjyghPNYH9zuETAy9FwSYwUdEdS4Mc/4KutkGAAbUuwhwEm0KRBjAGEFxQDd3cSNE238uazxURHa5g1y69G4ZyEEMx4zcpLLwsUjHTrCg+P13DDmLPf97t3w1ffwDffySk8t98Gd4yTc8svZNLTFZYssbBokZW//7bSsqWWpk11bNxY2crZt6+ePn08x9nJlRQVwcZNUmyWiU5//8qis21b54rO01FFqAdQn/7Edu6UltBu3eCrr2BH9lqeW/Ec65PXM7rtaO7rdj++Jwfy1Vca5s+X80LuuEMOlZ8ea+8MbDZ47z051nzHHfDaa7X2PsrIkA74ZnOFyGzeXD7lXQhkZ8Pw4aDXS+HtiVbbi4X6dP9eiJzr+q/cAX9thVWrrOzebeeB+4z4+2vRauQf7alLjaby67wi2HgcVkVCbjToVkP7NOjYBDo0g/ZNoWkE7EuEhSthSRqYWoC9BaAAh8DHDOYwaKMTxCRbWf15Ibfd4sWMGT7nHDo2mWDOXIXnXhBk52i58gqFN2bo6Nq1+tfFaoX4P6R1dMlv0KunHK6/wUVOne6koECwfLmV48cVeve+OK2cIEf41q6rKNu2S0PMpZfAgH4VotOdxhdVhHoAnjwntCoyM2Xs+YwMGU+0RQtYvWc/zy38jHXFcxFFYfTU3c/zI+7g6kHhNf+BHzwI998vPXI++kiqLZUzKC6Wc0SPHYPly+VUBBXXos4JdT/n6z+//97MQw8Vs359IO3a1W5s+p9cGH8Q8qwwOA2se2H3EUhMAS8DtG0C7ZqCnx4yMmGTGY42A0KAdPA6ARZfEBaB1w6BfaOVkYO1PPqInh7dNeUPx4cOwcefwiefCoqKFdq2sbLwFyOtWtXNNJWVBd/Pk4I0YR9cPwrG3QaRjeSzv9VaurRVfl1p3ynbIiKgZQvZ958xquUALBY5R//AQRk1xGCAPr2hY0c1hN3ZsNvldK1y0ble/jd06ihFZ1lp0sTdLa1AnRPqIdRHS4rNJtN8fvONtIquXAmDB8Mt48yIuAXM3f1puXX0/u73M7DpwJpN8BYC5syBJ5+UkyDfe0+Ol6tUwmqVen3FCjkFoU0bd7fo4qM+3r8XEue6/hs22Bg8OJ+ff/Zn2DDjWc5QPWwCPj0JzyVD3wB4pwk000shuueoFKV7jsC2g3D0BMQ1hqDGkBQGWWFANmjTwEsHJh+BNkGgrAeSNbSN0xAeBhv+EzSOtpOWWsys9724+27Hh3rauVMO1//wk5yipNdLYXfGUlf1dq1WzvE/eAgKCmS33KK5LC1bVl5GRp59aNdkkqK7TGgmHShdPyjFk9Eoz9OqpXzg/m+j7O+6d5OCtKxER9f9migKpKbKNiQmQkqqHEWzWiuKxVK6bjtl3Vp5XaORn7lxDMTEQEy0XDaOke30qmHW69MRQl6L7Gz5YHHiZMXw+voNUoj26V0hOPv0dpzV226Hz3+Eo2kQHAAhQRASKJfBgRXrQQE1G85XLaEeQH3+E/vxRzh6FG655czOYH/mfj7b+hlzt88lzDeM+7vdzx1d7jhj7ug5SU+HRx6RKveDD6QbvEolhICpU+HTT+H336F3b3e36OKiPt+/FwLnuv6//27h6FGF8eMdN3E6ywovpMCXGfBoI3guGgJOM7Aez4b1e2F9glxuPAaadmBtDcIKpILIFNLrPkcQccTOwAANW1YXEROj4auv/Gje3LO9DoWQQujQITh0WIrSQ4dKl4dllmYvLzkNqkykms0VYjMlRXpZt2opQ/K1KhWcsbFyGRVVWcwoCuzfDxv+qyi798jj+vSG3r2gTy/o3r1q7+2y9iYmQmJSqeAsC4d3QLataVNoHStFo7e3FN0GgxTEldb1VW+32+VfVkqqLMnJcpmaWmFBjomWYQ/LBGpMtBTyhYWyfdk5FSIzO+fMbRaLFLvBwTLvS7eucGlfKTo7dZIPC47mwFG48xk4kQlXXAI5+ZCTV7HMzZfrdrtsW6B/ZZEaEgg3XQM3DDvz3KoI9QAu9D8xk83EwoSFfLLlEzakbGB029E80usR+jbuW/2T/PKLDJA5ZAjMnq1OgqyC996TDmM//gjDqrjZVZzDhX7/ejruuv47iuDRo5Bogjcaw23hci5pVVissP0grNkDP6bAVm+w+QFpYMwRWPUg9OC/VzC+h4YxQzX06Fg/HCXPhskkDRRl4vTQYSlKY1tViM7IyLp9xsJC2LylsjDNyoLOnaQwbdBAWlkTk2TJyZF1to6VJTa2Yr1Fi7pbKs+Gosg41ykpkJwilympFcsTJyHAXzrShobK2JphYacsQyEstHRfqPz7c0VkFEWBj76HZ2bC3dfDa5PA7yx+FUJAYZEUo7mnidScfOjeHgZWYSBRRagHcDH9ie3P3M8nWz7h862f0y2yG5P7TWZIyyHVG3I6fhzuvrvU/fMruOwy5ze4njF/vgwy8NFHcPvt7m7NxcHFdP96Iu68/kLAj9kw6RjEGGFWU+hRjTmSQsCvB+HVnbDZDJosUE4IMGjQKEAS+GRBrxZw0+Vw81A5BKpyboSQQ/llgjQzU8a1bB0rxW9sLASo17FaHE2FuyfDwWPw5WtweQ1sRjVBFaEeQH1zTHIEOSU5zN40m/f+e48mQU2Y3G8yo+JGnT/wvRBSYT39tLSMvvqqYx9f09Lko6ezHoldwF9/wejR8MILct6uivNQHZPcjyf0n0V2mZ3pf8fhplCYHAWtqxnQO6UYPjoAHyRCuACRBkdKQFMISiZgBixgsENkAPRtBf3ioFNzOe80Irj61kSbDdJOwrE0SD0BOq20wPn7yhLgB/5+ct3Hu35bYlVqhxAw52d4fIYcQn/rGQiso3A/nCGXzU9LQqM6JnkIF7MlpchSxBfbvmDmupn4Gfx45tJnuLXTrRh153Ei2L9fRqQ3m2UU/I4da98IIaRye/ddWLpUTqpcsuT8aZtcQV4ebN8uUzjt3ClnpZfdVlUtS9dzcgTr10FMEw0dLw1CEx4mJxCFnbYMD5fjO86YSHSRcDHfv57Aua5/sl0hX0ArnQYvFyiqwyZ4MRXmZ8E1wfB0lAx6Xx1yLPBaAsxKguGNwJoFfx6Am1uB7jj8sxuO5YLVAGhBawfFDD4aaBkGPZpBp8YQ5gdeQGa2FJvJ6XAsvUJ4AkQ3lEVRoLAYCooqljabPEarLRWnfqcI1FKxGtkAmkRC02hoGgVNoiCmkeq5Xt9JOwH3TYXt++DzV2HYwDOPsQpBoYACISgQUCgEBYp8XVi676RZsDsTduULjlnB5CW4VNGzpvuZ/zOqJdQDUP/EwGK38N3O73h97euUWEuYdMkk7u12L76GcwT2tFrhpZdkQvWXX5Y5NmvilldcLAXsu+/KeFMPPgh33SXz2m/dCsuWyclLriI9XSarP7UcOiRnsXftCp07V+TjO/UPtWz9tGVGpoYPZimMHJhHt6ZZcmwq67RlUZF8T3BwZWEaEyNjeZSVpk2lB4D6L3MG6v3rXs51/WcXW3m2wIIJaKbT0FqnpY1eQ6xOSxu9ljY6DVFajcM90JPN8O5x6U3f3U+K0WFB1bMsHimCqTthYSqMbgQ7k6DYAu8OhuGtIOEAzF0Ki9ZBUhYENIRiPdj0gA2wyGK0Q0MjtGogY5r2aguXdpLi8VzPnGazFKTl4rSoQqQWFkN+IaSflF7Sx9LkMuW4FLRRDSoL0/Jl6bYAJ4RzUqk7QsC8JfDwS3DNIHj/eelQlJknoz6sTlVYYrSxp7ENc3CFrPPXQIBGgx9gt2goMCkUKwo2nR29jx2dzo6f3k6gl53B+PGBX8gZdasi1ANQ/8QqsCt2Fu5byIw1M0jOT2Zi74lM6DWBYO/gs79p3bqKaPhz50rRdi5SU6Vz0yefSLE1cSKMHVuRgkRR4Jln5LmWLJGplxxNWpps96mC88QJOYGpa9fKJbwG0QRO45dfYPx46QFaZT5pk0kK0lPFaUaGnDF/7Jj0LDh2TF4zIaQQPVWclgnUVq1kCqeLEPX+dS/nu/6KEKQqgv02QaJdYb9NYb9dkGhTOKYI/DTQWqeltV5DG52W1qXiNE6vxaeO4jTHBh+fgPdOQIQenoqEm8PAWI1n5c3ZMGk77MmDwcHwx07oGwXvDIbWofKYtBOw8j8pGELDpOd+QgasTYJNhyElT8YuNdihMB8wQ1wD6NpCBtwPCwQ/b/D1Kl16V/1af5ZZUkKAyQI5BXAwBZKOweFUOHYcUk/CiWzIyIXsAhnaKMBbWmt7tIUOraFDLLRrVffhXpXaczIL7nsOVm+HW8eA0Q92HYZdyYITzez4DLVhibXTIkPHAKGQX2hl20k7WQE2fJraINgGfjZ0Xja0egUUDQF2PdFaPc21ehqgR2uFzoo313mf+SekilAPQP0TOxMhBH8c/IMZ/85gW/o2xvccz+i2o2ke3Jxw3/AzLRcFBVJMLlggk6qPHXvmSTdulFbPX36RsUcnToRBg85unvjgA2kV/fZbuO46x3wwiwXefBNmzJCi7VSx2bmzwyM/CwFXXAFdusD//leHE9ls0lJ77FhlcVpWkpJkTNeXX77oJpKp9697qcv1LxGCJJtgv10h6RRxut+uIIAHfQw86qsnUle3YPEmBb7OhLfSoUSBJxrBvQ3ODO10OkLAb2nw9A6wKhALrNwPj3WHqX0h4DxT1/OKYVsybD4iy4ZDcDQbgrzAXws6ARo7YAObFWxmMJVAiVl69Jdh0J8iTL3AYoP8YigoBrtScUyADwT6yWWADwT4Viz1WtiyH7YfhlBf8NNA/gnIyZDW0g6xpcK0VJzGtZTzUquDokBRqeX21KkFxSXyc5TF9rRYT3ldtm6tWC/bp9XIKQeREdK6G9lALkOD63/3JgTEb4a1e+CP/2BzIihaCA2ATi0gqqNCZhcrmxvYCNJpuNagJ8Ku8Ls+m7SAAmxFXlhseuzFenR5ejQZegoOGQgq0tM6TE+DCC3CR0NKCRw3FKJrnEJ40xO0y2rGnIZNz2iPKkI9APVP7NysS17HG2vfYH3yejKKM/Az+NEsuBnNgpvRPLi5XIbIZevVe/GbMBFNWSgnf38pTN97T3rV3323jDvasmX1Kl+4UFpZX38dHn64bh9k40a49165/vnn0KtX3c5XTXbvhp49pbHVacbKhAQZF2rQIPjss4tq2F69f92LM66/EIJ/rQozi6z8ZbFzm4+eJ30NtNHXTYzaBSzOgTfSZGin8Q1lrNGG57ldbAp8fgim7YZob1CypIXxzUFwS7uaCaPcYth6VIrTY9mQlgvpeXKZlgtmGwT6QFQQRARAhD+E+UKwDwQapfBtEQ5NwiDQt0JoelUzF0BOASzfBEs2wLJN0sraoyXEBIJSBPsPwe5EKSJbNpGCNDS4VFgWVgjMU8VmUXHF+b29Kuaw+vqA0XBKMcqlQX/a9lPifxoNYLPD8QzpxJV2Uk4/yC+U+8oEaWQERDWsLFSjG8rpDq6w7AoBWTnyIaAs3azm9HS0ZdtK09L+uQ2mfQWpmeALpKfCxFtg9BjBIoONn6w2DguFoGIdljwDxcJOUHQWfuH5FKUGkpsQDnkGGpRepzSTfDiKMEKgAGseZKQrBDfLpNnAFLSNCrjU2pA7jdG01VZ9UVQR6gGof2LVp9BSyNHcoxzOPcyR3CMczjnMkbzSZe4Rckw5xJr9+fJXDXHpVox6L3x8AzFMfFLO96zN9V2/HkaMkO9/442azTsFOe/y+efh449lVPmnnpI9ngt55BEZL2/pUic+yaelwdVXQ6NG8NNPF00sFPX+dS/Ovv57bApvFVn5wWRjmJeOp/0M9DbULUijELC6AN5Mh3/y4I4IeDISYs9j+Suwwsx98HYidPSTsTdb+MOIVtCtoSwN/OrWrpziCkFaXvIqxGpKjiyRQdC5MXSOkaVTDLRpdPah+6qw2qRFbsl6KUqPnYTLusDw3tCrlQzQvmu/FIBljlFl3vwBfrIYDWCyQ5EF8ksgMx9O5MiEAfnFpwm0U0Ta6a+1WtBQ0b3bFWlhtZcWk1lafguLpegtKpGl2CQtxyYzlJjAngdBBjkHtmnUmXNkm0ZDg7Bz/40IIWNtJqefUo5XrB8rnYd7qrX6nHghU8gaABPQAWIuEXh3UTjmZ8Pqb0Nj1cAJA0qKHi+9Hf+4LHyb51KS6k/h7gga2430CJfW8lWHwQhM6gr3dQRfPWRhZjFpLBRpYNUSvC2a7d9Hsm+/gQEd4fHrYXgVM9tUEeoBeEKIkQuFPFMeR3KPcCTnMPoff2ZLXgKvBe+iVUQbRseNZlTbUXRu2LnmjghJSdLS16OHnCvqXc1xoj/+gAcekHNPP/vMbfMms7PldNO5c2H4cCdWlJ8v40Pl5Mj0TY0aObEy96OGaHI/ruo/U+wK7xVb+bTERje9lkl+Bq426urs1LSrGGamw09ZMKEhTI8B//MIudRieGE3fH8UegdAqAl2n4SkHIj2h26NKkRpt4YQHeDYh8/8EtiVCjuSYUeKXO5KlWKtfdQp4rSxFKeh1RTGiSkVgnTtHulQNaIvtGgEJ3KlsDyeUyEyj+dAbqH8bBFB0CgUGgbLZaNQaakVApTSwCGKcsr6WV4LAQIZvkqrkUHhddozi/bU16XHpGbC3D/k+y5vD20iIDe3woHraKqcg+llhManRBdoGC63lwnM5ONS6Ab4yeMaR0oR27hRxevGkdLyatBXbnvZZ1IU2JIEM+bLIfcbRkBaHPytEwhvG/YgG3graDP0KOl6yNfia1Dwis3CPy4Hv0w/RuaH83CMN9GnfX9WO8zdBS+tE4Q3yaf7wBSS/DPorgnheqLpQxg65A/uyHH5fbaOhqE9K59HDdHkIaiWFOeSZ8pjadJSFu5byNKkpTTwa8CouFGMbjuavo37otVU07J58qS0iHp5waJFMrTR2cjKgieekMP5b7whhWhNLagO5qOPZCCB3budHAbVYpHTHtatg+XLpfq9gFHvX/fi6uufowg+KbHyfrGNCC086WtgrLceQx1V3p5iePAIHDHLoPcjQ84vHHflwqQdsCkbprWHWxpDQiZsPQ5bT8iSkAVhPhWCtGvpskWwY4WpXYGDJytE6c5SkXosG2JCpChtFi5DSIX6QZh/6fKU10E+UswBZOfD8s3w2wYpNhuFnCYyQ6Bh6bbwoJpZYJ2J1Sbb/MnvsGKHtOo+cA1c0U3+BZSYpNg8mlohTE9kQcOwygKzcaTMw14b9hyBqXNg+S5o2w9SWkJmhECntyEaWdHZoE2OgegcPSknNezLttPq0hzsHbNpb/dhmjGcrpqzB7o1Y+dPTvKjSOGo3UTGrkYEHonmla6+DG5as9+Vagn1ANQ/MddRYi3hr0N/sXDfQhbvX4xBa+C6uOsYFTeKy5pfdv74pMXF0ukpMVGGcGrWrPJ+IeCHH+DRR6VX/YcfSiuoB2C3y7zKY8dK53+noigweTJ88QX89ptzIgx4COr9617cdf1NQvB1iY3/FVsxC5joa+AeHz0BZ8vdWQ0UAV9lwlPHoK8/zGoGzarxwLg8XXrSmxWY2RlGRlcIgWIr7DxZIUq3noDdGeCjh6ZB0MgPIv0h0u+Udf/Sdb/zOz+dj5wi2JkixWlKDmQXQVYhZBeXLosgq0g6O2k0EOJ7pjgN84Nwf7kefkop2+floVPQD6fDZ0vhy3jp0HXf1XDXUCmcHY0QsHw7vPADbEkFbRugM4hwgcFgxxpuwUsDsclGCvfqSMnXMKi5QmSfHLZGZtFG68UkIujN2cMiHsfEL6SwhHTC8WIM0QyhIcKiZ9ZWePM/6BQBrw6AS6v5t6eKUA9A/RNzDzbFxpqja1i4byEL9y2kwFzA8NbDGddpHFe2vPLsFlK7HR57THrZ//abVHYAyckyHtLGjTBrFtxwg8e5Uq5eDddcIzV0ZKQLKpw1C6ZMge+/l1bkCxD1/nUv7r7+diFYZLYzs8hKkl3hUV8DT/sZ6hTehWBAmwAAl7xJREFUKcsKzyTLoPfPR0tvesN5BlJsCnx5GJ7fBXGB8HYX6H4WsWO2SQtpcgEcL4T0IkgvhOOnLovAYgc/Q2WhGukPrUKgQzi0D6/bHNQyhIAic4UgLReqpa+zCiGzUK5nlq0XQl6JfH+Ad4VQDQ+oWA/xlfsCvMHfq2L99Ne+xnMPVNkV2b5CMxSaSpenvS4yy++gVQNoGwnNwyusulYbLF4Hn/4Oq3bBtX3ggeFweZfaD5DZFTiaBasS4ddtEL8HSuwQ2AlsncGsEygaO9oGFoQGvHcbuMSkp2+khm5RCseb5PK5IYtIDEwinAH4oaHq36yC4CdS+JTD9CaUG4imC8FnHJ9rgrc3wTuboX8MvNJfTg05F6oI9QDc3YmqSG/YzWmb+SXhF+Zun0uAVwDje4znzi53EuJzZoBdhIC33pLB8ufPlyGLnn0Wrr9ebg8Lc/2HqCY33SSntH71lYsq/OUXmcj+7bfltIQLDPX+dS+ecv2FEKy2KjxTYCFPCD4P9OJSY93GiP8tgAcPy/WPmkH/any8fCu8ngDvJsKYGJjRCWLOkfPjbAgBOabThGkhpBVCYo60ph7NhwhfKUbLRGlZCa1m2tK6YLFVCNbMU4VqgVzPKYaCUpFYYDpz3VyaHUqjAT9jhSg16k8RnWYosVTU6WOUAra8eMv3+pdajJNOygLQuiHENZKitGypV+C7v6R1NNAX7hsGdwyRMVtPp8AEhzPhUKZcHs6oeH00WwrRcF84aQf/S6GoMSg2AcKOMcqK3kcwxmRgUrCe9mEaNBrBcgp4lQx80fAEEQzF/6ziEyCZYmawjwzMTCWOrlTxf3gaGcXwxgb4cBtc3QJe6g/tzhLuWhWhHoCndKIqEovdwoKEBczeNJstaVu4peMtTOg5ga6RXc88eP58uOMOGcT900/hyitd3+AacuwYtG0L//wjM5S6hDVrYORIGebqxRc9zkJcF9T717142vW3CcF7xTamF1q4x0fPK/5G/OswRG9VZPalF1PhxjB4szGEV2P4+VgRTNkFC1PgiTbwTBz4O3jYusAMe7NgT6YUpXsyZUktlBbTMmFatuzUAHw9aOjcaqsQpaeKU4v9TKHp7wV+XhXWzXNhs0uxuO84JKRXLBPSpfW2aZgUqAYFDh6F/UcAHaAvLYbSYoRyfagtLbrSYgDCgTggFDBBlNGOdxML+V4Kz/oZechXj29pX7sPEy9ykiTMPE0E1xNU7jxUFfZS6+dnHGY4kTxIC3yo2UNVagG8uh6+3CnDiT3a48xjVBHqAXhaJ6pSwY7jO/ho80d8u/NbOjbsyISeExjTbgze+lO84w8ckGPbfg4Yl3IRL74ondc3bHChv9TevTJJwBVXyGxVF0gsUfX+dS+eev0TbQr355tJtgs+DfRisFfdrKJHzfDoEfi3UArRuyKkF/b52JgFT2yHg4Xwcge4q3n1hFRdyDHB3kzYnVkhUHdnyu3dG0K/GFkujZGW1Asds01+9mwTHMyC3cch8SQcypFZrbJMoDeAzgA2jUxuUGyX+jPMByJKi7c3WA2QboSjvlBihBALXBtp52iYhR12hSd9DTzqayifm5yLnXfIYD553E4IjxBG4HnE5DGKeZUEsrEyhTi6Elynz384V853blmFEVUVoR6Ap3aiKhXkmfL4asdXfLjpQ7JKsri367080OMBmgU3c3fTakVJibSGTp8Od97pwopTU2Woq5gY+PFHh2eIcgfq/etePPn6K0LwcYmNyYUWbvLWM9PfSFAdrKIAi7PhkaPQxAgfNYeO1RBxQsCCFJl5yU8P/+sCV7o4epoQcvh+TTL8myJLQha0Ca0Qpf1iHO+17woKzPDbQfjnKGSWSLGZY6oQnsWl8Tx9DRDqDSGlJfSUZUM/WRr5QQNfsGrgkAm25sDGbJnG1WwEn1Aw6WGoL9wQZecnrZWVVjuP+hp40tdASOnvy45gHrn8j0w64c3zNKAV5/YysyP4kWQ+4zAjieIBWuBdQ+tnTVFFqAfgyZ2oSmWEEPxz+B8+3PwhvyX+xtCWQxnfczxXtLgCvVbv7ubViJ9/lqPjiYm1i+Ffa/LyYNQoKCyUw/ROjRflfNT7173Uh+t/xK7wQL6ZBJvgw0Ajw73q1lcU2uGlVJh1HB5pBM9FQWA1Tmm2w+wD8PIe6BsOk9tCv3D3ib6sEliXWiFKN6VLy1+/GOnY0i9GDuGfL1GVEHLagtVeulTkkHqAEQKd1L3kmWHJAfh5Pyw/BLEhcE1LOQ3hdIFZJjqr+tqFgJNm2JItQ21tLF1mW6BjEPQIAYM/rLbAcTtMbAR3NxC8Y7LwUbGNB331PO1npMEpDzf/Ucx0TlCMwgs05PJzOB2VcbTU+plXav3sXEfrZ/nnQ7CMRILw4lKanbFfFaEegBqsvn6Skp/CJ5s/4bOtn1FkLeLSxpcyoOkABjQdQM+onnjpPVtcCQGXXy7j78+c6eLKzWbo1k3GUr3nHhdX7jjUYPXup770n0II5phsTCqwcI2XjncDvAiro1V0VzE8cgT2lMBz0fBgA/CqxlB7llk6L31xGKK84aFWMK4ZBLp5hkyJFTYdrxCla1NAARoHVAjLU8Vm2WubUvX5NEiHmEuioW8U9I2Wltfaiu4cE/yaBD/thz+PQLswGNMGrm8DcVX4oioCTpogpQRSiiG1pGL91G0ldmgdAD1DZekVCh2C4Nc8eC0NcmwwKRLubwBb7HbuyzcTodXweaAXcaco9DSszOAkKyjiYcK4mxC8OPcPwo7gB5L5nMNcRzT309xh1k8rdj5nE1tI5SkG0IaISvvVYPUeQn14klc5O3bFzq6Tu1hzdA2rj61m9dHV5Jny6B3TmwFNBtC/aX/6xvQlwMvz0lju3Cmdk3bscENM+TlzZCD/vXvdHsi/Lqj3r3upb9c/za7wUL6FjTY7swK8GONdN6uoELAsD549BgUKvBwDt4RVb75oiQ1+SIaPDsCefLi1iRSkXc7vAO0S7IqcS5peCEadDFNVtjTowFi6rLS9bF0HJ4tgQxqsT5MW183HZXzUMkHaNwp6RZ47FmpWCSxKlBbPv49CxwYwrBX0awJ+3lLQZ5oh0wIZpspCM7UEbAIivCDaR0YpiDlleeq2MqcxsyJjxb6RJl8/EwV3hIMFwZRCC1+ZbLzib2SCjx5dqZo2ofAJ2XxMFsMI4BkiaMj5nyiOUMSr7KMAK1NpS0eC6vqVlZOHibdYjRk7TzOAcKr2mVAtoR5AfetEVc6NEILErETWHFvD6qNSlKbkp9AtshsDmg6gf5P+9GvSjzBfzwjjNGECHDkiHZVcitkMLVrIVE7XXuviyh2Hev+6l/p4/YUQzDfZeazAzACjjg8CjDSqo7eQXcB3mfB8CoTo4bXGcFVQ9a1+W7KlGJ13DDoFw/hWcENj8PaQTES1QRFyCkKJXTr75FtgRwZsOgHbTkqBm2GCJkHSaaZpMMQEgh15zJ4cSC+RXvHeRrACeaVhnfz0EGaEcC9ZwoxVi80on+pdwyI7fHoS3kqX39/kKLgpDPQa+NNs54F8M811Gj4N9KJlqfVTIEMuvcJJwtAzjYZ05/yxsWwozCeZLznCqFLrp5cD534eIYc3WEUsYUygL16c/UFLFaEeQH3sRFVqxrG8Y9JSenQ1q4+tJjErkRmXz+CZfs5OXXR+srIgNha++UYGsncpb74Jv/4K//7r4oodh3r/upf6fP1PKoLH8s38abEzM8DIOG89+jpO0DQp8NEJeCUNOvnAG02gVw38/3It8PURKUgzzNKb/sFW0NLDfAiFkO07WgTHiuFosQxLdbS49HURZFkqv0evAR+dFIXeOvDWym1Wu5wKUGiGfBMIBRr5QOdw6BcJsUGlYtMIYaWC08dBLgDJZpibCe8dh+ZeMDUKrg2RluxcRTCpwMLPZhtv+Bu5z0ePtvT3kYqVZ0hnX2nIpTEEoT3PvE+gNN7nbvKxMZU4h1o/Af4jmVms4zracT0dzjsXVRWhHkB97kRVasfW9K0M/XYoD/V4iBcHvYjGze6gH34I774r88obz5O51KHk5UHjxjLH/CWXuLBix6Hev+7lQrj+i0w2niiQiulhXwN3++gJruN80TwbvJkuY4wOC4JXG0ObGgSQFwJWZUgxuigVBjWAh1rC8KjzOwo5CrsCO3JhV96ZIvNYMZjs0vrYxBea+pUufaGJn1w28Jai00cn58pWp91mm7SYOjN01Ekr/JwN87JgQyEMDoQnI+GKwArL9RKzjfH5FjrotXwSaKTJKZbyxeTzHMcZTgCTaXDekEtl7CSXKeyhL6FMorVDrZ8Cwc/sZjF7eYRL6E3jar1PFaEewIXQiarUnL0Ze7ni6ysY22Esbw15y61C1GaTfkLjxsFTT7m48qefhqQkWLjQxRU7BvX+dS8XyvW3lab/fL/Yyg6bwp3eeh7xNdCqjoovzSI96edmwJ0RMC0aImv4oHm8BD4/BJ8ekuJ0WCT0DpMONO0CHRd3VBGwMxdWnISVJ2F1hnRM6hIsRWW50CwVmY19wbeeBCXJtcGiHCk8/8mHPv4wNgzGhEKDU6ZvZiqCiQVmlpntvB1g5HZvffl/Qz52XuAEqyjiDRoxhOr7GSwilfc5wARaMpro81ooa4IZGx+wngNk8QwDaVaNrEplqCLUA7hQOlGVmnMg+wCDvx7M1a2uZvY1s8+er94FrFwpp2YmJkIjV8YQTE2Fli2ld1SbNi6s2DGo9697uRCv/yarFKM/m+wMMeqY6GdgkEFbpwfV/SXwXAoszZVhfp6OhKAaCjibAn8ch5UZMgj+5hy5vXuIFKS9wqB3qBSH1WmqImBXrjzfypPS8mpToH+EtLxe1kAKUFdZXh1NsR1+y5XCc1kutPORwvOmMGhShTPUTyYbj+Sb6WvU8WGAkchT1P0miplIGi3x4i0iaXCOeZanYkHhbRJZTSav0qHOgedPJ5Mi3mAV3uiZxACC8D7/m05BFaEewIXYiapUn6O5Rxn89WD6NenHF9d+gU7rPk+AG26Q8ePnzHFxxXfdJTMoffqpiyuuO+r9617Odf3tCLTgUKuPK0m1K3xUYuOTYisxOi2P+uoZ663Huw5i9L9C6Um/s0TOP5zQsHphnarCrkBCvoxtuTFbCtOdeXL+ZK/QCmHaMxRCjFJ07smTgnNFqeg0K9A/XArOQQ2gW0j9FZ0AFgX+yJPCc3EOxBil8Lw57OzTIY7bFR4psLDaYuf9QC9u9NKVP3BYEbxHJl+QzdNEcAch1Zr7CRXzP20IXqMDDWsoEM/HfjKYyWp6EMM99MBQi+F9VYR6AOqfmEpaQRpXfH0FnRp24ptR32DQuSdg39Gj0K6dnKLZv78LK96zRwYsPXzYxWbYuqPev+7lXNd/Npl8SDaR6IlETyMMpesVy0boCUTr0UK1WAi+K7HxfrGVDEXwoK+BB330tfaoFwKW58GzyXLu6MsxcEs46BxwCYptsC2nsjA9VASt/CHXKvf3iygVnRHQPVSGVaqPFNhlOtUjpWV7MSzIhgCdFJ1jw6DzOazCltKMWi8VWrjSqOP9QC8iTpkLfBgLE0nDjOA9omhznoxHp7KLPKaym56E8rSD538CrOAgn7OJW+nKMFrX+v7xCBF66NChap1Qq9XSrFmzurTJKSiKwsCBA0lISODBBx/klVdeKd/n5eVF3759AbjyyiuZOnXqGe9X/8RUADKKMrjymytpGtyUH8b8UDk/vQt55x2YPVuOjvtVHdrNOQwfDp07w6uvurDSuuPu+1ftP89+/YtQSMVKGlaOYytfpmMlHRvp2ChCwQ8tjdATdYpQjcFAW7yJxYj3eQJ+uwohBH9a7LxXbGOFxc5N3noe89XTxVA7gWEX8H2mHKYP1sMbjWFoDcI6VZdMs8wCFGSQVtH6IjpPF5nlxSKXWTbw10IzL1lae8PoUOjrf+44rUIIfjHbmVJowRt4I8DIsFPSKQkEP5LHS5zkZoJ4ioga/QZ/JY13SeIBWnAjMQ59wLKj8C3bWMEhnqAfnYis0/k8QoRqteef6yKEwMfHh6KiIoc0ztEkJyfz999/c+DAgUqdaExMDCkpKed8r7v/xFQ8h5ySHK767iqCvYNZeNNCfA1OdNE8C4oCgwZBp07wwQcurHjVKrjuOjh2DAI8L7D/2XD3/av2n7W//gJBAUolgVq2PIKFfZgpQqE5RtriRRzetMOLOLxohN6t1tN9NoVZxVa+LrExwKjjOT8DfY21E6MmBT48Aa+mScvdG42hp4eFZHIF+0rgqww5nH7EAtmlIrO5V4XQPL2E6Gom2tdZ7DxdaOGwXfCin4E7fSqH5crBzrOksw0T/yOS/mcJ8l4VVhTeJYkVZPAy7eleAweh6lCEhXf4lwyKeJaBRFL3/s6Z/We1pzz7+vqyZ8+ecx4jhKBLly51bZPTaNy46nAEOTk5DBo0CF9fX1577TU6d+7s4pap1CdCfEL4c9yfDP9+OMO+G8ZvY39zeaYlrVbOCe3SRaZ4HzzYRRUPGCDTNn3xBUyc6KJK6z9q/1l7NGgIREcgOlpXMdQpEKRhYx9mEjCxFxMLyOMwFgLREoc3bfEqLa61msbptcwO9GK6v5F3iqxclWuit0HLVD8jA2soRr218EQk3B0hwzoNSoBrgmFGY2jlngEZl5FlhflZMkvRzmIYGQJPR0Gsd+1E5tk4YFOYXGgh3mJnkq+BJ3wN+J9mLl1DEU+STle8WU4zQqsvo8jCzHPsoQQ7X9CDSAfP/8zDxIv8RRh+zGAofrgyll8tEdVk7Nix1Tru1ltvre4pz2DevHmiX79+IiAgQADCarVW2q8oinjhhRdEZGSk8PX1Ff379xe7du2qUR1z5swRU6dOrbTt5MmTQgghNm3aJNq0aVPl+/Ly8gQg8vLyalSfyoVLkaVIXPn1laL3Z71FdnG2W9rw4YdCNGkihEt/lj/9JETjxkJYLC6stG64+/5V+0/XX/9iYRc7RLGYL3LENHFc3CiOiE5iv2guEsQIcVgsF/lCEYrL2iOEEFl2RbxQYBYhJwrFoKxi8afJJhSldm1IMQtx70EhvP8TYvwhIY7Xn9uxWpjtQizKEmLUfiGM/wlxyW4hPjkhRLb1/O+tKZl2RUzMNwnf44XivjyTSLPZzzimRNjFS+K4aCf2i/kip8a/nT0iT1wr1oppYo8oETZHNb2cXFEiHhdLxNtijbCJM9tfF5x5/1b7cfD777+v1nHffvttLaSwJCQkhPHjx/Puu+9Wuf+tt97iyy+/JD4+nszMTC699FKGDh1KYWFh+TF9+vQ5o4waNeqc9UZERADQo0cPfHx8yMjIqPVnULl48DX48uvYX2ng14DLv76cjCLX/24efFAaJp94woWVjholo+X/+KMLK63fqP2n6/FBSyd8uIlgptOQH2jKdmL5l5aMJpCpHOc6jrKGIgSu8c8N1Wp40d/IoXBfrjDquDnPRL8cE8vMNkQNfYSjjfBZC9jaEdKs0Go7TE+RcyTrK0LA5kJ49AhEb4OJx6CDL+zpBGvbw/0NZLpMR2ESgreKLMRmFpNoE/wX5sOngV6Vwi4BHMPCdRxlMyX8RjNuIrhG0zx+I52H2cbNxDCNtng72AGpzALamGAe5RJ0HjI/ulrURLEmJiaKn3/+WezYscPhavhUVqxYUeWTfLNmzcS7775b/tpqtYrw8HDx9ddfV/vcpz/JFxQUCJtNPpUcOXJENG/eXNjtZz5FuNuSouK5WGwWccOPN4h2s9uJtPw0l9d/9KgQgYFC/P67Cyv98EMhOnUSopZWHFfjCfev2n96Vv9ZJOziQ5EpOon94iZxVGwWRS5vQ75dEW8UmkWDE4WiV2axWFRirbVldG2+EJfuFiJisxCz0oU4WY8soylmIV5PFaLdDiECNgpx90EhVuUJYXdS92JXFPFdsVU0P1kkumVKi/TZ2CSKRFeRKKaL48JSQ+unTSjibZEohorVYqPIqmuzq8SZFtAy3GoJvaY0UfXcuXMZO3Ys69ev55FHHuG1115zojQ+k7y8PI4cOUKvXr3Kt+n1erp27cq2bduqdY6xY8cyc+ZMvvvuO6644gqsVit79+6lR48eDBgwgLFjx/LFF1+g1dajpwgVt2PQGfj++u/pEdWDAXMHcCinep7QjqJJE+ktf999kJPjokrvvBPS0+HPP11UYf1E7T89F1+0PEQYq2lJL3y4nRTuJpk9mFzWhgCthqf9jByK8OVmbz3jCyx0zzbxs8mGUkPL6CUBsKYdfN4CPsuABluh4Ra4PAEeOQKfnIB/CyDH5pzPUhNybbAyH95Jh6H7oPl2WJEvY6Ie7wZftIABgef2Xq8tqyx2+mabeLbQwnR/A5tCvbnCq2rL5GLyuJ1kHiecaTTEUAPrpw2Fl0ngP7L5kh70JNRRH6GcPExMr68W0FLOa9g2m80AfPXVV6xevRpfX18URaFfv35MnjzZ6Q0sIz8/H4Dg4OBK20NCQsr3nY958+adsa1Xr17V7oQBpkyZgrE0cffQoUMZOnRotd+rcuGi1+qZM3IOT8Y/Sa/PevHTDT9xWfPLXFb/XXfBL7/Ao4/CN9+4oEIfH3jkEXjzTRgyxAUV1pz4+Hji4+MBsFgsbmmD2n9WxhP7zyB0PFEaYPwjsrieowzGnycIp2UNYj7WBT+Nhsf9DDzoq+eLEpmj/qVCmOxn5HpvHcZqet1oNHBtiCz5NthbAntKYHcJ/JItU4OmWSHSAB18oL0vtPeR6+18INDB6TWFkPVtK4JtxbC9dHnYDI2N0NUXrgyCL1vI6QXOYo9N4WeTjZ9NNpIVwTN+Bh7zNeB7lusqELxHFl+QzUdEM5CahSGwojCNvSRTzId0JdQJDkJlArQpwTziBAHqqv7zvD85b29vcnJyCA0NxWQy4evri81mQ6dzbcaYsrAAubm5lbbn5OQQHR3tsnbMmDFDDdGkUiVajZZ3rnqHDg06MHzecGZeOZOHejzkknzzGg189hl06CDTu59nGp9jGD8eXn8dtm6VSe09jFNFTn5+PrNnz3Z5G9T+szKe3H+Goec5GnIPobxPFsM4wnUE8ijhxOCaxBQ+Gg0P+xq4z0fPnBIbzxdZeKRAcJ2Xnhu9dVxu1FUKFXQuAvXQJ0CWU8mxSWG6p1guv8+S6ydtUhg2NkKoXs69DNHJZegp66e/LsvkpAg4YJIis0x0biuCTBu08YaufjIX+0MNoYsvhDv5kp4qPA/ZBcO8dDznb+Qao+4Mj/dTMaHwDMfZQgm/0LTKiAznwoyd59hDJmY+oCtBTvjtOFuAguv6z/OK0JkzZzJmzBj8/f3p2LEjvXv35tChQ0ybNs0pDTobQUFBNGvWjE2bNpUHRrbZbGzfvp1x48a5rB1TpkxhxIgRHvEEr+KZ3NPtHuLC4xj942h2HN/BrKtnYdQ5P1RGVJSMGfrAA9CvH5T6iziPsDC45x6YOROqsJJ5CvHx8SxZssQtdav9Z2XqQ/8ZiYHXaMQDhPIumQzmEGMJZjxh1c4FXle8NBoe9DVwv4+eDVaFH0027s63YBWC0d56bvTWM8CgRVeLB9wQPfQLkOVUMqxSlKZZpFDNscsYnEdLswzl2GTJLt1XrMj3+WqlIM2zg1VAR19p4RwZAtOjoZMv+Lnomau2wrOMLGzcTyoKgkU0JbyG33cJdp5lFyXYeZ8uBNRTAXoqzu4/qxWs3mazsWHDBlJTUwkNDaVPnz4EOCFQtd1ux2q1snr16nKvTZ1Oh9FoRKvVMnPmTGbNmsXSpUtp2bIlr7zyCnPnzmX//v34+zs3aq+7g12r1D+O5R3juvnX4W/05+cbf6aBXwOn1ykEjBkj44j++KPjs6qcweHDEBcH+/ZB8+ZOrqz2uPP+VfvP+t1/7sfM/8hgDUXcSQgPEkaQg72bq4NdCP4tFaS/mGzoNBrGeOm40VtPX4MWrQtGXE7FokgxWiZMA3QQ5+36TEtVCc8bvPXVFp5lJGHmblLojDdvEVnjWLJF2JjETrRoeJOO+DnhgcXVArQMp96/Dnd1qgNz5swRwBllxYoVQggZ5+75558XDRs2FD4+PqJ///5i586dLmmbJ3p3qng+RZYicdNPN4km7zQR29K3uaTOEyeECA8XYt48l1QnxM03C/HIIy6qrHZcDPev2n86l22iWIwVR0VnsV98JrKEyUmeyNXBqijiT5NN3JdnEuEnCkWTk0XiyXyT+M9S+7ij9Ykkq11MLzCLDhlFwvd4obg+p0TML7GKglq6068WhaKD2C/+J04Key1ix+YLi7hXbBaPiW1OiQEqhPSCnyiWiHec6AV/Npx5/9ZYhI4ePdrhjagPlH0JEyZMEMuXL3d3c1TqEYqiiFdXvyr8Z/iLn/b85JI6f/5ZiNBQIdLTXVDZli1C+PoKkZnpgspqzvLly8WECRM8QgSp/Wf97j8VoYgVokAMFYfEJSJJLBC5tRItjsSiKGKpySruzDWJ4BOFomVGkXg23ywOWN0nkp1BkaKIr4ot4rKsYuF9vFCMqqPwLONbkS3ixD7xi8it1ftzhFncITaKp8QOYXaSOMwVJeIxsUS8I/51uQB1dv9Z7dzxZfTt25f169c7xgxbj6jPw0kqnsGv+3/ltgW38Xifx5k2aBpajXOHUm65BQoLYfFiFwzLX3EFDBwIzz/v5Ipqh6fcv2r/eWH0n3LOYD7/I4MgdDxLAwbUIH+4szAJQbzZzjyTjV/Ndq710vGUn4HuBtdPH3AEQgg22RS+LLEx32SjiVbD3T4GbvXRE1HH+E12BK9xkl/I51Oi6Ylvjc+RhZlH2UFzfJlGOwxOGB7Pw8Q0/qIZITxCX7eEYXLm/VvjT+MKT18VlQuRa9tcy/p71vPtrm8Z8+MYCi2F539THfjgA9i0Cb7+2qnVSJ56CmbNgpISF1RWf1H7zwsDLRpGE8TftOA6AnmEVG7jGLtdGGO0Krw1GkZ665kf7E1CuA+NtBoGZZsYklPCn2Z7jbMyuYsMRfBukZXOWSUMyZHX9I8Qb3aE+TDRz1BnAVqEwgOk8g9FLKJprQToSUxMYBtt8Gf6BSxAnc2F94mczJQpU8pjZ6mo1JT2Ddqz8d6N5JnzuOSLSzicc9hpdYWGwuefw2OPQXKy06qRDBkCkZHw1VdOrqjmxMfHM2XKFHc3Q4ULr//0Rsv9pQHv2+PNGI7yGGkk4564tKfSVKfl3UAvjkT40s+g49Y8E72yTfxgsmHzQDFqF4KlZhs35JpoklHMr2Ybk/wMpEX48nGgF70MOoc8xKVj5QaOUozCQprStBYxPNMoYTzb6EIwz9EW/QUsQJ3df9Z4OP6SSy5h3bp1zmqPx3KhDSepuBebYuPJ+Cf5btd3/HzjzwxqNshpdd19N6SmwvLlTh6W/+47mD5desq7OA7m+fCU+1ftPy/s/jMVK2+TwW8UcCvBPEwYoS4K63Q+ioRgbomNt4usaDXwhK+BO330+LjZOn/QpjDXZOOrEhsCuMNHz53eelrpHSu6TCgsJp//kcnl+PEyjWqUAamMYxTzKNsZSAQTaVWjHPLVwYbCag7zM7toTYRHWEA9aji+vpjzVVQ8Gb1Wz3vD3uONK97gmu+v4ee9PzutrnfegYQE+PRTp1UhufFGMJth0SInV1R/UfvPC5toDPyPKBbRlINYGMghZpNFCYq7m4afRsMEXwP7w3140c/IpyU2mmcU82qhhRzFub9LIQTpdoWVFjufFFt5ssDM8BwTsZnFdMgqIcGm8HGgkcPhPrzib3SoAM3AxttkcCkH+YJsniGC12opQA9RxHi2MYSGDhegVuzEk8gj/Moi9nADHT1CgDqbGltCL1Yulid5FdezNGkpN/50I/Oun8eINiOcUsdff8ksSklJ0KiRU6qQvPuuDFy/YYMLvKGqj3r/updzXf9fSOVPTtCWANoSSBwBxOCD1sEWJnewjiJeI4MT2BhJIFcRQFe8PeKzCSGIt9h5q8jKJpvCvT56JvoaaKyrvegpEYIkm2C/XSHRppQu5et8AU20GlrrNcTptLTRa2mt09LFoCXcCUni92LiS3L4lXwuwZd7CKUfvrUWjvsp4HF2MIZo7qKZwwSoGRt/cYDF7MUPI9fTgb40cbn4PEQKBgw0puEZ+5zZf6oitJqUfQkTJkzw+IwfKvWPRfsWcduC2/jlxl8Y2so5v62rroJLL3WyA3tBgVS569dDp05OrKj6lGX8mD17tipC3cS5+s90TGwlh30UkEA+SRTihY44AmhLQOkykIZ4OXzo0xUoCFZSxG/k8xeF+KLlKgK4igB64oPOAz7TRqudmUVWfjfbaaqTV/n0otWAhqr3aYBMRXBUEfhpoI1OWy42W+u1tNFpiNVrz5qr3VEoCFZQxBdks5USRhPE3YTQqoapN08ngXwmsoNxNOE2mjqkrSVYiSeRJewjFB+upwO9aOzyBxQFhT/5jz/5jxu5kl60r7Tf2f1nrURoUlISjz76KBs3bqSgoKDSPmcmuncnqiVFxdn8sPsH7vn1HpaMXcJlzS9z+PmXLIGHHpJJjgzOzNs8dqzMnjRjhhMrqRmedP+q/ee5r78VhYMUsY98EkqF6WGKCURfbikts5qG1sKpxJ1YEKyniOUU8AeFaIArCWAYAfTFt1ZDxI7koE3hoF05M+PBqUVUfk3pMkSroY1OQ6RW4/IoEMUo/EIec8ihEIXbCeYWgh0yH3cv+TzODu6mGTfRuM7nK8LCMvbzG/uIJIAxdKQbUW55wMqjkG/4nRwKuJMRVVpBwQMtoX369CEmJoZbbrkFP7/KsdEuVAuhJ/2JqVy4fL3jax5e+jBLb11Kvyb9HHpuux1atZKp3seMceipK/PrrzBxIhw86DFD8p50/6r9Z82vvwk7SRSyl/xSi2kBxyimDf48SxxtcHwaVGdjQ7CJYpZTwDIKMaOUC9J++OJ1gc8FdATpWPmKHOaRSwwG7iGU4QRidJCg21tqAb3HAQK0ADO/sY9l7KcZIYyhAx1p5Dbr/l4O8Q1LaUdzbuBKvM/xQOdxIjQwMJDs7Gz0es/w+nMFnvQnpnJh8+mWT3nqz6f4c9yf9Iru5dBzv/kmLFsGK1Y49LSVMZvlkPzy5dC7txMrqj6edP+q/adjrn8hNn4kmW84xk005m6aYaynwk1BsI0SllHAMgrIQ+Fy/BmGP5fhX+M85hc6SZiZRRbLKWAgftxDKL3xcaig20Mej7OTe2nOjcTU+jy5lLCEBOJJojXhjKED7c5icXQFNuwsYTXr2MkNDKYXHc77Ho/yjgfo0KED6enpDm1IfeFCi3On4nnc3/1+XrnsFYZ+O5Rt6dsceu577pE+Q7t3O/S0lfHygtGjpYOSB+BpcULV/tMx/ac/eu6mOZ/RnY1kcyeb2EOeQ87tarRo6I4vz9GQf2nJPJoQg4E3yKAnB3iGdNZThMLF7cKRiY2pHGc4RwhEyx805zNi6FMHh6Oq2F0qQO+vowDdyXEeZQnJ5PECg3mBwW4VoBnk8C7fk8QxnmJctQSox8UJBdi9ezeTJ09m3LhxNDrN1XbAgAEOa5wn4UmWFJWLg7fWvcXr/77OyjtX0qHB+TuL6nLXXeDjAx9+6LBTnsmff8Idd8go+R4QM9ST7l+1/3T89beh8D3JzOEI1xPNfTTHC/f/7uqKQLADEwvJYwkF+KBhJEGMJrDOzjb1CRMKX5LDR2TRB18m04AWTpoPvIs8nmAHD9KC6+sgQNdxlNms5z56MYgWDmxh7dhCAj/wB73pyLUMwFCD+bIeNxz/ww8/cM8991BcXFz5ZBoNdrvdYY3zJDzpT0zl4uGV1a8wa+MsVt25irjwOIecc/NmGDRIBrAPCnLIKc/EZoPoaJg/Hy5zvJNVTfGk+1ftP513/Q9TxAz2UYCVKcTRiWCn1eVqrAhWUchC8vmTQtrgxXUEci2BRHhIQHxHIxAsoYA3OEkwOp6jAX3xO/8ba0mFAG3J9UTX+jxL2c/3bOcJ+tGtDudxBGYs/Mzf7OIAtzKMjrSq8Tk8bjj+qaeeYsaMGeTm5mK1WsvLherZqaLiLp4b8Bz3dbuPwV8P5mD2QYecs0cP6NDByTnl9XoZvH7+fCdWUj9R+0/n0Rw/PqYb1xLFRHbwDkmUcGEIewMariCA2USziVbcRjB/UEBfDnAnySwm3yOC4juKLRQziqO8ykkeJ4IlNHOJAB1fBwEqEHzPdn5iFy8w2O0CNJWTzOQbMsnlGe6slQB1NrWyhJYp4osJT7KkqFxcCCF46s+n+GnvT6y+czVNg+sep+6bb+DVV2UmJac5sK9dC9deC+npYHRvKB1Pun/V/tM11/8YxbzGPjIwM4U4uhHiknpdTQpWFpPHAvI5gY2rCGAUgfTB1yNikNaUY1h4gwxWUMgDhHEfofg62TFrJ7k8yU4m0JLraikc7Sh8wkZ2ks7zXE40zhpmOj8CwRq28SurGUxPhtIXbR2uocdZQocNG8batWsd2pD6guqYpOJqNBoNM6+cyYjWI7j868tJzU+t8zlvuAGys+Hvvx3QwLPRty/4+8t0TW7E0xyT1P7TNf1nE3yZTVduojFPs4uZ7KcIm0vqdiUxGJhAOH/RnO9pTABaHiONSznIDE6yFxOiHjg05WFnBicZwmH80LKCFjxGuNMF6A5yeYKdTKBVrQWoGRszWU0SmbzKULcK0CJK+IJF/Ml/PMj1DOPSOglQj3RMevTRR/n222+5/vrriYyMrLTvpZdecljjPAlPsqSoXJwoQuGBJQ+w+thqVt25ikb+dcu/OWUK7N3r5FTvTz8tLaHffOPESs6PJ92/av/p+uufSgmvsY9USniWOHoT6vI2uBIrgn8pYhH5/EEBTTAykkBGEkg0zsxUUXOsCL4nl3fJpB1eTKUB7fB2Sd3byWUSO3mEVowkqlbnKMDM66xEg4ZnGYi/Gx3GSjDzDt8RRjC3MQw/fBxyXo9zTLrsLI4GGo2Gf/75p86N8kTc3YmqqADYFTt3Lb6LrelbWXfPOgK9av9bPHYMYmMhMRGaOiYT3Zls2wYDBsCJE+Dr66RKzo8n3b9q/+me668gWEwaszlIL0JpRwCN8SUGH2LwuSC86auiCIU/KGAR+ayliO74cB1BXE0AQW76zFYEezCxmRK+JxcNMJUGXIafy4K3byOXp9jJo7Ti2loK0CyKeZl/iCSAiVyKlxsdxBQUPmUBArif0Q7NPe9xIvRixBM6URUVAJti44qvr6BHVA/eGvJWnc41ahTExcFrrzmocacjhKzg1VednKbp3Kj3r3vxpOt/HBOLSCOZYlIoIYUSSrDTEC+i8aExvjQuFaaN8SUK7wtGoJ7Exm/ks5h8EjBzOX5cRxCX4efUDE152NlKCZspYTPF7MCENxq648NgAriBIJemLN1GDk+xi4nEMpzI87+hClLI4xX+oQtR3EdPh4q+2rCQFezlEE9wGz4OtsaqItQD8KROVEVl14ld9Pq8F9sf2E6b8Da1Ps/ff8PNN8twnt7OGgGbPh127YJffnFSBedHvX/diydff4EgCwvJlJBCcelSrqdQghmFBnjRGF+a4csAIuhKcL10+jmVg5hZTD6LyCcXO9cQwEiC6II3XmhqbZEUCJKxlgrOErZQQiJmmmGgO770wIce+NACI1o3XMOt5PB0HQXofjJ4jZVcRWtuopPbUm+WsZ5dLGYlT3IbEU5wwFNFqAfgyZ2oysXJw0sf5lDOIZbeurTW5xAC2rWDyZPh9tsd2LhT2bcPunSBkyfBTfeOev+6l/p6/QWCTCwkl4rTRApYSQZaNFxOA66gAR0IdLsIqQsCwVZMLCKP3yggBzt6wA8t/mgJQFe+Ll9rq9x3Elu5pTMXOx3wpge+dMeH7vh4RCzTVWTwMgk8TizX1FKAbiaFd1nLbXTlKlo7uIU15yApfMTP3M8oWuOceVWqCPUA6msnqnLhkl2STeysWL4Z9Q1Xx15d6/N88IH0G/rvPwc27nS6doXHH3ei0j036v3rXi6k629DYRu5/MlJVpGBH3quKBWksfjXa0FqR5CHnUIUClAoQjltXe47vRRgJwQd3fGlJz50wtuj8t3bEXzKIRaQynO0ZSARtTrPPxzkCzbxMJfQlyYObmXNySKPt/iGq7mU/nR1Wj3OvH/d/2hSz5gyZQojRoxg6NCh7m6KykVOqE8oL1/2Mo/HP84VLa7AqKtdLM7bb5eW0E2boGdPBzeyjJtvloHr3SBC4+PjWbJkicvrVTmTC6H/1KOlJ6H0JJRJtGYj2fzFSR5iGw3wKhWkDWmK+xzxaosODaHoL6jYAblYmMZeTmLmM7rTrBYB7wWCRexlIXuYzCA6ULfIJI7AhIXPWEA34pwqQJ3df6qW0GpyIT3Jq1w42BQb3T7pxu2db2fSJZNqfZ4JE6CoCObOdVzbKnHkCLRuDWlpEB7upErOjnr/upeL4fqXYGcdWfzFCdaTTVN8uZIGDKYhkS4KOaRSmf0UMJndxBHAVOLwq4XdTSD4mm2s4TBTuIwWHiDRFQSfswgLFh5iDDonO855VLD63NxczGazQxuhoqJSO/RaPe8Pe5+XVr3E8cLjtT7P+PHwww+QmenAxp1Ks2YyX6gbnZM8AbX/vHDxQcdgGvAaHfmNS7mJGLaSy41s4AG28AcnsF1AaTU9naWkM55tjCKKV2lfKwFqR+Ej/uM/jvEyQzxCgAL8xhpOkMldXOt0AepszitCv/vuO6Kjoxk2bBiff/45nTt3plOnTvz000+uaJ+Kisp5GNRsEENbDWXK37XPatG+PfTpA1984cCGnc7YsTBvnhMr8DzU/vPixB89VxPJ23RmCZcwmIZ8yiFuYAPzSb4gMzd5ClYUZrKfWRzkdTowjqa1mqdrxc7b/EsimbzMECIJcEJra84m9rCW7dzPaIcFo3cn5x2O79WrF3/99RcFBQV06dKFhIQEfH19GTJkCP/++6+r2ul2LobhJJX6y5HcI7T/sD0r71hJz+jaTez85Rd48kk4eBB0zni4Pn4cGjeWQ/PRtUuPV1vcdf+q/adE7T+lc8wqMphHMkcpZiRR3EgMEW7MsHOhkYGZqezGisIMOtZ6GkQJVmaymmKsTOUyAjzkOzpMGrP5kXsYSVuau6xetw7H+/n5ERgYSHR0NO3atSM8PBxfX1+8vDzjS1FRUYFmwc14su+TPLr8URRRuyG/kSPBZoPff3dw48po1AgGDoQff3RSBZ6H2n+qlKErDev0Kd14i44kU8wNbOAl9pJEobubV+/ZRi53sZkm+PIx3WotQAsw8xJ/oyCYxmCPEaA55PM5CxlOf5cKUGdzXhFqsVhQFPmn9ssp87lsNnU4QUXFk3jm0mdIyU/hu53f1er9ej08+CDMnu3ghp3KRTYkr/afKqejQUMngnmdjnxNT3zQcT9beIzt/Ec2AtVXuCYIBD+QzJPs4B6aMZW4Wme4yqaYF/iTEHyYwmX4YHBwa2uHGQufsZCOtGIg3dzdHIdy3uH43NxcgoODK22zWCzs2LGDnk6L5+J5qMNJKvWB+bvn80T8E+x/eD8BXjWfw3TiBDRpIhMctXZGHOacHGkR3bsXWrZ0QgVV4677V+0/JWr/eW5ysLCAVH4hlTCM3EITrqABBg+KtemJlGDnDfazlRxepQMdCar1uY5TwEv8Q3sa8CC93Z6GswwFwRx+pZBiJnAjejc4Irl1OP70DhTAaDRW6kAPHDjA4cOHHdowFRWVmnNT+5toFdqKGWtm1Or9DRvKFO8ffujghpUREgJDh8qYoRcBav+pUh1CMHIPzVlIX64nhq84wvWs52uOcoxi1TpaBSmU8ABbOImJOfSokwA9Sg7P8Qc9ieEh+niMAAVYzlpSOME9jHSLAHU2tbrSd999d/mk+vnz59OmTRtiY2OZdxEMs02ZMoX4+Hh3N0NFpUo0Gg3vXfUe7/33HgeyD9TqHBMmwJw5UOisaWplgetdRHx8PFOm1D5ygKNR+0+1/zwbXui4jii+pzdP0YYt5DCOjYxkHS+wh4WkcoSii1qUCgTLOM49bKY7IbxPF8LqMG9zPxlM4y+uojV30s0t+ezPxlb2sZIt3M9o/N2U/MDZ/WetgtVHRkZy8OBBfH196d27N5MmTSIwMJCnnnqKnTt3OqOdbkcdTlKpTzyw5AGOFx1n8c2La/xeIaB7d3jgAVkcTmEhNGgAGzdChw5OqOBMPOn+VftPtf+sCWbs7CWfbeSylVx2k48fOroQTNfS0hw/jxJPziKRAt4mieOYmEgsg2qZfrOM7aTzFqu5lS4Mo42DWukYjnGc95nPnYygA66bulQVHpc7vqwxBQUFNGnShKysLLRaLcHBweTm5jq0gZ6C2omq1CcyijKInRXLjzf8yJCWQ2r8/i++gHffhZ07QeOM/7abboLYWHjlFSec/Ew86f5V+0+1/6wLFhQSSkXpNnLZRR5e6OhCULkwbYX/BSVK87HyGYf5jXRuJIY7aIpvHbOOr+cYH7COB+jNAA/yNhcItrKPn/iLK+nNYHq5u0melzs+IiKChIQEdu/eTZ8+fdBqtRQVFaFxyr+ViopKTYnwi+DFQS8ycflEdjy4A4OuZl6eY8fCU0/BmjUwYIATGjh2rAxK+vLLTlK5novaf6rUBSNaOhNMZ4K5E7ChsI8CtpHLRrL5jMPo0HANkYyjCSEY3d3kWqMg+J10PuIQbQjgK3rSxAHD0n9zgDls4XH60YMYB7TUMeRTyI/8xSFSuYkhdPUw66wzqJUInThxIj169ADknCaA1atX0759e8e1TEVFpU6M7zmeT//f3p3HRVXuDxz/DPu+CAi4ICAqiuKCGxLiglLumuWemaWmtpndCjUtzfu7Zab3Ztm1q6m55L6VkbtmLrjivgGKosi+yD48vz/QSQJZZ5gZeN695kWcOec53zkz8/jl2c6Z//Jt+Le80/mdCh1rYQGvvQbffKOhJPT552HsWDh1CmrRLHGQ9eff5aHECINK3dVGAiMMaIktLbFlDI3Ip4DLpLGKO7zEcYbRgBG4YVXFlsPqdoU0vuI6SeTxIc3oiqNaPiPbucxmLvIx3fDBWQ2RVt3TrZ/NaMTHjMNaS2NAq1uluuOhcEankZER7u7uAFy/fp3c3FxaVtMYr+omu5MkfbTn1h5e2vgSN966gZNlxcZP3boFzZvDnTuFqyqp3bhxhbPlFy7UQOFF6dr3V9aff9nMRfZxk440pCMNaYajTs1O1mcXSGUpkdwkg9G4MZQGmOv4DOsUcvmeKH7jAaNwYzRumKkh5izy+IFwzhLLDLrTGAc1RFt1ha2fe4gklpcI1snWT50bE1ob6do/YpJUXoPWD8LZ0pnv+39f4WN79IC+fQt7ztXu998LE9GYGDDQbNIhv7/aVdr1z0VJBPc5yV1OcRcFCjrQgI40oBUuGOt40qTrBIJwkvmeSOLIYSyNGEg9TNSU6CsRXCWNDJQ0xBxnzDCsRIulEsEOYvmeSFphyzs0oYGa7o1+k0QWcRRHLHiLLjjoQCujQHCaK2xiH964M5SeWpsBXxadSELr1atHbGxsmfu5ublx586dKgema+Q/YpK+upV0i1bfteLoa0dp69q2QseuXFnYUHn+vAYCy8+HevVg48bC23lqkLa/v7L+LN/1V1LAFeI5SQwnieERubSjPp1oSFvq6cwdbPSRQHCEBP5LFI/IZzwePI8zRpVIRmPJ4iTJnCSJ0yQDYIsx98lGAdTDnIaY0wBzGmLx+Kc5dZ+RoF4glYVcJ5183qMJAThW9eUChWNKd3KFn4lgKC0ZSAudaGV/0voZ9bj1s40Otn4+TSeSUHNzc7799lvK2v29994jNTVVLcGpS0FBAUFBQVy5coVJkyYx76kZuZcvX+aDDz4gKysLU1NTdu/eXWIZ2v5HTJKqInRfKEfuHOHwq4crNAEmPb2wK/7oUWjTRgOBTZkCSiUsXaqBwv+i7e+vrD8rfv0FgkiSOEkMJ7hLHOn44kpHGtKB+thU8t7gtZ0SwT4e8gNRGACv40EP6pY6mz6DfE6TTPjjxPM+2bTCho7UoSN1aIY1hijIp4AHZHOXLGLIIoZM7j3+//tkYwDUx5wGTyWml0ljHw8ZQyNG0rDSt9z8u2Sy+A9/8oAM3iWApmpKbKtCIDjFFTbrQevn03QiCXV3dy/XP16mpqZcvXq1yoGpW0xMDPv27ePmzZuqSjQvL4+QkBA2btyIg0Pp40O0/Y+YJFVFRm4GPt/68G6nd3nP/70KHfvKK+DgAF9/rYHAjhyBwYPh/n0w1lwrl7a/v7L+rPr1v0fa44Q0hkiS8MaJttSjEXa4YYcDFnJyUwXkU8AvPGAF0dhizAQ86IIDisfJ5BXSOUkSJ0niMuk0wJwO2NOJOrTBDssKTnTKp4D7TyWod8nkLlnYYMxEPHBVU9c7wBnu8Q3H8MWFN+iIpQ6sEJD6uPUzmlhephet0cR9kTVDJ5Zoio6OVuuJq1vDhg2LbTt+/DhWVla88cYbJCYm8uqrrzJu3DgtRCdJmmVlYsXPQ3+m56qedGrQiS4Nu5T72LFjC1dU+uILDeSJAQFgbg5798ILL6i5cN0h68+qq48Ng/FhMD4kkslJYrhIHAeI5AHpmGFEQ2xxw46G2OGGLQ2xw1a2mJbICAMGUo/ncWYbsXzOVRpgjgMmnCYFQxS0x55+uPIpPrhU8ToaYUBDLGiIBf5qeg1/l4eSNZxjLzd5nQ4E4aH1P0yebv1sjgcfM04vWj+ri86s2bB+/XqWLFnC+fPnSU9PJy8vDyOjv8ITQjBnzhyWLVtGamoqfn5+fPvtt1WaTXrv3j3Cw8OJiIjAwsKCrl270qVLF5o10+3xGZJUGZ0bdGZ+j/kM2zSMsxPP4mhRvu6p7t3BzAx++w3691dzUAYGhQvXr19fo5NQTatt9acDFrxAM9VdbnJRco9UYkjlDimcI5adXCGeR9hgWiwxdcMOCzm+FCi8VegwGtIfV7YRSx4FjMWdpnq24P090ljEHyhQ8AUvUA/t91imk8l6wogmlhGE6HTrZz55GGCIQTWPmdWZJNTe3p7JkyeTlZXF+PHjiz2/YMECli9fTlhYGF5eXnz22WeEhIRw7do1rKysAOjcuXOx41xdXdm6dWuJ56xTpw6dOnXCyalw6Zpu3boREREhk1Cpxnq709scuXOE0VtG8+uoXzFQlF3hGBjAmDGwapUGklAoTEJ79oTly8FQzoSujNpef5pgiAd18KBOke2Z5HH3cWIaQwp/cocYIkghm/rY0ARHvHCgCQ64YVerZ+JbYMRI3LQdRoUJBAeIZDmn6E0TRtBaJ97H69xhFbvwoB6hvIalGocbqJsSJTtZgQuN8Cekek8udMyBAwcEIPLy8opsd3d3F4sWLVL9npeXJxwdHcWqVavKXfaKFSvEjBkzVL+npqaKVq1aiaysLKFUKkVgYKA4e/ZsicempqYKQKSmplbsBUmSjknJShFe//YScw/NLfcxV68KYWoqRGKiBgLKyyss/OpVDRReqLZ8f2X9WT5JIlOEixixVpwVn4q9Yoz4WQwXa8XH4jexXJwSR0SUeCDSRIEo0HaoUikyRI5YKI6I8WKTOCvuaTscIYQQ+UIpdorD4n3xtfhDnNX5z1CBUIpdYqVYIf4pssSjEvfR5PdXZ1pCS5Oamkp0dDQdO/51D1UjIyPatm3L2bNnGTNmTJlljBgxgoiICDIzMzl+/Di7d+/GxsaG2bNnExwcTH5+Pv3796eNRqYAS5LusDWzZeNLGwlYHkCXhl3o4dGjzGOaNSucHf/zz/Dmm2oOyMgIWrYsvFG97IVQO1l/FmePOe1poLplYwGC+6RzgwRukshOrnCbFCwwxguHx62ljrhhRwEFZKMkmzxyyCf7qUfJv+eRj6AZjrSlHg2x1fo4xZrgGvEs5ij1sGEBfbDTgZbGRFJZyS5yyOV9RuOqAzPySyMQ7GMLsdxmBO9gpoWxqnqRhKalpQFgZ2dXZLu9vb3qubKsW7euxO0vvvgiL774YpXikyR908alDYtCFjFy80jOTjyLq7VrmceMHVu4bqjak1AAX9/CJPSllzRQeO0m68+yGaCgPjbUx4ZueAKF40yjSeYGCdwgkcNE8YAMFIApRpipHsaYYvj459PbjbDCArPH/8xeIo6ficAGU9pQj3bUoxUucu3TCipAsJ3LbOICw2lNX7x1YuzqWa6xnjDa4c1gumOiB+/rUXZzg/OM5F2stDSGVi1JaEFBAXv27MHIyIiePXuqo8giniwJkJKSUmR7cnIy9evXV/v5ShMaGoqJSeFyDyEhIYSEVPP4CUlSk9fbvc6RO0cYvnk4+17Zh5FB6dXBsGHw3ntw7ZoGGix9feHAAbUWGRYWRlhYGAC5ublqLVudZP2pm0wwpCmORdaXrMp97gfSglyUXOYhZ4nlJ87xkAzVUlOylbRsaWTzDce4Sxqf0Usnbr2ZSx5b2M9ZrjGCEJ1feP6JUxzgHEcYztvYlnAdq63+VEef/rBhw8Rbb70l2rVrJ/Ly8sTixYsrXVZpY5qeLjcvL084OTlVaExTVejamCZJUoeMnAzRYkkL8fHej8u1/9ChQoSGaiCQffuEcHfXQMGFdPn7K+vP2uuBSBO7xTUxXxwQI8Q6MVFsEUvFcXFC3BGZIlfb4emUKyJOTBBbxBfikMgQOdoORwghxD3xUHwu/ie+FmtEotCfz3aEOCYWiQ9ErIgu1/6a/P6qZS5+XFwc//73v7GxscHIyIhdu3ZVuAylUkl2drYq487JySE7O5uCggIAJk+ezIIFC7h48SJZWVnMnj0bY2NjBg8erI6XUG6hoaGqvw4kSd9Zmliy6aVN/Ofkf/jl+i9l7j92LKxeDY+/lurTqhVER4MG7hYUFhZGaGio2stVF1l/1l7OWPM8TfmYbvzIS0yiMyYY8RPnGMcm5rCXfdwim3xth6o1T7rf57KfATRnOoFaX3y+8BaoZ1nIGlrTlLcYTh0dWBKqPK5znn1sYjBv4EqjMvfXeP1Z3mw1LCzsmc/16tVLpKeni+7duwshhAgKCqpwNrxixQoBFHscOHBACCFEQUGBmDVrlnB2dhbm5uYiMDBQREREVPg8lSX/kpdqsjURa0Sdf9UR0cml/2WcmyuEk5MQe/dqIAhXVyH++EMDBWv/+yvrT1l/VtQDkS52iStimtglRov14r/ihLglNLE8he5KE9livjgg3hRbxXURr+1whBBCZIhM8YPYKmaKb8V1cVvb4VRIlLgqvhbvixuiYt99TX5/y33bTjs7O9WYov/+979MmDBB9dyBAwf46KOPSE1N5cMPP2T16tXs379fTWmybtD2bf8kSdMm7ZrEuQfnODzuMCaGz25peO89SEwsXDdUrZ5/HgYMgMmT1Vyw9r+/sv6U9WdlCQQ3SWQvN/mDaBpgSzBNeI5GNXpS03USWMgRPKjDFDpjham2Q+IWd1nFLupTl5E8r1d3Poolig18Sy9exocOFTpWJ+4dX79+fc6fP4+joyM2NjbFZlVGR0ezfv16cnJymDBhAq6uZc+21SdP3oQpU6bQv39/nR5QL0mVkZ2fTZf/dSHQLZDFLyx+5n7nzhXebTMuDh6vc64eH35Y2B2/dKkaCy3sTtq5cydLlizRWhIk609Zf6pDJnkcIYo93OQB6QTiTjBeOjFBR10Egl1cZT3nGU5r+uGt9claBRTwO8fZy0n605WutNV6TBURTyzr+TcB9KEdXSt0rKbrz3InobNnz+a///0vzz//PD///DN79uyhbdu2WFjoz18CVSH/kpdqg1tJt/D7rx8/DPiBoS2GPnO/1q1h2rTCMaJqs2YNLFkCf/6pxkILafv7K+tPWX+qk0BwiyT2cZMjRFMPG4Lx4jnc9fp2pBnksITjRJHENAKLrEygLalksIpfSCWdV+lPA5y1HVKFJBPPOhbThufowvOVKkMnWkIB9uzZw65du/jmm2+wtLQkKyuL5s2b06FDB9XDz89PrQHqClmJSrXF1itbeXX7q5x64xRNHJqUuM/ChbBrF6i11/jCBejSpbA11EC99y/Whe+vrD9l/akJWeTxB7fZyw3ukUYAjQjGCy8c9Kq17gYJLOQPGmHHFPyx1oHu9ytEsZpfaYEHLxGMqZYnRFVUBqmsZRFetKI7gyv9edCZJPQJT09Pbt26xZUrVzh16hTh4eGEh4er7qhRE8nuJKk2eT/sffZF7ePY+GOYGxe/E8mDB+DmBjduQKOyJ1iWT25uYf/+1avg6ammQnWjO/5psv6U9aemRJLEXm5yhCjMMcYHZ9XDBSudTEoFgl+5xlrO8TK+DKC51uNUouQX/uAIZxlKMJ1oqdV4KiOLR6xjMa404nlGoKByf9jrTHd8eeTn52NkpBc3Yaow+Ze8VJvkKfMI+jEIHycflg1YVuI+ffuCvz/MnKnGE7duDZ9+CoMGqbFQ/fj+yvpTUpcc8rlBApd4yCXiuE4CNpjqVFKaQz73SWcjF7hJIu/xHN44aS2eJ5JI5Ud2kUsurzIAFz0cb5tLNhtYghV2DOBVDDCsUnma/P6qtcarqRWoJNU2xobG/Dz0Z1ovbc2AZgPo36x/sX3Gji1MQGfMAIW6/i17cvtONSeh+kDWn5K6mGJES1xoiQtQNCk9TBQ/EF4tSWkO+Twgnfukq34++f8ksjDHiNa48iUvYIOZWs9dGRHcYC2/0ZZmenPrzb/LJ4+t/IAJZvTjlSonoJoma70KCg0NfWZ30tOLRUtSdTExMcHMTP0VeEPbhnzR6wum7p5Kd4/uWJkUnQo/YABMnAjHjxe2iKqFr29hgWr0pDtJ0r7S6k9Jc8qblLbAGWcsMcQAQwwwevz463eF6vei2w14RG6RRPM+aSSRhRlGuGCN6+OHN06q320x03rXO0Ae+ezgECe4yHBCaIe3tkOqlAIK2MUq8sjhZaZipIYkWtP1p1q742uyspqjs7Oz8fDw4MGDB1qITqrNXFxciIqK0kgiWiAK6PZjNzrU68BXIV8Ve37SpMKfaltV6fffYcqUwsGmaiS7g7VLXn/d9nRSmkIW+RSQTwFKCshHPP5ZUOSnEkE+StXz5hg/Ti6tcMFGlXTa6Uii+SzxJLOCnSiAV+mPE/baDqnSDrCNW1xgFNMwx1Jt5epNd3xtlpuby4MHD4iJiZGVrFRt0tLSaNiwIbm5uRpJQg0UBnzf73v8/uvHaN/RtHVtW+T5V14pHBu6aBGo5fS+vnDrFmRkqHkRUkmSnuXvLaW1xWmusJ7f8acV/emKsR6nROf4g0ucUHsCqmn6e8V1lI2NjUxCpRqluVNzpneZzoRdEzg+/jiGBn+NMfL3Bycn2LEDXn5ZDSdzdgZHR7h0CTp1UkOBkiRJReWSx2b2c57rvEJfWuGl7ZCqJIrLHGQbQ3kTex2Y3FUR6l2MT5KkGik0MJSU7BSWhC8psl2hKGwNVdstPBWKvyYnSZIkqdl9EviKn3hAAv9grN4noA+5xw5+JIQRNKCxtsOpMJmEVlBoaChhYWHaDkOSqpWZkRlL+y5l5v6ZxKTGFHluzJjCoZxxcWo6mZqT0LCwMEJDQ9VWnlR5sv6UtOkWd/mKn2hJY95mOHXQ717LDFLZwvd0pCfN0cyNLjRdf8qJSeVU1sBcOfBe0obq/tyN3TaWtJw0tg7bWmR7jx7Qr1/hrTyr7McfYcUKOHRIDYUVkt9P7ZLXX9K2JFJZwE+8QBcCaVv2AToulxzWsxhH6vECozQ6+UuT31/ZEipJUrkt6LWAI7ePsO3qtiLbX3kFVq5U00l8feH8eZB/H0uSpAY55LKMrbShaY1IQAso4BdWYYIZIQzX6dUHyiKTUEmSys3J0okFvRcw9deppOekq7a/+CLcvAnnzqnhJC1aFM6Oj4kpe19JkqRSFCD4id2YY8aL9NB2OGpxkG0kEccgXsdQz+eXyyRUkqQKGdt6LE0cmjBz/1/367S2LkxE1TJBycwMmjWTk5MkSaqyMI4RQxyvMQBDHb97UHmc4TCXCWcIEzHDQtvhVJlMQiuotg6sj42NZezYsTg6OmJtbc2wYcNISUnR2PliYmIYOnQotra22NjYMGTIEO7cuVPp8rp160a3bt3UF2AtplAoWNp3KcvOLCP8Xrhq+9ixsGYN5OWp4SRqnJwkJybpjtpaf0racZ7r7CecCQzGqgYkbJFc4jA7GMwb1bYUk5yYpCNq88SkqKgoAgMD6dChA5MmTSI+Pp6pU6fSr18/fvrpJ7WfLzMzk9atW2Nqasq8efNQKBTMnDmTzMxMIiIisLSs+EK8TxLQgwcPqjdYLdPm5+7Tg5+y7do2wt8Ix8jAiIICcHeHb78tnKRUJf/8Z+G40PXr1RFqjf5+6gN5/aXqdo+HLGItY+iLL020HU6VPeQe61hECCPwpl21nltOTJK0RgjBiBEjaNOmDVu2bCEkJITRo0fz9ttvs3HjRrKzs9V+zmXLlhEZGcm2bdsYNGgQAwcOZMeOHdy+fZvvv/9e7eeTKuej5z4iKy+Lf5/4NwAGBjB6tJomKMm1QmuNPPJRotR2GFINkk4my9hKMJ1qRAKaTgqbWUonelV7AqppMgmVSrV161ZOnDjBwoULUSj+moHn5uZGbm4usbGxaj/njh076Ny5M15efy0i7OHhQUBAANu3by/z+PXr1+Pt7Y2pqSk+Pj5s3bq1xP3Onz/PgAEDsLe3x9zcnICAAI4cOVJsv3Xr1uHt7Y2ZmRmtWrVix44dsnsfMDUy5ft+3zP74Gxup9wGCrvkd+6EhIQqFu7rC9eugQb+yJF0y0FOs4CfuIu6FpqVarN8lCxnO2640pvO2g6nynLJYQv/xYPmdKKXtsNRO5mEaoEQkJam2Ye6BlksX74cf39/PD09yc/PVz0yMjIAMDIqOjNPCFFkv2c9lMpnt3xcunSJli1bFtvu4+PD5cuXS4137969jBw5kiZNmrBlyxY++OAD3nnnHa5du1ZkvzNnztClSxeSkpJYtmwZmzdvxsHBgeDgYE6fPq3ab8+ePYwaNQpvb2+2bNnC9OnTeffdd7l+/XqZ1642CHIP4uUWLzPl1ykIIWjWrPBWnitWVLHgBg3Axqbw9p1Sjdad9rSkMV+zll85Sr5sFZUqSSDYxF5yyGUUz+v10kVQuBTTLn7EDAt6MUzvX0+JhFQuqampAhCpqamVer7ovkIUpomae5QjjDLl5OQICwsLAZT4MDY2Fnl5eUWOOXDgwDP3f/oRFBT0zPMaGxuLDz/8sNj2GTNmCENDw1Jj7tKli2jevLlQKpWqbceOHSt2zh49eghvb2+Rk5Oj2pafny+8vb3FwIEDVdv8/f2Fj4+PKCgoUG07depUma+hulTkc6cpCY8ShOMXjmLjpY1CCCE2bBDCw0OI/PwqFhwUJMTy5VWOTwjduE61WXmu/x3xQPyfWCH+KVaIO+JBNUYn1RSHxRkRKr4RiaJmfM/3ik3if2KeyBKPtBqHJutP/V5gSgtCQ0Pp378/ISEhlS7D2hpSU9UY1DPOUVWXL18mMzOTJUuW0LFjxyLPDR8+HHt7+2ItoX5+foSHh1MWa3UE+DdKpZLw8HA++ugjDAz+auTv3Lkz7u7uqt+zsrI4dOgQoaGhGBgYkJ+fr3ouODiYNWvWqMo7deoUH3/8cZGhCH5+fnh4eKg9fn3lYOHAwt4LeXv32/Ty7MWgQba88w6EhUGfPlUoWE3jQsPCwti5c2eVy5GqrrT6syHOvM8Y9nCcRaylBx3oTWeM9XwdxMpKI4No7uOIHS44YlATW8HU6Dq32c4hJjNU72/HCXCKA1zlNKN4X6tLMWm6/qyd3+4qmD9/fpVnhykUhT2Nui46OhqA5557Dl9fX9X2uLg4oqKiGDFiRLFjrKysaNOmTZllP53U/Z29vT3JycnFticlJWFvb//M4xISEsjLy8PZ2bnYc09vS0pKQqlUMnfuXObOnVtiWQUFBary6tatW2p5Eoz2Hc3K8yuZsX8G3/T5hgkTYMkSNSSh69ZVObaQkBD8/f1ZsmRJlcuSqqas+tMIQ14ggFZ4sYbfiOAGo3gBN1yqfO488rnATU5wkWhiccQOVxxxwQFXHHHFEXtstNbl+YgsbhLDde5wnTs8JAkXHEgkFWOMaEJDmuBGE9xwpk7N7JqtpHiSWc4OXqQnnjTQdjhVoiSfA2zjCqd4kUnY4aDVeDRdf8okVHqmJy2EhoZFF/hdvXo1CoWCV199tdgxhw4donv37mWWHRQU9Mzlknx8fLhUwljAy5cv06JFi2eW6ejoiLGxMXFxxSc4xMXF0ahRIwDs7OwwMDBgypQpvPLKKyWWZWBgoCrv4cOHJZbn5ub2zFhqG4VCwXd9v6PN920Y7TuaN97ozD//CZGR4OlZyUJ9feGjjwpHmJTyR4tU8zTAmemMZg8nWMw6utOeEPwr3CoqEMQQxwkucporWGJOJ1rSl+dIIIUHJHCbB5zgIvEkY4wRLk8lpi444ooDdlirPenLIodb3OU6t7lBDLHE44IDTXGjP4F40RALzFCi5A4PuEEMEdxgGwcxxxSvp5JSJ+xqbVKaRQ7L2EoHfPCnlbbDqZI0ktnBcgpQMoYPtJ6AVgeZhErP9KQL+9KlS/j4+ADw4MED/vWvfzFhwgQaN25c7Bh1dMcPGDCA6dOnExkZiefjDCY6OpqjR4/yf//3f888ztDQkA4dOrBp0ybmzJmj6pI/ceIE0dHRqiTU0tKSwMBAzp8/T7t27Yp03f+9vPbt27N582bmzJmjar09ffo0UVFRMgn9myYOTQh9LpSJuyZyduJZBgww4Pvv4V//qmSBPj6QlAQPHoCrq1pjlXSfIYY8TxdVq+gFbjKS52lE2Z+FdDI5xWVOcIFEUmmLNxMYjAf1Vcna31tX88gnnmTuk8B9EojkLkc5TwIpmGKMCw4444AlZphjhhmmmJf4MMMUk2Ld5znkEsk9bhDDDe4QwwMcsKMpbvSiE01oiDXF10A2xBAP6uNBfXrTmTzyucMDrnOH01xhE/uwwlyVkDalIQ7YVf7CV5MscjjLVRJJpQHONMKlwi3RBQhW8wu2WDGIbpoLthpEc41d/EgTWtOTFzHCWNshVQu5WH051cbF6oUQ+Pj4kJOTw9dff01OTg6zZs3C3t6effv2YWGhmXEqjx49onXr1pibm6sWq581axbp6elERERgZWX1zGP37t1L79696devHxMnTiQ+Pp7Zs2eTm5tLs2bNVK2vZ86coWvXrvj7+zN+/HhcXV1JSEjgzJkzKJVKVbK7Z88eevfuzcCBA5kwYQIJCQnMmTOHrKwsmjdvzv79+4HCFuCePXuyfPnyZ7auaoKufe5ylbk0WNiAn4f+DNHdeekluHu38E6cldK0KfznP1CFMdige9eptqnq9VeiZC8n+Z3jBOHHC3Qp1iqqRMllojjBBS4RhQf16ERL2tAUU0wqHXse+cSRyH0SiSeJTHLIJkf1M4tsssgli2yyyUEACsAMU1WiaogBscRjgxVNHyeLTXDDnqqPjc8lj2hiVcntbe5jixXu1KMBdWmICw2oiyXmVT5XVRUguEUMx7nAOa7jggP1cOIucdwnAQvMccMFN1xo9PhnSYn5E7s4wlmuMo3ROvH6KkNQwHH2cII99GQorXRwWSlN1p+yJVR6JoVCwdatW3njjTd4+eWXcXR0ZPTo0cyaNUtjCSgUtlTu37+f9957jzFjxiCEoGfPnixatKjUBBT+mlg0Z84chgwZgpeXF4sWLWLx4sVF9mvXrh3h4eF8+umnvP3226SmpuLk5ES7du2YNGmSar9evXqxZs0aPv30UwYPHoyXlxdfffUVn332Gba2tqr9hBAolUoKCgrUezH0jImhCSNajmB1xGr+N6A7devChg1Q6bzc17fwzklVTEIl/WaIISH4P24V3c0FbjKK53GnHvdJ4AQXCecSRhjSkZYMpBtOPHv8eEUYY0QDnGlA2ePACxDkkPs4Of3rkUsebrjggK3au81NMKYpjWhKYU9PDrlEEcsdHnCbB/zBeZJIpQ62NMSZho9fS0PqlprgqVMSaZzkIie4SDa5dKAF0xhFff4ab59LHvd4qIr7DFd5SBL2WD9OTF1VCao5ppzmCoc5o9cJaDaZ/MpqEnjACN7BmYbaDqnayZbQcqqNLaFSye7evYuXlxczZsxg1qxZWo1FFz93p2JP0WNlDx5Mf8Dy7y1YvRpOnKhkYXPnFi5aX8Xbw+ridapN1Hn9lRSwj5OEcQwn7IgnBV+a0ImWNMUNA7n8dTGPyOIuD4nhweOfccSTjB3WqoS0IS40xBkbLNWSKOeRTwQ3OM4FbhKDN+50phU+NMYIw7ILoLDLPuZxUhrDA+7wgCTSqIs9KWQwjv74UHxYmD6II4btLMcRF/owRqsz4MsiW0IlSUuysrKYNm0awcHBODo6EhkZyRdffIGFhQWvv/66tsPTSX6uftS3qc/2q9sZM2YEH30Ep05B+/aVKMzXFzZuVHuMkv4yxIDedKYVXtzhAa3wwoLKjveoHSwxpxmNaPa4tRQKE7y7POQuccQQxxmu8ZBETDHBAVvqYIvD48fT/1/a0IYnE8GOc4HTXMEaCzrRitH0wZbSe7FKYo5pkVZegHQecYc4jDEssl2fXOAY+9hMZ3rTiWAUtfgPJ5mESlIpDA0NefDgAVOnTiUxMVE1qWnjxo24yskyJVIoFIzxHcPqiNWMaDWCMWPgu+/gf/+rRGG+vnDlCuTmgknlx/VJNc+TZZWkyjHH9PGyT391AeeQSwIpJJJKEmkkkso17pBEKomkkkMulpg/lZjaPP5px0OSOM4FEkmhLd5M5EU8qKf24QfWWOJDZZfc0K588tjHJm4QwSDewJ1m2g5J62QSKkmlMDExeea956VnG+07mjkH5xCXEcebbzrTuTN8+SXUqVPBgho1AnNzuHq1MCGVJEljTDGhPnWLjNV8QiDIJJvExwnpk8T0ErdIJBVrLOlO+ypPBKupUkhkB//DACPG8g+s1TRmWd/JJLSC1HHHJEmq6dxs3QhwC2DdxXW82/ld/Pzgxx9h2rQKFmRgAK1aFd45qZJJqLxjku6Q9af+UqDAEnMsH89gl8ovkkv8wmpa0J5uDMJQj1IvTdefcmJSOcmJSZIu0uXP3fKzy/nm5DecmXiG9eth5ky4fr0wr6yQN98svA/tF19UOhZdvk61gbz+Um2USTrhHOAsR+jNMFpQmYHx2qfJ72/tHQ0rSZJGDW0xlCsJV7j08BJDhkBGBuzZU4mCnizTJEmSpOMKUHKTC2zjB77jE+4TzSje09sEVNNkEipJkkbYmNowyHsQqyNWY2ICb7wB335biYJaty7sjpckSdJRiTzgINtZymz2sglHXHmNUIbzNk7U03Z4Okt/BiZIkqR3xviOYeKuiXze43MmTDCkcWO4fbtwvlG5tWxZeOvOhw+hbvEJE5IkSdqQQxZXOcsFjvOQuzTBlz6Mxo2mcr3acpJJqCRJGtO7cW9ylbkcjD5IT8+e9O0L338P8+dXoBAbG/DwgAsXoGdPjcUqSZJUFkEBMdzkAie4zjkccKEVnRnKJJ1ecF5XySRUkiSNMTIwUt3Gs6dnT6ZMgZEjYfZsMDWtQEG+voVd8jIJlSSpmgkEqSRxmZNc5CS5ZNOCDoxiGnWpr+3w9JpMQiVJ0qhXWr9C0I9BfNv3W3r0sMDeHjZtglGjKlDIkyRUkiRJDQSCXLJ5RDqZpPOINB6pfqY93vbXdkEBnrSgG4NojI9eLbOky2rNVSwoKCAoKIgrV64wadIk5s2bB8Dp06d5//33VfscO3aMhw8fYm8vF5KVJHVo69IWN1s3tl3dxshWI5k8uXCCUoWT0F9+0ViMUulk/SnVBA+5xz42kU4yj0gnnzwMMcQSGyywxhIbLLHGClvq0gBL1bbChwkV6b6RyqPWJKEGBgasXbuWffv2cfPmTdV2Pz8/Dh48CMDevXtZtGiRrEAlSY2e3MZz1flVjGw1krFjITQUzp2DNm3KWUjr1nDpEuTng1GtqbZ0hqw/JX2XzEM28i0+dCSAPqqE0xRztd9aVCq/WjV9q2HDhqU+/+OPP/Lqq69WTzCSVIuMajWKfVH7uJ9+Hzu7wnGhFVquydMTDA0LV7uXtELWn5K+SiOZDSyhJR0JYgBuNMEBZ8ywkAmolulUErp+/XoCAwOxsbFBoVCQn59f5HkhBLNnz6ZevXpYWlrStWtXLl68qJZzp6WlcejQIQYMGKCW8mqa2NhYxo4di6OjI9bW1gwbNoyUlBSNne/AgQM899xzmJubU6dOHcaMGUNcXFyx/WJiYhg6dCi2trbY2NgwZMgQ7ty5U+nzduvWjW7dulUhcqkkDW0b0rVRV9ZdXAfAlCmwZg2U+yNkaFi4VJMcF/pMsv6UpOIekc5GluBOc7oyQCadOkanklB7e3smT57MokWLSnx+wYIFLF++nLCwMBISEggICCAkJISMjAzVPp07dy72GDx4cJnn3rBhAwMHDsTExERdL6fGiIqKomPHjqSlpbFmzRq+++47wsLCmDp1qkbOd+TIEXr37o2dnR2bN29m8eLFHD58mJ49e5KTk6PaLzMzkx49enD16lVWrlzJ6tWruXHjBt27d+fRo0caiU2qvDG+Y1gdsRoo7IZv3RpWrqxAAXJyUqlk/SlJReWQxSa+oy4N6MXLMgHVRUIHHThwQAAiLy+vyHZ3d3exaNEi1e95eXnC0dFRrFq1qtxlr1ixQsyYMaPY9oCAABEeHv7M41JTUwUgUlNTK/W8viooKBCdOnUSffv2FQUFBarts2bNEiYmJiIrK0vt5+zZs6do3Lhxkfc/PDxcAGLJkiWqbYsWLRIGBgbixo0bqm2RkZHC0NBQfPXVV5U6d1BQkAgKCqp07NVNnz53adlpwnyeuYh4ECGEEOKnn4Ro2lSIpz5Wpfv3v4Xo27dS59an61RV+lh/SpK65YocsUZ8LTaJ70S+yCv7AOmZNPn91amW0NKkpqYSHR1Nx44dVduMjIxo27YtZ8+eLVcZI0aM4Msvv2TNmjUEBweTl5cHwM2bN0lNTaV9e3lv17/bunUrJ06cYOHChSgUf/0V6ebmRm5uLrGxsWo/5/Hjx+nVqxdGT01Aad++PQ4ODmzdulW1bceOHXTu3BkvLy/VNg8PDwICAti+fXuZ51m/fj3e3t6Ympri4+NTpOy/O3/+PAMGDMDe3h5zc3MCAgI4cuRIkX3WrVuHt7c3ZmZmtGrVih07dsju/adYm1qrbuMJMHQoJCXBvn3lLEC2hFaarD+l2kRJPtv5HwoUDOA1uZySDtObdyYtLQ0AOzu7Itvt7e1Vz5Vl3bp1JW738vLiwoULVYqvIoQQpOema/Qc1ibWRZLGylq+fDn+/v54enoWGWP2pAvP6G8zlYUQKJXKMstVKBQYGhqW+JyhoWGJ3XqmpqZFxrBdunSJgQMHFtvPx8eHjRs3lnr+vXv3MnLkSPr27ctXX31FfHw877zzDnl5eTRr1qzIvmfOnCEwMJC2bduybNkyLCwsWLp0KcHBwfz555/4+fmxZ88eRo0axYABA1i4cCHx8fG8++67ZGdn07Rp0zKvR23xSutXGL9jPP/s+U9MTQ15/fXCCUrBweU42NcXYmIgORnkDOwKqUn1pySVpoACfmEVmWQwjKkYI4eI6DK9SUJtbGwAik2GSU5Opn796rtjQWhoqCpBCgkJISQkpMJlpOemY/t/tuoOrYjUj1KxMbWpUhm5ubkcOHCAzMxMjI2Niz1vbGxMvXr1imw7dOgQ3bt3L7PsoKAg1dIuf9esWTOOHz9eZNvt27e5f/9+kTiSkpJKXA6mTp06JCcnl3r+2bNn4+3tzfbt2zEwKOwQ8Pb2xt/fv1gS+sEHH+Dm5sb+/fuLvPctW7Zk7ty5bNu2jdmzZ9OiRQu2bt2qSv5btmxJ+/btZRL6lGDPYJQFSg5EHyDYM5hJk6BJE7h7Fxo0KONge3to2LCwNTQoqMxzhYWFERYWBhR+lmuzmlR/StKzCAS/s54E7jOcdzDFXNsh6a3qqj/1Jgm1tbXF3d2d8PBw/P39AcjPz+fcuXOMGTOm2uKYP3++qkKvLGsTa1I/SlVTRM8+R1VdvnyZzMxMlixZUqQbD2D48OHY29sXawn18/MjPDy87Pisnx3fO++8w+jRo5k5cyZvv/02SUlJTJgwAQMDA1XCWBVKpZLw8HA++uijIuV17twZd3f3IvtmZWVx6NAhQkNDMTAwKNIaHBwczJo1a1AqlZw6dYqPP/64SOuzn58fHh4eVY63JjEyMGJkq5GsOr+KYM9gGjWC558vvJ/83LnlKOBJl3w5ktCnk5y0tDSWLFlSxej1ly7XnwIhJ4xIVSYQHGQbd7jOCN7FAitth6TXqqv+1KkkVKlUkpeXp8q6c3JyyM/Px8TEBAMDAyZPnsyCBQvo0aMHjRs3Zt68eRgbG5dr9qa6hIaG0r9//yr9Ba9QKKrcSlkdoqOjAXjuuefw9fVVbY+LiyMqKooRI0YUO8bKyoo25ViBvLShAqNGjeLq1assWLCAzz//HIVCwbBhw+jTp0+R7nh7e/sSWzyf1UL6REJCAnl5eTg7Oxd77u/bkpKSUCqVzJ07l7nPyJKelFe3bt0yy5MKZ8kHrgjku77fYWliyeTJMHYszJgBZmZlHFyJcaFhYWHs3Lmz8gHrCX2tP89yhBhuEEAfHHGttlikmuU4v3OF04zgHayx03Y4NYbG60+1T3WqghUrVgig2OPAgQNCiMKZ2rNmzRLOzs7C3NxcBAYGioiIiGqJrTbOjt+4caMAxMWLF4ts//LLL4WhoaG4efNmsWOezMwt61GeGegZGRkiIiJCPHjwQAghhLe3txgzZozq+e7du4uAgIBixwUFBYmuXbs+s9z8/HxhbGwsZs2aVew5d3f3IrFlZGQIAwMD8dZbb4nw8PASH0/K++STT4qV5+HhodHZ9vr4uSsoKBAtv20pVp9fLYQQQqkUwte3cPJ7mdatE6JjxwqfUx+vU0Xpa/2ZJpLF7+Jn8ZV4T+wUK0WSiKuWmKSa47Q4KP4tPhQPxT1th1IjabL+1KkkVJfVxiT0ybJIP//8s2rb/fv3haOjo3jzzTdLPCYtLe2ZydrTj6tXr1Yolt27dwtAHD16VLXt66+/FoaGhuLWrVuqbVFRUcLIyEgsWLCg1PK6dOkimjdvLpRKpWrb8ePHS0yQnyS1T+/7d/7+/sLHx6fIMlanTp0qd8JdWfr6ufvXH/8SvVf3Vv2+ebMQ9eoJUeaKX5cuCWFhIUR+foXOp6/XqaYoz/VPEQlit1gjvhLvid1ijUgRidUYoaSvLogTYpGYLu6JKG2HUmPJJFQHPHkTpkyZIn777bdnPl+T/pErKCgQzZs3F56enmL79u1iw4YNolmzZqJz587i0aNHGjvvmTNnxOeffy52794tdu/eLWbMmCFMTEzEP/7xjyL7ZWRkiMaNG4uWLVuKbdu2ie3btwtfX1/h4eEh0tPTSz3Hnj17hEKhEP379xe7du0SK1asEG5ubsLFxaVY0nj69GlhaWkpgoODxbp168TBgwfFpk2bRGhoqPjwww+FEEL8/vvvAhADBw4Uv/zyi1i5cqXw8PAQLi4uonv37kXKO3jwoDA0NBQrV66s8rXS189dTGqMMPrMSNxLK2y5UCqFaNVKiP/8p4wD8/KEMDER4vr1cp/rt99+E1OmTNHL61RTlFV/Pi1RxImd4kexUEwTe8QGkS5SqilK3aEUSpEh0kSSeCgKRHkX0q1+OSJbXBVnxE7xowgT66v9vbouzouvxfsiWlyr1vPWJpquPxVCCKGhnv4aJS0tDVtbW1JTU0ucmFTW8/rq2rVrvPHGG5w8eRJHR0dGjx7NrFmzsLS01Ng5L126xMSJE7l48SI5OTk0b96ct956i3HjxhXb986dO7z33nvs2bMHIQQ9e/Zk0aJFxSYYlWTdunXMmTOH6OhovLy8mDdvHosXLwYoNnP/ypUrfPrpp+zfv5/U1FScnJxo164dkyZNok+fPgCsXbuWTz/9tEh5n332Ge7u7kXWID148CDdu3dnxYoVVb7Xtj5/7oJXBfO81/NM7zIdgM2b4e234datMsaGtmtXOID0xRfLfS59vk41QWWufzyxHGU3UVymDYF0oicWVH7CZQFK4onlPrdJJh5DDDHCGCNMHv80xgijMn/n8SSqpydT/X1i1d+fEwiyyCSLDLJ4RCYZj///6d//2p5NFiBQYEB9POjFyzozXjaHLG5ykRucJ4or2FCHprQmmYdEcpn2dKcjPTGhrAHeVXOba2xlGX15hSb4ln2AVGmarD9lElpOtTUJlSrv7t27eHl5MWPGDGbNmqWRc+jz527V+VV8dewrzk86D0BBQeHtPCdOLLy3/DONG1e4VNNnn5X7XPp8nWqCqlz/B9zhKL8Swy38CKIDPTDDotRjBII0knnAbWK5zX2iiSMGAwxxwQ0HXChAST55KMknn1zyyHvq9zzyyC3yez55VbkEKDDAHEsssMIcS8wf/yz8/cm2or8LCjjKbs7xB350w58Qrax7mUkGN7nAdc5zm2s44EJTWtOU1kWS41iiOMh2kokngBfwxR8DSl4PurKyeMQlTnKUXwnmJXzoWPZBUpXIJFQHPHkTpkyZUuLsePmPXO2WlZXFtGnTCA4OxtHRkcjISL744gvi4uK4dOkSrq6aacXQ589dRm4GzgucOTb+GL7OhS0ZmzbBu+/CzZultIZ+/TUcOgTbtpXrPE9mdy5ZskQvr1NNUFb9WR73iOQPfiGOu3SgB34EqVrbcsjiAXe4/zjhjOU2WTzCCVdccceVRrjSCAecUVC5Zd4EAiX5qv9/evvf9yxpuwmmlT53HHfZw89kkkEwQ/HEp1LlVEQGqdwgguuc5y43caYhTR4nnvY4PfM4geAGERxmBwoMCGIAjWlZpWW4BIJ7RHGeo1znHM40pAM9ZAtoNdB0/SmT0HKSLaFSaXJzcxk2bBjHjx8nMTERS0tLAgMDmT9/Pi1bttTYefX9czd6y2hcrVz5sveXQGFraOvWMGlSKa2h+/bBG29AZGS5z6Pv10nfqfP63+Y6f7CLZOJxx5uH3COROGywe5xsFiadzjSsUXfLERRwnmMcZgeNaEYPhqh9KaI0krjOea5znliiqY8HTWlNE1pjQ8XuUqZEyXmOcozfqIMz3RiEK40qVEY2mVwmnPP8STop+NCR1nTRmaEJtYVsCdUBMgmVdJG+f+7CboYxbvs4Yt6LwdCgsNvuSWvorVtgalrCQfHxULcupKZCOV+zvl8nfafu6y8QRHGZWKJxpiGuuGNF7XhfH5HGQbZxkwsE0Id2dK1Sl3cayVznHFc5Sxx3aEgTmtIaL3zVck1zyOIk+zjNQRrTkkD6Y4fDM/cXCGKJ5jxHucZZ6lKf1gTQjLY16o8KfaLJ+lOnFqvXB+pYrF6SpEI9PXsCsC9qH70b9wZgyBD49FP43/9g8uQSDnJyAhcXuHgRunQp8xy1ZbF6faCu+lOBAk98qqVbWtdYYkNfXuE219nDBi5xkt4Mr1ArYwapXOMc1zjLfaJpgBet6MwQJqj9TkOmmBNIP9oQwB/8ygrm04bn6ExvzPlrgmsOWVxStXom04IOjOZ9nKhXSumSpmm6/pQtoeUkW0IlXVQTPnfTf59O3KM4Vg9erdq2cSNMm1Y4NrTE1tCQEBg8uLDfvhxqwnXSZ/L6a0Y+eZxkHyfYQ0s6EUi/Z07ayiCN648Tz1iiaEBjmtGWJrTGsgqrDlTUQ+5xiO3c5zad6U19PIjgGNc4iyOuqlZPE0r64kvaILvjdYBMQiVdVBM+dxfiLtD5f50JfyOcFk4tgMKxob6+heNC33yzhIM++AAePYJvvy3XOWrCddJn8vprVjLx7GUjD7lHdwbTHD8UKHhEGtc5zzXOco9I6uFBM9rSlDZaH74QzVUOsp1UEmhBB1oTQF3qazUmqWSyO16SpBqrlXMrpnWexqD1gzj5xknszOwwMIBPPoH334fXXiuhNbR1a/juO63EK0m6xh4nhvIm1zjLfrZwgWOAgrvcxBV3vGlLP8Ziha22Q1Vxx5uxNKOAAgzVvIyTVHH55FBAPiZobg3wksgktILkmFBJUr9Pu3/KubhzjNw8kp0jdmJoYMjQoYVjQ5cvL6E11Nsbrl0rV9lyTKjukPWn5ihQ4E07PGhOOPsxw4I+jMK6grPaq5MChUxAq4lAkEMaGcSRzgMyiCvy/1kk05IXac2IIsfJMaE6QnbHS7qoJn3uUrNT6fhDR4Y2H8rnPT8HYMMGmD4dbtz4W2vohQsQGAgpKeUquyZdJ30kr78kVY8c0kkminTiyOCB6mcGD8kjEzNsscIZK1yw/ttPM2xLXM9VdsdLklTj2ZrZsm3YNjr/rzNtXNrwks9LqtbQFSvKPQdJkiSp1hEIbrGXM6zGFCuscMEKZxxpgjvPYf34d2PMtR1qETIJlSRJZzR3as7qwasZtWUUzRyb4evsyyefFM5DGjfuGTPlJUmSarEMHnKC70gjlgDepT7ttB1SuVXuHmKSJEkaMqDZAD7o8gGD1g8iKSuJoUPB2hp+/FHbkUmSJOkOQQHX2M0vTMMKF/qyUK8SUJBJqCRJOmhm15m0dmnN8E3DEYp8Zs2Czz+H3FxtRyZJkqR9adxnD7O5wk6C+AedmFjtM9vVQSahFRQaGkpYWJi2w5CkGs1AYcCqQau4l36Pj/d+zEsvgZVV4djQigoLCyM0NFT9QUoVJutPSaqaApRcYQe/Mp06uNOXr3DBV2Pn03T9KZPQCpo/f36tXF4kNjaWsWPH4ujoiLW1NcOGDSOlnDOTK+ru3bu89dZb+Pv7Y2FhgUKhIDo6usR9jx49Su/evalbty7W1ta0a9eO5cuXF9svJiaGoUOHYmtri42NDUOGDOHOnTtVirNbt25069atSmVIz2Ztas22Ydv44ewP/Hx5LZ98AvPnV7w1NCQkhPnz52smSKlCamv9KUnqkEoMvzOTG+yhBzNoz3iNTzTSdP0pk1CpTFFRUXTs2JG0tDTWrFnDd999R1hYGFOnTtXI+W7evMmGDRuwt7cnMDDwmftFREQQHBxMXl4ey5YtY8uWLXTo0IHx48fz3VMLmWdmZtKjRw+uXr3KypUrWb16NTdu3KB79+48evRII69BUo8mDk1YO2QtE3ZOoPFzZ7CwkGNDJUmqXQrI5yKb2c2H1KU5fVhAXVpoOyy1kLPjpVIJIRgxYgRt2rRhy5YtKBSFa4hdv36df/3rX/zwww+YmZmp9Zxdu3YlLi4OgB9++IHff/+9xP3Wr1+PUqlk586dWFlZAdCrVy8iIiJYtWoVbz5e4XzZsmVERkZy7do1vLy8APD19aVJkyZ8//33TJs2Ta3xS+r1QpMXmNl1JkM3DSY09BSfz3Ri3FYw1nZgkiTplXyyiefq4+WL6qLQg3a4ZKI5zrcoySWYOTjSVNshqZXuvwOSVm3dupUTJ06wcOFCVQIK4ObmRm5uLrGxsWo/p4FB+T6Wubm5GBsbY25etDvC1taWgoIC1e87duygc+fOqgQUwMPDg4CAALZv316uc61fvx5vb29MTU3x8fFh69atJe53/vx5BgwYgL29Pebm5gQEBHDkyJFi+61btw5vb2/MzMxo1aoVO3bskN37pfgw4EM61e/EeuUwzC3zKOfbJkmSRD45XGEn25jMcb5jF++ygVf4jY84zndc5RcecJFs0rQdqoqSPCL4mTBCqUdbXuDLGpeAgmwJ1Q4hID1ds+ewtgZF8TsfVNTy5cvx9/fH09OT/Px81faMjAwAjIyKfoSEECiVyjLLVSgUGBpW7XZtr776Kt999x1vv/02M2bMwMLCgo0bN7Jv3z5Wr16t2u/SpUsMHDiw2PE+Pj5s3LixzPPs3buXkSNH0rdvX7766ivi4+N55513yMvLo1mzZqr9zpw5Q2BgIG3btmXZsmVYWFiwdOlSgoOD+fPPP/Hz8wNgz549jBo1igEDBrBw4ULi4+N59913yc7OpmnTmlfJqINCoWDFwBX4/88fj4kfsOxf43kRSri3hyRJUiEledxkL5fYghl2dGYy9fFDoCSN+6RwmxRu84AIrrKLR8Rjhh32NMION+we/7SlAYaYaDRWQQFp3COea8RzjTguYoIlvZlHHTw1em5tkkloBanl3sfp6WBrq76gSpKaClW8vVZubi4HDhwgMzMTY+PinZ/GxsbUq1evyLZDhw7RvXv3MssOCgri4MGDVYqvZcuWHDx4kMGDB/Ptt9+qYlq6dCnDhw9X7ZeUlIS9ffH7J9epU4fk5OQyzzN79my8vb3Zvn27qpXW29sbf3//IknoBx98gJubG/v378fEpLDCCgkJoWXLlsydO5dt27apymvRogVbt25VtS63bNmS9u3byyS0FJYmlmwbvo0Oyzrg6+lI7hkoz9r18t7xuqOk+lMgSrxVoCRVlpI8IjnARTZjjCXtGU9DOqk+ZwqMsKMhdjQEnlMdl8sjUoh5nJze4SZ7SeEO+WRjjQs21MeGeo8f9bGhPqZYVyrGPLJI5CbxXCOBayRwHSV5OOCFE81oz3jq0QYDLadpmq4/ZRJaQfPnz6/6vVOtrQuTRE2yrtwX42mXL18mMzOTJUuW0LFjxyLPDR8+HHt7+2ItoX5+foSHh5cjvKrHd+PGDV588UV8fHxYunQp5ubmbN++nUmTJmFmZsaoUaOqfA6lUkl4eDgfffRRkWECnTt3xt3dXfV7VlYWhw4dIjQ0FAMDgyKtxsHBwaxZs0ZV3qlTp/j444+LDG/w8/PDw8OjyvHWdJ72nvw89Gc+vD2A7OOGGBdAWaM3QkJC8Pf3Z8mSJdUTpPRMJdWf1/iVeK7QgTcwQ8N/nEs1WgFKojjEBTZhiDFteYVG+Jd77KcJltTFm7p4q7YJBJkkksId0rhHGrFEcZg0YskiGVOssVYlpvVUiaoVzhg+HrkuEDwigYTHrZwJXCOZaMypgxNNcaUNvgzDHnetJ51/p+n6U7debW2hUFS5lbI6PFkW6bnnnsPX9691yOLi4oiKimLEiBHFjrGysqJNmzZllq1Qw1CB0NBQjI2N2bVrl6qltmfPniQmJvLOO+8wYsQIDAwMsLe3L7HF81ktpE9LSEggLy8PZ2fnYs89vS0pKQmlUsncuXOZO3duiWUVFBSoyqtbt26p5UnPFuwZzJSOkxH/W8ihwwV07yaHtuuzRgTwkMvs4j068DqN6KLtkGqNApSkcIdEbpLIDZKIxA1/WvJitZz/EluJ5AB2uFMHTxzwxB5PTLGqUDkFKLnNUS6wEYHAl2G48xwGVG3IF4ACBZY4YoljsbsR5ZFJGrGPH/dIIpJo/iCd+xSQjxXOWOJEKnfJJpU6eOBIM1owCEeaYoljlePTdzIJlZ7pSWve38durl69GoVCwauvvlrsmOrsjr9w4QKtW7cuNlSgY8eOrF27locPH+Li4oKPjw+XLl0qdvzly5dp0aL0ZS4cHR0xNjZWzdZ/WlxcHI0aNQLAzs4OAwMDpkyZwiuvvFJiWQYGBqryHj58WGJ5bm5upcYjFXqlzSgy+Ip/bzxN924dtB2OVAXm2BHIdG5zlJP8lzscpwOvY4Zm/lBPJpoEruNBEEblGtBRMzxpjUvkBoncIOFx0qnAEAca40gTvOnHGVZiiAnN6a/ReK7yC5fZTnvG84iHJHKDG4TxiHisqEsdPKlD48c/PUvs9hYUcIfjRLABJTm0ZCieBFVba6IxFjjghQNeRbYLCnhEIuncI4N4bKiHA1616vNWXjIJlZ7pSXfzpUuX8PHxAeDBgwf861//YsKECTRu3LjYMdXZHe/i4sK5c+fIzc1VjcEEOHHiBGZmZtSpUweAAQMGMH36dCIjI/H0LBzgHR0dzdGjR/m///u/Us9haGhIhw4d2LRpE3PmzFF1yZ84cYLo6GhVEmppaUlgYCDnz5+nXbt2z5zhb2hoSPv27dm8eTNz5sxRtQifPn2aqKgomYSWk5GBEUYGxvx2azc5OR0wlXW7XlOgwJ3ncMaHkyxjF+/SkQm40Vlt50jmNhfYQCxnsaQul9lGe8ZTHz+1nUOX5PKIRG6SwI3HLZ03ySEdexrhgBeN6UknJmJD/SLd1bY0YB+fYYIljemhkdgiOcR51tOTWcVmfGeTRjJRJBFJEre4yR4yeIgldamDhyo5zSebC2wkh3Ra8iKN6aHq/tY2BQZY4YQVTtoORecphBBC20Hog7S0NGxtbUlNTS1xTGhZz+sjIQQ+Pj7k5OTw9ddfk5OTw6xZs7C3t2ffvn1YWFho7NybNm0CYN++fSxdupRvv/0WJycnnJycCAoKUu3z0ksv0bt3byZPnoy5uTk7duxgyZIlvPfeeyxcuBCAR48e0bp1a8zNzZk3bx4KhYJZs2aRnp5ORESEao3RZ9m7dy+9e/emX79+TJw4kfj4eGbPnk1ubi7NmjVTteieOXOGrl274u/vz/jx43F1dSUhIYEzZ86gVCpVCe+ePXvo3bs3AwcOZMKECSQkJDBnzhyysrJo3rw5+/fvBwpblXv27Mny5cuf2bpaEz935XLhAjldOmE71pf1wccZNKj03WvtddIRFbn+AkE0f3CK/+FKazrweqUnfwCkEMMFNnCP03jRkxYMxgxbbrGPc6ylLs3xYxxWFB8io0+ySCaOSzzkMnFcJo17WOGEA01wwAtHmmCPR7la4+K4yEH+D3+mqvUPAYC7hPMHiwjiQ1zLebvJHDJIJpJEIkkmkiQiySeXFgykCb00PnO9ttNo/SmkcklNTRWASE1NrdTz+urq1asiMDBQmJqaivr164sPP/xQZGRkaPy8QImPoKCgIvv9+uuvIigoSDg6OgorKyvRunVrsWTJEpGfn19kv9u3b4shQ4YIa2trYWVlJQYOHCiioqLKHc/atWtF06ZNhYmJiWjRooXYsmWLCAoKKhbP5cuXxbBhw4STk5MwMTER9evXF/379xe//PJLkf3WrFlTrLw2bdqIQYMGqfY5cOCAAMSKFSueGVdN/dyVKSJCKG2shWK2geg/7GGZu9fa66QjKnP9M0WSOCj+KTaJ18QdcbzC50wRMeKIWCjWiRHihPiveCQSiu2TJVLEMbFErBMjxQWxSeSL3AqfR1seiQQRKQ6J4+I7sV28JdaIl8Sv4gNxSqwQd8QJkSVSqlR+jAgX68RIESvOqyliIR6IC2K9GCVui2NqK1PSPE3Wn7IltJxqY0uoVH3u3r2Ll5cXM2bMYNasWeU+rtZ+7i5ehIAAWs5twrWV75Kwf3Spq57V2uukIyp7/QtbRY9wiuXUow3tGV9mq2ga97jARu5wAk+60ZIhWJbRLRrPVcL5gXxy6cB4XGld7hiryyPiieMyD7lEHJd5xEPs8cAZH+rSgro0xwRLtZ4zisOcZFmJ3eYVlchN9vEZfryqsW5+STM0WX/KMaGSVM2ysrKYNm0awcHBODo6EhkZyRdffIGFhQWvv/66tsPTK0Na9eGbdr+yZctoxo3TdjSSuilQ4EFXnGnJSb5nF+/RiYk0oPhktDTuc5GN3OYYngTRn3+Xe0yeE948z7+4zm8c4StcaY0fr2KBQ5XiV5KnGo9ZwJNl2xSq/yjjZwFKkojkIZd4RAJ1aIwzLWjPa9TFG2M0NyQKwIOu5PKIA8wnmE+xp1GlyknlLgf4nFa8JBNQqQiZhFaQWharl2o1Q0NDHjx4wNSpU0lMTFRNatq4cSOurq7aDk+v9GnShwUN+vDTGiXjxpW8HItcrF53VLb+tKAOQXxEFIc4xjfUw4/2jMMUa9KJ4yKbiOYPPAikP4uwouLLnRlgiDd9aUQXzrCKnbxDK17Cm77lnm2tJJcEbj5urbxEAtcxwgwnmmGEKYXdjgWPfwrEU6ONxFM/BX91UNrTiA5MwIlmGGNe/KQa1owXyCWD/cylN59jXcFrm0E8+/iMJvTW+Ix7Sf00XX/K7vhykt3xki6qtZ+7x93xyuQknL5wJu37Hdw52oW/3cBLpdZeJx2hzuufSSIn+J5konDGhzucwJ0AWjIUa1zUFHHh5JxwfgAUdOANnCm+nFs+OSRwg4ePJwTFcx0TLHGmBXXxwZkW2NBA7+8IJRCc4UfuEk4v5mFBnXIdl0UKe5iFC7504HW9vw61leyOlyRJKoGhgSEvNA3hzx67Wb++C9OmaTsiSdMscKAbHxPFIRK4QV++woZn/PVRBc60pA8LuMovHGQ+DehIa4aRQRxxXCaOSyRyA1NsqEsLGvEcHZmANfVqXLKlQEE7xpJLJvuZSy8+K3NsbmE3/jwcaEwHxte4ayKph7zViCRJeq2PVx9E41/56SdtRyJVFwUKPOlGR97QSAL6hAFGtGAg/VlMAblsZwrHWEIGD/EkiH58zWC+5znepQm9Hq+5WTOTLQUGdGISNrhygPnkkfXMffPJ4SD/xJw6+DO13LfNlGof2RIqSZJeC/EKYWz+WO7fvs+VK640b67tiKSaxgIHAplODhmYYFljE82yGGBIAO9ykH9ymC/oxsfF1uhUkscRFqDAgEDe17l7oUu6Rf55IkmSXnO0cKRD/Q74DvmNNWu0HY1Uk5liVWsT0CcMMaEr/yCPbP5gEQUoVc8VoOQY/yGLFIL4UN6mUiqTTEIlSdJ7fbz6YOzzK2vXgpxqKUmaZYw53QklnfucYCmCAgSCU/yPJKLpwUy1r1kq1UyynVzN0tLStB2CVIvIz1uhPk36sODYAhSpeRw7ZkyXLtqOSJJqNlOs6cEs9jCTM6zEEBPucYbezMWMUu4cIUlPkUmompiYmODi4kLDhg21HYpUy7i4uGBiUrvvndzWtS3mRuZ0GPEna9YEySRUkqqBBXXowSf8zkwEBfRmbpl3p5Kkp9WKJLSgoICgoCCuXLnCpEmTmDdvHgBCCN5++21OnjyJEILhw4czrZJrvJiZmREVFUVubq46Q5ekMpmYmGBmZqbtMLTKQGHA817Pk1tnNxs+DGLRIjA21nZUNUN11J+S/rLGhRDmI1BijbzZhlQxtSIJNTAwYO3atezbt4+bN2+qtp8/f56zZ89y4sQJ8vPzad68OePGjcPe3r5S5zEzM6v1yYAkaUufJn2Yd3ge5ub/x++/Q9++2o6oZqiu+lPSX1bU1XYIkp6qNROTSuomb9CgAaampuTk5JCZmYmJiQmmpnI2nyTpo16evbgcf5m+I2PkLHk1k/WnJEmaoDNJ6Pr16wkMDMTGxgaFQkF+fn6R54UQzJ49m3r16mFpaUnXrl25ePFilc7p4OBAy5YtadKkCU2aNGHixIlYWFhUqUxJf4WFhWk7BKkK7M3t6dKwC07+u9m+HTIytB1R9ZH1p+6pbfWJfL1SZehMEmpvb8/kyZNZtGhRic8vWLCA5cuXExYWRkJCAgEBAYSEhJDx1L80nTt3LvYYPHjwM8+5Z88erl27RmRkJFFRUaxcuZKoqCh1vzRJT8hKRf/1adKHiKxf8fSEbdu0HU31kfWn7qlt9Yl8vVJl6EwSGhISwogRI/D09Czx+W+//Zbp06fTqlUrzM3NmTt3Lrm5uWzdulW1z/Hjx4s9nn7+74QQ2NvbY2RkhLm5Oebm5qSnp6v9tWmKJr4EVSmzoseWZ/+y9int+co+pyvk+1vGe/i31j4oTEL3Ru6lXftdtapLXtafkiTpI72YmJSamkp0dDQdO3ZUbTMyMqJt27acPXuWMWPGlFnGiBEjiIiIIDMzk+PHj7N792569erFxo0b6dKlC/n5+fTo0QNfX98SjxePV8DWpXUZd+7cib+/v86UWdFjy7N/WfuU9nxFn8vNzZXvrxqP1ej7m5HBzrw8/P/2fjUya4QNNsSmL+Pg3q7cvAl16/71vRW1cCV7WX9qh67VJ5omX2/NpdH6U+iYAwcOCEDk5eWptt25c0cA4vLly0X2ffnll8X48eOrJa6YmBgById8yIceP2JiYqqlvtAWWX/Kh3zIh6Yemqg/9aIl1MbGBoCUlJQi25OTk6lfv361xFCvXj1iYmKwtrZGoajd9w6WJH0jhCA9PZ169eppO5RqJ+tPSZKqQpP1p14koba2tri7uxMeHq7qmsvPz+fcuXPl6kpSBwMDAxo0aFAt55IkSf1sbW21HYJWyPpTkqSq0lT9qTMTk5RKJdnZ2ao7DuXk5JCdnU1BQQEAkydPZsGCBVy8eJGsrCxmz56NsbFxqbM3JUmSagNZf0qSpI90piV09erVjBs3TvW7lZUVAAcOHKBbt25Mnz6d9PR0goODSUtLo3379vz222+q/SRJkmorWX9KkqSPFELUwumikiRJkiRJklbpTHe8PisoKCAwMBBHR0dmzpyp7XAkNTt9+jQBAQF07dqVgIAATpw4oe2QJDVKTk6mS5cuBAUF0b59e3766Sdth1SjCA3crUkXDR48GIVCwd69ewGIjo5GoVBgaWmJlZWV6pGamqrlSCsvOTmZiRMnUr9+faysrOjVqxdXr15VPX/nzh369euHtbU1jo6OTJ06VTVERB+UduexyMhIAgICcHR0xMbGhsaNGzN37lzVkBeAOXPmYGhoWOT9HjFihDZeSrmUdae1NWvW0KpVK2xsbKhfvz7vvvsuOTk5qudzcnKYMmUKjo6OWFtb069fP2JiYioUg0xC1cDAwIC1a9eyYMECbYciaUC9evXYvXs3hw8f5vvvv+ett97SdkiSGtna2nL48GEOHTrEvn37+Mc//qHtkGqU8tytSd+tWrWKzMzMEp87f/48GRkZqoc+T5B79dVXuX37NufPnychIQEfHx969erFo0ePKCgooF+/ftSpU4d79+5x+vRpDh8+zAcffKDtsMuttDuPOTk5sXz5cuLi4khLS2PPnj2sXbuWJUuWFNnP39+/yPu9bt26aoq+4kp7vefPn2fMmDHMnDmTlJQU/vzzT8LCwvj0009V+0ybNo0jR45w+vRp7t27R506dRgwYECRxLxMal/0qRZbsWKFmDFjhrbDkDTo2rVrolOnTtoOQ9KQu3fvit69e2s7jBrF3d1dLFq0SPV7Xl6ecHR0FKtWrdJiVOoTExMjGjZsKG7fvi0AsWfPHiGEEFFRUQIQN27c0HKE6pGRkSEMDAzEsWPHVNuysrKEoaGh+Omnn8TBgweFkZGRiI+PVz2/bds2YWFhIbKysrQRcqWVtN7u30VGRooWLVqIKVOmqLbNnj1bBAQEVEeIalXS692yZYuws7Mrst/7778vnn/+eSFE4Xtvbm4utm3bpno+Pj5eGBkZicOHD5f73LIllLKbpEUt6U6qqdT1/ubl5TFlyhQ55ELHqOP9jY+Pp2vXrvj6+vLiiy9WZ/g1Wll3a9J3Qghee+01Zs6ciZubW4n7BAUF4ejoSJcuXUq9Dao+EEIUuWvOk9/PnDnDuXPn8PT0xNHRUfV8hw4dyMzM5Pr169oIVyMCAwMxNzfH09OTtLQ0pkyZUuT5s2fP4uTkRKNGjRg5ciRRUVFairRqQkJCaNKkCWvWrEGpVHLr1i127tzJkCFDALh27RpZWVlFvtuOjo54eHhU6Lstk1BKb5KG2tGdVJOp4/1VKpWMGjWKl19+mX79+lVT5FJ5qOP9dXJy4vDhw1y5coV58+aRlJRUTdHXbE9u92dnZ1dku729fY245eF3332HEIIJEyYUe87R0ZE///yTqKgoYmJiePPNNxk+fDi//vqrFiKtOktLS4KDg/nkk0+Ii4vj0aNH/OMf/0AIQVpaGmlpaSW+z1Czbtd65MgRMjIyOHr0KGPGjKFu3bqq54YOHcrly5d5+PAhf/75JwqFguDgYL3MFSwsLHj99deZOnUqpqameHl50blzZ1577TVAfd9tmYRSmPGPGDECT0/PEp//9ttvmT59Oq1atcLc3Jy5c+eSm5ur93/V1hZVfX8LCgoYO3YsnTp14o033qjO0KVyqOr7+/RAeysrK0xNTTE3N6+W2Gu60u7W9OQ5fXXr1i3mzp3LDz/8UOLzVlZW+Pv7Y2Jigrm5OWPGjGHEiBF6PfHtp59+ol69evj5+eHl5YW9vT3e3t6qyTolvc+A3r/Xf2doaEiXLl2ws7Mr8gdIy5YtadSoEQqFgvr167N8+XLu3bvHn3/+qcVoK2flypV8+OGHbN++ndzcXGJjY0lMTGTUqFGA+r7bMgktQ3m7k0aMGMGXX37JmjVrCA4OJi8vTxvhShVUnvd3w4YNbNmyhZ07d9KtWzcGDhyorXClCirP+3v+/Hm6du1K9+7dCQ4OZu7cuTIJVZOn79b0xJO7NbVt21aLkVXdkSNHSExMxM/PD0dHR1U39IsvvlhiyygUTmIVerwqYt26dVm5ciV3797l/v37TJ06laioKHr27EmbNm2IiooiMTFRtf+pU6ewsLCgadOmWoxac/Ly8rh27dozn1coFCgUCr18z0+dOkXXrl3p2rUrBgYGuLq6MmHCBLZv3w5As2bNMDc3L/LdTkhIIDo6ukLfbZ1ZrF5XlbfJWZdnwEnPVp73d/jw4QwfPry6Q5PUoDzvb8eOHTl8+HB1h1ZrPLlbU48ePWjcuDHz5s2rEXdrevnllwkODi6yrWHDhnz//ff07t2bI0eO4OjoSNOmTVEqlWzevJm1a9eyYcMGLUVcddeuXcPe3p66dety8+ZN3nzzTXr06EFwcDAFBQV4e3vz/vvv85///Ifk5GQ++eQTxo8fj5mZmbZDLxelUkleXl6RO4/l5+djYmLCvn37sLS0xM/PD0NDQ44cOcLixYt59dVXVcdv2LCBHj164OjoSFxcHB988AHOzs506dJFS6+odKW93sDAQCZOnMiff/6Jv78/CQkJ/PDDD/j5+QFgZmbGuHHj+OSTT2jTpg329va8//77tGjRgoCAgHLHIFtCy1CTu5Mk+f7WdPL91b7p06fz6quvEhwcjIODA0eOHKkRd2uysLCgQYMGRR5QOBa0Tp06XLt2jX79+mFjY4OLiwuLFy9m9erVDBgwQMuRV97Ro0dp164dFhYWdO/enbZt27J582agsJV3586dxMfH4+rqStu2bXnuuef48ssvtRx1+a1evRpzc3NCQkKAwiEV5ubmHD58mPT0dCZNmoSDgwMODg5MmTKFt99+m/nz56uO/+mnn2jevDkWFha0a9eOvLw89u7di7W1tbZeUqlKe70vv/wyc+bM4bXXXsPW1paWLVtiamrKmjVrVMcvXLiQgIAA2rZti6urKwkJCezcuRMDgwqklpWYzV9jPWtZBnd3d7F48WLV73l5ecLJyanGLDFSW8j3t2aT768kSZJ+kS2hFDZJZ2dnF2mSzs7OVi24+qQ76eLFi2RlZTF79uwa0Z1UW8j3t2aT768kSZJ+kmNCKWySHjdunOr3J91EBw4coFu3bkyfPp309HSCg4NJS0ujffv2NaI7qbaQ72/NJt9fSZIk/aQQQg+nbUmSJEmSJEl6TXbHS5IkSZIkSdVOJqGSJEmSJElStZNJqCRJkiRJklTtZBIqSZIkSZIkVTuZhEqSJEmSJEnVTiahkiRJkiRJUrWTSagkSZIkSZJU7WQSKkmSJEk1WFZWFu+99x6urq7Y2tryzjvv8Kwlwt3d3TEzM8PFxeWZ5eXn56NQKDh48KCGIi5qxIgRWFpaolAouHnzZrWcU6oeMgmVJEmSpBps6NCh3Lp1i6tXr3Ls2DGWLVvGxo0bn7n/N998w4MHD6oxwtKtW7eOS5cuaTsMSQPkbTslSZIkqYZas2YNhw8f5s6dO9ja2mJra0uPHj3Yv38/L7/8srbDk2o52RIq1Vrl6XaqLNl9JEmSLli4cCEjR47E3t5etc3e3p7s7Oxyl/Hw4UOGDBmCnZ0dnp6e/Pzzz8X2yc7OJjQ0lMaNG2Nvb0/Xrl05e/as6vm4uDgGDRqkKmPNmjXV2qUv6SaZhEp6qXnz5piZmWFlZVXksWvXrgqVU9Fup8GDBzNo0KASn/voo49o0aIFILuPJEnSvpiYGM6cOUPfvn2LbM/KyqJevXrlLmf06NFkZmYSGRnJqVOnWLduXbF9Jk2axMmTJzl06BDx8fG8/PLLhISEkJKSAsDIkSNRKpVERUVx+vRpNmzYUKXXJtUMsjte0jupqalcu3aNvXv30qNHj2o995tvvkmfPn24e/cuDRo0UG3Pzc1l+fLlzJo1q1rjkSRJepajR48CMGbMGBQKhWp7RkYGO3bsKFcZ9+7dY8+ePURERFCnTh0A/vnPf/LLL7+o9klMTGTlypVcvXpVVS9OnTqVxYsXs2vXLrp168b+/fu5dOmSqkX2888/L3cMUs0lW0IlvRMeHo4QgtatW2uk/NK6lXr16oWnpyfLli0rcsymTZvIzMxk7NixGolJkiSpomJjY3FzcyM1NZWUlBRSUlK4cOEClpaW9OzZs1xl3L17FwAPDw/Vtqf/H1ANOerUqRN2dnaqx71797h79y737t0DoFGjRqpj3N3dq/LSpBpCJqGS3jl58iT169fHwcFBI+WX1q2kUCiYNGkSP/zwA/n5+apjli5dyqhRo7CxsdFITJIkSRVlYWGBpaVlkW2LFy9mwoQJmJqalquMJy2b0dHRqm1P/z+gGlcfERGhSnZTUlLIzMzko48+on79+gDcvn1bdczT/y/VXjIJlfTOyZMnefjwYZG/uO3s7NiyZUuVy37SrbRkyRIaNGiAkZERU6dOxdbWVjXedNy4cSQnJ6u6ki5dusSRI0eYPHlylc8vSZKkLt26dSMyMpLDhw+TnZ3N0qVL+fXXX/n000/LXUb9+vXp2bMn//jHP0hOTiY5OZnQ0NAi+zRq1IhBgwYxZcoUVXKZnp7O7t27uX//Pg0aNKBbt258/PHHqgR15syZan2tkn6SSaikd06ePMm8efOK/MWdkpLCkCFDqlx2Wd1KUDizdNiwYSxduhQobAX19/fX2PAASZKkyvD29ubLL79k6NChODs7c/DgQfbt24eFhUWFyvnpp58wMTHB3d2ddu3aMWzYsGL7rF27Fj8/P3r16oW1tTXNmjVj2bJlqkXx165dixCCRo0a0bZtWwYMGACAmZkZUNgD9cILL1TxFUv6RiGeddsESdJBMTExuLm5cejQIbp27VriPvv37+ef//wnubm5ODs7s3btWoyMis/Bc3d3Z+bMmbz++uuqbbdv38bd3Z3bt2/j5ub2zDjCw8Pp1KkTZ8+eJSgoiG+++YbRo0cX2Sc6OhoPDw9u3LiBl5dXJV+xJElS9WnWrBmxsbHY2tqq/vDWhHPnztG2bVtiY2NxdXUtdd9Ro0axa9cucnJyuHz5Mp6enhqLS6pesiVU0isnT57EyMiI9u3bl/h8VFQUH330EZs3b+bQoUM4OzuzZ8+ecpdfVrfSEx06dMDPz48hQ4ZgYmLCSy+9VLUXJkmSpAOuXbtGenq62hPQixcvcubMGQoKCrh79y7Tpk2je/fuZSagULjgfmpqKtnZ2TIBrWFkEirplZMnT6JUKqlbt26R9UGff/55ANavX09KSgoDBgygW7du/PHHHyW2gpamrG6lJyZPnkxkZCSvvfZauQf5S5Ik1UapqakMHz4ca2tr/Pz8cHR0ZM2aNdoOS9Iy2R0v1Shz5syhYcOGjB8/Hihcv9PExKTEfTXZ7SS7jyRJkiSpdDIJlWqU2NhYRo0aRUFBAcbGxvTr1493331X22FJkiRJkvQ3MgmVJEmSJEmSqp0cEypJkiRJkiRVO5mESpIkSZIkSdVOJqGSJEmSJElStZNJqCRJkiRJklTtZBIqSZIkSZIkVTuZhEqSJEmSJEnVTiahkiRJkiRJUrWTSagkSZIkSZJU7WQSKkmSJEmSJFW7/weF/VKT1xjnUAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f = plt.figure(figsize=(9, 7.5))\n", + "gsp1 = f.add_gridspec(2, 3, hspace=0, bottom=0.07, top=0.95, left=0.1, right=0.9)\n", + "\n", + "gsp10 = gsp1[0, :].subgridspec(1, 3, hspace=0)\n", + "gsp11 = gsp1[1, :].subgridspec(1, 2, hspace=0)\n", + "\n", + "# Create axes in the grid spec\n", + "axs10 = [f.add_subplot(gsp10[i]) for i in range(3)]\n", + "axs11 = [f.add_subplot(gsp11[i]) for i in range(2)]\n", + "\n", + "f.subplots_adjust(wspace=0.4)\n", + "v_x, v_y, f_mat = mms.vdf_projection(\n", + " vdf_e, time, np.vstack([x, y, -z]), sc_pot, e_lim=15\n", + ")\n", + "axs10[0], caxs10 = plot_projection(\n", + " axs10[0], v_x, v_y, f_mat * 1e12, vlim=12e3, clim=[-18, -13], cbar_pos=\"top\"\n", + ")\n", + "axs10[0].set_xlabel(\"$V_{E}~[\\\\mathrm{Mm}~\\\\mathrm{s}^{-1}]$\")\n", + "axs10[0].set_ylabel(\"$V_{E\\\\times B}~[\\\\mathrm{Mm}~\\\\mathrm{s}^{-1}]$\")\n", + "caxs10.set_xlabel(\"$\\\\mathrm{log}_{10}f_e~[\\\\mathrm{s}^{3}~\\\\mathrm{m}^{-6}]$\")\n", + "\n", + "v_x, v_y, f_mat = mms.vdf_projection(\n", + " vdf_e, time, np.vstack([y, z, -x]), sc_pot, e_lim=15\n", + ")\n", + "axs10[1], caxs11 = plot_projection(\n", + " axs10[1], v_x, v_y, f_mat * 1e12, vlim=12e3, clim=[-18, -13], cbar_pos=\"top\"\n", + ")\n", + "axs10[1].set_xlabel(\"$V_{E\\\\times B}~[\\\\mathrm{Mm}~\\\\mathrm{s}^{-1}]$\")\n", + "axs10[1].set_ylabel(\"$V_{B}~[\\\\mathrm{Mm}~\\\\mathrm{s}^{-1}]$\")\n", + "caxs11.set_xlabel(\"$\\\\mathrm{log}_{10}f_e~[\\\\mathrm{s}^{3}~\\\\mathrm{m}^{-6}]$\")\n", + "\n", + "v_x, v_y, f_mat = mms.vdf_projection(\n", + " vdf_e, time, np.vstack([z, x, -y]), sc_pot, e_lim=15\n", + ")\n", + "axs10[2], caxs12 = plot_projection(\n", + " axs10[2], v_x, v_y, f_mat * 1e12, vlim=12e3, clim=[-18, -13], cbar_pos=\"top\"\n", + ")\n", + "axs10[2].set_xlabel(\"$V_{B}~[\\\\mathrm{Mm}~\\\\mathrm{s}^{-1}]$\")\n", + "axs10[2].set_ylabel(\"$V_{E}~[\\\\mathrm{Mm}~\\\\mathrm{s}^{-1}]$\")\n", + "caxs12.set_xlabel(\"$\\\\mathrm{log}_{10}f_e~[\\\\mathrm{s}^{3}~\\\\mathrm{m}^{-6}]$\")\n", + "\n", + "\n", + "axs11[0].loglog(\n", + " vdf_e_pad.energy.data[idx, :],\n", + " 1e12 * vdf_e_pad.data.data[idx, :, 0],\n", + " label=\"$\\\\theta = 0~\\\\mathrm{deg.}$\",\n", + ")\n", + "axs11[0].loglog(\n", + " vdf_e_pad.energy.data[idx, :],\n", + " 1e12 * vdf_e_pad.data.data[idx, :, 9],\n", + " label=\"$\\\\theta = 90~\\\\mathrm{deg.}$\",\n", + ")\n", + "axs11[0].loglog(\n", + " vdf_e_pad.energy.data[idx, :],\n", + " 1e12 * vdf_e_pad.data.data[idx, :, -1],\n", + " label=\"$\\\\theta = 180~\\\\mathrm{deg.}$\",\n", + ")\n", + "\n", + "axs11[0].legend(loc=\"lower left\")\n", + "axs11[0].set_xlim([1e1, 1e3])\n", + "axs11[0].set_xlabel(\"$E_e~[\\\\mathrm{eV}]$\")\n", + "axs11[0].set_ylim([1e-18, 1e-13])\n", + "axs11[0].set_ylabel(\"$f_e~[\\\\mathrm{s}^{3}~\\\\mathrm{m}^{-6}]$\")\n", + "\n", + "\n", + "colors = pl.cm.jet(np.linspace(0, 1, len(vdf_e_pad.energy[idx, :])))\n", + "for i_en in range(len(vdf_e_pad.energy[idx, :])):\n", + " axs11[1].semilogy(\n", + " vdf_e_pad.theta.data[idx, :],\n", + " 1e12 * vdf_e_pad.data.data[idx, i_en, :],\n", + " color=colors[i_en],\n", + " label=f\"{vdf_e_pad.energy.data[idx, i_en]:5.2f} eV\",\n", + " )\n", + "\n", + "axs11[1].set_xlim([0, 180.0])\n", + "axs11[1].set_xlabel(\"$\\\\theta~[\\\\mathrm{deg.}]$\")\n", + "axs11[1].set_ylim([1e-18, 1e-13])\n", + "axs11[1].set_ylabel(\"$f_e~[\\\\mathrm{s}^{3}~\\\\mathrm{m}^{-6}]$\")\n", + "\n", + "axs11[1].set_xticks([0, 45, 90, 135, 180])\n", + "make_labels(axs10, (0.03, 0.90), pad=0, color=\"w\")\n", + "make_labels(axs11, (0.03, 0.94), pad=3, color=\"k\")\n", + "f.suptitle(time[0])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/01_mms/example_mms_particle_pad.ipynb b/docs/examples/01_mms/example_mms_particle_pad.ipynb new file mode 100644 index 00000000..5045b696 --- /dev/null +++ b/docs/examples/01_mms/example_mms_particle_pad.ipynb @@ -0,0 +1,567 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Particle Pitch Angle Distribution\n", + "author: Louis Richard\\\n", + "Calculate and plot electron and ion pitch angle distributions from particle brst data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pyrfu import mms, pyrf\n", + "from pyrfu.plot import plot_line, plot_spectr, make_labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define spacecraft index, data path and time interval" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "ic = 3 # Spacecraft number\n", + "mms.db_init(\"/Volumes/mms\")\n", + "tint = [\"2015-10-30T05:15:20.000\", \"2015-10-30T05:16:20.000\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Particle distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:35:40] INFO: Loading mms3_dis_dist_brst...\n", + "[09-Jun-23 11:35:40] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_dist.py:68: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[09-Jun-23 11:35:41] INFO: Loading mms3_des_dist_brst...\n" + ] + } + ], + "source": [ + "# vdf_i = mms.get_data(\"pdi_fpi_brst_l2\", tint, ic)\n", + "vdf_i, vdf_e = [mms.get_data(f\"pd{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Particle energy fluxes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:35:47] INFO: Loading mms3_dis_energyspectr_omni_brst...\n", + "[09-Jun-23 11:35:47] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[09-Jun-23 11:35:47] INFO: Loading mms3_des_energyspectr_omni_brst...\n" + ] + } + ], + "source": [ + "def_omni_i, def_omni_e = [\n", + " mms.get_data(f\"def{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Particle moments" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:35:47] INFO: Loading mms3_dis_numberdensity_brst...\n", + "[09-Jun-23 11:35:47] INFO: Loading mms3_des_numberdensity_brst...\n", + "[09-Jun-23 11:35:47] INFO: Loading mms3_dis_bulkv_gse_brst...\n", + "[09-Jun-23 11:35:47] INFO: Loading mms3_des_bulkv_gse_brst...\n", + "[09-Jun-23 11:35:47] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[09-Jun-23 11:35:47] INFO: Loading mms3_dis_temptensor_gse_brst...\n", + "[09-Jun-23 11:35:47] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[09-Jun-23 11:35:47] INFO: Loading mms3_des_temptensor_gse_brst...\n", + "[09-Jun-23 11:35:47] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n" + ] + } + ], + "source": [ + "n_i, n_e = [mms.get_data(f\"n{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]\n", + "v_xyz_i, v_xyz_e = [mms.get_data(f\"v{s}_gse_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]\n", + "t_xyz_i, t_xyz_e = [mms.get_data(f\"t{s}_gse_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Other variables" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:35:48] INFO: Loading mms3_fgm_b_dmpa_brst_l2...\n", + "[09-Jun-23 11:35:48] INFO: Loading mms3_fgm_b_gse_brst_l2...\n", + "[09-Jun-23 11:35:48] INFO: Loading mms3_edp_scpot_brst_l2...\n" + ] + } + ], + "source": [ + "b_xyz, b_gse = [mms.get_data(f\"b_{cs}_fgm_brst_l2\", tint, ic) for cs in [\"dmpa\", \"gse\"]]\n", + "scpot = mms.get_data(\"v_edp_brst_l2\", tint, ic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute parallel and perpendicular electron and ion temperatures" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:35:48] INFO: Using averages in resample\n", + "[09-Jun-23 11:35:48] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "t_fac_i, t_fac_e = [\n", + " mms.rotate_tensor(t_xyz, \"fac\", b_xyz, \"pp\") for t_xyz in [t_xyz_i, t_xyz_e]\n", + "]\n", + "\n", + "t_para_i, t_para_e = [t_fac[:, 0, 0] for t_fac in [t_fac_i, t_fac_e]]\n", + "t_perp_i, t_perp_e = [t_fac[:, 1, 1] for t_fac in [t_fac_i, t_fac_e]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute pitch-angle distributions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rebin VDFs to 64 energy channels" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "vdf_e64_i, vdf_e64_e = [mms.vdf_to_e64(vdf) for vdf in [vdf_i, vdf_e]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Electron and ion pitch angle distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:35:49] INFO: User defined number of pitch angles.\n", + "[09-Jun-23 11:35:49] INFO: Using averages in resample\n", + "[09-Jun-23 11:35:51] INFO: User defined number of pitch angles.\n", + "[09-Jun-23 11:35:51] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "pad_i, pad_e = [\n", + " mms.get_pitch_angle_dist(vdf, b_xyz, tint, angles=n)\n", + " for vdf, n in zip([vdf_e64_i, vdf_e64_e], [18, 24])\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PSD -> DEF" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "pad_def_i, pad_def_e = [mms.psd2def(pad) for pad in [pad_i, pad_e]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Split in low energy, middle energy and high energy groups" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:36:20] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_37994/393131314.py:5: RuntimeWarning: Mean of empty slice\n", + " np.nanmean(pad.data[:, : idx[0], :], axis=1), coords=coords, dims=dims\n", + "\n", + "[09-Jun-23 11:36:20] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_37994/393131314.py:8: RuntimeWarning: Mean of empty slice\n", + " np.nanmean(pad.data[:, idx[0] : idx[1], :], axis=1), coords=coords, dims=dims\n", + "\n", + "[09-Jun-23 11:36:20] WARNING: /var/folders/2t/0_80h219537d9f7j3ytlqtgh0000gn/T/ipykernel_37994/393131314.py:11: RuntimeWarning: Mean of empty slice\n", + " np.nanmean(pad.data[:, idx[1] :, :], axis=1), coords=coords, dims=dims\n", + "\n" + ] + } + ], + "source": [ + "def split_energy(pad, idx):\n", + " coords = [pad.time.data, pad.theta.data[0, :]]\n", + " dims = [\"time\", \"theta\"]\n", + " pad_lowen = xr.DataArray(\n", + " np.nanmean(pad.data[:, : idx[0], :], axis=1), coords=coords, dims=dims\n", + " )\n", + " pad_miden = xr.DataArray(\n", + " np.nanmean(pad.data[:, idx[0] : idx[1], :], axis=1), coords=coords, dims=dims\n", + " )\n", + " pad_higen = xr.DataArray(\n", + " np.nanmean(pad.data[:, idx[1] :, :], axis=1), coords=coords, dims=dims\n", + " )\n", + "\n", + " energy = pad.energy.data[0, :]\n", + " e_int = {\n", + " \"lowen\": \"{:6.2f} eV - {:6.2f} eV\".format(energy[0], energy[idx[0] - 1]),\n", + " \"miden\": \"{:6.2f} eV - {:6.2f} eV\".format(energy[idx[0]], energy[idx[1] - 1]),\n", + " \"higen\": \"{:6.2f} eV - {:6.2f} eV\".format(energy[idx[1]], energy[-1]),\n", + " }\n", + " return pad_lowen, pad_miden, pad_higen, e_int\n", + "\n", + "\n", + "pad_lowen_i, pad_miden_i, pad_higen_i, e_int_i = split_energy(pad_def_i, [21, 42])\n", + "pad_lowen_e, pad_miden_e, pad_higen_e, e_int_e = split_energy(pad_def_e, [21, 42])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "legend_options = dict(frameon=False, loc=\"upper left\", bbox_to_anchor=(1.0, 1.0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot Ion data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:36:21] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[09-Jun-23 11:36:21] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[09-Jun-23 11:36:23] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[09-Jun-23 11:36:23] INFO: Substituting symbol \\perp from STIXGeneral\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(7, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "plot_line(axs[0], b_xyz)\n", + "plot_line(axs[0], pyrf.norm(b_xyz), color=\"k\")\n", + "axs[0].set_ylabel(\"$B_{DMPA}$\" + \"\\n\" + \"[nT]\")\n", + "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\", \"$|\\\\mathbf{B}|$\"], **legend_options)\n", + "\n", + "plot_line(axs[1], n_i)\n", + "axs[1].set_ylabel(\"$n_{i}$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", + "\n", + "plot_line(axs[2], v_xyz_i)\n", + "axs[2].set_ylabel(\"$V_{i}$\" + \"\\n\" + \"[km s$^{-1}$]\")\n", + "axs[2].legend([\"$V_{x}$\", \"$V_{y}$\", \"$V_{z}$\"], **legend_options)\n", + "\n", + "axs[3], caxs3 = plot_spectr(\n", + " axs[3], def_omni_i, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\", clim=[2e3, 2e8]\n", + ")\n", + "plot_line(axs[3], t_para_i)\n", + "plot_line(axs[3], t_perp_i)\n", + "axs[3].set_yscale(\"log\")\n", + "axs[3].set_ylabel(\"$E_i$\" + \"\\n\" + \"[eV]\")\n", + "caxs3.set_ylabel(\n", + " \"$\\\\mathrm{DEF}$\"\n", + " + \"\\n\"\n", + " + \"$[\\\\mathrm{cm}^{-2}~\\\\mathrm{s}^{-1}~\\\\mathrm{sr}^{-1}]$\"\n", + ")\n", + "axs[3].legend([\"$T_{||}$\", \"$T_{\\perp}$\"], ncol=2, loc=\"lower left\", frameon=True)\n", + "\n", + "axs[4], caxs4 = plot_spectr(axs[4], pad_lowen_i, cscale=\"log\", cmap=\"Spectral_r\")\n", + "axs[4].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", + "caxs4.set_ylabel(\n", + " \"$\\\\mathrm{DEF}$\"\n", + " + \"\\n\"\n", + " + \"$[\\\\mathrm{cm}^{-2}~\\\\mathrm{s}^{-1}~\\\\mathrm{sr}^{-1}]$\"\n", + ")\n", + "axs[4].text(0.05, 0.1, e_int_i[\"lowen\"], transform=axs[4].transAxes)\n", + "\n", + "axs[5], caxs5 = plot_spectr(axs[5], pad_miden_i, cscale=\"log\", cmap=\"Spectral_r\")\n", + "axs[5].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", + "caxs5.set_ylabel(\n", + " \"$\\\\mathrm{DEF}$\"\n", + " + \"\\n\"\n", + " + \"$[\\\\mathrm{cm}^{-2}~\\\\mathrm{s}^{-1}~\\\\mathrm{sr}^{-1}]$\"\n", + ")\n", + "axs[5].text(0.05, 0.1, e_int_i[\"miden\"], transform=axs[5].transAxes)\n", + "\n", + "axs[6], caxs6 = plot_spectr(axs[6], pad_higen_i, cscale=\"log\", cmap=\"Spectral_r\")\n", + "axs[6].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", + "caxs6.set_ylabel(\n", + " \"$\\\\mathrm{DEF}$\"\n", + " + \"\\n\"\n", + " + \"$[\\\\mathrm{cm}^{-2}~\\\\mathrm{s}^{-1}~\\\\mathrm{sr}^{-1}]$\"\n", + ")\n", + "axs[6].text(0.05, 0.1, e_int_i[\"higen\"], transform=axs[6].transAxes)\n", + "\n", + "make_labels(axs, [0.02, 0.85])\n", + "\n", + "f.align_ylabels(axs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot Electron data" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 11:36:24] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[09-Jun-23 11:36:24] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[09-Jun-23 11:36:26] INFO: Substituting symbol \\perp from STIXGeneral\n", + "[09-Jun-23 11:36:26] INFO: Substituting symbol \\perp from STIXGeneral\n" + ] + }, + { + "data": { + "image/png": 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RxdBQFmVJojk4mIVVktgCWEgRsdiKiuKIokTCr58+sZBycOD/FBcXXhwd+b2jI+DszG0+Q6FQQCqVwtfXF1paWskRVSJKFVVMWmJjY/H69Wt4eXnB3NwcJUuWhKWlJRwdHVGiRAk4OTlBKz6eRebLl3xOUVEpi68vn59MxkuSP8zMOHquqws4OEBWujSQkAAzX1/2SdIjf1NTPgcrK/ZdQgKLRYUCiIlhnxoass+SXpMWQ0PAxAQoXRqwtuZjGRvzTYuuLi9JQrp4cfatvz/g4wOEhLCf4+L4NWmJi+NjOjiwSLaxYTGrpcX7JyTwsU1NU8R6YiLvK5Xya2go/x0dzfvFxXGbO3eA06fZZyVK8I2PqSn7ytKS/zY05MXKis9FR4f7SHoCoVSyX0ND2d8xMZD9d1NllpjI11fp0oCrK1ChAvebQShAoVDg9evXeP36Nby9vSGXyxEXFwd9fX2YmprC3Nw8+dXMxARmkZEwCw+HaVAQdJOuV6mU7Xj/nm1TKgFTUygqV0aAvj7eWFjARy7HNU9PbNq8GQDg6uqKa9euwdTUNF9HiEU0TX0I36oH4Vf1oOoIcEEhMDAQxYsXx5w5czB79uxU24YPH46///4br1+/hrOzM4KCglCpUiWcOXMGI0aMwIwZM9C9e3eV2SIiwPkFQ0MWoI6O6jtGVBTw4gWLY5kMePMGeP0auHiRRcenTyw27ew4WlyuHODgAG0nJ1g6OMCyVCkWXr6+3Ed0NAvK/wZ+QUuLhbyREVCqFFC1KvcbGMhi5vp1FosPH3Lk1ciIBZW1NQszExN+dXPjdSYmLDxtbICKFfm9VMpC09cX+PiRBaO9fYrgtbTkdXmJgwMvqkJHJ0UQW1tn3nb4cL7h8fEBvL3584uKYkEbHs6R5ogIwM8PuHuX3xPxzUBEBN9oSCQshi0t+TXpJiApyhwayp/dy5f8+eno8HVatixQpgyL47JlAVdXaFtaooKdHSqULcsCPSqKP6/373m5eZM/+3fv+PNLTOS+Spfmpxr6+vyZm5oC7dolpwQhMhLanp4o4eODEo8eATIZBtWqhVWdO2OwUolDJ07A1tYWADBo0CBs2LBBRIUFAoEgn/Hw4UMAgI6ODm7dugUACAgIwMGDB/Hvv/9i+vTpcHZ2BgDY2tpi/PjxaNCgAbZu3apS8QuICHCekK0IcH5BKmWR/Po1CxVfXxZXSa/6+iyU7O1ZJBkZ8ULEkTpDQxbGSSkEJibc1tSUX+3tWczWqMFC+yuSikRkQn1k6NvISBbZHz8CXl4sar29+fN+/ZoF9ZckRdRLl0556uDiwjdJzs4cVc8JRHwDt20bMGECAEAJIHjECPjo6qLn8eOoVbcuDhw4kNPTVjvimlUfwrfqQfhVPRTVCPDixYsxffr05PcSiQTm5uaoWbMmRo0ahR49eiRvCwsLQ4sWLRAQEIB9+/ahcePGKrVFCOA8oEAJ4AKE+GJWH7n2rVLJIjkp7SMpxURdxMQACxcCR4/yUwsvL/hHRcHh7VtUrlwZd+/ehb6+vvqOn0PENas+hG/Vg/CreiiqAji7SKVStGrVCmPGjIGpqSkWLlyIu3fvqrTyUP6tfyUQCAoeWlqcwmBnxykN6s7HNTICFizggYwHDwJ378K+QgV8rFQJT548gYGBAfz8/NRrg0AgEAhURnR0NNq3b4+hQ4diyJAh6N69O/T19bFjxw6VHkfkAAsEgsKDjg7w779wKlsWHgAaAWjZsiWeP3+uacsEAoFAkA2MjY1x/fr1VOu+fK8KRARYIBAULszMgKAgNARwHIDfx4/JM+4JBAKBQAAIASwQCAojWlpAVBTaApBGR8M6q2oaAoFAIChSCAEsEAgKJ8bG0Ll+Hf319BAREQFfX19NWyQQCASCfIIQwAKBoPBSpw52JiQAANavX69hYwQCgUCQXxACWCAQFF50dHhCDQALFizQsDECgUAgyC8IASwQCAo3p07h5H9/RkZGatQUgUAgEOQPhAAWCASFnnb/vXbs2FGjdggEAoEgfyAEsEAgKPRIrl0DAFy9ehWvXr3SsDUCgUAg0DRCAGeDbt26QSKR4Pz588nrLl++jBo1asDIyAhlypTBunXrNGihQCDIlJo18b///jx27JhGTREIBAKB5hECOAt27NiBmJiYVOs+fPiADh064Ntvv0VERAS2b9+OadOm4fDhwxqyUiAQZIqhIVoB6AjAMDBQ09YIBAKBQMMIAZwJnz59wowZM7Bp06ZU67dv347y5ctjzJgx0NPTQ9OmTTF8+HCsWbNGQ5YKBIIs+eMPRAM4d+KEpi0RCAQCgYYRAjgDiAjDhw/HjBkzULJkyVTbPD09UadOnVTrateujYcPH+aliQKBICf89BMuATj26pWYGlkgEAg0yObNmyGRSJIXLS0t2Nraolu3bnj//n2e2KCTJ0cpgKxbtw5EhO+++y7NNplMhvLly6daZ2FhAZlMlmF/JiYmogSTihH+VB+F1bdmZmYAgN9//x0jR47M8+MXVr/mB4Rv1YPwq3qIjo7WtAka5eHDhzA2Nk4eW5WYmIj79+9jypQpCAkJwbX/Bi6rEyGA0+Hdu3eYP38+bt26le52MzMzREREpFoXHh6e/OOaHnFxcZg7dy709PQAAO7u7mjZsqXKbBYIBFlztGZNdLl/Hxs3btSIABYIBEWX8+fP4+LFiwCEAPb09ETlypVRr1695HWNGzfGhQsXcOnSpTyxQQjgdLh27RpCQ0NRs2bNVOt79OiBPn36oFq1ajh69Giqbffu3UP16tUz7FMul2P58uWZimRB7hA+VR+FzbfNLl3CSgArHR01em6Fza/5CeHb7BESEoKlS5fC3t4egwYNgrW1dabthV+/nu7du6N79+4AAF9fX2zfvl2zBmkIIsLjx4/Rv3//NNv8/Pzg6uqaJ3YIAZwOvXv3ThOddXJywoYNG9C6dWtERkZiyZIlWLduHb799lvcvn0bW7duxdatWzVksUAgyBZz5qDUnDlIePtW05YIBBohKCgIlpaW6Nu3Ly5cuAArKyvs3r0b8+bNg62tLXR1dVG5cuUM979w4QICAwPRpk0bxMbGwsLCAsbGxnl4BoKCzps3bxAVFYVKlSpBLpeDiODr64uVK1fixYsXOHnyZNadqAAJEVGeHKmAI5FIcO7cuWRhfPnyZUyYMAEvX76EnZ0dpkyZgtGjR6e7r0wmg5OTE3x8fMRdtApJyrkWPlU9hdm3jyUSNAEQoYGvvsLsV02T33x7+fJl1KlTB0ZGRpo2JZmoqCiYmpoCAOzs7LBw4UJ0794dJUuWhJ6eHkJDQwEA/v7+KF68OIDUfvX394erqytiY2Mhl8uT+12/fj2+//77PD6bgo2vry8cHR0hlUrzzTWbV+zbtw99+vRJs97e3h779+9Hw4YN88QOEQHOJl/eJzRr1kxUfRAICiC2f/4J6U8/IT4+Hvr6+po2R1AIOX36NNq3b4+5c+di1qxZmjYnmQMHDqB06dI4fPgwKleuDG1tbQBAYGAgDAwMoFQqUb9+fTRu3BgTJkzAx48f4eDggA4dOuDt27fo2LEj3N3dMW/ePNy6dQt9+/bFqVOn8O2338LY2BgDBw7U8BkWXYgIkQnqG7BoqmcKiUSikr48PT0hkUjg4eEBPT09KJVKeHt7Y8aMGejXrx+ePXuWfKOmTlQugD98+IBHjx7h0aNHePLkCfbt26fqQwgEAkGusba3hxaAgIAAlCpVStPmCAoZly9fRvv27QEAy5cvR4UKFdCrVy8NWwV4eXnh559/xp9//olq1aql2mZoaAgA0NbWxtSpU9GzZ08sXrwYlSpVwtWrVzFz5kxERkaiR48emD9/Ptzc3FClShUAQN++fSGXyzFkyBA0bdoUTk5OeX1qAgCRCZEwX2yutv6l06Qw01dNpPrhw4dwdnZGgwYNktfVqVMHOjo66NGjB86ePYuePXuq5FiZoRIBPGbMGDx+/Bh+fn4oXbo03r9/j2nTpqF+/fqq6F4gEAhUho6+PkoCePvqlRDAApWiUCgwbtw4TJs2Db/++ivc3d3Ru3dvNG7cGF26dEGNGjXQpEmT5MhrXhAcHIy4uDj07NkTAwYMSHfg0ef06NEDSqUyOdonk8ng7e0NZ2fnDHN9+/bti99//x1LliwRE0JpCFM9U0inSdXav6rw9PRMJX6TMDAwAADExsYiLCwMTk5OCA8PT66e9f3336Ns2bKYOnWqSuxQiQC+ceMG2rRpg4ULF0JLSwvt2rVLt36uQCAQaBxDQ1QDcHrfPrRo3VrT1ggKEZs3b0Z0dDTmzJkDfX19eHh4YMuWLfD29sa+ffswY8YM6OnpwdjYGFWrVkX58uUxf/58JCQk4M2bN6hbty60tFQzP9X58+exdu1aHDlyBACn7S1fvjxb+375qLt06dKZDnTT0dHB9u3b0ahRI1SoUAFjxozJtd2C3CGRSFQWoVUngYGBCAgISH6C8DkHDhyAlpYWGjRoAEtLS5QtWxYPHjxAvXr1cO3aNdy5cwdr165VmS0q+U978OABXFxc0K5dO1y5ckVleSICgUCgclq0QF8bG5z7rx6nQKAKIiIiMGPGDKxYsSI5t1xPTw+jRo3CkiVLcPv2bYSFheHo0aP466+/ULNmTTx9+hQVK1ZE5cqV0aJFC9SvXx+7d+/+KjuUSiWmTp2KNm3awNraGp8+fUJ0dDQuXryYHElLYuG1hbBdZotJ/5v0VccEgEqVKuH06dOYOHEiOnfuDC8vr6/uU1D4SBo7paOjg1u3buHWrVs4cuQIBg0ahG3btmHq1KlwdnYGADRt2hQeHh5ISEjAqFGjsH79eujoqC5zVyU9SSQSjBgxAn379sWiRYvw8uVLvH37Fi4uLqroXiAQCFRKw2bN8Hj/frx58wblypXTtDmCQsBPP/2EatWqoXPnzhm2MTQ0RLNmzQAAXbt2hVKpxI4dO6Crq4vOnTtj9erVGDVqFHR0dHKdN7xo0SLs3bsXL168SDNj6efsfLwTv178FdMbTccij0X4+9HfcDRzhJ2xHU72PwltrZynadSvXx8fPnzADz/8AGdnZ3z33Xdo0qQJ7Ozs0LRpU+jq6ubqnASFB09PTwDAjBkzMGPGDEgkEpibm6NmzZo4cOAAevTokdy2SZMm2L17N6KiotC8eXPUrVtXpbaopQzahw8f8OuvvyIyMjLNhBFFEVEGTT3kt7JHhYnC7lvl/v3Q7t0bAE/1amJikifHLex+VSUnTpzA0qVLERgYCKlUCltbWzg4OEBbWxtNmjSBra0tbGxsULVqVZibpwz+0YRvL1++jK5du+Lly5fJ5cNyy+HDhzFs2DDcv38/ORKWXUJDQ1GiRAlcu3YNtWvXzrDdy5CXqLC2AkbWGImNnTbiefBzPAp4hFh5LEadHAWPYR6oXYL3z+01+/btW/z000949uwZYmJioKenh+HDh2P+/PniKTGKdhm07BIQEAA3NzdYWVnh4cOHKveTSiLAXl5e+Ouvv6BQKNChQwe0bNkS//77L27fvq2K7gUCgUClaNWtCzcALwFcPHcOnbt107RJgv/Ytm0b/vrrL7x79w6zZs3CN998A2NjY3z69Anh4eGIj4/HpUuXEBUVBV9fX7x69QpEhFq1aqFjx47o378/XFxc8kxkERGmT5+OyZMnf7X4BYBu3brh0qVL6NOnD65fv56jUn0///wzqlevnqH4VZISzf9ujvt+9zGq1iisac8D1r6x+Qbf2HwDADj04hCufriKEmYlUNwk9+fj4uKC06dPAwASExOxePFizJo1C25ubnj69Ck6dOiAxo0b57p/QeHHzs4Oenp6aptFVyUR4Nq1a6NHjx6wtrbG7t27MWjQIAwdOjRb+2Y3T0hLSwulS5fOvZEaRESA1YOIpqmPQu9bhQKXdXTQAoASgLe3d55UhPgavz579gyxsbGoVauWSmxRKpU4ceIE/Pz8MGzYMI3XRA4JCcHKlSuxevVqzJs3D4MGDYKtrW2W+8nlckREROD06dM4duwYjhw5AhMTEzRo0AAWFhYwNzdH27ZtUb58ebVMsXrq1CkMGzYM7969U9mThPj4eDRo0ADt2rXDggULALDQnj17NtauXYvSpUujZMmSWL9+Pezs7ADwDG0tW7bEqVOn0K5du3T7fRz4GFXXV8XhPofRxbVLujcJy64vw5TzUwAAxQyKYajbUExpOAX21vYIiArA5HOT0d2tO7pVyPlN44kTJ9CpUycAQMWKFVG1alXMmjUrz6a+zU+ICHDWrF+/HhcuXMD+/fvVcwBSAU2aNEn+Oy4ujtq0aZPtfSUSCWlpaWW6SCQSMjIyUoWpGkEqlZKZmRlJpVJNm1KokEqlwqdqokj4duVKOgUQAOrSpUueHDK3fh0wYADhP1tPnz5Nx44dox9++CHL/aKjo6latWo0Y8YM+vTpEz1//pwOHjxI/fv3Jysrq+Q+9+7dm5vTURnLli0jIyMjat26Nd25cydXfST5NjY2lu7cuUOrV6+mhQsX0k8//UQuLi6kr69PTZs2pV9++YVWrFhBoaGhKrG9d+/e9Ouvv6qkr8+5dOkSWVpaUmxsLEVFRVHz5s3J0tKS1q9fTytWrKA6deoQAPL39ydvb2/S19en9evXZ9rn8uvLqe2/bTNtExoTSks9llJIdAhd/3idKq+qTL+c/IWIiCaemUiYA2q0tVGuz+vw4cP0+++/J197PXr0yHVfBZlPnz4RgML/PZsLXrx4QRUqVCB3d3cKDw9X23FUIoArVqxIV69eJT8/PyIicnd3z/a+xsbG5O3tneny/v17Mjc3V4WpGkEIYPVQJESahigyvt23j3YCVKNMmTw5XG786uPjQwBo0qRJNGHChGThACBLEXzgwIFU7ZMWGxsbatOmDW3YsIGaNGlCrVq1ordv337NqWWLwMBAUiqVqdZ5enqSvr4+eXh4fFXfWfk2KCiIFi1aRJ06daKGDRuSnZ0d9ejRg7p3707h4eGkUCjo+PHjNGjQIDp58mS2jhkVFUVGRkb06NGjr7I9PZRKJdWoUYNMTU2pXbt2VKtWLfLy8kreLpPJyM3NjXr06EHjx4/PUkgqlUqq9Fcl2vZwW47sOPP0DBWbU4w67upINkttaP6V+aQ7T5ci4yNzc1rJtpQtW5YWL15Menp69P79+1z3lRcolUoaOXIktWnTJs31m1uEANY8KhHAv/76K3Xo0IEcHBzI2NiYzMzMaMKECXT06NEs9+3Xr1+2jjFgwICvNVNjCAGsHoqMSNMARcm3Hzp3Jm2JhKKjo9V+rNz4ddq0afT5w7pDhw7R8ePHacqUKQSA4uLiMtx3+fLl1L17d3r69Cm9ffuWfv31VwKQ6lyPHDlCAMja2poSEhLS7Ucmk9F3332XHOTIDomJieTl5UUDBgwgCwsLKlGiRLIAr1SpEgGgcuXKEQCaMGFCtvvNiJz4VqlU0r59+2j27NnUvn17KleuHJUvX55sbGzoxx9/JH19fapVqxY9ePAgU8GzY8cOqlixospE0ZdER0eTg4MDAUglfpMICgoiS0tLAkBXrlzJtK+bPjfJfJE5RSfk7DqXSqV0//19WnxtMU353xSKio+ikqtK0gWvCznqJyPq1KlDe/bsUUlf6mL//v1kYGBAAOjjx48q6VMIYM2jEgH8OUFBQXT27FlasmRJtsVtYUcIYPVQlERaXlOUfKvct49K6OrSuXPn1H6s3Pi1TZs29Pvvv6dZr1QqydbWljw8POjbb78lAwMDev78eao2Y8eOTSMuZTJZmn6Cg4PJ0dGRevToQdOmTaMjR47Qli1baN26dVS3bl1q0aIFAaDp06dnaa9CoaBOnToli9369evT7du36dSpU3TixAnq378/jRkzho4fP0779u2jK1euqERA5vaaTUhIoI0bN9LWrVuTbyaCgoJo9OjRJJFIyNHRkUaOHEm3b99O3icmJoY2bdpEpqamtGHDhnT7nXlxJu1+sjt3J/MZd+/epa1bt2a4PTY2NlNRluTbwYcH0+gTo3N8/PT82n1vd8IcUGBUYI77+5JBgwbR3Llzv7ofdbFnzx4yMTGhPXv2UJUqVWjbtm0q6VcIYM2jEgHs7u5OkyZNop07d9Lz589z/GVmamqqCjPyLUIAq4eiJNLymiLl2w8faKxEQgP79lX7obLj18jISFIoFDRp0iT67rvvSFdXl968eZNu2549e1LFihUJADVu3DhNNLJ9+/a0evXqbNl28uRJ6ty5c7JwNTU1JQCkq6tLtWvXpjVr1lD16tXT3Vcul9PSpUupefPmBIB0dHToxx9/pMGDB1NsbGy2jv+1qOOajYmJobNnz9KgQYNIT0+PbGxsSF9fn0qUKEEVKlSgXbt2pbtfvDyeMAeEOaCJZyaqLUKcFRe9LpLBAgMqu7osGf9mTG9Dc57mkp5fvcO9yXaZLW24l774zwnLli2jFi1afHU/OWXs2LH04MGDTNscPXqU9PX1afduvpFZunQpaWtrZ3jD8fz5cwoMzPimICIigk6dOkUJCQl048YNIYA1jEoEcIUKFahJkyY0ceJEatmyJZUsWZLq1atHo0aNytb+JiYmqjAj3yIEsHooUiItjylSvlUq6Zm9PRno6VFwcDDt3r07W+lbuSEjv0ZERFBiYiJduHCBAJC5uTkBoDJlytD8+fMz7M/Ly4t69OhBK1eupPj4eDIzM6N79+4lb7e0tEwVucwOYWFhFBERQSEhIakGx/n5+ZGWllaawWOxsbHUunVrcnZ2pnHjxtHKlSvJ09MzR8dUBeq+Zl++fEnDhw+nw4cP09q1aykmJibddsHRwVRqVSkyWGBA93zvEeaAfjn/CymUCrXZlh6fpJ/Idpktzbs8jw4+P0j3fO9lvVM6ZOTXZdeXZTmgLjv4+fmRtrZ2psJRlSiVSvrrr78IAA0cODDDduvWrSMjIyNavnx5qvXDhw8nHR0d6tu3L/32228UFhZGRES3bt1KvvmztLQkd3d36tChA/Xu3Zs8PDzo5MmTZGhoSADI1taWatWqJQSwhlGJAJbL5fTHH39QkyZN6NSpU0TE4f0TJ05ka38RARbkhiIl0vKYIufbb7+llqVKkbGxcXIENKePOg8ePEgLFizItE1GfjUyMko+7tSpU2nq1Km0Y8eOHB2fiKhPnz7J6Qdjx44lABQREZHjfjLCzc2NDh8+nGrdgAEDqH79+mlSK/Ka/HDN+kf6U9c9XQlzQMuuLyMiojW316R6n1e0+LsFDT0y9Kujzxn59X34ezJcYEie/l9/s1OvXj3atGnTV/eTHf755x8CkPzkJL0Bj1KplAwNDenSpUtptikUCrp79y7Z29uToaEhubq60sSJEwkALVu2jJ49e0bnzp2j6dOnU5UqVUhPT48AkJaWFq1YsYLCwsJo+vTpNHXqVCGANYxKZ4KTSqWYP38+Xr16hUWLFqFSpUrZ2k9fXx+//vprpm1mzZqlChM1gqgDrB4Kfa1aDVLkfLtnD27PnIl6b98C4OndtbS0cOrUKbRu3TpbXSTVVM3sKzUjvybtu3nzZgwaNAh6eno5PgUAePfuXaop6K2trREcHJyrvtJj9OjR0NXVxerVqwEAly5dQteuXfHq1SuVTALxNWjymj315hQGHx6M0NhQ6Gjp4OrQq6jvVD95+x3fO2i2vRn+aPcHRtQYoXZ7Dr84jKFHh+Lj+I8wNzDPeodMyMyvk/83GctvLsfK1isxtNpQWBha5OoYixcvxvXr13H8+PGvsjUrTp48iY4dO2LVqlX48ccfk6dmTkxMhI5Oyrxg06ZNw61bt3D58uUM+1IqlQgPD0eVKlXg5+eHHTt2YODAgWlqK8fFxYGIEBcXBwuLFP+IOsCaRyUCOCoqCs+fP8ezZ8/w9OlTnD9/Hs2bN8fvv/+erf11dHRQv379DLdLJBJcvXr1a83UGEIAq4ciJ9LykCLn27AwoEQJRF69iikbNmDVypVY/scfuHfvHo4cOZKtLnIrgCMiImBhYYHffvsNv/zyS+7P4T8sLS1RvHhxvHjxAmZmZpBKpV/dZxL79+/H/Pnz8fjxYxARGjVqhA4dOqjE7q/lc98GRQfBM8ATL0Newi/SD7GJsbA1tsWo2qNgaWiZaT9KUiI6IRoEgpl+1td/nDwOrmtcMaTqEIysMRJO5k7ptjv26hj6H+yPI32PoGXZljk/wWyy8uZKzLw0E1s7b0WfSn2+ur/MvgvCY8Mx8vhIHHxxEADgauWK3hV7Y1qjaTDSNcr2MV68eIEaNWogKCgIpqamX21zejx69AhNmzbF+vXr0bdvXwDAxYsX0bJlS5w7dw4tWrQAwFOjOzk54cSJE2jUqFGW/YaEhGDnzp0YN25cjuwRAljzqEQAa2tro3HjxujXrx9q1aqFSpUq5WhWITMzs+R/ssKIEMDqociJtDykSPp24EBg507+u0sX3JsxAy1btkRwcHBypCgjAgMDUbx4cVhYWCAsLCzDdun59fHjx2jcuLHKhKqfnx90dHTw888/o1y5cip9epZ0nhEREbh58yb69+8Pb29vjV4n0QnRWH5jOd74v4FcKcftkNvwjvCGs4UzKtpWRAnTEjDUMYRnoCeufbgGW2OeXa6GfQ20cW4DiUSCUbVGQSKRwCvcC/0P9sdt39vQkmihT8U+qO1QG6Nrj4a+Tvq/aVsfbsWKmyvwZNQTaEm0MrV13d11WHJ9CZ6OfgoTPdXMGvc51z9eR9udbXF24Fk0cGqgkj6z810gi5dhxY0VMNU3xcEXByFXynF16FUY6hpm6xhJN1NNmzbFwoULVWL358jlclSoUAG9e/fGb7/9lmrb9OnT8fTp0+To8++//47du3fj1q1bap1OWwhgzaMSATxjxgx4enri2bNnsLS0RNWqVVGtWjVUq1YNTZo0yXJ/IYAFuaFIirR0CA4Gzp0D7t0DFArAzg5wcQGMjICQEODVK+DJE+DxYyAwEDA0BMzMAEdHwMICCA8HKlYEqlYFbG15fenSMpiYFDHf3roFfPYkSrFiBRyXLcPu3bvRrFmzDHeTy+VwdXWFl5cXbG1tERgYmGHb9K7ZkydPYurUqXj69OnXn0Me4OjoiBUrVmDs2LFYsGABRo4cqTFb7vrexcDDA2FtZI0OTh2gp6OHbxy/QaOSjdKN3r4Le4eHAQ9haWiJs2/P4tTbU3gT+gYtyrZAKfNS2PFoB4ZWG4pxdcchJjEGB18cxJGXR1DRtiL+7fYvtLW00/RZc2NNfF/ze3xX87ss7VWSEk23N0Vth9pY2WalSnyQhHeEN8qsLoPva36P9R3Xq6zfnH7PJigS0HR7U7hauWJbl23ZFpFnz57FkCFD8O7dOxgbG+fa3vTYuXMnZsyYgdevX6e5mQ0ICICzszPOnz+P2rVrw8XFBcuWLUOvXr1UasOXCAGseXSybpI1SXOVA0BwcDAePnwIT09PrF+/PlsCWIVpyAJBoUOpBAICgLdv+TUyEoiKAp4/Z8329CmL1wYNAD09fn/4MBAXB1hbA+XLA61aAZMnA8WLA/Hx3Me7d0B0NFCsGIvn06dZMPv4ALGxwDffsIiOiQFevgT09QFdXUAuZ7t0dVlIm5kBrq5A3bpA8+ZAuXIadVfuqVeP7yamTwdsbaE9bRoatmqF27dvZyqAZ86cCS8vLzSe0Bgeqz2gVCqhpZV5JPBzfHx84OSU/mPz/Ejnzp3Rt29fDBkyBCNGqD+fNT0USgUWeSzCwmsLMavpLExuMBnRUdEAMhdqzpbOcLZ0BgC4l3HHklZL4B/pj/X31sNH5oPzg8+jnmO95PZVi1fFmNpj4LbWDTrzdbC4xWL0qtgLZS3KAgA8Pnrgdehr9KvUL1t2a0m0sKnTJtTcWBP9KvVD7RK1c+uCVChJie9PfA8TPRPMbjpbJX3mFj1tPRzodQC1NtXC3md70bdS32zt16pVK1haWuLEiRPo0yf7qRvR0dEZCub379/j/v37GDt2LFavXp3uk5zixYtj4sSJGDNmDMqXLw8A6NatW7aPLyi4qCQCrFAosHz5cly9ehWGhoZo1aoVhg4dmu00CA8Pj2zl2hRURARYPRTWCHBYGHD3LnDzJuDhAdy+zULV0RFwcGDBaWzMUd66dYHGjTnqq0pevZLh/n0gLs4MOjocIZbLgcREIGmsSEICC+nwcODZM7bz+nWgenVg0CCgWTMW0Wp8iqhe3N2xzNwcVxITceTIkVSDZD7Hzc0NBm0M8Mj4EbAICAsLSzXY5XPSu2Z/+eUXhIaGYsOGDao/BzWQmJiI//3vf2jbti20tdNGRNXNo4BHmHFpBl6GvMTenntRw74GAPV+H4TGhGLFzRVY5LEIAKdPPA9+jjh5HBY0X4Bfm2Q+iPtL5l+Zj/3P9+PGtzdUkgrx192/sPzGcjz8/uFXD3r7ktz6dcejHZh5aSZe/fgKBjoG2dpn1qxZePnyJfbt25dl2/3792PixIn49OkThgwZgjVr1sDEJMWXV65cSb5x3bZtG4YOHZphX5GRkcnnl1VbVeH76RMcnZxEBFiDqEQAT506FVKpFP3790dYWBjOnj2LW7du4eLFixn+EKSHUqnEP//8gzt37iAyMjLVth07dnytmRpDCGD1UBAFsJ8fcPAgEBHB4tHLi5e4OI7MBgfz+rJlgTp1gKZN+am8qytgkL3fEJWQW9+GhQH79vFy+zaL9MGDgTZtWEQXKDE8cyYu3b0L97NnUalSJZQqVQpjxoxBu3btkpsQEfSM9CAfLkeXxl1wbPAxPHv0DBUqVEi3y/T8OmjQILi6umLGjBnqPZ8CTkBUAMadGYfjr45jSNUhmNd8HmyMbZK358X3ARFh/b31uPHpBvpX6g97U3tUtaua41zRBEUC3P92R3mr8tjaZSvkSjnCY8Mx7fw03PK9hTsj7sBYL3tpABFxEai6viqWtlyqkkFvX5JbvypJicbbGsNY1xiH+hzKltB/9OgRGjZsiJCQEBhk8oX37t07VKlSBb/++isaNmyIH374AR07dsSyZcuS23Tt2hXW1tYYP358tipSffr0CZaWljAyyv7gva/B99gxOHbpIgSwJlFFLbUGDRqkWbdt2zYaM2ZMjvr5/vvvydLSknr16kVDhw5NtRRkRB3gtKhiYqT8UPczPSIiiHbvJurTh8jVlah1a6LatYmKFyfS1iZq3pxo8GCi0aOJli0jOniQ6NQpoosXiR4/JvqvrrpGUYVvo6OJtm4latuWyMiIqGxZPu9164iePlXNNaBWjh6lYBeX5Pq8SYutrS2NGDGCfv/9d3J1dSUAdPzRcTrz5gwZuhjSunXrMuwyPb82bdqUtm/fru6zyTFKpZIS5AmaNoMSFYm0xGMJGf1mRN33dqeAyIB02+XX74OM8JH6ULHFxWjT/U1UbX01whxQyVUlyXKJJa29szbb/Yw5OYYab21McoVcLXZ+jV9lcTKqu6kuzbo4K1vtlUolubi4ZDjDXlKbXr160YgRI5LXnT59mgwMDOjx48dERDRz5kwCkOkU0fd879Gb0PRnWMwLPo0fX6TrAG/atCnV96pEIiEbGxvq2rUreXl55YkNKhHAzZs3T3d93bp1c9SPlZUVvX79WhUm5SuyK4A/fCBas4ZFQp06ROXKEQ0fTvTwYd7YqU6iooh27iQaOZLPS1eXyMaGqFYtol69iKZMIVq/nujoUaJHj4j8/Iji4zPvU50/eMHBRPfvE12+THTrFtufGVFRRHPm8LkBLHxnz+bzWbeOaN8+ops3iQLS/+3Od6jat5GRLPKnTydq2ZLI0JDIwYFowACijRuJnjwhUqhooiyFIvXndf8+0dixuegoNJRIIqE5kyYRAJowYUIaMZy0fIz4SLc/3SaTjibUpk2bDLtMz6+lSpVKt+C+JvD096Tg6GDa4bmDKv9VmTAH1P9g/zyfxSyJd2HvqPr66lRhTQW6/vF6pm0LmgAmItrzZA9hDqjrnq4UGBVICqWC/nn0DzmvdqZ4ecoXoFwhp0RFIh19eZQOPDtASqWSjr08Rhe9LpLhAkN6HvRcbTZ+rV8vvb9Exr8ZZ1tszp49m/r06ZPutnHjxiX/z/n4+CSvVygU1LdvXwKQPNFFZvG9iNiI5KmqNXWT96l27SItgEePHk3GxsZ08+ZNunnzJl29epVWrVpFurq61KhRozyxQSUpEJUqVcK2bdtQuXLlVI8tWrRogQsXLmS7HwcHB3z8+DHDXLuCSlIKRKNGPvj1VzM0aMD5k9evA5cu8WCkBw94kFOjRvzY280NMDXlgUmbNwPVqnFeZZ8+PLCpoHD3LrBmDXDkCODkBLRrx+fn4sKP+r29gffvefHy4tegICCpIpSZGWBiArRuDWzdmvoRem4ezUVEcEWE16+B0FC2QVs7JR3h0ydOU/DzA6ysuGJCbCy3NTcHSpcGunQBOnXiNIXgYGDLFmDTJs53nTwZaNIEsMy81Gi+R92Pk+PjU65/Dw/gzh0eVNe0KdCyJVCjBg/sM8nkqWl8PH8uhoZczQIALl/ma0Uu5wF5EycCHTvytqSc6kmTcmBo5coInTwZ1kOG4NTZUyAFQWmtxGmf07h0+xJernoJIoIiQYF3Ye9QcW5F6GzWQVhYWPJ3YUJCAhQKBQwNDdP41dfXF05OTvD19YW9vX0Ovag6XgS/wMxLM5PruVoaWmJ4teFQkAKrbq3Cnh571PJ4PSOICNs9t2PC2QkYWGUgVrRekWEZsiQKYkoUwDnNFW0rQkeLf/cSFYmou7kudLR0oKutCxsjG2hraePQi0Pp7r+oxSJMazRNbfapwq+jToyCh48HfnP/DZ1dO2fa9saNG+jcuTOCgoLSDCatXr06Pnz4gIkTJ6abMjRv3jzMnj0bffr0wZ49e9Lt/6P0IwYcGoB4eTzehb9DWGwYwqeGo5hBsVyfX47x9YXvN9/AUSYrsikQDRs2hFKpxM2bN1Ot79SpEy5duoSoqCi126ASATxz5kw8fPgQjx8/hqmpaXIZtB07duSotM+8efNgamqKCRMmfK1J+YokATx8uA+2bDFD2bIswMzNeXR+uXJAlSo8mCk94RQWBuzfD/zzDwuFdu1YDHfsmLd5oTnh+HFg3jweHDVyJDBgAFC7dvZzQBMS+LyDg1mMDhoELFvG+aQAVzrYvFmGkBAgIMAMISEshoyNWQxZWLAvzc1Z1Hp7cwWF4GDA3p7Fqo0NCyyFgvcrW5ZFup0dfx7m/40lIeL9AgK4pNi2bfw5hIZyZYQOHYBx4/jzK1A5rpmQ12IiMRHw9AQuXACuXOG/AwP5czQ15ZsUgP1taMifybNnfJ0AwLBh/P8wdCjQogUwcyave/w47bGUyhx8TkOHAiVL4nDt6hjxbASUEiUi4iJStksBxAH0FyEkJgQ2S23gsNkBW7dsRZs2bRAVFYW6desiLCwMT548SZ7hzczMDBs2bMCmTZtQqVIlbN++PRdeUw0DDg3Arie74GzhjEkNJqGWQy2UMC0Be1MW5NsebsPkc5OxrsM69KqontJQ0jgpZPEyxMnjULpYafx68Vcsu7EMf7T9Az/W+TFbObYFVQCnR1RCFNbdXQevcC+c8zqHd+Hv0L1Cd4yvOx7FDIrh8MvDaFKqCRxMHVDeqrxabVGFX6MTovH7rd+x0GMhjvc7Dvcy7hm2TUxMhIWFBW7cuIEqVaqk2mZjY4PTp0+jVq1a6e4bExODoUOHYvHixShbtmy6bcafGY+9z/bi6tCrCI4JRsOtDTGm9hisab8m1+eXYxYvhu/ly3A8e7ZICmAigpmZGfr3759m8G/NmjUBAPfv31e7HSqdChlIXQbt4cOH2L17d7b3bdy4Me7cuQMnJyc4ODik2lZYZoKTycxw9SpHt3IzQv7dO+Dff1kMh4SklLdydeWoV2QkLwBXDTAz4wFWAJe0UrdAS0wExo8H9uwB5sxhwZokJL+GEyeAXr1YABkbs/gcNEgGFxfA1tYMVlYcsY2JYeEcEZHy6ugIlCnDwrZcOS77pQpkMq6IkEdjJvKU/CAmAgJ4CQsDkgJBcXEckZfLgUqV+InBoEHcBgAOHAB69OC/AwO57NulSxyxTwooBAfn4CnKqlWIOHsUq6Y2xbyr8wAAHcp1wN6ee6Gvo49OuzshOiEaV4ddhVwph8ECAwwJGIJP7z5h8+bNmD17Nl6/fg1LS0uEhITAxMQEzZs3R506ddCzZ0+MGTMG06dPV3nd0+wii5fBfLE5qhWvhmvDrmU4UGnNnTUYe3os/H/2h52JakuOnHl7Bl32dEGCIgE6WjpQkhJOZk44N+gcylllv6Zefrhm1YF/pD9ehb5Cs9LNNHJ8Vfp1+Y3l2Oa5DY9+eJQc8U6Ptm3bol27dqlmVwsLC4OVlRVCQkJgZWWVq+P7SH1Q8a+KODfoHOo61gUA3Pe7j8bbGuPVj68ynMVPpRABrq7wnTwZjt99VyQF8OvXr+Hq6oo//vgDo0aNAhHB19cXK1euxObNm3Hy5Ek0b95c7XbkWgBfvXoViYmJcHd3V9lsKXPnzs1w2+zZmq1t+DWoowoEEdeA3bOHUwOSfty1tDhiplSmCOEkzM2BChWAypU5sla2LEc6XVxYUBga5k4cEgH+/izM16zhvo8dA0qV+urTTEVMDEdgpVKukCCXF84fvPxAQRMTDx/yde/iknq9XM43KX36cGWKJGJi+HpPD2mcFHKlHApSYO/q7/D9zKOwmgoMa/wTDHUM8UvjX5JLTcmVcmhLtJO/A6uur4qxlcZiw8QNuHv3LmrUqIEDBw7AxMQE48ePR+nSpbFlyxbEx8dj3LhxmDNnjhq8kX0WXVuETQ82wWucV5Zt3f92R6fynTChvuqe0B15eQT9D/bHmvZr0K9SP+hp6+GD9AOczJygq5357HtfUtCu2YKCKv2aqEiE1VIrrO+4Hv0r98+w3fLly7Fnzx54eHjAwMAAo0ePxvHjx6FUKuHr65urYwdFB6HfwX4oYVoCO7qlrirV/2B/mOmbqXQCkQy5ehXo1g2+9+7BsWxZ1QlgorQ/+qrE1FRlEbR9+/alW+vZ3t4e+/fvR8OGDVVynKzIlQCePn06lixZAoCjtgcPHsQPP/yAd+/eoX///pg8ebLKDS3IqLsMWmIiR8V0dDhCmvS4WCZjYayvz/8bnz7xY+MnT1hEvnsHPHrEObdJuLlxZK1kSc53tbbmKNujR/w4WakESpRg8RAdzf17eXEt2CZNOBWgc+eUWrHqRPzgqY/C5tvYWBa9n0d+Y2PTphBtur8J353gGb0aOjXEq5fXEbwMOOwGRP6zBYNrDc/0ODMvzsSToCc40vcIYmJiYGhomCpAIJPJcPDgQaxZswanT5+Gra2tys4xp7wPfw+XP11wZsAZtHJulWX7NXfW4MTrEzgz8EyOjkNE6QZJNt7fiAlnJ+DPdn9iePXM/ZodCts1m19QtV/Hnh4Lj48eePD9gwzbxMTEoGnTpujYsSPkcnnyZFs1atTI1aNxIkLtTbVRwqwEtnbeCiuj1BHks2/P4sfTP+LNT29y3HeOGToUMDeH75Qpqp0JTiZTzePWjJBK+ZGyCvjll1+wePFieHh4QE9PD0qlEt7e3pgxYwbi4uLw7NkzmJqaquRYmZErAVyiRAn89ddfaNSoESZNmoRHjx7ByMgItWrVwj///IM//vgDAwYMyFZfDg4O8PPzy7JdyZIl8fHjx5yami/I73WAo6N5BjGZjAcmvX4NfPjAebOhoSyGq1ThRV+fhXRsLN8QGhmxUHZz49SEvET84KmPwurbz3XYli088K5aNc79TtQNhu1yFqRV7arCUumGGdXWo97i/jA6fpp3ksn4ws+ADxEfUO7PcvAe7w0HU4c02/OTX3vs64EERQKO9zuerfb3/e6jxY4WCJsaBi1JyuCkXU924Yr3FVgYWqB3xd7JE1PEJMag6vqqeBf2Dn0r9YWLpQvmNpsLiUSCax+uoc2/bXCozyG0dWmrkvPJT74tTKjar0+DnqLe5nqQTpOmO7V0EocOHUKPpHwmAOvWrUPjxo1RsWLFHB/z0ItD+PHUj3g39h0MddM++gmICoD9CntETY/Kdv3lXCGT8SCUGzfga22tWgFcgCLA7dq1w9u3b/HmTeobjqTPfP/+/ejZs6dKjpUZuYrTRUREoEuXLgCAZcuWwdbWFsHBwbCyskL79u0xf/78bAvg8PBwbNu2LcvpkKVJZQEEKidJuFpZcfRWICisbNnCg+x27AC+/TZlfck6nvDtUAv1Hetje6ddiPAujbp1gUsA7iwYj9pJAnj/fmB4SrTSy4ufiOjp8Q2jRFIKjUo2wv5n+zGu3jjkVzw+euDM2zN4Pvp5tvepbFcZcfI4vA17mzzwat+zfRh1chRG1RqFwOhANNnWBHt77kXzMs1Ra2MtRCVE4VCfQ/D46IFVt1bhgf8DtCrbCvOuzsPUhlNVJn4FBQc3azcoSIHXoa9RwSb9CWMArgawatUqNGjQADVr1sz1rIOR8ZGYcXEGZjSZka74BQA7YzsUMyiGN2FvUK14tVwdJ0sCA4GBAzlaVLUqkMtUjgyRSFQWoVU3np6eaNCgQZr1SZVzYmNjAQDDhg3Dw4cPAbAG9Pf3h1QqzfYsw1mSm9pppqamqd6bmZkl/y2Xy8nOzi7bfZUqVYpKly6d5eLq6pobU/MFYiIM9VAQ634WFAq7b2NiuF5z8tJ1CNmP7UUnTypp8mReN3YsUbNmROX13vOK/v0prowb3TobQdHRRGfPpuxvZsavbm5E6++up+rrq6dbXzQ3flWqeMYQaZyUXP90pWXXl+V43/qb69MOzx1ERPQq5BWZLTKjIy+OJG/f+3QvmSw0oQZbGpD5InN6HPA4edvfnn8T5oC67O5CLXe0pIjYiK8/mc8o7NesplCHXxtuaUiYA1p/d71K+/0SpVJJnXZ1ohZ/t0hVVzk96m2uR7uf7FaPITExRPb2/CVx8CAREX369KlI1gEOCAggADRnzpw024YNG0ZaWlr09u3bVOs/fvxI1apVo+PHj6vUllxFgBMTE3H16lVUrVoV5ubmqe7MtLW1k9V7dvD29s6NCQKBQJBrPh8A9+Pyy1gb+Q/0TjxFhz8kMDfnQM3q1VxdwsqqFNxxAWWU9bD2vSWC2wyAmfYJKBQpfZw4wTWIN24EBlQZgPX316Pr3q74p9s/sDTMfVFohVIBp1VOaFG2BeY1m4cyFmVy3Zc0TorxZ8fjps9NlDQvifH1xue4jzol6uDU21Oo61gXXfd0xfc1v0cXty7J23tX7I1bn25h1a1VePvTWzhbOidvG1x1MHp90yvDKJyg6PBr4xlov6sdrn68iu9rfa+24yzyWIR7fvfwbPQz6GnrZdrWzdoNL0Neqt6IgAAefW5hkfno2yJCUkRXR0cHt27dAgAEBATg4MGD+PfffzF9+nQ4O6d8b7x69Qo9e/bE2rVr0aRJE9UakxvV7ObmRlpaWqSlpUWlS5cmfX19WrFiBd24cYPi4+PTRIiLOiICrB5ExEd9FAXfKpW8zL8yn/od6EdERAMHcpDm1auUdh8+EK1cyetHNXxEiboGtOefBHrxgujTJ6IzZ7hdRAS3CQwkCo4Opg47O5DjSke653svua+c+HXN7TXUfmf75BmrbJfZUnRCdK7ONVGRSN32dCPMAbX7tx35yfxy1c/5d+dJa64WYQ6oz/4+lKhITNNGoVRkOF2xOikK16wmULVfIyKIuncnMmywlRpuTH8WWVVw5MURMltkRmffns1W+yUeS6jbnm6qNeL+faLmzYlMTNLMcV9UI8CLFi1KMwVysWLFqEWLFnTgwIFUbe/fv08VK1ak+/fvq8WWXE+FLJVK6dy5c7Rw4ULq2rUrOTg4kEQiIX19fdLW1laljfkWpVJJs2bNInt7ezIyMqLGjRvTkydP0rQTAlg9iB889VGUfNt7f29aeHUhERHFxfGU1V+iUBC9fv3fHwYGRP37p9tX+fJEW7b8t49SQb+c/4WcVztTWAz/+GXXr2ffniWdeTo08NBAOvfuHK25vYYabmlIRr8Z0bUP13J0fv97+79kEf3A70GO9k2PREViusJX0xSlazYvUZVfPTyISpcm0tEhqlKFqHqXa2T4i2OO+ujRg2jYMCKZjN+/fcs3sZ8jV8gpNCaUbJfZ0l93/sp233c+3SHzReaqm/Y7Lo5PGCBK59F9URXA2eXKlStUqVIlevnypdqOkWsBnB4+Pj504MABmjp1qiq7zbcsXbqUHB0d6fHjxxQTE0PTpk0jBwcHioyMTNVOCGD1IH7w1EdR8W10QjRZLbGiy+8vZ3+nDx+IjI2J7t5Ns+noUSIjI6KLF/m9Qqmg7nu7k9saN1IoFdny64Z7G8h8kTkturYo1fqwmDByWulE1dZXo9CYUAqJDsnS1JiEGDL6zYgwB7Tt4bZsn2JBpKhcs3mNKvy6axf/y8ycSfTkCYvWR+/8CHNAdx7GkFJJJJenv29kJEeMU+Xsux0iVDhAAFH79kQfPxI9D3qefKOXtMQlxmXbxtjEWJLMkZCvzPerzpWIiBYsILK0JKpalSgx/ZtFIYAzx9ramhwdHalq1apUtWpVqlatGkVH5+4JWEaoVAAXNUqXLk2///578vvExESytramHTt2pGonBLB6ED946qOo+PavO39R9fXVcz7QbMIEonbt0oafiOivv4gcHIhCQ/l9gjyBLJdY0qX3l7L06wWvC2T0mxFd8b6S7vaYhBiyW2ZHmAPSmqtFb0LfZGrm/mf7yfVPV5UPpMuPFJVrVtUolalTfr4kPb9m53JKSCDy9iaaN4/I3Jzo2LEvj6sknVlG1KTHM2rShEhbm2jGjJT+d+wgWrXqC+HbfCZVWNokWeC27eND1tZE5coRtdiSki7UfHvzVIMzv0QuJ9q4kZ/2hHx2H1liRYkcP2FJJjaWaNEiPlGAqF8/ouDgL86ZU0CIhADOD+TBdAWFE6lUCm9vb9SpUyd5nY6ODqpXr46HDx9i0KBBGrROIBBkh+fBz+FeJhezWU6eDNSoAaxYAUyalGrTDz8AZ88C33/Ps8/pauviuxrf4YcTP6BKsSqoaV8Tk5pPSlMD1S/SDz339cSadmvQpFT6gz0MdQ1xduBZeEd44/DLw1h4bSG2dtmaoZl3fe+iaammKputU1D4OHSIp5nfswc4eJBrYterB7i68vV7+jRw5EhKha1Tp4Du3fnV3Z3XEXGJWyMjwMMDuHkT2LAB+PiR+7t6levIf45EIoGzpQuuPn0LXa9vMHYssGgRsHcvVwyTybg0Z4POL3GjRkq5tBcxQPXi1eFg6oDIUgOwfuYEbF9UAye8/wf9tQFYusAMAzoawtKSJ4nS/WJCQU9PYOxY4MEDroEPcGWyqVMBZ0tnvA17i0YlG+XckYMHc5lEAFi1Chg/Ptk3333Hx713jzc3a8YTRwk0ixDAuSSpOHixL+YOtrCwSN72OSYmJohUZ5HqIojwp/ooqL6NTYxFs7+bwc3KDZs7b85yOt1PwZ/QvHTzdP9nM8XYmBVDjx5A167AFzO6rVoFtGzJP+bt2wOjq4zGlddXUFy3OA4/PoxzXuewss1KlC5WGgmKBGy6vwl7nu1Bh1Id0MO5R6b2lDEqk7w0394cA2IGYGydsenWVH308RG6unbN+fkVQHJyzRIB8fFpZwIsimzbBrRpA4waBVSuzLN7Hj/OxQuqVweioiLRoQNf8rNn8w1emTLA5s1ArVrAypXcR0QE92dtDdSsCSxcyNf+5zOTfkk16wqoPPQ1praUoXx5YMwY4O5dFr6lSgE2tgqUXl0XZkipbzu61mhMbTQVngGemHVpFoYfGIKGzRuj5POWiJAbYua0RMyclpjcvlEj4K+/gHPnuO9z54DWrbkmuI4OMHcun+eUKUCbZRXwyvcVZGVz+P8SGsp3vceO8R3AkCHJJ7x8Od9IlC3Lk+4ALL6joqJzdgyByhECOJckzdwSkfRf/x/h4eEoUaJEmvZxcXGYO3cu9PS4FIu7uztatmypdjsFgqLEoReHYKJrgo/Sj2i8rTFG1xqNwdUGZ9j+k+wTnMydcnew6tU5VLZ3L/DTT6k2WVkBP/8MLFkCtGgBmBuY48zAM4iMjERsYizWPFqDVv+0gnsZdyTIE+AV7oVWZVphYv2J2T586WKlMajScGx7shEnXp/A5PqTMaTaEHhHeKNUsVIw0TPBo8BHmNN0Tu7OT0UolYDWfxPHEQEvXnB0sGlTvndI2pZX3LrFQfugIGDiRBZDZcvylO+7d/NsmBIJC7zixfPWtrzm5k0WYx4egIkJT+iipcU3B/7+PMvny5fsk8BAoHdv3u/QIaBdO44Ev30LrFvHEeOQEH7N7mda2rw0oo0+oDzPqwIbGxbNSRx+cYyP1/sQKttWRoIiAWYG/Ntbw74Gjvc7jj9u/4EXIS+w+4cfUWoisGABz9qrVAK//MJCvHVrFuiVK/NMxOPGpUxq9scf/PrwIfDesxyiq1/PuSPPnoW8cjX8L7opHDo0xd4Z/B1w5gzw6hWXR2zXDjh//jwuXryIhg2B6Ojo5IiwQENoOgejIFO6dGlavXp18vvExESysbEROcB5hMj5Ux8F1bdV11WlLQ+2kDROSj329iDMAUXGR6bbVqlUktFvRvQ08GnuD3jgAI/0TmcET0ICUa1aRG3aELVowQOBIiJS/HrP9x5NPTeV6m2uR15hXrk6fKlSRB26y8h5tXOqwT+t/2lNi68tplKrSpFckcHookxQKIi8vDitMbe8esW5mVpaRJ06sZuaNuX0yGLFuBqAnh6PE9q+PffHSSKra1Ym40oEFhZc1u7YMSJbW57EZORIIkNDor59if7+m2jAACI7O6IpU7isXULaOU3yjLAwom3biH7+mUvuJSbyefz+O9G6dUTff8/nVKEC0fTp2cvPJeLPuFYtot9+y7zd536dMYNo/X9zV5w8SbRwIVHAV1S823hvI7X+p3WG26uuq0rr7q7L/QGI/zUXLkybg/wlb94Q6Zb1IKtFtjnOmVfWq0d/VlibnKsskfDnMXMm0aNH6e8jcoA1jxDAX8HSpUvJycmJnjx5QjExMfTLL7+IKhB5SEEVaQUBVfpWqVTSq5BXtP3hdvr57M/U70A/6negHw09MpTGnx5PK2+spMMvDtN9v/sUFBWU6wFbr0Nek958PZLGpdhd6a9KtOfJnnTbB0UFEeaAouKjcnU8ImJlVLJkSu2zL3j/nmjcOKKpU1lo2dpKaedO1fj16VP+sTUwYNFz4tUJ6rK7C3l4BpDpfCvCHNAtn1s56vP5c6LBg1N+xG1tiSZPJkoafB0byyJs0yau7OTlRfTuHYtLuZy37d5NtHYti9u6dfncO3Qg2rqVqEkTFm9ELJBv3WLxa2rK9xJfQ0bXrFxOtHMnkbU1n9eIESnblEoepNW/P9HDh6nX79zJ5boAFsP//vt19uUEpZKLjWzfzuLW2Zlo0CCupGBoSGRlReTuTlSnDo+12rWLqGVLtjUroZfErl1EJUqkfLYZoc7v2QteF6js6rLpbnsR/IL05+uTLE6mlmOnx9ARMSSZrU3PfT9mex9lOBcAdy0WQL6+qQfVZYYQwJpHpEB8BZMmTUJkZCRatmwJmUyGWrVq4cyZMzAxMdG0aQKBWohJjEFoTCjkSjn0tPWgr6OP2MRYyJVyJCoTEZMYgwRFAiLiIvAs6Bnu+9/HrU+34Bvpixr2NVDNrhoq21aGlkQL8Yp4hMWG4erHq/j70d/wkfkgLDYM+tr6sDG2gaWhJawMrVDOshx6ftMTLcq2gJYk42erh14cQsuyLWGmn5Iv2Na5LUYeH4mz786mGSzmHeENGyMbGOsZ594huro8IG7bNmD48DSbS5cGfv+d//7uO242Zw7QsCHnOH4Nc+YAAwbwYKTLl4EOzTvAhTqgQgWA7M+gZksv1ClRN8t+kgYwjR4N7NrFKRuXLwMNGvCgqPnzgYsXOT2gWzfAx4dzP318+PG3lhY/bjYx4ZzKEiWAp0/5MXm3bqmPNWxYyt9Jj73r1uVJsvr358FIFSt+nV8+59EjTtOOiQHWrgV69kz9eF4iAWbOTLufRML29OsH+PlxburAgfwI/cvBXKpk1SoeCBYTA8TFca7thg08SA3gc7h5kwdQfZm/3K8f8O23wNatPBjN2hqoWjX94+zezZ/Fpk3cVlO4WLrAO8IbCYqENDO1HXx+EG1c2sBU3zTP7Fm60BB7Frvhu9kPcHKFU/LAvyQUCk4PSfJZUBCw96cn6CFxwIWndnBwyDNTBSpAQkSkaSMKOzKZDE5OTvDx8UnOHRZ8PUkDe4RPcw8RQRovxSfZJ/hIfaCtpQ0HUwfERMXgWdAz3Au9h9u+t/E48DESlSkDS7Ql2lCQItV7HS0dGOkaQVdbF+b65nCzdkMN+xqoU6IOmpZqmi2hGZ0QDd9IX4TEhCA0JhRhsWF4GPAQe5/thaWhJcbWGYue3/SElZFVqv1CY0LhusYVmztvRle3rsnrT785jfa7OKnw2ehn+Mbmm+Rt+57tw4qbK3B7xO3cuo8JCmLV9/w5UK5cpk2lUhmmTgUuXTLDkyecc5kbEhJYcD57xoUojI35dcAAXr9wIQu1FSuAvn3T7k/EOa7nzgHXrvGApxYtgD//5FlbPyc+nsXfgQNA27Y8QErni9DJp0+cQ1u9OtuSmwFmv/7Kfd+6xTYNH84ibf78lHzNz+3/cl3S90FCghnOnQMOH+abgylTgOnT01YDyCnTpgGXLnE+7Jfn/7XExPANyOnTLGCdnPgGIac+PHkS6NiR90tM5NzmZs2AZcv4/P38WBi7uHDua//+Wfepzu9ZJSmhPU8b5wedR4uyLVJtq7GhBsbXG4/BVTPO4VcHPf4djJuny0B5YS7at+f0/urVeduPP/JNSOfOnO984wbws8VWzHH5FyZ3LuboOL6+vnB0dIRUKhW/YRpCCOA8QAjg7JN0OWanbJMQwDnDP9If9/3v477ffdzxuwOvcC98kn1CVEIUTPRM4GjmCLlSjsCoQBgpjeBm7YbqpaqjrmNd1LCvASNdIxjoGMDCwAJaEi0oSYl4RTwMdQzVXmYrXh6Pfx//iw33N+Cu313Ym9hjc+fNaF+uPeRKOYYfHQ6/SD+cH3w+zX4/nf4JCYoEnH57Gg2cGuBwn8MAgKXXl+K+/33s7bn36w3s0oXDgwsWZNpMJpNBLgdq1DDDn38CnTrl7nBPngD163Pkdts2YMcOjtLa2QFHj3KEef16Xn/jRup9ExKA334D5s3j4/fvD9jb82j5pBH7X5KYyH117cqDe9RBYiIPVvL25gFXU6eySHdz4yixtTVHzXft4rJcOjoc3S1XDihZEvjwQYYTJ4AHD8xQqRJHnzt35ooEqiA2lj/imTN5kL+qIGLRKpVytYCvjSJGRgKmpjzo63//4yjvw4eAXM7HAHhA1smTaW8i0kPd37OSuWyEcpYy+XvEK9wLbmvcEDgpEBaGFmo5bkasvrUa//M6h14JJ7B/Pw9kGz2a/98GDOCbMmdnvulzdQXGPv0OWqbGHL7PAUIAax4hgPMAIYBT4x3hjROvTyAoOgghMSHwi/SDf5Q//CP9ERAVAIlEAkczR9RzrIfh1YZnWKdVCOAUEhQJ2Pt0L06+OYm3YW+hp60HJ3MnWBlaITA6EPf97uOj9CNcrV1R074m6pSog/JW5eFk5gQnc6dUaQNA/vZtRFwEjr48itGnRmNpy6U4+OIgXoW+ws1vb6Kkecl09znz9gza7WwHAFDMUmDUiVHY+GAjpjWchkUtF329UVu2AH//zQVPMyHJr7/9ZgY/P+Cff/7b8HmphGwwfTrw+DGLmKdPWZgNG8bCMCiIxeHjxyxqpdIUoXPmDIsfc3MWxt98k+lh8pyEBE6dMDZmcR4eDixdyoL440d+bd2ao8NxcSwYg4I4FaNUKRlatQLatzeDjY167Fu1im8wLl9WXZ/HjvH5vHnDqSCqhgi4coV9qlBw+sSjR2kj/Rmh7u+Ct2FvUWdTHSxuuRjf1fwOALD8xnKc9zqPMwPPqOWYmeHx0QM99/WE/8/+UCgk2LmTrzM/Py5jtnnzFzeK5cpxnlOHDjk6jhDAmkcI4DwgPQEcEReBDxEfIIuXwUDHANZG1ohMiER0QjQcTB1gb2qfJieqoPNJ9gmrb63G2rtr0bBkQziaOcLWyDb5fO1N7GFvag8lKfEh4gMuvL+ADfc3oI1zG2zqtClNLlh+Fmm5QRonxXWf63jo/xA+Mh+ExoYiJCYEITEhCIsNQynzUqhoUxHf2HwDEz0TxCTGAGDxu+nBJmhJtNC/cn9Usq2EBEUCvCO8ER4bDhtjG1S1q4p6jvWynU9XEHx74vUJdNrNIdSH3z9EteLVMmwbmxgLo4WcuOcxzAONtnGh++1dtmNINRWE816/Timimsmz9iS/vnxphtatWdSZzRjLuQdPngCVKmV5KCJ+PL5sGUdkibjW6J07nAfavTu3i49n0ePlxRFSuZwffbdrx+2NvyL1OT+SF9dsUBCnJzx9mmW2S7aIiOA83V9/5Rzx/Ehe+HXfs32Ycm4K3o97D4lEgvpb6mN4teEYWXOk2o6ZEbGJsbBYYoGH3z9Mt7Z2Kl694lyj4GCkSRjOAiGANY8QwHlAkgC2m2mHUnal4BXuBa9wLxQzKAYzfTPEJMYgPDYcRrpGMNEzQWB0IJSkhI2RDUqYlUDjko3Ro0IPNCrZKM3sUQWBJ4FPMPvybJx8cxLtXNphZpOZqOmQveeSQdFBGHBoAAKiAnBl6BVYGlombysIIi0pxzYgKgBRCVF4HvwcD/wfQK6UQ1uijZjEGHhLveEV7oUPER9Q1qIsajrURGnz0rA2sk5eihkUg3eEN54FP8Pz4OeIk8fBUNcQEkggkUjQxrkNvq3+rcquj4LgW4AH5WU3BUNJSjT/uznaubTD9AvTAWQtnLONXM6Jl2/e8MilDEjyq6mpGRo2BCI/huNxkB0kiYnAyJFcMDQLzpzhjIvw8KwHMFWowHnA7dtz5GrkSCAsTD2RRk2TV9ds376AoyPfRHwNRECfPjwb2YkT2UtH0AR54dekG9Q6JepgSoMpGHh4ID6O/wibBB2NXKw99vWAq5UrFrZYmHnDqVP5DjNpBrgcIASw5hFVIPKQ39x/QwRFoHSx0qhhXyPVQJ7Pc1/lSjmCooPgF+mHj9KPOPv2LHrt7wWJRIJh1YZhfvP5Wc5wlR8IjArErEuzsOPxDvxQ8we8+vEVShcrnaM+bI1tcXrAafTa3wsdd3XE+o7rUcm2EoKig3Dt7TVI46WIRCTehb/DPb97SFQmQq6Uw0DHAFVsq6Ba8WqoVrwaqthVyTL6GS+Px7FXx/A+4j3u+d2Dmb4ZKttWxoAqA2BtZJ3pvuGx4bjhcwPvI97jk+wT3oa9xfuI93gV8grRidEw1DGEqb4pyhQrg3qO9WCkawQlKWGrZ4u6jnVR1qIsyluVh6OZY4bHaFiyYY58VxQw0s3+EHYtiRZKmpfEdZ/rsDexx7F+x1QjfgHOOXB0BD584Gmn3r7lIfkdOqSrbCQSHky12PkAgktUh+2+NUCdOjzKJpMyA8+fcwR37Njsjd53c+PgdNu2bNaaNYVT/OYlY8dyGsbQodkK2GfIqlU8AYWnZ/4Vv3mFoa4hVrZeiYn/m4ie+3vih6ojYLP/JOf1lCrFN5crVwKNG3OCs5oZVm0YxpwagwXuCzKtPINz53iUpaBAIiLAeYAqcoAVSgWufbyG8WfGQ09bD1s6b0Flu8oqtlR1HHx+EMOODkOLsi2wtOVSlLP6uueFsYmxGH1qNPY+3QsdLR3EyeNQ3aI6LAwtYGxqDAcTBzRwagBDXUPoaOkgKiEKjwMfwzPAE54BnvCP8oeTmRPKWZWDhYEF4hXxkMXLEJUQhZLmJWGiZ4Jz786hmEExVLarjGp21RCTGIPrPtfxKPAROrt2hrGuMbQkWtCSaEGulON16GvI4mWIk8fhefBzOFs6s4g1dUSpYqVQzrIcXK1d4WjmCHN9c7UPFFMlBSUCnFMm/W8Stnlug6uVK258eyPrHXJC06Y8+mzlSv5RXL+eRzdt2JCcFiGTyYADB2D2449AgwaQ3X6OIxVnYPDNUZzX4OLCpQsyuFaGDAHu3eNH8Nm5nEaP5lSI+/e5SkNAAGBoqMqTzj/k5TU7axanfd++zfc9OeXuXa7OcOkS3/fkZ/LSr7c/3QaBUPuvo9BetJhLbyxenNKgTx+eglzNJCgSYL/CHkf7HkWjko3SbxQeziMzP33iUaQ5pKhHgCUSCbZt24ahQ4cCAObMmYO5c+emaqOrqwtra2vUqlULkydPRuPGjTPtI6eICHABQVtLG81KN8P14dex4OoC1NtSD/Obz8dPdX7Kd9Hgc+/OYfCRwfi327/oVqFb1jtkA0NdQ2zrsg0bOm7Ay5CXKGZQDMUkxQBk/MXct1JK/aeg6CC8CH6Bt2FvIY2XwkDHAKZ6pjDWM8aHiA+QxksxpPsQuJdxT3PHf8HrAi57X4ZcKQeBIFfKoVAq0KdiHxQzKAY9bT3UcqiFEmZpp8AW5C+KGRRDWGwYXK1dVd95qVJcxHX4cC6xMHIk4O7OuQejRnG+oELBlSJWrADevoV35QFYdGIQBkskXP+rfHkeSNe0aZrug4L4t//x4+xHDIsXB2bP5r/v3Cm84jevmTuXdU+/fjzALDvjF319AUtLHvQ2ezZPw5zfxW9eU9exLueG7BvI5T769WNnt28PXLjA4fI8QE9bD72+6YXpF6bjeL/jKGZQLG2jEyf4aU0uxK8geyQmJsLf3x/Hjx/H6dOncfPmTdSqVUtl/QsBXMAw1jPGopaL0LF8Rww7OgyLPRZjYJWBqGFfA+b65khUJiIyPhL2pvZws3aDd4Q3Tr05hefBz9GoZCP8UOuHNCP+Vcll78vosa8HNnTcoDLx+zl62nqoYsePiJMiE9nB1tgWtsa2aFo6rbDIihZlW6SpUSkomJjo8SQ17qXd1dD5fxPgLPwvb9DJiUc3LVrEtcP69OGBMi1aAD/8AEgksPYDXq3mOrBGVlac3HvyZLoC+MQJrkfqmgPtbmvLrzt2ALVrf+X5CZKRSDidpEoVYMIEYPXqzNtLpSmRYmdnYNAgTh8VpIOnJ5f76NqV3+vpcemNjx/Z4YmJX1/UORuMqzsOHXZ1wPgz47G96/a0DXbvTpmhRKBSZs+ejTlz5iAiIgJ9+/bF2bNnIZfLsWfPHiGABZwP+vLHl7jsfRn/PP4HG+9vhDReCh0tHZjqmcJH5gOvcC+UMC2B+k710ax0Mxx+eRh/3P4Do2uPTm7XtHTT5MkBiAgJigTo6+hn2444eRzehb3D8+Dn2Oq5FVc/XMWqNqswsMpAdZ26QJBrxtcbj7DYsFSTZaiMpFkL7OxS1vXvz8m3ffpwfS8nJ56d4L8Qrr09R2W9vf8rSda2LRfpXbo0TffHjnFd25xQrBi/9umT47MRZIGREXD+PM9cFx7OT+u/LCv3+jWnqyxfztUeRozgtNbCVoFDpezdyzXwPn9cYWzMT0d0dHjQWUZ3gUQ8kDRpRpivoIJNBRzofQBNtzfF2vZrU0/k8+EDR6Q3bfqqYwgyp1ixYujSpQvOnj0LAIiLi1Np/0IAF2C0JFpwL+MO9zLpR7MUSkWqqgAT6k3Azic7cfz1cQBcdmvi/yaijXMbxCTG4HHgYwTHBKOWQy24l3aHm7UbnMyd4GrlCj1tPShJCQJBFi/D8+DnWHt3La5+uAodLR2UsyyHjuU74u+uf8PW2DZPzl8gyA3zms9TT8fTpvEItc/R0+OUhogIzkf44qmFRMJ6OTj4vxUtW6bMv/vZjAjR0TzeJot5NtLQuzcP2MrtjHOCzCldmnXQ/Pmcn339eoqvFQq+n3n/nuvGBgVxCoQgE5RKflyxdWvabdraXHuuf39OKRoxIm2b7dv56crHj3wj+ZVUL14dtRxq4ef//Yz1HdenbNi4kQe4lhBpb+pEKpXi+PHjye+7dOmi0v6FAC7EfFkSSyKRYGCVgamisx8iPmDTg01wMnPC7KazYW9qj2sfruHqh6u45XsL3hHe8I7wTtWPvrY+yliUQd+KffFX+7/gYulSoAZ4CQRqwdYWaNUq7XoDAxa/mewWGPjfG0tLnvbs2DH+If+Ps2f5EXrFijkzSUtLiC51U68eV8Fq0oQrRKz/TycdPMhP6+Pj+b24CckGjx/zVHYtMkg5692bp+IbOZL//nz8x8OHnH/fsCGnIT1/zvNhZ4foaK7h/fnTG/Bv5uIWi9Hm3zb4o90fXJs/Jobz+pNnsckfEBEiIyPV1r+pqWme/c7PnTs3zYC4yZMno1V6369fgRDARZxSxUphgXvqsFJZi7KpJgdIUCSAiCCRSCCBBDpaOkLwCgQqIpUABrh82sKFwMCBuPHYBPfucZ7phAmiXFa+ICyMZyEB+IPr1QtG7drh4EFOUe3dm8c+7tsHfP+9EL454soVnr4woxzfGTM4r75FC2DOHK64AnCSdY0a/ATl5ElOeD9yhMsRurhkfsy4OL6L8fYGQkIA/dQpgLVL1IaJngmueF9BK+dWPBLVzi79m10NEhkZCXNzc7X1r+lqFcuWLYOLiwu+U+GMMdmfe1NQZNHT1oO+jj70tPWgq60rxK9AoELs7PjxeDJDhyK2WHH87rIGTZoA48bxk+H+/TVmYtEkIICnuP3hByA0lGvJLVnCM2GcPMni99EjHrj44QNKleLgZO/eXOTjyBGgTRtNn0QB4/x5oHnzzNtIJPy5rFsHPHjAeb9bt/Ldx7lzfMfx6BH/w5QrB/TsyTkqERHp9zdjBucbW1mlO6GFlkQLrZxb4ZL3JY7+LluWPIg1P2FqagqpVKq2xTQP6i8nMXv2bBAR4uLisGvXruT106dPh1KpVNlxRARYIBAINEjx4l9EgLW1scv4OwwJnojOsx2x4G1fRETpoEYNjZmYmidPWATMns0lDQoj+/fz3MTlyrGgsv5vIpwGDTilZf/+lIFYQ4fyI/mTJ/HTT7rYv58reP36K6DCAeuFn6goFrBJ0fXMqFKFS2nUrMmlUZ4+5fzfz1myBHj5Enj3jnPqT57kCHNShJeIH7+EhPDNzfXrXLGlXTsWw5/RuGRjbPPcBhz4ma+F4cNVc84qRCKRFLp6wvr6+ujXrx/Gjh2LkJAQhIWFISgoCMUzSSnLCSICLBAIBBrEzo6DjUl8/AjMut0BFsowlJ09CFsk32L/HoXmDPyS8eNZqDRsyHmXBZ3wcM7pfPyY3x84wCPa1qzh4slXrvD2f/9lkXThQuoqBKtX8x3M999DX49w+zbrqblz812QMH9z+DDfcLi5Za/9X3+xowcP5uvwy0ckjo68/eFD4OJFHpXYujXPXvL6Nc8oExLCbatX59QjMzNg8uQ0h2pcsjHufLqN+H270bXaS6x6uO4rT5bH3wgyJz4+Hrt370bIf5+Tvr4+LFQ4laWIAAsEAoEG+TICvHw50LCLNbBbDjx6BEnz5tDu0gXo3l1zRiahVLJwuHSJCxN36cLRt4ISeUpI4BzeO3c4YhgSwrmf5uZcXsvAgAdhHTzIpbgArj7w7bcZ92luDpw+DdSvz1PEzZ+fF2dS+Ni2jWvEZRcdHc77zc6jEQMDvnFp147zfZNo3ZqPK5FwXbu//+bo8vTpLMb/w8XSBT94W4Ok/nhRqiTOXvwFDqYO6F2xd65SAuPl8ZhwdkKO9ysqpDcIDgBGjRoFff3sl2nNCiGABQKBQIM4OLAe27mTo8GbNwM3boCFV40a/Cj+3LmcC+DNm1mc5bZY/65dHJXbuzdlqjMvLx40VKkSR82uXOHyb3/9lbtjqAsiduqDByxyt23jc/D35zxOPT3OUbCx4aV7dx5I5eXFkcMvqgFkiYMDcOYMR8XLleOoZEEhOpp90aQJcO0aT0/Xr1/e23D9unrr6pqZ8TTjnp5cmvDwYY72flZuEOXL88jFnj35+v+v7Ipkzx6s2ifDyXEd8fLnYzjw/AAGHxmM0NhQjK49OsemzLg4I82Mo4K0SCQSmJqa4ptvvsHAgQMxatQo1R6ABGpHKpWSmZkZSaVSTZtSqJBKpcKnakL4Vj2k59fISCJWbER6ekRr136x0/btRI0bZ/8g69cT7dqV0ml2iYsj6tyZyNCQl6T9TUyITpzgNjt3EtWsmbLPu3dExsZEly9n/zhqQiqVkjQoiM+/cmUiMzOitm2JevRgf5w7R/T0KdGnT0QfPqjHiAMHiIoVI3r9mt+HhRHFxqrnWEkolURHjxLJZDnb7+VLomvXiJo1I3J2Tvm8AaI//yRq147o/Pm8+S44c4aodGk+F00TG0s0cCD7oWNHokmTiKytifbvT9Vs39N9ZL3UmlbcWEGX3l/Kdvfe4d6kN1+Prj65SgCK7PcsANq2bZtG+xARYIFAINAgn09YdfEiBxFTUbEi8OwZS5OsHrcmJqaqHwwdHU5b0MpGtOn33zmi26EDL7q6nOv6ww+cE/viBU//2rFjyj5ly/KEA/36ce1iTY36SkgAjh/n/JHERK4Z168fP9bOS7p3Bzw82G9TpvBALEdHYMMGoH179Rxz1Srg5595ur81a1IG7KVHbCxXTLC25moWAEd+PT05NcTUFLh1K8XWK1c4Irx8OQ84Uxfnz3MJs/yQNG1gwDV+x4/nGRkvXOBIcY8eqZp1r9AdAw4NwM//+xkAEP1LNIx007/eHvg/wK1Pt9C/cn8su7EMbV3aoqxFWXWfiSArvkp+C7KFiACrBxGlVB/Ct+ohI7/u30/05EkGO0VFcTTq3LmsD/DbbylRvGrV+PXFi6z3CwwkMjUlunQp7TalkqhbN6LixTmq+vFj2u1Ll3LUePRo7iuvUCqJduwgsrcnaaVKJP39d6LExLw7fnokJBANHcq+nzCBaNgwIiMjops3c9ff8+dEV66kvy02liPOGzfyMQCiBQtStsfFEd25QxQUxH6ZOJHbWFsTDRpEtGlT+v568YIjypcvk7RyZZI2asTXYWwskVyeu/PIjCpViPbsUX2/aiYsJoxkcTKyX25P7Xe2p7CYsORtwdHBtMRjCTXe2pgkcyRUYkUJwhyQZI6EHgc8pk+fPokIsIYjwEIA5wFCAKsHIdLUh/Ctesi1X5s1I+rdO/1tL15wikJsLJGVFQvlCxdYiJYtS7RmTeZ9v3rFoqhnz4zb+Plxm7lzM27j6UlUrx6Rtja3+/SJBWB8fMb7KJWZb88MpZJo3DgiGxui/ftJGh6ev67ZsBQxRMuWEZUqxWI2J9y9m3JDc/Vq2u3bthFVqMC+UCqJFi8m0tEhun+fha+LC6eoAClpLefP5yjVQPrqFUnr1ydydWWxXbKkalMVvLyIdHWJIiJU12cec9f3LhksMCCXP1xo7uW51GlXJzL6zYhMF5rSN2u/IR+pDymVSjr95jQ99H9IRCQEsBDARQMhgNWDEGnqQ/hWPeTar7dusfj4Mlp34ACRRMLCpnp1oho1iBSKlO1TpnAEMjOWLeNocVZCVCbLWvjI5Zwn/Hk+afPmRM+esYgmIvL25lzctWuJ6tThNiNG5ExUvX5N5O5OVKYMCyjK59esQkE0eDCL0UOHsrePUsk3MADnodrYJJ8rEfHf+vpE69al3u/XXzmf1tKS6Kef+DOJiCC6fp0oOjrHpkulUpKGhBANH57ymR49muN+MmTVKqLWrVXXn4aIjI+k6uurU5t/2tDia4vJ098z0/ZFXQDnByRERBrMwCgSyGQyODk5wcfHp9AVqtYkMpkMAIRP1YDwrXrItV+VSq6XNm0a52c6OQHGxkCzZpyvGxDAeZT79/NI9iSuXOF5eZs3B5o25XzOY8eAtWt5e2Iij45ftgz48UfVnCTAZSzu3uUyU7/+yiXTEhPZbh8fbmNuzsds355H3Q8ZAvz0U+pR+Z9DxJNw7N4N/PEHT0bw22/JJdgKxDW7axdXGXj2DChZMvO2169zvrW/P+dwlyvHvps8mXOMJ03ikm1JubtJxMUBGzdyPu2PP351Xm2yX3V1Ob/Zw4NL3x08mP4OcjnnnmeXIUM4l3z27K+ys6Dh6+sLR0dHjU8xXKTRtAIvCogIsHrI1xGfAo7wrXr4Kr+OHp06svriBb++epX5fg0acLsqVVL27diRqFy5lPfe3rmzKbsolUT+/kRjxhDNm8fVBz6PVN+5wxFsPT2iH39MHQ2Oi+MqE02acJ5rjx4cEf+CAnPNDh9O1LRp1hH3Xr04gpuEUkm0bx9H1EuWJBowIFcR3ZySxq8vXnDkOb2qExcu8BMJExOiunWJVq/mVJgk5HKi48c5J3nAAKLbt/n6/PdftZ9HfkNEgDWPiADnASICrB4KRMSngCJ8qx6+yq8xMVynNiSEI7pJKJWZR/kSEjiCqKMDvH0LrF/PtX1dXfn90aNA1ao5t0cdvH4NtGrF0+L26MHVCjZs4OjxmDEcATc3T3fXAnPNRkVx5N7VlWeX+/KzS0wEFiwA/vyTZ6dzdNSImUmk69dixXhKYT8/rlcNAJ8+8SQSc+bw+V25wrV2PT2Bffu4/c8/85OB8uX5sx41iq/FM2eA2rXz+Mw0i4gAax4hgPMAIYDVQ4H5wSuACN+qB5X59eJFfsw9axbwzTcqsCwf4efHs80lPWLv2xdo2zbLR/kF6poNDOSScd2787lVrQrY23MZs59/5jSQEyd4whENk65fT57k9AxPz5Sbp+nTgefP+YbqczZs4JJi2tosjGfNAurWTdnu6Ai8f5+ztIlCgBDAmqdoXXECgUBQGHB356Uw4uAAdO7MS2HFzo4F7i+/AP3780xorq5AeDjPVtakCc9Wl1/p0AHo2pXzsatWBS5fBhYv5jz0L/n+e2DQIK4rravL61q35oLXs2fz9iImfgX5A3HVCQQCgUCQ11StypFUgFNRTp5kMWxjo1m7ssuUKSxkhwzhqPWUKUCLFum3/XJCkrNn+fX77wvO+QoKHWIyaoFAIBAINImLCzBuXMESg/XrA926cQqOoyMwb17O+7Czy94shQKBGhARYIFAIBAIBDlnwwbOYe7VKyW9QSAoIAgBLBAIBAKBIOcYGnLahkBQABHPHgQCgUAgEAgERQohgAUCgUAgEAgERQohgAUCgUAgEAgERQohgAUCgUAgEAgERQohgDPh5s2bKF++PCQSCQwMDNCgQQMolUoAwNmzZ2FlZQWJRAItLS3UqVMH8fHxGrZYIBAIBAKBQJAVQgBnwM2bN9GmTRuEh4ejYsWKmDhxIlatWgWJRAKpVIqOHTvC3t4eAQEBOH36NB4+fIhWrVpl2J9cLs9D64sO59ObeUigEoRv1YPwq/oQvlUPwq+CwogQwBkwefJkGBsbY9OmTbC2toaWlhbq1q0LiUSCxYsXQy6X4/z587Czs0ObNm0wZMgQeHh4IC4uLt3+hABWDxcvXtS0CYUW4Vv1IPyqPoRv1YPwq6AwIgRwOsTExODGjRsoVqwYFi5ciOvXr2PLli04ePAgAI4OGxkZoXjx4sn7dOrUCUSEBw8eaMpsgUAgEAgEAkE2KFITYQwdOhR///13htubNm2Ky5cv49q1ayAihIaGYseOHZg0aRJsbGzQr18/XLlyBZGRkTAwMEi1b8mSJQEAAQEBafpVKBQwMjLC69evYWJiotqTKsLExMQgJiYGL1++1LQphQ7hW/Ug/Ko+hG/Vg/CreggKCgLA+kCgGSRERJo2Iq+IiorKMEUBAHR1dWFkZIRq1arh+fPnmDJlCpYsWYJmzZqhUaNGuHv3LqpVq4bbt2/j7t27iI6OTt736NGj6Nq1K65fv44GDRqk6vfu3bto1aoVYmNjk9fp6OhAR6dI3X+oHBMTE8jl8kw/U0HuEL5VD8Kv6kP4Vj0Iv6oOuVyenA6pq6uL6Oho3LlzB7Vr19awZUWTIiWAs4O3tzfKlCkDLS0t6OnpwdjYGFKpFLq6utDS0sKYMWOgpaWFxYsXIyAgAHZ2dgCAkSNHYsuWLYiJiUkTHQ4PD0fx4sXx7t07mJmZaeK0CiWRkZGYO3culi9frmlTCh3Ct+pB+FV9CN+qB+FX9eDv7w83NzeEhYXBwsJC0+YUSUQI8gucnJzg4+ODLVu2YO3atfjnn38we/Zs2NjY4Ny5c+jevTvc3NywfPlytGrVCufPn8ejR4/w999/o2HDhmnELwBoa2sDAMzMzIQAVjF6enrCp2pC+FY9CL+qD+Fb9SD8qnoiIyMBpOgDQd4jBPAXaGtrw9HREbNnz4aenh5GjBgBf39/WFtbY+/evahbty4A4Pjx4+jfvz/s7OwgkUhQq1YtnDt3TsPWCwQCgUAgEAiyQgjgTJg+fTqmT5+e7ra2bdsiLCws232JfF/14O7urmkTCi3Ct+pB+FV9CN+qB+FXQWFE5ADnATKZLDm1QjxGUh0ymQwAhE/VgPCtehB+VR/Ct+pB+FU9+Pr6wtHREVKpVPhWQ4g6wAKBQCAQCASCIoUQwAKBQCAQCASCIoUQwAKBQCAQCASCIoUQwAKBQCAQCASCIoUQwPmY69evo2rVqlAqlZgzZw4aNWqUYdvo6GiUKlUKr1+/zkMLBQKBQCAQCAoeQgDnY3766SfMnTsXWlpZf0zGxsaYOHEifv755zywTCAQCAQCgaDgIgRwPuXSpUsICAhAp06dsr3P4MGDcf78eREFFggEAoFAIMgEIYDzKQcPHkSLFi3STJM4ffp02Nraonjx4pg8eTISExOTt1lYWKB27do4fPhwXpsrEAgEAoFAUGAQAjifcu/ePVSqVCnVutu3b0NHRwc+Pj64fPkyDh48iKVLl6ZqU6VKFdy9ezcvTRUIBAKBQCAoUAgBnE8JCwuDubl5qnWWlpaYM2cO9PX14ebmhsmTJ2Pr1q2p2pibm+doimaBQCAQCASCooaOpg0QpI+lpSWkUmmqdU5OTqlSIsqUKQMfH59UbaRSKSwtLfPERoFAIBAIBDnj40dg3z5NWyEQEeB8Ss2aNfHs2bNU63x8fKBQKJLfe3t7w9HRMVWbJ0+eoFatWnlio0AgEAgEgpyxbx9w6ZKmrRAIAZxP6d69Oy5cuJBK8IaFhWHevHmIj4/Hq1evsGzZMgwbNix5e0REBO7cuYOuXbtqwGKBQCAQCARZceAA0Lq1pq0QCAGcT2nRogVsbW1x/Pjx5HV169ZFQkICHB0d0aRJE3Tt2hXTpk1L3r5jxw60aNECbm5umjBZIBAIBIIih1KZ/bY+PsD9+0CLFuqzR5A9JEREmjaisCOTyeDk5AQfHx+YmZllez8PDw+MGTMGDx8+zHIyjOjoaFSsWBFnz56Fq6vr15pcIJDJZACQI58KsofwrXoQflUfwrfqQfg1cw4eBH78ETh6FKhTJ+32sDBAJgOcnIC5c4FbtwCFAtixwxeOjo6QSqXCtxpCDILLxzRq1AiPHj3KVltjY2N4e3ur1yCBQCAQCAQAgMhIYOxYoGFDoGXLtKkNMTFAq1bAu3dAu3Yc+e3YEejSRXM2C1IQKRACgUAgEAgEOeDoUcDdHShXDti/H9iwAejWDVi6FIiNBYKCgN69ASMjYN484MoV4MQJYOVKoGlTTVsvAIQAFggEAoFAIMg2MTFA//5Ar17AkSOARAL06wecOQPs3QuYmHDKg74+cPgwR4l9fYHy5TVtueBzRAqEQCAQCAQCQTY5dQpwdAQmT2bxm0TjxsC9e1znNy4O+Hw4zuftBPkDIYAFAoFAIBAIssmBAxz9TU/USiRAqVJ5b5Mg5wgBLBAIBAKBoEgTEgJcuwZUqABkVkk0KopzeT088s42gXoQAlggEAgEAkGRpn9/4M0bICAAePQo43zdsWOBmjWBqlXz1j6B6hGD4LJBt27dIJFIcP78eQA8BbFEIoGxsTFMTEySF6lUqmFLBQKBQCAQ5IRLl4A7d7hM2bBhwLhxQHozJBw9Chw/DuzcKXJ6CwNCAGfBjh07EBMTk+62R48eISoqKnkxNzfPY+sEAoFAIBAAQGIiR2ivXMnZfrNnA5MmAZaWwIIFwN27LHa/5PhxYPhwHgAnKPgIAZwJnz59wowZM7Bp0yZNmyIQCAQCQZFBLgd+/x3o0YNnWouIyLy9UgkMHAjs28fpDOHh2TvO48cseMeM4feWlsDixcD48Vzu7PNI8K1bQP36uTgZQb5ECOAMICIMHz4cM2bMQMmSJdNt07RpU1hbW6NBgwY4fPhwHlsoEAgEAkHhIy6OS4pt3Ag0aMC5udWqAcHBGe/zxx8sZJ88AapXB775BmjfnqchzowNG4C+fQELi5R1w4cDxYsD5uZcz/fUKUAqBZ4/FwK4MCEGwWXAunXrQET47rvv0myztrbGjRs3ULNmTSgUChw4cAB9+/bF4cOH0b59+3T7MzExQWRkpLrNLlIIf6oP4Vv1IPyqPoRv1YMm/Lp+Pb9evgwYGAAjRgA//MDL1q1p82+fPAEWLQL+/Zcnn9i6lfN5164Fvv2WhbS2dtrjxMTwRBW7d6cVykeOAP7+nBs8bBgf+5tvAEPDrEV1doiOjv76TgRfhRDA6fDu3TvMnz8ft27dSne7iYkJ6n92Gzho0CBcuHAB//77b4YCOC4uDnPnzoWenh4AwN3dHS1btlS98QKBQCAQFFAiIoA1a4C//mLxC7DgXbIEaNUKmDaNpxbW1+dtly4Bo0ZxDm/durzOwABo2BCoWBHo2hWoU4dze9u1S32ss2eBEiU4uvwlRkaAszMvISE8xXGXLl93bufPn8fFixcBCAGcH5AQpTfWsWizfft2fPfddzAzM0teFxoaCjMzM/Tp0wcbN25Ms8/w4cMRGxuL3bt3p9kmk8ng5OQEHx+fVH0Kvg7Zf7fhwqeqR/hWPQi/qg/hW/WQ13794w9g/36uyfslHz8C3btzZHbYMBa95cuzOB06NP3+FAqOKC9aBLx7lyKcAU6RaNKERXVmxMWxmJ4yBfj++1yfWip8fX3h6OgIqVQqrlkNIQRwOsTExCAsLCzVOicnJ+zevRutW7fGs2fPYG1tjfLly0OhUODgwYMYNmwY9u3bh86dO6fpTwhg9SB+8NSH8K16EH5VH8K36iGv/dqsGc+yljQo7Uvkco76jh7NA9ScnICLFzMvS6ZUAlWqAD/9lCJgg4I4+vv2bfZmbouMBIyNAS0VjZwSAljziBSIdDAyMoKRkVGa9dbW1rC0tMSrV68wdOhQBAQEQF9fH+XLl8c///yTrvgVCAQCgUCQNcHBPMPazp0Zt9HR4VSIs2eBnj2BlSuzrsmrpQXMnMkl0tzdgXLlgL//5gF22Z222NQ0++chKBgIAZxNPg+UjxgxAiNGjNCgNQKBQKAZiIDJk4Hbt7lUVI8emrZIUFg4dgyoVYsjs1lRtizw4EH2++7dm9u7u7N4XrEC2Lw597YKCj6iDJpAIBAIss3p08A//wCVKnHupUCgKg4d4hxfdSCRcH3fHj2AGjW4zFmHDuo5lqBgIASwQCAQCLIFEfDbb8DEiTyo6PFjro0qUB9EwC+/pF8DNzGRB421acO5sQUZqRQ4fx7o1k19x5BIgFWrgOnTeZINMZ1x0UYIYIFAIBBkiwcPgKdPuexUsWIsVrZv17RVhZs7d1Jq3H4OETBgAJcLe/iQB4IVZE6dAlxdOT9XnUgkPPVxs2bqPY4g/yMEsEAgEAiyxY0bQKNGQNKg9VGjuMSUn59m7SrM/P03YGsL7NmTso6IRdzdu1wubOBAYNcuzdmoCg4fVm/0VyD4EiGABQKBQACAhVVmPHjA+ZNJNG4MdOwITJjA74OCuKxUZsTEAI8eAR8+8GxdiYlfZ3NhJi6OZynbvJmjvO/fA9HRwLhxPLvZ8eOAjQ3Qrx/nz8bGatri3BEfz7nlQgAL8hIhgAUCgUCAefMAFxcWpRnx4AFQs2bqdStX8pS1o0cD9erx8vw5C5ovxfCJE/yYu2FDoHRpFtBDh2YtvIsqhw5x9LdjR6BtW/ZXqVLArVscja9UidvVqgU4OgLbtmnW3txy9Sqn1FStqmlLBEUJIYAFAoGgCOHtzTNqfc6KFcDatVxXtVEjniFryZLUA9xiY4Fnz1JHgAEeTe/hAZw7B3TqxIK2YkVg5Ejgm294VH94OHD0KNCnDzB/PiCT8aCt9+95399/V/NJq4mzZ4GoqOy1JUp/IFtm/PUXp5lIJFwbd/t24OBBLkFXtmxKO4mEP8Nff+UofEHj5Em+5sSgNEFeIgSwQCAQFBHu3wcqV2bx5OQEfPcdi9pZs1igLlkCPHnCtVJv3eKJAh4/5n2fPOEonZNT2n7LlQNevwZWr+bSaHfvssj28wMSEgBLSxbXO3awQNbSArS1ATs7Fr9r1xa8KPDlyxyV3bEj67YKBUfIS5Rg0ZwdHj3iiPuQIfze1BRo2RJo2jR9odiuHW9btCjbp5BvOHlSlCQT5D15KoC9vLyytXh7e+elWQKBQFBokMt5YFrPnjywKIngYI7QzpoFRETw4Ko3bzii27s3py4AnAYxaRLvO3kyl9i6cwe4coXTHzKK0iWt19LiR/JaWoC1NU9u8P49HzO9STPatQMCAznHtSBAxI/shw0Dqlfn88uKX37h6XuXLOHPZfNmnp43M/78E+jfH7CwyL5to0YB+/Zl3Xd+4u1bvllq0ULTlgiKGnk6E5yLiwskWTzjICIYGhoiOjo6j6wSCASCwsO0acCRIyxqBw8G/vc/oH594OefgTp1WNxKJBzlbdgQWL6c0xXS45dfAAMDTovQ0wM2bMi5PVpanO+bEQYGQOfOwP79adMr8hvv3gEjRnBU/LvvWARXrswpHUmVMb7kyROu1XvvHqeGlCsH/PQTpzKMGcOz6R05kpLPC3Dk/N9/c35T4O7OqSo3b/JnWxC4e5dvJIyNNW2JoKiRpwLYyMgIz549y7QNEaFatWp5Y5BAIBAUIoiAAweAdes4survzwOpYmI4xeH589QRXH19zhvNCImEhXO3blxtwNRUPXb37s1icOJEPk56PHkCbN0KdO3KaQRBQSycW7YEjIzUY9fnnDkD9O3LtXePHUvxhbMz32T07Jn+fhMnsuCtWJHfd+zIg9m6dOFzadiQqzqcP5/y2axezZH3ChVyZqOuLvd54EDBEcCenmLwm0Az5KkA7ty5M0qVKpVlu44dO+aBNQKBQFC4ePGChWFSkf9u3TjCeOUKC90SJXLX7+cDrtRBx45c7qtVK2DmTCA0lMXcsGG8ff9+zh1u3ZoHSzVowJHXiROBgACOauvpcU3cyEjub/TojKOyOeXwYWDQIC491r9/6m19+gDLlrGg1dVNvc3Li32/c2fq9ebmLKg/fOBBhOXL8zn27s0R3I0b+YYlN/TvD/TqxRHqnApoTeDpyaJdIMhrJEQFbehBwUMmk8HJyQk+Pj4wU9U3sgAymQwAhE/VgPCtelC3X5ctY8F14gS/j43liKqJCT++z8+PmRMTOeXi3DmOrj5/ztPW1qvHOcX//ssRX4WCB9ABHPG+eROYOhUwNZWhc2dAV9cMW7ZwvdzTp/n8Fy3iwXs+Plw+rE8fFtNmZixCHzzgCPOYMWnTNc6c4dzlXbtY5H5JbCynlnTtyhUuPmf2bB7MduRI5ue+Zw9HiZ894+MtWcIz7uW2KsKsWRwt37YNcHDgnODKlXPXl7qu2ZgYwNCQB0IePcppOkUJX19fODo6QiqViu9ZDZGnEeAkIiIiYGhoCH19fU0cXiAQCAolJ0+yuEvC0BD4/nt+xJyfxS/A0dNly1Le/+9/XBlALgemTGHxC6SIX4AFYoMGHPn9T6fBzIxzn3/4gc+7alVOBdHT4yoWbdpwebERI1gYGhlxDmpcHEeT//knpf/jxzntYevW9MUvwD7euROoW5dnZHN15fVKJQ80XL0663Pv04dTVbp0YTu+++7rSoLNncuVN3r14iocSdP/TpmS+z5Vwfv3/Fk5OrJtI0YAISG5F+cCwdeQJxHgnTt3YsqUKahSpQp69OiB+fPnw8DAAAsWLECvXr3UfXiNIyLA6kFEKdWH8K16UKdfnz/nQWTv3wP29irvXiMEBLCQtLfPWhB+6VsiTivYtQvYtCltbnFUFIvDYsV4oJ6XF9ctvnqVJ5+ws+OKGIsXc/pDVowZw9UMjh/n93fucJQ5KIjFd1ZERXH6x65dnMpiaZn1PlkRHc3n9vw50Lw531QkVfvILqq6ZoODOcobF8eD/L79lqthJJXQK2qICLDmyRMBXKdOHZw/fx6RkZGoVq0aXrx4ASMjI7Ru3RoeHh7qPrzGEQJYPQiRpj6Eb9WDTCZDXBxw5YoZtLW5bquV1df1+egRC6b//Y9F4pYtqrG1oKGKa3bYMJ5sQk+PxauvL9dOzk40NiSEBfORI5yDPXs28OoVpzfkBCL1TAgxeTKXHPu8NF52+Bq/JqkLIs7vtrJif8hkfOPRpQs/mdi1K8ddF3iEANY8eZICYWxsDDMzM5iZmeGbb76BtbU1AIgUCIFAUKR48oSrCBgY8KP8+Hjg4kWgZMnc9eflxcJCqeSBY5/P3CbIOevX84xq9+/zDHb792dfjFpb86C8mTM5inziBDBhQs5tUNdsaOPHc8WKV69S0jTSg4hvAnx9eeDkixdAmTLZG1D48SNHzKtV4wojf//NN2i9e7P4PnKEI9LFinH7PXs4IiwQaII8mQgjISEByv8qcx88eDB5vVwuz4vDCwQCgcaJiuLKBEOHsqhImnHta7LABgzg/NEPH3hmsoIw6j8/o6/PqQetWvENRdu2Odt//Hi+CZk7l4VfTvdXJyVKcAWNQ4cybzd2LA9GXLuWJ05p3RqYNy/r/sPC+Hy9vbmix7ZtXIpt4UIWwhs3pi2jZ2iYs4k+BAJVkicR4JMnT0JLi7V2UvQ3ISEBy5cvz4vDCwQCgcaZPZtLXo0bx1E+iYSrE9jZcU6kg0PO+rtzhysFnD3Lj5GbNlWP3UWV7OTtfomZGU+NPHUq0KQJR4XzEy1acMWF6dPT3/7wIafQPH8OhIezGN64kSPZxYvzDVuDBunvO38+V9o4fjz1QEWAU0ty40+BQJ2IMmh5gMgBVg8iT1V9CN+qlk+fuNbrxYsyfPNNar/Wr8+VGoYOTbvfkiX8yHjy5LTbhgxhwfXnn+qzuyCRn65ZIl608uQZa/Z5/pxLyoWHc7T7c/z8uIpFzZqpq3HIZDKcPQts3WqGy5d5Frxy5VLvGx/PN3CHDokbsewicoA1j0b+PXukNyG8QCAQFEKUSo64de7MVQa+pG1brv36JXfvcg7mH39wH0kkJnKe6r59nFIhyH9IJPlP/AKcImNmBty+nXr9jh2ch06UfnS4TRuuqTxoEKdHfE5UFEeVLSw46i0QFBQ08i/q5+enicMKBAJBnuLnx3mUN29yukN6tG3LFRw+HxIRGckR3hkzWGDcucPrFQrO+920iQWJyPkV5ASJhNMYTp9OWffhA0/CcewYcOlS5uXX5s7lm7W//uJrcfFintWub19g+HD1DeATCNSBRibCkIj/EoFAUMg5dIjTGjp1Au7d45HvSZM1fE6tWvz4eMIEbvvwIU8WUKIEC2Bvb+DAAW43YgTn/Xp45L/8UkHB4PvveYIRJyeeLGTYMB5I2b591vva27MA7tKFr1cHB+D6da5q4uamftsFAlWiEQEsEAgEhZmzZ/lx8c6dPEVuZmhrc8msunV59HyzZrx+925AR4ejaz17crm0+Hjg/HkhfgW5p2lTfuLQqROwdy+XLjt6NPv7N2zIVUzCw4GyZfkaFQgKIvkwSyl/MHfuXDg7O8Pc3BzW1tZo06YNPD09U7V5/PgxmjRpAmNjYzg4OGDOnDkQYwoFgqJNYiJPZbt6ddbiN4nSpblsVlK098SJFJHbti1w7hxP73vlSs6rRQgEX9KgAXDjBtfg/fvvtOXJssLGhgd1CvErKMjkWADv27cPABAREZHrgxYEkdi3b1/cu3cPUqkUfn5+aN26Ndq0aQOFQgEAiIyMRJs2bdCwYUOEhITg7Nmz2Lx5M37//XfNGi4QCDTKrl08wn7YsJztV7w4YGKS/rb69bnGrIj8ClSFqysPhhMD1wRFlRwL4KRyHR07dkSVKlXQp08fLF26FBcuXMh2Hzdv3szpYfMcV1dXWPxXoZuIoK2tjaCgIISFhQEADh06BIVCgfnz58PQ0BCVK1fG5MmTsWbNGk2aLRAINMzSpcCUKWlroQoEAoEg/5DjBxht/5vaxsPDA0qlEi9evMD9+/dx/P/snXdYFMcbx7939HYI2EBU7F3sNXajscceo7GmGHtN1BhroiY/Yy9RE3uNPfbYe1cUe0URBQTpcFx7f3/M7e0td0dR4BDm8zz73O3u7O7sbPvOO++8s28fWrRokekZtCYHDhxA7969ERMTA5lMhtGjR6NAgQIAgICAAFSvXh22Rm1AtWvXxrNnzxAbG8vj+nE4eZCICBZrtUcPa+eEw+FwOKmRbgEcFhaGlStXgohQtWpV1KlTBz4+PqhUqRIqVaqEvn37ZujAjx8/xogRI3DlyhXExcVJ1qlUqgztK6to164doqOj8e7dO6xbtw6+vr6GdbGxscgnDGiuR7AYmxPArq6uJufJ+TB4eWYdvGzfj+vXWdMyYD7iAy/XrIOXbdbAyzVrSEhIsHYW8jzpFsBdu3ZFYmIiihcvjjVr1uDFixfw9vZG7dq1sWfPngwf+KuvvoKvry9WrVoFFxeXDG+fnXh6emLkyJHw8PBA2bJl4e/vD4VCgVevXknSRUVFATA/EpFSqcT06dNhrx8Psnnz5mjZsmXWZ57D4WQbDx/ycFAcDsc8x44dw4kTJwBwAZwTSLcADggIQFhYmEGsvn37FpcvX8a1a9fe68D37t3DuXPnJC4EORmdTge1Wo3Hjx/D398f1apVw6ZNm6DRaAzncO3aNZQsWdKsANZoNJg7dy53jcgCeJlmHbxsM8aDB2yY2LSKjZdr1sHLNmvg5frhdOnSBV26dAHAhkJeu3atdTOUx0l3J7hSpUpJojcUKFAA7du3x7Rp097rwJUrV8abN2/ea9vsYOHChQgLCwPAxP6QIUNgb2+Phg0bAmA3so2NDaZOnYqkpCTcuXMHc+fOxdChQ62ZbQ6HY0Xu3gUqVbJ2LjgcDoeTFukWwD/88ANmWxrL8z1YuXIlhgwZgn/++QdnzpyRTDmBo0ePomrVqnBxcUHVqlURGhqKY8eOwdvbGwDg5uaGI0eO4MyZM/Dy8kLLli0xcOBAjB492so553A41oCIC2AOh8P5WEi3/8HXX38NnU6Ha9euoVu3bqhTpw6qVKkCufz9xtK4e/cuTp48iQMHDkiWy2QyQ6xda7J///4001StWhVnz57NhtxwOJycTlgYEBkJVKxo7ZxwOBwOJy3SrV7PnDmDBQsWwNfXF0uWLEGtWrXg5uaGTz755L0OPH78eMyaNQvR0dFQq9WGKadEgOBwOHmPW7eAkSOBNWsyvm1gIFCiBJDD+/RyOBwOBxmwANeuXRu1a9c2zCuVSty4cQPXr19/rwPHxMRgxIgR77Uth8PhZDZbtgBffw20bw8MHw60bp2xYYcvXgTq1cu6/HE4HA4n83g//wUAjo6OaNCgAYYPH/5e27dp0wbnz59/38NzOBxOphEYCAwaBOzcCWzbBnToAEyalLF9XLgANGiQNfnjcDgcTuZitRhkBQsWRIcOHdC1a1dDxzKBGTNmWClXHA4nL7J4MfDll4B+oEvMmcN8eYcNA2rVSnt7rZZZgGfNytp8cjgcDidzsJoADgwMhL+/P548eYInT54YlstkMmtlicPh5EHevQM2bmQCVqB4cWDMGGDUKODsWSCt19K9e0wEV62apVnlcDgcTiZhNQF88uRJax2aw+FwDKxYway8/v7S5T/+CKxbB8yenbY7xLlzQN26wEcyrg+Hw+Hked7bB/hD2bBhAwICAiTLbt68iU2bNlknQxwOJ88RHw/88Qfw00+m61xdgX37gN9+YyLZmDdvgH//BQICmFAeOxbo1StbsszhcDicTMBq9orp06fj3LlzkmXe3t7o3r07evfubaVccTicvMTSpWzo4latzK/39wf27wc6dQJCQ4GpU4GYGKB5cyAuDoiIANq0Ac6cSZ+vMIfD4XByBlYTwOHh4ShcuLBkWeHChQ3DD3M4HE5WotUCS5YwEZyaj2+jRszFoXFjNtrbf/+xeL/79rHt3nMsIA6Hw+FYEau9ur29vfHo0SPJskePHqFgwYJWyhGHw8lLHD7MBG3btmmnrVgR2LMHmD+fxfr95x/AxoaLXw6Hw/lYsdrru3v37ujbty9u3ryJhIQE3Lx5E/3790ePHj2slSUOh5OHWLUKGDAg/R3XPvkEiIoC5s1j/sEcDofD+XixmgCePHkySpcujZo1a0KhUKBWrVooWbIkpkyZYq0scTicPEJSEvPt7dcvY9txiy+Hw+HkDqzmA+zo6IiNGzdi4cKFeP78Ofz8/JA/f35rZYfD4eQh7t9nVtxSpaydEw6Hw+FYA6tHrfTy8oKXl5e1s8HhcPIQd+8ClSunPcAFh8PhcHIn2dqg5+Pjk650xYoVy+KccDicvMzdu0ClStbOBYfD4XCsRbZagKOiorBmzRoQUarpYmJisilHHA4nL3LnjuXYvxwOh8PJ/WSrAC5UqBBmzJiRZjpvb+9syA2Hw8mr3L0LjBlj7VxwOBwOx1pkqwAOCgrKzsNxOByOCfHxQFAQd4HgcDicvAwP6sPhcPIU9+4BXl4AH3OHw+Fw8i5cAHM4nDzFgwdAhQo8AgSHw+HkZbgA5nA4eYrHj4GyZa2dCw6Hw+FYEy6AORxOnuLRIy6AORwOJ6/DBTCHw8lTPH4MlClj7VxwOBwOx5pwAWyBrVu3olGjRlAoFJDJZNBoNJL1MpkMTk5OcHV1NUyBgYFWyi2Hw0kPRNwCzOFwOJwcMBRyTsXDwwNDhgxBUlISBg0aZDbNvn370LJly2zOGYfDeV9CQ4GEBKBUKWvnhMPhcDjWhAtgC7Ru3RoAcOrUKetmhMPhZBqPHgHFigFOTtbOCYfD4XCsCXeB+AD69OkDLy8v1KhRA6tWrbJ2djgcThpw/18Oh8PhANwC/N4cO3YMDRo0gI2NDY4dO4bevXtDo9Hg+++/N5ve1dUVcXFx2ZzL3A0vz6wjt5bt48dA+fJAbKx1jp9byzUnwMs2a+DlmjUkJCRYOwt5Hi6A35MWLVoY/rdt2xYjR47Ehg0bLApgpVKJ6dOnw97eHgDQvHlz7j/M4WQz9+4BrVpZOxccDicvcuzYMZw4cQIAF8A5AS6AMwm5XA4isrheo9Fg7ty5UCgU2ZirvAEv06wjN5UtEXDhAjBlCmDt08pN5ZrT4GWbNfBy/XC6dOmCLl26AABCQkKwdu1a62Yoj8N9gC2g1WqhVCqhUqkAAMnJyVAqldDpdLhx4wauX78OlUoFjUaD//77DwsWLECvXr2snGsOh2OJZ8+AuDigalVr54TD4XA41oZbgC2wYcMGDBgwwDDv6uoKADh58iTi4uLwww8/IDg4GLa2tihevDhmzZqFwYMHWyu7HA4nDa5eBfz9AQcHa+eEw+FwONaGC2AL9O/fH/3797e4vkOHDtmXGQ6H88FcuwbUqmXtXHA4HA4nJ8BdIDgcTp7g6lWgdm1r54LD4XA4OQEugDkcTq5HpwNu3gRq1rR2TjgcDoeTE+ACmMPh5HqePweSk4EKFaydEw6Hw+HkBLgA5nA4uZ6AAKBiRUAfhpvD4XA4eRwugDkcTq4nIACoVs3aueBwOBxOToELYA6Hk+vhApjD4XA4xnABzOFwcj1cAHM4HA7HGC6AORxOriYyEnj1io8Ax+FwOBwRLoA5HE6uZvt2Zv318LB2TjgcDoeTU+ACmMPh5Fo0GuB//wPGj7d2TjgcDoeTk+ACmMPh5Fp27mSDYPToYe2ccDgcDicnwQUwh8PJtWzeDHz9NWBra+2ccDgcDicnwQUwh8PJlSQmAkePAp9/bu2ccDgcDienwQUwh8PJlRw9Cvj4sBHgOBwOh8MxhgtgDoeTK9m9m1l/ZTJr54TD4XA4OQ3uGcfhcHIdFy8C//wDXLli7ZxwOBwOJyfCLcAcDidXER8PfPklMHMmULmytXPD4XA4nJwIF8AcDidX8fvvgLc3MHq0tXPC4XA4nJwKd4HgcDi5htu3gT/+AE6eBOS8es/hcDgcC3ABzOFwPmpiY4E+fYBnz4DHj9mob3XqWDtXHA6Hw8nJcAHMMUAErF0LXLsGuLoCbm6mvwoFUK0amycy38OeCDh1CggIAPLlA2rUYE3S+fNzqxwncyFiA10kJABz5gA1a7J7jcPhcDic1OACOA/z5Alw+DAbJataNWD+fODsWaB3b9aRKDQUiItj/4Xfd+/YpFCwgQY6dwZq1QIePACePgWKFAEuXAAiIoDGjYHISGDECLZt4cJAt25sWNqGDbkY5rwfOh1w/z5w+TLw11/Aq1fA9etAgQLWzhmHw+FwPha4AP4ISUxkzb2hoUBUFBMEn38OODhY3kawyh46xIRqaCgTDY0aMevZmDFAz57AjRtMqKbG/ftAdDRgYwPs2sUGHChaFOjUCQgOBmbPBlq3BpydxWMrlcD588D27Uw0u7uLae7cAcLCmKguUgQoVIidi68vUK8esyAbi+XQUObjGRsLJCeLVmgbG5aPsmWZ4D5yBNBqmUXQw4OlTU5mebGzYxZtBwdWHnfvsubziAigeHGgfXsm1AsWNC3HoCDg+XNApWJ5uXmTXZNChYBSpQAvL3Y+zs7MJ/XSJVbGnTsDAwawwRkyEyIW7uv0aeDNG8DTk+W7UCH26+PDzkkmY2ljY4Fbt1jZCPlr3JiVU8GCQIUKrCyFfQNs26QkQKNh28XEsP3Y2AD29mzSaoF799j9mJTEyqBGDbZt4cJAyZIfdp4PHwKLFwMbNwJqNbP2du4MDB7MriWHw+FwOOlFRiR84vIWYWFhGD16NI4fP47k5GRUqFABc+bMQZMmTQAAp06dwpgxY/DgwQMUKlQIP/zwA77//nvD9snJyRg9ejTWrl0LpVIJmUyGWrVq4e+//0blFLGXYmNjUbRoUQQHB0OhUBiWh4cDO3eyj3e5ckCJEkwE2tub5jciAtixgwnOkycBR0dR2EVFMYE4cSITlQULMtFx9y7w339MoN29C7x4AXTtygSOqysTzYLVTKfLPousSgXs388sxYmJQJUq7Fzi4oCQECaclEqW3/PnmaDr3ZsJvOfP2fpq1YDChWNhZwfodKxMNRq2zePHTOB+9hkToSEhTLA5OjLB6+DAxFpsLDt+yZJstLBy5Vh5PH4MbNnChKGPD+DkJIq8t2/ZdStWjO3Hw4MJMYWCieEnT5jV29ubifBSpYDmzZlQ3LQJOHECaNYMaNqU7cPbm4lFlQpo0oSdQ1ISE546HfsV/icns/vAx4dd3+hoVhaTJ7PzbtmSVRqiotjy8HA2hYWx8xPuCY2GVTQcHdlv48asnKOjgdevWXo/v1g4OwPBwQrI5ew8bt9m2wJsX4IbjErFJrmcVT6EsipQgFWy7OxYPsqUAdq0YfdngQLs/AsUYOuEisaDB+ya3LvHXGhCQthxkpPZuh49gO+/B2rXZi0XHxuxsbEAIHkPcDIHXrZZAy/XrCEkJAS+vr6IiYnhZWsl8qwA7tq1K8LDw7Fr1y54enpiwYIFmDZtGl68eIG4uDhUrFgRv//+O7755htcvHgRHTt2xNq1a9G5c2cAwNChQ7Fr1y4AwK5du7BkyRIcO3YMNjY2ePToEVxdXQ3HEgTw9u3B8PBQ4J9/mHXywQOgbl0mbh4+ZMIJYFbMChWYmLG1ZWJw507A358J2E6dmGgTLJ8aDbByJZtu3WLb5MvHrI7Nm7NjFCvGrGUf23OmUrERvbZtYwKvenXAz48JR0svZp2OlYm5ikRGiIpiQiw5WRR5bm7MKu3k9H77DAoC9u5l4v/1azY5OjIhef++mE4uZ9fX+NfenlUG3rxh6T08mLAeMwYYMsRynuLjWRna2zOx7uHBrNSpnfft27FQKgEfHwWSk1mloF498ZiurlL/b0GkC5bjlMTGsnv+2DH2PzwcePmS/RYowEQ3wO57FxcmpKtXZ5br+Hi23wYN0m6dyOlwMZF18LLNGni5Zg1cAFufPCuA/f39MXDgQIwcORIAEB8fDzc3N1y6dAmHDx/Gnj17cPPmTUP60aNH4/bt2zh+/DiUSiU8PT3h5uaGSZMmYeTIkYiIiIC3tzdcXFywePFifPXVV4ZtY2Nj4e3tDYXiDZRKBZo3Z73WBTEnkJjIxMeTJ0wcv37NLJVETPSmp2d7UhITSOHhzLLq4pJZJZbziI2NxbFjx9ClSxdrZyVTiIxk18vRMfV0Oh0Tn1k5xG92l61Ox35zu194brtncxK8bLMGXq5ZAxfA1ieXf24s8+OPP2LPnj148+YN1Go1li5dilKlSqFq1aoICAhAnRRqs3bt2gZB/PDhQyQlJSE8PNyQLn/+/ChRogQKFy4sEc4CGo0GDx8ygbtzJ7PGGotfgDXXFynCmsK/+w6YPh345Rfg11/TH9bJyYlZh+vVy93iV+DEiRPWzkKm4eWVtvgFRKtwVpOdZSuX537xK5Cb7tmcBi/brIGXKyc38hF60WUODRs2xIYNG+Dj4wMbGxt4enpi9+7dcHJyQmxsLMqWLStJ7+HhYWgKEn4BIF++fJI0iYmJkvUcDofD4XA4nJxFnhTAOp0OzZs3R+PGjREZGQmFQoEDBw6gbdu2OH36NBQKBaKjoyXbREVFGZopjJsrjNNFRUVBLpebNGdotVo4Ozub+AZzPozExEQkJibiwYMH1s5KroOXbdbAyzXr4GWbNfByzRrCw8MBMH3AsQ55UgBHRUXh2bNn2LFjBzw9PQEAnTp1QqlSpXDkyBFUq1YNe/fulWxz7do1VK9eHQBQrlw5ODk5wc3NDVevXkX9+vURERGBoKAgODs7G9IJPHnyBESEhg0bGpbZ2trC9mPsxp6DcHV1hUajwc6dO62dlVwHL9usgZdr1sHLNmvg5Zp5aDQaaPShdOzs7AAwfVC7dm1rZivPkmc7wVWsWBENGjTAvHnz4OrqioMHD6Jr1644cOAAypQpgwoVKuCPP/7AoEGDcPnyZXTo0AGrV682dAQYOnQodu/eDQDYvXs3Fi9ejKNHj5qNAhEVFYXChQvj6dOn3Nk9E4mLi8P06dMxd+5ca2cl18HLNmvg5Zp18LLNGni5Zg1v3rxB+fLl8e7dO3h4eFg7O3mSPGuC3Lt3L8aPH4/SpUtDqVSiaNGiWLx4MVq2bAkAOHjwIEaPHo0xY8agUKFCmDVrlqQX7Lx580BEWLt2LerXr2+IA/zXX3+ZuDnY6GNDKRQKLoDfh8RE1qNPiJllhL29PS/TLIKXbdbAyzXr4GWbNfByzXzi4uIAiPqAk/3kWQFcpkwZ7Nmzx+L6pk2bmo3mIODg4IBly5Zh2bJlWZA7jgS1mv2GhfHxbjkcDofD4XwweSTwkPXh/r6ZQHKyyaLmzZtbISN5A162WQMv16yDl23WwMuVkxvhAjib4AL4AxB6yZoRwILLCifz4WWbNfByzTp42WYNvFw5uREugDk5H2GYMDMCmMPhcDgcDiejcAHMyflwAczhcDgcDicT4QKYk/MRXCBUKuvmg8PhcDgcTq6AC2BOzodbgDkcDofD4WQiXADnYM6fPw9/f3/oBAFogVevXsHX1xeRkZHZlLNsRjh/PmQkh8PhcDicTIAL4BzM8OHDMX36dMjlqV8mX19fdO/eHdOmTcuejGU3gvDlApjD4XA4HE4mwAVwDuXkyZMIDQ1Fhw4d0pX+m2++wZo1axAdHZ21GbMGggVYP4Y6h8PhcDgczofABXAOZefOnWjRooVkmMR169ahWrVqcHd3R6FChTBy5EjDuooVK8LT0xNHjhyxRnazFu4CweFwOBwOJxPhAjiHcu3aNVSuXNkw/9dff2HcuHGYO3cuIiMj8eTJE3Tp0kWyTdWqVXH16tXszmrWIwhfbgHmcDgcDoeTCfDhyXIo7969g7u7u2F+wYIF+PHHHw0j8ri5uaFJkyaSbdzd3fHu3btszWe2wC3AHA6Hw+FwMhFuAc6heHp6IiYmxjD//PlzlCtXLtVtYmJi4OnpmdVZy364DzCHw+FwOLmCHj16oHjx4hbXP3nyBPb29hg8eHCW5oML4BxKzZo1cffuXcO8n58fHj16lOo2gYGBqFWrVlZnLfvhUSA4HA6Hw8kVVKpUCcHBwYiPjze7fuLEiXBycsKMGTOyNB9cAOdQunTpguPHj0OrF30jR47Eb7/9hhMnTkCr1SIuLg6nT582pL9//z4iIiLQunVra2U56+AWYA6Hw+FwcgWVK1cGEeH+/fsm6y5duoQdO3Zg4sSJKFiwYJbmgwvgHEqLFi1QsGBB7Nu3DwDw7bffYvbs2Rg1ahQ8PDxQpkwZ7Nmzx5B+1apVGDBgADw8PKyU4yyE+wBzOBwOh5MrEDr437t3z2Td+PHjUbx4cYwaNSrL88E7weVgFi9ejKFDh6Jjx46Qy+UYNGgQBg0aZJIuJCQE27dvx82bN62Qy2yAR4HgcDgcDidXULp0aTg4OEjcPAFgz549OHfuHDZv3gxHR8cszwcXwDmYTz75BLdu3UozXZEiRRAcHJwNObIS3ALM4XA4HI5FiIC4uKzbv5sbIJNlzr5sbGxQvnx5iQVYo9FgwoQJqFu3Lr744ovMOVAacAHMyflwH2AOh8PhcCwSFwcYRU7NdGJiAIUi8/ZXuXJlXLhwwTC/atUqPHz4EOfPn4css5R2GnABzMn5cAswh8PhcDgWcXNjIjUr95+ZVKpUCZs3b0ZiYiJ0Oh2mT5+O7t27o0GDBpl7oFTgApiT8+Fh0DgcDofDsYhMlrkW2qzGOBLE3r17ER0djd9++02SZvDgwXBxccEff/yBsLAwtGzZEr/99hvatm2bKXngUSA4OR/uAsHhcDgcTq5BiARx/PhxzJs3D8OHD0eJEiUkaWbMmIENGzbgxo0baNOmDaZPn55p4hfgFmDOxwB3geBwOBwOJ9fg5+cHFxcXTJ06Fa6urpg8ebJJmoIFC2LUqFFo0KABVq9ejS5dumRqHrgFmJPz4WHQOBwOh8PJNchkMlSsWBFKpRLTpk2Du5kefO/evcP27dvh4eGBokWLZnoeuAAG0LlzZ8hkMhw7dsyw7NSpU6hRowacnZ1RokQJLF++XLJNcnIyhg4divz588PNzQ3t27fP3aHIrAl3geBwOBwOJ1dx5coVEBGGDh1qsi4mJgafffYZRo0ahaVLl2L06NEgokw9fp4XwOvXr0diYqJk2YsXL9CuXTsMGjQI0dHRWLt2LSZMmIDdu3cb0owZMwZnz57F9evXERISAk9PT3Ts2BE6QaxxMg+hTNVq6+aDw+FwOBxOlpKQkIC2bduif//+6NevH7p06QIHBwesX78+U4+Tp32AX716hcmTJ+PcuXMoXry4YfnatWtRtmxZQ62kSZMmGDhwIJYsWYLOnTtDqVRizZo12LJli2G7efPmwdvbG+fPn0ejRo2scj65FsEFIjnZuvngfNy8eQPcuwe0aGHtnHA4HA7HAi4uLjh//rxkWcr5zCDPWoCJCAMHDsTkyZNRrFgxybqAgADUqVNHsqx27dqGoYYfPnyIpKQkSZr8+fOjRIkSuXc4YmsiWIBVKuvmg/Nx07Mn0LKltXPB4XA4nBxAnrUAL1++HESEb7/91mRdbGwsypYtK1nm4eGB2NhYw3oAyJcvn8U0KXF1dUVcVo5TmJtRKlmAQyLAqHx5eWYdubJshaGMLDyj2UGuLNccAi/brIGXa9aQkJBg7SzkefKkAH769ClmzpyJS5cumV2vUCgQHR0tWRYVFQWFPsq08BsdHQ0nJyezaVKiVCoxffp02NvbAwCaN2+OltwalT64BZiTGURFWTsHHA4nD3Ps2DGcOHECABfAOYE8KYDPnj2LyMhI1KxZU7K8a9eu6NmzJ6pVq4a9e/dK1l27dg3Vq1cHAJQrVw5OTk64evUqOnbsCACIiIhAUFCQIU1KNBoN5s6da1Egc1LBxoZZ7eLjzQ51w8s068hVZRsWxipROeCcclW55jB42WYNvFw/nC5duhhi2YaEhGDt2rXWzVAeJ08K4B49ephYX4sWLYoVK1agVatWiIuLw2+//Ybly5dj0KBBuHz5MlavXo3Vq1cDABwdHTFgwABMmTIF1apVg4eHB8aOHYuKFSuiYcOG2Xsyly8D/v6Ao2P2Hjc74RZgDofD4XA4mUie7ATn7OwMX19fyQSwjmyenp4oXrw4Dh48iJUrV8Ld3R1fffUVZs2aJRmFZN68eWjYsCGqV68Ob29vREREYN++fZDLs7lI69UD5s7N3mNmNzwKBCczyOQYkhwOh8P5eMmTFmBzpAyw3LRp01QjOjg4OGDp0qVYunRpVmctbWJirJ2DrIVbgDnvS/PmwPDhQOfO1s4Jh8PhcHIQedIC/FFz5gyLZ5qX4AKY876cPAkIA9jIZNbNC4fD4XByDNwC/LHRpAnQrBnQoAFQrhxblts/7FotIJdzFwhOxtC36miLFINMx2v7mYFaq8aNNzdQ17eutbPC4XA4H0S2CuBnz56lK51cLoefn1/WZsYa7N4NdOwIeHh82H7i4oBffxXn0+vbqNGw6WPrMKfTAU5O3ALMyRh63/Ff5tggPBYwOCvNnAn8/HPmHy82Fjh+PFe7W0w9NRWzz80GTeX+1BwO5+MmWwVw6dKlIUvDWklEcHJyyp0x8vr3B9q0AQ4e/LD9qNXS+fQK4EGDgPXrP77OQNYUwC9fAl99BZw+nf3H5limVy/AzQ1YudJyGqHzJIATJyC2lEyZkjUC+H//A3755eN7vjJAYHigtbPA4XA4mUK2CmBnZ2fcvXs31TREhGrVqmVPhqzBoUPA48dAmTLvvw+1GnB2BhITxfn0cPHi+x/Tmmi1zGodH5/9xz58mPldc3IWW7eyX6WSVerMYSSANZpsyFNud0UCIJdxRxIOh5M7yNa3WceOHVG8ePFUJz8/P7Rv3z47s5X93L79Ydu/eCGKXwBIr7VcPwrdR4c1LcCCiMrFVj2rQcQs635+QIqBZ9LNjh2W1xkJYLUaUKuz+Bra2GTt/nMANjJ2jlqdNo2UHA6HY54ePXqgePHiFtc/efIE9vb2GDx4cJbmI1sF8ObNm9OVbuPGjVmcEyuTkc5cBw4Awk2QlMR+Uwre9FpG7ezSf9ychDUFsBCBQih7TuZx4wbQtCmr0K1bl7Ft8+Vjv40bW04jXDswAWw0mzXkAQEsWIAT1YlppORwOBzzVKpUCcHBwYi3oF0mTpwIJycnzJgxI0vzYdX2LK02j1oRlMr0px02DFixgv3/7z9xub+/+D+3C2DBBUKjyQYVk4K3b9lvbvRJtyZEzL1EICIiY9t7egLFigGpWBFSukAQsthFwTbnBtVJUCUgWfPhUVS4AOZwOB9K5cqVQUS4f/++ybpLly5hx44dmDhxIgoWLJil+bCqAG7Tpo01D28dGjfOmAXYzY39+vgAf/wBODiw+Ro1xDS5XACTVoewOCc2k91W4OnT2a81/I9zM8+eAZMni/MZDXGnUgFly6bu3GskgMPDM5i/9yEHW4BdZ7vC8dcPj/6iJVamCWpeIeRwOO9H5cqVAQD37t0zWTd+/HgUL14co0aNyvJ8ZIvJYsqUKSbLiAhPnz7NjsPnHBo2BPLnz5gFWAiZ9uYNm/z8gKAgFplgzRq2Lr3iLDN8gGUyYPt2oFu3D99XOnnxXIf7953QBmBll51h3Bo1As6eFS3AGg0wdCiwZMlHW6FIk23bgG+/BYKDs+4YcXHS+Yxa9tVq5haTWitSdrcw5VABbDzKZZI6CU52TmbXpxWhBwCUGvbuSlBxAczhcN6P0qVLw8HBwSQowp49e3Du3Dls3rwZjtnwnc8WC/Dy5ctRunRplCpVyjCVLl0azs7O2XH4nIO7OxNvGbF2RUebX27sApFe/1R5Jl3uffs+aHMdZUzsxMdqoYT+YRAsstmFEGFDuGZv3rDQW7ltNL5q1YBx49j/K1ey/nhJSdKKTEbFqlrNts9JAlhwgchhrl3G7gqRSZEm60svLo0vd32Zrn0JApi7QHA4OQsiQmxybJZNlIkdwW1sbFC+fHmJBVij0WDChAmoW7cuvvjii0w7VmpkiwW4QoUKaNGiBYoUKSJZfuTIkew4fM7Bzo65MGTEAhwTI50vW5ZZgD09xWWJ6fwYZVaYprCw995URzrYzLDB0xFPUdKjpMn6X8/8ioCwAGzvvl3cRqMTBXB2uyIIwle4ZoILRg619r03t24BkZHA3LnZ48ualAQUKQIIrUAZtQCrVMwCnFoIQL0QlSOb/MaFe0LIWw7BWPRGJkbCV+ErWf8s6hmeRT3Dlq5b0tyXwQLMXSA4nBxFnCoO7nPcs2z/MRNioHBQZNr+KleujAsXLhjmV61ahYcPH+L8+fPpao3KDLJFAJ8+fdrsCaU3KkSuYe9e4PvvM2YBTkxkH1bBqlS7NpCy4pBeASxAJIrh2bPZ6HSVKqV/+w+IiCBYjsITwk0E8KPIR5h/ab6JlUqn0UENO8SWrgFF9ervfez3QhC+KYVwemMvf0wILTJZLYCJgPnzgZAQcVlKAaxSsYFjpk4Vh/w2RnCBSK0yqX9mbJA9FtnbEXdR1ThvVkalVeFC8AW4O7jD08kT+Z3z48jTI/Av7J/2xhZIUrNnn1uAORwRlVYFexvrhhl1s3dDzISYtBN+wP4zk0qVKmHz5s1ITEyETqfD9OnT0b17dzRo0CBTj5Ma2eICkV1q/qMgoxbgpCSxIxxg3o83oxZg4ybaSZOAWbPSnx/gvTuiqbQqXH99nWUlRY/8iMQIlFtSzuyHlTRa6CBHrGfx7O8EJwhf4VewQOfGYZkFASxYMrMq4kZAALB/v/Q5SOkTfP06sGULUL48mydi4dIEVCoxMogl3lMAP4p8lKH0Ai/iQ8S85QBWXl+JZuuaITIpEl5OXvi05Kd4m/D2g/bJfYA5HCk77+2Ewy8O1s4GZDIZFA6KLJsyW8cZR4L4/fffER0djd9++82w/t27d3BxcYHK6H363XffSdJ8KFaJAtG1a1drHNb6LFvGBGxGPpBJSYCrqzhvSQCnxz9HuIFTWi+9vNKfH3Pbp5NZZ2eh6bqmAIC7b+/i+LPjhnXBMazDlVCLvhgsjlpHGh10kEMjs898y2tEBBu+1hKC8BVG0RM6w1lL5GzcmHXHTmkBzqil/9UrqUi1hLkWkNhY6byxsL10Cdi9m3UABZiwJUq3D3BGBHCMMgbllpTDq9hX6d5GwM5e76aTQwSwRsfKcNx/4+Dl7AVXe9cPttwqNUrYye24BZjD0XM7LGMDWx19epRXICFGgjh+/DjmzZuH4cOHo0SJEob1np6eKFmyJG7cuAEAOHv2LK5cuYKxY8dmWh6sIoBfv35tjcNan88+Y+IivZ1k1GqWNi0BrNNJP7pPn6bu7yukFSzHxv7EqSGI7PcUoaHxoYb/g/4dhJYbWhrmQ+KY9cxWzsTXmP/G4LONn8F3ni+iVa+hhQ3UMrvMFxfnzgE//wy8fGlYpNaq0W5zOyRrkqFN0pfRzJnsV7AAZ6AMElQJaLi64YePnkXEon8cOPBh+7GE0GwvCOCMutY0acJEakoxmxJjYV20KPMFjo2VVuKMj12/PvDwIfsfFyeWfTqjQAgCOD1xgJM0LG8qbcbvM3sbZgXSqT483m5m4GjLBPmtsFso4FwAznbOSNS8v3A98uQInkc/h5ezF/cB5nD0ENh7K72dxFptbIVZZ2fhyqts6Gycg/Hz84OLiwumTp0KJycnTDYOi6mnSZMmOHfuHFQqFb7//nv8+eefsM1EFz2rCOA86xLh4sKal1NrtjVGEAppCWBAKhj0NSaLvsaCiBQiGRjvPzVBIeQ7E0VoSGwITjw/gXdJ7wAANnLW/C6DDEeeHkFIXAhCk+9ABznUEC3Az6OeZ06vVKGMjIbijVJG4eDjgwiODUZifJSY9sEDiQV42dVl6L+nf5qHeB79HBeCLyBe9YEd+ARhmcFwdvGq+NTFt+DqIFiAhXLNqAAW0ru7s7KyhFIJVKjA7qOHD5mFV6OR3q9RUdJtJk1ivyEh4v1nJIAjEiMw6fgkdN/eXdzmPSzAwmARwoAPGcFBxl7ML8IfZ3jbrMDBRmyWLedVjgngD7Dc7nvEor94OnnmeQvWXzf+QoIqATvu7cAXO8Qe60qN0vAu4+Rs7r29B9n099ciWp0W7Ta3w723LJJBRiqF75LepevbkZuRyWSoWLEilEolpk2bBnd30w58jRs3xvnz5zFr1iw0a9YMdevWzdQ8WHUgjDyHg0PGLMCCj2RaPsAAYBxPT/CnTGmJE0bbEixogiVeEBRPnrD8WRLoQjoj6+f6W+sxeH/6xutO6fcLAL7zfdFifQuceXGGHUJveRMswgBAOi20sIEKovtIyUUlsffBXpP9ZZh3+o/ViBGGRULT8fpDc+BmrPVr1ZL4AE89NRXrbqU9hK9w3h/cbByp7xyYwQgU1VdUR4v1LSwnEM5JuPaCEM2oADaOi5wyeokxSiUT23Z2TMQWKMCWC/drXJzlkStiY8X7z9ERpNWi9cbWaLymMWafm40d93aIaVMTwBYqT4IFWK3NeCuHIIDbr2ud4W1TQ6lR4ujTox+0j0oFK8HZzhmHHh/C+ZfnDcuNK2VrA9YiPMHyiCHCs+nl5JWnXSA23t6Ib/Z9gz8u/oHu27tj291tuPTqEs69PIdu/3SD1+8ZdCn7ANRaNQ49PpRtx8sstDotZpyeAa1Oi67/dMW2O9uyPQ9zL8wFAMQmp9FilYKn754iMjESQdFBOPj4IHbe3wkAiEqKMpueiAzGGuEZehOfy8JovidXrlwBEWHo0KFm1zdu3BgnT57Ehg0b8Ouvv2b68bkAzk5kstQFZkqSktg2xj3KLQngRo1EwSwIiZQCOCRFJx1BEAvbCYLnkbQT0Ou418ziY0YATz4xGSuur8CKaytStXysubkGf17/EwBQwLmAyfpTQacAANHKaNjIbPAyRnRJiNI+h06ugwp2kmO/TfywDj0AzIZ0Ezr6zOjzt3RFQgIbIAIAVKp0xzNW61ieE9QJeBjxENdeXzNJE5UUhchE0xitEgRRmMFhmZ+8e4LTL05bTiAM97x7N9C5s0EA99vSA/Muzkv/gYyFeWqVvKQk6OwdsWIFMHEiIHN0ANnZASdPAv/+CygUwMiR5rdNKYDVKvz39D/cjzAdUjNVAWyhg58Q5eB9XCDkek1tp0Xa1zIDLLi0AK02tspwi4dwHwNA+fzl4WznjDhVHD5Z84lhudtssXI9YO8AbL+7HZZI1rL7wsHWIde4QGh1WkPH3PTy1e6vAAB3wu8YltX/uz4arWlkqMiP/2/8B+ctSZ2ETbc3wW+BH0JiQ8ymsf/FHm03t81wbHVr8/jdY0w9NRXhCeHYdX8Xfjn7C8YeGYsV11bgh6M/ZLiF4U3cmwxXBMIS2LvffY57uocJ//Panyi9uDQ6be2E0otLS9bdeHNDMv/03VPMOD0Dfff0he98FnpQeC/sfZgJxps8QKFChWBvb4+5c+dCoci8EGwCVhHAmRlQ+aMjIwI4LIyNHGdkWTt41A4BARa8EIRmY8H6ZmyFCwsTBZQgIPTrH7+5y14Agmh5KxWWReYVYS994aBmDj74wGB4/e6FSssqYeDegYblawPWotPWTrj+RvzIlPAoYbL90yhxVEBhuFWBJF04dO7BUBGzAAvWOcH36n2ITIxkH41z5yTL3yW9kwgHALi5do7J9m3WtEz3R0cQVQmqBFRZXgW1V9U2eQYqL6+MYguKpb6j2bMBAIlvExCeEI5jz46l6/hmCQ427A+rVxsW07VrhopTckw0dj/Ynf79BQWJ8ymjOhijVCI0xgmDBwNz9EWr1QL44gugUycx3ZQpzFK7zcg6FBcHJCeDZDLo7B2h06RiqdV3yDMrgC0IdMGyaUkAP333FB22dDBrkSW9m4m9FngY+dByvlKyahWwYoXZVeEJ4Zh4fCIAQD5DjrD4MCSpk/As6pnF3UUroyGbLjNYcz8v/znq+dYzqXiaq9xodBooNUqceH7CZJ3wXFQqUOm9LcBandZQtjfe3MCd8DvpFh9ZwYbbG1BrVS28iJZ23gwIDYBsuszkOY1LFu/rm6E3TfYXp2Lr516c+8GitOs/XdFndx+8iHlhtgJrbMm3JJBzKq/jWOujcC/cCb+DeZfmYfCBwfjfhf/BdbYrIhIj0r2/b/d/i7ab22YoD0JLH4B0ua0QEb4/8D0A4HzweZP1n2/73HBe8ap4fLnrS0w9NRUbb280LDc27HDSZsWKFWjSpAk6d+6cJfu3igC+ePFi2olyIUoloCGb9LtABAUBJUpILGurNjtjyhSJy6qIIEz1o8cNXtYO516eQ0RiBDT16ojphMEH9AL4YOAu/HTiJ3F7M2HaXsa8FC2PSiUuBl/ElJNTDNZNgXtv72FNABuiOV4VjwF7B+Dfh/9i6dWlhjS1fWoDAJa0WWL2tPM75wcgdoizJwdobVVIJhtArUZQdJD0tLUqtNnURmJZHbh3IMYcGQOtTgvZdBnC4llt/13SOySoEpD/f/nh/6c/Ip8EAr/+Cri7IyA0AF6/e5n46sbVqGySR3stExoA+/ikVqkTmtV77+ptKK8rIdIOEK/jXiNRnQgiMjk/A//+CwAYO/sQhh8ajk83fGrxmGmyYIHoV2vkA64JjcCpHawC1OkhYCOz7G5hOOenT4FixUD29lC/03+0LAxY8uhlNFavDkWCVjrMpa3OjJAVKn49erDWi6ZNmQBOSEACOePQUVtoUwhgSSxOfbSZb/AXCDI4wei+tlAJTa0T3K3QWyi9uDT2P9qPnjt6mm6sEQXwg4hUfKBT8u23wGDzbkQXgi9I5gPDA7H06lKUWlQKF4Mv4mGEqdAWhNGlkEtoXLwxNnbeCACo4V0DAOCr8EVofCjG/mfam3rUkVHovas3WqxvAd95vhIRF54QjjWd1iC/c36zFuC45DhD+kR1IqadmoaJxyZi4+2NhjTzL82Hwy8OGHZwGGqurIkqy6tgyskpJvs69uyYwZr6Pgw7OAyy6TL8+/BfHHh0ABOPTTSbTii/cUfHSZYL12/U4VGSiCCCy1Ntn9p48u5JqnmIV8UjSZ2ECccmYMyRMZBNl2HexXmSd8WJ5yckQlZY1nZTW1x6dcmwLGXzepI6SWLJT61ClBMJDAsEILWipyQj0RVilOxblpFKh3ElLi0B/DzqudlKbVmvsgCAt+PZO/NV7CuotCr4LfAzeccDrD+IQBO/JunOa17jwYMHqFixIrZv345Vq1Zl2XFyjAvEkydP8Pz587QTfsTUrAmMnWALnSp9FmDd2wjcCSuAMxdEERIPVwQEME1ggtCpaMECAED+Z6HY82APaq6sCdsgseaZMGY467ihF8AOGmaZMRbASo0SN9/cBF68gJOKNXsaLMwJCWiwugFmnpkpiexgzOmg0zj38pzJ8v7V+qO4e3EAQLn8pgMc1AwBKsgLAQACvw/E1gbzUSK+AHSlj+Gaw1+ASoXhh4YDYBYklVaFvQ/24vCTw5h6aioAVrNfE7AG8y/NN3wYroRcQbImGV6/e8F1NhN8d8LvMKt46dJAcrLhZbzz3k5Jni5F3IRu4ULJMicj3eU73xdTT03FoceHzDbdCT5md9+KftrRymhodBocenxI8iI+8PgASiw0tZADABwdca+wI1zLbU13RAnjj6gx2x6Lll1KFKMy2EGDphHs/HvdZZ0SZ5yeIbGGERFGHhoJ7z+82UavmEAIaloN9ovyQ9usJWLeBDH3ncuXcSv0lsFqX39DZVR/MhmR2mQgDQt+stYWW7cCgYEAfHyY+89vvwGJiUiEM4KCbUwswELHr7590ygYC5VQ4fqlrNgBwOIriw3/o5RRUGvV2P9oP+6/Ze4XggXYTgcTcZSsSTa0BADsQ2ksMFXO0jiiwgc9pQU+OCbYIPIbrG6A8kvLm+RTsDI9inwEf6cScJGx9N5u3jg34Bxexb4Sr50Zdt3fBYD54QvnBrAoLt6u3vB29TaELTRGMUeBcf8xIfnNvm8w/fR0zDk/B1/t/gpJ6iToSIeDjw8CgKRCfOCxNKqJjnT4dMOnaLJWFAiLLy9GXHIc3ia8xekgZg3NNycfvtr9Fe6Gs+cqWhmNEgtKQDZdZth/p62d0H5Le8w5P8ekknro8SEsuboELUq0kFRYiAi9dvYCACy6skhSURAqp9/W/NawzNIAAbHJsRj33zj8dv43zL80HwAw9r+xkM+Q43Xca4TEhqDF+hb4ZM0nePruKWKTY3En/A6+2/8dDj05hChlFErkY+8C40r5rdBb6Li1o+RY5gTw3AtzsfPeTuy+v9tQkdKR7oNaYG+8uWE2ljQRZUh8Pn7HOooe3b8Q63aJy8t5id8EQSQDwL6H+9B7V28AUv/8eFU8Bu8fjLMvzwIAemzvYTB2mCM0PhSy6TKUXVxWYvXvuaOniQh+l/TOEI6z5KKS6Ltb+lK5N+QeGhZtCIAZbcp6lUWMMgYrrq0wDOY0qu4oQ/peO3vh7Iuz6F6xO1a0X4Fl7ZZZzGdeRxgm+fjx48iXL1+WHee9BbBOp8ORI0dw/PjxtBObYeDAgTinb37eunUrypUrhzJlymDLlrSH4/xYefUK0MAWqkQNEtWJ+PXMr5IX2/5H+w1NWUSEO4EhuPfCGVdDixrSxMMVwfpvz5/4DhMhDmLR8q/GOLz3D8O8ezL7GBo3u1CD+tjqG4Vtd7dBG8UeeActsODyAsQLEQ+USgzYOwA1VtYA/Pyw7IDeshYVxfwz1WqUcJYOa52SRVcWGZpM25dtDwCY0XQGZreYDVd7JkCdbE1Hy7q2Cpg8pRAuDrqI8l7l0LPVaHQMfAWVDRBt/w6PX9/BkadsJLwDjw/g233fYmMgszA52jri6tKfMHyh2Amp7BJWQ++4tSMcf01hddQCXklAVCF3QKnE3VD2wv39wu+SdD+e/hkXbPRNjCtXIqRuRbiogYlnAAf9u3jmmZlou7ktfjrxE4KigyCbLoNsugwanQadt5k233y26TPYzbRD281tseDSAsPyjlvYh034+P/39D/IpstgO0UGKJUIVzjARQXcv3MSNA2A0Vjq5qj/d33D/692f4WlV5ZCNl2GB7GssvnqbRy2HLgKrYVXgQwyQ8UiSZMEtVYN+Qw5Fl1ZhLCEMAw9MBRLd00AAAzNzywej+9Hw3243qJWrx4GTamGKf/9jqV/R0GVEILqYQRF9FGgcECqeR946Vv06gVUrQpcuQI8TSjMzjcxEQlwwfUAG6iUUqGq0WlARNiwIdVdW/QBFvzKVVpTH++Urjn2v9ijw5YOqLmyJgCAtKIF+N7be5Jm6dYbW6P4guKG+cWXFyMkLsRQ6UrWJBssXrfDbiPfb/mQrEnG+lvrJcd8GfPSxP2g5MKSWBcgdsYUmltDYkOw6It1wJAhhnX5HPOZPe9dPXbh4TBTC5ex1etN3BsUdi2MygUrSypzgGh5m39pPorMK4LNgdJRPp1nOcNmhg1OBp1EKY9SknV3397F0ANDDZU1Y8vZi+gXSFInYcThEVh0eREmHJuApuuaQjZdhpjkGGy8vRGVl1eGbLoMVZZXgYZE44KPm4/kOF/u+hLnXp5DkjoJh58cRtvNbRGviseQ2kNwJ/wO2m1uh023N5nEgNboNFBpVZh/cT5W3ViF1R1X4+saX+PxcCbifv/0d3Sv2B3VCleTbBebHIvb4eatmMuuLsOtsFsA2Htw78O9qL6iOqosr2KoPO3ovsMwbPWE4xNQeVllDN4/GNVWVJO4P3Uu3xkD/x0oMUaotWqMPzoe3bZ3Q5d/uqDp2qYAgFYbWqHB6vcfaavmypoGH2hjuv7TFS6zXCQVptQQ7uFuG66jr76IGhZtiO9rfY+9X+zF4JqDMerIKGy9sxVEhKVXl2Jz4GbcDb8L+1/sERgWCLVWDbfZblhxXXQf2nl/J6osr4LIxEjMOTcHrTe2xqbbmwzrhU6yj989RnCsWIm7+/Yu61StrxwsvrwYXr97ocHqBobtr76+KjmHYu7FYCcX3RMfRT5C+y3tMeIw61DdtkxbdKvYzbB+652tWHZtGUp5lMK3Nb/Nu9GwchDvLYC//PJLHDhwAD/88AM0Gg0WLVqUoe0PHTqEGjVYk9z8+fOxdetWHDhwALMFv8SPACLC1KlT4ePjAxcXFzRu3Bh37lhu0gHAohkoNXCZ5YLJJydj30MWWuja62vosKUDpp6aitjkWBx9dhTbH81CIpwxCbOwFazJNR5ic/X3+BNzMBFPwYYUjogJxZV3TMRp3dzhqgK232OdWiL0WvNN1ZJIima1U3VUBNR2NnDUfzNeRehdI774AnvuilbQorGAndwOqqmTgdhYUL588Hps6nM2uKbYjLvr/i7sur8LLUu2xISGTCCNqDsChV0Lw8XeBQDgZGd+uNj8iVGo51sPL74XfW/VciDCGdBcvYz6ej3f/ClQf81x0N5/0dSvKUpde4baw2ZhwXhT/0VzFNAbayvs+wwAsPD07+YTyoAbZZmV5/nbx1A62cEjCZh1AqiTohgWXl6IlddXivOXpJZjcxhXggS/5jMvzuC/p/+h9UYm5p301yjGxQmuKsA5VG+tsDCEdXhCuEnz+cbbGzHs0DAArDwBoMRiBSLtDiMBzmb3c/y5WMH9/fzvko8NACy7tgyv71zCxirAIVbXwOxKUmvmpb+A/DN3Yv4tT/ysd2W8XwBAp0GAaygg08IWarx1En2glTbAC5eXgIyJyrp1gXrnWa9t/PMPEuGMd3G2CHoiCmAnWycka5MRHBMC2McjOLU+E1ot1Fo1tDqtxAf1cSQTNS3Wt0CnraI/8uqbqy2Ooia4TUBvAf5fk1+w9+Fe+M73hUqrwq77u3D6xWm8TXyLtpvaouj8ooZKlv+fbFhi0v+XTZdh5OGRknU3vxN9TYNjgxGbHItKBcTr/jz6Ofrv7W+YFwSw0GkNF8T7QBDAghuSQIdyHVDWqyyKuUv90IXySFInISY5BkWfvkVRG0+EJ4Tj63+/BsAiwUw6Psnk+JYQhmLuXF6sGC67tgz1/66PTls7of7f9VHQpSB8Fb649OqS4T6+F3HPpBKSkhF1RuDhsId4NfoVrn97HX2q9kGF/BUAMAEy7+I8rLu1Dm02tQEAHP3qKJr6NQUAHHx8EH129zH44hd2LQyAicljz45hzH9jEJsci/pFWaWytGdp0FTC4FqD8U/3f3Dzu5uImyj6CDdb18xsNJHqhavj17O/ot3mdmhbpi18Fb4Y+99YiRW3oEtBdK3YFR5OHoZld9/eNXn+AFFMev/hjZPPTyIoOggvY14aXMgAwMuZRaY4/vw4Lr26BB3pMtz5T8Cce9CeB3ug1ChRcVnFdO0jUZ0IWy3wSQB7j10cdBHnBp7DyHoj0bFcR1T3ZkPe99rZC+WXljcYPeacZ9+E3y/8btLCuLXrVgCsEjv5xGRMPD4R/z39DwsuL8DlV5dx/+19Q2frlHQr3QkDagwE5HIkjh+FTUf+Z1jXZ3cfSdrA7wOxtO1SuNi7oKh7Uck6oWwcbR0xo+kM1C5S23AflfRg32lzfWA41uG9BXBYWBgWLVoEhUIBW1tb7N+/P0PbJyYmwtnZGXFxcXj06BG6du2K1q1b46XRgAQ5nblz52L16tU4cuQIIiIi0LBhQ7Ru3RrxFvwfAWYBDo4Woyz8ffNv+P/pj9qrasNObocNtzfAfY47emzvASc1kCS3hQoOuAn2QjAWwAKVXI7jnSOzPN14fhkAcDKuFlyN3lMHywAJ/YfiRaLMENpLExWJGHcnOOjFVWKi2GnOOUF8cWtlwNFnR2F78TLWVANe+LigjJlO7iPqjpDMb7i9Afff3keFAhVQzL0YFA4KqFRAWDA7B0dbRzwc9hAhY0Jw87ubKB3FbkdXsPIrvkL8qKpsgHdOQIUI4IK+z9a6vcB3N4B/twLD6wxH/N0AAMyibY6dW4H8Rh4KBROASCcgVq/XnDTAdzW/M7vtyPM/AwCWnJkLpaMN/KLZ8mhH07TGloWxDcehborW4mlNpknm/7n7j9ljCuIXEF0ukhydMPAmkN/YCKjv1Djr7CwUX1Acn274FIXmFkLD1Q3N7hdg5QkAigRnOKmBd27SjkjLKphG6ph+errB/cSYYjHAS6MQjudRWLLeloCxF2/iyWLghwvABV+g/+cAvG8C47yBqbbQNp+Kgo4nDdsEFgLOV6kO+Imdf6KgFwNLlyIRztBBDv+kh5DrDbUj6o5AMfdiKF79CeD5GBeKAmtszfkKATqtBva/2KPXzl6SlgFj6//+R+I7bdC/g3Dg8QF8UfkLhIwxrfwlqZMMFuCqnhXgl88PALMKdf2H+SLX8qmFwPBAk20BwM1RVOvCR/ph5EMMrzNcYll8GfMS/9z9B21KtzG5V08FncIfF/7A6/gUAtQonJ0ghPZ8sQcXB4l9MQRfb6Hj0ZE+R/BV1a8QEBYA7z+84TzLGf5vAM9PPkWh/5jP6t83/4ZSo0S/Pf3w23nT4UkfD3+M24NFC+jwOsPxaNgjLGu7DBcGXsCunrvwfKTU7e3fh8zP/cvKXyIuOQ5f7PzCMGDO5sDNWHdrHYbXGY5PijH/11P9TuHRMPY+7VC2A76u8TXKepVFEUURFHYtjA2dN+De0HvY03MP2pVph4jECEOFUwYZWpZsCU8nT+imSK39TrZOCB4djBXtV+Bm6E1Ds7qDjSPK5zd1OxEQWrcAVgk1tvJ1q9gNLUq0wM+NfzYsm9BwAsbWF10swsax4wjHqFKwisVjAcCjYY+wvN1yw3zz9c3RaE0jBIYHopxXOUNFR6lR4r+n/xnS2cywQa1VtdLlDnEq6BQqLRMrXM520sryk3dP0t0h2f9Pf8w8OR31jz/E8MuARl889YpI47t+VfUrzGzGBh8yHppcMBhtvL0Rzdc3BwBM/GQingx/gh6VeoCmEgq7FjZEHAKYcane3/VQcVlF7Ly/02wrSEuPmuL5zV2IS1NNXXwAoJlfM1QuWBlDarNWlR8b/mhoORlXn7V6NS/RHImTElHTpybsbezxZuwbjKgzAvNbMzcYwarPsT7vLYDt7OwQHx9veMBVGRwcoUCBArh//z4OHz6MevXqQS6XIyEh4aNqFli2bBnGjRuHKlWqwMnJCTNnzoRKpcLu3RZ6zju/hUcBG7yJFyMexJw7jlIn2UdiSdslhhpkTHIMnNVAkjdrUtKA1ebjwCyRn38u7jZ5fAnEOTAB7KomXCoC/IsOEDTNr81/hTvZYevlEtgavtGwXHPkMF7CzmABToyPNuzTMwkorDdmaPV3SYwDML8ecFMXggJmOoELll1jpjWdBk8nT7wY9QIymQyLFgE/TGAHVL71RlmvsvBx80G1wtVweQX7CLki3qSJWm0DvErFouf68DnymfbdAwAUcikERzXQ5QHQOU503bjWegdeuwFKvaHESc2a4VI20QKAEMLYE45IshcFMJm5XZ9HsY/6Z6WZZbmWkR5Z0mYJxtQfI+msZRwBwxJOGkAHoPyzd/BUAsWjjVbqYxn/dOInvIx5ma7oEMI1LfnSB05KR4QqpJaqf+q8hQ5Iy00XALtX3hpd+sQEv1TTX/IF4h1SLGw8CxhZCmv9gcnNgFZCK6viFWCXCCheQdtOFN8JcjvcBfso549lZflr81/h6eQJOEYD7sFwUgNxNmZqKAASk9jNnbKzWkoLKCANJ9anSh+TpnUAGHJwCKDV37MqFR4NewRvV2+DZbRukbq4/PVlvBz1EsGjgxEzIQZbu241WB9lMhlG1GEVyG4VuyF8XDgqF6yM72t9LznO0WdH8TTqKcp4lcHA6gMl65qta4ZxR8fhddxrifXPuFOro60jaCrBx80H1QtXx6clPzUcHxCtia1KtUKLEi2w6/4uQ9N6WSEMdYwYXtF4OHMAmNNiDkbVHYUmxZugtGdplPZkoaJCx4ZiUZtFKONVBoVcCxmsqEJFwZgB1QZgbIOxGN/AfCixL6t8iV6VmY9uE78mKONVBqFjQ7Gs3TKJxdSYTuU7YXGbxbgScgXzL81HWa+yCBgcYFgvk8mwoPUCBI0Mwtvxb/F0xFPYym3Ru0pvvIp9hYH/DkQFu8+Q/Gva8VsDvzet5GzovAHbu2/Hsb7H0LlCZ4SMCQFNJTQq3gjf1foO5wYwa2ZBl4IAYOgnMbnxZPSpKlogzw44azgGTSWU8SqDEh4lMOkT0VjgYOOAi8EXUd+3Pg71PoSQMSGIVkZLKtQCUUrm9qbSitF1vtjxhaGDL8A6CsYmxxoqDs52zixiS3AwEBKCKvPKpFkmKq0K5ZeUx+2w29i2fRpG/hmAaqHApqr6BCkGbXKyczK5/h3KdkBMcozhngKAJ8OfYFaLWSjlWcpwD39e7nMAQIX8FWAjs8Hp/qcl7nbGQlWgqp15UTqs9jDJfMrOn3Y2doaOcDObM8FevXB1Ex2zsM1CdCzXEQHfBaBVqVZmj8XJft5bAE+cOBEtWrTA69evsWbNGsjlGdvVqFGjUKtWLfTv3x9D9D5qZ86cQSULTbo5jZiYGAQFBaFOHTG6gq2tLapXr46bN03D4wAARpSGstx22Bppu6urgF3/AOcGnDM0lQCsBuykARJLXsDwEQQ3WxbSqFIjZsHp2VMaElhlAxS08YKrCohwkiNO5gxXFeAhK4a2ZdqizgsNErUOiHQCCiQyS2i+ZMBJpYJDRAlULlgZu25vNezPMwloEsT+q21kAAGKZGYtfeucwgKpp6BLQdTyqQUAGF1vNGgq4esaX0vSxMcD0DFrU81KHjh5Eriub4kTysUOapORwNRy4EoqFedWncdhlpHnQ/g4MaB/LZ9a6OXCLAwr+4pxTm1vBSJf/WYgOWtyd1azzn5PRjxBPd96JseY9QlwqGEhRMiVKBHNltkYXcsjfY6gkEshnA8+j6KKoijvxaw4cgIQ5QcA6FKhC9zsXTGtovnA35ZwUjOhfrcgs9LXjnDEpSJAktweCeEhqcbNPNz7MADpQCSC1b+s9jmckhxxoKx0m1gH9nKw1QGYqYR/IX9xpdIdx/uKwiefklWOAAA3BiLJOfUYnheKWl43oDPwaxMgWvhede4H/OSCfqtnALVFq05i/mB0HceaFO21MjQ+QbCR27AOSR7PAIcYOGqAeHlKpc0oU4EJ4AIuzNKt1CihIx3iVfGS51Cj00iC9AtNnoKIFO6TC8EXDC4QUCphZ2OHN/FvDKOnnR94HnKZ3PBhVDgo0LNyT2xoxspRBxkWtlkImkrY3n07CrgUQOD3gahQgDXfy2VyiT9hw6INUadIHbwa/QpJPxkNLQ3mQyw8h6yAzMcOd7B1wIr2KyRi+dKgSzjdn1ndy3qVlTR3ewg6OjYWsROYCG6/pT1+bPgj3v3wDuHjwvHjJz9i/mfzcar/KQBMyNBUQiHXQmbzAABj64+Fm70blrVlnYJWtF8BX4UvJjWahI2dN+LfL/7F6o6s2adqoaqoU6QOvq/1Pd79IHZaSm3/AiU8SmB4neF4Hfca4+qPQ9VCVSXrR9YbieL5iiO/c354u7FOgsaVelWCC6DMlzJyogmVC1bG1W+uSiyNKV1OUlaiGhZrCJrKapsPhz3Ews+Y65SjrSM2dN6AtZ3WwkZmg4ZFG+Lt+LeoXFAamWZm85lInJSIGt418DTqKX6/8DvqF60PL2cvsxW2ogp2HwuDdlRYWgEdt3ZEojoR2+5uM3T+Ati9CsBgBc+vsmWhCevUAXx9cSOFV0a/Pf3w84mfceTJEex/tB99d/eFwy8OBn9yIV62bywQLhSvmdjmDrYOoKkE5U9K0FRCX3/WCW1IrSF4PeY11n++HqU8TY0VS9stxZuxb3D92+sIGROCxsUb41hfZhTY2nUrxtQfg509dkreYbWdTPeTtMEP81vPg2qyCs+a7cUxz9GG+9AcwtDjhVws34v+hf2llVOOVUlTtX777beIMTOqU7NmzbBt2zb0798fL1++xKZNm8xsbZlhw4bh1q1buHv3Ljp06AAAKFWqFP788880tswZxOpjpabsoejh4WFYZ45or/PIBy9DDV+gQdEGEutTB90aOD9oiSSNB846TEBsc9Y0a9fuR8DpHVq1Akp9Pw7owHojq2yAxLPdkD8ReOesQ3yBl3BVAX0Sr8Hd3h3esYRrz/LhpTtrsvbSC9hlFQvAVWeH7d23412M2Hu2cDyQoP9uauw94KICbAiIcWSuCJ5hRXC1SwzCxoXh6+pfo4Z3DdYJ7ZuroKmEea3nITER+DvFWBJ2dgDudwEWMH+3jRvZAGsaDRArZ+HP5NBB90rajCs02afFzMbst4CTFwZU7YfRFwBvp4JY3VjfOdBoIA1ERqJomZoYVXcUEu2YlVUQkod3szb9qkbRqc4NboPzuiBcfReIkgms80P5fOzF+VeHv9CwaEODwAmODTaIpa8qfwkEM3eEuHcuwKlTmNh1Ppo9A5In6y0f8eJLM3Ss2JlFGJLXSQMk2QHfdACUchu0deqIMFcg2lmFZotqGiJbpGTup3PRunRr0FSCbqqo1gWrv4dSiwK29ghzATYbfVOT9eXtHQdAaw8HyieulOlQ2aWZYTaf0sgVJK4IEhtYfoZl04CdKeq4nk6epgmjpZbYdXekoXCqJb7CFfu5SLYB7J+IeTn94jTQeiwKyF/DRQ3E25gXwDb2TAC72LEv8IZbG2Azwwbvkt5JmnjtZtpJAtwbN1++Gv0Kf7Ri99WL6BcIjtBfN30YQoFPS35qGOI7JVdO6R/EpCSz6wW0U7TY3n27wX1GECVFFEXgaOuIuIlxWNxmMYbWZhWrX2quQ6vX+ljFDubLAGCiUP2z+EzU9a2LxsXZQ1S7iCja+lTtg+X19SMxxcTAzUGMfFDLpxY8nDwMlYmMMrfVXMROjEWbMm1QyqMU7GzYsyWTydC7am90KNcBA6oPAE0l3Bp8y1CRsGTtJRIHTUyJcP26V+puPoEZYiaw759jVDUAQECAdP2QIcDly9JldhovRCujcenVJQytPdRsxBtLlPUqa3Ju/ar1g/pnNWQymSFMpDFymRxOdk6Y0XSGYVmdIqJxZmazmfjfp//DgtYLAAB/d5S+mJ9FPcPtsNtwmcWeByEKChEZWkCECBjV7ukrHqHsfq9gFK63Qv4KWH9rPX45+ws+2/QZOmzpgA23xR6pCz9baHDBax7EjCkAUh110sGW3b/uDuyd7OPmA283b3zlb9oZTyiLwq6F4WTnZKgYNSjaADSV0LNyTzjZOaFLhS6SbWxjRN/tpHx6F72nQbANDYddsholun+DFiPmo5Jn6tdxVN1RBqHOyfmkWRW5du0aypUrh/nz56NXr16SdX5+fpgwYcJ7H7x0aelIKmXLlrWQMuchjEoSneJjFxUVhSJFTCMkuOpjrWqcAbWjBxy0ciigQKzQrB8Xh0K2haAAW/D1wASscbZBfEEtnrn8iRd1FThWAXihXAFFlyAEho9EiMcqKGoCgAJFk4F/3mwB3iiwvCagqr4I8tMKOMkJ6mgZYhUKnG+2H07uCrgC6PEEiFUAYaXeQfesBHzsfVDQphB2568ETf7HKJEYg1AXlsYr2RmlEjQsr/ZAnBtQ8EEDhL0EyhZ3xB9N/4COdCbCf+lSNtBBd6NvjY0NCyQBnRegiMXr12w+PByIdisKhb0KCXDG6nEP8J3RyC829oACMJSXuw6Ic5WKPlXJ8vjz0TKMVDSHMvAxFhQZAlzajfUDvREbEcEOFBeHAjYFUNKjJGJfJgL580P3qAIiPFbDQwfUvByO2Ff7IDt7EbEKBWzVHlAgClA7wut1WyhwHslOgNoWUCucMKL6KNQokIjupbtDq9RiR8cdmP1rWwzuMx9xdk6IVSjg41kWiuBnABSYM4OwoJsSUCiweT/ww9gkKP55CJAcGFwTn5b8FE46J8N9YEO20EKDfDogIh9gZw+8cc0HrxuHkVhagddeQEE1IJRUKY9SeBr1FDLI8GvzX9G8WHPJdfmnfiC0Hvfx4vTXiFUA+XSAjUM+qJyVQEIJxCqY+4adjSviXIE7q4GiilgU2CPHuigFhrcDYGuLMmXiUOWH5nihuQZ7W0CuqwEFbgB2NoCNeF9/3QH4a594jfa0OIG+x5sz3xEZMwXNbTwXo46MklzLUh7V8ZSiTZ6jnl8Cq/YDrgBu2v+CyHwKuD/pDge/WMTGAtPqTcO8S/Pw5NksAArsdLdFrNzUd8bVIwIKKCBXsedw3P5xhjKv7VUbEVHiV/1Z2DPxeqhsEKsWy1OTpGHrtEBgzAN8qlCAIiIhi43Fzf43YSuzhb26MMq5P8SmAx7wLiF1Nbl54iVaCvd5ZKR0OGkz+Dr4suMlA9HRsWjVCtixA8iXD+hbvi8iEyPxOuI1Ak8XQMLWMMQqFEDJkqajQqaT/V32I79zflQoUAHxl341PEOIjUX0qGiEJYShkEuhVCv96cVT7okb/W+kua9ffmHF9OOPrK6xbh2wcCGwZ08cihVjle5p04DDh1n0kJo1mRG8fHmgV5leqOVVC3KVHLEq0+P88w+LBtmnD3DxIlC7Nnt/HZj0EvuGXkejwuvw7l1nQ3FqNMCmTcDt28BBFuENu3cDT4JtDffM5yU+z5TySQ8+9j5QQIFJn0xCMcdihuOOqMbca55HPYcCCtiq7Qz5G7JrCBRQID4uHgoo4KgCbr+4jabeTdFvdz9cCboCV7ji8evHUECBfJFadl8ZIcwd7n4YVZab91ue0XQG+lfojzDPLYhVsOg1Ca5AbEFHNviSu7vZ7QTsNfZQQAFPuWemlOeKFUCxRAWaqL0RGx6u/zABePQEakGbDBoEXLwIAiBTKIC7d1lsfgtMbzAd0CFd+UvI4IienMwnTQvwtWvXMHHiRAwePBifffZZro/Vm17c3d3h5+eHq1fF0CgajQYBAQGoXr26SXqlUgnlcSVu3FBiYWgEhucfjs2fi83xgwYBTrZSH9oiFGeI3qC1AV4IRoEyR9Bxm+VRb2zLlEWCHeCqBnbvdEDoU2aeU5c7hHh7wI6AcfoWrmRbQK4iXL8OlHkrw5PK1/C6QAy84wCHh62gBVA4WoXGL4A4O+a9kGAPuKhleP2avfTnzAE0arnJ2ALmAmLYpDCEJVy5i6+xEqNHAwnJ7ONvAy3eXGGdIVfnGwMAcNTrhj+GMd/BL0p/bthHvD6CgcrOCYmJ7OXk2LgOgpaxoTH7lPpctDCo1bjR9zz2P60HxMVB4+iKtStdobQF/mj8C/x+nMW+fnrUT/XlvPgx/l3G3ChURtXGyvkrYHAt0Uzsau+KzbuBKvM3wcdV34xq68QELgAHe5kQaQe3CgHr1smAhMJAYkH82+EKxldchktGoXu1yfa4+/1d1D43ydDRzAvRAICSEbaIdWCuKQJCMzKB8JX/VyiiECtjSxZqMax7AURfb416Hszc654MuOrsoLQB7OJEK7TxOVbFLXzzJBifp4iS9eL33cDzZnBTAqWTpwKrT6F2LRmMPC0gixbL5lD/bWhWVe8vKBOdiwWLY+uSoo/iwSF/Yn+v/YbzEYhIEaxCbQPYJzsYyvSbGt9AZuSWorSRvuJ2lioOLQBbW+bPaBybV2B+6/m4PEg06b2OFVsjUvr2GYdKE05JtngR7t3RoaRHSRTLVwyBpxNxFXVQtp2Rr2R0NHDvHlpe/IXtBzKzw3KnRGhVcLF3wbt3LOy3YH0MDQUe3fLCjBpr8fKFDQpBvz/b929ybVS8kcENA9HRzJqs7+Qrk8lQ2LVwpvXb0OmY+NTpRIN4RASwZg17lwi2hhUrgCVL2ECGVaoAc+eyhp2ePYH69Zn4BYDPPmODCbZrB3z6KXOFdrRzRJVCokALDWWnc+wYULQoMHYsG55bo2Gx1jdsAK5dA3p1ccdvb/rht8QRkgE2J09mv2+MXINHjAAW/Y9ZyN1t80sssVlNUUVx1HXuhQH+A82ut9UxkffvHvGe2BQobb19vASwWb0WN97cwImgE5DpAJkO+PH4jwAAG6XpyH13vr+D4NHByOeYD/90Ezv1BnwXAFsNCxc5oPoAAMCPK8XQjYl2gNrBGdGvE01aTlIiuKOkx90lPfzyC/Dr6jZY8vcb6bGNW0z0g3bdRwWEohCUr80PmEFxlju+G3Ps2DFMmjQJkyZN+qgiXuVaKJ2EhIRQjx49yNnZmWbNmkVqtTq9m+Zafv/9dypatCgFBgZSYmIiTZo0iXx8fCguLk6SLiYmhhQKBe2/vZ86fAF6WMCXiIiuXiUi1mJHANG7d0To/RlhRCkqj3tEAPUc4EaYhjQnYT/RFerR1BNTyP87UIQT22/NMjFEACkmgJwmsXTP8oHCnEHN+oIeyUpSyZJEe8q50qjWoGlNQH95fkIDivWlUBfQOzt7GvcpaGsJT8I00ICOoAsuLWn8eMNhJVNSEjtvf382r9OJZbFggZiuRw+iY2huOP+rqEkEUDycaRGG0f8wlipX0hEB9LD3dMI00JAVE4gAUoa8pBiFgmIUCvoPLYkACq/agvTjk0mnOXOIZs0iAki3azfp/tnOlletSle+XUUocoluFwTF7Nxssm1R2RNCsTNs1jmcMA00/DOjNKdOSa51SGwIEUBRLRqSKjGeCKA7A/5HKHib0Ho0DR9OFLzyIBFAF1CPACIFookA2jjyChUooN+1cG1/tiUiouX4hn5vIL3Wr2SF6FAp0NftZTT3/FzSaDVEZLSt/ulOTiaKjyfa6zecCKAffyRK7NqNkmxAf1UHvfYtTm2/BP1p24cIINWihaSYxsqWABqJ+XSgkA+7TtNAGFJRLKL+n1CyHDS83VMCiL4f/Y7W3lxL69aEkhfeUqfaIWJZvXhBRETf/vstrb6xmjANJJ8uJ51OR/MvzieNVkOYBvpk9SeSMsU00KRjkwjT2P1KAGlkoC7butBrV1BtxQ76RL+JVqclr/FiGfUrMFZyPYc3qsPuFRsFYRqo9sraps+Sfj/Gy+RTHMit02STd8DjyMeGNDMbgZRyORFAPbCVXq46TDsmXqM/hwUajn/5UhQtXx5D2nr1JfmKtfckGj06zXeOTqejK6+uEBHR7dts87ULooiCgyW3rp8fUV+sZTPNmqW5XyKisDDTZTExRJ07s2dY3akL3UN5Sm7+meSZJiLSaokGDSK6fJkoNpYte/tWfPbnziXy8DDdf0KC+H/ECJZd4b1CRDRsmHhOX33F9p/y8d61i+izz4gUihhSKGIIINJfBsl04gTb57Vr7LxUKra8cmUxTbNmRK6uRIsXs/lWrdjvMCwyJCrv8pI2bSJasUK6/ylTiA4fNlo2DVRpbuN0lX1axMURVaxI9Phx6umuXmXHvn/f/Pq7D5LZ/Zr/vtnvyKi9Q4gAOlmczdtMt6FDFRV0qqzCkOZglyqmhWuMRkPXQq7RnbA7REQU8UkNUnq4sXXh4ZLtOvcAPUdxWldhlunHIgXBMcGEaaAEVYLFNHFxRPPnE2k0qZeTcB9tQU8igG41HWHI09ixRATQTnQ2LNuCnnQLVaiX2z6Kj2ev/V9+Yau/HaQhAujxv/dMniGdjuXl7l32e+kSkSCdXr16RQAoJiYm9cxysox091zz8fHBtm3bsGfPHqxduxbVqlXDBaP4knmRcePGoX///mjZsiW8vLxw9uxZHD582ODukJKSHiWhkQOkZlYjabQ0QkQE0HbTEHRc9D8M78b8q9b//hih36c+clz4uHDgBOsB5n7/EioWrIR4exjCoD1/zMynajnzIwWAEtHAnE+AlqoNsCUdnj0D7DQyqOUsNJgXReMPl3PIpwScNVq4qID4558Dd3og3h7wcojD//5nmheAuTMYn59gGf7vP2DUKDHdl18COqNGCDVY5lyQiNY4gk/HVMX2Hcy6VLZoEuqfSEQDWxZCyCGJnVx1XMc8MCuxpVi2mDDBMOzvlAkqLJjNzEv04AHmrnQDQuogMdIfblrT5ucI8sa9w43YTCLzcZT4I6cYUUwIg6TWygAlMwneDiB4hXvD88hPcHQEwl4yC4oTWD48wawKvRfWwVsh1Gw86w2O68zHuxLu41YKw4cTJaNBMLBqP2F43eEgnQ3i9K5s9mqW1yNHmEHDwwOooWNDRb/97W847dyBIIUdPN4Wgr0mGUm2AM1dBjx7BrvhIxA8OhhKfYuEE5IgMas+bifm4VZ32OsA/8buOHYMmDXFA/2q9YO9YyFEIj8S3H3QEfpxu/WdsVZ0WIEB1QfAx80H7cq0g0wmw6h6o2Ajt8H9ofex9wvpON/aKVr80pxZSh0LsQ49SbaAj6sPVDaAfZL4vMllctgbXRKljdQ6Kdeya1JAy5ooUw4qkS+hDq5cYfsp41nG0HlJd7Mv4vayXt52djDc+6U9SyNxUiJ29dgFOx2wqkRJjMEf6I+1KPrNZ+g6uxaubBdHnPqy1Vv8+COQeIlFfjmBZphTciXcVO+A+fMNlmxLyGQyeCpZnsLDgXbYj36jPJj50oigIMADUQAA0mjw0HSMCwlHjwKFCpn6t965A7TZ/Q0OLn4K2727EIH8iHgeC7mcDfR38iQLQtKyJXM9qFsX6KfYhfolQlGgALPWBu+4jPHjdAiMKgLtb+JL48kTwMWFGd/i4gAhlPz//gc4QAldkaKwDbxpSL9hA9s/AMjl7LFOTAQ6dwYOHWLW8H79gOLF2fK4OFZGcXFAr17MyjtnDutzULYsIIywatxSdeIEWz9cH3Dkv/+Atm2BxRBDPNZJOIHevYHvvmPP1vXrwDffADNmMKszg/BwEdA83ExEGbCgB198wcrbOIBScjIQWaA8NLOkMckf3tfB895ZnDpl8RICAO7rx6H45Rf23n3wgJXBH3+w61WpvD0w/wUQoQ/lppbGYveV5wMgdkjWkhYNXgHVjQb8TIgSOxh37sGer/jN/+LvkbehvXkbsLVF1T8P4nrns0j28YPXuRtwiIpj91bBgpLjkUtpJMAFnq/1A6sYuQ7cvg2cPatPR8CLQF/ETIgxCcP26hWz9APMHWb0aDY6O8DK8+xZ1lLwRD84o0Yj3kcJYO+4qqcW4Sf8gvbYx8oKhH8hjrb3AsURgfxwiHsLV1fgRNPpODb5JLajGw79zcIiDuz4FoUKsX3PmcPKWy5nDTCVKrHfevWAzZvZaVZMX8hkTlbyPqo5OTmZpk+fTi4uLvTtt99mtijPdQgW4OjoaPr6uyr0zLEYERH9u1NlqGEOwRI6dIgM85pt24nq1BH3oYwxW2NPUuvNrVeusG179aI99/dQwXFsPw5IokJ4QwSQ7c9SC+LgdqBNE3YTAeSPm3TYz4W+bQ/q3RkUVKWYpKa+ulhPiuk/nFD8FLVs3JDCPcqYGAGE6do1liVvb72Fai07FeM02wsMprs/b6Ej+NRgAT6FxoYEL1CUmS1JXyajRpGvL5EcrLZNN29SjEJBXopwqog7RADdLNeTAKIouJvPGEBfyTbQAPxtmG+G4wQQnUQTOtp3vSTtGMwl6C3ahsUtf6D+nYz2eeSI4Rr98APRhEksf3tsWlM+vCMCaDx+o9dyH4qDC02ZQtQd24gAeoCyBBBVww3D/gzHkWkJ0BHArGv3HKvR9IbrKCgqyJA2EY6G/zodUdOmbPZYwAPq7biAumCH5PRfFP+ECKC/MYAIoMP4lN7CiwigH2dILVUxMTH05s4zWoevaDp+pv3eBYkA8nW5Qkn+takImMXRG3oLb4oWoS1b2OInT4h2LXjBZlKYRzRaDWl12nQ/RwP3DKTNtzcTHTpEFYeAFl9eTFEFSlMzHKeGDcV0xUaJJ93JV9pMMaZeLbGsp4FKLChheJb8p31FANHw4eK+lCoVW9+jKwFEjx6ZN6pef32d5tcFzS1dnmY3OWRy392oPpDi7T3oO8UyGqf4zbA8RO5LKz8/YJg/+Z+KiJg1tHZtohYtpEV75gxLmpxM1Lw5UQKcTO8dEAGs5URp60xRFeoZjHSXLzOrqkBwMLNKLVkibrtmjbj+4F72jhJaZ2biJwpEJcmxvvxS/C+DVpKfHs2YxW8t+hIBdA01aMUKohs3iIYOZUnLlWO/NlCTPZTkjRDxvgLIFy+pKgLoDQoRoCM/P/OGwpiYGIvWtJUrxXJxQBIBzCJtfB6RkSztwq6nqSBCDctDG4qWQKpYkX7DeMOsiwuRNuglHdirluxLyP/mzuvNWm2fPBHT1qsnLj9/jl23xzZl6cYN1nD17BlRwJSdRADNGhlKtGcP6bQ6GjOGbR8QQHT9OrOy/ybeWjR4sCHLZl+H9pNB+QZWJM+Zxajp8s9p+u71dGrnBSKALvuwZ2LK4QmGljbhOVldTdxJvQqjLb5rU04b0Ju9q2xsiLp2JQKoAu7QbVSmSIgX4/5dreHeAIgCA4mOHROv0c2bREePEgV/2p8erDpDrXGIAKIyZYjaVXxG7ogigGjZMqKpU6XZEFpNnJBArog1rLitaED58I4Aot27iQYOJDo77ZhhfcK4KaQbMpR2KfpJtiOABuIvIoB+wBzKj3DJ8WyhIickGObtkGz4r1BwC7C1QUYS63Q6evDgAf3zzz/0008/kb+/P8nl8qzKW65BEMAxMTF0889LFAJvmj6daNu8V5IHadxIURDTzJlEbdpI9iO8gOqsqkOzzsyiXjt6iSvvMBFIe/fSo4hHZPsz28+/f4aQL14SAeS/rKpEAK8b0IfCD4htikdLMPeGRv3Nv8B0XzGBUBYPSG3rSEyg6agMHkqSHjzIspQvn+X3IQGkUyjoXZ3Who9lgGtDUn3algigKZhmdOIgWr2abGzEbbUjR1OMQkEKRTTZQ0kE0H60ZS85VLZ44GnF/qZv8adhviLukExGdBCf0TeQtml287tKzs4sCyNH6hfXWEm9upg5WZKe2050pgIIIwJoEn4hjdyWCKCffybqg/WkApt3RSw1h/iiNZftV6+IHsvL0M25xyQHOocGdLMQ+x8XZ1q+xvtzcSF6VqwJEUDL8R0RQPMxUtwghSuHICYWYjgttB9HZ3xqEAH0PzCXgkD/LwkgqoRAioGbyT1/7Zo+HwKHD6f/gUkHwTHBpNVpKdK7IrXGIWrQQFxXarhYEK39pAJ4XJ2aYtlMAxX6XyGae34uvUuMkpRfmzZEFy6w5nwMqUiouUKyvmlTaX4CwwJpWS3QguqVSBcaZnIR//vhKAUVrmMQE8brlgy8bvh/ZMVz+hy7CCByRSwBTKQKLF/Okl66xH4j4GnY1hYqCg4m6t6dyB1RRAA9zl+XIkrWYmlfxNNq9Cc5NAQQTZggZmP6dCJfvCRAR79W+4ceP9DQT41OUwCqSvLa2u08BaOIyT1apQqRF97SGvST3HtV5cz9I6g0c3U6hcaGbVIUA8W4+xpmKkF0GwkYvJzCRswkAuj2jof08KH5eyImIIBiXr82uy4hge3uazAlXExfvx/OvIKocmV9wlgmbv5BN3b/arXSTM6apa8A6Mjbm2j2bCICKGb8TAKImjRhycrhPhFAPbHF8Byo1cxFQaUi+uefFM8rMVEvuEO9hK9hnS1UtKLIdCKABmEVEUBlnV5aesVRmzbml8+fz+6loL0BpLRzoT0OzOAweoya5HItuSKWWnqz78g9lKdlSw+Tyqc4xSgUFKHwMHx//qko7nTfpvXmD5bKZAM1TR/wnAig/Ag3WS8IUXPTvn3s1wORkhXeCGHlCNARfEqC8QBgRpMfS2yjflhDX2MlFQc7tuB+9wJFiYi5T7x7Z/RMb2H3YOKG7cxnZv9+irX1oLq4aDiuqoC3SSbl0NCGDcwFMMqhoGH5D5hDBNDACheoUyeiqoozlFcFcPfu3alYsWIW1z9+/Jjs7Ozou+++y9J8IK0EixYtoq+//ppq165Nzs7OJJPJqFChQtS6dWv68ccfaevWre914EePHtFnn31Gnp6eZGdnJ5lyG8YCOHDrHYqBG/s4IIDibPMZHpCUDzX98YdkP9/8+w01X9fc/EGeP2fbHDsmLlMoKOnKbSqBp6SGDZVZVIYwDfTIpwlLu349RZ+6aTjeqeKgPp1B56/sMv/2KcI+fE5IMLy82oBZr1rhsCHZtm3sZW5nZ/k9mPJt3QDn6JlzRcMXfmGj7eJ5aDREOp3hgylsG67wopIlY6h4cbbsMFpRlSpEx9HM7EFPgp13ApzoLioQAeSOKGrUiGgPOtJ51Ccl7A3pky7fMvgzEhFNm0YE/3XUpYfRfv/9l4iIoqKERcyCsx1dqQiCTfLw3XdEg7CK4mSuRABNwxSJIDdXVj4+RK/gQ882X2QZ0a/I75JIHSZVpQRbGb14Yb58hfklhWbQi8LMDB8zYCQRmPXbkMD4zU+iAP4N4+lv56F0qUB7IoB+BvsQU79+VKMGUVOcIHXxUhl7IDKRiKLVqAP2SgRwz/qiyG1SWiqAv25QTyybaSC3WW605uYaevPGtNwVCqIQgyFSRzJoDesEAbxuHVHVqkSPIh7R6mqgubX92QqViujmTdL160f/DdlNERFEyg7dJAI4Guw34nYIacCcVl/WZZYxodJaAGEUdCGEiJg2EyqBfzGjEwWjiCHDhz1ZhbhrV6IyeEhqG3v6/ZO9FFnMn1wRS1v7HzLs2/g8A1CVAv3aEQFUAk+JAJre9ASrhKYolNbF71M8nGn9evaqGTWKCen4p6H0aNhCQ7r4An6kdXUz2f4UGlMEPKkSAsnJiSg6mk1CK5Uw/ZPvG3G+aFEymDvHjyd6+JCZ4gW0WqLatVnZNmli/kZRKun2baI3ZRoRATTVfzcBoh9w9erEHmJ9TTekTBMKDSXmty6TsRrdyJGsOQugixP3ku7UaYlAXriQWdLtkEwtcJSJHfxFAFH//pbfhQDRJ6xxhvzwjAigREcPAojehuskCefgByKAGuOUxX0NHiy2wDRsaPKqMtlgMmYY3kGSd4J+OqJoSWGK/LTh5haq2OVXdp8q9OtfvjSbiaSSFen3wnPpV0wkAijqaSRFeJWh0KK1aCaryxgMJz9gDulq1DBsG2pflI7NvECXL7NGQIC1LHgjhMrjHjXHMWqKE5LjbXYaQBuXRkuWFUMQeSOE4guVlCwXro0wfYHNZm8ZnUZLd6duE5sbnj4ljcxG9K1v0ICZnc1dhOLFzZY1AURNmpBWS/RntzWUVwXwtGnTSCaTmfSXEujWrRspFAoKM9cxIRNBWglKly5N3bp1o19//ZUOHDhAry3UsDNK3bp1qWvXrrRz5046fPiwZMptGAvgh0dfkBYyAnRUH+fpta1o9aiHC9IHxah5PU3C9FYnY0teiRJEJ05QWTygJJkjnXx+khZcXEC0dy9Lu2gRJVwWLS3nfUF9eziwbYU8CCYngGj1aurYkf1VOzhTedyjHthqWC8IhL//Zh81Sy/oBjgnzrRoYfivgVw0bRkLeT0uLmyVkP6VwptCQmIoLo4tO4xW9PYtUYdar80eeBc+N/zfjq6GptDYWKJ3yEcEUDjyi9vcuyc5vlZLhMpbqF0vo/3u3k0zZoizjkgkAmgbuhs+ZsZTjw6JNARL6L5DVbN5tFRmUXCnhEu3JdfG0ZFI9ZwJlpvXteThQVQXF+lfMLGaCEfD9pKdubpS7Pc/0MuX+uU2Nmbv2ZiYGJqGKbTVdRBdLNCBCKBfMEncz+HDRJs3S9tws5m3JetQN/xD9evrF9y/LznXehWkneAcvugllvU01slnS+AWCggwLXNnZ/F2HIO5kusjdLprx3QjBUUF0abKoFkNalrO7NixZi3ApFYbBLBWzhRub2wgAuhz6Cuj58/TLqN66ahR7PeprfgBTnLLT0TMAvwJzlCMRzEaUeYgqW0dJMfriD20/MszZu+NhEHMJGqo6KSYmlVi75mHt5LE80pMFNN4ebFCcXY2u73wnBFAX3TXGBROfMNWTDQAdAW16JnCn56gJC0a9lDcvkoVplRdXfUZ13PzJhHAytbLi5lhhfegoAQBInt7IjdRlF/7O4BIf/qDBkqFJhUqRLR6Nftfvrx4LGF/DRqw30qVxG1iY+n2baJt6G5YNgILLD7TKac+fYiOzLtjWBC9aT9r/zdK9Ax+RAA9cKlB+9GWRvntptBQtnrECKLGjXQUOn8zkVZLpUoR/fknqy/s22d0H+bPb3JwwRXtDiqS6rP2knX352+lGIWC1qMPDQer5NR0OEpndkeYdGojgOjHH4levyadjijp703Sa6Vn+XLmthETw9xyDBdCmAYOFBMLD6HRdLpAV5NluhQ9H+MHGPWgrFyZfUCcnWnTl/sl6cxkzzwREURgLWfq/oPYB8HQaxmm57Btm/i/Xj3pfUNEr+bMobwqgHfs2EEA6MqVKybrLl68SABo9uzZWZ6P9F76TMfNzS3PRJIwFsDPrzMrrzPiqSX+o6d2ZdnLFqA73aeRtmQp0VwQGJj+gwhVZWMB7O9PtHcvVUIgxcpSNFMDRLNmkeq2KBgeu7jR/ln6F8/UqcyplYh1KaK6YgABAABJREFUhdZXfD7/nCVPKlSMPsEZutVatLCtwDfUoAHRokVMhziJ7on04IH4fygWizN160pfGq9esbZnM05+gksFARRRshY9U/iJLw/hJU7Mamfsi0gALcRwQ/MhAbQZXxAgnqI2P3uRqWyNMm3Gea/ViN30aR9xv1d+2G6w3gBkaNLbgp5UBg+l5wbQr5hIozCPjrp3ZU6eKdaXlT82zMqgJRfEkRfesgVPn4rXDmC6NZLdT6f2x1HRokQXIFo4b6EKAUSe+bQmx6FffpHsKyWCAL7YaTaFf/olXS7AXFNWYZB0P337EnXokMbNmXWEVG9HQ7DEoMF1jx5L8lezsuijGO3iQDY9RIdVoUl3zaVddOwY81kfrU/esSOrF/TpQ5L92UJFFXCXABYpQPB/XbT6Ne2oAJrStIrlzG7cyETakSPs3lq/nmjnTiIigwA2fODlTYnA/McJoPujV9B0I00qPIcHnLoYFmptbIl0OvriC6Iu2EFhxWuZWLuMp8lV9tDahiuly0uUYM+B3kWHAKKlSw3/G9dnJtPIwBDxvITu8ABRaKhBKBgmZ2cWEmLhQot5ER7GV48SaBJ+oSRHdwpAVYp/adQqttk0Sgv9+COzDpcoQTHPnlHMlCmpH+P0aRYVBmDOscnJlBCvI21N0Tecfv9duk2nTuK5ajRM5Kfcb5UqrDmApPfLZXk9k6TCPRNXqCS928JazipV0u//yhXD9yBdk95349Ur/StTMIQIHTHMUaSI4b2b9OVA031OnUqjKhw2uAjEPHggqbRp2ncSX886HdGmTUTt2pH68nVSHjsrPZZOJ4YFSgshbBBA1K0bW9ajh8VzV/+5imjIEGZxEZb7+zOxKYQUMX6/Cd/Ir7+WrPv66/RlT2guOIcG7B4hEisTgsGmenXWPPTzz9Lj79pFdO6c+GxcuECvWremvCqAHzx4QABo7dq1Jus++eQTKl68OCWl9775AEy/fNlE/fr16eXLl9Y6fLZiLIBfv1CRFjLyxUvqiD30qlB1lqhYMaJvvmHWi4H6l1KKZulUEZriAgLEZQ0aEG3eTBNa3yClq6c0/f79RLGxJoKB9u5N9TCd9f1BdNWr063ppq4S7u7s78GDYucW4f0zaRJrwZTED0rpKJxKGJz795k2Xpt/DF1GbXqYr6xEAFPLloa0U6awZYmffU47J10jeyipIEINx0n+4iupgbdoUcO6DtBbyPVhu4y58fqGIRQXQfTxA4hmzCD6a8QtIoC2ogdVQiDFw5l0Rr2EFmI4TcVUevNZf6JVq0zKjyBaGQVr6xvoP4jh4eK5Asz/UMn8n/euDKV8+Yjiq4oCmPnCEe3eGG96nLNnJftKiaFD0cKFRJ060dsi/kQA7UYn6X4cHCj9X5DM50nb4TQXYwwCWP34uSR//tWGG/7fK2RHmCRaJg2dSUsfpLZtiT6praS7XX8mOTSk0xHVqGHU6VL4WOp9SAdhFTVsSPT99/pVTpG0rwzrWGqR5GSK+fNPihF6WxmhlZmJ25Vi8kYIlS/Pnqtq1djic8V6kXbWbDFd//7U+0sdDcMielm1LTXByfSLKWFqLHZGpZ9+IiIiXZEiRMWKUatWRNFQkCbAqHLepw9rd58/X38yKSpcwn1robmcAObnIzzLP/3EBE6J0uK+nJzY//r1zW/fpIl4z4aGStcNGMDuY0GgEjHzo7C+l75V4Phx5kpGROQodjClwYOlF0voodWBtYrQ8uWi0aJXL5O8AUQTftRR8os3lPTHUqK4OLo1UW9J/v57ckQizZih3/epU6wScvGi5bJKORm/p26x9w+1bcsEm4Dxc+7iwqwU+vmJdfVuDytXEtWqRXT6NAUGkuFZj4mMpJi2bSVlnWXMNrqX790T/69aRcnDWWtO8vqtzLRtLJCmTWPp7rDQa/TY6Lvm7y+mK1VKWnZpfO9SohMqAsJ2jx8z646AUinGYDt9mn1nAdbrUUB/7FcKBeVVAazRaMjBwYHGjx8vWb57924CQJs3m3dLyWxSeVtnLYGBgdS+fXvatm0bnT59WjLlNowFcFQUURCK0ZkZJ0k17VcydF/392eW1vbtWcBLIFUxaBbh5S3wKWvWohkzmFXBHEFB0heCUacuc+g777J89+1r9mUPEP3vf0Tft35KZw/EmMZk1PvRmUxmmuLNET9qEj1EGbqtqCIVwC1aGNIkJrJl6q07DKttoBaPZdzERiTx5fr1C31XYTPuPlqdlnYsGWNI+yU2Gna5fOZbw/K96EATWt9gURaEnhsATcAs2uvRj72whYwBEuuYByJNLNjG98Pj33fR663650THmm63/P6SbGyIkmroBUKxYqSr34Dq1SN6fpXl67GstPRDaHz8FBjExPr17IOnr6jc8WhIVFLqU0eTJqXrumUFD3v+TCvwjUEAK+89leStUq3v6dv2oKqDQQXGSaOgGARwieOS8tZ5eBDt308dOxIVhtSdRuUsWsI6dtAZBPAWWVeKcgDNbNE71fxailRg3HybsuOZMH2GgzRwIFHPnkTu7sxKrevchQlPIV2xYrSmLnNbetigv9TdKL3TJCM3F4HoaKKYGOrcmShI7ie2NB09KoonY4z3Z4yv3uXr1SvLae7eZctKmfEtj4khE38Vf38ilUpatjt2sHVLlli+GCkroMZs3CiGUViwQLru5k0WeHjPHrb+xQsmyCyU5/kFV0gn+G8BUoulftJqiVUUDh4UzcHCesHcb2n680+W/s4dUZQXLMgEf2CgtEIiCPU3bwwC+ecON9myFB1h6c0botOnxXIVwuEI/j9ZQXKy2IO2pt6Xv5e+s7dg3TZnGVyzhq0zDiytNRNhRhLSB+aDX6eG4PNkKdCyOYwrIkSGY+dlAUxE5O/vT+3atTPMq9VqKleuHNWtW5d0GdU+74nply+b2Lp1K7m4uJBMJpNMuTGqhLEATk5mgz8oq9UVX95ErMZevDh72K9dYzFtPhThxVm4MPvwmCPlh+jo0VR3efQoUevWxJp5BNGob5p841fXYNDt14/YH3PN44Ifna2+mdXYuTE9zJxJoShIVxQ1xZdHu3ZsPxYAiHr3JvE4KXuXCr6jvr6ipcq4+70R2jOiqOiLtYZdPmgoNicqG7ekiS0u02sUNkRd1/n60mz8SJdRmzUbEonOnEYVkUD76pLOKLdQJdWyUctsadk4NhCFqo7ex6xtW9aqQERJj9j5PEUJ03KuYn7fho/e/v1iPDuAqGxZcZQAT30EgoULLeYtq3nSZyr9hYFUty6bT7glbdEoN9TyoDGGZUXPm/XXjo8nijhyzaLwKIGnYnHq/6xqujHV/FoM1SX0bgPoUVVT/0YCE8AjRrBWX8Mt3K4d0bJl9PgrvX9E1aoGkX6h02yqgxT+k7Vrs+bvBQuYs7C5c7txgzXXCpZbI/r0IQp0qMGeNWNhtW2bNKFxJwBjjhzRh04Qy4zMhdJ8+dJsBVSy/7AwZn3Td6QxKdv0fESFXo4lS5pff+OG5VEVkpOZ+4dOJ4aZMJqUI39ghgJjP2HAdAQNQFJJptq1SX9CbH6rvp+FEN7ht99MQ2gY+5sCRBMnEpUuLb4LUh7P6JxiruufGQstspJyBUh0uM9ChEqOEIYnLVQqqZU1NTZsYHEMgYwbmdbro14kJ2dsO2N69yYaNoxebdqUuQJYp2P3TFZNmSxKe/fuTSVKlDDML1u2jADQ+fPnM/U4qZHugTAym/Hjx2PWrFmIjo6GWq02TCrjqOC5EHt7oPGA0nAI0I9fKoyrWaQI8OIF4OrKBq8fO/bDD7ZnD/sNDbU8HGrK5Xamg0EY07IlcPiwPp0wvHC+fMDs2ShcvyRu3GCL1q3TbxAZaboTYTzkv/4CbtxgkewzgqMj8tvFIL+3vbhs//5U9xMaqg98f/ky4OhoGJTBQHl9YHg3N8DXF1i+HPDxMbsvuZ04EoYNxFEX7HVKw38HbRLGjVDBs7A9UIANSiGrWhUeiII/bgE1arCESv02Xl6GbSurbuLwXyHivHckMG+exXNTy+yxYC4b7MT2HhtgAU2bAi9fAiVLwiGWja7hrUg03fjcOeD1a9PlAp6ebJxXe3t2nd++BRQKNmyvcH+lCG6fncjsbCCHOEjHiyCdZL0mPW84jQPyIRrk6SlZ7OICeCWbKZu5c/GiZFP8iN/wBbZgd9UphlU1PvUyTZ8RVq9GmYXD2P8ibBjrC749ALDg/K6u4iuj+5lh7Po5OKB0b31k/8ePYZfIBhMILtoQSrBh0N/41EA9u+tsaNdz54CRI4H+/aXHrlmT/fr6Ag0bGu5bY1xcgFjHQmykgd9+Y2mvXwc6dZImdHdnYxrrpNcDrVqxESwAlo+DB4HfpYM+AGADe3h7Wy4nd3d235Urx96Z5kjPEM0+PmykiJs3za+vXt10/HYBe3vgp5/YcZydgZAQNnZzYiJQpAgcBnwJNGgA3L0r3e6770z3ZTyolJBvhYK9H7t1Y/MzZrCRM0aPZucPAE2asN+ePaX7K1pUHPnh4EE2yklrcahx43NSVCsJ/Pknu5bpIeU1zQr8/YHGjU3vUUvY2QGlzA86YkKfPsDx46x8MjqM9xdfAPv2mX4/MsLGjcDixeK1yyzi4th9kVWTMMpSJlGpUiUEBQUhMTER8fHxmD59Orp3744GDRpk6nFS4/0Hif9AYmJiMGLEiLQT5kLsqlYQZ4QHUPjYuLllzUGdLYySllIAWxLKKbGzAxIS2G/z5uxlkpgIDw8zxw0KAkqUYEPw2NgAaibW0KMH4OSUcs9p4+gIG3UyvLwd0k6rp5AwilqdOiwvloS+MMTV4MGWd2b08WiEsziGlghGMWiTjUbsS0qC5x8/ATol4OcHbN8OPH+OFm+uwuGmSrweQ4eyay/MFy0KBAfDPiFKzNKb10x5WCBZZwd7qFDI7h1kwvB7wgk/fw7Z/XsAAEetmfHqFQo2WaJcOfarUgH58zOx7OLCxIdQhpYEQjZgYyuHDbRQq9l3fsV4HYzHkUtVAJMMkBGgccTZfW8hG+HOhjUzrCfTyoGDAzB6NFxP3sJ3z1biO6wE9HUOTdESqDGy0fudiPAeGDCAKdyGDdmH8vhxJM6+wpLoBbAwsmKL+0vZn/z52fBnAJCUxK7P6tV4EfQJwhAGAPCuUwyXdteQHjOlcCxRAjhwwKzwFXBxAW4XaYMGs6exsipfXqzMWTonSzRsmPr67CKV880QxhXmV6/EfVsaMvPGDfZu8PQEAgOBRo3YsGWCuAWYAAdYRdnB6H136RJ7j7q6susPAN9/zyruNWuKFeoJE9iwZOHhwO7d5r8Dcrl5UW6J1Commcnp01m3b5ks/YLZGDs7oH37zM9PZuDmJtaOs2r/mUjlypVBRLh//z727t2L6Oho/Pbbb5I0AwYMwE195TQmJgZv3rxBTEwMHBzS/+1PDatZgNu0aYPz589b6/DWZeBA8b/wkRCsT5l5k23ZIv63JKBSCkFzFltL28XEACNGsBeziwuQkIB8+YBPPjFK5+wMPHrE/icksF+1monm9xG/gLjd+z4ExYpZFsBGQ3FaxKiSMABrsR/shehgoxf23bqxj9uZM+zDI5OxZR4eKO0RKd1H5crMsiOXM8ElWIvCwoDatcVjWqrAgA0hbQc1QtX6j56fH7MAC3z1FQBAJpR/RvD0BAoXZv+Fchc+9B4ezPLU6D1FXyYg11uAb9wA2rUDHj/KgAVYo79/NE5w1UQzC/fKleL6fv3YWLLG2NgAcjm8XE1bqmJ2HE21opJu3N2ZddTPDxg0CFo5u1dlILi4ABUqpEjfsiUTops2sfkZM4AePfDdYBl+X60Xd3prsoQUwyfDzc2opmgeT08grnhlJn5r1mQinWMZHx+gSxdgJhtGGy4uTJACbCxcQezu3y9Wus1ZBlO+63x8WKXHy4uNRw2wd4xKxaz8wvtFOK6d3fu/b415+tSoeY+To5DJRINGVkwZtZanQeXKlQEAx48fx7x58zB8+HCUKFFCkmbNmjUICAjAvn37kC9fPuzYsSPTxC9gRQtwwYIF0aFDB3Tt2hXeKWqUM2bMsFKusglji5twUwk19swUwCVLiv8tfZhTWnyrVk3fvu3smGAThFm+fEB0NAD2Hj53jsTjCqIyLo6du0qVpqtFqjiyZt33boaqWlUUdcb4+gKffpr29iksnlURiF87XIIv6QVwiqZ0Ax4ezIUAsHz+wnWaNQv4/HPg6lU2b2wVSoF7AXvYvzUSZOfOmRc8ALPoCk3Q6cXPj/mQCOVepoy4buvWjO0rk7Gxk0vcUIzdIYDUBXCbJ1ocqgB4OOstJ+7uUmvmhg2Ga03DR0C2eJHo9pNSGANw9MoE8WsGrUwUwB3jNqGYQwhGRf8A5APQu7f4DBo/i2CPWt8BNkDMfCbmU1KiBLB0KWuFANL17hk5ElAPbgLcO8Nqupn8UcyV7NzJfn/+md1jQ4cCn30mitqSJYFnz9j1ePaMPW8ZoWlT9i425vPPgfv32ft9/Xqx0tqvX+ouT2lh/E3hcD4APz8/uLi4YOrUqXB1dcXkyZPNpnv48CG6deuGpUuXonHjxpmaB6sJ4MDAQPj7++PJkyd4IvgqAZDllRfqn3+yGr9c/4UWRJMlf7b3wdhqaGm/ggBesQKoUiX9L19BwAnHyJ+f+b+BGRrcECeuv8ea4BEUxETmb7+ZitdVq8SySIsPFcDmfA4B5jObnvvPjJvIJM0M0TeuUyfm03fnjjRRwYJAcLDFfQCQlkGBAqyy8MUXzGJuAXtnZgE2YO5aN2wInD8PTJ9u6i+YFjt3sg/zt9+yea8P9HPNROR2NhIBLINUCKhS8c7o8UCNQxWAs8cVwJEYJhxTVky0WmD5csgGDwYWLxKXz5/PfM6NmhxdCn6AAK5eXazspEArY/fKnn9UsOnRBwDgPmYUuw7GfQWEZzGlH9KoUZaPKzSfA+JzlQqurgBc5Va1+n/UODuzQvT3F5fdvAnMns2WpdLSkyHkcrFfg74FCACwdm3m7J/D+UBkMhkqVqyIq1evYu7cuXA3Y+S5ceMG+vbti/Xr16OGJVerD8BqAnjp0qWoWLGiyfLDhw9bITdW4LvvmADOSguwsUBMywJcpgxQv3769y1sZyyA9dZNIsADeh9WBwdg6lT2X6jhHT9uKtK+/jr9xxaa8j6kI4I50lv50lsFE+EEZySxZYcOiU2XBQqw5mjjjxzA2q71VvJ0+VrHxDBBJliQLGFvLxXAwrV+/Vq0/AjC7n2aQX182DRgAPDLLznK6mdjJ5dYfYX/yrJV4PgoEFGp6IlYvQGugIcDuy7u7ubvKaEML14Uz71ZM1aZEMTl4MEf9uyePCn6xqdAcIGwUSeLC4XOVsbXM4UFOF0YNyemtwLKeT/q1TNf+VQomADmcPIYV65csbjuzJkzGDp0KHbu3IlyQl+UTMZqb7yOHTsiMoW/6cWLF9G7d28r5chKCM3JggXY2CLzoRh/0CwJYLmc9czOaO3KnAU4IQGoXBkJCUYC2Diqx6lT7Pe//z5MvH6oBfhD0QtgjWMKES/cz25uzM0ipaDIn1+s6KQmgAVfzjdv0pcfOzs4wEgcCfv29gZ27QKWLTO9Xu/D+PGigM8hpLQAy6ED5c+PV/tvwV4ok6R8ZrcVBLCrK0QXCHOuKUKrSL16QN264nJjV6bu3d/7HACw59OCcNXoLcBINrrGr16xDm/GAlj4n5GoHII7z5dfpt7xk/PhXLyYujWew+EY6Nq1K6Kjo9GzZ09Uq1YN1atXR6LggpZJWM0C3K9fP3Ts2BEnT56Evb097ty5g06dOmHFihXWylL2ExIi+nYKAtjYv/JDMfZzTc214siRjO9bEAop83/3LhISgKLQN/UnJaW+/fsgCOB0NNlmCXqBKXd0AJRGy4VOZoIjv6uraae6IkWYUE7t/L/8kollC2HYTLCzgwv0xx45UrpOCA23fz/7/dCOMDnI+gsANg42JhZgmVwOWzsZ1NBXkCIqoGYdFa6/uQ4AmPUJMOkcEKO/fZycwIS9r69ppertW8uVUmMBnIqP9odi66S/V4w7z44Zw37NPQNpdGSTIPiOLlli6jrB4XA4VuKt0F8mC7GaBfjnn3+Gn58fBgwYgOfPn6N169aYOXMmugkxD/MCPj6iZbZQIdZ5KUUvyA/C1VX0K8yM3unGCAJOsFoZWTS/6JCAHlUfshlLUSU+xHqbVS4Q6UVvNXONfiVdHh7Owp05GJsWUyDkOa3QYa1asQgR6eHWLQzBMvZ/2jTzaYTjZlWYPSshhEET6P+VDpDLpQZ2na2kb8GUZuw33AVAlB/T9OYswM2apd4iY3x9s1AAo2AKQduwoehfbhzCS7D8WuqEaQ7Bb91alUkOh8OxElZ1+lq9ejWeP3+OatWqYciQIfguI7EIcyNZERdT+EhnlQA2YzUq4x2Pr9qwDnF4+1ZqNTxzRrr9+5BDXCAefG4UTSFfPmYBNhaY5iJKCHnOZEtqc+hDIVkKESMst+KgFVmBjYMN2uMA+mEtevYEhg+VCuCKTs2xefQQyTZaG+BoSUBOADROLIb1zp1SH+ApU4ATJ1I/uPGAAFkogJtu/Br7fjwnhrYrV44J9pUrpW42RYsyi25GfHkFC3AmhhbicDicj4FsdYGYMmWKybLKlSsjKCgIycnJhvW5PgxadiII4MyIAWmMPuKDRAATMQGRkMCalL28WCgeHx/m7gGIAuNj9gHWq6ty3zRGjPYV3PdtZJbC6GipAP7rLxaY3pisEBqDByN89T4UVIVY3r8wekJm+pjnAGzsmNjrh3WYGd6fiVK53FC/mln6OLpUAf64OFeynU7GBHC1yk7A8+dsoUwmVszSU0EzrkykNpjIB5LPU44OcxoCPzVgfvSChTe9LQSpIYh43gGOw+HkMbJVAJ89e9bs8nLlyhnW5ZkwaNmFIIBTxon8UOzsmNhKGRfSxQWIj2disFAhFgKtRQvmHqBQZExgWEIQ89YagUx/XJmtDdxd9c3v5gYysbU17eyWFaK9VSsU3LYN0NlaFjIZiT7xEWFrz66FDnI2YqxOB8hkhtO05NYqCGAXeydRBCYkiJ0E09PZomZNVhHcvz97LKh9+rAhVIXWnMyozNSoYb2KJIfD4ViRbP0anhRGrOFkH4JlytzADx/CX3+x2MEpKyxOTkwAx8QwC9m9e6xpNiiIid7QUJYuMyzA1qosCerK1la0rAoVjbR8bLNCKHl6AlFRqe+7bl3g2rXMP7aVkdsywf9JIxlaDAFwllmAhXqA4KJOKeID62RA06KN0LPpFKCU3lWlc2exUiW0cKSFl5f5QSayggoVWKdKoSUtMwSwn580ugSHw+HkEXi7V25HLmcfuC+/zNz9ymTmrYlv3jCLb3y8GPKrSBHmBlGgQOZYgAUBbGULMGxtAaVS/A+kLYAz2xcbEK3PqQmZX39lIjmXIbNl18LBUf8q07tAuLqy8VaqVEmxwb0uLJkM+LLSF2hZ1GjYWV9f9rt9u+XOhDkBYfCNjMT75XA4HI6EbBXAPukM61SsWLEszkkew94+e62lvr4s/JnQMch4WF5B+H6IBViwdFpbANvZiWHehHjHafmCZkWoqfTuMze6Fwmm3tOn2a9eAMtkwA8/mKmjnZrOkskAGZE4NPWzZ2Kabt3SH4LOGgiRVXLj9eRwOJxsIltdIKKiorBmzRpQGv6oMUbDi3I+MubNY0Pu3r8vWqiMBbAgfD/EAix8+K0tgG1sRAEsWLvTOq+sEMCCBTi7muJzEsK1ECogegFskXDWcUwnAxvmOCKCVdQyM/xgVvPrr0Dr1tbOBYfD4XzUZKsALlSoULoiPHh7e2dDbjhZgkLB/BSVytQtwJlhvbKWALazY52HypcXO0utXAmMHp32tkOGZL7lzskJKF0a+N//Mne/HwMpxW5aAliPVqZPm5z88cXALV4c+Oora+eCw+FwPmqy1QUiKCgIz58/T3N68OBBtuRn4cKFkMlkmDx5smT5y5cv0b59e7i5uSF//vwYNmwYVMZD+gJYunQp/Pz84OzsjBo1auCMEN82ryMIYOMR4LJKAGd2x770IpMB16+zc23WjLWze3qmL46zjw8wc2bm5+fxY+mgCHmFlJUgCwLYuNXJ5e5QZgHW6QC1+sNaIzgcDofzUZJnO8E9fPgQCxcuRJUUvWR0Oh3at28PT09PhISE4Pr16zhz5gzGjx9vSLN9+3ZMmjQJ69atQ3R0NAYNGoS2bdsiODg4u08j51GmDBul6t070QJs7BebWWLjxQugU6fM2deH8McfYvM7J/t5TwuwQQA/esQFMIfD4eRB8qQA1mq16Nu3L+bNmwfPFMOGnj17Fvfv38e8efOgUChQvHhxzJw5E3/99ReU+h7/y5Ytw8CBA9GkSRPY29tj6NChKFOmDNauXWuFs8lhVK3KrL/JyUDbtkDKSoEgNj408H6xYjmnE1BOyUdeJKUFOJ0joRkE8IAB4kAYHA6Hw8kz5EkBPHv2bJQqVQqff/65ybqAgACULFkS+Y1ibNauXRuJiYl49OiRIU2dOnUk29WuXRs3b97M0nx/FMjlogDx8BBDSwkIArho0ezNFyd3YiyAtVrLLhBm4gBLhjLmcDgcTp4i1wwL1b9/f6xbt87i+iZNmuDUqVMICAjAypUrLYrV2NhY5EsRX9ND33M/NjY21TTPjEMppcDV1RVxcXHpOJNcgDCalr098wc2hoi5LtSrZ7oug+SZ8rQCH03ZqtWii01kJOt86eRkcm85ah2hgAJQxMLJVg6VswKxKpW47Qfei+nloynXjxBetlkDL9esISEhwdpZyPPkGgG8ZMkSzJ071+J6Ozs7qNVq9O3bFwsWLICXELYqBQqFAtHCsLF6ovQDCCj0H0tLaRSpxIBVKpWYPn067PVhwJo3b46WLVumdVq5D5kMWL/e2rng5BaMLcAajUUL8Ki6o3Di+QlsBQAZQQdw320Oh5OtHDt2DCdOnADABXBOINcIYFdXV7gKw9FaICgoCIGBgfj222/x7bffAmAxh69cuYLdu3fj7t27qFatGp4/f47IyEiDSL527RqcnZ1RtmxZAEC1atVw9epV9OrVy7Dva9euoXPnzhaPrdFoMHfu3FRFcq5BsKZl07nmiTK1Ejm+bI2tvQ4OLCKHsWVXT8+aPdGzZk+s7A5oNTrIk2OhSE7O9ntVIMeX60cML9usgZfrh9OlSxd06cJGowwJCeH9hqxMnvIBLlq0KIKDgxEQEGCYatWqhW+++QbHjh0DADRq1Ajly5fH2LFjERcXh5cvX2LKlCkYNGgQHPXxQocMGYLVq1fj7NmzUKlUWL58OR49eoT+/ftb8ew4nDyIcQQHlSpdUSBk0PsAC8NYczgcDifPkWsswOnBxsYGvik6ZTk4OMDNzc0w+IZcLse+ffswZMgQeHt7w8HBAb169cL/jAYZ6N69O8LCwtCnTx+Eh4ejQoUKOHDgAIryjl0cTvYiDIsNMH/gdAhgkhETwMnJWZs3DofD4eRY8pQANsepU6dMlhUvXhwHDhxIdbthw4Zh2LBhWZSrjxwbG9Yjn8PJaowFcDotwAC3AHM4HE5eJ88LYE4WcO8eFxec7MFYACclZUwAcwswh8Ph5Fm4AOZkPvrOghxOlmMsgOPiuADmcDgcTrrIU53gOBxOLsOcAE7HyHw6GYC9e9nMmzdZkzcOh8Ph5Fi4AOZwOB8vxgI4NpZZdY2XmcEQBUKgcOEsyRqHw+Fwci5cAHM4nI+XlBbghATAxSXVTQgkFcAcDofDyXNwAczhcD5ejAVwfHy6BDAALoA5HA4nj8MFMIfD+XgxHghj5EggMTFNAex8axRq+dbO4oxxOBwOJyfDBTCHw/l4kcmkwxinxwIcWQ5NSzbP2nxxOBwOJ0fDBTCHw8k9vHsHODunmkSrhRgqbdmyrM8Th8PhcHIcXABzOJzcw5kzQKVKqSbRaADY6kOgp5GWw+FwOLkTLoA5HM7HDZH4/+1boGLFVJNrNADs7dmMLR8LiMPhcPIiXABzOJzcQ2Jimi4QEgFs3ImOw+FwOHkGLoA5HE7uIo1OcFotxPBpXABzOBxOnoQLYA6Hk7tITyc4bgHmcDicPA0XwBwO5+PG2AcYSFMAA+AWYA6Hw8njcAHM4XByF+kRtdwCzOFwOHka3gWaw+HkGU6e1BuIn+jr/o6OVs0Ph8PhcKwDF8AcDifP0LSp/s/1GPabP7+1ssLhcDgcK8JdIDgczseNsQ9weuP6xugF8P/ZO+84O6ry/7/P1Nu3Z9MrSQglEASkSw9dAmIhohH8KhYQFL9fFQXF9lMQK4gIUjdSxChEeu+dUEIgvSebzbbbp57fH+feu9kkQMA0wrxfr33tzsyZc54558zcs3M/z/NEEoiIiIiIjyTRAjgiImLHIR7ftHKHHQbDhm1ZWyIiIiIitluiBXBERMSOg7aJj7SPfxyWLt2ytkREREREbLdEC+CIiIgdh01dAEdEREREfKSJPi0iIiJ2HITY1hZERERERHwI+MgtgIMg4MILL2TEiBGk02nGjx/PX/7yl35lli5dygknnEA6naa5uZlvfvObuK7br8wVV1zByJEjSSQS7LXXXjz++ONb8zIiIiI2RrQAjoiIiIjYBD5yC+Arr7ySq6++mpkzZ5LL5bj66qs577zzuP/++wEIw5ATTjiBxsZGVqxYwUsvvcTjjz/Od7/73Vodt99+Oz/4wQ+44YYb6Onp4ayzzuK4445j2bJl79iu7/tb/No+ijz44IPb2oQdlg9N364bBeJDIIH40PTrh5Cob7cMUb9G7Ihs/58Wm5n58+dz0EEHsfvuuwPwiU98gl133ZVXXnkFgCeeeII5c+Zw+eWXk8lkGDFiBD/96U+55pprKJfLgFpEn3nmmXziE5/Asiy+8Y1vMHbsWK6//vp3bDdaAG8ZHn744W1twg7Lh7JvPwRvgD+U/fohIerbLUPUrx8tfvazn7H//vtz8MEH89hjj21rc7YYH7kF8Fe+8hXmzp3LK6+8QhiGPPTQQyxYsIBjjz0WgFmzZjF69Gia1wmQv88++1AsFpk7d26tzL777tuv3n322ae2iI6IiNiKHHEEpNPq7w/BAjgiIiJie2XWrFk8/PDDPP3009x5552cd955BEGwrc3aIuwwmeCmTZvGDTfc8I7HP/GJT/Doo48yatQojj76aPbee2+EEGiaxu9+9zsmTpwIQDabpb6+vt+5DQ0NtWPvVmbhwoUbbTsIAhKJBHPnziWVSn3AK4xYn2KxSLFY5K233trWpuxwfKj69pe/VDKIAw6AoUNhO7b5Q9WvHzKivt0yRP26ZVizZg3Auy4uy+XyBv5HVSzLIrYFUrnPnTuXvfbaCyEEDQ0NWJbF3LlzmTBhwmZva1uzwyyA//SnP3HZZZe943GzkvHpG9/4Bq+99hpvv/02Y8aM4fXXX+fkk09G0zTOPvtsMpkMPT09/c7t7u4GIJPJ1H5vrEz1+PrMnz8fy7I44ogjPuDVRWyMVCqF7/vccccd29qUHY4PZd8KAfPmqRi/2ykfyn79kBD17ZYh6tctQ/Vl2Pz589lnn302OF4ulxkYr6OXjS+ABw4cyKJFizZYBN9yyy1cccUVvPrqq+RyOTzPw1gnQ6aUkh//+Mf89a9/pbe3l4997GNceeWV7LbbbgDsvvvuXHbZZZTLZdrb25k9ezadnZ2b67K3K3aYBXAqldqkt6svvvgiZ555JjvttBMAEydO5OSTT+bf//43Z599NnvuuSeLFi2is7OTpqam2jmJRIJx48YBsOeee/LCCy/wuc99rl+9U6ZM2WibO+20E6VSiVNOOQXLsnixYyHf+dSXeKUxz5FDXV7tNOkqC5wQWuOSL4w7EYCHl9/Fy12CCyaewOtd/6SzZHDokJMAuHrOTL4y4QS+9OtHGX1knr2aJMePOJEe59/U25/klvl3sawg6Mga7DHIY+pOJ25g1zVv3cWXd1b7H195J0+v0fjenidw3dt3sbIomNgomd8rOH/iCVz15kyOHVbin4vinD/xBP74xkzihuTLO5/IWf98jMN2z/L5sSfy+d8+wc3nHwzA2T95kKsuPpI/vjGTvZoDZnfrFDw4f+IJPLz8Lg4feiI/emEmP93nBAC+/POHuebCw2vHAB5cfhfzs4J6S/LZnU7kt6/N5PyJJ3DufQ/yy4P25+yLzuOm3/y1dk1/fGMm5+x2Qm27Wr7KT1+eSWscsh5cMPEELn11Jt/d4wTuWnIXvS4023DMcNX2VW/O5OxdTuDOxXexpkytr/73iXv59cHH1Oq86s2ZtMYlU0ad2K+dH+2l2v3D6zM5d/c+G9blitkz+cauJ/C9J+/h/x10bG3/ysJtPLwyzoAYHD1sw7EDmPqHJ2g79+B+/VXl89Of5ObTD9roeVUufVXZ7YZQ9ATn7n4CPe5dzOqQ7FN/BJ/5328z84q/1MouXh7niuP7/om76s2ZDEuqeQdw7n0P8ofJR27QTnWuAty7VPVldY4DXPjsf/j5fsfXtq97+y6enJHi2u8dBsAlL83koo+dUKvn0ldnEkhIGtTG+vWuf7J74yk8vvJObns1wZ+O3dCOd2PGorv6jd878dTqf3PgwE++r7qr3Lv0Lg5uOpzLL7+cH/3oRx+ojk1hY/NhY2xsvJ5p/xd/n5Xqt79t/l1ICZ8f27/Ox1feySGDT9o8RgPffugBdh9a5kvjVTsvdvyLvVtO3qRzb1twF8cP/O/7dl7PPxhb/6lNLv+N/zzEFccf8b764rYFdzEgJmvP8n8svItPje7r29c6/8nEplPIe/eQMo99p2o2YE7PHUyoP3WTywM8uuLOmh0AL62dwcea+3+OFQqFWr/eteQuThxxIncuvouTRvafD+vfxxvjlvl38dmNfBZtLqrjsf7f78Y373mQPx175AZ9sSm8tHYG3WWDIyv327rP8Y09Ux599NGaprZYLNLW1lZbi6yP67r04vI780Di6y3VSvict/opXNfdYAHc0NDA17/+dUqlEmedddYG9V522WX87W9/47777mOnnXbikksuYfLkybz99tukUikmTJjA//zP/zB58mSGDBnCXnvtxZAhQ95Xv3xokB8xvva1r8m9995bLl68WEop5ZtvvilHjx4tL7zwQimllEEQyN13311+8YtflNlsVi5ZskTuscce8pxzzqnVcdttt8m6ujr5+OOPS8dx5JVXXimTyaRcunTpRtvs7e2VlmXJnzx5tXy2/W/yxrdvrh37xpNt8tXOa+Q/Ftws7116g7xt/s3y/mU3SCmlvHfpDfL+ZTfIJ1ZdJw/77kx5zZy+826br/4+febttX1fuO9WKaWUP3mpbaN2/PY1dc5f3lS/v/zodPmrWX11fv+5/uf9eXbfsZ+93P/YT15qk+c/3SZ///rN/fZ/7Yk2+Y0nVdkHl19f23/Ri33nf/nR6bW//7Pkxo3a+o8F/ev94fNt8ua5N/fru97eXjnisJOklFL+4pU2ecEzbbVrXNfuo/42U37jyTZ5zZyb5f8+q+z4+7yb5flP99l0wTN9f/9nyY39+rq67/ev3ywfXH69PPL8O6WUUh5/9j+llGos1u2rMx/6e82mm+f2HbvxbdX+T17a0M5fvNImn1h13QZjIKWU5z/dVrNbSjWO9y+7oTYHLntV/e4oXS+//5wq+8Pn2+Rf3rxZXvfWzfKHz6tz//JmfzvXb39dznnwennY6ZOllLI2ntVrXb+slLLWhpRq7NY9Z91z123vT2/cvNG21x2L02feLg/84T1SStlv7Nfn8O/cJaWU8tgb73zHMu/GuU9t/J55N9ad3+vfB+tTnU+9vb3yG9/4xvtu692oPi82RrWPq7/XpTp/1+Wks/6x+Qx7nxx97r//q/P/m76tPof+veim931u9f6r8vd57z4XNsYra6+t/V19vlz3lqpn3Xn2bty1eOPP0ipPr77uPeuotr0u3374+k3q14O/f3e/7c7yTf1s2tgcfD+s/xm0qdy7tP/9UX0Gfvpfd9T2rWvbYytVfz+yYtP6fd1nxyf+7z+1v8Olv33X85YvXy4B2dvbu9Hjvb29EpDXJg6Vf08e2e/n2sSh73qulFI+8sgjEpCe5/XbP3LkSPm73/2utu15nmxubpY33rjh/Fm+fLmcPHnyu17Hh5kd5g3wpvLrX/+a733vexx00EF0d3fT1NTEqaeeWntroGkad911F1//+tcZNGgQtm3zuc99jksvvbRWx2mnnUZ7ezuf//znWbNmDRMmTOA///kPw4YN21aXFREREREREbGDYRgCYz3nXkN+MGff3t5eFi9e3M+J3zAMJk2axCuvvMIZZ5wBwLHHHovjOKRSKf785z9/cOO3cz5yC+BUKsWf/vQn/vSnP71jmREjRvCf//znXev55je/yTe/+c1NblcIwb1/vI26KRNpnLTXJp8X8e40TZi0rU3YYZmw327b2oQdksMPP3xbm7DDEvXtliHq183Lfffdt8maak0XaOstgLUPuACuOvJvzIm/egzgnnvu+UD1f9j4yC2AtxW2bXPMOZ9mv7EGc3u2tTU7Ds27RAvgLcUu++++rU3YITnyyPenT47YdKK+3TJE/bp5mTx5Mrvttht//etf37OsYQgMTfCys5ZX3LUA+DL8QO1WHfU35sS/w+p834WPXBzgiIiIiIiIiIgPA6YhMA3Bx5MtnN0wgX3izWjaB3sDXFdXx8iRI3nhhRdq+3zfZ9asWUya9NF7mSSkXDePaMSWIJvNMmHCBD5/7UVMGhLjhbVw8MCQeVmNXRt85vcaTBs/hd++PoOBcbjnujh/uKjI82sgYQYUPZ2X1wpGZ2BJDsbVQ9oMKfsahibpKAtGp33m9Bi82amxdHGGwyb1sG+Lx/MdJl/bZQoPr7iDW2Yl+PI+OfYd8GleWXsrHSWTl9cKul341cdVmZc6NL67Z58X8E9emsHqtTEaGsr8Yt8p3LP0nzywxODyg0/it6/PwPHhe5Om8ODyOzhyqPJAvn/ZPzl62Cmc89B/GD/QZWUBfrGvqvOmuTN4uxd+ts8U/t8rM7ANOH/3Kdw0dwZnjFNl/j5/BocNLnDbwiSzl8X40l55FmQNpo7ts+vbT9xJs+EA0FRvMDAOOuBJmDJqCncvmcFxI1T5mYtn8EY3jK2DU0dP4fq3ZzC+zueRVQaj0pA2YK0D08ZP4U+zZzA6BceNmELbvBl0u/DNXafws5dncOhgn79ckWHiqUWcEH64V/9+6i3qXH7wSVw6awYpC/Zt8fhYy6dr7acsVde6/O8z/+JTY1z2HfBpplz3GDO+9AkAzn1kJuWizn5jypy58xQunTWDcfXwyZF95z+5+nbaiwanjp7Cb16bweAEzOmBS/aewud+8gB/v/ioWtlLZ80gbvZv/+4l6vpWFqAlDquLsCavYxghI22PM8Yew+/nPciIFPzzqUZO3L+L19oNfvcJ5dn8lf/cw9XHH8uls2YwOgOtCZ/2osEb3WBqMCxJbUx/+/oMBsToN4YzF8/gnnk2Vxx1XG3fbQtm8OkxU7jgqX8zJBPW5tfG+L/nZvCrj0/hC3c8wI2nHsXf589gUQ4OGhhwyKA+T/4Lnvo3lx343pEbnl9zG/sO+DSd5Vtpin1mo2UeXnEHhw95f57265LL5QBIp9M82347+7WettFys7tvYdeGz2702JOrb+eggadx6awZ/e7V9+LPb87gkEHORuutXvtf5szgqxP66vzT7Bm1OXPP0n9y7PBT3rOdR1f+g0Mb6yB2VG2MNgfVmKhXvzWTU0aVeaXDpqMkOH2sigSSz+eBvvBSL3T8i33WiSLR7fyLBvvk9avdKDMWzWTKqL7ILd+4+yEOHlfiszttPJrLulz91ky+svN65ZxHwD6stvn4qjs5ZFBfxIFn1/yL/Qb0t239Mu/E46vupN7ymdikxubN7jvYpaH/HP3qnY/wpf16N2hjY3avf361X38/71EunPTO1/+n2TP55q59x7/060c59TN5ThjxzuecPfNhrjqhT17xj4Uz+dToDcs/vOIupl+f5PBPF2vjvSncOHcmXxinyj+w/C6e7xCMr4NPjT6Br/zsIa7+4REb7fv1uW3BTD49pq/dRdl/MCqzYbSQs2c+zNn79bJnJZLGO8Xq/e3rM/hcy/6MHz+e3t7ejYZQzWaz1NXVMWP4kSQ1s7b/xVIHzxTXcFdu6UbPDYIAz/N4/PHHmTx5Mvl8Hl3XsSwLTdO49NJL+eMf/8jdd9/NmDFj+NnPfsb1119fiwLxUSKSQGwlyuUyD199O2+kDFaXIHX0rqQqyTciIiIiIt6ZcrnMxIkTaW9vB+Cn6xz7P772Lmee8x7b78y569V7J/Ddd22rj59uUrlvrLe9MdvWL/NufGudv8/b4Ojd79hGH312b3g+wFXvcV2/Wu/4g3+Er73HOcPWO3z+u5S/76r3Gu8NuXAj5attDPtLdc97z4vvbFDP+Rstp7yHzgWgtbWV1157rbYIfvDBB3n44Yd5ae18Fscffc82oSKB0Pve+OquQHuX7+5vuukmvvSlL9W2q4vaRx55hEMPPZQLLriAXC7HkUceSTabZe+99+bee+/9yC1+IVoAbzV83+ewcz7Lx0fEeapd8PEhAXN6NOwk6J5BJpNBxE2MhMQNLVIZF6sAtinwPR0KGkZCgi8wExLLCgl8DVOT6JrAToHmGgR5HVe3EHGDWEoi8qpuq8fAMy1iKbUdKxtYmgEFjVCjVoa83u8/Shkz8S2TMBaoMkmDwDZr9uJT2189r/q3b5mIuCQM+rRHesIgdITajpsIQx3TE33nGwmDZFpHxFXbsZSB7hv97ApsE0yVQUeLG5gJ0AXIUNVnrmOPmTSgLDASstaWnVLXZiQkpgm6Tu2azGRfOaErW2XMxE6CG1oQ95DrXFO1n4JA77suSxJLyZotlAXCYsP/1mMmsVRIJpPB0eza8cA28TwDLeHX6jQr9lexcwYGRu24kZDIsrLXlVb/tuImwuzfvpk00A0gFOgJCVIQeDrCDBG2JJ1OI2MmekLiaDZaom/sATzT6te2nQQDA1kSSA30dewVcRM9Ljdo37fMfvuMyjwIbBMRD2vza2OEMXWuayg7jISB9AR2UmwwV96pjnWJFVXbjmWQiW28vNVjbFJd70UmkyFWeOe64t47H7NzfWP+fmwRcYN4KtjoOdVr1+L92xXrtLHuPf5u2L0GmYwFsUxtjDYH7e3tLFu2bLPVFxGxJclmswwbNoxYLFabs6eccgqnnHIKl7w8nbNaP8H111//nvWYpsBcZwF8QP0A9kg38O/epRstP23aNKZNm/aO9QkhuOSSS7jkkkve1/XsiEQL4IiIiIiIDwWZTCZaAEd8pFj/DfCzuTU8mW3fhhbtOEQa4K1A9T/Ba5++FBlLEjdCWuI+T662GF8X8sJaHYDjhjmsLJgMTXncv9zGWPAWV/zwBm5/7CLmZ9VXKCPSHrYuuXmezUv/eyVdQ4bw/Z+eRIMocNZhP+CJh59mdfoZ3uoxOH54nv8sTfGVCVM59Kp7ufCENQxKeMzpjnHamKn87vU2ztt9Kid95Z988tslLA0CCXN6BL/6+OncNLeN+TmBLtTb1fmdOq6jE4v7XHv4Z/m/56YzqUliapDzYFAiIOvqtCZ8luQM3BBWFAUX7XV6rS++9fR0PjvGJW0GlAKNt7otlhYEuzeE5DzB3y9P8PnvFOlxoRxAZ1lwyCCfzrJOS9zH1iXDUy6/eS3FEY1lXlij89NDvgjACx3X8fwai/t+H+dbF+foKhssLcB3Jk7lta5reXp1jDVlwbKcYL/WkJaYJJDQURbs3eJwz7IYZ47P8fCKFAMTPqEUPLPGYFRKMrdX8OpfY9x59Sl899npjEyrcyfUB7y4VpXxQjhj3FQAHlpxA7qAub0Gs7sFvz/gdP7wRhudjmCf5oATRpzBVW+2YWrQGg95YrXOoIQkpsMzy016btMZ92WHS/c7nSMumMlDl53At5+ZzuX7n87jq24g62o8t0ZnXJ3k5ivS3Pd7pRU89R8z+NaBWVYWDF7rEgxPSRZkBWPrJJYGi/KCY4a6vNltcsPDzXzrmLU81S54bXY9yWdLNH7WZ1AmYJjuEdMhkzGYmxV8f8/Tufil6fzkY6dz0lf+yeHnlFhREJgajMlIXusSfG/PHP9clGJur+CQgZJTR6u+uOrNNpIGvNKpHuIHtkpWl2BNWbCww6SzI8YRe2TpcQQ710v+8UqSSTsVuWiv07l6jjr3X2/E2XlomSOGeKwoGHSUYXaXRk+Xze1TlPbxhK/NYOaf+/SmMxbdzJ8fbWCnsb2kTfjVx/vm4bo80349+7dOq20f941/cfcVJ9e2b5nfxmd3UtfSNq+NqWOncsfCttr1Ady+oI3TxqjtmUtu4oQRZ9A2r420KTlp5Of7PQvgnd9qA9w0t403ukU/e29f0EanA2fvMnWj51TbBLhv2Y1MHvYFAK6Y3cY3dt34OevyyVv+zb8/26eT/vWrbfzvHn3n3bn4Zk4a+fna9Ve5/u02po1Xz5KEAXG9cg/IR0AozWt13gA8sPxGjhqqbFtR+AtDkl/tZ8e6ZWcsupkpo1TfVfWQ76SXjIjY3lh/zq57X0657V/8cdw8hk363/fUAD+y5zGkdLPfsXzgcdise6P74b8kigKxHXP1j2/k5HNOQXs3wU+FeMLmlC8fw3e+852tYFlERERERETElsY0hJJBVH5eKHRw1aq3trVZOwTRAng7ZeELs+nu6GXPwzY9acYRpx7Igw8+yLIFq7egZRERERERERFbA8MU/X4OaWnl/NG7bGuzdggiDfB2yuyHXmCPA3ZF09X/KIEfcNvlt/HMv58gDEPGHHsY6wdXSdcn2WeffXjinpfY6/NDt77REREREREREZsN3ZDoRp9S9cnONTzSGb3k2hxEb4C3IqtLggkNZUamXbxQsFtDQN7TGJWSHDXEZW6PxZs9GllXY+nshYzceQh1lqTgaTxz07959u7nOOfaC3n0jV8zPKPx7HPPst8whwY7JGFIXu3SybfGePDpxfS40FE22XeAw0MrbuCUgzrZo8nnxY4YNzybZkXhL2gCfjlrOl/8XokVRUHCgJFpn4wFv3u9DU3ATmlJ0pDs3ewzrjng4FEOHx8U8O1npnPKKIdOBwYnPR5ZYvK3l1OsKELJV7GFx2R89h/gA0orePWcNg4ZKLH1kN0az2JpzmJw0mffFh8vFDy+WnD29/IUfBiaDBkYh4mNkgVZnXIAv3m4kXorYNbaBCcM9zE0yeCkqnvmkptoLxrc+lgzx51XYmydQ0vcZ2QKznr4Fn75VD31FuzZGHLmzg7j6z2KvmDn+jINFjy/xmbvZp8eR2dcvcvKgsHQpMtFe53OgHjAUUM9zvi/Em90XcuJIzz2avY4d7epaEKyc13I4UMczhg3lWvfauOrT0wnYUiGJF1mdwv2bZE80349s7sFQxKSu5bq/OiF6dRbcOdlMa58vJ4jh/js1eyzICs4bbxT0//et+xG9vpUlm8/o3THj6+6gaFJl6Epj5/uczpLC4JdP5vnzsU3c8a9t3PBwb0cMuiLeCFMGeUwJhMwsVE9POssyUGtPi+vNTlr56lc+qmVLMjBzvWS/ztmLZ/63zINyYCBCcnUsVN4rl2gCUlXWc3fQwZ6XPzSdEZNK/PI72O4IXysWWmf9x8gOfvOgRw7rMjkoQE9rtKfPr7qBjodNbe+OqHIrNcbOHX0VAIJU0aWOXiEy/8c1AvAwIQkYUgy9S4DYsrmWZ2Cnepcdh5aptOBvKcxLOWTNmGfASFf3jvPD56fTtu8Nmb+eQq/frUNgGNumMnCnKDu4TyFosEnBgUb3I9XvanK7t86jTsX38zFL00HqOl/Zy65iRmLbubx1X0OKFPHTuWsh2+p6X9/OWs6l7w8ndPGTGXmkpsAalrcqWOn1vS/V8xuq7W3Lr95rY2vPzm9tn31nDbG1bscPLDP3itmK33xuvrfm+a2ccfCvvqqbQI1nSHA3F7BQytuqG2fNuOf3L1U2XnxS9O5aa6qw3uivyvI/+4xlavn9NVfvY6pY6fy7Wf67J02fioPLL+RhAFfmTC1poE/7V/dtTJVTS/AUUO/wBWz27j+7baa/veEtjsBpcVet2yX09fvty64lYiIDyMvdtzMfctu5LZ5Vm2f8xiIlo37JKyP0Pr/HNwygG/vNGFLmfuRIloAb6eUswWS6URte+YtT3LktOMYvNMQLNvkU+eeSqouscF5djKBky1sTVMjIiIiIiIitgCaLjf6E/HfEy2At1NimSSFXLG23bGqm+ahLbVtTddoGdy0wXlOoYidSW4VGyMiIiIiPhhvvfUWQoj3/Dn++OO3tak7HB+mvtd0WZNB6Ibkqe52Lp8/Z1ubtUMQaYC3Er7v88/f3oY5ZTcOPHy39yzfOn4US+euqG23DGqgc0VHbTsMQtau6trgvI4Fyxj+8T02j9EREREREVuEgQMH8swzz9S2X3zxRc455xx+/vOfc/jhh/crF7F52dZ937H0FX7yk7s3qaxmSLR1NMCHDmrhY8313Lx0yRaxbVuxcOHCTSqnaRojR47cLG1GC+CtRH19PZ88+1QmjRAU8z5lV+L4Gp4r8QMo6z5+USBL4BR8xh2wFw/++mrKWRcnLjn65P3593V3M27SzvQYaW7/7UxyPQV8N8Qt+JT9gOKaIqtnL+Cwc7+ALPmU8kp/6ziSsAj5nI9f1DF9l0IuICz6yAA8HWQJPBMc30eWINTBDyGUEDrgWAGyJAl8kAJEGcp5n6CoUc776I6H4UrCoo9jBoRFiWP5eFKQzWbxiz5BCJ4PJc0na2XxCj6Or+Lteq5EK4NbCPHLGm4Y4vkaIRC44Otg+S7FvI9b0NGMEL/kE5TA18ANQgDswCEo+hRyAU7ex3NAd0IMF7yij6tLyoFPIMErCopWgFfwCVxwtICiDCgFEr+oUcwFZPUsTsFH6BKvBEXTp+xAoEmycXXMdSX5nE/MU9epVfqm6AWIsodfhLLmo5UFQRG0MiDBs8HUPYSvxlxqElHpA1EOyWYrbZclQqoxKds+hTCg5EuyZhZZ8hAOuAWJ7qixyMaVHSXTx3ElfkkSBOAGEscMCYqSbDZLOe8jSxqBr67dL6n2Qx1yuRya4+MWNERZ1GyhBMIFU9cQZQ+vAIGjxtX01LxyCz5+SSMIoGyp+eQZUDQDrMAlm80SFtX8DIo6biAJS4JAA1dKdEcQFH2y2Sxa2aOc96Gkobmqb8puiF9Uc9MRAZQkvq3iZsqSOs/yXcKSj6Gr+tyCX4vBW6XaBoBb8KEk+pVxC0qbrpX779cdr7YtSx4yVG1vrI1120Kofl2XsKTmS/W8oOhTtn3c9fatX69f9PF03rG9KqLs4axjl+F6fXaWPPxKHZbmvmv/rF/nuvudgl+bU1UM13vXvvCNPttN363Nx2y87xx/nfaD4oYa7g879fX17LfffrXtF154AYCTTz6ZXXaJvPy3JFuz78t5H9/20Z2+58iQkbtywTlT+etf//qe5+u6RF9P8qCHO54EYqeddkII8a5lpJTE43EKhc0j84wSYWwFstkso0aN4pDjJmJaBqaQfOzgXRm33x6UfY2EGbAoa7BTxicAhiU9ioHg9GN+zqFfOpmvf2ZXessh1176T+7713OEoeTwUw9h3itvM36f3Tj2Kyr4//P/uJfZz77B0ZdcwG4NIQ22T9nXcEJBTJfM6zWYl4UzdipTCDSkhFUFCycEW4PGmE85EPzxgWYuP2UlxUDQ6xgsyhm0xELa/pDg3P/NkjJCbl0Yo6ugc9IYl8U5nda4pL0kGJkOcAONettnVdFkt4YyPa5Oe9GkFKjkFsrZziVlhHS7Ok12gCuh1zEo+hqPrtIYlICUASPSATE9JG6EzO+16SzDzg0+zTGfrh7oKBkQNxiecpnU/Bn+Pn8GuzU6GJqks2zgh4LHVul8cqRLTA9psAPcQOCHAk3Am90xhqZcbpgTZ1A65DsTp3DRizMYnYbWuKQp5nPPMpMJ9TAgHrCioLNLg8vDKy2OHuqwe+Nn+c1rMzh4oEfR13hytc4Rg32EgLuXGUxqUtc8scmhbZ7NPi0wKuOS9zTiuqTX1UkYIaMyDllH582eGANiPj2uztqyoDUuGZZ2aC+a2JpkYc4gpsOcHjVmL82pY8DAIp8Y4eGGsCQHRw31GBD3eHltAh2oj/kYAvZrPY07Fs5gcMKnPuazoCeGpUteXKuSjdRZAasKFpNaHM79exN77NzLXkN1Yob656KzrDOu3uGuxTafHFUm56oELsvyJi3xgCEJj5yv8Y+FFkcOCViW12mOSbocQaMtkVLwcicMjIOhwZ7NLktyFk0xn/aiwd4tJbKeRsHTuW+5QcqENSVU38fg3oUmp4wv05rweGB5jEdeaGTX3bpqbTXZcNX9zVx88iqSZsDsrhinjl43McYMnlkt2LlBcubOU9a9Rbl7yQyOG9F/3zvxhTse4MZTj9pg/1fPvZO//OGk2vZtC2Ywc1aKxuYyv/vEiQDc/ubtHDOkiXTd4f3Onbl4BieMnNLv3E+PmcKMRTOYMmrD/QDPrL6dnKeTNgP2H3ha7di69rWXbuaxlUnm9MDFH5vCb16bwU4Z+OTIjV/rD1+Ywc/26X/sweV3sLygkTap9eff588gptPPtur+z+00hS9ceC83/vwYbpo7gzPGbVjmuRUGv/vEiXzhjgfQn/M541tFFuc0ztx5Cs+svp39B57W75xqMqEdOfD/WWedxfTp08nn8+i6vq3N+UixJfq+msjiwO/dwlEnlDl/9yk8+OCD/PmfV7Oow+Tgwc1cf/3175kIY87JR5I2+yfCyHkeE/714A51P6RSKWbPnv2uZaSU7LnnnvT09GyWNqM3wFsJ3/c558enkUzHyVSyoHWWDUJfwzYFum9gp1QmtmQ6RPoa5/z8DC793s18+8zdCOMxvv+rz3LZ7z9FydeY12vTmvBZmrPodQVOscyMGx7ghhnf4kXdxEoGxGIgfQ0CQcyQ6J6BdAWJtEfo64QSTAyCAEwdYnGQvsDRbZJpHelrOKaBERhY8RAvNImlDBJmgIyZBL6OnQzRAx0rIdGFwE4KCDRiMVV3Iq1TdgxMYeD5oAdgGBBLhSTMgJJjkIiBHgoc08D3NGRMR8Qlugl2UhAz1ALY8Aw0DewkJOJQ8CSmUAvgWCokk8lgJAziqQBDk9i6gR4KZMwgngqJ6yGpGDiBwAtVhjvLVccC24S4qkPGTPSExEpI1ScxEyMhsRMCXerEUiHETeKpQD184iaxlCTwNGRMjaMm+s4zAoinAsLKdjwV4rkaMUNScnRiZkgq7eNbOqZrYCfAcnR0TWAlJPFUgC0MbF2iBwaGDrIsQAdXtwhsDyMhCUPAF8RSkmQixCwa6ALsGJgatf6xUxCPgekZWLpE5jXsJMRtgYVBKu3jahbEDKykjl1ZABuaTjwVVK7dx6ssgI3QwE4IEqkQ39MJYyZ2UqCHOlZcousCKyYJpUAWBFpComsQT4WYgYEdBwODZFrHc3UCT0fGTLDUW149ITHjENgmdtInnpSIuImj2f3aMmNQ1uzKHBUYZaPfh4ORMAhiGloi3OBDw0wam/xB4hrWRsv6gblBe65hEdhBbb8RN0inNzx//faNhFEbr43tB7DzBo6rY1uiX9l17SsYOkbCQJZFba6aCfmO1xrGzA2OWUkDXWoYZl8GOyOh5uH6ZWs2hMoGPbFhvxoJg8A2a7YaocBKGmiBRiaTwc5v+ljsSLz66qvstttu0eJ3G7Al+97BQsTVM+CUU04hu2uJ257P8LPD9+L6669/z/N1g35h0B5Z1cH968ghdxROOukkRowY8Z7lTjjhhM3WZrQA3kr4vs+VP7+DgyfvydFHb1oIk933Hcf/3fELNK0E4buXtRMxbnnuNwxJury4aVKaiIiIiB0OKWE9pclmJ52G9/i29n3h+z6zZ89m6tT3Tlu9PSOlJOduuc5PW+n3/Jr8/bIt+r7jtZf5yRN3bVJZTesf9eGIoc3sO6Ce6+ct3VLmbROmT5/+3oWAm2++ebO1GS2AtxKGYfD1C08lmY4DO56eLSIiImJ7IJeDurot20ZvL2zOl9Rvv/025XKZPffcc4Nj06ZNY9q0aRx66KEb3d6eyLk56v7fluv83u/1krE377cD79b36zNv3jwSiQRDhgz5r9psmbgXFx/+5U3SAK/vBAeg7YAa4PUJgmCLfxsSLYC3Ii0xj+aUTnvJxNIkk5olS/Mu3Y7OAQPLZF0dz9dIGFD0IWWGnDyyQMHT1FfBQtJRNtBQX7HXWz5DB+qsLPrE9ZA53XEaYxo710l0ob5ytnXJ+PoyPY7Ofq0B+w6ARTmbvVs8FuUE+wwo0F4yWVUw6XF0nEDw1aPW4gSCF9bEaLQlY+t8vBBOOL/E/F4LS4Pzds/xn6UpAPZvdWi0fUY4Bgt6bUZmXJbnTXZucFicsxiS9JjY5NHjGpR8DU1ISr7GzCU2Rw31mNdrY2oVO12Dr+/icM1bCcZmQsbXl1lVNGmO+aRNiz2aHJ5ujxFKQcoos3tTkU6RwNYlr3Vdy6RmweqiScnXaE14pMyQz+0UsKDXYlTGxfY0FudsRqYdFuUsRqRdir7GJ0f72HrILfPbOHtCiayn82KHhR+ajMlI9mx20ISkMRYQ10NOH1PkkZVxluRupsESPLHaYnhScvxwDycQDE+5/M/OPu0lg3m9Bp1lnVNHeZgaZF2NhCF5bo3JwDgMTHg8vCLFXs0lOh3Yo8kjYwUcMDDk8ZVxHlgeY2xGkvcEI1IBjbEAsBiW8tmruYuUGTKnR+ekEQWOGSZ5tj2l5kbKoyXmkzID7l2WwgtvYO8Wj6fb46wuGqwpw17NPiePhLgRkvc06u2Aeb0Gh+3dzcikSoCw72UP8cvPLWdCg8NTq232aQl5YU2MSc0O3Y7Ofq1F1pZNNCEJJXxjlyJv9cQoB9BgB4xIB8zptul04NL9TqdtXhuhBFsPWVOGcfUBXWWDGYsT7NPiszBrMKlJsrwoOGOsyxtdJqGEr08q0Gj7ZD2dvZt99jtlFSsLBoOSHvV2QHvR5H+ObaezrPNmt8nQZMALHdfxi0ca+OmRnUwdexYDEzdwxyKTX86aji4k/7uHeutz6uip/HLWdL6/5+nItX9DNJ9Zu29//WobnWXBySNd9m+dxtT9svz8lelcOOl0rn2rjV0aPK56NcHtN32mds5Nc9uYNn4qMy69gxuu+TQPrbiBI4Z8kSmjphDKJ7hv2Y1MHvYFfv7KdM4Ym+e0MV+pnfvF+2/jhqOncsv8NjrKcOfim0maAUcM+SJ3X6IzrZKf4tDBX6ydc8nL07lor6kcfvU9pJ93ObRwL2cf2clnd/oqumjj8v1V0P11E00cc8NM7v1i/68T/3Rg3/G2eW0UfPjKhL4kGzMW3cySvGBgHHQBf3hDGdNgwRnjprIgK7hpbht3XzGVN7quZbfGvg+wB5bfyOqizlk7T2Vu73Rumd/GwbsLvn/G6Ty68gZeWB6D3eDx1SaHDu7/7Lxl/qYnwkin1QJ1S5JOb976Zs2aBcAee3y4I/ikrTS939tynZ+2NnPH8/76/r777mPo0KHvawG8y8Fd7Nloc+hV9/Lo2ccQ0+F7R3a957e6VbT1MsHBR2MBfOyxx3L//fdv0TaiBfBWwvd9Lv3JDI49fiITDtpzW5sTERERsUMixOZ9O7s1ePXVVxFCfOgXwEKIzf6GdkuzLfr++Udf5+ZHntqkshtLfLEjJcK46KKLNtgnpWTBggVbvO1oAbyVMAyD7148heaGGO2lbW1NRERERMT2wqxZsxg5cuRH0vlvW7Mt+n7fQ3fnc4ccuWlh0DbyBnhHCoP25z//md/85jesH5Askdgw0+3mJloAR0REREREbENeffVVDjzwwG1txkeS7b3vhaZ+1t+3ozBhwgSOOOKIDWQl99133xZvewfqxu0b3/f56UX/5v773iTvadh6SCA98p7O0KRHygjJWAG2Lul1BSkzYGQ6IJTQGING2yfv65hCkrECBiU82ksmC7IhPY5BKdAwNElX2WBsfZmirzEq7WHrSmjUUVa62G5HJ2WGaEJnUMKjx9UZmnSZ0FDC1kPG1DmMznh0Ogbj6z1CKehxdBKGZOd6H0uDkRmXR1em2HeAgxNoeKGgo2wyu8tmUNIjrof89ck6WmIerQkfXwrWVtofX++xKGuyc73L2bvkyFgBBw7UiBuSvKfTEvNImQEtccnYOoeCp6EJKHgava7A1CTHD88zNOkyOG7wYkeCoUkVV9cUEi8UNNhBTffc4+jE9ZARaQ9NQI9rUG8H9LgGrXGfjpKKIdwS97F1yR5NJdpLJv9cZHPwoDITGsqMSIXUWz49jsEjKy1WFVVMxj2aPEakXeZnBYcOconpKpby6IzDG11xVhRMFucMPtbi0hr3aYkrLfXqokHRF4xISQo+3Lc8xrCUz6udcfZo8sh6OkOTMK9X6Vs/PsBjz+YiuzSUyHs6th7SFAvRBIxIu3ysReOggQ5ZT2dp3mZUxmFp3qLJ9qmzJKZmcsywPCkzpNMx+PiAMpOaS6wuKm9qTUjynkZr3GN4yiGUgiOHlElZAU+uvp6rpi2hxzFIGiED4zC2zgGg29FZWTB4uydGveWT83TqrYB6O2BUxmWvZh9DU+M6LOXTZMMz7dczocFhj+YyTqCxV7NH1tUZW+9w2GCHkWmHXRtd1pTh+OGlinbdYUJDmSFJl1VFk3rLZ06PTsZS/eAEGvVWQNIMmNjk4oWCmA7PdRgUPI1PTCjw3JoYb3RdyxFDvsiVB53OwQPdmv63Ss5Vv6v632vfUvrWF3+tc/xwj+V5Ne5Tx/adVw5gPwMmDvS4/u02Ln5JeTL/5T6VtvwLPyhz+4I2Lv7nIJ5bcx3Xvz0DTRzMDRcY/OGNNkamJN86twFQGtk7FrZxw9Gf5vq322iwA87bfSrLCoIjhii9721tn+X2BW386IXpzFjU5w190V6nM/U//2DAIz38+5pTmXZYJzO/p449ury/I0nbPHVd9hMOj668AYDfvd5W+339221cMbuNqWOn8o9n62vn/ea1NtpLgn891cQjqwSvdApynuDc3aYSSDjr4Vs4eqiKR/1a17Xs1ngWS3JmTSd86QNNPLhY9eGEesm/fqTx/T2V5rizbLDfMDUAKyox7r94/221tpcVNq/n//bE6tWrWbNmzYde/vBhZGv0/T4DQurtgLOP7OT6t9sYmvK45Lq3+clPfrJJ5wtDQ5jr/Rg7ztLtscce26imelOjQvw3RG+AtxKGYfDNH57K8GaT5ZsniUlERERExIecgQMHbvD1b8TWYVv1/ZC99+TiLx64SRIITF39VHhgYQd3z99x4gBv7rB274cd59+IiIiIiIiIiIjNyH/+8x/2228/9ttvP37zm9/wf//3f7Xtl19+eYu3L0zR7+3v0eNbueTI8Vu83W3FqaeeutXait4AR0RERERERERshOOPP57jjz9+m7VflUBUeWD+Gv7zVvs2s2dLs3Llyq3WVvQGeCvh+z5/+tkdPPbgu+e6joiIiIiIiNhxWfHirE3XAJt6/x9dV4kAdlC2piQiWgBvJQzD4Ne//AxNe+3N2DqfjCVYUdBYmDWUE5Kv4YeC8fUe9TaEUqAJnZiuJkPKDBmRkjTGIG6Y6EIyNOnS4+i0xpWz2piMQ9HXyLoqscb8rMmQZEiPoxzddm1IMDjhsXujgSTE1CQ9jsHSvEXB1xmU9EibAa1x5Zg0POWyW2MJJxCkzYA3uw0mNpXwQ8GEBoe8pzEy49Dj6NRbPgcPKjAq7aALyfeO7GJebwxQTlaNtq/6QVjsO6CEG0pWFi28UNDtFDE1yaqiSbdj4IWCKSOLNNg+TqhR8jXyvs6ujQ4t8RjzemN0lE1Wlz0mNJTJejqj0g6GJmmNexiapBRo7N1SZs9mF1sPyboaTbZPvaWShrTGPdpLBrYuaS+ZNNrKCc6TgqQZ8tUJBeJ6iC8FI9MuWU9nUMJlvwGq3OKcRb3l0+3ofHaMS2dZZ3x9GVNIFudsMhVnsIMGKkcugJaYcrobnfEq7QqOGFJg8tAyI9MOti4ZnPDIexq9bkDJ19CFSjjgh4KspzOxqaicxurKDE261FsB83qV4yGo5BJOoJwKAymY22sxt1ej6Ctnwp0yyjFyecHkyzsX6Cor57bhKbfWxphMmZKvUWcFVKYfk5qL9Lg6TTGfeb02hw0u0hr32bulhCbACTWcQCU5WZC18UJBS8zDDwVOIGi0ffYdUGR4ymFlwaTHMWiJqXm2umiQdXWcQKO9ZGLrISeNUNeZ9zTiekiXY/DwiiStcY+0CWeMDai3lGPh4ISLEwqGJj1KvsYhg0LG1buMy0jG1pU5b/epTGp2eGxVjHm9f+Xnr0znoIHTeKHjOh5fdQMzl9wEwK4NkjsXK8cy2XUjjy62uHPxzdzW9lkOGfRFThszlZvmtnHtW21cOOl0nltzHTEdRNM0vj3EY1AiYFRK8pvX2vj8EUqjd+uPdO5eZHD0gWv5+IAv4Ycwp+fvTL/1dM7dbSpTx06lY5LKnDVzqcGpowfzTPv16ALq7YCr5ygHslvmt/HzV6bzm9fauOKBJo4b7tJeUoNz0B/v56a5bbQd/yn+foUHwKK84Mt/8Dnr4VvYpVllnvzlrOkc9Mf7aY4F3Ln4Znr3TlL0NZ5pv56sJ/jus9M5ckiZciVR5c9fmc5XP9FTe4bVW/DVgQHfP34N9Rb8Yt/T2aNRFR6SDNilOeDQwV9kbJ3PxMazeHL19f0cBpNPFBmQ8Wvbt/z9cxz0x/s54PcP8Oyavg+9vxysHONWLE1xy3x1/Yt7ProfVSeffDIjR458x+2I7ZsBMcl1s2PYuiSQUPI19j1yN045Z/dNq8DS+v+YlQ+FiP+aSAIRERERERGxnXLyySe/63bEjk1V+1vl6N0Gsd9Ozfzl6UXb0Kodg2gBHBERERERERGxHSIMJX2ocv+bq5n52optaNGWZWtG5fjofq8UEREREREREbE9Y2r9fo7eYzA/+9Se29qqLcYzzzyz1doSMgpAuMXJZrMMGjSIU6fuw4FHT+Kk43YmpqdwgiKWHieQHj2OT3MswVs9SofbEpdYWpxQBnihSjxg6wncsIQhLEIC2oshzbGQmJGivVggYwV0lA1a45KSH5I0TaSU6MKg4JdJmQmW5pVuOGMFGJpkYFwghEBKSSgDDM0CoOg79LgqsUFVn1tvBSzKWYxKq2QDXigYkozhhiVMLca8Xo/x9TGcoICpqd8lXyNpCpbmdQYnXPKeTiihwZZoQqfoK01gl2PQGvfoKJu0Fw1Gpl00ISttW7TGPZxA1M6b26tRF5RJmTqxVMib3THG1jlYmmBtWSNlBszrjTEo4TEgLlhTUglE/FCQNAWGsAikz7PtFhObiiwvqDZSZsjinM2ghIupqSQSC7IanWWdwUmvknBBEMqAxTkbTUh2yujM6pSMyTj0uAY9jkoyUmdJVpd0BsYD1pQ1bE1SZ0nWlLWaxnpt2UATYGthRcssebNbRxOQMgPynk570eCwIT7LCwErCyZ7NZfpdUVtvEMpqLeUNtkJBJqAhGGjCR0vLJN1JbYusTTB/KxZ0WlruKFkXq/NzvVl2ksmrXEPLxSUfI013ZLhKVgWWGRdpR8eW6fKJYywNlZLcxbD0y62poK968KkHPiUfI0GWyPvBdh6yEsdSQ4aBF5YZnFOZ0TKx9AsVhd9WhMaoQyY022SNFXdpqaSmlTtsnVVJpSwvGAxts4klAGLcqp83DBZVVT656U5i/H1ZVJmgBcKFuds8p7GAQPLLM2r/sx7GlkPxtX5zOo0OG/3qZz18C1ce/hnAbj+7TZGZ3yqGUe9UFQSmQieWm0R01UilKyrEsG83asx47EWRozOAjBtjyJL8zpj63z+/GKKT+9W4tFVGsc1l7B0yZvlOANiEEi480LB5y8NWFPS+NxO9dj68dy+oI0DBuYZkvwqj6+6gVDCWz0GX5nQypce6GLiQI+PNfscOviLnP6Z6Rz7U8mojErWsaYMezb5ZF2NE0acwQPLb2RMxuGljgRJM8QPBUNTLnO6beqskOUFjUnNLi92WLzcrnPt4Z/l7Z5reKMrzuOrBb8/4HTm9f6VsXX/w0F/vJ8vHdXBmQMlL3oeV76a4LojP8Pjq25Q7Q0bzOmfa2f6rafzu9fbOHCgy1vdFmeMU1rgL95/G5/fpUzW1TE1yfj6Mo+sjHP2LlOZ1/tXHlmZ4K0ewRfGlbl/uc2gOAxO+uyTnkJdXR29vb1RuuCIDwXZbJa6ujrunXslLQ02SUP5XpR8jXnPzOK6W17l9X8/9I5zunp+558/TSZu9j9W8mj62m075P3geR7f+ta3uPzyy4nFYlu0regN8FbCMAy+e/EUDjx8t21tSkRERERERMQ2Yr/DdueAs07bpLLC6B8H+P45q/nhHbO2rIHbENM0ueWWW7b44heiBXBERERERERExPZJNRRQ5WfypCH8/PS9trVVW5TJkydz3333bfF2Iie4iIiIiIiIiIjtkPWjQFT37cg0NzfzqU99ipNOOolRo0ahaX3Xe8kll2y2diIN8FYgm80ybNgwXp1/GaGtYrUGUhBWYgIOSibJujnihompxQhCD1tP4oVlDM1S2lxhUQpySEJyro+th5iaiSZ0TM3GDUsEoU/MSFH282hCRxIipSRp1uMExZq+1wkKSCkp+lBvx3GDEssKBqYmGZyAmJ6iFGQRaBR9n7ihIaWkHEiWFyxSZsjgBKwpSZpiPnlPw9QkTqB+J01BwZM0x+rIed0EUmBpKq6xEIIex6fRtgEQQkMgcMMSPQ7Uq904QUgooc5KkvMK6ELpcUMZsCRvMDTpsqITGm1JY32KxTmXUWnVD11l8KSgyfbRhbpxlB7Ww9Ktmi7aDUvE9BSvd/lMaAgRaCwvBMT1kIwVkPc01pZNxtVpLCv4DE6Aqdk4QZG1ZY2BCQM/dOl1BUvzFmPryti6rNiv4jQmTZOc62Noan/CsOkoeTTYkpVFncGJgJwHTqCRsQKeWJVkj6YyKwomk5p9Xu/SGZr0aIzplHwV59YJBYuzNh9vVfreetug4HnUWUmKfh4AXWjYeqJiS5FeV9Bgq/HMezrDUkmW5QsMiIvaHHKCIoH0CCW4BY3Z3ToTh/hYmmB1SWdY0mBhLqDJ9nECQY9rMK5OY0XRY2ClnnXnWNaVZCyBpcV5sSNgdVHn6GFqfJpiKtZxa7yOcqBsDqSKgVzVKwMUfUhbBraWJOd1V+rWCCpPrVKgMSqdwQmKFU2wYGjSxdRMluSVNjqUgn8vibNvi8+EBo+XOmwSRsiyvMGAeMDDKw1+9XEVe/bltX/jm23DuON/FnLTvBTlQHDRXqdz09w27l9osd8wl6QBI9M+RV/FqI4bIXN6NCYPLXPX0jh7NAb8+somLvhaJwtzGocMclias5jVpbFfuoQbClaGNi0xSUvcxw8FF/1rIN86Zi2njp5a087aesi8nhg9LuzW6NJZNjhiSCOf+IPNs+cfybVvtZHzlN73Z3e38OCXj+MPb7SRMWHXRpeSr7GyYBBImNDg8ORqm5gOQ5Mhx2YE/+wSLM7D2IxkWUGwc73Sbr+wVkcXMHmow8cHfIlvPT2drstCvvNXlxmLY/zkY6dzxr23c9rODl2OoNOB70ycyoxFN3Ps8Dqebe+i6Gs0xXxmLrH56T6qb2cuuYkTRpxR0xffv0Lw6dEey/IGc3sFRwzxmNdrMDwVVvT2Pp+/fiQ/OWUVmlPg8DHfYNmyZTuc5jFix6T6ud+77G8sTYQ8vDLG5KElCr5Go+2zckWRAyec/54a4O6/n0EmYdX23/fycma+sJQr756zQ2qAAQ477LCN7hdC8PDDD2+2dqI3wBERERER2zWmaTBw4ECGDRu2rU2JiNhkmgY0YlkG4H7wSgxD/VSYvO9I9t9tMFfePee/N3A75ZFHHtkq7UQL4IiIiIiI7Ro7ZrJo0SLmdFzNm91xRmUcir6K6FHw4Y1uwS4xn8ZYwGLPIm3C1LGf4asP/ZO/7B1w7aoQTUCjpaJJaUKyqiTIe2DrcNigMmPrv8i3nr6N3x/waZ5YNZ2DB51ea/+Z9jYeW2WyZ2NI0gz57SONnPixXgbFJYcPaeSFjrXct9xgUqMkbkjq7IA1RQMvhNZEgBOo6DS2BoYGzXGfOjPg23cM5p7TOnnW8fnPUpNDBwUsK6hvrSwNDhpYZITM8I8On0+N+QyPrpyOrUve6jGI6/DZFo0b20Nue6oRgHOP7MQJBCuK8MVxKWZ3d2Nokj2bzuA3r93GA8818u1j1nLbmzEOGeXQXhJMalLRcTQBRw/7LP9edCufHPUZZiy8lYFJn0VrBB0OdGIwIiWJ6dAaD2kvabTEJH99OsPJe+WY0yMYk5HslAmY3a3z0i+h54Q03zi4h3o7YFnOIuvBzg0eHUWDOlu1OyDmkQ9UVJAlWYsRGZdeR8fWJfN6DZYVBA8/28TXjl7L8IzHrQssjh6iMhD2OBojMyqjZMYKCKWgvWjwx3ub+fZxHTy4UuOzY9S3IQcPOp1ZnTfxx+/YXPTHAgMTg3mmvZPGWMD1b9tMGelz8KChPL9mGU22x1/eSrBXk2RFUXDUUId/LLQZkZIcOqjEmLppfOGuO/n8nkXuX6Fx6iivkiE1ZF6vYEXeYJ8BReblU8RiFvj/xQJY18HQN9z3EWL+/Pnous6oUaM2a73RAngr4fs+P/vRbRx09K4cNXnCtjYnIiIi4kNFLBYjlYmT8OOkMhrC00noBqEnsF1BPO6RiAXEPJu4CZlMBiuZIJMJiOfUAjhhg6lJNCGJ6xq+BzEdUhmxTvkMyXy831fLyWKcWM4ikQ5ImiFGPEE85ZFIhGQyCZLlOHbSJJGWxI2QpB2Q0E28EJIJHyMQFAydmA6GJknGfVJWgBFPkskUSZZ97KRFMu0TF2pxY2mQzkgyMkGi7Cu7cnFsXRL3TeI6ZDIa8YKyByCRLqIHgpgmlF1+GVOTZDIZYqkERjxJIl3ATMSJp3RiuiCZVuH9NKH6LJFO1H4nkx7xoiBmgI1JvLIATiYC4rpOIh5iJhLEUwG2J4inJMm0T8wzME0wEwmSaYeUHZDAwnMFqbRLQTdIVhbAqbgBvloAJ6RFKq3jW2oBHA9MYkJU7C6SSrvYSZtkWoXPdC2dZNolcHWSlQVwQjcq5fNYSZ1kWkfzNTKZDCk3jmnGSGdCMokEyWKRVCzASsZIpj0ymSTJUpxUzMBOJtT1aoJUWsNOxoinJKmM6iczkSCRlpU2VCr5dCYkGQoSwiSdkVi+teFEBh57cDb3/PvVTZv4xkYWwOtvb0ZeeuklvvOd7wAQhiHPPPMMa9asoaGhYYu1uT5nnnkmZ555JgcddBC33HILU6dORQjBTTfdxOc+97nN1k60AN5KGIbBD3/6aUIbINzW5kRERERERERsAz5x5K6MnTCKW2947L0LG3o/CcR9zy9m5lMLt5htH/vYx3j00UcBePDBB/nd7363VRe/APfccw9/+tOfAPjtb3/LLbfcQiaT4bvf/e5mXQBHTnBbgaoY/rm3fkJDfQpDqP8KTU3FuSsHeeJGmkD6SBmiCZ1ykMfUYmhCx9ZUAgxdmDhBAU30/fcnhNbPqa3qgJY0lVNSVzlgQDwOQCgDdM3ED11MzabbyZKxEvQ4JQbEG1lT6iJpCuZ0m+xc7+KGkoKn0RTT0IROID06SoKUqb5+snVJyVcOSYMSjQTSo7OcI2Uqh7OYHsfUYqwt9wLKmcnS4gg0vLCMrScp+r3E9FTNKc3UYpSCLKEMiOsZhBAE0scPXQLpYQgLNyxh60nyWR9fuiTTyklPCIGtJyn7eWJGilAGuEEJXTNqfR5KZXuv65CxrIrTV4ClxQmkT95TiS5AOeL5oSBjWXhhuZYUwdBU/0spkYQYmkUQ+gghMLUYblAikCGGpmMICyFUEgdTsyn5OQAsPU6PU8LWQxZkYziBYGKT+pqsOr4aOjmvTKLy8Jvbq7FTxgMgZqQIQg9Ti9Hj9lBv1eOEBTR03LCkHA4rUQ51YSCROEEZW48RSA+BhhCCku8R0w3Vt5pFyfewNEEu56EJXb2NCQOyrk69rRwDC75O0ghIGCGWrs4xNUne02iwLZygXPtKUhcaumYQ01OsLvbgh4JSoNEc80mZCQLpkdDryHmd2Hqi1lea0GuOoSVfOealzVjtnigH+UpijABbTyDpe4xJGWLrSdaUuliat2iM+cT1kIVZm90aS7SXTAxNsjxvEUrIexpeCE0x9Y/pwIRHIAUpI+DN7jjLCnDIIIfleYvFeUE5gOOGlcl5Ok4gOHTwF3ly9fUA7N86hEdXrmR10aAcQNvf6tn5pF4mNkpGpQOcgs8/f6zzf3/2WJq3mNOjM64upOBptMR9FmQNhqcCMlbAoqzFiLRHICGUgkMGfZE53dcwoeHLvNZ1LU22z2f+NoqfnLKKBVmDr0yYygPLb2R1UcfUqJ3bXjTJWAFL8zqHDS7yRleck0Z+nmvfauOwwUUCqRLRZMyAlUWTx1ebfHJEGU3A82tsBsYlJ438PAALs3/lze4YrQnlCJkwVPKRoi/Yub5MIFXykkdWWgyKw/yc4GNNIY+u0hhXJ+l0BEcOcai3Am5dmODjLepN3pCkx3NrYgxKhMzt1Th+eJH2kkoa4IXKYdjWJYMSHncvi/Hp0QXe7I5z67lw+h8l9XbAi0s1dmt0KRgxmmI+j62yOHKIQ9HXagl9sp5Oj6PGreRrteQmeU9jYU4wZWSRp1YnCKRKUnLWzlNpm9dG2pQ8u0ZjTEbSaEu6HFGTAbTGVTKRs0cmeKArV3tOxA2VmKYp5rM8b2HrIX97OcWZe+WJG5KOksHQlMvDK20mDy3T7eiEUhA3QpbkTPZoKrM4Z2Hr6t7Ke4IB8YAbX0uwz3CHF38CX/mjz+tdBgcOdAglWJrkuTUx3uoRnDhCOZU6geCf34EDfyYwNfVmOaZDwYdp46fy61fbmDKyyMKsjRNotCY8nlxt8bFmnzuXmPx41xN4dOU/8Cyb+1cIPjkiYFVR45arM0z7Wpa7LtI46uKQQYmQkq/xyCrBMUMDThhxBn94o42JjT6zuw2OHlrknmUJdsqEDE+5vN0TI26oc3Kectx8pt3ikEEOPY7OX19J0nb8p/jDG23slAlJGCErCgbj6l26yjpFX0MX8PfX43xyQpkGO+DVLp3ZF4ec/kfJ2DqHlUWT9qJJOYCBCZ/VRYPdm8qAckCf020xLOWzLG8wvt5j75aBzOlZgR8KVhZMmmIqkU/aDJi1NsGgpIetq89FW5cUfUHCkDTHPHQBozM78UbXQhbnLHpdjdEZldjKlwJNSPxQkPd06m2fpo6QoTt/5b2d4O4/h0zS7n+s4NBw9B+3uBPc5z//eU4++WQ+9alPbbE2NkY16U0ul2P48OF0dnaiaRr19fX09PRstnZ27FgaEREREREREREfUoSub/Tnnbjllls4+OCDyWTUCyS/km21ipSSiy++mMGDB5NMJjnkkEN44403Nqgnm83y2GOPcdJJJ232a3ovWlpamDNnDvfeey/77bcfmqZRKBQQQmzWdiIJRERERERERETE9ojePwqE2he8Y/GGhga+/vWvUyqVOOusszY4ftlll/G3v/2N++67j5122olLLrmEyZMn8/bbb5NKpWrlbrvtNj75yU9iWRvXMW9JzjvvPPbee29ALegBHn/8cXbdddfN2k70BjgiIiIiIiIiYnuk6gS3/s87MHnyZD73uc8xevTojR6/8sorueCCC9h9992Jx+P89Kc/xXVdZsyY0a/c9ddfz7Rp0zbnlWwy3/zmN3n11VeZPXs2J554IgBjxozhqquu2qztRAvgrUjSzBDXlV4nbmTwpdK1ri5JAunjBiVyXgFTi1W0sgI3KCGRlaQUSksrkYQyqJ1Tbw3C0uPYekhMTxE3QiwtjpSS1kSyov8sYutJBAJbT+CFDhkrgUDQGm8ikB6NsRSrihoj0w6GZpEyEwxMxNGFwcqij0DD1pU3dZ2VRBcmgYR6W/13GspAJS3QE6TMuj59qh2nJV6PpcXxQxchBEJoGJqFqSltUznIkzKblP4THVtPKi0rOgKBLgzieoacV8bSlE2a0InpKdVfutIWCwQZqwVT2ATSo+hTKW8ikX266kpf6cIEwNAs8l5AwkBpj9GwNEHSFBiaVSmrkTQzlPyQhFFXa9MQFrqm9M1VjXHR14jrGUwthi6UBlYiSRh16JpR0a6G2HqMsXVl9mqmZlvB89CFSUhAnZUkkB5Jo55xdaFqR1f9aOtJykEeSxMU/O6aPjpuZJBS1rTEBb9cSQQi6Cx7JI0GJCGhDMhYaWw9QdpsJqanaLAbiBtpNKGTdyVxPYOtx2pzqt42GBgPaIrF6Sib6MLE1CS6MGmN1yEQmJpJU0wjbiRryVgCqZKfxI2Q1rjSDFua0ogXg14sPY4TFOkoKW1vECotsi5MUmYCU6vcIxV9szrXxtYTtXkkZUggvcq1lklbhkpOokmaYwkmDxumxtAIGRALaYr5DE+7FHyY1FzmlU6lDa23AhZnLXauH82k5iL7t7rkPZ2kGXDUkBKfHFHiF1+LMySpNNv3LbuRjBXwx+cyeGGZlrjPuHqXA1pLnPO1br63Z56d61XijLQZ8rVfl5k+P0G3o9NkQ1PM54CBeQYlPCY1OzTFVPKOBjug6AsOSibJWAF3L72J5QWLOd3XEErBopzNSYd0MLvbYEzG542ua+lxdA4eVGBoyiNuhCzPm4zKONi65OMDyjyyMkFjzOeOhSphxi9fSbE4Z/NMu0XcCNm5vszSbp1/LIzR4+h4IWSsgLd7rmHmkpt4cEWClUWNuT0Wqwomlia5d7lFgx0wxBrOa51xQgn7tvg0xUJiugo5dsool7N3GczIlKS9aPJGV5yjhrgcNXQwAG/3xBhf73HruXDe7lN5oyteCWsFKTOkJe6zuqjzYkeMgwd6vNaZwNZDvnRlyJ7NKszV2DqVcCbrajiBxitLY5XnkmBt2UATkqU5iwVZdS9MbCpR9DUeX6WSAC3NC26en+SMgQZDkgHDUwEPLL+RAfGA0RmHL44rskuDxy4NZYanAtaWVZu3zomxc73P471ZdmkoMSTpUW8rzXHKDMm6qr1ACha/lmZNSSduqGQfWVdn72af5phPgx0wPOXS4+gsyAkKvsbgpMfwlEvGChiV8dAEtB0iOXxImeS3BD2Ozjd2ncp/lsZ4pt2mFGh0OnDpbjbtRYOSrzGmzuGbV3mMyQQ8264xIB5QDpT+975lNzIqBfN6Y2hCJfBZWTAp+ILXugwObJX8e/EMWhMenQ4cMzQk62p8rMXhK9/I0unAF37l0xoPVYKkpEfSgG5H4+6lNzG3V3D1+Sa6gLN/OoiDBjqMSju83RNjbH2ZJ1frjK0vM7HJwdZDJtQHdJbVc+tTu5SZ030N5+42lYEJj0EVG865eRitCZ+mWMCEhhLnfTzL3Kxgt8YS+w3wOfm3ggkNZTodg5QZMjztYmkqgc6BAwsUPJ2XO2LcNM9m75Yy83oNDhxYpDXuMbt7JaaQeKFgcFLdQ2kz4Oo5SZ5sV8+GZ9rVG1FbD2mKBawsmDiBxvNrEviheibsM8DhqKEOnWWdLsdgz0QTXWWDRVmbrKvhh4J2LbdpCwdNbPznA9Db28vixYvZd999a/sMw2DSpEm88sortX3z58+nt7e39hZ2W7DTTjsxcuTI2va4cePYbbfdNmsbkQQiIiIiIiIiImJ7ZL0oEGqfv/Gy70E2mwWgvr6+3/6GhobaMVCLz9dff/0DtfFhIloAR0RERERERERsj1QkD/c9MZf7npgHgOt9sAVwNWLE+pEUuru7GTJkyH9l5oeRSAIREREREREREbE9UskEN/mwCVx+0UlMPnQ8iA+2dKurq2PkyJG88MILtX2+7zNr1iwmTZq0uSz+0BAtgLcigQxqsWqLfi8poxFN6AxN2ggElh6n0W6i4HcDKsavqdm12L9VvWtVZ6TKCIp+D4H0SJl1OEGxooF0MDW7Fis1ZqRUvOCKfjhlNiJliC5MSkEOiURDZ0TKJmmaSELyXhFfqrYGJTQC6ZE2IaYbPL8mQBcGti4JQh8nKNT0r1V9stLOGkip4qu6YQlDs5QeV4YVPbCGJMTS4+S8tQghSJoN6ELFpvWkU4lbHEMS0hJrxgsdDGERhJ7SSMuQpNGg9LXCxA1LCKGhC5OmWBroi60b01N4oUNzbChFvxdTs2vxcuvtOLpmVOIMJ3BDiROEuEEJJyjihhIvLJMylW5ZXX9f7FqAuJ4mZqRoiqnYy05YqMQY9ij7+Vo8Z0uLEzeS6MIgYdRh60kVAxqd1ngrQC2GsKqnWGnTRCCQlWQqKbORenugij9ciaPshy6a0JGEJI16Mla6ot9NU2+r8am2GcoAgVY719ISBNInbTTSGlda4YRRR4OdURpmvY6M1QLA6LSK3xw3MrXwNJYex9LjGJqFQJA2m2vHfOnSHGsikIKYnqppfas677TZREs8hSRkbVnFqtaFgReWKzGlleY3kB5CiNqYBtJHQ8fUYsT0FLaeJKanCEL1lqTB1mrjXtXK97oCLxS0xHyGpwJaExqTmlQmqefXJDhtgEUpyBJIiBshjbZPwpDsXCyS9XQuv67AG11xxtereLl+KBg3uMz83jXYmuS1TouspzOikiHK1iWjMi5jMgZlX6PbhXH1Ds91COb3WiwvWDihoDXu0Wj7rCyY7DNAY2ydwyw3R6Pt0xTzaU2oONC2pmIH11swJAEJQ5IyA8bWO+Q9nfH1koHxgLlZwYiUwf6trZQCjX0GlBmadGmK+cxcYPLJkT5xI2T/VpeleZsF2Rhn7Vbi3N0KDEp47N/qcOjgL+KEAlOTNNlw2OAiuzY6ZKyAppjPQa0+gRQQhuzdUiZphIxMO3Q7Gns0Boytc3iz28QNSoyrdxmU9Biedmm0VXzvvVvg1MEZWmI+X7oy5IrZbXSUBXcsitfi5dZbAXWWZK+WMsNTDqtLgsEJFZNVPW+gHAhumBMnkEp3fMkhXQC80GEwocEjYSg9aCBhysghZKyAwUmPL4wr0BL3mTw0oMmWPF4oM75eZVDbo8nH1CRv98Qo+BqDEm6tL3QBBw4czPl758hYSrv6Vo+KQfxGl8XojI8mJGPrykxsKtJgB/z5a8vYo7mMEwgCCYcNbiWU8MSqJKD01ic0JdizMcTWJcNTOk0xk8EJpRd+cIXBC+U8ppCcMtLnhheThPJBjh9e5uSReYYmXSY1BawQa2lN+Jw4ooHFWZuMGVBvBxwxJGROj86ItE/bvDYu/PtQRmZcTE1iapIJDWUGJz1mL4uxOCeY/nKCppjPQytihBL2aCqRMJSWOm6E7NXssUtDiYU5jaFJl4VZk5NHusR0pW3/7BiX6beeTtqE88/pZm6P0imPyjgAfGq00ugXfY2JZoa4EXLY4CGsKBh8rKVIYwzmdF+DpUk6ygafHl3kolNXMTAecEgqQSlQcaIn1EkW5ZS+dnDS4+6lSUq+ioHcZPuMbyhzXGOCUAoGJVz2by3xjV2K9Lg6Bw0sMzozkryvMTAe1OJNN8d8MqbKVnfBHkW+t2eO3epHcvjgMo0xn86ywdKcxfC0S8II2a+1QK/bTsJQz+ZeN2CXhjI71weskqvpdpTvyT4DHJpjPmudTfwCXjf6/2g6aO+8dAuCgHK5jOuqz27HcSiXy4ShsuvrX/86l112GW+88QalUomLL74Y0zSZMmXKptmzlQiCd450sbmIFsAREREREREREdsj6y2AJx++G7/4/jvH5r3pppuIx+NMnjwZgFQqRTwe5/HHHwfgggsuYNq0aRx55JE0NTXxxBNPcO+99/YLgbY9cOyxx27xNiINcERERERERETE9oimg7ZOKuRH3+Su+9/ZQW3atGnvGr5MCMEll1zCJZdcslnMO/TQQ2upkwHa2tqYOnXqJp9/0UUXbbBPSsmCBQs2h3nvyjZfAB9yyCGbVC4Wi3H//fdvYWsiIiIiIiIiIrYTqm9/q2g6bOaMaP8NL7/8cr/tc845530tgP/85z/zm9/8Billv/2JRGKz2PduCLl+q1sZ27b5wQ9+8K5lpJRcfvnl/cJ0fJjIZrMMGjSIqV84mGOO34vDj94dKUMMzarpNQUaIUEljmoMgcALHTShV/Swfk1nCmBqNm5YIu/1kjDUVxde6BA30nihQyiVRlciaxrUajteWFb6YmEjhEY5yGMIixAVg7cUZHGCEEsTxIwUWTdH0ogR0qfxrepmvdAjbSqtaK+bJWnEiBsZ8l4Xlh6v6X8tLY4v3Uo8XqX/1YWJEOp6TS1Gl7OcemsQUob0uKuJG2ksLYETqBSIujAJZUDR78XWE5RyAokkldFrGlCJxA8dnLBIwqij4HUTEtR0t7owEELDDUr40sWuaF4tPV6Jqau0yEmjAVOLUQqymMJG10yybgcps5GC303SaKiVF4iahhbACVQsZ1tP4lfGsBzka/2ga6a6Bi1BSFDTOHuhitfrhy4Jo46c10ncSCNQOulA+ujCqPVDKINaX5aDEo32QCRKpwyQNBpqOudQBrXxU/XX18pZWuXaK+MVSJ9s1lGxe+tSBFJpaZ2wgIbeF/9Ys5AyrM1TTejkvS4SRp3Sb4dOrc91Tem+S0EOgcAJ1PhI1PnV6/LCMpYep+D1IAnJmC340q3Nt6LfS9pswg1LJI0GAulV+tmtjX11HEIC8l4XTlCm0R6IG5YAsPUkS3LtNNhKu72mrPFmV4xRGbcWo3h5waLeChgfH8TqYCW2nqDo57F0dc/GjQxOUEAXJlnXpehrZKyAhVmbXRrU2DyxKsERQ2Xtek3Npqs3T1xP49slkqZJV1nFujU0ia1LdCGx9RiN9hC0coEV4WoCKRjmJ+iMBXSUPAIpaE1oaOi4YYkFWZsxGYeBWUlnvbrXW2NjyPtdBNKj5OeotwdiCIs3e5aTMQOGJIfT7aykKTaM+b1LGJTIUA7yeKHHkKAemW6hy1lOIH1e7TQYX19mbdlgUMKjvWQyOAHdTsh4Lc1i0Uvc0Ch4krRl0CIbWSt6KPoOliZYW1Z9k3V1WuKSFQWNuB4yIG6xrOAzMB4wUAygnQ5aYiN5ZOUKhiY9bD0klIK0ZVDyPYaHday1yjS7MVYZPWRdnfF1Y1haUJ7xnd0SV0JjncbYzE5Q6uG5fA/j6izm9rokjZDBSXUvNMeG01leRq/rEDdUrN57lsWZPLREvR1g60nyXhFb1yj5IWlTaeyXFyxGp3WaXJt5MsuI1BC63ZU4Qchw2cBiuhkp61htZDE1m5xXpsc1GJygcq8GaAJCCQkjRcYawOriQpJmJYa0E4NYhg65prYgsPUEneUcmpBkPR1TSDJWQNHXGJMZhRc6dDnLKfkadZaNRNJZ9kiZAQPiI3h8VTsNdkDaDMh5Op1lg6FJl1Kg0RzzKfkaTTGTt3pUvRrghoK8p+IVx1yH2V1xJg3zKPgaHSWD0ZXY0i+sSTI87WLrIaaQtJdMNAGDEi4Fvy9ZQ73lszhnMzrjoAvJkOQ4ZnUuqmhuEzR4MV5ze2iyfRrsVlYXO2iKpXmzu4yhSYYmXVYVTcbWpZFS4oQFCp5HTBfomoFA47GVBocODmhybRaSrcWsB2qfNYaw6HaKjEyPottZiRc6CCFosAYzt3cVTTHlu2IIC1tPkih5IDTW6L0MCOognmGtt5Imexgd5cUAtMRG0uOuqjyP1edtphRQTqXodduRUtaedep5HOORB95g5r9e4ca/PURvb28tOsP664a6ujp6Fv6JTDre/1iuRP3ob77juVuTTCbTb23W0NBAd3f3Jp9/yCGH8Pe//32DKBSnn34606dP32x2boxt/gbYsiwuvvji9yz3+9//fitYs+UwDIMf/2wqqbRdW4hEREREREREfLQ4/Kjd2WW3Mdz4t4feu7Aw+ksgHn6du+595V1O2LaI9/l2+rHHHtvoOVt68QvbwQJ4xYoVm1RuyZIlW9iSLYvv+/z4h221N8ARERERERERHz0efuB1Zv5r0xaxQjcQulnbPuaovTjg4ztz5bWbsHjeCjiO00/HWyqVNtD1vpve+P0umDcnm3UBHIYhDzzwAIZhcMQRR2zSOZlMhttvv51TTjkFXX/n/Nbb+jX/f0v0BjgiIiIiIiLifb0B1tZ7A/zgLO6656UtaN37Y//99+eJJ56obe+33379tt/vAvfUU0/ljjvu2Gz2vRubdQF8+umnM2DAAJ566imee+45rrzySs4999z3PG/atGmcd955/M///A9f+cpXGDx48OY0a7sgegMcERERERER8X7eACO0fokvJh+1F/t/fGeu+Ot9W8i698e6ESA2BytXrtys9b0bm9UJ7rDDDuORRx6p/T766KM3KXJDT08P1113HVdffTULFizgxBNP5Otf//omv0Xe3slmswwbNox5i6/GTkqSRgNuWMIP3drb4KpzEUAQegTSx9TsimNbpuZM5kvlNOcGyqHH0CwAvLCMqcUoB3liego/dInpqZpjkETWnOKqjmgFrxtbT1IO8th6onZ+1QEvrqcxNJtAesrJS9h0lNfSaNdXHMsMQhlgCAtDsykFWXRhqMQGQkPKsFZn1WGqut/Wk2hCV0kMhF65ZhXkX6skPxAIfOnWkjs4YYG02QygnK8KNuWwQHN9XS3Jh60nKfnZWpIEgUo8UnW0qvWPdAlCj4RRT9HvwdKVs50XlkkY9ejCoOB31+oxtRiGsCrXqL6OCmWAqcUIpY9E1hzILD1OEKprsfREzV43LNXq04VZc+7T0JFIDM2iHOSJ65nKGCgnNSG0SnKTcqW9oGanGxTVNQdZYnqaQHrKoVEG6JqJGxSRKEcsQ7Nrc0ETOlm3g0Z7SM1xTjlm2rhhESdvUg5yDGkaghuWaok3pAzRhEEo/co8krVxqjrTVedk1blP/e3UkrCYmk05yJM2m2vXqAsTJyzUEpnE9QzlIIcuzNrcCWWAJvRaH1h6nED6lcQgyo5QBhhSI9T63jrkvLXUGS240sHMdeGk62r9b2nxitOnSjRSZ7WS9zpJGHWEMiDp6xDLQOCCbql2pER2LkI0jqAQ5khks/SkTOpLknyyL7GKLgzivgZWAoRGgHIu1It5kgObKHjdZMoSDIuyre6lkp+lzmrFCIFynlLMqCTEiaFj0OOtJmk0UPKzCCFIFRwKyTgJow4t1wnJRsqUibkhvXoJS4sTWzEP0TIG4vXk/LUApEUGZAhCw9dA71yGaBqFI9V42KFGaJjkva6aE27GaIL8WpAhxWQcW1fJG3TXpWyExERC9VM5T5fl1Jx2Jeo5UE28Y2p2bV6bWgw3KFIO8jQECUjUk3U71P0pPRJ6HT1uB02xwbX7xHZ9evQi9VojyBBXk8rJtJgi63cyNFUPsYoDEpK02UzZz1Wel6ptK9eNsNN0a8o5tSFI0GOU8UOXZi9Bh6meE2lSFCgRM9LoGPR67dT5Nr2GQ2bFUkTTCLBTdNNTc+xM58sE9a1q3i57FdEwDC+RxHQ98rqLG5ZoNAdRDHM15+BykCeh12HmunDTDSphUK4LMgPwQgcpQ3q9dmwtia0nWVFYyYB4I0mjgbzfSVzP1O75UAasKi4lY6UJZVBLnOSFKpnGwEScop8n7+kMTKRqDtaWpj5/qs/Ihbk8LTGPQi5A13TqM/GKo7aGQNQcQQPpq+Q0FcfYjlKZelvd0yqRjVN5FsaxtHgtqZPsXIJb34Kp2bXnYjnIE9NS5P0u5WwtBGmRoSzcmnO45pRwTGUDgLn4dYrDx1IMesmYA/BDB18qp9iYniLm+BDL4ErlrOsEBZK+Tt5Qzr2B9MgUfXriKrlOZvVKSDXj1jVVnjc25NZSilvEA4P2cBXNsRHouS66bI9GoxUKXeTjBqnAgtCHeD0UupAdC5Aj91Kfa7lOumyPhFGHISzaVxYYOuzU93SC6109nUwmsd6xInUDT98unOBuv/12TjvttNr2ggULGDNmTG37sssu44ILLtjk+g444ACefvrpzWrjO7FZE2GYpkk+n6+98q5mInkv6uvrOf/885kzZw733nsvuq5z3HHHsfPOO/P73/+e3t7ezWlmRERERERERMT2T1UCUfm576FX+cHFbdvaqhpnnXVWv+199tmn3/bmije8JdisC+Dvf//7HHHEEaxcuZLrrrsO7V3S9b0Thx9+OLfddhtLly7ljDPO4LLLLmPo0KGb08xtgu/7XHThjTx0/6vb2pSIiIiIiIiIbcT9973IT35y3aYV1vX+meCO+Ti/+MWZW9bA98H6IoL32t6e+EAL4Lvvvnuj+w877DBuvfVWpk2bxtKlS2lr++D/pSxatIi3336bzs5OBgwY8IHr2V4wDINLfv4Fjjh6j21tSkRERERERMQ24ujJe3PxxV/atMLrvQFe3yluW7O+k9t7bb8XW3PB/IE0wOsHPv7Xv/7FySef/F8bUygUuPnmm7nqqqt4/fXXOeaYY/j617/Oscceu01DZfy3rKsBbqyrxxAWTljE0uIU/R5sPYkbltTvoIgm1OQOpY8mDGwRQwqhdKHSqWnypAwJCWo6y6reM6GlCURY0w0LodW0WIH0alo8iRr6qpbKCgXSsJGESiuMgRMWa7qpah1VPWrSaABAE3qfzlRYIMANSkoDhyQIvVrSj0B6NV1xTEv1aaClwMdfR7urVZIplNGEUdMvKw2qjoFBNucREpDJ2DXtJ0IjEGGtzWoSiepvLZTKoUDT8EMXA6PmYOBJB4Hopyutal8FGqZmE0gfQ1iVpB6GSuihKa1b0e8llAEJo66W4KR63UYIoa7X7AAo+dla4PSqhraaRCOQHpaeIAi9mpbX0hNIGdbKVMd1XS15VR8rZVhLPlG1oTreVarzx9aSFP0ekkYDnnQwMenMquQodXWxmo5w3XFWSUSStbmiZmelj4VeS8YiEJVkKzlieho/dDA0uzaeJT9b09tWtcMlP4svXeJ6pqJPVBrrqgYQwAhC9SHguxC4SLeAiDco/Z0MIVC/ZboFIaXS47kF6FoF6QYo9EJ9K5SzEK9HZFpBt6CcRXYvU7pfTYPe1dDVC00NYMZU3e3tMKBFdaKdUnY4eUSiQf0dV/pamesAvwyFHkg1QilLtqzSmmZsT5XzyrXfomEYaAZyzlPQNABKWagbqOoIQzAq+sIwRCSb1Bj2rFB2xip1eWVE3SBkobOm8cUrQ6IemkbCslmIhmFIJ6fqcIuqDlC265X7oZJ5SiSa1HVrmqqzgki3IjsWqGNuse8czYByFtE6Xo2NDJG5dkRmUOVEDZw8stiNSDQgi91gJRBWEtm9DNEyBrlmHmLwrshsO6J+iNrfNALZuQRhxtU4emWlrS72QLoZCl3k4iORhS4yaQNhJVWfADK7SrXtlVWfABgxhBnvsy/dqq7FK4NhIXtXQTGHaB6urjtRr/pdN1X7+S5E61hkvA5R6lXzLV5pb+0iiKWUbYl6VWfgg2Eh6gapv3UDvLK6ttbxqs3OJWp/dg0UitAyWPVt4KvzMgOQa+ap6w5DROMw5Oq3Ea3jkZ2L1NzQDDUPYxnk2gWqv5tHk/XWKO14kEVK5QNg60kMYRFIr+bXUE2Y5IVO7ZnQky2hoRNPhVh6gqLfQ53VipSSkKDm26H7Pp4ukDKk4HeTMOopBVniegY3LGJrSSzHUf2pW7WxEPE6ZDmHiNdV5pqae7J7GRR7EAPHq7lmWOpYx0rE0PGgG2pODN0D/HLffOlaourXNHUP6QbkuiHThIg3IMu90NuhngNWAhFT7cpce62vZe+q2pwk1azuwd5OxNBdkPkORP0QKPYgfQeRbFLzGFQ53QInr+rKtKr5ohlqDN2ielYZcZYvWMywPS94bw1w711kMsn1jhWoqztxu9AAr78ebGxspKur6x2Pb098oH8j1l8zn3nmmf0WwMcdd9w7viXeGK+99hpXXXUVbW1txGIxzjzzTGbMmMHIkSM/iHnbJVUJxEknHsBxxxywrc2JiIiIiIiI2Abc9+As/nHH45tUtpr1s3bufS8w865nt5Rp75v3igO8qb5g78b8+fPRdZ1Ro0b913WtywdaAK//Nnb9BfGTTz75vurbc889OeCAA7jyyis57bTTsCzrg5i1XVOVQDTW1W9rUyIiIiIiIiK2EZOP3JNdR9Vzzc3vvQhefwF81NF78fH9xnPllXduSRM3mfeKA7z//vu/7zrPPPNMzjzzTA466CBuueUWpk6dihCCm266ic997nObxW7YTHGA/1t5wqxZs5g4ceLmMCUiIiIiIiIiYocgrISAXH/f9sLmjgMMcM899/CnP/0JgN/+9rfccsstZDIZvvvd727WBfAH0gDH43HuvfdeJk6cSENDw2bVfDz99NNcf/31rFq1irvuuouXX36ZYrHIQQcd9IHq2x6oaoCXLbmJTLyS7a6i10LTkIaNcEt9GjSzovkt59Q+31U/dgKcYk2Th1HR31kJVR8QagItCGp6QMyY0pxVz42llC5sXX2gbhAYBrrrIkvdCCOu6g8rN5mmKa2b7yhbESBQNlX1TrrRV94tqvqrekiqZY0+DZ7vqrbtlNqWYV8Zw1K2Bb6yf12EpnRWXplsrwOGTaY+1qcJdIt9dYa+sq/SNzUtngz7+qwaYDz0a/FaQ00orXCoNG0mZp9+sFq+Ehe2Vper4shiWMhK2krhO6rPdaOmAaxqKwkr5wn6j1NFn0zg9/VLtc8q/VGzr9ofVXuq4+q7qq2KBhPdUse1dfpWaFDOEsRT6FLpMoHaHMwWVXuZxorG1S0i7aQa++q1V9ssZVW9VTurdtipvjGo9J1SVQeqzXJW9UF1DlXnY6oRyvm+cRSV/qrEmAWQgYMQOjLw1Lk9XdDQrMpU+9opQrJetbHgLWRHJ/gBZFKIZKIyBibUpyv62hBcB3pyoAlkqQzFMlRjClsmuF5l/Cr6+aZ69Xd17jfWgaGra+nsVu1V0TSyeTXGmSGVcmUH4nHozar6NQ3Z2VPRm+q1+1B6HhgGYlBL/zpB2duYgbU9qnzFXum4CNtS9jVm1G/XU7YmYrVt2d2LME1kECBME3wfGQSgaYiGDMTsira3pGzMq7kucwVVv+updi2zck0utDRW5mIAlg35vDqWTCsNtVF5Dq5/LZlU37gITT2vKmNOd6+6Nk2r1KtsJZSQSpB1dZAhGa8AjQ2qfTMGq1ep9gxDlY/Z6nzfh7qMaif01VjHLNWmYUFPhypn6H39VhlHYjbEk+oeA/A8dZ26AZ1rlW3V/q7e79X5ITTI5aGhEXp7IJlQc7Y6fqCu01djIB0HkU5CKlGZj5oav3hG6cvtBKxarezUhOpDUGMW+ioGtJVQtlY05NVni/RKffdtRZes/raURlzTyHar8c6kK5r7VDNkVyNlULsHhRlHOjkQGsJOK5sqzyuZa0foKv6y7FnRp9GHSr9W+qm5CXp6lP69+myu2tvTo8aviqH3zZ2GZvUMMGN9/Wkl1HOp0oe1a84WVB+lEpCo6GqrtgS+Oh6rzOnK/VgbkzBU92tj4zr3dKXPPK/veqo/mqbqKrtqvOrq+9rKZlmRlwzb78fvqQFu77plo3GAWxs/u11ogKssXLiQhx56iM7OTpqbmzn88MMZPXr0B6pLaZ97yeVyDB8+nM7OTjRNo76+np6ens1m8weKAjFy5EgOP/xwmpubGT58OMVikd/85jc89dRTlMvlD2zMrbfeyrHHHgvAY489Bqj0yuvnlf4w4vs+P7jwWu57YPtJYRgRERERERGxdbnvqfn85Hf3blLZqgRi/Z/tiR/96EeMGzeOr33ta/zud7/jq1/9KuPHj+fCCy/8QPW1tLTU8kLst99+aJpGoVDY7MEQPpAEYs6cOWSzWZ5//nleeOEFnn/+eS6//HJWrVqFaZoEwQcbnJ/97Gfcc889HHDAAdx+++0A7L777rzxxhsfqL7tCcMw+MXPz+p7AxwRERERERHxkWPygTux2x5juOaW93Zm29iCd3taAN999938/ve/5+qrr+b0008nFotRLpeZPn06559/PgcccADHH3/8+6rzvPPOY++99wbglltuAeDxxx9n11133ay2f2ANcCaT4cgjj+TII4+s7Vu+fDnPPfccL7zwwgeqc9myZRxwgIqQUF3pW5aF7/sf1MyIiIiIiIiIiA8lKtRkuMG+7YVrrrmGX/3qV5x5Zl9yjmo0L8/zuOaaa973Avib3/wmxxxzDIZh1KKBjRkzhquuumpzmr55nOCqDB06lKFDh3Lqqad+oPNHjhzJK6+8wqRJk2r7XnrppQ+sI4mIiIiIiIiI+LCyvb8Bfumll7j66qs3euzUU0/l5z//+Qeqd6edduq3PW7cuA9Uz7ux/aQTAb797W8zZcoULrzwQnzf5+abb+anP/0pP/7xj7e1aZsFmW0HkUL6TsXJy0VYSUTV0c3Jq2NVh6GqA1T1p9ipRPfFTuUAV3XuqjjwCDOOJjQV6LvqaKBpqu6qk5WTA0AYtnJi0G3wNbRCCekWlcONrDgwVh22NAPKOdBNZSv0OX9ohgr07ZdVORn2OVJoBpS61W+hqTZDDyGUk5As90K5V5UNw4rDQSUAf6xOOTQ5eXV9VUcxXzleSSeHLDmqX4Wt+jPfoQKOV2yTvqOC3Vf6WvqOclYzY6ofqv1acegS8QZk4KDZaZUoQDMxzRgEBWUr9DnCVftUhohYHdIvqcDp+V7lBFJNxOCV1gmOTp9zh2Ygqo5z1f7UjD7HMq/ctw1Ir6TqdfJo1SxAVQc4o8+JrOqIUrVVWMla4gBhp5UzW+ipRAG+i57vUXaWe9WcqgZ0L6n5J7u7wYwhjDiiOq5VJzcZqj7VNPBLfc6aVkK1XXU4XMfpUgQuum4hq3N5vaD4BC6iWnexG2FUnGcq86YWcN4rI4WmnNayeeSKdmjsVbaUHeXAks0rh7d4jGDOEsKuErLgo7Xk0BIm0gsRCQPRkK44LYVQLCNzJQgkMpTInIsMZOX+0pFlH8yqI2uI3tyLDCW4Aega2rDGmoOWXNlVGbsQEdORXkgYVIL8+3nlKFN1zKo60vkBwVo1L4SpI0w1lmHORZga2truvnvF0Psc87p7CVf3QFBxjtQFBBJhakgvRBvS0OfMta4DmaYRrsnWzpNeoP7WBcLU0QZVHNCqjkRVp7AwJGzPVcqpNoQmQBeqve7K/VJ1OlvXUa4nC6FEegGiUh5NIGwDGuuUQ57j9rtOoevI9rWVfpf9neHCEBrrkMSUA19MQm9OOeiBcir0/b7r1jS1DVCfUXUZBkIIZIcLlln5u2tDZ7uqc1pzQ99zCypjqVfayyL0Pq2i9KpzWyIG1vc5ZrWvrTlTEobI7nyt72U5UGMI4AZow5tgVUefM59lgrtS2eN6yNU9iJTV18cp5TQldB1ZLFeuW/Q5d4VS1dXV2zePytVnuuoPWZdV92FBQiGHbKo4tvWsqDyvNGTZqThyV5wfNYGMx2vPn+q9L8NQfZ6sWqMcOjVNOfStS6msvvktL+9zQq3Ou55cfyfEqp2hGuvqXBG6juzs7nOYq8y16pyt0aEh6tLK8VPXkVKqMfc8KBmqX6rOcmW34mCo2hXVbajNv9o1re3uPz/DsHaPi9ZmNa8r94XMbVr8ge19AZzNZmlubt7osebm5u02CQZsZwvgL3zhC4RhyG9/+1t83+fiiy/mW9/61mYNe7Gt8H2fH1xyGycd9zGO/sTO29qciIiIiIiIiG3AfS8s4Z9PL9mksnK9MGgPP/A69/3nlS1l2vsmXPcfiw9wfFuyXS2AAaZNm8a0adO2tRmbHcMw+MVFn6aurvIGOCIiIiIiIuIjx+R9RrDbzsO55s5X37NsSFBLda+2ww3S2m9LyuXyu0bq2hyZ4LYU23wBPHjwYFauXPme5YYPH87SpUu3gkUREREREREREdue9SUQUgbblRPc+pngNnZ8e2WbL4C7u7u57rrrNkinvD69vb1byaItSK4DSU5pocolpR2yK0kEoE9bWtPbVf5zMiwoFtTfmqZ0XDG7T49m20pnZVgVjbAK5E++qMoBhL3qvFRCJWtYV2dc1daWS336yao2sapTTCbU/5zVhBfV5A4yhFIlQH5VH1gsqzKhVAHAK/opKTTwPKRlK+1mVY/mev01U5aJrAZtr/ZNRftbo5iDnooWy5N9mkg/QCaSNT2yLPX0JUbwXXAcCLv77F1HTyY9ZbcsdIFuIavJI2QIpcKG4+muhZiFdPLgu7X+l8WePrsLPX1jFPh9yUAMC1nO9k9MUUtcYagg7hWNcVXfJkvZvsQbutJV1xJIVAOyu06fNs7Qkfmu2jXKajIPzVB1VeealVDJBoKemq5SBpUxCnWwEkhd6cP7JeYAlWyi2rfV+dmxWo1huq6fHrmaUAPdgHyuT8NZDTxfCTovvT5toNStmqZcylAlEAhDKLtKdxeGsKYLf0kWkVBaM+n4CFMnzLnozXHQNdzZa5Fln1JOJ76mbyy1lIXemEOkLWQ5QDo+YY9T02aGRQ/pSQJfYFgS3xXohkSGgiAQWHV5CEKlAw4kZm+5pt8MOkp9Gs9A9ZkrLNA1vLW9fXrdQOlhq3X4naovNUPWNMBBSSI0iTmkqHS6ALpWOw4QtBdUHZU6AUTMQHoBRntBaUrdythZmioTSPxqf1R1wNV8L7qonSc0pdWt6pkB/OW5iu63L/mGMDUIJXpHJVmGF9a0rMJWHzfB2mKtPkKJLPuImAGmhtGag+o1BVL1X1UH3VHqu/bq/Kj0md5awA8MhKkRWEFf31b6BV2DoKLjruiP1XnZfn1Y1W8KUyNoV30tYgay7COdQNkSSPSWXP8ECxXtLkDY4/Qbh+p1AhiD1BhVddNArS+CtaU+O9aZUwBGdxlhG0jHR0uYfclOvBC8AH9VQenZNYHWGEfEdETMQNi60rhD3+cF9Nle1YRX70E/UM9sTUNk86qcoyELBWRB6e9FPI4sFCvlffX5sE7iDhJVP5Ggv1bW95Gre9S2qRH2On39A+iNsdp7TVmZF7Vh6SjV9gtT69NHBxK9te8eFDGjMq9D0DU19v+fvTMPt6Mo8/+nqno5y703+0YSSCDs+47sIIqgooi7oIA76ug4qDM4iiLqqIw6zk8dR8UFRdwGFdkEZBFECDth30JISMh2l7P2VvX7o7r7nHPvDYlAEgz9fZ77nHu6u6reWrrPe+p83++bIePFp+tXThjuHE85/KYdI/u9dM2IfM1C515Sk0s2LiBbL4Bp2EQY8fLsXtJd9ipINM6c9J4IEkxiSBob5sSOdoCPOGZn9jlgHj/63nUbVH5jY2NkgttU2OwO8IwZMzjnnHPWe92sWbM2gTUFChQoUKBAgQIvDkRaEOlOUOX1V9/H1Zetnzrxj4Jbb72Vj3zkIyil+MxnPpMnQzvxxBO5+OKLN2rbm90BXrx48eY2oUCBAgUKFChQ4EWHWAviLgf4kKN3Y4/9tuOn379+M1r1wuFf/uVfOP/883Fdlw9/+MMsX76c008//QVNebwubHYHuECBAgUKFChQoMBYxKN2gP9yzSKuvXzL2QFWSuUZ3i699FJOPvlkhoeHX/C0x+OhcIA3EawM2v/x2sN34Nj9ts51BzO+Va4XGFouUc6lzfibQdDRxcyuTSFUyrPy3E45sBzJRsorSzU8RTvs8F+hw72VAlNr2LKea/+s4aCN5YM5TqpDqTr6mO0g1zYUQmCSpMMlA6iUEG5qV8aLiwct1zNJNTwzHl3aJ+G6UGrnfGC0sXZUyva1Hdp2GxrhOJhWkuq0plqu7aCjxQhd7aY8NGPsmGV6oCn3FK/ZGddsrLMxqrc64wHWdikRI6Iz7tCpK9OdrLds+WY71eGMO2NY8oFGZwxKfmfc6k2Q9Y7tmU2Ogr50DEper3ZnGGFqDYTvd67P6s343116pblmbslP2+twsGmkbUeiMxeZFmbG1011c7Nx7OHx1xp2reX6zl0cQcCkmp/GGBim92Hn1Xtty9ZHnGBqqS5tO7RttiLipTXL213dwiQG04wQJYekniCX1xG+Q3056MSlVVOELU0SCqRjcP2E8poRVFmk3FBDu6HQsUglTBVhSxFHAqUMcSRyWrOOBf5ggtGp/quGgXAYndi+NIcdjJYIaTBaWSlvz8EYcFc3SOJOn5Mo5Y9qQbtmOZTSMTiu5f5GgW20Wmv3ULBlykcW0tAc8ki6kmYKCUoZQFEeaiIleZvKMfmwNof8fLnk/N8UfUMdreqsvJAmLyeVtU9IkNIgpMZo8FfW8nKZfZlNQaMzXkksMNpFOgblJFRXr8mv64EStIYkRoseG7OxrqwZJJQeQhoC3Rt13hx28jqFDHObdCIorRrE8UyvXLWbzt9alY5VSByKtC17f5ZWdrRNu+0RwuT9E9LOaxx2OlNaVUdK0jXRmX8hTbpesmN2vSSJsLTaWgPl2DGUyuSPJq3tGDQGXbxyjJBQWd1CuBJRcUEJ1OS65aFmvOsowbQTq02dvtp+mFw7GlfhbJVyVmMXPdRGV4zlKyuBroW2rBJjONtycsoBzmIRwPLrm/ZeBRAV1+pyRzpfz870suXHprrQphnl49MY7HzeScfkY5jEgvLyeo/+tZD2XhZS0FzbGWPl2OuVY9erN6nRsS/jlicG2e/RXmnH0nEzLq9A+un5rH9pf007RgcaIaG22kWna7Qzt/ZN/4pV+b18zZI1XLJ0DRuC0RSIg47and32XcCFP7xug8q/2BHHMfV6nb6+PlzX5Re/+AXvfOc7ufXWWzd624UDvIngOA5f+vDRDPT5HSe3QIECBQoUKPCSwtFzprLzrOn89P71awHr9C/DTX9exPVXbDk7wN/61rdoNBr09fUBIKXkggsu4Fe/+tVGb7twgAsUKFCgQIECBV6EiE0vB/jAI3dn130X8MsfXrsZrXrhsM8++4w5JoTgLW95y0Zve/QPTQUKFChQoECBAgVeBMg4wN1/3Q7xloaTTjppk7VV7AAXKFCgQIECBQq8CDGaA3zLtfdy05/u2nwGbWRsSGK0FwqFA7wJYYZGMEnJBgOFEcZR0EgDr7qDrrLArHbQCcrKeMNx3AlAyxIkZMFJntsJUsqSWMiuTX6tMc3WWOHyLFgsjDC1lg2Y6BYwlwKTBaNlAV9ZABnYgKQ08CALmjNBbAXls34CPZEbWf+yYKruhBSOYwO8clF61wba1Rqd60fq0DQYKcGzAVomOxdFHdvzICzZCaDL2ujOUZ4Ff2Xj2C3ingnGZ8j+18b2LQsYzILfuvtab/YkpuhuS1QredCbSZI8iM8kST6OeTBd2h5S2OAyKW0AWhzbsckSirQDTDvo2K8NtNLEEq2uYLau+mg00wDBTmCkiW2fTJzY8/n8yE7ikmwssrnMzqf2mu5guQzZ+3bQk8zTdI9ZlrwljmG41rtGmi1MkCYlSAy6FtqAnDS4B2xwGvWEoCVJRiSurwkaDkkiiNqSqC1JIhuc5pU1QUPhVayNcSgIWxIdizzALAokYQscVxBHvQl7OgFPneQYOrZBMO2GQqfBOdkQtFJhfbnWxxiBEAZjbBmAODK0Wtl4CcpllZdPtCFo+HmZfOzSIKzhtZo46gS3OY7AK0mUFJRraaCTsbYp1+QBZLUhg9a9/coQNv38/yyoKwv4atQMStogLalEHqQF4JV1msOlY6dMg+XCVhoIqTvj6bg2UK5VU/kyyPophL2m3bCBYd3Hsj60aoqG69q8DWnAYFZPuy7z8RLC2qATOw5+VeG4hiSxQY7ddTdH0rlSdqx0bO2VSuCVdZoQpTM2GbKARSEMSSQJA1tfog19A6qnjW4ETdHJq6ENOgGtDVIK+mslpDL5OGZrE2wgYW0IymUbTNgacXB8jeMmNihu7ZC1J02+YTTEkUQIQ7vuUBmIkY5BxyIfByENeq0N/A2lj6mFRF6CrLgYbUhWN3sC5uhJLlLNOpEH1pkgQTdjGkMuSSzwym3aNTcP9DMaqsMRUSDRsbJ5gaJSPr71tSp/BGRrRUh7r5cndEV+5mvNvjaHVX4sC3KU6TxXJ8Z5/XHk5GvS9TWNoXIa4GnHW2uRz3epP8mDMZNEkoR+vs4HV3XmLZvLzJbJ0yp5G3FkWCk3bBd3tAzavkfswU77bM/FP/7zBpX/R8OmUH/IUDjABQoUKFCgQIECL0KM3gHOjhV4/igc4AIFChQoUKBAgRchRusAZ8c2Jnzf52UvexkAr3jFK/j0pz+9UdvbXCgc4AIFCmxRMAY24a9oBQoUKLDRMJoCccf197Dwmjs3apvTpk3juuuu26htrAs9evIbGYUDvClRa4CxHF7TTqxotys7iSVG8SV1M0ZWnJxElHGyUBKSIBf7RkkrfD5QyvmYJtJWqNyVoFIB9MQg3BCUsBzOSFsbypbfq4cDTDtO7VKQdGwRpXSpKIHwu5JFgG0rsv8LKTDtOD8mJybWRlf2JqcA2343VMrT850OP1gKyw/N+McZx7jRxqRcO+Mmeb3CVR3uL/Rylbu5yWlb2ZibxCD8LAlJ2r8sSUg3B1jbRAvk5bvGIysru+rOknJkHOOMmxtGmJSPnPXNtIOO3d0JLrL+p9eaUtThB6d88rxcluiimwOc1ZPxhLP+ZVzzbC6zax1lvcgksYk7ZPca7Yjb53Vk77sStZjEILp501J22dRpy0SpmL6Unf+zpCHZuGHXmHAluhblXEMTafSw5R4vHZmKSiIueugY/v369/DUaScw0iozkRpJyinUgcAYQdiCKNboBPxA4vqSKNAksU12ELZSqnTK+U20od3USCl6uLIynRup7LU6McSRIghMyuXt9F8nlhMY9VmeZ4xGJ5ZPmvGEwd5ylsdrj9Uc0XNNuyl7htraly6ZpkZrQxRlCRMEvm9trPZZLmR2zk3tTXSn3GhIKWg3Zd6+4wqCwNjHT1pOKnudlLbOrI3+CSrlQvbe444raDc7nM2oi1MtlaB/QCEVqHH4ka3Uzu58QNnYVvoUrZJEKvAbMp8XgGa9Y0NvAglbznUFSdr/jNOsNTTqST4OcayJIpPPg1+SOI7I7XEcgVSgE4jjJG8rCJKc5xxHhvqwJNGd8c/GVipBq9n17O/iACcaymtkPpdSZTZmPGjN0FCC7wscR1DtU3glB9+3nR2YGpEkIk+qApY7a4ygXZc9PNnuRCPVITtPDcdy5xNiHLeNkNAY8tGxwPGM5dRqSBIHHQv6Bu09aYxIE52AjhVx5FBfq4hiQ7XfpTZk8r7GkaF/wCeKDWHbHg+DzngMDQX5/67Tue90AhMmdXjy3ZAShgfDnrUgu+Z30hQnbzuO7H0jlcB1BbXhsKeM1gbPl4SBpn9ApaEmhjjS+f0qpaA2kuTzkiS99qxe1YkJiCPDSHXDRLgi3Ut52P2wPdlur+257IKrN6j8c8Hg4CBHHnkklUqFL3/5y+y5554bra3RuPnmmzdZW4UMWoECBf7hseN3L2DB/17Ev1//HgDm/ugP7PrLX/J4bavNbFmBAgUKPHdYB3js37pw0UUXcdhhhzEwMIAQgjjuDRI0xnD22Wez1VZbUa1WOfzww1m0aFHPNYsXL+a6667jnHPO2SR6vJsLhQNcoECBf2jE2j7GplfW8r3jzuMnr/xCfu6wy36wucwqUKBAgeeN2Ix1fuNnYQlMmjSJM844g29+85vjnj/vvPM4//zzufLKK1m9ejWHHHIIxx57LPV6Pb9m2rRpAOy3336Uy2VWrVr1QnZpvXjkkUc47rjjmDJlCp7n9fy9kCgoEAUKFPiHxsOr59jX974D1zMkjZjZlZWsbfTx1qvPZRNSygoUKFDgBUWoQemxx9aFY489FmCdHN7vfOc7nHnmmey+++4AfOELX+AHP/gBF198Maeccgr1ep1yuYxSiieffJLh4WGmTJnyQnRlg3HKKacwZ84cvv/971OtVjdaO4UDvAlhogTTijDtBNOOrS6jkkDUe2GiQVlOpE61Fk07RjfjHm6rCWKEFIiSg5ECEWlESeX8YtOMLH/WlTlnUijLxxWZbm+kEf2WH2yaEboZ2XNgea6Jsa8pb6+7fN6vIOm0oSy32LRjTDtBQ85DHo3chrSNrM6M75lxdU3KmxJaW35pENs2Us6c8dMxUQITJAgZ5e97x9XkXGXTfT7jYEY65VWHto9d5U1iUq1LDWFiedhK2P/TcgDCjRC+k/NZTTvpcLVTHjZukmotd3GVuzWRoUeTdwzHNvu/69qM423Hzs4/7S4SmhKIOLH9aEUdPnE21tk8g9XTFRkHNx3LTHc61fvMxiYbz3wMSXWN07XVMwdS5Fq93TxiE3We8La+JF97JqsjTDDZ+S7NUSEFH73sQwC4JRBKokqSA2c/QK1VQhvFqsZEvvXwWzl2+k3s138/cajQKU9Xa0OrBeVAEsUdTh90uH8Z1zeOs+OdLsWxQeqM+2lotyBoa8vz7eIGZrzA0NEkGoZbvT9LStnhkNrp7XAFwXJnAWojvcRCnUC0ju2gsK2JI5Fyd+OuMmOvzzixsueeMT11Z9zfzKasnoyT2o12S2ePsTH9DFJup1Sprm4mMa1ED6+4txy5nm7OseyqPwgMQUWitaHcjvNrpBQ9XNLRaLV0yuW177Nx1okhjk3PfCS6w+V23E6dmQ2WB571pTM33VrQ3e/lqOfT6HnJb+8o46CnbXTNVc4pjQ3Nuu3v8FDSw8meNOgQR5bDXaooymWrTey4gtpwks+DnVdbXxQZJk21WuCtkkscGYbbCt93cVzB4Jo4v9bzZb7+o0gzYVJnl66zVuz52nCYj1UY6Hyeg0AzPJik96DJxzlDN1ccQHVoyzlXO+eEd92zWTnVxee1r3bus3a6efdSip72uttKEhhcE5NkWr9Jb3vd/2fvM6xZ2bsOGxv4xTwxXY/XrmPPBcPDwyxevJgDDjggP+Y4DnvvvTd33nknp5xyCvfffz/vf//76e/vJ45jfvjDHyJH3+AbGffffz833ngjjrNxXdTCAd5EiOOYT//iDl6zzxxeuePMzW1OgQJbBO5ZPo+bntyVn5/0pTHnKm7AZH+YPyw9nB89cgJxJPlDcgTvmfVr+lmzGawtUKBAAVhk1nBHe3CDro00OHrsseeCkZERACZOnNhzfNKkSfm5Aw44gDvv3LgqE+vDbrvtxvLly5k7d+5GbadwgDcRHMfhi2/bh4GSa3doCxQo8Lzx18U7c+i8RZy4802YaOz5tcEEzr77AwA8MDKf29buykH9d/GycuEAFyhQYPNgNzGFeaXpXBcuWe+1ccr7ffCme3jwr/fYY+HY7HcbgoGBAQCGhoZ6jg8ODjJ79uznVOfGwP/+7/9yxhlncMoppzBzZu+G4eGHH/6CtVM4wJsIxQ5wgQIvPD52yfuZUKqv8/w3Dvo6//y3jwNw29pdAWgk5U1iW4ECBQqMh79nBzjUIDVs+7I92PZle/DQX+/hgevveE7tTpgwgXnz5rFw4cI80UUcx9x1112ccsopz6nOjYH77ruPa6+9lksvvbTnuBCCZLS+3PNAoQKxiZDtAB+754vnW1aBAv/I+PPDNojjj6eevc5r3rrtVQDM71uWHxuO+jauYQUKFCjwLNhNTOENpR036NosFXL2l2jRI8U+GkmS0G63CUPLtw6CgHa7nWtyn3HGGZx33nksWrSIVqvF2Wefjeu6nHjiic+7Xy8UPvGJT/ClL32JoaEhoijK/7I+vVAodoA3EeI45tMX3M5r9prNK7afbhNFpIEpwlU2cUAWWJT+b6IEKtjkFGADwHL6RBr4lAaSiTTRRBYchDa2nKtIGftpUBGQKBsw1o4x2uTfgnTNLi7dTpNXdAfBkSYf8GzSDVxl28oCt7LgJikhSvLgL5oRlDpRBFlfIQuhSAPrtMFkbbZjayNguoLjUFGeYMM0IgyutUmm9iZp8FXJsXZnP4lnmQKyKB6yIK20fSnSxCF0koRolSehyILY8oA9bEBjXn+kIckC4RxIovSaLElImkAiTViSJXgwLRsMZ9KxGy85R3Y8+58kscGAaVCCCWI7Bu04759NHCJ6IyWiTsCaHas0wCzptIMW+Tkcg4kTjLBriagjRE9i0IHpmpfOOOXrJTGdBCbZPHdfP05URx5IKDuJW6Br/acwieG+Fdtw2s8/ykHzHmT/+Y9D4ubjiCsROsBxwU93CyaXRniibr98/sfj7+Xkwy4lCGwyjDDQtMgE7buDwWxAiw2M6g2I6w5Yy+A4wgbWaBv8kpWDToBO4ggUkDQ6y1FJugK/DI7TKZcFYtlAI5MH4mWQqhMh7vlZkgxbxnVt4JbrdALPxoNNBAG4neCdLLCrOxgoDwhLegPzrB29CT2CNGAtpjdu044p+RhJ2RsMFgQ6H/tudN2646LV0oTSBjXR7r5wfE+hO3iu1dS5DSrovS4LfHNdkZaxwYpxZMYEAeZzKTvBcEra5BqJztZMd3BXr21u13hmbUMnAFDrUYGAXWshWx/ZPOjE2iiV4OmnsmQQdi1XqzJ/H6b3QHdwnq3fBlxKJQiraT9q9rnmOiIPjszGsTtAcHhwVICn6gQWZgiC7FynvUY9ydfF6E2+LAlIt33Z2GT3RBaYlo2tlKJnTLvjuKQShO3OvZqVB4gije/L8ZPeSPJ7KQ94TANYwSZIyY7Z9d25J4O2ya+7R6/hjmDDOcCya0lvc9AezNxtAbf8avxEGBdccAGnnXZa/r6vz37hv/baaznyyCM588wzqdVqHHPMMYyMjLDffvtxxRVX5Ne9GDA8PMw//dM/bfR2ih3gTQTHcTj3jXvxyt0LYf4CBZ4P1jaqnPi9TwFwznE/26Ay8wee7nlfSKMVKFBgc2EPOYU3VjdwBzjp/Xvopnu46r9/uc7rTz31VIwxY/6OPPJIwNIIzjnnHFasWEGz2eSGG27IJdFeLDjuuOO46aabNno7xQ5wgQIF/mFwy+MLOOyr5/Ka3Rfy63d9BYHOlNfWiXtPfCsTqg32HngQjeAzd53B7jf+jtsPOGHTGF2gQIECzxGR6d0BjjU8RxGIfxhMnz6d1772tZx00knMmjWr59w555zzgrVTOMCbCHEc8++/uSunQLwU8OTgdKZOqNNf6g3Pf2D5bH6+8HB2nrmUqX0jnPLjj/HHD3+ZA7d7dDNZWmBTotYqcf2iHbn3qbn8z1Uv56zX/563Hvw3BkpNAJ5cNQUpEq68aw8mVhoct8vtXH3/Hvz4piO45K79AfjqiT9FSoPZgE+CqaVhHEfzzu0uA+Capftz3er9qcUVqqK50fpZoECBAuPhHr2G2xpDG3RtqEF0PedmH7AHU3ddwF2/HZ8CsSXg3nvvZc899+TRRx/l0Uc7foEQY/MJPB8IY4ofAzc2RkZGmDt3Lk985VX0O7LD49WWzyq6ebyQJktQECWIktPhojajTsIDbSDRtHQJpQx+xdYjKm5at+lwdLvaAnJ+r03qoBFV+z3ItJNOYgPoJEjoQmZnbitYW7tIWVcs2pNP/N+7eHiVzdB19yc/QrUSMnvCWoQwzPi3nzDcGpvd5bw3/oS3H3QjU/tq9oCyST4I03GgwzM1UcK/Xvwm7lg6n+vu2ZvLP/4ljtm1K595NzltHOTJKbrq7JS1x0XJ6ZmbfGzCrmQP2Xx1l8u5rilPN8o41bKT8GN0gght8nkW2dxIYTnCQZo0JUmTS7jSJtLo4gmbZpy30923fBy6+dw5n1j38JuFFOAphBSMRJYnPuAmeTkzSnxyzPiNl/kgG28pCBKPelTmqM+dxYNPj08FOvWIG/jx9euWuZk5YZCbP/VvzJ5o+XMd/nKHM6ybMaZpk33o4YCgoXB9TbuhMBradYddLvkVv9j3k2zrL6XV1D1JK2SXaL7vC6QSKCfltCqNYwQCQUtrVhMwrVXOb7PhoVSUPzE5JzeD1oZ22d6fZk0v2dR1Rc6V9Lr4jqMTYYSBGZMwIVvi5XKHb5gNiU4sF7lR1/m57sQcWR9HI7OlUU/GJKXIbMrsHI/jmXFV7fmuMUhsuYzGPjoZxOikFd22Wm50h2/cXTYMNEHFJmDwm72BMqPnATrL1CZDGN333nJZW9085Iw7O7p/6xrP7NZotcZ/HnWX6+ZTZ+21m73luvmpAKWy7DrXe00Q9F6b8ZShs64sX7ir/qTDdQ2rnuVA18KcD93N/x49Dq1mJ9FJd5uZnR3+t+jhh3ePzej5Lff0r7N2oVNuvAQv1T7Vc303j//ZyvnjrJkM655DWy4bh8yzyny2VlMzpCdyX7QH59U+zcDAMkZG5jA8PJzLk3VjZGSECRMm8O4rvo9XreTHl9xyD4tvup37Lr56nWULbBiKHeDNiMdWz+CpVZM5cof7n3Md87/yI/r9Fqfsfy3vP/RKtqo08nNB7OB7vTfrMd/5AkcuWMRnXv3r59zms2FNo48T//df0UZx9it/zuf/9A72/Op/5+dP2uuv1Nolhr55Citqk1hRn0TFaXP3knm894IP8sd79+XKj53L+r7o3fHkfH5w4zH5+/f9+H08/rUOaX5NvY/J1Trd1dy1ZBtKbsROs8byQZcPTWKric8elGAMGCP4z8tei+vEfOioK3BG56h8gdAMPCrlcYRtx8H//W0/9p39CJ/99Un8x5t/wYz+oXGvG2pUOP8vR/H2g25k5sThMeeNgRsf3JHDd3n4+ZhOnEgcpXn0mRn0l1o8smIWf7pvD778h9cBMKHSYOHXPsdeWy9GCKg3PT7/6xP5xqXH8ePrD2fX2U/x8eMu48id72fp2snMm7qKtcNl+kpt5k1bYzMPPk9M8mrcObwz205fut5rV/gt3r/3zUyIXe6YsJYZQYn3PrU9582/n7ZK2HFkgF/97QgwgtgoHLFpdb6NsR+ysdCcu+/d3DxjJXusmcxZt+xJNSke8S80VseT+cXgG3hX/4/wxIbdowW2fITGxTMJvx45iR8OvSc/fuqEH9EnG4wEJX7afM+z1DA+okQgks4n2az99mTKzttz38Vb7g7wBRdcwO67785ee+2VH7vzzju5//77ecc73vGCtVPsAG8CjIyMMGvWLN71sjm8erdZHLPddNY0+jni61/g0VVbEXz9pPXuAC98YjsqpsGcvmdYU+vnbT//JEHscv/KbfJ2vvq6H7EmmMgnX/V7mm2Puf/6vxy63f3sNfcJhpsVjtrubk7/xceYWh1m2Zfe/XftAA+2qkwsNcA8+w7wV646iSvv34trzvg0JIZ7l2/D9295FbcvXcDtTy0A4F9f+RvOef2vU2UEkatWLFk7he0/8/94x4F/wVUJQsI9y7Zh7sQ1XHzHAfzqo9/ixP0W8szafuZ89DvMnr6Cz5/4K97zvY7je/TOi3jvEdfwtv/5KJ989R+YXKnzwxuO4pFnenlE577hF5xx9FX0l9tccufevOH/fYI//8vnmNY/zA5brUCIzg7wXx7Ynkvu2pdHnp7BJXfsu855XvjZT7H77KdQmc/xHHeAb31sOw7+t88QXnQai57emkneCMKVzJm0BiLNkuGpDPRHxJFguw9+lUZQ6syZ0Pzfh87jNXvfxT1PzeWOp7bj4O0f5rbF2/Gu//lgft2cyWuY3j+M70T88oz/YubAIPc9PZe9z/4qa7//PnCjv2sHWEcwWCvzlctfxzeuePU6xwig8ePTKfWbrt3bNP2qdpBJjBxH5zFXypCixwF+rjvAn77jA1z9zEFctf97aLd6d4CNMihEviv31Z0W8bOtHwdgVrvM8lILgIPXTuOIZ2by5Z3vpRwroqUHc8jvz+bcWd/4u3aAEyN5/7Jvc/r0C5njPc3j8XYcPuFWBlSNJH72HeDIOLxn8X/x6Zlf4/59r+cbe97X0+aPLz2M7VsDxQ7wKDyXHeAYlx+vfht/GH41dd2JmP/Y5G/wqr4/5fb+vTvAxoCQEum51HQ/0/3Bnh3gB9vbs6rZz/6l2/IyxQ7wC7sDfG+4Jw1T5SD/r8CG7wA/Gu/Ar1onM6IHeCTZuee6D076LpfXX8XiaH5+bAfnAc7o+yZrktu42Qxydf2J9e4Av/135/fsAC9beDdL/nY7D/3hT1vsDvCCBQu48cYbe5JgrFixgkMPPbSHEvF8UTjAmwDdFIiycNj3c//BAys6Kf7OOPRSzj3+As685N2cf/MrOHm/a1kyNJ1X77KQkw+5kX/+9Wn86rZDAJg9sJplI1PzsiftdiOfOuo3XPbI/lx05+E8+Mxc+ktNau0K20x+hmN3uYv/vfHYce362cnn8artbqN/spWt+cPt+/LuX/4Tt//TR5gzYU3uAN+xbDsO/vZ/ArDyM29loC+wP8kb+PltR7A2GODGR3fmlsU7sGJkEld86HMcOf+eHmqAUYpaUKbshnhObH/CH+UAA5z1+7dz3pWv67HTUTGek3Dw9g/zkw/8D7M//G0qXpu/nf0JZk8dxjdwzh9O4quX9ZbLcPTOi1BS86b9b2aHmcv57MVv5oaHdlnvvM2dspq3Hvw3vnbJa3qOX/upz3PUV85eR1v3cvFH/pOKH4KSLHx8W+54bBuO2e1epk+sc9Zv3sppR93AvgueBOC+p2Zz+d17gjG87oA7WTDzGc74wal8/6ojee1+d3DJbfv01L/L7KXcv2zOuG0vmL6cR1daR993QoLYG3PNdZ85hyO/8Nkxx3942ndY2+znE788hfu+9klmTVuVO8DNpkuEQ1m0GW6VOeaLn+a+ZXN428F/Zeupq/nGZccRxm5e1zZTVjHSLnP0zvdx+E4PsNucpdz79Fx2nr2cif0t9t3mCURJjXGAkQIiPcbRhhfOAR4mwh0pUWuU2Xv4ME6e+zs+tLg/d4Av3PFxvr37AxyxfAafv2NfWv0hxx1+NZ95ZHd2aAywX20yK72AuoqYXxsgjgyHH3I9Q1W7oz7vD1/hgvYNuQPcSMp8dsknWB7OYIqzhjfO/QM7lB/j2iUHcVd7D06e+AseC7flq/4hOK99N/FTh8Kit0F9BjOGJ/OTBf+E643wyUMX8sEHdmL6YJm/zHyG3VdNYtG0Qa6eFnLHRQvp63uc+j/txWdu24tXLZ7NiBvy/qP+yu4rJ3PWXXvkDvCKcCoBZeY6SxhMJnJt82huC/ZnB/9RbmvujRIJ2kjml5bw1ikXs1X86HN2gBu6QlU2WaFn0TBV5rmLUTrOHWAhYIjJTFaD+S8+L1YH2EjFp5/+HLc27Rfgz087m1ub+3Np4zVURJPfzjkJIcZ3gEeSPso0qJkJXLX2YJbHsxhKJhIZjxtbh46x7dqdX0c7drls+BiOqN7Ivz39OR4JFvDTmSczWa7ly2vP4ql4Lq+v/pZXlP+EEOCUXJZHM9nKXQ5aI0ThAMP6HWBj4ItDZ7MwtEkhtlGP87kJ/8qMcm1MXRmy9lYl0/jAsFWhmSOfZJ7zBANug6Or17JbqfNF9JFwATOdFTjtkZ56hiuSU5dcsV4H+K3/96MeBxggbDS56A2nbbEO8MDAQJ6auRv9/f3Uauuem78XhQO8CZA5wPd/4TVc+Nfj+eQv38EHD7+cR1dtxay+Nfx04dHrLPuqXe/kivv2BuCi077GW3/0CQBu/tiZzJ+4nAmlJsIRrGpMYOcvfYd60Mly9cUTfs4nXvUHwlhx15PbUKJFHEkO+tbXe9p4+N8/wNZTV3P8tz/Dnx/ZE4DGl09iOKrypavezC/vOIxVjQn59d9743/z+NBWfOXqk8bY+8NTv83JB/4FYbo0iV3Vc41ItXaFK3vFUBP7QFo6PJXJ5RrN0OOx1TM5cIfHueIZlxM+cT4A20xdxV3n/ivasU/eBU//gq9P248FK17B3MlrefSZGUztr1Fr+Ow1ZzGlqu7SM7Y7Ln++dxde9bV/y236zYe+zscufBczBwa57ckFPfbuNvcpPvaaK9l62hqO3uV+TKR5ZniASaU6v114ANtMXkkz8PH9hI/89DQcFfPK3e5loNziM799c17PDjOX8/AK66B+4nWXcvfirbnqnl0xZtQHtD8Ch/4HXPOlcdcEpUEQBlqTWfRfZ7HTrKe54+G57Dn3SR5bOZ1dPnFefumNn/s8//STUzhmj/s564TfU/XaRIFk8eqpTC0P8egzMzn4S1/sqX73uUu49LNfph25vP28j3Hb49uOb8co/NsJv+ejx1/J1H6bma1bFzhx4OmgydalvpzbHCkBccIq3WYrt4qJYoQYpQHcvdOVzWGYjBGG7eZjm0hjaiEogW5G6LVtRMUlGYkZmHE1J9VmcezwTN43504ADhyayn/ctQ8P9I1wxn5/67S3fG92NCEL/EG+85S9B4UwGGMdOKMhCB32vvoXtHWZbV79HlZPfpSTwjr3B7MQSR8LHzsR7n8Tu/c/wn217egbqAOakXo/njdE+Pp3w5SHYNpD446pe9fJ7N0oc+sh31/nuM+9/Fyemr+QOTP/xp/uPgC0XU+PlEd43UHXsteyHRl+8AQOG6rw0yWnQ2mI3b3HuZdpMO0BmHY/PPwaiMrwyk/grFlAjID6LA6p3E374dczcdrdHFVexH9t9zQT/ZWsqDbYd+1UTn1sAXvWJlF3Yvpjl0QbjIF/eehTXD9yyBhb3zb1Yj614Mck2nD+02/k20vfyb799/Kl7b/ODG9trs2coVtv2Sv1OpaqyzFvNXW+u15q9dISypXe+yvRJi87ur3xyj3c2IY33/stAD4z///xpllX5V8oalGJ1zz4Mz4159sc0n8bk/ya5YGmTd46vDvvue/cceuf7y/hiWBrtistZra/gnnV5fxqxXFIGdKMBwADh34Z3DbTb/wII3E/u1Yf4vb6nj31CDRmlKLpj3Y6k2nuGvrlCEtGptAnG0x2hsbYUEkdRJ307tSD5R0nGoKyi5S941rpsxfnfPOu8qP5ytDRSu7Wqu7m2+sEmvVk3C9TUgmemDnCfROHOHTldGa2ep3B9qhd9e6y3XaO/iLXbmkebc/ntMf/m0/PPA+N4Msr/gVPBJw37xx2qC5mkjvS07cr1x7Bz1e8ln0qd/OLwTczIEe4eNu34aTnS5V17xw36ronRGLQF7z+nkvX6wC/6Vc/wa309jlqNvn1m9+1xTrAO+64I5dccgk77LBDfuzhhx/muOOO47HHHnvB2ikc4E2AzAEeGXkKGOB/Tv4epx14jQ12ascMNqt898bj+N5Nr+Lw7e/jlP2upVIKufqhvfjylSdx2A4PcPKBN/Cufa/izT/4BIfMv5+PHnFJzw6rkIIVIxPZ5nM/5MMvv5yjt7+HIxcsoq9id0NMYvKkCYl0uOaxPfnAhR/k6ZEpfPLlv2VCpcU3/vxazj72F3zk/z4AwC4zlnD/M1uz9+zHuPT9n+fBpbP4tytO55YlHf3CSz/0BSrlmAO3eTgV8+/s6q3PAV4lQ6qOh0g0T5gmu6r0Ru5ybu5NhvlW7SF+XHuMUx7+Cg/UQ84/bDU7T1vN0kATodlh+S95bXUOH5uwM/OcPmY6JW4P1vIyd6qtx1OQGGoipiwUjrA7263QpexFaC2QpkP/CBxDM/aZWIYn107DTHkCIQXz3T6E6SSUIDGsTQIqRlISCpSkHpWY9N4frGMlGA5/14e54eFt4eZ/sYf6lnPm6efxwO0n8EjlTvYN9uUvLGLp0Wfwn5OP5b+GbwYE39hqX15Vmc/Pnqrx/uav8hrfOGEe+/iTOXNgF0Q69COtEu22i/Jgan8dnWhWyZAZojQ2mQdgkCxasTX7fuHT8NbXwQVXMZA6sSMjA7z9kBupiRZH7fgIA9LhnYffhJSGKFYsXj2V8y45nu+++8dWkkzA6iRgmlPqcYC/VXuQf1l5G/FO76ROzINJjYMeuYTXDWzN70eWALCd189DO7zBCvyPcoCfiVvU4ogFpYHn5ACPVCCpJcydfl1exguqhL7lzM+v97Gi1GJq6PPpG97FBx78BP7JrySY+ii/Xfg6dpOS1cEETv/b2Rwx/Xb+bZcf0Yx8fvDo6/n+E2/gjpe/jZ/2NTl392vWMfcWA9g1PkJnd6P/b+/nfRNvYsETB7CVM8zgzKeZHpS5YNZifjHbUi/4zYWww6UQVjlx0eFcvMtf2e7RQ2Hbq3jsoB9Rqk/h+Eu+whe3+gNGd5JvfHlA8/M9rgP/hds1WRemtkucccur+M6Tp7J6ylL2XDORuye0eY15grfrx/jc4Nt4ePkrePvUq3isuTW3jOzJLhPv5v5DvgdRle1WzWX68u2ZHLgMxRO4qXYAu1cfYCt3BYuaO7I6nsIBA3dz68ieHDRwFyfPuoQ/rDqKRfUdOGzyVew+7QFmDjxMO4xYNnkFA7HLvJE+Zjglnm7NxK+sZG4kuWNkJx5uz2V41n0E/Ys5aW3MkB8y4kY8MGmIbUf6md6sMEP08evFb+d7y96GJOHC3f+FnapPICU9lJIftI/gR1u1obIaYQTntlawZMkreKrS4rLBg2HXX7HL1pezeNoT7DZSxmBQBuY0qrxm8VyafsQOwxOY7vqctc0K/rjjrWPGVmlJkmphvfnat/CxwTr3J1N4z7T5sNsv2G759hy08E38fMm71jk/H5z2Q946+f96jm1KB/jxcB7TynWmeWuf1QEOZMJSP+TH26/gup3GxsfsPDSBo5/eioOe3IE/7ngP9w0M8nS1hQAabsyseplth/qZXa9SUYqWk3D0slnMCMoEKmFWq8xtU9aw3YoBfrrs3TzR3povz7HSWiuiabzl8R8DMNkZ5PLd34lOYFFrJ7byn+HVi36a27FX+R4+N+uLDKh6Z4d7IzjAJ154QY8DvOLOO1l26208dvm6d4//0fHv//7vXH311Xz3u99lhx124OGHH+ZDH/oQRxxxBF/+8pdfsHYKB3gTIOMAh/pEDp2/D3/80O24Jsod4Ax/ip8hEoZXq5mWNiAkD62Zw85zltsP9izr1zjqAxkf8//px3l3/wIqiRyTXS4rb3de7U7bl/90Ep//kyWVv3nvG/jpO77JD/56DB/+vzMAePu+13H+2+zuh2knCFdy+QP7suNWT7PdjJUgu7KbpTZcFaygnkS8Xs1apwN8S7yGw9Zew2vLcwhNwpXt5dwy41h2cSfgKIVKDEuTJtsu/8OY8dzDm8RQEjCUBviMMMLBpWn8tb2q57pFs1/LDqLK1clK/lB/iv+pPQLAru4EXluZwz8P7Mxk5ROYhDtba9hLTOC/Gw9zYfAkiyL7k/YXpuzFZ9bc1VPv8ZXZfGHyXlxSf4rPDd3DNqpKaDSL555IS2qiej+DzSrHPfAkX5i8J6/cZglLBydxYfgYX3V+B8BO0QIedF84LtOXpuzNWWvuZNHc1zJDlZmIy78P381h5ekMxgGnrLyJaMHJfHTlQmZIn5f509hR9DFLlXN+8u3tNRy04gr2c2bx8KVns/3hP+aO0i0cV92Ky5rLmKp8Fs45nv8afpB7gkF28SbgCMlgEnJZcymrkoCKUDRNQrTgZDCwImhy9DNX8UhkHbCdvQk8EI4NwBuNrVSZtw9syytLs3gybvDeVTcDEG138nNygGdMvo5GGpzma0kgNR99anf+6/y7+P1Rb+FbO97Eglo/731ie/a76Y8A/O3Qt3HQLT+EqML00hpWtqfk7e03+T4OnnI333rk7cyrLOPqwz9ATUV8TUgOeWIf9q3eT11OYdnM+zl3wb08Vq1x8Nrp7NqaydxGFVEPqGqHA5bPQikou53HsF+xu8wAN5RHeFBX+frlv+dj8y7kl8teyfJwRu4I4g9z9AkncMzS+Xzx1u9w3cGnU5VtOxQJvH/R2cwureTkbb5B0w9pOCHLqg20hllBmQmxx43VEjvJEaaFLjvXJpAIgycVBsMjg/083V+j2pjCXWYq2wWG8xZ/lCXhHMDATr+DOX+D/qdhzw1LSrJeaAlLDsP1hpm+djbDSYX4iVey1WOHMrLgGlbPvRMGt4VdfgOz7syLjfflYgzq0+2vJ9VV676mG08dhHPH6fyu+gcen7SKXYcnMC0u5Q7wn7ZaxtkH3bmeSmDnkQk82D/MzkMTWFlqs7ocrPPaMx7bkZqKMAJOWLI1f5izhJ/Nf5zpzRIrK+0x1x+5bCaLpgyyuhSw1fAUzv/LoTRaM7hq8DB80caJW5Rlm3OX218P3zXlQk6f+nNg0znAj4QLeM8T/wXA93f8FHtU7idwE+6YtoaDVk+jGfr8Ys4j/GyXx2mVGz1lp9//So5+cic+qB/hqzM1l+575Zj6hVbIdj+J1wJn3WM7Bq1J/Nuv38ur3EXU3IhF0wZ5qtJkpZzKb1eeyJSZNzMxFjzy2FvgIasbft4BL+c3uz9I043Yb/lUAifh1q1WMadWxZMCx0i2Hx5gm3ofjw6M8Jv5i1kwMkCp5dAfOWw73M/qZQHXB6t48oHh9TrAr/3Jz8bdAb7kXSdvsQ5wu93mPe95DxdeeGEuffa2t72N73//+5TL5fWU3nAUDvAmQL4DfMYIu/ZPINaaz5d347Xl2VzXWM4xzgwA/BH77TwYeANrRMh3w8f5Qut+JIIdVD9fL+/B0WIaK5I2M2WpxwHWAv6crOQ1zZv4WN+OfLWyJ8NRiOtI7o2HOUBOHtcBXjPSx0X3HMHRO93L3P6V9PntvN629nBkkisdZA4wkNcxngPsrbBZaoJJJ+UOcGw0CkGdmMU02W/tnzZ4/N5U3YYJjscPhh/pOb5BH3jPgonSY0iH+EgCNFvJMk/r1nOqazReVd6KK1pWbeJIfzrXBSs3qNxk5XPq5O3ZvzKVtz153bjXKATJOlK8/r341eRDuS1aw7HV2fy49jgXNOyO4/Md2zf0bc0MWeK7I+MrSuxemsTBfTP53uoHeM3A1tzUWMF2Xj+3tdawozfAQ+H47Z4//WAOkpNZ4A8guugR63KAf958gvdxD4kwnFmfxztbs5lSq9AIwW27HH3V93iiMYfr9z+ZCarGcNLPUQt/wg/2/Bwvm3wPfx48gC8/9B6Wtmbwzvl/5OT5l/HrJcfw/UffAMC/7vhDDpl8FztPWAxAfUjmqX79rud0tivb9F3Lfx0M8p/3pbLpZTN0O8A6FsSJYI8bL+bKg97PFLOSa9YczH4D93Lhitdy28hufG+nsyg5mnfe/1VeNe0mTp1jvzT+Zc1efPyBT3LZfh+kFK7t4dNm7W5IENxotCKXduLyuN6Bx1rb8HBzPnfVdmFuaSnHbfsz9uy/j3oUIyRsXevjyf46g37IguF+frngCWq+4ImBtdTdiJ1HJvDRh3ehlCjW+G1WiDa3Txzi9ilD3DJ7GRMDDz+xz5xnupw/P1HMG55C06+zQ20C266YwXard2OkMZvJQYO7h/ZhrSgTGklr+mKmzv4rcWM69293C647wikrJ7Hj6lk8vHonvurPZ3YouH/NwTC4HZTXgF/D2euHbL3zhTw+cfW4azHDGx+bx8fv3hWDHdNr+qusGljLyuEFHDVwD/utmYLTxTvVCQx6AZ5WPNVXZ5tWH9fPXMGdM9fw9iXz2a05Kf8ZPwtoazYTq3kt4dpZy/nZTo+x89oJvPXhbdm61cfKSU3edtj1NJ2Ef7tnd97w5Ly0vMkd0tsbe3Jt7TBubuzPb7ezO8WZA9yIfP5W34/b6ntw0tTLWFB+8jk7wGvrPve2dmGSGuL81e+gTza5p70rx0y4gSZ9XLH2CH6zy/t5276PMjL/5t7BXLsdXPMlpoTwofb93Dx8ODfVD6KpOw7gyyfexNMzHsGfdw2tm87iicZ2hMbyv6c4a5iohpnsrKEmHJbGW1HXFZj8KPQ9gzPwJPHcv8HNH4ej/x12sveKm0iicRR9vNULCKc+/42KA1dO487JawidThsD7QFG/mNkvQ7wcT+8cFwH+PJ3v32LdYAzrFmzhieeeIJ58+YxderU9Rf4O1E4wJsA3Q4wpbHnX6lm4AvJJfHy9db1Pnc+/xs9kb8/UEziFrNu+a4SijYJny/vxkliFse3bmQ/NZlL4qf5N28nThFzWevEnNG+k8+7u1Aj4vXMQgjBFeYZjlDTuFcPM1OUuL29hhN9G4D1Rf0QXwge4FPlnbg6eobbY2vDPFVlcdL5Bv+L8oF8KriXJbpJGUWrK23XQd5U/hau5t8HduPckUWMh0tnHMUr+2bnjveyVp0VSQuTGKYwgX8fvItJrmaX0iRWxi3mOlWuaS3n/QM7IDX8YOQRDqvO4MFgmP3LU3lz3zwAlgR1jlj+J5YmYxMhTBQuP5l2CKHQXFB/nJ/PPpxSKu3wldX38u+r1r/jsy58cMIOnDt9H4QU3BsMsas/kcVBjV2rk3i4Ncxu1cm5dnObhGfCFo04ZJfSRLQxnPrUX/ja7AOZoXyyW3dF2GKHR35L0/Q6KwvcfrZ2qtwRrOUN/VszUXhs6/TxQDDMZa1lPBHXx7Xxx9MP5p9W2vnYqeTxwYEdWBQP86OhR1irQ97RP589/Emc3L8tn15zJ5c1lvHGvm3Yz5/C6Sv/yi7eBO5Pd3lfV5nD6QMLOG/4AX4x8zDmPPEb3jVhO86fdwQ4CuIk/+JEnFIyIo2JNXeHg9zRWoPGcGljGZc0O5JlM1WJ3089gn28yQDoRNudgsSQhAnUQp5WAdu17W7RIckkLl9tA5jaDUUUSOJA8sjgVhxz/f+y/8C9fH67/+LjD5+FQfC7Az4GgHINDVPhXxd9lO8e8GUcmWCM4GePH8sjta359A4/wBiRU382hgOsNTieTfoRjPp+lqQOiJKCy4eO4hfLjueifT5JLa7wvns+y67VRzlr+x/kag4vhAOsE+v4ZD/3dnNxM3vaTT2Gc5mhXJE9dndjXRzgtkz4zc5PcMjK6exQG0AgNgoHeEU4je8/8w7267ub1291A1IYlpYb1J2Y2a0Kl261lOWVJpfNWMbOgxP48N27MLdL01wqejjAQK54Ac8edJcFenXrAGcOcKupexQ8Rit5ZPzoH237CD+b/zg//cthzGpVehxggKYu8eonf8C2p+/G6omreeXyrfj4Pbvy4+Vv4n9WWKd47/47+erO/0rYNITG4Ksqd09rsNpfweFPzWRi6OH3SRTWhmZS4v9WvYr/Xv5uxoMk4Y2TL+GDM3/MimnDvG+nVdRm3QftCfA/d8Gkxzl8m4vY87HteF31FnwZjuEA3xvtxa21PTlp2uXM9Hq/kDwXDvDDzXkMJKvR1VV86tCFBCphvxVTefOD86kELlVfoRR4WhFoTeQlXLvVCh7rH2G7FRM46slZCASDfoCfKPq1/XzwK7aNETfCMRJHC3xtbWjUNVIahr2IhhfhxlXedPdl63WAj/neRbjlUQ5wq8nV73/rFu8Ab2wUDvAmQOYAP/61V2F8OGzpFTwYr39n7ez+3Xh9eS4NpXkiqnPK6o2fGzvDDrKfh/WG8wZnqzLLkvXvnh7pTeeo0gxOnDCPnfsmgTYIZRM7rGzUCYSmFofspAbA0Nlx9h3LWU15paKkGImsksCASmkk2QOui3uaJRrJIEr2YWQijTEGIQQ61qxK2nx61Z2c1Lc1r/Rnoapuvqst0p3urA0TxKANq3TAsA6ZlfisNSEDwmHA8xFC8GRU5+mgyUyvzKLWIKviNqdM2A7XcxC+k3OkM4oKYB3CbCc986KcVFMtjjvHtEnPKXssjDCJYU3cpipdSlJhUocwT6CR1Zu9Rpo4SfhDYynbuv0gYQ9nopW4U5InmhED0mVKiZ7d/TiMcbyOtmx30g6AxGikhpZJaOiYaU4pb1e4kmeSgImuj18q9dSbOb/5J372Pu2zSQw60fxw+BFubq7ipyNjAyGOq87m8saynmNThcd1+mDmewOIhnWMkuGQKJAksSBsKe5ZuR3HXdvRqr7x5acyu2o/ZF1foxyDVAYhQUiD0QKd6nImsejJRteu2fUlpHVau6E11JWLkODV7I6eSJeVckz+3ivrvM4ksfVn0npRO+P3Wqdb6876WRzP5ZV//jbf3e+LfPfRN9FOPH77sjNt4GNbYozIg/jy+RMGxzN5v/LjqUMftlTPtcZYe4wWeOUkt6PTR/s+CtbNhXT9LqlFSc/4PVs55Zq8rdHtRoGk6VkHuBL2OsBZe91tZeMeBXKd2QRdX+dlRJdZRkMcjV9OSHBcPeb67vbWhY6dvXNh9LOX6y5rMHx8u7u4ZNoytm328aknduGQFbMwGO4dGGRC5PGBSS5Lt/8ze9Sr3D1xkFLsYK74Ol8WT3NAZTGHzgxg54vzugcYgMRhRK3taXNgaDbtv5xFcsd7SYwLMmb/g88mJmbfyXfz2IzHiP0aylhe70DicuX0VIP9iSM5/Jp/YysSztz+p/Srxrh9zNQgutdMNs7ZuMaBzB8b6xqX8ZC1N1o9Qmsojc3TlCN8lo85L/3SO959FjR7v+ytdgRH3/D79TrAR337VzijHOC41eTaD715i3KAt9pqK55++un1Xrf11luzZMmSF6TNwgHeBMhl0M57FQNlD8KEJNGEaFQC17RX0C8cDnSnUI9jJkjXOkY53cB+CA03W/w5fIbXl+Zggpg4vZ8SDCXPJUhifM9FlBwazTaOEEjXQWM4fvk17KMm8c3Ww5xZ2pF9vcl8r/kYxmjmqSrDxNwWr2V/dwotEyONYEC4LDVNykKxOG7wiLY7hjNFia9W9+TNA/N4LKyxfWkA4ShGRIRIrFO03ZMX89G+HdlbTeKuZJC3l+fhKsksUe5kHPM7Dl+W8Sx30lKu88Z0gHPkQWE6l2UTFWe9DnCeoS/oysDWrY/clXktz7Tmyo3mAPcg3R16Nge4W2Ghx34pGEk/HAZ83euoppno8nKjHODutnN0OcBW/FWA5/7dDnB3vX9pPsPPRh7nktpTrEzG8iLB/vpxV98xbF1zkL6DrtuA0NEOcBxIfrf4MAbDAV4/+zoG/EbusPwjOcBeWXPC9V/nnqEdqKomvzvs42xbWYaQpnCAeWk4wGCp8b+ctoRvb/0wz/htJocea71eaTi+vgRG5sL8a+D4D8O0B3tOO9ecgzs4nxYOA1NWMOHhY5jY1Dx14A8Zak+DCU/CttfAJPtr5N5DU7lzHVSRvYYmM7/Zx5TIY8A4vH/p9rQSDwGU3fBZ+1g4wBM47Ju/6XGA19x3O2vuvoVl1/9xi3KAy+Uy3/nOd1ifS/rP//zPDA+vP45kQ1A4wJsAeSKMg+fwmr3n8MqdZvQGsnVzGdObcTwHuDtgzjqLXY5IxsN1JaLk5NfmZaPEOj1d9dvUukmHy9vljPXYkZZfnDSYKysolX7Il1QXp1j2ptsFCJOOU0mXk1U4wIUD/Dwd4LzetL06MY04YrrxMbEmboY4QmLaMclwsF4HOA5F/oEqpPmHdID9ii20pt1PRbXwRIzRonCAX2IOsNadNbnMb3JreZC6E3P0qplcM305AZojHjyULzx+Bk1T5tStf89u02/hj/PuYUZQYkGjnz1rU2m1XZ5szKCvv06/16I/DIhiwX8sfh/31nfge3udQ3PCU3x52/sYdiKGnYjv33cg0waraMfgjJJ3lBKUO9aRfbY+bqkO8E2DK7lmZBW/WfbYeh3gl533W5xyr0Fxq8HNZ560RTnA8+bNywPeng2+7/Pggw+u97oNQZEncxPBcRzOffNe+Q7wPyLmqWd5KhQosBnRJ136HMd+4RHCSt29RDHJs9SldTl2BV46mB1UeM1IJ2vd25Zuax2+8nLO3+OzPQ7iR7rkLREaX0YsqDxF03GRqWOihOYzC77XKdfu43v3H9jTZgBjnN8CvThk0nR2nDaD3yxbv6ZtHEuI5NhjWxgWL168ydssHOBNCOFIZMXB+AqZ7pzlov9pMgiRSTspme9Wdu+gma6dvRxdKXZNpJF9HlScnl1Boe1urWnHnd3IbMc4q8vr2u3p3skFxGjpKWXLmUjb3VLo7JRmO3a+glG7oyIxiEqaNSzbwUz7KByV73CaqCOKbkOQPQSh3TV1pd1BlA5Ca4TXKYejIIzAT3dL093lfIsg3SUSMsnbzXZURUl1dlkB/K7bo1K2u7CAcGx6auIEHIVQ9if47u+uJunaDXZJ+5bal9oofCfdER31MJOis/ObIR+bca7V2u7YZTvH3bupjkJ0V5XtorrK2qvtGhSuhEinu9PpeyERvk4D1dJxUaPGyFG2b6OR7kznO755P5zODnA6fvnxzOY46VyXrXebPsoe7+R67bSVzr9w07WX/ergq7y/0pP2XpMC2YwwUYLj2d3RJBL5rmLmO2e7o9IxOG7vLmISW2H/bIcW7K6ockxej0qlzbKdKqMhSX898UnyXVawO8BZ/dkumUl38owRKFdjtMDxZc8uZreTm/NAu3Z6s7Yjv+tXpq4ydqe6t2/dr447atc9rR+snePtkAK4gUEIM6YMgPMsu3JeqMfsnGXvs3Lras+4EiEMlTDpadst9baX7ZrLdGd8tH1Zm6Pt7N59TsJ190F5vTvV3eWz9sbDaDu7ke3EZ7aN3mHsHpsM2S5yFOgxcwv2/3zN6LE7b1GaCS57jlVKnV8g7a8UIl/D2f9GC9xS9y8JvfPV7XCPLh8F6+6/65tx5x0gLul1ftlz/d57sKdcpPNfM7rtGG3naHhlMebXkg0p53iy59rmOI/N8ZBEkh4B4exYgeeNwgEuUKBAgQIFChR4EULHIOKxxwo8fxQOcIECBQoUKFCgwIsQUhvkqPgHMzoeosBzQuEAFyhQoECBAgUKvAghI40alaSjJ4i7wHNG4QBvQoiqD56bcy9FyUeEUYdPmiJPYVzyLe8040vGieVCZgkEMkiZc0QF5Nf3sLqyhAOuzDNo5eczzmTGl03t6+bndicp6InmD2JE2evYkV4v0jaFb+sR3fzNjA9b8nr7kPF4pbDj0q0SkPUp4w2XPMiiht2klxea1SXdjgJE9o0555x28Y+zMnGSl+tJ0pDyhnPlgqx8GKUhw/4YFYRsjvP2MuWGkp+W77r1svHNxjTjCmdjFsdWLd/xbZvjzX83B9pzO2Wz9dV9bW5nypv10jmqdF3XFpbvqzIer+xdE922d49jdzvdfe+ex+yc5/ZyfyFd5/FYrm93Hdl1PX3XPXxhoawSgAbkBN9y0FPlE6kEWglEpPHcGMeNibs4dd08S6ecKqmk2Qoy3ruMEnAVKkrsbkzKt5ddag5KjY0uD6X97bKSKpdk3EOpuriuXZHySZzxVdMp9Xo5mxmnUEirVAGMyxF1fY3WjOEuQketYjyepFW/6OYPi/za0SoX3TzKeJQSQjeyPoyHOFw3t3G8clnfY1+SpDzJcinu4Vo6nmY0Xzp7r8bhOI83Lt1luu3sbif7P2uvu2yG8drLMB4HOCs/mk8+Gh0Oea+CBFi1g/FUCbrbHJ8DnPKjnZQDHHe41aPnHroeo2G6RkxvnUJYPv1ou422yV7ckhzDQc/6/2y88dF82O75ytZv9xx1lxvdzmj+/vjtdZRYoMMNh7Hz28N/HjW/pQ0MFFSJRsWjHOBk3eNRYMNROMCbCHEc8+mfLuQ1B2zNsfvM3dzmFChQoECBAgU2A65dvorLlq/ZoGudyODIjsO79vHbWfPoLRvLtJcUCgd4E8FxHL74zv0ZqHjrv7hAgQIFChQosEXiqFnT2GXmDH720OL1XqviXgqE1AZRbAC/ICgc4AIFChQoUKBAgRchnEj37ACrWI8Jiivw3FA4wJsSvg+eMz5nsotfmmv0StHhjHqj9GK7OZY591Z2eKfdfNUu3q0YzeXstiHjY3bzZruzksUJoqJ6OKHCc3vb6s7sVXE7WrAZ7xd6+bdOF+dYd2nGjua1dvNJM1uzXNAOgOrVlc0wOgNanNgxzXijpGW7x7N7TjLbujOXZTZkXNs46fyf9R0sl3g0/3Y0B7d77rL2s/qy/7t1h0ujfkHwXGgHnT5kbWfj7bm9c9gzNtmxbE67bDXSlq+UuvrTNY6ZzVlbcRcPO5+7Djd9DOfbcRBCdNJedq/dfB6618aorHFed39k1xxp0JkecozwrdY1kcYQQ2J1qaXvYFxbpyoZZFeWRZRKdblVnhkv09nOkWl0lxSiKyDFVZ0sZDmfPuXrSW1wMip0qkMtJF2ZErP2nbyccE3erqIT/NLNc9SxsNzKXLfbdF67MiKOx/OEDm85t7uLgzmaxwy902yvH3uNcrv1b3vLKWf8D++MszmaZp7dJt0Z+kZzYR3XEKVjWtK9iYZG807zjJV0dI7HpbePY2e3PvJ4ZXrKdbWTn0vHc/SYSNnLH8/a6i7X/egefStnXNrx5yJdMyk3vBuOl2Wr1GP4sBlvPJI2i6CvLQd49FyMbU/kNmY84G7u8GhedLaG43Wsi6z/Y46lbSjHjOF4r6tc97h3c7xHc+OF7NWg7h437Yke3eOe+p+lD8rpvVZtoJSZHMUBnjF7LyZP24Gn7r1swyoosE4UDnCBAgUKFChQoMCLEE6kcbo4DyufupMVS27bjBZtOSjSiRQoUKBAgQIFCrwIoeKk52/WrD3Ybe+3bm6ztggUDvAmQhwXqVs2Bq65bfHmNmGLxTW3P7m5Tdgice3iVZvbhC0W1y0pxnZj4PqninHdXHAi3fO3+sk7eXDhhZvbrC0ChQO8iVA4wBsHf7598eY2YYvFn+8oHOCNgeueLJyJjYXCAd44KBzgzYdMBzj7mz19d/bc/c2b26wtAgUHeFOiJ0lD+t0jC1LKEhxkyL6adEc8ZAFFAI7bezyD53clEEgTOUgFcVqnFvZ/R3WSLGTtZDZmyILVsmNZYFaeTMIdJ0CqKxArDw7rCn7LAtW6E090I034kV/jjArO6g7oMgJMV9BBz/jJNIGE6D0nTdf4mK650L3JKbqjU7KkF1LaQeweszDqClRMxyMLRBmVHMOmZWBsFEu+Lrpsqzd7x6x77eTBil1rKU5s/XKcQKc4AR3Zct3BehLyALgw6tg5XjBg1ufuNZG9hlFX0g3ZWbvZ2s6CG7NAtjTJhekew2wc4sQG3o3G6IQm3e1n5boDHbNrsyQVYTK2zsQmdTHaIGRXkCNYm8EG0I2G6h1j4as8NamsuDZQrWce0qQJaIQQCCGQftZuep0aNUbagJIItD2XtZVeKzIhfCWRUWKDW7N6Et1JlZoF2Llpcppk/CCd0QFN2fuegK60vGRsUomesUnMuIkSZBYU1lUXdAW0KYFTHmvbqN7b/0YtUanssAkF0icfV5MF/ub/ZwGcAqNNlt8ENeq+ycuNGk+BtV8qkN196UI+F3TCEbP+OmV6xkDS/V709EvQGZ8s0Csb++5HVSdwriswLQ+sFIhMQUCRH8uh0oMudk11rS2j7Vw7wq4Dr89alZdLetem7aPGUXacrVJB1wik/RTK2tCdzlcqcGVvUo7xggxHB/lJRCcRxag1kaG7nizATdJJJ2zbM2OCO+31uie4MLuWLLhunCC70R9pGZTbCapbVxKW8TA6FfLyFfewbNntG1S2wLOjcIA3AZIkQWvN0tU1BtrxWEc4c4DHDSkex1kafTxzTKHXSY67Pohzp0BDktgH2Poc4O4MYN1PkdyZFL1taGMd1rhLYSFTI5Cic72fZgNLEhDdygOpAxhrW09mQ3ZNFHfaBeqhpN6KWLqqNv649WRLy8ahq+5Y985Bt+PXPc5C2LYz57vb6W+HUArte6Vsn7KI3dEOcDYG3XO3LlWOsMvh7HaAM5WEPDRedNoc7fx2O4uZ4+w5Y5/QUtr28ix0knpbdMZ2dJ9HO8CZ09wOO19asmMi/ZLS/dqtCCJFZ16zuS9HvfZ125utkW5HMU4ginrHAyBOMGECicEE1kE2gf2CZYKko6hgUie5xwFO7etygIUQneu70KNmEWlMZMbOfdqPutTUw5hltfYoh2wcBzjr+3jKIV3rx8RJbz1a9zgXvWP5HOWTRo3LOh3gbtvHq2OcMc4duPG+vP0daEhDPUxYVmuPcoDH/p+97/RhPAdYjB3P0faPY/O62hijSDP6+GiMGut1KWCMzp4G5PUJKTCjnkc9fX2WtZXZ3j2uPeXG81DTsRrt4I7u53jnTdztZG6YA7wuZZNRJo1xgG3Z3jrGd4DNGAd4XQ5uVvbZznc7wCvTTHBJMs6X8y60a2tJVDN/P6m8FX3zJ/PIY1c/a7kC64cwo5/mBV5wLFy4kAMOOGBzm1GgQIECBQoUeBHh1ltvZf/99x9zfGhoiHmTZjBMOG45KSVr1qxh4sSJG9nCLRfFDvAmwIIFCwD4p0u+RXWgzD5TNLtNbhJ1fXttx5J+LyHSAimgGUmkgEeGSwQJ9Hua3Sa3GA4UUsBA+hNjYnReZnnTY21bEWmBNrDLpDZemkHGlYZWLCk7mlZsv3naDUnDcOQgMUwtxQgkzdh+We9zYSgQTOr6RXo4gNWBg6cMk70YKQyNSKEBXxoiLZhZcam6k5HpV+12UidK2sQmJNYJnvKohTGtWNLSkslejDZQixW1UNGIJJEWDIbQTmBGCS573GXGhJjJvmFaCab4Bkcazv/Kr3nTP7+F0EA93Th0JfgKJrimI1krDLEWTC4lBIlgKFAoAasDe77fsRuXVVczHEhqMSypCRoJ9LsQpbLG1fQn4d0nJzw6ojhyVounmx5uukNQjyRLGzL/ZXSSZ9uWAoLE9ikxcNsKxewBTZSAm27oDkV2TmaUIdK2P30uNGPYqmJYGwiG0mehJ2GwKRkoa14xO0k3bg3aCGqhpJXA1JImTCSRhgl+ktvUiCTDkaDfgX4voRVLnmoIqo79uXN5C56sC27931+y33vfwvx+Q2JgWgn6HFjWhAmenZuSgkZsxzxINzLaCczvg+EIKgpiY6+pRfaXWV9BLRIMpmM/wbPjGmqYWYZVbdvvqb7dEO737Jrt9xJ8ZdimL8BTHhLFZDUVs+w+WDOMiSP7S4qX6k8P12FCn13MtQZ6JIREEy+rEa9uM/yMx8RZIe62E5CTSgjP7ky3b1lOu6aIQ4mUhr5tFWpSCTlQQtdCTCtC9nmYxBA8NETQUDglTRIKKrMksuqhWxHJUEjQUIyssr/QxKGkOini3Dse4dN77QBAeSBBuZrBpz2m75hgooT2sKQ8VdBabfDKGmeah7/rVKLHh0EJ1JQKycoGyZo2oqJ46ibF1G0CSlMl7rYTEK4iWdlAjwQEaxL8KQrdjAkbgqitiEPwSlaP1a1ogroiagu8SvpMiaHU17srFTYVpf6YsKGQjkF5mY6uRpbtx4iJEuI2xIFESGjXJU4q/ZztttkfIgxaC6K21S+uTLT6sm7FLtCoafAmO+im/WXAxAY14BKujXMqQNiSlKZ1+uX323JfWPgI5xyxA6Li2J34KAEl0a1ODEa75uBXk3xXTziCqCVwXJ3vDoYtiVfWxKHVtK0POvgVzfAzLjoRJJGwP2lrmLRViPIMSSgoT7cVqAGPZCggHNHo2O4g2lfQsegZE7di1w5YqoHfD+1hkV+XxILaagfH13n5+qCkOtH+PK8TgVQG5WrK/Tof425t3GZNUapq3JKmNMHgzO6zFIeypSnFS0fQ6QNUDniYZgyuQg146EbIZ//8MJ8/YgdESSLLLsnKJmp6Bd2K7K8e7QQd6nwsM73gTKfXaEGSdI4niR3vsKVyHeuoLXE8TRxKwpbM14vra5KoMx9CGpQDrZrMJbOFMARNhePrnh3zfOc31fH1KoYkBq9kx85oGFnt0q4Lmg1Nq6Wp9En6+xV+X4zjGqRj8rmQjqE55JBEkkZDE7Rsmf5+Raul8X1Bq6WJgs69o7VBSkEUd/Yam42EwWbImfw19w9GQ0rJMCHfdA+hPMpVaxHzsegm5Hi/GhfYYBQO8CaASnlWfrVMqa9CpT+hf8D0OMDOKAdYpg5wRZdQiaDqJfQPQJI6wP3jOMAjyqPtZg6woG9A4Hc5wCqWVByNSh1glTrASegghaE/dYBVbJ20fhfiQNDf5QAnAbTaDr4y9PvWARajHOD+ikufW0GmhDYv0YSJIDYqd4AJY1QsUYmk349JDJhIoUOFSR3gdiAQCVTK4FVd/GpMqWQol6BS0rjS4LoOlf4KjoakywEuKah4OneAlbC2VUsJTiIIPesAl1PB/oprHciqq4k8RRyBbwRxDL4HMoGSC6XUAa72x5S1Q/8AVFXHATaRpCxU7mxWfJM7wCqxfUoMeFWF36eRXQ6wF9o5KVVAaYhCmzsiiaBUNfiuwE8frJ4ET0j8sqbaH/c4wEloOd/VcoKT2LGs+nFuk4kkUSipuIaqlyBiSVlIyk5n7DwjUI6LV61Q6rMOcLlkx6kkoeyBSB1gnTrAIvOZEqj0QxhCxbHOvI4tS0EJWyaKBH769Cn5dlylhnIZSg6UXaiU7JeWzMZq6gD398vcAR5QFcxICYI2JpLWEM9Nk1+EUE058UmEjox1gEsusRejXZcBT+OWXGTZQ/jWAfY8B9dTxEYilaHfd1AlF1l20bHBGJBl1zrAnkM7UrieJkZQ9RWy5NomPU07UhjXOsCRlvS5dt7602MVT6BcTey6DPgCIwWuJ6n4Ascz+J7G8V38ikdUcq0DXHZJSi6JHyN8hz5HMeAllHyJW3atA1xy0YEm8AS+76ATCCNBmChiI/A86yh4nrbPjETipQkTEiEoe70/LQeRouzZV+l0OL6Oq5HpRBopiDVERiIluK7KE010HGDr+OhEECUS6RiqnnWMXD91gGOD57tk+SyMNCjfJfQ6XNgwkZS6+uX7aTklGfAdhO9iZEoLUpLu3Biu51DyOj9rC1cQJaMc4ETipXMqJQjXpeQlaNclEYLEiNSxs1/QHM8QI6j46bO15JL4CaGn0dK2lUhbVyJ7HeCsHbD9833wPJFflwiBcVzc1D4tBDiSPkejtUAL6wA7rqbsJvkYdzvAynUouQmepyn5Bqfk9jrAvosOUx6772ISrANcsmveUyIdV7u+k/Se0BqQGmMEGp2P5QY7wIkikXYswyR1gI0kjFW6XsB1NQkSz02IjUwdYIPjqh4H2HUUrrseB9jVJELgeUnuAGvHxXEEQmmk0lSVpM9RlFzRcYBFxwGWjkNiJEJpHKWRUlNVCqk0vhJIqQm7qBqa1AHupm8IQTuNCcn8g3Wh33WpiF5XzTEConUUKLDBKBzgTQgp7YaErzSJEQSJxJXWufBVtktod2mhE7+TGKhHipFQ0UokZaVpxtbhUQIiLXClIUhsnRnVKtACKQRKQDMWxFrQTJ1fbeyHVdkxOOnDQQqFMQZfWad6OJSpU6WRwjqv2ii0sYEA9VgigdgIZOpgKmHQJAgE2iRoExPpgMTEOU9SIAkSQZjandXjSbur2+dqgkQAkrWBoJ1AGCjaSUwjFiTG4EqDr0weYlFSducROnEpkRYEiaDi2A+nSNtdxyAReZKmxFgHbcAlH0Mp7DEpIdTQjKw/5Uo7R15G1zWwtOGhja3TkYYVTUU7gYnpDunaQFB1bJ3Z7u2AC76nqYW9MTSTPNueJw2eBDf9AKy61p52+sWk0eo8MAcbipHQMJDu5HbT+7K5dqVhbdthwEsIEmkdcEn+BWwwkDRiO4at9KEaxAINVFOneCQQhNqwqm2/GCWmQ4+168seqzr2fTtN1jYSQVnZflVTZ7gRd+bMTc/JRDCQOkvTS9maF/S5dl31uRpXQsWxa9NX0n7BcktQ6oO+ENEOoFpJP3WVXRtaWw+73sS0Y0ykkRN9WNnC8ezuXM4FbsYIVyJc68AlsXUsSAxychlTsxNoIo2JEkTFRfpQIiFJBOWJGlEpoYOYeE1IHMl8d6s1bHfwdCwwCOIo40LaD3PlgOxzSQYTmy0r0jieQJVFyivW+YIVJYWuhdZhJ90hiwUmiDG1EDmjiuz30M3ItteOiZq2L0ks8g/+PMtV6pCAdZhkynuMQ5E7ulIZdH6NIA47jk0WJGYiA9idy8QIwpbE8RLrZKRtJpHdVY/aMg+wMxoMWbY6+4TQtTDfOc3GHOycOJ5BKoNJs/cZLdBBkjtDxt6QufOb9VG4kqRlcEsa6RhEyWZYNEFsbUvrNhq8PoMOBMYIGzLhmPy84xmawyrfebXjaedRpDe0iRJINK6vCWJlM7mlY5g52dlubtjs3cUzUYLWyga7eYYkEnk5xzX5symD43ZIp1oL4iD9hU8awpZCuRqlDMrtBHqZSIMS6GaUcp0FopL+1KXsuJkgRjclJtI5b1hIu55ExbVr0JUYVyJcD6li2+8U0jGQrjeAOBQoB5SjcR1DFNg1oNOxBzseuouTazREgcznRycCoe11UVtCSefzrtJd5GxNCWmPub4mjkT+ZSxbt5mjnESW1ytVGosq7fuoLVFOx6FOEnvfGtO5PopsnXFsUBktWgkbHx4ZgkDTP6DSMAx7bRAY1kP77YHjCJyuWJm7otXcFheqHC8Eiv3zAv/Q2Ofw3Te3CVsstjlwj81twhaJw6ZM29wmbLE4Zuspm9uELRLHbDt1c5vwkoXjCBy387dfZRrv7Nthc5u1RaDYAS7wD419jtiDLnpfgRcQhQO8cXDY1OmAXu91Bf5+FA7wxsErtp2GWYeEXoGNC9cV+a+B+bENUL8osH4UDnCBAgUKFChQoMCLEMoVOKMcYFU4wC8IXrIUiIsuuojDDjuMgYEBhBBjMrUFQcBZZ53FNttsQ7VaZZtttuGnP/1pft4Yw9lnn81WW21FtVrl8MMPZ9GiRZu6GwUKFChQoECBLRSOk9Ig0r87w9X8ZOThzW3WFoGXrAM8adIkzjjjDL75zW+Oe/5Nb3oTCxcu5JprrqFer7Nw4UIOPPDA/Px5553H+eefz5VXXsnq1as55JBDOPbYY6nX65uoBwUKFChQoECBLRmuIywNIv07eMJ0PjB9x81t1haBlywF4thjjwXguuuuG3Pummuu4aqrruLJJ59k+vTpAEyfPj3/H+A73/kOZ555JrvvboOwvvCFL/CDH/yAiy++mFNOOWXcNmsROBE0UkWHMJUvy9CMJaEWNGKZqkQI1gaClW3B/D5DI5YESfadRafJqyStROZKDu3E/ikBI6ECjx6lBjS00jqCRNJKNM3Y6tZWnIggESTGQWOVIrSx1w+4CZGWrAkcRkJbPta2zkx5wqoMKKpuiCdHkEKhTUKYtAi1SfWHFZEOqMcuQZKpV6hceWIkVKltgmYsWNESTPQMw0MekyYFDAVWJaAZZ3rFIld/aCedhF5WFUKkigTCqmJpO17thPx9Nl4jEfjKKkUMpVq9zciqIYDVqAVbX8W1er9Jqv6Q9SM71k6s0oE2VqlGCat0kGXIXdHqZKTNNHTrbWnloVIN59AGaVtVLwkrUp3QqIs62m45eH5CLYIsD6hO25cChkPbb5UrUKhc7SF7bcZWAaKR9lMJq/3cbjlIGVnViVjQjm0dti8i10P2pK1rJBK5+oMdK1vXSAQ1QareYYPMgXycVPrDS1tCLRQ0fJP3O9LQSqyKRklZNQttrEoGBPiqTSseoTxpLqY8YAVss5DtsIloNaDZsu+VQkmJaYXo1S0r19SWVv0gMeihAOFKUC64iiTV7HQ8u6AyxQFRUoimRFRcm2bWd5Cp/l7cApXYiHmdRqELSR6ZLtMsVCrVFXW8TlYqx9eYKJUmofNqtBmTwtg0IqtWUbU2uKUglQcT4CprY+Sm8mQRScsQhxKjZa43a4ztf65+kGra6q6IfUiT7qUR+mFLETYlpf6EJJIoJyFoSfyUz5wkkrAp87qzeqwclUHT0bU1xio8xIEkassxygg67a+QVi7LRIktkz7njBEkLZNm9rJR/TKVJbNqFF3Z8UhVAbCR/EanN2WiISFXr8iUFYwWEBmkY6/PVBqENKnAiMmVN6AjzSaEydUquiVSXF+TJAKlrIRej1qBsUoSWR+yMpm+bxLblLndkmbZvMSRRAhDokVHkYOO5JdUBse3GsOZ4odVubB2CqziCaTKJs1UAqYWdjLhaWPXmq8gSHqOZ33Ms7qla18Iq4iR2SklxJFVp+hOPyykwXENwrdqFWD7ZNdhp79SWsa8kLZPcWTH0t5LqQRaKVMJUV0Z3uxrpiJhHPI1no1RHEmSWBBFmkZdUy5L/FSOL4kkWmtAdPoRW5UT5RjiukEnhjgy+XrNXqW0KhGjk6hKKdBpqmnH3TApM8cVOKNSbo+mRBR4bnjJ7gA/G6666irmz5/PV77yFWbNmsXcuXM57bTTWL16NQDDw8MsXry4J7ub4zjsvffe3HnnnS+YHffc+gife/1Z6U347LjvzsW88/B/JQ6LiLACBQoUKFBgS4CUYty/As8fhQM8DlavXs0DDzxAEAQ8+uij3HbbbSxdujTf2R0ZGQEYk4Jw0qRJ+bkXAt/89M943YffsEHZXnbdex7bbL8Vf7rgTy9Y+wUKFChQoECBzQfl9sqgLWyu4n9WPri5zdoiUDjA4yALjPvqV79KtVplxowZnHPOOVx55ZU0m00GBgYAm6u7G4ODg/m554vbbnyAtatG2POofTa4zKvffgRX/vTK/CeWAgUKFChQoMA/LkZzgA+fOoOPbbPL5jZri8BLlgP8bNhnn/GdTiEExhgmTJjAvHnzWLhwIS972csAiOOYu+66a538X4Cb/ueXCMdlcZ/h+FftxP5HrjuJw7V/vJ19D90ZqWTOE/rjL//KBd/5E8ufWs3MrSbyoU++muPf0AnM2+eQnRlePcziRY+z/V7j5xcvUKBAgQIFCmx63N5exd9aq0gSiMyGpYNzXKv+0HNMFBSIFwIvWQc4SRKiKCIMbXrTIAiI4xjP8zjxxBOZPXs2Z511Fv/xH/9Bo9Hgc5/7HMcffzzVahWAM844g/POO4+jjz6a7bbbjnPPPRfXdTnxxBPX2eYhH3gLTqXCy7fSbDsQpMFC4+OBuxdz6Ks6jvj1v72Bi//7Yr70ww+zx95zefD2R/jAW77FjFmT2eUAGxHqlTxmzptZOMAFChQoUKDAiwz7lqaxUzyZIDA0kohr9LL1lrGp0nuTkKSJ3gs8T7xkHeALLriA0047LX/f19cHwLXXXsuRRx7JVVddxUc+8hGmTp3KwMAAxx9/PF/96lfz688880xqtRrHHHMMIyMj7LffflxxxRV5PeNhpCXxpGRtYJgcKmJtI+NdaYNqHWmVEqQwDK5tIMt9NGJY24bf//By3vaR1zJ9pwUMhoY5e+7CUa89gIt+djOf3HtnW3+o8KplVq9p2Mj8UOFKQ5RGB7dimSo1SCJtVRx8JWjGVsVBCoNO1ScGvITBQKGNoM/VBIkk1oKhQNGKJdoIqm5CK1a5MoVVkBCUHYeSaiKFIkjajISKQCsibevuczUjoaQRKapuwkioGPCsOkaQCBqxVRJopq8jkWDlsgr9AxGlcsyaUkzVsePWiMlVIBqxyFUSWgkoYUhMR7GgncCAawPqE2MDwVe1BK6CZU2raCBTxYShQDASwciQj5SGSjUi8jQlG6xMPZKsCex4ZPWVlFU9aCeCxNhxLynbdnY8XwtDPnogpFl36RsIabccSjejNCgAAQAASURBVMQ0Gy4MhLjKMNS0Kg++Y4g0NBsuOhFoLZDSUBtx6R+A5a0YV5qO+kdkFRQyJYV+t3ecsnErKfCkZCiEWghRYiPhbb89HMcqhHgyohYKSunTIkqs+kYjgqpr52hN256rptesCTp9zRQ1EtNRw9BplHscSxxH43s6tVfkY1ly0roDmOQZSkoQJA5SQCOW+MqgxBoSZwL+wFSM0RgMrvSRYQBhExPUIGzChBDhufDUctT8yYgnhlGesdHk6aSaIMY0I8LBBJ0o/GpiVQwqLqLkYIitKsH8CZhaiJxaQTwxDK5EVRSyGeUqAFKCkJqgoahMiIkCSama4Pga6ahOlHwsSISdT5RElBxEaCdSKZOrP4iSQgcxsuLa+if6mEijmzFSGZSrERUPWbETIFL5EOVqhDRpJL2xfXZMrjyQqVK0apKw5eCVdRo9b+9pz0nyyPxMtcKqHtg6jBZWSYNelQKvnBAFTt5HTRr172scV+dKCEIYdCJwHN2lBJHWbaxiQxxJRGwImxLpGEDbSHxlciWGrP3svSNNnrksOy5cgxCgtbTz5GYKBh3FC5m2n9kNIB1DEtnof6PBSHsPGiNwPbtGPMeOMVLY9RRpcBWmHkGqfhFHgiRVEQDs+g9l3p7tO/k5gCQShC2rbBCHEtDEoSTRVrlCJ5mKhO2nSdVHpLTjp7vGx7Yn8PrSfkcaUVKYdgKR7YdMlRaEqyDRdgwz5YbEzjuyI9NiupUglMS0Y6QUxJFEKUMUyFwBQWuB1L2KHUIaJFZVIQo6jEzbF6ugoTzTs7ZcX2O0wPF0ur7T50wicEsaKa0gjHLsq46tEolVNlEIYWwgmepVbFDpq1QivX8NSShI0jUQx3b+4sgglX0Fu0ObsQ5dV5Boqxaik1TpIUV34JrWBr1hG8BIZXJb82OmcIBfCLxkHeBTTz2VU089dZ3nd9ppJ6666qp1nhdCcM4553DOOedsBOugf2IfjVozf796yQr+55yL+P4Xf50+Tw061uz1st6c4K16i8qE6kaxqUCBAgUKFCiw6SBH7QDfsGol16x6ZjNatOXgJesAv9ixYPd5LHlkGRkJon/qRN7ziddz2OsPw1cGVxocadIdXntNGISsenIFc3fddrPZXaBAgQIFChR4YSCVSX/5sDhy1jT2nTqRny95cjNatWWgcIBfpDjkVfvytX/+PickGlAccfKx/Pybf2DG/Nnsutc2hFHMIw8twVOC7XafD8C9Nz9A/5SBwgEuUKBAgQIFtgAox/71HNsCGRCPP/74Bl0npWTevHkvSJuFA/wixd6H7srEKf0suu4OJh24P0ee/Cp2ml3hO5/+MSuWrEQpybY7zeb9n3p9XuZPF13HUScfa5UjChQoUKBAgQL/0BiXA6y3PA94wYIFiPWoWxhjKJfLNBqNF6TNwgHexBgZ9hia1mIoDTCTwjCibSpdmxZYEmkbzHXKZ97F/3z2J5yw175M9CX7vvpw9j7+cHxlCBLBRD8h0oJVLcEj9yzmiYeW8tovfCgPwmpEEnCItcgD4eqRDWCDTlBUpEmD8RRS2GC2emTTLlubbFtDgWI4tKmES9oGJGVphF0JtUhRUlBxFL6MkEKjjWJV26Y9zmwAG6A3HAqasV2Ca9u2rsGQPBVwFohWVlAZCWm3FFIa1rQ7QXA2/bFNh9yMoDdBjsBLA6uUgHZs+9kdIDYSQRTAFJ88+K4R23bXri6lgVqKOBZ4nqblJzT8hGltW18WcAY21XUjFixvwkAa/DAUiDygK82my1AIYSAZGfKJI4FULu2Wot1SlMoJ7ZZDPZJ4fkJ9xKMpDY5rG2m3FEGgUMpQr3loLVgxJUAJG8SXGBs0qHUn0G8ksoFrSnQCACNtA9a0MQwFNvBwKA1cCwPF4NoSnp8ukAkRcSRZ0UwYcG2AYTtJUxfHNvgujmT6kDZZzAzakAd5hhqakQ3aaTZcKtWIdstBa0F9xKVUTnBcTbUS5zaORDZoEWzZigtVxyCFXTM2DXfEtNJaHLmGWAscafCVpOJMoNI3EVWdjBlZbiOcABpNhOuiZlSojtTsT4tSIpTAYIOMXF+jlKEx5OBXE0RJIaf2QTuEqRMhjJBbVzC1Bv6xO2GWryF+dMgGJmmD8BWqT4EUVLFjF4cCv6yRPpQnanQAjSEH6dggsFJ/AolEN2NA2JSyUiNciW5G6FqErLg2wMpTkNgUtYQamQVgAXhpUJMSeZpbIS2PUAJJZBDCBjO5vi2rY4HrG8JWGuRl0hTMkjwAyyvrPFhLOgYljE0Tjc4DhsAGLmlt0nSxNnAuSdd9lgpaSCABt2TTCSs3S+Nr8iCuLAAObKrbJJL5MRsISh40laedTgP1sjTORoNwBQJrq4nAmK6gs8TksfSia8+gOxgvSQNDEzKbrJ2ZXXl/UjuUNphmbFMHt+M85a+JMps6qahHp1JOYhsMadJUvYmWdsxEJ8jNcQ1xCL5v0wor19qSpft1S2mK39AGDErHBnLl6ZGlTXusAxvQaRpZim8HR0eYyCBcB9OO7RySpIGdTv5wNdrkDz3TjEEKDGDaMXEo8gC+KO7diOnMv32eJbFA+jJPae24Gp0IotCW6w66tOm6NVracRPSoFJpsKxer6zzVN+dlMwif3YmiT1mjMhTfgcNSW0kIQxsYJvjijGKC1oLkrZNXW7P2QA4z5c06po4MpQrkigNilMSsnyscWSgnKVEzoLv0mxuCtgAyf5sHnuObYFBcJVKhfvuu+9ZrzHGsNdee71gbRYO8IsYO+23I2dc9GWWbcCXne12n88XLz+PNUHHIStQoECBAgUK/OPCUiBGyaBtef4vJ5xwAttss816r3vNa17zgrVZOMAFChQoUKBAgQIvQghMvoMPcO3Tq7ly6crNaNHGwYUXXrhB1/3sZz97wdp8yZJFL7roIg477LA87XEcx+Ned/vtt+O6LoceemjP8SAI+NCHPsTUqVPp7+/nNa95DU899dSmML1AgQIFChQo8BJARoHI/l6+9RQ+s9/2m9usjY4k2UCh5OeBl6wDPGnSJM444wy++c1vrvOadrvNqaeeyhFHHDHm3Mc//nH+8pe/cPvtt7Ns2TImT57MCSecgNYF/6BAgQIFChQo8PyR6QB3/43mBG+JOO644zZ6Gy9ZCsSxxx4LwHXXXbfOaz796U/z8pe/nIkTJ3L11Vfnx9vtNj/60Y/4xS9+kXNWvv71rzNr1ixuuukmDjvssI1qe4ECBQoUKFBgy8e4KhBbEAn4s5/97Jhjxhgee+yxjd72S9YBXh9uuOEG/vjHP3LnnXf2pEAGeOihh2i1WhxwwAH5salTpzJ//nzuvPPOdTrAw4M+wlOMRIIn65KqYxUMbLS+fc2UGbLXWgirnykDLWaW7bEoELgSBkOVR/5bNQSb2nckguklwcq2QQUyVzywKWZtO5nSQp5EQwsSo3I1gVZuh8CTgsFA0kqsggGkqWqVVT6wqhM2Qt+mwnVoxhIlDFJYxYes/XYCI6FkWZrkTgloJQJPmvS8IEqsbWvbgmbdpVWOqdZChod8KtWYKFVqSIyNrm3EWT3QCiRaC8ol24FMCQFsEPNgaPuXYajuIKWh7SRpumDBUNBRLBhcWyIMFI6rqVQjfD+hUo1ZUwlZPqKAhJLTUZUYCiCIBUPG9l2JTFnCqiaAVVlotRzKaaxwfcSl0XBxHE2rZe0BCMMS5XKMlAatRX6umV6rtVVUWLGqBLSpOra9RtwZ64keoGF1Q9JX0tTbVq1BJ4KasgoOjRiCUBKGijBQ6DT9bBzZgRpsZMcFWid2PBxNHEt0kr5qQRgoPD9BSkNSjm0dsY1kDwNFHEviSKTX2jJBWm+cRoy3W/aRVKlGdq3rBCXsWrPr1UbhKwGuFNQjj1W+Q5+r03Vtx3jrvmEGvLX0u1Pp65sG0gGvCq0WOA5qUglvShvZ59lF5Dp20KTEmdUHSuC2Y2R/BVFxwXFgchkxoR8qZVg7bP+fPhkxaQLulNWIRU8TL63blLFpSmKn4uBgI+/RxtaVaGSk6fdD4hYYBG4lVZBwBQobaS7SiH2jwUSJTcmsBITpzSmltdO3Cg2Z6oOuhch+DxPZlMMokaZ/7XxwOp7BOGlaWs9gUskFx7PR+a6v8zS7OhF45SSN7rf2CGVTzNqIepvOFjqpbU2WUjdtN1NKsMoMJo/Sz1Jiu36aQrr7gz2P5LdqE2DT2ZouNRkhDSpVSNBaYoxJ0wan7UWCLBWxUtm5VGXANRBbdQAdC6Rjn62qy4bMxkyxwaZVtuOUTYFORD4mJGkK5KijPKDI1DE612pt59fxtB0jx6oQ2JTTdg4cbJpfrU0+x9IxNgVwugPolTVRYFMqo8jnS0jb12yOhMj6LvAHRJ7q2PbJpMoQqV2pmoNyOimITZRY1QhAIPP+dcvu5KodXXOdKVwYTZ5+m3Q8MkUKm2pb5PMvHQORyP/vrkdiFSGsbSY/budTAxIdWzWYbI4yxYdMOiwbG/JymZqEQKWpsbMdVuWZPH1z2JLpfNhycWTXtuMKosigE0OUjoXWBq0Nvm/7GcedNSUlKAUlT5IXeBZku749x7YgGbTvfve7/Od//idmlLJFpVLZ6G0XDvA4qNfrnH766Zx//vnjTsLIyAgAEydO7Dk+adKk/FyBAgUKFChQoMDzgXAEwu3VxxVarOPqfzzsvPPOvPzlL2f27Nk9x6+88sqN3nbhAI+DM888k+OPP57DDz983PMDAwMADA0NUS6X8+ODg4P5ufFw34U/A+WxalLE3ofvzv5H7PHCGl6gQIECBQoUeFHi9vYqbm2sJokNidyweCHhKrtj331sC9oBvv7668dNgLGhqhDPB4UDPA6uuOIKhoaG8gloNptEUcTUqVP529/+xo477ki5XGbhwoWccMIJAKxevZrFixez9957r7PeXd9+MsLrY98dGkzxt5wFXKBAgQIFChR4duxbmsZuYgrtliZUCVeGS9dbRridpDYAVz2+isse2XJk0NaX/W1j4iWrApEkCe12mzC0ZLEgCGi322it+dvf/saiRYu46667uOuuu/jABz7A3nvvzV133cW8efMolUqcdtppfPazn2XJkiXUajX+5V/+hV122YVDDjlkM/esQIECBQoUKLBFwJU9f6/YcQaff8VOm9uqjYaTTjppk7X1knWAL7jgAsrlcq4G0dfXR7lc5oYbbmDmzJnMmTMn/xsYGMDzPObMmYPj2E3zr3/96xxyyCHsvffezJo1i9WrV3PJJZcg5Ut2SAsUKFCgQIECLyCEo3IaRP7nqPUX/AfF008/vcnaeslSIE499VROPfXUDbr2c5/7HJ/73Od6jvm+z7e//W2+/e1vb3CbQaBQQrKmbdUfSgqUMERaIIVVfACoutCIbET/2qak1XIIA8WaIE6j4DtIjP1imKk6tBKrMvB0ZKi4GpVmkGmkUczaBinn6gTZsUZsI+xLyuSv7UQQahhwrUJDlEbhZwoDA65hKOi8jxLByhbMqhjWBDJXYEgM1CJB1TG0EoEShhVN2/9MhSBTpwDbl1bbqgY0Gw7tlkLFNlpYa5G2Y5UA2gl5Pa12R60A7DhIZfC8xEYUS0McSVp0os0zhYK10kaaJ+k4tlsOjmvyPPJArmjguJo1bRt1bJUjbB892VHvSIxVVsjamVkxPLPWpVSOc0WEVsvBdTWNupurIGTqDlJauxt1W0ZrQRzJ3J5SOWZobYk4kqxZVcJxNEOuRkrDQCWh3s5UHTSen9BuOYSh7SuQqzUMYVUdsuNak6teZOoTmUpDmEbPh4EijiTtVuch7PmaOBL5F8Bmw6FUtnVXqhHtlsr71UqVHsJA2TmJO18acwWMwCeKJEoZKtWYOBZUqnHe93bLwXE0kyuaklKUHJWvn6pjGArKzBsImeKvxe+rEukalfIAVKrglhD9HqLiIqeVMbUQE8QI38EEMWpGBTyF8JVVXSj5MFIHKTFSIMIYM1wDz0WM1K16w7TJOAtC9FCAiTRCClC2X7LfBSlJltch0YhUJUD2eagosGoLE3yINMJ3kBWBroWIkgNSoEdie/NpjUFimrFtA9DDgVWAyCLv2zG6bh8kyaoWgLUFg9bkUfj2eKqikEbbx6FAuQbl2mh9t5QqGcQCpwxxyxCHMq9jtOS51h21g0yxwKpECCTWRq+cRuYL+145vRJPUnZstOvJtq+TjhJDdp1ytT2fdBQaMhjTq04glcmvs8cEcUSuUKBcjTFWAcKqBNh+GGNtyO4Ft2Tstbq3/jiUVikj0IhIdx58SJJEEAWyxz7X1yRRl3pEWo8xvf3MynhlnashKAccP0HHAr+a4JYEQSMdly61AOWYXM1DuSY/ZiKDaUZp/cbameiucTSdeZd2XTiewWTyQVpZVYiUlypcqwohXIGltdq1lrUJnT5CZ93FkV1LUhmSVHkhiWS+TrK5SVLFDCHsejJakMT0rHu7XuwYZ8ez8o5rx0A4qQpJdtzXqJai2m+VQuJQoBM7/92Z12wbJlWxAMeVtJoaqUAqgVT2Vleye30KPF/kChMOoBOQCsoViVSCdmPDEj0IV/ZQIABEsuVutG1KSsRL1gEuUKBAgQIFChR4UUOJjpZn97ECzxuFA1ygQIECBQoUKPAixEttB3hTonCACxQoUKBAgQIFXoTIOMA9x+INk1D7R8TohBgbE4UDXKBAgQIFChQo8GJEpgDRjXjL3QG++eabN1lbW+4oFihQoECBAgUK/ANjjALEOIkxtiREUcQZZ5xBu93e6G29JHeAL7roIr797W9z9913U6vViKIolze75ZZbOPfcc1m4cCHNZpNtttmGj3/845x22mk9dXz729/ma1/7GitXrmSnnXbim9/85jozx3WjXvOotyXtRFN1AESuIDAUCCquoZ10lAWkMtRrXhrdX8+Di7OI97Ky/w82bOR+fcQjDCSer1nmB5RTdYVaaOsGIOqoFmTqDgDt2Co5uApCbRUgogRWxiLn3GeKD0rAmrYg0lbtAKCd5l6P6qkKg4aqQ94frSVxlF7bcqwCQ2iPdasBSGkIAkW75eD5CYNLfOYPrWZFawJhIGnWXdrK5HXVM+WAtC6tBc2GVQkAqGuXKJL4vlWD0ImwqgWZMkYa/bxap4oIoSQMFEkiqI94uQIDWPWEMFSEU9sMD3lU+yJKZRvN6zg675dM7ctUDR4dEQwPeUjpEcedcdBa0G45xLG1WyeCvoGQkSEfz0/y482Gm9vpuDpfE46rCUNlVRYaDp6vqddcnDQavFl3UgUMCBuSIMgenFZ5olqNcgWHOLbnMwWKbjWNOBJIZRha69Oou0ya0s5VLKQ0uepDnEbsA6x6ppwqb4hcySRTpNBa0G46eR878+bmKhOVvigfw1I5pjbS+dlPSoPratZKw8DEMB//MFT090WpGodHUBVMK9dITIznlFHlAXA8xLSJyGeaqMkl4qa9IUzTSq/IiT4mMYhJ/SAF1JtW3UEbZJT0BKCYVI5IKAV9FavcQJzeLBqjDSZSkCSYxGDqEcJNMO0YXJVGpItcGUIoAVJ0eH8yjY5f20ZWHEBjGjE6iFN1B/DLmjgUyCBBr2qhmxHJqhbRUIyUAhNpdCK7lE+seUIYnLJViZCRpjLB2p3EIlVjsNdkkfZJ1NkvCRpdCiAVTRyRlxXSrrdyf2zfC5FH3kOmEGEVCboVD+z9AGhh1Qo0oFLlAmOPdStZWKUHew8Y3VFwyNDTpmvHAcjvh8wWIUGUHEw7RlRchBKYdowCdKCJkTi+Lev6OrcjDmRPG0BHqUXbvunYzp8QBul2lB6gVyUhsxdt0r4LSO9hB41xsjLa2u905tLxNMrVxIFEuYYIiXIMfjXJ1QwytQwhDGqCh/AVRhuEFFbdIV0P2RyqLjUJKcnXvHBVuj7tqz2WyhClaiMAjmclgHSQrovEjkM27nEkrapD+swbrSjSsybiTJ3C9ks6Bp2us6jdUSUxWnStMdJj9v8ktkoW2dgZLXIlEce1tjmuyJ952XPb9TVRIPM+2bVtUI6k2YD+AUUcpX12rSKETsDzDXFs+xtHBteVgDXOL0kgYUa/A4+uu98ZbCrkURzgaMsNgnNdl4suuojvfOc7G72tl+QO8KRJkzjjjDP45je/OebcmjVrOOmkk7jnnnsYHh7mW9/6Fh/96Ef53e9+l1/z61//mrPOOouf/OQnDA0N8e53v5vjjz+ep556atN1okCBAgUKFCiwZWNUIowr71vOp39z1+a2aqPi2GOP5corr9zo7bwkd4Cz5BfXXXfdmHPHH398z/ujjjqKo48+mmuvvZbXv/71AHznO9/h9NNP54gjjgDgQx/6ED/4wQ/48Y9/zGc+85mNanuBAgUKFChQ4KWB0ZSHV+2/NQfvOpPvXvnQZrRq42Lq1Km88Y1v5IQTTmD+/Pk9CcbOOeecF6ydl6QD/PdgZGSEW265JXd+Ae666y7e97739Vy3//77c+edd25i6woUKFCgQIECWywcZf9GH9uCsWjRIvbbbz+efvrpnsxwL3SSjMIBfhaEYchb3vIWdtppJ04++eT8+MjICBMnTuy5dtKkSTz++OOb2MICBQoUKFCgwBYLNY4DrLZsB/jaa6/dJO0UDvA60Gw2ecMb3kAYhlxyySV5kBzAwMAAQ0NDPdcPDg4yMDCwia0sUKBAgQIFCmyxcBz713Nsy9UBHg+PPvooSinmz5//gtZbOMDjYHBwkFe/+tVMnjyZ3/3ud5RKpZ7ze+21FwsXLuRtb3tbfuy2227jxBNPfNZ6F/385wjHZZmfMGvvPZm2xz5UqjHNhkPfQES7pRiRVmkAbPR9u6UIA8XQ2hKrJ4a5ioFUNlJ/sOXQNxDSbjmEQ5I4tlHzg2t92i2F5+tcDaGdlsnqCNLXTH0hShJ8x6AETPShFnYUJ7L09q22Io5lXk+75dBu2W+jMrW9lvY3DBTLAplH1kaRpFyOqdW8XAUhjiXtpl2Gnp/guNbekSGfUiWmPuIh0PitGL8RobVgzeoSWgvK5ThXLcj6nakJeH7C4NrOvOlEUCrbKHetbZ8rVasyEMcSx9HEsaRUjnM1hji2ahBuK0a6IBJDHAuGIltv1lajbggDq8KhtbDqDGl9mfKB1sIqTASKuNH5Gac0saOCYOoGowSrG2WktmOrgoR22YHQIBI7j6FWeb2ZDVoLhod8KtWIMFT5WACUyjEjwz6e16u4kJUHqyzRPU71mofnJzQbrq0zUGn0vI14btTdVL0iU4HoXJfNfaae4aXqG+2mQ6Uvoll30TG4QYKpGeKyQA0Z9ERJsEZiFPhBRDtWaKejlpHZmqkZgFV+GFxboq8/zNdAGCiiJKCkDOCy08Qm2iS0RZ2+6hRMUEOUS7naA4mxEfGutIoJtQjZ74LnQhxjWhG6GSOUIFlaA1ch+z2rFCDXgudi0mtRohNdr+2NYxpR+n+SqkIkmMjkEchag66FmCBG+I5VgOj37E2n0wj3KEE3gUSTrG4hKi6UFLoZ4UzxSAYDdDPCtGPCOsShIA4cvLLGd5Jc+UA5Jld3MEagA4MqA0rguFaxI1MAEMKkahAaHdioe+VA1JaELYXRoDyDaQiU2/uBLB2DN0mRjMS5gkgSSVxf20h6JXJFAKMFrq/HKAGITLmgSz3CcTprFay6g3AdTJR0pHGgRw3AaPJz0pcI1/RcK0p2zEmkVeFwJQLHKoFog9IGPFDputPaxiM5nlV3SCJpVQTKoAODcFO1FmkAnSo1WOUIo1MlBidTI7DHsr67JZ0qGBhU1/g4jh0fv6zz8QTyMfX6BHFLo5OOCofra2S/B6kSiAJESSErrq040nbN6whRcXGioEvtwY6pcBUyShC+fUZnKiUmSKxaRmIwkbZ/uleFgyRdazJV2xAdhRgdC5SbqmmEEsfTRG2Zn8vUFqJQ2nWqSddm5/4XEnQMOpH5+OXqHpgeBQirEGFItMSVOp9H5Riqk+znQruhcKUhaqeKG561XylDrDsqFvb+0PQpQRTIXLUjU+YwGpJc0QgadY3vC4JA8tehZ/jbqlV4viBqbaAT68hxKBBbtn7B6aefzumnn86hhx7KRRddxDve8Q6EEFxwwQU9ftfzxUvSAU6ShCiKCEMrnxQEAXEc43keK1eu5JWvfCU777wzP/vZz3Bdd0z5M844g/e+972ceOKJHHjggfzwhz/k4Ycf5tRTT33Wdue/7lSccpUJE4P8gVagQIECBQoU2PJx0MB0DhqYzsBEST2OuHDZ4vUXkrLj2Xcf24Jx+eWX8//+3/8D4Bvf+AYXXXQRAwMDfOITnygc4OeLCy64oEfXt6+vD7C8k+uvv557772Xxx57jEmTJuXXHHbYYVx++eUAvOlNb+KZZ57h5JNPZuXKley8885ceumlzJ07d9N2pECBAgUKFCiw5eIlGATXbDapVCrUajUefvhhTjrpJKSUvOUtb3lB23lJOsCnnnrqOndrjzzySM4+++z11vHhD3+YD3/4wy+wZQUKFChQoECBAhZCKZtoZ9SxLRnTpk3jgQceYNGiRRx00EFIKWk0GoUKRIECBQoUKFCgwEsCapwgOJVsHls2ET72sY+x3377ATZzL8ANN9zArrvu+oK2UzjAmxBSkgZNuXkK2FaWOrducnJ/fcSlVE7SFLAJk6e28mAzz08DrbRJA7gEQ2tLtFs2dW8YKjzPpvTtHwgZHvLw06A6KQ3TZ0W0W3ba+/2YRiTpr9oggFaaShM6AXBg4yJqWeriNMip2XDz4DbHMbRbWeCVQ6vl4KeBX426Sxja1LalckwYKOo1z6atDRSipXHjGL8VE5QdWo5Caon2scFxoUFqQ6vqEvmKVc9U8qCydsvJg82yAKzMpiyVcH3Ew3F1HtyWBfyFgcrLZkFh2fEwULmtpXJMWyukNpiyTXPquLbeUjmm3XLo6w/zIK0soA7I68xSOjfrLlIZRNKpKwsAlLEm8W3Anav/P3vvHe9JVd//P0+ZmU+/9+7dXVh6WUAUkK5YEAkRsUaIJRoU4ZfyRU00AaPGqMEEoyEajUqiBHuJRo1dLFHEDgpRsSBd2rLltk+bmVN+f5yZufduoejC4jLPfXwed++U0+fzOfd8zuv19uSJQhqHNo48E6jcYduK1oYUk1nG7Qg7Dy4CNXCs1y2yBRnK1TJVSNZSQNnq5CzcHmEjicodUWYxqyXjka7q2uqEeLb9hbj62Wga5meTqj7OCcS8o9+LcVZUgiSTS5wVVR+UbV7Wv9E0SOUZ9iPUwCKkIEot2jimbx8w6kTkqcLJMLaTkUEZx7AbyuKkIHYeJwXaONKmDm0SSfrNqBrTzgqkHIS+sZ6B8VifY1zxgSEkQiV4QghXnwZhGoAv4o/bDUNEo4uY74NzuE3jIGAjhCvGeexChmhr8ms2offoIhrFueKh8eniB1QZgrf66cqQwQ7vglgJQlxyEUlkEgRIzvogOgL80OAx+LHBZIIoKsptfRBxyRQzgmwkyVOJzQTehxDEUeLwXmCLIinlsTaE6Q1hZl0h4BFVCNlSJGdtEHNZG0Ry44EkG8lKzOSLkL/ehXDBS0PoQhDDSbcoHCpFUuG9kEJ4t/j+GLooCMmiyFehdEUhXBJFWN4QOtpW4aCX4rciplu8VyIUIJf3SXXv2CDQIaxvIZAMbViEAbZBTOadRxLOO+srgVyo35ZlEtLjjUTHRYhkB0p4hAIvw/+lBNVR2L7FUrZveJV6EVu2uxdL0g5l01h8HkL7Aug1YWtfOTaD4M0Xgj+/RBi4OA2QrQg7Mw73LQ17XN63NPwxQG6DaDCSYB2ioyuxp4hUEO5GCpFb1FSE62dkfVEJCU0u0LHDFGK3bCirUMc2l0HMmRWiNB1EduX7alSEsbb54niypmoVJGCLkMFCLA+77azA52G8q8jhTBBieidQcRDiSQk2E5WQT0eLYZilDmM7agTxnm4uEWYW5arGeCIxqaDVc0E06iBu5rhRVdi75kG4BeLFL34xT3ziE9Fas88++wCw//778+///u/bNZ96AlxTU1NTU1NT80DkQegCAbB27dplvx944IHbPY96AlxTU1NTU1NT80DkQbgCfH9RT4Brampqampqah6IbLYH+JJvXsNnv/azHVignYedfx29pqampqampuZ3kXIFuHidfOLBnP+KJ+/oUu0UPGgnwB/96Ed57GMfS6/XQwiBMcs3pP/4xz/m+OOPp91us9tuu/G6170O75dEGvKe1772tey22260222OP/54fvrTn97f1aipqampqanZWQmKuy1fOznW3vdOFzt/K26Dqakpzj77bEajEWedddaycwsLC5x88smcccYZXHLJJVx77bWccsopTExM8LKXvQyACy64gIsvvphLLrmEtWvXct5553HyySfzy1/+sgqssTnDoUY3giPCcBCFkLmRw+SSqRUp/YUQdU5Kz8J8hFKefEmo25lNDXTk6HYzdBGgblS4CGSZoj8f0+llDAdBFb8wHwcXhBVjskwxMZmyYV2zCoXcaBqajcVBpiPH/FCxsmOD2LcQtQ/zEIozyxTDQXCDGI007U7OaKhRKoT+HY105aoAwYmhdEfQkWM4iNDaIeYd8SBHaktzkOOUQFpPliiagxwTyeByoSXJyOAKJbzTEikN6bqQvpgUwVUgtYyHTRotw7BfNEzhkiGlx6SCoY0Qo+C0II1DWM98GqNyh+iI6r5oZPA9GcpuACmQzhGPDDaXeCAnHJtvJ0jjGG7SEIsqDHJ5X6uTMx5qnIG4GdwRxMghnYeBxUSSeJSjc4eTAqcEWVMT9XPSpiZKLcnIYHRoj2RDiitU2FHhMhAN8uAIkYJserReDG88HuqgmHYhDLFylmgQJPd5rGAWFJascMYYj5qVIwQEZ4e5jTE2UdX4gODIIOeDjDszMrwXZ558JJAdD5nHIDGbCvV+OzhrRAsG19Uo4+gMcvJYEaWWUSei2c+hA7JQpicjg4kkvU1jTKE4z5NwvXQhRK3OHSaStBYy0oYmbWp0L4RlbrYMd3YMqxown3lA0o0sNmkgx3OwYgKxfhPu13MhbPHYQixDKONBjl0/QhYhjEPo4iIscenykFvc+tCW5vY+ItGoFY0qlKy3vgpjHB5qEdLOF9XoZRhcKUOIWa8WVeqLoZOLus/bEE63UMdHSuDHhtGCorPSYTJBNlIMZjXZUGLz4MoghKfVWwwvWzoilE4Jzi06CzhXqN91cIAoy2eQ4X4R3jfcUjV9cT2y+H8clPomE0TDPIRL1sHVQUeAZdn9JSYPLhPWChQgpYBIoSJfhOQtQvFGEj82YJe4ODiPz0M42rIesggn7F2odxmq1o/NYjp5cOEQ1uGK484IFAYihR/moAQyEiH0NABF2PdIgpK4hYzIhXEgpAS5GAa7bGOTl6F4i3DO8aLjj449KBBSIBo6WCP0LUqFMMHOBXcEWbRpVrS3LtwETCZwTqFGjrgT3EV07JHdGNHW+EGoS9niciLBjy1uIa3CR5ehkdUu7VCPYR7aNbdUa2QyhD0WUiyO66KPRCRDqPBWFBw6unHVPyUiShDt8IyIWxYYz4bj1ojK3cHnonJZKPsRwvhxThTOKVQhh8tQyFKGdhAy3BtcIlzlFhHGoKgcNWzh/mALF5TOtK3GjtQeVYwfqXx1j1IeHbvQbxF4p6rr46YjTxfXEhttuzxv5ZETBqU9KvLYXKATRzO6hxM8pcI2iM2P7eSccsopfPnLX75P83jQToBPPvlkAL7xjW9sce6Tn/wk1lpe//rXo7Xm0EMP5dxzz+Vtb3tbNQF+5zvfyTnnnMOhhx4KwOtf/3ouuugiPvWpT3H66affb/Woqampqamp2UnZ2orvA2gF+IQTTlg2j/rQhz7E8573vHt8/2te85otjnnvue6667ZH8e6SB+0WiLviqquu4ogjjkAv2Xh+zDHHcP311zM/P8/c3Bw33ngjxx57bHVea80RRxzBlVdeuSOKXFNTU1NTU7OzIeXWXw8QfvSjHy37/SUvecm9uv/CCy9k7dq17L///tVr7dq1tFqt7VnMrbJD/oy4/vrr79F1UsrKBPn+ZH5+nsnJyWXHpqamqnPlXuCtXTM/P39/FLGmpqampqZmZ+cBvgK8OUu1UveEgw8+mN/7vd9j9913X3b8kksu2Z7F2io7pBXXrl17tzGdvfc0m00Gg8H9VKpFer0et9xyy7JjMzMz1bmyg2dnZ7e4ZvNOXMqvP38xUkcIPJMPOZoVDztq+xa8pqampqam5gHJ/966gf+9ZQNSQbbZPultIrYyARYP3Anw3c3tNufSSy/d6j0f/vCHt1eRtskOacVWq8XVV199l9d47zn88MPvnwJtxuGHH86HPvQhjDHVNogrrriC/fbbj16vB8A+++zD5ZdfznHHHQeAMYarrrrqLvf/7vnkM9GNNlKWfyHdu7+Uampqampqan43OXH3lTxu9Sp04ljIDBf/8td3e49QGqGizY7l91UR7zVpmi7bxzsajbbY13veeedt8/57O2HenuyQCfDTnvY09t5777u97ilPecp9VgZrLXmek2UZEDrRGEMcx5x66qm84hWv4LWvfS2vfvWrue6667jgggt46UtfWt1/9tlnc8EFF3DiiSey//778w//8A9EUcQznvGMbebpR2CEKIKEl0ra4KqwcUODJLGV64PWjjRVwX0hVUjpiZOgGh2NNE0Mo5FeVOxXSv7Qpc4J5mcTOr2M/kKMc4Jm01ST76X3lur+Mp8NfYWOHK3Ik7sQN72/EKO1YzCIqnRKB4rRSNNsmip/KX2VdjYqnBykwEWCsVT0BmOSkaFTOBmULhDl/00kMVoSZZa0qZHOM+wlIf2BQkkX3CNSgYgcwnkyJRgajSykw05LnINkEBwVyHzlZiCsx0ahXHmioO9JTE7eDfXxfQ9NgXSezqYxo3ZUlM8hra/KhfM0+jk2kkTzlv5kAsaHjfXOk26UNEYZaVPjU4+MHNo4WgsZdkkoS6Ml2jjU2JGMDHFq0caRJYrGICdKQ35pU9OZS5mfatAc5OSJIo9VcLJIBM4Gxb6YD64S8aQN/eE8ZMVwsx6nBNqEa/KmRhX5pVJU6vSy70gE0jicDunatsJFAplahPW0+qEcyjiypqa5Lgvplm3tPNHIoIwjjxXdO0boPNRTJ8FdA2DcjujMjkmbEV6GMeALx4s8UVgtQxrF/wGk8zSL/tXGMUwUduRxLqLRNLDLmLhoZuMEUii8dzCcDZ6aUuKtD44LsVzi8ODwwxwXHiSwHp87xFLltZKQO1w/Q04mwbFhvMRKsXy+rQMl8anB5+GYlMF5oXQJENIjEoUo7vf5EnV4sUpkc0nUE9C3CBncKEwWHCFcP3wYOivIx5JRX5DnjjYSE7vgilAo55UKinsIqnlZ+AOUThBCepTyy5whJEExL7VARQ4hJd6CEMFVwjtRKfatAQh1y9PgHqEih5a+cpWwhZOEKt53SlcaU6rzbXj2BMGtQUShn0Qk8cV7hncelwYnAJyvyrB0e6SvlPiFA0TV3sX9JpRF48hGqnKLMLlHKgMIdBNEQyO7MX5sEFExrhsqOCUAMpG41FWuEmXfL82/7AOpfChjUR7R0GAdoqERicK70D/WCLwPzgfLy03lAFFekw5k4aRgw7UKZCeuyhk6XuBzh4wUbi7FbhgiEo3r56FdnUevaIRrWhHmzkEY96lDJhJsGPPhORDVc1L+lK3QRuXvoqXDc2WLVywRrQgRKeTQ0GBEPiicQ2Q5dkTlUlKOBak9NgtOEXkqMRk0E4f3W06aSgcIZ5e7boR+CGMsjGmByxbvF5LimShcQGJf9AOkA4mQECVumXNHNqL6vxCebCTRkS/Sgjix4WPelQ4siw4gQganDnVPF8Ae4FsgjjvuOC677LLq90c+8pHLfr+3E9zTTjuNT3ziE9utfHfFDmnFe7q0/cEPfvA+K8MHPvABXvjCF1a/l9ZlX//61znhhBO45JJLeNGLXsT09DS9Xo8///M/rxwgAM455xwWFhY46aSTmJ+f5+ijj+ZLX/rSNi3QampqampqamruFUpvxQbtgTMB3pqT1m/Dbbfdtl3TuyseOFLC+5kzzjgD7/0WrxNOOAGAww47jMsuu4zhcMgdd9zB6173umV/yQghOO+887jjjjsYDod885vfrCzRampqampqamp+ax7ggTA+/vGPL/t9c/uyCy644F6ld39uifiNJsD//d//zdlnn82ZZ57Jq1/9ar70pS+R5w+cPSk1NTU1NTU1Nb/zlCvAm78eIGweSOyYY45Z9vtd7f/d0dzrCfBLX/pSXvCCF/DrX/8aYwy//OUvedrTnsbq1av5m7/5G8bj8X1RzpqampqampqaBxdSbWUF+IETCW5z27O7+/2BxL2eAL/vfe/jiiuu4LOf/Szvf//7+fjHP0673eYnP/kJ1lqOPvpo1q1bd7fprF+/ns985jMYY+722pqampqampqaBx0P8C0Qm29ZuLvf7477c8J8ryfAzWaTPffcc9kxIQR77LEHF1xwAS9+8YuXicW2xQte8AL6/T6vetWr7m0RfmdJJixSB9cFnXik87Q6Oa1OjtYOrV3l5lD+dC4o81vtHCk9SWIrx4Vm0zC9Kqy4j0ea3mRKHNsqrTixZIWTRImUIa566QbRbFgi5WlFfvH6yJHo4ACRF8rZLJU0moapFSlaO6IovJqtcKwsa6ebIaWn3clDuXuGtB2RNzXCepAC4TxOCkadiEEvrhwO+pMJM7u2GbcjfFMi1wQ1eJ4oZla3iBNL3HR4JYKrhBIkIxOcDbQkbjrirkNYT6Np0JFD7QJx0yGboe5SBjeIRjs4K+i2R3QEdlIhRg7fkyTTDh0F54P5FQ3ypmY4meALd4P+RHCkiFKLjSRpO2LYi9FJqJco3QSUYNiLQx2ahTuHFKQNTZao8P+mrlwlrJZI54PDBJCMDON2aKMos8H5IVY0BznSeZKRIRkZvBLB+cEJfD+UwTcldlNwwRA2uFUoE1wodO4whZNCNDL4pgx5SsGwHwWVtAGVO1RqUblDGoeNJGQhX2UcUWarOnlZ9EnRRnlRn/L4uB0hnQ+/K1G5fQx7MVlTYyLJ3MoWedEupQNE2tShf6UI7WM9yriQf2qxWqILNbpKLdKFPu72FrdkLeSKRHlyV3w7ZQ1kOcQRIpKQBeU7SixzgqiIFEIKRFEm1PKfdt0QPw5tUV0jBT63hRLeVcetWXRMEHLxjd67JQ4UeeE84Xx1HAhOEaWqXUnyNPShHxukDK4Mzgrywm3CucVvSn2lRveFQl4UqvvgKGCyRbeBxTItqumXpuFsqIP34f4yDVdc64tzzixeV7oWuOI8UKUT2oLKAUBHof44H1w6xia4bOSuaFe3RRlLnKNKc+n/SwcKkwdnCp/7yokjuAuE9rAmOGmUbUvxnlX2B0oERwW32SpXVffFMQQU7gwepT1J06G0R+rgaFHWFyVDHoAfW7wLrgdlGy0dK2VbLuYbHEKykQr1cmLZWPbOV23nB3kYk2NLPhSQW0wenB3IlzTaEkeUcmy61OGGpnAzcaFPijYQkQxOKIVDRnWv3WwiY4tnoaGC+0VRn6VOCcG5obg8E9gsODuYwrXBWbHoUFK4YAjhK5cTCK4iS90zvFscC0KEsZqNZJWXWuLu4F34nFA6vI+UZVFL0s9GclmfVH0d++pY2c+h/8O1sqif0r7K857gt/HvvsJ7z0te8hIe8YhHcOyxx/LmN7/5Pstra3z3u9+93/K6139GPPvZz+aP//iPef/731954i7lhS98Ia985SvvNp08z9l333355je/eW+LUFNTU1NTU1Oz0+NxeNwWx+4r/u///o8rr7yS73//+xhjOPjgg3nhC19YRcPdnLvzAS6tZn8brr32WpRS7Lvvvr91Wku51yvAb3jDG8jznD333JOzzz6bz33uc8uWrH/wgx8Qx/HdpvPWt76V73//+7zhDW+4t0W431i3bh3Pfe5z2WWXXZicnOS4447j0ksvrc5/4xvf4Mgjj6TVarHvvvty4YUX7sDS1tTU1NTU1OxMOG+3+rqv2GOPPUiShDRNGQ6HxHFMkiTbvL70AS5fpQ9w+SqDhd0bzjzzTL71rW8B8NGPfpSDDjqIAw44gI985CO/cb22xr1eAW40Gnz+85/nQx/6EG95y1v493//dwD23Xdf2u02119/Pf/8z/98t+k89KEP5aEPfei9L/H9yNlnn82dd97JT3/6U1asWMG//uu/8pSnPIWbbrqJhYUFnvzkJ/OmN72JP/mTP+G73/0uT3va09h1113vMhhGTU1NTU1NTc09wXm3xYTXbb5faQkf/ehHecc73sH//d//sbCwQJ7nVURbCFscXve61/Hud7+bubk5jjrqKN75zndyyCGHADA9Pc0hhxzCAQccQJqm/O3f/i2tVmub+W1vH2CAL37xi7z97W8H4C1veQsf/ehH6fV6nHvuufzRH/3RdsvnN95J/bznPY/nPe953HLLLXz729/mtttuo9VqccIJJ3DQQQdttwLuSK699lrOPPNMVq1aBcCf/dmfcc455/CrX/2KL33pSxx44IG86EUvAuBxj3scZ555Jm9/+9vrCXBNTU1NTU3Nb43D4rBbHNsWU1NTnH322YxGoy0syiD48l588cVccsklrF27lvPOO4+TTz6ZX/7yl3Q6Hb7yla/wy1/+kuuvv54sy3jsYx/LU5/61LvdfnD99dfzta99jY0bN7Jy5UpOPPFE9ttvv9+ozsPhkFarxcLCAtdccw2nnXYaUkqe/exn/0bpbYvfWkq4xx57bPdCPVD4m7/5G9797nfzrGc9i5UrV/KOd7yD/fffn8MOO4x/+qd/4thjj112/THHHMP73ve+HVTampqampqamp2JrW15uKstECeffDKw7ZXZd77znZxzzjlV4K7Xv/71XHTRRXzqU5/i9NNPx3vP1NQUWmuUUjSbTRYWFu6yjH/3d39XbWdduXIl69evR0rJy1/+cv7xH//xnla1YtWqVfz85z/npz/9KY985CORUjIYDLZ7kIwHjpfGA5BHP/rRfOADH2C33XZDKcWKFSv41Kc+RbPZZH5+ngMPPHDZ9VNTU8zPz28zPSk9vW7KeKRDXPGE4NgQha8zjJE0m4Y8l9X1WaZoNg1puuj755wgToLTg3OCdicnyxTjkabVzukvxMSJrdKQyhPHtlDPSjrdnPEopJdbQZYqpPToKDhHJNozSiU6ckTKk9rFQRdinEuyVFax0aX0NJq2cpgwxjHoR9U9OnKYVODVovvEqB3Oy0LpLq3FaEk0Mti2QipPlipMUqigVwqSJLhfzKcJ405EPDJkiQrOAKmlMWkYDiJ8U5ClVK4WQW0u6PQyslSRjWRwwtBg8iXb4FVQVJfK6zixZCOJNA6VO/IktFnpPgBgtKzq5Pueibkxg90b2CXp2rYC46v/20girCePFcnIMOwGpwizJM2ybZwUNAc5o3ZEb9OYYTdGGYfVEicXXSbEpIAUfFPiDMiscJ5wwW1k2IuJUku/qYkKt4TSmUIrh3PB5cMVSnkZeYxUxInFDEJeyNB/qY4qBTgEl40818SRY6zjkL8J7ZWMDDaSwUnCeURxXx6HfiudJNqpZdSJyGOFMg4vBcq4aqx4KYhSi1OiavtNu7TQeXCjGBUuE7YpaTRzOt2MtoaVDUdc9I8QMqinsxSkREx0kd25ZXUBEA2N7EbIlq4cB3yh/ieWiEguuj5ECrWyuXid88tV8Grx2REyKMCtKRT/LijgJQJyF+5NDaAXy1SkFSXBHUJIguLdekBUz6AoFOtCeKSCSIsir0UnAaByQXCOQpW+WBYKZbnJRVW20r2h/MZVao8QHqXC/70TyCWK+eqYdMEdwkigcJ7wQc1fKv51HFwrlqr/lQrpmEyglUXIRccFUbhqeAc4j7WyuH5JmxauAKVTgBA+OEEU+Zd4J1DK40Ron1Kxb01wznBuUfnvc4cb5vjcYcc2uBhECp/bymHBOQGlG4URSMJ7YdTRaGcrdwBrCoeHqn+Lfo1kqKdbUt7CtQBZ9IddPm7K95il/VTmIyKJG5rgciJFGK9KVm4POnL4HKKExfyLOmJ95RhicolyHqmD6wK5h2L1UeCQE82QB+BTA5nDpQaplrh1WBfq2/KFg0T52RQcG5QunUnCGLB56XxROIdYUbk3hPqDA4QI40TIsk0W+1cucWgoxxeATELdAOKWC+4fS8ZK6SYhVah75VjiRFUeWzlSSIR0hWtHkWeRVzZSoe+KMemLOslifEtlq3Tujns7Ab4r5ubmuPHGG5ct3mmtOeKII7jyyis5/fTT+f3f/30+/vGP86hHPQpjDCeeeCKHHXbYNtP8whe+wFvf+lbe9a538dznPpdGo8F4PObDH/4wL3vZy3jUox7Fk5/85HtVzpe+9KUcffTRQNjSAfDNb36Thz3sYb9BrbfNDpkA77bbbvco3vNee+3FzTfffD+UaEucc5x44okcf/zxbNy4kV6vx+c//3me9KQncemll9Lr9ZidnV12z8zMzFadMWpqampqampq7i3lBPirX76K//3KjwFIs98s8m65QDc5Obns+NLFOyklF1100T1O86KLLuKNb3wjZ555ZnWs0Whw5plnkuc5F1100b2eAL/4xS/miU98Ilpr9tlnHwD233//SnO2vdghE+CZmRne85733K3h8dzc3P1Uoi2ZmZnh+uuv57//+79ZsWIFAE9/+tPZf//9ueSSSzj88MP59Kc/veyeK664giOOOGKbad7w6feiIo01khUPO5Kphx59n9ahpqampqam5oHB12/bwNdv34DSnnF2z7x8yz3AJz7hUE58Qti2MD8/5KJ///K9zr9coNva4t3uu+9+r9MD+OEPf8i73vWurZ477bTTfqMtEABr165d9vvm37hvD3bIBHiXXXa5R/Gh16xZcz+UZutMT09z8MEH8453vIM3v/nNdDodvvCFL3D11Vdz1FFHccABB/DGN76RCy+8kLPOOovvf//7XHzxxVx88cXbTHPfp59Bo9tc3AJRU1NTU1NT86Dg8but5MQ9VhI3LZsWHBf/8td3e8+9dYG4KyYmJthnn324/PLLK3syYwxXXXUVp59++m+U5vz8PCtXrtzquZUrV97lttAdzQ6ZAN944407Itt7zac//WnOPfdc1q5dy3g8Zs899+Tf/u3fOOmkk4Cw9+VlL3sZf/VXf8Uuu+zC+eefz6mnnrqDS11TU1NTU1OzM+C3sgfY38UeYGsteZ5XASjSNMUYQxzHSCk5++yzueCCCzjxxBPZf//9+Yd/+AeiKPqN3aucu+vJ+N2d35HUIri74IADDuB//ud/tnn+hBNO4Morr7z/ClRTU1NTU1PzoGFzEdzXv/JTLvnCVdu8/gMf+AAvfOELq987nU647+tf54QTTuCcc85hYWGBk046ifn5eY4++mi+9KUvVdfdW8bj8bLIb5uzPSLB3VfUE+D7kUbToiNHAxOcEfKgvI+i4AQxN5vQbudMTGYMB5pG0xKlrnJnkDI4EzgraHVyTC4xRtJoWlrtvHKQaLVztHYkiWVuNkFrh4w93V5euDRI4iT8VeZsUPaXThClelZHjkZhPJErT6ttaCaLf8lJ6RmPFDoK18eJxRRK1zgOD+v0qjHjkWJuNoEmYeuHdAy7wZEgberK6SBKLNo47KRCS0eWKnTk0ElQiuvIYYxkPAwK+Si1SOuxkWRcuBmMR7oqe1a4ZnS6GcNBhMkl/dmIuBnS1JEr3DQsjaZhPNLongs/i76pHCJiQXM2Z35FA6TAWI9vytAnGxypkUS5C2VpR1X/QFDdZyOJ1NBoBVcKUVwbpRYbycodQRbK8PZChpMC6TyjdkSzn2O1xEuBiWR1TjpPaz5j2ItxjmpM6JZDSk9/PqYzmRfOF6JyZTBaYhNFp5fhXHABmVwxLsaFZTzSNJqGLA3OIrIJjDytyRwpPcYEp4wsVVU9pfTBNUMqMhQgwEDe1ahRqK82Dl24QSjjkDbUO2tqRm2F1ZI8UVU500YYHzp3jNoReaIQdnHrUHshoz+Z0Ojn2LbCGYgjR6udM9EMYzWSIWiolp6m6qGEhiyHdgsmQazsIpUA6/HWg7TB2WFFA9FrgHO4mTEiNcE5QgYlu2xp3IJD7dVDNBR+IQuqe7nE9SGSuNQhSieThobcIh0I6SqFO4Q0XVo4POSLH3bOCFRTICmU9Co8C3Yk0HFwnZDdGD82qJEnbjk6SJR2y9wmZOFwYIZhXOvIoRqLz7PNZeG4EFwKSpeI8opSaa9jj/cCFQVHiihZLGtwgwCHJ0pc4fAQ+qt0KigpXQukhNIlAoIrgyiV92k4J3Wo91KHjbJupWMCFGp7B54lzhJq0RnCu6DCL+tWqv9LZb5Svno/s1YsOhP0c0QkEFKQ9SFK8sq1wYzA5opykUvHHpNDrH1oq9wGR4mi/WwusTnIPORlBh5nJTEZoqHxY4P3crHNizpZI4r28pXzQYlSHlTRprnEW48bGtxcimhpZKLxeXDUcAsZfmyQnQifGmQrCs4MqcVuGhcJCkwukIrKMUFIXzmW+DT8jvTLnVLGtnKScLkLz5MLLhZCCVwk8WODGxrIw+dFs2uqujgrqn6txn/hACEkpflE1S+ydBIpxlbpRLJ0jJVlh8Ldo0hfx77o90WHE+/LsR8cJZwR1bNT9oEzohgbRX8aST6WRI3F5610OanGvll0mgDQUfhMy8aGe8LmE+DHnXQwRx67D+/5j69v9fozzjiDM844Y5vpCSE477zz7tE21HtCGQnurs4/UKknwDU1NTU1NTU1D0A8Do/b4tgDhfsiEtz9hbz7S2pqampqampqau5vcie2+tpZ+MEPfsAjHvEIHvWoR/HFL36xOn5/RNStV4Brampqampqah6AGCcwSya83/zq1Xzti/+3A0u0ffnrv/5rLr74YqIo4sUvfjG33347Z5555hZWbfcF9QS4pqampqampuYBiNlsxfe4Ew/h0KP354MXfWPHFWo7opSqIrx9/vOf54//+I+Zm5vb7mGPt0Y9Ab4f6U1kRC3NwnxEs2mQyqN1EA85J9B6TKMZBHKdXlaI5EQlTtORYzqpolkSNS1KWG6ZC2GRSwGTK0JlxrFDqnEhNIE1K1NmBqoKk6wETCdwy1w4piPHZBx0C00FMkRopRV5ZC+jF8FU7Bhbx3wOUkXLQgk3miHd/kKE1kGMBNBqDxgONFMrxuS5ZNzW9BdivBV45ZmYTMlSRZwYTO6YXjXC5JLRSFfpdwvB1gwNxkONWCkYjhLixNJp5hgjqzyzbDH8cRAJGmTbF+3tmN3UoDeRolQImdpsGaT0dHsZC/NxlaeUHt0Jgrj+6gat4rpONyPLFHFs2eSa9JoZOnLMzybYpmJycozWQYjXaBr0CleFiZ63CVkh2MoThcpdEAFmITxx2tQoE455KXCF8C0thH5l+ORkZHBKkDbCcTeCYR6hUoucDGOm1ckrMaBOwhgyeRDABVHl8roo5ckLYWYlaitCR0eFMKusU5YpepNpFX60HFOtTk6jaTAmiK2k8phIIpwnlREqd2jjaAzyEF4ZMNGWAr9BNw5hnZ2oytJo5lWIUucEZlqzsjsK4j2b0mgalPLsuvuAI6Y91sNenQwpwpjWMkYKhdcKRqMwaLVCrJqALA+CzNk0hLptRTDZBa2RbkMIWes8FHXx1iNXhDCwoqGDmGhscHOFWKUI8S0222QmIoW0phhfIexruZtPLgm9Wz2g1X0Sn1t8XoQKzkNY4WbXVvdEiaDRtlU4YQAdF2HWM4FJF9sTgohHLAkZ64xYFJJ5gROL6ZRCoXy8PAT6UrwTmELXoych6y+ey1NZvQ8tpilAekwmq3C4jhCCWC4REZWhj8sQyd6LKlSvZzFsbilMWlresm3L68rQy9YuucZJVBGOvuwD56jETiDwoyD8M5kkH2uE9ChdiNNyWQm1nFkU/IlI4FIq4VsZvliqELrZZIuiKe8EYuQodyWWYa1LYZUdlcKqIrR1McxMJrE2iAmr9+I8COD82IB12IWsCndMPwj4RCQhF0F0l7tQz6HBmiB0FFLi/aIALBupKjy0d6EtdLMYozlFiGODncuW9UclcHPghjkuDWM36YVzslOEVR+5EIq4qLczYayW7SqlD2GM3XJhpSzEiwA2K8ZEMQaq56m4xzlQKuQbxTY8e0aQj+UyAV7Zn+WzUPZROY5NFsRy2UghpCduhnDI6UAtCUm9+HyU75FlKOuUILwc300gsJKtbXnYmbZAGGPo9/t0Oh2iKOIjH/kIz3/+8/nBD35wn+dd7wG+C7773e9y4okn0u12mZyc5FGPelTlaffjH/+Y448/nna7zW677cbrXve6u41sV1NTU1NTU1NzTzEIjN/sxc4zAX7b297GYDCofpdS8oEPfID//M//vM/zrleAt8F3v/tdTjnlFN72trfxuc99jjiO+eEPf4gQgoWFBU4++WTOOOMMLrnkEq699lpOOeUUJiYmeNnLXraji15TU1NTU1OzE7D5CvB3//enXPblq3ZcgbYzRx555BbHhBA8+9nPvs/zrleAt8HLX/5yzjrrLJ7//OfTarXQWvOIRzwCIQSf/OQnsdby+te/nmazyaGHHsq5557L29/+9h1d7JqampqampqdBFuI4MrXMSccyp+98g93dLHuM0477bT7La96ArwVhsMh3/nOd1BKceyxxzI9Pc1RRx3FJz7xCQCuuuoqjjjiCLReXEA/5phjuP766x/Qca9rampqampqfnfY2W3QNue222673/Kqt0BshU2bNuGc433vex+f+9znOOKII/jMZz7Dc57zHC699FLm5+eZnJxcds/U1BQA8/Pz9Hq9HVDqmpqampqamp2J3C0K35ce21m5P9wfSuoJ8FbodrtACCl4zDHHAHDqqafy+Mc/nv/5n/+h1+txyy23LLtnZmYG4C4nvz/90AdRcWjyPY85jIMfdwixhKwYzP2xpNNwWA+pEazuWhZy6EYwMNCLYHUzqNuth7aGsYWsa5lNLJGkClM7NZnhPMzNxWSZZMXKMW0NUdcyMCH9yYZnMvHcucQVYjIBJTwDI2iokH4sYWxgMvE0FGxMBVLA7CZFp5tVKleDZDwKrgNSeVZ2LJsK94PSdUBKz3XXTNKbTMN1S9wXksSiu6Ex4sSR58E9YTiIaLUNGzc0QhtPpkgVXB16E2kVAllKj1KeZtMws6lBq51X7gul+l1KT28yJSpU38H1wOJscKxoNg0jCicNKQp3CluV3zlR5ZHnkhUrgwtBKGNehZTOUoUxEgy0CzeMPC/CJyvPsB/Uz3lTo1JLVro8JAo7kpX7g5eC/kRC2tSkTU3cdFgTXCHKsMGlzYGOHESC3mQIawxgtKz6B0Du4gFDnFja7RwdedoYnAOtPVkqSYuyD/tRcGBoOnxK5URStnUZcrp0aWgW7g/WLrZ3fyGu3CR6kynZgiBTCmUceRzKb7QkyixWS2wkEW2PH4Rw0lovvtM3mqZw7AhK8t5khjGycrcox93ubZguwvyuaECi2qR2gLIOTD+EQu52QMeIvXVovw2zSGORLQ1agdZUtgWdFrIMcew8PjX4sUV2I4gjyji4oqGwM+PwINklTg6RWv5GULg7CBdU/nHThdC0LY0rwtHmqSTuhJDJLvWVo4CQFGGBIUoc2UiiTY61kihxNLqWzoqcPA1lz4rnUWmPM0uehSIksC/HSSYrxX8ZzrkM2aqKELPOBWcGqT02W3RfKF0hhPTkqaLZNUHtn4W0vfPB6WCJo0QZnrgMIVz+lAhMLqtyCFE6ToA1VA4QZVheW4W2dcvSqcY7i64BZehlIX2l6i/xXmAtyCTk69Og/C9dJ8r7AdJBcJnRiavSCWGgF10r8lQi8uBaUfZB+f5RhtRdLLMgT0MfCQkqckU5F50qspFER36Z00XVli64XWSj4LQx3CSKvFRxTQjjLCU452l2Q9jlMDAkOI/NgktBCNdMNRZKxoMQmt6Z4AwR68IpYmDwuQUl8cMcWziJlP1Wth1AviCrto8SgzMquFE4jzWL4Y6X3iNk6NvSTUK64L5RnReLwvPS9aGkDElc1qV0/VF4VORJB8HFYTivK8cKKSEdBleSMk3vwLI4BvJUkqcwGjraHcnCJkmz46v2q8a09ehIYPIwzq3z/GBhPd+dX0+WOry6Z6L5zX2Ay2M1vz31BHgrTExMsP/++2/zL5HDDz+cD33oQxhjqm0QV1xxBfvtt99dToAPf8FzSbpNgOKD3W7z2pqampqampqdh0dPreaxK3dhYc5iY8t/rbvxbu/Z3Af4R5f+mB989cr7sJQPHuo9wNvgJS95Ce9973u56qqrcM7xmc98hksvvZRTTz2VU089FaUUr33taxmNRvz0pz/lggsu4EUvetGOLnZNTU1NTU3NToLzYtnr8OMfzh+fe987JOwo7k872XoFeBv85V/+JcPhkKc+9anMzs5ywAEH8F//9V884hGPAOCSSy7hRS96EdPT0/R6Pf78z/+8tkCrqampqamp2W6EPcCbB8LYQYW5H/jud797v+VVT4Dvgle+8pW88pWv3Oq5ww47jMsuu+x+LlFNTU1NTU3Ng4UHmwju/qTeAlFTU1NTU1NT8wDE+MVJcPkyO3nQ2V/96leccsopTE9PE8fxstf2pF4Bvh9ptnMme7pye1ACGio4LwxyyGNHLwoOD7PFPpio+BOlqaAdwapGeAAiGe6VhQq2HQkWMhhoR9y2NFU4b3sZw0FEQ8F0wxNJuGMoGCtPL4bVDbjqtuBg0FDQi8I1sQxOENOJJ3fBiaKtgyMEeMZG0OlmlStAQ8H8ELSWaG2YmkrZteWx3lEIfZlNBYn27LJmSJxYtHb0FyIaTYvWwRWiUTgJmFzSbBkgqKSnJjOGA40pnRSkp124PCSJZWEhRmtHnNjggrFizGikaTYNaapoNg3OCbR24WdZ7qbF5JJWx4S0Y4OOHHOzCc4KGk1TOR1IFZwPOt2cudmYKHI0mpaF+YhG01TOCFq70OZNg9bhGmNEpcCH4GQRnCLAyeDUkMeKiQ0j8lgxakdI50lGJvxfw+T0GGcF83kS3B/iUI9We8x4pEN+kaPZNEgVnChk21ftMLOpwfSqEXEc2mBqKmUw1FWbNBuWjRsbgA19UDgEaO0wPYmOgsuFjkI79xfiqt5lnq3EFM4dmoX5mE43I8uCGj1LFeO2QjrPuBuTDHLSdkScWJAS4QSxDGnrniOOLe1OSM8YQaebh/pGYUyuaoISltwWrijFs7N/17NrK2doJLFs0lITaBHj59eBGcM4AzmEqRa0J8ID1slgJgoOEJM92DQX3CKMhUYMUiCmimvXbUDskoQyt1v4mTmKgYofbMAPDSiJwFUOEEItUba3ItxChncCpT1RT+FTC5EslOsekwl0GsZmPpa0dwHSRdFs6R4BnsGMJmo4UIJkpUbt0kIv5NgNQ3RkGA9UpYaXNjhPOBvU+sEZISjdXXG8dHVwsnCLKJT/skgjHZRuDOC1xwvQsUcU6UjlSUfBjUYIXzkDSOnwbjOXAFfkSeHO4EAUinxYdH0oa+4KFwTvgEKpb3NBuZbjXXAzKN83lPYI4StnASc8FA4W5bGyzLK4VmkHyOA0oT3ZSBaOCIXbTS7IhhIxDMcbHbfMgUDI0D869thcYrPgRpDlwckhTwVKB5eMsqwArnBCkMpjTOFGUbxfZCOFjgy2cDspnQbKfivrK6WnvylCKY/JQz6ucEAoXSWSpkNHEiKJaEX43BIllmzGYg1VmwlF4RwBjbat2r69m0K0Gth1A1w/g0ji50bYUXBCEHLRUWIpNi/cKFQYE0L64GiyZDmzdBsp2w+o6lC6kpT1L50yhAz9W6biC7cSIcO91hSOGl5Uxi6hnGFsj+Y0Jvc0Or4652z5GSGqce2dwKSSNA05tTuSQd/hnEfKYrxIT248Jg+vOJE454tx4xkDcSLJUsfsfM49IXOg3JbHdmZOP/109thjD9797nfTbrfvs3zqCXBNTU1NTU1NzQOQ3MESN0h+9q0f8+Nv/GjHFeh+4Gc/+xnf+ta3lgUbuy+ot0DU1NTU1NTU1DwAyV1Y8S1fax91GE/6i2ft6GLdpxxyyCHcfvvt93k+9QpwTU1NTU1NTc0DkHwrWyB2dhHcu971Ls4++2xOP/10dt1112Xnjj/++O2WT70CfA94xjOegRCCr371q9Wxb3zjGxx55JG0Wi323XdfLrzwwh1YwpqampqampqdjcyJrb52Zq6++mq+/vWv85znPIcTTjihej3+8Y/frvnUE+C74f3vfz/D4XDZsZtuuoknP/nJnHXWWczOzvLe976XV7ziFXzqU5/aQaWsqampqamp2dnY3AFia7ZoOxvnnnsu559/PrOzs+R5Xr2yLNuu+dRbIO6CW265hVe/+tV861vfYu+9966Ov/e97+XAAw+sIr897nGP48wzz+Ttb387z3jGM7aZ3kTiaSiIW45uDA3l6UUwMMFlIXdBpdrVHiXC/2MZlO0NDdOJZ2Uj6KGb2iOFJ5Ie42JmMo/1gtVdy9jCqmbIK3OCSObs3vasakC3UNBvHAsmY89E7Gk0DW0N0w3oRRQuENDSnlguOlEATMQO6yUNDZ2Gq8qvBEy1LQuRw1nBqmZwmLDeUwrguzGMDbByTFOBFNBuBseKpQyMJSrqPbcQYfJQvkbTEifj4EpgJL3JlFbkGdugEI4TSxxbnBNkqSqut7SsCe4QTjAeaaaaaeU20W4ZIlm8qdigos7S5QXS2jMeBSeIVsewohfcCNotQ5pJGk3DeKQxuSBNFROTGUlig/tEy9Bq52SZQhdK8UE/OESUbhSls8KQiLndWsRzOcnIMJxMyJvhEe10ssr5YjiIIApqdKCqb28yrdwq4thV5U6KujeahlY7uFq02kGB3G2byo1kwyA4bJhc027njEc6OEkoD4bKaaJsIyk945Gm082IE1elmWWKyRUpvcmscLowjIYapTyDZhTaNHJI6en2BjgHJpdI6YmT8M5e1qNMU4ngQtJQ0I3D2N6ns6i8b2pHJD3Ww6qmYbeWRgnNFJNAhNYx2BkY9/EbZyBtIRoxNDpFJyuIo+DsIARuMMZvGiA7MTRixKoV0GmBjhFxFGTorWb4udCHRgJZXina3TA4mMhIgfN458OAL36KSGLTQq2eaLL1GfF0VPRpcC8YLWiE9MHlQIb+1lGRJh4RSXzuMLksFPMeuaKBiBRyQiKkwM6M8QtBwS516QQggotA4SBgbKFg90Exb03pjiBRsQ99oYNy35qlKnyBySRRo1DCI0jawYXFpMHZxDuB9sHVQghRuTtAUNubkawcFEr3B1O4NAjpKxeCUB5RuQBYI1C+cK9wAmGp3B6qPDabJEjlK2eKpQ4QpUNG6VABvhiDECUOWzhxOBvaLR/L4DqQg4qCY4KUHhV5dOyqttaRw0QCFYvKqSFPw/+dAakX3S5k4VIQxspi25TlNVkoW3BioHJYKB09IPRl3LSM5jSqcLnJfOGYQahLWVciiYgUoqEQkcTMpZUriVQe3QSXgkxAWo/S4XPH5BK1poeIJG7TCDfMEVKQ9SEfqyqv0H/hpyrGjnMCiYclb68iUpCHNhPSY9PFAWIyEdxNyv7ToW3sUieIIi0VOexo+XreYv7Lx0HpfuKMIE+Dk8N45ACFjgp3CgoXERnGRemWImRwd2h3FKNR8axbSFNPlrrKnULK0FfDfmg360BVxXOMRm7z4blNjIXcbnlsZ2Zubo6/+Iu/uM/zqVeAt4H3njPPPJNXv/rV7LXXXsvOXXXVVRx77LHLjh1zzDFceWUdn7umpqampqZm+5D55SK4zIVjOzOnnHIK3/72t+/zfOoV4G1w4YUX4r3nT//0T7c4Nz8/z4EHHrjs2NTUFPPz8/dX8Wpqampqamp2cpwPr5IbvvdjrvvWD3dcge4HVq9ezVOf+lROO+001qxZs+zceeedt93yqSfAW+G6667j9a9/Pd/73ve2er7X6zE7O7vs2MzMDL1e734oXU1NTU1NTc2DgawIDlOy+7GHsfJha7nqE1/d9k2/4/zkJz/h4Q9/ONdeey3XXnttdVyI7Sv+qyfAW+Gyyy5j48aNHHXUUcuOn3baaTz72c/m8MMP59Of/vSyc1dccQVHHHHEXad74X+howgPPPTRh3HIYw/d3kWvqampqampeQDyg8F6rhhtIMschnu2j8E4kG7LYzszX//61++XfOoJ8FZ41rOexUknnbTs2J577sl//Md/8IQnPIGFhQXe+MY3cuGFF3LWWWfx/e9/n4svvpiLL774LtN97P97Ns1OC+eDIIx7+ADU1NTU1NTU/G5zbHsVj5pczcK8ZYThsws33+09uRUIK7Y4tjPzgQ98gEMPPZTDDz+8OnbllVfys5/9jOc973nbLZ96ArwVWq0WrVZri+MrV65kxYoVrFixgi984Qu87GUv46/+6q/YZZddOP/88zn11FPvMt2JICjHeuhFnpEVdCNPW4evOSYTaGuPFJDrRccGJcI9kzHs0jL0c0kvtjgvaCrHbGwBxcaUynGhoYJqfjIJv/ei4OqwomG4YxQRtTzTCUwmlslE0o1hMvbs2gry0tQKNowl3SjsP0qUp7kkHqMS4dXQi/HiZ7Kg0I+kZzrxTDcc87kkkuHaSHryokzTCWxMYdKLUhjP2C7WFYITxSi2rFhpWd30bGjnOCeIE8t4pFnR8LSjoJBV0+NKZF++OejIMdkxDHNBUijNS+eGFS3HyDqaKrSV9TAwvqiXZTiIKlcJoHJyiGPLdAOGuUWKoCyPVemy4IlTV5QvOEA0mpaJpmMcO9JMsjAf0k0SS5ZZTB5cJHThniGVR3bA5IoVnVGhPA917vYyOt2chfkYCHkDtNs5UyvGrFg1ZjzSSOnR2lX3lZhc0m0bXMsgBaxuBleOsh9zF8pZlsO5MXkuUcozGmmahQNEo2kxRlT9HseOTi9DidCvA+OwPqQ7bhmsD3kbI2k0Q3k63QwdOSIJaaGwHo807eL6hlocY5EM5QtuFZ5dm8GNZK9uhnECLT3TiSnGrWRFo0NbTxFZj5/5NQBCxXDLTfiZOegPg4y624bhOAzgLC+tDfDW4lOLWz+EzCJ77XDNhlmY7EKrAeMsDFoT7hFJjO8PgzNDaiuVvk8NPveVyt3kEiFCvw3nItqTOa6fMe5rpMyxRi6q5q0AG5wW/DAlH2ukssjcYnONd6HONhPBbWAE2np8bkN5izeDdKCWqekr14DiZz4ufhZlloUK3hCU99YJhAzlrtwSHAjll6n+TSbQsScfy1Dm4lxeXJ8jl7hAFMdSQSyDy0GU+KpMEL72dVA5NpTKfak9zoT7VfF/HTl07HHOV21fllfK4G6htEfpUuEf7lNJuD5KXOW4UL535E5W6ejYYbLgbKG0JxtJpASTCnzLFe4ai+4MJdU4KNqirH/ppmGNWOIE4QFRXZcOQnsJCYO+pTURnB7KsVGmXbojhHoJstSjnazqWo69kG6otwREJMN4xSHkYnq66QCBTEBIAZFCRhJvPbpfPCdI5ERCfscY8GRDFRw4xOKijneh78qfzgi8hKRtQ79ZAdbh7WI5y351VlSOJUKE8jgjlrl3LG3j8vfF/wc3D0d4FEJbB7egPJWoaLFsJvfkuWc0crSVQiIqF43SfcM7iLoOk0OShASVFIyHtnIpSVNfOD0IRsVxHYkl7g/hGlvkOTb3cAU4l8hcbnFsZ+bv//7v+da3vrXs2Jo1a3jmM59ZT4B3BN4vH6wnnHBC7fpQU1NTU1NTc59hjEQaucWxnZk777xziwhwu+66K+vWrduu+ezcrVhTU1NTU1NT8zuKNTJ8g7bkZXfyCfCaNWu45pprlh275pprWL169XbNZ+duxZqampqampqa31GMEVt97cw885nP5PnPfz5XXnklg8GAK6+8kjPOOINnPetZ2zWfegvEg4A7f3UTn/uPj3LbL25idsMcL73oFax95CHLrhnND3jvBe/lZ9+8EiEERz/+4Tzrb8+gO93eZrrXf/tH/Nd/foINv76DRqfNQ//g9zjuBU9fds0d193KZ/71o/zq8l/ggek9VvPCt5/L9O6T97oe33/n+7n9qp/zh+/5xy3OXf0/X+X77/44z//kv4FO7nXa+TjjC6+7kA2/uonZW+/k4D88laPOfPaya378oY9zw9cuJZtfAKVY/ZB9OfpP/ojpA/YBwKQZ3/6ndzB7440s3L6Oh5x2Kkef9eyt5Lac8cwGbvif/2TDj3+Et4ZkxSoOfP4raR20JwBX/svfMHfdz5GRRhCiWK097YXsddKT73U9bW5436kv4ajTn8bvv+CULc5/4s9eQ2/v/XjkX/zJvU67pqampmb7YnKJeJDtAX71q1/NjTfeyFFHHVVZn/3RH/0Rr3nNa7ZrPvUE+H6kG3liHcK1tgqRW1yM44aCO8fhZ0OB8+FcWy+GSk6URwtPL7YkMoQ+7cWWFUV4ZCUUTgjaURC9NRQ0lafX1Bz7hGM48m9O5S+e+jo6xblIBmEawMf+9p1om/OR77wJgFf/2b/z7lf8B296318ym6pKANfUjoZS3PGza/nc376N09/0YtY+9khu/NnNfOSlb6LRTHjCC55IL4K5W+/gLc8/j6f/+dP5y3/5f9Bo8Iuf3UKn06Ctg/BsaDwtHQRwZVsMclGJ21a2HQt5qP/BT/89rv7kl+j/8ld0DjqAXhFOOpOCX3z6azzsSY9lqhMzMJ5WZKt2jWUZFjqI5No6hKLOivaeTDwLFvY87ECOPO0kvvHO/yJJLI2mqfpOSs+BT3gEj/7j32f/NS2u3WS5/tOX8KWX/xPP++Q7mOh6RiPDmocfwJHPOZFvv+PjOCuIY4sSQS+VxI6p6ZT+fIRzgm43Y9PtKVf+8zmsOfY4jn7tf9KYaDNYdzuNTpNmkb8QngOe9gccfdazaXVyNq1vFAK4lDh2NJqG4SBiRcMzjnJyF/p2bEP9hrmgG3sGBta0NQ9/2gn84jP/y+l//kTGdnH83Xj1Tdz5s+s4/tz/D60drXYI11yyYuU4hDNdEi46TixSelY3IFKhP1qRYCELv0cmhJlWkUMKx2xRp7YuQhtrAM/YgE0MURHaNLfBKWUqDqLQZjFelYBebGlHjsnYkiiJQNKJdkEKhXEZHdmD4Tw+XYB1t4HW4Bx+/Sbc7XPYDUPUVAMZR4huG29tEPYMx2Vn42aDotQ7HwRvxuJHY7AWEUVBCJflMD+A2Xm8c/iZBXzu8LlFNQUudaTzqghfK8hGsgqTW+KsIB8GsY0rQhOXQp5SKFWKvkwuESPw3lXnAPJUEreCiMsN8/C1ng3qRp+H0Nbeh5DWIXRwEEvZLAiNxgNFlDi8D2KyMj+AuOnIUxlCD2sK0VAhzHMhNKwfKJJ2Wd5wfXkdsCz8cRB1hZDMZYhhgCjxy8RuUnpEMRay4WICIdSxr8oWxFK+CG9Lda7Ma2ko5TJMbdkfpaAPQlnkEtFYWY6y7OVzYLNSQOiRclFsFwSAQdSnIodwi6GbSxGYlGU/ByGdkKH/bSpw1hMli/1d9quKPEpT5OVxRZpl/+s4hGLWia/SBYHJQ/lEEb5XRb6qSzi2RG1sPSiJkA6pQohtYDF2r3WIiQQZSYQSITy4CmG4y3EeyrwoMizJRmqZcM3ZIJDUURB6eufxwxylFeNB6HCloTVhGfdl1edShrYtzxtT9HvRt3aJKLAMYSxk8TzJxX71PpRDxyE8uYqh2VLEDUE29qSpo9UWi2GpvShCQzu8g7hpyUahnGXIY5M73GJTYZ0vnjGPyYEo9G+WetLUkzhBnvtS73i35LmEzSa8+U4+AW40Gnzwgx/krW99KzfccAP77LMPK1eu3O751BPgBwGr99udIw7ZjTWtrQcQn7t9Pdd/5ype/t//yOR0F4AXvOq5nPOUv+XOWzcSr9xy381Pvno5ex95MIc8/igyJ9j1oH044ukncNV/f5knvOCJALznXz7FwY94KE8660nBEcDCbgfutUVaSxnND/jymz/GDd+9imyYsuZh+3PcS8+A6VVM7rUba454GFd98ms85pUHVPfcdNU13Hndr3ny+X/5G7YQREnMMX90Cs6DjuOtXjO512404vBGj/dIJRnNzJMu9ElW9NBJzCF/+CSaiUPG/3OP8r3py/9D1Onx0OedyfxcghCO1urd0doB+W9cnzuuvpbL3vlRZq7/NbrZ4GGnPIZDn38qoHj4H5zI9z/wWX75/Z+x99EPre754Se+yu4PP4gV++9Flv7GWdfU1NTUbCeslYgle343/ORH3HnVD3Zgie4/pqenmZ6evs/S37n/jKi5R6z/1U2oOGL3g/auju1z8N7oSHPd1Tdt/SYfJoHLD3lmb1lHOhgB8MNvXk13qsv5zz+f/+/oP+PVT30F3/6vbUev8d7z3pf9K+lgxJ9/+Hz+8gtvZ+X+e/KFl/8zNg8rhwc/7fe55mvfYzzfr+77wX9/jX2PPYTJPXbdVtLbjeu/fSWvfsyf8u8nnsElb/4Qhz/7FJqTv3kEwE0/u4rWql254i3n891z/5DvveZPuOkLH8a75X+s3PCVL/HxZ5/Bx0//a376wQ9gRqNtpjlz82186i/P5+Cn/x7/32ffyZPf+ndc960fcdWHPwvAxJpVHPiYI7j0v75W3TPqD/npJd/h8FNP2layNTU1NTX3M86JZa8VDzuKtae+YEcXa7uz22673aPr9trrrhfR7g31CvBWeMUrXsHnP/95brrpJtrtNieccAJvetOb2HPPPatrbr75Zs4++2wuvfRSkiThOc95Dm9+85uJt7F6+EAmHYxI2s0tjrd6LQb9rU+0Dj7hSC770Jf4yVcvZ+3xR3Hbz27iqs9cCsB4MIKVDeY29fn2p7/Fyy58GYc+8qH89Ipf8ZY/u4DWRIdTnvHILdK89Rc3cuNV1/CSL7+L1kSD3MLx/+/Z/PBjl3DDj6+DPQ9mr0cdRWOiwzVf/CYHnvUkBrML/OSr3+fpr3/x9m2UbbDfo4/gH771Ln5+64BbvnoZcmrFb5Ve1p9n7oZrePifvZS1L/g7sjtv5MfveB0qUjzsmX8AwAF/+HxW7rcrE6sSxndez//+w7sYrl/HI8/5662mefUnv8K+jz6Stb93HFJBd9dVHP28p/Cdiz4B/y+kecwzT+IjL/sX/mDDHL2VE3zvM98mbiYc+PhjGfxWNaqpqamp2V6YXILe+fcAz8zM8J73vGcLy9nNmZub22551hPgrSCE4L3vfS+HHnoow+GQs88+m6c+9alcddVVADjneMpTnsLhhx/OrbfeyszMDE996lM599xzeetb37pjC/8bkLSb1artUobzQ9qdLSfGAPsecRB/8Pdn89V3/w8f+/uLmNpzV44+7ff45kWfotkNwrlWt8nBxx3CoY8+FCXgoGMewpFPOo4ff+3yrU6AN9y0DmcdFz7tJYWN+SKb7tiI2BOkUhz29Mdz9Wf+lyefeQqXf/oy2lM91j7mSLJt1O89z30583dsAOChJz+a3/+bs+5N82yVRq/DI//oZN5wwp/y1N13ZdcD9777m7aCbrTo7XMAezzmBObnNJ0992O345/C+iu/A8UEeOqAg4mbBiEN02v35rAzXsi3znsdNk0hjrZIc/aWO7jtyp9z45MKIZsHvMMteWNZe9xhTK2Z5nuf+gZP+JOn883/+hoPf9oJqEj/Njsvampqamq2I8YI2MIHeOdzgdhll10477zz7va6NWvWbLc86wnwVnjDG95Q/T+OY17+8pdzxBFHMDMzw9TUFJdddhk///nP+d///V96vR69Xo/Xv/71PPe5z+WNb3wjjUZjB5b+3rPqgL2xWc6tv7yZwx8Vvoa48Rc3Y3LD/g/b9sTuob//SB71lEeQOcEwh6+85QOsOeQA4mYCeA48ZO9KwXlP6K6cQEWaF3/pQnpNTW6DcG0hh8OnPZffGa477Okn8t33fJrrfnA13/vv/+WYUx+PVBK2vsWZF374TctEcNsL7zzOWOZuueM3ngB3996fwa3b2GayDco23VZVWismOej3H8Vj/uZPtxDBLU3j+GedyDc+8lX2O+Igbr/uVk694JzfqA41NTU1NfcNxmwpgtsZA2HceOON93ue9QT4HvDlL3+Zvffem6mpKQCuuuoq9ttvv2WqxGOOOYbhcMg111zDYYcdttV0VibQbYWJ2FTiME7Qiy25E6RWsLohWdMKxydicF6QKMfISJra0YsdU4nBekFbN/B4ItlhVWOeRDlu6iuc90QSJmJPJ3Iooejnjg4ZK1RYI22JjAmRMh17ViSeQw5YySHHP5wvvOXDPOWws3DAR974IY496Qj23nsK4wxaehIVwhz3YsfKHG6/+gYOOGIf5keGG7/+A/7vs5fy4gvPZY+WZ3XT8vw/ezwv/5N3c8eVP+GIRx3ED39wA//3pe/xp2/4E1Y1DVOJYD5TJCo4Y+z62IP48oF78O03X8xpL3sWzakJNs0M+PX3fsbUkw9lt04T5zytFVM87PFH8YXz/5OZOzby9Oc9Ht3xZC44DmROoIRnYARTcThehtbNHVWo3enCLS2E8PU0fY73nlg4WsqyZyMl8zKsigKXffCLPOLJxzG9okd/0xzfeNvH0ZHmUY86gHbHs5AJjEyDW4e0dNsp09GYSS1oqIj5nMpZQsqg2j7kGb/HV879W+av/horH/5YFm67lTu+/XkOePKTWLl6xIZfDxnfcD1Thx9EuysZ3nwrP3n/e9nzuKNotGOmJoNaTaqscFSARz/7JD549vmsfdTDOeD4o+hpyfj2O7njxnXsc+LDK/eM33vm4/js2z/BR17zLh7+uMPZba9VLGRFWzUs7aatwlTryDHVtlgf3BtyF0JB54UKu6FDmu3iZ1MFp5PcUTlNWA+TiaCpfBV+uvxZXhPCHYf/t3UI3y1FcIJoFU4kvdiyIjFMxD2kUMSyRZI7cAZkjN/wK+jPwaY5/MbZEL64P4T+ELeQ4TaN8WOLjhVyOMKnBqFEcH6IFbKl8QsZ3nmklPiFEcwt4G/fiEg0XitoNRC7rMTfug67boBMDfb2PrIV4foZPneISCBzz3guhKceLyisEcQtRzYMzg1RItGxwxlBnkqykULHrlDIl0r/5WGMnQmOEi4tQkxngnSgEMKTDA0+UiEcMpAPQzr5GFSxOysbSnS8GJZ5bsaw616L37lEiSeHKryv0h4VOWwuq/yl9IVbQ+EqIIKjgsll4e6w3MFC6sKpIadyT3AuuC8o7fFR6VAR0rcutJMQHhUX4Y6LdhEytIl3wYEhSjxRwxVOFYvOAMElQBShpSnOlU4CvnJSEJVLACBFCJNsi1DfS1bavFtsk6XOEixpK2tECLPrBNaW94nqOu+o2i5quODyUNRdx6EcSduRjYLzRqNr8S6E39WRR8gQhrhqexHcDsq+lNqjo8V6VU4ILpxTyqOmkuDwoAQ+d5Q2NVHiCgeS4BIhygc4USFsciuCYY4bGmRLI4r45MKly9q2rLOQHqlC3+RjucwNogxR7ccWlAghhx2V04LJwrgw2VLHi9CI+bg8FlxHKrcPqBxIvAMKl5PSvSRqhHHsnCzGayhrnocQ2qVrQxmOXKgQtrl0ZglhvgW58SSqHMMhtLFNPVnq0Fog1ZaLPsGBRFB8lCCVQG1x1daxuWRZPOXyWM1vTT0Bvhu++tWv8vd///d84hOfqI7Nz88zOTm57Lpycjw/P39/Fu8esfHWDZx50suq31/1/DcD8MK/fjp/8+qnAHDmP/0/Pn7++3jSUa8C4Njfezh/8veLG+2//InvcsHL38d3bn4nAM46PvmG9/HvN9yGd559D92XF73zHPY/8sDqnhOfdAQv/oc/5s0vfw+b7pxl1e4ref6rnssjnngssGQ5skApyWs/8Ddc9M+f5J+e8zr6Mws0e232PuIhPOMpy/+oeMSzTuI///R8HvmkRzKxcmLZ6uZvyj8+9Vw23Ra2Stx45S/5zvs+y95HHswL3vVqAG76wU/4wfs/TT5KSdoN1h66H2f++yvprZqqHIUufOY5zN0e0rjt/37JDz/4WfY76iE898K/A+Bb//KfzN++kZP+8ZUArFi7P48851x+8pEPsvDm/6AxOcm+v38yBzz1KYDBZhk/+fB/8Z1/vhXvHe0Vk+x6zCM58gXP2OYK8G4P3Z/n/Nsr+eZ/fJyv/PN7cMYyuWYlD/2D5QK37lSXI05+BD/4zLd4/mt2PlFFTU1Nze86zoAwWx6r+e2pJ8B3wec+9zn++I//mA9+8IM88YlPrI73ej1mZ2eXXTszM1Od2xYf/ZePEcca6+FRJx7Cwx/78Puk3Juzco9VfPqGD7BPL2U6MfxitsEgV6xoGMqJaHuyw4ve/CIePj3GAetHmpGRlPsKnnDacTz1WY+kXCXSkeZlHz6PVY3gU9xUbHUSespzjueU5xyPEtDPJamV1arh1uhMtDntlc/nea9+frV6OLaCpOFYqs7a/5iH8cFrPghsv20Nr73kXwEYmLCloxUtrkwCnPov5zKZwO4tz8YUVjfgluHy3cov+fRbaWi4s9hS3daFz3CxQfkxf30W45Feds+ao4/hgBMPpz8f9vM6J4qVHmivXs0T/uWf0JGj1c5pKLhjfYOoae7Sqmz3Q9byrLeFSbYrVlrnt7K39/nn/zkvfMOf01Bw+7aNJWpqampqfkt+OF7Pj7INWAuWu/ggXILKHUptdu1dfYjW3GPqCfA2+NCHPsTZZ5/Nxz72MU4++eRl5w4//HBuuOEGNm7cWHnUXXHFFbRaLQ488MCtJQfAc/76WXR7rSVbIO7TKtTU1NTU1NQ8QDiqsYpj2qtIU0+K4fPDX9/tPco69OaTBVtPHrYH9UaSrfD2t7+dF7/4xXzuc5/bYvIL8NjHPpaHPOQh/PVf/zULCwvcfPPNvOY1r+Gss876nRPA1dTU1NTU1DwwUbkPq8DLXttRzf0gpp4Ab4WXvOQl9Pt9TjnlFDqdTvW67LLLAJBS8tnPfpb169ezZs0ajjjiCB7zmMfwz//8zzu45DU1NTU1NTU7C9q4rb5qfnvqLRBb4e6MmAH23ntvPv/5z9+rdDuRp6kdTaATOfq5pB055jNJJ/LMZbJSugOkVmC9oKld4b5giQsptxQKKRSJatPUfWYzQVPBKIhqAYikR0tPWwe3iahQ0/dih/OCTuRIlMP5sE+0pT2JCi4UkQQjPS3t6OeKlnbYQsWb2vCzG4W9peV2pEhCLMP2jqYO90YSEuVIbXCyKNuhFwd3C+sdqihXme5ULMgcNKOQJoS6TyeChVzQizx54exQumS0dLBLUwJmMo8S0NaeyTjsTe5F4WfZNu0lI7+hgtNBWQclPMM8OEk0lCj20Ib/9yLPROyZz8uyBpeJdlHO3AkGRTlKYZwSQaXc1jC2QX3faucYIzFG0OnlQc1vJFJ60lShI8f8XOjrLFUwUmjtaLRsESY5MEolrlBf9yKDcyK4K2hYyEK9Rjb8HOaCcVEo6wUjGxxDhia0QVt7nBeMLTSi0JfzefipemH/8cBAVDg3KLFoGbxr0xdlCC4n08miw0O59zg4PPjKWaKhwhi1PvT92IYx1dRhDAbnk+CSkiiPEp6mcjS1oxs1SFQbLUIb+cHtkA2DHP+O2/FzC9AfYu9YQAFufR+3kGE3DMP1Y4NdN8CPDX6Yo9Z08LnDz6WwqoV3Hj/McYBoKCQbseuDYwSAXtPBr5/D3tbHbhoj51LcMFRUSBEaCTBZcDAwNij306GgXHewmcDZoGg3ucDmslLui2I8lWpvmYBJJVFiKlV8noY08zQ4Sjgr8LnBzaXhK9JI4YwoXA4EOvJko6DG9y44NyjtkVJUyvzgNOBROjgXLHdXCO4IZXoU90BwMCjfN7NiL3lwOvBFncHL0Ba+cAwIdQ3uBFIHx4BwXVl/KueAkO/iMyuEL9oabL78XOk8Uf6/dLJY6lJgl7g7OCNAeyShHVQU0g7KfZalbQs3ijJtUTyKsnCScEVbLXWFcDa0vbMCS3BsqOrqQt1C2Vx1rGrTJfMcIUH6cH1VNy8QLLYFLNa1dJdQkUfHnrjpkNLjU4+IZHgIlYDMISKJTGRwL0kicB6ZaJzNEQ0drieMbdmNwbrgFNGN8QtZqF8xPqq2Y9H1QccOFbmqXnpNh+z6IeSWrL/ozBHuLcdYUWe5+Hnsi2ho5ZgoHUbyLDiQOLPUcUOgIleNh9Ixw7vw7FTjRQVXBikF45FDR45Osnw8hes8UkOSKJz1y9weoig8w1EksC64QwCYPLhySBnaJE0dUSRw1pPfQ/GKMlvuAfb1BHi7UE+Aa2pqampqamoegGjj0HKzCW89Ad4u1BPgmpqampqampoHIs4jlqwWr7/5Su688fIdWKCdh3oPcE1NTU1NTU3NAxCdu2WvNWsezkOPeu6OLtZOQb0CXFNTU1NTU1PzAEQZi5J22TFn7Daurrk31CvANb/T/OjSH+/oIuy0XPfdum3vCy7beOeOLsJOy9du2bCji7BT8uVfrNvRRXjQsvkKcPmq+e2pJ8D3I3O5YCaVjIxEisU9PSMjmc8UsYR25EiULxTwwf0BILWLXWWdQYkIJSK0iAtHCF+JepUA5wVSQKJ8FaEtdRKBZD5bnn9LB7V+JD2R9Mxmmo1jxdCEPJ0HLUv1vqefi8pRYWyDu4EUwSFgPi/zDo4IALOpqtwldmvntKPw/05k6cWWRPmqnmU6jSVOA9aHVyyhG3nuGAq6USjLD7/5UxLlGS9xvxjb4EZQtsWqhueOEVVkucwtuhCUbdSJLEqEPHoRTDc8DQXTiWcy9kwn5f+Dg0fpcjAwIc2BWXSW6Mbh/2XZx1YQS5jNYDiIyFLJeKRptwyDfsTG9Q2kCu4Po5FGSk+WKrQOjhEL88HpQEeuikyXpQpjBEkcVMs6cqG8jdDfg8J5YWBgKoY75jTzs0mlkA5uFyGCX0OFNlECxgZ2b3tiCb/8zo/pheB0DHNBu4iMNzbQjhYj/00uUUzP56GPIhncHOwSobMSoQ+ioh9zF8aVKcaJ9cGRYmSCY4grxmSiPB1tsT64oiTKk7sU7x1SKLSMQ2xQU4TbK/fLSYlfyMAY/NhAbnFzaXCAmEnxY4soLS1yVw0gPzaV0bycSPALGWL3lYiGwq4b4mbT4CixfhScEeZS7LoBbtM4pBceUkSigmrdBmV6nkpy47lsw/rK3cBkAlMo0ktngNFCSENIsCao4rO+WOJ84Kt7faEsV8qjIo/sxLihwaWh/CpyqGJc5mlwnBDCo+JSpS5xzldK9zyVlYOCUsEVwrvgYqC0r9wThPSVOj84ALCsDqWThSscGGThjKBiT5Qs5ieWuCzIIn3vwksqT5S4cL9aPBecLYJDhJC+cgXwTvC/t2xc4nxBddxksnKbKB0oFh0tWOYQEdwXQr/kqSxcMQr3lML9xbrC0cKJohxUbh4mk+RjGepblD1PQ7vnqcQ7lrk7lO1ZuSCUbSx81XdREq6xpnSOCA4PQoR+WOoqUaan49B2Og756sKFAevw6eIbpmioZeE081mDaGnkZIKcTBANxVeuWYdQAp+7MLbb4Q1WJDq4jbhFd4rg3LDotLE4ZsIraVtcPwt9mAeXkbjlcEXfelekoT1Rw6Hi0G9KQ5Q4okaoR9x01dgRMrSbThw6Du2honDeZqGfrQ196owgbhbPtwQlBUoKkiS4M0AYW3ErPFflsyCkp7PCIJfMmhotiSp+N7knTT3Oekzui98dWfEsZqmrnpMsXe4icVco64ITxNJXHQhju1BvgaipqampqampeQCic4feLGyyr1eAtwv1BPh+wNqwbLfpjk002iMSCapt6GcK1zBsGGmcF2QWmv0c68IKqvOCgZFkVuC8IFnIEC2Bc5aGNsEHWI6Yyzdx59CxcTYhtWEVc6SApmWQSzaNBe2+JY0tvuFYPxeW9UxiGUWWmTvG5B7aC57bR2PuHEVsGIWh0VjI6OeKPDaMraShHJtmEhYMpAq0hFhA5sMKpxTQbIJpGGxs2JBGjIwgiy1zRX1HVqKAWDlGVmKL1ZdBLpECZgaCLApOL7ZId31umZlX5B7mR4JNA4+LYdQfs+H2TfQNlPbNs2NIGmE10UYQK5gdQiIhLVZ/iRfLnepQlk1jWV1T1iWRkPtQ19zBWINoODaOJbGA2dHiSnWiYD4LK++jHIY2tI8vVlH7QxgMIkZDhWs4VJox2pQihSd2GeNNIYqgUi6s6Iws47FkvBCj8oy+HCOFZ9iPcU5gjKDvx9VK+4wN4bUX+oJMhfYYW1ARDOY06Vgzr8ZIWfRdElZ6hybUNfcwPxSQeOZTQTYaMb9uI6mDfiaYG3n6A5AxGA0Laai3iKHZXFxBEgmMDIyj4Jc8KlaKCytREglDHa7J4uC5bH1or3LspnHhT90wWC/oK8vAKprSkTUsAkk/ksRqiBQKv3ETpMOwVLZuHj8YwCjFzg5RGyLczBA/yskXxggpcJlDzwlUS+FnR0gNfmTwgwyRG+zcCJ9ZVEOCsag7F3CbhuTzY0Qi0TMq+AZnBtMfI7TEG4dqCNxCGtrBGAYDx3gYVshGY0Wae4bGcMd4iLKehrIoDaORpD2w5OPC17dYyktHkkQ4EmEZDg2JdAjpGQwNTRXeV9ZlFjO0JNLRnpfYflgJl86S9w0LQ4fJi1WwPKxARjjyscR7mMkcFN693gki68KqpvAM+jnpUJF4S55KspEkHRXfDNlildZ6EmmxhY/1cKjCqrcVaOeq60p05DG5wHvIx5JYOCLriHFkQ1mtxsaE58DkEjMWy/x9Y8KKodKhHg1lMamknxlu7Y+X+ci60ou3sI4yxaquNRKlXbWiXJYx8ZZ0oMLKvASlHdZI8qFgOFbE0jGXSRoilKU1tMTCYTPIxqEfIdhXZSOFGQvyTKBzz3AcVvelAu1dWJEsvtXRPtSvoSzWCvKxJJEOa0O6/aEhG0uyscRkoH3hYbvE41hZRzYO/RPL0PZChG8LRoMc7wQLCxaRKaQPq8BCCuz8GJ9avHGYTBDPO3QscKPwVVJ/bLh1ZoiZH6E3RYixxs4OEWmEmR+T982yFeCyzcPvYUU2HYa69/s50lvSviXKHeOBQmWe8VBhM1F9W5COJEUT4z0oFVa8y7ZRdvEbEe8E2ViGNrRU94VV55C/KPzY80zQHxjSYfDEniu/gZEwl1sGmaI1hliEFWBrBUqFVejIeTaNHdYE/+w09wxzi7CCgXGV53tqim9UrKdhJJpwLHOe2AiGzmGLfbzl/GBbjPubsGq47Fhux3d5T809Q/h7EvWh5rfi8ssv59hjj93Rxaipqampqal5APGDH/yAY445Zovjs7Oz7DO1C3NkW71PSsnGjRuZnJy8j0u481KvAN8PrF27FoBXf/mtdLtNnrHPiFuGmo6ypD789bkyyVnIFLHyjIxkLlc4L2hHltv6Mfv3xrQix082toiV46DJMXeOw9KidYLcCXIHC5liZdMwyCWTiWWPdoYUituGkl2bOXOpYiKxLGSKhnYsZAoHOARtZXFltDcnmEk1u7Uy2lGItqal5/vrWnQiz67tHCkgs4KWWtyr7Dzs2m7TUF0yN0SiMD7DuAwtY5y3RLJBagfMphlaesZF2rHy3DKI6UaWDWNdrKp6UivYq5OyIY2YGSt2beUMTdj3/LbXf5I/e9UfVm3tPLSiEF0uFmFvcTuyjIxkKkmw3uBxLGSeBaOYTgw3LiR04/BX+EIWjinhmcs1V89oDl2REyvPQqZY3chpR5bZVNOOLBNJh42jAQOrkHhM0X4LWVjt2K2VsWAUC1lo9zIK2lUbYvbqWCIZVr53bedcelvMcbvkXDcf8bCplF/MJEwmropS19SOW/uaBQN7tsM+6n27KdYLHJBIz3XzCQdNjlkwitwJhsWq+q7NjBWNJsZl5C7nlkHCvl2H9TmbxppdWxE3DwxdbfnVXIN25HjL6z/Bi//2D4lkGJMHT40Y5pK+VYyNJFGO2VSxbzdlNtdE0lfjqikdG1Nd/b5nO2Mm08ymisnEIkWIUrhprNmznfHrQcyuzZyRkyjhyaykpS0OwdhIUivYvzemoZpomaBlTCwbqHXXMfq3L9Jfr/jFFYL5Wcv0Ks1g4OjPW056tkPGktuuTti4zrHvoY4bfiI59GSHWtkgfvLR+H0PZy67M+wnFjGtm38FUQRz/RASD2B6N4RuQNQAqSHtA+B/fQ2sXA02x99wE+LgwxCNDn7hTsLS7gJkKRiL3zSL6HR41Vu+zPkve0IYCN023LkRJjowuRIRd/A3/wpaSVjySjPojxAPPRp3xTcRK6fwd2xErF4BExOI7ircL/4PMdmDlWtCGZMOpH18PoThXNiIm+ehPGmKn51H9LoQ61DPPAdjQStIEtg4F8oWa8gMTK+AmVnEXgfgZ26F9gSivRLyMfR2gf5G/HgW0d017MUWEj93W8jvtlvDQ7lqGkYDWHssYjTPHaxnl41DaHZDOkkPxnOhrccjaHUgbsHsnTC5OrRL1sdP7YaYuxPm1yEm1uA33gLtHmLFXsy4Tfzz332a81/3R8yoIb14Fc7b4n3BkYzGjJsxiWzB7T8vNigrfpK0WN0I77+rWi2sy1mR7M7Xbr2N1S3D2t4UrVEOzuDLfG/9FaMDDqF53c9CmZMY4gYk7dDnUoavXqIGjBfC/vRmFwZz4TohwBR9Yk3og+4kLMzC5OoQ1bAMqTcaQxRh9jkEfUMQpv5oYheOWrkXI7PA0MyxYv0mNq6aZEWyOwDXzd3MDQsJqRWsbBqUgF2aGVPJBC/8ZJPfO3SeAyccV2xQrGp42hqO22XI+nHEJ26IufKLUxxxygyb3uI46jWCT77lYxz7p89mbc/z0xnBkdOey25V/O3R83zu5jYHT1qmE8OVGxMOmAhf+awbam4dwi0DwYsf1ufLt7R5+j4Kj+ea2YxYOfZsKzrRNMaltF2CH80iuqvYlK9jRRbjTUY2sYKfbFrPfr2IhmoTyQTvPbkb09ITCAQ4w4xZTyTDt2hDM8uPN8asbhlWN3J68WpunN/E2oldUVIzk95WfHYadmnuh99wHdxyS3g2Vk7h+/3wjExNQdxETOwW+ilp4zdch2iuAO/wC+tgNMB+52f41CJaEUILXPG1l5puhrGR5+FZkxLzi3Xkv15gITMc9J/fquYHmyOlZI6MtyaPobnZVG2E4S/TbyGXbkiuudfUE+D7AaXCZKjRbtLotOj2oKM0HW2JXBjA3YbGZyqEfDUSk6kiXLGlJWI6PUE7crSyJolydHuCYVRsZVgyAbaZot00kEvaiaXbCSGTO0rSbWlsqugmFp8pmtrhywmwF7T14gQ4coJsrOm0FZ3IoYtJamvYohU5Om1dhS9u6+UT4F67RUO1gtgMhfGa3CkimVQT4LF1mFSFcM1F2onytGVMJ7aMosUJsLaCblcyHkdkkaLT1og8TICjWNPuNqu2dj4ICZXwxIWorxNZlJF0l0yAfebxuaLbMLRJ6BQTYJcpOo0wATaZpplHtLsZifLhXFPTiSwmDT97SYs0cohiQl4KulwxAe60FT5X4d4lE+DmOKHdNUQSfC7ptDWNTkK7m9FyMZ2epGUatBO7bALcEhEmh3YnhAju9OSyCXDLJ3R7Ap+HCbAoJsCdlqLXaJE7Re40bZnQ7Tms12SxptuK6EgTxpsLE2AdhbaNpEcYSbcHMpdgFKqYAOexotOTmCxMgF0xrprKMR5rbPF7p6PIUk0Wq6JOoW/SSNPpqNDvLY2yYQKcWkk7CuNRGRnGQE/QUE0i2SgmwE3UsEmUaESkaCuJkYK2ikBanBD0YodMJPM6Yqwc3cjSVopeYlGNiLjbwPfa+Ky1OAHuNCCOwObQCAJEuk1E1FwyAS7C1nYS6DbBKnw7QfSaiEYLTzNMbnQOqQgT4DRBdBLiSNHrJMUEuAGDBDqNkEfSCmm2GmFCGknAIXotXDtBdBr4dozoJNBtLDtelTFpQRrGOCoNE71MgdYQCXyehvvjKLwytTgBbiSQjosJcARZHsqYJ4heC28a0G4iOi3IJfTaIEf4KEX0WosTYFfUv5OEh7LbAGWh10ZEhgFNepkL+6VyAY0mRGHrCJGHVhPiJtgl7ZJafK+N8E3woe4+K8rTa2HdmDjR9HotrIJe3F4yAbYkkSRuxiSyDf1GqdCjkzTpNsP7b6/VwriMXtKmPd+k0zL0ei1aUTEBLvOdaxD1WjQ7YcJFo5wAN0OfSwkqDuMlysHIUFeZFhPg8BwtToBVGEeMw8/MhxcQ9otFmF4bXeTX6Tbp9dpExqJMRm/cIOs16SXtcN43aZGgrKBdTIC7LUUvaRE1mzQ6hnbX0hhrmg1PU0O35xlGEUk7QSctknZKHFmaHYmKNEm7RbPjSTJBs+OJ24puL6fZadHuGjoNQzNt0O6GbRMtpWlIQYKg27M0Oy16vTABbtsgjO52FN2ohXEqTICjFNFtk+dNelmCN4qs16KdN+n1IhqqRSQbeO/InaSl29UE2JpBNQFWJqWdxXRahm5T04tbdBjR67VQMsKk4TPDOkOv2canTegk+Dw8W97n1TNGHMZXNQFOm4hWK0yACePaNiO8lIhmhIgkrggDrVpxGBtZ8TxJiWlE5Mni1KucH2yLXhTREsunapEXkN7lbTX3gHoCfD+yRyvsnZzLBM7DLYO4ckC4bRjjfNgLuKJhcL64ph+zqhn2zaYuTBSlgE1pmHAEN4egml831Ew3LDctRKxuhjf+m/sxvSgo6EdGMrKS0VDSjSybUh0mIsIzcqJymsicIJaeFQ2DcYL1Y01TOTQwVUzIbhtErGoaepGlbxSZ85Wi37iMTAwxLnx14/FIoRiaucq9IndjRlaTWsL2fhecLhLlmc9U2EslYP1Is6ppuG4+WA00tWe2qjuVM0W4T9LSnkEuaUeOhTxMosjDG8ztw5xebFk3inA+TK5nM1X88SCYTcN1s5kiLfavtXXogz06GSMjuGUQ4XzMHu2M2UyTuz4jK9k4VqxqGlIruWMY9nQ3tePHG1t0otLBAFo69Jn1ob6DXJAoxw3zMQdNONaPNFOJ4+aF0O+/7mv27IR0N/YjxjbUObVhVdR6waZiMj7IFbu1c25YiBkZSSR9VbcViSS1QzyO+UwxGRtmUpCFjHpgxsykCfPFH2G/7msE4Q+cfi5pacev5hJGJnyzAKEM1guun09oR45BHtKaTRWq4WlHrnAXCeOwU/xhklpBU3tmivZeN4pIlGdkJc6DKcb5IFfMFH8kOQ9DI7F+XLiOKMa2T6ezCr13D7VpgW4vQkmYmFS0OpI4kXiXoVa2aHZz4hmFd5Y4kYjCMQHnCDvSQYkIKRR0JoqNjMWKZBwhoiZ+OBMmNkGijojbsGIF2Ayak4g9ixVQW2x6zseLsnznEMUfrACM0/CBOBzj8xzRSMAa/NztYSKaFauyjThcB+F4fxiOOR9WSnuEMrkgnfc2R8TFRDQfheNSFmkl4XetC2sXHc5pXUyAi5+T3eK64oM5SyHLQ/2HY+jF+PECOIPDIG2KaExU9fTp3GK9ZZjAlw4dYrwQ2lypsMI7mg+rY1KHa6IG1V982TCsmikNNkP0dgEkQkb41iTe5tBsg45Banp6NV6Gj7TyWybvHW3ZxQgBPvS1ECJcl/WhvYLdW5Z1Q00vtty4MGJNyzGy87QjRy+yZG5ECx3yK/qeLEcKhVh7FP76H4XyNnrhZ9JCtKbx2SDUAUK90mFo33bxR5R34aeQMBrBwkxo9/F80f5xaJNGDN2V5G6MjqOQBjC2fUZ2galkN8SKDokMe+NTNyAt/hBf1TS0tGPjWBefNzPsuY/hh7fGXHm75+qrVnL8Y+9guhE+jzaNNU/eK6fxBzMcMe358d8qZjPInWDfrmc2gwMngtPMflMO6wW7tcJE/TvrGjxk0lT6ldQKphMYW48SEasanpFZQMmweLKyYVAiwXkTJq7OI9SipYx3OSJq4rxh745BiSZKLD5DHo8waWhbpZFWEckGUoRvHQ+Y6KOLb6/aOqMXWxbyDUSywcjkOA8TcTs8v86FPzo3zoTnTetiwlqMy9JuYuFO0A2wGd6k4RmMg9BENjSiFeHz4C7jhzk+t8g1E/hb1xXPV45byBDlGL8H6EigxfLrtb/n99dsm3oCXPM7zdGPO3RHF2Gn5bDHHraji7BTcvIj993RRdhpOfnkLfdS1vz27H9c/V6wo9BaoDebMGtXT4C3B/UEuOZ3mqNPOBQ2s4ip2T6ECXDdttubkx+5344uwk7LyScfA6O5HV2MnY61xx0G1Hr5HYGSVF7DAD9MN/CDcR1MZ3tQ76CuqampqampqXkAoiIRtkEUr0d0VnHmioN2dLF2Ch60E+CPfvSjPPaxj6XX6yGEwBiz7HyaprzqVa9i7733pt1us/fee/P+97+/Ou+957WvfS277bYb7Xab448/np/+9Kf3dzVqampqampqdlK0LrZBLHvt6FLtHDxoJ8BTU1OcffbZ/Ou//utWzz/zmc/k8ssv52tf+xr9fp/LL7+cRzziEdX5Cy64gIsvvphLLrmEDRs28OhHP5qTTz6Zfr9/P9WgpqampqamZmcm0oIo2uyl6z3A24MH7d8RJ598MgDf+MY3tjj3ta99ja985SvcdNNNrF69GoDVq1dX/wd45zvfyTnnnMOhhwYR1utf/3ouuugiPvWpT3H66advNc914xCV7PZhxHzhNJA5WNkILgFKeJras76IwrZprJnPIXcRnSi4L9w5kkzEwVXBeUGiPLkLSvs7RwrroaGgn8vKKWGjE4U7Q8R8JmlqTz9XOB/UvZH0DAtbq0iGiGdpYUelpa/syABm0qAKXtWAdcOINAl/Q41MUPY7D5vSlG6UYstoVlYyNBLjI5rK0dSzrBvFzKahDJOJZZMJdl0jIwp3gWDNNrYwl0XBL9fAqkYoh5aeTuTYOFb04tB2G8aqumZogqPAxrFir27G+pEmUZ4NY818YVFWqpUX8uB00M8FAwNtrZiIQz5jG17D2ZhOFBwMcgdSRDgvGEXBjWFkJLcPgkJ5/VjQ1rCQS1Y2XIik5+DWoWSv4FLEHSPB0EgmY5gv+iKSAiVCX1oPsZSMLXzvzoi92p6ZDH7dF6xqem5dL9mz41k30kQyOFVMJpZ+rsldiBzYz8P9AOtGGiVycq+ZTTW5C/Z5TR1cSDaMw5i7dbD4lhDU37JwrFC0dHCV2DQWDE0Ye8aFn3eOVOXK0dIwnykWcliReNaPBb0ojE8lQntOxJ6hEcQy2NalVqBE6JdyTIaxEyLrLeSwohHcSIxLaeoRsWzSSabRe3Tpji17pGPysaQ5McYZwaqBIt6ng1zRYPrAFKk9EwcqFjaCWtXEDwu7KqChOmRuREMV/rPj+VAZ5wt7qw4ihLgKTgtRMyjDG73g9mALl4OosP+SEtBByT+7AFLgvUeULgxS4Edjqo+xLIcYGAzD/1sNaLWDm4OzwXVieirkkeXBoUGrRXU6hDKowrZNxcFlwHnAhTTn+5CZ4EbRaoAxIQ1jCqcIA90JmJsNTgy6CbENCvk4Co4Gzi1xeNCoLMM7h7cjhNT48VxwzEj7wQlB3wGFywZak8aapD9E6QjRbON9kZ53od2dqVwOUBp8HNwxFIhxP9jADTYiWqEtPFQOExIVLA6bXdqFG0Kk2iA02oPPBsSdlaEOzR4IiVARzmdMNyzzmWL3tiArInNNxsFqMBIJPu8X7Wnw6QJ0WigRBb/nRgJxK9TbpIVThAz9EbcgI9RL6ZC3ycInr8lAhjSJo6KvQrtix5Bl4VgUgXe09ERwr2h3UMIXdmAzOG9R7RVk6c04bNEWwV+8EwWnBimCO87IyODi0M4ZG+DwDezV8XQj2KWZF+/lkoaG20ewMYV0QXLoKsuPNwVXh1vmFEfsarh2oyI6qHxPFEzF4bMDwmfJfB5cdBoKcpfTixVaxiGCqcpIio2tzlukKJ4Vm0E+ph1NIdI+3qbEapKJeDUN1SV1A5RIQAQnGCskykPmwvuBxxHJFlrEtCOBcY5I+yJPWblEDPLgNGN8yI98HMZ/HIEUiDJsHEXfjftLHGBkcJ6webDvy8aIpBjjblEz4a0PbhALg/D/4pyIFKKhEenyb523hY4EWm0mgrsXLhI12+ZBuwJ8V3zlK19h33335Y1vfCNr1qxhzz335IUvfCEbNmwAYG5ujhtvvHFZdDetNUcccQRXXnnldivHL354Da/7g1fhigfn4//+BV762Bfx3EP+P7731at4x999gPdc8Kntll9NTU1NTU3NA4fN9wDrSKCiegK8PagnwFthw4YN/PznPydNU6699lquuOIKbrnllmpld35+HmCLEIRTU1PVue3Be/7+fTz9xacipWTDHZu46PyP8Rfv/Cs+/NOLeORJh/Pcv3w6/33Rl9lw+8x2y7OmpqampqbmgcHmk9/yVfPb86DdAnFXlMK4N73pTbRaLdrtNueddx6PfvSjGQ6H9HrB8Hx2dnbZfTMzM+y+++7bTPezb/0YKtJ8u+F52KMP42GPefg2r73q2z9ndsMcD3/8kQDccfMGvPfsffA+1TVTK3sc/bhD+Mz7v8afvfIPt5FSTU1NTU1NzY7mkh/9mi99+Vf43JLewy0Qkdpyz29EPQHeHtQT4K1w5JFHbvW4EGEf38TEBPvssw+XX345xx13HADGGK666qpt7v8FeOpfPou43eLwaVPsAd52GS77whUcctzDkErytY99g/983fsAeNGxf4oAPnfNfwBw7AmH8tELv1hPgGtqampqah7AnHzknpwYJbh+xtzcmHf93y13e4+OgvPDsmOingBvDx60E2BrLXmek2VBQJGmKcYY4jjmGc94BrvvvjuvetWr+Kd/+icGgwGve93reNKTnkS7HVRMZ599NhdccAEnnngi+++/P//wD/9AFEU84xnP2GaeYwvrB4KHTYWwuLePYHUjCIbKkLEbxjCVOK6+6kYOP+loBgaO/8MT2H2f1bz6j87n1Ze+m9VtyXwWxEsr99+bm6+9nfWzGZnQNFQQL0kRREWTiWWQBxFaJ3IMTRB7NXUQHs1lgtVNx8iE3TDG+Sok8sDKSgQ3mVhyV4ocgigpkoJEiSp8bj8XISSvUTjvmM/jIFoqBFmR9IyMpmlDfrOpQhdip1IEeMtAFQI4ipC3kDmYz4MILneChoJu8RVQ7oJooyxXU8HQiCqUdFM7fjXbYFXTsH6kizYI4jolQj+U6c7nId/JOPy+kAcRyGQMIwszWRB6zOcwbSXzWQiBnKggppvLQh8v5KEP2hpUGkRkmQt9nbmQpvUhvdJcPivKvzEXNJSnoYKYDqChPBtTuH0Y6j62gtsWJJlz9CLNVGIZGoEUQeRi/WK5F/IgQhkZyW3DiE7kuGkhhCJtKCqhoitEhwt5uG/9OJTxjqFgMvFFOwVx3x0jQSSh7YMg8OaBKIQuob+sh1KzMTQhzYUcuhFV30ZGcPNAMBV7BkZW15cizoU8HJgPuhyUgPlMkqqy3wVSpEx0NWrPNUSNhCnWFU9aEIK5uZToIdMACCmYcpvQe0yw20HrkZ0OvhQdeUdDdTE+Q8sYn/YhG0OnCeMiPK930OjAaB7R6IZwx0kLskEQ0OgexA28t+EYBEHPcFCEGVYwTvGAiCL8aByukbKI5y0LcZQqBGc5dHUIQ9zqBiGOLIQ5w3G4xlh8f31IpwiljG7ghUCUgjitFvNvNSFbCPXSCgxBtNNIQojlYQbNZiGOK0LAdqdg/R0UjQiZCeIxXYSsdQbMOIRhzgZBJJaPgqCpFMuVoZ3jiEgm0F1Nf3Ad06NRUS+9eK2QiyGYi/DO4Y0pg6Qb8iryEJ1VMHc7KAeDTYjeakQpuLOGWDYRyCA+TAehjHiElAjdxGd3wtSeSD8mkg0iOUIQxLhjG9x8erENYXrtpjAOynplORERxO3wBHu3KJAiCPdEcwpvRmEcSF2I4Hz4fz4O/diIFz8gpAhxZ0rBXJ6HdnMO/Gx4q5AS0j7d5gTWhXDMUigwY5TQSBQSxXTDsKads2kcwt1DeA8fGklDwc398P50xJqcXZvh+VrIFZOxxTjBZOzpRTCdwLx07N6CO0fh+VzTs/y6Lzhud4OWMS0dhHClULqpQ1j0qTiIlacST6ziMHyFCuUUaVV2Kcr+NqFdpKbRWIm3Mwid4LxFywThPYlsh/DHukG5CGp8hpIROMJPIFFtFvINWC9IivDKoW8VIztffe5EsgGyEZ5tN7P4TMYRJEno16gRyuZl6JdsgB/NhX4dj8Lz4jyiGeH7YT7hxwbRUCEscpYv9nEcgRL4scGbexZkSGqP1ItBSC7beCdfL5/Jmt+KB+0e4A984AM0m83KDaLT6dBsNvnmN79Ju93mK1/5CldffTUrV67k0EMPZc899+R973tfdf8555zDGWecwUknncT09DSXXXYZX/rSl+h0OtulfP25Aa1u626vK69ZmK3t12pqampqanYmpPLLXo9bvYpzDnrIji7WTsGDdgX4jDPO4Iwzztjm+Yc85CF85Stf2eZ5IQTnnXce55133n1QOuhMtBkuDO/2uvKa7mSH2fukJDU1NTU1NTU7AiE9UvotjtX89jxoJ8APdPY9ZF9uu/bWu73upl/ewm77raHZbjB79/Plmpqampqamt8RpFq+BQJA+v+fvTePl6MqE/efc2rp6uX23bIHCEsAUYKJ7CIQgTEiygwwio6gCD9niThuoOOMIwguo/JFnGEZFVEGcVBHHReUuLGpiIABARVkCUsg2829t/v2Vss5vz/equ7cLCRotgn15NOfvl3LOafOOdWpW7fe5931LoAfe+yxLdpOa82ee+65VerML4B3Ug571SFccf7nMInhuZ5UWXrbbznkLw7Zfg3LycnJycnJ2S446ePjk5btete/zJ07VxKQPAfWWorFIo1GY6vUmV8A76Qc+PIDqQ5XeeCW3zDjpI1f4I6P1Lj3tvv51I2f2M6ty8nJycnJydnWZM/+Tlpmdr0r4FKpxIMPPvic21hrmT9//larM78A3o6s7Si8EqxoOngaxkIxHwSORL43Qomkz1Lgvulf3sJ1F13Ly45/GTPmv5j//uNXWN6UiN1Oorj2su/y2rNeBUPDjKRR+yMdKXe4YFnbUYyHLtOKCWs7irUdh6qXmQo0nhbzxGhH0gZrJel5HSVmhMRC0VGUXHimIQaFzIZQdi1lV1IjR0aW+Roi45BYxV59HUm/rBVjHTElZOmIQcrL7AlPTEiK3KontgCtpB9WpRaE4YJlLJS00e0EZpcsq9sw4GsaMYym/daIJXo5cCTYOrHQTjQVz7KyKfWNdRzaidgFxKggxoFGbGknYjdY1Zb00pmZoB5BLVKSEVZbljcUVU/z+IRiv6plPJQUysMF2efRmmKfquXJCXkHWFZX9PkwLbAMF2B1CzwHWolU0k6gAZRdSyMWO8SqFgwUxJBR9SxrGppiwdDqaCbqPq7XZkULSq78hWBNW3dTWSc2S2UshonA0URG0YgkXXQ7gRlF0IliIlKT9vvtWknJXIvkeAJX+t7XtmvbiNKxWNWWtmUpl2uRwtdijQC6761E7BbtRFIlP1qXORSanvkhq78WyVwQS0V6bsSwuuUyHCR0EsXUYsxwIWEsXMHwbruhqmW8wO9FcMcJrBmFagU1PIAeqOEZizOrHz+x6CklbCeWCHsToxxfIsI7TYnm9wNJYVwKeulrlaRCte06qtjfTcFLoQStmkSMtyckYhzSCPE0lXBoQOtuGmIF2JFRKAYS6b92HEoBtt5AFTJ7QygR5mFTXsUidDpiDojTOux6keRZ+tZCBVrjqbkhtUZAaqMIe9vFiZTnOuC7qUki/W8hjECnwbWBP7muzM6QxN1+QWsxHGSfM6ODsfKutaS91T6DhSoKB1tfKfVlNoioLX2fpp3G9VFuQaLuIU0fLNYI2x6XCP04xHbqqE5FPrdroF20mx5XOgeV44G1oFQv9TLyn6rWTmrGaadp1Bt4upe6VxUHsc2RdMzTPslSYBsjbcqWOX6a1jdGOQWs0lJfkqbatUa2CXzp46DQS6Grda9Ps1TVxkh5YROKAzD6LAMFF08HGJtIOmnHp6DKKGtxtIenA2IDZU9SPA8HMZ1ETDxlVwwQM0qWpyYUs0uWWiTndys1/2TfG1OLlr7UfDO7bNP/FxRFR1Kch0kIeFR9wzMNl4ECPD3hsVslYrQj6eoBwiRkqOCilVgvNGJlUMicENNG7xyj0+ymyPZ0Qc4vFaFM1jchulglsTGu8jFIGSqJwLbxtMuAP4NWUsfYBIXCUS6OcnGVT8ERM0PJ6ceOPSvnbZygikHPxJKdR2m7bNJBuX5af2pxaXd6qbsBFYjRQrU11liUoyZbTcJIvtQ8B+VumYNgfQsE7JqPQJx88snMmTNns9u99rWv3Wp15hfAOzH7Hrw/5//Px7v/V63PmR96CxXP8kz+7G9OTk5OTs4uhzwCMfmCd1d8BOKrX/3qFm33la98ZavV+YLVoOXk5OTk5OTk7MxobTdQoa1vhdiaWGt55zvfyeGHH85hhx3GpZdeus3qei6S5DkyhW0lXrAXwDfccANHH310N+1xnP1JcT3uuecePM/jFa94xaTlnU6Hd7zjHUyZMoW+vj5e+9rX8tRTT22Ppufk5OTk5OS8ANCuxVnvtf4jEVuT++67j6VLl3LnnXfyy1/+kquuuorR0dFtVt+mOPHEE7d5HS/YRyAGBwdZvHgxrVaLc845Z6PbtNttzjrrLI499lja7fakde9973u5/fbbueeeexgcHOTcc8/l5JNP5p577kFv6pmFnJycnJycnJwtZKPPAG/DILjddtuNQqFAp9Oh0+ng+z6FQmGb1ffhD394g2XWWh599NFtVmfGC/ZKbdGiRbzpTW9i77333uQ2//Iv/8Lxxx+/wd3fdrvNl770JS6++GLmzJlDtVrl0ksv5YEHHuAXv/jFtm56Tk5OTk5OzgsAx7EbfW2Kzf1121rLBRdcwKxZsyiXyxxzzDE88MAD3fXDw8MceOCB7Lvvvuy777783d/9HaXS5rPS/qlcddVVzJ07l3322af7mjt37jatM+MFewd4c9x22218//vfZ+nSpXzqU5+atO6hhx6i1Wpx2GGHdZdNmTKFvfbai6VLl3L00UdvtMzVdYeSoxgNLb7uRciPhfJzYsUKkS2PjETVZ0aGcvpboKNgVZs0Ul+RWInYjQyMdMTGsKyu2Ldfonm1chjpwLQAnm1J2WVXrBOOEhNEI4ZnW4oBX8pwlMVREsDdSuS9nWhGOgpjYXVLYYKeFSCx0IzF4DAcWGqRw0Sk0coy2nGIjaLsGVY0HWaUepYHX5NGIEu0fy3KrALyvqqlaLvwyIhDuZgw6EtbG7EisWIVCBxwnCxXvSJwYNC3qbkAQqNoJ7JuuCCmjEYs5oN2As0IPK0oezDWgXooZgbPgRVNsTskVowMoZHx0mm/PT6hGPRtd4wiI31bi9J+Sk0WgSvjN9KR9j497hDHmqAYs3uf5clRh2l9CSNtRdWDSEvf1ENpY1JUxLGmHmv8QoLrGiZqPmG1Q2QUK1rSJhCrRTuWOgNH+hAsVU+OWStpw0hHjjubN3uULSMdReDC42sd9hpKGOvIMRsDxpV5oZXUMbtku1aMPl/aGrhSlqct9VAxtdj7on62JeaHJJK+WNVSVH3wtPQfZMYHhUmjnMXKkc6FNrQTh7n9Ea1Ys9J4zCqFqJkHQGUNVAclQtsaiRxpjHUlmqpawXEdlOfhpEYCpTsS7d2qQVAh0AGEY+mEjmF4UKwCgG2NQie1FVgDoYdNol5Ev52ARhOiSKL3RauSfjkMQBihSgFUq9Imv4GKY4kg9z0wBltvyHsnRIVK7AATLfA97KplXZPCBlGxxoDndS0NqtOAoNIzD5jUJa5Vz0rhe2IhaKcmC9eVctupeUJr0KmBQGtZH0ZiiuhMiFjBGulvx5dI+cyKkRkiXF+OodmWcpRGKY21lsDpg5IPtWelHO32TBup5UGMFD7WJtJn61ooHD99721rx5fL8tQUoCpTe1YBa7A2SS0CftcwgdZo66BQuFpMIK5u0EkURdeg8FBKy/FFbTkWN5L+i9rYidXSZ4UYm3SkTVasITbpyDFk8wl6cyIJe2OvXVBpX4ZpGe1Was9Yx66RjbvndY0HWjm4BuzEs/jVqRA20YUyrvZJbMhgIftOSSgWxXwwVPAJXPme6fPl/Bbzj2bATzAuzCh6rGjJ+Vd2YcCH1W35P6QWWaYFMBZaIqMoewlF1zBYSPC0ZVY5wlioeIaCY5mIdPr/lkXj9H5WDkopXF2QPu7O5xg6YtqwSYTqpBYUx8fGLZRTEINHHOJoF8IJMX5YOT7iEJImRS8g8GcR25DERrjWx9UFSCaoeCVqYRNHyzgShz3jRnZ3tWsyCXpz2qbnQzbXgwJ29drJ55Ax4GiUAzYyqDjBdhKUo7BRhAocVMdBKY8t4fneAd7cX7cvueQSrrnmGpYsWcLcuXO56KKLWLRoEQ899BCVSoUf//jHPPTQQzz22GOEYcjRRx/N6173Ovbaa68tau/z5YADDuD4449n9uzZk5YvWbJkm9S3Li/YO8DPxcTEBGeffTZf+MIXNvpbSK1WA2BgYGDS8sHBwe66nJycnJycnJw/B+VqlLfe6zkUapv76/aVV17Jeeedx7x58ygWi1x88cWEYci3v/1tQO4QDw4O4rouxWKRYrFIvV7fJscGcOutt25w8QtbboX4c8jvAG+E8847j9e85jUcc8wxG11frVYBGBsbo1gsdpePjo52122Me665Aa/g8EhgmfeKgxg8+KVbt+E5OTk5OTk5OyVL7lvOkl8+ge0khInZ/A4AjpbX+sv+BMbHx1m2bNmkv167rsuCBQtYunQpZ555Jn/xF3/BN77xDV7+8pcTxzHHHXccBx100J9U35awuexv25L8Angj3HTTTYyNjXV/A2k2m0RRxJQpU/jVr37F/vvvT7FY5K677uLkk08GYM2aNSxbtowFCxZsstyDz34jpYGAecMGX/ceR8jJycnJycnZtVn00tmcMLWfZLxDrRPxubue2Ow+ylMob/IFrzJ/2kXjlvz1WmvN1Vdf/SeVvzU47bTT+OY3v7ld6nrBXgAnSUIURYShPMvT6XSI4xjf9/nVr3416cHxSy+9lJ///Od861vfYsaMGbiuy9ve9jY+/OEPM3/+fAYHB3nf+97Hi1/8Yo466qgddUg5OTk5OTk5uxJpytYfP7Kanzy6GmDL7x6vx7p/vV6X0dHRjT6GsCN45plntltdL9hngK+77jqKxSKLFi0CoFKpUCwWue2225gxYwa77bZb91WtVvF9n9122w03TRN66aWXctRRR7FgwQJmzpzJmjVr+N73vpcr0HJycnJycnK2Csp1UJ7Dqw6YwadeO49PvXYeFy168Z9UVn9/P3vuuSd33XVXd1kcx9x7773P+dfr7cn2fCTiBXsH+KyzzuKss87aom0vvPBCLrzwwknLCoUCV1xxBVdcccUW19lXSih4ltUtxUDBdi0O65oUfK0YCyUyHmCkLVG4gQvtRNGIACxh+ieQqie53Zc3FVEi5oJlNcWsiuRq1woasdQ10unV04hl/7IrtoQsqr8Rk5oVxC5BLO0IjWK4ID9r1TNYiF2h97nswvKGRPpORJrIKJqxGA1G2prEktohZN8smFUrulHHq1pibQjKpH2gKAYJnVixIpao5YGCGAPaCTw5IeYCEBNBU0HRkbJGQzEXVD2JdF7ezNoo+w748MyIz6zhkFoIZY9uPzYiaLdcnCCmnUjZYx3F1GKv7cMF6TM//b2nESvGUgvCQIF0vGT87h/RzBs2NGKFX0jQ2mISxaqWZPYxVtqYWDFaBI70y0jdZcCP6SvHNFoOYcehvz9k5Yoi9VAxFlo83du3HUOfb3lyQjGzpLrWjNUtGCjY1Poh/bIuTzZUtw9dz/BUvTdH2onsu6qlmFa0NCKZcyB9tTp9nMdzZM4OB4rhQOZ6n2+7hojVLbF0rGqLXePZJpRdMXDIHLI0I5nrjpL5XXR6thFPw7MNj7JnKDiGyCjgEXy/iDdlKtZavDSqPJiyDzTHJNo/bIrRodVAVUrY8TrUJqDdEcND1EIFfdjGCDTrYlVIDRIkMax4GkpFaLZAa2zfhESEtztQKkuUeLsjEeBhagkIE4ksL1ahrKEtke24PtCQCPKW+MVto5lG/TtifQBYuQYAFZdhrA6VkpQ/0YRSIBHnY2NSH/TMCVFbjndc7hZJZLuBNmJ+mGj2jBBd44OCdlpOGEs7II14T3rlxElqwWj2jiUzb2RtALEWhE3pkzBK1SMFHFxQ4GofEtMzP5h0P8eVcVK9SHulHKx2e2Wm9Sm/DxuHoI1YFRLk+DMrhInFCqE0yvFk37AJgYttjUtdzTEG+mYA0K+mE5oWvi7iqhqu9im5AziJke3Ddu9YANselza5jtSb9QlgoxbKL2NNQ9bFYWoIsGI5MEb2ixMg7J2ExohJRGvkgEjnUwhJjHILWGsou4NgNRVvGNK/Yrq4QIhSCk8VmF5sUnAMzVin54vm2aZ8Ue1Vke/9smvp8yw6/S6o+gkjbRdj5buxHon5oeIljIUOgdP7rh/wpSsKjnx3FV2xQngaXG3x0u+0kmu68gutZF652qJQOMoTGwSqa/SwNkEpR+ax62PrK9P50EmnmEFl9o+ohg0bMsbFfllmjCyLW6h2Dc/x8Vwf7VXRyqHoVolNiKcbYqUw6xg6tAa/0PtZa2mLicU+kY2RX4LGWphoohwHyoXeHNYSqGaNlUcXtEJPqUCzjSq4qJKHNhZtt8zlmwW+Zfz4oZV8//crNrn9c/11W2vN4sWLueSSSzjuuOPYZ599+OhHP4rneZxyyilb1J5diRfsBXBOTk5OTk5Ozs7M+hfArzpwJkfMncLn7nh8o9tfd911vO1tb+t+rlQqANx8880sXLiQ8847j3q9zgknnECtVuOQQw7hpptu6m73QiL/e31OTk5OTk5Ozs6I68if1tZ9ZX+h2QhnnXUW1toNXgsXLgTkEYOLLrqIFStW0Gw2ue2225g3b952OpjNY7fwzvjWIL8DnJOTk5OTk5OzE7L+HeAfPfgs3//t9gsU297ccccd262u/A5wTk5OTk5OTs5OyPpJMBbNn83HXj9/RzdrmxFFEYsXL6bdbm/zuvIL4JycnJycnJycnRFfg++s99p1L908z+OGG24gCIJtXtcL8hGIG264gSuuuIL77ruPer1OFEVdvdmdd97JRz/6Ue666y6azSZz5szhve9976SHygGuuOIKPv3pT7Nq1Spe9KIXcdlll20yc1zG48sq7LGflTzoWqLcQaJqq55lrKMm/Ury5ISiE4sxInBs19zQiMUGUfbEItFO0vWRmBsKrmV1C6YWYdC3lFx4tKYYKEDR6UXyg0TlN2Koh2KMKDqWKIFmBLPLthu9GziWsTRY+akJiQAODandQdHnS0TxiqZiRsky0nbwNHQSySFfcsWc4CgxStTTgPNapGjHYhhIrNgf2onkqH9yQuwRK8Y8gqJE6hZcS+BKLvknG4p6KGWu25fDgbTJc8SIMK0IyxtiI0isWDbGW5r+okTtzhoOacRih8hMD2HHISjGGKP4w9MBpXLMjP6YRgxr24qZqQkhcGQ/rWB1W45lKJC+rYU9y4VnIY40Y6HtmiFczxBHuvvzqppLuRQTGWg05WeAcilmpCPHGMcarS2jowXiWNNOYEVLxj1wes9ONWLFcCDHHzgynoEr/e1rGTtjxJ7RiKTPxQ4BnVhhEunQYsEwGkLVg8frirIrfdznw9p2z8QRGhj05XNWb2KzdvXm9FhHMRTY7n6dWFF2bdd2kjiKZSMeew5HjITgKEXg9MwVj9bFvLFfwdKIHGqhg7GKxMYM+B1aicbTE2hlmVVyKRb6sIRUSgMSRR6HUCyj2h0xLcSJRJsnMbYzIT+P1cWWUAokgr/RxD67Wj5PNMF1od6QAwojMKtRfRWZBJkpwXV6keSZHaDRTCP/Q7EvtDsopbBaSZnNVhph7ok9YR2loq1PoFx59s9ai2qHUNI948TIqLSv3cKmNgrbaKE8D3xXJmJmi0i/64gTqFbElKDTk7vZxjaacjxx0rNaGAPtdELXar2yAh+KRYngNyY1OBhQWiwJGVpDsw2dCVAax8Rix9iYQcIaiczXYoKwTgflFsDEWBNJXSBmhjhcx6rgy75K99pDCG76H6lfEmtD1h7E1uDatJ8nVuGXh/CdIqXQQhRDe1yMAo1xGTPXgaAgxz+6Mt2vKXXZ9FUU16q1RuYTpF8EqY1Du2DC1Cih0zmS/pzNH+hZRYyBsNPrT6Vxx1ZhATduiyFDaWjX5L1VQ3sOw0GcGh0UjUjjqISJyGWgkFB0FZFRJFZTcMTSYKyc861EM1gwGKsYKkBsFAXH4qemmew7wFGyrhVrcA1Tg4jIKqpeQi1y6CSKqp/QSTSO8ii68n3m6yL9vkPgVFBK4ygXbVXPCBJ3sCqdA2Es/ZXOBZSWcYzaMseilpheHBebhJPsIYCc414AE238Yj9oF6dYxbhFLAYVtmSc/ACqWf+HMoaOi3KLMu/ijlgmXB8Vh9isXXGCTRI5BzObirGoMP1SXfdZ3XTOOdNFb+QOFdgSslTIGUvuW87373l6i/b9v8qiRYtYsmRJV1O7rXhBXgAPDg6yePFiWq0W55xzzqR1IyMjnHbaaXzxi19k6tSp3HLLLfzlX/4lg4OD/NVf/RUA3/jGN/jnf/5nvvvd73LkkUfyhS98gde85jX8/ve/Z/fdd98BR5STk5OTk5Ozq7H+M8CvPmR3Xv7i6Vz1o4d2YKu2LVOmTOGv//qvOfnkk9lrr70m5Ve46KKLtlo9L8gL4Oy3iltuuWWDda95zWsmfX7lK1/Jcccdx80339y9AL7yyis5++yzOfbYYwF4xzvewdVXX82Xv/xl/vVf/3Wbtj0nJycnJyfnBUL2V4L1l+3CPPDAAxxyyCE888wzkzLDbe0kGS/IC+DnQ61W48477+xe/ALce++9/O3f/u2k7Q499FCWLl26nVuXk5OTk5OTs8viTtaeLfnNU3z/10/uwAZte26++ebtUs+u/WvEn0kYhpx++um86EUv4owzzugur9VqDAwMTNp2cHCQWq22nVuYk5OTk5OTs8viOL2LYNdh0WF78rG3HbGjW7VdeeSRR3j88Y0n/vhzyO8Ab4Jms8mpp55KGIZ873vf6wbJAVSrVcbGxiZtPzo6SrVafc4yn73piyz7fsBgOeFFRx3Ei18xj0acBXIpSh6MpcFOY6EEHFU9y7IRlz0GY0ZDCTBa1YI9KpJ2sp4uG+tIQNNIG1odzVDJUPUk4K3sQdWXOIwnJySVbyOWgK1GDM1IAtckjbAEj0WmF0hW9SQAwtcwGsLsci+4aqQjgU4SvKbSADXS9LsQGUUtUiRWgvxmlCStbWgk4K0ZSVrjyChC0wtoy4LbtIK+StQNCpOgO/BLCmMkGMsYCeRqxBKsVQulrVECtaiX9rkWSeBh4NANgBsoWP44pplVsRgjwR1aW8qlmCQNXBua0qbdkvH3NYxOeLSHOkRG+nOfqqQKHutIMFhkeoFf5TR+IwvarYfQSiDsOEzUPQaGOjRaDoPlBFNIqE949PdF9JVjjJVgOGMUcaSpDkgq0GbDpdIX0W5JJVmwXeQophYtbUfqyYIUWwmksXbUImnfgE93vgwHdIPsAFY9W6JUjimVI6peOndrDsUgkTbFiuUN6etSuv7p1QXK0zqEHUmfmgXKrKo7jIUJU4vSPwMFy4pxlz2HY0baYBLFmoZiesUQuBJYN1SNWNWGGaV0XCMJBM3SKbcT+PlKl2mBBMc90/TYvWxZ3ZKAn4IjY9tJYnYvjxC4FerU6Oubio3bEhTlrukFHbVbMtEcF0bHsaMSbKT6ypL2uNXGrqmjqhFmrIUupUFYI+v8wtvuQFBADfb3Ut76npTf6fS2yep0HSk7S3vbTAPlssCz7OfA76bMtZ0QWkYC1UoBqp2WpTW2E6KMxXY6kuI5rceWiihd7tY3KbAtTFPAqjQIqx1Ke+JEygGUVrJvsy1pm9c9njDqBcuVgjTFM720wHEoAXe+7R1vY0T6OQ5RcZhuG0k66SyITLuS2jgjCcEtSLpbtwhpOl0bdyYHvWU/Z8F00A1Cs0ZSJFvo1Zseu137RNoHTaivlKCp7DiyFMZhlKapTk+kch/UxyUgLu0LgoL0RdYGE/dekKZBNpISO+urLMgtTuR/Y9ftpao2VgIHjUkDL9f02tZY2+1HCe7T2LHlqMpU8EtopSk4ZQYLDTxtqXgJrnaoeIaiY3i26TGzFNGKA7SSFMgFJ0tlbKl42TkkwXIFxzIcGFqxpuhKCvLs+6LgyPautug0DndaYNDI9/zUIEIpj6pfwtU+sQ0pun1o5eLpAtpYwEp/a1fGzEkD+6I2RA1JTb7OuFoTy/rOBIQdoCPbZv2jdO89CWXskwjCBrTH0eVhKsVh7PgzUB5Kx6AtY+OXJNit0Cft0Bqao1itUfT15pbrw7QpqLWjoJSc7yDnSRyjKrp33rkOhBFL7nmKH933DAQFwtY6c/y5cN1e4Gp3mdn4trsIZ599NmeffTaveMUruOGGG3jzm9+MUorrrruON73pTVutnvwCeCOMjo5y0kknMTQ0xP/+7/9uoOOYP38+d91116SBuPvuuzebS/vlf386o80h9pvVJnAhjVHNycnJycnJ2cVZtGA3Fh06B4b6qa2uceV379/8Tq7eMPObu2v/8f6HP/whl19+OQCf+cxnuOGGG6hWq5x//vlb9QJ41+7FTZAkCe12mzCU38A6nQ7tdhtjDCtWrODYY49l991359vf/vZGXXSLFy/mmmuu4fbbbycMQ6666ioefvhhzjrrrO18JDk5OTk5OTm7LO7kRyCW3PMU//LF7ZctbUfQbDYplUrU63UefvhhTjvtNBYtWsSTT27dZ59fkHeAr7vuukle30qlAsiD17feeiv3338/jz76KIODg91tjj76aH74wx8C8PrXv56VK1dyxhlnsGrVKg444ABuvPHGXIGWk5OTk5OTs/VY7xGIRUfN5cj5u3Plt+/bgY3atkydOpXf//73PPDAAxxxxBForWk0GrkFYmtw1llnbfJu7cKFC7ngggs2W8a5557Lueeeu5VblpOTk5OTk5MjKO2gHGeDZbsy7373uznkkEMASVwGcNttt/GSl7xkq9bzgrwAzsnJycnJycnZ6VlPg9Zdtgtz7rnn8upXvxrXddlzzz0B2GefffjP//zPrVpPfgG8HWlFij1ntAmNBIRPLfZSSwKp0cGyuqUIHIniHwogKEoq3Gy7vfosiZXUw2UXRkP5s4Axklq24BsasaS2HShY2oniqbqkoB0oSBrhagHGlBgcfG2ZWrSMdBTTAkstSlP9psHLWkka3Kov7V3VEnNAI5a0um3EuOAoaCRiGBhtS5sGfSmnFolRohbJ8iwt8uyy5fdjihklOX6QNMWVwHRTb2oFfkHSg2Ypl8c6Ut7qFvR5Ul7Z7ZkNIiPHn6WS7vMnp0sue9K/K5qKWRVLO+6VMaMkx5NY8IY6FB0RBQQuOAnMmRrSiGWMBgpQjyRdb8kDnZooGqklIzN7+D7MrCYELgQJVL2EsSDB0xA58h5HmlI5YtAXK0Or7aC1xXUNvi/H7/sJQ9WIibZmj92aNGLSFNawqi39O60ox+koMUB0YkU5sHgODDvSnowsbXG75XZTPQ8MdZioefgFzUjHsGcfPIv0TSuBtWOa3folCtnXlkfqDpVqL530cEFsIWUXqqUkTUUtyxsxzOiPeWZCUfIss/oMK5qKyMDqusJzLJ1QU/ANUSLjmKSxorXU9DGmJAJ9RKlu6upVbctwQdKFDxc0joKC4+AoS9ltMFjQ9LmS9lf5ZazvgVbYegMVJ2l624acmBNNbDOS1MSuC3GMGe+gPY1txyTtGNWMIbHET9fRfR5OYlFBG4pBKqlPUyG305TBmYFBW1kexj0DRNfKEIlNIfs5TlOsZuaG9jo2icDHjo6jpg6hQrBRJOWtWiv2h8SiPAeqFTFE4IObRqg3W5AUUQUfO15HNdvSF1HUsxI02+B7sq/jgCsSers2NR+k/wHbKEJBmlYZMR54kpKZOP0C8T0IihC2JSI/aktUfpYiup3aIlxHou6TUGwAjt9Lc5ym+7VxS2wB69ofklheJrUIZIYAm6YhjsO07xUEFaya6KXLzcrRutfmYtwrN4qk7eVSelK5adtcMVc4LgQS9d9NYZylP85SLzfbcmyZ3WPd8c3mhe/1LmrCSNLrrmMDUZkNQk+IGSSzXiSx1BeHUChhwwZKu7iFKkWnD60cAicitiG+LrJnn0ahmFZMcFSRijfGRORQcCT1sactZTehoA0TsUPZk3THAAXHUHAMnUTjaUvRNUzEmoqXUPUS+RzJOqU0JddQsBA4CoWkPJbvjCKxddDKkRTIWDFwmFjSTuvUohE2JS15NoeyNOE67M0z6BoWCNOxzOZFlvY6my/ZfHNcbGsclcTgl3vbeEWs0r25ZGL5AsrKMaZnl3D9Xlpma6DVkvPCGFRQgL6yzOl0nG2rhdpnj975HhRQjscW8QK8AAaYO3fupM/77bffVq8jvwDOycnJycnJydkZcZ1JzwAv+fkf+d7Nf9iBDdp1eEFaIHJycnJycnJydnq0mvRadMx+fPx9r9rRrdolyO8A5+Tk5OTk5OTsjDgbSYTh5JduW4O8F3NycnJycnJydkZeoM8AJ0mC42zb43zBPgJxww03cPTRR1OtVlFKEWcBGym//e1vOeaYYyiXy8yaNYsLL7wQa3uZ26y1XHDBBcyaNYtyucwxxxzDAw88sL0PIycnJycnJ2dXRbsbf+3inHjiidu8jl2/FzfB4OAgixcvptVqcc4550xaV6/XWbRoEWeddRZLlizhkUce4cQTT6S/v5/3vOc9AFxyySVcc801LFmyhLlz53LRRRexaNEiHnrooW5ijQ3qDCzt1C7gKBhpq66tAMQykFgxMwwHEtQ61oGw4zClkpCkqeSXN5RE2weWJycUw4HYEMquWB9CQ7pMiXEigT2rthv9P9ZROMp2o+v7/J6VITJQdKSMsgsjHYnSLXvgactIU1H1xBwQKTmWyIDjSIS+o+DRmrQvsVAP5RhrkdgVRppSrqMgsmJhmJ0F4lqYVgRPG8ZCGJtwmT0QM16XaNmhwUiMBal9IttnLKRrkQgcSzPS7Fm11EKYURL7wVgnPc5YzA3GQjOStjQjmFoUg0CUpNYGLeNirIzDQCWWYzbyOUoDtNuJmB4CR8rxtHyuRXQtFZ6GRtTr49FQyi27sl8t6s0RYxSN2DLgAyTodEyMEevEWEsTGcNQybCq5tJXjmm0HPxy0i2jFkqZZVeOA9cSuLCiKfXVQ0VQtDQi6Yt2AtOqMU5qkxgejnmmkOA5lqlFWNsWgwXIsRd8w0gbZpdhVUsxpWyoRzDo9/ojNLCmoaWdrd5x+lq2qY0VMH0hkTEMFGQuxpGm4CZMrxhGOrK9o2T7h0YcppRN9ziqnrxrBX2+pRnJcfX56fzzoBE7HDAAQ/1t2okhCVycYj+2Xe/J5VetxU4bQhkLsZgZzESIGeugI4MeDLCtCBsl2NT8AGDGOth6iPI0ZqwDiUX1+TgzErAWpX2JAM/u1MSxRO+3Q2yUoLw0qj2zAGQ/m/SkNLZnAVjXHuA6aSS8lvY2mqhyCeoTECcka5oorbDGovt8VBjJ9mEExorJoVQUU0QnhGYLG5hueV1jQfY5jrFBAWWM3ADI2hojnd9sQVnJsjVjUClBYGTfMOoZI9qt1IoxIWaFzDYRFFIrRpSub4ktojpNouvT6H0LG5oPXF9+Dju98tz0PNCm149Z/wMwIW+uL+YEY1NDA702hxEMDEjZWomNojgA7RqqPCz7Ky3GgLAJlQpMTPTakMTSf2GzV4YxYrsIfOnDzFww0Je2V4vVIGr3xlvrdNzS/mync0ErqFak/e0QJsakvCH5UrSuj8MQviPmCqMSQtPC0wGBU8E1QGMtVAbpq0xhpPMUiZUvIWs1fR6ExlB0DVrB2o5LZBQVz9KKxe6Q2N7/WwDPNOU7erdySDPW1CPN7LJhZUsxUNAkNqKgSiQmQmsHV/lo5chYZ+MIYl6Iw3VsC07vHMj6QmvwPHDMhss7HXnXocwXvyTlWQMq0xqVxDjRqYPro9xir27tyvbal31TYwl+CVXsl5+9ABUBysHaBDUwG+uP9qwUVcRkktVrDApQ1enYqCXLHRf0xq8TNsBxNnzkYRvfGd2efPjDH95gmbWWRx99dJvX/YK9A7xo0SLe9KY3sffee2+w7lvf+hZJknDxxRdTLBaZN28e559/fjc3NcCVV17Jeeedx7x58ygWi1x88cWEYci3v/3t7XkYOTk5OTk5ObsqO/kd4IULF076fP311z+v/a+66irmzp3LPvvs033NnTuXUqm0FVu5cXaeXtyJuPfee1mwYAHuOg+eH3rooTz22GPUajWstSxbtozDDjusu951XRYsWMDSpUs588wzd0Szc3JycnJycnYlHHfSHeAlP3uA7/1o50mD/Jvf/GbS53e+8528+c1v3uL9DzjgAI4//nhmz549afmSJUu2Svueix1yAfzYY49t0XZa624WkO1JrVZjYGBg0rLBwcHuuuxZ4I1tU6vVtkcTc3JycnJycnZ11rvju+iE+Rx5+P5cec0tO65Nz8G6sVJbwq233opSaoPlX/3qV7dWkzbJDrkAnjt37kYPeF2stRSLRRqNxnZqVY9qtcrTTz89adno6Gh3XTbAY2NjG2yz/m8x63LblV/Duh4W2PPwg9j/5Qdt1Xbn5OTk5OTk7Jws+dkDLPnpb0EpwijZ/A7Qyzy4/rKdlM1d2/25229NdkgvlkolHnzwwefcxlrL/Pnzt0+D1mP+/Plcf/31xHHcfQzi7rvvZu+996ZarQKw5557ctddd3HkkUcCEMcx995773M+/nDM4tOxQakbBJeTk5OTk5PzwmDRcQey6Jj9wHGpTXS48pqbN7uP0i5Ke+stizax9fan0+lMCmRrtVobBLZddNFFW1zeaaedxje/+c2t1r7nYodcAJ988snMmTNns9u99rWv3WZtSJKEKIoIQ8kJ3+l0iOMY3/c59dRT+ad/+icuuOACPvShD/Hoo49yySWX8O53v7u7/+LFi7nkkks47rjj2GefffjoRz+K53mccsopz1nvQEHMAJHpXQTXI+jzxA7gKNkGxEYwrQhROcHTEKjM8qAoeVByxXIw1lFoBa1EIvETK7aDxPYi8UfS9PTtNBB2VUt1rQ2BC+1EUfUsgdOzFYx0JDc8pLaFjmK04dAoJMwoSVkmjQbOIvZHGw5+IemaEuKoF2f52GqHWYMRkYGxWPYru7CqJfaB7JjHOoqSZyn3xSQWtLb0lWNW1R12608YDaGBGAUcJWWMdaTd9VD2zYwNmSEicKS9jhJ7gK/FWgFiLFjeUAwUukH+JBZKDnRCTVA0tBMpL3DkmLWSvp0zYHiqrnDSYO7VLTFstBOIkp7FoOqJmaMdp/YHV+bB1KIFFCMd8AtJan+QcgquJTIwXIDIyDi7aWR2aGC4LyZwAJKu3SFSloEClFO7yLrjn5k5+nw5yHYCT03IsuGC9NVwQfpy3wHDiqZirCP7ZXNioCC2jUYs7+30JkaUiL2iz5cyAwfG1haADmHooLVl9kDM6pZioGCZOqXdLXesI8cKUsdYCGNrA4JizHBfTNWDp8c8VsQJQTEm7DisjSxxLHMrKMa0Wy4DlZh6CFOL4Gvpu4Ij5XYShUP6p8R2DSaaMNQPz6wS80ExkL/sTDSx7TiN5jcQJ9hGBFpjowSMxWY6lnSi6IECydo2OjI4xojhIYv0jxNIzyHC9D+tOAaleqYFY8RI0e70bAhdE4Dq7TvUn5abftZa9ikG6c9igDATIcpzsO1ELBBaY7Um+51bOQ44DrbTkXqzSPr1MVasBVpjk3SgS2mkuzEQSxutH6EKBWyS1pexrsUAxEaRxD3bQnf9OuaLzNaQWR5ietYH48rP7ZbsG60T/Z8ZJ9Zvf1ae1qmlIT154xBqjZ6JIU7W6X8tVgfXgVIZHBfll7EmhvJQao+IIdQyn5SZPGZZ/2Rffdkx6nTMtZV+H+gDP5Bj6h6jmXwMcdztfxUU5AvEdaX+dL1tNMF1UMODEIcopwBhE6dQwndKhEkTR3m42se1Gju+HFY9jZoFyi/hOQW0dUhshKM9wqRFxSthbEJk2lQ9GfvIKIquYWXTo+ia1BpjaKHxtEWr3vnma8va9P8cay1aOWgcLJaOaaBxKDv9YkvIjgWkP227Z4VwHTFd+B62E6JCBf1yEwrHhU7cs5xk55Obfkn7rvRp2IGgKGU67jpl+yjliKkBxDyRmiOUWwBrsHEH5ZdRflmsEJ2Jnq0DUEG/zAfXR3lFKSOzWqSmEdsel3VBFeX42FD+qq28LTQ5bCzobSe6A3zkkUdy++23dz8fccQRkz4/3zu8zzzzzFZr2+bYIb24pc92fOUrX9lmbbjuuut429ve1v2cqctuvvlmFi5cyJIlS3jHO97B8PAw1WqVv//7v+8q0ADOO+886vU6J5xwArVajUMOOYSbbrppkwq0nJycnJycnJznxXpBcN1lOwm33HLLVi1vez4S8YLVoJ111llYazd4ZUqPgw46iNtvv51ms8mKFSu48MILJw2MUoqLLrqIFStW0Gw2ue2225g3b94OOpqcnJycnJycXQ6lN/7aSfjGN74x6fP6/t5LLrlkezbnefEn9eL//M//sHjxYs4++2w+9KEPcdNNNxFFO88zKTk5OTk5OTk5/+fJ7gCv/9pJWD+R2KGHHjrp8/N5/nd787wvgN/97nfz1re+laeeeoo4jnnooYc4+eSTmTZtGh/4wAdot9vbop05OTk5OTk5OS8stLORRBg7Tya49bVnm/v8fMvbljzvXyOuvfZa7r77bg444IDussHBQe6//34uu+wyDjnkEH76058yffr05yxn9erV3HHHHbzmNa+ZlHAiJycnJycnJyeHDYLgliy5i+9975c7sEGTWf+Z3c193hx33HHHn92mLeV53wEuFovsvvvuk5Yppdhtt9245JJLOPfccycFi22Kt771rUxMTPDP//zPz7cJ/2cJHLEP1Ce8NHpfIvWH0+Dl4UBsASCR+FVPov0TK1H3JQ9qkWJ22eJry1gIjVii6quemBR0akZoxGKXcD3J2x440Of3LAeBK9tPK8r2YnJQPNtStBLFSEdRdjNbgMIYKHtQDMRIMdaB2WVLK7UD+OlMKhclYni87uEoqV87Fu1YpvZH1CO67WlGihVjHoO+tE2OW46n7MLKtT6NGFzXEBmolhJWtaXdZVfKGC6I/SJwpL88p3eMmU2jHcPyMbdrjQgc2SczLlQ9MTyY1MyRbdOOYc6AYaAg9Xi61/ayB/1FQ5SIVaGRHlfJszRiMEbhOTAUSN8kVmwXiZV9o0TMCe1Evhx0OgaZbWIosNQbrvR1mI2/ZUrZUEznUTuR4y27cnxeGoEdOLJ9ZGB6xVDyepaN0EhfR0b2y0wcY2HahrS8rM1lt9cfmfygz5fl0OuvPl8MEFEiZY7UXWbObGGMYqgqj0c1YjCJIkz72VgYm3DR2uJrmNaXMDbh0gk1Wltm9MfdY8lMIJ6WOeh6hnIpxi/IfJtWjak1Hfp86Zd2ohjwoRZqWqktIrZifMmi7VW5KJ9Tk0D2Ra20wrYTbGYncNb5mtQKlepbVOBgo4QkDXe3UWqAWDeSv2t+SMAYieYvFCSSH3rv0LNLxIm8QDqpHcq7salFQPfaHSddC4LtxOD3TgAbJRIhDxIdH8cS6xBF2FZbyo3jXn2ZUSFrb1a/kZNDOU5vu4y0fNtoikVh3WPO2pi1PYzSOpNeHWGU1r+OrcH3JDLfml5kvtby2WTWDKfbrm67Xad7nN36srq7dcW95XEidoswgtqEfLZWljVTI0PYFhtA2BADwLpt6A6c6Y2LsWIF0FpMANb0TBuloiwPfPALqbUgTMtcx4Sx7vhm9gqtpX2ttuwLsm82dmEEnY7UaeLun8gVCk8HuNrHWgPNMTmOZhvbqUMc4igPaw0aB60clFIYm6BQBE6FgmMoe6r7fTqQnnNaWTqJppNoVjZlHrRiTcGxlFzDQCFhuBCjlEKh0crBYlBoHO2RKCPt1etYLaK2jLnWMibZXMrmltbQasl+YZNJhFFvbLPtvWDSOEq/pvs2x2R99kytJ4YTVehL550vNggQo4NKx7Q9AdrF2qRXpuvL/m76n0qhJJ91aoXILmDTeaL8MluKXe/fqxYdwsc+fs7md9yFeOSRR3j88ce3ernP+9br6aefzhlnnMF//dd/dZ246/K2t72ND37wg5stJ4oi9tprL2677bbn24ScnJycnJycnF0eQ4Ih2WDZzsLmPMCZavb5cPbZZ3P22Wfzile8ghtuuIE3v/nNKKW47rrreNOb3rRV2g1/wh3gT3ziE0RRxO67787ixYv5/ve/P+mZjV//+tf4vr/Zcj772c9y55138olPfOL5NmG7sXLlSv7mb/6G6dOnMzAwwJFHHsmtt97aXX/LLbfwspe9jFKpxF577cVVV121A1ubk5OTk5OTsythbLLR185C5gHOXpkHOHtlycKeDz/84Q952cteBsBnPvMZbrjhBm688catfr34vO8AB0HAjTfeyPXXX89nPvMZ/vM//xOAvfbai3K5zGOPPcanP/3pzZbz4he/mBe/+MXPv8XbkcWLF7Nq1SoeeOABhoaGuOyyy3jta1/LE088Qb1e56STTuJTn/oUb3/727njjjs4+eSTmTFjxmaTYeTk5OTk5OTkbA5jzQYXvMaaTWy9/dnaHmCAZrNJqVSiXq/z8MMPc9ppp6G15vTTT9+q9fzJ0WdvfvObefOb38zTTz/NL37xC5555hlKpRILFy5k//3335pt3GE88sgjnH322UydOhWAv/u7v+O8887jj3/8IzfddBP77bcf73jHOwA49thjOfvss7n88svzC+CcnJycnJycPxtjY4yNN1i2s/HYY4/x05/+lJGREaZMmcJxxx3H3nvv/SeVNXXqVH7/+9/zwAMPcMQRR6C1ptFobPUkGX+2fmG33Xbb6lflOwsf+MAH+MIXvsAb3vAGpkyZwhVXXME+++zDQQcdxL/9279x2GGHTdr+0EMP5dprr91kec0YdASzBqNuzMNwYKmHCl9PjnkYCmBtG6b6Fl8rnm3Ks/3TAttNP1v15POzLUWSpiQ2abreWpQGvjkS8DRQsBLMZiXAqRllAVCWx+tS/9SipNsNHFnXShT1UAKjSp7U3+dJcN2gD2OhSgOgIDCSGreVSIBUE9lWgq2sBI+lAVVtye4paZaDiNBIGuiiA7MHTJrOGCrVMA3CMgwHsLzRCxIM0pk70pbjyQLLvDQOJXCzQD2JCZo9EOM50sZSWoYErWUpiXtxLe1E1jlKAteqnpTfSNMYN2JJpzy12Kuzmj718+SEBMyFriVK6KYgLnu9NmUUXMtYpxdsNhaCURAhdfiFhEbLYY/BBEdJ27NU2mGa6ngkDQpst1z2GIwJTS+AMjuGLOW2p2Ws6qHsr9PgtsAFJ50PjUjauqqlusGUWcpiL00fPbUITQUjHUkznaVEDhJ66bWLMY2Ww2BZ0mLP6I/xNdSainYCYcfB9QxBMWYgDcpbMe7SV5Yv9oLfoRH3jmP6UEiYjk82r2SeW8brHqYYM7OaUPVsN7ivEUvAjlbQ7ztEpo0brxNENTQEA2sl6EorCVZKg7pU4Eiq0qCAaicoL31aLOvQqHdHxplSIlnZgHRe2SRBxbqXnnWdQC3lOthGUwLh1l2fBu/YyKDcdRRHxkC7g40S1ISkvO2mfG2HvWCwdB/bjlHZybFuymYD1nXTQLSOLK9WJNiLdYLK2p1eHeulY7ZRJP3TDUDTvaAtX0uQXJaWtt3pBm4RpMcaGwjjyV90voeNIknT7KdfDI7bCw7zAmjVJKioG8SUfnl2A8/Sydxs94LpsiC4wO+lpXZdeWWBcIEv9WqdrnNk3LrBdbYXqKZdCaTKxsSkwWbWSGrmLBDNdSaneS6VZVml1AvOywITjZFjzRz6SoOJpJ/jJE3ZnPT6v7POl0coQYy2E04uz2isTeS4jEGjUMrDVYkEgQYVSDoww0hq32IVZRsYEhzloVA4ysPYRFInax9XO8QmwdUOnUTSHnva4ipLbOX/lKEgZrgQ42pLZBS1yKHgGFztoNAkNkIrF9JAVEd5aBzpwyzwLevPLCV0UJB01Wk/KKXk3MrmlWt6/d49/nTOV0q9FNPrBhUqDa0GeJ4EDWZjm9VtTDcYjiRNY1yZ2guc80uSvjkrO5uncQi6LcFxcQsVOqA01kQSNFd0oDOBDRsSAGeNpNbeAjb2yMPO9AgEwL/+6792H0+YMmUKq1evRmvN+9//fj72sY897/Le/e53c8ghhwBwww03AHDbbbfxkpe8ZOs1mhdwJrgt4aijjiIIAmbNmkWxWOT//b//x7XXXkuxWKRWqzEwMDBp+8HBQWq12o5pbE5OTk5OTs4uRRYEt/5rW3HPPfewcOFCFi5cyDHHHIPneYyOjm5y+x/84Ad89rOf5fOf/zwTExOsWLGCRqPB5z73OS6//HJuvPHG592Gc889l/vuu48HH3yQ173udQDss88+3UdutxY7RMA7a9Ysnnnmmc1ut8cee/Dkk09uhxZtiDGG4447jmOOOYaRkRGq1So33ngjr3nNa7j11lupVquMjY1N2md0dHSjZoyM26/6Gp7vYS3MOfwg9jrioG18FDk5OTk5OTk7A0t+9gA/uu0hQNFpdza7PWz/O8AHH3xw97nen/zkJ1x22WUMDg5ucvurr76aT37yk5x99tndZUEQcPbZZxNFEVdffTUnnXTS827H3LlzJ33eb7/9nncZm2OHXACPjo7ypS99abMZP8bHx7dTizZkdHSUxx57jP/5n/9haGgIgL/8y79kn332YcmSJcyfP5/vfOc7k/a5++67WbBgwSbLPPofTqfSV5K/TG6/ZCc5OTk5OTk5O5hFxx3Iq199OCjN+OgYV37xJ5vdJ/P/rr9se/DlL3+Zs8466zm3ueeee/j85z+/0XWnnXban/QIxPZih1wAT58+fYvyQ8+cOXM7tGbjDA8Pc8ABB3DFFVdw6aWXUqlU+MEPfsCDDz7IwQcfzL777ssnP/lJrrrqKs455xzuvPNOrrnmGq655pod1uacnJycnJycXYfna4G44YYbuOKKK7jvvvuo1+tEUTQp2661lgsvvJAvfOELjI+Pc/DBB3PllVdy4IEHTiqnVqtx6623bvaaplarMWXKlI2umzJlyk79WOgOuQBetmzZjqj2efOd73yH888/n7lz59Jut9l99935j//4D0444QRAnn15z3vew3vf+16mT5/Oxz/+cU499dQd3OqcnJycnJycXQG7kUcg7HM8AjE4OMjixYtptVqcc86GGeMuueQSrrnmGpYsWcLcuXO56KKLWLRoEQ899BCVSqW73de//nX+8i//crN5HYzZ9MX4lqzfkeyQC+D/K+y777787//+7ybXL1y4kKVLl25xeZV1TAJBmsq3nUjq30aseGJMM6VsqPoS6T+jZEmsoham6WY927U9DKZpjbPHKcppIHAjkm33qMj2y+oSQZxYhZeaHgC8RBE4WarjXtrgPfvEMtGIJVo/Sy1b9WxqE5BUzGJKUF3DQNWTtnZTPA9G7NVnGelI6luN2B+yuqIEZpYsyxtifMgC1xuxFBgZmBZkdcu64YIYKVY0xZLRTmCPitS7rC7rHWUZLog1Y9AXS0LgiKUh67vVLXmfVhQbwuwy1FLDQjtNkVz2egaHxMp2WX/0+VA20o4ZJSCRY1vRVGgtfdPqaKqewVFynKtaqms10FrMCVnfayXHO1HzqVRDjFG0Wy6ua/ALCSuaYnnYZ0rMI2s12rEMBZIKO+srrW3X/NFOJK12YqWNVU/6y1g5pm66Z1dsIO1Y2rByQjOrz1B2LSuaMg6ttkPQl3QNDABPTYgJQit4ZkKxZ9WyvNGbj4EjfWi8hMCBelOlYw1xrBksJ0zUxBAxUncZI8ZRks44G6NGLONTD2UeZKYMLzWUNCMZk736LE+qiNllOYdCoxjQlulFQyvWlFxDwTF4ukjR+thwHCpDqYYkQM2Z3Tsw3YFOAT1QQPX5YoGollFxjDK2l2M7Maj+ANuO0Z6D7vMx4+nzfK4LaTpdpZQYDtZPMey6km5Xa+lErSWNcabtyCLZgwBVDKSMoj/ZAJFFvmfluq60N7HYKEFXfEnlHCfQbqbGhxZMpOljfQ/leVhSs8FEc520yQlo2zVLSHrfNJq+UurV7Ur7lZeaIXwPyiVorpXjanekP7K0wlk53f4W40JXbWSsfFFkaZDjUKL2/WByNH/WTteRsl2nV3bWd1m65a5pI13fPSbTs2esm7rZdYB0WfYfd9QWc4DrYFc8JCaFOEyXt8TGEUYy5o6TpmRO6/fD3rFmZYYRlNL6iXo2jw1SSKfv2pGxqk+kx5TOtThGFXzsuvtlhg+lxVagXZQxGCWWh1BZnP7pOP2zIWwyYSdQKKy1GJLUAuGilUNswtTe4OBpTTuJKTqKeuSglWVWHzxa07jaUgsdKp5hahARGUXZTdIhlfTSjnaJjMw1lf0zCTZqQdxBlYclRbN2e3aMOAHf7c2NLB13ag/pEvi9eV0pieEhiSWFeWZ20I6kJI7aXasGYSRWhr7p2JYEeSnHk21AzCPalc9+KTV2tCEOxe6gNLY93rVYWBBbRKuGjRNpR2q3sFmZ7Ra2b1DKao2xJTzfZ4AXLVoEbNrPe+WVV3Leeecxb948AC6++GKuvvpqvv3tb3PmmWd2t/vyl7/MZZddttn2tdvtSZnf1udPyQS3vcgvgHNycnJycnJydkK2ZhDc+Pg4y5Ytm6RwdV2XBQsWsHTp0u4F8COPPML4+HhXRfZcZJngnmv9zkp+AZyTk5OTk5OTsxOSWEOy3jO/63/eUrLncTencJ07dy7333//FpX552aC+/Wvf8073/lOHMfhX//1XznxxBMBOOWUU/j2t7/9Z5W9OfIL4JycnJycnJycnZDIKCKjuPUnD3L7Tx8EIAz/tExwmaZ1YwrX2bNnb2SPbc/73vc+rrnmGjzP49xzz+XZZ5/l7LPP3qCN24L8AjgnJycnJycnZyckTi+AX37cgbz8ODE1TNRaXPeFW593Wf39/ey5557cdddd3UcT4jjm3nvvnfT87/bEcZxuhrcbb7yRM844g/Hx8a2e9nhj5JngcnJycnJycnJ2QmKjJr1u+fGDfPojm340IEkS2u12N/is0+nQbre7NobFixdzySWX8MADD9BqtbjgggvwPI9TTjlluxzP+sRxzMSEBHh6nsd///d/c8899/DrX/96m9ed3wHejnRimN1vqYeKQd9Si1Qa8S42hd36JQK/ESv6fPnTRy0Ug0GjSdewUHQkAr7qkeZezyL+VdeyEKZB132pwcTXYnGYGsDyJgSO1DOtYhnwxcrQVopiaoYIHMuKlnwe9GE0VOxWsiRp8pLIiEViOFBEBpqR2B4yu0DgShu0EkOEp2EsVF1bReBbAkfsBL4Wu4VED0u7aiFd28TyhuKAsuX3Y4pqajCoh3JsgQP1SPoi2z6zSoRGTALtGMqutHtGEZY3FVEi5oeyl7bRh+UN6Vut5JUF5HuptWFqUQKK+3zL6pZidhnGOmJcCBxLn295ckLGcs6AYUU6Zo1YMRaKxSA0sGrcoVxMumVPtDXGN0wZDFPThKXgRhgLq1cHTJ3aJijGrGrB3CHD8oZYEuJI0180qcGjQ9llkpVjeUMxnJotIiMGiBklMUMkVsZ8bVOzW7/h6XEpS2t4vK7QChotB61tWh4sH3MZ7ou71oy1bcXufVb6oCCB7ivGXaJi3LOBaNi731IPpU7XNYw2HGYNhzRi6CvHVNPxGuvI9lFq5libBmwPB2LRyGwpZdd2LSiehpcMWnwtBpCSC1OLMQXHMOAnTAl8QhMRmQ7YWKK8HQ/bHJPBLPVJdLh2oTmGMgYbRiitUeUSBGJT0GYcjMFGBlUMIE6wgYMZ66ACB3dmGRW44Dqo1HRgk6T3bq1YIdZ5ByTyP4xQRQ87If9h2ShBOUoi3UsB1JOeWaDkSaS/72HHmyjfkzJ8D6pl7NN1VMkD30GBDEpQkG3andRCkVoTtE5/TqPqM1tDtswY2TezWGgtJonsZ9eRqPysnCBVyWglxoRWG+W6XUOCWAsK3fptFKFcB9sJpa3tjkTxx6kaxXElql5p8Pzez2G7Z2jQarKxIU56xorM/oCZvD57dx2Io8lZidx1jidOxC5gjNgs4hjadSBNC7tOn9lIxtGmZai4IGPX7ojlQSvseF2MGZPaEcuc8NIvojhJj1/3xsBobKcz2aQRRhDG3XrXNWIoryjz2XGlbFy0SVBKEyZNatFqPF2g6FYJ4xZVbyojnTV4OkLpIgOFmTTjMcKkRWIMnvYYLMyiHq0hNi1KrqGTKHynjKvbtGJNK5Z7aY7SaMewuu3h6YhOoqn6CbEJwYHExmjlSCKHsCmv0kDanyXo1MWyETal7yDt+6Q3N7rjRzqu6RyvNSBIDSIg75khI5tL6fnWLau+BkqDYvUAbBxK/cagTPqYgePL+tRKYpujk4wQ+CUpe3y1mB/G6ulYTsgcycanOz+TdO402RIiq4hs727o4cfN4yWHzOW/v3jLRre/7rrreNvb3tb9nKnNbr75ZhYuXMh5551HvV7nhBNOoFarccghh3DTTTdNUqBtT/793/+dRqPRrV9rzXXXXcfXv/71bV53fgf4Objjjjs47rjj6OvrY2BggJe//OXd36J++9vfcswxx1Aul5k1axYXXnjhZjPb5eTk5OTk5ORsKbFVG9wFju2mHw8466yzsNZu8Fq4cCEASikuuugiVqxYQbPZ5Lbbbusq0XYEL3vZy5g+ffqkZUopTj/99G1ed34BvAnuuOMOTjzxRM466yxWrlzJmjVr+MxnPoNSinq9zqJFizjqqKNYs2YNS5Ys4eqrr94iZ15OTk5OTk5OzpaQPQOcvW7/6YP8+8Xf2tHN2macdtpp262u/AJ4E7z//e/nnHPO4S1veQulUgnXdTn88MNRSvGtb32LJEm4+OKLKRaLzJs3j/PPP5/LL798Rzc7JycnJycnZxfBWDXpddjCefz9P//1jm7WNuOZZ57ZbnXlF8Abodls8stf/hLHcTjssMMYHh7m4IMP5pvf/CYA9957LwsWLJiUX/vQQw/lscce26nzXufk5OTk5OT83yFa7w5w9tpV2R72h4w8CG4jrF27FmMM1157Ld///vdZsGAB3/3ud3njG9/IrbfeSq1W26hIGkQ0nbn2cnJycnJycnL+VCIjr4y7brmfO3587w5rz65EfgG8Efr6+gB5mPzQQw8F4NRTT+WVr3wl//u//0u1WuXpp5+etM/oqEQGP9fF78+u/BrFwEMD+x81jzmHv5ThgtgWQiO2gnasaMRiLwgcSw2Fpy17VMQi0E6DYKcGFldbHGXp8xzaiZgBak2HxCbsUZGI+UHfEhqJlvd1L+DZUfI5Wz5QEEtEyZUA1rGwZ28A2KNsWZ2mMx9pK/p8SztRXZtE4PaCdj0Nu5XE8lCLLCMdCFIbBEj0/rRA6qlFlrIrlom+dH1mvhgLxZIxoyRlSnliY/B1r7zhgpgsapGi6snxQmp/CCzaUwz40neOEiPEWKKo+tCX2hJmlixVT0wDK5oKR1laiaLqgeeIpaAdw559YhxY3ZLxCRwZH0fBWEfhazE9jHV6/VwL02B1I+PjFxIiA8YoWm2NX5BBnWhrCr7pGjy0gunT2pRdMS74rmVFU+ZBZMAvGpkTsfRNX3qMw75lpK0InN4xj7Slz9qJvCdWjmm3foOvxVox0pbtio4cy7Q+aVfVlzpmD8SMdCBpwR4VsY+saokFI0pgdtHiTIkJHEtiZe6NhjIm7USsGat0QtkVU8lsR+wOgSv7ZwaRaZ4lNIrdKzIWzzYVw4GMcdmV8a1F8KJ+sYdUPEPFM0RGYSwUXcPUIGI4GKTPm0JkOnjWwY4/C/VVUB4C10f1z4RCGnGuNHhFbGkAVV0jk3l4JsQhqh1KdH8YoYyR6P5aAzWtjIpWoQYrqJmpaSGN/LfW9swDris/FwNUnIgRIVtnrESwBwVUxUgkO2AmQvSQlojxteO9KHLfE0NAGKH6JZLdjtel3qCAMxSgKn7vSyeMYKi/Z28oeVKn74lZINum2cZmOhnPphYK3TMsZPW76RdEO5S2ZJ+1lj5zHXm1Q2kbHTFFpOVYOpC4PVND2g82SVBxerykFgjSCP5SWSL641C2j2OpKyhMNj5025r+t6ZVzxKg07tKxvQi8aH7bqMIRUGOKfBTc4MRC4SfmhvaIZQC7MiYGC36q9hGU9oQJz2bRmiwgHIdmGjKNu0Q22hDOUj7MbVNxDG4LjYzb7jr9U24jqUijuXnsXq3zRjb69tWW/raC4htiMYhNC00DpFpY7HUozUU3SpaOXRMg6LTh4tL4LgkVmwp1ho0Mt6dRBM4Ll6zwVBlNoEzjrEJE9FaPFXA0y3WxorpJRmregSFVEVTixwqrpyXvlbdlL5Ka6w1Yk8AsSkUB+UcdHw57uw/E9/rjbPvpfMrnXNx2LM8aD1pPGU+rGORiKLe+GvdG6+JJra8Ghpj6bZ+1yJhwyaq2C/GhyQ1u3TqYnywBloNsT6MrIK+SnoetaTt2fhpjW00UYUCNklYcsej/GjpcggjwmTLguazwLeMBcccxH4L9uW71/50i/bP2TT5BfBG6O/vZ5999tnkrfj58+dz/fXXE8dx9zGIu+++m7333vs5L4CP/ofTmTJYwkuVTdnFbE5OTk5OTs6uzaLD9+LVJxyIrTeodwxX/s9vNrtPvJFHHuJd+BGI7WnTyp8B3gTvfOc7+fKXv8y9996LMYbvfve73HrrrZx66qmceuqpOI7DBRdcQKvV4oEHHuCSSy7hHe94x45udk5OTk5OTs4uwvrP/v76lvv50ie/saObtc244447tltd+R3gTfCud72LZrPJ6173OsbGxth333352te+xuGHHw7AkiVLeMc73sHw8DDVapW///u/5z3vec8ObnVOTk5OTk7OrkJsJt/xnfeKl7LPS/flpq/8ZAe2atcgvwB+Dj74wQ/ywQ9+cKPrDjroIG6//fbt3KKcnJycnJycFwqhBddsuGxX5o9//CP/+I//yK9//Wvq9fqkdVmK561BfgGck5OTk5OTk7MTEq9ngciW7cqceeaZ7LbbbnzhC1+gXC5vs3ryC+DtyJRAbAHtBIYLvQj9KBELQzNSlDyJupdId/mzRz0U60LJFevASAcGlfwKOBQkPN1wqHpiAfB0QtmDxCpmFGWbNChXIv8dsQiA2BPqkQSsNmJFlMD84YiHx13aCYRGMeBbljcVjdgyLYBVbWlblK4bC2W/tpUA86onVgcvNQ5Uvd57LZLlJm2Hqy3TAmnctEDaMhqKnSEzQfhaLBOOguHAMtaR7TOzxaAvxxUZUiOFmALqkWKgIIHsIOaAwIFnWwpf225ZiVWUPQkq7vPpBhuMtBV7VCbbDOpGopw9LRaD4QIsbwCIpWA4sASxQit4bFwRRxqvlDCj1AvknlJJGOsoCq7FcSx4Cc1IUS1YtDKMrC0wbbhDJ1ZoLSaNRgwzSpY+X/qi7FoeGnGYM2BwUrtEGWhGMgZlV/YxJjVDxDC1KLaLPiU/Z4aIRqyohTC7bGlEYrHwHJhaNKxuKfp8aERQ9sQQMbss5SVWys7m12DRoieZPuS96skcmZbO/cCRuTmzaLvH5WtY3lAMBb39+9J55CjbtZ9k9ordy5aKZxgoJFTchI7RTC9GOMpDKwdXB5TdmXjWgdoqPKWxjRGxPaxaC7N8ieoOqrSSGkXHx7ge2nFRegoU+yUyHbDahRnTUGFbDA1ZFHm5BJUSjudBpSSGgD6ZOzaKeiaCLErddbGd9M5FVoY2PWtCs937WWuUlv3seB3b6KTGh1i200omVKUEYYRdPY7SGnwPtcdUVLmEbbXFYFAKpD5joZpGqsexmA7G6j0LQRqRbo1FGSsWgzCSfTIyG0ScpO8xZIHCmQXC98Qo8exqqcP3sEFBouNLxTT6Pj0ZTCJ9EqeT1fO67enaD7K+yvoyi96PE+mLdfvTGLEhpBH/yvPW6eteVH7XaBEn3W0JI6zWqInemNjRcRmL1Bpho0hOgjWj2HaCHalBZFAlD6pliOnZKNJ3u3INthNjm6lFoF7DNGP0QAHlaWwjAt9Beb1wHNVfkn7M+kor6b+0n22j2TMZaNWzb2iN1QrluISmhaM8jE1AgVYO7WSCwKnQ701HxR2sW0CZhJiYyERoBRZDbEMi28FiGCikFx8mhqiN1g6lxMN6A5S9QYrOWmaWIwq6939NLXQoOoaSayi5YLEk1pLYCEe5WGtkvipXbArtCajEoF2UV8QmoRxfpdQbb9eVPjEbufLzvXVsEEp+9r3ePMvsIOvOlbF614Ki4qd6BpcggZKGsA2Oi3V9lHZ7fZCEYp8Ama/1CWljK7U/xEn33LeNJiqdhzZJeuaSZhtqE9hoy27jhgacdQ77wZ//lvtu3nzw3P9lfve73/Hzn/98Uq6FbUEeBJeTk5OTk5OTsxOSeYCz134vP4jXvusNO7pZ25QDDzyQZ599dpvXk98BzsnJycnJycnZCTFW/vK1/rJdmc9//vMsXryYM888kxkzZkxad8wxx2y1evIL4JycnJycnJycnZBovUcgsmW7Mg8++CA333wzN95446TlSimSZOslUMgfgdgCTjnlFJRS/OQnPe3ILbfcwste9jJKpRJ77bUXV1111Q5sYU5OTk5OTs6uRmjURl+7Mueffz4f//jHGRsbI4qi7mtrGiAgvwO8Wf7rv/6LZrM5adkTTzzBSSedxKc+9Sne/va3c8cdd3DyySczY8YMTjnllB3U0pycnJycnJxdifXvAD/yy9/yh9vu2XEN2g6Mj4/zj//4j9u8nvwC+Dl4+umn+dCHPsTPf/5z5syZ013+5S9/mf3226+b+e3YY4/l7LPP5vLLL3/OC+DxjmL3qmWGJ1H6A75Expc8aCeKwMnMDIqqZxnwJfp9pCPrHCVR8Y5SVH1DJ5HfAqcXDVqBrx3KgZgL9ijbrmXCURIc61gxL8woKsZCumaE4YJNI/4tkVG0E4nKHw5gLJT1QGpYgMAV08JYqBguWNppavZ6JLaGAR+GgwStLGvbLlUPKp60C+R5pqovf8YoOIpWrImMmCJmFC3LmzBQEdvC8qYce58nbenzoehYaqmxoOwZYqOop599P7UmFC0jHalrIDVFeFqsEpkFIzRSVp8nhgFjxWSQWDmuRizrtYK5fZZHUEwLpKx6JF9Mw4EYLBIrfbOqJcaEgYIlKCUkVsZwatGyoqkou6C1pRMrhgLbXQewtqnZbapEJvsFMTXU0npCIzYQz5Exm9VnCBzbnTeOgj3KlrFQjr/qWUIj82gExeqWbLdH2bKqDatbioGCZXZJ9hlL7Rt9niUysKKpmFGyXXNIO5H50I5lDKQPbdc4UXKhGcu2mWFCp9YHT1tWtxVTA8vMouznaZgSGDxtGe04DPiWWiTHUXYzqwfMKELBMRirKKYyzE6imFqMmRpEeNrDUS7DwV6pBSDGji2HcAQ7PibR3bPnYJc9jir42OUrUSDbzgpxdQHaLUId42ofRylUUAXtYtvjKK8I1enQHMOWx8UO4aT6C78EpbJEjLsOdrSGmjYVWCMR35m9YN1o/jiRSPAwkpfr9KwHWmPGWuhqAVUNxOIQFLrfH7YTo9w0wl2vY5doRKhCEyol1PRheS8FYqoAbH0ibUfci6gH7FgTFTjYVgRaYSMDYTpwIIMRx916cB2xXcRxz3Kg08+VEjTbKN/DRhFmbQvVl6C8UPbJ+sP3UsNBLMc3VuuaGazvoQIf2h2JoC8WsVEkxoA4kaj9OJFxzOwNWR9nBoqsrVnUv+umZgkH0L3jyY4hmwvIn1dto4mqlNIviHR8+srYkTFodzDPjmPqISpV66iSh0oNDMShjJfvybvrguuitMasEYuDaUYorTBjHZSjsIlFRanSBrFwOH1FmS/Ndtd4YWoddMUHjLSrnd4J00qMFFGENlbMG0lMqdAv1gJcaE2A1hSDGWJdaI1DHKL8EliDqzSz1TTwSySOxrEa7Tj4upiaGzysmUDFIU6hAKZDnzsAtVX0+5aqPyX9Pm1hbIJWTQJHoZWDUgqNWDcG/JkoFG0zgacLci4pDY7GNkdR1enYsCHLoHeM2Xh5AbQavXFZ17iRbZONZ9Z36553rtP93J0/zTY2jKA2AUP9UG+gZqcGibiDGljHggGgXeh0oG8QGuNijpho9qwTWV3GoPr7pH2VEqrZhr5K2gYj50tny/6UHydiisqYc/hBzHjJXO76xq6bCOPEE0/kF7/4BUcdddQ2rSe/AN4E1lrOPvtsPvShD7HHHntMWnfvvfdy2GGHTVp26KGHcu21127PJubk5OTk5OTswoQW1AssEca0adN43etex2mnncbMmTMnrbvooou2Wj35BfAmuOqqq7DW8rd/+7cbrKvVauy3336Tlg0ODlKr1bZX83JycnJycnJ2cSIjyvD1l+3K3H///bz0pS/lkUce4ZFHHukuV2rrPvucXwBvhEcffZSLL76YX/3qVxtdX61WGRsbm7RsdHSUarX6nOXecuXX6Cu5+Br2OfIgDjjqoK3V5JycnJycnJydmCW/fIQlv3gUoohwfbfZJojMhneAd/UL4Jtvvnm71JNfAG+E22+/nZGREQ4++OBJy0877TROP/105s+fz3e+851J6+6++24WLFjwnOUuXHw6u08t0pc+A5yTk5OTk5PzwmDRy+ey6Ii9sY0mtU7Cld9cutl9XogXwNdddx3z5s1j/vz53WVLly7ld7/7HW9+85u3Wj35BfBGeMMb3sAJJ5wwadnuu+/O5z73OV71qldRr9f55Cc/yVVXXcU555zDnXfeyTXXXMM111zznOVOL0lgUz2SoC5HSUBVFmjVl6axHfQl+ChLmzzgW/asWMpeQiNymBpYCo4hMprhQkwnUcRGMaMowWRZIFM1TfHbTmCwYHCUpehaJiJNxbPUQgdXS3rlyEgwWNVP2LOiuwF0u2cxAFYCsRox3QA7T9tuSmdPS+BVVoan5ZjKnqHoyraeVt0srq62FBzL6pZL1U+6Pxccy4CvusF7s0vyW7KnLbuXFcsmJDjQ05aZpYSiazFWUhPXQp2mMlYkVlIVD6TxStLnEmA4qyTBfqHp9VNiYXZJjgVkefY5cKQfpwW9FMJ9nhx7aGya3leC9GaUZPm9HQkqLLuS0tpRsm6krRhIUzyHBqYVJYWzo8BzDCNtSe8swW6WwFWMdSQNdBZgFhkJsiu7Uk4pTRUcGgkaa3cDJtJytfRZFljWiBWz0zHMvkirnk0DKaX+sivHMdKRMZ+dBsTVIymj6vVSbE8LoN+3dBLFcCBj34w1jrJMRA5F1zDoa7SCWeWIZqzwNGglKY0B1rQdBv3euVIpSGBnLdQMFAwlV8rP6pgaRPR5gzjKRSsH6muwUUvSHT/1pKQefWYVVCuowMc8tBzdX6BzzwoKaSpc1ViL5wWSJrkwHddqSDoSpNKZkLL6+iQop1BBeQG2vlKC4BwXVejDAjSXS9DNRBParXVS+aadO9GUn7OUrRNilbGNDmrmMEw00+A3g23H2IKD7SQorVBZKmJjZZnT6aZCVoWCpDz2s9S+cS8FcxaEFccwmk4I15XAonQTGxlUxcc2Y0nLm+W4dgy2maACF61DqU9rSfWbBRiBBHB5aVBPmrrYrl4rwXGdBGs6qMCFUALAus7NSilNnRySrGniTJGUzgxUIYy7ddi0/bbRlEigNHDQZseqNZg0mClOusF0SilJU5ulbc6CpLSSPlj3PZE0x7J+3fTC6wTu9fdJm9odbDOCKCEZlz5T7RjdnwYqZmMep2lvHUkXbWqdbpCh8rQEGzblRLLtBOsoSYWcfunbRlsC5cY7UgdI0FzBkcC5VtRLoaxVmnc+xkyEaNfBLv+dtKNQkJTfa2swaxaMLYegChNrwHGhPIRSDngBtjUO7To6bkOhD98L8AsViNuyPloDfhkvSiRgDUBpZhTnQmsMGzYoFfqwQR/9foKTGFCaRENiIjwdwDMPYIFgeC/ojEkQXCdN61wfxzquLDOx9J+xaeBlnKYbbqTnggFk3tlOiMq2y+ZBO5QU3GnKaOU42CRBZedgnMj8SYPfTD3CNiOcLN1yNn6+h407kp45bKCCfqnD96SNQRFqNTnXsgA4rSVKPDsXs7YN9ffqdD3ouNCK2BLiSKMjvcGyXZmPfOQj/PznP5+0bObMmbz+9a/PL4C3NaVSiVKptMHyKVOmMDQ0xNDQED/4wQ94z3vew3vf+16mT5/Oxz/+cU499dQd0NqcnJycnJycXZE41ui4d8G7/O57efpXu7YGbdWqVRtkgJsxYwYrV67cqvXkF8BbiLWTn9dZuHAhS5du/s8XOTk5OTk5OTl/CkmsJ93xnf7SlzG074t46Hs/2oGt2rbMnDmThx9+eJJs4OGHH2batGlbtZ5d+z56Tk5OTk5OTs7/UYxRG33tyrz+9a/nLW95C0uXLqXRaLB06VLOOuss3vCGN2zVevI7wLsgd3z/Dn50/Y95+g9P0mq0ufZ310Jh4yfMo/c/zj+d9hF2P3AfPvjVD3eXj60c4Qf/76ssu+f3hJ2QeUfN48wPvQV/eHCT9TbGG3zxY9fym5uXohQcefxLeftH3kqpKg8SJ4nhuku/yc3/+0smxhpMmTHAa952Iqe9deGfdJyf+8j13P+rP/CFH23oBbz5hp/yzc98g8//8t8pBP5G9n5uwnbIv7//P1n2uydY/dQqTv2Hkznxna+ftM17X/0BVi1fQ2ZmMcYStUPedfm78A4WT/T9tyzle1d8k5EnVxD0lXnpKcdzxFv/clI5v7jhx9xy7Y3UR2oMzJnFie89g7mHHrDJtq16YgXf+OT1PPnbP2KNZc5L9uJ17/sb9nmx+KpNYvj2v/8Pd3zvFzTGGvRPG+DYM0/ksL8+/nn3A8B3Pv0Vlt3zez70zY9tsO62r/2U7372G1x9x2fBK2xk75ycnJycP5U4VqhYbbBsV+ZDH/oQy5Yt4+CDD+6qz970pjfx4Q9/eDN7Pj/yO8C7IOX+Mse96QTe+qEzn3O7sBPyH+d/ngMOe9Gk5cYYrn3XpXiBz+U3X8rnfv7vaK249B8u3eBRkHX5/PuvZHzNONf+/NNc/4tPMbq6xqXv+3x3/Q+u+wk3/fctfPiL7+V7D/0n53/qrXzp4q9w9633/0nH+ZozjuPR3z3F7+55ZIN1N//3T3n5KUf/SRe/AErBvgv25S0XncPeB+290W0uvemTfPrXX+Q/f/NFrrjni7z2XadTHqjw0mNfCsBTDzzKF9/77xz39lO4+Oef562XvYd7vraEe752U7eMB358Jz/8j6/zNx/9Oz5wyxd48UnHcsN7Ps34ipFNtu1L51+OV/D46I8+w0d+djmz9t2Nzy3+NCYNuvrZV3/CLV+/mXdddR6fX3o1Z3zkHL79qa/w0C//tH4+8vXH8fQfnuSx+zbs59tu+Cmv+DP6OScnJydn08SR3uhrVyYIAr7yla+wevVq7rzzTlauXMlXvvIVisXiVq0nvwO8HSmktoAs9a6ne6lfq77hgVHN/v0JiVUYC630wfd+X6wPUoah6FoG/CQ1HkiUf8UzDBUcSYl7woF0EsWTv3kAgN0qMVPKcuFqrKLgGMY6Dt/9969x8NEvxi2Xue/nDzLow1Ah5tZ7VvPMH57g7MvPxxYCBguGv/3gX3P2Me/nmd/+kQMP3Y92IpaEWgRTA8ua5av57a33cdmNH2XOzBLGwnkXv4E3vfJCkpGVTN1tmNVPruQlh+7HHvvvQcGJOeSoF7HX/rN59qHHGTrxRak5wrKi5VNNs5qGEw1uuOTr3H/bvTQaHfaYtw//8JG3Upw5jT33ncHLXnEAP7z+Zxxx5F5oZWnFmkeWPsLTDz/FR69+B0VX0u1WfbFTuNrF05YodJgWQMkVu0ZsxFDRijUlV0HJZ+7bX03FM/zwKo+ia5gayLaOAq8pxoqqp7pmiF//z09Z+NcLKQY++2jLN356F/sf9iKOeNXBDPhQeMmeHHrqQu7+xo941VsXkVjFN7/xYw567bEcdMQBjHQUJ731L3jsh7dwz3dv49Rz/2pSKuKya6Wvn1zJa//hFOZMCXhiQnHMacfy0/+6ib72ONW+fsaWr2DeYfvx4gN3JzLw0iMOYPbc3Vjzx2V4r5rHcEFsHTqde4kFt9Xgxn//Gr/+2X20mh1eNH9v3vvxM5g1cyZTD5zFvJe/mJ9/7Se865K5jHSkTY8sfZhn/vgU//yf/0jJNVR9S9U3DBdiJmJNJ9Fd20c2R6cEEcYqPC3zMbHgaof9BkLGOg6etrjpupJrmFpUVL2EjlEUXYWrLLFVlL0qFW+IxMZo5WDHHpdUtBNNzB+flrLXNNGVDk5fmWRlA1MPmXgyQQ+sQZc81Piz4PrQaRJUZ4JpQtjEJpFEyrv+pIh34jQ1a3MMVZ0uEeuNtbB2HPvsamwzQu2eBm5kNoaJZprGN8auSe0PJrUnAGrteDeFr21JmtxkZRPd58sXRm0CVXCxY01JU1xyu+UpY1KTgcVGCcq3EmkeO0DSsyBkNoosFXJQ6pkRQOwPocI2I/RQ+h9MlA5OlrY4K0NrTDOGMBErRcmDxIiJIrFoYzDNWFL+pul9SVP92lQ5kt27sp1YLAgdMSqQHU8sxgXV34fNbAzGSP94upfyuFRIzRudnsnB91I7hAHtgU77hCxdcYRN88qqgi/pj8MoNWuk9Y/VxSISJ9hWq5vmmTiRvvIcTL2JLrl0/ws1Rsa/knTrtuN1SWO8toXyHEk73U7tH2VRz9h2jI2MzMfAwdQjnClikrDNCOtpdNpXWf8nqxvdPlMlD2cowKb9TxhhH31Stg0KqL4ytj6BMjGsXQusgYEBmctRG+v4KO1CIp/RaZrs5qjML5DlY6ux1sjPhYpYIxprUYDt1CEOsXGIikN00sGGTYhDdBLLXTZrsM+sTNMTG0nJnKUjXrMW2+mIzaHdEatCpST2isDHjtQlrXCmEEqNHLguynG689xaK2mzs3aHUr5Nl1nfg1Zbyte6ew6BRuS6AADyU0lEQVTYtvRtlhpcyk9TeA+E4JdRxUGwBlUaxLZq0k9hU1IhZ+aQLB2y501OdQ69FMlhDNUyhJG0fQuIIg3rXfBGu/gFcMbw8DDDw8PbrPwXRi/mbMD9d/6BO358H2/7wF9vcpt17/YaIz8ve3DZRrd97HdP4Pkee714TnfZfgfujue7PPyAfCGf9OaFLH9sBY89uAxjDHff/juWP7GKo46ft8n6L118Ga2JFtf8+EKu/Pl/MGvf3fm3t19CHMmX1ilvfSU//s5djI9OdPf74fU/46CjDmT2XtO3rDO2Ar/71YM88/izHPfG4ya1n/VumFtjGX96JZ2GqKGeeegJZr1kn0nb7Hng3qx4aNkm6zr57/+SO7/3cxq1BmGrw61f/xkvOng/BqeKpucv3vjKSf384B0PsOqplRx49Es3Wp61lv849zKa9Raf+u7H+Pfb/4M999+df37rZd1+fs0Zx/PrH97JxFivn3/23z9jwdEvYeae26+fc3Jycl5IJIkmjie/kmTXu3SbNWvWFm23xx57bLU68zvAL0CajTaXnvdFPviZcwiKGz63OWPPGczYexY/uOwGdv/IW3FbMV/65DdQStFKL9w2KHOiTalvwz9P9FVLNCbEGzpjjyksOOZA3nfyh1FKobTiHy/6G/Z9ye5scKUIPP7gMh76zcNcfdfnqA461CY8Tn7XG7j1+iU8/ttHmbtwH45atID+wQrfu+EXnPr2E6mNTvCLG3/Nuy4798/rpOfJzV/9CfOPOYipu/eiVA9c+DKuvP4m7v3xXRy96GU8+eAT3POdWwFoT7SolEq0Gy2CvsnKvVK1TOeJVZusa97R8/j1j+/mLQv+ARRMmT2VD33xvO766btPZcExB/JPf/WvkPbz33zwDHbbf+NfHE//fhl/XPowH/3yVRi/RDuBM9//er537Y94+N5H2ffgF3HYCS+jMlDhlm/expFnvIb6aJ07b7qTD1y++M/ptpycnJyc5yCONLi7vgd4dHSUL33pS8/5mCXA+Pj4VqszvwDeCP/0T//EjTfeyBNPPEG5XGbhwoV86lOfYvfdd+9u8+STT7J48WJuvfVWCoUCb3zjG7n00kvx/Z3/Wcj/uPDrHPrKl/LSI/ank2y43nEdFl/xPr72qa/ygRPPx/Mc3vD3r+Ge2x6gMljZaJmlSkCzvuHFcb3WpFwJpN5/uY5Hfvc0V/7007x4/yGW/eFpPnDWf1Bw4W/OPnaDfVc8sRITG95x9D+ilcVY1b1MXrtiBNgH13U49cyj+da1t3LK//dqfvT126kO93HwcQuAjafLefvxH2Tl0yNYYOFfHcX5n3rLFvTaplm7cpSlP/sN51/17knL937Z/vx/n/wHbvz8//LfF17NlD1mcPjrj+dnn/82xTQwMCgXadebk/Zr1hoUyht/1qlRa/CJt3ycY04/no9d/wGWt11++/3b+Jc3XMSVP/oYw9MH+fyHr+XJh57isz/+NMO7T+ex3z/FZ9/xGRI0p751w0C41U9KP7/1iHeTffdkf6Ze8+xa9kXmxMI3vJIf3/AzjnjzidzyzdupDlU57PgF5EkNc3JycrYNcawg1hsu28WYPn06F120YUD7+sycOXOr1ZlfAG8EpRRf/vKXmTdvHs1mk8WLF/O6172Oe++9F5Agsde+9rXMnz+f5cuXMzo6yute9zrOP/98PvvZz+7Yxm8Bd9x8P/XxFrd+5w4s0G6FJHHCOYf+Pd/66T9BZU+m7jGdt3zmPcwsSha5p/7wBJ1mh5cc8ZKNlrn3i+cQhRHLfv8kMw+RCfrHB58iCmP2O1DuPD503zJOeMMxzNxzOkrFzH3x7hz96gXc+sOlG70AHpjSj+u5fO7OK9lz0PL0hMdIRzKP1SIgvfQ69S3H8oVLb+Se23/HjdffzKvf9Eq0o9nUBfAXfvoJaqFDZOQZYNjIbwHPg5997WcMzRhi/rEvJVzvl9dDTzyCea86ggFfsqp9/d++woyX7ItfLJBYmLX/HJ793WNw2pHdfZ548DH2PubQjda14olVNMYb/MXbTiIoBbhGcezpx/Ptz3ydB3/9EMe87ggevf9xXnX6MczccwaRgT1etAcLjj+Y+352z0YvgKvD/Tiey1d/czkd/G7mwIpnuv0E8Mo3vJLvXvUd/vCrB/nJDT/jlae/EsfR3ccvc3JycnK2LuvfAR79/d2svf9XO7BF24Zly5Zt9zp3vfvoW4FPfOITHHzwwfi+z8DAAO9///u57777GB0dBeD222/n97//PZdeeinVapU5c+Zw8cUXc/XVV9Nut3dw60U3FnZC4lCuTKIwptOOupaAL/7gQ/znjz/G1T++iKuWXMyr/uY45hwwh09992PM3kMeOH/6oSdp1RoYY3j8d0/y/867mle9+Xhm7Dljo3VO220KB7/ypXzp4//N2EidsZE6n7nw6xy9aD4zdpMy5x2+H7d+5w5WPb0GgGUPP8PPlyzlgJfO2WiZLzpkf3bbbzeuueBLjK6pAdAcb/Crm35Np9Xpbjd1xgALT5zPp99/LSufXsOr3rjwz+9EIOpEhO0QayzGSJ9G4eT7nUmc8LOv38Kxpx+H1pNPJ2MMj/32UZI4odPq8Ovv3s7d37mVI//hjd1tXn76Cdz7vVv5491/II5i7vj6T1i1bAUHvfaYjbZp9j4z6Rvs46f/9UPCjvzicvv/3EKr0WavA+QXjQMO3Z9bv3MHq5dLPy9/dDlLf/Yb9njJXhstc++X7c9u++7GFf9yLeMj8uelifEGt95416R+Hpg2yKEnHMxXP3INq5av5pVveOXz7NGcnJycnOdDvN4zwH37Hsbs15yzo5u1S5DfAd4CfvSjHzFnzhwGB8WBe++997L33nszZcqU7jaHHnoozWaThx9+mIMOOmij5RQdmFG21COFo6DgWIaDhLGOg6Ms+1YtM8sRtdCh6ic0IodnGh57Vjt0Eo2jLFpBLXQYLGggpur7uLqDo2z3bt3N/3M7H3v3F7v1vnru3wFw3ffex/HH7cvAnBITkUOc2iamD/k8Hjjss+cArtthSpBw509+w51f+xGdRpvh6QO8+o3HcuL/9zpQYlNorx7h/zv+g1z61fcy9Yj9SCx8+D/ezpUfvo5TD/8ACsXxrz6QD33qzZQ8uRD/54tP5dMXfot/Of0iJsabDAyW+YvXvYwP/MuJVIKYipdgrGJOxaEZa6qB4bNfO4/Pfep/WXzSRYyMNKj0l3jJYS/iiONfSsUz7FYO6SSac/7uWP7mtZdy7OsOZ87sPjwd0UnEeGGsouonlF1DxzNU/YSKZ0isGDQy80AjchgoJF0bwZuOPZ8VT4uO7Hd3PcRXL7+R+Ufuz3XfP5/IKCqe4Z4f3c3EWJ2//Jtj0QqGA4PEC2pm+DFXfuxannn0Gay17HngXpx75XlMO2g/pgUwFlpe/prD8SfGueYDV1FfW2P63rN49+fOY9+5QzgKfG258OXncMaFZ7PPKUdR9gLO+8J5fOPSr/P/HfkurDHMnDON8//jHczdfwYFJ+HcD7+eqz/+DS5440eojTWpDlY47FWH8jf/+FcMBzHgopVlsGAxFkqu4t/++/18/bJv8q9/fQHjayeoDpQ4+Mh9OfE1L8F6GmMVkfE48czjueDNn+CVJx/GnrP7KDhif8isDrNKIa52AEPRMRjr0kk0w0FMYhVDhQKR6VBwSl2DQ8GRR0A8bSm7MlcKjqXgaDqJoeh6hElIwZFHaRSKPm8KutOS3+DjBnbFKmyjCWvHCX8vY5aMdvD2qqIbTeJnJ3Cnlamv8Sg9UUN5Grc+IZHba8ex5eWooB+8ACZWQ6MJJQNJjE1CVNCPbY5iH3sUVShIRLzSsGYE++xqOveuhMQSvDS9HW6MRIBrRbKmKRH/qfUAR4nNIJE+0/1g6yEUHMx4B9OM0X0+ZryDNja1BMhXtalH6Knpc/ta98wEgPIiiVzPotFdpxftbqRdthWi/DTSHjFPmNUt9NQiuuKn5WgIlShq4liMFr4nZWsDYYIZl1+MbJSgPAc73kEFLnE9TPdPjQrpNrrk9SwQYQS+JwaIZoQZ0+g+X6wDxoLriuVA666dwbYibGRQBVeWhVHaNkfalkbjq76yRNr7HgS+bBf40vZYrABKqZ6lA6ScZhtbKoKx2CiCtfKLYLKyids3AWGEaabj105QjhKbR2K7BgDTjHDaHam7JnPLJrZn3Egsykn7pJ2g+gvgaHRqiABQgYOqpO32pF9sZLpzpWsMaURQ9lCJxYx1sMb2th2dwDYi9EABG0aYFWM4/X3YegNVLsHEhPSbJ9YHG7Wg04SwA3GM9QKxHCShvFuDHRmTx6LaHZjiytwH7NonoFiVbRvj2GL6ONfoWnmfEFOCHa9jnx2Rfmu2wfekLSU5p+3yNdjla1BFDwaqqMGq2CsCX8aiGEBfBZoTMpalQNra6YiJIkntG2GEjSJUdg60O+k5oGTOm3TepIYI0xQLh/I0ycoGeqiIk84t22qjvAAcF9sYQfXPlH5qtyCoiEGmPp6eH4GYNVxX3rWSPk7rIZ3zmQVDlUtiddkCkkiDozdclvNnk18Ab4af/OQnfOQjH+Gb3/xmd1mtVmNgYGDSdtnFca1W257N2ygnvfEVnPTGVzCzFNKKNYlVVLxMm7bhYwFvP/+veNO7T00v3ITj3n4Krz/3r5hRlItDR1kmIt3dZtrsYb75+y8wWOidxNXBCh//3N8y4CeUPUVsEpqxJk6rLFUC3vPxMznvE2cyuxzSSRSxURQ8w8aC4AD6Bsr87YVv4YDPvp7lDZ/EQifRxEYhribhyGP2Z+maL7K65W6yrOfLN+66BEdBM1ZkRietJj8yccxJh3LMSYfSiHqPCmS4nssnvnVRuh80Y2jE2eMbPU58y6s48o2vop3I/3NVr/f/HcAn7/wipXXO1H0O2od/ufaDFBxLyTVERmGs6rarWA5458fO5Lx/O4O1bZeiK9o7YeNP7Pb1l3nXR8/gXR89g1asGAqSrnqvtc4u8458MTcv/zIgx5OTk5OTs+3QsUHHk/9zsfHGH+/LeX7kF8DPwfe//33OOOMMvvKVr/DqV7+6u7xarTI2NjZp2+zxiGq1usnyvn7p1ykHLmEC8485iMMWblz/lZOTk5OTk7NrseQXj7DkV49DFBMmW3ajRkcGx1nvAnj9uy05fxL5BfAmuP7661m8eDFf//rXWbRo0aR18+fP5/HHH2dkZKQrab777rsplUrst99+myzzDe99AzOGit1HILbWncqcnJycnJycnZtFR81l0SsPgIkWtTDhyq/dtdl9tLVoYzdYlvPnkz9IshEuv/xyzj33XL7//e9vcPELcPTRR/OiF72I973vfdTrdZ588kk+/OEPc8455xAEwQ5ocU5OTk5OTs6uhhNZnMis98ovgLcG+QXwRnjnO9/JxMQEJ554IpVKpfu6/fbbAdBa873vfY/Vq1czc+ZMFixYwCte8Qo+/elP7+CW5+Tk5OTk5OwquLHZ6Gtb8rvf/Y6TTjqJ4447jhNPPHGb1rUjyR+B2Aiby0QCMGfOHG688cbnVe60omGgkKCVpuIZXG0pOJbIwGAhoZNo5laHCU0LTxXomCa1MGGfvkEslvFwDZ1EM1SIKbqDJHYtvq5SKJQwNmG3cp2CY/C0RzuJCRyXqu8TmTaeLuFrSa7g6QlKboKxCVo5TEQxM0uWNW2X6cV+xsIGc/scCo5l72qHVqIlop+IRuTQSTIJt2VKEFFwxALgKI/ERhScMr4uEpoWJVfRSZq42kehmBq0cLWPtS5FV6Fx0MrB10WaehxHeUwvhbjKYpDf0MqeOGlnliIio3CUZSx0mRrEVLx+Sq4cR9WPqXotdGrL8LRFKwdrLe3EUnQ1U4IQR7uMdSIqnkGhaSeWkusSFTrUQjFAREYMErFRTAkSPG1Z3faYXozwtWKw0MJRmtGOYiJ2KLmWxELJlS+mpyf81GSQoJXF01ALNf2+YiAWg0S/L4/CTC3GGOtRdqHqGzxtGetI/ydWjAidpLft1CKsbrnsVhEDRjGts+yK3WKs4zARO7RizVAgkWoSAAnTixFlT0wVE5Hulj21GDNciJmIHDpGsUelRGTaxCYkMkoMDZ6YSaYXJYqvGWsqXtI1QGgFQ4XdUCgCp4axCVMCCI0kSHGUx3CwO7EJcbWPsQkWi69rKBQDvlghHOWS2BhHuRg3wdUF8MDVPo5y5fxc+yS2MwHWQKGCXbkGM9LErG0x8WSC0pYk8qiubWNHajTXOpRosObZgIEZDtq1BM+u7kZpq72QaPi4A81G12CAiaE9gY1DGF1L8run0X0+eqiKfehx1PQphA+uofZIQhwqZq2rwnNdMBalFfHKZjfS37YTlFbyHF/XChCjSh7JeAfbTjD1UMroQ6wLicWMt3GmlCSKPUltBa6TmguSnnkiTuSV2RKyZev+GdUYSAysl1JVD6QJWBKLNVYi1123a40gjFCBi13TEqtB2e0dU5+PHWtBdpyRSS0JWupCY5sRVPx1liHtb8coY7GtNJlOZMTqMVaHagVb66R9lqBSC4DyPKzWvQj7iWavvVkEfqkoJgDfndQHNlonOt+IScGZrbBxDLGDGRWdZfLsBM7UYtqXiRgt+nzsqJH2GLF7KEAFrvQrpONisM1YjrnkoTzdtWeQmO4YmLZBGYsKHGwj6hovSOyklyq4aXtNb4w6MTie9FdqGLH1EFMPsZHBAczaFrrRhHYHG0aoalnKiNpiLChWxQARRqAVyi9j2+NYE4vpoLFWTAyOgx0dRw0M9OwiYxNiTkliMV+4DvapZ8XU4DrS9602NNvpPDXQHpe5/sgKnCNfjK1PED20Fj1URA8YtBmH/j4ZL9chWV7D3Ws3cNzUaJL2XRh3DRvKcbBZ3zTb4Ml8MBMhekoltTMkabvc7lwxa1vduYsj84uJJnZkTMpVDngBqtAnRoxCCYIihE0oDsD4mBxnkvaVTi+pXF9sEcbIuZPJ0kOxqthGE9SWXX458UaeAd6GF8BRFHHuuefyjW98o/uI565Kfgc4JycnJycnJ2cnxI0NbrTeaxteAP/qV7+iUqnw9re/nWOPPZYvfelL26yuHU1+AZyTk5OTk5OTsxOiYyN3gdd5ra9FW5cbbriBo48+mmq1ilKKOJ7sq7TWcsEFFzBr1izK5TLHHHMMDzzwQHf98uXLueuuu/jc5z7HD37wAy6//HIeeuihbXZ8O5L8AjgnJycnJycnZydkwwA4eW2KwcFBFi9ezGWXXbbR9ZdccgnXXHMNS5YsYc2aNRx11FEsWrSIiYkJAIaGhjj88MOZOnUq5XKZhQsX8tvf/nZbHNoOJ78AzsnJycnJycnZCXHjZKOvTbFo0SLe9KY3sffee290/ZVXXsl5553HvHnzKBaLXHzxxYRhyLe//W0AjjjiCB577DHa7TbGGO666y723XffbXJsO5r8Ajjn/zQ//dF9O7oJuyw/+/EDm98o53nz4z+u2tFN2GVZcvsfd3QTdkmW3PPUjm7CCxYn3sgd4D/xGeDx8XGWLVvGYYcd1l3mui4LFixg6dKlgCTzuuCCCzjhhBN4+ctfzqJFi5g/f/7WOJSdjtwCsR1Z29aUE8VwmmY2MoqqlxAbha8tRSempPso6T5QmoItU3RXUFZlsIbQLdNJWjjKUtBlWqpO2RvE6bRpOBFF1zBc2J2OaaT2AQdfl2glNQKngq+LaOWgUMQ27Ebar2o1mFZUzKm4VLwhhgvjdMqqazQoOIaSayg4Bg1opZkaxKxuuxQcS8UrYa1BKwePAgWnjKMkZ72nAwpOmdhIVHsjblN0AxQKR3nd9mjlUqIfrRyKzmo8bSm6UqaxIRVvCFhLJ1GUXJeq36HqD3HbTx/i1a8+HK0cmvE4WolZQ9ot9XZMTMl18XQBrSRCveSO4+sirvYpOAkFp0wrruEXExpRxNqOy4AvhgOFxtEexopZo9+fzmjnGVzto1VExU2o+gkFJyAyEavbLlOLcddG0Ujztk9EmuEgYWpR+nW047CqpdmtYmjEEjyfpZaeiDRaWVwtgesVrzcWWkHRNUyk6ZeHCoaia6h4Ym4AGC7ENB1NPXKIjOqWW07tI51E04o1nrZUPIOnLI7SDBSgERk8VeDWn/yOv3jVAlwd4SofYzsMFWL6fBdjEyJjGSj00Yhq9HmDWAyeFluANg4ld4BmPEbRqWIQ6whxiKtdiNry27cbUPGGGO08Q9Gt4igPaw0Fp4wmTd8ctyWi3negPYFSWiLUG3WJwAZsK8SsbWHqIUmkAIV2LJ01Ce7aNmFLw1oIO5Y4VHRGXfpG66iCK1H8nSYE6f2ANPocpcUyUayiCn2YRx5DaUWysoGOE+zaBmreAdQf7jC+0qd/uhgXlOdJFLpWkurG0V3jgo0MP/7DCha9bLZsl1jM2hbuXgNiGnjYEI4meHvJwNtGBL4jEf/tRMwKWoOnU6uBEYOC58NAn7S/2e596bgOqlzCuq5E8hubRqXHmGaMBrFTRAYTJaigLTYCY1EFRyLmfQ9VDLBphH1mGLBRgvY1zpQSydo2ynMwzQjtp5H5nhbLRYQcQ5iaKUoBlALMY2tl7KIEVfK65gTCSI7JdaBSEvNEwcE2Y3TJwzbaKMBm5oeJptgHWhFLbnuYRYfvmRogAhkHwK5eK9uVAlShgNJajBPGYMea4Gjss6sh8GHtOGa8je4PaK1IcPcKMeMdnMGAZLQtFowoQXka247Re87CRpGM1UCxZyIICug+D7NWQ5SgpxYJH1yDrohxQHmOWCE8B2d6Id2mJDaHDE9j1jTB18RP1tADBWwjFvtEM0K1ZQxJLHoowKz9/9l773i7qjL//712Ob3cc3vNTbnpJCGQABEIAQJRFBQzqAwoxTZfdOanDuoMo+Jg+47DqDNfO0gVYUR6jQECSSCEJCSB9HJvbm49t5zed1m/P3ZyQyA0DUXYb177Rc7eq++193nuOuv5PCUnzwEVEaErKCEPwu9HprKHFEEqhnNUVSH8MWQi7qiLlCrIwIAz7wNVUMrByCiPrtjNkvnjsEdyqC0ZJ28ogBxNIQJ+0HVkvoDweZ2+l8qOIseBeqy+rHOPVYGdKCICOvZwEVURY/PVGsiBbaMEtEMKEprmzCOf11FVgEOKJD4PZHPg9TrpszmnzlIZGQocqL+IzBYRugpVYezBDEqrOqbQYmcNZ4yqfVj9uUNjrwgnv1BA9ThKF0LBUAVasNoZW1VHBnxOH71eqJQclYhcDoKBA+8R7ZBKyVDCaXPA54z1GwyEpZk2mjg6KhCZTAaAqqqqw87HYrGxawBLly5l6dKlf1Edf0u4BrCLi4uLi4uLy7sQxZCo2PTHX6A//iIAtm2+Tq4jE4lEAEilUoedTyaTtLS0/FXt/FvENYDfBizL+atydDCBz/RS0Gw8BzRmS7pFqqJR0By92HA25GQSCqasMDySoLesApKknSRTLuFVbSpeP2kjQdHrQ62UKagmOSNJyeOnLAtjGr8eUaBoZ/EpJXTFN7ZS6qwAW6hCJZ4rYPoFqtDIe3QGckmGShp+1cZzQI83q0m8qk26rFKwFEyvRaKsgt8koJdA2gjhrPx4lSKq0DFkGV14saSBKR3dzUwlQ9ZTObQCjDK2AmzKCgoKg/kEmuKscgqhIKVNQJcUjBRlS+DXVEzbJOuRZHMF+npHnH5ZWXKV4tgKsK6WsewKZcvCq6oHVmydFcOCkcWrFg+s7Fp4lSIlK4eNRcEwSVVUDO+hFWBFUclVLIK6Qk5XSVeSqIpOsmRgS8h7bDyqF9MuM1pSKZgqipAIAUVTQREwmlexfTaFAyvr6YpKoiSIlyskkh68CvizzottpKgd0DF2Fu08B/pkek2nrAOr74YNmt/Ap0kCegnTLpEqOQtuJdPRKDalQBMHtHoDBhVLULEEiYqGT7XxqBKPsCl5AQEFA0w/5LIl+voS2NJAFTrxfAWfYlPyqdjSIlMGM1CiaOTI6DYSm5Dm1FOw0viUEgUrg0A5sAJsU1BwtDLtAzqsqhcEpCqj+NXKmM6vquiHVoCtsrNSp/sQlQIIBZlNQCbtrK56fNijeexUATtnkClYBx4hiW7bBJMFRgpFdFsyVFEJ5S0qBQV7NI/wqMiKhTKQAu8B7d1E2llR8ngOrADbCE8FeziLnSggCxW0wTT2aB5lIEUiXyJdssgXTCqDacgWnHIUgRzJYScKWOkioqQiTZtc2aQv5fRDGCZYNortrI5WMkXKOYtgsnDwxoNHAQvsVAFFFyjSRpYMhKKDbWOlCgjDRAnnEBVxSHPUo4OmQMVEViqQyiFzJYTmAdPCShYQJQ2ZLTsr4abzDhI+FbtoIjwqwhTg0RAFC1koQiaHnXT6I00bxTJRPQIzXUANqJiZIgoWGDaipCIME1m2ELYJFRtMGyXkBb+BlSwgiwbCpyIsG2Uoe0Cf1sROFJzPxTJYFrJQRhYtFMNAmjaiaEIFyBehUARDIEfz5PIVeuMZZ+z8ZeceGgYykYF8CfwGwuNo8cpSySl7NI80JYphgNcDpbIzppZNNlcinChgZ8so0qaSLaH5FcxsCcU0QRF44xmkaWInCyheBdQDq5deA8oVrJQzHxSvoJwtOfdaFWgegZEtgaaiJZ2VWeHVoOSsWlM2EdLCThWclX+PhrBN7HQBu3JgjgvhjLclUbwCmSqDpjj3wTJRDmh0q/EMciQHqorwpsFw2k4RRCGAHDxwrlQBeWjeYxkwnCFXMukdzmEn86jxtKNnmzOQw1mEdmAFeCiLMBTkcA7KZWf1/YDesp0sOP1SQaZKiJKOnS6iD6aRIzmMdBFZsVC9oPoVRNw5LwZSWIkC6kDKGRPDPNR2RSBHs4iCo38tkzkEutNPW3PmaqKAqBgITQEDZ155dTjwnWykCwhNQQ2p2CnnlwRtKINMOavJSt8ohARU8qD7MTFQ084vF0LVkJmU0x6Px9EC9pYhk4FAxZmX4LxLdA1GMs4KcN5EDufISnGYffBqlAoJLNVHLNhCbKJjpBpWiV1dj71mviMRjUYZP34869atY8GCBQCYpsmmTZv49Kc//abL+5tHurzlPPfccxLn9w73cA/3cA/3cA/3cA8JyOeee+6IdkMymZRRPK+aTwghk8nkK/KZpimLxaJctmyZBGQul5PFYlFaliWllPLHP/6xbGtrky+++KIsFAryqquuks3NzTKbzb6VZtC7EncF+G2go6MDgP0bvk/gyQ3onzgXe+UTEA6izD+V8u/uQJ9cg/LB/wHAuuuLqEt/g9zw79h7+1A/9CEIn4/94JdQTluMvelZRCiEzOVQTv0PAOTm7yPmfMv598jNiNpLsFd9E+XU/8Be8XWU0/8T++l/RTn5R8jB6xCNn8d+6hsop/3YybP/fxDj/mmszZVffxr9grOxH1+F+onfYq/4OsbzvXjOnoWYNAsUDfuJhxAnnYyovpDKrz+N5x9uhcID2FvXQa6I3ZtAPW3OWLkH+2U//s+I2ccg6i6D4kPIQgJR82nsR/9pbAwArHv+AfX8XyP3/w/mik3oF5yPzAwiGj/vJCg+xFXfe5of/vCHrzn+9tpvoZz4/bFxAaj85hI8X7wZmbgduXc7yvxrnLQHx2rNVYi2VtC9iIbPYj/8ZZRzfj5Wpuz/NaL5Hw593vczCNcgaj7t1KMHnAvRCw5vTPEh8H/4UNsOlpu5CyLOnit71TehWEY5+2eH7kvmLuytGxCRMGLmvyJ7fg5Do4jjr3bqT9wO+VFE25edz7v/E7m9E3HKQsiPgj+MqL0E4+bL0T+xFJnscfbU+QOIpi9S/n8X4f3spzDuvAftnFMRdZdx1VVXvWJsZfx3yA0bDx+Ll8wduf5qxLx/H+uHMm8h+D/stM8qOff8wDjIYgq5cyvKAqcOe/lXUc76qXO99Aj25qed+9b5U8TErx5+T9dchewfRT3nPIr/dSOVEYPoz+6D7D0QPt9py/CNyHUbUM75OdZdX0Q5eR4Y5UNjFP8douGzr5gvuSvPQ/FrBL53N3BoHhauXkrg3+96RfrkP34MTZeEf3LfK64Vr1mK/zuH53n5uB4sH6Bw1ccI/PDeV5Qz1u8/fwXl7J8BkDfuZ0c6w/G1F49dT3/lo0S+8wlE9UVj5156T8bObfweYu63kYnbENUXOff/H2877B7Yj/8zypn/dXi+vf+FmPTPh7X55cjEbYhANficEKpy4DeIpi++ep8e/2dENHRo3qz4OuVn9+H/1zuxH/0nxIRWxNRvHCr/Jc/xyzk4tvaDX0Ia1mFttB/8EspHfkHxmqV4ZtaiLv3Nq7bp9Tj4vjv4jj1iv146n19+bdU3QVFQTv6R8/nRf0K0NCJHktgDKdQTpyEm/bPT3+3/FxQFuXc/xCIIXR8bq/hnl9Lwu1fOSWDsfT/2+alvUHluP76v3/Gm+3ukd8ER63yVPsv9/+P8MmGZiMlfP/TsHXwf5u5DmiVE1SeP+MyMlZO4Hbr3IOZ++8CJlZTsPAIFz47nETP/9U33DaD0n5/C9/U7MG64DKEraJ/+HXLvfzkr5uO/8or05q2fRfv07w71+8EvsfuXCaY+fPvYPHts5lJOvlDQt06l477/fUUZT33g45y39fEx++DlKIpCmgr/7T0F/8t+rC9i8v+VV6O8NPLkAW699VYuu+yysc+hkPOr8ooVK1i0aBFXXnkl2WyWxYsXk8lkmDdvHo8++uhYuvcTrgH8NqCqzk+5kaCHyN+dgty1ETFzIrJYQgzvJvLlv0dKCwp/gEoB2RBF7Py/mLv60C+5CPu5FeDZhlgwCzwGMhZCBAMwoYly8Cm8iTi01iKU5RCsRnaNIPv/HdFaj1CWI+fNRD75NcSEVuS2H6DMPgn54g8xP/wxtI0/ZnTmZGoiAVLeB6hKpEnEQtScOx+5exu0xhBd/4WUJsrXLkaWs1AccDo2eRxYKWT4OcTFZ0HXf0FTCzLoBV1BtNZB63jI3eKEk/z4ecjyH2FcA3JgP3L9P6KMb4LGWuj6L7IfOwfDvpeIpx5tcA/MGI/c9gNSc2dRrQuEXwF/M4XAE/hTCUTDBFD3YfseomIVqcvDYMBAV3zUbN9DcdbxSGkTOWEBKd9DRAb3I3y3gCdA9v+7EI/6FN6KjnX6h1A3/pi7Y9P5+ILjeSxzLyecfgpRGWZUjqIrjxI9+ThI/hbROB28H8YIzUaXa7lnf5yPtNeitYxjvS2Z6LmX9XqUJVV+7FCMx/vuZbE9ghj3NaR8AhmuI28uI6xWQz6BPHUectsPEFNmkvMvIzTYhzh1EWge1mTvZkHHHNZm7uLEhkZo/Tly4JcI7ypkQyNEAk7euuoDzj4mdP0Xu9rbmKIpsPhU5LPPQVUYZd5U8D5NoSeHP7cfuX8/yimLwSgxoN1ObW0QvSqCPbcdEfZD5U8oWi+RSITOzHVM6Isjpn8LlHHIs2IU/I8R1D8OgBQ2IhJBdl8Lxx1P3r+ckOWBcz7BSHk/tdoapOkHU1AMPMHTgxkWNtXgldXY7ZOB57CkibZgLsR/gZh0AoTryZx2Cqa8n1h1mBv6H+Aj7UUa/J+DzP+SPeM0VKHjTwwR/uKZyJ1dKGIZNDZT4im8m9ZiH3caam099rP/ijhpJqPjaql+diN0fwPrgx9Dj3z1SI8rWpUPoSv4D+yXswIe1EiEyE+XHzH98FCQYlmh5UD6gxj2o3hrgni9qzB151Wrdm9G27eCsHkvwhOE0FL42LlwIG/oYzMxfn0J3m/cc1hZMvV7hC+KJW3uH72f4+sKtA0O4pk0HqEuo2TlCKhRGo6rY29Up82/nBABhq1B6s54pUFiDQ2jRiJIO4yIRCgWLPw8guxode7l6E2I86/DsB9FVz7oZCo9gF0sUAo8QWhyC8rL+gsgX/wuMl+EwfUoH/ukc1KZSM67nKwxQmPKQFS3k1byAJh2hZrzr0P2/xwRiUDuLoxzz6d4VjeGci/VU9spzJhDUHfqkhu+5YTjLf+PMx8BKZ9AiDMA0I3tzj7Hv78VuftHTpkH23bKXEQkQuTa5ch1Vx127UjIzVcj5hz+h4P97DdRTvoP5MVnISIRKh88F32X0xa54VuI479/KPFHPg6sAu+hP3jLPz4f7zfuwcoVUD95s1PPuqsQH78QmYmTCEB1Tx/Z9klIHmawkGPqtFkQvZD0nDvQFR++lU+Q8z1KxPMJ9Cn62DwFkD0/Q7R9BVs+hrFtAO+HXtLHD51PccP/Oyz9YX27+zKUj994xGsej4fIgWdctF/p1PXSfx/ou1wwFyu0Bk1Zgv3YP6Is/n8ADE5sQFU0fGqIsB7BDM1AqzwFwVpgLQgfOV8AWz5Kw7Uve86s5awe7uOUWJ3zfLc14x29g7tzQZbWaoTWrcRafC5afRVCLOP+0TLz6/M0GRFSfqjyXHhofDZ+GzH3e6/oX/gTJ5DxPUxsWj2irQnMe53tR4oCff+DTGdRTvoP7Ae/gHLmh5EXnIXwPAW+c52xC3iYNNUkEolgBzyI7p+Q7rNp+PHjDC74KOHunyBmffdQhbm7GNjlbHc7aB+8GhFdJ/CysMm6FFA+cvpLL72USy+99FXLE0JwzTXXcM0117xmve8HXAPYxcXFxcXFxeVdiKYLNCEOPyfFq6R2eTO4BrDL3zRnnDX7nW7Ce5azzp77TjfhPcnZc5rf6Sa8Z1myaPo73YT3JEuWLHmnm/C+RdMEmvIyA9h2DeCjgWsAu/xNs/jsY7F5bS9al7+Ms5Yc90434T3JkmPff3JDbxdLFs14p5vwnsQ1gN85dE2gv8wA1l0D+KjgGsAuLi4uLi4uLu9CVJ1XrACrf1kcDJeX8b4NhXzHHXdw6qmnEolEEEJgmocLS5fLZa666ira29sJBoO0t7dzyy23jF2XUnL11VfT3NxMMBhk4cKFbNniho51cXFxcXFxOTpomnD2AR84NlZGuCm9651u1nuC960BHIvFuOKKK/jZz352xOsXXHAB69at4/HHHyeXy7Fu3TpOPPHEsevXXnstN9xwA8uWLWNkZISTTz6ZJUuWkMvlXr1SfxVoHieUYyiAiIaRwwlkoge5fi309UIyjdWTgkgQ/ZKLkDvXI4IBx9M/XOuEZRxJQnMb8dow3h0bEZ4gsqsXWckj1y93wn3WVSOTaWT3VuTWnSgfWopMZhBBP3KkC2kYqA/+CeqqqUkVsVpnEn3kCejvp3r7bmRfHBQF0dIAtTWgqdhrVkK5AIMjMDiCzBeQXb2I3i3Q04tMpiGbRPh9jgi4z4MI1iBibYhIEwSrHfm0zl5ESwPCr8PUY5wQkc1thAf2U+1t5bbdozA8DIBy/CJiiYwTojPRg8wNEygapMJeyCWIlAVFM0O1twW7po0G/yR8aojK7JMIFA32ZpNIyyA60IdywmJEVQsoGhERxlexeVGRGHYJc+4pnNxYQHjDnNXaRlSGidsDVG/fja546YoGETUT6DN7kQO/JGuMIAe3s7xPQc+lyUWCzA9WUe1t5Wy9gMwNI15YxWKZAFWD8kOQ7EXsWQeALaQzF0ZGEe3tYJb45loDfCFkfhSEwkmKCdLmxGg9d3QnSFfuoDukIbNxhDeMiDYhVBXZF0fu63XumaYyVQQPBBQoYXalnFC4lTxycCe+j8+BQnFMAUJm4jSW/GgfXYTMxiGRZiSik4uG2ZMewpLL8Ws2yYnj6cxcB/4IWBWCpsqa+E1Qfoj8lJnI1O8ptUwmG/AQ7N2L5fVBdoiaRBaKGYxoLWg+AiLIWTVRPP27WTYQR/RvQ8kMoyUHEY3TGW1rRsZ3kjbiRLQaYv39YBhcHhymwYyyrOcWpG0Q0moISK/TT9NEtDUid2xEKiqp8iAyX0DNZ5CZOKML5yGmnUzOKCPqqjGWnEuy3I9pLwMgZ9zF/txvIedILylRL7JkIvv+B7nuKpQZEwHIG3ePPcqG/Shy6LcAWKYgXGtQNO8bOwcwUupGbQggPX60coln4oMQqka01CG3bWY/o07C6CEvdWvvMPbXLj2Q/2aGS45X/pD3QHhjW3J6i0q1twaZzbM1Kdk8mmfNoE7Q9iJ0hYmRJkL6UpbHh6nNWqQqt7MzdT2dmeuQ8gmnj+ObyRt3kws6YWW1cRG2mDl6Jh0Q2q+qR+7/CXvSvWP9HiUJqawT0hoYKPwGWMEde24jXryeePF65x3T3IDZlWLt0I3I/T9hRMsR0KI07uyFphn0Wf082O2Exq7OVhgsXIcIN4C1nDvjJf64d5SAFuWuLicUcuCF5yF7p9P/SBAxZw4EfHRmrsN+8AsIcQZbEo4klahuR8onKFsPkR8/BZn6/djYioapAGSNO6Gh1jmZvv3ADX1k7FrWOFBXyJEylIlDCx9i4iTn/81fpmI9jCefRbRMds69VAECoJSBSuGwU/rsRgBSHz0TCvcge34GE6cgN68C26RiF8HnJ+L5BJFEiinRiUijyEDhN0Q9nyJQkaAoLO91Akyotf5D98J+HNH2FV5I/A5FLEY/fhyWfImiQjGD1hrm1bA+9kmK5kuk/MoPvSJNtmnc2L/zzRMOjctxZ46Ni1ZxAsooiz/OUPEGABoDn6dOayasX0DWuBN1tAfDozNQ7gSjBIEqQoZCeGQISg9Qsh4Aa7nTnmKGU6pqWJse4slEgm3JYczacdgSCgEvYtokcsYo/Y1VIBQKpmDDcID9apZowULu/hF5427k0G8Rc7/HXZ23vbLzFZM/dVpYvSloOfC8T5npBNyoqUe0NDnpqsIUdAn5FKbHMzZXlI9dzFCXD5n6PcrHHFlCj1fBtJcRrrahuhqKztjmjLso+jRqavVXvRcvRdfFYccHqur5P43T3lBel9fmfWsAL1myhAsvvJCJEye+4trjjz/O8uXLue222+jo6EAIQX19PVOnTh1L88tf/pIrr7ySWbNm4ff7+d73vkelUuGee+55RXkuLi4uLi4uLm8WTRNHPFz+et63BvBrsXz5ciZMmMB//Md/0NTURFtbG5dddhkjIyMApNNp9u3bxwknnDCWR9M05s6dy8aNG49aO55+ZhvHfuI32LZ83bT5osH4OV9jV/foUavfxcXFxcXF5Z1D1Q/fAqHpAlV3DeCjgWsAH4GRkRG2b99OuVxmz549rF+/nt7e3rFY2ZlMBoCqqqrD8sVisbFrR4N//Off8t1/OA1Fef3JHvTrfPWKD3Llfz9x1Op3cXFxcXFxeedQ1CMfLn89rgrEETjoGPfjH/+YQCBAMBjkmmuu4eSTT6ZQKDiRhoBUKnVYvmQySUvLq0scXXXNH/EoFmQyLFkyhyUnTXjVtCue7WRwMMW5p00B+forwACf+eTJ/Ou//y+7ukaY0l79hvK4uLi4uLi4vPUsW7aMBx65CQWFrswbc2TTVYH+si0POu4K8NHANYCPwHHHHVn/VAiBlJJoNMr48eNZt24dCxYsAMA0TTZt2jS2SnwkfvidTxDRysh93YiGWqgYr5r2rj9v5czTZ6OqCpgWhmHxL9/5X/5w11qsUpnPXtrPE+t28aFj2/j3HzlOGLGqIPOnN3HP8q1883On/hUj4OLi4uLi4nI0WbJkCSefkUMVGg//8ec8mN//unk0/ZV7fl8eGe5os23bNr7+9a9TLBbxer088sgjb2l97xTv2y0QlmVRKpWoHPBYLZfLlEolbNvm/PPPp6WlhauuuopSqcTo6Cjf/e53OeeccwgGgwBcccUVXHvttWzZsoViscjVV1+Nruucf/75r15pdhiMEmLGHCiVHQ92RcEePxfR1gi1VdA+Ge2kqWzY0MUxM9qhVEb2DfKj797Jffc8y5P3XknPM1fhFSYb1+2DRArZvxcxfRoUU0w7aQbrt8dB0xDRMOVpc1k96wQoZiBXAFtirdyIiIahKgLDCeSuXagvPoNy5geddlaFEZPbEcfNR8ZHkes2O8oSbU2OCoBHdxQfNBUxczr5xjYnZnrFQHb3IffuB1tib+tEbnkGmeiGcg60JcjtL6LMngy5AjJXgUoBMXUOItbGE1odbFnJxIgJsSgynUV2Pu94bze3OW3zBJBGEU3xkNRLrCnkaQp8EXW0B7VUwJImezJpvOUKaa/FbH8tmCUqrdORm1ch/VGwTVA0pC/M9KoW/IUS6uo/019QkZU85BKg+2jQ2xATZ+BLDjP++a0UfCotdjVoHsJ6LXJ3J7885e/BE2C4mEIO74XOdYjGfyBZHYH6BjINjZBKYT+9jKcrNkRr2DhSQBGLQdEozTwOuXcvctdOfnlykzMe1e1IXxhRM8EpU1H4xKQGylaB8b42rOoW5I6N2I89iiyXHZWQKVNQzv47aByHHNoH4yZBJoN2fDsym3PuTyKJEAI0DdmzDTnYiWw9Bvo6kTt2QG834tgZeJUghl1mZrmMms/Q4J+EQNAcbCRhxqGvB9m/g2lVOjI/SshQEMEafJvXUTDTiKbpKDvWgubhWTXEFiND3kxCrBWpqMhUH5SKLG5t4gmiSGk590Ta1IpaRO0EIqkMMtkDuQJDzTUASKNIwVQQ3jCmrIBtk4qFkbu7kemsozpSKdLQM+TMr0oecjmCWgz78bsZTwzROhnPcA+70graAS3NkL4Un6rRaSd4eP+teM+eie+0caB6GJ011VHXAIL6x8ce5XQljqj/AgCqJhEH3qQ9jnAAsuvHaIoHPCrEdyEHdwJgVDdjbetF1MQI6QHixevJGXcBK6D0AEqVF1NWkN3XUmuGeH5YpWjeR51vPKbHg8wbeJUAuuJDtLWwsKqK5oDBeeMvxvJ4wJZ41Q+TN+7Gr9lQO5GIXkdzMEprsAVRzjsNDPjQFC9hrXasT8dUT6Qt6agWGHYJ0TqHsq0QSCXYnb6OmLcZPDrBbB4UhabAF5FS8kinjpSS+qIHKgbWY+vRjm+nwW8gok1UeRoRzz0GHh1TVmjxdzCzukzV0DCZaIhGfwdyx0ZQNebX5zmzpUBkZIQvtPlItDRBJAiBKjKVPzrvTd0HngATTC9i1hQq1sMcox4YeO+HYd96UpUBQvpSKKTG+rff6AMgrF/As17nF7KEz3KUMfQPAeBXI+SNFKOlW5ATjweg31scK0P4Y84/CveQMYYh8klk93anrPLvHSWRA2oi0izTpyTAfpzRkqMkIWZNJ1m+jeoCPJ7M8JBdQ/nX9yBmnggD+2lMGcide5HyCUSkAbn8Lujr5pdbw+zP/RZZykKpzJI2H7/YehtK1AvGI6wbCoJyJnLkBqo8FrvT10EmR8E81H/CFyBLhwcNWjt049i/9VwaP76xzwXVWaDZlz2gbFJ6gIKZHrse0pceeBbuAHH6oUJ95zJQ+A1ypAshBLvT1yETtzBqxima92FJE4b6MewSTYEvUtAslvUPsKmYRDZNI68aPBtPUJB5ynaBgk9lS2GIE1SBJeG42sspW3kumHQRgZKF7B0gZoeo87XxeDrHue1+PtLaSEtwgqOuZFpoipdUNMi64Rt5Ifky4zF9O3g0LplSi1obgHwCGWsmONQH0RrwBMDrzC8xoZ0AfsSEb6ApSwjrFxwo5ED/LRPjV78Gj05VTEUrlwjXGoiWfyKp5MbGrWTl8PrfmBGraPKIx1uFYRh8+ctf5pZbbuGJJ554zxq/8D42gG+99Vb8fv9YhJtQKITf72flypUEg0GWL1/O1q1bqa2tZdasWbS1tXHzzTeP5b/yyiu59NJLWbx4MTU1NaxatYpHH32UUCh0VNqXyJaJRgOH2vvUXr62dDZTOxrxejS+/U+Lqa5+paRNJBogkSq84ryLi4uLi4vL3xaKKo94vFU8++yzhEIhPv/5z3Paaadx4403vn6mv1HetwbwpZdeipTyFceiRYsAmDZtGsuXLyeXy9Hf38/1119PdfWhfbVCCK655hoGBwcpFAqsXLmSWbNmHbX2VYe9pNOHDNm+RIFx9YcMXlVVaGurf0W+TLpAdVXgFeddXFxcXFxc/rZQVImqHX68lgH8ekG+Xi+IV19fH+vWreM3v/kNDz/8MD//+c/ZuXPnW9a/dxJ3D/C7lOM6atm6fT+c5Qint1QH2D+UHbtu2za9vcPQ0X5Yvm1b9/PhU6a8rW11cXFxcXFxOfocacuD8hqO8QeDfBWLRT772c++4vpLg3h1dHRwzTXXsGTJEnbu3EkoFKK6upoTTzyRuro6ABYtWsQLL7xwWByEt4LOzs43lE5RFMaPH39U6nzfrgC/2zn/AxN4fMVmLMvZqHjxaZP42T0vsGvvIJWKyff/3+OMjh4uuZZKF9iwbg8f++Dsd6LJLi4uLi4uLkcRVZVHPF6N1wryBa8fxOukk06is7NzzCdq3bp1TJ48+S3p20vp6Ohg8uTJr3l0dHQwc+bMo1anuwL8LuXMY1upr4/ywOo9fHRaNf9y/iySJYuF5/4Y27L47KdOZO7cDnz6IUHAW/60jtNOn8W0yY1QyL5G6S4uLi4uLi7vdt7sCvBr8XpBvD796U8TiUS4+uqrWbx4MaZpcu6553Lsscf+NV14QwQCAbZu3fqaaaSUR7Ut7grw20kqg+wZQD6zGiIRqBiI2cehvLAawlHkxu1gmdi7urEHM/z0Q5P47nWrsSsmnroqfvaLzxHffyvxP17O9/7lo3Tt7KYt4oNYLfl0mp/+6jH++6f/hGycguwZQHTMRhMeTh3pQqYHEFMd3WH15FlYm/dCJgctrYimOmibhP3UcmS+gHxxF2ge5DOrEZPbQVORgylHtq2nC1rbkekCsm8YPAGC+3YhiyXw6IhoBGlJxDFTUI7pAJ8XEapDxjuRozch6qohFEZm89gFw1Fj6NvN4/FBzujfyejkCZzqV2E4gWhrRDRPwV75AHLzZvAGELE2HskJwkWbWMVDwRTY8jGy0Qh4Aph2mVnVbcjRbiLbt4MvQrmmCa/6YWgbB53rWGWoyN7NsGsNejZJxidQ5sznuGANcu9ukDZUCtiP3oXMxMEooZx4OgFDQPgCGBlEIBAtDdjyMQyPzgTbD6ksydYWCua9xPI2om4SQS0Gto0y/1ROqKtHeMOcOrhnLC68L5Nm6LhpiHEtUEyNeRwLswyFFKJukqOEse0Z6v2Xg/4h1IGdSMNAtLcgvF7nnnXuRXZvgHyCysTZyJ1bsTfvwe4agoAfofnHlDRE61RkMoNonoLYshqZzSOmTnGUI4YThIcHiWUrpGtiWMEIZStPrKQgUDDtCmLaPMS42cSo4t60Ar4Icv8LEPDRqDZje/3kOqZS8mrkDIVjIq1U9fZCYj+ilEU2TkG0zkAVGmdWV0F6ELOmFdSzyCklZH4UuWULpOKI9uk0+D+H7O5Bxpo5b3wDcsd69FIRmRsmpjeCoiAHRhHHnMaodCIhipkdzngGfASkFzFzOjJUgzTLMDrEpEiZO7udyI5y5Abq/ZczwdA5py7iKGZUR1mvenluSEG0NEDBWR3ZNHoD4KgkDBWdf1umQNNtfJvXMc5/QNvbH2GkaKFObgHdh6ibxMKmS9DTIygBHQolPIqfVBlCIkTOSDBg96NMGUdIqwHTAqvCnJoyfu2j3L4nzvU7RlDnTsKnhskZo5BIMCrStAYd7fEXEz2IsIcHu28lZyZY0NDizNPR/YT1C9CH9xOXjqKF7Bng/n2psVeTOrWRm3b2g6oxUPgN21Jp0uYwx3qjlGJ1NPijKOUi5AqYVfVQKnNX522IUpabz/4E1d4WRLgOEQ2jjK+FkSRV3jDLswYPdA9DYy0oCtrgHkqywHBRA28AVeiQG4GAD8Mu41MFezI65LPIUprqnAmlMqawiXg+geiYS9IPIlRHJuRBjjuWZ+LDZPwq8eL1wAriDTEa/J8DoFjfSrx4Paa9jHGhLzhKDeYyFjRcCoBXCSDEGWQqfyRr3MlIaT8+LUSN7zOkKoMAtAS/iCWXQ/E+XjAybBq9gYJH0F8wkanfIybNwZaPUe29mAdHCxByfEVkdSstwS+yPd1F1ig58zJUR0xG2IrBifUhPuwvojYEHMWTqiqwTEQ4iI3FPfH8mFTmFTOz7Mt6eSCjIGJRDLtEqiJQqv0k7QTn+nKUrAcgGSekBxgp6Ri7E4RGhsfu8UDhN6h1fgA6M9cBcILqKBGUrAdA97F80Jkfa+I3EdA+Rl/+N2xJOHlyaoXGwOcB2J68HsoPARD1fIo18Zug9ACrB2+iL/8bmtRWAL5wf4z2UAv/m1Cp8X0Gf6FEWK/FnH4CXjVIzrgLnxripAYPc6rHs6K/j6ASZlFjK/7dW6jyNGLaFY7RI4hQHWd4DJb13ELI1EhVbkfG9yAmTqKoQ9qIc2bLJQSLBoPlfTzRN4CIRSFahScxQMzbzPy6y/j345qc+9P1Y+TQb5H7d0JbB4pQIRJE7u5Eyacd9SFpkwt6YXTowHd4CrQl9OV/g9z9I7LGnc55cxnSFghvGLU9BrE6FEUgOzejeWywlhPT6lk5cDNy/08IajHqGvQjGAmvRNV4xR5g9S9cunyjQbyWLl3K6tWrefbZZ/m3f/u3v6yyN8l5551He3v7ax7jx4/nIx/5yFGr010BfhdzyuQ6Nv3+cmRfnGyhwlPLt/DB85spFSt89wf3YUvJB2c7X37BgJfOu76AMnUctrRep2QXFxcXFxeXdzuK4ji9regfYUW/80d7xbb/orL+0iBebwd/+MMf3lC63//+90etzvftCvDreUoeZMOGDei6zimnnHLY+XK5zJe+9CVqa2sJh8N85CMfoaen5y1rr21Lvv+zZdS2fYa2T93Euk3dPHj1B6kKet6yOl1cXFxcXFzeOQ5ugThzXA3fP2kq3z9pKt+e95ftyX1pEK+DHAziNXfu3KPV5KOCZb31C3nvWwP4oKfkz372s1dNUyqVuPTSSznttNNece1rX/saq1atYsOGDfT19VFdXc15552H/Rf+ZfZ6RENe1j70z2Tit5O87wusvOernDj1lTJoLi4uLi4uLu8NhOLsyjp4PNk/wg+e3/2q6V8ryBf8hUG83gE+9KEPveV1vG+3QBwMgPHkk0++app/+7d/48wzz6SqqorHHnts7HypVOLGG2/k9ttvp73dkSH7yU9+QlNTE08//TSnnuqGIXZxcXFxcXH563i5E9zi8TWc0BzldzuO/IvzrbfeymWXXTb2+WBwrhUrVrBo0SKuvPJKstksixcvJpPJMG/evKMaxOvN8p3vfOcV56SU7N279y2v+327Avx6rFy5kgcffJAf/vCHr7i2c+dOisXiYZ6UtbW1TJgwgY0bN76dzXRxcXFxcXF5j/Jy+bMVfSN8f92rrwC/XpCvtzqI15vlV7/6FR0dHUyaNGns6OjoIBB46wN6uQbwEcjlclx++eVcd911R7wJb9ST8hV4NERLA8pZH4VEEjmcgO7diPHToFxwPKVH+hC6ihUvoDRGsHb0QijgeLrv3ANmBVFXjaif7JzLVpB7diOaZ0LFQG5dhUj2w2kfQeaGUfu2Y2/rgsEhzMc2Ym3YjezuR2mswh4tILdsddqhKChLzkdMmeKU29WNmDUFbBs0FTtdBo8OjU3Itc8h/DrC70FufQESafDoyHgSNBWhq44HrWlCrBZZzjp1jPQhGieCLwIjyUNx1+Mj6ArQ3EhNqgjFDOlZMyFag9y1GdHWhHLqB2F4GLl3PcfVlpCVPI9nCpwZ9sGaZY4Hr6rhV0I8PdhPuqEB2icjdz2Dd982hks3OmMMLAz6wBMgOb4dOdhJpCyQlkFOM1k3cRqoGjIzgJjcjmw9BhFuIK0Wkb3bkN3Xkp40CeWZP0M4Rt5Mou1cD6N9YNvEPM0ElDDE9yNTfWh5Zz7kfSp6vBOEwqrGDoZFAso5yI3QkLGc+6n7EN4wcmg3MtWHHN4L0qbScSxipvOrguz7H0T9ZMSMOcj4CDTWQyrDtmnTkNv3Ittm05kZgFwBaUtkyaQ87wMYgSBy724q805Fdm5B+H3IkS4olBAtDcjtOyAVR6YzYNuImgkEtRgKKrszI0jLwKsEqNeaWDaSBtVDQTWYESshR7tItrZAwzgyIo/oeQEAb7wbryodJY1MDqH7WZ1OIvq3OWVwujMXgtVoA7uQqd+jKz6Id5P4wPHg8yPTA8jt30e0NJAs96P0vIh97ELnd8HEAAU7i5jcjmhrQG5bRVh31AbEuOMZjHmduVnMQKUAneugkELO+AADBZ0LJl3kPJeFFHnjbkR1O1QKyHwBe9cA830h7utWoWk8qM5e+zm2zS+23kZjThD1NLA/91sCUQtv0OKG4HRS1gh5427KdS1Mj30OMeN4VlUEea/gwe5bnXtoSxg3nuW9RX7yYoBlA3FC+lIau+LQNoGtyS4Ix7AjdXxzdRSMR1AFHFNtIJNphDiDGmKISXOo8X2Grmw/ABPCAWTJZHy4QoP/c6wf7nXUBbIjjmKF5ZxfN3wjhAJ8cJyHOzudvGLqFC6dehGj1WEa/ZOJ6M7+u7uGSihCI6JEwXcudqKItncjdu8oZ7YqmF4fQ8Ub0JODLI8PQyiAHEwh5k6nSqtnsTXEWa1+CFZDxWC9HsSnhDir9TMIf4yQvpS7hivQOI6s4Tj4zIzpkMkjok3ko1EwLdTMMAXzXvBFiHmasdetwqc6kTGPq/Vzyb0efGqIslWgoeI/9C43EhRNm4KZJm/cjWGXQNH4+rOOw02qMsyWxO/wqSH+4c8Cj+on2rMfCvdQ7b14rBwl7qxGza7+LMfEWnhuKM20aBOYFV4oJXgm3sum0Rv4SFszv952YEy3rGa0dAu1fpX2zj5GS7dAtBE5vJeY18SUFUrVDaiNYUQgBuFaKBVh3DhUcRanNR+o3LbxKH7m1UXxqjYyX8Cj+LlwUh4rnies1yIa/wFv707weollSixouBTPGTMR1e1I+QRl6yFGSxqipZbd6etIVRz5zKfKjgqETz2XUZHmrNbPALCg4VLWDt1IS/CLfKT90wCEbB9ly1F+UATg/TBF8z62J6+nwW/wtY1ZTmm8lHp/O2UNCtEq7vw7Px71HD7VcRH37/s9SBtVKui5NIZdIqBFUSyLiF5P1hjhzJZLHBWeUgY5nEAObCe8ewc7rTRb8oOwfx9L2j4Dug/TroA/CPk0uuIjXrBZ1nMLeEP4tTDH1YI0DOjrR9R9jnXD+wDYlOhisHAdsi8O5QKyux/53BqnPFuinPIhkDYyE6dY10womUS0TIbyQ4gp85EDv6TW18r6qkaCT610nmlVx7bBfuFZxIRWRP0XnDkwwTEs5f6NyL4XmV9XBYC24hE0zxuTMju4AnzwOHtiNf9+yqQ3lPdvgenTp3PmmWdyySWXHHa8HUb5+3YLxGtx5ZVXcs4557Bw4cIjXn+pJ6Xff+hlm0wmx665uLi4uLi4uPw1CE1B6IfWKpfvG+HhvcOvkeNvi6eeegohxCvOv1FViL8G1wA+Ao8++iipVGrsBhQKBQzDoLa2lmeffZapU6fi9/tZt24d5513HgAjIyPs27fvNT0pr/rpcjx+DyK8hrOnBjh7UtXb0R0XFxcXFxeXd5jlf97IirtXQCrN0J70G8ukq85xgLMmN3DiuBp+u7n3LWrl28uRjN+3i/etAWxZFoZhHOYpaZomHo+HZ5999jBZtJ/85CesXr2au+++m8bGRjRN47LLLuM73/kOxx57LLFYjH/+539mxowZnHzyya9a5w+/ehaR2giidRZy27POT9guLi4uLi4u73nOOnsu5x4TQe7qYuPIXv7Qt+918whdHLYCDCDsd85ofKtZunQpd91119tS1/t2D/Ctt96K3+8fU4MIhUL4/X5WrlxJY2Mjra2tY0ckEsHj8dDa2oqmOX8z/OQnP+Hkk09m7ty5NDU1MTIywgMPPICivG+H1MXFxcXFxeUocnALxMHjse4Rrn5y1zvdrLeM/v7+t62u96219nqeki/lu9/9LqtXrz7snNfr5Re/+AWjo6Pkcjkeeugh2traXrNO0TwZcgXsVY+MOZ5RE0MOdiJq2p3QxKYFtVXox7dQXtOD0hLD7k0iJo5DWfhhZOcWMC1kohvlrI86YYfHtSB7NqOccBrGtHkkAiC2PYOoaoFMBhENIKbOQW0IotQGEOEQ5o446nEdiHAQMXkqZIaQzz8Fo0PYowVQFKwnN4GqoSz5BNKwkMk0DMUdR7eGWuf/kzuQ+QLlh7agTBnneEfYNvaqR+GYhZBNQnoU48RF4PMipYVc8wxEQtiJotNf22Zh0yWIxulOyMlUgtAfH8Z67GmszZ2Ihg7k4PYDaSUNacMJi1noh0AVyoJzCOlLkYke5OB2Th3pIvLceoqhEFQ3UB4/g9qcdMaiaQrrygUo5aha+zykshCogtE+Qlo18/dsQw7sQlS3k50wGfHcY6Bq7EoXnfs1kiIajztOOqE6hospxPRvIVpmgC0p23lyVsoZq2ScrA+IRvCqQUSkCfwRTvVIx1kr8klEyyxkQwdy53oqikTaBngCyA0bIFKP7NuBvuYJZNc6Cua9iIapyFQfFDOImioyDY2IWVOYsXoNVEUQ/duYtmsP2DZmZwpt0Sx8PTtRV/8Z0daCvuwBqG9wQlf7I5TnnYzc3Y1y/AJnLEwLCnnkjqfR4p081tfrhK01S46D2OBOlrR9Btm7ma3JJPtzHhCK4zAU3084nUG0zsGrBCnWt7KwoRUG9jn3fmAPc2qCiPrJBHUbw37UCYMb7wWhkPILx0nJo7M3YyPCDYi6SY4jZqyZ6u4ejJZpqKUC9uZnSI5rw58YQm7fi4iGsWcsQE8OOs4t2SEa1u90HGkqeeTAMGL8cVDIY0mT42ovZ+XAzawcuBkRa0NVdEp2DhlpgFQG9ZyFyMwApzdJhCc4FmIZafOx8TlE/RfwqgHyhkKsuYy3LcBnx0f4416bQDZHpuLs0ZN7XqBoCoJliS0F5WgMdXo7cusWJkXLfHy8yZKo1wmlms3D6AAzYxOglKNgpvnhyUko5fhUx0VOKO1wiE2jN/B8bpjt5VEq1sM0BRy/g+eGnF+zjqn+LGuHbuTEWKPjXFUx0BUvomYCGI9QtgQiFqUrm2F8pOI4NiVGeLD7VvxqmIpdZIoBEb2ekxsLaIoHtCUgV6B0NEI4htJaQ5WnEXWkmxcTKjtUm4VNNU6fSyZydzdy6ypKLZPpzKaQzz0HtTXMK6VBnO6MpeZhqHgDSydeBKUMsc59NPgnEaMK6muR6QGCqSRyNIXQ/BTNDPHSXkQpi2ioZVlPknXDPcSLae4+M4UlTbxSIxf0Ok6vgKZ4aC861QULZbxqEBOTr8zKsnLgZpoDk2kMaDzWN8ptZ3qJJXN8astkpD+Kff/n+K8XbnMyWxVkNg7248SL+5hVLfjZliS5aJjJkVomR02OjbWDtPmHGY5zpWifTskqck+XB3yOk96+7G5G62Lsz3mp8jTiVYPYQznkjs0w3OM4E7+4lXXDN7I7bWCnisjufnTFR0D7mPPsjWZ4aiDPxIgTljhR7iNduQNKZQhUIWLO95BMprl/IEm8uJecMUqdX2LvGiCsw+RomJL1AIuaL+Gq55ytfjW+z2Df/zmwHwfgxPpDUlrx4vWMylHn+QR0xXHgMuwy06MTCOqCnyz4exLl3/Ng9wj3dKUYLY8gkQwUfgPAue3NoGjIno0gbXakhhHxPRQoIrLDhIcHkanfO06rgfMR0TAiWAMtE5kUaWNipAaaHc9AW1WprfgYivkgkUZTlqAK6TjIlTJE1Rqq8zairhpZLiNHbqA5UGFL4ndMiVbT6BnnhDj3RxCTnH/r255DjmaQyR7nHZtPEDAEGCXwhsCqcOf+BNgmnj2baA9JCqctYqR0M3Suo6a17EyVNduc0Mc1Gvbzq/HVa4i6SbwYiuLb/SJ22yzE9Ml0/PLIPkavsBt09bDj7OlNfO9DM99Q3r9F3s4tEe/bLRAuLi4uLi4uLu9qPIpzvBT7fbt2eVRxDWAXFxcXFxcXl3chB7c+HHbOcg3go4FrALu4uLi4uLi4vBs5GAv5AH/ePshDL759+2TfbqR8Y/rIRwP3zwgXFxcXFxcXl3chL3WAE7rCktnNfP/jc97pZr1lrFmz5m2ryzWAXVxcXFxcXFzehbzcAD7Sloj3EoZhcMUVV1Aqld7yut67o/guxLjzflAU7J6EE864pQEAe8NO5PA+xPTJThjaumrMrYPoE6JU1u5H+DRk537s++9AzPwAcmAIsknklmfQT54KlukoQ+RH0XesJ/bs8xAOYS9/ANFxPAT8yL1bkGUT8YEPYHf2ogQ0xLipyHIF+8kDChdVYfDoqB9ZDIk0VjyP7I9jr30UtSGIvTfuhGU2Lex9A45n/8YXEOEQ+oQo2BLaOpCWpHL62Yh80vnpJpXFWyw54VDzCacu08LszWI9tRlKFWT3tchNTzkhfzUVZWIjSsiD0hpDBmNYqzZDYz2yqxeMErJrM2Lyv2L98TbIDCK3fx/KOUdJoq0DEQzgj++HSgHv82uQzzwDsWrk9g3Myw0jaidgnrwYJs9g1BiAWAPyuWXsO2YKYtIJYJQI50rQMQ2ZHyVROiBE7vOAP4JyyunIdB8Tg+ORw9c7YY2POwNvvJvgSBzluIWsCjUTSmdhOIEmFZA29oYnEf4oPiWE7P85KWsEW1qIOYvQh/ZhReqQ0UaUhWcjvGHwehGTJiHGzSVgqsj0AFgmItqEtCwiGzc54TtPX4Lw+2BwCKrCiKkTUJtDyL09iOo2xOQOyGWhOgqVAsqxpyD3deF5egVi6iRk5zZHl7pUdsJVV1WDJ8Di7H4nTLUn4ISHbprueJx7AkyJ+jmruQXMCnLot46KSc4J8aw9/hD+dMoJxQuIKSc699yuIAd3Mq+uBr1cRvbvckJsZ9NU9fcRfGEjmBbjwxaE6x2P7O5+5DOroVAiUe4DTwAxaTIBLeo8LyedgNzZhfL8U5AdQUyeiCwknXqnTUJ4wwhdRw7vRbTPZk08DsDCpktY2HQJMtVHpjKMd982RCmLuT+Ded+TAHygMU/KU6HG8NOXd7zZ+wseMpU/ck9XH9OqJjpz37LBdy4eBQZ9RaIe59kWs05xPNNDSzlv/MX4cjkn3DROWN3j6yToPtYm+lk3cRoymXFCNgerCRVNWnwTQfNg2stIG3GorSJVVjnOG2ZytA2Peg6RkqRsPYRXldjDRToz13FirBH0D4HuA9MiZgXAEyBhj5KpqMj4CA1+yfy6y/BKDUIBplaVCOofx2sCRglRKdKV1R3Pf/tx1g7vg5EksqEDEQ1j2GWs2nbGhSpMrRqP17CRo0lE2INoqGVrSxvPxFNMr2pB1FQ5odxtk6xxJ0/23wy+CHVlj6PYYJmI8bMcD3BpIzducea/UBA1VRCqJqjHaKCOPnsQMW0BmiIpmgp1/hBUCoS0asrCJKxfwKYRlSf7byaWyCC8YSxpOI+vGkJTPIyWNSZHSzzR30Ot7xJsCbJvG6LxH7j9vAj9hd2U1w3w5ZlVULjHeXdlk6TMIVo849iTsTi9uUIoncX7rBMOF0Xh/1t7IEKXtZxLnhXU+Jq5sMOD3N1NIF9gfPgL1GQNTqhvIVMZJl2Jo9SHoNlR1yi1TEbEosyvu4wT68ejTm+H6igrB8pUrIcZLFyHqInwgUYNw34UJeyhnhqink8BMBwE2b3Jac7xCzhv/MVEPfXU+D6DTw2hzhpPw0iWM/+7Gl3x8ePNt/HDE/6eO/Y4ahfiAx+AUoYn+2/m/n2/d8JmF+4hoEWp8X2G5b1FLvnzH9kwHACgYhdZM9xDUIsBUO29mPMnXEzMazEu9AUEgnVDQWdMkr0QvgBR3U45FGGOZSJqJxAoGhCuR4TqEP4YhGrpzFyHLJaQtkHKL1BTcQIly9kOAKQrcQhUUT+UAkWhYN6LV5WsHrwJmegB9SySIQ25swsSaTKRALvTPhoCCqrQkfGdThj4UB1UDOTe/VBdjWiqcd4X9ZMpNo1H5obBX4Xs34rh83N6iwn+KkRTB3X9wwT3bMeWFrJnAADZN4zS0YhMdFNVo6AcvxCtNQyhaiaGqxCT56Mk+sAyqdzz9BszHDwKeNSXHe9d003Xde644w58Pt9bXtd7dxRfgzvuuINTTz2VSCSCEOKwoBdr167l3HPPpbGxkUgkwqxZs7jxxhtfUcYvfvELxo8fTyAQ4LjjjmPlypVvZxdcXFxcXFxc3uO8XAf4z9sG+NafNr/TzXpLWbJkCcuWLXvL63lfOsHFYjGuuOIKisUin/3sZw+7Njo6ytKlS/nd735HXV0dTz75JB/96EeJxWJ87GMfA+DOO+/kqquu4v7772fBggVcd911nHPOOWzfvv11tYBdXFxcXFxcXN4IL9/y8MF5bXxgRgO/+vPOd7BVby21tbX83d/9Heeddx4TJkw4LMDYNddcc9TqeV8awAejvz355JOvuHbOOecc9vn000/njDPOYMWKFWMG8C9/+Usuv/xyTjvtNAC+9KUvcf3113PTTTfx7W9/+y1tu4uLi4uLi8v7BE11jpefew+zZcsW5s2bR39//2GR4Y52kIz3pQH8ZshkMqxdu3bM+AXYtGkTX/jCFw5LN3/+fDZu3Pg2t87FxcXFxcXlPYumOcdh5+x3pi1vEytWrHhb6nlf7gF+o1QqFT75yU8ybdo0Lr744rHzmUyGqqqqw9LGYjEymczb3EIXFxcXFxeX9yyqemgV+OChvrdXgF/Onj176OrqOurlugbwq1AoFDjvvPMol8s88MADaC/5CywSiZBKpQ5Ln0wmiUQir1nmdx7bwde+9Se+/uA2Hn1wg+M56o+AKkARyI1bsHfsQ27dgzRslIn1qLV+ZMnE3NiDaGlAbl4FAZ/jjZ3NI7N5SGWRL+5yvPEHhsHndTypiwZyx3OgqZjP7kIaNiTjyGwFszeL/fRTCK8H5ZgOGE4gR1OYj20CwB4toDaHIJEG28ZOlVE/tBDZ1YuYNA5l2njw6NiDGcTsk1FPm4PM5hCeIOLvLsY72IXcufGQd+zwXsiOwOAQoqkea98InhNaseIFlDM+jNy+F2kYyI1PYD+/A9Hehmiqw9wRRz51P+rkFhgZBZ8H0TKL4uRjwFyGMr0dKS1E61RE3SRk72YopJDd/eANIWJtoCiIpnoYGHSUJPri2CuWoQ/tQ2heYs+/6LRt8gwmxEeQ259G7nsRskPIjRsQvihnVwYQrcdArBoRa0OOdkOpCLkRKGYY8jjx7EXdJEiMILNxTk31IGomOPd2y2rsDU9jn3AGqzNpRx2hXCAaj6Om4uzL7oZCFnWkG8MuU/JqlPweRFULomk6cv9G7JWPQmIAuvc59SfS2PsGkF292JufRWZzEPJDKIzc24N+8d8jZnY4nsxGCdkziGhvh0IJ+8mHETVViOkzkHv3Ye/aj2hvhqoI9ovrIZWgGK1CTF3AqcYoFDMwMsjzqT4iIyOgeYgqMWSi21Gk2LUT8/ku5N4e7A0rEVMnQG8XQvcju3qRL6yEGadQVZQQrkUTHqz770ZMPA4KJahpojJuJigC0TKN+uGMM0bSBuXAz15tE2iIJ5G71oKi4Vn2EGSTEO9HHD8LaRiOGkp3D8IfhVQG+cIO5OAe0FRnfiR7GBcqjz2TeeNuROsc0hULMWEeWa2CNqMRtc6PiDQxLvQFLNugEgjSnJUIf4z9WQ8FM02iLNiV7kRrjyCCOpnKH9EVKFsCr63wZP/NyEycTOWPGPajPN53MyPeCmL6NOzuEUZKN1OTNTC8XjoiKidgQKGIaOhABGIM6XksBWQhiUTSmQF7/FwWNV8CoVo0y2ZZzy10ksGrfpiZMYE0LEbLGiXFhPJDDBu9iNbJ5HQblDOp9l7MwqYA2JIG/+fIVP5I0hpBNM9kcvTzkL0TFAWrcTJ4P8zc2hp0xQfSxqtK8HkRu9Yis3m0oX1ouRRFS0GxJfjOhZEUGDayu49j9AinNTWiWxJpWWBLdkWrCFs+FjVfwqZEF1akjs4MyH29rE4nebC7l7WZEcTk8QCIQAw5moJihmTZ+Sm0v+AB4JRGL6c1OT4XonE6Tw2M4DVsLLmcs1o/w2BBQ9ROAKGwJm6DULi3K4Mc7aYjUkOTfwpnxiLEi9dzQr1BZfwxVKyHuacrTliPobVHWBMfAd1HORxFjJtNVcUDuo/ja5sZH3bm0P45U2mgDpno4d/nOXP1m+uHufnsTyClTcUqYqeKDHsrVKyHEbWTUMslwukMMe9FmPtS0Dobua8LW1qYmw980WeGIBKEVIaFTV705CBhvQZSWRRUimaGytYRlo+mSZZvg5oman3jEC0zWDd8I7KQZKh4A4/2ZAHwqkEIBbDbZnH95fsYLHTyjdnN/Hb7bWwcFcjh6xE1E3i+kOT4ugDnjW9hft1lDIkkezNOGedPuJibz/4EF0y6iNWDN1Hru4TJUUHFLnLrrttYPXgT6codnNYcY6DwGzRlCRGP5dwjf5Rk+TbWlXIYdgkRbSEv88j8KPbjd5P2C2yPFzm8m4nhDsS4NjArVHV2IaodJRVR1QLgqJoUUjDuOKiO4FcjGLbAlgIi9WQqf6S64kVmi8hchajlZ1HzJdT5LsMz1E2poR0iEVA1ZL4ApuV8VxSKEKhCjuzFn8vxUMGL0LwQrgXAsEsY0VrwBHguXA8+D3XJAiIaxh8xkQUT0d6MrB1PpN5A9m5Dnd6EHN5LSImwbNk6/vmae1n6vT/zrTs3vaa9MMbLjN9lG3v5t5vXvrG8f6NcfvnlrF7tqFPdcccdTJ06lcmTJ3P77bcf1XrcLRBHIJlM8uEPf5jq6mruvffeV8hxHHvssaxbt44LL7xw7Nz69es5//zzX7PcH3xmPpGQzzFgS+XXTOvi4uLi4uLy3mHJ2cezZPFclg2NMuc/buK3m3tfP5OmHrYFYsmCSSyY3cov73vxLWzpO8sjjzzCz3/+cwB++tOfcscddxCJRPj6179+mN311/K+XAG2LItSqUSlUgGgXC5TKpWwbZvBwUFOO+002trauOeee46oRXfFFVdwww03sGrVKiqVCr/61a/YtWsXl1566dvcExcXFxcXF5f3LC/f/nAkp7j3GIVCgUAgQDabZdeuXSxdupQlS5awf//+o1rP+3IF+NZbb+Wyyy4b+xwKhQBn4/VTTz3Fiy++yN69e4nFYmNpTj31VB555BEALrjgAuLxOBdffDFDQ0NMnz6dhx56yJVAc3FxcXFxcTl6KIpzvPzce5i6ujq2b9/Oli1bOOmkk1AUhXw+76pAHA0uvfTSV12tXbRoEVdfffXrlvHlL3+ZL3/5y0e5ZS4uLi4uLi4uDkJRES9zehPKe3sF+Ctf+Qrz5s0DnD3AACtXrmTmzJlHtZ73pQHs4uLi4uLi4vKu532oA/zlL3+ZD37wg2iaxvjx4wGYNGkSv/71r49qPe/tdfR3GVZ/Fqs7CbZNafleKut6kRs3IrwasquX8vODyLI1lr6yci+ybGLFC2jT67H39iO7+0DTMNbuwd4/7HjxDyecn0QSI4hwENnpKAMIr4Y45hQqK3ahzmxBPXOBE/NcVfAunQ+mSeWJHcj4iOMJO5RAnVCN3Pw8Sl0Idf40ME2wbdSmEPL5jaCpyJ4BxDGnQCaHUu3Huv9OZFcvyoKzsB97CNYuB18Emc0hh1OOOsFICmvFJuw9Pch0BrU2gGhpQD++hcr1t0KpgvncXmR8BGXeDMwHn8F8sdvpVyaHsa7TcRxUFOyH/hfPLX/E/vM9yL44dO3E+P2fsO+/A7ltN5QLiAmtmHf9Geuh+5w2x0cwVm7HvP9pqBhYQ3lIJZDbN4JtgzeAfOF55N79VB7YgLVmGyTSWPtGkMkexKRvOooE8TjW3bchqtuQL+5CjuyHZJq6VAm5dwtyyzPYm/cg9+5GxkewH78bmc4iu/uw9sThppv5wI5NUExBIoPs2o9cv5a2R5/BXrsV4v14RvvwxrtRUJHpAeTOZyCTJ37KbMgVnD4X8oi5x6BMG4+1dxjh9yMHRpF7e5Dbd2PvH8W4+TYYSiB7+pBbdiEmtWGveBY5MAS2pHL/OuSLLwAgmmqcedA7AqksFEr4E0PYKx9A1E+GTBKAucP90NOL0P3YD/4RuWkjxk1/QMyYgzZvEsa6HoTX66iHRILYW9ZjDWYdZZJNKxzP7eEelGf+jNLRhux8HntXN3LlKrzlCrK7H/u5p7DXvoDcthm5Yxdi0jjs4RzWQ08g9/Y49yafQKYKkCtgb+tCbngR4fc5KiYVA2kUnTqroxAMU5w5F8wKeAKMDzsa3kPFGxBCgWKKyXotOStFWK/F7k8iZk0BobB68CYUoWJjIWoncNdwhaW1GpriYelEhSmZDNKwURuC5M0kYV0S0r2kZZo5NRoM7CNStNCVD3JmyyVsSQCVAsrkJqKeBrLRCJtGB9iVtomHhOPs4o8g86OsGvAipQ2FFIZdospjoZYKFMx72ZLspKSYKAIm9MWx5HK6cybWSJH5NePwFStglKijlkooSqjohHvfNHoDndkUVAyyxp2E0xmqRpPIPRtIlm/DDEaoKBI1OcDjfTfjkx6eH+klbY3S4DfA53E85qNhirWNrCkWmV39WTJWEkoPQCiAqAog2ppYV8phSZOSYiL8jvNvSzAGvnPZNHoDcyRohjkm8nGyR2FBg+TEWCNyZxfSKjOqZJFDaUZEioAWJaeZzKttx1YEIb0aIc4gU8lStgssbmnF9vpRbbin6/csai6AZSKTPZzTVItMD/CJSQ2QGSKAH0oZZKqPGm8bybLN8t4EenKQpW3VhFMp7ETpwIvbxKeEnLKG9yJTfTzQPYyUEuEJMm7jDoo6lCJRHu+1GS7dyH8cGwXgwe4c1TkTO1XmxYSKvmcjOTMBug9qJwJQeDEFe9ZCqUxAq0KtDTgqDukB593k0dEVL8WqajxqADSViOcThEeGUKp9nNX6GTTFg9D9KPk0ttePLUHE2hBCcH7zoa18cjiB0vMis6s/S72/nc7sHr4w/SKumqs47SllmKt52JHKcVeno7pRL2OMlhyD6+H9t46V5VUlALW+S3g2Lvn0lIs4pfFSNo6UyRkJNMWDlE8AsCXxO+yNq9mRMpgfqmZrMkNJs9k4mkE0/gPKSYuIFiWKZSEiTVDOwfAw1IxnVVUbhv0opr0M1ANrdoHzkeE6hDgD+eIuhG3RHIyyMFYN/fuIeD4Bmsf5Dgzo5DWLvvxveLL/ZkTtBDTFC5UyFUU6q6uNtchUH9iSlJ2AQh727uDUJh2AeMDCkgZNgS9SsnLI0S6mRD2OOoRZcd6nRRX1lFkQrkcRKsW0SnHCdEikHTWgcs5peymDJQXe+Y1vzHB4H+4BBujo6BgzfgGmTJnCMcccc1TrcFeAXVxcXFxcXFzejbxMBcI5Z74zbXmP4RrALi4uLi4uLi7vRl624rts1S4eeGL7O9ig9w7uFggXFxcXFxcXl3cjL4sEt+T06fzwG+e80616T/C+NYDvuOMOTj31VCKRCEIITPPwnxReeOEFFi5cSDAYpLm5me9+97tIKceuSym5+uqraW5uJhgMsnDhQrZs2fJ2d8PFxcXFxcXlvYqqHfl4j2NZ1usn+it53xrAsViMK664gp/97GevuJbNZlmyZAknn3wyIyMjLFu2jOuvv/6wtNdeey033HADy5YtY2RkhJNPPpklS5aQy+Xevk64uLi4uLi4vHd5nxrAH/rQh97yOt63BvCSJUu48MILmThx4iuu3X333ViWxfe+9z38fj+zZs3i61//+lhoPoBf/vKXXHnllcyaNQu/38/3vvc9KpUK99xzz6vWaecrlLeOUlnTDbqK8KnIZA6qoxhr96M1hbBTZUpP94EqEEEdbVoD1kiByoZ+yi8MY3ZnyN/xInaqjNmZwl6+EpnOY+cqyPgI1tb9iJAH0VSHNZDD+t9b8cxtgkwOuWYdYkIrSl0IubMLe6SAPr0GSmVEMICYNQVz17CjAhAJgt+PaGvC+sBZEAkhl3wc0dKAaG8ma6cQc2Yh2ppQjulATGhFpvvAozte+Lu3Y77QC7ZEDqYo3rURadjYqTJ4dOxEkeJNz4yFhDa7UgivRmVDv6NGUDCQ2QrYNsbuBFpHDXI0Bakssmhg9mYpP9eL1ZPGeHIbansMaUmMjQNYK19Ajiad+tJlirc/hz2UwxopIsIezM19GLuSlP73WcrLtiL7RrCfXo+oqQJAn1EHHhXZ1YvaHEWufxHz95/GfuwBKk9spbJpCHvFY9jpMvbarZjP7ECuex6ZzWO90IWVKCFqqhDhENa+EURNFdZADnViHdZIEWPDfuzVG5DDo9hdQ5Sf6UbURLDieeTubujrQT7/AtoDd4HmIT1hIsYTW6j9w5PYu/ZjxQvI7Z1YK9Yhk2lnrEaTYNjIbBFr7zCVHQlnTowmsXf2Ieqqkds7UWZNRA6msPrT6AsmOfcrlcXaNYC5pR9l6jhkpoTx9A6sB59EBAPYzz4OgNy9D5lMY+3oxbzrAbBtREsDdqqM9fByR5mkYFB5bBvWC12OJ3/PEGZXGtkzQOHGNcgdu5Db98JIisqT25B9cYQqQFNJffVXyLKJuaUfY1cSbImIRR1lkJYYSnst5dVdWCNF5K4u7HQZamKYnWlQhOPhHvWCaUF6GAolhK5jNEzEn05BpYAIxJDyCdbEb6IrKwgYAjwBKl4vIdvHsp4elCo/srsfmRng5IZxxLzNPD2YANXDkjYf0jbwq2FS5SyiaTpCV1Cnt+PXIpw3/mJqfZcQ0muIWQGwbeTIfkrWA8AKFjVfAok0sm+Yrcl+IiLMlKif2dUR/mdL0HlRqGdhVjdz3vgaUpVBkDb+jc+xL+tB5obRFR9toSC+sslZrZ9BTJ6Psm0N06qq0TuqGCjtAaDgU9lejFOxi8h4JwCjJZWp0UbMzgRh/QJHKaD5yxCtIua9CE1ZwrbUIAiFM5rbqAiL+XWXEVViDBQcj3gxcQ7U1bEjlSCoWwwUfkPE8wkMj45oa0TMmgZVYeIFRy0jZyQQE2cgWhooWTls+RhFUyEfjYKiML/uMsjkKEWivJiQ5CkiCwaZgEaNqEGZOo7No/D0oEEwnUaIMxAoFM0MZeshxoW+QNnKM1LuJl2JI7NxGgMmjaIejBKibhL2hifBF6I33wmRejal+5C5YUSsDc2ymSqCRDw2uWgYhAL5FKiCUxobKelApeCoBFQM7k2CaQssaSB3bna8/IXC430ZTmkqUu1tBaPEyoGbOW98FUgbraOGCeEKouMEQmoVMj3AxtF9AIRObeCpYDNoKsIsg6aSqSiIaBPYEnxeKnYR/3A/y3qGHQWb9O2I+snoJ04AIKxfwOO5CrIwilIpU+WxkNufI6BGKWm2o+hhKs79aXa0VFcOxOnKelg5cDPRio4QZ1DQnV8592U8LGyu8Nvtt4FQmBlzoqaeM66Vm3bexpP9NzMh7JgPWxK/Y16dAcCynls4zSeoyZSp810Gif2ULUGqoiKmOvUWdMloScO7aS2nNF5KyXoAWc7yvFFk+WAczAoyM4AslkgbcebVRdFtgSp00jLtPCPZO9k4uo+CeS94dGR8J2G9FpnsgVCAwcJ15DULMakNsyvFtlSaiKeaE+urwKywdmgQMjk8wguREAyOkK6tQcydTTRnIMbNhglTeKLPZNhToKHkRRwwl54fKYLq4T83C0S4AYpFUAS+oAUT55MPBxks7KH5OIusMYKYPwcDAxSNjMzyZL5C0VQorxt8VVvhMBQVFO1lx7tHBWLRokWHfb7tttveVP7vfOc7rzi+/e1vs3fv3qPYyiPzvjWAX4tNmzYxd+5ctJd4Xs6fP5/Ozk4ymQzpdJp9+/ZxwgknjF3XNI25c+eycePGd6LJLi4uLi4uLu813oEVYK/Xy6JFi1i0aBE/+MEPXjPt888/f9jnf/zHf3xTdf3qV7+io6ODSZMmjR0dHR0EAoE33e43yzuyjt7Z2fmG0imKcpgO3NtFJpOhqqrqsHMHwyJnMpmxvcBHSpPJZN6OJrq4uLi4uLi81zm46vvyc28hdXV1PPnkk39R3pf6Sr0Rpk+fzplnnklLS8th55ctW/YX1f9meEcM4I6OjteN6SylxO/3k8/n36ZWHSISidDb23vYuWQyOXbt4A1OpVKvSPPym+ji4uLi4uLi8hchFOd4+bm3kGQyyaJFiwgEAvzoRz9izpw5bzjv69l2L+epp546Yp4//OEPb6qcv4R3xAAOBAJs3br1NdNIKTn22GPfnga9jGOPPZbbbrsN0zTHtkGsX7+eiRMnEolEABg/fjzr1q1jwYIFAJimyaZNm/j0pz/9quV+++GtaHkTJahzZkuMD9aNe+s74+Li4uLi4vKO89ifN3H7/c9TsgR/3rD7jWVSjrDl4TVWgO+44w5+8YtfsHnzZrLZLIZhHLadU0rJd7/7Xa677jrS6TTHH388v/zlLw+LsrZv3z7q6upYv349n/zkJ9mxY8er1lcul/nOd74z9rlYLB72GeCaa6551fxv1mA+mrwjBvB5551He3v766b7yEc+8pa1wbIsDMOgUnE295fLZUzTxOPx8PGPf5x/+Zd/4eqrr+Zb3/oWe/fu5dprr+UrX/nKWP4rrriCa6+9ljPOOINJkybx/e9/H13XOf/881+1zu+dMxP/QBGtKYidrbxlfXNxcXFxcXF5d7H47GOpPm4qoyWNk3/yO367ufd18whFQyj6y84Zr5r+oMJVsVjks5/97Cuuv1TBqqOjg2uuuYYlS5awc+dOQqEQ4GyBAJg3bx5+v5/h4eGxcy9nwYIFrFq1auzzSSeddNjnN2vgLl26lLvuuutN5flLeUec4N7o0vbvf//7t6wNt956K36/nyVLlgAQCoXw+/2sXLmScDjMsmXLWLlyJTU1NSxevJjLL7+cr371q2P5r7zySi699FIWL15MTU0Nq1at4tFHHx2bQEfCGsijt4QQikAJaBR35bBTZYp/egG1IUBlxyjlbUlQFaz+HHaiRGnZbufzSMEpRBWofoHaEESfWo3Rlcbck0JprMLc3IcIeyDgx1i+GbUljDKp2VFaqI5iF0yobsDqSWFnDZRxNVBbhblrGHtPDzI+gr5kLnbWQEydjnx+K7JnANMug2mhDO5GdvdDTROh/Z2QSUJ1BGobQdeRu3Yi2lsQ4SAUitipMtKwEbVhlICGUu1DbY/BSAqlNYbvrEmOekS2gtGVdhQjDIvinZsQAR10FW1itaN4sWsYqyeFsStBeUPcGQvDBkUgfBrGljiVrSOobWEqLwxRWd1FZUcCWTAQqqCyK4ksmVCxsEaK2AUTrSmE0FWEriDLFqU7nsPcPuQoS+xJknqon8r6PipbRpw0+TJaUwgl5MHcn0ZpiWEXDNSmEKKhFnLOPTJ2JTCWbaTw+7WoHQ1Yz+9xFAsAO1tBn9mA8GpQKIEq0JpClFfsRfg0x6N5OIGxc8RRhejqIrLyGdTWMEIRiKAX4VWxBnIojVWImiqkYQNg9mZRxjchDRvfwnGY3RnMPaOIgIaxdg9UDMr3bECZ3o4VL1B+aCuYFtKwsBMlcpuzoAgKqwcQAd2ZH6EA5pZ+5N4eyusGMFY7CgNGV9o5Ht0IqvOCM/dnKA0amAM5SusGMbcPMfLAEOUR01H+MAX2vhGsgRyVF4ZQYz7MbYMYe1IAhBc3Yw0XKbyYcu5VqYxMZyk9uA1jcx+lh3egRr3o02qw948iSyZyyy70kydSWt2LCAeRJRMxuR10HTF3OgAFK42ouZQ1hTyYFWws5tbWcEJdO9IXJmOnURUdpM3JjUFHcD4UwG6aiqgUKZoZMhUVbBNFqIiqFlYP5hjvaaGs2IgPnIg0DKr0RgCW996CMtrDstGs48U/kqQ7GydnJDDsRxHTT0DoKq1BFUo5/txjIYTC/zergBjfym27byNrjKAbFjtSEtk2m+ycY2kKGsitW9ANi6FiliHhbMtK20nEpLlsTSZRoj6aqGddMUO6Emd6uA1N8SDqxwNwRlDFW3bUVfLG3ciB7djyMRgdYfXgTVC8j0mRMMIXJm3E0RP9AKwZHeC4A6oEct+LmPetYm7NeIqm4njfD/0WTXigVMFe9Txk8kyKlOnO7qPWdwmydzfkCmxJQNnKE9QtQqaGzA4DICaN45l4ikXNlxDUP45orUWgYOgqdmcvZ0YCzK0tQDLOw/tvRQjBM/Ecj/clAFCESp3eSszTjKhqYUEkRpxhRvQC0h9FtLdDMcO44GRGgpJjay5HeIJgmTw+NASRRo6rDRFWqhx1iOaZKCEPWjaBTwkxLIfAEwDT4pQmgwsmXURT4IuOckN7MwKFKq+FJjyoNqzM5vBrtjP23jAAUY8XmeyhLEsQa3XGEzA7U2iKhFKFtclBUBTObLlkTLmDXIGo51OIUB0h3UYogueNIgWK2LsGGCrewP37fs9xtc56lqlrdETboWJgyDKa8HBKYy1y/wsQa4ZKgZUDN3NmyyVMqyqxsOkSzECIVOV28maSYW+FM1slawZ1zm3PcX88S2Pg8we+xU7nQ+OKTI8ZbHOmH/GiRr3/ch7efyuLW5tguAcRbmC4dCNbhcUpjSFOabwU4Q2zoOFSAiLIOeM+jZg8i7VDN6IrPqgUmGtW8Gs2Mt2HiLaAabI7XcKSBiVRoWzn8anOWMpQDZOjYQJqFDw6ItIElukoZ3h87Eh5CAz3Q8VEmz+e+XWXsWG44CiiFJIM5HVkXxw5tBu5swuzN4tAsNnjQ8TaHMWY6jbOn9DCzpRABGu4f5/znjq1qRkRa+OHx9chE90QjlKZfRJ6ow9hGfzkxTJRTz3eBa3sy6rIvfsoW3lkbphwrsRxtX6WTrwI34env6qtcBivUIA4wp7gl/BaClfw+gpWuVxuTIO3u7ubdDpNTU3Nq9b35JNPsmLFilc9nnjiiTfWzwP09/e/qfR/De9bFYhLL70UKeUrjoOSHrNnz2bVqlUUCgUGBwf57ne/e9hfMkIIrrnmGgYHBykUCqxcuZJZs2a9Q71xcXFxcXFxec9xFFUg3oiC1bZt25g3bx4LFy7kwgsv5He/+x2K8uqm4p133nnY55fLl1177bVvqo1v55aI960B7OLi4uLi4uLyruZNrgC/FgdVql5LweqEE05g48aNrFy5kmeeeYbTTz/9Nct8+TaL+fPnH/b5tfb/vtP8RaP4pz/9iSeeeIJSqURzczOnnHIKZ555Jrquv35mFxcXFxcXFxeX10dVQdVY9uf1LPvzBgAqlVffA/xaHHTiP5oKVi+XPXu9z+8m3vQK8Fe+8hUuueQSenp6ME2TnTt3ct5551FfX883v/lNSqXSGypneHiY+++/H9M033SjXVxcXFxcXFze8xxY8V3ywZP4yU++xE9+8iV++MPPv36+IxCNRscUrA5yUMFq7ty5f1GZL9+y8HqfX4+302B+0wbwzTffzPr163nggQe45ZZbuPPOOwkGg7z44otYlsW8efOIx+OvW84ll1xCLpfjqquu+osa/reICGiOw5AqsEaKhE6sRZZNxzkoXUboKmpEo5x0nJqkYSNtiRrzodUHwbAOlKM7YXhTZTwnT0BtCx8KEVu2sHYPoZ81B9FUjyw7ahPm890oExuxHn4S7biJ2Iki1u4hhNeLCOiIhhgiGIBozYFwshXEpDaUs87FN9CFmNCKqJ8MhSJyx1bHQSmZAW8AuX0bZHNOuN10FkIBrM5h1OYQ6szxyHQerT2KnShhbh+C6ijFR3Yhs0VkVy/mUB6l2odS6x8bK1kwsBNFsg92IcsWKApWPI8VzzvjMbEKtSnkhPu1JGpDEM+UauzhAr4lk7FzBv4PNGP259HGRdBaw4iwBztXQan2UcqraCdMdirz6MiSiT6pCq09gtoUQo16qbpoKp5Fkw/dC8NGjG9An17jOAq2N2MnSlAVxt43AIrAGingP2cyWmsY/5kTwLZRYj6kYVNZ041neg3G1jhEgpj9eexEERHU0SdE0RbNwtg5gmioRWsNo06qQ/YNI2JRJ5ywYSPzZdTT5mBnK8ihNBRKTnjlTf1OGOLVu9A6arD6MugToqh1fifUsGFh5yoIn0b8P9cjSybeU9sxNuxH1ETQZ9QR+6eTkH1xPG1BxzEtV8B6bgfGrgSYJt75TegLp6A0hPGeOh6hK2gzG1GbQ47zWUBDKGCUFfSWENqsZkLVBt56HTw6nvEhjL0prJEinuOaKD0/hNYaJv1iEWPHKNZADm1chNAH6lGqvE6YZp8Xz5QYSsjjzEug8Od9CJ+G1h7B2DwIponvAy1IKVGqvJSuX4Hc34eobof28UT7egA4SVcoBwIYdold6SHixb2I7DCRooWaSyEHtrMrnYaqCKKumjv3DrEmNcyGkaTj56dqPNabI1MZZmpViYzIc/ueFGb9eJT5i5FI7thzG3NrJdlIiFMaDzjEVkfJmwohfSm68kGs+/4EPg8CAYEqzh0fIVAo0+CdAIbBRZMvotrTguzdwinRGIpYTDiVomgqiGAAObSbydHPUzBN4sXr8apBEiLNzFgMY0+SjGYwv+4ymowImBWGigMQbWT14E2IYA2oHvQTO9ibdbyYlGIWNA1VgOULENYvgICjZpOJOn2YXR1BbnsWKgalybNQm0KsH+nmxLrxbBrNglDYOLoPMX4aoj6KmDSb6YEmJgbHsz15vRNaemcXi5ovwS892FIgkz3YMccRjKpqzvAf+KIsPYDw+4hoNei2QBnfjBWqIqLXISafxGlNQVYO7KcpYHDOOEd2Uld8yKHdPDmwn0xlGOkL06C3oQoN+dT9iFAdDAxhYVKnNvJ4383Ikf0UfCpnKI7WvEBQkHmEoiPTfShhD7mAh4wxjIKKWVWPGDfdCfF78J0+9TgA8maS6VUKnVmLMhXKlsCWIKWN/fwaZCJPjdaA0P0IBEIITNX5+i10VwhoNuK4+eiKdBww5WNQVTf2ftqS+B2EL2BRJISVLFHtNTFs59kXQnBsbQFxQC9WUzwkyr0QCWHYJQy7hK54oaYFueNFsG10RTJSuhlNUaF4H3szPQgEzw+rDBYl+7IVFjQarB4Icd74iwG4p8txSm/wfw5wfJABTqoP8/D+Wzm1yYcqNCodx7J8NI2UksnRRl5IZECuQBad+VaUjhPewZ/xy1Ye0XTFgbDYdTAySkUTYEumRP3szWTxqeeyIzWMV2rkjbsp23k2DBcgN+I4H4eWsiW9H7l3MwSrWdBQi9U4GfO53Rhru1g3fCOToyXHmdAT5IJJFyFmdlCua0FMaEU7ppmoiDJnXycrB/ezqZKlYKahkOLYmjC2x0u1z+SuzttQSwVkohuZHnC+EwFvoYBn4SQ6C/v5zmQdRWhUnh/gRH8IFAVd8ZGPRilHYyzvNVjee8vr2gtjKMphx7LlG7jqWze8anLLsiiVSocpXJVKJWzbuWEHFay2bNlCsVjk6quvfl0Fq7eTNWvWvG11vektEH6/n7a2tsPOCSFobW3l2muvpaOjg69+9auvq/RgGAYTJkxg5cqVb7YJLi4uLi4uLi7veWxpYUtr7PNZZx/HiSdN5Re/uO+I6W+99VYuu+zQH2kHlalWrFjBokWLuPLKK8lmsyxevJhMJsO8efNeV8HqtXg9HeCDhvhfw549e1BVlQkTJvzVZb2UN70C/MlPfpKLL774VUP+XnbZZTzyyCOvW85///d/s3btWn70ox+92Sa4uLi4uLi4uLznOWgAHzwefXQtV111/aumfz2Fq6OtYHVQB/jgcVAH+OBxMFjYm+Hyyy9n9erVgBPYY+rUqUyePJnbb7/9L27nkXjTK8A/+tGPWLp0KW1tbVx00UWcc845h+3ZeO655/B4PK9bzowZM5gxY8abrf5tJR6P89WvfpXHH3+ccrnM9OnT+b//9/9y2mmnAY7+3de+9jV27NhBQ0MD3/jGN/g//+f/vMOtdnFxcXFxcXkvYEv7sBXgxWfP5YSTpvKrXz74DrbqEE8++eRRL/ORRx7h5z//OQA//elPueOOO4hEInz961/nwgsvPGr1vGkD2Ofz8dBDD3Hbbbfx05/+lF//+tcATJgwgWAwSGdnJ//5n/951Br4TnLFFVcwNDTEli1bqK6u5mc/+xkf+chH6O7uJpvN8uEPf5gf//jHfP7zn2fNmjWcd955NDY2vmv20ri4uLi4uLj87SIP/Pfyc+82Ojs7efzxxxkdHaW2tpYzzjjjVYNxvB6FQoFAIEA2m2XXrl0sXboURVH45Cc/eVTb/BeHQr7ooou46KKL6O3t5emnn6a/v59AIMCiRYuYOnXq0WzjO8aePXu4/PLLx0IAfvGLX+TKK69k9+7dPProo0yZMoUvfelLAJx22mlcfvnl/PznP3cNYBcXFxcXF5e/Glua2PKQWtbyP2/k4Qefewdb9Eq+/e1vj21nra2tZXh4GEVR+MY3vsEPfvCDN11eXV0d27dvZ8uWLZx00kkoikI+nz/qQTL+6kAYra2tfPKTn+SrX/0qX/ziF98zxi/AN7/5Te69914GBgYwDINf/OIXTJo0idmzZ7Np06bDoqmAIwB9MJrKkbAzFbQJUcd7vj2ClSxh9ufRWwLYBUeFQJZNPH4bz8xatNYQam0AaViYQ3n0jhhqbQAl7EEJe7ATJYTfh9WfwzOzFrUhABULtS2K/cJuKit3QCaH0ZVGm1iN7BvGTpeQo0m0OS1oHz3V8Sw9gLluF3JfFztPWwD+CHh0MmqZbEMzomUGFFKISeMQddVg2zA0inz+BeTAKHJnF6JlGpU/b0OOptCO70CdWIexajvm/gxYNmpDEGN/BhJplICOCPuxM2WUkAe9I4bQVbQmp8/WSBG1OUT44tmoMR/WQA5pS/wXn4g+vw2hO6oQ2rgIdqKIunA2drpMeXcGSmV8H2jBHMjhnV2HnS47/S+YYEmwJOEpfuxd+9Fn1UPFwE6UUGoDlDfEsdNlp60beyGRRmsNI7MVrHgec30X1kAO79+dgNy7H312I1QMRCyEuWcULIkdzyILBlZfBkoVRwHh5AnoU2IYXWk8i2ZAJo82rQHtmGZH4aA2gL1lD54PHkvpT+ud+TKYQrTUYW3uRJ9R54RcbqpB7t6HflyrEwI5FMC/9FjUWj+e6TV4Fk6DUgVtXgdKtR9jZwIUxQmRXbEwe7PUf6rFUYeIRfGcN5/ivduwh3LYu7rBo6PPqsdzjKNQInwqgU+f4HimJ0sIXXfmjO3cT3PrIEpjFUpAR416CUwL463VUJtDpG7fhdYaxntiC/buAaRh4z21Ha0piLFzBCWkgyKo+6fjUGv9aOMOhDXVlbFQ0eZ6R91EndmCZ2YtqILAOR1O6OzqKHzj89g9CQgFIJNDOefjeJdMQ3i9TujaUg58IbCW83TFxlsxkdLGtAWNSiNoHghU0SlTEG3kuNrLIVdAxkf4VHOAE+tbmV9Xx+ktXh7s7uW88RdTsYv4NYWIGuPSFh/Jcj85O4ONxQca81R5Gnkh4YTFltkcYuI859/yCeTw9YiwM/Y1vs+AtMlUhrHC1fQV9yAHhhgo/AY5uB1SWTYVRimY94JlMrXKB23jEI1TWRO/iW1JPw3+z2HaFapLKp2ZUZSwh7Bey0jpZmS/E5EpUdaoWEW8qoRCCrlvEzKZpjfnIV4TZGdlFGybBQ2XsjW5n3jxeijeR1RvIKQ7IVDzZors9JkQ8FGycqAI5vtCbEp0EfFYYJlMiUZBOO8Te/0qTF0Ds0JX1otoaYD6apLl2zBUQc5Q2Orzs2rACXtqr34OciOsHboRfOdCSzNy+9OUhYm9u5dVA/0YsoxhlwkqYU6VWSZHG7HkcgAMu4QI1XFaUxvhbAHS/chkDzHvRYimOghfAG2tKOk4KBpntlwCNS0EpBfZOIU1Q9141SCa8CDzo9h1E5C2RGLjVYPUlHXWDg0iMwPc1XkbAPHi9QwrKagYhPVaajwtzK9qxCt8nNX6GWbEQlTsIqKtCdFcTcZOg6Khp0dY3tuDpizhrs7bqLpgIgVTgd3bGBdSoVRGQQWj5LxncwX2ZT3kjLt4PJ1DnxSj3l/vzKmSSUSvZ1zoC1Qp1YiqFipWkYpVQbRMxrJNilaWZLnfeQ+oKiW/h8GCxpYE7M+p/GhnnqlVnyPq+RRL2tpoC/roiITQhIe8CcOlG3mw+1bOn+CoQQwWrsOnhpwxBOLFUWbEioT1C9g02oW3UGBxSyv1/stZOzTCvLpGhsv76A9YPNl/MwOFIRYGPKQ8FU6svwx/OgXARkWjZGURsxfhUfygCCIjI0wMh1g3fCPHRppYMzrA7kwKVegsar6EUb0IAT9rh25kemwcVFUhfFE0xUPOTKC2x1BjPo6JVbN5NAC5u0h5TYrmfaB58OVyYFrY+0aQmhfZN8ipuX5mxprxjwyCojn3cNdaZlVbnDMuhO0LIhunQDEFxQyj1WHwhVAWnkuqopL3qXiySfSp1c73aKGIt2ISKlTw7tvGue0x5tSY2J1Db8gOefke4DPPms3V3/v7N5T37eDhhx/mv//7v/ntb39LLpdjcHCQfD7P/9/efYfHUdyPH3/PXi+6k069WZbkXuWOcQEMxrglwYaAMcVgIARIgQRCgISWkA7kCwkEiOkB8oMAwYaYZrDBxg33XiRZVu93p+u38/tjbRm5gAGDDczrefaB2zI7Ozu3Gu/NfOYf//gHDzzwAAsWLPjMaf70pz9l+PDhzJkzh6uvvhqAxYsX079//2Oa98/9BviLyMvLO6r5nrt168aePXu+ghwd3pgxY3jqqafIy8vDZDLh8/l46aWXcDgc+P1+evXq1WX/j8+moiiKoiiK8kXoJNFJHrLuRPHoo4/yhz/8gcsuu6xznd1u57LLLiMej/Poo48yderUz5Tmtddey1lnnYXZbKZ79+4AlJaWdna5PVaOSwO4tbWVxx577FMDHre3t39FOTqUrutMmDCB8ePH09zcjMfjYcGCBUyZMoX33nsPj8dz2NlU9s+0cji3L92JY28jMhhjYq8sJvbO/pKvQlEURVGUE8HCxdt5/f4PieodaBsrjuqYg8Ogvf3GOl5fsPpLyuFnt3r1ah5++OHDbps5c+bn6gIB0KNHjy6fD37heCwclwZwdnb2Uc0PnZub+xXk5vBaW1vZvXs3L7zwAj6fD4Dvfve7lJaWsnDhQsrKynjlla5x+FatWvWJs6ncfnIPsk7vTnJvAKmfeJ3YFUVRFEX5ckwa34sJFw+kLVaHed7rPLhox6cec3AD+LSJAxg2qoRHH3rzy8zqUfP7/WRkZBx2W0ZGxgn9q/hxaQBXVFQcj9N+Junp6fTt25e//e1v3HPPPbjdbl577TU2bdrEsGHD6NmzJ3/4wx948MEHmTt3LsuXL2fevHnMm3fkGVoURVEURVGOljwoDNr+dSeK/TPMfd7tx9NxaQB/XbzyyivccMMN9OjRg0gkQmFhIffffz9nnHEGYHT+vu6667j++uvJzs7m7rvvZsaMGcc514qiKIqifBMc3Af4nTc38saCtccvQweJRCJdZn472LGYCe7L8oWjQHyT9ezZk5dffpmGhgb8fj+bNm3iyiuv7Nx+6qmnsmbNGsLhMBUVFZ2jFY/EcWo3hM2M5dR+aF4bmttqjNwfnI25IIV4eTuO04uxlWWB1YRlYhkmnx3r0FwsxV5M+SkIu4n4ngBoAnOJl8SGSiwDskns8aO3RcGkIf0RRLoHzWsjsbvFiKJQ1WZEQQDE0CEk1lUjP1yNbG7F1CsXWdNi/Leyjj47d8OOzWC3kRII4Una0N9/A1mxCVJTCZX0RXaEweOGRBKtey6iMBd9xXtYB2SQWFWOrKwh8r/txMvbsYwqRg/GELmZWPtnIPr3wDooE1E2GJISc54brZsRrcHUOwfrKT2wX3ASppJMRFEhWqYD2+gCLD3SkOV7IZEg2RjGlO1CZPuwntEHWvyYct3Y+qUh8nNI7GlH89oQWV6sM08Cs8mIGLEvEoS5xEti94E+5prPjnbK6dhG5aFlpyDcVswFKZDlQ+uVi5bhQEaSmPvlYh7ZE3QJiSQkkoiiPGR7B+ayQizje6EV+tBDccwTBoPZhDZ6PIQiaCUF2L4zBDHkNMTIIQhvCsLrQcaT4HGj9SuBUATb5H5o3XMhlkSkedEK0oxynnIuJBJgt4HbiWlkH0S3vugVtWhZ+6axLO2DKC1E1jZgmjQW69hiZDyJeXAxWo8cHN/pi4wmsEwdgUwmkY0tOM4ZhHnOXLQehYgBfRG5WZgmnYIoykE7/RRkNGrkozgLintBJAqhCHoghnlwvjHKHozIIkMKjXJ3mnEPTsE6cQDJqna03vlYT++DSE8DqwlTmh3HzCHIcJzktiq0Pvmg64hCo9uTuZsHUTYA84A8I9JGKAIeF+Y+2YhMH9bTeoHVgg0rppP6IvKzia+tQbZUGhEgnHbQE4jcvoicvsjWKoake5B7N+PQ3Azy5RM260RtVpa11NPN3Q2RVgjJNyESRZhMJB1uVjZWURuq5t+7Ykws8FHd8Q+cZi9ZUTuYJiJdaTy0xY5AEIg3URGwEU12MDYjl5pQs3FPEjH6pPp4r7aKuC8PUlOIL99JbegfEPZj0Wy8XN5AfsIDusSi2cHfihg8jrL0YkKJdkTu1Vg1B3L7drBM5iQzdHPHiCYX4NY8vNsRY0BKHsJpQSBwW3yIvreyI1pHb286S+sbGZF5KTIRJdpjEKIwlympVrKDkgy7FVnfxPt1jzMwEqYxLEBohJN+pNSJJV8jK5FCiiUDkZ9NmvQgCnPpsJvo7U2nwGUDTcOdtCK3rAF/EFra0WUS2VJJgTuGbG4luWEvG1oSbGipYWxmPgMcGfT36bxf9zjCJKCwrPP7KPdUIfqPoz5cjXbGWHqnRvAGougyye6OCkRufyoCtSypreH9useJ6xFIySCqd/Cy34zuzUZYXTRHnkTuqiIYfxGRmk88xUeECK/teYqQy0lU09EiHYzOKqItVkco2Y5wpRNO+CGWJMVyLjbTVEIuB2Nz5oAjlfi+F1rrm634bAVgtWCu3kog2UKAYGckjJS4Gaf5e+BJB7sNt8VH3JUCmsbEjDTQ32ZmyWzia2oZmzMHWduIw5RCclcTiNNA6kbEFbOJk3MEW9r8dMRNJPa040xa8FrPB6Aj0QqJhRD2I802OhKtOMwuZP1ucpw98NkuxGPNQnhykeEI9miCs4svZExONgPSfNyUVsPb1U/QFnsWwn6aIiFsJhdpEY3R2WEyOgTjci3UhR4BoCZkwms9n1jyNd7c+yRb2+zYTUZ4qkG+bkScdt7Yu5f68KOk2hIE481k2i+lJmTFrEl8djcy1EqqdRZrm+cZ15lYSInHgTcsSZIgLqMQi4PQ8FgzsZl00HVOspkp8xUjpU5r9BlS//MWAD5bAq12G9Q1QDSI1lxFqkiF1BRMQ0qJ61GmdLsI3eUl1ZKDIw60tBJ1e9B3VqGl2oybardBIoklnsTvS4VoEJ81H9HrZDLt3XFgJxhv4f26akT+QNA00usbke21yKZdDEnvTjDRAt484xkudbRTpyCbypF1u426AFhNjk9tf+x3cBSIU8/oyy/v+N5RH/9lO3gmuIOXzzMT3FdFvQFWFEVRFEU5AR3cB3j/uhPFlzET3FdFvQFWFEVRFEU5AcV1cdjlm2LFihWMGjWKk08+mddff71z/VcxoZh6A6woiqIoinIC0qWx7PfeW5t4+/V1xy9Dx9jPfvYz5s2bh8Vi4dprr6W2tpbLLrvskDCzXwbVAFYURVEURTkBJQ5643vyhAEMGl7KU4+8dxxzdeyYTKbOGd4WLFjAhRdeSHt7+zGf9vhwVBcIRVEURVGUE1BCF4ddvikSiQTBYBAAi8XCs88+y+rVq1mxYsWXfm7VAP4Ey5YtY8KECaSkpJCamsrJJ5/cGdNu/fr1jB8/HpfLRV5eHrfffvunzmxHigtRlG+MGh5cjPnkPpj6FEAsjrCbcMwcYkQNyHCieWz7oh2EjAgBXjsyGAOnHdc1p2Ie2ROR5sZUkAqJBOZcN6ZsF1qKBVGQAaEIyfoQJCWaz4GM68ao/aFFyN27MPfPQeRnI4ryoM2PNqIv2pDRCK8T2doOpX3Am0MgxYnc/hFoYl/UhxjOjhD4g4iBZeB2ImsbkYEg2oSzkXEdy7i+RuSBAjeOcQUIiwXLKf1J7qhGaALsxjzvwpONaZARpUB4UyCpI/oPQNhsyOp6hMuJ3LEb04BuYLdhGtYbUVxAYmczlu+chLCbIBgiuW6PkWenHfOAPGQ0irmsG+ZhPYxoAE2tiN7FEE9iHZKL9ZxRiBQHlmH54HEjhg/E/N3TkXW7iW9qMkYCZ6ShDShFWCyI0lK0Pt33RbDoC7k54E2F1BRE72JkbSNaSQGyugnRZyAixY15yhhkYwvoOvqGVYj8bGRlNURiyLZqsHtIDBuPrG9CO208mwcNNPJqtSCyM5DV9ZjG9Ef0HGHUj8G9kdUbELlZiEFD0foNRRQPRu7dhijKQQwZgHDZQE8gO8JoZ0yHaBCsFrSSAiOv/QaArhufU9IgEkVWNSDGTQNNQ/Qdidy6HRIJPtTNaINOIuhNMX5/S01BZKdDsAmy0hGD+2DqkW1EbbBa0E47Ce20s8EfxNLLhzaoJ5ZJQ5DRGOZzJ6GNOANt0EmIgaMxT5+CafoEyMlC69Md06h+iJJSRN9SyMpDG9UfMfokRGYp2uiJYLchRo5GpLiNsolG0YaORzjsyLotRv7sVix90knm9CS5oQLZ3AZWJyRivFu7B/QEzkAQUTIctNNZ01yDLpPYTE5G7drEnuAe/PFGljVVI4YMQEqJKZFgb9BKqi2Fc0pMtMVqyddyWFgVJJripT32HEII5vYOkJAxWiJBClwxbCYXr9U0kOnYF5lD03AmTJyadwlmzQrBEOZembxX40b6azEJC9v9gojbjXDY+bBeQ+T3ISaShBLtJGUcWfcQG1paEaXdAVilSwb45mLTNeIiSVICZjsymmBtSzl2aUVW3UdHwoS9qZZTUz20xZ6l1W3Gtncb6DoBh4ZwpmMzuUjuaMBp1iHQzp6glTq9jpge5pWKJvZ2VBN1OmmN1YA3nQ5THBJJdvjbsLfUs7ElicwsYVl7MzjtyLgOTjuW1jqiWYUkdIFe0YTms+O2JOmT6gOpszHcRHWHpF+aGTF8IGx+n55eC5Hkq9DSjlz3Lt1cPUm+sYSckAXpK2BPsIb6sAXZUkm208WYnGwG+ux4rFnQXodJWBiTEyMQb6LJFiOmh0nWtOOO6MjqrVg0O7b2VoZnxnFFjed12AIJGUOXSdxmX2cUh/2RARL6QuwmN9HkAgA8Vh1dvsWILAvBeDN43DRleknRUhFoyPZq1rf8Exlu56Xyp4060OZHtOwlnPDzQSQBQiMmo7xb8wSJWuOPP047zo4QIsUK+tuQiEF6FugSn+1CRqRkcGrevug0Uof469jOHoZAUBGuBIsd9q7HJCwk9BikpNEU2cNT25/BEk8a1+UP0m6OsrDqSQSCcDIADg8JXRBO+EnY7PS05bCptZ56awe9bZkIWwpxPUqOvYT1Lf+kMWwGuYj2eD1DMiQnZQs2tVp5t+YJltbvZUVDC8MyJeGEziDfXNJss5G1f2dE5qW0RU2YhBl0IyKRTdOhvZEO2WFEbfDkYErqxPWI8fcppy8rGysY5JtL3GIi4LSCvw6raQorGpKInFQIhenpvYKtDjtixETWJ0PUOeMsb62DlnaSmyrY1BoimlyAqNlMQsaM8isoZre/Fm1QT+Pvct1WkntaqC/KJm4x4bVk0+YA2V6NbK8mqncg22vxmNMZW7kZWbuFmM1mRD0BZGYJIhIga3uVEZWkuAS5fSWyrdqIRlM6HJGSjVVzkBrR0HKOPGvsx8WlOOzyTfF///d/dHR0dH7WNI2nnnqKf/7zn1/6uVUD+AiWLVvG5MmTmTNnDvX19TQ1NXHvvfcihCAQCDBp0iTGjBlDU1MTCxcu5NFHH+W+++473tlWFEVRFOUbIiEP8wb4G9QAHjp0KNnZ2V3WCSE477zzvvRzqwbwEdx4443MnTuXiy++GKfTidlsZtSoUQgh+M9//kMymeSuu+7C4XAwcOBAbrjhBh544IHjnW1FURRFUb4hEoeJAPFN6gJxsJkzZ35l51IN4MMIhUIsXboUk8nEyJEjSU9PZ9iwYbz44osArF27liFDhmA2HxhDOGLECHbv3n1Cz3utKIqiKMrXx8GN3/ff3sgDv3nxeGfrS1NTU/OVnUtFgTiMlpYWdF3niSeeYP78+QwZMoT//ve/nH/++bz33nv4/X5SU1O7HJOWlgaA3+/H4zm6vj2KoiiKoihHcvCgt2GnDKLP0J689Pg7xzFXX56vIvrDfuoN8GGkpKQAMGfOHEaMGIHZbGbGjBmcdtppvPzyy3g8nkNi1LW2tgKoxq+iKIqiKMfEN30ijONJNYAPw+v1UlpaesR/iZSVlbFmzRoSiUTnulWrVlFSUvKJDeBbHlvO9fe/y/UPvM8bzamIXiOQUiLGjUGbMM6Y913TkOE4oqwfaBr2m6+FYAgtzQ4mAU4Hsroe2RFCO2k8Ij0NMtIQdpMxatlpR5R2R28OYZsyCM3nQA/GMA8vRku1IWubEYX5JHc1oVfUInqfbBxfOtIYrTp0CKJsMK8HBSI1H/dHqyHNa0Qn6DYMbG7khx8gho2APbuMyACaUU76a88jctPRt1einXIyoU1+8LjRd9WA3YrWLRPT4BJkZRWmqd9FVm81ImIUpKBvr8R0xnBoqoPSPojexYhBxsh/dN2IvpDdDX37Hsx5Lkgm0FKs4HaiZTgQg4x52QlFjCUSJdB/IKJ7ERQWIGsbsYzvi8jOgNRMRKbPiHzQpw+0tiOXrwC7DeuEPohePYzjdB0Ku0E8ApEYpKaAzQ1VeyEaQthsB6I2RKNooweje7OhqDsiNR9twHCWlo0ytneEEEP6khg2HtIKECYL5oYKRHoqVJXTb/t2sLvB4zIibORng8OYL15k+sBkhpZ2Er2HQ6CBbXqApM2OrKxGOOxgstJ8xmjk+rVG5Ik3XuH5eC4kklDYDVlZw8sBK9qZfwO7FZFejHA50cYOQ3S00hAuR7ZWGZE34nH6pdlJ2J24m5uN87cFILMQhIYo7YmsbYBIlI7uvRC5vSAUoDlahTZhMmLEYJZ4CgDQTjoLkVGM3LkCWb2VFtGOvmUtaGbjfnlciOwS5O5d4LSTTC800jNZkTWbkMFGhMsJu7Yi43FI8xIbfSqytQrcTqI5xeDxGN+RKediWv0epsElaCNPQ9ZvY0+ynlOz86B8N7TX0ZKoJ5J8lUJXglCy3YiKMepMSjxX8HZ1jCEZ6chN2xAuByRinFXoYG1TjGgyREXARIc5ydnFF2I3TcfT3II/1ojD7KI95qfEcwVOM8T1CJNdSba1RcBsBstkkjY729oeRUvEwWknsb2R8/PdJHJ7YRJmfll2gTFy3+1kQn4KMlBPc7QKAIfJg/DmMsjnQ27bCcDwRBjCr4BlMlLquC06CRnDUppG39RsMFvB7qZ7ioOAz0fAkmBjSxSfOZtYQV9k+V5SohpxpwuXOQ1h0ch1xhG5PShwx0iz5bE3GGJigYUSzxVY68tZ35yExjpclhmIAaPom5rNBrOJbu4oukwyOjXTuOf9io3oKnYvtkA7/dOyjXtS1otubhNOsxfiERJSkGmP47NdiL5sHaLPaDRhwm6abkSn0QQ7/Dsx9cyHaBCBRk/vFWTY4zQ6dTRMxJIh5lcm0drrkTLJ/MomVjRYSLXOYpdfJ9f5A8yDi40ICN50KoM7WZFIkpVI4f1gAJuu4cCOqaEcm8mJqakS9AQ3Lo+Cz0tDeB6mhnI0fyO2uE4yNZtT81J4paKOVOnBm3QgG1tIteayvHkvgXgTIjWf+n1RK77TPZuA14NI84KeYEtbB8My0iAWYnVTA6emODFlu4zveXoafqcZrSSHtkSDEV0g0IooLiCceIW41cL7dVHI8hmRZJIJ5K4qbCYXmoCIBWhr44blCUKJBFTvxWFKYVaPDN5sbEa2VEJGGhbNzknZViMaAkAixrhcD7lkYWqtBZubVGuSPUETYQuE7WbSQwJZtYaBWJiUm017vJ7dfljRoOGTXoZn2hiZ5cUfMxFNCuwmN06zmfmVT0HkVURuXwDG5GhGPXek8lHTPArcXkRWCZvb2lnWUk8w0QLmSdhNbuSmnSB1entdBOMvsrCqESEEbwdjvFvzBGc6EvjHjkYM7sNre56ir7cYIkEGpXYnw17EiMxCRO9iNJ+L0ZmFdCRawWQlrkfQV7wHLbX0taUjW9tJrtuNyO6N5rSQueADLNEorTEjUozuzabD5cDeEUKk5iOby9HKxoJ5XxmGIoiMYsTOlcSfeQ58aVha60hkdAOrBeIRpL8epM7C15bxw5/8getvX84vH1h8VO2Rb1sf4K+SagAfwY9+9CMef/xx1q5di67r/Pe//+W9995jxowZzJgxA5PJxG233UY4HGbjxo38+c9/5pprrvnENO+++hTu+fV3ueeu85k0acRXdCWKoiiKohxvk84YzF1/nMU999zDb2YMPqpjvulxgA/2qeFkjyHVB/gIfvKTnxAKhZg+fTptbW307NmT559/nlGjRgGwcOFCrrnmGtLT0/F4PFx11VVcd911xznXiqIoiqJ8U8R1DunyENePU2a+AsuWLfvKzqUawJ/gl7/8Jb/85S8Pu23QoEEsWbLkK86RoiiKoijfFro0loPXKV+c6gKhKIqiKIpyAopJiOkHLd/wBvCOHTuYPHky6enpWK3WLsuxpBrAXzWzFer3IlsqkVaHMYgs7Efu2AZWMzjtmKZMQ+6sgFgc/f3XIRJF5Ocg8jOMgRG6bkwX3FaNDAQhEjWm0dUEmM3Iyiq0XC+RF1aTqGxHc5oR2eloTjPayUOR5ZWYJwxCRpLIHR8aaW1bBi2tyI/WIFd9xOT6LUSTIUR+LnSEENkZ6EteRXhzEf17I9LnQHFvY8CZxw1tAURRHtqYyWhlvZEbN+K5aCBiQH9ESS4Ew2hloyCRQORmIas2s8SdB5nGlMzaoJ6E80oQBb2Ra1chfEVGGSWTiJ69jakzNQ2R5kaUdgOzFa1HoTGNcy9jeufQKaciOyLgdpI4fSop69ZCoB25u9wYWDZ4HBR2Q6TmQ04WonepcU/sVkTfUjakpIHVgj8jA7m7HNF3NIT9iLz+xlTALidy8XvGoLrqeiJDRhEv6IdsbEEUF4PFjikagcweYHUiq3cwJlALiaQxkCstD3P5OoSUyGiAYHo6YuQkcDsR+bnI5asROb3BakF2hKC2wRi8YzYjsnsTHjiMxbX14M2hV0cIrb0e0b83ZKQTzcwn/aPNRhmVlCL69+S88A6ENwVh94LTzvc8CeSG241r7mhBjJxENKcYGWwkY80WaGqAou4QiuBN2DDt+oiAzwepPiIDhxvTIAeDvBOzIIqLwGzGVVeFjLQjispIt+SCphHMymXsmmUQDEN7HbJ2C7QFWOHNwWfJZVvvnhALQaiD1rw8AERRISKtENOaJcimcoTFYdRzsw3RZ4gxuDM3E5yp2Gp2IXfshNRMbC310NJi1PumcsTA0Yi+w5DN5WBz082cS1iGaBzUC5HXn/XNSSLJINlhC3aTm23+ct5vrKY1+gxOs0402WFM59vqJ2LVCCXbOUV0kBVzMNCXhtXkNKbEjbwKOX3wWDPZE4xh0SSR5KtsbbOytL4NYXUxKutSREkpLdGn0Vqq6RXHuJ+ahnloEbvjDZhWLOKf28IAuC0zIasAZyQJgM3kYlNrKx7r93muOsj7de1GPQJEaj4rgy20RJ/Gqgv6pbnRhAkZimMzTYV4hHdiFkzCjNXkYHVjiGhSGAM6AZGfg6zdTjjhR4gJaN0zWN/sQEYD5DgECT1KqSeDhIwh5TuI1HxGZxtTrK9sfAzZuIvlDU0MMjnZE7TREq0mYTEbU303tpD4qBIcHoQnl5WNjcZg0kSS1Y2CjngrsqOZsvTLyHf0oDnyJNrJQ3iztpaNLVEIvkji/c3gdOA060Z6ub1g7SKS8k26ufPIsHdjfUsIp9/PrB7ZhN1udG82I7M6mJZlDEQelpHHtGf+C1k+2q1x/tXuIq4LhqRnszgYAkCabbTrrYjUfLzW8/lP0MVuEWRMtkSvbCLLWojILAWh0WGKk5QJnB1h0mxJQqY4f9ttTIPrjzUwIM3L+mYHd35Uy+D0BP/eVY8pHMRqcpDcUQ0WO6OyumMzuWi3JSnxJMBsxeSz7/vbYOKd6gTCZMIijAG2stJ4xocS7ViSkindjMGlenYp0uZCRhM42loocJVgN02H7DxGZ0nsJoEMBHHpNszRCBNzcwl6XAghcPj9pFgysDVW44s7oLERZ0fYGFga8SP3rmNNk5PhQuKIJLCvWQ6JCNGC3pSboxBqwxuzUOKByc44sn4bXt1FIN7EiKwEp+RmEE76yUqkMLVbvvHsTsRYVv84C6vAJCxQvZuhthTsphRkaxXD2+pwmI2BnHH9f8T0MDKS5M2aajwRyUdNQUo9UVIsGQzLtJJhTyBSsvEsXorcUclpeR4j75F2SMawxIx7JatqEaWFfNRSCUDA5yOYaEH0H0By6SbQdRIfVRLb2gKhNkSWF9PU79JhTqLLJD7bhfy3oh53RAfNzJ5wOSKtkGXtzciM7jRFGoyBbu11kJKGqVcubwkfOy1JLNpZiG6DoKEJkZKJbK9FtteSYb+ExbVPYDnv3KNqMiT0/d0gjOWj99bz3J///RkbHl8vF110ES6Xi0ceeYRXX321y3IsqS4QiqIoiqIoJ6CYDqaP9fntPWYQ3Qb34J1/vXX8MvUl27x5M++//36Xyca+DOoNsKIoiqIoygkorh9++SYbMGAAtbW1X/p51BtgRVEURVGUE1DiMA3exDe8Afzwww9z9dVXc9FFF5GTk9Nl2/jx44/ZeVQDWFEURVEU5QQU00HTD133TbZp0yYWLVrEggULuqwXQpBMJo/ZeVQXiKNw9tlnI4TgrbcO9Ll59913GTp0KE6nk+LiYh588MHjmENFURRFUb5pjtdUyE1NTaSlpfHoo49+6ec62A033MDdd99NW1sb8Xi8c4nFYsf0PKoB/CmefPJJQqFQl3WVlZVMnTqVuXPn0tbWxuOPP85NN93ESy+99Ilp6btrkTt3G9PnerKhdjNEYoj8gQiHHX3DbkRRIdJfi8j0IeubwG6DvCxkux+RmwWxOESiyKpaovk9jSlzrRZwOhBpKciGdmRFPcldTZi7ebD0TUdGkuD2kaj0I7ftRCs7GVHYD9PJ/Y1RskNPNzKYnUuyvAXsVsjyGVNH2pzszMsGVwrakNHIUCsioxj93Z9CJIgMdCAcdkR+Nsk1O5AVH0EsjujXD/wdCGcaIi+bxJCxYLFDbhY4UyAjn9HZ2cZ0nuEIkdIBONpakNEA2G3GtJ2JGKKbER0AkxU6WiASRR80Flw+Yxrdnt0hIx3RZyCuQAfaqEGITB+W6q2IgaNBl4iSYsTEmch1SyAaQm5fiVyzkdasDGisM4IqurwMDLaD046nvg5txAQSGuDJIiD9vGHPRwyfiCgtBLsHzGbsG1Zh0ewQDEFDPQgNaXNB3VZk7RZEXimyxwj8pT1YklVijDLO6Ibcuw7iEdwVO5Hr30Vkl/CBJRXRswjd7kJW1yNysxAjJyFrNkFWBlidONas4PSsLEjEjDx0tBDJLQarE1tbM6J3fzCboHovctce5ICxyI4QMlBv5LGpmk35hZBdQNTlBqlj27MFkXMVIs1Lsv9oY+rVgf3BeTbrfNl4wklERrFxnRY7WC1MaKuESBjcTrA5jcgCUicsQ8hIAPf2LSQmfQfRbwR+hwn27EH0H8HIjkZkaxV900oJZ+ZBaiZpwQQyHkaWVxoRUqxmmrLSjCga3YcSdjlBTxhl2X0o4VQfAGJAGXpGEa0eO2TmINsDiJyrkK1V6EvfQbgzIRYiabNjM7nIbGgBk5lTPW42tkQRZhs72gP08pYwpr2ahnCYSYUX4wlE0AafhDZ6Igk9xm8+svGBcIJ3FptaW/HHGrCZpiJbKhHJOLJuG2WuTGpDFjrirQxKN1GcEkO601ne8Bhy5w681myELQVhdRn3rq4JmtpwW0zER4znij5uALa1PWpEVzFbEVk9AfigzopseZLpRQ5GZFmQ9U2sbZ4HNjfDYwE2tybAMpk3qmKIlr0A7PY/ArqOzSTRZRJbu5GvUk8UbG6sgVbj2ZKWjUekQGIh+DuYWJCHSC8mxZKORbMjkaRYMqgP7zKib0gzlPRiROalEA7jsSZZ6I8TjGukWnMw7d1kRKXRNEw+O7K5nJAeYGxOETQ3IIr6MyrbQlQPIXxFEF3AS5W1eK3ZEAlzhi1GS8RMmzWG+aTeiKwS8tqiiMH9MLfW8UFuKaakjq0jiJaI08urI1zp7PKXYxZWTGIiee1x3m9vZX7lU5ilxvzZ3yHx5mrMwsrsbDP1YQvb22spcMVoi5pIyjgWzU6tXsN9G55hhjfOC+VOZvecjX9xIzHioCeQOzbQFmvEFgqBdxan5l1Ce6yea/rPRt9Zg6+2DpNmYWJBHjcOTsFny+f8HrOR7bXYO0LoLWGiKV5iyTB7O3Ybj1vH5fhNUfRQHNnwMGiCCflmcDuN6aZ7noRI8yJys/aVUZDWaA20BTAFWhDhdkRuOjK9G6FEOxWBhxGeXMoyokSSEiJRomaQu9fRFK9Bw2REnWmvQ2z9kHhmN+SmlVBQjIyHwWSl0mNHeHOZWuSBxhr09R8SGzoGGuuw1ZVTHBXgMKJsZEbtRNMywWzlnxV+sqJ2MiIWrNJERshkTJsuJhjP+G3LOcmkY9FgeUOEjj4DebM9THusHpFZiigcwOBoCK8/jCZMRJMdiIwUokmB3y4Yn12AzWTE/lpeH6cpYmZtsJ7khKmINC97OxrYnWjinY4kb9Y3gtWJRbMZ0YvW76DUY3x2W3xkJjzoGUVomW6qRRPmEh/WQZnEHA6i75cbf2sAi2Ynrv+Ps4vzabaE+X/1ETLsWRBqI9WaQEsmsZvMrCksZovegUgrxH/SCM7IL6Cn9woAZNMuRK/BYJmMyLiMVXavUXb2OL/fGjiqNkj84BBoX1Ef4DvuuINx48Z9+Sc6jPb2dn784x/j8XgwmUxdlmNJNYA/wd69e7n11lt55JFHuqx//PHH6dWrF9dccw1Wq5VTTjmFyy67jAceeOA45VRRFEVRlG+aePLwy5dp8+bNBINBhg4d+uWe6AgmT57MBx988KWfRzWAj0BKyWWXXcatt95Kt27dumxbu3YtI0eO7LJuxIgRrFmz5qvMoqIoiqIo32BxeZgoEJ8wEcZzzz3HuHHj8Hg8CCFIJBJdtkspue2228jLy8PlcjF+/Hg2btzYZZ9bbrmFO+6448u4nKOSlZXF9OnTueKKK/j1r3/dZTmW1CC4I3jwwQeRUnLllVcess3v99OrV68u69LS0vD7/V9V9hRFURRF+YaL6SA+wyC4tLQ0rr76asLhMHPnzj1k+5///GfmzZvHwoUL6dGjB3feeSeTJk1i27ZtuN1u/ve//9GrV69DXvx9lTZs2MDgwYPZuXMnO3fu7FwvxLHt+6wawIexa9cu7rrrLj788MPDbvd4PLS1tXVZ19raisfj+cR0b3l+Dba0HeBycNY5Sc4cknqMcqwoiqIoyols2TsbeXb59bRENrC56eiO0SUk5aHrjmTSpEmAMVD/cP7+97/z85//nIEDBwJw11138eijj/LSSy9x0UUXsXr1apYvX85ZZ53Fzp07sdvtlJSUMGHChKPL8DGwaNGir+Q8qgF8GEuWLKG5uZlhw4Z1WT9z5kzOO+88ysrKeOWVV7psW7VqFUOGDPnEdH973hC8vfIQuZmI7mXIxl3HPO+KoiiKopx4Rk8YwI/Pu5QtrY/y1AZY+f8+fTa3+GHeAH/eQXDt7e1UVFR06cJpNpsZMmQIa9as4aKLLuKWW27hlltuAeD222+noKDgK238Ajz11FMMHDiQsrKyznVr1qxh8+bNzJ49+5idR/UBPozvf//77N69m7Vr13YuAP/4xz/4/e9/z5w5c9i6dSsPPvggsViMJUuWMG/ePK655ppPTFc7fRzCm4L+0VYwWZFbt4M31RjpX9QTkeo0RsLvrUG2+sFsRgiBKMw3IkAEQ5DlI9nQQXDaZGzrV4DZjKxuMiJDtAbQxg1FG9ILU+9s9PYoyfoQoiAD/d33wSQQmT709R8iKzchq+shEkPuXmFED2hvxjx1FGLAAETBYHzWfIgE6enuDs5UZDTAC20W3mxuh2AIkdUTkemDnv2QHSE0jw2aWhF9xyA3b0YM7GNEdXBnYF73AbJiAyKzFCx25JYNmKs2QiiA6NUDe2M1BJpI+vIRKS5ETl9jNLwn15hDfft2CAYQI4ejbfwA4SskXjIYgiE6svKhaidy8zqobQBPuhFFoHHXvnna22DvevShpyArq8CXQeCsM0jbuBXZ72TQBMQjCG8upOcac9fHQiRlApIx3IEwZ2ptEPEjigciN64FXUeU9IPWveC0g9mEcKXDzuWwdy/4/fgdJrRoGG9YMj6nG68nvMhkHNFtCIncXkRLBkBWNnLHBsbYzRCJoUXDaOPPguxuSCQiNR9SMoyIHHZjdDLxCPHULHCk4o83QiRIs0tATQXx0RPA7UAbfSqifiei70BE3kDEsBHgcFCckkaNI461oZKojICmITfcTnl+Ni3RamRto1H2kVcp91uRNbuQa5dgqt+FXPmRUU9icURWCdrwCQhnOsJXhNyzHqvJifAVgtuJNZ4kYtWwm9zg86KvXWpEjEjGkDWbCCf8hFJ9ULeHQIoTbeBwwlYN0Xc0UkpkczkSiSMUAe8sZN1uRMVHOOr3GBFAKnewoaUKX9QCyRiitCey5gFoqEeb8D2kvx7aWtCaq/DHG6lK9yD99YTsJsZm5iPrdjNET0D9dkT3MvJcXv5b8TSx1Ezk1jXIj94jrke4c7hGtiMOQEmKiXRTJgAiqyf6Gy9BNAh6gsHphfhag/hs+WQ6Uqnq2MmIzELQNPyxBkjG0LesBft0kvUhyEhFEyZspqk4zN9FtjxJqm3f7bVaqI3uJi0Q46cDZxP2eHAlTDSGg4ix49kbtCAbdqDn9aUsPYW4/j8md3Mh0ucgdYnbYuLF6jYKXDE8gQhLohK32UdcFzy1o8aop6kpYLYSElEC0o/IzaQtVkdMRmmM1GONJ3lwcwfRZAc5ziuMe2KyQN0emiJPQHouZd58Ts/PpsAdoy68FzpCEIlBLE6yKUQyowhHMMgzO2oAkI278AbjOM1e6GghqEU4O92COdgGbi8io5QxOTqplhwwm5AtlbRm+MDhQVgclHqixDVJyGmjOrYHiU5FvJZnd7kIxJt4aPMzhLMKKHDFiCY1aqO7eb/ucTSnBVc4TrM1ylh3CgPMblwWwbSii1hUU4+jtQlNmPhe9w4QGj8ZkMpHTfNob7Bg2bgcGWiEnCzy6tpotcaIJo3YpLnOH7Db/wjxna08n8zHbpqOFg7QHKnBH2/kL+ufMa67uZL21UHspulYmvdS3WElmgwRS75GiiUDy+hSqK+BzBy8EYEo6Mme4MMkSCBrG8HhwpzUabKE2N4eQ5w8lna7BJMVbdTpaNEwbpx0dxRREa+lMWwmmtSITZyCtb4cGeggw34Jzro9EA0icnqj9xmFtSMARd0h2GREsNm5g+4pV1JvCWBrawZNILoXYdUcRsUMdSB8RfhlANwzwerEHk3QmuHjshwJlTvQvdnGvh0tyKotRJMLCNo1I5qRnmBmSR4WTeJqrGViqst4Pjg8yNot0NqCSC8mGG9GIsFsYlrRRaRYMiAWItORCokYZRkxoklBmTsbi3YW+Dz0tGTgtpgYleXm9Pw85N51yHXvIgMdoBvRUBZUxlhav5e404UpEiJZ1YbD7CJZ2YrmsWHR7NiG5kAshMsyg0C8FSl1FtfuwWPN4tzS2TjN32OP1k6Gw8Tu0B6yHJcxpLqSDIcJbFPxLF2OEBOM7wgg8n+MDDYCoMu3aAwb7xz7pl3OWYXRo2qPJOLaYZfPY383zdTU1C7rj9SF8/bbb+fyyy//XOf6Iu64445DJsDIzc3ltttuO6bnUW+AD8PpdOJ0Og9Zn5GRgc/nw+fz8dprr3Hddddx/fXXk52dzd13382MGTOOQ24VRVEURfkmSiQ0tIRG9aq11KxeD4Aej3+utPZ30zxcF878/PwvlM9jqaGh4ZAGcE5ODvX19cf0PKoBfJSk7Nrp5tRTT1VRHxRFURRF+dIkE8Yb3+zBQ8kebIQli4dCbHv1jc+cltfrpXv37qxcuZLRo0cDkEgkWLt2LRdddNExzfcXkZuby/bt27sEG9i+fTtZWVnH9DyqC4SiKIqiKMoJKJHQuix7V67jo8eePeL+yWSSSCTSOWtaNBolEomg60bH4auvvpo///nPbNy4kXA4zG233YbFYuHss8/+Sq7naJx77rlcfPHFrFmzho6ODtasWcOcOXP4/ve/f0zPo94AfwPddM8bvPb2Zipr2nHZzJwyvIjfnzOQvI/ts6emjatvfYXF66qx2S2cN2UQ9/ziLKz7unvdc+8LPPPsInbuqMJmtzFySDG//9UMyC0xjm8IMsAzDfQkaBokdeKJJAldUl87Et8R8jb/tZXcdufT7NhZS2qqk6tmjeKX10/r3H7vvS/y3D/ns7P6T9gsGiOH9+L3v5pBv89ZFn99/H1+9+Ai9iy//ZDK/r+lu/jOSb+n/O2fkV/s+sxpb99Zy613PcfSD7bQHoqRm5vO5TMH8/OZZewP1nLVj/7O088uAgRJKTEJ6OiIcu2Pvsuf7jVC1Jx25VMs3VCN1ayBZsx084fbvs/VP78EgGRS5/ZfPcO/n3yblkCMvKwUfvzz73P1eSM68yKl5M+PL+XhVx+hZm8jXq+bH889hV9c993D5v21tzfyl8ceZsPancTjCYp65vLzX05ndtaBUnr/1WXc88QC1m2rJdARI/qfS7F85lI6YMbcR5AIXvrnoX3Kbrr9WV5ZuIYt67/6aTcVRVFOVIm4QMQPhP/KGDAUb0kfdr3+v8Pu/9RTT3HppZd2fna7jZkmFy1axKmnnsrPf/5zAoEAZ5xxBn6/n+HDh/O///2vc78Twa233kpFRQXDhg3rDH02a9asYx4HWL0B/gYSwLwbz6D611NYd8skhIDv/eHtzu26Lpl+9dP4UuxUPXkhq17+EUtWVXDDnxZ27hOJxLnv3muoXXU7lbufpnevXCbO/AuxiPGvym5ZboL++QSW/ZLAnofxL7iKKcMLmTy6hIwM72HztXLDXs6Z/Ud+9fPptLx+DS8/PIf/e+ID/u+f73buE43EuffGSdRVPkXFh7fRp1c+E2f8iXA0cdg0P80lM4YRDMX4z//WH7LtoRdWM/07o8nP/uTwdUfS2tbB2JN6s/Tv36d9+c08//hP+evjS/nrcysPnOP+qwlWP0Yw8BovbnmED16/FYDZF3YdVXvDj6cSWHIjwaZ/E2z6N1fPPb1z29//+RaP/fMtXr1/FoGWF/nHny7g59c/zBuLNnTu88vrn+bZ1zbwzLM34V/9KzavuIcppw84ct7bQ1x51RS2LbqRpsU3MvfaiVxx4YOs2lrXuY/L6+KHs0Zy73VnfK7yOdhVF49l/psb2FvT2mV9LJZg3tPvccWVZx2T8yiKonxTHPwGuGb1OjY8/cwR958zZ44xaPWg5dRTTwWMWLp33nkndXV1hEIhFi9e3BkS7URht9t5+umnaWxsZPny5dTX1/P000/jcDiO6XlUA/irFA5AZibaaWPQV7yByPQhUrKNyATtdYjJ34eWJshIQzjsiBQXZPmgoRESCchIh1AEU78CUip3gdsJFgsi3QN2GyIzFblpG789rTvDx/XDNSib9CIvN157BusqWwlkuSAS4/3aCFt2NfKXS0bgLc2nW7CNO84dzD9f/IiIcCB37uDmX5zLoJOycaSmYfdXcOs1E6hraKfPsrcp9USR4Rj60teQu4yRxTWeDOavrOKHU/oh67YgigsgGESuXo2wOKCgiL+8W8Wpw7rxve+Nx1SYw5ACD3OnDeSBh9+GUABSMrjlyjGMnzkFR+oMHKk+bv3ledQ1tLM1vZ8RoaG5AfK6Q7AFq7BBcQ9MdQ3c8q/t9Jz9OL6Jf+WUKb9lzfJNAKTqCc6fcRIPPb8WseR1tIHDkTt20v7RJhZ8sJNLrzwNeo2GUAcAHyY1RFohsrUKkzAb5ws24c/MIm6zIcs3QCSKSPMyMjXKjyaXUNirAJFbRFmhxoSzR/Luzg5kfRMp5TvAbCXscoKeIMcZ5+//eI2RA/IYlQ92zY3csROZ4kVYnYQHD4dEDLlrDYTbCCf9yEA9O7dXcfKYvgwcXoo02+g3eSD9i9JYu6ONrPYEO1stzPvHO9z/9E8YOXQAmtOBJcfDoJNPIpaaCRE/tNWDw4MrHCff1YvTLx3N+ZMG4xv0a8y9hvCjs8fTp2ce72+uR7ZWgX06P581nAuuu4KS4cbDUZt6PggNMeBk5PZ1YLLSaPYjO5oRGd3QVi1i9549fP8XL5FVfDG52bO5ZPafaUw6EQ4HE08vo6RbOr/990p8IXC2NkFqKm+8tJ6OcJTLLzgDhEYW6ZCIoW1fgdy1kae2P0Nb9+6I7J40padAoAEKixkcj9FoDUEygbCl0ORzQVFPZOVqMJlZ4CwCzUxq1ExCF5CM4TB5kGvfQ9Y3Q2UFkYxcsE3l37ti9E6NYJNm8LgQ/YaRZpvNLn+Int4rAPA1NIHZDkB9vAoxsA+i6OfgPBtTsA2RezUAKZZz6WbKJqHHEN2LWVgFsqMZrd9QCL0EgOjdl/TafQM64q+TTM0m22G8GbdoZ5Hr/AEI4/HsTJh4s7mdInc+cuNabCYJeoJIIoDbMhNdJnGavMimeWgZbrLMuUwssNDdVYrwFTE+uwCAnt4ruNATAMtkRFEhhNqIJINUBvzgdiIQSCTb2uyQiDGlMGx8B4DywC42tZZDdgGp1hyQOmEi1IQqSbUmMQmJ3meUERHFasGU6yaYaEHYUjizMGZEMGnxIzzZ2E3TkQ27CSXaqTa1EXSYIdgOAlJtucj2auS2ckRef9LCgNRZHgmR6/wBv1jegmPHRvI6BIuqHXRPuZJfD72AdFshVzatxlGzm6IOnZkls8l2lDLW5cK/qI6420u6/WLqtBbwnIfLnAZgRAzI6E6243K6p1yJ3LoBm2kqQzMuIyUjjr61AuG7GJFeDKEwaVEzNtPUzkd6iaMAGYgxvcj4wyybK8l3/QCvNZtLe0uEMw1ZVYfTt2/u2voaRvuyybLkY40nCSf9aGUngy4RmaXo61eAZqaboxhzhx8xtIxETg9a9GYy7JdQ6tEgGSOcCCD3rCdsSiC3rYJoiBf3NKEJmJiZTg9vEba920AzIxxGnZVV1TTRgqzYgNZezw7pN6LAbCs3IojUNtMWe5ZwQqc9xUZzUQEi71po3EljSQHtRd3BNJGo3kFc/x9vNrcjg42k1Tfw1yrjLZ1JTER/dz6yYACi7xisjVXYTW5EVgmYrGxp3cWorFSSOT2JORyYNSskE0ZgW48HuXkJCT2Gt7kV0aM7SfkmbbFaIhZISViRDTvIdlzOmU6d91sbIfkmtAVYHmzDbfHh1jwE4k2IrJ5GZB6LBa1XNza1xjm/x2zCCQ2LNJG0OzH1ziGtpobE3gCB1yoRYgKxDY3Itmpk8+OsaXLyxt5msh1xLNpZNITnAdDNfSUtkSQlGC9OxOA7yLQbb1y1U+/b9wzIADCOCbbTHnsO0VrD+Nx9g+uTb1KWXnRUzYZ4QiMeP7Ck9RtGj3MuOapjv+7S09MZPnw4GRkZX0r6qgH8LfDGBzspynCR5jbiLK3dVE1JTgoZXnvnPiN6ZRIKRdm+6/CjLN94bwsup41ehWmH3f6Pp5fSLTuFs0YcefYYKQ8dTKhLyc69bQSChw8J88bba3G5bPTqdeQRqj+84SlWrNzKu8//kIb3buDcyQM567LHaGsPA3D1paexeMkGNlc0dx7zyLPL6VHo49QJx+5fvolEkhXvb2VIWelhtwcDYf718kf88Nxhh2x78B8LyM+8kD5Df8xN975JsONAeVx54Rh27qhhzcZqdF1n8aLN7KppY/JZRizHt9/dgNtt543X19Ct6HzyRtzJBd//IxUVRz9idk9VE9t21jKkV/ZnvOoDorEEk8+8lZzcNF5Y9jteWPFHzGYTs696GDDePPzgwpP5z1NLSCQOTGb/0MMLOOf80Xg8h0ZeURRF+TZLHvQGuG7tGrY+/9TxztYxl5eX9+k7wTGdoU41gA/jpptuYuDAgXg8HnJzc5k1axZVVVVd9tmzZw/Tpk0jJSWFjIwMrr322s5O5yeStzbUcOff3uFvl5/Uuc4fjJK6rzG83/7GsT8QPiSN9Zur+eFNz3Hvb87D5Ti0F2g8nuSfzy3jB9MHfuJUhaMnDmHRygr+89/lJBJJVm2q4bFXN+zLU+TQ826q4qofP8Tv/nwxLtfhf/pobgnyxPPL+Nv9P6YgNxWzWePai0bjTbEz/23jLfDQwUWMGtmHf7xidINIJHT++dxyrjp3+BHz+llJKbnqlv+QiCf52XUzD7vPgueXYrOaOO/M/l3W3/WbOezYMo+q+id5/onreeODnVz+8wODHIoLMzj9jDJGfOev2GyTuOB79/GbK8cyaJDRH7up2U8gEOaj1eWsXfMwW96+AYfDxvSZd5JMJvk07e0dzLjor8ycNpxTh33+h8uCD3YRDIS46/ezcbhsOF12fv+7y3nrvc3srTdiTF567kgC7SH++7oRQWXTtlqWvL+RS6/8agOtK4qifB0cHP83tfcIir9z6acf+DXT2trKY489xrx58z5xaW9vP2bnVIPgDkMIweOPP87AgQMJhUJcffXVTJ8+vXNCDF3XmTZtGmVlZVRXV9Pa2sr06dO54YYb+Otf/3p8M/8x81dUcvG97/HUH8/lrMwDt9rjttF20BvX1n2fPSkOoKNz/YqPypk2+wHu+PlULr9wHHLL5kPO89Lra2ltC3Pp5E8eqjZgZC+e+u3Z/OZP/+Hyijp6FqZx1Ywy7pq3lDRv1wbuihUrmDrzz9x56ywuuuzIjaOd5Q0AjBp9rTEgDwkIYvEke2vbOvf74VXT+OmP7+cPoSgLPthFeyDCJdMHHzHdKRc/wpJVFaDrjBtZwnNvHDkAdzKpc8WP57Fq7R7++fINpKQ4OdxEPS88/i5zzh2J3db1azf65H5YdEGIMIMHdueeG89i4hVP8vdwFAdw7c3Ps3ZHA1vfuZHScRN5b9X7zD37Xkwlr/KDGX3wpBhvTm+5/Rx8Pg8y6OC3v7+YHkWXs2N7DYNLc4+Y98bGRs6achu9e+Ty2N+ugPJtR9z30+zY00J9fRtF2Zej73vTbxIaNpuZPXV+CoC0VCdnfm84/3j8HWZMHsBDT33A6JP6MmDQ8Zt3XlEU5USl66Dr4pB13zTZ2dnceeedn7pfbu6R/559VqoBfBi/+93vOv/farVy4403MmTIEFpbW0lLS2PJkiVs2bKFd955B4/Hg8fj4a677uKCCy7gD3/4A3a7/RNS/2o8M3891/xlEc/+ZDyTJ/ZDX7u9c1tZ/3zK6wM0+yNk7OtZsGpHI06njV6l2VC1G4D33tnIxef9lXtvn8Gc808+4rkefGIJ359eRrrXAYlPfuP4/Un9Oe+H5yGbysHfwXW/f43RA/JwOqyd+7z97gZmXnQF9951LpdePpV2Dn0rvV9OltEPa/3ahykUNeAPGrMOAdgd0GBMuH7e90/lZ9c9wLMvLOXZV9Zx/nfKSPXYCR4h3deevIJkQX9M21aCy8mhc+QYorEEs37xItVtMd59/ofUZh9+AOC7766jfHstP3x0zicVDwCaZjzs9vcWWbV+DxfNPYse3TNACPoPLOS743rwyn+X8oMZfRhaZrwJ/qS374dTtbeJiTPGMf7kUh7884Vf+OegnHQX3YqyWLPlXra3h0nogtGZhciWSqjc3bnfeZedxoVn3s26TVU8/Z9VPPB/137BMyuKonwzJeIamA88nVu3rKJlw4fHMUdfjoqKiq/8nKoLxFF44403KCoqIi3N6P+6du1aSkpKunTMHjFiBKFQiO3btx8pma/M397Yyo/uXsB/fz2JSYMP7Ts7bmQJfQpS+fk/PyTQEWVPQ4Dbn17F3ItOw243uji89PJSLvz+PTxyz0XMOe+kQ9LYb3NVG+8t28EPLx77qfnSdZ0VG6pJJJKEwjGefHUdj726nt/9cHznPi8t+IizZ/+ZRx99lDmzPj3NosIMvje5jGuuvZ/KvUZ0gUAwyuvvbaO2/sBPJXa7lTmT+/Pbv7zMOx9VcfVFYz417U8TDMWYeu2/aPGHees/N+BLPXIf1r8/9CqjT+tPSbf0LuvrWzpY+L9VdHREkFKyaUsVP/vjQr4zcQBOp9GQHzuqlOefW9x5fdu31vDfJTsZNqwnAGNG96FsSAm/u/MF2tuDBDui/PqWpxk8qJievQ7fr2rn9jrGTLqNKVOm8PADP0TTDn0UJJM6kUiMWNyIwBGNxohE4p3xJA8247TexONJfnP7vwn6QwA0NLTy/Esruuw3YGgxwwZ3Z+alD2G1mDj3nHFHLDdFUZRvs0Syax/glJ4jyZ8y93hn6xtBNYA/xVtvvcUdd9zBQw891LnO7/cfdi7t/duOqCMIrS1gtiNsVvC4abFGEYXGK3353n/BakHWNiAbW4z4up4siMWR1fUQ2fcmNJEEpwtROAAKyxBpXgiG0Pc2g9PBT55YSTAYZept/8N76XOklN2Od86zLFm7F6wWtJZWXr7hNJoiSfLOvI9h17/CmP45/PEHI6CtEeFN4fobH6GjI8qlP3mClJ4/J6XHdaQUX8tj5UmyHeloA3oQGnUy/9gcZmhJOqOG90AbNgbsVuSHqyEY4pl/L8cz8zFw+RB2L6eZI/zoTwvxFV5C9oi7eGz+Bl5e8BvG/uByRMEARGo+P/v1v+noiDJnzoWkFF6FO/N88nxz+Ne9jxrRJHK6QSwENqdRPvV7eeauaQwYUsCZs/+BZ/Tv6HPWvTzy2GKk1BF9h0FzLQSb+OH1M6nY08jIocUMHVgAeYW4mxqhYhVoGmGXk+GZOcjGCugIYWqthbQMcPlwmlOxRKOIXiPQRp8KZhP/2RTknRUVrNhUS16/n5DS/1ZO6nYt/QddQWT8BONetlTSXtnIy68s5aYrJoHQ0MvGg80NwSai3Xty582PkFd4PtlpF/DdGXdy8lllPPHMr3GavWx12PnjbedwUp8Mxp3/CCkpU5j9vfs5c+ZJTP7hKcQyCxFWB/Of+jGmYAeF3c6j9JQ/Emtp4bn//ALHug8hHuHueR8yYPwdYLETSQaZ98e3qNrbzMMPP4g76wJSCi4npeByrrrnHdpcJioCD/PyP9/G4Z7GWef+BQC3ZzrO3jexcMlqKO1DR6KVTD2VJmcSvDmk9OnJsvk3sGBtE5ecdieTel7NySdfy5KVFWA1Q1sjRMIMrK3mh+cNZXdlE5ddfDo2hxO3xWdEwNBMxv11eBC5PQj0H8iFGUlSLBlIh5eOeJxk4UBEWiHoCewmN5G8UmRrFRmBJMJkA7sRz3JamhWkTq2phe4pxbwRsSL3dU4RPUqQjS3Y6ytBLqKnN0FtyALmSei9RuK3SWTTPIbU76Ut9izR5AKIRpE1Rp/1bMflyNUHwtAlXB6Qi4DTjO/y5pWYNAv1HhNJCcKdCVYn1bIB89Bi6lxJEkWDjIMtkzFrkz72oFhk5NG776c++3Qm5uXTkWhF9ChhSIbkHZmCy2JMvW4zTUUiIb07+pnfY32gBrNmRVZvoCm6h3fr9mLWrCyufQLqa6kIPEyjz80j7Wk0R8KEkxrRPkNIra/HqjmYWHAxODzkOPd1Swq+SHHCxgDfXOqdSRIyhp5RRHusgW7uK+nm6klr1ExdaDcizUt4zHi0bjnsaI/w/2qCRhSA9CKwmolrxs8adbmpZIbN5DpLccc1ZFUtBFvQmqsQZgfalBnIjmY+jCZIvvoOPb3GP8zvGX0BW3Ny2GkTnOc98MuQP96INngEotv1iOzeAESSQUg5F80kMa98F7n6VnKcRkQPt8l4fou9G9HEgTB/sVHGP8bbYs9Su92OcFjQP/wFcssKiMTAc55xf6v/b9+9s++7By7CiVcQhT+FwP/DFA7is13IbhGERALbwEyIvw4+H3L3R8jVi5DbVuGo3kniuf9npNmwA+FyIpNRdgQr0D94B+prMa95H1/cAeFXyLBfArt3kuO8AtHzlzikFfIK0d9/i1PzknRzX0nCYsYkJhppVpQjA8bvXNroKcbxiQQ0VlG67CMS/11E7PSzSMo3EQ4rLnMaGfZ0vNVVWDWHkefy3WQ5LsPz0Vpk5Z/JtF+KuW4nZ8TraEwxgzOVn4Q2gK4j255GNvsRrTU0Ripo8Joxd/gJ2jXkji3UhCzYWxsxrVyENSGxm6bToQcQxcMhGISsPHxtIRpT7YiMYi5/u4U0cxav72lHdjTz0wofL5U/DdEgqdYkHXoAWVXLqKxLcWxaA0LDaz2fWr2GUK8BiAGjEQNOZkysBbnpTsbmeIkQYVPrHqJvbkeu20akIYHFY8RfNxd5EOlFSF8B07I8TCu6CK9VQvx1shyXddaT3qmXI0MfC+cYXcDH2ULGC4Asx2XGfTJ5EL6LcTXWAhCWIWA8RyN5UB/gRFwjGVdNt2NBdYH4BPPnz+fCCy/k6aef5qyzDsQo9Xg8h51Le/+24y353nXE3t2Bpa/RUJXbyiE1BVoOvBEtynQz/55z933ojvxoLcJ6oDrs2PUEMT2MsyOMjIehtR4yC+k4aDDa/X+6FP2tw/fJmX1Wf2b/4EDIIIvFzPL/XAttAXA7wGoh3q3rgLDdy+9E+IqQwUbkuo9InjqF2tAeCutbD06+k8Nu4Ze/Ope7LxgMZhPYbMjNOxAHdUco6Z5J4q0fIQYOgvAn/EPlKF1ywSlMungIOXE3smIrorgfOFMhESPEga4gOdlpRKMLEf56pL9rZIaiwgyWv3odAKJwMHL127QO6EOKduBtsttl54HbvsPfSkcaIcA8OTRHq9gdOHCOvNw0XvjbRUSK+uCI6cimckK5mdBSCcDNP53KLb84v3P/eQ9cwXUPzGFQSh7EI8hIAKp3g9lM27595lwwjjnj8hC9hzO/vo2pWjMEg4R69IOO0GHLJD83jbN+dRX3nJQLiYhRzprZCB/3MZfOHMalV33PaLAqiqIoh6UldLRE11/dZOIb2An4OFAN4CN45plnuPrqq/n3v//NpEmTumwrKyujvLyc5uZm0tONn7RXrVqF0+nsMnf1wW6+902sFhO4PEzq72PStKFf6jUoiqIoinJiWPjuFt5YdT0JfRd64s2jOkaL65hMBxq8bTtX0brtm9cH+HhQDeDDeOCBB/jVr37F/PnzGTfu0P6J48aNo0+fPvzsZz/j/vvvp7W1lV//+tfMnTv3EwfA3X3dRDxOK+R2h8odX+IVKIqiKIpyIpl0al/OuuBawolXiIdO429/+9unHmNK6pg+9sY3vftQvLm9qV/56peZ1W8F1ZHkMH70ox8RDAaZPHkybre7c1myZAkAmqbx6quv0tjYSG5uLkOGDGHs2LH86U9/Os45VxRFURTlm8Icl5jj+kGL/PQDlU+l3gAfxsGzlR1OUVERCxYs+NT9FEVRFEVRPg9TomsXCFB9gI8V9Qb4KyS69YfUVDpSXMagNH8QfywEQjNGvZcNR27Zhb6rHu20yYgRE5HbNiHrm8BqQV+1GUIRRG6mMeI22Ag1G8FpB7sVrUceRKIQCmMdX4reEkH0Hg2aMCImZPmQre3oO41Z7WRHiLaygWB1QiSKvnEXtLQj+o/D1FCOU7jQ161Ebt8CKWkQaMLV3o5z63pIy8CdtCLrdyOK8kFPoK/7EDFgiBHVIqcA0Wcwcu065Oq3ebM9DMEWaGqFbt0RRYMQ3YZg2f4RfPQOsm4LsrUK2lqQNZsIeTyIQWWY/U0U7qqG9Hx0TyZhbyoE29HNFoLxFrAao8NTtmyiqjALQhGQOmLYMAiHkbvWIWsbkXW7IRI0ImhEgiQK+iF37aDN50Xk9MZfVEww0YJZWBGlv0D0HEE0NR1MZkjGMDftQbZUIjuakbEO8OWCO4P2mDAiOphNRFxO/Hq7Mbq7YS+xbv2NAWCJMCLQiAw2Es7tjla7jUhqOu8GQhBsgpQMRF5/ZO0WsFvxSS+yoxna6+i9fDW0NyMKBxDSA2BzI/euI92az4A0HzbTVAi1sSKRRGT3wPbW/yCZ4EObD+dHK4x7Huugxpkk6DCD47tYP1iE8GQzsLUJLJORbdXG4LrCHqAJUgNRujuK2K7FEAPHE7OYyHYmEHn9ISMHVySJ8ORSE2oGZyqZtu7Ium1QX4uwuvhLrxiI02jVW8CZity0EtF3NLGSQYjSkTSVFrIhPQth99JhNyHLV2GPQ4slTFTvQCbj0FSHjHXw5t44wpmGOakjpKQomMC0ZTmyuZxgZjbu2irslVvBYkdmdDe+aKEAcsUawk47xCO4LT60WJQztTY0cQaiR3822I1BmNjcdCRaGVu+kVPzLkH/8BeYxERSAiFIxCASw1u1B8uClxA9f4nI/3Hn9zm69sCARrM2CcRpB77r/UcRS4awmZxcENvFwvYoEQvk1bUhq+vJcpRgrj8QG/nj4g8aEWfkrlUHVmqnk2abjfDkkmG/hNPzLwH9bd6ufoJg/EX+U16LEBMw1e0g2yFpCDcgCgaTEbVyat4laMFWxudeQlVpPo0RC2m2PK4stNPTewUjMi/Fjh3hzUUIY9KZhNBJs83G3lQL7pkI38XIHb8jOyixx8EkJpK9Y6+Rz5ZKBvjmku/6AfqgsTg7QiQ+KmdE5qV8rzgdtzkdWb0DMnMQGLGqM+3dERYHMT2MDLWSWFeNTISRO7ehb1xB8pUXEMLEQF8K8fJ2fLYLO4uib9rl9PRegci8vHOdJ5wkkeLbV/jGfXCavweANd1CYm0F5OZ07i8rVxtV5Z6uLzHspukApJqzSMtNIMaMRes3FH37HsSIu5ErbzZOsa8e6ItfxVycilmbhH3DKiNiRN1OcJ6N3HMPJc5uaBP+D6lL9CWvI1euQ7b6wWlHDL4DgiFMU8ZDIonIvZrIQGN2yhxnKqJvKc29SxDDfgPumeD4rnFvRpz6sXphhngEkZlOuv1iYJHx/aj8M6LbMLRhp6INHdulXBJLt5J4bz3RD/ZgufxZ7KbpmMREosursWhn4VzyHrK2EY/1+8jGXciOEJE7pxsx1nUdueZXJN9Zjii+kcyKGqgqR99VAz4vwuoi8kE1JGNk2i8lq64FuXk1MT2MKCllQs025JIPqOxXQpvwQ+RVakLNyE1LEL1GGdnMuYqMddtIPPf/eOyM8yARo9gTg0SM34ywcHbxhYQy81jXbMdlmYF2xlT+ufUZcNrxJ5oJxl8k1/kDnIEgsr0afcEL6MvWIdsDJGUC6weLGOSbi7zjSsTAXnjP7YmwGE0hLdWBdHgRNZupp5GEvtCIHGKZfMj3VORcdeCDbWrXjSnndvlo2f7RvopnNFwd5u8ekt6RHPz2t23nKsrfe+Koj1eOTDWAFUVRFEVRTkDavj7A+5fs/DJ6j5p9vLP1jaC6QCiKoiiKopyAzHEdszioC0RcdYE4FlQDWFEURVEU5QQkdInQ5SHrlC9OdYFQvtYWLjn+U09/Uy15e+PxzsI30sI11cc7C99YCxcuPN5Z+EZ6c2fD8c7Ct5Y5kTzsonxxqgH8VXJMJf7iYtwJM7K2EbweipJORLfriT/7JkKY0E6bhHnm2eD4LrKtGuGwgy7RRp+OdvIQMJuIPrsUkVmKyLjMGIzhtCPSvEReXo//xR2I9DQCz20isq4Z2VaNdsrpJDdVI/r0RzjsaD0K0U4dC2azMbDGdzHkZBiD5kb9HjgNNA1Zswla2kmu24P+3jJE4U8RGZchm9vQ31uGbKmk+e53ke1+4i8uRhv7F4Qwoe/Yi8i8HNlSRXxNLWLE3Zy+9n1E8Y3QrRskE2Cfbgy+SstA9BwINjci83JE31vRl200pnkVGiL1QmRtAyL1QrRgK07z95CNLehPPo5r7Wre2JiGbGxBNrbQzX0lFBSTeHO1Ma3uripEUV9E/wGI3regL12D6FOKKPwp5tWL0foPJ802G/29/+Ep30VGyAStxsAe/b/PYY8mjDxlXYnIuhJZVQ16ApE+h+Trb4N2Oj1eW4z+1quI/r/G1moMHBGlgyAawmaaatwjk61zClWn+XuIvGtxxOFUtx25oxKReqExY9revYj+v0ZfsQhhdqC/9zZYLSTeWosMGdeOfbox3ap2OvZgAFn7d/DOYqQmkeFWcDrA5WPk4neNOufyITIuI9/1A1xN+wZtWY0pf0XpLwCQ23cit5UjUi9E9L4FkXEZ+jv/ZfMHGnLDYiytdYzIvBRME42BRynnQjRIT+8VRl0Rp4HZCgXFxmDDqDH1ampTs3GevkPAPMmYsnfTEtLf/4hBvrlgn47LMgNR+gtk9WZSP1iBPRRBrl2F3mcUfo+T7yz8LwgN+dF7yF0rYM8ecNrR33qfFMu54ExFZPfonJKWlHMJ//1txJjROMzfRW7diru11RhgaTOmSJYVWxnkm4t2xv2IjMuMgTQnGyEM5W5jqlKRPgeRcxUyEERWVqN959FDv863zz/yd90+HYf5u8b3q/84zjQH0WUSUfoL3tjSgElMRORefdhDLT983shD71sO2Sbrd3b+f0SG8Fh1NGFihq0Z/Y1rwOok23E56fY0EKd1zjwoG4wBd/kLPmCEzYlpxSJwz0R/Z9+gPtNE8M5Cf80Y2KP97yVk+R8ROVcR+e2+ATuRqPE96Gg2pgLOMCYBij38MgD6G9egbV8BkSDmM8qMZN99DbngOWR1PcmF73dO+WyOhCDlXOym6YiMyzBlu4zvARD+12rjJ17vLNyWmchQ/JByiP7x7C6fY0+8iFmbdPgGsCaIl7cj1x74B50ovhEA171HmIxAO52CFxYg0ueA5zxM5xmDjsSIu7vsJutbiG1tMf6/uh7tpD8gev7S+FxTD2bjei2XP0vs/XIwm9BO+gOYjR9fRf9fI3KuQgz7DXLbb3GYv0ty/kJSLOciin5uTF18EIt2YFZSTBMRedcSXbBu34rTkLWNhO5bSOjnvzMGbzm7llV8ewuWy5/FcdcCGi46i/AtUwBw3LUA/eW5aBP+r/P7IPKuJb58D68ubwBNQxTfiKyup+ZFY2ZOuaMCMeQuTBc8iSj6OTjPpq3Oily+gvoLJxvlnOXDZ7sQkXUleFxoMx6jqMVPqnUWG0MN9PReQfKjncbzft/ARu2kPyCjRiNPdjRTln4ZkYfmG995wGWZweyeszu3z+0z2xi4F5G4LTONC22vQ65dhyjMJVnfgXbyn4xtMaM+Oc3fg1AY7cwDsXjrnt5rDFhes5Fsx+WYtUnI1bcevo58BqKvkYZct/kzH2tK6JjiB5bGyjVsXvWvL5wnRTWAFUVRFEVRTkjmhN5lKcgayJAB3z/e2fpGUH2AvwL74wr7/X5ikThWfwi9I4oIRIAwQjuwHs1shEXT/Uh/GIJRZCiG5g8hAxFkKEYsEifmD0HMb6QfiEAwSjiaIBZLIoJR/LEEmga6P4wwxYmH45gDYeiIQkyHQBjZEUXzG2noHVGS4TgW//40wxA1zpcMx9FCGqaP7StDMbRAmEAsgbkjRiISx+r3gz9EMhTD5PcjAxFikTg2vx89FEPz+410hYZw7vv/eASRsCHDYYTTSP/A8WGExY/eYRxLIATSj94RRQ/HMQWjxGIx/B1JSOid6Sf2XWtnucXCCJufZCiGFogg/H5kMIrw708vhghGwBYGQJj35zcECX/nfTTuWRhh9ZMIxzD7jTSFWes8t7D6IWjcK7GvvPbnu8u6cAjikQPXFgzty5NxfZo/hB6KgTCTDMcx+8MI84G8AEb9iIQRrn1lKfV997TDuNaOKOzP0757Klz70+96XcQSB62LEYsJ/MGuaXQKhyB+YN3++0owAliNMt5/vf4QsC8PwShy/zV//FoCkX3rQ+gdMaS/g2A8RCIcx+IPITuiYI0Y9TcQQf94HYlZkJEw0tqBJvyEogniAeOe779n6DqYkwirv7OcD2d/3etSNro8JL+fTQcyECZsC5Ew+4nFdfyfM72P16FIMkQwEMZvshHfV37762cgHkJaDtyD/f81ri/UWQf0g+7F/u+pHooh9h0TicSJ+Q+UmwyEIRIBQPj9RPd/x/d/j7QwBCP76nIMwjFICvRwHPP+c4W6frf2l7veESUcS2Db9/0CCEUTJA4qr/3n3C+27/kTi8UOKdtwNI6MS2KhQ+vdF5UMxQjHEugfe8Z1luVB37NIJI51/3MwGDmkDu5fl/jYtR+t/ffIOG+McDSB0MQh5QZdyzMQSyCjgvj+Yw9TRtFInJiu4+84UDcC8Tj+w1wzQCAexxWKEYgncPi7Pvf2X+P+dUF/GL/Zbzyz/R2gHUgrvu/vkdz37AtH45357FJuH/uuax9/1uz/TtiixPf/fdpXPpr/wPNI+A+kbVxXqMt1fdLz4rP6eLr76+mnzTuQDIfQDpr4IpkIH5P8fNsJeTSzPihfyN69eyksLDze2VAURVEU5QRSVVVFQUHBIesjkQjFxcXU1dUd9ricnBzKy8ux2+1fdha/sVQD+Cug6zo1NTWkpKQghDje2VEURVEU5TiSUhIIBMjLy0PTDt8bNRKJEIvFDrvNarWqxu8XpBrAiqIoiqIoyreKGgSnKIqiKIqifKuoBrCiKIqiKIryraIawIqiKIqiKMq3imoAHyM33XQTAwcOxOPxkJuby6xZs6iqquqyz549e5g2bRopKSlkZGRw7bXXHrGDu3LAHXfcQWlpKV6vl4yMDCZNmsTatWu77LN+/XrGjx+Py+UiLy+P22+//VPDyyhdnX322QgheOuttzrXvfvuuwwdOhSn00lxcTEPPvjgcczh18ftt9+OyWTC7XZ3LrNmzercruprV1JKbrvtNvLy8nC5XIwfP56NGz82cYUQOByOLuW5YcOGI6YXDoc599xz6dmzJ5qmceuth5/MYNmyZUyYMIGUlBRSU1M5+eST0XX9mF/f18Fzzz3HuHHj8Hg8CCFIJBJdtqs6+/motsGJSzWAjxEhBI8//jhNTU1s2bIFIQTTp0/v3K7rOtOmTcPn81FdXc3q1atZvHgxN9xww3HM9dfD+eefz6pVq2hvb6empoYzzzyTSZMmkUwaMwUFAgEmTZrEmDFjaGpqYuHChTz66KPcd999xzfjXyNPPvkkoVCoy7rKykqmTp3K3LlzaWtr4/HHH+emm27ipZdeOk65/HoZPXo0wWCwc3n22WcBVV8P589//jPz5s1j4cKFNDU1MWbMGCZNmkQwGOzc59VXX+1SngMHDjxiekIITj75ZB5++GFGjhx52H2WLVvG5MmTmTNnDvX19TQ1NXHvvfd+ayP1pKWlcfXVVx+2Hqo6+/mptsEJTCpfijVr1khAtrS0SCmlfPfdd6XZbJaNjY2d+7z88svS6XTKcDh8vLL5tROJROS9994rAdnQ0CCllPLxxx+XmZmZMh6Pd+533333yZKSkuOVza+VqqoqWVhYKCsrKyUg33zzTSmllLfffrssKyvrsu9Pf/pTOWHChOORza+V2267TY4ZM+aw21R9PVT37t3lfffd1/k5Ho/LjIwM+eSTT0opZZd6+Vmdcsop8pZbbjlk/dixY+X111//+TL8DbZo0SIJdKmfqs4eO6ptcOJQb4C/JG+88QZFRUWkpaUBsHbtWkpKSsjIyOjcZ8SIEYRCIbZv3368svm1sWDBAlJTU7Hb7Vx//fVcd911ZGZmAkbZDhkyBLP5wMSGI0aMYPfu3Z97xq1vCykll112GbfeeivdunXrsm3t2rWHvD0bMWIEa9as+Sqz+LW1Zs0aMjMzKSoq4oILLqC8vBxQ9fVg7e3tVFRUdKlrZrOZIUOGdKlrF154Ienp6QwdOpRHHnmkSxr/+te/SE1NPepzhkIhli5dislkYuTIkaSnpzNs2DBefPHFL3w930Sqzh47qm1w4lAN4C/BW2+9xR133MFDDz3Uuc7v9x/ygN7/BVAPkE83depU2traaG5u5i9/+QujR4/u3KbK9vN78MEHkVJy5ZVXHrLtSOWqyvTTnXPOOWzevJmGhgaWLl2KEIIzzjiDYDCo6utB9l/zJ9W1t956i/Lycmpra/nNb37DjTfe2KU/+gUXXEBbW9tRn7OlpQVd13niiSf429/+Rn19PbfccguzZs1i2bJlX/iavmlUnT02VNvgxKIawMfY/PnzOeecc3j66ac566yzOtd7PJ5DHtCtra2d25Sj4/P5+MlPfsLll1/OunXrAFW2n9euXbu46667ePTRRw+7/Ujlqsr00w0YMICioiKEEOTn5zNv3jyqq6tZunSpqq8H2X/Nn1TXTj/9dBwOB1arlSlTpvCTn/yEp5566nOfMyUlBYA5c+YwYsQIzGYzM2bM4LTTTuPll1/+3Ol+U6k6+8WptsGJRzWAj6FnnnmG2bNn8/zzz3P22Wd32VZWVkZ5eTnNzc2d61atWoXT6aRXr15fdVa/1nRdJx6Ps2PHDsAo2zVr1nQZtbxq1SpKSkrUA+QTLFmyhObmZoYNG0ZGRkbnT3AzZ87kyiuvpKysjJUrV3Y5ZtWqVQwZMuR4ZPdrTQiBEAIppaqvB/F6vXTv3r1LXUskEp0/ux+OpmlfKAKB1+ultLT0Wzvg7bNSdfaLUW2DE9Tx7YL8zXH//ffL1NRUuXjx4sNuTyaTcuDAgfKSSy6Rfr9fVlZWysGDB8sf/ehHX3FOv37uu+8+WVdXJ6WUsqGhQV5xxRXS6/XKmpoaKaWUfr9f5uTkyJtvvlmGQiG5YcMGWVhYKP/yl78cz2yf8Do6OmRVVVWXBZDPPvusbG5ulhUVFdLhcMi///3vMhqNysWLF0uv1ytffPHF4531E97zzz/fOailrq5OXnTRRbKoqEj6/X5VXw/jj3/8oywsLJQbNmyQoVBI3nzzzTIvL08GAgG5evVquWrVKhmNRmU8HpcLFy6UaWlp8q9//esnphmJRGQ4HJbjxo2TN910kwyHwzIajXZuv++++2R2drZcs2aNTCaT8pVXXpE2m01++OGHX/blnpASiYQMh8Ny4cKFEpDBYFCGw2GZTCZVnf0CVNvgxKUawMcIIM1ms3S5XF2Wj1f6iooKOWXKFOlyuaTP55PXXHONjEQixzHXXw9Tp06VWVlZ0ul0ypycHDl9+nS5cuXKLvusW7dOjh07VjocDpmdnS1vu+02qev6ccrx1xcHjbZftGiRLCsrk3a7XRYVFcm//e1vxzF3Xx/Tp0+XGRkZ0uFwyLy8PHn++efLHTt2dG5X9bUrXdflr371K5mdnS0dDoccN26cXL9+vZRSyv/+97+yT58+0uVySa/XKwcNGiQffPDBLsc//fTT0uVydVlXVFQkgS7LKaec0mWfu+++WxYUFEi32y2HDBkiX3755S/1Ok9kjz322CHlBchFixZJKVWd/bxU2+DEJaRUkawVRVEURVGUbw/VB1hRFEVRFEX5VlENYEVRFEVRFOVbRTWAFUVRFEVRlG8V1QBWFEVRFEVRvlVUA1hRFEVRFEX5VlENYEVRFEVRFOVbRTWAFUVRFOU4uummmxg4cCAej4fc3FxmzZpFVVVVl3327NnDtGnTSElJISMjg2uvvZZYLNa5/e233+b0008nPT0dIQQ7d+485Dzdu3fHbrfjdrs7l/nz539q/urq6vjBD35AQUEBDoeDgoICrrrqKurr64/6GufMmcOFF174qfu98sorDBo0iLS0NFJTUxk6dCgvvPBCl31aW1uZPXs2Xq+X1NRUZs+efch0wgdbv34948ePx+VykZeXx+23395lNsHbb78dk8nUpWxmzZr1iWk+99xzjBs3Do/HgxCiy0x5+0WjUW6++WaKiopwuVwUFRXx5JNPfmo5KF8+1QBWFEVRlONICMHjjz9OU1MTW7ZsQQjB9OnTO7frus60adPw+XxUV1ezevVqFi9ezA033NC5j8vl4uKLL/7UxtUDDzxAMBjsXKZNm/aJ+9fV1TFy5Eiqqqp4++23CQaDvPPOO+zZs4dRo0Z9pkbw0Rg+fDivv/46LS0ttLa2ct9993HRRRexbt26zn0uvPBC6uvr2bVrFzt37qS+vp5LLrnkiGkGAgEmTZrEmDFjaGpqYuHChTz66KPcd999XfYbPXp0l7J59tlnPzGvaWlpXH311Yek83HnnnsuK1eu7Cy7lStXMmrUqKMqC+VLdpwn4lAURVEU5WPWrFkjAdnS0iKllPLdd9+VZrO5c3ptKaV8+eWXpdPplOFwuMux5eXlEugy8+B+RUVF8pFHHvlMebniiitkSUlJl2mkpTSmmi4pKZE/+MEPOtdVVVXJCy64QObn58uUlBQ5ePBguXr1avnb3/5Wms3mLjOiVVZWfuq5k8mkXLJkibTZbPL//b//J6U0Zk0D5Nq1azv3W7t2rQSOmObjjz8uMzMzZTwe71x33333yZKSks7Pt912mxwzZszRFcpBFi1aJIEu6Usp5VtvvSXtdrusr6//XOkqXy71BlhRFEVRTiBvvPEGRUVFpKWlAbB27VpKSkrIyMjo3GfEiBGEQiG2b9/+mdK++eab8fl8DBgwgD/+8Y/E4/FP3H/+/PnMmjULq9XaZb3NZuP888/v7EIRDoeZMGECVquVtWvX0tbWxr/+9S/S09O5+eabmT17Nuedd17n29Vu3bod8Zzt7e2kpqZis9kYN24cI0aMYOrUqZ1lYbPZGDx4cOf+gwcP7jzv4axdu5YhQ4ZgNps7140YMYLdu3fj9/s7161Zs4bMzEyKioq44IILKC8v75LOoEGD+P3vf/+J5fVxb775JsXFxfzhD38gNzeXwsJCLr30Upqamo46DeXLoxrAiqIoinKCeOutt7jjjjt46KGHOtf5/X5SU1O77Le/cfzxBtyneeKJJ9i1axcNDQ089NBD/P3vf+fWW2/9xGMaGxvJz88/7LaCggIaGhoAWLBgAc3NzTz00ENkZGSgaRr9+vWjqKjoqPO3n9frpa2tjUAgwAsvvMCUKVM6G+B+vx+v13vIMampqUcsi6Mpv3POOYfNmzfT0NDA0qVLEUJwxhlnEAwGO49Zv349N91001Ffx/4uLdFolJ07d7Jq1Sr27t3LRRdddNRpKF8e1QBWFEVRlBPA/PnzOeecc3j66ac566yzOtd7PJ5DBnm1trZ2bjtap5xyCikpKZjNZsaOHcvtt9/OU089BcCSJUu6DABbsmQJAJmZmVRXVx82vb1795KVlQVAeXk53bt3x2azHXV++vfv33m+q6666pDtdrudmTNnsmTJEv7+9793Xm97e/sh+7a1tR2xLI6m/AYMGEBRURFCCPLz85k3bx7V1dUsXbr0qK/ncOcVQvDHP/4Rl8tFdnY2d955JwsXLiQUCn3udJVjQzWAFUVRFOU4e+aZZ5g9ezbPP/88Z599dpdtZWVllJeX09zc3Llu1apVOJ1OevXq9bnPqWlaZySEcePGdRkANm7cOACmTp3K888/3yXiBEAsFuP555/v7JrQvXt3KioqDtnv4+c62KZNmzrP9/E33geLx+Ns27YNMMoiGo2yfv36zu3r168nFotRVlZ22OPLyspYs2ZNlygNq1atoqSk5IiNZiEEQogukSI+q6FDhx4x7S+SrnKMHOc+yIqiKIryrXb//ffL1NRUuXjx4sNuTyaTcuDAgfKSSy6Rfr9fVlZWysGDB8sf/ehHXfYJh8Ny69atEpCbNm2S4XBYJhIJKaWU27dvl4sXL5bhcFgmk0m5bNkyWVxcLK+77rpPzFt1dbXMz8+X06ZNk9u2bZPJZFJu375dTps2TRYWFsra2loppZShUEiWlpbKuXPnysbGRqnruty0aZOsqKiQUkr5y1/+Up500kmd+TmSJ554Qm7btk0mEgkZCoXkgw8+KDVNk/Pnz+/cZ8qUKXLixImysbFRNjY2yokTJ8rp06cfMU2/3y9zcnLkzTffLEOhkNywYYMsLCyUf/nLXzr3ef755zsHGdbV1cmLLrpIFhUVSb/ff8R0E4mEDIfDcuHChRKQwWCws3yllDIYDMqCggL5k5/8RIbDYdnU1CTPPPNMOW3atE8sA+WroRrAiqIoinIcAV0iJOxfPt4grqiokFOmTJEul0v6fD55zTXXyEgk0rl9fySCg5fHHntMSinl8uXL5aBBg6Tb7ZYpKSmyT58+8re//a2MxWKfmr/q6mp5+eWXy9zcXGmz2WRubq684oorOhu/+1VWVsrzzjtP5uTkyJSUFFlWViY/+ugjKaURneKkk06Sqamp0uv1HjFiw69//WvZvXt36XQ6pc/nkyeddJJ87rnnuuzT3NwsZ82aJT0ej/R4PPKCCy6Qra2tn3gN69atk2PHjpUOh0NmZ2fL2267Teq63rl9+vTpMiMjQzocDpmXlyfPP//8QyJp9OvXT/72t7/t/PzYY48dtswXLVrUuc+WLVvkGWecIV0ul8zNzZVz586Vzc3Nn5hX5ashpFTv4RVFURRFUZRvD9UHWFEURVEURflWUQ1gRVEURVEU5VtFNYAVRVEURVGUbxXVAFYURVEURVG+VVQDWFEURVEURflWUQ1gRVEURVEU5VtFNYAVRVEURVGUbxXVAFYURVEURVG+VVQDWFEURVEURflWUQ1gRVEURVEU5Vvl/wN0ncaQHzPkRgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(7, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "plot_line(axs[0], b_xyz)\n", + "plot_line(axs[0], pyrf.norm(b_xyz), color=\"k\")\n", + "axs[0].set_ylabel(\"$B_{DMPA}$\" + \"\\n\" + \"[nT]\")\n", + "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\", \"$|\\\\mathbf{B}|$\"], **legend_options)\n", + "\n", + "plot_line(axs[1], n_i)\n", + "axs[1].set_ylabel(\"$n_{i}$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", + "\n", + "plot_line(axs[2], v_xyz_e)\n", + "axs[2].set_ylabel(\"$V_{e}$\" + \"\\n\" + \"[km s$^{-1}$]\")\n", + "axs[2].legend([\"$V_{x}$\", \"$V_{y}$\", \"$V_{z}$\"], **legend_options)\n", + "\n", + "axs[3], caxs3 = plot_spectr(\n", + " axs[3], def_omni_e, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\"\n", + ")\n", + "plot_line(axs[3], t_para_e)\n", + "plot_line(axs[3], t_perp_e)\n", + "axs[3].set_ylabel(\"$E_e$\" + \"\\n\" + \"[eV]\")\n", + "caxs3.set_ylabel(\n", + " \"$\\\\mathrm{DEF}$\"\n", + " + \"\\n\"\n", + " + \"$[\\\\mathrm{cm}^{-2}~\\\\mathrm{s}^{-1}~\\\\mathrm{sr}^{-1}]$\"\n", + ")\n", + "axs[3].legend([\"$T_{||}$\", \"$T_{\\perp}$\"], ncol=2, loc=\"upper right\", frameon=True)\n", + "\n", + "axs[4], caxs4 = plot_spectr(axs[4], pad_lowen_e, cscale=\"log\", cmap=\"Spectral_r\")\n", + "axs[4].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", + "caxs4.set_ylabel(\n", + " \"$\\\\mathrm{DEF}$\"\n", + " + \"\\n\"\n", + " + \"$[\\\\mathrm{cm}^{-2}~\\\\mathrm{s}^{-1}~\\\\mathrm{sr}^{-1}]$\"\n", + ")\n", + "axs[4].text(0.05, 0.1, e_int_e[\"lowen\"], transform=axs[4].transAxes)\n", + "\n", + "axs[5], caxs5 = plot_spectr(axs[5], pad_miden_e, cscale=\"log\", cmap=\"Spectral_r\")\n", + "axs[5].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", + "caxs5.set_ylabel(\n", + " \"$\\\\mathrm{DEF}$\"\n", + " + \"\\n\"\n", + " + \"$[\\\\mathrm{cm}^{-2}~\\\\mathrm{s}^{-1}~\\\\mathrm{sr}^{-1}]$\"\n", + ")\n", + "axs[5].text(0.05, 0.1, e_int_e[\"miden\"], transform=axs[5].transAxes)\n", + "\n", + "axs[6], caxs6 = plot_spectr(axs[6], pad_higen_e, cscale=\"log\", cmap=\"Spectral_r\")\n", + "axs[6].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", + "caxs6.set_ylabel(\n", + " \"$\\\\mathrm{DEF}$\"\n", + " + \"\\n\"\n", + " + \"$[\\\\mathrm{cm}^{-2}~\\\\mathrm{s}^{-1}~\\\\mathrm{sr}^{-1}]$\"\n", + ")\n", + "axs[6].text(0.05, 0.1, e_int_e[\"higen\"], transform=axs[6].transAxes)\n", + "\n", + "make_labels(axs, [0.02, 0.85])\n", + "\n", + "f.align_ylabels(axs)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/01_mms/example_mms_polarizationanalysis.ipynb b/docs/examples/01_mms/example_mms_polarizationanalysis.ipynb new file mode 100644 index 00000000..9fdfa9fa --- /dev/null +++ b/docs/examples/01_mms/example_mms_polarizationanalysis.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Polarization Analysis\n", + "author: Louis Richard\\\n", + "Perform polarization analysis on burst mode electric and magnetic fields. Plots spectrograms, ellipticity, wave-normal angle, planarity, degree of polarization (DOP), phase speed, and normalized Poynting flux along B. Time selections should not be too long (less than 20 seconds), otherwise the analysis will be very slow. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from scipy import constants\n", + "from pyrfu import mms, pyrf\n", + "from pyrfu.plot import plot_line, plot_spectr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define time interval and spacecraft index" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# mms.db_init(\"/Volumes/mms\")\n", + "tint = [\"2015-10-30T05:15:42.000\", \"2015-10-30T05:15:54.00\"]\n", + "tint_long = pyrf.extend_tint(tint, [-100, 100])\n", + "ic = 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data from SDC" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[01-Dec-23 00:07:42] INFO: Loading mms3_mec_r_gse...\n", + "[01-Dec-23 00:07:49] INFO: Loading mms3_fgm_b_gse_brst_l2...\n", + "[01-Dec-23 00:07:55] INFO: Loading mms3_edp_dce_gse_brst_l2...\n", + "[01-Dec-23 00:09:08] INFO: Loading mms3_scm_acb_gse_scb_brst_l2...\n" + ] + } + ], + "source": [ + "r_xyz = mms.get_data(\"r_gse_mec_srvy_l2\", tint_long, ic, from_sdc=True)\n", + "b_xyz = mms.get_data(\"b_gse_fgm_brst_l2\", tint, ic, from_sdc=True)\n", + "e_xyz = mms.get_data(\"e_gse_edp_brst_l2\", tint, ic, from_sdc=True)\n", + "b_scm = mms.get_data(\"b_gse_scm_brst_l2\", tint, ic, from_sdc=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute electron cylotron frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "b_si = pyrf.norm(b_xyz) * 1e-9\n", + "w_ce = constants.elementary_charge * b_si / constants.electron_mass\n", + "f_ce = w_ce / (2 * np.pi)\n", + "f_ce_01 = f_ce * 0.1\n", + "f_ce_05 = f_ce * 0.5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Polarization Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[01-Dec-23 00:09:17] INFO: Interpolating b and e to 2x e sampling\n", + "[01-Dec-23 00:09:18] INFO: ebsp ... calculate E and B wavelet transform ... \n", + "[01-Dec-23 00:09:29] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/pyrf/ebsp.py:803: RuntimeWarning: invalid value encountered in divide\n", + " np.trace(np.matmul(s_mat_avg, s_mat_avg), axis1=1, axis2=2)\n", + "\n", + "[01-Dec-23 00:09:29] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/pyrf/ebsp.py:824: RuntimeWarning: invalid value encountered in divide\n", + " np.trace(np.matmul(s_mat_avg, s_mat_avg), axis1=1, axis2=2)\n", + "\n" + ] + } + ], + "source": [ + "polarization_options = dict(freq_int=[10, 4000], polarization=True, fac=True)\n", + "polarization = pyrf.ebsp(e_xyz, b_scm, b_xyz, b_xyz, r_xyz, **polarization_options)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unpack polarization analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "frequency = polarization[\"f\"]\n", + "time = polarization[\"t\"]\n", + "b_sum = polarization[\"bb_xxyyzzss\"][..., 3]\n", + "b_per = polarization[\"bb_xxyyzzss\"][..., 0] + polarization[\"bb_xxyyzzss\"][..., 1]\n", + "e_sum = polarization[\"ee_xxyyzzss\"][..., 3]\n", + "e_per = polarization[\"ee_xxyyzzss\"][..., 0] + polarization[\"ee_xxyyzzss\"][..., 1]\n", + "ellipticity = polarization[\"ellipticity\"]\n", + "dop = polarization[\"dop\"]\n", + "thetak = polarization[\"k_tp\"][..., 0]\n", + "planarity = polarization[\"planarity\"]\n", + "pflux_z = polarization[\"pf_xyz\"][..., 2]\n", + "pflux_z /= np.sqrt(\n", + " polarization[\"pf_xyz\"][..., 0] ** 2\n", + " + polarization[\"pf_xyz\"][..., 1] ** 2\n", + " + polarization[\"pf_xyz\"][..., 2] ** 2\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate phase speed v_ph = E/B." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "v_ph = np.sqrt(e_sum / b_sum) * 1e6\n", + "v_ph_perp = np.sqrt(e_per / b_per) * 1e6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove points with very low B amplitutes" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "b_sum_thres = 1e-7\n", + "\n", + "ellipticity.data[b_sum < b_sum_thres] = np.nan\n", + "thetak.data[b_sum < b_sum_thres] = np.nan\n", + "dop.data[b_sum < b_sum_thres] = np.nan\n", + "planarity.data[b_sum < b_sum_thres] = np.nan\n", + "pflux_z.data[b_sum < b_sum_thres] = np.nan\n", + "v_ph.data[b_sum < b_sum_thres] = np.nan\n", + "v_ph_perp.data[b_sum < b_sum_thres] = np.nan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot figure" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$S_{||}/|S|$\\n ')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(8, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "axs[0], caxs0 = plot_spectr(\n", + " axs[0], b_sum, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\"\n", + ")\n", + "plot_line(axs[0], f_ce, color=\"w\")\n", + "plot_line(axs[0], f_ce_01, color=\"w\")\n", + "plot_line(axs[0], f_ce_05, color=\"w\")\n", + "axs[0].set_ylabel(\"$f$ [Hz]\")\n", + "caxs0.set_ylabel(\"$B^2$\" + \"\\n\" + \"[nT$^2$ Hz$^{-1}$]\")\n", + "\n", + "axs[1], caxs1 = plot_spectr(\n", + " axs[1], e_sum, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\"\n", + ")\n", + "plot_line(axs[1], f_ce, color=\"w\")\n", + "plot_line(axs[1], f_ce_01, color=\"w\")\n", + "plot_line(axs[1], f_ce_05, color=\"w\")\n", + "axs[1].set_ylabel(\"$f$ [Hz]\")\n", + "caxs1.set_ylabel(\"$E^2$\" + \"\\n\" + \"[mV$^2$ m$^{-2}$ Hz$^{-1}$]\")\n", + "\n", + "axs[2], caxs2 = plot_spectr(\n", + " axs[2], ellipticity, yscale=\"log\", cmap=\"RdBu_r\", clim=[-1, 1]\n", + ")\n", + "plot_line(axs[2], f_ce, color=\"w\")\n", + "plot_line(axs[2], f_ce_01, color=\"w\")\n", + "plot_line(axs[2], f_ce_05, color=\"w\")\n", + "axs[2].set_ylabel(\"$f$ [Hz]\")\n", + "caxs2.set_ylabel(\"Ellipticity\" + \"\\n\" + \" \")\n", + "\n", + "axs[3], caxs3 = plot_spectr(\n", + " axs[3], thetak * 180 / np.pi, yscale=\"log\", cmap=\"Spectral_r\", clim=[0, 90]\n", + ")\n", + "plot_line(axs[3], f_ce, color=\"w\")\n", + "plot_line(axs[3], f_ce_01, color=\"w\")\n", + "plot_line(axs[3], f_ce_05, color=\"w\")\n", + "axs[3].set_ylabel(\"$f$ [Hz]\")\n", + "caxs3.set_ylabel(\"$\\\\theta_k$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", + "\n", + "axs[4], caxs4 = plot_spectr(axs[4], dop, yscale=\"log\", cmap=\"Spectral_r\", clim=[0, 1])\n", + "plot_line(axs[4], f_ce, color=\"w\")\n", + "plot_line(axs[4], f_ce_01, color=\"w\")\n", + "plot_line(axs[4], f_ce_05, color=\"w\")\n", + "axs[4].set_ylabel(\"$f$ [Hz]\")\n", + "caxs4.set_ylabel(\"DOP\" + \"\\n\" + \" \")\n", + "\n", + "\n", + "axs[5], caxs5 = plot_spectr(\n", + " axs[5], planarity, yscale=\"log\", cmap=\"Spectral_r\", clim=[0, 1]\n", + ")\n", + "plot_line(axs[5], f_ce, color=\"w\")\n", + "plot_line(axs[5], f_ce_01, color=\"w\")\n", + "plot_line(axs[5], f_ce_05, color=\"w\")\n", + "axs[5].set_ylabel(\"$f$ [Hz]\")\n", + "caxs5.set_ylabel(\"Planarity\" + \"\\n\" + \" \")\n", + "\n", + "\n", + "axs[6], caxs6 = plot_spectr(axs[6], v_ph, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", + "plot_line(axs[6], f_ce, color=\"w\")\n", + "plot_line(axs[6], f_ce_01, color=\"w\")\n", + "plot_line(axs[6], f_ce_05, color=\"w\")\n", + "axs[6].set_ylabel(\"$f$ [Hz]\")\n", + "caxs6.set_ylabel(\"E/B\" + \"\\n\" + \"[m s$^{-1}$]\")\n", + "\n", + "axs[7], caxs7 = plot_spectr(axs[7], pflux_z, yscale=\"log\", cmap=\"RdBu_r\", clim=[-1, 1])\n", + "plot_line(axs[7], f_ce, color=\"w\")\n", + "plot_line(axs[7], f_ce_01, color=\"w\")\n", + "plot_line(axs[7], f_ce_05, color=\"w\")\n", + "axs[7].set_ylabel(\"$f$ [Hz]\")\n", + "caxs7.set_ylabel(\"$S_{||}/|S|$\" + \"\\n\" + \" \")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/01_mms/example_mms_reduced_electron_dist.ipynb b/docs/examples/01_mms/example_mms_reduced_electron_dist.ipynb new file mode 100644 index 00000000..3ed6f429 --- /dev/null +++ b/docs/examples/01_mms/example_mms_reduced_electron_dist.ipynb @@ -0,0 +1,511 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c02659ce", + "metadata": {}, + "source": [ + "# Reduced Electron Velocity Distribution Function using Monte-Carlo integration\n", + "\n", + "author: Louis Richard\\\n", + "Example to compute and plot reduced electron distributions from FPI (based on Khotyaintsev et al., 2020, PRL)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "90737244", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from pyrfu import mms, pyrf\n", + "from pyrfu.plot import plot_spectr, plot_line\n", + "\n", + "mms.db_init(\"/Volumes/mms\")" + ] + }, + { + "cell_type": "markdown", + "id": "44ffc375", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "markdown", + "id": "25a92e7d", + "metadata": {}, + "source": [ + "### Define time interval and spacecraft index" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0de83044", + "metadata": {}, + "outputs": [], + "source": [ + "tint = [\"2015-12-02T01:14:55.500\", \"2015-12-02T01:14:56.600\"]\n", + "\n", + "tint_long = [\"2015-12-02T01:14:45.500\", \"2015-12-02T01:15:02.500\"]\n", + "mms_id = 4" + ] + }, + { + "cell_type": "markdown", + "id": "5230ae36", + "metadata": {}, + "source": [ + "### Get magnetic field in spacecraft coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "82c83897", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 09:48:37] INFO: Loading mms4_fgm_b_dmpa_brst_l2...\n" + ] + } + ], + "source": [ + "b_dmpa = mms.get_data(\"b_dmpa_fgm_brst_l2\", tint_long, mms_id)" + ] + }, + { + "cell_type": "markdown", + "id": "8690c845", + "metadata": {}, + "source": [ + "### Get electric field in spacecraft coordinates and spacecraft potential" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c0b08a37", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 09:48:37] INFO: Loading mms4_edp_dce_dsl_brst_l2...\n", + "[09-Jun-23 09:48:37] INFO: Loading mms4_edp_scpot_brst_l2...\n" + ] + } + ], + "source": [ + "e_dsl = mms.get_data(\"e_dsl_edp_brst_l2\", tint_long, mms_id)\n", + "scpot = mms.get_data(\"v_edp_brst_l2\", tint_long, mms_id)" + ] + }, + { + "cell_type": "markdown", + "id": "f687130b", + "metadata": {}, + "source": [ + "### Get the electron velocity distribution skymap and the uncertainties ($\\delta f / f = 1/\\sqrt{n}$ with $n$ the number of counts)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3104a6d0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 09:48:38] INFO: Loading mms4_des_dist_brst...\n", + "[09-Jun-23 09:48:38] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_dist.py:68: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[09-Jun-23 09:48:41] INFO: Loading mms4_des_disterr_brst...\n" + ] + } + ], + "source": [ + "vdf_e = mms.get_data(\"pde_fpi_brst_l2\", tint_long, mms_id)\n", + "vdf_e_err = mms.get_data(\"pderre_fpi_brst_l2\", tint_long, mms_id)" + ] + }, + { + "cell_type": "markdown", + "id": "04731e8f", + "metadata": {}, + "source": [ + "### Ignore phase-space density for one count level (also makes function faster)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8b7e220e", + "metadata": {}, + "outputs": [], + "source": [ + "vdf_e.data.data[vdf_e.data.data < 1.1 * vdf_e_err.data.data] = 0.0" + ] + }, + { + "cell_type": "markdown", + "id": "c3a29bcd", + "metadata": {}, + "source": [ + "### Define coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4f48f418", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 09:48:45] INFO: Using averages in resample\n", + "[09-Jun-23 09:48:45] INFO: Using averages in resample\n", + "[09-Jun-23 09:48:45] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "x_hat = pyrf.resample(b_dmpa, vdf_e)\n", + "x_hat.data /= pyrf.norm(pyrf.resample(b_dmpa, vdf_e)).data[:, np.newaxis]\n", + "y_hat = pyrf.resample(pyrf.cross(e_dsl, b_dmpa), vdf_e)\n", + "y_hat.data /= pyrf.norm(y_hat).data[:, np.newaxis]\n", + "z_hat = pyrf.cross(x_hat, y_hat)" + ] + }, + { + "cell_type": "markdown", + "id": "f38beed6", + "metadata": {}, + "source": [ + "### Create transformation matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c69d13fc", + "metadata": {}, + "outputs": [], + "source": [ + "xyz = np.transpose(np.stack([x_hat.data, y_hat.data, z_hat.data]), [1, 2, 0])\n", + "xyz = pyrf.ts_tensor_xyz(vdf_e.time.data, xyz)" + ] + }, + { + "cell_type": "markdown", + "id": "c2b62886", + "metadata": {}, + "source": [ + "### Define velocity grid parallel to the magnetic field" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "58cc4197", + "metadata": {}, + "outputs": [], + "source": [ + "vpara_lim = np.array([-10e3, 10e3], dtype=np.float64)\n", + "vg_para = np.linspace(vpara_lim[0], vpara_lim[1], 100)" + ] + }, + { + "cell_type": "markdown", + "id": "71228141", + "metadata": {}, + "source": [ + "### Reduce distribution along the magnetic field" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "76aa3423", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 09:48:45] INFO: Using averages in resample\n", + "100%|█████████████████████| 566/566 [00:20<00:00, 27.97it/s]\n" + ] + } + ], + "source": [ + "f1dpara = mms.reduce(\n", + " vdf_e, dim=\"1d\", xyz=xyz, n_mc=200, vg=vg_para * 1e3, sc_pot=scpot, lower_e_lim=30.0\n", + ")\n", + "f1dpara = f1dpara.assign_coords(vx=f1dpara.vx.data / 1e3)" + ] + }, + { + "cell_type": "markdown", + "id": "1968d826", + "metadata": {}, + "source": [ + "### Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d5354171", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(16771.05203125, 16771.05204398148)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(2, sharex=\"all\", figsize=(9, 6.5))\n", + "f.subplots_adjust(hspace=0.0, left=0.15, right=0.85, bottom=0.08, top=0.95)\n", + "plot_line(axs[0], b_dmpa)\n", + "plot_line(axs[0], pyrf.norm(b_dmpa), color=\"k\")\n", + "axs[0].set_ylim([-5, 40])\n", + "axs[0].set_ylabel(\"$B_{dmpa}~[\\mathrm{nT}]$\")\n", + "\n", + "axs[1], cax1 = plot_spectr(\n", + " axs[1], f1dpara, cscale=\"log\", clim=[1e-2, 1e0], cmap=\"Spectral_r\"\n", + ")\n", + "axs[1].set_ylim([-9.9, 9.9])\n", + "axs[1].set_ylabel(\"$v_\\\\parallel~[\\\\mathrm{Mm}~\\\\mathrm{s}^{-1}]$\")\n", + "cax1.set_ylabel(\"$F_e~[\\\\mathrm{s}~\\\\mathrm{m}^{-4}]$\")\n", + "\n", + "axs[-1].set_xlim(pyrf.iso86012datetime64(np.array(tint)))" + ] + }, + { + "cell_type": "markdown", + "id": "12d97045", + "metadata": {}, + "source": [ + "### " + ] + }, + { + "cell_type": "markdown", + "id": "119570f3", + "metadata": {}, + "source": [ + "## 2D projection of the ion velocity distribution functions" + ] + }, + { + "cell_type": "markdown", + "id": "754aeb6f", + "metadata": {}, + "source": [ + "### Define times for 2D projection" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6efa9608", + "metadata": {}, + "outputs": [], + "source": [ + "t = [\n", + " \"2015-12-02T01:15:02.020000000\",\n", + " \"2015-12-02T01:14:56.440\",\n", + " \"2015-12-02T01:14:56.380\",\n", + " \"2015-12-02T01:14:56.290\",\n", + " \"2015-12-02T01:14:55.810\",\n", + " \"2015-12-02T01:14:55.600\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "659eca74", + "metadata": {}, + "source": [ + "### Reduce ion distributions in 2d plane $(B,E\\times B)$" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6934f567", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████| 4/4 [00:02<00:00, 1.96it/s]\n", + "100%|█████████████████████████| 4/4 [00:02<00:00, 1.94it/s]\n", + "100%|█████████████████████████| 4/4 [00:01<00:00, 2.06it/s]\n", + "100%|█████████████████████████| 4/4 [00:01<00:00, 2.04it/s]\n", + "100%|█████████████████████████| 4/4 [00:02<00:00, 1.86it/s]\n", + "100%|█████████████████████████| 4/4 [00:02<00:00, 1.74it/s]\n" + ] + } + ], + "source": [ + "f2d = []\n", + "\n", + "for t_ in t:\n", + " t_ = pyrf.extend_tint([t_, t_], [-0.06, 0.06])\n", + " tmp = mms.reduce(\n", + " pyrf.time_clip(vdf_e, t_),\n", + " xyz=pyrf.time_clip(xyz, t_),\n", + " dim=\"2d\",\n", + " base=\"cart\",\n", + " n_mc=200 * 5,\n", + " vg=vg_para * 1e3,\n", + " )\n", + " f2d.append(tmp.assign_coords(vx=tmp.vx.data / 1e3, vy=tmp.vy.data / 1e3))" + ] + }, + { + "cell_type": "markdown", + "id": "6f183278", + "metadata": {}, + "source": [ + "### Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c3c85eb6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.02, 0.5, '$v_{E\\\\times B}~[\\\\mathrm{Mm}~\\\\mathrm{s}^{-1}]$')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(2, 3, sharex=\"all\", sharey=\"all\", figsize=(9, 6.5))\n", + "f.subplots_adjust(wspace=0, hspace=0, left=0.11, right=0.97, bottom=0.1, top=0.85)\n", + "\n", + "\n", + "axs[0, 0], cax0 = plot_spectr(\n", + " axs[0, 0],\n", + " f2d[0].mean(axis=0),\n", + " cscale=\"log\",\n", + " clim=[1e-10, 3e-7],\n", + " cmap=\"Spectral_r\",\n", + " colorbar=\"top\",\n", + ")\n", + "axs[0, 0].set_xlim([-1e4, 1e4])\n", + "axs[0, 0].set_ylim([-1e4, 1e4])\n", + "\n", + "for f_2d, ax in zip(f2d[1:], axs.flatten()[1:]):\n", + " ax = plot_spectr(\n", + " ax,\n", + " f_2d.mean(axis=0),\n", + " cscale=\"log\",\n", + " clim=[1e-10, 3e-7],\n", + " cmap=\"Spectral_r\",\n", + " colorbar=\"none\",\n", + " )\n", + " ax.set_xlim([-9.9, 9.9])\n", + " ax.set_ylim([-9.9, 9.9])\n", + "\n", + "pos_axs02 = axs[0, 2].get_position()\n", + "pos_cax0 = cax0.get_position()\n", + "x0 = pos_cax0.x0\n", + "y0 = pos_cax0.y0 + 0.03\n", + "width = pos_axs02.x0 + pos_axs02.width - pos_cax0.x0\n", + "height = 0.02\n", + "cax0.set_position([x0, y0, width, height])\n", + "cax0.set_xlabel(\"$F_e~[\\\\mathrm{s}~\\\\mathrm{m}^{-5}]$\")\n", + "\n", + "f.supxlabel(\"$v_{\\\\parallel}~[\\\\mathrm{Mm}~\\\\mathrm{s}^{-1}]$\")\n", + "f.supylabel(\"$v_{E\\\\times B}~[\\\\mathrm{Mm}~\\\\mathrm{s}^{-1}]$\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/01_mms/example_mms_reduced_ion_dist.ipynb b/docs/examples/01_mms/example_mms_reduced_ion_dist.ipynb new file mode 100644 index 00000000..95e496d4 --- /dev/null +++ b/docs/examples/01_mms/example_mms_reduced_ion_dist.ipynb @@ -0,0 +1,650 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "68c3f088", + "metadata": {}, + "source": [ + "# Reduced Ion Velocity Distribution Function using Monte-Carlo integration\n", + "\n", + "author: Louis Richard\\\n", + "Example to compute and plot reduced ion distributions from FPI" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "80192ac2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from pyrfu import mms, pyrf\n", + "from pyrfu.plot import plot_spectr, plot_line\n", + "\n", + "mms.db_init(\"/Volumes/mms\")" + ] + }, + { + "cell_type": "markdown", + "id": "b0c6036e", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "markdown", + "id": "45b32ac5", + "metadata": {}, + "source": [ + "### Define time interval and spacecraft index" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8c409e7e", + "metadata": {}, + "outputs": [], + "source": [ + "tint = [\"2015-12-28T03:57:10\", \"2015-12-28T03:59:00\"]\n", + "mms_id = 2" + ] + }, + { + "cell_type": "markdown", + "id": "ea250e78", + "metadata": {}, + "source": [ + "### Get magnetic field in spacecraft coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ef29c16a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 09:47:17] INFO: Loading mms2_fgm_b_dmpa_brst_l2...\n" + ] + } + ], + "source": [ + "b_dmpa = mms.get_data(\"b_dmpa_fgm_brst_l2\", tint, mms_id)" + ] + }, + { + "cell_type": "markdown", + "id": "85ae72f7", + "metadata": {}, + "source": [ + "### Get spacecraft attitude (for conversion between Geocentric Solar Ecliptic to spacecrat coordinates)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "16322530", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 09:47:18] INFO: Loading ancillary defatt files...\n", + "[09-Jun-23 09:47:24] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/load_ancillary.py:106: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " data_frame = data_frame.append(data_frame_dict[k])\n", + "\n" + ] + } + ], + "source": [ + "defatt = mms.load_ancillary(\"defatt\", tint, mms_id)" + ] + }, + { + "cell_type": "markdown", + "id": "3db22f49", + "metadata": {}, + "source": [ + "### Get the ion velocity distribution skymap and the uncertainties ($\\delta f / f = 1/\\sqrt{n}$ with $n$ the number of counts)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "66e16bd3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 09:47:24] INFO: Loading mms2_dis_dist_brst...\n", + "[09-Jun-23 09:47:24] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_dist.py:68: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[09-Jun-23 09:47:25] INFO: Loading mms2_dis_disterr_brst...\n" + ] + } + ], + "source": [ + "vdf_i = mms.get_data(\"pdi_fpi_brst_l2\", tint, mms_id)\n", + "vdf_i_err = mms.get_data(\"pderri_fpi_brst_l2\", tint, mms_id)" + ] + }, + { + "cell_type": "markdown", + "id": "ff050f73", + "metadata": {}, + "source": [ + "### Ignore phase-space density for one count level (also makes function faster)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "915b7e3f", + "metadata": {}, + "outputs": [], + "source": [ + "vdf_i.data.data[vdf_i.data.data < 1.1 * vdf_i_err.data.data] = 0.0" + ] + }, + { + "cell_type": "markdown", + "id": "3be9397f", + "metadata": {}, + "source": [ + "### " + ] + }, + { + "cell_type": "markdown", + "id": "e2d56108", + "metadata": {}, + "source": [ + "## Define the coordinates system for projection of the distribution function" + ] + }, + { + "cell_type": "markdown", + "id": "769f1d87", + "metadata": {}, + "source": [ + "### Shock normal vector in GSE (get this from irf_shock_normal or irf_shock_gui)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "09281abb", + "metadata": {}, + "outputs": [], + "source": [ + "n_vec = np.array([0.9580, -0.2708, -0.0938])\n", + "n_vec /= np.linalg.norm(n_vec)" + ] + }, + { + "cell_type": "markdown", + "id": "9aab5bab", + "metadata": {}, + "source": [ + "### Upstream magnetic field in GSE\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "aa634640", + "metadata": {}, + "outputs": [], + "source": [ + "b_u = [-1.0948, -2.6270, 1.6478]" + ] + }, + { + "cell_type": "markdown", + "id": "e1b4c1bb", + "metadata": {}, + "source": [ + "### $t_2$ vector in GSE (same vectors as in [Johlander et al. 2016, PRL])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "df1e12d6", + "metadata": {}, + "outputs": [], + "source": [ + "t2_vec = np.cross(n_vec, b_u) / np.linalg.norm(np.cross(n_vec, b_u))" + ] + }, + { + "cell_type": "markdown", + "id": "e91c9326", + "metadata": {}, + "source": [ + "### $t_1$ vector in GSE\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5d840178", + "metadata": {}, + "outputs": [], + "source": [ + "t1_vec = np.cross(t2_vec, n_vec)" + ] + }, + { + "cell_type": "markdown", + "id": "00c98f2e", + "metadata": {}, + "source": [ + "### Construct the time series for $n$, $t_1$, and $t_2$" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bb8c5a1d", + "metadata": {}, + "outputs": [], + "source": [ + "n_t = len(b_dmpa.time.data)\n", + "n_gse = pyrf.ts_vec_xyz(b_dmpa.time.data, np.tile(n_vec[np.newaxis, :], [n_t, 1]))\n", + "t1_gse = pyrf.ts_vec_xyz(b_dmpa.time.data, np.tile(t1_vec[np.newaxis, :], [n_t, 1]))\n", + "t2_gse = pyrf.ts_vec_xyz(b_dmpa.time.data, np.tile(t2_vec[np.newaxis, :], [n_t, 1]))" + ] + }, + { + "cell_type": "markdown", + "id": "c0d2bb26", + "metadata": {}, + "source": [ + "### Transform the vectors into spacecraft coordinates using the spacecraft attitude" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d726d00e", + "metadata": {}, + "outputs": [], + "source": [ + "n_dmpa = mms.dsl2gse(n_gse, defatt)\n", + "t1_dmpa = mms.dsl2gse(t1_gse, defatt)\n", + "t2_dmpa = mms.dsl2gse(t2_gse, defatt)" + ] + }, + { + "cell_type": "markdown", + "id": "ddf54a3c", + "metadata": {}, + "source": [ + "### Create the transformation matrices from spacecraft coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4ab69c97", + "metadata": {}, + "outputs": [], + "source": [ + "nt1t2 = np.transpose(np.stack([n_dmpa.data, t1_dmpa.data, t2_dmpa.data]), [1, 2, 0])\n", + "nt1t2 = pyrf.ts_tensor_xyz(b_dmpa.time.data, nt1t2)\n", + "\n", + "t1t2n = np.transpose(np.stack([t1_dmpa.data, t2_dmpa.data, n_dmpa.data]), [1, 2, 0])\n", + "t1t2n = pyrf.ts_tensor_xyz(b_dmpa.time.data, t1t2n)\n", + "\n", + "t2nt1 = np.transpose(np.stack([t2_dmpa.data, n_dmpa.data, t1_dmpa.data]), [1, 2, 0])\n", + "t2nt1 = pyrf.ts_tensor_xyz(b_dmpa.time.data, t2nt1)" + ] + }, + { + "cell_type": "markdown", + "id": "3399657a", + "metadata": {}, + "source": [ + "### Define velocity grid along the three vectors" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "803ba764", + "metadata": {}, + "outputs": [], + "source": [ + "vn_lim = np.array([-800.0, 800.0], dtype=np.float64)\n", + "vt1_lim = vn_lim\n", + "vt2_lim = vn_lim + 300.0\n", + "\n", + "vg_1d_n = 1e3 * np.linspace(vn_lim[0], vn_lim[1], 100)\n", + "vg_1d_t1 = 1e3 * np.linspace(vt1_lim[0], vt1_lim[1], 100)\n", + "vg_1d_t2 = 1e3 * np.linspace(vt2_lim[0], vt2_lim[1], 100)" + ] + }, + { + "cell_type": "markdown", + "id": "ba64fe6e", + "metadata": {}, + "source": [ + "### Reduce distribution along the three vectors" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8243e46b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 09:47:26] INFO: Using averages in resample\n", + "100%|█████████████████████| 733/733 [00:11<00:00, 61.85it/s]\n", + "[09-Jun-23 09:47:38] INFO: Using averages in resample\n", + "100%|█████████████████████| 733/733 [00:09<00:00, 73.39it/s]\n", + "[09-Jun-23 09:47:48] INFO: Using averages in resample\n", + "100%|█████████████████████| 733/733 [00:08<00:00, 84.67it/s]\n" + ] + } + ], + "source": [ + "n_mc = 200\n", + "\n", + "f1dn = mms.reduce(vdf_i, projection_dim=\"1d\", xyz=nt1t2, n_mc=n_mc, vg=vg_1d_n)\n", + "f1dt1 = mms.reduce(vdf_i, projection_dim=\"1d\", xyz=t1t2n, n_mc=n_mc, vg=vg_1d_t1)\n", + "f1dt2 = mms.reduce(vdf_i, projection_dim=\"1d\", xyz=t2nt1, n_mc=n_mc, vg=vg_1d_t2)" + ] + }, + { + "cell_type": "markdown", + "id": "addde2fa", + "metadata": {}, + "source": [ + "### Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "80b58834", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(4, sharex=\"all\", figsize=(9, 6.5))\n", + "f.subplots_adjust(hspace=0.0, left=0.15, right=0.85, bottom=0.08, top=0.95)\n", + "\n", + "plot_line(axs[0], b_dmpa)\n", + "plot_line(axs[0], pyrf.norm(b_dmpa), color=\"k\")\n", + "axs[0].set_ylabel(\"$B_{dmpa}~[\\mathrm{nT}]$\")\n", + "\n", + "axs[1], cax1 = plot_spectr(axs[1], f1dn, cscale=\"log\", clim=[1e-4, 1e3], cmap=\"jet\")\n", + "axs[1].set_ylim(vn_lim)\n", + "axs[1].set_ylabel(\"$v_n~[\\\\mathrm{km}~\\\\mathrm{s}^{-1}]$\")\n", + "axs[1].set_yticks([-500.0, 0.0, 500.0])\n", + "\n", + "axs[2] = plot_spectr(\n", + " axs[2], f1dt1, cscale=\"log\", clim=[1e-4, 1e3], cmap=\"jet\", colorbar=\"none\"\n", + ")\n", + "axs[2].set_ylim(vt1_lim)\n", + "axs[2].set_ylabel(\"$v_{t1}~[\\\\mathrm{km}~\\\\mathrm{s}^{-1}]$\")\n", + "axs[2].set_yticks([-500.0, 0.0, 500.0])\n", + "\n", + "axs[3] = plot_spectr(\n", + " axs[3], f1dt2, cscale=\"log\", clim=[1e-4, 1e3], cmap=\"jet\", colorbar=\"none\"\n", + ")\n", + "axs[3].set_ylim(vt2_lim)\n", + "axs[3].set_ylabel(\"$v_{t2}~[\\\\mathrm{km}~\\\\mathrm{s}^{-1}]$\")\n", + "axs[3].set_yticks([0.0, 500.0, 1000.0])\n", + "\n", + "pos_axs3 = axs[3].get_position()\n", + "pos_cax1 = cax1.get_position()\n", + "x0 = pos_cax1.x0\n", + "y0 = pos_axs3.y0\n", + "width = pos_cax1.width\n", + "height = pos_cax1.y0 + pos_cax1.height - y0\n", + "cax1.set_position([x0, y0, width, height])\n", + "\n", + "cax1.set_ylabel(\"$F_i~[\\\\mathrm{s}~\\\\mathrm{m}^{-4}]$\")\n", + "f.align_ylabels(axs)" + ] + }, + { + "cell_type": "markdown", + "id": "a8bc41ff", + "metadata": {}, + "source": [ + "## " + ] + }, + { + "cell_type": "markdown", + "id": "4a20d4d5", + "metadata": {}, + "source": [ + "## 2D projection of the ion velocity distribution functions" + ] + }, + { + "cell_type": "markdown", + "id": "5b229d3e", + "metadata": {}, + "source": [ + "### Define the center time $\\pm 1~\\mathrm{s}$ (to average the distributions)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e224c209", + "metadata": {}, + "outputs": [], + "source": [ + "t_2d = np.datetime64(\"2015-12-28T03:57:40.300\")\n", + "t_2d = pyrf.extend_tint([t_2d, t_2d], [-1, 1])" + ] + }, + { + "cell_type": "markdown", + "id": "8d6bd797", + "metadata": {}, + "source": [ + "### Define the velocity grid for 2D projection" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "311cefb5", + "metadata": {}, + "outputs": [], + "source": [ + "vg_2d = np.linspace(-1500, 1500, 200) * 1e3" + ] + }, + { + "cell_type": "markdown", + "id": "cb48882d", + "metadata": {}, + "source": [ + "### Reduce ion distributions in 2d planes $(n, t_1)$, $(t_2, n)$, and $(t_1, t_2)$" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "f1790338", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 09:47:58] INFO: Using averages in resample\n", + "100%|███████████████████████| 13/13 [00:02<00:00, 6.25it/s]\n", + "[09-Jun-23 09:48:00] INFO: Using averages in resample\n", + "100%|███████████████████████| 13/13 [00:00<00:00, 27.64it/s]\n", + "[09-Jun-23 09:48:01] INFO: Using averages in resample\n", + "100%|███████████████████████| 13/13 [00:00<00:00, 26.88it/s]\n" + ] + } + ], + "source": [ + "f2Dnt1 = mms.reduce(\n", + " pyrf.time_clip(vdf_i, t_2d),\n", + " xyz=nt1t2,\n", + " dim=\"2d\",\n", + " base=\"cart\",\n", + " n_mc=n_mc * 5,\n", + " vg=vg_2d,\n", + ")\n", + "f2Dt2n = mms.reduce(\n", + " pyrf.time_clip(vdf_i, t_2d),\n", + " xyz=t2nt1,\n", + " dim=\"2d\",\n", + " base=\"cart\",\n", + " n_mc=n_mc * 5,\n", + " vg=vg_2d,\n", + ")\n", + "f2Dt1t2 = mms.reduce(\n", + " pyrf.time_clip(vdf_i, t_2d),\n", + " xyz=t1t2n,\n", + " dim=\"2d\",\n", + " base=\"cart\",\n", + " n_mc=n_mc * 5,\n", + " vg=vg_2d,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d5f42e72", + "metadata": {}, + "source": [ + "### Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "02793fc8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, '$F_i~[\\\\mathrm{s}^2~\\\\mathrm{m}^{-5}]$')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(1, 3, figsize=(9, 3.5))\n", + "f.subplots_adjust(wspace=0.6, left=0.11, right=0.97, bottom=0.2, top=0.78)\n", + "axs[0], cax0 = plot_spectr(axs[0], f2Dnt1.mean(axis=0), cscale=\"log\", colorbar=\"top\")\n", + "axs[0].set_xlim([-1e3, 1e3])\n", + "axs[0].set_ylim([-1e3, 1e3])\n", + "axs[0].set_aspect(\"equal\")\n", + "axs[0].set_xlabel(\"$v_{n}~[\\\\mathrm{km}~\\\\mathrm{s}^{-1}]$\")\n", + "axs[0].set_ylabel(\"$v_{t1}~[\\\\mathrm{km}~\\\\mathrm{s}^{-1}]$\")\n", + "\n", + "axs[1] = plot_spectr(axs[1], f2Dt2n.mean(axis=0), cscale=\"log\", colorbar=\"none\")\n", + "axs[1].set_xlim([-0.5e3, 1.5e3])\n", + "axs[1].set_ylim([-1e3, 1e3])\n", + "axs[1].set_aspect(\"equal\")\n", + "axs[1].set_xlabel(\"$v_{t2}~[\\\\mathrm{km}~\\\\mathrm{s}^{-1}]$\")\n", + "axs[1].set_ylabel(\"$v_{n}~[\\\\mathrm{km}~\\\\mathrm{s}^{-1}]$\")\n", + "\n", + "axs[2] = plot_spectr(axs[2], f2Dt1t2.mean(axis=0), cscale=\"log\", colorbar=\"none\")\n", + "axs[2].set_xlim([-1e3, 1e3])\n", + "axs[2].set_ylim([-0.5e3, 1.5e3])\n", + "axs[2].set_aspect(\"equal\")\n", + "axs[2].set_xlabel(\"$v_{t1}~[\\\\mathrm{km}~\\\\mathrm{s}^{-1}]$\")\n", + "axs[2].set_ylabel(\"$v_{t2}~[\\\\mathrm{km}~\\\\mathrm{s}^{-1}]$\")\n", + "\n", + "pos_axs2 = axs[2].get_position()\n", + "pos_cax0 = cax0.get_position()\n", + "x0 = pos_cax0.x0\n", + "y0 = pos_cax0.y0 + 0.03\n", + "width = pos_axs2.x0 + pos_axs2.width - pos_cax0.x0\n", + "height = 0.02\n", + "cax0.set_position([x0, y0, width, height])\n", + "\n", + "cax0.set_xlabel(\"$F_i~[\\\\mathrm{s}^2~\\\\mathrm{m}^{-5}]$\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/01_mms/example_mms_walen_test.ipynb b/docs/examples/01_mms/example_mms_walen_test.ipynb new file mode 100644 index 00000000..3d2e588b --- /dev/null +++ b/docs/examples/01_mms/example_mms_walen_test.ipynb @@ -0,0 +1,549 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Walen Test\n", + "author: Louis Richard\\\n", + "Example code to perform Walen test; only for burst mode MMS data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "\n", + "from pyrfu import mms, pyrf\n", + "from scipy import constants\n", + "from pyrfu.plot import plot_line, plot_spectr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define spacecraft index, time intervals, jet direction and trasnformation matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mms.db_init(\"/Volumes/mms\")\n", + "\n", + "mms_id = 1\n", + "j_sign = 1 # +/-1 for jet direction\n", + "\n", + "# time = irf_time('2015-11-30T00:23:55.200Z', 'utc>epochtt');\n", + "trans_matrix = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) # in GSE\n", + "\n", + "# Plot\n", + "tint = [\"2015-11-30T00:23:48.000\", \"2015-11-30T00:24:01.000\"]\n", + "# reference region\n", + "tint_ref = [\"2015-11-30T00:23:49.000\", \"2015-11-30T00:23:50.000\"]\n", + "# Test region\n", + "tint_walen = [\"2015-11-30T00:23:50.000\", \"2015-11-30T00:23:54.000\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PSD" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 13:39:03] INFO: Loading mms1_dis_dist_brst...\n", + "[09-Jun-23 13:39:03] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_dist.py:68: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n" + ] + } + ], + "source": [ + "vdf_i = mms.get_data(\"pdi_fpi_brst_l2\", tint, mms_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Moments" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 13:39:05] INFO: Loading mms1_dis_numberdensity_brst...\n", + "[09-Jun-23 13:39:05] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n", + "[09-Jun-23 13:39:05] INFO: Loading mms1_des_numberdensity_brst...\n", + "[09-Jun-23 13:39:05] INFO: Loading mms1_dis_bulkv_gse_brst...\n", + "[09-Jun-23 13:39:05] INFO: Loading mms1_dis_prestensor_gse_brst...\n", + "[09-Jun-23 13:39:05] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/get_ts.py:58: UserWarning: Epoch_plus_var/Epoch_minus_var units are not clear, assume s\n", + " warnings.warn(message)\n", + "\n" + ] + } + ], + "source": [ + "n_i = mms.get_data(\"ni_fpi_brst_l2\", tint, mms_id)\n", + "n_e = mms.get_data(\"ne_fpi_brst_l2\", tint, mms_id)\n", + "v_gse_i = mms.get_data(\"vi_gse_fpi_brst_l2\", tint, mms_id)\n", + "p_gse_i = mms.get_data(\"pi_gse_fpi_brst_l2\", tint, mms_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fields" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 13:39:05] INFO: Loading mms1_fgm_b_gse_brst_l2...\n" + ] + } + ], + "source": [ + "b_gse = mms.get_data(\"b_gse_fgm_brst_l2\", tint, mms_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load defatt files" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 13:39:06] INFO: Loading ancillary defatt files...\n", + "[09-Jun-23 13:39:11] WARNING: /usr/local/lib/python3.10/site-packages/pyrfu/mms/load_ancillary.py:106: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " data_frame = data_frame.append(data_frame_dict[k])\n", + "\n" + ] + } + ], + "source": [ + "defatt = mms.load_ancillary(\"defatt\", tint, mms_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute omnidirectionnal differential energy flux (DEF)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def_omni_i = mms.vdf_omni(mms.psd2def(vdf_i))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rotate pressure tensor into Field Aliigned Coordinates (FAC)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 13:39:11] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "p_fac_i = mms.rotate_tensor(p_gse_i, \"fac\", b_gse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Alpha: pressure anisotropy factor" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 13:39:11] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "alpha_ = pyrf.pres_anis(p_fac_i, b_gse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### gse to new123" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "b_123 = pyrf.new_xyz(b_gse, trans_matrix)\n", + "v_123_i = pyrf.new_xyz(v_gse_i, trans_matrix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reference(MSH) region; in New frame(123);" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "b_ref = pyrf.time_clip(b_123, tint_ref)\n", + "b_ref = np.nanmean(b_ref.data, axis=0)\n", + "\n", + "v_i_ref = pyrf.time_clip(v_123_i, tint_ref)\n", + "v_i_ref = np.nanmean(v_i_ref.data, axis=0)\n", + "\n", + "n_i_ref = pyrf.time_clip(n_i, tint_ref)\n", + "n_i_ref = np.nanmean(n_i_ref.data, axis=0)\n", + "\n", + "alpha_ref = pyrf.time_clip(alpha_, tint_ref)\n", + "alpha_ref = np.nanmean(alpha_ref.data, axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Vipred1: delta_B / sqrt(rho1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[09-Jun-23 13:39:11] INFO: Using averages in resample\n" + ] + } + ], + "source": [ + "b_123 = pyrf.resample(b_123, n_i)\n", + "v_123_i = pyrf.resample(v_123_i, n_i)\n", + "\n", + "tmp_1 = (b_123 - b_ref) * 21.8 / np.sqrt(n_i_ref)\n", + "v_i_pred1 = pyrf.resample(tmp_1, v_123_i) * j_sign + v_i_ref" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Vipred2: $B_2 / \\sqrt{\\rho_2} - B_1 / \\sqrt{\\rho_1}$ [Phan et al, 2004]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "tmp_2 = 21.8 * (1 - alpha_) * b_123 / np.sqrt(n_i_ref * (1 - alpha_ref))\n", + "v_i_pred2 = tmp_2 - 21.8 * np.sqrt(1 - alpha_ref) * b_ref / np.sqrt(n_i_ref)\n", + "v_i_pred2 *= j_sign\n", + "v_i_pred2 += v_i_ref" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Vipred2: $\\sqrt{1 - \\alpha_2} B_2 / \\sqrt{\\rho_2} - \\sqrt{1 - \\alpha_1} B_1 / \\sqrt{\\rho_1}$" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "v_i_pred3 = 21.8 * (1 - alpha_) * b_123 / np.sqrt(n_i)\n", + "v_i_pred3 -= 21.8 * np.sqrt(1 - alpha_ref) * b_ref / np.sqrt(n_i_ref)\n", + "v_i_pred3 *= j_sign\n", + "v_i_pred3 += v_i_ref" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Slope & CC" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "v_123_i_w = pyrf.time_clip(v_123_i, tint_walen)\n", + "v_i_pred1_w = pyrf.time_clip(v_i_pred1, tint_walen)\n", + "v_i_pred2_w = pyrf.time_clip(v_i_pred2, tint_walen)\n", + "v_i_pred3_w = pyrf.time_clip(v_i_pred3, tint_walen)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "p_ = [np.polyfit(v_i_pred2_w.data[:, i], v_123_i_w.data[:, i], 1) for i in range(3)]\n", + "slope_2 = [p_[i][0] for i in range(3)]\n", + "\n", + "corr_ = [np.corrcoef(v_i_pred2_w.data[:, i], v_123_i_w.data[:, i]) for i in range(3)]\n", + "cc_2 = [corr_[i][0, 1] for i in range(3)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(7, sharex=\"all\", figsize=(9, 13))\n", + "f.subplots_adjust(hspace=0, left=0.1, right=0.82, bottom=0.05, top=0.95)\n", + "\n", + "plot_line(axs[0], b_gse)\n", + "plot_line(axs[0], pyrf.norm(b_gse), color=\"k\")\n", + "axs[0].set_ylim([-98, 148])\n", + "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], ncol=3, frameon=False)\n", + "axs[0].set_ylabel(\"$B$ [nT]\")\n", + "axs[0].set_title(f\"MMS-{mms_id:d}\")\n", + "\n", + "plot_line(axs[1], n_i, color=\"tab:blue\", label=\"$N_i$\")\n", + "plot_line(axs[1], n_e, color=\"tab:red\", label=\"$N_i$\")\n", + "axs[1].legend(ncol=3, frameon=False)\n", + "axs[1].set_ylabel(\"$N$ [cm$^{-3}$]\")\n", + "\n", + "\n", + "axs[2], caxs2 = plot_spectr(axs[2], def_omni_i, yscale=\"log\", cscale=\"log\")\n", + "axs[2].set_yticks(np.logspace(1, 4, 4))\n", + "axs[2].set_ylabel(\"$W_i$ [eV]\")\n", + "caxs2.set_ylabel(\"DEF\" + \"\\n\" + \"[(cm$^2$ s sr)$^{-1}$]\")\n", + "\n", + "plot_line(axs[3], b_123)\n", + "axs[3].set_ylim([-98, 148])\n", + "axs[3].legend([\"$B_1$\", \"$B_2$\", \"$B_3$\"], ncol=3, frameon=False)\n", + "axs[3].set_ylabel(\"$B$ [nT]\")\n", + "axs[3].text(\n", + " 1.01,\n", + " 0.75,\n", + " np.array2string(trans_matrix[0, :], separator=\",\", precision=2),\n", + " color=\"tab:blue\",\n", + " transform=axs[3].transAxes,\n", + ")\n", + "axs[3].text(\n", + " 1.01,\n", + " 0.50,\n", + " np.array2string(trans_matrix[1, :], separator=\",\", precision=2),\n", + " color=\"tab:green\",\n", + " transform=axs[3].transAxes,\n", + ")\n", + "axs[3].text(\n", + " 1.01,\n", + " 0.25,\n", + " np.array2string(trans_matrix[2, :], separator=\",\", precision=2),\n", + " color=\"tab:red\",\n", + " transform=axs[3].transAxes,\n", + ")\n", + "\n", + "\n", + "plot_line(axs[4], v_123_i[:, 0], color=\"k\", label=\"FPI\")\n", + "plot_line(axs[4], v_i_pred2_w[:, 0], color=\"tab:red\", linestyle=\"-\", label=\"pred\")\n", + "plot_line(axs[4], v_i_pred2[:, 0], color=\"tab:red\", linestyle=\"--\")\n", + "axs[4].legend(ncol=3, frameon=False)\n", + "axs[4].set_ylim([-240, 240])\n", + "axs[4].set_ylabel(\"$V_1$ [km s$^{-1}$]\")\n", + "axs[4].text(\n", + " 1.01, 0.75, f\"slope = {slope_2[0]:3.2f}\", color=\"k\", transform=axs[4].transAxes\n", + ")\n", + "axs[4].text(1.01, 0.25, f\"cc = {cc_2[0]:3.2f}\", color=\"k\", transform=axs[4].transAxes)\n", + "axs[4].axvspan(\n", + " mdates.datestr2num(tint_ref[0]),\n", + " mdates.datestr2num(tint_ref[1]),\n", + " color=\"tab:red\",\n", + " alpha=0.2,\n", + ")\n", + "axs[4].axvspan(\n", + " mdates.datestr2num(tint_walen[0]),\n", + " mdates.datestr2num(tint_walen[1]),\n", + " color=\"yellow\",\n", + " alpha=0.2,\n", + ")\n", + "\n", + "\n", + "plot_line(axs[5], v_123_i[:, 1], color=\"k\", label=\"FPI\")\n", + "plot_line(axs[5], v_i_pred2_w[:, 1], color=\"tab:red\", linestyle=\"-\", label=\"pred\")\n", + "plot_line(axs[5], v_i_pred2[:, 1], color=\"tab:red\", linestyle=\"--\")\n", + "axs[5].legend(ncol=3, frameon=False)\n", + "axs[5].set_ylim([-240, 240])\n", + "axs[5].set_ylabel(\"$V_2$ [km s$^{-1}$]\")\n", + "axs[5].text(\n", + " 1.01, 0.75, f\"slope = {slope_2[1]:3.2f}\", color=\"k\", transform=axs[5].transAxes\n", + ")\n", + "axs[5].text(1.01, 0.25, f\"cc = {cc_2[1]:3.2f}\", color=\"k\", transform=axs[5].transAxes)\n", + "\n", + "\n", + "plot_line(axs[6], v_123_i[:, 2], color=\"k\", label=\"FPI\")\n", + "plot_line(axs[6], v_i_pred2_w[:, 2], color=\"tab:red\", linestyle=\"-\", label=\"pred\")\n", + "plot_line(axs[6], v_i_pred2[:, 2], color=\"tab:red\", linestyle=\"--\")\n", + "axs[6].legend(ncol=3, frameon=False)\n", + "axs[6].set_ylim([-240, 240])\n", + "axs[6].set_ylabel(\"$V_3$ [km s$^{-1}$]\")\n", + "axs[6].text(\n", + " 1.01, 0.75, f\"slope = {slope_2[2]:3.2f}\", color=\"k\", transform=axs[6].transAxes\n", + ")\n", + "axs[6].text(1.01, 0.25, f\"cc = {cc_2[2]:3.2f}\", color=\"k\", transform=axs[6].transAxes)\n", + "\n", + "f.align_ylabels(axs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/01_mms/index.rst b/docs/examples/01_mms/index.rst new file mode 100644 index 00000000..b5dcf8be --- /dev/null +++ b/docs/examples/01_mms/index.rst @@ -0,0 +1,22 @@ +MMS examples gallery +==================== + +.. nbgallery:: + :glob: + + ./example_mms_b_e_j + ./example_mms_ebfields + ./example_mms_edr_signatures + ./example_mms_eis + ./example_mms_electron_psd + ./example_mms_feeps + ./example_mms_hpca + ./example_mms_ipshocks + ./example_mms_ohmslaw + ./example_mms_particle_deflux + ./example_mms_particle_distributions + ./example_mms_particle_pad + ./example_mms_reduced_ion_dist + ./example_mms_reduced_electron_dist + ./example_mms_polarizationanalysis + ./example_mms_walen_test \ No newline at end of file diff --git a/docs/examples/02_dispersion/.ipynb_checkpoints/example_dispersion_one_fluid-checkpoint.ipynb b/docs/examples/02_dispersion/.ipynb_checkpoints/example_dispersion_one_fluid-checkpoint.ipynb new file mode 100644 index 00000000..76332fb7 --- /dev/null +++ b/docs/examples/02_dispersion/.ipynb_checkpoints/example_dispersion_one_fluid-checkpoint.ipynb @@ -0,0 +1,192 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# One Fluid Dispersion Relation\n", + "author: Louis Richard\n", + "\n", + "Solves one fluid dispersion relation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pyrfu.dispersion import one_fluid_dispersion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Local conditions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Magnetic field" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "b_0 = 10e-9" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Angle of propagation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "theta = 5.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Particles" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "ions = {\"n\": 10e6, \"t\": 10, \"gamma\": 1}\n", + "electrons = {\"n\": 10e6, \"t\": 10, \"gamma\": 1}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve one fluid dispersion relation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "wc_1, wc_2, wc_3 = one_fluid_dispersion(10e-9, 5, ions, electrons)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Dispersion relations: $\\\\theta_{kB}$ = 5.00')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "v_a, c_s, wc_p = [wc_1.attrs[k] for k in [\"v_a\", \"c_s\", \"wc_p\"]]\n", + "\n", + "k = wc_1.k.data\n", + "\n", + "f, ax = plt.subplots(1, figsize=(8, 8))\n", + "ax.plot(k * v_a / wc_p, wc_1 / wc_p, color=\"k\")\n", + "ax.plot(k * v_a / wc_p, wc_2 / wc_p, color=\"tab:blue\")\n", + "ax.plot(k * v_a / wc_p, wc_3 / wc_p, color=\"tab:red\")\n", + "ax.plot(\n", + " k * v_a / wc_p,\n", + " v_a * k / wc_p,\n", + " color=\"tab:green\",\n", + " linestyle=\"--\",\n", + " label=\"$\\\\omega = V_A k$\",\n", + ")\n", + "ax.plot(\n", + " k * v_a / wc_p,\n", + " c_s * k / wc_p,\n", + " color=\"tab:purple\",\n", + " linestyle=\"--\",\n", + " label=\"$\\\\omega = c_s k$\",\n", + ")\n", + "\n", + "ax.set_xlim([0, 7])\n", + "ax.set_ylim([0, 6])\n", + "ax.legend()\n", + "ax.set_xlabel(\"$k V_A / \\\\Omega_{ci}$\")\n", + "ax.set_ylabel(\"$\\\\omega / \\\\Omega_{ci}$\")\n", + "ax.set_title(f\"Dispersion relations: $\\\\theta_{{kB}}$ = {theta:3.2f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/02_dispersion/example_dispersion_one_fluid.ipynb b/docs/examples/02_dispersion/example_dispersion_one_fluid.ipynb new file mode 100644 index 00000000..473c265e --- /dev/null +++ b/docs/examples/02_dispersion/example_dispersion_one_fluid.ipynb @@ -0,0 +1,192 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# One Fluid Dispersion Relation\n", + "author: Louis Richard\n", + "\n", + "Solves one fluid dispersion relation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load IGRF coefficients ...\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pyrfu.dispersion import one_fluid_dispersion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Local conditions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Magnetic field" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "b_0 = 10e-9" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Angle of propagation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "theta = 5.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Particles" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "ions = {\"n\": 10e6, \"t\": 10, \"gamma\": 1}\n", + "electrons = {\"n\": 10e6, \"t\": 10, \"gamma\": 1}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve one fluid dispersion relation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "wc_1, wc_2, wc_3 = one_fluid_dispersion(10e-9, 5, ions, electrons)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "v_a, c_s, wc_p = [wc_1.attrs[k] for k in [\"v_a\", \"c_s\", \"wc_p\"]]\n", + "\n", + "k = wc_1.k.data\n", + "\n", + "f, ax = plt.subplots(1, figsize=(9, 7))\n", + "f.subplots_adjust(hspace=0, left=0.07, right=0.93, bottom=0.10, top=0.95)\n", + "ax.plot(k * v_a / wc_p, wc_1 / wc_p, color=\"k\")\n", + "ax.plot(k * v_a / wc_p, wc_2 / wc_p, color=\"tab:blue\")\n", + "ax.plot(k * v_a / wc_p, wc_3 / wc_p, color=\"tab:red\")\n", + "ax.plot(\n", + " k * v_a / wc_p,\n", + " v_a * k / wc_p,\n", + " color=\"tab:green\",\n", + " linestyle=\"--\",\n", + " label=\"$\\\\omega = V_A k$\",\n", + ")\n", + "ax.plot(\n", + " k * v_a / wc_p,\n", + " c_s * k / wc_p,\n", + " color=\"tab:purple\",\n", + " linestyle=\"--\",\n", + " label=\"$\\\\omega = c_s k$\",\n", + ")\n", + "\n", + "ax.set_xlim([0, 7])\n", + "ax.set_ylim([0, 6])\n", + "ax.legend()\n", + "ax.set_xlabel(\"$k V_A / \\\\Omega_{ci}$\")\n", + "ax.set_ylabel(\"$\\\\omega / \\\\Omega_{ci}$\")\n", + "ax.set_title(f\"Dispersion relations: $\\\\theta_{{kB}}$ = {theta:3.2f}\")\n", + "\n", + "f.savefig(\"../../_static/example_dispersion_one_fluid_nb_thumbnail.png\", dpi=100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs/examples/02_dispersion/index.rst b/docs/examples/02_dispersion/index.rst new file mode 100644 index 00000000..182f10c1 --- /dev/null +++ b/docs/examples/02_dispersion/index.rst @@ -0,0 +1,7 @@ +Dispersion relations examples gallery +===================================== + +.. nbgallery:: + :glob: + + ./example_dispersion_one_fluid \ No newline at end of file diff --git a/docs/examples/index.rst b/docs/examples/index.rst new file mode 100644 index 00000000..512b77c2 --- /dev/null +++ b/docs/examples/index.rst @@ -0,0 +1,12 @@ +Pyrfu examples +============== + +In this section, we provide simple and practical examples on how to use +the different subpackages of the ``pyrfu`` package. + +.. toctree:: + :maxdepth: 1 + + ./00_overview/index + ./01_mms/index + ./02_dispersion/index \ No newline at end of file diff --git a/docs/source/index.rst b/docs/index.rst similarity index 55% rename from docs/source/index.rst rename to docs/index.rst index 8d5df3dd..6bc656b6 100644 --- a/docs/source/index.rst +++ b/docs/index.rst @@ -6,23 +6,25 @@ Welcome to pyrfu's documentation! ================================= -.. include:: ../../README.rst - :end-before: end-marker-intro-do-not-remove - - .. toctree:: - :maxdepth: 1 - :caption: Contents: + :titlesonly: + :hidden: + :maxdepth: 2 - getting_started - modules - examples + installation + examples/index + dev/index contributing +.. include:: ../README.rst + :start-after: start-marker-intro-do-not-remove + :end-before: end-marker-intro-do-not-remove + + + +Examples +======== +See :doc:`here ` for a complete list of examples. -Indices and tables -================== -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` +:doc:`Go to developers doc ` diff --git a/docs/installation.rst b/docs/installation.rst new file mode 100644 index 00000000..f937a7ee --- /dev/null +++ b/docs/installation.rst @@ -0,0 +1,47 @@ +.. highlight:: shell + +============ +Installation +============ + + +Stable release +-------------- + +To install Pyrfu, run this command in your terminal: + +.. code-block:: console + + $ python -m pip install pyrfu + # or + $ python -m pip install --user pyrfu + +This is the preferred method to install Pyrfu, as it will always install the most +recent stable release. + +If you don't have `pip`_ installed, this `Python installation guide`_ can guide +you through the process. + +.. _pip: https://pip.pypa.io +.. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/ + + +From sources +------------ + +The sources for Pyrfu can be downloaded from the `Github repo`_. + +You can either clone the public repository: + +.. code-block:: console + + $ git clone git://github.com/louis-richard/irfu-python + +Once you have a copy of the source, you can install it with: + +.. code-block:: console + + $ python -m pip install . + + +.. _Github repo: https://github.com/louis-richard/irfu-python \ No newline at end of file diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index a9dfdab4..00000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=. -set BUILDDIR=_build - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% - -:end -popd \ No newline at end of file diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 00000000..fba93db8 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,8 @@ +nbsphinx==0.9.2 +numpydoc==1.5.0 +pydata-sphinx-theme==0.13.3 +sphinx==7.0.0 +sphinx-codeautolink==0.15.0 +sphinx-copybutton==0.5.2 +sphinx-gallery==0.13.0 +sphinxcontrib-apidoc==0.3.0 diff --git a/docs/source/examples.rst b/docs/source/examples.rst deleted file mode 100644 index b67b7a5f..00000000 --- a/docs/source/examples.rst +++ /dev/null @@ -1,24 +0,0 @@ -Examples -========= - -In this section, we provide simple and practical examples on how to use -the different subpackages of the ``pyrfu`` package. - -.. toctree:: - :maxdepth: 1 - - examples/01_mms/example_mms_b_e_j.ipynb - examples/01_mms/example_mms_ebfields.ipynb - examples/01_mms/example_mms_edr_signatures.ipynb - examples/01_mms/example_mms_eis.ipynb - examples/01_mms/example_mms_electron_psd.ipynb - examples/01_mms/example_mms_feeps.ipynb - examples/01_mms/example_mms_hpca.ipynb - examples/01_mms/example_mms_ohmslaw.ipynb - examples/01_mms/example_mms_particle_deflux.ipynb - examples/01_mms/example_mms_particle_distributions.ipynb - examples/01_mms/example_mms_particle_pad.ipynb - examples/01_mms/example_mms_polarizationanalysis.ipynb - examples/01_mms/example_mms_walen_test.ipynb - examples/02_dispersion/example_dispersion_one_fluid.ipynb - diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst deleted file mode 100644 index 984ba6e2..00000000 --- a/docs/source/getting_started.rst +++ /dev/null @@ -1,10 +0,0 @@ -Getting started -=============== - -Installation ------------- - -.. include:: ../../README.rst - :start-after: start-marker-install-do-not-remove - :end-before: end-marker-install-do-not-remove - diff --git a/docs/source/modules.rst b/docs/source/modules.rst deleted file mode 100644 index 0e09008f..00000000 --- a/docs/source/modules.rst +++ /dev/null @@ -1,7 +0,0 @@ -Modules -======== - -.. toctree:: - :maxdepth: 1 - - _generated/modules diff --git a/docs/source/quick-overview.rst b/docs/source/quick-overview.rst deleted file mode 100644 index 3f9751c7..00000000 --- a/docs/source/quick-overview.rst +++ /dev/null @@ -1,36 +0,0 @@ -############## -Quick overview -############## - -Here are some quick examples of what you can do with :py:`pyrfu`. Everything is explained in much more detail in the rest of the -documentation. - - -Define a time interval ----------------------- - -Time intervals are defined as list of two string where the strings are the begining and the end -time of the interval in isot format. -You can make a DataArray from scratch by supplying data in the form of a numpy -array or list, with optional *dimensions* and *coordinates*: - -.. ipython:: python - - tint = ["2015-10-30T05:15:40.000", "2015-10-30T05:15:55.000"] - -In this case, we have generated a 2D array, assigned the names *x* and *y* to the two dimensions respectively and associated two *coordinate labels* '10' and '20' with the two locations along the x dimension. If you supply a pandas :py:class:`~pandas.Series` or :py:class:`~pandas.DataFrame`, metadata is copied directly: - -.. ipython:: python - - xr.DataArray(pd.Series(range(3), index=list("abc"), name="foo")) - -Here are the key properties for a ``DataArray``: - -.. ipython:: python - - # like in pandas, values is a numpy array that you can modify in-place - data.values - data.dims - data.coords - # you can use this dictionary to store arbitrary metadata - data.attrs \ No newline at end of file diff --git a/examples/00_overview/.ipynb_checkpoints/quick-overview-checkpoint.ipynb b/examples/00_overview/.ipynb_checkpoints/quick-overview-checkpoint.ipynb deleted file mode 100644 index 96f09350..00000000 --- a/examples/00_overview/.ipynb_checkpoints/quick-overview-checkpoint.ipynb +++ /dev/null @@ -1,2420 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MMS in pyRFU\n", - "Louis RICHARD (louis.richard@irfu.se)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Getting Started\n", - "To get up and running with Python, virtual environments and pyRFU, see: \\\n", - "https://pyrfu.readthedocs.io/en/latest/getting_started.html#installation\n", - "\n", - "Python 3.7 or later is required; we recommend installing Anaconda to get\n", - "everything up and running." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Virtual environments\n", - "It's best to setup and use virtual environments when using Python - these allow you to avoid common dependency problems when you install multiple packages\\\n", - "`python -m venv pyrfu-tutorial`\\\n", - "Then, to run the virtual environment, on Mac and Linux :\\\n", - "`source pyrfu-tutorial/bin/activate`\\\n", - "To exit the current virtual environment, type `deactivate`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install pyRFU\n", - "`pip install pyrfu`\n", - "### Upgrade pyRFU\n", - "`pip install pyrfu --upgrade`\n", - "### Local data directory\n", - "We use environment variables to set the local data directories:\\\n", - "data_path (root data directory for all missions in pyRFU) e.g., if you set data_path=\"/Volumes/mms\", your data will be stored in /Volumes/mms\n", - "\n", - "The load routines supported include:\n", - "- Fluxgate Magnetometer (FGM)\n", - "- Search-coil Magnetometer (SCM)\n", - "- Electric field Double Probe (EDP)\n", - "- Fast Plasma Investigation (FPI)\n", - "- Hot Plasma Composition Analyzer (HPCA)\n", - "- Energetic Ion Spectrometer (EIS)\n", - "- Fly's Eye Energetic Particle Sensor (FEEPS)\n", - "- Ephemeris and Coordinates (MEC)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import MMS routines" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pyrfu import mms" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define time interval" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "tint = [\"2019-09-14T07:54:00.000\", \"2019-09-14T08:11:00.000\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup the MMS data path\n", - "The MMS data path can be setup using mms.db_init" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "mms.db_init(\"/Volumes/mms\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data\n", - "Keywords to access data can be found in the help of mms.get_data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function get_data in module pyrfu.mms.get_data:\n", - "\n", - "get_data(var_str, tint, mms_id, verbose: bool = True, data_path: str = '')\n", - " Load a variable. var_str must be in var (see below)\n", - " \n", - " Parameters\n", - " ----------\n", - " var_str : str\n", - " Key of the target variable (use mms.get_data() to see keys.).\n", - " tint : list of str\n", - " Time interval.\n", - " mms_id : str or int\n", - " Index of the target spacecraft.\n", - " verbose : bool, Optional\n", - " Set to True to follow the loading. Default is True.\n", - " data_path : str, Optional\n", - " Path of MMS data. If None use `pyrfu.mms.mms_config.py`\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.DataArray or xarray.Dataset\n", - " Time series of the target variable of measured by the target\n", - " spacecraft over the selected time interval.\n", - " \n", - " See also\n", - " --------\n", - " pyrfu.mms.get_ts : Read time series.\n", - " pyrfu.mms.get_dist : Read velocity distribution function.\n", - " \n", - " Examples\n", - " --------\n", - " >>> from pyrfu import mms\n", - " \n", - " Define time interval\n", - " \n", - " >>> tint_brst = [\"2019-09-14T07:54:00.000\", \"2019-09-14T08:11:00.000\"]\n", - " \n", - " Index of MMS spacecraft\n", - " \n", - " >>> ic = 1\n", - " \n", - " Load magnetic field from FGM\n", - " \n", - " >>> b_xyz = mms.get_data(\"B_gse_fgm_brst_l2\", tint_brst, ic)\n", - "\n" - ] - } - ], - "source": [ - "help(mms.get_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load magnetic field from (FGM)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:55:15: Loading mms1_fgm_b_gse_srvy_l2...\n" - ] - } - ], - "source": [ - "b_xyz = mms.get_data(\"b_gse_fgm_srvy_l2\", tint, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load ions and electrons bulk velocity, number density and DEF (FPI)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:55:16: Loading mms1_dis_numberdensity_fast...\n", - "09-Dec-21 10:55:19: Loading mms1_des_numberdensity_fast...\n", - "09-Dec-21 10:55:23: Loading mms1_dis_bulkv_gse_fast...\n", - "09-Dec-21 10:55:31: Loading mms1_des_bulkv_gse_fast...\n", - "09-Dec-21 10:55:39: Loading mms1_dis_energyspectr_omni_fast...\n", - "09-Dec-21 10:55:45: Loading mms1_des_energyspectr_omni_fast...\n" - ] - } - ], - "source": [ - "n_i, n_e = [mms.get_data(f\"n{s}_fpi_fast_l2\", tint, 1) for s in [\"i\", \"e\"]]\n", - "# n_i, n_e = [mms.get_data(\"n{}_fpi_fast_l2\".format(s), tint, 1) for s in [\"i\", \"e\"]]\n", - "v_xyz_i, v_xyz_e = [mms.get_data(f\"v{s}_gse_fpi_fast_l2\", tint, 1) for s in [\"i\", \"e\"]]\n", - "def_omni_i, def_omni_e = [mms.get_data(f\"def{s}_fpi_fast_l2\", tint, 1) for s in [\"i\", \"e\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load electric field (EDP)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:55:52: Loading mms1_edp_dce_gse_fast_l2...\n" - ] - } - ], - "source": [ - "e_xyz = mms.get_data(\"e_gse_edp_fast_l2\", tint, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot overview" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu.plot import plot_line, plot_spectr" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'DEF\\n[kev/(cm$^2$ s sr keV)]')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "\n", - "legend_options = dict(frameon=True, loc=\"upper right\")\n", - "\n", - "fig, axs = plt.subplots(7, sharex=\"all\", figsize=(8, 11))\n", - "fig.subplots_adjust(bottom=.05, top=.95, left=.11, right=.89, hspace=0)\n", - "\n", - "# magnetic field\n", - "plot_line(axs[0], b_xyz)\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], ncol=3, **legend_options)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "\n", - "# electric field\n", - "plot_line(axs[1], e_xyz)\n", - "axs[1].legend([\"$E_x$\", \"$E_y$\", \"$E_z$\"], ncol=3, **legend_options)\n", - "axs[1].set_ylabel(\"$E$ [mV.m$^{-1}$]\")\n", - "\n", - "# number density\n", - "plot_line(axs[2], n_i, color=\"tab:red\")\n", - "plot_line(axs[2], n_e, color=\"tab:blue\")\n", - "axs[2].legend([\"$Ions$\", \"$Electrons$\"], ncol=2, **legend_options)\n", - "axs[2].set_ylabel(\"$n$ [cm$^{-3}$]\")\n", - "\n", - "# Ion bulk velocity\n", - "plot_line(axs[3], v_xyz_i)\n", - "axs[3].legend([\"$V_{i,x}$\", \"$V_{i,y}$\", \"$V_{i,z}$\"], ncol=3, **legend_options)\n", - "axs[3].set_ylabel(\"$V_i$ [km.s$^{-1}$]\")\n", - "\n", - "# Ion DEF\n", - "axs[4], caxs4 = plot_spectr(axs[4], def_omni_i, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[4].set_ylabel(\"$E_i$ [eV]\")\n", - "caxs4.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "\n", - "# Electron bulk velocity\n", - "plot_line(axs[5], v_xyz_e)\n", - "axs[5].legend([\"$V_{e,x}$\", \"$V_{e,y}$\", \"$V_{e,z}$\"], ncol=3, **legend_options)\n", - "axs[5].set_ylabel(\"$V_e$ [km.s$^{-1}$]\")\n", - "\n", - "# Electron DEF\n", - "axs[6], caxs6 = plot_spectr(axs[6], def_omni_e, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[6].set_ylabel(\"$E_e$ [eV]\")\n", - "caxs6.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data for all spacecraft" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spacecaft position (MEC)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:55:54: Loading mms1_mec_r_gse...\n", - "09-Dec-21 10:55:59: Loading mms2_mec_r_gse...\n", - "09-Dec-21 10:56:04: Loading mms3_mec_r_gse...\n", - "09-Dec-21 10:56:09: Loading mms4_mec_r_gse...\n" - ] - } - ], - "source": [ - "r_mms = [mms.get_data(\"r_gse_mec_srvy_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Magnetic field (FGM)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:56:14: Loading mms1_fgm_b_gse_srvy_l2...\n", - "09-Dec-21 10:56:16: Loading mms2_fgm_b_gse_srvy_l2...\n", - "09-Dec-21 10:56:18: Loading mms3_fgm_b_gse_srvy_l2...\n", - "09-Dec-21 10:56:19: Loading mms4_fgm_b_gse_srvy_l2...\n" - ] - } - ], - "source": [ - "b_mms = [mms.get_data(\"b_gse_fgm_srvy_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f, axs = plt.subplots(3, sharex=\"all\", figsize=(6.5, 5))\n", - "f.subplots_adjust(hspace=0)\n", - "\n", - "labels = [\"MMS{:d}\".format(i + 1) for i in range(4)]\n", - "legend_options = dict(ncol=4, frameon=True, loc=\"upper right\")\n", - "\n", - "for ax, j, c in zip(axs, [0, 1, 2], [\"x\", \"y\", \"z\"]):\n", - " for i, b in enumerate(b_mms):\n", - " plot_line(ax, b[:, j])\n", - " \n", - " ax.legend(labels, **legend_options)\n", - " ax.set_ylabel(\"$B_{}$ [nT]\".format(c))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/00_overview/quick-overview.ipynb b/examples/00_overview/quick-overview.ipynb deleted file mode 100644 index 96f09350..00000000 --- a/examples/00_overview/quick-overview.ipynb +++ /dev/null @@ -1,2420 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MMS in pyRFU\n", - "Louis RICHARD (louis.richard@irfu.se)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Getting Started\n", - "To get up and running with Python, virtual environments and pyRFU, see: \\\n", - "https://pyrfu.readthedocs.io/en/latest/getting_started.html#installation\n", - "\n", - "Python 3.7 or later is required; we recommend installing Anaconda to get\n", - "everything up and running." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Virtual environments\n", - "It's best to setup and use virtual environments when using Python - these allow you to avoid common dependency problems when you install multiple packages\\\n", - "`python -m venv pyrfu-tutorial`\\\n", - "Then, to run the virtual environment, on Mac and Linux :\\\n", - "`source pyrfu-tutorial/bin/activate`\\\n", - "To exit the current virtual environment, type `deactivate`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install pyRFU\n", - "`pip install pyrfu`\n", - "### Upgrade pyRFU\n", - "`pip install pyrfu --upgrade`\n", - "### Local data directory\n", - "We use environment variables to set the local data directories:\\\n", - "data_path (root data directory for all missions in pyRFU) e.g., if you set data_path=\"/Volumes/mms\", your data will be stored in /Volumes/mms\n", - "\n", - "The load routines supported include:\n", - "- Fluxgate Magnetometer (FGM)\n", - "- Search-coil Magnetometer (SCM)\n", - "- Electric field Double Probe (EDP)\n", - "- Fast Plasma Investigation (FPI)\n", - "- Hot Plasma Composition Analyzer (HPCA)\n", - "- Energetic Ion Spectrometer (EIS)\n", - "- Fly's Eye Energetic Particle Sensor (FEEPS)\n", - "- Ephemeris and Coordinates (MEC)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import MMS routines" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pyrfu import mms" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define time interval" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "tint = [\"2019-09-14T07:54:00.000\", \"2019-09-14T08:11:00.000\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup the MMS data path\n", - "The MMS data path can be setup using mms.db_init" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "mms.db_init(\"/Volumes/mms\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data\n", - "Keywords to access data can be found in the help of mms.get_data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function get_data in module pyrfu.mms.get_data:\n", - "\n", - "get_data(var_str, tint, mms_id, verbose: bool = True, data_path: str = '')\n", - " Load a variable. var_str must be in var (see below)\n", - " \n", - " Parameters\n", - " ----------\n", - " var_str : str\n", - " Key of the target variable (use mms.get_data() to see keys.).\n", - " tint : list of str\n", - " Time interval.\n", - " mms_id : str or int\n", - " Index of the target spacecraft.\n", - " verbose : bool, Optional\n", - " Set to True to follow the loading. Default is True.\n", - " data_path : str, Optional\n", - " Path of MMS data. If None use `pyrfu.mms.mms_config.py`\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.DataArray or xarray.Dataset\n", - " Time series of the target variable of measured by the target\n", - " spacecraft over the selected time interval.\n", - " \n", - " See also\n", - " --------\n", - " pyrfu.mms.get_ts : Read time series.\n", - " pyrfu.mms.get_dist : Read velocity distribution function.\n", - " \n", - " Examples\n", - " --------\n", - " >>> from pyrfu import mms\n", - " \n", - " Define time interval\n", - " \n", - " >>> tint_brst = [\"2019-09-14T07:54:00.000\", \"2019-09-14T08:11:00.000\"]\n", - " \n", - " Index of MMS spacecraft\n", - " \n", - " >>> ic = 1\n", - " \n", - " Load magnetic field from FGM\n", - " \n", - " >>> b_xyz = mms.get_data(\"B_gse_fgm_brst_l2\", tint_brst, ic)\n", - "\n" - ] - } - ], - "source": [ - "help(mms.get_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load magnetic field from (FGM)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:55:15: Loading mms1_fgm_b_gse_srvy_l2...\n" - ] - } - ], - "source": [ - "b_xyz = mms.get_data(\"b_gse_fgm_srvy_l2\", tint, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load ions and electrons bulk velocity, number density and DEF (FPI)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:55:16: Loading mms1_dis_numberdensity_fast...\n", - "09-Dec-21 10:55:19: Loading mms1_des_numberdensity_fast...\n", - "09-Dec-21 10:55:23: Loading mms1_dis_bulkv_gse_fast...\n", - "09-Dec-21 10:55:31: Loading mms1_des_bulkv_gse_fast...\n", - "09-Dec-21 10:55:39: Loading mms1_dis_energyspectr_omni_fast...\n", - "09-Dec-21 10:55:45: Loading mms1_des_energyspectr_omni_fast...\n" - ] - } - ], - "source": [ - "n_i, n_e = [mms.get_data(f\"n{s}_fpi_fast_l2\", tint, 1) for s in [\"i\", \"e\"]]\n", - "# n_i, n_e = [mms.get_data(\"n{}_fpi_fast_l2\".format(s), tint, 1) for s in [\"i\", \"e\"]]\n", - "v_xyz_i, v_xyz_e = [mms.get_data(f\"v{s}_gse_fpi_fast_l2\", tint, 1) for s in [\"i\", \"e\"]]\n", - "def_omni_i, def_omni_e = [mms.get_data(f\"def{s}_fpi_fast_l2\", tint, 1) for s in [\"i\", \"e\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load electric field (EDP)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:55:52: Loading mms1_edp_dce_gse_fast_l2...\n" - ] - } - ], - "source": [ - "e_xyz = mms.get_data(\"e_gse_edp_fast_l2\", tint, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot overview" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu.plot import plot_line, plot_spectr" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. 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" icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'DEF\\n[kev/(cm$^2$ s sr keV)]')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "\n", - "legend_options = dict(frameon=True, loc=\"upper right\")\n", - "\n", - "fig, axs = plt.subplots(7, sharex=\"all\", figsize=(8, 11))\n", - "fig.subplots_adjust(bottom=.05, top=.95, left=.11, right=.89, hspace=0)\n", - "\n", - "# magnetic field\n", - "plot_line(axs[0], b_xyz)\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], ncol=3, **legend_options)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "\n", - "# electric field\n", - "plot_line(axs[1], e_xyz)\n", - "axs[1].legend([\"$E_x$\", \"$E_y$\", \"$E_z$\"], ncol=3, **legend_options)\n", - "axs[1].set_ylabel(\"$E$ [mV.m$^{-1}$]\")\n", - "\n", - "# number density\n", - "plot_line(axs[2], n_i, color=\"tab:red\")\n", - "plot_line(axs[2], n_e, color=\"tab:blue\")\n", - "axs[2].legend([\"$Ions$\", \"$Electrons$\"], ncol=2, **legend_options)\n", - "axs[2].set_ylabel(\"$n$ [cm$^{-3}$]\")\n", - "\n", - "# Ion bulk velocity\n", - "plot_line(axs[3], v_xyz_i)\n", - "axs[3].legend([\"$V_{i,x}$\", \"$V_{i,y}$\", \"$V_{i,z}$\"], ncol=3, **legend_options)\n", - "axs[3].set_ylabel(\"$V_i$ [km.s$^{-1}$]\")\n", - "\n", - "# Ion DEF\n", - "axs[4], caxs4 = plot_spectr(axs[4], def_omni_i, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[4].set_ylabel(\"$E_i$ [eV]\")\n", - "caxs4.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "\n", - "# Electron bulk velocity\n", - "plot_line(axs[5], v_xyz_e)\n", - "axs[5].legend([\"$V_{e,x}$\", \"$V_{e,y}$\", \"$V_{e,z}$\"], ncol=3, **legend_options)\n", - "axs[5].set_ylabel(\"$V_e$ [km.s$^{-1}$]\")\n", - "\n", - "# Electron DEF\n", - "axs[6], caxs6 = plot_spectr(axs[6], def_omni_e, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[6].set_ylabel(\"$E_e$ [eV]\")\n", - "caxs6.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data for all spacecraft" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spacecaft position (MEC)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:55:54: Loading mms1_mec_r_gse...\n", - "09-Dec-21 10:55:59: Loading mms2_mec_r_gse...\n", - "09-Dec-21 10:56:04: Loading mms3_mec_r_gse...\n", - "09-Dec-21 10:56:09: Loading mms4_mec_r_gse...\n" - ] - } - ], - "source": [ - "r_mms = [mms.get_data(\"r_gse_mec_srvy_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Magnetic field (FGM)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:56:14: Loading mms1_fgm_b_gse_srvy_l2...\n", - "09-Dec-21 10:56:16: Loading mms2_fgm_b_gse_srvy_l2...\n", - "09-Dec-21 10:56:18: Loading mms3_fgm_b_gse_srvy_l2...\n", - "09-Dec-21 10:56:19: Loading mms4_fgm_b_gse_srvy_l2...\n" - ] - } - ], - "source": [ - "b_mms = [mms.get_data(\"b_gse_fgm_srvy_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f, axs = plt.subplots(3, sharex=\"all\", figsize=(6.5, 5))\n", - "f.subplots_adjust(hspace=0)\n", - "\n", - "labels = [\"MMS{:d}\".format(i + 1) for i in range(4)]\n", - "legend_options = dict(ncol=4, frameon=True, loc=\"upper right\")\n", - "\n", - "for ax, j, c in zip(axs, [0, 1, 2], [\"x\", \"y\", \"z\"]):\n", - " for i, b in enumerate(b_mms):\n", - " plot_line(ax, b[:, j])\n", - " \n", - " ax.legend(labels, **legend_options)\n", - " ax.set_ylabel(\"$B_{}$ [nT]\".format(c))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/.ipynb_checkpoints/example_mms_b_e_j-checkpoint.ipynb b/examples/01_mms/.ipynb_checkpoints/example_mms_b_e_j-checkpoint.ipynb deleted file mode 100644 index 3c64d89f..00000000 --- a/examples/01_mms/.ipynb_checkpoints/example_mms_b_e_j-checkpoint.ipynb +++ /dev/null @@ -1,1330 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# B, E, J\n", - "author: Louis Richard\\\n", - "Plots of B, J, E, JxB electric field, and J.E. Calculates J using Curlometer method. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu.mms import get_data, db_init\n", - "from pyrfu.plot import plot_line\n", - "from pyrfu.pyrf import resample, avg_4sc, edb, c_4_j, norm, convert_fac, dot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define time interval, data path and spacecraft indices" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "tint = [\"2016-06-07T10:00:00.000\", \"2016-06-07T12:00:00.000\"]\n", - "db_init(\"/Volumes/mms\")\n", - "ic = np.arange(1, 5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load background magnetic field (FGM)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:57:43: Loading mms1_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 10:57:45: Loading mms2_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 10:57:48: Loading mms3_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 10:57:50: Loading mms4_fgm_b_dmpa_srvy_l2...\n" - ] - } - ], - "source": [ - "b_mms = [get_data(\"b_dmpa_fgm_srvy_l2\", tint, i) for i in ic]\n", - "b_mms = [resample(b_xyz, b_mms[0]) for b_xyz in b_mms]\n", - "b_xyz = avg_4sc(b_mms)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load electric field (EDP)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:57:52: Loading mms1_edp_dce_dsl_fast_l2pre...\n", - "09-Dec-21 10:57:54: Loading mms2_edp_dce_dsl_fast_l2pre...\n", - "09-Dec-21 10:57:56: Loading mms3_edp_dce_dsl_fast_l2pre...\n", - "09-Dec-21 10:57:59: Loading mms4_edp_dce_dsl_fast_l2pre...\n" - ] - } - ], - "source": [ - "e_mms = [get_data(\"e2d_dsl_edp_fast_l2pre\", tint, i) for i in ic]\n", - "e_mms = [resample(e_xyz, e_mms[0]) for e_xyz in e_mms]\n", - "e_xyz = avg_4sc(e_mms)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load ion number density (FPI)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:58:01: Loading mms1_hpca_hplus_number_density...\n", - "09-Dec-21 10:58:03: Loading mms2_hpca_hplus_number_density...\n", - "09-Dec-21 10:58:04: Loading mms3_hpca_hplus_number_density...\n", - "09-Dec-21 10:58:05: Loading mms4_hpca_hplus_number_density...\n" - ] - } - ], - "source": [ - "n_mms_i = [get_data(\"nhplus_hpca_srvy_l2\", tint, i) for i in ic]\n", - "n_mms_i = [resample(n_i, n_mms_i[0]) for n_i in n_mms_i]\n", - "\n", - "n_i = avg_4sc(n_mms_i)\n", - "n_i = resample(n_i, b_mms[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load spacecraft position (MEC)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:58:06: Loading mms1_mec_r_gse...\n", - "09-Dec-21 10:58:11: Loading mms2_mec_r_gse...\n", - "09-Dec-21 10:58:15: Loading mms3_mec_r_gse...\n", - "09-Dec-21 10:58:20: Loading mms4_mec_r_gse...\n" - ] - } - ], - "source": [ - "r_mms = [get_data(\"r_gse_mec_srvy_l2\", tint, i) for i in ic]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute current density, Hall electric field and E.J" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Compute current density using curlometer" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "j_xyz, div_b, _, jxb_xyz, _, _ = c_4_j(r_mms, b_mms)\n", - "div_over_curl = div_b.copy()\n", - "div_over_curl.data = abs(div_over_curl.data) / norm(j_xyz)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Transform current density into field-aligned coordinates" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "j_fac = convert_fac(j_xyz, b_xyz,[1, 0, 0])\n", - "j_xyz.data *= 1e9\n", - "j_fac.data *= 1e9" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Hall electric field" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "jxb_xyz.data /= n_i.data[:, None]\n", - "jxb_xyz.data /= 1.6e-19 * 1000 # Convert to (mV/m)\n", - "jxb_xyz.data[abs(jxb_xyz.data) > 100.] = np.nan # Remove some questionable fields" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute E.J" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "j_xyz = resample(j_xyz, e_xyz)\n", - "e_dot_j = dot(e_xyz, j_xyz) / 1000 # J (nA/m^2), E (mV/m), E.J (nW/m^3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot figure" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "legend_options = dict(ncol=4, loc=\"upper right\", frameon=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; 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" event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - 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" if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(16959.416666666668, 16959.5)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(8, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(hspace=0, left=.15, right=.85)\n", - "plot_line(axs[0], b_xyz)\n", - "axs[0].set_ylabel(\"$B_{DMPA}$\" + \"\\n\" + \"[nT]\")\n", - "axs[0].legend([\"$B_{x}$\",\"$B_{y}$\",\"$B_{z}$\"], **legend_options)\n", - "axs[0].set_ylim([-70, 70])\n", - "\n", - "labels = []\n", - "for i, n in enumerate(n_mms_i):\n", - " plot_line(axs[1], n)\n", - " labels.append(\"MMS{:d}\".format(i + 1))\n", - "\n", - "axs[1].set_ylabel(\"$n_i$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", - "axs[1].set_yscale(\"log\")\n", - "axs[1].set_ylim([1e-4, 10])\n", - "axs[1].legend(labels, **legend_options)\n", - "\n", - "plot_line(axs[2], j_xyz)\n", - "axs[2].set_ylabel(\"$J_{DMPA}$\" + \"\\n\" + \"nA m$^{-2}$\")\n", - "axs[2].legend([\"$J_{x}$\",\"$J_{y}$\",\"$J_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[3], j_fac)\n", - "axs[3].set_ylabel(\"$J_{FAC}$\" + \"\\n\" + \"nA m$^{-2}$\")\n", - "axs[3].legend([\"$J_{\\\\perp 1}$\",\"$J_{\\\\perp 2}$\",\"$J_{||}$\"], **legend_options)\n", - "\n", - "plot_line(axs[4], div_over_curl)\n", - "axs[4].set_ylabel(\"$\\\\frac{|\\\\nabla . B|}{|\\\\nabla \\\\times B|}$\")\n", - "\n", - "plot_line(axs[5], e_xyz);\n", - "axs[5].set_ylabel(\"$E_{DSL}$\" + \"\\n\" +\"[mV m$^{-1}$\")\n", - "axs[5].legend([\"$E_{x}$\",\"$E_{y}$\",\"$E_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[6], jxb_xyz)\n", - "axs[6].set_ylabel(\"$J \\\\times B/n_{e} q_{e}$\" + \"\\n\" + \"[mV m$^{-1}$]\")\n", - "\n", - "plot_line(axs[7], e_dot_j)\n", - "axs[7].set_ylabel(\"$E . J$\" + \"\\n\" +\"[nW m$^{-3}$]\")\n", - "\n", - "axs[0].set_title(\"MMS - Current density and fields\")\n", - "\n", - "f.align_ylabels(axs)\n", - "axs[-1].set_xlim(tint)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/.ipynb_checkpoints/example_mms_ebfields-checkpoint.ipynb b/examples/01_mms/.ipynb_checkpoints/example_mms_ebfields-checkpoint.ipynb deleted file mode 100644 index 011f90ca..00000000 --- a/examples/01_mms/.ipynb_checkpoints/example_mms_ebfields-checkpoint.ipynb +++ /dev/null @@ -1,1429 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# EB Fields\n", - "author: Louis Richard\\\n", - "Plots E and B time series and of burst mode electric field in GSE coordinates and field-aligned coordinates. Plots spectrograms of paralleland perpendicular electric fields and fluctuating magnetic field." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from astropy import constants\n", - "from pyrfu.mms import get_data, db_init\n", - "from pyrfu.plot import plot_line, plot_spectr\n", - "from pyrfu.pyrf import wavelet, norm, convert_fac, filt, resample, ts_scalar" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define time interval, data path and spacecraft index" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "tint = [\"2015-10-30T05:15:20.000\", \"2015-10-30T05:16:20.000\"]\n", - "db_init(\"/Volumes/mms\")\n", - "ic = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load FGM data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:05:28: Loading mms1_fgm_b_gse_brst_l2...\n" - ] - } - ], - "source": [ - "b_xyz = get_data(\"b_gse_fgm_brst_l2\", tint, ic)\n", - "b_mag = norm(b_xyz)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load EDP data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:05:29: Loading mms1_edp_dce_gse_brst_l2...\n" - ] - } - ], - "source": [ - "e_xyz = get_data(\"e_gse_edp_brst_l2\", tint, ic)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load SCM data" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:05:30: Loading mms1_scm_acb_gse_scb_brst_l2...\n" - ] - } - ], - "source": [ - "b_scm = get_data(\"b_gse_scm_brst_l2\", tint, ic)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load FPI data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:05:32: Loading mms1_des_numberdensity_brst...\n" - ] - } - ], - "source": [ - "n_e = get_data(\"ne_fpi_brst_l2\", tint, ic)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute low and high frequency electric field and magnetic field fluctuations in FAC" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rotate E and B into field-aligned coordinates" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "e_xyzfac = convert_fac(e_xyz, b_xyz,[1, 0, 0])\n", - "b_scmfac = convert_fac(b_scm, b_xyz,[1, 0, 0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Bandpass filter E and B waveforms" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "fmin, fmax = [.5, 1000] # Hz\n", - "e_xyzfac_hf = filt(e_xyzfac, fmin, 0, 3)\n", - "e_xyzfac_lf = filt(e_xyzfac, 0, fmin, 3)\n", - "b_scmfac_hf = filt(b_scmfac, fmin, 0, 3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Wavelet transform of the electric field and the magnetic field fluctuations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute wavelet transforms" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "nf = 100\n", - "e_cwt, b_cwt = [wavelet(field, f_range=[fmin, fmax], n_freqs=nf) for field in [e_xyzfac, b_scm]]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Compress wavelet transform" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:06:18: :13: RuntimeWarning: Mean of empty slice\n", - " e_cwt_x[i, :] = np.squeeze(np.nanmean(e_cwt.x[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "09-Dec-21 11:06:18: :14: RuntimeWarning: Mean of empty slice\n", - " e_cwt_y[i, :] = np.squeeze(np.nanmean(e_cwt.y[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "09-Dec-21 11:06:18: :15: RuntimeWarning: Mean of empty slice\n", - " e_cwt_z[i, :] = np.squeeze(np.nanmean(e_cwt.z[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "09-Dec-21 11:06:25: :31: RuntimeWarning: Mean of empty slice\n", - " b_cwt_x[i, :] = np.squeeze(np.nanmean(b_cwt.x[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "09-Dec-21 11:06:25: :32: RuntimeWarning: Mean of empty slice\n", - " b_cwt_y[i, :] = np.squeeze(np.nanmean(b_cwt.y[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "09-Dec-21 11:06:25: :33: RuntimeWarning: Mean of empty slice\n", - " b_cwt_z[i, :] = np.squeeze(np.nanmean(b_cwt.z[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n" - ] - } - ], - "source": [ - "nc = 100\n", - "\n", - "# Number of frequencies\n", - "nf = e_cwt.x.shape[1]\n", - "\n", - "idxs = np.arange(int(nc / 2), len(e_cwt.time) - int(nc / 2), step=nc).astype(int)\n", - "\n", - "e_cwt_times = e_cwt.time[idxs]\n", - "\n", - "e_cwt_x, e_cwt_y, e_cwt_z = [np.zeros((len(idxs), nf)) for _ in range(3)]\n", - "\n", - "for i, idx in enumerate(idxs):\n", - " e_cwt_x[i, :] = np.squeeze(np.nanmean(e_cwt.x[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - " e_cwt_y[i, :] = np.squeeze(np.nanmean(e_cwt.y[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - " e_cwt_z[i, :] = np.squeeze(np.nanmean(e_cwt.z[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "options = dict(coords=[e_cwt_times, e_cwt.frequency], dims=[\"time\", \"frequency\"])\n", - "e_perp_cwt = xr.DataArray(e_cwt_x + e_cwt_y, **options)\n", - "e_para_cwt = xr.DataArray(e_cwt_z, **options)\n", - "\n", - "# Number of frequencies\n", - "nf = b_cwt.x.shape[1]\n", - "\n", - "idxs = np.arange(int(nc / 2), len(b_cwt.time) - int(nc / 2), step=nc).astype(int)\n", - "\n", - "b_cwt_times = b_cwt.time[idxs]\n", - "\n", - "b_cwt_x, b_cwt_y, b_cwt_z = [np.zeros((len(idxs), nf)) for _ in range(3)]\n", - "\n", - "for i, idx in enumerate(idxs):\n", - " b_cwt_x[i, :] = np.squeeze(np.nanmean(b_cwt.x[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - " b_cwt_y[i, :] = np.squeeze(np.nanmean(b_cwt.y[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - " b_cwt_z[i, :] = np.squeeze(np.nanmean(b_cwt.z[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "options = dict(coords=[b_cwt_times, b_cwt.frequency], dims=[\"time\", \"frequency\"])\n", - "b_cwt = xr.DataArray(b_cwt_x + b_cwt_y + b_cwt_z, **options)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute characteristic frequencies" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "me = constants.m_e.value\n", - "mp = constants.m_p.value\n", - "qe = constants.e.value\n", - "eps0 = constants.eps0.value\n", - "mu0 = constants.mu0.value\n", - "mp_me = mp / me\n", - "b_si = b_mag * 1e-9\n", - "w_pe = np.sqrt(resample(n_e, b_xyz) * 1e6 * qe ** 2 /(me * eps0))\n", - "w_ce = qe * b_si / me\n", - "w_pp = np.sqrt(resample(n_e, b_xyz) * 1e6 * qe ** 2/ (mp * eps0))\n", - "f_ce = w_ce / (2 * np.pi)\n", - "f_pe = w_pe / (2 * np.pi)\n", - "f_cp = f_ce / mp_me\n", - "f_pp = w_pp / (2 * np.pi)\n", - "f_lh = np.sqrt(f_cp * f_ce /(1 + f_ce ** 2. / f_pe ** 2) + f_cp ** 2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot figure" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "legend_options = dict(ncol=4, loc=\"upper right\", frameon=True)\n", - "spectr_options = dict(yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(7, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(hspace=0, left=.15, right=.85)\n", - "\n", - "plot_line(axs[0], b_xyz)\n", - "plot_line(axs[0], b_mag)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "labels = [\"$B_x$\", \"$B_y$\", \"$B_z$\", \"$|B|$\"]\n", - "axs[0].legend(labels, **legend_options)\n", - "\n", - "plot_line(axs[1], e_xyzfac_lf)\n", - "axs[1].set_ylabel(\"$E$ [mV m$^{-1}$]\")\n", - "labels = [\"$E_{\\\\perp 1}$\", \"$E_{\\\\perp 2}$\", \"$E_{||}$\"]\n", - "axs[1].legend(labels, **legend_options)\n", - "\n", - "plot_line(axs[2], e_xyzfac_hf)\n", - "axs[2].set_ylabel(\"$E$ [mV m$^{-1}$]\")\n", - "labels = [\"$E_{\\\\perp 1}$\", \"$E_{\\\\perp 2}$\", \"$E_{||}$\"]\n", - "axs[2].legend(labels, **legend_options)\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], e_perp_cwt, **spectr_options)\n", - "axs[3].set_ylabel(\"$f$ [Hz]\")\n", - "caxs3.set_ylabel(\"$E_{\\\\perp}^2$\" + \"\\n\" + \"[mV$^{2}$ m$^{-2}$ Hz$^{-1}$]\")\n", - "plot_line(axs[3], f_lh, color=\"k\")\n", - "plot_line(axs[3], f_ce, color=\"tab:blue\")\n", - "plot_line(axs[3], f_pp, color=\"tab:red\")\n", - "labels = [\"$f_{lh}$\", \"$f_{ce}$\", \"$f_{pp}$\"]\n", - "axs[3].legend(labels, **legend_options)\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], e_para_cwt, **spectr_options)\n", - "axs[4].set_ylabel(\"$f$ [Hz]\")\n", - "caxs4.set_ylabel(\"$E_{||}^2$\" + \"\\n\" + \"[mV$^{2}$ m$^{-2}$ Hz$^{-1}$]\")\n", - "plot_line(axs[4], f_lh, color=\"k\")\n", - "plot_line(axs[4], f_ce, color=\"tab:blue\")\n", - "plot_line(axs[4], f_pp, color=\"tab:red\")\n", - "labels = [\"$f_{lh}$\", \"$f_{ce}$\", \"$f_{pp}$\"]\n", - "axs[4].legend(labels, **legend_options)\n", - "\n", - "plot_line(axs[5], b_scmfac_hf)\n", - "axs[5].set_ylabel(\"$\\\\delta B$ [nT]\")\n", - "labels = [\"$B_{\\\\perp 1}$\", \"$B_{\\\\perp 2}$\", \"$B_{||}$\"]\n", - "axs[5].legend(labels, **legend_options)\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], b_cwt, **spectr_options)\n", - "axs[6].set_ylabel(\"$f$ [Hz]\")\n", - "caxs6.set_ylabel(\"$B^2$\" + \"\\n\" + \"[nT$^{2}$ Hz$^{-1}$]\")\n", - "plot_line(axs[6], f_lh, color=\"k\")\n", - "plot_line(axs[6], f_ce, color=\"tab:blue\")\n", - "plot_line(axs[6], f_pp, color=\"tab:red\")\n", - "labels = [\"$f_{lh}$\", \"$f_{ce}$\", \"$f_{pp}$\"]\n", - "axs[6].legend(labels, **legend_options)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/.ipynb_checkpoints/example_mms_edr_signatures-checkpoint.ipynb b/examples/01_mms/.ipynb_checkpoints/example_mms_edr_signatures-checkpoint.ipynb deleted file mode 100644 index 3252ca29..00000000 --- a/examples/01_mms/.ipynb_checkpoints/example_mms_edr_signatures-checkpoint.ipynb +++ /dev/null @@ -1,1461 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# EDR Signatures\n", - "Author: Louis Richard\\\n", - "A routine to compute various parameters used to identify electron diffusion regions for the four MMS spacecraft. \n", - "\n", - "Quantities calculated so far are:\n", - "- sqrt(Q) : Based on Swisdak, GRL ,2016. Values around 0.1 indicate electron agyrotropies. Computed based on the off-diagonal terms in the pressure tensor for Pe_perp1 = Pe_perp2. \n", - "- Dng: Based on Aunai et al., 2013; Computed based on the off-diagonal terms in the pressure tensor for Pe_perp1 = Pe_perp2. Similar to sqrt(Q) but with different normalization. Calculated but not plotted. \n", - "- AG^(1/3): Based on Che et al., POP, 2018. Constructed from determinant of field-aligned rotation of the electron pressure tensor (Pe_perp1 = Pe_perp2). \n", - "- A phi_e/2 = abs(Perp1-Perp2)/(Perp1+Perp2): This is a measure of electron agyrotropy. Values of O(1) are expected for EDRs. We transform the pressure tensor into field-aligned coordinates such that the difference in Pe_perp1 and Pe_perp2 is maximal. This corresponds to P23 being zero. (Note that this definition of agyrotropy neglects the off-diagonal pressure terms P12 and P13, therefore it doesn't capture all agyrotropies.)\n", - "- A n_e = T_parallel/T_perp: Values much larger than 1 are expected. Large T_parallel/T_perp are a feature of the ion diffusion region. For MP reconnection ion diffusion regions have A n_e ~ 3 based on MMS observations. Scudder says A n_e ~ 7 at IDR-EDR boundary, but this is extremely large for MP reconnection.\n", - "- Mperp e: electron Mach number: bulk velocity divided by the electron thermal speed perpendicular to B. Values of O(1) are expected in EDRs (Scudder et al., 2012, 2015). \n", - "- J.E': J.E > 0 is expected in the electron diffusion region, corresponding to dissipation of field energy. J is calculated on each spacecraft using the particle moments (Zenitani et al., PRL, 2011). \n", - "- epsilon_e: Energy gain per cyclotron period. Values of O(1) are expected in EDRs (Scudder et al., 2012, 2015). \n", - "- delta_e: Relative strength of the electric and magnetic force in the bulk electron rest frame. N. B. Very sensitive to electron moments and electric field. Check version of these quantities (Scudder et al., 2012, 2015). \n", - " \n", - "Notes: \n", - "kappa_e (not yet included) is taken to be the largest value of epsilon_e and delta_e at any given point. Requires electron distributions with version number v2.0.0 or higher. Calculations of agyrotropy measures (1)--(3) become unreliable at low densities n_e <~ 2 cm^-3, when the raw particle counts are low. Agyrotropies are removed for n_e < 1 cm^-3" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import tqdm\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from astropy import constants\n", - "from pyrfu.mms import get_data, rotate_tensor, db_init\n", - "from pyrfu.plot import make_labels, pl_tx\n", - "from pyrfu.pyrf import (resample, norm, cross, dot, trace, calc_sqrtq, \n", - " calc_dng, calc_ag, calc_agyro)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time interval selection and data path" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "tint = [\"2015-12-14T01:17:38.000\", \"2015-12-14T01:17:41.000\"]\n", - "db_init(\"/Volumes/mms\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load fields" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:09:20: Loading mms1_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 11:09:23: Loading mms2_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 11:09:25: Loading mms3_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 11:09:27: Loading mms4_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 11:09:29: Loading mms1_edp_dce_dsl_brst_l2...\n", - "09-Dec-21 11:09:31: Loading mms2_edp_dce_dsl_brst_l2...\n", - "09-Dec-21 11:09:32: Loading mms3_edp_dce_dsl_brst_l2...\n", - "09-Dec-21 11:09:33: Loading mms4_edp_dce_dsl_brst_l2...\n" - ] - } - ], - "source": [ - "b_mms = [get_data(\"b_dmpa_fgm_srvy_l2\", tint, i) for i in range(1, 5)]\n", - "e_mms = [get_data(\"e_dsl_edp_brst_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load particles moments" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:09:35: Loading mms1_des_numberdensity_brst...\n", - "09-Dec-21 11:09:37: Loading mms2_des_numberdensity_brst...\n", - "09-Dec-21 11:09:39: Loading mms3_des_numberdensity_brst...\n", - "09-Dec-21 11:09:41: Loading mms4_des_numberdensity_brst...\n", - "09-Dec-21 11:09:43: Loading mms1_des_bulkv_dbcs_brst...\n", - "09-Dec-21 11:09:47: Loading mms2_des_bulkv_dbcs_brst...\n", - "09-Dec-21 11:09:52: Loading mms3_des_bulkv_dbcs_brst...\n", - "09-Dec-21 11:09:56: Loading mms4_des_bulkv_dbcs_brst...\n", - "09-Dec-21 11:10:00: Loading mms1_dis_bulkv_dbcs_brst...\n", - "09-Dec-21 11:10:05: Loading mms2_dis_bulkv_dbcs_brst...\n", - "09-Dec-21 11:10:08: Loading mms3_dis_bulkv_dbcs_brst...\n", - "09-Dec-21 11:10:12: Loading mms4_dis_bulkv_dbcs_brst...\n", - "09-Dec-21 11:10:17: Loading mms1_des_temptensor_dbcs_brst...\n", - "09-Dec-21 11:10:24: Loading mms2_des_temptensor_dbcs_brst...\n", - "09-Dec-21 11:10:32: Loading mms3_des_temptensor_dbcs_brst...\n", - "09-Dec-21 11:10:40: Loading mms4_des_temptensor_dbcs_brst...\n", - "09-Dec-21 11:10:47: Loading mms1_des_prestensor_dbcs_brst...\n", - "09-Dec-21 11:10:55: Loading mms2_des_prestensor_dbcs_brst...\n", - "09-Dec-21 11:11:02: Loading mms3_des_prestensor_dbcs_brst...\n", - "09-Dec-21 11:11:09: Loading mms4_des_prestensor_dbcs_brst...\n" - ] - } - ], - "source": [ - "n_mms_e = [get_data(\"ne_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "v_mms_e = [get_data(\"ve_dbcs_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "v_mms_i = [get_data(\"vi_dbcs_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "t_mms_e = [get_data(\"te_dbcs_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "p_mms_e = [get_data(\"pe_dbcs_fpi_brst_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Resample to DES sampling frequency" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:11:15: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "e_mms = [resample(e_xyz, n_e) for e_xyz, n_e in zip(e_mms, n_mms_e)]\n", - "b_mms = [resample(b_xyz, n_e) for b_xyz, n_e in zip(b_mms, n_mms_e)]\n", - "v_mms_i = [resample(v_xyz_i, n_e) for v_xyz_i, n_e in zip(v_mms_i, n_mms_e)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Rotate pressure and temperature tensors" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are most unequal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are most unequal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are most unequal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are most unequal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Transforming tensor into field-aligned coordinates.\n" - ] - } - ], - "source": [ - "p_mms_e_pp = [rotate_tensor(p_xyz, \"fac\", b_xyz, \"pp\") for p_xyz, b_xyz in zip(p_mms_e, b_mms)]\n", - "p_mms_e_qq = [rotate_tensor(p_xyz, \"fac\", b_xyz, \"qq\") for p_xyz, b_xyz in zip(p_mms_e, b_mms)]\n", - "t_mms_e_fac = [rotate_tensor(t_xyz, \"fac\", b_xyz) for t_xyz, b_xyz in zip(t_mms_e, b_mms)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute tests for EDR" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Q and Dng from Pepp" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "sqrtq_mms = [calc_sqrtq(p_pp) for p_pp in p_mms_e_pp]\n", - "dng_mms = [calc_dng(p_pp) for p_pp in p_mms_e_pp]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute agyrotropy measure AG1/3" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "ag_mms = [calc_ag(p_pp) for p_pp in p_mms_e_pp]\n", - "ag_cr_mms = [ag ** (1 / 3) for ag in ag_mms]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute agyrotropy Aphi from Peqq" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "agyro_mms = [calc_agyro(p_qq) for p_qq in p_mms_e_qq]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simple fix to remove spurious points" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "for sqrtq, dng, agyro, ag_cr in zip(sqrtq_mms, dng_mms, agyro_mms, ag_cr_mms):\n", - " for coeff in [sqrtq, dng, agyro, ag_cr]:\n", - " coeff_data = coeff.data.copy()\n", - " for ii in range(len(coeff_data) - 1):\n", - " if coeff[ii] > 2 * coeff[ii - 1] and coeff[ii] > 2 * coeff[ii + 1]:\n", - " coeff_data[ii] = np.nan\n", - " \n", - " coeff.data = coeff_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Remove all points corresponding to densities below 1cm^-3" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "for n_e, sqrtq, dng, agyro, ag_cr in zip(n_mms_e, sqrtq_mms, dng_mms, agyro_mms, ag_cr_mms):\n", - " sqrtq.data[n_e.data < 1] = np.nan\n", - " dng.data[n_e.data < 1] = np.nan\n", - " agyro.data[n_e.data < 1] = np.nan\n", - " ag_cr.data[n_e.data < 1] = np.nan" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute temperature ratio An" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "t_rat_mms = [p_pp[:, 0, 0] / p_pp[:, 1, 1] for p_pp in p_mms_e_pp]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute electron Mach number" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "qe, me = [constants.e.value, constants.m_e.value]\n", - "v_mms_e_mag = [norm(v_xyz_e) for v_xyz_e in v_mms_e]\n", - "v_mms_e_per = [np.sqrt((t_fac_e[:, 1, 1] + t_fac_e[:, 2, 2]) * qe / me) for t_fac_e in t_mms_e_fac]\n", - "m_mms_e = [1e3 * v_e_mag / v_e_perp for v_e_mag, v_e_perp in zip(v_mms_e_mag, v_mms_e_per)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute current density and J.E" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Current density in nA m^-2\n", - "j_mms_moms = [1e18 * qe * n_e * (v_xyz_i - v_xyz_e) for n_e, v_xyz_i, v_xyz_e in zip(n_mms_e, v_mms_i, v_mms_e)]\n", - "vexb_mms = [e_xyz + 1e-3 * cross(v_xyz_e, b_xyz) for e_xyz, v_xyz_e, b_xyz in zip(e_mms, v_mms_e, b_mms)]\n", - "# J (nA/m^2), E (mV/m), E.J (nW/m^3)\n", - "edotj_mms = [1e-3 * dot(vexb_xyz, j_xyz) for vexb_xyz, j_xyz in zip(vexb_mms, j_mms_moms)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculate epsilon and delta parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "w_mms_ce = [1e-9 * qe * norm(b_xyz) /me for b_xyz in b_mms]\n", - "edotve_mms = [dot(e_xyz, v_xyz_e) for e_xyz, v_xyz_e in zip(e_mms, v_mms_e)]\n", - "eps_mms_e = [np.abs(6 * np.pi * edotve_xyz /(w_ce * trace(t_fac_e))) for edotve_xyz, w_ce, t_fac_e in zip(edotve_mms, w_mms_ce, t_mms_e_fac)]\n", - "delta_mms_e = [1e-3 * norm(vexb_xyz) / (v_xyz_e_per * norm(b_xyz) * 1e-9) for vexb_xyz, v_xyz_e_per, b_xyz in zip(vexb_mms, v_mms_e_per, b_mms)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot figure" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - 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' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(9, sharex=\"all\", figsize=(6.5, 11))\n", - "f.subplots_adjust(bottom=.1, top=.95, left=.15, right=.85, hspace=0)\n", - "\n", - "pl_tx(axs[0], b_mms, 2)\n", - "axs[0].set_ylabel(\"$B_{z}$ [nT]\")\n", - "labels = [\"MMS{:d}\".format(ic) for ic in range(1, 5)]\n", - "axs[0].legend(labels, **legend_options)\n", - "\n", - "pl_tx(axs[1], sqrtq_mms, 0)\n", - "axs[1].set_ylabel(\"$\\sqrt{Q}$\")\n", - "\n", - "pl_tx(axs[2], ag_cr_mms, 0)\n", - "axs[2].set_ylabel(\"$AG^{1/3}$\")\n", - "\n", - "pl_tx(axs[3], agyro_mms, 0)\n", - "axs[3].set_ylabel(\"$A\\Phi_e / 2$\")\n", - "\n", - "pl_tx(axs[4], t_rat_mms, 0)\n", - "axs[4].set_ylabel(\"$T_{e||}/T_{e \\perp}$\")\n", - "\n", - "pl_tx(axs[5], m_mms_e, 0)\n", - "axs[5].set_ylabel(\"$M_{e \\perp}$\")\n", - "\n", - "pl_tx(axs[6], edotj_mms, 0)\n", - "axs[6].set_ylabel(\"$E'.J$ [nW m$^{-3}$]\")\n", - "\n", - "pl_tx(axs[7], eps_mms_e, 0)\n", - "axs[7].set_ylabel(\"$\\epsilon_{e}$\")\n", - "\n", - "pl_tx(axs[8], delta_mms_e, 0)\n", - "axs[8].set_ylabel(\"$\\delta_{e}$\")\n", - "\n", - "make_labels(axs, [0.025, 0.83])\n", - "axs[-1].set_xlim(tint)\n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/.ipynb_checkpoints/example_mms_eis-checkpoint.ipynb b/examples/01_mms/.ipynb_checkpoints/example_mms_eis-checkpoint.ipynb deleted file mode 100644 index 0066271c..00000000 --- a/examples/01_mms/.ipynb_checkpoints/example_mms_eis-checkpoint.ipynb +++ /dev/null @@ -1,2497 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Energetic Ion Spectrometer (EIS)\n", - "author: Louis Richard" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu import mms\n", - "from pyrfu.plot import plot_line, plot_spectr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define path to data, time interval and spacecraft index" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "data_path = \"/Users/louisr/Documents/PhD/Y2/jet_fronts/data\"\n", - "tint_long = [\"2017-07-23T16:10:00\", \"2017-07-23T18:10:00\"]\n", - "mms_id = 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Magnetic field in GSE coordinates" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 23:51:09: Loading mms2_fgm_b_gse_srvy_l2...\n" - ] - } - ], - "source": [ - "# Single spacecraft\n", - "b_gse = mms.get_data(\"b_gse_fgm_srvy_l2\", tint_long, mms_id, data_path=data_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Single spacecraft H$^+$ (PHxTOF and ExTOF), He$^{n+}$ (ExTOF), O$^{n+}$ (ExTOF) and electrons differential particle fluxes for all 6 telescopes." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_spin...\n", - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_sector...\n", - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_proton_t0_energy_dminus...\n", - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_proton_t0_energy_dplus...\n", - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_proton_P4_flux_t0...\n", - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_look_t0...\n", - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_proton_P4_flux_t1...\n", - "18-Jun-21 23:51:11: Loading mms2_epd_eis_phxtof_look_t1...\n", - "18-Jun-21 23:51:11: Loading mms2_epd_eis_phxtof_proton_P4_flux_t2...\n", - "18-Jun-21 23:51:11: Loading mms2_epd_eis_phxtof_look_t2...\n", - "18-Jun-21 23:51:11: Loading mms2_epd_eis_phxtof_proton_P4_flux_t3...\n", - "18-Jun-21 23:51:12: Loading mms2_epd_eis_phxtof_look_t3...\n", - "18-Jun-21 23:51:12: Loading mms2_epd_eis_phxtof_proton_P4_flux_t4...\n", - "18-Jun-21 23:51:12: Loading mms2_epd_eis_phxtof_look_t4...\n", - "18-Jun-21 23:51:12: Loading mms2_epd_eis_phxtof_proton_P4_flux_t5...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_phxtof_look_t5...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_extof_spin...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_extof_sector...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_extof_proton_t0_energy_dminus...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_extof_proton_t0_energy_dplus...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_extof_proton_P4_flux_t0...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_extof_look_t0...\n", - "18-Jun-21 23:51:14: Loading mms2_epd_eis_extof_proton_P4_flux_t1...\n", - "18-Jun-21 23:51:14: Loading mms2_epd_eis_extof_look_t1...\n", - "18-Jun-21 23:51:15: Loading mms2_epd_eis_extof_proton_P4_flux_t2...\n", - "18-Jun-21 23:51:15: Loading mms2_epd_eis_extof_look_t2...\n", - "18-Jun-21 23:51:15: Loading mms2_epd_eis_extof_proton_P4_flux_t3...\n", - "18-Jun-21 23:51:16: Loading mms2_epd_eis_extof_look_t3...\n", - "18-Jun-21 23:51:16: Loading mms2_epd_eis_extof_proton_P4_flux_t4...\n", - "18-Jun-21 23:51:16: Loading mms2_epd_eis_extof_look_t4...\n", - "18-Jun-21 23:51:17: Loading mms2_epd_eis_extof_proton_P4_flux_t5...\n", - "18-Jun-21 23:51:17: Loading mms2_epd_eis_extof_look_t5...\n", - "18-Jun-21 23:51:18: Loading mms2_epd_eis_extof_spin...\n", - "18-Jun-21 23:51:18: Loading mms2_epd_eis_extof_sector...\n", - "18-Jun-21 23:51:18: Loading mms2_epd_eis_extof_oxygen_t0_energy_dminus...\n", - "18-Jun-21 23:51:18: Loading mms2_epd_eis_extof_oxygen_t0_energy_dplus...\n", - "18-Jun-21 23:51:18: Loading mms2_epd_eis_extof_oxygen_P4_flux_t0...\n", - "18-Jun-21 23:51:18: Loading mms2_epd_eis_extof_look_t0...\n", - "18-Jun-21 23:51:19: Loading mms2_epd_eis_extof_oxygen_P4_flux_t1...\n", - "18-Jun-21 23:51:19: Loading mms2_epd_eis_extof_look_t1...\n", - "18-Jun-21 23:51:20: Loading mms2_epd_eis_extof_oxygen_P4_flux_t2...\n", - "18-Jun-21 23:51:20: Loading mms2_epd_eis_extof_look_t2...\n", - "18-Jun-21 23:51:21: Loading mms2_epd_eis_extof_oxygen_P4_flux_t3...\n", - "18-Jun-21 23:51:21: Loading mms2_epd_eis_extof_look_t3...\n", - "18-Jun-21 23:51:22: Loading mms2_epd_eis_extof_oxygen_P4_flux_t4...\n", - "18-Jun-21 23:51:22: Loading mms2_epd_eis_extof_look_t4...\n", - "18-Jun-21 23:51:23: Loading mms2_epd_eis_extof_oxygen_P4_flux_t5...\n", - "18-Jun-21 23:51:23: Loading mms2_epd_eis_extof_look_t5...\n", - "18-Jun-21 23:51:24: Loading mms2_epd_eis_extof_spin...\n", - "18-Jun-21 23:51:24: Loading mms2_epd_eis_extof_sector...\n", - "18-Jun-21 23:51:24: Loading mms2_epd_eis_extof_alpha_t0_energy_dminus...\n", - "18-Jun-21 23:51:24: Loading mms2_epd_eis_extof_alpha_t0_energy_dplus...\n", - "18-Jun-21 23:51:24: Loading mms2_epd_eis_extof_alpha_P4_flux_t0...\n", - "18-Jun-21 23:51:24: Loading mms2_epd_eis_extof_look_t0...\n", - "18-Jun-21 23:51:25: Loading mms2_epd_eis_extof_alpha_P4_flux_t1...\n", - "18-Jun-21 23:51:25: Loading mms2_epd_eis_extof_look_t1...\n", - "18-Jun-21 23:51:26: Loading mms2_epd_eis_extof_alpha_P4_flux_t2...\n", - "18-Jun-21 23:51:26: Loading mms2_epd_eis_extof_look_t2...\n", - "18-Jun-21 23:51:26: Loading mms2_epd_eis_extof_alpha_P4_flux_t3...\n", - "18-Jun-21 23:51:27: Loading mms2_epd_eis_extof_look_t3...\n", - "18-Jun-21 23:51:27: Loading mms2_epd_eis_extof_alpha_P4_flux_t4...\n", - "18-Jun-21 23:51:27: Loading mms2_epd_eis_extof_look_t4...\n", - "18-Jun-21 23:51:28: Loading mms2_epd_eis_extof_alpha_P4_flux_t5...\n", - "18-Jun-21 23:51:28: Loading mms2_epd_eis_extof_look_t5...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_spin...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_sector...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_electron_t0_energy_dminus...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_electron_t0_energy_dplus...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_electron_P4_flux_t0...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_look_t0...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_electron_P4_flux_t1...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_look_t1...\n", - "18-Jun-21 23:51:30: Loading mms2_epd_eis_electronenergy_electron_P4_flux_t2...\n", - "18-Jun-21 23:51:30: Loading mms2_epd_eis_electronenergy_look_t2...\n", - "18-Jun-21 23:51:30: Loading mms2_epd_eis_electronenergy_electron_P4_flux_t3...\n", - "18-Jun-21 23:51:30: Loading mms2_epd_eis_electronenergy_look_t3...\n", - "18-Jun-21 23:51:31: Loading mms2_epd_eis_electronenergy_electron_P4_flux_t4...\n", - "18-Jun-21 23:51:31: Loading mms2_epd_eis_electronenergy_look_t4...\n", - "18-Jun-21 23:51:31: Loading mms2_epd_eis_electronenergy_electron_P4_flux_t5...\n", - "18-Jun-21 23:51:31: Loading mms2_epd_eis_electronenergy_look_t5...\n" - ] - } - ], - "source": [ - "# Proton\n", - "dpf_phxtof_allt_proton = mms.get_eis_allt(\"flux_phxtof_proton_srvy_l2\", tint_long, mms_id,\n", - " data_path=data_path)\n", - "dpf_extof_allt_proton = mms.get_eis_allt(\"flux_extof_proton_srvy_l2\", tint_long, mms_id,\n", - " data_path=data_path)\n", - "# Oxygen\n", - "dpf_extof_allt_oxygen = mms.get_eis_allt(\"flux_extof_oxygen_srvy_l2\", tint_long, mms_id,\n", - " data_path=data_path)\n", - "# Helium\n", - "dpf_extof_allt_helium = mms.get_eis_allt(\"flux_extof_alpha_srvy_l2\", tint_long, mms_id,\n", - " data_path=data_path)\n", - "# Electron\n", - "dpf_een_allt_electron = mms.get_eis_allt(\"flux_electronenergy_electron_srvy_l2\", tint_long, \n", - " mms_id, data_path=data_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Post-processing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute H$^+$ combined PHxTOF and ExTOF differential particle flux for all 6 telescopes" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "dpf_eis_allt_proton = mms.eis_combine_proton_spec(dpf_phxtof_allt_proton, dpf_extof_allt_proton)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spin average H+, He𝑛+ (ExTOF) and O𝑛+ (ExTOF) differential particle fluxes for all 6 telescopes" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Helium\n", - "dpf_eis_allt_proton_spin = mms.eis_spin_avg(dpf_eis_allt_proton)\n", - "\n", - "# Helium\n", - "dpf_extof_allt_helium_spin = mms.eis_spin_avg(dpf_extof_allt_helium)\n", - "\n", - "# Oxygen\n", - "dpf_extof_allt_oxygen_spin = mms.eis_spin_avg(dpf_extof_allt_oxygen)\n", - "\n", - "# Electron\n", - "dpf_een_allt_electron_spin = mms.eis_spin_avg(dpf_een_allt_electron)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute H$^+$, He$^{n+}$ (ExTOF) and O$^{n+}$ (ExTOF) omni-directional differential particle fluxes with an without spin averaging" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Helium\n", - "dpf_eis_omni_proton = mms.eis_omni(dpf_eis_allt_proton)\n", - "dpf_eis_omni_proton_spin = mms.eis_omni(dpf_eis_allt_proton_spin)\n", - "\n", - "# Helium\n", - "dpf_extof_omni_helium = mms.eis_omni(dpf_extof_allt_helium)\n", - "dpf_extof_omni_helium_spin = mms.eis_omni(dpf_extof_allt_helium_spin)\n", - "\n", - "# Oxygen\n", - "dpf_extof_omni_oxygen = mms.eis_omni(dpf_extof_allt_oxygen)\n", - "dpf_extof_omni_oxygen_spin = mms.eis_omni(dpf_extof_allt_oxygen_spin)\n", - "\n", - "# Electron\n", - "dpf_een_omni_electron = mms.eis_omni(dpf_een_allt_electron)\n", - "dpf_een_omni_electron_spin = mms.eis_omni(dpf_een_allt_electron_spin)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Apply H$^+$ omni-directional differential particle flux cross calibration correction" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "dpf_eis_omni_proton_corr = mms.eis_proton_correction(dpf_eis_omni_proton)\n", - "dpf_eis_omni_proton_spin_corr = mms.eis_proton_correction(dpf_eis_omni_proton_spin)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot in a MMS SDC Quicklook fashion" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. 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position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
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" if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - 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" el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(5, sharex=\"all\", figsize=(8.5, 10))\n", - "f.subplots_adjust(bottom=.05, top=.95, left=.13, right=.87, hspace=0)\n", - "\n", - "plot_line(axs[0], b_gse)\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], frameon=True, loc=\"upper right\", ncol=3)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "\n", - "# Electron spin averaged\n", - "axs[1], caxs1 = plot_spectr(axs[1], dpf_een_omni_electron_spin, yscale=\"log\", cscale=\"log\")\n", - "caxs1.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[1].set_ylabel(\"$E_{e}$ [keV]\")\n", - "\n", - "# Proton spin averaged\n", - "axs[2], caxs2 = plot_spectr(axs[2], dpf_eis_omni_proton_spin_corr, yscale=\"log\", cscale=\"log\")\n", - "caxs2.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[2].set_ylabel(\"$E_{H^{+}}$ [keV]\")\n", - "\n", - "# Helium spin averaged\n", - "axs[3], caxs3 = plot_spectr(axs[3], dpf_extof_omni_helium_spin, yscale=\"log\", cscale=\"log\")\n", - "caxs3.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[3].set_ylabel(\"$E_{He^{n+}}$ [keV]\")\n", - "\n", - "# Oxygen spin averaged\n", - "axs[4], caxs4 = plot_spectr(axs[4], dpf_extof_omni_oxygen_spin, yscale=\"log\", cscale=\"log\")\n", - "caxs4.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[4].set_ylabel(\"$E_{O^{n+}}$ [keV]\")\n", - "\n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute H$^{+}$ (PHxTOF and ExTOF), He$^{n+}$, O$^{n+}$ and electron pitch angle distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 23:51:32: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n", - "18-Jun-21 23:51:32: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/mms/eis_pad.py:113: RuntimeWarning: Mean of empty slice\n", - " pa_flux[i, j, :] = np.nanmean(flux_file[i, ind, :], axis=0)\n", - "\n" - ] - } - ], - "source": [ - "# Low energy proton\n", - "dpf_phxtof_pad_proton = mms.eis_pad(dpf_phxtof_allt_proton, vec=b_gse, energy=[10, 50])\n", - "\n", - "# High energy proton\n", - "dpf_extof_pad_proton = mms.eis_pad(dpf_extof_allt_proton, vec=b_gse)\n", - "\n", - "# Helium\n", - "dpf_extof_pad_helium = mms.eis_pad(dpf_extof_allt_helium, vec=b_gse)\n", - "\n", - "# Oxygen\n", - "dpf_extof_pad_oxygen = mms.eis_pad(dpf_extof_allt_oxygen, vec=b_gse)\n", - "\n", - "# Electron\n", - "dpf_een_pad_electron = mms.eis_pad(dpf_een_allt_electron, vec=b_gse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spin average H$^+$, He$^{n+}$ and O$^{n+}$ pitch angle distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 23:51:38: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/mms/eis_pad_spinavg.py:47: RuntimeWarning: Mean of empty slice\n", - " spin_sum_flux[i, :, :] = np.nanmean(inp.data[idx_, :, :], axis=0)\n", - "\n" - ] - } - ], - "source": [ - "# Low energy proton\n", - "dpf_phxtof_pad_proton_spin = mms.eis_pad_spinavg(dpf_phxtof_pad_proton, dpf_phxtof_allt_proton.spin)\n", - "\n", - "# High energy proton\n", - "dpf_extof_pad_proton_spin = mms.eis_pad_spinavg(dpf_extof_pad_proton, dpf_extof_allt_proton.spin)\n", - "\n", - "# Helium\n", - "dpf_extof_pad_helium_spin = mms.eis_pad_spinavg(dpf_extof_pad_helium, dpf_extof_allt_helium.spin)\n", - "\n", - "# Oxygen\n", - "dpf_extof_pad_oxygen_spin = mms.eis_pad_spinavg(dpf_extof_pad_oxygen, dpf_extof_allt_oxygen.spin)\n", - "\n", - "# Electron\n", - "dpf_een_pad_electron_spin = mms.eis_pad_spinavg(dpf_een_pad_electron, dpf_een_allt_electron.spin)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f, axs = plt.subplots(6, sharex=\"all\", figsize=(8.5, 10))\n", - "f.subplots_adjust(bottom=.05, top=.95, left=.13, right=.87, hspace=0)\n", - "\n", - "plot_line(axs[0], b_gse)\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], frameon=True, loc=\"upper right\", ncol=3)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "\n", - "axs[1], caxs1 = plot_spectr(axs[1], dpf_een_pad_electron_spin.mean(dim=\"energy\", skipna=True), \n", - " cscale=\"log\")\n", - "caxs1.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[1].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "e_min, e_max = list(dpf_een_pad_electron.energy.data[[0, -1]])\n", - "axs[1].text(.02, .1, f\"{e_min:3.1f} keV < $E_{{e}}$ < {e_max:3.1f} keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[1].transAxes)\n", - "\n", - "axs[2], caxs2 = plot_spectr(axs[2], dpf_extof_pad_proton_spin.mean(dim=\"energy\", skipna=True), \n", - " cscale=\"log\")\n", - "caxs2.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[2].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "e_min, e_max = list(dpf_extof_pad_proton.energy.data[[0, -1]])\n", - "axs[2].text(.02, .1, f\"{e_min:3.1f} keV < $E_{{H^+}}$ < {e_max:3.1f} keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[2].transAxes)\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], dpf_phxtof_pad_proton_spin.mean(dim=\"energy\", skipna=True), \n", - " cscale=\"log\")\n", - "caxs3.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[3].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "e_min, e_max = list(dpf_phxtof_pad_proton.energy.data[[0, -1]])\n", - "axs[3].text(.02, .1, f\"{e_min:3.1f} keV < $E_{{H^+}}$ < {e_max:3.1f} keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[3].transAxes)\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], dpf_extof_pad_helium_spin.mean(dim=\"energy\", skipna=True), \n", - " cscale=\"log\")\n", - "caxs4.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[4].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "e_min, e_max = list(dpf_extof_pad_helium_spin.energy.data[[0, -1]])\n", - "axs[4].text(.02, .1, f\"{e_min:3.1f} keV < $E_{{He^{{n+}}}}$ < {e_max:3.1f} keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[4].transAxes)\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], dpf_extof_pad_oxygen_spin.mean(dim=\"energy\", skipna=True), \n", - " cscale=\"log\")\n", - "caxs5.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[5].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "e_min, e_max = list(dpf_extof_pad_oxygen_spin.energy.data[[0, -1]])\n", - "axs[5].text(.02, .1, f\"{e_min:3.1f} keV < $E_{{O^{{n+}}}}$ < {e_max:3.1f} keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[5].transAxes)\n", - "\n", - " \n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/.ipynb_checkpoints/example_mms_electron_psd-checkpoint.ipynb b/examples/01_mms/.ipynb_checkpoints/example_mms_electron_psd-checkpoint.ipynb deleted file mode 100644 index c36884c0..00000000 --- a/examples/01_mms/.ipynb_checkpoints/example_mms_electron_psd-checkpoint.ipynb +++ /dev/null @@ -1,1196 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Electron PSD\n", - "author: Louis Richard\n", - "\n", - "Script to plot electron PSD around pitch angles 0, 90, and 180 deg and PSD versus pitch angle L1b brst data " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from astropy.time import Time\n", - "from pyrfu.pyrf import resample, time_clip\n", - "from pyrfu.mms import get_data, get_pitch_angle_dist, db_init" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define time interval and spacecraft index" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "db_init(\"/Volumes/mms\")\n", - "tint_r = [\"2015-10-30T05:15:20.000\", \"2015-10-30T05:16:20.000\"]\n", - "mms_id = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:15:26: Loading mms1_des_dist_brst...\n", - "09-Dec-21 11:15:40: Loading mms1_fgm_b_dmpa_brst_l2...\n", - "09-Dec-21 11:15:42: Loading mms1_edp_scpot_brst_l2...\n" - ] - } - ], - "source": [ - "vdf_e = get_data(\"pde_fpi_brst_l2\", tint_r, mms_id)\n", - "b_xyz = get_data(\"b_dmpa_fgm_brst_l2\", tint_r, mms_id)\n", - "sc_pot = get_data(\"v_edp_brst_l2\", tint_r, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert PSD units to s^3/km^6" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "vdf_e.data.data *= 1e36" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resample spacecraft potential to VDF sampling" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:15:43: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "sc_pot = resample(sc_pot, vdf_e.data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Produce a single PAD at a selected time" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "tint = \"2015-10-30T05:15:45.731587000\"\n", - "\n", - "pad_vdf_e = get_pitch_angle_dist(vdf_e, b_xyz, tint=tint_r)\n", - "pad_vdf_e = pad_vdf_e.sel(time=tint)\n", - "\n", - "idx = np.argmin(abs(sc_pot.time.data - Time(tint, format=\"isot\").datetime64))\n", - "energy_pad = pad_vdf_e.energy.data[0, :] - sc_pot.data[idx, ...]\n", - "thetas_pad = pad_vdf_e.theta.data[0, ...]\n", - "pad_data_e = pad_vdf_e.data.data[0, ...]\n", - "\n", - "pad_data_e[pad_data_e == 0.] = np.nan" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; 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" event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - 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" icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, '05:15:45.731 UT')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(ncols=2, sharey=\"all\", figsize=(8, 6))\n", - "f.subplots_adjust(wspace=.06)\n", - "\n", - "y_range = [10 ** 2, 10 ** np.ceil(np.nanmax(np.nanmax(np.log10(pad_data_e))))]\n", - "\n", - "for i, color, ang in zip([0, 6, -1], [\"k\", \"tab:red\", \"tab:blue\"], [0, 90, 180]):\n", - " axs[0].loglog(energy_pad, pad_data_e[:, i], color=color, label=f\"{ang} deg\")\n", - " \n", - "axs[0].set_xlim([5, 3e4])\n", - "axs[0].set_ylim(y_range)\n", - "axs[0].set_xticks([1e0, 1e1, 1e2, 1e3, 1e4, 1e5])\n", - "axs[0].set_xlim([5, 3e4])\n", - "axs[0].set_ylabel(\"$f_e$ [s$^3$ km$^{-6}$]\")\n", - "axs[0].set_xlabel(\"$E$ [eV]\")\n", - "axs[0].legend(loc=\"upper right\", frameon=True)\n", - "axs[0].grid(which=\"major\")\n", - "axs[0].set_title(\"{} UT\".format(tint[11:23]))\n", - "\n", - "colors = pl.cm.jet(np.linspace(0, 1, len(energy_pad)))\n", - "\n", - "for (i, energy), color in zip(enumerate(energy_pad), colors):\n", - " axs[1].semilogy(thetas_pad, pad_data_e[i, :], color=color)\n", - " \n", - "axs[1].set_xlim([0, 180])\n", - "axs[1].set_xlabel(\"$\\\\theta$ [deg.]\")\n", - "axs[1].set_xticks([0, 45, 90, 135, 180])\n", - "axs[1].grid(which=\"major\")\n", - "axs[1].set_title(\"{} UT\".format(tint[11:23]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/.ipynb_checkpoints/example_mms_feeps-checkpoint.ipynb b/examples/01_mms/.ipynb_checkpoints/example_mms_feeps-checkpoint.ipynb deleted file mode 100644 index 85ebc01b..00000000 --- a/examples/01_mms/.ipynb_checkpoints/example_mms_feeps-checkpoint.ipynb +++ /dev/null @@ -1,3002 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fly’s Eye Energetic Particle Spectrometer (FEEPS)\n", - "author: Louis Richard\n", - "\n", - "Routines :\n", - "* feeps_correct_energies\n", - "* feeps_flat_field_corrections\n", - "* feeps_omni\n", - "* feeps_pad\n", - "* feeps_pad_spinavg\n", - "* feeps_remove_bad_data\n", - "* feeps_remove_sun\n", - "* feeps_sector_spec\n", - "* feeps_spin_avg\n", - "* feeps_split_integral_ch\n", - "* get_feeps_alle\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu import mms\n", - "from pyrfu.plot import plot_line, plot_spectr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define data path, time interval and spacecraft index" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "data_path = \"/Users/louisr/Documents/PhD/Y2/jet_fronts/data\"\n", - "tint_long = [\"2017-07-23T16:10:00\", \"2017-07-23T18:10:00\"]\n", - "mms_id = 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Magnetic field in GSM" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 21:44:33: Loading mms2_fgm_b_bcs_srvy_l2...\n", - "18-Jun-21 21:44:33: Loading mms2_fgm_b_gsm_srvy_l2...\n" - ] - } - ], - "source": [ - "b_bcs = mms.get_data(\"b_bcs_fgm_srvy_l2\", tint_long, mms_id, data_path=data_path)\n", - "b_gsm = mms.get_data(\"b_gsm_fgm_srvy_l2\", tint_long, mms_id, data_path=data_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Electron and ion differential particle flux for all FEEPS sensors" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function get_feeps_alleyes in module pyrfu.mms.get_feeps_alleyes:\n", - "\n", - "get_feeps_alleyes(tar_var, tint, mms_id, verbose: bool = True, data_path: str = '')\n", - " Read energy spectrum of the selected specie in the selected energy\n", - " range for all FEEPS eyes.\n", - " \n", - " Parameters\n", - " ----------\n", - " tar_var : str\n", - " Key of the target variable like\n", - " {data_unit}{specie}_{data_rate}_{data_lvl}.\n", - " tint : list of str\n", - " Time interval.\n", - " mms_id : int or float or str\n", - " Index of the spacecraft.\n", - " verbose : bool, Optional\n", - " Set to True to follow the loading. Default is True.\n", - " data_path : str, Optional\n", - " Path of MMS data. Default uses `pyrfu.mms.mms_config.py`\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.Dataset\n", - " Dataset containing the energy spectrum of the available eyes of the\n", - " Fly's Eye Energetic Particle Spectrometer (FEEPS).\n", - " \n", - " Examples\n", - " --------\n", - " >>> from pyrfu import mms\n", - " \n", - " Define time interval\n", - " \n", - " >>> tint_brst = [\"2017-07-23T16:54:24.000\", \"2017-07-23T17:00:00.000\"]\n", - " \n", - " Read electron energy spectrum for all FEEPS eyes\n", - " \n", - " >>> feeps_all_eyes = mms.get_feeps_alleyes(\"fluxe_brst_l2\", tint_brst, 2)\n", - "\n" - ] - } - ], - "source": [ - "help(mms.get_feeps_alleyes)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_spinsectnum...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_pitch_angle...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_3...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_4...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_5...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_11...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_12...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_3...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_4...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_5...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_11...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_12...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_spinsectnum...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_pitch_angle...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_top_count_rate_sensorid_6...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_top_count_rate_sensorid_7...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_top_count_rate_sensorid_8...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_bottom_count_rate_sensorid_6...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_bottom_count_rate_sensorid_7...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_bottom_count_rate_sensorid_8...\n" - ] - } - ], - "source": [ - "# Electron\n", - "dpf_feeps_alle_e = mms.get_feeps_alleyes(\"cpse_srvy_l2\", tint_long, mms_id, data_path=data_path)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_i = mms.get_feeps_alleyes(\"cpsi_srvy_l2\", tint_long, mms_id, data_path=data_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "b_gsm.data.ndim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Post-processing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Correct energy table" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_correct_energies in module pyrfu.mms.feeps_correct_energies:\n", - "\n", - "feeps_correct_energies(feeps_alle)\n", - " Modifies the energy table in FEEPS spectra (intensity, count_rate,\n", - " counts) using the function: mms_feeps_energy_table (which is s/c, sensor\n", - " head and sensor ID dependent)\n", - " \n", - " Parameters\n", - " ----------\n", - " feeps_alle : xarray.Dataset\n", - " Dataset containing the energy spectrum of the available eyes of the\n", - " Fly's Eye Energetic Particle Spectrometer (FEEPS).\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.Dataset\n", - " Dataset containing the energy spectrum of the available eyes of the\n", - " Fly's Eye Energetic Particle Spectrometer (FEEPS) with corrected\n", - " energy table.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_correct_energies)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_alle_e = mms.feeps_correct_energies(dpf_feeps_alle_e)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_i = mms.feeps_correct_energies(dpf_feeps_alle_i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Apply flat field correction to electron and ion differential particle flux spectra" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_flat_field_corrections in module pyrfu.mms.feeps_flat_field_corrections:\n", - "\n", - "feeps_flat_field_corrections(inp_alle)\n", - " Apply flat field correction factors to FEEPS ion/electron\n", - " data. Correct factors are from the gain factor found in:\n", - " FlatFieldResults_V3.xlsx from Drew Turner, 1/19/2017\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_alle : xarray.Dataset\n", - " Dataset containing the energy spectrum of the available eyes of the\n", - " Fly's Eye Energetic Particle Spectrometer (FEEPS).\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.Dataset\n", - " Dataset containing the energy spectrum of the available eyes of the\n", - " Fly's Eye Energetic Particle Spectrometer (FEEPS) with corrected\n", - " data.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_flat_field_corrections)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_alle_e = mms.feeps_flat_field_corrections(dpf_feeps_alle_e)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_i = mms.feeps_flat_field_corrections(dpf_feeps_alle_i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Remove bad data" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_remove_bad_data in module pyrfu.mms.feeps_remove_bad_data:\n", - "\n", - "feeps_remove_bad_data(inp_dataset)\n", - " This function removes bad eyes, bad lowest energy channels based on\n", - " data from Drew Turner\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_dataset : xarray.Dataset\n", - " Dataset with all active telescopes data.\n", - " \n", - " Returns\n", - " -------\n", - " inp_dataaset_clean_all : xarray.Dataset\n", - " Dataset with all active telescopes data where bad eyes and lab lowest\n", - " energy channels are set to NaN.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_remove_bad_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_alle_e = mms.feeps_remove_bad_data(dpf_feeps_alle_e)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_i = mms.feeps_remove_bad_data(dpf_feeps_alle_i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Split the last integral channel from the electron and ion differential particle flux spectra" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_split_integral_ch in module pyrfu.mms.feeps_split_integral_ch:\n", - "\n", - "feeps_split_integral_ch(inp_dataset)\n", - " This function splits the last integral channel from the FEEPS spectra,\n", - " creating 2 new DataArrays\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_dataset : xarray.Dataset\n", - " Energetic particles energy spectrum from FEEPS.\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.Dataset\n", - " Energetic particles energy spectra with the integral channel removed.\n", - " out_500kev : xarray.Dataset\n", - " Integral channel that was removed.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_split_integral_ch)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_alle_e_clean, dpf_feeps_alle_e_500kev = mms.feeps_split_integral_ch(dpf_feeps_alle_e)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_i_clean, dpf_feeps_alle_i_500kev = mms.feeps_split_integral_ch(dpf_feeps_alle_i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Remove sunlight contamination" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_remove_sun in module pyrfu.mms.feeps_remove_sun:\n", - "\n", - "feeps_remove_sun(inp_dataset)\n", - " Removes the sunlight contamination from FEEPS data.\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_dataset : xarray.Dataset\n", - " Dataset of energy spectrum of all eyes.\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.Dataset\n", - " Dataset of cleaned energy spectrum of all eyes.\n", - " \n", - " See also\n", - " --------\n", - " pyrfu.mms.get_feeps_alleyes : Read energy spectrum for all FEEPS eyes.\n", - " \n", - " Examples\n", - " --------\n", - " >>> from pyrfu import mms\n", - " \n", - " Define time interval\n", - " \n", - " >>> tint = [\"2017-07-18T13:04:00.000\", \"2017-07-18T13:07:00.000\"]\n", - " \n", - " Spacecraft index\n", - " \n", - " >>> mms_id = 2\n", - " \n", - " Load data from FEEPS\n", - " \n", - " >>> cps_i = mms.get_feeps_alleyes(\"CPSi_brst_l2\", tint, mms_id)\n", - " >>> cps_i_clean, _ = mms.feeps_split_integral_ch(cps_i)\n", - " >>> cps_i_clean_sun_removed = mms.feeps_remove_sun(cps_i_clean)\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_remove_sun)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_alle_e_clean_sun_removed = mms.feeps_remove_sun(dpf_feeps_alle_e_clean)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_i_clean_sun_removed = mms.feeps_remove_sun(dpf_feeps_alle_i_clean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute the electron and ion omni-directional flux for all 24 sensors" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_omni in module pyrfu.mms.feeps_omni:\n", - "\n", - "feeps_omni(inp_dataset)\n", - " Calculates the omni-directional FEEPS spectrogram.\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_dataset : xarray.Dataset\n", - " Dataset with all active telescopes data.\n", - " \n", - " Returns\n", - " -------\n", - " flux_omni : xarray.DataArray\n", - " Omni-directional FEEPS spectrogram.\n", - " \n", - " Notes\n", - " -----\n", - " The dataset can be raw data, but it is better to remove bad datas,\n", - " sunlight contamination and split before.\n", - " \n", - " See Also\n", - " --------\n", - " pyrfu.mms.get_feeps_alleyes, pyrfu.mms.feeps_remove_bad_data,\n", - " pyrfu.mms.feeps_split_integral_ch, pyrfu.mms.feeps_remove_sun\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_omni)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_omni_e = mms.feeps_omni(dpf_feeps_alle_e_clean_sun_removed)\n", - "\n", - "# Ion\n", - "dpf_feeps_omni_i = mms.feeps_omni(dpf_feeps_alle_i_clean_sun_removed)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creates sector-spectrograms with FEEPS data" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_sector_spec in module pyrfu.mms.feeps_sector_spec:\n", - "\n", - "feeps_sector_spec(inp_alle)\n", - " Creates sector-spectrograms with FEEPS data (particle data organized\n", - " by time and sector number)\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_alle : xarray.Dataset\n", - " Dataset of energy spectrum of all eyes.\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.Dataset\n", - " Sector-spectrograms with FEEPS data for all eyes.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_sector_spec)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 21:44:36: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/mms/feeps_sector_spec.py:54: RuntimeWarning: Mean of empty slice\n", - " sector_spec[i, s_] = np.nanmean(sensor_data[c_start:spin, :],\n", - "\n" - ] - } - ], - "source": [ - "# Electron\n", - "dpf_feeps_alle_ss_e_clean_sun_removed = mms.feeps_sector_spec(dpf_feeps_alle_e_clean_sun_removed)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_ss_i_clean_sun_removed = mms.feeps_sector_spec(dpf_feeps_alle_i_clean_sun_removed)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute the electron and ion pitch angle distribution for energies below and above 100 keV" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_pad in module pyrfu.mms.feeps_pad:\n", - "\n", - "feeps_pad(inp_dataset, b_bcs, bin_size: float = 16.3636, energy: list = None)\n", - " Compute pitch angle distribution using FEEPS data.\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_dataset : xarray.Dataset\n", - " Energy spectrum of all eyes.\n", - " b_bcs : xarray.DataArray\n", - " Time series of the magnetic field in spacecraft coordinates.\n", - " bin_size : float, optional\n", - " Width of the pitch angles bins. Default is 16.3636.\n", - " energy : array_like, optional\n", - " Energy range of particles. Default is [70., 600.]\n", - " \n", - " Returns\n", - " -------\n", - " pad : xarray.DataArray\n", - " Time series of the pitch angle distribution.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_pad)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 21:44:36: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n", - "18-Jun-21 21:44:38: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n", - "18-Jun-21 21:44:40: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n", - "18-Jun-21 21:44:42: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n", - "18-Jun-21 21:44:44: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "dpf_feeps_pad_i_070_100 = mms.feeps_pad(dpf_feeps_alle_i_clean_sun_removed, b_bcs, \n", - " energy=[70, 100])\n", - "\n", - "# Electron\n", - "dpf_feeps_pad_e_050_100 = mms.feeps_pad(dpf_feeps_alle_e_clean_sun_removed, b_bcs, \n", - " energy=[50, 100])\n", - "dpf_feeps_pad_e_100_200 = mms.feeps_pad(dpf_feeps_alle_e_clean_sun_removed, b_bcs, \n", - " energy=[100, 200])\n", - "\n", - "# Ion\n", - "dpf_feeps_pad_i_070_100 = mms.feeps_pad(dpf_feeps_alle_i_clean_sun_removed, b_bcs, \n", - " energy=[70, 100])\n", - "dpf_feeps_pad_i_100_200 = mms.feeps_pad(dpf_feeps_alle_i_clean_sun_removed, b_bcs, \n", - " energy=[100, 200])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot in a MMS SDC Quicklook fashion" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. 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" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'MMS 2')" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(7, sharex=\"all\", figsize=(8.5, 10))\n", - "f.subplots_adjust(left=.13, right=.87, bottom=.07, top=.95, hspace=0)\n", - "\n", - "plot_line(axs[0], b_gsm)\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], loc=\"upper right\", ncol=3, frameon=True)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "\n", - "axs[1], caxs1 = plot_spectr(axs[1], dpf_feeps_omni_e, yscale=\"log\", cscale=\"log\")\n", - "caxs1.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[1].set_ylabel(\"$E_e$ [keV]\")\n", - "\n", - "axs[2], caxs2 = plot_spectr(axs[2], dpf_feeps_pad_e_050_100, cscale=\"log\")\n", - "caxs2.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[2].set_ylim([0, 180])\n", - "axs[2].set_yticks([45, 90, 135])\n", - "axs[2].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], dpf_feeps_pad_e_100_200, cscale=\"log\")\n", - "caxs3.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[3].set_ylim([0, 180])\n", - "axs[3].set_yticks([45, 90, 135])\n", - "axs[3].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], dpf_feeps_omni_i[:, 1:], yscale=\"log\", cscale=\"log\")\n", - "caxs4.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[4].set_ylabel(\"$E_i$ [keV]\")\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], dpf_feeps_pad_i_070_100, cscale=\"log\", clim=[3e-1, 1e2])\n", - "caxs5.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[5].set_ylim([0, 180])\n", - "axs[5].set_yticks([45, 90, 135])\n", - "axs[5].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], dpf_feeps_pad_i_100_200, cscale=\"log\")\n", - "caxs6.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[6].set_ylim([0, 180])\n", - "axs[6].set_yticks([45, 90, 135])\n", - "axs[6].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "\n", - "f.align_ylabels(axs)\n", - "f.suptitle(f\"MMS {mms_id:d}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spin average omni-directional differential particle flux and pitch angle distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_spin_avg in module pyrfu.mms.feeps_spin_avg:\n", - "\n", - "feeps_spin_avg(flux_omni, spin_sectors)\n", - " spin-average the omni-directional FEEPS energy spectra\n", - " \n", - " Parameters\n", - " ----------\n", - " flux_omni : xarray.DataArray\n", - " Omni-direction flux.\n", - " spin_sectors : xarray.DataArray\n", - " Time series of the spin sectors.\n", - " \n", - " Returns\n", - " -------\n", - " spin_avg_flux : xarray.DataArray\n", - " Spin averaged omni-directional flux.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_spin_avg)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_omni_e_spin = mms.feeps_spin_avg(dpf_feeps_omni_e, dpf_feeps_alle_e.spinsectnum)\n", - "\n", - "# Ion\n", - "dpf_feeps_omni_i_spin = mms.feeps_spin_avg(dpf_feeps_omni_i, dpf_feeps_alle_i.spinsectnum)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_pad_spinavg in module pyrfu.mms.feeps_pad_spinavg:\n", - "\n", - "feeps_pad_spinavg(pad, spin_sectors, bin_size: float = 16.3636)\n", - " Spin-average the FEEPS pitch angle distributions.\n", - " \n", - " Parameters\n", - " ----------\n", - " pad : xarray.DataArray\n", - " Pitch angle distribution.\n", - " spin_sectors : xarray.DataArray\n", - " Time series of the spin sectors.\n", - " bin_size : float, Optional\n", - " Size of the pitch angle bins\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.DataArray\n", - " Spin averaged pitch angle distribution.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_pad_spinavg)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_pad_e_050_100_spin = mms.feeps_pad_spinavg(dpf_feeps_pad_e_050_100, dpf_feeps_alle_e.spinsectnum)\n", - "dpf_feeps_pad_e_100_200_spin = mms.feeps_pad_spinavg(dpf_feeps_pad_e_100_200, dpf_feeps_alle_e.spinsectnum)\n", - "\n", - "# Ion\n", - "dpf_feeps_pad_i_070_100_spin = mms.feeps_pad_spinavg(dpf_feeps_pad_i_070_100, dpf_feeps_alle_i.spinsectnum)\n", - "dpf_feeps_pad_i_100_200_spin = mms.feeps_pad_spinavg(dpf_feeps_pad_i_100_200, dpf_feeps_alle_i.spinsectnum)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - 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" if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'MMS 2 spin averaged')" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(7, sharex=\"all\", figsize=(8.5, 10))\n", - "f.subplots_adjust(left=.13, right=.87, bottom=.07, top=.95, hspace=0)\n", - "\n", - "plot_line(axs[0], b_gsm)\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], loc=\"upper right\", ncol=3, frameon=True)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "\n", - "axs[1], caxs1 = plot_spectr(axs[1], dpf_feeps_omni_e_spin, yscale=\"log\", cscale=\"log\")\n", - "caxs1.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[1].set_ylabel(\"$E_e$ [keV]\")\n", - "\n", - "axs[2], caxs2 = plot_spectr(axs[2], dpf_feeps_pad_e_050_100_spin, cscale=\"log\")\n", - "caxs2.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[2].set_ylim([0, 180])\n", - "axs[2].set_yticks([45, 90, 135])\n", - "axs[2].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "axs[2].text(.02, .1, \"50 keV < $E_e$ < 100 keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[2].transAxes)\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], dpf_feeps_pad_e_100_200_spin, cscale=\"log\")\n", - "caxs3.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[3].set_ylim([0, 180])\n", - "axs[3].set_yticks([45, 90, 135])\n", - "axs[3].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "axs[3].text(.02, .1, \"100 keV < $E_e$ < 200 keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[3].transAxes)\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], dpf_feeps_omni_i_spin[:, 1:], yscale=\"log\", cscale=\"log\")\n", - "caxs4.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[4].set_ylabel(\"$E_i$ [keV]\")\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], dpf_feeps_pad_i_070_100_spin, cscale=\"log\", clim=[3e-1, 1e2])\n", - "caxs5.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[5].set_ylim([0, 180])\n", - "axs[5].set_yticks([45, 90, 135])\n", - "axs[5].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "axs[5].text(.02, .1, \"70 keV < $E_e$ < 100 keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[5].transAxes)\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], dpf_feeps_pad_i_100_200_spin, cscale=\"log\")\n", - "caxs6.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[6].set_ylim([0, 180])\n", - "axs[6].set_yticks([45, 90, 135])\n", - "axs[6].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "axs[6].text(.02, .1, \"100 keV < $E_i$ < 200 keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[6].transAxes)\n", - "\n", - "f.align_ylabels(axs)\n", - "f.suptitle(f\"MMS {mms_id:d} spin averaged\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/.ipynb_checkpoints/example_mms_hpca-checkpoint.ipynb b/examples/01_mms/.ipynb_checkpoints/example_mms_hpca-checkpoint.ipynb deleted file mode 100644 index 19f3af70..00000000 --- a/examples/01_mms/.ipynb_checkpoints/example_mms_hpca-checkpoint.ipynb +++ /dev/null @@ -1,1240 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hot Plasma Composition Analyzer\n", - "author: Louis Richard\\\n", - "HPCA summary plot" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu import mms\n", - "from pyrfu.plot import plot_line, plot_spectr, make_labels" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define spacecraft index, time interval and species" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "ic = 1\n", - "mms.db_init(\"/Volumes/mms\")\n", - "tint = [\"2017-07-23T16:54:00.000\", \"2017-07-23T17:00:00.000\"]\n", - "species = {\"hplus\": \"$H^+$\", \"heplus\": \"$He^+$\", \"heplusplus\": \"$He^{2+}$\", \"oplus\": \"$O^+$\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load HPCA moments" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:18:14: Loading mms1_hpca_hplus_number_density...\n", - "09-Dec-21 11:18:15: Loading mms1_hpca_heplus_number_density...\n", - "09-Dec-21 11:18:17: Loading mms1_hpca_heplusplus_number_density...\n", - "09-Dec-21 11:18:18: Loading mms1_hpca_oplus_number_density...\n", - "09-Dec-21 11:18:20: Loading mms1_hpca_hplus_ion_bulk_velocity...\n", - "09-Dec-21 11:18:21: Loading mms1_hpca_heplus_ion_bulk_velocity...\n", - "09-Dec-21 11:18:23: Loading mms1_hpca_heplusplus_ion_bulk_velocity...\n", - "09-Dec-21 11:18:25: Loading mms1_hpca_oplus_ion_bulk_velocity...\n" - ] - } - ], - "source": [ - "n_hpca = [mms.get_data(f\"n{s}_hpca_srvy_l2\", tint, ic) for s in species.keys()]\n", - "v_xyz_hpca = [mms.get_data(f\"v{s}_dbcs_hpca_srvy_l2\", tint, ic) for s in species.keys()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load HPCA ion" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:18:28: Loading mms1_hpca_hplus_flux...\n", - "09-Dec-21 11:18:30: Loading mms1_hpca_heplus_flux...\n", - "09-Dec-21 11:18:33: Loading mms1_hpca_heplusplus_flux...\n", - "09-Dec-21 11:18:36: Loading mms1_hpca_oplus_flux...\n" - ] - } - ], - "source": [ - "flux_hpca = [mms.get_data(f\"dpf{s}_hpca_srvy_l2\", tint, ic) for s in species.keys()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load magnetic field" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:18:39: Loading mms1_fgm_b_gse_srvy_l2...\n" - ] - } - ], - "source": [ - "b_xyz = mms.get_data(\"b_gse_fgm_srvy_l2\", tint, ic)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute ion fluxes" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:18:41: :6: RuntimeWarning: Mean of empty slice\n", - " out = xr.DataArray(np.nanmean(flux.data, axis=1), coords=coords, dims=dims, attrs=flux.attrs)\n", - "\n" - ] - } - ], - "source": [ - "def calc_hpca_flux(flux):\n", - " flux.data[flux.data <= 0] = np.nan\n", - " coords = [flux.time.data, flux.ccomp.data]\n", - " dims = [\"time\", \"energy\"]\n", - "\n", - " out = xr.DataArray(np.nanmean(flux.data, axis=1), coords=coords, dims=dims, attrs=flux.attrs)\n", - " return out\n", - "\n", - "flux_hpca = [calc_hpca_flux(flux) for flux in flux_hpca]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "legend_options = dict(ncol=max([3, len(species.keys())]), frameon=True, loc=\"upper right\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - 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height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - 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" // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([,\n", - " ,\n", - " , ,\n", - " , ],\n", - " dtype=object)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(6, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(bottom=.1, top=.95, left=.15, right=.85, hspace=0)\n", - "\n", - "plot_line(axs[0], b_xyz)\n", - "axs[0].set_ylabel(\"$B_{GSE}$\" + \"\\n\" + \"[nT]\")\n", - "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\"], **legend_options)\n", - "\n", - "labels = []\n", - "for n, s in zip(n_hpca, species.keys()):\n", - " plot_line(axs[1], n, linestyle=\"-\", marker=\"o\")\n", - " labels.append(species[s])\n", - " \n", - "axs[1].set_ylabel(\"$n$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", - "axs[1].legend(labels, **legend_options)\n", - "\n", - "caxs = [None] * len(species)\n", - "for s, ax, cax, flux in zip(species.keys(), axs[2:], caxs, flux_hpca):\n", - " ax, cax = plot_spectr(ax, flux, yscale=\"log\", cscale=\"log\", cmap=\"viridis\")\n", - " ax.set_ylabel(\"$E$\" + \"\\n\" + \"[eV]\")\n", - " cax.set_ylabel(\"flux\" + \"\\n\" + \"[1/cc s sr eV]\")\n", - " ax.text(0.05, 0.1, species[s], transform=ax.transAxes)\n", - " \n", - "f.align_ylabels(axs)\n", - "make_labels(axs, [0.02, 0.85])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/.ipynb_checkpoints/example_mms_ohmslaw-checkpoint.ipynb b/examples/01_mms/.ipynb_checkpoints/example_mms_ohmslaw-checkpoint.ipynb deleted file mode 100644 index 18ed8335..00000000 --- a/examples/01_mms/.ipynb_checkpoints/example_mms_ohmslaw-checkpoint.ipynb +++ /dev/null @@ -1,1449 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Ohm's Law\n", - "Author: Louis Richard\\\n", - "Compute the terms in the generalized Ohm's law equation: Ion convection, Hall, and electron pressure divergence terms. Hall and pressure terms are computed using four-spacecraft methods. The observed electric fields and convection terms are averaged over the four spacecraft. Terms computed in GSE coordinates." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from astropy import constants\n", - "from pyrfu.mms import get_data, db_init\n", - "from pyrfu.plot import plot_line\n", - "from pyrfu.pyrf import (resample, avg_4sc, extend_tint, cross, c_4_grad, \n", - " ts_vec_xyz, c_4_j)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define time interval and data path" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "db_init(\"/Volumes/mms\")\n", - "tint = [\"2015-10-30T05:15:40.000\", \"2015-10-30T05:15:55.000\"]\n", - "tint_long = extend_tint(tint, [-60, 60])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load all data and constants" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define constants" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "qe = constants.e.value\n", - "me = constants.m_e.value" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load FPI data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:10:26: Loading mms1_des_numberdensity_brst...\n", - "09-Dec-21 12:10:29: Loading mms2_des_numberdensity_brst...\n", - "09-Dec-21 12:10:31: Loading mms3_des_numberdensity_brst...\n", - "09-Dec-21 12:10:33: Loading mms4_des_numberdensity_brst...\n", - "09-Dec-21 12:10:35: Loading mms1_des_bulkv_gse_brst...\n", - "09-Dec-21 12:10:41: Loading mms2_des_bulkv_gse_brst...\n", - "09-Dec-21 12:10:46: Loading mms3_des_bulkv_gse_brst...\n", - "09-Dec-21 12:10:52: Loading mms4_des_bulkv_gse_brst...\n", - "09-Dec-21 12:10:57: Loading mms1_des_prestensor_gse_brst...\n", - "09-Dec-21 12:11:06: Loading mms2_des_prestensor_gse_brst...\n", - "09-Dec-21 12:11:16: Loading mms3_des_prestensor_gse_brst...\n", - "09-Dec-21 12:11:25: Loading mms4_des_prestensor_gse_brst...\n", - "09-Dec-21 12:11:34: Loading mms1_dis_numberdensity_brst...\n", - "09-Dec-21 12:11:36: Loading mms2_dis_numberdensity_brst...\n", - "09-Dec-21 12:11:38: Loading mms3_dis_numberdensity_brst...\n", - "09-Dec-21 12:11:40: Loading mms4_dis_numberdensity_brst...\n", - "09-Dec-21 12:11:42: Loading mms1_dis_bulkv_gse_brst...\n", - "09-Dec-21 12:11:47: Loading mms2_dis_bulkv_gse_brst...\n", - "09-Dec-21 12:11:52: Loading mms3_dis_bulkv_gse_brst...\n", - "09-Dec-21 12:11:56: Loading mms4_dis_bulkv_gse_brst...\n" - ] - } - ], - "source": [ - "n_mms_e = [get_data(\"ne_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "v_mms_e = [get_data(\"ve_gse_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "p_mms_e = [get_data(\"pe_gse_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "\n", - "n_mms_i = [get_data(\"ni_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "v_mms_i = [get_data(\"vi_gse_fpi_brst_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load FGM data" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:12:01: Loading mms1_fgm_b_gse_brst_l2...\n", - "09-Dec-21 12:12:03: Loading mms2_fgm_b_gse_brst_l2...\n", - "09-Dec-21 12:12:04: Loading mms3_fgm_b_gse_brst_l2...\n", - "09-Dec-21 12:12:06: Loading mms4_fgm_b_gse_brst_l2...\n" - ] - } - ], - "source": [ - "b_mms = [get_data(\"b_gse_fgm_brst_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load spacecraft position" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:12:08: Loading mms1_mec_r_gse...\n", - "09-Dec-21 12:12:14: Loading mms2_mec_r_gse...\n", - "09-Dec-21 12:12:20: Loading mms3_mec_r_gse...\n", - "09-Dec-21 12:12:26: Loading mms4_mec_r_gse...\n" - ] - } - ], - "source": [ - "r_mms = [get_data(\"r_gse_mec_srvy_l2\", tint_long, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load Electric field" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:12:32: Loading mms1_edp_dce_gse_brst_l2...\n", - "09-Dec-21 12:12:33: Loading mms2_edp_dce_gse_brst_l2...\n", - "09-Dec-21 12:12:35: Loading mms3_edp_dce_gse_brst_l2...\n", - "09-Dec-21 12:12:37: Loading mms4_edp_dce_gse_brst_l2...\n" - ] - } - ], - "source": [ - "e_mms = [get_data(\"e_gse_edp_brst_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resample and compute averages" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:12:38: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "n_mms_e = [resample(n_e, n_mms_e[0]) for n_e in n_mms_e]\n", - "v_mms_e = [resample(v_xyz_e, n_mms_e[0]) for v_xyz_e in v_mms_e]\n", - "p_mms_e = [resample(p_xyz_e, n_mms_e[0]) for p_xyz_e in p_mms_e]\n", - "n_mms_i = [resample(n_i, n_mms_e[0]) for n_i in n_mms_i]\n", - "v_mms_i = [resample(v_xyz_i, n_mms_e[0]) for v_xyz_i in v_mms_i]\n", - "r_mms = [resample(r_xyz, n_mms_e[0]) for r_xyz in r_mms]\n", - "b_mms = [resample(b_xyz, n_mms_e[0]) for b_xyz in b_mms]\n", - "e_mms = [resample(e_xyz, n_mms_e[0]) for e_xyz in e_mms]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "n_e_avg = avg_4sc(n_mms_e)\n", - "b_xyz_avg = avg_4sc(b_mms)\n", - "e_xyz_avg = avg_4sc(e_mms)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute convection terms" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute ion convection term (MMS average)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "evxb_mms_i = [1e-3 * cross(v_xyz_i, b_xyz) for v_xyz_i, b_xyz in zip(v_mms_i, b_mms)]\n", - "evxb_xyz_i = avg_4sc(evxb_mms_i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute electron convection term (MMS average)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "evxb_mms_e = [1e-3 * cross(v_xyz_e, b_xyz) for v_xyz_e, b_xyz in zip(v_mms_e, b_mms)]\n", - "evxb_xyz_e = avg_4sc(evxb_mms_e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute pressure divergence term" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "p_mms_xx = [1e-9 * p_xyz[:, 0, 0] for p_xyz in p_mms_e]\n", - "p_mms_yy = [1e-9 * p_xyz[:, 1, 1] for p_xyz in p_mms_e]\n", - "p_mms_zz = [1e-9 * p_xyz[:, 2, 2] for p_xyz in p_mms_e]\n", - "p_mms_xy = [1e-9 * p_xyz[:, 0, 1] for p_xyz in p_mms_e]\n", - "p_mms_xz = [1e-9 * p_xyz[:, 0, 2] for p_xyz in p_mms_e]\n", - "p_mms_yz = [1e-9 * p_xyz[:, 1, 2] for p_xyz in p_mms_e]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "ep_xyz_xx = c_4_grad(r_mms, p_mms_xx, \"grad\")\n", - "ep_xyz_yy = c_4_grad(r_mms, p_mms_yy, \"grad\")\n", - "ep_xyz_zz = c_4_grad(r_mms, p_mms_zz, \"grad\")\n", - "ep_xyz_xy = c_4_grad(r_mms, p_mms_xy, \"grad\")\n", - "ep_xyz_xz = c_4_grad(r_mms, p_mms_xz, \"grad\")\n", - "ep_xyz_yz = c_4_grad(r_mms, p_mms_yz, \"grad\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "ep_x = -(ep_xyz_xx[:, 0] + ep_xyz_xy[:, 1] + ep_xyz_xz[:, 2]) / (n_e_avg * 1e6 * qe)\n", - "ep_y = -(ep_xyz_xy[:, 0] + ep_xyz_yy[:, 1] + ep_xyz_yz[:, 2]) / (n_e_avg * 1e6 * qe)\n", - "ep_z = -(ep_xyz_xz[:, 0] + ep_xyz_yz[:, 1] + ep_xyz_zz[:, 2]) / (n_e_avg * 1e6 * qe)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "ep_xyz = ts_vec_xyz(ep_xyz_xx.time.data, np.vstack([ep_x.data, ep_y.data, ep_z.data]).T)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Hall term and current density using curlometer" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "j_xyz, div_b, b_xyz, jxb_xyz, div_t_shear, div_pb = c_4_j(r_mms, b_mms)\n", - "jxb_xyz.data /= n_e_avg.data[:, None] * qe * 1e6\n", - "jxb_xyz.data *= 1e3\n", - "j_xyz.data *= 1e9" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "e_lhs = ts_vec_xyz(e_xyz_avg.time.data, e_xyz_avg.data - evxb_xyz_i.data)\n", - "e_rhs = ts_vec_xyz(e_xyz_avg.time.data,jxb_xyz.data + ep_xyz.data);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot figure" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "legend_options = dict(ncol=3, loc=\"upper right\", frameon=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'MMS - 4 Spacecraft average')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(5, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(hspace=0, left=.15, right=.85)\n", - "\n", - "plot_line(axs[0], b_xyz_avg)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "axs[0].legend([\"$B_{x}$\",\"$B_{y}$\",\"$B_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[1], j_xyz)\n", - "axs[1].set_ylabel(\"$J$ [nA m$^{-2}$]\")\n", - "axs[1].legend([\"$J_{x}$\",\"$J_{y}$\",\"$J_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[2], e_xyz_avg)\n", - "axs[2].set_ylabel(\"$E$ [mV m$^{-1}$]\")\n", - "axs[2].legend([\"$E_{x}$\",\"$E_{y}$\",\"$E_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[3], e_xyz_avg[:, 0])\n", - "plot_line(axs[3], jxb_xyz[:, 0])\n", - "plot_line(axs[3], evxb_xyz_i[:, 0])\n", - "plot_line(axs[3], ep_xyz[:, 0])\n", - "plot_line(axs[3], evxb_xyz_e[:, 0])\n", - "axs[3].set_ylabel(\"$E_x$ [mV m$^{-1}$]\")\n", - "labels = ['$E$','$J \\\\times B/q_{e}n$','$-V_{i} \\\\times B$','$-\\\\nabla \\\\cdot P_{e}/q_{e}n$','$-V_{e} \\\\times B$']\n", - "axs[3].legend(labels, **legend_options)\n", - "\n", - "plot_line(axs[4], e_lhs[:, 0], color=\"k\")\n", - "plot_line(axs[4], e_rhs[:, 0], color=\"tab:red\")\n", - "axs[4].set_ylabel(\"$E_x$ [mV m$^{-1}$]\")\n", - "labels = ['$E+V_{i} \\\\times B$', '$J \\\\times B/q_{e}n - \\\\nabla \\cdot P_{e}/q_{e}n$']\n", - "axs[4].legend(labels, **legend_options)\n", - "\n", - "axs[0].set_title('MMS - 4 Spacecraft average')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/.ipynb_checkpoints/example_mms_particle_deflux-checkpoint.ipynb b/examples/01_mms/.ipynb_checkpoints/example_mms_particle_deflux-checkpoint.ipynb deleted file mode 100644 index 83257a1b..00000000 --- a/examples/01_mms/.ipynb_checkpoints/example_mms_particle_deflux-checkpoint.ipynb +++ /dev/null @@ -1,1373 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Particle Differential Energy Fluxes\n", - "author: Louis Richard\\\n", - "Load brst particle distributions and convert to differential energy fluxes. Plots electron and ion fluxes and electron anisotropies." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu import mms\n", - "from pyrfu.pyrf import norm, resample\n", - "from pyrfu.plot import plot_line, plot_spectr, make_labels\n", - "from astropy import constants" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define data path, spacecraft index and time interval" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "mms.db_init(\"/Volumes/mms\")\n", - "ic = 3 # Spacecraft number\n", - "tint = [\"2015-10-30T05:15:20.000\", \"2015-10-30T05:16:20.000\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Particle distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:37:17: Loading mms3_dis_dist_brst...\n", - "09-Dec-21 11:37:23: Loading mms3_des_dist_brst...\n" - ] - } - ], - "source": [ - "vdf_i, vdf_e = [mms.get_data(f\"pd{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Particle moments" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:37:35: Loading mms3_dis_numberdensity_brst...\n", - "09-Dec-21 11:37:37: Loading mms3_des_numberdensity_brst...\n", - "09-Dec-21 11:37:38: Loading mms3_dis_bulkv_gse_brst...\n", - "09-Dec-21 11:37:42: Loading mms3_des_bulkv_gse_brst...\n", - "09-Dec-21 11:37:48: Loading mms3_dis_temptensor_gse_brst...\n", - "09-Dec-21 11:37:55: Loading mms3_des_temptensor_gse_brst...\n" - ] - } - ], - "source": [ - "n_i, n_e = [mms.get_data(f\"n{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]\n", - "v_xyz_i, v_xyz_e = [mms.get_data(f\"v{s}_gse_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]\n", - "t_xyz_i, t_xyz_e = [mms.get_data(f\"t{s}_gse_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Other variables" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:38:02: Loading mms3_fgm_b_dmpa_brst_l2...\n", - "09-Dec-21 11:38:04: Loading mms3_fgm_b_gse_brst_l2...\n", - "09-Dec-21 11:38:05: Loading mms3_edp_dce_gse_brst_l2...\n", - "09-Dec-21 11:38:07: Loading mms3_edp_scpot_brst_l2...\n", - "09-Dec-21 11:38:08: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "b_xyz, b_gse = [mms.get_data(f\"b_{cs}_fgm_brst_l2\", tint, ic) for cs in [\"dmpa\", \"gse\"]]\n", - "e_xyz = mms.get_data(\"e_gse_edp_brst_l2\", tint, ic)\n", - "scpot = mms.get_data(\"v_edp_brst_l2\", tint, ic)\n", - "scpot = resample(scpot, n_e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute moments " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "qe = constants.e.value\n", - "mp = constants.m_p.value\n", - "v_av = 0.5 * mp * (1e3 * norm(v_xyz_i)) ** 2 / qe" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute parallel and perpendicular electron and ion temperatures" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:38:08: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n" - ] - } - ], - "source": [ - "t_fac_i, t_fac_e = [mms.rotate_tensor(t_xyz, \"fac\", b_xyz, \"pp\") for t_xyz in [t_xyz_i, t_xyz_e]]\n", - "\n", - "t_para_i, t_para_e = [t_fac[:, 0, 0] for t_fac in [t_fac_i, t_fac_e]]\n", - "t_perp_i, t_perp_e = [t_fac[:, 1, 1] for t_fac in [t_fac_i, t_fac_e]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Differential Energy Fluxes" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def_omni_i, def_omni_e = [mms.vdf_omni(mms.psd2def(vdf)) for vdf in [vdf_i, vdf_e]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Pitch-Angle Distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined number of pitch angles.\n" - ] - } - ], - "source": [ - "def_pad_e = mms.psd2def(mms.get_pitch_angle_dist(vdf_e, b_xyz, tint, angles=13))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute parallel/anti-parallel and parallel+anti-parallel/perpandicular" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:38:18: :5: RuntimeWarning: divide by zero encountered in true_divide\n", - " psd_parapar = def_pad_e.data.data[:, :, 0] / def_pad_e.data.data[:, :, -1]\n", - "\n", - "09-Dec-21 11:38:18: :5: RuntimeWarning: invalid value encountered in true_divide\n", - " psd_parapar = def_pad_e.data.data[:, :, 0] / def_pad_e.data.data[:, :, -1]\n", - "\n", - "09-Dec-21 11:38:18: :6: RuntimeWarning: divide by zero encountered in true_divide\n", - " psd_parperp = (def_pad_e.data.data[:, :, 0] + def_pad_e.data.data[:, :, -1]) / (2 * def_pad_e.data.data[:, :, 7])\n", - "\n", - "09-Dec-21 11:38:18: :6: RuntimeWarning: invalid value encountered in true_divide\n", - " psd_parperp = (def_pad_e.data.data[:, :, 0] + def_pad_e.data.data[:, :, -1]) / (2 * def_pad_e.data.data[:, :, 7])\n", - "\n" - ] - } - ], - "source": [ - "def calc_parapar_parperp(pad):\n", - " coords = [pad.time.data, pad.energy.data[0, :]]\n", - " dims = [\"time\", \"energy\"]\n", - "\n", - " psd_parapar = def_pad_e.data.data[:, :, 0] / def_pad_e.data.data[:, :, -1]\n", - " psd_parperp = (def_pad_e.data.data[:, :, 0] + def_pad_e.data.data[:, :, -1]) / (2 * def_pad_e.data.data[:, :, 7])\n", - " \n", - " psd_parapar = xr.DataArray(psd_parapar, coords=coords, dims=dims)\n", - " psd_parperp = xr.DataArray(psd_parperp, coords=coords, dims=dims)\n", - " \n", - " return psd_parapar, psd_parperp\n", - "\n", - "vdf_parapar_e, vdf_parperp_e = calc_parapar_parperp(def_pad_e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "legend_options = dict(frameon=True, loc=\"upper right\", ncol=3)\n", - "e_lim_i = [min(def_omni_i.energy.data), max(def_omni_i.energy.data)]\n", - "e_lim_e = [min(def_omni_e.energy.data), max(def_omni_e.energy.data)]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; 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height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(8, sharex=\"all\", figsize=(6.5, 11))\n", - "f.subplots_adjust(bottom=.1, top=.95, left=.15, right=.85, hspace=0)\n", - "\n", - "plot_line(axs[0], b_xyz)\n", - "axs[0].set_ylabel(\"$B_{DMPA}$\" + \"\\n\" + \"[nT]\")\n", - "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[1], v_xyz_i)\n", - "axs[1].set_ylabel(\"$V_{i}$\" + \"\\n\" + \"[km s$^{-1}$]\")\n", - "axs[1].legend([\"$V_{x}$\", \"$V_{y}$\", \"$V_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[2], n_i, color=\"tab:blue\")\n", - "plot_line(axs[2], n_e, color=\"tab:red\")\n", - "axs[2].set_yscale(\"log\")\n", - "axs[2].set_ylabel(\"$n$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", - "axs[2].legend([\"$n_i$\", \"$n_e$\"], **legend_options)\n", - "\n", - "plot_line(axs[3], e_xyz)\n", - "axs[3].set_ylabel(\"$E$\" + \"\\n\" + \"[mV m$^{-1}$]\")\n", - "axs[3].legend([\"$E_{x}$\", \"$E_{y}$\", \"$E_{z}$\"], **legend_options)\n", - "\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], def_omni_i, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[4].set_ylabel(\"$E_i$\" + \"\\n\" + \"[eV]\")\n", - "caxs4.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[4].set_ylim(e_lim_i)\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], def_omni_e, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "plot_line(axs[5], scpot)\n", - "plot_line(axs[5], t_para_e)\n", - "plot_line(axs[5], t_perp_e)\n", - "axs[5].set_ylabel(\"$E_e$\" + \"\\n\" + \"[eV]\")\n", - "caxs5.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[5].legend([\"$\\phi$\", \"$T_{||}$\",\"$T_{\\perp}$\"], **legend_options)\n", - "axs[5].set_ylim(e_lim_e)\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], vdf_parapar_e, yscale=\"log\", cscale=\"log\", clim=[1e-2, 1e2], cmap=\"RdBu_r\")\n", - "plot_line(axs[6], scpot)\n", - "axs[6].set_ylabel(\"$E_e$\" + \"\\n\" + \"[eV]\")\n", - "caxs6.set_ylabel(\"$\\\\frac{f_{||+}}{f_{||-}}$\" + \"\\n\" + \" \")\n", - "axs[6].legend([\"$V_{SC}$\", \"$T_{||}$\",\"$T_{\\perp}$\"], **legend_options)\n", - "axs[6].set_ylim(e_lim_e)\n", - "\n", - "axs[7], caxs7 = plot_spectr(axs[7], vdf_parperp_e, yscale=\"log\", cscale=\"log\", clim=[1e-2, 1e2], cmap=\"RdBu_r\")\n", - "plot_line(axs[7], scpot)\n", - "axs[7].set_ylabel(\"$E_e$\" + \"\\n\" + \"[eV]\")\n", - "caxs7.set_ylabel(\"$\\\\frac{f_{||+}+f_{||-}}{2 f_{\\perp}}$\" + \"\\n\" + \" \")\n", - "axs[7].legend([\"$V_{SC}$\"], **legend_options)\n", - "axs[7].set_ylim(e_lim_e)\n", - "\n", - "make_labels(axs, [0.02, 0.85])\n", - "\n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/.ipynb_checkpoints/example_mms_particle_distributions-checkpoint.ipynb b/examples/01_mms/.ipynb_checkpoints/example_mms_particle_distributions-checkpoint.ipynb deleted file mode 100644 index e8e27822..00000000 --- a/examples/01_mms/.ipynb_checkpoints/example_mms_particle_distributions-checkpoint.ipynb +++ /dev/null @@ -1,2676 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Particle Distributions\n", - "author: Louis Richard\\\n", - "Example showing how to you can work with particle distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import xarray as xr\n", - "import matplotlib.pylab as pl\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu import mms, pyrf\n", - "from pyrfu.plot import plot_line, plot_spectr, plot_projection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define spacecraft index, time interval and data path" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "mms_id = 3\n", - "tint = [\"2015-12-02T01:14:15.000\", \"2015-12-02T01:15:13.000\"]\n", - "mms.db_init(\"/Volumes/mms\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Velocity Distribution Functions (VDFs)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:07:47: Loading mms3_dis_dist_brst...\n", - "09-Dec-21 12:07:53: Loading mms3_des_dist_brst...\n" - ] - } - ], - "source": [ - "vdf_i = mms.get_data(\"pdi_fpi_brst_l2\", tint, mms_id)\n", - "vdf_e = mms.get_data(\"pde_fpi_brst_l2\", tint, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load supporting information" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:04: Loading mms3_fgm_b_dmpa_brst_l2...\n", - "09-Dec-21 12:08:06: Loading mms3_edp_dce_dsl_brst_l2...\n", - "09-Dec-21 12:08:08: Loading mms3_edp_scpot_brst_l2...\n" - ] - } - ], - "source": [ - "b_xyz = mms.get_data(\"b_dmpa_fgm_brst_l2\", tint, mms_id)\n", - "e_xyz = mms.get_data(\"e_dsl_edp_brst_l2\", tint, mms_id)\n", - "sc_pot = mms.get_data(\"v_edp_brst_l2\", tint, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example operations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Omnidirectional differential energy flux" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "vdf_e_omni = mms.vdf_omni(vdf_e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Construt pitchangle distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:10: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined number of pitch angles.\n" - ] - } - ], - "source": [ - "vdf_e_pad = mms.get_pitch_angle_dist(vdf_e, b_xyz, tint=tint, angles=24)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Limit energy range" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective eint = [10.96, 191.15]\n" - ] - } - ], - "source": [ - "vdf_e_lowen = mms.vdf_elim(vdf_e, [0, 200])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Change units to differential energy flux" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "vdf_e_deflux = mms.psd2def(vdf_e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Change units to particle energy flux" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "vdf_e_dpflux = mms.psd2dpf(vdf_e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Resample energy to 64 energy levels, reduces the time resolution" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "vdf_e_e64 = mms.vdf_to_e64(vdf_e)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:25: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective eint = [20.40, 191.15]\n", - "notice : User defined number of pitch angles.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:28: :2: RuntimeWarning: Mean of empty slice\n", - " vdf_e_pa_lowen_spectr = xr.DataArray(np.nanmean(vdf_e_pa_lowen.data, axis=1),\n", - "\n", - "09-Dec-21 12:08:28: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective eint = [216.45, 1790.88]\n", - "notice : User defined number of pitch angles.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:31: :7: RuntimeWarning: Mean of empty slice\n", - " vdf_e_pa_miden_spectr = xr.DataArray(np.nanmean(vdf_e_pa_miden.data, axis=1),\n", - "\n" - ] - } - ], - "source": [ - "vdf_e_pa_lowen = mms.get_pitch_angle_dist(mms.vdf_elim(vdf_e_e64, [20, 200]), b_xyz, tint=tint, angles=18)\n", - "vdf_e_pa_lowen_spectr = xr.DataArray(np.nanmean(vdf_e_pa_lowen.data, axis=1), \n", - " coords=[vdf_e_pa_lowen.time.data, vdf_e_pa_lowen.theta.data[0, :]], \n", - " dims=[\"time\", \"theta\"])\n", - "\n", - "vdf_e_pa_miden = mms.get_pitch_angle_dist(mms.vdf_elim(vdf_e_e64, [200, 2000]), b_xyz, tint=tint, angles=18)\n", - "vdf_e_pa_miden_spectr = xr.DataArray(np.nanmean(vdf_e_pa_miden.data, axis=1), \n", - " coords=[vdf_e_pa_miden.time.data, vdf_e_pa_miden.theta.data[0, :]], \n", - " dims=[\"time\", \"theta\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:32: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective eint = [20.40, 191.15]\n", - "notice : User defined number of pitch angles.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:35: :22: RuntimeWarning: Mean of empty slice\n", - " vdf_e_pa_lowen_spectr = xr.DataArray(np.nanmean(vdf_e_pa_lowen.data, axis=1),\n", - "\n", - "09-Dec-21 12:08:35: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective eint = [216.45, 1790.88]\n", - "notice : User defined number of pitch angles.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:38: :38: RuntimeWarning: Mean of empty slice\n", - " vdf_e_pa_miden_spectr = xr.DataArray(np.nanmean(vdf_e_pa_miden.data, axis=1),\n", - "\n", - "09-Dec-21 12:08:39: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined pitch angle limits.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:42: :54: RuntimeWarning: Mean of empty slice\n", - " vdf_e_lowan_spectr = xr.DataArray(np.nanmean(vdf_e_lowan.data, axis=2),\n", - "\n", - "09-Dec-21 12:08:42: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined pitch angle limits.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:46: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined pitch angle limits.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:49: :84: RuntimeWarning: Mean of empty slice\n", - " vdf_e_higan_spectr = xr.DataArray(np.nanmean(vdf_e_higan.data, axis=2),\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.03, 0.1, '165$^\\\\circ$ < $\\\\theta$ < 180$^\\\\circ$')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "\n", - "n_panels = 7;\n", - "f, axs = plt.subplots(n_panels, sharex=\"all\", figsize=(8.5, 11))\n", - "f.subplots_adjust(hspace=0, left=.13, right=.87, bottom=.05, top=.95)\n", - "\n", - "plot_line(axs[0], b_xyz)\n", - "plot_line(axs[0], pyrf.norm(b_xyz))\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\", \"$|B|$\"], bbox_to_anchor=(1, 1.05))\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "axs[0].set_title(f\"MMS{mms_id:d}\")\n", - "\n", - "\n", - "axs[1], caxs1 = plot_spectr(axs[1], mms.vdf_omni(vdf_e), yscale=\"log\", \n", - " cscale=\"log\", cmap=\"jet\")\n", - "axs[1].set_yticks(np.logspace(1, 4, 4))\n", - "caxs1.set_ylabel(\"PSD\" + \"\\n\" + \"[s$^{3}$ cm$^{-6}$]\")\n", - "axs[1].set_ylabel(\"$E_e$ [eV]\")\n", - "\n", - "e_lim = [20, 200]\n", - "vdf_e_pa_lowen = mms.get_pitch_angle_dist(mms.vdf_elim(vdf_e_e64, e_lim), b_xyz, tint=tint, angles=18)\n", - "vdf_e_pa_lowen_spectr = xr.DataArray(np.nanmean(vdf_e_pa_lowen.data, axis=1), \n", - " coords=[vdf_e_pa_lowen.time.data, vdf_e_pa_lowen.theta.data[0, :]], \n", - " dims=[\"time\", \"theta\"])\n", - "\n", - "axs[2], caxs2 = plot_spectr(axs[2], vdf_e_pa_lowen_spectr, \n", - " cscale=\"log\", cmap=\"jet\")\n", - "axs[2].set_yticks([0, 45, 90, 135])\n", - "caxs2.set_ylabel(\"PSD\" + \"\\n\" + \"[s$^{3}$ cm$^{-6}$]\")\n", - "axs[2].set_ylabel(\"$\\\\theta$ [deg.]\")\n", - "\n", - "axs[2].text(.03, .1, f\"{e_lim[0]:d} < $E_e$ < {e_lim[1]:d} eV\", transform=axs[2].transAxes,\n", - " bbox=dict(boxstyle=\"square\", ec=(1., 1., 1.), fc=(1., 1., 1.)))\n", - "\n", - "\n", - "e_lim = [200, 2000]\n", - "vdf_e_pa_miden = mms.get_pitch_angle_dist(mms.vdf_elim(vdf_e_e64, e_lim), b_xyz, tint=tint, angles=18)\n", - "vdf_e_pa_miden_spectr = xr.DataArray(np.nanmean(vdf_e_pa_miden.data, axis=1), \n", - " coords=[vdf_e_pa_miden.time.data, vdf_e_pa_miden.theta.data[0, :]], \n", - " dims=[\"time\", \"theta\"])\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], vdf_e_pa_miden_spectr, \n", - " cscale=\"log\", cmap=\"jet\")\n", - "axs[3].set_yticks([0, 45, 90, 135])\n", - "caxs3.set_ylabel(\"PSD\" + \"\\n\" + \"[s$^{3}$ cm$^{-6}$]\")\n", - "axs[3].set_ylabel(\"$\\\\theta$ [deg.]\")\n", - "\n", - "axs[3].text(.03, .1, f\"{e_lim[0]:d} < $E_e$ < {e_lim[1]:d} eV\", transform=axs[3].transAxes,\n", - " bbox=dict(boxstyle=\"square\", ec=(1., 1., 1.), fc=(1., 1., 1.)))\n", - "\n", - "\n", - "pa_lim = [0, 15]\n", - "vdf_e_lowan = mms.get_pitch_angle_dist(mms.vdf_to_e64(vdf_e), b_xyz, tint=tint, angles=pa_lim)\n", - "vdf_e_lowan_spectr = xr.DataArray(np.nanmean(vdf_e_lowan.data, axis=2), \n", - " coords=[vdf_e_lowan.time.data, vdf_e_lowan.energy.data[0, :]], \n", - " dims=[\"time\", \"energy\"])\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], vdf_e_lowan_spectr, yscale=\"log\", \n", - " cscale=\"log\", cmap=\"jet\")\n", - "caxs4.set_ylabel(\"PSD\" + \"\\n\" + \"[s$^{3}$ cm$^{-6}$]\")\n", - "axs[4].set_ylabel(\"$E_e$ [eV]\")\n", - "\n", - "axs[4].text(.03, .1, f\"{pa_lim[0]:d}$^\\\\circ$ < $\\\\theta$ < {pa_lim[1]:d}$^\\\\circ$\", \n", - " transform=axs[4].transAxes,\n", - " bbox=dict(boxstyle=\"square\", ec=(1., 1., 1.), fc=(1., 1., 1.)))\n", - "\n", - "pa_lim = [75, 105]\n", - "vdf_e_midan = mms.get_pitch_angle_dist(mms.vdf_to_e64(vdf_e), b_xyz, tint=tint, angles=pa_lim)\n", - "vdf_e_midan_spectr = xr.DataArray(np.nanmean(vdf_e_midan.data, axis=2), \n", - " coords=[vdf_e_midan.time.data, vdf_e_midan.energy.data[0, :]], \n", - " dims=[\"time\", \"energy\"])\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], vdf_e_midan_spectr, yscale=\"log\", \n", - " cscale=\"log\", cmap=\"jet\")\n", - "caxs5.set_ylabel(\"PSD\" + \"\\n\" + \"[s$^{3}$ cm$^{-6}$]\")\n", - "axs[5].set_ylabel(\"$E_e$ [eV]\")\n", - "\n", - "axs[5].text(.03, .1, f\"{pa_lim[0]:d}$^\\\\circ$ < $\\\\theta$ < {pa_lim[1]:d}$^\\\\circ$\", \n", - " transform=axs[5].transAxes,\n", - " bbox=dict(boxstyle=\"square\", ec=(1., 1., 1.), fc=(1., 1., 1.)))\n", - "\n", - "pa_lim = [165, 180]\n", - "vdf_e_higan = mms.get_pitch_angle_dist(mms.vdf_to_e64(vdf_e), b_xyz, tint=tint, angles=pa_lim)\n", - "vdf_e_higan_spectr = xr.DataArray(np.nanmean(vdf_e_higan.data, axis=2), \n", - " coords=[vdf_e_higan.time.data, vdf_e_higan.energy.data[0, :]], \n", - " dims=[\"time\", \"energy\"])\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], vdf_e_higan_spectr, yscale=\"log\", \n", - " cscale=\"log\", cmap=\"jet\")\n", - "caxs6.set_ylabel(\"PSD\" + \"\\n\" + \"[s$^{3}$ cm$^{-6}$]\")\n", - "axs[6].set_ylabel(\"$E_e$ [eV]\")\n", - "\n", - "axs[6].text(.03, .1, f\"{pa_lim[0]:d}$^\\\\circ$ < $\\\\theta$ < {pa_lim[1]:d}$^\\\\circ$\", \n", - " transform=axs[6].transAxes,\n", - " bbox=dict(boxstyle=\"square\", ec=(1., 1., 1.), fc=(1., 1., 1.)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Project distribution onto the (E, ExB), (ExB, B), (B, E)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute pitchangle distribution with 17 angles" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:49: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined number of pitch angles.\n" - ] - } - ], - "source": [ - "vdf_e_pad = mms.get_pitch_angle_dist(vdf_e, b_xyz, tint, angles=17)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Resample background magnetic field, electric field and ExB drift" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:58: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "b_0 = pyrf.resample(b_xyz, vdf_e)\n", - "e_0 = pyrf.resample(e_xyz, vdf_e)\n", - "exb = pyrf.cross(e_0, b_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - 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' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; 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position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; 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" icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:58: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/mms/vdf_projection.py:37: RuntimeWarning: invalid value encountered in arccos\n", - " if abs(np.rad2deg(np.arccos(np.dot(vec, coord_sys[:, i])))) > 1.:\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, '2015-12-02T01:14:55.176087000')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "\n", - "idx = 1339\n", - "x = e_0.data[idx, :]\n", - "y = exb.data[idx, :]\n", - "z = b_0.data[idx, :]\n", - "time = list(pyrf.datetime642iso8601(vdf_e.time.data[idx]))\n", - "\n", - "f = plt.figure(figsize=(8.5, 9))\n", - "gsp1 = f.add_gridspec(2, 3, hspace=0, bottom=.07, top=.99, left=.1, right=.9)\n", - "\n", - "gsp10 = gsp1[0, :].subgridspec(1, 3, hspace=0)\n", - "gsp11 = gsp1[1, :].subgridspec(1, 2, hspace=0)\n", - "\n", - "# Create axes in the grid spec\n", - "axs10 = [f.add_subplot(gsp10[i]) for i in range(3)]\n", - "axs11 = [f.add_subplot(gsp11[i]) for i in range(2)]\n", - "\n", - "f.subplots_adjust(wspace=.4)\n", - "v_x, v_y, f_mat = mms.vdf_projection(vdf_e, time, np.vstack([x, y, -z]), sc_pot, e_lim=15)\n", - "axs10[0], caxs10 = plot_projection(axs10[0], v_x, v_y, f_mat * 1e12, vlim=12e3, \n", - " clim=[-18, -13], cbar_pos=\"top\")\n", - "axs10[0].set_xlabel(\"$V_{E}$ [Mm s$^{-1}$]\")\n", - "axs10[0].set_ylabel(\"$V_{E\\\\times B}$ [Mm s$^{-1}$]\")\n", - "caxs10.set_xlabel(\"log$_{10} f_e$ [s$^{3}$ m$^{-6}$]\")\n", - "\n", - "v_x, v_y, f_mat = mms.vdf_projection(vdf_e, time, np.vstack([y, z, -x]), sc_pot, e_lim=15)\n", - "axs10[1], caxs11 = plot_projection(axs10[1], v_x, v_y, f_mat * 1e12, vlim=12e3, \n", - " clim=[-18, -13], cbar_pos=\"top\")\n", - "axs10[1].set_xlabel(\"$V_{E\\\\times B}$ [Mm s$^{-1}$]\")\n", - "axs10[1].set_ylabel(\"$V_{B}$ [Mm s$^{-1}$]\")\n", - "caxs11.set_xlabel(\"log$_{10} f_e$ [s$^{3}$ m$^{-6}$]\")\n", - "\n", - "v_x, v_y, f_mat = mms.vdf_projection(vdf_e, time, np.vstack([z, x, -y]), sc_pot, e_lim=15)\n", - "axs10[2], caxs12 = plot_projection(axs10[2], v_x, v_y, f_mat * 1e12, vlim=12e3, \n", - " clim=[-18, -13], cbar_pos=\"top\")\n", - "axs10[2].set_xlabel(\"$V_{B}$ [Mm s$^{-1}$]\")\n", - "axs10[2].set_ylabel(\"$V_{E}$ [Mm s$^{-1}$]\")\n", - "caxs12.set_xlabel(\"log$_{10} f_e$ [s$^{3}$ m$^{-6}$]\")\n", - "\n", - "\n", - "axs11[0].loglog(vdf_e_pad.energy.data[idx, :], vdf_e_pad.data.data[idx, :, 0], \n", - " label=\"$\\\\theta = 0$ deg.\")\n", - "axs11[0].loglog(vdf_e_pad.energy.data[idx, :], vdf_e_pad.data.data[idx, :, 9], \n", - " label=\"$\\\\theta = 90$ deg.\")\n", - "axs11[0].loglog(vdf_e_pad.energy.data[idx, :], vdf_e_pad.data.data[idx, :, -1], \n", - " label=\"$\\\\theta = 180$ deg.\")\n", - "\n", - "axs11[0].legend()\n", - "axs11[0].set_xlim([1e1, 1e3])\n", - "axs11[0].set_xlabel(\"$E_e$ [eV]\")\n", - "axs11[0].set_ylim([1e-31, 2e-26])\n", - "axs11[0].set_ylabel(\"$f_e$ [s$^{3}$ cm$^{-6}$]\")\n", - "\n", - "\n", - "colors = pl.cm.jet(np.linspace(0, 1, len(vdf_e_pad.energy[idx, :])))\n", - "for i_en in range(len(vdf_e_pad.energy[idx, :])):\n", - " axs11[1].semilogy(vdf_e_pad.theta.data[idx, :], vdf_e_pad.data.data[idx, i_en, :], \n", - " color=colors[i_en], label=f\"{vdf_e_pad.energy.data[idx, i_en]:5.2f} eV\")\n", - " \n", - "axs11[1].set_xlim([0, 180.])\n", - "axs11[1].set_xlabel(\"$\\\\theta$ [deg.]\")\n", - "axs11[1].set_ylim([1e-31, 2e-26])\n", - "axs11[1].set_ylabel(\"$f_e$ [s$^{3}$ cm$^{-6}$]\")\n", - "\n", - "axs11[1].set_xticks([0, 45, 90, 135, 180])\n", - "\n", - "f.suptitle(time[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/.ipynb_checkpoints/example_mms_particle_pad-checkpoint.ipynb b/examples/01_mms/.ipynb_checkpoints/example_mms_particle_pad-checkpoint.ipynb deleted file mode 100644 index 6581f140..00000000 --- a/examples/01_mms/.ipynb_checkpoints/example_mms_particle_pad-checkpoint.ipynb +++ /dev/null @@ -1,2429 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Particle Pitch Angle Distribution\n", - "author: Louis Richard\\\n", - "Calculate and plot electron and ion pitch angle distributions from particle brst data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu import mms\n", - "from pyrfu.plot import plot_line, plot_spectr, make_labels" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define spacecraft index, data path and time interval" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "ic = 3 # Spacecraft number\n", - "mms.db_init(\"/Volumes/mms\")\n", - "tint = [\"2015-10-30T05:15:20.000\", \"2015-10-30T05:16:20.000\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Particle distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:42:10: Loading mms3_dis_dist_brst...\n", - "09-Dec-21 11:42:15: Loading mms3_des_dist_brst...\n" - ] - } - ], - "source": [ - "# vdf_i = mms.get_data(\"pdi_fpi_brst_l2\", tint, ic)\n", - "vdf_i, vdf_e = [mms.get_data(f\"pd{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Particle energy fluxes" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:42:27: Loading mms3_dis_energyspectr_omni_brst...\n", - "09-Dec-21 11:42:30: Loading mms3_des_energyspectr_omni_brst...\n" - ] - } - ], - "source": [ - "def_omni_i, def_omni_e = [mms.get_data(f\"def{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Particle moments" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:42:33: Loading mms3_dis_numberdensity_brst...\n", - "09-Dec-21 11:42:35: Loading mms3_des_numberdensity_brst...\n", - "09-Dec-21 11:42:36: Loading mms3_dis_bulkv_gse_brst...\n", - "09-Dec-21 11:42:40: Loading mms3_des_bulkv_gse_brst...\n", - "09-Dec-21 11:42:45: Loading mms3_dis_temptensor_gse_brst...\n", - "09-Dec-21 11:42:52: Loading mms3_des_temptensor_gse_brst...\n" - ] - } - ], - "source": [ - "n_i, n_e = [mms.get_data(f\"n{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]\n", - "v_xyz_i, v_xyz_e = [mms.get_data(f\"v{s}_gse_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]\n", - "t_xyz_i, t_xyz_e = [mms.get_data(f\"t{s}_gse_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Other variables" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:42:59: Loading mms3_fgm_b_dmpa_brst_l2...\n", - "09-Dec-21 11:43:00: Loading mms3_fgm_b_gse_brst_l2...\n", - "09-Dec-21 11:43:02: Loading mms3_edp_scpot_brst_l2...\n" - ] - } - ], - "source": [ - "b_xyz, b_gse = [mms.get_data(f\"b_{cs}_fgm_brst_l2\", tint, ic) for cs in [\"dmpa\", \"gse\"]]\n", - "scpot = mms.get_data(\"v_edp_brst_l2\", tint, ic)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute parallel and perpendicular electron and ion temperatures" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:43:03: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n" - ] - } - ], - "source": [ - "t_fac_i, t_fac_e = [mms.rotate_tensor(t_xyz, \"fac\", b_xyz, \"pp\") for t_xyz in [t_xyz_i, t_xyz_e]]\n", - "\n", - "t_para_i, t_para_e = [t_fac[:, 0, 0] for t_fac in [t_fac_i, t_fac_e]]\n", - "t_perp_i, t_perp_e = [t_fac[:, 1, 1] for t_fac in [t_fac_i, t_fac_e]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute pitch-angle distributions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rebin VDFs to 64 energy channels" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "vdf_e64_i, vdf_e64_e = [mms.vdf_to_e64(vdf) for vdf in [vdf_i, vdf_e]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Electron and ion pitch angle distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined number of pitch angles.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:43:06: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined number of pitch angles.\n" - ] - } - ], - "source": [ - "pad_i, pad_e = [mms.get_pitch_angle_dist(vdf, b_xyz, tint, angles=n) for vdf, n in zip([vdf_e64_i, vdf_e64_e], [18, 24])]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PSD -> DEF" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "pad_def_i, pad_def_e = [mms.psd2def(pad) for pad in [pad_i, pad_e]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Split in low energy, middle energy and high energy groups" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:43:35: :4: RuntimeWarning: Mean of empty slice\n", - " pad_lowen = xr.DataArray(np.nanmean(pad.data[:, :idx[0], :], axis=1), coords=coords, dims=dims)\n", - "\n", - "09-Dec-21 11:43:35: :5: RuntimeWarning: Mean of empty slice\n", - " pad_miden = xr.DataArray(np.nanmean(pad.data[:, idx[0]:idx[1], :], axis=1), coords=coords, dims=dims)\n", - "\n", - "09-Dec-21 11:43:35: :6: RuntimeWarning: Mean of empty slice\n", - " pad_higen = xr.DataArray(np.nanmean(pad.data[:, idx[1]:, :], axis=1), coords=coords, dims=dims)\n", - "\n" - ] - } - ], - "source": [ - "def split_energy(pad, idx):\n", - " coords = [pad.time.data, pad.theta.data[0, :]]\n", - " dims = [\"time\", \"theta\"]\n", - " pad_lowen = xr.DataArray(np.nanmean(pad.data[:, :idx[0], :], axis=1), coords=coords, dims=dims)\n", - " pad_miden = xr.DataArray(np.nanmean(pad.data[:, idx[0]:idx[1], :], axis=1), coords=coords, dims=dims)\n", - " pad_higen = xr.DataArray(np.nanmean(pad.data[:, idx[1]:, :], axis=1), coords=coords, dims=dims)\n", - " \n", - " energy = pad.energy.data[0, :]\n", - " e_int = {\"lowen\": \"{:6.2f} eV - {:6.2f} eV\".format(energy[0], energy[idx[0] - 1]),\n", - " \"miden\": \"{:6.2f} eV - {:6.2f} eV\".format(energy[idx[0]], energy[idx[1] - 1]),\n", - " \"higen\": \"{:6.2f} eV - {:6.2f} eV\".format(energy[idx[1]], energy[-1])}\n", - " return pad_lowen, pad_miden, pad_higen, e_int\n", - "\n", - "pad_lowen_i, pad_miden_i, pad_higen_i, e_int_i = split_energy(pad_def_i, [21, 42])\n", - "pad_lowen_e, pad_miden_e, pad_higen_e, e_int_e = split_energy(pad_def_e, [21, 42])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "legend_options = dict(frameon=True, loc=\"upper right\", ncol=3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot Ion data" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(7, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(bottom=.1, top=.95, left=.15, right=.85, hspace=0)\n", - "\n", - "plot_line(axs[0], b_xyz)\n", - "axs[0].set_ylabel(\"$B_{DMPA}$\" + \"\\n\" + \"[nT]\")\n", - "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[1], n_i)\n", - "axs[1].set_ylabel(\"$n_{i}$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", - "\n", - "plot_line(axs[2], v_xyz_i)\n", - "axs[2].set_ylabel(\"$V_{i}$\" + \"\\n\" + \"[km s$^{-1}$]\")\n", - "axs[2].legend([\"$V_{x}$\", \"$V_{y}$\", \"$V_{z}$\"], **legend_options)\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], def_omni_i, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "plot_line(axs[3], t_para_i)\n", - "plot_line(axs[3], t_perp_i)\n", - "axs[3].set_ylabel(\"$E_i$\" + \"\\n\" + \"[eV]\")\n", - "caxs3.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[3].legend([\"$T_{||}$\",\"$T_{\\perp}$\"], **legend_options)\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], pad_lowen_i, cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[4].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "caxs4.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[4].text(0.05, 0.1, e_int_i[\"lowen\"], transform=axs[4].transAxes)\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], pad_miden_i, cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[5].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "caxs5.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[5].text(0.05, 0.1, e_int_i[\"miden\"], transform=axs[5].transAxes)\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], pad_higen_i, cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[6].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "caxs6.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[6].text(0.05, 0.1, e_int_i[\"higen\"], transform=axs[6].transAxes)\n", - "\n", - "make_labels(axs, [0.02, 0.85])\n", - "\n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot Electron data" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(7, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(bottom=.1, top=.95, left=.15, right=.85, hspace=0)\n", - "\n", - "plot_line(axs[0], b_xyz)\n", - "axs[0].set_ylabel(\"$B_{DMPA}$\" + \"\\n\" + \"[nT]\")\n", - "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[1], n_e)\n", - "axs[1].set_ylabel(\"$n_{e}$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", - "\n", - "plot_line(axs[2], v_xyz_e)\n", - "axs[2].set_ylabel(\"$V_{e}$\" + \"\\n\" + \"[km s$^{-1}$]\")\n", - "axs[2].legend([\"$V_{x}$\", \"$V_{y}$\", \"$V_{z}$\"], **legend_options)\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], def_omni_e, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "plot_line(axs[3], t_para_e)\n", - "plot_line(axs[3], t_perp_e)\n", - "axs[3].set_ylabel(\"$E_e$\" + \"\\n\" + \"[eV]\")\n", - "caxs3.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[3].legend([\"$T_{||}$\",\"$T_{\\perp}$\"], **legend_options)\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], pad_lowen_e, cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[4].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "caxs4.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[4].text(0.05, 0.1, e_int_e[\"lowen\"], transform=axs[4].transAxes)\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], pad_miden_e, cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[5].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "caxs5.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[5].text(0.05, 0.1, e_int_e[\"miden\"], transform=axs[5].transAxes)\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], pad_higen_e, cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[6].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "caxs6.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[6].text(0.05, 0.1, e_int_e[\"higen\"], transform=axs[6].transAxes)\n", - "\n", - "make_labels(axs, [0.02, 0.85])\n", - "\n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/.ipynb_checkpoints/example_mms_polarizationanalysis-checkpoint.ipynb b/examples/01_mms/.ipynb_checkpoints/example_mms_polarizationanalysis-checkpoint.ipynb deleted file mode 100644 index a2a93334..00000000 --- a/examples/01_mms/.ipynb_checkpoints/example_mms_polarizationanalysis-checkpoint.ipynb +++ /dev/null @@ -1,1309 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Polarization Analysis\n", - "author: Louis Richard\\\n", - "Perform polarization analysis on burst mode electric and magnetic fields. Plots spectrograms, ellipticity, wave-normal angle, planarity, degree of polarization (DOP), phase speed, and normalized Poynting flux along B. Time selections should not be too long (less than 20 seconds), otherwise the analysis will be very slow. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from astropy import constants\n", - "from pyrfu.mms import get_data, db_init\n", - "from pyrfu.plot import plot_line, plot_spectr\n", - "from pyrfu.pyrf import extend_tint, norm, ebsp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define time interval and spacecraft index" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "db_init(\"/Volumes/mms\")\n", - "tint = [\"2015-10-30T05:15:42.000\", \"2015-10-30T05:15:54.00\"]\n", - "tint_long = extend_tint(tint, [-100, 100])\n", - "ic = 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:45:45: Loading mms3_mec_r_gse...\n", - "09-Dec-21 11:45:50: Loading mms3_fgm_b_gse_brst_l2...\n", - "09-Dec-21 11:45:51: Loading mms3_edp_dce_gse_brst_l2...\n", - "09-Dec-21 11:45:52: Loading mms3_scm_acb_gse_scb_brst_l2...\n" - ] - } - ], - "source": [ - "r_xyz = get_data(\"r_gse_mec_srvy_l2\", tint_long, ic)\n", - "b_xyz = get_data(\"b_gse_fgm_brst_l2\", tint, ic)\n", - "e_xyz = get_data(\"e_gse_edp_brst_l2\", tint, ic)\n", - "b_scm = get_data(\"b_gse_scm_brst_l2\", tint, ic)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute electron cylotron frequency" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "me = constants.m_e.value\n", - "qe = constants.e.value\n", - "b_si = norm(b_xyz) * 1e-9\n", - "w_ce = qe * b_si / me\n", - "f_ce = w_ce / (2 * np.pi)\n", - "f_ce_01 = f_ce * .1\n", - "f_ce_05 = f_ce * .5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Polarization Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:45:54: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/ebsp.py:77: UserWarning: Interpolating b and e to 2x e sampling\n", - " warnings.warn(\"Interpolating b and e to 2x e sampling\",\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ebsp ... calculate E and B wavelet transform ... \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:48:08: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/ebsp.py:180: RuntimeWarning: Mean of empty slice\n", - " out[i, :] = np.nanmean(data[il:ir, :], axis=0)\n", - "\n" - ] - } - ], - "source": [ - "polarization_options = dict(freq_int=[10, 4000], polarization=True, fac=True)\n", - "polarization = ebsp(e_xyz, b_scm, b_xyz, b_xyz, r_xyz, **polarization_options)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Unpack polarization analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "frequency = polarization[\"f\"]\n", - "time = polarization[\"t\"]\n", - "b_sum = polarization[\"bb_xxyyzzss\"][..., 3]\n", - "b_per = polarization[\"bb_xxyyzzss\"][..., 0] + polarization[\"bb_xxyyzzss\"][..., 1]\n", - "e_sum = polarization[\"ee_xxyyzzss\"][..., 3]\n", - "e_per = polarization[\"ee_xxyyzzss\"][..., 0] + polarization[\"ee_xxyyzzss\"][..., 1]\n", - "ellipticity = polarization[\"ellipticity\"]\n", - "dop = polarization[\"dop\"]\n", - "thetak = polarization[\"k_tp\"][..., 0]\n", - "planarity = polarization[\"planarity\"]\n", - "pflux_z = polarization[\"pf_xyz\"][..., 2] \n", - "pflux_z /= np.sqrt(polarization[\"pf_xyz\"][..., 0] ** 2 + polarization[\"pf_xyz\"][..., 1] ** 2 + polarization[\"pf_xyz\"][..., 2] ** 2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculate phase speed v_ph = E/B." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "v_ph = np.sqrt(e_sum / b_sum) * 1e6\n", - "v_ph_perp = np.sqrt(e_per / b_per) * 1e6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Remove points with very low B amplitutes" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "b_sum_thres = 1e-7\n", - "\n", - "ellipticity.data[b_sum < b_sum_thres] = np.nan\n", - "thetak.data[b_sum < b_sum_thres] = np.nan\n", - "dop.data[b_sum < b_sum_thres] = np.nan\n", - "planarity.data[b_sum < b_sum_thres] = np.nan\n", - "pflux_z.data[b_sum < b_sum_thres] = np.nan\n", - "v_ph.data[b_sum < b_sum_thres] = np.nan\n", - "v_ph_perp.data[b_sum < b_sum_thres] = np.nan" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot figure" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. 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" icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$S_{||}/|S|$\\n ')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(8, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(left=.15, right=.85, hspace=0.1)\n", - "\n", - "axs[0], caxs0 = plot_spectr(axs[0], b_sum, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "plot_line(axs[0], f_ce, color=\"w\")\n", - "plot_line(axs[0], f_ce_01, color=\"w\")\n", - "plot_line(axs[0], f_ce_05, color=\"w\")\n", - "axs[0].set_ylabel(\"$f$ [Hz]\")\n", - "caxs0.set_ylabel(\"$B^2$\" + \"\\n\" +\"[nT$^2$ Hz$^{-1}$]\")\n", - "\n", - "axs[1], caxs1 = plot_spectr(axs[1], e_sum, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "plot_line(axs[1], f_ce, color=\"w\")\n", - "plot_line(axs[1], f_ce_01, color=\"w\")\n", - "plot_line(axs[1], f_ce_05, color=\"w\")\n", - "axs[1].set_ylabel(\"$f$ [Hz]\")\n", - "caxs1.set_ylabel(\"$E^2$\" + \"\\n\" + \"[mV$^2$ m$^{-2}$ Hz$^{-1}$]\")\n", - "\n", - "axs[2], caxs2 = plot_spectr(axs[2], ellipticity, yscale=\"log\", cmap=\"RdBu_r\", clim=[-1, 1])\n", - "plot_line(axs[2], f_ce, color=\"w\")\n", - "plot_line(axs[2], f_ce_01, color=\"w\")\n", - "plot_line(axs[2], f_ce_05, color=\"w\")\n", - "axs[2].set_ylabel(\"$f$ [Hz]\")\n", - "caxs2.set_ylabel(\"Ellipticity\" + \"\\n\" + \" \")\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], thetak * 180 / np.pi, yscale=\"log\", cmap=\"Spectral_r\", clim=[0, 90])\n", - "plot_line(axs[3], f_ce, color=\"w\")\n", - "plot_line(axs[3], f_ce_01, color=\"w\")\n", - "plot_line(axs[3], f_ce_05, color=\"w\")\n", - "axs[3].set_ylabel(\"$f$ [Hz]\")\n", - "caxs3.set_ylabel(\"$\\\\theta_k$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], dop, yscale=\"log\", cmap=\"Spectral_r\", clim=[0, 1])\n", - "plot_line(axs[4], f_ce, color=\"w\")\n", - "plot_line(axs[4], f_ce_01, color=\"w\")\n", - "plot_line(axs[4], f_ce_05, color=\"w\")\n", - "axs[4].set_ylabel(\"$f$ [Hz]\")\n", - "caxs4.set_ylabel(\"DOP\" + \"\\n\" + \" \")\n", - "\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], planarity, yscale=\"log\", cmap=\"Spectral_r\", clim=[0, 1])\n", - "plot_line(axs[5], f_ce, color=\"w\")\n", - "plot_line(axs[5], f_ce_01, color=\"w\")\n", - "plot_line(axs[5], f_ce_05, color=\"w\")\n", - "axs[5].set_ylabel(\"$f$ [Hz]\")\n", - "caxs5.set_ylabel(\"Planarity\" + \"\\n\" + \" \")\n", - "\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], v_ph, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "plot_line(axs[6], f_ce, color=\"w\")\n", - "plot_line(axs[6], f_ce_01, color=\"w\")\n", - "plot_line(axs[6], f_ce_05, color=\"w\")\n", - "axs[6].set_ylabel(\"$f$ [Hz]\")\n", - "caxs6.set_ylabel(\"E/B\" + \"\\n\" + \"[m s$^{-1}$]\")\n", - "\n", - "axs[7], caxs7 = plot_spectr(axs[7], pflux_z, yscale=\"log\", cmap=\"RdBu_r\", clim=[-1, 1])\n", - "plot_line(axs[7], f_ce, color=\"w\")\n", - "plot_line(axs[7], f_ce_01, color=\"w\")\n", - "plot_line(axs[7], f_ce_05, color=\"w\")\n", - "axs[7].set_ylabel(\"$f$ [Hz]\")\n", - "caxs7.set_ylabel(\"$S_{||}/|S|$\" + \"\\n\" + \" \")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/.ipynb_checkpoints/example_mms_walen_test-checkpoint.ipynb b/examples/01_mms/.ipynb_checkpoints/example_mms_walen_test-checkpoint.ipynb deleted file mode 100644 index f170c9db..00000000 --- a/examples/01_mms/.ipynb_checkpoints/example_mms_walen_test-checkpoint.ipynb +++ /dev/null @@ -1,1459 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Walen Test\n", - "author: Louis Richard\\\n", - "Example code to perform Walen test; only for burst mode MMS data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates\n", - "\n", - "from pyrfu import mms, pyrf\n", - "from scipy import constants\n", - "from pyrfu.plot import plot_line, plot_spectr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define spacecraft index, time intervals, jet direction and trasnformation matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "mms.db_init(\"/Volumes/mms\")\n", - "\n", - "mms_id = 1\n", - "j_sign = 1 # +/-1 for jet direction\n", - "\n", - "#time = irf_time('2015-11-30T00:23:55.200Z', 'utc>epochtt');\n", - "trans_matrix = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) # in GSE\n", - "\n", - "# Plot\n", - "tint = [\"2015-11-30T00:23:48.000\", \"2015-11-30T00:24:01.000\"]\n", - "# reference region\n", - "tint_ref = [\"2015-11-30T00:23:49.000\", \"2015-11-30T00:23:50.000\"]\n", - "# Test region\n", - "tint_walen = [\"2015-11-30T00:23:50.000\", \"2015-11-30T00:23:54.000\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PSD" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:49:22: Loading mms1_dis_dist_brst...\n" - ] - } - ], - "source": [ - "vdf_i = mms.get_data(\"pdi_fpi_brst_l2\", tint, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Moments" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:49:29: Loading mms1_dis_numberdensity_brst...\n", - "09-Dec-21 11:49:31: Loading mms1_des_numberdensity_brst...\n", - "09-Dec-21 11:49:33: Loading mms1_dis_bulkv_gse_brst...\n", - "09-Dec-21 11:49:36: Loading mms1_dis_prestensor_gse_brst...\n" - ] - } - ], - "source": [ - "n_i = mms.get_data(\"ni_fpi_brst_l2\", tint, mms_id)\n", - "n_e = mms.get_data(\"ne_fpi_brst_l2\", tint, mms_id)\n", - "v_gse_i = mms.get_data(\"vi_gse_fpi_brst_l2\", tint, mms_id)\n", - "p_gse_i = mms.get_data(\"pi_gse_fpi_brst_l2\", tint, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fields" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:49:43: Loading mms1_fgm_b_gse_brst_l2...\n" - ] - } - ], - "source": [ - "b_gse = mms.get_data(\"b_gse_fgm_brst_l2\", tint, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load defatt files" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:49:46: Loading ancillary defatt files...\n" - ] - } - ], - "source": [ - "defatt = mms.load_ancillary(\"defatt\", tint, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute omnidirectionnal differential energy flux (DEF)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def_omni_i = mms.vdf_omni(mms.psd2def(vdf_i))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rotate pressure tensor into Field Aliigned Coordinates (FAC)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:50:06: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : Transforming tensor into field-aligned coordinates.\n" - ] - } - ], - "source": [ - "p_fac_i = mms.rotate_tensor(p_gse_i, \"fac\", b_gse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Alpha: pressure anisotropy factor" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "alpha_ = pyrf.pres_anis(p_fac_i, b_gse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### gse to new123" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "b_123 = pyrf.new_xyz(b_gse, trans_matrix)\n", - "v_123_i = pyrf.new_xyz(v_gse_i, trans_matrix)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reference(MSH) region; in New frame(123);" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "b_ref = pyrf.time_clip(b_123, tint_ref)\n", - "b_ref = np.nanmean(b_ref.data, axis=0)\n", - "\n", - "v_i_ref = pyrf.time_clip(v_123_i, tint_ref)\n", - "v_i_ref = np.nanmean(v_i_ref.data, axis=0)\n", - "\n", - "n_i_ref = pyrf.time_clip(n_i, tint_ref)\n", - "n_i_ref = np.nanmean(n_i_ref.data, axis=0)\n", - "\n", - "alpha_ref = pyrf.time_clip(alpha_, tint_ref)\n", - "alpha_ref = np.nanmean(alpha_ref.data, axis=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Vipred1: delta_B / sqrt(rho1)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:50:08: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "b_123 = pyrf.resample(b_123, n_i)\n", - "v_123_i = pyrf.resample(v_123_i, n_i)\n", - "\n", - "tmp_1 = (b_123 - b_ref) * 21.8 / np.sqrt(n_i_ref)\n", - "v_i_pred1 = pyrf.resample(tmp_1, v_123_i) * j_sign + v_i_ref" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Vipred2: $B_2 / \\sqrt{\\rho_2} - B_1 / \\sqrt{\\rho_1}$ [Phan et al, 2004]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "tmp_2 = 21.8 * (1 - alpha_) * b_123 / np.sqrt(n_i_ref * (1 - alpha_ref))\n", - "v_i_pred2 = (tmp_2 - 21.8 * np.sqrt(1 - alpha_ref) * b_ref / np.sqrt(n_i_ref))\n", - "v_i_pred2 *= j_sign\n", - "v_i_pred2 += v_i_ref" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Vipred2: $\\sqrt{1 - \\alpha_2} B_2 / \\sqrt{\\rho_2} - \\sqrt{1 - \\alpha_1} B_1 / \\sqrt{\\rho_1}$" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "v_i_pred3 = 21.8 * (1 - alpha_) * b_123 / np.sqrt(n_i)\n", - "v_i_pred3 -= 21.8 * np.sqrt(1 - alpha_ref) * b_ref / np.sqrt(n_i_ref)\n", - "v_i_pred3 *= j_sign\n", - "v_i_pred3 += v_i_ref" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Slope & CC" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "v_123_i_w = pyrf.time_clip(v_123_i, tint_walen)\n", - "v_i_pred1_w = pyrf.time_clip(v_i_pred1, tint_walen)\n", - "v_i_pred2_w = pyrf.time_clip(v_i_pred2, tint_walen)\n", - "v_i_pred3_w = pyrf.time_clip(v_i_pred3, tint_walen)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "p_ = [np.polyfit(v_i_pred2_w.data[:, i], v_123_i_w.data[:, i], 1) for i in range(3)]\n", - "slope_2 = [p_[i][0] for i in range(3)]\n", - "\n", - "corr_ = [np.corrcoef(v_i_pred2_w.data[:, i], v_123_i_w.data[:, i]) for i in range(3)]\n", - "cc_2 = [corr_[i][0, 1] for i in range(3)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(7, sharex=\"all\", figsize=(8.5, 11))\n", - "f.subplots_adjust(bottom=.05, top=.95, left=.12, right=.88, hspace=0)\n", - "\n", - "plot_line(axs[0], b_gse)\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], ncol=3)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "axs[0].set_title(f\"MMS-{mms_id:d}\")\n", - "\n", - "plot_line(axs[1], n_i, color=\"tab:blue\", label=\"$N_i$\")\n", - "plot_line(axs[1], n_e, color=\"tab:red\", label=\"$N_i$\")\n", - "axs[1].legend(ncol=3)\n", - "axs[1].set_ylabel(\"$N$ [cm$^{-3}$]\")\n", - "\n", - "\n", - "axs[2], caxs2 = plot_spectr(axs[2], def_omni_i, yscale=\"log\", cscale=\"log\")\n", - "axs[2].set_yticks(np.logspace(1, 4, 4))\n", - "axs[2].set_ylabel(\"$W_i$ [eV]\")\n", - "caxs2.set_ylabel(\"DEF\" + \"\\n\" + \"[(cm$^2$ s sr)$^{-1}$]\")\n", - "\n", - "plot_line(axs[3], b_123)\n", - "axs[3].legend([\"$B_1$\", \"$B_2$\", \"$B_3$\"], ncol=3)\n", - "axs[3].set_ylabel(\"$B$ [nT]\")\n", - "axs[3].text(1.01, .75, np.array2string(trans_matrix[0, :], separator=\",\", precision=2), color=\"tab:blue\", transform=axs[3].transAxes)\n", - "axs[3].text(1.01, .50, np.array2string(trans_matrix[1, :], separator=\",\", precision=2), color=\"tab:green\", transform=axs[3].transAxes)\n", - "axs[3].text(1.01, .25, np.array2string(trans_matrix[2, :], separator=\",\", precision=2), color=\"tab:red\", transform=axs[3].transAxes)\n", - "\n", - "\n", - "plot_line(axs[4], v_123_i[:, 0], color=\"k\", label=\"FPI\")\n", - "plot_line(axs[4], v_i_pred2_w[:, 0], color=\"tab:red\", linestyle=\"-\", label=\"pred\")\n", - "plot_line(axs[4], v_i_pred2[:, 0], color=\"tab:red\", linestyle=\"--\")\n", - "axs[4].legend(ncol=3)\n", - "axs[4].set_ylabel(\"$V_1$ [km s$^{-1}$]\")\n", - "axs[4].text(1.01, .75, f\"slope = {slope_2[0]:3.2f}\", color=\"k\", transform=axs[4].transAxes)\n", - "axs[4].text(1.01, .25, f\"cc = {cc_2[0]:3.2f}\", color=\"k\", transform=axs[4].transAxes)\n", - "axs[4].axvspan(mdates.datestr2num(tint_ref[0]), mdates.datestr2num(tint_ref[1]), color=\"tab:red\", alpha=.2)\n", - "axs[4].axvspan(mdates.datestr2num(tint_walen[0]), mdates.datestr2num(tint_walen[1]), color=\"yellow\", alpha=.2)\n", - "\n", - "\n", - "plot_line(axs[5], v_123_i[:, 1], color=\"k\", label=\"FPI\")\n", - "plot_line(axs[5], v_i_pred2_w[:, 1], color=\"tab:red\", linestyle=\"-\", label=\"pred\")\n", - "plot_line(axs[5], v_i_pred2[:, 1], color=\"tab:red\", linestyle=\"--\")\n", - "axs[5].legend(ncol=3)\n", - "axs[5].set_ylabel(\"$V_2$ [km s$^{-1}$]\")\n", - "axs[5].text(1.01, .75, f\"slope = {slope_2[1]:3.2f}\", color=\"k\", transform=axs[5].transAxes)\n", - "axs[5].text(1.01, .25, f\"cc = {cc_2[1]:3.2f}\", color=\"k\", transform=axs[5].transAxes)\n", - "\n", - "plot_line(axs[6], v_123_i[:, 2], color=\"k\", label=\"FPI\")\n", - "plot_line(axs[6], v_i_pred2_w[:, 2], color=\"tab:red\", linestyle=\"-\", label=\"pred\")\n", - "plot_line(axs[6], v_i_pred2[:, 2], color=\"tab:red\", linestyle=\"--\")\n", - "axs[6].legend(ncol=3)\n", - "axs[6].set_ylabel(\"$V_3$ [km s$^{-1}$]\")\n", - "axs[6].text(1.01, .75, f\"slope = {slope_2[2]:3.2f}\", color=\"k\", transform=axs[6].transAxes)\n", - "axs[6].text(1.01, .25, f\"cc = {cc_2[2]:3.2f}\", color=\"k\", transform=axs[6].transAxes)\n", - "\n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/example_mms_b_e_j.ipynb b/examples/01_mms/example_mms_b_e_j.ipynb deleted file mode 100644 index 3c64d89f..00000000 --- a/examples/01_mms/example_mms_b_e_j.ipynb +++ /dev/null @@ -1,1330 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# B, E, J\n", - "author: Louis Richard\\\n", - "Plots of B, J, E, JxB electric field, and J.E. Calculates J using Curlometer method. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu.mms import get_data, db_init\n", - "from pyrfu.plot import plot_line\n", - "from pyrfu.pyrf import resample, avg_4sc, edb, c_4_j, norm, convert_fac, dot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define time interval, data path and spacecraft indices" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "tint = [\"2016-06-07T10:00:00.000\", \"2016-06-07T12:00:00.000\"]\n", - "db_init(\"/Volumes/mms\")\n", - "ic = np.arange(1, 5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load background magnetic field (FGM)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:57:43: Loading mms1_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 10:57:45: Loading mms2_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 10:57:48: Loading mms3_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 10:57:50: Loading mms4_fgm_b_dmpa_srvy_l2...\n" - ] - } - ], - "source": [ - "b_mms = [get_data(\"b_dmpa_fgm_srvy_l2\", tint, i) for i in ic]\n", - "b_mms = [resample(b_xyz, b_mms[0]) for b_xyz in b_mms]\n", - "b_xyz = avg_4sc(b_mms)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load electric field (EDP)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:57:52: Loading mms1_edp_dce_dsl_fast_l2pre...\n", - "09-Dec-21 10:57:54: Loading mms2_edp_dce_dsl_fast_l2pre...\n", - "09-Dec-21 10:57:56: Loading mms3_edp_dce_dsl_fast_l2pre...\n", - "09-Dec-21 10:57:59: Loading mms4_edp_dce_dsl_fast_l2pre...\n" - ] - } - ], - "source": [ - "e_mms = [get_data(\"e2d_dsl_edp_fast_l2pre\", tint, i) for i in ic]\n", - "e_mms = [resample(e_xyz, e_mms[0]) for e_xyz in e_mms]\n", - "e_xyz = avg_4sc(e_mms)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load ion number density (FPI)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:58:01: Loading mms1_hpca_hplus_number_density...\n", - "09-Dec-21 10:58:03: Loading mms2_hpca_hplus_number_density...\n", - "09-Dec-21 10:58:04: Loading mms3_hpca_hplus_number_density...\n", - "09-Dec-21 10:58:05: Loading mms4_hpca_hplus_number_density...\n" - ] - } - ], - "source": [ - "n_mms_i = [get_data(\"nhplus_hpca_srvy_l2\", tint, i) for i in ic]\n", - "n_mms_i = [resample(n_i, n_mms_i[0]) for n_i in n_mms_i]\n", - "\n", - "n_i = avg_4sc(n_mms_i)\n", - "n_i = resample(n_i, b_mms[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load spacecraft position (MEC)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 10:58:06: Loading mms1_mec_r_gse...\n", - "09-Dec-21 10:58:11: Loading mms2_mec_r_gse...\n", - "09-Dec-21 10:58:15: Loading mms3_mec_r_gse...\n", - "09-Dec-21 10:58:20: Loading mms4_mec_r_gse...\n" - ] - } - ], - "source": [ - "r_mms = [get_data(\"r_gse_mec_srvy_l2\", tint, i) for i in ic]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute current density, Hall electric field and E.J" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Compute current density using curlometer" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "j_xyz, div_b, _, jxb_xyz, _, _ = c_4_j(r_mms, b_mms)\n", - "div_over_curl = div_b.copy()\n", - "div_over_curl.data = abs(div_over_curl.data) / norm(j_xyz)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Transform current density into field-aligned coordinates" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "j_fac = convert_fac(j_xyz, b_xyz,[1, 0, 0])\n", - "j_xyz.data *= 1e9\n", - "j_fac.data *= 1e9" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Hall electric field" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "jxb_xyz.data /= n_i.data[:, None]\n", - "jxb_xyz.data /= 1.6e-19 * 1000 # Convert to (mV/m)\n", - "jxb_xyz.data[abs(jxb_xyz.data) > 100.] = np.nan # Remove some questionable fields" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute E.J" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "j_xyz = resample(j_xyz, e_xyz)\n", - "e_dot_j = dot(e_xyz, j_xyz) / 1000 # J (nA/m^2), E (mV/m), E.J (nW/m^3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot figure" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "legend_options = dict(ncol=4, loc=\"upper right\", frameon=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; 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" event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(16959.416666666668, 16959.5)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(8, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(hspace=0, left=.15, right=.85)\n", - "plot_line(axs[0], b_xyz)\n", - "axs[0].set_ylabel(\"$B_{DMPA}$\" + \"\\n\" + \"[nT]\")\n", - "axs[0].legend([\"$B_{x}$\",\"$B_{y}$\",\"$B_{z}$\"], **legend_options)\n", - "axs[0].set_ylim([-70, 70])\n", - "\n", - "labels = []\n", - "for i, n in enumerate(n_mms_i):\n", - " plot_line(axs[1], n)\n", - " labels.append(\"MMS{:d}\".format(i + 1))\n", - "\n", - "axs[1].set_ylabel(\"$n_i$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", - "axs[1].set_yscale(\"log\")\n", - "axs[1].set_ylim([1e-4, 10])\n", - "axs[1].legend(labels, **legend_options)\n", - "\n", - "plot_line(axs[2], j_xyz)\n", - "axs[2].set_ylabel(\"$J_{DMPA}$\" + \"\\n\" + \"nA m$^{-2}$\")\n", - "axs[2].legend([\"$J_{x}$\",\"$J_{y}$\",\"$J_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[3], j_fac)\n", - "axs[3].set_ylabel(\"$J_{FAC}$\" + \"\\n\" + \"nA m$^{-2}$\")\n", - "axs[3].legend([\"$J_{\\\\perp 1}$\",\"$J_{\\\\perp 2}$\",\"$J_{||}$\"], **legend_options)\n", - "\n", - "plot_line(axs[4], div_over_curl)\n", - "axs[4].set_ylabel(\"$\\\\frac{|\\\\nabla . B|}{|\\\\nabla \\\\times B|}$\")\n", - "\n", - "plot_line(axs[5], e_xyz);\n", - "axs[5].set_ylabel(\"$E_{DSL}$\" + \"\\n\" +\"[mV m$^{-1}$\")\n", - "axs[5].legend([\"$E_{x}$\",\"$E_{y}$\",\"$E_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[6], jxb_xyz)\n", - "axs[6].set_ylabel(\"$J \\\\times B/n_{e} q_{e}$\" + \"\\n\" + \"[mV m$^{-1}$]\")\n", - "\n", - "plot_line(axs[7], e_dot_j)\n", - "axs[7].set_ylabel(\"$E . J$\" + \"\\n\" +\"[nW m$^{-3}$]\")\n", - "\n", - "axs[0].set_title(\"MMS - Current density and fields\")\n", - "\n", - "f.align_ylabels(axs)\n", - "axs[-1].set_xlim(tint)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/example_mms_ebfields.ipynb b/examples/01_mms/example_mms_ebfields.ipynb deleted file mode 100644 index 011f90ca..00000000 --- a/examples/01_mms/example_mms_ebfields.ipynb +++ /dev/null @@ -1,1429 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# EB Fields\n", - "author: Louis Richard\\\n", - "Plots E and B time series and of burst mode electric field in GSE coordinates and field-aligned coordinates. Plots spectrograms of paralleland perpendicular electric fields and fluctuating magnetic field." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from astropy import constants\n", - "from pyrfu.mms import get_data, db_init\n", - "from pyrfu.plot import plot_line, plot_spectr\n", - "from pyrfu.pyrf import wavelet, norm, convert_fac, filt, resample, ts_scalar" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define time interval, data path and spacecraft index" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "tint = [\"2015-10-30T05:15:20.000\", \"2015-10-30T05:16:20.000\"]\n", - "db_init(\"/Volumes/mms\")\n", - "ic = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load FGM data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:05:28: Loading mms1_fgm_b_gse_brst_l2...\n" - ] - } - ], - "source": [ - "b_xyz = get_data(\"b_gse_fgm_brst_l2\", tint, ic)\n", - "b_mag = norm(b_xyz)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load EDP data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:05:29: Loading mms1_edp_dce_gse_brst_l2...\n" - ] - } - ], - "source": [ - "e_xyz = get_data(\"e_gse_edp_brst_l2\", tint, ic)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load SCM data" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:05:30: Loading mms1_scm_acb_gse_scb_brst_l2...\n" - ] - } - ], - "source": [ - "b_scm = get_data(\"b_gse_scm_brst_l2\", tint, ic)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load FPI data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:05:32: Loading mms1_des_numberdensity_brst...\n" - ] - } - ], - "source": [ - "n_e = get_data(\"ne_fpi_brst_l2\", tint, ic)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute low and high frequency electric field and magnetic field fluctuations in FAC" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rotate E and B into field-aligned coordinates" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "e_xyzfac = convert_fac(e_xyz, b_xyz,[1, 0, 0])\n", - "b_scmfac = convert_fac(b_scm, b_xyz,[1, 0, 0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Bandpass filter E and B waveforms" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "fmin, fmax = [.5, 1000] # Hz\n", - "e_xyzfac_hf = filt(e_xyzfac, fmin, 0, 3)\n", - "e_xyzfac_lf = filt(e_xyzfac, 0, fmin, 3)\n", - "b_scmfac_hf = filt(b_scmfac, fmin, 0, 3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Wavelet transform of the electric field and the magnetic field fluctuations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute wavelet transforms" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "nf = 100\n", - "e_cwt, b_cwt = [wavelet(field, f_range=[fmin, fmax], n_freqs=nf) for field in [e_xyzfac, b_scm]]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Compress wavelet transform" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:06:18: :13: RuntimeWarning: Mean of empty slice\n", - " e_cwt_x[i, :] = np.squeeze(np.nanmean(e_cwt.x[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "09-Dec-21 11:06:18: :14: RuntimeWarning: Mean of empty slice\n", - " e_cwt_y[i, :] = np.squeeze(np.nanmean(e_cwt.y[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "09-Dec-21 11:06:18: :15: RuntimeWarning: Mean of empty slice\n", - " e_cwt_z[i, :] = np.squeeze(np.nanmean(e_cwt.z[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "09-Dec-21 11:06:25: :31: RuntimeWarning: Mean of empty slice\n", - " b_cwt_x[i, :] = np.squeeze(np.nanmean(b_cwt.x[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "09-Dec-21 11:06:25: :32: RuntimeWarning: Mean of empty slice\n", - " b_cwt_y[i, :] = np.squeeze(np.nanmean(b_cwt.y[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "09-Dec-21 11:06:25: :33: RuntimeWarning: Mean of empty slice\n", - " b_cwt_z[i, :] = np.squeeze(np.nanmean(b_cwt.z[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n" - ] - } - ], - "source": [ - "nc = 100\n", - "\n", - "# Number of frequencies\n", - "nf = e_cwt.x.shape[1]\n", - "\n", - "idxs = np.arange(int(nc / 2), len(e_cwt.time) - int(nc / 2), step=nc).astype(int)\n", - "\n", - "e_cwt_times = e_cwt.time[idxs]\n", - "\n", - "e_cwt_x, e_cwt_y, e_cwt_z = [np.zeros((len(idxs), nf)) for _ in range(3)]\n", - "\n", - "for i, idx in enumerate(idxs):\n", - " e_cwt_x[i, :] = np.squeeze(np.nanmean(e_cwt.x[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - " e_cwt_y[i, :] = np.squeeze(np.nanmean(e_cwt.y[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - " e_cwt_z[i, :] = np.squeeze(np.nanmean(e_cwt.z[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "options = dict(coords=[e_cwt_times, e_cwt.frequency], dims=[\"time\", \"frequency\"])\n", - "e_perp_cwt = xr.DataArray(e_cwt_x + e_cwt_y, **options)\n", - "e_para_cwt = xr.DataArray(e_cwt_z, **options)\n", - "\n", - "# Number of frequencies\n", - "nf = b_cwt.x.shape[1]\n", - "\n", - "idxs = np.arange(int(nc / 2), len(b_cwt.time) - int(nc / 2), step=nc).astype(int)\n", - "\n", - "b_cwt_times = b_cwt.time[idxs]\n", - "\n", - "b_cwt_x, b_cwt_y, b_cwt_z = [np.zeros((len(idxs), nf)) for _ in range(3)]\n", - "\n", - "for i, idx in enumerate(idxs):\n", - " b_cwt_x[i, :] = np.squeeze(np.nanmean(b_cwt.x[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - " b_cwt_y[i, :] = np.squeeze(np.nanmean(b_cwt.y[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - " b_cwt_z[i, :] = np.squeeze(np.nanmean(b_cwt.z[idx - int(nc / 2) + 1:idx + int(nc / 2), :], axis=0))\n", - "\n", - "options = dict(coords=[b_cwt_times, b_cwt.frequency], dims=[\"time\", \"frequency\"])\n", - "b_cwt = xr.DataArray(b_cwt_x + b_cwt_y + b_cwt_z, **options)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute characteristic frequencies" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "me = constants.m_e.value\n", - "mp = constants.m_p.value\n", - "qe = constants.e.value\n", - "eps0 = constants.eps0.value\n", - "mu0 = constants.mu0.value\n", - "mp_me = mp / me\n", - "b_si = b_mag * 1e-9\n", - "w_pe = np.sqrt(resample(n_e, b_xyz) * 1e6 * qe ** 2 /(me * eps0))\n", - "w_ce = qe * b_si / me\n", - "w_pp = np.sqrt(resample(n_e, b_xyz) * 1e6 * qe ** 2/ (mp * eps0))\n", - "f_ce = w_ce / (2 * np.pi)\n", - "f_pe = w_pe / (2 * np.pi)\n", - "f_cp = f_ce / mp_me\n", - "f_pp = w_pp / (2 * np.pi)\n", - "f_lh = np.sqrt(f_cp * f_ce /(1 + f_ce ** 2. / f_pe ** 2) + f_cp ** 2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot figure" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "legend_options = dict(ncol=4, loc=\"upper right\", frameon=True)\n", - "spectr_options = dict(yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(7, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(hspace=0, left=.15, right=.85)\n", - "\n", - "plot_line(axs[0], b_xyz)\n", - "plot_line(axs[0], b_mag)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "labels = [\"$B_x$\", \"$B_y$\", \"$B_z$\", \"$|B|$\"]\n", - "axs[0].legend(labels, **legend_options)\n", - "\n", - "plot_line(axs[1], e_xyzfac_lf)\n", - "axs[1].set_ylabel(\"$E$ [mV m$^{-1}$]\")\n", - "labels = [\"$E_{\\\\perp 1}$\", \"$E_{\\\\perp 2}$\", \"$E_{||}$\"]\n", - "axs[1].legend(labels, **legend_options)\n", - "\n", - "plot_line(axs[2], e_xyzfac_hf)\n", - "axs[2].set_ylabel(\"$E$ [mV m$^{-1}$]\")\n", - "labels = [\"$E_{\\\\perp 1}$\", \"$E_{\\\\perp 2}$\", \"$E_{||}$\"]\n", - "axs[2].legend(labels, **legend_options)\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], e_perp_cwt, **spectr_options)\n", - "axs[3].set_ylabel(\"$f$ [Hz]\")\n", - "caxs3.set_ylabel(\"$E_{\\\\perp}^2$\" + \"\\n\" + \"[mV$^{2}$ m$^{-2}$ Hz$^{-1}$]\")\n", - "plot_line(axs[3], f_lh, color=\"k\")\n", - "plot_line(axs[3], f_ce, color=\"tab:blue\")\n", - "plot_line(axs[3], f_pp, color=\"tab:red\")\n", - "labels = [\"$f_{lh}$\", \"$f_{ce}$\", \"$f_{pp}$\"]\n", - "axs[3].legend(labels, **legend_options)\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], e_para_cwt, **spectr_options)\n", - "axs[4].set_ylabel(\"$f$ [Hz]\")\n", - "caxs4.set_ylabel(\"$E_{||}^2$\" + \"\\n\" + \"[mV$^{2}$ m$^{-2}$ Hz$^{-1}$]\")\n", - "plot_line(axs[4], f_lh, color=\"k\")\n", - "plot_line(axs[4], f_ce, color=\"tab:blue\")\n", - "plot_line(axs[4], f_pp, color=\"tab:red\")\n", - "labels = [\"$f_{lh}$\", \"$f_{ce}$\", \"$f_{pp}$\"]\n", - "axs[4].legend(labels, **legend_options)\n", - "\n", - "plot_line(axs[5], b_scmfac_hf)\n", - "axs[5].set_ylabel(\"$\\\\delta B$ [nT]\")\n", - "labels = [\"$B_{\\\\perp 1}$\", \"$B_{\\\\perp 2}$\", \"$B_{||}$\"]\n", - "axs[5].legend(labels, **legend_options)\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], b_cwt, **spectr_options)\n", - "axs[6].set_ylabel(\"$f$ [Hz]\")\n", - "caxs6.set_ylabel(\"$B^2$\" + \"\\n\" + \"[nT$^{2}$ Hz$^{-1}$]\")\n", - "plot_line(axs[6], f_lh, color=\"k\")\n", - "plot_line(axs[6], f_ce, color=\"tab:blue\")\n", - "plot_line(axs[6], f_pp, color=\"tab:red\")\n", - "labels = [\"$f_{lh}$\", \"$f_{ce}$\", \"$f_{pp}$\"]\n", - "axs[6].legend(labels, **legend_options)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/example_mms_edr_signatures.ipynb b/examples/01_mms/example_mms_edr_signatures.ipynb deleted file mode 100644 index 4d1ebb94..00000000 --- a/examples/01_mms/example_mms_edr_signatures.ipynb +++ /dev/null @@ -1,1461 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# EDR Signatures\n", - "Author: Louis Richard\\\n", - "A routine to compute various parameters used to identify electron diffusion regions for the four MMS spacecraft. \n", - "\n", - "Quantities calculated so far are:\n", - "- sqrt(Q) : Based on Swisdak, GRL ,2016. Values around 0.1 indicate electron agyrotropies. Computed based on the off-diagonal terms in the pressure tensor for Pe_perp1 = Pe_perp2. \n", - "- Dng: Based on Aunai et al., 2013; Computed based on the off-diagonal terms in the pressure tensor for Pe_perp1 = Pe_perp2. Similar to sqrt(Q) but with different normalization. Calculated but not plotted. \n", - "- AG^(1/3): Based on Che et al., POP, 2018. Constructed from determinant of field-aligned rotation of the electron pressure tensor (Pe_perp1 = Pe_perp2). \n", - "- A phi_e/2 = abs(Perp1-Perp2)/(Perp1+Perp2): This is a measure of electron agyrotropy. Values of O(1) are expected for EDRs. We transform the pressure tensor into field-aligned coordinates such that the difference in Pe_perp1 and Pe_perp2 is maximal. This corresponds to P23 being zero. (Note that this definition of agyrotropy neglects the off-diagonal pressure terms P12 and P13, therefore it doesn't capture all agyrotropies.)\n", - "- A n_e = T_parallel/T_perp: Values much larger than 1 are expected. Large T_parallel/T_perp are a feature of the ion diffusion region. For MP reconnection ion diffusion regions have A n_e ~ 3 based on MMS observations. Scudder says A n_e ~ 7 at IDR-EDR boundary, but this is extremely large for MP reconnection.\n", - "- Mperp e: electron Mach number: bulk velocity divided by the electron thermal speed perpendicular to B. Values of O(1) are expected in EDRs (Scudder et al., 2012, 2015). \n", - "- J.E': J.E > 0 is expected in the electron diffusion region, corresponding to dissipation of field energy. J is calculated on each spacecraft using the particle moments (Zenitani et al., PRL, 2011). \n", - "- epsilon_e: Energy gain per cyclotron period. Values of O(1) are expected in EDRs (Scudder et al., 2012, 2015). \n", - "- delta_e: Relative strength of the electric and magnetic force in the bulk electron rest frame. N. B. Very sensitive to electron moments and electric field. Check version of these quantities (Scudder et al., 2012, 2015). \n", - " \n", - "Notes: \n", - "kappa_e (not yet included) is taken to be the largest value of epsilon_e and delta_e at any given point. Requires electron distributions with version number v2.0.0 or higher. Calculations of agyrotropy measures (1)--(3) become unreliable at low densities n_e <~ 2 cm^-3, when the raw particle counts are low. Agyrotropies are removed for n_e < 1 cm^-3" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import tqdm\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from astropy import constants\n", - "from pyrfu.mms import get_data, rotate_tensor, db_init\n", - "from pyrfu.plot import make_labels, pl_tx\n", - "from pyrfu.pyrf import (resample, norm, cross, dot, trace, calc_sqrtq, \n", - " calc_dng, calc_ag, calc_agyro)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time interval selection and data path" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "tint = [\"2015-12-14T01:17:38.000\", \"2015-12-14T01:17:41.000\"]\n", - "db_init(\"/Volumes/mms\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load fields" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:09:20: Loading mms1_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 11:09:23: Loading mms2_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 11:09:25: Loading mms3_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 11:09:27: Loading mms4_fgm_b_dmpa_srvy_l2...\n", - "09-Dec-21 11:09:29: Loading mms1_edp_dce_dsl_brst_l2...\n", - "09-Dec-21 11:09:31: Loading mms2_edp_dce_dsl_brst_l2...\n", - "09-Dec-21 11:09:32: Loading mms3_edp_dce_dsl_brst_l2...\n", - "09-Dec-21 11:09:33: Loading mms4_edp_dce_dsl_brst_l2...\n" - ] - } - ], - "source": [ - "b_mms = [get_data(\"b_dmpa_fgm_srvy_l2\", tint, i) for i in range(1, 5)]\n", - "e_mms = [get_data(\"e_dsl_edp_brst_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load particles moments" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:09:35: Loading mms1_des_numberdensity_brst...\n", - "09-Dec-21 11:09:37: Loading mms2_des_numberdensity_brst...\n", - "09-Dec-21 11:09:39: Loading mms3_des_numberdensity_brst...\n", - "09-Dec-21 11:09:41: Loading mms4_des_numberdensity_brst...\n", - "09-Dec-21 11:09:43: Loading mms1_des_bulkv_dbcs_brst...\n", - "09-Dec-21 11:09:47: Loading mms2_des_bulkv_dbcs_brst...\n", - "09-Dec-21 11:09:52: Loading mms3_des_bulkv_dbcs_brst...\n", - "09-Dec-21 11:09:56: Loading mms4_des_bulkv_dbcs_brst...\n", - "09-Dec-21 11:10:00: Loading mms1_dis_bulkv_dbcs_brst...\n", - "09-Dec-21 11:10:05: Loading mms2_dis_bulkv_dbcs_brst...\n", - "09-Dec-21 11:10:08: Loading mms3_dis_bulkv_dbcs_brst...\n", - "09-Dec-21 11:10:12: Loading mms4_dis_bulkv_dbcs_brst...\n", - "09-Dec-21 11:10:17: Loading mms1_des_temptensor_dbcs_brst...\n", - "09-Dec-21 11:10:24: Loading mms2_des_temptensor_dbcs_brst...\n", - "09-Dec-21 11:10:32: Loading mms3_des_temptensor_dbcs_brst...\n", - "09-Dec-21 11:10:40: Loading mms4_des_temptensor_dbcs_brst...\n", - "09-Dec-21 11:10:47: Loading mms1_des_prestensor_dbcs_brst...\n", - "09-Dec-21 11:10:55: Loading mms2_des_prestensor_dbcs_brst...\n", - "09-Dec-21 11:11:02: Loading mms3_des_prestensor_dbcs_brst...\n", - "09-Dec-21 11:11:09: Loading mms4_des_prestensor_dbcs_brst...\n" - ] - } - ], - "source": [ - "n_mms_e = [get_data(\"ne_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "v_mms_e = [get_data(\"ve_dbcs_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "v_mms_i = [get_data(\"vi_dbcs_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "t_mms_e = [get_data(\"te_dbcs_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "p_mms_e = [get_data(\"pe_dbcs_fpi_brst_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Resample to DES sampling frequency" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:11:15: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "e_mms = [resample(e_xyz, n_e) for e_xyz, n_e in zip(e_mms, n_mms_e)]\n", - "b_mms = [resample(b_xyz, n_e) for b_xyz, n_e in zip(b_mms, n_mms_e)]\n", - "v_mms_i = [resample(v_xyz_i, n_e) for v_xyz_i, n_e in zip(v_mms_i, n_mms_e)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Rotate pressure and temperature tensors" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are most unequal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are most unequal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are most unequal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are most unequal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Transforming tensor into field-aligned coordinates.\n" - ] - } - ], - "source": [ - "p_mms_e_pp = [rotate_tensor(p_xyz, \"fac\", b_xyz, \"pp\") for p_xyz, b_xyz in zip(p_mms_e, b_mms)]\n", - "p_mms_e_qq = [rotate_tensor(p_xyz, \"fac\", b_xyz, \"qq\") for p_xyz, b_xyz in zip(p_mms_e, b_mms)]\n", - "t_mms_e_fac = [rotate_tensor(t_xyz, \"fac\", b_xyz) for t_xyz, b_xyz in zip(t_mms_e, b_mms)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute tests for EDR" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Q and Dng from Pepp" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "sqrtq_mms = [calc_sqrtq(p_pp) for p_pp in p_mms_e_pp]\n", - "dng_mms = [calc_dng(p_pp) for p_pp in p_mms_e_pp]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute agyrotropy measure AG1/3" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "ag_mms = [calc_ag(p_pp) for p_pp in p_mms_e_pp]\n", - "ag_cr_mms = [ag ** (1 / 3) for ag in ag_mms]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute agyrotropy Aphi from Peqq" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "agyro_mms = [calc_agyro(p_qq) for p_qq in p_mms_e_qq]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simple fix to remove spurious points" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "for sqrtq, dng, agyro, ag_cr in zip(sqrtq_mms, dng_mms, agyro_mms, ag_cr_mms):\n", - " for coeff in [sqrtq, dng, agyro, ag_cr]:\n", - " coeff_data = coeff.data.copy()\n", - " for ii in range(len(coeff_data) - 1):\n", - " if coeff[ii] > 2 * coeff[ii - 1] and coeff[ii] > 2 * coeff[ii + 1]:\n", - " coeff_data[ii] = np.nan\n", - " \n", - " coeff.data = coeff_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Remove all points corresponding to densities below 1cm^-3" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "for n_e, sqrtq, dng, agyro, ag_cr in zip(n_mms_e, sqrtq_mms, dng_mms, agyro_mms, ag_cr_mms):\n", - " sqrtq.data[n_e.data < 1] = np.nan\n", - " dng.data[n_e.data < 1] = np.nan\n", - " agyro.data[n_e.data < 1] = np.nan\n", - " ag_cr.data[n_e.data < 1] = np.nan" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute temperature ratio An" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "t_rat_mms = [p_pp[:, 0, 0] / p_pp[:, 1, 1] for p_pp in p_mms_e_pp]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute electron Mach number" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "qe, me = [constants.e.value, constants.m_e.value]\n", - "v_mms_e_mag = [norm(v_xyz_e) for v_xyz_e in v_mms_e]\n", - "v_mms_e_per = [np.sqrt((t_fac_e[:, 1, 1] + t_fac_e[:, 2, 2]) * qe / me) for t_fac_e in t_mms_e_fac]\n", - "m_mms_e = [1e3 * v_e_mag / v_e_perp for v_e_mag, v_e_perp in zip(v_mms_e_mag, v_mms_e_per)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute current density and J.E" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Current density in nA m^-2\n", - "j_mms_moms = [1e18 * qe * n_e * (v_xyz_i - v_xyz_e) for n_e, v_xyz_i, v_xyz_e in zip(n_mms_e, v_mms_i, v_mms_e)]\n", - "vexb_mms = [e_xyz + 1e-3 * cross(v_xyz_e, b_xyz) for e_xyz, v_xyz_e, b_xyz in zip(e_mms, v_mms_e, b_mms)]\n", - "# J (nA/m^2), E (mV/m), E.J (nW/m^3)\n", - "edotj_mms = [1e-3 * dot(vexb_xyz, j_xyz) for vexb_xyz, j_xyz in zip(vexb_mms, j_mms_moms)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculate epsilon and delta parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "w_mms_ce = [1e-9 * qe * norm(b_xyz) /me for b_xyz in b_mms]\n", - "edotve_mms = [dot(e_xyz, v_xyz_e) for e_xyz, v_xyz_e in zip(e_mms, v_mms_e)]\n", - "eps_mms_e = [np.abs(6 * np.pi * edotve_xyz /(w_ce * trace(t_fac_e))) for edotve_xyz, w_ce, t_fac_e in zip(edotve_mms, w_mms_ce, t_mms_e_fac)]\n", - "delta_mms_e = [1e-3 * norm(vexb_xyz) / (v_xyz_e_per * norm(b_xyz) * 1e-9) for vexb_xyz, v_xyz_e_per, b_xyz in zip(vexb_mms, v_mms_e_per, b_mms)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot figure" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "legend_options = dict(ncol=4, frameon=True, loc=\"upper right\")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. 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' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(9, sharex=\"all\", figsize=(6.5, 11))\n", - "f.subplots_adjust(bottom=.1, top=.95, left=.15, right=.85, hspace=0)\n", - "\n", - "pl_tx(axs[0], b_mms, 2)\n", - "axs[0].set_ylabel(\"$B_{z}$ [nT]\")\n", - "labels = [\"MMS{:d}\".format(ic) for ic in range(1, 5)]\n", - "axs[0].legend(labels, **legend_options)\n", - "\n", - "pl_tx(axs[1], sqrtq_mms, 0)\n", - "axs[1].set_ylabel(\"$\\sqrt{Q}$\")\n", - "\n", - "pl_tx(axs[2], ag_cr_mms, 0)\n", - "axs[2].set_ylabel(\"$AG^{1/3}$\")\n", - "\n", - "pl_tx(axs[3], agyro_mms, 0)\n", - "axs[3].set_ylabel(\"$A\\Phi_e / 2$\")\n", - "\n", - "pl_tx(axs[4], t_rat_mms, 0)\n", - "axs[4].set_ylabel(\"$T_{e||}/T_{e \\perp}$\")\n", - "\n", - "pl_tx(axs[5], m_mms_e, 0)\n", - "axs[5].set_ylabel(\"$M_{e \\perp}$\")\n", - "\n", - "pl_tx(axs[6], edotj_mms, 0)\n", - "axs[6].set_ylabel(\"$E'.J$ [nW m$^{-3}$]\")\n", - "\n", - "pl_tx(axs[7], eps_mms_e, 0)\n", - "axs[7].set_ylabel(\"$\\epsilon_{e}$\")\n", - "\n", - "pl_tx(axs[8], delta_mms_e, 0)\n", - "axs[8].set_ylabel(\"$\\delta_{e}$\")\n", - "\n", - "make_labels(axs, [0.025, 0.83])\n", - "axs[-1].set_xlim(tint)\n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/examples/01_mms/example_mms_eis.ipynb b/examples/01_mms/example_mms_eis.ipynb deleted file mode 100644 index 3bf14eab..00000000 --- a/examples/01_mms/example_mms_eis.ipynb +++ /dev/null @@ -1,2492 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Energetic Ion Spectrometer (EIS)\n", - "author: Louis Richard" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu import mms\n", - "from pyrfu.plot import plot_line, plot_spectr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define path to data, time interval and spacecraft index" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "db_init(\"/Volumes/mms\")\n", - "tint_long = [\"2017-07-23T16:10:00\", \"2017-07-23T18:10:00\"]\n", - "mms_id = 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Magnetic field in GSE coordinates" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 23:51:09: Loading mms2_fgm_b_gse_srvy_l2...\n" - ] - } - ], - "source": [ - "# Single spacecraft\n", - "b_gse = mms.get_data(\"b_gse_fgm_srvy_l2\", tint_long, mms_id, data_path=data_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Single spacecraft H$^+$ (PHxTOF and ExTOF), He$^{n+}$ (ExTOF), O$^{n+}$ (ExTOF) and electrons differential particle fluxes for all 6 telescopes." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_spin...\n", - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_sector...\n", - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_proton_t0_energy_dminus...\n", - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_proton_t0_energy_dplus...\n", - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_proton_P4_flux_t0...\n", - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_look_t0...\n", - "18-Jun-21 23:51:10: Loading mms2_epd_eis_phxtof_proton_P4_flux_t1...\n", - "18-Jun-21 23:51:11: Loading mms2_epd_eis_phxtof_look_t1...\n", - "18-Jun-21 23:51:11: Loading mms2_epd_eis_phxtof_proton_P4_flux_t2...\n", - "18-Jun-21 23:51:11: Loading mms2_epd_eis_phxtof_look_t2...\n", - "18-Jun-21 23:51:11: Loading mms2_epd_eis_phxtof_proton_P4_flux_t3...\n", - "18-Jun-21 23:51:12: Loading mms2_epd_eis_phxtof_look_t3...\n", - "18-Jun-21 23:51:12: Loading mms2_epd_eis_phxtof_proton_P4_flux_t4...\n", - "18-Jun-21 23:51:12: Loading mms2_epd_eis_phxtof_look_t4...\n", - "18-Jun-21 23:51:12: Loading mms2_epd_eis_phxtof_proton_P4_flux_t5...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_phxtof_look_t5...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_extof_spin...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_extof_sector...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_extof_proton_t0_energy_dminus...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_extof_proton_t0_energy_dplus...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_extof_proton_P4_flux_t0...\n", - "18-Jun-21 23:51:13: Loading mms2_epd_eis_extof_look_t0...\n", - "18-Jun-21 23:51:14: Loading mms2_epd_eis_extof_proton_P4_flux_t1...\n", - "18-Jun-21 23:51:14: Loading mms2_epd_eis_extof_look_t1...\n", - "18-Jun-21 23:51:15: Loading mms2_epd_eis_extof_proton_P4_flux_t2...\n", - "18-Jun-21 23:51:15: Loading mms2_epd_eis_extof_look_t2...\n", - "18-Jun-21 23:51:15: Loading mms2_epd_eis_extof_proton_P4_flux_t3...\n", - "18-Jun-21 23:51:16: Loading mms2_epd_eis_extof_look_t3...\n", - "18-Jun-21 23:51:16: Loading mms2_epd_eis_extof_proton_P4_flux_t4...\n", - "18-Jun-21 23:51:16: Loading mms2_epd_eis_extof_look_t4...\n", - "18-Jun-21 23:51:17: Loading mms2_epd_eis_extof_proton_P4_flux_t5...\n", - "18-Jun-21 23:51:17: Loading mms2_epd_eis_extof_look_t5...\n", - "18-Jun-21 23:51:18: Loading mms2_epd_eis_extof_spin...\n", - "18-Jun-21 23:51:18: Loading mms2_epd_eis_extof_sector...\n", - "18-Jun-21 23:51:18: Loading mms2_epd_eis_extof_oxygen_t0_energy_dminus...\n", - "18-Jun-21 23:51:18: Loading mms2_epd_eis_extof_oxygen_t0_energy_dplus...\n", - "18-Jun-21 23:51:18: Loading mms2_epd_eis_extof_oxygen_P4_flux_t0...\n", - "18-Jun-21 23:51:18: Loading mms2_epd_eis_extof_look_t0...\n", - "18-Jun-21 23:51:19: Loading mms2_epd_eis_extof_oxygen_P4_flux_t1...\n", - "18-Jun-21 23:51:19: Loading mms2_epd_eis_extof_look_t1...\n", - "18-Jun-21 23:51:20: Loading mms2_epd_eis_extof_oxygen_P4_flux_t2...\n", - "18-Jun-21 23:51:20: Loading mms2_epd_eis_extof_look_t2...\n", - "18-Jun-21 23:51:21: Loading mms2_epd_eis_extof_oxygen_P4_flux_t3...\n", - "18-Jun-21 23:51:21: Loading mms2_epd_eis_extof_look_t3...\n", - "18-Jun-21 23:51:22: Loading mms2_epd_eis_extof_oxygen_P4_flux_t4...\n", - "18-Jun-21 23:51:22: Loading mms2_epd_eis_extof_look_t4...\n", - "18-Jun-21 23:51:23: Loading mms2_epd_eis_extof_oxygen_P4_flux_t5...\n", - "18-Jun-21 23:51:23: Loading mms2_epd_eis_extof_look_t5...\n", - "18-Jun-21 23:51:24: Loading mms2_epd_eis_extof_spin...\n", - "18-Jun-21 23:51:24: Loading mms2_epd_eis_extof_sector...\n", - "18-Jun-21 23:51:24: Loading mms2_epd_eis_extof_alpha_t0_energy_dminus...\n", - "18-Jun-21 23:51:24: Loading mms2_epd_eis_extof_alpha_t0_energy_dplus...\n", - "18-Jun-21 23:51:24: Loading mms2_epd_eis_extof_alpha_P4_flux_t0...\n", - "18-Jun-21 23:51:24: Loading mms2_epd_eis_extof_look_t0...\n", - "18-Jun-21 23:51:25: Loading mms2_epd_eis_extof_alpha_P4_flux_t1...\n", - "18-Jun-21 23:51:25: Loading mms2_epd_eis_extof_look_t1...\n", - "18-Jun-21 23:51:26: Loading mms2_epd_eis_extof_alpha_P4_flux_t2...\n", - "18-Jun-21 23:51:26: Loading mms2_epd_eis_extof_look_t2...\n", - "18-Jun-21 23:51:26: Loading mms2_epd_eis_extof_alpha_P4_flux_t3...\n", - "18-Jun-21 23:51:27: Loading mms2_epd_eis_extof_look_t3...\n", - "18-Jun-21 23:51:27: Loading mms2_epd_eis_extof_alpha_P4_flux_t4...\n", - "18-Jun-21 23:51:27: Loading mms2_epd_eis_extof_look_t4...\n", - "18-Jun-21 23:51:28: Loading mms2_epd_eis_extof_alpha_P4_flux_t5...\n", - "18-Jun-21 23:51:28: Loading mms2_epd_eis_extof_look_t5...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_spin...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_sector...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_electron_t0_energy_dminus...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_electron_t0_energy_dplus...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_electron_P4_flux_t0...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_look_t0...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_electron_P4_flux_t1...\n", - "18-Jun-21 23:51:29: Loading mms2_epd_eis_electronenergy_look_t1...\n", - "18-Jun-21 23:51:30: Loading mms2_epd_eis_electronenergy_electron_P4_flux_t2...\n", - "18-Jun-21 23:51:30: Loading mms2_epd_eis_electronenergy_look_t2...\n", - "18-Jun-21 23:51:30: Loading mms2_epd_eis_electronenergy_electron_P4_flux_t3...\n", - "18-Jun-21 23:51:30: Loading mms2_epd_eis_electronenergy_look_t3...\n", - "18-Jun-21 23:51:31: Loading mms2_epd_eis_electronenergy_electron_P4_flux_t4...\n", - "18-Jun-21 23:51:31: Loading mms2_epd_eis_electronenergy_look_t4...\n", - "18-Jun-21 23:51:31: Loading mms2_epd_eis_electronenergy_electron_P4_flux_t5...\n", - "18-Jun-21 23:51:31: Loading mms2_epd_eis_electronenergy_look_t5...\n" - ] - } - ], - "source": [ - "# Proton\n", - "dpf_phxtof_allt_proton = mms.get_eis_allt(\"flux_phxtof_proton_srvy_l2\", tint_long, mms_id)\n", - "dpf_extof_allt_proton = mms.get_eis_allt(\"flux_extof_proton_srvy_l2\", tint_long, mms_id)\n", - "# Oxygen\n", - "dpf_extof_allt_oxygen = mms.get_eis_allt(\"flux_extof_oxygen_srvy_l2\", tint_long, mms_id)\n", - "# Helium\n", - "dpf_extof_allt_helium = mms.get_eis_allt(\"flux_extof_alpha_srvy_l2\", tint_long, mms_id)\n", - "# Electron\n", - "dpf_een_allt_electron = mms.get_eis_allt(\"flux_electronenergy_electron_srvy_l2\", tint_long, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Post-processing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute H$^+$ combined PHxTOF and ExTOF differential particle flux for all 6 telescopes" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "dpf_eis_allt_proton = mms.eis_combine_proton_spec(dpf_phxtof_allt_proton, dpf_extof_allt_proton)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spin average H+, He𝑛+ (ExTOF) and O𝑛+ (ExTOF) differential particle fluxes for all 6 telescopes" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Helium\n", - "dpf_eis_allt_proton_spin = mms.eis_spin_avg(dpf_eis_allt_proton)\n", - "\n", - "# Helium\n", - "dpf_extof_allt_helium_spin = mms.eis_spin_avg(dpf_extof_allt_helium)\n", - "\n", - "# Oxygen\n", - "dpf_extof_allt_oxygen_spin = mms.eis_spin_avg(dpf_extof_allt_oxygen)\n", - "\n", - "# Electron\n", - "dpf_een_allt_electron_spin = mms.eis_spin_avg(dpf_een_allt_electron)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute H$^+$, He$^{n+}$ (ExTOF) and O$^{n+}$ (ExTOF) omni-directional differential particle fluxes with an without spin averaging" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Helium\n", - "dpf_eis_omni_proton = mms.eis_omni(dpf_eis_allt_proton)\n", - "dpf_eis_omni_proton_spin = mms.eis_omni(dpf_eis_allt_proton_spin)\n", - "\n", - "# Helium\n", - "dpf_extof_omni_helium = mms.eis_omni(dpf_extof_allt_helium)\n", - "dpf_extof_omni_helium_spin = mms.eis_omni(dpf_extof_allt_helium_spin)\n", - "\n", - "# Oxygen\n", - "dpf_extof_omni_oxygen = mms.eis_omni(dpf_extof_allt_oxygen)\n", - "dpf_extof_omni_oxygen_spin = mms.eis_omni(dpf_extof_allt_oxygen_spin)\n", - "\n", - "# Electron\n", - "dpf_een_omni_electron = mms.eis_omni(dpf_een_allt_electron)\n", - "dpf_een_omni_electron_spin = mms.eis_omni(dpf_een_allt_electron_spin)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Apply H$^+$ omni-directional differential particle flux cross calibration correction" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "dpf_eis_omni_proton_corr = mms.eis_proton_correction(dpf_eis_omni_proton)\n", - "dpf_eis_omni_proton_spin_corr = mms.eis_proton_correction(dpf_eis_omni_proton_spin)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot in a MMS SDC Quicklook fashion" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. 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position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
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" if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - 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" el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(5, sharex=\"all\", figsize=(8.5, 10))\n", - "f.subplots_adjust(bottom=.05, top=.95, left=.13, right=.87, hspace=0)\n", - "\n", - "plot_line(axs[0], b_gse)\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], frameon=True, loc=\"upper right\", ncol=3)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "\n", - "# Electron spin averaged\n", - "axs[1], caxs1 = plot_spectr(axs[1], dpf_een_omni_electron_spin, yscale=\"log\", cscale=\"log\")\n", - "caxs1.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[1].set_ylabel(\"$E_{e}$ [keV]\")\n", - "\n", - "# Proton spin averaged\n", - "axs[2], caxs2 = plot_spectr(axs[2], dpf_eis_omni_proton_spin_corr, yscale=\"log\", cscale=\"log\")\n", - "caxs2.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[2].set_ylabel(\"$E_{H^{+}}$ [keV]\")\n", - "\n", - "# Helium spin averaged\n", - "axs[3], caxs3 = plot_spectr(axs[3], dpf_extof_omni_helium_spin, yscale=\"log\", cscale=\"log\")\n", - "caxs3.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[3].set_ylabel(\"$E_{He^{n+}}$ [keV]\")\n", - "\n", - "# Oxygen spin averaged\n", - "axs[4], caxs4 = plot_spectr(axs[4], dpf_extof_omni_oxygen_spin, yscale=\"log\", cscale=\"log\")\n", - "caxs4.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[4].set_ylabel(\"$E_{O^{n+}}$ [keV]\")\n", - "\n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute H$^{+}$ (PHxTOF and ExTOF), He$^{n+}$, O$^{n+}$ and electron pitch angle distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 23:51:32: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n", - "18-Jun-21 23:51:32: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/mms/eis_pad.py:113: RuntimeWarning: Mean of empty slice\n", - " pa_flux[i, j, :] = np.nanmean(flux_file[i, ind, :], axis=0)\n", - "\n" - ] - } - ], - "source": [ - "# Low energy proton\n", - "dpf_phxtof_pad_proton = mms.eis_pad(dpf_phxtof_allt_proton, vec=b_gse, energy=[10, 50])\n", - "\n", - "# High energy proton\n", - "dpf_extof_pad_proton = mms.eis_pad(dpf_extof_allt_proton, vec=b_gse)\n", - "\n", - "# Helium\n", - "dpf_extof_pad_helium = mms.eis_pad(dpf_extof_allt_helium, vec=b_gse)\n", - "\n", - "# Oxygen\n", - "dpf_extof_pad_oxygen = mms.eis_pad(dpf_extof_allt_oxygen, vec=b_gse)\n", - "\n", - "# Electron\n", - "dpf_een_pad_electron = mms.eis_pad(dpf_een_allt_electron, vec=b_gse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spin average H$^+$, He$^{n+}$ and O$^{n+}$ pitch angle distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 23:51:38: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/mms/eis_pad_spinavg.py:47: RuntimeWarning: Mean of empty slice\n", - " spin_sum_flux[i, :, :] = np.nanmean(inp.data[idx_, :, :], axis=0)\n", - "\n" - ] - } - ], - "source": [ - "# Low energy proton\n", - "dpf_phxtof_pad_proton_spin = mms.eis_pad_spinavg(dpf_phxtof_pad_proton, dpf_phxtof_allt_proton.spin)\n", - "\n", - "# High energy proton\n", - "dpf_extof_pad_proton_spin = mms.eis_pad_spinavg(dpf_extof_pad_proton, dpf_extof_allt_proton.spin)\n", - "\n", - "# Helium\n", - "dpf_extof_pad_helium_spin = mms.eis_pad_spinavg(dpf_extof_pad_helium, dpf_extof_allt_helium.spin)\n", - "\n", - "# Oxygen\n", - "dpf_extof_pad_oxygen_spin = mms.eis_pad_spinavg(dpf_extof_pad_oxygen, dpf_extof_allt_oxygen.spin)\n", - "\n", - "# Electron\n", - "dpf_een_pad_electron_spin = mms.eis_pad_spinavg(dpf_een_pad_electron, dpf_een_allt_electron.spin)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f, axs = plt.subplots(6, sharex=\"all\", figsize=(8.5, 10))\n", - "f.subplots_adjust(bottom=.05, top=.95, left=.13, right=.87, hspace=0)\n", - "\n", - "plot_line(axs[0], b_gse)\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], frameon=True, loc=\"upper right\", ncol=3)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "\n", - "axs[1], caxs1 = plot_spectr(axs[1], dpf_een_pad_electron_spin.mean(dim=\"energy\", skipna=True), \n", - " cscale=\"log\")\n", - "caxs1.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[1].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "e_min, e_max = list(dpf_een_pad_electron.energy.data[[0, -1]])\n", - "axs[1].text(.02, .1, f\"{e_min:3.1f} keV < $E_{{e}}$ < {e_max:3.1f} keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[1].transAxes)\n", - "\n", - "axs[2], caxs2 = plot_spectr(axs[2], dpf_extof_pad_proton_spin.mean(dim=\"energy\", skipna=True), \n", - " cscale=\"log\")\n", - "caxs2.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[2].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "e_min, e_max = list(dpf_extof_pad_proton.energy.data[[0, -1]])\n", - "axs[2].text(.02, .1, f\"{e_min:3.1f} keV < $E_{{H^+}}$ < {e_max:3.1f} keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[2].transAxes)\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], dpf_phxtof_pad_proton_spin.mean(dim=\"energy\", skipna=True), \n", - " cscale=\"log\")\n", - "caxs3.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[3].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "e_min, e_max = list(dpf_phxtof_pad_proton.energy.data[[0, -1]])\n", - "axs[3].text(.02, .1, f\"{e_min:3.1f} keV < $E_{{H^+}}$ < {e_max:3.1f} keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[3].transAxes)\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], dpf_extof_pad_helium_spin.mean(dim=\"energy\", skipna=True), \n", - " cscale=\"log\")\n", - "caxs4.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[4].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "e_min, e_max = list(dpf_extof_pad_helium_spin.energy.data[[0, -1]])\n", - "axs[4].text(.02, .1, f\"{e_min:3.1f} keV < $E_{{He^{{n+}}}}$ < {e_max:3.1f} keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[4].transAxes)\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], dpf_extof_pad_oxygen_spin.mean(dim=\"energy\", skipna=True), \n", - " cscale=\"log\")\n", - "caxs5.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[5].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "e_min, e_max = list(dpf_extof_pad_oxygen_spin.energy.data[[0, -1]])\n", - "axs[5].text(.02, .1, f\"{e_min:3.1f} keV < $E_{{O^{{n+}}}}$ < {e_max:3.1f} keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[5].transAxes)\n", - "\n", - " \n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/examples/01_mms/example_mms_electron_psd.ipynb b/examples/01_mms/example_mms_electron_psd.ipynb deleted file mode 100644 index 69a814ff..00000000 --- a/examples/01_mms/example_mms_electron_psd.ipynb +++ /dev/null @@ -1,1196 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Electron PSD\n", - "author: Louis Richard\n", - "\n", - "Script to plot electron PSD around pitch angles 0, 90, and 180 deg and PSD versus pitch angle L1b brst data " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from astropy.time import Time\n", - "from pyrfu.pyrf import resample, time_clip\n", - "from pyrfu.mms import get_data, get_pitch_angle_dist, db_init" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define time interval and spacecraft index" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "db_init(\"/Volumes/mms\")\n", - "tint_r = [\"2015-10-30T05:15:20.000\", \"2015-10-30T05:16:20.000\"]\n", - "mms_id = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:15:26: Loading mms1_des_dist_brst...\n", - "09-Dec-21 11:15:40: Loading mms1_fgm_b_dmpa_brst_l2...\n", - "09-Dec-21 11:15:42: Loading mms1_edp_scpot_brst_l2...\n" - ] - } - ], - "source": [ - "vdf_e = get_data(\"pde_fpi_brst_l2\", tint_r, mms_id)\n", - "b_xyz = get_data(\"b_dmpa_fgm_brst_l2\", tint_r, mms_id)\n", - "sc_pot = get_data(\"v_edp_brst_l2\", tint_r, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert PSD units to s^3/km^6" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "vdf_e.data.data *= 1e36" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resample spacecraft potential to VDF sampling" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:15:43: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "sc_pot = resample(sc_pot, vdf_e.data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Produce a single PAD at a selected time" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "tint = \"2015-10-30T05:15:45.731587000\"\n", - "\n", - "pad_vdf_e = get_pitch_angle_dist(vdf_e, b_xyz, tint=tint_r)\n", - "pad_vdf_e = pad_vdf_e.sel(time=tint)\n", - "\n", - "idx = np.argmin(abs(sc_pot.time.data - Time(tint, format=\"isot\").datetime64))\n", - "energy_pad = pad_vdf_e.energy.data[0, :] - sc_pot.data[idx, ...]\n", - "thetas_pad = pad_vdf_e.theta.data[0, ...]\n", - "pad_data_e = pad_vdf_e.data.data[0, ...]\n", - "\n", - "pad_data_e[pad_data_e == 0.] = np.nan" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, '05:15:45.731 UT')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(ncols=2, sharey=\"all\", figsize=(8, 6))\n", - "f.subplots_adjust(wspace=.06)\n", - "\n", - "y_range = [10 ** 2, 10 ** np.ceil(np.nanmax(np.nanmax(np.log10(pad_data_e))))]\n", - "\n", - "for i, color, ang in zip([0, 6, -1], [\"k\", \"tab:red\", \"tab:blue\"], [0, 90, 180]):\n", - " axs[0].loglog(energy_pad, pad_data_e[:, i], color=color, label=f\"{ang} deg\")\n", - " \n", - "axs[0].set_xlim([5, 3e4])\n", - "axs[0].set_ylim(y_range)\n", - "axs[0].set_xticks([1e0, 1e1, 1e2, 1e3, 1e4, 1e5])\n", - "axs[0].set_xlim([5, 3e4])\n", - "axs[0].set_ylabel(\"$f_e$ [s$^3$ km$^{-6}$]\")\n", - "axs[0].set_xlabel(\"$E$ [eV]\")\n", - "axs[0].legend(loc=\"upper right\", frameon=True)\n", - "axs[0].grid(which=\"major\")\n", - "axs[0].set_title(\"{} UT\".format(tint[11:23]))\n", - "\n", - "colors = pl.cm.jet(np.linspace(0, 1, len(energy_pad)))\n", - "\n", - "for (i, energy), color in zip(enumerate(energy_pad), colors):\n", - " axs[1].semilogy(thetas_pad, pad_data_e[i, :], color=color)\n", - " \n", - "axs[1].set_xlim([0, 180])\n", - "axs[1].set_xlabel(\"$\\\\theta$ [deg.]\")\n", - "axs[1].set_xticks([0, 45, 90, 135, 180])\n", - "axs[1].grid(which=\"major\")\n", - "axs[1].set_title(\"{} UT\".format(tint[11:23]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/examples/01_mms/example_mms_feeps.ipynb b/examples/01_mms/example_mms_feeps.ipynb deleted file mode 100644 index 1b40f6e7..00000000 --- a/examples/01_mms/example_mms_feeps.ipynb +++ /dev/null @@ -1,3002 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fly’s Eye Energetic Particle Spectrometer (FEEPS)\n", - "author: Louis Richard\n", - "\n", - "Routines :\n", - "* feeps_correct_energies\n", - "* feeps_flat_field_corrections\n", - "* feeps_omni\n", - "* feeps_pad\n", - "* feeps_pad_spinavg\n", - "* feeps_remove_bad_data\n", - "* feeps_remove_sun\n", - "* feeps_sector_spec\n", - "* feeps_spin_avg\n", - "* feeps_split_integral_ch\n", - "* get_feeps_alle\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu import mms\n", - "from pyrfu.plot import plot_line, plot_spectr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define data path, time interval and spacecraft index" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "db_init(\"/Volumes/mms\")\n", - "tint_long = [\"2017-07-23T16:10:00\", \"2017-07-23T18:10:00\"]\n", - "mms_id = 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Magnetic field in GSM" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 21:44:33: Loading mms2_fgm_b_bcs_srvy_l2...\n", - "18-Jun-21 21:44:33: Loading mms2_fgm_b_gsm_srvy_l2...\n" - ] - } - ], - "source": [ - "b_bcs = mms.get_data(\"b_bcs_fgm_srvy_l2\", tint_long, mms_id)\n", - "b_gsm = mms.get_data(\"b_gsm_fgm_srvy_l2\", tint_long, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Electron and ion differential particle flux for all FEEPS sensors" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function get_feeps_alleyes in module pyrfu.mms.get_feeps_alleyes:\n", - "\n", - "get_feeps_alleyes(tar_var, tint, mms_id, verbose: bool = True, data_path: str = '')\n", - " Read energy spectrum of the selected specie in the selected energy\n", - " range for all FEEPS eyes.\n", - " \n", - " Parameters\n", - " ----------\n", - " tar_var : str\n", - " Key of the target variable like\n", - " {data_unit}{specie}_{data_rate}_{data_lvl}.\n", - " tint : list of str\n", - " Time interval.\n", - " mms_id : int or float or str\n", - " Index of the spacecraft.\n", - " verbose : bool, Optional\n", - " Set to True to follow the loading. Default is True.\n", - " data_path : str, Optional\n", - " Path of MMS data. Default uses `pyrfu.mms.mms_config.py`\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.Dataset\n", - " Dataset containing the energy spectrum of the available eyes of the\n", - " Fly's Eye Energetic Particle Spectrometer (FEEPS).\n", - " \n", - " Examples\n", - " --------\n", - " >>> from pyrfu import mms\n", - " \n", - " Define time interval\n", - " \n", - " >>> tint_brst = [\"2017-07-23T16:54:24.000\", \"2017-07-23T17:00:00.000\"]\n", - " \n", - " Read electron energy spectrum for all FEEPS eyes\n", - " \n", - " >>> feeps_all_eyes = mms.get_feeps_alleyes(\"fluxe_brst_l2\", tint_brst, 2)\n", - "\n" - ] - } - ], - "source": [ - "help(mms.get_feeps_alleyes)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_spinsectnum...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_pitch_angle...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_3...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_4...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_5...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_11...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_top_count_rate_sensorid_12...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_3...\n", - "18-Jun-21 21:44:34: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_4...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_5...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_11...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_electron_bottom_count_rate_sensorid_12...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_spinsectnum...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_pitch_angle...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_top_count_rate_sensorid_6...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_top_count_rate_sensorid_7...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_top_count_rate_sensorid_8...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_bottom_count_rate_sensorid_6...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_bottom_count_rate_sensorid_7...\n", - "18-Jun-21 21:44:35: Loading mms2_epd_feeps_srvy_l2_ion_bottom_count_rate_sensorid_8...\n" - ] - } - ], - "source": [ - "# Electron\n", - "dpf_feeps_alle_e = mms.get_feeps_alleyes(\"cpse_srvy_l2\", tint_long, mms_id)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_i = mms.get_feeps_alleyes(\"cpsi_srvy_l2\", tint_long, mms_id)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "b_gsm.data.ndim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Post-processing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Correct energy table" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_correct_energies in module pyrfu.mms.feeps_correct_energies:\n", - "\n", - "feeps_correct_energies(feeps_alle)\n", - " Modifies the energy table in FEEPS spectra (intensity, count_rate,\n", - " counts) using the function: mms_feeps_energy_table (which is s/c, sensor\n", - " head and sensor ID dependent)\n", - " \n", - " Parameters\n", - " ----------\n", - " feeps_alle : xarray.Dataset\n", - " Dataset containing the energy spectrum of the available eyes of the\n", - " Fly's Eye Energetic Particle Spectrometer (FEEPS).\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.Dataset\n", - " Dataset containing the energy spectrum of the available eyes of the\n", - " Fly's Eye Energetic Particle Spectrometer (FEEPS) with corrected\n", - " energy table.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_correct_energies)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_alle_e = mms.feeps_correct_energies(dpf_feeps_alle_e)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_i = mms.feeps_correct_energies(dpf_feeps_alle_i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Apply flat field correction to electron and ion differential particle flux spectra" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_flat_field_corrections in module pyrfu.mms.feeps_flat_field_corrections:\n", - "\n", - "feeps_flat_field_corrections(inp_alle)\n", - " Apply flat field correction factors to FEEPS ion/electron\n", - " data. Correct factors are from the gain factor found in:\n", - " FlatFieldResults_V3.xlsx from Drew Turner, 1/19/2017\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_alle : xarray.Dataset\n", - " Dataset containing the energy spectrum of the available eyes of the\n", - " Fly's Eye Energetic Particle Spectrometer (FEEPS).\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.Dataset\n", - " Dataset containing the energy spectrum of the available eyes of the\n", - " Fly's Eye Energetic Particle Spectrometer (FEEPS) with corrected\n", - " data.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_flat_field_corrections)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_alle_e = mms.feeps_flat_field_corrections(dpf_feeps_alle_e)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_i = mms.feeps_flat_field_corrections(dpf_feeps_alle_i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Remove bad data" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_remove_bad_data in module pyrfu.mms.feeps_remove_bad_data:\n", - "\n", - "feeps_remove_bad_data(inp_dataset)\n", - " This function removes bad eyes, bad lowest energy channels based on\n", - " data from Drew Turner\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_dataset : xarray.Dataset\n", - " Dataset with all active telescopes data.\n", - " \n", - " Returns\n", - " -------\n", - " inp_dataaset_clean_all : xarray.Dataset\n", - " Dataset with all active telescopes data where bad eyes and lab lowest\n", - " energy channels are set to NaN.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_remove_bad_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_alle_e = mms.feeps_remove_bad_data(dpf_feeps_alle_e)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_i = mms.feeps_remove_bad_data(dpf_feeps_alle_i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Split the last integral channel from the electron and ion differential particle flux spectra" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_split_integral_ch in module pyrfu.mms.feeps_split_integral_ch:\n", - "\n", - "feeps_split_integral_ch(inp_dataset)\n", - " This function splits the last integral channel from the FEEPS spectra,\n", - " creating 2 new DataArrays\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_dataset : xarray.Dataset\n", - " Energetic particles energy spectrum from FEEPS.\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.Dataset\n", - " Energetic particles energy spectra with the integral channel removed.\n", - " out_500kev : xarray.Dataset\n", - " Integral channel that was removed.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_split_integral_ch)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_alle_e_clean, dpf_feeps_alle_e_500kev = mms.feeps_split_integral_ch(dpf_feeps_alle_e)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_i_clean, dpf_feeps_alle_i_500kev = mms.feeps_split_integral_ch(dpf_feeps_alle_i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Remove sunlight contamination" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_remove_sun in module pyrfu.mms.feeps_remove_sun:\n", - "\n", - "feeps_remove_sun(inp_dataset)\n", - " Removes the sunlight contamination from FEEPS data.\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_dataset : xarray.Dataset\n", - " Dataset of energy spectrum of all eyes.\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.Dataset\n", - " Dataset of cleaned energy spectrum of all eyes.\n", - " \n", - " See also\n", - " --------\n", - " pyrfu.mms.get_feeps_alleyes : Read energy spectrum for all FEEPS eyes.\n", - " \n", - " Examples\n", - " --------\n", - " >>> from pyrfu import mms\n", - " \n", - " Define time interval\n", - " \n", - " >>> tint = [\"2017-07-18T13:04:00.000\", \"2017-07-18T13:07:00.000\"]\n", - " \n", - " Spacecraft index\n", - " \n", - " >>> mms_id = 2\n", - " \n", - " Load data from FEEPS\n", - " \n", - " >>> cps_i = mms.get_feeps_alleyes(\"CPSi_brst_l2\", tint, mms_id)\n", - " >>> cps_i_clean, _ = mms.feeps_split_integral_ch(cps_i)\n", - " >>> cps_i_clean_sun_removed = mms.feeps_remove_sun(cps_i_clean)\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_remove_sun)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_alle_e_clean_sun_removed = mms.feeps_remove_sun(dpf_feeps_alle_e_clean)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_i_clean_sun_removed = mms.feeps_remove_sun(dpf_feeps_alle_i_clean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute the electron and ion omni-directional flux for all 24 sensors" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_omni in module pyrfu.mms.feeps_omni:\n", - "\n", - "feeps_omni(inp_dataset)\n", - " Calculates the omni-directional FEEPS spectrogram.\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_dataset : xarray.Dataset\n", - " Dataset with all active telescopes data.\n", - " \n", - " Returns\n", - " -------\n", - " flux_omni : xarray.DataArray\n", - " Omni-directional FEEPS spectrogram.\n", - " \n", - " Notes\n", - " -----\n", - " The dataset can be raw data, but it is better to remove bad datas,\n", - " sunlight contamination and split before.\n", - " \n", - " See Also\n", - " --------\n", - " pyrfu.mms.get_feeps_alleyes, pyrfu.mms.feeps_remove_bad_data,\n", - " pyrfu.mms.feeps_split_integral_ch, pyrfu.mms.feeps_remove_sun\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_omni)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_omni_e = mms.feeps_omni(dpf_feeps_alle_e_clean_sun_removed)\n", - "\n", - "# Ion\n", - "dpf_feeps_omni_i = mms.feeps_omni(dpf_feeps_alle_i_clean_sun_removed)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creates sector-spectrograms with FEEPS data" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_sector_spec in module pyrfu.mms.feeps_sector_spec:\n", - "\n", - "feeps_sector_spec(inp_alle)\n", - " Creates sector-spectrograms with FEEPS data (particle data organized\n", - " by time and sector number)\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_alle : xarray.Dataset\n", - " Dataset of energy spectrum of all eyes.\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.Dataset\n", - " Sector-spectrograms with FEEPS data for all eyes.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_sector_spec)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 21:44:36: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/mms/feeps_sector_spec.py:54: RuntimeWarning: Mean of empty slice\n", - " sector_spec[i, s_] = np.nanmean(sensor_data[c_start:spin, :],\n", - "\n" - ] - } - ], - "source": [ - "# Electron\n", - "dpf_feeps_alle_ss_e_clean_sun_removed = mms.feeps_sector_spec(dpf_feeps_alle_e_clean_sun_removed)\n", - "\n", - "# Ion\n", - "dpf_feeps_alle_ss_i_clean_sun_removed = mms.feeps_sector_spec(dpf_feeps_alle_i_clean_sun_removed)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute the electron and ion pitch angle distribution for energies below and above 100 keV" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_pad in module pyrfu.mms.feeps_pad:\n", - "\n", - "feeps_pad(inp_dataset, b_bcs, bin_size: float = 16.3636, energy: list = None)\n", - " Compute pitch angle distribution using FEEPS data.\n", - " \n", - " Parameters\n", - " ----------\n", - " inp_dataset : xarray.Dataset\n", - " Energy spectrum of all eyes.\n", - " b_bcs : xarray.DataArray\n", - " Time series of the magnetic field in spacecraft coordinates.\n", - " bin_size : float, optional\n", - " Width of the pitch angles bins. Default is 16.3636.\n", - " energy : array_like, optional\n", - " Energy range of particles. Default is [70., 600.]\n", - " \n", - " Returns\n", - " -------\n", - " pad : xarray.DataArray\n", - " Time series of the pitch angle distribution.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_pad)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "18-Jun-21 21:44:36: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n", - "18-Jun-21 21:44:38: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n", - "18-Jun-21 21:44:40: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n", - "18-Jun-21 21:44:42: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n", - "18-Jun-21 21:44:44: /Users/louisr/opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "dpf_feeps_pad_i_070_100 = mms.feeps_pad(dpf_feeps_alle_i_clean_sun_removed, b_bcs, \n", - " energy=[70, 100])\n", - "\n", - "# Electron\n", - "dpf_feeps_pad_e_050_100 = mms.feeps_pad(dpf_feeps_alle_e_clean_sun_removed, b_bcs, \n", - " energy=[50, 100])\n", - "dpf_feeps_pad_e_100_200 = mms.feeps_pad(dpf_feeps_alle_e_clean_sun_removed, b_bcs, \n", - " energy=[100, 200])\n", - "\n", - "# Ion\n", - "dpf_feeps_pad_i_070_100 = mms.feeps_pad(dpf_feeps_alle_i_clean_sun_removed, b_bcs, \n", - " energy=[70, 100])\n", - "dpf_feeps_pad_i_100_200 = mms.feeps_pad(dpf_feeps_alle_i_clean_sun_removed, b_bcs, \n", - " energy=[100, 200])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot in a MMS SDC Quicklook fashion" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. 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" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'MMS 2')" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(7, sharex=\"all\", figsize=(8.5, 10))\n", - "f.subplots_adjust(left=.13, right=.87, bottom=.07, top=.95, hspace=0)\n", - "\n", - "plot_line(axs[0], b_gsm)\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], loc=\"upper right\", ncol=3, frameon=True)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "\n", - "axs[1], caxs1 = plot_spectr(axs[1], dpf_feeps_omni_e, yscale=\"log\", cscale=\"log\")\n", - "caxs1.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[1].set_ylabel(\"$E_e$ [keV]\")\n", - "\n", - "axs[2], caxs2 = plot_spectr(axs[2], dpf_feeps_pad_e_050_100, cscale=\"log\")\n", - "caxs2.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[2].set_ylim([0, 180])\n", - "axs[2].set_yticks([45, 90, 135])\n", - "axs[2].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], dpf_feeps_pad_e_100_200, cscale=\"log\")\n", - "caxs3.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[3].set_ylim([0, 180])\n", - "axs[3].set_yticks([45, 90, 135])\n", - "axs[3].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], dpf_feeps_omni_i[:, 1:], yscale=\"log\", cscale=\"log\")\n", - "caxs4.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[4].set_ylabel(\"$E_i$ [keV]\")\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], dpf_feeps_pad_i_070_100, cscale=\"log\", clim=[3e-1, 1e2])\n", - "caxs5.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[5].set_ylim([0, 180])\n", - "axs[5].set_yticks([45, 90, 135])\n", - "axs[5].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], dpf_feeps_pad_i_100_200, cscale=\"log\")\n", - "caxs6.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[6].set_ylim([0, 180])\n", - "axs[6].set_yticks([45, 90, 135])\n", - "axs[6].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "\n", - "f.align_ylabels(axs)\n", - "f.suptitle(f\"MMS {mms_id:d}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spin average omni-directional differential particle flux and pitch angle distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_spin_avg in module pyrfu.mms.feeps_spin_avg:\n", - "\n", - "feeps_spin_avg(flux_omni, spin_sectors)\n", - " spin-average the omni-directional FEEPS energy spectra\n", - " \n", - " Parameters\n", - " ----------\n", - " flux_omni : xarray.DataArray\n", - " Omni-direction flux.\n", - " spin_sectors : xarray.DataArray\n", - " Time series of the spin sectors.\n", - " \n", - " Returns\n", - " -------\n", - " spin_avg_flux : xarray.DataArray\n", - " Spin averaged omni-directional flux.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_spin_avg)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_omni_e_spin = mms.feeps_spin_avg(dpf_feeps_omni_e, dpf_feeps_alle_e.spinsectnum)\n", - "\n", - "# Ion\n", - "dpf_feeps_omni_i_spin = mms.feeps_spin_avg(dpf_feeps_omni_i, dpf_feeps_alle_i.spinsectnum)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function feeps_pad_spinavg in module pyrfu.mms.feeps_pad_spinavg:\n", - "\n", - "feeps_pad_spinavg(pad, spin_sectors, bin_size: float = 16.3636)\n", - " Spin-average the FEEPS pitch angle distributions.\n", - " \n", - " Parameters\n", - " ----------\n", - " pad : xarray.DataArray\n", - " Pitch angle distribution.\n", - " spin_sectors : xarray.DataArray\n", - " Time series of the spin sectors.\n", - " bin_size : float, Optional\n", - " Size of the pitch angle bins\n", - " \n", - " Returns\n", - " -------\n", - " out : xarray.DataArray\n", - " Spin averaged pitch angle distribution.\n", - "\n" - ] - } - ], - "source": [ - "help(mms.feeps_pad_spinavg)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Electron\n", - "dpf_feeps_pad_e_050_100_spin = mms.feeps_pad_spinavg(dpf_feeps_pad_e_050_100, dpf_feeps_alle_e.spinsectnum)\n", - "dpf_feeps_pad_e_100_200_spin = mms.feeps_pad_spinavg(dpf_feeps_pad_e_100_200, dpf_feeps_alle_e.spinsectnum)\n", - "\n", - "# Ion\n", - "dpf_feeps_pad_i_070_100_spin = mms.feeps_pad_spinavg(dpf_feeps_pad_i_070_100, dpf_feeps_alle_i.spinsectnum)\n", - "dpf_feeps_pad_i_100_200_spin = mms.feeps_pad_spinavg(dpf_feeps_pad_i_100_200, dpf_feeps_alle_i.spinsectnum)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - 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"%matplotlib notebook\n", - "f, axs = plt.subplots(7, sharex=\"all\", figsize=(8.5, 10))\n", - "f.subplots_adjust(left=.13, right=.87, bottom=.07, top=.95, hspace=0)\n", - "\n", - "plot_line(axs[0], b_gsm)\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], loc=\"upper right\", ncol=3, frameon=True)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "\n", - "axs[1], caxs1 = plot_spectr(axs[1], dpf_feeps_omni_e_spin, yscale=\"log\", cscale=\"log\")\n", - "caxs1.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[1].set_ylabel(\"$E_e$ [keV]\")\n", - "\n", - "axs[2], caxs2 = plot_spectr(axs[2], dpf_feeps_pad_e_050_100_spin, cscale=\"log\")\n", - "caxs2.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[2].set_ylim([0, 180])\n", - "axs[2].set_yticks([45, 90, 135])\n", - "axs[2].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "axs[2].text(.02, .1, \"50 keV < $E_e$ < 100 keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[2].transAxes)\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], dpf_feeps_pad_e_100_200_spin, cscale=\"log\")\n", - "caxs3.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[3].set_ylim([0, 180])\n", - "axs[3].set_yticks([45, 90, 135])\n", - "axs[3].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "axs[3].text(.02, .1, \"100 keV < $E_e$ < 200 keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[3].transAxes)\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], dpf_feeps_omni_i_spin[:, 1:], yscale=\"log\", cscale=\"log\")\n", - "caxs4.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[4].set_ylabel(\"$E_i$ [keV]\")\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], dpf_feeps_pad_i_070_100_spin, cscale=\"log\", clim=[3e-1, 1e2])\n", - "caxs5.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[5].set_ylim([0, 180])\n", - "axs[5].set_yticks([45, 90, 135])\n", - "axs[5].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "axs[5].text(.02, .1, \"70 keV < $E_e$ < 100 keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[5].transAxes)\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], dpf_feeps_pad_i_100_200_spin, cscale=\"log\")\n", - "caxs6.set_ylabel(\"Flux\" + \"\\n\" + \"[(cm$^2$ s sr keV)$^{-1}$]\")\n", - "axs[6].set_ylim([0, 180])\n", - "axs[6].set_yticks([45, 90, 135])\n", - "axs[6].set_ylabel(\"$\\\\theta$ [$^{\\\\circ}$]\")\n", - "axs[6].text(.02, .1, \"100 keV < $E_i$ < 200 keV\", \n", - " bbox=dict(fc=(1, 1, 1)), transform=axs[6].transAxes)\n", - "\n", - "f.align_ylabels(axs)\n", - "f.suptitle(f\"MMS {mms_id:d} spin averaged\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/examples/01_mms/example_mms_hpca.ipynb b/examples/01_mms/example_mms_hpca.ipynb deleted file mode 100644 index 03c75d92..00000000 --- a/examples/01_mms/example_mms_hpca.ipynb +++ /dev/null @@ -1,1240 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hot Plasma Composition Analyzer\n", - "author: Louis Richard\\\n", - "HPCA summary plot" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu import mms\n", - "from pyrfu.plot import plot_line, plot_spectr, make_labels" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define spacecraft index, time interval and species" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "ic = 1\n", - "mms.db_init(\"/Volumes/mms\")\n", - "tint = [\"2017-07-23T16:54:00.000\", \"2017-07-23T17:00:00.000\"]\n", - "species = {\"hplus\": \"$H^+$\", \"heplus\": \"$He^+$\", \"heplusplus\": \"$He^{2+}$\", \"oplus\": \"$O^+$\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load HPCA moments" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:18:14: Loading mms1_hpca_hplus_number_density...\n", - "09-Dec-21 11:18:15: Loading mms1_hpca_heplus_number_density...\n", - "09-Dec-21 11:18:17: Loading mms1_hpca_heplusplus_number_density...\n", - "09-Dec-21 11:18:18: Loading mms1_hpca_oplus_number_density...\n", - "09-Dec-21 11:18:20: Loading mms1_hpca_hplus_ion_bulk_velocity...\n", - "09-Dec-21 11:18:21: Loading mms1_hpca_heplus_ion_bulk_velocity...\n", - "09-Dec-21 11:18:23: Loading mms1_hpca_heplusplus_ion_bulk_velocity...\n", - "09-Dec-21 11:18:25: Loading mms1_hpca_oplus_ion_bulk_velocity...\n" - ] - } - ], - "source": [ - "n_hpca = [mms.get_data(f\"n{s}_hpca_srvy_l2\", tint, ic) for s in species.keys()]\n", - "v_xyz_hpca = [mms.get_data(f\"v{s}_dbcs_hpca_srvy_l2\", tint, ic) for s in species.keys()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load HPCA ion" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:18:28: Loading mms1_hpca_hplus_flux...\n", - "09-Dec-21 11:18:30: Loading mms1_hpca_heplus_flux...\n", - "09-Dec-21 11:18:33: Loading mms1_hpca_heplusplus_flux...\n", - "09-Dec-21 11:18:36: Loading mms1_hpca_oplus_flux...\n" - ] - } - ], - "source": [ - "flux_hpca = [mms.get_data(f\"dpf{s}_hpca_srvy_l2\", tint, ic) for s in species.keys()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load magnetic field" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:18:39: Loading mms1_fgm_b_gse_srvy_l2...\n" - ] - } - ], - "source": [ - "b_xyz = mms.get_data(\"b_gse_fgm_srvy_l2\", tint, ic)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute ion fluxes" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:18:41: :6: RuntimeWarning: Mean of empty slice\n", - " out = xr.DataArray(np.nanmean(flux.data, axis=1), coords=coords, dims=dims, attrs=flux.attrs)\n", - "\n" - ] - } - ], - "source": [ - "def calc_hpca_flux(flux):\n", - " flux.data[flux.data <= 0] = np.nan\n", - " coords = [flux.time.data, flux.ccomp.data]\n", - " dims = [\"time\", \"energy\"]\n", - "\n", - " out = xr.DataArray(np.nanmean(flux.data, axis=1), coords=coords, dims=dims, attrs=flux.attrs)\n", - " return out\n", - "\n", - "flux_hpca = [calc_hpca_flux(flux) for flux in flux_hpca]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "legend_options = dict(ncol=max([3, len(species.keys())]), frameon=True, loc=\"upper right\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - 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We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - 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" // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([,\n", - " ,\n", - " , ,\n", - " , ],\n", - " dtype=object)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(6, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(bottom=.1, top=.95, left=.15, right=.85, hspace=0)\n", - "\n", - "plot_line(axs[0], b_xyz)\n", - "axs[0].set_ylabel(\"$B_{GSE}$\" + \"\\n\" + \"[nT]\")\n", - "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\"], **legend_options)\n", - "\n", - "labels = []\n", - "for n, s in zip(n_hpca, species.keys()):\n", - " plot_line(axs[1], n, linestyle=\"-\", marker=\"o\")\n", - " labels.append(species[s])\n", - " \n", - "axs[1].set_ylabel(\"$n$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", - "axs[1].legend(labels, **legend_options)\n", - "\n", - "caxs = [None] * len(species)\n", - "for s, ax, cax, flux in zip(species.keys(), axs[2:], caxs, flux_hpca):\n", - " ax, cax = plot_spectr(ax, flux, yscale=\"log\", cscale=\"log\", cmap=\"viridis\")\n", - " ax.set_ylabel(\"$E$\" + \"\\n\" + \"[eV]\")\n", - " cax.set_ylabel(\"flux\" + \"\\n\" + \"[1/cc s sr eV]\")\n", - " ax.text(0.05, 0.1, species[s], transform=ax.transAxes)\n", - " \n", - "f.align_ylabels(axs)\n", - "make_labels(axs, [0.02, 0.85])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/examples/01_mms/example_mms_ohmslaw.ipynb b/examples/01_mms/example_mms_ohmslaw.ipynb deleted file mode 100644 index 18ed8335..00000000 --- a/examples/01_mms/example_mms_ohmslaw.ipynb +++ /dev/null @@ -1,1449 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Ohm's Law\n", - "Author: Louis Richard\\\n", - "Compute the terms in the generalized Ohm's law equation: Ion convection, Hall, and electron pressure divergence terms. Hall and pressure terms are computed using four-spacecraft methods. The observed electric fields and convection terms are averaged over the four spacecraft. Terms computed in GSE coordinates." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from astropy import constants\n", - "from pyrfu.mms import get_data, db_init\n", - "from pyrfu.plot import plot_line\n", - "from pyrfu.pyrf import (resample, avg_4sc, extend_tint, cross, c_4_grad, \n", - " ts_vec_xyz, c_4_j)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define time interval and data path" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "db_init(\"/Volumes/mms\")\n", - "tint = [\"2015-10-30T05:15:40.000\", \"2015-10-30T05:15:55.000\"]\n", - "tint_long = extend_tint(tint, [-60, 60])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load all data and constants" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define constants" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "qe = constants.e.value\n", - "me = constants.m_e.value" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load FPI data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:10:26: Loading mms1_des_numberdensity_brst...\n", - "09-Dec-21 12:10:29: Loading mms2_des_numberdensity_brst...\n", - "09-Dec-21 12:10:31: Loading mms3_des_numberdensity_brst...\n", - "09-Dec-21 12:10:33: Loading mms4_des_numberdensity_brst...\n", - "09-Dec-21 12:10:35: Loading mms1_des_bulkv_gse_brst...\n", - "09-Dec-21 12:10:41: Loading mms2_des_bulkv_gse_brst...\n", - "09-Dec-21 12:10:46: Loading mms3_des_bulkv_gse_brst...\n", - "09-Dec-21 12:10:52: Loading mms4_des_bulkv_gse_brst...\n", - "09-Dec-21 12:10:57: Loading mms1_des_prestensor_gse_brst...\n", - "09-Dec-21 12:11:06: Loading mms2_des_prestensor_gse_brst...\n", - "09-Dec-21 12:11:16: Loading mms3_des_prestensor_gse_brst...\n", - "09-Dec-21 12:11:25: Loading mms4_des_prestensor_gse_brst...\n", - "09-Dec-21 12:11:34: Loading mms1_dis_numberdensity_brst...\n", - "09-Dec-21 12:11:36: Loading mms2_dis_numberdensity_brst...\n", - "09-Dec-21 12:11:38: Loading mms3_dis_numberdensity_brst...\n", - "09-Dec-21 12:11:40: Loading mms4_dis_numberdensity_brst...\n", - "09-Dec-21 12:11:42: Loading mms1_dis_bulkv_gse_brst...\n", - "09-Dec-21 12:11:47: Loading mms2_dis_bulkv_gse_brst...\n", - "09-Dec-21 12:11:52: Loading mms3_dis_bulkv_gse_brst...\n", - "09-Dec-21 12:11:56: Loading mms4_dis_bulkv_gse_brst...\n" - ] - } - ], - "source": [ - "n_mms_e = [get_data(\"ne_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "v_mms_e = [get_data(\"ve_gse_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "p_mms_e = [get_data(\"pe_gse_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "\n", - "n_mms_i = [get_data(\"ni_fpi_brst_l2\", tint, i) for i in range(1, 5)]\n", - "v_mms_i = [get_data(\"vi_gse_fpi_brst_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load FGM data" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:12:01: Loading mms1_fgm_b_gse_brst_l2...\n", - "09-Dec-21 12:12:03: Loading mms2_fgm_b_gse_brst_l2...\n", - "09-Dec-21 12:12:04: Loading mms3_fgm_b_gse_brst_l2...\n", - "09-Dec-21 12:12:06: Loading mms4_fgm_b_gse_brst_l2...\n" - ] - } - ], - "source": [ - "b_mms = [get_data(\"b_gse_fgm_brst_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load spacecraft position" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:12:08: Loading mms1_mec_r_gse...\n", - "09-Dec-21 12:12:14: Loading mms2_mec_r_gse...\n", - "09-Dec-21 12:12:20: Loading mms3_mec_r_gse...\n", - "09-Dec-21 12:12:26: Loading mms4_mec_r_gse...\n" - ] - } - ], - "source": [ - "r_mms = [get_data(\"r_gse_mec_srvy_l2\", tint_long, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load Electric field" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:12:32: Loading mms1_edp_dce_gse_brst_l2...\n", - "09-Dec-21 12:12:33: Loading mms2_edp_dce_gse_brst_l2...\n", - "09-Dec-21 12:12:35: Loading mms3_edp_dce_gse_brst_l2...\n", - "09-Dec-21 12:12:37: Loading mms4_edp_dce_gse_brst_l2...\n" - ] - } - ], - "source": [ - "e_mms = [get_data(\"e_gse_edp_brst_l2\", tint, i) for i in range(1, 5)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resample and compute averages" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:12:38: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "n_mms_e = [resample(n_e, n_mms_e[0]) for n_e in n_mms_e]\n", - "v_mms_e = [resample(v_xyz_e, n_mms_e[0]) for v_xyz_e in v_mms_e]\n", - "p_mms_e = [resample(p_xyz_e, n_mms_e[0]) for p_xyz_e in p_mms_e]\n", - "n_mms_i = [resample(n_i, n_mms_e[0]) for n_i in n_mms_i]\n", - "v_mms_i = [resample(v_xyz_i, n_mms_e[0]) for v_xyz_i in v_mms_i]\n", - "r_mms = [resample(r_xyz, n_mms_e[0]) for r_xyz in r_mms]\n", - "b_mms = [resample(b_xyz, n_mms_e[0]) for b_xyz in b_mms]\n", - "e_mms = [resample(e_xyz, n_mms_e[0]) for e_xyz in e_mms]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "n_e_avg = avg_4sc(n_mms_e)\n", - "b_xyz_avg = avg_4sc(b_mms)\n", - "e_xyz_avg = avg_4sc(e_mms)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute convection terms" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute ion convection term (MMS average)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "evxb_mms_i = [1e-3 * cross(v_xyz_i, b_xyz) for v_xyz_i, b_xyz in zip(v_mms_i, b_mms)]\n", - "evxb_xyz_i = avg_4sc(evxb_mms_i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute electron convection term (MMS average)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "evxb_mms_e = [1e-3 * cross(v_xyz_e, b_xyz) for v_xyz_e, b_xyz in zip(v_mms_e, b_mms)]\n", - "evxb_xyz_e = avg_4sc(evxb_mms_e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute pressure divergence term" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "p_mms_xx = [1e-9 * p_xyz[:, 0, 0] for p_xyz in p_mms_e]\n", - "p_mms_yy = [1e-9 * p_xyz[:, 1, 1] for p_xyz in p_mms_e]\n", - "p_mms_zz = [1e-9 * p_xyz[:, 2, 2] for p_xyz in p_mms_e]\n", - "p_mms_xy = [1e-9 * p_xyz[:, 0, 1] for p_xyz in p_mms_e]\n", - "p_mms_xz = [1e-9 * p_xyz[:, 0, 2] for p_xyz in p_mms_e]\n", - "p_mms_yz = [1e-9 * p_xyz[:, 1, 2] for p_xyz in p_mms_e]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "ep_xyz_xx = c_4_grad(r_mms, p_mms_xx, \"grad\")\n", - "ep_xyz_yy = c_4_grad(r_mms, p_mms_yy, \"grad\")\n", - "ep_xyz_zz = c_4_grad(r_mms, p_mms_zz, \"grad\")\n", - "ep_xyz_xy = c_4_grad(r_mms, p_mms_xy, \"grad\")\n", - "ep_xyz_xz = c_4_grad(r_mms, p_mms_xz, \"grad\")\n", - "ep_xyz_yz = c_4_grad(r_mms, p_mms_yz, \"grad\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "ep_x = -(ep_xyz_xx[:, 0] + ep_xyz_xy[:, 1] + ep_xyz_xz[:, 2]) / (n_e_avg * 1e6 * qe)\n", - "ep_y = -(ep_xyz_xy[:, 0] + ep_xyz_yy[:, 1] + ep_xyz_yz[:, 2]) / (n_e_avg * 1e6 * qe)\n", - "ep_z = -(ep_xyz_xz[:, 0] + ep_xyz_yz[:, 1] + ep_xyz_zz[:, 2]) / (n_e_avg * 1e6 * qe)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "ep_xyz = ts_vec_xyz(ep_xyz_xx.time.data, np.vstack([ep_x.data, ep_y.data, ep_z.data]).T)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Hall term and current density using curlometer" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "j_xyz, div_b, b_xyz, jxb_xyz, div_t_shear, div_pb = c_4_j(r_mms, b_mms)\n", - "jxb_xyz.data /= n_e_avg.data[:, None] * qe * 1e6\n", - "jxb_xyz.data *= 1e3\n", - "j_xyz.data *= 1e9" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "e_lhs = ts_vec_xyz(e_xyz_avg.time.data, e_xyz_avg.data - evxb_xyz_i.data)\n", - "e_rhs = ts_vec_xyz(e_xyz_avg.time.data,jxb_xyz.data + ep_xyz.data);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot figure" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "legend_options = dict(ncol=3, loc=\"upper right\", frameon=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
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"mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'MMS - 4 Spacecraft average')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(5, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(hspace=0, left=.15, right=.85)\n", - "\n", - "plot_line(axs[0], b_xyz_avg)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "axs[0].legend([\"$B_{x}$\",\"$B_{y}$\",\"$B_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[1], j_xyz)\n", - "axs[1].set_ylabel(\"$J$ [nA m$^{-2}$]\")\n", - "axs[1].legend([\"$J_{x}$\",\"$J_{y}$\",\"$J_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[2], e_xyz_avg)\n", - "axs[2].set_ylabel(\"$E$ [mV m$^{-1}$]\")\n", - "axs[2].legend([\"$E_{x}$\",\"$E_{y}$\",\"$E_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[3], e_xyz_avg[:, 0])\n", - "plot_line(axs[3], jxb_xyz[:, 0])\n", - "plot_line(axs[3], evxb_xyz_i[:, 0])\n", - "plot_line(axs[3], ep_xyz[:, 0])\n", - "plot_line(axs[3], evxb_xyz_e[:, 0])\n", - "axs[3].set_ylabel(\"$E_x$ [mV m$^{-1}$]\")\n", - "labels = ['$E$','$J \\\\times B/q_{e}n$','$-V_{i} \\\\times B$','$-\\\\nabla \\\\cdot P_{e}/q_{e}n$','$-V_{e} \\\\times B$']\n", - "axs[3].legend(labels, **legend_options)\n", - "\n", - "plot_line(axs[4], e_lhs[:, 0], color=\"k\")\n", - "plot_line(axs[4], e_rhs[:, 0], color=\"tab:red\")\n", - "axs[4].set_ylabel(\"$E_x$ [mV m$^{-1}$]\")\n", - "labels = ['$E+V_{i} \\\\times B$', '$J \\\\times B/q_{e}n - \\\\nabla \\cdot P_{e}/q_{e}n$']\n", - "axs[4].legend(labels, **legend_options)\n", - "\n", - "axs[0].set_title('MMS - 4 Spacecraft average')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/example_mms_particle_deflux.ipynb b/examples/01_mms/example_mms_particle_deflux.ipynb deleted file mode 100644 index 07785f58..00000000 --- a/examples/01_mms/example_mms_particle_deflux.ipynb +++ /dev/null @@ -1,1373 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Particle Differential Energy Fluxes\n", - "author: Louis Richard\\\n", - "Load brst particle distributions and convert to differential energy fluxes. Plots electron and ion fluxes and electron anisotropies." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu import mms\n", - "from pyrfu.pyrf import norm, resample\n", - "from pyrfu.plot import plot_line, plot_spectr, make_labels\n", - "from astropy import constants" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define data path, spacecraft index and time interval" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "mms.db_init(\"/Volumes/mms\")\n", - "ic = 3 # Spacecraft number\n", - "tint = [\"2015-10-30T05:15:20.000\", \"2015-10-30T05:16:20.000\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Particle distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:37:17: Loading mms3_dis_dist_brst...\n", - "09-Dec-21 11:37:23: Loading mms3_des_dist_brst...\n" - ] - } - ], - "source": [ - "vdf_i, vdf_e = [mms.get_data(f\"pd{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Particle moments" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:37:35: Loading mms3_dis_numberdensity_brst...\n", - "09-Dec-21 11:37:37: Loading mms3_des_numberdensity_brst...\n", - "09-Dec-21 11:37:38: Loading mms3_dis_bulkv_gse_brst...\n", - "09-Dec-21 11:37:42: Loading mms3_des_bulkv_gse_brst...\n", - "09-Dec-21 11:37:48: Loading mms3_dis_temptensor_gse_brst...\n", - "09-Dec-21 11:37:55: Loading mms3_des_temptensor_gse_brst...\n" - ] - } - ], - "source": [ - "n_i, n_e = [mms.get_data(f\"n{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]\n", - "v_xyz_i, v_xyz_e = [mms.get_data(f\"v{s}_gse_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]\n", - "t_xyz_i, t_xyz_e = [mms.get_data(f\"t{s}_gse_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Other variables" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:38:02: Loading mms3_fgm_b_dmpa_brst_l2...\n", - "09-Dec-21 11:38:04: Loading mms3_fgm_b_gse_brst_l2...\n", - "09-Dec-21 11:38:05: Loading mms3_edp_dce_gse_brst_l2...\n", - "09-Dec-21 11:38:07: Loading mms3_edp_scpot_brst_l2...\n", - "09-Dec-21 11:38:08: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "b_xyz, b_gse = [mms.get_data(f\"b_{cs}_fgm_brst_l2\", tint, ic) for cs in [\"dmpa\", \"gse\"]]\n", - "e_xyz = mms.get_data(\"e_gse_edp_brst_l2\", tint, ic)\n", - "scpot = mms.get_data(\"v_edp_brst_l2\", tint, ic)\n", - "scpot = resample(scpot, n_e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute moments " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "qe = constants.e.value\n", - "mp = constants.m_p.value\n", - "v_av = 0.5 * mp * (1e3 * norm(v_xyz_i)) ** 2 / qe" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute parallel and perpendicular electron and ion temperatures" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:38:08: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n" - ] - } - ], - "source": [ - "t_fac_i, t_fac_e = [mms.rotate_tensor(t_xyz, \"fac\", b_xyz, \"pp\") for t_xyz in [t_xyz_i, t_xyz_e]]\n", - "\n", - "t_para_i, t_para_e = [t_fac[:, 0, 0] for t_fac in [t_fac_i, t_fac_e]]\n", - "t_perp_i, t_perp_e = [t_fac[:, 1, 1] for t_fac in [t_fac_i, t_fac_e]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Differential Energy Fluxes" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def_omni_i, def_omni_e = [mms.vdf_omni(mms.psd2def(vdf)) for vdf in [vdf_i, vdf_e]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Pitch-Angle Distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined number of pitch angles.\n" - ] - } - ], - "source": [ - "def_pad_e = mms.psd2def(mms.get_pitch_angle_dist(vdf_e, b_xyz, tint, angles=13))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute parallel/anti-parallel and parallel+anti-parallel/perpandicular" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:38:18: :5: RuntimeWarning: divide by zero encountered in true_divide\n", - " psd_parapar = def_pad_e.data.data[:, :, 0] / def_pad_e.data.data[:, :, -1]\n", - "\n", - "09-Dec-21 11:38:18: :5: RuntimeWarning: invalid value encountered in true_divide\n", - " psd_parapar = def_pad_e.data.data[:, :, 0] / def_pad_e.data.data[:, :, -1]\n", - "\n", - "09-Dec-21 11:38:18: :6: RuntimeWarning: divide by zero encountered in true_divide\n", - " psd_parperp = (def_pad_e.data.data[:, :, 0] + def_pad_e.data.data[:, :, -1]) / (2 * def_pad_e.data.data[:, :, 7])\n", - "\n", - "09-Dec-21 11:38:18: :6: RuntimeWarning: invalid value encountered in true_divide\n", - " psd_parperp = (def_pad_e.data.data[:, :, 0] + def_pad_e.data.data[:, :, -1]) / (2 * def_pad_e.data.data[:, :, 7])\n", - "\n" - ] - } - ], - "source": [ - "def calc_parapar_parperp(pad):\n", - " coords = [pad.time.data, pad.energy.data[0, :]]\n", - " dims = [\"time\", \"energy\"]\n", - "\n", - " psd_parapar = def_pad_e.data.data[:, :, 0] / def_pad_e.data.data[:, :, -1]\n", - " psd_parperp = (def_pad_e.data.data[:, :, 0] + def_pad_e.data.data[:, :, -1]) / (2 * def_pad_e.data.data[:, :, 7])\n", - " \n", - " psd_parapar = xr.DataArray(psd_parapar, coords=coords, dims=dims)\n", - " psd_parperp = xr.DataArray(psd_parperp, coords=coords, dims=dims)\n", - " \n", - " return psd_parapar, psd_parperp\n", - "\n", - "vdf_parapar_e, vdf_parperp_e = calc_parapar_parperp(def_pad_e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "legend_options = dict(frameon=True, loc=\"upper right\", ncol=3)\n", - "e_lim_i = [min(def_omni_i.energy.data), max(def_omni_i.energy.data)]\n", - "e_lim_e = [min(def_omni_e.energy.data), max(def_omni_e.energy.data)]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - 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" icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(8, sharex=\"all\", figsize=(6.5, 11))\n", - "f.subplots_adjust(bottom=.1, top=.95, left=.15, right=.85, hspace=0)\n", - "\n", - "plot_line(axs[0], b_xyz)\n", - "axs[0].set_ylabel(\"$B_{DMPA}$\" + \"\\n\" + \"[nT]\")\n", - "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[1], v_xyz_i)\n", - "axs[1].set_ylabel(\"$V_{i}$\" + \"\\n\" + \"[km s$^{-1}$]\")\n", - "axs[1].legend([\"$V_{x}$\", \"$V_{y}$\", \"$V_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[2], n_i, color=\"tab:blue\")\n", - "plot_line(axs[2], n_e, color=\"tab:red\")\n", - "axs[2].set_yscale(\"log\")\n", - "axs[2].set_ylabel(\"$n$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", - "axs[2].legend([\"$n_i$\", \"$n_e$\"], **legend_options)\n", - "\n", - "plot_line(axs[3], e_xyz)\n", - "axs[3].set_ylabel(\"$E$\" + \"\\n\" + \"[mV m$^{-1}$]\")\n", - "axs[3].legend([\"$E_{x}$\", \"$E_{y}$\", \"$E_{z}$\"], **legend_options)\n", - "\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], def_omni_i, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[4].set_ylabel(\"$E_i$\" + \"\\n\" + \"[eV]\")\n", - "caxs4.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[4].set_ylim(e_lim_i)\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], def_omni_e, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "plot_line(axs[5], scpot)\n", - "plot_line(axs[5], t_para_e)\n", - "plot_line(axs[5], t_perp_e)\n", - "axs[5].set_ylabel(\"$E_e$\" + \"\\n\" + \"[eV]\")\n", - "caxs5.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[5].legend([\"$\\phi$\", \"$T_{||}$\",\"$T_{\\perp}$\"], **legend_options)\n", - "axs[5].set_ylim(e_lim_e)\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], vdf_parapar_e, yscale=\"log\", cscale=\"log\", clim=[1e-2, 1e2], cmap=\"RdBu_r\")\n", - "plot_line(axs[6], scpot)\n", - "axs[6].set_ylabel(\"$E_e$\" + \"\\n\" + \"[eV]\")\n", - "caxs6.set_ylabel(\"$\\\\frac{f_{||+}}{f_{||-}}$\" + \"\\n\" + \" \")\n", - "axs[6].legend([\"$V_{SC}$\", \"$T_{||}$\",\"$T_{\\perp}$\"], **legend_options)\n", - "axs[6].set_ylim(e_lim_e)\n", - "\n", - "axs[7], caxs7 = plot_spectr(axs[7], vdf_parperp_e, yscale=\"log\", cscale=\"log\", clim=[1e-2, 1e2], cmap=\"RdBu_r\")\n", - "plot_line(axs[7], scpot)\n", - "axs[7].set_ylabel(\"$E_e$\" + \"\\n\" + \"[eV]\")\n", - "caxs7.set_ylabel(\"$\\\\frac{f_{||+}+f_{||-}}{2 f_{\\perp}}$\" + \"\\n\" + \" \")\n", - "axs[7].legend([\"$V_{SC}$\"], **legend_options)\n", - "axs[7].set_ylim(e_lim_e)\n", - "\n", - "make_labels(axs, [0.02, 0.85])\n", - "\n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/examples/01_mms/example_mms_particle_distributions.ipynb b/examples/01_mms/example_mms_particle_distributions.ipynb deleted file mode 100644 index e8e27822..00000000 --- a/examples/01_mms/example_mms_particle_distributions.ipynb +++ /dev/null @@ -1,2676 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Particle Distributions\n", - "author: Louis Richard\\\n", - "Example showing how to you can work with particle distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import xarray as xr\n", - "import matplotlib.pylab as pl\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu import mms, pyrf\n", - "from pyrfu.plot import plot_line, plot_spectr, plot_projection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define spacecraft index, time interval and data path" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "mms_id = 3\n", - "tint = [\"2015-12-02T01:14:15.000\", \"2015-12-02T01:15:13.000\"]\n", - "mms.db_init(\"/Volumes/mms\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Velocity Distribution Functions (VDFs)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:07:47: Loading mms3_dis_dist_brst...\n", - "09-Dec-21 12:07:53: Loading mms3_des_dist_brst...\n" - ] - } - ], - "source": [ - "vdf_i = mms.get_data(\"pdi_fpi_brst_l2\", tint, mms_id)\n", - "vdf_e = mms.get_data(\"pde_fpi_brst_l2\", tint, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load supporting information" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:04: Loading mms3_fgm_b_dmpa_brst_l2...\n", - "09-Dec-21 12:08:06: Loading mms3_edp_dce_dsl_brst_l2...\n", - "09-Dec-21 12:08:08: Loading mms3_edp_scpot_brst_l2...\n" - ] - } - ], - "source": [ - "b_xyz = mms.get_data(\"b_dmpa_fgm_brst_l2\", tint, mms_id)\n", - "e_xyz = mms.get_data(\"e_dsl_edp_brst_l2\", tint, mms_id)\n", - "sc_pot = mms.get_data(\"v_edp_brst_l2\", tint, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example operations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Omnidirectional differential energy flux" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "vdf_e_omni = mms.vdf_omni(vdf_e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Construt pitchangle distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:10: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined number of pitch angles.\n" - ] - } - ], - "source": [ - "vdf_e_pad = mms.get_pitch_angle_dist(vdf_e, b_xyz, tint=tint, angles=24)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Limit energy range" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective eint = [10.96, 191.15]\n" - ] - } - ], - "source": [ - "vdf_e_lowen = mms.vdf_elim(vdf_e, [0, 200])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Change units to differential energy flux" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "vdf_e_deflux = mms.psd2def(vdf_e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Change units to particle energy flux" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "vdf_e_dpflux = mms.psd2dpf(vdf_e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Resample energy to 64 energy levels, reduces the time resolution" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "vdf_e_e64 = mms.vdf_to_e64(vdf_e)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:25: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective eint = [20.40, 191.15]\n", - "notice : User defined number of pitch angles.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:28: :2: RuntimeWarning: Mean of empty slice\n", - " vdf_e_pa_lowen_spectr = xr.DataArray(np.nanmean(vdf_e_pa_lowen.data, axis=1),\n", - "\n", - "09-Dec-21 12:08:28: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective eint = [216.45, 1790.88]\n", - "notice : User defined number of pitch angles.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:31: :7: RuntimeWarning: Mean of empty slice\n", - " vdf_e_pa_miden_spectr = xr.DataArray(np.nanmean(vdf_e_pa_miden.data, axis=1),\n", - "\n" - ] - } - ], - "source": [ - "vdf_e_pa_lowen = mms.get_pitch_angle_dist(mms.vdf_elim(vdf_e_e64, [20, 200]), b_xyz, tint=tint, angles=18)\n", - "vdf_e_pa_lowen_spectr = xr.DataArray(np.nanmean(vdf_e_pa_lowen.data, axis=1), \n", - " coords=[vdf_e_pa_lowen.time.data, vdf_e_pa_lowen.theta.data[0, :]], \n", - " dims=[\"time\", \"theta\"])\n", - "\n", - "vdf_e_pa_miden = mms.get_pitch_angle_dist(mms.vdf_elim(vdf_e_e64, [200, 2000]), b_xyz, tint=tint, angles=18)\n", - "vdf_e_pa_miden_spectr = xr.DataArray(np.nanmean(vdf_e_pa_miden.data, axis=1), \n", - " coords=[vdf_e_pa_miden.time.data, vdf_e_pa_miden.theta.data[0, :]], \n", - " dims=[\"time\", \"theta\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:32: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective eint = [20.40, 191.15]\n", - "notice : User defined number of pitch angles.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:35: :22: RuntimeWarning: Mean of empty slice\n", - " vdf_e_pa_lowen_spectr = xr.DataArray(np.nanmean(vdf_e_pa_lowen.data, axis=1),\n", - "\n", - "09-Dec-21 12:08:35: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective eint = [216.45, 1790.88]\n", - "notice : User defined number of pitch angles.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:38: :38: RuntimeWarning: Mean of empty slice\n", - " vdf_e_pa_miden_spectr = xr.DataArray(np.nanmean(vdf_e_pa_miden.data, axis=1),\n", - "\n", - "09-Dec-21 12:08:39: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined pitch angle limits.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:42: :54: RuntimeWarning: Mean of empty slice\n", - " vdf_e_lowan_spectr = xr.DataArray(np.nanmean(vdf_e_lowan.data, axis=2),\n", - "\n", - "09-Dec-21 12:08:42: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined pitch angle limits.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:46: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined pitch angle limits.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:49: :84: RuntimeWarning: Mean of empty slice\n", - " vdf_e_higan_spectr = xr.DataArray(np.nanmean(vdf_e_higan.data, axis=2),\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.03, 0.1, '165$^\\\\circ$ < $\\\\theta$ < 180$^\\\\circ$')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "\n", - "n_panels = 7;\n", - "f, axs = plt.subplots(n_panels, sharex=\"all\", figsize=(8.5, 11))\n", - "f.subplots_adjust(hspace=0, left=.13, right=.87, bottom=.05, top=.95)\n", - "\n", - "plot_line(axs[0], b_xyz)\n", - "plot_line(axs[0], pyrf.norm(b_xyz))\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\", \"$|B|$\"], bbox_to_anchor=(1, 1.05))\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "axs[0].set_title(f\"MMS{mms_id:d}\")\n", - "\n", - "\n", - "axs[1], caxs1 = plot_spectr(axs[1], mms.vdf_omni(vdf_e), yscale=\"log\", \n", - " cscale=\"log\", cmap=\"jet\")\n", - "axs[1].set_yticks(np.logspace(1, 4, 4))\n", - "caxs1.set_ylabel(\"PSD\" + \"\\n\" + \"[s$^{3}$ cm$^{-6}$]\")\n", - "axs[1].set_ylabel(\"$E_e$ [eV]\")\n", - "\n", - "e_lim = [20, 200]\n", - "vdf_e_pa_lowen = mms.get_pitch_angle_dist(mms.vdf_elim(vdf_e_e64, e_lim), b_xyz, tint=tint, angles=18)\n", - "vdf_e_pa_lowen_spectr = xr.DataArray(np.nanmean(vdf_e_pa_lowen.data, axis=1), \n", - " coords=[vdf_e_pa_lowen.time.data, vdf_e_pa_lowen.theta.data[0, :]], \n", - " dims=[\"time\", \"theta\"])\n", - "\n", - "axs[2], caxs2 = plot_spectr(axs[2], vdf_e_pa_lowen_spectr, \n", - " cscale=\"log\", cmap=\"jet\")\n", - "axs[2].set_yticks([0, 45, 90, 135])\n", - "caxs2.set_ylabel(\"PSD\" + \"\\n\" + \"[s$^{3}$ cm$^{-6}$]\")\n", - "axs[2].set_ylabel(\"$\\\\theta$ [deg.]\")\n", - "\n", - "axs[2].text(.03, .1, f\"{e_lim[0]:d} < $E_e$ < {e_lim[1]:d} eV\", transform=axs[2].transAxes,\n", - " bbox=dict(boxstyle=\"square\", ec=(1., 1., 1.), fc=(1., 1., 1.)))\n", - "\n", - "\n", - "e_lim = [200, 2000]\n", - "vdf_e_pa_miden = mms.get_pitch_angle_dist(mms.vdf_elim(vdf_e_e64, e_lim), b_xyz, tint=tint, angles=18)\n", - "vdf_e_pa_miden_spectr = xr.DataArray(np.nanmean(vdf_e_pa_miden.data, axis=1), \n", - " coords=[vdf_e_pa_miden.time.data, vdf_e_pa_miden.theta.data[0, :]], \n", - " dims=[\"time\", \"theta\"])\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], vdf_e_pa_miden_spectr, \n", - " cscale=\"log\", cmap=\"jet\")\n", - "axs[3].set_yticks([0, 45, 90, 135])\n", - "caxs3.set_ylabel(\"PSD\" + \"\\n\" + \"[s$^{3}$ cm$^{-6}$]\")\n", - "axs[3].set_ylabel(\"$\\\\theta$ [deg.]\")\n", - "\n", - "axs[3].text(.03, .1, f\"{e_lim[0]:d} < $E_e$ < {e_lim[1]:d} eV\", transform=axs[3].transAxes,\n", - " bbox=dict(boxstyle=\"square\", ec=(1., 1., 1.), fc=(1., 1., 1.)))\n", - "\n", - "\n", - "pa_lim = [0, 15]\n", - "vdf_e_lowan = mms.get_pitch_angle_dist(mms.vdf_to_e64(vdf_e), b_xyz, tint=tint, angles=pa_lim)\n", - "vdf_e_lowan_spectr = xr.DataArray(np.nanmean(vdf_e_lowan.data, axis=2), \n", - " coords=[vdf_e_lowan.time.data, vdf_e_lowan.energy.data[0, :]], \n", - " dims=[\"time\", \"energy\"])\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], vdf_e_lowan_spectr, yscale=\"log\", \n", - " cscale=\"log\", cmap=\"jet\")\n", - "caxs4.set_ylabel(\"PSD\" + \"\\n\" + \"[s$^{3}$ cm$^{-6}$]\")\n", - "axs[4].set_ylabel(\"$E_e$ [eV]\")\n", - "\n", - "axs[4].text(.03, .1, f\"{pa_lim[0]:d}$^\\\\circ$ < $\\\\theta$ < {pa_lim[1]:d}$^\\\\circ$\", \n", - " transform=axs[4].transAxes,\n", - " bbox=dict(boxstyle=\"square\", ec=(1., 1., 1.), fc=(1., 1., 1.)))\n", - "\n", - "pa_lim = [75, 105]\n", - "vdf_e_midan = mms.get_pitch_angle_dist(mms.vdf_to_e64(vdf_e), b_xyz, tint=tint, angles=pa_lim)\n", - "vdf_e_midan_spectr = xr.DataArray(np.nanmean(vdf_e_midan.data, axis=2), \n", - " coords=[vdf_e_midan.time.data, vdf_e_midan.energy.data[0, :]], \n", - " dims=[\"time\", \"energy\"])\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], vdf_e_midan_spectr, yscale=\"log\", \n", - " cscale=\"log\", cmap=\"jet\")\n", - "caxs5.set_ylabel(\"PSD\" + \"\\n\" + \"[s$^{3}$ cm$^{-6}$]\")\n", - "axs[5].set_ylabel(\"$E_e$ [eV]\")\n", - "\n", - "axs[5].text(.03, .1, f\"{pa_lim[0]:d}$^\\\\circ$ < $\\\\theta$ < {pa_lim[1]:d}$^\\\\circ$\", \n", - " transform=axs[5].transAxes,\n", - " bbox=dict(boxstyle=\"square\", ec=(1., 1., 1.), fc=(1., 1., 1.)))\n", - "\n", - "pa_lim = [165, 180]\n", - "vdf_e_higan = mms.get_pitch_angle_dist(mms.vdf_to_e64(vdf_e), b_xyz, tint=tint, angles=pa_lim)\n", - "vdf_e_higan_spectr = xr.DataArray(np.nanmean(vdf_e_higan.data, axis=2), \n", - " coords=[vdf_e_higan.time.data, vdf_e_higan.energy.data[0, :]], \n", - " dims=[\"time\", \"energy\"])\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], vdf_e_higan_spectr, yscale=\"log\", \n", - " cscale=\"log\", cmap=\"jet\")\n", - "caxs6.set_ylabel(\"PSD\" + \"\\n\" + \"[s$^{3}$ cm$^{-6}$]\")\n", - "axs[6].set_ylabel(\"$E_e$ [eV]\")\n", - "\n", - "axs[6].text(.03, .1, f\"{pa_lim[0]:d}$^\\\\circ$ < $\\\\theta$ < {pa_lim[1]:d}$^\\\\circ$\", \n", - " transform=axs[6].transAxes,\n", - " bbox=dict(boxstyle=\"square\", ec=(1., 1., 1.), fc=(1., 1., 1.)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Project distribution onto the (E, ExB), (ExB, B), (B, E)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute pitchangle distribution with 17 angles" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:49: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined number of pitch angles.\n" - ] - } - ], - "source": [ - "vdf_e_pad = mms.get_pitch_angle_dist(vdf_e, b_xyz, tint, angles=17)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Resample background magnetic field, electric field and ExB drift" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:58: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "b_0 = pyrf.resample(b_xyz, vdf_e)\n", - "e_0 = pyrf.resample(e_xyz, vdf_e)\n", - "exb = pyrf.cross(e_0, b_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 12:08:58: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/mms/vdf_projection.py:37: RuntimeWarning: invalid value encountered in arccos\n", - " if abs(np.rad2deg(np.arccos(np.dot(vec, coord_sys[:, i])))) > 1.:\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, '2015-12-02T01:14:55.176087000')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "\n", - "idx = 1339\n", - "x = e_0.data[idx, :]\n", - "y = exb.data[idx, :]\n", - "z = b_0.data[idx, :]\n", - "time = list(pyrf.datetime642iso8601(vdf_e.time.data[idx]))\n", - "\n", - "f = plt.figure(figsize=(8.5, 9))\n", - "gsp1 = f.add_gridspec(2, 3, hspace=0, bottom=.07, top=.99, left=.1, right=.9)\n", - "\n", - "gsp10 = gsp1[0, :].subgridspec(1, 3, hspace=0)\n", - "gsp11 = gsp1[1, :].subgridspec(1, 2, hspace=0)\n", - "\n", - "# Create axes in the grid spec\n", - "axs10 = [f.add_subplot(gsp10[i]) for i in range(3)]\n", - "axs11 = [f.add_subplot(gsp11[i]) for i in range(2)]\n", - "\n", - "f.subplots_adjust(wspace=.4)\n", - "v_x, v_y, f_mat = mms.vdf_projection(vdf_e, time, np.vstack([x, y, -z]), sc_pot, e_lim=15)\n", - "axs10[0], caxs10 = plot_projection(axs10[0], v_x, v_y, f_mat * 1e12, vlim=12e3, \n", - " clim=[-18, -13], cbar_pos=\"top\")\n", - "axs10[0].set_xlabel(\"$V_{E}$ [Mm s$^{-1}$]\")\n", - "axs10[0].set_ylabel(\"$V_{E\\\\times B}$ [Mm s$^{-1}$]\")\n", - "caxs10.set_xlabel(\"log$_{10} f_e$ [s$^{3}$ m$^{-6}$]\")\n", - "\n", - "v_x, v_y, f_mat = mms.vdf_projection(vdf_e, time, np.vstack([y, z, -x]), sc_pot, e_lim=15)\n", - "axs10[1], caxs11 = plot_projection(axs10[1], v_x, v_y, f_mat * 1e12, vlim=12e3, \n", - " clim=[-18, -13], cbar_pos=\"top\")\n", - "axs10[1].set_xlabel(\"$V_{E\\\\times B}$ [Mm s$^{-1}$]\")\n", - "axs10[1].set_ylabel(\"$V_{B}$ [Mm s$^{-1}$]\")\n", - "caxs11.set_xlabel(\"log$_{10} f_e$ [s$^{3}$ m$^{-6}$]\")\n", - "\n", - "v_x, v_y, f_mat = mms.vdf_projection(vdf_e, time, np.vstack([z, x, -y]), sc_pot, e_lim=15)\n", - "axs10[2], caxs12 = plot_projection(axs10[2], v_x, v_y, f_mat * 1e12, vlim=12e3, \n", - " clim=[-18, -13], cbar_pos=\"top\")\n", - "axs10[2].set_xlabel(\"$V_{B}$ [Mm s$^{-1}$]\")\n", - "axs10[2].set_ylabel(\"$V_{E}$ [Mm s$^{-1}$]\")\n", - "caxs12.set_xlabel(\"log$_{10} f_e$ [s$^{3}$ m$^{-6}$]\")\n", - "\n", - "\n", - "axs11[0].loglog(vdf_e_pad.energy.data[idx, :], vdf_e_pad.data.data[idx, :, 0], \n", - " label=\"$\\\\theta = 0$ deg.\")\n", - "axs11[0].loglog(vdf_e_pad.energy.data[idx, :], vdf_e_pad.data.data[idx, :, 9], \n", - " label=\"$\\\\theta = 90$ deg.\")\n", - "axs11[0].loglog(vdf_e_pad.energy.data[idx, :], vdf_e_pad.data.data[idx, :, -1], \n", - " label=\"$\\\\theta = 180$ deg.\")\n", - "\n", - "axs11[0].legend()\n", - "axs11[0].set_xlim([1e1, 1e3])\n", - "axs11[0].set_xlabel(\"$E_e$ [eV]\")\n", - "axs11[0].set_ylim([1e-31, 2e-26])\n", - "axs11[0].set_ylabel(\"$f_e$ [s$^{3}$ cm$^{-6}$]\")\n", - "\n", - "\n", - "colors = pl.cm.jet(np.linspace(0, 1, len(vdf_e_pad.energy[idx, :])))\n", - "for i_en in range(len(vdf_e_pad.energy[idx, :])):\n", - " axs11[1].semilogy(vdf_e_pad.theta.data[idx, :], vdf_e_pad.data.data[idx, i_en, :], \n", - " color=colors[i_en], label=f\"{vdf_e_pad.energy.data[idx, i_en]:5.2f} eV\")\n", - " \n", - "axs11[1].set_xlim([0, 180.])\n", - "axs11[1].set_xlabel(\"$\\\\theta$ [deg.]\")\n", - "axs11[1].set_ylim([1e-31, 2e-26])\n", - "axs11[1].set_ylabel(\"$f_e$ [s$^{3}$ cm$^{-6}$]\")\n", - "\n", - "axs11[1].set_xticks([0, 45, 90, 135, 180])\n", - "\n", - "f.suptitle(time[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/example_mms_particle_pad.ipynb b/examples/01_mms/example_mms_particle_pad.ipynb deleted file mode 100644 index 0a114270..00000000 --- a/examples/01_mms/example_mms_particle_pad.ipynb +++ /dev/null @@ -1,2429 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Particle Pitch Angle Distribution\n", - "author: Louis Richard\\\n", - "Calculate and plot electron and ion pitch angle distributions from particle brst data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu import mms\n", - "from pyrfu.plot import plot_line, plot_spectr, make_labels" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define spacecraft index, data path and time interval" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "ic = 3 # Spacecraft number\n", - "mms.db_init(\"/Volumes/mms\")\n", - "tint = [\"2015-10-30T05:15:20.000\", \"2015-10-30T05:16:20.000\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Particle distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:42:10: Loading mms3_dis_dist_brst...\n", - "09-Dec-21 11:42:15: Loading mms3_des_dist_brst...\n" - ] - } - ], - "source": [ - "# vdf_i = mms.get_data(\"pdi_fpi_brst_l2\", tint, ic)\n", - "vdf_i, vdf_e = [mms.get_data(f\"pd{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Particle energy fluxes" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:42:27: Loading mms3_dis_energyspectr_omni_brst...\n", - "09-Dec-21 11:42:30: Loading mms3_des_energyspectr_omni_brst...\n" - ] - } - ], - "source": [ - "def_omni_i, def_omni_e = [mms.get_data(f\"def{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Particle moments" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:42:33: Loading mms3_dis_numberdensity_brst...\n", - "09-Dec-21 11:42:35: Loading mms3_des_numberdensity_brst...\n", - "09-Dec-21 11:42:36: Loading mms3_dis_bulkv_gse_brst...\n", - "09-Dec-21 11:42:40: Loading mms3_des_bulkv_gse_brst...\n", - "09-Dec-21 11:42:45: Loading mms3_dis_temptensor_gse_brst...\n", - "09-Dec-21 11:42:52: Loading mms3_des_temptensor_gse_brst...\n" - ] - } - ], - "source": [ - "n_i, n_e = [mms.get_data(f\"n{s}_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]\n", - "v_xyz_i, v_xyz_e = [mms.get_data(f\"v{s}_gse_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]\n", - "t_xyz_i, t_xyz_e = [mms.get_data(f\"t{s}_gse_fpi_brst_l2\", tint, ic) for s in [\"i\", \"e\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Other variables" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:42:59: Loading mms3_fgm_b_dmpa_brst_l2...\n", - "09-Dec-21 11:43:00: Loading mms3_fgm_b_gse_brst_l2...\n", - "09-Dec-21 11:43:02: Loading mms3_edp_scpot_brst_l2...\n" - ] - } - ], - "source": [ - "b_xyz, b_gse = [mms.get_data(f\"b_{cs}_fgm_brst_l2\", tint, ic) for cs in [\"dmpa\", \"gse\"]]\n", - "scpot = mms.get_data(\"v_edp_brst_l2\", tint, ic)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute parallel and perpendicular electron and ion temperatures" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:43:03: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n", - "notice : Transforming tensor into field-aligned coordinates.\n", - "notice : Rotating tensor so perpendicular diagonal components are equal.\n" - ] - } - ], - "source": [ - "t_fac_i, t_fac_e = [mms.rotate_tensor(t_xyz, \"fac\", b_xyz, \"pp\") for t_xyz in [t_xyz_i, t_xyz_e]]\n", - "\n", - "t_para_i, t_para_e = [t_fac[:, 0, 0] for t_fac in [t_fac_i, t_fac_e]]\n", - "t_perp_i, t_perp_e = [t_fac[:, 1, 1] for t_fac in [t_fac_i, t_fac_e]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute pitch-angle distributions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rebin VDFs to 64 energy channels" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "vdf_e64_i, vdf_e64_e = [mms.vdf_to_e64(vdf) for vdf in [vdf_i, vdf_e]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Electron and ion pitch angle distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined number of pitch angles.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:43:06: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : User defined number of pitch angles.\n" - ] - } - ], - "source": [ - "pad_i, pad_e = [mms.get_pitch_angle_dist(vdf, b_xyz, tint, angles=n) for vdf, n in zip([vdf_e64_i, vdf_e64_e], [18, 24])]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PSD -> DEF" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "pad_def_i, pad_def_e = [mms.psd2def(pad) for pad in [pad_i, pad_e]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Split in low energy, middle energy and high energy groups" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:43:35: :4: RuntimeWarning: Mean of empty slice\n", - " pad_lowen = xr.DataArray(np.nanmean(pad.data[:, :idx[0], :], axis=1), coords=coords, dims=dims)\n", - "\n", - "09-Dec-21 11:43:35: :5: RuntimeWarning: Mean of empty slice\n", - " pad_miden = xr.DataArray(np.nanmean(pad.data[:, idx[0]:idx[1], :], axis=1), coords=coords, dims=dims)\n", - "\n", - "09-Dec-21 11:43:35: :6: RuntimeWarning: Mean of empty slice\n", - " pad_higen = xr.DataArray(np.nanmean(pad.data[:, idx[1]:, :], axis=1), coords=coords, dims=dims)\n", - "\n" - ] - } - ], - "source": [ - "def split_energy(pad, idx):\n", - " coords = [pad.time.data, pad.theta.data[0, :]]\n", - " dims = [\"time\", \"theta\"]\n", - " pad_lowen = xr.DataArray(np.nanmean(pad.data[:, :idx[0], :], axis=1), coords=coords, dims=dims)\n", - " pad_miden = xr.DataArray(np.nanmean(pad.data[:, idx[0]:idx[1], :], axis=1), coords=coords, dims=dims)\n", - " pad_higen = xr.DataArray(np.nanmean(pad.data[:, idx[1]:, :], axis=1), coords=coords, dims=dims)\n", - " \n", - " energy = pad.energy.data[0, :]\n", - " e_int = {\"lowen\": \"{:6.2f} eV - {:6.2f} eV\".format(energy[0], energy[idx[0] - 1]),\n", - " \"miden\": \"{:6.2f} eV - {:6.2f} eV\".format(energy[idx[0]], energy[idx[1] - 1]),\n", - " \"higen\": \"{:6.2f} eV - {:6.2f} eV\".format(energy[idx[1]], energy[-1])}\n", - " return pad_lowen, pad_miden, pad_higen, e_int\n", - "\n", - "pad_lowen_i, pad_miden_i, pad_higen_i, e_int_i = split_energy(pad_def_i, [21, 42])\n", - "pad_lowen_e, pad_miden_e, pad_higen_e, e_int_e = split_energy(pad_def_e, [21, 42])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "legend_options = dict(frameon=True, loc=\"upper right\", ncol=3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot Ion data" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. 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height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(7, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(bottom=.1, top=.95, left=.15, right=.85, hspace=0)\n", - "\n", - "plot_line(axs[0], b_xyz)\n", - "axs[0].set_ylabel(\"$B_{DMPA}$\" + \"\\n\" + \"[nT]\")\n", - "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[1], n_i)\n", - "axs[1].set_ylabel(\"$n_{i}$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", - "\n", - "plot_line(axs[2], v_xyz_i)\n", - "axs[2].set_ylabel(\"$V_{i}$\" + \"\\n\" + \"[km s$^{-1}$]\")\n", - "axs[2].legend([\"$V_{x}$\", \"$V_{y}$\", \"$V_{z}$\"], **legend_options)\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], def_omni_i, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "plot_line(axs[3], t_para_i)\n", - "plot_line(axs[3], t_perp_i)\n", - "axs[3].set_ylabel(\"$E_i$\" + \"\\n\" + \"[eV]\")\n", - "caxs3.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[3].legend([\"$T_{||}$\",\"$T_{\\perp}$\"], **legend_options)\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], pad_lowen_i, cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[4].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "caxs4.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[4].text(0.05, 0.1, e_int_i[\"lowen\"], transform=axs[4].transAxes)\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], pad_miden_i, cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[5].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "caxs5.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[5].text(0.05, 0.1, e_int_i[\"miden\"], transform=axs[5].transAxes)\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], pad_higen_i, cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[6].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "caxs6.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[6].text(0.05, 0.1, e_int_i[\"higen\"], transform=axs[6].transAxes)\n", - "\n", - "make_labels(axs, [0.02, 0.85])\n", - "\n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot Electron data" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(7, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(bottom=.1, top=.95, left=.15, right=.85, hspace=0)\n", - "\n", - "plot_line(axs[0], b_xyz)\n", - "axs[0].set_ylabel(\"$B_{DMPA}$\" + \"\\n\" + \"[nT]\")\n", - "axs[0].legend([\"$B_{x}$\", \"$B_{y}$\", \"$B_{z}$\"], **legend_options)\n", - "\n", - "plot_line(axs[1], n_e)\n", - "axs[1].set_ylabel(\"$n_{e}$\" + \"\\n\" + \"[cm$^{-3}$]\")\n", - "\n", - "plot_line(axs[2], v_xyz_e)\n", - "axs[2].set_ylabel(\"$V_{e}$\" + \"\\n\" + \"[km s$^{-1}$]\")\n", - "axs[2].legend([\"$V_{x}$\", \"$V_{y}$\", \"$V_{z}$\"], **legend_options)\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], def_omni_e, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "plot_line(axs[3], t_para_e)\n", - "plot_line(axs[3], t_perp_e)\n", - "axs[3].set_ylabel(\"$E_e$\" + \"\\n\" + \"[eV]\")\n", - "caxs3.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[3].legend([\"$T_{||}$\",\"$T_{\\perp}$\"], **legend_options)\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], pad_lowen_e, cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[4].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "caxs4.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[4].text(0.05, 0.1, e_int_e[\"lowen\"], transform=axs[4].transAxes)\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], pad_miden_e, cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[5].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "caxs5.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[5].text(0.05, 0.1, e_int_e[\"miden\"], transform=axs[5].transAxes)\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], pad_higen_e, cscale=\"log\", cmap=\"Spectral_r\")\n", - "axs[6].set_ylabel(\"$\\\\theta$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "caxs6.set_ylabel(\"DEF\" + \"\\n\" + \"[kev/(cm$^2$ s sr keV)]\")\n", - "axs[6].text(0.05, 0.1, e_int_e[\"higen\"], transform=axs[6].transAxes)\n", - "\n", - "make_labels(axs, [0.02, 0.85])\n", - "\n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/examples/01_mms/example_mms_polarizationanalysis.ipynb b/examples/01_mms/example_mms_polarizationanalysis.ipynb deleted file mode 100644 index a2a93334..00000000 --- a/examples/01_mms/example_mms_polarizationanalysis.ipynb +++ /dev/null @@ -1,1309 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Polarization Analysis\n", - "author: Louis Richard\\\n", - "Perform polarization analysis on burst mode electric and magnetic fields. Plots spectrograms, ellipticity, wave-normal angle, planarity, degree of polarization (DOP), phase speed, and normalized Poynting flux along B. Time selections should not be too long (less than 20 seconds), otherwise the analysis will be very slow. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from astropy import constants\n", - "from pyrfu.mms import get_data, db_init\n", - "from pyrfu.plot import plot_line, plot_spectr\n", - "from pyrfu.pyrf import extend_tint, norm, ebsp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define time interval and spacecraft index" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "db_init(\"/Volumes/mms\")\n", - "tint = [\"2015-10-30T05:15:42.000\", \"2015-10-30T05:15:54.00\"]\n", - "tint_long = extend_tint(tint, [-100, 100])\n", - "ic = 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:45:45: Loading mms3_mec_r_gse...\n", - "09-Dec-21 11:45:50: Loading mms3_fgm_b_gse_brst_l2...\n", - "09-Dec-21 11:45:51: Loading mms3_edp_dce_gse_brst_l2...\n", - "09-Dec-21 11:45:52: Loading mms3_scm_acb_gse_scb_brst_l2...\n" - ] - } - ], - "source": [ - "r_xyz = get_data(\"r_gse_mec_srvy_l2\", tint_long, ic)\n", - "b_xyz = get_data(\"b_gse_fgm_brst_l2\", tint, ic)\n", - "e_xyz = get_data(\"e_gse_edp_brst_l2\", tint, ic)\n", - "b_scm = get_data(\"b_gse_scm_brst_l2\", tint, ic)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute electron cylotron frequency" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "me = constants.m_e.value\n", - "qe = constants.e.value\n", - "b_si = norm(b_xyz) * 1e-9\n", - "w_ce = qe * b_si / me\n", - "f_ce = w_ce / (2 * np.pi)\n", - "f_ce_01 = f_ce * .1\n", - "f_ce_05 = f_ce * .5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Polarization Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:45:54: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/ebsp.py:77: UserWarning: Interpolating b and e to 2x e sampling\n", - " warnings.warn(\"Interpolating b and e to 2x e sampling\",\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ebsp ... calculate E and B wavelet transform ... \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:48:08: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/ebsp.py:180: RuntimeWarning: Mean of empty slice\n", - " out[i, :] = np.nanmean(data[il:ir, :], axis=0)\n", - "\n" - ] - } - ], - "source": [ - "polarization_options = dict(freq_int=[10, 4000], polarization=True, fac=True)\n", - "polarization = ebsp(e_xyz, b_scm, b_xyz, b_xyz, r_xyz, **polarization_options)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Unpack polarization analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "frequency = polarization[\"f\"]\n", - "time = polarization[\"t\"]\n", - "b_sum = polarization[\"bb_xxyyzzss\"][..., 3]\n", - "b_per = polarization[\"bb_xxyyzzss\"][..., 0] + polarization[\"bb_xxyyzzss\"][..., 1]\n", - "e_sum = polarization[\"ee_xxyyzzss\"][..., 3]\n", - "e_per = polarization[\"ee_xxyyzzss\"][..., 0] + polarization[\"ee_xxyyzzss\"][..., 1]\n", - "ellipticity = polarization[\"ellipticity\"]\n", - "dop = polarization[\"dop\"]\n", - "thetak = polarization[\"k_tp\"][..., 0]\n", - "planarity = polarization[\"planarity\"]\n", - "pflux_z = polarization[\"pf_xyz\"][..., 2] \n", - "pflux_z /= np.sqrt(polarization[\"pf_xyz\"][..., 0] ** 2 + polarization[\"pf_xyz\"][..., 1] ** 2 + polarization[\"pf_xyz\"][..., 2] ** 2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculate phase speed v_ph = E/B." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "v_ph = np.sqrt(e_sum / b_sum) * 1e6\n", - "v_ph_perp = np.sqrt(e_per / b_per) * 1e6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Remove points with very low B amplitutes" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "b_sum_thres = 1e-7\n", - "\n", - "ellipticity.data[b_sum < b_sum_thres] = np.nan\n", - "thetak.data[b_sum < b_sum_thres] = np.nan\n", - "dop.data[b_sum < b_sum_thres] = np.nan\n", - "planarity.data[b_sum < b_sum_thres] = np.nan\n", - "pflux_z.data[b_sum < b_sum_thres] = np.nan\n", - "v_ph.data[b_sum < b_sum_thres] = np.nan\n", - "v_ph_perp.data[b_sum < b_sum_thres] = np.nan" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot figure" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$S_{||}/|S|$\\n ')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(8, sharex=\"all\", figsize=(6.5, 10))\n", - "f.subplots_adjust(left=.15, right=.85, hspace=0.1)\n", - "\n", - "axs[0], caxs0 = plot_spectr(axs[0], b_sum, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "plot_line(axs[0], f_ce, color=\"w\")\n", - "plot_line(axs[0], f_ce_01, color=\"w\")\n", - "plot_line(axs[0], f_ce_05, color=\"w\")\n", - "axs[0].set_ylabel(\"$f$ [Hz]\")\n", - "caxs0.set_ylabel(\"$B^2$\" + \"\\n\" +\"[nT$^2$ Hz$^{-1}$]\")\n", - "\n", - "axs[1], caxs1 = plot_spectr(axs[1], e_sum, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "plot_line(axs[1], f_ce, color=\"w\")\n", - "plot_line(axs[1], f_ce_01, color=\"w\")\n", - "plot_line(axs[1], f_ce_05, color=\"w\")\n", - "axs[1].set_ylabel(\"$f$ [Hz]\")\n", - "caxs1.set_ylabel(\"$E^2$\" + \"\\n\" + \"[mV$^2$ m$^{-2}$ Hz$^{-1}$]\")\n", - "\n", - "axs[2], caxs2 = plot_spectr(axs[2], ellipticity, yscale=\"log\", cmap=\"RdBu_r\", clim=[-1, 1])\n", - "plot_line(axs[2], f_ce, color=\"w\")\n", - "plot_line(axs[2], f_ce_01, color=\"w\")\n", - "plot_line(axs[2], f_ce_05, color=\"w\")\n", - "axs[2].set_ylabel(\"$f$ [Hz]\")\n", - "caxs2.set_ylabel(\"Ellipticity\" + \"\\n\" + \" \")\n", - "\n", - "axs[3], caxs3 = plot_spectr(axs[3], thetak * 180 / np.pi, yscale=\"log\", cmap=\"Spectral_r\", clim=[0, 90])\n", - "plot_line(axs[3], f_ce, color=\"w\")\n", - "plot_line(axs[3], f_ce_01, color=\"w\")\n", - "plot_line(axs[3], f_ce_05, color=\"w\")\n", - "axs[3].set_ylabel(\"$f$ [Hz]\")\n", - "caxs3.set_ylabel(\"$\\\\theta_k$\" + \"\\n\" + \"[$^\\\\circ$]\")\n", - "\n", - "axs[4], caxs4 = plot_spectr(axs[4], dop, yscale=\"log\", cmap=\"Spectral_r\", clim=[0, 1])\n", - "plot_line(axs[4], f_ce, color=\"w\")\n", - "plot_line(axs[4], f_ce_01, color=\"w\")\n", - "plot_line(axs[4], f_ce_05, color=\"w\")\n", - "axs[4].set_ylabel(\"$f$ [Hz]\")\n", - "caxs4.set_ylabel(\"DOP\" + \"\\n\" + \" \")\n", - "\n", - "\n", - "axs[5], caxs5 = plot_spectr(axs[5], planarity, yscale=\"log\", cmap=\"Spectral_r\", clim=[0, 1])\n", - "plot_line(axs[5], f_ce, color=\"w\")\n", - "plot_line(axs[5], f_ce_01, color=\"w\")\n", - "plot_line(axs[5], f_ce_05, color=\"w\")\n", - "axs[5].set_ylabel(\"$f$ [Hz]\")\n", - "caxs5.set_ylabel(\"Planarity\" + \"\\n\" + \" \")\n", - "\n", - "\n", - "axs[6], caxs6 = plot_spectr(axs[6], v_ph, yscale=\"log\", cscale=\"log\", cmap=\"Spectral_r\")\n", - "plot_line(axs[6], f_ce, color=\"w\")\n", - "plot_line(axs[6], f_ce_01, color=\"w\")\n", - "plot_line(axs[6], f_ce_05, color=\"w\")\n", - "axs[6].set_ylabel(\"$f$ [Hz]\")\n", - "caxs6.set_ylabel(\"E/B\" + \"\\n\" + \"[m s$^{-1}$]\")\n", - "\n", - "axs[7], caxs7 = plot_spectr(axs[7], pflux_z, yscale=\"log\", cmap=\"RdBu_r\", clim=[-1, 1])\n", - "plot_line(axs[7], f_ce, color=\"w\")\n", - "plot_line(axs[7], f_ce_01, color=\"w\")\n", - "plot_line(axs[7], f_ce_05, color=\"w\")\n", - "axs[7].set_ylabel(\"$f$ [Hz]\")\n", - "caxs7.set_ylabel(\"$S_{||}/|S|$\" + \"\\n\" + \" \")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/01_mms/example_mms_walen_test.ipynb b/examples/01_mms/example_mms_walen_test.ipynb deleted file mode 100644 index f170c9db..00000000 --- a/examples/01_mms/example_mms_walen_test.ipynb +++ /dev/null @@ -1,1459 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Walen Test\n", - "author: Louis Richard\\\n", - "Example code to perform Walen test; only for burst mode MMS data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load IGRF coefficients ...\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates\n", - "\n", - "from pyrfu import mms, pyrf\n", - "from scipy import constants\n", - "from pyrfu.plot import plot_line, plot_spectr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define spacecraft index, time intervals, jet direction and trasnformation matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "mms.db_init(\"/Volumes/mms\")\n", - "\n", - "mms_id = 1\n", - "j_sign = 1 # +/-1 for jet direction\n", - "\n", - "#time = irf_time('2015-11-30T00:23:55.200Z', 'utc>epochtt');\n", - "trans_matrix = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) # in GSE\n", - "\n", - "# Plot\n", - "tint = [\"2015-11-30T00:23:48.000\", \"2015-11-30T00:24:01.000\"]\n", - "# reference region\n", - "tint_ref = [\"2015-11-30T00:23:49.000\", \"2015-11-30T00:23:50.000\"]\n", - "# Test region\n", - "tint_walen = [\"2015-11-30T00:23:50.000\", \"2015-11-30T00:23:54.000\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PSD" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:49:22: Loading mms1_dis_dist_brst...\n" - ] - } - ], - "source": [ - "vdf_i = mms.get_data(\"pdi_fpi_brst_l2\", tint, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Moments" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:49:29: Loading mms1_dis_numberdensity_brst...\n", - "09-Dec-21 11:49:31: Loading mms1_des_numberdensity_brst...\n", - "09-Dec-21 11:49:33: Loading mms1_dis_bulkv_gse_brst...\n", - "09-Dec-21 11:49:36: Loading mms1_dis_prestensor_gse_brst...\n" - ] - } - ], - "source": [ - "n_i = mms.get_data(\"ni_fpi_brst_l2\", tint, mms_id)\n", - "n_e = mms.get_data(\"ne_fpi_brst_l2\", tint, mms_id)\n", - "v_gse_i = mms.get_data(\"vi_gse_fpi_brst_l2\", tint, mms_id)\n", - "p_gse_i = mms.get_data(\"pi_gse_fpi_brst_l2\", tint, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fields" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:49:43: Loading mms1_fgm_b_gse_brst_l2...\n" - ] - } - ], - "source": [ - "b_gse = mms.get_data(\"b_gse_fgm_brst_l2\", tint, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load defatt files" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:49:46: Loading ancillary defatt files...\n" - ] - } - ], - "source": [ - "defatt = mms.load_ancillary(\"defatt\", tint, mms_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute omnidirectionnal differential energy flux (DEF)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def_omni_i = mms.vdf_omni(mms.psd2def(vdf_i))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rotate pressure tensor into Field Aliigned Coordinates (FAC)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:50:06: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "notice : Transforming tensor into field-aligned coordinates.\n" - ] - } - ], - "source": [ - "p_fac_i = mms.rotate_tensor(p_gse_i, \"fac\", b_gse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Alpha: pressure anisotropy factor" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "alpha_ = pyrf.pres_anis(p_fac_i, b_gse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### gse to new123" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "b_123 = pyrf.new_xyz(b_gse, trans_matrix)\n", - "v_123_i = pyrf.new_xyz(v_gse_i, trans_matrix)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reference(MSH) region; in New frame(123);" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "b_ref = pyrf.time_clip(b_123, tint_ref)\n", - "b_ref = np.nanmean(b_ref.data, axis=0)\n", - "\n", - "v_i_ref = pyrf.time_clip(v_123_i, tint_ref)\n", - "v_i_ref = np.nanmean(v_i_ref.data, axis=0)\n", - "\n", - "n_i_ref = pyrf.time_clip(n_i, tint_ref)\n", - "n_i_ref = np.nanmean(n_i_ref.data, axis=0)\n", - "\n", - "alpha_ref = pyrf.time_clip(alpha_, tint_ref)\n", - "alpha_ref = np.nanmean(alpha_ref.data, axis=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Vipred1: delta_B / sqrt(rho1)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "09-Dec-21 11:50:08: /opt/anaconda3/lib/python3.8/site-packages/pyrfu/pyrf/resample.py:177: UserWarning: Using averages in resample\n", - " warnings.warn(\"Using averages in resample\", UserWarning)\n", - "\n" - ] - } - ], - "source": [ - "b_123 = pyrf.resample(b_123, n_i)\n", - "v_123_i = pyrf.resample(v_123_i, n_i)\n", - "\n", - "tmp_1 = (b_123 - b_ref) * 21.8 / np.sqrt(n_i_ref)\n", - "v_i_pred1 = pyrf.resample(tmp_1, v_123_i) * j_sign + v_i_ref" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Vipred2: $B_2 / \\sqrt{\\rho_2} - B_1 / \\sqrt{\\rho_1}$ [Phan et al, 2004]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "tmp_2 = 21.8 * (1 - alpha_) * b_123 / np.sqrt(n_i_ref * (1 - alpha_ref))\n", - "v_i_pred2 = (tmp_2 - 21.8 * np.sqrt(1 - alpha_ref) * b_ref / np.sqrt(n_i_ref))\n", - "v_i_pred2 *= j_sign\n", - "v_i_pred2 += v_i_ref" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Vipred2: $\\sqrt{1 - \\alpha_2} B_2 / \\sqrt{\\rho_2} - \\sqrt{1 - \\alpha_1} B_1 / \\sqrt{\\rho_1}$" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "v_i_pred3 = 21.8 * (1 - alpha_) * b_123 / np.sqrt(n_i)\n", - "v_i_pred3 -= 21.8 * np.sqrt(1 - alpha_ref) * b_ref / np.sqrt(n_i_ref)\n", - "v_i_pred3 *= j_sign\n", - "v_i_pred3 += v_i_ref" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Slope & CC" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "v_123_i_w = pyrf.time_clip(v_123_i, tint_walen)\n", - "v_i_pred1_w = pyrf.time_clip(v_i_pred1, tint_walen)\n", - "v_i_pred2_w = pyrf.time_clip(v_i_pred2, tint_walen)\n", - "v_i_pred3_w = pyrf.time_clip(v_i_pred3, tint_walen)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "p_ = [np.polyfit(v_i_pred2_w.data[:, i], v_123_i_w.data[:, i], 1) for i in range(3)]\n", - "slope_2 = [p_[i][0] for i in range(3)]\n", - "\n", - "corr_ = [np.corrcoef(v_i_pred2_w.data[:, i], v_123_i_w.data[:, i]) for i in range(3)]\n", - "cc_2 = [corr_[i][0, 1] for i in range(3)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "f, axs = plt.subplots(7, sharex=\"all\", figsize=(8.5, 11))\n", - "f.subplots_adjust(bottom=.05, top=.95, left=.12, right=.88, hspace=0)\n", - "\n", - "plot_line(axs[0], b_gse)\n", - "axs[0].legend([\"$B_x$\", \"$B_y$\", \"$B_z$\"], ncol=3)\n", - "axs[0].set_ylabel(\"$B$ [nT]\")\n", - "axs[0].set_title(f\"MMS-{mms_id:d}\")\n", - "\n", - "plot_line(axs[1], n_i, color=\"tab:blue\", label=\"$N_i$\")\n", - "plot_line(axs[1], n_e, color=\"tab:red\", label=\"$N_i$\")\n", - "axs[1].legend(ncol=3)\n", - "axs[1].set_ylabel(\"$N$ [cm$^{-3}$]\")\n", - "\n", - "\n", - "axs[2], caxs2 = plot_spectr(axs[2], def_omni_i, yscale=\"log\", cscale=\"log\")\n", - "axs[2].set_yticks(np.logspace(1, 4, 4))\n", - "axs[2].set_ylabel(\"$W_i$ [eV]\")\n", - "caxs2.set_ylabel(\"DEF\" + \"\\n\" + \"[(cm$^2$ s sr)$^{-1}$]\")\n", - "\n", - "plot_line(axs[3], b_123)\n", - "axs[3].legend([\"$B_1$\", \"$B_2$\", \"$B_3$\"], ncol=3)\n", - "axs[3].set_ylabel(\"$B$ [nT]\")\n", - "axs[3].text(1.01, .75, np.array2string(trans_matrix[0, :], separator=\",\", precision=2), color=\"tab:blue\", transform=axs[3].transAxes)\n", - "axs[3].text(1.01, .50, np.array2string(trans_matrix[1, :], separator=\",\", precision=2), color=\"tab:green\", transform=axs[3].transAxes)\n", - "axs[3].text(1.01, .25, np.array2string(trans_matrix[2, :], separator=\",\", precision=2), color=\"tab:red\", transform=axs[3].transAxes)\n", - "\n", - "\n", - "plot_line(axs[4], v_123_i[:, 0], color=\"k\", label=\"FPI\")\n", - "plot_line(axs[4], v_i_pred2_w[:, 0], color=\"tab:red\", linestyle=\"-\", label=\"pred\")\n", - "plot_line(axs[4], v_i_pred2[:, 0], color=\"tab:red\", linestyle=\"--\")\n", - "axs[4].legend(ncol=3)\n", - "axs[4].set_ylabel(\"$V_1$ [km s$^{-1}$]\")\n", - "axs[4].text(1.01, .75, f\"slope = {slope_2[0]:3.2f}\", color=\"k\", transform=axs[4].transAxes)\n", - "axs[4].text(1.01, .25, f\"cc = {cc_2[0]:3.2f}\", color=\"k\", transform=axs[4].transAxes)\n", - "axs[4].axvspan(mdates.datestr2num(tint_ref[0]), mdates.datestr2num(tint_ref[1]), color=\"tab:red\", alpha=.2)\n", - "axs[4].axvspan(mdates.datestr2num(tint_walen[0]), mdates.datestr2num(tint_walen[1]), color=\"yellow\", alpha=.2)\n", - "\n", - "\n", - "plot_line(axs[5], v_123_i[:, 1], color=\"k\", label=\"FPI\")\n", - "plot_line(axs[5], v_i_pred2_w[:, 1], color=\"tab:red\", linestyle=\"-\", label=\"pred\")\n", - "plot_line(axs[5], v_i_pred2[:, 1], color=\"tab:red\", linestyle=\"--\")\n", - "axs[5].legend(ncol=3)\n", - "axs[5].set_ylabel(\"$V_2$ [km s$^{-1}$]\")\n", - "axs[5].text(1.01, .75, f\"slope = {slope_2[1]:3.2f}\", color=\"k\", transform=axs[5].transAxes)\n", - "axs[5].text(1.01, .25, f\"cc = {cc_2[1]:3.2f}\", color=\"k\", transform=axs[5].transAxes)\n", - "\n", - "plot_line(axs[6], v_123_i[:, 2], color=\"k\", label=\"FPI\")\n", - "plot_line(axs[6], v_i_pred2_w[:, 2], color=\"tab:red\", linestyle=\"-\", label=\"pred\")\n", - "plot_line(axs[6], v_i_pred2[:, 2], color=\"tab:red\", linestyle=\"--\")\n", - "axs[6].legend(ncol=3)\n", - "axs[6].set_ylabel(\"$V_3$ [km s$^{-1}$]\")\n", - "axs[6].text(1.01, .75, f\"slope = {slope_2[2]:3.2f}\", color=\"k\", transform=axs[6].transAxes)\n", - "axs[6].text(1.01, .25, f\"cc = {cc_2[2]:3.2f}\", color=\"k\", transform=axs[6].transAxes)\n", - "\n", - "f.align_ylabels(axs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/02_dispersion/example_dispersion_one_fluid.ipynb b/examples/02_dispersion/example_dispersion_one_fluid.ipynb deleted file mode 100644 index 915263c5..00000000 --- a/examples/02_dispersion/example_dispersion_one_fluid.ipynb +++ /dev/null @@ -1,1148 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# One Fluid Dispersion Relation\n", - "author: Louis Richard\n", - "\n", - "Solves one fluid dispersion relation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "from pyrfu.dispersion import one_fluid_dispersion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Local conditions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Magnetic field" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "b_0 = 10e-9" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Angle of propagation" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "theta = 5." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Particles" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "ions = {\"n\": 10e6, \"t\": 10, \"gamma\": 1}\n", - "electrons = {\"n\": 10e6, \"t\": 10, \"gamma\": 1}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve one fluid dispersion relation" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "wc_1, wc_2, wc_3 = one_fluid_dispersion(10e-9, 5, ions, electrons)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox â‰Ĩ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; 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" icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Dispersion relations: $\\\\theta_{kB}$ = 5.00')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib notebook\n", - "v_a, c_s, wc_p = [wc_1.attrs[k] for k in [\"v_a\", \"c_s\", \"wc_p\"]]\n", - "\n", - "k = wc_1.k.data\n", - "\n", - "f, ax = plt.subplots(1, figsize=(8, 8))\n", - "ax.plot(k * v_a / wc_p, wc_1 / wc_p, color=\"k\")\n", - "ax.plot(k * v_a / wc_p, wc_2 / wc_p, color=\"tab:blue\")\n", - "ax.plot(k * v_a / wc_p, wc_3 / wc_p, color=\"tab:red\")\n", - "ax.plot(k * v_a / wc_p, v_a * k / wc_p, color=\"tab:green\", linestyle=\"--\", \n", - " label=\"$\\\\omega = V_A k$\")\n", - "ax.plot(k * v_a / wc_p, c_s * k / wc_p, color=\"tab:purple\", linestyle=\"--\", \n", - " label=\"$\\\\omega = c_s k$\")\n", - "\n", - "ax.set_xlim([0, 7])\n", - "ax.set_ylim([0, 6])\n", - "ax.legend()\n", - "ax.set_xlabel(\"$k V_A / \\\\Omega_{ci}$\")\n", - "ax.set_ylabel(\"$\\\\omega / \\\\Omega_{ci}$\")\n", - "ax.set_title(f\"Dispersion relations: $\\\\theta_{{kB}}$ = {theta:3.2f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/README.rst b/examples/README.rst deleted file mode 100644 index dd30bbc4..00000000 --- a/examples/README.rst +++ /dev/null @@ -1,4 +0,0 @@ -Examples -================== - -This directory contains a list of examples showing the use of the `pyrfu` package: diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..debcf049 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,171 @@ +[build-system] +requires = [ + "setuptools>=42", + "wheel==0.38.1", +] +build-backend = "setuptools.build_meta" + +[project] +name = "pyrfu" +version = "2.4.13" +description = "Python Space Physics (RymdFysik) Utilities" +readme = "README.rst" +authors = [{ name = "Louis RICHARD", email = "louir@irfu.se" }] +license = { file = "LICENSE.txt" } +classifiers = [ + "Development Status :: 2 - Pre-Alpha", + "Environment :: Other Environment", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: MIT License", + "Natural Language :: English", + "Operating System :: Unix", + "Operating System :: MacOS", + "Operating System :: MacOS :: MacOS X", + "Operating System :: Microsoft", + "Operating System :: Microsoft :: MS-DOS", + "Operating System :: Microsoft :: Windows", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering", + "Topic :: Scientific/Engineering :: Physics", +] +dependencies = [ + "boto3>=1.34.0", + "botocore>=1.34.0", + "cdflib>=1.2.0", + "keyring>=24.2.0", + "geopack>=1.0.10", + "matplotlib>=3.8.0", + "numba>=0.59.0", + "numpy>=1.22", + "pandas>=2.2.0", + "pycdfpp>=0.6.0", + "python-dateutil>=2.9.0", + "requests>=2.31.0", + "scipy>=1.11.2", + "tqdm>=4.66.2", + "xarray>=2024.1.0", +] +requires-python = ">=3.9,<3.13" + +[project.optional-dependencies] +git = [ + "pre-commit", +] +style = [ + "black", + "flake8", + "isort", + "pylint", +] +tests = [ + "ddt", + "pytest", + "pytest-cov", +] +docs = [ + "nbsphinx>=0.9.2", + "numpydoc>=1.5.0", + "pydata-sphinx-theme>=0.13.0", + "sphinx>=7.0.0", + "sphinx-codeautolink>=0.15.0", + "sphinx-copybutton>=0.5.0", + "sphinx-gallery>=0.13.0", + "sphinxcontrib-apidoc>=0.3.0", +] +dev = [ + "pyrfu[docs]", + "pyrfu[git]", + "pyrfu[style]", + "pyrfu[tests]", +] + +[project.urls] +homepage = "https://pypi.org/project/pyrfu/" +documentation = "https://pyrfu.readthedocs.io/en/latest/" +source = "https://github.com/louis-richard/irfu-python" +issue-tracker = "https://github.com/louis-richard/irfu-python/issues" + +[tool.setuptools] +include-package-data = true + +[tool.setuptools.packages.find] +where = ["."] + +[tool.setuptools.package-data] +"*" = ["*.json", "*.csv"] + +[tool.pytest.ini_options] +filterwarnings = [ + "ignore::RuntimeWarning", +] + +[tool.black] +py36 = true +include = '\.pyi?$' +exclude = ''' +/( + \.git + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | _build + | buck-out + | build + | dist + + # The following are specific to Black, you probably don't want those. + | blib2to3 + | tests/data +)/''' + +[tool.isort] +profile = "black" + +[tool.pylint."MESSAGES CONTROL"] +disable = """ + missing-module-docstring, + too-many-arguments, + too-many-locals, + too-many-lines, + too-many-statements, + too-many-branches, + too-many-nested-blocks, + invalid-name, + duplicate-code, + not-an-iterable, + fixme +""" +ignore = """ + ancillary.json, + config.json, + feeps_bad_data.json, + igrf13coeffs.csv, + mms_keys.json, + transformation_indices.json, + test_pyrf.py, + test_dispersion.py, + test_solo.py, + test_mms.py, + test_models.py, + test_plot.py, +""" +ignored-modules = "scipy,cdflib" + +[tool.bumpver] +current_version = "2.4.13" +version_pattern = "MAJOR.MINOR.PATCH" +commit_message = "Bump version {old_version} -> {new_version}" +commit = true +tag = false +push = false + +[tool.bumpver.file_patterns] +"pyproject.toml" = ['current_version = "{version}"', 'version = "{version}"'] +"pyrfu/__init__.py" = ["{version}", "Copyright 2020-YYYY"] diff --git a/pyrfu/__init__.py b/pyrfu/__init__.py index b8cfbb8d..27ed68ec 100644 --- a/pyrfu/__init__.py +++ b/pyrfu/__init__.py @@ -1,15 +1,13 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -import unittest -import numpy as np - - -from pyrfu import mms, pyrf, models, dispersion +from pyrfu import dispersion, lp, maven, mms, models, plot, pyrf __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2024" __license__ = "MIT" -__version__ = "2.3.9" +__version__ = "2.4.13" __status__ = "Prototype" + +__all__ = ["dispersion", "lp", "maven", "mms", "models", "plot", "pyrf"] diff --git a/pyrfu/dispersion/__init__.py b/pyrfu/dispersion/__init__.py index c0643126..9af8ecbb 100644 --- a/pyrfu/dispersion/__init__.py +++ b/pyrfu/dispersion/__init__.py @@ -1,13 +1,16 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +from .disp_surf_calc import disp_surf_calc + # @Louis Richard from .one_fluid_dispersion import one_fluid_dispersion -from .disp_surf_calc import disp_surf_calc __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" + +__all__ = ["disp_surf_calc", "one_fluid_dispersion"] diff --git a/pyrfu/dispersion/disp_surf_calc.py b/pyrfu/dispersion/disp_surf_calc.py index 92a03309..1acebbe3 100644 --- a/pyrfu/dispersion/disp_surf_calc.py +++ b/pyrfu/dispersion/disp_surf_calc.py @@ -9,9 +9,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.11" +__version__ = "2.4.2" __status__ = "Prototype" @@ -39,10 +39,10 @@ def _calc_e(diel_tensor): e_z = np.ones(e_y.shape) e_per = np.sqrt(e_x * np.conj(e_x) + e_y * np.conj(e_y)) - e_tot = np.sqrt(e_x * np.conj(e_x) + e_y * np.conj(e_y) + e_z**2) e_pol = -2 * np.imag(e_x * np.conj(e_y)) / e_per**2 + e_tot = np.sqrt(e_x * np.conj(e_x) + e_y * np.conj(e_y) + e_z**2) - return e_x, e_y, e_z, e_per, e_tot, e_pol + return e_x, e_y, e_z, e_per, e_pol, e_tot def _calc_b(kc_x_mat, kc_z_mat, w_final, e_x, e_y, e_z): @@ -53,7 +53,9 @@ def _calc_b(kc_x_mat, kc_z_mat, w_final, e_x, e_y, e_z): b_par = np.sqrt(b_z * np.conj(b_z)) b_per = np.sqrt(b_x * np.conj(b_x) + b_y * np.conj(b_y)) b_pol = -2 * np.imag(b_x * np.conj(b_y)) / b_per**2 - b_tot = np.sqrt(b_x * np.conj(b_x) + b_y * np.conj(b_y) + b_z * np.conj(b_z)) + b_tot = np.sqrt( + b_x * np.conj(b_x) + b_y * np.conj(b_y) + b_z * np.conj(b_z), + ) return b_x, b_y, b_z, b_par, b_per, b_pol, b_tot @@ -64,7 +66,9 @@ def _calc_s(e_x, e_y, e_z, b_x, b_y, b_z): s_y = e_z * np.conj(b_x) - e_x * np.conj(b_z) s_z = e_x * np.conj(b_y) - e_y * np.conj(b_x) s_par = np.abs(s_z) - s_tot = np.sqrt(s_x * np.conj(s_x) + s_y * np.conj(s_y) + s_z * np.conj(s_z)) + s_tot = np.sqrt( + s_x * np.conj(s_x) + s_y * np.conj(s_y) + s_z * np.conj(s_z), + ) return s_par, s_tot @@ -203,11 +207,16 @@ def disp_surf_calc(kc_x_max, kc_z_max, m_i, wp_e): diel_tensor = _calc_diel(kc_, w_final, theta_, wp_e, wp_i, wc_i) - e_x, e_y, e_z, e_per, e_tot, e_pol = _calc_e(diel_tensor) + e_x, e_y, e_z, _, e_pol, e_tot = _calc_e(diel_tensor) e_par = (kc_x_mat * e_x + kc_z_mat * e_z) / kc_ - b_x, b_y, b_z, b_par, b_per, b_pol, b_tot = _calc_b( - kc_x_mat, kc_z_mat, w_final, e_x, e_y, e_z + b_x, b_y, b_z, b_par, _, b_pol, b_tot = _calc_b( + kc_x_mat, + kc_z_mat, + w_final, + e_x, + e_y, + e_z, ) dk_x, dk_z = [kc_x_mat[1], kc_z_mat[1]] @@ -219,7 +228,14 @@ def disp_surf_calc(kc_x_max, kc_z_max, m_i, wp_e): s_par, s_tot = _calc_s(e_x, e_y, e_z, b_x, b_y, b_z) # Compute ion and electron velocities - v_ex, v_ey, v_ez, v_ix, v_iy, v_iz = _calc_vei(m_i, wc_i, w_final, e_x, e_y, e_z) + v_ex, v_ey, v_ez, v_ix, v_iy, v_iz = _calc_vei( + m_i, + wc_i, + w_final, + e_x, + e_y, + e_z, + ) # Ratio of parallel and perpendicular to B speed vepar_perp = v_ez * np.conj(v_ez) @@ -241,7 +257,13 @@ def disp_surf_calc(kc_x_max, kc_z_max, m_i, wp_e): # Continuity equation dn_e_n, dn_i_n, dne_dni = _calc_continuity( - kc_x_mat, kc_z_mat, w_final, v_ex, v_ez, v_ix, v_iz + kc_x_mat, + kc_z_mat, + w_final, + v_ex, + v_ez, + v_ix, + v_iz, ) dn_e_n_db_b = dn_e_n / b_tot diff --git a/pyrfu/dispersion/one_fluid_dispersion.py b/pyrfu/dispersion/one_fluid_dispersion.py index c4ddbce5..8741f049 100644 --- a/pyrfu/dispersion/one_fluid_dispersion.py +++ b/pyrfu/dispersion/one_fluid_dispersion.py @@ -4,21 +4,19 @@ # 3rd party imports import numpy as np import xarray as xr - from scipy import constants, optimize __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.11" +__version__ = "2.4.2" __status__ = "Prototype" def _disprel(w, *args): - k, theta = args[0:2] - v_a, c_s = args[2:4] - wc_e, wc_p = args[4:6] + assert len(args) == 6, "not enougth arguments" + k, theta, v_a, c_s, wc_e, wc_p = args theta = np.deg2rad(theta) l_00 = 1 diff --git a/pyrfu/lp/__init__.py b/pyrfu/lp/__init__.py index 5256a336..80f87991 100644 --- a/pyrfu/lp/__init__.py +++ b/pyrfu/lp/__init__.py @@ -7,8 +7,6 @@ # 3rd party imports import numpy as np -from scipy import constants - # Local imports from ..pyrf import estimate from .photo_current import photo_current @@ -16,63 +14,15 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" +__all__ = ["LangmuirProbe", "photo_current", "thermal_current"] -class Plasma(object): - r"""Describes plasma model consisting of several plasma components where - each component is characterized by charge size and sign, density, mass of - particles, temperature and drift velocity.""" - - def __init__( - self, - name: str = "", - n: Union[float, list] = None, - mp: Union[float, list] = None, - qe: Union[int, list] = None, - t: Union[float, list] = None, - ): - r"""Setup plasma properties. They can be a single number applicable to - all plasma components or a vector of the length equal to the number of - plasma components. - - Parameters - ---------- - name : str - Name of the plasma. - n : float or list - Species number densities. - mp : float or list - Species masses in terms of proton mass. - qe : float or list - Species charges in terms of elementary charge. - t : float or float - Species temperatures. - """ - - self.name = name - self.n = np.atleast_1d(n) - self.mp = np.atleast_1d(mp) - self.qe = np.atleast_1d(qe) - self.t = np.atleast_1d(t) - - # Computes mass and charge in SI units - self._m() - self._q() - - def _m(self): - self.m = self.mp * constants.proton_mass - self.m[self.m == 0] = constants.electron_mass - - def _q(self): - self.qe * constants.elementary_charge - - -class LangmuirProbe(object): +class LangmuirProbe: r"""Defines either spherical, cylindrical, conical or spherical + cylindrical/conical probes. Probe belonging to LangmuirProbe is defined with properties.""" @@ -119,49 +69,47 @@ def __init__( assert not r_wire or np.atleast_1d(r_wire) <= 2, message self.name = name - self.surface = surface - self.r_sphere = r_sphere - self.r_wire = np.atleast_1d(r_wire) - self.l_wire = l_wire + self.wire = {"l": l_wire, "r": np.atleast_1d(r_wire)} + self.sphere = {"r": r_sphere, "surface": surface} self.s_photoemission = s_photoemission # Probe type according to the specified parameters: Sphere + Wire or # Wire or Sphere - self._get_probe_type() + self.get_probe_type() # Get probe area - self._get_probe_area() + self.get_probe_area() # Get probe capacitance - self._get_probe_capa() + self.get_probe_capa() - def _get_probe_type(self): + def get_probe_type(self): r"""Define probe type according to the specified parameters.""" - if self.r_sphere and self.r_wire and self.l_wire: + if self.sphere["r"] and self.wire["r"] and self.wire["l"]: self.probe_type = "sphere+wire" - elif self.r_wire and self.l_wire: + elif self.wire["r"] and self.wire["l"]: self.probe_type = "wire" - elif self.r_sphere: + elif self.sphere["r"]: self.probe_type = "sphere" else: self.probe_type = None - def _get_probe_area(self): + def get_probe_area(self): r"""Computes probe area.""" a_sphere_sunlit, a_sphere_total = [0, 0] a_wire_sunlit, a_wire_total = [0, 0] - if isinstance(self.r_sphere, (float, int)): - a_sphere_sunlit = np.pi * self.r_sphere**2 + if isinstance(self.sphere["r"], (float, int)): + a_sphere_sunlit = np.pi * self.sphere["r"] ** 2 a_sphere_total = 4 * a_sphere_sunlit if ( - self.r_wire - and isinstance(self.r_wire, (float, int, list)) - and self.l_wire - and isinstance(self.l_wire, (float, int)) + self.wire["r"] + and isinstance(self.wire["r"], (float, int, list)) + and self.wire["l"] + and isinstance(self.wire["l"], (float, int)) ): - a_wire_sunlit = 2 * np.mean(self.r_wire) * self.l_wire + a_wire_sunlit = 2 * np.mean(self.wire["r"]) * self.wire["l"] a_wire_total = np.pi * a_wire_sunlit self.area = { @@ -173,7 +121,7 @@ def _get_probe_area(self): self.area["total_sunlit"] = self.area["total"] / self.area["sunlit"] self.area["sunlit_total"] = 1 / self.area["total_sunlit"] - def _get_probe_capa(self): + def get_probe_capa(self): r"""Computes probe capacitance. Raises @@ -187,30 +135,36 @@ def _get_probe_capa(self): """ c_wire = 0 - c_sphere = estimate("capacitance_sphere", self.r_sphere) + c_sphere = estimate("capacitance_sphere", self.sphere["r"]) if ( - self.r_wire - and isinstance(self.r_wire, (float, int, list)) - and self.l_wire - and isinstance(self.l_wire, (float, int)) + self.wire["r"] + and isinstance(self.wire["r"], (float, int, list)) + and self.wire["l"] + and isinstance(self.wire["l"], (float, int)) ): - if self.l_wire > 10 * list([self.r_wire]): - c_wire = estimate("capacitance_wire", np.mean(self.r_wire), self.l_wire) - elif self.l_wire > list(self.r_wire): + if self.wire["l"] > 10 * list([self.wire["r"]]): + c_wire = estimate( + "capacitance_wire", + np.mean(self.wire["r"]), + self.wire["l"], + ) + elif self.wire["l"] > list(self.wire["r"]): c_wire = estimate( - "capacitance_cylinder", np.mean(self.r_wire), self.l_wire + "capacitance_cylinder", + np.mean(self.wire["r"]), + self.wire["l"], ) else: raise ValueError( - "estimate of capacitance for cylinder " "requires length > radius" + "estimate of capacitance for cylinder requires length > radius", ) self.capacitance = np.sum([c_sphere, c_wire]) - def _get_probe_surface_photoemission(self): + def get_probe_surface_photoemission(self): r"""Computes (or get) surface photo emission.""" - if self.surface: + if self.sphere["surface"]: self.s_photoemission = self.s_photoemission else: - self.s_photoemission = photo_current(1.0, 0.0, 1.0, self.surface) + self.s_photoemission = photo_current(1.0, 0.0, 1.0, self.sphere["surface"]) diff --git a/pyrfu/lp/photo_current.py b/pyrfu/lp/photo_current.py index 7780b3e5..f4cf2bbb 100644 --- a/pyrfu/lp/photo_current.py +++ b/pyrfu/lp/photo_current.py @@ -2,20 +2,27 @@ # -*- coding: utf-8 -*- # Built-in imports +import logging from typing import Union # 3rd party imports import numpy as np - from scipy import interpolate __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" +logging.captureWarnings(True) +logging.basicConfig( + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, +) + surface_materials = [ "cluster", "themis", @@ -80,9 +87,11 @@ def photo_current( if not iluminated_area and not u and not distance_sun: for surf in surface_materials: j0 = photo_current(1, 0, 1, surf) - print(f"{surf}: Io= {j0 * 1e6:3.2f} uA/m2") + logging.info( + "%(surf)s: Io= %(i0)3.2f uA/m2", {"surf": surf, "i0": j0 * 1e6} + ) - return + return None # Assert than u is an array u = np.atleast_1d(u) @@ -101,7 +110,6 @@ def photo_current( j_photo[u >= 0] *= a_ + b_ elif flag.lower() == "1ev": - j_photo = np.ones(u.shape) # initialize to current valid for negative potentials diff --git a/pyrfu/lp/thermal_current.py b/pyrfu/lp/thermal_current.py index 2a98492d..3d3b8f75 100644 --- a/pyrfu/lp/thermal_current.py +++ b/pyrfu/lp/thermal_current.py @@ -6,49 +6,47 @@ # 3rd party imports import numpy as np - -from scipy.special import erf -from scipy.constants import elementary_charge, Boltzmann +from scipy import constants, special __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" -def _spherical_body(z, u, x_, i_p): +def _spherical_body(z, u, x, i_p): j_thermal = np.zeros(u.shape) if z > 0: - j_thermal[u >= 0] = i_p * np.exp(-x_[u >= 0]) - j_thermal[u < 0] = i_p * (1 - x_[u < 0]) + j_thermal[u >= 0] = i_p * np.exp(-x[u >= 0]) + j_thermal[u < 0] = i_p * (1 - x[u < 0]) elif z < 0: - j_thermal[u >= 0] = i_p * (1 + x_[u >= 0]) - j_thermal[u < 0] = i_p * np.exp(x_[u < 0]) + j_thermal[u >= 0] = i_p * (1 + x[u >= 0]) + j_thermal[u < 0] = i_p * np.exp(x[u < 0]) return j_thermal -def _cylindrical_body(z, u, x_, i_p): +def _cylindrical_body(z, u, x, i_p): sq = np.zeros(u.shape) j_thermal = np.zeros(u.shape) - sq[u < 0] = np.sqrt(abs(-x_[u < 0])) - sq[u >= 0] = np.sqrt(abs(+x_[u >= 0])) - erfv = erf(sq) + sq[u < 0] = np.sqrt(abs(-x[u < 0])) + sq[u >= 0] = np.sqrt(abs(+x[u >= 0])) + erfv = special.erf(sq) if z > 0: - j_thermal[u >= 0] = i_p * np.exp(-x_[u >= 0]) + j_thermal[u >= 0] = i_p * np.exp(-x[u >= 0]) c_0 = (2.0 / np.sqrt(np.pi)) * sq[u < 0] - c_1 = np.exp(-x_[u < 0]) * (1.0 - erfv[u < 0]) + c_1 = np.exp(-x[u < 0]) * (1.0 - erfv[u < 0]) j_thermal[u < 0] = i_p * (c_0 + c_1) elif z < 0: - j_thermal[u < 0] = i_p * np.exp(x_[u < 0]) + j_thermal[u < 0] = i_p * np.exp(x[u < 0]) c_0 = (2.0 / np.sqrt(np.pi)) * sq[u >= 0] - c_1 = np.exp(x_[u >= 0]) * (1.0 - erfv[u >= 0]) + c_1 = np.exp(x[u >= 0]) * (1.0 - erfv[u >= 0]) j_thermal[u >= 0] = i_p * (c_0 + c_1) return j_thermal @@ -98,29 +96,33 @@ def thermal_current( # If zero density return zero current if n == 0 or t == 0: - return + return None # Is the body moving with a velocity, V, with respect to the plasma ? # Criteria set such that it is considered important if V > 0.1 * V_th. - if v < 0.1 * np.sqrt(Boltzmann * t / m): + if v < 0.1 * np.sqrt(constants.Boltzmann * t / m): # Ratio of potential to thermal energy. - x_ = elementary_charge * u / (Boltzmann * t) + x = constants.elementary_charge * u / (constants.Boltzmann * t) # Total current to/from body. - i_p = np.sqrt(t * Boltzmann / (2.0 * np.pi * m)) - i_p *= a * n * elementary_charge + i_p = np.sqrt(t * constants.Boltzmann / (2.0 * np.pi * m)) + i_p *= a * n * constants.elementary_charge else: - x_ = (elementary_charge / (m * v**2 / 2 + Boltzmann * t)) * u - i_p = np.sqrt(v**2 / 16 + t * Boltzmann / (2.0 * np.pi * m)) - i_p *= a * n * elementary_charge + x = ( + constants.elementary_charge / (m * v**2 / 2 + constants.Boltzmann * t) + ) * u + i_p = np.sqrt( + v**2 / 16 + t * constants.Boltzmann / (2.0 * np.pi * m), + ) + i_p *= a * n * constants.elementary_charge if p_type == "sphere": # Spherical body case. - j_thermal = _spherical_body(z, u, x_, i_p) + j_thermal = _spherical_body(z, u, x, i_p) else: # Cylindrical body case. - j_thermal = _cylindrical_body(z, u, x_, i_p) + j_thermal = _cylindrical_body(z, u, x, i_p) return j_thermal diff --git a/pyrfu/maven/__init__.py b/pyrfu/maven/__init__.py new file mode 100644 index 00000000..d4491934 --- /dev/null +++ b/pyrfu/maven/__init__.py @@ -0,0 +1,17 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +from .db_init import db_init +from .download_data import download_data + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.10" +__status__ = "Prototype" + +__all__ = [ + "db_init", + "download_data", +] diff --git a/pyrfu/maven/config.json b/pyrfu/maven/config.json new file mode 100644 index 00000000..2ce7d171 --- /dev/null +++ b/pyrfu/maven/config.json @@ -0,0 +1 @@ +{"local_data_dir": "/Volumes/maven"} diff --git a/pyrfu/maven/db_init.py b/pyrfu/maven/db_init.py new file mode 100644 index 00000000..7018e1b5 --- /dev/null +++ b/pyrfu/maven/db_init.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import json + +# Built-in imports +import os + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.10" +__status__ = "Prototype" + + +def db_init(local_data_dir): + r"""Setup the default path of MAVEN data. + + Parameters + ---------- + local_data_dir : str + Path to the data. + + """ + + # Normalize the path and make sure that it exists + local_data_dir = os.path.normpath(local_data_dir) + assert os.path.exists(local_data_dir), f"{local_data_dir} doesn't exists!!" + + # Path to the configuration file. + pkg_path = os.path.dirname(os.path.abspath(__file__)) + + # Read the current version of the configuration + with open(os.path.join(pkg_path, "config.json"), "r", encoding="utf-8") as fs: + config = json.load(fs) + + # Overwrite the configuration file with the new path + with open(os.path.join(pkg_path, "config.json"), "w", encoding="utf-8") as fs: + config["local_data_dir"] = local_data_dir + json.dump(config, fs) diff --git a/pyrfu/maven/download_data.py b/pyrfu/maven/download_data.py new file mode 100644 index 00000000..1a9792f2 --- /dev/null +++ b/pyrfu/maven/download_data.py @@ -0,0 +1,185 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Built-in imports +import json +import logging +import os +import warnings +from datetime import datetime, timedelta +from shutil import copy, copyfileobj +from tempfile import NamedTemporaryFile + +# 3rd party imports +import numpy as np +import requests +import tqdm + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.10" +__status__ = "Prototype" + +logging.captureWarnings(True) +logging.basicConfig( + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, +) + + +LASP_PUBL = "https://lasp.colorado.edu/maven/sdc/public/files/api/v1/" + + +def _login_lasp(user: str, password: str): + r"""Login to LASP colorado.""" + + session = requests.Session() + session.auth = (user, password) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=ResourceWarning) + _ = session.post("https://lasp.colorado.edu", verify=True, timeout=5) + + return session + + +def _construct_url(tint, var, lasp_url): + r"""Construct the url that return a json-formatted string of science + filenames that are available for download according to: + https://lasp.colorado.edu/mms/sdc/team/about/how-to/ + """ + + tint = np.array(tint).astype(" GSE, -1 GSE -> DSL. Default is 1. + Direction of transformation. +1 DSL -> GSE, -1 GSE -> DSL. + Default is 1. Returns ------- @@ -77,25 +87,23 @@ def dsl2gse(inp, defatt, direction: int = 1): """ if isinstance(defatt, xr.Dataset): - x = np.cos(np.deg2rad(defatt.z_dec)) * np.cos(np.deg2rad(defatt.z_ra.data)) - y = np.cos(np.deg2rad(defatt.z_dec)) * np.sin(np.deg2rad(defatt.z_ra.data)) - z = np.sin(np.deg2rad(defatt.z_dec)) - sax_gei = np.transpose( - np.vstack([defatt.time.data.astype("int") / 1e9, x, y, z]) + x = np.cos(np.deg2rad(defatt.z_dec)) * np.cos( + np.deg2rad(defatt.z_ra.data), ) - sax_gse = cotrans(sax_gei, "gei>gse") - sax_gse = ts_vec_xyz( - (sax_gse[:, 0] * 1e9).astype("datetime64[ns]"), sax_gse[:, 1:] + y = np.cos(np.deg2rad(defatt.z_dec)) * np.sin( + np.deg2rad(defatt.z_ra.data), ) - + z = np.sin(np.deg2rad(defatt.z_dec)) + sax_gei = ts_vec_xyz(defatt.time.data, np.transpose(np.vstack([x, y, z]))) + sax_gse = cotrans(sax_gei, "gei>gse") spin_ax_gse = resample(sax_gse, inp, f_s=calc_fs(inp)) spin_axis = spin_ax_gse.data elif isinstance(defatt, (np.ndarray, list)) and len(defatt) == 3: - spin_axis = defatt + spin_axis = np.atleast_2d(defatt) else: - raise ValueError("unrecognized DEFATT/SAX input") + raise TypeError("DEFATT/SAX input must be xarray.Dataset or vector") # Compute transformation natrix transf_mat = _transformation_matrix(spin_axis, direction) diff --git a/pyrfu/mms/dsl2gsm.py b/pyrfu/mms/dsl2gsm.py index 1936ee63..bc937dcb 100644 --- a/pyrfu/mms/dsl2gsm.py +++ b/pyrfu/mms/dsl2gsm.py @@ -6,13 +6,16 @@ import xarray as xr # Local imports -from ..pyrf import cotrans, resample, sph2cart, ts_vec_xyz, calc_fs +from ..pyrf.calc_fs import calc_fs +from ..pyrf.cotrans import cotrans +from ..pyrf.resample import resample +from ..pyrf.ts_vec_xyz import ts_vec_xyz __author__ = "Louis Richard" __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.26" +__version__ = "2.4.2" __status__ = "Prototype" @@ -22,7 +25,7 @@ def _transformation_matrix(spin_axis, direction): a = 1.0 / np.sqrt(r_y**2 + r_z**2) out = np.zeros((len(a), 3, 3)) out[:, 0, :] = np.transpose( - np.stack([a * (r_y**2 + r_z**2), -a * r_x * r_y, -a * r_x * r_z]) + np.stack([a * (r_y**2 + r_z**2), -a * r_x * r_y, -a * r_x * r_z]), ) out[:, 1, :] = np.transpose(np.stack([0.0 * a, a * r_z, -a * r_y])) @@ -45,7 +48,8 @@ def dsl2gsm(inp, defatt, direction: int = 1): defatt : xarray.Dataset or array_like Spacecraft attitude. direction : {1, -1}, optional - Direction of transformation. +1 DSL -> GSE, -1 GSE -> DSL. Default is 1. + Direction of transformation. +1 DSL -> GSE, -1 GSE -> DSL. + Default is 1. Returns ------- @@ -75,25 +79,23 @@ def dsl2gsm(inp, defatt, direction: int = 1): """ if isinstance(defatt, xr.Dataset): - x = np.cos(np.deg2rad(defatt.z_dec)) * np.cos(np.deg2rad(defatt.z_ra.data)) - y = np.cos(np.deg2rad(defatt.z_dec)) * np.sin(np.deg2rad(defatt.z_ra.data)) - z = np.sin(np.deg2rad(defatt.z_dec)) - sax_gei = np.transpose( - np.vstack([defatt.time.data.astype("int") / 1e9, x, y, z]) + x = np.cos(np.deg2rad(defatt.z_dec)) * np.cos( + np.deg2rad(defatt.z_ra.data), ) - sax_gsm = cotrans(sax_gei, "gei>gsm") - sax_gsm = ts_vec_xyz( - (sax_gsm[:, 0] * 1e9).astype("datetime64[ns]"), sax_gsm[:, 1:] + y = np.cos(np.deg2rad(defatt.z_dec)) * np.sin( + np.deg2rad(defatt.z_ra.data), ) - + z = np.sin(np.deg2rad(defatt.z_dec)) + sax_gei = ts_vec_xyz(defatt.time.data, np.transpose(np.vstack([x, y, z]))) + sax_gsm = cotrans(sax_gei, "gei>gsm") spin_ax_gsm = resample(sax_gsm, inp, f_s=calc_fs(inp)) spin_axis = spin_ax_gsm.data elif isinstance(defatt, (np.ndarray, list)) and len(defatt) == 3: - spin_axis = defatt + spin_axis = np.atleast_2d(defatt) else: - raise ValueError("unrecognized DEFATT/SAX input") + raise TypeError("DEFATT/SAX input must be xarray.Dataset or vector") # Compute transformation matrix transf_mat = _transformation_matrix(spin_axis, direction) diff --git a/pyrfu/mms/eis_ang_ang.py b/pyrfu/mms/eis_ang_ang.py index 50ec4495..146febea 100644 --- a/pyrfu/mms/eis_ang_ang.py +++ b/pyrfu/mms/eis_ang_ang.py @@ -8,11 +8,14 @@ import numpy as np import xarray as xr +# Local imports +from .dsl2gse import dsl2gse + __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -34,7 +37,21 @@ def _check_spin(spin): return spin_inds, len_spin -def eis_ang_ang(inp_allt, en_chan: list = None): +def _combine_attrs(inp_allt_attrs): + attrs = {} + for k in inp_allt_attrs[0]: + allt_attrs = [inp_allt_attr[k] for inp_allt_attr in inp_allt_attrs] + is_same = [allt_attr == allt_attrs[0] for allt_attr in allt_attrs[1:]] + + if all(is_same): + attrs[k] = allt_attrs[0] + else: + continue + + return attrs + + +def eis_ang_ang(inp_allt, en_chan: list = None, defatt: xr.Dataset = None): r"""Generates EIS angle-angle distribution. Parameters @@ -64,10 +81,22 @@ def eis_ang_ang(inp_allt, en_chan: list = None): for i, scope in enumerate(scopes): d_xyz = inp_allt[f"look_{scope}"] - # Domain [-180,180], 0 = sunward (GSE) - phi[i, :] = np.rad2deg(np.arctan2(d_xyz.data[:, 1], d_xyz.data[:, 0])) - # Domain [-90,90], Positive is look direction northward - theta[i, :] = 90.0 - np.rad2deg(np.arccos(d_xyz[:, 2])) + + # If defatt is given get angles in spacecraft coordinates system + if defatt is not None: + d_xyz = dsl2gse(d_xyz, defatt, -1) + coordinates_system = "DBCS>Despun Body Coordinate System" + else: + coordinates_system = "GSE>Geocentric Solar Magnetospheric" + + # Domain [-180, 180], 0 = sunward (GSE) + # phi[i, :] = (np.rad2deg(np.arctan2(d_xyz.data[:, 1], d_xyz.data[:, 0]))) + # Domain [0, 360], 0 = sunward (GSE) + phi[i, :] = np.rad2deg(np.arctan2(d_xyz.data[:, 1], d_xyz.data[:, 0])) + 180.0 + # Domain [-90, 90], Positive is look direction northward + # theta[i, :] = 90.0 - np.rad2deg(np.arccos(d_xyz[:, 2])) + # Domain [0, 180], Positive is look direction northward + theta[i, :] = np.rad2deg(np.arccos(d_xyz[:, 2])) spin_ = inp_allt.spin.data sect_ = inp_allt.sector.data @@ -79,9 +108,11 @@ def eis_ang_ang(inp_allt, en_chan: list = None): # Minus 80 plus min_pol_edges = -80.0 + 160.0 * np.arange(n_pol) / n_pol max_pol_edges = -80.0 + 160.0 * (np.arange(n_pol) + 1) / n_pol + mid_pol_edges = min_pol_edges + 90.0 / n_pol min_azi_edges = -180.0 + 360.0 * np.arange(n_azi) / n_azi max_azi_edges = -180.0 + 360.0 * (np.arange(n_azi) + 1) / n_azi + mid_azi_edges = min_azi_edges + 180.0 / n_azi out_data = np.zeros((n_spins, n_en, n_azi, n_pol)) @@ -89,31 +120,88 @@ def eis_ang_ang(inp_allt, en_chan: list = None): for i, spin_ind in enumerate(spin_inds): t_inds = np.where(spin_ == spin_[spin_ind])[0] - for t_ind, (i_s, scope) in itertools.product(t_inds, enumerate(scopes)): + for t_ind, (i_s, scope) in itertools.product( + t_inds, + enumerate(scopes), + ): cond_azi = np.logical_and( - phi[i_s, t_ind] > min_azi_edges, phi[i_s, t_ind] < max_azi_edges + phi[i_s, t_ind] > min_azi_edges, + phi[i_s, t_ind] < max_azi_edges, ) a_idx = np.where(cond_azi)[0] cond_pol = np.logical_and( - theta[i_s, t_ind] > min_pol_edges, theta[i_s, t_ind] < max_pol_edges + theta[i_s, t_ind] > min_pol_edges, + theta[i_s, t_ind] < max_pol_edges, ) p_idx = np.where(cond_pol)[0] out_data[i, :, a_idx, p_idx] = inp_allt[scope].data[t_ind, en_chan] + # Setup attributes (that of all telescopes + delta energies and + # particle species) + attrs = {k: inp_allt.attrs[k] for k in ["delta_energy_plus", "delta_energy_minus"]} + attrs = {"species": inp_allt.attrs["species"], **attrs} + attrs = { + **attrs, + **_combine_attrs([inp_allt[scope].attrs for scope in scopes]), + } + attrs = {k: attrs[k] for k in sorted(attrs)} + out = xr.DataArray( out_data, coords=[ time_data, inp_allt.energy.data[en_chan], - min_azi_edges + 180.0 / n_azi, - min_pol_edges + 90.0 / n_pol, + mid_azi_edges + 180.0, + mid_pol_edges + 90.0, ], dims=["time", "energy", "phi", "theta"], + attrs=attrs, ) - out.attrs["energy_dminus"] = inp_allt.energy_dminus.data - out.attrs["energy_dplus"] = inp_allt.energy_dplus.data + # Fill coordinates attributes + out.time.attrs = inp_allt.time.attrs # time + out.energy.attrs = inp_allt.energy.attrs # energy + + # Fill azimuthal angles attributes + out.phi.attrs = { + "CATDESC": f"{attrs['CATDESC'][0][:4]} EPD-EIS sky-map azimuth angles", + "COORDINATE_SYSTEM": coordinates_system, + "DELTA_MINUS_VAR": " ", + "DELTA_PLUS_VAR": " ", + "FIELDNAM": f"{attrs['CATDESC'][0][:4]} EPD-EIS phi", + "FILLVAL": -1e31, + "FORMAT": "E12.2", + "LABLAXIS": "phi", + "REPRESENTATION_1": "t", + "SCALETYP": "linear", + "SI_CONVERSION": "0.0174532925>rad", + "UNITS": "deg", + "VALIDMAX": 360.0, + "VALIDMIN": 0.0, + "VAR_NOTES": "Azimuth angles in the instrument frame", + "VAR_TYPE": "support_data", + } + + # Fill elevation angles attributes + out.theta.attrs = { + "CATDESC": f"{attrs['CATDESC'][0][:4]} EPD-EIS sky-map zenith angles", + "COORDINATE_SYSTEM": coordinates_system, + "DELTA_MINUS_VAR": " ", + "DELTA_PLUS_VAR": " ", + "FIELDNAM": f"{attrs['CATDESC'][0][:4]} EPD-EIS theta", + "FILLVAL": -1e31, + "FORMAT": "E12.2", + "LABLAXIS": "theta", + "REPRESENTATION_1": "t", + "SCALETYP": "linear", + "SI_CONVERSION": "0.0174532925>rad", + "UNITS": "deg", + "VALIDMAX": 180.0, + "VALIDMIN": 0.0, + "VAR_NOTES": "Pixel zenith angles", + "VAR_TYPE": "support_data", + } return out diff --git a/pyrfu/mms/eis_combine_proton_pad.py b/pyrfu/mms/eis_combine_proton_pad.py index 5be90261..152c1812 100644 --- a/pyrfu/mms/eis_combine_proton_pad.py +++ b/pyrfu/mms/eis_combine_proton_pad.py @@ -8,16 +8,17 @@ import numpy as np import xarray as xr +from .eis_combine_proton_spec import eis_combine_proton_spec +from .eis_omni import eis_omni + # Local imports from .eis_pad import eis_pad -from .eis_omni import eis_omni -from .eis_combine_proton_spec import eis_combine_proton_spec __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -32,7 +33,10 @@ def _despin(inp, spin_nums): for i, spin_strt in enumerate(spin_starts): with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) - pad_ds[i, :, :] = np.nanmean(inp.data[c_strt : spin_strt + 1, :, :], axis=0) + pad_ds[i, :, :] = np.nanmean( + inp.data[c_strt : spin_strt + 1, :, :], + axis=0, + ) c_strt = spin_strt + 1 out = xr.DataArray( @@ -87,7 +91,7 @@ def eis_combine_proton_pad( """ if energy is None: - energy = [55, 800] + energy = [phxtof_allt.energy.data[0], extof_allt.energy.data[-1]] # set up the number of pa bins to create n_pabins = int(180.0 / pa_width) @@ -101,15 +105,13 @@ def eis_combine_proton_pad( extof_pad = eis_pad(extof_allt, vec, energy, pa_width) # Compute combined PHxTOF and ExTOF omni-directional energy spectrum. - proton_combined_spec = eis_omni(eis_combine_proton_spec(phxtof_allt, extof_allt)) + proton_combined_spec = eis_omni( + eis_combine_proton_spec(phxtof_allt, extof_allt), + ) data_size = [len(phxtof_pad), len(extof_pad)] - if data_size[0] == data_size[1]: - time_data = phxtof_pad.time.data - phxtof_pad_data = phxtof_pad.data - extof_pad_data = extof_pad.data - elif data_size[0] > data_size[1]: + if data_size[0] > data_size[1]: time_data = extof_pad.time.data phxtof_pad_data = phxtof_pad.data[: data_size[1], ...] extof_pad_data = extof_pad.data @@ -118,7 +120,9 @@ def eis_combine_proton_pad( phxtof_pad_data = phxtof_pad.data extof_pad_data = extof_pad.data[: data_size[0], ...] else: - raise ValueError + time_data = phxtof_pad.time.data + phxtof_pad_data = phxtof_pad.data + extof_pad_data = extof_pad.data cond_ = np.logical_and( proton_combined_spec.energy.data > energy[0], @@ -134,7 +138,8 @@ def eis_combine_proton_pad( phxtof_pad.energy.data > energy[0], ) cond_extof = np.logical_and( - extof_pad.energy.data > 82, extof_pad.energy.data < energy[1] + extof_pad.energy.data > 82, + extof_pad.energy.data < energy[1], ) phxtof_taren = np.where(cond_phxtof)[0] @@ -145,10 +150,11 @@ def eis_combine_proton_pad( proton_pad[..., : phxtof_taren.size] = phxtof_pad_data[..., phxtof_taren] for (i, phxtof_en), extof_en in zip( - enumerate(phxtof_taren_cross), extof_taren_cross + enumerate(phxtof_taren_cross), + extof_taren_cross, ): temp_ = np.stack( - [phxtof_pad_data[..., phxtof_en], extof_pad_data[..., extof_en]] + [phxtof_pad_data[..., phxtof_en], extof_pad_data[..., extof_en]], ) idx_l, idx_r = [phxtof_taren.size, phxtof_taren.size + i + 1] proton_pad[..., idx_l:idx_r] = np.nanmean(temp_, axis=0)[:, :, None] diff --git a/pyrfu/mms/eis_combine_proton_skymap.py b/pyrfu/mms/eis_combine_proton_skymap.py index eb11eb64..3eb02486 100644 --- a/pyrfu/mms/eis_combine_proton_skymap.py +++ b/pyrfu/mms/eis_combine_proton_skymap.py @@ -1,21 +1,25 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +from .eis_ang_ang import eis_ang_ang + # Local imports from .eis_combine_proton_spec import eis_combine_proton_spec -from .eis_ang_ang import eis_ang_ang from .eis_skymap import eis_skymap __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" def eis_combine_proton_skymap( - phxtof_allt, extof_allt, en_chan: list = None, to_psd: bool = True + phxtof_allt, + extof_allt, + en_chan: list = None, + to_psd: bool = True, ): r"""Combines ExTOF and PHxTOF proton energy spectra and generate proton skymap distribution. diff --git a/pyrfu/mms/eis_combine_proton_spec.py b/pyrfu/mms/eis_combine_proton_spec.py index 8f58a5d4..930deef9 100644 --- a/pyrfu/mms/eis_combine_proton_spec.py +++ b/pyrfu/mms/eis_combine_proton_spec.py @@ -5,18 +5,11 @@ import numpy as np import xarray as xr -from cdflib import cdfread - -# Local imports -from ..pyrf import datetime642iso8601 - -from .list_files import list_files - __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -33,10 +26,14 @@ def _check_time(proton_phxtof, proton_extof): phxtof_inds = [] for i_t in range(data_size[1]): dt_dummy = np.min( - np.abs(proton_extof.time.data[i_t] - proton_extof.time.data) + np.abs( + proton_extof.time.data[i_t] - proton_extof.time.data, + ), ) t_ind = np.argmin( - np.abs(proton_extof.time.data[i_t] - proton_extof.time.data) + np.abs( + proton_extof.time.data[i_t] - proton_extof.time.data, + ), ) if dt_dummy == 0: extof_inds.append(i_t) @@ -62,10 +59,14 @@ def _check_time(proton_phxtof, proton_extof): phxtof_inds = [] for i_t in range(data_size[1]): dt_dummy = np.min( - np.abs(proton_extof.time.data[i_t] - proton_phxtof.time.data) + np.abs( + proton_extof.time.data[i_t] - proton_phxtof.time.data, + ), ) t_ind = np.argmin( - np.abs(proton_extof.time.data[i_t] - proton_phxtof.time.data) + np.abs( + proton_extof.time.data[i_t] - proton_phxtof.time.data, + ), ) if dt_dummy == 0: extof_inds.append(i_t) @@ -91,10 +92,14 @@ def _check_time(proton_phxtof, proton_extof): phxtof_inds = [] for i_t in range(data_size[0]): dt_dummy = np.min( - np.abs(proton_phxtof.time.data[i_t] - proton_phxtof.time.data) + np.abs( + proton_phxtof.time.data[i_t] - proton_phxtof.time.data, + ), ) t_ind = np.argmin( - np.abs(proton_phxtof.time.data[i_t] - proton_phxtof.time.data) + np.abs( + proton_phxtof.time.data[i_t] - proton_phxtof.time.data, + ), ) if dt_dummy == 0: phxtof_inds.append(i_t) @@ -118,29 +123,20 @@ def _check_time(proton_phxtof, proton_extof): return time_data, phxtof_data, extof_data -def _get_energy_dplus_dminus(eis_allt, data_path): - tint = list(datetime642iso8601(eis_allt.time.data[[0, -1]])) - - name_ = eis_allt.t0.attrs["FIELDNAM"] - - mms_id = int(name_.split("_")[0][-1]) - - var = {"inst": "epd-eis", "lev": "l2"} - - if "brst" in name_: - var["tmmode"] = "brst" - else: - var["tmmode"] = "srvy" - - var["dtype"] = name_.split("_")[-5] - - files = list_files(tint, mms_id, var, data_path=data_path) - - with cdfread.CDF(files[0]) as file: - d_plus = file.varget(eis_allt.t0.energy.attrs["DELTA_PLUS_VAR"]) - d_minus = file.varget(eis_allt.t0.energy.attrs["DELTA_MINUS_VAR"]) +def _combine_attrs(attrs1, attrs2): + attrs = {} + for k in attrs1: + if k.lower() == "global": + attrs[k] = _combine_attrs(attrs1[k], attrs2[k]) + elif k not in ["delta_energy_plus", "delta_energy_minus"]: + if attrs1[k] == attrs2[k]: + attrs[k] = attrs1[k] + else: + attrs[k] = [attrs1[k], attrs2[k]] + else: + continue - return d_plus, d_minus + return attrs def eis_combine_proton_spec(phxtof_allt, extof_allt): @@ -166,35 +162,59 @@ def eis_combine_proton_spec(phxtof_allt, extof_allt): scopes_extof = list(filter(lambda x: x[0] == "t", extof_allt)) assert scopes_extof == scopes_phxtof - data_path = extof_allt.attrs["data_path"] - dp_phxtof, dm_phxtof = _get_energy_dplus_dminus(phxtof_allt, data_path) - dp_extof, dm_extof = _get_energy_dplus_dminus(extof_allt, data_path) + # Get energy deltas for PHxTOF and ExTOF + delta_energy_plus_phxtof = phxtof_allt.attrs["delta_energy_plus"] + delta_energy_minus_phxtof = phxtof_allt.attrs["delta_energy_minus"] + + delta_energy_plus_extof = extof_allt.attrs["delta_energy_plus"] + delta_energy_minus_extof = extof_allt.attrs["delta_energy_minus"] out_keys = list(filter(lambda x: x not in scopes_extof, extof_allt)) out_dict = {k: extof_allt[k] for k in out_keys if k != "sector"} - comb_en_low, comb_en_hig = [None] * 2 + energy_combined_low, energy_combined_hig = [None] * 2 - time_sect, _, extof_sect = _check_time(phxtof_allt["sector"], extof_allt["sector"]) - sect = xr.DataArray(extof_sect, coords=[time_sect], dims=["time"]) + time_sect, _, extof_sect = _check_time( + phxtof_allt["sector"], + extof_allt["sector"], + ) + sect = xr.DataArray( + extof_sect, + coords=[time_sect], + dims=["time"], + attrs=extof_allt["sector"].attrs, + ) _, _, extof_spin = _check_time(phxtof_allt["spin"], extof_allt["spin"]) - spin = xr.DataArray(extof_spin, coords=[time_sect], dims=["time"]) + spin = xr.DataArray( + extof_spin, + coords=[time_sect], + dims=["time"], + attrs=extof_allt["spin"].attrs, + ) for scope in scopes_phxtof: proton_phxtof = phxtof_allt[scope] proton_extof = extof_allt[scope] - time_data, phxtof_data, extof_data = _check_time(proton_phxtof, proton_extof) + time_data, phxtof_data, extof_data = _check_time( + proton_phxtof, + proton_extof, + ) + + energy_phxtof = proton_phxtof.energy.data + energy_extof = proton_extof.energy.data - en_phxtof, en_extof = [proton_phxtof.energy.data, proton_extof.energy.data] - idx_phxtof = np.where(en_phxtof < en_extof[0])[0] - cond_ = np.logical_and(en_phxtof > en_extof[0], en_phxtof < en_phxtof[-1]) + idx_phxtof = np.where(energy_phxtof < energy_extof[0])[0] + cond_ = np.logical_and( + energy_phxtof > energy_extof[0], + energy_phxtof < energy_phxtof[-1], + ) idx_phxtof_cross = np.where(cond_)[0] - idx_extof_cross = np.where(en_extof < en_phxtof[-2])[0] - idx_extof = np.where(en_extof > en_phxtof[-2])[0] + idx_extof_cross = np.where(energy_extof < energy_phxtof[-2])[0] + idx_extof = np.where(energy_extof > energy_phxtof[-2])[0] n_phxtof = idx_phxtof.size n_phxtof_cross = idx_phxtof_cross.size @@ -202,41 +222,85 @@ def eis_combine_proton_spec(phxtof_allt, extof_allt): n_en = n_phxtof + n_phxtof_cross + n_extof - comb_en, comb_en_low, comb_en_hig = [np.zeros(n_en) for _ in range(3)] + energy_combined = np.zeros(n_en) + energy_combined_low, energy_combined_hig = [np.zeros(n_en) for _ in range(2)] - comb_array = np.zeros((len(time_data), n_en)) - comb_array[:, :n_phxtof] = phxtof_data[:, idx_phxtof] - comb_en[:n_phxtof] = en_phxtof[idx_phxtof] - comb_en_low[:n_phxtof] = comb_en[:n_phxtof] - dm_phxtof[idx_phxtof] - comb_en_hig[:n_phxtof] = comb_en[:n_phxtof] + dp_phxtof[idx_phxtof] + data_combined = np.zeros((len(time_data), n_en)) + data_combined[:, :n_phxtof] = phxtof_data[:, idx_phxtof] + energy_combined[:n_phxtof] = energy_phxtof[idx_phxtof] + energy_combined_low[:n_phxtof] = ( + energy_combined[:n_phxtof] - delta_energy_minus_phxtof[idx_phxtof] + ) + energy_combined_hig[:n_phxtof] = ( + energy_combined[:n_phxtof] + delta_energy_plus_phxtof[idx_phxtof] + ) - for (i, i_phx), i_ex in zip(enumerate(idx_phxtof_cross), idx_extof_cross): + for (i, i_phx), i_ex in zip( + enumerate(idx_phxtof_cross), + idx_extof_cross, + ): idx_ = n_phxtof + i - comb_array[:, idx_] = np.nanmean( - np.vstack([phxtof_data[:, i_phx], extof_data[:, i_ex]]), axis=0 + data_combined[:, idx_] = np.nanmean( + np.vstack([phxtof_data[:, i_phx], extof_data[:, i_ex]]), + axis=0, ) - comb_en_low[idx_] = np.nanmin( - [en_phxtof[idx_] - dm_phxtof[idx_], en_extof[i] - dm_extof[i]] + energy_combined_low[idx_] = np.nanmin( + [ + energy_phxtof[idx_] - delta_energy_minus_phxtof[idx_], + energy_extof[i] - delta_energy_minus_extof[i], + ], ) - comb_en_hig[idx_] = np.nanmax( - [en_phxtof[idx_] + dp_phxtof[idx_], en_extof[i] + dp_extof[i]] + energy_combined_hig[idx_] = np.nanmax( + [ + energy_phxtof[idx_] + delta_energy_plus_phxtof[idx_], + energy_extof[i] + delta_energy_plus_extof[i], + ], + ) + energy_combined[idx_] = np.sqrt( + energy_combined_low[idx_] * energy_combined_hig[idx_], ) - comb_en[idx_] = np.sqrt(comb_en_low[idx_] * comb_en_hig[idx_]) - comb_array[:, -n_extof:] = extof_data[:, idx_extof] - comb_en[-n_extof:] = en_extof[idx_extof] - comb_en_low[-n_extof:] = en_extof[idx_extof] - dm_extof[idx_extof] - comb_en_hig[-n_extof:] = en_extof[idx_extof] + dp_extof[idx_extof] + data_combined[:, -n_extof:] = extof_data[:, idx_extof] + energy_combined[-n_extof:] = energy_extof[idx_extof] + energy_combined_low[-n_extof:] = ( + energy_extof[idx_extof] - delta_energy_minus_extof[idx_extof] + ) + energy_combined_hig[-n_extof:] = ( + energy_extof[idx_extof] + delta_energy_plus_extof[idx_extof] + ) + + attrs = _combine_attrs( + phxtof_allt[scope].attrs, + extof_allt[scope].attrs, + ) out_dict[scope] = xr.DataArray( - comb_array, coords=[time_data, comb_en], dims=["time", "energy"] + data_combined, + coords=[time_data, energy_combined], + dims=["time", "energy"], + attrs=attrs, ) out_dict["sector"] = sect out_dict["spin"] = spin - out_dict["energy_dminus"] = comb_en_low - out_dict["energy_dplus"] = comb_en_hig - comb_allt = xr.Dataset(out_dict) + # Combine attributes for all telescopes and set energy deltas as attributes + attrs = _combine_attrs(phxtof_allt.attrs, extof_allt.attrs) + attrs = { + "delta_energy_minus": energy_combined_low, + "delta_energy_plus": energy_combined_hig, + **attrs, + } + + # Create Dataset + comb_allt = xr.Dataset(out_dict, attrs=attrs) + comb_allt.time.attrs = _combine_attrs( + phxtof_allt.time.attrs, + extof_allt.time.attrs, + ) + comb_allt.energy.attrs = _combine_attrs( + phxtof_allt.energy.attrs, + extof_allt.energy.attrs, + ) return comb_allt diff --git a/pyrfu/mms/eis_moments.py b/pyrfu/mms/eis_moments.py index da9c47f9..8cd0537d 100644 --- a/pyrfu/mms/eis_moments.py +++ b/pyrfu/mms/eis_moments.py @@ -4,22 +4,25 @@ # 3rd party imports import numpy as np import xarray as xr - from scipy import constants, integrate # Local imports -from ..pyrf import ts_scalar, resample +from ..pyrf.resample import resample +from ..pyrf.ts_scalar import ts_scalar __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" def eis_moments( - inp, specie: str = "proton", n_bg: xr.DataArray = None, p_bg: xr.DataArray = None + inp, + specie: str = "proton", + n_bg: xr.DataArray = None, + p_bg: xr.DataArray = None, ): r"""Computes the partial moments given the omni-directional differential particle flux and the ion specie under the assumption of angular isotropy @@ -111,7 +114,8 @@ def _int(flux_c, energy_c, order): v_i = ts_scalar(inp.time.data, v_i * 1e-3) # km s^-1 p_i = ts_scalar(inp.time.data, p_i * 1e9) # nPa t_i = ts_scalar( - inp.time.data, t_i * constants.Boltzmann / constants.elementary_charge + inp.time.data, + t_i * constants.Boltzmann / constants.elementary_charge, ) # eV if n_bg is not None and p_bg is not None: diff --git a/pyrfu/mms/eis_omni.py b/pyrfu/mms/eis_omni.py index c5d1c72c..39145419 100644 --- a/pyrfu/mms/eis_omni.py +++ b/pyrfu/mms/eis_omni.py @@ -1,11 +1,15 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +# 3rd party imports +import numpy as np +import xarray as xr + __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -49,19 +53,25 @@ def eis_omni(eis_allt, method: str = "mean"): scopes = list(filter(lambda x: x[0] == "t", eis_allt)) - flux_omni = None + flux_omni = np.zeros_like(eis_allt[scopes[0]].data) for scope in scopes: - try: - flux_omni += eis_allt[scope].copy() - except TypeError: - flux_omni = eis_allt[scope].copy() + flux_omni += eis_allt[scope].data.copy() + + # Why?? + # try: + # flux_omni += eis_allt[scope].data.copy() + # except TypeError: + # flux_omni = eis_allt[scope].data.copy() if method.lower() == "mean": - flux_omni.data /= len(scopes) + flux_omni /= len(scopes) + + # Get dimensions, coordinates and attributes based on first telescope + dims = eis_allt[scopes[0]].dims + coords = [eis_allt[scopes[0]][k] for k in dims] + attrs = eis_allt[scopes[0]].attrs - flux_omni.name = "flux_omni" - flux_omni.attrs["energy_dplus"] = eis_allt.energy_dplus.data - flux_omni.attrs["energy_dminus"] = eis_allt.energy_dminus.data + flux_omni = xr.DataArray(flux_omni, coords=coords, dims=dims, attrs=attrs) return flux_omni diff --git a/pyrfu/mms/eis_pad.py b/pyrfu/mms/eis_pad.py index f034f184..809f56df 100644 --- a/pyrfu/mms/eis_pad.py +++ b/pyrfu/mms/eis_pad.py @@ -9,24 +9,31 @@ import xarray as xr # Local imports -from ..pyrf import ts_vec_xyz, normalize, resample +from ..pyrf.normalize import normalize +from ..pyrf.resample import resample +from ..pyrf.ts_vec_xyz import ts_vec_xyz __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" def _calc_angle(look_, vec): vec_hat = normalize(vec) - theta_ = np.rad2deg(np.pi - np.arccos(np.sum(vec_hat.data * look_.data, axis=1))) + theta_ = np.rad2deg( + np.pi - np.arccos(np.sum(vec_hat.data * look_.data, axis=1)), + ) return theta_ def eis_pad( - inp_allt, vec: xr.DataArray = None, energy: list = None, pa_width: int = 15 + inp_allt, + vec: xr.DataArray = None, + energy: list = None, + pa_width: int = 15, ): r"""Calculates Pitch Angle Distributions (PADs) using data from the MMS Energetic Ion Spectrometer (EIS) @@ -65,7 +72,7 @@ def eis_pad( vec = resample(vec, inp_allt.time) if energy is None: - energy = [55, 800] + energy = inp_allt.energy.data[[0, -1]] # set up the number of pa bins to create n_pabins = int(180.0 / pa_width) @@ -79,8 +86,8 @@ def eis_pad( pa_file = np.zeros([len(time_), len(scopes)]) - e_minu = inp_allt.energy.data + inp_allt.energy_dminus.data - e_plus = inp_allt.energy.data + inp_allt.energy_dplus.data + e_minu = inp_allt.energy.data + inp_allt.delta_energy_minus + e_plus = inp_allt.energy.data + inp_allt.delta_energy_plus cond_low = np.logical_and(e_minu >= energy[0], e_minu <= energy[1]) cond_hig = np.logical_and(e_plus >= energy[0], e_plus <= energy[1]) @@ -104,7 +111,10 @@ def eis_pad( flux_file[flux_file == 0] = np.nan - for (i), (j, pa_lbl) in itertools.product(range(len(time_)), enumerate(pa_label)): + for (i), (j, pa_lbl) in itertools.product( + range(len(time_)), + enumerate(pa_label), + ): cond_ = np.logical_and( pa_file[i, :] + pa_hangw >= pa_lbl - delta_pa, pa_file[i, :] - pa_hangw < pa_lbl + delta_pa, diff --git a/pyrfu/mms/eis_pad_combine_sc.py b/pyrfu/mms/eis_pad_combine_sc.py index b283e0df..a2c94408 100644 --- a/pyrfu/mms/eis_pad_combine_sc.py +++ b/pyrfu/mms/eis_pad_combine_sc.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -52,7 +52,12 @@ def eis_pad_combine_sc(pads): pa_label = 180.0 * np.arange(n_pabins) / n_pabins + size_pabin / 2.0 allmms_pad = np.zeros( - (ref_probe.shape[0], ref_probe.shape[1], ref_probe.shape[2], len(pads)) + ( + ref_probe.shape[0], + ref_probe.shape[1], + ref_probe.shape[2], + len(pads), + ), ) for i_pad, pad_ in enumerate(pads): diff --git a/pyrfu/mms/eis_pad_spinavg.py b/pyrfu/mms/eis_pad_spinavg.py index ad7cf65e..82c8cd02 100644 --- a/pyrfu/mms/eis_pad_spinavg.py +++ b/pyrfu/mms/eis_pad_spinavg.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -38,7 +38,7 @@ def eis_pad_spinavg(inp, spin_nums): spin_times = np.zeros(len(spin_starts), dtype="= energy[0], energies <= energy[1]) + np.logical_and(energies >= energy[0], energies <= energy[1]), )[0] with warnings.catch_warnings(): @@ -110,18 +113,24 @@ def _pa_flux(pa_times, pa_bins, pa_labels, dpa, dflux, d_type): # Now loop through PA bins and time, find the telescopes where there is # data in those bins and average it up! - for pa_idx, ipa in itertools.product(range(len(pa_times)), range(n_pabins)): + for pa_idx, ipa in itertools.product( + range(len(pa_times)), + range(n_pabins), + ): if not np.isnan(dpa[pa_idx, :][0]): with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) ind = np.where( (dpa[pa_idx, :] + dangresp >= pa_labels[ipa] - delta_pa) - & (dpa[pa_idx, :] - dangresp < pa_labels[ipa] + delta_pa) + & (dpa[pa_idx, :] - dangresp < pa_labels[ipa] + delta_pa), ) if ind[0].size != 0: if len(ind[0]) > 1: - pa_flux[pa_idx, ipa] = np.nanmean(dflux[pa_idx, ind[0]], axis=0) + pa_flux[pa_idx, ipa] = np.nanmean( + dflux[pa_idx, ind[0]], + axis=0, + ) else: pa_flux[pa_idx, ipa] = dflux[pa_idx, ind[0]] @@ -130,7 +139,12 @@ def _pa_flux(pa_times, pa_bins, pa_labels, dpa, dflux, d_type): return pa_flux -def feeps_pad(inp_dataset, b_bcs, bin_size: float = 16.3636, energy: list = None): +def feeps_pad( + inp_dataset, + b_bcs, + bin_size: float = 16.3636, + energy: list = None, +): r"""Compute pitch angle distribution using FEEPS data. Parameters @@ -172,13 +186,28 @@ def feeps_pad(inp_dataset, b_bcs, bin_size: float = 16.3636, energy: list = None pa_data_map = _pa_data_map(idx_maps, d_type, d_rate) dpa, dflux = _dpa_dflux( - inp_dataset, pitch_angles, pa_data_map, energy, d_type, mms_id + inp_dataset, + pitch_angles, + pa_data_map, + energy, + d_type, + mms_id, ) - pa_flux = _pa_flux(pitch_angles.time, pa_bins, pa_labels, dpa, dflux, d_type) + pa_flux = _pa_flux( + pitch_angles.time, + pa_bins, + pa_labels, + dpa, + dflux, + d_type, + ) pad = xr.DataArray( - pa_flux, coords=[time, pa_labels], dims=["time", "theta"], attrs=attrs + pa_flux, + coords=[time, pa_labels], + dims=["time", "theta"], + attrs=attrs, ) return pad diff --git a/pyrfu/mms/feeps_pad_spinavg.py b/pyrfu/mms/feeps_pad_spinavg.py index 24f62e67..dfd6c4ff 100644 --- a/pyrfu/mms/feeps_pad_spinavg.py +++ b/pyrfu/mms/feeps_pad_spinavg.py @@ -7,14 +7,13 @@ # #rd party imports import numpy as np import xarray as xr - from scipy import interpolate __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -64,12 +63,17 @@ def feeps_pad_spinavg(pad, spin_sectors, bin_size: float = 16.3636): for i, spin in enumerate(spin_starts): with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) - spin_avg_flux[i, :] = np.nanmean(data[c_start : spin + 1, :], axis=0) + spin_avg_flux[i, :] = np.nanmean( + data[c_start : spin + 1, :], + axis=0, + ) spin_times[i] = times[c_start] # rebin and interpolate to new_bins spin_avg_interp = interpolate.interp1d( - np.arange(n_angs), spin_avg_flux[i, :], fill_value="extrapolate" + np.arange(n_angs), + spin_avg_flux[i, :], + fill_value="extrapolate", ) rebinned_data[i, :] = spin_avg_interp(srx) @@ -80,7 +84,9 @@ def feeps_pad_spinavg(pad, spin_sectors, bin_size: float = 16.3636): c_start = spin + 1 out = xr.DataArray( - rebinned_data, coords=[spin_times, new_bins], dims=["time", "theta"] + rebinned_data, + coords=[spin_times, new_bins], + dims=["time", "theta"], ) return out diff --git a/pyrfu/mms/feeps_pitch_angles.py b/pyrfu/mms/feeps_pitch_angles.py index eff53ce4..0f282bf6 100644 --- a/pyrfu/mms/feeps_pitch_angles.py +++ b/pyrfu/mms/feeps_pitch_angles.py @@ -5,16 +5,16 @@ import numpy as np import xarray as xr -# Local imports -from ..pyrf import resample +from ..pyrf.resample import resample +# Local imports from .feeps_active_eyes import feeps_active_eyes __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" # Rotation matrices for FEEPS coord system (FCS) into body coordinate system @@ -24,14 +24,14 @@ [1.0 / np.sqrt(2.0), -1.0 / np.sqrt(2.0), 0], [1.0 / np.sqrt(2.0), 1.0 / np.sqrt(2.0), 0], [0, 0, 1], - ] + ], ) t_bot = np.array( [ [-1.0 / np.sqrt(2.0), -1.0 / np.sqrt(2.0), 0], [-1.0 / np.sqrt(2.0), 1.0 / np.sqrt(2.0), 0], [0, 0, -1], - ] + ], ) # the following 2 hash tables map TOP/BOTTOM telescope #s to index of the @@ -160,7 +160,10 @@ def feeps_pitch_angles(inp_dataset, b_bcs): d_rate = inp_dataset.attrs["tmmode"] mms_id = inp_dataset.attrs["mmsId"] - tint = np.datetime_as_string(np.hstack([np.min(times), np.max(times)]), "ns") + tint = np.datetime_as_string( + np.hstack([np.min(times), np.max(times)]), + "ns", + ) eyes = feeps_active_eyes(inp_dataset.attrs, list(tint), mms_id) diff --git a/pyrfu/mms/feeps_remove_bad_data.py b/pyrfu/mms/feeps_remove_bad_data.py index ed15dc19..bec573cd 100644 --- a/pyrfu/mms/feeps_remove_bad_data.py +++ b/pyrfu/mms/feeps_remove_bad_data.py @@ -1,28 +1,32 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import datetime +import json + # Built-in imports import os -import json -import datetime # 3rd party imports import numpy as np # Local imports -from ..pyrf import iso86012datetime, datetime642iso8601 +from ..pyrf.datetime642iso8601 import datetime642iso8601 +from ..pyrf.iso86012datetime import iso86012datetime __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" def _bad_vars(bad_data): bad_vars_top = list(filter(lambda x: x not in [6, 7, 8], bad_data["top"])) - bad_vars_bot = list(filter(lambda x: x not in [6, 7, 8], bad_data["bottom"])) + bad_vars_bot = list( + filter(lambda x: x not in [6, 7, 8], bad_data["bottom"]), + ) bad_vars = [ *[f"top-{x}" for x in bad_vars_top], @@ -116,7 +120,9 @@ def feeps_remove_bad_data(inp_dataset): root_path = os.path.dirname(os.path.abspath(__file__)) - with open(os.path.join(root_path, "feeps_bad_data.json")) as file: + with open( + os.path.join(root_path, "feeps_bad_data.json"), "r", encoding="utf-8" + ) as file: feeps_bad_data = json.load(file) bad_data_table = feeps_bad_data["bad_data_table"] diff --git a/pyrfu/mms/feeps_remove_sun.py b/pyrfu/mms/feeps_remove_sun.py index 5177ab70..cddd65e3 100644 --- a/pyrfu/mms/feeps_remove_sun.py +++ b/pyrfu/mms/feeps_remove_sun.py @@ -10,9 +10,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/mms/feeps_sector_spec.py b/pyrfu/mms/feeps_sector_spec.py index 2da6ed37..8addcbb5 100644 --- a/pyrfu/mms/feeps_sector_spec.py +++ b/pyrfu/mms/feeps_sector_spec.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -51,7 +51,10 @@ def feeps_sector_spec(inp_alle): # find the sectors for this spin spin_sect = sector_data[c_start:spin] - sector_spec[i, spin_sect] = np.nanmean(sensor_data[c_start:spin, :], axis=1) + sector_spec[i, spin_sect] = np.nanmean( + sensor_data[c_start:spin, :], + axis=1, + ) c_start = spin diff --git a/pyrfu/mms/feeps_spin_avg.py b/pyrfu/mms/feeps_spin_avg.py index 865ecac2..f7a801f3 100644 --- a/pyrfu/mms/feeps_spin_avg.py +++ b/pyrfu/mms/feeps_spin_avg.py @@ -10,9 +10,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -44,7 +44,10 @@ def feeps_spin_avg(flux_omni, spin_sectors): for i, spin_start in enumerate(spin_starts[1:-1]): with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) - spin_avg[i, :] = np.nanmean(data[c_start : spin_start + 1, :], axis=0) + spin_avg[i, :] = np.nanmean( + data[c_start : spin_start + 1, :], + axis=0, + ) c_start = spin_start + 1 spin_avg_flux = xr.DataArray( diff --git a/pyrfu/mms/feeps_split_integral_ch.py b/pyrfu/mms/feeps_split_integral_ch.py index 07157122..d9cbf5fc 100644 --- a/pyrfu/mms/feeps_split_integral_ch.py +++ b/pyrfu/mms/feeps_split_integral_ch.py @@ -6,9 +6,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/mms/fft_bandpass.py b/pyrfu/mms/fft_bandpass.py index 04f627bc..ae67cfd4 100644 --- a/pyrfu/mms/fft_bandpass.py +++ b/pyrfu/mms/fft_bandpass.py @@ -5,13 +5,15 @@ import numpy as np # Local imports -from ..pyrf import calc_fs, ts_scalar, ts_vec_xyz +from ..pyrf.calc_fs import calc_fs +from ..pyrf.ts_scalar import ts_scalar +from ..pyrf.ts_vec_xyz import ts_vec_xyz __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/mms/fk_power_spectrum_4sc.py b/pyrfu/mms/fk_power_spectrum_4sc.py index 14a3c555..3ebde449 100644 --- a/pyrfu/mms/fk_power_spectrum_4sc.py +++ b/pyrfu/mms/fk_power_spectrum_4sc.py @@ -3,22 +3,32 @@ # Built-in imports import bisect -import pdb +import logging # 3rd party imports import numpy as np import xarray as xr # Local imports -from ..pyrf import resample, avg_4sc, time_clip, wavelet +from ..pyrf.avg_4sc import avg_4sc +from ..pyrf.resample import resample +from ..pyrf.time_clip import time_clip +from ..pyrf.wavelet import wavelet __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2022" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.22" +__version__ = "2.4.2" __status__ = "Prototype" +logging.captureWarnings(True) +logging.basicConfig( + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, +) + def fk_power_spectrum_4sc( e, @@ -47,7 +57,8 @@ def fk_power_spectrum_4sc( Positions of the four spacecraft. b : list of xarray.DataArray background magnetic field in the same coordinates as r. Used to - determine the parallel and perpendicular wave numbers using 4SC average. + determine the parallel and perpendicular wave numbers using 4SC + average. tints : list of str Time interval over which the power spectrum is calculated. To avoid boundary effects use a longer time interval for e and b. @@ -85,8 +96,8 @@ def fk_power_spectrum_4sc( >>> tint = ["2015-10-16T13:05:24.00", "2015-10-16T13:05:50.000"] >>> ic = range(1, 5) - >>> b_fgm_mms = [get_data("b_gse_fgm_brst_l2", tint, i) for i in ic] - >>> b_scm_mms = [get_data("b_gse_scm_brst_l2", tint, i) for i in ic] + >>> b_fgm = [get_data("b_gse_fgm_brst_l2", tint, i) for i in ic] + >>> b_scm = [get_data("b_gse_scm_brst_l2", tint, i) for i in ic] Load spacecraft position @@ -95,14 +106,14 @@ def fk_power_spectrum_4sc( Convert magnetic field fluctuations to field aligned coordinates - >>> b_scm_fac = [convert_fac(b_scm, b_fgm) for b_scm, b_fgm in zip(b_scm_mms, b_fgm_mms)] - >>> b_scm_par = [b_scm[:, 0] for b_scm in b_scm_fac] + >>> b_fac = [convert_fac(b_s, b_f) for b_s, b_f in zip(b_scm, b_fgm)] + >>> b_par = [b_s[:, 0] for b_s in b_fac] Compute dispersion relation >>> tint = ["2015-10-16T13:05:26.500", "2015-10-16T13:05:27.000"] - >>> pwer = fk_power_spectrum_4sc(b_scm_par, r_gse_mms, b_fgm_mms, tint, 4, - ... 500, 2, 10, 2) + >>> pwer = fk_power_spectrum_4sc(b_par, r_gse, b_fgm, tint, 4, 500, 2, + ... 10, 2) """ @@ -117,20 +128,26 @@ def fk_power_spectrum_4sc( times = e[0].time use_linear = df is not None - idx = time_clip(e[0].time, list(tints)) + # idx = time_clip(e[0].time, list(tints)) # If odd, remove last data point (as is done in irf_wavelet) - if len(idx) % 2: - idx = idx[:-1] + # if len(idx) % 2: + # idx = idx[:-1] if use_linear: - cwt_options = dict( - linear=df, return_power=False, wavelet_width=5.36 * w_width, cut_edge=False - ) + cwt_options = { + "linear": df, + "return_power": False, + "wavelet_width": 5.36 * w_width, + "cut_edge": False, + } else: - cwt_options = dict( - nf=num_f, return_power=False, wavelet_width=5.36 * w_width, cut_edge=False - ) + cwt_options = { + "nf": num_f, + "return_power": False, + "wavelet_width": 5.36 * w_width, + "cut_edge": False, + } w = [wavelet(e[i], **cwt_options) for i in range(4)] @@ -162,22 +179,28 @@ def fk_power_spectrum_4sc( lb, ub = [int(pos_avm - cav / 2), int(pos_avm + cav / 2)] cx12[m, :] = np.nanmean( - w[0].data[lb:ub, :] * np.conj(w[1].data[lb:ub, :]), axis=0 + w[0].data[lb:ub, :] * np.conj(w[1].data[lb:ub, :]), + axis=0, ) cx13[m, :] = np.nanmean( - w[0].data[lb:ub, :] * np.conj(w[2].data[lb:ub, :]), axis=0 + w[0].data[lb:ub, :] * np.conj(w[2].data[lb:ub, :]), + axis=0, ) cx14[m, :] = np.nanmean( - w[0].data[lb:ub, :] * np.conj(w[3].data[lb:ub, :]), axis=0 + w[0].data[lb:ub, :] * np.conj(w[3].data[lb:ub, :]), + axis=0, ) cx23[m, :] = np.nanmean( - w[1].data[lb:ub, :] * np.conj(w[2].data[lb:ub, :]), axis=0 + w[1].data[lb:ub, :] * np.conj(w[2].data[lb:ub, :]), + axis=0, ) cx24[m, :] = np.nanmean( - w[1].data[lb:ub, :] * np.conj(w[3].data[lb:ub, :]), axis=0 + w[1].data[lb:ub, :] * np.conj(w[3].data[lb:ub, :]), + axis=0, ) cx34[m, :] = np.nanmean( - w[2].data[lb:ub, :] * np.conj(w[3].data[lb:ub, :]), axis=0 + w[2].data[lb:ub, :] * np.conj(w[3].data[lb:ub, :]), + axis=0, ) power_avg[m, :] = np.nanmean(fk_power[lb:ub, :], axis=0) @@ -228,7 +251,7 @@ def fk_power_spectrum_4sc( r[1][ii, :] - r[0][ii, :], r[2][ii, :] - r[0][ii, :], r[3][ii, :] - r[0][ii, :], - ] + ], ) for jj in range(num_f): m = np.linalg.solve(dr, [dt2[ii, jj], dt3[ii, jj], dt4[ii, jj]]) @@ -259,7 +282,7 @@ def fk_power_spectrum_4sc( dk = 2 * k_max / num_k # Sort power into frequency and wave vector - print("notice : Computing power versus kx,f; ky,f, kz,f") + logging.info("Computing power versus kx,f; ky,f, kz,f") power_k_x_f, power_k_y_f, power_k_z_f = [np.zeros((num_f, num_k)) for _ in range(3)] power_k_mag_f = np.zeros((num_f, num_k)) @@ -288,7 +311,7 @@ def fk_power_spectrum_4sc( idx_max_freq = bisect.bisect_left(frequencies, np.max(f_range)) idx_f = idx_f[idx_min_freq:idx_max_freq] - print("notice : Computing power versus kx,ky; kx,kz; ky,kz\n") + logging.info("Computing power versus kx,ky; kx,kz; ky,kz") power_k_x_k_y = np.zeros((num_k, num_k)) power_k_x_k_z = np.zeros((num_k, num_k)) power_k_y_k_z = np.zeros((num_k, num_k)) @@ -306,7 +329,9 @@ def fk_power_spectrum_4sc( power_k_x_k_z[k_z_number, k_x_number] += np.real(power_avg[:, nn]) power_k_y_k_z[k_z_number, k_y_number] += np.real(power_avg[:, nn]) - power_k_perp_k_par[k_par_number, k_perp_number] += np.real(power_avg[:, nn]) + power_k_perp_k_par[k_par_number, k_perp_number] += np.real( + power_avg[:, nn], + ) power_k_x_k_y /= np.max(power_k_x_k_y) power_k_x_k_z /= np.max(power_k_x_k_z) diff --git a/pyrfu/mms/get_data.py b/pyrfu/mms/get_data.py index 2edec38e..2df7283e 100644 --- a/pyrfu/mms/get_data.py +++ b/pyrfu/mms/get_data.py @@ -2,42 +2,44 @@ # -*- coding: utf-8 -*- # Built-in imports -import os import json import logging +import os -# Local imports -from ..pyrf import dist_append, ts_append, ttns2datetime64 +# 3rd party imports +import requests +from botocore.exceptions import ClientError -from .tokenize import tokenize -from .list_files import list_files -from .get_ts import get_ts +# Local imports +from ..pyrf.dist_append import dist_append +from ..pyrf.ts_append import ts_append +from ..pyrf.ttns2datetime64 import ttns2datetime64 +from .db_init import MMS_CFG_PATH from .get_dist import get_dist +from .get_ts import get_ts +from .list_files import list_files +from .list_files_aws import list_files_aws +from .list_files_sdc import _login_lasp, list_files_sdc +from .tokenize import tokenize __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" logging.captureWarnings(True) logging.basicConfig( - format="%(asctime)s: %(message)s", datefmt="%d-%b-%y %H:%M:%S", level=logging.INFO + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, ) def _var_and_cdf_name(var_str, mms_id): var = tokenize(var_str) - - root_path = os.path.dirname(os.path.abspath(__file__)) - - with open(os.sep.join([root_path, "mms_keys.json"]), "r") as json_file: - keys_ = json.load(json_file) - - var["dtype"] = keys_[var["inst"]][var_str.lower()]["dtype"] - cdf_name = f"mms{mms_id}_{keys_[var['inst']][var_str.lower()]['cdf_name']}" - + cdf_name = f"mms{mms_id}_{var['cdf_name']}" return var, cdf_name @@ -49,7 +51,61 @@ def _check_times(inp): return out -def get_data(var_str, tint, mms_id, verbose: bool = True, data_path: str = ""): +def _list_files_sources(source, tint, mms_id, var, data_path): + if source == "local": + file_names = list_files(tint, mms_id, var, data_path) + sdc_session, headers = None, {} + elif source == "sdc": + file_names = [file.get("url") for file in list_files_sdc(tint, mms_id, var)] + sdc_session, headers, _ = _login_lasp() + elif source == "aws": + file_names = [file.get("s3_obj") for file in list_files_aws(tint, mms_id, var)] + sdc_session, headers = None, {} + else: + raise NotImplementedError("AWS is not yet implemented!!") + + return file_names, sdc_session, headers + + +def _get_file_content_sources(source, file_name, sdc_session, headers): + if source == "local": + file_path = os.path.normpath(file_name) + with open(file_path, "rb") as file: + file_content = file.read() + elif source == "sdc": + try: + response = sdc_session.get(file_name, timeout=None, headers=headers) + response.raise_for_status() # Raise an HTTPError for bad responses + file_content = response.content + except requests.RequestException as e: + print(f"Error retrieving file from {file_name}: {e}") + elif source == "aws": + try: + response = file_name.get() + file_content = response["Body"].read() + except ClientError as err: + if err.response["Error"]["Code"] == "InternalError": # Generic error + logging.error("Error Message: %s", err.response["Error"]["Message"]) + + response_meta = err.response.get("ResponseMetadata") + logging.error("Request ID: %s", response_meta.get("RequestId")) + logging.error("Http code: %s", response_meta.get("HTTPStatusCode")) + else: + raise err + else: + raise NotImplementedError(f"Resource {source} is not yet implemented!!") + + return file_content + + +def get_data( + var_str, + tint, + mms_id, + verbose: bool = True, + data_path: str = "", + source: str = "", +): r"""Load a variable. var_str must be in var (see below) Parameters @@ -63,7 +119,9 @@ def get_data(var_str, tint, mms_id, verbose: bool = True, data_path: str = ""): verbose : bool, Optional Set to True to follow the loading. Default is True. data_path : str, Optional - Path of MMS data. If None use `pyrfu/mms/config.json` + Local path of MMS data. Default uses that provided in `pyrfu/mms/config.json` + source: {"local", "sdc", "aws"}, Optional + Ressource to fetch data from. Default uses default in `pyrfu/mms/config.json` Returns ------- @@ -90,7 +148,7 @@ def get_data(var_str, tint, mms_id, verbose: bool = True, data_path: str = ""): Load magnetic field from FGM - >>> b_xyz = mms.get_data("B_gse_fgm_brst_l2", tint_brst, ic) + >>> b_xyz = mms.get_data("b_gse_fgm_brst_l2", tint_brst, ic) """ @@ -98,22 +156,37 @@ def get_data(var_str, tint, mms_id, verbose: bool = True, data_path: str = ""): var, cdf_name = _var_and_cdf_name(var_str, mms_id) - files = list_files(tint, mms_id, var, data_path) + # Read the current version of the MMS configuration file + with open(MMS_CFG_PATH, "r", encoding="utf-8") as fs: + config = json.load(fs) + + source = source if source else config.get("default") + + file_names, sdc_session, headers = _list_files_sources( + source, tint, mms_id, var, data_path + ) - assert files, "No files found. Make sure that the data_path is correct" + assert file_names, "No files found. Make sure that the data_path is correct" if verbose: logging.info("Loading %s...", cdf_name) out = None - for file in files: + for file_name in file_names: + file_content = _get_file_content_sources( + source, file_name, sdc_session, headers + ) + if "-dist" in var["dtype"]: - out = dist_append(out, get_dist(file, cdf_name, tint)) + out = dist_append(out, get_dist(file_content, cdf_name, tint)) else: - out = ts_append(out, get_ts(file, cdf_name, tint)) + out = ts_append(out, get_ts(file_content, cdf_name, tint)) out = _check_times(out) + if sdc_session: + sdc_session.close() + return out diff --git a/pyrfu/mms/get_dist.py b/pyrfu/mms/get_dist.py index 54e4a0ba..d7371da8 100644 --- a/pyrfu/mms/get_dist.py +++ b/pyrfu/mms/get_dist.py @@ -1,117 +1,37 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -# Built-in import -import re -import pdb -import warnings - # 3rd party imports import numpy as np - -from cdflib import CDF, cdfepoch +from pycdfpp import load # Local imports -from ..pyrf import ( - ts_skymap, - iso86012datetime64, - datetime642ttns, - cdfepoch2datetime64, - extend_tint, - time_clip, -) +from ..pyrf.datetime642iso8601 import datetime642iso8601 +from ..pyrf.iso86012datetime64 import iso86012datetime64 +from ..pyrf.time_clip import time_clip +from ..pyrf.ts_skymap import ts_skymap +from .get_ts import _get_epochs +from .get_variable import _pycdfpp_attributes_to_dict __author__ = "Louis Richard" __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.26" +__version__ = "2.4.2" __status__ = "Prototype" # Keys of the global attributes to keep from CDF informations -Globkeys = ["CDF", "Version", "Encoding", "Checksum", "Compressed", "LeapSecondUpdated"] - - -def _shift_epochs(file, epoch): - r"""Shift times for particles.""" - - epoch_shifted = epoch["data"].copy() - - try: - delta_minus_var = { - "data": file.varget(epoch["attrs"]["DELTA_MINUS_VAR"]), - "attrs": file.varget(epoch["attrs"]["DELTA_MINUS_VAR"]), - } - delta_plus_var = { - "data": file.varget(epoch["attrs"]["DELTA_PLUS_VAR"]), - "attrs": file.varget(epoch["attrs"]["DELTA_PLUS_VAR"]), - } - - delta_vars = [delta_minus_var, delta_plus_var] - flags_vars = [1e3, 1e3] # Time scaling conversion flags - - for i, delta_var in enumerate(delta_vars): - if isinstance(delta_var["attrs"], dict) and "UNITS" in delta_var["attrs"]: - if delta_var["attrs"]["UNITS"].lower() == "s": - flags_vars[i] = 1e3 - elif delta_var["attrs"]["UNITS"].lower() == "ms": - flags_vars[i] = 1e0 - else: - message = " units are not clear, assume s" - warnings.warn(message) - else: - message = "Epoch_plus_var/Epoch_minus_var units are not clear, assume s" - warnings.warn(message) - - flag_minus, flag_plus = flags_vars - t_offset = ( - delta_plus_var["data"] * flag_plus - delta_minus_var["data"] * flag_minus - ) - t_offset = np.timedelta64(int(np.round(t_offset, 1) * 1e6 / 2), "ns") - t_diff = ( - delta_plus_var["data"] * flag_plus - delta_minus_var["data"] * flag_minus - ) - t_diff = np.timedelta64(int(np.round(t_diff, 1) * 1e6 / 2), "ns") - t_diff_data = np.median(np.diff(epoch["data"])) / 2 - - if t_diff_data != np.mean(t_diff): - t_offset = t_diff_data - - epoch_shifted += t_offset - - return {"data": epoch_shifted, "attrs": epoch["attrs"]} - - except KeyError: - return {"data": epoch_shifted, "attrs": epoch["attrs"]} - - -def _get_epochs(file, cdf_name, tint): - r"""Get epochs form cdf and shift if needed.""" - - depend0_key = file.varattsget(cdf_name)["DEPEND_0"] - - out = {"data": file.varget(depend0_key, starttime=tint[0], endtime=tint[1])} - - if file.varinq(depend0_key)["Data_Type_Description"] == "CDF_TIME_TT2000": - try: - out["data"] = cdfepoch2datetime64(out["data"]) - except TypeError: - pass +Globkeys = [ + "CDF", + "Version", + "Encoding", + "Checksum", + "Compressed", + "LeapSecondUpdated", +] - # Get epoch attributes - out["attrs"] = file.varattsget(depend0_key) - # Shift times if particle data - is_part = re.search("^mms[1-4]_d[ei]s_", cdf_name) # Is it FPI data? - is_part = is_part or re.search("^mms[1-4]_hpca_", cdf_name) # Is it HPCA data? - - if is_part: - out = _shift_epochs(file, out) - - return out - - -def get_dist(file_path, cdf_name, tint): +def get_dist(file_path, cdf_name, tint: list = None): r"""Read field named cdf_name in file and convert to velocity distribution function. @@ -121,7 +41,7 @@ def get_dist(file_path, cdf_name, tint): Path of the cdf file. cdf_name : str Name of the target variable in the cdf file. - tint : list of str + tint : list of str, Optional Time interval. Returns @@ -139,90 +59,93 @@ def get_dist(file_path, cdf_name, tint): elif "_des_" in cdf_name: specie = "electrons" else: - raise AttributeError("Couldn't get the particle species from file name!!") + raise AttributeError( + "Couldn't get the particle species from file name!!", + ) - tint_org = tint - tint = extend_tint(tint, [-1, 1]) - tint = list(datetime642ttns(iso86012datetime64(np.array(tint)))) + # Check time interval type + # Check time interval + if tint is None: + tint = ["1995-10-06T18:50:00.000000000", "2200-10-06T18:50:00.000000000"] + elif isinstance(tint, (np.ndarray, list)): + if isinstance(tint[0], np.datetime64): + tint = datetime642iso8601(np.array(tint)) + elif isinstance(tint[0], str): + tint = iso86012datetime64( + np.array(tint), + ) # to make sure it is ISO8601 ok!! + tint = datetime642iso8601(np.array(tint)) + else: + raise TypeError("Values must be in datetime64, or str!!") + else: + raise TypeError("tint must be array_like!!") - with CDF(file_path) as file: - # Get the relevant CDF file information and add the global attributes - glob_attrs = {k: file.cdf_info()[k] for k in Globkeys} - glob_attrs = {**glob_attrs, **file.globalattsget()} - glob_attrs = {**glob_attrs, **{"tmmode": tmmode, "species": specie}} + # Load CDF file + file = load(file_path) - # Get VDF zVariable attributes and sort them - dist_attrs = file.varattsget(cdf_name) - dist_attrs = {k: dist_attrs[k] for k in sorted(dist_attrs)} + # with CDF(file_path) as file: + # Get the relevant CDF file information (zVariables) + z_vars = [z_var[0] for z_var in file.items()] - # Get CDF keys to Epoch, energy, azimuthal and elevation angle zVariables - depends_keys = [dist_attrs[f"DEPEND_{i:d}"] for i in range(4)] - depend0_key, depend1_key, depend2_key, depend3_key = depends_keys + # Get the global attributes + glob_attrs = _pycdfpp_attributes_to_dict(file.attributes) + glob_attrs = {**glob_attrs, **{"tmmode": tmmode, "species": specie}} - # Get coordinates attributes and sort them - coords_attrs = [file.varattsget(k) for k in depends_keys] - coords_attrs = [{k: attrs[k] for k in sorted(attrs)} for attrs in coords_attrs] - coords_names = ["time", "phi", "theta", "energy"] - coords_attrs = {k: attrs for k, attrs in zip(coords_names, coords_attrs)} + # Get VDF zVariable attributes + dist_attrs = _pycdfpp_attributes_to_dict(file[cdf_name].attributes) - times = _get_epochs(file, cdf_name, tint) + # Get CDF keys to Epoch, energy, azimuthal and elevation angle + # zVariables + depends_keys = [dist_attrs[f"DEPEND_{i:d}"] for i in range(4)] + + # Get coordinates attributes + coords_attrs = {} + + for n, k in zip(["time", "phi", "theta", "energy"], depends_keys): + coords_attrs[n] = _pycdfpp_attributes_to_dict(file[k].attributes) + + times = _get_epochs(file, cdf_name) + + # If something time is None means that there is nothing interesting + # in this file so leave!! + if times["data"] is not None: times = times["data"] + else: + return None - dist = file.varget(cdf_name, starttime=tint[0], endtime=tint[1]) - dist = np.transpose(dist, [0, 3, 1, 2]) - phi = file.varget(depend1_key, starttime=tint[0], endtime=tint[1]) - theta = file.varget(depend2_key) - energy = file.varget(depend3_key, starttime=tint[0], endtime=tint[1]) - - if tmmode == "brst": - en0_name = "_".join( - [ - cdf_name.split("_")[0], - cdf_name.split("_")[1], - "energy0", - cdf_name.split("_")[-1], - ] - ) - en1_name = "_".join( - [ - cdf_name.split("_")[0], - cdf_name.split("_")[1], - "energy1", - cdf_name.split("_")[-1], - ] - ) - - e_step_table_name = "_".join( - [ - cdf_name.split("_")[0], - cdf_name.split("_")[1], - "steptable_parity", - cdf_name.split("_")[-1], - ] - ) - - step_table = file.varget( - e_step_table_name, starttime=tint[0], endtime=tint[1] - ) - - if en0_name not in file.cdf_info()["zVariables"]: - if energy.ndim == 1: - energy0 = energy - energy1 = energy - elif energy.shape[0] == 1: - energy0 = energy[0, :] - energy1 = energy[0, :] - else: - energy0 = energy[1, :] - energy1 = energy[0, :] - else: - energy0 = file.varget(en0_name) - energy1 = file.varget(en1_name) + dist = np.transpose(file[cdf_name].values, [0, 3, 1, 2]) + phi, theta, energy = [np.squeeze(file[k].values) for k in depends_keys[1:]] - # Overwrite energy to make sure that energy0 and energy1 are used instead - energy = None + if tmmode == "brst": + en0_name = "_".join( + [ + cdf_name.split("_")[0], + cdf_name.split("_")[1], + "energy0", + cdf_name.split("_")[-1], + ], + ) + en1_name = "_".join( + [ + cdf_name.split("_")[0], + cdf_name.split("_")[1], + "energy1", + cdf_name.split("_")[-1], + ], + ) + + e_step_table_name = "_".join( + [ + cdf_name.split("_")[0], + cdf_name.split("_")[1], + "steptable_parity", + cdf_name.split("_")[-1], + ], + ) - elif tmmode == "fast": + step_table = file[e_step_table_name].values + + if en0_name not in z_vars: if energy.ndim == 1: energy0 = energy energy1 = energy @@ -232,49 +155,63 @@ def get_dist(file_path, cdf_name, tint): else: energy0 = energy[1, :] energy1 = energy[0, :] - - step_table = np.zeros(len(times)) - else: - raise ValueError("Invalid sampling mode!!") - - d_en_name = "_".join( - [ - cdf_name.split("_")[0], - cdf_name.split("_")[1], - "energy_delta", - cdf_name.split("_")[-1], - ] - ) - - if d_en_name in file.cdf_info()["zVariables"]: - glob_attrs["delta_energy_plus"] = file.varget( - d_en_name, starttime=tint[0], endtime=tint[1] - ) - glob_attrs["delta_energy_minus"] = file.varget( - d_en_name, starttime=tint[0], endtime=tint[1] - ) + energy0 = file[en0_name].values + energy1 = file[en1_name].values + + # Overwrite energy to make sure that energy0 and energy1 + # are used instead + energy = np.tile(energy0, (len(step_table), 1)) + energy[step_table == 1] = np.tile(energy1, (int(np.sum(step_table)), 1)) + + elif tmmode == "fast": + phi = np.tile(phi, (len(times), 1)) + + if energy.ndim == 1: + energy0 = energy + energy1 = energy + elif energy.shape[0] == 1: + energy0 = energy[0, :] + energy1 = energy[0, :] else: - glob_attrs["delta_energy_plus"] = None - glob_attrs["delta_energy_minus"] = None - - # Sort the global attributes - glob_attrs = {k: glob_attrs[k] for k in sorted(glob_attrs)} - - out = ts_skymap( - times, - dist, - energy, - phi, - theta, - energy0=energy0, - energy1=energy1, - esteptable=step_table, - attrs=dist_attrs, - coords_attrs=coords_attrs, - glob_attrs=glob_attrs, - ) + energy0 = energy[1, :] + energy1 = energy[0, :] - out = time_clip(out, tint_org) + step_table = np.zeros(len(times)) + + else: + raise ValueError("Invalid sampling mode!!") + + d_en_name = "_".join( + [ + cdf_name.split("_")[0], + cdf_name.split("_")[1], + "energy_delta", + cdf_name.split("_")[-1], + ], + ) + + if d_en_name in z_vars: + glob_attrs["delta_energy_plus"] = file[d_en_name].values + glob_attrs["delta_energy_minus"] = file[d_en_name].values + else: + glob_attrs["delta_energy_plus"] = None + glob_attrs["delta_energy_minus"] = None + + out = ts_skymap( + times, + dist, + energy, + phi, + theta, + energy0=energy0, + energy1=energy1, + esteptable=step_table, + attrs=dist_attrs, + coords_attrs=coords_attrs, + glob_attrs=glob_attrs, + ) + + out = time_clip(out, tint) return out diff --git a/pyrfu/mms/get_eis_allt.py b/pyrfu/mms/get_eis_allt.py index 08c185af..12372e05 100644 --- a/pyrfu/mms/get_eis_allt.py +++ b/pyrfu/mms/get_eis_allt.py @@ -4,20 +4,27 @@ # 3rd party imports import xarray as xr -# Local imports -from .list_files import list_files from .db_get_ts import db_get_ts from .db_get_variable import db_get_variable +# Local imports +from .list_files import list_files + __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" -def get_eis_allt(tar_var, tint, mms_id, verbose: bool = True, data_path: str = ""): +def get_eis_allt( + tar_var, + tint, + mms_id, + verbose: bool = True, + data_path: str = "", +): r"""Read energy spectrum of the selected specie in the selected energy range for all telescopes. @@ -79,6 +86,9 @@ def get_eis_allt(tar_var, tint, mms_id, verbose: bool = True, data_path: str = " # before. files = list_files(tint, mms_id, var, data_path=data_path) + if not files: + raise FileNotFoundError("no files for these inputs!!") + file_version = int(files[0].split("_")[-1][1]) var["version"] = file_version @@ -105,12 +115,12 @@ def get_eis_allt(tar_var, tint, mms_id, verbose: bool = True, data_path: str = " ) # Names of the energy spectra in the CDF (one for each telescope) - cdfnames = ["{}_{}{:d}".format(pref, suf, t) for t in range(6)] + cdfnames = [f"{pref}_{suf}{t:d}" for t in range(6)] spin_nums = db_get_ts(dset_name, f"{pref}_spin", tint, data_path=data_path) sectors = db_get_ts(dset_name, f"{pref}_sector", tint, data_path=data_path) - e_minu = db_get_variable( + e_minus = db_get_variable( dset_name, f"{pref}_{specie}_t0_energy_dminus", tint, @@ -132,10 +142,14 @@ def get_eis_allt(tar_var, tint, mms_id, verbose: bool = True, data_path: str = " scope_key = f"t{i:d}" outdict[scope_key] = db_get_ts( - dset_name, cdfname, tint, verbose=verbose, data_path=data_path + dset_name, + cdfname, + tint, + verbose=verbose, + data_path=data_path, ) outdict[scope_key] = outdict[scope_key].rename( - {"time": "time", "Energy": "energy"} + {"time": "time", "Energy": "energy"}, ) outdict[f"look_{scope_key}"] = db_get_ts( @@ -147,14 +161,19 @@ def get_eis_allt(tar_var, tint, mms_id, verbose: bool = True, data_path: str = " ) e_plus = e_plus.assign_coords(x=outdict["t0"].energy.data) - e_minu = e_minu.assign_coords(x=outdict["t0"].energy.data) + e_minus = e_minus.assign_coords(x=outdict["t0"].energy.data) e_plus = e_plus.rename({"x": "energy"}) - e_minu = e_minu.rename({"x": "energy"}) - - outdict["energy_dplus"] = e_plus - outdict["energy_dminus"] = e_minu + e_minus = e_minus.rename({"x": "energy"}) + + # glob_attrs = {**outdict["spin"].attrs["GLOBAL"], **var} + glob_attrs = { + "delta_energy_plus": e_plus.data, + "delta_energy_minus": e_minus.data, + "species": specie, + **outdict["spin"].attrs["GLOBAL"], + } # Build Dataset - out = xr.Dataset(outdict, attrs=var) + out = xr.Dataset(outdict, attrs=glob_attrs) return out diff --git a/pyrfu/mms/get_feeps_alleyes.py b/pyrfu/mms/get_feeps_alleyes.py index 6ddd2034..a845a811 100644 --- a/pyrfu/mms/get_feeps_alleyes.py +++ b/pyrfu/mms/get_feeps_alleyes.py @@ -4,15 +4,16 @@ # 3rd party imports import xarray as xr +from .db_get_ts import db_get_ts + # Local imports from .feeps_active_eyes import feeps_active_eyes -from .db_get_ts import db_get_ts __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" data_units_keys = { @@ -43,7 +44,14 @@ def _tokenize(tar_var): return var, data_units -def _get_oneeye(tar_var, e_id, tint, mms_id, verbose: bool = True, data_path: str = ""): +def _get_oneeye( + tar_var, + e_id, + tint, + mms_id, + verbose: bool = True, + data_path: str = "", +): mms_id = int(mms_id) var, data_units = _tokenize(tar_var) @@ -65,18 +73,28 @@ def _get_oneeye(tar_var, e_id, tint, mms_id, verbose: bool = True, data_path: st raise ValueError("Invalid format of eye id") out = db_get_ts( - dset_name, f"mms{mms_id:d}_{pref}_{suf}", tint, verbose, data_path=data_path + dset_name, + f"mms{mms_id:d}_{pref}_{suf}", + tint, + verbose, + data_path=data_path, ) out.attrs["tmmode"] = var["tmmode"] out.attrs["lev"] = var["lev"] out.attrs["mms_id"] = mms_id out.attrs["dtype"] = var["dtype"] - out.attrs["species"] = "{}s".format(var["dtype"]) + out.attrs["species"] = f"{var['dtype']}s" return out -def get_feeps_alleyes(tar_var, tint, mms_id, verbose: bool = True, data_path: str = ""): +def get_feeps_alleyes( + tar_var, + tint, + mms_id, + verbose: bool = True, + data_path: str = "", +): r"""Read energy spectrum of the selected specie in the selected energy range for all FEEPS eyes. @@ -140,19 +158,32 @@ def get_feeps_alleyes(tar_var, tint, mms_id, verbose: bool = True, data_path: st out_dict = { "spinsectnum": db_get_ts( - dset_name, f"mms{mms_id:d}_{pref}_spinsectnum", tint, data_path=data_path + dset_name, + f"mms{mms_id:d}_{pref}_spinsectnum", + tint, + data_path=data_path, ), "pitch_angle": db_get_ts( - dset_name, f"mms{mms_id:d}_{pref}_pitch_angle", tint, data_path=data_path + dset_name, + f"mms{mms_id:d}_{pref}_pitch_angle", + tint, + data_path=data_path, ), } for e_id in e_ids: out_dict[e_id] = _get_oneeye( - tar_var, e_id, tint, mms_id, verbose, data_path=data_path + tar_var, + e_id, + tint, + mms_id, + verbose, + data_path=data_path, + ) + + out_dict[e_id] = out_dict[e_id].rename( + {out_dict[e_id].dims[1]: f"energy_{e_id}"} ) - dims = {o: n for o, n in zip(out_dict[e_id].dims, ["time", f"energy_{e_id}"])} - out_dict[e_id] = out_dict[e_id].rename(dims) out = xr.Dataset(out_dict) diff --git a/pyrfu/mms/get_feeps_omni.py b/pyrfu/mms/get_feeps_omni.py index 322df8d3..78d5d9c4 100644 --- a/pyrfu/mms/get_feeps_omni.py +++ b/pyrfu/mms/get_feeps_omni.py @@ -1,19 +1,20 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -# Local imports -from .get_feeps_alleyes import get_feeps_alleyes +from .feeps_omni import feeps_omni from .feeps_remove_bad_data import feeps_remove_bad_data -from .feeps_split_integral_ch import feeps_split_integral_ch from .feeps_remove_sun import feeps_remove_sun -from .feeps_omni import feeps_omni from .feeps_spin_avg import feeps_spin_avg +from .feeps_split_integral_ch import feeps_split_integral_ch + +# Local imports +from .get_feeps_alleyes import get_feeps_alleyes __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -46,7 +47,7 @@ def get_feeps_omni( Spin average the omni-directional flux. Default is False. Returns - -------- + ------- flux_omni : xarray.DataArray Energy spectrum of the target data unit for the target specie in omni direction. @@ -60,7 +61,13 @@ def get_feeps_omni( """ # Get all telescopes - dataset_feeps = get_feeps_alleyes(tar_var, tint, mms_id, verbose, data_path) + dataset_feeps = get_feeps_alleyes( + tar_var, + tint, + mms_id, + verbose, + data_path, + ) # Remove bad eyes and bad energy channels (lowest) dataset_feeps_washed = feeps_remove_bad_data(dataset_feeps) @@ -75,6 +82,9 @@ def get_feeps_omni( spec_feeps_omni = feeps_omni(dataset_feeps_clean_sun_removed) if spin_avg: - spec_feeps_omni = feeps_spin_avg(spec_feeps_omni, dataset_feeps.spinsectnum) + spec_feeps_omni = feeps_spin_avg( + spec_feeps_omni, + dataset_feeps.spinsectnum, + ) return spec_feeps_omni diff --git a/pyrfu/mms/get_hpca_dist.py b/pyrfu/mms/get_hpca_dist.py index 5315dfbf..966a8f3f 100644 --- a/pyrfu/mms/get_hpca_dist.py +++ b/pyrfu/mms/get_hpca_dist.py @@ -6,9 +6,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" mass_and_charge = { @@ -74,7 +74,10 @@ def _get_dphi(azimuth, full, out_phi, inp): if out_phi.ndim == 4: out_dphi = np.median(np.diff(azimuth.data[full, ...], axis=1), axis=1) out_dphi = np.transpose(out_dphi, [0, 2, 1]) - dphi_reform = np.reshape(out_dphi, [full.size, energy_len, theta_len, 1]) + dphi_reform = np.reshape( + out_dphi, + [full.size, energy_len, theta_len, 1], + ) out_dphi = np.repeat(dphi_reform, phi_len, axis=3) elif out_phi.ndim == 3: out_dphi = np.median(np.diff(azimuth.data[full, ...], axis=1), axis=0) @@ -91,8 +94,8 @@ def get_hpca_dist(inp, azimuth): r"""Returns pseudo-3D particle data structures containing mms hpca data for use with spd_slice2d. - Paramters - --------- + Parameters + ---------- inp : xarray.DataArray HPCA ion spec azimuth : xarray.DataArray @@ -105,6 +108,7 @@ def get_hpca_dist(inp, azimuth): """ + # check if the time series is monotonic to avoid doing incorrect # calculations when there's a problem with the CDF files time_data = azimuth.time.data @@ -174,7 +178,10 @@ def get_hpca_dist(inp, azimuth): else: continue - out_data[i, ...] = np.transpose(inp.data[start_idx:end_idx, :, :], [2, 0, 1]) + out_data[i, ...] = np.transpose( + inp.data[start_idx:end_idx, :, :], + [2, 0, 1], + ) out = { "data": out_data, diff --git a/pyrfu/mms/get_pitch_angle_dist.py b/pyrfu/mms/get_pitch_angle_dist.py index 6a51da69..45bd1427 100644 --- a/pyrfu/mms/get_pitch_angle_dist.py +++ b/pyrfu/mms/get_pitch_angle_dist.py @@ -1,6 +1,8 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import logging + # Built-in imports import warnings @@ -9,15 +11,24 @@ import xarray as xr # Local imports -from ..pyrf import time_clip, resample, normalize +from ..pyrf.normalize import normalize +from ..pyrf.resample import resample +from ..pyrf.time_clip import time_clip __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" +logging.captureWarnings(True) +logging.basicConfig( + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, +) + def get_pitch_angle_dist(vdf, b_xyz, tint: list = None, **kwargs): r"""Computes the pitch angle distributions from l1b brst particle data. @@ -36,8 +47,8 @@ def get_pitch_angle_dist(vdf, b_xyz, tint: list = None, **kwargs): pad : xarray.DataArray Particle pitch angle distribution - Other Paramters - --------------- + Other Parameters + ---------------- angles : int or float or list of ndarray User defined angles. meanorsum : {'mean', 'sum', 'sum_weighted'} @@ -77,13 +88,13 @@ def get_pitch_angle_dist(vdf, b_xyz, tint: list = None, **kwargs): d_angles = 180 / n_angles angles_v = np.linspace(d_angles, 180, n_angles) d_angles = d_angles * np.ones(n_angles) - print("notice : User defined number of pitch angles.") + logging.info("User defined number of pitch angles.") elif isinstance(kwargs["angles"], (list, np.ndarray)): angles_v = kwargs["angles"] d_angles = np.diff(angles_v) angles_v = angles_v[1:] - print("notice : User defined pitch angle limits.") + logging.info("User defined pitch angle limits.") else: raise ValueError("angles parameter not understood.") @@ -102,7 +113,9 @@ def get_pitch_angle_dist(vdf, b_xyz, tint: list = None, **kwargs): if vdf.phi.data.ndim == 1: phi = np.tile(vdf.phi.data, (len(time), 1)) phi = xr.DataArray( - phi, coords=[time, np.arange(len(phi))], dims=["time", "idx1"] + phi, + coords=[time, np.arange(len(phi))], + dims=["time", "idx1"], ) else: phi = vdf.phi @@ -128,13 +141,16 @@ def get_pitch_angle_dist(vdf, b_xyz, tint: list = None, **kwargs): b_vec = normalize(b_xyz) b_vec_x = np.transpose( - np.tile(b_vec.data[:, 0], [n_en, n_phi, n_theta, 1]), [3, 0, 1, 2] + np.tile(b_vec.data[:, 0], [n_en, n_phi, n_theta, 1]), + [3, 0, 1, 2], ) b_vec_y = np.transpose( - np.tile(b_vec.data[:, 1], [n_en, n_phi, n_theta, 1]), [3, 0, 1, 2] + np.tile(b_vec.data[:, 1], [n_en, n_phi, n_theta, 1]), + [3, 0, 1, 2], ) b_vec_z = np.transpose( - np.tile(b_vec.data[:, 2], [n_en, n_phi, n_theta, 1]), [3, 0, 1, 2] + np.tile(b_vec.data[:, 2], [n_en, n_phi, n_theta, 1]), + [3, 0, 1, 2], ) x_vec = np.zeros((len(time), n_phi, n_theta)) @@ -151,7 +167,8 @@ def get_pitch_angle_dist(vdf, b_xyz, tint: list = None, **kwargs): np.sin(np.deg2rad(theta.data[:, None])).T, ) z_vec[i, ...] = np.dot( - -np.ones((n_phi, 1)), np.cos(np.deg2rad(theta.data[:, None])).T + -np.ones((n_phi, 1)), + np.cos(np.deg2rad(theta.data[:, None])).T, ) if tint is not None: @@ -159,16 +176,22 @@ def get_pitch_angle_dist(vdf, b_xyz, tint: list = None, **kwargs): else: energy = vdf.energy.data - x_mat = np.squeeze(np.transpose(np.tile(x_vec, [n_en, 1, 1, 1]), [1, 0, 2, 3])) - y_mat = np.squeeze(np.transpose(np.tile(y_vec, [n_en, 1, 1, 1]), [1, 0, 2, 3])) - z_mat = np.squeeze(np.transpose(np.tile(z_vec, [n_en, 1, 1, 1]), [1, 0, 2, 3])) + x_mat = np.squeeze( + np.transpose(np.tile(x_vec, [n_en, 1, 1, 1]), [1, 0, 2, 3]), + ) + y_mat = np.squeeze( + np.transpose(np.tile(y_vec, [n_en, 1, 1, 1]), [1, 0, 2, 3]), + ) + z_mat = np.squeeze( + np.transpose(np.tile(z_vec, [n_en, 1, 1, 1]), [1, 0, 2, 3]), + ) theta_b = np.rad2deg( np.arccos( x_mat * np.squeeze(b_vec_x) + y_mat * np.squeeze(b_vec_y) - + z_mat * np.squeeze(b_vec_z) - ) + + z_mat * np.squeeze(b_vec_z), + ), ) dists = [vdf0.data.copy() for _ in range(n_angles)] @@ -183,10 +206,12 @@ def get_pitch_angle_dist(vdf, b_xyz, tint: list = None, **kwargs): warnings.simplefilter("ignore", category=RuntimeWarning) if mean_or_sum == "mean": pad_arr[i] = np.squeeze( - np.nanmean(np.nanmean(dists[i], axis=3), axis=2) + np.nanmean(np.nanmean(dists[i], axis=3), axis=2), ) elif mean_or_sum == "sum": - pad_arr[i] = np.squeeze(np.nansum(np.nansum(dists[i], axis=3), axis=2)) + pad_arr[i] = np.squeeze( + np.nansum(np.nansum(dists[i], axis=3), axis=2), + ) else: raise ValueError("Invalid method") @@ -196,13 +221,19 @@ def get_pitch_angle_dist(vdf, b_xyz, tint: list = None, **kwargs): pad = xr.Dataset( { - "data": (["time", "idx0", "idx1"], np.transpose(pad_arr, [0, 2, 1])), + "data": ( + ["time", "idx0", "idx1"], + np.transpose(pad_arr, [0, 2, 1]), + ), "energy": (["time", "idx0"], np.tile(energy, (len(pad_arr), 1))), - "theta": (["time", "idx1"], np.tile(pitch_angles, (len(pad_arr), 1))), + "theta": ( + ["time", "idx1"], + np.tile(pitch_angles, (len(pad_arr), 1)), + ), "time": time, "idx0": np.arange(len(energy)), "idx1": np.arange(len(pitch_angles)), - } + }, ) pad.attrs = vdf.attrs @@ -210,4 +241,8 @@ def get_pitch_angle_dist(vdf, b_xyz, tint: list = None, **kwargs): pad.attrs["delta_pitchangle_minus"] = d_angles * 0.5 pad.attrs["delta_pitchangle_plus"] = d_angles * 0.5 + pad.time.attrs = vdf.time.attrs + pad.energy.attrs = vdf.energy.attrs + pad.data.attrs["UNITS"] = vdf.data.attrs["UNITS"] + return pad diff --git a/pyrfu/mms/get_ts.py b/pyrfu/mms/get_ts.py index 71feb4da..a1f67fc2 100644 --- a/pyrfu/mms/get_ts.py +++ b/pyrfu/mms/get_ts.py @@ -8,23 +8,19 @@ # 3rd party imports import numpy as np import xarray as xr - -from cdflib import CDF, cdfepoch +from pycdfpp import DataType, load, to_datetime64 # Local imports -from ..pyrf import ( - datetime642iso8601, - iso86012datetime64, - cdfepoch2datetime64, - extend_tint, - time_clip, -) +from ..pyrf.datetime642iso8601 import datetime642iso8601 +from ..pyrf.iso86012datetime64 import iso86012datetime64 +from ..pyrf.time_clip import time_clip +from .get_variable import _pycdfpp_attributes_to_dict __author__ = "Louis Richard" __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.26" +__version__ = "2.4.2" __status__ = "Prototype" @@ -34,13 +30,15 @@ def _shift_epochs(file, epoch): epoch_shifted = epoch["data"].copy() try: + delta_minus_key = epoch["attrs"]["DELTA_MINUS_VAR"] delta_minus_var = { - "data": file.varget(epoch["attrs"]["DELTA_MINUS_VAR"]), - "attrs": file.varget(epoch["attrs"]["DELTA_MINUS_VAR"]), + "data": file[delta_minus_key].values, + "attrs": _pycdfpp_attributes_to_dict(file[delta_minus_key].attributes), } + delta_plus_key = epoch["attrs"]["DELTA_PLUS_VAR"] delta_plus_var = { - "data": file.varget(epoch["attrs"]["DELTA_PLUS_VAR"]), - "attrs": file.varget(epoch["attrs"]["DELTA_PLUS_VAR"]), + "data": file[delta_plus_key].values, + "attrs": _pycdfpp_attributes_to_dict(file[delta_plus_key].attributes), } delta_vars = [delta_minus_var, delta_plus_var] @@ -60,14 +58,16 @@ def _shift_epochs(file, epoch): warnings.warn(message) flag_minus, flag_plus = flags_vars + t_offset = ( delta_plus_var["data"] * flag_plus - delta_minus_var["data"] * flag_minus ) - t_offset = np.timedelta64(int(np.round(t_offset, 1) * 1e6 / 2), "ns") + + t_offset = (np.round(t_offset, 1) * 1e6 / 2).astype("timedelta64[ns]") t_diff = ( delta_plus_var["data"] * flag_plus - delta_minus_var["data"] * flag_minus ) - t_diff = np.timedelta64(int(np.round(t_diff, 1) * 1e6 / 2), "ns") + t_diff = (np.round(t_diff, 1) * 1e6 / 2).astype("timedelta64[ns]") t_diff_data = np.median(np.diff(epoch["data"])) / 2 if t_diff_data != np.mean(t_diff): @@ -81,34 +81,43 @@ def _shift_epochs(file, epoch): return {"data": epoch_shifted, "attrs": epoch["attrs"]} -def _get_epochs(file, cdf_name, tint): +def _get_epochs(file, cdf_name): r"""Get epochs form cdf and shift if needed.""" - depend0_key = file.varattsget(cdf_name)["DEPEND_0"] + depend0_key = file[cdf_name].attributes["DEPEND_0"][0] - out = {"data": file.varget(depend0_key, starttime=tint[0], endtime=tint[1])} + out = { + "data": file[depend0_key].values, + } - if file.varinq(depend0_key)["Data_Type_Description"] == "CDF_TIME_TT2000": + if file[depend0_key].type == DataType.CDF_TIME_TT2000: try: - out["data"] = cdfepoch2datetime64(out["data"]) - except TypeError: - pass + out["data"] = to_datetime64(out["data"]) + + # Get epoch attributes + out["attrs"] = _pycdfpp_attributes_to_dict(file[depend0_key].attributes) - # Get epoch attributes - out["attrs"] = file.varattsget(depend0_key) + # Shift times if particle data + is_part = re.search( + "^mms[1-4]_d[ei]s_", + cdf_name, + ) # Is it FPI data? + is_part = is_part or re.search( + "^mms[1-4]_hpca_", + cdf_name, + ) # Is it HPCA data? - # Shift times if particle data - is_part = re.search("^mms[1-4]_d[ei]s_", cdf_name) # Is it FPI data? - is_part = is_part or re.search("^mms[1-4]_hpca_", cdf_name) # Is it HPCA data? + if is_part: + out = _shift_epochs(file, out) - if is_part: - out = _shift_epochs(file, out) + except TypeError: + pass return out def _get_depend_attributes(file, depend_key): - attributes = file.varattsget(depend_key) + attributes = _pycdfpp_attributes_to_dict(file[depend_key].attributes) # Remove spaces in label try: @@ -123,31 +132,30 @@ def _get_depend_attributes(file, depend_key): return attributes -def _get_depend(file, cdf_name, tint, dep_num=1): +def _get_depend(file, cdf_name, dep_num=1): out = {} - try: - depend_key = file.varattsget(cdf_name)[f"DEPEND_{dep_num:d}"] - except KeyError: - depend_key = file.varattsget(cdf_name)[f"REPRESENTATION_{dep_num:d}"] + if f"DEPEND_{dep_num:d}" in file[cdf_name].attributes: + depend_key = file[cdf_name].attributes[f"DEPEND_{dep_num:d}"][0] + elif f"REPRESENTATION_{dep_num:d}" in file[cdf_name].attributes: + depend_key = file[cdf_name].attributes[f"REPRESENTATION_{dep_num:d}"][0] + else: + raise KeyError(f"no DEPEND_{dep_num:d}/REPRESENTATION_{dep_num:d} attributes") if depend_key == "x,y,z": out["data"] = np.array(depend_key.split(",")) out["attrs"] = {"LABLAXIS": "comp"} else: - try: - out["data"] = file.varget(depend_key, starttime=tint[0], endtime=tint[1]) - except IndexError: - out["data"] = file.varget(depend_key) - - out["data"] = file.varget(depend_key) + out["data"] = file[depend_key].values if len(out["data"]) == 1: out["data"] = out["data"][0] - if len(out["data"]) == 4 and all(out["data"] == ["x", "y", "z", "r"]): - out["data"] = out["data"][:-1] + if len(out["data"]) == 4 and all( + out["data"].astype(str) == ["x", "y", "z", "r"] + ): + out["data"] = out["data"].astype(str)[:-1] elif out["data"].ndim == 2: if len(out["data"].flatten()) == 3: @@ -163,7 +171,7 @@ def _get_depend(file, cdf_name, tint, dep_num=1): return out -def get_ts(file_path, cdf_name, tint): +def get_ts(file_path, cdf_name, tint: list = None): r"""Reads field named cdf_name in file and convert to time series. Parameters @@ -172,7 +180,7 @@ def get_ts(file_path, cdf_name, tint): Path of the cdf file. cdf_name : str Name of the target variable in the cdf file. - tint : list of str + tint : list of str, Optional Time interval. Returns @@ -182,70 +190,85 @@ def get_ts(file_path, cdf_name, tint): """ - # Extend time interval by 1s and convert time interval to epochs - tint_org = tint.copy() - tint = extend_tint(tint, [-1.0, 1.0]) - tint = list(datetime642iso8601(iso86012datetime64(np.array(tint)))) - tint = list(map(cdfepoch.parse, tint)) + # Check time interval type + # Check time interval + if tint is None: + tint = ["1995-10-06T18:50:00.000000000", "2200-10-06T18:50:00.000000000"] + elif isinstance(tint, (np.ndarray, list)): + if isinstance(tint[0], np.datetime64): + tint = datetime642iso8601(np.array(tint)) + elif isinstance(tint[0], str): + tint = iso86012datetime64( + np.array(tint), + ) # to make sure it is ISO8601 ok!! + tint = datetime642iso8601(np.array(tint)) + else: + raise TypeError("Values must be in datetime64, or str!!") + else: + raise TypeError("tint must be array_like!!") out_dict = {} time, depend_1, depend_2, depend_3 = [{}, {}, {}, {}] - with CDF(file_path) as file: - var_attrs = file.varattsget(cdf_name) - glb_attrs = file.globalattsget() - out_dict["attrs"] = {**var_attrs, **glb_attrs} - out_dict["attrs"] = {k: out_dict["attrs"][k] for k in sorted(out_dict["attrs"])} + # Load CDF file + file = load(file_path) - assert "DEPEND_0" in var_attrs and "epoch" in var_attrs["DEPEND_0"].lower() + var_attrs = _pycdfpp_attributes_to_dict(file[cdf_name].attributes) + glb_attrs = _pycdfpp_attributes_to_dict(file.attributes) + out_dict["attrs"] = {"GLOBAL": glb_attrs, **var_attrs} + out_dict["attrs"] = {k: out_dict["attrs"][k] for k in sorted(out_dict["attrs"])} - time = _get_epochs(file, cdf_name, tint) + assert "DEPEND_0" in var_attrs and "epoch" in var_attrs["DEPEND_0"].lower() - if time["data"] is None: - return None + time = _get_epochs(file, cdf_name) - if "DEPEND_1" in var_attrs or "REPRESENTATION_1" in var_attrs: - depend_1 = _get_depend(file, cdf_name, tint, 1) + if time["data"] is None: + return None - elif "afg" in cdf_name or "dfg" in cdf_name: - depend_1 = {"data": ["x", "y", "z"], "attrs": {"LABLAXIS": "comp"}} + if "DEPEND_1" in var_attrs or "REPRESENTATION_1" in var_attrs: + depend_1 = _get_depend(file, cdf_name, 1) - if "DEPEND_2" in var_attrs or "REPRESENTATION_2" in var_attrs: - depend_2 = _get_depend(file, cdf_name, tint, 2) + elif "afg" in cdf_name or "dfg" in cdf_name: + depend_1 = { + "data": ["x", "y", "z"], + "attrs": {"LABLAXIS": "comp"}, + } - if depend_2["attrs"]["LABLAXIS"] == depend_1["attrs"]["LABLAXIS"]: - depend_1["attrs"]["LABLAXIS"] = "rcomp" - depend_2["attrs"]["LABLAXIS"] = "ccomp" + if "DEPEND_2" in var_attrs or "REPRESENTATION_2" in var_attrs: + depend_2 = _get_depend(file, cdf_name, 2) - if "DEPEND_3" in var_attrs or "REPRESENTATION_3" in var_attrs: - if "REPRESENTATION_3" in var_attrs: - assert out_dict["attrs"]["REPRESENTATION_3"] != "x,y,z" + if depend_2["attrs"]["LABLAXIS"] == depend_1["attrs"]["LABLAXIS"]: + depend_1["attrs"]["LABLAXIS"] = "rcomp" + depend_2["attrs"]["LABLAXIS"] = "ccomp" + + if "DEPEND_3" in var_attrs or "REPRESENTATION_3" in var_attrs: + if "REPRESENTATION_3" in var_attrs: + assert out_dict["attrs"]["REPRESENTATION_3"] != "x,y,z" - depend_3 = _get_depend(file, cdf_name, tint, 3) + depend_3 = _get_depend(file, cdf_name, 3) - if depend_3["attrs"]["LABLAXIS"] == depend_2["attrs"]["LABLAXIS"]: - depend_2["attrs"]["LABLAXIS"] = "rcomp" - depend_3["attrs"]["LABLAXIS"] = "ccomp" + if depend_3["attrs"]["LABLAXIS"] == depend_2["attrs"]["LABLAXIS"]: + depend_2["attrs"]["LABLAXIS"] = "rcomp" + depend_3["attrs"]["LABLAXIS"] = "ccomp" - if "sector_mask" in cdf_name: - cdf_name_mask = cdf_name.replace("sector_mask", "intensity") - depend_1_key = file.varattsget(cdf_name_mask)["DEPEND_1"] + if "sector_mask" in cdf_name: + cdf_name_mask = cdf_name.replace("sector_mask", "intensity") + depend_1_key = file.varattsget(cdf_name_mask)["DEPEND_1"] - depend_1["data"] = file.varget(depend_1_key) - depend_1["attrs"] = file.varattsget(depend_1_key) + depend_1["data"] = file[depend_1_key].values + depend_1["attrs"] = _pycdfpp_attributes_to_dict(file[depend_1_key].attributes) - depend_1["attrs"]["LABLAXIS"] = depend_1["attrs"]["LABLAXIS"].replace( - " ", "_" - ) + depend_1["attrs"]["LABLAXIS"] = depend_1["attrs"]["LABLAXIS"].replace(" ", "_") - if "edp_dce_sensor" in cdf_name: - depend_1["data"] = ["x", "y", "z"] - depend_1["attrs"] = {"LABLAXIS": "comp"} + if "edp_dce_sensor" in cdf_name: + depend_1["data"] = ["x", "y", "z"] + depend_1["attrs"] = {"LABLAXIS": "comp"} - out_dict["data"] = file.varget(cdf_name, starttime=tint[0], endtime=tint[1]) + out_dict["data"] = file[cdf_name].values - if out_dict["data"].ndim == 2 and out_dict["data"].shape[1] == 4: - out_dict["data"] = out_dict["data"][:, :-1] + if out_dict["data"].ndim == 2 and out_dict["data"].shape[1] == 4: + out_dict["data"] = out_dict["data"][:, :-1] + # depend_1["data"] = depend_1["data"][:-1] if out_dict["data"].ndim == 2 and not depend_1: depend_1["data"] = np.arange(out_dict["data"].shape[1]) @@ -262,19 +285,15 @@ def get_ts(file_path, cdf_name, tint): coords_attrs = [time["attrs"], depend_1["attrs"]] elif time and depend_1 and depend_2 and not depend_3: - if depend_1["attrs"]["LABLAXIS"] == depend_2["attrs"]["LABLAXIS"]: - depend_1["attrs"]["LABLAXIS"] = "rcomp" - depend_2["attrs"]["LABLAXIS"] = "ccomp" - - dims = ["time", depend_1["attrs"]["LABLAXIS"], depend_2["attrs"]["LABLAXIS"]] + dims = [ + "time", + depend_1["attrs"]["LABLAXIS"], + depend_2["attrs"]["LABLAXIS"], + ] coords_data = [time["data"], depend_1["data"], depend_2["data"]] coords_attrs = [time["attrs"], depend_1["attrs"], depend_2["attrs"]] elif time and depend_1 and depend_2 and depend_3: - if depend_2["attrs"]["LABLAXIS"] == depend_3["attrs"]["LABLAXIS"]: - depend_2["attrs"]["LABLAXIS"] = "rcomp" - depend_3["attrs"]["LABLAXIS"] = "ccomp" - dims = [ "time", depend_1["attrs"]["LABLAXIS"], @@ -298,7 +317,10 @@ def get_ts(file_path, cdf_name, tint): raise NotImplementedError out = xr.DataArray( - out_dict["data"], coords=coords_data, dims=dims, attrs=out_dict["attrs"] + out_dict["data"], + coords=coords_data, + dims=dims, + attrs=out_dict["attrs"], ) for dim, coord_attrs in zip(dims, coords_attrs): @@ -306,6 +328,6 @@ def get_ts(file_path, cdf_name, tint): out[dim].attrs = {k: coord_attrs[k] for k in sorted(coord_attrs)} # Time clip to original time interval - out = time_clip(out, tint_org) + out = time_clip(out, tint) return out diff --git a/pyrfu/mms/get_variable.py b/pyrfu/mms/get_variable.py index e309fe29..62cd4487 100644 --- a/pyrfu/mms/get_variable.py +++ b/pyrfu/mms/get_variable.py @@ -4,17 +4,35 @@ # 3rd party imports import numpy as np import xarray as xr - -from cdflib import cdfread +from pycdfpp import _pycdfpp, load, to_datetime64 __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" +def _pycdfpp_attributes_to_dict(attributes): + attributes_dict = {} + + for k in attributes: + tmp = [attributes[k][i] for i in range(len(attributes[k]))] + + if np.size(tmp) == 1: + if isinstance(tmp[0], (list, np.ndarray)) and isinstance( + tmp[0][0], _pycdfpp.tt2000_t + ): + attributes_dict[k] = to_datetime64(tmp[0][0]) + else: + attributes_dict[k] = tmp[0] + else: + attributes_dict[k] = tmp[:] + + return attributes_dict + + def get_variable(file_path, cdf_name): r"""Reads field named cdf_name in file and convert to DataArray. @@ -32,12 +50,16 @@ def get_variable(file_path, cdf_name): """ - with cdfread.CDF(file_path) as file: - var_data = file.varget(cdf_name) - var_atts = file.varattsget(cdf_name) + file = load(file_path) + + var_data = file[cdf_name].values + var_attributes = _pycdfpp_attributes_to_dict(file[cdf_name].attributes) out = xr.DataArray( - var_data, coords=[np.arange(len(var_data))], dims=["x"], attrs=var_atts + var_data, + coords=[np.arange(len(var_data))], + dims=["x"], + attrs=var_attributes, ) return out diff --git a/pyrfu/mms/hpca_calc_anodes.py b/pyrfu/mms/hpca_calc_anodes.py index 2d05a4ac..9f9b629f 100644 --- a/pyrfu/mms/hpca_calc_anodes.py +++ b/pyrfu/mms/hpca_calc_anodes.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" anodes_theta = np.array( @@ -30,7 +30,7 @@ 348.75000, 168.75000, 146.25000, - ] + ], ) @@ -71,7 +71,9 @@ def hpca_calc_anodes(inp, fov: list = None, method: str = "mean"): updated_spectra = inp.data[:, anodes_in_fov, :].sum(axis=1) out = xr.DataArray( - updated_spectra, coords=[times, energies], dims=["time", "energy"] + updated_spectra, + coords=[times, energies], + dims=["time", "energy"], ) return out diff --git a/pyrfu/mms/hpca_energies.py b/pyrfu/mms/hpca_energies.py index 92572c54..6c2532c0 100644 --- a/pyrfu/mms/hpca_energies.py +++ b/pyrfu/mms/hpca_energies.py @@ -3,13 +3,14 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" def hpca_energies(): + r"""Construct Hot Plasma Composition Analyser (HPCA) energy bins""" return [ 1.35500, 1.57180, diff --git a/pyrfu/mms/hpca_pad.py b/pyrfu/mms/hpca_pad.py index 58483431..2ea87c8a 100644 --- a/pyrfu/mms/hpca_pad.py +++ b/pyrfu/mms/hpca_pad.py @@ -7,17 +7,19 @@ # 3rd party imports import numpy as np import xarray as xr - from scipy import interpolate # Local imports -from ..pyrf import time_clip, resample, normalize, ts_scalar +from ..pyrf.normalize import normalize +from ..pyrf.resample import resample +from ..pyrf.time_clip import time_clip +from ..pyrf.ts_scalar import ts_scalar __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -134,7 +136,9 @@ def hpca_pad(vdf, saz, aze, b_xyz, elim=None): t0_start = t0_[0] t0_ -= t0_start tck_ = interpolate.interp1d( - np.arange(0, n_en * len(t0_), n_en), t0_, fill_value="extrapolate" + np.arange(0, n_en * len(t0_), n_en), + t0_, + fill_value="extrapolate", ) t1_tt = tck_(np.arange(0, n_en * len(t0_))) + t0_start t1_tt = t1_tt.astype("datetime64[ns]") diff --git a/pyrfu/mms/hpca_spin_sum.py b/pyrfu/mms/hpca_spin_sum.py index f49b4442..1ad42bc5 100644 --- a/pyrfu/mms/hpca_spin_sum.py +++ b/pyrfu/mms/hpca_spin_sum.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.12" +__version__ = "2.4.2" __status__ = "Prototype" @@ -40,13 +40,17 @@ def hpca_spin_sum(inp, saz, method: str = "mean"): out_data = [] for i, spin in enumerate(spin_starts[:-1]): if method == "mean": - out_data.append(inp[spin : spin_starts[i + 1]].mean(dim="time").data) + out_data.append( + inp[spin : spin_starts[i + 1]].mean(dim="time").data, + ) elif method == "sum": - out_data.append(inp[spin : spin_starts[i + 1]].sum(dim="time").data) + out_data.append( + inp[spin : spin_starts[i + 1]].sum(dim="time").data, + ) else: raise ValueError("Invalid method") - out_time = np.stack([t for t in az_times[spin_starts[:-1]]]) + out_time = np.stack(az_times[spin_starts[:-1]]) out_data = np.stack(out_data) coords = [inp.coords[k].data for k in inp.dims[1:]] coords = [out_time, *coords] diff --git a/pyrfu/mms/lh_wave_analysis.py b/pyrfu/mms/lh_wave_analysis.py index 028c1c4e..8a3081c0 100644 --- a/pyrfu/mms/lh_wave_analysis.py +++ b/pyrfu/mms/lh_wave_analysis.py @@ -4,26 +4,23 @@ # 3rd party imports import numpy as np import xarray as xr - from scipy import constants # Local imports -from ..pyrf import ( - filt, - calc_dt, - resample, - convert_fac, - ts_scalar, - extend_tint, - time_clip, - ts_vec_xyz, -) +from ..pyrf.calc_dt import calc_dt +from ..pyrf.convert_fac import convert_fac +from ..pyrf.extend_tint import extend_tint +from ..pyrf.filt import filt +from ..pyrf.resample import resample +from ..pyrf.time_clip import time_clip +from ..pyrf.ts_scalar import ts_scalar +from ..pyrf.ts_vec_xyz import ts_vec_xyz __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -181,7 +178,10 @@ def lh_wave_analysis( phi_e_best = ts_scalar(phi_bs.time.data, phi_e_best) v_best = vph_vec[corr_vpos] - options = dict(coords=[phi_bs.time, ["Ebest", "Bs"]], dims=["time", "comp"]) - phi_eb = xr.DataArray(np.vstack([phi_e_best.data, phi_bs.data]).T, **options) + options = {"coords": [phi_bs.time, ["Ebest", "Bs"]], "dims": ["time", "comp"]} + phi_eb = xr.DataArray( + np.vstack([phi_e_best.data, phi_bs.data]).T, + **options, + ) return phi_eb, v_best, dir_best, thetas, corrs diff --git a/pyrfu/mms/list_files.py b/pyrfu/mms/list_files.py index 6504b5ba..c89dba67 100644 --- a/pyrfu/mms/list_files.py +++ b/pyrfu/mms/list_files.py @@ -1,32 +1,39 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import bisect +import datetime +import json + # Built-in imports import os import re -import json -import bisect -import datetime # 3rd party imports +import numpy as np from dateutil import parser -from dateutil.rrule import rrule, DAILY +from dateutil.rrule import DAILY, rrule + +# Local imports +from ..pyrf.datetime642iso8601 import datetime642iso8601 +from ..pyrf.iso86012datetime64 import iso86012datetime64 +from .db_init import MMS_CFG_PATH __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2022" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.11" +__version__ = "2.4.11" __status__ = "Prototype" def list_files(tint, mms_id, var, data_path: str = ""): - """Find files in the data directories of the target instrument, data type, - data rate, mms_id and level during the target time interval. + r"""Find available files in the data directories of the target instrument, + data type, data rate, mms_id and level during the target time interval. Parameters ---------- - tint : list + tint : array_like Time interval mms_id : str or int Index of the spacecraft @@ -41,7 +48,7 @@ def list_files(tint, mms_id, var, data_path: str = ""): Returns ------- - files : list + file_names : list List of files corresponding to the parameters in the selected time interval @@ -49,23 +56,36 @@ def list_files(tint, mms_id, var, data_path: str = ""): # Check path if not data_path: - pkg_path = os.path.dirname(os.path.abspath(__file__)) - # Read the current version of the MMS configuration file - with open(os.path.join(pkg_path, "config.json"), "r") as fs: + with open(MMS_CFG_PATH, "r", encoding="utf-8") as fs: config = json.load(fs) - data_path = os.path.normpath(config["local_data_dir"]) + data_path = os.path.normpath(config["local"]) else: data_path = os.path.normpath(data_path) # Make sure that the data path exists assert os.path.exists(data_path), f"{data_path} doesn't exist!!" + # Check time interval + if isinstance(tint, (np.ndarray, list)): + if isinstance(tint[0], np.datetime64): + tint = datetime642iso8601(np.array(tint)) + elif isinstance(tint[0], str): + tint = iso86012datetime64( + np.array(tint), + ) # to make sure it is ISO8601 ok!! + tint = datetime642iso8601(np.array(tint)) + else: + raise TypeError("Values must be in datetime64, or str!!") + else: + raise TypeError("tint must be array_like!!") + files_out = [] if not isinstance(mms_id, str): mms_id = str(mms_id) + # directory and file name search patterns: # - assume directories are of the form: # (srvy, SITL): spacecraft/instrument/rate/level[/datatype]/year/month/ @@ -99,7 +119,7 @@ def list_files(tint, mms_id, var, data_path: str = ""): date.strftime("%Y"), date.strftime("%m"), date.strftime("%d"), - ] + ], ) else: local_dir = os.sep.join( @@ -111,7 +131,7 @@ def list_files(tint, mms_id, var, data_path: str = ""): level_and_dtype, date.strftime("%Y"), date.strftime("%m"), - ] + ], ) if os.name == "nt": @@ -136,7 +156,7 @@ def list_files(tint, mms_id, var, data_path: str = ""): "timetag": "", "full_name": this_file, "file_size": "", - } + }, ) in_files = files_out @@ -156,7 +176,7 @@ def list_files(tint, mms_id, var, data_path: str = ""): parser.parse(matches.groups()[0]).timestamp(), file["timetag"], file["file_size"], - ) + ), ) # sort in time @@ -173,13 +193,21 @@ def list_files(tint, mms_id, var, data_path: str = ""): files_in_interval = [] for file in sorted_files[idx_min:]: files_in_interval.append( - {"file_name": file[0], "timetag": file[2], "file_size": file[3]} + { + "file_name": file[0], + "timetag": file[2], + "file_size": file[3], + }, ) else: files_in_interval = [] for file in sorted_files[idx_min - 1 :]: files_in_interval.append( - {"file_name": file[0], "timetag": file[2], "file_size": file[3]} + { + "file_name": file[0], + "timetag": file[2], + "file_size": file[3], + }, ) local_files = [] @@ -190,4 +218,6 @@ def list_files(tint, mms_id, var, data_path: str = ""): if file["file_name"] in file_names: local_files.append(file["full_name"]) - return sorted(local_files) + file_names = sorted(local_files) + + return file_names diff --git a/pyrfu/mms/list_files_ancillary.py b/pyrfu/mms/list_files_ancillary.py index 95263463..23c3a74c 100644 --- a/pyrfu/mms/list_files_ancillary.py +++ b/pyrfu/mms/list_files_ancillary.py @@ -1,121 +1,138 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -# Built-in imports -import os -import re -import glob -import json -import bisect -import fnmatch -import datetime - -# 3rd party imports -import pandas as pd - -from dateutil import parser -from dateutil.rrule import rrule, DAILY -from ..pyrf import iso86012datetime - -__author__ = "Louis Richard" -__email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2023" -__license__ = "MIT" -__version__ = "2.3.26" -__status__ = "Prototype" - - -def list_files_ancillary(tint, mms_id, product, data_path: str = ""): - r"""Loads ancillary data. - - Parameters - ---------- - tint : list of str - Time interval - mms_id : str or int - Spacecraft index - product : {"predatt", "predeph", "defatt", "defeph"} - Ancillary type. - data_path : str, Optional - Path of MMS data. If None use `pyrfu.mms.mms_config.py` - - Returns - ------- - files_names : list - Ancillary files in interval. - - """ - # Check path - if not data_path: - pkg_path = os.path.dirname(os.path.abspath(__file__)) - - # Read the current version of the MMS configuration file - with open(os.path.join(pkg_path, "config.json"), "r") as fs: - config = json.load(fs) - - data_path = os.path.normpath(config["local_data_dir"]) - else: - data_path = os.path.normpath(data_path) - - # Make sure that the data path exists - assert os.path.exists(data_path), f"{data_path} doesn't exist!!" - - if isinstance(mms_id, int): - mms_id = str(mms_id) - - tint = iso86012datetime(tint) - - # directory and file name search patterns - # For now - # -all ancillary data is in one directory: - # mms\ancillary - # -assume file names are of the form: - # SPACECRAFT_FILETYPE_startDate_endDate.version - # where SPACECRAFT is [MMS1, MMS2, MMS3, MMS4] in uppercase - # and FILETYPE is either DEFATT, PREDATT, DEFEPH, PREDEPH in uppercase - # and start/endDate is YYYYDOY - # and version is Vnn (.V00, .V01, etc..) - dir_pattern = os.sep.join([data_path, "ancillary", f"mms{mms_id}", product]) - file_pattern = "_".join( - ["MMS{}".format(mms_id), product.upper(), "???????_???????.V??"] - ) - - files_in_tint = [] - out_files = [] - - files = glob.glob(os.sep.join([dir_pattern, file_pattern])) - - # find the files within the time interval - fname_fmt = ( - f"MMS{mms_id}_{product.upper()}" + f"_([0-9]{{7}})_([0-9]{{7}}).V[0-9]{{2}}" - ) - - if os.name == "nt": - full_path = os.sep.join([re.escape(dir_pattern) + os.sep, fname_fmt]) - else: - full_path = os.sep.join([re.escape(dir_pattern), fname_fmt]) - - file_regex = re.compile(full_path) - - for file in files: - time_match = file_regex.match(file) - if time_match is not None: - start_time = pd.to_datetime(time_match.group(1), format="%Y%j") - end_time = pd.to_datetime(time_match.group(2), format="%Y%j") - if start_time < tint[1] and end_time >= tint[0]: - files_in_tint.append(file) - - # ensure only the latest version of each file is loaded - for file in files_in_tint: - this_file = file[0:-3] + "V??" - versions = fnmatch.filter(files_in_tint, this_file) - if len(versions) > 1: - # only grab the latest version - out_files.append(sorted(versions)[-1]) - else: - out_files.append(versions[0]) - - files_names = list(set(out_files)) - files_names.sort() - - return files_names +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Built-in imports +import datetime +import fnmatch +import glob +import json +import os +import re + +# 3rd party imports +import numpy as np +import pandas as pd + +# Local imports +from ..pyrf.datetime642iso8601 import datetime642iso8601 +from ..pyrf.iso86012datetime import iso86012datetime +from ..pyrf.iso86012datetime64 import iso86012datetime64 +from .db_init import MMS_CFG_PATH + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.11" +__status__ = "Prototype" + + +def list_files_ancillary(tint, mms_id, product, data_path: str = ""): + r"""Find available ancillary files in the data directories for the target product + type. + + Parameters + ---------- + tint : list of str + Time interval + mms_id : str or int + Spacecraft index + product : {"predatt", "predeph", "defatt", "defeph"} + Ancillary type. + data_path : str, Optional + Path of MMS data. If None use `pyrfu.mms.mms_config.py` + + Returns + ------- + file_names : list + Ancillary files in interval. + + """ + + # Check path + if not data_path: + # Read the current version of the MMS configuration file + with open(MMS_CFG_PATH, "r", encoding="utf-8") as fs: + config = json.load(fs) + + data_path = os.path.normpath(config["local"]) + else: + data_path = os.path.normpath(data_path) + + # Make sure that the data path exists + assert os.path.exists(data_path), f"{data_path} doesn't exist!!" + + if isinstance(mms_id, int): + mms_id = str(mms_id) + + # Check time interval type + if isinstance(tint, (np.ndarray, list)): + if isinstance(tint[0], np.datetime64): + tint = datetime642iso8601(tint) + tint = iso86012datetime(tint) + elif isinstance(tint[0], str): + tint = iso86012datetime64( + np.array(tint), + ) # to make sure it is ISO8601 ok! + tint = datetime642iso8601(tint) + tint = iso86012datetime(tint) + elif isinstance(tint[0], datetime.datetime): + pass + else: + raise TypeError("Values must be in datetime, datetime64, or str!!") + else: + raise TypeError("tint must be a DataArray or array_like!!") + + # PAD time interval to handle ancillary file start after midnight + tint = [tint[0] - datetime.timedelta(days=1), tint[1]] + + # directory and file name search patterns + # For now + # -all ancillary data is in one directory: + # mms\ancillary + # -assume file names are of the form: + # SPACECRAFT_FILETYPE_startDate_endDate.version + # where SPACECRAFT is [MMS1, MMS2, MMS3, MMS4] in uppercase + # and FILETYPE is either DEFATT, PREDATT, DEFEPH, PREDEPH in uppercase + # and start/endDate is YYYYDOY + # and version is Vnn (.V00, .V01, etc..) + dir_pattern = os.sep.join([data_path, "ancillary", f"mms{mms_id}", product]) + file_pattern = "_".join([f"MMS{mms_id}", product.upper(), "???????_???????.V??"]) + + files_in_tint = [] + out_files = [] + + files = glob.glob(os.sep.join([dir_pattern, file_pattern])) + + # find the files within the time interval + fname_fmt = f"MMS{mms_id}_{product.upper()}_([0-9]{{7}})_([0-9]{{7}}).V[0-9]{{2}}" + + if os.name == "nt": + full_path = os.sep.join([re.escape(dir_pattern) + os.sep, fname_fmt]) + else: + full_path = os.sep.join([re.escape(dir_pattern), fname_fmt]) + + file_regex = re.compile(full_path) + + for file in files: + time_match = file_regex.match(file) + if time_match is not None: + start_time = pd.to_datetime(time_match.group(1), format="%Y%j") + end_time = pd.to_datetime(time_match.group(2), format="%Y%j") + if start_time < tint[1] and end_time >= tint[0]: + files_in_tint.append(file) + + # ensure only the latest version of each file is loaded + for file in files_in_tint: + this_file = file[0:-3] + "V??" + versions = fnmatch.filter(files_in_tint, this_file) + if len(versions) > 1: + # only grab the latest version + out_files.append(sorted(versions)[-1]) + else: + out_files.append(versions[0]) + + file_names = list(set(out_files)) + file_names.sort() + + return file_names diff --git a/pyrfu/mms/list_files_ancillary_sdc.py b/pyrfu/mms/list_files_ancillary_sdc.py new file mode 100644 index 00000000..16f9909f --- /dev/null +++ b/pyrfu/mms/list_files_ancillary_sdc.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Built-in imports +import warnings +from datetime import datetime, timedelta + +# 3rd party imports +import numpy as np + +# Local imports +from .list_files_sdc import _login_lasp + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.11" +__status__ = "Prototype" + + +def _construct_url_json_list(tint, mms_id, product, lasp_url): + r"""Construct the url that return a json-formatted string of science + filenames that are available for download according to: + https://lasp.colorado.edu/mms/sdc/team/about/how-to/ + """ + + tint = np.array(tint).astype(" 13: - [ - is_brst_data, - flag_same_e, - flag_de, - step_table, - energy0, - delta_v0, - energy1, - delta_v1, - q_e, - sc_pot, - p_mass, - flag_inner_electron, - w_inner_electron, - phi_tr, - theta_k, - int_energies, - vdf, - delta_ang, - ] = arguments - else: - [ - is_brst_data, - flag_same_e, - flag_de, - energy, - delta_v, - q_e, - sc_pot, - p_mass, - flag_inner_electron, - w_inner_electron, - phi_tr, - theta_k, - int_energies, - vdf, - delta_ang, - ] = arguments - - if is_brst_data: - if not flag_same_e or not flag_de: - energy = energy0 - delta_v = delta_v0 - - if step_table[time_idx]: - energy = energy1 - delta_v = delta_v1 - - velocity = np.real(np.sqrt(2 * q_e * (energy - sc_pot.data[time_idx]) / p_mass)) - velocity[ - energy - sc_pot.data[time_idx] - flag_inner_electron * w_inner_electron < 0 - ] = 0 - - if is_brst_data: - phi_j = phi_tr[time_idx, :] - else: - phi_j = phi_tr - - phi_j = np.deg2rad(phi_j[:, np.newaxis]) - the_k = np.deg2rad(theta_k) - ones_ = np.ones(phi_j.shape) - - n_psd = 0 - v_psd = np.zeros(3) - p_psd = np.zeros((3, 3)) - h_psd = np.zeros(3) - - psd2_n_mat = np.dot(ones_, np.sin(the_k)) - psd2_v_x_mat = -np.dot(np.cos(phi_j), np.sin(the_k) * np.sin(the_k)) - psd2_v_y_mat = -np.dot(np.sin(phi_j), np.sin(the_k) * np.sin(the_k)) - psd2_v_z_mat = -np.dot(ones_, np.sin(the_k) * np.cos(the_k)) - psd_mf_xx_mat = np.dot(np.cos(phi_j) ** 2, np.sin(the_k) ** 3) - psd_mf_yy_mat = np.dot(np.sin(phi_j) ** 2, np.sin(the_k) ** 3) - psd_mf_zz_mat = np.dot(ones_, np.sin(the_k) * np.cos(the_k) ** 2) - psd_mf_xy_mat = np.dot(np.cos(phi_j) * np.sin(phi_j), np.sin(the_k) ** 3) - psd_mf_xz_mat = np.dot(np.cos(phi_j), np.cos(the_k) * np.sin(the_k) ** 2) - psd_mf_yz_mat = np.dot(np.sin(phi_j), np.cos(the_k) * np.sin(the_k) ** 2) - - for i in int_energies: - tmp = np.squeeze(vdf[time_idx, i, :, :]) - # n_psd_tmp1 = tmp .* psd2_n_mat * v(ii)^2 * delta_v(ii) * delta_ang; - # n_psd_e32_phi_theta(nt, ii, :, :) = n_psd_tmp1; - # n_psd_e32(nt, ii) = n_psd_tmp - - # number density - n_psd_tmp = np.nansum(np.nansum(tmp * psd2_n_mat, axis=0), axis=0) - n_psd_tmp *= delta_v[i] * delta_ang * velocity[i] ** 2 - n_psd += n_psd_tmp - - # Bulk velocity - v_temp_x = np.nansum(np.nansum(tmp * psd2_v_x_mat, axis=0), axis=0) - v_temp_x *= delta_v[i] * delta_ang * velocity[i] ** 3 - - v_temp_y = np.nansum(np.nansum(tmp * psd2_v_y_mat, axis=0), axis=0) - v_temp_y *= delta_v[i] * delta_ang * velocity[i] ** 3 - - v_temp_z = np.nansum(np.nansum(tmp * psd2_v_z_mat, axis=0), axis=0) - v_temp_z *= delta_v[i] * delta_ang * velocity[i] ** 3 - - v_psd[0] += v_temp_x - v_psd[1] += v_temp_y - v_psd[2] += v_temp_z - - # Pressure tensor - p_temp_xx = np.nansum(np.nansum(tmp * psd_mf_xx_mat, axis=0), axis=0) - p_temp_xx *= delta_v[i] * delta_ang * velocity[i] ** 4 - - p_temp_xy = np.nansum(np.nansum(tmp * psd_mf_xy_mat, axis=0), axis=0) - p_temp_xy *= delta_v[i] * delta_ang * velocity[i] ** 4 - - p_temp_xz = np.nansum(np.nansum(tmp * psd_mf_xz_mat, axis=0), axis=0) - p_temp_xz *= delta_v[i] * delta_ang * velocity[i] ** 4 - - p_temp_yy = np.nansum(np.nansum(tmp * psd_mf_yy_mat, axis=0), axis=0) - p_temp_yy *= delta_v[i] * delta_ang * velocity[i] ** 4 - - p_temp_yz = np.nansum(np.nansum(tmp * psd_mf_yz_mat, axis=0), axis=0) - p_temp_yz *= delta_v[i] * delta_ang * velocity[i] ** 4 - - p_temp_zz = np.nansum(np.nansum(tmp * psd_mf_zz_mat, axis=0), axis=0) - p_temp_zz *= delta_v[i] * delta_ang * velocity[i] ** 4 - - p_psd[0, 0] += p_temp_xx - p_psd[0, 1] += p_temp_xy - p_psd[0, 2] += p_temp_xz - p_psd[1, 1] += p_temp_yy - p_psd[1, 2] += p_temp_yz - p_psd[2, 2] += p_temp_zz - - h_psd[0] = v_temp_x * velocity[i] ** 2 - h_psd[1] = v_temp_y * velocity[i] ** 2 - h_psd[2] = v_temp_z * velocity[i] ** 2 +logging.captureWarnings(True) +logging.basicConfig( + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, +) + + +@numba.jit(cache=True, fastmath=True, nogil=True, parallel=True, nopython=True) +def _moms( + energy, + delta_v, + q_e, + sc_pot, + p_mass, + flag_inner_electron, + w_inner_electron, + phi, + theta, + int_energies, + vdf, + delta_ang, +): + n_psd = np.zeros(vdf.shape[0]) + v_psd = np.zeros((vdf.shape[0], 3)) + p_psd = np.zeros((vdf.shape[0], 3, 3)) + h_psd = np.zeros((vdf.shape[0], 3)) + + for i_t in numba.prange(vdf.shape[0]): + energy_correct = energy[i_t, :] - sc_pot[i_t] + + velocity = np.sqrt(2 * q_e * energy_correct / p_mass) + velocity[energy_correct < flag_inner_electron * w_inner_electron] = 0 + + phi_i = np.deg2rad(phi[i_t, :, :]) + theta_i = np.deg2rad(theta[i_t, :, :]) + + psd2n_mat = np.ones(theta_i.shape) * np.sin(theta_i) + + # Particle flux and heat flux vector + psd2v_x_mat = -np.cos(phi_i) * np.sin(theta_i) ** 2 + psd2v_y_mat = -np.sin(phi_i) * np.sin(theta_i) ** 2 + psd2v_z_mat = -np.ones(theta_i.shape) * np.sin(theta_i) * np.cos(theta_i) + + psd2p_xx_mat = np.cos(phi_i) ** 2.0 * np.sin(theta_i) ** 3 + psd2p_yy_mat = np.sin(phi_i) ** 2.0 * np.sin(theta_i) ** 3 + psd2p_zz_mat = np.ones(theta_i.shape) * np.sin(theta_i) * np.cos(theta_i) ** 2 + psd2p_xy_mat = np.cos(phi_i) * np.sin(phi_i) * np.sin(theta_i) ** 3 + psd2p_xz_mat = np.cos(phi_i) * np.sin(phi_i) ** 2 * np.cos(theta_i) + psd2p_yz_mat = np.sin(phi_i) * np.sin(phi_i) ** 2 * np.cos(theta_i) + + for i_e in int_energies: + tmp = vdf[i_t, i_e, :, :] + # n_psd_tmp1 = tmp .* psd2_n_mat * v(ii)^2 * delta_v(ii) * delta_ang; + # n_psd_e32_phi_theta(nt, ii, :, :) = n_psd_tmp1; + # n_psd_e32(nt, ii) = n_psd_tmp + + # number density + n_psd_tmp = np.nansum(tmp * psd2n_mat * delta_ang[i_t]) + n_psd_tmp *= delta_v[i_t, i_e] * velocity[i_e] ** 2 + n_psd[i_t] += n_psd_tmp + + # Bulk velocity + v_temp_x = np.nansum(tmp * psd2v_x_mat * delta_ang[i_t]) + v_temp_x *= delta_v[i_t, i_e] * velocity[i_e] ** 3 + + v_temp_y = np.nansum(tmp * psd2v_y_mat * delta_ang[i_t]) + v_temp_y *= delta_v[i_t, i_e] * velocity[i_e] ** 3 + + v_temp_z = np.nansum(tmp * psd2v_z_mat * delta_ang[i_t]) + v_temp_z *= delta_v[i_t, i_e] * velocity[i_e] ** 3 + + v_psd[i_t, 0] += v_temp_x + v_psd[i_t, 1] += v_temp_y + v_psd[i_t, 2] += v_temp_z + + # Pressure tensor + p_temp_xx = np.nansum(tmp * psd2p_xx_mat * delta_ang[i_t]) + p_temp_xx *= delta_v[i_t, i_e] * velocity[i_e] ** 4 + + p_temp_xy = np.nansum(tmp * psd2p_xy_mat * delta_ang[i_t]) + p_temp_xy *= delta_v[i_t, i_e] * velocity[i_e] ** 4 + + p_temp_xz = np.nansum(tmp * psd2p_xz_mat * delta_ang[i_t]) + p_temp_xz *= delta_v[i_t, i_e] * velocity[i_e] ** 4 + + p_temp_yy = np.nansum(tmp * psd2p_yy_mat * delta_ang[i_t]) + p_temp_yy *= delta_v[i_t, i_e] * velocity[i_e] ** 4 + + p_temp_yz = np.nansum(tmp * psd2p_yz_mat * delta_ang[i_t]) + p_temp_yz *= delta_v[i_t, i_e] * velocity[i_e] ** 4 + + p_temp_zz = np.nansum(tmp * psd2p_zz_mat * delta_ang[i_t]) + p_temp_zz *= delta_v[i_t, i_e] * velocity[i_e] ** 4 + + p_psd[i_t, 0, 0] += p_temp_xx + p_psd[i_t, 0, 1] += p_temp_xy + p_psd[i_t, 0, 2] += p_temp_xz + p_psd[i_t, 1, 1] += p_temp_yy + p_psd[i_t, 1, 2] += p_temp_yz + p_psd[i_t, 2, 2] += p_temp_zz + + h_psd[i_t, 0] = v_temp_x * velocity[i_e] ** 2 + h_psd[i_t, 1] = v_temp_y * velocity[i_e] ** 2 + h_psd[i_t, 2] = v_temp_z * velocity[i_e] ** 2 return n_psd, v_psd, p_psd, h_psd @@ -171,7 +143,7 @@ def psd_moments(vdf, sc_pot, **kwargs): Time series of the spacecraft potential. Returns - -------- + ------- n_psd : xarray.DataArray Time series of the number density (1rst moment). v_psd : xarray.DataArray @@ -197,11 +169,11 @@ def psd_moments(vdf, sc_pot, **kwargs): en_channels : array_like Set energy channels to integrate over [min max]; min and max between must be between 1 and 32. - partial_moments : ndarray or xarray.DataArray - Use a binary array (or DataArray) (pmomsarr) to select which psd - points are used in the moments calculation. pmomsarr must be a - binary array (1s and 0s, 1s correspond to points used). Array (or - data of Dataarray) must be the same size as vdf.data. + partial_moments : numpy.ndarray or xarray.DataArray + Use a binary array to select which psd points are used in the moments + calculation. `partial_moments` must be a binary array (1s and 0s, + 1s correspond to points used). Array (or data of Dataarray) must be the same + size as vdf.data. inner_electron : {"on", "off"} inner_electrontron potential for electron moments. @@ -228,74 +200,58 @@ def psd_moments(vdf, sc_pot, **kwargs): """ - flag_de, flag_same_e, flag_inner_electron = [False] * 3 - # [eV] sc_pot + w_inner_electron for electron moments calculation w_inner_electron = 3.5 - vdf.data.data *= 1e12 - # Check if data is fast or burst resolution - field_name = vdf.attrs["FIELDNAM"] - - if "brst" in field_name: + if "brst" in vdf.data.attrs["FIELDNAM"].lower(): is_brst_data = True - print("notice : Burst resolution data is used") - elif "fast" in field_name: + logging.info("Burst resolution data is used") + elif "fast" in vdf.data.attrs["FIELDNAM"].lower(): is_brst_data = False - print("notice : Fast resolution data is used") + logging.info("Fast resolution data is used") else: raise TypeError("Could not identify if data is fast or burst.") - phi = vdf.phi.data - theta_k = vdf.theta + theta = vdf.theta.data particle_type = vdf.attrs["species"] + assert particle_type[0].lower() in ["e", "i"], "invalid particle type" - if is_brst_data: - step_table = vdf.attrs["esteptable"] - energy = None - energy0 = vdf.attrs["energy0"] - energy1 = vdf.attrs["energy1"] - e_tmp = energy1 - energy0 + vdf_data = vdf.data.data * 1e12 # In SI units - if all(e_tmp) == 0: - flag_same_e = 1 - else: - step_table = None - energy = vdf.energy - energy0 = None - energy1 = None - e_tmp = energy[0, :] - energy[-1, :] - - if all(e_tmp) == 0: - energy = energy[0, :] - else: - raise TypeError("Could not identify if data is fast or burst.") + step_table = vdf.attrs["esteptable"] + energy = vdf.energy.data + energy0 = vdf.attrs["energy0"] + energy1 = vdf.attrs["energy1"] + e_tmp = energy1 - energy0 + + flag_same_e = all(e_tmp) == 0 # resample sc_pot to same resolution as particle distributions - sc_pot = resample(sc_pot, vdf.time) + sc_pot = resample(sc_pot, vdf.time).data if "energy_range" in kwargs: if ( isinstance(kwargs["energy_range"], (list, np.ndarray)) and len(kwargs["energy_range"]) == 2 ): - if not is_brst_data: - energy0 = energy + # if not is_brst_data: + # energy0 = energy # e_min_max = kwargs["energy_range"] # start_e = bisect.bisect_left(energy0, e_min_max[0]) # stop_e = bisect.bisect_left(energy0, e_min_max[1]) - print("notice : Using partial energy range") + logging.info("Using partial energy range") - if "no_sc_pot" in kwargs: - if isinstance(kwargs["no_sc_pot"], bool) and not kwargs["no_sc_pot"]: - sc_pot.data = np.zeros(sc_pot.shape) - print("notice : Setting spacecraft potential to zero") + no_sc_pot = kwargs.get("no_sc_pot", False) + if no_sc_pot: + sc_pot = np.zeros(sc_pot.shape) + logging.info("Setting spacecraft potential to zero") int_energies = np.arange( - kwargs.get("en_channels", [0, 32])[0], kwargs.get("en_channels", [0, 32])[1] + kwargs.get("en_channels", [0, 32])[0], + kwargs.get("en_channels", [0, 32])[1], ) if "partial_moments" in kwargs: @@ -304,31 +260,35 @@ def psd_moments(vdf, sc_pot, **kwargs): partial_moments = partial_moments.data # Check size of partial_moments - if partial_moments.shape == vdf.data.shape: + if partial_moments.shape == vdf_data.shape: sum_ones = np.sum( - np.sum(np.sum(np.sum(partial_moments, axis=-1), axis=-1), axis=-1), + np.sum( + np.sum(np.sum(partial_moments, axis=-1), axis=-1), + axis=-1, + ), axis=-1, ) sum_zeros = np.sum( - np.sum(np.sum(np.sum(-partial_moments + 1, axis=-1), axis=-1), axis=-1), + np.sum( + np.sum(np.sum(-partial_moments + 1, axis=-1), axis=-1), + axis=-1, + ), axis=-1, ) - if (sum_ones + sum_zeros) == vdf.data.size: - print( - "notice : partial_moments is correct. Partial moments " - "will be calculated" + if (sum_ones + sum_zeros) == vdf_data.size: + logging.info( + "partial_moments is correct. Partial moments will be calculated" ) - vdf.data = vdf.data * partial_moments + vdf_data = vdf_data * partial_moments else: - print( - "notice : All values are not ones and zeros in " - "partial_moments. Full " + "moments will be calculated" + logging.info( + "All values are not ones and zeros in partial_moments. " + "Full moments will be calculated" ) else: - print( - "notice : Size of partial_moments is wrong. Full moments " - "will be calculated" + logging.info( + "Size of partial_moments is wrong. Full moments will be calculated" ) tmp_ = kwargs.get("inner_electron", "") @@ -340,44 +300,63 @@ def psd_moments(vdf, sc_pot, **kwargs): if particle_type[0] == "e": p_mass = constants.electron_mass - print("notice : Particles are electrons") - elif particle_type[0] == "i": - p_mass = constants.proton_mass - sc_pot.data = -1.0 * sc_pot.data - print("notice : Particles are ions") + logging.info("Particles are electrons") else: - raise ValueError("Could not identify the particle type") + p_mass = constants.proton_mass + sc_pot *= -1.0 + logging.info("Particles are ions") # angle between theta and phi points is 360/32 = 11.25 degrees - delta_ang = np.deg2rad(11.25) ** 2 + phi = vdf.phi.data - if is_brst_data: - phi_tr = vdf.phi + if "delta_phi_minus" in vdf.attrs and "delta_phi_plus" in vdf.attrs: + delta_phi_minus = vdf.attrs["delta_phi_minus"] + delta_phi_plus = vdf.attrs["delta_phi_plus"] + delta_phi = delta_phi_plus + delta_phi_minus + delta_phi = np.tile(delta_phi[:, :, np.newaxis], (1, 1, vdf_data.shape[3])) else: - phi_tr = phi - phi_size = phi_tr.shape + delta_phi = np.deg2rad(np.median(np.diff(phi[0, :]))) + delta_phi = delta_phi * np.ones( + (vdf_data.shape[0], vdf_data.shape[2], vdf_data.shape[3]) + ) + + if "delta_theta_minus" in vdf.attrs and "delta_theta_plus" in vdf.attrs: + delta_theta_minus = vdf.attrs["delta_theta_minus"] + delta_theta_plus = vdf.attrs["delta_theta_plus"] + delta_theta = delta_theta_plus + delta_theta_minus + delta_theta = np.tile( + delta_theta[np.newaxis, np.newaxis, :], + (vdf_data.shape[0], vdf_data.shape[2], 1), + ) + else: + delta_theta = np.deg2rad(np.median(np.diff(theta))) + delta_theta = delta_theta * np.ones( + (vdf_data.shape[0], vdf_data.shape[2], vdf_data.shape[3]) + ) + + delta_ang = delta_phi * delta_theta - if phi_size[1] > phi_size[0]: - phi_tr = phi_tr.T + phi_mat = np.tile(phi[:, :, np.newaxis], (1, 1, vdf_data.shape[3])) + theta_mat = np.tile( + theta[np.newaxis, np.newaxis, :], (vdf_data.shape[0], vdf_data.shape[2], 1) + ) - if "delta_energy_minus" in vdf.attrs and "delta_energy_plus" in vdf.attrs: - energy_minus = vdf.attrs["delta_energy_plus"] - energy_plus = vdf.attrs["delta_energy_plus"] + energy_minus = vdf.attrs["delta_energy_plus"] + energy_plus = vdf.attrs["delta_energy_plus"] - flag_de = True - else: - energy_minus, energy_plus = [None, None] + energy_correct = energy - sc_pot[:, np.newaxis] + velocity = np.sqrt(2 * q_e * energy_correct / p_mass) + velocity[energy_correct < flag_inner_electron * w_inner_electron] = 0 # Calculate speed widths associated with each energy channel. if is_brst_data: # Burst mode energy/speed widths - if flag_same_e and flag_de: - energy = energy0 + if flag_same_e: energy_upper = energy + energy_plus energy_lower = energy - energy_minus v_upper = np.sqrt(2 * q_e * energy_upper / p_mass) v_lower = np.sqrt(2 * q_e * energy_lower / p_mass) delta_v = v_upper - v_lower - delta_v0, delta_v1 = [None, None] + delta_v = np.tile(delta_v, (vdf_data.shape[0], 1)) else: energy_all = np.hstack([energy0, energy1]) energy_all = np.log10(np.sort(energy_all)) @@ -406,80 +385,42 @@ def psd_moments(vdf, sc_pot, **kwargs): delta_v0 = (v0upper - v0lower) * 2.0 delta_v1 = (v1upper - v1lower) * 2.0 - delta_v = None + delta_v = np.tile(delta_v0, (vdf_data.shape[0], 1)) + delta_v[step_table == 1, :] = delta_v1 else: # Fast mode energy/speed widths - energy_all = np.log10(energy) + energy_all = np.log10(energy[0, :]) temp0 = 2 * energy_all[0] - energy_all[1] temp33 = 2 * energy_all[31] - energy_all[30] energy_all = np.hstack([temp0, energy_all, temp33]) diff_en_all = np.diff(energy_all) - energy_upper = 10 ** (np.log10(energy) + diff_en_all[1:33] / 4) - energy_lower = 10 ** (np.log10(energy) - diff_en_all[0:32] / 4) + energy_upper = 10 ** (np.log10(energy[0, :]) + diff_en_all[1:33] / 4) + energy_lower = 10 ** (np.log10(energy[0, :]) - diff_en_all[0:32] / 4) v_upper = np.sqrt(2 * q_e * energy_upper / p_mass) v_lower = np.sqrt(2 * q_e * energy_lower / p_mass) delta_v = (v_upper - v_lower) * 2.0 delta_v[0] = delta_v[0] * 2.7 - delta_v0, delta_v1 = [None, None] - - theta_k = theta_k.data[np.newaxis, :] - - if is_brst_data: - args_ = ( - is_brst_data, - flag_same_e, - flag_de, - step_table, - energy0, - delta_v0, - energy1, - delta_v1, - q_e, - sc_pot.data, - p_mass, - flag_inner_electron, - w_inner_electron, - phi_tr.data, - theta_k, - int_energies, - vdf.data.data, - delta_ang, - ) - else: - args_ = ( - is_brst_data, - flag_same_e, - flag_de, - energy, - delta_v, - q_e, - sc_pot.data, - p_mass, - flag_inner_electron, - w_inner_electron, - phi_tr.data, - theta_k, - int_energies, - vdf.data, - delta_ang, - ) - - pool = mp.Pool(mp.cpu_count()) - res = pool.starmap(_moms, [(nt, args_) for nt in range(len(vdf.time))]) - out = np.vstack(res) - - n_psd = np.array(out[:, 0], dtype="float") - v_psd = np.vstack(out[:, 1][:]) - p_psd = np.vstack(out[:, 2][:]) - p_psd = np.reshape(p_psd, (len(n_psd), 3, 3)) - h_psd = np.vstack(out[:, 3][:]) - p2_psd = np.zeros((len(vdf.time), 3, 3)) - - pool.close() + delta_v = np.tile(delta_v, (vdf_data.shape[0], 1)) + + n_psd, v_psd, p_psd, h_psd = _moms( + energy, + delta_v, + q_e, + sc_pot, + p_mass, + flag_inner_electron, + w_inner_electron, + phi_mat, + theta_mat, + int_energies, + vdf_data, + delta_ang, + ) # Compute moments in SI units p_psd *= p_mass v_psd /= n_psd[:, np.newaxis] + p2_psd = np.zeros_like(p_psd) p2_psd[:, 0, 0] = p_psd[:, 0, 0] p2_psd[:, 0, 1] = p_psd[:, 0, 1] p2_psd[:, 0, 2] = p_psd[:, 0, 2] diff --git a/pyrfu/mms/psd_rebin.py b/pyrfu/mms/psd_rebin.py index 924d9986..5ff83b9d 100644 --- a/pyrfu/mms/psd_rebin.py +++ b/pyrfu/mms/psd_rebin.py @@ -3,20 +3,19 @@ # 3rd party import numpy as np -import xarray as xr # Local imports -from ..pyrf import calc_dt +from ..pyrf.calc_dt import calc_dt __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" -def psd_rebin(vdf, phi, energy0, energy1, step_table): +def psd_rebin(vdf, phi, energy0, energy1, esteptable): r"""Converts burst mode distribution into 64 energy channel distribution. Functions takes the burst mode distribution sampled in two energy tables and converts to a single energy table with 64 energy channels. Time @@ -28,22 +27,22 @@ def psd_rebin(vdf, phi, energy0, energy1, step_table): Time series of the particle distribution. phi : xarray.DataArray Time series of the phi angles. - energy0 : xarray.DataArray or ndarray + energy0 : numpy.ndarray Energy table 0. - energy1 : xarray.DataArray or ndarray + energy1 : numpy.ndarray Energy table 1. - step_table : xarray.DataArray + esteptable : numpy.ndarray Time series of the stepping table between energies (burst). Returns ------- - time_r : ndarray + time_r : numpy.ndarray Revised time steps. - vdf_r : ndarray + vdf_r : numpy.ndarray Rebinned particle distribution. - energy_r : ndarray + energy_r : numpy.ndarray Revised energy table. - phi_r : ndarray + phi_r : numpy.ndarray Time series of the recalculated phi angle. Notes @@ -53,17 +52,9 @@ def psd_rebin(vdf, phi, energy0, energy1, step_table): """ - if isinstance(energy0, xr.DataArray): - energy0 = energy0.data - else: - pass - - if isinstance(energy1, xr.DataArray): - energy1 = energy1.data - else: - pass - - step_table = step_table.data + assert isinstance(energy0, np.ndarray), "energy0 must be a numpy.ndarray" + assert isinstance(energy1, np.ndarray), "energy1 must be a numpy.ndarray" + assert isinstance(esteptable, np.ndarray), "step_table must be a numpy.ndarray" # Sort energy levels energy_r = np.sort(np.hstack([energy0, energy1])) @@ -72,8 +63,8 @@ def psd_rebin(vdf, phi, energy0, energy1, step_table): delta_t = calc_dt(vdf.data) time_r = vdf.time.data[:-1:2] + int(delta_t * 1e9 / 2) - vdf_r = np.zeros((len(time_r), 64, 32, 16)) - phi_r = np.zeros((len(time_r), 32)) + vdf_r = np.zeros((len(time_r), 64, *vdf.data.shape[2:])) + phi_r = np.zeros((len(time_r), vdf.data.shape[2])) phi_s = np.roll(phi.data, 2, axis=1) phi_s[:, 0] = phi_s[:, 0] - 360 @@ -84,9 +75,13 @@ def psd_rebin(vdf, phi, energy0, energy1, step_table): if phi.data[idx, 0] > phi.data[idx + 1, 0]: phi_r[new_el_num, :] = (phi.data[idx, :] + phi_s[idx + 1, :]) / 2 - vdf_temp = np.roll(np.squeeze(vdf.data.data[idx + 1, ...]), 2, axis=1) + vdf_temp = np.roll( + np.squeeze(vdf.data.data[idx + 1, ...]), + 2, + axis=1, + ) - if step_table[idx]: + if esteptable[idx]: vdf_r[new_el_num, 1:64:2, ...] = vdf.data.data[idx, ...] vdf_r[new_el_num, 0:63:2, ...] = vdf_temp else: @@ -97,7 +92,7 @@ def psd_rebin(vdf, phi, energy0, energy1, step_table): phi_r[new_el_num, :] = phi.data[idx, :] + phi.data[idx + 1, :] phi_r[new_el_num, :] /= 2 - if step_table[idx]: + if esteptable[idx]: vdf_r[new_el_num, 1:64:2, ...] = vdf.data.data[idx, ...] vdf_r[new_el_num, 0:63:2, ...] = vdf.data.data[idx + 1, ...] else: diff --git a/pyrfu/mms/read_feeps_sector_masks_csv.py b/pyrfu/mms/read_feeps_sector_masks_csv.py index ba3101e4..1e80f80c 100644 --- a/pyrfu/mms/read_feeps_sector_masks_csv.py +++ b/pyrfu/mms/read_feeps_sector_masks_csv.py @@ -1,21 +1,24 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import csv + # Built-in imports import os -import csv # 3rd party imports import numpy as np # Local imports -from ..pyrf import datetime642unix, unix2datetime64, iso86012datetime64 +from ..pyrf.datetime642unix import datetime642unix +from ..pyrf.iso86012datetime64 import iso86012datetime64 +from ..pyrf.unix2datetime64 import unix2datetime64 __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -53,24 +56,17 @@ def read_feeps_sector_masks_csv(tint): date = datetime642unix(iso86012datetime64(np.array(tint)[0])) nearest_date = dates[np.argmin((np.abs(np.array(dates) - date)))] nearest_date = unix2datetime64(np.array(nearest_date)) - str_date = nearest_date.astype(" thresh_e)[0][0] f_3d[:e_min_idx] = 0.0 @@ -171,15 +194,8 @@ def reduce(vdf, xyz: np.ndarray, dim: str = "1d", base: str = "pol", **kwargs): energy[energy < 0] = 0.0 # Convert energy to velocity (relativistically correct) - speed = constants.speed_of_light * np.sqrt( - 1 - - 1 - / ( - energy * constants.electron_volt / (m_p * constants.speed_of_light**2) - + 1 - ) - ** 2 - ) # m/s + gamma = 1 + electron_volt * energy / (m_p * speed_of_light**2) + speed = speed_of_light * np.sqrt(1 - 1 / gamma**2) # m/s # azimuthal angle phi = vdf_phi.data[i_t, :].astype(np.float64) # in degrees @@ -190,49 +206,46 @@ def reduce(vdf, xyz: np.ndarray, dim: str = "1d", base: str = "pol", **kwargs): theta = np.deg2rad(theta - 90.0) # in radians # Set velocity projection grid. - if speed_grid_edges is not None: - speed_grid = speed_grid_edges[:-1] + 0.5 * np.diff(speed_grid_edges) - elif speed_grid is not None: - speed_grid = speed_grid - else: + if speed_grid is None: if base == "pol": if dim == "1d": speed_grid = np.hstack((-np.flip(speed), speed)) - elif dim in ["2d", "3d"]: - speed_grid = speed else: - raise ValueError("Invalid projection dimension!!") - elif base == "cart": - speed_grid = speed_grid_cart + # speed_grid = speed + raise NotImplementedError( + "2d projection on polar grid is not ready yet!!", + ) else: - raise ValueError("Invalid base!!") - - options = dict( - xyz=xyz_ts[i_t, ...], - n_mc=n_mc, - weight=weight, - v_lim=v_lim, - a_lim=a_lim, - projection_dim=dim, - projection_base=base, - speed_grid_edges=speed_grid_edges, - ) + speed_grid = speed_grid_cart + else: + pass + + options = { + "xyz": xyz_ts[i_t, ...], + "n_mc": n_mc, + "weight": weight, + "v_lim": v_lim, + "a_lim": a_lim, + "projection_dim": dim, + "projection_base": base, + "speed_grid_edges": speed_grid_edges, + } tmpst = int_sph_dist(f_3d, speed, phi, theta, speed_grid, **options) - if dim == "1d": - f_g[i_t, :] = tmpst["f"] - all_v["v"][i_t, :] = tmpst["v"] - elif dim == "2d" and base == "cart": - f_g[i_t, :, :] = tmpst["f"] - all_v["vx"][i_t, :] = tmpst["vx"] - all_v["vy"][i_t, :] = tmpst["vy"] - elif dim == "3d" and base == "cart": - f_g[i_t, ...] = tmpst["f"] - all_v["vx"][i_t, :] = tmpst["vx"] - all_v["vy"][i_t, :] = tmpst["vy"] - all_v["vz"][i_t, :] = tmpst["vz"] - else: - raise NotImplementedError("Invalid dimension") + f_g[i_t, ...] = tmpst["f"] + + for i in range(n_pr): + all_v[f"v{chr(120 + i)}"][i_t, :] = tmpst[f"v{chr(120 + i)}"] / 1e3 # km/s + + # Build output as a time series with dimensions: + # - (time x vx) for 1D reduced distribution + # - (time x vx x vy) for 2D reduced distribution + coords = [ + vdf_time.data, + *[all_v[f"v{chr(120 + i)}"][0, :] for i in range(n_pr)], + ] + dims = ["time", *[f"v{chr(120 + i)}" for i in range(n_pr)]] + out = xr.DataArray(f_g, coords=coords, dims=dims) - return all_v, f_g + return out diff --git a/pyrfu/mms/remove_edist_background.py b/pyrfu/mms/remove_edist_background.py index a39925dd..62f26e26 100644 --- a/pyrfu/mms/remove_edist_background.py +++ b/pyrfu/mms/remove_edist_background.py @@ -6,29 +6,29 @@ import os # 3rd party imports -from cdflib import cdfread import numpy as np +from cdflib import cdfread - -from scipy import constants +from ..pyrf.datetime642iso8601 import datetime642iso8601 +from ..pyrf.time_clip import time_clip +from ..pyrf.ts_skymap import ts_skymap +from .db_get_ts import db_get_ts # Local imports -from ..pyrf import datetime642iso8601, time_clip, ts_skymap -from .db_get_ts import db_get_ts +from .db_init import MMS_CFG_PATH from .get_data import get_data - __author__ = "Louis Richard" __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.26" +__version__ = "2.4.2" __status__ = "Prototype" def remove_edist_background(vdf, n_sec: float = 0.0, n_art: float = -1.0): - r"""Remove secondary photoelectrons from electron distribution function according - to [1]_. + r"""Remove secondary photoelectrons from electron distribution function + according to [1]_. Parameters ---------- @@ -37,8 +37,8 @@ def remove_edist_background(vdf, n_sec: float = 0.0, n_art: float = -1.0): n_sec : float, Optional Artificial secondary electron density (isotropic). Default is 0. n_art : float, Optional - Artificial photoelectron density (sun-angle dependant), Default is ephoto_scale - from des-emoms GlobalAttributes. + Artificial photoelectron density (sun-angle dependant), + Default is ephoto_scale from des-emoms GlobalAttributes. Returns ------- @@ -51,11 +51,11 @@ def remove_edist_background(vdf, n_sec: float = 0.0, n_art: float = -1.0): References ---------- - .. [1] Gershman, D. J., Avanov, L. A., Boardsen,S. A., Dorelli, J. C., Gliese, U., - Barrie, A. C.,... Pollock, C. J. (2017). Spacecraft and instrument - photoelectrons measured by the dual electron spectrometers on MMS. Journal - of Geophysical Research:Space Physics,122, 11,548–11,558. - https://doi.org/10.1002/2017JA024518 + .. [1] Gershman, D. J., Avanov, L. A., Boardsen,S. A., Dorelli, J. C., + Gliese, U., Barrie, A. C.,... Pollock, C. J. (2017). Spacecraft + and instrument photoelectrons measured by the dual electron + spectrometers on MMS. Journal of Geophysical Research:Space + Physics,122, 11,548–11,558. https://doi.org/10.1002/2017JA024518 """ @@ -63,7 +63,7 @@ def remove_edist_background(vdf, n_sec: float = 0.0, n_art: float = -1.0): tint = list(datetime642iso8601(vdf.time.data[[0, -1]])) # Get spacecraft index from VDF metadata - mms_id = vdf.attrs["CATDESC"].split(" ")[0].lower() + mms_id = vdf.data.attrs["CATDESC"].split(" ")[0].lower() mms_id = int(mms_id[-1]) vdf_tmp = time_clip(vdf, tint) @@ -72,34 +72,27 @@ def remove_edist_background(vdf, n_sec: float = 0.0, n_art: float = -1.0): dataset_name = f"mms{mms_id}_fpi_brst_l2_des-dist" startdelphi_count = db_get_ts( - dataset_name, f"mms{mms_id}_des_startdelphi_count_brst", tint, verbose=False + dataset_name, + f"mms{mms_id}_des_startdelphi_count_brst", + tint, + verbose=False, ) - # Load the elctron number density to get the name of the photoelectron model file, - # and the photoelectron scaling factor + # Load the elctron number density to get the name of the photoelectron + # model file, and the photoelectron scaling factor n_e = get_data("ne_fpi_brst_l2", tint, mms_id, verbose=False) - photoe_scle = n_e.attrs["Photoelectron_model_scaling_factor"] + photoe_scle = n_e.attrs["GLOBAL"]["Photoelectron_model_scaling_factor"] photoe_scle = float(photoe_scle) # Load the model internal photoelectrons - bkg_fname = n_e.attrs["Photoelectron_model_filenames"] - - # Check path - # Guess data path from CDF attributes - data_path = str(vdf.attrs["CDF"]).split(f"mms{mms_id}/fpi")[0] + bkg_fname = n_e.attrs["GLOBAL"]["Photoelectron_model_filenames"] - # Check if path exists if not use the default - if not os.path.exists(data_path): - pkg_path = os.path.dirname(os.path.abspath(__file__)) + # Read the current version of the MMS configuration file + with open(MMS_CFG_PATH, "r", encoding="utf-8") as fs: + config = json.load(fs) - # Read the current version of the MMS configuration file - with open(os.path.join(pkg_path, "config.json"), "r") as fs: - config = json.load(fs) - - data_path = os.path.normpath(config["local_data_dir"]) - else: - data_path = os.path.normpath(data_path) + data_path = os.path.normpath(config["local"]) bkg_fname = os.path.join(data_path, "models", "fpi", bkg_fname) @@ -116,7 +109,7 @@ def remove_edist_background(vdf, n_sec: float = 0.0, n_art: float = -1.0): else: n_photo = photoe_scle - for i in range(len(vdf.time.data)): + for i, _ in enumerate(vdf.time.data): istartdelphi_count = startdelphi_count.data[i] iebgdist = int(np.fix(istartdelphi_count / 16)) diff --git a/pyrfu/mms/remove_idist_background.py b/pyrfu/mms/remove_idist_background.py index 70bebc71..545288cc 100644 --- a/pyrfu/mms/remove_idist_background.py +++ b/pyrfu/mms/remove_idist_background.py @@ -3,20 +3,20 @@ # 3rd party imports import numpy as np - from scipy import constants __author__ = "Louis Richard" __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.26" +__version__ = "2.4.2" __status__ = "Prototype" def remove_idist_background(vdf, def_bg): - r"""Remove the mode background population due to penetrating radiation `def_bg` - from the ion velocity distribution function `vdf` using the method from [1]_. + r"""Remove the mode background population due to penetrating radiation + `def_bg` from the ion velocity distribution function `vdf` using the + method from [1]_. Parameters ---------- @@ -32,26 +32,27 @@ def remove_idist_background(vdf, def_bg): References ---------- - .. [1] Gershman, D. J., Dorelli, J. C., Avanov,L. A., Gliese, U., Barrie, A., - Schiff, C.,et al. (2019). Systematic uncertainties in plasma parameters - reported by the fast plasma investigation on NASA's magnetospheric - multiscale mission. Journal of Geophysical Research: Space Physics, 124, - https://doi.org/10.1029/2019JA026980 + .. [1] Gershman, D. J., Dorelli, J. C., Avanov,L. A., Gliese, U., + Barrie, A., Schiff, C.,et al. (2019). Systematic uncertainties + in plasma parameters reported by the fast plasma investigation on + NASA's magnetospheric multiscale mission. Journal of Geophysical + Research: Space Physics, 124, https://doi.org/10.1029/2019JA026980 """ - # Tile background flux to number of energy channels of the FPI-DIS instrument - def_bg_tmp = np.tile(def_bg[:, np.newaxis], (1, vdf.energy.shape[1])) + # Tile background flux to number of energy channels of the + # FPI-DIS instrument + def_bg_tmp = np.tile(def_bg.data[:, np.newaxis], (1, vdf.energy.shape[1])) - # Convert differential energy flux (cm^2 s sr)^{-1} of the background population - # (penetrating radiations) to phase-space density (s^3 m^{-6}) + # Convert differential energy flux (cm^2 s sr)^{-1} of the background + # population (penetrating radiations) to phase-space density (s^3 m^{-6}) coeff = constants.proton_mass / (constants.elementary_charge * vdf.energy.data) vdf_bg = def_bg_tmp.copy() * 1e4 / 2 vdf_bg *= coeff**2 vdf_bg /= 1e12 - # Tile the background phase-space density to number of azimuthal and elevation - # angles channels of the FPI-DIS instrument + # Tile the background phase-space density to number of azimuthal + # and elevation angles channels of the FPI-DIS instrument vdf_bg = np.tile( vdf_bg[:, :, np.newaxis, np.newaxis], (1, 1, vdf.phi.shape[1], vdf.theta.shape[0]), diff --git a/pyrfu/mms/remove_imoms_background.py b/pyrfu/mms/remove_imoms_background.py index 5d9fbce8..1cfc51fb 100644 --- a/pyrfu/mms/remove_imoms_background.py +++ b/pyrfu/mms/remove_imoms_background.py @@ -3,24 +3,24 @@ # 3rd party imports import numpy as np - from scipy import constants # Local imports -from ..pyrf import ts_tensor_xyz +from ..pyrf.ts_tensor_xyz import ts_tensor_xyz __author__ = "Louis Richard" __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.26" +__version__ = "2.4.2" __status__ = "Prototype" def remove_imoms_background(n_i, v_gse_i, p_gse_i, n_bg_i, p_bg_i): - r"""Remove the mode background population due to penetrating radiation from the - the moments (density `n_i`, bulk velocity `v_gse_i` and pressure tensor `p_gse_i`) - of the ion velocity distribution function using the method from [1]_. + r"""Remove the mode background population due to penetrating radiation + from the the moments (density `n_i`, bulk velocity `v_gse_i` and + pressure tensor `p_gse_i`) of the ion velocity distribution function + using the method from [1]_. Parameters ---------- @@ -46,11 +46,11 @@ def remove_imoms_background(n_i, v_gse_i, p_gse_i, n_bg_i, p_bg_i): References ---------- - .. [1] Gershman, D. J., Dorelli, J. C., Avanov,L. A., Gliese, U., Barrie, A., - Schiff, C.,et al. (2019). Systematic uncertainties in plasma parameters - reported by the fast plasma investigation on NASA's magnetospheric - multiscale mission. Journal of Geophysical Research: Space Physics, 124, - https://doi.org/10.1029/2019JA026980 + .. [1] Gershman, D. J., Dorelli, J. C., Avanov,L. A., Gliese, U., Barrie, + A., Schiff, C.,et al. (2019). Systematic uncertainties in plasma + parameters reported by the fast plasma investigation on NASA's + magnetospheric multiscale mission. Journal of Geophysical + Research: Space Physics, 124, https://doi.org/10.1029/2019JA026980 """ @@ -80,7 +80,8 @@ def remove_imoms_background(n_i, v_gse_i, p_gse_i, n_bg_i, p_bg_i): p_bkg_mat *= p_bg_i.data[:, None, None] p_gse_i_new -= p_bkg_mat - # Fill the lower left off diagonal terms using symetry of the pressure tensor + # Fill the lower left off diagonal terms using symetry of the + # pressure tensor p_gse_i_new[:, 1, 0] = p_gse_i_new[:, 0, 1] p_gse_i_new[:, 2, 0] = p_gse_i_new[:, 0, 2] p_gse_i_new[:, 2, 1] = p_gse_i_new[:, 1, 2] diff --git a/pyrfu/mms/rotate_tensor.py b/pyrfu/mms/rotate_tensor.py index 5ec72e54..c32f32a3 100644 --- a/pyrfu/mms/rotate_tensor.py +++ b/pyrfu/mms/rotate_tensor.py @@ -3,42 +3,56 @@ # 3rd party imports import numpy as np +import xarray as xr # Local imports -from ..pyrf import resample, ts_tensor_xyz, calc_fs +from ..pyrf.calc_fs import calc_fs +from ..pyrf.resample import resample +from ..pyrf.ts_tensor_xyz import ts_tensor_xyz __author__ = "Louis Richard" __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.26" +__version__ = "2.4.2" __status__ = "Prototype" -def rotate_tensor(*args): +def rotate_tensor(inp, rot_flag, vec, perp: str = "pp"): """Rotates pressure or temperature tensor into another coordinate system. Parameters ---------- PeIJ/Peall : xarray.DataArray - Time series of either separated terms of the tensor or the complete tensor. - If columns (PeXX,PeXY,PeXZ,PeYY,PeYZ,PeZZ) + Time series of either separated terms of the tensor or the complete + tensor. If columns (PeXX,PeXY,PeXZ,PeYY,PeYZ,PeZZ) - flag : str + rot_flag : str Flag of the target coordinates system : - * "fac" : Field-aligned coordinates, requires background magnetic field - Bback, optional flag "pp" - :math:`\\mathbf{P}_{\\perp 1} = \\mathbf{P}_{\\perp2}` or "qq" - :math:`\\mathbf{P}_{\\perp 1}` and - :math:`\\mathbf{P}_{\\perp 2}` are most unequal, sets P_{23} to - zero. + * "fac" : Field-aligned coordinates, requires background + magnetic field Bback, optional flag "pp" + :math:`\\mathbf{P}_{\\perp 1} = \\mathbf{P}_{\\perp2}` + or "qq" :math:`\\mathbf{P}_{\\perp 1}` and + :math:`\\mathbf{P}_{\\perp 2}` are most unequal, sets + P_{23} to zero. - * "rot" : Arbitrary coordinate system, requires new x-direction xnew, new - y and z directions ynew, znew (if not included y and z - directions are orthogonal to xnew and closest to the original - y and z directions) + * "rot" : Arbitrary coordinate system, requires new x-direction + xnew, new y and z directions ynew, znew (if not + included y and z directions are orthogonal to xnew + and closest to the original y and z directions) + + * "gse" : GSE coordinates, requires MMS spacecraft number + + vec : xarray.DataArray or numpy.ndarray + Vector or coordinates system to rotate the tensor. If vec is timeseries of a + vector tensor is rotated in field aligned coordinates. If vec is a + numpy.ndarray rotates to a time independant coordinates system. + + perp : str, Optional + Flag for perpandicular components of the tensor. Default is pp. + * "pp" : perpendicular diagonal components are equal + * "qq" : perpendicular diagonal components are most unequal - * "gse" : GSE coordinates, requires MMS spacecraft number 1--4 MMSnum Returns ------- @@ -61,52 +75,24 @@ def rotate_tensor(*args): >>> # Compute ion temperature in field aligned coordinates >>> t_xyzfac_i = mms.rotate_tensor(t_xyz_i, "fac", b_xyz, "pp") - TODO : change input, add check that vectors are orthogonal L145 + TODO : implement method "gse" """ - nargin = len(args) + assert isinstance(rot_flag, str), "flag must be a string" + assert rot_flag.lower() in ["fac", "rot", "gse"], "flag must be fac, rot or gse" - # Check input and load pressure/temperature terms - if isinstance(args[1], str): - rot_flag = args[1] - rot_flag_pos = 1 - p_all = args[0] - p_times = p_all.time.data - - if p_all.data.ndim == 3: - p_tensor = p_all - else: - p_tensor = np.reshape(p_all.data, (p_all.shape[0], 3, 3)) - p_tensor = ts_tensor_xyz(p_times, p_tensor) - else: - raise SystemError("critical','Something is wrong with the input.") + assert isinstance(perp, str), "perp must be a string" + assert perp.lower() in ["pp", "qq"], "perp must be pp or qq" - ppeq, qqeq = [0, 0] + # Check input and load pressure/temperature terms + inp_times = inp.time.data + n_t = len(inp_times) - rot_mat = np.zeros((len(p_times), 3, 3)) + rot_mat = np.zeros((n_t, 3, 3)) if rot_flag[0] == "f": - if nargin == rot_flag_pos: - raise ValueError("B TSeries is missing.") - - b_back = args[rot_flag_pos + 1] - b_back = resample(b_back, p_tensor, f_s=calc_fs(p_tensor)) - - if nargin == 4: - if isinstance(args[3], str) and args[3][0] == "p": - ppeq = 1 - elif isinstance(args[3], str) and args[3][0] == "q": - qqeq = 1 - else: - raise ValueError("Flag not recognized no additional rotations applied.") - - if nargin == 9: - if isinstance(args[8], str) and args[8][0] == "p": - ppeq = 1 - elif isinstance(args[8], str) and args[8][0] == "q": - qqeq = 1 - else: - raise ValueError("Flag not recognized no additional rotations applied.") + assert isinstance(vec, xr.DataArray) + b_back = resample(vec, inp, f_s=calc_fs(inp)) b_vec = b_back / np.linalg.norm(b_back, axis=1, keepdims=True) @@ -120,17 +106,10 @@ def rotate_tensor(*args): rot_mat[:, 0, :], rot_mat[:, 1, :], rot_mat[:, 2, :] = [r_x, r_y, r_z] elif rot_flag[0] == "r": - if nargin == rot_flag_pos: - raise ValueError("Vector(s) is(are) missing.") - - vectors = list(args[rot_flag_pos + 1 :]) - - if len(vectors) == 1: - r_x = vectors[0] - - if len(r_x) != 3: - raise TypeError("Vector format not recognized.") + assert isinstance(vec, np.ndarray) + if vec.ndim == 1 and vec.shape[0] == 3: + r_x = vec r_x /= np.linalg.norm(r_x, keepdims=True) r_y = np.array([0, 1, 0]) r_z = np.cross(r_x, r_y) @@ -138,44 +117,44 @@ def rotate_tensor(*args): r_y = np.cross(r_z, r_x) r_y /= np.linalg.norm(r_y, keepdims=True) - elif len(vectors) == 3: - r_x, r_y, r_z = [r / np.linalg.norm(r, keepdims=True) for r in vectors] + elif vec.ndim == 2 and vec.shape[0] == 3 and vec.shape[1] == 3: + r_x = vec[:, 0] / np.linalg.norm(vec[:, 0], keepdims=True) + r_y = vec[:, 1] / np.linalg.norm(vec[:, 1], keepdims=True) + r_z = vec[:, 2] / np.linalg.norm(vec[:, 2], keepdims=True) else: raise TypeError("Vector format not recognized.") - rot_mat[:, 0, :] = np.ones((len(p_times), 1)) * r_x - rot_mat[:, 1, :] = np.ones((len(p_times), 1)) * r_y - rot_mat[:, 2, :] = np.ones((len(p_times), 1)) * r_z + rot_mat[:, 0, :] = np.ones((n_t, 1)) * r_x + rot_mat[:, 1, :] = np.ones((n_t, 1)) * r_y + rot_mat[:, 2, :] = np.ones((n_t, 1)) * r_z else: - raise ValueError("Flag is not recognized.") + raise NotImplementedError("gse is not yet implemented!!") - p_tensor_p = np.zeros((len(p_times), 3, 3)) + p_tensor_p = np.zeros((n_t, 3, 3)) - for i in range(len(p_times)): + for i in range(n_t): rot_temp = np.squeeze(rot_mat[i, :, :]) p_tensor_p[i, :, :] = np.matmul( - np.matmul(rot_temp, np.squeeze(p_tensor.data[i, :, :])), + np.matmul(rot_temp, np.squeeze(inp.data[i, :, :])), np.transpose(rot_temp), ) - if ppeq: + if perp.lower() == "pp": thetas = 0.5 * np.arctan( - (p_tensor_p[:, 2, 2] - p_tensor_p[:, 1, 1]) / (2 * p_tensor_p[:, 1, 2]) + (p_tensor_p[:, 2, 2] - p_tensor_p[:, 1, 1]) / (2 * p_tensor_p[:, 1, 2]), ) + thetas[np.isnan(thetas)] = 0.0 for i, theta in enumerate(thetas): - if np.isnan(theta): - theta = 0 - rot_temp = np.array( [ [1, 0, 0], [0, np.cos(theta), np.sin(theta)], [0, -np.sin(theta), np.cos(theta)], - ] + ], ) p_tensor_p[i, :, :] = np.matmul( @@ -183,9 +162,9 @@ def rotate_tensor(*args): np.transpose(rot_temp), ) - if qqeq: + else: thetas = 0.5 * np.arctan( - (2 * p_tensor_p[:, 1, 2]) / (p_tensor_p[:, 2, 2] - p_tensor_p[:, 1, 1]) + (2 * p_tensor_p[:, 1, 2]) / (p_tensor_p[:, 2, 2] - p_tensor_p[:, 1, 1]), ) for i, theta in enumerate(thetas): @@ -194,7 +173,7 @@ def rotate_tensor(*args): [1, 0, 0], [0, np.cos(theta), -np.sin(theta)], [0, np.sin(theta), np.cos(theta)], - ] + ], ) p_tensor_p[i, :, :] = np.matmul( @@ -203,11 +182,6 @@ def rotate_tensor(*args): ) # Construct output - p_new = ts_tensor_xyz(p_times, p_tensor_p) - - try: - p_new.attrs["units"] = args[0].attrs["units"] - except KeyError: - pass + p_new = ts_tensor_xyz(inp_times, p_tensor_p, attrs=inp.attrs) return p_new diff --git a/pyrfu/mms/scpot2ne.py b/pyrfu/mms/scpot2ne.py index 37cd22ed..5627d114 100644 --- a/pyrfu/mms/scpot2ne.py +++ b/pyrfu/mms/scpot2ne.py @@ -4,24 +4,28 @@ # 3rd party imports import numpy as np import xarray as xr - from scipy import constants, optimize # Local imports -from ..pyrf import trace, ts_tensor_xyz, ts_scalar, resample +from ..pyrf.resample import resample +from ..pyrf.trace import trace +from ..pyrf.ts_scalar import ts_scalar +from ..pyrf.ts_tensor_xyz import ts_tensor_xyz __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" def _f_one_pop(x, *args): i_e, i_aspoc, sc_pot_r = args return np.nansum( - np.abs(i_e.data + i_aspoc.data - (x[0] * np.exp(-sc_pot_r.data / x[1]))) + np.abs( + i_e.data + i_aspoc.data - (x[0] * np.exp(-sc_pot_r.data / x[1])), + ), ) @@ -32,8 +36,8 @@ def _f_two_pop(x, *args): i_e.data + i_aspoc.data - (x[0] * np.exp(-sc_pot_r.data / x[1])) - + (x[2] * np.exp(-sc_pot_r.data / x[3])) - ) + + (x[2] * np.exp(-sc_pot_r.data / x[3])), + ), ) @@ -89,11 +93,9 @@ def scpot2ne(sc_pot, n_e, t_e, i_aspoc: xr.DataArray = None): else: i_aspoc = ts_scalar(n_e.time.data, np.zeros(len(n_e))) - # Check format of electron temperature - if t_e.ndim == 3 and t_e.shape[1:] == (3, 3): - t_e = trace(ts_tensor_xyz(t_e.time.data, t_e.data)) / 3 - else: - raise IndexError("Te format not recognized") + # Check format of electron temperature (tensor) + assert t_e.ndim == 3 and t_e.shape[1:] == (3, 3), "te must be timeseries of tensor" + t_e = trace(ts_tensor_xyz(t_e.time.data, t_e.data)) / 3 # Define constants m_e = constants.electron_mass @@ -110,7 +112,10 @@ def scpot2ne(sc_pot, n_e, t_e, i_aspoc: xr.DataArray = None): # First a simple fit of Iph to Ie using 1 photoelectron population opt_p1 = optimize.fmin( - _f_one_pop, x0=[500.0, 3.0], args=(i_e, i_aspoc, sc_pot_r), maxfun=5000 + _f_one_pop, + x0=[500.0, 3.0], + args=(i_e, i_aspoc, sc_pot_r), + maxfun=5000, ) # Fit of Iph to Ie for two photoelectron populations @@ -123,7 +128,9 @@ def scpot2ne(sc_pot, n_e, t_e, i_aspoc: xr.DataArray = None): i_ph0, t_ph0, i_ph1, t_ph1 = opt_p2 v_eth = np.sqrt(2 * q_e * resample(t_e, sc_pot).data / m_e) - n_esc = i_ph0 * np.exp(-sc_pot.data / t_ph0) + i_ph1 * np.exp(-sc_pot.data / t_ph1) + n_esc = i_ph0 * np.exp(-sc_pot.data / t_ph0) + i_ph1 * np.exp( + -sc_pot.data / t_ph1, + ) n_esc /= s_surf * q_e * v_eth * (1 + sc_pot.data / resample(t_e, sc_pot).data) n_esc *= 2 * np.sqrt(np.pi) * 1e-12 n_esc = ts_scalar(sc_pot.time.data, n_esc) diff --git a/pyrfu/mms/spectr_to_dataset.py b/pyrfu/mms/spectr_to_dataset.py index e4bec1f4..ab78cdae 100644 --- a/pyrfu/mms/spectr_to_dataset.py +++ b/pyrfu/mms/spectr_to_dataset.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/mms/sun/MMS1_FEEPS_ContaminatedSectors_20151111.csv b/pyrfu/mms/sun/MMS1_FEEPS_ContaminatedSectors_20151111.csv index 44ca6f7e..77a190e1 100644 --- a/pyrfu/mms/sun/MMS1_FEEPS_ContaminatedSectors_20151111.csv +++ b/pyrfu/mms/sun/MMS1_FEEPS_ContaminatedSectors_20151111.csv @@ -1,64 +1,64 @@ -0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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-all_params_scalars = [ +ALL_PARAMS_SCALARS = [ "ni", "nbgi", "pbgi", @@ -54,7 +56,7 @@ "epsd", ] -all_params_vectors = [ +ALL_PARAMS_VECTORS = [ "r", "sti", "vi", @@ -70,14 +72,24 @@ "errqe", "b", "e", + "hmfe", "e2d", "es12", "es34", ] -all_params_tensors = ["pi", "partpi", "pe", "partpe", "ti", "partti", "te", "partte"] +ALL_PARAMS_TENSORS = [ + "pi", + "partpi", + "pe", + "partpe", + "ti", + "partti", + "te", + "partte", +] -hpca_params_scalars = [ +HPCA_PARAMS_SCALARS = [ "nhplus", "nheplus", "nheplusplus", @@ -96,7 +108,7 @@ "azimuth", ] -hpca_params_tensors = [ +HPCA_PARAMS_TENSORS = [ "vhplus", "vheplus", "vheplusplus", @@ -111,7 +123,24 @@ "toplus", ] -instruments = [ +PARAMS_SCALARS = [*ALL_PARAMS_SCALARS, *HPCA_PARAMS_SCALARS] +PARAMS_VECTORS = [*ALL_PARAMS_VECTORS] +PARAMS_TENSORS = [*ALL_PARAMS_TENSORS, *HPCA_PARAMS_TENSORS] +ALL_PARAMETERS = [*PARAMS_SCALARS, *PARAMS_VECTORS, *PARAMS_TENSORS] + + +COORDINATE_SYSTEMS = [ + "gse", + "gsm", + "dsl", + "dbcs", + "dmpa", + "ssc", + "bcs", + "par", +] + +INSTRUMENTS = [ "mec", "fpi", "edp", @@ -125,22 +154,22 @@ "dsp", ] -coordinate_systems = ["gse", "gsm", "dsl", "dbcs", "dmpa", "ssc", "bcs", "par"] -data_lvls = ["ql", "sitl", "l1b", "l2a", "l2pre", "l2", "l3"] +SAMPLING_RATES = ["brst", "fast", "slow", "srvy"] + +DATA_LVLS = ["ql", "sitl", "l1b", "l2a", "l2pre", "l2", "l3"] def _tensor_order(splitted_key): - param_low = splitted_key[0].lower() + param = splitted_key[0].lower() + assert param in ALL_PARAMETERS, f"invalid PARAM : {param}" - if param_low in all_params_scalars or param_low in hpca_params_scalars: + if param in PARAMS_SCALARS: tensor_order = 0 - elif param_low in all_params_vectors or param_low in hpca_params_tensors: + elif param in ALL_PARAMS_VECTORS: tensor_order = 1 - elif param_low in all_params_tensors: - tensor_order = 2 else: - raise ValueError(f"invalid PARAM : {param_low}") + tensor_order = 2 return tensor_order @@ -167,43 +196,44 @@ def tokenize(var_str): """ splitted_key = var_str.split("_") + assert len(splitted_key) == 4 or len(splitted_key) == 5 tensor_order = _tensor_order(splitted_key) - coordinate_system = [] - idx = 0 + if len(splitted_key) == 5 and tensor_order > 0: + parameter, coordinates_system, instrument, data_rate, data_level = splitted_key + assert coordinates_system in COORDINATE_SYSTEMS, "invalid coord. sys." + else: + parameter, instrument, data_rate, data_level = splitted_key + coordinates_system = [] - if tensor_order > 0: - coordinate_system = splitted_key[idx + 1] - assert coordinate_system in coordinate_systems, "invalid coord. sys." - idx += 1 + assert parameter in ALL_PARAMETERS, "invalid parameter" - instrument = splitted_key[idx + 1] - assert instrument in instruments, "invalid INSTRUMENT" + # Instrument + assert instrument in INSTRUMENTS, "invalid INSTRUMENT" - idx += 1 + # Sampling rate + assert data_rate in SAMPLING_RATES, "invalid sampling mode" - tmmode = splitted_key[idx + 1] - idx += 1 + # Data level + assert data_level in DATA_LVLS, "invalid DATA_LEVEL level" - if tmmode not in ["brst", "fast", "slow", "srvy"]: - tmmode = "fast" - idx -= 1 - warnings.warn("assuming TM_MODE = FAST", UserWarning) + root_path = os.path.dirname(os.path.abspath(__file__)) - if len(splitted_key) == idx + 1: - data_lvl = "l2" # default - else: - data_lvl = splitted_key[idx + 1] - assert data_lvl in data_lvls, "invalid DATA_LEVEL level" + with open( + os.sep.join([root_path, "mms_keys.json"]), "r", encoding="utf-8" + ) as json_file: + keys_ = json.load(json_file) res = { "param": splitted_key[0], "to": tensor_order, - "cs": coordinate_system, + "cs": coordinates_system, "inst": instrument, - "tmmode": tmmode, - "lev": data_lvl, + "tmmode": data_rate, + "lev": data_level, + "dtype": keys_[instrument][var_str.lower()]["dtype"], + "cdf_name": keys_[instrument][var_str.lower()]["cdf_name"], } return res diff --git a/pyrfu/mms/vdf_elim.py b/pyrfu/mms/vdf_elim.py index 6806c1b6..4e6f17aa 100644 --- a/pyrfu/mms/vdf_elim.py +++ b/pyrfu/mms/vdf_elim.py @@ -8,19 +8,20 @@ import numpy as np # Local imports -from ..pyrf import ts_skymap +from ..pyrf.ts_skymap import ts_skymap __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.10" +__version__ = "2.4.2" __status__ = "Prototype" - logging.captureWarnings(True) logging.basicConfig( - format="%(asctime)s: %(message)s", datefmt="%d-%b-%y %H:%M:%S", level=logging.INFO + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, ) @@ -47,6 +48,7 @@ def vdf_elim(vdf, e_int): n_etables = unique_etables.shape[0] e_int = list(np.atleast_1d(e_int)) + e_int.sort() # energy interval if len(e_int) == 2: @@ -62,7 +64,12 @@ def vdf_elim(vdf, e_int): e_levels = list(e_levels.astype(np.int64)) e_min = np.min(energy.data[:, e_levels]) e_max = np.max(energy.data[:, e_levels]) - print(f"Effective eint = [{e_min:5.2f}, {e_max:5.2f}]") + logging.info( + "Effective eint = [%(e_min)5.2f, %(e_max)5.2f]", + {"e_min": e_min, "e_max": e_max}, + ) + energies = energy.data[:, e_levels] + data = vdf.data.data[:, e_levels, ...] else: # pick closest energy level @@ -74,25 +81,41 @@ def vdf_elim(vdf, e_int): e_diff = e_diff1 e_levels = int(np.where(e_diff == np.min(e_diff))[0][0]) - print( - f"Effective energies alternate in time between " - f"{energy.data[0, e_levels]:5.2f} and " - f"{energy.data[1, e_levels]:5.2f}" + logging.info( + "Effective energies alternate in time between %(e0)5.2f and %(e1)5.2f", + {"e0": energy.data[0, e_levels], "e1": energy.data[1, e_levels]}, ) + energies = energy.data[:, e_levels] + energies = energies[:, np.newaxis] + data = vdf.data.data[:, e_levels, ...] + data = data[:, np.newaxis, ...] + + # Data attributes + data_attrs = vdf.data.attrs + + # Coordinates attributes + coords_attrs = {k: vdf[k].attrs for k in ["time", "energy", "phi", "theta"]} + + # Global attributes + glob_attrs = vdf.attrs + + # Get energies levels + energy_0 = glob_attrs.get("energy0", unique_etables[0, :])[e_levels] + energy_1 = glob_attrs.get("energy1", unique_etables[0, :])[e_levels] + esteptable = glob_attrs.get("esteptable", np.zeros(len(vdf.time))) vdf_e_clipped = ts_skymap( vdf.time.data, - vdf.data.data[:, e_levels, ...], - energy=energy.data[:, e_levels], - phi=vdf.phi.data, - theta=vdf.theta.data, + data, + energies, + vdf.phi.data, + vdf.theta.data, + energy0=energy_0, + energy1=energy_1, + esteptable=esteptable, + attrs=data_attrs, + coords_attrs=coords_attrs, + glob_attrs=glob_attrs, ) - energy_0 = vdf.attrs.get("energy0", unique_etables[0, :])[e_levels] - energy_1 = vdf.attrs.get("energy1", unique_etables[0, :])[e_levels] - esteptable = vdf.attrs.get("esteptable", np.zeros(len(vdf.time))) - vdf_e_clipped.attrs["energy0"] = energy_0 - vdf_e_clipped.attrs["energy1"] = energy_1 - vdf_e_clipped.attrs["esteptable"] = esteptable - return vdf_e_clipped diff --git a/pyrfu/mms/vdf_omni.py b/pyrfu/mms/vdf_omni.py index 7f774428..2b98d39a 100644 --- a/pyrfu/mms/vdf_omni.py +++ b/pyrfu/mms/vdf_omni.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -37,6 +37,8 @@ def vdf_omni(vdf, method: str = "mean"): """ + assert method.lower() in ["mean", "sum"], "invalid method!!" + time = vdf.time.data energy = vdf.energy.data @@ -46,22 +48,30 @@ def vdf_omni(vdf, method: str = "mean"): sine_theta = np.ones((np_phi, 1)) * np.sin(np.deg2rad(thetas)) solid_angles = dangle * dangle * sine_theta - all_solid_angles = np.tile(solid_angles, (len(time), energy.shape[1], 1, 1)) + all_solid_angles = np.tile( + solid_angles, + (len(time), energy.shape[1], 1, 1), + ) if method.lower() == "mean": dist = vdf.data.data * all_solid_angles omni = np.squeeze(np.nanmean(np.nanmean(dist, axis=3), axis=2)) omni /= np.mean(np.mean(solid_angles)) - elif method.lower() == "sum": + else: dist = vdf.data.data omni = np.squeeze(np.nansum(np.nansum(dist, axis=3), axis=2)) - else: - raise ValueError("invalid method!!") energy = np.mean(energy[:2, :], axis=0) + # Use global and zVariable attributes + attrs = {**vdf.data.attrs, **vdf.attrs} + attrs = {k: attrs[k] for k in sorted(attrs)} + out = xr.DataArray( - omni, coords=[time, energy], dims=["time", "energy"], attrs=vdf.attrs + omni, + coords=[time, energy], + dims=["time", "energy"], + attrs=attrs, ) return out diff --git a/pyrfu/mms/vdf_projection.py b/pyrfu/mms/vdf_projection.py index f3064807..1133ffb9 100644 --- a/pyrfu/mms/vdf_projection.py +++ b/pyrfu/mms/vdf_projection.py @@ -2,27 +2,36 @@ # -*- coding: utf-8 -*- # Built-in imports -import warnings import itertools +import logging # 3rd party imports import numpy as np import xarray as xr - from scipy import constants -# Local imports -from ..pyrf import iso86012datetime64, time_clip, ts_scalar, ts_skymap +from ..pyrf.iso86012datetime64 import iso86012datetime64 +from ..pyrf.time_clip import time_clip +from ..pyrf.ts_scalar import ts_scalar +from ..pyrf.ts_skymap import ts_skymap +# Local imports from .psd_rebin import psd_rebin __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.10" +__version__ = "2.4.2" __status__ = "Prototype" +logging.captureWarnings(True) +logging.basicConfig( + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, +) + def _coord_sys(coord_sys): x_vec = coord_sys[0, :] / np.linalg.norm(coord_sys[0, :]) @@ -35,19 +44,17 @@ def _coord_sys(coord_sys): for i, vec, comp in zip([0, 1, 2], [x_vec, y_vec, z_vec], ["x", "y", "z"]): if abs(np.rad2deg(np.arccos(np.dot(vec, coord_sys[:, i])))) > 1.0: - msg = " ".join( - [ - "In making 'xyz' a right handed orthogonal", - f"coordinate system, {comp} (in-plane {i:d}) was", - "changed from", - np.array2string(coord_sys[:, i]), - "to", - np.array2string(x_vec), - "Please verify that this is according to your", - "intentions.", - ] + logging.warning( + "In making xyz a right handed orthogonal coordinate system, %(comp)s " + "(in-plane %(i)d) was changed from %(x_old)s to %(x_new)s. Please " + "verify that this is according to your intentions.", + { + "comp": comp, + "i": i, + "x_old": np.array2string(coord_sys[:, i]), + "x_new": np.array2string(x_vec), + }, ) - warnings.warn(msg, UserWarning) changed_xyz[i] = True return x_vec, y_vec, z_vec, changed_xyz @@ -67,7 +74,7 @@ def _init(vdf, tint): else: phi = vdf.phi - theta = vdf.theta + theta = vdf.theta.data polar = np.deg2rad(theta) azimuthal = np.deg2rad(phi) step_table = vdf.attrs.get("esteptable", np.zeros(len(vdf.time))) @@ -81,22 +88,24 @@ def _init(vdf, tint): [ 10 ** (np.log10(energy0) - diff_energ), 10 ** (np.log10(energy0[-1]) + diff_energ), - ] + ], ) energy1_edges = np.hstack( [ 10 ** (np.log10(energy1) - diff_energ), 10 ** (np.log10(energy1[-1]) + diff_energ), - ] + ], ) if tint is not None and len(tint) == 1: - t_id = np.argmin(np.abs(vdf.time.data - iso86012datetime64(np.array(tint))[0])) + t_id = np.argmin( + np.abs(vdf.time.data - iso86012datetime64(np.array(tint))[0]), + ) dist = vdf.data.data[t_id, ...] dist = dist[None, ...] step_table = step_table[t_id] - azimuthal = azimuthal[t_id, ...] + azimuthal = azimuthal[t_id, ...].data if step_table.data: energy_edges = energy1_edges @@ -110,38 +119,53 @@ def _init(vdf, tint): azimuthal = time_clip(azimuthal, tint) if len(dist.time) > 1 and list(energy0) != list(energy1): - print("notice: Rebinning distribution.") + logging.info("Rebinning distribution.") temp = ts_skymap( dist.time.data, dist.data, time_clip(vdf.energy, tint).data, np.rad2deg(azimuthal.data), - theta.data, + theta, + ) + newt, dist, energy, phi = psd_rebin( + temp, + phi, + energy0, + energy1, + step_table, ) - newt, dist, energy, phi = psd_rebin(temp, phi, energy0, energy1, step_table) dist = ts_skymap( - newt, dist, np.tile(energy, (len(newt), 1)), phi, theta.data + newt, + dist, + np.tile(energy, (len(newt), 1)), + phi, + theta, ) dist = time_clip(dist.data, tint) azimuthal = xr.DataArray( - phi, coords=[newt, np.arange(phi.shape[1])], dims=["time", "odx"] + phi, + coords=[newt, np.arange(phi.shape[1])], + dims=["time", "odx"], ) len_e = dist.shape[1] energy_edges = np.hstack( [ 10 ** (np.log10(energy) - diff_energ / 2), 10 ** (np.log10(energy[-1]) + diff_energ / 2), - ] + ], ) else: if all(step_table.data): energy_edges = energy1_edges else: energy_edges = energy0_edges + + dist = dist.data.data + azimuthal = azimuthal.data else: raise ValueError("Invalid time interval") - return dist, polar.data, azimuthal.data, energy_edges, len_e + return dist, polar, azimuthal, energy_edges, len_e def _cotrans(dist, polar, azimuthal, x_vec, y_vec, z_vec, e_lim, bin_corr): @@ -175,7 +199,7 @@ def _cotrans(dist, polar, azimuthal, x_vec, y_vec, z_vec, e_lim, bin_corr): new_z_mat = np.reshape(new_tmp_z, (x_mat.shape[0], x_mat.shape[1])) elevation_angle = np.arctan( - new_z_mat / np.sqrt(new_x_mat**2 + new_y_mat**2) + new_z_mat / np.sqrt(new_x_mat**2 + new_y_mat**2), ) plane_az = np.arctan2(new_y_mat, new_x_mat) + np.pi @@ -191,7 +215,7 @@ def _cotrans(dist, polar, azimuthal, x_vec, y_vec, z_vec, e_lim, bin_corr): geo_factor_bin_size = np.ones(pol_mat.shape) f_mat[i, ...] = _cotrans_jit( - dist.data[i, ...], + dist[i, ...], elevation_angle, e_lim, plane_az, @@ -214,7 +238,10 @@ def _cotrans_jit( ): out = np.zeros((dist.shape[1], dist.shape[0])) # azimuthal, energy - for i_en, i_az in itertools.product(range(dist.shape[0]), range(dist.shape[1])): + for i_en, i_az in itertools.product( + range(dist.shape[0]), + range(dist.shape[1]), + ): # dist.data has dimensions nT x nE x nAz x nPol c_mat = dist[i_en, ...].copy() c_mat = c_mat * geo_factor_elev * geo_factor_bin_size @@ -285,7 +312,14 @@ def vdf_projection( azimuthal = np.ones((len(dist), 1)) * azimuthal f_mat = _cotrans( - dist, polar, azimuthal, x_vec, y_vec, z_vec, e_lim, bins_correction + dist, + polar, + azimuthal, + x_vec, + y_vec, + z_vec, + e_lim, + bins_correction, ) if len(dist) == 1: f_mat = np.squeeze(f_mat) diff --git a/pyrfu/mms/vdf_reduce.py b/pyrfu/mms/vdf_reduce.py index ede12844..4f4074cd 100644 --- a/pyrfu/mms/vdf_reduce.py +++ b/pyrfu/mms/vdf_reduce.py @@ -4,17 +4,16 @@ # 3rd party imports import numpy as np import xarray as xr - -from scipy import interpolate, constants +from scipy import constants, interpolate # Local imports -from pyrfu.pyrf import cart2sph, sph2cart, resample, time_clip +from pyrfu.pyrf import cart2sph, resample, sph2cart, time_clip __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2022" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.12" +__version__ = "2.4.2" __status__ = "Prototype" __all__ = ["vdf_frame_transformation", "vdf_reduce"] @@ -123,7 +122,8 @@ def _interp_skymap_cart(vdf, energy, phi, theta, grid_cart): theta_grid = np.rad2deg(theta_grid) grid_sphe = np.transpose( - np.stack([energy_grid, phi_grid, theta_grid]), [1, 2, 3, 0] + np.stack([energy_grid, phi_grid, theta_grid]), + [1, 2, 3, 0], ) # Interpolate the skymap distribution onto the spherical grid @@ -175,7 +175,9 @@ def vdf_frame_transformation(vdf, v_gse): phi = vdf.phi.data[i, :] phi_mat, en_mat, theta_mat = np.meshgrid(phi, energy, theta) - v_mat = np.sqrt(2 * en_mat * constants.electron_volt / constants.proton_mass) + v_mat = np.sqrt( + 2 * en_mat * constants.electron_volt / constants.proton_mass, + ) v_xyz = sph2cart(np.deg2rad(phi_mat), np.deg2rad(theta_mat), v_mat) grid_cart = np.stack( @@ -183,10 +185,16 @@ def vdf_frame_transformation(vdf, v_gse): v_xyz[0] - v_gse.data[i, 0, None, None, None], v_xyz[1] - v_gse.data[i, 1, None, None, None], v_xyz[2] - v_gse.data[i, 2, None, None, None], - ] + ], ) - out_data[i, ...] = _interp_skymap_cart(vdf_data, energy, phi, theta, grid_cart) + out_data[i, ...] = _interp_skymap_cart( + vdf_data, + energy, + phi, + theta, + grid_cart, + ) out = vdf.copy() out.data.data = out_data @@ -195,7 +203,13 @@ def vdf_frame_transformation(vdf, v_gse): def vdf_reduce( - vdf, tint, dim, x_vec, z_vec: list = None, v_int: list = None, n_vpt: int = 100 + vdf, + tint, + dim, + x_vec, + z_vec: list = None, + v_int: list = None, + n_vpt: int = 100, ): r"""Interpolate the skymap distribution onto the velocity grid defined by the velocity interval `v_int` along the axes `x_vec` and `z_vec`, @@ -260,7 +274,11 @@ def vdf_reduce( if dim.lower() == "2d": dv_ = np.abs(np.diff(v_z)[0]) red_vdf = np.nansum(interp_vdf, axis=-1) * dv_ - out = xr.DataArray(red_vdf, coords=[v_x / 1e6, v_y / 1e6], dims=["vx", "vy"]) + out = xr.DataArray( + red_vdf, + coords=[v_x / 1e6, v_y / 1e6], + dims=["vx", "vy"], + ) elif dim.lower() == "1d": dv_ = np.abs(np.diff(v_y)[0] * np.diff(v_z)[0]) red_vdf = np.nansum(np.sum(interp_vdf, axis=-1), axis=-1) * dv_ diff --git a/pyrfu/mms/vdf_to_e64.py b/pyrfu/mms/vdf_to_e64.py index e6f4b999..019e22e8 100644 --- a/pyrfu/mms/vdf_to_e64.py +++ b/pyrfu/mms/vdf_to_e64.py @@ -4,16 +4,16 @@ # 3rd party imports import numpy as np -# Local imports -from ..pyrf import ts_skymap +from ..pyrf.ts_skymap import ts_skymap +# Local imports from .psd_rebin import psd_rebin __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -43,6 +43,36 @@ def vdf_to_e64(vdf_e32): energy_r = np.tile(energy_r, (len(vdf_r), 1)) + # Data attributes + data_attrs = vdf_e32.data.attrs + + # Coordinates attributes + coords_attrs = {k: vdf_e32[k].attrs for k in ["time", "energy", "phi", "theta"]} + + # Global attributes + glob_attrs = vdf_e32.attrs + + # update delta_energy + if "delta_energy_plus" in glob_attrs and "delta_energy_minus" in glob_attrs: + log_energy = np.log10(energy_r[0, :]) + log10_energy = np.diff(log_energy) + log10_energy_plus = log_energy + 0.5 * np.hstack( + [log10_energy, log10_energy[-1]], + ) + log10_energy_minus = log_energy - 0.5 * np.hstack( + [log10_energy[0], log10_energy], + ) + + energy_plus = 10**log10_energy_plus + energy_minus = 10**log10_energy_minus + delta_energy_plus = energy_plus - energy_r + delta_energy_minus = abs(energy_minus - energy_r) + delta_energy_plus[-1] = np.max(vdf_e32.attrs["delta_energy_minus"][:, -1]) + delta_energy_minus[0] = np.min(vdf_e32.attrs["delta_energy_minus"][:, 0]) + + glob_attrs["delta_energy_plus"] = delta_energy_plus + glob_attrs["delta_energy_minus"] = delta_energy_minus + vdf_e64 = ts_skymap( time_r, vdf_r, @@ -51,25 +81,10 @@ def vdf_to_e64(vdf_e32): vdf_e32.theta.data, energy0=energy_r[0, :], energy1=energy_r[0, :], - esteptable=vdf_e32.attrs.get("esteptable"), + esteptable=np.zeros(len(time_r)), + attrs=data_attrs, + coords_attrs=coords_attrs, + glob_attrs=glob_attrs, ) - # update delta_energy - log_energy = np.log10(energy_r[0, :]) - log10_energy = np.diff(log_energy) - log10_energy_plus = log_energy + 0.5 * np.hstack([log10_energy, log10_energy[-1]]) - log10_energy_minus = log_energy - 0.5 * np.hstack([log10_energy[0], log10_energy]) - - energy_plus = 10**log10_energy_plus - energy_minus = 10**log10_energy_minus - delta_energy_plus = energy_plus - energy_r - delta_energy_minus = abs(energy_minus - energy_r) - delta_energy_plus[-1] = np.max(vdf_e32.attrs["delta_energy_minus"][:, -1]) - delta_energy_minus[0] = np.min(vdf_e32.attrs["delta_energy_minus"][:, 0]) - vdf_e64.attrs["delta_energy_plus"] = delta_energy_plus - vdf_e64.attrs["delta_energy_minus"] = delta_energy_minus - vdf_e64.attrs["esteptable"] = np.zeros(len(time_r)) - vdf_e64.attrs["species"] = vdf_e32.attrs["species"] - vdf_e64.attrs["UNITS"] = vdf_e32.attrs["UNITS"] - return vdf_e64 diff --git a/pyrfu/mms/whistler_b2e.py b/pyrfu/mms/whistler_b2e.py index 8aa9d67c..e30ba9d1 100644 --- a/pyrfu/mms/whistler_b2e.py +++ b/pyrfu/mms/whistler_b2e.py @@ -3,17 +3,16 @@ # 3rd party imports import numpy as np - from scipy import constants # Local imports -from ..pyrf import plasma_calc +from ..pyrf.plasma_calc import plasma_calc __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -55,26 +54,26 @@ def whistler_b2e(b2, freq, theta_k, b_mag, n_e): raise IndexError("B2 and freq lengths do not agree!") # Calculate cold plasma parameters - r = 1 - fpe**2 / (freq * (freq - fce)) - l = 1 - fpe**2 / (freq * (freq + fce)) - p = 1 - fpe**2 / freq**2 - d = 0.5 * (r - l) - s = 0.5 * (r + l) - - n2 = r * l * np.sin(theta_k) ** 2 - n2 += p * s * (1 + np.cos(theta_k) ** 2) + rr = 1 - fpe**2 / (freq * (freq - fce)) + ll = 1 - fpe**2 / (freq * (freq + fce)) + pp = 1 - fpe**2 / freq**2 + dd = 0.5 * (rr - ll) + ss = 0.5 * (rr + ll) + + n2 = rr * ll * np.sin(theta_k) ** 2 + n2 += pp * ss * (1 + np.cos(theta_k) ** 2) n2 -= np.sqrt( - (r * l - p * s) ** 2 * np.sin(theta_k) ** 4 - + 4 * (p**2) * (d**2) * np.cos(theta_k) ** 2 + (rr * ll - pp * ss) ** 2 * np.sin(theta_k) ** 4 + + 4 * (pp**2) * (dd**2) * np.cos(theta_k) ** 2, ) - n2 /= 2 * (s * np.sin(theta_k) ** 2 + p * np.cos(theta_k) ** 2) + n2 /= 2 * (ss * np.sin(theta_k) ** 2 + pp * np.cos(theta_k) ** 2) - e_temp1 = (p - n2 * np.sin(theta_k) ** 2) ** 2.0 * ((d / (s - n2)) ** 2 + 1) + ( + e_temp1 = (pp - n2 * np.sin(theta_k) ** 2) ** 2.0 * ((dd / (ss - n2)) ** 2 + 1) + ( n2 * np.cos(theta_k) * np.sin(theta_k) ) ** 2 - e_temp2 = (d / (s - n2)) ** 2 * ( - p - n2 * np.sin(theta_k) ** 2 - ) ** 2 + p**2 * np.cos(theta_k) ** 2 + e_temp2 = (dd / (ss - n2)) ** 2 * ( + pp - n2 * np.sin(theta_k) ** 2 + ) ** 2 + pp**2 * np.cos(theta_k) ** 2 e2 = (constants.speed_of_light**2 / n2) * e_temp1 / e_temp2 * b2.data e2 *= 1e-12 diff --git a/pyrfu/models/__init__.py b/pyrfu/models/__init__.py index 4af72e05..e00e9292 100644 --- a/pyrfu/models/__init__.py +++ b/pyrfu/models/__init__.py @@ -1,21 +1,15 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -# -# MIT License -# -# Copyright (c) 2020 Louis Richard -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so. - -"""__init__.py -@author: Louis Richard -""" # @Louis Richard from .igrf import igrf from .magnetopause_normal import magnetopause_normal + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.2" +__status__ = "Prototype" + +__all__ = ["igrf", "magnetopause_normal"] diff --git a/pyrfu/models/igrf.py b/pyrfu/models/igrf.py index ef9b8f21..5375f5f1 100644 --- a/pyrfu/models/igrf.py +++ b/pyrfu/models/igrf.py @@ -8,9 +8,15 @@ # 3rd party imports import numpy as np import pandas as pd - from scipy import interpolate +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.2" +__status__ = "Prototype" + def igrf(time, flag): r"""Returns magnetic dipole latitude and longitude of the IGRF model @@ -22,12 +28,10 @@ def igrf(time, flag): flag : str Default is dipole. - Returns ------- lambda : ndarray latitude - phi : ndarray longitude @@ -35,7 +39,9 @@ def igrf(time, flag): # Root path # root_path = os.getcwd() - path = os.sep.join([os.path.dirname(os.path.abspath(__file__)), "igrf13coeffs.csv"]) + path = os.sep.join( + [os.path.dirname(os.path.abspath(__file__)), "igrf13coeffs.csv"], + ) df = pd.read_csv(path) # construct IGRF coefficient matrices @@ -43,9 +49,7 @@ def igrf(time, flag): years_igrf[-1] = float(years_igrf[-1].split("-")[0]) + 5.0 # read in all IGRF coefficients from file - i_igrf = df.loc[ - 1:, - ].values + i_igrf = df.loc[1:,].values i_igrf[:, -1] = i_igrf[:, -2] + 5.0 * i_igrf[:, -1].astype(np.float64) h_igrf = i_igrf[i_igrf[:, 0] == "h", 1:].astype(np.float64) g_igrf = i_igrf[i_igrf[:, 0] == "g", 1:].astype(np.float64) @@ -59,29 +63,39 @@ def igrf(time, flag): year_ref_unix = year_ref_unix.astype("datetime64[ns]").astype(np.int64) / 1e9 if np.min(year_ref) < np.min(years_igrf): - message = ( - "requested time is earlier than the first available IGRF " - "model from extrapolating in past.. " + warnings.warn( + "requested time is earlier than the first available IGRF model; " + "extrapolating in past", + category=UserWarning, ) - warnings.warn(message, category=UserWarning) - - assert flag == "dipole", "input flag is not recognized" - tck_g0_igrf = interpolate.interp1d( - years_igrf, g_igrf[0, 2:], kind="linear", fill_value="extrapolate" - ) - tck_g1_igrf = interpolate.interp1d( - years_igrf, g_igrf[1, 2:], kind="linear", fill_value="extrapolate" - ) - tck_h0_igrf = interpolate.interp1d( - years_igrf, h_igrf[0, 2:], kind="linear", fill_value="extrapolate" - ) + if flag.lower() == "dipole": + tck_g0_igrf = interpolate.interp1d( + years_igrf, + g_igrf[0, 2:], + kind="linear", + fill_value="extrapolate", + ) + tck_g1_igrf = interpolate.interp1d( + years_igrf, + g_igrf[1, 2:], + kind="linear", + fill_value="extrapolate", + ) + tck_h0_igrf = interpolate.interp1d( + years_igrf, + h_igrf[0, 2:], + kind="linear", + fill_value="extrapolate", + ) - g01 = tck_g0_igrf(year_ref + (time - year_ref_unix) / (365.25 * 86400)) - g11 = tck_g1_igrf(year_ref + (time - year_ref_unix) / (365.25 * 86400)) - h11 = tck_h0_igrf(year_ref + (time - year_ref_unix) / (365.25 * 86400)) - lambda_ = np.arctan(h11 / g11) - phi = np.pi / 2 - phi -= np.arcsin((g11 * np.cos(lambda_) + h11 * np.sin(lambda_)) / g01) + g01 = tck_g0_igrf(year_ref + (time - year_ref_unix) / (365.25 * 86400)) + g11 = tck_g1_igrf(year_ref + (time - year_ref_unix) / (365.25 * 86400)) + h11 = tck_h0_igrf(year_ref + (time - year_ref_unix) / (365.25 * 86400)) + lambda_ = np.arctan(h11 / g11) + phi = np.pi / 2 + phi -= np.arcsin((g11 * np.cos(lambda_) + h11 * np.sin(lambda_)) / g01) + else: + raise NotImplementedError("input flag is not recognized") return np.rad2deg(lambda_), np.rad2deg(phi) diff --git a/pyrfu/models/magnetopause_normal.py b/pyrfu/models/magnetopause_normal.py index 5144bca5..1a983a91 100644 --- a/pyrfu/models/magnetopause_normal.py +++ b/pyrfu/models/magnetopause_normal.py @@ -2,20 +2,26 @@ # -*- coding: utf-8 -*- # Built-in imports -import warnings +import logging # 3rd party imports import numpy as np - from scipy.optimize import fminbound __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2022" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.22" +__version__ = "2.4.2" __status__ = "Prototype" +logging.captureWarnings(True) +logging.basicConfig( + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, +) + def _magnetopause(theta, *args): r0, alpha, x0, y0 = args @@ -33,31 +39,34 @@ def _magnetopause(theta, *args): def _bow_shock(r_xy, *args): - x, y = r_xy + x, y = r_xy[:, 0], r_xy[:, 1] x0, y0 = args out = (x - x0) ** 2 + (y - y0) ** 2 return out def magnetopause_normal( - r_gsm, b_z_imf, p_sw, model: str = "mp_shue1997", m_alfven: float = 4.0 + r_gsm, + b_z_imf, + p_sw, + model: str = "mp_shue1997", + m_alfven: float = 4.0, ): r"""Computes the distance and normal vector to the magnetopause for - Shue et al., 1997 or Shue et al., 1998 model. Or bow shock for - Farris & Russell 1994 model. + [1]_ or [2]_ model. Or bow shock for [3]_ model. Parameters ---------- r_gsm : array_like - GSM position in Re (if more than 3 values assumes that 1st is time). + GSM position in Re. b_z_imf : float IMF Bz in nT. p_sw : float Solar wind dynamic pressure in nPa. model : {"mp_shue1997", "mp_shue1998", "bs97", "bs98"}, Optional Name of model : - * 'mp_shue1997' : Shue et al., 1997 (Default) - * 'mp_shue1998' : Shue et al., 1998 + * 'mp_shue97' : Shue et al., 1997 (Default) + * 'mp_shue98' : Shue et al., 1998 * 'bs97' : Bow shock, Farris & Russell 1994 * 'bs98' : Bow shock, Farris & Russell 1994 m_alfven : float, Optional @@ -93,11 +102,13 @@ def magnetopause_normal( """ - if model in ["mp_shue1998", "bs98"]: + if model.lower() in ["mp_shue98", "bs98"]: + logging.info("Shue et al., 1998 model used.") alpha = (0.58 - 0.007 * b_z_imf) * (1.0 + 0.024 * np.log(p_sw)) - r0 = (10.22 + 1.29 * np.tanh(0.184 * (b_z_imf + 8.14))) * p_sw ** (-1.0 / 6.6) - warnings.warn("Shue et al., 1998 model used.", UserWarning) - else: + r0 = 10.22 + 1.29 * np.tanh(0.184 * (b_z_imf + 8.14)) + r0 *= p_sw ** (-1.0 / 6.6) + elif model.lower() in ["mp_shue97", "bs97"]: + logging.info("Shue et al., 1997 model used.") alpha = (0.58 - 0.01 * b_z_imf) * (1.0 + 0.01 * p_sw) if b_z_imf >= 0: @@ -105,7 +116,8 @@ def magnetopause_normal( else: r0 = (11.4 + 0.140 * b_z_imf) * p_sw ** (-1.0 / 6.6) - warnings.warn("Shue et al., 1997 model used.", UserWarning) + else: + raise NotImplementedError(f"Invalid model : {model}") # Spacecraft position r1_x, r1_y, r1_z = r_gsm @@ -114,7 +126,7 @@ def magnetopause_normal( if model[:2].lower() == "mp": # Magnetopause - theta_min, min_val, ierr, numfunc = fminbound( + theta_min, min_val, _, _ = fminbound( _magnetopause, x1=-np.pi / 2, x2=np.pi / 2, @@ -141,13 +153,13 @@ def magnetopause_normal( n_vec = np.stack([x_n, y_n, z_n]) / min_dist # if statement to ensure normal is pointing away from Earth - if np.sqrt(r0_x**2 + r0_y**2) > r0 * (2 / (1 + np.cos(theta_min))) ** alpha: - n_vec *= -1 - min_dist *= -1 + fact = r0 * (2 / (1 + np.cos(theta_min))) ** alpha - np.sqrt(r0_x**2 + r0_y**2) + n_vec *= np.sign(fact) + min_dist *= np.sign(fact) else: # Bow shock - warnings.warn("Farris & Russell 1994 bow shock model used.", UserWarning) + logging.info("Farris & Russell 1994 bow shock model used.") gamma = 5 / 3 mach = m_alfven @@ -170,7 +182,7 @@ def magnetopause_normal( min_val = np.min(_bow_shock(r_xy, *args_bow_shock)) min_pos = np.argmin(_bow_shock(r_xy, *args_bow_shock)) - d_vec = np.stack([x - r0_x, y - r0_y]) + d_vec = np.transpose(np.vstack([x - r0_x, y - r0_y])) d_min = d_vec[min_pos, :] x_n = d_min[0] / np.linalg.norm(d_min) @@ -183,8 +195,7 @@ def magnetopause_normal( n_vec = np.stack([x_n, y_n, z_n]) # if statement to ensure normal is pointing away from Earth - if n_vec[0] < 0: - n_vec *= -1 - min_dist *= -1 + min_dist *= np.sign(n_vec[0]) + n_vec *= np.sign(n_vec[0]) return min_dist, n_vec diff --git a/pyrfu/plot/__init__.py b/pyrfu/plot/__init__.py index c728bc70..e6f37158 100644 --- a/pyrfu/plot/__init__.py +++ b/pyrfu/plot/__init__.py @@ -1,41 +1,276 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +# Built-in imports +import os +import shutil +import warnings + # 3rd party imports -import matplotlib.pyplot as plt +import cycler +import matplotlib as mpl +from matplotlib import style # Local imports -from .plot_line import plot_line -from .plot_spectr import plot_spectr -from .mms_pl_config import mms_pl_config -from .pl_scatter_matrix import pl_scatter_matrix -from .zoom import zoom +from .add_position import add_position +from .annotate_heatmap import annotate_heatmap from .colorbar import colorbar from .make_labels import make_labels +from .mms_pl_config import mms_pl_config +from .pl_scatter_matrix import pl_scatter_matrix from .pl_tx import pl_tx -from .add_position import add_position +from .plot_ang_ang import plot_ang_ang +from .plot_clines import plot_clines +from .plot_heatmap import plot_heatmap +from .plot_line import plot_line from .plot_magnetosphere import plot_magnetosphere from .plot_projection import plot_projection +from .plot_reduced_2d import plot_reduced_2d +from .plot_spectr import plot_spectr from .plot_surf import plot_surf from .span_tint import span_tint -from .plot_clines import plot_clines -from .plot_sitl_overview import plot_sitl_overview -from .plot_ang_ang import plot_ang_ang -from .plot_heatmap import plot_heatmap -from .annotate_heatmap import annotate_heatmap -from .plot_reduced_2d import plot_reduced_2d +from .zoom import zoom __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" -# Setup plotting style -plt.style.use("classic") -plt.rcParams["figure.facecolor"] = "1" -plt.rcParams["mathtext.sf"] = "sans" -plt.rcParams["mathtext.fontset"] = "dejavusans" +__all__ = [ + "add_position", + "annotate_heatmap", + "colorbar", + "make_labels", + "mms_pl_config", + "pl_scatter_matrix", + "pl_tx", + "plot_ang_ang", + "plot_clines", + "plot_heatmap", + "plot_line", + "plot_magnetosphere", + "plot_projection", + "plot_reduced_2d", + "plot_spectr", + "plot_surf", + "set_color_cycle", + "span_tint", + "use_pyrfu_style", + "zoom", +] + +stylesheets = os.path.join(os.path.dirname(os.path.abspath(__file__)), "stylesheets") +style.core.USER_LIBRARY_PATHS.append(stylesheets) +style.core.reload_library() + +EXTRA_COLORS = { + "pyrfu:bg": "#eeeeee", + "pyrfu:fg": "#444444", + "pyrfu:bgAlt": "#e4e4e4", + "pyrfu:red": "#af0000", + "pyrfu:green": "#008700", + "pyrfu:blue": "#005f87", + "pyrfu:yellow": "#afaf00", + "pyrfu:orange": "#d75f00", + "pyrfu:pink": "#d70087", + "pyrfu:purple": "#8700af", + "pyrfu:lightblue": "#0087af", + "pyrfu:olive": "#5f7800", + "on:bg": "#1b2b34", + "on:fg": "#cdd3de", + "on:bgAlt": "#343d46", + "on:fgAlt": "#d8dee9", + "on:red": "#ec5f67", + "on:orange": "#f99157", + "on:yellow": "#fac863", + "on:green": "#99c794", + "on:cyan": "#5fb3b3", + "on:blue": "#6699cc", + "on:pink": "#c594c5", + "on:brown": "#ab7967", + "series:cyan": "#54c9d1", + "series:orange": "#eca89a", + "series:blue": "#95bced", + "series:olive": "#ceb776", + "series:purple": "#d3a9ea", + "series:green": "#9bc57f", + "series:pink": "#f0a1ca", + "series:turquoise": "#5fcbaa", + "transparent": "#ffffff00", +} + +mpl.colors.EXTRA_COLORS = EXTRA_COLORS +mpl.colors.colorConverter.colors.update(EXTRA_COLORS) +mpl.colormaps.register( + name="parula", + cmap=mpl.colors.LinearSegmentedColormap.from_list( + "parula", + [ + (0.0592, 0.3599, 0.8684), + (0.078, 0.5041, 0.8385), + (0.0232, 0.6419, 0.7914), + (0.1802, 0.7178, 0.6425), + (0.5301, 0.7492, 0.4662), + (0.8186, 0.7328, 0.3499), + (0.9956, 0.7862, 0.1968), + (0.9764, 0.9832, 0.0539), + ], + N=2560, + ), +) +mpl.colormaps.register( + name="hot_desaturated", + cmap=mpl.colors.LinearSegmentedColormap.from_list( + "hot_desaturated", + [ + (0.2784, 0.2784, 0.8588), + (0.0000, 0.0000, 0.3569), + (0.0000, 1.0000, 1.0000), + (0.0000, 0.5000, 0.0000), + (1.0000, 1.0000, 0.0000), + (1.0000, 0.3765, 0.0000), + (0.4196, 0.0000, 0.0000), + (0.8784, 0.2980, 0.2980), + ], + N=2560, + ), +) + +mpl.colormaps.register( + name="hot_white_desaturated", + cmap=mpl.colors.LinearSegmentedColormap.from_list( + "hot_white_desaturated", + [ + (1.0000, 1.0000, 1.0000), + (0.2784, 0.2784, 0.8588), + (0.0000, 0.0000, 0.3569), + (0.0000, 1.0000, 1.0000), + (0.0000, 0.5000, 0.0000), + (1.0000, 1.0000, 0.0000), + (1.0000, 0.3765, 0.0000), + (0.4196, 0.0000, 0.0000), + (0.8784, 0.2980, 0.2980), + ], + N=2560, + ), +) + + +def set_color_cycle(pal=None): + r"""Sets color cycle. + + Parameters + ---------- + pal : {"Pyrfu", "Oceanic", "Tab", None}. Optional + The palette to use. "Tab" provides the default matplotlib palette. Default is + None (resets to default palette). + """ + + if pal.lower() == "pyrfu": + colors = [ + "pyrfu:blue", + "pyrfu:green", + "pyrfu:red", + "pyrfu:fg", + "pyrfu:orange", + "pyrfu:purple", + "pyrfu:yellow", + "pyrfu:lightblue", + "pyrfu:olive", + ] + cmap = "hot_white_desaturated" + elif pal.lower() == "oceanic": + colors = [ + "on:green", + "on:red", + "on:blue", + "on:cyan", + "on:orange", + "on:pink", + "on:yellow", + ] + cmap = "ocean" + elif pal.lower() == "tab" or pal.lower() == "tableau" or pal.lower() == "mpl": + colors = [ + "tab:blue", + "tab:orange", + "tab:green", + "tab:red", + "tab:purple", + "tab:brown", + "tab:pink", + "tab:gray", + "tab:olive", + "tab:cyan", + ] + cmap = "Spectral_r" + else: + colors = [ + "series:cyan", + "series:orange", + "series:blue", + "series:olive", + "series:purple", + "series:green", + "series:pink", + ] + cmap = "jet" + + mpl.rcParams["axes.prop_cycle"] = cycler.cycler(color=colors) + mpl.rcParams["image.cmap"] = cmap + + +def use_pyrfu_style( + name="classic", color_cycle="pyrfu", fancy_legend=False, usetex=False +): + r"""Setup plot style. + + Parameters + ---------- + name : str, Optional + Name of the style sheet. Default is "classic" (see also + https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html). + color_cycle : str, Optional + Name of the color cycle to use. Default is "pyrfu". + fancy_legend : bool, Optional + Use matplotlib's fancy legend frame. Default is False. + usetex : bool, Optional + Use LaTeX installation to set text. Default is False. + If no LaTeX installation is found, this package will fallback to usetex=False. + """ + + if usetex: + if ( + shutil.which("pdflatex") is None + or shutil.which("dvipng") is None + or shutil.which("gs") is None + ): + warnings.warn( + "No LaTeX installation found pyrfu.plot is falling back to usetex=False" + ) + usetex = False + style.use(name) + set_color_cycle(color_cycle) + mpl.rcParams["xtick.minor.visible"] = True + mpl.rcParams["ytick.minor.visible"] = True + if not usetex: + mpl.rcParams["text.usetex"] = False + mpl.rcParams["mathtext.sf"] = "sans" + mpl.rcParams["mathtext.fontset"] = "dejavusans" + else: + mpl.rcParams["text.latex.preamble"] = "\n".join( + [ + r"\usepackage{amsmath}", + r"\usepackage{physics}", + r"\usepackage{siunitx}", + r"\usepackage{bm}", + ] + ) + if not fancy_legend: + mpl.rcParams["legend.frameon"] = False + mpl.rcParams["legend.fancybox"] = False + mpl.rcParams["legend.framealpha"] = 0.75 diff --git a/pyrfu/plot/add_position.py b/pyrfu/plot/add_position.py index 0ff077f5..013a0555 100644 --- a/pyrfu/plot/add_position.py +++ b/pyrfu/plot/add_position.py @@ -3,7 +3,6 @@ # 3rd party imports import numpy as np - from matplotlib.dates import num2date # Local imports @@ -11,14 +10,18 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" def add_position( - ax, r_xyz, spine: float = 20, position: str = "top", fontsize: float = 10 + ax, + r_xyz, + spine: float = 20, + position: str = "top", + fontsize: float = 10, ): r"""Add extra axes to plot spacecraft position. @@ -50,7 +53,9 @@ def add_position( ticks_labels = [] for ticks_ in r_ticks: - ticks_labels.append(f"{ticks_[0]:3.2f}\n{ticks_[1]:3.2f}\n{ticks_[2]:3.2f}") + ticks_labels.append( + f"{ticks_[0]:3.2f}\n{ticks_[1]:3.2f}\n{ticks_[2]:3.2f}", + ) axr = ax.twiny() axr.spines[position].set_position(("outward", spine)) diff --git a/pyrfu/plot/annotate_heatmap.py b/pyrfu/plot/annotate_heatmap.py index ea26d404..68324541 100644 --- a/pyrfu/plot/annotate_heatmap.py +++ b/pyrfu/plot/annotate_heatmap.py @@ -6,14 +6,13 @@ # 3rd party imports import numpy as np - from matplotlib import ticker __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -23,7 +22,7 @@ def annotate_heatmap( valfmt: str = "{x:.2f}", textcolors: tuple = ("black", "white"), threshold: float = None, - **textkw + **textkw, ): r"""Annotate a heatmap. @@ -64,7 +63,7 @@ def annotate_heatmap( # Set default alignment to center, but allow it to be # overwritten by textkw. - kw = dict(horizontalalignment="center", verticalalignment="center") + kw = {"horizontalalignment": "center", "verticalalignment": "center"} kw.update(textkw) # Get the formatter in case a string is supplied diff --git a/pyrfu/plot/colorbar.py b/pyrfu/plot/colorbar.py index fefdcbb4..951bbbe4 100644 --- a/pyrfu/plot/colorbar.py +++ b/pyrfu/plot/colorbar.py @@ -6,9 +6,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/plot/make_labels.py b/pyrfu/plot/make_labels.py index bf06446a..3aa75afe 100644 --- a/pyrfu/plot/make_labels.py +++ b/pyrfu/plot/make_labels.py @@ -6,9 +6,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -36,7 +36,7 @@ def make_labels(axs, pos, pad: float = 0, **kwargs): axis.text2D( pos[0], pos[1], - "({})".format(label), + f"({label})", transform=axis.transAxes, **kwargs, ) @@ -44,7 +44,7 @@ def make_labels(axs, pos, pad: float = 0, **kwargs): axis.text( pos[0], pos[1], - "({})".format(label), + f"({label})", transform=axis.transAxes, **kwargs, ) diff --git a/pyrfu/plot/mms_pl_config.py b/pyrfu/plot/mms_pl_config.py index 3672c934..e6aa595f 100644 --- a/pyrfu/plot/mms_pl_config.py +++ b/pyrfu/plot/mms_pl_config.py @@ -1,18 +1,18 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import matplotlib.pyplot as plt + # 3rd party imports import numpy as np -import matplotlib.pyplot as plt __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" -plt.style.use("seaborn-ticks") colors = ["tab:blue", "tab:green", "tab:red", "k"] markers = ["s", "d", "o", "^"] @@ -41,7 +41,15 @@ def mms_pl_config(r_mms): delta_r = r_xyz - np.tile(r_xyz, (4, 1)) fig = plt.figure(figsize=(9, 9)) - gs0 = fig.add_gridspec(3, 3, hspace=0.3, left=0.1, right=0.9, bottom=0.1, top=0.9) + gs0 = fig.add_gridspec( + 3, + 3, + hspace=0.3, + left=0.1, + right=0.9, + bottom=0.1, + top=0.9, + ) gs00 = gs0[0, :].subgridspec(1, 3, wspace=0.35) gs10 = gs0[1:, :].subgridspec(1, 1, wspace=0.35) @@ -62,7 +70,9 @@ def mms_pl_config(r_mms): for ax, idx_, idy_, x_lb, y_lb in zip(axs_, idxs_, idys_, x_lbs, y_lbs): for i, marker in enumerate(markers): ax.scatter( - r_xyz[i, idx_] / r_earth, r_xyz[i, idy_] / r_earth, marker=marker + r_xyz[i, idx_] / r_earth, + r_xyz[i, idy_] / r_earth, + marker=marker, ) ax.add_artist(earth) @@ -75,23 +85,32 @@ def mms_pl_config(r_mms): axs3.view_init(elev=13, azim=-20) for i, marker in enumerate(markers): - options = dict(s=50, marker=marker) + options = {"s": 50, "marker": marker} axs3.scatter(delta_r[i, 0], delta_r[i, 1], delta_r[i, 2], **options) - options = dict(color=colors[i], marker=marker, zdir="z", zs=-30) + options = {"color": colors[i], "marker": marker, "zdir": "z", "zs": -30} axs3.plot([delta_r[i, 0]] * 2, [delta_r[i, 1]] * 2, **options) axs3.plot([delta_r[i, 0]] * 2, [delta_r[i, 2]] * 2, **options) axs3.plot([delta_r[i, 1]] * 2, [delta_r[i, 2]] * 2, **options) - options = dict(color="k", linestyle="--", linewidth=0.5) + options = {"color": "k", "linestyle": "--", "linewidth": 0.5} axs3.plot( - [delta_r[i, 0]] * 2, [delta_r[i, 1]] * 2, [-30, delta_r[i, 2]], **options + [delta_r[i, 0]] * 2, + [delta_r[i, 1]] * 2, + [-30, delta_r[i, 2]], + **options, ) axs3.plot( - [delta_r[i, 0]] * 2, [-30, delta_r[i, 1]], [delta_r[i, 2]] * 2, **options + [delta_r[i, 0]] * 2, + [-30, delta_r[i, 1]], + [delta_r[i, 2]] * 2, + **options, ) axs3.plot( - [-30, delta_r[i, 0]], [delta_r[i, 1]] * 2, [delta_r[i, 2]] * 2, **options + [-30, delta_r[i, 0]], + [delta_r[i, 1]] * 2, + [delta_r[i, 2]] * 2, + **options, ) for idx_0, idx_1 in zip([0, 1, 2, 0, 1, 2], [1, 2, 0, 3, 3, 3]): @@ -105,9 +124,9 @@ def mms_pl_config(r_mms): axs3.set_xlim([-30, 30]) axs3.set_ylim([30, -30]) axs3.set_zlim([-30, 30]) - axs3.set_xlabel("$\\Delta X$ [km]") - axs3.set_ylabel("$\\Delta Y$ [km]") - axs3.set_zlabel("$\\Delta Z$ [km]") + axs3.set_xlabel(r"$\Delta X$ [km]") + axs3.set_ylabel(r"$\Delta Y$ [km]") + axs3.set_zlabel(r"$\Delta Z$ [km]") axs3.legend(["MMS1", "MMS2", "MMS3", "MMS4"], frameon=False) diff --git a/pyrfu/plot/pl_scatter_matrix.py b/pyrfu/plot/pl_scatter_matrix.py index dc0b58d5..df3b668d 100644 --- a/pyrfu/plot/pl_scatter_matrix.py +++ b/pyrfu/plot/pl_scatter_matrix.py @@ -4,9 +4,10 @@ # Built-in imports import warnings +import matplotlib.pyplot as plt + # 3rd party imports import xarray as xr -import matplotlib.pyplot as plt # Local imports from ..pyrf import histogram2d @@ -14,14 +15,17 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" def pl_scatter_matrix( - inp1, inp2: xr.DataArray = None, pdf: bool = False, cmap: str = "jet" + inp1, + inp2: xr.DataArray = None, + pdf: bool = False, + cmap: str = "jet", ): r"""Produces a scatter plot of each components of field inp1 with respect to every component of field inp2. If pdf is set to True, the scatter @@ -57,20 +61,46 @@ def pl_scatter_matrix( assert isinstance(inp1, xr.DataArray) and isinstance(inp2, xr.DataArray) if not pdf: - fig, axs = plt.subplots(3, 3, sharex="all", sharey="all", figsize=(16, 9)) + fig, axs = plt.subplots( + 3, + 3, + sharex="all", + sharey="all", + figsize=(16, 9), + ) fig.subplots_adjust( - left=0.1, right=0.9, bottom=0.1, top=0.9, hspace=0.05, wspace=0.05 + left=0.1, + right=0.9, + bottom=0.1, + top=0.9, + hspace=0.05, + wspace=0.05, ) for i in range(3): for j in range(3): - axs[j, i].scatter(inp1[:, i].data, inp2[:, j].data, marker="+") + axs[j, i].scatter( + inp1[:, i].data, + inp2[:, j].data, + marker="+", + ) out = (fig, axs) else: - fig, axs = plt.subplots(3, 3, sharex="all", sharey="all", figsize=(16, 9)) + fig, axs = plt.subplots( + 3, + 3, + sharex="all", + sharey="all", + figsize=(16, 9), + ) fig.subplots_adjust( - left=0.1, right=0.9, bottom=0.1, top=0.9, hspace=0.05, wspace=0.3 + left=0.1, + right=0.9, + bottom=0.1, + top=0.9, + hspace=0.05, + wspace=0.3, ) caxs = [[None] * 3] * 3 @@ -79,7 +109,10 @@ def pl_scatter_matrix( for j in range(3): hist_ = histogram2d(inp1[:, i], inp2[:, j]) axs[j, i], caxs[j][i] = plot_spectr( - axs[j, i], hist_, cmap=cmap, cscale="log" + axs[j, i], + hist_, + cmap=cmap, + cscale="log", ) axs[j, i].grid() diff --git a/pyrfu/plot/pl_tx.py b/pyrfu/plot/pl_tx.py index 04ab41a3..e43bd6f8 100644 --- a/pyrfu/plot/pl_tx.py +++ b/pyrfu/plot/pl_tx.py @@ -1,23 +1,19 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import matplotlib.dates as mdates +import matplotlib.pyplot as plt + # 3rd party imports import numpy as np -import matplotlib as mpl -import matplotlib.pyplot as plt -import matplotlib.dates as mdates __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" -plt.style.use("seaborn-ticks") -colors_ = ["tab:blue", "tab:green", "tab:red", "k"] -plt.rc("axes", prop_cycle=mpl.cycler(color=colors_)) - def pl_tx(axis, inp_list, comp, **kwargs): r"""Line plot of 4 spacecraft time series. @@ -41,16 +37,21 @@ def pl_tx(axis, inp_list, comp, **kwargs): if axis is None: _, axis = plt.subplots(1) - for inp in inp_list: + colors = ["blue", "green", "red", "k"] + + for inp, color in zip(inp_list, colors): if len(inp.shape) == 3: - data = np.reshape(inp.data, (inp.shape[0], inp.shape[1] * inp.shape[2])) + data = np.reshape( + inp.data, + (inp.shape[0], inp.shape[1] * inp.shape[2]), + ) elif len(inp.shape) == 1: data = inp.data[:, np.newaxis] else: data = inp.data time = inp.time - axis.plot(time, data[:, comp], **kwargs) + axis.plot(time, data[:, comp], color=color, **kwargs) locator = mdates.AutoDateLocator(minticks=3, maxticks=7) formatter = mdates.ConciseDateFormatter(locator) diff --git a/pyrfu/plot/plot_ang_ang.py b/pyrfu/plot/plot_ang_ang.py index 3a4b0e90..c178d95b 100644 --- a/pyrfu/plot/plot_ang_ang.py +++ b/pyrfu/plot/plot_ang_ang.py @@ -5,10 +5,11 @@ import bisect import warnings +import matplotlib.pyplot as plt + # 3rd party imports import numpy as np import xarray as xr -import matplotlib.pyplot as plt # Local imports from ..pyrf import datetime642iso8601, time_clip @@ -16,9 +17,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -44,7 +45,9 @@ def _time_avg(vdf, tint): raise TypeError("Invalid time interval format") vdf_new = xr.DataArray( - vdf_data, coords=[vdf_ener, vdf_azim, vdf_thet], dims=["energy", "phi", "theta"] + vdf_data, + coords=[vdf_ener, vdf_azim, vdf_thet], + dims=["energy", "phi", "theta"], ) return vdf_new @@ -59,14 +62,19 @@ def _energy_avg(vdf, en_range): en_range[1] = np.max(vdf.energy.data[-1], en_range[-1]) idx = np.where( - np.logical_and(vdf.energy.data > en_range[0], vdf.energy.data < en_range[1]) + np.logical_and( + vdf.energy.data > en_range[0], + vdf.energy.data < en_range[1], + ), )[0] assert idx, "Energy range is not covered by the instrument" out_data = np.nanmean(vdf.data[idx, ...], axis=0) out = xr.DataArray( - out_data, coords=[vdf.phi.data, vdf.theta.data], dims=["phi", "theta"] + out_data, + coords=[vdf.phi.data, vdf.theta.data], + dims=["phi", "theta"], ) return out diff --git a/pyrfu/plot/plot_clines.py b/pyrfu/plot/plot_clines.py index 5e10d8f2..51105fcd 100644 --- a/pyrfu/plot/plot_clines.py +++ b/pyrfu/plot/plot_clines.py @@ -1,22 +1,22 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -# 3rd party import -import numpy as np import matplotlib.pyplot as plt +# 3rd party import +import numpy as np from matplotlib.cm import get_cmap -from matplotlib.colors import LogNorm from matplotlib.colorbar import ColorbarBase +from matplotlib.colors import LogNorm # Local imports from .plot_line import plot_line __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/plot/plot_heatmap.py b/pyrfu/plot/plot_heatmap.py index ea29c312..1dc8f795 100644 --- a/pyrfu/plot/plot_heatmap.py +++ b/pyrfu/plot/plot_heatmap.py @@ -3,14 +3,13 @@ # 3rd party imports import numpy as np - from mpl_toolkits.axes_grid1 import make_axes_locatable __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -21,7 +20,7 @@ def plot_heatmap( col_labels, cbar_kw: dict = None, cbarlabel: str = "", - **kwargs + **kwargs, ): r"""Creates a heatmap from a numpy array and two lists of labels. diff --git a/pyrfu/plot/plot_line.py b/pyrfu/plot/plot_line.py index 59ae0954..11372c53 100644 --- a/pyrfu/plot/plot_line.py +++ b/pyrfu/plot/plot_line.py @@ -2,17 +2,18 @@ # -*- coding: utf-8 -*- # 3rd party imports -import numpy as np import matplotlib as mpl -import matplotlib.pyplot as plt import matplotlib.dates as mdates -import matplotlib.ticker as ticker +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker +import numpy as np +import xarray as xr __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -21,30 +22,44 @@ def plot_line(axis, inp, **kwargs): Parameters ---------- - axis : matplotlib.pyplot.subplotsaxes - Axis + axis : matplotlib.axes._axes.Axes + Single axis where to plot inp. If None creates a new figure with a single axis. inp : xarray.DataArray Time series to plot Other Parameters ---------------- **kwargs - Keyword arguments control the Line2D properties. + Keyword arguments control the line properties. See matplotlib.lines.Line2D + for reference. Returns ------- - axs : - Axes. + axs : matplotlib.axes._axes.Axes + Axis with matplotlib.lines.Line2D. """ if axis is None: _, axis = plt.subplots(1) - - if len(inp.shape) == 3: - data = np.reshape(inp.data, (inp.shape[0], inp.shape[1] * inp.shape[2])) else: + if not isinstance(axis, mpl.axes.Axes): + raise TypeError("axis must be a matplotlib.axes._axes.Axes") + + if not isinstance(inp, xr.DataArray): + raise TypeError("inp must be an xarray.DataArray object!") + + if inp.data.ndim < 3: data = inp.data + elif inp.data.ndim == 3: + data = np.reshape( + inp.data, + (inp.shape[0], inp.shape[1] * inp.shape[2]), + ) + else: + raise NotImplementedError( + f"plot_line cannot handle {inp.data.ndim} dimensional data" + ) time = inp.time axis.plot(time, data, **kwargs) @@ -56,6 +71,6 @@ def plot_line(axis, inp, **kwargs): axis.xaxis.set_major_formatter(formatter) axis.grid(True, which="major", linestyle="-", linewidth="0.2", c="0.5") - axis.yaxis.set_major_locator(ticker.MaxNLocator(4)) + axis.yaxis.set_major_locator(mticker.MaxNLocator(4)) return axis diff --git a/pyrfu/plot/plot_magnetosphere.py b/pyrfu/plot/plot_magnetosphere.py index 3e966d56..72b3ce8b 100644 --- a/pyrfu/plot/plot_magnetosphere.py +++ b/pyrfu/plot/plot_magnetosphere.py @@ -6,32 +6,50 @@ # 3rd party imports import numpy as np - from geopack import geopack from matplotlib.patches import Wedge # Local imports -from ..pyrf import magnetosphere, datetime642unix, iso86012datetime64 +from ..pyrf import datetime642unix, iso86012datetime64, magnetosphere __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" logging.captureWarnings(True) logging.basicConfig( - format="%(asctime)s: %(message)s", datefmt="%d-%b-%y %H:%M:%S", level=logging.INFO + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, ) def _add_earth(ax=None, **kwargs): theta1, theta2 = 90.0, 270.0 - nightside_ = Wedge((0.0, 0.0), 1.0, theta1, theta2, fc="k", ec="k", **kwargs) - dayside_ = Wedge((0.0, 0.0), 1.0, theta2, theta1, fc="w", ec="k", **kwargs) + nightside_ = Wedge( + (0.0, 0.0), + 1.0, + theta1, + theta2, + fc="k", + ec="k", + **kwargs, + ) + dayside_ = Wedge( + (0.0, 0.0), + 1.0, + theta2, + theta1, + fc="w", + ec="k", + **kwargs, + ) for wedge in [nightside_, dayside_]: ax.add_artist(wedge) + return [nightside_, dayside_] @@ -40,16 +58,19 @@ def _add_field_lines(ax, tint): ut = datetime642unix(iso86012datetime64(np.array(tint)))[0] _ = geopack.recalc(ut) - x_lines_m, y_lines_m, z_lines_m = [[], [], []] - x_lines_p, y_lines_p, z_lines_p = [[], [], []] + x_lines_m, z_lines_m = [[], []] + x_lines_p, z_lines_p = [[], []] - xx_gsm, zz_gsm = np.meshgrid(np.linspace(-30, 6, 19), np.linspace(-5, 5, 10)) + xx_gsm, zz_gsm = np.meshgrid( + np.linspace(-30, 6, 19), + np.linspace(-5, 5, 10), + ) xx_gsm = np.reshape(xx_gsm, (xx_gsm.size,)) zz_gsm = np.reshape(zz_gsm, (zz_gsm.size,)) for x_gsm, z_gsm in zip(xx_gsm, zz_gsm): y_gsm = 0 - _, _, _, xx, yy, zz = geopack.trace( + _, _, _, xx, _, zz = geopack.trace( x_gsm, y_gsm, z_gsm, @@ -64,7 +85,7 @@ def _add_field_lines(ax, tint): x_lines_m.append(xx) z_lines_m.append(zz) - _, _, _, xx, yy, zz = geopack.trace( + _, _, _, xx, _, zz = geopack.trace( x_gsm, y_gsm, z_gsm, @@ -85,10 +106,13 @@ def _add_field_lines(ax, tint): for xx, zz in zip(x_lines_p, z_lines_p): ax.plot(xx, zz, color="k", linewidth=0.2) - return - -def plot_magnetosphere(ax, tint, colors: list = None, field_lines: bool = True): +def plot_magnetosphere( + ax, + tint, + colors: list = None, + field_lines: bool = True, +): r"""Plot magnetopause, bow shock and earth. Parameters diff --git a/pyrfu/plot/plot_projection.py b/pyrfu/plot/plot_projection.py index cf6e7bc4..38be5063 100644 --- a/pyrfu/plot/plot_projection.py +++ b/pyrfu/plot/plot_projection.py @@ -1,15 +1,16 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import matplotlib.pyplot as plt + # 3rd party imports import numpy as np -import matplotlib.pyplot as plt __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -57,7 +58,12 @@ def plot_projection( clim = [None, None] image = axis.pcolormesh( - v_x / 1e3, v_y / 1e3, np.log10(f_mat.T), cmap=cmap, vmin=clim[0], vmax=clim[1] + v_x / 1e3, + v_y / 1e3, + np.log10(f_mat.T), + cmap=cmap, + vmin=clim[0], + vmax=clim[1], ) axis.set_xlim([-vlim / 1e3, vlim / 1e3]) axis.set_ylim([-vlim / 1e3, vlim / 1e3]) @@ -66,12 +72,21 @@ def plot_projection( f = plt.gcf() pos = axis.get_position() if cbar_pos == "top": - caxis = f.add_axes([pos.x0, pos.y0 + pos.height + 0.01, pos.width, 0.01]) - f.colorbar(mappable=image, cax=caxis, ax=axis, orientation="horizontal") + caxis = f.add_axes( + [pos.x0, pos.y0 + pos.height + 0.01, pos.width, 0.01], + ) + f.colorbar( + mappable=image, + cax=caxis, + ax=axis, + orientation="horizontal", + ) caxis.xaxis.set_ticks_position("top") caxis.xaxis.set_label_position("top") elif cbar_pos == "right": - caxis = f.add_axes([pos.x0 + pos.width + 0.01, pos.y0, 0.01, pos.height]) + caxis = f.add_axes( + [pos.x0 + pos.width + 0.01, pos.y0, 0.01, pos.height], + ) f.colorbar(mappable=image, cax=caxis, ax=axis) else: raise ValueError("invalic position") diff --git a/pyrfu/plot/plot_reduced_2d.py b/pyrfu/plot/plot_reduced_2d.py index 723c2828..9a9806a9 100644 --- a/pyrfu/plot/plot_reduced_2d.py +++ b/pyrfu/plot/plot_reduced_2d.py @@ -1,17 +1,17 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -# 3rd party imports -import numpy as np import matplotlib.pyplot as plt +# 3rd party imports +import numpy as np from matplotlib import colors __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/plot/plot_sitl_overview.py b/pyrfu/plot/plot_sitl_overview.py deleted file mode 100644 index 5efe901f..00000000 --- a/pyrfu/plot/plot_sitl_overview.py +++ /dev/null @@ -1,360 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -# Built-in imports -import os - -# 3rd party imports -import numpy as np -import matplotlib.pyplot as plt -import matplotlib.image as mimg -import matplotlib.dates as mdates - -# Local imports -from ..mms import get_data, get_feeps_omni -from ..pyrf import iso86012datetime64, datetime642iso8601, iso86012datetime, date_str - -from .plot_line import plot_line -from .plot_spectr import plot_spectr -from .plot_magnetosphere import plot_magnetosphere -from .span_tint import span_tint - -__author__ = "Louis Richard" -__email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" -__license__ = "MIT" -__version__ = "2.3.7" -__status__ = "Prototype" - - -def _tcut_edges(tint): - tint = iso86012datetime64(np.array(tint)) - tint[0] += np.timedelta64(1, "s") - tint[1] -= np.timedelta64(1, "s") - tint = list(datetime642iso8601(tint)) - return tint - - -def _get_sclocs(data_rate, tint, mms_id, data_path): - r_xyz = get_data(f"r_gse_mec_{data_rate}_l2", tint, mms_id, data_path=data_path) - return r_xyz - - -def _get_fields(data_rate, tint, mms_id, data_path): - if data_rate == "fast": - data_ratb = "srvy" - else: - data_ratb = data_rate - - b_xyz = get_data(f"b_gse_fgm_{data_ratb}_l2", tint, mms_id, data_path=data_path) - e_xyz = get_data(f"e_gse_edp_{data_rate}_l2", tint, mms_id, data_path=data_path) - return b_xyz, e_xyz - - -def _get_momnts(data_rate, tint, mms_id, data_path): - n_i = get_data(f"ni_fpi_{data_rate}_l2", tint, mms_id, data_path=data_path) - n_e = get_data(f"ne_fpi_{data_rate}_l2", tint, mms_id, data_path=data_path) - - v_xyz_i = get_data(f"vi_gse_fpi_{data_rate}_l2", tint, mms_id, data_path=data_path) - v_xyz_e = get_data(f"ve_gse_fpi_{data_rate}_l2", tint, mms_id, data_path=data_path) - return n_i, n_e, v_xyz_i, v_xyz_e - - -def _get_spectr(data_rate, tint, mms_id, data_path): - if data_rate == "fast": - data_ratp = "srvy" - else: - data_ratp = data_rate - - # FPI-DIS and FPI-DES differential energy flux - def_i = get_data(f"defi_fpi_{data_rate}_l2", tint, mms_id, data_path=data_path) - def_e = get_data(f"defe_fpi_{data_rate}_l2", tint, mms_id, data_path=data_path) - - if data_rate == "brst": - dpf_i = get_feeps_omni( - f"fluxi_{data_ratp}_l2", tint, mms_id, data_path=data_path - ) - dpf_e = get_feeps_omni( - f"fluxe_{data_ratp}_l2", tint, mms_id, data_path=data_path - ) - else: - dpf_i, dpf_e = [None, None] - - return def_i, def_e, dpf_i, dpf_e - - -def _init_fig(): - fig = plt.figure(figsize=(16 * 1.2, 9 * 1.2)) - gsp1 = fig.add_gridspec( - 18, 4, top=0.95, bottom=0.05, left=0.08, right=0.92, wspace=1, hspace=0.1 - ) - - gsp10 = gsp1[:5, :2].subgridspec(1, 2, hspace=0) - gsp11 = gsp1[6:, :2].subgridspec(6, 1, hspace=0) - gsp20 = gsp1[:, 2:].subgridspec(9, 1, hspace=0) - - # Create axes in the grid spec - axs10 = [fig.add_subplot(gsp10[i]) for i in range(2)] - axs11 = [fig.add_subplot(gsp11[i]) for i in range(6)] - axs20 = [fig.add_subplot(gsp20[i]) for i in range(9)] - - return fig, axs10, axs11, axs20 - - -def _plot_scps(axs, r_xyz): - tint = list(datetime642iso8601(r_xyz.time.data[[0, -1]])) - r_avg = np.mean(r_xyz.data / 6371, axis=0) - - field_lines = [False, False] - for i, y_axis in enumerate(["$Y$ [$R_E$]", "$Z$ [$R_E$]"]): - plot_magnetosphere(axs[i], tint, field_lines=field_lines[i]) - axs[i].invert_xaxis() - axs[i].plot( - r_avg[0], - r_avg[i + 1], - marker="^", - color="tab:red", - linestyle="", - label="MMS", - ) - axs[i].set_xlim([-30, 25]) - axs[i].set_ylim([-25, 25]) - axs[i].set_aspect("equal") - axs[i].set_xlabel("$X$ [$R_E$]") - axs[i].set_ylabel(y_axis) - axs[i].invert_xaxis() - - return axs - - -def _plot_fast(axs, fields, momnts, spectr): - b_xyz, _ = fields - n_i, n_e, v_xyz_i, v_xyz_e = momnts - def_i, def_e, _, _ = spectr - - plot_line(axs[0], b_xyz) - axs[0].set_ylabel("$B$" + "\n" + "[nT]") - axs[0].legend( - ["$B_x$", "$B_y$", "$B_z$"], - frameon=False, - loc="upper left", - bbox_to_anchor=(1.0, 1.0), - ) - - plot_line(axs[1], n_i) - plot_line(axs[1], n_e) - axs[1].set_ylabel("$n$" + "\n" + "[cm$^{-3}$]") - axs[1].legend( - ["$n_{i}$", "$n_{e}$"], - frameon=False, - loc="upper left", - bbox_to_anchor=(1.0, 1.0), - ) - - plot_line(axs[2], v_xyz_i) - axs[2].set_ylabel("$V_i$" + "\n" + "[km s$^{-1}$]") - axs[2].legend( - ["$V_{i,x}$", "$V_{i,y}$", "$V_{i,z}$"], - frameon=False, - loc="upper left", - bbox_to_anchor=(1.0, 1.0), - ) - - axs[3], caxs3 = plot_spectr(axs[3], def_i, yscale="log", cscale="log") - axs[3].set_ylabel("$E_i$" + "\n" + "[eV]") - caxs3.set_ylabel("DEF" + "\n" + "[(cm$^2$ s sr)$^{-1}$]") - - plot_line(axs[4], v_xyz_e) - axs[4].set_ylabel("$V_e$" + "\n" + "[km s$^{-1}$]") - axs[4].legend( - ["$V_{e,x}$", "$V_{e,y}$", "$V_{e,z}$"], - frameon=False, - loc="upper left", - bbox_to_anchor=(1.0, 1.0), - ) - - axs[5], caxs5 = plot_spectr(axs[5], def_e, yscale="log", cscale="log") - axs[5].set_ylabel("$E_e$" + "\n" + "[eV]") - caxs5.set_ylabel("DEF" + "\n" + "[(cm$^2$ s sr)$^{-1}$]") - - axs[-1].get_shared_x_axes().join(*axs) - - for ax in axs[:-1]: - ax.xaxis.set_ticklabels([]) - - return axs - - -def _plot_brst(axs, fields, momnts, spectr): - b_xyz, e_xyz = fields - n_i, n_e, v_xyz_i, v_xyz_e = momnts - def_i, def_e, dpf_i, dpf_e = spectr - - plot_line(axs[0], b_xyz) - axs[0].set_ylabel("$B$" + "\n" + "[nT]") - axs[0].legend( - ["$B_x$", "$B_y$", "$B_z$"], - frameon=False, - loc="upper left", - bbox_to_anchor=(1.0, 1.0), - ) - - plot_line(axs[1], n_i) - plot_line(axs[1], n_e) - axs[1].set_ylabel("$n$" + "\n" + "[cm$^{-3}$]") - axs[1].legend( - ["$n_{i}$", "$n_{e}$"], - frameon=False, - loc="upper left", - bbox_to_anchor=(1.0, 1.0), - ) - - plot_line(axs[2], v_xyz_i) - axs[2].set_ylabel("$V_i$" + "\n" + "[km s$^{-1}$]") - axs[2].legend( - ["$V_{i,x}$", "$V_{i,y}$", "$V_{i,z}$"], - frameon=False, - loc="upper left", - bbox_to_anchor=(1.0, 1.0), - ) - - axs[3], caxs3 = plot_spectr(axs[3], dpf_i[:, 1:], yscale="log", cscale="log") - axs[3].set_ylabel("$E_i$" + "\n" + "[keV]") - caxs3.set_ylabel("Intensity" + "\n" + "[(cm$^2$ s sr keV)$^{-1}$]") - - axs[4], caxs4 = plot_spectr(axs[4], def_i, yscale="log", cscale="log") - axs[4].set_ylabel("$E_i$" + "\n" + "[eV]") - caxs4.set_ylabel("DEF" + "\n" + "[(cm$^2$ s sr)$^{-1}$]") - - plot_line(axs[5], v_xyz_e) - axs[5].set_ylabel("$V_e$" + "\n" + "[km s$^{-1}$]") - axs[5].legend( - ["$V_{e,x}$", "$V_{e,y}$", "$V_{e,z}$"], - frameon=False, - loc="upper left", - bbox_to_anchor=(1.0, 1.0), - ) - - axs[6], caxs6 = plot_spectr(axs[6], dpf_e[:, 1:], yscale="log", cscale="log") - axs[6].set_ylabel("$E_e$" + "\n" + "[keV]") - caxs6.set_ylabel("Intensity" + "\n" + "[(cm$^2$ s sr keV)$^{-1}$]") - - axs[7], caxs7 = plot_spectr(axs[7], def_e, yscale="log", cscale="log") - axs[7].set_ylabel("$E_e$" + "\n" + "[eV]") - caxs7.set_ylabel("DEF" + "\n" + "[(cm$^2$ s sr)$^{-1}$]") - - plot_line(axs[8], e_xyz) - axs[8].set_ylabel("$E$" + "\n" + "[mV m$^{-1}$]") - axs[8].legend( - ["$E_x$", "$E_y$", "$E_z$"], - frameon=False, - loc="upper left", - bbox_to_anchor=(1.0, 1.0), - ) - - axs[-1].get_shared_x_axes().join(*axs) - - for ax in axs[:-1]: - ax.xaxis.set_ticklabels([]) - - return axs - - -def _add_logo(fig, path, loc=None): - if loc is None: - loc = [-0.015, 0.885, 0.1, 0.1] - im = mimg.imread(path) - # put a new axes where you want the image to appear - # (x, y, width, height) - imax = fig.add_axes(loc) - # remove ticks & the box from imax - imax.set_axis_off() - # print the logo with aspect="equal" to avoid distorting the logo - imax.imshow(im, aspect="equal") - - -def plot_sitl_overview( - tint_brst, - title, - mms_id: int = 2, - data_path: str = "/Volumes/mms", - fig_path: str = "figures", -): - r"""Creates overview plot from SITL selections. - - Paramters - --------- - tint_brst : list - Time interval selected. - mms_id : int, Optional - Spacecraft index. Default is 1. - data_path : str, Optional - Path to MMS data. Default is /Volumes/ - - Returns - ------- - fig : matplotlib.figure - Figure. - axs : list - All axes. - - """ - - file_name = "IRF_logo_blue_on_white.jpg" - logo_path = os.sep.join( - [os.path.dirname(os.path.abspath(__file__)), "logo", file_name] - ) - - tint_dt = iso86012datetime(np.array(tint_brst)) - t_start = np.datetime64( - f"{tint_brst[0][:11]}{tint_dt[0].hour - tint_dt[0].hour % 2:02}:00:00" - ) - - if t_start + np.timedelta64(2, "h") < np.datetime64(tint_brst[1]): - tint_tmp0 = np.array([tint_brst[0], t_start + np.timedelta64(2, "h")]) - tint_tmp1 = np.array([tint_tmp0[1], np.datetime64(tint_brst[1])]) - plot_sitl_overview( - list(datetime642iso8601(tint_tmp0)), title, mms_id, data_path - ) - plot_sitl_overview( - list(datetime642iso8601(tint_tmp1)), title, mms_id, data_path - ) - else: - tint_fast = np.array([t_start, t_start + np.timedelta64(2, "h")]) - tint_fast = list(datetime642iso8601(tint_fast)) - tint_fast = _tcut_edges(tint_fast) - tint_brst = _tcut_edges(tint_brst) - - r_xyz = _get_sclocs("srvy", tint_fast, mms_id, data_path) - - fields_fast = _get_fields("fast", tint_fast, mms_id, data_path) - fields_brst = _get_fields("brst", tint_brst, mms_id, data_path) - - momnts_fast = _get_momnts("fast", tint_fast, mms_id, data_path) - momnts_brst = _get_momnts("brst", tint_brst, mms_id, data_path) - - spectr_fast = _get_spectr("fast", tint_fast, mms_id, data_path) - spectr_brst = _get_spectr("brst", tint_brst, mms_id, data_path) - - fig, axs10, axs11, axs20 = _init_fig() - _ = _plot_scps(axs10, r_xyz) - axs11 = _plot_fast(axs11, fields_fast, momnts_fast, spectr_fast) - axs20 = _plot_brst(axs20, fields_brst, momnts_brst, spectr_brst) - - axs11[-1].set_xlim(mdates.datestr2num(tint_fast)) - axs20[-1].set_xlim(mdates.datestr2num(tint_brst)) - fig.align_ylabels(axs11) - fig.align_ylabels(axs20) - - span_tint(axs11, tint_brst, ec="k", fc="tab:purple", alpha=0.2) - - fig.suptitle(title) - - _add_logo(fig, logo_path) - - tint_iso = datetime642iso8601(iso86012datetime64(np.array(tint_brst))) - pref = date_str([f"{t_[:-3]}" for t_ in list(tint_iso)], 4) - fig.savefig(os.path.join(fig_path, f"{pref}_mms{mms_id}_overview.png")) - - # return fig, [*axs10, *axs11, *axs20] - return diff --git a/pyrfu/plot/plot_spectr.py b/pyrfu/plot/plot_spectr.py index a882ec0b..750cac14 100644 --- a/pyrfu/plot/plot_spectr.py +++ b/pyrfu/plot/plot_spectr.py @@ -2,20 +2,16 @@ # -*- coding: utf-8 -*- # 3rd party imports +import matplotlib as mpl import matplotlib.pyplot as plt -import matplotlib.dates as mdates -import matplotlib.colors as mcolors -import matplotlib.ticker as ticker __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.11" +__version__ = "2.4.2" __status__ = "Prototype" -plt.style.use("seaborn-ticks") - def plot_spectr( axis, @@ -23,9 +19,9 @@ def plot_spectr( yscale: str = "linear", cscale: str = "linear", clim: list = None, - cmap: str = "", - colorbar: bool = True, - **kwargs + cmap: str = None, + colorbar: str = "right", + **kwargs, ): r"""Plot a spectrogram using pcolormesh. @@ -43,8 +39,9 @@ def plot_spectr( C-axis bounds. Default is None (autolim). cmap : str, Optional Colormap. Default is "jet". - colorbar : bool, Optional - Flag for colorbar. Set to False to hide. + colorbar : str, Optional + Location of the colorbar with respect to the axis. + Set to "none" to hide. Other Parameters ---------------- @@ -67,56 +64,110 @@ def plot_spectr( else: fig = plt.gcf() - if not cmap: - cmap = "jet" + if not cmap or isinstance(cmap, str): + cmap = mpl.cm.get_cmap(cmap) + else: + raise TypeError( + "cmap must be a string. " + "To add a custom colormap use mpl.colormaps.register(custom)." + ) if cscale == "log": if clim is not None and isinstance(clim, list): - options = dict(norm=mcolors.LogNorm(vmin=clim[0], vmax=clim[1]), cmap=cmap) + options = { + "norm": mpl.colors.LogNorm(vmin=clim[0], vmax=clim[1]), + "cmap": cmap, + } else: - options = dict(norm=mcolors.LogNorm(), cmap=cmap) + options = {"norm": mpl.colors.LogNorm(), "cmap": cmap} else: if clim is not None and isinstance(clim, list): - options = dict(cmap=cmap, vmin=clim[0], vmax=clim[1]) + options = {"cmap": cmap, "vmin": clim[0], "vmax": clim[1]} else: - options = dict(cmap=cmap, vmin=None, vmax=None) + options = {"cmap": cmap, "vmin": None, "vmax": None} x_data, y_data = [inp.coords[inp.dims[0]], inp.coords[inp.dims[1]]] image = axis.pcolormesh( - x_data.data, y_data.data, inp.data.T, rasterized=True, shading="auto", **options + x_data.data, + y_data.data, + inp.data.T, + rasterized=True, + shading="auto", + **options, ) if x_data.dtype == "= n or maxlags < 1: - raise ValueError(f"maxlags must be None or strictly positive < {n:d}") + if maxlags >= n_t or maxlags < 1: + raise ValueError(f"maxlags must be None or strictly positive < {n_t:d}") - lags = np.arange(-maxlags, maxlags + 1) + lags = np.linspace(-float(maxlags), float(maxlags), 2 * maxlags + 1, dtype=int) lags = lags * calc_dt(inp) - out_data = np.zeros_like(x) + out_data = np.zeros((maxlags + 1, x.shape[1])) for i in range(x.shape[1]): correls = np.correlate(x[:, i], x[:, i], mode="full") @@ -59,18 +65,20 @@ def autocorr(inp, maxlags: int = None, normed: bool = True): if normed: correls /= np.sqrt(np.dot(x[:, i], x[:, i]) ** 2) - correls = correls[n - 1 - maxlags : n + maxlags] + correls = correls[n_t - 1 - maxlags : n_t + maxlags] out_data[:, i] = correls[lags >= 0] if inp.ndim == 1: - out = xr.DataArray(np.squeeze(out_data), coords=[lags[lags >= 0]], dims=["lag"]) - elif inp.ndim == 2: + out = xr.DataArray( + np.squeeze(out_data), + coords=[lags[lags >= 0]], + dims=["lag"], + ) + else: out = xr.DataArray( out_data, coords=[lags[lags >= 0], inp[inp.dims[1]].data], dims=["lag", inp.dims[1]], ) - else: - raise ValueError("invalid shape!!") return out diff --git a/pyrfu/pyrf/average_vdf.py b/pyrfu/pyrf/average_vdf.py index 6f888055..c4ac9737 100644 --- a/pyrfu/pyrf/average_vdf.py +++ b/pyrfu/pyrf/average_vdf.py @@ -12,11 +12,11 @@ __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.26" +__version__ = "2.4.2" __status__ = "Prototype" -def average_vdf(vdf, n_pts): +def average_vdf(vdf, n_pts, method: str = "mean"): r"""Time averages the velocity distribution functions over `n_pts` in time. Parameters @@ -25,6 +25,8 @@ def average_vdf(vdf, n_pts): Time series of the velocity distribution function. n_pts : int Number of points (samples) of the averaging window. + method : {'mean', 'sum'}, Optional + Method for averaging. Use 'sum' for counts. Default is 'mean'. Returns ------- @@ -33,9 +35,10 @@ def average_vdf(vdf, n_pts): """ - assert n_pts % 2 != 0, "The number of distributions to be averaged must be an odd" + # Check input type + assert isinstance(vdf, xr.Dataset), "vdf must be a xarray.Dataset" - assert np.median(vdf.energy.data[0, :] - vdf.energy.data[0, :]) == 0 + assert n_pts % 2 != 0, "The number of distributions to be averaged must be an odd" n_vdf = len(vdf.time.data) times = vdf.time.data @@ -51,9 +54,21 @@ def average_vdf(vdf, n_pts): for i, avg_ind in enumerate(avg_inds): l_bound = int(avg_ind - pad_value) r_bound = int(avg_ind + pad_value) - vdf_avg[i, ...] = np.nanmean(vdf.data.data[l_bound:r_bound, ...], axis=0) - energy_avg[i, ...] = np.nanmean(vdf.energy.data[l_bound:r_bound, ...], axis=0) - phi_avg[i, ...] = np.nanmean(vdf.phi.data[l_bound:r_bound, ...], axis=0) + if method == "mean": + vdf_avg[i, ...] = np.nanmean(vdf.data.data[l_bound:r_bound, ...], axis=0) + elif method == "sum": + vdf_avg[i, ...] = np.nansum(vdf.data.data[l_bound:r_bound, ...], axis=0) + else: + raise NotImplementedError("method not implemented feel free to do it!!") + + energy_avg[i, ...] = np.nanmean( + vdf.energy.data[l_bound:r_bound, ...], + axis=0, + ) + phi_avg[i, ...] = np.nanmean( + vdf.phi.data[l_bound:r_bound, ...], + axis=0, + ) # Attributes glob_attrs = vdf.attrs # Global attributes @@ -61,8 +76,11 @@ def average_vdf(vdf, n_pts): coords_attrs = {k: vdf[k].attrs for k in ["time", "energy", "phi", "theta"]} # Get delta energy in global attributes for selected timestamps - glob_attrs["delta_energy_minus"] = glob_attrs["delta_energy_minus"][avg_inds] - glob_attrs["delta_energy_plus"] = glob_attrs["delta_energy_plus"][avg_inds] + if "delta_energy_minus" in glob_attrs: + glob_attrs["delta_energy_minus"] = glob_attrs["delta_energy_minus"][avg_inds, :] + + if "delta_energy_plus" in glob_attrs: + glob_attrs["delta_energy_plus"] = glob_attrs["delta_energy_plus"][avg_inds, :] glob_attrs["esteptable"] = glob_attrs["esteptable"][: len(avg_inds)] diff --git a/pyrfu/pyrf/avg_4sc.py b/pyrfu/pyrf/avg_4sc.py index 468794bd..95d6e7ab 100644 --- a/pyrfu/pyrf/avg_4sc.py +++ b/pyrfu/pyrf/avg_4sc.py @@ -1,15 +1,18 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +# 3rd party imports +import xarray as xr + # Local imports from .calc_fs import calc_fs from .resample import resample __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -44,8 +47,18 @@ def avg_4sc(b_list): """ - b_list = [resample(b, b_list[0], f_s=calc_fs(b_list[0])) for b in b_list] + # Check input type + assert isinstance(b_list, list), "b_list must be a list" + + b_list_r = [] + + for b in b_list: + if isinstance(b, (xr.DataArray, xr.Dataset)): + b_list_r.append(resample(b, b_list[0], f_s=calc_fs(b_list[0]))) + else: + raise TypeError("elements of b_list must be DataArray or Dataset") - b_avg = sum(b_list) / len(b_list) + # Average the resamples time series + b_avg = sum(b_list_r) / len(b_list_r) return b_avg diff --git a/pyrfu/pyrf/c_4_grad.py b/pyrfu/pyrf/c_4_grad.py index a53c859e..26336917 100644 --- a/pyrfu/pyrf/c_4_grad.py +++ b/pyrfu/pyrf/c_4_grad.py @@ -8,19 +8,20 @@ import numpy as np import xarray as xr -# Local imports -from .resample import resample -from .c_4_k import c_4_k -from .normalize import normalize from .avg_4sc import avg_4sc -from .dot import dot +from .c_4_k import c_4_k from .cross import cross +from .dot import dot +from .normalize import normalize + +# Local imports +from .resample import resample __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -30,19 +31,18 @@ def _to_ts(out_data, b_dict): elif len(out_data.shape) == 2: out = xr.DataArray( - out_data, coords=[b_dict["1"].time, ["x", "y", "z"]], dims=["time", "comp"] + out_data, + coords=[b_dict["1"].time, ["x", "y", "z"]], + dims=["time", "comp"], ) - elif len(out_data.shape) == 3: + else: out = xr.DataArray( out_data, coords=[b_dict["1"].time, ["x", "y", "z"], ["x", "y", "z"]], dims=["time", "vcomp", "hcomp"], ) - else: - raise TypeError("Invalid type") - return out @@ -104,6 +104,12 @@ def c_4_grad(r_list, b_list, method: str = "grad"): """ + assert isinstance(r_list, list) and len(r_list) == 4, "r_list must a list of s/c" + assert isinstance(b_list, list) and len(b_list) == 4, "b_list must a list of s/c" + + assert isinstance(method, str), "method must be a string" + assert method.lower() in ["grad", "div", "curl", "bdivb", "curv"], "Invalid method" + # Resample with respect to 1st spacecraft r_list = [resample(r, b_list[0]) for r in r_list] b_list = [resample(b, b_list[0]) for b in b_list] @@ -158,14 +164,11 @@ def c_4_grad(r_list, b_list, method: str = "grad"): out_data[:, i] = np.sum(b_avg.data * grad_b[:, i, :], axis=1) # Curvature - elif method.lower() == "curv": + else: b_hat_list = [normalize(b) for b in b_list] out_data = c_4_grad(r_list, b_hat_list, method="bdivb").data - else: - raise ValueError("Invalid method") - out = _to_ts(out_data, b_dict) return out diff --git a/pyrfu/pyrf/c_4_j.py b/pyrfu/pyrf/c_4_j.py index d9f25786..4a79d8f3 100644 --- a/pyrfu/pyrf/c_4_j.py +++ b/pyrfu/pyrf/c_4_j.py @@ -11,15 +11,15 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.10" +__version__ = "2.4.2" __status__ = "Prototype" def c_4_j(r_list, b_list): r"""Calculate current density :math:`J` from using 4 - spacecraft technique [4]_, the divergence of the magnetic field + spacecraft technique [1]_, the divergence of the magnetic field :math:`\nabla . B`, magnetic field at the center of mass of the tetrahedron, :math:`J \times B` force, part of the divergence of stress associated with @@ -37,7 +37,7 @@ def c_4_j(r_list, b_list): \nabla P_b = \nabla \frac{B^2}{2\mu_0} The divergence of the magnetic field is current density units as - it shows the error on the estimation of the current density [5]_ . + it shows the error on the estimation of the current density [2]_ . Parameters ---------- @@ -73,12 +73,12 @@ def c_4_j(r_list, b_list): References ---------- - .. [4] Dunlop, M. W., A. Balogh, K.-H. Glassmeier, and P. Robert + .. [1] Dunlop, M. W., A. Balogh, K.-H. Glassmeier, and P. Robert (2002a), Four-point Cluster application of magnetic field analysis tools: The Curl- ometer, J. Geophys. Res., 107(A11), 1384, doi : https://doi.org/10.1029/2001JA005088. - .. [5] Robert, P., et al. (1998), Accuracy of current determination, + .. [2] Robert, P., et al. (1998), Accuracy of current determination, in Analysis Methods for Multi-Spacecraft Data, edited by G. Paschmann and P. W. Daly, pp. 395–418, Int. Space Sci. Inst., Bern. url : http://www.issibern.ch/forads/sr-001-16.pdf @@ -98,17 +98,17 @@ def c_4_j(r_list, b_list): Load magnetic field and spacecraft position - >>> b_mms = [mms.get_data("B_gse_fgm_srvy_l2", tint, i) for i in mms_list] - >>> r_mms = [mms.get_data("R_gse", tint, i) for i in mms_list] + >>> b_mms = [mms.get_data("b_gse_fgm_srvy_l2", tint, i) for i in mms_list] + >>> r_mms = [mms.get_data("r_gse_mec_srvy_l2", tint, i) for i in mms_list] Compute current density, divergence of b, etc. using curlometer technique - >>> j_xyz, _, b_xyz, _, _, _ = pyrf.c_4_j(r_mms, - b_mms) + >>> j_xyz, _, b_xyz, _, _, _ = pyrf.c_4_j(r_mms, b_mms) """ - mu0 = constants.mu_0 + assert isinstance(r_list, list) and len(r_list) == 4, "r_list must a list of s/c" + assert isinstance(b_list, list) and len(b_list) == 4, "b_list must a list of s/c" b_avg = 1e-9 * avg_4sc(b_list) @@ -116,18 +116,18 @@ def c_4_j(r_list, b_list): div_b = c_4_grad(r_list, b_list, "div") # to get right units - div_b *= 1.0e-3 * 1e-9 / mu0 + div_b *= 1.0e-3 * 1e-9 / constants.mu_0 # estimate current j [A/m2] j = c_4_grad(r_list, b_list, "curl") - j.data *= 1.0e-3 * 1e-9 / mu0 + j.data *= 1.0e-3 * 1e-9 / constants.mu_0 # estimate jxB force [T A/m2] jxb = cross(j, b_avg) # estimate divTshear = (1/muo) (B*div)B [T A/m2] div_t_shear = c_4_grad(r_list, b_list, "bdivb") - div_t_shear.data *= 1.0e-3 * 1e-9 * 1e-9 / mu0 + div_t_shear.data *= 1.0e-3 * 1e-9 * 1e-9 / constants.mu_0 # estimate divPb = (1/muo) grad (B^2/2) = divTshear-jxB div_pb = div_t_shear.copy() diff --git a/pyrfu/pyrf/c_4_k.py b/pyrfu/pyrf/c_4_k.py index 47dca74f..f90fd598 100644 --- a/pyrfu/pyrf/c_4_k.py +++ b/pyrfu/pyrf/c_4_k.py @@ -10,9 +10,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -45,10 +45,14 @@ def c_4_k(r_list): mms_list_r3 = np.roll(mms_list, 3) for i, alpha, beta, gamma in zip( - mms_list_r0, mms_list_r1, mms_list_r2, mms_list_r3 + mms_list_r0, + mms_list_r1, + mms_list_r2, + mms_list_r3, ): dr_jk_x_dr_jm = cross( - r_list[beta] - r_list[alpha], r_list[gamma] - r_list[alpha] + r_list[beta] - r_list[alpha], + r_list[gamma] - r_list[alpha], ) dr12 = r_list[i] - r_list[alpha] diff --git a/pyrfu/pyrf/c_4_v.py b/pyrfu/pyrf/c_4_v.py index b9339513..59ad6990 100644 --- a/pyrfu/pyrf/c_4_v.py +++ b/pyrfu/pyrf/c_4_v.py @@ -3,14 +3,13 @@ # 3rd party imports import numpy as np - from scipy import interpolate __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -21,12 +20,18 @@ def _get_vol_ten(r_xyz, time): tckr_x, tckr_y, tckr_z = [[], [], []] for i in range(4): - tckr_x.append(interpolate.interp1d(r_xyz[i].time.data, r_xyz[i].data[:, 0])) - tckr_y.append(interpolate.interp1d(r_xyz[i].time.data, r_xyz[i].data[:, 1])) - tckr_z.append(interpolate.interp1d(r_xyz[i].time.data, r_xyz[i].data[:, 2])) + tckr_x.append( + interpolate.interp1d(r_xyz[i].time.data, r_xyz[i].data[:, 0]), + ) + tckr_y.append( + interpolate.interp1d(r_xyz[i].time.data, r_xyz[i].data[:, 1]), + ) + tckr_z.append( + interpolate.interp1d(r_xyz[i].time.data, r_xyz[i].data[:, 2]), + ) r_xyz[i] = np.array( - [tckr_x[i](time[0]), tckr_y[i](time[0]), tckr_z[i](time[0])] + [tckr_x[i](time[0]), tckr_y[i](time[0]), tckr_z[i](time[0])], ) # Volumetric tensor with SC1 as center. diff --git a/pyrfu/pyrf/calc_ag.py b/pyrfu/pyrf/calc_ag.py index 63e11bd8..18fca9f0 100644 --- a/pyrfu/pyrf/calc_ag.py +++ b/pyrfu/pyrf/calc_ag.py @@ -3,12 +3,16 @@ # 3rd party imports import numpy as np +import xarray as xr + +# Local imports +from .ts_scalar import ts_scalar __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -67,9 +71,16 @@ def calc_ag(p_xyz): """ + # Check input type + assert isinstance(p_xyz, xr.DataArray), "p_xyz must be a xarray.DataArray" + + # Check import shape + message = "p_xyz must be a time series of a tensor" + assert p_xyz.data.ndim == 3 and p_xyz.shape[1] == 3 and p_xyz.shape[2] == 3, message + # Diagonal and off-diagonal terms - p_11, p_22, _ = [p_xyz[:, 0, 0], p_xyz[:, 1, 1], p_xyz[:, 2, 2]] - p_12, p_13, p_23 = [p_xyz[:, 0, 1], p_xyz[:, 0, 2], p_xyz[:, 1, 2]] + p_11, p_22, _ = [p_xyz.data[:, 0, 0], p_xyz.data[:, 1, 1], p_xyz.data[:, 2, 2]] + p_12, p_13, p_23 = [p_xyz.data[:, 0, 1], p_xyz.data[:, 0, 2], p_xyz.data[:, 1, 2]] det_p = p_11 * (p_22**2 - p_23**2) det_p -= p_12 * (p_12 * p_22 - p_23 * p_13) @@ -78,5 +89,6 @@ def calc_ag(p_xyz): det_g = p_11 * p_22**2 agyrotropy = np.abs(det_p - det_g) / (det_p + det_g) + agyrotropy = ts_scalar(p_xyz.time.data, agyrotropy) return agyrotropy diff --git a/pyrfu/pyrf/calc_agyro.py b/pyrfu/pyrf/calc_agyro.py index 641a8182..f5459544 100644 --- a/pyrfu/pyrf/calc_agyro.py +++ b/pyrfu/pyrf/calc_agyro.py @@ -3,12 +3,16 @@ # 3rd party imports import numpy as np +import xarray as xr + +# Local imports +from .ts_scalar import ts_scalar __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -58,9 +62,17 @@ def calc_agyro(p_xyz): """ + # Check input type + assert isinstance(p_xyz, xr.DataArray), "p_xyz must be a xarray.DataArray" + + # Check import shape + message = "p_xyz must be a time series of a tensor" + assert p_xyz.data.ndim == 3 and p_xyz.shape[1] == 3 and p_xyz.shape[2] == 3, message + # Parallel and perpendicular components - p_perp_1, p_perp_2 = [p_xyz[:, 1, 1], p_xyz[:, 2, 2]] + p_perp_1, p_perp_2 = [p_xyz.data[:, 1, 1], p_xyz.data[:, 2, 2]] - agyro = np.abs(p_perp_1 - p_perp_2) / (p_perp_1 + p_perp_2) + agyrotropy = np.abs(p_perp_1 - p_perp_2) / (p_perp_1 + p_perp_2) + agyrotropy = ts_scalar(p_xyz.time.data, agyrotropy) - return agyro + return agyrotropy diff --git a/pyrfu/pyrf/calc_dng.py b/pyrfu/pyrf/calc_dng.py index d8de7fe8..b5ca1e83 100644 --- a/pyrfu/pyrf/calc_dng.py +++ b/pyrfu/pyrf/calc_dng.py @@ -3,12 +3,16 @@ # 3rd party imports import numpy as np +import xarray as xr + +# Local imports +from .ts_scalar import ts_scalar __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -65,13 +69,22 @@ def calc_dng(p_xyz): """ + # Check input type + assert isinstance(p_xyz, xr.DataArray), "p_xyz must be a xarray.DataArray" + + # Check import shape + message = "p_xyz must be a time series of a tensor" + assert p_xyz.data.ndim == 3 and p_xyz.shape[1] == 3 and p_xyz.shape[2] == 3, message + # Parallel and perpendicular components - p_para, p_perp = [p_xyz[:, 0, 0], (p_xyz[:, 1, 1] + p_xyz[:, 2, 2]) / 2] + p_para = p_xyz.data[:, 0, 0] + p_perp = (p_xyz.data[:, 1, 1] + p_xyz.data[:, 2, 2]) / 2 # Off-diagonal terms - p_12, p_13, p_23 = [p_xyz[:, 0, 1], p_xyz[:, 0, 2], p_xyz[:, 1, 2]] + p_12, p_13, p_23 = [p_xyz.data[:, 0, 1], p_xyz.data[:, 0, 2], p_xyz.data[:, 1, 2]] d_ng = np.sqrt(8 * (p_12**2 + p_13**2 + p_23**2)) d_ng /= p_para + 2 * p_perp + d_ng = ts_scalar(p_xyz.time.data, d_ng) return d_ng diff --git a/pyrfu/pyrf/calc_dt.py b/pyrfu/pyrf/calc_dt.py index 80e8957d..62ca9768 100644 --- a/pyrfu/pyrf/calc_dt.py +++ b/pyrfu/pyrf/calc_dt.py @@ -3,12 +3,13 @@ # 3rd party imports import numpy as np +import xarray as xr __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -17,7 +18,7 @@ def calc_dt(inp): Parameters ---------- - inp : xarray.DataArray + inp : xarray.DataArray or xarray.Dataset Time series of the input variable. Returns @@ -27,6 +28,9 @@ def calc_dt(inp): """ + message = "Input must be a time series" + assert isinstance(inp, (xr.Dataset, xr.DataArray)), message + out = np.median(np.diff(inp.time.data)).astype(np.float64) * 1e-9 return out diff --git a/pyrfu/pyrf/calc_fs.py b/pyrfu/pyrf/calc_fs.py index 40f304cb..a06c0cfe 100644 --- a/pyrfu/pyrf/calc_fs.py +++ b/pyrfu/pyrf/calc_fs.py @@ -3,12 +3,13 @@ # 3rd party imports import numpy as np +import xarray as xr __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -17,7 +18,7 @@ def calc_fs(inp): Parameters ---------- - inp : xarray.DataArray + inp : xarray.DataArray or xarray.Dataset Time series of the input variable. Returns @@ -27,6 +28,9 @@ def calc_fs(inp): """ + message = "Input must be a time series" + assert isinstance(inp, (xr.Dataset, xr.DataArray)), message + out = 1 / (np.median(np.diff(inp.time.data)).astype(np.float64) * 1e-9) return out diff --git a/pyrfu/pyrf/calc_sqrtq.py b/pyrfu/pyrf/calc_sqrtq.py index 1a758c8c..dbeada59 100644 --- a/pyrfu/pyrf/calc_sqrtq.py +++ b/pyrfu/pyrf/calc_sqrtq.py @@ -3,12 +3,16 @@ # 3rd party imports import numpy as np +import xarray as xr + +# Local imports +from .ts_scalar import ts_scalar __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -64,13 +68,22 @@ def calc_sqrtq(p_xyz): """ + # Check input type + assert isinstance(p_xyz, xr.DataArray), "p_xyz must be a xarray.DataArray" + + # Check import shape + message = "p_xyz must be a time series of a tensor" + assert p_xyz.data.ndim == 3 and p_xyz.shape[1] == 3 and p_xyz.shape[2] == 3, message + # Parallel and perpendicular components - p_para, p_perp = [p_xyz[:, 0, 0], (p_xyz[:, 1, 1] + p_xyz[:, 2, 2]) / 2] + p_para = p_xyz.data[:, 0, 0] + p_perp = (p_xyz.data[:, 1, 1] + p_xyz.data[:, 2, 2]) / 2 # Off-diagonal terms - p_12, p_13, p_23 = [p_xyz[:, 0, 1], p_xyz[:, 0, 2], p_xyz[:, 1, 2]] + p_12, p_13, p_23 = [p_xyz.data[:, 0, 1], p_xyz.data[:, 0, 2], p_xyz.data[:, 1, 2]] sqrt_q = np.sqrt(p_12**2 + p_13**2 + p_23**2) sqrt_q /= np.sqrt(p_perp**2 + 2 * p_perp * p_para) + sqrt_q = ts_scalar(p_xyz.time.data, sqrt_q) return sqrt_q diff --git a/pyrfu/pyrf/cart2sph.py b/pyrfu/pyrf/cart2sph.py index 8224a089..ca10f849 100644 --- a/pyrfu/pyrf/cart2sph.py +++ b/pyrfu/pyrf/cart2sph.py @@ -6,9 +6,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/cart2sph_ts.py b/pyrfu/pyrf/cart2sph_ts.py index 0b7aee42..d0cdcc66 100644 --- a/pyrfu/pyrf/cart2sph_ts.py +++ b/pyrfu/pyrf/cart2sph_ts.py @@ -3,15 +3,16 @@ # 3rd party imports import numpy as np +import xarray as xr # Local imports from .ts_vec_xyz import ts_vec_xyz __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -37,8 +38,14 @@ def cart2sph_ts(inp, direction_flag: int = 1): """ - if inp.attrs["TENSOR_ORDER"] != 1 or inp.data.ndim != 2: - raise TypeError("Input must be vector field") + # Check input type + assert isinstance(inp, xr.DataArray), "inp must be a xarray.DataArray" + + # Check that inp is a vector time series + assert inp.data.ndim == 2 and inp.shape[1] == 3, "inp must be a vector time series" + + # Check direction +/-1 + assert direction_flag in [-1, 1], "direction_flag must be +/-1" if direction_flag == -1: r_data = inp.data[:, 0] @@ -52,7 +59,7 @@ def cart2sph_ts(inp, direction_flag: int = 1): x_data = r_data * cos_the * cos_phi y_data = r_data * cos_the * sin_phi - out_data = np.hstack([x_data, y_data, z_data]) + out_data = np.transpose(np.vstack([x_data, y_data, z_data])) else: xy2 = inp.data[:, 0] ** 2 + inp.data[:, 1] ** 2 diff --git a/pyrfu/pyrf/cdfepoch2datetime64.py b/pyrfu/pyrf/cdfepoch2datetime64.py index c3754a8f..41903891 100644 --- a/pyrfu/pyrf/cdfepoch2datetime64.py +++ b/pyrfu/pyrf/cdfepoch2datetime64.py @@ -3,14 +3,13 @@ # 3rd party imports import numpy as np - from cdflib import cdfepoch __author__ = "Louis Richard" __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.26" +__version__ = "2.4.2" __status__ = "Prototype" @@ -51,7 +50,12 @@ def _compose_date( nanoseconds, ] - dates = sum([np.asarray(v, dtype=t) for t, v in zip(types, vals) if v is not None]) + dates_list = [] + for t, v in zip(types, vals): + if v is not None: + dates_list.append(np.asarray(v, dtype=t)) + + dates = sum(dates_list) return dates @@ -61,7 +65,7 @@ def cdfepoch2datetime64(epochs): Parameters ---------- - epochs : array_like + epochs : float or int or array_like CDF epochs to convert. Returns @@ -71,7 +75,8 @@ def cdfepoch2datetime64(epochs): """ - times = cdfepoch.breakdown(epochs, to_np=True) + # Check input type + times = cdfepoch.breakdown(epochs) times = np.transpose(np.atleast_2d(times)) times = _compose_date(*times).astype("datetime64[ns]") diff --git a/pyrfu/pyrf/compress_cwt.py b/pyrfu/pyrf/compress_cwt.py index f857f5a9..3fa1180c 100644 --- a/pyrfu/pyrf/compress_cwt.py +++ b/pyrfu/pyrf/compress_cwt.py @@ -4,13 +4,24 @@ # 3rd party import import numba import numpy as np +import xarray as xr +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.2" +__status__ = "Prototype" -@numba.jit(nopython=True, parallel=True) + +@numba.jit(cache=True, fastmath=True, nopython=True, parallel=True) def _compress_cwt_1d(cwt, nc: int = 100): nf = cwt.shape[1] idxs = np.arange( - start=int(nc / 2), stop=len(cwt) - int(nc / 2), step=nc, dtype=np.int64 + start=int(nc / 2), + stop=len(cwt) - int(nc / 2), + step=nc, + dtype=np.int64, ) cwt_c = np.zeros((len(idxs), nf)) @@ -27,7 +38,7 @@ def compress_cwt(cwt, nc: int = 100): Parameters ---------- - cwt : xarray.DataArray + cwt : xarray.Dataset Wavelet transform to compress. nc : int, Optional Number of time steps for averaging. Default is 100. @@ -45,8 +56,13 @@ def compress_cwt(cwt, nc: int = 100): """ + assert isinstance(cwt, xr.Dataset), "cwt must be an xarray.Dataset" + indices = np.arange( - int(nc / 2), len(cwt.time.data) - int(nc / 2), step=nc, dtype=np.int64 + int(nc / 2), + len(cwt.time.data) - int(nc / 2), + step=nc, + dtype=np.int64, ) cwt_t = cwt.time.data[indices] diff --git a/pyrfu/pyrf/convert_fac.py b/pyrfu/pyrf/convert_fac.py index 2d1bc1a3..a2e57708 100644 --- a/pyrfu/pyrf/convert_fac.py +++ b/pyrfu/pyrf/convert_fac.py @@ -5,16 +5,17 @@ import numpy as np import xarray as xr +from .calc_fs import calc_fs + # Local imports from .resample import resample from .ts_vec_xyz import ts_vec_xyz -from .calc_fs import calc_fs __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -71,49 +72,66 @@ def convert_fac(inp, b_bgd, r_xyz: list = None): """ - assert r_xyz is None or isinstance(r_xyz, (xr.DataArray, list, np.ndarray)) + # Check input type + assert isinstance(inp, xr.DataArray), "inp must be a xarray.DataArray" + assert isinstance(b_bgd, xr.DataArray), "b_xyz must be a xarray.DataArray" - if r_xyz is None: - r_xyz = np.array([1, 0, 0]) + assert r_xyz is None or isinstance( + r_xyz, + (xr.DataArray, list, np.ndarray), + ) if len(inp) != len(b_bgd): b_bgd = resample(b_bgd, inp, f_s=calc_fs(inp)) time, inp_data = [inp.time.data, inp.data] - # Normalize background magnetic field - b_hat = b_bgd / np.linalg.norm(b_bgd, axis=1, keepdims=True) + if r_xyz is None: + r_xyz = np.array([1, 0, 0]) if isinstance(r_xyz, xr.DataArray): r_xyz = resample(r_xyz, b_bgd, f_s=calc_fs(b_bgd)) - elif len(r_xyz) == 3: + elif isinstance(r_xyz, (list, np.ndarray)) and len(r_xyz) == 3: r_xyz = np.tile(r_xyz, (len(b_bgd), 1)) - # Perpendicular - r_perp_y = np.cross(b_hat, r_xyz, axis=1) - r_perp_y /= np.linalg.norm(r_perp_y, axis=1, keepdims=True) - r_perp_x = np.cross(r_perp_y, b_bgd, axis=1) - r_perp_x /= np.linalg.norm(r_perp_x, axis=1, keepdims=True) - - assert inp_data.shape[1] in [1, 3], "Invalid dimension of inp" + if b_bgd.ndim == 2 and b_bgd.shape[1] == 3: + # Normalize background magnetic field + b_hat = b_bgd / np.linalg.norm(b_bgd, axis=1, keepdims=True) + + # Perpendicular + r_perp_y = np.cross(b_hat, r_xyz, axis=1) + r_perp_y /= np.linalg.norm(r_perp_y, axis=1, keepdims=True) + r_perp_x = np.cross(r_perp_y, b_bgd, axis=1) + r_perp_x /= np.linalg.norm(r_perp_x, axis=1, keepdims=True) + r_para = b_hat + elif b_bgd.ndim == 3 and b_bgd.shape[1] == 3 and b_bgd.shape[2] == 3: + r_perp_x = ts_vec_xyz(b_bgd.time.data, b_bgd.data[:, 0]) + r_perp_y = ts_vec_xyz(b_bgd.time.data, b_bgd.data[:, 1]) + r_para = ts_vec_xyz(b_bgd.time.data, b_bgd.data[:, 2]) + else: + raise TypeError("b_bgd must be a vector or a tensor time series") - if inp_data.shape[1] == 3: - out_data = np.zeros(inp.shape) + if inp_data.ndim == 2 and inp_data.shape[1] == 3: + out_data = np.zeros(inp.shape, dtype=inp_data.dtype) out_data[:, 0] = np.sum(r_perp_x * inp_data, axis=1) out_data[:, 1] = np.sum(r_perp_y * inp_data, axis=1) - out_data[:, 2] = np.sum(b_hat * inp_data, axis=1) + out_data[:, 2] = np.sum(r_para * inp_data, axis=1) - # xarray - out = xr.DataArray(out_data, coords=[time, inp.comp], dims=["time", "comp"]) + # To xarray + out = ts_vec_xyz(time, out_data, attrs=inp.attrs) - else: - out_data = np.zeros([3, inp_data.shape[0]]) + elif inp_data.ndim == 1: + out_data = np.zeros([inp_data.shape[0], 2]) - out_data[:, 0] = inp[:, 0] * np.sum(r_perp_x * r_xyz, axis=1) - out_data[:, 1] = inp[:, 0] * np.sum(b_hat * r_xyz, axis=1) + out_data[:, 0] = inp * np.sum(r_perp_x * r_xyz, axis=1) + out_data[:, 1] = inp * np.sum(r_para * r_xyz, axis=1) - out = ts_vec_xyz(time, out_data, attrs=inp.attrs) + out = xr.DataArray( + out_data, coords=[time, ["perp", "para"]], dims=["time", "comp"] + ) + else: + raise TypeError("inp must be a vector or scalar") return out diff --git a/pyrfu/pyrf/corr_deriv.py b/pyrfu/pyrf/corr_deriv.py index a12f8833..1d08a9b5 100644 --- a/pyrfu/pyrf/corr_deriv.py +++ b/pyrfu/pyrf/corr_deriv.py @@ -9,9 +9,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/cotrans.py b/pyrfu/pyrf/cotrans.py index 9644d9a7..4d64edb9 100644 --- a/pyrfu/pyrf/cotrans.py +++ b/pyrfu/pyrf/cotrans.py @@ -1,9 +1,10 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import json + # Built-in imports import os -import json # 3rd party imports import numpy as np @@ -11,14 +12,15 @@ # Local imports from ..models import igrf - +from .ts_tensor_xyz import ts_tensor_xyz from .ts_vec_xyz import ts_vec_xyz +from .unix2datetime64 import unix2datetime64 __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -47,11 +49,11 @@ def _dipole_direction_gse(time, flag: str = "dipole"): cos_phi * np.cos(np.deg2rad(lambda_)), cos_phi * np.sin(np.deg2rad(lambda_)), np.sin(np.deg2rad(phi)), - ] + ], ).T - dipole_direction_gse_ = cotrans( - np.hstack([time[:, None], dipole_direction_geo_]), "geo>gse" + ts_vec_xyz(unix2datetime64(time), dipole_direction_geo_), + "geo>gse", ) return dipole_direction_gse_ @@ -66,6 +68,8 @@ def _transformation_matrix(t, tind, hapgood, *args): transf_mat_out[:, 2, 2] = np.ones(len(t)) for j, t_num in enumerate(tind[::-1]): + assert abs(t_num) in list(range(1, 6)), "t_num must be +/- 1, 2, 3, 4, 5" + if t_num in [-1, 1]: if hapgood: theta = 100.461 + 36000.770 * t_zero + 15.04107 * ut @@ -93,7 +97,9 @@ def _transformation_matrix(t, tind, hapgood, *args): # Suns mean longitude m_long = 280.460 + 36000.772 * t_zero + 0.04107 * ut l_sun = m_long - l_sun += (1.915 - 0.0048 * t_zero) * np.sin(np.deg2rad(m_anom)) + l_sun += (1.915 - 0.0048 * t_zero) * np.sin( + np.deg2rad(m_anom), + ) l_sun += 0.020 * np.sin(np.deg2rad(2 * m_anom)) else: # Source: United States Naval Observatory, Astronomical @@ -113,8 +119,8 @@ def _transformation_matrix(t, tind, hapgood, *args): elif t_num in [-3, 3]: dipole_direction_gse_ = _dipole_direction_gse(t, "dipole") - y_e = dipole_direction_gse_[:, 2] # 1st col is time - z_e = dipole_direction_gse_[:, 3] + y_e = dipole_direction_gse_[:, 1] # 1st col is time + z_e = dipole_direction_gse_[:, 2] psi = np.rad2deg(np.arctan(y_e / z_e)) transf_mat = _triang(-psi * np.sign(t_num), 0) # inverse if -3 @@ -123,24 +129,21 @@ def _transformation_matrix(t, tind, hapgood, *args): dipole_direction_gse_ = _dipole_direction_gse(t, "dipole") mu = np.arctan( - dipole_direction_gse_[:, 1] - / np.sqrt(np.sum(dipole_direction_gse_[:, 2:] ** 2, axis=1)) + dipole_direction_gse_[:, 0] + / np.sqrt(np.sum(dipole_direction_gse_[:, 1:] ** 2, axis=1)), ) mu = np.rad2deg(mu) transf_mat = _triang(-mu * np.sign(t_num), 1) - elif t_num in [-5, 5]: + else: lambda_, phi = igrf(t, "dipole") transf_mat = np.matmul(_triang(phi - 90, 1), _triang(lambda_, 2)) if t_num == -5: transf_mat = np.transpose(transf_mat, [0, 2, 1]) - else: - raise ValueError - - if j == len(tind): + if j == 0: transf_mat_out = transf_mat else: transf_mat_out = np.matmul(transf_mat, transf_mat_out) @@ -149,7 +152,7 @@ def _transformation_matrix(t, tind, hapgood, *args): def cotrans(inp, flag, hapgood: bool = True): - r"""Coordinate transformation GE0/GEI/GSE/GSM/SM/MAG + r"""Coordinate transformation GE0/GEI/GSE/GSM/SM/MAG as described in [1]_ Parameters ---------- @@ -192,40 +195,41 @@ def cotrans(inp, flag, hapgood: bool = True): Compute the dipole direction in GSE - >>> dipole = cotrans(b_gse.time, - 'dipoledirectiongse') + >>> dipole = cotrans(b_gse.time, 'dipoledirectiongse') References ---------- - .. [17] Hapgood 1997 (corrected version of Hapgood 1992) Planet.Space - Sci..Vol. 40, No. 5. pp. 71l - 717, 1992 - - .. [18] USNO - AA 2011 & 2012 + .. [1] Hapgood 1997 (corrected version of Hapgood 1992) Planet.Space Sci..Vol. + 40, No. 5. pp. 71l - 717, 1992 """ + assert isinstance(inp, xr.DataArray), "inp must be a xarray.DataArray" + assert inp.ndim < 3, "inp must be scalar or vector" + if ">" in flag: ref_syst_in, ref_syst_out = flag.split(">") else: ref_syst_in, ref_syst_out = [None, flag.lower()] - if isinstance(inp, xr.DataArray): - if "COORDINATE_SYSTEM" in inp.attrs: - ref_syst_internal = inp.attrs["COORDINATE_SYSTEM"].lower() - ref_syst_internal = ref_syst_internal.split(">")[0] - else: - ref_syst_internal = None - - if ref_syst_in is not None and ref_syst_internal is not None: - message = "input ref. frame in variable and input flag differs" - assert ref_syst_internal == ref_syst_in, message - elif ref_syst_in is None and ref_syst_internal is not None: - ref_syst_in = ref_syst_internal.lower() - elif ref_syst_in is None and ref_syst_internal is None: - raise ValueError("input reference frame undefined") + if "COORDINATE_SYSTEM" in inp.attrs: + ref_syst_internal = inp.attrs["COORDINATE_SYSTEM"].lower() + ref_syst_internal = ref_syst_internal.split(">")[0] + else: + ref_syst_internal = None + if ref_syst_in is not None and ref_syst_internal is not None: + message = "input ref. frame in variable and input flag differs" + assert ref_syst_internal == ref_syst_in, message + flag = f"{ref_syst_in}>{ref_syst_out}" + elif ref_syst_in is None and ref_syst_internal is not None: + ref_syst_in = ref_syst_internal.lower() flag = f"{ref_syst_in}>{ref_syst_out}" + elif flag.lower() == "dipoledirectiongse": + flag = flag.lower() + elif ref_syst_in is None and ref_syst_internal is None: + raise ValueError(f"Transformation {flag} is unknown!") if ref_syst_in == ref_syst_out: return inp @@ -234,24 +238,13 @@ def cotrans(inp, flag, hapgood: bool = True): j2000 = 946727930.8160001 # j2000 = Time("J2000", format="jyear_str").unix - if isinstance(inp, xr.DataArray): - time = inp.time.data - t = time.view("i8") * 1e-9 - - # Terrestial Time (seconds since J2000) - tts = t - j2000 - inp_ts = inp - inp = inp.data - - elif isinstance(inp, np.ndarray): - time = (inp[:, 0] * 1e9).astype("datetime64[ns]") - t = inp[:, 0] - # Terrestial Time (seconds since J2000) - tts = t - j2000 - inp_ts = None - inp = inp[:, 1:] - else: - raise TypeError("invalid inpu") + time = inp.time.data + t = (time.astype(np.int64) * 1e-9).astype(np.float64) + + # Terrestial Time (seconds since J2000) + tts = t - j2000 + inp_ts = inp + inp = inp.data if hapgood: day_start_epoch = time.astype("datetime64[D]") @@ -297,34 +290,23 @@ def cotrans(inp, flag, hapgood: bool = True): root_path = os.path.dirname(os.path.abspath(__file__)) file_name = "transformation_indices.json" - with open(os.sep.join([root_path, file_name])) as file: + with open(os.sep.join([root_path, file_name]), "r", encoding="utf-8") as file: transformation_dict = json.load(file) tind = transformation_dict[flag] - elif flag == "dipoledirectiongse": - out_data = _dipole_direction_gse(t) - return ts_vec_xyz(inp.time.data, out_data) + transf_mat = _transformation_matrix(t, tind, hapgood, *args_trans_mat) - else: - raise ValueError(f"Transformation {flag} is unknown!") - - transf_mat = _transformation_matrix(t, tind, hapgood, *args_trans_mat) + if inp.ndim == 1: + out = ts_tensor_xyz(inp_ts.time.data, transf_mat) - if inp.ndim == 2: - out = np.einsum("kji,ki->kj", transf_mat, inp) - elif inp.ndim == 1: - out = transf_mat - else: - raise ValueError - - if inp_ts is not None: - out_data = out - out = inp_ts.copy() - out.data = out_data - out.attrs["COORDINATE_SYSTEM"] = ref_syst_out.upper() + else: + out_data = np.einsum("kji,ki->kj", transf_mat, inp) + out = inp_ts.copy() + out.data = out_data + out.attrs["COORDINATE_SYSTEM"] = ref_syst_out.upper() else: - out = np.hstack([t[:, None], out]) + out = _dipole_direction_gse(t) return out diff --git a/pyrfu/pyrf/cross.py b/pyrfu/pyrf/cross.py index e1f7b8b2..b7387838 100644 --- a/pyrfu/pyrf/cross.py +++ b/pyrfu/pyrf/cross.py @@ -3,6 +3,7 @@ # 3rd party imports import numpy as np +import xarray as xr # Local imports from .resample import resample @@ -10,9 +11,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -58,6 +59,14 @@ def cross(inp1, inp2): """ + # Check type + assert isinstance(inp1, xr.DataArray), "inp1 must be a xarray.DataArray" + assert isinstance(inp2, xr.DataArray), "inp2 must be a xarray.DataArray" + + # Check inputs are vectors + assert inp1.ndim == 2 and inp1.shape[1] == 3, "inp1 must be a vector" + assert inp2.ndim == 2 and inp2.shape[1] == 3, "inp1 must be a vector" + if len(inp1) != len(inp2): inp2 = resample(inp2, inp1) diff --git a/pyrfu/pyrf/date_str.py b/pyrfu/pyrf/date_str.py index b8a52b18..8653795b 100644 --- a/pyrfu/pyrf/date_str.py +++ b/pyrfu/pyrf/date_str.py @@ -6,9 +6,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -33,6 +33,17 @@ def date_str(tint, fmt: int = 1): """ + # Check input + assert isinstance(tint, list), "tint must be a list" + assert isinstance(tint[0], str), "1st element of tint must be a string" + assert isinstance(tint[1], str), "2nd element of tint must be a string" + assert fmt in range(1, 5), "fmt must be 1, 2, 3, or 4" + + assert len(tint[0]) > 25, "tint[0] must be in %Y-%m-%dT%H:%M:%S.%f format" + assert len(tint[1]) > 25, "tint[1] must be in %Y-%m-%dT%H:%M:%S.%f format" + + tint = [t_[:26] for t_ in tint] + start_time = datetime.strptime(tint[0], "%Y-%m-%dT%H:%M:%S.%f") end_time = datetime.strptime(tint[1], "%Y-%m-%dT%H:%M:%S.%f") @@ -42,13 +53,17 @@ def date_str(tint, fmt: int = 1): out = start_time.strftime("%y%m%d%H%M%S") elif fmt == 3: out = "_".join( - [start_time.strftime("%Y%m%d_%H%M%S"), end_time.strftime("%H%M%S")] + [ + start_time.strftime("%Y%m%d_%H%M%S"), + end_time.strftime("%H%M%S"), + ], ) - elif fmt == 4: + else: out = "_".join( - [start_time.strftime("%Y%m%d_%H%M%S"), end_time.strftime("%Y%m%d_%H%M%S")] + [ + start_time.strftime("%Y%m%d_%H%M%S"), + end_time.strftime("%Y%m%d_%H%M%S"), + ], ) - else: - raise ValueError("Unknown format") return out diff --git a/pyrfu/pyrf/datetime2iso8601.py b/pyrfu/pyrf/datetime2iso8601.py index 768619b6..e146fb6d 100644 --- a/pyrfu/pyrf/datetime2iso8601.py +++ b/pyrfu/pyrf/datetime2iso8601.py @@ -2,13 +2,16 @@ # -*- coding: utf-8 -*- # 3rd party imports +import datetime + +import numpy as np import pandas as pd __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -27,14 +30,20 @@ def datetime2iso8601(time): """ - if isinstance(time, list): + # Check input type + message = "time must be array_like or datetime.datetime" + assert isinstance(time, (list, np.ndarray, datetime.datetime)), message + + if isinstance(time, (np.ndarray, list)): return list(map(datetime2iso8601, time)) + assert isinstance(time, datetime.datetime), "time datetime.datetime" + time_datetime = pd.Timestamp(time) # Convert to string datetime_str = time_datetime.strftime("%Y-%m-%dT%H:%M:%S.%f") - tt2000 = f"{datetime_str}{time_datetime.nanosecond:03d}" + time_iso8601 = f"{datetime_str}{time_datetime.nanosecond:03d}" - return tt2000 + return time_iso8601 diff --git a/pyrfu/pyrf/datetime642iso8601.py b/pyrfu/pyrf/datetime642iso8601.py index 54214358..74b96b62 100644 --- a/pyrfu/pyrf/datetime642iso8601.py +++ b/pyrfu/pyrf/datetime642iso8601.py @@ -6,9 +6,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -31,8 +31,13 @@ def datetime642iso8601(time): """ - # Convert to required precision - time_datetime64 = time.astype(" 0: - b_mag[indices] = np.ones(len(indices)) * 1e-3 + b_mag[indices] = np.ones((len(indices), 1)) * 1e-3 b_hat = b_bgd / b_mag b_hat = resample(b_hat, inp) a_para = dot(b_hat, inp) - a_perp = inp.data - (b_hat * np.tile(a_para.data, (3, 1)).T) + a_perp = inp - (b_hat.data * np.tile(a_para.data[:, np.newaxis], (1, 3))) alpha = [] else: b_bgd = resample(b_bgd, inp) b_tot = np.sqrt(b_bgd[:, 0] ** 2 + b_bgd[:, 1] ** 2) - b_bgd /= b_tot[:, np.newaxis] + b_bgd /= b_tot.data[:, np.newaxis] a_para = inp[:, 0] * b_bgd[:, 0] + inp[:, 1] * b_bgd[:, 1] a_perp = inp[:, 0] * b_bgd[:, 1] - inp[:, 1] * b_bgd[:, 0] diff --git a/pyrfu/pyrf/dist_append.py b/pyrfu/pyrf/dist_append.py index 7b703d8c..6f6e350a 100644 --- a/pyrfu/pyrf/dist_append.py +++ b/pyrfu/pyrf/dist_append.py @@ -3,6 +3,7 @@ # 3rd party imports import numpy as np +import xarray as xr # Local imports from .ts_skymap import ts_skymap @@ -11,7 +12,7 @@ __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.26" +__version__ = "2.4.2" __status__ = "Prototype" @@ -36,9 +37,14 @@ def dist_append(inp0, inp1): """ + # Check input type + assert isinstance(inp1, xr.Dataset), "inp1 must be a xarray.Dataset" + if inp0 is None: return inp1 + assert isinstance(inp0, xr.Dataset), "inp0 must be a xarray.Dataset" + # Global attributes glob_attrs = inp0.attrs @@ -47,10 +53,11 @@ def dist_append(inp0, inp1): time = np.hstack([inp0.time.data, inp1.time.data]) # Azimuthal angle - if inp0.phi.ndim == 2: - phi = np.vstack([inp0.phi.data, inp1.phi.data]) - else: - phi = inp0.phi.data + # if inp0.phi.ndim == 2: + # phi = np.vstack([inp0.phi.data, inp1.phi.data]) + # else: + # phi = inp0.phi.data + phi = np.vstack([inp0.phi.data, inp1.phi.data]) # Elevation angle theta = inp0.theta.data @@ -64,7 +71,10 @@ def dist_append(inp0, inp1): if "delta_energy_plus" in glob_attrs: delta_energy_plus = np.vstack( - [inp0.attrs["delta_energy_plus"].data, inp1.attrs["delta_energy_plus"].data] + [ + inp0.attrs["delta_energy_plus"].data, + inp1.attrs["delta_energy_plus"].data, + ], ) glob_attrs["delta_energy_plus"] = delta_energy_plus @@ -73,31 +83,28 @@ def dist_append(inp0, inp1): [ inp0.attrs["delta_energy_minus"].data, inp1.attrs["delta_energy_minus"].data, - ] + ], ) glob_attrs["delta_energy_minus"] = delta_energy_minus - # Energy - if inp0.attrs["tmmode"] == "brst": - step_table = np.hstack([inp0.attrs["esteptable"], inp1.attrs["esteptable"]]) - - out = ts_skymap( - time, - data, - None, - phi, - theta, - energy0=inp0.energy0, - energy1=inp0.energy1, - esteptable=step_table, - attrs=data_attrs, - coords_attrs=coords_attrs, - glob_attrs=glob_attrs, - ) - - else: - energy = np.vstack([inp0.energy.data, inp1.energy.data]) - - out = ts_skymap(time, data, energy, phi, theta) + step_table = np.hstack( + [inp0.attrs["esteptable"], inp1.attrs["esteptable"]], + ) + + energy = np.vstack([inp0.energy.data, inp1.energy.data]) + + out = ts_skymap( + time, + data, + energy, + phi, + theta, + energy0=inp0.energy0, + energy1=inp0.energy1, + esteptable=step_table, + attrs=data_attrs, + coords_attrs=coords_attrs, + glob_attrs=glob_attrs, + ) return out diff --git a/pyrfu/pyrf/dot.py b/pyrfu/pyrf/dot.py index b1bdc300..ebbc7f27 100644 --- a/pyrfu/pyrf/dot.py +++ b/pyrfu/pyrf/dot.py @@ -10,9 +10,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/dynamic_press.py b/pyrfu/pyrf/dynamic_press.py index 03065552..74fa1470 100644 --- a/pyrfu/pyrf/dynamic_press.py +++ b/pyrfu/pyrf/dynamic_press.py @@ -3,18 +3,18 @@ # 3rd party imports import numpy as np - +import xarray as xr from scipy import constants __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" -def dynamic_press(n_s, v_xyz, specie: str = "i"): +def dynamic_press(n_s, v_xyz, specie: str = "ions"): r"""Computes dynamic pressure. Parameters @@ -23,8 +23,8 @@ def dynamic_press(n_s, v_xyz, specie: str = "i"): Time series of the number density of the specie. v_xyz : xarray.DataArray Time series of the bulk velocity of the specie. - specie : {"i", "e"}, Optional - Specie. default "i". + specie : {"ions", "electrons"}, Optional + Specie. Default "ions". Returns ------- @@ -45,27 +45,35 @@ def dynamic_press(n_s, v_xyz, specie: str = "i"): Load ion bulk velocity and remove spintone - >>> v_xyz_i = mms.get_data("Vi_gse_fpi_fast_l2", tint, mms_id) - >>> st_xyz_i = mms.get_data("STi_gse_fpi_fast_l2", tint, mms_id) + >>> v_xyz_i = mms.get_data("vi_gse_fpi_fast_l2", tint, mms_id) + >>> st_xyz_i = mms.get_data("sti_gse_fpi_fast_l2", tint, mms_id) >>> v_xyz_i = v_xyz_i - st_xyz_i Ion number density - >>> n_i = mms.get_data("Ni_fpi_fast_l2", tint, mms_id) + >>> n_i = mms.get_data("ni_fpi_fast_l2", tint, mms_id) Compute dynamic pressure - >>> p = pyrf.dynamic_press(n_i, v_xyz_i, specie="i") + >>> p = pyrf.dynamic_press(n_i, v_xyz_i, specie="ions") """ - if specie == "i": + # Check input + assert isinstance(n_s, xr.DataArray), "n_s must be a xarray.DataArray" + assert isinstance(v_xyz, xr.DataArray), "v_xyz must be a xarray.DataArray" + assert isinstance(specie, str), "specie must be a str" + assert specie.lower() in ["ions", "electrons"], "specie must be ions or electrons" + + # Check n_s and v_xyz shapes + assert n_s.ndim == 1, "n_s must be a scalar" + assert v_xyz.ndim == 2 and v_xyz.shape[1] == 3, "v_xyz must be a vector" + + if specie.lower() == "ions": mass = constants.proton_mass - elif specie == "e": - mass = constants.electron_mass else: - raise ValueError("Unknown specie") + mass = constants.electron_mass - p_dyn = n_s * mass * np.linalg.norm(v_xyz, axis=0) ** 2 + p_dyn = n_s * mass * np.linalg.norm(v_xyz, axis=1) ** 2 return p_dyn diff --git a/pyrfu/pyrf/e_vxb.py b/pyrfu/pyrf/e_vxb.py index 045bab08..ea9436c6 100644 --- a/pyrfu/pyrf/e_vxb.py +++ b/pyrfu/pyrf/e_vxb.py @@ -3,6 +3,7 @@ # 3rd party imports import numpy as np +import xarray as xr # Local imports from .resample import resample @@ -10,9 +11,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -50,8 +51,8 @@ def e_vxb(v_xyz, b_xyz, flag: str = "vxb"): Load magnetic field and electric field - >>> b_xyz = mms.get_data("B_gse_fgm_srvy_l2", tint, mms_id) - >>> e_xyz = mms.get_data("E_gse_edp_fast_l2", tint, mms_id) + >>> b_xyz = mms.get_data("b_gse_fgm_srvy_l2", tint, mms_id) + >>> e_xyz = mms.get_data("e_gse_edp_fast_l2", tint, mms_id) Compute ExB drift velocity @@ -59,29 +60,30 @@ def e_vxb(v_xyz, b_xyz, flag: str = "vxb"): """ - input_v_cons = v_xyz.size == 3 - estimate_exb = flag.lower() == "exb" + assert isinstance(flag, str) and flag.lower() in ["exb", "vxb"], "Invalid flag" + assert isinstance(b_xyz, xr.DataArray), "b_xyz must be a xarray.DataArray" - if estimate_exb: + if isinstance(v_xyz, (list, np.ndarray)) and v_xyz.ndim == 1 and len(v_xyz) == 3: + v_xyz = ts_vec_xyz(b_xyz.time.data, np.tile(v_xyz, (len(b_xyz), 1))) + elif isinstance(v_xyz, xr.DataArray): b_xyz = resample(b_xyz, v_xyz) + else: + raise TypeError("v_xyz must be xarray.DataArray or array_like constant vector") + if flag.lower() == "exb": res = 1e3 * np.cross(v_xyz.data, b_xyz.data, axis=1) res /= np.linalg.norm(b_xyz.data, axis=1)[:, None] ** 2 attrs = {"UNITS": "km/s", "FIELDNAM": "Velocity", "LABLAXIS": "V"} else: - if input_v_cons: - res = np.cross(np.tile(v_xyz, (len(b_xyz), 1)), b_xyz.data) - res *= (-1) * 1e-3 - - else: - b_xyz = resample(b_xyz, v_xyz) - - res = np.cross(v_xyz.data, b_xyz.data) - res *= (-1) * 1e-3 + res = -1e-3 * np.cross(v_xyz.data, b_xyz.data) - attrs = {"UNITS": "mV/s", "FIELDNAM": "Electric field", "LABLAXIS": "E"} + attrs = { + "UNITS": "mV/s", + "FIELDNAM": "Electric field", + "LABLAXIS": "E", + } out = ts_vec_xyz(b_xyz.time.data, res, attrs) diff --git a/pyrfu/pyrf/eb_nrf.py b/pyrfu/pyrf/eb_nrf.py index 3073195e..399a3046 100644 --- a/pyrfu/pyrf/eb_nrf.py +++ b/pyrfu/pyrf/eb_nrf.py @@ -7,15 +7,13 @@ # Local imports from .resample import resample -from .dot import dot -from .normalize import normalize -from .cross import cross +from .ts_vec_xyz import ts_vec_xyz __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -30,8 +28,13 @@ def eb_nrf(e_xyz, b_xyz, v_xyz, flag=0): Time series of the magnetic field. v_xyz : xarray.DataArray Normal vector. - flag : int or ndarray - to fill. + flag : str or ndarray + Method flag : + * a : L is along b_xyz, N closest to v_xyz and M = NxL + * b : N is along v_xyz, L is the mean direction of b_xyz in plane perpendicular + to N, and M = NxL + * numpy,ndarray : N is along v_xyz , L is closest to the direction specified by + L_vector (e.g., maximum variance direction), M = NxL Returns ------- @@ -39,40 +42,49 @@ def eb_nrf(e_xyz, b_xyz, v_xyz, flag=0): to fill. """ + # Check inputs + assert isinstance(e_xyz, xr.DataArray), "e_xyz must be a xarray.DataArray" + assert isinstance(b_xyz, xr.DataArray), "b_xyz must be a xarray.DataArray" + assert isinstance(v_xyz, xr.DataArray), "v_xyz must be a xarray.DataArray" + + assert e_xyz.ndim == 2 and e_xyz.shape[1] == 3, "e_xyz must be a vector" + assert b_xyz.ndim == 2 and b_xyz.shape[1] == 3, "e_xyz must be a vector" + assert v_xyz.ndim == 2 and v_xyz.shape[1] == 3, "e_xyz must be a vector" - assert isinstance(flag, (int, np.ndarray)), "Invalid flag type" + assert isinstance(flag, (str, np.ndarray, list)), "Invalid flag type" - if isinstance(flag, int): - flag_cases = ["a", "b"] - flag_case = flag_cases[flag] + if isinstance(flag, str): + assert flag.lower() in ["a", "b"], "flag must be a or b" + flag_case = flag l_direction = None else: - assert np.size(flag) == 3 + flag = np.array(flag) + assert flag.ndim == 1 and len(flag) == 3, "array_like flag must be a vector!" l_direction = flag flag_case = "c" if flag_case == "a": - b_data = resample(b_xyz, e_xyz).data + b_xyz = resample(b_xyz, e_xyz) - n_l = b_data / np.linalg.norm(b_data, axis=0)[:, None] - n_n = np.cross(np.cross(b_data, v_xyz), b_data) - n_n = n_n / np.linalg.norm(n_n)[:, None] + n_l = b_xyz.data / np.linalg.norm(b_xyz.data, axis=1, keepdims=True) + n_n = np.cross(np.cross(b_xyz.data, v_xyz.data), b_xyz.data) + n_n /= np.linalg.norm(n_n, axis=1, keepdims=True) n_m = np.cross(n_n, n_l) # in (vn x b) direction - elif flag_case == "b": - n_n = v_xyz / np.linalg.norm(v_xyz) - n_m = normalize(cross(n_n, b_xyz.mean(dim="time"))) - n_l = cross(n_m, n_n) + n_n = v_xyz.data / np.linalg.norm(v_xyz, axis=1, keepdims=True) + n_m = np.cross(n_n, np.mean(b_xyz.data, axis=0)) + n_m /= np.linalg.norm(n_m, axis=1, keepdims=True) + n_l = np.cross(n_m, n_n) else: - n_n = normalize(v_xyz) - n_m = normalize(np.cross(n_n, l_direction)) - n_l = cross(n_m, n_n) + n_n = v_xyz.data / np.linalg.norm(v_xyz, axis=1, keepdims=True) + n_m = np.cross(n_n, l_direction) + n_m /= np.linalg.norm(n_m, axis=1, keepdims=True) + n_l = np.cross(n_m, n_n) # estimate e in new coordinates - e_lmn = np.hstack([dot(e_xyz, vec) for vec in [n_l, n_m, n_n]]) - - out = xr.DataArray(e_xyz.time.data, e_lmn, e_xyz.attrs) + e_lmn = np.vstack([np.sum(e_xyz.data * vec, axis=1) for vec in [n_l, n_m, n_n]]) + out = ts_vec_xyz(e_xyz.time.data, np.transpose(e_lmn), e_xyz.attrs) return out diff --git a/pyrfu/pyrf/ebsp.py b/pyrfu/pyrf/ebsp.py index f34f4f23..2a8e0afb 100644 --- a/pyrfu/pyrf/ebsp.py +++ b/pyrfu/pyrf/ebsp.py @@ -2,94 +2,102 @@ # -*- coding: utf-8 -*- # Built-in imports +import logging import os -import bisect import warnings # 3rd party imports +import numba import numpy as np import xarray as xr - from scipy import fft +from .calc_fs import calc_fs +from .cart2sph import cart2sph +from .convert_fac import convert_fac +from .resample import resample + # Local imports from .ts_time import ts_time from .ts_vec_xyz import ts_vec_xyz -from .resample import resample -from .iso2unix import iso2unix -from .start import start -from .end import end -from .cart2sph import cart2sph -from .calc_fs import calc_fs -from .convert_fac import convert_fac from .unix2datetime64 import unix2datetime64 -from .datetime642iso8601 import datetime642iso8601 __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" +logging.captureWarnings(True) +logging.basicConfig( + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, +) -def _checksampling(e_xyz, delta_b, b_bgd, full_b, flag_no_resamp): + +def _checksampling(e_xyz, db_xyz, b_xyz, b_bgd, flag_no_resamp): assert e_xyz is not None - fs_e, fs_b = [calc_fs(e_xyz), calc_fs(delta_b)] + fs_e, fs_b = [calc_fs(e_xyz), calc_fs(db_xyz)] - resample_b_options = dict(f_s=fs_b) - resample_e_options = dict(f_s=fs_e) + resample_b_options = {"f_s": fs_b} if flag_no_resamp: assert fs_e == fs_b fs_ = fs_e else: if fs_b > 1.5 * fs_e: - e_xyz = resample(e_xyz, delta_b, **resample_b_options) - b_bgd = resample(b_bgd, delta_b, **resample_b_options) + e_xyz = resample(e_xyz, db_xyz, **resample_b_options) + b_bgd = resample(b_bgd, db_xyz, **resample_b_options) fs_ = fs_b - warnings.warn("Interpolating e to b", UserWarning) + logging.info("Interpolating e to b") elif fs_e > 1.5 * fs_b: - delta_b = resample(delta_b, e_xyz, **resample_e_options) - b_bgd = resample(b_bgd, e_xyz, **resample_e_options) + db_xyz = resample(db_xyz, e_xyz) + b_bgd = resample(b_bgd, e_xyz) fs_ = fs_e - warnings.warn("Interpolating b to e", UserWarning) - elif fs_e == fs_b and len(e_xyz) == len(delta_b): + logging.info("Interpolating b to e") + elif fs_e == fs_b and len(e_xyz) == len(db_xyz): fs_ = fs_e else: fs_ = 2 * fs_e - - nt = np.min([end(e_xyz), end(delta_b)]) - np.max( - [start(e_xyz), start(delta_b)] + start_time = np.max( + [ + e_xyz.time.data[0].astype(np.float64) / 1e9, + db_xyz.time.data[0].astype(np.float64) / 1e9, + ] ) - nt /= 1 / fs_ - t = np.linspace( - np.max([start(e_xyz), start(delta_b)]), - np.min([end(e_xyz), end(delta_b)]), - int(nt), + end_time = np.min( + [ + e_xyz.time.data[-1].astype(np.float64) / 1e9, + db_xyz.time.data[-1].astype(np.float64) / 1e9, + ] ) + nt = np.floor((end_time - start_time) * fs_).astype(np.int64) + + t = np.linspace(start_time, end_time, nt) + t = ts_time(t) e_xyz = resample(e_xyz, t) b_bgd = resample(b_bgd, t) - full_b = resample(full_b, t) - delta_b = resample(delta_b, t) + b_xyz = resample(b_xyz, t) + db_xyz = resample(db_xyz, t) - warnings.warn("Interpolating b and e to 2x e sampling", UserWarning) + logging.info("Interpolating b and e to 2x e sampling") - return e_xyz, delta_b, b_bgd, full_b, fs_ + return e_xyz, db_xyz, b_xyz, b_bgd, fs_ def _b_elevation(b_x, b_y, b_z, angle_b_elevation_max): # Remove the last sample if the total number of samples is odd - if len(b_x) % 2: - b_x = b_x[:-1, :] - b_y = b_y[:-1, :] - b_z = b_z[:-1, :] + b_x = b_x[: int(2 * (len(b_x) // 2))] + b_y = b_y[: int(2 * (len(b_y) // 2))] + b_z = b_z[: int(2 * (len(b_z) // 2))] angle_b_elevation = np.arctan(b_z / np.sqrt(b_x**2 + b_y**2)) angle_b_elevation = np.rad2deg(angle_b_elevation) @@ -99,42 +107,24 @@ def _b_elevation(b_x, b_y, b_z, angle_b_elevation_max): def _freq_int(freq_int, delta_b): - pc12_range, pc35_range, other_range = [False, False, False] - - if isinstance(freq_int, str): - if freq_int.lower() == "pc12": - pc12_range = True - - freq_int = [0.1, 5] - - delta_t = 1 # local - - tint = np.round([start(delta_b), end(delta_b)]) - tint = list(datetime642iso8601(unix2datetime64(tint))) + start_time = delta_b.time.data[0].astype(np.float64) / 1e9 + end_time = delta_b.time.data[-1].astype(np.float64) / 1e9 - elif freq_int.lower() == "pc35": - pc35_range = True + pc12_range, other_range = [False, False] + if isinstance(freq_int, str): + if freq_int.lower() == "pc35": freq_int = [0.002, 0.1] delta_t = 60 # local - - tint = 60 * np.array( - [np.round(start(delta_b) / 60), np.round(end(delta_b) / 60)] - ) - tint = datetime642iso8601(unix2datetime64(tint)) - else: - raise ValueError("Invalid format of interval") + pc12_range = True - fs_out = 1 / delta_t + freq_int = [0.1, 5.0] - nt = np.round((iso2unix(tint[1]) - iso2unix(tint[0])) / delta_t) - nt = nt.astype(np.int64) # local + delta_t = 1 # local - out_time = np.linspace(iso2unix(tint[0]), iso2unix(tint[1]), nt) - out_time += delta_t / 2 - out_time = out_time[:-1] + fs_out = 1 / delta_t else: if freq_int[1] >= freq_int[0]: other_range = True @@ -142,21 +132,21 @@ def _freq_int(freq_int, delta_b): fs_out = freq_int[1] / 5 delta_t = 1 / fs_out # local - - nt = np.round((end(delta_b) - start(delta_b)) / delta_t) - nt = nt.astype(np.int64) # local - - out_time = np.linspace(start(delta_b), end(delta_b), nt) - out_time += delta_t / 2 - out_time = out_time[:-1] else: raise ValueError("FREQ_INT must be [f_min f_max], f_min idx_nan[i + 1]: + out[i : int(min([i + censure[j], n_data])), j] = np.nan + + return out + + +def ebsp(e_xyz, db_xyz, b_xyz, b_bgd, xyz, freq_int, **kwargs): """Calculates wavelet spectra of E&B and Poynting flux using wavelets (Morlet wavelet). Also computes polarization parameters of B using SVD [7]_. SVD is performed on spectral matrices computed from the time series @@ -215,9 +221,9 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): ---------- e_xyz : xarray.DataArray Time series of the wave electric field. - delta_b : xarray.DataArray + db_xyz : xarray.DataArray Time series of the wave magnetic field. - full_b : xarray.DataArray + b_xyz : xarray.DataArray Time series of the high resolution background magnetic field used for E.B=0. b_bgd : xarray.DataArray @@ -273,13 +279,13 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): ---------------- polarization : bool Computes polarization parameters. Default False. - noresamp : bool + no_resample : bool No resampling, E and delta_b are given at the same time line. Default False. fac : bool Uses FAC coordinate system (defined by b0 and optionally xyz), otherwise no coordinate system transformation is performed. Default - False. + True. de_dot_b0 : bool Computes dEz from delta_b dot B = 0, uses full_b. Default False. full_b_db : bool @@ -332,42 +338,49 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): """ - assert isinstance(delta_b, xr.DataArray), "delta_b must be a DataArray" - assert isinstance(full_b, xr.DataArray), "full_b must be a DataArray" + assert isinstance(db_xyz, xr.DataArray), "delta_b must be a DataArray" + assert isinstance(b_xyz, xr.DataArray), "full_b must be a DataArray" assert isinstance(b_bgd, xr.DataArray), "b0 must be a DataArray" - assert isinstance(xyz, xr.DataArray), "xyz must be a DataArray" + + message = "freq_int must be a string or array_like" + assert isinstance(freq_int, (list, np.ndarray, str)), message + + if isinstance(freq_int, (list, np.ndarray)): + assert len(freq_int) == 2, "freq_int list must contain two elements" + else: + assert freq_int in ["pc12", "pc35"], "string freq_int must be pc12 or pc35" # Compute magnetic field fluctuations sampling frequency - fsb = calc_fs(delta_b) + fs_b = calc_fs(db_xyz) # Below which we cannot apply E*B=0 - angle_b_elevation_max = 15 + angle_b_elevation_max = 15.0 want_ee = e_xyz is not None - res = dict( - t=None, - f=None, - flagFac=0, - bb_xxyyzzss=None, - ee_xxyyzzss=None, - ee_ss=None, - pf_xyz=None, - pf_rtp=None, - dop=None, - dop2d=None, - planarity=None, - ellipticity=None, - k_tp=None, - full_b=full_b, - b0=b_bgd, - r=xyz, - ) + res = { + "t": None, + "f": None, + "flagFac": 0, + "bb_xxyyzzss": None, + "ee_xxyyzzss": None, + "ee_ss": None, + "pf_xyz": None, + "pf_rtp": None, + "dop": None, + "dop2d": None, + "planarity": None, + "ellipticity": None, + "k_tp": None, + "full_b": b_xyz, + "b0": b_bgd, + "r": xyz, + } want_polarization = kwargs.get("polarization", False) flag_no_resample = kwargs.get("no_resample", False) - flag_want_fac = kwargs.get("fac", False) + flag_want_fac = kwargs.get("fac", True) flag_de_dot_b0 = kwargs.get("de_dot_b0", False) flag_full_b_db = kwargs.get("full_b_db", False) @@ -380,36 +393,34 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): fac_matrix = kwargs.get("fac_matrix", None) if flag_want_fac and fac_matrix is None: - if b_bgd is None: - raise ValueError("ebsp(): at least b0 should be given for option FAC") - if xyz is None: - print("convert_fac : assuming s/c position [1 0 0] for estimating " "FAC") + logging.info( + "convert_fac : assuming s/c position [1 0 0] for estimating FAC" + ) xyz = [1, 0, 0] - xyz = ts_vec_xyz(delta_b.time.data, np.tile(xyz, (len(delta_b), 1))) + xyz = ts_vec_xyz(db_xyz.time.data, np.tile(xyz, (len(db_xyz), 1))) + else: + assert isinstance(xyz, xr.DataArray), "xyz must be a DataArray" - xyz = resample(xyz, delta_b, **{"f_s": fsb}) + xyz = resample(xyz, db_xyz, **{"f_s": fs_b}) - b_bgd = resample(b_bgd, delta_b, **{"f_s": fsb}) + b_bgd = resample(b_bgd, db_xyz, **{"f_s": fs_b}) if flag_full_b_db: - full_b = delta_b - res["full_b"] = full_b - delta_b = delta_b - b_bgd - - if flag_de_dot_b0 and full_b is None: - raise ValueError("full_b must be given for option de_dot_b0=0") + b_xyz = db_xyz + res["full_b"] = b_xyz + db_xyz = db_xyz - b_bgd - any_range, freq_int, out_sampling, out_time = _freq_int(freq_int, delta_b) - pc12_range, pc35_range, other_range = any_range + any_range, freq_int, out_sampling, out_time = _freq_int(freq_int, db_xyz) + pc12_range, other_range = any_range if want_ee: # Check the sampling rate - temp_ = _checksampling(e_xyz, delta_b, b_bgd, full_b, flag_no_resample) - e_xyz, delta_b, b_bgd, full_b, in_sampling = temp_ + temp_ = _checksampling(e_xyz, db_xyz, b_xyz, b_bgd, flag_no_resample) + e_xyz, db_xyz, b_xyz, b_bgd, in_sampling = temp_ else: - in_sampling = calc_fs(delta_b) + in_sampling = calc_fs(db_xyz) e_xyz = None @@ -417,36 +428,35 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): raise ValueError("F_MAX must be lower than the Nyquist frequency") if want_ee and e_xyz.shape[1] < 3 and not flag_de_dot_b0: - raise ValueError( - "E must have all 3 components or flag de_dot_db=0 " "must be given" + raise TypeError( + "E must have all 3 components or flag de_dot_db=0 must be given" ) - if len(delta_b) % 2: - delta_b = delta_b[:-1, :] + if len(db_xyz) % 2: + db_xyz = db_xyz[:-1, :] b_bgd = b_bgd[:-1, :] if fac_matrix is None: xyz = xyz[:-1, :] else: - fac_matrix["t"] = fac_matrix["t"][:-1, :] - - fac_matrix["rotMatrix"] = fac_matrix["rotMatrix"][:-1, :, :] + fac_matrix = fac_matrix[:-1, ...] if want_ee: e_xyz = e_xyz[:-1, :] - in_time = delta_b.time.data.astype(np.int64) * 1e-9 + in_time = db_xyz.time.data.astype(np.float64) / 1e9 b_x, b_y, b_z = [None, None, None] idx_b_par_spin_plane = None if flag_de_dot_b0: - b_x, b_y, b_z = [full_b[:, i].data for i in range(3)] + b_x, b_y, b_z = [b_xyz[:, i].data for i in range(3)] # Remove the last sample if the total number of samples is odd - temp_ = _b_elevation(b_x, b_y, b_z, angle_b_elevation_max) - _, idx_b_par_spin_plane = temp_ + # temp_ = _b_elevation(b_x, b_y, b_z, angle_b_elevation_max) + # angle_b_elevation, idx_b_par_spin_plane = temp_ + _, idx_b_par_spin_plane = _b_elevation(b_x, b_y, b_z, angle_b_elevation_max) # If E has all three components, transform E and B waveforms to a magnetic # field aligned coordinate (FAC) and save eisr for computation of e_sum. @@ -457,24 +467,31 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): if flag_want_fac: res["flagFac"] = True - time_b0 = b_bgd.time.data.astype(np.int64) * 1e-9 + time_b0 = b_bgd.time.data.astype(np.float64) / 1e9 - if want_ee: - if not flag_de_dot_b0: - eisr2 = e_xyz[:, :2] + if want_ee and not flag_de_dot_b0: + eisr2 = e_xyz[:, :2] + idx_nan_e = np.isnan(e_xyz.data) + idx_nan_eisr2 = np.isnan(eisr2.data) - if e_xyz.shape[1] < 3: - raise TypeError("E must be a 3D vector to be rotated to " "FAC") + if fac_matrix is None: + e_xyz = convert_fac(e_xyz, b_bgd, xyz) + else: + e_xyz = convert_fac(e_xyz, fac_matrix) - if fac_matrix is None: - e_xyz = convert_fac(e_xyz, b_bgd, xyz) - else: - e_xyz = convert_fac(e_xyz, fac_matrix) + else: + idx_nan_e = np.full((len(in_time), 3), False) + eisr2 = None + idx_nan_eisr2 = np.full((len(in_time), 2), False) if fac_matrix is None: - delta_b = convert_fac(delta_b, b_bgd, xyz) + db_xyz = convert_fac(db_xyz, b_bgd, xyz) else: - delta_b = convert_fac(delta_b, fac_matrix) + db_xyz = convert_fac(db_xyz, fac_matrix) + else: + idx_nan_e = np.full((len(in_time), 3), False) + eisr2 = None + idx_nan_eisr2 = np.full((len(in_time), 2), False) # Find the frequencies for an FFT of all data and set important parameters nd2 = len(in_time) / 2 @@ -498,7 +515,6 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): a_number = freq_number a_ = np.logspace(a_min, a_max, int(a_number)) - a_ = np.flip(a_) # Maximum frequency w_0 = in_sampling / 2 @@ -507,38 +523,26 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): sigma = morlet_width / w_0 # Make the FFT of all data - idx_nan_b = np.isnan(delta_b.data) - - delta_b.data[idx_nan_b] = 0 + idx_nan_b = np.isnan(db_xyz.data) - sw_b = fft.fft(delta_b.data, axis=0, workers=os.cpu_count()) - # sw_b = pyfftw.interfaces.numpy_fft.fft(delta_b, axis=0, threads=n_threads) + db_xyz.data[idx_nan_b] = 0 - idx_nan_e, idx_nan_eisr2 = [None, None] + swb = fft.fft(db_xyz.data, axis=0, workers=os.cpu_count()) sw_e, sw_eisr2 = [None, None] if want_ee: - print("ebsp ... calculate E and B wavelet transform ... ") - - idx_nan_e = np.isnan(e_xyz.data) - - e_xyz.data[idx_nan_e] = 0 + logging.info("ebsp ... calculate E and B wavelet transform ... ") + e_xyz.data[idx_nan_e] = 0.0 sw_e = fft.fft(e_xyz.data, axis=0, workers=os.cpu_count()) - # sw_e = pyfftw.interfaces.numpy_fft.fft(e_xyz, axis=0, - # threads=n_threads) if flag_want_fac and not flag_de_dot_b0: - idx_nan_eisr2 = np.isnan(eisr2.data) - - eisr2.data[idx_nan_eisr2] = 0 + eisr2.data[idx_nan_eisr2] = 0.0 sw_eisr2 = fft.fft(eisr2.data, axis=0, workers=os.cpu_count()) - # sw_eisr2 = pyfftw.interfaces.numpy_fft.fft(eisr2, axis=0, - # threads=n_threads) else: - print("ebsp ... calculate B wavelet transform ....") + logging.info("ebsp ... calculate B wavelet transform ....") # Loop through all frequencies n_data, n_freq, n_data_out = [len(in_time), len(a_), len(out_time)] @@ -571,63 +575,52 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): censure = np.floor(2 * a_ * out_sampling / in_sampling * n_wave_period_to_average) - for ind_a, f_a in enumerate(a_): + for ind_a, a_0 in enumerate(a_): + new_freq_mat = w_0 / a_0 + # resample to 1 second sampling for Pc1-2 or 1 minute sampling for # Pc3-5 average top frequencies to 1 second/1 minute below will be # an average over 8 wave periods. first find where one sample is less # than eight wave periods - if f_a / n_wave_period_to_average > out_sampling: + if frequency_vec[ind_a] / n_wave_period_to_average > out_sampling: av_window = 1 / out_sampling else: - av_window = n_wave_period_to_average / f_a + av_window = n_wave_period_to_average / frequency_vec[ind_a] # Get the wavelet transform by backward FFT - w_exp_mat = np.exp(-sigma * sigma * ((a_[ind_a] * w_ - w_0) ** 2) / 2) - w_exp_mat2 = np.tile(w_exp_mat, (2, 1)).T - w_exp_mat = np.tile(w_exp_mat, (3, 1)).T + w_exp_mat = np.exp(-sigma * sigma * ((a_0 * w_ - w_0) ** 2) / 2) + w_exp_mat2 = np.tile(w_exp_mat[:, np.newaxis], (1, 2)) + w_exp_mat = np.tile(w_exp_mat[:, np.newaxis], (1, 3)) - wb = fft.ifft(np.sqrt(1) * sw_b * w_exp_mat, axis=0, workers=os.cpu_count()) - # wb = pyfftw.interfaces.numpy_fft.ifft(np.sqrt(1) * sw_b * w_exp_mat, - # axis=0, threads=n_threads) + wb = fft.ifft(np.sqrt(1) * swb * w_exp_mat, axis=0, workers=os.cpu_count()) + wb = np.array(wb) # Make sure it's an array (scipy.fft.ifft returns Any type) wb[idx_nan_b] = np.nan we, w_eisr2 = [None, None] if want_ee: we = fft.ifft(np.sqrt(1) * sw_e * w_exp_mat, axis=0, workers=os.cpu_count()) - # arg_ = np.sqrt(1) * sw_e * w_exp_mat - # we = pyfftw.interfaces.numpy_fft.ifft(arg_, axis=0, - # threads=n_threads) - + we = np.array(we) we[idx_nan_e] = np.nan if flag_want_fac and not flag_de_dot_b0: w_eisr2 = fft.ifft( np.sqrt(1) * sw_eisr2 * w_exp_mat2, axis=0, workers=os.cpu_count() ) - # arg_ = np.sqrt(1) * sw_eisr2 * w_exp_mat2 - # w_eisr2 = pyfftw.interfaces.numpy_fft.ifft(arg_, axis=0, - # threads=n_threads) + w_eisr2 = np.array(w_eisr2) w_eisr2[idx_nan_eisr2] = np.nan - new_freq_mat = w_0 / a_[ind_a] - new_freq_mat = np.flip(new_freq_mat) - # Power spectrum of E and Poynting flux - - if want_ee: - # Power spectrum of E, power = (2*pi)*conj(W).*W./new_freq_mat - if flag_want_fac and not flag_de_dot_b0: - sum_power_eisr2 = np.sum( + # Power spectrum of E, power = (2*pi)*conj(W).*W./new_freq_mat + power_2e_isr2_plot[:, ind_a] = np.sum( 2 * np.pi * (w_eisr2 * np.conj(w_eisr2)) / new_freq_mat, axis=1 ) else: - sum_power_eisr2 = np.sum( + # Power spectrum of E, power = (2*pi)*conj(W).*W./new_freq_mat + power_2e_isr2_plot[:, ind_a] = np.sum( 2 * np.pi * (we * np.conj(we)) / new_freq_mat, axis=1 ) - power_2e_isr2_plot[:, ind_a] = sum_power_eisr2 - # Compute Ez from dE * B = 0 if flag_de_dot_b0: we_re, we_im = [np.real(we), np.imag(we)] @@ -640,15 +633,17 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): if flag_want_fac: if fac_matrix is None: - arg_ = ts_vec_xyz(time_b0, np.hstack([we[:, :2], we_z])) + tmp = np.vstack([np.transpose(we[:, :2]), np.transpose(we_z)]) + arg_ = ts_vec_xyz(time_b0, np.transpose(tmp)) we = convert_fac(arg_, b_bgd, xyz) else: - arg_ = ts_vec_xyz(time_b0, np.hstack([we[:, :2], we_z])) + tmp = np.vstack([np.transpose(we[:, :2]), np.transpose(we_z)]) + arg_ = ts_vec_xyz(time_b0, np.transpose(tmp)) we = convert_fac(arg_, fac_matrix) - - we = we[:, 1:] else: - we = np.hstack([we[:, :2], we_z]) + we = np.transpose( + np.vstack([np.transpose(we[:, :2]), np.transpose(we_z)]) + ) power_e = 2 * np.pi * (we * np.conj(we)) / new_freq_mat power_e = np.vstack([power_e.T, np.sum(power_e, axis=1)]).T @@ -715,16 +710,14 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): # Polarization parameters if want_polarization: # Construct spectral matrix and average it - s_mat = np.zeros((3, 3, n_data), dtype="complex128") + s_mat = np.zeros((n_data, 3, 3), dtype="complex128") for i in range(3): for j in range(3): - s_mat[i, j, :] = ( + s_mat[:, i, j] = ( 2 * np.pi * (wb[:, i] * np.conj(wb[:, j])) / new_freq_mat ) - s_mat = np.transpose(s_mat, [2, 0, 1]) - # Averaged s_mat s_mat_avg = np.zeros((n_data_out, 3, 3), dtype="complex128") @@ -759,16 +752,15 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): a_mat[3:6, ...] = -np.imag(np.transpose(s_mat_avg, [1, 2, 0])) for i in range(n_data_out): - if np.isnan(a_mat[..., i]).any(): - u_mat[..., i], w_mat[..., i], v_mat[..., i] = [ - np.nan, - np.nan, - np.nan, - ] + if np.sum(np.isnan(a_mat[..., i])) != 0: + u_mat[..., i] = np.nan + w_mat[..., i] = np.nan + v_mat[..., i] = np.nan else: - u_mat[..., i], w_mat[..., i], v_mat[..., i] = np.linalg.svd( - a_mat[..., i], full_matrices=False - ) + uu_, ww_, vv_ = np.linalg.svd(a_mat[..., i], full_matrices=False) + u_mat[..., i] = uu_ + w_mat[..., i] = ww_ + v_mat[..., i] = vv_ # compute direction of propagation sign_kz = np.sign(v_mat[2, 2, :]) @@ -804,8 +796,7 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): ellipticity[:, ind_a] = ellipticity_local - # DOP = sqrt[(3/2.*trace(SM^2)./(trace(SM))^2 - 1/2)]; - # Samson, 1973, JGR + # DOP = sqrt[(3/2.*trace(SM^2)./(trace(SM))^2 - 1/2)]; Samson, 1973, JGR dop = np.sqrt( (3 / 2) * ( @@ -873,91 +864,42 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): idx_nan_b = np.sum(idx_nan_b, axis=1) > 0 idx_nan_eisr2 = np.sum(idx_nan_eisr2, axis=1) > 0 - n_data2 = len(power_2b_plot) + n_power_b = len(power_2b_plot) + if pc12_range or other_range: censure3 = np.floor(1.8 * a_) - elif pc35_range: - censure3 = np.floor(0.4 * a_) else: - raise ValueError("Invalid range") - - for i in range(len(idx_nan_b) - 1): - if idx_nan_b[i] < idx_nan_b[i + 1]: - for j in range(len(a_)): - censure_index_front = np.arange(np.max([i - censure3[j], 0]), i) - - power_bx_plot[censure_index_front, j] = np.nan - power_by_plot[censure_index_front, j] = np.nan - power_bz_plot[censure_index_front, j] = np.nan - power_2b_plot[censure_index_front, j] = np.nan - - s_plot_x[censure_index_front, j] = np.nan - s_plot_y[censure_index_front, j] = np.nan - s_plot_z[censure_index_front, j] = np.nan - - if idx_nan_b[i] > idx_nan_b[i + 1]: - for j in range(len(a_)): - censure_index_back = np.arange(i, np.min([i + censure3[j], n_data2])) - - power_bx_plot[censure_index_back, j] = np.nan - power_by_plot[censure_index_back, j] = np.nan - power_bz_plot[censure_index_back, j] = np.nan - power_2b_plot[censure_index_back, j] = np.nan - - s_plot_x[censure_index_back, j] = np.nan - s_plot_y[censure_index_back, j] = np.nan - s_plot_z[censure_index_back, j] = np.nan - - n_data3 = len(power_2e_plot) - - for i in range(len(idx_nan_e) - 1): - if idx_nan_e[i] < idx_nan_e[i + 1]: - for j in range(len(a_)): - censure_index_front = np.arange(np.max([i - censure3[j], 1]), i) - - power_ex_plot[censure_index_front, j] = np.nan - power_ey_plot[censure_index_front, j] = np.nan - power_ez_plot[censure_index_front, j] = np.nan - power_2e_plot[censure_index_front, j] = np.nan - - power_2e_isr2_plot[censure_index_front, j] = np.nan - - s_plot_x[censure_index_front, j] = np.nan - s_plot_y[censure_index_front, j] = np.nan - s_plot_z[censure_index_front, j] = np.nan - - elif idx_nan_e[i] > idx_nan_e[i + 1]: - for j in range(len(a_)): - censure_index_back = np.arange(i, np.min([i + censure3[j], n_data3])) - - power_ex_plot[censure_index_back, j] = np.nan - power_ey_plot[censure_index_back, j] = np.nan - power_ez_plot[censure_index_back, j] = np.nan - power_2e_plot[censure_index_back, j] = np.nan - - power_2e_isr2_plot[censure_index_back, j] = np.nan - - s_plot_x[censure_index_back, j] = np.nan - s_plot_y[censure_index_back, j] = np.nan - s_plot_z[censure_index_back, j] = np.nan - - else: - continue - - n_data4 = len(power_2e_isr2_plot) - - for i in range(len(idx_nan_eisr2) - 1): - if idx_nan_eisr2[i] < idx_nan_eisr2[i + 1]: - for j in range(len(a_)): - censure_index_front = np.arange(np.max([i - censure3[j], 0]), i) - - power_2e_isr2_plot[censure_index_front, j] = np.nan + censure3 = np.floor(0.4 * a_) - elif idx_nan_eisr2[i] > idx_nan_eisr2[i + 1]: - for j in range(len(a_)): - censure_index_back = np.arange(i, np.min([i + censure3[j], n_data4])) + # Censure magnetic fied + power_bx_plot = _censure_plot(power_bx_plot, idx_nan_b, censure3, n_power_b, a_) + power_by_plot = _censure_plot(power_by_plot, idx_nan_b, censure3, n_power_b, a_) + power_bz_plot = _censure_plot(power_bz_plot, idx_nan_b, censure3, n_power_b, a_) + power_2b_plot = _censure_plot(power_2b_plot, idx_nan_b, censure3, n_power_b, a_) + + # Censure electric field + n_power_e = len(power_2e_plot) + power_ex_plot = _censure_plot(power_ex_plot, idx_nan_e, censure3, n_power_e, a_) + power_ey_plot = _censure_plot(power_ey_plot, idx_nan_e, censure3, n_power_e, a_) + power_ez_plot = _censure_plot(power_ez_plot, idx_nan_e, censure3, n_power_e, a_) + power_2e_plot = _censure_plot(power_2e_plot, idx_nan_e, censure3, n_power_e, a_) + + power_2e_isr2_plot = _censure_plot( + power_2e_isr2_plot, idx_nan_e, censure3, n_power_e, a_ + ) - power_2e_isr2_plot[censure_index_back, j] = np.nan + # Censure poynting flux + s_plot_x = _censure_plot(s_plot_x, idx_nan_b, censure3, n_power_b, a_) + s_plot_x = _censure_plot(s_plot_x, idx_nan_e, censure3, n_power_e, a_) + s_plot_y = _censure_plot(s_plot_y, idx_nan_b, censure3, n_power_b, a_) + s_plot_y = _censure_plot(s_plot_y, idx_nan_e, censure3, n_power_e, a_) + s_plot_z = _censure_plot(s_plot_z, idx_nan_b, censure3, n_power_b, a_) + s_plot_z = _censure_plot(s_plot_z, idx_nan_e, censure3, n_power_e, a_) + + n_power_2e_isr2 = len(power_2e_isr2_plot) + power_2e_isr2_plot = _censure_plot( + power_2e_isr2_plot, idx_nan_eisr2, censure3, n_power_2e_isr2, a_ + ) power_bx_plot = _average_data(power_bx_plot, in_time, out_time) power_by_plot = _average_data(power_by_plot, in_time, out_time) @@ -970,9 +912,9 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): # Output res["t"] = unix2datetime64(out_time) - res["f"] = frequency_vec + res["f"] = frequency_vec[::-1] res["bb_xxyyzzss"] = xr.DataArray( - bb_xxyyzzss, + bb_xxyyzzss[:, ::-1, ...], coords=[res["t"], res["f"], ["xx", "yy", "zz", "ss"]], dims=["time", "frequency", "comp"], ) @@ -989,6 +931,8 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): s_plot_x = np.real(_average_data(s_plot_x, in_time, out_time)) s_plot_y = np.real(_average_data(s_plot_y, in_time, out_time)) s_plot_z = np.real(_average_data(s_plot_z, in_time, out_time)) + + # TODO: check that it's correct (MATLAB weird stuff) s_azimuth, s_elevation, s_r = cart2sph(s_plot_x, s_plot_y, s_plot_z) ee_xxyyzzss = _ee_xxyyzzss( @@ -1012,19 +956,19 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): res["ee_ss"] = power_2e_isr2_plot.astype(np.float64) res["ee_xxyyzzss"] = xr.DataArray( - ee_xxyyzzss, + ee_xxyyzzss[:, ::-1, ...], coords=[res["t"], res["f"], ["xx", "yy", "zz", "ss"]], dims=["time", "frequency", "comp"], ) res["pf_xyz"] = xr.DataArray( - poynting_xyz, + poynting_xyz[:, ::-1, ...], coords=[res["t"], res["f"], ["x", "y", "z"]], dims=["time", "frequency", "comp"], ) res["pf_rtp"] = xr.DataArray( - poynting_r_th_ph, + poynting_r_th_ph[:, ::-1, ...], coords=[res["t"], res["f"], ["rho", "theta", "phi"]], dims=["time", "frequency", "comp"], ) @@ -1048,23 +992,29 @@ def ebsp(e_xyz, delta_b, full_b, b_bgd, xyz, freq_int, **kwargs): # Output res["dop"] = xr.DataArray( - np.real(dop_3d), coords=[res["t"], res["f"]], dims=["time", "frequency"] + np.real(dop_3d[:, ::-1]), + coords=[res["t"], res["f"]], + dims=["time", "frequency"], ) res["dop2d"] = xr.DataArray( - np.real(dop_2d), coords=[res["t"], res["f"]], dims=["time", "frequency"] + np.real(dop_2d[:, ::-1]), + coords=[res["t"], res["f"]], + dims=["time", "frequency"], ) res["planarity"] = xr.DataArray( - planarity, coords=[res["t"], res["f"]], dims=["time", "frequency"] + planarity[:, ::-1], coords=[res["t"], res["f"]], dims=["time", "frequency"] ) res["ellipticity"] = xr.DataArray( - ellipticity, coords=[res["t"], res["f"]], dims=["time", "frequency"] + ellipticity[:, ::-1], + coords=[res["t"], res["f"]], + dims=["time", "frequency"], ) res["k_tp"] = xr.DataArray( - k_th_ph_svd_fac, + k_th_ph_svd_fac[:, ::-1, ...], coords=[res["t"], res["f"], ["theta", "phi"]], dims=["time", "frequency", "comp"], ) diff --git a/pyrfu/pyrf/edb.py b/pyrfu/pyrf/edb.py index 56715efa..0f467426 100644 --- a/pyrfu/pyrf/edb.py +++ b/pyrfu/pyrf/edb.py @@ -3,6 +3,7 @@ # 3rd party imports import numpy as np +import xarray as xr # Local imports from .resample import resample @@ -11,9 +12,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -79,6 +80,10 @@ def edb(e_xyz, b_bgd, angle_lim: float = 20.0, flag_method: str = "E.B=0"): """ + assert isinstance(e_xyz, xr.DataArray), "e_xyz must be a xarray.DataArray" + assert isinstance(b_bgd, xr.DataArray), "b_bgd must be a xarray.DataArray" + assert isinstance(angle_lim, (int, float)), "angle_lime must be int or float" + flag_method, default_value = _check_method(flag_method) # Make sure that background magnetic field sampling matches the @@ -91,27 +96,40 @@ def edb(e_xyz, b_bgd, angle_lim: float = 20.0, flag_method: str = "E.B=0"): if flag_method.lower() == "e.b=0": # Calculate using assumption E.B=0 - b_angle = np.arctan2(b_data[:, 2], np.linalg.norm(b_data[:, :2], axis=1)) + b_angle = np.arctan2( + b_data[:, 2], + np.linalg.norm(b_data[:, :2], axis=1), + ) b_angle = np.rad2deg(b_angle) ind = np.abs(b_angle) > angle_lim if True in ind: - e_data[ind, 2] = -np.sum(e_data[ind, :2] * b_data[ind, :2], axis=1) + e_data[ind, 2] = -np.sum( + e_data[ind, :2] * b_data[ind, :2], + axis=1, + ) e_data[ind, 2] /= b_data[ind, 2] else: # Calculate using assumption that E field along the B projection is # coming from parallel electric field - b_angle = np.arctan2(b_data[:, 2], np.linalg.norm(b_data[:, :2], axis=1)) + b_angle = np.arctan2( + b_data[:, 2], + np.linalg.norm(b_data[:, :2], axis=1), + ) b_angle = np.rad2deg(b_angle) ind = np.abs(b_angle) < angle_lim if True in ind: e_data[ind, 2] = np.sum(e_data[ind, :2] * b_data[ind, :2], axis=1) e_data[ind, 2] *= b_data[ind, 2] - e_data[ind, 2] /= np.linalg.norm(b_data[:, :2], axis=1) ** 2 + e_data[ind, 2] /= np.linalg.norm(b_data[ind, :2], axis=1) ** 2 b_angle = ts_scalar(e_xyz.time.data, b_angle, {"UNITS": "degrees"}) - e_data = ts_vec_xyz(e_xyz.time.data, e_data, {"UNITS": e_xyz.attrs["UNITS"]}) + e_data = ts_vec_xyz( + e_xyz.time.data, + e_data, + e_xyz.attrs, + ) return e_data, b_angle diff --git a/pyrfu/pyrf/end.py b/pyrfu/pyrf/end.py index 5961e71c..87c8d8d0 100644 --- a/pyrfu/pyrf/end.py +++ b/pyrfu/pyrf/end.py @@ -3,12 +3,13 @@ # 3rd party import import numpy as np +import xarray as xr __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -17,7 +18,7 @@ def end(inp): Parameters ---------- - inp : xarray.DataArray + inp : xarray.DataArray or xarray.Dataset Time series of the input variable. Returns @@ -27,6 +28,9 @@ def end(inp): """ + message = "inp must be a xarray.DataArray or xarray.Dataset" + assert isinstance(inp, (xr.DataArray, xr.Dataset)), message + out = inp.time.data[-1].astype(np.int64) / 1e9 return out diff --git a/pyrfu/pyrf/estimate.py b/pyrfu/pyrf/estimate.py index 1e59e5e7..e4b73228 100644 --- a/pyrfu/pyrf/estimate.py +++ b/pyrfu/pyrf/estimate.py @@ -3,14 +3,13 @@ # 3rd party imports import numpy as np - from scipy import constants __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -23,15 +22,13 @@ def _estimate_capa_sphe(radius): def _estimate_capa_wire(radius, length): - if not radius or radius == 0 or not length: - out = None - elif length and radius and length >= 10 * radius: + if length and radius != 0 and length >= 10 * radius: l_ = np.log(length / radius) out = length / l_ * (1 + 1 / l_ * (1 - np.log(2))) out *= 2 * np.pi * constants.epsilon_0 else: raise ValueError( - "capacitance_wire requires length at least 10 times " "the radius!" + "capacitance_wire requires length at least 10 times the radius!", ) return out diff --git a/pyrfu/pyrf/extend_tint.py b/pyrfu/pyrf/extend_tint.py index cde5034b..50512c6b 100644 --- a/pyrfu/pyrf/extend_tint.py +++ b/pyrfu/pyrf/extend_tint.py @@ -4,15 +4,16 @@ # 3rd party imports import numpy as np +from .datetime642iso8601 import datetime642iso8601 + # Local imports from .iso86012datetime64 import iso86012datetime64 -from .datetime642iso8601 import datetime642iso8601 __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -23,7 +24,7 @@ def extend_tint(tint, ext: list = None): ---------- tint : list of str Reference time interval to extend. - ext : list of float or list of int + ext : list of float or list of float Number of seconds to extend time interval [left extend, right extend]. @@ -46,14 +47,25 @@ def extend_tint(tint, ext: list = None): """ + # Set default extension if ext is None: - ext = [-60, 60] - - # Convert extension to timedelta64 in s units - ext = np.array(ext).astype("timedelta64[s]") - - # Original time interval to datetime64 format in ns units - tint_ori = iso86012datetime64(np.array(tint)) + ext = [-60.0, 60.0] + + # Make sure tint and ext are 2 elements array_like + message = "must be array_like with 2 elements" + assert isinstance(tint, (np.ndarray, list)) and len(tint) == 2, f"tint {message}" + assert isinstance(ext, (np.ndarray, list)) and len(ext) == 2, f"ext {message}" + + # Convert extension to timedelta64[ns] + ext = (np.array(ext) * 1e9).astype("timedelta64[ns]") + + # Original time interval to datetime64[ns] + if isinstance(tint[0], np.datetime64): + tint_ori = tint + elif isinstance(tint[0], str): + tint_ori = iso86012datetime64(np.array(tint)) + else: + raise TypeError("Invalid time format!! Must be datetime64 or str!!") # New time interval in iso 8601 format tint_new = list(datetime642iso8601(tint_ori + ext)) diff --git a/pyrfu/pyrf/filt.py b/pyrfu/pyrf/filt.py index 60134a14..b2e57e9d 100644 --- a/pyrfu/pyrf/filt.py +++ b/pyrfu/pyrf/filt.py @@ -4,14 +4,13 @@ # 3rd party imports import numpy as np import xarray as xr - from scipy import signal __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -22,29 +21,37 @@ def _ellip_coefficients(f_min, f_max, order): if f_min == 0: if order == -1: order, f_max = signal.ellipord( - f_max, np.min([f_max * 1.1, 0.9999]), 0.5, 60 + f_max, + np.min([f_max * 1.1, 0.9999]), + 0.5, + 60, ) num1, den1 = signal.ellip(order, 0.5, 60, f_max, btype="lowpass") elif f_max == 0: if order == -1: order, f_min = signal.ellipord( - f_min, np.min([f_min * 1.1, 0.9999]), 0.5, 60 + f_min, + np.min([f_min * 1.1, 0.9999]), + 0.5, + 60, ) num1, den1 = signal.ellip(order, 0.5, 60, f_min, btype="highpass") else: if order == -1: - order, f_max = signal.ellipord( - f_max, np.min([f_max * 1.3, 0.9999]), 0.5, 60 + order1, f_max = signal.ellipord( + f_max, + np.min([f_max * 1.3, 0.9999]), + 0.5, + 60, ) + order2, f_min = signal.ellipord(f_min, f_min * 0.75, 0.5, 60) + else: + order1, order2 = [order, order] - num1, den1 = signal.ellip(order, 0.5, 60, f_max) - - if order == -1: - order, f_min = signal.ellipord(f_min, f_min * 0.75, 0.5, 60) - - num2, den2 = signal.ellip(order, 0.5, 60, f_min) + num1, den1 = signal.ellip(order1, 0.5, 60, f_max) + num2, den2 = signal.ellip(order2, 0.5, 60, f_min) return num1, den1, num2, den2 @@ -96,11 +103,17 @@ def filt(inp, f_min: float = 0.0, f_max: float = 1.0, order: int = -1): """ + assert isinstance(inp, xr.DataArray), "inp must be a xarray.DataArray" + f_samp = 1 / (np.median(np.diff(inp.time)).astype(np.int64) * 1e-9) # Data of the input inp_data = inp.data + assert isinstance(f_min, (int, float)), "f_min must be int or float" + assert isinstance(f_max, (int, float)), "f_max must be int or float" + assert isinstance(order, (int, float)), "order must be int or float" + f_min, f_max = [f_min / (f_samp / 2), f_max / (f_samp / 2)] f_max = np.min([f_max, 1.0]) @@ -120,10 +133,19 @@ def filt(inp, f_min: float = 0.0, f_max: float = 1.0, order: int = -1): out_data[:, i_col] = signal.filtfilt(num1, den1, inp_data[:, i_col]) if num2 is not None and den2 is not None: - out_data[:, i_col] = signal.filtfilt(num2, den2, out_data[:, i_col]) + out_data[:, i_col] = signal.filtfilt( + num2, + den2, + out_data[:, i_col], + ) if inp_data.shape[1] == 1: out_data = out_data[:, 0] - out = xr.DataArray(out_data, coords=inp.coords, dims=inp.dims, attrs=inp.attrs) + out = xr.DataArray( + out_data, + coords=inp.coords, + dims=inp.dims, + attrs=inp.attrs, + ) return out diff --git a/pyrfu/pyrf/find_closest.py b/pyrfu/pyrf/find_closest.py index ef2fad59..c93c8c6a 100644 --- a/pyrfu/pyrf/find_closest.py +++ b/pyrfu/pyrf/find_closest.py @@ -3,14 +3,13 @@ # 3rd party imports import numpy as np - from scipy import interpolate __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -46,13 +45,19 @@ def find_closest(inp1, inp2): while flag: flag_t1 = np.zeros(inp1.shape) tckt1 = interpolate.interp1d( - inp1, np.arange(nt1), kind="nearest", fill_value="extrapolate" + inp1, + np.arange(nt1), + kind="nearest", + fill_value="extrapolate", ) flag_t1[tckt1(inp2)] = 1 flag_t2 = np.zeros(inp2.shape) tckt2 = interpolate.interp1d( - inp2, np.arange(nt2), kind="nearest", fill_value="extrapolate" + inp2, + np.arange(nt2), + kind="nearest", + fill_value="extrapolate", ) flag_t2[tckt2(inp1)] = 1 diff --git a/pyrfu/pyrf/get_omni_data.py b/pyrfu/pyrf/get_omni_data.py index 4421cff9..a95a54ee 100644 --- a/pyrfu/pyrf/get_omni_data.py +++ b/pyrfu/pyrf/get_omni_data.py @@ -1,9 +1,10 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import datetime + # Built-in imports import urllib -import datetime # 3rd party imports import numpy as np @@ -14,9 +15,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -155,7 +156,7 @@ def get_omni_data(variables, tint, database: str = "omni_hour"): """ - tint = iso86012datetime64(np.array(tint)).astype("gsm", "gsm>gse"], "invalid flag" + assert isinstance(inp, xr.DataArray), "inp must be a xarray.DataArray" + assert inp.ndim == 2 and inp.shape[1] == 3, "inp must be a vector" + + message = "flag must be a string gse>gsm or gsm>gse" + assert isinstance(flag, str) and flag.lower() in ["gse>gsm", "gsm>gse"], message out = cotrans(inp, flag) diff --git a/pyrfu/pyrf/histogram.py b/pyrfu/pyrf/histogram.py index ccb044f1..ba351ed9 100644 --- a/pyrfu/pyrf/histogram.py +++ b/pyrfu/pyrf/histogram.py @@ -1,28 +1,19 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -# Built-in imports -from typing import Union - # 3rd party imports import numpy as np import xarray as xr __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.14" +__version__ = "2.4.2" __status__ = "Prototype" -def histogram( - inp, - bins: Union[str, int, np.ndarray, list] = 100, - y_range: tuple = None, - weights=None, - density: bool = True, -): +def histogram(inp, bins=100, y_range=None, weights=None, density=True): r"""Computes 1D histogram of the inp with bins bins Parameters @@ -33,15 +24,15 @@ def histogram( Number of bins. Default is ``bins=100``. y_range : (float, float), Optional The lower and upper range of the bins. If not provided, range - is simply ``(a.min(), a.max())``. Values outside the range are + is simply ``(inp.min(), inp.max())``. Values outside the range are ignored. The first element of the range must be less than or equal to the second. `range` affects the automatic bin computation as well. While bin width is computed to be optimal based on the actual data within `range`, the bin count will fill the entire range including portions containing no data. weights : array_like, Optional - An array of weights, of the same shape as `a`. Each value in - `a` only contributes its associated weight towards the bin count + An array of weights, of the same shape as `inp`. Each value in + `inp` only contributes its associated weight towards the bin count (instead of 1). If `density` is True, the weights are normalized, so that the integral of the density over the range remains 1. @@ -60,8 +51,16 @@ def histogram( """ + # Check input + assert isinstance(inp, xr.DataArray), "inp must be a xarray.DataArray" + assert inp.ndim == 1, "inp must be a scalar time series" + hist, bins = np.histogram( - inp.data, bins=bins, range=y_range, weights=weights, density=density + inp.data, + bins=bins, + range=y_range, + weights=weights, + density=density, ) bin_center = (bins[1:] + bins[:-1]) * 0.5 diff --git a/pyrfu/pyrf/histogram2d.py b/pyrfu/pyrf/histogram2d.py index 1714ab97..1af47a9d 100644 --- a/pyrfu/pyrf/histogram2d.py +++ b/pyrfu/pyrf/histogram2d.py @@ -1,9 +1,6 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -# Built-in imports -from typing import Union - # 3rd party imports import numpy as np import xarray as xr @@ -13,20 +10,13 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.14" +__version__ = "2.4.2" __status__ = "Prototype" -def histogram2d( - inp1, - inp2, - bins: Union[str, int, tuple] = 100, - y_range: tuple = None, - weights=None, - density: bool = True, -): +def histogram2d(inp1, inp2, bins=100, y_range=None, weights=None, density=True): r"""Computes 2d histogram of inp2 vs inp1 with nbins number of bins. Parameters @@ -84,7 +74,7 @@ def histogram2d( >>> b_mag = pyrf.norm(b_xyz) >>> j_mag = pyrf.norm(j_xyz) - Histogram of |J| vs |B| + Histogram of J vs B >>> h2d_b_j = pyrf.histogram2d(b_mag, j_mag) @@ -95,12 +85,21 @@ def histogram2d( inp2 = resample(inp2, inp1) h2d, x_edges, y_edges = np.histogram2d( - inp1.data, inp2.data, bins=bins, range=y_range, weights=weights, density=density + inp1.data, + inp2.data, + bins=bins, + range=y_range, + density=density, + weights=weights, ) x_bins = x_edges[:-1] + np.median(np.diff(x_edges)) / 2 y_bins = y_edges[:-1] + np.median(np.diff(y_edges)) / 2 - out = xr.DataArray(h2d, coords=[x_bins, y_bins], dims=["x_bins", "y_bins"]) + out = xr.DataArray( + h2d, + coords=[x_bins, y_bins], + dims=["x_bins", "y_bins"], + ) return out diff --git a/pyrfu/pyrf/increments.py b/pyrfu/pyrf/increments.py index 45186a19..2f7a523d 100644 --- a/pyrfu/pyrf/increments.py +++ b/pyrfu/pyrf/increments.py @@ -4,14 +4,13 @@ # 3rd party imports import numpy as np import xarray as xr - from scipy.stats import kurtosis __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -40,32 +39,27 @@ def increments(inp, scale: int = 10): """ - if inp.data.ndim == 1: + assert isinstance(inp, xr.DataArray), "inp must be a xarray.DataArray" + assert inp.ndim < 4, "inp must ber a scalar, vector or tensor" + + if inp.ndim == 1: data = inp.data[:, np.newaxis] else: data = inp.data - delta_inp = data[scale:, :] - data[:-scale, :] + # Compute the increments + delta_inp = data[scale:, ...] - data[:-scale, ...] - result = np.array(delta_inp) + # Compute kurtosis of the increments + kurt = kurtosis(delta_inp, axis=0, fisher=False) - cols = [inp.coords[dim].data for dim in inp.dims] + times, *comp = [inp.coords[dim].data for dim in inp.dims] - if inp.data.ndim == 1: - result = xr.DataArray( - np.squeeze(result), - coords=[cols[0][0 : len(delta_inp)]], - dims=inp.dims, - attrs=inp.attrs, - ) - else: - result = xr.DataArray( - np.squeeze(result), - coords=[cols[0][0 : len(delta_inp)], *cols[1:]], - dims=inp.dims, - attrs=inp.attrs, - ) - - kurt = kurtosis(result, axis=0, fisher=False) + result = xr.DataArray( + np.squeeze(delta_inp), + coords=[times[0 : len(delta_inp)], *comp], + dims=inp.dims, + attrs=inp.attrs, + ) return kurt, result diff --git a/pyrfu/pyrf/int_sph_dist.py b/pyrfu/pyrf/int_sph_dist.py index 921459f8..453271f2 100644 --- a/pyrfu/pyrf/int_sph_dist.py +++ b/pyrfu/pyrf/int_sph_dist.py @@ -3,13 +3,19 @@ # Built-in imports import random - -from math import cos, sin, asin, sqrt +from math import asin, cos, sin, sqrt # Third party imports import numba import numpy as np +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.2" +__status__ = "Prototype" + def int_sph_dist(vdf, speed, phi, theta, speed_grid, **kwargs): r"""Integrate a spherical distribution function to a line/plane. @@ -37,7 +43,8 @@ def int_sph_dist(vdf, speed, phi, theta, speed_grid, **kwargs): # Coordinates system transformation matrix xyz = kwargs.get("xyz", np.eye(3)) - # Number of Monte Carlo iterations and how number of MC points is weighted to data. + # Number of Monte Carlo iterations and how number of MC points is + # weighted to data. n_mc = kwargs.get("n_mc", 10) weight = kwargs.get("weight", None) @@ -53,27 +60,26 @@ def int_sph_dist(vdf, speed, phi, theta, speed_grid, **kwargs): speed_edges = kwargs.get("speed_edges", None) speed_grid_edges = kwargs.get("speed_grid_edges", None) - # Azimuthal and elevation angles steps. Assumed to be constant if not provided. + # Azimuthal and elevation angles steps. Assumed to be constant + # if not provided. d_phi = np.abs(np.median(np.diff(phi))) * np.ones_like(phi) d_phi = kwargs.get("d_phi", d_phi) d_theta = np.abs(np.median(np.diff(theta))) * np.ones_like(theta) d_theta = kwargs.get("d_theta", d_theta) # azimuthal angle of projection plane - n_az_g = 32 + n_az_g = len(phi) d_phi_g = 2 * np.pi / n_az_g phi_grid = np.linspace(0, 2 * np.pi - d_phi_g, n_az_g) + d_phi_g / 2 phi_grid = kwargs.get("phi_grid", phi_grid) - # Overwrite projection dimension if azimuthal angle of projection plane is not - # provided. Set the azimuthal angle grid width. - if phi_grid is None or projection_dim == "1d": - projection_dim = "1d" - d_phi_grid = 1.0 - elif phi_grid is not None and projection_dim.lower() in ["2d", "3d"]: + # Overwrite projection dimension if azimuthal angle of projection + # plane is not provided. Set the azimuthal angle grid width. + if phi_grid is not None and projection_dim.lower() in ["2d", "3d"]: d_phi_grid = np.median(np.diff(phi_grid)) else: - raise RuntimeError("1d projection with phi_grid provided doesn't make sense!!") + projection_dim = "1d" + d_phi_grid = 1.0 # Make sure the transformation matrix is orthonormal. x_phat = xyz[:, 0] / np.linalg.norm(xyz[:, 0]) # re-normalize @@ -106,7 +112,7 @@ def int_sph_dist(vdf, speed, phi, theta, speed_grid, **kwargs): d_v_grid = np.diff(speed_grid_edges) else: mean_diff = np.mean(np.diff(speed_grid)) - msg = "For a cartesian grid (default), all velocity bins must be equal!!" + msg = "For a cartesian grid, all velocity bins must be equal!!" assert (np.diff(speed_grid) / mean_diff - 1 < 1e-2).all(), msg d_v_grid = mean_diff @@ -116,41 +122,18 @@ def int_sph_dist(vdf, speed, phi, theta, speed_grid, **kwargs): if weight == "lin": n_mc_mat = np.ceil(n_sum / np.sum(vdf) * vdf) elif weight == "log": - n_mc_mat = np.ceil(n_sum / np.sum(np.log10(vdf + 1)) * np.log10(vdf + 1)) + n_mc_mat = np.ceil( + n_sum / np.sum(np.log10(vdf + 1)) * np.log10(vdf + 1), + ) else: n_mc_mat = np.zeros_like(vdf) n_mc_mat[vdf != 0] = n_mc n_mc_mat = n_mc_mat.astype(int) - if projection_base == "pol": - # Area or line element (primed) - d_a_grid = speed_grid ** (int(projection_dim[0]) - 1) * d_phi_grid * d_v_grid - d_a_grid = d_a_grid.astype(np.float64) - - if projection_dim == "1d": - f_g = mc_pol_1d( - vdf, - speed, - phi, - theta, - d_v, - d_v_m, - d_phi, - d_theta, - speed_grid_edges, - d_a_grid, - v_lim, - a_lim, - n_mc_mat, - r_mat, - ) - else: - raise NotImplementedError("2d projection on polar grid is not ready yet!!") - - elif projection_base == "cart" and projection_dim == "2d": + if projection_base == "cart" and projection_dim == "2d": d_a_grid = d_v_grid**2 - f_g = mc_cart_2d( + f_g = _mc_cart_2d( vdf, speed, phi, @@ -168,7 +151,7 @@ def int_sph_dist(vdf, speed, phi, theta, speed_grid, **kwargs): ) elif projection_base == "cart" and projection_dim == "3d": d_a_grid = d_v_grid**3 - f_g = mc_cart_3d( + f_g = _mc_cart_3d( vdf, speed, phi, @@ -185,11 +168,33 @@ def int_sph_dist(vdf, speed, phi, theta, speed_grid, **kwargs): r_mat, ) else: - raise ValueError("Invalid base!!") + # Area or line element (primed) + d_a_grid = speed_grid ** (int(projection_dim[0]) - 1) * d_phi_grid * d_v_grid + d_a_grid = d_a_grid.astype(np.float64) - if projection_dim == "1d": - pst = {"f": f_g, "v": speed_grid, "v_edges": speed_grid_edges} - elif projection_dim == "2d" and projection_base == "cart": + if projection_dim == "1d": + f_g = _mc_pol_1d( + vdf, + speed, + phi, + theta, + d_v, + d_v_m, + d_phi, + d_theta, + speed_grid_edges, + d_a_grid, + v_lim, + a_lim, + n_mc_mat, + r_mat, + ) + else: + raise NotImplementedError( + "2d projection on polar grid is not ready yet!!", + ) + + if projection_dim == "2d" and projection_base == "cart": pst = { "f": f_g, "vx": speed_grid, @@ -208,13 +213,13 @@ def int_sph_dist(vdf, speed, phi, theta, speed_grid, **kwargs): "vz_edges": speed_grid_edges, } else: - raise NotImplementedError("2d projection on polar grid is not ready yet!!") + pst = {"f": f_g, "vx": speed_grid, "vx_edges": speed_grid_edges} return pst -@numba.jit(fastmath=True) -def mc_pol_1d( +@numba.jit(cache=True, nogil=True, parallel=True, nopython=True) +def _mc_pol_1d( vdf, v, phi, @@ -253,12 +258,12 @@ def mc_pol_1d( vg_egdes : double Bin centers of the velocity of the projection grid. d_a_grid : double - Bin centers of the azimuthal angle of the projection in radians in the span - [0,2*pi]. If this input is given, the projection will be 2D. If it is omitted, - the projection will be 1D. + Bin centers of the azimuthal angle of the projection in radians in + the span [0,2*pi]. If this input is given, the projection will be 2D. + If it is omitted, the projection will be 1D. v_lim : double - Limits on the out-of-plane velocity interval in 2D and "transverse" velocity - in 1D. + Limits on the out-of-plane velocity interval in 2D and "transverse" + velocity in 1D. a_lim : double Angular limit in degrees, can be combined with v_lim. n_mc : double @@ -289,13 +294,11 @@ def mc_pol_1d( c_ijk = dtau_ijk / n_mc_ijk f_ijk = vdf[i, j, k] - for l in range(n_mc_ijk): + for _ in range(n_mc_ijk): d_v_mc = -random.random() * d_v[i] - d_v_m[0] d_phi_mc = (random.random() - 0.5) * d_phi[j] d_the_mc = (random.random() - 0.5) * d_theta[k] - # printf("%f, %f\n", d_v_m[0], d_v_mc) - # convert instrument bin to cartesian velocity v_mc = v[i] + d_v_mc phi_mc = phi[j] + d_phi_mc @@ -326,8 +329,8 @@ def mc_pol_1d( return f_g -@numba.jit(fastmath=True) -def mc_cart_3d( +@numba.jit(cache=True, nogil=True, parallel=True, nopython=True) +def _mc_cart_3d( vdf, v, phi, @@ -366,12 +369,12 @@ def mc_cart_3d( vg_egdes : double Bin centers of the velocity of the projection grid. d_a_grid : double - Bin centers of the azimuthal angle of the projection in radians in the span - [0,2*pi]. If this input is given, the projection will be 2D. If it is omitted, - the projection will be 1D. + Bin centers of the azimuthal angle of the projection in radians in + the span [0,2*pi]. If this input is given, the projection will be 2D. + If it is omitted, the projection will be 1D. v_lim : double - Limits on the out-of-plane velocity interval in 2D and "transverse" velocity - in 1D. + Limits on the out-of-plane velocity interval in 2D and "transverse" + velocity in 1D. a_lim : double Angular limit in degrees, can be combined with v_lim. n_mc : double @@ -403,7 +406,7 @@ def mc_cart_3d( c_ijk = dtau_ijk / n_mc_ijk f_ijk = vdf[i, j, k] - for l in range(n_mc_ijk): + for _ in range(n_mc_ijk): d_v_mc = -random.random() * d_v[i] - d_v_m[0] d_phi_mc = (random.random() - 0.5) * d_phi[j] d_the_mc = (random.random() - 0.5) * d_theta[k] @@ -439,8 +442,8 @@ def mc_cart_3d( return f_g -@numba.jit(nopython=True, parallel=True, fastmath=True) -def mc_cart_2d( +@numba.jit(cache=True, nogil=True, parallel=True, nopython=True) +def _mc_cart_2d( vdf, v, phi, @@ -479,11 +482,12 @@ def mc_cart_2d( vg_egdes : double Bin centers of the velocity of the projection grid. d_a_grid : double - Bin centers of the azimuthal angle of the projection in radians in the span - [0,2*pi]. If this input is given, the projection will be 2D. If it is omitted, - the projection will be 1D. + Bin centers of the azimuthal angle of the projection in radians in + the span [0,2*pi]. If this input is given, the projection will be 2D. + If it is omitted, the projection will be 1D. v_lim : double - Limits on the out-of-plane velocity interval in 2D and "transverse" velocity + Limits on the out-of-plane velocity interval in 2D and "transverse" + velocity in 1D. a_lim : double Angular limit in degrees, can be combined with v_lim. @@ -516,7 +520,7 @@ def mc_cart_2d( c_ijk = dtau_ijk / n_mc_ijk f_ijk = vdf[i, j, k] - for l in range(n_mc_ijk): + for _ in range(n_mc_ijk): d_v_mc = -random.random() * d_v[i] - d_v_m[0] d_phi_mc = (random.random() - 0.5) * d_phi[j] d_the_mc = (random.random() - 0.5) * d_theta[k] diff --git a/pyrfu/pyrf/integrate.py b/pyrfu/pyrf/integrate.py index 51ac47e0..ff4e6a11 100644 --- a/pyrfu/pyrf/integrate.py +++ b/pyrfu/pyrf/integrate.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -54,11 +54,16 @@ def integrate(inp, time_step: float = None): """ - time_tmp = inp.time.data.astype(np.int64) * 1e-9 - data_tmp = inp.data - unit_tmp = inp.attrs["UNITS"] + assert isinstance(inp, xr.DataArray), "inp must be xarray.DataArray" - data = np.hstack([time_tmp, data_tmp]) + time_tmp = inp.time.data.astype(np.float64) * 1e-9 + + if inp.data.ndim == 1: + data_tmp = inp.data[:, np.newaxis] + else: + data_tmp = inp.data + + data = np.transpose(np.vstack([time_tmp, np.transpose(data_tmp)])) delta_t = np.hstack([0, np.diff(data[:, 0])]) @@ -76,7 +81,6 @@ def integrate(inp, time_step: float = None): x_int[j_ok, j] = np.cumsum(data[j_ok, j] * delta_t[j_ok]) - out = xr.DataArray(data[:, 1:], coords=inp.coords, dims=inp.dims) - out.attrs["UNITS"] = unit_tmp + "*s" + out = xr.DataArray(np.squeeze(data[:, 1:]), coords=inp.coords, dims=inp.dims) return out diff --git a/pyrfu/pyrf/iplasma_calc.py b/pyrfu/pyrf/iplasma_calc.py index ae04d41e..e63ec178 100644 --- a/pyrfu/pyrf/iplasma_calc.py +++ b/pyrfu/pyrf/iplasma_calc.py @@ -3,14 +3,13 @@ # 3rd party imports import numpy as np - from scipy import constants __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -18,7 +17,9 @@ def _print_header(): print("=" * 70) print("IRFU plasma calculator, relativistic effects not fully included") print("velocities, gyroradia are relativistically correct") - print("can somebody fix relativstically correct frequencies Fpe, Fce,.. ?") + print( + "can somebody fix relativstically correct frequencies Fpe, Fce,.. ?", + ) print("=" * 70) @@ -59,7 +60,7 @@ def _print_other(n_d, eta, p_mag): print("\nOther parameters: ") print("*" * 17) print( - f"{'N_deb':>5} = {n_d:>6.2E} {'':<6} " f"# number of particle in Debye sphere" + f"{'N_deb':>5} = {n_d:>6.2E} {'':<6} " f"# number of particle in Debye sphere", ) print(f"{'eta':>5} = {eta:>6.2E} {'Ohm m':<6} # Spitzer resistivity") print(f"{'P_B':>5} = {p_mag:>6.2E} {'Pa':<6} # Magnetic pressure") @@ -97,7 +98,9 @@ def iplasma_calc(output: bool = False, verbose: bool = True): b_0 = float(input(f"{'Magnetic field in nT [10] ':<34}: ") or "10") * 1e-9 n_hplus = float(input(f"{'H+ desity in cc [1] ':<34}: ") or "1") * 1e6 - t_e = float(input(f"{'Electron temperature in eV [100] ':<34}: ") or "10") + t_e = float( + input(f"{'Electron temperature in eV [100] ':<34}: ") or "10", + ) t_i = float(input(f"{'Ion temperature in eV [1000] ':<34}: ") or "1000") n_i, n_e = [n_hplus] * 2 diff --git a/pyrfu/pyrf/iso86012datetime.py b/pyrfu/pyrf/iso86012datetime.py index c6bf0ae4..7fea878a 100644 --- a/pyrfu/pyrf/iso86012datetime.py +++ b/pyrfu/pyrf/iso86012datetime.py @@ -9,9 +9,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -20,7 +20,7 @@ def iso86012datetime(time): Parameters ---------- - time : ndarray or list + time : ndarray or list or str Time Returns @@ -31,7 +31,7 @@ def iso86012datetime(time): """ # Make sure that str is in ISO8601 format - time = np.array(time).astype(">> b_mag = pyrf.norm(b_xyz) >>> j_mag = pyrf.norm(j_xyz) - Mean value of |J| for 10 bins of |B| + Mean value of J for 10 bins of B >>> m_b_j = pyrf.mean_bins(b_mag, j_mag) """ - if inp1 is None: - inp1 = inp0 + assert isinstance(inp0, xr.DataArray), "inp0 must be xaray.DataArray" + assert isinstance(inp1, xr.DataArray), "inp1 must be xaray.DataArray" + + assert inp0.ndim == 1, "inp0 must be a scalar" + assert inp1.ndim == 1, "inp1 must be a scalar" x_sort = np.sort(inp0.data) x_edge = np.linspace(x_sort[0], x_sort[-1], bins + 1) @@ -90,8 +93,12 @@ def mean_bins(inp0, inp1, bins: int = 10): bins = x_edge[:-1] + np.median(np.diff(x_edge)) / 2 - out_dict = {"data": (["bins"], y_avg), "sigma": (["bins"], y_std), "bins": bins} + out_dict = { + "data": (["bins"], y_avg), + "sigma": (["bins"], y_std), + "bins": bins, + } out = xr.Dataset(out_dict) - return bins, y_avg, out + return out diff --git a/pyrfu/pyrf/mean_field.py b/pyrfu/pyrf/mean_field.py index 0bfa350d..2fd8b80f 100644 --- a/pyrfu/pyrf/mean_field.py +++ b/pyrfu/pyrf/mean_field.py @@ -6,9 +6,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/medfilt.py b/pyrfu/pyrf/medfilt.py index 16ea495c..3d31a381 100644 --- a/pyrfu/pyrf/medfilt.py +++ b/pyrfu/pyrf/medfilt.py @@ -1,17 +1,17 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -# 3rd party imports -import xarray as xr import numpy as np +# 3rd party imports +import xarray as xr from scipy import signal __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/median_bins.py b/pyrfu/pyrf/median_bins.py index 428c525d..f53e9e1f 100644 --- a/pyrfu/pyrf/median_bins.py +++ b/pyrfu/pyrf/median_bins.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -66,12 +66,18 @@ def median_bins(inp0, inp1, bins: int = 10): >>> b_mag = pyrf.norm(b_xyz) >>> j_mag = pyrf.norm(j_xyz) - Median value of |J| for 10 bins of |B| + Median value of J for 10 bins of B >>> med_b_j = pyrf.mean_bins(b_mag, j_mag) """ + assert isinstance(inp0, xr.DataArray), "inp0 must be xaray.DataArray" + assert isinstance(inp1, xr.DataArray), "inp1 must be xaray.DataArray" + + assert inp0.ndim == 1, "inp0 must be a scalar" + assert inp1.ndim == 1, "inp1 must be a scalar" + x_sort = np.sort(inp0.data) x_edge = np.linspace(x_sort[0], x_sort[-1], bins + 1) @@ -87,8 +93,12 @@ def median_bins(inp0, inp1, bins: int = 10): bins = x_edge[:-1] + np.median(np.diff(x_edge)) / 2 - out_dict = {"data": (["bins"], y_med), "sigma": (["bins"], y_std), "bins": bins} + out_dict = { + "data": (["bins"], y_med), + "sigma": (["bins"], y_std), + "bins": bins, + } out = xr.Dataset(out_dict) - return bins, y_med, out + return out diff --git a/pyrfu/pyrf/movmean.py b/pyrfu/pyrf/movmean.py index bbc296bf..3e8d2b58 100644 --- a/pyrfu/pyrf/movmean.py +++ b/pyrfu/pyrf/movmean.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -51,7 +51,7 @@ def movmean(inp, n_pts: int = 100): Running average the pressure tensor over 10s >>> fs = pyrf.calc_fs(p_xyz_i) - >>>> p_xyz_i = pyrf.movmean(p_xyz_i, int(10 * fs)) + >>> p_xyz_i = pyrf.movmean(p_xyz_i, int(10 * fs)) """ diff --git a/pyrfu/pyrf/mva.py b/pyrfu/pyrf/mva.py index 9b730008..0feff3df 100644 --- a/pyrfu/pyrf/mva.py +++ b/pyrfu/pyrf/mva.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -58,6 +58,8 @@ def mva(inp, flag: str = "mvar"): """ + assert flag.lower() in ["mvar", "=0", "td"], "invalid method!!" + inp_data = inp.data n_t = inp_data.shape[0] @@ -67,14 +69,11 @@ def mva(inp, flag: str = "mvar"): m_mu_nu_m = np.mean(inp_data[:, idx_1] * inp_data[:, idx_2], 0) m_mu_nu_m -= np.mean(inp_data, 0)[idx_1] * np.mean(inp_data, 0)[idx_2] - elif flag.lower() == "td": - m_mu_nu_m = np.mean(inp_data[:, idx_1] * inp_data[:, idx_2], 0) - else: - raise ValueError("invalid flag") + m_mu_nu_m = np.mean(inp_data[:, idx_1] * inp_data[:, idx_2], 0) m_mu_nu = np.array( - [m_mu_nu_m[[0, 3, 4]], m_mu_nu_m[[3, 1, 5]], m_mu_nu_m[[4, 5, 2]]] + [m_mu_nu_m[[0, 3, 4]], m_mu_nu_m[[3, 1, 5]], m_mu_nu_m[[4, 5, 2]]], ) # Compute eigenvalues and eigenvectors @@ -126,7 +125,7 @@ def mva(inp, flag: str = "mvar"): m_mu_nu_m -= inp_data_2_m[idx_1] * inp_data_2_m[idx_2] m_mu_nu = np.array( - [m_mu_nu_m[[0, 3, 4]], m_mu_nu_m[[3, 1, 5]], m_mu_nu_m[[4, 5, 2]]] + [m_mu_nu_m[[0, 3, 4]], m_mu_nu_m[[3, 1, 5]], m_mu_nu_m[[4, 5, 2]]], ) lamb, lmn = np.linalg.eig(m_mu_nu) @@ -134,7 +133,7 @@ def mva(inp, flag: str = "mvar"): lamb, lmn = [lamb[lamb.argsort()[::-1]], lmn[:, lamb.argsort()[::-1]]] # Force the maximum variance direction to be positive - #lmn[:, 0] *= np.sign(lmn[np.argmax(lmn[:, 0]), 0]) + # lmn[:, 0] *= np.sign(lmn[np.argmax(lmn[:, 0]), 0]) # lamb[2], lmn[:, 2] = [l_min, np.cross(lmn[:, 0], lmn[:, 1])] elif flag.lower() == "td": @@ -150,7 +149,7 @@ def mva(inp, flag: str = "mvar"): m_mu_nu_m -= inp_data_2_m[idx_1] * inp_data_2_m[idx_2] m_mu_nu = np.array( - [m_mu_nu_m[[0, 3, 4]], m_mu_nu_m[[3, 1, 5]], m_mu_nu_m[[4, 5, 2]]] + [m_mu_nu_m[[0, 3, 4]], m_mu_nu_m[[3, 1, 5]], m_mu_nu_m[[4, 5, 2]]], ) lamb, lmn = np.linalg.eig(m_mu_nu) @@ -158,6 +157,8 @@ def mva(inp, flag: str = "mvar"): lamb, lmn = [lamb[lamb.argsort()[::-1]], lmn[:, lamb.argsort()[::-1]]] lamb[2], lmn[:, 2] = [l_min, np.cross(lmn[:, 0], lmn[:, 1])] + else: + pass out_data = (lmn.T @ inp_data.T).T diff --git a/pyrfu/pyrf/mva_gui.py b/pyrfu/pyrf/mva_gui.py new file mode 100644 index 00000000..8d4a1061 --- /dev/null +++ b/pyrfu/pyrf/mva_gui.py @@ -0,0 +1,294 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# 3rd party imports +import matplotlib.pyplot as plt +import numpy as np +from matplotlib import dates, gridspec +from matplotlib.widgets import Button, SpanSelector + +# Local imports +from ..plot import plot_line +from .mva import mva +from .new_xyz import new_xyz +from .norm import norm +from .time_clip import time_clip + +__author__ = "Atlas Silverhult" +__email__ = "atlas.silverhult.9977@student.uu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.2" +__status__ = "Prototype" + +__all__ = ["mva_gui"] + + +def mva_gui(inp): + r"""GUI to interactively perform minimum variance analysis (MVA) on + time series data by selecting the time interval to apply MVA on. + The return of this function is a callback to the GUI object + and class attributes like the minimum variance direction vector are accesable + through this callback by the method get_minvar(). + + + Parameters + ---------- + inp : xarray.DataArray + Time series of the quantity to load into GUI and perform MVA on. + + Returns + ------- + mva_callback : + Returns MvaGui object to access attributes. In order to keep + GUI responsive and interactive, a reference to this object is needed. + + """ + + mva_callback = MvaGui(inp) + + return mva_callback + + +class MvaGui: + r"""Class to display and update GUI elements of MVA.""" + + def __init__(self, b): + # Time series data + self.b = b + self.t = self.b.time.data + + # Figure and GUI + self.fig = self.init_fig(self.b) + + axreset = self.fig.add_axes([0.91, 0.66, 0.04, 0.03]) + self.resetbutton = Button(axreset, "Reset", hovercolor="0.75") + self.resetbutton.on_clicked(self.reset_selection) + + self.span = SpanSelector( + self.fig.axes[0], + self.update_onselect, + "horizontal", + useblit=True, + props={"alpha": 0, "facecolor": "black"}, + interactive=True, + minspan=0, + drag_from_anywhere=True, + ) + + # Normal vec - to be calculated + self.minvar = None + self.errors = None + + @staticmethod + def init_fig(b_xyz): + r"""Initialize figure window with time series. Returns figure object + to access the axes. + """ + + fig = plt.figure(figsize=(10, 8)) + legend_options = { + "ncol": 1, + "loc": "center left", + "frameon": True, + "framealpha": 1, + "bbox_to_anchor": (1, 0.5), + } + gs = gridspec.GridSpec( + 3, 3, top=0.9, left=0.05, right=0.9, hspace=0.3, wspace=0.35 + ) + # Time series data + ax1 = fig.add_subplot(gs[0, :]) + + # MVA transformed frame + fig.add_subplot(gs[1, :]) + + # Min/Max hodogram + fig.add_subplot(gs[2, 0]) + + # Interm/Max hodogram + fig.add_subplot(gs[2, 1]) + + # Text display + ax5 = fig.add_subplot(gs[2, 2]) + ax5.axis("off") + + # Plot b_xyz in the first axis + plot_line(ax1, b_xyz) + plot_line(ax1, norm(b_xyz), color="black") + ax1.set_ylabel("$B$ [nT]") + + ax1.set_title("Time series for magnetic field") + b_labels = ["$B_x$", "$B_y$", "$B_z$", "$|B|$"] + ax1.legend(b_labels, **legend_options) + return fig + + def reset_selection(self): + r"""Resets MVA selection. Event is passed on from button click but has + no meaning or use here. + """ + + ax3 = self.fig.axes[2] + ax4 = self.fig.axes[3] + ax5 = self.fig.axes[4] + self.span.set_visible(False) + self.update_fig(self.b) + ax3.clear() + ax4.clear() + for txt in ax5.texts: + txt.set_visible(False) + plt.draw() + + def update_onselect(self, tmin, tmax): + r"""This function is called when a span is selected with spanselector.""" + + indmin, indmax = np.searchsorted(dates.date2num(self.t), (tmin, tmax)) + indmax = min(len(self.t) - 1, indmax) + + region_t = self.t[indmin:indmax] + + # convert times to datetime64 + tmin = np.datetime64(dates.num2date(tmin).replace(tzinfo=None)) + tmax = np.datetime64(dates.num2date(tmax).replace(tzinfo=None)) + + b_xyz_clip = time_clip(self.b, [tmin, tmax]) + if len(region_t) >= 2: + self.update_fig(b_xyz_clip) + self.fig.canvas.draw_idle() + + @staticmethod + def force_positive(mva_frame, b_xyz): + r"""Force maximum variance direction to be positive.""" + + frame = mva_frame[2] + frame[:, 0] *= np.sign(max(frame[:, 0], key=abs)) + + # Keep frame right-handed + frame[:, 2] = np.cross(frame[:, 0], frame[:, 1]) + b_lmn = new_xyz(b_xyz, frame) + return b_lmn, mva_frame[1], frame + + def update_fig(self, b_xyz_clip): + r"""Update figure based on the clipped time series""" + + legend_options = { + "ncol": 1, + "loc": "center left", + "frameon": True, + "framealpha": 1, + "bbox_to_anchor": (1, 0.5), + } + + # Perform MVA on the clipped time series + b_lmn_clip, lamb_clip, frame_clip = self.force_positive( + mva(b_xyz_clip), b_xyz_clip + ) + + # Pick out time series for each component of the new magnetic field + b_1 = b_lmn_clip[:, 0] + b_2 = b_lmn_clip[:, 1] + b_3 = b_lmn_clip[:, 2] + + # Pick out each eigenvector as the columns of frame_clip + v1 = frame_clip[:, 0] + v2 = frame_clip[:, 1] + v3 = frame_clip[:, 2] + + # Define axse of figure + ax2 = self.fig.axes[1] + ax3 = self.fig.axes[2] + ax4 = self.fig.axes[3] + ax5 = self.fig.axes[4] + + # Clear axis graphs from previous selection + ax2.clear() + ax3.clear() + ax4.clear() + + # Plot b in MVA frame given by frame_clip + b_new = new_xyz(self.b, frame_clip) + plot_line(ax2, b_new) + plot_line(ax2, norm(b_new), color="black") + b2_labels = ["max", "interm", "min", "abs"] + ax2.legend(b2_labels, **legend_options) + ax2.set_ylabel("$B$ [nT]") + ax2.set_title("MVA frame") + + # Update B3/B1 hodogram + ax3.axis("equal") + ax3.plot(b_3, b_1, c="black") + ax3.set_ylabel("max") + ax3.set_xlabel("min") + + # Update B2/B1 hodogram + ax4.axis("equal") + ax4.plot(b_2, b_1, c="black") + ax4.set_ylabel("max") + ax4.set_xlabel("interm") + + # Text + for txt in ax5.texts: + txt.set_visible(False) + + val_textstring = ( + f"$\\lambda_1$ = {np.round(lamb_clip[0], 2)}\n" + f"$\\lambda_2$ = {np.round(lamb_clip[1], 2)}\n" + f"$\\lambda_3$ = {np.round(lamb_clip[2], 2)}" + ) + ratio_textstring = ( + f"\n" + f"$\\lambda_1 / \\lambda_2$ =" + f" {np.round(lamb_clip[0] / lamb_clip[1], 1)}\n" + f"$\\lambda_2 / \\lambda_3$ = {np.round(lamb_clip[1] / lamb_clip[2], 1)}" + ) + vec_textstring = ( + f"\n" + f"$x_1$ = {np.round(v1, 2)}\n" + f"$x_2$ = {np.round(v2, 2)}\n" + f"$x_3$ = {np.round(v3, 2)}" + ) + fontsize = "large" + ax5.text( + 0, + 1, + val_textstring, + fontsize=fontsize, + ha="left", + va="top", + transform=ax5.transAxes, + ) + ax5.text( + 0, + 0.5, + ratio_textstring, + fontsize=fontsize, + ha="left", + va="center", + transform=ax5.transAxes, + ) + ax5.text( + 0, + 0, + vec_textstring, + fontsize=fontsize, + ha="left", + va="center", + transform=ax5.transAxes, + ) + + # Set minimum variance vector + self.minvar = v3 + + return b_lmn_clip, lamb_clip, frame_clip + + def get_minvar(self): + r"""Access the minimum varience direction vector from mva_gui_class object + via this method. + """ + + if self.minvar is not None: + out = self.minvar + else: + raise RuntimeError("Normal has not been calculated yet") + + return out diff --git a/pyrfu/pyrf/new_xyz.py b/pyrfu/pyrf/new_xyz.py index a82ccbba..d53ee6a9 100644 --- a/pyrfu/pyrf/new_xyz.py +++ b/pyrfu/pyrf/new_xyz.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -60,6 +60,11 @@ def new_xyz(inp, trans_mat): else: out_data = (trans_mat.T @ inp.data.T).T - out = xr.DataArray(out_data, coords=inp.coords, dims=inp.dims, attrs=inp.attrs) + out = xr.DataArray( + out_data, + coords=inp.coords, + dims=inp.dims, + attrs=inp.attrs, + ) return out diff --git a/pyrfu/pyrf/norm.py b/pyrfu/pyrf/norm.py index def0722f..e5fb45c3 100644 --- a/pyrfu/pyrf/norm.py +++ b/pyrfu/pyrf/norm.py @@ -4,11 +4,14 @@ # 3rd party imports import numpy as np +# Local imports +from .ts_scalar import ts_scalar + __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -47,6 +50,6 @@ def norm(inp): """ - out = np.sqrt(np.sum(inp**2, axis=1)) + out = ts_scalar(inp.time.data, np.linalg.norm(inp.data, axis=1), attrs=inp.attrs) return out diff --git a/pyrfu/pyrf/normalize.py b/pyrfu/pyrf/normalize.py index 3842ca41..6aabbc36 100644 --- a/pyrfu/pyrf/normalize.py +++ b/pyrfu/pyrf/normalize.py @@ -6,9 +6,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/optimize_nbins_1d.py b/pyrfu/pyrf/optimize_nbins_1d.py index 25146963..035d314c 100644 --- a/pyrfu/pyrf/optimize_nbins_1d.py +++ b/pyrfu/pyrf/optimize_nbins_1d.py @@ -6,9 +6,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -33,10 +33,10 @@ def optimize_nbins_1d(x, n_min: int = 1, n_max: int = 100): References ---------- - _[1] Rudemo, M. (1982) Empirical Choice of Histograms and Kernel Density + .. [1] Rudemo, M. (1982) Empirical Choice of Histograms and Kernel Density Estimators. Scandinavian Journal of Statistics, 9, 65-78. - _[2] Shimazaki H. and Shinomoto S., A method for selecting the bin size + .. [2] Shimazaki H. and Shinomoto S., A method for selecting the bin size of a time histogram Neural Computation (2007) Vol. 19(6), 1503-1527 """ @@ -48,9 +48,9 @@ def optimize_nbins_1d(x, n_min: int = 1, n_max: int = 100): # Bin size vector ds_x = (x_max - x_min) / ns_x - cs_x = np.zeros(d_x.shape) + cs_x = np.zeros(ds_x.shape) # Computation of the cost function to x and y - for i, n_x in enumumerate(ns_x): + for i, n_x in enumerate(ns_x): k_i = np.histogram(x, bins=n_x) # The mean and the variance are simply computed from the # event counts in all the bins of the 1-dimensional histogram. @@ -64,6 +64,6 @@ def optimize_nbins_1d(x, n_min: int = 1, n_max: int = 100): # combination of i and j that produces the minimum cost function idx_min = np.argmin(cs_x) # get the index of the min Cxy - opt_n_x = ns_x[idx_min] + opt_n_x = int(ns_x[idx_min]) return opt_n_x diff --git a/pyrfu/pyrf/optimize_nbins_2d.py b/pyrfu/pyrf/optimize_nbins_2d.py index 6faa615e..e5817946 100644 --- a/pyrfu/pyrf/optimize_nbins_2d.py +++ b/pyrfu/pyrf/optimize_nbins_2d.py @@ -8,9 +8,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -80,7 +80,7 @@ def optimize_nbins_2d(x, y, n_min: list = None, n_max: list = None): # Optimal Bin Size Selection # get the index in x and y that produces the minimum cost function - n_x = n_x[np.where(c_xy == np.min(c_xy))[0][0]] - n_y = n_y[np.where(c_xy == np.min(c_xy))[1][0]] + n_x = int(n_x[np.where(c_xy == np.min(c_xy))[0][0]]) + n_y = int(n_y[np.where(c_xy == np.min(c_xy))[1][0]]) return n_x, n_y diff --git a/pyrfu/pyrf/pid_4sc.py b/pyrfu/pyrf/pid_4sc.py index a419a386..c69360b5 100644 --- a/pyrfu/pyrf/pid_4sc.py +++ b/pyrfu/pyrf/pid_4sc.py @@ -7,18 +7,17 @@ # Local imports from ..mms.rotate_tensor import rotate_tensor - -from .c_4_grad import c_4_grad from .avg_4sc import avg_4sc +from .c_4_grad import c_4_grad from .trace import trace -from .ts_tensor_xyz import ts_tensor_xyz from .ts_scalar import ts_scalar +from .ts_tensor_xyz import ts_tensor_xyz __author__ = "Louis Richard" __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.26" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/plasma_beta.py b/pyrfu/pyrf/plasma_beta.py index be6e5536..45a8bc4e 100644 --- a/pyrfu/pyrf/plasma_beta.py +++ b/pyrfu/pyrf/plasma_beta.py @@ -1,18 +1,20 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +# 3rd party imports +import numpy as np +from scipy import constants + # Local imports from ..mms import rotate_tensor - -from .norm import norm from .resample import resample from .ts_scalar import ts_scalar __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -44,15 +46,13 @@ def plasma_beta(b_xyz, p_xyz): p_fac = rotate_tensor(p_xyz, "fac", b_xyz, "pp") # Scalar temperature - p_mag = (p_fac[0, 0] + (p_fac[1, 1] + p_fac[2, 2]) / 2) / 2 + p_tot = (p_fac.data[:, 0, 0] + p_fac.data[:, 1, 1] + p_fac.data[:, 2, 2]) / 3 # Magnitude of the magnetic field - b_mag = norm(b_xyz) + b_mag = np.linalg.norm(b_xyz.data, axis=1) + p_mag = 1e-18 * b_mag**2 / (2 * constants.mu_0) # Compute plasma beta - beta = p_mag / (b_mag * 1e-5) ** 2 - - time = b_xyz.time.data - beta = ts_scalar(time, beta) + beta = ts_scalar(b_xyz.time.data, p_tot / p_mag) return beta diff --git a/pyrfu/pyrf/plasma_calc.py b/pyrfu/pyrf/plasma_calc.py index 9da1c6dc..c8d94813 100644 --- a/pyrfu/pyrf/plasma_calc.py +++ b/pyrfu/pyrf/plasma_calc.py @@ -4,7 +4,6 @@ # 3rd party imports import numpy as np import xarray as xr - from scipy import constants # Local imports @@ -12,9 +11,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -131,27 +130,15 @@ def plasma_calc(b_xyz, t_i, t_e, n_i, n_e): mp_me = m_p / m_e # Resample all variables with respect to the magnetic field - n_t = len(b_xyz) - - if len(t_i) != n_t: - t_i = resample(t_i, b_xyz).data - - if len(t_e) != n_t: - t_e = resample(t_e, b_xyz).data - - if len(n_i) != n_t: - n_i = resample(n_i, b_xyz).data - - if len(n_e) != n_t: - n_e = resample(n_e, b_xyz).data + t_i = resample(t_i, b_xyz).data + t_e = resample(t_e, b_xyz).data + n_i = resample(n_i, b_xyz).data + n_e = resample(n_e, b_xyz).data # Transform number density and magnetic field to SI units n_i, n_e = [1e6 * n_i, 1e6 * n_e] - if b_xyz.ndim == 2: - b_si = 1e-9 * np.linalg.norm(b_xyz, axis=1) - else: - b_si = 1e-9 * np.linalg.norm(b_xyz, axis=1) + b_si = 1e-9 * np.linalg.norm(b_xyz, axis=1) w_pe = np.sqrt(n_e * q_e**2 / (m_e * ep0)) # rad/s w_ce = q_e * b_si / m_e # rad/s @@ -212,7 +199,7 @@ def plasma_calc(b_xyz, t_i, t_e, n_i, n_e): "rho_e": (["time"], rho_e), "rho_p": (["time"], rho_p), "rho_s": (["time"], rho_s), - } + }, ) return out diff --git a/pyrfu/pyrf/poynting_flux.py b/pyrfu/pyrf/poynting_flux.py index 17e63bd0..53edd2a8 100644 --- a/pyrfu/pyrf/poynting_flux.py +++ b/pyrfu/pyrf/poynting_flux.py @@ -14,9 +14,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/pres_anis.py b/pyrfu/pyrf/pres_anis.py index 296e4c11..31f1c9b1 100644 --- a/pyrfu/pyrf/pres_anis.py +++ b/pyrfu/pyrf/pres_anis.py @@ -3,17 +3,17 @@ # 3rd party imports import numpy as np - from scipy import constants # Local imports from .resample import resample +from .ts_scalar import ts_scalar __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -67,16 +67,17 @@ def pres_anis(p_fac, b_xyz): """ - b_xyz = resample(b_xyz, p_fac) - # Get parallel and perpendicular pressure - p_para = p_fac[:, 0, 0] - p_perp = (p_fac[:, 1, 1] + p_fac[:, 2, 2]) / 2 + p_para = p_fac.data[:, 0, 0] + p_perp = (p_fac.data[:, 1, 1] + p_fac.data[:, 2, 2]) / 2 - # Load permittivity - mu0 = constants.mu_0 + # Compute magnetic pressure + b_xyz = resample(b_xyz, p_fac) + b_mag = np.linalg.norm(b_xyz.data, axis=1) + p_mag = 1e-18 * b_mag**2 / (2 * constants.mu_0) # Compute pressure anistropy - p_anis = 1e9 * mu0 * (p_para - p_perp) / np.linalg.norm(b_xyz) ** 2 + p_anis = (1e-9 * (p_para - p_perp) / 2) / p_mag + p_anis = ts_scalar(p_fac.time.data, p_anis) return p_anis diff --git a/pyrfu/pyrf/psd.py b/pyrfu/pyrf/psd.py index 2a2c303e..d8127753 100644 --- a/pyrfu/pyrf/psd.py +++ b/pyrfu/pyrf/psd.py @@ -2,19 +2,18 @@ # -*- coding: utf-8 -*- # Built-in imports -import warnings +import logging # 3rd party imports import numpy as np import xarray as xr - from scipy import signal __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -81,7 +80,7 @@ def psd( if n_fft < n_persegs: n_fft = n_persegs - warnings.warn("nfft < n_persegs. set to n_persegs", UserWarning) + logging.warning("nfft < n_persegs. set to n_persegs") f_samp = 1e9 / np.median(np.diff(inp.time.data)).astype(np.float64) diff --git a/pyrfu/pyrf/pvi.py b/pyrfu/pyrf/pvi.py index 8bccadf4..7fbc3ee5 100644 --- a/pyrfu/pyrf/pvi.py +++ b/pyrfu/pyrf/pvi.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -49,7 +49,10 @@ def pvi(inp, scale: int = 10): time = inp.coords[inp.dims[0]].data result = xr.DataArray( - result, coords=[time[0 : len(delta_inp)]], dims=[inp.dims[0]], attrs=inp.attrs + result, + coords=[time[0 : len(delta_inp)]], + dims=[inp.dims[0]], + attrs=inp.attrs, ) result.attrs["units"] = "dimensionless" diff --git a/pyrfu/pyrf/pvi_4sc.py b/pyrfu/pyrf/pvi_4sc.py index 921aef26..b34e9040 100644 --- a/pyrfu/pyrf/pvi_4sc.py +++ b/pyrfu/pyrf/pvi_4sc.py @@ -4,16 +4,17 @@ # 3rd party imports import numpy as np +from .dot import dot +from .norm import norm + # Local imports from .resample import resample -from .norm import norm -from .dot import dot __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/read_cdf.py b/pyrfu/pyrf/read_cdf.py index 1a57063a..87453dc1 100644 --- a/pyrfu/pyrf/read_cdf.py +++ b/pyrfu/pyrf/read_cdf.py @@ -4,8 +4,7 @@ # 3rd party imports import numpy as np import xarray as xr - -from cdflib import cdfread, cdfepoch +from cdflib import cdfepoch, cdfread from dateutil import parser # Local imports @@ -15,7 +14,7 @@ __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.26" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/remove_repeated_points.py b/pyrfu/pyrf/remove_repeated_points.py index ae0dca7b..9ad22223 100644 --- a/pyrfu/pyrf/remove_repeated_points.py +++ b/pyrfu/pyrf/remove_repeated_points.py @@ -5,15 +5,16 @@ import numpy as np import xarray as xr +from .ts_scalar import ts_scalar + # Local imports from .ts_vec_xyz import ts_vec_xyz -from .ts_scalar import ts_scalar __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/resample.py b/pyrfu/pyrf/resample.py index a2cf8c38..993ac288 100644 --- a/pyrfu/pyrf/resample.py +++ b/pyrfu/pyrf/resample.py @@ -1,25 +1,29 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -import pdb - # Built-in imports import bisect -import warnings +import logging # 3rd party imports import numpy as np import xarray as xr - from scipy import interpolate __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" +logging.captureWarnings(True) +logging.basicConfig( + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, +) + def _guess_sampling_frequency(ref_time): r"""Compute sampling frequency of the time line.""" @@ -49,9 +53,7 @@ def _guess_sampling_frequency(ref_time): cur += 1 if not_found: - raise RuntimeError( - "Cannot guess sampling frequency. Tried {:d} times".format(max_try) - ) + raise RuntimeError(f"Cannot guess sampling frequency. Tried {max_try:d} times") return sfy @@ -85,11 +87,13 @@ def _average(inp_time, inp_data, ref_time, thresh, dt2): for j, stdd in enumerate(std_): if not np.isnan(stdd): idx_r = bisect.bisect_right( - inp_data[idx, j + 1] - mean_[j], thresh * stdd + inp_data[idx, j + 1] - mean_[j], + thresh * stdd, ) if idx_r: out_data[i, j + 1] = np.mean( - inp_data[idx[idx_r], j + 1], axis=0 + inp_data[idx[idx_r], j + 1], + axis=0, ) else: out_data[i, j + 1] = np.nan @@ -130,7 +134,7 @@ def _resample_dataarray(inp, ref, method, f_s, window, thresh): if len(inp_time) / (inp_time[-1] - inp_time[0]) > 2 * sfy: flag_do = "average" - warnings.warn("Using averages in resample", UserWarning) + logging.info("Using averages in resample") else: flag_do = "interpolation" else: @@ -159,12 +163,21 @@ def _resample_dataarray(inp, ref, method, f_s, window, thresh): for k in list(inp.dims)[1:]: coord.append(inp.coords[k].data) - out = xr.DataArray(out_data, coords=coord, dims=inp.dims, attrs=inp.attrs) + out = xr.DataArray( + out_data, + coords=coord, + dims=inp.dims, + attrs=inp.attrs, + ) return out tck = interpolate.interp1d( - inp_time, inp.data, kind=method, axis=0, fill_value="extrapolate" + inp_time, + inp.data, + kind=method, + axis=0, + fill_value="extrapolate", ) out_data = tck(ref_time) @@ -190,7 +203,10 @@ def _resample_dataset(inp, ref, **kwargs): out_dict = {**out_dict, **{k: inp[k] for k in ndepnd_zvars}} # Find array_like attributes - arr_attrs = filter(lambda x: isinstance(inp.attrs[x], np.ndarray), inp.attrs) + arr_attrs = filter( + lambda x: isinstance(inp.attrs[x], np.ndarray), + inp.attrs, + ) arr_attrs = list(arr_attrs) # Initialize attributes dictionary with non array_like attributes @@ -200,14 +216,17 @@ def _resample_dataset(inp, ref, **kwargs): for k in arr_attrs: attr = inp.attrs[k] - # If array_like attributes have one dimension equal to time length assume - # time dependent. One option would be move the time dependent array_like - # attributes to time series to zVaraibles to avoid confusion + # If array_like attributes have one dimension equal to time length + # assume time dependent. One option would be move the time dependent + # array_like attributes to time series to zVaraibles to avoid + # confusion if attr.shape[0] == len(inp.time.data): coords = [np.arange(attr.shape[i + 1]) for i in range(attr.ndim - 1)] dims = [f"idx{i:d}" for i in range(attr.ndim - 1)] attr_ts = xr.DataArray( - attr, coords=[inp.time.data, *coords], dims=["time", *dims] + attr, + coords=[inp.time.data, *coords], + dims=["time", *dims], ) out_attrs[k] = _resample_dataarray(attr_ts, ref, **kwargs).data else: @@ -222,7 +241,12 @@ def _resample_dataset(inp, ref, **kwargs): def resample( - inp, ref, method: str = "", f_s: float = None, window: int = None, thresh: float = 0 + inp, + ref, + method: str = "", + f_s: float = None, + window: int = None, + thresh: float = 0, ): r"""Resample inp to the time line of ref. If sampling of X is more than two times higher than Y, we average X, otherwise we interpolate X. @@ -249,10 +273,6 @@ def resample( out : xarray.DataArray Resampled input to the reference time line using the selected method. - TODO - ---- - Make the resampling VDF (xarray.Dataset) compliant. - Examples -------- @@ -268,8 +288,8 @@ def resample( Load magnetic field and electric field - >>> b_xyz = mms.get_data("B_gse_fgm_srvy_l2", tint, mms_id) - >>> e_xyz = mms.get_data("E_gse_edp_fast_l2", tint, mms_id) + >>> b_xyz = mms.get_data("e_gse_fgm_srvy_l2", tint, mms_id) + >>> e_xyz = mms.get_data("e_gse_edp_fast_l2", tint, mms_id) Resample magnetic field to electric field sampling @@ -277,13 +297,14 @@ def resample( """ - options = dict(method=method, f_s=f_s, window=window, thresh=thresh) + message = "Invalid input type. Input must be xarray.DataArary or xarray.Dataset" + assert isinstance(inp, (xr.DataArray, xr.Dataset)), message + + options = {"method": method, "f_s": f_s, "window": window, "thresh": thresh} if isinstance(inp, xr.DataArray): out = _resample_dataarray(inp, ref, **options) - elif isinstance(inp, xr.Dataset): - out = _resample_dataset(inp, ref, **options) else: - raise TypeError("Invalid input type. Input must be a xarray!!") + out = _resample_dataset(inp, ref, **options) return out diff --git a/pyrfu/pyrf/shock_models_parameters.json b/pyrfu/pyrf/shock_models_parameters.json new file mode 100644 index 00000000..763e179e --- /dev/null +++ b/pyrfu/pyrf/shock_models_parameters.json @@ -0,0 +1,44 @@ +{ + "farris": { + "eps": 0.81, + "l": 24.8, + "x_0": 0.0, + "y_0": 0.0, + "alpha": 3.8 + }, + "slavin_holzer": { + "eps": 1.16, + "l": 23.3, + "x_0": 3.0, + "y_0": 0.0, + "alpha": "NaN" + }, + "peredo": { + "eps": 0.98, + "l": 26.1, + "x_0": 2.0, + "y_0": 0.3, + "alpha": 3.2 + }, + "fairfield_meridian_4o": { + "eps": 1.02, + "l": 22.3, + "x_0": 3.4, + "y_0": 0.3, + "alpha": 4.8 + }, + "fairfield_meridian_no_4o": { + "eps": 1.05, + "l": 20.5, + "x_0": 4.6, + "y_0": 0.4, + "alpha": 5.2 + }, + "formisano_unnorm": { + "eps": 0.97, + "l": 22.8, + "x_0": 2.6, + "y_0": 1.1, + "alpha": 3.6 + } +} \ No newline at end of file diff --git a/pyrfu/pyrf/shock_normal.py b/pyrfu/pyrf/shock_normal.py new file mode 100644 index 00000000..38b1da1c --- /dev/null +++ b/pyrfu/pyrf/shock_normal.py @@ -0,0 +1,426 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import json + +# Built-in imports +import os + +# Thrid party imports +import numpy as np +import xarray as xr +from scipy import constants, interpolate, optimize +from scipy.spatial.transform import Rotation as R + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.2" +__status__ = "Prototype" + +__all__ = ["shock_normal"] + + +def shock_normal(spec, leq90: bool = True): + r"""Calculates shock normals with different methods. Normal vectors are calculated + by methods described in [1]_ and references therein. + + The data can be averaged values or values from the time series in matrix format. + If series is from time series all parameters are calculated from a random + upstream and a random downstream point. This can help set errorbars on shock + angle etc. The time series input must have the same size (up- and downstream can + be different), so generally the user needs to resample the data first. + + Parameters + ---------- + spec : dict + Hash table with: + * b_u : Upstream magnetic field (nT). + * b_d : Downstream magnetic field. + * v_u : Upstream plasma bulk velocity (km/s). + * v_d : Downstream plasma bulk velocity. + * n_u : Upstream number density (cm^-3). + * n_d : Downstream number density. + * r_xyz : Spacecraft position in time series format of 1x3 vector. Optional. + * d2u : Down-to-up, is 1 or -1. Optional. + * dt_f : Time duration of shock foot (s). Optional. + * f_cp : Reflected ion gyrofrequency (Hz). Optional. + * n : Number of Monte Carlo particles. Optional, default is 100. + + leq90 : bool, Optional + Force angles to be less than 90 (default). For leq90 = 0, angles can be between + 0 and 180 deg. For time series input and quasi-perp shocks,leq90 = 0 is + recommended. + + Returns + ------- + out : dict + Hash table with: + * n : Hash table containing normal vectors (n always points toward the + upstream region). + From data: + * mc : Magnetic coplanarity (10.14) + * vc : Velocity coplanarity (10.18) + * mx_1 : Mixed method 1 (10.15), [2]_ + * mx_2 : Mixed method 2 (10.16), [2]_ + * mx_3 : Mixed method 3 (10.17), [2]_ + From models (only if r_xyz is included in spec): + * farris : [3]_ + * slho : [4]_ + * per : [5]_, (z = 0) + * fa4o : [6]_ + * fan4o : [6]_ + * foun : [7]_ + + * theta_bn : Angle between normal vector and b_u, same fields as n. + * theta_vn : Angle between normal vector and v_u, same fields as n. + * v_sh : Hash table containing shock velocities: + * gt : Using shock foot thickness (10.32). [8]_ + * mf : Mass flux conservation (10.29). + * sb : Using jump conditions (10.33). [9]_ + * mo : Using shock foot thickness + * info : Hash table containing some more info: + * msh : Magnetic shear angle. + * vsh : Velocity shear angle. + * cmat : Constraints matrix with normalized errors. + * sig : Scaling factor to fit shock models to sc position. Calculated + from (10.9-10.13) in [1]_ + + + References + ---------- + .. [1] Schwartz, S. J. (1998), Shock and Discontinuity Normals, Mach Numbers, and + Related Parameters, ISSI Scientific Reports Series, vol. 1, pp. 249–270. + .. [2] Abraham-Shrauner, B. (1972), “Determination of magnetohydrodynamic shock + normals”, Journal of Geophysical Research, vol. 77, no. 4, p. 736. + doi:10.1029/JA077i004p00736. + .. [3] Farris, M. H., Petrinec, S. M., and Russell, C. T. (1991), The thickness of + the magnetosheath: Constraints on the polytropic index”, Geophysical + Research Letters, vol. 18, no. 10, pp. 1821–1824. doi:10.1029/91GL02090. + .. [4] Slavin, J. A. and Holzer, R. E. (1981), Solar wind flow about the + terrestrial planets, 1. Modeling bow shock position and shape, Journal of + Geophysical Research, vol. 86, no. A13, pp. 11401–11418. + doi:10.1029/JA086iA13p11401. + .. [5] Peredo, M., Slavin, J. A., Mazur, E., and Curtis, S. A. (1995), + Three-dimensional position and shape of the bow shock and their variation + with AlfvÊnic, sonic, and magnetosonic Mach numbers and interplanetary + magnetic field orientation, Journal of Geophysical Research, vol. 100, + no. A5, pp. 7907–7916. doi:10.1029/94JA02545. + .. [6] Fairfield, D. H. (1971), Average and unusual locations of the Earth's + magnetopause and bow shock, Journal of Geophysical Research, vol. 76, + no. 28, p. 6700, 1971. doi:10.1029/JA076i028p06700. + .. [7] Formisano, V. (1979), Orientation and Shape of the Earth's Bow Shock in + Three Dimensions, Planetary and Space Science, vol. 27, no. 9, + pp. 1151–1161. doi:10.1016/0032-0633(79)90135-1. + .. [8] Gosling, J. T. and Thomsen, M. F. (1985), Specularly reflected ions, shock + foot thicknesses, and shock velocity determination in space, Journal of + Geophysical Research, vol. 90, no. A10, pp. 9893–9896. + doi:10.1029/JA090iA10p09893. + .. [9] Smith, E. J. and Burton, M. E. (1988), Shock analysis: Three useful new + relations, Journal of Geophysical Research, vol. 93, no. A4, pp. 2730–2734. + doi:10.1029/JA093iA04p02730. + + + + """ + + # Check input + assert isinstance(spec, dict), "spec must be a dictionary" + + if spec["b_u"].ndim > 1 or spec["b_d"].ndim > 1: + n_bu = len(spec["b_u"]) + n_bd = len(spec["b_d"]) + + # randomize points upstream and downstream + n = int(np.floor(spec.get("n", 10.0))) + idt_u, idt_d = [np.random.rand(n) for _ in range(2)] + + tmp_spec = {} + for i in range(n): + f_bu = interpolate.interp1d(np.linspace(0, 1, n_bu), spec["b_u"], axis=0) + tmp_spec["b_u"] = f_bu(idt_u[i]) + f_vu = interpolate.interp1d(np.linspace(0, 1, n_bu), spec["v_u"], axis=0) + tmp_spec["v_u"] = f_vu(idt_u[i]) + f_nu = interpolate.interp1d(np.linspace(0, 1, n_bu), spec["n_u"], axis=0) + tmp_spec["n_u"] = f_nu(idt_u[i]) + + f_bd = interpolate.interp1d(np.linspace(0, 1, n_bd), spec["b_d"], axis=0) + tmp_spec["b_d"] = f_bd(idt_d[i]) + f_vd = interpolate.interp1d(np.linspace(0, 1, n_bd), spec["v_d"], axis=0) + tmp_spec["v_d"] = f_vd(idt_d[i]) + f_nd = interpolate.interp1d(np.linspace(0, 1, n_bd), spec["n_d"], axis=0) + tmp_spec["n_d"] = f_nd(idt_d[i]) + + # normal vector, according to different models + normal = {} + + b_u, b_d = np.array(spec["b_u"]), np.array(spec["b_d"]) + v_u, v_d = np.array(spec["v_u"]), np.array(spec["v_d"]) + + delta_b = b_d - b_u + delta_v = v_d - v_u + spec["delta_b"] = delta_b + spec["delta_v"] = delta_v + + # magenetic coplanarity + mc = np.cross(np.cross(b_d, b_u), delta_b) + mc /= np.linalg.norm(mc, keepdims=True) + normal["mc"] = mc + + # velocity coplanarity + vc = delta_v / np.linalg.norm(delta_v, keepdims=True) + normal["vc"] = vc + + # mixed methods + mx_1 = np.cross(np.cross(b_u, delta_v), delta_b) + mx_1 /= np.linalg.norm(mx_1) + normal["mx_1"] = mx_1 + mx_2 = np.cross(np.cross(b_d, delta_v), delta_b) + mx_2 /= np.linalg.norm(mx_2) + normal["mx_2"] = mx_2 + mx_3 = np.cross(np.cross(delta_b, delta_v), delta_b) + mx_3 /= np.linalg.norm(mx_3) + normal["mx_3"] = mx_3 + + # Load shock + # pkg_path = os.path.join(os.getcwd(), "sandbox", "sbox") + pkg_path = os.path.dirname(os.path.abspath(__file__)) + with open( + os.path.join(pkg_path, "shock_models_parameters.json"), "r", encoding="utf-8" + ) as fs: + shock_models_params = json.load(fs) + + if "r_xyz" in spec: + # info + info = {k: {} for k in shock_models_params["farris"]} + sig = {} + + # overwrite alpha to azimuthal angle of the bulk velocity for + # Slavin-Holzer model + alpha_sh = -np.rad2deg(np.arctan(spec["v_u"][1] / spec["v_u"][0])) + shock_models_params["slavin_holzer"]["alpha"] = alpha_sh + + for m in shock_models_params: + for k in info: + info[k][m] = shock_models_params[m][k] + + normal[m], sig[m] = _shock_model(spec, *shock_models_params[m].values()) + + info["sig"] = sig + else: + info = {} + + # make sure all normal vectors are pointing upstream based on delta_sv, + # should work for IP shocks also + for n_ in normal.values(): + if np.sum(delta_v * n_) < 0: + n_ *= -1 + + # Shock normal to magnetic field and velocity angles + theta_bn = _shock_angle(spec, normal, "b", leq90) + theta_vn = _shock_angle(spec, normal, "v", leq90) + + # Magnetic and velocity shear angles + info["msh"] = _shear_angle(b_u, b_d) + info["vsh"] = _shear_angle(v_u, v_d) + + # Constraint matrix + info["cmat"] = _constraint_matrix(spec, normal) + + # Shock speed + v_sh = {} + for m in ["gt", "mf", "sb", "mo"]: + v_sh[m] = _shock_speed(spec, normal, theta_bn, m) + + out = { + "info": info, + "n": normal, + "theta_bn": theta_bn, + "theta_vn": theta_vn, + "v_sh": v_sh, + } + + return out + + +def _shear_angle(au, ad): + theta = np.arccos(np.sum(au * ad) / (np.linalg.norm(au) * np.linalg.norm(ad))) + return np.rad2deg(theta) + + +def _shock_angle(spec, n, field, leq90): + if field.lower() == "b": + a = spec["b_u"] + else: + a = spec["v_u"] + + theta = {} + + for fname in n: + tmp = np.rad2deg(np.arccos(np.sum(a * n[fname]) / np.linalg.norm(a))) + + if tmp > 90.0 and leq90: + theta[fname] = 90.0 - tmp + else: + theta[fname] = tmp + + return theta + + +def _shock_model(spec, *args): + eps, l_, x_0, y_0, alpha = args + + # Rotation matrix + rot_mat = R.from_euler("z", alpha, degrees=True).as_matrix() + + # offset from GSE + r_0 = np.array([x_0, y_0, 0]) + + # sc position in GSE (or GSM or whatever) in Earth radii + if isinstance(spec["r_xyz"], xr.DataArray): + # Time series + r_sc = np.mean(spec["r_xyz"].data, axis=0) / 6371.0 + elif isinstance(spec["r_xyz"], (np.ndarray, list)) and len(spec["r_xyz"]) == 3: + # Array like + r_sc = spec["r_xyz"] / 6371.0 + else: + raise TypeError("r_xyz must be a time series or a vector!!") + + def fval(sig, *args): + rot_mat, r_sc, r_0, l_ = args + + # sc position in the natural system (cartesian) + r_p = np.matmul(rot_mat, r_sc) - sig * r_0 + + # sc polar angle in the natural system + theta_p = np.arctan2(np.sqrt(r_p[1] ** 2 + r_p[2] ** 2), r_p[0]) + + # minimize |LH-RH| in eq 10.22 + res = np.abs(sig * l_ / np.linalg.norm(r_p) - 1 - eps * np.cos(theta_p)) + return res + + # find the best fit for sigma + sig_0 = optimize.minimize(fval, 1, args=(rot_mat, r_sc, r_0, l_)) + # to make sure it finds the largest sigma + sig_0 = optimize.minimize(fval, 2 * sig_0.x[0], args=(rot_mat, r_sc, r_0, l_)) + + # calculate normal + r_p0 = np.matmul(rot_mat, r_sc) - sig_0.x[0] * r_0 + + # gradient to model surface + grad_s_x = (r_p0[0] * (1 - eps**2) + eps * sig_0.x[0] * l_) * np.cos( + np.deg2rad(alpha) + ) + r_p0[1] * np.sin(np.deg2rad(alpha)) + grad_s_y = -(r_p0[0] * (1 - eps**2) + eps * sig_0.x[0] * l_) * np.sin( + np.deg2rad(alpha) + ) + r_p0[1] * np.cos(np.deg2rad(alpha)) + grad_s_z = r_p0[2] + + grad_s = np.array([grad_s_x, grad_s_y, grad_s_z], dtype=np.float32) + grad_s /= np.linalg.norm(r_p0, keepdims=True) * 2 * sig_0.x[0] * l_ + + # normal vector + n = grad_s / np.linalg.norm(grad_s, keepdims=True) + + return n, sig_0.x[0] + + +def _shock_speed(spec, n, theta_bn, method): + if method.lower() == "gt" and "f_cp" in spec and "dt_f" in spec and "d2u" in spec: + v_sh = _speed_gosling_thomsen(spec, n, theta_bn) + elif method.lower() == "mf": + v_sh = _speed_mass_flux(spec, n) + elif method.lower() == "sb": + v_sh = _speed_smith_burton(spec, n) + elif method.lower() == "mo" and "f_cp" in spec and "dt_f" in spec and "d2u" in spec: + v_sh = _speed_moses(spec, n, theta_bn) + else: + v_sh = {k: 0.0 for k in n} + + return v_sh + + +def _speed_gosling_thomsen(spec, n, theta_bn): + v_sh = {} + + for k, nvec in n.items(): + theta = np.deg2rad(theta_bn[k]) + nvec = n[k] + + # Notation as in (Gosling and Thomsen 1985) + w = 2 * np.pi * spec["f_cp"] + t1 = np.arccos((1 - 2 * np.cos(theta) ** 2) / (2 * np.sin(theta) ** 2)) / w + x_0 = w * t1 * (2 * np.cos(theta) ** 2 - 1) + 2 * np.sin(theta) ** 2 * np.sin( + w * t1 + ) + x_0 /= w * spec["dt_f"] + + # the sign of Vsh in this method is ambiguous, assume n points upstream + v_sh[k] = ( + spec["d2u"] * np.sum(spec["v_u"] * nvec) * (x_0 / (1 + spec["d2u"] * x_0)) + ) + + return v_sh + + +def _speed_mass_flux(spec, n): + rho_u = spec["n_u"] * constants.proton_mass + rho_d = spec["n_d"] * constants.proton_mass + + v_sh = {} + + for k, nvec in n.items(): + v_sh[k] = ( + rho_d * np.sum(spec["v_d"] * nvec) - rho_u * np.sum(spec["v_u"] * nvec) + ) / (rho_d - rho_u) + + return v_sh + + +def _speed_smith_burton(spec, n): + v_sh = {} + + for k, nvec in n.items(): + v_sh[k] = np.sum(spec["v_u"] * nvec) + np.linalg.norm( + np.cross(spec["delta_v"], spec["b_d"]) + ) / np.linalg.norm(spec["delta_b"]) + + return v_sh + + +def _speed_moses(spec, n, theta_bn): + v_sh = {} + + for k, nvec in n.items(): + theta = np.deg2rad(theta_bn[k]) + theta_vn = np.arccos(np.sum(nvec * spec["v_u"]) / np.linalg.norm(spec["v_u"])) + + # Notation as in (Moses et al., 1985) + w = 2 * np.pi * spec["f_cp"] + x = 0.68 * np.sin(theta) ** 2 * np.cos(theta_vn) / (w * spec["dt_f"]) + + v_sh[k] = np.sum(spec["v_u"] * nvec) * (x / (1 + spec["d2u"] * x)) + + return v_sh + + +def _constraint_matrix(spec, n): + u_vecs = [ + spec["delta_b"], + np.cross(spec["b_d"], spec["b_u"]), + np.cross(spec["b_u"], spec["delta_v"]), + np.cross(spec["b_d"], spec["delta_v"]), + np.cross(spec["delta_b"], spec["delta_v"]), + ] + + c_mat = np.zeros((len(u_vecs), len(n))) + + for i, u in enumerate(u_vecs): + for j, nvec in enumerate(n.values()): + c_mat[i, j] = np.sum(u * nvec) / np.linalg.norm(u) + + # fields that are by definition zero are set to 0 + c_mat[[0, 0, 0, 0, 1, 2, 2, 3, 3, 4, 4], [0, 2, 3, 4, 0, 1, 2, 1, 3, 1, 4]] = 0.0 + + return c_mat diff --git a/pyrfu/pyrf/shock_parameters.py b/pyrfu/pyrf/shock_parameters.py new file mode 100644 index 00000000..99b4266b --- /dev/null +++ b/pyrfu/pyrf/shock_parameters.py @@ -0,0 +1,318 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Built-in imports +import logging + +# Third party imports +import numpy as np +from scipy import constants + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.2" +__status__ = "Prototype" + +__all__ = ["shock_parameters"] + +logging.captureWarnings(True) +logging.basicConfig( + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, +) + + +def shock_parameters(spec): + r"""Calculate shock related plasma parameters. + + Parameters + ---------- + spec : dict + Hash table with parameters input with fixed names. After the parameter name, + a name of the region can be given, e.g. "u" and "d". All parameters except b + are optional. + + Returns + ------- + out : dict + Hash table with derived plasma parameters from hash table spec with measured + plasma parameters. + + """ + + id_b = list(filter(lambda x: x[0].lower() == "b", spec)) + regions = [id_[1:] for id_ in id_b if len(id_) > 1] + regions = regions if len(regions) > 1 else [""] + + spec["ref_sys"] = spec.get("ref_sys", "sc") + assert spec["ref_sys"].lower() in ["sc", "nif"], "Invalid reference frame" + + if spec["ref_sys"].lower() == "nif": + if "nvec" not in spec: + logging.warning("Setting shock speed, nvec to [1, 0, 0]") + spec["nvec"] = np.array([1, 0, 0]) + + if "v_sh" not in spec: + logging.warning("Setting shock speed, Vsh, to 0.") + spec["v_sh"] = 0.0 + + dspec = {} + + # Alfven speed + if f"n{regions[0]}" in spec: + for region in regions: + dspec[f"v_a{region}"] = _v_alfv(spec[f"b{region}"], spec[f"n{region}"]) + + # Sound speed + if f"t_i{regions[0]}" in spec and f"t_e{regions[0]}" in spec: + for region in regions: + dspec[f"v_ts{region}"] = _v_sound( + spec[f"t_i{region}"], spec[f"t_e{region}"] + ) + + # Fast speed + if ( + f"n{regions[0]}" in spec + and f"v{regions[0]}" in spec + and f"t_i{regions[0]}" in spec + and f"t_e{regions[0]}" in spec + ): + for region in regions: + dspec[f"v_f{region}"] = _v_fast( + spec[f"b{region}"], + spec[f"n{region}"], + spec[f"v{region}"], + spec[f"t_i{region}"], + spec[f"t_e{region}"], + ) + + # Normal incidence frame velocity + if f"v{regions[0]}" in spec and "nvec" in spec and "v_sh" in spec: + for region in regions: + dspec[f"v_nif{region}"] = _nif_speed( + spec[f"v{region}"], spec["v_sh"], spec["nvec"] + ) + + # de Hoffman-Teller frame velocity + if f"v{regions[0]}" in spec and "nvec" in spec and "v_sh" in spec: + for region in regions: + dspec[f"v_htf{region}"] = _htf_speed( + spec[f"b{region}"], spec[f"v{region}"], spec["v_sh"], spec["nvec"] + ) + + # Ion gyrofrequency + for region in regions: + dspec[f"f_cp{region}"] = _ion_gyro_freq(spec[f"b{region}"]) + + # Ion inertial length + if f"n{regions[0]}" in spec: + for region in regions: + dspec[f"l_i{region}"] = _ion_in_len(spec[f"n{region}"]) + + if f"n{regions[0]}" in spec: + for region in regions: + dspec[f"r_cp{region}"] = _ion_gyro_rad( + spec[f"b{region}"], spec[f"v{region}"] + ) + + # Alfven Mach number + if f"n{regions[0]}" in spec and f"v{regions[0]}" in spec: + for region in regions: + dspec[f"m_a{region}"] = _alfv_mach( + spec[f"b{region}"], + spec[f"n{region}"], + spec[f"v{region}"], + spec["ref_sys"], + spec["v_sh"], + spec["nvec"], + ) + + # Sonic Mach number + if ( + f"v{regions[0]}" in spec + and f"t_i{regions[0]}" in spec + and f"t_e{regions[0]}" in spec + ): + for region in regions: + dspec[f"m_s{region}"] = _sonic_mach( + spec[f"v{region}"], + spec[f"t_i{region}"], + spec[f"t_e{region}"], + spec["ref_sys"], + spec["v_sh"], + spec["nvec"], + ) + + if ( + f"n{regions[0]}" in spec + and f"v{regions[0]}" in spec + and f"t_i{regions[0]}" in spec + and f"t_e{regions[0]}" in spec + ): + for region in regions: + dspec[f"m_f{region}"] = _fast_mach( + spec[f"b{region}"], + spec[f"n{region}"], + spec[f"v{region}"], + spec[f"t_i{region}"], + spec[f"t_e{region}"], + spec["ref_sys"], + spec["v_sh"], + spec["nvec"], + ) + + if f"n{regions[0]}" in spec and f"t_i{regions[0]}" in spec: + for region in regions: + dspec[f"beta_i{region}"] = _beta( + spec[f"b{region}"], spec[f"n{region}"], spec[f"t_i{region}"] + ) + + if f"n{regions[0]}" in spec and f"t_e{regions[0]}" in spec: + for region in regions: + dspec[f"beta_e{region}"] = _beta( + spec[f"b{region}"], spec[f"n{region}"], spec[f"t_e{region}"] + ) + + return dspec + + +def _ion_gyro_freq(b): + b_si = 1e-9 * np.linalg.norm(b) + w_cp = constants.elementary_charge * b_si / constants.proton_mass + return w_cp / (2 * np.pi) + + +def _ion_in_len(n): + n_si = 1e6 * n + w_pp = np.sqrt( + n_si + * constants.elementary_charge**2 + / (constants.proton_mass * constants.epsilon_0) + ) + l_i = constants.speed_of_light / w_pp + return l_i + + +def _ion_gyro_rad(b, v): + b_si = 1e-9 * np.linalg.norm(b) + e_i = 0.5 * constants.proton_mass * np.linalg.norm(v) ** 2 / constants.electron_volt + v_tp = constants.speed_of_light * np.sqrt( + 1 + - 1 + / ( + e_i + * constants.elementary_charge + / (constants.proton_mass * constants.speed_of_light**2) + + 1 + ) + ** 2 + ) + gamma_p = 1 / np.sqrt(1 - (v_tp / constants.speed_of_light) ** 2) + rho_p = ( + constants.proton_mass + * constants.speed_of_light + / (constants.elementary_charge * b_si) + * np.sqrt(gamma_p**2 - 1) + ) + return rho_p + + +def _v_alfv(b, n): + b_si = 1e-9 * np.linalg.norm(b) + n_si = 1e6 * n + return b_si / np.sqrt(constants.mu_0 * n_si * constants.proton_mass) + + +def _v_sound(t_i, t_e): + t_i_si = t_i * constants.electron_volt + t_e_si = t_e * constants.electron_volt + return np.sqrt((t_e_si + 3 * t_i_si) / constants.proton_mass) + + +def _v_fast(b, n, v, t_i, t_e, theta=None): + if theta is None: + theta = np.arccos(np.sum(b * v) / (np.linalg.norm(b) * np.linalg.norm(v))) + else: + theta = np.deg2rad(theta) + + v_a = _v_alfv(b, n) + c_s = _v_sound(t_i, t_e) + c_ms0 = np.sqrt(v_a**2 + c_s**2) + v_f = np.sqrt( + c_ms0**2 / 2 + + np.sqrt(c_ms0**4 / 4 - v_a**2.0 * c_s**2 * np.cos(theta) ** 2) + ) + return v_f + + +def _nif_speed(v, v_sh, nvec): + v_si = 1e3 * v + v_sh_si = 1e3 * v_sh + v_nif = v_si - (np.sum(v_si * nvec) - v_sh_si) * nvec + return v_nif + + +def _htf_speed(b, v, v_sh, nvec): + v_si = 1e3 * v + v_sh_si = 1e3 * v_sh + + # first get the velocity in a shock rest frame + v_in_shock_rest_frame = v_si - v_sh_si * nvec + + # then get the dHT frame speed in the shock rest frame + v_htf_srf = np.cross(nvec, np.cross(v_in_shock_rest_frame, b)) / np.sum(b * nvec) + + # then the dHT frame speed in the sc frame is the shock speed plus the dHT frame + # speed in the shock rest frame (I think) + v_htf = v_sh_si * nvec + v_htf_srf + return v_htf + + +def _alfv_mach(b, n, v, ref_sys, v_sh, nvec): + v_si = 1e3 * v + v_sh_si = 1e3 * v_sh + + if ref_sys.lower() == "nif" and nvec is not None: + m_a = np.abs(np.sum(v_si * nvec) - v_sh_si) / _v_alfv(b, n) + else: + m_a = np.linalg.norm(v_si) / _v_alfv(b, n) + + return m_a + + +def _sonic_mach(v, t_i, t_e, ref_sys, v_sh, nvec): + v_si = 1e3 * v + v_sh_si = 1e3 * v_sh + + if ref_sys.lower() == "nif" and nvec is not None: + m_s = np.abs(np.sum(v_si * nvec) - v_sh_si) / _v_sound(t_i, t_e) + else: + m_s = np.linalg.norm(v_si) / _v_sound(t_i, t_e) + + return m_s + + +def _fast_mach(b, n, v, t_i, t_e, ref_sys, v_sh, nvec): + v_si = 1e3 * v + v_sh_si = 1e3 * v_sh + + if ref_sys.lower() == "nif" and nvec is not None: + theta_bn = np.rad2deg(np.arccos(np.sum(b * nvec) / np.linalg.norm(b))) + m_f = np.abs(np.sum(v_si * nvec) - v_sh_si) / _v_fast( + b, n, v, t_i, t_e, theta_bn + ) + else: + m_f = np.linalg.norm(v_si) / _v_fast(b, n, v, t_i, t_e) + + return m_f + + +def _beta(b, n, t_s): + b_si = 1e-9 * np.linalg.norm(b) + n_si = 1e6 * n + t_s_si = t_s * constants.electron_volt + beta_s = (n_si * t_s_si) / (b_si**2 / (2 * constants.mu_0)) + return beta_s diff --git a/pyrfu/pyrf/solid_angle.py b/pyrfu/pyrf/solid_angle.py index 4afe5da7..99faf36f 100644 --- a/pyrfu/pyrf/solid_angle.py +++ b/pyrfu/pyrf/solid_angle.py @@ -3,12 +3,13 @@ # 3rd party imports import numpy as np +import xarray as xr __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -32,30 +33,42 @@ def solid_angle(inp0, inp1, inp2): """ + message = "must be array_like of xarray.DataArray" + assert isinstance(inp0, (list, np.ndarray, xr.DataArray)), f"inp0 {message}" + assert isinstance(inp1, (list, np.ndarray, xr.DataArray)), f"inp2 {message}" + assert isinstance(inp2, (list, np.ndarray, xr.DataArray)), f"inp2 {message}" + + inp0, inp1, inp2 = [np.atleast_2d(inp) for inp in [inp0, inp1, inp2]] + + message = "must be a vector" + assert inp0.shape[1] == 3, f"inp0 {message}" + assert inp1.shape[1] == 3, f"inp1 {message}" + assert inp2.shape[1] == 3, f"inp2 {message}" + # Calculate the smaller angles between the vectors around origin - acos_12 = np.arccos(np.sum(inp2 * inp1)) - acos_02 = np.arccos(np.sum(inp0 * inp2)) - acos_01 = np.arccos(np.sum(inp1 * inp0)) + acos_12 = np.arccos(np.sum(inp2 * inp1, axis=1)) + acos_02 = np.arccos(np.sum(inp0 * inp2, axis=1)) + acos_01 = np.arccos(np.sum(inp1 * inp0, axis=1)) # Calculate the angles in the spherical triangle (Law of Cosines) alpha = np.arccos( (np.cos(acos_12) - np.cos(acos_02) * np.cos(acos_01)) - / (np.sin(acos_02) * np.sin(acos_01)) + / (np.sin(acos_02) * np.sin(acos_01)), ) beta = np.arccos( (np.cos(acos_02) - np.cos(acos_12) * np.cos(acos_01)) - / (np.sin(acos_12) * np.sin(acos_01)) + / (np.sin(acos_12) * np.sin(acos_01)), ) gamma = np.arccos( (np.cos(acos_01) - np.cos(acos_02) * np.cos(acos_12)) - / (np.sin(acos_02) * np.sin(acos_12)) + / (np.sin(acos_02) * np.sin(acos_12)), ) # Calculates the Surface area on the unit sphere (solid angle) angle = alpha + beta + gamma - np.pi # Calculate the sign of the area var = np.cross(inp2, inp1) - div = np.sum(var * inp0) + div = np.sum(var * inp0, axis=1) sgn = np.sign(div) # Solid angle with sign taken into account diff --git a/pyrfu/pyrf/sph2cart.py b/pyrfu/pyrf/sph2cart.py index 07cb42c3..5b9c230a 100644 --- a/pyrfu/pyrf/sph2cart.py +++ b/pyrfu/pyrf/sph2cart.py @@ -6,9 +6,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/st_diff.py b/pyrfu/pyrf/st_diff.py index 3efc0542..e014c93a 100644 --- a/pyrfu/pyrf/st_diff.py +++ b/pyrfu/pyrf/st_diff.py @@ -4,17 +4,18 @@ # 3rd party imports import numpy as np +from .avg_4sc import avg_4sc + # Local imports from .c_4_grad import c_4_grad from .gradient import gradient -from .avg_4sc import avg_4sc from .ts_vec_xyz import ts_vec_xyz __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/start.py b/pyrfu/pyrf/start.py index 3e96fb71..9807a1e0 100644 --- a/pyrfu/pyrf/start.py +++ b/pyrfu/pyrf/start.py @@ -3,12 +3,13 @@ # 3rd party import import numpy as np +import xarray as xr __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -27,6 +28,12 @@ def start(inp): """ + # Check input type + assert isinstance(inp, xr.DataArray), "inp must be a xarray.DataArray" + + # Make sure this is a time series + assert list(inp.dims)[0] == "time", "inp must be a time series" + out = inp.time.data[0].astype(np.int64) * 1e-9 return out diff --git a/pyrfu/pyrf/struct_func.py b/pyrfu/pyrf/struct_func.py index 5fd0e18d..25071ed4 100644 --- a/pyrfu/pyrf/struct_func.py +++ b/pyrfu/pyrf/struct_func.py @@ -7,9 +7,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2022" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.23" +__version__ = "2.4.2" __status__ = "Prototype" @@ -30,7 +30,8 @@ def struct_func(inp, scales, order): A list or an array containing the scales to calculate. order : int Order of the exponential of the structure function. - + ncut : int, Optional + Number of standard deviation to cut (Kiyani et al., XXXX) Returns ------- values : xarray.DataArray @@ -42,30 +43,26 @@ def struct_func(inp, scales, order): """ if scales is None: - scales = [1] - - data = inp.data + scales = np.arange(1, len(inp) // 2) - if data.ndim == 1: - data = np.transpose(np.atleast_2d(data)) + if inp.ndim == 1: + data = inp.data[:, np.newaxis] + else: + data = inp.data result = [] for scale in scales: - result.append( - np.nanmean(np.abs(data[scale:, :] - data[:-scale, :]) ** order, axis=0) - ) - - if inp.data.ndim == 1: - result = xr.DataArray( - np.squeeze(result), coords=[scales], dims=["scale"], attrs=inp.attrs - ) - else: - result = xr.DataArray( - np.squeeze(result), - coords=[scales, inp.coords[inp.dims[1]]], - dims=["scale", inp.dims[1]], - attrs=inp.attrs, - ) + increment = np.abs(data[scale:, ...] - data[:-scale, ...]) + result.append(np.nanmean(increment**order, axis=0)) + + _, *comp = [inp.coords[dim].data for dim in inp.dims] + + result = xr.DataArray( + np.squeeze(np.stack(result)), + coords=[scales, *comp], + dims=["scales", *inp.dims[1:]], + attrs=inp.attrs, + ) result.attrs["order"] = order diff --git a/pyrfu/pyrf/t_eval.py b/pyrfu/pyrf/t_eval.py index 717b48f2..e5063306 100644 --- a/pyrfu/pyrf/t_eval.py +++ b/pyrfu/pyrf/t_eval.py @@ -10,9 +10,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -42,7 +42,9 @@ def t_eval(inp, times): if inp.ndim == 2: out = xr.DataArray( - inp.data[idx, :], coords=[times, inp.comp], dims=["time", "comp"] + inp.data[idx, :], + coords=[times, inp.comp], + dims=["time", "comp"], ) else: out = xr.DataArray(inp.data[idx], coords=[times], dims=["time"]) diff --git a/pyrfu/pyrf/time_clip.py b/pyrfu/pyrf/time_clip.py index da3bfd4b..a766374a 100644 --- a/pyrfu/pyrf/time_clip.py +++ b/pyrfu/pyrf/time_clip.py @@ -3,7 +3,6 @@ # Built-in imports import bisect -import datetime # 3rd party imports import numpy as np @@ -14,9 +13,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.14" +__version__ = "2.4.2" __status__ = "Prototype" @@ -50,7 +49,10 @@ def time_clip(inp, tint): out_dict[k] = inp[k] # Find array_like attributes - arr_attrs = filter(lambda x: isinstance(inp.attrs[x], np.ndarray), inp.attrs) + arr_attrs = filter( + lambda x: isinstance(inp.attrs[x], np.ndarray), + inp.attrs, + ) arr_attrs = list(arr_attrs) # Initialize attributes dictionary with non array_like attributes @@ -60,14 +62,17 @@ def time_clip(inp, tint): for a in arr_attrs: attr = inp.attrs[a] - # If array_like attributes have one dimension equal to time length assume - # time dependent. One option would be move the time dependent array_like - # attributes to time series to zVaraibles to avoid confusion + # If array_like attributes have one dimension equal to time + # length assume time dependent. One option would be move the time + # dependent array_like attributes to time series to zVaraibles to + # avoid confusion if attr.shape[0] == len(inp.time.data): coords = [np.arange(attr.shape[i + 1]) for i in range(attr.ndim - 1)] dims = [f"idx{i:d}" for i in range(attr.ndim - 1)] attr_ts = xr.DataArray( - attr, coords=[inp.time.data, *coords], dims=["time", *dims] + attr, + coords=[inp.time.data, *coords], + dims=["time", *dims], ) out_attrs[a] = time_clip(attr_ts, tint).data else: @@ -81,35 +86,35 @@ def time_clip(inp, tint): if isinstance(tint, xr.DataArray): t_start, t_stop = tint.time.data[[0, -1]] - - elif isinstance(tint, np.ndarray): - if isinstance(tint[0], datetime.datetime) and isinstance( - tint[-1], datetime.datetime - ): - t_start, t_stop = [tint.time[0], tint.time[-1]] - + elif isinstance(tint, (np.ndarray, list)): + if isinstance(tint[0], np.datetime64): + t_start, t_stop = tint + elif isinstance(tint[0], str): + t_start, t_stop = iso86012datetime64(np.array(tint)) else: - raise TypeError("Values must be in Datetime64") - - elif isinstance(tint, list): - t_start, t_stop = iso86012datetime64(np.array(tint)) - + raise TypeError("Values must be in datetime64, or str!!") else: - raise TypeError("invalid tint") + raise TypeError("tint must be a DataArray or array_like!!") - idx_min = bisect.bisect_left(inp.time.data, np.datetime64(t_start)) - idx_max = bisect.bisect_right(inp.time.data, np.datetime64(t_stop)) + idx_min = bisect.bisect_left(inp.time.data, t_start) + idx_max = bisect.bisect_right(inp.time.data, t_stop) - coord = [inp.time.data[idx_min:idx_max]] + coords = [inp.time.data[idx_min:idx_max]] + coords_attrs = [inp.time.attrs] if len(inp.coords) > 1: for k in inp.dims[1:]: - coord.append(inp.coords[k].data) + coords.append(inp.coords[k].data) + coords_attrs.append(inp.coords[k].attrs) out = xr.DataArray( - inp.data[idx_min:idx_max, ...], coords=coord, dims=inp.dims, attrs=inp.attrs + inp.data[idx_min:idx_max, ...], + coords=coords, + dims=inp.dims, + attrs=inp.attrs, ) - out.time.attrs = inp.time.attrs + for i, k in enumerate(inp.dims): + out[k].attrs = coords_attrs[i] return out diff --git a/pyrfu/pyrf/timevec2iso8601.py b/pyrfu/pyrf/timevec2iso8601.py index 79d0c89e..8d075b64 100644 --- a/pyrfu/pyrf/timevec2iso8601.py +++ b/pyrfu/pyrf/timevec2iso8601.py @@ -6,9 +6,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" diff --git a/pyrfu/pyrf/trace.py b/pyrfu/pyrf/trace.py index 12d05491..eb7cdd55 100644 --- a/pyrfu/pyrf/trace.py +++ b/pyrfu/pyrf/trace.py @@ -4,11 +4,14 @@ # 3rd party imports import xarray as xr +# Local imports +from .ts_scalar import ts_scalar + __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -52,15 +55,16 @@ def trace(inp): """ - inp_data = inp.data - out_data = inp_data[:, 0, 0] + inp_data[:, 1, 1] + inp_data[:, 2, 2] + # Check input type + assert isinstance(inp, xr.DataArray), "inp must be a xarray.DataArray" - # Attributes - attrs = inp.attrs + # Check that inp is a tensor + message = "inp must be a time series of a tensor" + assert inp.ndim == 3 and inp.shape[1] == 3 and inp.shape[1] == 3, message - # Change tensor order from 2 (matrix) to 0 (scalar) - attrs["TENSOR_ORDER"] = 0 + out_data = inp.data[:, 0, 0] + inp.data[:, 1, 1] + inp.data[:, 2, 2] - out = xr.DataArray(out_data, coords=[inp.time.data], dims=["time"], attrs=attrs) + # Construct time series + out = ts_scalar(inp.time.data, out_data, inp.attrs) return out diff --git a/pyrfu/pyrf/ts_append.py b/pyrfu/pyrf/ts_append.py index 79b6ab4b..c8467e1e 100644 --- a/pyrfu/pyrf/ts_append.py +++ b/pyrfu/pyrf/ts_append.py @@ -7,20 +7,20 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" -def ts_append(inp1, inp2): +def ts_append(inp0, inp1): r"""Concatenate two time series along the time axis. Parameters ---------- - inp1 : xarray.DataArray + inp0 : xarray.DataArray Time series of the first input (early times). - inp2 : xarray.DataArray + inp1 : xarray.DataArray Time series of the second input (late times). Returns @@ -34,60 +34,60 @@ def ts_append(inp1, inp2): """ - if inp1 is None: - return inp2 + if inp0 is None: + return inp1 out_data = {} - if inp1.data.ndim != 1: - out_data["data"] = np.vstack([inp1, inp2]) + if inp0.data.ndim != 1: + out_data["data"] = np.vstack([inp0, inp1]) else: - out_data["data"] = np.hstack([inp1, inp2]) + out_data["data"] = np.hstack([inp0, inp1]) out_data["attrs"] = {} - for k in inp1.attrs: - if isinstance(inp1.attrs[k], np.ndarray): - out_data["attrs"][k] = np.hstack([inp1.attrs[k], inp2.attrs[k]]) + for k in inp0.attrs: + if isinstance(inp0.attrs[k], np.ndarray): + out_data["attrs"][k] = np.hstack([inp0.attrs[k], inp1.attrs[k]]) else: - out_data["attrs"][k] = inp1.attrs[k] + out_data["attrs"][k] = inp0.attrs[k] - depends = [{} for _ in range(len(inp1.dims))] + depends = [{} for _ in range(len(inp0.dims))] - for i, dim in enumerate(inp1.dims): + for i, dim in enumerate(inp0.dims): if i == 0 or dim == "time": - depends[i]["data"] = np.hstack([inp1[dim].data, inp2[dim].data]) + depends[i]["data"] = np.hstack([inp0[dim].data, inp1[dim].data]) # add attributes depends[i]["attrs"] = {} - for k in inp1[dim].attrs: + for k in inp0[dim].attrs: # if attrs is array time append - if isinstance(inp1[dim].attrs[k], np.ndarray): + if isinstance(inp0[dim].attrs[k], np.ndarray): depends[i]["attrs"][k] = np.hstack( - [inp1[dim].attrs[k], inp2[dim].attrs[k]] + [inp0[dim].attrs[k], inp1[dim].attrs[k]], ) else: - depends[i]["attrs"][k] = inp1[dim].attrs[k] + depends[i]["attrs"][k] = inp0[dim].attrs[k] else: - # Use values of other coordinates of inp1 assuming equal to inp2 - depends[i]["data"] = inp1[dim].data + # Use values of other coordinates of inp0 assuming equal to inp1 + depends[i]["data"] = inp0[dim].data # add attributes depends[i]["attrs"] = {} - for k in inp1[dim].attrs: - depends[i]["attrs"][k] = inp1[dim].attrs[k] + for k in inp0[dim].attrs: + depends[i]["attrs"][k] = inp0[dim].attrs[k] # Create DataArray out = xr.DataArray( out_data["data"], coords=[depend["data"] for depend in depends], - dims=inp1.dims, + dims=inp0.dims, attrs=out_data["attrs"], ) diff --git a/pyrfu/pyrf/ts_scalar.py b/pyrfu/pyrf/ts_scalar.py index 64eee5fb..1c86bf79 100644 --- a/pyrfu/pyrf/ts_scalar.py +++ b/pyrfu/pyrf/ts_scalar.py @@ -2,13 +2,14 @@ # -*- coding: utf-8 -*- # 3rd party imports +import numpy as np import xarray as xr __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -17,9 +18,9 @@ def ts_scalar(time, data, attrs: dict = None): Parameters ---------- - time : ndarray + time : numpy.ndarray Array of times. - data : ndarray + data : numpy.ndarray Data corresponding to the time list. attrs : dict, Optional Attributes of the data list. @@ -31,10 +32,15 @@ def ts_scalar(time, data, attrs: dict = None): """ + # Check input type + assert isinstance(time, np.ndarray), "time must be a numpy.ndarray" + assert isinstance(data, np.ndarray), "data must be a numpy.ndarray" + + # Check input shape must be (n, ) assert data.ndim == 1, "Input must be a scalar" assert len(time) == len(data), "Time and data must have the same length" - if attrs is None: + if attrs is None or not isinstance(attrs, dict): attrs = {} out = xr.DataArray(data, coords=[time[:]], dims="time", attrs=attrs) diff --git a/pyrfu/pyrf/ts_skymap.py b/pyrfu/pyrf/ts_skymap.py index 9928f2c1..2a573227 100644 --- a/pyrfu/pyrf/ts_skymap.py +++ b/pyrfu/pyrf/ts_skymap.py @@ -9,7 +9,7 @@ __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.26" +__version__ = "2.4.2" __status__ = "Prototype" @@ -47,24 +47,38 @@ def ts_skymap(time, data, energy, phi, theta, **kwargs): """ - # Check if even (odd) time step energy channels energy1 (energy0), and energy - # step table are provided. - energy0 = kwargs.get("energy0", None) - energy1 = kwargs.get("energy1", None) - esteptable = kwargs.get("esteptable", None) + # Check input type + assert isinstance(time, np.ndarray), "time must be numpy.ndarray" + assert isinstance(data, np.ndarray), "data must be numpy.ndarray" + assert isinstance(energy, np.ndarray), "energy must be numpy.ndarray" + assert isinstance(phi, np.ndarray), "phi must be numpy.ndarray" + assert isinstance(theta, np.ndarray), "theta must be numpy.ndarray" + + # Check if even (odd) time step energy channels energy1 (energy0), and + # energy step table are provided. + energy0 = kwargs.get("energy0", energy[0, :]) + energy1 = kwargs.get("energy1", energy[1, :]) + esteptable = kwargs.get("esteptable", np.zeros(len(time))) + + # Check that energy0 and energy1 + # assert isinstance(energy0, np.ndarray), "energy0 must be 1D numpy.ndarray" + # assert energy0.ndim == 1, "energy0 must be 1D numpy.ndarray" + # assert energy0.shape[0] == energy.shape[1], "energy0 is not consistent with time" + # assert isinstance(energy1, np.ndarray), "energy1 must be 1D numpy.ndarray" + # assert energy1.ndim == 1, "energy1 must be 1D numpy.ndarray" + # assert energy1.shape[0] == energy.shape[1], "energy1 is not consistent with time" + + # Check esteptable + assert isinstance(esteptable, np.ndarray), "esteptable must be 1D numpy.ndarray" + assert esteptable.ndim == 1, "esteptable must be 1D numpy.ndarray" + assert esteptable.shape[0] == len(time), "esteptable is not consistent with time" + attrs = kwargs.get("attrs", {}) coords_attrs = kwargs.get("coords_attrs", {}) glob_attrs = kwargs.get("glob_attrs", {}) - if energy is None: - assert energy0 is not None and energy1 is not None and esteptable is not None - - energy = np.tile(energy0, (len(esteptable), 1)) - - energy[esteptable == 1] = np.tile(energy1, (int(np.sum(esteptable)), 1)) - - if phi.ndim == 1: - phi = np.tile(phi, (len(time), 1)) + # Check attributes are dictionaries + assert isinstance(attrs, dict) out_dict = { "data": (["time", "idx0", "idx1", "idx2"], data), @@ -77,18 +91,27 @@ def ts_skymap(time, data, energy, phi, theta, **kwargs): "idx2": np.arange(len(theta)), } + # Construct global attributes and sort them + # remove energy0, energy1, and esteptable from global attrs to overwrite + overwrite_keys = ["energy0", "energy1", "esteptable"] + glob_attrs = {k: glob_attrs[k] for k in glob_attrs if k not in overwrite_keys} glob_attrs = { + "energy0": energy0, + "energy1": energy1, + "esteptable": esteptable, **glob_attrs, - **{"energy0": energy0, "energy1": energy1, "esteptable": esteptable}, } + glob_attrs = {k: glob_attrs[k] for k in sorted(glob_attrs)} + + # Create Dataset out = xr.Dataset(out_dict, attrs=glob_attrs) - # Set coordinates attributes + # Sort and fill coordinates attributes for k in coords_attrs: - out[k].attrs = coords_attrs[k] + out[k].attrs = {m: coords_attrs[k][m] for m in sorted(coords_attrs[k])} - # Set data attributes - out.data.attrs = attrs + # Sort and fill data attributes + out.data.attrs = {k: attrs[k] for k in sorted(attrs)} return out diff --git a/pyrfu/pyrf/ts_spectr.py b/pyrfu/pyrf/ts_spectr.py new file mode 100644 index 00000000..afdfd80c --- /dev/null +++ b/pyrfu/pyrf/ts_spectr.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# 3rd party imports +import numpy as np +import xarray as xr + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.2" +__status__ = "Prototype" + + +def ts_spectr(time, ener, data, comp_name: str = "energy", attrs: dict = None): + r"""Create a time series containing a spectrum + + Parameters + ---------- + time : numpy.ndarray + Array of times. + ener : numpy.ndarray + Y value of the spectrum (energies, frequencies, etc.) + data : numpy.ndarray + Data of the spectrum. + attrs : dict, Optional + Attributes of the data list. + + Returns + ------- + out : xarray.DataArray + Time series of a spectrum + + """ + + # Check input type + assert isinstance(time, np.ndarray), "time must be a numpy.ndarray" + assert isinstance(ener, np.ndarray), "time must be a numpy.ndarray" + assert isinstance(data, np.ndarray), "data must be a numpy.ndarray" + + # Check input shape must be (n, m, ) + assert data.ndim == 2, "Input must be a spectrum" + assert len(time) == data.shape[0], "len(time) and data.shape[0] must be equal" + assert len(ener) == data.shape[1], "len(ener) and data.shape[1] must be equal" + + if attrs is None or not isinstance(attrs, dict): + attrs = {} + + out = xr.DataArray(data, coords=[time, ener], dims=["time", comp_name], attrs=attrs) + out.attrs["TENSOR_ORDER"] = 0 + + return out diff --git a/pyrfu/pyrf/ts_tensor_xyz.py b/pyrfu/pyrf/ts_tensor_xyz.py index b7c9f3aa..73349b56 100644 --- a/pyrfu/pyrf/ts_tensor_xyz.py +++ b/pyrfu/pyrf/ts_tensor_xyz.py @@ -2,13 +2,14 @@ # -*- coding: utf-8 -*- # 3rd party imports +import numpy as np import xarray as xr __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -31,10 +32,15 @@ def ts_tensor_xyz(time, data, attrs: dict = None): """ + # Check input type + assert isinstance(time, np.ndarray), "time must be a numpy.ndarray" + assert isinstance(data, np.ndarray), "data must be a numpy.ndarray" + + # Check input shape must be (n, 3, 3) assert data.ndim == 3 and data.shape[1:] == (3, 3) assert len(time) == len(data), "Time and data must have the same length" - if attrs is None: + if attrs is None or not isinstance(attrs, dict): attrs = {} out = xr.DataArray( diff --git a/pyrfu/pyrf/ts_time.py b/pyrfu/pyrf/ts_time.py index 66d1269b..47a11d5b 100644 --- a/pyrfu/pyrf/ts_time.py +++ b/pyrfu/pyrf/ts_time.py @@ -7,13 +7,13 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" -def ts_time(time): +def ts_time(time, attrs: dict = None): r"""Creates time line in DataArray. Parameters @@ -30,8 +30,13 @@ def ts_time(time): assert isinstance(time, np.ndarray) - time = (time * 1e9).astype("datetime64[ns]") + if time.dtype == np.float64: + time = (time * 1e9).astype("datetime64[ns]") + elif time.dtype == "datetime64[ns]": + pass + else: + raise TypeError("time must be in unix (float64) or numpy.datetime64") - out = xr.DataArray(time, coords=[time], dims=["time"]) + out = xr.DataArray(time, coords=[time], dims=["time"], attrs=attrs) return out diff --git a/pyrfu/pyrf/ts_vec_xyz.py b/pyrfu/pyrf/ts_vec_xyz.py index fdc361da..e62adf57 100644 --- a/pyrfu/pyrf/ts_vec_xyz.py +++ b/pyrfu/pyrf/ts_vec_xyz.py @@ -2,13 +2,14 @@ # -*- coding: utf-8 -*- # 3rd party imports +import numpy as np import xarray as xr __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -31,14 +32,22 @@ def ts_vec_xyz(time, data, attrs: dict = None): """ + # Check input type + assert isinstance(time, np.ndarray), "time must be a numpy.ndarray" + assert isinstance(data, np.ndarray), "data must be a numpy.ndarray" + + # Check input shape must be (n, 3) assert data.ndim == 2 and data.shape[1] == 3 assert len(time) == len(data), "Time and data must have the same length" - if attrs is None: + if attrs is None or not isinstance(attrs, dict): attrs = {} out = xr.DataArray( - data, coords=[time[:], ["x", "y", "z"]], dims=["time", "comp"], attrs=attrs + data, + coords=[time[:], ["x", "y", "z"]], + dims=["time", "comp"], + attrs=attrs, ) out.attrs["TENSOR_ORDER"] = 1 diff --git a/pyrfu/pyrf/ttns2datetime64.py b/pyrfu/pyrf/ttns2datetime64.py index 265eab71..c92469d9 100644 --- a/pyrfu/pyrf/ttns2datetime64.py +++ b/pyrfu/pyrf/ttns2datetime64.py @@ -2,6 +2,7 @@ # -*- coding: utf-8 -*- # 3rd party imports +import numpy as np from cdflib import cdfepoch # Local imports @@ -9,9 +10,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -31,6 +32,9 @@ def ttns2datetime64(time): """ + message = "time must be float, int, or array_like" + assert isinstance(time, (float, int, list, np.ndarray)), message + # time_tt2000 = cdfepoch.breakdown_tt2000(time) @@ -38,6 +42,6 @@ def ttns2datetime64(time): time_iso8601 = timevec2iso8601(time_tt2000) # - time_datetime64 = time_iso8601.astype(" 0: @@ -277,16 +316,20 @@ def wavepolarize_means(b_wave, b_bgd, min_psd: float = 1e-25, nop_fft: int = 256 gamma = np.pi + (np.pi + np.arctan(upper[j, k] / lower[j, k])) lambda_u[k, xyz, :] = np.exp(-0.5 * gamma * 1j) * lambda_u[k, xyz, :] - helic[j, k, xyz] = np.sqrt(np.sum(np.real(lambda_u[k, xyz, :3]) ** 2)) - helic[j, k, xyz] /= np.sqrt(np.sum(np.imag(lambda_u[k, xyz, :3]) ** 2)) + helic[j, k, xyz] = np.sqrt( + np.sum(np.real(lambda_u[k, xyz, :3]) ** 2), + ) + helic[j, k, xyz] /= np.sqrt( + np.sum(np.imag(lambda_u[k, xyz, :3]) ** 2), + ) helic[j, k, xyz] = np.divide(1, helic[j, k, xyz]) # ELLIPTICITY CALCULATION upper_e = np.sum( - np.imag(lambda_u[k, xyz, :3]) * np.real(lambda_u[k, xyz, :3]) + np.imag(lambda_u[k, xyz, :3]) * np.real(lambda_u[k, xyz, :3]), ) lower_e = np.sum(np.real(lambda_u[k, xyz, :2]) ** 2) - np.sum( - np.imag(lambda_u[k, xyz, :2]) ** 2 + np.imag(lambda_u[k, xyz, :2]) ** 2, ) if upper_e > 0: @@ -298,12 +341,14 @@ def wavepolarize_means(b_wave, b_bgd, min_psd: float = 1e-25, nop_fft: int = 256 lambda_u_rot = np.exp(-0.5 * gamma_rot * 1j) * lam ellip[j, k, xyz] = np.sqrt(np.sum(np.imag(lambda_u_rot) ** 2)) - ellip[j, k, xyz] /= np.sqrt(np.sum(np.real(lambda_u_rot) ** 2)) + ellip[j, k, xyz] /= np.sqrt( + np.sum(np.real(lambda_u_rot) ** 2), + ) ellip[j, k, xyz] *= -( np.imag(e_spec_mat[k, 0, 1]) * np.sin(wave_angle[j, k]) ) ellip[j, k, xyz] /= np.abs( - np.imag(e_spec_mat[k, 0, 1]) * np.sin(wave_angle[j, k]) + np.imag(e_spec_mat[k, 0, 1]) * np.sin(wave_angle[j, k]), ) # AVERAGING HELICITY AND ELLIPTICITY RESULTS @@ -336,15 +381,31 @@ def wavepolarize_means(b_wave, b_bgd, min_psd: float = 1e-25, nop_fft: int = 256 helicity[power_spec < min_psd] = np.nan # Save as DataArrays - b_psd = xr.DataArray(power_spec, coords=[time_line, freq_line], dims=["t", "f"]) + b_psd = xr.DataArray( + power_spec, + coords=[time_line, freq_line], + dims=["t", "f"], + ) wave_angle = xr.DataArray( - wave_angle * 180 / np.pi, coords=[time_line, freq_line], dims=["t", "f"] + wave_angle * 180 / np.pi, + coords=[time_line, freq_line], + dims=["t", "f"], ) ellipticity = xr.DataArray( - ellipticity, coords=[time_line, freq_line], dims=["t", "f"] + ellipticity, + coords=[time_line, freq_line], + dims=["t", "f"], + ) + deg_pol = xr.DataArray( + deg_pol, + coords=[time_line, freq_line], + dims=["t", "f"], + ) + helicity = xr.DataArray( + helicity, + coords=[time_line, freq_line], + dims=["t", "f"], ) - deg_pol = xr.DataArray(deg_pol, coords=[time_line, freq_line], dims=["t", "f"]) - helicity = xr.DataArray(helicity, coords=[time_line, freq_line], dims=["t", "f"]) b_psd.f.attrs["units"] = "Hz" wave_angle.f.attrs["units"] = "Hz" diff --git a/pyrfu/pyrf/waverage.py b/pyrfu/pyrf/waverage.py index 43fc6f75..eadc846c 100644 --- a/pyrfu/pyrf/waverage.py +++ b/pyrfu/pyrf/waverage.py @@ -6,9 +6,9 @@ __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2021" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.7" +__version__ = "2.4.2" __status__ = "Prototype" @@ -37,7 +37,7 @@ def waverage(inp, f_sampl: float = None, n_pts: int = 7): f_sampl = 1e9 / (inp.time.data[1] - inp.time.data[0]).view("i8") n_data = np.round( - 1e-9 * (inp.time.data[-1] - inp.time.data[0]).view("i8") * f_sampl + 1e-9 * (inp.time.data[-1] - inp.time.data[0]).view("i8") * f_sampl, ) inp_data = inp.data diff --git a/pyrfu/solo/__init__.py b/pyrfu/solo/__init__.py index 92cb67ab..1e5e3f6c 100644 --- a/pyrfu/solo/__init__.py +++ b/pyrfu/solo/__init__.py @@ -2,13 +2,14 @@ # -*- coding: utf-8 -*- from .db_init import db_init -from .read_tnr import read_tnr -from .read_tnr import read_tnr from .read_lfr_density import read_lfr_density +from .read_tnr import read_tnr __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2022" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.22" +__version__ = "2.4.2" __status__ = "Prototype" + +__all__ = ["db_init", "read_tnr", "read_lfr_density"] diff --git a/pyrfu/solo/db_init.py b/pyrfu/solo/db_init.py index a7645c21..8c629492 100644 --- a/pyrfu/solo/db_init.py +++ b/pyrfu/solo/db_init.py @@ -1,15 +1,16 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import json + # Built-in imports import os -import json __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2022" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.22" +__version__ = "2.4.2" __status__ = "Prototype" @@ -25,16 +26,18 @@ def db_init(local_data_dir): # Normalize the path and make sure that it exists local_data_dir = os.path.normpath(local_data_dir) - assert os.path.exists(local_data_dir), f"{local_data_dir} doesn't exists!!" + assert os.path.exists( + local_data_dir, + ), f"{local_data_dir} doesn't exists!!" # Path to the configuration file. pkg_path = os.path.dirname(os.path.abspath(__file__)) # Read the current version of the configuration - with open(os.path.join(pkg_path, "config.json"), "r") as fs: + with open(os.path.join(pkg_path, "config.json"), "r", encoding="utf-8") as fs: config = json.load(fs) # Overwrite the configuration file with the new path - with open(os.path.join(pkg_path, "config.json"), "w") as fs: + with open(os.path.join(pkg_path, "config.json"), "w", encoding="utf-8") as fs: config["local_data_dir"] = local_data_dir json.dump(config, fs) diff --git a/pyrfu/solo/read_lfr_density.py b/pyrfu/solo/read_lfr_density.py index 4c9e84e2..fba78f51 100644 --- a/pyrfu/solo/read_lfr_density.py +++ b/pyrfu/solo/read_lfr_density.py @@ -10,28 +10,30 @@ # 3rd party imports import numpy as np - from cdflib import cdfepoch from dateutil import parser -from dateutil.rrule import rrule, DAILY +from dateutil.rrule import DAILY, rrule -from ..pyrf import datetime2iso8601, read_cdf, ts_append, ts_scalar +from ..pyrf import read_cdf, ts_append, ts_scalar __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2022" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.22" +__version__ = "2.4.2" __status__ = "Prototype" logging.captureWarnings(True) logging.basicConfig( - format="%(asctime)s: %(message)s", datefmt="%d-%b-%y %H:%M:%S", level=logging.INFO + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, ) def _list_files_lfr_density_l3(tint, data_path: str = "", tree: bool = False): - """Find files in the L2 data repo corresponding to the target time interval. + """Find files in the L2 data repo corresponding to the target time + interval. Parameters ---------- @@ -50,13 +52,20 @@ def _list_files_lfr_density_l3(tint, data_path: str = "", tree: bool = False): """ + # Check input types + assert isinstance(tint, (list, np.ndarray)), "tint must be array_like" + assert len(tint) == 2, "tint must contain two elements" + assert isinstance(tint[0], str), "tint[0] must be a string" + assert isinstance(tint[1], str), "tint[1] must be a string" + assert isinstance(tree, bool), "tree must be a boolean" + # Check path if not data_path: # pkg_path = os.path.dirname(os.path.abspath(__file__)) pkg_path = os.path.dirname(os.path.abspath(__file__)) # Read the current version of the MMS configuration file - with open(os.path.join(pkg_path, "config.json"), "r") as fs: + with open(os.path.join(pkg_path, "config.json"), "r", encoding="utf-8") as fs: config = json.load(fs) data_path = os.path.normpath(config["local_data_dir"]) @@ -69,7 +78,7 @@ def _list_files_lfr_density_l3(tint, data_path: str = "", tree: bool = False): files_out = [] # directory and file name search patterns: - # - assume directories are of the form: [data_path]/L3/lfr_density/year/month/ + # - assume directories are of the form: [path]/L3/lfr_density/year/month/ # - assume file names are of the form: # solo_L3_rpw-bia-density-cdag_YYYYMMDD_version.cdf @@ -88,13 +97,15 @@ def _list_files_lfr_density_l3(tint, data_path: str = "", tree: bool = False): "lfr_density", date.strftime("%Y"), date.strftime("%m"), - ] + ], ) else: local_dir = data_path if os.name == "nt": - full_path = os.sep.join([re.escape(local_dir) + os.sep, file_name]) + full_path = os.sep.join( + [re.escape(local_dir) + os.sep, file_name], + ) else: full_path = os.sep.join([re.escape(local_dir), file_name]) @@ -113,9 +124,9 @@ def _list_files_lfr_density_l3(tint, data_path: str = "", tree: bool = False): # sort in time if len(files_out) > 1: - return sorted(files_out) - else: - return files_out + files_out = sorted(files_out) + + return files_out def read_lfr_density(tint, data_path: str = "", tree: bool = False): @@ -152,7 +163,7 @@ def read_lfr_density(tint, data_path: str = "", tree: bool = False): # Get time from Epoch epoch = data_l3["epoch"].data - time = cdfepoch.to_datetime(epoch, to_np=True) + time = cdfepoch.to_datetime(epoch) # Get density data and contruct time series. density = data_l3["density"].data diff --git a/pyrfu/solo/read_tnr.py b/pyrfu/solo/read_tnr.py index c3f14a95..75e629c1 100644 --- a/pyrfu/solo/read_tnr.py +++ b/pyrfu/solo/read_tnr.py @@ -11,29 +11,31 @@ # 3rd party imports import numpy as np import xarray as xr - from cdflib import cdfepoch from dateutil import parser -from dateutil.rrule import rrule, DAILY +from dateutil.rrule import DAILY, rrule from scipy import integrate from ..pyrf import read_cdf, ts_append __author__ = "Louis Richard" __email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2022" +__copyright__ = "Copyright 2020-2023" __license__ = "MIT" -__version__ = "2.3.22" +__version__ = "2.4.2" __status__ = "Prototype" logging.captureWarnings(True) logging.basicConfig( - format="%(asctime)s: %(message)s", datefmt="%d-%b-%y %H:%M:%S", level=logging.INFO + format="[%(asctime)s] %(levelname)s: %(message)s", + datefmt="%d-%b-%y %H:%M:%S", + level=logging.INFO, ) def _list_files_tnr_l2(tint, data_path: str = "", tree: bool = False): - """Find files in the L2 data repo corresponding to the target time interval. + """Find files in the L2 data repo corresponding to the target time + interval. Parameters ---------- @@ -57,7 +59,7 @@ def _list_files_tnr_l2(tint, data_path: str = "", tree: bool = False): pkg_path = os.path.dirname(os.path.abspath(__file__)) # Read the current version of the MMS configuration file - with open(os.path.join(pkg_path, "config.json"), "r") as fs: + with open(os.path.join(pkg_path, "config.json"), "r", encoding="utf-8") as fs: config = json.load(fs) data_path = os.path.normpath(config["local_data_dir"]) @@ -76,6 +78,12 @@ def _list_files_tnr_l2(tint, data_path: str = "", tree: bool = False): file_name = r"solo_L2_rpw-tnr-surv.*_([0-9]{8})_V[0-9]{2}.cdf" + # Check tint + assert isinstance(tint, (list, np.ndarray)), "tint must be array_like" + assert len(tint) == 2, "tint must contain two elements" + assert isinstance(tint[0], str), "tint[0] must be a string" + assert isinstance(tint[1], str), "tint[1] must be a string" + d_start = parser.parse(parser.parse(tint[0]).strftime("%Y-%m-%d")) until_ = parser.parse(tint[1]) - datetime.timedelta(seconds=1) days = rrule(DAILY, dtstart=d_start, until=until_) @@ -83,13 +91,21 @@ def _list_files_tnr_l2(tint, data_path: str = "", tree: bool = False): for date in days: if tree: local_dir = os.sep.join( - [data_path, "L2", "thr", date.strftime("%Y"), date.strftime("%m")] + [ + data_path, + "L2", + "thr", + date.strftime("%Y"), + date.strftime("%m"), + ], ) else: local_dir = data_path if os.name == "nt": - full_path = os.sep.join([re.escape(local_dir) + os.sep, file_name]) + full_path = os.sep.join( + [re.escape(local_dir) + os.sep, file_name], + ) else: full_path = os.sep.join([re.escape(local_dir), file_name]) @@ -108,9 +124,9 @@ def _list_files_tnr_l2(tint, data_path: str = "", tree: bool = False): # sort in time if len(files_out) > 1: - return sorted(files_out) - else: - return files_out + files_out = sorted(files_out) + + return files_out def read_tnr(tint, sensor: int = 4, data_path: str = "", tree: bool = False): @@ -146,9 +162,10 @@ def read_tnr(tint, sensor: int = 4, data_path: str = "", tree: bool = False): """ - files = _list_files_tnr_l2(tint, data_path, tree) + # Check input types + assert isinstance(sensor, int), "sensor must integer" - assert files, "No files found. Make sure that the data_path is correct" + files = _list_files_tnr_l2(tint, data_path, tree) # Initialize spectrum output to None out = None @@ -194,7 +211,7 @@ def read_tnr(tint, sensor: int = 4, data_path: str = "", tree: bool = False): idx_l, idx_r = [xdelta_sw[inswn] + 1, xdelta_sw[inswn + 1]] sweep_num[idx_l:idx_r] += sweep_num[xdelta_sw[inswn]] - timet_ = cdfepoch.to_datetime(epoch_, to_np=True) + timet_ = cdfepoch.to_datetime(epoch_) sens0_, sens1_ = [np.where(confg_[:, i] == sensor)[0] for i in range(2)] @@ -265,6 +282,6 @@ def read_tnr(tint, sensor: int = 4, data_path: str = "", tree: bool = False): ), ) - out = out[np.argsort(out.time.data)] + out = out[np.argsort(out.time.data)] return out diff --git a/pyrfu/stylesheets/aps.mplstyle b/pyrfu/stylesheets/aps.mplstyle new file mode 100644 index 00000000..8d0b4539 --- /dev/null +++ b/pyrfu/stylesheets/aps.mplstyle @@ -0,0 +1,74 @@ +## ************************************************************************************* +## * Matplotlib style sheet for APS publications * +## author : Louis Richard +## email : louisr@irfu.se +## ************************************************************************************* + +## Fonts +font.family: serif +font.style: normal +font.variant: normal +font.weight: normal +font.stretch: normal +font.size: 8.0 +#font.serif: Times, Computer Modern Roman +#font.sans-serif: Helvetica, Computer Modern Sans serif + +## LaTeX +#text.usetex: True +#text.latex.preamble: \usepackage{bm} +pgf.texsystem: xelatex + +## Axes +axes.labelsize: 8 +axes.linewidth: .5 +axes.xmargin: 0 +axes.ymargin: 0 + +## Save +savefig.dpi: 600 +savefig.format: eps +# savefig.bbox: tight + +## Legend +legend.loc: best +legend.frameon: False # if True, draw the legend on a background patch +legend.framealpha: 0.8 # legend patch transparency +legend.facecolor: inherit # inherit from axes.facecolor; or color spec +legend.edgecolor: 0.8 # background patch boundary color +legend.fancybox: False # if True, use a rectangle box for the legend background +legend.shadow: False # if True, give background a shadow effect +legend.numpoints: 1 # the number of marker points in the legend line +legend.scatterpoints: 1 # number of scatter points +legend.markerscale: 0.9 # the relative size of legend markers vs. original +legend.fontsize: 8 +legend.title_fontsize: None # None sets to the same as the default axes. + +## Dimensions as fraction of fontsize: +legend.borderpad: 0.2 # border whitespace +legend.labelspacing: 0.2 # the vertical space between the legend entries +legend.handlelength: 0.1 # the length of the legend lines +#legend.handleheight: 0.4 # the height of the legend handle +legend.handletextpad: 0.1 # the space between the legend line and legend text +legend.borderaxespad: 0.4 # the border between the axes and legend edge +legend.columnspacing: 0.8 # column separation + +## Ticks +xtick.labelsize: 8 +xtick.major.size: 2 +xtick.minor.size: 1.2 +xtick.direction: in +xtick.top: True + +ytick.labelsize: 8 +ytick.major.size: 2 +ytick.minor.size: 1.2 +ytick.direction : in +ytick.right: True + +## Lines +lines.linewidth: 0.6 +lines.markersize: 3 + +## Errorbars +errorbar.capsize: 2 diff --git a/pyrfu/tests/__init__.py b/pyrfu/tests/__init__.py index d4e8eca2..15b6c3fb 100644 --- a/pyrfu/tests/__init__.py +++ b/pyrfu/tests/__init__.py @@ -1,13 +1,194 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -# -# MIT License -# -# Copyright (c) 2020 Louis Richard -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so. + +# Built-in imports +import string + +# 3rd party imports +import numpy as np +import xarray as xr + +# Local imports +from .. import pyrf + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.2" +__status__ = "Prototype" + + +__all__ = [ + "generate_timeline", + "generate_data", + "generate_ts", + "generate_spectr", + "generate_vdf", +] + + +def generate_timeline( + f_s, + n_pts: int = 10000, + dtype="datetime64[ns]", + ref_time: str = "2019-01-01T00:00:00.000000000", +): + r"""Generate timeline for testings + + Parameters + ---------- + f_s + n_pts + dtype + ref_time + + Returns + ------- + + """ + ref_time = np.datetime64(ref_time) + times = [ref_time + np.timedelta64(int(i * 1e9 / f_s), "ns") for i in range(n_pts)] + times = np.array(times).astype(dtype) + return times + + +def generate_data(n_pts, tensor_order: int = 0): + r"""Generate data (numpy.ndarray) for testings + + Parameters + ---------- + n_pts + tensor_order + + Returns + ------- + data + """ + + data = np.random.random((n_pts, *([3] * tensor_order))) + + return data + + +def generate_ts(f_s, n_pts, tensor_order: int = 0, attrs: dict = None): + r"""Generate timeseries for testings + + Parameters + ---------- + f_s + n_pts + tensor_order + attrs + + Returns + ------- + + """ + if attrs is None: + attrs = {} + + time = generate_timeline(f_s, n_pts) + data = generate_data(n_pts, tensor_order=tensor_order) + + if tensor_order == 0: + out = pyrf.ts_scalar(time, data, attrs=attrs) + elif tensor_order == 1: + out = pyrf.ts_vec_xyz(time, data, attrs=attrs) + elif tensor_order == 2: + out = pyrf.ts_tensor_xyz(time, data, attrs=attrs) + else: + coords = [time, *[np.arange(data.shape[i]) for i in range(1, tensor_order + 1)]] + dims = ["time", *list(string.ascii_lowercase[8 : 8 + tensor_order])] + out = xr.DataArray(data, coords=coords, dims=dims, attrs=attrs) + + out.time.attrs = {"UNITS": "I AM GROOT!!"} + + return out + + +def generate_spectr(f_s, n_pts, shape, attrs=None): + r"""Generates spectrum for testings + + Parameters + ---------- + f_s + n_pts + shape + attrs + + Returns + ------- + + """ + out = pyrf.ts_spectr( + generate_timeline(f_s, n_pts), + np.arange(shape), + np.random.random((n_pts, shape)), + attrs=attrs, + ) + return out + + +def generate_vdf( + f_s, + n_pts, + shape, + energy01: bool = False, + species: str = "ions", + units: str = "s^3/cm^6", +): + r"""Generate skymap for testings + + Parameters + ---------- + f_s + n_pts + shape + energy01 + species + + Returns + ------- + + """ + times = generate_timeline(f_s, n_pts) + + phi = np.arange(shape[1]) + phi = np.tile(phi, (n_pts, 1)) + theta = np.arange(shape[2]) + data = np.random.random((n_pts, *shape)) + + if energy01: + energy0 = np.linspace(0, shape[0], shape[0], endpoint=False) + energy1 = np.linspace(0, shape[0], shape[0], endpoint=False) + 0.5 + esteptable = np.arange(n_pts) % 2 + energy = np.tile(energy0, (n_pts, 1)) + energy[esteptable == 1, :] = np.tile(energy1, (np.sum(esteptable), 1)) + else: + energy = np.linspace(0, shape[0], shape[0], endpoint=False) + energy = np.tile(energy, (n_pts, 1)) + energy0 = energy[0, :] + energy1 = energy[1, :] + esteptable = np.zeros(n_pts) + + attrs = {"UNITS": units} + glob_attrs = { + "species": species, + "delta_energy_plus": np.ones((n_pts, shape[0])), + "delta_energy_minus": np.ones((n_pts, shape[0])), + } + + out = pyrf.ts_skymap( + times, + data, + energy, + phi, + theta, + energy0=energy0, + energy1=energy1, + esteptable=esteptable, + attrs=attrs, + glob_attrs=glob_attrs, + ) + return out diff --git a/pyrfu/tests/test_dispersion.py b/pyrfu/tests/test_dispersion.py new file mode 100644 index 00000000..d9b4eee5 --- /dev/null +++ b/pyrfu/tests/test_dispersion.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Built-in imports +import random +import unittest + +# 3rd party imports +import numpy as np +import xarray as xr +from ddt import data, ddt + +# Local imports +from .. import dispersion + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.4" +__status__ = "Prototype" + + +class DispSurfCalcTestCase(unittest.TestCase): + def test_disp_surf_calc_output(self): + kx, kz, wf, extra_param = dispersion.disp_surf_calc( + random.random(), random.random(), random.random(), random.random() + ) + self.assertIsInstance(kx, np.ndarray) + self.assertIsInstance(kz, np.ndarray) + self.assertIsInstance(wf, np.ndarray) + self.assertIsInstance(extra_param, dict) + + +@ddt +class OneFluidDispersionTestCase(unittest.TestCase): + @data( + ( + random.random(), + random.random(), + {"n": random.random(), "t": random.random(), "gamma": random.random()}, + {"n": random.random(), "t": random.random(), "gamma": random.random()}, + random.randint(10, 1000), + ) + ) + def test_one_fluid_dispersion_output(self, value): + b_0, theta, ions, electrons, n_k = value + result = dispersion.one_fluid_dispersion(b_0, theta, ions, electrons, n_k) + self.assertIsInstance(result[0], xr.DataArray) + self.assertIsInstance(result[1], xr.DataArray) + self.assertIsInstance(result[2], xr.DataArray) + + +if __name__ == "__main__": + unittest.main() diff --git a/pyrfu/tests/test_mms.py b/pyrfu/tests/test_mms.py new file mode 100644 index 00000000..665d2949 --- /dev/null +++ b/pyrfu/tests/test_mms.py @@ -0,0 +1,1073 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Built-in imports +import itertools +import json +import os +import random +import string +import unittest + +# 3rd party imports +import numpy as np +import requests +import xarray as xr +from ddt import data, ddt, idata, unpack + +# Local imports +from .. import mms, pyrf +from ..mms.psd_moments import _moms +from . import ( + generate_data, + generate_spectr, + generate_timeline, + generate_ts, + generate_vdf, +) + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2024" +__license__ = "MIT" +__version__ = "2.4.2" +__status__ = "Prototype" + +TEST_TINT = ["2019-01-01T00:00:00.000000000", "2019-01-01T00:10:00.000000000"] + + +def generate_feeps(f_s, n_pts, data_rate, dtype, lev, mms_id): + var = {"tmmode": data_rate, "dtype": dtype, "lev": lev} + eyes = mms.feeps_active_eyes(var, TEST_TINT, mms_id) + keys = [f"{k}-{eyes[k][i]}" for k in eyes for i in range(len(eyes[k]))] + feeps_dict = {k: generate_spectr(f_s, n_pts, 16, f"energy-{k}") for k in keys} + feeps_dict["spinsectnum"] = pyrf.ts_scalar( + generate_timeline(f_s, n_pts), np.tile(np.arange(12), n_pts // 12 + 1)[:n_pts] + ) + + feeps_alle = xr.Dataset(feeps_dict) + feeps_alle.attrs = {"mmsId": mms_id, **var} + + return feeps_alle + + +def generate_eis(f_s, n_pts, data_rate, dtype, lev, specie, data_unit, mms_id): + pref = f"mms{mms_id:d}_epd_eis" + pref = f"{pref}_{data_rate}_{lev}_{dtype}" + + if data_rate == "brst": + pref = f"{pref}_{data_rate}_{dtype}" + else: + pref = f"{pref}_{dtype}" + + suf = f"{specie}_P1_{data_unit.lower()}_t" + + keys = [f"{pref}_{suf}{t:d}" for t in range(6)] + + spin_nums = pyrf.ts_scalar( + generate_timeline(f_s, n_pts), + np.sort(np.tile(np.arange(n_pts // 12 + 1), (12,)))[1 : n_pts + 1], + ) + sectors = pyrf.ts_scalar( + generate_timeline(f_s, n_pts), + np.tile(np.arange(12), n_pts // 12 + 1)[1 : n_pts + 1], + ) + + if dtype.lower() == "extof": + energies = np.array( + [ + 47.645324, + 54.928681, + 62.419454, + 70.833554, + 80.315371, + 91.00098, + 103.018894, + 116.554129, + 131.801143, + 148.970297, + 168.295534, + 190.060874, + 214.590996, + 242.245343, + 273.466432, + 308.768669, + 348.73539, + 394.035378, + 445.404668, + 503.597543, + 569.429005, + 643.764143, + 727.683404, + 822.660211, + 930.654627, + ] + ) + else: + energies = np.array( + [ + 10.51516, + 11.509144, + 12.612351, + 13.817409, + 15.111664, + 16.55435, + 18.134081, + 19.857029, + 21.774935, + 23.807037, + 26.021971, + 28.526016, + 31.215776, + 34.228877, + 37.604494, + 41.116729, + 45.29041, + 51.412368, + 58.570702, + 65.951929, + 75.09237, + ] + ) + + eis_dict = {"spin": spin_nums, "sector": sectors} + + for i, k in enumerate(keys): + eis_dict[f"t{i:d}"] = generate_spectr(f_s, n_pts, len(energies), "energy") + eis_dict[f"look_t{i:d}"] = generate_ts(f_s, n_pts, tensor_order=1) + + # glob_attrs = {**outdict["spin"].attrs["GLOBAL"], **var} + glob_attrs = { + "delta_energy_plus": 0.5 * np.ones(len(energies)), + "delta_energy_minus": 0.5 * np.ones(len(energies)), + "species": specie, + "randattrs": "".join(random.choice(string.ascii_lowercase) for _ in range(10)), + } + + # Build Dataset + eis = xr.Dataset(eis_dict, attrs=glob_attrs) + eis = eis.assign_coords(energy=energies) + + return eis + + +def _mms_keys(): + test_path = os.path.dirname(os.path.abspath(__file__)) + + root_path = os.path.join(os.path.split(test_path)[0], "mms") + + with open( + os.sep.join([root_path, "mms_keys.json"]), "r", encoding="utf-8" + ) as json_file: + keys_ = json.load(json_file) + + all_keys = list( + np.hstack([list(instrument.keys()) for instrument in keys_.values()]) + ) + return all_keys + + +@ddt +class CalcEpsilonTestCase(unittest.TestCase): + @data( + ( + generate_vdf(64.0, 100, (32, 16, 16), energy01=False, species="bazinga"), + generate_vdf(64.0, 100, (32, 16, 16), energy01=False, species="bazinga"), + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=0), + ), + ( + generate_vdf(64.0, 100, (32, 16, 16), energy01=False, species="ions"), + generate_vdf(64.0, 100, (32, 16, 16), energy01=False, species="ions"), + generate_ts(32.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=0), + ), + ) + @unpack + def test_calc_epsilon_input(self, vdf, model_vdf, n_s, sc_pot): + with self.assertRaises(ValueError): + mms.calculate_epsilon(vdf, model_vdf, n_s, sc_pot) + + @data( + ( + generate_vdf(64.0, 100, (32, 16, 16), energy01=False, species="ions"), + generate_vdf(64.0, 100, (32, 16, 16), energy01=False, species="ions"), + {}, + ), + ( + generate_vdf(64.0, 100, (32, 16, 16), energy01=False, species="electrons"), + generate_vdf(64.0, 100, (32, 16, 16), energy01=False, species="electrons"), + {}, + ), + ( + generate_vdf(64.0, 100, (32, 16, 16), energy01=True, species="ions"), + generate_vdf(64.0, 100, (32, 16, 16), energy01=True, species="ions"), + {}, + ), + ) + @unpack + def test_calc_epsilon_output(self, vdf, model_vdf, kwargs): + mms.calculate_epsilon( + vdf, + model_vdf, + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=0), + **kwargs, + ) + + +class DbInitTestCase(unittest.TestCase): + def test_db_init_inpput(self): + with self.assertRaises(NotImplementedError): + mms.db_init(default="bazinga!") + + def test_db_init_output(self): + self.assertIsNone(mms.db_init(local=os.getcwd())) + + +@ddt +class Def2PsdTestCase(unittest.TestCase): + @data( + ("I AM GROOT!!", "s^3/cm^6"), + ("ions", "bazinga"), + ) + @unpack + def test_def2psd_input(self, species, units): + with self.assertRaises(ValueError): + mms.def2psd(generate_vdf(64.0, 100, (32, 32, 16), False, species, units)) + + @idata( + itertools.product( + [ + "ions", + "ion", + "protons", + "proton", + "alphas", + "alpha", + "helium", + "electrons", + "e", + ], + ["keV/(cm^2 s sr keV)", "eV/(cm^2 s sr eV)", "1/(cm^2 s sr)"], + ) + ) + @unpack + def test_def2psd_output(self, species, units): + vdf = generate_vdf(64.0, 100, (32, 32, 16), False, species, units) + result = mms.def2psd(vdf) + self.assertIsInstance(result, xr.Dataset) + + spectr = generate_spectr(64.0, 100, 32, {"species": species, "UNITS": units}) + result = mms.def2psd(spectr) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class Dpf2PsdTestCase(unittest.TestCase): + @data( + ("I AM GROOT!!", "s^3/cm^6"), + ("ions", "bazinga"), + ) + @unpack + def test_dpf2psd_input(self, species, units): + with self.assertRaises(ValueError): + mms.dpf2psd(generate_vdf(64.0, 100, (32, 32, 16), False, species, units)) + + @idata( + itertools.product( + [ + "ions", + "ion", + "protons", + "proton", + "alphas", + "alpha", + "helium", + "electrons", + "e", + ], + ["1/(cm^2 s sr keV)", "1/(cm^2 s sr eV)"], + ) + ) + @unpack + def test_dpf2psd_output(self, species, units): + vdf = generate_vdf(64.0, 100, (32, 32, 16), False, species, units) + result = mms.dpf2psd(vdf) + self.assertIsInstance(result, xr.Dataset) + + spectr = generate_spectr(64.0, 100, 32, {"species": species, "UNITS": units}) + result = mms.dpf2psd(spectr) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class Dsl2GseTestCase(unittest.TestCase): + def test_dsl2gse_input(self): + with self.assertRaises(TypeError): + mms.dsl2gse( + generate_ts(64.0, 42, tensor_order=1), np.random.random((42, 3)), 1 + ) + + @data( + xr.Dataset({"z_dec": generate_ts(64.0, 42), "z_ra": generate_ts(64.0, 42)}), + np.random.random(3), + ) + def test_dsl2gse_output(self, value): + result = mms.dsl2gse(generate_ts(64.0, 42, tensor_order=1), value, 1) + self.assertIsInstance(result, xr.DataArray) + result = mms.dsl2gse(generate_ts(64.0, 42, tensor_order=1), value, -1) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class Dsl2GsmTestCase(unittest.TestCase): + def test_dsl2gsm_input(self): + with self.assertRaises(TypeError): + mms.dsl2gsm( + generate_ts(64.0, 42, tensor_order=1), np.random.random((42, 3)), 1 + ) + + @data( + xr.Dataset({"z_dec": generate_ts(64.0, 42), "z_ra": generate_ts(64.0, 42)}), + np.random.random(3), + ) + def test_dsl2gsm_output(self, value): + result = mms.dsl2gsm(generate_ts(64.0, 42, tensor_order=1), value, 1) + self.assertIsInstance(result, xr.DataArray) + result = mms.dsl2gsm(generate_ts(64.0, 42, tensor_order=1), value, -1) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class EisCombineProtonPadTestCase(unittest.TestCase): + @idata( + itertools.product( + ["srvy", "brst"], + ["proton", "alpha", "oxygen"], + ["flux", "cps", "counts"], + ) + ) + @unpack + def test_eis_combine_proton_pad_output(self, tmmode, specie, unit): + mms_id = random.randint(1, 5) + phxtof_allt = generate_eis( + 64.0, 100, tmmode, "phxtof", "l2", specie, unit, mms_id + ) + extof_allt = generate_eis( + 64.0, 100, tmmode, "extof", "l2", specie, unit, mms_id + ) + result = mms.eis_combine_proton_pad(phxtof_allt, extof_allt) + self.assertIsInstance(result, xr.DataArray) + + @idata(itertools.product([99, 100], repeat=2)) + @unpack + def test_eis_combine_proton_pad_input(self, n_phxtof, n_extof): + phxtof_allt = generate_eis( + 64.0, n_phxtof, "brst", "phxtof", "l2", "proton", "flux", 1 + ) + extof_allt = generate_eis( + 64.0, n_extof, "brst", "extof", "l2", "proton", "flux", 1 + ) + result = mms.eis_combine_proton_pad(phxtof_allt, extof_allt) + self.assertIsInstance(result, xr.DataArray) + + @data(None, [1, 0, 0], generate_ts(64.0, 10, tensor_order=1)) + def test_eis_combine_proton_pad_vec(self, vec): + phxtof_allt = generate_eis( + 64.0, 100, "brst", "phxtof", "l2", "proton", "flux", 1 + ) + extof_allt = generate_eis(64.0, 100, "brst", "extof", "l2", "proton", "flux", 1) + result = mms.eis_combine_proton_pad(phxtof_allt, extof_allt, vec) + self.assertIsInstance(result, xr.DataArray) + + def test_eis_combine_proton_pad_options(self): + phxtof_allt = generate_eis( + 64.0, 100, "brst", "phxtof", "l2", "proton", "flux", 1 + ) + extof_allt = generate_eis(64.0, 100, "brst", "extof", "l2", "proton", "flux", 1) + result = mms.eis_combine_proton_pad(phxtof_allt, extof_allt, None, despin=True) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class EisCombineProtonSpecTestCase(unittest.TestCase): + @idata( + itertools.product( + ["srvy", "brst"], + ["proton", "alpha", "oxygen"], + ["flux", "cps", "counts"], + ) + ) + @unpack + def test_eis_combine_proton_spec_output(self, tmmode, specie, unit): + mms_id = random.randint(1, 5) + phxtof_allt = generate_eis( + 64.0, 100, tmmode, "phxtof", "l2", specie, unit, mms_id + ) + extof_allt = generate_eis( + 64.0, 100, tmmode, "extof", "l2", specie, unit, mms_id + ) + result = mms.eis_combine_proton_spec(phxtof_allt, extof_allt) + self.assertIsInstance(result, xr.Dataset) + + @idata(itertools.product([99, 100], repeat=2)) + @unpack + def test_eis_combine_proton_spec_ctimes(self, n_phxtof, n_extof): + phxtof_allt = generate_eis( + 64.0, n_phxtof, "brst", "phxtof", "l2", "proton", "flux", 1 + ) + extof_allt = generate_eis( + 64.0, n_extof, "brst", "extof", "l2", "proton", "flux", 1 + ) + result = mms.eis_combine_proton_spec(phxtof_allt, extof_allt) + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class EisOmniTestCase(unittest.TestCase): + @idata( + itertools.product( + ["srvy", "brst"], + ["extof", "phxtof"], + ["proton", "alpha", "oxygen"], + ["flux", "cps", "counts"], + ) + ) + @unpack + def test_eis_omni_output(self, tmmode, dtype, specie, unit): + eis = generate_eis( + 64.0, 100, tmmode, dtype, "l2", specie, unit, random.randint(1, 4) + ) + result = mms.eis_omni(eis, "mean") + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class EisPadTestCase(unittest.TestCase): + @data(None, [1, 0, 0], generate_ts(64.0, 10, tensor_order=1)) + def test_eis_pad_output(self, vec): + eis = generate_eis(64.0, 100, "brst", "extof", "l2", "proton", "flux", 1) + result = mms.eis_pad(eis, vec) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class EisPadSpinAvgTestCase(unittest.TestCase): + @idata( + itertools.product( + ["srvy", "brst"], + ["extof", "phxtof"], + ["proton", "alpha", "oxygen"], + ["flux", "cps", "counts"], + ) + ) + @unpack + def test_eis_pad_spin_avg_output(self, tmmode, dtype, specie, unit): + eis = generate_eis( + 64.0, 100, tmmode, dtype, "l2", specie, unit, random.randint(1, 4) + ) + eis_pad = mms.eis_pad(eis) + result = mms.eis_pad_spinavg(eis_pad, eis.spin) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class EisProtonCorrectionTestCase(unittest.TestCase): + def test_eis_proton_correction_dataarray(self): + flux_eis = generate_spectr(64, 100, 16, "energy") + result = mms.eis_proton_correction(flux_eis) + self.assertIsInstance(result, xr.DataArray) + + @idata( + itertools.product( + ["srvy", "brst"], + ["extof", "phxtof"], + ["proton", "alpha", "oxygen"], + ["flux", "cps", "counts"], + ) + ) + @unpack + def test_eis_proton_correction_dataset(self, tmmode, dtype, specie, unit): + flux_eis = generate_eis( + 64.0, 100, tmmode, dtype, "l2", specie, unit, random.randint(1, 4) + ) + result = mms.eis_proton_correction(flux_eis) + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class EisSpinAvgTestCase(unittest.TestCase): + @idata( + itertools.product( + ["mean", "sum"], + ["srvy", "brst"], + ["extof", "phxtof"], + ["proton", "alpha", "oxygen"], + ["flux", "cps", "counts"], + ) + ) + @unpack + def test_eis_spin_avg_output(self, method, tmmode, dtype, specie, unit): + eis_allt = generate_eis( + 64.0, 100, tmmode, dtype, "l2", specie, unit, random.randint(1, 4) + ) + result = mms.eis_spin_avg(eis_allt, method) + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class MakeModelVDFTestCase(unittest.TestCase): + @data( + (generate_vdf(64.0, 100, (32, 16, 16), species="ions"), False), + (generate_vdf(64.0, 100, (32, 16, 16), species="electrons"), False), + (generate_vdf(64.0, 100, (32, 16, 16), species="ions"), True), + ) + @unpack + def test_make_Model_vdf_output(self, vdf, isotropic): + result = mms.make_model_vdf( + vdf, + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=2), + isotropic, + ) + self.assertIsInstance(result, xr.Dataset) + + +class HpcaEnergiesTestCase(unittest.TestCase): + def test_hpca_energies_output(self): + result = mms.hpca_energies() + self.assertIsInstance(result, list) + + +@ddt +class MakeModelKappaTestCase(unittest.TestCase): + @data( + (generate_vdf(64.0, 100, (32, 16, 16), species="bazinga"), random.random()), + ) + @unpack + def test_make_model_kappa_input(self, vdf, kappa): + with self.assertRaises(ValueError): + mms.make_model_kappa( + vdf, + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=0), + kappa, + ) + + @data( + (generate_vdf(64.0, 100, (32, 16, 16), species="ions"), random.random()), + (generate_vdf(64.0, 100, (32, 16, 16), species="electrons"), random.random()), + (generate_vdf(64.0, 100, (32, 16, 16), species="ions"), random.random()), + ) + @unpack + def test_make_model_kappa_output(self, vdf, kappa): + result = mms.make_model_kappa( + vdf, + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=0), + kappa, + ) + + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class Psd2DefTestCase(unittest.TestCase): + @data( + ("I AM GROOT!!", "s^3/cm^6"), + ("ions", "bazinga"), + ) + @unpack + def test_psd2def_input(self, species, units): + with self.assertRaises(ValueError): + mms.psd2def(generate_vdf(64.0, 100, (32, 32, 16), False, species, units)) + + @idata( + itertools.product( + [ + "ions", + "ion", + "protons", + "proton", + "alphas", + "alpha", + "helium", + "electrons", + "e", + ], + ["s^3/cm^6", "s^3/m^6", "s^3/km^6"], + ) + ) + @unpack + def test_psd2def_output(self, species, units): + vdf = generate_vdf(64.0, 100, (32, 32, 16), False, species, units) + result = mms.psd2def(vdf) + self.assertIsInstance(result, xr.Dataset) + + spectr = generate_spectr(64.0, 100, 32, {"species": species, "UNITS": units}) + result = mms.psd2def(spectr) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class Psd2DpfTestCase(unittest.TestCase): + @data( + ("I AM GROOT!!", "s^3/cm^6"), + ("ions", "bazinga"), + ) + @unpack + def test_psd2dpf_input(self, species, units): + with self.assertRaises(ValueError): + mms.psd2dpf(generate_vdf(64.0, 100, (32, 32, 16), False, species, units)) + + @idata( + itertools.product( + [ + "ions", + "ion", + "protons", + "proton", + "alphas", + "alpha", + "helium", + "electrons", + "e", + ], + ["s^3/cm^6", "s^3/m^6", "s^3/km^6"], + ) + ) + @unpack + def test_psd2dpf_output(self, species, units): + vdf = generate_vdf(64.0, 100, (32, 32, 16), False, species, units) + result = mms.psd2dpf(vdf) + self.assertIsInstance(result, xr.Dataset) + + spectr = generate_spectr(64.0, 100, 32, {"species": species, "UNITS": units}) + result = mms.psd2dpf(spectr) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class PsdMomentsTestCase(unittest.TestCase): + @data( + (generate_vdf(64.0, 100, (32, 32, 16)), "brst"), + (generate_vdf(64.0, 100, (32, 32, 16), species="electrons"), "brst"), + (generate_vdf(64.0, 100, (32, 32, 16), energy01=False), "brst"), + (generate_vdf(64.0, 100, (32, 32, 16), energy01=False), "fast"), + (generate_vdf(64.0, 100, (32, 32, 16), energy01=True), "brst"), + ) + @unpack + def test_psd_moments_input(self, vdf, data_rate): + delta_theta = 0.5 * np.ones(vdf.data.shape[3]) + vdf.attrs["delta_theta_minus"] = delta_theta + vdf.attrs["delta_theta_plus"] = delta_theta + + delta_phi = 0.5 * np.ones((vdf.data.shape[0], vdf.data.shape[2])) + vdf.attrs["delta_phi_minus"] = delta_phi + vdf.attrs["delta_phi_plus"] = delta_phi + vdf.data.attrs["FIELDNAM"] = f"MMS1 FPI/DIS {data_rate}SkyMap dist" + mms.psd_moments(vdf, generate_ts(64.0, 100, tensor_order=0)) + + @data({"energy_range": [1, 1000]}, {"no_sc_pot": True}) + def test_psd_moments_options(self, options): + vdf = generate_vdf(64.0, 100, (32, 32, 16)) + vdf.data.attrs["FIELDNAM"] = "MMS1 FPI/DIS brstSkyMap dist" + mms.psd_moments(vdf, generate_ts(64.0, 100, tensor_order=0), **options) + + @data( + ( + np.random.random((100, 32)), # energy + np.random.random((10000, 32)), # delta_v + random.random(), # q_e + np.random.random(100), # sc_pot + random.random(), # p_mass + random.choice([True, False]), # flag_inner_electron + random.random(), # w_inner_electron + np.random.random((100, 32, 16)), # phi + np.random.random((100, 32, 16)), # theta + np.arange(32), # int_energies + np.random.random((100, 32, 32, 16)), # vdf + np.random.random((100, 32, 16)), # delta_ang + ) + ) + def test_moms(self, value): + result = _moms.__wrapped__(*value) + self.assertIsInstance(result[0], np.ndarray) + self.assertIsInstance(result[1], np.ndarray) + self.assertIsInstance(result[2], np.ndarray) + self.assertIsInstance(result[3], np.ndarray) + + +@ddt +class PsdRebinTestCase(unittest.TestCase): + @data(generate_vdf(64.0, 100, (32, 32, 16), energy01=True, species="ions")) + def test_psd_rebin_output(self, vdf): + result = mms.psd_rebin( + vdf, + vdf.phi, + vdf.attrs["energy0"], + vdf.attrs["energy1"], + vdf.attrs["esteptable"], + ) + self.assertIsInstance(result[0], np.ndarray) + self.assertEqual(len(result[0]), 50) + self.assertIsInstance(result[1], np.ndarray) + self.assertListEqual(list(result[1].shape), [50, 64, 32, 16]) + self.assertIsInstance(result[2], np.ndarray) + self.assertEqual(len(result[2]), 64) + self.assertIsInstance(result[3], np.ndarray) + self.assertListEqual(list(result[3].shape), [50, 32]) + + +@ddt +class FeepsActiveEyesTestCase(unittest.TestCase): + @idata(itertools.product(["srvy", "brst"], ["electron", "ion"], ["sitl", "l2"])) + @unpack + def test_feeps_active_eyes_output(self, data_rate, dtype, lev): + result = mms.feeps_active_eyes( + {"tmmode": data_rate, "dtype": dtype, "lev": lev}, + TEST_TINT, + random.randint(1, 4), + ) + self.assertIsInstance(result, dict) + + result = mms.feeps_active_eyes( + {"tmmode": data_rate, "dtype": dtype, "lev": lev}, + pyrf.iso86012datetime64(np.array(TEST_TINT)), + str(random.randint(1, 4)), + ) + self.assertIsInstance(result, dict) + + +@ddt +class FeepsCorrectEnergiesTestCase(unittest.TestCase): + @idata(itertools.product(["srvy", "brst"], ["electron", "ion"])) + @unpack + def test_feeps_correct_energies_output(self, data_rate, dtype): + # Generate fake FEEPS data + feeps_alle = generate_feeps( + 64.0, 100, data_rate, dtype, "l2", random.randint(1, 4) + ) + + result = mms.feeps_correct_energies(feeps_alle) + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class FeepsFlatFieldCorrectionsTestCase(unittest.TestCase): + @idata(itertools.product(["srvy", "brst"], ["electron", "ion"])) + @unpack + def test_feeps_flat_field_corrections_output(self, data_rate, dtype): + # Generate fake FEEPS data + feeps_alle = generate_feeps( + 64.0, 100, data_rate, dtype, "l2", random.randint(1, 4) + ) + + result = mms.feeps_flat_field_corrections(feeps_alle) + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class FeepsOmniTestCase(unittest.TestCase): + @idata(itertools.product(["srvy", "brst"], ["electron", "ion"])) + @unpack + def test_feeps_omni_output(self, data_rate, dtype): + # Generate fake FEEPS data + feeps_alle = generate_feeps( + 64.0, 100, data_rate, dtype, "l2", random.randint(1, 4) + ) + feeps_alle, _ = mms.feeps_split_integral_ch(feeps_alle) + + result = mms.feeps_omni(feeps_alle) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class FeepsPadTestCase(unittest.TestCase): + @idata(itertools.product(["srvy", "brst"], ["electron", "ion"])) + @unpack + def test_feeps_pad_ouput(self, data_rate, dtype): + # Generate fake FEEPS data + feeps_alle = generate_feeps( + 64.0, 100, data_rate, dtype, "l2", random.randint(1, 4) + ) + feeps_alle, _ = mms.feeps_split_integral_ch(feeps_alle) + + result = mms.feeps_pad(feeps_alle, generate_ts(64.0, 100, tensor_order=1)) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class FeepsPadSpinAvgTestCase(unittest.TestCase): + @idata(itertools.product(["srvy", "brst"], ["electron", "ion"])) + @unpack + def test_feeps_pad_spin_avg(self, data_rate, dtype): + # Generate fake FEEPS data + feeps_alle = generate_feeps( + 64.0, 100, data_rate, dtype, "l2", random.randint(1, 4) + ) + feeps_alle, _ = mms.feeps_split_integral_ch(feeps_alle) + + feeps_pad = mms.feeps_pad(feeps_alle, generate_ts(64.0, 100, tensor_order=1)) + result = mms.feeps_pad_spinavg(feeps_pad, feeps_alle.spinsectnum) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class FeepsPitchAnglesTestCase(unittest.TestCase): + @idata(itertools.product(["srvy", "brst"], ["electron", "ion"])) + @unpack + def test_feeps_pitch_angles_output(self, data_rate, dtype): + # Generate fake FEEPS data + feeps_alle = generate_feeps( + 64.0, 100, data_rate, dtype, "l2", random.randint(1, 4) + ) + feeps_alle, _ = mms.feeps_split_integral_ch(feeps_alle) + + result = mms.feeps_pitch_angles( + feeps_alle, generate_ts(64.0, 100, tensor_order=1) + ) + self.assertIsInstance(result[0], xr.DataArray) + + +@ddt +class FeepsRemoveBadDataTestCase(unittest.TestCase): + @idata(itertools.product(["srvy", "brst"], ["electron", "ion"])) + @unpack + def test_feeps_remove_bad_data_output(self, data_rate, dtype): + # Generate fake FEEPS data + feeps_alle = generate_feeps( + 64.0, 100, data_rate, dtype, "l2", random.randint(1, 4) + ) + + result = mms.feeps_remove_bad_data(feeps_alle) + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class FeepsRemoveSunTestCase(unittest.TestCase): + @idata(itertools.product(["srvy", "brst"], ["electron", "ion"])) + @unpack + def test_feeps_remove_sun(self, data_rate, dtype): + # Generate fake FEEPS data + feeps_alle = generate_feeps( + 64.0, 100, data_rate, dtype, "l2", random.randint(1, 4) + ) + feeps_alle, _ = mms.feeps_split_integral_ch(feeps_alle) + + result = mms.feeps_remove_sun(feeps_alle) + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class FeepsSpinAvgTestCase(unittest.TestCase): + @idata(itertools.product(["srvy", "brst"], ["electron", "ion"])) + @unpack + def test_feeps_spin_avg(self, data_rate, dtype): + # Generate fake FEEPS data + feeps_alle = generate_feeps( + 64.0, 100, data_rate, dtype, "l2", random.randint(1, 4) + ) + feeps_alle, _ = mms.feeps_split_integral_ch(feeps_alle) + feeps_omni = mms.feeps_omni(feeps_alle) + + result = mms.feeps_spin_avg(feeps_omni, feeps_alle.spinsectnum) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class FeepsSplitIntegralChTestCase(unittest.TestCase): + @idata(itertools.product(["srvy", "brst"], ["electron", "ion"])) + @unpack + def test_feeps_split_integral_ch(self, data_rate, dtype): + feeps_alle = generate_feeps( + 64.0, 100, data_rate, dtype, "l2", random.randint(1, 4) + ) + mms.feeps_split_integral_ch(feeps_alle) + + +@ddt +class FkPowerSpectrum4scTestCase(unittest.TestCase): + @data((None, None), (random.random(), None), (None, [0.1, 1])) + @unpack + def test_fk_power_spectrum_4sc(self, df, f_range): + e_mms = [generate_ts(64.0, 100, tensor_order=0) for _ in range(4)] + r_mms = [generate_ts(64.0, 100, tensor_order=1) for _ in range(4)] + b_mms = [generate_ts(64.0, 100, tensor_order=1) for _ in range(4)] + + result = mms.fk_power_spectrum_4sc( + e_mms, r_mms, b_mms, TEST_TINT, df=df, f_range=f_range + ) + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class ReduceTestCase(unittest.TestCase): + @data("s^3/cm^6", "s^3/m^6", "s^3/km^6") + def test_reduce_units(self, value): + vdf = generate_vdf(64.0, 42, [32, 32, 16], energy01=True, species="ions") + vdf.data.attrs["UNITS"] = value + result = mms.reduce(vdf, np.eye(3), "1d", "cart") + self.assertIsInstance(result, xr.DataArray) + + @data( + (False, "ions", np.eye(3), "1d", "cart"), + (False, "electrons", np.eye(3), "1d", "cart"), + (True, "ions", np.eye(3), "1d", "cart"), + (True, "electrons", np.eye(3), "1d", "cart"), + (False, "electrons", generate_ts(64.0, 42, tensor_order=2), "1d", "cart"), + (False, "ions", np.eye(3), "1d", "pol"), + # (False, "ions", np.eye(3), "2d", "pol"), mc_pol_2d NotImplementedError + ) + @unpack + def test_reduce_output(self, energy01, species, xyz, dim, base): + vdf = generate_vdf(64.0, 42, [32, 32, 16], energy01, species) + result = mms.reduce(vdf, xyz, dim, base) + self.assertIsInstance(result, xr.DataArray) + + @data( + ("1d", "cart", {"vg": np.linspace(-1, 1, 42)}), + ("1d", "cart", {"lower_e_lim": generate_ts(64.0, 42)}), + ("1d", "cart", {"vg_edges": np.linspace(-1.01, 1.01, 102)}), + ) + @unpack + def test_reduce_options(self, dim, base, options): + vdf = generate_vdf(64.0, 42, [32, 32, 16], energy01=False, species="ions") + xyz = np.eye(3) + result = mms.reduce(vdf, xyz, dim, base, **options) + self.assertIsInstance(result, xr.DataArray) + + @data( + ("ions", "s^3/m^6", np.array([1, 0, 0]), "1d", "pol", {}), + ("I AM GROOT", "s^3/m^6", np.eye(3), "1d", "pol", {}), + ("ions", "bazinga", np.eye(3), "1d", "pol", {}), + ("ions", "s^3/m^6", np.eye(3), "2d", "pol", {}), + ("ions", "s^3/m^6", np.eye(3), "1d", "pol", {"lower_e_lim": generate_data(42)}), + ) + @unpack + def test_reduce_input(self, species, units, xyz, dim, base, options): + vdf = generate_vdf(64.0, 42, [32, 32, 16], energy01=True, species=species) + vdf.data.attrs["UNITS"] = units + with self.assertRaises((TypeError, ValueError, NotImplementedError)): + mms.reduce(vdf, xyz, dim, base, **options) + + +@ddt +class RotateTensorTestCase(unittest.TestCase): + @data(("rot", generate_data(100, tensor_order=1)), ("gse", None)) + @unpack + def test_rotate_tensor_input(self, flag, vec): + with self.assertRaises((TypeError, NotImplementedError)): + mms.rotate_tensor(generate_ts(64.0, 100, tensor_order=2), flag, vec) + + @data( + ("fac", generate_ts(64.0, 100, tensor_order=1), "pp"), + ("fac", generate_ts(64.0, 100, tensor_order=1), "qq"), + ("rot", np.random.random(3), "pp"), + ("rot", np.random.random((3, 3)), "pp"), + ) + @unpack + def test_rotate_tensor_output(self, rot_flag, vec, perp): + result = mms.rotate_tensor( + generate_ts(64.0, 100, tensor_order=2), rot_flag, vec, perp + ) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class SpectrToDatasetTestCase(unittest.TestCase): + @data(generate_spectr(64.0, 100, 10)) + def test_spectr_to_dataset_output(self, spectr): + result = mms.spectr_to_dataset(spectr) + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class Scpot2NeTestCase(unittest.TestCase): + @data(None, generate_ts(64.0, 100, tensor_order=0)) + def test_scpot2ne_output(self, i_aspoc): + result = mms.scpot2ne( + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=2), + i_aspoc, + ) + self.assertIsInstance(result[0], xr.DataArray) + self.assertIsInstance(result[1], float) + self.assertIsInstance(result[2], float) + self.assertIsInstance(result[3], float) + self.assertIsInstance(result[4], float) + + +@ddt +class VdfElimTestCase(unittest.TestCase): + @data( + random.randint(0, 15), + random.randint(0, 15) + 0.4, + [random.randint(0, 15), random.randint(16, 31)], + ) + def test_vdf_elim_output(self, e_int): + result = mms.vdf_elim( + generate_vdf(64.0, 42, [32, 32, 16], energy01=True), e_int + ) + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class VdfOmniTestCase(unittest.TestCase): + @data("mean", "sum") + def test_vdf_omni_output(self, method): + result = mms.vdf_omni(generate_vdf(64.0, 100, (32, 32, 16)), method) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class TokenizeTestCase(unittest.TestCase): + @data(*random.choices(_mms_keys(), k=10)) + def test_tokenize(self, var_str): + result = mms.tokenize(var_str) + self.assertIsInstance(result, dict) + + +@ddt +class ListFilesTestCase(unittest.TestCase): + @data(*random.choices(_mms_keys(), k=10)) + def test_list_files(self, var_str): + mms.list_files(TEST_TINT, random.randint(1, 4), mms.tokenize(var_str)) + + +@ddt +class ListFilesSdcTestCase(unittest.TestCase): + @data(*random.choices(_mms_keys(), k=10)) + def test_list_files_sdc(self, var_str): + try: + mms.list_files_sdc(TEST_TINT, random.randint(1, 4), mms.tokenize(var_str)) + except requests.exceptions.ReadTimeout: + pass + + +@ddt +class ListFilesAncillaryTestCase(unittest.TestCase): + @data("predatt", "predeph", "defatt", "defeph") + def test_list_files_ancillary(self, product): + mms.list_files_ancillary(TEST_TINT, random.randint(1, 4), product) + + +@ddt +class ListFilesAncillarySdcTestCase(unittest.TestCase): + @data("predatt", "predeph", "defatt", "defeph") + def test_list_files_ancillary_sdc(self, product): + try: + mms.list_files_ancillary_sdc(TEST_TINT, random.randint(1, 4), product) + except requests.exceptions.ReadTimeout: + pass + + +if __name__ == "__main__": + unittest.main() diff --git a/pyrfu/tests/test_models.py b/pyrfu/tests/test_models.py new file mode 100644 index 00000000..a2705410 --- /dev/null +++ b/pyrfu/tests/test_models.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Built-in imports +import random +import unittest + +# 3rd party imports +import numpy as np +from ddt import data, ddt, unpack + +# Local imports +from .. import models +from . import generate_timeline + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.4" +__status__ = "Prototype" + + +@ddt +class IgrfTestCase(unittest.TestCase): + @data( + ( + generate_timeline(64.0, 100, ref_time="1789-07-14T00:00:00.000000000"), + "dipole", + ), + ) + @unpack + def test_igrf_input_time(self, timeline, flag): + with self.assertWarns(UserWarning): + models.igrf(timeline.astype(np.int64) / 1e9, flag) + + @data((generate_timeline(64.0, 100), "bazinga!")) + @unpack + def test_igrf_input_flag(self, timeline, flag): + with self.assertRaises(NotImplementedError): + models.igrf(timeline.astype(np.int64) / 1e9, flag) + + @data((generate_timeline(64.0, 100), "dipole")) + @unpack + def test_igrf_output(self, timeline, flag): + result = models.igrf(timeline.astype(np.int64) / 1e9, flag) + self.assertIsInstance(result[0], np.ndarray) + self.assertListEqual(list(result[0].shape), list(timeline.shape)) + self.assertIsInstance(result[1], np.ndarray) + self.assertListEqual(list(result[1].shape), list(timeline.shape)) + + +@ddt +class MagnetopauseNormalTestCase(unittest.TestCase): + @data( + (np.random.rand(3), random.randint(1, 10), random.randint(1, 10), "bazinga!!") + ) + @unpack + def test_magnetopause_normal_input(self, r_gsm, b_z_imf, p_sw, model): + with self.assertRaises(NotImplementedError): + models.magnetopause_normal(r_gsm, b_z_imf, p_sw, model) + + @data( + (np.random.rand(3), random.randint(1, 10), random.randint(1, 10), "mp_shue97"), + (np.random.rand(3), random.randint(1, 10), random.randint(1, 10), "bs97"), + (np.random.rand(3), -random.randint(1, 10), random.randint(1, 10), "bs97"), + (np.random.rand(3), random.randint(1, 10), random.randint(1, 10), "mp_shue98"), + (np.random.rand(3), random.randint(1, 10), random.randint(1, 10), "bs98"), + ) + @unpack + def test_magnetopause_normal_output(self, r_gsm, b_z_imf, p_sw, model): + models.magnetopause_normal(r_gsm, b_z_imf, p_sw, model) + + +if __name__ == "__main__": + unittest.main() diff --git a/pyrfu/tests/test_plot.py b/pyrfu/tests/test_plot.py new file mode 100644 index 00000000..2d5d4417 --- /dev/null +++ b/pyrfu/tests/test_plot.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Built-in imports +import random +import unittest + +# 3rd party imports +import matplotlib as mpl +import matplotlib.pyplot as plt +from ddt import data, ddt, unpack + +# Local imports +from .. import plot +from . import generate_data, generate_ts + + +@ddt +class PlotLineTestCase(unittest.TestCase): + @data((0.0, generate_ts(64.0, 100)), (plt.subplots(3)[1], generate_ts(64.0, 100))) + @unpack + def test_plot_line_axis_type(self, axis, inp): + with self.assertRaises(TypeError): + plot.plot_line(axis, inp) + + @data((plt.subplots(1)[1], generate_data(100))) + @unpack + def test_plot_line_inp_type(self, axis, inp): + with self.assertRaises(TypeError): + plot.plot_line(axis, inp) + + @data( + (plt.subplots(1)[1], generate_ts(64.0, 100, tensor_order=random.randint(3, 10))) + ) + @unpack + def test_plot_line_inp_shape(self, axis, inp): + with self.assertRaises(NotImplementedError): + plot.plot_line(axis, inp) + + @data( + (None, generate_ts(64.0, 100, tensor_order=random.randint(0, 2))), + (plt.subplots(1)[1], generate_ts(64.0, 100, tensor_order=random.randint(0, 2))), + ( + plt.subplots(3)[1][0], + generate_ts(64.0, 100, tensor_order=random.randint(0, 2)), + ), + ) + @unpack + def test_plot_line_output(self, axis, inp): + result = plot.plot_line(axis, inp) + self.assertIsInstance(result, mpl.axes.Axes) + + +if __name__ == "__main__": + unittest.main() diff --git a/pyrfu/tests/test_pyrf.py b/pyrfu/tests/test_pyrf.py index af248dae..ed92496d 100644 --- a/pyrfu/tests/test_pyrf.py +++ b/pyrfu/tests/test_pyrf.py @@ -1,57 +1,2445 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -# -# MIT License -# -# Copyright (c) 2020 Louis Richard -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so. - -from pyrfu import mms, pyrf +# Built-in imports +import builtins +import datetime +import itertools +import random import unittest +from unittest import mock +# 3rd party imports import numpy as np +import xarray as xr +from ddt import data, ddt, idata, unpack +# Local imports +from .. import pyrf +from ..pyrf.compress_cwt import _compress_cwt_1d +from ..pyrf.ebsp import _average_data, _censure_plot, _freq_int +from ..pyrf.int_sph_dist import _mc_cart_2d, _mc_cart_3d, _mc_pol_1d +from ..pyrf.wavelet import _power_c, _power_r, _ww +from . import generate_data, generate_timeline, generate_ts, generate_vdf -class TestPyrf(unittest.TestCase): - def setUp(self): - """integration test setup.""" - tint = ["2019-09-14T08:00:00.000", "2019-09-14T08:00:30.000"] - self.b_xyz = mms.get_data("B_gse_fgm_brst_l2", tint, 2) - self.e_xyz = mms.get_data("E_gse_edp_brst_l2", tint, 2) +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.2" +__status__ = "Prototype" - def test_calc_fs(self): - """integration test on sampling frequency computation""" - fs = pyrf.calc_fs(self.b_xyz) - self.assertEqual(np.round(fs), 128.0) +class AutoCorrTestCase(unittest.TestCase): + def test_autocorr_input_type(self): + self.assertIsNotNone(pyrf.autocorr(generate_ts(64.0, 100, tensor_order=0))) + self.assertIsNotNone(pyrf.autocorr(generate_ts(64.0, 100, tensor_order=0), 25)) + self.assertIsNotNone( + pyrf.autocorr(generate_ts(64.0, 100, tensor_order=0), 25, True) + ) + + def test_autocorr_input_shape(self): + self.assertIsNotNone(pyrf.autocorr(generate_ts(64.0, 100, tensor_order=0))) + self.assertIsNotNone(pyrf.autocorr(generate_ts(64.0, 100, tensor_order=1))) + + def test_autocorr_input_values(self): + with self.assertRaises(ValueError): + pyrf.autocorr(generate_ts(64.0, 100, tensor_order=0), 100) + + def test_autocorr_output_type(self): + self.assertIsInstance( + pyrf.autocorr(generate_ts(64.0, 100, tensor_order=0)), xr.DataArray + ) + self.assertIsInstance( + pyrf.autocorr(generate_ts(64.0, 100, tensor_order=1)), xr.DataArray + ) + + def test_autocorr_output_shape(self): + result = pyrf.autocorr(generate_ts(64.0, 100, tensor_order=0)) + self.assertEqual(result.ndim, 1) + self.assertEqual(result.shape[0], 100) + + result = pyrf.autocorr(generate_ts(64.0, 100, tensor_order=0), 25) + self.assertEqual(result.ndim, 1) + self.assertEqual(result.shape[0], 26) + + result = pyrf.autocorr(generate_ts(64.0, 100, tensor_order=1)) + self.assertEqual(result.ndim, 2) + self.assertEqual(result.shape[0], 100) + self.assertEqual(result.shape[1], 3) + + +class AverageVDFTestCase(unittest.TestCase): + def test_average_vdf_input_type(self): + with self.assertRaises(AssertionError): + pyrf.average_vdf(0, 3) + pyrf.average_vdf(np.random.random((100, 32, 32, 16)), 3) + pyrf.average_vdf(generate_vdf(64.0, 100, [32, 32, 16]), [3, 5]) + + def test_average_vdf_values(self): + with self.assertRaises(AssertionError): + pyrf.average_vdf(generate_vdf(64.0, 100, [32, 32, 16]), 2) + + def test_average_vdf_output_type(self): + self.assertIsInstance( + pyrf.average_vdf(generate_vdf(64.0, 100, [32, 32, 16]), 3), xr.Dataset + ) + + def test_average_vdf_output_meta(self): + avg_inds = np.arange(1, 99, 3, dtype=int) + result = pyrf.average_vdf(generate_vdf(64.0, 100, [32, 32, 16]), 3) + + self.assertIsInstance(result.attrs["delta_energy_plus"], np.ndarray) + self.assertEqual(result.attrs["delta_energy_plus"].ndim, 2) + self.assertEqual(len(result.attrs["delta_energy_plus"]), len(avg_inds)) + + self.assertIsInstance(result.attrs["delta_energy_minus"], np.ndarray) + self.assertEqual(result.attrs["delta_energy_minus"].ndim, 2) + self.assertEqual(len(result.attrs["delta_energy_minus"]), len(avg_inds)) + + +class Avg4SCTestCase(unittest.TestCase): + def test_avg_4sc_input(self): + self.assertIsNotNone( + pyrf.avg_4sc( + [ + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=0), + ] + ) + ) + self.assertIsNotNone( + pyrf.avg_4sc( + [ + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + ] + ) + ) + self.assertIsNotNone( + pyrf.avg_4sc( + [ + generate_ts(64.0, 100, tensor_order=2), + generate_ts(64.0, 100, tensor_order=2), + generate_ts(64.0, 100, tensor_order=2), + generate_ts(64.0, 100, tensor_order=2), + ] + ) + ) + + with self.assertRaises(TypeError): + pyrf.avg_4sc( + [ + generate_data(100, tensor_order=2), + generate_data(100, tensor_order=2), + generate_data(100, tensor_order=2), + generate_data(100, tensor_order=2), + ] + ) + + def test_avg_4sc_output(self): + result = pyrf.avg_4sc( + [ + generate_ts(64.0, 100, tensor_order=2), + generate_ts(64.0, 100, tensor_order=2), + generate_ts(64.0, 100, tensor_order=2), + generate_ts(64.0, 100, tensor_order=2), + ] + ) + + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3, 3]) + + +class C4GradTestCase(unittest.TestCase): + def test_c_4_grad_input(self): + with self.assertRaises(AssertionError): + pyrf.c_4_grad( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + ) + pyrf.c_4_grad([], []) + + pyrf.c_4_grad( + [generate_ts(64.0, 100, tensor_order=1) for _ in range(4)], + [generate_ts(64.0, 100, tensor_order=1) for _ in range(4)], + 0, + ) + + pyrf.c_4_grad( + [generate_ts(64.0, 100, tensor_order=1) for _ in range(4)], + [generate_ts(64.0, 100, tensor_order=1) for _ in range(4)], + "bazinga", + ) + + def test_c_4_grad_output(self): + r_mms = [generate_ts(64.0, 100, tensor_order=1) for _ in range(4)] + b_mms = [generate_ts(64.0, 100, tensor_order=1) for _ in range(4)] + n_mms = [generate_ts(64.0, 100, tensor_order=0) for _ in range(4)] + + result = pyrf.c_4_grad(r_mms, b_mms, "grad") + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3, 3]) + + result = pyrf.c_4_grad(r_mms, b_mms, "div") + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual( + list(result.shape), + [ + 100, + ], + ) + + result = pyrf.c_4_grad(r_mms, b_mms, "curl") + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + result = pyrf.c_4_grad(r_mms, b_mms, "bdivb") + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + result = pyrf.c_4_grad(r_mms, b_mms, "curv") + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + result = pyrf.c_4_grad(r_mms, n_mms, "grad") + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + +class C4JTestCase(unittest.TestCase): + def test_c_4_j_input(self): + with self.assertRaises(AssertionError): + pyrf.c_4_j( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + ) + pyrf.c_4_j([], []) + + def test_c_4_j_output(self): + r_mms = [generate_ts(64.0, 100, tensor_order=1) for _ in range(4)] + b_mms = [generate_ts(64.0, 100, tensor_order=1) for _ in range(4)] + j, div_b, b_avg, jxb, div_t_shear, div_pb = pyrf.c_4_j(r_mms, b_mms) + + self.assertIsInstance(j, xr.DataArray) + self.assertListEqual(list(j.shape), [100, 3]) + + self.assertIsInstance(div_b, xr.DataArray) + self.assertListEqual( + list(div_b.shape), + [ + 100, + ], + ) + + self.assertIsInstance(b_avg, xr.DataArray) + self.assertListEqual(list(b_avg.shape), [100, 3]) + + self.assertIsInstance(jxb, xr.DataArray) + self.assertListEqual(list(jxb.shape), [100, 3]) + + self.assertIsInstance(div_t_shear, xr.DataArray) + self.assertListEqual(list(div_t_shear.shape), [100, 3]) + + self.assertIsInstance(div_pb, xr.DataArray) + self.assertListEqual(list(div_pb.shape), [100, 3]) + + +class CalcAgTestCase(unittest.TestCase): + def test_calc_ag_input_type(self): + self.assertIsNotNone(pyrf.calc_ag(generate_ts(64.0, 100, tensor_order=2))) + + with self.assertRaises(AssertionError): + # Raises error if input is not a xarray + pyrf.calc_ag(0.0) + pyrf.calc_ag(generate_data(100)) + + def test_calc_ag_output_type(self): + result = pyrf.calc_ag(generate_ts(64.0, 100, tensor_order=2)) + + # Output must be a xarray + self.assertIsInstance(result, xr.DataArray) + + def test_calc_ag_output_shape(self): + result = pyrf.calc_ag(generate_ts(64.0, 100, tensor_order=2)) + self.assertEqual(result.ndim, 1) + self.assertEqual(len(result), 100) + + def test_calc_ag_dims(self): + result = pyrf.calc_ag(generate_ts(64.0, 100, tensor_order=2)) + self.assertListEqual(list(result.dims), ["time"]) + + def test_calc_ag_meta(self): + result = pyrf.calc_ag(generate_ts(64.0, 100, tensor_order=2)) + self.assertEqual(result.attrs["TENSOR_ORDER"], 0) + + +class CalcAgyroTestCase(unittest.TestCase): + def test_calc_agyro_input_type(self): + self.assertIsNotNone(pyrf.calc_agyro(generate_ts(64.0, 100, tensor_order=2))) + + with self.assertRaises(AssertionError): + # Raises error if input is not a xarray + pyrf.calc_agyro(0.0) + pyrf.calc_agyro(generate_data(100)) + + def test_calc_agyro_output_type(self): + result = pyrf.calc_agyro(generate_ts(64.0, 100, tensor_order=2)) + + # Output must be a xarray + self.assertIsInstance(result, xr.DataArray) + + def test_calc_agyro_output_shape(self): + result = pyrf.calc_agyro(generate_ts(64.0, 100, tensor_order=2)) + self.assertEqual(result.ndim, 1) + self.assertEqual(len(result), 100) + + def test_calc_agyro_dims(self): + result = pyrf.calc_agyro(generate_ts(64.0, 100, tensor_order=2)) + self.assertListEqual(list(result.dims), ["time"]) + + def test_calc_agyro_meta(self): + result = pyrf.calc_agyro(generate_ts(64.0, 100, tensor_order=2)) + self.assertEqual(result.attrs["TENSOR_ORDER"], 0) + + +class CalcDngTestCase(unittest.TestCase): + def test_calc_dng_input_type(self): + self.assertIsNotNone(pyrf.calc_dng(generate_ts(64.0, 100, tensor_order=2))) + + with self.assertRaises(AssertionError): + # Raises error if input is not a xarray + pyrf.calc_dng(0.0) + pyrf.calc_dng(generate_data(100)) + + def test_calc_dng_output_type(self): + result = pyrf.calc_dng(generate_ts(64.0, 100, tensor_order=2)) + + # Output must be a xarray + self.assertIsInstance(result, xr.DataArray) + + def test_calc_dng_output_shape(self): + result = pyrf.calc_dng(generate_ts(64.0, 100, tensor_order=2)) + self.assertEqual(result.ndim, 1) + self.assertEqual(len(result), 100) + + def test_calc_dng_dims(self): + result = pyrf.calc_dng(generate_ts(64.0, 100, tensor_order=2)) + self.assertListEqual(list(result.dims), ["time"]) + + def test_calc_dng_meta(self): + result = pyrf.calc_dng(generate_ts(64.0, 100, tensor_order=2)) + self.assertEqual(result.attrs["TENSOR_ORDER"], 0) + + +class CalcDtTestCase(unittest.TestCase): + def test_calc_dt_input_type(self): + self.assertIsNotNone(pyrf.calc_dt(generate_ts(64.0, 100, tensor_order=0))) + self.assertIsNotNone(pyrf.calc_dt(generate_ts(64.0, 100, tensor_order=1))) + self.assertIsNotNone(pyrf.calc_dt(generate_ts(64.0, 100, tensor_order=2))) + + with self.assertRaises(AssertionError): + # Raises error if input is not a xarray + pyrf.calc_dt(0) + pyrf.calc_dt(generate_data(100)) + + def test_calc_dt_output_type(self): + self.assertIsInstance(pyrf.calc_dt(generate_ts(64.0, 100)), float) + + +class CalcFsTestCase(unittest.TestCase): + def test_calc_fs_input_type(self): + self.assertIsNotNone(pyrf.calc_fs(generate_ts(64.0, 100))) + + with self.assertRaises(AssertionError): + # Raises error if input is not a xarray + pyrf.calc_fs(0) + pyrf.calc_fs(generate_data(100)) + + def test_calc_fs_output_type(self): + self.assertIsInstance(pyrf.calc_fs(generate_ts(64.0, 100)), float) + + +class CalcSqrtQTestCase(unittest.TestCase): + def test_calc_sqrtq_input_type(self): + self.assertIsNotNone(pyrf.calc_sqrtq(generate_ts(64.0, 100, tensor_order=2))) + + with self.assertRaises(AssertionError): + # Raises error if input is not a xarray + pyrf.calc_sqrtq(0.0) + pyrf.calc_sqrtq(generate_data(100)) + + def test_calc_sqrtq_output_type(self): + result = pyrf.calc_sqrtq(generate_ts(64.0, 100, tensor_order=2)) + + # Output must be a xarray + self.assertIsInstance(result, xr.DataArray) + + def test_calc_sqrtq_output_shape(self): + result = pyrf.calc_sqrtq(generate_ts(64.0, 100, tensor_order=2)) + self.assertEqual(result.ndim, 1) + self.assertEqual(len(result), 100) + + def test_calc_sqrtq_dims(self): + result = pyrf.calc_sqrtq(generate_ts(64.0, 100, tensor_order=2)) + self.assertListEqual(list(result.dims), ["time"]) + + def test_calc_sqrtq_meta(self): + result = pyrf.calc_sqrtq(generate_ts(64.0, 100, tensor_order=2)) + self.assertEqual(result.attrs["TENSOR_ORDER"], 0) + + +class Cart2SphTestCase(unittest.TestCase): + def test_cart2sph_output(self): + result = pyrf.cart2sph(1.0, 1.0, 1.0) + self.assertIsInstance(result[0], np.float64) + self.assertIsInstance(result[1], np.float64) + self.assertIsInstance(result[2], np.float64) + + result = pyrf.cart2sph( + np.random.random(100), np.random.random(100), np.random.random(100) + ) + self.assertIsInstance(result[0], np.ndarray) + self.assertListEqual( + list(result[0].shape), + [ + 100, + ], + ) + self.assertIsInstance(result[1], np.ndarray) + self.assertListEqual( + list(result[1].shape), + [ + 100, + ], + ) + self.assertIsInstance(result[2], np.ndarray) + self.assertListEqual( + list(result[2].shape), + [ + 100, + ], + ) + + +class Cart2SphTsTestCase(unittest.TestCase): + def test_cart2sph_ts_input(self): + with self.assertRaises(AssertionError): + pyrf.cart2sph_ts(0.0) + pyrf.cart2sph_ts(generate_data(100, tensor_order=1)) + pyrf.cart2sph_ts(generate_ts(64.0, 100, tensor_order=0)) + pyrf.cart2sph_ts(generate_ts(64.0, 100, tensor_order=1), 2) + + def test_cart2sph_ts_output(self): + result = pyrf.cart2sph_ts(generate_ts(64.0, 100, tensor_order=1), 1) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + result = pyrf.cart2sph_ts(generate_ts(64.0, 100, tensor_order=1), -1) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + +class CdfEpoch2Datetime64TestCase(unittest.TestCase): + def test_cdfepoch2datetime64_input_type(self): + ref_time = 599572869184000000 + self.assertIsNotNone(pyrf.cdfepoch2datetime64(ref_time)) + time_line = np.arange(ref_time, int(ref_time + 100)) + self.assertIsNotNone(pyrf.cdfepoch2datetime64(time_line)) + self.assertIsNotNone(pyrf.cdfepoch2datetime64(list(time_line))) + + def test_cdfepoch2datetime64_output_type(self): + ref_time = 599572869184000000 + self.assertIsInstance(pyrf.cdfepoch2datetime64(ref_time), np.ndarray) + time_line = np.arange(ref_time, int(ref_time + 100)) + self.assertIsInstance(pyrf.cdfepoch2datetime64(time_line), np.ndarray) + self.assertIsInstance(pyrf.cdfepoch2datetime64(list(time_line)), np.ndarray) + + def test_cdfepoch2datetime64_output_shape(self): + ref_time = 599572869184000000 + self.assertEqual(len(pyrf.cdfepoch2datetime64(ref_time)), 1) + time_line = np.arange(ref_time, int(ref_time + 100)) + self.assertEqual(len(pyrf.cdfepoch2datetime64(time_line)), 100) + self.assertEqual(len(pyrf.cdfepoch2datetime64(list(time_line))), 100) + + +@ddt +class CompressCwtTestCase(unittest.TestCase): + @data(([], 10), (np.random.random((100, 100)), 100)) + @unpack + def test_compress_cwt_input(self, cwt, nc): + with self.assertRaises(AssertionError): + pyrf.compress_cwt(cwt, nc) + + def test_compress_cwt_output(self): + times = generate_timeline(64.0, 1000) + freqs = np.logspace(0, 3, 100) + cwt_x = xr.DataArray( + np.random.random((1000, 100)), coords=[times, freqs], dims=["time", "f"] + ) + cwt_y = xr.DataArray( + np.random.random((1000, 100)), coords=[times, freqs], dims=["time", "f"] + ) + cwt_z = xr.DataArray( + np.random.random((1000, 100)), coords=[times, freqs], dims=["time", "f"] + ) + cwt = xr.Dataset({"x": cwt_x, "y": cwt_y, "z": cwt_z}) + result = pyrf.compress_cwt(cwt, 10) + self.assertIsInstance(result[0], np.ndarray) + + self.assertIsInstance(result[1], np.ndarray) + self.assertIsInstance(result[2], np.ndarray) + + def test_compress_cwt_1d(self): + result = _compress_cwt_1d.__wrapped__( + np.random.random((1000, 100)), random.randint(2, 100) + ) + self.assertIsInstance(result, np.ndarray) + + +class ConvertFACTestCase(unittest.TestCase): + def test_convert_fac_input(self): + with self.assertRaises(AssertionError): + pyrf.convert_fac(0, 0) + pyrf.convert_fac( + generate_data(100, tensor_order=1), generate_data(100, tensor_order=1) + ) + + with self.assertRaises(TypeError): + pyrf.convert_fac( + generate_ts(64.0, 100, tensor_order=2), + generate_ts(64.0, 100, tensor_order=1), + ) + + with self.assertRaises(TypeError): + pyrf.convert_fac( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=0), + ) + + def test_convert_fac_output(self): + result = pyrf.convert_fac( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + ) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + result = pyrf.convert_fac( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 98, tensor_order=1), + ) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + result = pyrf.convert_fac( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + np.random.random(3), + ) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + result = pyrf.convert_fac( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + ) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + result = pyrf.convert_fac( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 97, tensor_order=1), + ) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + result = pyrf.convert_fac( + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + ) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 2]) + + +@ddt +class CotransTestCase(unittest.TestCase): + @data( + (0.0, "gse>gsm", True), + (generate_data(100), "gse>gsm", True), + (generate_ts(64.0, 100, tensor_order=1), "gsm", True), + (generate_ts(64.0, 100, tensor_order=2), "gse>gsm", True), + ( + generate_ts(64.0, 100, tensor_order=1, attrs={"COORDINATE_SYSTEM": "gse"}), + "gsm>sm", + True, + ), + ) + @unpack + def test_cotrans_input(self, inp, flag, hapgood): + with self.assertRaises((TypeError, IndexError, ValueError, AssertionError)): + pyrf.cotrans(inp, flag, hapgood) + + @idata(itertools.product(["gei", "geo", "gse", "gsm", "mag", "sm"], repeat=2)) + def test_cotrans_output_trans(self, value): + transf = f"{value[0]}>{value[1]}" + + inp = generate_ts(64.0, 100, tensor_order=1) + result = pyrf.cotrans(inp, transf, hapgood=True) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + result = pyrf.cotrans(inp, transf, hapgood=False) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + inp.attrs["COORDINATE_SYSTEM"] = value[0] + result = pyrf.cotrans(inp, transf, hapgood=False) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + result = pyrf.cotrans(inp, value[1], hapgood=False) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + result = pyrf.cotrans(inp, value[1], hapgood=True) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + def test_cotrans_output_exot(self): + inp = generate_ts(64.0, 100, tensor_order=0) + result = pyrf.cotrans(inp, "gse>gsm", hapgood=True) + self.assertIsInstance(result, xr.DataArray) + result = pyrf.cotrans(inp, "dipoledirectiongse", hapgood=True) + self.assertIsInstance(result, xr.DataArray) + + +class CrossTestCase(unittest.TestCase): + def test_cross_input(self): + with self.assertRaises(AssertionError): + pyrf.cross( + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=0), + ) + pyrf.cross( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=0), + ) + pyrf.cross( + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=1), + ) + + def test_cross_output(self): + result = pyrf.cross( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + ) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + result = pyrf.cross( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 97, tensor_order=1), + ) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + +@ddt +class DateStrTestCase(unittest.TestCase): + def test_date_str_input(self): + with self.assertRaises(AssertionError): + pyrf.date_str("2019-01-01T00:00:00") + pyrf.date_str([np.datetime64("2019-01-01T00:00:00"), "2019-01-01T00:10:00"]) + pyrf.date_str(["2019-01-01T00:00:00", "2019-01-01T00:10:00"], 1) + + tint = ["2019-01-01T00:00:00.000000000", "2019-01-01T00:10:00.000000000"] + pyrf.date_str(tint, 0) + pyrf.date_str(tint, 5) + + @idata(range(1, 5)) + def test_date_str_output(self, value): + tint = ["2019-01-01T00:00:00.000000000", "2019-01-01T00:10:00.000000000"] + result = pyrf.date_str(tint, value) + self.assertIsInstance(result, str) + + +class Datetime2Iso8601TestCase(unittest.TestCase): + def test_datetime2iso8601_input_type(self): + ref_time = datetime.datetime(2019, 1, 1, 0, 0, 0, 0) + time_line = [ref_time + datetime.timedelta(seconds=i) for i in range(10)] + self.assertIsNotNone(pyrf.datetime2iso8601(ref_time)) + self.assertIsNotNone(pyrf.datetime2iso8601(time_line)) + + def test_datetime2iso8601_output_type(self): + ref_time = datetime.datetime(2019, 1, 1, 0, 0, 0, 0) + time_line = [ref_time + datetime.timedelta(seconds=i) for i in range(10)] + self.assertIsInstance(pyrf.datetime2iso8601(ref_time), str) + self.assertIsInstance(pyrf.datetime2iso8601(time_line), list) + + def test_datetime2iso8601_output_shape(self): + ref_time = datetime.datetime(2019, 1, 1, 0, 0, 0, 0) + time_line = [ref_time + datetime.timedelta(seconds=i) for i in range(10)] + + # ISO8601 contains 29 characters (nanosecond precision) + self.assertEqual(len(pyrf.datetime2iso8601(ref_time)), 29) + self.assertEqual(len(pyrf.datetime2iso8601(time_line)), 10) + + +@ddt +class Datetime642Iso8601TestCase(unittest.TestCase): + @data( + datetime.datetime(2019, 1, 1, 0, 0, 0), + "2019-01-01T00:00:00.000000000", + ) + def test_datetime642iso8601_input(self, value): + with self.assertRaises(TypeError): + pyrf.datetime642iso8601(value) + + @data(np.datetime64("2019-01-01T00:00:00.000000000"), generate_timeline(64.0, 100)) + def test_datetime642iso8601_output(self, value): + self.assertIsInstance(pyrf.datetime642iso8601(value), np.ndarray) + + +@ddt +class Datetime642TtnsTestCase(unittest.TestCase): + @data( + datetime.datetime(2019, 1, 1, 0, 0, 0), + "2019-01-01T00:00:00.000000000", + ) + def test_datetime642ttns_input(self, value): + with self.assertRaises(TypeError): + pyrf.datetime642ttns(value) + + @data(np.datetime64("2019-01-01T00:00:00.000000000"), generate_timeline(64.0, 100)) + def test_datetime642ttns_output(self, value): + self.assertIsInstance(pyrf.datetime642ttns(value), np.ndarray) + + +@ddt +class Datetime642UnixTestCase(unittest.TestCase): + @data( + datetime.datetime(2019, 1, 1, 0, 0, 0), + "2019-01-01T00:00:00.000000000", + ) + def test_datetime642unix_input(self, value): + with self.assertRaises(TypeError): + pyrf.datetime642unix(value) + + @data(np.datetime64("2019-01-01T00:00:00.000000000"), generate_timeline(64.0, 100)) + def test_datetime642unix_output(self, value): + self.assertIsInstance(pyrf.datetime642unix(value), np.ndarray) + + +@ddt +class DecParPerpTestCase(unittest.TestCase): + @data( + ( + generate_data(100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + False, + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_data(100, tensor_order=1), + False, + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + 0, + ), + ( + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=1), + False, + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=0), + False, + ), + ) + @unpack + def test_dec_par_perp_input(self, inp, b_bgd, flag_spin_plane): + with self.assertRaises(AssertionError): + pyrf.dec_par_perp(inp, b_bgd, flag_spin_plane) + + @data( + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + False, + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1) * 1e-4, + False, + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + True, + ), + ) + @unpack + def test_dec_par_perp_output(self, inp, b_bgd, flag_spin_plane): + a_para, a_perp, alpha = pyrf.dec_par_perp(inp, b_bgd, flag_spin_plane) + self.assertIsInstance(a_para, xr.DataArray) + self.assertIsInstance(a_perp, xr.DataArray) + + +@ddt +class DistAppendTestCase(unittest.TestCase): + @data( + (None, generate_vdf(64.0, 100, [32, 32, 16])), + (generate_vdf(64.0, 100, [32, 32, 16]), generate_vdf(64.0, 100, [32, 32, 16])), + ) + @unpack + def test_dist_append_output(self, inp0, inp1): + result = pyrf.dist_append(inp0, inp1) + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class DynamicPressTestCase(unittest.TestCase): + @data( + ( + generate_data(100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=1), + "ions", + ), + ( + generate_ts(64.0, 100, tensor_order=0), + generate_data(100, tensor_order=1), + "ions", + ), + ) + @unpack + def test_dynamic_press_input(self, n_s, v_xyz, specie): + with self.assertRaises(AssertionError): + pyrf.dynamic_press(n_s, v_xyz, specie) + + @data("ions", "electrons") + def test_dynamic_press_output(self, value): + result = pyrf.dynamic_press( + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=1), + value, + ) + self.assertIsInstance(result, xr.DataArray) + self.assertEqual(result.ndim, 1) + + +@ddt +class EVxBTestCase(unittest.TestCase): + @data( + ( + generate_data(100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + "vxb", + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_data(100, tensor_order=1), + "vxb", + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + "bazinga", + ), + ) + @unpack + def test_e_vxb_input(self, v_xyz, b_xyz, flag): + with self.assertRaises((TypeError, AssertionError)): + pyrf.e_vxb(v_xyz, b_xyz, flag) + + @data( + (generate_ts(64.0, 100, tensor_order=1), "vxb"), + (generate_ts(64.0, 100, tensor_order=1), "exb"), + (np.random.random(3), "vxb"), + (np.random.random(3), "exb"), + ) + @unpack + def test_e_vxb_output(self, v_xyz, flag): + result = pyrf.e_vxb(v_xyz, generate_ts(64.0, 100, tensor_order=1), flag) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), [100, 3]) + + +@ddt +class EbNRFTestCase(unittest.TestCase): + @data("a", "b", np.random.random(3)) + def test_eb_nrf_output(self, value): + result = pyrf.eb_nrf( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + value, + ) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class EdbTestCase(unittest.TestCase): + @data("e.b=0", "e_perp+nan", "e_par") + def test_edb_output(self, value): + pyrf.edb( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + random.random() * 90, + value, + ) + + +@ddt +class EbspTestCase(unittest.TestCase): + @data( + ( + None, + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + [1e0, 1e1], + {}, + ), + ( + generate_ts(64.0, 97, tensor_order=1), + generate_ts(64.0, 98, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 120, tensor_order=1), + [1e0, 1e1], + {}, + ), + ( + generate_ts(99.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + [1e0, 1e1], + {}, + ), + ( + generate_ts(40.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(40.0, 100, tensor_order=1), + generate_ts(40.0, 100, tensor_order=1), + generate_ts(40.0, 100, tensor_order=1), + [1e0, 1e1], + {}, + ), + ( + generate_ts(64.0, 97, tensor_order=1), + generate_ts(64.0, 97, tensor_order=1), + generate_ts(64.0, 97, tensor_order=1), + generate_ts(64.0, 97, tensor_order=1), + generate_ts(64.0, 97, tensor_order=1), + [1e0, 1e1], + {}, + ), + ( + generate_ts(64.0, 97, tensor_order=1), + generate_ts(64.0, 97, tensor_order=1), + generate_ts(64.0, 97, tensor_order=1), + generate_ts(64.0, 97, tensor_order=1), + generate_ts(64.0, 97, tensor_order=1), + [1e0, 1e1], + {"fac_matrix": generate_ts(64.0, 100, tensor_order=2)}, + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + None, + [1e0, 1e1], + {}, + ), + ) + @unpack + def test_ebsp_input_pass(self, e_xyz, db_xyz, b_xyz, b_bgd, xyz, freq_int, options): + result = pyrf.ebsp(e_xyz, db_xyz, b_xyz, b_bgd, xyz, freq_int, **options) + self.assertIsInstance(result, dict) + + @data( + ( + generate_ts(64.0, 100, tensor_order=1), + None, + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + [1e0, 1e1], + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + None, + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + [1e0, 1e1], + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + None, + generate_ts(64.0, 100, tensor_order=1), + [1e0, 1e1], + ), + ( + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + [1e0, 1e1], + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + [1e1, 1e0], + ), + ( + generate_ts(64.0, 100, tensor_order=1)[:, :2], + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + [1e0, 1e1], + ), + ) + @unpack + def test_ebsp_input_fail(self, e_xyz, db_xyz, b_xyz, b_bgd, xyz, freq_int): + with self.assertRaises((AssertionError, TypeError, IndexError, ValueError)): + pyrf.ebsp(e_xyz, db_xyz, b_xyz, b_bgd, xyz, freq_int) + + @data( + { + "polarization": False, + "no_resample": False, + "fac": True, + "de_dot_b0": False, + "full_b_db": False, + "nav": 8, + "fac_matrix": None, + "m_width_coeff": 1, + }, + { + "polarization": True, + "no_resample": False, + "fac": True, + "de_dot_b0": False, + "full_b_db": False, + "nav": 8, + "fac_matrix": None, + "m_width_coeff": 1, + }, + { + "polarization": False, + "no_resample": True, + "fac": True, + "de_dot_b0": False, + "full_b_db": False, + "nav": 8, + "fac_matrix": None, + "m_width_coeff": 1, + }, + { + "polarization": False, + "no_resample": False, + "fac": False, + "de_dot_b0": False, + "full_b_db": False, + "nav": 8, + "fac_matrix": None, + "m_width_coeff": 1, + }, + { + "polarization": False, + "no_resample": False, + "fac": False, + "de_dot_b0": True, + "full_b_db": False, + "nav": 8, + "fac_matrix": None, + "m_width_coeff": 1, + }, + { + "polarization": False, + "no_resample": False, + "fac": True, + "de_dot_b0": True, + "full_b_db": False, + "nav": 8, + "fac_matrix": None, + "m_width_coeff": 1, + }, + { + "polarization": False, + "no_resample": False, + "fac": True, + "de_dot_b0": False, + "full_b_db": True, + "nav": 8, + "fac_matrix": None, + "m_width_coeff": 1, + }, + { + "polarization": False, + "no_resample": False, + "fac": True, + "de_dot_b0": False, + "full_b_db": False, + "nav": random.randint(2, 50), + "fac_matrix": None, + "m_width_coeff": 1, + }, + { + "polarization": False, + "no_resample": False, + "fac": True, + "de_dot_b0": True, + "full_b_db": False, + "nav": 8, + "fac_matrix": generate_ts(64.0, 100, tensor_order=2), + "m_width_coeff": 1, + }, + { + "polarization": False, + "no_resample": False, + "fac": True, + "de_dot_b0": False, + "full_b_db": False, + "nav": 8, + "fac_matrix": generate_ts(64.0, 100, tensor_order=2), + "m_width_coeff": 1, + }, + { + "polarization": False, + "no_resample": False, + "fac": True, + "de_dot_b0": False, + "full_b_db": False, + "nav": 8, + "fac_matrix": None, + "m_width_coeff": random.random(), + }, + ) + def test_ebsp_options(self, value): + result = pyrf.ebsp( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + [1e0, 1e1], + **value, + ) + + self.assertIsInstance(result, dict) + self.assertIsInstance(result["bb_xxyyzzss"], xr.DataArray) + + @data("pc12", "pc35", [1e0, 1e1]) + def test_ebsp_freq_int_pass(self, value): + self.assertIsNotNone( + pyrf.ebsp( + generate_ts(64.0, 100000, tensor_order=1), + generate_ts(64.0, 100000, tensor_order=1), + generate_ts(64.0, 100000, tensor_order=1), + generate_ts(64.0, 100000, tensor_order=1), + generate_ts(64.0, 100000, tensor_order=1), + value, + ) + ) + + @data(random.random(), np.random.random(3), "bazinga", [1, 100]) + def test_ebsp_freq_int_fail(self, value): + with self.assertRaises((AssertionError, ValueError)): + pyrf.ebsp( + generate_ts(64.0, 10000, tensor_order=1), + generate_ts(64.0, 10000, tensor_order=1), + generate_ts(64.0, 10000, tensor_order=1), + generate_ts(64.0, 10000, tensor_order=1), + generate_ts(64.0, 10000, tensor_order=1), + value, + ) + + @data(([0.32, 3.2], generate_ts(64.0, 100000, tensor_order=1))) + @unpack + def test_average_data(self, freq_int, data): + _, _, _, out_time = _freq_int(freq_int, data) + in_time = data.time.data.astype(np.float64) / 1e9 + + result = _average_data.__wrapped__(data.data, in_time, out_time, None) + self.assertIsInstance(result, np.ndarray) + self.assertListEqual(list(result.shape), [len(out_time), 3]) - def test_calc_dt(self): - """integration test on sampling tme step computation""" - dt = pyrf.calc_dt(self.b_xyz) + @data(([0.32, 3.2], generate_ts(64.0, 100000, tensor_order=1))) + @unpack + def test_censure_plot(self, freq_int, data): + _, _, out_sampling, out_time = _freq_int(freq_int, data) + a_ = np.logspace(1, 2, 12) + idx_nan = np.full(len(data), False) + idx_nan[np.random.randint(100000, size=100)] = True + censure = np.floor(2 * a_ * out_sampling / 64.0 * 8) + result = _censure_plot.__wrapped__( + np.random.random((len(out_time), len(a_))), + idx_nan, + censure, + len(data), + a_, + ) + self.assertIsInstance(result, np.ndarray) + self.assertListEqual(list(result.shape), [len(out_time), len(a_)]) + + +class EndTestCase(unittest.TestCase): + def test_end_input(self): + with self.assertRaises(AssertionError): + pyrf.end(generate_timeline(64.0, 100)) + + def test_end_output(self): + pyrf.end(generate_ts(64.0, 100)) + + +@ddt +class EstimateTestCase(unittest.TestCase): + @data( + ("bazinga", random.random(), None), + ("capacitance_wire", 0, random.random()), + ("capacitance_wire", random.randint(1, 9), random.randint(1, 9)), + ("capacitance_cylinder", random.randint(20, 100), random.randint(1, 9)), + ) + @unpack + def test_estimate_input(self, what_to_estimate, radius, length): + with self.assertRaises((NotImplementedError, ValueError)): + pyrf.estimate(what_to_estimate, radius, length) + + @data( + ("capacitance_disk", random.random(), None), + ("capacitance_sphere", random.random(), None), + ("capacitance_wire", random.random(), random.randint(10, 100)), + ("capacitance_cylinder", random.randint(1, 9), random.randint(40, 100)), + ("capacitance_cylinder", random.randint(1, 9), random.randint(5, 26)), + ) + @unpack + def test_estimate_output(self, what_to_estimate, radius, length): + result = pyrf.estimate(what_to_estimate, radius, length) + self.assertIsInstance(result, float) + + +@ddt +class ExtendTintTestCase(unittest.TestCase): + def test_extend_tint_input(self): + with self.assertRaises(TypeError): + pyrf.extend_tint([0, 0], None) + + @data( + ( + ["2019-01-01T00:00:00.000000000", "2019-01-01T00:10:00.000000000"], + [-random.random(), random.random()], + ), + (["2019-01-01T00:00:00.000000000", "2019-01-01T00:10:00.000000000"], None), + ( + [ + np.datetime64("2019-01-01T00:00:00.000000000"), + np.datetime64("2019-01-01T00:10:00.000000000"), + ], + None, + ), + ) + @unpack + def test_extend_tint_ouput(self, tint, ext): + pyrf.extend_tint(tint, ext) + + +@ddt +class FiltTestCase(unittest.TestCase): + @data( + (generate_data(100), 0, random.randint(1, 22), random.choice(range(1, 10, 2))), + ( + generate_ts(64.0, 100), + "bazinga", + random.randint(1, 22), + random.choice(range(1, 10, 2)), + ), + ( + generate_ts(64.0, 100), + random.randint(1, 22), + "bazinga", + random.choice(range(1, 10, 2)), + ), + (generate_ts(64.0, 100), 0, random.randint(1, 22), "ORDEEERRRR"), + ) + @unpack + def test_filt_input(self, inp, f_min, f_max, order): + with self.assertRaises(AssertionError): + pyrf.filt(inp, f_min, f_max, order) + + @data( + ( + generate_ts(64.0, 100, tensor_order=0), + 0, + 1, + -1, + ), + ( + generate_ts(64.0, 100, tensor_order=1), + 0, + 1, + -1, + ), + ( + generate_ts(64.0, 100, tensor_order=1), + 0, + random.randint(2, 22), + -1, + ), + ( + generate_ts(64.0, 100, tensor_order=1), + 0, + random.randint(2, 22), + random.choice(range(1, 10, 2)), + ), + ( + generate_ts(64.0, 100, tensor_order=1), + random.randint(2, 22), + 0, + -1, + ), + ( + generate_ts(64.0, 100, tensor_order=1), + random.randint(2, 22), + 0, + random.choice(range(1, 10, 2)), + ), + ( + generate_ts(64.0, 100, tensor_order=1), + random.randint(2, 11), + random.randint(12, 22), + -1, + ), + ( + generate_ts(64.0, 100, tensor_order=1), + random.randint(2, 11), + random.randint(12, 22), + random.choice(range(1, 10, 2)), + ), + ) + @unpack + def test_filt_output(self, inp, f_min, f_max, order): + result = pyrf.filt(inp, f_min, f_max, order) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class GradientTestCase(unittest.TestCase): + @data( + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=2), + ) + def test_gradient_output(self, value): + value.attrs = {"UNITS": "bazinga"} + result = pyrf.gradient(value) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual(list(result.shape), list(value.shape)) + + +@ddt +class Gse2GsmTestCase(unittest.TestCase): + @data( + (generate_data(100, tensor_order=1), "gse>gsm"), + (generate_ts(64.0, 100, tensor_order=0), "gse>gsm"), + (generate_ts(64.0, 100, tensor_order=1), "bazinga"), + ) + @unpack + def test_gse2gsm_input(self, inp, flag): + with self.assertRaises(AssertionError): + pyrf.gse2gsm(inp, flag) + + @data( + (generate_ts(64.0, 100, tensor_order=1), "gse>gsm"), + (generate_ts(64.0, 100, tensor_order=1), "gsm>gse"), + ) + @unpack + def test_gse2gsm_output(self, inp, flag): + pyrf.gse2gsm(inp, flag) + + +@ddt +class HistogramTestCase(unittest.TestCase): + @data( + (random.randint(2, 100), None, None, None), + (np.sort(np.random.random(10)), None, None, None), + ("fd", None, None, None), + ("auto", np.sort(np.random.random(2)), None, None), + (100, None, np.random.random(1000), None), + ("auto", None, None, True), + ) + @unpack + def test_histogram_output(self, bins, y_range, weights, density): + result = pyrf.histogram( + generate_ts(64.0, 1000), bins, y_range, weights, density + ) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class Histogram2DTestCase(unittest.TestCase): + @data( + (random.randint(2, 100), None, None, None), + (np.sort(np.random.random(100)), None, None, None), + (np.random.randint(2, 100, size=(2,)), None, None, None), + ([np.sort(np.random.random(100)) for _ in range(2)], None, None, None), + (100, np.sort(np.random.random((2, 2)), axis=1), None, None), + (100, None, np.random.random(1000), None), + (100, None, None, True), + ) + @unpack + def test_histogram2d_output(self, bins, y_range, weights, density): + result = pyrf.histogram2d( + generate_ts(64.0, 1000), + generate_ts(64.0, 900), + bins, + y_range, + weights, + density, + ) + self.assertIsInstance(result, xr.DataArray) + result = pyrf.histogram2d( + generate_ts(64.0, 1000), + generate_ts(64.0, 1000), + bins, + y_range, + weights, + density, + ) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class IncrementsTestCase(unittest.TestCase): + @data( + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=2), + ) + def test_increments_output(self, value): + result = pyrf.increments(value, random.randint(1, 50)) + self.assertIsInstance(result[0], np.ndarray) + self.assertIsInstance(result[1], xr.DataArray) + + +@ddt +class IntSphDistTestCase(unittest.TestCase): + @data( + {"projection_base": "pol", "projection_dim": "2d"}, + ) + def test_int_sph_dist_input(self, value): + vdf = np.random.random((51, 32, 16)) + speed = np.linspace(0, 1, 51) + phi = np.arange(32) + theta = np.arange(16) + speed_grid = np.linspace(-1, 1, 101) + + with self.assertRaises((RuntimeError, NotImplementedError)): + pyrf.int_sph_dist(vdf, speed, phi, theta, speed_grid, **value) + + @data( + {}, + {"weight": "lin"}, + {"weight": "log"}, + {"speed_edges": np.linspace(-0.01, 1.01, 52)}, + {"speed_grid_edges": np.linspace(-1.01, 1.01, 102)}, + { + "phi_grid": np.arange(0, 32), + "projection_base": "cart", + "projection_dim": "2d", + }, + {"projection_base": "cart", "projection_dim": "3d"}, + ) + def test_int_sph_dist_output(self, value): + vdf = np.random.random((51, 32, 16)) + speed = np.linspace(0, 1, 51) + phi = np.arange(32) + theta = np.arange(16) + speed_grid = np.linspace(-1, 1, 101) + result = pyrf.int_sph_dist(vdf, speed, phi, theta, speed_grid, **value) + self.assertIsInstance(result, dict) + + @data( + ( + np.random.random((51, 32, 16)), + np.linspace(0, 1, 51), + np.arange(32), + np.arange(16), + np.ones(51) * 0.02, + np.ones(51) * 0.01, + np.ones(32), + np.ones(16), + np.linspace(-1.01, 1.01, 102), + np.ones(101) * 0.02 * np.pi / 16, + np.array([-np.inf, np.inf]), + np.array([-np.pi, np.pi]), + np.ones((51, 32, 16), dtype=np.int64) * 10, + np.eye(3), + ) + ) + def test_mc_pol_1d(self, value): + vdf, *args = value + vdf[vdf < 1e-2] = 0 + self.assertIsInstance(_mc_pol_1d.__wrapped__(vdf, *args), np.ndarray) + + @data( + ( + np.random.random((51, 32, 16)), + np.linspace(0, 1, 51), + np.arange(32), + np.arange(16), + np.ones(51) * 0.02, + np.ones(51) * 0.01, + np.ones(32), + np.ones(16), + np.linspace(-1.01, 1.01, 102), + 0.02**2, + np.array([-np.inf, np.inf]), + np.array([-np.pi, np.pi]), + (np.ones((51, 32, 16), dtype=np.int64) * 10).astype(int), + np.eye(3), + ) + ) + def test_mc_cart_2d(self, value): + vdf, *args = value + vdf[vdf < 1e-2] = 0 + self.assertIsInstance(_mc_cart_2d.__wrapped__(vdf, *args), np.ndarray) + + @data( + ( + np.random.random((51, 32, 16)), + np.linspace(0, 1, 51), + np.arange(32), + np.arange(16), + np.ones(51) * 0.02, + np.ones(51) * 0.01, + np.ones(32), + np.ones(16), + np.linspace(-1.01, 1.01, 102), + 0.02**2, + np.array([-np.inf, np.inf]), + np.array([-np.pi, np.pi]), + (np.ones((51, 32, 16), dtype=np.int64) * 10).astype(int), + np.eye(3), + ) + ) + def test_mc_cart_3d(self, value): + vdf, *args = value + vdf[vdf < 1e-2] = 0 + self.assertIsInstance(_mc_cart_3d.__wrapped__(vdf, *args), np.ndarray) + + +@ddt +class IntegrateTestCase(unittest.TestCase): + @data( + generate_ts(64.0, 100, tensor_order=0), generate_ts(64.0, 100, tensor_order=1) + ) + def test_integrate_output(self, value): + result = pyrf.integrate(value) + self.assertIsInstance(result, xr.DataArray) + + +class IPlasmaCalcTestCase(unittest.TestCase): + def test_iplasma_calc_output(self): + with mock.patch.object(builtins, "input", lambda _: random.randint(10, 100)): + result = pyrf.iplasma_calc(True, True) + self.assertIsInstance(result, dict) + + result = pyrf.iplasma_calc(False, False) + self.assertIsNone(result) + + +@ddt +class Iso86012DatetimeTestCase(unittest.TestCase): + @data( + "2019-01-01T00:00:00.000000000", + ["2019-01-01T00:00:00.000000000", "2019-01-01T00:10:00.000000000"], + np.array(["2019-01-01T00:00:00.000000000", "2019-01-01T00:10:00.000000000"]), + ) + def test_iso86012datetime(self, value): + result = pyrf.iso86012datetime(value) + self.assertIsInstance(result, list) + + +@ddt +class Iso86012Unix(unittest.TestCase): + @data( + "2019-01-01T00:00:00.000000000", + ["2019-01-01T00:00:00.000000000", "2019-01-01T00:10:00.000000000"], + np.array(["2019-01-01T00:00:00.000000000", "2019-01-01T00:10:00.000000000"]), + ) + def test_iso86012unix_output(self, value): + result = pyrf.iso86012unix(value) + self.assertIsInstance(result, np.ndarray) + + +@ddt +class Iso86012TimeVec(unittest.TestCase): + @data( + "2019-01-01T00:00:00.000000000", + ["2019-01-01T00:00:00.000000000", "2019-01-01T00:10:00.000000000"], + np.array(["2019-01-01T00:00:00.000000000", "2019-01-01T00:10:00.000000000"]), + ) + def test_iso86012timevec_output(self, value): + result = pyrf.iso86012timevec(value) + self.assertIsInstance(result, np.ndarray) + + +@ddt +class LowPassTestCase(unittest.TestCase): + @data( + generate_ts(64.0, 10000, tensor_order=0), + generate_ts(64.0, 10000, tensor_order=1), + generate_ts(64.0, 10000, tensor_order=2), + ) + def test_lowpass_output(self, value): + pyrf.lowpass(value, random.random(), 64.0) + + +@ddt +class LShellTestCase(unittest.TestCase): + @data("gei", "geo", "gse", "gsm", "mag", "sm") + def test_l_shell_output(self, value): + result = pyrf.l_shell( + generate_ts(64.0, 100, tensor_order=1, attrs={"COORDINATE_SYSTEM": value}) + ) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class MeanTestCase(unittest.TestCase): + @data(None, generate_ts(64.0, 100, tensor_order=1)) + def test_mean_output(self, value): + result = pyrf.mean( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + value, + ) + self.assertIsInstance(result, xr.DataArray) + + +class MeanBinsTestCase(unittest.TestCase): + def test_mean_bins_output(self): + result = pyrf.mean_bins( + generate_ts(64.0, 100), generate_ts(64.0, 100), random.randint(2, 20) + ) + self.assertIsInstance(result, xr.Dataset) + + +class MedianBinsTestCase(unittest.TestCase): + def test_median_bins_output(self): + result = pyrf.median_bins( + generate_ts(64.0, 100), generate_ts(64.0, 100), random.randint(2, 20) + ) + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class MvaTestCase(unittest.TestCase): + @data("mvar", "=0", "td") + def test_mva_output(self, method): + result = pyrf.mva(generate_ts(64.0, 100, tensor_order=1), method) + self.assertIsInstance(result[0], xr.DataArray) + self.assertIsInstance(result[1], np.ndarray) + self.assertIsInstance(result[2], np.ndarray) + + +@ddt +class NewXyzTestCase(unittest.TestCase): + @data( + generate_ts(64.0, 100, tensor_order=1), generate_ts(64.0, 100, tensor_order=2) + ) + def test_new_xyz_output(self, inp): + result = pyrf.new_xyz(inp, np.random.random((3, 3))) + self.assertIsInstance(result, xr.DataArray) + self.assertEqual(result.ndim, inp.ndim) + + +class NormTestCase(unittest.TestCase): + def test_norm_output(self): + result = pyrf.norm(generate_ts(64.0, 100, tensor_order=1)) + self.assertIsInstance(result, xr.DataArray) + self.assertEqual(result.ndim, 1) + self.assertEqual(len(result), 100) + + +@ddt +class PlasmaBetaTestCase(unittest.TestCase): + @data( + (generate_ts(64.0, 100, tensor_order=1), generate_ts(64.0, 100, tensor_order=2)) + ) + @unpack + def test_plasma_beta_output(self, b_xyz, p_xyz): + result = pyrf.plasma_beta(b_xyz, p_xyz) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class StructFuncTestCase(unittest.TestCase): + @data( + (generate_ts(64.0, 100, tensor_order=0), None, random.randint(1, 100)), + (generate_ts(64.0, 100, tensor_order=1), None, random.randint(1, 100)), + (generate_ts(64.0, 100, tensor_order=2), None, random.randint(1, 100)), + ( + generate_ts(64.0, 100, tensor_order=1), + np.random.randint([1] * 50, [50] * 50), + 1, + ), + ) + @unpack + def test_struct_func_output(self, inp, scales, order): + result = pyrf.struct_func(inp, scales, order) + self.assertIsInstance(result, xr.DataArray) + + +class TraceTestCase(unittest.TestCase): + def test_trace_input(self): + self.assertIsNotNone(pyrf.trace(generate_ts(64.0, 100, tensor_order=2))) + + with self.assertRaises(AssertionError): + pyrf.trace(generate_data(100, tensor_order=2)) + pyrf.trace(generate_ts(64.0, 100, tensor_order=0)) + pyrf.trace(generate_ts(64.0, 100, tensor_order=1)) + + def test_trace_output(self): + result = pyrf.trace(generate_ts(64.0, 100, tensor_order=2)) + self.assertIsInstance(result, xr.DataArray) + self.assertListEqual( + list(result.shape), + [ + 100, + ], + ) + + +class OptimizeNbins1DTestCase(unittest.TestCase): + def test_optimize_nbins_1d(self): + result = pyrf.optimize_nbins_1d( + generate_ts(64.0, 1000), + n_min=random.randint(2, 10), + n_max=random.randint(20, 100), + ) + + self.assertIsInstance(result, int) + + +class OptimizeNbins2DTestCase(unittest.TestCase): + def test_optimize_nbins_2d(self): + result = pyrf.optimize_nbins_2d( + generate_ts(64.0, 1000), + generate_ts(64.0, 1000), + n_min=[random.randint(2, 10), random.randint(2, 10)], + n_max=[random.randint(20, 100), random.randint(20, 100)], + ) + self.assertIsInstance(result[0], int) + self.assertIsInstance(result[1], int) + + +class Pid4SCTestCase(unittest.TestCase): + def test_pid_4sc_output(self): + result = pyrf.pid_4sc( + [generate_ts(64.0, 100, tensor_order=1) for _ in range(4)], + [generate_ts(64.0, 100, tensor_order=1) for _ in range(4)], + [generate_ts(64.0, 100, tensor_order=2) for _ in range(4)], + [generate_ts(64.0, 100, tensor_order=1) for _ in range(4)], + ) + self.assertIsInstance(result[0], xr.DataArray) + self.assertIsInstance(result[1], xr.DataArray) + + +class PlasmaCalcTestCase(unittest.TestCase): + def test_plasma_calc_output(self): + result = pyrf.plasma_calc( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=0), + ) + self.assertIsInstance(result, xr.Dataset) + + +@ddt +class ResampleTestCase(unittest.TestCase): + @data( + (generate_ts(64.0, 100), generate_ts(640.0, 1000)), + (generate_ts(640.0, 1000), generate_ts(64.0, 100)), + (generate_vdf(64.0, 100, [32, 32, 16]), generate_ts(640.0, 1000)), + (generate_ts(64.0, 100), generate_ts(640.0, 2)), + (generate_ts(64.0, 100), generate_ts(640.0, 1)), + ) + @unpack + def test_resample_output(self, inp, ref): + result = pyrf.resample(inp, ref) + self.assertIsInstance(result, type(inp)) + + +@ddt +class PoyntingFluxTestCase(unittest.TestCase): + @data( + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + ), + ( + generate_ts(128.0, 200, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(128.0, 200, tensor_order=1), + ), + ) + @unpack + def test_poynting_flux_output(self, e_xyz, b_xyz): + result = pyrf.poynting_flux(e_xyz, b_xyz, None) + self.assertIsInstance(result[0], xr.DataArray) + self.assertIsInstance(result[1], xr.DataArray) + + result = pyrf.poynting_flux( + e_xyz, b_xyz, generate_ts(64.0, 100, tensor_order=1) + ) + self.assertIsInstance(result[0], xr.DataArray) + self.assertIsInstance(result[1], xr.DataArray) + self.assertIsInstance(result[2], xr.DataArray) + + +class PresAnisTestCase(unittest.TestCase): + def test_pres_anis_output(self): + result = pyrf.pres_anis( + generate_ts(64.0, 100, tensor_order=2), + generate_ts(64.0, 100, tensor_order=1), + ) + self.assertIsInstance(result, xr.DataArray) + + +@ddt +class ShockNormalTestCase(unittest.TestCase): + def test_shock_normal_input(self): + with self.assertRaises(AssertionError): + pyrf.shock_normal([]) + + with self.assertRaises(TypeError): + pyrf.shock_normal( + { + "b_u": np.random.random(3), + "b_d": np.random.random(3), + "v_u": np.random.random(3), + "v_d": np.random.random(3), + "n_u": random.random(), + "n_d": random.random(), + "r_xyz": random.random(), + } + ) + + @data( + { + "b_u": np.random.random(3), + "b_d": np.random.random(3), + "v_u": np.random.random(3), + "v_d": np.random.random(3), + "n_u": random.random(), + "n_d": random.random(), + }, + { + "b_u": np.random.random((2, 3)), + "b_d": np.random.random((2, 3)), + "v_u": np.random.random((2, 3)), + "v_d": np.random.random((2, 3)), + "n_u": np.random.random((2, 1)), + "n_d": np.random.random((2, 1)), + }, + { + "b_u": np.random.random(3), + "b_d": np.random.random(3), + "v_u": np.random.random(3), + "v_d": np.random.random(3), + "n_u": random.random(), + "n_d": random.random(), + "r_xyz": np.random.random(3), + }, + { + "b_u": np.random.random(3), + "b_d": np.random.random(3), + "v_u": np.random.random(3), + "v_d": np.random.random(3), + "n_u": random.random(), + "n_d": random.random(), + "r_xyz": generate_ts(64.0, 100, tensor_order=1), + }, + { + "b_u": np.random.random(3), + "b_d": np.random.random(3), + "v_u": np.random.random(3), + "v_d": np.random.random(3), + "n_u": random.random(), + "n_d": random.random(), + "r_xyz": generate_ts(64.0, 100, tensor_order=1), + "d2u": random.choice([-1, 1]), + "dt_f": random.random(), + "f_cp": random.random(), + }, + ) + def test_shock_normal_ouput(self, value): + result = pyrf.shock_normal(value) + self.assertIsInstance(result, dict) + self.assertIsInstance(result["v_sh"], dict) + + +@ddt +class ShockParametersTestCase(unittest.TestCase): + def test_shock_parameters_input(self): + with self.assertRaises(AssertionError): + pyrf.shock_parameters( + { + "b": np.random.random(3), + "n": random.random(), + "v": np.random.random(3), + "t_i": random.random(), + "t_e": random.random(), + "v_sh": random.random(), + "nvec": np.random.random(3), + "ref_sys": "bazinga", + } + ) + + @data( + { + "b": np.random.random(3), + "n": random.random(), + "v": np.random.random(3), + "t_i": random.random(), + "t_e": random.random(), + "ref_sys": "nif", + }, + { + "b": np.random.random(3), + "n": random.random(), + "v": np.random.random(3), + "t_i": random.random(), + "t_e": random.random(), + "v_sh": random.random(), + "nvec": np.random.random(3), + "ref_sys": "nif", + }, + { + "b": np.random.random(3), + "n": random.random(), + "v": np.random.random(3), + "t_i": random.random(), + "t_e": random.random(), + "v_sh": random.random(), + "nvec": np.random.random(3), + "ref_sys": "sc", + }, + ) + def test_shock_parameters_output(self, value): + pyrf.shock_parameters(value) + + +@ddt +class SolidAngleTestCase(unittest.TestCase): + @data( + tuple(np.random.random(3) for _ in range(3)), + tuple(generate_data(100, tensor_order=1) for _ in range(3)), + tuple(generate_ts(64.0, 100, tensor_order=1) for _ in range(3)), + ) + @unpack + def test_solid_angle_ouput(self, inp0, inp1, inp2): + result = pyrf.solid_angle(inp0, inp1, inp2) + self.assertIsInstance(result, np.ndarray) + + +@ddt +class Sph2CartTestCase(unittest.TestCase): + @data( + tuple(generate_data(100, tensor_order=0) for _ in range(3)), + tuple(generate_ts(64.0, 100, tensor_order=0) for _ in range(3)), + ) + @unpack + def test_sph2cart_output(self, azimuth, elevation, r): + self.assertIsNotNone(pyrf.sph2cart(azimuth, elevation, r)) + + +class StartTestCase(unittest.TestCase): + def test_start_input_type(self): + self.assertIsNotNone(pyrf.start(generate_ts(64.0, 100, tensor_order=0))) + self.assertIsNotNone(pyrf.start(generate_ts(64.0, 100, tensor_order=1))) + self.assertIsNotNone(pyrf.start(generate_ts(64.0, 100, tensor_order=2))) + + with self.assertRaises(AssertionError): + pyrf.start(0) + pyrf.start(generate_timeline(64.0, 100)) + + def test_start_output(self): + result = pyrf.start(generate_ts(64.0, 100, tensor_order=0)) + self.assertIsInstance(result, np.float64) + self.assertEqual( + np.datetime64(int(result * 1e9), "ns"), + np.datetime64("2019-01-01T00:00:00.000"), + ) + + +@ddt +class TsAppendTestCase(unittest.TestCase): + @data( + generate_ts(64.0, 100, tensor_order=0), + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=2), + ) + def test_ts_append_output(self, value): + value.attrs = { + "bazinga": "This is my spot!!", + "I AM GROOT": "I AM STEVE ROGERS", + "random": np.random.random(100), + } + value.time.attrs = { + "bazinga": "This is my spot!!", + "I AM GROOT": "I AM STEVE ROGERS", + "random": np.random.random(100), + } + + result = pyrf.ts_append(None, value) + self.assertIsInstance(result, xr.DataArray) + self.assertEqual(result.ndim, value.ndim) + + result = pyrf.ts_append(value, value) + self.assertIsInstance(result, xr.DataArray) + self.assertEqual(result.ndim, value.ndim) + + +@ddt +class TimeClipTestCase(unittest.TestCase): + @data( + [ + datetime.datetime(2019, 1, 1, 0, 0, 0, 312), + datetime.datetime(2019, 1, 1, 0, 0, 0, 468), + ], + "2019-01-01T00:00:00.312", + ) + def test_time_clip_input(self, value): + with self.assertRaises(TypeError): + pyrf.time_clip(generate_ts(64.0, 100), value) + + @data(generate_ts(64.0, 100), generate_vdf(64.0, 100, (32, 32, 16))) + def test_time_clip_output(self, value): + result = pyrf.time_clip( + value, ["2019-01-01T00:00:00.312", "2019-01-01T00:00:00.468"] + ) + self.assertIsInstance(result, type(value)) + + result = pyrf.time_clip( + value, + [ + np.datetime64("2019-01-01T00:00:00.312"), + np.datetime64("2019-01-01T00:00:00.468"), + ], + ) + self.assertIsInstance(result, type(value)) + + result = pyrf.time_clip(value, generate_ts(64.0, 20)) + self.assertIsInstance(result, type(value)) + + +@ddt +class TsTimeTestCase(unittest.TestCase): + def test_ts_skymap_input_type(self): + with self.assertRaises(AssertionError): + pyrf.ts_time(np.datetime64("1789-07-14T00:00:00.000000000"), {}) + + @data(generate_timeline(64.0, 100, dtype=np.int64)) + def test_ts_time_inpu_datatype(self, timeline): + with self.assertRaises(TypeError): + pyrf.ts_time(timeline, {}) + + @data( + generate_timeline(64.0, 100, dtype=np.float64) / 1e9, + generate_timeline(64.0, 100, dtype=np.datetime64), + ) + def test_ts_time_output(self, timeline): + result = pyrf.ts_time(timeline, {}) + self.assertIsInstance(result, xr.DataArray) + + +class TsSkymapTestCase(unittest.TestCase): + def test_ts_skymap_input_type(self): + with self.assertRaises(AssertionError): + pyrf.ts_skymap(0, 0, 0, 0, 0) + + def test_ts_skymap_output_type(self): + result = pyrf.ts_skymap( + generate_timeline(64.0, 100), + np.random.random((100, 32, 32, 16)), + np.random.random((100, 32)), + np.random.random((100, 32)), + np.random.random(16), + ) + self.assertIsInstance(result, xr.Dataset) + + def test_ts_skymap_output_shape(self): + result = pyrf.ts_skymap( + generate_timeline(64.0, 100), + np.random.random((100, 32, 32, 16)), + np.random.random((100, 32)), + np.random.random((100, 32)), + np.random.random(16), + ) + self.assertEqual(result.data.ndim, 4) + self.assertListEqual(list(result.data.shape), [100, 32, 32, 16]) + self.assertEqual(result.energy.ndim, 2) + self.assertListEqual(list(result.energy.shape), [100, 32]) + self.assertEqual(result.phi.ndim, 2) + self.assertListEqual(list(result.phi.shape), [100, 32]) + self.assertEqual(result.theta.ndim, 1) + self.assertListEqual(list(result.theta.shape), [16]) + + def test_ts_skymap_output_meta(self): + result = pyrf.ts_skymap( + generate_timeline(64.0, 100), + np.random.random((100, 32, 32, 16)), + np.random.random((100, 32)), + np.random.random((100, 32)), + np.random.random(16), + ) + self.assertListEqual( + list(result.attrs.keys()), ["energy0", "energy1", "esteptable"] + ) + self.assertListEqual( + list(result.attrs["energy0"].shape), + [ + 32, + ], + ) + self.assertListEqual( + list(result.attrs["energy1"].shape), + [ + 32, + ], + ) + self.assertListEqual( + list(result.attrs["esteptable"].shape), + [ + 100, + ], + ) + + for k in result: + self.assertEqual(result[k].attrs, {}) - self.assertEqual(int(dt * 1e9), 7813000) - def test_cross(self): - """integration test on vector cross product computation""" - exb = pyrf.cross(self.e_xyz, self.b_xyz) - res = pyrf.dot(exb, self.e_xyz) - res = np.mean(res) - self.assertTrue(res < 1e-5) +class TsScalarTestCase(unittest.TestCase): + def test_ts_scalar_input_type(self): + with self.assertRaises(AssertionError): + pyrf.ts_scalar(0, 0) + pyrf.ts_scalar( + list(generate_timeline(64.0, 100)), + list(generate_data(100, tensor_order=0)), + ) - def test_resample(self): - """integration test on time resampling""" - e_xyz = pyrf.resample(self.e_xyz, self.b_xyz) + def test_ts_scalar_input_shape(self): + with self.assertRaises(AssertionError): + # Raises error if data and timeline don't have the same size + pyrf.ts_scalar( + generate_timeline(64.0, 100), generate_data(99, tensor_order=0) + ) + # Raises error if vector as input + pyrf.ts_scalar( + generate_timeline(64.0, 100), generate_data(100, tensor_order=1) + ) + # Raises error if tensor as input + pyrf.ts_scalar( + generate_timeline(64.0, 100), generate_data(100, tensor_order=2) + ) - self.assertTrue( - (pyrf.resample(e_xyz, self.b_xyz).time.data == self.b_xyz.time.data).all() + def test_ts_scalar_output_type(self): + result = pyrf.ts_scalar( + generate_timeline(64.0, 100), generate_data(100, tensor_order=0) ) + self.assertIsInstance(result, xr.DataArray) + + def test_ts_scalar_output_shape(self): + result = pyrf.ts_scalar( + generate_timeline(64.0, 100), generate_data(100, tensor_order=0) + ) + self.assertEqual(result.ndim, 1) + self.assertEqual(len(result), 100) + + def test_ts_scalar_dims(self): + result = pyrf.ts_scalar( + generate_timeline(64.0, 100), generate_data(100, tensor_order=0) + ) + self.assertListEqual(list(result.dims), ["time"]) + + def test_ts_scalar_meta(self): + result = pyrf.ts_scalar( + generate_timeline(64.0, 100), generate_data(100, tensor_order=0) + ) + self.assertEqual(result.attrs["TENSOR_ORDER"], 0) + + +@ddt +class TsSpectrTestCase(unittest.TestCase): + @data( + (0, np.random.random(10), np.random.random((100, 10)), "energy", None), + (generate_timeline(64.0, 100), 0, np.random.random((100, 10)), "energy", None), + (generate_timeline(64.0, 100), 0, np.random.random((100, 10)), "energy", None), + ( + generate_timeline(64.0, 100), + np.random.random(10), + np.random.random((98, 10)), + "energy", + None, + ), + ( + generate_timeline(64.0, 100), + np.random.random(10), + np.random.random((100, 9)), + "energy", + None, + ), + ) + @unpack + def test_ts_spectr_input(self, time, ener, data, comp_name, attrs): + with self.assertRaises(AssertionError): + pyrf.ts_spectr(time, ener, data, comp_name, attrs) + + def test_ts_spectr_output(self): + result = pyrf.ts_spectr( + generate_timeline(64.0, 100), + np.random.random(10), + np.random.random((100, 10)), + ) + self.assertIsInstance(result, xr.DataArray) + + +class TsVecXYZTestCase(unittest.TestCase): + def test_ts_vec_xyz_input_type(self): + with self.assertRaises(AssertionError): + pyrf.ts_vec_xyz(0, 0) + pyrf.ts_vec_xyz( + list(generate_timeline(64.0, 100)), + list(generate_data(100, tensor_order=1)), + ) + + def test_ts_vec_xyz_input_shape(self): + with self.assertRaises(AssertionError): + # Raises error if data and timeline don't have the same size + pyrf.ts_vec_xyz( + generate_timeline(64.0, 100), generate_data(99, tensor_order=1) + ) + # Raises error if vector as input + pyrf.ts_vec_xyz( + generate_timeline(64.0, 100), generate_data(100, tensor_order=0) + ) + # Raises error if tensor as input + pyrf.ts_vec_xyz( + generate_timeline(64.0, 100), generate_data(100, tensor_order=2) + ) + + def test_ts_vec_xyz_output_type(self): + result = pyrf.ts_vec_xyz( + generate_timeline(64.0, 100), generate_data(100, tensor_order=1) + ) + self.assertIsInstance(result, xr.DataArray) + + def test_ts_vec_xyz_output_shape(self): + result = pyrf.ts_vec_xyz( + generate_timeline(64.0, 100), generate_data(100, tensor_order=1) + ) + self.assertEqual(result.ndim, 2) + self.assertEqual(result.shape[0], 100) + self.assertEqual(result.shape[1], 3) + + def test_ts_vec_xyz_dims(self): + result = pyrf.ts_vec_xyz( + generate_timeline(64.0, 100), generate_data(100, tensor_order=1) + ) + self.assertListEqual(list(result.dims), ["time", "comp"]) + + def test_ts_vec_xyz_meta(self): + result = pyrf.ts_vec_xyz( + generate_timeline(64.0, 100), generate_data(100, tensor_order=1) + ) + self.assertEqual(result.attrs["TENSOR_ORDER"], 1) + + +class TsTensorXYZTestCase(unittest.TestCase): + def test_ts_tensor_xyz_input_type(self): + with self.assertRaises(AssertionError): + pyrf.ts_tensor_xyz(0, 0) + pyrf.ts_tensor_xyz( + list(generate_timeline(64.0, 100)), + list(generate_data(100, tensor_order=2)), + ) + + def test_ts_tensor_xyz_input_shape(self): + with self.assertRaises(AssertionError): + # Raises error if data and timeline don't have the same size + pyrf.ts_tensor_xyz( + generate_timeline(64.0, 100), generate_data(99, tensor_order=2) + ) + # Raises error if vector as input + pyrf.ts_tensor_xyz( + generate_timeline(64.0, 100), generate_data(100, tensor_order=0) + ) + # Raises error if tensor as input + pyrf.ts_tensor_xyz( + generate_timeline(64.0, 100), generate_data(100, tensor_order=1) + ) + + def test_ts_tensor_xyz_output_type(self): + result = pyrf.ts_tensor_xyz( + generate_timeline(64.0, 100), generate_data(100, tensor_order=2) + ) + self.assertIsInstance(result, xr.DataArray) + + def test_ts_tensor_xyz_output_shape(self): + result = pyrf.ts_tensor_xyz( + generate_timeline(64.0, 100), generate_data(100, tensor_order=2) + ) + self.assertEqual(result.ndim, 3) + self.assertEqual(result.shape[0], 100) + self.assertEqual(result.shape[1], 3) + self.assertEqual(result.shape[2], 3) + + def test_ts_tensor_xyz_dims(self): + result = pyrf.ts_tensor_xyz( + generate_timeline(64.0, 100), generate_data(100, tensor_order=2) + ) + self.assertListEqual(list(result.dims), ["time", "comp_h", "comp_v"]) + + def test_ts_tensor_xyz_meta(self): + result = pyrf.ts_tensor_xyz( + generate_timeline(64.0, 100), generate_data(100, tensor_order=2) + ) + self.assertEqual(result.attrs["TENSOR_ORDER"], 2) + + +@ddt +class Ttns2Datetime64TestCase(unittest.TestCase): + @data( + int(random.random() * 1e12), + [int(random.random() * 1e12), int(random.random() * 1e12)], + np.array([int(random.random() * 1e12), int(random.random() * 1e12)]), + ) + def test_ttns2datetime64_output(self, value): + result = pyrf.ttns2datetime64(value) + self.assertIsInstance(result, np.ndarray) + + +@ddt +class WaveletTestCase(unittest.TestCase): + @data( + (generate_ts(64.0, 100, tensor_order=2), {}), + (generate_ts(64, 100, tensor_order=1), {"linear": [random.randint(10, 100)]}), + ) + @unpack + def test_wavelet_input(self, inp, options): + with self.assertRaises(TypeError): + pyrf.wavelet(inp, **options) + + @data( + (generate_ts(64, 100, tensor_order=0), {}), + (generate_ts(64, 99, tensor_order=0), {}), + (generate_ts(64, 100, tensor_order=1), {}), + (generate_ts(64, 100, tensor_order=1), {"linear": True}), + (generate_ts(64, 100, tensor_order=1), {"linear": random.randint(10, 100)}), + ( + generate_ts(64, 100, tensor_order=1), + {"linear": random.randint(10, 100), "return_power": False}, + ), + ) + @unpack + def test_wavelet_output(self, inp, options): + self.assertIsNotNone(pyrf.wavelet(inp, **options)) + + @data( + ( + np.random.random((100, 1)), + np.random.random((1, 200)), + random.random(), + np.random.random((100, 1)), + random.randint(16, 96), + ) + ) + @unpack + def test_ww(self, s_ww, scales_mat, sigma, frequencies_mat, f_nyq): + self.assertIsNotNone( + _ww.__wrapped__(s_ww, scales_mat, sigma, frequencies_mat, f_nyq) + ) + + @data( + ( + np.random.random((100, 3)) + np.random.random((100, 3)) * 1j, + np.random.random((100, 3)), + ) + ) + @unpack + def test_power_r(self, power, new_freq_mat): + self.assertIsNotNone(_power_r.__wrapped__(power, new_freq_mat)) + + @data( + ( + np.random.random((100, 3)) + np.random.random((100, 3)) * 1j, + np.random.random((100, 3)), + ) + ) + @unpack + def test_power_c(self, power, new_freq_mat): + self.assertIsNotNone(_power_c.__wrapped__(power, new_freq_mat)) + + +@ddt +class VhtTestCase(unittest.TestCase): + @data( + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + True, + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 103, tensor_order=1), + True, + ), + ( + generate_ts(64.0, 100, tensor_order=1), + generate_ts(64.0, 100, tensor_order=1), + False, + ), + ) + @unpack + def test_vht_output(self, e, b, no_ez): + result = pyrf.vht(e, b, no_ez) + + self.assertIsInstance(result[0], np.ndarray) + self.assertIsInstance(result[1], xr.DataArray) + self.assertIsInstance(result[2], np.ndarray) if __name__ == "__main__": diff --git a/pyrfu/tests/test_solo.py b/pyrfu/tests/test_solo.py new file mode 100644 index 00000000..9e9f645f --- /dev/null +++ b/pyrfu/tests/test_solo.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Built-in imports +import os +import random +import unittest + +# 3rd party imports +import numpy as np +from ddt import data, ddt, unpack + +# Local imports +from .. import solo + +__author__ = "Louis Richard" +__email__ = "louisr@irfu.se" +__copyright__ = "Copyright 2020-2023" +__license__ = "MIT" +__version__ = "2.4.4" +__status__ = "Prototype" + + +class DbInitTestCase(unittest.TestCase): + def test_db_init_inpput(self): + with self.assertRaises(AssertionError): + solo.db_init("/Volumes/solo/remote/data") + + def test_db_init_output(self): + self.assertIsNone(solo.db_init(os.getcwd())) + + +@ddt +class ReadLFRDensityTestCase(unittest.TestCase): + @data( + ([], ".", False), + ([np.datetime64("2023-01-01T00:00:00"), "2023-01-01T00:10:00"], ".", False), + (["2023-01-01T00:00:00", np.datetime64("2023-01-01T00:10:00")], ".", False), + (["2023-01-01T00:00:00", "2023-01-01T00:10:00"], "/bazinga", False), + (["2023-01-01T00:00:00", "2023-01-01T00:10:00"], ".", "i am groot"), + ) + @unpack + def test_read_lfr_density_input(self, tint, data_path, tree): + with self.assertRaises(AssertionError): + solo.read_lfr_density(tint, data_path, tree) + + def test_read_lfr_density_output(self): + tint = ["2023-01-01T00:00:00", "2023-01-01T00:10:00"] + self.assertIsNone(solo.read_lfr_density(tint)) + + +@ddt +class ReadTNRTestCase(unittest.TestCase): + @data( + ([], 1, "."), + ([np.datetime64("2023-01-01T00:00:00"), "2023-01-01T00:10:00"], 1, "."), + (["2023-01-01T00:00:00", np.datetime64("2023-01-01T00:10:00")], 1, "."), + (["2023-01-01T00:00:00", "2023-01-01T00:10:00"], random.random(), "."), + (["2023-01-01T00:00:00", "2023-01-01T00:10:00"], 1, "/bazinga"), + ) + @unpack + def test_read_tnr_input(self, tint, sensor, data_path): + with self.assertRaises(AssertionError): + solo.read_tnr(tint, sensor, data_path) + + @data( + (["2023-01-01T00:00:00", "2023-01-01T00:10:00"], 1, ""), + (["2023-01-01T00:00:00", "2023-01-01T00:10:00"], 2, ""), + ) + @unpack + def test_read_tnr_output(self, tint, sensor, data_path): + self.assertIsNone(solo.read_tnr(tint, sensor, data_path)) + + +if __name__ == "__main__": + unittest.main() diff --git a/requirements.txt b/requirements.txt index 24cd585d..10d1f4e7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,14 +1,15 @@ -# cat requirements.txt -cdflib>=0.4.7 -geopack>=1.0.9 -matplotlib>=3.5.2 -numba>=0.54.1 -numpy>=1.20.3 -pandas>=1.3.4 -python-dateutil>=2.8.2 -requests>=2.26.0 -scipy>=1.7.3 -Sphinx>=4.3.0 -tqdm>=4.62.3 -xarray>=0.20.1 - +boto3>=1.34.0 +botocore>=1.34.0 +cdflib>=1.2.0 +geopack>=1.0.10 +keyring>=24.2.0 +matplotlib>=3.8.0 +numba>=0.59.0 +numpy>=1.22 +pandas>=2.2.0 +pycdfpp>=0.6.0 +python-dateutil>=2.9.0 +requests>=2.31.0 +scipy>=1.11.2 +tqdm>=4.66.2 +xarray>=2024.1.0 diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index a5bdf2fa..00000000 --- a/setup.cfg +++ /dev/null @@ -1,79 +0,0 @@ -[metadata] -name = pyrfu -version = attr: pyrfu.__version__ -url = https://pypi.org/project/pyrfu -author = Louis Richard -author_email = louisr@irfu.se -description = Python Space Physics (RymdFysik) Utilities -long_description = file: README.rst -license = MIT -classifiers = - Development Status :: 2 - Pre-Alpha - Environment :: Other Environment - Intended Audience :: Science/Research - License :: OSI Approved :: MIT License - Natural Language :: English - Operating System :: MacOS - Operating System :: MacOS :: MacOS X - Operating System :: Microsoft - Operating System :: Microsoft :: MS-DOS - Operating System :: Microsoft :: Windows - Programming Language :: Python - Programming Language :: Python :: 3 - Programming Language :: Python :: 3 :: Only - Programming Language :: Python :: 3.7 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 - Topic :: Scientific/Engineering - Topic :: Scientific/Engineering :: Physics -project_urls = - Documentation = https://pyrfu.readthedocs.io/en/latest/ - Release notes = https://pypi.org/project/pyrfu/#history - Source = https://github.com/louis-richard/irfu-python - -[options] -# NOTE: Can not use "file: requirements.txt" since -# (1) the "file:" syntax is not permitted for "install_requires", and -# (2) using the "file:" syntax would violate the intent of install_requires -# and requirements.txt. -install_requires = - cdflib - geopack - matplotlib - numba - numpy - pandas - python-dateutil - requests - scipy - Sphinx - tqdm - xarray - -python_requires = >=3.7 -packages = find: -# When the django-admin.py deprecation ends, remove "scripts". -include_package_data = true - - -[pylint] -disable = C0114, C0303, E1101, R0913, R0914, R0915 -# C0114 : missing-module-docstring -# E1101 : no-member -# R0913 : Too many arguments -# R0914 : Too many local variables -# R0915 : too-many-statements -# W0632 : unbalanced-tuple-unpacking -good-names = x, i, j, k -extension-pkg-whitelist = numpy, matplotlib.cm -ignored-modules = numpy, matplotlib.cm -ignored-classes = numpy, matplotlib.cm -ignore = tests - -[build_sphinx] -source-dir = docs/source -build-dir = docs/build -all_files = 1 - -[upload_sphinx] -upload-dir = docs/build/html \ No newline at end of file diff --git a/setup.py b/setup.py deleted file mode 100644 index 7eb9c4a7..00000000 --- a/setup.py +++ /dev/null @@ -1,77 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -# Built-in imports -import pathlib - -from setuptools import setup, find_packages - -# 3rd party imports -from sphinx.setup_command import BuildDoc - -__author__ = "Louis Richard" -__email__ = "louisr@irfu.se" -__copyright__ = "Copyright 2020-2023" -__license__ = "MIT" -__version__ = "2.3.27" -__status__ = "Prototype" - -HERE = pathlib.Path(__file__).parent - -VERSION = __version__ -PACKAGE_NAME = "pyrfu" -AUTHOR = "Louis RICHARD" -AUTHOR_EMAIL = "louir@irfu.se" -URL = "https://github.com/louis-richard/irfu-python" - -LICENSE = "MIT License" -DESCRIPTION = "Python Space Physics (RymdFysik) Utilities" - -with open("README.rst", "r") as fh: - LONG_DESCRIPTION = fh.read() - - -INSTALL_REQUIRES = [ - "cdflib", - "geopack", - "matplotlib", - "numba", - "numpy", - "pandas", - "python-dateutil", - "requests", - "scipy", - "Sphinx", - "tqdm", - "xarray", -] - -PYTHON_REQUIRES = ">=3.7" - - -cmdclass = {"build_sphinx": BuildDoc} - -setup( - name=PACKAGE_NAME, - version=VERSION, - description=DESCRIPTION, - long_description=LONG_DESCRIPTION, - author=AUTHOR, - license=LICENSE, - author_email=AUTHOR_EMAIL, - url=URL, - install_requires=INSTALL_REQUIRES, - python_requires=PYTHON_REQUIRES, - packages=find_packages(), - include_package_data=True, - cmdclass=cmdclass, - # these are optional and override conf.py settings - command_options={ - "build_sphinx": { - "project": ("setup.py", PACKAGE_NAME), - "version": ("setup.py", VERSION), - "release": ("setup.py", VERSION), - "source_dir": ("setup.py", "docs"), - } - }, -)