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Scisco
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Merge pull request #112 from developmentseed/develop
version 0.8.0 release
2 parents 5dfbaf8 + 51b7ebe commit 6353cbd

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.travis.yml

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language: python
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sudo: false
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cache:
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directories:
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- ~/.cache/pip
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env:
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global:
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- PIP_WHEEL_DIR=$HOME/.cache/pip/wheels
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- PIP_FIND_LINKS=file://$HOME/.cache/pip/wheels
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virtualenv:
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system_site_packages: true
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addons:
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apt:
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packages:
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- libgdal1h
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- gdal-bin
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- libgdal-dev
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- libatlas-dev
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- libatlas-base-dev
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- gfortran
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- python-numpy
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- python-scipy
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python:
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- '2.7'
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before_install:
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- sudo add-apt-repository -y ppa:ubuntugis/ppa
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- sudo apt-get update -qq
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- sudo apt-get install libgdal1h gdal-bin libgdal-dev libcurl4-gnutls-dev
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- wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
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- chmod +x miniconda.sh
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- "./miniconda.sh -b"
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- export PATH=/home/travis/miniconda/bin:$PATH
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- conda update --yes conda
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- sudo rm -rf /dev/shm
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- sudo ln -s /run/shm /dev/shm
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- pip install -U pip
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- pip install wheel
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install:
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- conda install --yes python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib scikit-image
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six nose dateutil
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- conda install --yes -c https://conda.binstar.org/osgeo gdal
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- pip install --install-option="--no-cython-compile" cython
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- pip uninstall requests --yes
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- pip install requests==2.5.3
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- pip install -r requirements/travis.txt
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- "pip wheel -r requirements/dev.txt"
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- "pip install -r requirements/dev.txt"
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script:
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- nosetests
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deploy:
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provider: pypi
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user: devseed
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tags: true
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repo: developmentseed/landsat-util
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branch: master
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after_deploy:
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if [ "$TRAVIS_BRANCH" == "master" ]; then
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echo "Start Docker Hub Push"
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VER=$(python -c "import landsat; print landsat.__version__")
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docker build . -t developmentseed/landsat-util:$VER
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docker login -e ${DOCKER_EMAIL} -u ${DOCKER_USER} -p ${DOCKER_PASSWORD}
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docker push developmentseed/landsat-util:$VER
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fi

CHANGES.txt

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Changes
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=======
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0.8.0 (2015-09-22)
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------------------
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- Improved docs
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- Add `--ndvi` flag
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- Handle downloading new bands (10, 11, QA)
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- Improved color correction
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- Remove noise in pansharpened image processing
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0.7.0 (2015-05-29)
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------------------
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- New documentation

README.rst

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To run the documentation locally::
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$ pip install -r requirements/dev.txt
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$ cd docs
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$ make html
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+++++++++
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See `CHANGES.txt <CHANGES.rst>`_.
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docs/installation.rst

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$: pip install landsat-util
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Ubuntu 14.10
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Ubuntu 14.04
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++++++++++++
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Use pip to install landsat-util. If you are not using virtualenv, you might have to run ``pip`` as ``sudo``::

docs/notes.rst

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- Image processing is a very heavy and resource consuming task. Each process takes about 5-10 mins. We recommend that you run the processes in smaller badges. Pansharpening, while increasing image resolution 2x, substantially increases processing time.
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- Landsat-util requires at least 2GB of Memory (RAM).
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- Make sure to read over the `section on returned products <overview.html#a-note-on-returned-products>`_ as it is different depending on scene acquisition date.

docs/overview.rst

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Process *and* pansharpen a downloaded image::
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$: landsat process path/to/LC80090452014008LGN00.tar.bz --pansharpen
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A note on returned products
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++++++++++++++++
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Scenes acquired after 2015 will be downloaded from `AWS Public Data Sets <http://aws.amazon.com/public-data-sets/landsat/>`_ while scenes acquired before 2015 will be downloaded from `Google Earth Engine <https://earthengine.google.org/>`_. AWS provides the bands separately and so landsat-util will also pass along the bands individually if requested. In the case of Google Earth Engine, only the full, compressed image bundle is available (including all bands and metadata) and will be downloaded no matter what bands are requested.

landsat/__init__.py

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__version__ = '0.7.0'
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__version__ = '0.8.0'

landsat/decorators.py

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import warnings
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import rasterio
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def rasterio_decorator(func):
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def wrapped_f(*args, **kwargs):
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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with rasterio.drivers():
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return func(*args, **kwargs)
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return wrapped_f

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