From 52bbc9d3965aabd39dc1f438ee163e5265cf522c Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 17:03:18 -0700 Subject: [PATCH 01/26] Organize doc env removed some unused things --- doc/environment.yml | 35 ++++++++++++++++++++++++----------- 1 file changed, 24 insertions(+), 11 deletions(-) diff --git a/doc/environment.yml b/doc/environment.yml index ea6e02ff..95efec8b 100644 --- a/doc/environment.yml +++ b/doc/environment.yml @@ -1,19 +1,32 @@ -name: xrft +name: xrft-doc dependencies: - - python=3.7 - - pandas - - xarray + - python=3.8 + # + # xrft - dask - - scipy - numpy - - pytest - - numpydoc - - sphinx + - pandas + - scipy + - xarray + # + # xrft extras + - cftime + # + # docs - ipython - - jupyter + - jupyterlab - matplotlib - - pandoc + - numpydoc + - sphinx + # + - pip - pip: - - docrep + # xrft itself + - '-e ../' + # + # xrft extras + - numpy_groupies + # + # docs - nbsphinx - sphinxcontrib-apidoc From c65f7eab63c09b6b24763520f65a1df17b38cf48 Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 17:12:28 -0700 Subject: [PATCH 02/26] Add sphinx-autobuild for docs work --- doc/environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/environment.yml b/doc/environment.yml index 95efec8b..c6ce58f6 100644 --- a/doc/environment.yml +++ b/doc/environment.yml @@ -29,4 +29,5 @@ dependencies: # # docs - nbsphinx + - sphinx-autobuild - sphinxcontrib-apidoc From 463e3aaa4a867b18ce8df926fe22252928a7ed52 Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 17:19:58 -0700 Subject: [PATCH 03/26] Fix extlinks `issue` --- .gitignore | 3 +++ doc/conf.py | 8 ++++++++ 2 files changed, 11 insertions(+) diff --git a/.gitignore b/.gitignore index c5dc8e96..5e2887a0 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +# Local docs build +doc/_build/ + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/doc/conf.py b/doc/conf.py index c7742a84..6ea512bd 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -101,6 +101,14 @@ todo_include_todos = False +# -- Extension configuration + +extlinks = { + "issue": ("https://github.com/xgcm/xrft/issues/%s", "GH#"), + "pull": ("https://github.com/xgcm/xrft/pull/%s", "PR#"), +} + + # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for From 4d1b6d95f72ca569c356934b67e735be3956040c Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 17:23:25 -0700 Subject: [PATCH 04/26] Remove unused sphinxcontrib-apidoc --- doc/conf.py | 2 -- doc/environment.yml | 1 - 2 files changed, 3 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index 6ea512bd..be782043 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -41,13 +41,11 @@ "sphinx.ext.extlinks", "sphinx.ext.viewcode", "sphinx.ext.intersphinx", - "sphinxcontrib.apidoc", "numpydoc", "nbsphinx", "IPython.sphinxext.ipython_console_highlighting", ] -# apidoc_module_dir = '../xrft' # never execute notebooks: avoids lots of expensive imports on rtd # https://nbsphinx.readthedocs.io/en/0.2.14/never-execute.html nbsphinx_execute = "never" diff --git a/doc/environment.yml b/doc/environment.yml index c6ce58f6..ee1c6cb4 100644 --- a/doc/environment.yml +++ b/doc/environment.yml @@ -30,4 +30,3 @@ dependencies: # docs - nbsphinx - sphinx-autobuild - - sphinxcontrib-apidoc From 0ee72bf80f98f3997ef53d2fccea1d3835822224 Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 17:25:07 -0700 Subject: [PATCH 05/26] RTD theme in conf --- doc/conf.py | 2 +- doc/environment.yml | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/conf.py b/doc/conf.py index be782043..5ba679bf 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -112,8 +112,8 @@ # 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 = 'alabaster' +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 diff --git a/doc/environment.yml b/doc/environment.yml index ee1c6cb4..0fc838de 100644 --- a/doc/environment.yml +++ b/doc/environment.yml @@ -30,3 +30,4 @@ dependencies: # docs - nbsphinx - sphinx-autobuild + - sphinx_rtd_theme From f3191e357b7d9883dc6f53fe3fa24ff0bba38067 Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 18:26:32 -0700 Subject: [PATCH 06/26] Fixes for current Sphinx warnings --- doc/conf.py | 2 +- doc/whats-new.rst | 2 +- xrft/padding.py | 12 ++++++++---- xrft/xrft.py | 5 +++-- 4 files changed, 13 insertions(+), 8 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index 5ba679bf..6a503c7f 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -124,7 +124,7 @@ # 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"] +# html_static_path = ["_static"] # -- Options for HTMLHelp output ------------------------------------------ diff --git a/doc/whats-new.rst b/doc/whats-new.rst index 8ab5644c..058acc2c 100644 --- a/doc/whats-new.rst +++ b/doc/whats-new.rst @@ -6,7 +6,7 @@ What's New .. _whats-new.0.3.0: v0.3.0 (18 February 2021) ----------------------- +------------------------- Enhancements ~~~~~~~~~~~~ diff --git a/xrft/padding.py b/xrft/padding.py index c4b4937b..837bbcfb 100644 --- a/xrft/padding.py +++ b/xrft/padding.py @@ -36,6 +36,7 @@ def pad( {dim: pad} is a shortcut for pad_before = pad_after = pad mode : str, default: "constant" One of the following string values (taken from numpy docs). + - constant: Pads with a constant value. - edge: Pads with the edge values of array. - linear_ramp: Pads with the linear ramp between end_value and the @@ -98,6 +99,10 @@ def pad( ------- da_padded : :class:`xarray.DataArray` + See Also + -------- + xrft.padding.unpad + Examples -------- @@ -300,17 +305,16 @@ def unpad(da, pad_width=None, **pad_width_kwargs): {dim: pad} is a shortcut for pad_before = pad_after = pad. If ``None``, then the *pad_width* for each coordinate is read from their ``pad_width`` attribute. - **pad_width_kwargs (optional) + **pad_width_kwargs : dict, optional The keyword arguments form of ``pad_width``. Pass ``pad_width`` or ``pad_width_kwargs``. - See also + See Also -------- - :func:`xrft.pad` + xrft.padding.pad Examples -------- - >>> import xarray as xr >>> da = xr.DataArray( ... [[1, 2, 3], [4, 5, 6], [7, 8, 9]], diff --git a/xrft/xrft.py b/xrft/xrft.py index fb7bc622..85a5ae92 100644 --- a/xrft/xrft.py +++ b/xrft/xrft.py @@ -675,9 +675,10 @@ def power_spectrum( Calculates the power spectrum of da. .. math:: - da' = da - \overline{da} + da' = da - \overline{da} + .. math:: - ps = \mathbb{F}(da') {\mathbb{F}(da')}^* + ps = \mathbb{F}(da') {\mathbb{F}(da')}^* Parameters ---------- From 6e6d5789be8634e39639243e9b56a7a9fb3a934c Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 18:52:54 -0700 Subject: [PATCH 07/26] xref; version; copyright --- doc/conf.py | 52 +++++++++++++++++++++++++++++++--------------------- 1 file changed, 31 insertions(+), 21 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index 6a503c7f..42028f6d 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -17,12 +17,15 @@ # 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 -import xrft - +# import os +# import sys +# # sys.path.insert(0, os.path.abspath('.')) +import datetime + +import xrft + # -- General configuration ------------------------------------------------ @@ -46,17 +49,11 @@ "IPython.sphinxext.ipython_console_highlighting", ] -# never execute notebooks: avoids lots of expensive imports on rtd -# https://nbsphinx.readthedocs.io/en/0.2.14/never-execute.html -nbsphinx_execute = "never" - # Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] +# templates_path = ["_templates"] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: -# -# source_suffix = ['.rst', '.md'] source_suffix = [".rst", ".md"] # The master toctree document. @@ -64,7 +61,7 @@ # General information about the project. project = "xrft" -copyright = "2018, xrft developers" +copyright = f"2018\u2013{datetime.datetime.now().year}, xrft developers" author = "xrft developers" # The version info for the project you're documenting, acts as replacement for @@ -72,9 +69,9 @@ # built documents. # # The short X.Y version. -version = "0.1" +version = ".".join(xrft.__version__.split(".")[:2]) # The full version, including alpha/beta/rc tags. -release = "0.1" +release = xrft.__version__.split("+")[0] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. @@ -99,13 +96,32 @@ todo_include_todos = False -# -- Extension configuration +# -- Extension configuration ---------------------------------------------- extlinks = { "issue": ("https://github.com/xgcm/xrft/issues/%s", "GH#"), "pull": ("https://github.com/xgcm/xrft/pull/%s", "PR#"), } +# never execute notebooks: avoids lots of expensive imports on rtd +# https://nbsphinx.readthedocs.io/en/0.2.14/never-execute.html +nbsphinx_execute = "never" + +intersphinx_mapping = { + "python": ("https://docs.python.org/3/", None), + "xarray": ("https://xarray.pydata.org/en/stable/", None), + "numpy": ("https://numpy.org/doc/stable", None), + "matplotlib": ("https://matplotlib.org/stable/", None), + "dask": ("https://docs.dask.org/en/latest", None), +} + +numpydoc_xref_param_type = True +numpydoc_xref_aliases = { + "DataArray": "xarray.DataArray", + "Dataset": "xarray.Dataset", + "scalar": ":term:`scalar`", +} + # -- Options for HTML output ---------------------------------------------- @@ -181,9 +197,3 @@ "Miscellaneous", ), ] - -# 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) -# } From 8bb2948801e6d51f92b399886e99d8ac50908ef4 Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 19:16:49 -0700 Subject: [PATCH 08/26] WIP: docstring work --- doc/conf.py | 1 + xrft/xrft.py | 64 ++++++++++++++++++++++++++++++++-------------------- 2 files changed, 41 insertions(+), 24 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index 42028f6d..514848c3 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -111,6 +111,7 @@ "python": ("https://docs.python.org/3/", None), "xarray": ("https://xarray.pydata.org/en/stable/", None), "numpy": ("https://numpy.org/doc/stable", None), + "scipy": ("https://docs.scipy.org/doc/scipy", None), "matplotlib": ("https://matplotlib.org/stable/", None), "dask": ("https://docs.dask.org/en/latest", None), } diff --git a/xrft/xrft.py b/xrft/xrft.py index 85a5ae92..0de4485c 100644 --- a/xrft/xrft.py +++ b/xrft/xrft.py @@ -778,7 +778,7 @@ def cross_spectrum( **kwargs, ): """ - Calculates the cross spectra of da1 and da2. + Calculates the cross spectra of `da1` and `da2`. .. math:: da1' = da1 - \overline{da1};\ \ da2' = da2 - \overline{da2} @@ -787,26 +787,39 @@ def cross_spectrum( Parameters ---------- - da1 : `xarray.DataArray` - The data to be transformed - da2 : `xarray.DataArray` - The data to be transformed + da1 : xarray.DataArray + The data to be transformed. + da2 : xarray.DataArray + The data to be transformed. dim : str or sequence of str, optional - The dimensions along which to take the transformation. If `None`, all + The dimensions along which to take the transformation. If ``None``, all dimensions will be transformed. real_dim : str, optional Real Fourier transform will be taken along this dimension. scaling : str, optional - If 'density', it will normalize the output to power spectral density - If 'spectrum', it will normalize the output to power spectrum - window_correction : boolean - If True, it will correct for the energy reduction resulting from applying a non-uniform window. - This is the default behaviour of many tools for computing power spectrum (e.g scipy.signal.welch and scipy.signal.periodogram). - If scaling = 'spectrum', correct the amplitude of peaks in the spectrum. This ensures, for example, that the peak in the one-sided power spectrum of a 10 Hz sine wave with RMS**2 = 10 has a magnitude of 10. - If scaling = 'density', correct for the energy (integral) of the spectrum. This ensures, for example, that the power spectral density integrates to the square of the RMS of the signal (ie that Parseval's theorem is satisfied). Note that in most cases, Parseval's theorem will only be approximately satisfied with this correction as it assumes that the signal being windowed is independent of the window. The correction becomes more accurate as the width of the window gets large in comparison with any noticeable period in the signal. - If False, the spectrum gives a representation of the power in the windowed signal. - Note that when True, Parseval's theorem may only be approximately satisfied. - kwargs : dict : see xrft.dft for argument list + If ``'density'``, it will normalize the output to power spectral density. + If ``'spectrum'``, it will normalize the output to power spectrum. + window_correction : boolean, optional, default: False + If ``True``, it will correct for the energy reduction resulting from applying a non-uniform window. + This is the default behaviour of many tools for computing power spectrum + (e.g., :func:`scipy.signal.welch` and :func:`scipy.signal.periodogram`). + + - If ``scaling='spectrum'``, correct the amplitude of peaks in the spectrum. + This ensures, for example, that the peak in the one-sided power spectrum + of a 10 Hz sine wave with RMS\ :sup:`2` = 10 has a magnitude of 10. + - If ``scaling='density'``, correct for the energy (integral) of the spectrum. + This ensures, for example, that the power spectral density integrates to the square + of the RMS of the signal (i.e., that Parseval's theorem is satisfied). + + Note that in most cases, Parseval's theorem will only be approximately satisfied with this correction + as it assumes that the signal being windowed is independent of the window. + The correction becomes more accurate as the width of the window gets large + in comparison with any noticeable period in the signal. + + If ``False``, the spectrum gives a representation of the power in the windowed signal. + Note that when ``window_correction=True``, Parseval's theorem may only be approximately satisfied. + kwargs : dict, optional + See :func:`fft` for argument list. """ if not true_phase: @@ -864,6 +877,7 @@ def cross_spectrum( "window_correction can only be applied when windowing is turned on." ) else: + # FIXME: `da` not defined windows, _ = _apply_window(da, dim, window_type=kwargs.get("window")) cs = cs / (windows ** 2).mean() fs = np.prod([float(cs[d].spacing) for d in updated_dims]) @@ -875,6 +889,7 @@ def cross_spectrum( "window_correction can only be applied when windowing is turned on." ) else: + # FIXME: `da` not defined windows, _ = _apply_window(da, dim, window_type=kwargs.get("window")) cs = cs / windows.mean() ** 2 fs = np.prod([float(cs[d].spacing) for d in updated_dims]) @@ -887,10 +902,10 @@ def cross_spectrum( def cross_phase(da1, da2, dim=None, true_phase=False, **kwargs): - """ - Calculates the cross-phase between da1 and da2. + r""" + Calculates the cross-phase between `da1` and `da2`. - Returned values are in [-pi, pi]. + Returned values are in :math:`[-\pi, -\pi]`. .. math:: da1' = da1 - \overline{da1};\ \ da2' = da2 - \overline{da2} @@ -899,11 +914,12 @@ def cross_phase(da1, da2, dim=None, true_phase=False, **kwargs): Parameters ---------- - da1 : `xarray.DataArray` - The data to be transformed - da2 : `xarray.DataArray` - The data to be transformed - kwargs : dict : see xrft.dft for argument list + da1 : xarray.DataArray + The data to be transformed. + da2 : xarray.DataArray + The data to be transformed. + kwargs : dict, optional + See :func:`cross_spectrum` for argument list. """ if not true_phase: msg = ( From 989b335749067b077b2b7273ccad0cfbd6549814 Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 19:53:41 -0700 Subject: [PATCH 09/26] xrft.fft docstring --- xrft/xrft.py | 61 ++++++++++++++++++++++++++++++---------------------- 1 file changed, 35 insertions(+), 26 deletions(-) diff --git a/xrft/xrft.py b/xrft/xrft.py index 0de4485c..e0223ce3 100644 --- a/xrft/xrft.py +++ b/xrft/xrft.py @@ -241,7 +241,7 @@ def dft( da, dim=None, true_phase=False, true_amplitude=False, **kwargs ): # pragma: no cover """ - Deprecated function. See fft doc + Deprecated function. See :func:`fft`. """ msg = ( "This function has been renamed and will disappear in the future." @@ -271,6 +271,7 @@ def idft( def fft( da, + *, spacing_tol=1e-3, dim=None, real_dim=None, @@ -283,8 +284,8 @@ def fft( prefix="freq_", **kwargs, ): - """ - Perform discrete Fourier transform of xarray data-array `da` along the + r""" + Perform discrete Fourier transform of :class:`xarray.DataArray` `da` along the specified dimensions. .. math:: @@ -292,44 +293,52 @@ def fft( Parameters ---------- - da : `xarray.DataArray` - The data to be transformed - spacing_tol: float, optional + da : xarray.DataArray + The data to be transformed. + spacing_tol : float, optional Spacing tolerance. Fourier transform should not be applied to uneven grid but this restriction can be relaxed with this setting. Use caution. dim : str or sequence of str, optional - The dimensions along which to take the transformation. If `None`, all - dimensions will be transformed. If the inputs are dask arrays, the + The dimensions along which to take the transformation. If ``None``, all + dimensions will be transformed. If the inputs are Dask arrays, the arrays must not be chunked along these dimensions. real_dim : str, optional Real Fourier transform will be taken along this dimension. - shift : bool, default - Whether to shift the fft output. Default is `True`, unless `real_dim is not None`, - in which case shift will be set to False always. + shift : bool, default: True + Whether to shift the FFT output. + + .. note:: + If `real_dim` is not ``None``, `shift` will be set to ``False`` always. detrend : {None, 'constant', 'linear'} - If `constant`, the mean across the transform dimensions will be + If ``'constant'``, the mean across the transform dimensions will be subtracted before calculating the Fourier transform (FT). - If `linear`, the linear least-square fit will be subtracted before - the FT. For `linear`, only dims of length 1 and 2 are supported. + + If ``'linear'``, the linear least squares fit will be subtracted before + the FT. Only dims of length 1 and 2 are supported. + + Default (``None``): no detrending. window : str, optional Whether to apply a window to the data before the Fourier transform is taken. A window will be applied to all the dimensions in - dim. Please follow `scipy.signal.windows`' naming convention. - true_phase : bool, optional - If set to False, standard fft algorithm is applied on signal without consideration of coordinates. - If set to True, coordinates location are correctly taken into account to evaluate Fourier Tranforrm phase and - fftshift is applied on input signal prior to fft (fft algorithm intrinsically considers that input signal is on fftshifted grid). - true_amplitude : bool, optional - If set to True, output is multiplied by the spacing of the transformed variables to match theoretical FT amplitude. - If set to False, amplitude regularisation by spacing is not applied (as in numpy.fft) - chunks_to_segments : bool, optional - Whether the data is chunked along the axis to take FFT. + `dim`. Please follow :mod:`scipy.signal.windows`'s naming convention. + true_phase : bool, default: False + If ``False``, standard FFT algorithm is applied on signal without consideration of coordinates. + + If ``True``, coordinates' locations are correctly taken into account to evaluate Fourier Tranform phase and + ``fftshift`` is applied on input signal prior to FFT + (FFT algorithm intrinsically considers that input signal is on ``fftshift``\ed grid). + true_amplitude : bool, default: False + If ``True``, output is multiplied by the spacing of the transformed variables to match theoretical FT amplitude. + + If ``False``, amplitude regularisation by spacing is not applied (as in :mod:`numpy.fft`). + chunks_to_segments : bool, default: False + Whether chunks along the axis to take FFT should be stacked (Dask). prefix : str - The prefix for the new transformed dimensions. + For the new transformed dimensions. Returns ------- - daft : `xarray.DataArray` + daft : xarray.DataArray The output of the Fourier transformation, with appropriate dimensions. """ From 1374805a519b8fbf20d30c21a7913631e8ff868d Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 20:35:15 -0700 Subject: [PATCH 10/26] fit_loglog docstring --- doc/conf.py | 2 ++ xrft/xrft.py | 24 ++++++++++++------------ 2 files changed, 14 insertions(+), 12 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index 514848c3..53f04c7c 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -121,6 +121,8 @@ "DataArray": "xarray.DataArray", "Dataset": "xarray.Dataset", "scalar": ":term:`scalar`", + "ndarray": "numpy.ndarray", + "float64": "numpy.float64", } diff --git a/xrft/xrft.py b/xrft/xrft.py index e0223ce3..ad435ac3 100644 --- a/xrft/xrft.py +++ b/xrft/xrft.py @@ -1281,26 +1281,26 @@ def isotropic_cross_spectrum( def fit_loglog(x, y): """ - Fit a line to isotropic spectra in log-log space + Fit a line to isotropic spectra in log-log space. Parameters ---------- - x : `numpy.array` - Coordinate of the data - y : `numpy.array` - data + x : ndarray + Coordinate of the data. + y : ndarray + Data. Returns ------- - y_fit : `numpy.array` - The linear fit + y_fit : ndarray + The linear fit. a : float64 - Slope of the fit + Slope of the fit. b : float64 - Intercept of the fit + Intercept of the fit. """ - # fig log vs log p = np.polyfit(np.log2(x), np.log2(y), 1) - y_fit = 2 ** (np.log2(x) * p[0] + p[1]) + a, b = p + y_fit = 2 ** (np.log2(x) * a + b) - return y_fit, p[0], p[1] + return y_fit, a, b From 13de88925ccdb3719ff8c7c9a6b648376a2401da Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 21:15:51 -0700 Subject: [PATCH 11/26] xrft.ifft docstring --- xrft/xrft.py | 63 ++++++++++++++++++++++++++++++---------------------- 1 file changed, 36 insertions(+), 27 deletions(-) diff --git a/xrft/xrft.py b/xrft/xrft.py index ad435ac3..d8b899ce 100644 --- a/xrft/xrft.py +++ b/xrft/xrft.py @@ -257,7 +257,7 @@ def idft( daft, dim=None, true_phase=False, true_amplitude=False, **kwargs ): # pragma: no cover """ - Deprecated function. See ifft doc + Deprecated function. See :func:`ifft`. """ msg = ( "This function has been renamed and will disappear in the future." @@ -330,7 +330,7 @@ def fft( true_amplitude : bool, default: False If ``True``, output is multiplied by the spacing of the transformed variables to match theoretical FT amplitude. - If ``False``, amplitude regularisation by spacing is not applied (as in :mod:`numpy.fft`). + If ``False``, amplitude regularisation by spacing is not applied (as in :func:`numpy.fft.fft`). chunks_to_segments : bool, default: False Whether chunks along the axis to take FFT should be stacked (Dask). prefix : str @@ -490,7 +490,7 @@ def ifft( lag=None, **kwargs, ): - """ + r""" Perform inverse discrete Fourier transform of xarray data-array `daft` along the specified dimensions. @@ -499,40 +499,49 @@ def ifft( Parameters ---------- - daft : `xarray.DataArray` - The data to be transformed - spacing_tol: float, optional + daft : xarray.DataArray + The data to be transformed. + spacing_tol : float, optional Spacing tolerance. Fourier transform should not be applied to uneven grid but this restriction can be relaxed with this setting. Use caution. dim : str or sequence of str, optional - The dimensions along which to take the transformation. If `None`, all + The dimensions along which to take the transformation. If ``None``, all dimensions will be transformed. real_dim : str, optional Real Fourier transform will be taken along this dimension. - shift : bool, default - Whether to shift the fft output. Default is `True`. - chunks_to_segments : bool, optional - Whether the data is chunked along the axis to take FFT. - prefix : str - The prefix for the new transformed dimensions. - true_phase : bool, optional - If set to False, standard ifft algorithm is applied on signal without consideration of coordinates order. - If set to True, coordinates are correctly taken into account to evaluate Inverse Fourier Tranforrm phase and - fftshift is applied on input signal prior to ifft (ifft algorithm intrinsically considers that input signal is on fftshifted grid). - true_amplitude : bool, optional - If set to True, output is divided by the spacing of the transformed variables to match theoretical IFT amplitude. - If set to False, amplitude regularisation by spacing is not applied (as in numpy.ifft) - lag : None, float or sequence of float and/or None, optional - Output coordinates of transformed dimensions will be shifted by corresponding lag values and correct signal phasing will be preserved if true_phase is set to True. - If lag is None (default), 'direct_lag' attributes of each dimension is used (or set to zero if not found). - If defined, lag must have same length as dim. - If lag is a sequence, a None element means that 'direct_lag' attribute will be used for the corresponding dimension - Manually set lag to zero to get output coordinates centered on zero. + shift : bool, default: True + Whether to shift the FFT output. + true_phase : bool, default: False + If ``False``, standard IFFT algorithm is applied on signal without consideration of coordinates order. + If ``False``, coordinates are correctly taken into account to evaluate Inverse Fourier Tranform phase and + ``fftshift`` is applied on input signal prior to IFFT + (IFFT algorithm intrinsically considers that input signal is on ``fftshift``\ed grid). + true_amplitude : bool, default: False + If ``True``, output is divided by the spacing of the transformed variables to match theoretical IFT amplitude. + + If ``False``, amplitude regularisation by spacing is not applied (as in :func:`numpy.fft.ifft`). + lag : None, float, or sequence of (float or None), optional + Output coordinates of transformed dimensions will be shifted by corresponding lag values + and correct signal phasing will be preserved if `true_phase` is set to ``True``. + + If `lag` is ``None`` (default), the ``'direct_lag'`` attribute of each dimension is used + (or set to zero if not found). + + If defined, `lag` must have same length as `dim`. + + If `lag` is a sequence, a ``None`` element means that the ``'direct_lag'`` attribute will be used + for the corresponding dimension. + + Manually set `lag` to zero to get output coordinates centered on zero. + chunks_to_segments : bool, default: False + Whether chunks along the axis to take FFT should be stacked (Dask). + prefix : str + For the new transformed dimensions. Returns ------- - da : `xarray.DataArray` + da : xarray.DataArray The output of the Inverse Fourier transformation, with appropriate dimensions. """ From 50aa3ee1c5e8b76f2825092fbfcd3628a94a1529 Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 21:58:54 -0700 Subject: [PATCH 12/26] isotropic docstrings --- xrft/xrft.py | 198 ++++++++++++++++++++++++++++++++------------------- 1 file changed, 124 insertions(+), 74 deletions(-) diff --git a/xrft/xrft.py b/xrft/xrft.py index d8b899ce..46f888d3 100644 --- a/xrft/xrft.py +++ b/xrft/xrft.py @@ -703,21 +703,34 @@ def power_spectrum( da : `xarray.DataArray` The data to be transformed dim : str or sequence of str, optional - The dimensions along which to take the transformation. If `None`, all + The dimensions along which to take the transformation. If ``None``, all dimensions will be transformed. real_dim : str, optional Real Fourier transform will be taken along this dimension. scaling : str, optional - If 'density', it will normalize the output to power spectral density - If 'spectrum', it will normalize the output to power spectrum - window_correction : boolean - If True, it will correct for the energy reduction resulting from applying a non-uniform window. - This is the default behaviour of many tools for computing power spectrum (e.g scipy.signal.welch and scipy.signal.periodogram). - If scaling = 'spectrum', correct the amplitude of peaks in the spectrum. This ensures, for example, that the peak in the one-sided power spectrum of a 10 Hz sine wave with RMS**2 = 10 has a magnitude of 10. - If scaling = 'density', correct for the energy (integral) of the spectrum. This ensures, for example, that the power spectral density integrates to the square of the RMS of the signal (ie that Parseval's theorem is satisfied). Note that in most cases, Parseval's theorem will only be approximately satisfied with this correction as it assumes that the signal being windowed is independent of the window. The correction becomes more accurate as the width of the window gets large in comparison with any noticeable period in the signal. - If False, the spectrum gives a representation of the power in the windowed signal. - Note that when True, Parseval's theorem may only be approximately satisfied. - kwargs : dict : see xrft.dft for argument list + If ``'density'``, it will normalize the output to power spectral density. + If ``'spectrum'``, it will normalize the output to power spectrum. + window_correction : boolean, optional, default: False + If ``True``, it will correct for the energy reduction resulting from applying a non-uniform window. + This is the default behaviour of many tools for computing power spectrum + (e.g., :func:`scipy.signal.welch` and :func:`scipy.signal.periodogram`). + + - If ``scaling='spectrum'``, correct the amplitude of peaks in the spectrum. + This ensures, for example, that the peak in the one-sided power spectrum + of a 10 Hz sine wave with RMS\ :sup:`2` = 10 has a magnitude of 10. + - If ``scaling='density'``, correct for the energy (integral) of the spectrum. + This ensures, for example, that the power spectral density integrates to the square + of the RMS of the signal (i.e., that Parseval's theorem is satisfied). + + Note that in most cases, Parseval's theorem will only be approximately satisfied with this correction + as it assumes that the signal being windowed is independent of the window. + The correction becomes more accurate as the width of the window gets large + in comparison with any noticeable period in the signal. + + If ``False``, the spectrum gives a representation of the power in the windowed signal. + Note that when ``window_correction=True``, Parseval's theorem may only be approximately satisfied. + **kwargs : dict, optional + See :func:`fft` for argument list. """ if "density" in kwargs: @@ -836,7 +849,7 @@ def cross_spectrum( If ``False``, the spectrum gives a representation of the power in the windowed signal. Note that when ``window_correction=True``, Parseval's theorem may only be approximately satisfied. - kwargs : dict, optional + **kwargs : dict, optional See :func:`fft` for argument list. """ @@ -1029,7 +1042,7 @@ def _groupby_bins_agg( def isotropize(ps, fftdim, nfactor=4, truncate=False): - """ + r""" Isotropize a 2D power spectrum or cross spectrum by taking an azimuthal average. @@ -1040,15 +1053,15 @@ def isotropize(ps, fftdim, nfactor=4, truncate=False): Parameters ---------- - ps : `xarray.DataArray` + ps : xarray.DataArray The power spectrum or cross spectrum to be isotropized. - fftdim : list - The fft dimensions overwhich the isotropization must be performed. - nfactor : int, optional + fftdim : sequence of str + The dimensions over which the isotropization should be performed. + nfactor : int, default: 4 Ratio of number of bins to take the azimuthal averaging with the - data size. Default is 4. - truncate : bool, optional - If True, the spectrum will be truncated for wavenumbers larger than + data size. + truncate : bool, default: False + If ``True``, the spectrum will be truncated for wavenumbers larger than the Nyquist wavenumber. """ @@ -1089,7 +1102,7 @@ def isotropize(ps, fftdim, nfactor=4, truncate=False): def isotropic_powerspectrum(*args, **kwargs): # pragma: no cover """ - Deprecated function. See isotropic_power_spectrum doc + Deprecated function. See :func:`isotropic_power_spectrum`. """ msg = ( "This function has been renamed and will disappear in the future." @@ -1112,52 +1125,70 @@ def isotropic_power_spectrum( truncate=False, **kwargs, ): - """ + r""" Calculates the isotropic spectrum from the two-dimensional power spectrum by taking the azimuthal average. .. math:: - \text{iso}_{ps} = k_r N^{-1} \sum_{N} |\mathbb{F}(da')|^2 + \text{iso}_{ps} = k_r N^{-1} \sum_{N} |\mathbb{F}(da')|^2 where :math:`N` is the number of azimuthal bins. - Note: the method is not lazy does trigger computations. + .. warning:: + The method is not lazy, it does trigger computations. Parameters ---------- - da : `xarray.DataArray` - The data to be transformed - spacing_tol: float, optional + da : xarray.DataArray + The data to be transformed. + spacing_tol : float, optional Spacing tolerance. Fourier transform should not be applied to uneven grid but this restriction can be relaxed with this setting. Use caution. - dim : list, optional - The dimensions along which to take the transformation. If `None`, all - dimensions will be transformed. - shift : bool, optional - Whether to shift the fft output. - detrend : str, optional - If `constant`, the mean across the transform dimensions will be + dim : str or sequence of str, optional + The dimensions along which to take the transformation. If ``None``, all + dimensions will be transformed. If the inputs are Dask arrays, the + arrays must not be chunked along these dimensions. + shift : bool, default: True + Whether to shift the FFT output. + + .. note:: + If `real_dim` is not ``None``, `shift` will be set to ``False`` always. + detrend : {None, 'constant', 'linear'} + If ``'constant'``, the mean across the transform dimensions will be subtracted before calculating the Fourier transform (FT). - If `linear`, the linear least-square fit will be subtracted before - the FT. - density : list, optional - If true, it will normalize the spectrum to spectral density + + If ``'linear'``, the linear least squares fit will be subtracted before + the FT. Only dims of length 1 and 2 are supported. + + Default (``None``): no detrending. + scaling : str, optional + See :func:`power_spectrum`. window : str, optional Whether to apply a window to the data before the Fourier - transform is taken. Please adhere to scipy.signal.windows for naming convention. - nfactor : int, optional + transform is taken. A window will be applied to all the dimensions in + `dim`. Please follow :mod:`scipy.signal.windows`'s naming convention. + window_correction + See :func:`power_spectrum`. + + If true, it will normalize the spectrum to spectral density + nfactor : int, default: 4 Ratio of number of bins to take the azimuthal averaging with the - data size. Default is 4. - truncate : bool, optional - If True, the spectrum will be truncated for wavenumbers larger than + data size. + truncate : bool, default: False + If ``True``, the spectrum will be truncated for wavenumbers larger than the Nyquist wavenumber. + density : bool + If ``True``, normalize the spectrum to spectral density. + **kwargs : dict, optional + Passed on to :func:`power_spectrum`. Returns ------- - iso_ps : `xarray.DataArray` - Isotropic power spectrum + iso_ps : xarray.DataArray + Isotropic power spectrum. """ + # TODO: remove `density` from docstring and let `power_spectrum` handle this with its warning if "density" in kwargs: density = kwargs.pop("density") scaling = "density" if density else "false_density" @@ -1179,14 +1210,14 @@ def isotropic_power_spectrum( **kwargs, ) - fftdim = ["freq_" + d for d in dim] + fftdim = ["freq_" + d for d in dim] # FIXME: allow for non-default `prefix` return isotropize(ps, fftdim, nfactor=nfactor, truncate=truncate) def isotropic_crossspectrum(*args, **kwargs): # pragma: no cover """ - Deprecated function. See isotropic_cross_spectrum doc + Deprecated function. See :func:`isotropic_cross_spectrum`. """ msg = ( "This function has been renamed and will disappear in the future." @@ -1210,7 +1241,7 @@ def isotropic_cross_spectrum( truncate=False, **kwargs, ): - """ + r""" Calculates the isotropic spectrum from the two-dimensional power spectrum by taking the azimuthal average. @@ -1220,44 +1251,63 @@ def isotropic_cross_spectrum( where :math:`N` is the number of azimuthal bins. - Note: the method is not lazy does trigger computations. + .. warning:: + The method is not lazy, it does trigger computations. Parameters ---------- - da1 : `xarray.DataArray` - The data to be transformed - da2 : `xarray.DataArray` - The data to be transformed - spacing_tol: float (default) + da1 : xarray.DataArray + Data to be transformed. + da2 : xarray.DataArray + Data to be transformed. + + spacing_tol : float, optional Spacing tolerance. Fourier transform should not be applied to uneven grid but this restriction can be relaxed with this setting. Use caution. - dim : list (optional) - The dimensions along which to take the transformation. If `None`, all - dimensions will be transformed. - shift : bool (optional) - Whether to shift the fft output. - detrend : str (optional) - If `constant`, the mean across the transform dimensions will be + dim : str or sequence of str, optional + The dimensions along which to take the transformation. If ``None``, all + dimensions will be transformed. If the inputs are Dask arrays, the + arrays must not be chunked along these dimensions. + real_dim : str, optional + Real Fourier transform will be taken along this dimension. + shift : bool, default: True + Whether to shift the FFT output. + + .. note:: + If `real_dim` is not ``None``, `shift` will be set to ``False`` always. + detrend : {None, 'constant', 'linear'} + If ``'constant'``, the mean across the transform dimensions will be subtracted before calculating the Fourier transform (FT). - If `linear`, the linear least-square fit will be subtracted before - the FT. - density : list (optional) - If true, it will normalize the spectrum to spectral density - window : str (optional) + + If ``'linear'``, the linear least squares fit will be subtracted before + the FT. Only dims of length 1 and 2 are supported. + + Default (``None``): no detrending. + scaling : str, optional + See :func:`cross_spectrum`. + window : str, optional Whether to apply a window to the data before the Fourier - transform is taken. Please adhere to scipy.signal.windows for naming convention. - nfactor : int (optional) + transform is taken. A window will be applied to all the dimensions in + `dim`. Please follow :mod:`scipy.signal.windows`'s naming convention. + window_correction + See :func:`cross_spectrum`. + nfactor : int, default: 4 Ratio of number of bins to take the azimuthal averaging with the - data size. Default is 4. - truncate : bool, optional - If True, the spectrum will be truncated for wavenumbers larger than + data size. + truncate : bool, default: False + If ``True``, the spectrum will be truncated for wavenumbers larger than the Nyquist wavenumber. + density : bool + If ``True``, normalize the spectrum to spectral density. + **kwargs : dict, optional + Passed on to :func:`cross_spectrum`. Returns ------- - iso_cs : `xarray.DataArray` - Isotropic cross spectrum + iso_cs : xarray.DataArray + Isotropic cross spectrum. """ + # TODO: remove `density` from docstring and let `cross_spectrum` handle this with its warning if "density" in kwargs: density = kwargs.pop("density") scaling = "density" if density else "false_density" @@ -1283,7 +1333,7 @@ def isotropic_cross_spectrum( **kwargs, ) - fftdim = ["freq_" + d for d in dim] + fftdim = ["freq_" + d for d in dim] # FIXME: allow for non-default `prefix` return isotropize(cs, fftdim, nfactor=nfactor, truncate=truncate) From 6be4d89128a9bd5f9b8226536bf842c161d8f853 Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 22:09:47 -0700 Subject: [PATCH 13/26] detrend docstring --- xrft/detrend.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/xrft/detrend.py b/xrft/detrend.py index ce4dc4a8..aa639b98 100644 --- a/xrft/detrend.py +++ b/xrft/detrend.py @@ -16,14 +16,14 @@ def detrend(da, dim, detrend_type="constant"): ---------- da : xarray.DataArray The data to detrend - dim : str or list + dim : str or sequence of str, optional Dimensions along which to apply detrend. Can be either one dimension or a list with two dimensions. Higher-dimensional detrending is not supported. - If dask data are passed, the data must be chunked along dim. + If Dask data are passed, the array must be chunked along `dim`. detrend_type : {'constant', 'linear'} If ``constant``, a constant offset will be removed from each dim. - If ``linear``, a linear least-squares fit will be estimated and removed + If ``linear``, a linear least squares fit will be estimated and removed from the data. Returns @@ -33,8 +33,7 @@ def detrend(da, dim, detrend_type="constant"): Notes ----- - This function will act lazily in the presence of dask arrays on the - input. + This function will act lazily in the presence of Dask arrays in the input. """ if dim is None: @@ -46,7 +45,7 @@ def detrend(da, dim, detrend_type="constant"): if detrend_type not in ["constant", "linear", None]: raise NotImplementedError( "%s is not a valid detrending option. Valid " - "options are: 'constant','linear', or None." % detrend_type + "options are: 'constant', 'linear', or None." % detrend_type ) if detrend_type is None: From 60a9355c5fe7de2fc00c2057531c1a37d2d5a06d Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 22:25:28 -0700 Subject: [PATCH 14/26] padding docstrings some other things to fix, but would probably want to do so in xarray first, since mostly copied from there --- xrft/padding.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/xrft/padding.py b/xrft/padding.py index 837bbcfb..8662e633 100644 --- a/xrft/padding.py +++ b/xrft/padding.py @@ -1,5 +1,5 @@ """ -Functions to pad and unpad a N-dimensional regular grid +Functions to pad and unpad an *N*\-dimensional regular grid. """ import numpy as np from xarray.core.utils import either_dict_or_kwargs @@ -18,7 +18,7 @@ def pad( **pad_width_kwargs, ): """ - Pad array with evenly spaced coordinates + Pad array with evenly spaced coordinates. Wraps the :meth:`xarray.DataArray.pad` method but also pads the evenly spaced coordinates by extrapolation using the same coordinate spacing. @@ -27,15 +27,15 @@ def pad( Parameters ---------- - da : :class:`xarray.DataArray` + da : xarray.DataArray Array to be padded. The coordinates along which the array will be padded must be evenly spaced. pad_width : mapping of hashable to tuple of int Mapping with the form of {dim: (pad_before, pad_after)} describing the number of values padded along each dimension. {dim: pad} is a shortcut for pad_before = pad_after = pad - mode : str, default: "constant" - One of the following string values (taken from numpy docs). + mode : str, default: 'constant' + One of the following string values (taken from :func:`numpy.pad` doc). - constant: Pads with a constant value. - edge: Pads with the edge values of array. @@ -97,7 +97,7 @@ def pad( Returns ------- - da_padded : :class:`xarray.DataArray` + da_padded : xarray.DataArray See Also -------- @@ -284,21 +284,16 @@ def _pad_coordinates_callback(vector, iaxis_pad_width, iaxis, kwargs): def unpad(da, pad_width=None, **pad_width_kwargs): """ - Unpad an array and its coordinates + Unpad an array and its coordinates. Undo the padding process of the :func:`xrft.pad` function by slicing the - passed :class:`xarray.DataArray` and its coordinates. + passed :class:`xarray.DataArray` `da` and its coordinates. Parameters ---------- - da : :class:`xarray.DataArray` + da : xarray.DataArray Padded array. The coordinates along which the array will be padded must be evenly spaced. - - Returns - ------- - da_unpaded : :class:`xarray.DataArray` - Unpadded array. pad_width : mapping of hashable to tuple of int (optional) Mapping with the form of {dim: (pad_before, pad_after)} describing the number of values padded along each dimension. @@ -309,6 +304,11 @@ def unpad(da, pad_width=None, **pad_width_kwargs): The keyword arguments form of ``pad_width``. Pass ``pad_width`` or ``pad_width_kwargs``. + Returns + ------- + da_unpadded : xarray.DataArray + Unpadded array. + See Also -------- xrft.padding.pad From ef63ac9eb359f92928bb122fa6d33bbd9d4fc726 Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 22:58:20 -0700 Subject: [PATCH 15/26] autosummary tables on main API page --- .gitignore | 1 + doc/api.rst | 59 ++++++++++++++++++++++++++++++++++++++----------- doc/conf.py | 2 ++ xrft/detrend.py | 2 +- 4 files changed, 50 insertions(+), 14 deletions(-) diff --git a/.gitignore b/.gitignore index 5e2887a0..8ddb9bad 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,6 @@ # Local docs build doc/_build/ +doc/api/ # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/doc/api.rst b/doc/api.rst index 8005fb76..15ddabbb 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -4,7 +4,7 @@ API reference ############# -This page provides an auto-generated summary of xrft's API. For more details +This page provides a summary of xrft's top-level API. For more details and examples, refer to the relevant chapters in the main part of the documentation. @@ -15,26 +15,59 @@ documentation. missing data. It's the user's responsibility to ensure data are free of NaN or that NaNs have been filled somehow. -xrft -==== +FFT routines +============ -.. automodule:: xrft.xrft - :members: +.. autosummary:: + :toctree: api/ + + fft + ifft + power_spectrum + cross_spectrum + cross_phase + isotropize + isotropic_power_spectrum + isotropic_cross_spectrum + + +Deprecated names: + +.. autosummary:: + :toctree: api/ + + dft + idft + isotropic_powerspectrum + isotropic_crossspectrum -detrend -======= -You also may wish to use xrft's detrend function on its own. +Detrending +========== -.. automodule:: xrft.detrend - :members: +You may wish to use xrft's detrend function on its own. -padding +.. autosummary:: + :toctree: api/ + + detrend + +Padding ======= Pad and unpad arrays and its coordinates so they can be used for computing FFTs. -.. automodule:: xrft.padding - :members: +.. autosummary:: + :toctree: api/ + + pad + unpad + +Misc. +===== + +.. autosummary:: + :toctree: api/ + fit_loglog diff --git a/doc/conf.py b/doc/conf.py index 53f04c7c..1cfa26d9 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -125,6 +125,8 @@ "float64": "numpy.float64", } +autosummary_generate = True + # -- Options for HTML output ---------------------------------------------- diff --git a/xrft/detrend.py b/xrft/detrend.py index aa639b98..7b4e0f25 100644 --- a/xrft/detrend.py +++ b/xrft/detrend.py @@ -15,7 +15,7 @@ def detrend(da, dim, detrend_type="constant"): Parameters ---------- da : xarray.DataArray - The data to detrend + The data to detrend. dim : str or sequence of str, optional Dimensions along which to apply detrend. Can be either one dimension or a list with two dimensions. From a91ca876d3ff003217e8c9d733d3fd3021d69a49 Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 23:18:01 -0700 Subject: [PATCH 16/26] Edit docs pages --- doc/api.rst | 2 +- doc/conf.py | 1 + doc/environment.yml | 1 + doc/index.rst | 14 +++++++------- doc/installation.rst | 36 ++++++++++++++++++++++-------------- doc/whats-new.rst | 11 ++++++----- doc/why-xrft.rst | 16 ++++++++-------- 7 files changed, 46 insertions(+), 35 deletions(-) diff --git a/doc/api.rst b/doc/api.rst index 15ddabbb..c53b3f81 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -4,7 +4,7 @@ API reference ############# -This page provides a summary of xrft's top-level API. For more details +This page provides a summary of **xrft's top-level API**. For more details and examples, refer to the relevant chapters in the main part of the documentation. diff --git a/doc/conf.py b/doc/conf.py index 1cfa26d9..73b2b93d 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -47,6 +47,7 @@ "numpydoc", "nbsphinx", "IPython.sphinxext.ipython_console_highlighting", + "sphinx-prompt", ] # Add any paths that contain templates here, relative to this directory. diff --git a/doc/environment.yml b/doc/environment.yml index 0fc838de..74a7ab92 100644 --- a/doc/environment.yml +++ b/doc/environment.yml @@ -30,4 +30,5 @@ dependencies: # docs - nbsphinx - sphinx-autobuild + - sphinx-prompt - sphinx_rtd_theme diff --git a/doc/index.rst b/doc/index.rst index b3f32e30..51a15dbd 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -6,17 +6,17 @@ xrft: Fourier transforms for xarray data ============================================== **xrft** is a Python package for -taking the discrete Fourier transform (DFT) on xarray_ and dask_ arrays. +taking the discrete Fourier transform (DFT) on xarray_ and Dask_ arrays. It is: -- **Powerful**: It keeps the metadata and coordinates of the original xarray dataset and provides a clean work flow of DFT. -- **Easy-to-use**: It uses the native arguments of numpy FFT and provides a simple, high-level API. -- **Fast**: It uses the dask API of FFT and map_blocks to allow parallelization of DFT. +- **Powerful**: It keeps the metadata and coordinates of the original xarray dataset and provides a clean workflow of DFT. +- **Easy-to-use**: It uses the native arguments of NumPy FFT and provides a simple, high-level API. +- **Fast**: It uses the Dask API of FFT and ``map_blocks`` to allow parallelization of DFT. .. note:: xrft is at early stage of development and will keep improving in the future. - The discrete Fourier transform API should be quite stable, + The discrete Fourier transform API (:func:`xrft.fft`/:func:`xrft.ifft`) should be quite stable, but minor utilities could change in the next version. If you find any bugs or would like to request any enhancements, please `raise an issue on GitHub `_. @@ -50,5 +50,5 @@ Documentation api -.. _xarray: http://xarray.pydata.org -.. _dask: http://dask.pydata.org/en/latest/array-api.html +.. _xarray: https://xarray.pydata.org +.. _Dask: https://dask.pydata.org/en/latest/array-api.html diff --git a/doc/installation.rst b/doc/installation.rst index 1d56aad1..570e77b9 100644 --- a/doc/installation.rst +++ b/doc/installation.rst @@ -6,30 +6,38 @@ Installation The quickest way ---------------- -xrft is compatible both with Python 2 and 3. The major dependencies are xarray_ and dask_. -The best way to install them is using Anaconda_:: +xrft is compatible both with Python 2 and 3. The major dependencies are xarray_ and Dask_. +The best way to install them is using Anaconda_. - $ conda install -c conda-forge xarray dask xrft . +.. prompt:: bash -It is also possible to install from PyPI_ by:: + conda install -c conda-forge xarray dask xrft - $ pip install xrft . +It is also possible to install from PyPI_ by + +.. prompt:: bash + + pip install xrft Install xrft from GitHub repo ----------------------------- -To get the latest version:: +To get the latest version + +.. prompt:: bash + + git clone https://github.com/xgcm/xrft.git + cd xrft + python setup.py install . - $ git clone https://github.com/xgcm/xrft.git - $ cd xrft - $ python setup.py install . +Developers can track source code changes by -Developers can track source code changes by:: +.. prompt:: bash - $ git clone https://github.com/xgcm/xrft.git - $ cd xrft - $ python setup.py develop . + git clone https://github.com/xgcm/xrft.git + cd xrft + python setup.py develop . .. _xarray: http://xarray.pydata.org -.. _dask: http://dask.pydata.org/en/latest/ +.. _Dask: http://dask.pydata.org/en/latest/ .. _Anaconda: https://www.continuum.io/downloads .. _PyPI: https://pypi.org/ diff --git a/doc/whats-new.rst b/doc/whats-new.rst index 058acc2c..154ff9b7 100644 --- a/doc/whats-new.rst +++ b/doc/whats-new.rst @@ -11,14 +11,15 @@ v0.3.0 (18 February 2021) Enhancements ~~~~~~~~~~~~ -- Implemented the inverse discrete Fourier transform ``idft``. By `Frederic Nouguier `_ +- Implemented the inverse discrete Fourier transform ``idft``. By `Frederic Nouguier `_. -- Allowed windowing other than the Hann function. By `Takaya Uchida `_ +- Allowed windowing other than the Hann function. By `Takaya Uchida `_. - Allowed parallelization of isotropizing the spectrum via ``numpy_groupies``. - By `Takaya Uchida `_ + By `Takaya Uchida `_. -- Implemented proper amplitude correction for real Fourier transform and windowed data. By `Dougie Squire `_ +- Implemented proper amplitude correction for real Fourier transform and windowed data. + By `Dougie Squire `_. .. _whats-new.0.2.0: @@ -36,7 +37,7 @@ Enhancements By `Tom Nicholas `_. - Allowed ``isotropic_powerspectrum`` function to support arrays with up to four dimensions. (:issue:`9`) - By `Takaya Uchida `_ + By `Takaya Uchida `_. .. warning:: diff --git a/doc/why-xrft.rst b/doc/why-xrft.rst index 3e638416..97ff4509 100644 --- a/doc/why-xrft.rst +++ b/doc/why-xrft.rst @@ -5,21 +5,21 @@ For robustness and efficiency ----------------------------- In the field of Earth Science, we often take Fourier transforms of the variable of interest. -There has, however, not been an universal algorithm in which we calculate the transforms -and our aim is to stream line this process. +There has, however, not been a universal algorithm with which we calculate the transforms, +and our aim is to streamline this process. -We utilize the dask_ API to parallelize the computation to make it efficient for large data sets. +We utilize the Dask_ API to parallelize the computation to make it efficient for large data sets. For usability and simplicity ---------------------------- -The arguments in xrft rely on well-estabilished standards -(dask and numpy), so users don't need to learn a bunch of new syntaxes or even a new software stack. +The arguments in xrft rely on well-established standards +(Dask and NumPy), so users don't need to learn a bunch of new syntaxes or even a new software stack. -xrft can track the metadata in ``xarray.DataArray`` (:doc:`example <./MITgcm_example>`), -which makes it easy for large data sets. +xrft can track the metadata in :class:`xarray.DataArray`\s (:doc:`example <./MITgcm_example>`), +which makes it easy to use large data sets. The choice of Python and Anaconda also makes xrft :ref:`extremely easy to install `. -.. _dask: http://dask.pydata.org/en/latest/array-api.html +.. _Dask: http://dask.pydata.org/en/latest/array-api.html From dc238055e39615779c7c594d54aeed471474297a Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 23:21:14 -0700 Subject: [PATCH 17/26] codespell --- xrft/tests/test_padding.py | 2 +- xrft/xrft.py | 14 +++++++------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/xrft/tests/test_padding.py b/xrft/tests/test_padding.py index 3a279fae..9dda0edb 100644 --- a/xrft/tests/test_padding.py +++ b/xrft/tests/test_padding.py @@ -19,7 +19,7 @@ def sample_da_2d(): x = np.linspace(0, 10, 11) y = np.linspace(-4, 4, 17) z = np.arange(11 * 17, dtype=float).reshape(17, 11) - # Create one xr.DataArray for each coordiante and add spacing and + # Create one xr.DataArray for each coordinate and add spacing and # direct_lag attributes to them dx, dy = x[1] - x[0], y[1] - y[0] x = xr.DataArray( diff --git a/xrft/xrft.py b/xrft/xrft.py index 46f888d3..0c309842 100644 --- a/xrft/xrft.py +++ b/xrft/xrft.py @@ -324,7 +324,7 @@ def fft( true_phase : bool, default: False If ``False``, standard FFT algorithm is applied on signal without consideration of coordinates. - If ``True``, coordinates' locations are correctly taken into account to evaluate Fourier Tranform phase and + If ``True``, coordinates' locations are correctly taken into account to evaluate Fourier Transform phase and ``fftshift`` is applied on input signal prior to FFT (FFT algorithm intrinsically considers that input signal is on ``fftshift``\ed grid). true_amplitude : bool, default: False @@ -370,7 +370,7 @@ def fft( if chunks_to_segments: da = _stack_chunks(da, dim) - rawdims = da.dims # take care of segmented dimesions, if any + rawdims = da.dims # take care of segmented dimensions, if any if real_dim is not None: da = da.transpose( @@ -413,7 +413,7 @@ def fft( if not np.allclose(diff, diff[0], rtol=spacing_tol): raise ValueError( "Can't take Fourier transform because " - "coodinate %s is not evenly spaced" % d + "coordinate %s is not evenly spaced" % d ) if delta == 0.0: raise ValueError( @@ -514,7 +514,7 @@ def ifft( true_phase : bool, default: False If ``False``, standard IFFT algorithm is applied on signal without consideration of coordinates order. - If ``False``, coordinates are correctly taken into account to evaluate Inverse Fourier Tranform phase and + If ``False``, coordinates are correctly taken into account to evaluate Inverse Fourier Transform phase and ``fftshift`` is applied on input signal prior to IFFT (IFFT algorithm intrinsically considers that input signal is on ``fftshift``\ed grid). true_amplitude : bool, default: False @@ -631,7 +631,7 @@ def ifft( else: raise ValueError( "Can't take Fourier transform because " - "coodinate %s is not evenly spaced" % d + "coordinate %s is not evenly spaced" % d ) if np.abs(l) > spacing_tol: raise ValueError( @@ -856,7 +856,7 @@ def cross_spectrum( if not true_phase: msg = ( "true_phase flag will be set to True in future version of xrft.dft possibly impacting cross_spectrum output. " - + "Set explicitely true_phase = False in cross_spectrum arguments list to ensure future compatibility " + + "Set explicitly true_phase = False in cross_spectrum arguments list to ensure future compatibility " + "with numpy-like behavior where the coordinates are disregarded." ) warnings.warn(msg, FutureWarning) @@ -955,7 +955,7 @@ def cross_phase(da1, da2, dim=None, true_phase=False, **kwargs): if not true_phase: msg = ( "true_phase flag will be set to True in future version of xrft.dft possibly impacting cross_phase output. " - + "Set explicitely true_phase = False in cross_spectrum arguments list to ensure future compatibility " + + "Set explicitly true_phase = False in cross_spectrum arguments list to ensure future compatibility " + "with numpy-like behavior where the coordinates are disregarded." ) warnings.warn(msg, FutureWarning) From fc369765d34bf7857bbd7cff0ddedfb7bd4db3bc Mon Sep 17 00:00:00 2001 From: zmoon Date: Wed, 24 Nov 2021 23:29:01 -0700 Subject: [PATCH 18/26] update readme --- README.rst | 18 +++++++++--------- doc/index.rst | 2 +- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/README.rst b/README.rst index 8f80a732..6c19d66f 100644 --- a/README.rst +++ b/README.rst @@ -4,22 +4,22 @@ xrft: Fourier transforms for xarray data |pypi| |conda forge| |conda-forge| |Build Status| |codecov| |docs| |DOI| |license| |Code style| **xrft** is an open-source Python package for -taking the discrete Fourier transform (DFT) on xarray_ and dask_ arrays. +taking the discrete Fourier transform (DFT) on xarray_ and Dask_ arrays. .. _xarray: http://xarray.pydata.org/en/stable/ -.. _dask: https://dask.org +.. _Dask: https://dask.org It is: -- **Powerful**: It keeps the metadata and coordinates of the original xarray dataset and provides a clean work flow of DFT. -- **Easy-to-use**: It uses the native arguments of `numpy FFT`_ and provides a simple, high-level API. -- **Fast**: It uses the `dask API of FFT`_ and `map_blocks`_ to allow parallelization of DFT. +- **Powerful**: It keeps the metadata and coordinates of the original xarray dataset and provides a clean workflow of DFT. +- **Easy-to-use**: It uses the native arguments of `NumPy FFT`_ and provides a simple, high-level API. +- **Fast**: It uses the `Dask FFT API`_ and `map_blocks`_ to allow parallelization of DFT. -.. _numpy FFT: https://docs.scipy.org/doc/numpy/reference/routines.fft.html -.. _dask API of FFT: http://docs.dask.org/en/latest/array-api.html?highlight=fft#fast-fourier-transforms -.. _map_blocks: http://docs.dask.org/en/latest/array-api.html?highlight=map_blocks#dask.array.core.map_blocks +.. _NumPy FFT: https://docs.scipy.org/doc/numpy/reference/routines.fft.html +.. _Dask FFT API: https://docs.dask.org/en/latest/array-api.html#fast-fourier-transforms +.. _map_blocks: https://docs.dask.org/en/latest/generated/dask.array.map_blocks.html#dask.array.map_blocks -Please cite the `doi `_ if you find this +Please cite the `DOI `_ if you find this package useful in order to support its continuous development. Get in touch diff --git a/doc/index.rst b/doc/index.rst index 51a15dbd..fc01314c 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -11,7 +11,7 @@ It is: - **Powerful**: It keeps the metadata and coordinates of the original xarray dataset and provides a clean workflow of DFT. - **Easy-to-use**: It uses the native arguments of NumPy FFT and provides a simple, high-level API. -- **Fast**: It uses the Dask API of FFT and ``map_blocks`` to allow parallelization of DFT. +- **Fast**: It uses the Dask FFT API and ``map_blocks`` to allow parallelization of DFT. .. note:: From 79a2ba8ea110d8e6727732b417d931108e225ad2 Mon Sep 17 00:00:00 2001 From: zmoon Date: Thu, 25 Nov 2021 13:34:07 -0700 Subject: [PATCH 19/26] More concise deprecated fn docstrings --- xrft/xrft.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/xrft/xrft.py b/xrft/xrft.py index 0c309842..cd8c5275 100644 --- a/xrft/xrft.py +++ b/xrft/xrft.py @@ -241,7 +241,7 @@ def dft( da, dim=None, true_phase=False, true_amplitude=False, **kwargs ): # pragma: no cover """ - Deprecated function. See :func:`fft`. + Deprecated function; see :func:`fft`. """ msg = ( "This function has been renamed and will disappear in the future." @@ -257,7 +257,7 @@ def idft( daft, dim=None, true_phase=False, true_amplitude=False, **kwargs ): # pragma: no cover """ - Deprecated function. See :func:`ifft`. + Deprecated function; see :func:`ifft`. """ msg = ( "This function has been renamed and will disappear in the future." @@ -1102,7 +1102,7 @@ def isotropize(ps, fftdim, nfactor=4, truncate=False): def isotropic_powerspectrum(*args, **kwargs): # pragma: no cover """ - Deprecated function. See :func:`isotropic_power_spectrum`. + Deprecated function; see :func:`isotropic_power_spectrum`. """ msg = ( "This function has been renamed and will disappear in the future." @@ -1217,7 +1217,7 @@ def isotropic_power_spectrum( def isotropic_crossspectrum(*args, **kwargs): # pragma: no cover """ - Deprecated function. See :func:`isotropic_cross_spectrum`. + Deprecated function; see :func:`isotropic_cross_spectrum`. """ msg = ( "This function has been renamed and will disappear in the future." From 7d73dab3e3c38eeef7a7f74d5162240a4c04f416 Mon Sep 17 00:00:00 2001 From: zmoon Date: Thu, 25 Nov 2021 13:36:19 -0700 Subject: [PATCH 20/26] Tweak detrend docstring --- xrft/detrend.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/xrft/detrend.py b/xrft/detrend.py index 7b4e0f25..bbccc135 100644 --- a/xrft/detrend.py +++ b/xrft/detrend.py @@ -8,9 +8,9 @@ import scipy.linalg as spl -def detrend(da, dim, detrend_type="constant"): +def detrend(da, dim=None, detrend_type="constant"): """ - Detrend a DataArray + Detrend a :class:`~xarray.DataArray`. Parameters ---------- @@ -18,17 +18,20 @@ def detrend(da, dim, detrend_type="constant"): The data to detrend. dim : str or sequence of str, optional Dimensions along which to apply detrend. - Can be either one dimension or a list with two dimensions. - Higher-dimensional detrending is not supported. - If Dask data are passed, the array must be chunked along `dim`. + Default: :attr:`da.dims `. + + .. note:: + - Can be either **one** dimension or a list with **two** dimensions. + Higher-dimensional detrending is not supported. + - If Dask data are passed, the array must be chunked along `dim`. detrend_type : {'constant', 'linear'} - If ``constant``, a constant offset will be removed from each dim. - If ``linear``, a linear least squares fit will be estimated and removed + If ``'constant'``, a constant offset will be removed from each dim. + If ``'linear'``, a linear least squares fit will be estimated and removed from the data. Returns ------- - da : xarray.DataArray + da_dt : xarray.DataArray The detrended data. Notes From 52965d598923e94bf1f61866b7efe833ed07fff8 Mon Sep 17 00:00:00 2001 From: zmoon Date: Thu, 25 Nov 2021 14:53:53 -0700 Subject: [PATCH 21/26] chunk_example --- doc/chunk_example.ipynb | 5041 ++++++++++++++++++++++++++++++++++++--- xrft/xrft.py | 2 +- 2 files changed, 4756 insertions(+), 287 deletions(-) diff --git a/doc/chunk_example.ipynb b/doc/chunk_example.ipynb index c0168920..50c84d5f 100644 --- a/doc/chunk_example.ipynb +++ b/doc/chunk_example.ipynb @@ -6,13 +6,14 @@ "metadata": {}, "outputs": [], "source": [ + "import dask.array as dsar\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import numpy.testing as npt\n", "import xarray as xr\n", "import xrft\n", - "import dask.array as dsar\n", "from matplotlib import colors\n", - "import matplotlib.pyplot as plt\n", + "\n", "%matplotlib inline" ] }, @@ -31,32 +32,437 @@ "outputs": [ { "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "\n", + "array([ 0.0822994 , 0.08318687, 0.08423205, 0.08286732, 0.08314404,\n", + " 0.0833294 , 0.08355509, 0.08353916, 0.0832645 , 0.08389559,\n", + " 0.08328535, 0.08325178, 0.0832305 , 0.08298212, 0.08303852,\n", + " 0.08343363, 0.0825896 , 0.08329751, 0.08317598, 0.08358536,\n", + " 0.08369741, 0.0830625 , 0.08337486, 0.08417442, 0.08269168,\n", + " 0.08352632, 0.08301033, 0.08378805, 0.08328842, 0.08334037,\n", + " 0.08365589, 0.08328598, 16.08265403, 0.08328598, 0.08365589,\n", + " 0.08334037, 0.08328842, 0.08378805, 0.08301033, 0.08352632,\n", + " 0.08269168, 0.08417442, 0.08337486, 0.0830625 , 0.08369741,\n", + " 0.08358536, 0.08317598, 0.08329751, 0.0825896 , 0.08343363,\n", + " 0.08303852, 0.08298212, 0.0832305 , 0.08325178, 0.08328535,\n", + " 0.08389559, 0.0832645 , 0.08353916, 0.08355509, 0.0833294 ,\n", + " 0.08314404, 0.08286732, 0.08423205, 0.08318687])\n", + "Coordinates:\n", + " * freq_time (freq_time) float64 -0.5 -0.4844 -0.4688 ... 0.4531 0.4688 0.4844" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ps = ps.mean(['time_segment', 'y', 'x']).compute()\n", + "ps" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "ax.semilogx(ps.freq_time[n//8 + 1:], ps[n//8 + 1:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Two dimension\n", + "### Discrete Fourier Transform" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (time: 256, y: 128, x: 128)>\n",
+       "dask.array<xarray-<this-array>, shape=(256, 128, 128), dtype=float64, chunksize=(256, 32, 32), chunktype=numpy.ndarray>\n",
+       "Dimensions without coordinates: time, y, x
" + ], "text/plain": [ - "\n", - "dask.array\n", - "Coordinates:\n", - " * freq_time (freq_time) float64 -0.5 -0.4844 -0.4688 -0.4531 ...\n", - " freq_time_spacing float64 0.01562" + "\n", + "dask.array, shape=(256, 128, 128), dtype=float64, chunksize=(256, 32, 32), chunktype=numpy.ndarray>\n", + "Dimensions without coordinates: time, y, x" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ps = ps.mean(['time_segment','y','x'])\n", - "ps" + "da_chunked2 = da.chunk({'y': 32, 'x': 32})\n", + "da_chunked2" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:347: FutureWarning: Flags true_phase and true_amplitude will be set to True in future versions of xrft.dft to preserve the theoretical phasing and amplitude of Fourier Transform. Consider using xrft.fft to ensure future compatibility with numpy.fft like behavior and to deactivate this warning.\n", + " warnings.warn(msg, FutureWarning)\n" + ] }, { "data": { - "image/png": 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RLKEdi2ivIBbma5ZGPJGw9EksEUY7cvTR4axITsRrVmdpT+SC4X9dtxCPy8G3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\nzC75WFi9olTCWaZqr+nFjKnOcuk+EU2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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.semilogx(ps.freq_time[int(n/8)+1:], ps[int(n/8)+1:])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Two dimension\n", - "### Discrete Fourier Transform" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[[[[ 505.090962 +0.j , ..., 3.673241 +2.033024j],\n", - " ..., \n", - " [ 506.979486 +0.j , ..., 2.672219 +8.645102j]],\n", + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'fftn-b5d909a58bf2b5448926475664707cf4' (time: 256, y_segment: 4, freq_y: 32, x_segment: 4, freq_x: 32)>\n",
+       "array([[[[[ 5.12574137e+02+0.00000000e+00j,\n",
+       "           -3.91888705e+00-9.19831925e-01j, ...,\n",
+       "           -9.84527885e+00-6.15310238e+00j,\n",
+       "           -3.91888705e+00+9.19831925e-01j],\n",
+       "          [ 5.07347794e+02+0.00000000e+00j,\n",
+       "            3.79693809e+00+5.70092814e-01j, ...,\n",
+       "           -2.63561036e+00-3.02296894e+00j,\n",
+       "            3.79693809e+00-5.70092814e-01j],\n",
+       "          [ 5.03097187e+02+0.00000000e+00j,\n",
+       "            2.90490798e+00-1.47772821e+01j, ...,\n",
+       "            3.42125014e+00+9.14353115e-01j,\n",
+       "            2.90490798e+00+1.47772821e+01j],\n",
+       "          [ 5.16574333e+02+0.00000000e+00j,\n",
+       "            1.05921075e+01+1.69903252e+00j, ...,\n",
+       "            3.64428449e+00+1.39509922e+00j,\n",
+       "            1.05921075e+01-1.69903252e+00j]],\n",
+       "\n",
+       "         [[ 7.16306266e-01-2.73677635e+00j,\n",
+       "            1.03872840e+01-1.95456035e+00j, ...,\n",
+       "           -6.45706853e-02-4.37418156e+00j,\n",
+       "...\n",
+       "           -1.23134147e+00+2.77575997e+00j, ...,\n",
+       "           -3.77817981e+00+2.00753238e+00j,\n",
+       "           -4.66368067e+00-1.56960297e+00j]],\n",
+       "\n",
+       "         [[-3.85120827e-01-8.09457586e-01j,\n",
+       "            1.92525875e+00+8.00579049e+00j, ...,\n",
+       "            1.29216767e+00+1.21065275e+01j,\n",
+       "            2.61102376e+00-4.27162055e-01j],\n",
+       "          [ 1.00089606e+00+5.36388802e+00j,\n",
+       "            5.68732800e+00-7.11047435e+00j, ...,\n",
+       "           -1.71208012e+00+5.45064624e+00j,\n",
+       "            3.30031381e+00-1.16662691e+00j],\n",
+       "          [ 9.82555404e+00+2.88969155e+00j,\n",
+       "           -1.34389231e+01-1.88076983e+00j, ...,\n",
+       "            2.07255582e+00+6.63590027e+00j,\n",
+       "            2.17139125e+00-5.34713943e-01j],\n",
+       "          [ 1.84331751e+00+9.19765914e+00j,\n",
+       "            2.39901606e+00+4.15347887e+00j, ...,\n",
+       "            8.48760528e+00+4.94598333e+00j,\n",
+       "            1.27965378e+01+7.47818523e+00j]]]]])\n",
+       "Coordinates:\n",
+       "  * time       (time) int64 0 1 2 3 4 5 6 7 ... 248 249 250 251 252 253 254 255\n",
+       "  * y_segment  (y_segment) int32 0 1 2 3\n",
+       "  * x_segment  (x_segment) int32 0 1 2 3\n",
+       "  * freq_y     (freq_y) float64 0.0 0.03125 0.0625 ... -0.09375 -0.0625 -0.03125\n",
+       "  * freq_x     (freq_x) float64 0.0 0.03125 0.0625 ... -0.09375 -0.0625 -0.03125
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" + ], "text/plain": [ - "\n", - "dask.array\n", + "\n", + "array([[0.01206814, 0.01185798, ..., 0.0117029 , 0.01185798],\n", + " [0.01164809, 0.01132902, ..., 0.01182616, 0.0119674 ],\n", + " ...,\n", + " [0.01135967, 0.01194001, ..., 0.01155556, 0.01094249],\n", + " [0.01164809, 0.0119674 , ..., 0.01153195, 0.01132902]])\n", "Coordinates:\n", - " * freq_y (freq_y) float64 -0.5 -0.4844 -0.4688 -0.4531 -0.4375 ...\n", - " * freq_x (freq_x) float64 -0.5 -0.4844 -0.4688 -0.4531 -0.4375 ...\n", - " freq_y_spacing float64 0.01562\n", - " freq_x_spacing float64 0.01562" + " * freq_y (freq_y) float64 -0.5 -0.4844 -0.4688 ... 0.4531 0.4688 0.4844\n", + " * freq_x (freq_x) float64 -0.5 -0.4844 -0.4688 ... 0.4531 0.4688 0.4844" ] }, "execution_count": 14, @@ -477,56 +4949,53 @@ } ], "source": [ - "ps = xrft.power_spectrum(da.chunk({'time':1,'y':64,'x':64}), dim=['y','x'], \n", - " chunks_to_segments=True, window='True', detrend='linear')\n", - "ps = ps.mean(['time','y_segment','x_segment'])\n", + "ps = xrft.power_spectrum(\n", + " da.chunk({'time': 1, 'y': 64, 'x': 64}),\n", + " dim=['y', 'x'],\n", + " chunks_to_segments=True, window=\"hann\", detrend='linear'\n", + ")\n", + "ps = ps.mean(['time', 'y_segment', 'x_segment']).compute()\n", "ps" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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XGz8fwvckNZHbZm851xNh+mooDGzZcEx82V6uZ3K83RjlRfzdK7blFqhqtpWwEXji/7wV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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "fig, ax = plt.subplots()\n", - "ps.plot(ax=ax, norm=colors.LogNorm(), vmin=6.5e-4, vmax=7.5e-4)" + "ps.plot(norm=colors.LogNorm(), robust=True, size=5)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (xrft)", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "xrft" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -538,9 +5007,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.8.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/xrft/xrft.py b/xrft/xrft.py index cd8c5275..896232e0 100644 --- a/xrft/xrft.py +++ b/xrft/xrft.py @@ -44,7 +44,7 @@ def _fft_module(da): def _apply_window(da, dims, window_type="hann"): """Creating windows in dimensions dims.""" - if window_type == True: + if window_type is True: window_type = "hann" warnings.warn( "Please provide the name of window adhering to scipy.signal.windows. The boolean option will be deprecated in future releases.", From ddf95377f32237c20d1afdbebb46cf993abdc4b4 Mon Sep 17 00:00:00 2001 From: zmoon Date: Thu, 25 Nov 2021 15:00:39 -0700 Subject: [PATCH 22/26] Improve true_* FutureWarning I think this is what it meant at least --- doc/chunk_example.ipynb | 70 ++++++++++++++++++++--------------------- xrft/xrft.py | 14 +++++++-- 2 files changed, 47 insertions(+), 37 deletions(-) diff --git a/doc/chunk_example.ipynb b/doc/chunk_example.ipynb index 50c84d5f..b001cb1e 100644 --- a/doc/chunk_example.ipynb +++ b/doc/chunk_example.ipynb @@ -410,7 +410,7 @@ " ...,\n", " [0.08574744, 0.71378142, ..., 0.44964498, 0.44554641],\n", " [0.69503646, 0.92408159, ..., 0.8577461 , 0.82631412]]])\n", - "Dimensions without coordinates: time, y, x
      • " ], "text/plain": [ "\n", @@ -849,7 +849,7 @@ "}\n", "
      <xarray.DataArray (time: 256, y: 128, x: 128)>\n",
        "dask.array<xarray-<this-array>, shape=(256, 128, 128), dtype=float64, chunksize=(64, 128, 128), chunktype=numpy.ndarray>\n",
-       "Dimensions without coordinates: time, y, x
      " ], "text/plain": [ "\n", @@ -962,7 +962,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:347: FutureWarning: Flags true_phase and true_amplitude will be set to True in future versions of xrft.dft to preserve the theoretical phasing and amplitude of Fourier Transform. Consider using xrft.fft to ensure future compatibility with numpy.fft like behavior and to deactivate this warning.\n", + "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:352: FutureWarning: Flags true_phase and true_amplitude will be set to True in future versions of xrft.fft to preserve the theoretical phasing and amplitude of Inverse Fourier Transform. Consider setting them to ensure future compatibility with numpy.fft.fft-like behavior and to deactivate this warning.\n", " warnings.warn(msg, FutureWarning)\n" ] }, @@ -1366,7 +1366,7 @@ " * time_segment (time_segment) int32 0 1 2 3\n", " * y (y) int64 0 1 2 3 4 5 6 7 ... 120 121 122 123 124 125 126 127\n", " * x (x) int64 0 1 2 3 4 5 6 7 ... 120 121 122 123 124 125 126 127\n", - " * freq_time (freq_time) float64 0.0 0.01562 0.03125 ... -0.03125 -0.01562
      • " ], "text/plain": [ "\n", @@ -1897,7 +1897,7 @@ " [-4.49106525e-01+1.22839785j, -2.83221836e+00-1.15185018j,\n", " ..., -6.14847741e-01+2.56170558j,\n", " 9.94684786e-04-3.53138968j]]]])\n", - "Dimensions without coordinates: time_segment, freq_time, y, x
        • " ], "text/plain": [ "\n", @@ -2389,7 +2389,7 @@ " * time_segment (time_segment) int32 0 1 2 3\n", " * y (y) int64 0 1 2 3 4 5 6 7 ... 120 121 122 123 124 125 126 127\n", " * x (x) int64 0 1 2 3 4 5 6 7 ... 120 121 122 123 124 125 126 127\n", - " * freq_time (freq_time) float64 -0.5 -0.4844 -0.4688 ... 0.4688 0.4844" + " 0.4375 , 0.453125, 0.46875 , 0.484375])
        • " ], "text/plain": [ "\n", @@ -2919,7 +2919,7 @@ " 0.08389559, 0.0832645 , 0.08353916, 0.08355509, 0.0833294 ,\n", " 0.08314404, 0.08286732, 0.08423205, 0.08318687])\n", "Coordinates:\n", - " * freq_time (freq_time) float64 -0.5 -0.4844 -0.4688 ... 0.4531 0.4688 0.4844
        • " ], "text/plain": [ "\n", @@ -2980,7 +2980,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 9, @@ -3375,7 +3375,7 @@ "}\n", "
        <xarray.DataArray (time: 256, y: 128, x: 128)>\n",
        "dask.array<xarray-<this-array>, shape=(256, 128, 128), dtype=float64, chunksize=(256, 32, 32), chunktype=numpy.ndarray>\n",
-       "Dimensions without coordinates: time, y, x
        " ], "text/plain": [ "\n", @@ -3494,7 +3494,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:347: FutureWarning: Flags true_phase and true_amplitude will be set to True in future versions of xrft.dft to preserve the theoretical phasing and amplitude of Fourier Transform. Consider using xrft.fft to ensure future compatibility with numpy.fft like behavior and to deactivate this warning.\n", + "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:352: FutureWarning: Flags true_phase and true_amplitude will be set to True in future versions of xrft.fft to preserve the theoretical phasing and amplitude of Inverse Fourier Transform. Consider setting them to ensure future compatibility with numpy.fft.fft-like behavior and to deactivate this warning.\n", " warnings.warn(msg, FutureWarning)\n" ] }, @@ -3899,7 +3899,7 @@ " * y_segment (y_segment) int32 0 1 2 3\n", " * x_segment (x_segment) int32 0 1 2 3\n", " * freq_y (freq_y) float64 0.0 0.03125 0.0625 ... -0.09375 -0.0625 -0.03125\n", - " * freq_x (freq_x) float64 0.0 0.03125 0.0625 ... -0.09375 -0.0625 -0.03125
        • " ], "text/plain": [ "\n", @@ -4411,7 +4411,7 @@ " 2.39901606e+00+4.15347887e+00j, ...,\n", " 8.48760528e+00+4.94598333e+00j,\n", " 1.27965378e+01+7.47818523e+00j]]]]])\n", - "Dimensions without coordinates: time, y_segment, freq_y, x_segment, freq_x
          • " ], "text/plain": [ "\n", @@ -4905,11 +4905,11 @@ " [0.01164809, 0.0119674 , ..., 0.01153195, 0.01132902]])\n", "Coordinates:\n", " * freq_y (freq_y) float64 -0.5 -0.4844 -0.4688 ... 0.4531 0.4688 0.4844\n", - " * freq_x (freq_x) float64 -0.5 -0.4844 -0.4688 ... 0.4531 0.4688 0.4844
          • " ], "text/plain": [ "\n", @@ -4966,7 +4966,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, diff --git a/xrft/xrft.py b/xrft/xrft.py index 896232e0..1825e593 100644 --- a/xrft/xrft.py +++ b/xrft/xrft.py @@ -343,7 +343,12 @@ def fft( """ if not true_phase and not true_amplitude: - msg = "Flags true_phase and true_amplitude will be set to True in future versions of xrft.dft to preserve the theoretical phasing and amplitude of Fourier Transform. Consider using xrft.fft to ensure future compatibility with numpy.fft like behavior and to deactivate this warning." + msg = ( + "Flags true_phase and true_amplitude will be set to True in future versions of xrft.fft " + "to preserve the theoretical phasing and amplitude of Inverse Fourier Transform. " + "Consider setting them to ensure future compatibility with numpy.fft.fft-like behavior " + "and to deactivate this warning." + ) warnings.warn(msg, FutureWarning) if dim is None: @@ -546,7 +551,12 @@ def ifft( """ if not true_phase and not true_amplitude: - msg = "Flags true_phase and true_amplitude will be set to True in future versions of xrft.idft to preserve the theoretical phasing and amplitude of Inverse Fourier Transform. Consider using xrft.ifft to ensure future compatibility with numpy.ifft like behavior and to deactivate this warning." + msg = ( + "Flags true_phase and true_amplitude will be set to True in future versions of xrft.ifft " + "to preserve the theoretical phasing and amplitude of Inverse Fourier Transform. " + "Consider setting them to ensure future compatibility with numpy.fft.ifft-like behavior " + "and to deactivate this warning." + ) warnings.warn(msg, FutureWarning) if dim is None: From 4a78a7e4d2f0c9f49a5d2dd9f89f8888aa9fb7da Mon Sep 17 00:00:00 2001 From: zmoon Date: Thu, 25 Nov 2021 15:41:46 -0700 Subject: [PATCH 23/26] chunk_example --- doc/chunk_example.ipynb | 1084 ++++++++++++++++++--------------------- 1 file changed, 499 insertions(+), 585 deletions(-) diff --git a/doc/chunk_example.ipynb b/doc/chunk_example.ipynb index b001cb1e..4699fc89 100644 --- a/doc/chunk_example.ipynb +++ b/doc/chunk_example.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "outputs": [], "source": [ - "import dask.array as dsar\n", + "import dask.array\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import numpy.testing as npt\n", @@ -385,84 +385,36 @@ " fill: currentColor;\n", "}\n", "
          <xarray.DataArray (time: 256, y: 128, x: 128)>\n",
-       "array([[[0.37454012, 0.95071431, ..., 0.81801477, 0.86073058],\n",
-       "        [0.00695213, 0.5107473 , ..., 0.16949275, 0.55680126],\n",
+       "array([[[0.37454012, ..., 0.86073058],\n",
        "        ...,\n",
-       "        [0.90852403, 0.44136756, ..., 0.0516418 , 0.9962421 ],\n",
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-       "        [0.45946862, 0.00836947, ..., 0.97922982, 0.90108442]],\n",
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        "\n",
        "       ...,\n",
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-       "        [0.51225929, 0.4663246 , ..., 0.33682087, 0.15090019],\n",
-       "        ...,\n",
-       "        [0.05638765, 0.67847929, ..., 0.26920986, 0.87433108],\n",
-       "        [0.76589517, 0.95161275, ..., 0.55565673, 0.27902325]],\n",
-       "\n",
-       "       [[0.13304204, 0.36165999, ..., 0.9761357 , 0.64379419],\n",
-       "        [0.84308903, 0.7042752 , ..., 0.55923313, 0.15030696],\n",
+       "       [[0.13304204, ..., 0.64379419],\n",
        "        ...,\n",
-       "        [0.08574744, 0.71378142, ..., 0.44964498, 0.44554641],\n",
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-       "Dimensions without coordinates: time, y, x
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            <xarray.DataArray (time: 256, y: 128, x: 128)>\n",
        "dask.array<xarray-<this-array>, shape=(256, 128, 128), dtype=float64, chunksize=(64, 128, 128), chunktype=numpy.ndarray>\n",
-       "Dimensions without coordinates: time, y, x
            " ], "text/plain": [ "\n", @@ -943,7 +921,7 @@ "Dimensions without coordinates: time, y, x" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -955,15 +933,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:352: FutureWarning: Flags true_phase and true_amplitude will be set to True in future versions of xrft.fft to preserve the theoretical phasing and amplitude of Inverse Fourier Transform. Consider setting them to ensure future compatibility with numpy.fft.fft-like behavior and to deactivate this warning.\n", - " warnings.warn(msg, FutureWarning)\n" + "Wall time: 121 ms\n" ] }, { @@ -1321,92 +1298,72 @@ " fill: currentColor;\n", "}\n", "
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        "Coordinates:\n",
        "  * time_segment  (time_segment) int32 0 1 2 3\n",
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        "           9.94684786e-04-3.53138968j]]]])\n",
-       "Dimensions without coordinates: time_segment, freq_time, y, x
              • " ], "text/plain": [ "\n", - "array([[[[ 3.04275615e+01+0.j , 3.04070471e+01+0.j ,\n", - " ..., 3.13327034e+01+0.j ,\n", + "array([[[[ 3.04275615e+01+0.j , ...,\n", " 3.41680724e+01+0.j ],\n", - " [ 3.34108379e+01+0.j , 3.35497119e+01+0.j ,\n", - " ..., 2.99278169e+01+0.j ,\n", - " 3.33127071e+01+0.j ],\n", " ...,\n", - " [ 3.57882335e+01+0.j , 2.92711075e+01+0.j ,\n", - " ..., 3.41733335e+01+0.j ,\n", - " 3.76466842e+01+0.j ],\n", - " [ 3.66918724e+01+0.j , 3.00492889e+01+0.j ,\n", - " ..., 3.27852953e+01+0.j ,\n", + " [ 3.66918724e+01+0.j , ...,\n", " 3.39650329e+01+0.j ]],\n", "\n", - " [[-1.71780176e+00-3.8350854j , 2.11638519e+00-2.68663187j,\n", - " ..., -1.27213391e+00-1.0886499j ,\n", - " 1.32765261e+00-0.22620235j],\n", - " [-3.34655496e+00+0.73068476j, 1.12375993e+00+1.01184767j,\n", - " ..., 2.15842273e+00-0.53010756j,\n", - " 9.45156533e-01+0.39540872j],\n", - "...\n", - " [-2.42186392e+00+4.21358201j, -1.94794224e+00-1.50914707j,\n", - " ..., -6.10507815e-01+1.34491213j,\n", - " -3.25578483e+00-0.07834045j],\n", - " [-2.50536824e-01-1.47216093j, -1.05466916e+00-0.16700293j,\n", - " ..., 4.08207540e-01-1.1420307j ,\n", - " -1.23225413e+00+0.67961108j]],\n", - "\n", - " [[-2.34468531e+00+0.64660455j, -1.27249426e+00+3.00954048j,\n", - " ..., 1.65238922e+00-0.12094772j,\n", + " ...,\n", + "\n", + " [[-1.71780176e+00+3.8350854j , ...,\n", + " 1.32765261e+00+0.22620235j],\n", + " ...,\n", + " [-3.90552442e-01+0.96255549j, ...,\n", + " 1.89361607e+00-0.92510149j]]],\n", + "\n", + "\n", + " ...,\n", + "\n", + "\n", + " [[[ 3.20689773e+01+0.j , ...,\n", + " 3.35456208e+01+0.j ],\n", + " ...,\n", + " [ 3.19693891e+01+0.j , ...,\n", + " 3.08010838e+01+0.j ]],\n", + "\n", + " ...,\n", + "\n", + " [[-2.34468531e+00+0.64660455j, ...,\n", " -3.27757432e+00+0.38897244j],\n", - " [ 1.56963673e+00+0.59041267j, -7.72469058e-01-0.88308146j,\n", - " ..., -1.19544659e+00+0.36516077j,\n", - " 1.30093133e+00+2.11648012j],\n", " ...,\n", - " [-2.27508501e+00+1.29138639j, -1.67416002e+00+1.99822807j,\n", - " ..., 5.04822866e-01+0.73084585j,\n", - " -7.91053043e-01-2.55680915j],\n", - " [-4.49106525e-01+1.22839785j, -2.83221836e+00-1.15185018j,\n", - " ..., -6.14847741e-01+2.56170558j,\n", + " [-4.49106525e-01+1.22839785j, ...,\n", " 9.94684786e-04-3.53138968j]]]])\n", "Dimensions without coordinates: time_segment, freq_time, y, x" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = da_chunked.data\n", - "data_rs = data.reshape(4, n//4, n//2, n//2)\n", - "da_rs = xr.DataArray(data_rs, dims=['time_segment', 'time', 'y', 'x'])\n", - "da1 = xr.DataArray(\n", - " dsar.fft.fftn(data_rs, axes=[1]).compute(),\n", - " dims=['time_segment','freq_time','y','x']\n", + "shape, dims = (4, n//4, n//2, n//2), ['time_segment', 'time', 'y', 'x']\n", + "data_rs = data.reshape(*shape)\n", + "da_rs = xr.DataArray(data_rs, dims=dims)\n", + "da_ft2 = xr.DataArray(\n", + " dask.array.fft.fftn(data_rs, axes=[1]).compute(),\n", + " dims=[f\"freq_{dim}\" if dim in {\"time\"} else dim for dim in dims]\n", ")\n", - "da1" + "da_ft2" ] }, { @@ -2010,11 +1929,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "npt.assert_almost_equal(da1, daft.values)" + "npt.assert_almost_equal(da_ft1, da_ft2.values)" ] }, { @@ -2026,7 +1945,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -2389,7 +2308,7 @@ " * time_segment (time_segment) int32 0 1 2 3\n", " * y (y) int64 0 1 2 3 4 5 6 7 ... 120 121 122 123 124 125 126 127\n", " * x (x) int64 0 1 2 3 4 5 6 7 ... 120 121 122 123 124 125 126 127\n", - " * freq_time (freq_time) float64 -0.5 -0.4844 -0.4688 ... 0.4688 0.4844" + " 0.4375 , 0.453125, 0.46875 , 0.484375])
              • " ], "text/plain": [ "\n", @@ -2528,7 +2447,7 @@ " * freq_time (freq_time) float64 -0.5 -0.4844 -0.4688 ... 0.4688 0.4844" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -2547,7 +2466,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -2919,7 +2838,7 @@ " 0.08389559, 0.0832645 , 0.08353916, 0.08355509, 0.0833294 ,\n", " 0.08314404, 0.08286732, 0.08423205, 0.08318687])\n", "Coordinates:\n", - " * freq_time (freq_time) float64 -0.5 -0.4844 -0.4688 ... 0.4531 0.4688 0.4844
              • " ], "text/plain": [ "\n", @@ -2962,7 +2881,7 @@ " * freq_time (freq_time) float64 -0.5 -0.4844 -0.4688 ... 0.4531 0.4688 0.4844" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -2974,16 +2893,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, @@ -3016,7 +2935,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -3375,7 +3294,7 @@ "}\n", "
              <xarray.DataArray (time: 256, y: 128, x: 128)>\n",
        "dask.array<xarray-<this-array>, shape=(256, 128, 128), dtype=float64, chunksize=(256, 32, 32), chunktype=numpy.ndarray>\n",
-       "Dimensions without coordinates: time, y, x
              " ], "text/plain": [ "\n", @@ -3475,7 +3394,7 @@ "Dimensions without coordinates: time, y, x" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -3487,7 +3406,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -3853,145 +3772,145 @@ " fill: currentColor;\n", "}\n", "
              <xarray.DataArray 'fftn-b5d909a58bf2b5448926475664707cf4' (time: 256, y_segment: 4, freq_y: 32, x_segment: 4, freq_x: 32)>\n",
-       "array([[[[[ 5.12574137e+02+0.00000000e+00j,\n",
-       "           -3.91888705e+00-9.19831925e-01j, ...,\n",
-       "           -9.84527885e+00-6.15310238e+00j,\n",
-       "           -3.91888705e+00+9.19831925e-01j],\n",
-       "          [ 5.07347794e+02+0.00000000e+00j,\n",
-       "            3.79693809e+00+5.70092814e-01j, ...,\n",
-       "           -2.63561036e+00-3.02296894e+00j,\n",
-       "            3.79693809e+00-5.70092814e-01j],\n",
-       "          [ 5.03097187e+02+0.00000000e+00j,\n",
-       "            2.90490798e+00-1.47772821e+01j, ...,\n",
-       "            3.42125014e+00+9.14353115e-01j,\n",
-       "            2.90490798e+00+1.47772821e+01j],\n",
-       "          [ 5.16574333e+02+0.00000000e+00j,\n",
-       "            1.05921075e+01+1.69903252e+00j, ...,\n",
-       "            3.64428449e+00+1.39509922e+00j,\n",
-       "            1.05921075e+01-1.69903252e+00j]],\n",
-       "\n",
-       "         [[ 7.16306266e-01-2.73677635e+00j,\n",
-       "            1.03872840e+01-1.95456035e+00j, ...,\n",
-       "           -6.45706853e-02-4.37418156e+00j,\n",
+       "array([[[[[ 5.12574137e+02 +0.j        , ...,\n",
+       "           -3.91888705e+00 +0.91983192j],\n",
+       "          ...,\n",
+       "          [ 5.16574333e+02 +0.j        , ...,\n",
+       "            1.05921075e+01 -1.69903252j]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[ 7.16306266e-01 +2.73677635j, ...,\n",
+       "            1.03872840e+01 +1.95456035j],\n",
+       "          ...,\n",
+       "          [ 3.47657699e+00 -2.48778346j, ...,\n",
+       "           -7.61921613e+00 -2.29869668j]]],\n",
+       "\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "\n",
+       "        [[[ 5.21281617e+02 +0.j        , ...,\n",
+       "           -3.99828454e+00 +2.98342908j],\n",
        "...\n",
-       "           -1.23134147e+00+2.77575997e+00j, ...,\n",
-       "           -3.77817981e+00+2.00753238e+00j,\n",
-       "           -4.66368067e+00-1.56960297e+00j]],\n",
-       "\n",
-       "         [[-3.85120827e-01-8.09457586e-01j,\n",
-       "            1.92525875e+00+8.00579049e+00j, ...,\n",
-       "            1.29216767e+00+1.21065275e+01j,\n",
-       "            2.61102376e+00-4.27162055e-01j],\n",
-       "          [ 1.00089606e+00+5.36388802e+00j,\n",
-       "            5.68732800e+00-7.11047435e+00j, ...,\n",
-       "           -1.71208012e+00+5.45064624e+00j,\n",
-       "            3.30031381e+00-1.16662691e+00j],\n",
-       "          [ 9.82555404e+00+2.88969155e+00j,\n",
-       "           -1.34389231e+01-1.88076983e+00j, ...,\n",
-       "            2.07255582e+00+6.63590027e+00j,\n",
-       "            2.17139125e+00-5.34713943e-01j],\n",
-       "          [ 1.84331751e+00+9.19765914e+00j,\n",
-       "            2.39901606e+00+4.15347887e+00j, ...,\n",
-       "            8.48760528e+00+4.94598333e+00j,\n",
-       "            1.27965378e+01+7.47818523e+00j]]]]])\n",
+       "          [-8.64968474e+00 +2.13214317j, ...,\n",
+       "           -1.14615866e+00 -1.21148467j]]],\n",
+       "\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "\n",
+       "        [[[ 5.14176973e+02 +0.j        , ...,\n",
+       "            2.27243031e+00 +4.25132027j],\n",
+       "          ...,\n",
+       "          [ 5.06560025e+02 +0.j        , ...,\n",
+       "            2.06389069e+00 +1.91391656j]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[-3.85120827e-01 -0.80945759j, ...,\n",
+       "            2.61102376e+00 -0.42716206j],\n",
+       "          ...,\n",
+       "          [ 1.84331751e+00 +9.19765914j, ...,\n",
+       "            1.27965378e+01 +7.47818523j]]]]])\n",
        "Coordinates:\n",
        "  * time       (time) int64 0 1 2 3 4 5 6 7 ... 248 249 250 251 252 253 254 255\n",
        "  * y_segment  (y_segment) int32 0 1 2 3\n",
        "  * x_segment  (x_segment) int32 0 1 2 3\n",
        "  * freq_y     (freq_y) float64 0.0 0.03125 0.0625 ... -0.09375 -0.0625 -0.03125\n",
-       "  * freq_x     (freq_x) float64 0.0 0.03125 0.0625 ... -0.09375 -0.0625 -0.03125
              • " ], "text/plain": [ "\n", - "array([[[[[ 5.12574137e+02+0.00000000e+00j,\n", - " -3.91888705e+00-9.19831925e-01j, ...,\n", - " -9.84527885e+00-6.15310238e+00j,\n", - " -3.91888705e+00+9.19831925e-01j],\n", - " [ 5.07347794e+02+0.00000000e+00j,\n", - " 3.79693809e+00+5.70092814e-01j, ...,\n", - " -2.63561036e+00-3.02296894e+00j,\n", - " 3.79693809e+00-5.70092814e-01j],\n", - " [ 5.03097187e+02+0.00000000e+00j,\n", - " 2.90490798e+00-1.47772821e+01j, ...,\n", - " 3.42125014e+00+9.14353115e-01j,\n", - " 2.90490798e+00+1.47772821e+01j],\n", - " [ 5.16574333e+02+0.00000000e+00j,\n", - " 1.05921075e+01+1.69903252e+00j, ...,\n", - " 3.64428449e+00+1.39509922e+00j,\n", - " 1.05921075e+01-1.69903252e+00j]],\n", - "\n", - " [[ 7.16306266e-01-2.73677635e+00j,\n", - " 1.03872840e+01-1.95456035e+00j, ...,\n", - " -6.45706853e-02-4.37418156e+00j,\n", + "array([[[[[ 5.12574137e+02 +0.j , ...,\n", + " -3.91888705e+00 +0.91983192j],\n", + " ...,\n", + " [ 5.16574333e+02 +0.j , ...,\n", + " 1.05921075e+01 -1.69903252j]],\n", + "\n", + " ...,\n", + "\n", + " [[ 7.16306266e-01 +2.73677635j, ...,\n", + " 1.03872840e+01 +1.95456035j],\n", + " ...,\n", + " [ 3.47657699e+00 -2.48778346j, ...,\n", + " -7.61921613e+00 -2.29869668j]]],\n", + "\n", + "\n", + " ...,\n", + "\n", + "\n", + " [[[ 5.21281617e+02 +0.j , ...,\n", + " -3.99828454e+00 +2.98342908j],\n", "...\n", - " -1.23134147e+00+2.77575997e+00j, ...,\n", - " -3.77817981e+00+2.00753238e+00j,\n", - " -4.66368067e+00-1.56960297e+00j]],\n", - "\n", - " [[-3.85120827e-01-8.09457586e-01j,\n", - " 1.92525875e+00+8.00579049e+00j, ...,\n", - " 1.29216767e+00+1.21065275e+01j,\n", - " 2.61102376e+00-4.27162055e-01j],\n", - " [ 1.00089606e+00+5.36388802e+00j,\n", - " 5.68732800e+00-7.11047435e+00j, ...,\n", - " -1.71208012e+00+5.45064624e+00j,\n", - " 3.30031381e+00-1.16662691e+00j],\n", - " [ 9.82555404e+00+2.88969155e+00j,\n", - " -1.34389231e+01-1.88076983e+00j, ...,\n", - " 2.07255582e+00+6.63590027e+00j,\n", - " 2.17139125e+00-5.34713943e-01j],\n", - " [ 1.84331751e+00+9.19765914e+00j,\n", - " 2.39901606e+00+4.15347887e+00j, ...,\n", - " 8.48760528e+00+4.94598333e+00j,\n", - " 1.27965378e+01+7.47818523e+00j]]]]])\n", + " [-8.64968474e+00 +2.13214317j, ...,\n", + " -1.14615866e+00 -1.21148467j]]],\n", + "\n", + "\n", + " ...,\n", + "\n", + "\n", + " [[[ 5.14176973e+02 +0.j , ...,\n", + " 2.27243031e+00 +4.25132027j],\n", + " ...,\n", + " [ 5.06560025e+02 +0.j , ...,\n", + " 2.06389069e+00 +1.91391656j]],\n", + "\n", + " ...,\n", + "\n", + " [[-3.85120827e-01 -0.80945759j, ...,\n", + " 2.61102376e+00 -0.42716206j],\n", + " ...,\n", + " [ 1.84331751e+00 +9.19765914j, ...,\n", + " 1.27965378e+01 +7.47818523j]]]]])\n", "Coordinates:\n", " * time (time) int64 0 1 2 3 4 5 6 7 ... 248 249 250 251 252 253 254 255\n", " * y_segment (y_segment) int32 0 1 2 3\n", @@ -4000,19 +3919,19 @@ " * freq_x (freq_x) float64 0.0 0.03125 0.0625 ... -0.09375 -0.0625 -0.03125" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "daft = xrft.fft(da_chunked2, dim=['y','x'], shift=False , chunks_to_segments=True).compute()\n", - "daft" + "da_ft3 = xrft.fft(da_chunked2, dim=['y', 'x'], shift=False, chunks_to_segments=True).compute()\n", + "da_ft3" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -4370,149 +4289,150 @@ " fill: currentColor;\n", "}\n", "
              <xarray.DataArray (time: 256, y_segment: 4, freq_y: 32, x_segment: 4, freq_x: 32)>\n",
-       "array([[[[[ 5.12574137e+02+0.00000000e+00j,\n",
-       "           -3.91888705e+00-9.19831925e-01j, ...,\n",
-       "           -9.84527885e+00-6.15310238e+00j,\n",
-       "           -3.91888705e+00+9.19831925e-01j],\n",
-       "          [ 5.07347794e+02+0.00000000e+00j,\n",
-       "            3.79693809e+00+5.70092814e-01j, ...,\n",
-       "           -2.63561036e+00-3.02296894e+00j,\n",
-       "            3.79693809e+00-5.70092814e-01j],\n",
-       "          [ 5.03097187e+02+0.00000000e+00j,\n",
-       "            2.90490798e+00-1.47772821e+01j, ...,\n",
-       "            3.42125014e+00+9.14353115e-01j,\n",
-       "            2.90490798e+00+1.47772821e+01j],\n",
-       "          [ 5.16574333e+02+0.00000000e+00j,\n",
-       "            1.05921075e+01+1.69903252e+00j, ...,\n",
-       "            3.64428449e+00+1.39509922e+00j,\n",
-       "            1.05921075e+01-1.69903252e+00j]],\n",
-       "\n",
-       "         [[ 7.16306266e-01-2.73677635e+00j,\n",
-       "            1.03872840e+01-1.95456035e+00j, ...,\n",
-       "           -6.45706853e-02-4.37418156e+00j,\n",
+       "array([[[[[ 5.12574137e+02 +0.j        , ...,\n",
+       "           -3.91888705e+00 +0.91983192j],\n",
+       "          ...,\n",
+       "          [ 5.16574333e+02 +0.j        , ...,\n",
+       "            1.05921075e+01 -1.69903252j]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[ 7.16306266e-01 +2.73677635j, ...,\n",
+       "            1.03872840e+01 +1.95456035j],\n",
+       "          ...,\n",
+       "          [ 3.47657699e+00 -2.48778346j, ...,\n",
+       "           -7.61921613e+00 -2.29869668j]]],\n",
+       "\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "\n",
+       "        [[[ 5.21281617e+02 +0.j        , ...,\n",
+       "           -3.99828454e+00 +2.98342908j],\n",
        "...\n",
-       "           -1.23134147e+00+2.77575997e+00j, ...,\n",
-       "           -3.77817981e+00+2.00753238e+00j,\n",
-       "           -4.66368067e+00-1.56960297e+00j]],\n",
-       "\n",
-       "         [[-3.85120827e-01-8.09457586e-01j,\n",
-       "            1.92525875e+00+8.00579049e+00j, ...,\n",
-       "            1.29216767e+00+1.21065275e+01j,\n",
-       "            2.61102376e+00-4.27162055e-01j],\n",
-       "          [ 1.00089606e+00+5.36388802e+00j,\n",
-       "            5.68732800e+00-7.11047435e+00j, ...,\n",
-       "           -1.71208012e+00+5.45064624e+00j,\n",
-       "            3.30031381e+00-1.16662691e+00j],\n",
-       "          [ 9.82555404e+00+2.88969155e+00j,\n",
-       "           -1.34389231e+01-1.88076983e+00j, ...,\n",
-       "            2.07255582e+00+6.63590027e+00j,\n",
-       "            2.17139125e+00-5.34713943e-01j],\n",
-       "          [ 1.84331751e+00+9.19765914e+00j,\n",
-       "            2.39901606e+00+4.15347887e+00j, ...,\n",
-       "            8.48760528e+00+4.94598333e+00j,\n",
-       "            1.27965378e+01+7.47818523e+00j]]]]])\n",
-       "Dimensions without coordinates: time, y_segment, freq_y, x_segment, freq_x
                • " ], "text/plain": [ "\n", - "array([[[[[ 5.12574137e+02+0.00000000e+00j,\n", - " -3.91888705e+00-9.19831925e-01j, ...,\n", - " -9.84527885e+00-6.15310238e+00j,\n", - " -3.91888705e+00+9.19831925e-01j],\n", - " [ 5.07347794e+02+0.00000000e+00j,\n", - " 3.79693809e+00+5.70092814e-01j, ...,\n", - " -2.63561036e+00-3.02296894e+00j,\n", - " 3.79693809e+00-5.70092814e-01j],\n", - " [ 5.03097187e+02+0.00000000e+00j,\n", - " 2.90490798e+00-1.47772821e+01j, ...,\n", - " 3.42125014e+00+9.14353115e-01j,\n", - " 2.90490798e+00+1.47772821e+01j],\n", - " [ 5.16574333e+02+0.00000000e+00j,\n", - " 1.05921075e+01+1.69903252e+00j, ...,\n", - " 3.64428449e+00+1.39509922e+00j,\n", - " 1.05921075e+01-1.69903252e+00j]],\n", - "\n", - " [[ 7.16306266e-01-2.73677635e+00j,\n", - " 1.03872840e+01-1.95456035e+00j, ...,\n", - " -6.45706853e-02-4.37418156e+00j,\n", + "array([[[[[ 5.12574137e+02 +0.j , ...,\n", + " -3.91888705e+00 +0.91983192j],\n", + " ...,\n", + " [ 5.16574333e+02 +0.j , ...,\n", + " 1.05921075e+01 -1.69903252j]],\n", + "\n", + " ...,\n", + "\n", + " [[ 7.16306266e-01 +2.73677635j, ...,\n", + " 1.03872840e+01 +1.95456035j],\n", + " ...,\n", + " [ 3.47657699e+00 -2.48778346j, ...,\n", + " -7.61921613e+00 -2.29869668j]]],\n", + "\n", + "\n", + " ...,\n", + "\n", + "\n", + " [[[ 5.21281617e+02 +0.j , ...,\n", + " -3.99828454e+00 +2.98342908j],\n", "...\n", - " -1.23134147e+00+2.77575997e+00j, ...,\n", - " -3.77817981e+00+2.00753238e+00j,\n", - " -4.66368067e+00-1.56960297e+00j]],\n", - "\n", - " [[-3.85120827e-01-8.09457586e-01j,\n", - " 1.92525875e+00+8.00579049e+00j, ...,\n", - " 1.29216767e+00+1.21065275e+01j,\n", - " 2.61102376e+00-4.27162055e-01j],\n", - " [ 1.00089606e+00+5.36388802e+00j,\n", - " 5.68732800e+00-7.11047435e+00j, ...,\n", - " -1.71208012e+00+5.45064624e+00j,\n", - " 3.30031381e+00-1.16662691e+00j],\n", - " [ 9.82555404e+00+2.88969155e+00j,\n", - " -1.34389231e+01-1.88076983e+00j, ...,\n", - " 2.07255582e+00+6.63590027e+00j,\n", - " 2.17139125e+00-5.34713943e-01j],\n", - " [ 1.84331751e+00+9.19765914e+00j,\n", - " 2.39901606e+00+4.15347887e+00j, ...,\n", - " 8.48760528e+00+4.94598333e+00j,\n", - " 1.27965378e+01+7.47818523e+00j]]]]])\n", + " [-8.64968474e+00 +2.13214317j, ...,\n", + " -1.14615866e+00 -1.21148467j]]],\n", + "\n", + "\n", + " ...,\n", + "\n", + "\n", + " [[[ 5.14176973e+02 +0.j , ...,\n", + " 2.27243031e+00 +4.25132027j],\n", + " ...,\n", + " [ 5.06560025e+02 +0.j , ...,\n", + " 2.06389069e+00 +1.91391656j]],\n", + "\n", + " ...,\n", + "\n", + " [[-3.85120827e-01 -0.80945759j, ...,\n", + " 2.61102376e+00 -0.42716206j],\n", + " ...,\n", + " [ 1.84331751e+00 +9.19765914j, ...,\n", + " 1.27965378e+01 +7.47818523j]]]]])\n", "Dimensions without coordinates: time, y_segment, freq_y, x_segment, freq_x" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = da_chunked2.data\n", - "data_rs = data.reshape(256, 4, 32, 4, 32)\n", - "da_rs = xr.DataArray(data_rs, dims=['time', 'y_segment', 'y', 'x_segment', 'x'])\n", - "da2 = xr.DataArray(\n", - " dsar.fft.fftn(data_rs, axes=[2,4]).compute(),\n", - " dims=['time', 'y_segment', 'freq_y', 'x_segment', 'freq_x']\n", + "shape, dims = (256, 4, 32, 4, 32), ['time', 'y_segment', 'y', 'x_segment', 'x']\n", + "data_rs = data.reshape(*shape)\n", + "da_rs = xr.DataArray(data_rs, dims=dims)\n", + "da_ft4 = xr.DataArray(\n", + " dask.array.fft.fftn(data_rs, axes=[2, 4]).compute(),\n", + " dims=[f\"freq_{dim}\" if dim in {\"y\", \"x\"} else dim for dim in dims]\n", ")\n", - "da2" + "da_ft4" ] }, { @@ -4524,11 +4444,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "npt.assert_almost_equal(da2, daft.values)" + "npt.assert_almost_equal(da_ft3, da_ft4.values)" ] }, { @@ -4540,7 +4460,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -4898,18 +4818,14 @@ " fill: currentColor;\n", "}\n", "
                <xarray.DataArray 'rechunk-merge-66c3aaf087ca8fb2ba59ae3447c47a57' (freq_y: 64, freq_x: 64)>\n",
-       "array([[0.01206814, 0.01185798, ..., 0.0117029 , 0.01185798],\n",
-       "       [0.01164809, 0.01132902, ..., 0.01182616, 0.0119674 ],\n",
+       "array([[0.01206814, ..., 0.01185798],\n",
        "       ...,\n",
-       "       [0.01135967, 0.01194001, ..., 0.01155556, 0.01094249],\n",
-       "       [0.01164809, 0.0119674 , ..., 0.01153195, 0.01132902]])\n",
+       "       [0.01164809, ..., 0.01132902]])\n",
        "Coordinates:\n",
        "  * freq_y   (freq_y) float64 -0.5 -0.4844 -0.4688 ... 0.4531 0.4688 0.4844\n",
-       "  * freq_x   (freq_x) float64 -0.5 -0.4844 -0.4688 ... 0.4531 0.4688 0.4844
                • " ], "text/plain": [ "\n", - "array([[0.01206814, 0.01185798, ..., 0.0117029 , 0.01185798],\n", - " [0.01164809, 0.01132902, ..., 0.01182616, 0.0119674 ],\n", + "array([[0.01206814, ..., 0.01185798],\n", " ...,\n", - " [0.01135967, 0.01194001, ..., 0.01155556, 0.01094249],\n", - " [0.01164809, 0.0119674 , ..., 0.01153195, 0.01132902]])\n", + " [0.01164809, ..., 0.01132902]])\n", "Coordinates:\n", " * freq_y (freq_y) float64 -0.5 -0.4844 -0.4688 ... 0.4531 0.4688 0.4844\n", " * freq_x (freq_x) float64 -0.5 -0.4844 -0.4688 ... 0.4531 0.4688 0.4844" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -4960,16 +4874,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, From 0e28395fce2512648d196bd74df1f6641da1c0b5 Mon Sep 17 00:00:00 2001 From: zmoon Date: Sun, 28 Nov 2021 08:47:02 -0700 Subject: [PATCH 24/26] lint --- xrft/detrend.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/xrft/detrend.py b/xrft/detrend.py index bbccc135..27a4b4ee 100644 --- a/xrft/detrend.py +++ b/xrft/detrend.py @@ -20,7 +20,7 @@ def detrend(da, dim=None, detrend_type="constant"): Dimensions along which to apply detrend. Default: :attr:`da.dims `. - .. note:: + .. note:: - Can be either **one** dimension or a list with **two** dimensions. Higher-dimensional detrending is not supported. - If Dask data are passed, the array must be chunked along `dim`. From f386b0b8ff09b3f7b4a308636f9d232d6e4b735c Mon Sep 17 00:00:00 2001 From: zmoon Date: Sun, 28 Nov 2021 11:13:18 -0700 Subject: [PATCH 25/26] main nb work --- doc/DFT-iDFT_example.ipynb | 563 +++++++++++++++++++++++++------------ doc/environment.yml | 1 + xrft/xrft.py | 7 +- 3 files changed, 385 insertions(+), 186 deletions(-) diff --git a/doc/DFT-iDFT_example.ipynb b/doc/DFT-iDFT_example.ipynb index 7ddb7e7b..be599c10 100644 --- a/doc/DFT-iDFT_example.ipynb +++ b/doc/DFT-iDFT_example.ipynb @@ -13,14 +13,17 @@ "metadata": {}, "outputs": [], "source": [ + "import dask.array\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import numpy.fft as npft\n", "import numpy.testing as npt\n", "import xarray as xr\n", "import xrft\n", - "import numpy.fft as npft\n", - "import scipy.signal as signal\n", - "import dask.array as dsar\n", - "import matplotlib.pyplot as plt\n", + "from scipy import signal\n", + "\n", + "plt.rcParams.update({\"figure.autolayout\": True})\n", + "\n", "%matplotlib inline" ] }, @@ -28,12 +31,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this notebook, we provide examples of the discrete Fourier transform (DFT) and its inverse, and how `xrft` automatically harnesses the metadata. We compare the results to conventional `numpy.fft` (hereon `npft`) to highlight the strengths of `xrft`." + "In this notebook, we provide examples of the discrete Fourier transform (DFT) and its inverse, and how `xrft` automatically harnesses the metadata. We compare the results to conventional `numpy.fft` (hereon `npft`) routines to highlight the strengths of `xrft`." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "## A case with synthetic data\n", "\n", @@ -49,20 +54,22 @@ "k0 = 1/0.52\n", "T = 4.\n", "dx = 0.02\n", - "x = np.arange(-2*T,2*T,dx) \n", + "\n", + "x = np.arange(-2*T, 2*T, dx) \n", "y = np.cos(2*np.pi*k0*x) \n", - "y[np.abs(x)>T/2]=0.\n", - "da = xr.DataArray(y, dims=('x',), coords={'x':x})" + "y[np.abs(x) > T/2] = 0\n", + "\n", + "da = xr.DataArray(y, dims=('x',), coords={'x': x})" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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woYzXfP4+X52Lv3Q/9o0UrehU3CqW3Hy3r85V//wbbD4wZkWHEEI6CV2DZaNEcCeA0+r+vQDA7gZj34dJ5YFSyt21/+4D8Et4JYeEEELqaNQlsNdyaVhDnVR7dNKJGJyY8D0XhbEGOkII9FiMabTYuKuezWc3WmjcLdLmd2Gs2Lgdv+3vHCGEdBs2DNZTAJYKIRYLIZLwTNRdkwcJIfoAvAnAr+qO9QghZqi/A3grgHUW7okQQroKZUg+9obFE473pLz1MJedeaIVHWVI/uTS0yccT8VjiMcE+k8/wYqOMgrvv/i0CceFEOhJOjh/QZ8dnVo8f3jRqVPO9abiOHf+DEs6niH5/fPnTznXk3Kw5OQeKzrq/Vxz3rwp53pTcZw+O2tFR23A/JZzTp5yricZxyl9aSs6hBDSjRj/GkpKWRFCfBLAvQAcAHdIKV8UQny8dv622tB3AbhPSlnf+mgugF8KIdS9/ERKeY/pPRFCSLehJtZ/NWmtSyruIOEInH/aLCs6ypB88solE46rjM+rTplpRWesNoG/4Yozp5zrTcWxdK4t4+PpfPTyxVPO9aTiWHSSXePzgUtOn3KuNxVH36SNm6Oi4nlP/2lTzvUk40g6dra3VPG888KpxrTHYjyEENKNWMnzSylXAlg56dhtk/79IwA/mnRsM4DzbdwDIYR0MypDkvXp4NaTijcshQuL+hy/8rneVHz8PkxRn6MycPW0Ih6/Mr0eq/HUdHzK53rTNuNRz81fZ8eh3JTjURht+j1wrMVDCCHdiJ1fdRFCCGkpo4UKelNxxHzWJ/Wm4k3X5oTSKVYgBJBNTjU+nsFqvNYoDGqCPiM1tY15bzrecI1WVB0/ozAjFcdowU486vn7GrmkPYOlnr+fTm8qPp4ZNNYpqPfjb+RsvR9CCOlGaLAIIaQDGCtWfLM9gDI+9gxWbzKOWun2BHpSTtPmB6F0ChXEhNfUYjI24xlpmsGyF48yNo0yfyPWDLA7/pmT6Uk51ox2M2PaY/H9EEJIN0KDRQghHcBoqdKw612PxcyFZ+Qa69g0cj2pBkbOYsZnrFiBExO+Rs52PECDzFLa7vsB/Esre1MJewa4WTzJOEqVKkqVqhUtQgjpNmiwCCGkA1Algn70WC4RbEembKzYnnjGii56ko6vkbOa+StUEI8JpOL+Rs5eiWDztVEl147xaaajjnEdFiGE+EODRQghHUAzQzLDaibGRW966roooLbGx5bBKjWJx+Ian9Emz03FI6U01hlrkpHrTcVRdiWKFfPs0mixgoTT2MipezGlaaas1siDZYKEEOIPDRYhhHQAo01L9yyuJSpW0Nsgg2WzpG6kEBBPybVifEYLlYYb4/ak4qhUJYpWMj5uUyOn7sWUICPn3Yu5zmjRRdKJIRX3b3YCwFrZIyGEdBs0WIQQ0gE0y8RYXUtUqKAn2Z6MT7N43KpEoWxufMaarF2za0jKTeMBYMUEB70f717sxNOoVLTHomEkhJBuhAaLEEI6gKASwbGSHeMzWmyc8elNx1GVQL5sbhTGmmR8Zlg1Cs1LBL17sZFZcpusXXPG78WU0WIFM5pk5Lx7sRNPw++BxfdDCCHdCA0WIYR0AN4EvvHEWkogV7JgfJqsjeqxbHyaxQPYMQpBzUHUvRjrFCtN1q4lrOk0zcjVDNGIrfcTkCmzVZZKCCHdBg0WIYRMc4oVFyW32nRtFGA+gZdSeiVoDTM+tUyMhdKw0YC1XmqMKc3azttcG9U8Hmf8Xox1mr6fdhlTlZGzs0kzIYR0GzRYhBAyzVGZgqe2HvI9rybC316z0UinWKmiUpV4bscR3/Mqo/G9hzYb6UgpMVas4MXdw77nVTw//O0WIx3AMz4b9o74nlNG4d+f2GasM1asYOuBMd9zKp6fD+ww1hktVrD7SM73nDJe//3MLmOdsVIF+0eKvudUPP/z/KCxDgDcsmq9lc8hhJDpAg0WIYRMc1RG4sH1B3zPqwnvvz5mZhSUzqObDvrr1ErQfvqUmVFQRm5g22F/nVo8v3jazChIKTFWcvHcziHf82ot013PmRuF0WIFvxtsZOQ8nbvX7THWGSu62LivgZGrGeD7X9pnrDNarGDboeZG7qEN/t/HsNy6eoOVzyGEkOkCDRYhhExzgkrlGpWMhSVoTU2jkrGwtCueQrkKt9q48YctHZWRa0SjZhFRaKbTqMmGbZ2EE/PdhysKjbKlhBDSyQgbXafaTX9/vxwYGDjWt0EIIS3nllXrfX/Df+PVS3HT8mWB56lDnU7UIYSQ6YgQYq2Usj9wHA0WIYRMbx5cvx8fvuNJAMDWFddNOb9x3wje8vWHGp7XZWDrIbz7tscafs7uI3lctuIBY52XBodxza0PN/ycoVwZ5//DfcY62w/mcMVX1zT8nLJbxdKb7zbWOTBaRP8X72/6Ocv+7m6UKlUjnXzJxTmfvaepzoX/cB8O58pGOlJKnPHplZCysc7lKx7AriN5Ix0AWPSp/wFg9vwJIaRd6BoslggSQsg0J19qXlKXadBOOyxBbd6zSTslaEE6GVs65ebPLeHEkHCEsU5eoz2+jWeXC/geeDrm34VipYqg373aiKcTf8FLCCE62CsMJ4QQ0hKUIfnwpaf7ns8mvMnum5adZEXn/Ref5nteGZ/LzjzRSEcZkne/doHv+WQ8hnhM4ILTZhnpqHiuv+CUhmMyCQfnzu+xonPtefMajskmHJwyP21F5y3nnNxwTCbpYOnJvVZ0mn2fskkHp8/OGukUK1Wj6wkhZLpiJYMlhHi7EOIVIcRGIcSnfM6/WQgxJIR4tvbns7rXEkLI8Y6a8H7iyiW+55XxuXixofGpZXxuuOJM3/NJJwYnJnDhQlPj4+l85LJFDcdkkg7OO7XPSEcZuQ9c4m9MAS/jc9a8GUY6Kp4/ep2/MQW8eBbPMTM++bIXzzsvPLXhmGzSwYITMkY6Kp7rXt3EmCYdzJ1pZhh1Mn+EENKJGGewhBAOgG8DWA5gJ4CnhBB3SSl/N2now1LKd0S8lhBCjlvURLRR6VwqHoMQ5hNWZeQalX8JIZBJOIElfqY6gJdZMo1HdcJrqpO0GE+iuU6r3w8ApC28n6DvG+C9n/2j/vtk6ZIr02ARQroTGxmsiwFslFJullKWAPwMwPVtuJYQQo4LxkrKKPj/TkwIgWzCGc9wRCVX1JhYJx0UTHXGjULj3/Flk+bxqOuDjEJb4knEzQ1WzTBmEs2fm2k8YxpGLpu0Fw8hhHQbNgzWqQDqd53cWTs2mUuFEM8JIe4WQrwq5LWEEHLcki+5SMW98rxGZJLxtmRislYyPjWj0NTIWYynqVFoVzyOccZG3Wez/a5sxtPMMNrMyBFCSLdhw2D5/cSf3BroaQCnSynPB/BNAP8d4lpvoBA3CCEGhBAD+/fvj3yzhBDSaeRKbmDXtkwyFthtMFCnXEHSiSHuNP7RYKNEMK9VIhgbXxMWlaOGsblRaE88joX3094SwaB4bBlGQgjpNmwYrJ0A6lf2LgCwu36AlHJYSjla+/tKAAkhxEk619Z9xu1Syn4pZf+cOXMs3DYhhHQGnsFqvmQ2mzDP+ORLLrJNsiOAnczFWMlF0okh0cTIZW1ksIoamSUba71UZimg5NH8/ah4Wlta2a7MnzLQNlrlE0LIdMKGwXoKwFIhxGIhRBLA+wDcVT9ACDFPCCFqf7+4pntQ51pCCDneyZcrgXtDZSxNrJuVBwKW1kaVNOMxNVhlFwlHIBlvZuTsxAO0ce1a0xJO87VRWk0ukg5KlSrcavS9rFQ8ySZGmxBCOhHjLoJSyooQ4pMA7gXgALhDSvmiEOLjtfO3AXg3gL8QQlQA5AG8T3o7DPpea3pPhBDSTYwVXfQEGJKslTUxGsYnEcehsbyhTnDJox3j4yITYBhtrfWKx4KNnLU1ZU2yjJlas5NqVSLWZM1eM1RTlaCMHOA1EulNRZtKqKYqzZ4bIYR0IlY2Gq6V/a2cdOy2ur9/C8C3dK8lhBBylHzJ1TA+Do7kykY6OqWIXmbJfC2RTjw2mjUExmNjbZTm+8mXXUgpUSvoiKBTgRMTTTM+6j4KleB32VhHr/uiuqfIBqv23GmwCCHdBv+vRggh05xcWcMoWCpBC8wsWWkHX2maHQFq8VhYGxW0pkxlyryiimjkSjrxxCElUKxUDXS8Es5mBm08s2Tw7PIlFzHh7a/WCLUOrFAyiKf2PUrFm78jQgjpNGiwCCFkmqOTIbHTREGnW6GdUjeteAyNj248VRvGRyMeNTYquplMUx2VydQxcjmDTo/KBMYjljISQsh0hQaLEEKmOflS8Bosr6TOtNRNL1Nm3EShrGF8Eg7cqkTJNTE+laYt2pUOYJ7x0Tc+0d9RruSiJ6AcL1O3Nioq+XJF6/2oe4qKurZi0CiDEEKmIzRYhBAyzRkr6hifuJUugoGZpYSDSlWi1OKMjypBa7XxOZqJMc34BGfK1D1F16kENu2wkSkbK7YvHgBGnQgJIWQ6QoNFCCHTnLxGU4hs0kHZlSgbZXxCTKxNDEmxgkxAZilrQWesncYnaJ8yG89NJ56EuTH1jLZmPFYyWNG/s4QQMh2hwSKEkGlM2a2i7Eqt/akAw9IwnQ2NLWSWcmUXPRrNJwDzNUttiUenhNNCPLpr14Cjm/hGQadE0FbmDwAqLjNYhJDuggaLEEKmMUf3PmpuFNKGa4kqbhUlt6qR8YnV7stsLVGQUTCNx9MJs5aotfHYWuul031R3VNUdDJlR9+PeZMLrsEihHQbNFiEENIGblm1PtJ1ahL66MYDTcepCfF31myMpKMyEU9tPdR0nCpB+/7DmyPpVNwqSpUqntt+pOk4Fc+dj26NpAN4RuF3u4eajlGG5CdPbo+sky+72LB3pOkYlSn7z7U7IuvkyhVsPTjWdIwycr96ZldknXzJxe4jzTeTVvGsfGEwsg7XYBFCuhUaLEIIaQO3rt4Q6bqx2iR09cv7mo4bNySPbYukkyt6BuvhDXpG7idPRjMKysg9vqW5kVM6/7l2ZyQdtypRrFSxVtPI/erZ3ZF0AK8JyQu7hrV0Vr6wJ7JOruji5T1BRs7TWfVS8+9LM8ZKFWza39zIKZ0H1zf/vjSDa7AIId1KtO3XCSGEaFGsuPjBI1siX69bUpYOWKMVhG6JXFApXBDTLh5DHWXkgjCNB9Ar+zN9P4DeO0rFYxACMNimjGuwCCFdizDZxPFY0d/fLwcGBo71bRBCSFNuWbXeN3N149VLcdPyZdaupw51ulGHEEKmG0KItVLK/sBxNFiEENI61ry8Dx/90VMAgK0rrgt9/UPr9+NP7ngy8Pp1u4bwjm8+Elln7bZD+MPvPhZ4/eb9o7jqaw9G1nlpcBjX3Ppw4PV7hwu45B9XR9bZfjCHK766JvD6kUIZr/78fZF1DowW0f/F+wOvr1Ylzvj0ysg6hbKLsz9zj9b153zmHuTLbiQdKSXO/PRKVGWwTv8XV+HAaCmSDgC8+nP3YqToZRo3/eO1cGIi0ucQQki70DVYXINFCCEtRKd8rBm63eBMS8N0dYLantvSMY5Hs025aYmgbsljLCaQTkT/kRumK2BQB8BmFCtV6PacMHlHUsoJLd65DosQ0k1wDRYhhLSQUm3j37ecc3Kk6wu1SeiHLz296ThlFK4+O5qOMgrvv/g0LZ03Lj0pko6K590Xnaql8/ozZkfSUfFcf/4pTcfFnRiSTgyvXjAzmk4tnmvOmxc4NpNwsOzkGUY6Ot+jdMLB2fOi6aj386Zlwe83k3Bw5pyeSDplV8KtSiQdgVLt74QQ0i0wg0UIIS2kXMtgvWFJNEOiJtZ/8eYlTceprMXlhjp/9oYzmo5TWYtLFpsZnz+5bFHTcQknhoQjcOHCE6Lp1OJ538ULA8dmkg7OO6Uvmk4tnne/dkHg2GwyjmURjY/S+f0LmhtTT8fBGRGNz1HDOD9wbCYZx6knZI10TupNAfAMFyGEdAs0WIQQ0kJUBqu+HCoMamIdVMqmjE8+oo7KXASVfSXjMcRjIvJGtur+dErzMgkn8sa8uvEAniFpSzxJC/Fo6BjFUwrx3BJO5I2GVTy9aa+QhhksQkg3QYNFCCEtpFTLYBUMJ/DpZPP/XSedGGJCv8ZOV/MAACAASURBVD35FB1NI6fGmBoSnbblmaRjEI/33LXjiWqAx9+P7nOLGE8Ig5Vu6/sxM3Iz0gkAXINFCOkuaLAIIaSFKINlklmKCc9ANUMIgWwybjCxDmFIbGRitDJLJvG0KbMUxphaySwF/9jOtilTZvLc1PvpTTGDRQjpPqwYLCHE24UQrwghNgohPuVz/gNCiOdrfx4VQpxfd26rEOIFIcSzQgj2XieEdBXjJYIGE+tMwoEQwS2sM0lnfIIcWqd2XSquOYFvccmjGmMaT1DmDzAzJGGMXNbC+9HJLGWTcYP3UzPauiWChvGoEkFuNkwI6SaMuwgKIRwA3wawHMBOAE8JIe6SUv6ubtgWAG+SUh4WQlwD4HYAl9Sdv1JKecD0XgghZLphmsHKl13tdtgma28KZRfpRAwxjb2IMhYyS3pGwSCeUJmlOIby5Ug6+ZBrvXYd7ozMUqvXeqn3M1MZLGawCCFdhI0M1sUANkopN0spSwB+BuD6+gFSykellIdr/3wcQHC7JUII6QJUBstkwqtrsIzWRpVc7T2uMokY8pr7TE3RKbtIxWNam8oaldSFaqYRi9ysIUxTCKO1UbXrdN6RlbVeOvFYMHJqDZbLNViEkC7ChsE6FcCOun/vrB1rxJ8BuLvu3xLAfUKItUKIGxpdJIS4QQgxIIQY2L9/v9ENE0JIu7CxBkt3M1zTEkFdnWwybrRmKYxhNIkn6cQQD1i7BpiV1IXt7mdaIqirEzmeEJm/bCKOkltFxQ1vjiavwWIGixDSTdgwWH6/hvT9P6UQ4kp4But/1x2+XEp5EYBrAHxCCHGF37VSytullP1Syv45c+aY3jMhhLSFsmkGqxTG+JhlfNIJvR8JppmltsRTChePSSYmHhNIaBo549JKjTVlntGuohrBtIQ1cvXXhNIpTTJYXINFCOkibBisnQBOq/v3AgC7Jw8SQrwGwPcBXC+lPKiOSyl31/67D8Av4ZUcEkJIV2BjDZbOeiXArEQwbGYpejzVUBk5kzVl7Smt1I8nXXtuUYxPoaTXTRI4ao4iGZ+QJYJAtF8eTN4HixksQkg3YcNgPQVgqRBisRAiCeB9AO6qHyCEWAjgvwB8SEq5vu54jxBihvo7gLcCWGfhngghZFowbrBMMli6RiEZj7yWKNeuTFmpEsIwti+efNmFlFEyPhWtPbCUDgAUKuGfnYpHp5uk0onyjtQ1Wt0kE+Y6M8c3GuYaLEJI92DcRVBKWRFCfBLAvQAcAHdIKV8UQny8dv42AJ8FcCKA79R+OFSklP0A5gL4Ze1YHMBPpJT3mN4TIYRMF8abXBhksE7RNQqGbbNnpDWbXCQdo42Tw3RFVMZHx1hM1tE2ckkHUgLFSlX7mnGdkEZOXaPbUGRcJ0xGrvbZUdZ7qTV/YYycSaashyWChJAuxNhgAYCUciWAlZOO3Vb3948B+JjPdZsBnD/5OCGEdAvGGayQTS5MSupOnpHSGptNOshFNT5huhUmHVQjGp8wJYL1mZjQBivM+6nTOTGUSjjDaJLBCpcxNdApu0jGY+Mlj9xomBDSTVjZaJgQQog/xhmsUlW7BM20WUOYNUtuVY7HFk5H3yyNryWKahRCGFMAkVqb58vh3o93TfTMkpZOwiSe8DqR1mDV3k/c8Qx6mQaLENJF0GARQkgLqW9yEWWNT5iJdTbhoFKV450LwxDOkNRK0ErhdUJllgxL0ELHE8X4lFxkNLsVZg2aQkTJLEV9brrdF1Um0uT9ODGVweIaLEJI90CDRQghLUSZHbXGJwxSytAlgkD0kq3QJWgRNhvOhzAkxvFEKBGMoqOf8Ykb6YR9P5EzS6FLBKNl/jJJB/HahtNcg0UI6SZosAghpIWU6kxV2Alv2ZVwqzJ85iJi2+wwJYJAOwyJeQmalo6hkQufWYpoSCKs9QqvE/65Rc3IpetKBLkGixDSTdBgEUJIC6nPWuVCllKF2fQVqG9uEG4CX3arKLtyPJMThNHEuuyOl+QFoUrQoq4lyrbBmHoZOd14ohufQqlN8YR5PwZGrlB7PyqDxTVYhJBuggaLEEJaSH0jiNt+synUtWpN0G83HtAaryb63394cySdJ7ce0hqvJvp3Pro1lI5blShVqnh2+2Gt8coo/OTJ7aF0AM8ovLhrSGusiuc/1+4IrVMou1i/d1hrrDLKv3pmV2idfNnFlgNjWmOVMV35wmB4nZKLXYdzWmPV+7n/pb3hdcou9g0XuAaLENKV0GARQkgLKbtVzKjt9fNvj28Lda3KQKx+eZ/W+Oy4IQlnFFSm7OENekbuqCHZGUpHGbnHt4Qzcr96dnconWpVolCuYu32I3o6CWVI9oTSAbxn98IuPYOl4ln1kt77nKzz8p6RUDq/Wb8/tE6h7GLTfj0jl4rHEBPAo5sOhtbJl1zsOJznGixCSFdCg0UIIS2kVKmiL5uIdG3Y7my6a4EmE7YbYNi9ohSh44moE7aZSDoZ7UehakKiS9T3AyDUxs6peAwhtycbJ0w8QojI70iZba7BIoR0IyJK2+BjTX9/vxwYGDjWt0EIIYGc/Xd3o+Az4b/x6qW4afmyhtfdsmo9bl29Qfu6sOOpQ53ppHPlWXPww49e3PA6QgiZDggh1kop+wPH0WARQkjreNVn78H5p80aL6PauuI67Wt/u/EAPvD9J7Sv27R/FFd/7cHQOs9sP4x3fedR7ev2DBXw+i+vDq2zfu8I3nrLQ9rXDRfKeM3n7wuts/NwDm/4pzXa11XcKpbcfHdoncNjJVz4hVWhrjv7M3ejUK6G0ilVqlj2d+Hu77VfWIWDY6VQOgCw9OaVKLtS+7o3fuUB7DiUD61z/t/fh6F8GU9/Zjku+sIqfP73zsVHLl8c6jMIIaTd6BoslggSQkgLKbsSfZmIJYIhu7NFLddqV+leu+IJu2Fw3Ikh6YT/cRhlg90oMUXRiVLGqbpJhsH0O6dKBCssESSEdBF6vVgJIYSERkqJklsdN1hXnT0n1PVqEvqh1y/UGq8mu29adlIoHWVI3vu607TGqzVLl515YigdFc8fXHiq1viEE0M8JnDBabPC6dTWlP3ea+ZrX5NOxHDO/BnhdGrxvP1Vc7WvySQczJ+fDqWj3s9VZ5+sr5N0sPTk3kg6b1yq//3JJBycPjsbSkd1k7xk8ezxJhdcg0UI6SZosAghpEWoFu2qycVlZ4YzPmoC/+dvOlNrvGqicMkZIY1PzZD8qWaJVtLxusddtPCEcDq1eD546ena12QSDl69oC+Szntfp2dMAe/ZnT1vZjidWkbuXRct0L4mnXRwxpxwxkfp/N75+oYxk3AwZ0YqnE7tub3tVfO0r0knHMztC5fFqjeMTowZLEJI98ESQUIIaRGlWnMLlcEKW7qmxuuWYanucWE6zgHhNzQWQiCbjIcuXVP3FaasLJN0Qj+38XhCdAeMFE/I56bGRo4n5HMLW5KpukmG0ckavR8H8do+WGzTTgjpJmiwCCGkRaj1LNmEg3hMIBfW+JSOTkR1UG2zwxoFNT5Mu/K0gU6rjYIaH2YdklE8IdqvZ5MG7yekkWtHPKbvp5bA4kbDhJCuggaLEEJahMpgJeOONxGNOrGOt3ZirTJL2aR+1XgmGYucKcuGmcBHiaccIZ5ELHwmJkJGLp2IklmKEo9ZZkmXKMa0UPc9EEIg4QiWCBJCugoaLEIIaRFHDVbMMwoRDEk6EUMspr9rrDeBD5cNOGrk9H8kZBJO5IxcOuQEPrROxExZZJ2QhnHaxhOlhNNCyaMTE2xyQQjpKmiwCCGkRZRcbyKZjMciZbAKJTd0G+yoa5aSTgzxEK3KTTJLLZ/ARzQKoTNLEY1PO9aUmWSWWv3cJr+feCwWuj08IYRMZ6x0ERRCvB3ArQAcAN+XUq6YdF7Uzl8LIAfgI1LKp3Wu9WPvcAG3rFpv49YJIaRl7B8pAgCSjoicwQptsBIOcqVKOJ2SlykLQ9Q1S/GYQCKMkUs62DdSDq0DhF9T1jYjF1En7BqsyM1OQq7BypVdSCnh/ajX11GZTCcmsHbbIf5cJ4R0DcYGSwjhAPg2gOUAdgJ4Sghxl5Tyd3XDrgGwtPbnEgDfBXCJ5rVT2DdSxK2rN5jeOiGEtIWoGaxcyQ1VTgdEn8CHmVQD3sT60Fgp1DW5KBm5iBmSmECozYOjPLdc1DVLbSkRjEX6voXVSSccSAkUK1VtAzjZmCYcged2DuG5nUOh7pcQQqYrNkoELwawUUq5WUpZAvAzANdPGnM9gH+VHo8DmCWEmK95LSGEdDRJx4lcghbWkKSTDvLl8Guw2mF8CuXwhtHLLEWLRzejAiByCSfgtccPoxM6npDdJAHv/VSqEmVXXytqt0Ig3BYEfmuwCCGkm7BRIngqgB11/94JL0sVNOZUzWsBAEKIGwDcAADJeUvM7pgQQtrIB3/wBADgpN5kqOuiGZ8Y9g5FaabRpkxZ6DVl4TMx+XKEjJxBCWcoI5dwUHKrqLhV7TVvUbpJqveZL7vaJZmFKEYueVRnluY1Kp4fP74N339ki7YWIYQca5LzlrxWZ5yNDJbfT5bJq1UbjdG51jso5e1Syn4pZX/I+yOEkGPK1hXX4R2vmY+Z6USo6yKV7kVsbhBWJx2xWUNbMmWl8IYxnXBQrFRRDdHNLqqRA4BCJVxmKRUP101S3VeYdVhRu0kCCPWO1Ni/vHoptq64DltXXKd9LSGEHEtKezau1Rlnw2DtBHBa3b8XANitOUbnWkII6XiibTCrv65FEWm/rXatjSpXI68pkzKk8Ynw3ACgUAljFKqRSji9a/V1ChGNNoBQ34Uo3STTEXSidCskhJBOwkaJ4FMAlgohFgPYBeB9AP540pi7AHxSCPEzeCWAQ1LKQSHEfo1rp3DyjBRuvHqphVsnhJD2EGX/oyhrsDKJeKTucX2ZcNk1ZRjDdI/z2s6H+71epra5bqgmChEyS9k646O7oW+hHL77YtQ1S2G/B9lkBIMVoZuk0gkbjxPzNhhW8Gc6IaQT+Ot/0htnbLCklBUhxCcB3Auv1fodUsoXhRAfr52/DcBKeC3aN8Jr0/7RZtcGac6dmcZNy5eZ3johhLSNdLsySxHXLEVpPlGVQMmtIqW5NihfdkOvQ1OGLB+i7C/MWEWUTIxJiWA4nQiZsgile1FKRcfXYIXY3Fpl/uqNOX+mE0I6gb/WHGdlHywp5Up4Jqr+2G11f5cAPqF7LSGEdBvZRBylShVuVWp3TYs6gVfd48I0N4hSIuhdG85gRZ7Al12coHlNoexiVjaskYuQiYlogNW1YXSiNCEBIhjGNumEjYcQQjoJG2uwCCGEBDA+sW5xhiRqJiYb0fjkyvqbGnuGJNzv9VQ8YcorI8UTUSejWU6oiBJPweD9hDVyUeMJuwYrbDyEENJJ0GARQkgbUBNX3QmvW5UoVcKXhkXtHhc5cxG6BG16rlmKYki8NXJh1yzFx6/VpX2liFHWyEX4vkXI/BFCSCdBg0UIIW0grCGJ2mkt7MS6WpUoROhWGHnNUlTjE6p0L3y3wrbFE0XHZE1Zq9dgRS0RZAaLENLF0GARQkgbCDsRVSVkUdqah9FRbcmjro3SzcRIKduWKcuXKtHXlIUwCjmT9umtzjBG6O6XM1iLF9Ywhs2UEUJIJ8H/wxFCSBtQa07u+O0WrfFqYvzQ+v2hdJQh++Ejejpqov/4poOhdNTE+sePb9caX6xUISUwsO1wKB2Vifn5wA6t8crIPb/zSCgdZUh++cwu7WsKJRcvDQ6H0knXSiR//bz+lo/5kotN+0ZD6aj3c++Le/R1yi62HcyF0knVNiVe8/K+UDp7hgqhdAghpJOgwSKEkDagjMJ/PKVnFFRGYNXv9obSURPr/xjYGUrnNyGNnNLRNSTKyD0a1sjVjM+vnx/UGl9yq6hK4Kmt4YzcUUOi/7zzZRfP7hiKpLPmFf3nnS+7eDGskavpPLJR/3kXSi42hDRysZhAOhHDE1sOaV+TL7vYGtLIEUJIJ0GDRQghbSBsKVmYErIJOiFLvMKUkE3QCdmsIuzeXOM6YeMJsR+TiU7ZraJSlaF1orQnj/LsnJhAMj4931HU7zYhhHQKwtuiqrPo7++XAwMDx/o2CCFEi1tWrcetqzdMOX7j1Ut9N1gNO5461OlmHUIImS4IIdZKKfsDx9FgEUJI69lxKIc3fmUNAGDriusCx695eR8++qOntMdH1Xli80G89/bHQ+scGivhoi+s0r5u3a4hvOObj4TWKZRdnP2Ze7Sv27x/FFd97cHQOlJKLLn5brhVqXXd3uECLvnH1aF1AODVn78XI4WK1nWjxQrO+9y9kXQu/fJqDA4VtK6rViXO+PTKSDpXf+032LR/TPu6cz5zj1cmGFKHEEKONboGiyWChBDSBsKWCIbZiNZIp01lYVHjScVjEKL1OkKIUDFF1QHCPbtcSX8jZxOdqOWBQLjvXLUqjbQIIaQTCLdlOyGEkEioye4blpyoNV5NrD962aKW6qj1MB+4ZGEoHdU97pLFs7XGq3je078glI4yPmfPm6E1Xk3e33nBKaF0AG991KKTslpjVTzXvXpeaJ1M0sFZc3u1xqr389Zz54bWSSccnHFSj9ZYZRivPGtOaJ1MwsGCEzJaY9W2AJefqff9JISQToQGixBC2oBqbtC/SM+QqOYTn7hqSSSd1y0KZ7BuuOKMUDqqe9z5p83SGq/i+chli0PpAN4E/pz5M7XGqng++PrTw+skY1h6sp6RU/G853XhjCngxbNwtp6RU4bxXReeGl4n6WB2T1JrrIrnutdEM6ZzZqS0xo4bxleFN6aEENIpsESQEELagBMTSMVj2h3UVEYhG7LkT3WP097QuBxto2HAMwqtjgfwJvBhN2hmPLV4QupEiSfKc4sSDyGEdAo0WIQQ0iYyyfAT3nQ82oRXt/16vlbqFnZNlbqmLUYhGSKecpvjiaij/36i66RDGR+D5xbq/USPhxBCOgUaLEIIaRPZEBPefNlFOhFDLBaiw0ONaJmY8BXj6RCGMd/2zFKEeEIYrIJJ5i+C0Y4STyjj0wGZMkII6RRosAghpE2kk4521758yY00qQbCTeDzZRfJeAxORCNXCGEYAc9kRtFpi5ELYUiMSuqS4Yy2uia0TkK/VHT8/UQtedSNhyWChJDjABosQghpE2EMSa7kRi6jCpOJ8YxcNJ2wmYukE0PcCf9jx8uUVbXG5tu8liibiJBZSjgoaMfjle5FjqdNhlE7nrKKhz22CCHdCw0WIYS0iWzS0d4/KV+uRP4tfyYRC5WJiWrkQmXKShWkE9F+5GQSMX1jWnYRjwkkIhi5MIZEPd90srU6Jmu90mEyZWrNX8QMY8mtouIGmyyTeAghpFMwMlhCiNlCiFVCiA21/57gM+Y0IcQaIcRLQogXhRA31p37vBBilxDi2dqfa03uhxBCpjNtyyyFLEGLauRClYaVDUoeQz63yPGEKhGseB0boxi5tpUIOihWqqhWpbZOpLVeNbNUqAQbLJMMIyGEdAqmGaxPAVgtpVwKYHXt35OpAPgbKeU5AF4P4BNCiHPrzt8ipbyg9mel4f0QQsi0JUz3OKPMUiKunykzMHLZkGuWohvGeFuMaZgmJLmSi2zCgRDh164poy2lhvEpuYiJoxs7h0E9B7W5bzNMSwS9z6gEjjUxjIQQ0imYGqzrAdxZ+/udAN45eYCUclBK+XTt7yMAXgIQfsdEQgjpcMKVCEbPxIRr1lAxMHLhMktRys+UTpgSQdOSR13jkzZYuwYARY2MjzLaUYyc0tExjflSBSKikRvPYJVYIkgIIYC5wZorpRwEPCMF4ORmg4UQiwBcCOCJusOfFEI8L4S4w6/EsO7aG4QQA0KIgf379xveNiGEtJ+w7bmjN58I0T2u5CITsXQv3D5LJhmsMPFUjOKpSqCksZbIK3mM/n4APeOTM3w/ALSenZGRS4bTAWiwCCHdTaDBEkLcL4RY5/Pn+jBCQoheAL8A8FdSyuHa4e8COBPABQAGAXyt0fVSytullP1Syv45c+aEkSaEkGlBJhEP1XQgE6FDnacTrk17lNbpQPh28NGbdjioVCVKOmt8jIyPfsbHtDkIAK2W/QWTeJIhMlg2nptmPFH3dyOEkE4h8Ke3lPItjc4JIfYKIeZLKQeFEPMB7GswLgHPXP27lPK/6j57b92Y7wH4dZibJ4SQTkJlYqSUgZkCkwlvmO5xZpkyB2VXouxWA7v25Usu5s5MRdKpz8QkA0rYciUXvano+4cpnVkBY03WeqVDGblKW4yPUXOQ0PGwRTshpLsxLRG8C8CHa3//MIBfTR4gvFnEDwC8JKX8+qRz8+v++S4A6wzvhxBCpi2ZhAO3KlF2g9f45EombdpDdI+zsJZIZ71Xrmyw1iupr2O61kt9RqCOYUYO0HxubYrHRkZONx6WBxJCuh1Tg7UCwHIhxAYAy2v/hhDiFCGE6gh4OYAPAbjKpx37V4QQLwghngdwJYCbDO+HEEKmLWotTdCEt1qVKJSrRs0nAL3ucSYlgukQa2/ypWrktURhjY9xZknbKJhnyoIwKREM9X7K0dd6hS0RZAdBQki3Y5Snl1IeBHC1z/HdAK6t/f0RAL61MFLKD5noE0JIJ1E/Ee1DouE4ZYxsrL1pVo4lpbSy9kane1y+TaVuZs00wmTKLMSjmVma39f4u6Kjo5v5i7wWL2Q83AOLENLtmGawCCGEaJLVzCiMd1prcSamUK5CShiXCAbpSCmN2qeHy5TZKKnTa6YROZ6Qa6OM49Et4YycKYvp6xjEQwghnQINFiGEtAk1sQzakFVlAoxLBAONnHcf0bsI6k2sixXPyBmvWQrIkEgp29YUImfQFCLsmiXzTKbmflttWFNm0hyEEEI6BRosQghpE2oCe+ejW5uOUxP8NS/7NmYN1qlNeH/w8BYtnUc2Hoikowzjvz22tblOzRg9ueVgJB0Vz8+e2t50XMmtoiqBZ7YfiaZTM4y/eHpn4Nh8ycW6XUORdJTBuOvZ3cE6ZRev7BmJpKPezz3rBgPHFkoutuwfjaSj3s/ql/YGjPTi2Xk4F0mHEEI6BRosQghpE2pi/fOB5hN4VSK4ct0eI52fPrWj6ThlfO5/KZqRU+u7fvH0rqbj1H5PD66PZuTGDclzzY2CiufRTRGNXC2eewKee9mtolKVGNh2OJqOMiQaBjpfcvHcTjMj99CG4OeeK7v43WA0Ixd3Ykg6MTy2+VDg2HzJxcZ9Y5F0CCGkU6DBIoSQNqFb8hdUQhiE7pqqnEZTgmboxpM3jUf7ubUnHlMd3XgqbhUlN7i8rxEJJ4a45oa+5jHpTSdMv9uEENIJCCmD90mZbvT398uBgYFjfRuEEKLNLavW49bVG6Ycv/Hqpbhp+bLQ46hDneNJhxBCpgNCiLVSyv7AcTRYhBDSHnYfyeOyFQ8AALauuK7huF8/vxuf/MkzgeMase3gGN701d8EXr/m5X346I+eiqyzf6SI133p/sDrn9h8EO+9/fHIOrlSBed+9t7A69ftGsI7vvlIZB0pJc749EpI2fz6zftHcdXXHoysAwCv+uw9GCu5Ta/fN1zAxf+42kjn4i/dj30jxabXjxYrOO9zwc+3GVf+82+w5cBY0+urVe/5mugQQsixRNdgsUSQEELaRLtK0HS7wZnq6HaDy2l0l2tGOt6eeIQQWu9Ip8tgEDqb+prGA+i9Ixtle+16boQQ0gkYbTRMCCFEH2V8LjvzxKbjVLOGG964OJKOaj7xhiXNddTE+iOXLYqkoybVFy+e3XSciucDlyyMpBOLCWSTDs6aO6PpOBXPe/oXRNIBvGd3xkk9TceoeN55wSmRdXpSDk7s6W06Rhms6149L7JOJhnHYs143nru3Mg62aSDBSdkmo5R8bz5rDmRdQghpBOgwSKEkDaRiscgBPDa009oOk79pv+m5WdF0lHGp39Rc+Oj9i36xJVLIunEYl7G5/wFfU3HjRvGK86IpAN4E/hzTpnZdIyK5yOXRTOmSmfJyXrG54OvPz2yTibh4LTZ2aZj1PfgPa+LZkwBoCfpYHZPQkvnXReeGlknm4rjpN5U0zHq/Vz36vmRdQghpBNgiSAhhLQJVYKWDyj9UhN43c5sk3FiAulELLDETJ032fg1m3SCdWoT66gb2aprdZ9bq+PJW4jH09HbcNoknkzSwVhR77kZxZMIjufo++Hvdgkh3Q0NFiGEtJFs0glck5QvVZBJOBBCr8W2v05ce8Kr2zbcj4yOIandh+4aND+yiTjGinrxmBq54HhqOibxJOMaBtjCc9Mwpnbi0TDaKp4kpx6EkO6G/5cjhJA2kk44KGhkSEyyFoA3WQ6a8BbKLlLxGBzNvZL80MvEVGtjo2cusiknsElCwUJmqSeEMTWKR8f4WMmUxZEr62bKosejk2EcjyfBDBYhpLuhwSKEkDai95t+12hSDXhNFHRK6kyNnFYmplxB0oqR0yx1a3VGzlqJoF5mybTkMfB7YMOYpoK/BzbiIYSQToAGixBC2kgmEZyJyZdcI5MAeN3jxnSMnKGObgmacTwJnZI6FwlHIOFE/9GWTeq8H/PSvUyITJlp6V7QGqzxeExKK2vf62q18d6aNko4CSGkE6DBIoSQNpLWMFhWMksJZ3zi3Ih8uWI82dXNLJlnyjTiqa1dM9XRiSceE0jGzYxcOzJlmWRc2/hkDY0c0HyvKxtrvQghpBOgwSKEkDaiu/amHcYnX3KNO7rpZGJsxNOTcgIzct7aNbN4ssk4cgHNNKzEU8uUSdnY+ORLLpyYQNIwIwcAhUoT42Op5BFovjmy0mGJICGk2zEyWEKI2UKIVUKIDbX/+m7uIoTYKoR4QQjxrBBiIOz1hBDSLWS0StBsGB+9NVjGRk6jmUbeQgYrk4i3aU2Z1+UxyPgYx5OMQ0qgUK42HJMrucgad5PUMD4lFzHh7dMWlUztrfOTGAAAIABJREFU+9rsHbFNOyHkeME0g/UpAKullEsBrK79uxFXSikvkFL2R7yeEEI6Hj2jYF7q1pOMY0wns2RaUqfVTMNW6V4l0PiYtJwHPGMqJVCsNDc+NuIB0PQd5csVpC00IQGAXJN1WCoeEyPXoxNP7ZyJkSOEkE7A9P9y1wO4s/b3OwG8s83XE0JIR5FJxjTajVeNM0u6+zm1JeNTro5nOKKSSTqoBhgfG+3t1TqkZs/Oy/yZxwM0z/jYej8AmrZq90oe7cQTVCKYSTiIGXSTJISQTsDUYM2VUg4CQO2/JzcYJwHcJ4RYK4S4IcL1hBDSFWQSehkfO00h2lAimIzDrcrmxqdUMWqgABzNkAQbH9OMnGc0mm1qXLBg5HpUZikoHgsZuSAdO0ZOr0SQ668IIccDgb+yEkLcD2Cez6mbQ+hcLqXcLYQ4GcAqIcTLUsqHQlyPmjG7AQAWLlwY5lJCCJk21Hd1a/SbfFslaJWqRKlSbdjtzkaJoLq+WXmeLSPnfVYFs3uSvmPyJRcnz0gZ6gR3w/MMsGkzDWV8gjJLdjJyzY2PnRJO9VmNsFHCSQghnUDgTwgp5VsanRNC7BVCzJdSDgoh5gPY1+Azdtf+u08I8UsAFwN4CIDW9bVrbwdwOwD09/c3rkUhhJBpjJrIFiv+ZYAqG2ReInjUkCTj/obEVqYM8DarbdSlKG/BYOmU1OXKFuMJyMSc2Gtm5HQyS7Y2ggaaZ+RsGOB2xUMIIZ2AaYngXQA+XPv7hwH8avIAIUSPEGKG+juAtwJYp3s9IYR0E5mE97/dRr/pL1hqZR1UUletytpaL8NMTEqVhjXPxJiWCOp1w7Ow1itx1Jg2wkaJoG53P3U/kXVSwRm5dpU82lgjRwghnYCpwVoBYLkQYgOA5bV/QwhxihBiZW3MXACPCCGeA/AkgP+RUt7T7HpCCOlWxteqNJjwqglqq5sOqH2RWt0UQkppp/mEzgTeQkauJ6VTUmczngBj2qaMnHnbeb0SQdNMGSGEdAJGBktKeVBKebWUcmntv4dqx3dLKa+t/X2zlPL82p9XSSm/FHQ9IYR0K6rt9v99cJPveTWxf2j9fiMdNYH/wcObfc+rCffjmw4a6njx3PnoVt/zhXIVUgID2w5b0fnpk9t8z0spkSu7eH7nESs6/7l2Z8Mx+ZKLlwaHrejc9ezuhmNypQo27Rs106llwO5+YbDhmHzJxbaDOTOdWjyrX9rbcEyuXMGeoYKRDiGEdALcjIIQQtqIyvj82+Pbfc+rzNaq3zWeqGrpKEPy1A5/nZrB+o2hkVMZiV88vctfpxbPo5aM3F3P+RuFYsUzck9tNTNyKnN4z7o9Dcfkyy6e3TFkpDNuSF5uuPQY+ZKLFw2NnHo/D2040Fin7GKDoZFLODEkHIHHNjf+PWm+5GKroZEjhJBOgAaLEELaSFCJVLMSK5s6QXtx6RLUTa9t8QS0pNclaK1YqVJFpWreZ0mnVM7GO0rGY4gH7DsVtF+aLkGdCG29I0IIme6IZptDTlf6+/vlwMDAsb4NQggJxS2r1uPW1RumHL/x6qW4afmywPPUoc7xrEMIIccaIcRaKWV/4DgaLEIIaR8b9o5g+S3eNoBbV1w35fw96/bg4z9e2/C8LruO5HH5igcafs7DG/bjQz940ljnSK6EC/5hVcPPWbvtMP7wu48a65TdKpbefHfDz3l5zzDe/o2HjXUAYNnNd6PkVn0/Z8ehHN74lTVWdC74h/twJFf2/ZxDYyVc9IXGzzUMl694ALuO5H0/p1B2cfZn7rGis/zrD2LDvlHfz5FSYsnNd8OtSmMdQgg5VugaLJYIEkJIG+lNNy+pa7ZfUSidVHt0etqkk3BiDTdMtqkDHO0k6KtjqeQRaP6ObMbTNp0m3+1ipQrXQmklIYR0AmZ9gAkhhIRCGZI3Lj3J97yawN/wxsVmOrU1Pq9fPNv3/GjRWw/zkcsWGekknBhS8RjOO2Wm73k1gf/AJQuNdABgRiqOU+amfc+peN7Tv8BYpzcdx9yZ/joqnndecIq5TiqOM07q8T03WtO57tXzjHV6Ug5OOyHje26s9tzeeu5cY53eVBzzZvpvwKziefNZc4x1CCFkukODRQghbURtyHrRwhN8z48UvInoX7/1LCOduBNDOhHDqxf0+Z4fLZQBAH951RIjHcCbWJ81399gjdQm1h9/05nGOj2pOM6c0+t7brT23P70DWbGFPDe0Wmzs77n1Pv50KWLzHVScczu8c/KKUPyvovNjWlPKg63QfJopOh9D/7gInNj2pOMY0Y64XtOvZ/fe425MSWEkOkOSwQJIaSNODGBTMJpWJo1VqwgHhNINSmH06U3FR/P7EzRqXV0Cyrx06EnFW8aj02dhvEoHcMNmgHvuTWOxx0fY0qz5zZq8bl1WzyEEDLdocEihJA205uON1zLM1asoCcVhxDNW2tr6QRMeBOOPSMXbLCC25IH6zQ2pmoCPyNgjZsOeobRPJ4Zqfj4fTfSsWF82vV+ZqSD47HxfgghZLpDg0UIIW2mWWZptOhamVQDwUbBppFrNLEeLbpIOjGk4jYMVvAE3krGJx0fL22cjDo+I+VfCheGnpQznkGajCqps5VZavx+bBpTB2MlF37didUvFJjBIoQcD9BgEUJIm+lJOeNroCYzWixbNViNjMJooWKlnM7TcZpM4MtWsiOeTvOMXDIeQ8KxkJFLtifjo2N8bJYI+hkfmzo9qTjcqkShXJ1ybmTcMNr5LhBCyHSGBosQQtqMN+FttJbItWZIZgQYElvlWr3pRNN4glrTa+sEGJIZVjN/jdd6peIxxC0YuRkpr1TUN+NT01fdIE3oTcdRlUC+PDUmm5k/9fz93tHRtV7mmT9CCJnu0GARQkibCTIKtsqompYIluzp9DbNYNnMlDUvEbQdT9Vn36aRYsVqhlFKIFeaanxGi2WkE3aMXE8T4zOewbLwjpSO33fOZuaPEEKmOzRYhBDSZnpSzZtc2MosNeu6N1p07Rm5gJI6W4akNxVHruT6Gh+b8aiMW65BxsdWRq6ZIfHW4tnJ9qiyPL+s3FixgmzSgRMzX4vXLiNHCCHTHRosQghpM72p+HgTg8nYzPh4Xd0arPUqlK2V1PWmPePj+hofe4ZEGTU/czpatBdPUCbG5vsB4LtOzjOmdrI9yqj5fedsZkyblQh632sHMQtGjhBCpjs0WIQQ0mbaViKYjKNQrqLiTm06YHOtV3PjY7fkUX3mZFoRz4iPIRkpWCwRTDbLYNl8bs74Z07Vsdu1EmhiTNlBkBBynECDRQghbaYnFUexUkV5kvGRUlotqVMT6zGfNT42J7xBE+tei90Km+nYNKYNdUoVq10RgSYZH4ullUCz59aeeGx9rwkhZLpDg0UIIW2mkSHJl11UJayX1E2e8EopMVqy26wBaFCCVrBYgpZW8Uw1jDabT6jn729IXPSmba2Navzcxix3RQQaGB+LGblG3zd1jBksQsjxgpHBEkLMFkKsEkJsqP33BJ8xZwkhnq37MyyE+Kvauc8LIXbVnbvW5H4IIaQTUBPnW1dvmHBcTUyf2HzQio4yCt/9zcYJx3MlF1ICz2w/bEVHxfP9hzdPOF6tSoyVXKzbPWRFR2WW7nx065RzY8UKXtkzYkVHGYWfPLl9yrmRQgVb9o/a0am9n188vXPKudFiBTsO56zoqPfzq2d3+ersHS5Y0VHx3P3C4JRzY8UKDo+VrOgQQsh0xzSD9SkAq6WUSwGsrv17AlLKV6SUF0gpLwDwWgA5AL+sG3KLOi+lXGl4P4QQMu1Rv8n/4W+3TjiuuryteWW/VZ0fPz7RKKjMzCMb7Rg5pfMfAxONgurC9+SWQ1Z1fvnMRKNQrUrkSi6e2XHEqs6vn/c3Cut2D1vS8Urz7n1xr6/O+r12jJyKx+97NVaqYMsBO0Yum/Di8ftejRZd7DySt6JDCCHTHVODdT2AO2t/vxPAOwPGXw1gk5Rym6EuIYR0LI3WvDRqdR6VRqVfjRpsROVYx9Oo5X1UGsXjVqXvZr1RaVaaZ/MdZZMORIPmfTbfUSwmGm6MbPu7QAgh0xnht4O89sVCHJFSzqr792Ep5ZQywbrzdwB4Wkr5rdq/Pw/gIwCGAQwA+BsppW/NihDiBgA3AMDChQtfu20bPRohpPO4ZdX6KaWBAHDJ4tl4wifTc+PVS3HT8mXUoQ51CCHkGCOEWCul7A8cF2SwhBD3A5jnc+pmAHfqGiwhRBLAbgCvklLurR2bC+AAAAngCwDmSyn/NOim+/v75cDAQNAwQgiZlmzcN4q3fP1BAMDWFdeNH7/7hUH8xb8/PeV4VAaH8rj0yw9M+byH1u/Hn9zxpDWdoXwZ5//9fVM+b+22Q/jD7z5mTafsVrH05runfN5Lg8O45taHrekAwLK/uxulSnXC520/mMMVX11jVeeiL6zCobHShM87MFpE/xfvt6rzhn96ADsP5yd8Xr7k4pzP3mNV5223PIRX9o5M+LxqVWLJzStRlfZ0CCHkWKBrsAJb+kgp39JEZK8QYr6UclAIMR/AviYfdQ287NV4sXn934UQ3wPw66D7IYSQTmdW1r8L3VDef1PgyDqZpO/xI5Z1ZqTiiAlg8j7DR3J2dRJOzHcPMds6ADArk8C+keJEnbz9Jg2zMgkcmtT8oSXxZBPYeXjiGijb3zcA6PP5bo+WKlO+G4QQ0s2YrsG6C8CHa3//MIBfNRn7fgA/rT9QM2WKdwFYZ3g/hBAy7enLeJPQS8+YPeG4Mj7/z5vPtKKTTsSQdGJ47ekTCwvUxPpjb1xsRScWE5iZSeA1C/p8dT586elWdADv2Z09b4avzh9ffJpVnTPn9PjqvPu1C6zpzMwksHB2xlfn+gtOsabTl0lgfl96wjFlGK89z69IJbrOiT0Tjf1QzTC+5ZyTrekQQsh0xnRTihUAfi6E+DMA2wH8EQAIIU4B8H0p5bW1f2cBLAfw55Ou/4oQ4gJ4JYJbfc4TQkjXkXBi6Ek6OPeUqYYk4Qj87dvOsqIjhEBfNoFlc3sn6uS8ibUtHcDLxCw6caIhUZkYm2tt/IzCUM0ofOKqpdZ0ZmUTcGITO0OoeG644gyrOtVJpfoqno9ctsieTiaJbHJiO3ZlfD7wensGeFYmgWR84u9ulWF8T789A0wIIdMZI4MlpTwIrzPg5OO7AVxb9+8cgBN9xn3IRJ8QQjqVvkxiSonWkVwZfZkERKOWb5Z0hvJlZBIOUnH/jm82dQBghqWNeYN0VGbQls6uI5MMSYt0thwYa7nOzEwCQ/lJpZUtiqcd74cQQqYzpiWChBBCItCXTU5ZazOcL1ufhM7KJKboHMmVG64Di0pfNjllbddQvoyZ6fiUTJAJs7KJKTpHcmXEm7QIj0JfJjme6VO0wig0ej8AMCvrv4Yukk42gaF8CfWNrVoSTzaBXMlFqVIdP9aKeAghZDpDg0UIIceAvkwcw5ONQr5k3WA1yii0QmdyPEP5sm/TA1OdRvG0I/OXiseQTtjN/A0XyqhWpxqfmWnTKv6JOmV34j5eqkTQ5jtS36v6Z8cMFiHkeIMGixBCjgGzMskpXemG8mXrv+Xvy/pkSFqWKZvcDa/UsJNhVPqyCQzlyhMyMS2JJ5vAWMlF2a3PxJSsZ/5mZhKQEhgpHC3fO5IrY0Yqjrhj70f0rNrzqf8uDOXLcGICM5pseByWvtr3d6juu62+57afHSGETFdosAgh5BjglWz5lO5ZNz7JKZml4bz9EkEVz+RMjHWdTBIlt4pC+ajxGW5Bpkzd9+RMjG3DOGvckBzVaVc8KmNqM/M3q0EGy3bmjxBCpjM0WIQQcgzo81l7M5QrY2YLSvdGihVU3IlrYlpRIliV3p5H4zr51sTjfXZdhqRF8ajPbovOhIyPfZ2Z7Y6nPlPWAh1CCJnO0GARQsgxoC+bQLFSRaG2JqbiVjFSrLQkswQAw3UlaC0pRVSZi9zETIz1jFzDzFJrjMJknbZlylqQ+fPTaUVppZ8OywMJIccTNFiEEHIMUBPbf773FQBHDdAz2w+3ROcb968HABQrLvJlF+t2DbVE5zu/2QgAkFLiSK6MV/aMtETnew9tHj92JFea0urcls6dj24ZPzaUL2Pn4VxLdH7yxPbxY0dyJewdLjS6JJpOzeD8f2t3jB8bypdxeKzU6JJoOrV4/vuZXePHjuTKGC1UGl1CCCFdBw0WIYQcA1RG4fuPeBN49Rv/B9cfsKqjJtb/+ti2CTqPbjpoVUdlxH76pDeBz5VcVKoSA9taYxh/8bQ3gXerEiPFCp7badcwqnjuem5w/NhQvoyXBu0aRpV5u3vdnjqdCjbus2sYlc79L+2r0ylj2yG7hnFGOgEhgIc2HP0eD+XL2D1k1zASQsh0hgaLEEKOAZNLsyZ34GuVztCkdV+t0pm8V1WrdEYKZdQ1FGyZTrHiIldyG4yOzuQ1alLKCR34bJFNOkg4E5tZTF4DaAO/roSTm7kQQki3I2QrfjK1mP7+fjkwMHCsb4MQQiJxy6r1uHX1hsBxN169FDctX0Yd6lCHEEKmAUKItVLK/sBxNFiEENJ+hvJlnP/39wEA/v/27j/Iqvq84/j7wy5w+bVXDZHdCi3aqjGx+eFExsS2JsEIQkabaczojJZpm2biVGPS2oqxafojHXcwicGkmcQirZ0QbSQ2MrEYYYyO1h+AKBpECMENrOzyI8gFlcvusk//OGdXqOAy9Zx77u79vP7xnrsHnufrs9x7nnO+53w72udy5+MdfGXZ+sHtrPT09XPG3y4f/HuXPt3J9fesyzxORHDmlx+gp6+fjva5LH++i6uXrM08DsAH/vFBXnm9l472uTyyaRfzFq/KJc757Q/x8t4DdLTPZU3HHj713SdyiXPRrY+wacerdLTP5YXt+5hz26O5xPnkd/6HZ7bupaN9Lr/69WtccMvDucS5ctFTPLZ5Nx3tc9n96kE++NWVucQxM6u1422wPEXQzKwALaVmxo95Y12grkr1TVO4sjCmeRSTJ44d3O6uHMg8BoAk2sqlwe2uHO+5aS2PG3yd13iAYsazbySM54043b73yswaUHbLt5uZ2XGTRGu5RFO6yGt35QBTWkr80TlTM4/VVi4xYWzSzHVVqpw0YQxXnfdbmcdpbSnR25est9W9r8rY5lF87oLTMo/TVi6xa39y4D7QKFzz0d/JPE5ruUR5XPI1OdAo5DKeltJgsz0wnj/7vemZx2ltGUfzKBERg+PJ4/egrVxCJEsPDIzn8nOnZR7HzKxe+QqWmVlB2solJpXSA/h9VdrKpVzuTWktlxg3OjmA765UaW3JJ05buURTehWuqzIwnjMzj9NaLtGfzm7vrlSZPHEs18/KPk5buUS1t5+IoKtSZfyYJm6Y/a7M47SWSxzoPURPXz/dlSqjBDdefFbmcdrKJfrSpy52p4+Bv+HifMYTwO5XewavMP7lRb7nyswahxssM7OCtLaMG7yS0F2pHjFVLEtt5dLglYSBxicPreVx7KgcpL8/6K4cOGKqWJbaWkrsea2Hau+h3MdzsK+fva/30r0vGY+U/TTOtnKJCNi5v0pXpcrJk0o0N2X/9TxQj+5Kle5KlUmlZiaOzX4iy0A9uioH6KpUaR4lJk8YO8SfMjMbOTxF0MysIG3lEjv2H+TrD25Mp+5luybRgNZyicqBXhY88CLd+6rkcOwOJOPpOdTPzcs30FWp5nLwDm80Cu3LN9BdqdJ7qD+XOAONwi0/TeozMP0xawPjWbjyF3RXqgT5PHxqYDzf+dlmDvQeyuWeP0hOHAAsenQLY5ubGDe6iVGj8ollZlaP3GCZmRVkSrnEof7gWw9tBmDt1r25xBk8sH74lwDseS2fNbcGGoV/ffQlRjeJzkN5NQrJAfy/P/4rWkrN7Kv25RJnYDw/WLX1iKuAWRsYzz1Pd/Lb75zAjn0Hc4kzpSUZz4+f3c77ppbZ81o+61MN/L7d/3w3HzrtHew/mE99zMzqlacImpkVpK0ln6lt/9fAFYW8HT5Vrzen5gqOfEpdXs0VHDmenfvzaXrgyPHk+XS/KYf9vm3PMc4J40cztjk5vOjK8SmPZmb16m2tgyXpMuDvgbOAGRFx1MWpJM0GFgJNwKKIaE/fPwn4T2A60AF8OiJeGSqu18Eys+FuqEVZs1qM1XEcZyTGMTMrQk0WGpZ0FtAPfA+4/mgNlqQmYBPwcaATWA1cEREvSFoA7ImIdknzgRMj4oah4rrBMrOR4tJvP8a6zsrgdl6LsV5x+5M8seXXucf5zJ2rWblhZ+5xPn/XMyxbtz33ODfe+xx3rdqWe5x/+skL3PHYS7nH+caKTdx2WAOUV5zvPfJLbl7+Yu5xzMxqqSYLDUfEhojYOMRuM4DNEbElInqAu4FL059dCtyZvr4T+MO3k4+Z2XAz6+zWmsSZXaM4s95TmzgX1+z/W1tN4tRsPDWqT61+D8zM6lEt7sE6Bdh22HZn+h7AlIjoAkj/e/Kx/hJJn5W0RtKaXbt25ZasmVmt3LpiEwseOPIc1fT593Prik2Zx/nKsvU1ifPXS5+rSZyrl6ytSZx5i1fVJM6nvvtETeLMue3RmsT5yNcezj2OmVm9GnKKoKSVwNFORd0UEfel+zzMsacIXgbMiojPpNtXkdyvda2kvRFxwmH7vhIRJw6VtKcImtlIM33+/TWZRuU4jjMS45iZ1cLxThEc8jHtEXHh28ylE5h22PZUYGDi/A5JbRHRJakN2PmmP21mZmZmZjZM1GKK4GrgdEmnShoDXA4sS3+2DJiXvp4H3FeDfMzM6s51M093HMdxHDOzEeDtPkXwk8C3gHcCe4FnI2KWpN8geRz7nHS/OcA3SR7Tvjgi/jl9/x3AD4HfBLYCl0XEnqHieoqgmZmZmZnVUk0e014UN1hmZmZmZlZLNXlMu5mZmZmZmb3BDZaZmZmZmVlG3GCZmZmZmZllxA2WmZmZmZlZRtxgmZmZmZmZZWRYPkVQ0n5gY9F52KDJwO6ik7BBrkd9cT3qj2tSX1yP+uJ61BfXo76cGRGThtqpuRaZ5GDj8Twi0WpD0hrXo364HvXF9ag/rkl9cT3qi+tRX1yP+iLpuNaJ8hRBMzMzMzOzjLjBMjMzMzMzy8hwbbBuLzoBO4LrUV9cj/rietQf16S+uB71xfWoL65HfTmuegzLh1yYmZmZmZnVo+F6BcvMzMzMzKzuuMEyMzMzMzPLyLBtsCS9X9KTkp6VtEbSjKJzanSSrpW0UdJ6SQuKzsdA0vWSQtLkonNpZJJukfSipOck/ZekE4rOqRFJmp1+Rm2WNL/ofBqZpGmSfiZpQ/qdcV3RORlIapL0jKSfFJ2LgaQTJC1Nvz82SPpQ0Tk1MklfTD+vfi7pLkmlY+07bBssYAHwDxHxfuDv0m0riKSPApcC742I9wBfKzilhidpGvBxYGvRuRgrgLMj4r3AJuDGgvNpOJKagH8BLgbeDVwh6d3FZtXQ+oC/ioizgPOAv3A96sJ1wIaik7BBC4EHIuJdwPtwbQoj6RTg88AHI+JsoAm4/Fj7D+cGK4CW9HUZ2F5gLgZXA+0RcRAgInYWnI/BrcDfkPxbsQJFxIMR0ZduPglMLTKfBjUD2BwRWyKiB7ib5KSQFSAiuiJibfp6P8mB4ynFZtXYJE0F5gKLis7FQFIL8AfAHQAR0RMRe4vNquE1A+MkNQPjeYveYzg3WF8AbpG0jeRqic8IF+sM4PclPSXpEUnnFp1QI5N0CfByRKwrOhd7kz8FlhedRAM6Bdh22HYnPqCvC5KmAx8Anio2k4b3TZKTcv1FJ2IAnAbsAv4tnba5SNKEopNqVBHxMkm/sRXoAioR8eCx9m+uVWL/H5JWAq1H+dFNwEzgixHxI0mfJunwL6xlfo1miHo0AyeSTPU4F/ihpNPC6wDkZoh6fAm4qLYZNba3qkdE3JfucxPJ1KgltczNANBR3vPnU8EkTQR+BHwhIvYVnU+jkvQJYGdEPC3pI0XnY0ByXHUOcG1EPCVpITAf+HKxaTUmSSeSzHo4FdgL3CPpyoj4/tH2r+sGKyKO2TBJ+g+SucIA9+BL2rkboh5XA/emDdUqSf3AZJKzL5aDY9VD0u+SfACskwTJdLS1kmZERHcNU2wob/XvA0DSPOATwEyfeChEJzDtsO2peGp5oSSNJmmulkTEvUXn0+DOBy6RNAcoAS2Svh8RVxacVyPrBDojYuDK7lKSBsuKcSHwUkTsApB0L/Bh4KgN1nCeIrgduCB9/THgFwXmYvBjkjog6QxgDLC70IwaVEQ8HxEnR8T0iJhO8iF9jpur4kiaDdwAXBIRrxedT4NaDZwu6VRJY0huTl5WcE4NS8nZnzuADRHxjaLzaXQRcWNETE2/My4HHnJzVaz0O3ubpDPTt2YCLxSYUqPbCpwnaXz6+TWTt3joSF1fwRrCnwML0xvNqsBnC86n0S0GFkv6OdADzPNZerNB3wbGAivSq4pPRsTnik2psUREn6RrgJ+SPP1pcUSsLzitRnY+cBXwvKRn0/e+FBH/XWBOZvXmWmBJelJoC/AnBefTsNJpmkuBtSRT/Z8Bbj/W/vIxsJmZmZmZWTaG8xRBMzMzMzOzuuIGy8zMzMzMLCNusMzMzMzMzDLiBsvMzMzMzCwjbrDMzMzMzMwy4gbLzMzMzMwsI26wzMzMzMzMMuIGy8zMRjxJ50p6TlJJ0gRJ6yWdXXReZmY28nihYTMzawiSvgqUgHFAZ0TcXHBKZmY2ArnBMjOzhiBpDLAaqAIfjohDBadkZmYjkKcImplZozgJmAhMIrmSZWZmljlfwTIzs4YgaRlwN3Aq0BYR1xSckpmZjUDNRSdgZmaWN0l/DPRFxA8kNQGPS/pYRDxUdG5mZjay+AqWmZmZmZlZRnwPlpmZmZmZWUbcYJmZmZmZmWXEDZaZmZmZmVlG3GCkTMKGAAAAKklEQVSZmZmZmZllxA2WmZmZmZlZRtxgmZmZmZmZZcQNlpmZmZmZWUb+Fxw0oyEXQ0SRAAAAAElFTkSuQmCC\n", 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YYBER1YmeeAoTmkLDHptQjoQkkcaEpiBE9iVyE6LBwVY7bXHiKUSCPkSC+xK5CU3lS7DGR/Y1bZjjKU/COHQ+VFnGkzsPo+LoHk/uuCcMTbCaQuhNppHN1t8CWURElcIEi4ioTsQS6VFtbePLlCiMHxGntUxxJkSHJ4zlGs+4cAAB/75feWWpLCVS8PsELeF9idz4aBC9ibTW+93EClSW4ilj2P3LyhVHKWBvngUwiIjIxASLiKhOWJWloSY0hfQnJPH0sKqFGac8laWRCeOEaBnGk0iPWv1uQpOZ+OisxFgJ8PDKXwgpI4tEWt/8qFg8jXBgdOXPOgZd8rUiWp+LHs3VTCKiRsIEi4ioTuSvLAXKk5BUqII1MvFpjQbRN5BB2tBbick3nqwC+lL6KjGxRCZvHOsY9MXJP55yxGnJU/nTHYeIqNEwwSIiqhN5K0u5io/OFrSexOi5Xq1NQQxkskjqrMTkSRSsSkyv1kpMalTlbzBR0FiV64mnCo5HZ/WvJz66krkv8dFXWepJVGY8RESNhgkWEVEdUEoVrFwYWYW+AY2VmDyte+WqkIxMGOu54tNbwcpSI42HiKjRaEmwROQCEXlTRNaJyPV5nv83EXk592+liBgiMjH3XKeIvJZ7rkPH8RARNZr+lIFMVuHVrp5hj1stdjc/vEZLHCOr0JvMYFV377DHrcUofrx8rZY4gFkFWbe9b9hj1nhue/wtbXFiiTQ27ooPe8yq0P3yqbe1xelJpLE1lhj2mJWQ/O8zG7TG2dk3vFJljefu5zfpixNPozc5PJGyzs+9L3Zpi0NE1Gg8J1gi4gfwUwAXAjgKwBUictTQbZRS31NKnaCUOgHAlwE8rpTaPWSTs3PPt3s9HiKiRmRVDJ5at2vY49YF/K+e7tQSZ2/ugvq5t3cPe9yK89vnNmqJM5AxkEgbeGlTT944f+rQcwFvVf5WbhmeMFpx7ntps5Y4gHmO3tw2ImHMxXngtW5tcXoTaby9sz9vnIff2KYtTiyRRtee/Anjo2/u0BaHiKjR6KhgzQOwTim1XimVAnA3gEuLbH8FgD9oiEtENGb0FLiJ7MgWO+9x8rd+jZzz41WhFjPd44mnDKSN0fPTdI8nm1V5543pjgPk/ywMvceXtjh5xhMO+BEdsnohERGNJl4nRovI+wFcoJT6VO77jwKYr5S6Js+2TQC6ABxuVbBE5G0AewAoAP+jlLq9QJyrAVwNANOnTz9pwwZ97RZERLXslqVrcGue1rz5MyeOqjQBwLXnzsJ1C2YzDuPUdBwionojIivsdNzpSLA+AOD8EQnWPKXUF/Js+yEAH1FKvXvIYwcqpbaIyAEAlgL4glLqiWIx29vbVUcHp2sR0dixfNU2fPIu8+de56KFg49v3BXHmd97dNTjbj3z1i5c8YtnR73ezr4BtH9rmbY4r3b14JKf/GPU6yVSBuZ89UFtcdZt78N5Nz8+6vWUUjjsK4uRVXribI0lccp3lo+KAwDHfO0h9A1ktMTpTaZx3Ncfzhtn3reXYfveAS1xMkYWh9+wJG+cc77/GNbv7NcSh4ionthNsHS0CHYBOHjI99MAbCmw7eUY0R6olNqS++92APfBbDkkIqIhCq0S2KK5NaxgnHBl4kSCPvh9kvc5N/oLxBERNGscU99A4VX1dL53fcnCq0Xq/Cz0DxRejl/3Z46IqNHoSLBeADBLRGaKSAhmEnX/yI1EpBXAWQD+NuSxZhEZZ30N4F0AVmo4JiKihmIlJJ96x8xhjzeHzfkwpx22v5Y4VkLysVMPGfZ4OOBDwCdoP2Q/LXGsROGKeQcPe1xE0Bzy4/hprXri5MbzvrkHjXquJRzAUW3jNMUxE5JLjm8b9Vxz2I/DD2jWEsc6PxceM3XUcy3hAA6Z2KQljnUD5vPmHDDqueZQAAe2RrTEISJqRJ7/DKWUyojINQAeAuAHcKdS6nUR+Uzu+dtym74XwMNKqaFLH00BcJ+IWMfye6XUg16PiYio0VgX1l8aMdclHPAj6Bccf/AELXGshOSasw8f9rhV8Tn6wPFa4vTnLuCvPvOwUc+1hAOYNUVX4mPG+fjpM0c91xwOYMYkvYnPh+cfMuq5lnAArSNu3OyWNZ4Pth886rnmUAAhv57bW1rjec+JoxPTZo3jISJqRFrq/EqpxQAWj3jsthHf/xrAr0c8th7A8TqOgYiokVkVkqY8K7g1hwMFW+Gcsl4nX/tcSzgweBxeWa9jVeCGKsd48rXpNWsdTy5Onva5lojO8VjvW/44m3bHRz3uRl/Rz4Ff23iIiBqRnj91ERFRWfUlM2gJB+DLMz+pJRwoOjfHUZyBDESAptDoxMdMsArPNXLCukAfFx69jHlLJFBwjpbbOPkShXHhAPqSesZjvf95E7mQvgTLev/zxWkJBwYrg57jJK3zkz+R03V+iIgaERMsIqI60D+QyVvtAazER1+C1RIKINe6PUxz2F908QNHcZIZ+MRc1GIknePZW7SCpW88VmJTqPK3V1sCbAy+5kjNYb+2RLtYYtqs8fwQETUiJlhERHWgL5UpuOpds8bKhZnIFY6jM5FrDhdI5DRWfPoHMvD7JG8ip3s8QIHKUkTv+QHyt1a2hIP6EuBi4wkFkMpkkcpktcQiImo0TLCIiOqA1SKYT7PmFsFKVMr6Byoznv4BA80hf95ETmvlL5lBwCcIB/IncvpaBIvPjUoZehKfYnGsxzgPi4goPyZYRER1oFhCMk5rJcZAS2T0vCggN8dHV4KVKjIejXN8+oq8b9Z4lFKe4/QXqci1hANIGwoDGe/Vpb6BDIL+womcdSxeFa2U5RbyYJsgEVF+TLCIiOpAX9HWPY1ziQYyaClQwdLZUrc3WWI8KUNL4tOXzBS8MW5zOIBMVmFAS8XHKJrIWcfiValEzjwW73H6BgyE/D6EA/kXOwGgre2RiKjRMMEiIqoDxSoxWucSJTNoDlWm4lNsPEZWIZn2nvj0F5m7pjchSRcdDwAtSXCp82Mei57xFGoVbdaYMBIRNSImWEREdaBUi2B/Sk/i0zdQuOLTEgkgq4BE2nui0F+k4jNOa6JQvEXQPBYdlSWjyNw1/+CxeNU3kMG4IhU581j0jKfg50Dj+SEiakRMsIiI6oB5AV/4wlopIJ7SkPgUmRvVrDnxKTYeQE+iUGpxEOtYPMcZyBSZuxbUFqdoRS6XEO3VdX5KVMp0taUSETUaJlhERDVuIGMgZWSLzo0CvF/AK6XMFrSCFZ9cJUZDa1hfible1jZeFVt2XufcqOLj8Q8ei+c4Rc9PpRJTqyKn5ybNRESNhgkWEVGNsyoFL3Tuzvu8dSH800fXeYozkMkik1V4ZVNP3uetisYvnljvKY5SCv0DGby+pTfv89Z4fvWPtz3FAczEZ+22vXmfsxKF3z23wXOc/oEMOnf2533OGs+fOjZ5jtM3kMGWnnje56zE668vbfYcpz+VwY69A3mfs8bzwKvdnuMAwC1L12h5HSKiWsEEi4ioxlkVicfX7Mz7vHXB+5tnvCUKVpyn39qVP06uBe0PL3hLFKxErmPDnvxxcuO550VviYJSCv0pA690xfI+b81luv8V74lC30AGb3QXSuTMOEtWbvUcp3/AwLrtBRK5XAK8bNV2z3H6BjLYsLt4IvfE2vyfR6duXb5Wy+sQEdUKJlhERDWuVKtcoZYxp0rNqSnUMuZUpcaTTGdhZAsv/KErjlWRK6TQYhFuFItTaJEN3XGCfl/e+3C5UahaSkRUz0THqlOV1t7erjo6Oqp9GEREZXfL0jV5/8J/7bmzcN2C2SWfZxzGqcc4RES1SERWKKXaS27HBIuIqLY9vmYHrrzzeQBA56KFo55ft30vzrv5iYLP29XRuRvvv+2Zgq+zpSeB0xY94jnOqu5eXHjrkwVfJxZP4/hvPuw5zsZdcZz5vUcLvk7ayGLWDUs8x9nZN4D2by0r+jqz/3MJUpmspziJlIE5X32waJwTv/kw9sTTnuIopXDoVxZDqcJxTl/0CDb3JDzFAYAZ1z8AwNv7T0RUKXYTLLYIEhHVuESqeEtdtMBy2k6VWua9KaSnBa1UnKiuOOni71vQ70PQL57jJGwsj6/jvYuX+ByYcbx/FgYyWZT626uO8dTjH3iJiOzQ1xhORERlYSUkV556SN7nm4Lmxe5ZsydpiXPFvIPzPm8lPqcdtr+nOFZC8v6TpuV9PhTwIeATnHDwBE9xrPFcesKBBbeJBv04qq1ZS5yLjplacJumoB8HtkW0xDlvzgEFt4mG/Jh1QIuWOMU+T00hPw6Z2OQpzkAm62l/IqJapaWCJSIXiMibIrJORK7P8/w7RSQmIi/n/n3V7r5ERGOddcH7+bMPz/u8lfjMm+kx8clVfK4+87C8z4f8Pvh9ghOnT/AUx6rEXHXajILbREN+HHNQq6c4ViL34fn5E1PArPgcMXWcpzjWeD5wcv7EFDDHM3Oyt8QnkTbH854TDyq4TVPIj2n7RT3Fscaz8NgiiWnIjynjvSWMdip/RET1yHMFS0T8AH4KYAGALgAviMj9Sqk3Rmz6pFLqYpf7EhGNWdaFaKHWuXDABxHvF6xWIleo/UtEEA36S7b4eY0DmJUlr+OxVsIrGiekcTzB4nHKfX4AIKLh/JT6vAHm+dnRl/8+WXbF00ywiKgx6ahgzQOwTim1XimVAnA3gEsrsC8R0ZjQn7IShfx/ExMRNAX9gxUOt+IDNi6sQ34kvcYZTBQK/42vKeR9PNb+pRKFiownGPCeYOUSxmiw+PvmdTz9NhK5ppC+8RARNRodCdZBAIbedbIr99hIp4rIKyKyRESOdrgvEdGYlUgZCAfM9rxCoqFARSoxTVoqPrlEoWgip3E8RROFSo3H77liYx1nsftd6RxPsYRRZ0WOiKjR6Eiw8v3GH7k00IsADlFKHQ/gxwD+6mBfc0ORq0WkQ0Q6duzY4fZYiYjqTjxllFy1LRrylVxtsGScdAYhvw8Bf+FfDTpaBBO2WgR9g3PC3NqXMBZPFCozHr+G81PZFsFS49GVMBIRNRodCVYXgKEze6cB2DJ0A6VUr1KqL/f1YgBBEZlkZ98hr3G7UqpdKdU+efJkDYdNRFQfzASr+JTZpqD3ik8iZaCpSHUE0FO56E8ZCPl9CBZJ5Jp0VLAGbFSWdMz1sipLJVoevZ8fazzlba2sVOXPSqB1LJVPRFRLdCRYLwCYJSIzRSQE4HIA9w/dQESmiojkvp6Xi7vLzr5ERGNdIp0peW+oqKYL62LtgYCmuVEpm+PxmmClDQT9glCgWCKnZzxABeeuFW3h9D43ytYiFyE/UpksjKz7e1lZ4wkVSbSJiOqR51UElVIZEbkGwEMA/ADuVEq9LiKfyT1/G4D3A/isiGQAJABcrsw7DObd1+sxERE1kv4BA80lEpImLXNibCQ+wQB29yc8xind8qgn8TEQLZEw6prrFfCVTuS0zSkrUmWM5hY7yWYVfEXm7BVjLapSqiIHmAuJtITdXUpYi6oUe9+IiOqRlhsN59r+Fo947LYhX/8EwE/s7ktERPskUoaNxMePnnjaUxw7rYhmZcn7XCI749GxWEPJ8eiYG2Xz/CTSBpRSyDV0uIiTgd8nRSs+1nEkM6XPZeE49lZftI7JdYKVe9+ZYBFRo+FPNSKiGhdP20gUNLWglawsaVkOPlO0OgLkxqNhblSpOWVWpcxsqnAnnrIzngCUAgYyWQ9xzBbOYgnaYGXJw3uXSBnwiXl/tUKseWDJlIfx5D5H4UDxc0REVG+YYBER1Tg7FRI9iyjYWa1QT6ubrfF4THzsjierI/GxMR5rW7fsVjK9xrEqmXYSubiHlR6tJDDgspWRiKhWMcEiIqpxiVTpOVhmS53XVjd7lTLPiyikbSQ+QT+MrELK8JL4ZIou0W7FAbxXfOwnPu7PUTxloLlEO150yNwotxLpjK3zYx2TW9a+GQ8LZRAR1SImWERENa5/wE7iE9CyimDJylLQj0xWIVXmio/VglbuxGdfJcZrxad0pcw6JvdxMiUX7dBRKesfqNx4AHhaiZCIqBYxwSIiqnEJG4tCNIX8SBsKaU8VHwcX1l4SkoEMoiUqS00a4vRXMvEpdZ8yHe+bnfEEvSemZqJtczxaKljuP7NERLWICRYRUQ1LG1mkDWXr/lSAx9YwOzc01lBZiqcNNNtYfALwPmepIuOx08KpYTx2564B+27i64adFkFdlT8AyBisYBFRY2GCRURUw/bd+6h4ohDxOJcoY2SRMrI2Kj6+3HF5m0tUKlHwOh4zjpO5ROUdj665XnZWX7SOyS07lbJ958f7Ihecg0VEjYYJFhFRBdyydI2r/ayL0KfX7Sy6nXVB/LNH17mKY1UiXujcXXQ7qwXtjifXu4qTMbJIZbJ4ZWNP0e2s8dz1dKerOICZKLyxJVZ0Gysh+f3zG13HSaQNrN22t+g2VqXszys2uY4TT2fQuau/6DZWIve3lza7jpNIGdjSU/xm0tZ4Fr/W7ToO52ARUaNigkVEVAG3Ll/rar/+3EXo8tXbi243mJA8s8FVnPiAmWA9udZeIvf7590lClYi9+zbxRM5K86fV3S5imNkFQYyWaywmcj97eUtruIA5iIkr23utRVn8WtbXceJDxhYvbVUImfGWbqq+OelmP5UBm/tKJ7IWXEeX1P881IM52ARUaNyd/t1IiKyZSBj4JdPve16f7stZZESc7RKsdsiV6oVrpSaG4/HOFYiV4rX8QD22v68nh/A3jkKB3wQATzcpoxzsIioYYmXmzhWS3t7u+ro6Kj2YRARFXXL0jV5K1fXnjsL1y2YrW1/xmGcRoxDRFRrRGSFUqq95HZMsIiIyufR1dvx8V+/AADoXLTQ8f5PrNmBj935fMn9V26O4eIfP+U6zooNu/G+nz9Tcv/1O/pwzg8edx1nVXcvLrz1yZL7b+tNYv5/LXcdZ+OuOM783qMl99+bTOPYrz/sOs7OvgG0f2tZyf2zWYVDv7LYdZxk2sCRNz5oa/85Nz6IRNpwFUcphcO+shhZVTpO+7eWYmdfylUcADj2aw9h74BZaXzrvy6C3yeuXoeIqFLsJlicg0VEVEZ22seKsbsanNfWMLtxSi17riuO5/HYXKbca4ug3ZZHn08QCbr/letkVcBSKwAWM5DJwu6aE17OkVJq2BLvnIdFRI2Ec7CIiMoolbvx73lzDnC1fzJ3EXrlqYcU3c5KFM490l0cK1G4Yt7BtuKcMWuSqzjWeN4/9yBbcU45dKKrONZ4Lj3+wKLbBfw+hPw+HDttvLs4ufFceMzUkttGg37MPmCcpzh2PkeRoB9HTnUXxzo/Z80ufX6jQT8Om9zsKk7aUDCyCiG/IJX7moioUbCCRURURulcBesdh7tLSKwL68++8/Ci21lVi9M9xvnkOw4tup1VtZg/01vi87HTZhTdLuj3IegXnDh9P3dxcuO5fN70kttGQ34cc2Cruzi58bz/pGklt20KBTDbZeJjxbnkhOKJqRnHj0NdJj77Esa2kttGQwEctF+TpziTWsIAzISLiKhRMMEiIiojq4I1tB3KCevCulQrm5X4JFzGsSoXpdq+QgEfAj5xfSNb6/jstOZFg37XN+a1Ox7ATEgqMp6QhvHYiONpPCkH71vQ7/pGw9Z4WiJmIw0rWETUSJhgERGVUSpXwUp6vICPhIr/uA75ffCJ/eXJR8WxmchZ23hNSOwsWx4N+T2Mx3zfbY/HbQI8eH7svm8ux+MgwYpU9Px4S+TGRYIAOAeLiBoLEywiojKyEiwvlSWfmAlUMSKCplDAw4W1g4RERyXGVmXJy3gqVFlykphqqSyV/rXdVKFKmZf3zTo/LWFWsIio8WhJsETkAhF5U0TWicj1eZ7/sIi8mvv3tIgcP+S5ThF5TUReFhGuvU5EDWWwRdDDhXU06IdI6SWsoyH/4AWy4zi5/cIBmxfwZW55tLbxOp5SlT/AW0LiJJFr0nB+7FSWmkIBD+cnl2jbbRH0OB6rRZA3GyaiRuJ5FUER8QP4KYAFALoAvCAi9yul3hiy2dsAzlJK7RGRCwHcDmD+kOfPVkrt9HosRES1xmsFK5E2bC+H7WXuTTJtIBL0wWfjXkRRDZUle4mCh/E4qiwFEEukXcVJOJzrtXlPfVSWyj3Xyzo/460EixUsImogOipY8wCsU0qtV0qlANwN4NKhGyilnlZK7cl9+yyA0sstERE1AKuC5eWC126C5WluVMqwfY+raNCHhM37TI2KkzYQDvhs3VTWU0udo8U0fK4Xa3CyKISnuVG5/eycIy1zveyMR0MiZ83BMjgHi4gaiI4E6yAAm4Z835V7rJBPAlgy5HsF4GERWSEiVxfaSUSuFpEOEenYsWOHpwMmIqoUHXOw7N4M12uLoN04TaGApzlLThJGL+MJ+X0IlJi7BnhrqXO6up/XFkG7cVyPx0HlrykYQMrIImM4T45GzsFiBYuIGomOBCvfnyHz/qQUkbNhJlj/MeTh05VScwFcCODzInJmvn2VUrcrpdqVUu2TJ0/2esxERBWR9lrBSjlJfLxVfCJBe78SvFaWKjKelLPxeKnEBHyCoM1EznNrpY05ZWainUXWRdLiNJEbuo+jOKkRCRbnYBFRA9GRYHUBOHjI99MAbBm5kYgcB+AOAJcqpXZZjyultuT+ux3AfTBbDomIGoKOOVh25isB3loEnVaW3I8n66gi52VOWWVaK+2PJ5J739wkPsmUvdUkgX3JkavEx2GLIODujwcj74PFChYRNRIdCdYLAGaJyEwRCQG4HMD9QzcQkekA7gXwUaXUmiGPN4vIOOtrAO8CsFLDMRER1YTBBMtLBctuohAKuJ5LFK9UpSyVcZAwVm48ibQBpdxUfDK27oFlxQGAZMb5e2eNx85qklYcN+fI2sfWapJB73HGD95omHOwiKhxeF5FUCmVEZFrADwEwA/gTqXU6yLymdzztwH4KoD9Afws98sho5RqBzAFwH25xwIAfq+UetDrMRER1YrBRS48VLAOtJsoeFw2e1zE5iIXIb+nGyc7WRXRSnzsJBYj49hO5EJ+KAUMZLK29xmM4zCRs/axu6DIYBwnFbnca7uZ72XN+XOSyHmplDWzRZCIGpDnBAsAlFKLASwe8dhtQ77+FIBP5dlvPYDjRz5ORNQoPFewHC5y4aWl7oBxYVvbNoX8iLtNfJysVhjyI+sy8XHSIji0EuM4wXJyfobE2d9RFGcJo5cKlrOKqYc4aQOhgG+w5ZE3GiaiRqLlRsNERJSf5wpWKmu7Bc3rYg1O5iwZWTU4Nmdx7CdLg3OJ3CYKDhJTAK6WNk+knZ0fcx/3lSVbcYJexuM8jqs5WLnzE/CbCXqaCRYRNRAmWEREZTR0kQs3c3ycXFg3Bf3IZNXgyoVOOEtIci1oKedxHFWWPLagOR6Pm8QnZSBqc7XCJg+LQripLLl93+yuvmhVIr2cH7/PqmBxDhYRNQ4mWEREZWQlO9YcHyeUUo5bBAH3LVuOW9Bc3Gw44SAh8TweFy2CbuLYr/gEPMVxen5cV5Yctwi6q/xFQ34Ecjec5hwsImokTLCIiMooNSSpcnrBmzYUjKxyXrlwuWy2kxZBoBIJifcWNFtxPCZyzitLLhMSF3O9nMdx/r65rchFhrQIcg4WETUSJlhERGU0tGoVd9hK5eSmr8DQxQ2cXcCnjSzShhqs5JTi6cI6bQy25JVitaC5nUvUVIHE1KzI2R2P+8QnmarQeJycHw+JXDJ3fqwKFudgEVEjYYJFRFRGQxeCuO2xtxzta80J+se6nba2ty7073hyvas4z3futrW9daF/19OdjuIYWYVUJouXN+6xtb2VKPz++Y2O4gBmovD65pitba3x/HnFJsdxkmkDa7b12trWSpT/9tJmx3ESaQNv7+y3ta2VmC5+rdt5nJSBzXvitra1zs+yVducx0kb2N6b5BwsImpITLCIiMoobWQxLnevn/99doOjfa0KxPLV221t3zSYkDhLFKxK2ZNr7SVy+xKSLkdxrETu2bedJXJ/e3mLozjZrEIyncWKjT324gSthGSroziA+d69ttlegmWNZ+kqe+dzZJzVW/c6ivPYmh2O4yTTBt7aYS+RCwd88Anw9Fu7HMdJpAxs2pPgHCwiakhMsIiIyiiVyaK1KehqX6ers9mdCzSS09UAnd4ryuJ4PC7jOF1MJBJy96vQWoTELrfnB4CjGzuHAz44vD3ZICfjERHX58hKtjkHi4gakbhZNrja2tvbVUdHR7UPg4iopCP/cwmSeS74rz13Fq5bMLvgfrcsXYNbl6+1vZ/T7RmHcWopztlHTMavPj6v4H5ERLVARFYopdpLbscEi4iofI7+6oM4/uAJg21UnYsW2t73H+t24sN3PGd7v7d29OHcHzzuOM5LG/fgvT972vZ+W2NJnPKd5Y7jrNm2F++65Qnb+/Um0zju6w87jtO1J453/PejtvfLGFkcfsMSx3H29Kdw4k1LHe135I1LkExnHcVJZbKY/Z/Oju+km5ZiV3/KURwAmHXDYqQNZXu/M777CDbtTjiOc/w3HkYskcaLNy7A3JuW4uvvPgpXnT7T0WsQEVWa3QSLLYJERGWUNhRaoy5bBB2uzua2XatSrXuVGo/TGwYH/D6E/M5/Hbq5wa6bMbmJ46aN01pN0gmvnzmrRTDDFkEiaiD21mIlIiLHlFJIGdnBBOucIyc72t+6CP3oKdNtbW9d7J41e5KjOFZC8qGTD7a1vTVn6bTD9ncUxxrPZSceZGv7oN+HgE9wwsETnMXJzSl793FttveJBH2Y0zbOWZzceC44eortfaJBP9raIo7iWOfnnCMPsB8n5MesA1pcxTljlv3PTzToxyETmxzFsVaTnD9z4uAiF5yDRUSNhAkWEVGZWEu0W4tcnHaYs8THuoD/9FmH2dreWkRh/qEOE59cQvIJmy1aIb+5etzc6fs5i5Mbz0dOPcT2PtGgH8dOa3UV50Mn20tMAfO9O3LqeGdxchW5986dZnufSMiPQyc7S3ysOO8+3n7CGA36MXlc2Fmc3Pt2/tFTbe8TCfoxpdVZFWtowuj3sYJFRI2HLYJERGWSyi1uYVWwnLauWdvbbcOyVo9zsuIc4PyGxiKCplDAceuadVxO2sqiIb/j921wPA5WB3Q1Hofvm7Wt6/E4fN+ctmRaq0k6idPk6fz4EcjdB4vLtBNRI2GCRURUJtZ8lqagHwGfIO408UntuxC1w1o222miYG3vZLnyiIc45U4UrO2dzEPyNB4Hy683hTycH4eJXCXG4/X85ApYvNEwETUUJlhERGViVbBCAb95Ier2wjpQ3gtrq7LUFLLfNR4N+VxXypqcXMC7GU/axXiCPueVGBcVuUjQTWXJzXi8VZbscpOYJod8DkQEQb+wRZCIGgoTLCKiMtmXYPnMRMFFQhIJ+uDz2b9rrHkB76wasC+Rs/8rIRr0u67IRRxewDuO47JS5jqOw4SxZsfjpoVTQ8uj3ydc5IKIGgoTLCKiMkkZ5oVkKOBzVcFKpgzHy2C7nbMU8vsQcLBUuZfKUtkv4F0mCo4rSy4Tn0rMKfNSWSr3+zby/AR8PsfLwxMR1TItqwiKyAUAbgXgB3CHUmrRiOcl9/xFAOIArlJKvWhn33y29SZxy9I1Og6diKhsduwdAACE/OK6guU4wQr6EU9lnMVJmZUyJ9zOWQr4BEEniVzIj+17047jAM7nlFUskXMZx+kcLNeLnTicgxVPG1BKwfxVbz+OVcn0+wQrNuzm73UiahieEywR8QP4KYAFALoAvCAi9yul3hiy2YUAZuX+zQfwcwDzbe47yva9A7h1+Vqvh05EVBFuK1jxlOGonQ5wfwHv5KIaMC+sd/enHO0Td1ORc1kh8Qkc3TzYzfsWdztnqSItgj5XnzencSJBP5QCBjJZ2wngyMQ06Be80hXDK10xR8dLRFSrdLQIzgOwTim1XimVAnA3gEtHbHMpgN8o07MAJohIm819iYjqWsjvd92C5jQhiYT8SKSdz8GqROKTTDtPGM3Kkrvx2K2oAHDdwgmYy+M7ieN4PA5XkwTM85PJKqQN+7HcrlYIOLsFQb45WEREjURHi+BBADYN+b4LZpWq1DYH2dwXACAiVwO4GgBCUw/3dsRERBX0kV8+BwCY1BJytJ+7xMeHbTE3i2lUqFLmeE6Z80pMIu2iIuehhdNRIhf0I2VkkTGytue8uVlN0jqfibRhuyUz6SaRC+2LM8HmPtZ4fvvsBtzx1Nu2YxERVVto6uEn2dlORwUr32+WkbNVC21jZ1/zQaVuV0q1K6XaHR4fEVFVdS5aiIuPa8P4SNDRfq5a91wubuA0TsTlYg0VqZSlnCeMkaAfA5kssg5Ws3ObyAFAMuOsshQOOFtN0jouJ/Ow3K4mCcDRObK2/cK5s9C5aCE6Fy20vS8RUTWltq5bYWc7HQlWF4CDh3w/DcAWm9vY2ZeIqO65u8Gs/XktFlf326rU3Kh01vWcMqUcJj4u3jcASGacJApZVy2c5r724yRdJtoAHH0W3KwmGXERx81qhURE9URHi+ALAGaJyEwAmwFcDuCfRmxzP4BrRORumC2AMaVUt4jssLHvKAeMC+Pac2dpOHQiospwc/8jN3OwosGAq9XjWqPOqmtWwuhk9Thz2Xlnf9eL5m6u62gRBReVpaYhiY/dG/om085XX3Q7Z8np56Ap5CLBcrGapBXH6Xj8PvMGwxb+TieievDP/21vO88JllIqIyLXAHgI5lLrdyqlXheRz+Sevw3AYphLtK+DuUz7x4vtWyrmlPERXLdgttdDJyKqmEilKksu5yy5WXwiq4CUkUXY5tygRNpwPA/NSsgSDtr+nGxrcVOJ8dIi6CyOi0qZi9Y9N62ig3OwHNzc2qr8DU3M+TudiOrBP9vcTst9sJRSi2EmUUMfu23I1wrA5+3uS0TUaJqCAaQyWRhZZXvVNLcX8NbqcU4WN3DTImju6yzBcn0Bnzawn819kmkDE5qcJnIuKjEuE2BrXydx3CxCArhIGCsUx+l4iIjqiY45WEREVMLghXWZKyRuKzFNLhOfeNr+TY3NhMTZ3/Ws8Thpr3Q1HpdxojbbCS1uxpP0cH6cJnJux+N0DpbT8RAR1RMmWEREFWBduNq94DWyCqmM89Ywt6vHua5cOG5Bq805S24SEnOOnNM5S4HBfe2qXCuimzlyLj5vLip/RET1hAkWEVEFOE1I3K605vTCOptVSLpYrdD1nCW3iY+j1j3nqxVWbDxu4niZU1buOVhuWwRZwSKiBsYEi4ioApxeiFotZG6WNXcSx1qW3O3cKLuVGKVUxSpliVTG/ZwyB4lC3Mvy6eWuMLpY3S/uYS6e04TRaaWMiKie8CccEVEFWHNO7vzH27a2ty6Mn1izw1EcKyH71VP24lgX+s++tctRHOvC+rfPbrS1/UAmC6WAjg17HMWxKjF/6thka3srkXu1q8dRHCshue+lzbb3SaYMrOrudRQnkmuR/Pur9m/5mEgZeGt7n6M41vl56PWt9uOkDWzYFXcUJ5y7KfGjq7c7irM1lnQUh4ionjDBIiKqACtR+OML9hIFqyKw9I1tjuJYF9Z/7OhyFOcxh4mcFcduQmIlck87TeRyic/fX+22tX3KyCKrgBc6nSVy+xIS++93Im3g5U0xV3EefdP++51IG3jdaSKXi/PUOvvvdzJlYK3DRM7nE0SCPjz39m7b+yTSBjodJnJERPWECRYRUQU4bSVz0kI2LI7DFi8nLWTD4jhcrMLpvbkG4zgdj4P7MXmJkzayyGSV4zhulid38975fYJQoDbPkdvPNhFRvRDzFlX1pb29XXV0dFT7MIiIbLll6RrcunztqMevPXdW3husOt2ecRinkeMQEdUKEVmhlGovuR0TLCKi8tu0O44zvvsoAKBz0cKS2z+6ejs+/usXbG/vNs5z63fhQ7c/6zjO7v4U5t601PZ+KzfHcPGPn3IcJ5k2cOSND9reb/2OPpzzg8cdx1FK4fAblsDIKlv7betNYv5/LXccBwCO/fpD2JvM2NqvbyCDY772kKs4p35nObpjSVv7ZbMKh35lsas45/7gMby1o9/2fnNufNBsE3QYh4io2uwmWGwRJCKqAKctgk5uROspToXawtyOJxzwQaT8cUTE0ZjcxgGcvXfxlP0bOXuJ47Y9EHD2mctmladYRET1wNkt24mIyBXrYvcdh+9va3vrwvrjp80oaxxrPsyH5093FMdaPW7+zIm2trfG88H2aY7iWInPkVPH2dreunh/zwkHOooDmPOjZkxqsrWtNZ6Fx051HCca8uOIKS22trXOz7uOmuI4TiTox6GTmm1tayWMZx8x2XGcaNCPaftFbW1r3Rbg9MPsfT6JiOoREywiogqwFjdon2EvIbEWn/j8OYe7inPyDGcJ1tVnHuoojrV63PEHT7C1vTWeq06b6SgOYF7Az2kbb2tbazwfOeUQ53FCPsw6wF4iZ43ngyc7S0wBczzTJ9pL5KyE8b0nHuQ8TsiPic0hW9ta41l4nLvEdPK4sK1tBxPGo50npkRE9YItgkREFeD3CcIBn+0V1KyKQpPDlj9r9TjbNzROu7vRMGAmCuUeD2BewDu9QTPHkxuPwzhuxuPmfXMzHiKiesEEi4ioQqIh5xe8kYC7C167y68ncq1uTudUWftUJFEIORhPusLjcRnH/vlxHyfiKPHx8L45Oj/ux0NEVC+YYBERVUiTgwveRNpAJOiDz+dghYccd5UY5x3jEQcJY6LilSUX43GQYCW9VP5cJNpuxuMo8amDShkRUb1ggkVEVCGRkN/2qn2JlOHqohpwdgGfSBsIBXzwu0zkkg4SRsBMMt3EqUgi5yAh8dRSF3KWaFv7OI4TtN8qOnh+3LY82h0PWwSJaAxggkVEVCFOEpJ4ynDdRuWkEmMmcu7iOK1chPw+BPzOf+2YlbKsrW0TFZ5L1BR0UVkK+pG0PR6zdc/1eCqUMNoeT9oaD9fYIqLGxQSLiKhCmkJ+2/dPSqQzrv/KHw36HFVi3CZyjiplqQwiQXe/cqJBn/3ENG0g4BMEXSRyThIS6/2NhMobx8tcr4iTSpk1589lhTFlZJExSidZXsZDRFQvPCVYIjJRRJaKyNrcf/fLs83BIvKoiKwSkddF5Nohz31dRDaLyMu5fxd5OR4iolpWscqSwxY0t4mco9awtIeWR4fvm+vxOGoRzJgrNrpJ5CrWIujHQCaLbFbZjuNqrlcuWUpmSidYXiqMRET1wmsF63oAy5VSswAsz30/UgbAvyil5gA4BcDnReSoIc/fopQ6IfdvscfjISKqWU5Wj/NUWQoG7FfKPCRyTQ7nLLlPGAMVSUydLEISTxloCvoh4nzumpVoK2Uj8UkZ8Mm+Gzs7Yb0P1s19i/HaImi+Rqbktl4SRiKieuE1wboUwF25r+8C8J6RGyilupVSL+a+3gtgFQDnd0wkIqpzzloE3VdinC3WkPGQyDmrLLlpP7PiOGkR9NryaDfxiXiYuwYAAzYqPlai7SaRs+LYSRoTqQzEZSI3WMFKsUWQiAjwnmBNUUp1A2YiBeCAYhuLyAwAJwJ4bsjD14jIqyJyZ74WwyH7Xi0iHSLSsWPHDo+HTURUeU6X53a/+ISD1eNSBqIuW/ec3WfJSwXLyXgynsaTVUDKxlwis+XR/fkB7CU+cY/nB4Ct985TIhdyFgdggkVEja1kgiUiy0RkZZ5/lzoJJCItAO4B8CWlVG/u4Z8DOAzACQC6Afyg0P5KqduVUu1KqfbJkyc7CU1EVBOiwYCjRQeiLlaoM+M4W6bdzdLpgPPl4N0v2uFHJquQsjPHx1PiY7/i43VxEAC2luxPehlPyEEFS8f7ZnM8bu/vRkRUL0r+9lZKnVfoORHZJiJtSqluEWkDsL3AdkGYydXvlFL3DnntbUO2+QWAvzs5eCKiemJVYpRSJSsFXi54nawe561S5kfaUEgb2ZKr9iVSBqaMD7uKM7QSEyrRwhZPGWgJu79/mBVnQoltvcz1ijhK5DIVSXw8LQ7ieDxcop2IGpvXFsH7AVyZ+/pKAH8buYGYVxG/BLBKKXXziOfahnz7XgArPR4PEVHNigb9MLIKaaP0HJ94yssy7Q5Wj9Mwl8jOfK942sNcr5D9OF7nelmvUTKOx4ocYPN9q9B4dFTk7I6H7YFE1Oi8JliLACwQkbUAFuS+h4gcKCLWioCnA/gogHPyLMf+XRF5TUReBXA2gOs8Hg8RUc2y5tKUuuDNZhWS6aynxScAe6vHeWkRjDiYe5NIZV3PJXKa+HiuLNlOFLxXykrx0iLo6Pyk3c/1ctoiyBUEiajRearTK6V2ATg3z+NbAFyU+/opAHl7YZRSH/USn4iongy9EG1FsOB2VmKkY+5NsXYspZSWuTd2Vo9LVKjVzdtiGk4qZRrGY7Oy1NZa+LNiJ47dyp/ruXgOx8N7YBFRo/NawSIiIpuabFYUBldaK3MlJpnOQil4bhEsFUcp5Wn5dGeVMh0tdfYW03A9HodzozyPx24Lp+tKmc9+HA/jISKqF0ywiIgqxLqwLHVDVqsS4LlFsGQiZx6H+1UE7V1YD2TMRM7znKUSFRKlVMUWhYh7WBTC6Zwl75VMm/fbqsCcMi+LgxAR1QsmWEREFWJdwN71dGfR7awL/EdX512YtXSc3AXvL59821acp9btdBXHShj/95nO4nFyidHzb+9yFccaz90vbCy6XcrIIquAlzb2uIuTSxjvebGr5LaJlIGVm2Ou4lgJxv0vbykdJ23gza17XcWxzs+DK7tLbptMGXh7R5+rONb5Wb5qW4ktzfF07Ym7ikNEVC+YYBERVYh1Yf2njuIX8FaL4OKVWz3F+cMLm4puZyU+y1a5S+Ss+V33vLi56HbW/Z4eX+MukRtMSF4pnihY43n6LZeJXG48D5Z439NGFpmsQseGPe7iWAmJjQQ6kTLwSpe3RO6JtaXf93jawBvd7hK5gN+HkN+HZ9bvLrltImVg3fZ+V3GIiOoFEywiogqx2/JXqoWwFLtzquI2FiUoxu54El7HY/t9q8x4vMaxO56MkUXKKN3eV0jQ70PA5g19vY/J3uWE1882EVE9EKVK3yel1rS3t6uOjo5qHwYRkW23LF2DW5evHfX4tefOwnULZjvejnEYZyzFISKqBSKyQinVXnI7JlhERJWxpSeB0xY9AgDoXLSw4HZ/f3ULrvn9SyW3K2TDrn6c9b3HSu7/6Ort+PivX3AdZ8feAZz87WUl939u/S586PZnXceJpzI46qsPldx/5eYYLv7xU67jKKVw6FcWQ6ni+6/f0YdzfvC46zgAcPRXH0R/yii6//beJOb913JPceZ9exm27x0oun/fQAbHfK30+1vM2d9/DG/v7C+6fzZrvr9e4hARVZPdBIstgkREFVKpFjS7q8F5jWN3Nbi4jdXliokEKjMeEbF1juysMliKnZv6eh0PYO8c6Wjbq9T7RkRUDzzdaJiIiOyzEp/TDtu/6HbWYg1XnzHTVRxr8Yl3HF48jnVhfdVpM1zFsS6q582cWHQ7azwfnj/dVRyfT9AU8uOIKeOKbmeN54Pt01zFAcz37tBJzUW3scbznhMOdB2nOezH/s0tRbexEqyFx051HScaCmCmzfG866gpruM0hfyYtl+06DbWeN55xGTXcYiI6gETLCKiCgkHfBABTjpkv6LbWX/pv27BEa7iWIlP+4ziiY9136LPn324qzg+n1nxOX5aa9HtBhPGMw91FQcwL+DnHDi+6DbWeK46zV1iasU5/AB7ic9HTjnEdZxo0I+DJzYV3cb6HHzwZHeJKQA0h/yY2By0Fee9Jx7kOk5TOIBJLeGi21jnZ+Gxba7jEBHVA7YIEhFViNWClijR+mVdwNtdmW0kv08QCfpKtphZz3u58WtTyF86Tu7C2u2NbK197b5v5R5PQsN4zDj2bjjtZTzRkB/9A/beN0/jCZYez77zw7/tElFjY4JFRFRBTSF/yTlJiVQG0aAfIvaW2M4fJ2D7gtfusuH5RO0kJLnjsDsHLZ+mYAD9A/bG4zWRKz2eXBwv4wkFbCTAGt43G4mpnvHYSLSt8YR46UFEjY0/5YiIKigS9CNpo0LipWoBmBfLpS54k2kD4YAPfpv3SsrHXiUmm9vWfeWiKewvuUhCUkNlqdlBYuppPHYSHy2VsgDiabuVMvfjsVNhHBxPkBUsImpsTLCIiCrI3l/6DU8X1YC5iIKdljqviZytSkw6g5CWRM5mq1u5K3LaWgTtVZa8tjyW/BzoSEzDpT8HOsZDRFQPmGAREVVQNFi6EpNIGZ6SBMBcPa7fTiLnMY7dFjTP4wnaaakzEPQLgn73v9qaQnbOj/fWvaiDSpnX1r1Sc7AGx+OltTL3uc5mC99bU0cLJxFRPWCCRURUQREbCZaWylLQP3jhXEginfF8sWu3suS9UmZjPLm5a17j2BlPwCcIBbwlcpWolEVDAduJT5PHRA4ofq8rHXO9iIjqARMsIqIKsjv3phKJTyJleF7RzU4lRsd4msP+khU5c+6at/E0hQKIl1hMQ8t4cpUypQonPomUAb9PEPJYkQOAZKZI4qOp5REofnNkKw5bBImo0XlKsERkoogsFZG1uf/mvbmLiHSKyGsi8rKIdDjdn4ioUURttaDpSHzszcHynMjZWEwjoaGCFQ0GKjSnzFzlsVTi43k8oQCUApLpbMFt4ikDTZ5Xk7SR+KQM+MS8T5tb0dzntdg54jLtRDRWeK1gXQ9guVJqFoDlue8LOVspdYJSqt3l/kREdc9eouC91a05FEC/ncqS15Y6W4tp6Grdy5RMfLwsOQ+YialSwECmeOKjYzwAip6jRDqDiIZFSAAgXmQeljUeL4lcs53x5J7zksgREdUDrz/lLgVwV+7ruwC8p8L7ExHVlWjIZ2O58aznypLd+zlVpOKTzg5WONyKhvzIlkh8dCxvb81DKvbemZU/7+MBild8dJ0fAEWXajdbHvWMp1SLYDToh8/DapJERPXAa4I1RSnVDQC5/x5QYDsF4GERWSEiV7vYn4ioIUSD9io+ehaFqECLYCgAI6uKJz6pjKcFFIB9FZLSiY/XipyZaBS7qXFSQyLXbFWWSo1HQ0WuVBw9iZy9FkHOvyKisaDkn6xEZBmAqXmeusFBnNOVUltE5AAAS0VktVLqCQf7I5eYXQ0A06dPd7IrEVHNGLqqW6G/5OtqQctkFVKZbMHV7nS0CFr7F2vP05XIma+VwcTmUN5tEikDB4wLe4xTejU8MwH2upiGlfiUqizpqcgVT3z0tHBar1WIjhZOIqJ6UPI3hFLqvELPicg2EWlTSnWLSBuA7QVeY0vuv9tF5D4A8wA8AcDW/rl9bwdwOwC0t7cX7kUhIqph1oXsQCZ/G6BVDfLeIrgvIQkF8ickuiplgHmz2kKrFCU0JFh2WuriaY3jKVGJ2b/FWyJnp7Kk60bQQPGKnI4EuFLjISKqB15bBO8HcGXu6ysB/G3kBiLSLCLjrK8BvAvASrv7ExE1kmjQ/LFb6C/9SU1LWZdqqctmVW6ul8dKTNhqDSteifHaImhvNTwNc72C+xLTQnS0CNpd3c86HtdxwqUrcpVqedQxR46IqB54TbAWAVggImsBLMh9DxE5UEQW57aZAuApEXkFwPMAHlBKPVhsfyKiRjU4V6XABa91gVruRQes+yKVe1EIpZSexSfsXMBrqMg1h+201OkcT4nEtEIVOe/LzttrEfRaKSMiqgeeEiyl1C6l1LlKqVm5/+7OPb5FKXVR7uv1Sqnjc/+OVkp9u9T+RESNylp2+38efyvv89aF/RNrdniKY13A//LJ9Xmfty64n31rl8c45njueroz7/PJdBZKAR0b9miJ84fnN+R9XimFeNrAq109WuL8eUVXwW0SKQOrunu1xLn/5S0Ft4mnMnhre5+3OLkK2JLXugtuk0gZ2LAr7i1ObjzLV20ruE08ncHWWNJTHCKiesCbURARVZBV8fnfZzfmfd6qbC19o/CFqq04VkLywqb8cXIJ1mMeEzmrInHPi5vzx8mN52lNidz9r+RPFAYyZiL3Qqe3RM6qHD64cmvBbRJpAy9vinmKM5iQrC449RiJlIHXPSZy1vl5Yu3OwnHSBtZ6TOSCfh+CfsEz6wv/nTSRMtDpMZEjIqoHTLCIiCqoVItUsRYrnXFK3YvLrlKr6VVsPCWWpLer1FyxVCaLTNb7Okt2WuV0nKNQwIdAiftOlbpfml2lViLUdY6IiGqdFLs5ZK1qb29XHR0d1T4MIiJHblm6BrcuXzvq8WvPnYXrFswu+TzjMM5YjkNEVG0iskIp1V5yOyZYRESVs3bbXiy4xbwNYOeihaOef3DlVnzmtysKPm/X5p4ETl/0SMHXeXLtDnz0l897jtMTT+GEby4t+DorNuzB+37+tOc4aSOLWTcsKfg6q7f24oIfPuk5DgDMvmEJUkY27+ts2h3HGd99VEucE775MHri6byvs7s/hbk3FX5fnTh90SPY3JPI+zrJtIEjb3xQS5wFNz+Otdv78r6OUgqH37AERlZ5jkNEVC12Eyy2CBIRVVBLpHhLXbH7FTmKE65MnOYKxQn6fQVvmKwzDrBvJcG8cTS1PALFz5HO8VQsTpHP9kAmC0NDayURUT3wtg4wERE5YiUkZ8yalPd56wL+6jNmeouTm+NzysyJeZ/vGzDnw1x12gxPcYJ+H8IBH445cHze560L+A/Pn+4pDgCMCwdw4JRI3ues8XywfZrnOC2RAKaMzx/HGs97TjjQe5xwAIdOas77XF8uzsJjp3qO0xz24+D9onmf68+9b+86aornOC3hAKaOz38DZms87zxisuc4RES1jgkWEVEFWTdknTt9v7zP702aF6L//K4jPMUJ+H2IBH04dlpr3uf7kmkAwBfOOdxTHMC8sD6iLX+CtTd3Yf2Zsw7zHKc5HMBhk1vyPteXe98+8Q5viSlgnqODJzblfc46Px89dYb3OOEAJjbnr8pZCcnl87wnps3hAIwCxaO9A+bn4LK53hPT5lAA4yLBvM9Z5+fdx3lPTImIah1bBImIKsjvE0SD/oKtWf0DGQR8gnCRdji7WsKBwcrOqDi5Fd1KtfjZ0RwOFB2PzjgFx2PF8XiDZsB83wqPxxjcxqti71ufxvet0cZDRFTrmGAREVVYSyRQcC5P/0AGzeEARIovrW0rTokL3qBfXyJXOsEqvSx56TiFE1PrAn5ciTludthLGL2PZ1w4MHjcheLoSHwqdX7GRUqPR8f5ISKqdUywiIgqrFhlqW/A0HJRDZROFHQmcoUurPsGDIT8PoQDOhKs0hfwWio+kcBga+NI1uPjwvlb4ZxoDvsHK0gjWS11uipLhc+PzsTUj/6UgXyrE1t/UGAFi4jGAiZYREQV1hz2D86BGqlvIK01wSqUKPQlM1ra6cw4/iIX8Gkt1REzTvGKXCjgQ9CvoSIXqkzFx07io7NFMF/iozNOczgAI6uQTGdHPbd3MGHU81kgIqplTLCIiCrMvOAtNJfI0JaQjCuRkOhq12qJBIuOp9TS9LbjlEhIxmmt/BWe6xUO+BDQkMiNC5utonkrPrn41mqQXrREAsgqIJEePSadlT/r/c93jvbN9fJe+SMiqnVMsIiIKqxUoqCrjapoi2BKX5yWohUsnZWy4i2CuseTzXPfpr0DGa0VRqWAeGp04tM3kEYkqCeRay6S+AxWsDScIytOvs+czsofEVGtY4JFRFRhzeHii1zoqiwVW3Wvb8DQl8iVaKnTlZC0hAOIp4y8iY/O8VgVt3iBio+uilyxhMSci6en2mO15eWryvUPZNAU8sPv8z4Xr1KJHBFRrWOCRURUYS3hwOAiBiPprPiYq7oVmOuVTGtrqWuJmImPkTfx0ZeQWIlavuS0b0DfeEpVYnSeHwB558mZiameao+VqOX7zOmsmBZrETQ/1374NCRyRES1jgkWEVGFVaxFMBRAMp1Fxhi96IDOuV7FEx+9LY/Wa45UjvHszZOQ7E1qbBEMFatg6Xzf/IOvOTqO3lUrgSKJKVcQJKIxggkWEVGFNYcDGMhkkR6R+CiltLbUWRfW/Xnm+Oi84C11Yd2icbXCYnF0JqYF46QyWldFBIpUfDS2VgLF3rfKjEfX55qIqNYxwSIiqrBCCUkibSCroL2lbuQFr1IKfSm9izUABVrQkhpb0CLWeEYnjDoXn7De//wJiYGWiK65UYXft37NqyICBRIfjRW5Qp836zFWsIhorPCUYInIRBFZKiJrc//dL882R4jIy0P+9YrIl3LPfV1ENg957iIvx0NEVA+sC+dbl68d9rh1Yfrc+l1a4liJws8fWzfs8XjKgFLASxv3aIljjeeOJ9cPezybVehPGVi5JaYljlVZuuvpzlHP9Q9k8ObWvVriWInC75/fOOq5vckM3t7RpydO7vzc82LXqOf6BjLYtCeuJY51fv728ua8cbb1JrXEscaz5LXuUc/1D2Swpz+lJQ4RUa3zWsG6HsBypdQsAMtz3w+jlHpTKXWCUuoEACcBiAO4b8gmt1jPK6UWezweIqKaZ/0l/1f/6Bz2uLXK26Nv7tAa57fPDk8UrMrMU+v0JHJWnD92DE8UrFX4nn97t9Y49700PFHIZhXiKQMvberRGufvr+ZPFFZu6dUUx2zNe+j1bXnjrNmmJ5GzxpPvc9WfyuDtnXoSuaagOZ58n6u+AQNdPQktcYiIap3XBOtSAHflvr4LwHtKbH8ugLeUUhs8xiUiqluF5rwUWurcrUKtX4UW2HCr2uMptOS9W4XGY2RV3pv1ulWsNU/nOWoK+SEFFu/TeY58Pil4Y2TdnwUiolom+e4gb3tnkR6l1IQh3+9RSo1qExzy/J0AXlRK/ST3/dcBXAWgF0AHgH9RSuXtWRGRqwFcDQDTp08/acMG5mhEVH9uWbpmVGsgAMyfORHP5an0XHvuLFy3YDbjMA7jEBFVmYisUEq1l9yuVIIlIssATM3z1A0A7rKbYIlICMAWAEcrpbblHpsCYCcABeAmAG1KqU+UOuj29nbV0dFRajMiopq0bnsfzrv5cQBA56KFg48vea0bn/3di6Med6s7lsCp33lk1Os9sWYHPnbn89rixBJpHP+Nh0e93ooNu/G+nz+jLU7ayGLWDUtGvd6q7l5ceOuT2uIAwOz/XIJUJjvs9TbuiuPM7z2qNc7cm5Zid39q2Ovt7BtA+7eWaY3zjv9+BF17EsNeL5EyMOerD2qNc/4tT+DNbXuHvV42q3D4DYuRVfriEBFVg90Eq+SSPkqp84oE2SYibUqpbhFpA7C9yEtdCLN6NdhsPvRrEfkFgL+XOh4iono3oSn/KnSxRP6bAruOEw3lfbxHc5xx4QB8Aoy8z3BPXG+coN+X9x5iuuMAwIRoENv3DgyPk9C/SMOEaBC7Ryz+UJbxNAXRtWf4HCjdnzcAaM3z2e5LZUZ9NoiIGpnXOVj3A7gy9/WVAP5WZNsrAPxh6AO5pMzyXgArPR4PEVHNa42aF6GnHjpx2ONW4vO5dx6mJU4k6EPI78NJhwxvLLAurD91xkwtcXw+wfhoEMdNa80b58pTD9ESBzDfuyOnjssb55/mHaw1zmGTm/PGef9J07TFGR8NYvrEaN44l55woLY4rdEg2lojwx6zEsaLjsnXpOI+zv7NwxP7WC5hPG/OAdriEBHVMq83pVgE4E8i8kkAGwF8AABE5EAAdyilLsp93wRgAYBPj9j/uyJyAswWwc48zxMRNZyg34fmkB9HHTg6IQn6Bf92/hFa4ogIWpuCmD2lZXicuHlhrSsOYFZiZuw/PCGxKjE659rkSxRiuUTh8+fM0hZnQlMQft/wlSGs8Vx95qFa42RHtOpb47nqtBn64kRDaAoNX47dSnw+fIq+BHhCNIhQYPjfbq2E8YPt+hJgIqJa5inBUkrtgrky4MjHtwC4aMj3cQD759nuo17iExHVq9ZocFSLVk88jdZoEFJoyTdNcWKJNKJBP8KB/Cu+6YwDAOM03Zi3VByrMqgrzuaeEQlJmeK8vbO/7HHGR4OIJUa0VpZpPJU4P0REtcxriyAREbnQ2hQaNdemN5HWfhE6IRocFacnni44D8yt1qbQqLldsUQa4yOBUZUgLyY0BUfF6YmnESiyRLgbrdHQYKXPUo5EodD5AYAJTfnn0LmK0xRELJHC0IWtyjKepiDiKQOpTHbwsXKMh4ioljHBIiKqgtZoAL0jE4VESnuCVaiiUI44I8cTS6TzLnrgNU6h8VSi8hcO+BAJ6q389SbTyGZHJz7jI167+IfHSRvD7+NltQjqPEfW52roe8cKFhGNNUywiIiqYEI0NGpVulgirf2v/K1NeSokZauUjVwNL1VwJUO3WpuCiMXTwyoxZRlPUxD9KQNpY2glJqW98jc+GoRSwN7kvva9nnga48IBBPz6fkVPyL0/Qz8LsUQafp9gXJEbHjvVmvv8xoZ8tq3Pue73joioVjHBIiKqArNlK0/rnvbEJzSqstSb0N8iaI1nZCVGe5xoCCkji2R6X+LTW4ZKmXXcIysxuhPGCYMJyb44lRqPVTHVWfmbUKCCpbvyR0RUy5hgERFVQWueuTexeBrjy9C6t3cgg4wxfE5MOVoEs8q859FgnER5xmO+9pAKSZnGY712ReIMq/jojzO+0uMZWikrQxwiolrGBIuIqApam4IYyGSRzM2JyRhZ7B3IlKWyBAC9Q1rQytKKaFUu4sMrMdorcgUrS+VJFEbGqVilrAyVv3xxytFamS8O2wOJaCxhgkVEVAXWhe33H3oTwL4E6KWNe8oS54fL1gAABjIGEmkDKzfHyhLnZ4+tAwAopdATT+PNrXvLEucXT6wffKwnnhq11LmuOHc9/fbgY7FEGl174mWJ8/vnNg4+1hNPYVtvstAu7uLkEpy/rNg0+Fgskcae/lShXdzFyY3nry9tHnysJ55GXzJTaBcioobDBIuIqAqsisIdT5kX8NZf/B9fs1NrHOvC+jfPbBgW5+m3dmmNY1XE/vC8eQEfTxnIZBU6NpQnYbznRfMC3sgq7B3I4JUuvQmjNZ77X+kefCyWSGNVt96E0aq8LVm5dUicDNZt15swWnGWrdo+JE4aG3brTRjHRYIQAZ5Yu+9zHEuksSWmN2EkIqplTLCIiKpgZGvWyBX4yhUnNmLeV7nijLxXVbni7E2mMWRBwbLFGcgYiKeMAlu7N3KOmlJq2Ap8ujSF/Aj6hy9mMXIOoA75ViUcuZgLEVGjE1WO30xl1t7erjo6Oqp9GERErtyydA1uXb625HbXnjsL1y2YzTiMwzhERDVARFYopdpLbscEi4io8mKJNI7/xsMAgM5FC3HX05342v2vD36vSyqTxez/XDL4un9Z0YV//fMr2uMopXDEjQ8ilcmic9FCLHmtG5/93Yva4wDAid98GHviaXQuWojH1+zAlXc+X5Y4py96BJt7EuhctBAdnbvx/tueKUucd93yONZs60PnooV4Y0svLvrRk2WJ896f/QMvbexB56KF2LCrH2d977GyxPnIHc/hqXU70bloIXb2DaD9W8vKEoeIqNLsJlhsESQiqoLxkQCaQvvuC9QdS45q4dIhFPBhUkt48PutsYT2GAAgImhrjQx+313GOTdTW6ODX5drPACqM57eRhjPvjhbOfeKiMYgfbdvJyIi20QEU1sj8Odu8ro1lsCU8RG8b+407bHaWiNoDpvJXHcsiYnNIXz0lEO0x5k6PoJ0xrzf1tbeJMIBHz5z1qHa47S1RrBjr3nhbiUK15x9uPY4U1sjaI2avyatRKEs4xkfGUy2rfF88h0ztMeZOj6KgE+glBocTzk+B22tEQjMWw9Y47n85IO1xyEiqlWsYBERVUlbawTjIrkL+N4k2lojZZmbMrU1gmjQvIDfGkti6vjyxGlrjcCfq8J1x6zxHKE9ztTWCLK57vatsSQmtYTxr+frj9PWGkEynYVSCt2xJJpCfvzHBUdqjzO1NYJE2kAqk8XWWBI+Ab584RztcdpaI8jkVl3cmlsG/j8uLM94FICdfanBCuM/v4tzroho7GCCRURUJVPHRwcrCVtjyWGtYjq1tUYGKwlW4lMOU1uj2BYbQDarsDWWGNYqplPb+Ah296eQTBtlH89AJoueeBpbe83xiOhv42xrjUApYPveJLpjSRwwLoKAX/+vZ+t8bI0lsTWWxLhIAC1h/Y0s1vnojiXQHUsi4BNMag6X2IuIqHGwRZCIqEraWiPYtncAP3j4zVzrnt57ElmmtkYQS6Tx3QdXY2tvEmW4dgdgjidlZPGdJavQHUuW5eId2JcoLFqyCltjSaSNbFniWInC9x4yz4/V/qibNZ5bl63F1lgSCuVZfMoaz88eXYdE2ijLnD/A/MMBANzx5HqEA35Eg374fOWJRURUi5hgERFVyZTWCIyswo8fWQcAeHFjT1niDF5YP/YWAGB3f3nuuWUlCr948m0E/YIuo1yJgnkB/+unN2B8JIDeZKYscazx/P75jcOqgLpZ4/nzii4cNrkZ23oHyhJnynhzPH99eQuOn9aK3f3luT+V9Xl74LWtOPXQ/bF3oDznh4ioVrFFkIioStrGl6e1bSSrolBuQ1v10mVKroDhq9SVK7kCho9n+97yJD3A8PGUc3W/KUM+b1vKGGdCUxDhgHl50V3GVR6JiGqVp/tgicgHAHwdwBwA85RSeW9OJSIXALgVgB/AHUqpRbnHJwL4I4AZADoBfFAptadUXN4Hi4jqXambsuq6GSvjME4jxiEiqoaK3GhYROYAyAL4HwD/mi/BEhE/gDUAFgDoAvACgCuUUm+IyHcB7FZKLRKR6wHsp5T6j1JxmWARUaO49CdP4ZWu2OD35boZ6xW3P4tn1u8qe5xP3fUClq3aXvY4X/zDS7j/lS1lj/Ple1/FH57fVPY4N/39DfzyqbfLHufmpWvwoyEJULni/M/jb+E7S1aXPQ4RUSVV5EbDSqlVSqk3S2w2D8A6pdR6pVQKwN0ALs09dymAu3Jf3wXgPV6Oh4io3px/zNSKxLmgQnHOP7oycS6s2PvWVpE4FRtPhc5PpT4HRES1qBJzsA4CsGnI9125xwBgilKqGwBy/z2g0IuIyNUi0iEiHTt27CjbwRIRVcotS9fguw8O/xvVjOsfwC1L12iP87X7X69InH/7y6sVifPZ371YkThX3vl8ReK8/7ZnKhLnoh89WZE47/z+Y2WPQ0RUq0q2CIrIMgD5/hR1g1Lqb7ltHkPhFsEPADhfKfWp3PcfhTlf6wsi0qOUmjBk2z1Kqf1KHTRbBImo0cy4/oGKtFExDuM0Yhwiokqw2yJYcpl2pdR5Ho+lC8DBQ76fBsBqnN8mIm1KqW4RaQOwfdTeREREREREdaISLYIvAJglIjNFJATgcgD35567H8CVua+vBPC3ChwPEVHNufbcWYzDOIxDRNQAvK4i+F4APwYwGUAPgJeVUueLyIEwl2O/KLfdRQB+CHOZ9juVUt/OPb4/gD8BmA5gI4APKKV2l4rLFkEiIiIiIqqkiizTXi1MsIiIiIiIqJIqskw7ERERERER7cMEi4iIiIiISBMmWERERERERJowwSIiIiIiItKECRYREREREZEmdbmKoIjsBfBmtY+DBk0CsLPaB0GDeD5qC89H7eE5qS08H7WF56O28HzUliOUUuNKbRSoxJGUwZt2lkikyhCRDp6P2sHzUVt4PmoPz0lt4fmoLTwftYXno7aIiK37RLFFkIiIiIiISBMmWERERERERJrUa4J1e7UPgIbh+agtPB+1heej9vCc1Baej9rC81FbeD5qi63zUZeLXBAREREREdWieq1gERERERER1RwmWERERERERJrUbYIlIieIyLMi8rKIdIjIvGof01gnIl8QkTdF5HUR+W61j4cAEflXEVEiMqnaxzKWicj3RGS1iLwqIveJyIRqH9NYJCIX5H5GrROR66t9PGOZiBwsIo+KyKrc74xrq31MBIiIX0ReEpG/V/tYCBCRCSLyl9zvj1Uicmq1j2ksE5Hrcj+vVorIH0QkUmjbuk2wAHwXwDeUUicA+Grue6oSETkbwKUAjlNKHQ3g+1U+pDFPRA4GsADAxmofC2EpgGOUUscBWAPgy1U+njFHRPwAfgrgQgBHAbhCRI6q7lGNaRkA/6KUmgPgFACf5/moCdcCWFXtg6BBtwJ4UCl1JIDjwXNTNSJyEIAvAmhXSh0DwA/g8kLb13OCpQCMz33dCmBLFY+FgM8CWKSUGgAApdT2Kh8PAbcA+HeY/69QFSmlHlZKZXLfPgtgWjWPZ4yaB2CdUmq9UioF4G6YfxSiKlBKdSulXsx9vRfmheNB1T2qsU1EpgFYCOCOah8LASIyHsCZAH4JAEqplFKqp6oHRQEAUREJAGhCkdyjnhOsLwH4nohsglkt4V+Eq2s2gDNE5DkReVxETq72AY1lInIJgM1KqVeqfSw0yicALKn2QYxBBwHYNOT7LvCCviaIyAwAJwJ4rsqHMtb9EOYf5bJVPg4yHQpgB4Bf5do27xCR5mof1FillNoMM9/YCKAbQEwp9XCh7QOVOjA3RGQZgKl5nroBwLkArlNK3SMiH4SZ4Z9XyeMba0qcjwCA/WC2epwM4E8icqjifQDKpsT5+AqAd1X2iMa2YudDKfW33DY3wGyN+l0lj40AAJLnMf58qjIRaQFwD4AvKaV6q308Y5WIXAxgu1JqhYi8s8qHQ6YAgLkAvqCUek5EbgVwPYAbq3tYY5OI7Aez62EmgB4AfxaRjyilfptv+5pOsJRSBRMmEfkNzF5hAPgzWNIuuxLn47MA7s0lVM+LSBbAJJh/faEyKHQ+RORYmD8AXhERwGxHe1FE5imltlbwEMeUYv9/AICIXAngYgDn8g8PVdEF4OAh308DW8urSkSCMJOr3yml7q328YxxpwO4REQuAhABMF5EfquU+kiVj2ss6wLQpZSyKrt/gZlgUXWcB+BtpdQOABCRewGcBiBvglXPLYJbAJyV+/ocAGureCwE/BXmeYCIzAYQArCzmgc0VimlXlNKHaCUmqGUmgHzh/RcJlfVIyIXAPgPAJcopeLVPp4x6gUAs0RkpoiEYE5Ovr/KxzRmifnXn18CWKWUurnaxzPWKaW+rJSalvudcTmAR5hcVVfud/YmETki99C5AN6o4iGNdRsBnCIiTbmfX+eiyKIjNV3BKuH/Abg1N9EsCeDqKh/PWHcngDtFZCWAFIAr+Vd6okE/ARAGsDRXVXxWKfWZ6h7S2KKUyojINQAegrn6051KqderfFhj2ekAPgrgNRF5OffYV5RSi6t3SEQ15wsAfpf7o9B6AB+v8vGMWbk2zb8AeBFmq/9LAG4vtL3wGpiIiIiIiEiPem4RJCIiIiIiqilMsIiIiIiIiDRhgkVERERERKQJEywiIiIiIiJNmGARERERERFpwgSLiIiIiIhIEyZYREREREREmjDBIiKihiciJ4vIqyISEZFmEXldRI6p9nEREVHj4Y2GiYhoTBCRbwGIAIgC6FJKfafKh0RERA2ICRYREY0JIhIC8AKAJIDTlFJGlQ+JiIgaEFsEiYhorJgIoAXAOJiVLCIiIu1YwSIiojFBRO4HcDeAmQDalFLXVPmQiIioAQWqfQBERETlJiIfA5BRSv1eRPwAnhaRc5RSj1T72IiIqLGwgkVERERERKQJ52ARERERERFpwgSLiIiIiIhIEyZYREREREREmjDBIiIiIiIi0oQJFhERERERkSZMsIiIiIiIiDRhgkVERERERKTJ/wce8Zg0q5hIngAAAABJRU5ErkJggg==\n", 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                " ] @@ -74,10 +81,10 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize=(12,4)) \n", - "fig.set_tight_layout(True)\n", - "da.plot(ax=ax, marker='+', label='original signal') \n", - "ax.set_xlim([-8,8]);" + "fig, ax = plt.subplots(figsize=(12, 4)) \n", + "da.plot(ax=ax, marker='+', label='original signal')\n", + "ax.legend()\n", + "ax.set_xlim([-8, 8]);" ] }, { @@ -91,33 +98,49 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:352: FutureWarning: Flags true_phase and true_amplitude will be set to True in future versions of xrft.fft to preserve the theoretical phasing and amplitude of Inverse Fourier Transform. Consider setting them to ensure future compatibility with numpy.fft.fft-like behavior and to deactivate this warning.\n", + " warnings.warn(msg, FutureWarning)\n" + ] + } + ], "source": [ - "da_dft = xrft.dft(da, true_phase=True, true_amplitude=True) # Fourier Transform w/ consideration of phase\n", - "da_fft = xrft.fft(da) # Fourier Transform w/ numpy.fft-like behavior\n", + "# Fourier Transform w/ consideration of phase\n", + "da_dft = xrft.fft(da, true_phase=True, true_amplitude=True)\n", + "\n", + "# Fourier Transform w/ numpy.fft-like behavior\n", + "da_fft = xrft.fft(da)\n", + "\n", + "# With numpy.fft\n", "da_npft = npft.fft(da)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "k = da_dft.freq_x # wavenumber axis\n", - "TF_s = T/2*(np.sinc(T*(k-k0)) + np.sinc(T*(k+k0))) # Theoretical result of the Fourier transform" + "k = da_dft.freq_x # Wavenumber axis\n", + "\n", + "# Theoretical result of the Fourier transform\n", + "TF_s = T/2 * (np.sinc(T * (k - k0)) + np.sinc(T * (k + k0)))" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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                " ] @@ -129,24 +152,21 @@ } ], "source": [ - "fig, (ax1,ax2) = plt.subplots(figsize=(12,8), nrows=2, ncols=1)\n", - "fig.set_tight_layout(True)\n", + "fig, (ax1, ax2) = plt.subplots(figsize=(12, 8), nrows=2, ncols=1, sharex=True, sharey=True)\n", "\n", - "(da_dft.real).plot(ax=ax1, linestyle='-', lw=3, c='k', label='phase preservation') \n", - "((da_fft*dx).real).plot(ax=ax1, linestyle='', marker='+',label='no phase preservation') \n", - "ax1.plot(k, (npft.fftshift(da_npft)*dx).real, linestyle='', marker='x',label='numpy fft')\n", - "ax1.plot(k, TF_s.real, linestyle='--', label='Theory')\n", - "ax1.set_xlim([-10,10])\n", - "ax1.set_ylim([-2,2])\n", + "(da_dft.real).plot(ax=ax1, ls='-', lw=4, c='k', label='Phase preservation') \n", + "((da_fft*dx).real).plot(ax=ax1, ls='', marker='+', label='No phase preservation') \n", + "ax1.plot(k, (npft.fftshift(da_npft)*dx).real, 'x', label='numpy.fft')\n", + "ax1.plot(k, TF_s.real, '--', lw=2, label='Theory')\n", + "ax1.set_xlim((-10, 10))\n", + "ax1.set_ylim((-2, 2))\n", "ax1.legend()\n", "ax1.set_title('REAL PART')\n", "\n", - "(da_dft.imag).plot(ax=ax2, linestyle='-', lw=3, c='k', label='phase preservation') \n", - "((da_fft*dx).imag).plot(ax=ax2, linestyle='', marker='+', label='no phase preservation') \n", - "ax2.plot(k, (npft.fftshift(da_npft)*dx).imag, linestyle='', marker='x',label='numpy fft')\n", - "ax2.plot(k, TF_s.imag, linestyle='--', label='Theory')\n", - "ax2.set_xlim([-10,10])\n", - "ax2.set_ylim([-2,2])\n", + "(da_dft.imag).plot(ax=ax2, ls='-', lw=4, c='k', label='Phase preservation') \n", + "((da_fft*dx).imag).plot(ax=ax2, ls='', marker='+', label='No phase preservation') \n", + "ax2.plot(k, (npft.fftshift(da_npft)*dx).imag, 'x', label='numpy.fft')\n", + "ax2.plot(k, TF_s.imag, '--', lw=2, label='Theory')\n", "ax2.legend()\n", "ax2.set_title('IMAGINARY PART');" ] @@ -155,32 +175,47 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`xrft.dft`, `xrft.fft` (and `npft.fft` with careful `npft.fftshift`ing) all give the same amplitudes as theory (as the coordinates of the original data was centered) but the latter two get the sign wrong due to losing the phase information. It is perhaps worth noting that the latter two (`xrft.fft` and `npft.fft`) require the amplitudes to be multiplied by $dx$ to be consistent with theory while `xrft.dft` automatically takes care of this with the flag `true_amplitude=True`:\n", + "`xrft.fft` with phase preservation settings, `xrft.fft` with default settings, and `npft.fft` with careful `npft.fftshift`ing all give the same amplitudes as theory (as the coordinates of the original data was centered), but the latter two get the sign wrong due to losing the phase information. It is perhaps worth noting that the latter two (`xrft.fft` with default settings and `npft.fft`) require the amplitudes to be multiplied by $dx$ to be consistent with theory while `xrft.fft` can automatically take care of this with the flag `true_amplitude=True`:\n", "$$\\mathcal{F}(da)(f) = \\int_{-\\infty}^{+\\infty}da(x)e^{-2\\pi ifx} dx\n", - " \\rightarrow\n", - "\\text{xrft.dft}(da)(f[m]) = \\sum_n da(x[n]) e^{-2\\pi i f[m] x[n]} \\Delta x$$\n", + "\\\\\n", + "\\rightarrow\n", + "\\text{xrft.fft}(da)(f[m]) = \\sum_n da(x[n]) e^{-2\\pi i f[m] x[n]} \\Delta x$$\n", "\n", "**Perform the inverse transform**" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:589: FutureWarning: Default idft's behaviour (lag=None) changed! Default value of lag was zero (centered output coordinates) and is now set to transformed coordinate's attribute 'direct_lag', defaulting to zero if that attribute is not set.\n", + " warnings.warn(msg, FutureWarning)\n", + "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:560: FutureWarning: Flags true_phase and true_amplitude will be set to True in future versions of xrft.ifft to preserve the theoretical phasing and amplitude of Inverse Fourier Transform. Consider setting them to ensure future compatibility with numpy.fft.ifft-like behavior and to deactivate this warning.\n", + " warnings.warn(msg, FutureWarning)\n", + "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:589: FutureWarning: Default idft's behaviour (lag=None) changed! Default value of lag was zero (centered output coordinates) and is now set to transformed coordinate's attribute 'direct_lag', defaulting to zero if that attribute is not set.\n", + " warnings.warn(msg, FutureWarning)\n" + ] + } + ], "source": [ - "ida_dft = xrft.idft(da_dft, true_phase=True, true_amplitude=True) # Signal in direct space \n", + "# Signal in direct space\n", + "ida_dft = xrft.ifft(da_dft, true_phase=True, true_amplitude=True)\n", "ida_fft = xrft.ifft(da_fft)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
                " ] @@ -192,13 +227,12 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize=(12,4))\n", - "fig.set_tight_layout(True)\n", - "ida_dft.real.plot(ax=ax, linestyle='-', c='k', lw=4, label='phase preservation')\n", - "ax.plot(x, ida_fft.real, linestyle='', marker='+', label='no phase preservation', alpha=.6) # w/out the phase information, the coordinates are lost\n", - "da.plot(ax=ax, ls='--', lw=3, label='original signal')\n", - "ax.plot(x, npft.ifft(da_npft).real, ls=':', label='inverse of numpy fft')\n", - "ax.set_xlim([-8,8])\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "ida_dft.real.plot(ax=ax, ls='-', c='k', lw=4, label='Phase preservation')\n", + "ax.plot(x, ida_fft.real, '+', label='No phase preservation', alpha=.6)\n", + "da.plot(ax=ax, ls='--', lw=3, label='Original signal')\n", + "ax.plot(x, npft.ifft(da_npft).real, ':', label='numpy.fft')\n", + "ax.set_xlim((-8, 8))\n", "ax.legend(loc='upper left');" ] }, @@ -206,11 +240,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Although `xrft.ifft` misses the amplitude scaling (viz. resolution in wavenumber or frequency), since it is the inverse of the Fourier transform uncorrected for $dx$, the result becomes consistent with `xrft.idft`. In other words, `xrft.fft` (and `npft.fft`) misses the $dx$ scaling and `xrft.ifft` (and `npft.ifft`) misses the $df\\ (=1/(N\\times dx))$ scaling. When applying the two operators in conjuction by doing `ifft(fft())`, there is a $1/N\\ (=dx\\times df)$ factor missing which is, in fact, [arbitrarily included in the `ifft` definition as a normalization factor](https://numpy.org/doc/stable/reference/routines.fft.html#module-numpy.fft).\n", + "Although `xrft.ifft` with default settings misses the amplitude scaling (viz. resolution in wavenumber or frequency), since it is the inverse of the Fourier transform uncorrected for $dx$, the result becomes consistent with `xrft.ifft` with phase-preservation settings. In other words, `xrft.fft` (and `npft.fft`) misses the $dx$ scaling and `xrft.ifft` (and `npft.ifft`) misses the $df\\ (=1/(N\\times dx))$ scaling. When applying the two operators in conjuction by doing `ifft(fft())`, there is a $1/N\\ (=dx\\times df)$ factor missing which is, in fact, [arbitrarily included in the `ifft` definition as a normalization factor](https://numpy.org/doc/stable/reference/routines.fft.html#module-numpy.fft).\n", "By incorporating the right scalings in `xrft.dft` and `xrft.idft`, there is no more consideration of the number of data points ($N$):\n", "$$\\mathcal{F}^{-1}(\\mathcal{F}(da))(x) = \\frac{1}{2\\pi}\\int_{-\\infty}^{+\\infty}\\mathcal{F}(da)(f)e^{2\\pi ifx} df\n", - " \\rightarrow\n", - "\\text{xrft.idft}(\\text{xrft.dft}(da))(x[n]) = \\sum_m \\text{xrft.dft}(da)(f[m]) e^{2\\pi i f[m] x[n]} \\Delta f$$" + "\\\\\n", + "\\rightarrow\n", + "\\text{xrft.ifft}(\\text{xrft.fft}(da))(x[n]) = \\sum_m \\text{xrft.fft}(da)(f[m]) e^{2\\pi i f[m] x[n]} \\Delta f$$" ] }, { @@ -226,28 +261,28 @@ "source": [ "**Now let's shift the coordinates so that they are not centered.**\n", "\n", - "**This is where the `xrft` magic happens.** With the relevant flags, `xrft`'s dft can preserve information about the data's location in its original space. This information is not preserved in a `numpy` fourier transform. This section demonstrates how to preserve this information using the `true_phase=True`, `true_amplitude=True` flags." + "**This is where the `xrft` magic happens.** With the relevant flags, `xrft`'s FFT can preserve information about the data's location in its original space. This information is not preserved in a `numpy.fft` Fourier transform. This section demonstrates how to preserve this information using the `true_phase=True`, `true_amplitude=True` flags." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "nshift = 70 # defining a shift\n", + "nshift = 70\n", "x0 = dx*nshift \n", "nda = da.shift(x=nshift).dropna('x')" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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ePZunnnqqtF8ikWD16tWOx+3o6ChFqKyOu3TpUsvPnXnmmfzkJz8pRd5Wr17N4OAgH/jAB7jnnnvI5/Ns3ryZRx99tOpzq1ev5qijqk30wMAA/f39fPjDH+aHP/xhjebIkSPp6Ojg2WefBaiK3Nmxe/du2tvb6erqYuvWrcyf7//8ur3V5EIQBEHwkU39KcZ1xRnXbS52k8Ois7k/xbjuGPu0RYgEA2waRp1IMMCo9ghju2JsambxDjDpw8aiumtC1WbzPo3rjjG20wedQ041dDr2h3w5jcc0OWO744ztjJHJF9g52MDCO5eB7CAcdHz5eirqygoFzZb+lKHTFa+6xrpJ9cG4mYZJGKJjHDfF2O5YhcFqQmffyUYjjVGHWtbJ+UKq2FjkyHMM0zhy4vDomM/Coacb39GI/Wzb3Autx8yZM7nkkks49thjOe6447jssss4+uijHT9zzDHHcN555zF9+nQuuOACZs2aVUpT88JXv/pVrrvuOk444QTyeffxGEcccQT9/f0lM/S1r32NXbt2MWXKFKZPn86jjz7KmDFjuPvuu7nooouYNm0as2fPLjXVsOPcc8/lvvvuKzW5+PGPf8zixYuZNm0akydP5s4777T83GWXXcbkyZOZOXMmU6ZM4XOf+xy5XI6PfOQjHHbYYUydOpUrrriCk08+ufSZrVu3Eo/HGTu2ulPtnj17OOecc5g2bRonn3wyP/jBD2r0/vu//5vLL7+cOXPmoLV2vdfTp0/n6KOP5qijjuKzn/1sKW3ST/ZWDZYgCILgE5lcgR0D6WIEy1xUD09kaUt/ioNGtREIKPbraiKy5KqTZL+uKIGAYv/OGOt2DjZ2oFSf0aHuqAtg5V+NFupal7rjbSlFsOLs3xVjx0CaTK5AJFTn7xtNU3DEObD7TaPeJtkHI4w6CPP7GNsVIxIMlLRHj4haHs5V59APGSbB1BlpvL1zMEM2r6siWA09C1obx514IkQ7oftAywjWlAM66YiFGRENNRfBMlMCx84oR5r8JtVndPY78lx48X8NEzxcOgBH/R/oXQydYyGXMtIThXcFV111FVdddVXVexMnTmTFihVV761bt67081e+8hVuvPFGEokEH/jAB/jyl78MVEekKvefNWsWixYtAmDOnDlV0aVvfOMbgDGjyS5d8LOf/Sy///3vueyyyxgxYgS//OUva/b54Ac/yAsvvFDzvt15HH744Sxbtqxq39///vc1n7/xxhurXgcCAW666SZuuummmn0rI2CV/Pa3v+Vzn/tczftjx47l+eefd9Q86qijSud58803M2uW8ffLJZdcwiWXXFLar7I2yy4yaF57s0gESxAEocXYtieF1sbifUQ0REc0NGzGZ1N/srRwH9sZHzYjt6k/xdhOY0E6tivW3OI92mnUKMW6IZ+GbDnSsrk/RUc0xIhoiLFdMbQ27mdDOmCYnlgxpS5VbsVuRnf272zS+FjqlI1PyTB2xti3M1r1Xl1kE1DIGjqBIMQ6q3RKpr74He3fFWsujdNMQ4x3D18EK7mrfM9MneGILCWLpj7SYZteKbz3uPzyy5kxYwYzZ87kox/9KDNnzhxWvSuuuKKq3qjV6O7u5uKLL27osw8++CAzZsxgypQpPPHEE77PA2sEMViCIAgtxpaKOiLzv8ORIjiQzrEnlStFycxaouHArCkzdOLsSeUam1GV6jM61AHEimkir9xX2ry5P1mlY2o3pLN7E4TbyovqnvKQUdNM7ddp1GAZOg18R6l+6NtoGIVIu9F57/VFVdcDMLYrTjQUZPSISONGztQB496tf6a0eevuckTO/G9DOoUCbFlR/m5i3fDmS/UfxwupfiO6aOroAqwYhuYTqT4Y3GGY+ngxtLjqAf91hJbit7/9LUuXLmXVqlVcd911w64Xi8X4zGc+M+w6w8Wll17a0IBigAsvvJClS5eyYsUKHnzwQd4J83LFYAmCILQY5fQzH6IJDmzpr15UmzoNN2uwQetiHVHF4r1Svy6SfbBrnfGzaXxe+3tpc5WRayaylOqHPVuM1EPTLLzxeJXO6BFRIqEAo9ujhAKqQZ0+6N9oaChlGIUNz5V1dtea7caMXIUOGDpbyqlBNTqdDT5z6f6iToWR2/ka5Bsc+OxEss/oHAiWJthXnUojB7D2Ufv9BUF41yMGSxAEocUYGsFqKqWuHp1is4a3GmnW4MBbgxky+ULV4r1S3zNaG8YnVEyTMRe7FR3+NlcYuf2bNXKmTihqRLLy1jqBgGK/Rg1Jss+IWoUixut4d41OOKgY1W5s37/RNM7KVMSSTrnr3ub+2gjWtj0pcvlC8zpQlV7pC2ZzkNKzUDSOw9HhL9VndJMs6ajh61goCEJLIE0uBEEQWoytu1PEwgE6Y8Zf4ft3xtg+kCZf0AQD9jNGGtEBI80NyoZk6+40o+pt1uCok7bRqcMo9MyHVQ/Cxueg+yBj0Gv/RmPQ8Jgj4YEvUdAwLTGK/TovBKAzFqYtEqxfZ/UCeHOJUetl6uSz0LEfPPAlACbunEB433KHrP27Ymytp9bL1Nm2ymgzbuqogPG6qDNq2+Hs23EkgeL3vn9XlBc31NE0wtTp22i8/uNnYXevYRgLefjrlRAIEsvOAPZl3+J3tF9XjII2mmyY35snncRO4/W8q2Fwezmlbv7VEO3wZzBvz3x45S+w6UWjS+H/XmDMK8smjDlpxXvXtJZ5TRuegxH7lr+jXMp4Bv3SEQSh5RCDJQiC0GL0JbOMbIuUBjZ2tUXQ2pi91N0W8VUHYGRb2NCJR4rv+xsFMI/XXdTpLulkbT9Tw6S5MOE4ePg/YWAbXFQcpjn/Gti2Es79EbsTGRY+/Q/mVNyj7ni4fp1Jc8vHNQfKPvVjo2vhuT8C4KEVD3PCEJ26hhqbOuZxTZ3l98IT3yvpPHP3C3S3lY/bHY/Ql8hQKOiS6fKkYx73n4v1auufgQf+Pzj969C2D8v+3kMwsLZk6kvfUSLrzWCZOuZxL/y1Mfx313r4zcfgxKtg/yne74+b1r6T4cnvQ2IXfOZ3RgrivC9Df2/p3vmic+jp5eN++k/G+49+G954zD8dQRBaDkkRFARBaDH6Elm64uHS6+7iz32JOoyCB/oTGZSCjljR+BQNUL/vOsbxzEV7RyyEUoZ+XWSKrd0DFb87DLdBwajv6SvplO9dV1uk/vtWKBhRiiqdeFUdUV8iO0Qn3Nj3k01U60TajespGKl5fcls6XsB4zsqaBjI1FnTVKPTVn4fwwR3xcMlU29q9tX7HWUHy9dR+d+ijm+YxwuGyv8NRkvPgu86Q58Fv3WEdxwTJ05kx44dNe/ff//93HzzzQBs376d4447jqOPPponnnjCsm25G3fffTdXXnmlp303bdrExz72sbo1/DwHwUAMliAIQoux22JRDdBfTyTGA/3JLJ2xcCnt0DR1w6ED5esIBBSdsXD9OmY79kNOLb8XaYNRh1fpVJrTrniI3XXrFBfVBx1fodNuzI4C0rk8yWx+iE64fh1Ta2xFe+dw3BgCnDOutT9ZbbY74w2a4GwS9j2yQqfd0MmaOrma6zH169bpPshIcwTDAHdNGD6DdeiHyu+F40a66HDoTDyx/F6kHUYe7K+OUD89898W2fPOO49rr70WgIcffpgjjjiCl156iZNOOqkhg1UP48aN49577x1WDcEbYrAEQRBaDDOaYGL+XFeqmyedIZGytuHTAWq06tYxoyOHV9S7hNtgn4OqdKrMaTxSf8qjuah+3ykVOvHSINuhhtHU2ZPO1d8UIpOAg2ZX6LQbw4YzxchSIltK3TR0GjQ+mUFj6G9JJ17UGSzq+PTMZRIwZlJp8LOhc2DpenzDPN4R55Tfi7TDqEP81bEzcl3j/dUR6mf1Al8OMzg4yNlnn8306dOZMmVK1aDdW2+9lZkzZzJ16lRWrVoFlKM9S5cu5atf/Srz5s1jxowZXHPNNSSTSWbMmMGnPvUpAH79619z7LHHMmPGDD73uc+Rz+cB+MUvfsHhhx/OySefzFNPPWV5Xo899hgzZsxgxowZHH300ezZs4d169YxZYqRaptIJPinf/onpk2bxoUXXshxxx3H4sWLARgxYgT//u//zvTp05k9ezZbtxqjLR544IFSxO1DH/pQ6X2hfqQGSxAEocXoT2ZL6XQwvBGsSpMQDwcJB9Ww6ISDirZIsPReV7yJCFY4Xn4v3FbqUGcdwWpEJ1E+dkmn3ehQl8+VIlWdQyJlALtTOfZp91gnl88aw3+rdOKlc9Ba029nthuJYLXtU35dShEsR8pGVtSUdTWaLppNlNMCwTBa4fgwRLDMZ2HIvcv6PC8uY/UstPl/PYLBij+XW+K7sXUFPH2r+36dB8CUC2w3L1iwgHHjxvHggw8C0N9f7ng5evRoXnzxRe644w5uueUWfv7zn5e2zZgxg69//essXryY2267DYDbb7+dpUuXArBy5Up+//vf89RTTxEOh/nCF77Ab37zG04//XRuuOEGlixZQldXF6eeeipHH310zXndcsst3H777ZxwwgkMDAwQi1XXQt5xxx2MHDmSZcuWsWLFCmbMKP8CZXBwkNmzZ/Otb32Lr371q/zsZz/ja1/7GieeeCLPPvssSil+/vOf893vfpfvfe977vdQqEEiWIIgCC1GXyJbWuBCZVqYz80nhtR6KaXoijdQs+RRx6zvAcMo1K1jLnYrF/DhtlIUxrw/XUPSKxvWqTJyZeNTqvWqbHLRZjaFqOM7MmvKKhfvFbVRyWyebF4PSRc1dBoyjUNNApSigkPNdkc0RDDQgNnOJqrvGwyTwRo00hCDQ+rx9oqRazNq9Ap1RiuF5tm2Cl79i/EHyj9vW9XwIadOncrChQu55ppreOKJJ+jq6iptu+ACw5i9//3vZ926dXUd9+GHH2bJkiUcc8wxzJgxg4cffpjXX3+d5557jlNOOYUxY8YQiUS48MILLT9/wgkncNVVV/HjH/+Yvr6+miG9Tz75JJ/4xCcAmDJlCtOmTStti0QinHPOOTXn3tvby5lnnsnUqVP5r//6L1555ZW6rkkoIxEsQRCEFiKVzZPOFfyph3FhdzLL+JHVi+HutgZriVx0Kq/H0Inw5q46ow3ZBATCEKw4VqTNWARrbR3BaguTzhVIZfPEwsGhR7TRKZ5XpZGrMD79yWIjiCE6UOd3ZBeRK+pYNe0op3HWYeS0rjVYwQioYOkchjbtMMx2uLH0ymhX9XvhNv8jS9lk9fWUdIbByEH5+6/8OZuA6Ah/9d7rOESaanjgS750cjz88MNZsmQJ8+bN47rrruOMM87g+uuvByAaNcZVBINBcrn6Gptorbn44ov59re/XfX+X/7yl6pfNtlx7bXXcvbZZzNv3jxmz57NwoULq6JYTgPhw+HyL7Qqz/2LX/wiV111Feeddx6LFi3ixhtvrOuahDISwRIEQWghrOp7oqEgbZGg/5GlIVELoLFFtatOpqa9fFc81EANllV0pA10HvIZ+hJZ4uEg0VB1KiLUa3zMyFKl8Sl3wzO/h6ZrlrI2ETmAjItOPc9CLg26UG0SlDJeZxIUCprdqVoT3HCUMTLE+ETay9E6v8gMVt83KF2Pr2STgIKQtQkWWp9NmzbR1tbGpz/9ab7yla/w4osvNnyscDhMNmv8P3Paaadx7733sm3bNgDeeust1q9fz3HHHceiRYvYuXMn2WyWP/7xj5bHWrt2LVOnTuWaa65h1qxZpRowkxNPPJE//OEPALz66qssX77c9fz6+/s54IADAPjlL3/Z8HUKEsESBEFoKawW1eZrP5tP6GLEpyayVO88Jw9YzVLqjkfoT2bRWnv6bS5gvaguGZJBS8NY9zwnsKm7iZe29SWrW5kbOg3ULGWtUhHLtVH9heKzUKETCweJhgL1RRmtasrM19lB9qRyaG20tK+k4fo1qxRBcwCxX2ST1ma7kDVq24Jh68/VS2bQ0AlU/L664pkT3kYOP8uXwyxfvpyrr76aQCBAOBzmJz/5ScPHuvzyy5k2bRozZ87kN7/5Dd/85jc544wzKBQKhMNhbr/9dmbPns2NN97InDlzGDt2LDNnziw1v6jkhz/8IY8++ijBYJDJkyczd+5cNm/eXNr+hcbpuh0AACAASURBVC98gYsvvphp06Zx9NFHM23atKr0RituvPFGPv7xj3PAAQcwe/Zs3njjjYav9b2OGCxBEIQWohTBivuw2HVgIJ0jX9CWOj1b9/imA8Y1Tdqvo0YnX9AMpHOlOVyuWC2qK5o1WBnGxiJYCQjFIFCRUlia55SkP2nO82oyjbNk5CpMoznPKTtIfyZTdexKrboiS44GK1mKWFrp7KqnpsycHxYeaoLbhyFFMAHtY4boVBifeLdPOlZGLl7eJrx9TJrrvo8HzjzzTM4888ya9ytrrmbNmsWiRYsAuOSSS7jkkktqfgb4zne+w3e+853S6wsvvNCyxurSSy/l0ksvdTyvW2+tbeAxceJEVqxYAUAsFuPXv/41sViMtWvXctppp3HQQUZH1YGBgdJnPvaxj5VmZ51//vmcf/75Nccdeh2CO2KwBEEQWgizSUJNJKYt7OsA4FKkbGiKoM86YER1rHTM8/BusBIQH1n9XkXqXn/CqtargYG5Q+uVoKopRH8iTmcsVJofBg2m7pVSBIdqGd3w+jK1zTSM13WmcVo1BzFfJ/ssa71MnTd21BGlsbueSLE2Suty+/ZmsfqOKjsj+mawrHSGaXiyINRBIpHg1FNPJZs1MgF+8pOfEIl47GAqNI0vNVhKqbOUUj1KqTVKqWsttl+tlFpa/LNCKZVXSu1T3LZOKbW8uG2xH+cjCILwbsWqUYP52s8IlpNOQ/OcbMjlC+xJ5/yLLO1cU/2eGU147R8+RrCS0N9roaNg3ZOGzhDDGAoGGBEN1X89fRuNaFklkXbY+Lx/z4KpYxWJ2bK8rGNRj1f3fevbaGFO47BrvVEL5heZBOzoGaJT1F3t4wDabMI49yqd4n18fZF/OoJQJx0dHSxevJiXX36ZZcuWMXeuPxE9wRtNGyylVBC4HZgLTAYuUkpNrtxHa/1fWusZWusZwHXAY1rrtyp2ObW4fVaz5yMIgvBuxm6x29DAXA86NVGL4uvdqfo6ZtlhHsdOp+6Uuu1D2jGbUYs3Hi8207COlNWnMwi7htQmmPOcNj5v1HrFa39TXHeDkGwS9myujeoUjU9fMksooGiPVHc/rLuVfjYB/VbGpw12ri4PaLb4jnanshQK9t3KqnUGbXTajff9iviY88O2rBiiU9Rd+6g/OmA8czWmvhjBWv+0fzrvcZw64gnC3qDeZ9CPCNaxwBqt9eta6wxwD1CbwFnmIuB3PugKgiC85+hLZAkGFB3R6gzvhuY5uegYxx2aftbAPCdHHTPl0U7H4zUV8pBPQ2BI5ru5qC7kjFbjQ3TMeU71GZJkrY6pVdKpTWusO40zM+ioY86mGtoEpLutzsiSXYpguA0KefoTRmSpNo0zgtawO+VRy2p+GJRNsF9NIczjDL135vUV/PnlAFAcDTBEJxAwoo5+6ryHicVi7Ny5U0yW8LahtWbnzp01w5yd8KMG6wBgY8XrXuA4qx2VUm3AWcCVFW9r4CGllAZ+qrW+y+azlwOXAxx44IE+nLYgCELrsSeVZUQ0VLOo7oiFSOcKZHIFIqHmf3e2p7ho7ohV/zNhvt7jUwTLPI69jofFe898WPkA9L4A+xxszL8Bo8nBwHbY8Az5znHcwF1M29AFPReWCuCVUoyIhrzrrF4AvYuNWq9KncHtsPllCEa4rO9HRtpez6erCu07YiFv983U2bbSWLwP1dm5Bgo5PrzuZuaQhZ5cczq71gMK5n2lWme30ZHs/S9fz02hAbo3AEedXaUDxnc41Lha6gzuMF4v+rZhskyd1G7j/X/8B8S6jO5vjTYo6JkPr9wHm16C0YdX37vdm6H3eRhZ8Yw0qmVe0/pnoHNc7XfUu9io82pWR2D8+PH09vayffv2t/tUhPcwsViM8ePHe97fD4NlVZFq92uGc4GnhqQHnqC13qSU2hf4h1Jqldb68ZoDGsbrLoBZs2bJrzEEQXhPMpDOMyJa+1e3+d5gOkck1Hwh80DaWKC3D9Gq1PGDQTud4uJ9wIvOpLkw/lh45OuGoRo6XHTBdRQ2Leffcldww9TJTJl0cLVWNMRAurYNsqXOpLkw/xojFXGoztO3wit/4Zvqc3zgwNGcNGn6EJ0wm/o8dJYzdZ76kWEch+qs+BM8fgv/3f0ldoQynDvpxKrNHdEQg5kchYImEHBoGmHqLL8Xnvherc6GZ+H+L7LwgCv58YadXDS52iCYUVTX78jUWf+0YTjmfqe6GUnfRvj1BXDSl2H/qc7HcmPSXNh3Mjz5fUjuqr6mQgEe/Fejfq7ZAbST5sKhp8O8L0P/m7XHW3SzUYPlw6Db9zrhcJiDDz7YfUdBeAfhR4pgLzCh4vV4YJPNvp9gSHqg1npT8b/bgPswUg4FQRAECwbSWUuD1e51setZxzjOUC1TZ49POnvsdCJ1Xk+uOJsrYPHPWihKPm9t5EztgbTHNDetDS0VrN0WioHOM5DO2egE6/t+cml7nUKOgVSW9mjt9vZoCK0hkfVgGsG4noCNDpBOJWi3iJrW/cyZ31FoSIqg2cTDryYXps7QexcIQCBspJP6opMsH3cooah/OoIgtBx+GKwXgMOUUgcrpSIYJur+oTsppbqAk4G/VrzXrpTqMH8GzgBWDP2sIAiCYDCYzlsuqkf4bLAG0zni4WBVq/FKHb8jWEMNVjCgiIeD3nXMRfXBp9RuC8VIdB1qqQPQHg0y6CWCBWUTMMHid4GhKLp7IoOZnK0Jruu+5VIwdnrt+6EodE0gnU47mu267t3oI2x1MumEP6Y+l4auCcZxLXRK32GzmMc55IO120JR2O8on3SKdYgHnVC7LRiFUYf4oyMIQsvRdIqg1jqnlLoS+DsQBP5Ha/2KUurzxe13Fnf9CPCQ1rqyinU/4L7ib8VCwG+11guaPSdBEIR3K8bgXYvoSMxf4zOQzltHYXzWsUsRNLU8pe5B2fgc9qHabaEoA+0TjWNa6oTZ7bUpRMnIfcBCJ0ZuxDi0ttMJ1R/BsjRyMeieQGZTkhHRkTWbOyrSK/fzqrP/FAudKHRPILstyYhoR83mjnqfhVwKRh1a2xWxeD3+Gayi8bGqeQrHodunOm7zfN93ioVODEZO9EdHEISWw5dBw1rrecC8Ie/dOeT13cDdQ957HbD49ZwgCIJgxUA6x7ju2k5Gfqfu2Rq5YUoRtNOqP/3MostTKEY2YzRYsEvd81QbBWUjNzQKU3wvm07a60SMRiTZfIFw0EMCSS4FQYt6uuI15tIJSx0zvbIu4xNur32/mMqXTSdtUxEBBrw2PMmlre9bMAwoH1MEi9+lpVbU/1REm2eOrMdnShCEdx2+DBoWBEEQ9g6D6VxpAV3JcKTuWS2qo6EAwYDyVScYUEQtOh8aqXt1LN7B1vjki8bHMtUtUkfqnrmoDlrpxMhm0ygKzafu5XNGm2/LxbuhncsknVP3mjU+FTp2hhHqrMGyuh6ljPd9i2CZz4LNvfNdx8rIRSDv31w6QRBaCzFYgiAILYR9AwW/UwStjZzZ1txzzZILg+k87ZFgTQMF8DeClS9GE0bYpFfWVUcEtbOcAEJRcgVNFOtGJHV1RsybOjbXU9CofMZSp6MeHbA3PkXjUMikLCOMpgGvq37NyoyYWr5FltKgAjYzxOL+13pZ3TtTR2Y3CcJ7EjFYgiAILYLWmkGb1L1SiqBP86kGUtY6QHFulH9zsDpitUN5TZ26ojBgE1mKks8Yi+ERNtG/wXTO2yDT0qLaJoKVKxAj42iCPRmfrFOkLEour211SpGyTLMRLMM45DNJS7MdCgaIhQPeOzDaGTlTy0/jE4rX1nqBEVnyPUXQJvqnC5D3b/i3IAitgxgsQRCEFiGZzVPQ1vU97ZE6owkuDGasI2VQZ+qem45NKqKhE6rDJBTrlSxbZsfQ2RSgbWuJCtq4v+46zkYuW9BEsO8iCB6jjG5GrlCwjZSZ1+jJnGpdNFgWxicQhEAYnU3ZPgueZ4jBXoxgpRx09lIqovl8+KUlCEJLIQZLEAShRTAXzFaL3VAwQDwc9B5N8KDlvKj2MRXRSafZOiIwjE8+T0e4QMiiucSIemqWzAWzTepeLl8gpjLWqYj1RBlLOtapiNl8MRXRQqcjakQEPRmfXBrQtvdOh6LonHWKIDSQxrnXIlg2z0I4Vo4O+qED1mbb/N78Mo2CILQUYrAEQRBaBHMh22EbWaojmuBBy1nHP4NlFYUBfxfv2bxmn4h1CmBdqXsuDRSyeSOy5DSrzFOUsRQps+4imMsXiCrrFMFYOEBA1Rsps753WRUhQtYhmllPgxCbSBn4X4NlpxOMQiELhYIPOinjeHaDhs19BEF4zyEGSxAEoUUwF+Z2i92OWJ2DbG3I5gukc4Vh1wHDBDgZLLOtuSuOi/cYubxmZNT6OPUZn5R9A4WikYuQLUWRqnTqmRvlFMEKRsg6NNMwG5F4a6aRKZ27FRnCtjrQiAm2iyz53HzC7lkwI49+aOUyELIwwFDWlwiWILwnEYMlCILQIuwppv/Z1ywFfYksOQ3/BaOt+d5IEay7ZskxRbBAd9jaYJVniHlIrzQX71YNFIqRpTaVIRa2SEWM1DFDzKkFuFKktU/GJ+swMwpIEXHX8ZLy6NR2HnxuPpH2YHz8MFhJ54icXzqCILQcYrAEQRBaBDPCYhUdAf+Mj7dUxL2TIlh5Po64RLCyhQJdET8iWC61XgVNV6Rg2Xa+3Na8jgiWVX0PkCpGlhwbhDSb8gikdaio45Ai6KURSd7BMJr6frU1N7sIWmGmXPph5lyeOd90BEFoOcRgCYIgtAjlyJL1otqv1D2vqYie2po7YLadtzVYsTpT9xyiCbm8to1g1Z2651jrVaArZK1jtjX3w/gkC2GiyjoVEeqY7eVifJI6TFRZz9sydfy4HsIxo615wQfj7mSCS80n/IhguXRF9EtHEISWQwyWIAhCi2Cmltktdv2KLA24piLW0dbcAae286ZO5fk4kkvB9lXW24rGZ0bmRRsd4zo9p+7tWme9LRAknQ8wK2BzHhRniHk1crs3WzdQAJKFEFPVWtvvyHuKYAr6NjoYuRCHq17HKKOnrojZZFHHxpAEo8Z2M2WxUcy281tXWG839dcsbE4HjO9o51obneL9XPdE8zoAPfP9OY4gCHsFMViCIAgtgmttVD0d3RwwOxE6GTljv+a0BkqG0c4kBKvOx5FcGra+Yr0tGCGX10xKL7fRqTOCZbeoBhKFEEfp12y315W6t7vXXkeHmBzYaNl2Hox0Uc/X029vfBKFEAerLfZmO2I0Ism5NSLJpx11CMWM7c2m1OUzgIbNy6y3mymXrz/WnA4Y927HahudCKBg4/PN6wCsXuDPcQRB2CuIwRIEQWgRBtM5lIK2iE2KoE8RLHNhbjVjydQx9msugmV+3k5nRDH9zdUoFPJG6+2A9X0hHCebLxAJWKc0xsPBOtqap+11MAxJWNmnTo7wbLBSoOx1BvNhwjbXA2bqnofvJ++cujdYCBOgwIio9XLBcxqnS4pgIRhlIJ1DN5tSZ37eqssjlFMEtQ/jDHJp++9IKcNM+pHyuHtz85E9QRD2KjZ/AwmCIAjvNAbSOdojIcsGCmBER1JZI5pgF9nwqgNGdMJOBzwaEgfMz7fZ6pgRLAednvmw8m/Q+zyMPBge+JLx/uFnwaS50DOfwqr5zGQlIXVg7XaMtuau6ZU9840oQu9iiHdb6rB6AVNTSwhEYpY6xjV51Nm2yjByNjpHvfUSWmlbHdcUQVOnb4Pxet7VoGp1Dlm3hgwwcuFXIBy20Cl+R5kcXW0W9WCmzuBO4/WjN0GkrUZnfe8mcv0p4n+4mvFj96vR8UTPfHjlPtj0Eow+zPremc/KPhbPSj06qxfAhmehY6ztd2Q8KyOb11n/tPG60eMIgrDXEYMlCILQIiTSedvoFRiRGDBqmzqaMFiJkvGx1jJ1EplmI1jORq50PU46k+bCgbNh4Y2wZyuc+6Oa7QMHfYinXvgkH1L9tdsrtFx1Js2FBdcZqYgWOkyay8MvX8lcljjq9CUy7jpP3wqv/MVWZ9GGmzkt/TtbnZjX61nxZ3j8u3Cetc6zf/wdM3d8neBZ34K2kZY6AEm7ToKmzobn4P4r4ayboX1Uzfb7//gQH978JZ4/8P8y/qzT7c/biUlzYb8p8MQtkHjL+t4d+iGY9xXof9P23nnSOexMePBfjboxm++IR74J655qTmfSXMNYrX+68eMIgrDXkRRBQRCEFiGRdTFYEQ+GxKMO2EeWTJ2El/bcHnTiNtdk6rsauVzRsNik7iUzeTKECTqk7rVFgt4MYz5jDBq2IZEPEnJI3fNVR9nXPbVFgmTyXmqj3HUCCgI2qW6ev6NSKqL1fKqkNo4TVU2m1JmDk+1S9wIhQDWfIljScVhGBaNGZ0RBEN5ziMESBEFoEZKZnK3pgXLEqdnIUjKTRyksh+VW6jRr5MzP2zVQiIUDKOUQHTExF+8TT7LcnMjkyRIk0XWY7SHikZD7fSsU24iPP9Z2l8F8gP7YBAcdjwYrl4GxU203D+SD7AqNsZ0bVXoW3Do95tIw+nBHna1q3/I9ttFxfRbyGeiaYDvXK5EPskmPJhbwyWC97xTr7UoZDSj2t7+33nSK9+Og4+33CYaNVEU/6LJ/pgRBeOfhi8FSSp2llOpRSq1RSl1rsf0UpVS/Umpp8c/1Xj8rCIIgGCQyzhGs0mK3yfbpiUyetnDQttbLTx2AtrC1aVRKGal7bjr5Yhv3Qz5oo5Mjo8Okuu0Xu22RIClXHXNRPcd6c0EzmA8y2Da+OR0wjMK499tuTuQC9Ef2LxuKIZhRwZQX47PvZHudfIDtoX1dddyNXAa6DzRMhwWpfJA39WiiqsnIktlM4zCHNMNQxEglbEqneD9sTL2hE4VRhzSnY9ItBksQWomma7CUUkHgduB0oBd4QSl1v9b61SG7PqG1PqfBzwqCILznGczk6bTpuAflephmI1iJTI64Q6TMrxosM8XQLkXQ1HJPEXROPzMiWCGiAfvjGDou0ZOcy1DebJ6MDhNT9p3wPF2PqWXX0hzYnQsQigcM42Oxn+fvKJ8pthS30ckGCAVU2cQOwXsEK23o2Jj2RN44TgQPM88cdYrGx+HeEYz40A7e+ZkzdMKQ2t2cTpPDvAVBeHvwI4J1LLBGa/261joD3AOcvxc+KwiC8J4imcnZNoSAcj1M0zVYmbxt2h74WOvlkiJoanlavIN9+lkmT4YQMWW/ePeUumcu3m0MSSKTI0uIiJORi4SMAcsFl4VzPmMb7QEYzCnD+OSsI0ue00VdjNxgLkAoELA1JJ6NXC7jaEaSheJyxCZS5hnzPB1MI8FI8zqlZ8HJyEVtUyvr1hEEoaXww2AdAGyseN1bfG8oc5RSLyul5iuljqrzs4IgCO95vKYINt18IpMvLZytdTw2NvCgAxALOV+T5yYXdpGlTI4MISLY35e2iJdURDcdo9YrSs61NiqVc9AqFIy5Xg6L9z2lyJJd6l7RbGfd6tc8RLCCTjouXQQ96qTzkCVoaxg94zWC1bSRM3WG2cg1G2kTBOFtwQ+DZRXvH/ovy4vAQVrr6cCtwF/q+Kyxo1KXK6UWK6UWb9++veGTFQRBaFUSmbxzOp1vtVE5RyMXDCgioUDzOumcMeQ3YJ02BuWIjyOlCJZ1xGcwbXQRjDh0qGvzEilziY6YOqEAxvBjGx1wif7lnRfvhYJmTy5gzDqzWcCXdVy62OXSjsbHzciVoqauNVgpR8OYzhlpnNq3yJKD8QlFfYhgeYiUhSK2qZXedSSCJQitiB8GqxeorL4cD2yq3EFrvVtrPVD8eR4QVkqN9vLZimPcpbWepbWeNWbMGB9OWxAEobVIZHKlIb9W+NXdz0gRdC7RNQxJ823andIDAdrc5jmBa7pWIpsnq0OEHSJY8XCoDh2bVuNZI1JmGJ8mUupcUh5TuTxpHXaOLIU9RjNtarhM+rPK0ch5r/XKOkZ7MnlNhrA/ER+HWi+gWIPlUwTLMUWwWOvVTB2VRLAEoSXxw2C9ABymlDpYKRUBPgHcX7mDUmp/VWxHpZQ6tqi708tnBUEQBKNDXSpbcEzd86v5RNIlRdDU8kXHIVIGxdootzS3XAZQthGsZLE2ytFgRQIksnm002LYJYKVyBhGzjl1z0OUMeds5MymHUYNlo2R8xrNdEsRzDjXepWimZ6aXNibkUyuQJaQPymCTlElMJ4TvyJlbimCaKO1f7M6giC0FE13EdRa55RSVwJ/B4LA/2itX1FKfb64/U7gY8AVSqkckAQ+oY1/xSw/2+w5CYIgvNtIlob/Dn+K4KBLiqCp5dqa200nnbNt0V6l02SHusF0njQhQtq+u19bJES+oMnkC0TtasJKi+qYrU6mZHyaaD7hNpQ3kyejQwQD7imCjjpau0awBrKaYCDouND3XCcXGWG7OZ3Lk9YhVLNNIVxSHgF/mk/kUuVj2eoUz8OlYYkjYrAEoSVp2mBBKe1v3pD37qz4+TbgNq+fFQRBEKoxU72cjE8kGCAYUE03uTAiS15SBJuMYGXdI1ieUgTdOtRl8xAME9A5o4FEoDZ5w4zYJTN5e4OVc671MlIEw8UIll2KoNkgxOE7ckt5zFQYOTuDFfbQiKSkY309WmsSmTwqFHE0JG2eZpU5dyssRbD8qFlyanABxQhWszrFz7vVeoHx3ETaG9ORFEFBaEl8GTQsCIIgDC+myWhzMD5KqaIhcWls4EIik6fd1fh4qFnyouNWg+W1u5/DQjeRyREIx8r72uiAS/TPpUNdyfgEA65zoxyHDbt0qBsspjx66e7nrONc65XOFShoCISjjobEWyv9jGO0J5sv+FOD5SVF0K8mF4GwpVkvYRrXZsycRLAEoSURgyUIgtACJDLuKYIAsUjQvTW3A4WCJpl1bgdv6jSbImi0g3eOlMU8pwjaL96NKEy0vK8FcU+pey5NLkoGy934eGty4aQTNuZT2eiEg8o9mmku/B0MI0AgFHWMpLRFQh6GNDtHGc0IlvKjyYVrBCti1EUVmvhFRD7rIRXRTBFsIgolBksQWhIxWIIgCC2AuYB1TanzYkgcSOXyaI17imDYhy6CHmq92sIhMrkCeafBvC6L90Q6T8jcblMbVZkiaK/jXutVanLRzGBelw51ieK8rVDQvtbLjGY665h1RPaDkwGCYeeIj2vDE7PWy2kOVq5AliCq4EOK4N4wPrmUc4MLKH9/zUSwJEVQEFoSMViCIAgtgLmAdWuf3mx3v7LO8Bo5U8uLjrGvUyTGJYKVzROMuKUIeqxZckpFzOYgFCGglGuKYDNzsIx7oQiFnZs1uKbueRicDBAMOw/MjUeCzqmI+SygXWuw0jrcfJOLegxWM+bFJeXR0An7o2PSTLt3QRD2KmKwBEEQWgBz4e/WPr3NbbHrQtKjjuui2qOWW4qgp86I+axjl7ZkJmeYEWiufbrL4j2ZybvqeBrM6zIHy3wWghHn2ijX+jWXlMfBkk6suS6CLimPmVyBXMGYg9V0BMtLimDIj8iSc9S0WqeJNL9KcyYGSxBaBjFYgiAILYCXLoLgsa25A4MlneGNlGmtvbWD95S6l3JcVA+m84QiLgarpOMQKXNZvA+m84RKzTSsoxbRkPHPrrcUQfs5WICh5bB4j7mmCLrN9coVddybXDSX8mjolFIEmzESXudgmfs2rOOlHbyZiuhTkwvd3C80BEHYe4jBEgRBaAHKTS7cjE/IlxRB19qoYnTEcTCvA+lcoVjr5TVF0C2yZG98kll3g+Vdx6kdfK6sY1MbFQgo4m71ay4d6szPhsNuzSf8SREMR5x14m5t2l3awZv3PEsIXdCNGx+tPc7B8qMGy0uKoM9NLnRz3UEFQdh7iMESBEFoAUpt2r20NW+i+UTSo8GKR0JoDalsY4u+Uq2XqdMz30bHY1OIbfYz6hOZHJFI3HixdpHlPp4MVi4Nb73uoJOnLRaBQAg2Pmu7n3tKXRZ2v+moEw4qI0Vw88sOOi7d/fIZ6NvoGimLRGOwfZWDjpuRSxs6NgOaTZ0MYTrSWxo3WIUcoGHLcuf9zOtd80hjOmCc447V3nTWPdm4Tq547wAKEsEShFZBDJYgCEILUIoseajB8ieC5T5o2Ni/MTOXGJqKuHqBjU6xZsnumswOdQ6L6kQmTzhaXNyvf8pyn7in5hNZ2PGao048HDQW1m++aLufa/OJXBr6N3rT2WpvLN1T94o6LimC4Wgc3lrroBMimc1TsOv0mMs46pj3IqNDdGW22Eb/XDGN2aaXnPczI3ZvPNaYDhim0cF0AuXr3fh8EzqZ8rMgKYKC0DKIwRIEQdgLvLhhFwtf3drw5wczOSLBgDHE1oGYW7qWC17bwZdqlhrUKjftULBmoe1v5111zA51yvp8tdYkMnmipsGySbPydD35NCj7+19qOx+MOC6G3VPq0rbXU9YJGSlqLjrO3f3cBycDRCIxY2aUTTpoaXhyzu47KqbIOQxOhmKKYOV51YuZxhhwfnZLxqeZlLtcxvFZMM4jYEQzm9KpbHIhKYKC0Co4/4pSEARB8IWfPraWNdsG+NDk/Rr6fCqTdzU94CFdywXvKYIeIj4uOqcFljDrxd9BogdGHw4PfMnYePhZMGlulY5lpKxnPqx8AHpfgH0Otvx8Nq85hcWcsXEt5F6F7oMs9wsFA0SCAeuIT898I8LWuxjiIy0/DzAj8QxnZV+G9EaItNvuZxtlNHW2rTQW7zafP2TXk5xUeAxe2WMYn0Z1dq0HFDx4leXn9938KDeFHiC+TAHa0FHKUgeKKZKVkU9TZ3CH8fqRb0I4Xvv5dQ9xU+gPdKkBw8Q99DWIjqjZz5Ge+fDKfUb0yuZZKu336l/hzSWwzyH2+znprF4A65+BznHOOqsXGNGr9jGN62xeZrxe/zTMvwYmn+/9ngiC8LYhBksQBGEvkM4VmjM+2byrKkKIfAAAIABJREFU6QFjsZsraDK5ApFQ/UkKZmTFS5MLcKlZctF5uPB+vjDzXMZuvBsGtsG5P7LVsbx3k+bChOPg4f+0/bypc/xRn+aovh8bpsJiPzBT9yyM3KS5xp/518K2V20//6ieRWrCmZy470Ow5h8uOjbXM2kuPPVjWPlX28+/EDmWzZ3TOHfmRnjm9sZ1VvwJHr/F9vOrOk/g9vx+XHRCO8y/Gs76tmEchxCz6/Ro6mx4Fu7/Ipx1M7TtU/P53jGn8G+5Tg5Wm7mHH8Ep18LowyzPyZZJc2G/o+CJ70Fil+01MWkuHHwy/P062L3Zfj8nncPOMExpf6+zzqS58I8b4M3FjelMmguPfhte+TMcdDx88HpoH1XfcQRBeFuQFEFBEIS9QCZXINFU6l7edTYVGPUw0HhkyTRMMQ9zsCr3r5fSvK1gMe3MJtXN1ciZ6WQ26VolnUjISIVzSKlzbz7hnBaWyOSM7yjknCLYFgkZQ4kddezvfzJbUYPlkDbWFgmScOr06JLmliw+c8pM7XPpwGifxuncdt78jvIqTAEaH8xrtkN3S90rpQg2+P+jyzNXRSjaXHOKfAZQxs9SgyUILYMYLEEQhL1ApskIViqbdzU90HxtVCqbR6nyvCY3nUaHDZvnFwsVF//jjrbcL+Zag1Vc7E480VEnHgkYbcL3m2J7To61UYW8scAdf4zt55PZYhpnMGqknznpuDXT2N/+PJNmumgwAl0HOOrkC5ps3sZg5TOOkaKq6+maYDvPyZMJ7ppgb7CyZq1XlLeC+zVeg2V+7n2nOO8XCBrmaP/pzekcOMd932AYRh/RmI6pNar4LEkNliC0DGKwBEEQ9gKZfIF0rmDfac2F0mLXhWa7+5WiFkq56ISKOs1FsGKB4qJx7AzL/aKhAAHlEJEzF/2HfNBRx4j4RGFf+8WuY3c/U+cg60V1oaBJZQuGIQyGYZ/3NaYDxqJ63EzbzcmSTgQ6DzDqsCx1XKKZ+QzsO9lBp2jqQxHonmAbWYqHzWfB5pnLZ43P28zBMk16JBZjZ2jfxgfzmsbnsNOd91PKuHcOJtZZp3h+E09y3zcYhdH2ZtuT1pjiMytt2gWhZRCDJQiCsBfI5IxFcKORpaTnFMHma6O86DRt5MwIlillE7VQShXnObmln1kv3ks6piFxGczrrmMdhUkXv9+4Vx237n4Ow3JTpRTBcPW5WegA9umI+aztfavWcU4RNJ8522hmPmMMTrYx7aYBjEZjFDSND+Y1jY/boGFzn2YjZQ73zhcdcwRBuDjDTSJYgtAyiMESBEHYC2TyTRosM2rhgms9jKuOx1TEJnVKUYtgcdHoELWIR4IkbU2Cs/ExdUq1UY46IXvj47J4L6UihgNG3Y3Dotp1PpWL8SmZbbO9uo0h8ZS652BGyqmIHo2cU/TPSSdrDE4OhGNGDdbeMD6haBO1Xs7PXLVOpPG5XubgZPN7lhosQWgZxGAJgiDsBUoRrAYjSymPXQTjdh3d3mE6pRRBVTRYDotdx5qlkvGxnuVUbjsfco0mxMMBUg1GsMrdF0PGAt/JyIWDZHIF8lbpombUwsWQlGqjwFbLtrufiQfjU0qtBPsIVtiDkbOZgVWlEwgbDTmaTREc9gjWXo6UhSSCJQithi8GSyl1llKqRym1Ril1rcX2TymllhX/PK2Uml6xbZ1SarlSaqlSarEf5yMIgvBOo2VSBD3O23JdVLvpFKMWIcwIlv0i1FvqnnOKoNHkwnmx69jdz4wSuXTCi5nGJ59xHcxr+SyYUYt6Iks25tRTdz+3SFmkIkXQJhLj2Eq/pON+PaFggKwONd9F0JPxcTbBzjp7KUXQ/Fy4OCTbptZOEIR3Hk3PwVJKBYHbgdOBXuAFpdT9WutXK3Z7AzhZa71LKTUXuAs4rmL7qVrrHc2eiyAIwjsVM0WwUUOSyOQ8NrkoNjZwagPuqOMtRTAQUMTCgYYNY0mnUDxPl5S6ZluAl2qw3HRcI2XOOqVURIqRGIvITTxSbgoxIjrkn2GXxXsuXyCTLwxJEWw0dc89UjaudD1OOqHS/tYn7WLkihGsUFCRIdS4Icmlje6AAffn1zDBDRq5nLPZrtbxIVIWKhosiWAJQsvgRwTrWGCN1vp1rXUGuAc4v3IHrfXTWutdxZfPAuN90BUEQWgZsk2nCNZXg9WokUt5bHJhaIUabnJR0vFgsJwjWF5rozw0nwj7ECmragphE1lySt1zqykb2kyj8jNDMLv7WQ5PhjpSBJ2vx2zp30ytVywcJBgIkNZNGCxTx6UDJtBkBKuOSFnIOZrprGOmCJoGS2qwBKFV8MNgHQBsrHjdW3zPjv8LzK94rYGHlFJLlFKX231IKXW5UmqxUmrx9u3bmzphQRCEvU25yUX9hqQqauGCa92NC167CIKxyG+qW2EkWG497Vgb5WZ8lG3UopwiaDa5cI+UWQ7m9ViDVUpFBNsFvGMap8dIWcxD8wnXdFHXFMFCOeURbFMEAwFVrJNzaETioaYsFFBkCTaXIuglbQ9czbazTj0pgs7fkScd02BJm3ZBaBn8MFhWvyqy/FWNUupUDIN1TcXbJ2itZwJzgX9RSn3A6rNa67u01rO01rPGjBnT7DkLgiDsNQoVw16TmfrTfEpRi4j7X9mu9TAueJ23ZZxPsPFBw5mhESzn7n6OLcAdohYlQxKqqI2y1QmidbnlerWOR+NTR1tzy5Q6l8V7VVdEl+YTjjVYWkPBOYJVijIGAhBwjiy5Rhk9tIMPBRRp3WRtlJeoErh2enTVAe8pgpWfqUuneB/CkiIoCK2GHwarF5hQ8Xo8sGnoTkqpacDPgfO11jvN97XWm4r/3Qbch5FyKAiC8K7BjF5BY00uSvU9Efey2XAwQDionOcsOWoVPBssx0W1m04pguUhRTAcdB5i69ChLpXNEwsHCASKw2V1AfLWx2pzatxRl/Exm0I0kyJo0xVxaNt5sI0sxZ3MtotJ0FpXRzO91K85mmDr64EhNVg62HyKoBf2WhdB506PzjrSRVAQWhU/DNYLwGFKqYOVUhHgE8D9lTsopQ4E/gx8Rmu9uuL9dqVUh/kzcAawwodzEgRBeMdQZbAaqFmqWrx7wLGtuQetvZEiWFOD5dSm3a2LoJc6IvAwz6ncfMJSBzykCFZGsKwX1WUdpxRBm1qvjFUqovW9c+z06HI92bwmX9Blsx0Mu6ZxNtNFMBYxarBSOtzcoOF6UgSbqY0KhIzInquOc6dHVx2oiGBJiqAgtApNdxHUWueUUlcCfweCwP9orV9RSn2+uP1O4HpgFHCHMtI4clrrWcB+wH3F90LAb7XWC5o9J0EQhHcS2VyTEax6DVbEIeLjQE3UwoPOzoHGogDJbJ7OWLiiBstl0LDd4j2Xdm8Bbl5P1WDetpp9Y04Rn5yLwTLnbYVDUHAxPhGzKUT9Rs40S/FwyNXIlaKZTdR6lc2pc0pdMymCyUyetnCQXEGTJtj4YN56I1i6YDx/wTqXQvXqmJ+pl6FdBKVNuyC0DE0bLACt9Txg3pD37qz4+TLgMovPvQ5MH/q+IAjCu4nKCFYjEZ+qqIUH2iIhktn6F2M1UQtXnSC9TdRgxYamCGptWUtlLr6z+QLh4JB74LZ4z+ZLxsk1shR2qY1yiFqYn4lFApBzGcxbjGBZ1pW5dRGsjJQFQoByjSw567jND6tMEWzQBHtscpHM5EkXmuwiGO3wtm9l6/m6DVYdkbKQHymCUoMlCK2GL4OGBUEQBHsyTUawEpUNFDzg2NHNgWTdOqHGm2kMbXKBrvh5iI5j1z0PQ2zDQw1WA3OjPOgEFESCFal7doN5HVP3XFIEK6OZShkLeKfW83at9L2mPJaify4t7iMhm8HJeSO1zUMaZyioSOtgc+3T60kRhMbSERuKYDWgkxtqsCRFUBBaBTFYgiAIw0yVwWrAkNRbg9Vo84l6UxHbGkxFNLUMg1WxmHYbZNuI8clapAjaGAXnphDukbJ4OIhSynUwr6thhDpS95y77tk+C/V0RQTXwby2aakukbJCQZdmvAUDipQONVGDVY/xaTKyVK/BaqYGy3xupU27ILQMYrAEQRCGmXSTBqsmXcsFx6YQnnS8/dPQqI6pVTUHCxwMiVPNkgfjU9mowVGniQhWlY77YF6lGuvuV5WKaO7nYEhids0n6k4RdDZytk0uXK4nXRpBECQUCJAqFKOajdQb1WV8mmk+0UikrAkjZ854kxRBQWgZxGAJgiAMM9nKGqwm2rS3hb3VijTaRbAcHfGuk84VyBfq68RmRi2MCFbFedq1Gw87dd3zMUUw7NJF0GlmVCZfEe1xXlQrpYqt5+tPEayJZgbdUgTtdDzWelVek5uOZa2XS6SsQicUUKQLwerzq4e881yvKkLOdXLOOs5t56t1mklFLF6PEoMlCK2GGCxBEIRhpjJFMNVEZCnmucmFw0wiDzr1NLkAm2YNDlRGLbylCDrouBmfqsiSc1pY3FHHW4ogYNRGBVzamtvVLJldEW0GJ9fU44U8NJ9owPgkhqYiugzmtW1y4RIpM81sPBwkGFQkC6Hqz3lF68YiWA0buXojWI0auQio4v/3UoMlCC2DGCxBEIRhxuwiqFRjTS7qnoMVCTWUutdIrRfU3xmxqtarkAOKZmLNP+rXyWdhq/34RMs5WG883oBOBnauddapNKahKGx83nb/tohNI5J8FvrfdNQJB1W5m2IwAluWu+jYXE/fRg8pgqZOGHa8Zq8TDllHM0s6zpGyWMSIYCV1yNi/XkNSyAEaNr/sbX/TbK99tD4dMM5te49HneJ1b3i2MZ1d68sGS9q0C0LLIAZLEARhmDEjWJ2xcFNt2vdWF0GvBss8n3rTEWsMVjhubHj9MUedmntnRi0cDEbSKnVvwzP16YBhfBwMRlUqIhiGZNNLtvvbDmnOZ6B/Qx06Edi+0lkna5Py2O9gfEqjAUJlnV1v2A7mNY1YzS8Q8llHnWTG+H+jLVwcNFwIovs31l8bZRoyh3tehXk+656sT8fU2r7K275mNLP3hQZ0ssY9lxosQWg5xGAJgiAMM6bB6oqH606nA4uohQtmPYy2WQw76UB987YA6wW8k06mHLWgkC8bLJsUKDOylByqY0YtlL0hTGUL5ciSWXdjs1ANBhTRUMB+Dpayvy+poRGsYNRxQWyfulevTsSxu1w8YtNKv47aKGM/t/RKm/q1OppphAOKLA2mCJr7O9y7KszzacS05LPedcB47hpt2qECxXRRJSmCgtBC+DJoWBAEQbDHTBHsbgvTl6i/m1iiMgrjgXgkiNZGrVM9n6s3UtZwimBlpGz7KiMNKjMAow+DB75k7HT4WTBpblHHoslFz3xY+TfofR5GHmz5uVy+QCZfbKbRMx9WL4D1z0DnOMv9zWuqMgnm53oXQ7zb9nOJTJ5x3RU6m5cZg4AddfK1OttWGotqm8+VUh7N/XeuNUxCvTq7NgAKHvyykaFpoQPQvu4f8PpDsGezseFv/79hTobqDI1mmjqDO4zXj3wTIm22OvttfpQPrpnHQaFNxrzphf8J8a6a/S3pmQ+v3GdEr0YfbnsvqvZf9SBsfA5GTnTff+i92/AsdIz1prN6Aby5BKKd9etUPkMbnoX4SDjibOd7IQjCOwIxWIIgCMNMZQRrc3+q7s+nsnnPaXtQjjqksvUZs3rnYMUqdOqhSmfkRBh/LOzogT1b4dwf1exfvp6KKMCkuXDgbP4fe+8dZsd1nnm+p6pubHRARiMyAc0gWpQIUpIVKZISQYlWsD3rXY8sj3eWu17bQ2tle2TL2bJWY60tyWEtS7Zn6TQej4IZAYukKIpKpMBMEYEgiJxDNzrcVHXP/nHq3HhS1b2doO/3PHjQfSu8FQ7Q5+0vHDz8u9rj2nTGtok/2z8GnHpJub/ct0tnbBuw4zdErZfmuIbxkft/+0+BXfcYdSZKLWZbHvedPxdmQacjzbbc/4UvAY//seV+RDSTMdY87sWvAN/8I+DH9DoAkLnqduCa94gJ/r2/BLzrE0BxWbdOtuMdSZ1DTwD3/iJw26eAgeVanerl78a3+etx96uP4z34C+BtvwKseY3y2roY2wasuRb45qeBmXPaZ9G2/+Z3AQ/8X8DEEfv+bce9G3jgI6JOzEVnbBvw6P8NvPpYMp2xbcA3PgXsf1Qc9+CvAqOvdTueIIh5h1IECYIgZhkZwRoqZFKvg1V07OwHtCyYm9L4yIiRq05qg5WNa7CyRbFBkwIluyd26ch0NU/9bJRdEf2MMS0s30PqXr4rda//Ol3NNPysNRWxzptjsKljTnMT5tyD58UNSBpd9zQpghnNmGsslmtpB58VNVhVBM3auiQ0UgQd/514vrj/pKl7sutlkhRBy5jTElWb98M8qsEiiEUEGSyCIIhZpjWCVUpTG5UwRbBRs5QydS8XuNd6ieOSTfzaUgTrEZCJDdbo65T7Z30Pvse670dOqje9RXlcOb6utohckANWXaW9tmLW726lX6+LifX6G7THdTWfCLLAssv0OhmFDiDuac21CXRywNA6rVFoRP8631FUBZZv1urMqJppDG+wLtKsfEfDG5zawWd8UYMVDa1LvjCv3P+yt7sf4+eMz1qtE9//xjcm0Mkan7VRa/Q68TXzyWARxCKCDBZBEMQs02qwojpHLUrefMJ1bSqgpbtfwshSV9TCgjZq4aADxJPyqCZMAvOA0R9R7s8YU3fdk5Pdy29SHqeOYGWBlWPaa1PqyKiFZlLNOW9vBy91lm7S62gX5q0Ca9VGE4BCJwOM2I1PVyOSqGo0msr7Mek0xkKnTk0c51maXGR8+HGTi2hQr6NF7r/5Vvdj/Ayw+pqEOvFYuOStCXSywHK92TZqrd8qvvYogkUQiwkyWARBELNMTTa5KIhJZtLIUtoarDTt05PoNNu0J+wi2FobxSNRyO9njZPqfEaRUtfohGefvDfwM0CYVEd2wlNHYapRHXWuMHKGVuOilX7yhZNLqlTE1mtU6ACqyJJ94eR8Z2qlSUcXzYyq4v166ulGcx0sD0FssOocQJSyTbvrQsNy39Q6jgsNy32TRuSklrwf5hm7RRIEsbAgg0UQBDHLyAjWYD42WClqlhIZrLQ1WJ1pYbOoAwCFgInfyjPfOgktZD1FDZZ5Uq3simgxcrIpRLuOuaW5MhXRcj/5jI9KWEe9a2Fes/EpV6NGx762a9JoaaOZFiNXrnbU/bkauYQ6pWoE32NxGqiowapznj5FMJHxMY8FtU5aI5d04eRI/NtoGCyf2rQTxCKCDBZBEMQsU4nqyAZeS1vz5OtG5ZM0ueghgpVEJx/XaiWuwZJRiyA+zg+s0QRlxMcy2S3rUgSNRs4UwbKv5dSuUzUszBvXRoUJDUlXkwu3yJLSnNp0+mLk7JGyQsYHYwwZX6z3VPeCOTI+KSJLFrOt1unFyMXPzqMaLIJYTJDBIgiCmGWqYR053+sp4lNMk7qXRieBwQp8D1lfszCvRcf3GLIsnjB6gWg4YIksaVMENR3qZC1VsdOQ2HQSGjlpmLsNCdemdSlNcGfUQoHe+NhSBBVdBG2piJ0ROZOONHKqZycXKbbo+HHtX52lMD7hHEWWZNqnZsypddLcj9SJnx11ESSIRQUZLIIgiFmmGtaRCbzeaqNStGlP0z49SYogAOQzitQ9Rx0mzYcXzEptlLoGq5daL7NOIkOiMsGWSFm9zlGu1btTHluv0UVHallS95IYuUY0M0UqYiFuwx/EBitiZhOsJKoCYGIsuTLXKYJJuod2jjmqwSKIRUVfFhpmjN0G4HMAfAB/zTn/VMd2Fm+/HcAMgJ/lnD/tciyRgD3bzdvHttn3IQgVNHZ64orzr+Kd7AJGT57Hzd5uFF6dBCrDzse/sfYUrp1aBew57LT/cDnEzd4zWH70NDC02lnnmqldKGQ8YM+E8zHvCp7DJWf2A3sOdW/UjJtNZw7gVn8c2DshFmw9slP8pt4w2S1mfZyd7thuiVqojY85RbCYVdRgyWiCxvhoUxGB+J6KXccoTXBDR5PyGNp0utFGTROnIsaRFM2z00YzLSmCre3g/bgRRuSlNFh+FmBuHTDFRWeBmTlIEQxyIvpUj0Q6rJNOh9lmPnDiBfp/2Ab9rCLS0MO4WVaA8od5zwaLMeYD+AsAtwI4AuD7jLF7Oecvtey2DcDm+M8bAPwlgDc4Hku4sneHefvYNvs+BKGCxk5PjF04i+WoYPnRg7jZO4Ghw0eAqYLTsZwDb+WHMHZhGNjrZsrydY6bvcMYPfkq4A85X+frSsexpB4Ae191PubtOI5lE1lg7+7ujZpxs3n8LIa9CrDvADBxGDjyJHDZTUB1RqtTyPqYOa+JYGmiFjJdratmyZIiWIs4alEdGd9r19E201A1uXCLLLW1hLdFyqqaiFzrNXbqNLr7Kdqnm2qjOiNYng+AmZ9dVpNe6ZjyGPgyghWkq41K0uAC6E9tlJNOSzQzscGKn53nAadeApBsiYcfOuhnFZGGHsbNcI6NqD7vRwTrRgD7OOf7AYAx9s8A3geg1SS9D8DfcbG65vcYYyOMsVEAlzgc28XhczP4T//tmT5c+sXD0toJfOj408Z9vn/kD3DDBfM+BKGCxk5vFMohNnkMhQsTuJxNoDBxAQjdfgNe5xyXs/NYUR4HTuedjvEBXM7OYWj6PHDazcgBwLpwHAN+AJw+73zMJZhAtuIBp892b3zqbuD03q6PV5SnMMAj4LyMlHlW45PX1UYZohbNZhot2fB+VqxrxbnyuNaIT9NguaUIdnURlNeoQLkwr2szjTQpgq06nLu1g2/VYcypA6OyHXx2wElHpgg+9soEcofL+MYr7j/nb5x4Gauq53H/OfdjXn/hGDaWj+JfT7sfs2X6Jbxu8iy+cmEXap6+tqyVy2YO4oYLZ3HvxE6U/EGnY1ZVDuKm82fx9XP7cDpbxi1nT+D6C6dROlNyvtYfRuhnFZGG2Rg3/TBY6wC05q0cgYhS2fZZ53gsAIAxdieAOwGgMHo5XjjqnsJyMfOm8Em8v/ogLq+/iiFMwkMdrOM3XPK7S0vPNz5LkERB/BBDY6d/sAjAMeAGD2BnASj8iAoP8TETABz/22PymAsALrhf47UAWAmAIttPxzUAEGqOOfQd5TGXyy8OA4An0gQPPA7kh4F1rxe/TexA2z7dYhKyvofAbzVYLcYn6J4gy8l+uRphKN9hkpJ2EQS0nRGVjUhSdUWU16jWadZgtTRIqIcAuLk2qqZoeGJtpa+pk7PoLB8Q2zevGsRr1g3h7CRHpjSJF0ruP+c31SYR1iO8UHU/ZmlYxXA0hRdq7scUwwlsCqt49tg06qzsdEwYlXF5rYq9tXMY99waVVwWjeO1tSpype/h34ffxub6PizBjPJnPEE/q4h09DpuOBhG8myZals/DJbqWjr/9ev2cTlWfMj5FwB8AQC2bt3KH/2VdyS4xIuZdwD4NfHlfXeZd73jc/Z9CEIFjZ2+UAnr+PJTR/D6TUtx5Rq332TPVCLc8+xRvOGy5bh8pT4S0MlXnz6KjcsKuOES5f/9Sr781BFcumIAWzctdT7m0d2nENU53nW1otZLM26+vuskAOCWq1qOueStwNGnlOYK0HURrFrT3PKZjl5OrSl1CoNlbj6hWwfLZLDMkaU202hNETSlIqojSzlV8wmLYaxFddQi3t3wJFWDEIdmGkuFzprhPO7/pbcCT+4GSufxv7/9HdrjunhiL1BZjp99W4Jj9pSAvSfxgfe+3b12a9cU8MpR3PHem911ji8Fdr6EO952AzC8zu2Yo08DTz+DO97xS8DgH4rP6P9gO/SzikhDD+NmvPxn51Sf96OL4BEAG1q+Xw/gmOM+LscSBEFcFDS6pHUuLmsgrNfbjnXF9xjCKNlvuqOIJ9YJUuiEdY7AUxgfW31PLQJv7cRmawFejVDMdvwe0WZ8VE0hGu3g1VrqNu2OKYIq46NpAW6OlKnvx/NYd6fHho76fpSRMqllTBHULAZtWQcrn9DIKYkqyRpPAC3vKEG9l2XMqXXMJlitk6KZBkEQC4Z+RLC+D2AzY+xSAEcB/BSA/6Vjn3sB/GJcY/UGABOc8+OMsdMOxxKubLmtP/sQhAoaOz3jcY6vP7kT+aG1uGaL22+yz5+ZxiPPvoSr12/Gpg3KWlolO196EesKBbxpy+X2nSGM3EPffQoDK9bhui1rnXV2n9iPQ2encfuWa9U7KMbNt196EWsH83jHlitaPmUida1eFwX9HRSyPjgXUcDGhNxlEVtVmhuQvDaKeXGjB5WOKrIku+6pdYoNnZaUMce2822GxPNEkw9jB8ag437k5N2c8thlfAKHRZoTNrkoq5YGsHSUVBJVgYx7hBdARzTT0cikbaYhj3VFFWWk/4PdoOdEpCHluJmo/Om46vOeDRbnPGSM/SKAf4Oorf5bzvkPGGP/R7z98wAehGjRvg+iTft/MB3b6zX90KJJrUm8D0GooLHTMwzA9wIfmwY34INjVzsdcyp7Do/UC/i5S94AXLHCWeu54iCO5XL4+bEbnPYvlWt4pA68afQqYOwyZ51XXngOj585A4xpUqYU4+ZR5HDj0mXA2HXND/c9Iv6OqoDX3cyjNaWuabDMUQttdETqKNCmCFp0gGY6ntAxR0fUNVi2FEFFpEzun2SRZmtXREUzDUed8da25/W6MM2GZzfT2a0QSLcwb1QD8mmNT5LIknksqHXMpl6rA7Rr0f/BbtBzItKQctycK6mro/uyDhbn/EEIE9X62edbvuYAfsH1WIIgiIuVfMZvb81tQRtNsKDs6DZbOgkXGlZGLVonuxm9wZqpRhiRy0pZohZCR1eDlaDrniVqIUyfB681vdLVyLW2T0/TTEPubzAk+Yyn6VaYIBVR7l+dMuh0jLm6OVLGOddEGVO2T5+zFMGkOuaGJ2odShEkiMVMP2qwCIIgCEcES88pAAAgAElEQVSUC9ka0EYTLORVHd0MlFUNFFx1Ehg5QLHGEtCsB0paG2Vby0k1eQeS10bZGjUkjJRlfAbfYwkjS5p35Fi/1tSxpAhqI1hmI1fs0jHfTyWsg3OFqfezzXRRV3oyPglro+YqRdDLJFs4mSCIBQMZLIIgiDmkkPEbTRFc0DYcsOooGg4Y0EYtrDo+KmEddcfGHfqohbndeF4ZWXJfxLap4xpZ6lXHHB1hjMVRxtYarHhfL8E6WEDy9anS1Hq56iRIeSxr7ydNSl0vxmeBpghS9IogFi1ksAiCIOYQEU1w/828dlJt00mZiphGp/V4G9WojroyamFuCqFua55wsVzAGk3IK3VSNNNgTBilJG3No6poWKFo8tF6TUpzGibUAazGp3sdrCwQ6tPcuqKZjimPSp3W411YyMZnriJlBEEsGMhgEQRBzCGFjN9YN8mFkmqNJRedhCmCvegA7gZLpiLqJ9X9SxEsVzWL5QIpUgT1rbnLqlREII74mLruKdqnW9rO+x5Dxu9IG3OILKnX29KlCMapiEkXGu6MZlrawWvHXNKUunoE8LpWR0ta4+PacbBLZ5ZrvQiCWDCQwSIIgphDClkfMzX3FMH0kaUgmZGraTrUWXUUKXUGZnQ6crIvuwnadDgXk9ATLxq0DCmCB75l0WlN3asAZ/bpdVQ1WICYiB/ZqT2umOlsn14FJg5bdVhnXY6fBU7qG/B21UaFFWD8sHYCr1zXS+qc3SeevYJGlDGMtcKqRceQiggAe3fobqkdGVU79ozb/g2d2JDtf8z9mKgCnNqdTEdGMw8/kUCnCpw/kEyHIIgFAxksgiCIOUS5VpABbdTCquMljGBpohZWHUVKnVHHErU48LhRp3FPMmpx4jmjVl6nc/hJ5TG+x5ANvO5I2Rn9pFqZiii1juuvr6sRSVQFzh9MoZMBTu/RHtc15qKaMHKWFEGl8Zk4LJ69RgdoMcHSMGoiZfoarPi6Xvm67pbakRGoo0+77d/Qia/r4Hfcj4lqwKmXkukA4p6SXF9UBc69klyHIIgFARksgiCIOSRN+3Rl1MJBJ6xz1CK3eq+5qsEyNlAAhGky6bRO3gGxALCCep2jEta778fzATCA669XvKOO9ulM/1yUbecBMYHX3I/Q6WyfXtPWXzV0sortftZ4P13t0+Wz89QrtRjbwbcer9ABmpGpntrBA8Zn10ajOUjCKU1SHaC56HRS/IzxHal1kv1bJAhi4dCXdbAIgiAIN7rStSzMVDVRCwutC9lmfPuEUBqKNOtgieMTRrBadfZsB3bdBxz5PrDsMuC+u8TnW25rLP7YZuT2bAdeuhc4uhNYdrlyf6Vh3LNdpJ0dfgJYslp5nDymobN3h4h2FVdo95/prMGSx514UaSHGXTOTFWb+5/8gZi8a3VC9f2ce1WYBNf7OX9ATN7v/2Xt/QBAXi6cLI+bPCm+f+CjIv2x4zhZ7+a/vAM49RgwdVpsePj3xNpmGp2ue3rhS8CJ54FVV2nvqW3/F78sIoUrx+z7tx63Zztw6LvAyCY3nT07gEPfAYY3JNPZuwM4+gyQHXDT2btD3I+fc9chCGJBQQaLIAhiDkna3a9cUzRqcNGRqXvVCEN5ezcybUc3C/mETS6UOmPbgMvfCWz/NWDiKHDH57qOa0sRHNsGrNsKPPoJYPq0cn+tztg24Gu/CRx7Tnmc1CrV6s39H/gVUXuk2b/U+Y7kcd/7S+CF/2HRiZr7f+szwO4HDTp1FLItP7blcS/dAzz6Sb2OjGZecRsyY9uA5/8F+NZnjfeTDTwE0phLnSM7gX/9eeDW3weWrFTqAMCZde/E2jd8EDjwbWHibv80kB/q2l/ZFXFsGzB6HfDYp4Dps9prbNt/5ZXAtz8LlMbt+7cet+U24P6PAOMH3XQuvxnY/qvaMao9bmwb8NingX0Pu+mMbQO+/gmRuuiqQxDEgoJSBAmCIOaQQjbZulEz1TCdwepM17Lq9JgimFQnYVpYLo6mNBp3yPWyNOlazVovxe8Rk6TUcQ7Ua8a0MLHQsEono61X6tIBRFMIQ5pbqRqiqKv14nV984lOExxV41RJnY7G1FvWKutO46w0r0+BHAvajpLOKYLmdFEtjDWfnZNOfD+GZ6claYpgWEmXikgQxIKA/vUSBEHMIUlrltKmCCaujapGyAUePC95rVdSndbjGshOa6M/ojyusTBvq0kAgEvfrtzfaBj9rIh6aGhbpFnqbLhRuS/nXG+C/Syw9FKDTmf79Aqw5rXa/WdMxmd4g7U2qmFOwwqwYotZR/fcTDqdRi6siv1tBqvTnEojt/567TW2Ia/n8ne67d+ptfo1jjpxrdfGN6XQyQLLr3DfP6oCa1+fXIcgiAUBGSyCIIg5pJg0pU43qbaQdH0q7eTdQtL7aUYtNJElw2S3rX4tNE+qZatxrSExGIxiNmjRiaMWm96s3LcSioWT1etg5YCRDQYdRRfBdXpToeyK2KqjMT5d7yiqAauvTqGTjXU0a5V1LtIcVYFll2qjco26v87GHdKQrX2d9hrbkGNhy7vd9m8lyIlaLyedeCxc+rYUOllRX+iCXIJAY+oJglj4kMEiCIKYQ/JJm0KkrcHqjFo46SQvy21ELRLoADpDkjEu+ppvrV+zLWJr1DEvzNuuU2seo0DbahxwWpi3VIvAZWqfZeHkUs0QWWq9VoUO0NHdz7SgsW7MWRbmTaPjewzZziYsSRfmbXQrtNcadmEZC0qdpAsaS51QnVrZRdqFkwmCWDCQwSIIgphDpIlJEsFS1hFZSBrBKnV2wnPV6YxaWHUMkaUgZ5yEFrJ+d+qergW4rr5HHmOYVCt1goR1RA46+awPzkUUDJyLezdMqo0pgq3XqtABWiNYNp2wO20PsBssVa2XwfTIVMSuJQg8T7SQdzY+stYrjfExm+B2HWm2UxqspEYujWEkCGJBQAaLIAhiDpHrGCVpClHIJP+vOnmtV5i4wQUAZHwPgccSpQgGHlO3jneJ+DiusWSuwbLpeIpGDepJtbZph7w2HukX5m2NZtZDAFx7P3I/bdMOwBpZatZgmY2P1mw3jJz62XVFZ6Oqw/1oxlwiQ2KOMhpJpCPHQppIWRIjZ24OQhDEwocMFkEQxBzStRirBdFAIUXqXorufmkiWIA0Pm6d2Iw6flbboa6h01kbZYlgpUkRbNdxi5RpjRxgTd0r1SJr1CKM6qhGioWTW6/NRQewGh9h6pOnCMroWrn12Wkifw0d7VhIYEhkx71U3f0SGCzLmOubTi+RMoIgFgRksAiCIOYQaZacU+pqKVP3Oie7Djppar0AkYLWl6Ydfs6aUleqxUbOErVoNrnQRHysOp2RMrXxseq0nqODtpS6ho4mUmZap8ySIqhO3TMbnzQ1WF3RzKhiNAlaIye1kqTU+VnRiTIpaVIE09ZgGaKZbUgjZzCnBEEsbMhgEQRBzCFJ1qeqRXXUIq5ubGBBTpBdI2VpuxVKLVlbZdUxNdOwTHaLGb9lHayqiFr46nNJI6Y1JEadANWwjqjOe2+mIa9VQVuKoIyUaSbVZVtEzlVH7mcwWGWdqbdE5KRWI5rpoKM32wmaQkS19Ol0iXR6bKYBuJk5S/orQRALn54MFmNsGWPsIcbYy/HfSxX7bGCMPcoY28UY+wFj7K6Wbb/LGDvKGHs2/nN7L9dDEASx0EnS1tw4ebeQD+ZmvS2gI6WuFx2H5hOu0ZFSNQRjzQWKlTrahXnjRY3bIktpmmm4RZaETspFeVuP0aUIturU66LeK00ESy7M6xz9M3dFNKa/Jq1Z6sVgJa7BSpkiCLhpUZMLglj09BrB+hiARzjnmwE8En/fSQjgo5zzqwC8EcAvMMZaF+D4DOf8uvjPgz1eD0EQxIKmWRtlj/gY64gseB5DLvDcU/d6SRHMtKTuWXU0i/IC1mhCW/t0S6MGmX7W1aFO6oDHjSW6aYsyWlMELQsaA25tza0pjy61Xi465khZvc5FWqrRBNsiWPFzdeiKmNZst2HRMZKqi+BcGSyqwSKIxUqvBut9AO6Ov74bwPs7d+CcH+ecPx1/PQlgF4B1PeoSBEEsSpJEsIxRCwcKWd95Hay0zTQAMal21zEYucA+eW9rn26avBvTzyxtzVtbz/dUG2WOLOWVTS40kbKaMC1pUgTbdcxdEcuhNPWmyJJjgxBbF0FbimAS45M22mOJZrYRVnqr9QLcDJalsQpBEAufXg3Was75cUAYKQCrTDszxi4B8DoAT7R8/IuMsecZY3+rSjFsOfZOxthOxtjO06dP93jZBEEQ80MzgmWP+DQ71KU3Pi5Grl7nKNc0HepcdBI2ubA2NjCk7jUW5rXV99i6FQJuTSHCKgCmncCba6MSpAhauyLKmjJTMw21IfE9hqyMZlqiMMaUR3lckkYkljbtRhOcqMlF2giWjGY6jN9ea72AZBEsanJBEIsWq8FijD3MGHtR8ed9SYQYY0sAfBnAL3POL8Qf/yWAywFcB+A4gD/WHc85/wLnfCvnfOvKlSuTSBMEQSwY/Dh1b6bmkCJYMyzK60DBMXVPRi160+lDKqJD6l5U56hFdoMlFrHVRWEc25pXo2Z9jyZq0Ywypl+fqtSaIqiJys2YFmj2fNHwwxJZKlcja4c647pegNMaYuVqJEyyJcrYv3WwzOmiRpJElnqt9QISNrmgFEGCWKxYfy3KOb9Ft40xdpIxNso5P84YGwVwSrNfBsJc/SPn/Cst5z7Zss8XAdyf5OIJgiAWI66pe9bJroV868K8C0BHahkn74B2wtya6pa1RC1mahHyaSNYbSl1lkYNtRDZwIPv6Wq94LgOliWCZWp44tB8omGCramIvUWwitkApycr8T1z7bPjnFvSOJOkCJrNtpG2sVCcRZ0kRo5SBAlisdNriuC9AD4cf/1hAPd07sBEhfHfANjFOf+Tjm2jLd9+AMCLPV4PQRDEgqfQ2qzBgLGxgQPFrO+0DpZxsdw+6kgtbcqjdSHbljXELManVA317e2Trk9lSXPTPrcgoU7rtXVgHQu21vMyda9nHfNaZU0jZzaM1Ui0we/LOlg9NbmIj3Mxc5aFk806CVIEwwrgBYBHK+kQxGKl13+9nwJwK2PsZQC3xt+DMbaWMSY7Ar4ZwIcAvFPRjv2PGGMvMMaeB3ATgI/0eD0EQRALHteaJWs9jIPOjEO3QmOam7OO/X4453EzDcPkHTC0Gxc/skoy1c3Soc7e5MLS1rxnnXhSremMmAs8MCZ13IxPLx0YS1UXI2dopgGIZxfqTUJTR9Z6adYPa0RNDWa7XnNrPtGXyJLDWlh9iZQ5pghS9IogFjXpfprGcM7PArhZ8fkxALfHX38LgDJ5nXP+oV70CYIgFiMFx5S6ZrpWuv+q8xkfZ6bsvzG3poU56MjmE8q26DHVqI46t0zeAa1RaG83blkHS7dYLpC8fbopUmbS8cxGjjHWjGZGVWPUomxKEQQc1xALgSh+PxrTWLaNOauO1xGRUz87eypiS0qdLTrVtxRBC1EVyA2l1DGP7XadHpppEASxIKD4M0EQxBxTdIxg9Zoi2NbW3Kgjoha9LDQMAJXQ3FDDmoooJ9JO7cZtKYKWboUGna7aKOOCxoYIlucJ0+RaG2WqKauGohugr/mxbW0+EZv60Gx8nCJltvtpbabRS0QOsBufKAR4vQ8RLNfIUg/t4OU5rDo9NNMgCGJBQAaLIAhijsk71mCVbOlaFpwjZb2mImZaUvcM2CfVbgvmlquhWxfBlCmCsjlG2aEGa6Yamg2wra15m8FKuXCys07dWhvVa62XNIy80WrcnCJoXGgYsBufXluaJ62NSl3rlSRFkCJYBLHYIYNFEAQxx7g2hZipRgjiNYzS4Frr1Y8FjQH74sn2FuDxpPKVR4065UpVRC1OvKDVEi3ALc00DnxLrdPaPj2sAmf2ptMBhCE58pR2c0GOhagKjB+y6FiM3El9n6imTg0YP+xQG2V4R2f3aXWkOa1WyrFO2ghWbDb37tBqAWgaxqNPm/fTIa9v/zfs+0Y14ORL6XRkNPPw9+z7hhXg/P50OgRBLAjIYBEEQcwxrutGGet7HMgn0AF6a9Peeh4dTvU9AHDQbHwqlZL44Ngzyv3CqI5qVLennx1ST3YzvofAY83I0uld6vMgXtfLFsE6/qx2c1vq3vlXzTpGg5UBTu8x6HjNJhcTR8TaWRodwJK6N35QuzBv4x2Vy8CE3mA56QDAvkfU2yUyInRUb2KNSCN38Dv2faOq0cTatbJuRjCqAmf0JpYgiIUPGSyCIIg5ppANHFMEDXVEDhSzPqqhaIdt0wF6adMetJ1Hhz39LJ5U19W1XNIAVipl8QFT/whrGEZTmhsgomAaGtG/qAow/XMxpiICIlLkpFOx6phTEXMA1z//YjYQOmFFRFMsCyfng3TplcVshwnWGSyZ/mpr2W94duI64tQ+zViwIlP+DM8OgBiT9Vp6HUA8O9v9AHHDE5qeEcRipqcuggRBEERyChn3hYbTpu1JHXGeEIN5c30P0EOb9tauewaMLcD3bAd23Q8ceRJYeilw313i8y23AWPbGjo3e0/h2uf/EQhfBlZsUe5nTHPbs12knR36HjC4Rnk8ANzqP43b9v8DEOwFBke1+2lT96TOiRfEpFxz/JtqT+Kqqe8AL54SKWQGHeVYkDpn9wsToDn+NVPfwZXhdmBn1Xg9pbimzOtcOFnqTJ4U39//EVH31HH8pjPfxCeDL6P4RGwQHvotYSw69jOmCO7ZDrzwP8SzW3WV9lqxZzvw/L+IqNKqa/T76dizHdjzoBgLI5vMOrsfAA4/Yd7PpLN3h4heZZeYdeR+ucHkOgRBLBjIYBEEQcwxhayHGYe25jO2+h6rTrOWyGSwStUQjAH5TPpaL6BpoPQ6hkn12DbgiluBBz8qUtju+FzXLsVsgEfq1+Ptl9yEK2pfAmbOKfczTt7Htok///Zx4PjzyuMB4On8GxGufBfeMPh3opZIs582siR1vvv/Ai9+SXv83pG34IHqdbhty3eB/Y8adEK1kZM6L90DPPpJ7fHHVr8Df/L8WvzEa84g2PnX5vsx6RzZCfzrzwO3/j6wZGXXbpObbsVvPLYUb7wqxGU7PwG89zPKxhDGeryxbcDoa4HH/gswfVZ7rRjbBiy9BHji80BlUr+fjrFtwrzc/xGR9mjS2fRmYRYnT6TTGdsGPPZpYN/DZh2HsUkQxMKHYtAEQRBzTDEbIKpz1CJL6l7NsCivAwM518iSpUOdo07PXQQ9X0RXNCmC+YxYmLfWSBFUn8epaUdgT6mrSh1NvVK9zu21UUFWW68kdRptza0pgqZmGlmRfqZZmFdeY61S1t4P4JCW2uiGp17PaaDR5MLcrdDeTEOm7s1yiiBj8Vhw1Un/7xFB1p6KCIj0S0oRJIhFDf0LJgiCmGPyGTdD0msNlpyQ2wxWqdZjrVcCHXFdGi3GxIR8zbWazQzFjI9aNZ68X/YOo45xXS8/C6y8Uru5mPVRq8YGa+OblPvIdb/MXQRzIspi0JmRbefXvk67X9na5CILDG/QthuX6Z+1atl433bDmDPqSMMUVsvivjWmvdHkQrsmWmzM1r1efy1AsxbsilvM+5nwMyLF0KgT3+8lb+5BJwcsu8y8D+dCa9316XUIgph3yGARBEHMMXIC69LWvJcugsUEqXu96CRNETS3G88Aq/WT3UI2aEawNt9q1DHWlPlZYPkVBh1fmAQAuOStyn3k/doNyXrtZmGw4mYa62/Q7metxwtywMiG5gK/Ch0AwpxqDKyTjp+x6IhnXq2WRY2cQSfrewi0CyfHBmvtdfprAZrXseU2834m/BywSm8623Quu6kHnYzdYNVDABzY+KPpdQiCmHfIYBEEQcwxrc0nTFijCRaaBstu5OZKR+xvMT6a9DOpVauVm/sqdRyNj2Fx2bYIlmYBYGtXRHmsaWHerI9KGKEemhc0LlUjS0QuTqlziSzNok7RWSc01/wlXmg45QLA8liNYWzq1OZGR25Pu3AyQRALAjJYBEEQc0yShXl7Mz5uqXsztR6babh2EayFyAYe/M4Oda34duMTVuWk2rzGknVhXoPOQDZAKFMRNZNqN534fjS1UQPZABlECOt17f1wzjHjsg4WoJ3AD8TvN6xVjMZnxlb3J59FaDZYUdWiU43MRpsx8eysxie+DoOWFT/rYORkTZm+WYxdxzy2hU4f7ocgiHmHDBZBEMQcIw1J2bYwry2aYKGZimiOlJWrEQopOwgCgOcxp8WTyy41ZZaITzHro96IYGmMj1NkyWywClkfUc2tUYO1yQW49p4KWR9Z1BBFXKtTiziiOnczPpYIVmQxWCWb8WlElsy1XmGtajQjTtFZP+NmSJhvbNxhxRI1bejIfdMSZBMYxh6MHEEQ8w4ZLIIgiDnGJaXOKWrhqDNdsUeW0q6B1ao1XTEbOaeInCWNqpgNEFlTBB27CIZlg47dYBlbjUsaKXX62qgMQoR1bjeMPRmfFoNlSD+z1uNJHc2zk50e67WyMZ3Oqe7PNaWul7Q9IDY+NiMXG+ReI2X1mrZLptCRBqvHeyIIYl4hg0UQBDHHuHQRrEb1OGqR3vgUc4FVB+i9mYbQ8u06NQcdS7qWiGBVxaK8mlbW7imCJp0A9bAqMvu0KYJhY1+jDmBIqQuQQYhaVNfXetUca8oAa4pgvVYxTt7tqYjm2ijZ6TEKXVIEbWPBJaWu1nu0x8/ZI1ihOV3UTcdsgoUOpQgSxMUAGSyCIIg5xqWLoFOamwXX2qhSNdK3y3akmAncdHpMCytmfUSWhhAz1RC+x5DVdagDmtERw7pRWdTiyFIPTS4Ce2Qpx0JEdX2KoFszDbNOIevDR4QoCq3Gx6jjeYCXMRqSQjYAr1neUc0h/TUwp3ECENfRa7TH0vBE6PQjRdCcxtm2jZpcEMSihgwWQRDEHNNocmEwJE7pZxZ8jyEXeNZuhf2IYBWyPqatOqFDDZatNioAd4iOWBdO9uPaqLr6motZH1kWiuYTvaQiysm/JqWuLUVQFylzSkWUkTKXVES1YYzqHNWw7pi6ZzbBPLR3EbSbbZeapVrv0R4/45AiWAXAROQ0tY6LwTKnpRIEsTjoyWAxxpYxxh5ijL0c/71Us98BxtgLjLFnGWM7kx5PEARxMeESWWosxNpr6p5cZ8lAySV1z0HHunByrW7vVmgxWMWsLybDhglo2TUVEdBO4AvZAFmECCN9bVTZJRWxEbUwN7kIDU0unMaCrclFxm/ej6UropPxsbTS51HVXINVszTTaOg4GJ++pAjqOz02dbLahZPddMydHoWOrPWiGiyCWMz0GsH6GIBHOOebATwSf6/jJs75dZzzrSmPJwiCuChwadPejxRBQNT4mAyWjFoUM702uXBJEQztqYjW9uli8s57re+xGJIBGfHh0Haoc4tgxZNqjSEZiGuw+hYp09yP5zEMZeoWHRHNs5pghzXEeGiujXJucmEzWP1qcmHo9Ch0qr2n7QXmhidCpw/t4AmCmHd6NVjvA3B3/PXdAN4/x8cTBEEsOrK+B4+5pQj2o7ufKUXQaVHePugILZcarKxI29N0WitkA2RQQ8j0z8VaRyR1AEMES0R8qjzQRi3kO8oHLimC+hqsZqRM1w5ePFdjzZLnifQ1Q3RkKMMtOo6m3pJSN5DxwKOauZmG0zsyd3oE0Iws9YKl02P/dBwWT+7HwskEQcw7vRqs1Zzz4wAQ/71Ksx8H8DXG2FOMsTtTHE8QBHHRwBhDMRsYI1jNaEJv/03bUgTlpDo/F6mI1ciu04j4GJpCIEQVPUZHHNZzyiBEDXojV6qGyGc8eKaFkxtNLgxGjskaLFsEy5ZSZ474DGbqcQ1WD5Gyho7ejCzJcER1fVdEznmCdbBsCwD30fiY6rAic92fE5ZOj0InvgaPIlgEsZix/mqUMfYwgDWKTR9PoPNmzvkxxtgqAA8xxnZzzr+Z4HjExuxOANi4cWOSQwmCIBYcecvCvI36nj6k7pkiZY26mz6kItprsBy6FbYan0xeoSNS9yrcx4BJx5p+lm/qKBBNLmqomgyWUx2ROXWvmA3iboX61D332ihzB8ahDBc6GiPn1N4eEEahNG7QMRu5SlgH5446stOjrvapHwbLqbtfP5ppOHYR9DLaJQgIglgcWH9yc85v0W1jjJ1kjI1yzo8zxkYBnNKc41j89ynG2FcB3AjgmwCcjo+P/QKALwDA1q1bDZWoBEEQCx9bUwjnaIKDzslJfZpVP3VmahE458rufc5RC0tTiGI2QIaFqHD9b/hnqhGWFi2TYUvDAZm6V+HdJq9Vxz0VUT2p9j2Gol93S93rcWHeJUEdYc2u02ud3BKZiqhJc5tx1smh0elRV5PUj9oo3xxlFDp9qPVybXJBHQQJYtHT669I7gXw4fjrDwO4p3MHxtgAY2xQfg3gXQBedD2eIAjiYqSQmRuDVcj6mKnMbjt4eXxU56iE6tqpck1GLWwRH3uKYDaOYOlwagFuaThQjLsIlusmHZc0twBgnjmlLqiL+7HUevXa4n5JI7JkMT5O3f309zMYRE7NNKw60jjpDAnn/Y1gGVME+2B8XCJlYYXWwCKIi4BeDdanANzKGHsZwK3x92CMrWWMPRjvsxrAtxhjzwF4EsADnPMdpuMJgiAudgpxxEdHv2qjBizd/frVrXDAsrZXo6YsY/mxY2k40EgRNBifRJElXaQs10xFNOq4vJ8gb40smYzcTDVCxmfImBZOBuwRrEakTLdwsmPdn2UdrMFAGDmu0XEe27aUunoIQB+Rc8YlghVV+tMOHrCvg0URLIJY9PRksDjnZznnN3PON8d/n4s/P8Y5vz3+ej/n/LXxn2s4539oO54gCOJip5DxUXaJYPVofArOXQR7r/UCoDWNiaIjALDvEbVOvJ5T5sxL2lM4twAHgFcfN+jUMBFq6AkAACAASURBVDB5wKzj8n78DHDkKe3mJX6EwcoJg47DAs2AeHYnX9RuHggiLA9POKQiOryjsy9r140q+nWswxlU6urzuKcIxoZm7w71dmkmjz1rPo8NORb2f0O/T1QFTu3pTUd2ejz0XYNODTh/oDcdgiDmHaqiJAiCmAdEBEtvfEq1CFnfQ2CLWliwdhF0bWxgQR4/U1Hfk9OivEBzUr3/MbVOwMHAUTyrNxJuHepik3H4CeXmwPeQ9yIMT7/Smw4gIhfH9Sag6NexrHrcouNggP0scGq3dvOAX8eq+mn7QsM24xPkgPFDQF09rgaCOtayM5jRROWcm3bI69SY7UYk6NjT5vPYkGPuwLf1+0Q14NQPetMBxFg4arjeqAqc1Y85giAWB2SwCIIg5oGCtX26aAHeK8WsjzBeTFit0x+DJSfLuntKVEcEAFwzeffF55Hmx1c1FOlpVh0vro3SmARAGJ+I61uwz1RDt+fmZ7X3AwAFP7LouKYi5ow6RT9C3WHhZGcTrGs97wuTXdIZrKQpgrp7kgaL9fjvpKGj/jcCQETLetUBxLMzvKO+6RAEMa/0lhNCEARBpGLA0nxiqhJhSa73/6IH4nNMV0JkFcXzU3HEaUmPKYKtOirk5wOme9qzHXjpHuDoU8DyK4D77hKfb7kNGNsG7NmO5c9/FTd4u1Grb+7enkRn7w7g8JPAwEqlDvbuwNvYM6jVFdsb9xRhwPTcpM6JONqm0yl/D1XODDohBnIGMyJ1zr0qTIJG541nXkSJe+D33iX6aSh0fI8hF2gm+FJnKm74+8BHhanr0PmRYwdxAcDA458ABpd06TTGnO0dvfAl4MTzwMor1ffU2H6V9tlZ2bNd/Dn0XWBko1rHtD2Jzt4dIp0xU9C+Ixx9BsgW0+sQBLEgIINFEAQxDwzkAq0ZAeSkun8Ga6oSYulAt8Gajk2ecQLvwJIWHRVOk+qxbcClbwP+7TeAC8eBOz7Xtd1b9Rp8/7n/hBs4796eRGdsG/DQ7wBHdyp1sOU27Hrhp3ApP6PUARzekdR54gvAc/+k1hnbht0HP4IN575j0LEYOamz637gkd8D3vvZ9o6E8fb9//wnWDX+/2Hm3X+svO7pSoiBrK9ss9+mc+wZ4Ct3Ajf/DjA02rX9xDf/FcWjv479N/4+Xn/FOqUOYDHBY9uAddcDj/4hMH1a/eyWXQ587y+A8gXts7Mi7+mBXwHO7lPrXPJW4GsfV4/JpDqP/4kwU5qxgId+W6QQptUhCGJBQHFogiCIeWAwF2CqGoJrGgVMV0MsyfdusAZlZEnT6GI6TkXstdZriYMOAPs9yQWANWlUAa/B9xhqXH29zjpSS5ciGFWR9RhqmtQ9zjmmqyEGnXRyxlTEAa+GWl3//KcqjjpyYWZdZ0QvQsQ9rbGfqkQYzDt0ypPvKFSvrzbgifNfCNWm3ckEt+rUNal7Ul+T8pgI0zuSzTT6opOnFEGC+CGA/hUTBEHMAwO5AJzra5Ymy2FfUwSnyupJ9VzpyM+tkTLPB7wMsOZa9fZaGRnfw67C9crNTtERSZADlm/W6JQQ+AzPe1crN5dqop7JTScPDG/Qbi54IZ6NLtVun646RjP9nNDRGJ+iV8Nevl4bZbSmIkoCs06B1XCQr8J0VfPLAxk1dW1ysfa16u3S+Fxxi/WSrQR5YNWVGp34Pi95Sx90csBSzbvmXNzT+ht61yEIYl4hg0UQBDEPuNQsGdPCEuqYJ9W96zRTBNWGUX7uZOaCHLBKbWwQlhH4DM/kb1RunizL6Ijj+lTLL1Nvi6rI+B6eZGqj1zSMjvcztFa7ucBCPBltRhipIzVTZcd3FOSBkQ3atbDyrIYf8EuMaZz90Cl4NbzC12Gqoo6kTVVqblFTPwuAAWt+RL1dGp+x2+3XbCPIAyu2mHUuv7k/OiMasx1WAHBg04/2rkMQxLxCBosgCGIesNUsTVf6lCKYl0ZObXymK/2JYOUzHnyPGQ2jxxwXNA7y2uiIND4TVfV5phtGziXVzbAwb1hGxmeYqKp/TMr3NuhqSOo1dapbFCLr1VFBRvuOpiqhm07GnLqXZyEqyBoNlpsBljoaI4cQFWSMZtvp/TBmXqRZfp4p2M9lI8jpx5zU0bS3T66jH3Nin3zvOgRBzCtksAiCIOYBm8Ga7JPxaUaw1NGEyT5FsBhjGMj61uiItoFCK6ZJda2EjM9wvqY+TzNF0NHI1UoanTIC38P5mqbWq9EcxDGCBagn8FEFGd9DBVlMKt5RLaqjEtbdUwQBg/GposL1Rs7ZbDfuR/3scqiiwrNGs+0UYZRaWuNTFvVKXh/6dZlMfT+Nj9RR1V7KtvNksAhi0UMGiyAIYh4wpe5xzt3rYSzI9uu6aEK/IliAMI09R0cAazQh8DyMV9Q/viZdGyhIHTmp7SSqIBPrqBqRSDPkbOQA9T2FFQQe0xqfxDVlOh0AWdTiyJIudS9BiiAAhOpn59eriDxzpMzZ1NuMT5Bv75iYFmNkqZ8RrLxopV9XPBtp9vuhQxDEvEIGiyAIYh5odN1TTKrLtTrq3DHNzYI0AOZoQp8MVl7fej6RjjFFsAIvyGKqqq5XSmZIDNGEsIJMwFDiGZRr3VryvQ26piLG5+zWKSMTeLHx6X52iVIRZaqcSofzFoOlT0V0ekeeL6JGBhPMMrk+mm2NCQ4r/TMjcxbBMpjghpGjCBZBLHbIYBEEQcwDsr5KFU2Q0RHnNCoDge8hn/H6E02wMGCJYLlHLQzRhFoZyOSNtWu5wEPGpe28jCao2prXSgg8YXxUqXuJUxEBzaS6jIzHtAYrUSqin9XrRFURKUNGaYJl1LQvJjgsAUG+j2Zbk8YpI1j9wCmC1acUQUCM406iPkbKCIKYV8hgEQRBzANyYq6KJjQaNfShyQUgImEm4+O0xpKTjtlgOetYogm+wWBNJtKx1UYxlJFVRhkbqYhJ1qdSRrBkDZba+EgD7qYjI1hqIycMVlbZSl+2nXcecxZD4mXy+pb9SRq42IxPPyNYUVXdiCQsC/Pq9WHK1FirTHFPNWpyQRAXC2SwCIIg5oFGkwvFJLTRArwPbdqFlq/UCaM6yrV6H3UC4zpYzjpBTv0bfiBOP8ujXKsr25onajtv6oZXKyMIAoRQPztphpwiMb7ByMXrbVW42vg029s7RMpkW3NN+hljgJ9Vm9OpJKmVgNUEe5lCf5YGcIiU9QVb6l6/jJwcC6rx3UhFpAgWQSx2yGARBEHMA4WMD4+pa6OmkkzeHRjIqWujmulnvacimnSEVsJJta6teViGny3E51Q3hUhk5OJzKnUyeQBMk7qXsO08oDZycdt5fYpgAuPDmD7iE9+jn1Gn7k0nMXKA3vjUI6Aews/mMV3tVw2WLoJV7W8EC1BHlvqdiijP2UlENVgEcbFABosgCGIeYIxpa5amk6SfOaBL3ZuKJ8ALL0XQHE3IxAZrSjGBnywnST8zG58gJ42cWse57bxpfap44WRtimA5odnWGizxmZ8tqsdCQ8exsYrFyAXZgjFqmrjWS9mIZDaMj+bZ9cvIyTROXYog8wC/9+Y2BEHML2SwCIIg5gmt8UmarpVWpzw7Op1tzTnncZOLBNERQG9IYuOjTN2rJoyO6HRqJQTSyGlMsLOOaX2qWhl+HHkyjYWem0LE95jJ2VIEe1yfKr7HIFcw1hcmimbq2pqH5f5HsFTrovXTyMlGJLoUwX61nScIYl4hg0UQBDFP6FLq5ipFsN9GbiAXoM7R1dZctp1P1EUQ0EZIgpyY7Oq67vWlBiusIJszGKxqgpRHP9C3NY8qgJfBQE69blSiFEFA39Y8vsdMrqhJEUxj5PR1RJlcQT3mqlIngZFrOW8DzuPIUp8jWKp10fqqY/jlQVRtGjCCIBY1PRksxtgyxthDjLGX47+XKvYZY4w92/LnAmPsl+Ntv8sYO9qy7fZerocgCGIxoYssJZ7s2nTygSaakGCNJUcdoNuQJFrLCWhOQvf+W/e2sIKlU/sA6FP3EkewXn2se1tUQW7yYH90AHFPh5/o/jysABeOadcQm0rSdl7qHH9OoVMGxg8jm9ekCCY2WDng9F69Tq6IUi3qakSSPBUxHgu7H2z/PKoC4MCJ593O46qz75HubWEZOKO411Q6OQAMOPTd7m21EjBxpD86BEHMK71GsD4G4BHO+WYAj8Tft8E538M5v45zfh2A6wHMAPhqyy6fkds55w92Hk8QBHGxYkoRZAwoZvvTfELodK/l1P9URNl6Xm2wEkew9j/a/nm9DtRrGBzfrdQBZOpewlTEg9/p3lYrIzi/Dx7rQ4ogIO7p2DMKnRIwcRgDWf1YSKxz6qXuz8MyMHEY2VxB2RwkVSri+Ve7G5GEFaGTj+vXqu1aqVIRAWDfQx06cQTo2LNu57HqxGPhwOPd28IycGp3f3RkI5LDTyp0KuKZEgSx6OnVYL0PwN3x13cDeL9l/5sBvMI5P9ijLkEQxKJnIOdroxYDWccGCi462UDZ1rzvqYhx977Oe0qe5hZPdusdRkB2wvPVkbKozlGqJUkRjCfvXN2tkHm+1viIVMQEBjjId98PICIxnm+MZiYywEEe4AqdOEWwkFfXYKVKRQS6mzXE7yhfKLadt1MnkZEDFGMh1vX680uI5ljQPLt+6Ugt1ZiLKgDrow5BEPNGrz9VV3POjwMA5/w4Y2yVZf+fAvDfOj77RcbYzwDYCeCjnPPzqgMZY3cCuBMANm7c2NtVEwRBLAAG8xlcKKnTz/rV2U/oBI3zLh1o1nhMlvvbRXAwL9K+LpTao2UXyjV3nT3bgZfuAY4+BSy/ArjvLvH5wEqRPnX0KQQjl+OTwV/j2qeXAoM/CYxtA9BMP5PXYdXZu0Ok7Q2sateZPt34/A/8L2L1K3lgz083dABgslxLpnPiRfF9p86JFwHPx/954c8wU42APeMdOo5jQeqce1WYkU6dc68CXoAPHvtjjIUTqO8K4V3VzMqfLIu289aoqdSZPCm+v/8jwjBInfjz1+/7c3wyuID67jLwxh9v0wESvKMXviTSAFde2X5P516NP7+q+fmW29qenTN7tos/h74LjGxs15k61f15Lzp7d4ioW6bQ/Y46P0+rQxDEvGP9X5sx9jCANYpNH08ixBjLAvgxAL/e8vFfAvgDADz++48B/JzqeM75FwB8AQC2bt2q6NdKEASxuBgpZDBR6k7dG5+pYbjQv1bNI0VxrolSrc1gTcxUwZjjZDehTisTM+L7kYJDAf/YNuCym4Ad/xmYOArc8bnmtvHDwOP/D4Lpc/h49CH80iVX4KqxsebmUjXWcbifsW3iz8O/J8xUq069DjzwEWD8MP5q+C6sGyngTWNb2w4fL9WS6Tz5ReCZf2jXAYBvfArY/yju3fhreGL/Odw+9s5unWICnT3bgYd+G3jPZwCvJUnl6b8Dnvwivn/Nb+MTh3bhvZfeiqE2nSpGill71FTqHH8O+PJ/BN75m8Dw+ub2fQ8DO34dh9/8SfzG3mfx31e9ES1bm+/I9Z7W3wh8/feBqdPtz+7ULuCJzwOVye5nmhR5Tw/+GnBmT/v5yheAh34LmDzRP51vfRbYfX/3+b72m8Jk9apDEMS8YzVYnPNbdNsYYycZY6Nx9GoUwCnDqbYBeJpzfrLl3I2vGWNfBHC/22UTBEEsfoYLGZRqESphhFzQjBxcKPXXYMlzdRmfUg2DuQC+159URJMOAAy7TKoBERFhXndaWNxCm/kBhvLd5rShk+TZZYrdLcBrM+JvP8BwIeiKyFXDOmaqUTKd7IC61XhtBvAyGC5kunQAcU+rh5a468h1lsKS0GzolAAvwJB8RzM1DLUY64lSmPy5yfO2UisBYBgaGGhcfyuJ35G8H9078voX6UWmMDc6qrHAeeMdEQSx+Om1ButeAB+Ov/4wgHsM+/7P6EgPjE2Z5AMAXuzxegiCIBYNuoiPiCb0P4I13qVTw0ixf22hTTqAY2QJEI0AMkVg9dXtn9emxd9X3IKRYqZbR0bKkjy7bBFYdlmHTjyp3vQWjBSyjaiLRL6vRDqZAjC0rvvz6gywfitGCllMVsKuOjkRzUzwjjIDwPAGcd42nWlg9Wsa76BrzM1Ukxus4Q3ivG06M8DyKzAcj6vOdzQxU0Mu8JDPONYaZQoAGLDm2m4dANj8bvdrtpEdAJZvbv9MjoXL3t4/nUwBGO4odYiqwnRtfEP/dAiCmDd6NVifAnArY+xlALfG34MxtpYx1ugIyBgrxtu/0nH8HzHGXmCMPQ/gJgAf6fF6CIIgFg1yEtoZuZgo1dzS6Vx1DJGlfkbKChkfGZ8pdQKPJeuKmCkAKzonu3G05Mr3YFiRXpkuglUQ9TVtOvGk+opbjDpDiXQGgKFRIGqJXEQ1oF4DNr0ZwwURubjQsngy5zx5NDNTAEY2NO+hcU8lYP312rGQWCdbjHU6I1gzwJrXNKKVqrGdSIcxcU+rrurQiXWvfp/7uWxkCt1mezaMXKYoxkIr8n1d2kcjRxDEvNFTLJpzfhaiM2Dn58cA3N7y/QyA5Yr9PtSLPkEQxGJGTjRl5EUyPlNzT6dz0hFmbWKmPRIzPuNY3+MIYwzDhazyfkaKmWRdETNFRRQm/j47gOFCplsnaSoiIIyPKgoDAJkCRorlLp2JRh1RkshSnOpWmwH8uPpJ6maKjXONz1SxLK6TK9UiVKN68oic1GmlNtOh0/3sLlkxAGcaKYIdz642A2QKjdRT3VhIRKaouJ9psSiv388UwaJoNtGmU2pu66dOWBa1frJOrjHm+qhDEMS80WsEiyAIgkiJKl2rXItQCetzUoN1oVRLFoVx0uquWUqlo5xUz4gaFV9dsyQNZOKIT60kamAaOvGkOjuAoUIGlbCOcq1ZDyZNQ+KIj7yHTp1MoWm2W+4plU5GocN5w2A1dbrNdiIdPytaiqsiWJkBMMYwlA+6dUoJUxGB5jtq0yk1TWu/0Bk5oPn++oFxLJDBIoiLATJYBEEQ80SjZqnlt/yp6nssZAMPxayvjFo410U5MlLsrlkaL1WT62Q1BiuegKpqsCZKNRQyflvDECcdHokamIaOjCwVlHVyjXeUNEVQ3kNDpyUi1zedeILeGv0LK2LdpWxReT/1OseFcsKxwJh4dqooY1a+oywmOpYhEM00Eqa/ZlVRxunmM+0XqvuRxifoo5lTmeDZMHIEQcwbZLAIgiDmCVVkKVUdkQOdLeE556LWq49GTqUDxDVlSZtpZIrq6EjcGW+kkMVEqQbeEnlKl36mMj6lxjZZCzehiCwlbnIBtE/gay2piC3d/Tp1kqU8Krr71ZrpZ/mMj1zgtelMlkNw3qwJTKSlqvWK71WkcXY0CJlJ0cBFORZK/TcjmQFRExe1jN/qtND3+jhdUhosimARxMUEGSyCIIh5YjCfAWPqtLB+NrkAREOGVp2pSoiozvtu5JS1UWnW9ZKT6npLV73qTNvkPapzTFWaEZLxNE07VManOg0EecDzlHVy8jkmWj9MtkxvNQqNupuBFp2mIZG1XslS9wLAz7XXRtXa63s639F4Gh15vlaTUK+L9vCxaVU1CEn3jnTRzH6nCMqx0PrsZiMVUTPmADJYBHGRQAaLIAhinvA9hsFc0NZ8YjxNHZEDI8WMMjrSbyM33KEDiKhM4vvJFgFwMWGXtKQIDmvSK5PraFL3WlIRhU7zHV0o1TCUT7h+WKPJhcr4FFqimU3D2EwXTRpZKmiMXPOeek5FlOfTRORUOnL9sNTpom11cjOzkyIIdEf/+m16lGOuJNZ+C3L91SIIYl4gg0UQBDGPiDqV2a3BArqjCalajTvqtK7nFEZ1TFYSLmIL6FPdWqIwQEd6ZapIWUt3v4ZOyagzPlNN3uUxiNdz6krdE23IA9/Dklx7U4hUTS6A7pqlRq1XSwRLpZP0njrr5FqagzR0FPWFiXUyBVFDFlaan1VnI4KlSt2bBYOlHHOxTpJOmwRBLFjIYBEEQcwjnc0aUk9CbTodC+bOlpGT0Qm5npP8O1XdDQDsfqD5WXUGOLOnTafN+KRZoFnq7Pt6i840MH4QAJTNJ8bTrFPmeWJifeDbzc9qJWDqZGNSPVzoiDLG64cNJFk/DBA6x55p0ZkBxg+3mMasMuUxVQTr1EstOtNtOiOFDC6Ua6jXReQpVcoj0IxU7bpX/C3XDzu1K9l5rDrxWHj5a83PqjPAuf191onv59XHW3SmgQvH+qtDEMS8QQaLIAhiHlFFljwGLMn2cX0fxKl7c9BMo9OQpNaRv+V/JTY+9QiIKsDJHyh15NepI2UHO4zP2X0AxHvwWB90gNj4PN38vjoNTBxpfKsaC8OFhOuHAeKe4ucEQBisicNtUbkL/RgLmaIwH7JOrlaKdcS7GypkwLlootGbTjwWpPGRkZ/jzyY7j1UnHguvfrP5WW0GOL2nvzqeJ2r8Dj/RolNqmHqCIBY/ZLAIgiDmka6GAzNizSgvSX2Po0651lzPKXX6mQUZ2Tkf1yylrimTdSr1uCZJTqo9X6lTrkUo11KsHxbkRO1LvaWdeG1arLcFwPMYhguZhg6QMhUREBP41g51tVJDBxBRvr7ptN5PdUbco59p0WlNrRSaidNFGzVL000dqY9m7VhzLPSQ8ggAUdiu4/X3lxCN+5HPTq4f5iWMILrQ+Y5axhxBEIsf+tdMEAQxj6wczOH0ZLO25NRkGasG+1/ovjI+5+nJCjYsK+LUZBkAsGJJf7VadQDgVPz3qsG8+0n2bAd23Q8ceRJYegnw9x8Ezr8KhGVg7fXAfXdhZZ3jFn8FTk9ubtNLrLN3B3BkJ5AfEToTh4HqFLDyKuC+uwAAd+TX4+TkssZhpyYreNuWBM9N6pzaLVLbpE49BAZWNXTegS34+6mrWnTKjeeZSGf8kDByf/cB4MIRYRD8DHD/LwMAtlZfi7+prcZkuYbBfAanJisYygfIZxyNhNSZPiu+/6efAqZPNSNAX/8DwPNx5cCbAAzi9FQFl6wYaI6FoYTv6AdfFSmPKzaLZ3f2FaBeBTa8sfHssOU2YGyb+3lVOnt2AIe/BwyOCp3xg8Jgrb62vzp7dwDHnxeGSo6FsAyMbOqfDkEQ8woZLIIgiHlkdDiPqUrYmOyemChjzXCfi/djHQA4PlHGhmVFnJgoY8WSHLJBfxMZ1sQ6JybKbX/Lz50Y2yYmmA/+KnD2ZeBn7hEpb09+AahMAnd8Dj6A5158GCvi8x9PqzO2DfjWZ0St1394ACidBx7+XWDyJHDH5wAAB04/iYn4/JPlGqYqYeN5JtJ5/l+Ab30W+NBXxOc7fgM4+WJD5+z2XTh54ADqdQ7PYzg+UcbWTUuT6xz4tjBTP/lfgcII8J0/B37wlYZO9dmjwPefxckLZQzmMzg+UcZokjEndc69CvzTvwNu/zSw+mrgxa8Aj/0R8GN/BjCG7MlJ4GvfbLyb4xNlMIZkv0AY2wZc/k5g+68BE0fFszv0BPDcPwG1cuOeekbe09c/IZ7fz20X6Zvf/DQwc7b/Ojv/FnjqbnE/9Qh44KPCGPdLhyCIeYVSBAmCIOYRaaZOtExCR5P8ht+RpsEqNXTWjvRfZ1kxi6zv4ViLTtb3sHwgYVMIxoDCUiCMU+ZK4+Jvvzk5Hx3O41jjuQm9VPeUH+nWCZrXOzrU1JHvaXQkhQnOjzQXsg2rIi3Mb9epRnWcm6miXuc4eSGl2c4Pi7/LE/Hf423Pbc1Q02zLe0pkTCWFkeb55d9BttG0o2m2S42/Vy7JIeMnnHoEuTi9Mn5H8r6C/i4xAEA8uyiOKCvGXP90RkRXRM7j++FtY4EgiMUNGSyCIIh5pDWyVIvqOD1VSTfZtSAn6m2T6lkwcp7HsHo41xLBKmH1cC5dTVlhRKQIAmLyzjzgyvc2Nq8ZyrdM3mUEK4UhKYwAg2viyW48qb7iXU2d4TzOTFVQDeuN55cogtWqM7xBTKilSbj0bS06TbN9drqKWsR71BkX91QaBzbc2Ng82jEWRAQrhU5uuHk/gPh7zWsbmwdzAQayfu86gDAkyy6LdcaB7BJg7PZ057LpDK1v6gCzk6qXHwaG1orUwMaYe2f/dQiCmBfIYBEEQcwjrZGlkxfK4DxlFMbCklyAwXzQMCLHJkrpJ7sWRocLjUn1sYkyRodSpjzmh4Elq8TXpXEgNwRc9Z7G5rUjhbbJ+2AuwJJcisz3/AgwNCqaTsioxWs+2KKTB+eiJkpGylKZ0/wIMLJBaJTOi89aTIJ878fGSw2ddAZraaxzXtQQ1WvA5Tc1Nq8eFhGZ4+NlVMIIZ6YqyVIEJZ4HrHlN815K54FNb2psZoxhdKSA4+OtBivlWCiMAMPrmjr5kdkxPoWlwMDypjFlHnDNB+3HpdFpjIV4zF15R/91CIKYF8hgEQRBzCOrBvNgTEw+e4rCODA6nMfxiVJc8xXOqs6JXtPPADGJrlwQbcDL482UtJg1w3lMlkNMVUIcnyj1oCNT6sbjdLps2yK2rZElWUe0Oo3Bak2pk1EfqY2WlLoL5ZZIWYp3lCkCXkZoyMl7vvnscoGPFUuyOHGhhFMXKrFOD++oNB6/owttOvK8xy/0aSzIeylPdI2FvpEfFosaVybFe8oPCyPZb9rGgnxHw/r9CYJYVJDBIgiCmEeygYcVS3KNyTvQw2TXwpo4siTNz2xEyoSOMFj1OseJiTJG0+oUlorJ7g++IibXLWtGAc3ndGKiFOukjY7EjSR23Sd0ps806ohadY7Fzy51cxA5gX75ITGpHj/cZhRWDOQQxM0tUjUHkTAmzvvq40oded7jLWMutfEpjIgujJUJ0aShUydO40zVHKRTpzolukuWxoHzs7RmlLz+H3xV6EyenB0daUT37BA6F463mXqCIBY3ZLAIgiDmmdF4stvTpNpFZ6hDZxZqsKRONapj3+kpVKN6+qYd0pDseVAYhXjxX0lrs4aemoPISfUrrEHF8AAADJdJREFUjwqdicPtOi1Grqc6Itms4dB348n78cbaVEBcvzaUb5jtjM+SNweR5IdFa/NSfD/5TuNTiHV6SEUExHlPv6TVGR3O49RkBYfPxamVvegAwEv3iOYgp3enO4+rzr7YBJ8/MDs6uSEADDgQm+CpE22mniCIxQ0ZLIIgiHlmzVAex8ZLODZRwkDWx2CaOiIXnbhZw8FzYmHY1PUwVh1x3qcOnm/7PjHS+FRnRAe5ji5r8voPnp3prTmInOxGFWEUOnRks4Zj48KQ9GRMZfe48rgwXB2MDudxNK7BWj2UT7/gdH5E3E95HACL77FdR9R69Wjq88Oizfjkieb3LawZLoBz4Lkj47Fuj2OhMin+nq2Oe1InrIqxMBudCgHAD4DckpYxNwudCgmCmDd6+inOGPtJAL8L4CoAN3LOd2r2uw3A5wD4AP6ac/6p+PNlAP47gEsAHADw7zjn53u5JoIgiMXG1WuH8NCukyiHEa5eOwQ2S7/JvnrtEDgHvvjN/RguZNKn7lm4Zq2YzP/VY68I3dEh0+5q9mwHdj8IvPKIaD4RlsWivH//QdEcYMttWHvFuzFcyOCvH98PzsX9pdLZuwN49Ztionvoe0BhWZsOG9uGq9cO4ZHdJ3HkfAm3XzuaXufQEyJy9fDvANnBNh3EOv+y8zAOnMngug0p6oykzrFnRcreV+4URu4ff6JN55q1Q/j77x3EPz95CBuWFTCYz9jPrdI5tRuYOinO7wXAPb8ALN3UpgOIseB7DFtWL0l3T7vuExHGsCz+DK7penY9Ixcb3v91oDIloksDK2ZHZ+8Osd7WzBng6NPNha77qUMQxLzR669JXwTwQQB/pduBMeYD+AsAtwI4AuD7jLF7OecvAfgYgEc4559ijH0s/v4/93hNBEEQi4rbXrMGn334ZRw+V8KH33TJrOm8bfNKFDI+DpydwY+/fn3y9Ygc2bCsiKtHh/DS8Qu4enQIG5cXk59ELsj6/b8Bnvi8mLyvvwH4sT9t7BIAuOWq1fjy00dQyPh42+aV6XX27AAe+i3x2brrgQ98vm23d1+zBp94YBcA8b5S6xx/Hvjy/yo+W3kl8D/9fdtut12zBn/33YMo1yrYdm0POtNngf96m/hsZCPw77/cttstV6+G99UXcODsDO5822XpdeoR8Pm3APUQKC4HfuZf23b7kfXDGB3O48DZGbzlihUYKaaICEmtb38OeOYfgCAPrH098GN9XpRX6rzwJeCx/wKAiTH3/r+YHZ2D3wHuu0t8tuZa4Cf+tr86BEHMGz39dOWc7+Kc77HsdiOAfZzz/ZzzKoB/BvC+eNv7ANwdf303gPf3cj0EQRCLkbHVg7h0xQCAlJN3RwpZHzddKUzItlnUaT1/zzqj8bpKxeXKGhV5/puuXIlC1u9dJ7tEmbon38ulKwYwtnowvc6qqwDmiz+KTng3XroMS4sZZHyGd165Or3OwHIgK8YUisu7Nq9YksMNlywDIMxjajwfKC7T6jDGGufveWy3jYXeTuWkkx8WqXyzxZprxd9+VkQzCYK4aJjF/zkarAPQWjF8BMAb4q9Xc86PAwDn/DhjbJXuJIyxOwHcCQAbN26cpUslCIKYexhj+Pm3X45nj4xj/dIU0Z4E/OyPXopSNcJbNq+YVZ0fv349vv3KGXzw+vXpT7JnO7D7AbEm0ZLVohvefXe1pVC9ZfMK3DS2Ej/7o5f2prNnhzALQ2uVOuuXFvHTb9iI164fSZ/CKVPD/CywZCUwcbRLJ/A9/MJNV+DMVBXDhYRpe506ng8MrBRdETt0AOB/e+tlWL4ki9elSUVs1eFcpLhVp5Q6P/2Gjdh7chLvSZNa2aq16z7RVVIzFvrCnu3iD5h2LPRNR46F4XWiQchs6BAEMS8wzrl5B8YeBqD6tdPHOef3xPt8A8CvqGqw4jqtd3PO/2P8/Ycg6rV+iTE2zjkfadn3POd8qe2it27dynfuVJZ7EQRBEBcj990F3NHnlDDSWXw6c6l1sekQBNF3GGNPcc63dn5ujWBxzm/pUfsIgA0t368HcCz++iRjbDSOXo0CONWjFkEQBEEQBEEQxLwxF23avw9gM2PsUsZYFsBPAbg33nYvgA/HX38YwD1zcD0EQRDEYmPLbaRDOnOrdbHpEAQxZ1hTBI0HM/YBAH8GYCWAcQDPcs7fzRhbC9GO/fZ4v9sBfBaiTfvfcs7/MP58OYB/AbARwCEAP8k5P2fTpRRBgiAIgiAIgiDmE12KYE8Ga74gg0UQBEEQBEEQxHyiM1hzkSJIEARBEARBEATxQwEZLIIgCIIgCIIgiD5BBosgCIIgCIIgCKJPkMEiCIIgCIIgCILoE2SwCIIgCIIgCIIg+sSi7CLIGJsEsGe+r4NoYwWAM/N9EUQDeh8LD3onCw96JwsLeh8LD3onCw96JwuLTZzzlZ0fBvNxJX1gj6olIjF/MMZ20jtZOND7WHjQO1l40DtZWND7WHjQO1l40DtZHFCKIEEQBEEQBEEQRJ8gg0UQBEEQxP/f3v2F3l3XcRx/vtiaC9daEWFswSY0TW0uYWMZVm6li4nehUImBcVEh4ZR2yQpCBYa6kBvYltXioTrj4j//3UTG6aZtlZpu2jLQrvQAtExfXvx/SKC2343X/c55/d9Pq7O9/x+P3jCh53zfX+/n7MjSRrItA5YP28doPdwTSaL6zF5XJPJ45pMFtdj8rgmk8c1mQJT+Z9cSJIkSdIkmtY7WJIkSZI0cRywJEmSJGkgUztgJVmZZE+SZ5L8Icnq1k1jl2RTkr8l2ZfkxtY96iT5XpJK8rHWLWOX5KYkf03ybJJfJ1nUummMkqzvX6teSLK5dc/YJflkkseT7O/fP65p3SRIMifJH5Pc27pFkGRRkrv795D9ST7XuknHNrUDFnAj8OOqWgnc0B+rkSTnA5cAK6rqTOBnjZNEd+ICfAX4Z+sWAfAwcFZVrQD+Dmxp3DM6SeYAtwNfBc4ALktyRtuq0TsCXFdVnwbWAFe5JhPhGmB/6wi9YzvwQFWdDpyNazPRpnnAKmBh//jDwIsNWwRXAj+tqjcAquqlxj3q3AJ8n+7fixqrqoeq6kh/uAdY0rJnpFYDL1TVgao6DNxFd3FIjVTVv6vq6f7x/+lOHBe3rRq3JEuADcCO1i2CJAuBLwA7AarqcFW90rZKxzPNA9a1wE1JDtLdLfFKcFvLgfOS7E3yuySrWgeNXZKLgX9V1Z9at+iovgXc3zpihBYDB991fAhP5idGkqXAZ4G9bUtG71a6i3NvtQ4RAKcCLwO/6Ldt7khycusoHdvc1gHHk+QR4JSj/Oh6YB3w3araneRrdFP9l09k39jMsB5zgY/Qbe9YBfwyyanl9wC8r2ZYk63ABSe2SMdbk6r6bf8719Nti7rjRLYJgBzlOV+nJkCSBcBu4Nqq+l/rnrFKchHwUlU9leRLrXsEdOdY5wCbqmpvku3AZuCHbbN0LFP7PVhJXgUWVVUlCfBqVS2c6e/0/kjyAN0WwSf6438Aa6rq5aZhI5XkM8CjwGv9U0vottGurqr/NAsTSa4ANgLrquq1mX5fw+o/GP6jqrqwP94CUFXbmoaNXJIPAPcCD1bVza17xizJNuByuotA8+k+jvGrqvp607ARS3IKsKeqlvbH5wGbq2pD0zAd0zRvEXwR+GL/eC3wfMMWwW/o1oEky4F5wH+bFo1YVT1XVR+vqqX9C/Ih4ByHq7aSrAd+AFzscNXMk8CnkixLMg+4FLincdOo9RdJdwL7Ha7aq6otVbWkf++4FHjM4aqt/r37YJLT+qfWAX9pmKQZTPQWwRl8G9ieZC7wOvCdxj1jtwvYleTPwGHgCrcHSu9xG3AS8HB3TsmeqtrYNmlcqupIkquBB4E5wK6q2tc4a+w+T3fH5Lkkz/TPba2q+xo2SZNmE3BHf2HoAPDNxj06jqndIihJkiRJk2aatwhKkiRJ0kRxwJIkSZKkgThgSZIkSdJAHLAkSZIkaSAOWJIkSZI0EAcsSZIkSRqIA5YkSZIkDcQBS5I0GklWJXk2yfwkJyfZl+Ss1l2SpNnDLxqWJI1Kkp8A84EPAoeqalvjJEnSLOKAJUkalSTzgCeB14Fzq+rNxkmSpFnELYKSpLH5KLAA+BDdnSxJkgbjHSxJ0qgkuQe4C1gGfKKqrm6cJEmaRea2DpAk6URJ8g3gSFXdmWQO8Pska6vqsdZtkqTZwTtYkiRJkjQQP4MlSZIkSQNxwJIkSZKkgThgSZIkSdJAHLAkSZIkaSAOWJIkSZI0EAcsSZIkSRqIA5YkSZIkDeRtZ39SK7ctryQAAAAASUVORK5CYII=\n", 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\n", 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                " ] @@ -260,10 +295,9 @@ ], "source": [ "fig, ax = plt.subplots(figsize=(12,4))\n", - "fig.set_tight_layout(True)\n", - "da.plot(ax=ax, label='original (centered) signal') \n", - "nda.plot(ax=ax, marker='+', label='shifted signal', alpha=.6) \n", - "ax.set_xlim([-8,nda.x.max()])\n", + "da.plot(ax=ax, label='Original (centered) signal') \n", + "nda.plot(ax=ax, marker='+', label='Shifted signal', alpha=.6) \n", + "ax.set_xlim((-8, nda.x.max()))\n", "ax.legend();" ] }, @@ -276,33 +310,49 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:352: FutureWarning: Flags true_phase and true_amplitude will be set to True in future versions of xrft.fft to preserve the theoretical phasing and amplitude of Inverse Fourier Transform. Consider setting them to ensure future compatibility with numpy.fft.fft-like behavior and to deactivate this warning.\n", + " warnings.warn(msg, FutureWarning)\n" + ] + } + ], "source": [ - "nda_dft = xrft.dft(nda, true_phase=True, true_amplitude=True) # Fourier Transform w/ phase preservation \n", - "nda_fft = xrft.fft(nda) # Fourier Transform w/out phase preservation\n", + "# Fourier Transform w/ phase preservation\n", + "nda_dft = xrft.fft(nda, true_phase=True, true_amplitude=True)\n", + "\n", + "# Fourier Transform w/out phase preservation\n", + "nda_fft = xrft.fft(nda)\n", + "\n", + "# With numpy.fft\n", "nda_npft = npft.fft(nda)" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "nk = nda_dft.freq_x # wavenumber axis\n", - "TF_ns = T/2*(np.sinc(T*(nk-k0)) + np.sinc(T*(nk+k0)))*np.exp(-2j*np.pi*nk*x0) # Theoretical FT (Note the additional phase)" + "nk = nda_dft.freq_x # Wavenumber axis\n", + "\n", + "# Theoretical FT (note the additional phase)\n", + "TF_ns = T/2 * (np.sinc(T * (nk - k0)) + np.sinc(T * (nk + k0))) * np.exp(-2j*np.pi*nk*x0)" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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                " ] @@ -314,24 +364,21 @@ } ], "source": [ - "fig, (ax1,ax2) = plt.subplots(figsize=(12,8), nrows=2, ncols=1)\n", - "fig.set_tight_layout(True)\n", + "fig, (ax1, ax2) = plt.subplots(figsize=(12, 8), nrows=2, ncols=1, sharex=True, sharey=True)\n", "\n", - "(nda_dft.real).plot(ax=ax1, linestyle='-', lw=3, c='k', label='phase preservation') \n", - "((nda_fft*dx).real).plot(ax=ax1, linestyle='', marker='+',label='no phase preservation')\n", - "ax1.plot(nk, (npft.fftshift(nda_npft)*dx).real, linestyle='', marker='x',label='numpy fft')\n", - "ax1.plot(nk, TF_ns.real, linestyle='--', label='Theory')\n", - "ax1.set_xlim([-10,10])\n", - "ax1.set_ylim([-2.,2])\n", + "(nda_dft.real).plot(ax=ax1, ls='-', lw=4, c='k', label='Phase preservation') \n", + "((nda_fft*dx).real).plot(ax=ax1, ls='', marker='+',label='No phase preservation')\n", + "ax1.plot(nk, (npft.fftshift(nda_npft)*dx).real, 'x', label='numpy.fft')\n", + "ax1.plot(nk, TF_ns.real, '--', label='Theory')\n", + "ax1.set_xlim((-10, 10))\n", + "ax1.set_ylim((-2., 2))\n", "ax1.legend()\n", "ax1.set_title('REAL PART')\n", "\n", - "(nda_dft.imag).plot(ax=ax2, linestyle='-', lw=3, c='k', label='phase preservation') \n", - "((nda_fft*dx).imag).plot(ax=ax2, linestyle='', marker='+', label='no phase preservation') \n", - "ax2.plot(nk, (npft.fftshift(nda_npft)*dx).imag, linestyle='', marker='x',label='numpy fft')\n", - "ax2.plot(nk, TF_ns.imag, linestyle='--', label='Theory')\n", - "ax2.set_xlim([-10,10])\n", - "ax2.set_ylim([-2.,2.])\n", + "(nda_dft.imag).plot(ax=ax2, ls='-', lw=4, c='k', label='Phase preservation') \n", + "((nda_fft*dx).imag).plot(ax=ax2, linestyle='', marker='+', label='No phase preservation') \n", + "ax2.plot(nk, (npft.fftshift(nda_npft)*dx).imag, 'x', label='numpy.fft')\n", + "ax2.plot(nk, TF_ns.imag, '--', label='Theory')\n", "ax2.legend()\n", "ax2.set_title('IMAGINARY PART');" ] @@ -340,7 +387,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The expected additional phase (i.e. the complex term; $e^{-i2\\pi kx_0}$) that appears in theory is retrieved with `xrft.dft` but not with `xrft.fft` nor `npft.fft`. This is because in `npft.fft`, the input data is expected to be centered around zero. **In the current version of `xrft`, the behavior of `xrft.dft` defaults to `xrft.fft` so set the flags `true_phase=True` and `true_amplitude=True` in order to have the results matching with theory.**" + "The expected additional phase (i.e., the complex term; $e^{-i2\\pi kx_0}$) that appears in theory is retrieved with `xrft.dft` but not with `xrft.fft` nor `npft.fft`. This is because in `npft.fft` the input data is expected to be centered around zero. **In the current version of `xrft`, the behavior of `xrft.fft` defaults to this as well, so set the flags `true_phase=True` and `true_amplitude=True` in order to obtain results matching with theory.**" ] }, { @@ -352,22 +399,36 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:589: FutureWarning: Default idft's behaviour (lag=None) changed! Default value of lag was zero (centered output coordinates) and is now set to transformed coordinate's attribute 'direct_lag', defaulting to zero if that attribute is not set.\n", + " warnings.warn(msg, FutureWarning)\n", + "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:560: FutureWarning: Flags true_phase and true_amplitude will be set to True in future versions of xrft.ifft to preserve the theoretical phasing and amplitude of Inverse Fourier Transform. Consider setting them to ensure future compatibility with numpy.fft.ifft-like behavior and to deactivate this warning.\n", + " warnings.warn(msg, FutureWarning)\n", + "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:589: FutureWarning: Default idft's behaviour (lag=None) changed! Default value of lag was zero (centered output coordinates) and is now set to transformed coordinate's attribute 'direct_lag', defaulting to zero if that attribute is not set.\n", + " warnings.warn(msg, FutureWarning)\n" + ] + } + ], "source": [ - "inda_dft = xrft.idft(nda_dft, true_phase=True, true_amplitude=True) # Signal in direct space \n", + "# Signal in direct space\n", + "inda_dft = xrft.ifft(nda_dft, true_phase=True, true_amplitude=True)\n", "inda_fft = xrft.ifft(nda_fft)" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
                " ] @@ -379,15 +440,14 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize=(12,4))\n", - "fig.set_tight_layout(True)\n", - "inda_dft.real.plot(ax=ax, linestyle='-', c='k', lw=4, label='phase preservation')\n", - "ax.plot(x[:len(inda_fft.real)], inda_fft.real, linestyle='', marker='o', alpha=.7, \n", - " label='no phase preservation (w/out shifting)')\n", - "ax.plot(x[nshift:], inda_fft.real, linestyle='', marker='+', label='no phase preservation')\n", - "nda.plot(ax=ax, ls='--', lw=3, label='original signal')\n", - "ax.plot(x[nshift:], npft.ifft(nda_npft).real, ls=':', label='inverse of numpy fft')\n", - "ax.set_xlim([nda.x.min(),nda.x.max()])\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "inda_dft.real.plot(ax=ax, ls='-', c='k', lw=4, label='Phase preservation')\n", + "ax.plot(x[:len(inda_fft.real)], inda_fft.real, 'o', alpha=.7, \n", + " label='No phase preservation (w/out shifting)')\n", + "ax.plot(x[nshift:], inda_fft.real, '+', label='No phase preservation')\n", + "nda.plot(ax=ax, ls='--', lw=3, label='Original signal')\n", + "ax.plot(x[nshift:], npft.ifft(nda_npft).real, ':', label='numpy.fft')\n", + "ax.set_xlim((nda.x.min(), nda.x.max()))\n", "ax.legend(loc='upper left');" ] }, @@ -395,7 +455,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that we are only able to match the inverse transforms of `xrft.ifft` and `npft.ifft` to the data `nda` to it being Fourier transformed because we \"know\" the original data `da` was shifted by `nshift` datapoints as we see in `x[nshift:]` (compare the blue dots and orange crosses where without the knowledge of the shift, we may assume that the data were centered around zero). **Using `xrft.idft` along with `xrft.dft` with the flags `true_phase=True` and `true_amplitude=True` automatically takes care of the information of shifted coordinates.**" + "Note that we are only able to match the inverse transforms of `xrft.ifft` and `npft.ifft` to the data `nda` because we \"know\" the original data `da` was shifted by `nshift` datapoints as we see in `x[nshift:]` (compare the blue dots and orange crosses where, without the knowledge of the shift, we assume that the data were centered around zero). **Using `xrft.ifft` along with `xrft.fft` with the flags `true_phase=True` and `true_amplitude=True` automatically takes care of the information of shifted coordinates.**" ] }, { @@ -414,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -423,13 +483,11 @@ "
                \n", "\n", "\n", - "Show/Hide data repr\n", "\n", "\n", "\n", "\n", "\n", - "Show/Hide attributes\n", "\n", "\n", "\n", @@ -452,11 +510,29 @@ " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n", "}\n", "\n", + "html[theme=dark],\n", + "body.vscode-dark {\n", + " --xr-font-color0: rgba(255, 255, 255, 1);\n", + " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", + " --xr-font-color3: rgba(255, 255, 255, 0.38);\n", + " --xr-border-color: #1F1F1F;\n", + " --xr-disabled-color: #515151;\n", + " --xr-background-color: #111111;\n", + " --xr-background-color-row-even: #111111;\n", + " --xr-background-color-row-odd: #313131;\n", + "}\n", + "\n", ".xr-wrap {\n", + " display: block;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", "\n", + ".xr-text-repr-fallback {\n", + " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n", + " display: none;\n", + "}\n", + "\n", ".xr-header {\n", " padding-top: 6px;\n", " padding-bottom: 6px;\n", @@ -719,7 +795,8 @@ " grid-template-columns: 125px auto;\n", "}\n", "\n", - ".xr-attrs dt, dd {\n", + ".xr-attrs dt,\n", + ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", @@ -754,17 +831,34 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
                xarray.DataArray
                'air'
                • time: 2920
                • lat: 25
                • lon: 53
                • ...
                  [3869000 values with dtype=float32]
                  • lat
                    (lat)
                    float32
                    75.0 72.5 70.0 ... 20.0 17.5 15.0
                    standard_name :
                    latitude
                    long_name :
                    Latitude
                    units :
                    degrees_north
                    axis :
                    Y
                    array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "
                    <xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
+       "[3869000 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
                  • time
                    (time)
                    datetime64[ns]
                    2013-01-01 ... 2014-12-31T18:00:00
                    standard_name :
                    time
                    long_name :
                    Time
                    array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n",
        "       '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n",
        "       '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n",
-       "      dtype='datetime64[ns]')
                • long_name :
                  4xDaily Air temperature at sigma level 995
                  units :
                  degK
                  precision :
                  2
                  GRIB_id :
                  11
                  GRIB_name :
                  TMP
                  var_desc :
                  Air temperature
                  dataset :
                  NMC Reanalysis
                  level_desc :
                  Surface
                  statistic :
                  Individual Obs
                  parent_stat :
                  Other
                  actual_range :
                  [185.16 322.1 ]
                " + " dtype='datetime64[ns]')
              • long_name :
                4xDaily Air temperature at sigma level 995
                units :
                degK
                precision :
                2
                GRIB_id :
                11
                GRIB_name :
                TMP
                var_desc :
                Air temperature
                dataset :
                NMC Reanalysis
                level_desc :
                Surface
                statistic :
                Individual Obs
                parent_stat :
                Other
                actual_range :
                [185.16 322.1 ]
                • " ], "text/plain": [ "\n", @@ -787,7 +881,7 @@ " actual_range: [185.16 322.1 ]" ] }, - "execution_count": 4, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -799,7 +893,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -808,13 +902,11 @@ "
                \n", "\n", "\n", - "Show/Hide data repr\n", "\n", "\n", "\n", "\n", "\n", - "Show/Hide attributes\n", "\n", "\n", "\n", @@ -837,11 +929,29 @@ " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n", "}\n", "\n", + "html[theme=dark],\n", + "body.vscode-dark {\n", + " --xr-font-color0: rgba(255, 255, 255, 1);\n", + " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", + " --xr-font-color3: rgba(255, 255, 255, 0.38);\n", + " --xr-border-color: #1F1F1F;\n", + " --xr-disabled-color: #515151;\n", + " --xr-background-color: #111111;\n", + " --xr-background-color-row-even: #111111;\n", + " --xr-background-color-row-odd: #313131;\n", + "}\n", + "\n", ".xr-wrap {\n", + " display: block;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", "\n", + ".xr-text-repr-fallback {\n", + " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n", + " display: none;\n", + "}\n", + "\n", ".xr-header {\n", " padding-top: 6px;\n", " padding-bottom: 6px;\n", @@ -1104,7 +1214,8 @@ " grid-template-columns: 125px auto;\n", "}\n", "\n", - ".xr-attrs dt, dd {\n", + ".xr-attrs dt,\n", + ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", @@ -1139,7 +1250,30 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
                xarray.DataArray
                • freq_lat: 25
                • lon: 53
                • (55.72721061044916-46.861821150779946j) ... (57.947560549433405+48.51852927393743j)
                  array([[ 55.72721061-46.86182115j,  54.88410513-45.81648436j,\n",
+       "
                  <xarray.DataArray (freq_lat: 25, lon: 53)>\n",
+       "array([[ 55.72721061-46.86182115j,  54.88410513-45.81648436j,\n",
+       "         54.44861105-45.4792758j , ...,  63.78368643-52.78354988j,\n",
+       "         60.99712143-50.28091047j,  57.94756055-48.51852927j],\n",
+       "       [-38.90906614-66.9663849j , -38.90038095-67.92497252j,\n",
+       "        -38.544492  -68.17925905j, ..., -41.94737401-74.09175983j,\n",
+       "        -40.00936528-70.47655747j, -39.05651073-68.04032158j],\n",
+       "       [-77.82019891+12.50876021j, -79.02288653+10.06164636j,\n",
+       "        -80.34175059 +9.81548668j, ..., -86.83039434+26.4819109j ,\n",
+       "        -84.02694401+23.31755583j, -81.26451369+21.4268003j ],\n",
+       "       ...,\n",
+       "       [-77.82019891-12.50876021j, -79.02288653-10.06164636j,\n",
+       "        -80.34175059 -9.81548668j, ..., -86.83039434-26.4819109j ,\n",
+       "        -84.02694401-23.31755583j, -81.26451369-21.4268003j ],\n",
+       "       [-38.90906614+66.9663849j , -38.90038095+67.92497252j,\n",
+       "        -38.544492  +68.17925905j, ..., -41.94737401+74.09175983j,\n",
+       "        -40.00936528+70.47655747j, -39.05651073+68.04032158j],\n",
+       "       [ 55.72721061+46.86182115j,  54.88410513+45.81648436j,\n",
+       "         54.44861105+45.4792758j , ...,  63.78368643+52.78354988j,\n",
+       "         60.99712143+50.28091047j,  57.94756055+48.51852927j]])\n",
+       "Coordinates:\n",
+       "  * lon       (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "    time      datetime64[ns] 2013-01-01\n",
+       "  * freq_lat  (freq_lat) float64 -0.192 -0.176 -0.16 -0.144 ... 0.16 0.176 0.192
                  • lon
                    (lon)
                    float32
                    200.0 202.5 205.0 ... 327.5 330.0
                    standard_name :
                    longitude
                    long_name :
                    Longitude
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                    degrees_east
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                    X
                    array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
        "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
        "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
        "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
        "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
-       "       325. , 327.5, 330. ], dtype=float32)
                  • time
                    ()
                    datetime64[ns]
                    2013-01-01
                    standard_name :
                    time
                    long_name :
                    Time
                    array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
                  • freq_lat
                    (freq_lat)
                    float64
                    -0.192 -0.176 -0.16 ... 0.176 0.192
                    spacing :
                    0.016000000000000014
                    array([-0.192, -0.176, -0.16 , -0.144, -0.128, -0.112, -0.096, -0.08 , -0.064,\n",
+       "       325. , 327.5, 330. ], dtype=float32)
                • time
                  ()
                  datetime64[ns]
                  2013-01-01
                  standard_name :
                  time
                  long_name :
                  Time
                  array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
                • freq_lat
                  (freq_lat)
                  float64
                  -0.192 -0.176 -0.16 ... 0.176 0.192
                  spacing :
                  0.016000000000000014
                  direct_lag :
                  45.0
                  array([-0.192, -0.176, -0.16 , -0.144, -0.128, -0.112, -0.096, -0.08 , -0.064,\n",
        "       -0.048, -0.032, -0.016,  0.   ,  0.016,  0.032,  0.048,  0.064,  0.08 ,\n",
-       "        0.096,  0.112,  0.128,  0.144,  0.16 ,  0.176,  0.192])
                • " + " 0.096, 0.112, 0.128, 0.144, 0.16 , 0.176, 0.192])
                • " ], "text/plain": [ "\n", @@ -1193,13 +1327,13 @@ " * freq_lat (freq_lat) float64 -0.192 -0.176 -0.16 -0.144 ... 0.16 0.176 0.192" ] }, - "execution_count": 6, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Fda = xrft.dft(da.isel(time=0), dim=\"lat\", true_phase=True, true_amplitude=True)\n", + "Fda = xrft.fft(da.isel(time=0), dim=\"lat\", true_phase=True, true_amplitude=True)\n", "Fda" ] }, @@ -1207,27 +1341,33 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The coordinate metadata is lost during the DFT (or any Fourier transform) operation so we need to specify the `lag` to retrieve the latitudes back in the inverse transform. The original latitudes are centered around 45$^\\circ$ so we set the lag to `lag=45`." + "The coordinate metadata is lost during the DFT (or any Fourier transform). However, `xrft.fft` preserves the original `lag` in the `direct_lag` attribute of the new `freq_lat` coordinate. The original latitudes are centered around 45°. We could instead pass `lag=45` manually." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 18, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\zmoon\\git\\xrft\\xrft\\xrft.py:589: FutureWarning: Default idft's behaviour (lag=None) changed! Default value of lag was zero (centered output coordinates) and is now set to transformed coordinate's attribute 'direct_lag', defaulting to zero if that attribute is not set.\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, { "data": { "text/html": [ "
                \n", "\n", "\n", - "Show/Hide data repr\n", "\n", "\n", "\n", "\n", "\n", - "Show/Hide attributes\n", "\n", "\n", "\n", @@ -1250,11 +1390,29 @@ " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n", "}\n", "\n", + "html[theme=dark],\n", + "body.vscode-dark {\n", + " --xr-font-color0: rgba(255, 255, 255, 1);\n", + " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", + " --xr-font-color3: rgba(255, 255, 255, 0.38);\n", + " --xr-border-color: #1F1F1F;\n", + " --xr-disabled-color: #515151;\n", + " --xr-background-color: #111111;\n", + " --xr-background-color-row-even: #111111;\n", + " --xr-background-color-row-odd: #313131;\n", + "}\n", + "\n", ".xr-wrap {\n", + " display: block;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", "\n", + ".xr-text-repr-fallback {\n", + " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n", + " display: none;\n", + "}\n", + "\n", ".xr-header {\n", " padding-top: 6px;\n", " padding-bottom: 6px;\n", @@ -1517,7 +1675,8 @@ " grid-template-columns: 125px auto;\n", "}\n", "\n", - ".xr-attrs dt, dd {\n", + ".xr-attrs dt,\n", + ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", @@ -1552,88 +1711,111 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
                xarray.DataArray
                • lat: 25
                • lon: 53
                • (296.2900085449223-7.014888176295515e-16j) ... (238.5999908447269+4.1703029862596966e-16j)
                  array([[296.29000854-7.01488818e-16j, 296.79000854-2.41028295e-15j,\n",
-       "        297.1000061 -1.08051561e-15j, ..., 296.8999939 +2.07870428e-15j,\n",
-       "        296.79000854+1.39068454e-15j, 296.6000061 +1.98243140e-15j],\n",
-       "       [295.8999939 -1.36617001e-16j, 296.19998169-3.08854147e-15j,\n",
-       "        296.79000854-2.27797690e-16j, ..., 295.8999939 -2.06120304e-15j,\n",
-       "        295.8999939 -7.63307736e-16j, 295.19998169+2.90958929e-15j],\n",
-       "       [296.6000061 +2.18513309e-15j, 296.19998169-5.34587573e-16j,\n",
-       "        296.3999939 -1.70159409e-15j, ..., 295.3999939 -9.67078004e-16j,\n",
-       "        295.1000061 -2.97325892e-15j, 294.69998169+2.84108954e-15j],\n",
+       "
                  <xarray.DataArray (lat: 25, lon: 53)>\n",
+       "array([[296.29000854-2.03692639e-15j, 296.79000854-1.57260115e-15j,\n",
+       "        297.1000061 +9.03196374e-16j, ..., 296.8999939 +2.20263432e-15j,\n",
+       "        296.79000854+2.90702812e-15j, 296.6000061 +2.20980508e-15j],\n",
+       "       [295.8999939 -8.88584270e-16j, 296.19998169-3.39575537e-15j,\n",
+       "        296.79000854+5.61674624e-16j, ..., 295.8999939 -2.38676620e-15j,\n",
+       "        295.8999939 +1.24770331e-15j, 295.19998169+7.24456203e-16j],\n",
+       "       [296.6000061 +2.01993645e-15j, 296.19998169+1.97610868e-15j,\n",
+       "        296.3999939 -9.88054338e-16j, ..., 295.3999939 -9.94589070e-16j,\n",
+       "        295.1000061 -1.54617942e-15j, 294.69998169+2.49523612e-15j],\n",
+       "       ...,\n",
+       "       [250.        +1.48534887e-15j, 249.79998779+3.91026468e-15j,\n",
+       "        248.88999939-1.95513234e-15j, ..., 233.19999695-1.29608207e-16j,\n",
+       "        236.38999939-2.72809988e-16j, 241.69999695+2.26693997e-15j],\n",
+       "       [243.79998779-3.00722368e-17j, 244.5       -3.36123551e-15j,\n",
+       "        244.69999695-5.82693719e-17j, ..., 232.79998779+2.19593359e-15j,\n",
+       "        235.29998779+8.21315052e-16j, 239.29998779-9.67078004e-16j],\n",
+       "       [241.19999695-3.03635842e-15j, 242.5       -8.07397560e-16j,\n",
+       "        243.5       +6.76246713e-16j, ..., 232.79998779+2.36408527e-15j,\n",
+       "        235.5       +3.10286712e-15j, 238.59999084+1.36864817e-15j]])\n",
+       "Coordinates:\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "    time     datetime64[ns] 2013-01-01\n",
+       "  * lat      (lat) float64 15.0 17.5 20.0 22.5 25.0 ... 65.0 67.5 70.0 72.5 75.0
                  • lon
                    (lon)
                    float32
                    200.0 202.5 205.0 ... 327.5 330.0
                    standard_name :
                    longitude
                    long_name :
                    Longitude
                    units :
                    degrees_east
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                    X
                    array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
        "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
        "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
        "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
        "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
-       "       325. , 327.5, 330. ], dtype=float32)
                  • time
                    ()
                    datetime64[ns]
                    2013-01-01
                    standard_name :
                    time
                    long_name :
                    Time
                    array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
                  • lat
                    (lat)
                    float64
                    15.0 17.5 20.0 ... 70.0 72.5 75.0
                    spacing :
                    2.4999999999999964
                    array([15. , 17.5, 20. , 22.5, 25. , 27.5, 30. , 32.5, 35. , 37.5, 40. , 42.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)
                • time
                  ()
                  datetime64[ns]
                  2013-01-01
                  standard_name :
                  time
                  long_name :
                  Time
                  array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
                • lat
                  (lat)
                  float64
                  15.0 17.5 20.0 ... 70.0 72.5 75.0
                  spacing :
                  2.4999999999999964
                  array([15. , 17.5, 20. , 22.5, 25. , 27.5, 30. , 32.5, 35. , 37.5, 40. , 42.5,\n",
        "       45. , 47.5, 50. , 52.5, 55. , 57.5, 60. , 62.5, 65. , 67.5, 70. , 72.5,\n",
-       "       75. ])
                • " + " 75. ])
                • " ], "text/plain": [ "\n", - "array([[296.29000854-7.01488818e-16j, 296.79000854-2.41028295e-15j,\n", - " 297.1000061 -1.08051561e-15j, ..., 296.8999939 +2.07870428e-15j,\n", - " 296.79000854+1.39068454e-15j, 296.6000061 +1.98243140e-15j],\n", - " [295.8999939 -1.36617001e-16j, 296.19998169-3.08854147e-15j,\n", - " 296.79000854-2.27797690e-16j, ..., 295.8999939 -2.06120304e-15j,\n", - " 295.8999939 -7.63307736e-16j, 295.19998169+2.90958929e-15j],\n", - " [296.6000061 +2.18513309e-15j, 296.19998169-5.34587573e-16j,\n", - " 296.3999939 -1.70159409e-15j, ..., 295.3999939 -9.67078004e-16j,\n", - " 295.1000061 -2.97325892e-15j, 294.69998169+2.84108954e-15j],\n", + "array([[296.29000854-2.03692639e-15j, 296.79000854-1.57260115e-15j,\n", + " 297.1000061 +9.03196374e-16j, ..., 296.8999939 +2.20263432e-15j,\n", + " 296.79000854+2.90702812e-15j, 296.6000061 +2.20980508e-15j],\n", + " [295.8999939 -8.88584270e-16j, 296.19998169-3.39575537e-15j,\n", + " 296.79000854+5.61674624e-16j, ..., 295.8999939 -2.38676620e-15j,\n", + " 295.8999939 +1.24770331e-15j, 295.19998169+7.24456203e-16j],\n", + " [296.6000061 +2.01993645e-15j, 296.19998169+1.97610868e-15j,\n", + " 296.3999939 -9.88054338e-16j, ..., 295.3999939 -9.94589070e-16j,\n", + " 295.1000061 -1.54617942e-15j, 294.69998169+2.49523612e-15j],\n", " ...,\n", - " [250. +1.32015223e-15j, 249.79998779+5.34587573e-16j,\n", - " 248.88999939-1.80369123e-15j, ..., 233.19999695+9.67078004e-16j,\n", - " 236.38999939+3.00722368e-17j, 241.69999695+3.04528382e-15j],\n", - " [243.79998779-1.32169378e-16j, 244.5 -4.76080394e-15j,\n", - " 244.69999695-1.17678849e-15j, ..., 232.79998779+1.86297913e-15j,\n", - " 235.29998779-3.02882224e-16j, 239.29998779-9.67078004e-16j],\n", - " [241.19999695+5.26510200e-16j, 242.5 -1.34198513e-15j,\n", - " 243.5 -3.83146461e-16j, ..., 232.79998779+2.46618241e-15j,\n", - " 235.5 +3.62856196e-16j, 238.59999084+4.17030299e-16j]])\n", + " [250. +1.48534887e-15j, 249.79998779+3.91026468e-15j,\n", + " 248.88999939-1.95513234e-15j, ..., 233.19999695-1.29608207e-16j,\n", + " 236.38999939-2.72809988e-16j, 241.69999695+2.26693997e-15j],\n", + " [243.79998779-3.00722368e-17j, 244.5 -3.36123551e-15j,\n", + " 244.69999695-5.82693719e-17j, ..., 232.79998779+2.19593359e-15j,\n", + " 235.29998779+8.21315052e-16j, 239.29998779-9.67078004e-16j],\n", + " [241.19999695-3.03635842e-15j, 242.5 -8.07397560e-16j,\n", + " 243.5 +6.76246713e-16j, ..., 232.79998779+2.36408527e-15j,\n", + " 235.5 +3.10286712e-15j, 238.59999084+1.36864817e-15j]])\n", "Coordinates:\n", " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", " time datetime64[ns] 2013-01-01\n", " * lat (lat) float64 15.0 17.5 20.0 22.5 25.0 ... 65.0 67.5 70.0 72.5 75.0" ] }, - "execution_count": 8, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Fda_1 = xrft.idft(Fda, dim=\"freq_lat\", true_phase=True, true_amplitude=True, lag=45)\n", - "Fda_1" + "iFda = xrft.ifft(Fda, dim=\"freq_lat\", true_phase=True, true_amplitude=True)\n", + "iFda" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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                " ] @@ -1642,32 +1824,43 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
                " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "fig, (ax1,ax2) = plt.subplots(figsize=(12,4), nrows=1, ncols=2)\n", + "fig, (ax1, ax2) = plt.subplots(figsize=(12, 4), nrows=1, ncols=2)\n", + "\n", "da.isel(time=0).plot(ax=ax1)\n", - "Fda_1.real.plot(ax=ax2)" + "ax1.set_title(\"Original\", loc=\"right\")\n", + "\n", + "iFda.real.plot(ax=ax2)\n", + "ax2.set_title(\"Recovered\", loc=\"right\")\n", + "\n", + "(iFda.real - da.isel(time=0)).plot(size=4, center=0, cbar_kwargs=dict(label=\"Recovered minus original\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We see the inverse DFT of the Fourier transformed original temperature data returns the original data." + "We see the inverse DFT of the Fourier transformed original temperature data returns the original data to within float64 floating point rounding error." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1681,7 +1874,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.1" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/doc/environment.yml b/doc/environment.yml index 74a7ab92..55056121 100644 --- a/doc/environment.yml +++ b/doc/environment.yml @@ -29,6 +29,7 @@ dependencies: # # docs - nbsphinx + - pooch - sphinx-autobuild - sphinx-prompt - sphinx_rtd_theme diff --git a/xrft/xrft.py b/xrft/xrft.py index 1825e593..630d9955 100644 --- a/xrft/xrft.py +++ b/xrft/xrft.py @@ -580,7 +580,12 @@ def ifft( ] # real dim has to be moved or added at the end ! if lag is None: lag = [daft[d].attrs.get("direct_lag", 0.0) for d in dim] - msg = "Default idft's behaviour (lag=None) changed! Default value of lag was zero (centered output coordinates) and is now set to transformed coordinate's attribute: 'direct_lag'." + msg = ( + "Default idft's behaviour (lag=None) changed! " + "Default value of lag was zero (centered output coordinates) " + "and is now set to transformed coordinate's attribute 'direct_lag', " + "defaulting to zero if that attribute is not set." + ) warnings.warn(msg, FutureWarning) else: if isinstance(lag, float) or isinstance(lag, int): From fb7897dd7e2e9c7bb7a72db011a065eb4396eaef Mon Sep 17 00:00:00 2001 From: zmoon Date: Sun, 28 Nov 2021 15:49:48 -0700 Subject: [PATCH 26/26] fix final comparison plot in main example the latitude coordinates are different by floating point rounding error, needed to replace before subtracting --- doc/DFT-iDFT_example.ipynb | 89 ++++++++++++++++++++++---------------- 1 file changed, 51 insertions(+), 38 deletions(-) diff --git a/doc/DFT-iDFT_example.ipynb b/doc/DFT-iDFT_example.ipynb index be599c10..8e0d7f81 100644 --- a/doc/DFT-iDFT_example.ipynb +++ b/doc/DFT-iDFT_example.ipynb @@ -84,7 +84,7 @@ "fig, ax = plt.subplots(figsize=(12, 4)) \n", "da.plot(ax=ax, marker='+', label='original signal')\n", "ax.legend()\n", - "ax.set_xlim([-8, 8]);" + "ax.set_xlim((-8, 8));" ] }, { @@ -376,7 +376,7 @@ "ax1.set_title('REAL PART')\n", "\n", "(nda_dft.imag).plot(ax=ax2, ls='-', lw=4, c='k', label='Phase preservation') \n", - "((nda_fft*dx).imag).plot(ax=ax2, linestyle='', marker='+', label='No phase preservation') \n", + "((nda_fft*dx).imag).plot(ax=ax2, ls='', marker='+', label='No phase preservation') \n", "ax2.plot(nk, (npft.fftshift(nda_npft)*dx).imag, 'x', label='numpy.fft')\n", "ax2.plot(nk, TF_ns.imag, '--', label='Theory')\n", "ax2.legend()\n", @@ -469,7 +469,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Load atmosheric temperature from the NMC reanalysis." + "Load atmospheric temperature from the NMC reanalysis." ] }, { @@ -831,12 +831,19 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
                <xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
-       "[3869000 values with dtype=float32]\n",
+       "
                <xarray.DataArray 'air' (lat: 25, lon: 53)>\n",
+       "array([[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    , 238.59999],\n",
+       "       [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999, 239.29999],\n",
+       "       [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   , 241.7    ],\n",
+       "       ...,\n",
+       "       [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    , 294.69998],\n",
+       "       [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    , 295.19998],\n",
+       "       [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   , 296.6    ]],\n",
+       "      dtype=float32)\n",
        "Coordinates:\n",
        "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
        "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "    time     datetime64[ns] 2013-01-01\n",
        "Attributes:\n",
        "    long_name:     4xDaily Air temperature at sigma level 995\n",
        "    units:         degK\n",
@@ -848,25 +855,36 @@
        "    level_desc:    Surface\n",
        "    statistic:     Individual Obs\n",
        "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
              • time
                ()
                datetime64[ns]
                2013-01-01
                standard_name :
                time
                long_name :
                Time
                array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
              • long_name :
                4xDaily Air temperature at sigma level 995
                units :
                degK
                precision :
                2
                GRIB_id :
                11
                GRIB_name :
                TMP
                var_desc :
                Air temperature
                dataset :
                NMC Reanalysis
                level_desc :
                Surface
                statistic :
                Individual Obs
                parent_stat :
                Other
                actual_range :
                [185.16 322.1 ]
                • " ], "text/plain": [ - "\n", - "[3869000 values with dtype=float32]\n", + "\n", + "array([[241.2 , 242.5 , 243.5 , ..., 232.79999, 235.5 , 238.59999],\n", + " [243.79999, 244.5 , 244.7 , ..., 232.79999, 235.29999, 239.29999],\n", + " [250. , 249.79999, 248.89 , ..., 233.2 , 236.39 , 241.7 ],\n", + " ...,\n", + " [296.6 , 296.19998, 296.4 , ..., 295.4 , 295.1 , 294.69998],\n", + " [295.9 , 296.19998, 296.79 , ..., 295.9 , 295.9 , 295.19998],\n", + " [296.29 , 296.79 , 297.1 , ..., 296.9 , 296.79 , 296.6 ]],\n", + " dtype=float32)\n", "Coordinates:\n", " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + " time datetime64[ns] 2013-01-01\n", "Attributes:\n", " long_name: 4xDaily Air temperature at sigma level 995\n", " units: degK\n", @@ -887,7 +905,7 @@ } ], "source": [ - "da = xr.tutorial.open_dataset(\"air_temperature\").air\n", + "da = xr.tutorial.open_dataset(\"air_temperature\").air.isel(time=0)\n", "da" ] }, @@ -1273,7 +1291,7 @@ "Coordinates:\n", " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", " time datetime64[ns] 2013-01-01\n", - " * freq_lat (freq_lat) float64 -0.192 -0.176 -0.16 -0.144 ... 0.16 0.176 0.192
                • " ], "text/plain": [ "\n", @@ -1333,7 +1351,7 @@ } ], "source": [ - "Fda = xrft.fft(da.isel(time=0), dim=\"lat\", true_phase=True, true_amplitude=True)\n", + "Fda = xrft.fft(da, dim=\"lat\", true_phase=True, true_amplitude=True)\n", "Fda" ] }, @@ -1734,7 +1752,7 @@ "Coordinates:\n", " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", " time datetime64[ns] 2013-01-01\n", - " * lat (lat) float64 15.0 17.5 20.0 22.5 25.0 ... 65.0 67.5 70.0 72.5 75.0
                • " ], "text/plain": [ "\n", @@ -1803,16 +1821,6 @@ "execution_count": 19, "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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\n", @@ -1827,7 +1835,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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                " ] @@ -1841,13 +1849,18 @@ "source": [ "fig, (ax1, ax2) = plt.subplots(figsize=(12, 4), nrows=1, ncols=2)\n", "\n", - "da.isel(time=0).plot(ax=ax1)\n", + "da.plot(ax=ax1)\n", "ax1.set_title(\"Original\", loc=\"right\")\n", "\n", "iFda.real.plot(ax=ax2)\n", "ax2.set_title(\"Recovered\", loc=\"right\")\n", "\n", - "(iFda.real - da.isel(time=0)).plot(size=4, center=0, cbar_kwargs=dict(label=\"Recovered minus original\"))" + "(iFda.real.assign_coords(lat=da.lat[::-1]) - da).plot(\n", + " size=4, center=0, cbar_kwargs=dict(label=\"Recovered minus original\")\n", + ");\n", + "# ^ In `iFda`, latitudes are reversed, and float64 instead of float32,\n", + "# so in a normal subtraction (without the `.assign_coords`),\n", + "# only a few latitude values match." ] }, {