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Merge pull request xarray-contrib#24 from nicrie/develop
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nicrie authored Aug 22, 2022
2 parents b03704c + 0f9c32e commit b79b1cd
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1 change: 1 addition & 0 deletions README.rst
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:align: center
:width: 800
:alt: Comparison of standard, Varimax-rotated and Proxmax-rotated EOF analysis for temperature field over North America.

Example_ showing North American surface temperature decomposed via EOF analysis, Varimax rotation and Promax rotation.

.. _Example: https://xeofs.readthedocs.io/en/stable/auto_examples/1eof/plot_rotated_eof.html#sphx-glr-auto-examples-1eof-plot-rotated-eof-py
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5 changes: 1 addition & 4 deletions docs/api.rst
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**********************
Numpy ``np.ndarray``
Numpy | ``np.ndarray``
**********************
.. autosummary::
:toctree: _autosummary
:template: custom-class-template.rst
:recursive:


xeofs.models.EOF
xeofs.models.Rotator
xeofs.models.Bootstrapper
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:template: custom-class-template.rst
:recursive:


xeofs.pandas.EOF
xeofs.pandas.Rotator
xeofs.pandas.Bootstrapper
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:template: custom-class-template.rst
:recursive:


xeofs.xarray.EOF
xeofs.xarray.Rotator
xeofs.xarray.Bootstrapper
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4 changes: 2 additions & 2 deletions docs/conf.py
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sys.path.insert(0, os.path.abspath('..'))

#

# # prevent circular imports...
# import sphinx.builders.html
# import sphinx.builders.latex
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#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
language = 'en'

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
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3 changes: 2 additions & 1 deletion docs/index.rst
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******************

.. toctree::
:maxdepth: 2
:maxdepth: 3

why
installation
auto_examples/index
api
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30 changes: 30 additions & 0 deletions docs/why.rst
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##################
Why ``xeofs``?
##################

EOF analysis and related variants are typically based on a decomposition of a 2D matrix.
When working with Earth observation data, however, the underlying structure
of the data is often multi-dimensional, e.g. they can be described by spatial (longitude, latitude, height etc.),
temporal (time, steps, lead times in forecasts etc.) and/or other (variable, sensor etc.)
dimensions. ``xarray`` provides a great tool to handle these multi-dimensional
data sets in Python. Performing EOF analysis and similar techniques using ``xarray``
could therefore speed up analysis by automatically taking care of dimension labels.

Although there exist already numerous Python packages for EOF analysis in ``xarray``,
none did satify my personal needs which is why I tried to merge existing implementations and extend their funcionalities.


************************************
What can ``xeofs`` do for me?
************************************
* perform your analysis ``xarray`` (support for ``pandas`` and ``numpy`` is also provided)
* analyze multi-dimensional data sets without having to keep track of all dimensions yourself
* investigate the significance of your results
* *planned, but not implemented yet*: work with large data sets with the help of ``dask``

*******************
Supported methods
*******************
* EOF analysis / PCA
* Varimax/Promax rotation for better interpretability
* Maximum Covariance Analysis

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