Metadata-Version: 2.1
Name: wxee
Version: 0.3.2
Summary: Earth Engine to xarray interface
Home-page: https://github.com/aazuspan/wxee
Author: Aaron Zuspan
Author-email: aazuspan@gmail.com
License: GPLv3+
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        ------------
        
        .. image:: https://raw.githubusercontent.com/aazuspan/wxee/main/docs/_static/demo_001.gif
          :alt: Demo downloading weather data to xarray using wxee.
        
        
        What is wxee?
        -------------
        `wxee <https://github.com/aazuspan/wxee>`_ was built to make processing gridded, mesoscale time series data quick 
        and easy by integrating the data catalog and processing power of `Google Earth Engine <https://earthengine.google.com/>`_ with the 
        flexibility of `xarray <https://github.com/pydata/xarray>`_, with no complicated setup required. To accomplish this, wxee implements 
        convenient methods for data processing, aggregation, downloading, and ingestion.
        
        `wxee <https://github.com/aazuspan/wxee>`_ can be found in the `Earth Engine Developer Resources <https://developers.google.com/earth-engine/tutorials/community/developer-resources#python>`_!
        
        
        Features
        --------
        * Time series image collections to `xarray <https://wxee.readthedocs.io/en/latest/examples/image_collection_to_xarray.html>`_, `NetCDF <https://wxee.readthedocs.io/en/latest/examples/image_collection_to_xarray.html>`_, or `GeoTIFF <https://wxee.readthedocs.io/en/latest/examples/downloading_images_and_collections.html>`_ in one line of code
        * `Climatological anomalies <https://wxee.readthedocs.io/en/latest/examples/climatology_anomaly.html>`_ and temporal `aggregation <https://wxee.readthedocs.io/en/latest/examples/temporal_aggregation.html>`_, `interpolation <https://wxee.readthedocs.io/en/latest/examples/temporal_interpolation.html>`_, `smoothing <https://wxee.readthedocs.io/en/latest/generated/wxee.time_series.TimeSeries.rolling_time.html>`_, and `gap-filling <https://wxee.readthedocs.io/en/latest/generated/wxee.time_series.TimeSeries.fill_gaps.html>`_ in Earth Engine
        * `Color composite plots <https://wxee.readthedocs.io/en/latest/examples/color_composites.html>`_ from **xarray** datasets
        * Parallel processing for fast downloads
        
        
        To see some of the capabilities of wxee and try it yourself, check out the interactive notebooks `here <https://wxee.readthedocs.io/en/latest/examples.html>`_!
        
        Install
        ------------
        
        Pip
        ~~~
        
        .. code-block:: bash
        
           pip install wxee
        
        Conda
        ~~~~~
        
        .. code-block:: bash
        
            conda install -c conda-forge wxee
        
        From Source
        ~~~~~~~~~~~
        
        .. code-block:: bash
        
           git clone https://github.com/aazuspan/wxee
           cd wxee
           make install
        
        
        Quickstart
        ----------
        
        Setup
        ~~~~~
        Once you have access to Google Earth Engine, just import and initialize :code:`ee` and :code:`wxee`.
        
        .. code-block:: python
           
           import ee
           import wxee
        
           wxee.Initialize()
        
        
        Download Images
        ~~~~~~~~~~~~~~~
        
        Download and conversion methods are extended to :code:`ee.Image` and :code:`ee.ImageCollection` using the 
        :code:`wx` accessor. Just :code:`import wxee` and use the :code:`wx` accessor.
        
        xarray
        ^^^^^^
        
        .. code-block:: python
        
           ee.ImageCollection("IDAHO_EPSCOR/GRIDMET").wx.to_xarray()
        
        NetCDF
        ^^^^^^
        
        .. code-block:: python
        
           ee.ImageCollection("IDAHO_EPSCOR/GRIDMET").wx.to_xarray(path="data/gridmet.nc")
        
        GeoTIFF
        ^^^^^^^
        
        .. code-block:: python
        
           ee.ImageCollection("IDAHO_EPSCOR/GRIDMET").wx.to_tif()
        
        
        Create a Time Series
        ~~~~~~~~~~~~~~~~~~~~
        
        Additional methods for processing image collections in the time dimension are available through the :code:`TimeSeries` subclass.
        A :code:`TimeSeries` can be created from an existing :code:`ee.ImageCollection`...
        
        .. code-block:: python
        
           col = ee.ImageCollection("IDAHO_EPSCOR/GRIDMET")
           ts = col.wx.to_time_series()
        
        Or instantiated directly just like you would an :code:`ee.ImageCollection`!
        
        .. code-block:: python
        
           ts = wxee.TimeSeries("IDAHO_EPSCOR/GRIDMET")
        
        
        Aggregate Daily Data
        ~~~~~~~~~~~~~~~~~~~~
        
        Many weather datasets are in daily or hourly resolution. These can be aggregated to coarser resolutions using the :code:`aggregate_time`
        method of the :code:`TimeSeries` class.
        
        .. code-block:: python
        
           ts = wxee.TimeSeries("IDAHO_EPSCOR/GRIDMET")
           monthly_max = ts.aggregate_time(frequency="month", reducer=ee.Reducer.max())
        
        Calculate Climatological Means
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Long-term climatological means can be calculated using the :code:`climatology_mean` method of the :code:`TimeSeries` class.
        
        .. code-block:: python
        
           ts = wxee.TimeSeries("IDAHO_EPSCOR/GRIDMET")
           mean_clim = ts.climatology_mean(frequency="month")
        
        Contribute
        ----------
        
        Bugs or feature requests are always appreciated! They can be submitted `here <https://github.com/aazuspan/wxee/issues>`_. 
        
        Code contributions are also welcome! Please open an `issue <https://github.com/aazuspan/wxee/issues>`_ to discuss implementation, 
        then follow the steps below. Developer setup instructions can be found `in the docs <https://wxee.readthedocs.io/en/latest/contributing.html>`_.
        
        
        
        
        
Keywords: wxee,xarray,earth-engine
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Topic :: Scientific/Engineering :: GIS
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Typing :: Typed
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: doc
Provides-Extra: dev
Provides-Extra: test
