| Name | wxee JSON |
| Version |
0.5.0
JSON |
| download |
| home_page | None |
| Summary | Earth Engine to xarray interface |
| upload_time | 2025-08-23 02:31:43 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
earth-engine
time-series
xarray
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
.. image:: https://raw.githubusercontent.com/aazuspan/wxee/main/docs/_static/wxee.png
:alt: wxee .-- -..-
:width: 200
:target: https://github.com/aazuspan/wxee
|
.. image:: https://img.shields.io/badge/Earth%20Engine%20API-Python-green
:alt: Earth Engine Python
:target: https://developers.google.com/earth-engine/tutorials/community/intro-to-python-api
.. image:: https://img.shields.io/pypi/v/wxee
:alt: PyPI
:target: https://pypi.org/project/wxee/
.. image:: https://img.shields.io/conda/vn/conda-forge/wxee.svg
:alt: conda-forge
:target: https://anaconda.org/conda-forge/wxee
.. image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Open in Colab
:target: https://colab.research.google.com/github/aazuspan/wxee/blob/main/docs/examples/image_collection_to_xarray.ipynb
.. image:: https://readthedocs.org/projects/wxee/badge/?version=latest&style=flat
:alt: Read the Docs
:target: https://wxee.readthedocs.io/en/latest/?badge=latest
.. image:: https://github.com/aazuspan/wxee/actions/workflows/ci.yml/badge.svg
:alt: Build status
:target: https://github.com/aazuspan/wxee
------------
.. 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>`__ 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
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()
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>`__.
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To accomplish this, wxee implements \nconvenient methods for data processing, aggregation, downloading, and ingestion.\n\n`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>`_!\n\n\nFeatures\n--------\n* Time series image collections to `xarray <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\n* `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\n* `Color composite plots <https://wxee.readthedocs.io/en/latest/examples/color_composites.html>`_ from **xarray** datasets\n* Parallel processing for fast downloads\n\n\nTo 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>`__!\n\nInstall\n------------\n\nPip\n~~~\n\n.. code-block:: bash\n\n pip install wxee\n\nConda\n~~~~~\n\n.. code-block:: bash\n\n conda install -c conda-forge wxee\n\n\nQuickstart\n----------\n\nSetup\n~~~~~\nOnce you have access to Google Earth Engine, just import and initialize :code:`ee` and :code:`wxee`.\n\n.. code-block:: python\n \n import ee\n import wxee\n\n wxee.Initialize()\n\n\nDownload Images\n~~~~~~~~~~~~~~~\n\nDownload and conversion methods are extended to :code:`ee.Image` and :code:`ee.ImageCollection` using the \n:code:`wx` accessor. 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