========
xweights
========
.. image:: https://github.com/ludwiglierhammer/xweights/actions/workflows/ci.yml/badge.svg
:target: https://github.com/ludwiglierhammer/xweights/actions/workflows/ci.yml
.. image:: https://codecov.io/gh/ludwiglierhammer/xweights/branch/main/graph/badge.svg
:target: https://codecov.io/gh/ludwiglierhammer/xweights
.. image:: https://img.shields.io/pypi/v/xweights.svg
:target: https://pypi.python.org/pypi/xweights
.. image:: https://readthedocs.org/projects/xweights/badge/?version=latest
:target: https://xweights.readthedocs.io/en/latest/?version=latest
:alt: Documentation Status
.. image:: https://results.pre-commit.ci/badge/github/ludwiglierhammer/xweights/main.svg
:target: https://results.pre-commit.ci/latest/github/ludwiglierhammer/xweights/main
:alt: pre-commit.ci status
Python "xweights" contains functions to calculate grid weighted area means from predefined regions or from an user-given shapefile. This tool is a wrapper around the python package xESMF_.
* Free software: MIT license
* Documentation: https://xweights.readthedocs.io
Features
--------
* Calculate grid-weighted-means and save the output as CSV file
* As input you need a dataset dictionary. Values are xarray.Datasets and keys correspondin strings.
Installation
------------
**Note:** Before building `xweights` from source, you nedd **first** install `cartopy` by using conda:
.. code-block:: console
conda install -c conda-forge cartopy
You can install the package directly with pip:
.. code-block:: console
pip install xweights
If you want to contribute, I recommend cloning the repository and installing the package in development mode, e.g.
.. code-block:: console
git clone https://github.com/ludwiglierhammer/xweights.git
cd xweights
pip install -e .
In additon you have to install xESMF using _Conda:
.. code-block:: console
conda install -c conda-forge xesmf
This will install the package but you can still edit it and you don't need the package in your :code:`PYTHONPATH`
Requirements
------------
* python3.6 or higher
* numpy
* pandas
* geopandas
* xarray
* py-cordex
* xesmf
Contact
-------
In cases of any problems, needs or wishes do not hesitate to contact:
ludwig.lierhammer@hereon.de
Credits
-------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
.. _Conda: https://docs.conda.io/
.. _xESMF: https://xesmf.readthedocs.io
=======
History
=======
0.1.0 (2022-03-04)
------------------
* First release on PyPI.
0.1.1 (2022-07-01)
------------------
* adjusted to pre-commit
* use functions from pyhomogenize
0.1.2 (2022-07-08)
------------------
* change pyhomogenize version requirements
0.2.0 (2022-07-11)
------------------
* rename spatial_averager
* keep geometry attributes
0.2.1 (2022-07-11)
------------------
* read and write column name to attributes
0.2.2 (2022-07-12)
------------------
* add data and tables via pip install
0.2.3 (2023-01-26)
------------------
* remove cartopy from requirements.txt
0.2.4 (2023-03-13)
------------------
* using pycordex >= 0.5.1
0.2.5 (2023-08-23)
------------------
* adding new region: counties_merged (merge counties less than 400m2)
0.2.6 (2023-08-30)
------------------
* optionally: wite variable attributes to dataframe
0.3.0 (2023-09-15)
------------------
* added new regions: IPCC WG1 Reference Regions v4 from Atlas
* xweights/_io.py is no longer available
* xweights/_domains.py is no longer available
* function `spatial_averager` -> `spatial_averaging`
* function `compute_weighted_means`:
* optionally: set `averager_ds` to calculate a general xesmf.SpatialAverager
* parameter `shp` -> `gdf`
* parameter `input` -> `dataset_dict`
* parameter `dataset_dict` has to be a dictionary
* parameter `outdir` -> `output`
* function `compute_weighted_means_ds`: parameters are now similar to `compute_weighted_means`
* command-line interface is no longer available
Raw data
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"description": "========\nxweights\n========\n\n.. image:: https://github.com/ludwiglierhammer/xweights/actions/workflows/ci.yml/badge.svg\n :target: https://github.com/ludwiglierhammer/xweights/actions/workflows/ci.yml\n\n.. image:: https://codecov.io/gh/ludwiglierhammer/xweights/branch/main/graph/badge.svg\n :target: https://codecov.io/gh/ludwiglierhammer/xweights\n\n.. image:: https://img.shields.io/pypi/v/xweights.svg\n :target: https://pypi.python.org/pypi/xweights\n\n.. image:: https://readthedocs.org/projects/xweights/badge/?version=latest\n :target: https://xweights.readthedocs.io/en/latest/?version=latest\n :alt: Documentation Status\n\n.. image:: https://results.pre-commit.ci/badge/github/ludwiglierhammer/xweights/main.svg\n :target: https://results.pre-commit.ci/latest/github/ludwiglierhammer/xweights/main\n :alt: pre-commit.ci status\n\nPython \"xweights\" contains functions to calculate grid weighted area means from predefined regions or from an user-given shapefile. This tool is a wrapper around the python package xESMF_.\n\n* Free software: MIT license\n* Documentation: https://xweights.readthedocs.io\n\n\nFeatures\n--------\n\n* Calculate grid-weighted-means and save the output as CSV file\n\n* As input you need a dataset dictionary. Values are xarray.Datasets and keys correspondin strings.\n\n\nInstallation\n------------\n\n**Note:** Before building `xweights` from source, you nedd **first** install `cartopy` by using conda:\n\n.. code-block:: console\n\n conda install -c conda-forge cartopy\n\nYou can install the package directly with pip:\n\n.. code-block:: console\n\n pip install xweights\n\nIf you want to contribute, I recommend cloning the repository and installing the package in development mode, e.g.\n\n.. code-block:: console\n\n git clone https://github.com/ludwiglierhammer/xweights.git\n cd xweights\n pip install -e .\n\nIn additon you have to install xESMF using _Conda:\n\n.. code-block:: console\n\n conda install -c conda-forge xesmf\n\nThis will install the package but you can still edit it and you don't need the package in your :code:`PYTHONPATH`\n\n\nRequirements\n------------\n\n* python3.6 or higher\n\n* numpy\n\n* pandas\n\n* geopandas\n\n* xarray\n\n* py-cordex\n\n* xesmf\n\n\nContact\n-------\nIn cases of any problems, needs or wishes do not hesitate to contact:\n\nludwig.lierhammer@hereon.de\n\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n.. _Conda: https://docs.conda.io/\n.. _xESMF: https://xesmf.readthedocs.io\n\n\n=======\nHistory\n=======\n\n0.1.0 (2022-03-04)\n------------------\n\n* First release on PyPI.\n\n0.1.1 (2022-07-01)\n------------------\n\n* adjusted to pre-commit\n* use functions from pyhomogenize\n\n0.1.2 (2022-07-08)\n------------------\n\n* change pyhomogenize version requirements\n\n0.2.0 (2022-07-11)\n------------------\n\n* rename spatial_averager\n* keep geometry attributes\n\n0.2.1 (2022-07-11)\n------------------\n\n* read and write column name to attributes\n\n0.2.2 (2022-07-12)\n------------------\n\n* add data and tables via pip install\n\n0.2.3 (2023-01-26)\n------------------\n\n* remove cartopy from requirements.txt\n\n0.2.4 (2023-03-13)\n------------------\n\n* using pycordex >= 0.5.1\n\n0.2.5 (2023-08-23)\n------------------\n\n* adding new region: counties_merged (merge counties less than 400m2)\n\n0.2.6 (2023-08-30)\n------------------\n\n* optionally: wite variable attributes to dataframe\n\n0.3.0 (2023-09-15)\n------------------\n\n* added new regions: IPCC WG1 Reference Regions v4 from Atlas\n* xweights/_io.py is no longer available\n* xweights/_domains.py is no longer available\n* function `spatial_averager` -> `spatial_averaging`\n* function `compute_weighted_means`:\n\n * optionally: set `averager_ds` to calculate a general xesmf.SpatialAverager\n * parameter `shp` -> `gdf`\n * parameter `input` -> `dataset_dict`\n * parameter `dataset_dict` has to be a dictionary\n * parameter `outdir` -> `output`\n\n* function `compute_weighted_means_ds`: parameters are now similar to `compute_weighted_means`\n* command-line interface is no longer available\n",
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