===============================================================
xclim: Climate services library |logo| |logo-dark| |logo-light|
===============================================================
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`xclim` is an operational Python library for climate services, providing numerous climate-related indicator tools
with an extensible framework for constructing custom climate indicators, statistical downscaling and bias
adjustment of climate model simulations, as well as climate model ensemble analysis tools.
`xclim` is built using `xarray`_ and can seamlessly benefit from the parallelization handling provided by `dask`_.
Its objective is to make it as simple as possible for users to perform typical climate services data treatment workflows.
Leveraging xarray and dask, users can easily bias-adjust climate simulations over large spatial domains or compute indices from large climate datasets.
For example, the following would compute monthly mean temperature from daily mean temperature:
.. code-block:: python
import xclim
import xarray as xr
ds = xr.open_dataset(filename)
tg = xclim.atmos.tg_mean(ds.tas, freq="MS")
For applications where metadata and missing values are important to get right, xclim provides a class for each index
that validates inputs, checks for missing values, converts units and assigns metadata attributes to the output.
This also provides a mechanism for users to customize the indices to their own specifications and preferences.
`xclim` currently provides over 150 indices related to mean, minimum and maximum daily temperature, daily precipitation,
streamflow and sea ice concentration, numerous bias-adjustment algorithms, as well as a dedicated module for ensemble analysis.
.. _xarray: https://docs.xarray.dev/
.. _dask: https://docs.dask.org/
Quick Install
-------------
`xclim` can be installed from PyPI:
.. code-block:: shell
$ pip install xclim
or from Anaconda (conda-forge):
.. code-block:: shell
$ conda install -c conda-forge xclim
Documentation
-------------
The official documentation is at https://xclim.readthedocs.io/
How to make the most of xclim: `Basic Usage Examples`_ and `In-Depth Examples`_.
.. _Basic Usage Examples: https://xclim.readthedocs.io/en/stable/notebooks/usage.html
.. _In-Depth Examples: https://xclim.readthedocs.io/en/stable/notebooks/index.html
Conventions
-----------
In order to provide a coherent interface, `xclim` tries to follow different sets of conventions. In particular, input data should follow the `CF conventions`_ whenever possible for variable attributes. Variable names are usually the ones used in `CMIP6`_, when they exist.
However, xclim will *always* assume the temporal coordinate is named "time". If your data uses another name (for example: "T"), you can rename the variable with:
.. code-block:: python
ds = ds.rename(T="time")
.. _CF Conventions: http://cfconventions.org/
.. _CMIP6: https://clipc-services.ceda.ac.uk/dreq/mipVars.html
Contributing to xclim
---------------------
`xclim` is in active development and is being used in production by climate services specialists around the world.
* If you're interested in participating in the development of `xclim` by suggesting new features, new indices or report bugs, please leave us a message on the `issue tracker`_.
* If you have a support/usage question or would like to translate `xclim` to a new language, be sure to check out the existing |discussions| first!
* If you would like to contribute code or documentation (which is greatly appreciated!), check out the `Contributing Guidelines`_ before you begin!
.. _issue tracker: https://github.com/Ouranosinc/xclim/issues
.. _Contributing Guidelines: https://github.com/Ouranosinc/xclim/blob/main/CONTRIBUTING.rst
How to cite this library
------------------------
If you wish to cite `xclim` in a research publication, we kindly ask that you refer to our article published in The Journal of Open Source Software (`JOSS`_): https://doi.org/10.21105/joss.05415
To cite a specific version of `xclim`, the bibliographical reference information can be found through `Zenodo`_
.. _JOSS: https://joss.theoj.org/
.. _Zenodo: https://doi.org/10.5281/zenodo.2795043
License
-------
This is free software: you can redistribute it and/or modify it under the terms of the `Apache License 2.0`_. A copy of this license is provided in the code repository (`LICENSE`_).
.. _Apache License 2.0: https://opensource.org/license/apache-2-0/
.. _LICENSE: https://github.com/Ouranosinc/xclim/blob/main/LICENSE
Credits
-------
`xclim` development is funded through Ouranos_, Environment and Climate Change Canada (ECCC_), the `Fonds vert`_ and the Fonds d'électrification et de changements climatiques (FECC_), the Canadian Foundation for Innovation (CFI_), and the Fonds de recherche du Québec (FRQ_).
This package was created with Cookiecutter_ and the `audreyfeldroy/cookiecutter-pypackage`_ project template.
.. _audreyfeldroy/cookiecutter-pypackage: https://github.com/audreyfeldroy/cookiecutter-pypackage/
.. _CFI: https://www.innovation.ca/
.. _Cookiecutter: https://github.com/cookiecutter/cookiecutter/
.. _ECCC: https://www.canada.ca/en/environment-climate-change.html
.. _FECC: https://www.environnement.gouv.qc.ca/ministere/fonds-electrification-changements-climatiques/index.htm
.. _Fonds vert: https://www.environnement.gouv.qc.ca/ministere/fonds-vert/index.htm
.. _FRQ: https://frq.gouv.qc.ca/
.. _Ouranos: https://www.ouranos.ca/
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"description": "===============================================================\nxclim: Climate services library |logo| |logo-dark| |logo-light|\n===============================================================\n\n+----------------------------+-----------------------------------------------------+\n| Versions | |pypi| |conda| |versions| |\n+----------------------------+-----------------------------------------------------+\n| Documentation and Support | |docs| |discussions| |\n+----------------------------+-----------------------------------------------------+\n| Open Source | |license| |fair| |ossf| |zenodo| |pyOpenSci| |joss| |\n+----------------------------+-----------------------------------------------------+\n| Coding Standards | |black| |ruff| |pre-commit| |security| |fossa| |\n+----------------------------+-----------------------------------------------------+\n| Development Status | |status| |build| |coveralls| |\n+----------------------------+-----------------------------------------------------+\n\n`xclim` is an operational Python library for climate services, providing numerous climate-related indicator tools\nwith an extensible framework for constructing custom climate indicators, statistical downscaling and bias\nadjustment of climate model simulations, as well as climate model ensemble analysis tools.\n\n`xclim` is built using `xarray`_ and can seamlessly benefit from the parallelization handling provided by `dask`_.\nIts objective is to make it as simple as possible for users to perform typical climate services data treatment workflows.\nLeveraging xarray and dask, users can easily bias-adjust climate simulations over large spatial domains or compute indices from large climate datasets.\n\nFor example, the following would compute monthly mean temperature from daily mean temperature:\n\n.. code-block:: python\n\n import xclim\n import xarray as xr\n\n ds = xr.open_dataset(filename)\n tg = xclim.atmos.tg_mean(ds.tas, freq=\"MS\")\n\nFor applications where metadata and missing values are important to get right, xclim provides a class for each index\nthat validates inputs, checks for missing values, converts units and assigns metadata attributes to the output.\nThis also provides a mechanism for users to customize the indices to their own specifications and preferences.\n`xclim` currently provides over 150 indices related to mean, minimum and maximum daily temperature, daily precipitation,\nstreamflow and sea ice concentration, numerous bias-adjustment algorithms, as well as a dedicated module for ensemble analysis.\n\n.. _xarray: https://docs.xarray.dev/\n.. _dask: https://docs.dask.org/\n\nQuick Install\n-------------\n`xclim` can be installed from PyPI:\n\n.. code-block:: shell\n\n $ pip install xclim\n\nor from Anaconda (conda-forge):\n\n.. code-block:: shell\n\n $ conda install -c conda-forge xclim\n\nDocumentation\n-------------\nThe official documentation is at https://xclim.readthedocs.io/\n\nHow to make the most of xclim: `Basic Usage Examples`_ and `In-Depth Examples`_.\n\n.. _Basic Usage Examples: https://xclim.readthedocs.io/en/stable/notebooks/usage.html\n.. _In-Depth Examples: https://xclim.readthedocs.io/en/stable/notebooks/index.html\n\nConventions\n-----------\nIn order to provide a coherent interface, `xclim` tries to follow different sets of conventions. In particular, input data should follow the `CF conventions`_ whenever possible for variable attributes. Variable names are usually the ones used in `CMIP6`_, when they exist.\n\nHowever, xclim will *always* assume the temporal coordinate is named \"time\". 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