===============================================================
xclim: Climate services library |logo| |logo-dark| |logo-light|
===============================================================
+----------------------------+-----------------------------------------------------+
| Versions | |pypi| |conda| |versions| |
+----------------------------+-----------------------------------------------------+
| Documentation and Support | |docs| |discussions| |
+----------------------------+-----------------------------------------------------+
| Open Source | |license| |ossf-score| |zenodo| |pyOpenSci| |joss| |
+----------------------------+-----------------------------------------------------+
| Coding Standards | |ruff| |pre-commit| |ossf-bp| |fossa| |
+----------------------------+-----------------------------------------------------+
| Development Status | |status| |build| |coveralls| |
+----------------------------+-----------------------------------------------------+
`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")
`xclim` employs `black`_-like code formatting style, a modified `ruff`_ linting configuration, and (mostly) adheres to the `NumPy docstring`_ style. For more information on coding and development conventions, see the `Contributing Guidelines`_.
.. _black: https://black.readthedocs.io/en/stable/
.. _ruff: https://docs.astral.sh/ruff/
.. _CF Conventions: http://cfconventions.org/
.. _CMIP6: https://clipc-services.ceda.ac.uk/dreq/mipVars.html
.. _NumPy docstring: https://numpydoc.readthedocs.io/en/stable/format.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
Energy and Carbon Usage
-----------------------
The `xclim` development team is interested in thoroughly testing our software while also reducing the environmental impact of the software we develop.
This repository uses the `ECO-CI`_ tool to estimate and track the energy use and carbon emissions of our continuous integration workflows.
+---------------------------------------------------------------------------------------+
| `ECO-CI`_ Energy Use and Carbon Emissions from CI Workflows (since November 2024) |
+------------------+------------------+-------------------------------------------------+
| Testing suite | Energy Usage | |energy-last| |energy-average| |energy-total| |
| +------------------+-------------------------------------------------+
| (*main* branch) | Carbon Emissions | |carbon-last| |carbon-average| |carbon-total| |
+------------------+------------------+-------------------------------------------------+
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
.. _ECO-CI: https://www.green-coding.io/
.. _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/
.. |pypi| image:: https://img.shields.io/pypi/v/xclim.svg
:target: https://pypi.python.org/pypi/xclim
:alt: Python Package Index Build
.. |conda| image:: https://img.shields.io/conda/vn/conda-forge/xclim.svg
:target: https://anaconda.org/conda-forge/xclim
:alt: Conda-forge Build Version
.. |discussions| image:: https://img.shields.io/badge/GitHub-Discussions-blue
:target: https://github.com/Ouranosinc/xclim/discussions
:alt: Static Badge
.. |build| image:: https://github.com/Ouranosinc/xclim/actions/workflows/main.yml/badge.svg
:target: https://github.com/Ouranosinc/xclim/actions/workflows/main.yml
:alt: Build Status
.. |coveralls| image:: https://coveralls.io/repos/github/Ouranosinc/xclim/badge.svg
:target: https://coveralls.io/github/Ouranosinc/xclim
:alt: Coveralls
.. |docs| image:: https://readthedocs.org/projects/xclim/badge
:target: https://xclim.readthedocs.io/en/latest
:alt: Documentation Status
.. |zenodo| image:: https://zenodo.org/badge/142608764.svg
:target: https://zenodo.org/badge/latestdoi/142608764
:alt: DOI
.. |pyOpenSci| image:: https://tinyurl.com/y22nb8up
:target: https://github.com/pyOpenSci/software-review/issues/73
:alt: pyOpenSci
.. |joss| image:: https://joss.theoj.org/papers/10.21105/joss.05415/status.svg
:target: https://doi.org/10.21105/joss.05415
:alt: JOSS
.. |license| image:: https://img.shields.io/github/license/Ouranosinc/xclim.svg
:target: https://github.com/Ouranosinc/xclim/blob/main/LICENSE
:alt: License
.. |ossf-bp| image:: https://bestpractices.coreinfrastructure.org/projects/6041/badge
:target: https://bestpractices.coreinfrastructure.org/projects/6041
:alt: Open Source Security Foundation Best Practices
.. |ossf-score| image:: https://api.securityscorecards.dev/projects/github.com/Ouranosinc/xclim/badge
:target: https://securityscorecards.dev/viewer/?uri=github.com/Ouranosinc/xclim
:alt: Open Source Security Foundation Scorecard
.. |fossa| image:: https://app.fossa.com/api/projects/git%2Bgithub.com%2FOuranosinc%2Fxclim.svg?type=shield
:target: https://app.fossa.com/projects/git%2Bgithub.com%2FOuranosinc%2Fxclim?ref=badge_shield
:alt: FOSSA
.. |logo| image:: https://raw.githubusercontent.com/Ouranosinc/xclim/main/docs/logos/xclim-logo-small-light.png
:target: https://github.com/Ouranosinc/xclim
:alt: Xclim
:class: xclim-logo-small no-theme
.. |logo-light| image:: https://raw.githubusercontent.com/Ouranosinc/xclim/main/docs/logos/empty.png
:target: https://github.com/Ouranosinc/xclim
:alt:
:class: xclim-logo-small only-light-inline
.. |logo-dark| image:: https://raw.githubusercontent.com/Ouranosinc/xclim/main/docs/logos/empty.png
:target: https://github.com/Ouranosinc/xclim
:alt:
:class: xclim-logo-small only-dark-inline
.. |pre-commit| image:: https://results.pre-commit.ci/badge/github/Ouranosinc/xclim/main.svg
:target: https://results.pre-commit.ci/latest/github/Ouranosinc/xclim/main
:alt: pre-commit.ci status
.. |ruff| image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json
:target: https://github.com/astral-sh/ruff
:alt: Ruff
.. |status| image:: https://www.repostatus.org/badges/latest/active.svg
:target: https://www.repostatus.org/#active
:alt: Project Status: Active – The project has reached a stable, usable state and is being actively developed.
.. |versions| image:: https://img.shields.io/pypi/pyversions/xclim.svg
:target: https://pypi.python.org/pypi/xclim
:alt: Supported Python Versions
..
Energy Metrics by https://github.com/green-coding-solutions/eco-ci-energy-estimation
.. |energy-total| image:: https://api.green-coding.io/v1/ci/badge/get?repo=Ouranosinc/xclim&branch=main&workflow=5828641&mode=totals
:target: https://metrics.green-coding.io/ci.html?repo=Ouranosinc/xclim&branch=main&workflow=5828641
:alt: Energy Consumption (Total)
.. |energy-last| image:: https://api.green-coding.io/v1/ci/badge/get?repo=Ouranosinc/xclim&branch=main&workflow=5828641
:target: https://metrics.green-coding.io/ci.html?repo=Ouranosinc/xclim&branch=main&workflow=5828641
:alt: Energy Consumption (Last run)
.. |energy-average| image:: https://api.green-coding.io/v1/ci/badge/get?repo=Ouranosinc/xclim&branch=main&workflow=5828641&mode=avg&duration_days=90
:target: https://metrics.green-coding.io/ci.html?repo=Ouranosinc/xclim&branch=main&workflow=5828641
:alt: Energy Consumption (Last 90 days moving average)
.. |carbon-total| image:: https://api.green-coding.io/v1/ci/badge/get?repo=Ouranosinc/xclim&branch=main&workflow=5828641&mode=totals&metric=carbon
:target: https://metrics.green-coding.io/ci.html?repo=Ouranosinc/xclim&branch=festive-indices&workflow=5828641
:alt: Carbon Emissions (Total)
.. |carbon-last| image:: https://api.green-coding.io/v1/ci/badge/get?repo=Ouranosinc/xclim&branch=main&workflow=5828641&metric=carbon
:target: https://metrics.green-coding.io/ci.html?repo=Ouranosinc/xclim&branch=main&workflow=5828641
:alt: Carbon Emissions (Last run)
.. |carbon-average| image:: https://api.green-coding.io/v1/ci/badge/get?repo=Ouranosinc/xclim&branch=main&workflow=5828641&mode=avg&duration_days=90&metric=carbon
:target: https://metrics.green-coding.io/ci.html?repo=Ouranosinc/xclim&branch=main&workflow=5828641
:alt: Carbon Emissions (Last 90 days moving average)
Raw data
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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| |ossf-score| |zenodo| |pyOpenSci| |joss| |\n+----------------------------+-----------------------------------------------------+\n| Coding Standards | |ruff| |pre-commit| |ossf-bp| |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\". If your data uses another name (for example: \"T\"), you can rename the variable with:\n\n.. code-block:: python\n\n ds = ds.rename(T=\"time\")\n\n`xclim` employs `black`_-like code formatting style, a modified `ruff`_ linting configuration, and (mostly) adheres to the `NumPy docstring`_ style. For more information on coding and development conventions, see the `Contributing Guidelines`_.\n\n.. _black: https://black.readthedocs.io/en/stable/\n.. _ruff: https://docs.astral.sh/ruff/\n.. _CF Conventions: http://cfconventions.org/\n.. _CMIP6: https://clipc-services.ceda.ac.uk/dreq/mipVars.html\n.. _NumPy docstring: https://numpydoc.readthedocs.io/en/stable/format.html\n\nContributing to xclim\n---------------------\n`xclim` is in active development and is being used in production by climate services specialists around the world.\n\n* 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`_.\n * 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!\n\n* If you would like to contribute code or documentation (which is greatly appreciated!), check out the `Contributing Guidelines`_ before you begin!\n\n.. _issue tracker: https://github.com/Ouranosinc/xclim/issues\n.. _Contributing Guidelines: https://github.com/Ouranosinc/xclim/blob/main/CONTRIBUTING.rst\n\nHow to cite this library\n------------------------\nIf 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\n\nTo cite a specific version of `xclim`, the bibliographical reference information can be found through `Zenodo`_\n\n.. _JOSS: https://joss.theoj.org/\n.. _Zenodo: https://doi.org/10.5281/zenodo.2795043\n\nLicense\n-------\nThis 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`_).\n\n.. _Apache License 2.0: https://opensource.org/license/apache-2-0/\n.. _LICENSE: https://github.com/Ouranosinc/xclim/blob/main/LICENSE\n\nEnergy and Carbon Usage\n-----------------------\nThe `xclim` development team is interested in thoroughly testing our software while also reducing the environmental impact of the software we develop.\nThis repository uses the `ECO-CI`_ tool to estimate and track the energy use and carbon emissions of our continuous integration workflows.\n\n+---------------------------------------------------------------------------------------+\n| `ECO-CI`_ Energy Use and Carbon Emissions from CI Workflows (since November 2024) |\n+------------------+------------------+-------------------------------------------------+\n| Testing suite | Energy Usage | |energy-last| |energy-average| |energy-total| |\n| +------------------+-------------------------------------------------+\n| (*main* branch) | Carbon Emissions | |carbon-last| |carbon-average| |carbon-total| |\n+------------------+------------------+-------------------------------------------------+\n\nCredits\n-------\n`xclim` development is funded through Ouranos_, Environment and Climate Change Canada (ECCC_), the `Fonds vert`_ and the Fonds d'\u00e9lectrification et de changements climatiques (FECC_), the Canadian Foundation for Innovation (CFI_), and the Fonds de recherche du Qu\u00e9bec (FRQ_).\n\nThis package was created with Cookiecutter_ and the `audreyfeldroy/cookiecutter-pypackage`_ project template.\n\n.. _audreyfeldroy/cookiecutter-pypackage: https://github.com/audreyfeldroy/cookiecutter-pypackage/\n.. _CFI: https://www.innovation.ca/\n.. _Cookiecutter: https://github.com/cookiecutter/cookiecutter/\n.. _ECCC: https://www.canada.ca/en/environment-climate-change.html\n.. _ECO-CI: https://www.green-coding.io/\n.. _FECC: https://www.environnement.gouv.qc.ca/ministere/fonds-electrification-changements-climatiques/index.htm\n.. _Fonds vert: https://www.environnement.gouv.qc.ca/ministere/fonds-vert/index.htm\n.. _FRQ: https://frq.gouv.qc.ca/\n.. _Ouranos: https://www.ouranos.ca/\n\n.. |pypi| image:: https://img.shields.io/pypi/v/xclim.svg\n :target: https://pypi.python.org/pypi/xclim\n :alt: Python Package Index Build\n\n.. |conda| image:: https://img.shields.io/conda/vn/conda-forge/xclim.svg\n :target: https://anaconda.org/conda-forge/xclim\n :alt: Conda-forge Build Version\n\n.. |discussions| image:: https://img.shields.io/badge/GitHub-Discussions-blue\n :target: https://github.com/Ouranosinc/xclim/discussions\n :alt: Static Badge\n\n.. |build| image:: https://github.com/Ouranosinc/xclim/actions/workflows/main.yml/badge.svg\n :target: https://github.com/Ouranosinc/xclim/actions/workflows/main.yml\n :alt: Build Status\n\n.. |coveralls| image:: https://coveralls.io/repos/github/Ouranosinc/xclim/badge.svg\n :target: https://coveralls.io/github/Ouranosinc/xclim\n :alt: Coveralls\n\n.. |docs| image:: https://readthedocs.org/projects/xclim/badge\n :target: https://xclim.readthedocs.io/en/latest\n :alt: Documentation Status\n\n.. |zenodo| image:: https://zenodo.org/badge/142608764.svg\n :target: https://zenodo.org/badge/latestdoi/142608764\n :alt: DOI\n\n.. |pyOpenSci| image:: https://tinyurl.com/y22nb8up\n :target: https://github.com/pyOpenSci/software-review/issues/73\n :alt: pyOpenSci\n\n.. |joss| image:: https://joss.theoj.org/papers/10.21105/joss.05415/status.svg\n :target: https://doi.org/10.21105/joss.05415\n :alt: JOSS\n\n.. |license| image:: https://img.shields.io/github/license/Ouranosinc/xclim.svg\n :target: https://github.com/Ouranosinc/xclim/blob/main/LICENSE\n :alt: License\n\n.. |ossf-bp| image:: https://bestpractices.coreinfrastructure.org/projects/6041/badge\n :target: https://bestpractices.coreinfrastructure.org/projects/6041\n :alt: Open Source Security Foundation Best Practices\n\n.. |ossf-score| image:: https://api.securityscorecards.dev/projects/github.com/Ouranosinc/xclim/badge\n :target: https://securityscorecards.dev/viewer/?uri=github.com/Ouranosinc/xclim\n :alt: Open Source Security Foundation Scorecard\n\n.. |fossa| image:: https://app.fossa.com/api/projects/git%2Bgithub.com%2FOuranosinc%2Fxclim.svg?type=shield\n :target: https://app.fossa.com/projects/git%2Bgithub.com%2FOuranosinc%2Fxclim?ref=badge_shield\n :alt: FOSSA\n\n.. |logo| image:: https://raw.githubusercontent.com/Ouranosinc/xclim/main/docs/logos/xclim-logo-small-light.png\n :target: https://github.com/Ouranosinc/xclim\n :alt: Xclim\n :class: xclim-logo-small no-theme\n\n.. |logo-light| image:: https://raw.githubusercontent.com/Ouranosinc/xclim/main/docs/logos/empty.png\n :target: https://github.com/Ouranosinc/xclim\n :alt:\n :class: xclim-logo-small only-light-inline\n\n.. 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