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Derived climate variables built with xarray.

Project description

====== xclim

.. image:: _static/_images/xclim-logo.png :align: center :target: _static/_images/xclim-logo.png :alt: xclim

.. image:: :target: :alt: Python Package Index Build

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.. image:: :target: :alt: Coveralls

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.. image:: :target: :alt: Documentation Status

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xclim is a library of functions computing climate indices. It is based on xarray and can benefit from the parallelization provided by dask. It's objective is to make it as simple as possible for users to compute indices from large climate datasets, and for scientists to write new indices with very little boilerplate.

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.icclim.TG(ds.tas, freq='YS')

For applications where meta-data and missing values are important to get right, xclim also provides a class for each index that validates inputs, checks for missing values, converts units and assigns metadata attributes to the output. This provides a mechanism for users to customize the indices to their own specifications and preferences.

xclim is in intense development at the moment and is absolutely not production ready. We're aiming for an alpha release in Q1 2019. If you're interested in participating to the development, please leave us a message on the issue tracker.


This work is made possible by the Canadian Center for Climate Services.

This package was created with Cookiecutter_ and the audreyr/cookiecutter-pypackage_ project template.

.. _Cookiecutter: .. _audreyr/cookiecutter-pypackage:

======= History

0.7-beta (2019-02-05)

Major Changes:

  • Support for resampling of data structured using non-standard CF-Time calendars
  • Added several ICCLIM and other indicators
  • Dropped support for Python 3.4
  • Now under Apache v2.0 license
  • Stable PyPI-based dependencies
  • Dask optimizations for better memory management
  • Introduced class-based indicator calculations with data integrity verification and CF-Compliant-like metadata writing functionality

Class-based indicators are new methods that allow index calculation with error-checking and provide on-the-fly metadata checks for CF-Compliant (and CF-compliant-like) data that are passed to them. When written to NetCDF, outputs of these indicators will append appropriate metadata based on the indicator, threshold values, moving window length, and time period / resampling frequency examined.

0.6-alpha (2018-10-03)

  • File attributes checks
  • Added daily downsampler function
  • Better documentation on ICCLIM indices

0.5-alpha (2018-09-26)

  • Added total precipitation indicator

0.4-alpha (2018-09-14)

  • Fully PEP8 compliant and available under MIT License

0.3-alpha (2018-09-4)

  • Added icclim module
  • Reworked documentation, docs theme

0.2-alpha (2018-08-27)

  • Added first indices

0.1.0-dev (2018-08-23)

  • First release on PyPI.

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