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        "description": "pysteps - Python framework for short-term ensemble prediction systems\n=====================================================================\n\n.. start-badges\n\n.. list-table::\n    :stub-columns: 1\n    :widths: 10 90\n\n    * - docs\n      - |stable| |colab| |gallery|\n    * - status\n      - |test| |docs| |codecov| |codacy| |black|\n    * - package\n      - |github| |conda| |pypi| |zenodo|\n    * - community\n      - |slack| |contributors| |downloads| |license|\n\n\n.. |docs| image:: https://readthedocs.org/projects/pysteps/badge/?version=latest\n    :alt: Documentation Status\n    :target: https://pysteps.readthedocs.io/\n\n.. |test| image:: https://github.com/pySTEPS/pysteps/workflows/Test%20pysteps/badge.svg\n    :alt: Test pysteps\n    :target: https://github.com/pySTEPS/pysteps/actions?query=workflow%3A\"Test+Pysteps\"\n\n.. |black| image:: https://github.com/pySTEPS/pysteps/workflows/Check%20Black/badge.svg\n    :alt: Check Black\n    :target: https://github.com/pySTEPS/pysteps/actions?query=workflow%3A\"Check+Black\"\n\n.. |codecov| image:: https://codecov.io/gh/pySTEPS/pysteps/branch/master/graph/badge.svg\n    :alt: Coverage\n    :target: https://codecov.io/gh/pySTEPS/pysteps\n\n.. |github| image:: https://img.shields.io/github/release/pySTEPS/pysteps.svg\n    :target: https://github.com/pySTEPS/pysteps/releases/latest\n    :alt: Latest github release\n\n.. |conda| image:: https://anaconda.org/conda-forge/pysteps/badges/version.svg\n    :target: https://anaconda.org/conda-forge/pysteps\n    :alt: Anaconda Cloud\n\n.. |pypi| image:: https://badge.fury.io/py/pysteps.svg\n    :target: https://pypi.org/project/pysteps/\n    :alt: Latest PyPI version\n\n.. |license| image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\n    :alt: License\n    :target: https://opensource.org/licenses/BSD-3-Clause\n\n.. |slack| image:: https://pysteps-slackin.herokuapp.com/badge.svg\n    :alt: Slack invitation page\n    :target: https://pysteps-slackin.herokuapp.com/\n\n.. |contributors| image:: https://img.shields.io/github/contributors/pySTEPS/pysteps\n    :alt: GitHub contributors\n    :target: https://github.com/pySTEPS/pysteps/graphs/contributors\n\n.. |downloads| image:: https://img.shields.io/conda/dn/conda-forge/pysteps\n    :alt: Conda downloads\n    :target: https://anaconda.org/conda-forge/pysteps\n\n.. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg\n    :alt: My first nowcast\n    :target: https://colab.research.google.com/github/pySTEPS/pysteps/blob/master/examples/my_first_nowcast.ipynb\n\n.. |gallery| image:: https://img.shields.io/badge/example-gallery-blue.svg\n    :alt: pysteps example gallery\n    :target: https://pysteps.readthedocs.io/en/stable/auto_examples/index.html\n    \n.. |stable| image:: https://img.shields.io/badge/docs-stable-blue.svg\n    :alt: pysteps documentation\n    :target: https://pysteps.readthedocs.io/en/stable/\n    \n.. |codacy| image:: https://api.codacy.com/project/badge/Grade/6cff9e046c5341a4afebc0347362f8de\n   :alt: Codacy Badge\n   :target: https://app.codacy.com/gh/pySTEPS/pysteps?utm_source=github.com&utm_medium=referral&utm_content=pySTEPS/pysteps&utm_campaign=Badge_Grade\n\n.. |zenodo| image:: https://zenodo.org/badge/140263418.svg\n   :alt: DOI\n   :target: https://zenodo.org/badge/latestdoi/140263418\n\n.. end-badges\n\nWhat is pysteps?\n================\n\nPysteps is an open-source and community-driven Python library for probabilistic precipitation nowcasting, i.e. short-term ensemble prediction systems.\n\nThe aim of pysteps is to serve two different needs. The first is to provide a modular and well-documented framework for researchers interested in developing new methods for nowcasting and stochastic space-time simulation of precipitation. The second aim is to offer a highly configurable and easily accessible platform for practitioners ranging from weather forecasters to hydrologists.\n\nThe pysteps library supports standard input/output file formats and implements several optical flow methods as well as advanced stochastic generators to produce ensemble nowcasts. In addition, it includes tools for visualizing and post-processing the nowcasts and methods for deterministic, probabilistic, and neighbourhood forecast verification.\n\n\nQuick start\n-----------\n\nUse pysteps to compute and plot a radar extrapolation nowcast in Google Colab with `this interactive notebook <https://colab.research.google.com/github/pySTEPS/pysteps/blob/master/examples/my_first_nowcast.ipynb>`_.\n\nInstallation\n============\n\nThe recommended way to install pysteps is with `conda <https://docs.conda.io/>`_ from the conda-forge channel::\n\n    $ conda install -c conda-forge pysteps\n\nMore details can be found in the `installation guide <https://pysteps.readthedocs.io/en/stable/user_guide/install_pysteps.html>`_.\n\nUsage\n=====\n\nHave a look at the `gallery of examples <https://pysteps.readthedocs.io/en/stable/auto_examples/index.html>`__ to get a good overview of what pysteps can do.\n\nFor a more detailed description of all the available methods, check the  `API reference <https://pysteps.readthedocs.io/en/stable/pysteps_reference/index.html>`_ page.\n\nExample data\n============\n\nA set of example radar data is available in a separate repository: `pysteps-data <https://github.com/pySTEPS/pysteps-data>`_.\nMore information on how to download and install them is available `here <https://pysteps.readthedocs.io/en/stable/user_guide/example_data.html>`_.\n\nContributions\n=============\n\n*We welcome contributions!*\n\nFor feedback, suggestions for developments, and bug reports please use the dedicated `issues page <https://github.com/pySTEPS/pysteps/issues>`_.\n\nFor more information, please read our `contributors guidelines <https://pysteps.readthedocs.io/en/stable/developer_guide/contributors_guidelines.html>`_.\n\nGet in touch\n============\n\nYou can get in touch with the pysteps community on our `pysteps slack <https://pysteps.slack.com/>`_.\nTo get access to it, you need to ask for an invitation or you can use this `automatic invitation page <https://pysteps-slackin.herokuapp.com/>`_.\n\nReference publications\n======================\n\nPulkkinen, S., D. Nerini, A. Perez Hortal, C. Velasco-Forero, U. Germann,\nA. Seed, and L. Foresti, 2019:  Pysteps:  an open-source Python library for\nprobabilistic precipitation nowcasting (v1.0). *Geosci. Model Dev.*, **12 (10)**,\n4185\u20134219, `doi:10.5194/gmd-12-4185-2019 <https://doi.org/10.5194/gmd-12-4185-2019>`_.\n\nPulkkinen, S., D. Nerini, A. Perez Hortal, C. Velasco-Forero, U. Germann, A. Seed, and\nL. Foresti, 2019: pysteps - a Community-Driven Open-Source Library for Precipitation Nowcasting.\n*Poster presented at the 3rd European Nowcasting Conference, Madrid, ES*,\n`doi:10.13140/RG.2.2.31368.67840 <https://doi.org/10.13140/RG.2.2.31368.67840>`_.\n\n\n",
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