Metadata-Version: 2.1
Name: pysteps
Version: 1.6.1
Summary: Python framework for short-term ensemble prediction systems
Home-page: https://pysteps.github.io/
Author: PySteps developers
License: LICENSE
Project-URL: Source, https://github.com/pySTEPS/pysteps
Project-URL: Issues, https://github.com/pySTEPS/pysteps/issues
Project-URL: CI, https://github.com/pySTEPS/pysteps/actions
Project-URL: Changelog, https://github.com/pySTEPS/pysteps/releases
Project-URL: Documentation, https://pysteps.readthedocs.io
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Classifier: Topic :: Scientific/Engineering :: Hydrology
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: jsmin
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: jsonschema

pysteps - Python framework for short-term ensemble prediction systems
=====================================================================

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What is pysteps?
================

Pysteps is an open-source and community-driven Python library for probabilistic precipitation nowcasting, i.e. short-term ensemble prediction systems.

The 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.

The 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.


Quick start
-----------

Use 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>`_.

Installation
============

The recommended way to install pysteps is with `conda <https://docs.conda.io/>`_ from the conda-forge channel::

    $ conda install -c conda-forge pysteps

More details can be found in the `installation guide <https://pysteps.readthedocs.io/en/stable/user_guide/install_pysteps.html>`_.

Usage
=====

Have 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.

For a more detailed description of all the available methods, check the  `API reference <https://pysteps.readthedocs.io/en/stable/pysteps_reference/index.html>`_ page.

Example data
============

A set of example radar data is available in a separate repository: `pysteps-data <https://github.com/pySTEPS/pysteps-data>`_.
More information on how to download and install them is available `here <https://pysteps.readthedocs.io/en/stable/user_guide/example_data.html>`_.

Contributions
=============

*We welcome contributions!*

For feedback, suggestions for developments, and bug reports please use the dedicated `issues page <https://github.com/pySTEPS/pysteps/issues>`_.

For more information, please read our `contributors guidelines <https://pysteps.readthedocs.io/en/stable/developer_guide/contributors_guidelines.html>`_.

Get in touch
============

You can get in touch with the pysteps community on our `pysteps slack <https://pysteps.slack.com/>`_.
To get access to it, you need to ask for an invitation or you can use this `automatic invitation page <https://pysteps-slackin.herokuapp.com/>`_.

Reference publications
======================

Pulkkinen, S., D. Nerini, A. Perez Hortal, C. Velasco-Forero, U. Germann,
A. Seed, and L. Foresti, 2019:  Pysteps:  an open-source Python library for
probabilistic precipitation nowcasting (v1.0). *Geosci. Model Dev.*, **12 (10)**,
4185â€“4219, `doi:10.5194/gmd-12-4185-2019 <https://doi.org/10.5194/gmd-12-4185-2019>`_.

Pulkkinen, S., D. Nerini, A. Perez Hortal, C. Velasco-Forero, U. Germann, A. Seed, and
L. Foresti, 2019: pysteps - a Community-Driven Open-Source Library for Precipitation Nowcasting.
*Poster presented at the 3rd European Nowcasting Conference, Madrid, ES*,
`doi:10.13140/RG.2.2.31368.67840 <https://doi.org/10.13140/RG.2.2.31368.67840>`_.


