Metadata-Version: 2.1
Name: pysteps
Version: 1.4.0
Summary: Python framework for short-term ensemble prediction systems
Home-page: https://pysteps.github.io/
Author: PySteps developers
License: LICENSE
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
Classifier: Operating System :: OS Independent
Description-Content-Type: text/x-rst
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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    :alt: My first nowcast
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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.


Run your first nowcast!
-----------------------

Use pysteps to compute and plot an extrapolation nowcast in Google Colab with `this interactive notebook`__.

__ https://colab.research.google.com/github/pySTEPS/pysteps/blob/master/examples/my_first_nowcast.ipynb

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

You can get in touch with the pysteps community on our `pysteps slack`__. To get access to it, you need to ask for an invitation or you can use the automatic invitation page `here`__. This invite page can sometimes take a while to load so please be patient.

__ https://pysteps.slack.com/
__ https://pysteps-slackin.herokuapp.com/

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

To install pysteps please have a look at the `pysteps user guide`__.

__ https://pysteps.readthedocs.io/en/latest/user_guide/index.html

Use
===

You can have a look at the `gallery of examples`__ to get a better idea of how the library can be used.

__ https://pysteps.readthedocs.io/en/latest/auto_examples/index.html

For a more detailed description of the implemented functions, check the `pysteps reference page`__.

__ https://pysteps.readthedocs.io/en/latest/pysteps_reference/index.html

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

A set of example radar data is available in a separate repository: `pysteps-data`__. More information on how to download and install them are available here__.

__ https://github.com/pySTEPS/pysteps-data
__ https://pysteps.readthedocs.io/en/latest/user_guide/example_data.html#example-data

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

We welcome contributions, feedback, suggestions for developments and bug reports.

Feedback, suggestions for developments and bug reports can use the dedicated `Issues page`__.

__ https://github.com/pySTEPS/pysteps/issues

More information dedicated to developers is available in the `developer guide`__.

__ https://pysteps.readthedocs.io/en/latest/developer_guide/index.html

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. [source__]

__ 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. [source__]

__ https://www.researchgate.net/publication/332781022_pysteps_-_a_Community-Driven_Open-Source_Library_for_Precipitation_Nowcasting


