https://github.com/pySTEPS/pysteps
Python framework for short-term ensemble prediction systems.
https://github.com/pySTEPS/pysteps
advection ensemble-prediction forecast-verification hydrology nowcasting optical-flow precipitation rainfall rainfall-prediction stochastic-processes weather weather-radar
Last synced: 10 months ago
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Python framework for short-term ensemble prediction systems.
- Host: GitHub
- URL: https://github.com/pySTEPS/pysteps
- Owner: pySTEPS
- License: bsd-3-clause
- Created: 2018-07-09T09:32:49.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-10-22T13:34:18.000Z (about 1 year ago)
- Last Synced: 2024-10-29T21:02:16.699Z (about 1 year ago)
- Topics: advection, ensemble-prediction, forecast-verification, hydrology, nowcasting, optical-flow, precipitation, rainfall, rainfall-prediction, stochastic-processes, weather, weather-radar
- Language: Python
- Homepage: https://pysteps.github.io/
- Size: 3.28 MB
- Stars: 466
- Watchers: 17
- Forks: 168
- Open Issues: 48
-
Metadata Files:
- Readme: README.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Citation: CITATION.bib
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README
pysteps - Python framework for short-term ensemble prediction systems
=====================================================================
.. start-badges
.. list-table::
:stub-columns: 1
:widths: 10 90
* - docs
- |stable| |colab| |gallery|
* - status
- |test| |docs| |codecov| |codacy| |black|
* - package
- |github| |conda| |pypi| |zenodo|
* - community
- |contributors| |downloads| |license|
.. |docs| image:: https://readthedocs.org/projects/pysteps/badge/?version=latest
:alt: Documentation Status
:target: https://pysteps.readthedocs.io/
.. |test| image:: https://github.com/pySTEPS/pysteps/workflows/Test%20pysteps/badge.svg
:alt: Test pysteps
:target: https://github.com/pySTEPS/pysteps/actions?query=workflow%3A"Test+Pysteps"
.. |black| image:: https://github.com/pySTEPS/pysteps/workflows/Check%20Black/badge.svg
:alt: Check Black
:target: https://github.com/pySTEPS/pysteps/actions?query=workflow%3A"Check+Black"
.. |codecov| image:: https://codecov.io/gh/pySTEPS/pysteps/branch/master/graph/badge.svg
:alt: Coverage
:target: https://codecov.io/gh/pySTEPS/pysteps
.. |github| image:: https://img.shields.io/github/release/pySTEPS/pysteps.svg
:target: https://github.com/pySTEPS/pysteps/releases/latest
:alt: Latest github release
.. |conda| image:: https://anaconda.org/conda-forge/pysteps/badges/version.svg
:target: https://anaconda.org/conda-forge/pysteps
:alt: Anaconda Cloud
.. |pypi| image:: https://badge.fury.io/py/pysteps.svg
:target: https://pypi.org/project/pysteps/
:alt: Latest PyPI version
.. |license| image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg
:alt: License
:target: https://opensource.org/licenses/BSD-3-Clause
.. |contributors| image:: https://img.shields.io/github/contributors/pySTEPS/pysteps
:alt: GitHub contributors
:target: https://github.com/pySTEPS/pysteps/graphs/contributors
.. |downloads| image:: https://img.shields.io/conda/dn/conda-forge/pysteps
:alt: Conda downloads
:target: https://anaconda.org/conda-forge/pysteps
.. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: My first nowcast
:target: https://colab.research.google.com/github/pySTEPS/pysteps/blob/master/examples/my_first_nowcast.ipynb
.. |gallery| image:: https://img.shields.io/badge/example-gallery-blue.svg
:alt: pysteps example gallery
:target: https://pysteps.readthedocs.io/en/stable/auto_examples/index.html
.. |stable| image:: https://img.shields.io/badge/docs-stable-blue.svg
:alt: pysteps documentation
:target: https://pysteps.readthedocs.io/en/stable/
.. |codacy| image:: https://api.codacy.com/project/badge/Grade/6cff9e046c5341a4afebc0347362f8de
:alt: Codacy Badge
:target: https://app.codacy.com/gh/pySTEPS/pysteps?utm_source=github.com&utm_medium=referral&utm_content=pySTEPS/pysteps&utm_campaign=Badge_Grade
.. |zenodo| image:: https://zenodo.org/badge/140263418.svg
:alt: DOI
:target: https://zenodo.org/badge/latestdoi/140263418
.. end-badges
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 `_.
Installation
============
The recommended way to install pysteps is with `conda `_ from the conda-forge channel::
$ conda install -c conda-forge pysteps
More details can be found in the `installation guide `_.
Usage
=====
Have a look at the `gallery of examples `__ to get a good overview of what pysteps can do.
For a more detailed description of all the available methods, check the `API reference `_ page.
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 is available `here `_.
Contributions
=============
*We welcome contributions!*
For feedback, suggestions for developments, and bug reports please use the dedicated `issues page `_.
For more information, please read our `contributors guidelines `_.
Reference publications
======================
The overall library is described in
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)**,
4185–4219, doi:`10.5194/gmd-12-4185-2019 `_.
While the more recent blending module is described in
Imhoff, R.O., L. De Cruz, W. Dewettinck, C.C. Brauer, R. Uijlenhoet, K-J. van Heeringen,
C. Velasco-Forero, D. Nerini, M. Van Ginderachter, and A.H. Weerts, 2023:
Scale-dependent blending of ensemble rainfall nowcasts and NWP in the open-source
pysteps library. *Q J R Meteorol Soc.*, 1-30,
doi: `10.1002/qj.4461 `_.
Contributors
============
.. image:: https://contrib.rocks/image?repo=pySTEPS/pysteps
:target: https://github.com/pySTEPS/pysteps/graphs/contributors