Metadata-Version: 2.1
Name: wradlib
Version: 1.11.1
Summary: wradlib - An Open Source Library for Weather Radar Data Processing
Home-page: http://wradlib.org
Maintainer: wradlib developers
Maintainer-email: wradlib@wradlib.org
License: MIT
Download-URL: https://github.com/wradlib/wradlib
Platform: Linux
Platform: Mac OS-X
Platform: Unix
Platform: Windows
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
License-File: LICENSE.txt
Requires-Dist: dask
Requires-Dist: deprecation
Requires-Dist: gdal
Requires-Dist: h5py
Requires-Dist: h5netcdf
Requires-Dist: netCDF4
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: requests
Requires-Dist: scipy
Requires-Dist: xarray (>=0.17.0)
Requires-Dist: xmltodict
Provides-Extra: dev
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Requires-Dist: coverage ; extra == 'dev'
Requires-Dist: nbconvert ; extra == 'dev'
Requires-Dist: nbformat ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: pytest-cov ; extra == 'dev'
Requires-Dist: pytest-sugar ; extra == 'dev'
Requires-Dist: pytest-xdist ; extra == 'dev'
Requires-Dist: semver ; extra == 'dev'

wradlib is designed to assist you in the most important steps of
processing weather radar data. These may include: reading common data
formats, georeferencing, converting reflectivity to rainfall
intensity, identifying and correcting typical error sources (such as
clutter or attenuation) and visualising the data.


