{ "info": { "author": "Christian Chwala", "author_email": "christian.chwala@kit.edu", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "License :: OSI Approved :: BSD License", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Atmospheric Science" ], "description": "[![Build Status](https://travis-ci.org/pycomlink/pycomlink.svg?branch=master)](https://travis-ci.org/pycomlink/pycomlink)\n\npycomlink\n=========\n\nA python toolbox for deriving rainfall information from commerical microwave link (CML) data.\n\nInstallation\n------------\n\n`pycomlink` works with Python 2.7 and Python 3.6 and can be installed via `pip`.\n\n $ pip install pycomlink\n\nHowever, for using scientific Python packages it is in general recommended to \ninstall the [Anaconda Python distribution](https://store.continuum.io/cshop/anaconda/) and use\nits package manager `conda` for managing all Python packages. `pycomlink` is, however,\nnot yet installable via the Anaconda community package channel [conda-forge](https://conda-forge.org/).\nHence, it is recommended to install all `pycomlink` dependencies (listed in `requirements.txt`) \nvia `conda` and then use `pip` to install `pycomlink`. \n\nTo run the example notebooks you will also need the [Jupyter Notebook](https://jupyter.org/) \nand `ipython`, both also available via `conda` or `pip`.\n\nUsage\n-----\n\nThe following jupyter notebooks showcase some use cases of `pycomlink`\n\n * [How to do baseline determination](http://nbviewer.jupyter.org/github/pycomlink/pycomlink/blob/master/notebooks/Baseline%20determination.ipynb)\n * [How to do spatial interpolation of CML rainfall](http://nbviewer.jupyter.org/github/pycomlink/pycomlink/blob/master/notebooks/Spatial%20interpolation.ipynb)\n * [How to get started with your CML data from a CSV 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