Metadata-Version: 1.1
Name: pycomlink
Version: 0.2.2
Summary: Python tools for MW link data processing
Home-page: https://github.com/pycomlink/pycomlink
Author: Christian Chwala
Author-email: christian.chwala@kit.edu
License: BSD
Download-URL: https://github.com/pycomlink/pycomlink/archive/0.2.2.tar.gz
Description-Content-Type: UNKNOWN
Description: [![Build Status](https://travis-ci.org/pycomlink/pycomlink.svg?branch=master)](https://travis-ci.org/pycomlink/pycomlink)
        
        pycomlink
        =========
        
        A python toolbox for deriving rainfall information from commerical microwave link (CML) data.
        
        Installation
        ------------
        
        `pycomlink` works with Python 2.7 and Python 3.6 and can be installed via `pip`.
        
            $ pip install pycomlink
        
        However, for using scientific Python packages it is in general recommended to 
        install the [Anaconda Python distribution](https://store.continuum.io/cshop/anaconda/) and use
        its package manager `conda` for managing all Python packages. `pycomlink` is, however,
        not yet installable via the Anaconda community package channel [conda-forge](https://conda-forge.org/).
        Hence, it is recommended to install all `pycomlink` dependencies (listed in `requirements.txt`) 
        via `conda` and then use `pip` to install `pycomlink`. 
        
        To run the example notebooks you will also need the [Jupyter Notebook](https://jupyter.org/) 
        and `ipython`, both also available via `conda` or `pip`.
        
        Usage
        -----
        
        The following jupyter notebooks showcase some use cases of `pycomlink`
        
         * [How to do baseline determination](http://nbviewer.jupyter.org/github/pycomlink/pycomlink/blob/master/notebooks/Baseline%20determination.ipynb)
         * [How to do spatial interpolation of CML rainfall](http://nbviewer.jupyter.org/github/pycomlink/pycomlink/blob/master/notebooks/Spatial%20interpolation.ipynb)
         * [How to get started with your CML data from a CSV file](http://nbviewer.jupyter.org/github/pycomlink/pycomlink/blob/master/notebooks/Use%20CML%20data%20from%20CSV%20file.ipynb)
        
        Features
        --------
         * Read and write the [common data format `cmlh5` for CML data](https://github.com/cmlh5/cmlh5)
         * Quickly visualize the CML network on a dynamic map
         * Perform all required CML data processing steps to derive rainfall information from raw signal levels:
            * data sanity checks
            * wet/dry classification
            * baseline calculation
            * wet antenna correction
            * transformation from attenuation to rain rate
         * Generate rainfall maps from the data of a CML network
         * Validate you results against gridded rainfall data or rain gauges networks
        
Keywords: microwave links precipitation radar
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
