Python tools for MW link data processing
Project description
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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
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