pycomlink 0.3.1

Python tools for MW link data processing

[](https://travis-ci.org/pycomlink/pycomlink) [](https://mybinder.org/v2/gh/pycomlink/pycomlink/master)

pycomlink

A python toolbox for deriving rainfall information from commercial microwave link (CML) data.

Installation

pycomlink works with Python 3.6 and newer. It might still work with Python 2.7, but this is not tested. It can be installed via [conda-forge](https://conda-forge.org/):

$ conda install -c conda-forge pycomlink

If you are new to conda or if you are unsure, it is recommended to [create a new conda environment, activate it](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands), [add the conda-forge channel](https://conda-forge.org/) and then install.

Installation via pip is also possible:

$ pip install pycomlink

If you install via pip, there might be problems with some dependencies, though. E.g. the dependency pykrige may only install if scipy, numpy and matplotlib have been installed before.

To run the example notebooks you will also need the [Jupyter Notebook](https://jupyter.org/) and ipython, both also available via conda or pip.

If you want to clone the repository for developing purposes follow these steps (installation of Jupyter Notebook included):

$ cd WORKING_DIRECTORY $ git clone https://github.com/pycomlink/pycomlink.git $ conda env create –name ENV_NAME -file=environment_dev.yml $ conda activate ENV_NAME $ pip install -e WORKING_DIRECTORY/pycomlink

Usage

The following jupyter notebooks showcase some use cases of pycomlink

• [Basic example CML processing workflow](http://nbviewer.jupyter.org/github/pycomlink/pycomlink/blob/master/notebooks/Basic%20CML%20processing%20workflow.ipynb)

• more to come… (see some [notebooks with old outdated pycomlink API](https://github.com/pycomlink/pycomlink/tree/master/notebooks/outdated_notebooks))

Features

• 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