A python toolbox for deriving rainfall information from commerical microwave link (CML) data.
pycomlink works with Python 2.7, Python 3.6 and Python 3.7. It can be installed via conda-forge:
$ 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, add the conda-forge channel 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. Currently the dependency pykrige only installs if scipy, numpy and matplotlib have been installed before.
To run the example notebooks you will also need the Jupyter Notebook
and ipython, both also available via conda or pip.
The following jupyter notebooks showcase some use cases of pycomlink
- How to do baseline determination
- How to do spatial interpolation of CML rainfall
- How to get started with your CML data from a CSV file
- Read and write the common data format
cmlh5for CML data - 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