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
cmlh5
for 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