A python toolbox for deriving rainfall information from commercial microwave link (CML) data.
pycomlink
is tested with Python 3.7, 3.8 and 3.9. It should also work without problems on Python 3.6. It might still work with Python 2.7, but this is not tested. 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. 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
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):
$ git clone https://github.com/pycomlink/pycomlink.git
$ cd pycomlink
$ conda env create environment_dev.yml
$ conda activate pycomlink-dev
$ cd ..
$ pip install -e pycomlink
The following jupyter notebooks showcase some use cases of pycomlink
- Basic example CML processing workflow
- more to come... (see some notebooks with old outdated pycomlink API)
- Perform all required CML data processing steps to derive rainfall information from raw signal levels:
- data sanity checks
- anomaly detection
- 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
The documentation is hosted by readthedocs.org: https://pycomlink.readthedocs.io/en/latest/