Python tools for MW link data processing
Project description
A python toolbox for deriving rainfall information from commerical microwave link (CML) data.
Installation
pycomlink works with Python 2.7 and can be installed via pip. However, since one of its dependencies, numba is easiest to install via the [Anaconda Python distribution](https://store.continuum.io/cshop/anaconda/), we recommend to install Anaconda Python first and then do
$ conda install numba $ pip 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
Jupyter notebook on [how to get started with CML data from a CSV file](http://nbviewer.jupyter.org/github/pycomlink/pycomlink/blob/master/notebooks/Use%20CML%20data%20from%20CSV%20file.ipynb)
More examples to come…
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
Project details
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