cloudnetpy 1.55.0

Python package for Cloudnet processing

CloudnetPy

CloudnetPy is a Python software for producing vertical profiles of cloud properties from ground-based remote sensing measurements. The Cloudnet processing combines cloud radar, optical lidar, microwave radiometer and model data. Measurements and model data are brought into common grid and classified as ice, liquid, aerosol, insects, and so on. Then, geophysical products such as ice water content can be retrieved in the further processing steps. See Illingworth et al. (2007) for more details about the concept.

CloudnetPy is a rewritten version of the original Cloudnet Matlab code. CloudnetPy features several revised methods, extensive documentation, and more.

• CloudnetPy documentation: https://cloudnetpy.readthedocs.io/en/latest/
• Cloudnet data portal: https://cloudnet.fmi.fi

Installation

From PyPI

python3 -m pip install cloudnetpy

From the source

git clone https://github.com/actris-cloudnet/cloudnetpy
cd cloudnetpy/
python3 -m venv venv
source venv/bin/activate
python3 -m pip install --upgrade pip
python3 -m pip install .

Citing

If you wish to acknowledge CloudnetPy in your publication, please cite:

Tukiainen et al., (2020). CloudnetPy: A Python package for processing cloud remote sensing data. Journal of Open Source Software, 5(53), 2123, https://doi.org/10.21105/joss.02123

Contributing

We encourage you to contribute to CloudnetPy! Please check out the contribution guidelines about how to proceed.

Development

Follow the installation instructions from the source above but install with the development dependencies and pre-commit hooks:

python3 -m pip install -e .[dev,test]
pre-commit install

Run unit tests:

python3 -m pytest --flake-finder --flake-runs=2

Run single unit test:

python3 -m pytest tests/unit/test_hatpro.py

Run end-to-end tests:

python3 tests/e2e_test.py

for f in cloudnetpy/model_evaluation/tests/e2e/*/main.py; do $f; done

Force pre-commit checks (pylint, mypy, etc.) for all files:

pre-commit run --all

License

MIT