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# Ensemble Tools Feature Detection: `enstools.feature`
This package is a module of the enstools python package, developed within
the framework of [Waves to Weather - Transregional Collaborative Research
Project (SFB/TRR165)](https://wavestoweather.de). enstools can be fetched
from the public github repo [here](https://github.com/wavestoweather/enstools).
`enstools.feature` is a modular framework for identificaton and tracking of
meteorological structures with the aims of providing easy-to-use interfaces
for automatic parallelization and unified and readable output. For the latter
one, this framework uses `protobuf` as description typed structures, where
users can simply define descriptions for to-detected structures, and using them
for further statistical analyses.
# Installation instructions
We recommend using a conda environment. Install instructions from the public
enstools repo serve as base and have been adapted and extended.
conda create --name enstools-feature python=3.7
conda activate enstools-feature
# install requirements listed in given venv_setup.sh
pip install --upgrade pip
pip install wheel numpy==1.20.0
# integrate enstools
pip install -e git+https://github.com/wavestoweather/enstools.git@main#egg=enstools
# install requirements for enstools-feature, and install enstools in this environment
conda install --file requirements.txt
pip install -e .
Additionally, depending on the used feature identification strategies, additional packages may be required. // TODO
# Usage: Applying existing techniques
We provide some template files, which we recommend to copy to your own identification strategy.
TODO
# Usage: Adding techniques
TODO
# Acknowledgment and license
`enstools.feature` is a collaborative development within
Waves to Weather (SFB/TRR165) project, and funded by the
German Research Foundation (DFG).
A full list of code contributors can [CONTRIBUTORS.md](./CONTRIBUTORS.md). TODO
The code is released under an [Apache-2.0 licence](./LICENSE).