.. toctree:: :maxdepth: 1 :caption: globaltoc :hidden: api.rst ellboegen.rst
foehnix package provides a toolbox for automated probabilistic foehn wind classification based on two-component mixture models (foehn m**ix**ture models). foehnix models are a special case of the general flexible mixture model class (:cite:`fraley_modelbased_2002`, :cite:`leisch_flexmix_2004`, :cite:`grun_fitting_2007`, :cite:`grun_flexmix_2008`), an unsupervised statistical model to identify unobserveable clusters or components in data sets.
The application of mixture models for an automated classification of foehn winds has first been proposed by :cite:`plavcan_automatic_2013`. The "Community Foehn Classification Experiment" shows that the method performs similar compared to another semi-automatic classification, foehn experts, students, and weather enthusiasts (see :cite:`mayr_community_2018`)
Aim of this software package:
- provide easy-to-use functions for classification
- create probabilistic foehn classification
- easy scalability (can be applied to large data sets)
- reproducibility of the results
- create results which are comparable to other locations
- foehnix Python on Github
- R version of foehnix, also available on github.
- R foehnix documentation, currently more comprehensive than the Python documentation.
The package is not yet published via the Python Package Index (PyPi) but will be made available as soon as finished.
Currently the easiest way to install foehnix Python on Linux is via github and pip:
git clone https://github.com/matthiasdusch/foehnix-python
cd foehnix-python
pip install -e .Once the observation data have been imported, one can start doing the classification. The foehnix package comes with two demo data sets, one for Southern California (USA) and one for Tyrol (A). The documentation provides a walk-through on how to start using foehnix:
- Demo for :ref:`Ellbögen (Tyrol, A) <ellboegen-demo>`
- Demo for Viejas (California, USA)
.. bibliography:: references.bib :style: unsrt