foehnix - Python version

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A Toolbox for Automated Foehn Classification based on Mixture Models

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 ([1], [2], [3], [4]), 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 [5]. The “Community Foehn Classification Experiment” shows that the method performs similar compared to another semi-automatic classification, foehn experts, students, and weather enthusiasts (see [6])

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

Installation

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 .

Create classification

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:

_images/timeseries.png

References

[1]Chris Fraley and Adrian E. Raftery. Model-Based Clustering, Discriminant Analysis, and Density Estimation. Journal of the American Statistical Association, 97(458):611–631, June 2002. doi:10.1198/016214502760047131.
[2]Friedrich Leisch. FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R. Journal of Statistical Software, 11(1):1–18, October 2004. doi:10.18637/jss.v011.i08.
[3]Bettina Grün and Friedrich Leisch. Fitting finite mixtures of generalized linear regressions in R. Computational Statistics & Data Analysis, 51(11):5247–5252, July 2007. doi:10.1016/j.csda.2006.08.014.
[4]Bettina Grün and Friedrich Leisch. FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters. Journal of Statistical Software, 28(1):1–35, October 2008. doi:10.18637/jss.v028.i04.
[5]David Plavcan, Georg J. Mayr, and Achim Zeileis. Automatic and Probabilistic Foehn Diagnosis with a Statistical Mixture Model. Journal of Applied Meteorology and Climatology, 53(3):652–659, November 2013. doi:10.1175/JAMC-D-13-0267.1.
[6]Georg j. Mayr, David Plavcan, Laurence Armi, Andrew Elvidge, Branko Grisogono, Kristian Horvath, Peter Jackson, Alfred Neururer, Petra Seibert, James W. Steenburgh, Ivana Stiperski, Andrew Sturman, Željko Večenaj, Johannes Vergeiner, Simon Vosper, and Günther Zängl. The Community Foehn Classification Experiment. Bulletin of the American Meteorological Society, 99(11):2229–2235, August 2018. doi:10.1175/BAMS-D-17-0200.1.