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foehnix - Python version

Build Status codecov Repository Status License: MIT

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 mixture models). foehnix models are a special case of the general flexible mixture model class (Fraley 2002, Leisch 2004, Grün 2007, Grün 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 Plavcan et al. (2014). The "Community Foehn Classification Experiment" shows that the method performs similar compared to another semi-automatic classification, foehn experts, students, and weather enthusiasts (see Mayr 2019).

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

Important Links

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:

References

Mayr GJ, Plavcan D, Laurence A, Elvidge A, Grisogono B, Horvath K, Jackson P, Neururer A, Seibert P, Steenburgh JW, Stiperski I, Sturman A, Večenaj Ž, Vergeiner J, Vosper S, Zängl G (2018). The Community Foehn Classification Experiment. Bulletin of the American Meteorological Society, 99(11), 2229—2235, 10.1175/BAMS-D-17-0200.1

Plavcan D, Mayr GJ, Zeileis A (2014). Automatic and Probabilistic Foehn Diagnosis with a Statistical Mixture Model. Journal of Applied Meteorology and Climatology, 53(3), 652—659, 10.1175/JAMC-D-13-0267.1

Hastie T, Tibshirani R, Friedman J (2009). Fitting Logistic Regression Models. In The Elements of Statistical Learning (Chapter 4.4.1), 2nd edition, ISBN 978-0387848570. PDF download

Grün B, Friedrich L (2008). FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters. Journal of Statistical Software, Articles, 28(4), 1—35, doi:10.18637/jss.v028.i04

Grün B, Leisch F (2007). Fitting Finite Mixtures of Generalized Linear Regressions in _R_. Computational Statistics & Data Analysis, 51(11), doi:10.1016/j.csda.2006.08.014

Friedrich L (2004). FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R. Journal of Statistical Software, Articles, 11(8), 1—18, doi:10.18637/jss.v011.i08

Fraley C, Raftery AE (2000). Model-Based Clustering, Discriminant Analysis, and Density Estimation. Journal of the American Statistical Association, 97(458), 611—631, doi:10.1198/016214502760047131

McCullagh P, Nelder JA (1999). Likelihood functions for binary data. In Generalized Linear Models (Chapter 4.4), 2nd edition, ISBN 0-412-31760-5.