Model Analysis ToolKit (MATK) - Python toolkit for model analysis¶
Homepage: http://matk.lanl.gov or http://dharp.github.io/matk/
MATK facilitates model analysis within the Python computational environment. MATK expects a model defined as a Python function that accepts a dictionary of parameter values as the first argument and returns model results as a dictionary, array, integer, or float. Many model analyses are provided by MATK. These model analyses can be easily modified and/or extended within the Python scripting languange. New model analyses can easily be hooked up to a MATK model as well.
- Obtaining MATK
- Getting Started
- Examples
- Sampling
- Sampling with Gaussian distributions
- Subsetting samples based on model output
- Sampling with a discrete parameter
- Sampleset Restart
- Parameter Study
- Parameter Study with discrete parameter
- Sobol Sensitivity Analysis
- RBD Fast Sensitivity Analysis
- Calibration Using LMFIT
- Minimize residuals using inequality constraints
- Differential Evolution
- Linear Analysis of Calibration Using PYEMU
- Markov Chain Monte Carlo Using PYMC
- MCMC using emcee package
- External Simulator (Python script)
- External Simulator (FEHM Groundwater Flow Simulator)
- Running on Cluster
- Class Documentation
- Copyright and License
Contact¶
Send questions to dharp (at) lanl (dot) gov.
Other useful python modules:¶
PYEMU: Jeremy White’s linear-based computer model uncertainty analysis python module; used in Linear Analysis of Calibration Using PYEMU example.
Contributors¶
Dylan Harp, Los Alamos National Laboratory (Primary Developer)
Veronika Vasylkivska, National Energy Technology Laboratory
Mark Porter, Bureau of Reclamation
Bailian Chen, Los Alamos National Laboratory
David Dempsey, The University of Auckland
Seth King, National Energy Technology Laboratory