# Model Analysis ToolKit

## Abstract

MATK provides basic functionality to facilitate model analysis within the Python computational environment. Model analysis setup within MATK includes: - define parameters - define observations - define model (python function) - define samplesets (sets of parameter combinations) Currently supported functionality includes: - forward model runs - Latin-Hypercube sampling of parameters - multi-dimensional parameter studies - parallel execution of parameter samples - model calibration using internal Levenberg-Marquardt algorithm - model calibration using lmfit package - model calibration using levmar package - Markov Chain Monte Carlo using pymc package MATK facilitates model analysis using: - scipy - calibration (scipy.optimize) - rpy2 - Python interface to R

- Authors:

- Publication Date:

- Research Org.:
- Los Alamos National Laboratory

- Sponsoring Org.:
- USDOE

- OSTI Identifier:
- 1232133

- Report Number(s):
- MATK; 003429MLTPL00

C14023; LA-CC-13-132

- DOE Contract Number:
- AC52-06NA25396

- Resource Type:
- Software

- Software Revision:
- 00

- Software Package Number:
- 003429

- Software Package Contents:
- Open Source Software package available from Los Alamos National Laboratory at the following URL: https://github.com/dharp/MATK

- Software CPU:
- MLTPL

- Open Source:
- Yes

- Source Code Available:
- Yes

- Country of Publication:
- United States

### Citation Formats

```
Harp, Dylan R.
```*Model Analysis ToolKit*.
Computer software. *https://www.osti.gov//servlets/purl/1232133*. Vers. 00. USDOE. 15 May. 2015.
Web.

```
Harp, Dylan R. (2015, May 15). Model Analysis ToolKit (Version 00) [Computer software]. https://www.osti.gov//servlets/purl/1232133.
```

```
Harp, Dylan R. Model Analysis ToolKit.
Computer software. Version 00. May 15, 2015. https://www.osti.gov//servlets/purl/1232133.
```

```
@misc{osti_1232133,
```

title = {Model Analysis ToolKit, Version 00},

author = {Harp, Dylan R.},

abstractNote = {MATK provides basic functionality to facilitate model analysis within the Python computational environment. Model analysis setup within MATK includes: - define parameters - define observations - define model (python function) - define samplesets (sets of parameter combinations) Currently supported functionality includes: - forward model runs - Latin-Hypercube sampling of parameters - multi-dimensional parameter studies - parallel execution of parameter samples - model calibration using internal Levenberg-Marquardt algorithm - model calibration using lmfit package - model calibration using levmar package - Markov Chain Monte Carlo using pymc package MATK facilitates model analysis using: - scipy - calibration (scipy.optimize) - rpy2 - Python interface to R},

url = {https://www.osti.gov//servlets/purl/1232133},

doi = {},

year = {2015},

month = {5},

note =

}