Dakotathon: Difference between revisions

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The CSDMS Dakota Interface --
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'''Dakotathon''', for short -- is wunderbar.
 
[https://dakota.sandia.gov Dakota] is a software toolkit developed at Sandia National Laboratories
that provides an interface between models and a library of analysis methods,
including support for sensitivity analysis, uncertainty quantification, optimization, and calibration techniques.
Dakota is a powerful tool, but its learning curve is steep:
the user not only must understand the structure and syntax of the Dakota input file,
but also must develop intermediate code
that allows Dakota to set up and run a model,
read outputs from the model,
and calculate a response statistic from the outputs.
 
The CSDMS Dakota Interface, or '''Dakotathon''',
is a Python package that wraps and extends Dakota’s file-based user interface.
It simplifies the process of configuring and running a Dakota experiment,
and it allows a Dakota experiment to be scripted.
Dakotathon creates the Dakota input file and provides a generic analysis driver.
Any model componentized in the CSDMS modeling framework
automatically works with Dakotathon.
Dakotathon has a plugin architecture, so models not wrapped into the CSDMS modeling framework
can be accessed by Dakotathon by programmatically extending a template;
an example is provided in the Dakotathon distribution.
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Currently, six Dakota analysis methods have been implemented from the much larger Dakota library:
 
* [https://dakota.sandia.gov/sites/default/files/docs/6.1/html-ref/method-vector_parameter_study.html vector parameter study],
* [https://dakota.sandia.gov/sites/default/files/docs/6.1/html-ref/method-centered_parameter_study.html centered parameter study],
* [https://dakota.sandia.gov/sites/default/files/docs/6.1/html-ref/method-multidim_parameter_study.html multidim parameter study],
* [https://dakota.sandia.gov/sites/default/files/docs/6.1/html-ref/method-sampling.html sampling],
* [https://dakota.sandia.gov/sites/default/files/docs/6.1/html-ref/method-polynomial_chaos.html polynomial chaos], and
* [https://dakota.sandia.gov/sites/default/files/docs/6.1/html-ref/method-stoch_collocation.html stochastic collocation].
 
For instructions on installing and using Dakotathon,
please see Dakotathon's [[Model:Dakotathon|CSDMS Model page]].
 
Dakotathon has been installed in the '''wmt-testing''' instance on '''beach.colorado.edu'''.
To use it, login to '''beach''', and from a shell prompt execute:
 
source /home/csdms/wmt/_testing/conda/bin/activate
 
This configures the conda environment for this WMT executor.
Now, when you run Python or IPython,
the Dakotathon package will be available to import.
 
== Links ==
 
* The [[Model:Dakotathon|CSDMS Model page]] for Dakotathon
* The source code for Dakotathon can be found at https://github.com/csdms/dakota
** It includes [https://github.com/csdms/dakota/tree/master/examples examples]
* The latest developer documentation for Dakotathon is available at http://csdms-dakota.readthedocs.io
* The Dakota home page is https://dakota.sandia.gov
** It includes instructions for [https://dakota.sandia.gov/download.html downloading] and [https://dakota.sandia.gov/content/install-dakota installing] Dakota
* The Dakota 6.5 (released November 2016) documentation is available at https://dakota.sandia.gov//sites/default/files/docs/6.5/html-ref/index.html
* [[User:Mpiper|Mpiper]] gave a [[:File:Dakotathon-slides.pdf|talk]] on Dakotathon at the 2016 AGU Fall Meeting; a repository of experiments is [https://github.com/mdpiper/AGU-2016 here]
 
[[User:Mpiper|Mpiper]] ([[User talk:Mpiper|talk]]) 14:09, 27 December 2016 (MST)
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Latest revision as of 12:17, 28 December 2016

The CSDMS Dakota Interface

Dakotathon.jpg

Dakota is a software toolkit developed at Sandia National Laboratories that provides an interface between models and a library of analysis methods, including support for sensitivity analysis, uncertainty quantification, optimization, and calibration techniques. Dakota is a powerful tool, but its learning curve is steep: the user not only must understand the structure and syntax of the Dakota input file, but also must develop intermediate code that allows Dakota to set up and run a model, read outputs from the model, and calculate a response statistic from the outputs.

The CSDMS Dakota Interface, or Dakotathon, is a Python package that wraps and extends Dakota’s file-based user interface. It simplifies the process of configuring and running a Dakota experiment, and it allows a Dakota experiment to be scripted. Dakotathon creates the Dakota input file and provides a generic analysis driver. Any model componentized in the CSDMS modeling framework automatically works with Dakotathon. Dakotathon has a plugin architecture, so models not wrapped into the CSDMS modeling framework can be accessed by Dakotathon by programmatically extending a template; an example is provided in the Dakotathon distribution.

Currently, six Dakota analysis methods have been implemented from the much larger Dakota library:

For instructions on installing and using Dakotathon, please see Dakotathon's CSDMS Model page.

Dakotathon has been installed in the wmt-testing instance on beach.colorado.edu. To use it, login to beach, and from a shell prompt execute:

source /home/csdms/wmt/_testing/conda/bin/activate

This configures the conda environment for this WMT executor. Now, when you run Python or IPython, the Dakotathon package will be available to import.

Links

Mpiper (talk) 14:09, 27 December 2016 (MST)