Dakotathon: Difference between revisions

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[[File:Dakotathon.jpg|360px|right]]
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[https://dakota.sandia.gov Dakota] is a software toolkit, developed at Sandia National Laboratories,
[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,
that provides an interface between models and a library of analysis methods,
including support for sensitivity analysis, uncertainty quantification, optimization, and calibration techniques.
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''',
The CSDMS Dakota Interface, or '''Dakotathon''',
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It simplifies the process of configuring and running a Dakota experiment,
It simplifies the process of configuring and running a Dakota experiment,
and it allows a Dakota experiment to be scripted.
and it allows a Dakota experiment to be scripted.
Any model written in Python that exposes a Basic Model Interface (BMI),
Dakotathon creates the Dakota input file and provides a generic analysis driver.
as well as any model componentized in the CSDMS modeling framework,
Any model componentized in the CSDMS modeling framework
automatically works with Dakotathon.
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:


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


* vector parameter study,
This configures the conda environment for this WMT executor.
* centered parameter study,
Now, when you run Python or IPython,
* multidim parameter study,
the Dakotathon package will be available to import.
* sampling,
* polynomial chaos, and
* stochastic collocation.


== Links ==


Additional information:
* 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]


* The CSDMS [[Model:Dakotathon|Model page]] for Dakotathon
[[User:Mpiper|Mpiper]] ([[User talk:Mpiper|talk]]) 14:09, 27 December 2016 (MST)
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Latest revision as of 11:17, 28 December 2016

The CSDMS Dakota Interface

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)