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https://github.com/csdms/dakotathon
A Python API and BMI for the Dakota iterative systems analysis toolkit
https://github.com/csdms/dakotathon
bmi csdms dakota python sensitivity-analysis uncertainty-quantification
Last synced: 18 days ago
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A Python API and BMI for the Dakota iterative systems analysis toolkit
- Host: GitHub
- URL: https://github.com/csdms/dakotathon
- Owner: csdms
- License: mit
- Created: 2015-03-24T22:38:26.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2020-04-13T16:59:30.000Z (almost 5 years ago)
- Last Synced: 2024-01-26T00:37:31.267Z (12 months ago)
- Topics: bmi, csdms, dakota, python, sensitivity-analysis, uncertainty-quantification
- Language: Python
- Homepage:
- Size: 826 KB
- Stars: 12
- Watchers: 7
- Forks: 8
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Code of conduct: CODE-OF-CONDUCT.rst
Awesome Lists containing this project
README
[](https://travis-ci.org/csdms/dakotathon)
[](https://landscape.io/github/csdms/dakotathon/master)
[](https://coveralls.io/r/csdms/dakotathon?branch=master)
[](https://readthedocs.org/projects/csdms-dakota/?badge=latest)
[](https://anaconda.org/csdms-stack/dakotathon)
[](https://anaconda.org/csdms-stack/dakotathon)
[](https://anaconda.org/csdms-stack/dakotathon)
# Dakotathon
Dakotathon provides
a [Basic Model Interface](https://bmi.readthedocs.io)
and a Python API for a subset of the methods
included in the [Dakota](https://dakota.sandia.gov/)
iterative systems analysis toolkit,
including:
* [vector_parameter_study](https://dakota.sandia.gov/sites/default/files/docs/6.1/html-ref/method-vector_parameter_study.html),
* [centered_parameter_study](https://dakota.sandia.gov/sites/default/files/docs/6.1/html-ref/method-centered_parameter_study.html),
* [multidim_parameter_study](https://dakota.sandia.gov/sites/default/files/docs/6.1/html-ref/method-multidim_parameter_study.html),
* [psuade_moat](https://dakota.sandia.gov/sites/default/files/docs/6.1/html-ref/method-psuade_moat.html),
* [sampling](https://dakota.sandia.gov/sites/default/files/docs/6.1/html-ref/method-sampling.html),
* [polynomial_chaos](https://dakota.sandia.gov/sites/default/files/docs/6.1/html-ref/method-polynomial_chaos.html), and
* [stoch_collocation](https://dakota.sandia.gov/sites/default/files/docs/6.1/html-ref/method-stoch_collocation.html).
Dakotathon is currently beta-level software
supported on Linux and macOS.
API documentation is available at http://csdms-dakota.readthedocs.io.
## Installation
Install Dakotathon into an Anaconda Python distribution with
$ conda install -c csdms-stack dakotathon
or install from source with
$ git clone https://github.com/csdms/dakotathon.git
$ cd dakotathon
$ python setup.py install
Dakotathon requires Dakota 6.1 or greater.
Install Dakota through conda with
$ conda install -c csdms-stack -c conda-forge dakota
or, follow the instructions on the Dakota website
for [downloading](https://dakota.sandia.gov/download.html) and
[installing](https://dakota.sandia.gov/content/install-linux-macosx)
a precompiled Dakota binary for your system.
## Execution: standalone
Import Dakotathon into a Python session with:
>>> from dakotathon import Dakota
Create a `Dakota` instance,
specifying a Dakota analysis method:
>>> d = Dakota(method='vector_parameter_study')
To run a sample case,
create a Dakota input file
from the default vector parameter study
and call Dakota:
>>> d.write_input_file()
>>> d.run()
Dakota output is written to two files,
**dakota.out** (run information)
and
**dakota.dat** (tabular output),
in the current directory.
For more in-depth examples of using Dakotathon
as a standalone Python package,
see the Jupyter Notebooks
in the [examples](./examples) directory
of this repository.
### Note
If you're using Anaconda IPython on macOS,
include the `DYLD_LIBRARY_PATH` environment variable
in your session before calling the `run` method with:
```python
>>> from dakotathon.utils import add_dyld_library_path
>>> add_dyld_library_path()
```
See https://github.com/csdms/dakotathon/issues/17 for more information.
## Execution: in PyMT
Dakotathon can also be called as a component in
[PyMT](https://github.com/csdms/pymt).
For example,
to perform a centered parameter study on the Hydrotrend component,
start with imports:
```python
import os
from pymt.components import CenteredParameterStudy, Hydrotrend
from dakotathon.utils import configure_parameters
```
then create instances of the Hydrotrend and Dakota components:
```python
h, c = Hydrotrend(), CenteredParameterStudy()
```
Next,
set up a dict of parameters for the experiment:
```python
experiment = {
'component': type(c).__name__,
'run_duration': 10, # years
'auxiliary_files': 'HYDRO0.HYPS', # the default Waipaoa hypsometry
'descriptors': ['starting_mean_annual_temperature',
'total_annual_precipitation'],
'initial_point': [15.0, 2.0],
'steps_per_variable': [2, 5],
'step_vector': [2.5, 0.2],
'response_descriptors': ['channel_exit_water_sediment~suspended__mass_flow_rate',
'channel_exit_water__volume_flow_rate'],
'response_statistics': ['median', 'mean']
}
```
and use a helper function
to format the parameters for Dakota and for Hydrotrend:
```python
cparameters, hparameters = configure_parameters(experiment)
```
Set up the Hydrotrend component:
```python
cparameters['run_directory'] = h.setup(os.getcwd(), **hparameters)
```
Create the Dakota template file from the Hydrotrend input file:
```python
cfg_file = 'HYDRO.IN' # get from pymt eventually
dtmpl_file = cfg_file + '.dtmpl'
os.rename(cfg_file, dtmpl_file)
cparameters['template_file'] = dtmpl_file
```
Set up the Dakota component:
```python
c.setup(dparameters['run_directory'], **cparameters)
```
then initialize, run, and finalize the Dakota component:
```python
c.initialize('dakota.yaml')
c.update()
c.finalize()
```
Dakota output is written to two files,
**dakota.out** (run information)
and
**dakota.dat** (tabular output),
in the current directory.
For more in-depth examples of using Dakotathon with PyMT,
see the Python scripts
in the [examples](./examples) directory
of this repository.
## Contributing
Dakotathon is open source software,
released under an [MIT license](https://opensource.org/licenses/MIT).
[Contributions](./CONTRIBUTING.rst) are welcome.
Please note that this project is released with a
[Contributor Code of Conduct](./CODE-OF-CONDUCT.rst).
By participating in this project you agree to abide by its terms.