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Tools for sensitivity analysis. Contains Sobol, Morris, and FAST methods.

Project description

##Sensitivity Analysis Library (SALib)

Python implementations of commonly used sensitivity analysis methods. Useful in systems modeling to calculate the effects of model inputs or exogenous factors on outputs of interest.

**Requirements:** [NumPy](, [SciPy](

**Methods included:**
* Sobol Sensitivity Analysis ([Sobol 2001](, [Saltelli 2002](, [Saltelli et al. 2010](
* Method of Morris ([Morris 1991](, [Campolongo et al. 2007](
* Fourier Amplitude Sensitivity Test (FAST) ([Cukier et al. 1973](, [Saltelli et al. 1999](
* Delta Moment-Independent Measure ([Borgonovo 2007](, [Plischke et al. 2013](
* Derivative-based Global Sensitivity Measure (DGSM) ([Sobol and Kucherenko 2009](
* Metamodel-based Sobol Analysis (experimental). Uses RBF support vector regression from `scikit-learn`.

**Contributors:** [Jon Herman](, [Matt Woodruff](, [Chris Mutel](, [Fernando Rios](, [Dan Hyams](

### Create a parameter file

To get started, create a file describing the sampling ranges for the parameters in the model. Parameter files should be created with 3 columns: `[name, lower bound, upper bound]`:
P1 0.0 1.0
P2 0.0 5.0

Lines beginning with `#` will be treated as comments and ignored.

### Generate samples

From the command line:
python -m SALib.sample.saltelli \
-n 1000 \
-p ./SALib/test_functions/params/Ishigami.txt \
-o model_input.txt \

Other methods include `SALib.sample.morris_oat` and `SALib.sample.fast_sampler`. For an explanation of all command line options, [see the examples here](

Or, generate samples from Python:
from SALib.sample import saltelli
import numpy as np

param_file = '../../SALib/test_functions/params/Ishigami.txt'
param_values = saltelli.sample(1000, param_file, calc_second_order = True)
np.savetxt('model_input.txt', param_values, delimiter=' ')

Either way, this will create a file of sampled input values in `model_input.txt`.

### Run model
Here's an example of running a test function in Python, but this will usually be a user-defined model, maybe even in another language. Just save the outputs.

from SALib.test_functions import Ishigami
Y = Ishigami.evaluate(param_values)
np.savetxt('model_output.txt', Y, delimiter=' ')

### Analyze model output

From the command line:
python -m SALib.analyze.sobol \
-p ./SALib/test_functions/params/Ishigami.txt \
-Y model_output.txt \
-c 0 \

This will print indices and confidence intervals to the command line. You can redirect to a file using the `>` operator.

Or, from Python:
from SALib.analyze import sobol
import numpy as np
Si = sobol.analyze(param_file, 'model_output.txt', column = 0, print_to_console=False)
# Returns a dictionary with keys 'S1', 'S1_conf', 'ST', and 'ST_conf'
# e.g. Si['S1'] contains the first-order index for each parameter, in the same order as the parameter file

Check out the [examples]( for a full description of command line and keyword options for all of the methods.

### License
Copyright (C) 2013-2014 Jon Herman and others. Licensed under the GNU Lesser General Public License.

The Sensitivity Analysis Library is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

The Sensitivity Analysis Library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License
along with the Sensitivity Analysis Library. If not, see <>.

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