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https://github.com/SALib/SALib

Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.
https://github.com/SALib/SALib

global-sensitivity-analysis joss morris numpy python salib sensitivity-analysis sensitivity-analysis-library sobol uncertainty uncertainty-quantification

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Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.

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README 

        
## SALib: Advanced options



### Parameter files



In the parameter file, lines beginning with `#` will be treated as comments and ignored.

```

# name lower_bound upper_bound

P1 0.0 1.0

P2 0.0 5.0

P3 0.0 5.0

...etc.

```

Parameter files can also be comma-delimited if your parameter names or group names contain spaces. This should be detected automatically.



### Command-line interface



**Generate samples** (the `-p` flag is the parameter file)

```

salib sample saltelli \

     -n 1024 \

     -p ./src/SALib/test_functions/params/Ishigami.txt \

     -o model_input.txt

```



**Run the model** this will usually be a user-defined model, maybe even in another language. Just save the outputs.



**Run the analysis**

```

salib analyze sobol \

     -p ./src/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.



### Parallel indices calculation (Sobol method only)

```python

Si = sobol.analyze(problem, Y, calc_second_order=True, conf_level=0.95,

                   print_to_console=False, parallel=True, n_processors=4)

```



Other methods include Morris, FAST, Delta-MIM, and DGSM. For an explanation of all command line options for each method, [see the examples here](https://github.com/SALib/SALib/tree/main/examples).



### Groups of variables (Sobol and Morris methods only)

It is sometimes useful to perform sensitivity analysis on groups of input variables to reduce the number of model runs required, when variables belong to the same component of a model, or there is some reason to believe that they should behave similarly.



Groups can be specified in two ways for the Sobol and Morris methods. First, as a fourth column in the parameter file:

```

# name lower_bound upper_bound group_name

P1 0.0 1.0 Group_1

P2 0.0 5.0 Group_2

P3 0.0 5.0 Group_2

...etc.

```



Or in the `problem` dictionary:

```python

problem = {

  'groups': ['Group_1', 'Group_2', 'Group_2'],

  'names': ['x1', 'x2', 'x3'],

  'num_vars': 3,

  'bounds': [[-3.14, 3.14], [-3.14, 3.14], [-3.14, 3.14]]

}

```



`groups` is a list of strings specifying the group name to which each variable belongs. The rest of the code stays the same:



```python

param_values = saltelli.sample(problem, 1024)

Y = Ishigami.evaluate(param_values)

Si = sobol.analyze(problem, Y, print_to_console=True)

```



But the output is printed by group:

```

Group S1 S1_conf ST ST_conf

Group_1 0.307834 0.066424 0.559577 0.082978

Group_2 0.444052 0.080255 0.667258 0.060871



Group_1 Group_2 S2 S2_conf

Group_1 Group_2 0.242964 0.124229

```



The output can then be converted to a Pandas DataFrame for further analysis.



```python

total_Si, first_Si, second_Si = Si.to_df()

```



### Generating alternate distributions



In the [Quick Start](https://github.com/SALib/SALib/tree/main/README.rst) we

generate a uniform sample of parameter space.



```python

from SALib.sample import saltelli

from SALib.analyze import sobol

from SALib.test_functions import Ishigami

import numpy as np



problem = {

     'num_vars': 3,

     'names': ['x1', 'x2', 'x3'],

     'bounds': [[-3.14159265359, 3.14159265359],

               [-3.14159265359, 3.14159265359],

               [-3.14159265359, 3.14159265359]]

}



param_values = saltelli.sample(problem, 1024)

```



SALib is also capable of generating alternate sampling distributions by

specifying a `dist` entry in the `problem` specification.



As implied in the basic example, a uniform distribution is the default.



When an entry for `dist` is not 'unif', the `bounds` entry does not indicate

parameter bounds but sample-specific metadata.



`bounds` definitions for available distributions:



* unif: uniform distribution

    e.g. :code:`[-np.pi, np.pi]` defines the lower and upper bounds

* triang: triangular with lower and upper bounds, as well as

     location of peak

     The location of peak is in percentage of width

     e.g. :code:`[1.0, 3.0, 0.5]` indicates 1.0 to 3.0 with a peak at 2.0



     A soon-to-be deprecated two-value format assumes the lower bound to be 0

     e.g. :code:`[3, 0.5]` assumes 0 to 3, with a peak at 1.5

* norm: normal distribution with mean and standard deviation

* lognorm: lognormal with ln-space mean and standard deviation



An example specification is shown below:



```python

problem = {

     'names': ['x1', 'x2', 'x3'],

     'num_vars': 3,

     'bounds': [[-np.pi, np.pi], [1.0, 0.2], [3, 0.5]],

     'groups': ['G1', 'G2', 'G1'],

     'dists': ['unif', 'lognorm', 'triang']

}

```