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Kernel: Python 3 (Ubuntu Linux)

Sensitivity Analysis via SALib

http://salib.readthedocs.io/en/latest/

from SALib.sample import saltelli
from SALib.analyze import sobol
from SALib.test_functions import Ishigami
import numpy as np

# Define the model inputs
problem = {
    'num_vars': 3,
    'names': ['x1', 'x2', 'x3'],
    'bounds': [[-3.14159265359, 3.14159265359],
               [-3.14159265359, 3.14159265359],
               [-3.14159265359, 3.14159265359]]
}

# Generate samples
param_values = saltelli.sample(problem, 1000)

# Run model (example)
Y = Ishigami.evaluate(param_values)

# Perform analysis
Si = sobol.analyze(problem, Y, print_to_console=True)
Parameter S1 S1_conf ST ST_conf x1 0.307975 0.063927 0.560137 0.085162 x2 0.447767 0.054012 0.438722 0.041973 x3 -0.004255 0.053252 0.242845 0.026515 Parameter_1 Parameter_2 S2 S2_conf x1 x2 0.012205 0.086689 x1 x3 0.251526 0.103264 x2 x3 -0.009954 0.064820 
# Print the first-order sensitivity indices
print(Si['S1'])
[ 0.30797549 0.44776661 -0.00425452] 
print(Si['ST'])
[0.56013728 0.4387225 0.24284474] 
# second order ...
print(Si['S2'])
[[ nan 0.01220462 0.25152574] [ nan nan -0.00995392] [ nan nan nan]] 
print("x1-x2:", Si['S2'][0,1])
print("x1-x3:", Si['S2'][0,2])
print("x2-x3:", Si['S2'][1,2])
x1-x2: 0.012204615255332008 x1-x3: 0.2515257351736558 x2-x3: -0.009953919522955074