def _parallel_analyze(index, method, problem, samples, data, seed):
if method == 'sobol':
result = sobol.analyze(problem,
data,
calc_second_order=True,
print_to_console=False,
seed=seed)
elif method == 'fast':
result = fast.analyze(problem, data, print_to_console=False, seed=seed)
elif method == 'rbd-fast':
result = rbd_fast.analyze(problem,
samples,
data,
print_to_console=False,
seed=seed)
elif method == 'morris':
result = morris_analyze(problem,
samples,
data,
print_to_console=False,
seed=seed)
elif method == 'delta':
result = delta.analyze(problem,
samples,
data,
print_to_console=False,
seed=seed)
elif method == 'dgsm':
result = dgsm.analyze(problem,
samples,
data,
print_to_console=False,
seed=seed)
elif method == 'frac':
result = ff_analyze(problem,
samples,
data,
second_order=True,
print_to_console=False,
seed=seed)
else:
return 0
for key in result.keys():
result[key] = result[key].tolist()
with open('{:s}.json'.format(index), 'w') as outfile:
json.dump(result, outfile)
return 0
def _parallel_analyze(data):
seed = int(opts['seed'])
samples = population['problem', 'samples']
problem = population['problem', 'definition']
if opts['method'] == 'sobol':
return sobol.analyze(problem,
data,
calc_second_order=True,
print_to_console=False)
elif opts['method'] == 'fast':
return fast.analyze(problem, data, print_to_console=False, seed=seed)
elif opts['method'] == 'rbd-fast':
return rbd_fast.analyze(problem,
samples,
data,
print_to_console=False,
seed=seed)
elif opts['method'] == 'morris':
return morris_analyze(problem,
samples,
data,
print_to_console=False,
seed=seed)
elif opts['method'] == 'delta':
return delta.analyze(problem,
samples,
data,
print_to_console=False,
seed=seed)
elif opts['method'] == 'dgsm':
return dgsm.analyze(problem,
samples,
data,
print_to_console=False,
seed=seed)
elif opts['method'] == 'frac':
return ff_analyze(problem,
samples,
data,
second_order=True,
print_to_console=False,
seed=seed)
else:
return 0
'ax2_lable': 'Parameter index',
}
Si_con = fast.analyze(problem, Y_con, print_to_console=False)
f1, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
SS1 = (np.array(Si_con['S1'][1:])) / np.array(Si['S1'][1:])
SS2 = (np.array(Si_con['ST'][1:])) / np.array(Si['ST'][1:])
sns.barplot(np.arange(2, 22), np.abs(SS1), ax=ax1)
sns.barplot(np.arange(2, 22), np.abs(SS2), ax=ax2)
ax1.set_title('SS1')
ax2.set_title('SST')
ax2.set_xlabel('Sensitivity')
elif method_flag == 5:
Si = ff_analyze(problem,
param_values,
Y,
second_order=True,
print_to_console=False)
elif method_flag == 6:
Si = morris.analyze(problem,
param_values,
Y,
conf_level=0.95,
print_to_console=False,
num_levels=4,
grid_jump=2,
num_resamples=100)
figure_keys = {
'ax1_title': 'mu',
'ax2_title': 'sigma',