def morris():
from SALib.analyze import morris
from SALib.sample.morris import sample
problem = {
'num_vars': 5,
'names': ['HEME', 'K1', 'MPY', 'MPYgu', 'QCC'],
'groups': None,
'bounds': [[.4, .5], [10., 30.], [30., 40.], [15., 25.], [10., 15.]]
}
parameter_values = sample(problem,
N=10,
num_levels=4,
grid_jump=2,
optimal_trajectories=None)
parameters = assign_parameters()
schedule = get_default_schedule()
y_index = np.argmax(o["Curine"])
ys = []
for inputs in parameter_values:
for i in range(len(problem["names"])):
setattr(parameters, problem["names"][i], inputs[i])
_, __, ___, output = pbpk(parameters, schedule)
ys.append(output["Curine"][y_index])
sis = morris.analyze(problem,
parameter_values,
np.array(ys),
conf_level=0.95,
print_to_console=True,
num_levels=4,
grid_jump=2,
num_resamples=100)
import matplotlib.pyplot as plt
from SALib.plotting.morris import horizontal_bar_plot, covariance_plot, sample_histograms
fig, (ax1, ax2) = plt.subplots(1, 2)
horizontal_bar_plot(ax1, sis, {}, sortby='mu_star', unit=r"mg/g")
covariance_plot(ax2, sis, {}, unit=r"mg/g")
fig2 = plt.figure()
sample_histograms(fig2, parameter_values, problem, {'color': 'y'})
plt.show()
def test_morris_sample_histograms():
input_1 = [[0, 1 / 3.], [0, 1.], [2 / 3., 1.]]
input_2 = [[0, 1 / 3.], [2 / 3., 1 / 3.], [2 / 3., 1.]]
input_3 = [[2 / 3., 0], [2 / 3., 2 / 3.], [0, 2 / 3.]]
input_4 = [[1 / 3., 1.], [1., 1.], [1, 1 / 3.]]
input_5 = [[1 / 3., 1.], [1 / 3., 1 / 3.], [1, 1 / 3.]]
input_6 = [[1 / 3., 2 / 3.], [1 / 3., 0], [1., 0]]
input_sample = np.concatenate([input_1, input_2, input_3,
input_4, input_5, input_6])
problem = {'num_vars': 2, 'groups': None, 'names':['x1','x2']}
fig2 = plt.figure()
sample_histograms(fig2, input_sample, problem, {'color':'y'})
# Files with a 4th column for "group name" will be detected automatically, e.g.
# param_file = '../../SALib/test_functions/params/Ishigami_groups.txt'
# Generate samples
param_values = sample(problem, N=1000, num_levels=4,
optimal_trajectories=None)
# To use optimized trajectories (brute force method),
# give an integer value for optimal_trajectories
# Run the "model" -- this will happen offline for external models
Y = Sobol_G.evaluate(param_values)
# Perform the sensitivity analysis using the model output
# Specify which column of the output file to analyze (zero-indexed)
Si = morris.analyze(problem, param_values, Y, conf_level=0.95,
print_to_console=True,
num_levels=4, num_resamples=100)
# Returns a dictionary with keys 'mu', 'mu_star', 'sigma', and 'mu_star_conf'
# e.g. Si['mu_star'] contains the mu* value for each parameter, in the
# same order as the parameter file
fig, (ax1, ax2) = plt.subplots(1, 2)
horizontal_bar_plot(ax1, Si, {}, sortby='mu_star', unit=r"tCO$_2$/year")
covariance_plot(ax2, Si, {}, unit=r"tCO$_2$/year")
fig2 = plt.figure()
sample_histograms(fig2, param_values, problem, {'color': 'y'})
plt.show()
def runMorris(control_rows, output_dropdown, trajectories_text, levels_text,
grid_jump_text, run_morris_button, results_output):
inputs = [(control_row[0].value, control_row[1].value,
control_row[2].value) for control_row in control_rows
if control_row[0].value != no_selection]
target_parameters = dict([(input[0], (input[1], input[2]))
for input in inputs])
target_output_name = output_dropdown.value
n_trajectories = trajectories_text.value
num_levels = levels_text.value
grid_jump = grid_jump_text.value
run_morris_button.disabled = True
try:
problem, parameter_values, sis = hdm.run_salib_morris(
target_parameters,
target_output_name,
n_trajectories,
num_levels,
grid_jump,
1 # os.cpu_count()
)
except:
with results_output:
print("Invalid input", file=sys.stderr)
return
finally:
run_morris_button.disabled = False
from base64 import b64encode
header = ','.join([name for name in target_parameters.keys()])
lines = [','.join([str(d) for d in inputs]) for inputs in parameter_values]
csv = header + os.linesep + os.linesep.join(lines)
payload = b64encode(csv.encode())
payload = payload.decode()
fileName = "parameter_values.csv"
title = "Download design"
html = f'<a download="{fileName}" href="data:text/csv;charset=utf-8;base64,{payload}" target="_blank">{title}</a>'
link = widgets.HTML(html)
results_output.clear_output()
with results_output:
print("{0:<30} {1:>10} {2:>10} {3:>15} {4:>10}".format(
"Parameter", "Mu_Star", "Mu", "Mu_Star_Conf", "Sigma"))
num_vars = problem['num_vars']
for j in list(range(num_vars)):
print("{0:30} {1:10.3f} {2:10.3f} {3:15.3f} {4:10.3f}".format(
sis['names'][j], sis['mu_star'][j], sis['mu'][j],
sis['mu_star_conf'][j], sis['sigma'][j]))
display(link)
fig1, (ax1, ax2) = plt.subplots(1, 2)
horizontal_bar_plot(ax1, sis, {}, sortby='mu_star')
covariance_plot(ax2, sis, {})
fig2 = plt.figure()
sample_histograms(fig2, parameter_values, problem, {'color': 'y'})
fig1.tight_layout()
show_inline_matplotlib_plots()
# }
# Files with a 4th column for "group name" will be detected automatically, e.g.:
# param_file = '../../SALib/test_functions/params/Ishigami_groups.txt'
# Generate samples
param_values = sample(problem, N=1000, num_levels=4, grid_jump=2, \
optimal_trajectories=None)
# To use optimized trajectories (brute force method), give an integer value for optimal_trajectories
# Run the "model" -- this will happen offline for external models
Y = Sobol_G.evaluate(param_values)
# Perform the sensitivity analysis using the model output
# Specify which column of the output file to analyze (zero-indexed)
Si = morris.analyze(problem, param_values, Y, conf_level=0.95,
print_to_console=True,
num_levels=4, grid_jump=2, num_resamples=100)
# Returns a dictionary with keys 'mu', 'mu_star', 'sigma', and 'mu_star_conf'
# e.g. Si['mu_star'] contains the mu* value for each parameter, in the
# same order as the parameter file
fig, (ax1, ax2) = plt.subplots(1, 2)
horizontal_bar_plot(ax1, Si,{}, sortby='mu_star', unit=r"tCO$_2$/year")
covariance_plot(ax2, Si, {}, unit=r"tCO$_2$/year")
fig2 = plt.figure()
sample_histograms(fig2, param_values, problem, {'color':'y'})
plt.show()