def monte_carlo_sens_nonlin(Ns, jpdf, sample_method='R'):
N_prms = len(jpdf)
# 1. Generate sample matrices
XA, XB, XC = generate_sample_matrices_mc(Ns, N_prms, jpdf, sample_method)
# 2. Evaluate the model
Y_A, Y_B, Y_C = evaluate_non_additive_linear_model(XA, XB, XC)
# 3. Approximate the sensitivity indices
S, ST = calculate_sensitivity_indices_mc(Y_A, Y_B, Y_C)
return XA, XB, XC, Y_A, Y_B, Y_C, S, ST
def mc_sensitivity_linear(Ns, jpdf, w, sample_method='R'):
Nrv = len(jpdf)
# 1. Generate sample matrices
A, B, C = generate_sample_matrices_mc(Ns, Nrv, jpdf, sample_method)
# 2. Evaluate the model
Y_A, Y_B, Y_C = evaluate_linear_model(A, B, C, w)
# 3. Approximate the sensitivity indices
S, ST = calculate_sensitivity_indices_mc(Y_A, Y_B, Y_C)
return A, B, C, Y_A, Y_B, Y_C, S, ST
plt.plot(samples[k], Y_area[0] * unit_m2_cm2, '.', color=color)
plt.ylabel('Area [cm2]')
plt.ylim([3.45, 4.58])
xlbl = 'Z' + str(k)
plt.xlabel(xlbl)
plt.tight_layout()
# end scatter plots
# start Monte Carlo
pressure_range = np.linspace(45, 180, 100) * unit_mmhg_pa
Ns = 50000
sample_method = 'R'
number_of_parameters = len(jpdf)
# 1. Generate sample matrices
A, B, C = generate_sample_matrices_mc(Ns, number_of_parameters, jpdf,
sample_method)
plt.figure('mean')
print("\n Uncertainty measures (averaged)\n")
print('\n E(Y) | Std(Y) \n')
for model, name, color in [
(quadratic_area_model, 'Quadratic model', '#dd3c30'),
(logarithmic_area_model, 'Logarithmic model', '#2775b5')
]:
# evaluate the model for all samples
Y_A = model(pressure_range, A.T)
Y_B = model(pressure_range, B.T)
Y_C = np.empty((len(pressure_range), Ns, number_of_parameters))
for i in range(number_of_parameters):
Y_C[:, :, i] = model(pressure_range, C[i, :, :].T)