if random_sample == False:
n0 = 50 # number of samples in lam0 direction
n1 = 50 # number of samples in lam1 direction
n_samples = n0 * n1
else:
n_samples = 2.5E3
#set up samples
if random_sample == False:
vec0 = list(np.linspace(lam_domain[0][0], lam_domain[0][1], n0))
vec1 = list(np.linspace(lam_domain[1][0], lam_domain[1][1], n1))
vecv0, vecv1 = np.meshgrid(vec0, vec1, indexing='ij')
samples = np.vstack((vecv0.flat[:], vecv1.flat[:])).transpose()
else:
samples = calculateP.emulate_iid_lebesgue(lam_domain=lam_domain,
num_l_emulate=n_samples)
# QoI map
Q_map = np.array([[0.506, 0.463], [0.253, 0.918]])
# calc data
data = np.dot(samples, Q_map)
'''
Compute the output distribution simple function approximation by
propagating a different set of samples to implicitly define a Voronoi
discretization of the data space, and then propagating i.i.d. uniform
samples to bin into these cells.
Suggested changes for user:
See the effect of using different values for d_distr_samples_num.
if random_sample == False: n0 = 50 # number of samples in lam0 direction n1 = 50 # number of samples in lam1 direction n_samples = n0*n1 else: n_samples = 1E3 #set up samples if random_sample == False: vec0 = list(np.linspace(lam_domain[0][0], lam_domain[0][1], n0)) vec1 = list(np.linspace(lam_domain[1][0], lam_domain[1][1], n1)) vecv0, vecv1 = np.meshgrid(vec0, vec1, indexing='ij') samples = np.vstack((vecv0.flat[:], vecv1.flat[:])).transpose() else: samples = calculateP.emulate_iid_lebesgue(lam_domain=lam_domain, num_l_emulate = n_samples) # QoI function def QoI(x,y,lam1,lam2): z = np.sin(m.pi*x*lam1)*np.sin(m.pi*y*lam2) return z #np.vstack(z.flat[:]).transpose() ''' Suggested changes for user: Try setting QoI_num = 2. Play around with the x1, y1, and/or, x2, y2 values to try and "optimize" the QoI to give the highest probability region on the reference parameter above.
if random_sample == False: n0 = 50 # number of samples in lam0 direction n1 = 50 # number of samples in lam1 direction n_samples = n0*n1 else: n_samples = 2.5E3 #set up samples if random_sample == False: vec0=list(np.linspace(lam_domain[0][0], lam_domain[0][1], n0)) vec1 = list(np.linspace(lam_domain[1][0], lam_domain[1][1], n1)) vecv0, vecv1 = np.meshgrid(vec0, vec1, indexing='ij') samples=np.vstack((vecv0.flat[:], vecv1.flat[:])).transpose() else: samples = calculateP.emulate_iid_lebesgue(lam_domain=lam_domain, num_l_emulate = n_samples) # QoI map Q_map = np.array([[0.506, 0.463],[0.253, 0.918]]) # calc data data = np.dot(samples,Q_map) ''' Compute the output distribution simple function approximation by propagating a different set of samples to implicitly define a Voronoi discretization of the data space, and then propagating i.i.d. uniform samples to bin into these cells. Suggested changes for user:
if random_sample == False:
n0 = 50 # number of samples in lam0 direction
n1 = 50 # number of samples in lam1 direction
n_samples = n0 * n1
else:
n_samples = 1E3
#set up samples
if random_sample == False:
vec0 = list(np.linspace(lam_domain[0][0], lam_domain[0][1], n0))
vec1 = list(np.linspace(lam_domain[1][0], lam_domain[1][1], n1))
vecv0, vecv1 = np.meshgrid(vec0, vec1, indexing='ij')
samples = np.vstack((vecv0.flat[:], vecv1.flat[:])).transpose()
else:
samples = calculateP.emulate_iid_lebesgue(lam_domain=lam_domain,
num_l_emulate=n_samples)
# QoI function
def QoI(x, y, lam1, lam2):
z = np.sin(m.pi * x * lam1) * np.sin(m.pi * y * lam2)
return z #np.vstack(z.flat[:]).transpose()
'''
Suggested changes for user:
Try setting QoI_num = 2.
Play around with the x1, y1, and/or, x2, y2 values to try and
"optimize" the QoI to give the highest probability region
