def check_show_data(self, data, sample_nos, q_ref, save, qnums, showdim):
"""
Check to see that the :meth:`bet.postTools.plotDomains.scatter_rhoD` ran
without generating an error.
"""
try:
if data.shape[1] == 4:
data_obj_temp = sample.sample_set(4)
data_obj_temp.set_values(data)
plotDomains.scatter_rhoD(data_obj_temp, q_ref, sample_nos,
'output', self.rho_D, qnums, None, showdim, save,
False)
else:
data_obj_temp = sample.sample_set(data.shape[1])
data_obj_temp.set_values(data)
plotDomains.scatter_rhoD(data_obj_temp, q_ref, sample_nos,
None, None, qnums, None, showdim, save, False)
go = True
except (RuntimeError, TypeError, NameError):
print("ERROR")
print("data shape:", data.shape)
print("data ref:", q_ref)
print("samples nums:", sample_nos)
print("save:", save)
print("qnums:", qnums)
print("showdim:", showdim)
go = False
nptest.assert_equal(go, True)
def check_show_param(self, samples, sample_nos, p_ref, save, lnums,
showdim):
"""
Check to see that the :meth:`bet.postTools.plotDomains.scatter_rhoD` ran
without generating an error.
"""
try:
input_sample_set_temp = sample.sample_set(samples.shape[1])
input_sample_set_temp.set_values(samples)
disc_obj_temp = sample.discretization(input_sample_set_temp,
self.disc._output_sample_set)
plotDomains.scatter_rhoD(disc_obj_temp, p_ref, sample_nos, 'input',
self.rho_D, lnums, None, showdim, save, False)
go = True
except (RuntimeError, TypeError, NameError) as error:
print("ERROR:", error)
print("samples shape:", samples.shape)
print("param ref:", p_ref)
print("samples nums:", sample_nos)
print("save:", save)
print("lnums:", lnums)
print("showdim:", showdim)
go = False
nptest.assert_equal(go, True)
output_samples = samp.sample_set(QoI_indices_observe.size)
output_samples.set_values(
np.loadtxt("files/data.txt.gz")[:, QoI_indices_observe])
# Create discretization object
my_discretization = samp.discretization(input_sample_set=input_samples,
output_sample_set=output_samples)
# Load the reference parameter and QoI values
param_ref = np.loadtxt("files/lam_ref.txt.gz") # reference parameter set
# reference QoI set
Q_ref = np.loadtxt("files/Q_ref.txt.gz")[QoI_indices_observe]
# Plot the data domain
plotD.scatter_rhoD(my_discretization,
ref_sample=Q_ref,
io_flag='output',
showdim=2)
# Whether or not to use deterministic description of simple function approximation of
# ouput probability
deterministic_discretize_D = True
if deterministic_discretize_D == True:
simpleFunP.regular_partition_uniform_distribution_rectangle_scaled(
data_set=my_discretization,
Q_ref=Q_ref,
rect_scale=0.25,
cells_per_dimension=1)
else:
simpleFunP.uniform_partition_uniform_distribution_rectangle_scaled(
data_set=my_discretization,
Q_ref=Q_ref,
rho_left = np.all(np.greater_equal(outputs, rho_left), axis=1)
rho_right = np.all(np.less_equal(outputs, rho_right), axis=1)
inside = np.logical_and(rho_left, rho_right)
max_values = np.repeat(maximum, outputs.shape[0], 0)
return inside.astype('float64') * max_values
# Read in points_ref and plot results
ref_sample = mdat['points_true']
ref_sample = ref_sample[:, 14]
# Create input, output, and discretization from data read from file
input_sample_set = sample.sample_set(points.shape[0])
input_sample_set.set_values(points.transpose())
input_sample_set.set_domain(param_domain)
output_sample_set = sample.sample_set(Q.shape[1])
output_sample_set.set_values(Q)
my_disc = sample.discretization(input_sample_set, output_sample_set)
# Show the samples in the parameter space
pDom.scatter_rhoD(my_disc, rho_D=rho_D, ref_sample=ref_sample, io_flag='input')
# Show the corresponding samples in the data space
pDom.scatter_rhoD(output_sample_set,
rho_D=rho_D,
ref_sample=Q_ref,
io_flag='output')
# Show multiple data domains that correspond with the convex hull of samples in
# the parameter space
pDom.show_data_domain_multi(my_disc, Q_ref=Q_ref, showdim='all')
