def check_scatter_2D(self, sample_nos, p_ref, save):
"""
Check to see that the :meth:`bet.postTools.plotDomains.scatter_2D` ran
without generating an error.
"""
try:
plotDomains.scatter_2D(self.samples[:, [0, 1]], sample_nos,
self.P_samples, p_ref, save, False, 'XLABEL', 'YLABEL',
self.filename)
go = True
except (RuntimeError, TypeError, NameError):
go = False
nptest.assert_equal(go, True)
def check_scatter_2D(self, sample_nos, p_ref, save):
"""
Check to see that the :meth:`bet.postTools.plotDomains.scatter_2D` ran
without generating an error.
"""
try:
plotDomains.scatter_2D(self.samples[:, [0, 1]], sample_nos,
self.P_samples, p_ref, save, False,
'XLABEL', 'YLABEL', self.filename)
go = True
except (RuntimeError, TypeError, NameError):
go = False
nptest.assert_equal(go, True)
def check_scatter_2D(self, sample_nos, p_ref, save):
"""
Check to see that the :meth:`bet.postTools.plotDomains.scatter_2D` ran
without generating an error.
"""
try:
input_sample_set_temp = sample.sample_set(2)
input_sample_set_temp.set_values(
self.disc._input_sample_set.get_values()[:, [0, 1]])
plotDomains.scatter_2D(
input_sample_set_temp, sample_nos,
self.disc._input_sample_set.get_probabilities(), p_ref, save,
False, 'XLABEL', 'YLABEL', self.filename)
go = True
except (RuntimeError, TypeError, NameError):
go = False
nptest.assert_equal(go, True)
def check_scatter_2D(self, sample_nos, p_ref, save):
"""
Check to see that the :meth:`bet.postTools.plotDomains.scatter_2D` ran
without generating an error.
"""
try:
input_sample_set_temp = sample.sample_set(2)
input_sample_set_temp.set_values(self.disc._input_sample_set.get_values()[:, [0, 1]])
plotDomains.scatter_2D(
input_sample_set_temp,
sample_nos,
self.disc._input_sample_set.get_probabilities(),
p_ref, save, False, 'XLABEL', 'YLABEL', self.filename)
go = True
except (RuntimeError, TypeError, NameError):
go = False
nptest.assert_equal(go, True)
num_samples=num_iid_samples)
# Compute the simple function approximation to the distribution on the data space
simpleFunP.user_partition_user_distribution(my_discretization,
Partition_discretization,
Monte_Carlo_discretization)
# Calculate probabilities
calculateP.prob(my_discretization)
########################################
# Post-process the results (optional)
########################################
# Show some plots of the different sample sets
plotD.scatter_2D(my_discretization._input_sample_set,
filename = 'Parameter_Samples',
file_extension = '.eps')
plotD.scatter_2D(my_discretization._output_sample_set,
filename = 'QoI_Samples',
file_extension = '.eps')
plotD.scatter_2D(my_discretization._output_probability_set,
filename = 'Data_Space_Discretization',
file_extension = '.eps')
'''
Suggested changes for user:
At this point, the only thing that should change in the plotP.* inputs
should be either the nbins values or sigma (which influences the kernel
density estimation with smaller values implying a density estimate that
looks more like a histogram and larger values smoothing out the values
more).
'''
Suggested changes for user:
Try different reference parameters.
'''
# Define the reference parameter
param_ref = np.array([5.5, 4.5])
#param_ref = np.array([4.5, 3.0])
#param_ref = np.array([3.5, 1.5])
# Compute the reference QoI
Q_ref = my_model(param_ref)
# Create some plots of input and output discretizations
plotD.scatter_2D(input_samples,
ref_sample=param_ref,
filename='nonlinearMapParameterSamples',
file_extension='.eps')
if Q_ref.size == 2:
plotD.show_data_domain_2D(my_discretization,
Q_ref=Q_ref,
file_extension=".eps")
'''
Suggested changes for user:
Try different ways of discretizing the probability measure on D defined
as a uniform probability measure on a rectangle or interval depending
on choice of QoI_num in myModel.py.
'''
randomDataDiscretization = False
if randomDataDiscretization is False:
simpleFunP.regular_partition_uniform_distribution_rectangle_scaled(
'''
Suggested changes for user:
Try different reference parameters.
'''
# Define the reference parameter
#param_ref = np.zeros((1,num_KL_terms))
param_ref = np.ones((1, num_KL_terms))
# Compute the reference QoI
Q_ref = my_model(param_ref)
# Create some plots of input and output discretizations
if num_KL_terms == 2:
plotD.scatter_2D(input_samples,
ref_sample=param_ref[0, :],
filename='FEniCS_ParameterSamples',
file_extension='.eps')
if Q_ref.size == 2:
plotD.show_data_domain_2D(my_discretization,
Q_ref=Q_ref[0, :],
file_extension=".eps")
'''
Suggested changes for user:
Try different ways of discretizing the probability measure on D defined
as a uniform probability measure on a rectangle or interval depending
on choice of QoI_num in myModel.py.
'''
randomDataDiscretization = False
if randomDataDiscretization is False:
simpleFunP.regular_partition_uniform_distribution_rectangle_scaled(
input_samples, savefile='FEniCS_Example.txt.gz')
'''
Suggested changes for user:
Try different reference parameters.
'''
# Define the reference parameter
#param_ref = np.zeros((1,num_KL_terms))
param_ref = np.ones((1,num_KL_terms))
# Compute the reference QoI
Q_ref = my_model(param_ref)
# Create some plots of input and output discretizations
plotD.scatter_2D(input_samples, ref_sample=param_ref[0,:],
filename='FEniCS_ParameterSamples.eps')
if Q_ref.size == 2:
plotD.show_data_domain_2D(my_discretization, Q_ref=Q_ref[0,:],
file_extension="eps")
'''
Suggested changes for user:
Try different ways of discretizing the probability measure on D defined
as a uniform probability measure on a rectangle or interval depending
on choice of QoI_num in myModel.py.
'''
randomDataDiscretization = False
if randomDataDiscretization is False:
simpleFunP.regular_partition_uniform_distribution_rectangle_scaled(
data_set=my_discretization, Q_ref=Q_ref[0,:], rect_scale=0.1,
'''
Suggested changes for user:
Try different reference parameters.
'''
# Define the reference parameter
param_ref = np.array([5.5, 4.5])
#param_ref = np.array([4.5, 3.0])
#param_ref = np.array([3.5, 1.5])
# Compute the reference QoI
Q_ref = my_model(param_ref)
# Create some plots of input and output discretizations
plotD.scatter_2D(input_samples, ref_sample = param_ref,
filename = 'nonlinearMapParameterSamples',
file_extension='.eps')
if Q_ref.size == 2:
plotD.show_data_domain_2D(my_discretization, Q_ref = Q_ref, file_extension=".eps")
'''
Suggested changes for user:
Try different ways of discretizing the probability measure on D defined
as a uniform probability measure on a rectangle or interval depending
on choice of QoI_num in myModel.py.
'''
randomDataDiscretization = False
if randomDataDiscretization is False:
simpleFunP.regular_partition_uniform_distribution_rectangle_scaled(
data_set=my_discretization, Q_ref=Q_ref, rect_scale=0.25,
