def check_show_data(self, data, sample_nos, q_ref, save, qnums, showdim):
"""
Check to see that the :meth:`bet.postTools.plotDomains.show_data` ran
without generating an error.
"""
try:
if data.shape[1] == 4:
plotDomains.show_data(data, self.rho_D, q_ref,
sample_nos, save, False, qnums, showdim)
else:
plotDomains.show_data(data, None, q_ref,
sample_nos, save, False, qnums, showdim)
go = True
except (RuntimeError, TypeError, NameError):
print "ERROR"
print data.shape
print q_ref
print sample_nos
print save
print qnums
print showdim
go = False
nptest.assert_equal(go, True)
def check_show_data(self, data, sample_nos, q_ref, save, qnums, showdim):
"""
Check to see that the :meth:`bet.postTools.plotDomains.show_data` ran
without generating an error.
"""
try:
if data.shape[1] == 4:
plotDomains.show_data(data, self.rho_D, q_ref, sample_nos,
save, False, qnums, showdim)
else:
plotDomains.show_data(data, None, q_ref, sample_nos, save,
False, qnums, showdim)
go = True
except (RuntimeError, TypeError, NameError):
print "ERROR"
print data.shape
print q_ref
print sample_nos
print save
print qnums
print showdim
go = False
nptest.assert_equal(go, True)
def rho_D(outputs):
rho_left = np.repeat([Q_ref - .5 * bin_size], outputs.shape[0], 0)
rho_right = np.repeat([Q_ref + .5 * bin_size], outputs.shape[0], 0)
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
p_ref = mdat['points_true']
p_ref = p_ref[5:7, 15]
# Show the samples in the parameter space
pDom.show_param(samples=points.transpose(), data=Q, rho_D=rho_D, p_ref=p_ref)
# Show the corresponding samples in the data space
pDom.show_data(data=Q, rho_D=rho_D, Q_ref=Q_ref)
# Show the data domain that corresponds with the convex hull of samples in the
# parameter space
pDom.show_data_domain_2D(samples=points.transpose(), data=Q, Q_ref=Q_ref)
# Show multiple data domains that correspond with the convex hull of samples in
# the parameter space
pDom.show_data_domain_multi(samples=points.transpose(),
data=mdat['Q'],
Q_ref=mdat['Q_true'][15],
Q_nums=[1, 2, 5],
showdim='all')
QoI_indices=[0,1,2,3] # Indices for output data with which you want to invert
bin_ratio = 0.25 #ratio of length of data region to invert
data = dataf[:,QoI_indices]
Q_ref=Q_ref[QoI_indices]
dmax = data.max(axis=0)
dmin = data.min(axis=0)
dscale = bin_ratio*(dmax-dmin)
Qmax = Q_ref + 0.5*dscale
Qmin = Q_ref -0.5*dscale
def rho_D(x):
return np.all(np.logical_and(np.greater(x,Qmin), np.less(x,Qmax)),axis=1)
# Plot the data domain
plotD.show_data(data, Q_ref = Q_ref, rho_D=rho_D, 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:
(d_distr_prob, d_distr_samples, d_Tree) = simpleFunP.uniform_hyperrectangle(data=data,
Q_ref=Q_ref,
bin_ratio=bin_ratio,
center_pts_per_edge = 1)
else:
(d_distr_prob, d_distr_samples, d_Tree) = simpleFunP.unif_unif(data=data,
Q_ref=Q_ref,
M=50,
bin_ratio=bin_ratio,
num_d_emulate=1E5)
data = dataf[:, QoI_indices]
Q_ref = Q_ref[QoI_indices]
dmax = data.max(axis=0)
dmin = data.min(axis=0)
dscale = bin_ratio * (dmax - dmin)
Qmax = Q_ref + 0.5 * dscale
Qmin = Q_ref - 0.5 * dscale
def rho_D(x):
return np.all(np.logical_and(np.greater(x, Qmin), np.less(x, Qmax)), axis=1)
# Plot the data domain
plotD.show_data(data, Q_ref=Q_ref, rho_D=rho_D, 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:
(d_distr_prob, d_distr_samples, d_Tree) = simpleFunP.uniform_hyperrectangle(
data=data, Q_ref=Q_ref, bin_ratio=bin_ratio, center_pts_per_edge=1
)
else:
(d_distr_prob, d_distr_samples, d_Tree) = simpleFunP.unif_unif(
data=data, Q_ref=Q_ref, M=50, bin_ratio=bin_ratio, num_d_emulate=1e5
)
# calculate probablities making Monte Carlo assumption
(P, lam_vol, io_ptr) = calculateP.prob(
Q_ref = Q_ref[14, station_nums] # 15th/20
bin_ratio = 0.15
bin_size = (np.max(Q, 0)-np.min(Q, 0))*bin_ratio
points = mdat['points']
# Create kernel
maximum = 1/np.product(bin_size)
def rho_D(outputs):
rho_left = np.repeat([Q_ref-.5*bin_size], outputs.shape[0], 0)
rho_right = np.repeat([Q_ref+.5*bin_size], outputs.shape[0], 0)
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
p_ref = mdat['points_true']
p_ref = p_ref[:, 14]
# Show the samples in the parameter space
pDom.show_param(samples=points.transpose(), data=Q, rho_D=rho_D, p_ref=p_ref)
# Show the corresponding samples in the data space
pDom.show_data(data=Q, rho_D=rho_D, Q_ref=Q_ref)
# Show multiple data domains that correspond with the convex hull of samples in
# the parameter space
pDom.show_data_domain_multi(samples=points.transpose(), data=mdat['Q'],
Q_ref=mdat['Q_true'][15], Q_nums=[1,2,5], showdim='all')
