def check_show_param(self, samples, sample_nos, p_ref, save, lnums,
showdim):
"""
Check to see that the :meth:`bet.postTools.plotDomains.show_param` ran
without generating an error.
"""
try:
plotDomains.show_param(samples, self.data, self.rho_D, p_ref,
sample_nos, save, False, lnums, showdim)
go = True
except (RuntimeError, TypeError, NameError):
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.show_param` ran
without generating an error.
"""
try:
plotDomains.show_param(samples, self.data, self.rho_D, p_ref,
sample_nos, save, False, lnums, showdim)
go = True
except (RuntimeError, TypeError, NameError):
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')
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')
