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')