import bet.calculateP.simpleFunP as simpleFunP import bet.calculateP.calculateP as calculateP import bet.postProcess.plotP as plotP import bet.postProcess.plotDomains as plotD import bet.sample as samp import bet.sampling.basicSampling as bsam from myModel import my_model from Compute_Save_KL import computeSaveKL # Interface BET to the model. sampler = bsam.sampler(my_model) # Define the number of KL terms to use to represent permeability field num_KL_terms = 2 # Compute and save the KL expansion -- can comment out after running once computeSaveKL(num_KL_terms) # Initialize input parameter sample set object input_samples = samp.sample_set(num_KL_terms) # Set parameter domain KL_term_min = -3.0 KL_term_max = 3.0 input_samples.set_domain( np.repeat([[KL_term_min, KL_term_max]], num_KL_terms, axis=0)) ''' Suggested changes for user: Try with and without random sampling. If using regular sampling, try different numbers of samples
import bet.calculateP.calculateP as calculateP import bet.postProcess.plotP as plotP import bet.postProcess.plotDomains as plotD import bet.sample as samp import bet.sampling.basicSampling as bsam from lbModel import lb_model from myModel import my_model from Compute_Save_KL import computeSaveKL # Interface BET to the model. sampler = bsam.sampler(lb_model) # Define the number of KL terms to use to represent permeability field num_KL_terms = 2 # Compute and save the KL expansion -- can comment out after running once computeSaveKL(num_KL_terms) # Initialize input parameter sample set object input_samples = samp.sample_set(num_KL_terms) # Set parameter domain KL_term_min = -3.0 KL_term_max = 3.0 input_samples.set_domain(np.repeat([[KL_term_min, KL_term_max]], num_KL_terms, axis=0)) ''' Suggested changes for user: Try with and without random sampling.