queens package#
The QUEENS package.
Subpackages#
- queens.data_processors package
- Submodules
- queens.data_processors.csv_file module
CsvFileCsvFile.use_cols_lstCsvFile.filter_typeCsvFile.header_rowCsvFile.skip_rowsCsvFile.index_columnCsvFile.use_rows_lstCsvFile.filter_rangeCsvFile.filter_target_valuesCsvFile.filter_tolCsvFile.returned_filter_formatCsvFile.check_valid_filter_options()CsvFile.expected_filter_by_rangeCsvFile.expected_filter_by_row_indexCsvFile.expected_filter_by_target_valuesCsvFile.expected_filter_entire_fileCsvFile.filter_and_manipulate_raw_data()CsvFile.get_raw_data_from_file()
- queens.data_processors.numpy_file module
- queens.data_processors.pvd_file module
- queens.data_processors.txt_file module
- queens.distributions package
- Submodules
- queens.distributions.bernoulli module
- queens.distributions.beta module
- queens.distributions.categorical module
- queens.distributions.exponential module
- queens.distributions.free_variable module
- queens.distributions.lognormal module
- queens.distributions.mean_field_normal module
MeanFieldNormalMeanFieldNormal.standard_deviationMeanFieldNormal.cdf()MeanFieldNormal.draw()MeanFieldNormal.get_check_array_dimension_and_reshape()MeanFieldNormal.grad_logpdf()MeanFieldNormal.grad_logpdf_var()MeanFieldNormal.logpdf()MeanFieldNormal.ppf()MeanFieldNormal.update_mean()MeanFieldNormal.update_variance()
- queens.distributions.mixture module
- queens.distributions.multinomial module
- queens.distributions.normal module
- queens.distributions.particle module
- queens.distributions.uniform module
- queens.distributions.uniform_discrete module
- queens.drivers package
- Submodules
- queens.drivers.function module
- queens.drivers.jobscript module
JobOptionsJobscriptJobscript.input_templatesJobscript.data_processorJobscript.gradient_data_processorJobscript.jobscript_templateJobscript.jobscript_optionsJobscript.jobscript_file_nameJobscript.raise_error_on_jobscript_failureJobscript.create_input_templates_dict()Jobscript.get_read_in_jobscript_template()Jobscript.prepare_input_files()Jobscript.run()
- queens.drivers.mpi module
- queens.iterators package
- Subpackages
- queens.iterators.sobol_index_gp_uncertainty_utils package
- Submodules
- queens.iterators.sobol_index_gp_uncertainty_utils.estimator module
- queens.iterators.sobol_index_gp_uncertainty_utils.predictor module
- queens.iterators.sobol_index_gp_uncertainty_utils.sampler module
- queens.iterators.sobol_index_gp_uncertainty_utils.statistics module
- queens.iterators.sobol_index_gp_uncertainty_utils.utils_estimate_indices module
- queens.iterators.sobol_index_gp_uncertainty_utils package
- Submodules
- queens.iterators.adaptive_sampling module
AdaptiveSamplingAdaptiveSampling.likelihood_modelAdaptiveSampling.solving_iteratorAdaptiveSampling.num_new_samplesAdaptiveSampling.num_stepsAdaptiveSampling.seedAdaptiveSampling.restart_fileAdaptiveSampling.cs_div_criterionAdaptiveSampling.x_trainAdaptiveSampling.x_train_newAdaptiveSampling.y_trainAdaptiveSampling.model_outputsAdaptiveSampling.choose_new_samples()AdaptiveSampling.core_run()AdaptiveSampling.eval_log_likelihood()AdaptiveSampling.post_run()AdaptiveSampling.pre_run()AdaptiveSampling.write_results()
cauchy_schwarz_divergence()
- queens.iterators.bbvi module
BBVIBBVI.control_variates_scaling_typeBBVI.loo_cv_boolBBVI.random_seedBBVI.max_fevalBBVI.memoryBBVI.model_eval_iteration_periodBBVI.resampleBBVI.log_variational_matBBVI.grad_params_log_variational_matBBVI.log_posterior_unnormalizedBBVI.samples_listBBVI.parameter_listBBVI.log_posterior_unnormalized_listBBVI.essBBVI.sampling_boolBBVI.sample_setBBVI.core_run()BBVI.eval_log_likelihood()BBVI.get_importance_sampling_weights()BBVI.get_log_posterior_unnormalized()BBVI.get_log_prior()
- queens.iterators.bmfia module
BMFIABMFIA.X_trainBMFIA.Y_LF_trainBMFIA.Y_HF_trainBMFIA.Z_trainBMFIA.features_configBMFIA.hf_modelBMFIA.lf_modelBMFIA.coords_experimental_dataBMFIA.time_vecBMFIA.y_obs_vecBMFIA.x_colsBMFIA.num_featuresBMFIA.coord_colsBMFIA.calculate_initial_x_train()BMFIA.core_run()BMFIA.eval_model()BMFIA.evaluate_HF_model_for_X_train()BMFIA.evaluate_LF_model_for_X_train()BMFIA.expand_training_data()BMFIA.get_design_method()BMFIA.random_design()BMFIA.set_feature_strategy()BMFIA.update_probabilistic_mapping_with_features()
- queens.iterators.bmfmc module
- queens.iterators.classification module
ClassificationIteratorClassificationIterator.result_descriptionClassificationIterator.num_sample_pointsClassificationIterator.num_model_callsClassificationIterator.random_sampling_frequencyClassificationIterator.classifierClassificationIterator.visualization_objClassificationIterator.classification_functionClassificationIterator.samplesClassificationIterator.classified_outputsClassificationIterator.binarize()ClassificationIterator.core_run()ClassificationIterator.post_run()
default_classification_function()
- queens.iterators.control_variates module
ControlVariatesControlVariates.modelControlVariates.control_variateControlVariates.seedControlVariates.num_samplesControlVariates.expectation_cvControlVariates.outputControlVariates.num_samples_cvControlVariates.samplesControlVariates.use_optimal_num_samplesControlVariates.cost_modelControlVariates.cost_cvControlVariates.variance_cv_mean_estimatorControlVariates.core_run()ControlVariates.post_run()ControlVariates.pre_run()
- queens.iterators.data module
- queens.iterators.elementary_effects module
ElementaryEffectsElementaryEffects.num_trajectoriesElementaryEffects.local_optimizationElementaryEffects.num_optimal_trajectoriesElementaryEffects.num_levelsElementaryEffects.seedElementaryEffects.confidence_levelElementaryEffects.num_bootstrap_samplesElementaryEffects.result_descriptionElementaryEffects.samplesElementaryEffects.outputElementaryEffects.salib_problemElementaryEffects.siElementaryEffects.visualizationElementaryEffects.core_run()ElementaryEffects.post_run()ElementaryEffects.pre_run()ElementaryEffects.print_results()ElementaryEffects.process_results()
- queens.iterators.grid module
- queens.iterators.hamiltonian_monte_carlo module
HamiltonianMonteCarloHamiltonianMonteCarlo.max_stepsHamiltonianMonteCarlo.target_acceptHamiltonianMonteCarlo.path_lengthHamiltonianMonteCarlo.step_sizeHamiltonianMonteCarlo.scalingHamiltonianMonteCarlo.is_covHamiltonianMonteCarlo.init_strategyHamiltonianMonteCarlo.advi_iterationsHamiltonianMonteCarlo.init_mcmc_method()
- queens.iterators.latin_hypercube_sampling module
LatinHypercubeSamplingLatinHypercubeSampling.seedLatinHypercubeSampling.num_samplesLatinHypercubeSampling.num_iterationsLatinHypercubeSampling.result_descriptionLatinHypercubeSampling.criterionLatinHypercubeSampling.samplesLatinHypercubeSampling.outputLatinHypercubeSampling.core_run()LatinHypercubeSampling.post_run()LatinHypercubeSampling.pre_run()
- queens.iterators.least_squares module
- queens.iterators.metropolis_hastings module
MetropolisHastingsMetropolisHastings.num_chainsMetropolisHastings.num_samplesMetropolisHastings.proposal_distributionMetropolisHastings.result_descriptionMetropolisHastings.as_smc_rejuvenation_stepMetropolisHastings.tuneMetropolisHastings.scale_covarianceMetropolisHastings.num_burn_inMetropolisHastings.temperMetropolisHastings.gammaMetropolisHastings.tune_intervalMetropolisHastings.tot_num_samplesMetropolisHastings.chainsMetropolisHastings.log_likelihoodMetropolisHastings.log_priorMetropolisHastings.log_posteriorMetropolisHastings.seedMetropolisHastings.acceptedMetropolisHastings.accepted_intervalMetropolisHastings.core_run()MetropolisHastings.do_mh_step()MetropolisHastings.eval_log_likelihood()MetropolisHastings.eval_log_prior()MetropolisHastings.post_run()MetropolisHastings.pre_run()
- queens.iterators.metropolis_hastings_pymc module
MetropolisHastingsPyMCMetropolisHastingsPyMC.covarianceMetropolisHastingsPyMC.tune_intervalMetropolisHastingsPyMC.scalingMetropolisHastingsPyMC.eval_log_likelihood()MetropolisHastingsPyMC.eval_log_likelihood_grad()MetropolisHastingsPyMC.eval_log_prior_grad()MetropolisHastingsPyMC.init_distribution_wrapper()MetropolisHastingsPyMC.init_mcmc_method()MetropolisHastingsPyMC.post_run()
- queens.iterators.mlmc module
- queens.iterators.monte_carlo module
- queens.iterators.nuts module
- queens.iterators.optimization module
OptimizationOptimization.algorithmOptimization.boundsOptimization.consOptimization.initial_guessOptimization.jac_methodOptimization.jac_rel_stepOptimization.max_fevalOptimization.result_descriptionOptimization.verbose_outputOptimization.precalculated_positionsOptimization.solutionOptimization.objective_and_jacobianOptimization.check_precalculated()Optimization.core_run()Optimization.eval_model()Optimization.evaluate_fd_positions()Optimization.jacobian()Optimization.objective()Optimization.post_run()Optimization.pre_run()
- queens.iterators.points module
- queens.iterators.polynomial_chaos module
PolynomialChaosPolynomialChaos.seedPolynomialChaos.num_collocation_pointsPolynomialChaos.sampling_rulePolynomialChaos.polynomial_orderPolynomialChaos.result_descriptionPolynomialChaos.sparsePolynomialChaos.polynomial_chaos_approachPolynomialChaos.distributionPolynomialChaos.samplesPolynomialChaos.result_dictPolynomialChaos.core_run()PolynomialChaos.post_run()PolynomialChaos.pre_run()
create_chaospy_distribution()create_chaospy_joint_distribution()
- queens.iterators.reinforcement_learning module
ReinforcementLearningReinforcementLearning._interaction_stepsReinforcementLearning._modeReinforcementLearning.initial_observationReinforcementLearning.outputReinforcementLearning.result_descriptionReinforcementLearning.samplesReinforcementLearning.convert_to_numpy()ReinforcementLearning.core_run()ReinforcementLearning.interaction_stepsReinforcementLearning.modeReinforcementLearning.post_run()ReinforcementLearning.pre_run()ReinforcementLearning.update_samples_and_outputs()
- queens.iterators.reparameteriztion_based_variational module
- queens.iterators.sequential_monte_carlo module
SequentialMonteCarloSequentialMonteCarlo.plot_trace_everySequentialMonteCarlo.result_descriptionSequentialMonteCarlo.seedSequentialMonteCarlo.mcmc_kernelSequentialMonteCarlo.num_particlesSequentialMonteCarlo.num_variablesSequentialMonteCarlo.particlesSequentialMonteCarlo.weightsSequentialMonteCarlo.log_likelihoodSequentialMonteCarlo.log_priorSequentialMonteCarlo.log_posteriorSequentialMonteCarlo.essSequentialMonteCarlo.ess_curSequentialMonteCarlo.temperSequentialMonteCarlo.gamma_curSequentialMonteCarlo.gammasSequentialMonteCarlo.aSequentialMonteCarlo.bSequentialMonteCarlo.calc_new_ess()SequentialMonteCarlo.calc_new_gamma()SequentialMonteCarlo.calc_new_weights()SequentialMonteCarlo.core_run()SequentialMonteCarlo.draw_trace()SequentialMonteCarlo.eval_log_likelihood()SequentialMonteCarlo.eval_log_prior()SequentialMonteCarlo.post_run()SequentialMonteCarlo.pre_run()SequentialMonteCarlo.resample()SequentialMonteCarlo.update_ess()SequentialMonteCarlo.update_gamma()SequentialMonteCarlo.update_weights()
- queens.iterators.sequential_monte_carlo_chopin module
ParticlesChopinDistributionSequentialMonteCarloChopinSequentialMonteCarloChopin.result_descriptionSequentialMonteCarloChopin.seedSequentialMonteCarloChopin.num_particlesSequentialMonteCarloChopin.num_variablesSequentialMonteCarloChopin.max_fevalSequentialMonteCarloChopin.priorSequentialMonteCarloChopin.smc_objSequentialMonteCarloChopin.resampling_thresholdSequentialMonteCarloChopin.resampling_methodSequentialMonteCarloChopin.feynman_kac_modelSequentialMonteCarloChopin.num_rejuvenation_stepsSequentialMonteCarloChopin.waste_freeSequentialMonteCarloChopin.remove_zero_weight_particlesSequentialMonteCarloChopin.merge_identical_particlesSequentialMonteCarloChopin.core_run()SequentialMonteCarloChopin.eval_log_likelihood()SequentialMonteCarloChopin.get_particles_and_weights()SequentialMonteCarloChopin.initialize_feynman_kac()SequentialMonteCarloChopin.post_run()SequentialMonteCarloChopin.pre_run()
- queens.iterators.sobol_index module
SobolIndexSobolIndex.seedSobolIndex.num_samplesSobolIndex.calc_second_orderSobolIndex.skip_valuesSobolIndex.num_bootstrap_samplesSobolIndex.confidence_levelSobolIndex.result_descriptionSobolIndex.samplesSobolIndex.outputSobolIndex.salib_problemSobolIndex.num_paramsSobolIndex.parameter_namesSobolIndex.sensitivity_indicesSobolIndex.core_run()SobolIndex.get_all_samples()SobolIndex.plot_results()SobolIndex.post_run()SobolIndex.pre_run()SobolIndex.print_results()SobolIndex.process_results()
- queens.iterators.sobol_index_gp_uncertainty module
SobolIndexGPUncertaintySobolIndexGPUncertainty.result_descriptionSobolIndexGPUncertainty.num_procsSobolIndexGPUncertainty.samplerSobolIndexGPUncertainty.predictorSobolIndexGPUncertainty.index_estimatorSobolIndexGPUncertainty.statisticsSobolIndexGPUncertainty.calculate_second_orderSobolIndexGPUncertainty.calculate_third_orderSobolIndexGPUncertainty.resultsSobolIndexGPUncertainty.calculate_index()SobolIndexGPUncertainty.core_run()SobolIndexGPUncertainty.evaluate_statistics()SobolIndexGPUncertainty.post_run()SobolIndexGPUncertainty.pre_run()
- queens.iterators.sobol_sequence module
- Subpackages
- queens.models package
- Subpackages
- queens.models.likelihoods package
- queens.models.reinforcement_learning package
- queens.models.surrogates package
- Subpackages
- Submodules
- queens.models.surrogates.bayesian_neural_network module
- queens.models.surrogates.gaussian_neural_network module
- queens.models.surrogates.gaussian_process module
- queens.models.surrogates.heteroskedastic_gaussian_process module
- queens.models.surrogates.jitted_gaussian_process module
- queens.models.surrogates.variational_gaussian_process module
- Submodules
- queens.models.adjoint module
- queens.models.bmfmc module
BMFMCBMFMC.parametersBMFMC.interfaceBMFMC.features_configBMFMC.X_colsBMFMC.num_featuresBMFMC.high_fidelity_modelBMFMC.X_trainBMFMC.Y_HF_trainBMFMC.Y_LFs_trainBMFMC.X_mcBMFMC.Y_LFs_mcBMFMC.Y_HF_mcBMFMC.gammas_ext_mcBMFMC.gammas_ext_trainBMFMC.Z_trainBMFMC.Z_mcBMFMC.m_f_mcBMFMC.var_y_mcBMFMC.p_yhf_meanBMFMC.p_yhf_varBMFMC.predictive_var_boolBMFMC.p_yhf_mcBMFMC.p_ylf_mcBMFMC.no_features_comparison_boolBMFMC.eigenfunc_random_fieldsBMFMC.eigenvalsBMFMC.f_mean_trainBMFMC.y_pdf_supportBMFMC.lf_data_iteratorsBMFMC.hf_data_iteratorBMFMC.training_indicesBMFMC.uncertain_parametersBMFMC.visualizationBMFMC.build_approximation()BMFMC.calculate_extended_gammas()BMFMC.calculate_p_yhf_mean()BMFMC.calculate_p_yhf_var()BMFMC.compute_pyhf_statistics()BMFMC.compute_pymc_reference()BMFMC.get_hf_training_data()BMFMC.get_random_fields_and_truncated_basis()BMFMC.grad()BMFMC.input_dim_red()BMFMC.load_sampling_data()BMFMC.run_BMFMC()BMFMC.run_BMFMC_without_features()BMFMC.set_feature_strategy()BMFMC.update_probabilistic_mapping_with_features()
BmfmcInterfaceassemble_x_red_stdizd()linear_scale_a_to_b()project_samples_on_truncated_basis()
- queens.models.finite_difference module
- queens.models.logpdf_gp module
LogpdfGPLogpdfGP.approx_typeLogpdfGP.num_hyperLogpdfGP.num_optimizationsLogpdfGP.hmc_burn_inLogpdfGP.hmc_stepsLogpdfGP.prior_rateLogpdfGP.prior_gp_meanLogpdfGP.upper_boundLogpdfGP.quantileLogpdfGP.jitterLogpdfGP.x_trainLogpdfGP.y_trainLogpdfGP.scaler_xLogpdfGP.scaler_yLogpdfGP.num_dimLogpdfGP.hyperparametersLogpdfGP.chol_k_train_trainLogpdfGP.v_trainLogpdfGP.jit_func_generate_outputLogpdfGP.partial_hyperparameter_log_probLogpdfGP.batch_sizeLogpdfGP.calc_train_factor()LogpdfGP.evaluate_mean_and_std()LogpdfGP.generate_output_cfbgp()LogpdfGP.generate_output_cgpmap_2()LogpdfGP.generate_output_gpmap_1()LogpdfGP.grad()LogpdfGP.hyperparameter_log_likelihood()LogpdfGP.hyperparameter_log_prior()LogpdfGP.hyperparameter_log_prob()LogpdfGP.initialize()LogpdfGP.optimize_hyperparameters()LogpdfGP.sample_hyperparameters()
distances()rbf()rbf_by_dists()
- queens.models.simulation module
- Subpackages
- queens.parameters package
- Subpackages
- Submodules
- queens.parameters.parameters module
ParametersParameters.dictParameters.parameters_keysParameters.num_parametersParameters.random_field_flagParameters.namesParameters.draw_samples()Parameters.expand_random_field_realization()Parameters.grad_joint_logpdf()Parameters.inverse_cdf_transform()Parameters.joint_logpdf()Parameters.latent_grad()Parameters.sample_as_dict()Parameters.to_distribution_list()Parameters.to_list()
- queens.schedulers package
- queens.stochastic_optimizers package
- Submodules
- queens.stochastic_optimizers.adam module
- queens.stochastic_optimizers.adamax module
- queens.stochastic_optimizers.learning_rate_decay module
- queens.stochastic_optimizers.rms_prop module
- queens.stochastic_optimizers.sgd module
- queens.utils package
- Submodules
- queens.utils.ascii_art module
- queens.utils.classifier module
- queens.utils.cli module
- queens.utils.collection module
- queens.utils.config_directories module
- queens.utils.configure_tensorflow module
- queens.utils.exceptions module
- queens.utils.experimental_data_reader module
- queens.utils.fd_jacobian module
- queens.utils.gpflow_transformations module
- queens.utils.imports module
- queens.utils.injector module
- queens.utils.io module
- queens.utils.iterative_averaging module
- queens.utils.jax_minimize_wrapper module
- queens.utils.logger_settings module
- queens.utils.mcmc module
- queens.utils.metadata module
- queens.utils.numpy_array module
- queens.utils.numpy_linalg module
- queens.utils.path module
- queens.utils.pdf_estimation module
- queens.utils.plot_outputs module
- queens.utils.pool module
- queens.utils.printing module
- queens.utils.process_outputs module
- queens.utils.random_process_scaler module
- queens.utils.remote_build module
- queens.utils.remote_operations module
RemoteConnectionRemoteConnection.remote_pythonRemoteConnection.remote_queens_repositoryRemoteConnection.build_remote_environment()RemoteConnection.copy_to_remote()RemoteConnection.create_remote_directory()RemoteConnection.get_free_local_port()RemoteConnection.get_free_remote_port()RemoteConnection.open()RemoteConnection.open_port_forwarding()RemoteConnection.run_function()RemoteConnection.start_cluster()RemoteConnection.sync_remote_repository()
get_port()
- queens.utils.rsync module
- queens.utils.run_subprocess module
- queens.utils.sequential_monte_carlo module
- queens.utils.sobol_sequence module
- queens.utils.start_dask_cluster module
- queens.utils.valid_options module
- queens.variational_distributions package
- Submodules
- queens.variational_distributions.full_rank_normal module
FullRankNormalFullRankNormal.n_parametersFullRankNormal.conduct_reparameterization()FullRankNormal.construct_variational_parameters()FullRankNormal.draw()FullRankNormal.export_dict()FullRankNormal.fisher_information_matrix()FullRankNormal.grad_params_logpdf()FullRankNormal.grad_params_reparameterization()FullRankNormal.grad_sample_logpdf()FullRankNormal.initialize_variational_parameters()FullRankNormal.logpdf()FullRankNormal.pdf()FullRankNormal.reconstruct_distribution_parameters()FullRankNormal.total_grad_params_logpdf()
- queens.variational_distributions.joint module
JointJoint.distributionsJoint.n_parametersJoint.distributions_n_parametersJoint.distributions_dimensionJoint.construct_variational_parameters()Joint.draw()Joint.export_dict()Joint.fisher_information_matrix()Joint.grad_params_logpdf()Joint.initialize_variational_parameters()Joint.logpdf()Joint.pdf()Joint.reconstruct_distribution_parameters()
split_array_by_chunk_sizes()
- queens.variational_distributions.mean_field_normal module
MeanFieldNormalMeanFieldNormal.n_parametersMeanFieldNormal.conduct_reparameterization()MeanFieldNormal.construct_variational_parameters()MeanFieldNormal.draw()MeanFieldNormal.export_dict()MeanFieldNormal.fisher_information_matrix()MeanFieldNormal.grad_params_logpdf()MeanFieldNormal.grad_params_reparameterization()MeanFieldNormal.grad_sample_logpdf()MeanFieldNormal.initialize_variational_parameters()MeanFieldNormal.logpdf()MeanFieldNormal.pdf()MeanFieldNormal.reconstruct_distribution_parameters()MeanFieldNormal.total_grad_params_logpdf()
- queens.variational_distributions.mixture_model module
MixtureModelMixtureModel.n_componentsMixtureModel.base_distributionMixtureModel.n_parametersMixtureModel.construct_variational_parameters()MixtureModel.draw()MixtureModel.export_dict()MixtureModel.fisher_information_matrix()MixtureModel.grad_params_logpdf()MixtureModel.initialize_variational_parameters()MixtureModel.logpdf()MixtureModel.pdf()MixtureModel.reconstruct_distribution_parameters()
- queens.variational_distributions.particle module
ParticleParticle.particles_objParticle.dimensionParticle.construct_variational_parameters()Particle.draw()Particle.export_dict()Particle.fisher_information_matrix()Particle.grad_params_logpdf()Particle.initialize_variational_parameters()Particle.logpdf()Particle.pdf()Particle.reconstruct_distribution_parameters()
- queens.visualization package
- Submodules
- queens.visualization.bmfia_visualization module
- queens.visualization.bmfmc_visualization module
BMFMCVisualizationBMFMCVisualization.pathsBMFMCVisualization.save_boolsBMFMCVisualization.animation_boolBMFMCVisualization.predictive_varBMFMCVisualization.no_features_refBMFMCVisualization.plot_booleansBMFMCVisualization.from_config_create()BMFMCVisualization.plot_feature_ranking()BMFMCVisualization.plot_manifold()BMFMCVisualization.plot_pdfs()
- queens.visualization.classification module
- queens.visualization.gaussian_neural_network_vis module
- queens.visualization.gnuplot_vis module
- queens.visualization.grid_iterator_visualization module
GridIteratorVisualizationGridIteratorVisualization.saving_paths_listGridIteratorVisualization.save_boolsGridIteratorVisualization.plot_booleansGridIteratorVisualization.scale_types_listGridIteratorVisualization.var_names_listGridIteratorVisualization.from_config_create()GridIteratorVisualization.get_plotter()GridIteratorVisualization.plot_one_d()GridIteratorVisualization.plot_qoi_grid()GridIteratorVisualization.plot_two_d()
- queens.visualization.sa_visualization module
- queens.visualization.surrogate_visualization module
SurrogateVisualizationSurrogateVisualization.saving_pathsSurrogateVisualization.should_be_savedSurrogateVisualization.should_be_displayedSurrogateVisualization.parameter_namesSurrogateVisualization.figuresSurrogateVisualization.from_config_create()SurrogateVisualization.plot()SurrogateVisualization.plot_1d()SurrogateVisualization.plot_2d()SurrogateVisualization.plot_gp_from_gpflow()
convert_to_dict()
- queens.visualization.variational_inference_visualization module
Submodules#
queens.global_settings module#
Global Settings.
This module provides a context for QUEENS runs with exit functionality for logging and working with remote resources.
- class GlobalSettings(experiment_name, output_dir, debug=False)[source]#
Bases:
objectClass for global settings in Queens.
- experiment_name#
Experiment name of queens run
- Type:
str
- output_dir#
Output directory for queens run
- Type:
Path
- git_hash#
Hash of active git commit
- Type:
str
- debug#
True if debug mode is to be used
- Type:
bool
- result_file(extension: str, suffix: str = None) Path[source]#
Create path to a result file with a given extension.
- Parameters:
extension (str) – The extension of the file.
suffix (str, optional) – The suffix to be appended to the experiment_name i.e. the default stem of the filename.
- Returns:
Path – Path of the file.
queens.main module#
QUEENS main.
Main module of QUEENS containing the high-level control routine for input file workflow.
- run_iterator(iterator, global_settings)[source]#
Run the main queens iterator.
- Parameters:
iterator (Iterator) – Main queens iterator
global_settings (GlobalSettings) – settings of the QUEENS experiment including its name and the output directory