queens package#
The QUEENS package.
Subpackages#
- queens.data_processors package
- Submodules
- queens.data_processors.csv_file module
CsvFile
CsvFile.use_cols_lst
CsvFile.filter_type
CsvFile.header_row
CsvFile.skip_rows
CsvFile.index_column
CsvFile.use_rows_lst
CsvFile.filter_range
CsvFile.filter_target_values
CsvFile.filter_tol
CsvFile.returned_filter_format
CsvFile.check_valid_filter_options()
CsvFile.expected_filter_by_range
CsvFile.expected_filter_by_row_index
CsvFile.expected_filter_by_target_values
CsvFile.expected_filter_entire_file
CsvFile.filter_and_manipulate_raw_data()
CsvFile.get_raw_data_from_file()
- queens.data_processors.ensight_file module
EnsightFile
EnsightFile.experimental_data
EnsightFile.coordinates_label_experimental
EnsightFile.time_label_experimental
EnsightFile.external_geometry
EnsightFile.target_time_lst
EnsightFile.time_tol
EnsightFile.vtk_field_label
EnsightFile.vtk_field_components
EnsightFile.vtk_array_type
EnsightFile.geometric_target
EnsightFile.geometric_set_data
EnsightFile.filter_and_manipulate_raw_data()
EnsightFile.get_raw_data_from_file()
EnsightFile.read_geometry_coordinates()
- 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
MeanFieldNormal
MeanFieldNormal.standard_deviation
MeanFieldNormal.cdf()
MeanFieldNormal.draw()
MeanFieldNormal.get_check_array_dimension_and_reshape()
MeanFieldNormal.grad_logpdf()
MeanFieldNormal.grad_logpdf_var()
MeanFieldNormal.logpdf()
MeanFieldNormal.pdf()
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.fourc module
- queens.drivers.function module
- queens.drivers.jobscript module
JobOptions
Jobscript
Jobscript.input_templates
Jobscript.data_processor
Jobscript.gradient_data_processor
Jobscript.jobscript_template
Jobscript.jobscript_options
Jobscript.jobscript_file_name
Jobscript.raise_error_on_jobscript_failure
Jobscript.create_input_templates_dict()
Jobscript.get_read_in_jobscript_template()
Jobscript.prepare_input_files()
Jobscript.run()
- queens.drivers.mpi module
- queens.example_simulator_functions package
example_simulator_function_by_name()
- Submodules
- queens.example_simulator_functions.agawal09 module
- queens.example_simulator_functions.borehole83 module
- queens.example_simulator_functions.branin78 module
- queens.example_simulator_functions.currin88 module
- queens.example_simulator_functions.executable_park91a_hifi_on_grid_with_gradients module
- queens.example_simulator_functions.gardner14a module
- queens.example_simulator_functions.gaussian_logpdf module
- queens.example_simulator_functions.gaussian_mixture_logpdf module
- queens.example_simulator_functions.ishigami90 module
- queens.example_simulator_functions.ma09 module
- queens.example_simulator_functions.oakley_ohagan04 module
- queens.example_simulator_functions.parabola_residual module
- queens.example_simulator_functions.paraboloid module
- queens.example_simulator_functions.park91a module
- queens.example_simulator_functions.park91b module
- queens.example_simulator_functions.perdikaris17 module
- queens.example_simulator_functions.rezende15 module
- queens.example_simulator_functions.rosenbrock60 module
- queens.example_simulator_functions.sinus module
- queens.example_simulator_functions.sobol_g_function module
- queens.external_geometries package
- Submodules
- queens.external_geometries.fourc_dat module
FourcDat
FourcDat.path_to_dat_file
FourcDat.path_to_preprocessed_dat_file
FourcDat.coords_dict
FourcDat.list_geometric_sets
FourcDat.current_dat_section
FourcDat.desired_dat_sections
FourcDat.nodes_of_interest
FourcDat.new_nodes_lst
FourcDat.node_topology
FourcDat.line_topology
FourcDat.surface_topology
FourcDat.volume_topology
FourcDat.node_coordinates
FourcDat.element_centers
FourcDat.element_topology
FourcDat.original_materials_in_dat
FourcDat.list_associated_material_numbers
FourcDat.new_material_numbers
FourcDat.random_dirich_flag
FourcDat.random_transport_dirich_flag
FourcDat.random_neumann_flag
FourcDat.nodes_written
FourcDat.random_fields
FourcDat.check_if_in_desired_dat_section()
FourcDat.dat_sections
FourcDat.finish_and_clean()
FourcDat.get_coordinates_of_desired_geometric_sets()
FourcDat.get_current_dat_section()
FourcDat.get_desired_dat_sections()
FourcDat.get_elements_belonging_to_desired_material()
FourcDat.get_materials()
FourcDat.get_nodes_of_interest()
FourcDat.get_only_desired_coordinates()
FourcDat.get_only_desired_topology()
FourcDat.get_topology()
FourcDat.organize_sections()
FourcDat.read_external_data()
FourcDat.read_geometry_from_dat_file()
FourcDat.section_match_dict
FourcDat.write_random_fields_to_dat()
- 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
AdaptiveSampling
AdaptiveSampling.likelihood_model
AdaptiveSampling.initial_train_iterator
AdaptiveSampling.solving_iterator
AdaptiveSampling.num_new_samples
AdaptiveSampling.num_steps
AdaptiveSampling.seed
AdaptiveSampling.restart_file
AdaptiveSampling.cs_div_criterion
AdaptiveSampling.x_train
AdaptiveSampling.x_train_new
AdaptiveSampling.y_train
AdaptiveSampling.model_outputs
AdaptiveSampling.choose_new_samples()
AdaptiveSampling.core_run()
AdaptiveSampling.eval_log_likelihood()
AdaptiveSampling.get_particles_and_weights()
AdaptiveSampling.post_run()
AdaptiveSampling.pre_run()
AdaptiveSampling.write_results()
cauchy_schwarz_divergence()
- queens.iterators.bbvi module
BBVI
BBVI.control_variates_scaling_type
BBVI.loo_cv_bool
BBVI.random_seed
BBVI.max_feval
BBVI.memory
BBVI.model_eval_iteration_period
BBVI.resample
BBVI.log_variational_mat
BBVI.grad_params_log_variational_mat
BBVI.log_posterior_unnormalized
BBVI.samples_list
BBVI.parameter_list
BBVI.log_posterior_unnormalized_list
BBVI.ess
BBVI.sampling_bool
BBVI.sample_set
BBVI.core_run()
BBVI.eval_log_likelihood()
BBVI.get_importance_sampling_weights()
BBVI.get_log_posterior_unnormalized()
BBVI.get_log_prior()
- queens.iterators.bmfia module
BMFIA
BMFIA.X_train
BMFIA.Y_LF_train
BMFIA.Y_HF_train
BMFIA.Z_train
BMFIA.features_config
BMFIA.hf_model
BMFIA.lf_model
BMFIA.coords_experimental_data
BMFIA.time_vec
BMFIA.y_obs_vec
BMFIA.x_cols
BMFIA.num_features
BMFIA.coord_cols
BMFIA.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
ClassificationIterator
ClassificationIterator.result_description
ClassificationIterator.num_sample_points
ClassificationIterator.num_model_calls
ClassificationIterator.random_sampling_frequency
ClassificationIterator.classifier
ClassificationIterator.visualization_obj
ClassificationIterator.classification_function
ClassificationIterator.samples
ClassificationIterator.classified_outputs
ClassificationIterator.binarize()
ClassificationIterator.core_run()
ClassificationIterator.post_run()
default_classification_function()
- queens.iterators.control_variates module
ControlVariates
ControlVariates.model
ControlVariates.control_variate
ControlVariates.seed
ControlVariates.num_samples
ControlVariates.expectation_cv
ControlVariates.output
ControlVariates.num_samples_cv
ControlVariates.samples
ControlVariates.use_optimal_num_samples
ControlVariates.cost_model
ControlVariates.cost_cv
ControlVariates.variance_cv_mean_estimator
ControlVariates.core_run()
ControlVariates.post_run()
ControlVariates.pre_run()
- queens.iterators.data module
- queens.iterators.elementary_effects module
ElementaryEffects
ElementaryEffects.num_trajectories
ElementaryEffects.local_optimization
ElementaryEffects.num_optimal_trajectories
ElementaryEffects.num_levels
ElementaryEffects.seed
ElementaryEffects.confidence_level
ElementaryEffects.num_bootstrap_samples
ElementaryEffects.result_description
ElementaryEffects.samples
ElementaryEffects.output
ElementaryEffects.salib_problem
ElementaryEffects.si
ElementaryEffects.visualization
ElementaryEffects.core_run()
ElementaryEffects.post_run()
ElementaryEffects.pre_run()
ElementaryEffects.print_results()
ElementaryEffects.process_results()
- queens.iterators.grid module
- queens.iterators.hamiltonian_monte_carlo module
HamiltonianMonteCarlo
HamiltonianMonteCarlo.max_steps
HamiltonianMonteCarlo.target_accept
HamiltonianMonteCarlo.path_length
HamiltonianMonteCarlo.step_size
HamiltonianMonteCarlo.scaling
HamiltonianMonteCarlo.is_cov
HamiltonianMonteCarlo.init_strategy
HamiltonianMonteCarlo.advi_iterations
HamiltonianMonteCarlo.init_mcmc_method()
- queens.iterators.latin_hypercube_sampling module
LatinHypercubeSampling
LatinHypercubeSampling.seed
LatinHypercubeSampling.num_samples
LatinHypercubeSampling.num_iterations
LatinHypercubeSampling.result_description
LatinHypercubeSampling.criterion
LatinHypercubeSampling.samples
LatinHypercubeSampling.output
LatinHypercubeSampling.core_run()
LatinHypercubeSampling.post_run()
LatinHypercubeSampling.pre_run()
- queens.iterators.least_squares module
- queens.iterators.metropolis_hastings module
MetropolisHastings
MetropolisHastings.num_chains
MetropolisHastings.num_samples
MetropolisHastings.proposal_distribution
MetropolisHastings.result_description
MetropolisHastings.as_smc_rejuvenation_step
MetropolisHastings.tune
MetropolisHastings.scale_covariance
MetropolisHastings.num_burn_in
MetropolisHastings.temper
MetropolisHastings.gamma
MetropolisHastings.tune_interval
MetropolisHastings.tot_num_samples
MetropolisHastings.chains
MetropolisHastings.log_likelihood
MetropolisHastings.log_prior
MetropolisHastings.log_posterior
MetropolisHastings.seed
MetropolisHastings.accepted
MetropolisHastings.accepted_interval
MetropolisHastings.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
MetropolisHastingsPyMC
MetropolisHastingsPyMC.covariance
MetropolisHastingsPyMC.tune_interval
MetropolisHastingsPyMC.scaling
MetropolisHastingsPyMC.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
Optimization
Optimization.algorithm
Optimization.bounds
Optimization.cons
Optimization.initial_guess
Optimization.jac_method
Optimization.jac_rel_step
Optimization.max_feval
Optimization.result_description
Optimization.verbose_output
Optimization.precalculated_positions
Optimization.solution
Optimization.objective_and_jacobian
Optimization.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
PolynomialChaos
PolynomialChaos.seed
PolynomialChaos.num_collocation_points
PolynomialChaos.sampling_rule
PolynomialChaos.polynomial_order
PolynomialChaos.result_description
PolynomialChaos.sparse
PolynomialChaos.polynomial_chaos_approach
PolynomialChaos.distribution
PolynomialChaos.samples
PolynomialChaos.result_dict
PolynomialChaos.core_run()
PolynomialChaos.post_run()
PolynomialChaos.pre_run()
create_chaospy_distribution()
create_chaospy_joint_distribution()
- queens.iterators.reinforcement_learning module
ReinforcementLearning
ReinforcementLearning._interaction_steps
ReinforcementLearning._mode
ReinforcementLearning.initial_observation
ReinforcementLearning.output
ReinforcementLearning.result_description
ReinforcementLearning.samples
ReinforcementLearning.convert_to_numpy()
ReinforcementLearning.core_run()
ReinforcementLearning.interaction_steps
ReinforcementLearning.mode
ReinforcementLearning.post_run()
ReinforcementLearning.pre_run()
ReinforcementLearning.update_samples_and_outputs()
- queens.iterators.reparameteriztion_based_variational module
- queens.iterators.sequential_monte_carlo module
SequentialMonteCarlo
SequentialMonteCarlo.plot_trace_every
SequentialMonteCarlo.result_description
SequentialMonteCarlo.seed
SequentialMonteCarlo.mcmc_kernel
SequentialMonteCarlo.num_particles
SequentialMonteCarlo.num_variables
SequentialMonteCarlo.particles
SequentialMonteCarlo.weights
SequentialMonteCarlo.log_likelihood
SequentialMonteCarlo.log_prior
SequentialMonteCarlo.log_posterior
SequentialMonteCarlo.ess
SequentialMonteCarlo.ess_cur
SequentialMonteCarlo.temper
SequentialMonteCarlo.gamma_cur
SequentialMonteCarlo.gammas
SequentialMonteCarlo.a
SequentialMonteCarlo.b
SequentialMonteCarlo.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
ParticlesChopinDistribution
SequentialMonteCarloChopin
SequentialMonteCarloChopin.result_description
SequentialMonteCarloChopin.seed
SequentialMonteCarloChopin.num_particles
SequentialMonteCarloChopin.num_variables
SequentialMonteCarloChopin.n_sims
SequentialMonteCarloChopin.max_feval
SequentialMonteCarloChopin.prior
SequentialMonteCarloChopin.smc_obj
SequentialMonteCarloChopin.resampling_threshold
SequentialMonteCarloChopin.resampling_method
SequentialMonteCarloChopin.feynman_kac_model
SequentialMonteCarloChopin.num_rejuvenation_steps
SequentialMonteCarloChopin.waste_free
SequentialMonteCarloChopin.core_run()
SequentialMonteCarloChopin.eval_log_likelihood()
SequentialMonteCarloChopin.initialize_feynman_kac()
SequentialMonteCarloChopin.post_run()
SequentialMonteCarloChopin.pre_run()
- queens.iterators.sobol_index module
SobolIndex
SobolIndex.seed
SobolIndex.num_samples
SobolIndex.calc_second_order
SobolIndex.skip_values
SobolIndex.num_bootstrap_samples
SobolIndex.confidence_level
SobolIndex.result_description
SobolIndex.samples
SobolIndex.output
SobolIndex.salib_problem
SobolIndex.num_params
SobolIndex.parameter_names
SobolIndex.sensitivity_indices
SobolIndex.core_run()
SobolIndex.get_all_samples()
SobolIndex.plot_results()
SobolIndex.post_run()
SobolIndex.pre_run()
SobolIndex.print_results()
SobolIndex.process_results()
extract_parameters_of_parameter_distributions()
- queens.iterators.sobol_index_gp_uncertainty module
SobolIndexGPUncertainty
SobolIndexGPUncertainty.result_description
SobolIndexGPUncertainty.num_procs
SobolIndexGPUncertainty.sampler
SobolIndexGPUncertainty.predictor
SobolIndexGPUncertainty.index_estimator
SobolIndexGPUncertainty.statistics
SobolIndexGPUncertainty.calculate_second_order
SobolIndexGPUncertainty.calculate_third_order
SobolIndexGPUncertainty.results
SobolIndexGPUncertainty.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
BMFMC
BMFMC.parameters
BMFMC.interface
BMFMC.features_config
BMFMC.X_cols
BMFMC.num_features
BMFMC.high_fidelity_model
BMFMC.X_train
BMFMC.Y_HF_train
BMFMC.Y_LFs_train
BMFMC.X_mc
BMFMC.Y_LFs_mc
BMFMC.Y_HF_mc
BMFMC.gammas_ext_mc
BMFMC.gammas_ext_train
BMFMC.Z_train
BMFMC.Z_mc
BMFMC.m_f_mc
BMFMC.var_y_mc
BMFMC.p_yhf_mean
BMFMC.p_yhf_var
BMFMC.predictive_var_bool
BMFMC.p_yhf_mc
BMFMC.p_ylf_mc
BMFMC.no_features_comparison_bool
BMFMC.eigenfunc_random_fields
BMFMC.eigenvals
BMFMC.f_mean_train
BMFMC.y_pdf_support
BMFMC.lf_data_iterators
BMFMC.hf_data_iterator
BMFMC.training_indices
BMFMC.uncertain_parameters
BMFMC.visualization
BMFMC.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.evaluate()
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()
BmfmcInterface
assemble_x_red_stdizd()
linear_scale_a_to_b()
project_samples_on_truncated_basis()
- queens.models.finite_difference module
- queens.models.logpdf_gp module
LogpdfGP
LogpdfGP.approx_type
LogpdfGP.num_hyper
LogpdfGP.num_optimizations
LogpdfGP.hmc_burn_in
LogpdfGP.hmc_steps
LogpdfGP.prior_rate
LogpdfGP.prior_gp_mean
LogpdfGP.upper_bound
LogpdfGP.quantile
LogpdfGP.jitter
LogpdfGP.x_train
LogpdfGP.y_train
LogpdfGP.scaler_x
LogpdfGP.scaler_y
LogpdfGP.num_dim
LogpdfGP.hyperparameters
LogpdfGP.chol_k_train_train
LogpdfGP.v_train
LogpdfGP.jit_func_generate_output
LogpdfGP.partial_hyperparameter_log_prob
LogpdfGP.batch_size
LogpdfGP.calc_train_factor()
LogpdfGP.evaluate()
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
Parameters
Parameters.dict
Parameters.parameters_keys
Parameters.num_parameters
Parameters.random_field_flag
Parameters.names
Parameters.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()
from_config_create_parameters()
- 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.from_config_create module
- queens.utils.gpflow_transformations module
- queens.utils.imports module
- queens.utils.injector module
- queens.utils.input_to_script module
QueensPythonCode
QueensPythonCode.imports
QueensPythonCode.run_iterator
QueensPythonCode.load_results
QueensPythonCode.global_settings_context
QueensPythonCode.code
QueensPythonCode.parameters
QueensPythonCode.global_settings
QueensPythonCode.extern_imports
QueensPythonCode.create_main
QueensPythonCode.create_code_section()
QueensPythonCode.generate_code()
QueensPythonCode.generate_script()
VariableName
assign_variable_value()
create_initialization_call()
create_initialization_call_from_class_and_arguments()
create_script_from_input_file()
dict_replace_infs()
from_config_create_fields_code()
from_config_create_parameters()
from_config_create_script()
get_module_class()
insert_new_obj()
list_replace_infs()
stringify()
- 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
RemoteConnection
RemoteConnection.remote_python
RemoteConnection.remote_queens_repository
RemoteConnection.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
FullRankNormal
FullRankNormal.n_parameters
FullRankNormal.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
Joint
Joint.distributions
Joint.n_parameters
Joint.distributions_n_parameters
Joint.distributions_dimension
Joint.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
MeanFieldNormal
MeanFieldNormal.n_parameters
MeanFieldNormal.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
MixtureModel
MixtureModel.n_components
MixtureModel.base_distribution
MixtureModel.n_parameters
MixtureModel.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
Particle
Particle.particles_obj
Particle.dimension
Particle.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
BMFMCVisualization
BMFMCVisualization.paths
BMFMCVisualization.save_bools
BMFMCVisualization.animation_bool
BMFMCVisualization.predictive_var
BMFMCVisualization.no_features_ref
BMFMCVisualization.plot_booleans
BMFMCVisualization.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
GridIteratorVisualization
GridIteratorVisualization.saving_paths_list
GridIteratorVisualization.save_bools
GridIteratorVisualization.plot_booleans
GridIteratorVisualization.scale_types_list
GridIteratorVisualization.var_names_list
GridIteratorVisualization.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
SurrogateVisualization
SurrogateVisualization.saving_paths
SurrogateVisualization.should_be_saved
SurrogateVisualization.should_be_displayed
SurrogateVisualization.parameter_names
SurrogateVisualization.figures
SurrogateVisualization.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:
object
Class 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(input_file, output_dir, debug=False)[source]#
Do a QUEENS run.
- Parameters:
input_file (Path) – Path object to the input file
output_dir (Path) – Path object to the output directory
debug (bool) – True if debug mode is to be used
- 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