matflow.subset_simulation.subset_simulation_result.SubsetSimulationResult

class matflow.subset_simulation.subset_simulation_result.SubsetSimulationResult(pf: 'float', cov: 'float | None', converged: 'bool', thresholds: 'NDArray', level_covs: 'NDArray', levels: 'list[LevelSamplingResult]', num_fine_evals: 'int', num_coarse_evals: 'int', num_failed_per_level: 'NDArray', x_original: 'NDArray | None' = None, final_chain_seeds: 'NDArray | None' = None, final_chain_g: 'NDArray | None' = None, final_all_x: 'NDArray | None' = None, final_all_g: 'NDArray | None' = None, debug_data: 'dict[str, Any] | None' = None)

Bases: ParameterValue

Methods

dump_element_group_to_HDF5_group	Write a list (from an element group) of parameter values to an HDF5 group.
dump_to_HDF5_group	Write this parameter value to an HDF5 group.
prepare_JSON_dump	Prepare this parameter value for serialisation as JSON.
save_from_HDF5_group	Extract a parameter value from an HDF5 group.
save_from_JSON	Extract a parameter value from JSON data.
to_dict	Serialise this parameter value as a dictionary.

Attributes

component_acceptance_rates
debug_data
final_all_g
final_all_x
final_chain_g
final_chain_seeds
mean_jump_distances
num_conditional_levels
outer_move_rates
subset_acceptance_rates
x_original
pf
cov
converged
thresholds
level_covs
levels
num_fine_evals
num_coarse_evals
num_failed_per_level

Parameters:

• pf (float) –

• cov (float | None) –

• converged (bool) –

• thresholds (ndarray[tuple[Any, ...], dtype[_ScalarT]]) –

• level_covs (ndarray[tuple[Any, ...], dtype[_ScalarT]]) –

• levels (list[matflow.subset_simulation.subset_simulation_result.LevelSamplingResult]) –

• num_fine_evals (int) –

• num_coarse_evals (int) –

• num_failed_per_level (ndarray[tuple[Any, ...], dtype[_ScalarT]]) –

• x_original (ndarray[tuple[Any, ...], dtype[_ScalarT]] | None) –

• final_chain_seeds (ndarray[tuple[Any, ...], dtype[_ScalarT]] | None) –

• final_chain_g (ndarray[tuple[Any, ...], dtype[_ScalarT]] | None) –

• final_all_x (ndarray[tuple[Any, ...], dtype[_ScalarT]] | None) –

• final_all_g (ndarray[tuple[Any, ...], dtype[_ScalarT]] | None) –

• debug_data (dict[str, Any] | None) –

property component_acceptance_rates: ndarray[tuple[Any, ...], dtype[_ScalarT]]

converged: bool

cov: float | None

debug_data: dict[str, Any] | None = None

classmethod dump_element_group_to_HDF5_group(objs, group)

Write a list (from an element group) of parameter values to an HDF5 group.

Parameters:

• objs (list[Self]) –

• group (HDF5Group) –

dump_to_HDF5_group(group)

Write this parameter value to an HDF5 group.

Parameters:

group (HDF5Group) –

final_all_g: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

final_all_x: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

final_chain_g: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

final_chain_seeds: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

level_covs: ndarray[tuple[Any, ...], dtype[_ScalarT]]

levels: list[matflow.subset_simulation.subset_simulation_result.LevelSamplingResult]

property mean_jump_distances: ndarray[tuple[Any, ...], dtype[_ScalarT]]

num_coarse_evals: int

property num_conditional_levels: int

num_failed_per_level: ndarray[tuple[Any, ...], dtype[_ScalarT]]

num_fine_evals: int

property outer_move_rates: ndarray[tuple[Any, ...], dtype[_ScalarT]]

pf: float

prepare_JSON_dump()

Prepare this parameter value for serialisation as JSON.

Return type:

dict[str, Any]

classmethod save_from_HDF5_group(group, param_id, workflow)

Extract a parameter value from an HDF5 group.

Parameters:

• group (HDF5Group) –

• param_id (int) –

• workflow (Workflow) –

classmethod save_from_JSON(data, param_id, workflow)

Extract a parameter value from JSON data.

Parameters:

• param_id (int | list[int]) –

• workflow (Workflow) –

property subset_acceptance_rates: ndarray[tuple[Any, ...], dtype[_ScalarT]]

thresholds: ndarray[tuple[Any, ...], dtype[_ScalarT]]

to_dict()

Serialise this parameter value as a dictionary.

Return type:

dict[str, Any]

x_original: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None