gwkokab.analysis.core.synthetic_pe

Attributes

ErrorFunctionRegistryType

Type alias for the error function registry mapping.

Classes

SyntheticAnalyticalPE	Mixin class that provides a random number generator key and seed management for
SyntheticDiscretePE	Mixin class that provides a random number generator key and seed management for

Functions

calculate_js_metrics(→ numpy.ndarray)	Calculates the Jensen-Shannon divergence between the discrete and analytical
histogram_pdf(→ tuple[numpy.ndarray, numpy.ndarray, ...)	Build a normalized histogram-based discrete PDF.
js_divergence_from_histograms(→ numpy.ndarray)	Jensen-Shannon divergence using natural log.
synthetic_analytical_pe_main()
synthetic_discrete_pe_main()	Command-line interface for generating synthetic discrete parameter estimation

Module Contents

class gwkokab.analysis.core.synthetic_pe.SyntheticAnalyticalPE(filename: str, discrete_waveform: str, coords: tuple[str, Ellipsis] | None = None)

Bases: gwkokab.analysis.core.utils.PRNGKeyMixin

Mixin class that provides a random number generator key and seed management for classes that require random number generation.

This mixin allows classes to initialize a random key based on a seed and provides a property to access the current random key, which is automatically split to ensure independent random streams across different parts of the code.

generate_parameter_estimates()

coords = None

discrete_waveform

filename

waveform_name = 'GWKokabSyntheticAnalyticalPE'

class gwkokab.analysis.core.synthetic_pe.SyntheticDiscretePE(filename: str, dataset: Literal['events', 'buffer_events'], error_params_filename: str, size: int, derive_parameters: bool = False, coords: list[str] | None = None, is_delta_error: bool = False)

Bases: gwkokab.analysis.core.utils.PRNGKeyMixin

Mixin class that provides a random number generator key and seed management for classes that require random number generation.

This mixin allows classes to initialize a random key based on a seed and provides a property to access the current random key, which is automatically split to ensure independent random streams across different parts of the code.

generate_parameter_estimates()

Generates parameter estimates based on the defined error functions.

This method should be implemented in subclasses to generate parameter estimates using the error functions defined in the error_function_registry property.

coords = None

dataset

derive_parameters = False

property error_function_registry: ErrorFunctionRegistryType

Returns a dictionary mapping parameters to their corresponding error functions.

The keys of the dictionary can be either strings representing parameter names, tuples of strings representing multiple parameter names, or instances of the Parameters enum (P) or tuples of such instances. The values are callable functions that define how to apply errors to the corresponding parameters.

Returns:

A dictionary mapping parameters to their error functions.

Return type:

ErrorFunctionRegistryType

error_params_filename

filename

is_delta_error = False

root_dir

size

waveform_name = 'GWKokabSyntheticDiscretePE'

gwkokab.analysis.core.synthetic_pe.calculate_js_metrics(discrete_samples: numpy.ndarray, analytical_samples: numpy.ndarray, bins: int) → numpy.ndarray

Calculates the Jensen-Shannon divergence between the discrete and analytical samples for each parameter.

Parameters:

• discrete_samples (np.ndarray) – Discrete samples from the parameter estimation, shape (num_samples, num_parameters).

• analytical_samples (np.ndarray) – Analytical samples generated from the error model, shape (num_samples, num_parameters).

• bins (int) – Number of bins to use for the histogram estimation of the PDFs.

Returns:

Array of Jensen-Shannon divergences for each parameter.

Return type:

np.ndarray

gwkokab.analysis.core.synthetic_pe.histogram_pdf(samples: numpy.ndarray, bins: int, value_range: tuple[float, float], weights: numpy.ndarray | None = None) → tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]

Build a normalized histogram-based discrete PDF.

gwkokab.analysis.core.synthetic_pe.js_divergence_from_histograms(p: numpy.ndarray, q: numpy.ndarray) → numpy.ndarray

Jensen-Shannon divergence using natural log.

Range: [0, ln(2)].

gwkokab.analysis.core.synthetic_pe.synthetic_analytical_pe_main()

gwkokab.analysis.core.synthetic_pe.synthetic_discrete_pe_main()

Command-line interface for generating synthetic discrete parameter estimation samples.

type gwkokab.analysis.core.synthetic_pe.ErrorFunctionRegistryType = dict[str | Parameters | tuple[str, ...] | tuple[Parameters, ...], tuple[tuple[str, ...], Callable[..., np.ndarray]]]

Type alias for the error function registry mapping.