aspire_bilby.utils

Classes

Inputs
Functions
GlobalFunctions	Dataclass to store global functions.

Functions

update_global_functions(bilby_likelihood, ...)	Update the global functions for log likelihood and log prior.
get_aspire_functions(bilby_likelihood, bilby_priors, ...)	Get the log likelihood function for a bilby likelihood object.
get_prior_bounds(bilby_priors, parameters)	Get a dictionary of prior bounds.
get_periodic_parameters(bilby_priors)	Determine which parameters are periodic.
samples_from_bilby_result(result[, parameters, ...])	Get samples from a bilby result object.
samples_from_bilby_priors(bilby_priors, n_samples[, ...])	Get samples from a bilby prior object.
sample_missing_parameters(bilby_result, bilby_priors)	Sample the missing parameters from the bilby result.
temporary_logger_level(logger, level)	Temporarily set the logger level.
load_bilby_pipe_ini(config_file, data_dump_file[, ...])	Load a bilby_pipe ini file and return the likelihood and priors.
get_inputs_from_bilby_pipe_ini(config_file, data_dump_file)	Get the aspire inputs from a bilby_pipe ini file.
get_function_from_path(path)	Get a function from a module path.

Module Contents

class aspire_bilby.utils.Inputs

Bases: tuple

log_likelihood

log_prior

dims

parameters

prior_bounds

periodic_parameters

class aspire_bilby.utils.Functions

Bases: tuple

log_likelihood

log_prior

class aspire_bilby.utils.GlobalFunctions

Dataclass to store global functions.

bilby_likelihood: bilby.core.likelihood.Likelihood

bilby_priors: bilby.core.prior.PriorDict

parameters: list

use_ratio: bool

aspire_bilby.utils.update_global_functions(bilby_likelihood, bilby_priors, fixed_parameters, parameters, use_ratio)

Update the global functions for log likelihood and log prior.

Parameters:

• bilby_likelihood (bilby.core.likelihood.Likelihood)

• bilby_priors (bilby.core.prior.PriorDict)

• fixed_parameters (dict[str, float])

• parameters (list[str])

• use_ratio (bool)

aspire_bilby.utils.get_aspire_functions(bilby_likelihood, bilby_priors, parameters, use_ratio=False, likelihood_dtype='float64')

Get the log likelihood function for a bilby likelihood object.

Parameters:

• bilby_likelihood (Likelihood) – The bilby likelihood object.

• bilby_priors (PriorDict) – The bilby prior object.

• parameters (list[str]) – The parameters to evaluate the log likelihood and log prior for. The order should match the order of the parameters in the aspire sampler.

• use_ratio (bool) – Whether to use the log likelihood ratio function if available. If False, the log likelihood function is used instead.

• likelihood_dtype (str) – The dtype to use for the log likelihood values. Default is “float64”.

aspire_bilby.utils.get_prior_bounds(bilby_priors, parameters)

Get a dictionary of prior bounds.

Parameters:

• bilby_priors (bilby.core.prior.PriorDict)

• parameters (list[str])

Return type:

dict[str, numpy.ndarray]

aspire_bilby.utils.get_periodic_parameters(bilby_priors)

Determine which parameters are periodic.

Parameters:

bilby_priors (bilby.core.prior.PriorDict)

Return type:

list[str]

aspire_bilby.utils.samples_from_bilby_result(result, parameters=None, bilby_priors=None, sample_from_prior=None, conversion_function=None)

Get samples from a bilby result object.

Parameters:

• result (Result) – The bilby result object.

• parameters (str) – The parameters to read from the result. If None, all parameters will be read.

• bilby_priors (PriorDict) – The bilby prior object. If not specified, the initial result must contain all parameters.

• sample_from_prior (list[str]) – A list of parameters to explicitly sample from the prior rather reading from the result.

• conversion_function (Callable | None)

aspire_bilby.utils.samples_from_bilby_priors(bilby_priors, n_samples, parameters=None)

Get samples from a bilby prior object.

Parameters:

• bilby_priors (PriorDict) – The bilby prior object.

• n_samples (int) – The number of samples to draw.

• parameters (str) – The parameters to sample. If None, all parameters will be sampled.

aspire_bilby.utils.sample_missing_parameters(bilby_result, bilby_priors, parameters=None, parameters_to_sample=None)

Sample the missing parameters from the bilby result.

Parameters:

• bilby_result (Result) – The initial bilby result object.

• bilby_priors (PriorDict) – The bilby prior object.

• parameters (list[str]) – The parameters that should be present in the final set of samples. If not specified, the parameters will be inferred from the priors.

• parameters_to_sample (list[str]) – A list of parameters to explicitly sample from the prior rather reading from the result. Each entry can be a regex pattern to match multiple parameters.

Returns:

The samples from the bilby result and the missing parameters from the bilby priors. The order will be the same as parameters.

Return type:

pd.DataFrame

aspire_bilby.utils.temporary_logger_level(logger, level)

Temporarily set the logger level.

Example usage

```python with temporary_logger_level(logger, “DEBUG”):

# Do something

```

Parameters:

level (str | None)

aspire_bilby.utils.load_bilby_pipe_ini(config_file, data_dump_file, suppress_bilby_logger=True)

Load a bilby_pipe ini file and return the likelihood and priors.

Parameters:

• config_file (str)

• data_dump_file (str)

• suppress_bilby_logger (bool)

aspire_bilby.utils.get_inputs_from_bilby_pipe_ini(config_file, data_dump_file, use_ratio=False, suppress_bilby_logger=True)

Get the aspire inputs from a bilby_pipe ini file.

Returns:

A namedtuple with the log_likelihood and log_prior functions.

Return type:

namedtuple

Parameters:

• config_file (str)

• data_dump_file (str)

• use_ratio (bool)

• suppress_bilby_logger (bool)

aspire_bilby.utils.get_function_from_path(path)

Get a function from a module path.

Parameters:

path (str) – The path to the function, e.g. “module.submodule.function”.

Returns:

The function object.

Return type:

Callable