Samples Utilities

RIFT.misc.samples_utils.add_field(a, descr)

Returns a new structured array with additional fields.

The contents of “a” are copied over to the appropriate fields in the new array, while the new fields are left uninitialized. The original array “a” is not modified.

Args:

a (np.ndarray): A structured numpy array. descr (list): A numpy type description of the new fields to add

(e.g., [(‘field_name’, float)]).

Returns:

np.ndarray: A new structured array containing the original fields and the new fields.

Raises:

ValueError: If the input array a is not a structured numpy array.

RIFT.misc.samples_utils.alt_expand_samples(samples)

Llike standard_expand_samples, but targeted specifically for composite files

RIFT.misc.samples_utils.dump_pesummary_samples_to_file_as_rift(fname_h5, key, fname_out, no_drop=False, no_rename=False)

Converts posterior samples from a pesummary HDF5 file to the RIFT text format.

Reads samples from the specified key in the HDF5 file, renames fields based on remap_bilby_to_rift, and optionally drops metadata fields (e.g., SNR, recalib).

Args:

fname_h5 (str): Path to the input pesummary HDF5 file. key (str): The key within the HDF5 file where the samples are stored. fname_out (str): Path to the output text file. no_drop (bool, optional): If True, does not drop ‘recalib’ or ‘snr’ fields. Defaults to False. no_rename (bool, optional): If True, does not rename fields using remap_bilby_to_rift. Defaults to False.

Raises:

Exception: If the provided key is not found in the HDF5 file.

Example:

>>> import samples_utils
>>> samples_utils.dump_pesummary_samples_to_file_as_rift("metafile.h5", "bilby-IMRPhenomXPHM-SpinTaylor-3",'test.dat')

$ convert_output_format_inference2ile –posterior-samples test.dat –output-xml my.xml.gz

RIFT.misc.samples_utils.extract_combination_from_LI(samples_LI, p)

Extracts a specific parameter or computed combination from posterior samples.

This function reads known columns from the posterior samples. If the requested parameter p is not directly available, it attempts to compute it from standard quantities using predefined remapping and physics formulas (e.g., computing effective spin chi_eff from individual spin components).

Args:

samples_LI (np.recarray): A structured numpy array containing posterior samples. p (str): The name of the parameter or combination to extract.

Returns:

np.ndarray: An array containing the extracted or computed values for parameter p.

Returns an array of zeros if the parameter cannot be accessed to avoid hard failures.

RIFT.misc.samples_utils.fchip(sample)

Helper function to compute the effective spin chi_p for a single sample.

Designed for use with multiprocessing.Pool.map.

Args:

sample (tuple): A tuple containing [m1, m2, s1x, s1y, s1z, s2x, s2y, s2z].

Returns:

float: The computed chi_p value.

RIFT.misc.samples_utils.fchipavg(sample)

Helper function to compute the average effective spin chi_pavg for a single sample.

Designed for use with multiprocessing.Pool.map.

Args:

sample (tuple): A tuple containing [m1, m2, s1x, s1y, s1z, s2x, s2y, s2z].

Returns:

float: The computed chi_pavg value.

RIFT.misc.samples_utils.standard_expand_samples(samples)

Expands a sample set by adding standard derived parameters.

This function checks for the presence of certain base parameters and computes derived quantities if they are missing. This is commonly used for preparing samples for plotting (e.g., in plot_posterior_corner.py).

Added fields may include: - Mass parameters: mtotal, eta, m1, m2 (computed from mc and q). - Spin parameters: a1x, a1y, a2x, a2y, chi1_perp, chi2_perp, chi_eff

(computed from a1, theta1, phi1, etc.).

• Angles: phi1, phi2, phi12.

• Tidal parameters: lambdat, dlambdat (computed from lambda1, lambda2).

Args:

samples (np.recarray): A structured numpy array of posterior samples.

Returns:

np.recarray: The expanded structured array containing the original and derived fields.