poplar.selection

Functions for computing noise-realised quantities from optimal SNRs, for the purposes of estimating signal/population detectability.

Functions

`matched_filter_snr_from_optimal_snr`(optimal_snr[, ...])	Computes detection probabilities from optimal snr values with respect to a detection threshold by sampling
`detection_probability_from_optimal_snr`(optimal_snr, ...)	Computes detection probabilities from optimal snr values with respect to a detection threshold using the survival function of
`selection_function_from_optimal_snr`(optimal_snr, threshold)	Computes the selection function (i.e. the mean detection probability) from a set of optimal snr values with respect to a detection threshold using the survival function of

Module Contents

poplar.selection.matched_filter_snr_from_optimal_snr(optimal_snr: numpy.ndarray | torch.tensor | float, number_of_detectors=1)

Computes detection probabilities from optimal snr values with respect to a detection threshold by sampling a non-central chi-square distribution.

This function is not GPU-compatible and will therefore force synchronisation and movement of data between CPU and GPU. The outputs will be on the same device as the inputs.

Parameters:

optimal_snrUnion[np.ndarray, torch.tensor, float]: Optimal snr values to convert into matched filter snrs.
number_of_detectorsint, optional: The number of detectors in use, by default 1, by default 1

Returns:

matched_filter_snrs: np.ndarray or torch.tensor: Matched filter SNRs corresponding to the given optimal SNRs.

poplar.selection.detection_probability_from_optimal_snr(optimal_snr: numpy.ndarray | torch.tensor | float, threshold: float, number_of_detectors=1)

Computes detection probabilities from optimal snr values with respect to a detection threshold using the survival function of a non-central chi-square distribution.

This function is not GPU-compatible and will therefore force synchronisation and movement of data between CPU and GPU. The outputs will be on the same device as the inputs.

Parameters:

optimal_snrnp.ndarray or torch.tensor: Optimal snr values to convert into detection probabilities.
thresholdfloat: The detection threshold.
number_of_detectorsint, optional: The number of detectors in use, by default 1

Returns:

detection_probabilities: np.ndarray or torch.tensor: The resuling detection probablities for the given detection threshold.

poplar.selection.selection_function_from_optimal_snr(optimal_snr: numpy.ndarray | torch.tensor, threshold: float, number_of_detectors=1)

Computes the selection function (i.e. the mean detection probability) from a set of optimal snr values with respect to a detection threshold using the survival function of a non-central chi-square distribution.

This function is not GPU-compatible and will therefore force synchronisation and movement of data between CPU and GPU. The outputs will be on the same device as the inputs.

Parameters:

optimal_snrnp.ndarray or torch.tensor: Optimal snr values to convert into detection probabilities.
thresholdfloat: The detection threshold.
number_of_detectorsint, optional: The number of detectors in use, by default 1

Returns:

selection function: np.ndarray or torch.tensor: The resuling selection function for the given detection threshold.