sage.utils.get_testdata_snr

Filename = Foobar.py Description = Lorem ipsum dolor sit amet

Created on Tue Jan 17 14:59:18 2023

__author__ = nnarenraju __copyright__ = Copyright 2022, ProjectName __credits__ = nnarenraju __license__ = MIT Licence __version__ = 0.0.1 __maintainer__ = nnarenraju __email__ = nnarenraju@gmail.com __status__ = [‘inProgress’, ‘Archived’, ‘inUsage’, ‘Debugging’]

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Documentation: NULL

Functions

`optimise_fmin`(h_pol, signal_length, ...)	Iteratively lower the starting frequency until the waveform reaches `signal_length`.
`get_injection_snr`(args)	Compute the optimal (matched-filter) network SNR for a single injection.
`get_snrs`(injection_file, data_cfg[, dataset_dir])	injparams = {}

Module Contents

optimise_fmin(h_pol, signal_length, signal_low_freq_cutoff, sample_rate, waveform_kwargs)[source]

Iteratively lower the starting frequency until the waveform reaches signal_length.

Parameters:

h_pol (pycbc.TimeSeries) – Current polarisation (h_plus or h_cross) used to estimate the required frequency adjustment.
signal_length (float) – Required waveform duration in seconds.
signal_low_freq_cutoff (float) – Original low-frequency cutoff (Hz); used as the starting estimate.
sample_rate (float) – Sample rate (Hz).
waveform_kwargs (dict) – Keyword arguments passed to pycbc.waveform.get_td_waveform(). Updated in-place with the adjusted f_lower.

Returns:

h_plus, h_cross – Re-generated waveform meeting the duration requirement.

Return type:

pycbc.TimeSeries

get_injection_snr(args)[source]

Compute the optimal (matched-filter) network SNR for a single injection.

Parameters:: args (tuple) – (injection_values, data_cfg) where injection_values is a dict of source parameters (masses, spins, sky-location, etc.) and data_cfg is the dataset configuration object.
Returns:: Quadrature-summed network SNR \(\sqrt{\sum_d \rho_d^2}\).
Return type:: float

get_snrs(injection_file, data_cfg, dataset_dir=None)[source]

injparams = {} with h5py.File(injection_file, ‘r’) as fp:

params = list(fp.keys()) for param in params:

injparams[param] = fp[param][()]

injlen = len(injparams[‘tc’]) # Add injection times into injparams injparams[‘injection_time’] = injparams[‘tc’] injparams[‘tc’] = np.random.uniform(11.0, 11.2, injlen)