Source code for pycbc.waveform.supernovae

"""Generate core-collapse supernovae waveform for core bounce and
subsequent postbounce oscillations.
"""

import numpy
import h5py
from pycbc.types import TimeSeries

_pc_dict = {}


[docs]def get_corecollapse_bounce(**kwargs): """ Generates core bounce and postbounce waveform by using principal component basis vectors from a .hdf file. The waveform parameters are the coefficients of the principal components and the distance. The number of principal components used can also be varied. """ try: principal_components = _pc_dict['principal_components'] except KeyError: with h5py.File(kwargs['principal_components_file'], 'r') as pc_file: principal_components = numpy.array(pc_file['principal_components']) _pc_dict['principal_components'] = principal_components if 'coefficients_array' in kwargs: coefficients_array = kwargs['coefficients_array'] else: coeffs_keys = [x for x in kwargs if x.startswith('coeff_')] coeffs_keys = numpy.sort(numpy.array(coeffs_keys)) coefficients_array = numpy.array([kwargs[x] for x in coeffs_keys]) no_of_pcs = int(kwargs['no_of_pcs']) coefficients_array = coefficients_array[:no_of_pcs] principal_components = principal_components[:no_of_pcs] pc_len = len(principal_components) assert len(coefficients_array) == pc_len distance = kwargs['distance'] mpc_conversion = 3.08567758128e+22 distance *= mpc_conversion strain = numpy.dot(coefficients_array, principal_components) / distance delta_t = kwargs['delta_t'] outhp = TimeSeries(strain, delta_t=delta_t) outhc = TimeSeries(numpy.zeros(len(strain)), delta_t=delta_t) return outhp, outhc
# Approximant names ########################################################### supernovae_td_approximants = {'CoreCollapseBounce': get_corecollapse_bounce}