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Generalize time-domain waveform modes generation via lalsim #4973

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Dec 19, 2024
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Generalize time-domain waveform modes generation via lalsim #4973

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100 changes: 99 additions & 1 deletion pycbc/waveform/waveform_modes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -232,6 +232,89 @@ def get_nrhybsur_modes(**params):
get_nrsur_modes.__doc__ = _formatdocstr(get_nrsur_modes.__doc__)
get_nrhybsur_modes.__doc__ = _formatdocstr(get_nrhybsur_modes.__doc__)

def get_lalsimulation_approximant(approximant):
import lalsimulation as ls
return {
'EOBNRv2': ls.EOBNRv2,
'EOBNRv2HM': ls.EOBNRv2HM,
'IMRPhenomTPHM': ls.IMRPhenomTPHM,
'NRSur7dq2': ls.NRSur7dq2,
'NRSur7dq4': ls.NRSur7dq4,
'NRHybSur3dq8': ls.NRHybSur3dq8,
'pSEOBNRv4HM_PA': ls.pSEOBNRv4HM_PA,
'SEOBNRv4HM_PA': ls.SEOBNRv4HM_PA,
'SEOBNRv4P': ls.SEOBNRv4P,
'SEOBNRv4PHM': ls.SEOBNRv4PHM,
'SpinTaylorT1': ls.SpinTaylorT1,
'SpinTaylorT4': ls.SpinTaylorT4,
'SpinTaylorT5': ls.SpinTaylorT5,
'TaylorT1': ls.TaylorT1,
'TaylorT2': ls.TaylorT2,
'TaylorT3': ls.TaylorT3,
'TaylorT4': ls.TaylorT4,
}[approximant]

def get_lalsimulation_modes(**params):
"""Generates approximant waveform mode-by-mode.

All waveform parameters should be provided as keyword arguments.
Recognized parameters are listed below. Unrecognized arguments are ignored.

Parameters
----------
template: object
An object that has attached properties. This can be used to substitute
for keyword arguments. A common example would be a row in an xml table.
approximant : str
The approximant to generate. Must be available in ``lalsimulation``.
{delta_t}
{mass1}
{mass2}
{spin1x}
{spin1y}
{spin1z}
{spin2x}
{spin2y}
{spin2z}
{f_lower}
{f_ref}
{distance}
{mode_array}
{ell_max}
{approximant}

Returns
-------
dict :
Dictionary of ``(l, m)`` -> ``(h_+, -h_x)`` ``TimeSeries``.
"""
ell_max = 5
if 'ell_max' in params:
ell_max = params['ell_max']
laldict = _check_lal_pars(params)
ret = lalsimulation.SimInspiralChooseTDModes(
params['coa_phase'],
params['delta_t'],
params['mass1']*lal.MSUN_SI,
params['mass2']*lal.MSUN_SI,
params['spin1x'],
params['spin1y'],
params['spin1z'],
params['spin2x'],
params['spin2y'],
params['spin2z'],
params['f_lower'], params['f_ref'],
params['distance']*1e6*lal.PC_SI, laldict,
ell_max,
get_lalsimulation_approximant(params['approximant'])
)
hlms = {}
while ret:
hlm = TimeSeries(ret.mode.data.data, delta_t=ret.mode.deltaT,
epoch=ret.mode.epoch)
hlms[(ret.l, ret.m)] = (hlm.real(), hlm.imag())
ret = ret.next
return hlms

def get_imrphenomxh_modes(**params):
"""Generates ``IMRPhenomXHM`` waveforms mode-by-mode. """
Expand Down Expand Up @@ -269,8 +352,23 @@ def get_imrphenomxh_modes(**params):
return hlms


_mode_waveform_td = {'NRSur7dq4': get_nrsur_modes,
_mode_waveform_td = {'EOBNRv2': get_lalsimulation_modes,
'EOBNRv2HM': get_lalsimulation_modes,
'IMRPhenomTPHM': get_lalsimulation_modes,
'NRSur7dq2': get_lalsimulation_modes,
'NRSur7dq4': get_nrsur_modes,
'NRHybSur3dq8': get_nrhybsur_modes,
'pSEOBNRv4HM_PA': get_lalsimulation_modes,
'SEOBNRv4HM_PA': get_lalsimulation_modes,
'SEOBNRv4P': get_lalsimulation_modes,
'SEOBNRv4PHM': get_lalsimulation_modes,
'SpinTaylorT1': get_lalsimulation_modes,
'SpinTaylorT4': get_lalsimulation_modes,
'SpinTaylorT5': get_lalsimulation_modes,
'TaylorT1': get_lalsimulation_modes,
'TaylorT2': get_lalsimulation_modes,
'TaylorT3': get_lalsimulation_modes,
'TaylorT4': get_lalsimulation_modes,
}
_mode_waveform_fd = {'IMRPhenomXHM': get_imrphenomxh_modes,
}
Expand Down
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