def get_nrsur_modes(**params):
"""Generates NRSurrogate 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 one of the ``NRSur*`` models.
{delta_t}
{mass1}
{mass2}
{spin1x}
{spin1y}
{spin1z}
{spin2x}
{spin2y}
{spin2z}
{f_lower}
{f_ref}
{distance}
{mode_array}
Returns
-------
dict :
Dictionary of ``(l, m)`` -> ``(h_+, -h_x)`` ``TimeSeries``.
"""
laldict = _check_lal_pars(params)
ret = lalsimulation.SimInspiralPrecessingNRSurModes(
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,
getattr(lalsimulation, 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 test_waveform(**args):
flow = args['f_lower'] # Required parameter
dt = args['delta_t'] # Required parameter
fpeak = args['fpeak'] # A new parameter for my model
t = numpy.arange(0, 10, dt)
f = t / t.max() * (fpeak - flow) + flow
a = t
wf = numpy.exp(2.0j * numpy.pi * f * t) * a
# Return product should be a pycbc time series in this case for
# each GW polarization
#
#
# Note that by convention, the time at 0 is a fiducial reference.
# For CBC waveforms, this would be set to where the merger occurs
offset = -len(t) * dt
wf = TimeSeries(wf, delta_t=dt, epoch=offset)
return wf.real(), wf.imag()
