def get_interpolated_fd_waveform(dtype=numpy.complex64, return_hc=True, **params):
""" Return a fourier domain waveform approximant, using interpolation
"""
def rulog2(val):
return 2.0 ** numpy.ceil(numpy.log2(float(val)))
orig_approx = params["approximant"]
params["approximant"] = params["approximant"].replace("_INTERP", "")
df = params["delta_f"]
if "duration" not in params:
duration = get_waveform_filter_length_in_time(**params)
elif params["duration"] > 0:
duration = params["duration"]
else:
err_msg = "Waveform duration must be greater than 0."
raise ValueError(err_msg)
# FIXME We should try to get this length directly somehow
# I think this number should be conservative
ringdown_padding = 0.5
df_min = 1.0 / rulog2(duration + ringdown_padding)
# FIXME: I don't understand this, but waveforms with df_min < 0.5 will chop
# off the inspiral when using ringdown_padding - 0.5.
# Also, if ringdown_padding is set to a very small
# value we can see cases where the ringdown is chopped.
if df_min > 0.5:
df_min = 0.5
params["delta_f"] = df_min
hp, hc = get_fd_waveform(**params)
hp = hp.astype(dtype)
if return_hc:
hc = hc.astype(dtype)
else:
hc = None
f_end = get_waveform_end_frequency(**params)
if f_end is None:
f_end = (len(hp) - 1) * hp.delta_f
if "f_final" in params and params["f_final"] > 0:
f_end_params = params["f_final"]
if f_end is not None:
f_end = min(f_end_params, f_end)
n_min = int(rulog2(f_end / df_min)) + 1
if n_min < len(hp):
hp = hp[:n_min]
if hc is not None:
hc = hc[:n_min]
offset = int(ringdown_padding * (len(hp) - 1) * 2 * hp.delta_f)
hp = interpolate_complex_frequency(hp, df, zeros_offset=offset, side="left")
if hc is not None:
hc = interpolate_complex_frequency(hc, df, zeros_offset=offset, side="left")
params["approximant"] = orig_approx
return hp, hc
def get_interpolated_fd_waveform(dtype=numpy.complex64, return_hc=True,
**params):
""" Return a fourier domain waveform approximant, using interpolation
"""
def rulog2(val):
return 2.0 ** numpy.ceil(numpy.log2(float(val)))
orig_approx = params['approximant']
params['approximant'] = params['approximant'].replace('_INTERP', '')
df = params['delta_f']
if 'duration' not in params:
duration = get_waveform_filter_length_in_time(**params)
elif params['duration'] > 0:
duration = params['duration']
else:
err_msg = "Waveform duration must be greater than 0."
raise ValueError(err_msg)
#FIXME We should try to get this length directly somehow
# I think this number should be conservative
ringdown_padding = 0.5
df_min = 1.0 / rulog2(duration + ringdown_padding)
# FIXME: I don't understand this, but waveforms with df_min < 0.5 will chop
# off the inspiral when using ringdown_padding - 0.5.
# Also, if ringdown_padding is set to a very small
# value we can see cases where the ringdown is chopped.
if df_min > 0.5:
df_min = 0.5
params['delta_f'] = df_min
hp, hc = get_fd_waveform(**params)
hp = hp.astype(dtype)
if return_hc:
hc = hc.astype(dtype)
else:
hc = None
f_end = get_waveform_end_frequency(**params)
if f_end is None:
f_end = (len(hp) - 1) * hp.delta_f
if 'f_final' in params and params['f_final'] > 0:
f_end_params = params['f_final']
if f_end is not None:
f_end = min(f_end_params, f_end)
n_min = int(rulog2(f_end / df_min)) + 1
if n_min < len(hp):
hp = hp[:n_min]
if hc is not None:
hc = hc[:n_min]
offset = int(ringdown_padding * (len(hp)-1)*2 * hp.delta_f)
hp = interpolate_complex_frequency(hp, df, zeros_offset=offset, side='left')
if hc is not None:
hc = interpolate_complex_frequency(hc, df, zeros_offset=offset,
side='left')
params['approximant'] = orig_approx
return hp, hc
