def interpolate_complex_frequency(series,
delta_f,
zeros_offset=0,
side='right'):
"""Interpolate complex frequency series to desired delta_f.
Return a new complex frequency series that has been interpolated to the
desired delta_f.
Parameters
----------
series : FrequencySeries
Frequency series to be interpolated.
delta_f : float
The desired delta_f of the output
zeros_offset : optional, {0, int}
Number of sample to delay the start of the zero padding
side : optional, {'right', str}
The side of the vector to zero pad
Returns
-------
interpolated series : FrequencySeries
A new FrequencySeries that has been interpolated.
"""
new_n = int((len(series) - 1) * series.delta_f / delta_f + 1)
old_N = int((len(series) - 1) * 2)
new_N = int((new_n - 1) * 2)
time_series = TimeSeries(zeros(old_N),
delta_t=1.0 / (series.delta_f * old_N),
dtype=real_same_precision_as(series))
ifft(series, time_series)
time_series.roll(-zeros_offset)
time_series.resize(new_N)
if side == 'left':
time_series.roll(zeros_offset + new_N - old_N)
elif side == 'right':
time_series.roll(zeros_offset)
out_series = FrequencySeries(zeros(new_n),
epoch=series.epoch,
delta_f=delta_f,
dtype=series.dtype)
fft(time_series, out_series)
return out_series
def interpolate_complex_frequency(series, delta_f, zeros_offset=0, side='right'):
"""Interpolate complex frequency series to desired delta_f.
Return a new complex frequency series that has been interpolated to the
desired delta_f.
Parameters
----------
series : FrequencySeries
Frequency series to be interpolated.
delta_f : float
The desired delta_f of the output
zeros_offset : optional, {0, int}
Number of sample to delay the start of the zero padding
side : optional, {'right', str}
The side of the vector to zero pad
Returns
-------
interpolated series : FrequencySeries
A new FrequencySeries that has been interpolated.
"""
new_n = int( (len(series)-1) * series.delta_f / delta_f + 1)
samples = numpy.arange(0, new_n) * delta_f
old_N = int( (len(series)-1) * 2 )
new_N = int( (new_n - 1) * 2 )
time_series = TimeSeries(zeros(old_N), delta_t =1.0/(series.delta_f*old_N),
dtype=real_same_precision_as(series))
ifft(series, time_series)
time_series.roll(-zeros_offset)
time_series.resize(new_N)
if side == 'left':
time_series.roll(zeros_offset + new_N - old_N)
elif side == 'right':
time_series.roll(zeros_offset)
out_series = FrequencySeries(zeros(new_n), epoch=series.epoch,
delta_f=delta_f, dtype=series.dtype)
fft(time_series, out_series)
return out_series
outname = outdir + '/' + cname + "_t_realh" + ".dat"
np.savetxt(outname, list(zip(mk1.times, np.real(mk1.h))))
print("match between {} and the mk1:".format(approximant))
wf_dt = wf_t[1] - wf_t[0]
mk1_t = mk1.times
mk1_dt = mk1_t[1] - mk1_t[0]
#f_lower_int = phenom.HztoMf(sys['f_lower'], sys['mtot'])
ts1 = TimeSeries(np.real(wf_h), delta_t=wf_dt)
ts2 = TimeSeries(np.real(mk1.h), delta_t=mk1_dt)
tlen = max(len(ts1), len(ts2))
ts1.resize(tlen)
ts2.resize(tlen)
#ma, _ = pycbc.filter.match(ts1, ts2, low_frequency_cutoff=f_lower_int)
ma, _ = pycbc.filter.match(ts1, ts2)
print("\t {}".format(ma))
print('plotting aligned waveform')
#ts1, ts2 = pycbc.waveform.utils.coalign_waveforms(ts1, ts2, low_frequency_cutoff=f_lower_int)
ts1, ts2 = pycbc.waveform.utils.coalign_waveforms(ts1, ts2)
_, mk1_idx = ts2.abs_max_loc()
mk1_shift = ts2.sample_times[mk1_idx]
figname = outdir + '/' + "{}-vs-mk1".format(sys['name']) + '.png'
