def slice_and_taper(inj, ts):
"""Slices and tapers a timeseries based on the injection settings.
This assumes that ``set_ref_time`` has been applied to the timeseries
first. A copy of the time series will be returned even if no slicing
or tapering is done.
"""
try:
tstart = inj.slice_start
except AttributeError:
tstart = ts.start_time
try:
tend = inj.slice_end
except AttributeError:
tend = ts.end_time
ts = ts.time_slice(tstart, tend).copy()
# now taper
try:
twidth = inj.left_taper_width
except AttributeError:
twidth = 0
if twidth:
ts = wfutils.td_taper(ts, ts.start_time, ts.start_time+twidth,
side='left')
try:
twidth = inj.right_taper_width
except AttributeError:
twidth = 0
if twidth:
ts = wfutils.td_taper(ts, ts.end_time-twidth, ts.end_time,
side='right')
return ts
def get_fd_waveform_from_td(**params):
""" Return time domain version of fourier domain approximant.
This returns a frequency domain version of a fourier domain approximant,
with padding and tapering at the start of the waveform.
Parameters
----------
params: dict
The parameters defining the waveform to generator.
See `get_td_waveform`.
Returns
-------
hp: pycbc.types.FrequencySeries
Plus polarization time series
hc: pycbc.types.FrequencySeries
Cross polarization time series
"""
# determine the duration to use
full_duration = duration = get_waveform_filter_length_in_time(**params)
nparams = params.copy()
while full_duration < duration * 1.5:
full_duration = get_waveform_filter_length_in_time(**nparams)
nparams['f_lower'] -= 1
if 'f_fref' not in nparams:
nparams['f_ref'] = params['f_lower']
# We'll try to do the right thing and figure out what the frequency
# end is. Otherwise, we'll just assume 2048 Hz.
# (consider removing as we hopefully have better estimates for more
# approximants
try:
f_end = get_waveform_end_frequency(**params)
delta_t = (0.5 / pnutils.nearest_larger_binary_number(f_end))
except:
delta_t = 1.0 / 2048
nparams['delta_t'] = delta_t
hp, hc = get_td_waveform(**nparams)
# Resize to the right duration
tsamples = int(1.0 / params['delta_f'] / delta_t)
if tsamples < len(hp):
raise ValueError("The frequency spacing (df = {}) is too low to "
"generate the {} approximant from the time "
"domain".format(params['delta_f'], params['approximant']))
hp.resize(tsamples)
hc.resize(tsamples)
# apply the tapering, we will use a safety factor here to allow for
# somewhat innacurate duration difference estimation.
window = (full_duration - duration) * 0.8
hp = wfutils.td_taper(hp, hp.start_time, hp.start_time + window)
hc = wfutils.td_taper(hc, hc.start_time, hc.start_time + window)
# avoid wraparound
hp = hp.to_frequencyseries().cyclic_time_shift(hp.start_time)
hc = hc.to_frequencyseries().cyclic_time_shift(hc.start_time)
return hp, hc
def get_fd_waveform_from_td(**params):
""" Return time domain version of fourier domain approximant.
This returns a frequency domain version of a fourier domain approximant,
with padding and tapering at the start of the waveform.
Parameters
----------
params: dict
The parameters defining the waveform to generator.
See `get_td_waveform`.
Returns
-------
hp: pycbc.types.FrequencySeries
Plus polarization time series
hc: pycbc.types.FrequencySeries
Cross polarization time series
"""
# determine the duration to use
full_duration = duration = get_waveform_filter_length_in_time(**params)
nparams = params.copy()
while full_duration < duration * 1.5:
full_duration = get_waveform_filter_length_in_time(**nparams)
nparams['f_lower'] -= 1
if 'f_fref' not in nparams:
nparams['f_ref'] = params['f_lower']
# We'll try to do the right thing and figure out what the frequency
# end is. Otherwise, we'll just assume 2048 Hz.
# (consider removing as we hopefully have better estimates for more
# approximants
try:
f_end = get_waveform_end_frequency(**params)
delta_t = (0.5 / pnutils.nearest_larger_binary_number(f_end))
except:
delta_t = 1.0 / 2048
nparams['delta_t'] = delta_t
hp, hc = get_td_waveform(**nparams)
# Resize to the right duration
tsamples = int(1.0 / params['delta_f'] / delta_t)
if tsamples < len(hp):
raise ValueError("The frequency spacing (df = {}) is too low to "
"generate the {} approximant from the time "
"domain".format(params['delta_f'], params['approximant']))
hp.resize(tsamples)
hc.resize(tsamples)
# apply the tapering, we will use a safety factor here to allow for
# somewhat innacurate duration difference estimation.
window = (full_duration - duration) * 0.8
hp = wfutils.td_taper(hp, hp.start_time, hp.start_time + window)
hc = wfutils.td_taper(hc, hc.start_time, hc.start_time + window)
# avoid wraparound
hp = hp.to_frequencyseries().cyclic_time_shift(hp.start_time)
hc = hc.to_frequencyseries().cyclic_time_shift(hc.start_time)
return hp, hc
