def compress_waveform(htilde,
sample_points,
tolerance,
interpolation,
precision,
decomp_scratch=None,
psd=None):
"""Retrieves the amplitude and phase at the desired sample points, and adds
frequency points in order to ensure that the interpolated waveform
has a mismatch with the full waveform that is <= the desired tolerance. The
mismatch is computed by finding 1-overlap between `htilde` and the
decompressed waveform; no maximimization over phase/time is done, a
PSD may be used.
.. note::
The decompressed waveform is only garaunteed to have a true mismatch
<= the tolerance for the given `interpolation` and for no PSD.
However, since no maximization over time/phase is performed when
adding points, the actual mismatch between the decompressed waveform
and `htilde` is better than the tolerance, using no PSD. Using a PSD
does increase the mismatch, and can lead to mismatches > than the
desired tolerance, but typically by only a factor of a few worse.
Parameters
----------
htilde : FrequencySeries
The waveform to compress.
sample_points : array
The frequencies at which to store the amplitude and phase. More points
may be added to this, depending on the desired tolerance.
tolerance : float
The maximum mismatch to allow between a decompressed waveform and
`htilde`.
interpolation : str
The interpolation to use for decompressing the waveform when computing
overlaps.
precision : str
The precision being used to generate and store the compressed waveform
points.
decomp_scratch : {None, FrequencySeries}
Optionally provide scratch space for decompressing the waveform. The
provided frequency series must have the same `delta_f` and length
as `htilde`.
psd : {None, FrequencySeries}
The psd to use for calculating the overlap between the decompressed
waveform and the original full waveform.
Returns
-------
CompressedWaveform
The compressed waveform data; see `CompressedWaveform` for details.
"""
fmin = sample_points.min()
df = htilde.delta_f
sample_index = (sample_points / df).astype(int)
amp = utils.amplitude_from_frequencyseries(htilde)
phase = utils.phase_from_frequencyseries(htilde)
comp_amp = amp.take(sample_index)
comp_phase = phase.take(sample_index)
if decomp_scratch is None:
outdf = df
else:
outdf = None
hdecomp = fd_decompress(comp_amp,
comp_phase,
sample_points,
out=decomp_scratch,
df=outdf,
f_lower=fmin,
interpolation=interpolation)
kmax = min(len(htilde), len(hdecomp))
htilde = htilde[:kmax]
hdecomp = hdecomp[:kmax]
mismatch = 1. - filter.overlap(
hdecomp, htilde, psd=psd, low_frequency_cutoff=fmin)
if mismatch > tolerance:
# we'll need the difference in the waveforms as a function of frequency
vecdiffs = vecdiff(htilde, hdecomp, sample_points, psd=psd)
# We will find where in the frequency series the interpolated waveform
# has the smallest overlap with the full waveform, add a sample point
# there, and re-interpolate. We repeat this until the overall mismatch
# is > than the desired tolerance
added_points = []
while mismatch > tolerance:
minpt = vecdiffs.argmax()
# add a point at the frequency halfway between minpt and minpt+1
add_freq = sample_points[[minpt, minpt + 1]].mean()
addidx = int(round(add_freq / df))
# ensure that only new points are added
if addidx in sample_index:
diffidx = vecdiffs.argsort()
addpt = -1
while addidx in sample_index:
addpt -= 1
try:
minpt = diffidx[addpt]
except IndexError:
raise ValueError("unable to compress to desired tolerance")
add_freq = sample_points[[minpt, minpt + 1]].mean()
addidx = int(round(add_freq / df))
new_index = numpy.zeros(sample_index.size + 1, dtype=int)
new_index[:minpt + 1] = sample_index[:minpt + 1]
new_index[minpt + 1] = addidx
new_index[minpt + 2:] = sample_index[minpt + 1:]
sample_index = new_index
sample_points = (sample_index * df).astype(
real_same_precision_as(htilde))
# get the new compressed points
comp_amp = amp.take(sample_index)
comp_phase = phase.take(sample_index)
# update the vecdiffs and mismatch
hdecomp = fd_decompress(comp_amp,
comp_phase,
sample_points,
out=decomp_scratch,
df=outdf,
f_lower=fmin,
interpolation=interpolation)
hdecomp = hdecomp[:kmax]
new_vecdiffs = numpy.zeros(vecdiffs.size + 1)
new_vecdiffs[:minpt] = vecdiffs[:minpt]
new_vecdiffs[minpt + 2:] = vecdiffs[minpt + 1:]
new_vecdiffs[minpt:minpt + 2] = vecdiff(htilde,
hdecomp,
sample_points[minpt:minpt + 2],
psd=psd)
vecdiffs = new_vecdiffs
mismatch = 1. - filter.overlap(
hdecomp, htilde, psd=psd, low_frequency_cutoff=fmin)
added_points.append(addidx)
logging.info("mismatch: %f, N points: %i (%i added)" %
(mismatch, len(comp_amp), len(added_points)))
return CompressedWaveform(sample_points,
comp_amp,
comp_phase,
interpolation=interpolation,
tolerance=tolerance,
mismatch=mismatch,
precision=precision)
def compress_waveform(htilde, sample_points, tolerance, interpolation,
decomp_scratch=None):
"""Retrieves the amplitude and phase at the desired sample points, and adds
frequency points in order to ensure that the interpolated waveform
has a mismatch with the full waveform that is <= the desired tolerance. The
mismatch is computed by finding 1-overlap between `htilde` and the
decompressed waveform; no maximimization over phase/time is done, nor is
any PSD used.
.. note::
The decompressed waveform is only garaunteed to have a true mismatch
<= the tolerance for the given `interpolation` and for no PSD.
However, since no maximization over time/phase is performed when
adding points, the actual mismatch between the decompressed waveform
and `htilde` is better than the tolerance, using no PSD. Using a PSD
does increase the mismatch, and can lead to mismatches > than the
desired tolerance, but typically by only a factor of a few worse.
Parameters
----------
htilde : FrequencySeries
The waveform to compress.
sample_points : array
The frequencies at which to store the amplitude and phase. More points
may be added to this, depending on the desired tolerance.
tolerance : float
The maximum mismatch to allow between a decompressed waveform and
`htilde`.
interpolation : str
The interpolation to use for decompressing the waveform when computing
overlaps.
decomp_scratch : {None, FrequencySeries}
Optionally provide scratch space for decompressing the waveform. The
provided frequency series must have the same `delta_f` and length
as `htilde`.
Returns
-------
CompressedWaveform
The compressed waveform data; see `CompressedWaveform` for details.
"""
fmin = sample_points.min()
df = htilde.delta_f
sample_index = (sample_points / df).astype(int)
amp = utils.amplitude_from_frequencyseries(htilde)
phase = utils.phase_from_frequencyseries(htilde)
comp_amp = amp.take(sample_index)
comp_phase = phase.take(sample_index)
if decomp_scratch is None:
outdf = df
else:
outdf = None
out = decomp_scratch
hdecomp = fd_decompress(comp_amp, comp_phase, sample_points,
out=decomp_scratch, df=outdf, f_lower=fmin,
interpolation=interpolation)
mismatch = 1. - filter.overlap(hdecomp, htilde, low_frequency_cutoff=fmin)
if mismatch > tolerance:
# we'll need the difference in the waveforms as a function of frequency
vecdiffs = vecdiff(htilde, hdecomp, sample_points)
# We will find where in the frequency series the interpolated waveform
# has the smallest overlap with the full waveform, add a sample point
# there, and re-interpolate. We repeat this until the overall mismatch
# is > than the desired tolerance
added_points = []
while mismatch > tolerance:
minpt = vecdiffs.argmax()
# add a point at the frequency halfway between minpt and minpt+1
add_freq = sample_points[[minpt, minpt+1]].mean()
addidx = int(add_freq/df)
new_index = numpy.zeros(sample_index.size+1, dtype=int)
new_index[:minpt+1] = sample_index[:minpt+1]
new_index[minpt+1] = addidx
new_index[minpt+2:] = sample_index[minpt+1:]
sample_index = new_index
sample_points = (sample_index * df).astype(
real_same_precision_as(htilde))
# get the new compressed points
comp_amp = amp.take(sample_index)
comp_phase = phase.take(sample_index)
# update the vecdiffs and mismatch
hdecomp = fd_decompress(comp_amp, comp_phase, sample_points,
out=decomp_scratch, df=outdf, f_lower=fmin,
interpolation=interpolation)
new_vecdiffs = numpy.zeros(vecdiffs.size+1)
new_vecdiffs[:minpt] = vecdiffs[:minpt]
new_vecdiffs[minpt+2:] = vecdiffs[minpt+1:]
new_vecdiffs[minpt:minpt+2] = vecdiff(htilde, hdecomp,
sample_points[minpt:minpt+2])
vecdiffs = new_vecdiffs
mismatch = 1. - filter.overlap(hdecomp, htilde,
low_frequency_cutoff=fmin)
added_points.append(addidx)
logging.info("mismatch: %f, N points: %i (%i added)" %(mismatch,
len(comp_amp), len(added_points)))
return CompressedWaveform(sample_points, comp_amp, comp_phase,
interpolation=interpolation, tolerance=tolerance,
mismatch=mismatch)