def to_timeseries(self, delta_t=None):
""" Return the Fourier transform of this time series.
Note that this assumes even length time series!
Parameters
----------
delta_t : {None, float}, optional
The time resolution of the returned series. By default the
resolution is determined by length and delta_f of this frequency
series.
Returns
-------
TimeSeries:
The inverse fourier transform of this frequency series.
"""
from pycbc.fft import ifft
from pycbc.types import TimeSeries, real_same_precision_as
nat_delta_t = 1.0 / ((len(self)-1)*2) / self.delta_f
if not delta_t:
delta_t = nat_delta_t
# add 0.5 to round integer
tlen = int(1.0 / self.delta_f / delta_t + 0.5)
flen = int(tlen / 2 + 1)
if flen < len(self):
raise ValueError("The value of delta_t (%s) would be "
"undersampled. Maximum delta_t "
"is %s." % (delta_t, nat_delta_t))
if not delta_t:
tmp = self
else:
tmp = FrequencySeries(zeros(flen, dtype=self.dtype),
delta_f=self.delta_f, epoch=self.epoch)
tmp[:len(self)] = self[:]
f = TimeSeries(zeros(tlen,
dtype=real_same_precision_as(self)),
delta_t=delta_t)
ifft(tmp, f)
f._delta_t = delta_t
return f
def to_timeseries(self, delta_t=None):
""" Return the Fourier transform of this time series.
Note that this assumes even length time series!
Parameters
----------
delta_t : {None, float}, optional
The time resolution of the returned series. By default the
resolution is determined by length and delta_f of this frequency
series.
Returns
-------
TimeSeries:
The inverse fourier transform of this frequency series.
"""
from pycbc.fft import ifft
from pycbc.types import TimeSeries, real_same_precision_as
nat_delta_t = 1.0 / ((len(self)-1)*2) / self.delta_f
if not delta_t:
delta_t = nat_delta_t
# add 0.5 to round integer
tlen = int(1.0 / self.delta_f / delta_t + 0.5)
flen = tlen / 2 + 1
if flen < len(self):
raise ValueError("The value of delta_t (%s) would be "
"undersampled. Maximum delta_t "
"is %s." % (delta_t, nat_delta_t))
if not delta_t:
tmp = self
else:
tmp = FrequencySeries(zeros(flen, dtype=self.dtype),
delta_f=self.delta_f, epoch=self.epoch)
tmp[:len(self)] = self[:]
f = TimeSeries(zeros(tlen,
dtype=real_same_precision_as(self)),
delta_t=delta_t)
ifft(tmp, f)
return f
def to_frequencyseries(self, delta_f=None):
""" Return the Fourier transform of this time series
Parameters
----------
delta_f : {None, float}, optional
The frequency resolution of the returned frequency series. By
default the resolution is determined by the duration of the timeseries.
Returns
-------
FrequencySeries:
The fourier transform of this time series.
"""
from pycbc.fft import fft
if not delta_f:
delta_f = 1.0 / self.duration
# add 0.5 to round integer
tlen = int(1.0 / delta_f / self.delta_t + 0.5)
flen = int(tlen / 2 + 1)
if tlen < len(self):
raise ValueError("The value of delta_f (%s) would be "
"undersampled. Maximum delta_f "
"is %s." % (delta_f, 1.0 / self.duration))
if not delta_f:
tmp = self
else:
tmp = TimeSeries(zeros(tlen, dtype=self.dtype),
delta_t=self.delta_t,
epoch=self.start_time)
tmp[:len(self)] = self[:]
f = FrequencySeries(zeros(flen, dtype=complex_same_precision_as(self)),
delta_f=delta_f)
fft(tmp, f)
f._delta_f = delta_f
return f
def to_frequencyseries(self, delta_f=None):
""" Return the Fourier transform of this time series
Parameters
----------
delta_f : {None, float}, optional
The frequency resolution of the returned frequency series. By
default the resolution is determined by the duration of the timeseries.
Returns
-------
FrequencySeries:
The fourier transform of this time series.
"""
from pycbc.fft import fft
if not delta_f:
delta_f = 1.0 / self.duration
# add 0.5 to round integer
tlen = int(1.0 / delta_f / self.delta_t + 0.5)
flen = int(tlen / 2 + 1)
if tlen < len(self):
raise ValueError("The value of delta_f (%s) would be "
"undersampled. Maximum delta_f "
"is %s." % (delta_f, 1.0 / self.duration))
if not delta_f:
tmp = self
else:
tmp = TimeSeries(zeros(tlen, dtype=self.dtype),
delta_t=self.delta_t, epoch=self.start_time)
tmp[:len(self)] = self[:]
f = FrequencySeries(zeros(flen,
dtype=complex_same_precision_as(self)),
delta_f=delta_f)
fft(tmp, f)
return f
