def lal(self):
"""Produces a LAL frequency series object equivalent to self.
Returns
-------
lal_data : {lal.*FrequencySeries}
LAL frequency series object containing the same data as self.
The actual type depends on the sample's dtype. If the epoch of
self was 'None', the epoch of the returned LAL object will be
LIGOTimeGPS(0,0); otherwise, the same as that of self.
Raises
------
TypeError
If frequency series is stored in GPU memory.
"""
lal_data = None
if self._epoch is None:
ep = _lal.LIGOTimeGPS(0,0)
else:
ep = self._epoch
if self._data.dtype == _numpy.float32:
lal_data = _lal.CreateREAL4FrequencySeries("",ep,0,self.delta_f,_lal.SecondUnit,len(self))
elif self._data.dtype == _numpy.float64:
lal_data = _lal.CreateREAL8FrequencySeries("",ep,0,self.delta_f,_lal.SecondUnit,len(self))
elif self._data.dtype == _numpy.complex64:
lal_data = _lal.CreateCOMPLEX8FrequencySeries("",ep,0,self.delta_f,_lal.SecondUnit,len(self))
elif self._data.dtype == _numpy.complex128:
lal_data = _lal.CreateCOMPLEX16FrequencySeries("",ep,0,self.delta_f,_lal.SecondUnit,len(self))
lal_data.data.data[:] = self.numpy()
return lal_data
def lal(self):
""" Returns a LAL Object that contains this data """
lal_data = None
if type(self._data) is not _numpy.ndarray:
raise TypeError("Cannot return lal type from the GPU")
elif self._data.dtype == _numpy.float32:
lal_data = _lal.CreateREAL4FrequencySeries("", self._epoch, 0,
self.delta_f,
_lal.lalSecondUnit,
len(self))
elif self._data.dtype == _numpy.float64:
lal_data = _lal.CreateREAL8FrequencySeries("", self._epoch, 0,
self.delta_f,
_lal.lalSecondUnit,
len(self))
elif self._data.dtype == _numpy.complex64:
lal_data = _lal.CreateCOMPLEX8FrequencySeries(
"", self._epoch, 0, self.delta_f, _lal.lalSecondUnit,
len(self))
elif self._data.dtype == _numpy.complex128:
lal_data = _lal.CreateCOMPLEX16FrequencySeries(
"", self._epoch, 0, self.delta_f, _lal.lalSecondUnit,
len(self))
lal_data.data.data[:] = self._data
return lal_data
def lal_psd_estimate(numPoints, chan):
""" This function simulates the psd estimation as done in inspiral.c
"""
chan = chan.lal()
df = 4096 / numPoints
avgSpecParams = lal.AverageSpectrumParams()
spec = lal.CreateREAL4FrequencySeries("", chan.epoch, 0, df,
lal.lalSecondUnit, numPoints / 2 + 1)
avgSpecParams.method = lal.useMedian
avgSpecParams.plan = lal.CreateREAL4FFTPlan(numPoints, 1, 1)
avgSpecParams.overlap = numPoints / 2
avgSpecParams.window = lal.CreateHannREAL4Window(numPoints)
lal.REAL4AverageSpectrum(spec, chan, avgSpecParams)
return FrequencySeries(spec.data.data, delta_f=spec.deltaF)
