def multispec(data, doxs=1):
"""Compute power and cross spectra.
power, cross = multispec(data[, doxs=1])
data is expected to be a numeric sequence, representing a binned light
curve.
If doxs is false or data is one-dimensional, the cross spectra will not
be calculated as described below, and only the power spectra will be
returned.
If data is one-dimensional, its periodogram will be calculated.
If data is two-dimensional, it will be interpreted as an array of
one-dimensional curves. The periodogram will be calculated for each,
and the cross spectra of all but the first relative to the first.
If data has more than two dimensions, it will be interpreted as an array
of two-dimensional sets, and each will be treated as described above."""
# Figure out what we've got.
data = num.asarray(data)
npoints = data.shape[-1]
if len(data.shape) < 2:
doxs = 0
# crunch
transform = None
if num.__name__ == 'numarray':
transform = FFT.real_fft(data)
else:
transform = FFT.fft(data)
conj = num.conjugate(transform)
# Get the periodogram.
power = transform * conj
power = num.array(power.real)
if doxs:
# Calculate the cross spectrum.
cross = transform[..., :1, :] * conj[..., 1:, :]
return power, cross
else:
return power
def multispec(data, doxs=1):
"""Compute power and cross spectra.
power, cross = multispec(data[, doxs=1])
data is expected to be a numeric sequence, representing a binned light
curve.
If doxs is false or data is one-dimensional, the cross spectra will not
be calculated as described below, and only the power spectra will be
returned.
If data is one-dimensional, its periodogram will be calculated.
If data is two-dimensional, it will be interpreted as an array of
one-dimensional curves. The periodogram will be calculated for each,
and the cross spectra of all but the first relative to the first.
If data has more than two dimensions, it will be interpreted as an array
of two-dimensional sets, and each will be treated as described above."""
# Figure out what we've got.
data = num.asarray(data)
npoints = data.shape[-1]
if len(data.shape) < 2:
doxs = 0
# crunch
transform = None
if num.__name__ == 'numarray':
transform = FFT.real_fft(data)
else:
transform = FFT.fft(data)
conj = num.conjugate(transform)
# Get the periodogram.
power = transform * conj
power = num.array(power.real)
if doxs:
# Calculate the cross spectrum.
cross = transform[..., :1, :] * conj[..., 1:, :]
return power, cross
else:
return power
def __invert__(self):
return Matrix(conjugate(self))
def __invert__(self):
return Matrix(conjugate(self))
