def lombscargle_scipy(t, y, frequency, normalization='normalized',
center_data=True):
"""Lomb-Scargle Periodogram
This is a wrapper of ``scipy.signal.lombscargle`` for computation of the
Lomb-Scargle periodogram. This is a relatively fast version of the naive
O[N^2] algorithm, but cannot handle heteroskedastic errors.
Parameters
----------
t, y: array_like
times, values, and errors of the data points. These should be
broadcastable to the same shape.
frequency : array_like
frequencies (not angular frequencies) at which to calculate periodogram
normalization : string (optional, default='normalized')
Normalization to use for the periodogram
TODO: figure out what options to use
center_data : bool (optional, default=True)
if True, pre-center the data by subtracting the weighted mean
of the input data.
Returns
-------
power : array_like
Lomb-Scargle power associated with each frequency.
Units of the result depend on the normalization.
References
----------
.. [1] M. Zechmeister and M. Kurster, A&A 496, 577-584 (2009)
.. [2] W. Press et al, Numerical Recipies in C (2002)
.. [3] Scargle, J.D. 1982, ApJ 263:835-853
"""
if not HAS_SCIPY:
raise ValueError("scipy must be installed to use lombscargle_scipy")
t, y = np.broadcast_arrays(t, y)
frequency = np.asarray(frequency)
assert t.ndim == 1
assert frequency.ndim == 1
if center_data:
y = y - y.mean()
# Note: scipy input accepts angular frequencies
p = signal.lombscargle(t, y, 2 * np.pi * frequency)
if normalization == 'unnormalized':
pass
elif normalization == 'normalized':
p *= 2 / (t.size * np.mean(y ** 2))
else:
raise ValueError("normalization='{0}' "
"not recognized".format(normalization))
return p
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