"""Main Lomb-Scargle Implementation"""
from __future__ import print_function, division
import numpy as np
from .implementations import lombscargle, available_methods
from .implementations.mle import periodic_fit
from ... import units
from ...utils.compat.numpy import broadcast_arrays
def has_units(obj):
return hasattr(obj, 'unit')
def get_unit(obj):
return getattr(obj, 'unit', 1)
def strip_units(*arrs):
strip = lambda a: None if a is None else np.asarray(a)
if len(arrs) == 1:
return strip(arrs[0])
else:
return map(strip, arrs)
[docs]class LombScargle(object):
"""Compute the Lomb-Scargle Periodogram
This implementations here are based on code presented in [1]_ and [2]_;
if you use this functionality in an academic application, citation of
those works would be appreciated.
Parameters
----------
t : array_like or Quantity
sequence of observation times
y : array_like or Quantity
sequence of observations associated with times t
dy : float, array_like or Quantity (optional)
error or sequence of observational errors associated with times t
fit_mean : bool (optional, default=True)
if True, include a constant offset as part of the model at each
frequency. This can lead to more accurate results, especially in the
case of incomplete phase coverage.
center_data : bool (optional, default=True)
if True, pre-center the data by subtracting the weighted mean
of the input data. This is especially important if fit_mean = False
nterms : int (optional, default=1)
number of terms to use in the Fourier fit
Examples
--------
Generate noisy periodic data:
>>> rand = np.random.RandomState(42)
>>> t = 100 * rand.rand(100)
>>> y = np.sin(2 * np.pi * t) + rand.randn(100)
Compute the Lomb-Scargle periodogram on an automatically-determined
frequency grid & find the frequency of max power:
>>> frequency, power = LombScargle(t, y).autopower()
>>> frequency[np.argmax(power)]
1.0016662310392956
Compute the Lomb-Scargle periodogram at a user-specified frequency grid:
>>> freq = np.arange(0.8, 1.3, 0.1)
>>> LombScargle(t, y).power(freq)
array([ 0.0204304 , 0.01393845, 0.35552682, 0.01358029, 0.03083737])
If the inputs are astropy Quantities with units, the units will be
validated and the outputs will also be Quantities with appropriate units:
>>> from astropy import units as u
>>> t = t * u.s
>>> y = y * u.mag
>>> frequency, power = LombScargle(t, y).autopower()
>>> frequency.unit
Unit("1 / s")
>>> power.unit
Unit(dimensionless)
Note here that the Lomb-Scargle power is always a unitless quantity,
because it is related to the :math:`\\chi^2` of the best-fit periodic
model at each frequency.
References
----------
.. [1] Vanderplas, J., Connolly, A. Ivezic, Z. & Gray, A. *Introduction to
astroML: Machine learning for astrophysics*. Proceedings of the
Conference on Intelligent Data Understanding (2012)
.. [2] VanderPlas, J. & Ivezic, Z. *Periodograms for Multiband Astronomical
Time Series*. ApJ 812.1:18 (2015)
"""
available_methods = available_methods()
def __init__(self, t, y, dy=None,
fit_mean=True, center_data=True, nterms=1):
self.t, self.y, self.dy = self._validate_inputs(t, y, dy)
self.fit_mean = fit_mean
self.center_data = center_data
self.nterms = nterms
def _validate_inputs(self, t, y, dy):
# Validate shapes of inputs
if dy is None:
t, y = broadcast_arrays(t, y, subok=True)
else:
t, y, dy = broadcast_arrays(t, y, dy, subok=True)
if t.ndim != 1:
raise ValueError("Inputs (t, y, dy) must be 1-dimensional")
# validate units of inputs if any is a Quantity
if any(has_units(arr) for arr in (t, y, dy)):
t, y = map(units.Quantity, (t, y))
if dy is not None:
dy = units.Quantity(dy)
try:
dy = units.Quantity(dy, unit=y.unit)
except units.UnitConversionError:
raise ValueError("Units of dy not equivalent "
"to units of y")
return t, y, dy
def _validate_frequency(self, frequency):
frequency = np.asanyarray(frequency)
if has_units(self.t):
frequency = units.Quantity(frequency)
try:
frequency = units.Quantity(frequency, unit=1./self.t.unit)
except units.UnitConversionError:
raise ValueError("Units of frequency not equivalent to "
"units of 1/t")
else:
assert not has_units(frequency)
return frequency
def _validate_t(self, t):
t = np.asanyarray(t)
if has_units(self.t):
t = units.Quantity(t)
try:
t = units.Quantity(t, unit=self.t.unit)
except units.UnitConversionError:
raise ValueError("Units of t not equivalent to "
"units of input self.t")
return t
def _power_unit(self, norm):
if has_units(self.y):
if self.dy is None and norm == 'psd':
return self.y.unit ** 2
else:
return units.dimensionless_unscaled
else:
return 1
[docs] def autofrequency(self, samples_per_peak=5, nyquist_factor=5,
minimum_frequency=None, maximum_frequency=None):
"""Determine a suitable frequency grid for data.
Note that this assumes the peak width is driven by the observational
baseline, which is generally a good assumption when the baseline is
much larger than the oscillation period.
If you are searching for periods longer than the baseline of your
observations, this may not perform well.
Even with a large baseline, be aware that the maximum frequency
returned is based on the concept of "average Nyquist frequency", which
may not be useful for irregularly-sampled data. The maximum frequency
can be adjusted via the nyquist_factor argument, or through the
maximum_frequency argument.
Parameters
----------
samples_per_peak : float (optional, default=5)
The approximate number of desired samples across the typical peak
nyquist_factor : float (optional, default=5)
The multiple of the average nyquist frequency used to choose the
maximum frequency if maximum_frequency is not provided.
minimum_frequency : float (optional)
If specified, then use this minimum frequency rather than one
chosen based on the size of the baseline.
maximum_frequency : float (optional)
If specified, then use this maximum frequency rather than one
chosen based on the average nyquist frequency.
Returns
-------
frequency : ndarray or Quantity
The heuristically-determined optimal frequency bin
"""
baseline = self.t.max() - self.t.min()
n_samples = self.t.size
df = 1. / baseline / samples_per_peak
if minimum_frequency is not None:
f0 = minimum_frequency
else:
f0 = 0.5 * df
if maximum_frequency is not None:
Nf = int(np.ceil((maximum_frequency - f0) / df))
else:
Nf = int(0.5 * samples_per_peak * nyquist_factor * n_samples)
return f0 + df * np.arange(Nf)
[docs] def autopower(self, method='auto', method_kwds=None,
normalization='standard', **kwargs):
"""Compute Lomb-Scargle power at automatically-determined frequencies
Parameters
----------
method : string (optional)
specify the lomb scargle implementation to use. Options are:
- 'auto': choose the best method based on the input
- 'fast': use the O[N log N] fast method. Note that this requires
evenly-spaced frequencies: by default this will be checked unless
``assume_regular_frequency`` is set to True.
- 'slow': use the O[N^2] pure-python implementation
- 'cython': use the O[N^2] cython implementation. This is slightly
faster than method='slow', but much more memory efficient.
- 'chi2': use the O[N^2] chi2/linear-fitting implementation
- 'fastchi2': use the O[N log N] chi2 implementation. Note that this
requires evenly-spaced frequencies: by default this will be checked
unless ``assume_regular_frequency`` is set to True.
- 'scipy': use ``scipy.signal.lombscargle``, which is an O[N^2]
implementation written in C. Note that this does not support
heteroskedastic errors.
method_kwds : dict (optional)
additional keywords to pass to the lomb-scargle method
normalization : string (optional, default='standard')
Normalization to use for the periodogram.
Options are 'standard', 'model', or 'psd'.
**kwargs :
additional keyword arguments will be passed to autofrequency()
Returns
-------
frequency, power : ndarrays
The frequency and Lomb-Scargle power
"""
frequency = self.autofrequency(**kwargs)
power = self.power(frequency,
normalization=normalization,
method=method, method_kwds=method_kwds,
assume_regular_frequency=True)
return frequency, power
[docs] def power(self, frequency, normalization='standard', method='auto',
assume_regular_frequency=False, method_kwds=None):
"""Compute the Lomb-Scargle power at the given frequencies
Parameters
----------
frequency : array_like or Quantity
frequencies (not angular frequencies) at which to evaluate the
periodogram. Note that in order to use method='fast', frequencies
must be regularly-spaced.
method : string (optional)
specify the lomb scargle implementation to use. Options are:
- 'auto': choose the best method based on the input
- 'fast': use the O[N log N] fast method. Note that this requires
evenly-spaced frequencies: by default this will be checked unless
``assume_regular_frequency`` is set to True.
- 'slow': use the O[N^2] pure-python implementation
- 'cython': use the O[N^2] cython implementation. This is slightly
faster than method='slow', but much more memory efficient.
- 'chi2': use the O[N^2] chi2/linear-fitting implementation
- 'fastchi2': use the O[N log N] chi2 implementation. Note that this
requires evenly-spaced frequencies: by default this will be checked
unless ``assume_regular_frequency`` is set to True.
- 'scipy': use ``scipy.signal.lombscargle``, which is an O[N^2]
implementation written in C. Note that this does not support
heteroskedastic errors.
assume_regular_frequency : bool (optional)
if True, assume that the input frequency is of the form
freq = f0 + df * np.arange(N). Only referenced if method is 'auto'
or 'fast'.
normalization : string (optional, default='standard')
Normalization to use for the periodogram.
Options are 'standard', 'model', 'log', or 'psd'.
fit_mean : bool (optional, default=True)
if True, include a constant offset as part of the model at each
frequency. This can lead to more accurate results, especially in
the case of incomplete phase coverage.
center_data : bool (optional, default=True)
if True, pre-center the data by subtracting the weighted mean of
the input data. This is especially important if fit_mean = False
method_kwds : dict (optional)
additional keywords to pass to the lomb-scargle method
Returns
-------
power : ndarray
The Lomb-Scargle power at the specified frequency
"""
frequency = self._validate_frequency(frequency)
power = lombscargle(*strip_units(self.t, self.y, self.dy),
frequency=strip_units(frequency),
center_data=self.center_data,
fit_mean=self.fit_mean,
nterms=self.nterms,
normalization=normalization,
method=method, method_kwds=method_kwds,
assume_regular_frequency=assume_regular_frequency)
return power * self._power_unit(normalization)
[docs] def model(self, t, frequency):
"""Compute the Lomb-Scargle model at the given frequency
Parameters
----------
t : array_like or Quantity, length n_samples
times at which to compute the model
frequency : float
the frequency for the model
Returns
-------
y : np.ndarray, length n_samples
The model fit corresponding to the input times
"""
frequency = self._validate_frequency(frequency)
t = self._validate_t(t)
y_fit = periodic_fit(*strip_units(self.t, self.y, self.dy),
frequency=strip_units(frequency),
t_fit=strip_units(t),
center_data=self.center_data,
fit_mean=self.fit_mean,
nterms=self.nterms)
return y_fit * get_unit(self.y)