BoxLeastSquares¶
- class astropy.timeseries.BoxLeastSquares(t, y, dy=None)[source]¶
Bases:
BasePeriodogram
Compute the box least squares periodogram
This method is a commonly used tool for discovering transiting exoplanets or eclipsing binaries in photometric time series datasets. This implementation is based on the “box least squares (BLS)” method described in [1] and [2].
- Parameters:
- tarray_like,
Quantity
,Time
, orTimeDelta
Sequence of observation times.
- yarray_like or
Quantity
Sequence of observations associated with times
t
.- dy
float
, array_like, orQuantity
, optional Error or sequence of observational errors associated with times
t
.
- tarray_like,
References
[1]Kovacs, Zucker, & Mazeh (2002), A&A, 391, 369 (arXiv:astro-ph/0206099)
[2]Hartman & Bakos (2016), Astronomy & Computing, 17, 1 (arXiv:1605.06811)
Examples
Generate noisy data with a transit:
>>> rand = np.random.default_rng(42) >>> t = rand.uniform(0, 10, 500) >>> y = np.ones_like(t) >>> y[np.abs((t + 1.0)%2.0-1)<0.08] = 1.0 - 0.1 >>> y += 0.01 * rand.standard_normal(len(t))
Compute the transit periodogram on a heuristically determined period grid and find the period with maximum power:
>>> model = BoxLeastSquares(t, y) >>> results = model.autopower(0.16) >>> results.period[np.argmax(results.power)] 2.000412388152837
Compute the periodogram on a user-specified period grid:
>>> periods = np.linspace(1.9, 2.1, 5) >>> results = model.power(periods, 0.16) >>> results.power array([0.01723948, 0.0643028 , 0.1338783 , 0.09428816, 0.03577543])
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.day >>> y = y * u.dimensionless_unscaled >>> model = BoxLeastSquares(t, y) >>> results = model.autopower(0.16 * u.day) >>> results.period.unit Unit("d") >>> results.power.unit Unit(dimensionless)
Methods Summary
autoperiod
(duration[, minimum_period, ...])Determine a suitable grid of periods
autopower
(duration[, objective, method, ...])Compute the periodogram at set of heuristically determined periods
compute_stats
(period, duration, transit_time)Compute descriptive statistics for a given transit model
from_timeseries
(timeseries[, ...])Initialize a periodogram from a time series object.
model
(t_model, period, duration, transit_time)Compute the transit model at the given period, duration, and phase
power
(period, duration[, objective, method, ...])Compute the periodogram for a set of periods
transit_mask
(t, period, duration, transit_time)Compute which data points are in transit for a given parameter set
Methods Documentation
- autoperiod(duration, minimum_period=None, maximum_period=None, minimum_n_transit=3, frequency_factor=1.0)[source]¶
Determine a suitable grid of periods
This method uses a set of heuristics to select a conservative period grid that is uniform in frequency. This grid might be too fine for some user’s needs depending on the precision requirements or the sampling of the data. The grid can be made coarser by increasing
frequency_factor
.- Parameters:
- duration
float
, array_like, orQuantity
[:ref: ‘time’] The set of durations that will be considered.
- minimum_period, maximum_period
float
orQuantity
[:ref: ‘time’], optional The minimum/maximum periods to search. If not provided, these will be computed as described in the notes below.
- minimum_n_transit
int
, optional If
maximum_period
is not provided, this is used to compute the maximum period to search by asserting that any systems with at leastminimum_n_transits
will be within the range of searched periods. Note that this is not the same as requiring thatminimum_n_transits
be required for detection. The default value is3
.- frequency_factor
float
, optional A factor to control the frequency spacing as described in the notes below. The default value is
1.0
.
- duration
- Returns:
- periodarray_like or
Quantity
[:ref: ‘time’] The set of periods computed using these heuristics with the same units as
t
.
- periodarray_like or
Notes
The default minimum period is chosen to be twice the maximum duration because there won’t be much sensitivity to periods shorter than that.
The default maximum period is computed as
maximum_period = (max(t) - min(t)) / minimum_n_transits
ensuring that any systems with at least
minimum_n_transits
are within the range of searched periods.The frequency spacing is given by
df = frequency_factor * min(duration) / (max(t) - min(t))**2
so the grid can be made finer by decreasing
frequency_factor
or coarser by increasingfrequency_factor
.
- autopower(duration, objective=None, method=None, oversample=10, minimum_n_transit=3, minimum_period=None, maximum_period=None, frequency_factor=1.0)[source]¶
Compute the periodogram at set of heuristically determined periods
This method calls
BoxLeastSquares.autoperiod()
to determine the period grid and thenBoxLeastSquares.power()
to compute the periodogram. See those methods for documentation of the arguments.
- compute_stats(period, duration, transit_time)[source]¶
Compute descriptive statistics for a given transit model
These statistics are commonly used for vetting of transit candidates.
- Parameters:
- period
float
orQuantity
[:ref: ‘time’] The period of the transits.
- duration
float
orQuantity
[:ref: ‘time’] The duration of the transit.
- transit_time
float
orQuantity
orTime
The mid-transit time of a reference transit.
- period
- Returns:
- stats
dict
A dictionary containing several descriptive statistics:
depth
: The depth and uncertainty (as a tuple with twovalues) on the depth for the fiducial model.
depth_odd
: The depth and uncertainty on the depth for amodel where the period is twice the fiducial period.
depth_even
: The depth and uncertainty on the depth for amodel where the period is twice the fiducial period and the phase is offset by one orbital period.
depth_half
: The depth and uncertainty for a model with aperiod of half the fiducial period.
depth_phased
: The depth and uncertainty for a model with thefiducial period and the phase offset by half a period.
harmonic_amplitude
: The amplitude of the best fit sinusoidalmodel.
harmonic_delta_log_likelihood
: The difference in loglikelihood between a sinusoidal model and the transit model. If
harmonic_delta_log_likelihood
is greater than zero, the sinusoidal model is preferred.
transit_times
: The mid-transit time for each transit in thebaseline.
per_transit_count
: An array with a count of the number ofdata points in each unique transit included in the baseline.
per_transit_log_likelihood
: An array with the value of thelog likelihood for each unique transit included in the baseline.
- stats
- classmethod from_timeseries(timeseries, signal_column_name=None, uncertainty=None, **kwargs)¶
Initialize a periodogram from a time series object.
If a binned time series is passed, the time at the center of the bins is used. Also note that this method automatically gets rid of NaN/undefined values when initializing the periodogram.
- Parameters:
- signal_column_name
str
The name of the column containing the signal values to use.
- uncertainty
str
orfloat
orQuantity
, optional The name of the column containing the errors on the signal, or the value to use for the error, if a scalar.
- **kwargs
Additional keyword arguments are passed to the initializer for this periodogram class.
- signal_column_name
- model(t_model, period, duration, transit_time)[source]¶
Compute the transit model at the given period, duration, and phase
- Parameters:
- t_modelarray_like,
Quantity
, orTime
Times at which to compute the model.
- period
float
orQuantity
[:ref: ‘time’] The period of the transits.
- duration
float
orQuantity
[:ref: ‘time’] The duration of the transit.
- transit_time
float
orQuantity
orTime
The mid-transit time of a reference transit.
- t_modelarray_like,
- Returns:
- y_modelarray_like or
Quantity
The model evaluated at the times
t_model
with units ofy
.
- y_modelarray_like or
- power(period, duration, objective=None, method=None, oversample=10)[source]¶
Compute the periodogram for a set of periods
- Parameters:
- periodarray_like or
Quantity
[:ref: ‘time’] The periods where the power should be computed
- duration
float
, array_like, orQuantity
[:ref: ‘time’] The set of durations to test
- objective{‘likelihood’, ‘snr’}, optional
The scalar that should be optimized to find the best fit phase, duration, and depth. This can be either
'likelihood'
(default) to optimize the log-likelihood of the model, or'snr'
to optimize the signal-to-noise with which the transit depth is measured.- method{‘fast’, ‘slow’}, optional
The computational method used to compute the periodogram. This is mainly included for the purposes of testing and most users will want to use the optimized
'fast'
method (default) that is implemented in Cython.'slow'
is a brute-force method that is used to test the results of the'fast'
method.- oversample
int
, optional The number of bins per duration that should be used. This sets the time resolution of the phase fit with larger values of
oversample
yielding a finer grid and higher computational cost.
- periodarray_like or
- Returns:
- results
BoxLeastSquaresResults
The periodogram results as a
BoxLeastSquaresResults
object.
- results
- Raises:
ValueError
If
oversample
is not an integer greater than 0 or ifobjective
ormethod
are not valid.
- transit_mask(t, period, duration, transit_time)[source]¶
Compute which data points are in transit for a given parameter set
- Parameters:
- tarray_like or
Quantity
[:ref: ‘time’] Times where the mask should be evaluated.
- period
float
orQuantity
[:ref: ‘time’] The period of the transits.
- duration
float
orQuantity
[:ref: ‘time’] The duration of the transit.
- transit_time
float
orQuantity
orTime
The mid-transit time of a reference transit.
- tarray_like or
- Returns:
- transit_maskarray_like
A boolean array where
True
indicates and in transit point andFalse
indicates and out-of-transit point.