basecamp.py
 The Everest base class¶
The everest
engine. All everest
models
inherit from Basecamp
.

class
everest.basecamp.
Basecamp
¶ 
X
(i, j=slice(None, None, None))¶ Computes the design matrix at the given PLD order and the given indices. The columns are the PLD vectors for the target at the corresponding order, computed as the product of the fractional pixel flux of all sets of
n
pixels, wheren
is the PLD order.

apply_mask
(x=None)¶ Returns the outlier mask, an array of indices corresponding to the nonoutliers.
Parameters: x (numpy.ndarray) – If specified, returns the masked version of x
instead. DefaultNone

cdpps
¶ The string version of the current value of the CDPP in ppm. This displays the CDPP for each segment of the light curve individually (if breakpoints are present).

compute
()¶ Compute the model for the current value of lambda.

compute_joint
()¶ Compute the model in a single step, allowing for a light curvewide transit model. This is a bit more expensive to compute.

dir
¶ Returns the directory where the raw data and output for the target is stored.

fcor
¶ The CBVcorrected detrended flux.

flux
¶ The corrected/detrended flux. This is computed by subtracting the linear model from the raw SAP flux.

get_cdpp
(flux=None)¶ Returns the scalar CDPP for the light curve.

get_cdpp_arr
(flux=None)¶ Returns the CDPP value in ppm for each of the chunks in the light curve.

get_chunk
(b, x=None, pad=True)¶ Returns the indices corresponding to a given light curve chunk.
Parameters:

get_masked_chunk
(b, x=None, pad=True)¶ Same as
get_chunk()
, but first removes the outlier indices. :param int b: The index of the chunk to return :param numpy.ndarray x: If specified, applies the mask to arrayx
. DefaultNone

get_norm
()¶ Computes the PLD normalization. In the base class, this is just the sum of all the pixel fluxes.

get_weights
()¶ Computes the PLD weights vector
w
...warning :: Deprecated and not thoroughly tested.

lnlike
(model, refactor=False, pos_tol=2.5, neg_tol=50.0, full_output=False)¶ Return the likelihood of the astrophysical model model.
Returns the likelihood of model marginalized over the PLD model.
Parameters:  model (ndarray) – A vector of the same shape as self.time corresponding to the astrophysical model.
 refactor (bool) – Recompute the Cholesky decomposition? This typically does not need to be done, except when the PLD model changes. Default
False
.  pos_tol (float) – the positive (i.e., above the median) outlier tolerance in standard deviations.
 neg_tol (float) – the negative (i.e., below the median) outlier tolerance in standard deviations.
 full_output (bool) – If
True
, returns the maximum likelihood model amplitude and the variance on the amplitude in addition to the loglikelihood. In the case of a transit model, these are the transit depth and depth variance. DefaultFalse
.

logfile
¶ Returns the full path to the log file for the current run.

mask
¶ The array of indices to be masked. This is the union of the sets of outliers, bad (flagged) cadences, transit cadences, and
NaN
cadences.

norm
¶ The PLD normalization. Typically, this is just the simple aperture photometry flux (i.e., the sum of all the pixels in the aperture).

overfit
(tau=None, plot=True, clobber=False, w=9, **kwargs)¶ Compute the masked & unmasked overfitting metrics for the light curve.
This routine injects a transit model given by tau at every cadence in the light curve and recovers the transit depth when (1) leaving the transit unmasked and (2) masking the transit prior to performing regression.
Parameters:  tau – A function or callable that accepts two arguments, time and t0, and returns an array corresponding to a zeromean, unit depth transit model centered at t0 and evaluated at time. The easiest way to provide this is to use an instance of
everest.transit.TransitShape
. Default iseverest.transit.TransitShape(dur=0.1)
, a transit with solarlike limb darkening and a duratio of 0.1 days.  plot (bool) – Plot the results as a PDF? Default
True
 clobber (bool) – Overwrite the results if present? Default
False
 w (int) – The size of the masking window in cadences for computing the masked overfitting metric. Default 9 (about 4.5 hours for K2 long cadence).
Returns: An instance of everest.basecamp.Overfitting.
 tau – A function or callable that accepts two arguments, time and t0, and returns an array corresponding to a zeromean, unit depth transit model centered at t0 and evaluated at time. The easiest way to provide this is to use an instance of

plot_aperture
(axes, labelsize=8)¶ Plots the aperture and the pixel images at the beginning, middle, and end of the time series. Also plots a high resolution image of the target, if available.

plot_info
(dvs)¶ Plots miscellaneous detrending information on the data validation summary figure.
Parameters: dvs – A dvs.DVS
figure instance

search
(pos_tol=2.5, neg_tol=50.0, clobber=False, name='search', **kwargs)¶

season
¶ Return the current observing season.
For K2, this is the observing campaign, while for Kepler, it is the current quarter.

transit_model
¶


class
everest.basecamp.
Overfitting
(O1, O2, O3, O4, O5, pdf)¶ Stores information on the overfitting metrics for a light curve.

masked
(depth=0.01)¶ Return the masked overfitting metric for a given transit depth.

show
()¶ Show the overfitting PDF summary.

unmasked
(depth=0.01)¶ Return the unmasked overfitting metric for a given transit depth.
