JointLightCurve

class linea.JointLightCurve(light_curves)

Bases: object

Joint analysis object for multiple CHEOPS light curves.

Parameters

light_curveslist

List of CheopsLightCurve objects.

Methods Summary

combined_design_matrix(self[, design_matrices])	Generate the combined design matrix, from a list of design matrices, one per visit.
concatenate(self)	Concatenate light curves into a single ConcatenatedLightCurve.
from_example([norm])	Load example WASP-189 b light curves (NOTE: this is not real data).
plot(self[, ax])	Plot the light curve.
regress(self, design_matrix)	Regress the design matrix against the fluxes.

Methods Documentation

combined_design_matrix(self, design_matrices=None)

Generate the combined design matrix, from a list of design matrices, one per visit.

Parameters

design_matriceslist of ndarray (optional)

List of design matrices, one per visit. If None is supplied, fetch the design matrices from each of the CheopsLightCurve objects used to initialize the JointLightCurve.

Returns

Xndarray

Design matrix (concatenated column vectors of observables)

concatenate(self)

Concatenate light curves into a single ConcatenatedLightCurve.

Returns

cnamedtuple

Named tuple containing the concatenated contents of the JointLightCurve object.

classmethod from_example(norm=True)

Load example WASP-189 b light curves (NOTE: this is not real data).

Parameters

normbool

Normalize the fluxes such that the median flux is unity. Default is True.

plot(self, ax=None, **kwargs)

Plot the light curve.

Parameters

axAxes

Matplotlib axis instance on which to build the plot

kwargsdict

Further keyword arguments to pass to plot.

Returns

axAxes

Matplotlib axis instance with the light curve plotted on it.

regress(self, design_matrix)

Regress the design matrix against the fluxes.

Parameters

design_matrixndarray

Design matrix (concatenated column vectors of observables)

Returns

betasndarray

Least squares estimators \(\hat{\beta}\)

covndarray

Covariance matrix for the least squares estimators \(\sigma_{\hat{\beta}}^2\)