Cotrending Basis Vectors (corrections.CBV)

class corrections.CBV(filepath)[source]

Bases: object

Cotrending Basis Vector object.

sector

TESS Sector.

Type

int

cadence

TESS observing cadence in seconds.

Type

int

camera

TESS Camera (1-4).

Type

int

ccd

TESS CCD (1-4).

Type

int

cbv_area
Type

int

data_rel

TESS Data release number.

Type

int

version

TASOC version/data release number.

Type

int

filepath

Path to file where CVB is stored.

Type

str

time
Type

numpy.ndarray

cadenceno
Type

numpy.ndarray

cbv
Type

numpy.ndarray

cbv_s
Type

numpy.ndarray

inifit
Type

numpy.ndarray

priors

Code author: Mikkel N. Lund <mikkelnl@phys.au.dk>

Code author: Rasmus Handberg <rasmush@phys.au.dk>

__init__(filepath)[source]
fit(lc, use_bic=True, use_prior=False, cbvs=None, alpha=1.3, WS_lim=0.5, N_neigh=1000)[source]

Fit the CBV object to a lightcurve, and return the fitted cotrending-lightcurve and the fitting coefficients.

Parameters
  • lc (LightCurve) – Lightcurve to be cotrended.

  • use_bic (bool, optional) – Use the Bayesian Information Criterion to find the optimal number of CBVs to fit. Default=True.

  • use_prior (bool, optional) –

  • cbvs (int, optional) – Number of CBVs to fit to lightcurve. If use_bic=True, this indicated the maximum number of CBVs to fit.

Returns

Fitted lightcurve with the same length as lc. - list: Coefficients for each CBV. - dict: Diagnostics information about the fitting.

Return type

  • numpy.array

fitting_lh(lc, Ncbvs, start_guess=None)[source]
fitting_lh_spike(lc, Ncbvs, start_guess=None)[source]
fitting_pos_2(lc, Ncbvs, err, pos, wscale, N_neigh, logprior=None, start_guess=None)[source]
lsfit(lc, Ncbvs)[source]

Computes the weighted least-squares solution to a linear matrix equation.

Parameters
  • lc (LightCurve) – Lightcurve to fit.

  • Ncbvs (int) – Number of CBVs to include in fit.

Returns

Coefficients for CBV plus constant offset.

Return type

ndarray

lsfit_spike(lc, Ncbvs)[source]

Computes the least-squares solution to a linear matrix equation.

mdl(coeffs)[source]

Model lightcurve given CBV coefficients.

Parameters

coeffs (ndarray) – CBV coefficients and constant offset. Should be of length 2*N+1, where the first N coefficients are for the CBVs, the next N are for the Spike-CBVs, and the last element is a constant offset.

Returns

Model lightcurve, given the CBV coefficients provided. Will be in relative

flux around 1.

Return type

ndarray

mdl1d(coeff, ncbv)[source]
mdl_off(coeff, fitted, Ncbvs)[source]
mdl_spike(coeffs)[source]
negloglike(coeffs, lc)[source]

Negative log-likelihood function.

Parameters
  • coeffs (ndarray) – CBV coefficients.

  • lc (LightCurve) – Lightcurve to be fitted. Should be in relative flux around 1.

Returns

The negative log-likelihood of the coefficients, given the lightcurve.

Return type

float

remove_cols(indx_lowsnr)[source]
save_to_fits(output_folder, version=6)[source]

Save CBVs to FITS file.

Parameters
  • output_folder (str) – Path to directory where FITS file should be saved.

  • version (int) – Data release number to add to file header.

Returns

Path to the generated FITS file.

Return type

str

Raises

FileNotFoundError – If output_folder is invalid.

Code author: Rasmus Handberg <rasmush@phys.au.dk>

Code author: Nicholas Saunders <nksaun@hawaii.edu>