Documentation for PerturbationMatrix3D

Class to handle 3D perturbation matrices in PSFMachine

Parameters:

Name	Type	Description	Default
time	np.ndarray	Array of time values	required
dx	np.ndarray	Pixel positions in x separation from source center	required
dy	np.ndaray	Pixel positions in y separation from source center	required
other_vectors	list or np.ndarray	Other detrending vectors (e.g. centroids)	required
poly_order	int	Polynomial order to use for detrending, default 3	'3'
nknots	int	Number of knots for the cartesian spline	required
radius	float	Radius out to which to calculate the cartesian spline	required
focus	bool	Whether to correct focus using a simple exponent model	required
segments	bool	Whether to fit portions of data where there is a significant time break as separate segments	required
resolution	int	How many cadences to bin down via bin_method	required
bin_method	str	How to bin the data under the hood. Default is by mean binning.	'by'
focus_exptime	float	Time for the exponent for focus change, if used	required
degree	int	Polynomial degree used to build the row/column cartesian design matrix	required
knot_spacing_type	str	Type of spacing bewtwen knots used for cartesian design matrix, options are {"linear", "sqrt"}	required

fit(self, flux, flux_err=None, pixel_mask=None)

Fits flux to find the best fit model weights. Optionally will include flux

errors. Sets the self.weights attribute with best fit weights.

Parameters:

Name	Type	Description	Default
flux	Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]	Array of flux values. Should have shape ntimes x npixels.	required
flux_err	Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]	Optional flux errors. Should have shape ntimes x npixels.	None
pixel_mask	Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]	Pixel mask to apply. Values that are True will be used in the fit. Values that are False will be masked. Should have shape npixels.	None

model(self, time_indices=None)

Returns the best fit model at given time_indices.

Parameters:

Name	Type	Description	Default
time_indices	Optional[list]	Optionally pass a list of integers. Model will be evaluated at those indices.	None

Returns:

Type	Description
npt.ArrayLike	Array of values with the same shape as the flux used in self.fit

pca(self, y, ncomponents=3, smooth_time_scale=0)

Adds the first ncomponents principal components of y to the design

matrix. y is smoothen with a spline function and scale smooth_time_scale.

Parameters:

Name	Type	Description	Default
y	np.ndarray	Input flux array to take PCA of.	required
n_components	int	Number of components to take	required
smooth_time_scale	float	Amount to smooth the components, using a spline in time. If 0, the components will not be smoothed.	0

plot_model(self, time_index=0)

Plot perturbation model

Parameters:

Name	Type	Description	Default
time_index	int	Time index to plot the perturbed model	0