Documentation for PerturbationMatrix
Class to handle perturbation matrices in PSFMachine
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
time |
np.ndarray |
Array of time values |
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' |
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 |
required |
bin_method |
str |
How to bin the data under the hood. Default is by mean binning. Options are 'downsample' and 'bin' |
'by' |
focus_exptime |
float |
Time for the exponent for focus change, if used |
required |
bin_func(self, var, **kwargs)
Bins down an input variable to the same time resolution as self
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
var |
npt.ArrayLike |
Array of values with at least 1 dimension. The first dimension must be
the same shape as |
required |
Returns:
| Type | Description |
|---|---|
object |
An object function according to |
fit(self, flux, flux_err=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. |
required |
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]]] |
Optional flux errors. Should have shape ntimes. |
required |
Returns:
| Type | Description |
|---|---|
npt.ArrayLike |
Array with computed weights |
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 |
pca(self, y, ncomponents=5, 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 |
ncomponents |
int |
Number of principal components to use |
5 |
smooth_time_scale |
float |
Amount to smooth the components, using a spline in time. If 0, the components will not be smoothed. |
0 |
plot(self)
Plot basis vectors