regularizepsf.psf#

Representations of point spread functions.

Classes#

`SimpleFunctionalPSF`	Model for a simple PSF.
`VariedFunctionalPSF`	Model for a PSF that varies over the field of view.
`ArrayPSF`	A PSF represented as a set of arrays.

Functions#

`simple_functional_psf`(→ SimpleFunctionalPSF)	Decorate a SimpleFunctionalPSF.
`_varied_functional_psf`(→ VariedFunctionalPSF)
`varied_functional_psf`(→ VariedFunctionalPSF)	Decorate to create a VariedFunctionalPSF.

Module Contents#

class regularizepsf.psf.SimpleFunctionalPSF(function: collections.abc.Callable)[source]#

Model for a simple PSF.

Create a PSF object.

Parameters:

function –

Python function representing the PSF,: first two parameters must be x and y and must return an numpy array

_f: collections.abc.Callable#

_signature: inspect.Signature#

_parameters: set[str]#

__call__(row: numbers.Real | numpy.ndarray, col: numbers.Real | numpy.ndarray, **kwargs: dict[str, Any]) → numbers.Real | numpy.ndarray[source]#: Get the PSF value at (row, col).

property parameters: set[str]#: Get the parameters of this PSF.

as_array_psf(coordinates: list[tuple[int, int]], size: int, **kwargs) → ArrayPSF[source]#: Convert FunctionalPSF to an ArrayPSF.

property f: collections.abc.Callable#: Retrieve the PSF functional form for calling.

regularizepsf.psf.simple_functional_psf(arg: Any = None) → SimpleFunctionalPSF[source]#: Decorate a SimpleFunctionalPSF.

class regularizepsf.psf.VariedFunctionalPSF(vary_function: collections.abc.Callable, base_psf: SimpleFunctionalPSF, validate_at_call: bool = True)[source]#

Model for a PSF that varies over the field of view.

Create a VariedFunctionalPSF object.

Parameters:

vary_function (Callable) – function used to vary the parameters of the base_psf
base_psf (Callable) – base form of the PSF
validate_at_call (bool) – whether to check if parameters are valid at each call, turning off may be faster but is risky

_vary_function#

_base_psf#

validate_at_call = True#

parameterization_signature#

_origin_parameters: set[str]#

__call__(row: numbers.Real | numpy.ndarray, col: numbers.Real | numpy.ndarray) → numbers.Real | numpy.ndarray[source]#: Get the PSF value at (row, col).

property parameters: set[str]#: Get the parameters of this PSF.

simplify(row: int, col: int) → SimpleFunctionalPSF[source]#: Simplify this VariedFunctionalPSF to a SimpleFunctionalPSF by evaluating at (row, col).

as_array_psf(coordinates: list[tuple[int, int]], size: int, **kwargs) → ArrayPSF[source]#: Convert FunctionalPSF to an ArrayPSF.

regularizepsf.psf._varied_functional_psf(base_psf: SimpleFunctionalPSF) → VariedFunctionalPSF[source]#

regularizepsf.psf.varied_functional_psf(base_psf: SimpleFunctionalPSF = None) → VariedFunctionalPSF[source]#: Decorate to create a VariedFunctionalPSF.

class regularizepsf.psf.ArrayPSF(values_cube: regularizepsf.util.IndexedCube, fft_cube: regularizepsf.util.IndexedCube | None = None, workers: int | None = None)[source]#

A PSF represented as a set of arrays.

Initialize an ArrayPSF model.

Parameters:

values_cube (IndexedCube) – PSF model where keys are upper left coordinates of array patches in the image
fft_cube (IndexedCube) – fft of the model
workers (int | None) – Maximum number of workers to use for parallel computation of FFT. If negative, the value wraps around from os.cpu_count(). See scipy.fft.fft for more details. Only used if fft_cube is None.

_values_cube#

_fft_cube = None#

_workers = None#

property coordinates: list[tuple[int, int]]#: Get the keys of the PSF model, i.e., where it is evaluated as an array.

property values: numpy.ndarray#: Get the model values.

property fft_evaluations: numpy.ndarray#: Get the model values.

__getitem__(coord: tuple[int, int]) → numpy.ndarray[source]#: Evaluate the PSF model at specific coordinates.

fft_at(coord: tuple[int, int]) → numpy.ndarray[source]#: Retrieve the FFT evaluation at a coordinate.

save(path: pathlib.Path) → None[source]#

Save the PSF model to a file. Supports h5 and FITS.

Parameters:: path (pathlib.Path) – where to save the PSF model
Return type:: None

classmethod load(path: pathlib.Path) → ArrayPSF[source]#

Load the PSF model from a file. Supports h5 and FITS.

Parameters:: path (pathlib.Path) – where to load the PSF model from
Returns:: loaded model
Return type:: ArrayPSF

visualize_psfs(fig: matplotlib.figure.Figure | None = None, fig_scale: int = 1, edge_trim: int = 1, patch_stride: int = 1, imshow_args: dict | None = None) → None[source]#

Visualize the PSFs.

Parameters:

fig (mp.figure.Figure) – the figure to plot in
fig_scale (int) – increasing this will make the figure higher resolution
edge_trim (int) – how many pixels to drop on each side of the PSF for plotting
patch_stride (int) – multiple of how many patches to skip when plotting, 1 means no skipping, 2 plots every other, 3 every third
imshow_args (dict) – additional arguments for imshow

Return type:

None

visualize_ffts(fig: matplotlib.figure.Figure | None = None, fig_scale: int = 1, edge_trim: int = 1, patch_stride: int = 1, imshow_args: dict | None = None) → None[source]#

Visualize the FFT kernels.

Parameters:

fig (mp.figure.Figure) – the figure to plot in
fig_scale (int) – increasing this will make the figure higher resolution
edge_trim (int) – how many pixels to drop on each side of the PSF for plotting
patch_stride (int) – multiple of how many patches to skip when plotting, 1 means no skipping, 2 plots every other, 3 every third
imshow_args (dict) – additional arguments for imshow

Return type:

None

__eq__(other: ArrayPSF) → bool[source]#: Check equality between two ArrayPSFs.

property sample_shape: tuple[int, int]#: Get the sample shape for this PSF model.

__len__() → int[source]#: Get the number of coordinates evaluated in this model.