regularizepsf#

Global init.

Submodules#

Attributes#

__version__

Classes#

`ArrayPSFBuilder`	A builder that will take a series of images and construct an ArrayPSF to represent their implicit PSF.
`ArrayPSF`	A PSF represented as a set of arrays.
`ArrayPSFTransform`	Representation of a transformation from a source to a target PSF that can be applied to images.

Functions#

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

Package Contents#

class regularizepsf.ArrayPSFBuilder(psf_size: int)#

A builder that will take a series of images and construct an ArrayPSF to represent their implicit PSF.

Initialize an ArrayPSFBuilder.

_psf_size#

property psf_size#

Build the PSF model.

Parameters:: images (list[pathlib.Path] | np.ndarray | Generator) – images to use
Returns:: an array PSF and the counts of stars in each component
Return type:: (ArrayPSF, dict)

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

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#: Evaluate the PSF model at specific coordinates.

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

save(path: pathlib.Path) → None#

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#

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, all_patches: bool = False, imshow_args: dict | None = None) → None#: Visualize the PSF model.

visualize_ffts(fig: matplotlib.figure.Figure | None = None, fig_scale: int = 1, all_patches: bool = False, imshow_args: dict | None = None) → None#: Visualize the fft of the PSF.

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

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

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

regularizepsf.simple_functional_psf(arg: Any = None) → SimpleFunctionalPSF#: Decorate a SimpleFunctionalPSF.

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

class regularizepsf.ArrayPSFTransform(transfer_kernel: regularizepsf.util.IndexedCube)#

Representation of a transformation from a source to a target PSF that can be applied to images.

Initialize a PSFTransform.

Parameters:: transfer_kernel (TransferKernel) – the transfer kernel required by this ArrayPSFTransform

_transfer_kernel#

property psf_shape: tuple[int, int]#: Retrieve the shape of the individual PSFs for this transform.

property coordinates: list[tuple[int, int]]#: Retrieve the coordinates of the individual PSFs for this transform.

__len__() → int#: Retrieve the number of coordinates used to represent this transform.

classmethod construct(source: regularizepsf.psf.ArrayPSF, target: regularizepsf.psf.ArrayPSF, alpha: float, epsilon: float) → ArrayPSFTransform#

Construct an ArrayPSFTransform from a source to a target PSF.

Parameters:

source (ArrayPSF) – source point spread function
target (ArrayPSF) – target point spread function
alpha (float) – controls the “hardness” of the transition from amplification to attenuation
epsilon (float) – controls the maximum of the amplification

Returns:

corresponding ArrayPSFTransform instance

Return type:

ArrayPSFTransform

apply(image: numpy.ndarray, workers: int | None = None, pad_mode: str = 'symmetric') → numpy.ndarray#

Apply the PSFTransform to an image.

Parameters:

image (np.ndarray) – image to apply the transform to
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.
pad_mode (str) – how to pad the image when computing ffts, see np.pad for more details.

Returns:

image with psf transformed

Return type:

np.ndarray

visualize(fig: matplotlib.figure.Figure | None = None, fig_scale: int = 1, all_patches: bool = False, imshow_args: dict | None = None) → None#: Visualize the transfer kernels.

save(path: pathlib.Path) → None#

Save a PSFTransform to a file. Supports h5 and FITS.

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

classmethod load(path: pathlib.Path) → ArrayPSFTransform#

Load a PSFTransform object. Supports h5 and FITS.

Parameters:: path (pathlib.Path) – file to load the PSFTransform from
Return type:: PSFTransform

__eq__(other: ArrayPSFTransform) → bool#: Test equality between two transforms.

regularizepsf.__version__#