regularizepsf.corrector#

Classes#

CorrectorABC

FunctionalCorrector

A version of the PSF corrector that stores the model as a set of functions.

ArrayCorrector

A PSF corrector that is evaluated as array patches.

Functions#

calculate_covering(→ numpy.ndarray)

Determines the grid of overlapping neighborhood patches.

Module Contents#

class regularizepsf.corrector.CorrectorABC#
abstract save(path: str | pathlib.Path) → None#

Save the model to a file.

Parameters:

path (str or pathlib.Path) – where to save the model, suggested extension is “.psf”

Return type:

None

classmethod load(path: str | pathlib.Path) → CorrectorABC#
Abstractmethod:

Loads a model from the path.

Parameters:

path (str or pathlib.Path) – where to load the model from, suggested extension is “.psf”

abstract correct_image(image: numpy.ndarray, size: int, alpha: float = 0.5, epsilon: float = 0.05) → numpy.ndarray#

PSF correct an image according to the model.

Parameters:

  • image (2D float np.ndarray) – image to be corrected

  • size (int) – how big to make the patches when correcting an image, only used for FunctionalCorrector

  • alpha (float) – controls the “hardness” of the transition from amplification to attenuation, see notes

  • epsilon (float) – controls the maximum of the amplification, see notes

Returns:

a image that has been PSF corrected

Return type:

np.ndarray

abstract simulate_observation(image: numpy.ndarray) → numpy.ndarray#

Simulates on a star field what an observation using this PSF looks like.

Parameters:

image (2D float np.ndarray) – image of point source stars to simluate PSF for

Returns:

an image with the PSF applied

Return type:

np.ndarray

class regularizepsf.corrector.FunctionalCorrector(psf: regularizepsf.psf.PointSpreadFunctionABC, target_model: regularizepsf.psf.PointSpreadFunctionABC | None)#

Bases: CorrectorABC

A version of the PSF corrector that stores the model as a set of functions. For the actual correction, the functions must first be evaluated to an ArrayCorrector.

Initialize a FunctionalCorrector.

Parameters:

  • psf (SimplePSF or VariedPSF) – the model describing the psf for each patch of the image

  • target_model (SimplePSF or None) – the target PSF to use to establish uniformity across the image

_psf: regularizepsf.psf.PointSpreadFunctionABC#
_variable: bool#
_target_model: regularizepsf.psf.SimplePSF#
property is_variable: bool#
returns: True if the PSF model is varied (changes across the field-of-view)

and False otherwise

Return type:

bool

evaluate_to_array_form(x: numpy.ndarray, y: numpy.ndarray, size: int) → ArrayCorrector#

Evaluates a FunctionalCorrector to an ArrayCorrector.

Parameters:

  • x (np.ndarray) – the first dimension coordinates to evaluate over

  • y (np.ndarray) – the second dimension coordinates to evaluate over

  • size (int) – how large the patches in the PSF correction model shouuld be

Returns:

an array evaluated form of this PSF corrector

Return type:

ArrayCorrector

correct_image(image: numpy.ndarray, size: int, alpha: float = 0.5, epsilon: float = 0.05) → numpy.ndarray#

PSF correct an image according to the model.

Parameters:

  • image (2D float np.ndarray) – image to be corrected

  • size (int) – how big to make the patches when correcting an image, only used for FunctionalCorrector

  • alpha (float) – controls the “hardness” of the transition from amplification to attenuation, see notes

  • epsilon (float) – controls the maximum of the amplification, see notes

Returns:

a image that has been PSF corrected

Return type:

np.ndarray

save(path: str) → None#

Save the model to a file.

Parameters:

path (str or pathlib.Path) – where to save the model, suggested extension is “.psf”

Return type:

None

classmethod load(path: str) → FunctionalCorrector#

Loads a model from the path.

Parameters:

path (str or pathlib.Path) – where to load the model from, suggested extension is “.psf”

simulate_observation(image: numpy.ndarray, size: int) → numpy.ndarray#

Simulates on a star field what an observation using this PSF looks like.

Parameters:

  • image (2D float np.ndarray) – image of point source stars to simluate PSF for

  • size (int) – the PSF will be evaluated to size x size pixels box

Returns:

an image with the PSF applied

Return type:

np.ndarray

class regularizepsf.corrector.ArrayCorrector(evaluations: dict[Any, numpy.ndarray], target_evaluations: numpy.ndarray | dict[Any, numpy.ndarray])#

Bases: CorrectorABC

A PSF corrector that is evaluated as array patches.

Initialize an ArrayCorrector.

Parameters:

  • evaluations (dict) –

    evaluated version of the PSF as they vary over the image,

    keys should be (x, y) of the lower left pixel of each patch. values should be the np.ndarray that corresponds to that patch

  • target_evaluations (np.ndarray) – evaluated version of the target PSF

_evaluation_points: list[Any]#
_size#
_evaluations: dict[Any, numpy.ndarray]#
_target_evaluations#
psf_i_fft#
target_fft#
property evaluations: dict[Any, numpy.ndarray]#
property evaluation_points: list#
correct_image(image: numpy.ndarray, size: int | None = None, alpha: float = 0.5, epsilon: float = 0.05) → numpy.ndarray#

PSF correct an image according to the model.

Parameters:

  • image (2D float np.ndarray) – image to be corrected

  • size (int) – how big to make the patches when correcting an image, only used for FunctionalCorrector

  • alpha (float) – controls the “hardness” of the transition from amplification to attenuation, see notes

  • epsilon (float) – controls the maximum of the amplification, see notes

Returns:

a image that has been PSF corrected

Return type:

np.ndarray

__getitem__(xy: tuple[int, int]) → numpy.ndarray#
save(path: str) → None#

Save the model to a file.

Parameters:

path (str or pathlib.Path) – where to save the model, suggested extension is “.psf”

Return type:

None

classmethod load(path: str) → ArrayCorrector#

Loads a model from the path.

Parameters:

path (str or pathlib.Path) – where to load the model from, suggested extension is “.psf”

simulate_observation(image: numpy.ndarray) → numpy.ndarray#

Simulates on a star field what an observation using this PSF looks like.

Parameters:

image (2D float np.ndarray) – image of point source stars to simluate PSF for

Returns:

an image with the PSF applied

Return type:

np.ndarray

regularizepsf.corrector.calculate_covering(image_shape: tuple[int, int], size: int) → numpy.ndarray#

Determines the grid of overlapping neighborhood patches.

Parameters:

  • image_shape (tuple of 2 ints) – shape of the image we plan to correct

  • size (int) – size of the square patches we want to create

Returns:

an array of shape Nx2 where return[:, 0] are the x coordinate and return[:, 1] are the y coordinates

Return type:

np.ndarray