regularizepsf.fitter#

Attributes#

CoordinateIdentifier

Classes#

PatchCollectionABC

CoordinatePatchCollection

A representation of a PatchCollection that operates on pixel coordinates from a set of images.

Module Contents#

class regularizepsf.fitter.PatchCollectionABC(patches: dict[Any, numpy.ndarray], counts: dict[Any, int] | None = None)#
patches#
counts#
__len__() → int#
classmethod extract(images: list[numpy.ndarray], coordinates: list, size: int) → PatchCollectionABC#
Abstractmethod:

Construct a PatchCollection from a set of images using the specified coordinates and patch size.

Parameters:

  • images (list of np.ndarrays) – the images loaded

  • coordinates (list) –

    A list of coordinates for the lower left pixel of each patch,

    specified in each type of PatchCollection

  • size (int) – size of one side of the square patches extracted

Returns:

the square patches extracted into a PatchCollection

Return type:

np.ndarray

__getitem__(identifier: Any) → numpy.ndarray#

Access a patch with square brackets.

Parameters:

identifier (Any) – identifier for a given patch, specifically implemented for each PatchCollection

Returns:

a patch’s data

Return type:

np.ndarray

__contains__(identifier: Any) → bool#

Determines if a patch is in the collection.

Parameters:

identifier (Any) –

identifier for a given patch,

specifically implemented for each PatchCollection

Returns:

True if patch with specified identifier is in the collection,

False otherwise

Return type:

bool

add(identifier: Any, patch: numpy.ndarray, count: int | None = None) → None#

Add a new patch to the collection.

Parameters:

  • identifier (Any) –

    identifier for a given patch,

    specifically implemented for each PatchCollection

  • patch (np.ndarray) – the data for a specific patch

  • count (int) – Optionally, a corresponding item to add to the counts dictionary

Return type:

None

abstract average(corners: numpy.ndarray, step: int, size: int, mode: str) → PatchCollectionABC#

Construct a new PatchCollection where patches lying inside a new grid are averaged together.

Parameters:

  • corners (np.ndarray) – an Nx2 np.ndarray of the lower left corners of the new patch grid

  • step (int) – how far apart each corner patch is

  • size (int) – dimension of the new (size, size) shaped square patches

  • mode (str) – either average using “mean” or “median”

Returns:

a PatchCollection where data is sampled at the new grid

Return type:

PatchCollectionABC

abstract fit(base_psf: regularizepsf.psf.SimplePSF, is_varied: bool = False) → regularizepsf.psf.PointSpreadFunctionABC#
Parameters:

  • base_psf

  • is_varied

abstract save(path: str) → None#

Save the PatchCollection to a file.

Parameters:

path (str) – where to save the patch collection

Return type:

None

classmethod load(path: str) → PatchCollectionABC#
Abstractmethod:

Load a PatchCollection from a file.

Parameters:

path (str) – file path to load from

Returns:

the new patch collection

Return type:

PatchCollectionABC

keys() → list#

Get identifiers for all patches.

values() → list#

Get values of all patches.

items() → dict#

Get a dictionary like iterator over the patches.

_fit_lmfit(base_psf: regularizepsf.psf.SimplePSF, initial_guesses: dict[str, numbers.Real]) → dict[Any, lmfit.minimizer.MinimizerResult]#

Fit a patch using lmfit.

Parameters:

  • base_psf (SimplePSF) – the PSF model to use in fitting

  • initial_guesses (dict[str, Real]) – the initial guesses for all the PSF parameters

Returns:

keys are the identifiers, values are the MinimizerResults from lmfit

Return type:

dict

regularizepsf.fitter.CoordinateIdentifier#
class regularizepsf.fitter.CoordinatePatchCollection(patches: dict[Any, numpy.ndarray], counts: dict[Any, int] | None = None)#

Bases: PatchCollectionABC

A representation of a PatchCollection that operates on pixel coordinates from a set of images.

classmethod extract(images: list[numpy.ndarray], coordinates: list[CoordinateIdentifier], size: int) → PatchCollectionABC#

Construct a PatchCollection from a set of images using the specified coordinates and patch size.

Parameters:

  • images (list of np.ndarrays) – the images loaded

  • coordinates (list) –

    A list of coordinates for the lower left pixel of each patch,

    specified in each type of PatchCollection

  • size (int) – size of one side of the square patches extracted

Returns:

the square patches extracted into a PatchCollection

Return type:

np.ndarray

classmethod find_stars_and_average(images: list[str] | numpy.ndarray | collections.abc.Generator, psf_size: int, patch_size: int, interpolation_scale: int = 1, average_mode: str = 'median', percentile: float = 10, star_threshold: int = 3, star_mask: list[str] | numpy.ndarray | collections.abc.Generator | None = None, hdu_choice: int = 0) → CoordinatePatchCollection#

Load a series of images, finds stars in each and build a CoordinatePatchCollection with averaged stars.

Parameters:

  • images (List[str] or np.ndarray or Generator) – The images to be processed. Can be a list of FITS filenames, a numpy array of shape (n_images, ny, nx), or a Generator that yields each data array in turn.

  • psf_size (int) – size of the PSF model to use

  • patch_size (int) – square size that each PSF model applies to

  • interpolation_scale (int) –

    if >1, the image are first scaled by this factor.

    This results in stars being aligned at a subpixel scale

  • average_mode (str) – “median”, “percentile”, or “mean”: determines how patches are combined

  • percentile (float) – If average_mode is “percentile”, use this percentile value (from 0 to 100)

  • star_threshold (int) –

    SEP’s threshold for finding stars. See threshold

    in https://sep.readthedocs.io/en/v1.1.x/api/sep.extract.html#sep-extract

  • star_mask (List[str] or np.ndarray or Generator) – Masks to apply during star-finding. Can be a list of FITS filenames, a numpy array of shape (n_images, ny, nx), or a Generator that yields each mask array in turn. Where the mask pixel is True, the corresponding data array pixel will not be selected as a star. See mask in https://sep.readthedocs.io/en/v1.1.x/api/sep.extract.html#sep-extract for more details.

  • hdu_choice (int) –

    Which HDU from each image will be used,

    default of 0 is most common but could be 1 for compressed images

Returns:

An averaged star model built from the provided images

Return type:

CoordinatePatchCollection

Notes

Using an interpolation_scale other than 1

for large images can dramatically slow down the execution.

average(corners: numpy.ndarray, patch_size: int, psf_size: int, mode: str = 'median', percentile: float = 10) → PatchCollectionABC#

Construct a new PatchCollection where patches lying inside a new grid are averaged together.

Parameters:

  • corners (np.ndarray) – an Nx2 np.ndarray of the lower left corners of the new patch grid

  • step (int) – how far apart each corner patch is

  • size (int) – dimension of the new (size, size) shaped square patches

  • mode (str) – either average using “mean” or “median”

Returns:

a PatchCollection where data is sampled at the new grid

Return type:

PatchCollectionABC

static _validate_average_mode(mode: str, percentile: float) → None#

Determine if the average_mode is a valid kind.

_calculate_pad_shape(size: int) → tuple[int, int]#
fit(base_psf: regularizepsf.psf.SimplePSF, is_varied: bool = False) → regularizepsf.psf.PointSpreadFunctionABC#
Parameters:

  • base_psf

  • is_varied

to_array_corrector(target_evaluation: numpy.array) → regularizepsf.corrector.ArrayCorrector#

Convert a patch collection that has been averaged into an ArrayCorrector.

Parameters:

target_evaluation (np.ndarray) – the evaluation of the Target PSF

Returns:

An array corrector that can be used to correct PSFs

Return type:

ArrayCorrector

save(path: str) → None#

Save the CoordinatePatchCollection to a file.

Parameters:

path (str) – where to save the patch collection

Return type:

None

classmethod load(path: str) → PatchCollectionABC#

Load a PatchCollection from a file.

Parameters:

path (str) – file path to load from

Returns:

the new patch collection

Return type:

PatchCollectionABC