regularizepsf.builder#
Functions for building PSF models from images.
Classes#
A builder that will take a series of images and construct an ArrayPSF to represent their implicit PSF. |
Functions#
|
|
|
|
|
|
|
|
|
|
|
|
|
Module Contents#
- regularizepsf.builder._convert_to_generator(images: list[pathlib.Path] | numpy.ndarray | collections.abc.Generator, hdu_choice: int | None = None) collections.abc.Generator#
- regularizepsf.builder._scale_image(image, interpolation_scale)#
- regularizepsf.builder._find_patches(image, star_threshold, star_mask, interpolation_scale, psf_size, i)#
- regularizepsf.builder._find_matches(coordinate, x_bounds, y_bounds, psf_size)#
- regularizepsf.builder._average_patches_by_mean(patches, corners, x_bounds, y_bounds, psf_size)#
- regularizepsf.builder._average_patches_by_percentile(patches, corners, x_bounds, y_bounds, psf_size, percentile: float = 50)#
- regularizepsf.builder._average_patches(patches, corners, method='mean', percentile: float = None)#
- class regularizepsf.builder.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(images: list[str] | list[pathlib.Path] | numpy.ndarray | collections.abc.Generator, star_masks: list[str] | list[pathlib.Path] | numpy.ndarray | collections.abc.Generator | None = None, hdu_choice: int | None = 0, interpolation_scale: int = 1, star_threshold: int = 3, average_method: str = 'median', percentile: float = 50)#
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)