lightstack package

Submodules

lightstack.crop module

lightstack.crop.crop_from_radec(fits_path, ra, dec, size_arcsec, output_path=None)

Extract a square cutout from a FITS image centered on a given sky position.

Parameters:

fits_path (str) – Path to the FITS image.
ra (float) – Right Ascension of the center (in degrees).
dec (float) – Declination of the center (in degrees).
size_arcsec (float) – Size of the cutout (in arcseconds). The cutout is square.

Returns:

data_cut (2D numpy array) – Cropped image data.
header_cut (FITS header) – Updated header with corrected WCS reference (CRPIX).

lightstack.crop.crop_reg(fits_path, region)

Crops a FITS image using a DS9 region.

Parameters:

fits_path (str) – Path to the FITS file.
region (regions.Region) – Region object read from a DS9 region file.

Returns:

data_cut (numpy.ndarray) – Cropped image data.
header_cut (astropy.io.fits.Header) – Updated FITS header with adjusted CRPIX keywords.

Raises:

ValueError – If no image data extension is found in the FITS file.

lightstack.crop.crop_using_reference(fits_path, ref_cutout, output_path=None)

Crop a FITS image using the sky footprint of a reference cutout.

This function extracts a region from a target image such that it matches the sky coverage (RA/Dec bounding box) of a reference FITS cutout.

Parameters:

fits_path (str) – Path to the target FITS image to be cropped.
ref_cutout (str) – Path to the reference FITS cutout defining the sky region.
output_path (str, optional) – If provided, saves the cropped FITS to this path.

Returns:

data_cut (2D numpy array) – Cropped image data.
header_cut (FITS header) – Updated header with corrected WCS reference (CRPIX).

lightstack.crop.cut_region_2d(fits_file, x_start, x_end, y_start, y_end, output_path)

Cuts a spatial region from a 2D fits image.

Parameters:

fits_file (str) – Path to the input 2D fits image.
x_start (int) – Pixel indices for the x axis.
x_end (int) – Pixel indices for the x axis.
y_start (int) – Pixel indices for the y axis.
y_end (int) – Pixel indices for the y axis.
output_path (str) – Path to the output fits file.

Returns:

Saves the cut fits image.

Return type:

None

lightstack.crop.get_sky_bbox_from_cutout(fits_cut)

Get sky bounding box (RA, Dec) from a FITS cutout.

Parameters:: fits_cut (str) – Path to FITS file
Returns:: ra, dec – RA and Dec of the four corners
Return type:: arrays

lightstack.datacube module

lightstack.datacube.align_reproject_fits(fits_list, ref_file, method='interp', crop=1)

Align and optionally reproject FITS images to a common WCS. If an image already matches the reference WCS, shape and pixel scale, reprojection is skipped automatically. Area correction is only applied when input and output pixel scales differ.

Parameters:

fits_list (list of tuples) – [(fits_path, filter_name), …]
ref_file (str) – Reference FITS file.
method (str, optional) – Reprojection method: - “interp” (default) - “exact”
crop (int, optional) – Pixels to crop from borders after reprojection.

Returns:

aligned_list – [(aligned_fits_path, filter_name), …]

Return type:

list of tuples

lightstack.datacube.build_datacube(aligned_fits_files, reference_file, output_path)

Build a 3D datacube from aligned 2D FITS images.

Parameters:

aligned_fits_files (list of tuples) – List in format [(filename, filter_name), …].
reference_file (str) – FITS file used to define WCS and output shape.
output_path (str) – Path to save the output datacube.

Returns:

Saves the datacube to disk.

Return type:

None

lightstack.datacube.build_valid_datacube(cube_fits_file, output_cube, threshold=0.0, frac_valid=0.01)

Remove empty filters in a datacube, saves the new datacube and returns the valid filter names.

Parameters:

cube_fits_file (str) – Path to the input 3D fits datacube.
output_cube (str) – Path to save the filtered fits.
threshold (float) – Minimum flux value to consider a pixel valid.
frac_valid (float) – Minimum fraction of pixels above the threshold to consider the filter valid.

Returns:

cube_filtered (np.ndarray) – Datacube with only valid filters.
filters_valid (list) – List of valid filter names.
Saves the new datacube.

lightstack.datacube.cut_region_datacube(cube_fits_file, x_start, x_end, y_start, y_end, output_path)

Cuts a spatial region from a datacube.

Parameters:

cube_fits_file (str) – Path to the input 3D fits datacube.
x_start (int) – Pixel indices for the x axis.
x_end (int) – Pixel indices for the x axis.
y_start (int) – Pixel indices for the y axis.
y_end (int) – Pixel indices for the y axis.
output_path (str) – Path to the output fits file.

Returns:

Saves the cut datacube.

Return type:

None

lightstack.datacube.load_image_fits(folder_path)

lightstack.datacube.remove_filter(cube_fits_file, output_cube, filter_to_remove)

Removes a specific filter from a 3D datacube.

Parameters:

cube_fits_file (str) – Path to the original datacube.
output_cube (str) – Path to save the new datacube.
filter_to_remove (str) – Name of the filter to be removed.

lightstack.datacube.update_cube_header(cube_fits_file, output_file=None, redshift=None, ra_center=None, dec_center=None, use_brightest_pixel=False, overwrite=False)

Update a datacube header with astrophysical metadata.

By default, creates a new file with suffix ‘_more.fits’ instead of overwriting the original.

Parameters:

cube_fits_file (str) – Path to input datacube.
output_file (str or None) – Output FITS file. If None, creates ‘<original>_more.fits’.
redshift (float or None) – Galaxy redshift.
ra_center (float or None) – Galaxy center coordinates (deg).
dec_center (float or None) – Galaxy center coordinates (deg).
use_brightest_pixel (bool) – If True, estimate center from brightest pixel.
overwrite (bool) – Overwrite output file if it already exists.

lightstack.plot module

lightstack.plot.plot_datacube_filters(cube_fits_file, ncols=None, figsize=(15, 15), cmap='viridis', norm=None, stretch='log', min_percent=25.0, max_percent=99.98, save_path=None)

Plot all filters from a datacube as a grid of images.

Parameters:

cube_fits_file (str) – Path to the 3D FITS datacube.
ncols (int or None) – Number of columns in the grid. If None, a near-square layout is used.
figsize (tuple) – Figure size.
cmap (str) – Colormap.
norm (matplotlib.colors.Normalize or None) – Custom normalization. If None, uses simple_norm.
stretch (str) – Stretch for simple_norm (ignored if norm is provided).
min_percent (float) – Percentile limits for normalization.
max_percent (float) – Percentile limits for normalization.
save_path (str or None) – If provided, saves the figure.

lightstack.plot.plot_psf_grid(psf_dir=None, psf_files=None, ncols=None, figsize=(12, 12), norm=None, stretch='log', percent=99.0, cmap='viridis', save_path=None)

Plot a grid of PSFs from FITS files.

Parameters:

psf_dir (str, optional) – Directory containing PSF FITS files.
psf_files (list, optional) – List of PSF FITS file paths (overrides psf_dir).
ncols (int or None) – Number of columns in the grid. If None, chosen automatically.
figsize (tuple) – Figure size.
norm (astropy visualization norm, optional) – Custom normalization.
stretch (str) – Stretch for simple_norm if norm is None.
percent (float) – Percentile used in simple_norm.
cmap (str) – Colormap.
save_path (str, optional) – Path to save figure.

lightstack.plot.visualize_fits(fits_path, save_path=None, stretch='log', min_percent=25.0, max_percent=99.98, cmap='viridis', xlim=None, ylim=None)

Visualizes a FITS file with both pixel and RA/Dec axes.

Parameters:

fits_path (str) – Path to the FITS file.
save_path (str or None) – Path to save the output image. If None, the figure is shown but not saved.
stretch (str) – Stretch type for simple_norm (e.g., ‘linear’, ‘log’, ‘sqrt’).
min_percent (float) – Percentile limits for normalization.
max_percent (float) – Percentile limits for normalization.
cmap (str) – Colormap name.
xlim (tuple or None) – Pixel axis limits.
ylim (tuple or None) – Pixel axis limits.

lightstack.psf module

lightstack.psf.apply_kernel(image, kernel)

Convolve an image with a given kernel.

Parameters:

image (2D array)
kernel (2D array)

Returns:

image_conv

Return type:

2D array

lightstack.psf.build_kernel(psf_source, psf_ref, shape=(101, 101), eps=0.001)

Build a convolution kernel that transforms psf_source into psf_ref using Fourier fast transforms.

Parameters:

psf_source (2D array) – PSF of the original image.
psf_ref (2D array) – PSF of the reference resolution.
shape (tuple) – Shape (Ny, Nx) of the kernel.
eps (float) – Regularization parameter.

Returns:

kernel – Convolution kernel.

Return type:

2D array

lightstack.psf.centroid_weighted(psf, threshold=0.0001)

Compute a flux-weighted centroid of a PSF, ignoring low-level noise.

Parameters:

psf (2D ndarray) – Input PSF image.
threshold (float, optional) – Fraction of the maximum PSF value below which pixels are ignored. Default is 1e-4.

Returns:

ycen, xcen – Coordinates of the centroid (in pixel units).

Return type:

float

lightstack.psf.make_odd(psf)

Ensure that a PSF has odd dimensions by padding with zeros if necessary.

Parameters:: psf (2D ndarray) – Input PSF image.
Returns:: psf_padded – PSF with odd dimensions.
Return type:: 2D ndarray

lightstack.psf.psf_match_datacube(cube_path, kernel_dir, ref_filter='F444W', output_path=None, overwrite=True)

Apply PSF matching to a datacube using precomputed convolution kernels.

Each slice is convolved to match the PSF of a reference filter.

Parameters:

cube_path (str) – Path to input datacube FITS.
kernel_dir (str) – Directory containing kernel FITS files.
ref_filter (str) – Reference filter (e.g., “F444W”).
output_path (str or None) – Output FITS file. If None, adds ‘_psfmatched’.
overwrite (bool) – Overwrite output file.

Returns:

output_path – Path to saved PSF-matched datacube.

Return type:

str

lightstack.psf.resample_psf(input_path, output_path, zoom_factor=None, psf_pixel_scale=None, target_pixel_scale=None, order=3, normalize=True, make_odd_shape=True, header_comment=True)

Resample a PSF to match a target pixel scale, by directly providing a zoom factor or by specifying the original and target pixel scales. Always downsample to the worst resolution. This code has not been tested for upsampling, so it is not recommended!

Parameters:

input_path (str) – Path to input PSF FITS file.
output_path (str) – Path to save the resampled PSF.
zoom_factor (float, optional) – Zoom factor to apply. If None, it will be computed from pixel scales.
psf_pixel_scale (float, optional) – Original PSF pixel scale (arcsec/pixel).
target_pixel_scale (float, optional) – Target pixel scale (arcsec/pixel).
order (int, optional) – Interpolation order for scipy.ndimage.zoom. Default is 3 (cubic).
normalize (bool, optional) – If True, normalize PSF to unit sum. Default is True.
make_odd_shape (bool, optional) – If True, pad PSF to have odd dimensions. Default is True.
header_comment (bool, optional) – If True, add history information to FITS header.

Returns:

psf_resampled – Resampled PSF array.

Return type:

2D ndarray

lightstack.psf.save_kernel(kernel, output_path, header=None)

Save a convolution kernel to a FITS file.

Parameters:

kernel (2D array) – Convolution kernel.
output_path (str) – Output FITS path.
header (fits.Header or None) – Optional header to attach to the kernel.

lightstack.utils module

lightstack.utils.MJy_sr_to_jy(aligned_list)

Convert FITS images from MJy/sr to Jy/pixel using the PIXAR_SR keyword: Jy/pixel = (MJy/sr) * 1e6 * PIXAR_SR

Parameters:: aligned_list (list of tuples) – List in the form [(fits_path, filter_name), …].
Returns:: new_list – List in the form [(new_fits_path, filter_name), …] for the converted files.
Return type:: list of tuples

lightstack.utils.add_custom_dataset(dataset_name, filters, overwrite=False)

Add a custom dataset/survey to FILTER_DATABASE.

Parameters:

dataset_name (str) – Name of the telescope/survey (e.g. ‘EUCLID’, ‘ROMAN’).
filters (dict) –
Dictionary in format:

{

“FILTER1”: {
“lambda”: 5500.0 (in angstroms), “instrument”: “TelescopeCam”, “group”: “broad”, “output_group”: “Telescope”

}
overwrite (bool, optional) – If True, overwrite existing dataset.

Return type:

None

lightstack.utils.find_ext(hdul)

Finds the first FITS extension containing valid image data.

Parameters:: hdul (astropy.io.fits.HDUList) – Opened FITS file.
Returns:: ext – Index of the HDU containing image data. Returns None if not found.
Return type:: int or None

lightstack.utils.get_filter(fname): Infer filter name from filename using FILTER_DATABASE. Works for JWST, HST, LSST, JPAS and future surveys.

lightstack.utils.get_filter_info(filt): Return metadata for a given filter from FILTER_DATABASE.

lightstack.utils.get_pixel_scale(fits_path): Opens FITS file and computes pixel scale in arcsec/pixel using WCS. Assumes square pixels and no significant distortion.

lightstack.utils.get_pixel_scale_from_wcs(wcs): Compute pixel scale in arcsec/pixel from a WCS object. Assumes square pixels and no significant distortion.

lightstack.utils.pick_group(filt): Return a group for a filter.

lightstack.utils.pick_instrument(filt): Return a instrument for a filter.

lightstack.utils.pick_output_group(filt): Return output grouping for a filter.

lightstack.utils.save_fits(data, header, path)

Saves a FITS file to disk.

Parameters:

data (numpy.ndarray) – Image data.
header (astropy.io.fits.Header) – FITS header.
path (str) – Output FITS path.

lightstack.utils.sort_filters(filters): Sort filters by wavelength.

Module contents

Lightstack: Tools for processing, PSF matching, and visualization of multi-filter photometric datacubes using astronomical imaging (HST and JWST).

Main functionalities include: - Crop regions - Image alignment and photometric datacube construction - PSF matching using convolution kernels - Visualization of FITS images and datacubes

lightstack.MJy_sr_to_jy(aligned_list)

Convert FITS images from MJy/sr to Jy/pixel using the PIXAR_SR keyword: Jy/pixel = (MJy/sr) * 1e6 * PIXAR_SR

Parameters:: aligned_list (list of tuples) – List in the form [(fits_path, filter_name), …].
Returns:: new_list – List in the form [(new_fits_path, filter_name), …] for the converted files.
Return type:: list of tuples

lightstack.add_custom_dataset(dataset_name, filters, overwrite=False)

Add a custom dataset/survey to FILTER_DATABASE.

Parameters:

dataset_name (str) – Name of the telescope/survey (e.g. ‘EUCLID’, ‘ROMAN’).
filters (dict) –
Dictionary in format:

{

“FILTER1”: {
“lambda”: 5500.0 (in angstroms), “instrument”: “TelescopeCam”, “group”: “broad”, “output_group”: “Telescope”

}
overwrite (bool, optional) – If True, overwrite existing dataset.

Return type:

None

lightstack.align_reproject_fits(fits_list, ref_file, method='interp', crop=1)

Align and optionally reproject FITS images to a common WCS. If an image already matches the reference WCS, shape and pixel scale, reprojection is skipped automatically. Area correction is only applied when input and output pixel scales differ.

Parameters:

fits_list (list of tuples) – [(fits_path, filter_name), …]
ref_file (str) – Reference FITS file.
method (str, optional) – Reprojection method: - “interp” (default) - “exact”
crop (int, optional) – Pixels to crop from borders after reprojection.

Returns:

aligned_list – [(aligned_fits_path, filter_name), …]

Return type:

list of tuples

lightstack.apply_kernel(image, kernel)

Convolve an image with a given kernel.

Parameters:

image (2D array)
kernel (2D array)

Returns:

image_conv

Return type:

2D array

lightstack.build_datacube(aligned_fits_files, reference_file, output_path)

Build a 3D datacube from aligned 2D FITS images.

Parameters:

aligned_fits_files (list of tuples) – List in format [(filename, filter_name), …].
reference_file (str) – FITS file used to define WCS and output shape.
output_path (str) – Path to save the output datacube.

Returns:

Saves the datacube to disk.

Return type:

None

lightstack.build_kernel(psf_source, psf_ref, shape=(101, 101), eps=0.001)

Build a convolution kernel that transforms psf_source into psf_ref using Fourier fast transforms.

Parameters:

psf_source (2D array) – PSF of the original image.
psf_ref (2D array) – PSF of the reference resolution.
shape (tuple) – Shape (Ny, Nx) of the kernel.
eps (float) – Regularization parameter.

Returns:

kernel – Convolution kernel.

Return type:

2D array

lightstack.build_valid_datacube(cube_fits_file, output_cube, threshold=0.0, frac_valid=0.01)

Remove empty filters in a datacube, saves the new datacube and returns the valid filter names.

Parameters:

cube_fits_file (str) – Path to the input 3D fits datacube.
output_cube (str) – Path to save the filtered fits.
threshold (float) – Minimum flux value to consider a pixel valid.
frac_valid (float) – Minimum fraction of pixels above the threshold to consider the filter valid.

Returns:

cube_filtered (np.ndarray) – Datacube with only valid filters.
filters_valid (list) – List of valid filter names.
Saves the new datacube.

lightstack.centroid_weighted(psf, threshold=0.0001)

Compute a flux-weighted centroid of a PSF, ignoring low-level noise.

Parameters:

psf (2D ndarray) – Input PSF image.
threshold (float, optional) – Fraction of the maximum PSF value below which pixels are ignored. Default is 1e-4.

Returns:

ycen, xcen – Coordinates of the centroid (in pixel units).

Return type:

float

lightstack.crop_from_radec(fits_path, ra, dec, size_arcsec, output_path=None)

Extract a square cutout from a FITS image centered on a given sky position.

Parameters:

fits_path (str) – Path to the FITS image.
ra (float) – Right Ascension of the center (in degrees).
dec (float) – Declination of the center (in degrees).
size_arcsec (float) – Size of the cutout (in arcseconds). The cutout is square.

Returns:

data_cut (2D numpy array) – Cropped image data.
header_cut (FITS header) – Updated header with corrected WCS reference (CRPIX).

lightstack.crop_reg(fits_path, region)

Crops a FITS image using a DS9 region.

Parameters:

fits_path (str) – Path to the FITS file.
region (regions.Region) – Region object read from a DS9 region file.

Returns:

data_cut (numpy.ndarray) – Cropped image data.
header_cut (astropy.io.fits.Header) – Updated FITS header with adjusted CRPIX keywords.

Raises:

ValueError – If no image data extension is found in the FITS file.

lightstack.crop_using_reference(fits_path, ref_cutout, output_path=None)

Crop a FITS image using the sky footprint of a reference cutout.

This function extracts a region from a target image such that it matches the sky coverage (RA/Dec bounding box) of a reference FITS cutout.

Parameters:

fits_path (str) – Path to the target FITS image to be cropped.
ref_cutout (str) – Path to the reference FITS cutout defining the sky region.
output_path (str, optional) – If provided, saves the cropped FITS to this path.

Returns:

data_cut (2D numpy array) – Cropped image data.
header_cut (FITS header) – Updated header with corrected WCS reference (CRPIX).

lightstack.cut_region_2d(fits_file, x_start, x_end, y_start, y_end, output_path)

Cuts a spatial region from a 2D fits image.

Parameters:

fits_file (str) – Path to the input 2D fits image.
x_start (int) – Pixel indices for the x axis.
x_end (int) – Pixel indices for the x axis.
y_start (int) – Pixel indices for the y axis.
y_end (int) – Pixel indices for the y axis.
output_path (str) – Path to the output fits file.

Returns:

Saves the cut fits image.

Return type:

None

lightstack.cut_region_datacube(cube_fits_file, x_start, x_end, y_start, y_end, output_path)

Cuts a spatial region from a datacube.

Parameters:

cube_fits_file (str) – Path to the input 3D fits datacube.
x_start (int) – Pixel indices for the x axis.
x_end (int) – Pixel indices for the x axis.
y_start (int) – Pixel indices for the y axis.
y_end (int) – Pixel indices for the y axis.
output_path (str) – Path to the output fits file.

Returns:

Saves the cut datacube.

Return type:

None

lightstack.find_ext(hdul)

Finds the first FITS extension containing valid image data.

Parameters:: hdul (astropy.io.fits.HDUList) – Opened FITS file.
Returns:: ext – Index of the HDU containing image data. Returns None if not found.
Return type:: int or None

lightstack.get_filter(fname): Infer filter name from filename using FILTER_DATABASE. Works for JWST, HST, LSST, JPAS and future surveys.

lightstack.get_filter_info(filt): Return metadata for a given filter from FILTER_DATABASE.

lightstack.get_pixel_scale(fits_path): Opens FITS file and computes pixel scale in arcsec/pixel using WCS. Assumes square pixels and no significant distortion.

lightstack.get_pixel_scale_from_wcs(wcs): Compute pixel scale in arcsec/pixel from a WCS object. Assumes square pixels and no significant distortion.

lightstack.get_sky_bbox_from_cutout(fits_cut)

Get sky bounding box (RA, Dec) from a FITS cutout.

Parameters:: fits_cut (str) – Path to FITS file
Returns:: ra, dec – RA and Dec of the four corners
Return type:: arrays

lightstack.load_image_fits(folder_path)

lightstack.make_odd(psf)

Ensure that a PSF has odd dimensions by padding with zeros if necessary.

Parameters:: psf (2D ndarray) – Input PSF image.
Returns:: psf_padded – PSF with odd dimensions.
Return type:: 2D ndarray

lightstack.pick_group(filt): Return a group for a filter.

lightstack.pick_instrument(filt): Return a instrument for a filter.

lightstack.pick_output_group(filt): Return output grouping for a filter.

lightstack.plot_datacube_filters(cube_fits_file, ncols=None, figsize=(15, 15), cmap='viridis', norm=None, stretch='log', min_percent=25.0, max_percent=99.98, save_path=None)

Plot all filters from a datacube as a grid of images.

Parameters:

cube_fits_file (str) – Path to the 3D FITS datacube.
ncols (int or None) – Number of columns in the grid. If None, a near-square layout is used.
figsize (tuple) – Figure size.
cmap (str) – Colormap.
norm (matplotlib.colors.Normalize or None) – Custom normalization. If None, uses simple_norm.
stretch (str) – Stretch for simple_norm (ignored if norm is provided).
min_percent (float) – Percentile limits for normalization.
max_percent (float) – Percentile limits for normalization.
save_path (str or None) – If provided, saves the figure.

lightstack.plot_psf_grid(psf_dir=None, psf_files=None, ncols=None, figsize=(12, 12), norm=None, stretch='log', percent=99.0, cmap='viridis', save_path=None)

Plot a grid of PSFs from FITS files.

Parameters:

psf_dir (str, optional) – Directory containing PSF FITS files.
psf_files (list, optional) – List of PSF FITS file paths (overrides psf_dir).
ncols (int or None) – Number of columns in the grid. If None, chosen automatically.
figsize (tuple) – Figure size.
norm (astropy visualization norm, optional) – Custom normalization.
stretch (str) – Stretch for simple_norm if norm is None.
percent (float) – Percentile used in simple_norm.
cmap (str) – Colormap.
save_path (str, optional) – Path to save figure.

lightstack.psf_match_datacube(cube_path, kernel_dir, ref_filter='F444W', output_path=None, overwrite=True)

Apply PSF matching to a datacube using precomputed convolution kernels.

Each slice is convolved to match the PSF of a reference filter.

Parameters:

cube_path (str) – Path to input datacube FITS.
kernel_dir (str) – Directory containing kernel FITS files.
ref_filter (str) – Reference filter (e.g., “F444W”).
output_path (str or None) – Output FITS file. If None, adds ‘_psfmatched’.
overwrite (bool) – Overwrite output file.

Returns:

output_path – Path to saved PSF-matched datacube.

Return type:

str

lightstack.remove_filter(cube_fits_file, output_cube, filter_to_remove)

Removes a specific filter from a 3D datacube.

Parameters:

cube_fits_file (str) – Path to the original datacube.
output_cube (str) – Path to save the new datacube.
filter_to_remove (str) – Name of the filter to be removed.

lightstack.resample_psf(input_path, output_path, zoom_factor=None, psf_pixel_scale=None, target_pixel_scale=None, order=3, normalize=True, make_odd_shape=True, header_comment=True)

Resample a PSF to match a target pixel scale, by directly providing a zoom factor or by specifying the original and target pixel scales. Always downsample to the worst resolution. This code has not been tested for upsampling, so it is not recommended!

Parameters:

input_path (str) – Path to input PSF FITS file.
output_path (str) – Path to save the resampled PSF.
zoom_factor (float, optional) – Zoom factor to apply. If None, it will be computed from pixel scales.
psf_pixel_scale (float, optional) – Original PSF pixel scale (arcsec/pixel).
target_pixel_scale (float, optional) – Target pixel scale (arcsec/pixel).
order (int, optional) – Interpolation order for scipy.ndimage.zoom. Default is 3 (cubic).
normalize (bool, optional) – If True, normalize PSF to unit sum. Default is True.
make_odd_shape (bool, optional) – If True, pad PSF to have odd dimensions. Default is True.
header_comment (bool, optional) – If True, add history information to FITS header.

Returns:

psf_resampled – Resampled PSF array.

Return type:

2D ndarray

lightstack.save_fits(data, header, path)

Saves a FITS file to disk.

Parameters:

data (numpy.ndarray) – Image data.
header (astropy.io.fits.Header) – FITS header.
path (str) – Output FITS path.

lightstack.save_kernel(kernel, output_path, header=None)

Save a convolution kernel to a FITS file.

Parameters:

kernel (2D array) – Convolution kernel.
output_path (str) – Output FITS path.
header (fits.Header or None) – Optional header to attach to the kernel.

lightstack.sort_filters(filters): Sort filters by wavelength.

lightstack.update_cube_header(cube_fits_file, output_file=None, redshift=None, ra_center=None, dec_center=None, use_brightest_pixel=False, overwrite=False)

Update a datacube header with astrophysical metadata.

By default, creates a new file with suffix ‘_more.fits’ instead of overwriting the original.

Parameters:

cube_fits_file (str) – Path to input datacube.
output_file (str or None) – Output FITS file. If None, creates ‘<original>_more.fits’.
redshift (float or None) – Galaxy redshift.
ra_center (float or None) – Galaxy center coordinates (deg).
dec_center (float or None) – Galaxy center coordinates (deg).
use_brightest_pixel (bool) – If True, estimate center from brightest pixel.
overwrite (bool) – Overwrite output file if it already exists.

lightstack.visualize_fits(fits_path, save_path=None, stretch='log', min_percent=25.0, max_percent=99.98, cmap='viridis', xlim=None, ylim=None)

Visualizes a FITS file with both pixel and RA/Dec axes.

Parameters:

fits_path (str) – Path to the FITS file.
save_path (str or None) – Path to save the output image. If None, the figure is shown but not saved.
stretch (str) – Stretch type for simple_norm (e.g., ‘linear’, ‘log’, ‘sqrt’).
min_percent (float) – Percentile limits for normalization.
max_percent (float) – Percentile limits for normalization.
cmap (str) – Colormap name.
xlim (tuple or None) – Pixel axis limits.
ylim (tuple or None) – Pixel axis limits.