deepdisc.preprocessing.process

Functions

write_scarlet_results(datas, observation, ...[, catalog])	Saves images in each channel, with headers for each source in image,
write_scarlet_results_nomodels(datas, observation, ...)	Saves images in each channel, with headers for each source in image,
write_scarlet_results_HSC(datas, observation, ...[, ...])	Saves images in each channel, with headers for each source in image,

Module Contents

write_scarlet_results(datas, observation, starlet_sources, model_frame, catalog_deblended, segmentation_masks, outdir, filters, s, catalog=None)

Saves images in each channel, with headers for each source in image, such that the number of headers = number of sources detected in image.

Parameters:

• datas (array) – array of Data objects

• observation (scarlet function) – Scarlet observation objects

• starlet_sources (list) – List of ScarletSource objects

• model_frame (scarlet function) – Image frame of source model

• catalog_deblended (list) – Deblended source detection catalog

• catalog (pandas df) – External catalog of source detections

• segmentation_masks (list) – List of segmentation mask of each object in image

• outdir (str) – Path to HSC image file directory

• filters (list) – A list of filters for your images. Default is [‘g’, ‘r’, ‘i’].

• s (str) – File basename string

Returns:

filename – dictionary of all paths to the saved scarlet files for the particular dataset. Saved image and model files for each filter, and one total segmentation mask file for all filters.

Return type:

dict

write_scarlet_results_nomodels(datas, observation, starlet_sources, model_frame, segmentation_masks, outdir, filters, s, catalog=None, keys=None)

Saves images in each channel, with headers for each source in image, such that the number of headers = number of sources detected in image.

Parameters:

• datas (array) – array of Data objects

• observation (scarlet function) – Scarlet observation objects

• starlet_sources (list) – List of ScarletSource objects

• model_frame (scarlet function) – Image frame of source model

• catalog_deblended (list) – Deblended source detection catalog

• catalog (pandas df) – External catalog of source detections

• segmentation_masks (list) – List of segmentation mask of each object in image

• outdir (str) – Path to HSC image file directory

• filters (list) – A list of filters for your images. Default is [‘g’, ‘r’, ‘i’].

• s (str) – File basename string

Returns:

filename – dictionary of all paths to the saved scarlet files for the particular dataset. Saved image and model files for each filter, and one total segmentation mask file for all filters.

Return type:

dict

write_scarlet_results_HSC(datas, observation, starlet_sources, model_frame, segmentation_masks, outdir, filters, s, source_catalog=None)

Saves images in each channel, with headers for each source in image, such that the number of headers = number of sources detected in image.

Parameters:

• datas (array) – array of Data objects

• observation (scarlet function) – Scarlet observation objects

• starlet_sources (list) – List of ScarletSource objects

• model_frame (scarlet function) – Image frame of source model

• catalog_deblended (list) – Deblended source detection catalog

• source_catalog (pandas df) – External catalog of source detections

• segmentation_masks (list) – List of segmentation mask of each object in image

• outdir (str) – Path to HSC image file directory

• filters (list) – A list of filters for your images. Default is [‘g’, ‘r’, ‘i’].

• s (str) – File basename string

Returns:

filename – dictionary of all paths to the saved scarlet files for the particular dataset. Saved image and model files for each filter, and one total segmentation mask file for all filters.

Return type:

dict