make_SEP_catalog_from_arrays

grizli.prep.make_SEP_catalog_from_arrays(sci, err, mask, wcs=None, threshold=2.0, ZP=25, get_background=True, detection_params={'clean': True, 'clean_param': 1, 'deblend_cont': 0.001, 'deblend_nthresh': 32, 'filter_kernel': array([[0.0049, 0.0213, 0.0513, 0.0687, 0.0513, 0.0213, 0.0049], [0.0213, 0.0921, 0.2211, 0.296, 0.2211, 0.0921, 0.0213], [0.0513, 0.2211, 0.5307, 0.7105, 0.5307, 0.2211, 0.0513], [0.0687, 0.296, 0.7105, 0.9511, 0.7105, 0.296, 0.0687], [0.0513, 0.2211, 0.5307, 0.7105, 0.5307, 0.2211, 0.0513], [0.0213, 0.0921, 0.2211, 0.296, 0.2211, 0.0921, 0.0213], [0.0049, 0.0213, 0.0513, 0.0687, 0.0513, 0.0213, 0.0049]]), 'filter_type': 'conv', 'minarea': 9}, segmentation_map=False, exposure_footprints=None, verbose=True)

Make a catalog from arrays using sep

Parameters

sci2D array

Data array

err2D array

Uncertainties in same units as sci

maskbool array

sep masks values where mask > 0

wcsWCS

WCS associated with data arrays

threshfloat

Detection threshold for sep.extract

ZPfloat

AB magnitude zeropoint of data arrays

get_backgroundbool

not used

detection_paramsdict

Keyword arguments for sep.extract

segmentation_mapbool

Also create a segmentation map

exposure_footprintslist, None

An optional list of objects that can be parsed with sregion.SRegion. If specified, add a column nexp to the catalog corresponding to the number of entries in the list that overlap with a particular source position

verbosebool

Print status messages

Returns

tabTable

Source catalog

segarray, None

Segmentation map, if requested