multiband_catalog

grizli.pipeline.auto_script.multiband_catalog(field_root='j142724+334246', threshold=1.8, detection_background=True, photometry_background=True, get_all_filters=False, filters=None, det_err_scale=-inf, phot_err_scale=-inf, rescale_weight=True, run_detection=True, detection_filter='ir', detection_root=None, output_root=None, use_psf_filter=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}, phot_apertures=[<Quantity 0.36 arcsec>, <Quantity 0.500001 arcsec>, <Quantity 0.7000002 arcsec>, <Quantity 1.0000002 arcsec>, <Quantity 1.2 arcsec>, <Quantity 1.5 arcsec>, <Quantity 3. arcsec>], master_catalog=None, bkg_mask=None, bkg_params={'bh': 64, 'bw': 64, 'fh': 3, 'fw': 3, 'pixel_scale': 0.06}, use_bkg_err=False, aper_segmask=True, sci_image=None, prefer_var_image=True, clean_bkg=True)

Make a detection catalog and run aperture photometry on all available filter images with the SourceExtractor clone sep.

Parameters

field_rootstr

Rootname of detection images and individual filter images (and weights).

thresholdfloat

Detection threshold, see make_SEP_catalog.

detection_backgroundbool

Background subtraction on detection image, see get_background on make_SEP_catalog.

photometry_backgroundbool

Background subtraction when doing photometry on filter images, see get_background on make_SEP_catalog.

get_all_filtersbool

Find all filter images available for field_root

filterslist, None

Explicit list of filters to include, rather than all available

det_err_scalefloat

Uncertainty scaling for detection image, see err_scale on make_SEP_catalog.

phot_err_scalefloat

Uncertainty scaling for filter images, see err_scale on make_SEP_catalog.

rescale_weightbool

Rescale the weight images based on sep.Background.rms for both detection and filter images, see grizli.prep.make_SEP_catalog.

run_detectionbool

Run the source detection. Can be False if the detection catalog file (master_catalog) and segmentation image ({field_root}-{detection_filter}_seg.fits) already exist, i.e., from a separate call to make_SEP_catalog.

detection_filterstr

Filter image to use for the source detection. The default ir is the product of grizli.pipeline.auto_script.make_filter_combinations. The detection image filename will be {field_root}-{detection_filter}_drz_sci.fits and with associated weight image {field_root}-{detection_filter}_drz_wht.fits.

detection_rootstr, None

Alternative rootname to use for the detection (and weight) image, i.e., {detection_root}_drz_sci.fits. Note that the _drz_sci.fits suffixes are currently required by make_SEP_catalog.

output_rootstr, None

Rootname of the output catalog file to use, if desired other than field_root.

use_psf_filterbool

For HST, try to use the PSF as the convolution filter for source detection

detection_paramsdict

Source detection parameters, see make_SEP_catalog. Many of these are analogous to SourceExtractor parameters.

phot_apertureslist

Aperture diameters. If provided as a string, then apertures assumed to be in pixel units. Can also provide a list of elements with astropy.unit attributes, which are converted to pixels given the image WCS/pixel size. See make_SEP_catalog.

master_catalogstr, None

Filename of the detection catalog, if None then build as {field_root}-{detection_filter}.cat.fits

bkg_maskarray-like, None

Mask to use for the detection and photometry background determination, see make_SEP_catalog. This has to be the same dimensions as the images themselves.

bkg_paramsdict

Background parameters, analogous to SourceExtractor, see make_SEP_catalog.

use_bkg_errbool

Use the background rms array determined by sep for the uncertainties (see sep.Background.rms).

aper_segmaskbool

Use segmentation masking for the aperture photometry, see make_SEP_catalog.

sci_imagearray-like, None

Array itself to use for source detection, see make_SEP_catalog.

prefer_var_imagebool

If found, use _var.fits image for the full variance that includes the Poisson component

Returns

tabTable

Catalog with detection parameters and aperture photometry. This is essentially the same as the output for make_SEP_catalog but with separate photometry columns for each multi-wavelength filter image found.