Extended Aperture Photometry

Use case: Measure galaxy photometry in a field. Related to JDox Science Use Case #22.
Data: WST simulated NIRCam images from JADES JWST GTO extragalactic blank field.
(Williams et al. 2018) https://ui.adsabs.harvard.edu/abs/2018ApJS..236…33W.
Tools: photutils, matplotlib, scipy, scikit.
Cross-intrument: potentially MIRI.
Documentation: This notebook is part of a STScI’s larger post-pipeline Data Analysis Tools Ecosystem.

Introduction

This notebook uses photutils to detect objects/galaxies in NIRCam deep imaging. Detections are first made in a F200W image, then isophotal photometry is obtained in all 9 filters (F090W, F115W, F150W, F200W, F277W, F335M, F356W, F410M, F444W). The catalog is loaded back in and some simple analysis is performed on the full catalog and on an individual galaxy.

The notebook analyzes only the central 1000 x 1000 pixels (30” x 30”) of the full JADES simulation. These cutouts have been staged at STScI with permission from the authors (Williams et al.).

NOTE: The photometry is aperture matched, but no PSF corrections are made. For more accurate color measurements, PSF corrections should be implemented, given the large range of wavelengths (and thus PSF FWHM) spanning a factor of >4.

NOTE: The simulated JADES images have different units (e-/s) than JWST pipeline products (MJy/sr).

NOTE: An exposure map is missing but required to calculate flux uncertainties.

To Do

• PSF corrections

• Check accuracy of photometry against simulated JADES catalog

• Exposure map required for input to error calculation

• ABmag units cannot be written to ecsv file (astropy update coming soon)

• plot with text labels looks horrible (I wish cursor hover would show id number instead)

• Fix plot secondary axis: mag vs. flux

• requirements.txt file – but I don’t know what versions are “required”

• rest of Robel’s comments: https://github.com/spacetelescope/dat_pyinthesky/pull/82#pullrequestreview-355206337

Download WEBBPSF Data

import os
import tarfile
import urllib.request

boxlink = 'https://stsci.box.com/shared/static/34o0keicz2iujyilg4uz617va46ks6u9.gz'                                                           
boxfile = './webbpsf-data/webbpsf-data-1.0.0.tar.gz'

webbpsf_folder = './webbpsf-data'

# Check whether the specified path exists or not
isExist = os.path.exists(webbpsf_folder)
if not isExist:
  # Create a new directory because it does not exist 
  os.makedirs(webbpsf_folder)
    
urllib.request.urlretrieve(boxlink, boxfile)
gzf = tarfile.open(boxfile)
gzf.extractall(webbpsf_folder)

Import packages

import os

import numpy as np

from astropy.convolution import convolve
from astropy.io import fits
from astropy.stats import gaussian_fwhm_to_sigma
from astropy.table import QTable
import astropy.units as u
from astropy.visualization import make_lupton_rgb, SqrtStretch, ImageNormalize, simple_norm
import astropy.wcs as wcs

from photutils.background import Background2D, MedianBackground
from photutils.segmentation import (detect_sources, deblend_sources, SourceCatalog,
                                    make_2dgaussian_kernel, SegmentationImage)
from photutils.utils import calc_total_error

Matplotlib setup for plotting

There are two versions

• notebook – gives interactive plots, but makes the overall notebook a bit harder to scroll

• inline – gives non-interactive plots for better overall scrolling

import matplotlib.pyplot as plt
import matplotlib as mpl

# Use this version for non-interactive plots (easier scrolling of the notebook)
%matplotlib inline

# Use this version if you want interactive plots
# %matplotlib notebook

# These gymnastics are needed to make the sizes of the figures
# be the same in both the inline and notebook versions
%config InlineBackend.print_figure_kwargs = {'bbox_inches': None}

mpl.rcParams['savefig.dpi'] = 80
mpl.rcParams['figure.dpi'] = 80

Create list of images to be loaded and analyzed

baseurl = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/'

filters = 'F090W F115W F150W F200W F277W F335M F356W F410M F444W'.split()

# Data images [e-/s]
imagefiles = {}
for filt in filters:
    filename = f'jades_jwst_nircam_goods_s_crop_{filt}.fits'
    imagefiles[filt] = os.path.join(baseurl, filename)

# Weight images (Inverse Variance Maps; IVM)
weightfiles = {}
for filt in filters:
    filename = f'jades_jwst_nircam_goods_s_crop_{filt}_wht.fits'
    weightfiles[filt] = os.path.join(baseurl, filename)

Load detection image: F200W

filt = 'F200W'
infile = imagefiles[filt]
hdu = fits.open(infile)
data = hdu[0].data
imwcs = wcs.WCS(hdu[0].header, hdu)

weight = fits.open(weightfiles[filt])[0].data

Report image size and field of view

ny, nx = data.shape
pixscale = wcs.utils.proj_plane_pixel_scales(imwcs)[0] 
pixscale *= imwcs.wcs.cunit[0].to('arcsec')
outline = '%d x %d pixels' % (ny, nx)
outline += ' = %g" x %g"' % (ny * pixscale, nx * pixscale)
outline += ' (%.2f" / pixel)' % pixscale
print(outline)

1000 x 1000 pixels = 30" x 30" (0.03" / pixel)

Create color image (optional)

# 3 NIRCam short wavelength channel images
r = fits.open(imagefiles['F200W'])[0].data
g = fits.open(imagefiles['F150W'])[0].data
b = fits.open(imagefiles['F090W'])[0].data

rgb = make_lupton_rgb(r, g, b, Q=5, stretch=0.02)  # , minimum=-0.001

fig = plt.figure(figsize=(8, 8))
ax = plt.subplot(projection=imwcs)
plt.imshow(rgb, origin='lower')
plt.xlabel('Right Ascension')
plt.ylabel('Declination')
fig.tight_layout()
plt.subplots_adjust(left=0.15)

Detect Sources and Deblend using photutils

https://photutils.readthedocs.io/en/latest/segmentation.html

# For detection, requiring 5 connected pixels 2-sigma above background

# Measure background and set detection threshold
bkg_estimator = MedianBackground()
bkg = Background2D(data, (50, 50), filter_size=(3, 3), bkg_estimator=bkg_estimator)
threshold = bkg.background + (2. * bkg.background_rms)

# Before detection, smooth image with Gaussian FWHM = 3 pixels
smooth_kernel = make_2dgaussian_kernel(3.0, size=3)
convolved_data = convolve(data, smooth_kernel)

# Detect and deblend
segm_detect = detect_sources(convolved_data, threshold, npixels=5)

segm_deblend = deblend_sources(convolved_data, segm_detect, npixels=5, nlevels=32, contrast=0.001)

# Save segmentation map of detected objects
segm_hdu = fits.PrimaryHDU(segm_deblend.data.astype(np.uint32), header=imwcs.to_header())
segm_hdu.writeto('JADES_detections_segm.fits', overwrite=True)

Measure photometry (and more) in detection image

https://photutils.readthedocs.io/en/latest/segmentation.html#centroids-photometry-and-morphological-properties

#error = bkg.background_rms
# Input weight should be exposure map. Fudging for now.
error = calc_total_error(data, bkg.background_rms, weight/500)
#cat = source_properties(data-bkg.background, segm_deblend, wcs=imwcs, background=bkg.background, error=error)
cat = SourceCatalog(data-bkg.background, segm_deblend, wcs=imwcs, background=bkg.background, error=error)

Show detections alongside images (optional)

fig, ax = plt.subplots(2, 3, sharex=True, sharey=True, figsize=(9.5, 6))
# For RA,Dec axes instead of pixels, add: , subplot_kw={'projection': imwcs})

# Color image
ax[0, 0].imshow(rgb, origin='lower')
ax[0, 0].set_title('Color Image')

# Data
norm = simple_norm(data, 'sqrt', percent=99.)
ax[0, 1].imshow(data, origin='lower', cmap='Greys_r', norm=norm)
ax[0, 1].set_title('Detection Image F200W')

# Segmentation map
cmap = segm_deblend.make_cmap(seed=12345)
ax[0, 2].imshow(segm_deblend, origin='lower', cmap=cmap, interpolation='nearest')
ax[0, 2].set_title('Detections (Segmentation Image)')

# Weight
ax[1, 0].imshow(weight, origin='lower', cmap='Greys_r', vmin=0)
ax[1, 0].set_title('Weight Image F200W')

# RMS
ax[1, 1].imshow(bkg.background_rms, origin='lower', norm=None)
ax[1, 1].set_title('Background RMS')

# Total error including Poisson noise
norm = simple_norm(error, 'sqrt', percent=99.)
ax[1, 2].imshow(error, origin='lower', norm=norm)
ax[1, 2].set_title('RMS + Poisson noise')

fig.tight_layout()

View all measured quantities in detection image (optional)

cat.to_table()

QTable length=326

label	xcentroid	ycentroid	sky_centroid	bbox_xmin	bbox_xmax	bbox_ymin	bbox_ymax	area	semimajor_sigma	semiminor_sigma	orientation	eccentricity	min_value	max_value	local_background	segment_flux	segment_fluxerr	kron_flux	kron_fluxerr
			deg,deg					pix2	pix	pix	deg
int64	float64	float64	SkyCoord	int64	int64	int64	int64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64
1	466.24461350033647	8.7975642602371	53.17381009103326,-27.805320830365776	460	472	3	14	101.0	3.121883592723396	1.687718238373556	-44.478947493786606	0.8412740063312978	0.0006697164628791134	0.03003565456943476	0.0	0.8863594626861904	0.04333105379347733	1.1523646398846878	0.05542864325311204
2	104.80681791619067	18.082129411035602	53.17721522973072,-27.805243193448323	95	116	10	27	271.0	4.4330760205526705	2.8904310038200527	-26.54972210581966	0.7582062720085906	0.001169592682614256	0.05683289995260978	0.0	3.3850931796608226	0.10096273567434758	4.064307886560463	0.1178471432803697
3	15.04416396551755	13.21870964358083	53.178060898531776,-27.805283643045666	14	16	12	14	8.0	0.8501945455843772	0.6682678017840504	-38.1612276642421	0.6182042301587309	0.0035728101756059504	0.006067336431199741	0.0	0.04072228096572398	0.011222825585360583	0.10086850698470742	0.02315230578296353
4	710.3242889701858	14.259777138086877	53.17151058505514,-27.805275444028442	708	712	12	17	25.0	1.2749753643104456	1.117371994707132	-59.26178196559024	0.4816073152747641	0.0013530635966815864	0.015100956012602468	0.0	0.15496901383687856	0.018575211927226793	0.19011686546526946	0.023625589169720226
5	434.5294891713508	22.219091527997158	53.17410887322447,-27.8052089643638	424	447	14	30	268.0	3.9551276120227397	2.6287674709150584	-8.140060370882344	0.7471566761152122	-0.0003420958396561837	0.10656058422736421	0.0	3.6147863822998016	0.08773502088121837	4.278741209374845	0.10086436727140968
6	289.38802515014896	17.281765562153954	53.17547627056455,-27.805250009193262	287	291	15	20	17.0	1.2913044931147235	1.1978568863866517	68.12270923149859	0.37349256122504115	0.0019512862197305747	0.008797215945005498	0.0	0.08513804286457863	0.0166428570162664	0.34384198668200716	0.045989916939691816
7	640.4863091995317	17.773429154661983	53.17216853452775,-27.805246129692247	638	642	15	20	21.0	1.5124916692884618	0.8803106550282112	-64.72678377811528	0.8131696183154983	0.0003576250881105088	0.009946935910809692	0.0	0.09708323832036175	0.014462494201596655	0.17695836262605436	0.026075310664432968
8	762.762291737285	28.289914155884627	53.171016553902064,-27.805158549648304	754	771	21	36	200.0	3.160315774736445	2.3536095208280066	37.520056617399476	0.6673561574146197	-0.0001941792050655057	0.10556121967783572	0.0	3.8212752545169955	0.08847910158114891	3.9811392641181644	0.09150995786991047
9	801.0706722163886	32.58026118517374	53.17065564474934,-27.805122812753073	799	804	31	34	12.0	1.3349434908844906	0.6567170220834244	-28.75519616047975	0.8706270208190223	0.0018856854900614819	0.009336017794193286	0.0	0.05935203501404956	0.012189355644992149	0.17003504172320322	0.027778386430149567
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
317	638.7423056471345	555.4511129157931	53.1721846558466,-27.80076548202184	630	647	544	568	256.0	4.1970530491670495	1.8021821587433078	64.56727691953017	0.9031180841005669	0.0014816106246247486	0.2658198490126946	0.0	8.690357561191234	0.1588037351236894	8.781566974916299	0.16012188685841844
318	13.420742051379385	884.694395861645	53.17807529846922,-27.798021345154865	6	21	877	892	185.0	3.285450616131066	3.147908093244288	-13.393044858704108	0.2863139002521662	0.0014361034419094758	0.03495382468946091	0.0	2.117442472888257	0.10785932211319861	3.411588966877565	0.15467709845637975
319	7.027176577444122	891.9727894960166	53.17813552144662,-27.797960686059632	5	11	889	895	29.0	1.603195468602836	1.3128724093575075	-82.75208405786572	0.5739223050191917	0.0019018454919824644	0.01347560203904176	0.0	0.21412965179118829	0.035024475587970415	0.6893013683010651	0.07610234326451545
320	143.92969676102126	953.6973600108524	53.17684577662435,-27.797446433611444	139	149	950	957	59.0	2.6507581110195266	1.3964068465385502	25.19703083525805	0.849992199281631	0.0021617009219625062	0.021165230633174926	0.0	0.5681924075559929	0.05743803199966468	1.254760987505672	0.10492362090925443
321	142.746173256619	959.5389917866399	53.176856920479636,-27.797397752374	139	147	957	963	48.0	2.1427436610213637	1.2555061677914512	-35.47427950196171	0.8103588434156908	0.00047583665796055224	0.030453184966657037	0.0	0.5637982114834339	0.0568718012708143	0.8893801784930943	0.08214060528713435
322	790.415367777511	955.3106960545537	53.17075559998907,-27.797433388936582	784	796	951	959	93.0	2.621691845359079	1.8772136125476881	5.157966396104574	0.6980681000951601	0.0005767950066928708	0.04023169644016939	0.0	1.115611206251331	0.07893942785469334	1.6521126492979301	0.10804543306519865
323	797.3397409211948	965.5478941851635	53.17069036467991,-27.797348081794517	792	802	958	974	121.0	3.0304070507432326	1.7633197784086392	79.51657917516623	0.8132778374227926	0.002333420221800177	0.08761686954182783	0.0	2.116916544882111	0.10637880974470741	2.8478971911033626	0.1315925489047444
324	790.7983558965504	979.5822296300561	53.17075198078638,-27.79723112632807	781	799	972	989	133.0	4.506451359665357	1.493045462994405	-44.19824469199431	0.9435209394229406	-0.001248849458808359	0.06943444193177313	0.0	2.002750597650743	0.10372107669936124	2.813577038594517	0.13630578304670982
325	307.7502590912509	989.993370703415	53.175302486237165,-27.797144093448775	305	311	987	993	37.0	1.707727984108737	1.1914329585464343	-56.82602073196763	0.7164179156288306	0.0017972712412996086	0.030535860130823354	0.0	0.3875293679154765	0.046167097763049746	0.7369473920493916	0.07417814641467854
326	306.8506442261885	997.243644725512	53.1753109550711,-27.797083673855024	302	312	994	999	51.0	2.5021353669231527	1.4883885517566502	15.388098948641728	0.8038386961956926	0.002853197675265836	0.02047051119926145	0.0	0.5505454403242959	0.05460363335321445	0.7777606691520305	0.07156975624994025

Only keep some quantities

columns = 'label xcentroid ycentroid sky_centroid area semimajor_sigma semiminor_sigma ellipticity orientation gini'.split()
tbl = cat.to_table(columns=columns)
tbl.rename_column('semimajor_sigma', 'a')
tbl.rename_column('semiminor_sigma', 'b')

tbl

QTable length=326

label	xcentroid	ycentroid	sky_centroid	area	a	b	ellipticity	orientation	gini
			deg,deg	pix2	pix	pix		deg
int64	float64	float64	SkyCoord	float64	float64	float64	float64	float64	float64
1	466.24461350033647	8.7975642602371	53.17381009103326,-27.805320830365776	101.0	3.121883592723396	1.687718238373556	0.4593910412587602	-44.478947493786606	0.3750841422808698
2	104.80681791619067	18.082129411035602	53.17721522973072,-27.805243193448323	271.0	4.4330760205526705	2.8904310038200527	0.34798523859744157	-26.54972210581966	0.4292034166747856
3	15.04416396551755	13.21870964358083	53.178060898531776,-27.805283643045666	8.0	0.8501945455843772	0.6682678017840504	0.21398248759086114	-38.1612276642421	0.10521135944081007
4	710.3242889701858	14.259777138086877	53.17151058505514,-27.805275444028442	25.0	1.2749753643104456	1.117371994707132	0.12361287442487268	-59.26178196559024	0.33737600483155716
5	434.5294891713508	22.219091527997158	53.17410887322447,-27.8052089643638	268.0	3.9551276120227397	2.6287674709150584	0.33535204706817323	-8.140060370882344	0.5363680123723463
6	289.38802515014896	17.281765562153954	53.17547627056455,-27.805250009193262	17.0	1.2913044931147235	1.1978568863866517	0.07236682535090477	68.12270923149859	0.18041073594089005
7	640.4863091995317	17.773429154661983	53.17216853452775,-27.805246129692247	21.0	1.5124916692884618	0.8803106550282112	0.41797322067741016	-64.72678377811528	0.257339933672325
8	762.762291737285	28.289914155884627	53.171016553902064,-27.805158549648304	200.0	3.160315774736445	2.3536095208280066	0.2552612812799423	37.520056617399476	0.5417393494467531
9	801.0706722163886	32.58026118517374	53.17065564474934,-27.805122812753073	12.0	1.3349434908844906	0.6567170220834244	0.5080563135685205	-28.75519616047975	0.28793220448127643
...	...	...	...	...	...	...	...	...	...
317	638.7423056471345	555.4511129157931	53.1721846558466,-27.80076548202184	256.0	4.1970530491670495	1.8021821587433078	0.5706077246031069	64.56727691953017	0.6381184942652184
318	13.420742051379385	884.694395861645	53.17807529846922,-27.798021345154865	185.0	3.285450616131066	3.147908093244288	0.041864127316802446	-13.393044858704108	0.331692826709577
319	7.027176577444122	891.9727894960166	53.17813552144662,-27.797960686059632	29.0	1.603195468602836	1.3128724093575075	0.18109024440939903	-82.75208405786572	0.254070174248954
320	143.92969676102126	953.6973600108524	53.17684577662435,-27.797446433611444	59.0	2.6507581110195266	1.3964068465385502	0.47320472557133164	25.19703083525805	0.2823870637944277
321	142.746173256619	959.5389917866399	53.176856920479636,-27.797397752374	48.0	2.1427436610213637	1.2555061677914512	0.41406609169806174	-35.47427950196171	0.3421221334166607
322	790.415367777511	955.3106960545537	53.17075559998907,-27.797433388936582	93.0	2.621691845359079	1.8772136125476881	0.28396862664438105	5.157966396104574	0.37596577589838664
323	797.3397409211948	965.5478941851635	53.17069036467991,-27.797348081794517	121.0	3.0304070507432326	1.7633197784086392	0.4181244470190332	79.51657917516623	0.47716929153411286
324	790.7983558965504	979.5822296300561	53.17075198078638,-27.79723112632807	133.0	4.506451359665357	1.493045462994405	0.6686871012615841	-44.19824469199431	0.42319913307021234
325	307.7502590912509	989.993370703415	53.175302486237165,-27.797144093448775	37.0	1.707727984108737	1.1914329585464343	0.3023286087519126	-56.82602073196763	0.35854189153102795
326	306.8506442261885	997.243644725512	53.1753109550711,-27.797083673855024	51.0	2.5021353669231527	1.4883885517566502	0.4051526662256868	15.388098948641728	0.24216628412005226

Convert measured fluxes (data units) to magnitudes

https://docs.astropy.org/en/stable/units/

https://docs.astropy.org/en/stable/units/equivalencies.html#photometric-zero-point-equivalency

https://docs.astropy.org/en/stable/units/logarithmic_units.html#logarithmic-units

# not detected: mag =  99; magerr = 1-sigma upper limit assuming zero flux
# not observed: mag = -99; magerr = 0
def fluxes2mags(flux, fluxerr):
    nondet = flux < 0  # Non-detection if flux is negative
    unobs = (fluxerr <= 0) + (fluxerr == np.inf)  # Unobserved if flux uncertainty is negative or infinity

    mag = flux.to(u.ABmag)
    magupperlimit = fluxerr.to(u.ABmag)  # 1-sigma upper limit if flux=0

    mag = np.where(nondet, 99 * u.ABmag, mag)
    mag = np.where(unobs, -99 * u.ABmag, mag)

    magerr = 2.5 * np.log10(1 + fluxerr/flux) 
    magerr = magerr.value * u.ABmag

    magerr = np.where(nondet, magupperlimit, magerr)
    magerr = np.where(unobs, 0*u.ABmag, magerr)
    
    return mag, magerr

# Includes features I couldn't find in astropy:
# mag = 99 / -99 for non-detections / unobserved
# flux uncertainties -> mag uncertainties

Multiband photometry using isophotal apertures defined in detection image

(Similar to running SourceExtractor in double-image mode)

filters = 'F090W F115W F150W F200W F277W F335M F356W F410M F444W'.split()
for filt in filters:
    infile = imagefiles[filt]
    print(filt)
    print(infile)
    print(weightfiles[filt])
    hdu = fits.open(infile)
    data = hdu[0].data
    zp = hdu[0].header['ABMAG'] * u.ABmag  # zeropoint
    weight = fits.open(weightfiles[filt])[0].data
    
    # Measure background
    bkg = Background2D(data, (50, 50), filter_size=(3, 3), bkg_estimator=bkg_estimator)
    #error = bkg.background_rms
    error = calc_total_error(data, bkg.background_rms, weight/500)
                             
    # Measure properties in each image of previously detected objects 
    filtcat = SourceCatalog(data-bkg.background, segm_deblend, wcs=imwcs, background=bkg.background, error=error)

    # Convert measured fluxes to fluxes in nJy and to AB magnitudes
    filttbl = filtcat.to_table()
    tbl[filt+'_flux']    = flux    = filttbl['segment_flux']     * zp.to(u.nJy)
    tbl[filt+'_fluxerr'] = fluxerr = filttbl['segment_fluxerr'] * zp.to(u.nJy)

    mag, magerr = fluxes2mags(flux, fluxerr)
    #mag = mag * u.ABmag  # incompatible with file writing
    tbl[filt+'_mag']    = mag.value
    tbl[filt+'_magerr'] = magerr.value

F090W
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F090W.fits
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F090W_wht.fits

/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/astropy/units/function/logarithmic.py:67: RuntimeWarning: invalid value encountered in log10
  return dex.to(self._function_unit, np.log10(x))

F115W
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F115W.fits
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F115W_wht.fits

/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/astropy/units/function/logarithmic.py:67: RuntimeWarning: invalid value encountered in log10
  return dex.to(self._function_unit, np.log10(x))
/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/astropy/units/quantity.py:666: RuntimeWarning: invalid value encountered in log10
  result = super().__array_ufunc__(function, method, *arrays, **kwargs)

F150W
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F150W.fits
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F150W_wht.fits
F200W
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F200W.fits
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F200W_wht.fits
F277W
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F277W.fits
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F277W_wht.fits
F335M
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F335M.fits
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F335M_wht.fits
F356W
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F356W.fits
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F356W_wht.fits
F410M
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F410M.fits
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F410M_wht.fits

/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/astropy/units/function/logarithmic.py:67: RuntimeWarning: invalid value encountered in log10
  return dex.to(self._function_unit, np.log10(x))
/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/astropy/units/quantity.py:666: RuntimeWarning: invalid value encountered in log10
  result = super().__array_ufunc__(function, method, *arrays, **kwargs)

F444W
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F444W.fits
https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/nircam_photometry/jades_jwst_nircam_goods_s_crop_F444W_wht.fits

/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/astropy/units/function/logarithmic.py:67: RuntimeWarning: invalid value encountered in log10
  return dex.to(self._function_unit, np.log10(x))
/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/astropy/units/quantity.py:666: RuntimeWarning: invalid value encountered in log10
  result = super().__array_ufunc__(function, method, *arrays, **kwargs)

View complete results (optional)

tbl

QTable length=326

label	xcentroid	ycentroid	sky_centroid	area	a	b	ellipticity	orientation	gini	F090W_flux	F090W_fluxerr	F090W_mag	F090W_magerr	F115W_flux	F115W_fluxerr	F115W_mag	F115W_magerr	F150W_flux	F150W_fluxerr	F150W_mag	F150W_magerr	F200W_flux	F200W_fluxerr	F200W_mag	F200W_magerr	F277W_flux	F277W_fluxerr	F277W_mag	F277W_magerr	F335M_flux	F335M_fluxerr	F335M_mag	F335M_magerr	F356W_flux	F356W_fluxerr	F356W_mag	F356W_magerr	F410M_flux	F410M_fluxerr	F410M_mag	F410M_magerr	F444W_flux	F444W_fluxerr	F444W_mag	F444W_magerr
			deg,deg	pix2	pix	pix		deg		nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy
int64	float64	float64	SkyCoord	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64
1	466.24461350033647	8.7975642602371	53.17381009103326,-27.805320830365776	101.0	3.121883592723396	1.687718238373556	0.4593910412587602	-44.478947493786606	0.3750841422808698	9.172811374136076	0.8464272435968171	28.993743842627527	0.09583064229371432	15.251750665804146	0.735186612920272	28.441700757864936	0.051113935624583086	18.356035098509803	0.9076155669095672	28.240552801409102	0.052399286366677095	20.355880166244727	0.9951286984899145	28.128275286137136	0.05182128829099082	15.305414631812216	0.3724290991315014	28.437887251597694	0.02610307984312319	14.02083788279239	0.45364676842729956	28.533065080581018	0.03457285577386587	14.174014573163747	0.39992610811377877	28.52126781262446	0.03021030645164098	13.880611681957355	0.46501162852267625	28.543978488116917	0.035777045316298495	13.807578444079855	0.4986392632950047	28.54970620192208	0.038518276199782
2	104.80681791619067	18.082129411035602	53.17721522973072,-27.805243193448323	271.0	4.4330760205526705	2.8904310038200527	0.34798523859744157	-26.54972210581966	0.4292034166747856	66.57967761225385	2.436255936703974	26.841645780216258	0.039019197405152646	67.80807330092654	1.7819304802085372	26.82179648873538	0.028163638965809652	70.39338412925109	2.1096858375971363	26.78117038947336	0.032061379743408516	77.74109040131492	2.318681567876357	26.6733734305523	0.031909302523638994	72.90069026757769	0.8318824113256227	26.743170898753043	0.012319367708500414	75.27250253951928	1.070603105909879	26.708409112497115	0.015333670289260427	71.48792896636016	0.9342131216075662	26.764418211087325	0.014096626458061819	61.42767389099904	0.9993087828855896	26.929089824977595	0.017520685942298192	57.2203760546637	1.051093157627436	27.00612323080047	0.019763151293857934
3	15.04416396551755	13.21870964358083	53.178060898531776,-27.805283643045666	8.0	0.8501945455843772	0.6682678017840504	0.21398248759086114	-38.1612276642421	0.10521135944081007	0.30189458075047043	0.22003285638903264	32.700361707101834	0.5943870266742837	0.7673344575647303	0.20049710485441527	31.68753824783774	0.2520377003105847	0.7185263104580911	0.22413744736254168	31.758893311421605	0.2947853371046982	0.9352163612291753	0.25774023109081057	31.47271975993688	0.26428136354091386	0.34971362332966227	0.06900286982371183	32.54071862281815	0.19551879280610804	0.5607617346802088	0.11336299849737179	32.02805407366013	0.19990472716017532	0.2668657682095152	0.0705653665151826	32.83426782771905	0.2547307087052081	-0.06031654176708369	0.08275804453775322	99.0	34.10547444921151	0.11765422427560634	0.08200758112663485	33.723481188078146	0.5742186726193422
4	710.3242889701858	14.259777138086877	53.17151058505514,-27.805275444028442	25.0	1.2749753643104456	1.117371994707132	0.12361287442487268	-59.26178196559024	0.33737600483155716	2.6566836210797113	0.4371488133389892	30.33915040433974	0.1653923745984598	3.818279752499735	0.360557908564342	29.945330638706018	0.09796938884703618	3.9761821744276116	0.4246250023752303	29.901334315002583	0.110165165468641	3.5589744431503423	0.42659305165797884	30.02168782658959	0.12291324714108388	1.6962298219026282	0.12672768826931047	30.826288264009904	0.07822962940363362	1.590536535091481	0.16680910589172168	30.896140876425513	0.10828384762060109	1.9912255134558894	0.151127025768846	30.652198879435126	0.07942622592908809	1.9438353657449134	0.178896842573335	30.67835130168316	0.09558932550457658	1.6253103136417588	0.17102441486844794	30.8726592718879	0.1086274373485027
5	434.5294891713508	22.219091527997158	53.17410887322447,-27.8052089643638	268.0	3.9551276120227397	2.6287674709150584	0.33535204706817323	-8.140060370882344	0.5363680123723463	48.235021889265	1.7919984904520279	27.191593800137312	0.039605392585556634	54.2155157021799	1.3658851730937915	27.064691017070395	0.02701473844609659	58.2872223328999	1.623579946541796	26.9860666010322	0.02982943945067686	83.01615938264015	2.014897619560197	26.602093406179243	0.026037350500330014	133.10191702875915	1.0561372293002846	26.089539223653347	0.008581100944569356	133.48409670470673	1.2826536715221066	26.086426182655003	0.010383073588802694	144.05302153454628	1.238472358399449	26.003694069734205	0.009294542790143837	161.5683820059277	1.4303041800759473	25.879109060386504	0.009569320100872941	148.33362636536881	1.5722468769659783	25.971900964480472	0.01144758607362489
6	289.38802515014896	17.281765562153954	53.17547627056455,-27.805250009193262	17.0	1.2913044931147235	1.1978568863866517	0.07236682535090477	68.12270923149859	0.18041073594089005	1.1303554308347756	0.3821670960543563	31.266962436913506	0.3162170658541775	1.5011462606731298	0.28676161013445184	30.958942478071556	0.18980531870852316	1.045624005692517	0.28930296346025686	31.35156113668441	0.26520490437873934	1.955256158588051	0.38221513653203576	30.671990843785842	0.1938565595483074	1.188616449626284	0.1236824441824322	31.212395659255414	0.10747756611504025	1.7708471014756073	0.19746701583101794	30.77954733764033	0.11478335606840087	1.4921438342127515	0.15067991684808457	30.96547327829425	0.10445071085094704	1.1433927169620364	0.17563750503987804	31.25451144606716	0.15514831191999282	0.5926553032606401	0.13657056678097335	31.967994562614585	0.2251497308694742
7	640.4863091995317	17.773429154661983	53.17216853452775,-27.805246129692247	21.0	1.5124916692884618	0.8803106550282112	0.41797322067741016	-64.72678377811528	0.257339933672325	1.7318562374159874	0.35449519649495176	30.80372040476935	0.20218906642550222	1.643429843026024	0.24985580694618065	30.86062207735413	0.15366243535885116	1.636102104727731	0.2840661464858718	30.865473991673777	0.17382220324021894	2.2295861313548446	0.33214154219160175	30.529439364296703	0.15077176392081712	1.0399598684843063	0.09617790287548726	31.357458548945587	0.09603604488615963	1.6537574517445193	0.1507209599380506	30.853820464729573	0.094699691326289	1.2604572236829945	0.11591912740405148	31.14867972112842	0.09552272603001738	0.40788132175057795	0.11575451167421517	32.37366545505972	0.27123885135203707	0.5688140707837224	0.10865005565010251	32.01257417244966	0.18978993015591245
8	762.762291737285	28.289914155884627	53.171016553902064,-27.805158549648304	200.0	3.160315774736445	2.3536095208280066	0.2552612812799423	37.520056617399476	0.5417393494467531	52.884573303469274	1.7905819633079445	27.091677488449587	0.0361525510072172	55.84480400775534	1.340403855296752	27.0325430722947	0.025752332465672795	61.65651417767283	1.6299731205318744	26.925052580640507	0.028330057963110337	87.75832428916449	2.0319859659923534	26.54177919509308	0.024852875576127663	85.91084431487936	0.7872047738101092	26.564880031956896	0.009903344287860692	73.69676407989382	0.8932394257702934	26.73137895243849	0.013080521346957403	74.68199206314202	0.8363385018246197	26.716960265556335	0.012091217376552723	70.61910087228084	0.9001197500128227	26.777694540519555	0.013751473288938415	73.9961654528609	1.0387154150809097	26.726976963031056	0.015134956407915642
9	801.0706722163886	32.58026118517374	53.17065564474934,-27.805122812753073	12.0	1.3349434908844906	0.6567170220834244	0.5080563135685205	-28.75519616047975	0.28793220448127643	1.1246036170169136	0.3004786436259074	31.272501310051688	0.257101145144837	1.2797476107076189	0.2305526011723881	31.13218918151494	0.17984738982032794	1.2734342232303713	0.2595936177065242	31.13755870689588	0.20143381199573665	1.3630620117794083	0.2799372864607707	31.063710964299986	0.20280440914679781	1.082787068323561	0.10030137862329394	31.313642349074232	0.0961853826328532	0.6781367101230981	0.1161012153749803	31.821706862430293	0.17158341526256418	0.5758891043153808	0.08989342817146666	31.99915284535117	0.15748383733187596	1.1076176036929608	0.13520585441853786	31.289025376253583	0.12504898101684045	1.113433002949251	0.14205342023678058	31.283339775099364	0.1303698266479056
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
317	638.7423056471345	555.4511129157931	53.1721846558466,-27.80076548202184	256.0	4.1970530491670495	1.8021821587433078	0.5706077246031069	64.56727691953017	0.6381184942652184	120.11105123386255	3.14934982289772	26.201042579971478	0.02810152116245664	165.08884566980734	2.663462515685416	25.855705672804568	0.017376934181754438	210.82538772196136	3.5242585095562693	25.590192730758954	0.017999658448518377	199.580288319275	3.6470415652056585	25.64970588578926	0.019661163764616296	143.09847494169267	1.1923776093934584	26.010912486668634	0.009009485356804967	125.45620116997327	1.4001200026317673	26.15376966773193	0.01204994954280746	115.52181690810832	1.2496816373897297	26.243339973021605	0.011682107040887598	106.85877963435514	1.3121928145361486	26.32797447480941	0.01325130980906172	104.25292308993991	1.4630332682490081	26.354779398102927	0.015130755569956123
318	13.420742051379385	884.694395861645	53.17807529846922,-27.798021345154865	185.0	3.285450616131066	3.147908093244288	0.041864127316802446	-13.393044858704108	0.331692826709577	18.86684199878647	1.9652265112006322	28.210751968887884	0.10758295675190566	31.87824388885622	1.7141356267129144	27.641264027661734	0.05686594809349493	50.41198344566361	2.4111091317305364	27.14366553757101	0.050725097051390826	48.62858360693147	2.477066617075186	27.182770949596527	0.05394324520990566	36.759914186317175	0.8217034694312548	27.486563777794004	0.024002452426466658	33.4590525753193	1.007273298718009	27.588715901645074	0.03220337636185417	34.35293391400531	0.9128407808005035	27.560090415197354	0.028473986633162217	31.658008005086796	1.0140664111276134	27.648791038562205	0.0342328125312021	31.811892751708378	1.1073440643576584	27.643526225913767	0.03715062136419812
319	7.027176577444122	891.9727894960166	53.17813552144662,-27.797960686059632	29.0	1.603195468602836	1.3128724093575075	0.18109024440939903	-82.75208405786572	0.254070174248954	4.46599816086569	0.8826460510016783	29.775203650276346	0.19581292499061762	3.531493143831654	0.5906264829187411	30.03010408141747	0.1679055592950589	5.107472203839501	0.7850802445520096	29.629484970574794	0.15524361252227648	4.917640884310548	0.8043621780644387	29.67060797308068	0.16447818783217794	3.997287152346768	0.2843667768454504	29.895586630725628	0.07461553548507588	3.8377835048269504	0.3661371226929502	29.93979882029562	0.09893508954511895	3.4123744791286934	0.30628798885439634	30.067358286892155	0.09332518819096991	3.28526387152336	0.35498246466009514	30.10857435571395	0.111401289304535	3.979156125410199	0.40496953684556536	29.900522551559803	0.10523003616876628
320	143.92969676102126	953.6973600108524	53.17684577662435,-27.797446433611444	59.0	2.6507581110195266	1.3964068465385502	0.47320472557133164	25.19703083525805	0.2823870637944277	5.342339301100906	1.0819792938646076	29.58067133163447	0.20023900750272017	5.922064340105094	0.8036616820537128	29.468817196351594	0.13816512636426892	7.212792530814553	0.9996371118645621	29.254741399255266	0.14092055402277476	13.04894576803854	1.3191055611080584	28.61106143453415	0.10455611158757944	8.378792200159028	0.41344559288155197	29.092046450623155	0.052295014155148895	6.694537733317119	0.5044867944031559	29.335698515562246	0.07888264867898688	6.468840508085545	0.42544842119709825	29.372933891089975	0.06915759129508553	7.525370789524617	0.5284959208044608	29.208680241701657	0.07369133634046583	6.716742187814203	0.5392825802265447	29.332103302827207	0.08385019406434349
321	142.746173256619	959.5389917866399	53.176856920479636,-27.797397752374	48.0	2.1427436610213637	1.2555061677914512	0.41406609169806174	-35.47427950196171	0.3421221334166607	6.9362362752687154	1.1392607904446856	29.297190303788632	0.16511346295363777	7.903254851436269	0.8693971748460056	29.1554850335146	0.11331229123717934	10.187623103416623	1.1263006743581756	28.879817825921563	0.11385094732159436	12.948029906646463	1.3061016666971015	28.619490765397067	0.10434267352803422	9.023017764882551	0.41847771533872685	29.011620468937362	0.049222442862693484	5.447014735335707	0.45676894530082573	29.559603624665375	0.0874297151873417	6.472888687598323	0.41968911320564845	29.372254653387305	0.06820884577947971	6.488348644754336	0.4869395067625267	29.369664554504258	0.07856993742528491	5.76627926777331	0.5007325407405385	29.497760820098495	0.09041210263092743
322	790.415367777511	955.3106960545537	53.17075559998907,-27.797433388936582	93.0	2.621691845359079	1.8772136125476881	0.28396862664438105	5.157966396104574	0.37596577589838664	20.729595474485713	1.8703470332283096	28.108522932043783	0.0937912678974309	28.033949477325784	1.5380517606650033	27.780789284195393	0.05799107346193481	25.74353540142662	1.7358437874135597	27.873329527427543	0.07084681983859821	25.620810723619343	1.8129005233748652	27.87851782980156	0.0742292377178118	21.15342216887344	0.6264899234444314	28.086548407739905	0.03168871406956517	18.828253167515435	0.7475941257205683	28.212974926843625	0.04227634815304789	17.964611476673056	0.6596358378746424	28.263955427272954	0.039152252468897156	12.790064955174342	0.6722158462709531	28.632818124807635	0.05561473730016507	13.580929019180171	0.7400945376355254	28.567676301555696	0.05761144934684235
323	797.3397409211948	965.5478941851635	53.17069036467991,-27.797348081794517	121.0	3.0304070507432326	1.7633197784086392	0.4181244470190332	79.51657917516623	0.47716929153411286	45.619197067374266	2.651803696939604	27.25213090726468	0.061346664251011665	50.392274387451174	2.0188186593533626	27.14409009976304	0.04264814256300904	49.831752268463035	2.3567748829108166	27.156234602905972	0.050172204162419035	48.61650529342564	2.4430655896970648	27.183040657043588	0.05323355846509684	33.58368869698832	0.7779020039869248	27.58467901105726	0.024862162100306468	23.755213036076142	0.8523243778218815	27.960602676216816	0.03827306144215809	23.023865642202438	0.7592225503052475	27.994554394409516	0.03522501022197596	19.63315929047223	0.8078373356779335	28.167524524558377	0.043779690921258704	18.68731450154886	0.8633253572661256	28.221132763267338	0.049035202457045374
324	790.7983558965504	979.5822296300561	53.17075198078638,-27.79723112632807	133.0	4.506451359665357	1.493045462994405	0.6686871012615841	-44.19824469199431	0.42319913307021234	34.20989140732531	2.35362390972397	27.564620760991172	0.07224061933251429	36.57135465011538	1.7557905380331742	27.492147381341525	0.050913561018798605	42.68849561449194	2.2064786055668897	27.324222874751488	0.05471719103146702	45.99460251160774	2.382028845957703	27.243232824978676	0.05482188387776275	34.80004302822041	0.7822981639992332	27.54605054768332	0.02413684700611631	23.40568188262023	0.8462317452688508	27.976696754900892	0.03856178701393752	22.767378194807595	0.7438457465179379	28.006717445540055	0.034905540803272225	20.501256369918995	0.8205498509872132	28.120548808604052	0.0426087886820206	19.089604206836878	0.8667809534858215	28.198007689828906	0.048212383021397735
325	307.7502590912509	989.993370703415	53.175302486237165,-27.797144093448775	37.0	1.707727984108737	1.1914329585464343	0.3023286087519126	-56.82602073196763	0.35854189153102795	7.126198120593676	1.0919973784526646	29.26785526920454	0.1547964399285069	6.598188006277326	0.7664570069816592	29.351438284978528	0.11931783010156356	6.196539367146464	0.8658897601506255	29.419626967122117	0.1420122236941388	8.899889611694567	1.0602604803697635	29.02653845005323	0.12220315747396855	6.033610248611692	0.341615226376123	29.44855686812721	0.05979574121037518	4.942377643470794	0.40732286190369604	29.66516018385374	0.08598385736288833	7.335769245190643	0.41481931976688835	29.23638584585209	0.059722553991341215	4.176814226515838	0.3922167589581281	29.84788710139504	0.0974473596196291	4.604584237652296	0.4395488742519671	29.742023943768242	0.0989902908814326
326	306.8506442261885	997.243644725512	53.1753109550711,-27.797083673855024	51.0	2.5021353669231527	1.4883885517566502	0.4051526662256868	15.388098948641728	0.24216628412005226	8.140192313568475	1.1783072461952102	29.123413336752737	0.14677830932912375	10.621922820493326	0.9579591036984573	28.834492146285715	0.0937524739668958	10.735153071224346	1.1138972366308966	28.822979396580244	0.10718825502231771	12.643670520931202	1.2540115652526385	28.645317074711215	0.10267300679924708	9.553009517952841	0.41693523286415374	28.94964947388319	0.04638135299187599	9.153235549670372	0.5151314172293293	28.99606340336343	0.05944621805492528	10.167634753780487	0.47542004710482255	28.881950157725086	0.04961590355201817	7.872874739121797	0.5029271829323597	29.159666645795102	0.06723264138084344	7.568902388401964	0.5444025773994181	29.202417738905766	0.07541224065721708

Save photometry as output catalog

tbl.write('JADESphotometry.ecsv', overwrite=True)

!head -175 JADESphotometry.ecsv  # show the first 175 lines

# %ECSV 1.0
# ---
# datatype:
# - {name: label, datatype: int64}
# - {name: xcentroid, datatype: float64}
# - {name: ycentroid, datatype: float64}
# - {name: sky_centroid.ra, unit: deg, datatype: float64}
# - {name: sky_centroid.dec, unit: deg, datatype: float64}
# - {name: area, unit: pix2, datatype: float64}
# - {name: a, unit: pix, datatype: float64}
# - {name: b, unit: pix, datatype: float64}
# - {name: ellipticity, datatype: float64}
# - {name: orientation, unit: deg, datatype: float64}
# - {name: gini, datatype: float64}
# - {name: F090W_flux, unit: nJy, datatype: float64}
# - {name: F090W_fluxerr, unit: nJy, datatype: float64}
# - {name: F090W_mag, datatype: float64}
# - {name: F090W_magerr, datatype: float64}
# - {name: F115W_flux, unit: nJy, datatype: float64}
# - {name: F115W_fluxerr, unit: nJy, datatype: float64}
# - {name: F115W_mag, datatype: float64}
# - {name: F115W_magerr, datatype: float64}
# - {name: F150W_flux, unit: nJy, datatype: float64}
# - {name: F150W_fluxerr, unit: nJy, datatype: float64}
# - {name: F150W_mag, datatype: float64}
# - {name: F150W_magerr, datatype: float64}
# - {name: F200W_flux, unit: nJy, datatype: float64}
# - {name: F200W_fluxerr, unit: nJy, datatype: float64}
# - {name: F200W_mag, datatype: float64}
# - {name: F200W_magerr, datatype: float64}
# - {name: F277W_flux, unit: nJy, datatype: float64}
# - {name: F277W_fluxerr, unit: nJy, datatype: float64}
# - {name: F277W_mag, datatype: float64}
# - {name: F277W_magerr, datatype: float64}
# - {name: F335M_flux, unit: nJy, datatype: float64}
# - {name: F335M_fluxerr, unit: nJy, datatype: float64}
# - {name: F335M_mag, datatype: float64}
# - {name: F335M_magerr, datatype: float64}
# - {name: F356W_flux, unit: nJy, datatype: float64}
# - {name: F356W_fluxerr, unit: nJy, datatype: float64}
# - {name: F356W_mag, datatype: float64}
# - {name: F356W_magerr, datatype: float64}
# - {name: F410M_flux, unit: nJy, datatype: float64}
# - {name: F410M_fluxerr, unit: nJy, datatype: float64}
# - {name: F410M_mag, datatype: float64}
# - {name: F410M_magerr, datatype: float64}
# - {name: F444W_flux, unit: nJy, datatype: float64}
# - {name: F444W_fluxerr, unit: nJy, datatype: float64}
# - {name: F444W_mag, datatype: float64}
# - {name: F444W_magerr, datatype: float64}
# meta: !!omap
# - {date: '2023-12-13 15:12:59 UTC'}
# - version: {Python: 3.11.6, astropy: 6.0.0, bottleneck: null, gwcs: 0.20.0, matplotlib: 3.8.2, numpy: 1.26.2, photutils: 1.10.0, scipy: 1.11.4,
#     skimage: 0.22.0, sklearn: null}
# - {localbkg_width: 0}
# - {apermask_method: correct}
# - kron_params: !!python/tuple [2.5, 1.4, 0.0]
# - __serialized_columns__:
#     F090W_flux:
#       __class__: astropy.units.quantity.Quantity
#       unit: &id001 !astropy.units.Unit {unit: nJy}
#       value: !astropy.table.SerializedColumn {name: F090W_flux}
#     F090W_fluxerr:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F090W_fluxerr}
#     F115W_flux:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F115W_flux}
#     F115W_fluxerr:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F115W_fluxerr}
#     F150W_flux:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F150W_flux}
#     F150W_fluxerr:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F150W_fluxerr}
#     F200W_flux:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F200W_flux}
#     F200W_fluxerr:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F200W_fluxerr}
#     F277W_flux:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F277W_flux}
#     F277W_fluxerr:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F277W_fluxerr}
#     F335M_flux:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F335M_flux}
#     F335M_fluxerr:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F335M_fluxerr}
#     F356W_flux:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F356W_flux}
#     F356W_fluxerr:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F356W_fluxerr}
#     F410M_flux:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F410M_flux}
#     F410M_fluxerr:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F410M_fluxerr}
#     F444W_flux:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F444W_flux}
#     F444W_fluxerr:
#       __class__: astropy.units.quantity.Quantity
#       unit: *id001
#       value: !astropy.table.SerializedColumn {name: F444W_fluxerr}
#     a:
#       __class__: astropy.units.quantity.Quantity
#       unit: !astropy.units.Unit {unit: pix}
#       value: !astropy.table.SerializedColumn {name: a}
#     area:
#       __class__: astropy.units.quantity.Quantity
#       unit: !astropy.units.Unit {unit: pix2}
#       value: !astropy.table.SerializedColumn {name: area}
#     b:
#       __class__: astropy.units.quantity.Quantity
#       unit: !astropy.units.Unit {unit: pix}
#       value: !astropy.table.SerializedColumn {name: b}
#     ellipticity:
#       __class__: astropy.units.quantity.Quantity
#       unit: !astropy.units.Unit {unit: ''}
#       value: !astropy.table.SerializedColumn {name: ellipticity}
#     orientation:
#       __class__: astropy.units.quantity.Quantity
#       unit: &id002 !astropy.units.Unit {unit: deg}
#       value: !astropy.table.SerializedColumn {name: orientation}
#     sky_centroid:
#       __class__: astropy.coordinates.sky_coordinate.SkyCoord
#       dec: !astropy.table.SerializedColumn
#         __class__: astropy.coordinates.angles.core.Latitude
#         unit: *id002
#         value: !astropy.table.SerializedColumn {name: sky_centroid.dec}
#       frame: icrs
#       ra: !astropy.table.SerializedColumn
#         __class__: astropy.coordinates.angles.core.Longitude
#         unit: *id002
#         value: !astropy.table.SerializedColumn {name: sky_centroid.ra}
#         wrap_angle: !astropy.coordinates.Angle
#           unit: *id002
#           value: 360.0
#       representation_type: spherical
# schema: astropy-2.0
label xcentroid ycentroid sky_centroid.ra sky_centroid.dec area a b ellipticity orientation gini F090W_flux F090W_fluxerr F090W_mag F090W_magerr F115W_flux F115W_fluxerr F115W_mag F115W_magerr F150W_flux F150W_fluxerr F150W_mag F150W_magerr F200W_flux F200W_fluxerr F200W_mag F200W_magerr F277W_flux F277W_fluxerr F277W_mag F277W_magerr F335M_flux F335M_fluxerr F335M_mag F335M_magerr F356W_flux F356W_fluxerr F356W_mag F356W_magerr F410M_flux F410M_fluxerr F410M_mag F410M_magerr F444W_flux F444W_fluxerr F444W_mag F444W_magerr
1 466.24461350033647 8.7975642602371 53.17381009103326 -27.805320830365776 101.0 3.121883592723396 1.687718238373556 0.4593910412587602 -44.478947493786606 0.3750841422808698 9.172811374136076 0.8464272435968171 28.993743842627527 0.09583064229371432 15.251750665804146 0.735186612920272 28.441700757864936 0.051113935624583086 18.356035098509803 0.9076155669095672 28.240552801409102 0.052399286366677095 20.355880166244727 0.9951286984899145 28.128275286137136 0.05182128829099082 15.305414631812216 0.3724290991315014 28.437887251597694 0.02610307984312319 14.02083788279239 0.45364676842729956 28.533065080581018 0.03457285577386587 14.174014573163747 0.39992610811377877 28.52126781262446 0.03021030645164098 13.880611681957355 0.46501162852267625 28.543978488116917 0.035777045316298495 13.807578444079855 0.4986392632950047 28.54970620192208 0.038518276199782
2 104.80681791619067 18.082129411035602 53.17721522973072 -27.805243193448323 271.0 4.4330760205526705 2.8904310038200527 0.34798523859744157 -26.54972210581966 0.4292034166747856 66.57967761225385 2.436255936703974 26.841645780216258 0.039019197405152646 67.80807330092654 1.7819304802085372 26.82179648873538 0.028163638965809652 70.39338412925109 2.1096858375971363 26.78117038947336 0.032061379743408516 77.74109040131492 2.318681567876357 26.6733734305523 0.031909302523638994 72.90069026757769 0.8318824113256227 26.743170898753043 0.012319367708500414 75.27250253951928 1.070603105909879 26.708409112497115 0.015333670289260427 71.48792896636016 0.9342131216075662 26.764418211087325 0.014096626458061819 61.42767389099904 0.9993087828855896 26.929089824977595 0.017520685942298192 57.2203760546637 1.051093157627436 27.00612323080047 0.019763151293857934
3 15.04416396551755 13.21870964358083 53.178060898531776 -27.805283643045666 8.0 0.8501945455843772 0.6682678017840504 0.21398248759086114 -38.1612276642421 0.10521135944081007 0.30189458075047043 0.22003285638903264 32.700361707101834 0.5943870266742837 0.7673344575647303 0.20049710485441527 31.68753824783774 0.2520377003105847 0.7185263104580911 0.22413744736254168 31.758893311421605 0.2947853371046982 0.9352163612291753 0.25774023109081057 31.47271975993688 0.26428136354091386 0.34971362332966227 0.06900286982371183 32.54071862281815 0.19551879280610804 0.5607617346802088 0.11336299849737179 32.02805407366013 0.19990472716017532 0.2668657682095152 0.0705653665151826 32.83426782771905 0.2547307087052081 -0.06031654176708369 0.08275804453775322 99.0 34.10547444921151 0.11765422427560634 0.08200758112663485 33.723481188078146 0.5742186726193422
4 710.3242889701858 14.259777138086877 53.17151058505514 -27.805275444028442 25.0 1.2749753643104456 1.117371994707132 0.12361287442487268 -59.26178196559024 0.33737600483155716 2.6566836210797113 0.4371488133389892 30.33915040433974 0.1653923745984598 3.818279752499735 0.360557908564342 29.945330638706018 0.09796938884703618 3.9761821744276116 0.4246250023752303 29.901334315002583 0.110165165468641 3.5589744431503423 0.42659305165797884 30.02168782658959 0.12291324714108388 1.6962298219026282 0.12672768826931047 30.826288264009904 0.07822962940363362 1.590536535091481 0.16680910589172168 30.896140876425513 0.10828384762060109 1.9912255134558894 0.151127025768846 30.652198879435126 0.07942622592908809 1.9438353657449134 0.178896842573335 30.67835130168316 0.09558932550457658 1.6253103136417588 0.17102441486844794 30.8726592718879 0.1086274373485027
5 434.5294891713508 22.219091527997158 53.17410887322447 -27.8052089643638 268.0 3.9551276120227397 2.6287674709150584 0.33535204706817323 -8.140060370882344 0.5363680123723463 48.235021889265 1.7919984904520279 27.191593800137312 0.039605392585556634 54.2155157021799 1.3658851730937915 27.064691017070395 0.02701473844609659 58.2872223328999 1.623579946541796 26.9860666010322 0.02982943945067686 83.01615938264015 2.014897619560197 26.602093406179243 0.026037350500330014 133.10191702875915 1.0561372293002846 26.089539223653347 0.008581100944569356 133.48409670470673 1.2826536715221066 26.086426182655003 0.010383073588802694 144.05302153454628 1.238472358399449 26.003694069734205 0.009294542790143837 161.5683820059277 1.4303041800759473 25.879109060386504 0.009569320100872941 148.33362636536881 1.5722468769659783 25.971900964480472 0.01144758607362489
6 289.38802515014896 17.281765562153954 53.17547627056455 -27.805250009193262 17.0 1.2913044931147235 1.1978568863866517 0.07236682535090477 68.12270923149859 0.18041073594089005 1.1303554308347756 0.3821670960543563 31.266962436913506 0.3162170658541775 1.5011462606731298 0.28676161013445184 30.958942478071556 0.18980531870852316 1.045624005692517 0.28930296346025686 31.35156113668441 0.26520490437873934 1.955256158588051 0.38221513653203576 30.671990843785842 0.1938565595483074 1.188616449626284 0.1236824441824322 31.212395659255414 0.10747756611504025 1.7708471014756073 0.19746701583101794 30.77954733764033 0.11478335606840087 1.4921438342127515 0.15067991684808457 30.96547327829425 0.10445071085094704 1.1433927169620364 0.17563750503987804 31.25451144606716 0.15514831191999282 0.5926553032606401 0.13657056678097335 31.967994562614585 0.2251497308694742
7 640.4863091995317 17.773429154661983 53.17216853452775 -27.805246129692247 21.0 1.5124916692884618 0.8803106550282112 0.41797322067741016 -64.72678377811528 0.257339933672325 1.7318562374159874 0.35449519649495176 30.80372040476935 0.20218906642550222 1.643429843026024 0.24985580694618065 30.86062207735413 0.15366243535885116 1.636102104727731 0.2840661464858718 30.865473991673777 0.17382220324021894 2.2295861313548446 0.33214154219160175 30.529439364296703 0.15077176392081712 1.0399598684843063 0.09617790287548726 31.357458548945587 0.09603604488615963 1.6537574517445193 0.1507209599380506 30.853820464729573 0.094699691326289 1.2604572236829945 0.11591912740405148 31.14867972112842 0.09552272603001738 0.40788132175057795 0.11575451167421517 32.37366545505972 0.27123885135203707 0.5688140707837224 0.10865005565010251 32.01257417244966 0.18978993015591245
8 762.762291737285 28.289914155884627 53.171016553902064 -27.805158549648304 200.0 3.160315774736445 2.3536095208280066 0.2552612812799423 37.520056617399476 0.5417393494467531 52.884573303469274 1.7905819633079445 27.091677488449587 0.0361525510072172 55.84480400775534 1.340403855296752 27.0325430722947 0.025752332465672795 61.65651417767283 1.6299731205318744 26.925052580640507 0.028330057963110337 87.75832428916449 2.0319859659923534 26.54177919509308 0.024852875576127663 85.91084431487936 0.7872047738101092 26.564880031956896 0.009903344287860692 73.69676407989382 0.8932394257702934 26.73137895243849 0.013080521346957403 74.68199206314202 0.8363385018246197 26.716960265556335 0.012091217376552723 70.61910087228084 0.9001197500128227 26.777694540519555 0.013751473288938415 73.9961654528609 1.0387154150809097 26.726976963031056 0.015134956407915642

Reformat output catalog for readability (optional)

# Remove units (pixels) from area
tbl['area'] = tbl['area'].value.astype(int)

# Replace sky_centroid with ra, dec
tbl['ra'] = tbl['sky_centroid'].ra.degree
tbl['dec'] = tbl['sky_centroid'].dec.degree

columns = list(tbl.columns)
columns = columns[:3] + ['ra', 'dec'] + columns[4:-2]

tbl = tbl[columns]

for column in columns:
    tbl[column].info.format = '.4f'

tbl['ra'].info.format = '11.7f'
tbl['dec'].info.format = '11.7f'

tbl['label'].info.format = 'd'
tbl['area'].info.format = 'd'

tbl.write('JADESphotometry.cat', format='ascii.fixed_width_two_line', delimiter=' ', overwrite=True)

!head -10 JADESphotometry.cat  # show the first 10 lines

label xcentroid ycentroid          ra         dec area       a      b ellipticity orientation   gini F090W_flux F090W_fluxerr F090W_mag F090W_magerr F115W_flux F115W_fluxerr F115W_mag F115W_magerr F150W_flux F150W_fluxerr F150W_mag F150W_magerr F200W_flux F200W_fluxerr F200W_mag F200W_magerr F277W_flux F277W_fluxerr F277W_mag F277W_magerr F335M_flux F335M_fluxerr F335M_mag F335M_magerr F356W_flux F356W_fluxerr F356W_mag F356W_magerr F410M_flux F410M_fluxerr F410M_mag F410M_magerr F444W_flux F444W_fluxerr F444W_mag F444W_magerr
----- --------- --------- ----------- ----------- ---- ------- ------ ----------- ----------- ------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------
    1  466.2446    8.7976  53.1738101 -27.8053208  101  3.1219 1.6877      0.4594    -44.4789 0.3751     9.1728        0.8464   28.9937       0.0958    15.2518        0.7352   28.4417       0.0511    18.3560        0.9076   28.2406       0.0524    20.3559        0.9951   28.1283       0.0518    15.3054        0.3724   28.4379       0.0261    14.0208        0.4536   28.5331       0.0346    14.1740        0.3999   28.5213       0.0302    13.8806        0.4650   28.5440       0.0358    13.8076        0.4986   28.5497       0.0385
    2  104.8068   18.0821  53.1772152 -27.8052432  271  4.4331 2.8904      0.3480    -26.5497 0.4292    66.5797        2.4363   26.8416       0.0390    67.8081        1.7819   26.8218       0.0282    70.3934        2.1097   26.7812       0.0321    77.7411        2.3187   26.6734       0.0319    72.9007        0.8319   26.7432       0.0123    75.2725        1.0706   26.7084       0.0153    71.4879        0.9342   26.7644       0.0141    61.4277        0.9993   26.9291       0.0175    57.2204        1.0511   27.0061       0.0198
    3   15.0442   13.2187  53.1780609 -27.8052836    8  0.8502 0.6683      0.2140    -38.1612 0.1052     0.3019        0.2200   32.7004       0.5944     0.7673        0.2005   31.6875       0.2520     0.7185        0.2241   31.7589       0.2948     0.9352        0.2577   31.4727       0.2643     0.3497        0.0690   32.5407       0.1955     0.5608        0.1134   32.0281       0.1999     0.2669        0.0706   32.8343       0.2547    -0.0603        0.0828   99.0000      34.1055     0.1177        0.0820   33.7235       0.5742
    4  710.3243   14.2598  53.1715106 -27.8052754   25  1.2750 1.1174      0.1236    -59.2618 0.3374     2.6567        0.4371   30.3392       0.1654     3.8183        0.3606   29.9453       0.0980     3.9762        0.4246   29.9013       0.1102     3.5590        0.4266   30.0217       0.1229     1.6962        0.1267   30.8263       0.0782     1.5905        0.1668   30.8961       0.1083     1.9912        0.1511   30.6522       0.0794     1.9438        0.1789   30.6784       0.0956     1.6253        0.1710   30.8727       0.1086
    5  434.5295   22.2191  53.1741089 -27.8052090  268  3.9551 2.6288      0.3354     -8.1401 0.5364    48.2350        1.7920   27.1916       0.0396    54.2155        1.3659   27.0647       0.0270    58.2872        1.6236   26.9861       0.0298    83.0162        2.0149   26.6021       0.0260   133.1019        1.0561   26.0895       0.0086   133.4841        1.2827   26.0864       0.0104   144.0530        1.2385   26.0037       0.0093   161.5684        1.4303   25.8791       0.0096   148.3336        1.5722   25.9719       0.0114
    6  289.3880   17.2818  53.1754763 -27.8052500   17  1.2913 1.1979      0.0724     68.1227 0.1804     1.1304        0.3822   31.2670       0.3162     1.5011        0.2868   30.9589       0.1898     1.0456        0.2893   31.3516       0.2652     1.9553        0.3822   30.6720       0.1939     1.1886        0.1237   31.2124       0.1075     1.7708        0.1975   30.7795       0.1148     1.4921        0.1507   30.9655       0.1045     1.1434        0.1756   31.2545       0.1551     0.5927        0.1366   31.9680       0.2251
    7  640.4863   17.7734  53.1721685 -27.8052461   21  1.5125 0.8803      0.4180    -64.7268 0.2573     1.7319        0.3545   30.8037       0.2022     1.6434        0.2499   30.8606       0.1537     1.6361        0.2841   30.8655       0.1738     2.2296        0.3321   30.5294       0.1508     1.0400        0.0962   31.3575       0.0960     1.6538        0.1507   30.8538       0.0947     1.2605        0.1159   31.1487       0.0955     0.4079        0.1158   32.3737       0.2712     0.5688        0.1087   32.0126       0.1898
    8  762.7623   28.2899  53.1710166 -27.8051585  200  3.1603 2.3536      0.2553     37.5201 0.5417    52.8846        1.7906   27.0917       0.0362    55.8448        1.3404   27.0325       0.0258    61.6565        1.6300   26.9251       0.0283    87.7583        2.0320   26.5418       0.0249    85.9108        0.7872   26.5649       0.0099    73.6968        0.8932   26.7314       0.0131    74.6820        0.8363   26.7170       0.0121    70.6191        0.9001   26.7777       0.0138    73.9962        1.0387   26.7270       0.0151

Start new session and analyze results

Load catalog and segmentation map

# Catalog: ecsv format preserves units for loading in Python notebooks
tbl = QTable.read('JADESphotometry.ecsv')

# Reconstitute filter list
filters = []
for param in tbl.columns:
    if param[-4:] == '_mag':
        filters.append(param[:-4])

# Segmentation map
segmfile = 'JADES_detections_segm.fits'
segm = fits.open(segmfile)[0].data
segm = SegmentationImage(segm)

Plot number counts vs. magnitude

fig = plt.figure(figsize=(8, 4))

filt = 'F200W'
mag1 = tbl[filt + '_mag']

mag1 = mag1[(0 < mag1) & (mag1 < 90)]  # detections only
n = plt.hist(mag1, histtype='step', label=filt)

plt.xlabel('AB magnitude')
plt.ylabel('Number counts')
plt.legend()

<matplotlib.legend.Legend at 0x7fe7071d2710>

Plot F200W vs. F090W magnitudes and look for dropouts

#import mplcursors
# Would love a better solution here!

mag1 = tbl['F090W_mag']
mag2 = tbl['F200W_mag']

# Only plot detections in F200W
det2 = (0 < mag2) & (mag2 < 90)

mag1 = mag1[det2]
mag2 = mag2[det2]
ids = tbl['label'][det2]

plt.figure(figsize=(8, 4))

plt.plot(mag1, mag2, '.')

for i in range(len(mag1)):
    plt.text(mag1[i], mag2[i], ids[i])

plt.xlabel('F090W AB magnitude')
plt.ylabel('F200W AB magnitude')

Text(0, 0.5, 'F200W AB magnitude')

Look at one object

# Could select object by position
#x, y = 905, 276
#id = segm.data[y,x]

# Select by ID number
id = 261  # F090W dropout
obj = tbl[id-1]

obj

Row index=260

label	xcentroid	ycentroid	sky_centroid	area	a	b	ellipticity	orientation	gini	F090W_flux	F090W_fluxerr	F090W_mag	F090W_magerr	F115W_flux	F115W_fluxerr	F115W_mag	F115W_magerr	F150W_flux	F150W_fluxerr	F150W_mag	F150W_magerr	F200W_flux	F200W_fluxerr	F200W_mag	F200W_magerr	F277W_flux	F277W_fluxerr	F277W_mag	F277W_magerr	F335M_flux	F335M_fluxerr	F335M_mag	F335M_magerr	F356W_flux	F356W_fluxerr	F356W_mag	F356W_magerr	F410M_flux	F410M_fluxerr	F410M_mag	F410M_magerr	F444W_flux	F444W_fluxerr	F444W_mag	F444W_magerr
			deg,deg	pix2	pix	pix		deg		nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy
int64	float64	float64	SkyCoord	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64
261	713.6003231522444	868.2862731469344	53.17147927632734,-27.79815855888541	111.0	2.1957364237904744	1.871859861852015	0.14750247726881305	-17.338774337937068	0.546245283985238	83.21064528625877	3.5272542780378053	26.599552775218456	0.04507502799678764	84.18846423504833	2.5879973703149886	26.586868532007543	0.032873375443186685	84.4248171839189	3.054886364876655	26.583824677815212	0.038592934392782094	78.728836865202	3.131787729879179	26.659665411873668	0.04235304682595798	45.258779395038786	0.9397610471453727	27.260742907280413	0.022313544876442264	35.277809352747205	1.0051267218729336	27.53124597775305	0.03050203683042903	32.9130336382702	0.9126976829650096	27.606580215895672	0.029698203226779688	24.75353531926416	0.9197377117713978	27.915906915058187	0.039610013762955344	25.401315400116076	1.0402217740639617	27.887859482444238	0.043576230399079735

obj['ellipticity']

\[0.14750248 \; \mathrm{}\]

segmobj = segm.segments[segm.get_index(id)]
segmobj

<photutils.segmentation.core.Segment>
label: 261
slices: (slice(863, 874, None), slice(708, 720, None))
area: 111

Show the object in all the images

fig, ax = plt.subplots(2, len(filters)+1, figsize=(9.5, 3.5), sharex=True, sharey=True)

ax[0, 0].imshow(rgb[segmobj.slices], origin='lower', extent=segmobj.bbox.extent)
ax[0, 0].set_title('Color')

cmap = segm.make_cmap(seed=12345)  # ERROR
ax[1, 0].imshow(segm.data[segmobj.slices], origin='lower', extent=segmobj.bbox.extent, cmap=cmap,
               interpolation='nearest')
ax[1, 0].set_title('Segment')

for i in range(1, len(filters)+1):
    filt = filters[i-1]

    # Show data on top row
    data = fits.open(imagefiles[filt])[0].data
    stamp = data[segmobj.slices]
    norm = ImageNormalize(stretch=SqrtStretch())  # scale each filter individually
    ax[0, i].imshow(stamp, extent=segmobj.bbox.extent, cmap='Greys_r', norm=norm, origin='lower')
    ax[0, i].set_title(filt.upper())

    # Show weights on bottom row
    weight = fits.open(weightfiles[filt])[0].data
    stamp = weight[segmobj.slices]
    # set black to zero weight (no exposure time / bad pixel)
    ax[1, i].imshow(stamp, extent=segmobj.bbox.extent, vmin=0, cmap='Greys_r', origin='lower')

ax[0, 0].set_ylabel('Data')
ax[1, 0].set_ylabel('Weight')

Text(0, 0.5, 'Weight')

Plot SED (Spectral Energy Distribution)

fig, ax = plt.subplots(figsize=(8, 6))

for filt in filters:
    lam = int(filt[1:4]) / 100
    plt.errorbar(lam, obj[filt+'_flux'].value, obj[filt+'_fluxerr'].value, marker='.', c='b')

plt.axhline(0, c='k', ls=':')
plt.xlim(0, 5)
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Flux (nJy)')

mlim = 31.4
flim = mlim * u.ABmag
flim = flim.to(u.nJy).value

# Add AB magnitudes as secondary x-axis at right
# https://matplotlib.org/gallery/subplots_axes_and_figures/secondary_axis.html#sphx-glr-gallery-subplots-axes-and-figures-secondary-axis-py
def AB2nJy(mAB):
    m = mAB * u.ABmag
    f = m.to(u.nJy)
    f = f.value
    f = np.where(f > flim, f, flim)
    return f

def nJy2AB(F_nJy):
    f = F_nJy * u.nJy
    m = f.to(u.ABmag)
    m = m.value
    m = np.where(m < mlim, m, mlim)
    return m
    
plt.ylim(flim, plt.ylim()[1])

secax = ax.secondary_yaxis('right', functions=(nJy2AB, AB2nJy))
secax.set_ylabel('magnitude (AB)')

Text(0, 0.5, 'magnitude (AB)')

Magnitude conversion fails for flux <= 0

fig, ax = plt.subplots(figsize=(8, 6))

for filt in filters:
    lam = int(filt[1:4]) / 100
    plt.errorbar(lam, obj[filt+'_flux'].value, obj[filt+'_fluxerr'].value, marker='.', c='b')

plt.axhline(0, c='k', ls=':')
plt.xlim(0, 5)
plt.xlabel('Wavelength ($\mu$m)')
plt.ylabel('Flux (nJy)')

f0 = 10**(0.4 * 31.4)  # flux [nJy] at zero magnitude
b0 = 1.e-12  # this should be filter dependent

# Add AB magnitudes as secondary x-axis at right
# https://matplotlib.org/gallery/subplots_axes_and_figures/secondary_axis.html#sphx-glr-gallery-subplots-axes-and-figures-secondary-axis-py
def AB2nJy(m):
    f = np.sinh(-0.4 * m * np.log(10) - np.log(b0)) * 2 * b0 * f0
    return f

# Luptitudes
# https://www.sdss.org/dr12/algorithms/magnitudes/
def nJy2AB(f):
    m = -2.5 / np.log(10) * (np.arcsinh((f / f0) / (2 * b0)) + np.log(b0))
    return m

#plt.ylim(flim, plt.ylim()[1])

secax = ax.secondary_yaxis('right', functions=(nJy2AB, AB2nJy))
secax.set_ylabel('asinh magnitude (AB)')

Text(0, 0.5, 'asinh magnitude (AB)')