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 Gaussian2DKernel
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

import photutils
print('photutils', photutils.__version__)

from photutils import Background2D, MedianBackground, detect_sources, deblend_sources, SourceCatalog#, source_properties (new API)
from photutils.utils import calc_total_error

              

photutils 1.6.0

               /tmp/ipykernel_3720/473964775.py:16: DeprecationWarning: `photutils.Background2D` is a deprecated alias for `photutils.background.Background2D` and will be removed in the future. Instead, please use `from photutils.background import Background2D` to silence this warning.
  from photutils import Background2D, MedianBackground, detect_sources, deblend_sources, SourceCatalog#, source_properties (new API)
/tmp/ipykernel_3720/473964775.py:16: DeprecationWarning: `photutils.MedianBackground` is a deprecated alias for `photutils.background.MedianBackground` and will be removed in the future. Instead, please use `from photutils.background import MedianBackground` to silence this warning.
  from photutils import Background2D, MedianBackground, detect_sources, deblend_sources, SourceCatalog#, source_properties (new API)
/tmp/ipykernel_3720/473964775.py:16: DeprecationWarning: `photutils.detect_sources` is a deprecated alias for `photutils.segmentation.detect_sources` and will be removed in the future. Instead, please use `from photutils.segmentation import detect_sources` to silence this warning.
  from photutils import Background2D, MedianBackground, detect_sources, deblend_sources, SourceCatalog#, source_properties (new API)
/tmp/ipykernel_3720/473964775.py:16: DeprecationWarning: `photutils.deblend_sources` is a deprecated alias for `photutils.segmentation.deblend_sources` and will be removed in the future. Instead, please use `from photutils.segmentation import deblend_sources` to silence this warning.
  from photutils import Background2D, MedianBackground, detect_sources, deblend_sources, SourceCatalog#, source_properties (new API)
/tmp/ipykernel_3720/473964775.py:16: DeprecationWarning: `photutils.SourceCatalog` is a deprecated alias for `photutils.segmentation.SourceCatalog` and will be removed in the future. Instead, please use `from photutils.segmentation import SourceCatalog` to silence this warning.
  from photutils import Background2D, MedianBackground, detect_sources, deblend_sources, SourceCatalog#, source_properties (new API)

              

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)

              

../../_images/NIRCam_multiband_photometry_16_0.png

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
sigma = 3.0 * gaussian_fwhm_to_sigma  
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()

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

segm_deblend = deblend_sources(data, segm_detect, npixels=5, kernel=kernel, 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)

              

               WARNING: AstropyDeprecationWarning: "kernel" was deprecated in version 1.5 and will be removed in a future version. Instead, if filtering is desired, please input a convolved image directly into the "data" parameter. [warnings]
WARNING: AstropyDeprecationWarning: "kernel" was deprecated in version 1.5 and will be removed in a future version. Instead, if filtering is desired, please input a convolved image directly into the "data" parameter. [warnings]

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()

              

../../_images/NIRCam_multiband_photometry_22_0.png

View all measured quantities in detection image (optional) #

cat.to_table()

QTable length=327

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	0.055428643253112045
2	104.80681791619065	18.0821294110356	53.17721522973072,-27.805243193448323	95	116	10	27	271.0	4.43307602055267	2.8904310038200527	-26.549722105819686	0.7582062720085905	0.001169592682614256	0.05683289995260978	0.0	3.3850931796608226	0.10096273567434758	4.064307886560462	0.11784714328036967
3	15.04416396551755	13.21870964358083	53.178060898531776,-27.805283643045666	14	16	12	14	8.0	0.8501945455843772	0.6682678017840503	-38.16122766424209	0.618204230158731	0.0035728101756059504	0.006067336431199741	0.0	0.04072228096572398	0.011222825585360585	0.1008685069847074	0.023152305782963536
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	0.018575211927226797	0.19011686546526946	0.023625589169720226
5	434.52931528855504	22.221938204336567	53.17410887486057,-27.80520894064138	424	447	14	30	268.0	3.9541143711184557	2.6317387394125484	-8.16733448470208	0.7463356928864708	-0.0003420958396561837	0.10656058422736421	0.0	3.6163046777967858	0.08775119207431732	4.277364104456117	0.10081655301137071
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	0.014462494201596657	0.17695836262605436	0.026075310664432975
8	762.7604138509574	28.298129971963505	53.17101657158984,-27.805158481182374	754	771	21	36	201.0	3.160486625451918	2.3639871661119467	37.84866003483639	0.663718991542073	-0.0001941792050655057	0.10556121967783572	0.0	3.8253517307369536	0.08853590501277893	3.981147093372532	0.09151504375364357
9	801.0706722163886	32.58026118517374	53.17065564474934,-27.805122812753073	799	804	31	34	12.0	1.3349434908844906	0.6567170220834244	-28.755196160479745	0.8706270208190223	0.0018856854900614819	0.009336017794193286	0.0	0.05935203501404956	0.012189355644992149	0.1700350417232032	0.02777838643014956
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
318	638.7423056471345	555.4511129157931	53.1721846558466,-27.80076548202184	630	647	544	568	256.0	4.1970530491670495	1.802182158743307	64.56727691953016	0.903118084100567	0.0014816106246247486	0.2658198490126946	0.0	8.690357561191234	0.1588037351236894	8.781566974916299	0.16012188685841847
319	13.420348917244358	884.6989392529491	53.17807530216808,-27.798021307292935	6	21	877	892	184.0	3.2861175102309126	3.144571547794767	-12.816561509735381	0.290331667641004	0.0017370791511803075	0.03495382468946091	0.0	2.116006369446348	0.10780075329442451	3.410121934628213	0.15459247580504082
320	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	0.6893013683010649	0.07610234326451544
321	143.93471643650219	953.7173230226308	53.17684572931822,-27.79744626725717	139	149	950	957	58.0	2.6519955833889846	1.3830963239962937	24.76422639478917	0.8532326993749456	0.0021617009219625062	0.021165230633174926	0.0	0.5651410634421303	0.05723718654011642	1.2524114346118043	0.10432157051791156
322	142.746173256619	959.5389917866399	53.176856920479636,-27.797397752374	139	147	957	963	48.0	2.1427436610213637	1.2555061677914512	-35.4742795019617	0.8103588434156908	0.00047583665796055224	0.030453184966657037	0.0	0.5637982114834339	0.0568718012708143	0.8893801784930939	0.08214060528713434
323	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	0.07893942785469332	1.65211264929793	0.10804543306519863
324	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
325	790.7983558965504	979.5822296300561	53.17075198078638,-27.79723112632807	781	799	972	989	133.0	4.506451359665357	1.4930454629944043	-44.19824469199431	0.9435209394229406	-0.001248849458808359	0.06943444193177313	0.0	2.002750597650743	0.10372107669936124	2.813577038594517	0.13630578304670984
326	307.76973240382	989.9933237644321	53.17530230279065,-27.797144093853966	305	311	987	993	36.0	1.7091274365898566	1.1796163441788732	-57.75342780537102	0.7236317221355874	0.0017972712412996086	0.030535860130823354	0.0	0.38480474350746313	0.04596586978565647	0.7331589389247967	0.07386435404843976
327	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	0.054603633353214456	0.7815995757165104	0.07181097204346876

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=327

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	104.80681791619065	18.0821294110356	53.17721522973072,-27.805243193448323	271.0	4.43307602055267	2.8904310038200527	0.34798523859744146	-26.549722105819686	0.4292034166747856
3	15.04416396551755	13.21870964358083	53.178060898531776,-27.805283643045666	8.0	0.8501945455843772	0.6682678017840503	0.21398248759086125	-38.16122766424209	0.10521135944081007
4	710.3242889701858	14.259777138086877	53.17151058505514,-27.805275444028442	25.0	1.2749753643104456	1.117371994707132	0.12361287442487268	-59.26178196559024	0.33737600483155716
5	434.52931528855504	22.221938204336567	53.17410887486057,-27.80520894064138	268.0	3.9541143711184557	2.6317387394125484	0.33443029401596747	-8.16733448470208	0.5358632947802063
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	762.7604138509574	28.298129971963505	53.17101657158984,-27.805158481182374	201.0	3.160486625451918	2.3639871661119467	0.25201798132149345	37.84866003483639	0.5424978595419511
9	801.0706722163886	32.58026118517374	53.17065564474934,-27.805122812753073	12.0	1.3349434908844906	0.6567170220834244	0.5080563135685205	-28.755196160479745	0.28793220448127643
...	...	...	...	...	...	...	...	...	...
318	638.7423056471345	555.4511129157931	53.1721846558466,-27.80076548202184	256.0	4.1970530491670495	1.802182158743307	0.570607724603107	64.56727691953016	0.6381184942652184
319	13.420348917244358	884.6989392529491	53.17807530216808,-27.798021307292935	184.0	3.2861175102309126	3.144571547794767	0.04307391990562126	-12.816561509735381	0.3289496147364014
320	7.027176577444122	891.9727894960166	53.17813552144662,-27.797960686059632	29.0	1.603195468602836	1.3128724093575075	0.18109024440939903	-82.75208405786572	0.254070174248954
321	143.93471643650219	953.7173230226308	53.17684572931822,-27.79744626725717	58.0	2.6519955833889846	1.3830963239962937	0.47846959751387097	24.76422639478917	0.2767875433471226
322	142.746173256619	959.5389917866399	53.176856920479636,-27.797397752374	48.0	2.1427436610213637	1.2555061677914512	0.41406609169806174	-35.4742795019617	0.3421221334166607
323	790.415367777511	955.3106960545537	53.17075559998907,-27.797433388936582	93.0	2.621691845359079	1.8772136125476881	0.28396862664438105	5.157966396104574	0.37596577589838664
324	797.3397409211948	965.5478941851635	53.17069036467991,-27.797348081794517	121.0	3.0304070507432326	1.7633197784086392	0.4181244470190332	79.51657917516623	0.47716929153411286
325	790.7983558965504	979.5822296300561	53.17075198078638,-27.79723112632807	133.0	4.506451359665357	1.4930454629944043	0.6686871012615843	-44.19824469199431	0.42319913307021234
326	307.76973240382	989.9933237644321	53.17530230279065,-27.797144093853966	36.0	1.7091274365898566	1.1796163441788732	0.30981369854286145	-57.75342780537102	0.3499708478753841
327	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

              

               /usr/share/miniconda/lib/python3.9/site-packages/astropy/units/function/logarithmic.py:64: 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

              

               /usr/share/miniconda/lib/python3.9/site-packages/astropy/units/function/logarithmic.py:64: RuntimeWarning: invalid value encountered in log10
  return dex.to(self._function_unit, np.log10(x))
/usr/share/miniconda/lib/python3.9/site-packages/astropy/units/quantity.py:673: 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

              

               /usr/share/miniconda/lib/python3.9/site-packages/astropy/units/function/logarithmic.py:64: RuntimeWarning: invalid value encountered in log10
  return dex.to(self._function_unit, np.log10(x))
/usr/share/miniconda/lib/python3.9/site-packages/astropy/units/quantity.py:673: 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

              

               /usr/share/miniconda/lib/python3.9/site-packages/astropy/units/function/logarithmic.py:64: RuntimeWarning: invalid value encountered in log10
  return dex.to(self._function_unit, np.log10(x))
/usr/share/miniconda/lib/python3.9/site-packages/astropy/units/quantity.py:673: RuntimeWarning: invalid value encountered in log10
  result = super().__array_ufunc__(function, method, *arrays, **kwargs)

              

View complete results (optional) #

tbl

QTable length=327

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	0.8464272435968172	28.993743842627527	0.09583064229371434	15.251750665804146	0.735186612920272	28.441700757864936	0.051113935624583086	18.356035098509803	0.907615566909567	28.240552801409102	0.052399286366677095	20.355880166244727	0.9951286984899145	28.128275286137136	0.05182128829099082	15.30541463181222	0.37242909913150146	28.437887251597694	0.026103079843123198	14.02083788279239	0.4536467684272995	28.533065080581018	0.03457285577386587	14.174014573163747	0.3999261081137788	28.52126781262446	0.030210306451640984	13.880611681957355	0.46501162852267625	28.543978488116917	0.035777045316298495	13.807578444079855	0.4986392632950047	28.54970620192208	0.038518276199781994
2	104.80681791619065	18.0821294110356	53.17721522973072,-27.805243193448323	271.0	4.43307602055267	2.8904310038200527	0.34798523859744146	-26.549722105819686	0.4292034166747856	66.57967761225385	2.436255936703974	26.841645780216258	0.039019197405152646	67.80807330092654	1.7819304802085372	26.82179648873538	0.02816363896580966	70.39338412925109	2.1096858375971363	26.78117038947336	0.032061379743408516	77.74109040131492	2.318681567876357	26.6733734305523	0.031909302523638994	72.90069026757769	0.8318824113256227	26.743170898753043	0.012319367708500412	75.27250253951928	1.070603105909879	26.70840911249712	0.015333670289260427	71.48792896636016	0.9342131216075662	26.764418211087325	0.01409662645806182	61.42767389099904	0.9993087828855896	26.929089824977595	0.017520685942298195	57.2203760546637	1.051093157627436	27.00612323080047	0.019763151293857938
3	15.04416396551755	13.21870964358083	53.178060898531776,-27.805283643045666	8.0	0.8501945455843772	0.6682678017840503	0.21398248759086125	-38.16122766424209	0.10521135944081007	0.30189458075047043	0.22003285638903267	32.700361707101834	0.5943870266742839	0.7673344575647303	0.20049710485441535	31.68753824783774	0.2520377003105847	0.7185263104580911	0.22413744736254168	31.758893311421605	0.2947853371046982	0.9352163612291753	0.2577402310908106	31.47271975993688	0.2642813635409141	0.34971362332966227	0.06900286982371183	32.54071862281815	0.19551879280610807	0.5607617346802088	0.11336299849737182	32.02805407366013	0.19990472716017532	0.2668657682095152	0.07056536651518258	32.83426782771905	0.25473070870520814	-0.06031654176708369	0.08275804453775322	99.0	34.10547444921151	0.11765422427560634	0.08200758112663478	33.723481188078146	0.574218672619342
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	0.16539237459845982	3.818279752499735	0.360557908564342	29.94533063870602	0.09796938884703618	3.9761821744276116	0.4246250023752303	29.901334315002583	0.110165165468641	3.5589744431503423	0.42659305165797895	30.02168782658959	0.12291324714108388	1.6962298219026282	0.12672768826931047	30.826288264009904	0.07822962940363361	1.590536535091481	0.16680910589172165	30.896140876425513	0.10828384762060109	1.9912255134558894	0.151127025768846	30.652198879435126	0.07942622592908809	1.9438353657449134	0.17889684257333507	30.67835130168316	0.09558932550457658	1.6253103136417588	0.17102441486844797	30.8726592718879	0.1086274373485027
5	434.52931528855504	22.221938204336567	53.17410887486057,-27.80520894064138	268.0	3.9541143711184557	2.6317387394125484	0.33443029401596747	-8.16733448470208	0.5358632947802063	48.21536384426303	1.7917279405059294	27.19203637900948	0.03961537507908405	54.180491712205686	1.365540854656955	27.065392644670535	0.027025255794697643	58.30054045382708	1.6237335342389887	26.98581854813561	0.029825500753549224	83.05102812663696	2.015269002596905	26.601637467123535	0.026031288898904284	133.15988061559287	1.0563434518773878	26.089066507204098	0.008579048595350228	133.46712655021906	1.2825865885346561	26.086564223686825	0.01038384697219314	144.03906284324478	1.2384188265720026	26.003799282319697	0.009295039598996597	161.60492000841137	1.4304450626852654	25.87886355352026	0.009568104243562612	148.3405102917476	1.5722804535154722	25.971850578368716	0.011447300801342226
6	289.38802515014896	17.281765562153954	53.17547627056455,-27.805250009193262	17.0	1.2913044931147235	1.1978568863866517	0.07236682535090477	68.12270923149859	0.18041073594089005	1.1303554308347756	0.3821670960543562	31.266962436913506	0.3162170658541775	1.5011462606731298	0.28676161013445184	30.958942478071556	0.18980531870852319	1.045624005692517	0.28930296346025686	31.35156113668441	0.2652049043787394	1.955256158588051	0.38221513653203576	30.671990843785842	0.1938565595483074	1.188616449626284	0.12368244418243222	31.212395659255414	0.10747756611504046	1.7708471014756073	0.19746701583101797	30.77954733764033	0.11478335606840086	1.4921438342127515	0.15067991684808457	30.96547327829425	0.10445071085094702	1.1433927169620364	0.17563750503987802	31.25451144606716	0.15514831191999282	0.5926553032606401	0.13657056678097335	31.967994562614585	0.22514973086947426
7	640.4863091995317	17.773429154661983	53.17216853452775,-27.805246129692247	21.0	1.5124916692884618	0.8803106550282112	0.41797322067741016	-64.72678377811528	0.257339933672325	1.7318562374159874	0.3544951964949517	30.80372040476935	0.20218906642550227	1.643429843026024	0.24985580694618062	30.86062207735413	0.15366243535885116	1.636102104727731	0.28406614648587186	30.865473991673777	0.17382220324021894	2.2295861313548446	0.3321415421916018	30.529439364296703	0.15077176392081712	1.0399598684843063	0.09617790287548722	31.35745854894559	0.09603604488615963	1.6537574517445193	0.1507209599380506	30.853820464729573	0.094699691326289	1.2604572236829945	0.11591912740405148	31.14867972112842	0.09552272603001735	0.40788132175057795	0.11575451167421515	32.37366545505972	0.2712388513520371	0.5688140707837224	0.10865005565010255	32.01257417244966	0.18978993015591247
8	762.7604138509574	28.298129971963505	53.17101657158984,-27.805158481182374	201.0	3.160486625451918	2.3639871661119467	0.25201798132149345	37.84866003483639	0.5424978595419511	52.90258045317187	1.7914935315960525	27.091307859172854	0.036158543601744766	55.89021246540487	1.3413162730412038	27.031660598550253	0.025748965322108706	61.671458415942496	1.6305394839730292	26.92478945297725	0.028332995920166557	87.85194348648487	2.0332904975012602	26.540621566180963	0.024842449407777148	86.04008354445801	0.7878699856173026	26.563247941466912	0.009896854334870047	73.84294727616292	0.8942980843096469	26.729227444357107	0.013070161189289636	74.8225190478038	0.8372019588600241	26.714919186276617	0.012081025050606558	70.73942058751356	0.9010723240651843	26.775846254315468	0.013742667527438834	74.11117248879874	1.0396086825403072	26.725290789617205	0.015124537907185093
9	801.0706722163886	32.58026118517374	53.17065564474934,-27.805122812753073	12.0	1.3349434908844906	0.6567170220834244	0.5080563135685205	-28.755196160479745	0.28793220448127643	1.1246036170169136	0.3004786436259075	31.272501310051688	0.25710114514483706	1.2797476107076189	0.23055260117238813	31.13218918151494	0.179847389820328	1.2734342232303713	0.2595936177065242	31.13755870689588	0.20143381199573665	1.3630620117794083	0.2799372864607707	31.063710964299986	0.20280440914679781	1.082787068323561	0.10030137862329397	31.313642349074232	0.09618538263285319	0.6781367101230981	0.1161012153749804	31.821706862430297	0.17158341526256443	0.5758891043153808	0.08989342817146666	31.999152845351176	0.15748383733187593	1.1076176036929606	0.13520585441853786	31.289025376253583	0.12504898101684045	1.113433002949251	0.1420534202367806	31.283339775099364	0.1303698266479056
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
318	638.7423056471345	555.4511129157931	53.1721846558466,-27.80076548202184	256.0	4.1970530491670495	1.802182158743307	0.570607724603107	64.56727691953016	0.6381184942652184	120.11105123386255	3.14934982289772	26.201042579971478	0.02810152116245664	165.08884566980734	2.663462515685416	25.855705672804568	0.01737693418175444	210.82538772196136	3.5242585095562693	25.590192730758954	0.017999658448518377	199.580288319275	3.6470415652056585	25.64970588578926	0.0196611637646163	143.09847494169267	1.1923776093934582	26.010912486668634	0.009009485356804967	125.45620116997327	1.4001200026317673	26.15376966773193	0.01204994954280746	115.52181690810832	1.2496816373897293	26.243339973021605	0.011682107040887598	106.85877963435514	1.3121928145361486	26.32797447480941	0.013251309809061716	104.25292308993991	1.4630332682490081	26.354779398102927	0.015130755569956123
319	13.420348917244358	884.6989392529491	53.17807530216808,-27.798021307292935	184.0	3.2861175102309126	3.144571547794767	0.04307391990562126	-12.816561509735381	0.3289496147364014	18.876874905408805	1.9637765224375798	28.210174755454297	0.10745298001454875	31.863552850787098	1.7129106309870576	27.64176450279784	0.05685188066255499	50.359881042840634	2.409326829214983	27.144788261069834	0.05073969845611444	48.59560246237275	2.475721542185557	27.183507573384148	0.053950365684063833	36.65997645594317	0.8204411796141273	27.489519545724278	0.02403059943648705	33.29989013091067	1.0048745397608183	27.593892998487796	0.03227910956200879	34.258836181673466	0.9114471956318294	27.563068486705767	0.02850815562893502	31.59820599327046	1.0125589051776267	27.65084393501573	0.03424639870337512	31.743952751062974	1.10588219702589	27.645847489865684	0.037180469406228316
320	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	0.19581292499061764	3.531493143831654	0.5906264829187411	30.03010408141747	0.1679055592950589	5.107472203839501	0.7850802445520096	29.629484970574794	0.15524361252227648	4.917640884310548	0.8043621780644387	29.670607973080685	0.16447818783217788	3.997287152346768	0.28436677684545053	29.895586630725628	0.07461553548507587	3.8377835048269504	0.3661371226929502	29.93979882029562	0.09893508954511895	3.4123744791286934	0.30628798885439634	30.067358286892155	0.09332518819096991	3.28526387152336	0.3549824646600952	30.10857435571395	0.11140128930453498	3.979156125410199	0.40496953684556536	29.900522551559803	0.10523003616876629
321	143.93471643650219	953.7173230226308	53.17684572931822,-27.79744626725717	58.0	2.6519955833889846	1.3830963239962937	0.47846959751387097	24.76422639478917	0.2767875433471226	5.25267816437387	1.072891902455373	29.599048019845632	0.20179719652929543	5.833623054813162	0.797341658396814	29.485154091662512	0.1390958836944704	7.139888054579577	0.9936751649092471	29.265771493549583	0.14147361016217055	12.978869464075444	1.3144930012171665	28.616907838734512	0.1047438565382814	8.314911352869782	0.41141015610520926	29.100355941326036	0.05243396610496494	6.622274866960971	0.5011520705718457	29.347481992995288	0.07920442863493232	6.3589945647895725	0.4220001550022493	29.391528865549652	0.0697623810541305	7.4586002239091185	0.5253410862821729	29.218356675310666	0.07390001481101394	6.662546579409225	0.5365182891738445	29.34089935425313	0.08408957144589889
322	142.746173256619	959.5389917866399	53.176856920479636,-27.797397752374	48.0	2.1427436610213637	1.2555061677914512	0.41406609169806174	-35.4742795019617	0.3421221334166607	6.9362362752687154	1.1392607904446859	29.297190303788632	0.16511346295363782	7.903254851436269	0.8693971748460056	29.1554850335146	0.11331229123717931	10.187623103416623	1.1263006743581756	28.879817825921563	0.11385094732159436	12.948029906646463	1.3061016666971015	28.619490765397067	0.10434267352803422	9.023017764882551	0.4184777153387269	29.011620468937362	0.049222442862693484	5.447014735335707	0.45676894530082573	29.559603624665375	0.08742971518734169	6.472888687598323	0.4196891132056483	29.372254653387305	0.06820884577947949	6.488348644754336	0.4869395067625267	29.369664554504258	0.07856993742528491	5.76627926777331	0.5007325407405385	29.497760820098495	0.09041210263092743
323	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	0.09379126789743089	28.033949477325784	1.5380517606650033	27.780789284195393	0.05799107346193481	25.74353540142662	1.7358437874135597	27.873329527427543	0.07084681983859821	25.620810723619343	1.8129005233748647	27.87851782980156	0.0742292377178118	21.15342216887344	0.6264899234444314	28.086548407739905	0.03168871406956518	18.828253167515435	0.7475941257205684	28.212974926843625	0.0422763481530479	17.964611476673056	0.6596358378746425	28.263955427272954	0.039152252468897156	12.790064955174342	0.6722158462709532	28.632818124807635	0.055614737300165064	13.580929019180171	0.7400945376355254	28.567676301555696	0.05761144934684234
324	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	0.053233558465096836	33.58368869698832	0.7779020039869248	27.58467901105726	0.02486216210030647	23.755213036076142	0.8523243778218816	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	0.04903520245704538
325	790.7983558965504	979.5822296300561	53.17075198078638,-27.79723112632807	133.0	4.506451359665357	1.4930454629944043	0.6686871012615843	-44.19824469199431	0.42319913307021234	34.20989140732531	2.35362390972397	27.564620760991172	0.0722406193325143	36.57135465011538	1.7557905380331742	27.492147381341525	0.05091356101879861	42.68849561449194	2.2064786055668897	27.324222874751488	0.05471719103146702	45.99460251160774	2.382028845957703	27.243232824978676	0.054821883877762746	34.80004302822041	0.7822981639992332	27.54605054768332	0.02413684700611632	23.40568188262023	0.8462317452688509	27.976696754900892	0.03856178701393753	22.767378194807595	0.7438457465179379	28.006717445540055	0.03490554080327223	20.501256369918995	0.820549850987213	28.120548808604052	0.0426087886820206	19.089604206836878	0.8667809534858215	28.198007689828906	0.048212383021397735
326	307.76973240382	989.9933237644321	53.17530230279065,-27.797144093853966	36.0	1.7091274365898566	1.1796163441788732	0.30981369854286145	-57.75342780537102	0.3499708478753841	7.069500195287273	1.0858487812895934	29.276528222969418	0.15513459728045884	6.559003957176758	0.7627753334333799	29.357905267698598	0.11944685087513006	6.183740011134048	0.8634612094941424	29.42187194540521	0.14191362590829343	8.837316659881695	1.0556391356824744	29.03419895757817	0.12251412891582383	5.903891204071303	0.3375812207664306	29.472154135545033	0.060371775836015065	4.765659788621438	0.400524443536207	29.70469241084787	0.08761713475141415	7.125902945739509	0.40872358703286327	29.267900242506997	0.060554569012257996	4.037502746172116	0.3858677684749164	29.884717923170992	0.09910121989210244	4.493111158366823	0.4338262357870153	29.768632092362214	0.10007470185186942
327	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	0.09375247396689582	10.735153071224346	1.1138972366308966	28.822979396580244	0.10718825502231771	12.643670520931202	1.2540115652526385	28.645317074711215	0.10267300679924707	9.553009517952841	0.41693523286415374	28.94964947388319	0.04638135299187599	9.153235549670372	0.5151314172293293	28.99606340336343	0.05944621805492528	10.167634753780487	0.4754200471048225	28.881950157725086	0.04961590355201818	7.872874739121797	0.5029271829323597	29.159666645795102	0.06723264138084344	7.568902388401964	0.5444025773994181	29.202417738905766	0.07541224065721709

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:
#   __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.Latitude
#         unit: *id002
#         value: !astropy.table.SerializedColumn {name: sky_centroid.dec}
#       frame: icrs
#       ra: !astropy.table.SerializedColumn
#         __class__: astropy.coordinates.angles.Longitude
#         unit: *id002
#         value: !astropy.table.SerializedColumn {name: sky_centroid.ra}
#         wrap_angle: !astropy.coordinates.Angle
#           unit: *id002
#           value: 360.0
#       representation_type: spherical
#   apermask_method: correct
#   date: 2022-12-30 16:51:36 UTC
#   kron_params: !numpy.ndarray
#     buffer: !!binary |
#       QUFBQUFBQUFCRUJtWm1abVptYjJQd0FBQUFBQUFBQUE=
#     dtype: float64
#     order: C
#     shape: !!python/tuple [3]
#   localbkg_width: 0
#   version: {Python: 3.9.12, astropy: '5.2', bottleneck: 1.3.5, gwcs: 0.18.3, matplotlib: 3.6.2, numpy: 1.24.1, photutils: 1.6.0, scipy: 1.9.3,
#     skimage: 0.19.3, sklearn: 1.2.0}
# 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 0.8464272435968172 28.993743842627527 0.09583064229371434 15.251750665804146 0.735186612920272 28.441700757864936 0.051113935624583086 18.356035098509803 0.907615566909567 28.240552801409102 0.052399286366677095 20.355880166244727 0.9951286984899145 28.128275286137136 0.05182128829099082 15.30541463181222 0.37242909913150146 28.437887251597694 0.026103079843123198 14.02083788279239 0.4536467684272995 28.533065080581018 0.03457285577386587 14.174014573163747 0.3999261081137788 28.52126781262446 0.030210306451640984 13.880611681957355 0.46501162852267625 28.543978488116917 0.035777045316298495 13.807578444079855 0.4986392632950047 28.54970620192208 0.038518276199781994
2 104.80681791619065 18.0821294110356 53.17721522973072 -27.805243193448323 271.0 4.43307602055267 2.8904310038200527 0.34798523859744146 -26.549722105819686 0.4292034166747856 66.57967761225385 2.436255936703974 26.841645780216258 0.039019197405152646 67.80807330092654 1.7819304802085372 26.82179648873538 0.02816363896580966 70.39338412925109 2.1096858375971363 26.78117038947336 0.032061379743408516 77.74109040131492 2.318681567876357 26.6733734305523 0.031909302523638994 72.90069026757769 0.8318824113256227 26.743170898753043 0.012319367708500412 75.27250253951928 1.070603105909879 26.70840911249712 0.015333670289260427 71.48792896636016 0.9342131216075662 26.764418211087325 0.01409662645806182 61.42767389099904 0.9993087828855896 26.929089824977595 0.017520685942298195 57.2203760546637 1.051093157627436 27.00612323080047 0.019763151293857938
3 15.04416396551755 13.21870964358083 53.178060898531776 -27.805283643045666 8.0 0.8501945455843772 0.6682678017840503 0.21398248759086125 -38.16122766424209 0.10521135944081007 0.30189458075047043 0.22003285638903267 32.700361707101834 0.5943870266742839 0.7673344575647303 0.20049710485441535 31.68753824783774 0.2520377003105847 0.7185263104580911 0.22413744736254168 31.758893311421605 0.2947853371046982 0.9352163612291753 0.2577402310908106 31.47271975993688 0.2642813635409141 0.34971362332966227 0.06900286982371183 32.54071862281815 0.19551879280610807 0.5607617346802088 0.11336299849737182 32.02805407366013 0.19990472716017532 0.2668657682095152 0.07056536651518258 32.83426782771905 0.25473070870520814 -0.06031654176708369 0.08275804453775322 99.0 34.10547444921151 0.11765422427560634 0.08200758112663478 33.723481188078146 0.574218672619342

              

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
----- --------- --------- ----------- ----------- ---- ------- ------ ----------- ----------- ------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------ ---------- ------------- --------- ------------
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
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
 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
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
434.5293   22.2219  53.1741089 -27.8052089  268  3.9541 2.6317      0.3344     -8.1673 0.5359    48.2154        1.7917   27.1920       0.0396    54.1805        1.3655   27.0654       0.0270    58.3005        1.6237   26.9858       0.0298    83.0510        2.0153   26.6016       0.0260   133.1599        1.0563   26.0891       0.0086   133.4671        1.2826   26.0866       0.0104   144.0391        1.2384   26.0038       0.0093   161.6049        1.4304   25.8789       0.0096   148.3405        1.5723   25.9719       0.0114
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
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
762.7604   28.2981  53.1710166 -27.8051585  201  3.1605 2.3640      0.2520     37.8487 0.5425    52.9026        1.7915   27.0913       0.0362    55.8902        1.3413   27.0317       0.0257    61.6715        1.6305   26.9248       0.0283    87.8519        2.0333   26.5406       0.0248    86.0401        0.7879   26.5632       0.0099    73.8429        0.8943   26.7292       0.0131    74.8225        0.8372   26.7149       0.0121    70.7394        0.9011   26.7758       0.0137    74.1112        1.0396   26.7253       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 = photutils.segmentation.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 0x7fa7fccea5e0>

../../_images/NIRCam_multiband_photometry_46_1.png

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')

../../_images/NIRCam_multiband_photometry_48_1.png

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	159.85524275404117	861.9318626458592	53.176695835547065,-27.798211159078907	84.0	2.093686819569325	1.7905824482261299	0.14477063546951308	-19.690587301825413	0.45043886443149744	26.774434064613025	2.065811084448465	27.830699250373154	0.08069661955128594	28.175269127668553	1.539758498124517	27.775329817299298	0.057770163184494475	39.57360585028314	2.108483008751378	27.40648594019712	0.05635962794717501	44.290538079545186	2.343861005477472	27.284222608750856	0.05598857116137783	37.57756572414375	0.8148383871956767	27.462678392335242	0.023291662964628788	25.27509316486492	0.8308497106067699	27.893268087273935	0.03511654568010063	26.943645179708977	0.7886404460448742	27.82385912339398	0.0313232846940502	30.95946346003939	0.9375521233464907	27.67301643605517	0.03239156248336977	27.219608981522722	1.008787266766099	27.812795294671048	0.03951080740432303

obj['ellipticity']

\[0.14477064 \; \mathrm{}\]

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

               <photutils.segmentation.core.Segment>
label: 261
slices: (slice(857, 867, None), slice(155, 166, None))
area: 84

              

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')

../../_images/NIRCam_multiband_photometry_55_1.png

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)')

../../_images/NIRCam_multiband_photometry_57_1.png

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)')

../../_images/NIRCam_multiband_photometry_59_1.png

STScI JDAT Notebooks

Extended Aperture Photometry

Contents

Extended Aperture Photometry #

Introduction #

To Do #

Download WEBBPSF Data #

Import packages #

Matplotlib setup for plotting #

Create list of images to be loaded and analyzed #

Load detection image: F200W #

Report image size and field of view #

Create color image (optional) #

Detect Sources and Deblend using `photutils` #

Measure photometry (and more) in detection image #

Show detections alongside images (optional) #

View all measured quantities in detection image (optional) #

Only keep some quantities #

Convert measured fluxes (data units) to magnitudes #

Multiband photometry using isophotal apertures defined in detection image #

View complete results (optional) #

Save photometry as output catalog #

Reformat output catalog for readability (optional) #

Start new session and analyze results #

Load catalog and segmentation map #

Plot number counts vs. magnitude #

Plot F200W vs. F090W magnitudes and look for dropouts #

Look at one object #

Show the object in all the images #

Plot SED (Spectral Energy Distribution) #

Magnitude conversion fails for flux <= 0 #

STScI JDAT Notebooks

Extended Aperture Photometry

Contents

Extended Aperture Photometry #

Introduction #

To Do #

Download WEBBPSF Data #

Import packages #

Matplotlib setup for plotting #

Create list of images to be loaded and analyzed #

Load detection image: F200W #

Report image size and field of view #

Create color image (optional) #

Detect Sources and Deblend using photutils #

Measure photometry (and more) in detection image #

Show detections alongside images (optional) #

View all measured quantities in detection image (optional) #

Only keep some quantities #

Convert measured fluxes (data units) to magnitudes #

Multiband photometry using isophotal apertures defined in detection image #

View complete results (optional) #

Save photometry as output catalog #

Reformat output catalog for readability (optional) #

Start new session and analyze results #

Load catalog and segmentation map #

Plot number counts vs. magnitude #

Plot F200W vs. F090W magnitudes and look for dropouts #

Look at one object #

Show the object in all the images #

Plot SED (Spectral Energy Distribution) #

Magnitude conversion fails for flux <= 0 #

Detect Sources and Deblend using `photutils` #