Catalog Demo - GOODS-South

imaging catalog gds

(This page is auto-generated from the Jupyter notebook photometric-catalog-demo.ipynb.)

Show how to interact with the DJA/grizli photometric catalogs.

(little explanatory text for the quick demo)

%matplotlib inline

import os
import yaml

import numpy as np
import matplotlib.pyplot as plt

import warnings
warnings.filterwarnings('ignore')

import astropy.io.fits as pyfits

import grizli
import grizli.catalog
from grizli import utils

import eazy

print(f'grizli version: {grizli.__version__}')
print(f'eazy-py version: {eazy.__version__}')


grizli version: 1.10.dev2+g661e5ea
eazy-py version: 0.6.5

Set the field

Currently available:

  • gds = GOODS-South
  • gdn = GOODS-North
  • ceers-full = CEERS EGS
  • abell2744clu = Abell 2744 GLASS + UNCOVER + DD-2756
  • macs0647 = MACS 0647 cluster (Coe et al., GO-1433)
  • rxj2129 = RXJ 2129 cluster (Kelly et al., DD-2767)
  • sunrise = “Sunrise Arc” (WHL0137, Coe et al., GO-2282)
  • smacs0723 = SMACS 0723 cluster (Pontoppidan et al., DD-2736)
  • …
field = 'gds-grizli-v7.0'

url_path = 'https://s3.amazonaws.com/grizli-v2/JwstMosaics/v7'

Raw photometry

NB: All photometry given in fnu flux densities with units of microJansky (AB zeropoint = 23.9).

phot = utils.read_catalog(f'{url_path}/{field}_phot.fits')

Metadata

# General data of the source detection
for i, k in enumerate(phot.meta):
    print(f'{k:>36} = {phot.meta[k]}')
    if i > 70:
        print('...')
        break

                             VERSION = 1.2.1
                             MINAREA = 9
                               CLEAN = True
                             DEBCONT = 0.001
                            DEBTHRSH = 32
                         FILTER_TYPE = conv
                           THRESHOLD = 1.5
                            KRONFACT = 2.5
                               KRON0 = 2.4
                               KRON1 = 3.8
                             MINKRON = 8.750000000000059
                            TOTCFILT = F140W
                            TOTCWAVE = 13922.907
                                  ZP = 28.9
                                PLAM = 13922.907
                                 FNU = 1e-08
                                FLAM = 1.4737148e-20
                              UJY2DN = 99.99395614709495
                            DRZ_FILE = gds-grizli-v7.0-ir_drc_sci.fits.gz
                            WHT_FILE = gds-grizli-v7.0-ir_drc_wht.fits.gz
                            GET_BACK = True
                             BACK_BW = 50
                             BACK_BH = 50
                             BACK_FW = 3
                             BACK_FH = 3
                    BACK_PIXEL_SCALE = 0.04
                           ERR_SCALE = 0.8123676180839539
                            RESCALEW = True
                            APERMASK = True
                                GAIN = 2000.0
                              APER_0 = 9.00000000000006
                              ASEC_0 = 0.36
                              APER_1 = 12.50002500000008
                              ASEC_1 = 0.5000009999999999
                              APER_2 = 17.50000500000012
                              ASEC_2 = 0.7000002000000001
                              APER_3 = 25.00000500000017
                              ASEC_3 = 1.0000002
                CLEARP-F430M_VERSION = 1.2.1
                     CLEARP-F430M_ZP = 28.9
                   CLEARP-F430M_PLAM = 42816.84172704966
                    CLEARP-F430M_FNU = 1e-08
                   CLEARP-F430M_FLAM = 1.63095481504235e-22
                 CLEARP-F430M_uJy2dn = 99.99395614709495
               CLEARP-F430M_DRZ_FILE = gds-grizli-v7.0-clearp-f430m_drc_sci
               CLEARP-F430M_WHT_FILE = gds-grizli-v7.0-clearp-f430m_drc_wht
               CLEARP-F430M_GET_BACK = True
                CLEARP-F430M_BACK_BW = 50
                CLEARP-F430M_BACK_BH = 50
                CLEARP-F430M_BACK_FW = 3
                CLEARP-F430M_BACK_FH = 3
       CLEARP-F430M_BACK_PIXEL_SCALE = 0.04
              CLEARP-F430M_ERR_SCALE = 1.0
               CLEARP-F430M_RESCALEW = True
               CLEARP-F430M_APERMASK = True
                   CLEARP-F430M_GAIN = 2000.0
                 CLEARP-F430M_aper_0 = 9.00000000000006
                 CLEARP-F430M_asec_0 = 0.36
                 CLEARP-F430M_aper_1 = 12.50002500000008
                 CLEARP-F430M_asec_1 = 0.5000009999999999
                 CLEARP-F430M_aper_2 = 17.50000500000012
                 CLEARP-F430M_asec_2 = 0.7000002000000001
                 CLEARP-F430M_aper_3 = 25.00000500000017
                 CLEARP-F430M_asec_3 = 1.0000002
                CLEARP-F480M_VERSION = 1.2.1
                     CLEARP-F480M_ZP = 28.9
                   CLEARP-F480M_PLAM = 48151.90220745452
                    CLEARP-F480M_FNU = 1e-08
                   CLEARP-F480M_FLAM = 1.28956813045049e-22
                 CLEARP-F480M_uJy2dn = 99.99395614709495
               CLEARP-F480M_DRZ_FILE = gds-grizli-v7.0-clearp-f480m_drc_sci
               CLEARP-F480M_WHT_FILE = gds-grizli-v7.0-clearp-f480m_drc_wht
...

Photometric apertures

for i, k in enumerate(phot.meta):
    if k.startswith('APER_'):
        aper_index = k[-1]
        print(f"Aperture index {aper_index}: *diameter* = {phot.meta[k]:4.1f} pixels = {phot.meta[k.replace('APER','ASEC')]:.2f} arcsec")

Aperture index 0: *diameter* =  9.0 pixels = 0.36 arcsec
Aperture index 1: *diameter* = 12.5 pixels = 0.50 arcsec
Aperture index 2: *diameter* = 17.5 pixels = 0.70 arcsec
Aperture index 3: *diameter* = 25.0 pixels = 1.00 arcsec

# Columns for a particular filter + aperture
aper_index = '1'

cols = []

for k in phot.colnames:
    if k.startswith('f444w') & k.endswith(aper_index):
        cols.append(k)
        
phot[cols].info()

<GTable length=52427>
           name             dtype  unit    class     n_bad
-------------------------- ------- ---- ------------ -----
   f444w-clear_flux_aper_1 float64  uJy MaskedColumn   640
f444w-clear_fluxerr_aper_1 float64  uJy MaskedColumn   640
   f444w-clear_flag_aper_1   int16            Column     0
    f444w-clear_bkg_aper_1 float64  uJy MaskedColumn   641
   f444w-clear_mask_aper_1 float64            Column     0

Photometric bands

  • NIRCam filters generally have “clear” in the filter name, which is the element in the pupil wheel.
  • Filters that start with clearp are generally the long-wavelength NIRISS filters.
  • Filters with names that end in wn are the NIRISS versions, e.g., f200wn-clear for NIRISS and f200w-clear for NIRCam
  • HST filters ending in “u” are the WFC3/UVIS versions, e.g., f814wu
count = 0
for k in phot.colnames:
    if k.endswith('_flux_aper_1'):
        count += 1
        print(f"{count:>2} {k.split('_flux')[0]}")

 1 clearp-f430m
 2 clearp-f480m
 3 f090w-clear
 4 f105w
 5 f110w
 6 f115w-clear
 7 f115wn-clear
 8 f125w
 9 f140w
10 f150w-clear
11 f150wn-clear
12 f160w
13 f182m-clear
14 f200w-clear
15 f200wn-clear
16 f210m-clear
17 f277w-clear
18 f335m-clear
19 f336wu
20 f350lpu
21 f356w-clear
22 f410m-clear
23 f430m-clear
24 f435w
25 f444w-clear
26 f460m-clear
27 f475w
28 f480m-clear
29 f606w
30 f606wu
31 f775w
32 f814w
33 f814wu
34 f850lp
35 f850lpu

# Missing data are *masked*

fig, axes = plt.subplots(1,2,figsize=(10,5), sharex=True, sharey=True)
cosd = np.cos(np.nanmedian(phot['dec'])/180*np.pi)

axes[0].scatter(phot['ra'], phot['dec'], c=phot['f444w-clear_flux_aper_1'].mask)
axes[0].set_title('F444W (JADES + FRESCO)')

axes[1].scatter(phot['ra'], phot['dec'], c=phot['f277w-clear_flux_aper_1'].mask)
axes[1].set_title('F277W (JADES)')

axes[0].set_xlim(*axes[0].get_xlim()[::-1])
for ax in axes:
    ax.set_aspect(1./cosd)
    ax.grid()

png

# 5-sigma depth in the D=0.5" aperture
depth = 23.9 - 2.5*np.log10(phot['f444w-clear_flux_aper_1']*5)
fig, ax = plt.subplots(1,1,figsize=(6,5))
ax.set_aspect(1./cosd)
so = np.argsort(depth)
sc = ax.scatter(phot['ra'][so], phot['dec'][so], c=depth[so], vmin=26, vmax=31, alpha=0.5)

ax.set_xlim(*axes[0].get_xlim()[::-1])

cb = plt.colorbar(sc)
cb.set_label('raw depth, D=0.5" aperture')

png

Point sources

fig, ax = plt.subplots(1,1,figsize=(8,5))
in_jades = ~phot['f277w-clear_flux_aper_1'].mask
ax.scatter(phot['mag_auto'], phot['flux_radius'], alpha=0.2, c=in_jades, vmin=0, vmax=2, cmap='viridis')
ax.set_ylim(0,10)
ax.grid()
ax.set_xlim(17, 32)

ax.set_xlabel('mag_auto (detection band)')
ax.set_ylabel('flux_radius')


Text(0, 0.5, 'flux_radius')

png

Catalog with aperture corrections

  • {filter}_corr_{aper} = {filter}_corr_{aper} * flux_auto / flux_{aper} : Aperture corrected to the auto flux in the detection band
  • {filter}_tot_{aper} = {filter}_corr_{aper} * {filter}_tot_corr : Corrected for flux outside of the auto aperture. Not implemented for JWST, where corr = tot
if not os.path.exists(f'{field}-fix.photoz.tar.gz'):
    ! wget {url_path}/{field}-fix.photoz.tar.gz

! tar xzvf {field}-fix.photoz.tar.gz

x gds-grizli-v7.0-fix.eazypy.h5
x gds-grizli-v7.0-fix.eazypy.residuals.001.png
x gds-grizli-v7.0-fix.eazypy.residuals.002.png
x gds-grizli-v7.0-fix.eazypy.residuals.003.png
x gds-grizli-v7.0-fix.eazypy.zout.fits
x gds-grizli-v7.0-fix.eazypy.zphot.param
x gds-grizli-v7.0-fix.eazypy.zphot.translate
x gds-grizli-v7.0-fix.eazypy.zphot.zeropoint
x gds-grizli-v7.0-fix.zhist.png
x gds-grizli-v7.0-fix.zphot_zspec.png
x gds-grizli-v7.0-fix_phot_apcorr.fits

apc = utils.read_catalog(f'{field}-fix_phot_apcorr.fits')

aper_index = '1'

cols = []

for k in apc.colnames:
    if k.startswith('f444w') & k.endswith(aper_index):
        cols.append(k)
        
apc[cols].info()

<GTable length=52427>
        name          dtype  unit    class     n_bad
-------------------- ------- ---- ------------ -----
   f444w_flux_aper_1 float64  uJy MaskedColumn   640
f444w_fluxerr_aper_1 float64  uJy MaskedColumn   640
   f444w_flag_aper_1   int16      MaskedColumn     0
    f444w_bkg_aper_1 float64  uJy MaskedColumn   641
   f444w_mask_aper_1 float64            Column     0
        f444w_corr_1 float64  uJy       Column     0
       f444w_ecorr_1 float64  uJy       Column     0
         f444w_tot_1 float64  uJy       Column     0
        f444w_etot_1 float64  uJy       Column     0

Compare to Skelton 3D-HST

import grizli.catalog
s14 = grizli.catalog.query_tap_catalog(ra=np.nanmedian(phot['ra']), dec=np.nanmedian(phot['dec']),
                                       radius=10,
                                       vizier=True,
                                       db='"J/ApJS/214/24/3dhstall"')
len(s14)

idx, dr, dx, dy = utils.GTable(s14).match_to_catalog_sky(apc, get_2d_offset=True)

fig, ax = plt.subplots(1,1,figsize=(5,5))

ax.scatter(dx, dy, alpha=0.1)

has_match = dr.value < 0.3
ra_offset = np.nanmedian((apc['ra'] - s14['RAJ2000'][idx])[has_match])
dec_offset = np.nanmedian((apc['dec'] - s14['DEJ2000'][idx])[has_match])

s14['ra'] += ra_offset
s14['dec'] += dec_offset

idx, dr, dx, dy = utils.GTable(s14).match_to_catalog_sky(apc, get_2d_offset=True)
ax.scatter(dx, dy, alpha=0.1)

has_match = dr.value < 0.2

ax.set_xlim(-0.5, 0.5)
ax.set_ylim(*ax.get_xlim())
ax.grid()

ax.set_xlabel(r'$\Delta RA, arcsec')
ax.set_ylabel(r'$\Delta Dec, arcsec')


Query "J/ApJS/214/24/3dhstall" from VizieR TAP server
Launched query: 'SELECT TOP 1000000 * FROM "J/ApJS/214/24/3dhstall" WHERE RAJ2000 > 53.05300162385859 AND RAJ2000 < 53.24140686277677 AND DEJ2000 > -27.87878381298912 AND DEJ2000 < -27.712117146322456 '
------>http
host = tapvizier.u-strasbg.fr:80
context = /TAPVizieR/tap/sync
Content-type = application/x-www-form-urlencoded
200 200
[('date', 'Fri, 14 Jul 2023 22:54:49 GMT'), ('server', 'Apache/2.4.41 (Ubuntu) mod_jk/1.2.46 OpenSSL/1.1.1f'), ('vary', 'Accept-Encoding'), ('access-control-allow-origin', '*'), ('access-control-allow-credentials', 'true'), ('transfer-encoding', 'chunked'), ('content-type', 'application/x-votable+xml; serialization=TABLEDATA;charset=UTF-8')]
Retrieving sync. results...
Saving results to: sync_20230715005449.xml
Query finished.





Text(0, 0.5, '$\\Delta Dec, arcsec')

png

mag_s14_aper = 25 - 2.5*np.log10(s14['F160Wap'])
mag_jw_d0p7 = 23.9 - 2.5*np.log10(phot['f160w_flux_aper_2'])

mag_s14_tot = 25 - 2.5*np.log10(s14['F160W'])
mag_jw_tot = 23.9 - 2.5*np.log10(apc['f160w_tot_1'])

delta_mag_aper = mag_jw_d0p7 - mag_s14_aper[idx]
delta_mag_tot = mag_jw_tot - mag_s14_tot[idx]

# jh_jw = -2.5*np.log10(apc['f814w_tot_1']*0.847/apc['f160w_tot_1'])
# jh_s14 = -2.5*np.log10(s14['F814W']/s14['F160W'])

jh_jw = -2.5*np.log10(apc['f125w_tot_1']/apc['f160w_tot_1'])
jh_s14 = -2.5*np.log10(s14['F125W']/s14['F160W'])

delta_color = jh_jw - jh_s14[idx]

fig, axes = plt.subplots(3,1,figsize=(8,6), sharex=True, sharey=True)

axes[0].scatter(mag_jw_tot[has_match], delta_mag_aper[has_match], alpha=0.1)
axes[0].set_ylabel(r'$\Delta$mag F160W' + '\nD=0.7" aperture')

axes[1].scatter(mag_jw_tot[has_match], delta_mag_tot[has_match], vmax=1.5, alpha=0.1)
axes[1].set_ylabel(r'$\Delta$mag F160W' + '\n total corrected')

axes[2].scatter(mag_jw_tot[has_match], delta_color[has_match], alpha=0.1)
axes[2].set_ylabel(r'$\Delta$color' + '\nF125W - F160W')

axes[2].set_xlabel('mag, HST F160W')

for ax in axes:
    ax.set_xlim(18, 29)
    ax.set_ylim(-1,1)
    ax.grid()

png

Photometric redshifts

import eazy.hdf5

if not os.path.exists('templates'):
    eazy.symlink_eazy_inputs()
    
root = f'{field}-fix'

self = eazy.hdf5.initialize_from_hdf5(h5file=root+'.eazypy.h5')
self.fit_phoenix_stars()

zout = utils.read_catalog(root+'.eazypy.zout.fits')
self.cat = utils.read_catalog(root+'_phot_apcorr.fits')
cat = self.cat

Read default param file: /Users/gbrammer/miniconda3/envs/py39jw/lib/python3.9/site-packages/eazy/data/zphot.param.default
CATALOG_FILE is a table
   >>> NOBJ = 52427
f090w_tot_1 f090w_etot_1 (363): jwst_nircam_f090w
f105w_tot_1 f105w_etot_1 (202): hst/wfc3/IR/f105w.dat
f110w_tot_1 f110w_etot_1 (241): hst/wfc3/IR/f110w.dat
f115w_tot_1 f115w_etot_1 (364): jwst_nircam_f115w
f115wn_tot_1 f115wn_etot_1 (309): niriss-f115w
f125w_tot_1 f125w_etot_1 (203): hst/wfc3/IR/f125w.dat
f140w_tot_1 f140w_etot_1 (204): hst/wfc3/IR/f140w.dat
f150w_tot_1 f150w_etot_1 (365): jwst_nircam_f150w
f150wn_tot_1 f150wn_etot_1 (310): niriss-f150w
f160w_tot_1 f160w_etot_1 (205): hst/wfc3/IR/f160w.dat
f182m_tot_1 f182m_etot_1 (370): jwst_nircam_f182m
f200w_tot_1 f200w_etot_1 (366): jwst_nircam_f200w
f200wn_tot_1 f200wn_etot_1 (311): niriss-f200w
f210m_tot_1 f210m_etot_1 (371): jwst_nircam_f210m
f277w_tot_1 f277w_etot_1 (375): jwst_nircam_f277w
f335m_tot_1 f335m_etot_1 (381): jwst_nircam_f335m
f356w_tot_1 f356w_etot_1 (376): jwst_nircam_f356w
f410m_tot_1 f410m_etot_1 (383): jwst_nircam_f410m
f430m_tot_1 f430m_etot_1 (384): jwst_nircam_f430m
f435w_tot_1 f435w_etot_1 (233): hst/ACS_update_sep07/wfc_f435w_t81.dat
f444w_tot_1 f444w_etot_1 (377): jwst_nircam_f444w
f460m_tot_1 f460m_etot_1 (385): jwst_nircam_f460m
f475w_tot_1 f475w_etot_1 (234): hst/ACS_update_sep07/wfc_f475w_t81.dat
f480m_tot_1 f480m_etot_1 (386): jwst_nircam_f480m
f606w_tot_1 f606w_etot_1 (236): hst/ACS_update_sep07/wfc_f606w_t81.dat
f606wu_tot_1 f606wu_etot_1 (214): hst/wfc3/UVIS/f606w.dat
f775w_tot_1 f775w_etot_1 (238): hst/ACS_update_sep07/wfc_f775w_t81.dat
f814w_tot_1 f814w_etot_1 (239): hst/ACS_update_sep07/wfc_f814w_t81.dat
f814wu_tot_1 f814wu_etot_1 (217): hst/wfc3/UVIS/f814w.dat
f850lp_tot_1 f850lp_etot_1 (240): hst/ACS_update_sep07/wfc_f850lp_t81.dat
Set sys_err = 0.05 (positive=True)
Read PRIOR_FILE:  templates/prior_F160W_TAO.dat
Template grid: templates/sfhz/agn_blue_sfhz_13.param (this may take some time)
TemplateGrid: user-provided tempfilt_data
Process templates: 0.237 s


294it [00:02, 104.84it/s]


h5: read corr_sfhz_13_bin0_av0.01.fits
h5: read corr_sfhz_13_bin0_av0.25.fits
h5: read corr_sfhz_13_bin0_av0.50.fits
h5: read corr_sfhz_13_bin0_av1.00.fits
h5: read corr_sfhz_13_bin1_av0.01.fits
h5: read corr_sfhz_13_bin1_av0.25.fits
h5: read corr_sfhz_13_bin1_av0.50.fits
h5: read corr_sfhz_13_bin1_av1.00.fits
h5: read corr_sfhz_13_bin2_av0.01.fits
h5: read corr_sfhz_13_bin2_av0.50.fits
h5: read corr_sfhz_13_bin2_av1.00.fits
h5: read corr_sfhz_13_bin3_av0.01.fits
h5: read corr_sfhz_13_bin3_av0.50.fits
h5: read fsps_4590.fits
h5: read j0647agn+torus.fits
fit_best: 2.5 s (n_proc=5,  NOBJ=51074)
phoenix_templates: ./bt-settl_t400-7000_g4.5.fits

Adjusted zeropoints

The iterated eazy “zeropoint” adjustments are used as a crude PSF-matching correction for the simple aperture photometry catalogs. That is, all photometry is done on the native images and corrected to “total” using a single correction derived in the detection band. The encircled energy (for point sources) will be different for the different instruments / filters, and the eazy zeropoint adjustments are initialized with corrections that would be appropriate to put point sources on a common scale. Corrections to that are then derived based on the photo-z fits to the full catalog.

print('# ix filt f_number zp')
for i in np.argsort(self.lc):
    print(f"{i:2} {self.flux_columns[i].split('_')[0]:12} {self.f_numbers[i]:3} {self.zp[i]:.3f}")

# ix filt f_number zp
19 f435w        233 1.067
22 f475w        234 1.091
25 f606wu       214 0.929
24 f606w        236 0.911
26 f775w        238 0.865
27 f814w        239 0.847
28 f814wu       217 1.000
 0 f090w        363 0.931
29 f850lp       240 0.875
 1 f105w        202 0.932
 4 f115wn       309 0.872
 2 f110w        241 0.942
 3 f115w        364 0.876
 5 f125w        203 0.948
 6 f140w        204 0.965
 8 f150wn       310 0.878
 7 f150w        365 0.871
 9 f160w        205 0.983
10 f182m        370 0.909
11 f200w        366 0.903
12 f200wn       311 0.882
13 f210m        371 0.936
14 f277w        375 1.000
15 f335m        381 1.052
16 f356w        376 1.077
17 f410m        383 1.114
18 f430m        384 1.138
20 f444w        377 1.148
21 f460m        385 1.175
23 f480m        386 1.205

fig = eazy.utils.zphot_zspec(zout['z_phot'][has_match], zout['z_spec'][has_match], zmax=8)
fig.axes[0].set_title(f'JWST {field}')

fig = eazy.utils.zphot_zspec(s14['zpk'][idx][has_match], zout['z_spec'][has_match], zmax=8)
fig.axes[0].set_title(f'Skelton (2014)')

Text(0.5, 1.0, 'Skelton (2014)')

png

png

Plot some SEDs

# e.g., GOODS-S-9209 from Carnall et al. https://arxiv.org/pdf/2301.11413.pdf
ra, dec = 53.1082274, -27.8252019

dr = np.sqrt((zout['ra'] - ra)**2 + (zout['dec'] - dec)**2)

self.cat['z_spec'][np.argmin(dr)] = 4.6582

cat_id = zout['id'][np.argmin(dr)]

_ = self.show_fit(cat_id, zr=[3,7])

png

def quick_cutout(resp, sy=2, pl=2, size=1.5, scl=3, filters='f115w-clear,f277w-clear,f444w-clear'):
    """
    Make a cutout figure with the grizli cutout server
    """
    from PIL import Image
    import requests
    from io import BytesIO

    #rd = ds9.get('pan icrs').replace(' ',',')
    
    if isinstance(resp, int):
        id = resp
        ix = np.where(self.cat['id'] == id)[0][0]
    else:
        ix = resp['ix']
        id = resp['id']

    rd = f"{self.RA[ix]:.6f},{self.DEC[ix]:.6f}"
    
    
    print(rd)
    print(f"https://s3.amazonaws.com/grizli-v2/ClusterTiles/Map/gds/jwst.html?coord={rd}&zoom=6")

    url=f"https://grizli-cutout.herokuapp.com/thumb?coord={rd}&all_filters=True&size={size}&scl={scl}&asinh=True&filters={filters}&rgb_scl=1.0,0.95,1.2&pl={pl}"

    response = requests.get(url)
    img = Image.open(BytesIO(response.content))
    img.apply_transparency()

    fig, ax = plt.subplots(1,1, figsize=(sy*4, sy+0.2), sharex=True, sharey=True)
    data = np.array(img)
    black = data.max(axis=2) == 0
    for ioff in range(-2,3):
        black &= np.roll(black, ioff, axis=0)


    for i in range(3):
        data[:,:,i][black] = 255
    
    ax.imshow(data, interpolation='Nearest', origin='upper')
    
    #ax.text(0.5, 0.01, 'nrc', color='r', fontsize=8, ha='center',va='bottom',transform=ax.transAxes)        
    ax.set_xticklabels([])
    ax.set_yticklabels([])
    ax.axis('off')
    ax.text(0.05, -0.01, f"{root}   {self.cat['id'][ix]}   ({rd.replace(',','  ')})    z_phot={self.zbest[ix]:.3f}    logM={np.log10(zout['mass'][ix]):.1f}".replace('z_phot', r'$z_\mathrm{phot}$'),
            ha='left', va='top', transform=ax.transAxes, fontsize=7)
    
    ax.text(0.95, -0.01, filters.replace('-clear','').replace(',', ' '), ha='right', va='top', 
            fontsize=7, color='k', transform=ax.transAxes)
    
    fig.tight_layout(pad=0.2)
        
    return id, fig, img

if 1:
    id, fig, img = quick_cutout(_[1], pl=2, scl=4, sy=3, filters='f775w,f182m-clear,f444w-clear')

# id, fig, img = quick_cutout(_[1], pl=2, scl=5, sy=3)


53.108211,-27.825183
https://s3.amazonaws.com/grizli-v2/ClusterTiles/Map/gds/jwst.html?coord=53.108211,-27.825183&zoom=6

png

# Curtis Lake - JADES
src = utils.read_catalog('https://raw.githubusercontent.com/dawn-cph/jwst-sources/main/jwst-sources.csv')
idx, dr = zout.match_to_catalog_sky(src)

ecl = src['author'] == 'Emma Curtis-Lake'
ids_jades = zout['id'][idx][ecl]
z_jades = src['zspec'][ecl]

i = -1

self.cat['z_spec'][idx] = src['zspec']

for i in np.argsort(z_jades):
    id = ids_jades[i]
    _ = self.show_fit(id, show_fnu=True, maglim=(32,27))
    # id, fig, img = quick_cutout(_[1], pl=2, scl=8, sy=3, size=1, filters='f090w-clear,f115w-clear,f150w-clear,f200w-clear,f277w-clear,f356w-clear,f444w-clear')
    id, fig, img = quick_cutout(_[1], scl=10, size=1, filters='f115w-clear,f150w-clear,f200w-clear')


53.158837,-27.773500
https://s3.amazonaws.com/grizli-v2/ClusterTiles/Map/gds/jwst.html?coord=53.158837,-27.773500&zoom=6
53.164768,-27.774627
https://s3.amazonaws.com/grizli-v2/ClusterTiles/Map/gds/jwst.html?coord=53.164768,-27.774627&zoom=6
53.166346,-27.821558
https://s3.amazonaws.com/grizli-v2/ClusterTiles/Map/gds/jwst.html?coord=53.166346,-27.821558&zoom=6
53.149886,-27.776504
https://s3.amazonaws.com/grizli-v2/ClusterTiles/Map/gds/jwst.html?coord=53.149886,-27.776504&zoom=6

png

png

png

png

png

png

png

png

# More filters
id, fig, img = quick_cutout(_[1], pl=2, scl=8, sy=3, size=1, filters='f090w-clear,f115w-clear,f150w-clear,f200w-clear,f277w-clear,f356w-clear,f444w-clear')

53.149886,-27.776504
https://s3.amazonaws.com/grizli-v2/ClusterTiles/Map/gds/jwst.html?coord=53.149886,-27.776504&zoom=6

png