Minju Lee
imaging ALMA catalog release demo
(This page is auto-generated from the Jupyter notebook ecogal-dja-showcase.ipynb.)
Short description of the project:
ECOGAL (ECOology for Galaxies using ALMA archive and Legacy surveys) is an ALMA data-mining effort that uniformly reduces archival data, creates science-ready ALMA images, and links them to JWST/HST legacy datasets in well-studied survey fields.
This notebook provides an introduction to the ECOGAL catalogue, including how to query sources by position and retrieve the summary plots available for ALMA-detected galaxies with DJA spectra. The catalogue released with this post covers galaxies in the three ALMA-accessible CANDELS fields: COSMOS, GOODS-S, and UDS.
- This notebook was tested on python 3.12 version
- Some functions that are used in this notebook can be installed from : https://github.com/mjastro/ecogal
- or on terminal:
python -m pip install git+https://github.com/mjastro/ecogal.git
Additional documentation will be released soon. A complete description of the ALMA data reduction and catalogue construction is provided in Lee et al. (2025) [arXiv:2511.20751], and should be cited when using the ECOGAL data products. Users should also cite the appropriate survey references (including ALMA project IDs) when making use of the DJA data products.
%matplotlib inline
# libraries to install
if 0:
!python -m pip install git+https://github.com/mjastro/ecogal.git
!pip install tabulate
!pip install git+https://github.com/karllark/dust_attenuation.git
# setting the libraries
import ecogal
import numpy as np
import astropy
import os,sys
from astropy.coordinates import SkyCoord
from astropy import units as u
from astropy.table import Table
from astropy.utils.data import download_file
import astropy.constants as const
import matplotlib as mpl
import cmasher as cmr
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from astropy.coordinates import Angle
import shapely
from shapely import Point, Polygon
import warnings
from astropy.io import fits
from astropy.wcs import WCS
warnings.filterwarnings('ignore')
import pandas as pd
# cosmology
from astropy.cosmology import FlatLambdaCDM
cosmo = FlatLambdaCDM(H0=70, Om0=0.3, Tcmb0=2.725)
CACHE_DOWNLOADS = True
print(f'astropy version: {astropy.__version__}')
astropy version: 7.1.1
# Set plotting style
mpl.rcParams['axes.linewidth'] = 2
mpl.rcParams['axes.labelsize'] = 20
mpl.rcParams.update({'font.family':'serif'})
mpl.rcParams.update({'xtick.major.pad': '7.0'})
mpl.rcParams.update({'xtick.major.size': '7.5'})
mpl.rcParams.update({'xtick.major.width': '1.5'})
mpl.rcParams.update({'xtick.minor.pad': '7.0'})
mpl.rcParams.update({'xtick.minor.size': '3.5'})
mpl.rcParams.update({'xtick.minor.width': '1.0'})
mpl.rcParams.update({'ytick.major.pad': '7.0'})
mpl.rcParams.update({'ytick.major.size': '7.5'})
mpl.rcParams.update({'ytick.major.width': '1.5'})
mpl.rcParams.update({'ytick.minor.pad': '7.0'})
mpl.rcParams.update({'ytick.minor.size': '3.5'})
mpl.rcParams.update({'ytick.minor.width': '1.0'})
mpl.rcParams.update({'xtick.labelsize':14})
mpl.rcParams.update({'ytick.labelsize':14})
mpl.rcParams.update({'xtick.direction':'in'})
mpl.rcParams.update({'ytick.direction':'in'})
mpl.rcParams.update({'axes.labelsize' :18})
# to get DJA spectra information
from urllib import request
####Needed to load spectra
import msaexp
import msaexp.spectrum
#to get DJA slit information
import grizli
from grizli import utils
print(f'grizli version: {grizli.__version__}')
grizli version: 1.13.2
Catalogue data exploration
Read the table
# prior catalogue which includes all sources with flux constraints (including non-detection) based on the source positions determined by JWST/HST detection
version ='v1' #initial data release
URL_PREFIX = "https://s3.amazonaws.com/alma-ecogal/dr1"
file_cat = "ecogal_all_priors_"+version+".csv"
if 0:
# the latest zenodo (frozen) catalogue is available here (TBD)
# this include blind catalogue, and detection catalogue
URL_PREFIX = "https://zenodo.org/records/XXX"
table_url = f"{URL_PREFIX}/catalogue/{file_cat}"
tab = utils.read_catalog(download_file(table_url, cache=CACHE_DOWNLOADS), format='csv')
Column descriptions
columns_url = f"{URL_PREFIX}/catalogue/ecogal_{version}.columns.csv"
tab_columns = utils.read_catalog(download_file(columns_url, cache=CACHE_DOWNLOADS), format='csv')
# Set column metadata
for row in tab_columns:
col = row['column']
if row['unit'] != '--':
tab[col].unit = row['unit']
if row['description'] != '--':
tab[col].description = row['description']
tab.info()
<GTable length=258455>
name dtype unit description class n_bad
------------------ ------- ------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------ ------
projectID str14 ALMA project ID Column 0
target_alma str23 ALMA target names without space;if original ALMA program has a space for the target name this column does not include the space Column 0
small_mosaic_idx int64 mosaic identifier if it was obtained in the mosaic mode Column 0
field str6 legacy field names Column 0
id_ecogal int64 ecogal parant ID Column 0
id_new str13 ecogal parent full ID including the field name Column 0
frequency float64 GHz observed frequency Column 0
band str2 observed ALMA band Column 0
beam_maj float64 arcsec synthesized beam major axis in arcsec Column 0
frame str4 image frame Column 0
RA_parent float64 deg prior position from JWST/HST (RA) in degree Column 0
Dec_parent float64 deg prior position from JWST/HST (DEC) in degree Column 0
RA_peak_alma float64 deg ALMA peak position (RA) in degree Column 0
Dec_peak_alma float64 deg ALMA pak position (DEC) in degree Column 0
separation_prior float64 arcsec position offset between ALMA peak and the prior position Column 0
flux_peak float64 Jy peak ALMA flux in Jy Column 0
noise float64 Jy 1-sigma noise level (after correcting for the primary beam response) Column 0
sn float64 peak SNR Column 0
flux_aper float64 Jy flux from the aperture photometry MaskedColumn 1707
eflux_aper float64 Jy flux error from the aperture photometry Column 0
flux_peak_imfit float64 Jy peak flux from the 2D Gaussian fitting using imfit Column 0
flux_imfit float64 Jy flux from the 2D Gaussian fitting using imfit Column 0
eflux_imfit float64 Jy flux error from the 2D Gaussian fitting using imfit Column 0
fac_pbcor float64 primary beam response at the position of the source;1=center Column 0
id_3dhst_v4 float64 3D-HST(ver4.0) catalogue id when available MaskedColumn 142476
ecogal_ver str4 ecogal version Column 0
zsp_best_avail float64 best available spectroscopic redshift Column 0
zsp_best_survey str9 selected spectroscopic survey name Column 0
z_ver str4 DJA MSA version Column 0
objid float64 Unique source identifier MaskedColumn 248862
srcid float64 Source ID from APT plan MaskedColumn 248862
file str55 DJA filename MaskedColumn 248862
grating str5 NIRSpec grating MaskedColumn 248862
file_phot str44 Filename of the DJA photometric catalog MaskedColumn 249835
id_phot float64 ID number in the DJA photometric cadtalog MaskedColumn 249835
valid str5 Redshift matches best z from visual inspection Column 0
z_phot_eazy float64 photometric redshift from eazypy combining 3D-HST+DJA photometry Column 0
restU float64 flux density of the rest-frame U band from the photoz (eazypy) fit Column 0
restU_err float64 flux density error of the rest-frame U band from the photoz (eazypy) fit Column 0
restV float64 flux density of the rest-frame V band from the photoz (eazypy) fit Column 0
restV_err float64 flux density of the rest-frame V band from the photoz (eazypy) fit Column 0
restJ float64 flux density of the rest-frame J band from the photoz (eazypy) fit Column 0
restJ_err float64 flux density of the rest-frame J band from the photoz (eazypy) fit Column 0
mass_ez float64 solMass stellar mass from the photoz (eazypy) fit Column 0
sfr_ez float64 solMass / yr star-formation rate from the photoz (eazypy) fit Column 0
Av_ez float64 mag Av the photoz (eazypy) fit Column 0
lmass float64 solMass stellar mass in log from FAST++ fit (spec-z source only) MaskedColumn 8
l68_lmass float64 solMass 68% lower boundary of stellar mass in log from FAST++ run (spec-z source only) MaskedColumn 8
u68_lmass float64 solMass 68% upper boundary of stellar mass in log from FAST++ run (spec-z source only) MaskedColumn 8
lsfr float64 solMass / yr star-formation rate in log from FAST++ fit (spec-z source only) MaskedColumn 8
l68_lsfr float64 solMass / yr 68% lower boundary of star-formation rate in log from FAST++ fit (spec-z source only) MaskedColumn 8
u68_lsfr float64 solMass / yr 68% upper boundary of star-formation rate in log from FAST++ fit (spec-z source only) MaskedColumn 8
file_alma str137 ECOGAL ALMA fits file name (primary beam corrected) Column 0
RA_bdsf float64 deg ALMA position(RA) based on PYBDSF fit MaskedColumn 255029
DEC_bdsf float64 deg ALMA position(Dec) based on PYBDSF fit MaskedColumn 255029
Total_flux_pbcor float64 Jy Total flux based on PYBDSF fit (corrected for the primary beam response) MaskedColumn 255029
E_Total_flux_pbcor float64 Jy Total flux error based on PYBDSF fit (corrected for the primary beam response) MaskedColumn 255029
pbfac float64 ALMA primary beam response at the position of the detected source;1=phase center MaskedColumn 255029
Peak_flux float64 Jy Peak flux from PYBDSF fit (before the primary beam response) MaskedColumn 255029
E_Peak_flux float64 Jy Peak flux error from PYBDSF fit (before the primary beam response) MaskedColumn 255029
Maj float64 deg the FWHM of the major axis of the source MaskedColumn 255029
E_Maj float64 deg the 1-sigma error on the FWHM of the major axis of the source MaskedColumn 255029
Min float64 deg the FWHM of the minor axis of the source MaskedColumn 255029
E_Min float64 deg the 1-sigma error on the FWHM of the minor axis of the source MaskedColumn 255029
PA float64 deg the position angle of the major axis of the source measured east of north MaskedColumn 255029
E_PA float64 deg the 1-sigma error on the position angle of the major axis of the source MaskedColumn 255029
Maj_img_plane float64 deg the FWHM of the major axis of the source in the image plane MaskedColumn 255029
E_Maj_img_plane float64 deg the 1-sigma error of the major axis of the source in the image plane MaskedColumn 255029
Min_img_plane float64 deg the FWHM of the minor axis of the source in the image plane MaskedColumn 255029
E_Min_img_plane float64 deg the 1-sigma error of the minor axis of the source in the image plane MaskedColumn 255029
PA_img_plane float64 deg the position angle in the image plane of the major axis of the source measured east of north MaskedColumn 255029
E_PA_img_plane float64 deg the 1-sigma error of the image plane of the major axis of the source measured east of north MaskedColumn 255029
Isl_Total_flux float64 Jy the total integrated Stokes I flux density of the island in which the source is located (not primary beam corrected) MaskedColumn 255029
E_Isl_Total_flux float64 Jy the 1-sigma error on the total flux density of the island in which the source is located MaskedColumn 255029
Isl_rms float64 Jy / beam the average background rms value of the island (derived from the rms map) MaskedColumn 255029
S_Code str1 a code that defines the source structure; ‘S’ = a single-Gaussian source that is the only source in the island; ‘C’ = a single-Gaussian source in an island with other sources;'M’ = a multi-Gaussian source MaskedColumn 255029
Separation_bdsf float64 arcsec offset between the optical counterpart (matched within 0.8 arcsec) MaskedColumn 255029
zphot-zspec
### -- getting the unique source and spec-z sources
con_dup = np.array(tab.to_pandas()['id_new'].duplicated())
tab0 = tab[~con_dup]
len(tab0)
128125
con_z = tab0['zsp_best_avail']>0
con_z &= tab0['z_phot_eazy']>0
con_dja = tab0['zsp_best_survey']=='dja'
con_dja_z = np.logical_and(con_dja,con_z)
plt.scatter(tab0[con_z]['zsp_best_avail'], tab0[con_z]['z_phot_eazy'], s=1, zorder=100, label='All spec-z')
plt.scatter(tab0[con_dja_z]['zsp_best_avail'], tab0[con_dja_z]['z_phot_eazy'], marker='s',facecolor='None',edgecolor='grey',alpha=0.5, label='DJA spec-z')
plt.xlabel(r'$z_{\rm spec}$')
plt.ylabel(r'$z_{\rm phot}$')
plt.legend()
<matplotlib.legend.Legend at 0x331825df0>
Query if there is any ALMA coverage given the position
-
ALMA/ECOGAL metadata includes information of individual ALMA images such as regions, pixel scales, phase center position, etc.
-
This will allow you to check the ALMA/ECOGAL coverage for your source of interest.
# A complete version of the metadata
version='v1'
meta_file = "ecogal_"+version+"_metadata.fits"
table_url = f"{URL_PREFIX}/ancillary/{meta_file}"
meta = utils.read_catalog(download_file(table_url, cache=CACHE_DOWNLOADS), format='fits')
meta[:3]
GTable length=3
| file_alma | version | simple | bitpix | naxis | naxis1 | naxis2 | naxis3 | extend | bscale | bzero | bmaj | bmin | bpa | btype | object | bunit | equinox | radesys | lonpole | latpole | pc1_1 | pc2_1 | pc3_1 | pc1_2 | pc2_2 | pc3_2 | pc1_3 | pc2_3 | pc3_3 | ctype1 | crval1 | cdelt1 | crpix1 | cunit1 | ctype2 | crval2 | cdelt2 | crpix2 | cunit2 | ctype3 | crval3 | cdelt3 | crpix3 | cunit3 | pv2_1 | pv2_2 | restfrq | specsys | altrval | altrpix | velref | telescop | observer | date-obs | timesys | obsra | obsdec | obsgeo-x | obsgeo-y | obsgeo-z | instrume | distance | mpiprocs | chnchnks | memreq | memavail | useweigh | date | origin | almaid | is_mosaic | band | footprint | release | is_available | ra_center | dec_center | noise_fit | noise_tot | FoV_sigma |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bytes137 | bytes4 | bool | int64 | int64 | int64 | int64 | int64 | bool | float64 | float64 | float64 | float64 | float64 | bytes9 | bytes26 | bytes7 | float64 | bytes4 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | bytes8 | float64 | float64 | float64 | bytes3 | bytes8 | float64 | float64 | float64 | bytes3 | bytes4 | float64 | float64 | float64 | bytes2 | float64 | float64 | float64 | bytes4 | float64 | float64 | int64 | bytes4 | bytes15 | bytes26 | bytes3 | float64 | float64 | float64 | float64 | float64 | bytes4 | float64 | int64 | int64 | float64 | float64 | bool | bytes26 | bytes30 | bytes14 | bool | int64 | bytes461 | bytes3 | bool | float64 | float64 | float64 | float64 | float64 |
| 2011.0.00064.S___concat_all_6_AzTEC-3_0_b7_cont_noninter2sig.image.pbcor.fits | v1.0 | True | -32 | 3 | 1600 | 1600 | 1 | True | 1.0 | 0.0 | 0.0002005813188023 | 0.0001579564147525 | -53.50122070312 | Intensity | AzTEC-3 | Jy/beam | 2000.0 | FK5 | 180.0 | 2.586833336311 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | RA---SIN | 150.0890000048 | -2.777777777778e-05 | 801.0 | deg | DEC--SIN | 2.586833336311 | 2.777777777778e-05 | 801.0 | deg | FREQ | 296763686486.8 | 15646751913.56 | 1.0 | Hz | 0.0 | 0.0 | 296763686486.8 | LSRK | -0.0 | 1.0 | 257 | ALMA | riechers | 2012-04-11T01:22:13.632000 | UTC | 150.0890000048 | 2.586833336311 | 2225142.180269 | -5440307.370349 | -2481029.851874 | ALMA | 0.0 | 1 | 1 | 0.12359619 | 150.23436 | False | 2023-11-14T12:19:42.442402 | CASA 6.5.6-22 CASAtools:v1.0.0 | 2011.0.00064.S | False | 7 | ((150.088583,2.582778),(150.085914,2.584167),(150.084940,2.587250),(150.086331,2.589917),(150.089417,2.590889),(150.092086,2.589500),(150.093060,2.586417),(150.091669,2.583750),(150.088583,2.582778)) | N/A | True | 150.0890000048 | 2.5868333363110025 | 4.46259777172499e-05 | 5.5606829846510664e-05 | 8.332485222950787 |
| 2011.0.00064.S___concat_all_6_AzTEC-3_1_b7_cont_noninter2sig.image.pbcor.fits | v1.0 | True | -32 | 3 | 1600 | 1600 | 1 | True | 1.0 | 0.0 | 0.0002007880806923 | 0.0001580240825812 | -53.53054428101 | Intensity | AzTEC-3 | Jy/beam | 2000.0 | FK5 | 180.0 | 2.588527777932 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | RA---SIN | 150.0868750002 | -2.777777777778e-05 | 801.0 | deg | DEC--SIN | 2.588527777932 | 2.777777777778e-05 | 801.0 | deg | FREQ | 296763687761.7 | 15646752334.74 | 1.0 | Hz | 0.0 | 0.0 | 296763687761.7 | LSRK | -0.0 | 1.0 | 257 | ALMA | riechers | 2012-04-11T01:22:54.720000 | UTC | 150.0868750002 | 2.588527777932 | 2225142.180269 | -5440307.370349 | -2481029.851874 | ALMA | 0.0 | 1 | 1 | 0.12359619 | 150.1451 | False | 2023-11-14T13:30:14.601414 | CASA 6.5.6-22 CASAtools:v1.0.0 | 2011.0.00064.S | False | 7 | ((150.086458,2.584472),(150.083789,2.585861),(150.082815,2.588944),(150.084206,2.591611),(150.087292,2.592583),(150.089961,2.591194),(150.090935,2.588111),(150.089544,2.585444),(150.086458,2.584472)) | N/A | True | 150.0868750002 | 2.5885277779320006 | 6.49289000021855e-05 | 0.00011714215361280367 | 8.33248518715434 |
| 2011.0.00097.S___concat_all_10_COSMOSLowz_64_29_b7_cont_noninter2sig.image.pbcor.fits | v1.0 | True | -32 | 3 | 1844 | 1844 | 1 | True | 1.0 | 0.0 | 0.0001445855862565 | 0.0001385951704449 | 32.57455062866 | Intensity | COSMOSLowz_64 | Jy/beam | 2000.0 | FK5 | 180.0 | 2.193778888889 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | RA---SIN | 150.0951 | -2.777777777778e-05 | 923.0 | deg | DEC--SIN | 2.193778888889 | 2.777777777778e-05 | 923.0 | deg | FREQ | 341959206917.1 | 15956638554.12 | 1.0 | Hz | 0.0 | 0.0 | 341959206917.1 | LSRK | -0.0 | 1.0 | 257 | ALMA | nscoville | 2012-04-22T02:13:22.032000 | UTC | 150.0951 | 2.193778888889 | 2225142.180269 | -5440307.370349 | -2481029.851874 | ALMA | 0.0 | -- | -- | -- | -- | False | 2022-01-18T10:06:48.750999 | CASA 5.6.1-8 | 2011.0.00097.S | True | 7 | ((150.094822,2.190251),(150.092487,2.191390),(150.091570,2.194057),(150.092709,2.196390),(150.095378,2.197307),(150.097713,2.196168),(150.098630,2.193501),(150.097491,2.191168),(150.094822,2.190251)) | dr1 | True | 150.0951 | 2.1937788888889997 | 0.00013968738028111863 | 0.00014185431064106524 | 7.231210572315804 |
Use ecogal function : query metata data based on the coordinates
Use visualcheck.get_footprint function.
import ecogal.visualcheck as visualcheck
# get the metadata via DJA
ra, dec = 34.41887, -5.21965
fp,_ = visualcheck.get_footprint(ra,dec)
There are #15 ALMA projects overlapping
fp['file_alma','almaid','object','band']
GTable length=15
| file_alma | almaid | object | band |
|---|---|---|---|
| bytes137 | bytes14 | bytes26 | int64 |
| 2012.1.00245.S__all_SXDF-NB2315-2_b7_cont_noninter2sig.image.pbcor.fits | 2012.1.00245.S | SXDF-NB2315-2 | 7 |
| 2012.1.00245.S__all_SXDF-NB2315-3_b7_cont_noninter2sig.image.pbcor.fits | 2012.1.00245.S | SXDF-NB2315-3 | 7 |
| 2013.1.00742.S__all_SXDF-B3-NB2315-FoV1_b3_cont_noninter2sig.image.pbcor.fits | 2013.1.00742.S | SXDF-B3-NB2315-FoV1 | 3 |
| 2013.1.00781.S__all_SXDS-AzTEC23_b6_cont_noninter2sig.image.pbcor.fits | 2013.1.00781.S | SXDS-AzTEC23 | 6 |
| 2015.1.00442.S__all_SXDS-AzTEC28_b6_cont_noninter2sig.image.pbcor.fits | 2015.1.00442.S | SXDS-AzTEC28 | 6 |
| 2015.1.01074.S__all_UDSp_17_b7_cont_noninter2sig.image.pbcor.fits | 2015.1.01074.S | UDSp_17 | 7 |
| 2015.1.01528.S__all_UDS.0113_b7_cont_noninter2sig.image.pbcor.fits | 2015.1.01528.S | UDS.0113 | 7 |
| 2016.1.00434.S__all_UDS.0113_b7_cont_noninter2sig.image.pbcor.fits | 2016.1.00434.S | UDS.0113 | 7 |
| 2017.1.00562.S__all_NB2315_b3_cont_noninter2sig.image.pbcor.fits | 2017.1.00562.S | NB2315 | 3 |
| 2017.1.00562.S__all_NB2315_b6_cont_noninter2sig.image.pbcor.fits | 2017.1.00562.S | NB2315 | 6 |
| 2017.1.00562.S__all_U4-16795_b9_cont_noninter2sig.image.pbcor.fits | 2017.1.00562.S | U4-16795 | 9 |
| 2017.1.01027.S__all_U4-16504_b7_cont_noninter2sig.image.pbcor.fits | 2017.1.01027.S | U4-16504 | 7 |
| 2017.1.01027.S__all_U4-16795_b7_cont_noninter2sig.image.pbcor.fits | 2017.1.01027.S | U4-16795 | 7 |
| 2019.1.00337.S__all_AS2UDS0113.1_b3_cont_noninter2sig.image.pbcor.fits | 2019.1.00337.S | AS2UDS0113.1 | 3 |
| 2021.1.00705.S__all_UDS.0113_b4_cont_noninter2sig.image.pbcor.fits | 2021.1.00705.S | UDS.0113 | 4 |
Get ALMA/ECOGAL cutouts
Method 1. Using ecogal
Use ecogal.pbcor.show_all_cutouts function.
import ecogal.pbcor as ecogal_plot
[1] JWST RGB + ALMA cutout
# an example of many ALMA coverage
ra, dec = 34.41887, -5.21965
# this example can take a while because it downloads a lot of fits files
summary_cutouts = ecogal_plot.show_all_cutouts(ra,dec)
N=15
2013.1.00742.S__all_SXDF-B3-NB2315-FoV1_b3 b3 157x 157 0.04 dx=7.57"
2017.1.00562.S__all_NB2315_b3 b3 313x 313 0.02 dx=7.57"
2019.1.00337.S__all_AS2UDS0113.1_b3 b3 63x 63 0.10 dx=9.84"
2021.1.00705.S__all_UDS.0113_b4 b4 33x 33 0.20 dx=6.65"
2015.1.00442.S__all_SXDS-AzTEC28_b6 b6 157x 157 0.04 dx=8.32"
2013.1.00781.S__all_SXDS-AzTEC23_b6 b6 313x 313 0.02 dx=8.33"
2017.1.00562.S__all_NB2315_b6 b6 313x 313 0.02 dx=4.83"
2016.1.00434.S__all_UDS.0113_b7 b7 157x 157 0.04 dx=6.65"
2012.1.00245.S__all_SXDF-NB2315-3_b7 b7 313x 313 0.02 dx=9.65"
2015.1.01074.S__all_UDSp_17_b7 b7 313x 313 0.02 dx=9.75"
2015.1.01528.S__all_UDS.0113_b7 b7 313x 313 0.02 dx=6.66"
2012.1.00245.S__all_SXDF-NB2315-2_b7 b7 313x 313 0.02 dx=0.15"
2017.1.01027.S__all_U4-16504_b7 b7 63x 63 0.10 dx=9.77"
2017.1.01027.S__all_U4-16795_b7 b7 63x 63 0.10 dx=0.42"
2017.1.00562.S__all_U4-16795_b9 b9 313x 313 0.02 dx=0.06"
[2] Ready-made summary file
There are some summary png files in the repository made for sources with ALMA detection and DJA spectra (~120 unique sources in total with the first data release), which can be queried by the source position.
- the default searching area is 0.4 arcsec, that you can change with
r_search
ra, dec = 150.14325, 2.35599
_ = visualcheck.get_summary(ra,dec, r_search=0.5)
There are #10 ALMA projects overlapping
There are 9 ECOGAL+DJA cross-match!
https://s3.amazonaws.com/alma-ecogal/dr1/pngs/ecogal__0_all_filters_COSMOS.60520.png
A source found at a distance of = 0.13 arcsec
ra, dec = 34.27751, -5.22819
_ = visualcheck.get_summary(ra,dec)
There are #1 ALMA projects overlapping
There are 1 ECOGAL+DJA cross-match!
https://s3.amazonaws.com/alma-ecogal/dr1/pngs/ecogal__0_all_filters_UDS.104633.png
A source found at a distance of = 0.13 arcsec
Method 2: Getting cutouts and footprint via DJA API
- ALMA footprint can also be retrieved via DJA API: https://grizli-cutout.herokuapp.com/
- See also the instructions for accessing API (for other projects) : https://dawn-cph.github.io/dja/general/api_summary/
- The identifier for ECOCAL is
ecogal, followed by the output mode and coordinate information?ra=&dec=.
There are three different output modes:
output=footprint: footprint for ALMA coverageoutput=csv: metadataoutput=cutout: making a cutout image- for cutout module: specify ALMA file names. The file name is available in the ECOGAL catalogue or from the metadata, and the column name is
file_alma
- for cutout module: specify ALMA file names. The file name is available in the ECOGAL catalogue or from the metadata, and the column name is
footprint mode
https://grizli-cutout.herokuapp.com/ecogal?output=footprint&ra=34.48016&dec=-5.11252
cutout mode
Given the ALMA file name of 2015.1.01528.S__all_UDS.0424_b7_cont_noninter2sig.image.pbcor.fits
from IPython.display import display, Markdown, Latex
# getting the summary of the footprint for a given ra, dec
ra, dec = 34.48016,-5.11252
cord = SkyCoord(ra,dec, unit=(u.degree, u.degree))
ara = tab['RA_peak_alma']
adec = tab['Dec_peak_alma']
acord = SkyCoord(ara, adec, unit=(u.degree, u.degree))
#search for the matching source within 0.1 arcsec
con_pos = acord.separation(cord).arcsec<0.15
ecotb=tab[con_pos]
# see also the description in the https://dawn-cph.github.io/dja/blog/2025/05/01/nirspec-merged-table-v4/
#
cutout_URL = f"https://grizli-cutout.herokuapp.com/ecogal?output=footprint&ra={ra}&dec={dec}"
ecotb['Thumb'] = [
"<img src=\"{0}\" height=200px>".format(
cutout_URL.format(ra,dec)
)
for row in ecotb
]
df = ecotb['projectID','target_alma','id_new','band','beam_maj','sn','separation_prior','zsp_best_avail','z_phot_eazy','Thumb','file_alma'].to_pandas()
display(Markdown(df.to_markdown()))
| projectID | target_alma | id_new | band | beam_maj | sn | separation_prior | zsp_best_avail | z_phot_eazy | Thumb | file_alma | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023.1.01520.S | 0424.0 | UDS.105062 | b4 | 0.69 | 7.08 | 0.09 | 3.5433 | 3.47313 | 2023.1.01520.S__all_0424.0_b4_cont_noninter2sig.image.pbcor.fits | |
| 1 | 2015.1.01528.S | UDS.0424 | UDS.105062 | b7 | 0.21 | 19.15 | 0.05 | 3.5433 | 3.47313 | 2015.1.01528.S__all_UDS.0424_b7_cont_noninter2sig.image.pbcor.fits | |
| 2 | 2013.1.00781.S | SXDS-AzTEC28 | UDS.105062 | b6 | 0.32 | 18.72 | 0.06 | 3.5433 | 3.47313 | 2013.1.00781.S__all_SXDS-AzTEC28_b6_cont_noninter2sig.image.pbcor.fits |
Access the ALMA/ECOGAL image fits files
- All fits files (primary beam corrected) are available from the DJA repository (on AWS server) and a frozen version will be available on Zenodo.
#getting the corresponding file name from the catalogue
#some times the file name includes special string like "+", which should be parsed to download the fits file
idx = 2
file_alma = ecotb['file_alma'][idx]
encoded_filename = file_alma.replace("+", "%2B")
fits_URL = "https://s3.amazonaws.com/alma-ecogal/dr1/pbcor/"
almafits = fits.open(
download_file(str(os.path.join(fits_URL, encoded_filename)), cache=True)
)
img = almafits[0].data[0]
hdr = almafits[0].header
wcs_alma = WCS(hdr)
gid = tab[con_pos]['id_new'][idx]
band = tab[con_pos]['band'][idx]
zgal = tab[con_pos]['zsp_best_avail'][idx]
lmass = np.log10(tab[con_pos]['mass_ez'][idx])
sfr = tab[con_pos]['sfr_ez'][idx]
#get the noise of the map (after pb-correction)
noise = tab[con_pos]['noise'][idx]
##################
## plotting; think about adding a function
##################
x,y,_= wcs_alma.wcs_world2pix(ra,dec,0,0)
pixsz = np.abs(hdr['CDELT1']*3600)
imsz = 1/pixsz
noise_array = noise*np.arange(4,30,3)
cutout = img[int(y-imsz):int(y+imsz),int(x-imsz):int(x+imsz)]
fig=plt.figure(1,figsize=(5,5))
ax=plt.subplot(111)
ax.imshow(cutout, origin='lower')
ax.contour(cutout, levels = noise_array, colors='white')
ax.set_title(f'{gid},{band}')
ax.text(0.55,0.95, f'z={zgal}', transform=ax.transAxes, c='white')
ax.text(0.55,0.90, f'log(Mstar/Msun)={lmass:.2f}', transform=ax.transAxes, c='white')
ax.text(0.55,0.85, f'sfr={sfr:.2f} Msun/yr', transform=ax.transAxes, c='white')
#get beam size
bmaj = hdr['BMAJ']*3600
bmin = hdr['BMIN']*3600
pa = hdr['BPA']
theta = Angle(90+pa,'deg')
be=mpatches.Ellipse((imsz/6.,imsz/6.),bmaj/pixsz,bmin/pixsz,angle=theta.degree,lw=1,facecolor='grey',edgecolor='black',hatch='//')
ax.add_patch(be)
<matplotlib.patches.Ellipse at 0x346f95010>
Access the DJA files from the information available in the catalogue
## consider peak SNR>15 and DJA spectra at z>5
con_sn = tab0['sn']>15
con_sn &= tab0['zsp_best_survey']=='dja'
con_sn &= tab0['zsp_best_avail']>5
tab1 = tab0[con_sn]
filename = tab1['file'][0]
zgal = tab1['zsp_best_avail'][0]
filename, zgal
('capers-cos01-v4_prism-clear_6368_52597.spec.fits', 5.8351)
# prepare a line detectionary
# ** line dictionary -- for plot purpose (you can add more)
# ** lines in AA to overplot the emission lines
lam_file = Table.read("""line, wavelength_nm
H-alpha,656.46
H-beta,486.271
H-delta,410.1734
H-gamma,434.0472
Ly-alpha,121.567
[OII],372.71
[OII],372.986
[OII],733.1
[OII],732.0
[NeIII],386.986
[OIII],496.03
[OIII],500.824
[NII],654.986
[NII],658.527
[SII],671.827
[SII],673.267
Pa-alpha,1875
Pa-beta,1282
Pa-gamma,1093.8
Br-beta,2626
HeI,1083.0
[SIII],906.9
[SIII],953.0
[CI],985.0
[PII],1188
[FeII],1257
[FeII],1640
""", format="csv")
#lam_file = Table.read('opt_emission_lines.csv')
lines_dic = {'Ly-alpha':r'Ly$\alpha$', 'H-beta':r'H$\beta$', 'H-alpha': r'H$\alpha$ + [NII]',
'H-delta':r'H$\delta$','H-gamma':r'H$\gamma$', 'Na1D':'NaID',
'Pa-alpha':r'Pa$\alpha$','Pa-beta':r'Pa$\beta$','Pa-gamma':r'Pa$\gamma$','CaIIK':'Ca II K',
'[SiVI]':'[SiVI]','H2':r'H$_2$',
'Br-beta':r'Br$\gamma$','MgI':'MgI','NaI':'NaI','SiI':'SiI','HI':'[FeII]+HI+FeII',#'HI':'HI'-- for grating
'HeI':'HeI','FeII':'FeII','[FeII]':'[FeII]','[CI]':'[CI]','[PII]':'[PII]',
'[OII]': '[OII]', '[NII]':'[NII]', '[SII]':'[SII]', '[OIII]':'[OIII]','[NeIII]':'[NeIII]',
'[SIII]':'[SIII]'}
lam_file
Table length=27
| line | wavelength_nm |
|---|---|
| str8 | float64 |
| H-alpha | 656.46 |
| H-beta | 486.271 |
| H-delta | 410.1734 |
| H-gamma | 434.0472 |
| Ly-alpha | 121.567 |
| [OII] | 372.71 |
| [OII] | 372.986 |
| [OII] | 733.1 |
| [OII] | 732.0 |
| ... | ... |
| Pa-beta | 1282.0 |
| Pa-gamma | 1093.8 |
| Br-beta | 2626.0 |
| HeI | 1083.0 |
| [SIII] | 906.9 |
| [SIII] | 953.0 |
| [CI] | 985.0 |
| [PII] | 1188.0 |
| [FeII] | 1257.0 |
| [FeII] | 1640.0 |
set some functions to get DJA spectra
########################################
### Load the DJA spectra information:
########################################
def loadDJAspec(file_fullpath):
sh = msaexp.spectrum.SpectrumSampler(file_fullpath, err_median_filter=None)
wave = np.array(sh.spec["wave"])*1e4 ###in AA
fnu = sh.spec["flux"] ##uJy
fnu_err = sh.spec["full_err"] ##uJy
spec_flambda = np.array((fnu*u.uJy/(wave*u.AA)**2*const.c).to(u.erg/u.s/u.cm**2/u.AA)/(u.erg/u.s/u.cm**2/u.AA))
spec_flambda_err = np.array((fnu_err/(wave*u.AA)**2*const.c).to(u.erg/u.s/u.cm**2/u.AA)/(u.erg/u.s/u.cm**2/u.AA))
return(sh,wave,spec_flambda,spec_flambda_err)
#################################################
### settting up the DJA spectra information
#################################################
def get_dja_spectra(file, zsp, restframe=False):
#plotting in the restframe
root = file.split('_')[0]+'/'
basename = 'https://s3.amazonaws.com/msaexp-nirspec/extractions/'
fullname = basename+root+file
print(f'A galaxy at z={zsp}')
sampled,lam,galaxy,noise = loadDJAspec(fullname)
#deredshift
lam0 = lam/(1 + zsp)
if restframe:
lam /=(1 + zsp) # Compute approximate restframe wavelength
####mask areas
con_mask = noise==0
con_mask |= noise>galaxy[lam > 1100].max()
galaxy_masked = np.ma.masked_where(con_mask, galaxy)
galaxy0=galaxy_masked
noise0=noise
lam0=lam #in AA
return lam0, galaxy0, noise0
lam0, galaxy0, noise0 = get_dja_spectra(filename, zgal, restframe=True)
plt.plot(lam0[lam0>1300],galaxy0[lam0>1300])
plt.xlabel(f'Rest-Wavelength ($\AA$)')
plt.ylabel(f'Flux (erg/s/cm$^2$/$\AA$)')
##adding line identifiers
lines_plotted=[]
ticker_max = galaxy0.max()
text_hi = ticker_max+1e-20
for kk in range(len(lam_file)):
lam_idx = np.argmin(np.abs(lam_file['wavelength_nm'][kk]*10-lam0))
if lam_file['wavelength_nm'][kk]*10> lam0.min() and lam_file['wavelength_nm'][kk]*10<lam0.max() and noise0[lam_idx]>0:
plt.axvline(lam_file['wavelength_nm'][kk]*10, linestyle=':', color='darkgrey')
if lam_file['line'][kk] not in lines_plotted:
#lines_plotted.append(lam_file['line'][kk])
if lam_file['line'][kk] not in ['[NII]']:#if PRISM #'[FeII]','FeII'
plt.text(lam_file['wavelength_nm'][kk]*10+0.01, text_hi*(0.9-0.1*np.mod(kk,3)), lines_dic[lam_file['line'][kk]], rotation='vertical', fontsize=9)
A galaxy at z=5.8351
get source information with multiple DJA spectra
# Full DJA table
table_url = "https://s3.amazonaws.com/msaexp-nirspec/extractions/dja_msaexp_emission_lines_v4.4.csv.gz"
tab00 = utils.read_catalog(download_file(table_url, cache=True), format='csv')
con_sn = tab0['sn']>10
con_sn &= tab0['zsp_best_survey']=='dja'
con_sn &= tab0['zsp_best_avail']>4
tab0[con_sn]['file','objid']
GTable length=6
| file | objid |
|---|---|
| str55 | float64 |
| gto-wide-uds13-v4_prism-clear_1215_1951.spec.fits | 172449.0 |
| capers-cos01-v4_prism-clear_6368_52597.spec.fits | 141884.0 |
| rubies-uds23-v4_prism-clear_4233_166691.spec.fits | 149974.0 |
| capers-cos04-v4_prism-clear_6368_36571.spec.fits | 143258.0 |
| rubies-uds1-v4_prism-clear_4233_37108.spec.fits | 151060.0 |
| gto-wide-uds13-v4_prism-clear_1215_1472.spec.fits | 162424.0 |
## check the alternative spectra information
dja_uniq_id = 172449
print(dja_uniq_id)
#search for other dja spectra
msa = tab00[tab00['objid'] == dja_uniq_id]
msa
172449
GTable length=4
| file | srcid | ra | dec | grating | filter | effexptm | nfiles | dataset | msamet | msaid | msacnf | dithn | slitid | root | npix | ndet | wmin | wmax | wmaxsn | sn10 | flux10 | err10 | sn50 | flux50 | err50 | sn90 | flux90 | err90 | xstart | ystart | xsize | ysize | slit_pa | pa_v3 | srcypix | profcen | profsig | ctime | version | exptime | contchi2 | dof | fullchi2 | line_ariii_7138 | line_ariii_7138_err | line_ariii_7753 | line_ariii_7753_err | line_bra | line_bra_err | line_brb | line_brb_err | line_brd | line_brd_err | line_brg | line_brg_err | line_hb | line_hb_err | line_hd | line_hd_err | line_hei_1083 | line_hei_1083_err | line_hei_3889 | line_hei_3889_err | line_hei_5877 | line_hei_5877_err | line_hei_7065 | line_hei_7065_err | line_hei_8446 | line_hei_8446_err | line_heii_4687 | line_heii_4687_err | line_hg | line_hg_err | line_lya | line_lya_err | line_mgii | line_mgii_err | line_neiii_3867 | line_neiii_3867_err | line_neiii_3968 | line_neiii_3968_err | line_nev_3346 | line_nev_3346_err | line_nevi_3426 | line_nevi_3426_err | line_niii_1750 | line_niii_1750_err | line_oi_6302 | line_oi_6302_err | line_oii | line_oii_7325 | line_oii_7325_err | line_oii_err | line_oiii | line_oiii_1663 | line_oiii_1663_err | line_oiii_4363 | line_oiii_4363_err | line_oiii_4959 | line_oiii_4959_err | line_oiii_5007 | line_oiii_5007_err | line_oiii_err | line_pa10 | line_pa10_err | line_pa8 | line_pa8_err | line_pa9 | line_pa9_err | line_paa | line_paa_err | line_pab | line_pab_err | line_pad | line_pad_err | line_pag | line_pag_err | line_pfb | line_pfb_err | line_pfd | line_pfd_err | line_pfe | line_pfe_err | line_pfg | line_pfg_err | line_sii | line_sii_err | line_siii_9068 | line_siii_9068_err | line_siii_9531 | line_siii_9531_err | spl_0 | spl_0_err | spl_1 | spl_10 | spl_10_err | spl_11 | spl_11_err | spl_12 | spl_12_err | spl_13 | spl_13_err | spl_14 | spl_14_err | spl_15 | spl_15_err | spl_16 | spl_16_err | spl_17 | spl_17_err | spl_18 | spl_18_err | spl_19 | spl_19_err | spl_1_err | spl_2 | spl_20 | spl_20_err | spl_21 | spl_21_err | spl_22 | spl_22_err | spl_2_err | spl_3 | spl_3_err | spl_4 | spl_4_err | spl_5 | spl_5_err | spl_6 | spl_6_err | spl_7 | spl_7_err | spl_8 | spl_8_err | spl_9 | spl_9_err | zline | line_civ_1549 | line_civ_1549_err | line_h10 | line_h10_err | line_h11 | line_h11_err | line_h12 | line_h12_err | line_h7 | line_h7_err | line_h8 | line_h8_err | line_h9 | line_h9_err | line_ha | line_ha_err | line_hei_6680 | line_hei_6680_err | line_heii_1640 | line_heii_1640_err | line_nii_6549 | line_nii_6549_err | line_nii_6584 | line_nii_6584_err | line_oii_7323 | line_oii_7323_err | line_oii_7332 | line_oii_7332_err | line_sii_6717 | line_sii_6717_err | line_sii_6731 | line_sii_6731_err | line_siii_6314 | line_siii_6314_err | escale0 | escale1 | line_ciii_1906 | line_ciii_1906_err | line_niv_1487 | line_niv_1487_err | line_pah_3p29 | line_pah_3p29_err | line_pah_3p40 | line_pah_3p40_err | eqw_ariii_7138 | eqw_ariii_7753 | eqw_bra | eqw_brb | eqw_brd | eqw_brg | eqw_ciii_1906 | eqw_civ_1549 | eqw_ha_nii | eqw_hb | eqw_hd | eqw_hei_1083 | eqw_hei_3889 | eqw_hei_5877 | eqw_hei_7065 | eqw_hei_8446 | eqw_heii_1640 | eqw_heii_4687 | eqw_hg | eqw_lya | eqw_mgii | eqw_neiii_3867 | eqw_neiii_3968 | eqw_nev_3346 | eqw_nevi_3426 | eqw_niii_1750 | eqw_niv_1487 | eqw_oi_6302 | eqw_oii | eqw_oii_7325 | eqw_oiii | eqw_oiii_1663 | eqw_oiii_4363 | eqw_oiii_4959 | eqw_oiii_5007 | eqw_pa10 | eqw_pa8 | eqw_pa9 | eqw_paa | eqw_pab | eqw_pad | eqw_pag | eqw_pfb | eqw_pfd | eqw_pfe | eqw_pfg | eqw_sii | eqw_siii_9068 | eqw_siii_9531 | line_ha_nii | line_ha_nii_err | eqw_h10 | eqw_h11 | eqw_h12 | eqw_h7 | eqw_h8 | eqw_h9 | eqw_ha | eqw_hei_6680 | eqw_nii_6549 | eqw_nii_6584 | eqw_oii_7323 | eqw_oii_7332 | eqw_sii_6717 | eqw_sii_6731 | eqw_siii_6314 | sn_line | ztime | line_ci_9850 | line_ci_9850_err | line_feii_11128 | line_feii_11128_err | line_pii_11886 | line_pii_11886_err | line_feii_12570 | line_feii_12570_err | eqw_ci_9850 | eqw_feii_11128 | eqw_pii_11886 | eqw_feii_12570 | line_feii_16440 | line_feii_16440_err | line_feii_16877 | line_feii_16877_err | line_brf | line_brf_err | line_feii_17418 | line_feii_17418_err | line_bre | line_bre_err | line_feii_18362 | line_feii_18362_err | eqw_feii_16440 | eqw_feii_16877 | eqw_brf | eqw_feii_17418 | eqw_bre | eqw_feii_18362 | valid | objid | z_best | ztype | z_prism | z_grating | phot_correction | phot_flux_radius | phot_dr | file_phot | id_phot | phot_mag_auto | phot_f090w_tot_1 | phot_f090w_etot_1 | phot_f115w_tot_1 | phot_f115w_etot_1 | phot_f150w_tot_1 | phot_f150w_etot_1 | phot_f200w_tot_1 | phot_f200w_etot_1 | phot_f277w_tot_1 | phot_f277w_etot_1 | phot_f356w_tot_1 | phot_f356w_etot_1 | phot_f410m_tot_1 | phot_f410m_etot_1 | phot_f444w_tot_1 | phot_f444w_etot_1 | phot_Av | phot_mass | phot_restU | phot_restV | phot_restJ | z_phot | phot_LHa | phot_LOIII | phot_LOII | grade | zgrade | reviewer | comment | zrf | escale | obs_239_valid | obs_239_frac | obs_239_flux | obs_239_err | obs_239_full_err | obs_205_valid | obs_205_frac | obs_205_flux | obs_205_err | obs_205_full_err | obs_362_valid | obs_362_frac | obs_362_flux | obs_362_err | obs_362_full_err | obs_363_valid | obs_363_frac | obs_363_flux | obs_363_err | obs_363_full_err | obs_364_valid | obs_364_frac | obs_364_flux | obs_364_err | obs_364_full_err | obs_365_valid | obs_365_frac | obs_365_flux | obs_365_err | obs_365_full_err | obs_366_valid | obs_366_frac | obs_366_flux | obs_366_err | obs_366_full_err | obs_370_valid | obs_370_frac | obs_370_flux | obs_370_err | obs_370_full_err | obs_371_valid | obs_371_frac | obs_371_flux | obs_371_err | obs_371_full_err | obs_375_valid | obs_375_frac | obs_375_flux | obs_375_err | obs_375_full_err | obs_376_valid | obs_376_frac | obs_376_flux | obs_376_err | obs_376_full_err | obs_377_valid | obs_377_frac | obs_377_flux | obs_377_err | obs_377_full_err | obs_379_valid | obs_379_frac | obs_379_flux | obs_379_err | obs_379_full_err | obs_380_valid | obs_380_frac | obs_380_flux | obs_380_err | obs_380_full_err | obs_381_valid | obs_381_frac | obs_381_flux | obs_381_err | obs_381_full_err | obs_382_valid | obs_382_frac | obs_382_flux | obs_382_err | obs_382_full_err | obs_383_valid | obs_383_frac | obs_383_flux | obs_383_err | obs_383_full_err | obs_384_valid | obs_384_frac | obs_384_flux | obs_384_err | obs_384_full_err | obs_385_valid | obs_385_frac | obs_385_flux | obs_385_err | obs_385_full_err | obs_386_valid | obs_386_frac | obs_386_flux | obs_386_err | obs_386_full_err | rest_120_valid | rest_120_frac | rest_120_flux | rest_120_err | rest_120_full_err | rest_121_valid | rest_121_frac | rest_121_flux | rest_121_err | rest_121_full_err | rest_218_valid | rest_218_frac | rest_218_flux | rest_218_err | rest_218_full_err | rest_219_valid | rest_219_frac | rest_219_flux | rest_219_err | rest_219_full_err | rest_270_valid | rest_270_frac | rest_270_flux | rest_270_err | rest_270_full_err | rest_271_valid | rest_271_frac | rest_271_flux | rest_271_err | rest_271_full_err | rest_272_valid | rest_272_frac | rest_272_flux | rest_272_err | rest_272_full_err | rest_274_valid | rest_274_frac | rest_274_flux | rest_274_err | rest_274_full_err | rest_153_valid | rest_153_frac | rest_153_flux | rest_153_err | rest_153_full_err | rest_154_valid | rest_154_frac | rest_154_flux | rest_154_err | rest_154_full_err | rest_155_valid | rest_155_frac | rest_155_flux | rest_155_err | rest_155_full_err | rest_156_valid | rest_156_frac | rest_156_flux | rest_156_err | rest_156_full_err | rest_157_valid | rest_157_frac | rest_157_flux | rest_157_err | rest_157_full_err | rest_158_valid | rest_158_frac | rest_158_flux | rest_158_err | rest_158_full_err | rest_159_valid | rest_159_frac | rest_159_flux | rest_159_err | rest_159_full_err | rest_160_valid | rest_160_frac | rest_160_flux | rest_160_err | rest_160_full_err | rest_161_valid | rest_161_frac | rest_161_flux | rest_161_err | rest_161_full_err | rest_162_valid | rest_162_frac | rest_162_flux | rest_162_err | rest_162_full_err | rest_163_valid | rest_163_frac | rest_163_flux | rest_163_err | rest_163_full_err | rest_414_valid | rest_414_frac | rest_414_flux | rest_414_err | rest_414_full_err | rest_415_valid | rest_415_frac | rest_415_flux | rest_415_err | rest_415_full_err | rest_416_valid | rest_416_frac | rest_416_flux | rest_416_err | rest_416_full_err | beta | beta_ref_flux | beta_npix | beta_wlo | beta_whi | beta_nmad | dla_npix | dla_value | dla_unc | beta_cov_00 | beta_cov_01 | beta_cov_10 | beta_cov_11 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| str57 | int64 | float64 | float64 | str5 | str6 | float64 | int64 | str72 | str25 | int64 | int64 | int64 | int64 | str24 | int64 | int64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | int64 | int64 | int64 | int64 | float64 | float64 | float64 | float64 | float64 | float64 | str30 | float64 | float64 | int64 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | str5 | int64 | float64 | str1 | float64 | float64 | float64 | float64 | float64 | str44 | int64 | 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 | int64 | float64 | str4 | str93 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 |
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| -- | -- | -- | 31.543615 | 8.97263 | 475.65164 | 12.492301 | 1478.7195 | 20.716312 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | 2.6916635 | 1.0292606 | 1.7623694 | 1.2084138 | 0.15936823 | 0.7734618 | 0.109258264 | 1.507685 | 0.09797959 | 0.94181395 | 0.09681845 | 1.1777208 | 0.101651385 | 0.77827024 | 0.1148971 | 1.0161806 | 0.15089206 | 0.77205783 | 0.29294607 | -0.36806196 | 1.5045673 | -4700.389 | 2830.5718 | 0.3863113 | 2.5191631 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.22010241 | 1.9552373 | 0.1284232 | 2.357576 | 0.10218456 | 2.2045765 | 0.080455534 | 1.839689 | 0.07207858 | 1.9590814 | 0.071644664 | 1.7162542 | 0.07768974 | 1.4521762 | 0.08238754 | 4.622439 | -- | -- | -23.880041 | 10.763638 | -4.216068 | 10.945385 | -10.380039 | 10.463703 | 34.730713 | 12.585057 | 63.86072 | 11.191996 | -11.493438 | 10.910542 | 1681.4718 | 22.707413 | -3.8843367 | 13.712671 | -- | -- | 92.769104 | 13.244619 | 303.3422 | 14.018277 | 32.40743 | 14.736954 | 16.250576 | 15.096581 | 101.71479 | 13.087306 | 102.27705 | 12.364596 | -1.5309846 | 11.467584 | -0.009823302 | -0.121039405 | -- | -- | -- | -- | -- | -- | -- | -- | 33.2399 | 6.5497913 | -- | -- | -- | -- | -- | -- | -- | 224.49889 | 18.628468 | -- | -- | 48.388504 | -15.68925 | -- | -- | 5.4066896 | 80.58066 | -- | -- | 43.52189 | 14.867565 | 2.3390548 | -4.0933743 | -- | -- | 30.003298 | 341.56543 | -- | -- | -- | 15.96317 | 259.9941 | 822.1586 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -10.549018 | -1.8727355 | -4.633839 | 15.341046 | 28.01381 | -5.052083 | 1117.2476 | -2.8325858 | 61.117584 | 204.48856 | 32.12791 | 16.236942 | 77.48471 | 79.08182 | -1.009964 | 74.0 | 1737501800.0 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | True | 172449 | 4.6228543 | G | 4.621582 | 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0.005398401794143544 | 2775 | 1.0000088989496112 | 0.32547592596442326 | 0.005407843553604075 | 0.004276370382439811 | 2775 | 1.0004391319019352 | 0.5330539495518211 | 0.004904124090599388 | 0.0038212765386505553 | 2775 | 0.9675655946156906 | 0.6715857097916574 | 0.010553795880270628 | 0.00794324809030287 | 2775 | 0.9587468093273036 | 0.5833624760823622 | 0.019415353864716576 | 0.014090356633500783 | 2775 | 0.010634676241646013 | 0.0 | -1.0 | -1.0 | 2775 | 0.0 | 0.0 | -1.0 | -1.0 | 2775 | 0.0 | 0.0 | -1.0 | -1.0 | 2775 | 0.0 | 0.0 | -1.0 | -1.0 | 2775 | 0.5946532185930513 | 0.14378560988350406 | 0.08065630650619657 | 0.06433176255255493 | 2775 | 0.9991700000926202 | 0.43613237675563177 | 0.007582746197806663 | 0.0058824222012783854 | 2775 | 0.9993484777878848 | 0.5972217145015718 | 0.022166001353858905 | 0.016079926523872485 | -- | -- | 0 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
| gto-wide-uds13-v4_g235h-f170lp_1215_1951.spec.fits | 1951 | 34.35065901 | -5.14988767 | G235H | F170LP | 802.389 | 4 | jw01215013001_04101_00001_nrs2_f170lp_g235h_raw.70.1215_1951.fits | jw01215013001_01_msa.fits | 205 | 1 | 1 | 70 | gto-wide-uds13-v4 | 2243 | 1 | 1.66 | 2.5689142 | 2.0963542 | -0.4861431 | -0.7656741 | 1.2593989 | 0.49343532 | 0.8224826 | 1.5858558 | 1.5304534 | 2.459035 | 2.1690035 | 1 | 1367 | 2048 | 40 | 0.0 | 253.84996 | 0.0 | -0.001 | 0.4917679 | 1738073094.4070385 | 0.9.5.dev2+g80b81b4 | 1604.778 | 2913.6748 | 2243 | 2811.6072 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | 139.03279 | 84.556435 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | 153.81612 | 61.984585 | -- | -- | -- | -- | 4.0369225 | 78.60983 | 45.681705 | 80.60967 | -50.989677 | 87.71492 | 73.9701 | 77.34231 | -- | -- | -- | -- | 853.96075 | -- | -- | 94.13828 | -- | -- | -- | -30.710531 | 61.72949 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -1.0793935 | 2.6578977 | 16.855896 | 6.094411 | 1.0531783 | 5.0059447 | 1.0905654 | 3.1887467 | 1.2597305 | 4.4599857 | 2.4125624 | -0.024661088 | 12.698964 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.1934314 | -15.388187 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.417971 | 9.601779 | 2.1934223 | 8.963685 | 1.6506367 | 7.166141 | 1.3367475 | 8.372035 | 1.2125107 | 4.2663774 | 1.2317595 | 4.6491623 | 1.1557022 | 3.8828688 | 1.0895466 | 4.6232686 | -- | -- | 47.9303 | 61.00501 | -43.66452 | 73.04057 | -52.864967 | 69.1943 | 49.73135 | 74.62461 | -10.002572 | 82.228905 | -74.391235 | 64.71364 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | 0.008058175 | -0.11264437 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | 27.334742 | -- | -- | -- | -- | -- | -- | -- | 35.321674 | -- | -- | 0.9015683 | 10.652017 | -5.7512264 | 9.181498 | -- | -- | -- | 154.39067 | -- | -- | -- | -7.458391 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | 10.362592 | -9.0206 | -10.356026 | 11.604869 | -2.242879 | -16.51436 | -- | -- | -- | -- | -- | -- | -- | -- | -- | 9.1 | 1737732200.0 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | True | 172449 | 4.6228543 | G | 4.621582 | 4.6228543 | 1.85 | 5.58 | 0.07257426 | primer-uds-north-grizli-v7.2-fix_phot.fits | 39787 | 23.86 | 0.2917665994332862 | 0.0074583520942602035 | 0.375822323338598 | 0.007474258631804441 | 0.4462115844556865 | 0.006440427649332001 | 0.5739798944677847 | 0.0055131230600766225 | 0.8435675923509218 | 0.004741583591371624 | 1.0094882735221247 | 0.0047143753686044055 | 0.8893053372138517 | 0.007319798398806998 | 0.9324802470703117 | 0.006405565944800626 | 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| gto-wide-uds13-v4_g395h-f290lp_1215_1951.spec.fits | 1951 | 34.35065901 | -5.14988767 | G395H | F290LP | 875.333 | 4 | jw01215013001_07101_00001_nrs1_f290lp_g395h_raw.70.1215_1951.fits | jw01215013001_01_msa.fits | 207 | 1 | 1 | 70 | gto-wide-uds13-v4 | 2181 | 1 | 2.83 | 4.333257 | 3.0323372 | -0.78633463 | -15.713539 | 12.172805 | 0.023815606 | 0.3733426 | 18.183922 | 0.86755025 | 16.770237 | 29.427864 | 1613 | 1366 | 436 | 24 | 0.0 | 253.8499 | 0.0 | 0.0009999998 | 0.4917679 | 1738073098.9230382 | 0.9.5.dev2+g80b81b4 | 1750.666 | 2725.7173 | 2181 | 2691.6755 | 23.426165 | 412.583 | 19.938076 | 307.09857 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | 1451.3488 | 428.4478 | 99.84564 | 273.67517 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | 845.74225 | 630.42847 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -14.739792 | 16.296173 | 12.941133 | 1.0238339 | 3.397932 | -3.8911495 | 3.7097752 | 7.1149216 | 3.9599311 | -7.7766714 | 6.362927 | 37.82434 | 21.133274 | -1564.3295 | 707.8916 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 20.102354 | 9.97284 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 16.226713 | -5.647535 | 6.8985815 | 19.841732 | 5.6790743 | -17.208584 | 4.4015408 | 12.006447 | 3.8504024 | -5.2901826 | 3.5769703 | 10.483284 | 3.5806997 | -0.044838645 | 3.3536315 | 4.5170674 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -6.600751 | 413.02917 | 35.67981 | 398.38538 | -- | -- | 261.49884 | 355.15863 | 129.73485 | 455.60483 | -77.98808 | 304.73105 | 1188.4515 | 320.5895 | -734.2784 | 440.50098 | -461.14597 | 403.66617 | -347.5903 | 561.14636 | -0.05869624 | -0.35027233 | -- | -- | -- | -- | -- | -- | -- | -- | -53.510654 | 7.888203 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | 486.24625 | 435.344 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | 213.96024 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -1.9749123 | 5.7209954 | 94.409485 | 31.447783 | 70.47738 | -1292.1211 | -123.25613 | -80.68548 | -96.06516 | 3.7 | 1737732200.0 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | False | 172449 | 4.6228543 | G | 4.621582 | 4.6228543 | 1.85 | 5.58 | 0.07257426 | primer-uds-north-grizli-v7.2-fix_phot.fits | 39787 | 23.86 | 0.2917665994332862 | 0.0074583520942602035 | 0.375822323338598 | 0.007474258631804441 | 0.4462115844556865 | 0.006440427649332001 | 0.5739798944677847 | 0.0055131230600766225 | 0.8435675923509218 | 0.004741583591371624 | 1.0094882735221247 | 0.0047143753686044055 | 0.8893053372138517 | 0.007319798398806998 | 0.9324802470703117 | 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0.8378392845863539 | 0.5215868519557816 | 2181 | 0.9999177797792165 | 1.5695825550910243 | 0.7339878408469604 | 0.4392704336981855 | 2181 | 0.9899288507048044 | 0.3378114355915089 | 0.8727410400891275 | 0.4888449496559451 | 2181 | 0.7262410592787684 | 0.7287832570032731 | 1.6251451026126238 | 0.8896363318577546 | 2181 | 0.0002046558169208894 | 0.0 | -1.0 | -1.0 | 2181 | 8.095252757928491e-05 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.025478101225164525 | 0.0 | -1.0 | -1.0 | 2181 | 0.8291742799690571 | 0.46436990342848333 | 0.585386872116059 | 0.38255718808494726 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.15580905088338265 | -0.12238282400133234 | 1.1890295304975842 | 0.795285131134417 | 2181 | 1.000795535675803 | 0.9472319776531377 | 0.5981600454935136 | 0.37015996906099646 | 2181 | 0.788454047968125 | 0.4860997689158019 | 0.7950771131455208 | 0.4469232974920383 | 2181 | 3.0335519041963066e-05 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.0 | 0.0 | -1.0 | -1.0 | 2181 | 0.8620101263997639 | 0.32700304883301073 | 0.8894654801456295 | 0.4967532214376828 | -- | -- | 0 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
| gto-wide-uds13-v4_prism-clear_1215_1951.spec.fits | 1951 | 34.35065901 | -5.14988767 | PRISM | CLEAR | 802.389 | 3 | jw01215013001_03101_00002_nrs2_clear_prism_raw.70.1215_1951.fits | jw01215013001_01_msa.fits | 1 | 1 | 1 | 70 | gto-wide-uds13-v4 | 468 | 1 | 0.54912597 | 5.5018334 | 3.6847322 | 2.3243139 | 0.25463933 | 0.02617235 | 9.302495 | 0.52588886 | 0.056728948 | 15.833174 | 0.81377536 | 0.24621214 | 1 | 1359 | 506 | 28 | 0.0 | 253.85 | 0.0 | 0.001 | 0.4917679 | 1738073090.4590383 | 0.9.5.dev2+g80b81b4 | 2407.167 | 5617.831 | 468 | 613.4004 | 48.812813 | 22.620071 | -2.438987 | 20.292538 | -- | -- | -- | -- | -- | -- | -- | -- | 560.37775 | 46.52388 | 71.51085 | 62.164665 | -- | -- | -209.0797 | 133.7496 | 80.514854 | 28.133654 | 40.78571 | 23.218676 | 60.545902 | 23.915543 | 96.50616 | 43.993908 | 291.39352 | 75.85304 | -716.07837 | 280.87146 | 57.39623 | 118.88307 | 514.75586 | 138.25935 | 124.91836 | 69.47591 | 16.877195 | 89.80219 | -47.70294 | 94.665474 | 47.62188 | 198.28189 | 99.01638 | 32.63919 | 733.29956 | 52.67196 | 21.678513 | 83.931694 | -- | 202.1944 | 241.93048 | -169.24973 | 72.94585 | 932.3925 | 51.213474 | 2339.1604 | 70.73323 | -- | -12.027637 | 30.294546 | 260.65622 | 45.92269 | -66.22304 | 32.629578 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | 241.58337 | 27.795877 | 126.88221 | 33.298237 | 228.29633 | 42.907158 | 40.989212 | 24.233473 | 13.529544 | 1.5309826 | 0.066841386 | 1.214747 | 0.065447986 | 1.278438 | 0.06856148 | 1.1027563 | 0.070816785 | 1.0249417 | 0.063015215 | 0.89060324 | 0.06667906 | 0.86389846 | 0.07108439 | 0.651814 | 0.08272743 | 0.89211535 | 0.098206654 | 0.799955 | 0.1357211 | 7.2655287 | 22.44103 | 0.5588701 | 0.20240332 | 0.870177 | 0.2691064 | 0.25699463 | 0.49232814 | 2.700355 | 14.098136 | 0.95429015 | 10.180353 | 0.3941978 | 5.217168 | 0.22023936 | 3.3910584 | 0.15795249 | 2.8573809 | 0.12847427 | 2.1295457 | 0.094936535 | 1.6646644 | 0.06834415 | 4.621582 | -649.9793 | 196.06087 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -323.86383 | 244.54553 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -0.020892859 | -0.019805139 | 164.59029 | 193.0386 | 363.44467 | 219.52042 | -- | -- | -- | -- | 43.720543 | -2.5788078 | -- | -- | -- | -- | 17.805237 | -53.11091 | 2274.049 | 278.24475 | 25.7951 | -- | -70.51571 | 55.215572 | 35.792576 | 80.42899 | -28.525156 | 44.31549 | 115.00771 | -52.42033 | 12.157452 | 172.58456 | 43.14509 | 4.735929 | -13.835131 | 4.564733 | 28.052313 | 77.97484 | 235.53954 | 49.396652 | -- | 18.168468 | -67.426506 | 482.79144 | 1235.6241 | -14.254797 | 377.87656 | -86.09008 | -- | -- | -- | -- | -- | -- | -- | -- | 195.16728 | 151.96198 | 334.70682 | 2837.5889 | 58.07976 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | 48.9 | 1737732200.0 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | True | 172449 | 4.6228543 | G | 4.621582 | 4.6228543 | 1.85 | 5.58 | 0.07257426 | primer-uds-north-grizli-v7.2-fix_phot.fits | 39787 | 23.86 | 0.2917665994332862 | 0.0074583520942602035 | 0.375822323338598 | 0.007474258631804441 | 0.4462115844556865 | 0.006440427649332001 | 0.5739798944677847 | 0.0055131230600766225 | 0.8435675923509218 | 0.004741583591371624 | 1.0094882735221247 | 0.0047143753686044055 | 0.8893053372138517 | 0.007319798398806998 | 0.9324802470703117 | 0.006405565944800626 | 1.7697781011450084 | 32809883821.644165 | 0.50983775 | 0.8515203 | 1.7189896 | 4.3906884 | 1693531123.1206756 | 2439001724.140659 | 491718980.0611007 | 3 | 4.62158 | Auto | Redshift matches gto-wide-uds13-v3_g395h-f290lp_1215_1951 z=4.6200 | 4.621581822366623 | 0.9710891829132017 | 468 | 0.9993118773223624 | 0.29734528736854304 | 0.005072510088040735 | 0.005767177676298873 | 468 | 1.0049920554790184 | 0.39358077725769736 | 0.006754919246597596 | 0.007532034658250185 | 468 | 0.9994121589090459 | 0.2105169070779525 | 0.005740663780735214 | 0.006500276615422723 | 468 | 1.0048331387204024 | 0.3142774578677898 | 0.0056972625518462 | 0.006475589632160145 | 468 | 1.0057802954242325 | 0.3633855175643899 | 0.005552140260213317 | 0.0063232330890943475 | 468 | 0.9969378740446078 | 0.390086461267687 | 0.006031948428263683 | 0.006740462634462128 | 468 | 0.9995545389631847 | 0.47428699929668094 | 0.006087252864125372 | 0.006644708782519558 | 468 | 0.9949583358463919 | 0.4221695478639817 | 0.008193361034636096 | 0.008954026097921097 | 468 | 0.9966398915013757 | 0.5330687735195239 | 0.00920935617242841 | 0.010074424705055462 | 468 | 1.000135743102705 | 0.6585727186306961 | 0.00540349388040512 | 0.005876314991454699 | 468 | 1.0001377456066418 | 0.7394149423240386 | 0.006323697221645326 | 0.0065784364741576125 | 468 | 1.000013567062085 | 0.6166311528474346 | 0.008695988683221448 | 0.008120834396306063 | 468 | 0.9973999343526664 | 0.5123215054081025 | 0.009529315724051777 | 0.010162216289208869 | 468 | 0.9998409327402623 | 0.5626509249823465 | 0.007773287358672584 | 0.008041947249691669 | 468 | 0.9995329666665248 | 0.5396141362524208 | 0.008187183943740751 | 0.008220709080628089 | 468 | 0.9999658710844571 | 0.9343532785573141 | 0.009678591326381328 | 0.010472675567219238 | 468 | 1.0000143039433227 | 0.6198705997379221 | 0.010520714370935278 | 0.010078224493847163 | 468 | 0.9997544208857447 | 0.6046318093271135 | 0.016583968030603233 | 0.015668615213923796 | 468 | 1.0004403138230038 | 0.6104681852342965 | 0.020620481299690376 | 0.01906515776929085 | 468 | 0.9997586216471691 | 0.6181443627182708 | 0.02107673853011141 | 0.019261352578809964 | 468 | 1.0005685988794546 | 0.3069343338892747 | 0.005583251896278126 | 0.006349944821663527 | 468 | 1.0003104619770644 | 0.3761750772827621 | 0.004312906982944125 | 0.004881883975885983 | 468 | 0.9904131708376346 | 0.31802241951276733 | 0.006028062674911858 | 0.006853123554938171 | 468 | 1.0107784320042819 | 0.389690293439562 | 0.008403419750412264 | 0.009342099415807005 | 468 | 1.0065232768365464 | 0.29651259700246846 | 0.006942986285491834 | 0.007903390037513149 | 468 | 0.9847564979575222 | 0.32320605184509554 | 0.008470150253087333 | 0.009614490334158416 | 468 | 1.0157188080544226 | 0.35958587157221905 | 0.00870528122208533 | 0.009880207569668304 | 468 | 1.0088670429538373 | 0.3976870900143664 | 0.010650006076162495 | 0.011869349833618707 | 468 | 0.9996170106520132 | 0.490647426381474 | 0.006174141010544129 | 0.006749864291928956 | 468 | 1.000184752460022 | 0.5686809441913387 | 0.005280487501663225 | 0.005684499640823398 | 468 | 1.0001471164580011 | 0.617824119776292 | 0.005899318910584339 | 0.00616102326630729 | 468 | 1.0001279166565336 | 0.4770255075760639 | 0.0066422708680174735 | 0.00725540569775386 | 468 | 0.9999969865492299 | 0.6548236950365218 | 0.005124902270081213 | 0.005617270097245955 | 468 | 0.9999998515125955 | 0.7575954360860535 | 0.006838287497065963 | 0.007237138306888876 | 468 | 0.9997355434741737 | 0.6194714287917443 | 0.009074522727257303 | 0.008616895171326298 | 468 | 0.9214708571648356 | 0.668345653523944 | 0.02038897155485026 | 0.01830167340888231 | 468 | 0.0 | 0.0 | -1.0 | -1.0 | 468 | 0.0 | 0.0 | -1.0 | -1.0 | 468 | 0.0 | 0.0 | -1.0 | -1.0 | 468 | 1.027102687446468 | 0.3910516613690073 | 0.007945509661014401 | 0.008788183019400268 | 468 | 0.9984309746369571 | 0.5230435541267343 | 0.00887556980792251 | 0.009462919431837948 | 468 | 1.0043779357206608 | 0.6176813660935505 | 0.010698828562773089 | 0.010147776185419594 | -1.4565206479365085 | 0.3061252990650476 | 44 | 0.140362119861813 | 0.2567145919520727 | 1.195405434175183 | 15.0 | 18.563167366575303 | 5.144609137022997 | 0.0021474377023419544 | -0.0001561310838130919 | -0.00015613108381309188 | 1.803142058546427e-05 |
# see the description in the https://dawn-cph.github.io/dja/blog/2025/05/01/nirspec-merged-table-v4/
#
RGB_URL = "https://grizli-cutout.herokuapp.com/thumb?size=1.5&scl=2.0&asinh=True&filters=f115w-clear%2Cf277w-clear%2Cf444w-clear&rgb_scl=1.5%2C0.74%2C1.3&pl=2&coord={ra}%2C{dec}"
msa['metafile'] = [m.split('_')[0] for m in msa['msamet']]
SLIT_URL = "https://grizli-cutout.herokuapp.com/thumb?size=1.5&scl=4.0&invert=True&filters=f444w-clear&rgb_scl=1.5%2C0.74%2C1.3&pl=2&coord={ra}%2C{dec}&nirspec=True&dpi_scale=6&nrs_lw=0.5&nrs_alpha=0.8&metafile={metafile}"
FITS_URL = "https://s3.amazonaws.com/msaexp-nirspec/extractions/{root}/{file}"
msa['Thumb'] = [
"<img src=\"{0}\" height=200px>".format(
RGB_URL.format(**row['ra','dec'])
)
for row in msa
]
msa['Slit_Thumb'] = [
"<img src=\"{0}\" height=200px>".format(
SLIT_URL.format(**row['ra','dec','metafile'])
)
for row in msa
]
msa['Spectrum_fnu'] = [
"<img src=\"{0}\" height=200px>".format(
FITS_URL.format(**row['root','file']).replace('.spec.fits', '.fnu.png')
)
for row in msa
]
msa['Spectrum_flam'] = [
"<img src=\"{0}\" height=200px>".format(
FITS_URL.format(**row['root','file']).replace('.spec.fits', '.flam.png')
)
for row in msa
]
df = msa['root','file','z_best','phot_mass','eqw_ha_nii','Thumb','Slit_Thumb','Spectrum_fnu', 'Spectrum_flam'].to_pandas()
display(Markdown(df.to_markdown()))
| root | file | z_best | phot_mass | eqw_ha_nii | Thumb | Slit_Thumb | Spectrum_fnu | Spectrum_flam | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | excels-uds01-v4 | excels-uds01-v4_g235m-f170lp_3543_109269.spec.fits | 4.62285 | 3.28099e+10 | nan |  |
 |
||
| 1 | gto-wide-uds13-v4 | gto-wide-uds13-v4_g235h-f170lp_1215_1951.spec.fits | 4.62285 | 3.28099e+10 | nan |  |
 |
||
| 2 | gto-wide-uds13-v4 | gto-wide-uds13-v4_g395h-f290lp_1215_1951.spec.fits | 4.62285 | 3.28099e+10 | nan |  |
 |
||
| 3 | gto-wide-uds13-v4 | gto-wide-uds13-v4_prism-clear_1215_1951.spec.fits | 4.62285 | 3.28099e+10 | 2274.05 |  |
 |
# Reading the spectrum
for i in range(len(df)):
spec_file = df['file'][i]
row = msa[msa['file'] == spec_file][0]
spec = msaexp.spectrum.SpectrumSampler(FITS_URL.format(**row))
##
con_mask = spec['full_err']==0
con_mask |= spec['full_err']> spec['flux']
galaxy_masked = np.ma.masked_where(con_mask, spec['flux'])
plt.plot(spec['wave'], galaxy_masked,
label="{file}\nz={z_best:.3f}".format(**row), alpha=0.5)
plt.xlim([0.8,6])
plt.ylim(top=galaxy_masked.max()*2)
plt.semilogx()
plt.legend(fontsize=7, ncol=1)
<matplotlib.legend.Legend at 0x3117895e0>
