CDFS SWIRE master catalogue

Checks and diagnostics

from herschelhelp_internal import git_version
print("This notebook was run with herschelhelp_internal version: \n{}".format(git_version()))

This notebook was run with herschelhelp_internal version: 
33f5ec7 (Wed Dec 6 16:56:17 2017 +0000)

%matplotlib inline
#%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
plt.style.use('ggplot')

import locale
locale.setlocale(locale.LC_ALL, 'en_GB')

import os
import time
import itertools

from astropy.coordinates import SkyCoord
from astropy.table import Table
from astropy import units as u
from astropy import visualization as vis
import numpy as np
from matplotlib_venn import venn3

from herschelhelp_internal.masterlist import (nb_compare_mags, nb_ccplots, nb_histograms, find_last_ml_suffix,
                                              quick_checks)

OUT_DIR = os.environ.get('OUT_DIR', "./data")
SUFFIX = find_last_ml_suffix()
#SUFFIX = "_20170718"

master_catalogue = Table.read("{}/master_catalogue_cdfs-swire_{}.fits".format(OUT_DIR, SUFFIX))
print('Using masterlist ' + "{}/master_catalogue_cdfs-swire_{}.fits".format(OUT_DIR, SUFFIX))

Using masterlist ./data/master_catalogue_cdfs-swire_20180122.fits

0 - Quick checks

quick_checks(master_catalogue).show_in_notebook()

/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(328, False) will return an array of dtype('bool')
  format(shape, fill_value, array(fill_value).dtype), FutureWarning)
/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(328, 0) will return an array of dtype('int64')
  format(shape, fill_value, array(fill_value).dtype), FutureWarning)

Table shows only problematic columns.

<Table length=92>

idx	Column	All nan	#Measurements	#Zeros	#Negative	Minimum value
0	merr_ap_gpc1_y	False	177299	1	0	0.0
1	merr_ap_wfi_416nm	False	63486	0	4974	-104195.382812
2	f_wfi_416nm	True	0	0	0	0.0
3	ferr_wfi_416nm	True	0	0	0	0.0
4	m_wfi_416nm	True	0	0	0	0.0
5	merr_wfi_416nm	True	0	0	0	0.0
6	merr_ap_wfi_461nm	False	63483	0	2324	-3028.66308594
7	f_wfi_461nm	True	0	0	0	0.0
8	ferr_wfi_461nm	True	0	0	0	0.0
9	m_wfi_461nm	True	0	0	0	0.0
10	merr_wfi_461nm	True	0	0	0	0.0
11	merr_ap_wfi_485nm	False	63500	0	5509	-42080.0273438
12	f_wfi_485nm	True	0	0	0	0.0
13	ferr_wfi_485nm	True	0	0	0	0.0
14	m_wfi_485nm	True	0	0	0	0.0
15	merr_wfi_485nm	True	0	0	0	0.0
16	merr_ap_wfi_518nm	False	63501	0	3219	-2874.01708984
17	f_wfi_518nm	True	0	0	0	0.0
18	ferr_wfi_518nm	True	0	0	0	0.0
19	m_wfi_518nm	True	0	0	0	0.0
20	merr_wfi_518nm	True	0	0	0	0.0
21	merr_ap_wfi_571nm	False	62977	0	2103	-4300.08154297
22	f_wfi_571nm	True	0	0	0	0.0
23	ferr_wfi_571nm	True	0	0	0	0.0
24	m_wfi_571nm	True	0	0	0	0.0
25	merr_wfi_571nm	True	0	0	0	0.0
26	merr_ap_wfi_604nm	False	63500	0	2143	-2069.82080078
27	f_wfi_604nm	True	0	0	0	0.0
28	ferr_wfi_604nm	True	0	0	0	0.0
29	m_wfi_604nm	True	0	0	0	0.0
30	merr_wfi_604nm	True	0	0	0	0.0
31	merr_ap_wfi_646nm	False	63491	0	6128	-15116.0361328
32	f_wfi_646nm	True	0	0	0	0.0
33	ferr_wfi_646nm	True	0	0	0	0.0
34	m_wfi_646nm	True	0	0	0	0.0
35	merr_wfi_646nm	True	0	0	0	0.0
36	merr_ap_wfi_696nm	False	63501	0	1729	-1487.05578613
37	f_wfi_696nm	True	0	0	0	0.0
38	ferr_wfi_696nm	True	0	0	0	0.0
39	m_wfi_696nm	True	0	0	0	0.0
40	merr_wfi_696nm	True	0	0	0	0.0
41	merr_ap_wfi_753nm	False	63501	0	3255	-1593.04675293
42	f_wfi_753nm	True	0	0	0	0.0
43	ferr_wfi_753nm	True	0	0	0	0.0
44	m_wfi_753nm	True	0	0	0	0.0
45	merr_wfi_753nm	True	0	0	0	0.0
46	merr_ap_wfi_815nm	False	63255	0	1825	-3719.67797852
47	f_wfi_815nm	True	0	0	0	0.0
48	ferr_wfi_815nm	True	0	0	0	0.0
49	m_wfi_815nm	True	0	0	0	0.0
50	merr_wfi_815nm	True	0	0	0	0.0
51	merr_ap_wfi_856nm	False	63501	0	3318	-4676.421875
52	f_wfi_856nm	True	0	0	0	0.0
53	ferr_wfi_856nm	True	0	0	0	0.0
54	m_wfi_856nm	True	0	0	0	0.0
55	merr_wfi_856nm	True	0	0	0	0.0
56	merr_ap_wfi_914nm	False	63499	0	5206	-2929.66845703
57	f_wfi_914nm	True	0	0	0	0.0
58	ferr_wfi_914nm	True	0	0	0	0.0
59	m_wfi_914nm	True	0	0	0	0.0
60	merr_wfi_914nm	True	0	0	0	0.0
61	merr_ap_wfi_u	False	63149	0	1932	-2020.99658203
62	f_wfi_u	True	0	0	0	0.0
63	ferr_wfi_u	True	0	0	0	0.0
64	m_wfi_u	True	0	0	0	0.0
65	merr_wfi_u	True	0	0	0	0.0
66	merr_ap_wfi_b	False	63246	0	1383	-8119.59130859
67	f_wfi_b	True	0	0	0	0.0
68	ferr_wfi_b	True	0	0	0	0.0
69	m_wfi_b	True	0	0	0	0.0
70	merr_wfi_b	True	0	0	0	0.0
71	merr_ap_wfi_v	False	63500	0	1581	-2031.71240234
72	f_wfi_v	True	0	0	0	0.0
73	ferr_wfi_v	True	0	0	0	0.0
74	m_wfi_v	True	0	0	0	0.0
75	merr_wfi_v	True	0	0	0	0.0
76	merr_ap_wfi_r	False	63184	0	412	-143.632049561
77	merr_ap_wfi_i	False	63494	0	4736	-17235.6953125
78	f_wfi_i	True	0	0	0	0.0
79	ferr_wfi_i	True	0	0	0	0.0
80	m_wfi_i	True	0	0	0	0.0
81	merr_wfi_i	True	0	0	0	0.0
82	f_ap_vista_z	False	383235	7	0	0.0
83	f_vista_z	False	387023	7	0	0.0
84	f_ap_irac_i1	False	807017	0	1740	-18.1546001822
85	merr_ap_irac_i1	False	807017	0	1740	-2909.12932527
86	f_irac_i1	False	806985	0	3715	-105.264969317
87	merr_irac_i1	False	806985	0	3715	-14306.7009534
88	f_ap_irac_i2	False	766836	0	3025	-4.79809834087
89	merr_ap_irac_i2	False	766836	0	3025	-15785.0070522
90	f_irac_i2	False	766827	0	3720	-77.343976166
91	merr_irac_i2	False	766827	0	3720	-4194.91062114

I - Summary of wavelength domains

flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']

venn3(
    [
        np.sum(flag_obs == 4),
        np.sum(flag_obs == 2),
        np.sum(flag_obs == 6),
        np.sum(flag_obs == 1),
        np.sum(flag_obs == 5),
        np.sum(flag_obs == 3),
        np.sum(flag_obs == 7)
    ],
    set_labels=('Optical', 'near-IR', 'mid-IR'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/len(flag_obs)))
)
plt.title("Wavelength domain observations");

venn3(
    [
        np.sum(flag_det[flag_obs == 7] == 4),
        np.sum(flag_det[flag_obs == 7] == 2),
        np.sum(flag_det[flag_obs == 7] == 6),
        np.sum(flag_det[flag_obs == 7] == 1),
        np.sum(flag_det[flag_obs == 7] == 5),
        np.sum(flag_det[flag_obs == 7] == 3),
        np.sum(flag_det[flag_obs == 7] == 7)
    ],
    set_labels=('Optical', 'near-IR', 'mid-IR'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/np.sum(flag_det != 0)))
)
plt.title("Detection of the {} sources detected\n in any wavelength domains "
          "(among {} sources)".format(
              locale.format('%d', np.sum(flag_det != 0), grouping=True),
              locale.format('%d', len(flag_det), grouping=True)));

II - Comparing magnitudes in similar filters

The master list if composed of several catalogues containing magnitudes in similar filters on different instruments. We are comparing the magnitudes in these corresponding filters.

u_bands = ["OmegaCAM u", "WFI u"]
g_bands = ["OmegaCAM g",          "GPC1 g", "DECam g"]
r_bands = ["OmegaCAM r", "WFI r", "GPC1 r", "DECam r"]
i_bands = ["OmegaCAM i", "WFI i", "GPC1 i", "DECam i"]
z_bands = ["OmegaCAM z",          "GPC1 z", "DECam z", "VISTA z"]
y_bands = [                       "GPC1 y", "DECam y", "VISTA y"]

II.a - Comparing depths

We compare the histograms of the total aperture magnitudes of similar bands.

for bands in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    colnames = ["m_{}".format(band.replace(" ", "_").lower()) for band in bands]
    nb_histograms(master_catalogue, colnames, bands)

HELP warning: the column m_wfi_u (WFI u) is empty.

HELP warning: the column m_wfi_i (WFI i) is empty.

II.b - Comparing magnitudes

We compare one to one each magnitude in similar bands.

for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    for band1, band2 in itertools.combinations(band_of_a_kind, 2):
        
        basecol1, basecol2 = band1.replace(" ", "_").lower(), band2.replace(" ", "_").lower()
        
        col1, col2 = "m_ap_{}".format(basecol1), "m_ap_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (aperture)".format(band1), "{} (aperture)".format(band2)))
        
        col1, col2 = "m_{}".format(basecol1), "m_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (total)".format(band1), "{} (total)".format(band2)))

WFI u (aperture) - OmegaCAM u (aperture):
- Median: 0.30
- Median Absolute Deviation: 0.07
- 1% percentile: 0.026031494140625
- 99% percentile: 5.898116226196289

No sources have both OmegaCAM u (total) and WFI u (total) values.
GPC1 g (aperture) - OmegaCAM g (aperture):
- Median: -0.30
- Median Absolute Deviation: 0.27
- 1% percentile: -1.477933654785156
- 99% percentile: 1.4628313636779788

GPC1 g (total) - OmegaCAM g (total):
- Median: -0.07
- Median Absolute Deviation: 0.16
- 1% percentile: -1.296688632965088
- 99% percentile: 0.7950097846984904

DECam g (aperture) - OmegaCAM g (aperture):
- Median: -0.86
- Median Absolute Deviation: 0.07
- 1% percentile: -1.1990659713745118
- 99% percentile: 4.204625453948987

DECam g (total) - OmegaCAM g (total):
- Median: -0.10
- Median Absolute Deviation: 0.13
- 1% percentile: -1.087312183380127
- 99% percentile: 2.6726785278320295

DECam g (aperture) - GPC1 g (aperture):
- Median: -0.50
- Median Absolute Deviation: 0.34
- 1% percentile: -2.962065200805664
- 99% percentile: 2.009983825683591

DECam g (total) - GPC1 g (total):
- Median: -0.00
- Median Absolute Deviation: 0.13
- 1% percentile: -0.8025470733642578
- 99% percentile: 2.3840057277679465

WFI r (aperture) - OmegaCAM r (aperture):
- Median: 0.10
- Median Absolute Deviation: 0.13
- 1% percentile: -0.3106356048583985
- 99% percentile: 0.7375316619873049

WFI r (total) - OmegaCAM r (total):
- Median: -0.25
- Median Absolute Deviation: 0.15
- 1% percentile: -1.2209947586059569
- 99% percentile: 0.9199421310424809

GPC1 r (aperture) - OmegaCAM r (aperture):
- Median: -0.16
- Median Absolute Deviation: 0.22
- 1% percentile: -1.072125778198242
- 99% percentile: 1.2293571472167972

GPC1 r (total) - OmegaCAM r (total):
- Median: 0.08
- Median Absolute Deviation: 0.14
- 1% percentile: -0.922282371520996
- 99% percentile: 1.0283002471923828

DECam r (aperture) - OmegaCAM r (aperture):
- Median: -0.79
- Median Absolute Deviation: 0.07
- 1% percentile: -1.1389399719238282
- 99% percentile: 4.62564792633057

DECam r (total) - OmegaCAM r (total):
- Median: -0.10
- Median Absolute Deviation: 0.14
- 1% percentile: -1.0039896392822265
- 99% percentile: 2.9235151672363267

GPC1 r (aperture) - WFI r (aperture):
- Median: -0.25
- Median Absolute Deviation: 0.34
- 1% percentile: -1.379720687866211
- 99% percentile: 1.7204780578613306

GPC1 r (total) - WFI r (total):
- Median: 0.34
- Median Absolute Deviation: 0.14
- 1% percentile: -0.486072883605957
- 99% percentile: 1.257844467163085

DECam r (aperture) - WFI r (aperture):
- Median: -0.84
- Median Absolute Deviation: 0.10
- 1% percentile: -1.2890096092224121
- 99% percentile: -0.22158359527587562

DECam r (total) - WFI r (total):
- Median: 0.20
- Median Absolute Deviation: 0.13
- 1% percentile: -0.5266014671325684
- 99% percentile: 1.3607005310058575

DECam r (aperture) - GPC1 r (aperture):
- Median: -0.61
- Median Absolute Deviation: 0.26
- 1% percentile: -2.3846909523010256
- 99% percentile: 4.328889093399047

DECam r (total) - GPC1 r (total):
- Median: -0.18
- Median Absolute Deviation: 0.12
- 1% percentile: -0.9400785064697266
- 99% percentile: 2.704162712097168

WFI i (aperture) - OmegaCAM i (aperture):
- Median: -0.00
- Median Absolute Deviation: 0.16
- 1% percentile: -0.7342316436767578
- 99% percentile: 0.5792187499999985

No sources have both OmegaCAM i (total) and WFI i (total) values.
GPC1 i (aperture) - OmegaCAM i (aperture):
- Median: -0.17
- Median Absolute Deviation: 0.19
- 1% percentile: -0.9848984146118165
- 99% percentile: 0.6653031349182128

GPC1 i (total) - OmegaCAM i (total):
- Median: 0.05
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8688420867919922
- 99% percentile: 0.8083547973632813

DECam i (aperture) - OmegaCAM i (aperture):
- Median: -0.70
- Median Absolute Deviation: 0.09
- 1% percentile: -1.0822570419311524
- 99% percentile: 5.428895111083982

DECam i (total) - OmegaCAM i (total):
- Median: -0.13
- Median Absolute Deviation: 0.14
- 1% percentile: -1.020422763824463
- 99% percentile: 3.4286825561523466

GPC1 i (aperture) - WFI i (aperture):
- Median: -0.15
- Median Absolute Deviation: 0.32
- 1% percentile: -1.2702113151550294
- 99% percentile: 1.1307513236999518

No sources have both WFI i (total) and GPC1 i (total) values.
DECam i (aperture) - WFI i (aperture):
- Median: -0.64
- Median Absolute Deviation: 0.19
- 1% percentile: -1.623051071166992
- 99% percentile: 0.4383729934692382

No sources have both WFI i (total) and DECam i (total) values.
DECam i (aperture) - GPC1 i (aperture):
- Median: -0.51
- Median Absolute Deviation: 0.24
- 1% percentile: -1.5011099433898927
- 99% percentile: 5.05627010345459

DECam i (total) - GPC1 i (total):
- Median: -0.17
- Median Absolute Deviation: 0.10
- 1% percentile: -0.7834041595458985
- 99% percentile: 3.1839383888244632

GPC1 z (aperture) - OmegaCAM z (aperture):
- Median: 0.05
- Median Absolute Deviation: 0.14
- 1% percentile: -0.806519775390625
- 99% percentile: 0.6441340255737295

GPC1 z (total) - OmegaCAM z (total):
- Median: 0.17
- Median Absolute Deviation: 0.11
- 1% percentile: -0.6282771301269532
- 99% percentile: 0.7950209236145019

DECam z (aperture) - OmegaCAM z (aperture):
- Median: -0.54
- Median Absolute Deviation: 0.07
- 1% percentile: -0.8661718368530273
- 99% percentile: 5.566607856750493

DECam z (total) - OmegaCAM z (total):
- Median: 0.01
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8286170196533202
- 99% percentile: 3.3931300640106197

VISTA z (aperture) - OmegaCAM z (aperture):
- Median: 0.16
- Median Absolute Deviation: 0.09
- 1% percentile: -0.33622013092041014
- 99% percentile: 0.8982582283020007

VISTA z (total) - OmegaCAM z (total):
- Median: 0.05
- Median Absolute Deviation: 0.11
- 1% percentile: -0.8182351684570313
- 99% percentile: 0.7913664817810039

DECam z (aperture) - GPC1 z (aperture):
- Median: -0.47
- Median Absolute Deviation: 0.22
- 1% percentile: -1.5124702644348145
- 99% percentile: 4.874284849166879

DECam z (total) - GPC1 z (total):
- Median: -0.18
- Median Absolute Deviation: 0.10
- 1% percentile: -0.8591748046875
- 99% percentile: 2.946851081848142

VISTA z (aperture) - GPC1 z (aperture):
- Median: 0.12
- Median Absolute Deviation: 0.11
- 1% percentile: -0.8292744445800782
- 99% percentile: 0.7439142608642577

VISTA z (total) - GPC1 z (total):
- Median: -0.13
- Median Absolute Deviation: 0.10
- 1% percentile: -0.7600824356079102
- 99% percentile: 0.5232796287536614

VISTA z (aperture) - DECam z (aperture):
- Median: 0.63
- Median Absolute Deviation: 0.16
- 1% percentile: -1.0520355224609375
- 99% percentile: 1.5007448577880806

VISTA z (total) - DECam z (total):
- Median: 0.07
- Median Absolute Deviation: 0.19
- 1% percentile: -2.0058786392211916
- 99% percentile: 1.170486259460449

DECam y (aperture) - GPC1 y (aperture):
- Median: -0.50
- Median Absolute Deviation: 0.24
- 1% percentile: -2.5951641464233397
- 99% percentile: 0.7417213249206779

DECam y (total) - GPC1 y (total):
- Median: -0.12
- Median Absolute Deviation: 0.14
- 1% percentile: -1.0285805511474608
- 99% percentile: 0.9867723846435557

VISTA y (aperture) - GPC1 y (aperture):
- Median: 0.09
- Median Absolute Deviation: 0.16
- 1% percentile: -1.9527296447753906
- 99% percentile: 1.0630077362060548

VISTA y (total) - GPC1 y (total):
- Median: -0.19
- Median Absolute Deviation: 0.15
- 1% percentile: -1.0352452850341796
- 99% percentile: 0.7106719207763674

VISTA y (aperture) - DECam y (aperture):
- Median: 0.55
- Median Absolute Deviation: 0.34
- 1% percentile: -74.98888462066651
- 99% percentile: 2.082161674499512

VISTA y (total) - DECam y (total):
- Median: -0.04
- Median Absolute Deviation: 0.38
- 1% percentile: -3.277728614807129
- 99% percentile: 1.957741127014161

III - Comparing magnitudes to reference bands

Cross-match the master list to 2MASS to compare magnitudes.

master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])

III.b - Comparing J and K bands to 2MASS

The catalogue is cross-matched to 2MASS-PSC withing 0.2 arcsecond. We compare the UKIDSS total J and K magnitudes to those from 2MASS.

The 2MASS magnitudes are “Vega-like” and we have to convert them to AB magnitudes using the zero points provided on this page:

Band	Fν - 0 mag (Jy)
J	1594
H	1024
Ks	666.7

# The AB zero point is 3631 Jy
j_2mass_to_ab = 2.5 * np.log10(3631/1595)
k_2mass_to_ab = 2.5 * np.log10(3631/666.7)

twomass = Table.read("../../dmu0/dmu0_2MASS-point-sources/data/2MASS-PSC_CDFS-SWIRE.fits")
twomass_coords = SkyCoord(twomass['raj2000'], twomass['dej2000'])

idx, d2d, _ = twomass_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

twomass = twomass[mask]
ml_twomass_idx = idx[mask]

nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_vista_j'][ml_twomass_idx],
                labels=("2MASS J", "VISTA J (total)"))

VISTA J (total) - 2MASS J:
- Median: 0.03
- Median Absolute Deviation: 0.06
- 1% percentile: -1.2033101961423698
- 99% percentile: 1.0771015779970339

nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, master_catalogue['m_vista_ks'][ml_twomass_idx],
                labels=("2MASS Ks", "VISTA Ks (total)"))

VISTA Ks (total) - 2MASS Ks:
- Median: 0.03
- Median Absolute Deviation: 0.11
- 1% percentile: -1.1203042906816978
- 99% percentile: 1.2803789119092368

IV - Comparing aperture magnitudes to total ones.

nb_ccplots(
    master_catalogue['m_gpc1_r'],
    master_catalogue['m_ap_gpc1_r'] - master_catalogue['m_gpc1_r'],
    "r total magnitude (PanSTARRS)", "r aperture mag - total mag (PanSTARRS)",
    master_catalogue["stellarity"],
    invert_x=True
)

Number of source used: 148283 / 2168544 (6.84%)

V - Color-color and magnitude-color plots

nb_ccplots(
    master_catalogue['m_gpc1_g'] - master_catalogue['m_gpc1_i'],
    master_catalogue['m_vista_j'] - master_catalogue['m_vista_ks'],
    "g - i (PanSTARRS)", "J - Ks (VISTA)",
    master_catalogue["stellarity"]
)

Number of source used: 63057 / 2168544 (2.91%)

nb_ccplots(
    master_catalogue['m_gpc1_i'] - master_catalogue['m_irac_i1'],
    master_catalogue['m_gpc1_g'] - master_catalogue['m_gpc1_i'],
    "PanSTARRS i - IRAC1", "g - i (PanSTARRS)",
    master_catalogue["stellarity"]
)

Number of source used: 64707 / 2168544 (2.98%)

nb_ccplots(
    master_catalogue['m_omegacam_u'] - master_catalogue['m_omegacam_g'],
    master_catalogue['m_omegacam_g'] - master_catalogue['m_omegacam_r'],
    "u - g (ATLAS)", "g - r (ATLAS)",
    master_catalogue["stellarity"]
)

Number of source used: 18258 / 2168544 (0.84%)

nb_ccplots(
    master_catalogue['m_decam_g'] - master_catalogue['m_decam_r'],
    master_catalogue['m_decam_z'] - master_catalogue['m_decam_y'],
    "DECam g - DECam r", "DECam z - DECam Y",
    master_catalogue["stellarity"]
)

Number of source used: 826224 / 2168544 (38.10%)

nb_ccplots(
    master_catalogue['m_vista_j'] - master_catalogue['m_vista_ks'],
    master_catalogue['m_gpc1_g'] - master_catalogue['m_vista_j'],
    "J - Ks (VISTA)", "g - J (PanSTARRS, VISTA)",
    master_catalogue["stellarity"]
)

Number of source used: 64049 / 2168544 (2.95%)

nb_ccplots(
    master_catalogue['m_gpc1_i'] - master_catalogue['m_gpc1_z'],
    master_catalogue['m_gpc1_z'] - master_catalogue['m_vista_j'],
    "i - z (PanSTARRS)", "z - J (PanSTARRS, VISTA)",
    master_catalogue["stellarity"]
)

Number of source used: 101174 / 2168544 (4.67%)

nb_ccplots(
    master_catalogue['m_irac_i3'] - master_catalogue['m_irac_i4'],
    master_catalogue['m_irac_i1'] - master_catalogue['m_irac_i2'],
    "IRAC3 - IRAC4", "IRAC1 - IRAC2",
    master_catalogue["stellarity"]
)

Number of source used: 28901 / 2168544 (1.33%)