GAMA-15 master catalogue

Checks and diagnostics

In [1]:
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: 
44f1ae0 (Thu Nov 30 18:27:54 2017 +0000)
In [2]:
%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, venn2

from herschelhelp_internal.masterlist import (nb_compare_mags, nb_ccplots, nb_histograms, 
                                              find_last_ml_suffix, quick_checks)
In [3]:
OUT_DIR = os.environ.get('OUT_DIR', "./data")
SUFFIX = find_last_ml_suffix()
#SUFFIX = "20171016"

master_catalogue_filename = "master_catalogue_gama-15_{}.fits".format(SUFFIX)
master_catalogue = Table.read("{}/{}".format(OUT_DIR, master_catalogue_filename))

print("Diagnostics done using: {}".format(master_catalogue_filename))
Diagnostics done using: master_catalogue_gama-15_20171208.fits

0 - Quick checks

In [4]:
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(192, 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(192, 0) will return an array of dtype('int64')
  format(shape, fill_value, array(fill_value).dtype), FutureWarning)
Table shows only problematic columns.
Out[4]:
<Table length=42>
idxColumnAll nan#Measurements#Zeros#NegativeMinimum value
0merr_ap_decam_z0.03896657.00.079587.0-50978.4570312
1f_decam_z0.03896762.00.09978.0-43402.5859375
2merr_decam_z0.03896762.00.09978.0-5712.55664062
3f_suprime_g0.05913643.0125.00.00.0
4f_suprime_r0.05980327.036.00.00.0
5f_suprime_i0.06053662.023.00.00.0
6f_suprime_z0.05843018.0101.00.00.0
7f_suprime_y0.05412330.0181.00.00.0
8f_ap_kids_u0.04655771.024.0201112.0-3878.95507812
9ferr_ap_kids_u0.04655771.024.00.00.0
10merr_ap_kids_u0.04655747.00.0201112.0-100367.203125
11f_ap_kids_g0.06243042.015.061033.0-1348.33129883
12ferr_ap_kids_g0.06243042.015.00.00.0
13merr_ap_kids_g0.06243027.00.061033.0-117383.757812
14f_ap_kids_r0.06796708.012.020393.0-587.771179199
15ferr_ap_kids_r0.06796708.025.00.00.0
16merr_ap_kids_r0.06796696.013.020387.0-49138.28125
17f_ap_kids_i0.05919916.011.0104851.0-17.1428833008
18ferr_ap_kids_i0.05919916.011.00.00.0
19merr_ap_kids_i0.05919905.00.0104851.0-589336.75
20ferr_ap_gpc1_g0.01305233.0169.00.00.0
21m_ap_gpc1_g0.01305233.00.06.0-0.449443995953
22merr_ap_gpc1_g0.01305233.0169.00.00.0
23ferr_gpc1_g0.01323650.0955.00.00.0
24m_gpc1_g0.01323650.00.013.0-1.92199003696
25merr_gpc1_g0.01323650.0955.00.00.0
26ferr_ap_gpc1_r0.01371933.0890.00.00.0
27merr_ap_gpc1_r0.01371933.0890.00.00.0
28ferr_gpc1_r0.01370083.0689.00.00.0
29merr_gpc1_r0.01370083.0689.00.00.0
30ferr_ap_gpc1_i0.01386253.0302.00.00.0
31merr_ap_gpc1_i0.01386253.0302.00.00.0
32ferr_gpc1_i0.01387106.0152.00.00.0
33merr_gpc1_i0.01387106.0152.00.00.0
34ferr_ap_gpc1_z0.01381378.0471.00.00.0
35merr_ap_gpc1_z0.01381378.0471.00.00.0
36ferr_gpc1_z0.01382173.0293.00.00.0
37merr_gpc1_z0.01382173.0293.00.00.0
38ferr_ap_gpc1_y0.01368267.0665.00.00.0
39merr_ap_gpc1_y0.01368267.0665.00.00.0
40ferr_gpc1_y0.01352979.0339.00.00.0
41merr_gpc1_y0.01352979.0339.00.00.0

I - Summary of wavelength domains

In [5]:
flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']
In [6]:
venn2(
    [
        np.sum(flag_obs == 1),
        np.sum(flag_obs == 2),
        np.sum(flag_obs == 3)
    ],
    set_labels=('Optical', 'near-IR'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/len(flag_obs)))
)
plt.title("Wavelength domain observations");
In [7]:
venn2(
    [
        np.sum(flag_det[flag_obs == 3] == 1),
        np.sum(flag_det[flag_obs == 3] == 2),
        np.sum(flag_det[flag_obs == 3] == 3)
    ],
    set_labels=('near-IR', 'Optical'),
    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.

In [8]:
u_bands = [           "KIDS u"]
g_bands = [           "KIDS g", "SUPRIME g", "GPC1 g"]
r_bands = [           "KIDS r", "SUPRIME r", "GPC1 r"]
i_bands = [           "KIDS i", "SUPRIME i", "GPC1 i"]
z_bands = ["DECam z",           "SUPRIME z", "GPC1 z"]
y_bands = [                     "SUPRIME y", "GPC1 y"]

II.a - Comparing depths

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

In [9]:
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)

II.b - Comparing magnitudes

We compare one to one each magnitude in similar bands.

In [10]:
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)))
SUPRIME g (aperture) - KIDS g (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.15
- 1% percentile: -1.639749526977539
- 99% percentile: 0.9492234992980966
SUPRIME g (total) - KIDS g (total):
- Median: -0.28
- Median Absolute Deviation: 0.25
- 1% percentile: -2.564059658050537
- 99% percentile: 1.128276958465567
GPC1 g (aperture) - KIDS g (aperture):
- Median: -0.31
- Median Absolute Deviation: 0.38
- 1% percentile: -3.1371237373352048
- 99% percentile: 2.213497180938726
GPC1 g (total) - KIDS g (total):
- Median: -0.12
- Median Absolute Deviation: 0.25
- 1% percentile: -3.543268051147461
- 99% percentile: 1.5813409042358293
GPC1 g (aperture) - SUPRIME g (aperture):
- Median: -0.28
- Median Absolute Deviation: 0.43
- 1% percentile: -3.1680870056152344
- 99% percentile: 2.4179883003234885
GPC1 g (total) - SUPRIME g (total):
- Median: -0.04
- Median Absolute Deviation: 0.30
- 1% percentile: -3.2857778167724607
- 99% percentile: 1.7586553955078141
SUPRIME r (aperture) - KIDS r (aperture):
- Median: -0.06
- Median Absolute Deviation: 0.11
- 1% percentile: -0.8526588439941406
- 99% percentile: 0.8960708618164048
SUPRIME r (total) - KIDS r (total):
- Median: -0.19
- Median Absolute Deviation: 0.18
- 1% percentile: -1.4241687393188478
- 99% percentile: 1.1404187583923342
GPC1 r (aperture) - KIDS r (aperture):
- Median: -0.11
- Median Absolute Deviation: 0.21
- 1% percentile: -1.3515098571777342
- 99% percentile: 1.496448478698733
GPC1 r (total) - KIDS r (total):
- Median: 0.05
- Median Absolute Deviation: 0.13
- 1% percentile: -1.5403206634521482
- 99% percentile: 1.1657170867919837
GPC1 r (aperture) - SUPRIME r (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.23
- 1% percentile: -1.5872383117675781
- 99% percentile: 1.525237350463859
GPC1 r (total) - SUPRIME r (total):
- Median: 0.11
- Median Absolute Deviation: 0.15
- 1% percentile: -1.6637313461303709
- 99% percentile: 1.2613188362121595
SUPRIME i (aperture) - KIDS i (aperture):
- Median: -0.07
- Median Absolute Deviation: 0.18
- 1% percentile: -2.0469532012939453
- 99% percentile: 1.169138336181641
SUPRIME i (total) - KIDS i (total):
- Median: -0.27
- Median Absolute Deviation: 0.27
- 1% percentile: -3.016033172607422
- 99% percentile: 1.297071933746338
GPC1 i (aperture) - KIDS i (aperture):
- Median: -0.08
- Median Absolute Deviation: 0.16
- 1% percentile: -0.9477043151855469
- 99% percentile: 0.8186016082763672
GPC1 i (total) - KIDS i (total):
- Median: 0.05
- Median Absolute Deviation: 0.10
- 1% percentile: -0.8343009567260742
- 99% percentile: 0.6969107818603506
GPC1 i (aperture) - SUPRIME i (aperture):
- Median: -0.03
- Median Absolute Deviation: 0.18
- 1% percentile: -1.1367273139953613
- 99% percentile: 0.9213391876220709
GPC1 i (total) - SUPRIME i (total):
- Median: 0.20
- Median Absolute Deviation: 0.11
- 1% percentile: -0.8914554595947265
- 99% percentile: 0.7686335754394538
SUPRIME z (aperture) - DECam z (aperture):
- Median: 0.05
- Median Absolute Deviation: 0.22
- 1% percentile: -2.385577850341797
- 99% percentile: 1.2567932891845697
SUPRIME z (total) - DECam z (total):
- Median: 0.03
- Median Absolute Deviation: 0.13
- 1% percentile: -0.8609659385681152
- 99% percentile: 1.6053583717346078
GPC1 z (aperture) - DECam z (aperture):
- Median: 0.09
- Median Absolute Deviation: 0.12
- 1% percentile: -1.6480256843566894
- 99% percentile: 1.2552158927917487
GPC1 z (total) - DECam z (total):
- Median: 0.26
- Median Absolute Deviation: 0.12
- 1% percentile: -0.7882562637329102
- 99% percentile: 1.3734452438354419
GPC1 z (aperture) - SUPRIME z (aperture):
- Median: -0.04
- Median Absolute Deviation: 0.20
- 1% percentile: -1.1917795181274413
- 99% percentile: 1.2728551101684569
GPC1 z (total) - SUPRIME z (total):
- Median: 0.18
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0532553100585937
- 99% percentile: 0.9818671417236327
GPC1 y (aperture) - SUPRIME y (aperture):
- Median: -0.19
- Median Absolute Deviation: 0.28
- 1% percentile: -1.8443033599853516
- 99% percentile: 1.679386215209945
GPC1 y (total) - SUPRIME y (total):
- Median: 0.09
- Median Absolute Deviation: 0.23
- 1% percentile: -2.0898357391357423
- 99% percentile: 1.6052894592285112

III - Comparing magnitudes to reference bands

Cross-match the master list to SDSS and 2MASS to compare its magnitudes to SDSS and 2MASS ones.

In [11]:
master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])

III.a - Comparing u, g, r, i, and z bands to SDSS

The catalogue is cross-matched to SDSS-DR13 withing 0.2 arcsecond.

We compare the u, g, r, i, and z magnitudes to those from SDSS using fiberMag for the aperture magnitude and petroMag for the total magnitude.

In [12]:
sdss = Table.read("../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_GAMA-15.fits")
sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)

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

sdss = sdss[mask]
ml_sdss_idx = idx[mask]
In [13]:
for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands]:
    for band in band_of_a_kind:
        
        sdss_mag_ap_colname = "fiberMag_{}".format(band[-1])
        sdss_mag_tot_colname = "petroMag_{}".format(band[-1])
        master_cat_mag_ap_colname = "m_ap_{}".format(band.replace(" ", "_").lower())
        master_cat_mag_tot_colname = "m_ap_{}".format(band.replace(" ", "_").lower())
        
        # Set SDSS magnitudes to NaN when the magnitude  is negative as SDSS uses large
        # negative numbers for missing magnitudes
        sdss[sdss_mag_ap_colname][sdss[sdss_mag_ap_colname] < 0.] = np.nan
        sdss[sdss_mag_tot_colname][sdss[sdss_mag_tot_colname] < 0.] = np.nan
        
        sdss_mag_ap = sdss[sdss_mag_ap_colname]
        master_cat_mag_ap = master_catalogue[master_cat_mag_ap_colname][ml_sdss_idx]
    
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (fiberMag)".format(band[-1]), "{} (aperture)".format(band)))
    
        sdss_mag_tot = sdss[sdss_mag_tot_colname]
        master_cat_mag_tot = master_catalogue[master_cat_mag_tot_colname][ml_sdss_idx]
        
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (petroMag)".format(band[-1]), "{} (total)".format(band)))
KIDS u (aperture) - SDSS u (fiberMag):
- Median: 0.06
- Median Absolute Deviation: 0.42
- 1% percentile: -1.6777435874938966
- 99% percentile: 2.960469150543212
KIDS u (total) - SDSS u (petroMag):
- Median: 0.06
- Median Absolute Deviation: 0.42
- 1% percentile: -1.6777435874938966
- 99% percentile: 2.960469150543212
KIDS g (aperture) - SDSS g (fiberMag):
- Median: -0.21
- Median Absolute Deviation: 0.15
- 1% percentile: -1.0146734237670898
- 99% percentile: 0.9639924621582026
KIDS g (total) - SDSS g (petroMag):
- Median: -0.21
- Median Absolute Deviation: 0.15
- 1% percentile: -1.0146734237670898
- 99% percentile: 0.9639924621582026
/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:11: RuntimeWarning: invalid value encountered in less
/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:12: RuntimeWarning: invalid value encountered in less
SUPRIME g (aperture) - SDSS g (fiberMag):
- Median: -0.29
- Median Absolute Deviation: 0.16
- 1% percentile: -1.1014499282836914
- 99% percentile: 0.9030055809020996
SUPRIME g (total) - SDSS g (petroMag):
- Median: -0.29
- Median Absolute Deviation: 0.16
- 1% percentile: -1.1014499282836914
- 99% percentile: 0.9030055809020996
GPC1 g (aperture) - SDSS g (fiberMag):
- Median: -0.50
- Median Absolute Deviation: 0.29
- 1% percentile: -2.7221382522583006
- 99% percentile: 1.9057488632202144
GPC1 g (total) - SDSS g (petroMag):
- Median: -0.50
- Median Absolute Deviation: 0.29
- 1% percentile: -2.7221382522583006
- 99% percentile: 1.9057488632202144
KIDS r (aperture) - SDSS r (fiberMag):
- Median: -0.26
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9293296623229981
- 99% percentile: 0.3801915359497068
KIDS r (total) - SDSS r (petroMag):
- Median: -0.26
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9293296623229981
- 99% percentile: 0.3801915359497068
SUPRIME r (aperture) - SDSS r (fiberMag):
- Median: -0.31
- Median Absolute Deviation: 0.13
- 1% percentile: -0.9976704406738282
- 99% percentile: 0.5201725578308096
SUPRIME r (total) - SDSS r (petroMag):
- Median: -0.31
- Median Absolute Deviation: 0.13
- 1% percentile: -0.9976704406738282
- 99% percentile: 0.5201725578308096
GPC1 r (aperture) - SDSS r (fiberMag):
- Median: -0.38
- Median Absolute Deviation: 0.15
- 1% percentile: -1.4833747577667236
- 99% percentile: 0.9636315536499054
GPC1 r (total) - SDSS r (petroMag):
- Median: -0.38
- Median Absolute Deviation: 0.15
- 1% percentile: -1.4833747577667236
- 99% percentile: 0.9636315536499054
KIDS i (aperture) - SDSS i (fiberMag):
- Median: -0.26
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9333913230895996
- 99% percentile: 0.4504426956176766
KIDS i (total) - SDSS i (petroMag):
- Median: -0.26
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9333913230895996
- 99% percentile: 0.4504426956176766
SUPRIME i (aperture) - SDSS i (fiberMag):
- Median: -0.32
- Median Absolute Deviation: 0.12
- 1% percentile: -1.010098304748535
- 99% percentile: 0.5216443252563442
SUPRIME i (total) - SDSS i (petroMag):
- Median: -0.32
- Median Absolute Deviation: 0.12
- 1% percentile: -1.010098304748535
- 99% percentile: 0.5216443252563442
GPC1 i (aperture) - SDSS i (fiberMag):
- Median: -0.36
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0263115501403808
- 99% percentile: 0.4693428230285639
GPC1 i (total) - SDSS i (petroMag):
- Median: -0.36
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0263115501403808
- 99% percentile: 0.4693428230285639
DECam z (aperture) - SDSS z (fiberMag):
- Median: -0.37
- Median Absolute Deviation: 0.18
- 1% percentile: -1.444174976348877
- 99% percentile: 1.322802276611327
DECam z (total) - SDSS z (petroMag):
- Median: -0.37
- Median Absolute Deviation: 0.18
- 1% percentile: -1.444174976348877
- 99% percentile: 1.322802276611327
SUPRIME z (aperture) - SDSS z (fiberMag):
- Median: -0.21
- Median Absolute Deviation: 0.19
- 1% percentile: -1.2618183135986327
- 99% percentile: 0.9837712860107417
SUPRIME z (total) - SDSS z (petroMag):
- Median: -0.21
- Median Absolute Deviation: 0.19
- 1% percentile: -1.2618183135986327
- 99% percentile: 0.9837712860107417
GPC1 z (aperture) - SDSS z (fiberMag):
- Median: -0.30
- Median Absolute Deviation: 0.18
- 1% percentile: -1.4636115074157714
- 99% percentile: 1.100392208099355
GPC1 z (total) - SDSS z (petroMag):
- Median: -0.30
- Median Absolute Deviation: 0.18
- 1% percentile: -1.4636115074157714
- 99% percentile: 1.100392208099355

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

In addition, UKIDSS uses a K band whereas 2MASS uses a Ks (“short”) band, this page give a correction to convert the K band in a Ks band with the formula:

$$K_{s(2MASS)} = K_{UKIRT} + 0.003 + 0.004 * (J−K)_{UKIRT}$$
In [14]:
# 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)
In [15]:
twomass = Table.read("../../dmu0/dmu0_2MASS-point-sources/data/2MASS-PSC_GAMA-15.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]
In [16]:
nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_ukidss_j'][ml_twomass_idx],
                labels=("2MASS J", "UKIDSS J (total)"))
UKIDSS J (total) - 2MASS J:
- Median: 0.06
- Median Absolute Deviation: 0.06
- 1% percentile: -0.7037197442533326
- 99% percentile: 0.6006480420015505
In [17]:
ukidss_ks_like = master_catalogue['m_ukidss_k'] + 0.003 + 0.004 * (
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, ukidss_ks_like[ml_twomass_idx],
                labels=("2MASS Ks", "UKIDSS Ks-like (total)"))
UKIDSS Ks-like (total) - 2MASS Ks:
- Median: 0.13
- Median Absolute Deviation: 0.09
- 1% percentile: -0.804199426332334
- 99% percentile: 1.1058544402448145
In [18]:
nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_vircam_j'][ml_twomass_idx],
                labels=("2MASS J", "VISTA J (total)"))
VISTA J (total) - 2MASS J:
- Median: 0.04
- Median Absolute Deviation: 0.07
- 1% percentile: -1.0072000961210075
- 99% percentile: 1.1233207901094577
In [19]:
ukidss_ks_like = master_catalogue['m_vircam_k'] + 0.003 + 0.004 * (
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, ukidss_ks_like[ml_twomass_idx],
                labels=("2MASS Ks", "VIRCAM Ks (total)"))
VIRCAM Ks (total) - 2MASS Ks:
- Median: 0.06
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9966990270098248
- 99% percentile: 1.2020145327130711

IV - Comparing aperture magnitudes to total ones.

In [20]:
nb_ccplots(
    master_catalogue['m_suprime_r'],
    master_catalogue['m_ap_suprime_r'] - master_catalogue['m_suprime_r'],
    "r total magnitude (HSC)", "r aperture mag - total mag (HSC)",
    master_catalogue["stellarity"],
    invert_x=True
)
Number of source used: 5937538 / 14232880 (41.72%)

V - Color-color and magnitude-color plots

In [21]:
nb_ccplots(
    master_catalogue['m_suprime_g'] - master_catalogue['m_suprime_i'],
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'],
    "g - i (HSC)", "J - K (UKIDSS)",
    master_catalogue["stellarity"]
)
/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:2: RuntimeWarning: invalid value encountered in subtract
  from ipykernel import kernelapp as app
Number of source used: 68178 / 14232880 (0.48%)
In [22]:
nb_ccplots(
    master_catalogue['m_suprime_i'] - master_catalogue['m_vircam_j'],
    master_catalogue['m_suprime_g'] - master_catalogue['m_vircam_k'],
    "WFC i - IRAC1", "g - i (WFC)",
    master_catalogue["stellarity"]
)
Number of source used: 279701 / 14232880 (1.97%)
In [23]:
nb_ccplots(
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'],
    master_catalogue['m_suprime_g'] - master_catalogue['m_ukidss_j'],
    "J - K (UKIDSS)", "g - J (HSC, UKIDSS)",
    master_catalogue["stellarity"]
)
Number of source used: 68180 / 14232880 (0.48%)
In [24]:
nb_ccplots(
    master_catalogue['m_suprime_i'] - master_catalogue['m_suprime_z'],
    master_catalogue['m_suprime_z'] - master_catalogue['m_ukidss_j'],
    "i - z (HSC)", "z - J (HSC, UKIDSS)",
    master_catalogue["stellarity"]
)
/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:2: RuntimeWarning: invalid value encountered in subtract
  from ipykernel import kernelapp as app
Number of source used: 84746 / 14232880 (0.60%)
In [25]: