In [1]:
from herschelhelp_internal import git_version
print("This notebook was run with herschelhelp_internal version: \n{}".format(git_version()))
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 venn2, venn3
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 = "20170711"
master_catalogue_filename = "master_catalogue_elais-s1_{}.fits".format(SUFFIX)
master_catalogue = Table.read("{}/{}".format(OUT_DIR, master_catalogue_filename))
print("Diagnostics done using: {}".format(master_catalogue_filename))
0 - Quick checks¶
In [4]:
quick_checks(master_catalogue).show_in_notebook()
Out[4]:
I - Summary of wavelength domains¶
In [5]:
flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']
In [6]:
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");
In [7]:
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.
In [8]:
u_bands = []
g_bands = [ "DECam g"]
r_bands = ["VOICE r", "DECam r"]
i_bands = [ "DECam i"]
z_bands = [ "DECam z"]
y_bands = ["VISTA y", "DECam y"]
j_bands = ["VISTA j"]
h_bands = ["VISTA h"]
k_bands = ["VISTA k"]
II.a - Comparing depths¶
We compare the histograms of the total aperture magnitudes of similar bands.
In [9]:
for bands in [g_bands, r_bands, i_bands, z_bands, y_bands, j_bands, h_bands, k_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 [r_bands, y_bands, j_bands, h_bands, k_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)))
III - Comparing magnitudes to reference bands¶
Cross-match the master list to 2MASS magnitudes.
In [11]:
master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])
III.a - 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 [12]:
# 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 [13]:
twomass = Table.read("../../dmu0/dmu0_2MASS-point-sources/data/2MASS-PSC_ELAIS-S1.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 [14]:
nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_vista_j'][ml_twomass_idx],
labels=("2MASS J", "VISTA J (total)"))
Keeping only sources with good signal to noise ratio¶
From here, we are only comparing sources with a signal to noise ratio above 3, i.e. roughly we a magnitude error below 0.3.
To make it easier, we are setting to NaN in the catalogue the magnitudes associated with an error above 0.3 so we can't use these magnitudes after the next cell.
In [15]:
for error_column in [_ for _ in master_catalogue.colnames if _.startswith('merr_')]:
column = error_column.replace("merr", "m")
keep_mask = np.isfinite(master_catalogue[error_column])
keep_mask[keep_mask] &= master_catalogue[keep_mask][error_column] <= 0.3
master_catalogue[column][~keep_mask] = np.nan
IV - Comparing aperture magnitudes to total ones.¶
In [16]:
nb_ccplots(
master_catalogue['m_voice_r'],
master_catalogue['m_ap_voice_r'] - master_catalogue['m_voice_r'],
"r total magnitude (VOICE)", "r aperture mag - total mag (VOICE)",
master_catalogue["stellarity"],
invert_x=True
)
V - Color-color and magnitude-color plots¶
In [17]:
nb_ccplots(
master_catalogue['m_voice_b99'] - master_catalogue['m_voice_r'],
master_catalogue['m_vista_j'] - master_catalogue['m_vista_k'],
"b99 - r (VOICE)", "J - K (VISTA)",
master_catalogue["stellarity"]
)
In [18]:
nb_ccplots(
master_catalogue['m_voice_r'] - master_catalogue['m_irac1'],
master_catalogue['m_voice_b99'] - master_catalogue['m_voice_v'],
"VOICE r - IRAC1", "b99 - v (VOICE)",
master_catalogue["stellarity"]
)
In [19]:
nb_ccplots(
master_catalogue['m_vista_j'] - master_catalogue['m_vista_k'],
master_catalogue['m_vista_h'] - master_catalogue['m_vista_y'],
"J - Ks (VHS)", "H - Y (VIDEO)",
master_catalogue["stellarity"]
)
