SSDF master catalogue¶
Preparation of SSDF data¶
This catalogue comes from dmu0_SSDF.
The SSDF data consists in two catalogue of IRAC Ch1 and Ch2 fluxes: one for Ch1 detected sources and the other for Ch2 detected sources. For now, we are only using the Ch1 detected sources. TODO : We may find a way to merge the two catalogues and select the best flux for each source.
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))
from collections import OrderedDict
import os
from astropy import units as u
from astropy.coordinates import SkyCoord
from astropy.table import Column, Table
import numpy as np
from herschelhelp_internal.flagging import gaia_flag_column
from herschelhelp_internal.masterlist import nb_astcor_diag_plot, remove_duplicates
from herschelhelp_internal.utils import astrometric_correction, flux_to_mag
In [3]:
OUT_DIR = os.environ.get('TMP_DIR', "./data_tmp")
try:
os.makedirs(OUT_DIR)
except FileExistsError:
pass
RA_COL = "ssdf_ra"
DEC_COL = "ssdf_dec"
I - Column selection¶
In [4]:
#To begin lets take the irac_i1 selected sources. Perhaps we should merge in the irac_i2 slected sources.
imported_columns = OrderedDict({
"cntr": "ssdf_id",
"ra": "ssdf_ra",
"dec": "ssdf_dec",
"class_star": "ssdf_stellarity",
#"mag_aut1": "m_irac_i1",
#"magerr_aut1": "merr_irac_i1",
"flux_aut1": "f_irac_i1",
"fluxerr_aut1": "ferr_irac_i1",
#"mag_ap1_4": "m_ap_irac_i1", #Is this a 4 arcsec aperture (corrected) mag?
#"magerr_ap1_4": "merr_ap_irac_i1",
"flux_ap1_4": "f_ap_irac_i1",
"fluxerr_ap1_4": "ferr_ap_irac_i1",
#"mag_aut2": "m_irac_i2",
#"magerr_aut2": "merr_irac_i2",
"flux_aut2": "f_irac_i2",
"fluxerr_aut2": "ferr_irac_i2",
#"mag_ap2_4": "m_ap_irac_i2", #Is this a 4 arcsec aperture (corrected) mag?
#"magerr_ap2_4": "merr_ap_irac_i2",
"flux_ap2_4": "f_ap_irac_i2",
"fluxerr_ap2_4": "ferr_ap_irac_i2"
})
catalogue = Table.read("../../dmu0/dmu0_SSDF/data/SSDF-I1_20160530.fits")[list(imported_columns)]
for column in imported_columns:
catalogue[column].name = imported_columns[column]
epoch = 2012 #TODO: Check?
# Clean table metadata
catalogue.meta = None
#catalogue.add_column(Column([str(source) for source in catalogue['ssdf_ra', 'ssdf_dec']], dtype=str), name="ssdf_intid")
In [5]:
# band-flag columns
for col in catalogue.colnames:
if col.startswith('f_'):
errcol = "ferr{}".format(col[1:])
#We compute the magnitudes from the fluxes because the mags are Vega
mag, error = flux_to_mag(catalogue[col]* 1.e-6, catalogue[errcol] * 1.e-6)
catalogue.add_column(Column(mag , name="m{}".format(col[1:])))
catalogue.add_column(Column(error , name="m{}".format(errcol[1:])))
# Band-flag column
if "ap" not in col:
catalogue.add_column(Column(np.zeros(len(catalogue), dtype=bool), name="flag{}".format(col[1:])))
# TODO: Set to True the flag columns for fluxes that should not be used for SED fitting.
In [6]:
catalogue[:10].show_in_notebook()
Out[6]:
II - Removal of duplicated sources¶
We remove duplicated objects from the input catalogues.
In [7]:
SORT_COLS = ['merr_ap_irac_i1', 'merr_ap_irac_i2']
FLAG_NAME = 'ps1_flag_cleaned'
nb_orig_sources = len(catalogue)
catalogue = remove_duplicates(catalogue, RA_COL, DEC_COL, sort_col=SORT_COLS, flag_name=FLAG_NAME)
nb_sources = len(catalogue)
print("The initial catalogue had {} sources.".format(nb_orig_sources))
print("The cleaned catalogue has {} sources ({} removed).".format(nb_sources, nb_orig_sources - nb_sources))
print("The cleaned catalogue has {} sources flagged as having been cleaned".format(np.sum(catalogue[FLAG_NAME])))
III - Astrometry correction¶
We match the astrometry to the Gaia one. We limit the Gaia catalogue to sources with a g band flux between the 30th and the 70th percentile. Some quick tests show that this give the lower dispersion in the results.
In [8]:
gaia = Table.read("../../dmu0/dmu0_GAIA/data/GAIA_SSDF.fits")
gaia_coords = SkyCoord(gaia['ra'], gaia['dec'])
In [9]:
nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL],
gaia_coords.ra, gaia_coords.dec, near_ra0=True)
In [10]:
delta_ra, delta_dec = astrometric_correction(
SkyCoord(catalogue[RA_COL], catalogue[DEC_COL]),
gaia_coords, near_ra0=True
)
print("RA correction: {}".format(delta_ra))
print("Dec correction: {}".format(delta_dec))
In [11]:
catalogue[RA_COL] += delta_ra.to(u.deg)
catalogue[DEC_COL] += delta_dec.to(u.deg)
In [12]:
nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL],
gaia_coords.ra, gaia_coords.dec, near_ra0=True)
IV - Flagging Gaia objects¶
In [13]:
catalogue.add_column(
gaia_flag_column(SkyCoord(catalogue[RA_COL], catalogue[DEC_COL]), epoch, gaia)
)
In [14]:
GAIA_FLAG_NAME = "ssdf_flag_gaia"
catalogue['flag_gaia'].name = GAIA_FLAG_NAME
print("{} sources flagged.".format(np.sum(catalogue[GAIA_FLAG_NAME] > 0)))
V - Flagging objects near bright stars¶
VI - Saving to disk¶
In [15]:
catalogue.write("{}/SSDF.fits".format(OUT_DIR), overwrite=True)