{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EGS master catalogue\n", "## Preparation of CANDELS-EGS data\n", "\n", "CANDELS-EGS catalogue: the catalogue comes from `dmu0_CANDELS-EGS`.\n", "\n", "In the catalogue, we keep:\n", "\n", "- The identifier (it's unique in the catalogue);\n", "- The position;\n", "- The stellarity;\n", "- The magnitude for each band in 2 arcsec aperture (aperture 10).\n", "- The kron magnitude to be used as total magnitude (no “auto” magnitude is provided).\n", "\n", "We don't know when the maps have been observed. We will use the year of the reference paper." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This notebook was run with herschelhelp_internal version: \n", "0246c5d (Thu Jan 25 17:01:47 2018 +0000) [with local modifications]\n", "This notebook was executed on: \n", "2018-02-07 19:30:38.471667\n" ] } ], "source": [ "from herschelhelp_internal import git_version\n", "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))\n", "import datetime\n", "print(\"This notebook was executed on: \\n{}\".format(datetime.datetime.now()))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "#%config InlineBackend.figure_format = 'svg'\n", "\n", "import matplotlib.pyplot as plt\n", "plt.rc('figure', figsize=(10, 6))\n", "\n", "from collections import OrderedDict\n", "import os\n", "\n", "from astropy import units as u\n", "from astropy.coordinates import SkyCoord\n", "from astropy.table import Column, Table\n", "import numpy as np\n", "\n", "from herschelhelp_internal.flagging import gaia_flag_column\n", "from herschelhelp_internal.masterlist import nb_astcor_diag_plot, remove_duplicates\n", "from herschelhelp_internal.utils import astrometric_correction, flux_to_mag" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "OUT_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n", "try:\n", " os.makedirs(OUT_DIR)\n", "except FileExistsError:\n", " pass\n", "\n", "RA_COL = \"candels-egs_ra\"\n", "DEC_COL = \"candels-egs_dec\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## I - Column selection" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "imported_columns = OrderedDict({\n", " 'ID': \"candels-egs_id\",\n", " 'RA': \"candels-egs_ra\",\n", " 'DEC': \"candels-egs_dec\",\n", " 'CLASS_STAR': \"candels-egs_stellarity\",\n", " #HST data\n", " 'FLUX_APER_10_F606W': \"f_ap_acs_f606w\", \n", " 'FLUXERR_APER_10_F606W': \"ferr_ap_acs_f606w\", \n", " 'FLUX_AUTO_F606W': \"f_acs_f606w\", \n", " 'FLUXERR_AUTO_F606W': \"ferr_acs_f606w\",\n", " 'FLUX_APER_10_F814W': \"f_ap_acs_f814w\", \n", " 'FLUXERR_APER_10_F814W': \"ferr_ap_acs_f814w\", \n", " 'FLUX_AUTO_F814W': \"f_acs_f814w\", \n", " 'FLUXERR_AUTO_F814W': \"ferr_acs_f814w\",\n", " 'FLUX_APER_10_F125W': \"f_ap_wfc3_f125w\", \n", " 'FLUXERR_APER_10_F125W': \"ferr_ap_wfc3_f125w\", \n", " 'FLUX_AUTO_F125W': \"f_wfc3_f125w\", \n", " 'FLUXERR_AUTO_F125W': \"ferr_wfc3_f125w\",\n", " 'FLUX_APER_10_F140W': \"f_ap_wfc3_f140w\", \n", " 'FLUXERR_APER_10_F140W': \"ferr_ap_wfc3_f140w\", \n", " 'FLUX_AUTO_F140W': \"f_wfc3_f140w\", \n", " 'FLUXERR_AUTO_F140W': \"ferr_wfc3_f140w\",\n", " 'FLUX_APER_10_F160W': \"f_ap_wfc3_f160w\", \n", " 'FLUXERR_APER_10_F160W': \"ferr_ap_wfc3_f160w\", \n", " 'FLUX_AUTO_F160W': \"f_wfc3_f160w\", \n", " 'FLUXERR_AUTO_F160W': \"ferr_wfc3_f160w\",\n", " #CFHT Megacam\n", " 'CFHT_u_FLUX': \"f_candels-megacam_u\", # 9 CFHT_u_FLUX Flux density (in μJy) in the u*-band (CFHT/MegaCam) (3)\n", " 'CFHT_u_FLUXERR': \"ferr_candels-megacam_u\",# 10 CFHT_u_FLUXERR Flux uncertainty (in μJy) in the u*-band (CFHT/MegaCam) (3)\n", " 'CFHT_g_FLUX': \"f_candels-megacam_g\",# 11 CFHT_g_FLUX Flux density (in μJy) in the g'-band (CFHT/MegaCam) (3)\n", " 'CFHT_g_FLUXERR': \"ferr_candels-megacam_g\",# 12 CFHT_g_FLUXERR Flux uncertainty (in μJy) in the g'-band (CFHT/MegaCam) (3)\n", " 'CFHT_r_FLUX': \"f_candels-megacam_r\",# 13 CFHT_r_FLUX Flux density (in μJy) in the r'-band (CFHT/MegaCam) (3)\n", " 'CFHT_r_FLUXERR': \"ferr_candels-megacam_r\",# 14 CFHT_r_FLUXERR Flux uncertainty (in μJy) in the r'-band (CFHT/MegaCam) (3)\n", " 'CFHT_i_FLUX': \"f_candels-megacam_i\",# 15 CFHT_i_FLUX Flux density (in μJy) in the i'-band (CFHT/MegaCam) (3)\n", " 'CFHT_i_FLUXERR': \"ferr_candels-megacam_i\",# 16 CFHT_i_FLUXERR Flux uncertainty (in μJy) in the i'-band (CFHT/MegaCam) (3)\n", " 'CFHT_z_FLUX': \"f_candels-megacam_z\",# 17 CFHT_z_FLUX Flux density (in μJy) in the z'-band (CFHT/MegaCam) (3)\n", " 'CFHT_z_FLUXERR': \"ferr_candels-megacam_z\",# 18 CFHT_z_FLUXERR \n", " #CFHT WIRCAM\n", " 'WIRCAM_J_FLUX': \"f_candels-wircam_j\",# 29 WIRCAM_J_FLUX Flux density (in μJy) in the J-band (CFHT/WIRCam) (3)\n", " 'WIRCAM_J_FLUXERR': \"ferr_candels-wircam_j\",# 30 WIRCAM_J_FLUXERR Flux uncertainty (in μJy) in the J-band (CFHT/WIRCam) (3)\n", " 'WIRCAM_H_FLUX': \"f_candels-wircam_h\",# 31 WIRCAM_H_FLUX Flux density (in μJy) in the H-band (CFHT/WIRCam) (3)\n", " 'WIRCAM_H_FLUXERR': \"ferr_candels-wircam_h\",# 32 WIRCAM_H_FLUXERR Flux uncertainty (in μJy) in the H-band (CFHT/WIRCam) (3)\n", " 'WIRCAM_K_FLUX': \"f_candels-wircam_k\",# 33 WIRCAM_K_FLUX Flux density (in μJy) in the Ks-band (CFHT/WIRCam) (3)\n", " 'WIRCAM_K_FLUXERR': \"ferr_candels-wircam_k\",# 34 WIRCAM_K_FLUXERR \n", " #Mayall/Newfirm\n", " 'NEWFIRM_J1_FLUX': \"f_candels-newfirm_j1\",# 35 NEWFIRM_J1_FLUX Flux density (in μJy) in the J1-band (Mayall/NEWFIRM) (3)\n", " 'NEWFIRM_J1_FLUXERR': \"ferr_candels-newfirm_j1\",# 36 NEWFIRM_J1_FLUXERR Flux uncertainty (in μJy) in the J1-band (Mayall/NEWFIRM) (3)\n", " 'NEWFIRM_J2_FLUX': \"f_candels-newfirm_j2\",# 37 NEWFIRM_J2_FLUX Flux density (in μJy) in the J2-band (Mayall/NEWFIRM) (3)\n", " 'NEWFIRM_J2_FLUXERR': \"ferr_candels-newfirm_j2\",# 38 NEWFIRM_J2_FLUXERR Flux uncertainty (in μJy) in the J2-band (Mayall/NEWFIRM) (3)\n", " 'NEWFIRM_J3_FLUX': \"f_candels-newfirm_j3\",# 39 NEWFIRM_J3_FLUX Flux density (in μJy) in the J3-band (Mayall/NEWFIRM) (3)\n", " 'NEWFIRM_J3_FLUXERR': \"ferr_candels-newfirm_j3\",# 40 NEWFIRM_J3_FLUXERR Flux uncertainty (in μJy) in the J3-band (Mayall/NEWFIRM) (3)\n", " 'NEWFIRM_H1_FLUX': \"f_candels-newfirm_h1\",# 41 NEWFIRM_H1_FLUX Flux density (in μJy) in the H1-band (Mayall/NEWFIRM) (3)\n", " 'NEWFIRM_H1_FLUXERR': \"ferr_candels-newfirm_h1\",# 42 NEWFIRM_H1_FLUXERR Flux uncertainty (in μJy) in the H1-band (Mayall/NEWFIRM) (3)\n", " 'NEWFIRM_H2_FLUX': \"f_candels-newfirm_h2\",# 43 NEWFIRM_H2_FLUX Flux density (in μJy) in the H2-band (Mayall/NEWFIRM) (3)\n", " 'NEWFIRM_H2_FLUXERR': \"ferr_candels-newfirm_h2\",# 44 NEWFIRM_H2_FLUXERR Flux uncertainty (in μJy) in the H2-band (Mayall/NEWFIRM) (3)\n", " 'NEWFIRM_K_FLUX': \"f_candels-newfirm_k\",# 45 NEWFIRM_K_FLUX Flux density (in μJy) in the K-band (Mayall/NEWFIRM) (3)\n", " 'NEWFIRM_K_FLUXERR': \"ferr_candels-newfirm_k\",# 46 NEWFIRM_K_FLUXERR \n", " #Spitzer/IRAC\n", " 'IRAC_CH1_FLUX': \"f_candels-irac_i1\",# 47 IRAC_CH1_FLUX Flux density (in μJy) in the 3.6μm-band (Spitzer/IRAC) (3)\n", " 'IRAC_CH1_FLUXERR': \"ferr_candels-irac_i1\",# 48 IRAC_CH1_FLUXERR Flux uncertainty (in μJy) in the 3.6μm-band (Spitzer/IRAC) (3)\n", " 'IRAC_CH2_FLUX': \"f_candels-irac_i2\",# 49 IRAC_CH2_FLUX Flux density (in μJy) in the 4.5μm-band (Spitzer/IRAC) (3)\n", " 'IRAC_CH2_FLUXERR': \"ferr_candels-irac_i2\",# 50 IRAC_CH2_FLUXERR Flux uncertainty (in μJy) in the 4.5μm-band (Spitzer/IRAC) (3)\n", " 'IRAC_CH3_FLUX': \"f_candels-irac_i3\",# 51 IRAC_CH3_FLUX Flux density (in μJy) in the 5.8μm-band (Spitzer/IRAC) (3)\n", " 'IRAC_CH3_FLUXERR': \"ferr_candels-irac_i3\",# 52 IRAC_CH3_FLUXERR Flux uncertainty (in μJy) in the 5.8μm-band (Spitzer/IRAC) (3)\n", " 'IRAC_CH4_FLUX': \"f_candels-irac_i4\",# 53 IRAC_CH4_FLUX Flux density (in μJy) in the 8.0μm-band (Spitzer/IRAC) (3)\n", " 'IRAC_CH4_FLUXERR': \"ferr_candels-irac_i4\"# 54 IRAC_CH4_FLUXERR\n", " \n", " \n", " })\n", "\n", "\n", "catalogue = Table.read(\"../../dmu0/dmu0_CANDELS-EGS/data/hlsp_candels_hst_wfc3_egs-tot-multiband_f160w_v1_cat.fits\")[list(imported_columns)]\n", "for column in imported_columns:\n", " catalogue[column].name = imported_columns[column]\n", "\n", "epoch = 2011\n", "\n", "# Clean table metadata\n", "catalogue.meta = None" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:76: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:76: RuntimeWarning: divide by zero encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:80: RuntimeWarning: divide by zero encountered in true_divide\n", " errors = 2.5 / np.log(10) * errors_on_fluxes / fluxes\n" ] } ], "source": [ "# Adding flux and band-flag columns\n", "for col in catalogue.colnames:\n", " if col.startswith('f_'):\n", " \n", " errcol = \"ferr{}\".format(col[1:])\n", " \n", " # Some object have a magnitude to 0, we suppose this means missing value\n", " #catalogue[col][catalogue[col] <= 0] = np.nan\n", " #catalogue[errcol][catalogue[errcol] <= 0] = np.nan \n", " \n", "\n", " mag, error = flux_to_mag(np.array(catalogue[col])*1.e-6, np.array(catalogue[errcol])*1.e-6)\n", " \n", " # Fluxes are added in µJy\n", " catalogue.add_column(Column(mag, name=\"m{}\".format(col[1:])))\n", " catalogue.add_column(Column(error, name=\"m{}\".format(errcol[1:])))\n", " \n", " # Add nan col for aperture fluxes\n", " if ('wfc' not in col) & ('acs' not in col):\n", " catalogue.add_column(Column(np.full(len(catalogue), np.nan), name=\"m_ap{}\".format(col[1:])))\n", " catalogue.add_column(Column(np.full(len(catalogue), np.nan), name=\"merr_ap{}\".format(col[1:])))\n", " catalogue.add_column(Column(np.full(len(catalogue), np.nan), name=\"f_ap{}\".format(col[1:])))\n", " catalogue.add_column(Column(np.full(len(catalogue), np.nan), name=\"ferr_ap{}\".format(col[1:])))\n", " \n", " # Band-flag column\n", " if \"ap\" not in col:\n", " catalogue.add_column(Column(np.zeros(len(catalogue), dtype=bool), name=\"flag{}\".format(col[1:])))\n", " \n", "# TODO: Set to True the flag columns for fluxes that should not be used for SED fitting." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<Table length=10>\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
idxcandels-egs_idcandels-egs_racandels-egs_deccandels-egs_stellarityf_ap_acs_f606wferr_ap_acs_f606wf_acs_f606wferr_acs_f606wf_ap_acs_f814wferr_ap_acs_f814wf_acs_f814wferr_acs_f814wf_ap_wfc3_f125wferr_ap_wfc3_f125wf_wfc3_f125wferr_wfc3_f125wf_ap_wfc3_f140wferr_ap_wfc3_f140wf_wfc3_f140wferr_wfc3_f140wf_ap_wfc3_f160wferr_ap_wfc3_f160wf_wfc3_f160wferr_wfc3_f160wf_candels-megacam_uferr_candels-megacam_uf_candels-megacam_gferr_candels-megacam_gf_candels-megacam_rferr_candels-megacam_rf_candels-megacam_iferr_candels-megacam_if_candels-megacam_zferr_candels-megacam_zf_candels-wircam_jferr_candels-wircam_jf_candels-wircam_hferr_candels-wircam_hf_candels-wircam_kferr_candels-wircam_kf_candels-newfirm_j1ferr_candels-newfirm_j1f_candels-newfirm_j2ferr_candels-newfirm_j2f_candels-newfirm_j3ferr_candels-newfirm_j3f_candels-newfirm_h1ferr_candels-newfirm_h1f_candels-newfirm_h2ferr_candels-newfirm_h2f_candels-newfirm_kferr_candels-newfirm_kf_candels-irac_i1ferr_candels-irac_i1f_candels-irac_i2ferr_candels-irac_i2f_candels-irac_i3ferr_candels-irac_i3f_candels-irac_i4ferr_candels-irac_i4m_ap_acs_f606wmerr_ap_acs_f606wm_acs_f606wmerr_acs_f606wflag_acs_f606wm_ap_acs_f814wmerr_ap_acs_f814wm_acs_f814wmerr_acs_f814wflag_acs_f814wm_ap_wfc3_f125wmerr_ap_wfc3_f125wm_wfc3_f125wmerr_wfc3_f125wflag_wfc3_f125wm_ap_wfc3_f140wmerr_ap_wfc3_f140wm_wfc3_f140wmerr_wfc3_f140wflag_wfc3_f140wm_ap_wfc3_f160wmerr_ap_wfc3_f160wm_wfc3_f160wmerr_wfc3_f160wflag_wfc3_f160wm_candels-megacam_umerr_candels-megacam_um_ap_candels-megacam_umerr_ap_candels-megacam_uf_ap_candels-megacam_uferr_ap_candels-megacam_uflag_candels-megacam_um_candels-megacam_gmerr_candels-megacam_gm_ap_candels-megacam_gmerr_ap_candels-megacam_gf_ap_candels-megacam_gferr_ap_candels-megacam_gflag_candels-megacam_gm_candels-megacam_rmerr_candels-megacam_rm_ap_candels-megacam_rmerr_ap_candels-megacam_rf_ap_candels-megacam_rferr_ap_candels-megacam_rflag_candels-megacam_rm_candels-megacam_imerr_candels-megacam_im_ap_candels-megacam_imerr_ap_candels-megacam_if_ap_candels-megacam_iferr_ap_candels-megacam_iflag_candels-megacam_im_candels-megacam_zmerr_candels-megacam_zm_ap_candels-megacam_zmerr_ap_candels-megacam_zf_ap_candels-megacam_zferr_ap_candels-megacam_zflag_candels-megacam_zm_candels-wircam_jmerr_candels-wircam_jm_ap_candels-wircam_jmerr_ap_candels-wircam_jf_ap_candels-wircam_jferr_ap_candels-wircam_jflag_candels-wircam_jm_candels-wircam_hmerr_candels-wircam_hm_ap_candels-wircam_hmerr_ap_candels-wircam_hf_ap_candels-wircam_hferr_ap_candels-wircam_hflag_candels-wircam_hm_candels-wircam_kmerr_candels-wircam_km_ap_candels-wircam_kmerr_ap_candels-wircam_kf_ap_candels-wircam_kferr_ap_candels-wircam_kflag_candels-wircam_km_candels-newfirm_j1merr_candels-newfirm_j1m_ap_candels-newfirm_j1merr_ap_candels-newfirm_j1f_ap_candels-newfirm_j1ferr_ap_candels-newfirm_j1flag_candels-newfirm_j1m_candels-newfirm_j2merr_candels-newfirm_j2m_ap_candels-newfirm_j2merr_ap_candels-newfirm_j2f_ap_candels-newfirm_j2ferr_ap_candels-newfirm_j2flag_candels-newfirm_j2m_candels-newfirm_j3merr_candels-newfirm_j3m_ap_candels-newfirm_j3merr_ap_candels-newfirm_j3f_ap_candels-newfirm_j3ferr_ap_candels-newfirm_j3flag_candels-newfirm_j3m_candels-newfirm_h1merr_candels-newfirm_h1m_ap_candels-newfirm_h1merr_ap_candels-newfirm_h1f_ap_candels-newfirm_h1ferr_ap_candels-newfirm_h1flag_candels-newfirm_h1m_candels-newfirm_h2merr_candels-newfirm_h2m_ap_candels-newfirm_h2merr_ap_candels-newfirm_h2f_ap_candels-newfirm_h2ferr_ap_candels-newfirm_h2flag_candels-newfirm_h2m_candels-newfirm_kmerr_candels-newfirm_km_ap_candels-newfirm_kmerr_ap_candels-newfirm_kf_ap_candels-newfirm_kferr_ap_candels-newfirm_kflag_candels-newfirm_km_candels-irac_i1merr_candels-irac_i1m_ap_candels-irac_i1merr_ap_candels-irac_i1f_ap_candels-irac_i1ferr_ap_candels-irac_i1flag_candels-irac_i1m_candels-irac_i2merr_candels-irac_i2m_ap_candels-irac_i2merr_ap_candels-irac_i2f_ap_candels-irac_i2ferr_ap_candels-irac_i2flag_candels-irac_i2m_candels-irac_i3merr_candels-irac_i3m_ap_candels-irac_i3merr_ap_candels-irac_i3f_ap_candels-irac_i3ferr_ap_candels-irac_i3flag_candels-irac_i3m_candels-irac_i4merr_candels-irac_i4m_ap_candels-irac_i4merr_ap_candels-irac_i4f_ap_candels-irac_i4ferr_ap_candels-irac_i4flag_candels-irac_i4
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\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "catalogue[:10].show_in_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## II - Removal of duplicated sources" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We remove duplicated objects from the input catalogues." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The initial catalogue had 41457 sources.\n", "The cleaned catalogue has 41449 sources (8 removed).\n", "The cleaned catalogue has 8 sources flagged as having been cleaned\n" ] } ], "source": [ "SORT_COLS = ['ferr_ap_acs_f606w', 'ferr_ap_acs_f814w', 'ferr_ap_wfc3_f125w', 'ferr_ap_wfc3_f140w', 'ferr_ap_wfc3_f160w']\n", "FLAG_NAME = 'candels-egs_flag_cleaned'\n", "\n", "nb_orig_sources = len(catalogue)\n", "\n", "catalogue = remove_duplicates(catalogue, RA_COL, DEC_COL, sort_col=SORT_COLS,flag_name=FLAG_NAME)\n", "\n", "nb_sources = len(catalogue)\n", "\n", "print(\"The initial catalogue had {} sources.\".format(nb_orig_sources))\n", "print(\"The cleaned catalogue has {} sources ({} removed).\".format(nb_sources, nb_orig_sources - nb_sources))\n", "print(\"The cleaned catalogue has {} sources flagged as having been cleaned\".format(np.sum(catalogue[FLAG_NAME])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## III - Astrometry correction\n", "\n", "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." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gaia = Table.read(\"../../dmu0/dmu0_GAIA/data/GAIA_EGS.fits\")\n", "gaia_coords = SkyCoord(gaia['ra'], gaia['dec'])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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i4P1c2NYbZpuE/TUmWcuFrXvxWcx/5Mkuy00cEK3IMk0OlF5TLxRr9IW7jaQX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uC4W+EkKBrrckvRDZ6rxdJ0opqS4oxCXqFfCRa5mYBhQFfMS1w7FI\nI01O9KzHXJY7FMtIfBfwiFMSHND6XE8J6r3TCgMm28G6RBxnKNhz/As+b9xPvX2cA80f0WRLdlaI\nZDlvRkNrzZQpU84bbPSUNz7RA9kOxF3wmeDzXdQplFKUBBOZwTo7TE2sGVMZNMYTgUaBr/0y6DlW\nHt/qV87B5j3kWAUMzR/F0UiYA831+A1FjuXnG/lF+LvhwFKtNf/vsSo+bjxMjunn+/2uY2heSZKv\ncerPUa8BpRK/R1NZNDn1FPiLk3o9IXqq8wYa06ZNS0c7hMhOsRiqOQKGAVEPXZB30cHG1yJxp814\njbDrdBhoABT4ixndK7Gu0IHmJr5saaIxbqOUYqBpcSTawtBuuE7H/zRX89/1X2Aqg+Z4jG01nzA3\n78akXqN/ARxsSAQcpXkF+Mx6wMT14uT7ipJ6LSF6svMGGo888kg62iFEdrLjiSADQBmJx5cYaARN\ni2Y3hqkMXO2Ra17Y+SKuCye7MuPaw9Ukcv9JFnUdPmuuxvZcQlaQK/P7pL371NZum6VHPO0l/Rp9\nCqA4FzwNAWsoX0VMIvFmQv4SCiWbIUTSdGr11mSYOHEi1dXV7RZfq6ys5PLLL2f9+vWsX7+eo0eP\nUlxczNSpU6moqDjrYm27du1ixYoVfPLJJyiluOqqq1iwYAHXXXcdAA888ACvv/46ltX2VpcsWcJt\nt92WmpsU3YdlghNPLLilPTANmhyHmpiNoRX9cgOdronRK5CHoRQR1yHPCpw1m3GmgGHQO5BDc9xB\nozGBvjm5F3FT5/ZFuBZX65NdOzFqYk30CZ6RNWmJo2ps0KB7+6CwbbDkOi240TrMnFJM6/yDyc80\noqA/V+Zdxmct1eQaPr7V+6pLuaWz8p320vXJGZSSawjR06U90ABYtmwZ5eXl7bZv2LCBFStWsGrV\nKsrKyqiqqmLevHmEQiFmzZrVbv/6+nrmzp3L9OnTWblyJQBPP/00d999N9u2bSMUCgHw/e9/n+XL\nl6f2pkT3lBNEex64Hvj8uAE/XzQ0YyoFaA62RBhemN/p0xb5c+lscn5IXh7V0SjXFPWiyBcgz7I6\nlWk4HvGIutArALm+s48D904bvKCAeAfZBONoLDGKElBf2Xj5VuvjaP0+Gvb9Ga1drJwSir9Rjunv\nXBEgQynuGPi/aHZjBA0Ln5GRtyohRBJk1Zrutm2zcOFCrr/+ekzTpKysjAkTJvDuu+92uP/Bgwdp\nampixowZ5OXlkZeXx4wZM2hqauLzzz9Pb+NF95WXC4X5kBMk7uk2H8RxT2O7HnWxOJH4hRXhuliW\nYTIgN4+Bufnk+3ydCjKOtngcatbUxmBvgyYcP3tXRP+cIjytsd04PsPksjOzGVq3HUl5RhdO86G/\nAomuj3i4muiJPRfcztMppSiwghJkCNHFZSTQ2LJlC5MnT6asrIzy8nK2bt0KwJ133sntt9/eup/W\nmsOHD9OvX78Oz3P11VczZMgQ/vjHP9LU1EQ0GuVPf/oTQ4cOZcSIEa377d27lzvuuIPx48fzne98\nhzVr1uBeYGVGIU7nNxSBkzVjPK0JGAaHwjb1tsuRsEOTk53VOxsdME9mHEylaIydfd9CX5CxRQMZ\nWzSI0YX9WweutlIKr9CXKEDhaXSeCeapoEcbqnUWmtYeOru+zwgh0iztXxWGDx/OkCFDePTRR/H7\n/bz88stUVFSwYcMGxo4d22bflStXcuTIkdZukTMFAgHWrFnDvHnzWLduHQADBgxg9erV+P1+AAYO\nHEhLSwsLFixg0KBB/PWvf+XnP/85Sinuvvvu1N6s6HaUUlxZkEttzMZUClcrmpxEdsBQiibbo+DS\nxoqmRI4JkbjGUApXa/J8586GmMrANM8RIFwWwCu0EiMp89q+jeQPuIHGA2/iuVH8BYPIvWxcMm5B\nCNFFXdAy8ak2bdo0RowYwW9/+1sgsSLs8uXLqaysZPXq1Ywb1/EbVX19PVOnTuWWW27hnnvuAeD3\nv/89r7zyCn/+85/p1avj6n6PPPII27Zta82knI0sqibOp9GOUxONY6jEt/h8n0GfHH+mm9WO1prD\nLRrbg+IAFAdSm2VwnTDai2H6Q6gzMyJCiKyVkUXV0mHw4MFUV1cDEI1GmT9/Pjt27GDjxo1nDTIg\n0QXT0NDAwoULKSoqoqioiAULFhCLxdiyZcsFXU+IS1Hotwj5LSyVGGBZGszCdAaJTMzAfINhhUbK\ngwwA05eLFSiWIEMIkd5A49ChQyxdupTGxsY22/fv38+QIUNwXZeKigoikQgbN24872JunuehzyhR\nrLXGdV08z8N1XR577DGqqqo6vJ4QyVAStBiUH6Bvjj/ry/XHvYwnMIUQPUxaA42SkhK2bdvG0qVL\nqaurIxwO8+yzz3LgwAFmzpzJyy+/zMGDB1m9ejUFBR2nb2bNmsXatWsBuPHGG9Fas2LFCpqbm1vP\nB/Dtb38b0zT54osvWLx4Mfv378dxHLZu3corr7zCnDlz0nbfQmSa7bp8cKKB947Xs/NEA7YMhhZC\npEnax2js27ePxx9/nKqqKiKRCCNHjuT+++9n7NixTJo0icOHD2N2UADpww8/BBIFv6ZMmcJ9990H\nwPvvv8/TTz/NZ599RjQaZeTIkfzsZz9j/PjxADQ1NfHkk0/y9ttvU1tbS//+/bn33nsvqLS6jNEQ\n3cX+pjAnYk7r42K/xZWFeRlskRAiG6VijEZWDAbNVhJoiO7izECjV8DiigIJNIQQbXXbwaBCiNQa\nlBvEVArH8zBV4rEQQqSDlNwTogfwmQbX9S7E9XRr4a5k8JrBOw54YPQGI5S0UwshugnJaAjRgyQz\nyADwviJRflyB+1Vi3TkhhDidBBpCiIty5pInAEigIYQ4gwQaQnQjWmuqo2GORJqJuqldd0WpRFeJ\njid+VAGoC+iMlfHnQvQsMkZDiG7kULiJiBtHKUWjHeMbhcXtF0VLIrMEVCGgwQice98TsRb+p7kW\nT2sG5hYyKLcoZe0SQmQPyWgI0Y3EXLe1OqlWikiKsxoAhv/8QQbAvpZalFKYhsGhcAOOJ0XDhOgJ\nJNAQohv5egl7AKU1OWb2JC1Pr37uAZ50oQjRI2TPu5AQ4pINzC3gq1gEz/Mozg2mtNukswbnFvF5\nSy0emr6BAgIXGQTZbpxDkUR2ZFCwGF8WBVNCiPakMug5SGVQIZIr7nloND6j/TIDF0Jrzc66L/BI\nvG35lMm44kHJbKIQPZpUBhVCAIlppZEWCDdBV1ofzTKMiw4yAOLaI+adKqXe4tq4UrxDiKwmgYYQ\nmXYRScWWeog1gxOB5hMXdYpLEnXjGVkB1lIGQdPX+jjX9GVV95AQoj3p3BQiU6IxVGML4KFzcyE/\n94IPjduJOhYAngtuHCzfuY9JlupIpHU2S8jvp9h/AVNOkkQpxTWFA/giUgvA4Jxeabu2EOLiSKAh\nRDpofSoyOEk1taAMBZjQEkHnBMC8sG4Fwzyt3Le64MMume26hN04lpHIIjQ6dloDDQC/aXFlfp+0\nXlMIcfEk0BDJ43mJD1OV3PU0urS4izpei3Li6IAfXdqrw9+P0prO9H7kFUOkKRG/5OZBunoPTEPB\nac035LUWQpyHdG6K5AhHEt/QG5ohZme6NVlDNTShPA2miXLi0Njc+pzOz0N7Htr18HKDnUpLmCbk\nF0FBMfj8qWj5Wa6rDHr7A2itUUBpQJabF0Kcm2Q0xKVz3cSH6Ml0OjEHAmn89Mtmp4/SVKrt45wA\nOuhPbDO6Tsxf6PNTmM7oRgjRpXWddzeRvc5MnyspzfI1HSpIVOp0XbRSUJjfdgelulSQIYQQnSUZ\nDXHpDAMd8KNiDloBOZJOb+Wz0P37oD0vEVDImAYhRA8jgYZIjmAAHUzv7IMuQ6VxWogQQmQZCTSE\n6O5sBxWOAqDzguBLU8ENIYRAxmgI0b1pjWoOtz48/c9CCJEOEmgI0ZNonf565UKIHi3tXScTJ06k\nuroa44yR9pWVlVx++eWsX7+e9evXc/ToUYqLi5k6dSoVFRXt9v/arl27WLFiBZ988glKKa666ioW\nLFjAdddd17rP2rVr2bRpE0eOHKFfv37MmDGD2bNnp/I2RbbwPHAS5bLxWT1vhodSaJ+VmH6sQPv9\nMiBVCJFWGRmjsWzZMsrLy9tt37BhAytWrGDVqlWUlZVRVVXFvHnzCIVCzJo1q93+9fX1zJ07l+nT\np7Ny5UoAnn76ae6++262bdtGKBRi8+bNPPXUU6xatYrrrruO3bt3c8899xAKhZg2bVrK71VkWMym\ntZRlzO6ZM2IK8tCnB1tCCJFGWfX1zrZtFi5cyPXXX49pmpSVlTFhwgTefffdDvc/ePAgTU1NzJgx\ng7y8PPLy8pgxYwZNTU18/vnnALz00ktMnz6dCRMm4Pf7GT9+PNOnT2ft2rVpvDOREVqDp9s+7qnd\nBj5LggwhREZkJNDYsmULkydPpqysjPLycrZu3QrAnXfeye233966n9aaw4cP069fvw7Pc/XVVzNk\nyBD++Mc/0tTURDQa5U9/+hNDhw5lxIgR2LbNp59+ypgxY9ocN2bMGPbu3UskEkndTYrMUwrM0/6K\nK6ljIYQQ6Zb2QGP48OEMGzaMdevWsX37diZNmkRFRQVVVVXt9l25ciVHjhzhrrvu6vBcgUCANWvW\nsH37dsaPH8+1117LW2+9xTPPPIPf76e+vh7XdQmFQm2OKy4uxvM86uvrU3KPIosEA2CZiZ+glM2+\nFC021IahPtJzE0NCiM5Ley519erVbR7Pnz+ft956i02bNjF27FgAXNdl+fLlVFZW8rvf/Y6BAwd2\neK76+nrmzJnDLbfcwj333APA73//e+bMmcOf//zn87ZFybfbnqE7dRm4LkRjiT8HL3xZ+UtlN4Rp\nafFQpsLNCdKASVFOWi4thOjismKMxuDBg6murgYgGo0yf/58duzYwcaNGxk3btxZj9uyZQsNDQ0s\nXLiQoqIiioqKWLBgAbFYjC1btlBUVIRlWe0yF3V1dViWRXFxcUrvS4ikC0dR+uRyMieLcKVcLIZj\nxzEMlbhu1MaVjIYQ4gKlNdA4dOgQS5cupbGxsc32/fv3M2TIEFzXpaKigkgkwsaNGxk6dOg5z+d5\nHlrrxKJVJ2mtcV0Xz/Pw+/2MGjWKXbt2tTlu586djB49mkBASmaLLkRr1Gl/11W6Brd6ELQU3slr\nua4m2I2SREKI1EproFFSUsK2bdtYunQpdXV1hMNhnn32WQ4cOMDMmTN5+eWXOXjwIKtXr6agoKDD\nc8yaNat1xsiNN96I1poVK1bQ3Nzcej6Ab3/72wDMnj2bV199lXfeeQfbttmxYwevvfYac+bMScs9\nC5E0SqFNs3X2jDbN9AxuDfqxDE1vv0tQuRQV+siT4S5CiAuktE7vsK59+/bx+OOPU1VVRSQSYeTI\nkdx///2MHTuWSZMmcfjwYcwO+p0//PBDIFHwa8qUKdx3330AvP/++zz99NN89tlnRKNRRo4cyc9+\n9jPGjx/feuyGDRt44YUXOHbsGP3792fevHncdttt521rTU1Tku5aiCSyncT//Wlcs+TkUvcYRs8r\neiZED1Ja2vGX/EuR9kCjK5FAQwghRE+SikBDelqF6OniLrREUNpD+32QK9NJhBDJIzlQIXq6SDQx\n1MMwEmuixOOZbpEQohuRQENcuJ5cwrtbO+M19eQ1FkIkj3SdiAsTj59aBdU00zsQMUW01rg1n4Dd\njFE6EiOQ/L7JLiEYgJZETQ5tGd3itRVCZA8JNMSFcdxTUyldF7TV5dcNiR/8C/GaT1DKhJpP8I/+\nFwxfF6+tEnUTCYqcTlQM9fnQISuRrZIZJUKIJJN3FdFjuY1fJoIMQDtRdOOhDLfoElU7GIccjC8d\n1FG7c8cqJUGGECIl5J1FXBifmfimrEmsHdLFsxkAhr+gtaqsUgYqryTDLboEWmM0umAqMBWqyUXq\nhAshsoF0nYgLY1mJn27EuuI7qC/+go7HMEtHYgSLMt2ki6dU268NhoKuHwsKIbqB7vXJIUQnGL4A\n7uD/G88JY/q7/kBQ7zIfqiYOCnSJlQg2hBAiwyTQED2WE6sn0nIYZVjEYjXk5Q/F8uVlulkXL99E\n56dn2XghhLhQEmiIHsuxG1FG4p+AoSwcu/HiA41wHBX10ArItSAgw5+EEAJkMKjowQwzgNYeAFq7\nmFbw4k7kalTEA0OhlIKIVNYUQoivSUZD9FiBnD6Axo1HsXx5+APFnT9JR9VSZbKHEEK0kkCju3Ic\ncL3EbAS/r1tMR002pRTB3L4Xf4KmCMp2wVBo00K5ADrRdSKEEAKQQKN7ct3EipyoxLdt24GAP6NN\nsj0P19OAwmeA1dWLQ9lxVNwD8+R9KA9dfLLrRWZ7CCFEKwk0uiPPo00RhQwvhKa1xvU0xsmsSlx3\n0794EmAIIUQ7XfxrpeiQZZ0WXOhT37pF8vitxAJkrpf4XedmNmMkhBDZqlt+sezxlIKcQGLFVcNI\nrLaa0eYoTEO16TrJOnY8sTy6qRIl1i9EQU5rCXMZAyOEEB2TQKO7Ugp82bPct98wsjd/ZsdPrQvi\neGC4uNrEdcG0zhOnSYAhhBDnJIFGNnGcRBreJ7NE0so7bQyLUjhRTeRkMkhHITc/40khIYTosrL1\nO2bPE42hbAcVdyEczfgAzh7FNE4FG56Ho43WFdOVAU4nV1wXQghximQ0soXrtmYxFBrtedn1NToc\nwWhoRgd86OJQ98q4+MxEyO1pMH0YtoFrJ25R60SwIYQQ4uJIoJEtlMHXJSW1BrKpzkQshnnoCBgm\nqtFDO3G8y0oy3arkMk04GdcFAonXwHUTvViBQGabJoQQXVnaA42JEydSXV2NccYHaWVlJZdffjnr\n169n/fr1HD16lOLiYqZOnUpFRUW7/QE2b97M4sWL2213HIeKigoqKip44IEHeP3117Gstre6ZMkS\nbrvttuTe3KXICaBjNqATxbWyKGOgmsNgnPwUNgxUOJrZBqWYUpCTk+lWCCFE95CRjMayZcsoLy9v\nt33Dhg2sWLGCVatWUVZWRlVVFfPmzSMUCjFr1qx2+0+dOpWp/397dx4V1Xm/Afy5c2cDZIs7ta4R\nVHDFBaOJqWvUGFHilhgVrUE9VNOck0SrJD9rqqTGWuMalxgVa9wtTcQYPVWbutRaBXtarS3GXVCB\nCDLrnff3xwg6oqwzdwZ4PufMSebOnXvfR9D5znvf+76xsS7bLl68iDfeeANDhw4t3jZ8+HAkJye7\nP4g7SRJg9M2vziLAH7ib+3B0pIAwcs4IIiIqHx/qnwesVivee+89dO/eHbIsIzo6GjExMTh58mS5\n3qKDZdEAABr8SURBVG+32zFnzhxMmzYNLVq08HBrK0cIB6y2B7ApJm83pfyMBihNGkEE+EGEBsHR\nqL63W0RERNWEV3o00tLSsH79emRlZaFZs2aYMWMG+vfvjwkTJrjsJ4TAjRs3EB0dXa7jbtu2DWaz\nGZMnT3bZfvHiRYwdOxb//e9/UbduXYwcORI///nPIas82FIIgQfmbEiSBsKuwCHbYNAHqdqGSgvw\nhyPA39utqN4UxXkLs0Z2LnT3DD9aC2ETdtTRGmGU2XtERNWb6j0a4eHhaNmyJVJSUnD06FEMGDAA\niYmJOHfuXIl9V65ciZs3b5YoHJ6moKAAq1atwqxZs1wKiCZNmqBJkyb4zW9+g+PHj+P999/HmjVr\nsGHDBrfmKg+7YkbRGiSSJFevXg2qGocDUoEJkk2BZLYApqePc8mxFuBHuwlmhx3ZlvuwKHaVG0pE\n5F6SEN6fsGHEiBFo27YtFi5cCABQFAXJyclITU3FmjVr0Llz5zKPsWHDBuzatQtpaWll7rto0SIc\nPnwYhw4dKnW/O3fyyxegnBTFBpPlDiSNsyNJkjTwN9Swuzfo6axWSBZb8VMBAIEBJXa7bcqDHY7i\n54FaI4J17EkiInXUrx/o9mP6xO2tTZs2RVZWFgDAbDZj5syZuH79OrZv347mzZuX6xipqakYPHhw\nhc+nJlnWwaAPgdVeCEmSYNAFq94G8hJZBhyWR7ctP+P2Zb2shdVugUaS4BAOGDW8dEJE1Zuql06u\nXbuG+fPn4/79+y7bMzMz0axZMyiKgsTERJhMpgoVGZcvX8aFCxfQv39/l+2KouC3v/1ticsyRefz\nBp3WHwHGevA31IWs8Yk6j9QgyxD+RgiNBCFrAH/jU3d7Tl8HITp/GDU6NDAEwSDzd4SIqjdVC416\n9erh8OHDmD9/PnJzc1FYWIgVK1bg8uXLGD9+PLZs2YIrV65gzZo1CAx8evfNxIkTsWnTJpdt6enp\n0Gq1aN26tct2WZZx9epVJCUlITMzEzabDYcOHcKuXbsQHx/vsZxET6XTAQH+gL9fqfOkBOn8UM8Q\nyIGgRFQjqPp1yc/PDxs3bsTixYsxePBgmEwmtGvXDikpKWjZsiUSEhJw48YNxMTElHjv+fPnATh7\nRXJyclxey87ORlBQEHRPWa100aJFWLJkCeLj45GTk4OwsDD83//9H0aMGOGZkNWV1QbJZHaOHQjw\nA7S17Ju0EM6HL83ISkRUA/jEYFBf5e7BoD5LCEi5Pz72ISsgQio5fqTo18mHZjYtk9UK6d6PkISA\n0Osg6oZUr/YTEblJjR0MSl5W9G2+iKOStafFCslkBgQgDDrnJYJqQLr/ANLDIkuyKxCFJuclDiIi\nqjL2ExOg0UBodc5iw+GAeMolqPKQTBZnT4BGgmSxAg5H2W/yFkVxzmVhtRVNbfIY9mYQEbkLezTI\nKSgAouhDV1/DByEqCqT8Bw/Xgbc6iyybGZLDAWHQO+8IEYKXT4iI3ICFBjlJknPV2CoQfobilV2F\nQe+7Ayut9kdFxMMeGNGoHsTDHh3kP4AkHBAaGajjz4KDiKgKWGhUA7bCu7Cb7kDSGmEIag7JVz/4\nDHqIojU8ntVGh8P5mjczaDWAWQAayfVOE0kCzFbnn68kQwIgzBbA7+lzXhARUdlYaHiJEA44HHZo\nNLpSCwe7JQ/m3H9DkmQIswNwKDCGPq9iSyuotAJCUZz/dTicH+7e6vHQ6SD8hXN8hqxhIUFE5EEs\nNLzAodhgs90HoIEkCWh1wdBonr6SrGL5EZLkfE2SNHDYynnLrc3u/BCt7Ie5EIDJAggHIGsBYxXH\nbTx+V4skuT73BoP+6ZeKjHqIBw/Ha8gawGhQv21ERDUICw0vUBRTcfEASHAoZmg0JRfYAgCt8TlY\n8686ezSEAo0hpOwT5N2HZHP2Hog6fpX7xm62QsLDAZGKHcImOWe2rIrqMMBSlp0DY6tDW4mIqgEW\nGl4gOT/CAQBCCEjSs3sdZH0g/Ot1gNWUDVkbAH2dsNIPbrNBsivO3gwAUqEZojKFhiRQ3Eh39EBI\nkrN3peg4z+hpsSmA3e582VDFuqZKWGQQEbkFCw0vkHV1IGz5EEKBrNFC1pY+sZVsCIafoZwzdUoS\nHlUIqPwHpk4HYbJCkgQE3NCbAZR5GceuABZrcScKAC8XG0REVGUsNLxAkiTo9EGeObhWC+FnhGR2\nfmKLwErOcCnLQIARomjgpgrf8BWH612nig/P90VEROXDQqMmCvCHcMcU2pLkLDhUotU4p7gouutU\nq96piYjIQ1hokM+QZcBf7xynIWsAHX87iYiqPf5TTj5FllXtRCEiIg/z0TmiiYiIqCZgoUFEREQe\nw0KDiIiIPIaFBhEREXkMCw0iIiLyGN51UhsJ4Vx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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL], \n", " gaia_coords.ra, gaia_coords.dec)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RA correction: -0.07893977393678142 arcsec\n", "Dec correction: -0.034441607530766305 arcsec\n" ] } ], "source": [ "delta_ra, delta_dec = astrometric_correction(\n", " SkyCoord(catalogue[RA_COL], catalogue[DEC_COL]),\n", " gaia_coords\n", ")\n", "\n", "print(\"RA correction: {}\".format(delta_ra))\n", "print(\"Dec correction: {}\".format(delta_dec))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "catalogue[RA_COL].unit = u.deg\n", "catalogue[DEC_COL].unit = u.deg\n", "catalogue[RA_COL] = catalogue[RA_COL] + delta_ra.to(u.deg)\n", "catalogue[DEC_COL] = catalogue[DEC_COL] + delta_dec.to(u.deg)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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27MDQoUM1b6+kpASCIGDfvn3Ny5xOJ/bs2dPi9lwScLhawIFKJ+wuSTEAsPPB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\nauPDJIgOEaOyQY3rrXoAnUGyd5wMsSEip7bbm3riW2d3tID7fnJH25U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0RCR1rIWzTZiEhw2HoHhIplvUVUs8qDvSNkW4JQ6FkYw2KelCIYi3dKznEjZB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\niIhINCw0iIiISDQsNIiIiEg0LDSIiIhINCw0ABQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL], \n", " gaia_coords.ra, gaia_coords.dec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## IV - Flagging Gaia objects" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "catalogue.add_column(\n", " gaia_flag_column(SkyCoord(catalogue[RA_COL], catalogue[DEC_COL]), epoch, gaia)\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "160 sources flagged.\n" ] } ], "source": [ "GAIA_FLAG_NAME = \"candels-egs_flag_gaia\"\n", "\n", "catalogue['flag_gaia'].name = GAIA_FLAG_NAME\n", "print(\"{} sources flagged.\".format(np.sum(catalogue[GAIA_FLAG_NAME] > 0)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# V - Saving to disk" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "catalogue.write(\"{}/CANDELS-EGS.fits\".format(OUT_DIR), overwrite=True)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python (herschelhelp_internal)", "language": "python", "name": "helpint" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }