{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# GAMA-09 master catalogue\n", "\n", "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on GAMA-09." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This notebook was run with herschelhelp_internal version: \n", "017bb1e (Mon Jun 18 14:58:59 2018 +0100)\n" ] } ], "source": [ "from herschelhelp_internal import git_version\n", "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/seaborn/apionly.py:6: UserWarning: As seaborn no longer sets a default style on import, the seaborn.apionly module is deprecated. It will be removed in a future version.\n", " warnings.warn(msg, UserWarning)\n" ] } ], "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", "import os\n", "import time\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", "from pymoc import MOC\n", "\n", "from herschelhelp_internal.masterlist import merge_catalogues, nb_merge_dist_plot, specz_merge\n", "from herschelhelp_internal.utils import coords_to_hpidx, ebv, gen_help_id, inMoc" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n", "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n", "SUFFIX = os.environ.get('SUFFIX', time.strftime(\"_%Y%m%d\"))\n", "\n", "try:\n", " os.makedirs(OUT_DIR)\n", "except FileExistsError:\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## I - Reading the prepared pristine catalogues" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cfhtlens = Table.read(\"{}/CFHTLENS.fits\".format(TMP_DIR))\n", "cfhtls = Table.read(\"{}/CFHTLS.fits\".format(TMP_DIR))\n", "decals = Table.read(\"{}/DECaLS.fits\".format(TMP_DIR))\n", "hsc = Table.read(\"{}/HSC-SSP.fits\".format(TMP_DIR))\n", "kids = Table.read(\"{}/KIDS.fits\".format(TMP_DIR))\n", "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))\n", "las = Table.read(\"{}/UKIDSS-LAS.fits\".format(TMP_DIR))\n", "vhs = Table.read(\"{}/VISTA-VHS.fits\".format(TMP_DIR))\n", "viking = Table.read(\"{}/VISTA-VIKING.fits\".format(TMP_DIR))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## II - Merging tables\n", "\n", "We first merge the optical catalogues and then add the infrared ones: CFHTLenS, CFHTLS, DECaLS, HSC, KIDS, PanSTARRS, UKIDSS-LAS, VISTA-VHS, and VISTA-VIKING.\n", "\n", "At every step, we look at the distribution of the distances separating the sources from one catalogue to the other (within a maximum radius) to determine the best cross-matching radius." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CFHTLenS" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue = cfhtlens\n", "master_catalogue['cfhtlens_ra'].name = 'ra'\n", "master_catalogue['cfhtlens_dec'].name = 'dec'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add CFHTLS" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(cfhtls['cfhtls_ra'], cfhtls['cfhtls_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Given the graph above, we use 0.8 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, cfhtls, \"cfhtls_ra\", \"cfhtls_dec\", radius=0.8*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add DECaLS" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(decals['decals_ra'], decals['decals_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Given the graph above, we use 0.8 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, decals, \"decals_ra\", \"decals_dec\", radius=0.8*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add HSC-PSS" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(hsc['hsc_ra'], hsc['hsc_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Given the graph above, we use 0.8 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, hsc, \"hsc_ra\", \"hsc_dec\", radius=0.8*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add KIDS" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(kids['kids_ra'], kids['kids_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Given the graph above, we use 0.8 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, kids, \"kids_ra\", \"kids_dec\", radius=0.8*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add PanSTARRS" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(ps1['ps1_ra'], ps1['ps1_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Given the graph above, we use 0.8 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, ps1, \"ps1_ra\", \"ps1_dec\", radius=0.8*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add UKIDSS LAS" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(las['las_ra'], las['las_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Given the graph above, we use 0.8 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, las, \"las_ra\", \"las_dec\", radius=0.8*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add VHS" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(vhs['vhs_ra'], vhs['vhs_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Given the graph above, we use 1 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, vhs, \"vhs_ra\", \"vhs_dec\", radius=1.*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add VIKING" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(viking['viking_ra'], viking['viking_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Given the graph above, we use 1 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, viking, \"viking_ra\", \"viking_dec\", radius=1.*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cleaning\n", "\n", "When we merge the catalogues, astropy masks the non-existent values (e.g. when a row comes only from a catalogue and has no counterparts in the other, the columns from the latest are masked for that row). We indicate to use NaN for masked values for floats columns, False for flag columns and -1 for ID columns." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for col in master_catalogue.colnames:\n", " if \"m_\" in col or \"merr_\" in col or \"f_\" in col or \"ferr_\" in col or \"stellarity\" in col:\n", " master_catalogue[col].fill_value = np.nan\n", " elif \"flag\" in col:\n", " master_catalogue[col].fill_value = 0\n", " elif \"id\" in col:\n", " master_catalogue[col].fill_value = -1\n", " \n", "master_catalogue = master_catalogue.filled()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=10\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
idxcfhtlens_idradeccfhtlens_stellaritym_cfhtlens_umerr_cfhtlens_um_cfhtlens_gmerr_cfhtlens_gm_cfhtlens_rmerr_cfhtlens_rm_cfhtlens_imerr_cfhtlens_im_cfhtlens_zmerr_cfhtlens_zf_cfhtlens_uferr_cfhtlens_uflag_cfhtlens_uf_cfhtlens_gferr_cfhtlens_gflag_cfhtlens_gf_cfhtlens_rferr_cfhtlens_rflag_cfhtlens_rf_cfhtlens_iferr_cfhtlens_iflag_cfhtlens_if_cfhtlens_zferr_cfhtlens_zflag_cfhtlens_zcfhtlens_flag_cleanedcfhtlens_flag_gaiaflag_mergedcfhtls_idcfhtls_stellaritym_cfhtls_umerr_cfhtls_um_cfhtls_gmerr_cfhtls_gm_cfhtls_rmerr_cfhtls_rm_cfhtls_imerr_cfhtls_im_cfhtls_zmerr_cfhtls_zm_ap_cfhtls_umerr_ap_cfhtls_um_ap_cfhtls_gmerr_ap_cfhtls_gm_ap_cfhtls_rmerr_ap_cfhtls_rm_ap_cfhtls_imerr_ap_cfhtls_im_ap_cfhtls_zmerr_ap_cfhtls_zf_cfhtls_uferr_cfhtls_uflag_cfhtls_uf_cfhtls_gferr_cfhtls_gflag_cfhtls_gf_cfhtls_rferr_cfhtls_rflag_cfhtls_rf_cfhtls_iferr_cfhtls_iflag_cfhtls_if_cfhtls_zferr_cfhtls_zflag_cfhtls_zf_ap_cfhtls_uferr_ap_cfhtls_uf_ap_cfhtls_gferr_ap_cfhtls_gf_ap_cfhtls_rferr_ap_cfhtls_rf_ap_cfhtls_iferr_ap_cfhtls_if_ap_cfhtls_zferr_ap_cfhtls_zcfhtls_flag_cleanedcfhtls_flag_gaiadecals_idf_decam_gf_decam_rf_decam_zferr_decam_gferr_decam_rferr_decam_zf_ap_decam_gf_ap_decam_rf_ap_decam_zferr_ap_decam_gferr_ap_decam_rferr_ap_decam_zm_decam_gmerr_decam_gflag_decam_gm_decam_rmerr_decam_rflag_decam_rm_decam_zmerr_decam_zflag_decam_zm_ap_decam_gmerr_ap_decam_gm_ap_decam_rmerr_ap_decam_rm_ap_decam_zmerr_ap_decam_zdecals_stellaritydecals_flag_cleaneddecals_flag_gaiahsc_idm_ap_suprime_gmerr_ap_suprime_gm_suprime_gmerr_suprime_gm_ap_suprime_rmerr_ap_suprime_rm_suprime_rmerr_suprime_rm_ap_suprime_imerr_ap_suprime_im_suprime_imerr_suprime_im_ap_suprime_zmerr_ap_suprime_zm_suprime_zmerr_suprime_zm_ap_suprime_ymerr_ap_suprime_ym_suprime_ymerr_suprime_yhsc_stellarityf_ap_suprime_gferr_ap_suprime_gf_suprime_gferr_suprime_gflag_suprime_gf_ap_suprime_rferr_ap_suprime_rf_suprime_rferr_suprime_rflag_suprime_rf_ap_suprime_iferr_ap_suprime_if_suprime_iferr_suprime_iflag_suprime_if_ap_suprime_zferr_ap_suprime_zf_suprime_zferr_suprime_zflag_suprime_zf_ap_suprime_yferr_ap_suprime_yf_suprime_yferr_suprime_yflag_suprime_yhsc_flag_cleanedhsc_flag_gaiakids_idkids_stellaritym_kids_umerr_kids_um_kids_gmerr_kids_gm_kids_rmerr_kids_rm_kids_imerr_kids_if_ap_kids_uferr_ap_kids_uf_ap_kids_gferr_ap_kids_gf_ap_kids_rferr_ap_kids_rf_ap_kids_iferr_ap_kids_if_kids_uferr_kids_uflag_kids_uf_kids_gferr_kids_gflag_kids_gf_kids_rferr_kids_rflag_kids_rf_kids_iferr_kids_iflag_kids_im_ap_kids_umerr_ap_kids_um_ap_kids_gmerr_ap_kids_gm_ap_kids_rmerr_ap_kids_rm_ap_kids_imerr_ap_kids_ikids_flag_cleanedkids_flag_gaiaps1_idm_ap_gpc1_gmerr_ap_gpc1_gm_gpc1_gmerr_gpc1_gm_ap_gpc1_rmerr_ap_gpc1_rm_gpc1_rmerr_gpc1_rm_ap_gpc1_imerr_ap_gpc1_im_gpc1_imerr_gpc1_im_ap_gpc1_zmerr_ap_gpc1_zm_gpc1_zmerr_gpc1_zm_ap_gpc1_ymerr_ap_gpc1_ym_gpc1_ymerr_gpc1_yf_ap_gpc1_gferr_ap_gpc1_gf_gpc1_gferr_gpc1_gflag_gpc1_gf_ap_gpc1_rferr_ap_gpc1_rf_gpc1_rferr_gpc1_rflag_gpc1_rf_ap_gpc1_iferr_ap_gpc1_if_gpc1_iferr_gpc1_iflag_gpc1_if_ap_gpc1_zferr_ap_gpc1_zf_gpc1_zferr_gpc1_zflag_gpc1_zf_ap_gpc1_yferr_ap_gpc1_yf_gpc1_yferr_gpc1_yflag_gpc1_yps1_flag_cleanedps1_flag_gaialas_idm_ukidss_ymerr_ukidss_ym_ap_ukidss_ymerr_ap_ukidss_ym_ukidss_jmerr_ukidss_jm_ap_ukidss_jmerr_ap_ukidss_jm_ap_ukidss_hmerr_ap_ukidss_hm_ukidss_hmerr_ukidss_hm_ap_ukidss_kmerr_ap_ukidss_km_ukidss_kmerr_ukidss_klas_stellarityf_ukidss_yferr_ukidss_yflag_ukidss_yf_ap_ukidss_yferr_ap_ukidss_yf_ukidss_jferr_ukidss_jflag_ukidss_jf_ap_ukidss_jferr_ap_ukidss_jf_ap_ukidss_hferr_ap_ukidss_hf_ukidss_hferr_ukidss_hflag_ukidss_hf_ap_ukidss_kferr_ap_ukidss_kf_ukidss_kferr_ukidss_kflag_ukidss_klas_flag_cleanedlas_flag_gaiavhs_idvhs_stellaritym_vhs_ymerr_vhs_ym_ap_vhs_ymerr_ap_vhs_ym_vhs_jmerr_vhs_jm_ap_vhs_jmerr_ap_vhs_jm_vhs_hmerr_vhs_hm_ap_vhs_hmerr_ap_vhs_hm_vhs_kmerr_vhs_km_ap_vhs_kmerr_ap_vhs_kf_vhs_yferr_vhs_yflag_vhs_yf_ap_vhs_yferr_ap_vhs_yf_vhs_jferr_vhs_jflag_vhs_jf_ap_vhs_jferr_ap_vhs_jf_vhs_hferr_vhs_hflag_vhs_hf_ap_vhs_hferr_ap_vhs_hf_vhs_kferr_vhs_kflag_vhs_kf_ap_vhs_kferr_ap_vhs_kvhs_flag_cleanedvhs_flag_gaiaviking_idviking_stellaritym_viking_zmerr_viking_zm_ap_viking_zmerr_ap_viking_zm_viking_ymerr_viking_ym_ap_viking_ymerr_ap_viking_ym_viking_jmerr_viking_jm_ap_viking_jmerr_ap_viking_jm_viking_hmerr_viking_hm_ap_viking_hmerr_ap_viking_hm_viking_kmerr_viking_km_ap_viking_kmerr_ap_viking_kf_viking_zferr_viking_zflag_viking_zf_ap_viking_zferr_ap_viking_zf_viking_yferr_viking_yflag_viking_yf_ap_viking_yferr_ap_viking_yf_viking_jferr_viking_jflag_viking_jf_ap_viking_jferr_ap_viking_jf_viking_hferr_viking_hflag_viking_hf_ap_viking_hferr_ap_viking_hf_viking_kferr_viking_kflag_viking_kf_ap_viking_kferr_ap_viking_kviking_flag_cleanedviking_flag_gaia
degdegmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmaguJyuJyuJyuJyuJyuJyuJyuJy
0W2p3p3_84208136.57007116730503-1.46922177542553010.52438319.79440.001518.31210.000517.7660.0006nannan17.42340.000643.877310.060618747False171.854190.07914178False284.18390.15704581FalsenannanFalse389.61850.21531115FalseFalse2TruenannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
1W2p3p3_150700136.66092926730502-1.08012025942553010.9829120.22560.001719.53820.000819.30590.001119.14430.000819.08020.001229.4957850.046183255False55.5545350.04093409False68.8081360.069712095False79.850910.058836322False84.707110.09362176FalseFalse3FalsenannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
2W2p1p3_180414134.28387446730503-0.980803794925530.00038077719.51530.002218.88930.001118.71730.001518.54330.001318.76770.004856.738720.11496825False100.99040.102317154False118.3258360.16347319False138.892790.16630249False112.958750.4993865FalseFalse0FalsenannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
3W2m1p3_85804132.38221556730502-1.49509635842553010.028528221.53980.005320.58470.001519.55650.001119.25410.001518.96810.0028.7918320.04291716False21.189460.029274322False54.6260830.055343725False72.170430.099707134False93.920460.17300789FalseFalse0TruenannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
4W2m1p2_205391132.21779586730503-1.987024800425530.026268622.40170.005321.61450.002221.23040.002821.04910.002220.99060.00533.9748470.019403137False8.2072910.016630229False11.6906760.030149028False13.8152710.027993536False14.5800730.071172334FalseFalse0FalsenannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
5W2m0p3_124333133.47038076730502-1.356366537425530.046883322.04280.005421.6750.00321.13860.002520.79690.002420.88590.00715.5319650.027513688False7.76247740.021448517False12.7221320.029293792False17.4276980.03852361False16.0560910.10499627FalseFalse2FalsenannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
6W2p2p2_133751135.70847486730503-2.057977137425530.13805622.96890.006322.05660.002621.32030.002120.69650.001920.27570.0032.35743330.01367904False5.46209860.013080025False10.7616870.020814946False19.116120.033452533False28.1656570.077824585FalseFalse0FalsenannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
7W2m1p3_157146132.78117686730502-1.100725478425530.025308222.33680.006621.42970.002220.87120.001920.4420.002120.16140.00394.2196840.025650717False9.7301510.01971596False16.2749540.028480593False24.1657430.046740692False31.2924820.11240362FalseFalse0TruenannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
8W2p3p3_89016136.018186067305-1.440058984425530.0003255522.89280.008221.98790.003921.53230.004221.26090.00421.1390.00712.52860050.0190972False5.81888960.020901643False8.8527790.034245584False11.3668390.041876987False12.717440.083163686FalseFalse0FalsenannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
9W2p2p3_167403135.67937746730502-1.00910061442553010.027707422.52870.008321.90270.002921.54640.00420.98360.00420.84010.00693.53606370.027031729False6.2938980.016810993False8.7385550.03219403False14.6743770.054062407False16.7478830.10643505FalseFalse0FalsenannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "master_catalogue[:10].show_in_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## III - Merging flags and stellarity\n", "\n", "Each pristine catalogue contains a flag indicating if the source was associated to a another nearby source that was removed during the cleaning process. We merge these flags in a single one." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "flag_cleaned_columns = [column for column in master_catalogue.colnames\n", " if 'flag_cleaned' in column]\n", "\n", "flag_column = np.zeros(len(master_catalogue), dtype=bool)\n", "for column in flag_cleaned_columns:\n", " flag_column |= master_catalogue[column]\n", " \n", "master_catalogue.add_column(Column(data=flag_column, name=\"flag_cleaned\"))\n", "master_catalogue.remove_columns(flag_cleaned_columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each pristine catalogue contains a flag indicating the probability of a source being a Gaia object (0: not a Gaia object, 1: possibly, 2: probably, 3: definitely). We merge these flags taking the highest value." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "flag_gaia_columns = [column for column in master_catalogue.colnames\n", " if 'flag_gaia' in column]\n", "\n", "master_catalogue.add_column(Column(\n", " data=np.max([master_catalogue[column] for column in flag_gaia_columns], axis=0),\n", " name=\"flag_gaia\"\n", "))\n", "master_catalogue.remove_columns(flag_gaia_columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each prisitine catalogue may contain one or several stellarity columns indicating the probability (0 to 1) of each source being a star. We merge these columns taking the highest value." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cfhtlens_stellarity, cfhtls_stellarity, decals_stellarity, hsc_stellarity, kids_stellarity, las_stellarity, vhs_stellarity, viking_stellarity\n" ] } ], "source": [ "stellarity_columns = [column for column in master_catalogue.colnames\n", " if 'stellarity' in column]\n", "\n", "print(\", \".join(stellarity_columns))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# We create an masked array with all the stellarities and get the maximum value, as well as its\n", "# origin. Some sources may not have an associated stellarity.\n", "stellarity_array = np.array([master_catalogue[column] for column in stellarity_columns])\n", "stellarity_array = np.ma.masked_array(stellarity_array, np.isnan(stellarity_array))\n", "\n", "max_stellarity = np.max(stellarity_array, axis=0)\n", "max_stellarity.fill_value = np.nan\n", "\n", "no_stellarity_mask = max_stellarity.mask\n", "\n", "master_catalogue.add_column(Column(data=max_stellarity.filled(), name=\"stellarity\"))\n", "\n", "stellarity_origin = np.full(len(master_catalogue), \"NO_INFORMATION\", dtype=\"S20\")\n", "stellarity_origin[~no_stellarity_mask] = np.array(stellarity_columns)[np.argmax(stellarity_array, axis=0)[~no_stellarity_mask]]\n", "\n", "master_catalogue.add_column(Column(data=stellarity_origin, name=\"stellarity_origin\"))\n", "\n", "master_catalogue.remove_columns(stellarity_columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## IV - Adding E(B-V) column" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue.add_column(\n", " ebv(master_catalogue['ra'], master_catalogue['dec'])\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## V - Adding HELP unique identifiers and field columns" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue.add_column(Column(gen_help_id(master_catalogue['ra'], master_catalogue['dec']),\n", " name=\"help_id\"))\n", "master_catalogue.add_column(Column(np.full(len(master_catalogue), \"GAMA-09\", dtype='" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(specz['ra'], specz['dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue = specz_merge(master_catalogue, specz, radius=.8 * u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## VII - Choosing between multiple values for the same filter\n", "\n", "Both CFHTLenS and CFHTLS, and VISTA-VIKING and VISTA-VHS have measurements from the same camera and filters. We wish to choose the superior measurement where both are present." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### VII.a CFHTLenS and CFHTLS\n", "CFHTLS is optimised for deep photometry so we take that for " ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "megacam_origin = Table()\n", "megacam_origin.add_column(master_catalogue['help_id'])" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For Megacam band u:\n", "898336 sources with CFHTLS flux\n", "403760 sources with CFHTLenS flux\n", "386292 sources with CFHTLS and CFHTLenS flux\n", "898336 sources for which we use CFHTLS\n", "17468 sources for which we use CFHTLenS\n", "For Megacam band g:\n", "968859 sources with CFHTLS flux\n", "506111 sources with CFHTLenS flux\n", "486700 sources with CFHTLS and CFHTLenS flux\n", "968859 sources for which we use CFHTLS\n", "19411 sources for which we use CFHTLenS\n", "For Megacam band r:\n", "984214 sources with CFHTLS flux\n", "509786 sources with CFHTLenS flux\n", "489579 sources with CFHTLS and CFHTLenS flux\n", "984214 sources for which we use CFHTLS\n", "20207 sources for which we use CFHTLenS\n", "For Megacam band i:\n", "959749 sources with CFHTLS flux\n", "529203 sources with CFHTLenS flux\n", "498878 sources with CFHTLS and CFHTLenS flux\n", "959749 sources for which we use CFHTLS\n", "30325 sources for which we use CFHTLenS\n", "For Megacam band z:\n", "846472 sources with CFHTLS flux\n", "421512 sources with CFHTLenS flux\n", "396727 sources with CFHTLS and CFHTLenS flux\n", "846472 sources for which we use CFHTLS\n", "24785 sources for which we use CFHTLenS\n" ] } ], "source": [ "megacam_bands = ['u','g','r','i','z'] # Lowercase naming convention (k is Ks)\n", "for band in megacam_bands:\n", " print('For Megacam band ' + band + ':')\n", " # Megacam total flux \n", " has_cfhtls = ~np.isnan(master_catalogue['f_cfhtls_' + band])\n", " has_cfhtlens = ~np.isnan(master_catalogue['f_cfhtlens_' + band])\n", " has_both = has_cfhtls & has_cfhtlens\n", "\n", " print(\"{} sources with CFHTLS flux\".format(np.sum(has_cfhtls)))\n", " print(\"{} sources with CFHTLenS flux\".format(np.sum(has_cfhtlens)))\n", " print(\"{} sources with CFHTLS and CFHTLenS flux\".format(np.sum(has_both)))\n", "\n", "\n", " use_cfhtls = has_cfhtls \n", " use_cfhtlens = has_cfhtlens & ~has_both\n", "\n", " print(\"{} sources for which we use CFHTLS\".format(np.sum(use_cfhtls)))\n", " print(\"{} sources for which we use CFHTLenS\".format(np.sum(use_cfhtlens)))\n", "\n", " f_megacam = np.full(len(master_catalogue), np.nan)\n", " f_megacam[use_cfhtls] = master_catalogue['f_cfhtls_' + band][use_cfhtls]\n", " f_megacam[use_cfhtlens] = master_catalogue['f_cfhtlens_' + band][use_cfhtlens]\n", "\n", " ferr_megacam = np.full(len(master_catalogue), np.nan)\n", " ferr_megacam[use_cfhtls] = master_catalogue['ferr_cfhtls_' + band][use_cfhtls]\n", " ferr_megacam[use_cfhtlens] = master_catalogue['ferr_cfhtlens_' + band][use_cfhtlens]\n", " \n", " m_megacam = np.full(len(master_catalogue), np.nan)\n", " m_megacam[use_cfhtls] = master_catalogue['m_cfhtls_' + band][use_cfhtls]\n", " m_megacam[use_cfhtlens] = master_catalogue['m_cfhtlens_' + band][use_cfhtlens]\n", "\n", " merr_megacam = np.full(len(master_catalogue), np.nan)\n", " merr_megacam[use_cfhtls] = master_catalogue['merr_cfhtls_' + band][use_cfhtls]\n", " merr_megacam[use_cfhtlens] = master_catalogue['merr_cfhtlens_' + band][use_cfhtlens]\n", "\n", " flag_megacam = np.full(len(master_catalogue), False, dtype=bool)\n", " flag_megacam[use_cfhtls] = master_catalogue['flag_cfhtls_' + band][use_cfhtls]\n", " flag_megacam[use_cfhtlens] = master_catalogue['flag_cfhtlens_' + band][use_cfhtlens]\n", "\n", " master_catalogue.add_column(Column(data=f_megacam, name=\"f_megacam_\" + band))\n", " master_catalogue.add_column(Column(data=ferr_megacam, name=\"ferr_megacam_\" + band))\n", " master_catalogue.add_column(Column(data=m_megacam, name=\"m_megacam_\" + band))\n", " master_catalogue.add_column(Column(data=merr_megacam, name=\"merr_megacam_\" + band))\n", " master_catalogue.add_column(Column(data=flag_megacam, name=\"flag_megacam_\" + band))\n", "\n", " master_catalogue.remove_columns(['f_cfhtls_' + band, \n", " 'f_cfhtlens_' + band, \n", " 'ferr_cfhtls_' + band,\n", " 'ferr_cfhtlens_' + band, \n", " 'm_cfhtls_' + band, \n", " 'm_cfhtlens_' + band, \n", " 'merr_cfhtls_' + band,\n", " 'merr_cfhtlens_' + band,\n", " 'flag_cfhtls_' + band, \n", " 'flag_cfhtlens_' + band])\n", "\n", " origin = np.full(len(master_catalogue), ' ', dtype='= 2\n", "has_nir_flux = nb_nir_flux >= 2\n", "has_mir_flux = nb_mir_flux >= 2\n", "\n", "master_catalogue.add_column(\n", " Column(\n", " 1 * has_optical_flux + 2 * has_nir_flux + 4 * has_mir_flux,\n", " name=\"flag_optnir_det\")\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## IX - Cross-identification table\n", "\n", "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogue. This can be used to easily get additional information from them.\n", "\n", "For convenience, we also cross-match the master list with the SDSS catalogue and add the objID associated with each source, if any. **TODO: should we correct the astrometry with respect to Gaia positions?**" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "910 master list rows had multiple associations.\n" ] } ], "source": [ "#\n", "# Addind SDSS ids\n", "#\n", "sdss = Table.read(\"../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_GAMA-09.fits\")['objID', 'ra', 'dec']\n", "sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)\n", "idx_ml, d2d, _ = sdss_coords.match_to_catalog_sky(SkyCoord(master_catalogue['ra'], master_catalogue['dec']))\n", "idx_sdss = np.arange(len(sdss))\n", "\n", "# Limit the cross-match to 1 arcsec\n", "mask = d2d <= 1. * u.arcsec\n", "idx_ml = idx_ml[mask]\n", "idx_sdss = idx_sdss[mask]\n", "d2d = d2d[mask]\n", "nb_orig_matches = len(idx_ml)\n", "\n", "# In case of multiple associations of one master list object to an SDSS object, we keep only the\n", "# association to the nearest one.\n", "sort_idx = np.argsort(d2d)\n", "idx_ml = idx_ml[sort_idx]\n", "idx_sdss = idx_sdss[sort_idx]\n", "_, unique_idx = np.unique(idx_ml, return_index=True)\n", "idx_ml = idx_ml[unique_idx]\n", "idx_sdss = idx_sdss[unique_idx]\n", "print(\"{} master list rows had multiple associations.\".format(nb_orig_matches - len(idx_ml)))\n", "\n", "# Adding the ObjID to the master list\n", "master_catalogue.add_column(Column(data=np.full(len(master_catalogue), -1, dtype='>i8'), name=\"sdss_id\"))\n", "master_catalogue['sdss_id'][idx_ml] = sdss['objID'][idx_sdss]\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['cfhtlens_id', 'cfhtls_id', 'decals_id', 'hsc_id', 'kids_id', 'ps1_id', 'las_id', 'vhs_id', 'viking_id', 'help_id', 'specz_id', 'sdss_id']\n" ] } ], "source": [ "id_names = []\n", "for col in master_catalogue.colnames:\n", " if '_id' in col:\n", " id_names += [col]\n", " if '_intid' in col:\n", " id_names += [col]\n", " \n", "print(id_names)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue[id_names].write(\n", " \"{}/master_list_cross_ident_gama-09{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)\n", "id_names.remove('help_id')\n", "master_catalogue.remove_columns(id_names)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## X - Adding HEALPix index\n", "\n", "We are adding a column with a HEALPix index at order 13 associated with each source." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue.add_column(Column(\n", " data=coords_to_hpidx(master_catalogue['ra'], master_catalogue['dec'], order=13),\n", " name=\"hp_idx\"\n", "))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## XI - Renaming some columns" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We use vista_ks as filter ID for VISTA Ks\n", "for column in master_catalogue.colnames:\n", " if \"vista_k\" in column:\n", " master_catalogue[column].name = column.replace(\"vista_k\", \"vista_ks\")" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We use omegacam_XXX for the KIDS bands\n", "for column in master_catalogue.colnames:\n", " if \"_kids_\" in column:\n", " master_catalogue[column].name = column.replace(\"_kids_\", \"_omegacam_\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## XI - Saving the catalogue" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "columns = [\"help_id\", \"field\", \"ra\", \"dec\", \"hp_idx\"]\n", "\n", "bands = [column[5:] for column in master_catalogue.colnames if 'f_ap' in column]\n", "for band in bands:\n", " columns += [\"f_ap_{}\".format(band), \"ferr_ap_{}\".format(band),\n", " \"m_ap_{}\".format(band), \"merr_ap_{}\".format(band),\n", " \"f_{}\".format(band), \"ferr_{}\".format(band),\n", " \"m_{}\".format(band), \"merr_{}\".format(band),\n", " \"flag_{}\".format(band)] \n", " \n", "columns += [\"stellarity\", \"stellarity_origin\",\"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \"flag_optnir_obs\", \"flag_optnir_det\", \n", " \"zspec\", \"zspec_qual\", \"zspec_association_flag\", \"ebv\"]" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing columns: set()\n" ] } ], "source": [ "# We check for columns in the master catalogue that we will not save to disk.\n", "print(\"Missing columns: {}\".format(set(master_catalogue.colnames) - set(columns)))" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue[columns].write(\"{}/master_catalogue_gama-09{}.fits\".format(OUT_DIR, SUFFIX))" ] } ], "metadata": { "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": 2 }