{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# GAMA-15 master catalogue\n", "\n", "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on GAMA-15." ] }, { "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/pyenv/versions/3.7.2/lib/python3.7/site-packages/matplotlib/__init__.py:855: MatplotlibDeprecationWarning: \n", "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n", " \"found relative to the 'datapath' directory.\".format(key))\n", "/opt/pyenv/versions/3.7.2/lib/python3.7/site-packages/matplotlib/__init__.py:846: MatplotlibDeprecationWarning: \n", "The text.latex.unicode rcparam was deprecated in Matplotlib 2.2 and will be removed in 3.1.\n", " \"2.2\", name=key, obj_type=\"rcparam\", addendum=addendum)\n", "/opt/pyenv/versions/3.7.2/lib/python3.7/site-packages/seaborn/apionly.py:9: 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": [ "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", "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: DECaLS, HSC, KIDS, PanSTARRS, UKIDSS-LAS, 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": [ "### DECaLS" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue = decals\n", "master_catalogue['decals_ra'].name = 'ra'\n", "master_catalogue['decals_dec'].name = 'dec'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add HSC-PSS" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": 7, "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": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": 9, "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": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": 11, "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": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": 13, "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 VIKING" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": 15, "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": 16, "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": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=10\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
idxdecals_idradecf_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_gaiaflag_mergedhsc_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_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
degdegmagmagmagmagmagmagmagmaguJyuJyuJyuJyuJyuJyuJyuJy
033124905878220.361457870107050.1035012513952082705869.441260276.4960225.167.10285145.97961153.8942419733.06822460.38518694.0490.133897480.169413240.30457499.2781910.00010321455False8.6488380.00012576238False8.9440690.00017400977False13.1620137.3671945e-0613.0214578.189445e-0613.2207411.768948e-050.05False3False-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
133412900162220.43994103268030.3931443042398369310202.34956618.91261758.636.205383109.5049158.4319218893.2823251.61718278.060.138146530.169596570.3306541710.1708870.00012672211False8.9481510.00012428506False8.647560.00013632976False13.2092317.938839e-0612.9838677.919326e-0613.2451741.964121e-050.05False2False-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
232981300002221.40662561248152-0.3645660861060052146724.28890312.751095498.523.36754118.79906188.1736315659.1223006.61123442.640.142970430.19160350.5841653310.9837460.00017291604False9.0261460.00014487543False8.8009720.00018649676False13.4130819.912956e-0612.9953689.04222e-0612.9749832.7055376e-050.05False2False-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
333125105700220.849023096443910.09718165888867425118615.97152950.17156046.4413.48258122.88297731.67916511792.96110872.0018740.6620.146415830.179949610.31665311.2146450.0001234111False10.9386290.0001624377False10.9168660.00022041655False13.72094251.3479988e-0513.8092261.7970731e-0514.0461393.933359e-050.05False3True-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
433125303694221.37093692689320.057030402001199924.923084325.39141188.14061.38484561.76010926.654581513895.34216222.0434662.0320.178514750.2177290.850846622.169410.30541363False17.6189880.005872971False16.2128370.006081031False13.5428271.3948555e-0513.3747361.45725335e-0514.7285610.000198152850.05False3True-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
533125204409221.169533051188920.05766145651485957104837.305229673.92286804.3813.87977332.1802667.225811380.57412959.72612071.7320.15748790.196290020.5906601511.3487130.00014374436False10.4972190.00015212553False10.2560350.00025449222False13.7595891.5024753e-0513.618511.644473e-0513.6955765.3124197e-050.05False3False-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
632980606309219.58294621351456-0.1971826579550724nan101.924521095.2025nan1.7848981.361588610284.9464665.75542547.03930.152481080.196988120.14094181nannanFalse18.879310.019013366False16.301270.0013498197False13.8694941.6096752e-0514.72769454.583977e-0515.3849116.0079798e-050.05False2True-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
732693203856221.15035093922413-0.6778898976993764185.94748245.36035918.5731.23454831.99154584.121786611532.26009.60944091.94820.172321720.232027080.618084818.2265240.0072084535False17.9254910.008812725False16.4922180.004871875False13.7452191.6223783e-0514.4528844.1919564e-0514.8701740.00016399940.05False2True-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
834277502371221.856836640677952.0362493668662904798191.0699422.94317159.12166.2788192.4828599.2979527532.26229422.4912138.0790.413940550.593327760.622698379.1447330.00022618007False9.2881510.00029879718False10.1468050.00033992835False12.8003951.6323767e-0512.7283012.1894728e-0513.6896255.56996e-050.05False0False-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
933269200756221.169768677847170.1533586618682976nan248.356721023.8969nan1.345773.784260311284.4174415.0313794.0830.17831230.225143210.6618447nannanFalse17.9123080.005883276False16.374360.0040128147False13.7688021.7156413e-0514.7876655.5366807e-0514.9522320.000189397220.05False2True-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 17, "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": 18, "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": 19, "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": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "decals_stellarity, hsc_stellarity, kids_stellarity, las_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": 21, "metadata": { "collapsed": true }, "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": 22, "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": 23, "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-15\", dtype='" ] }, "metadata": { "needs_background": "light" }, "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": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue = specz_merge(master_catalogue, specz, radius=1. * u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## VII - Choosing between multiple values for the same filter\n", "\n", "In GAMA-15 we don't have any pairs of surveys from the same instruments. All we need to do is rename some columns to the name of the camera" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Rename columns for KIDS and VISTA-VIKING\n", "\n", "for band in ['u', 'g', 'r', 'i']:\n", " master_catalogue['f_ap_kids_' + band].name = 'f_ap_omegacam_' + band\n", " master_catalogue['ferr_ap_kids_' + band].name = 'ferr_ap_omegacam_' + band\n", " master_catalogue['m_ap_kids_' + band].name = 'm_ap_omegacam_' + band\n", " master_catalogue['merr_ap_kids_' + band].name = 'merr_ap_omegacam_' + band\n", " master_catalogue['f_kids_' + band].name = 'f_omegacam_' + band\n", " master_catalogue['ferr_kids_' + band].name = 'ferr_omegacam_' + band\n", " master_catalogue['m_kids_' + band].name = 'm_omegacam_' + band\n", " master_catalogue['merr_kids_' + band].name = 'merr_omegacam_' + band\n", " master_catalogue['flag_kids_' + band].name = 'flag_omegacam_' + band \n", "\n", "for band in ['z','y','j','h','k']:\n", " band_old = band\n", " if band == 'k':\n", " band = 'ks'\n", " master_catalogue['f_ap_viking_' + band_old].name = 'f_ap_vista_' + band\n", " master_catalogue['ferr_ap_viking_' + band_old].name = 'ferr_ap_vista_' + band\n", " master_catalogue['m_ap_viking_' + band_old].name = 'm_ap_vista_' + band\n", " master_catalogue['merr_ap_viking_' + band_old].name = 'merr_ap_vista_' + band\n", " master_catalogue['f_viking_' + band_old].name = 'f_vista_' + band\n", " master_catalogue['ferr_viking_' + band_old].name = 'ferr_vista_' + band\n", " master_catalogue['m_viking_' + band_old].name = 'm_vista_' + band\n", " master_catalogue['merr_viking_' + band_old].name = 'merr_vista_' + band\n", " master_catalogue['flag_viking_' + band_old].name = 'flag_vista_' + band" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## VIII.a Wavelength domain coverage\n", "\n", "We add a binary `flag_optnir_obs` indicating that a source was observed in a given wavelength domain:\n", "\n", "- 1 for observation in optical;\n", "- 2 for observation in near-infrared;\n", "- 4 for observation in mid-infrared (IRAC).\n", "\n", "It's an integer binary flag, so a source observed both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n", "\n", "*Note 1: The observation flag is based on the creation of multi-order coverage maps from the catalogues, this may not be accurate, especially on the edges of the coverage.*\n", "\n", "*Note 2: Being on the observation coverage does not mean having fluxes in that wavelength domain. For sources observed in one domain but having no flux in it, one must take into consideration de different depths in the catalogue we are using.*" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "decals_moc = MOC(filename=\"../../dmu0/dmu0_DECaLS/data/DECaLS_GAMA-15_MOC.fits\")\n", "hsc_moc = MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_wide_GAMA-15_MOC.fits\")\n", "kids_moc = MOC(filename=\"../../dmu0/dmu0_KIDS/data/KIDS-DR3_GAMA-15_MOC.fits\")\n", "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_GAMA-15_MOC.fits\")\n", "las_moc = MOC(filename=\"../../dmu0/dmu0_UKIDSS-LAS/data/UKIDSS-LAS_GAMA-15_MOC.fits\")\n", "viking_moc = MOC(filename=\"../../dmu0/dmu0_VISTA-VIKING/data/VIKING_GAMA-15_MOC.fits\")" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "was_observed_optical = inMoc(\n", " master_catalogue['ra'], master_catalogue['dec'],\n", " decals_moc + hsc_moc + ps1_moc + kids_moc) \n", "\n", "was_observed_nir = inMoc(\n", " master_catalogue['ra'], master_catalogue['dec'],\n", " las_moc + viking_moc\n", ")\n", "\n", "was_observed_mir = np.zeros(len(master_catalogue), dtype=bool)\n", "\n", "#was_observed_mir = inMoc(\n", "# master_catalogue['ra'], master_catalogue['dec'], \n", "#)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue.add_column(\n", " Column(\n", " 1 * was_observed_optical + 2 * was_observed_nir + 4 * was_observed_mir,\n", " name=\"flag_optnir_obs\")\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## VIII.b Wavelength domain detection\n", "\n", "We add a binary `flag_optnir_det` indicating that a source was detected in a given wavelength domain:\n", "\n", "- 1 for detection in optical;\n", "- 2 for detection in near-infrared;\n", "- 4 for detection in mid-infrared (IRAC).\n", "\n", "It's an integer binary flag, so a source detected both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n", "\n", "*Note 1: We use the total flux columns to know if the source has flux, in some catalogues, we may have aperture flux and no total flux.*\n", "\n", "To get rid of artefacts (chip edges, star flares, etc.) we consider that a source is detected in one wavelength domain when it has a flux value in **at least two bands**. That means that good sources will be excluded from this flag when they are on the coverage of only one band." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: Check the detection bands of the pristine catalogues\n", "# Check to use catalogue flags from HSC and PanSTARRS.\n", "nb_optical_flux = (\n", " 1 * ~np.isnan(master_catalogue['f_suprime_g']) +\n", " 1 * ~np.isnan(master_catalogue['f_suprime_r']) +\n", " 1 * ~np.isnan(master_catalogue['f_suprime_i']) +\n", " 1 * ~np.isnan(master_catalogue['f_suprime_z']) +\n", " 1 * ~np.isnan(master_catalogue['f_suprime_y']) +\n", " 1 * ~np.isnan(master_catalogue['f_gpc1_g']) +\n", " 1 * ~np.isnan(master_catalogue['f_gpc1_r']) +\n", " 1 * ~np.isnan(master_catalogue['f_gpc1_i']) +\n", " 1 * ~np.isnan(master_catalogue['f_gpc1_z']) +\n", " 1 * ~np.isnan(master_catalogue['f_gpc1_y']) +\n", " 1 * ~np.isnan(master_catalogue['f_decam_g']) +\n", " 1 * ~np.isnan(master_catalogue['f_decam_r']) +\n", " #1 * ~np.isnan(master_catalogue['f_decam_i']) +\n", " 1 * ~np.isnan(master_catalogue['f_decam_z']) +\n", " #1 * ~np.isnan(master_catalogue['f_decam_y']) +\n", " 1 * ~np.isnan(master_catalogue['f_omegacam_u']) +\n", " 1 * ~np.isnan(master_catalogue['f_omegacam_g']) +\n", " 1 * ~np.isnan(master_catalogue['f_omegacam_r']) +\n", " 1 * ~np.isnan(master_catalogue['f_omegacam_i']) \n", ")\n", "\n", "nb_nir_flux = (\n", " 1 * ~np.isnan(master_catalogue['f_ukidss_y']) +\n", " 1 * ~np.isnan(master_catalogue['f_ukidss_j']) +\n", " 1 * ~np.isnan(master_catalogue['f_ukidss_h']) +\n", " 1 * ~np.isnan(master_catalogue['f_ukidss_k']) +\n", " 1 * ~np.isnan(master_catalogue['f_vista_z']) +\n", " 1 * ~np.isnan(master_catalogue['f_vista_y']) +\n", " 1 * ~np.isnan(master_catalogue['f_vista_h']) +\n", " 1 * ~np.isnan(master_catalogue['f_vista_j']) +\n", " 1 * ~np.isnan(master_catalogue['f_vista_ks'])\n", ")\n", "\n", "nb_mir_flux = np.zeros(len(master_catalogue), dtype=bool)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "has_optical_flux = nb_optical_flux >= 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." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['decals_id', 'hsc_id', 'kids_id', 'ps1_id', 'las_id', 'viking_id', 'help_id', 'specz_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": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue[id_names].write(\n", " \"{}/master_list_cross_ident_gama-15{}.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": 36, "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 - Saving the catalogue" ] }, { "cell_type": "code", "execution_count": 37, "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\", \n", " \"flag_optnir_obs\", \"flag_optnir_det\", \"ebv\",\n", " \"zspec\", \"zspec_qual\", \"zspec_association_flag\"]" ] }, { "cell_type": "code", "execution_count": 38, "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": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue[columns].write(\"{}/master_catalogue_gama-15{}.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": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 2 }