{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# GAMA-12 master catalogue\n", "\n", "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on GAMA-12." ] }, { "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", "This notebook was executed on: \n", "2019-01-28 12:36:02.583222\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": [ { "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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Fu4gUj3nD3czCwG3AdcB5wLvN7LxT7JcAPgrsyHWRuZIO91z23CHVd9fIXUSKSTYj961Ah7vvc/dJ4OvAjafY71PAZ4CiPbPYOzxBtCJEbXzpK0JmWtugm3aISHHJJtzXAp0Zz7uCbbPM7FXAOnf/9xzWlnM9QxO01MRytq5M2rqGKpIOXWrNiEiRWPIJVTMLAZ8D/iyLfbeZ2S4z29XT07PUQy9Yz/BEzvvtABtbawDY0z2c8/cWEVmMbML9ELAu43lbsC0tAbwS+LGZHQCuAO471UlVd7/D3be4+5aWlpbFV71IPUP5CfezgnDvULiLSJHIJtx3ApvMbIOZRYF3AfelX3T3E+7e7O7t7t4OPALc4O678lLxEvQOT+T8ZCpAXWWE1kRM4S4iRWPecHf3aeBDwAPAs8A97v60mX3SzG7Id4G5Mj2TpG9kMi8jd0iN3jt6FO4iUhyymjbi7tuB7Sdt+/hp9r1m6WXlXv/IJO65n+OedlZrDfc+fgh3z/kJWxGRhSqbK1S70xcw5aEtA6lwH56Y5tjgRF7eX0RkIXI74buIvbiuTO5WhLxrx8HZxy/0jQKwp3uI1XXxnB1DRGQxymbkPrv0QE1+grc1aPfopKqIFIPyCfdg5N6cw5F7pppYBfFISOEuIkWhfMJ9aIKaWAVV0fx0osyM1kRc4S4iRaFswr13eJLmmvyM2tNaEjH2ajqkiBSBsgn3nqHxvE2DTGtNxOgdnmRgZDKvxxERmU8ZhXt+lh7INHtSVaN3ESmw8gr3PM1xT2tJpGbiqO8uIoVWFuE+MT3D4Ph0XtaVyVRfFdGMGREpCmUR7r3DqR54vtsyITPObK5RuItIwZVFuOfr3qmnclarwl1ECk/hnmObWms4dHyMkYnpvB9LROR0yiLc0+vK5LvnDrB5TS0ATx8ezPuxREROpyzCPT1yb8rzRUwAW17RAMDOA/15P5aIyOmUTbjXV0WIVYTzfqyG6ijnrEqwY7/CXUQKp2zCPd9z3DNt3dDIYwf6mZ5JLtsxRUQylcV67r3D+b86NdPWDY189ZEXeObIIBe21S/bcUXk5cYmZzjQN8KB3hGODY7TPzrFwMgk4ZDR1lBJW0Ml+3pGqK/Krm37nsvX57ni3CiLcO8ZnuCiZQzZrRsaAXh0f7/CXWQZzCSdw8fH2Nc7wr6eYfb1jLA3+O/RwfGX7GtAPBJmxp3J6dRv1yGDKzc287rNrcQj+W/fLofyCPdlWFcm06raOO1NVezY38/Nrz1z2Y4rUsqSSefwiTEO9I7OjsQP9KUeH+wbZTKjDZqIV1BfGWFNXZwL2upoqo7SVBOjrjJCZSRMOGS4O2NTM/SPTLJjfz8/7ehld+dx3nLhmpIYlGUV7mZ2LfB/gDDwZXf/9Emv/ylwMzAN9AB/4O4v5LjWRRkcn2J0cmZ2Ua/lsnVDI//xzDGSSScU0g2zRbI1OD7Fvp7UCDw9+t7XM8KBvhEmpl8M8IqQ0VQTpak6xuVnNtJcEwu+otTEKua9Ub2ZURVN3eOhraGKre2N/NsTh/nGzk4qI2E2rUrk+6Pm1bzhbmZh4DbgTUAXsNPM7nP3ZzJ22w1scfdRM/sj4O+Ad+aj4IXaG1wtemZLzbIed+uGJu7Z1cWe7mHOWb2y/5KI5NrQ+BQH+0fp7B/lQN8oL/SNsL93hL09I7NTlyHVLmmsjtJSE2NreyNNNTGaaqI018RIxCsIzRPgC7GusYqbrzqTL/24g3t2dfLh12+itjKSs/dfbtmM3LcCHe6+D8DMvg7cCMyGu7s/mLH/I8D7clnkUqSXAtjYUr2sx718tu/ep3CXsjM6Oc2hgTE6B0bpGhgLvlKPO/tHGRidesn+1dEwTTUx1jVU8ar1DbTURGlOxGisjlIRWr5JfdGKEO/Zup4v/XgvX9/ZyU1XbSC8Qn/zzibc1wKdGc+7gMvn2P8m4HunesHMtgHbANavX54zznt7RoiGQ6xvrFqW46W1NVSypi7Ojv39vP/V7ct6bJF8G5+a4dDxVFCnw3s2yPtH6TvphjUVIaO+KkpDVYRNrQkaq6M0VEdprIrSVBMtqpOYrbVxbrz4DL75WBc/fPYYbz5/daFLWpScnlA1s/cBW4CrT/W6u98B3AGwZcsWz+WxT6eje5j25ioqwss7pd/M2LqhkV/s7cPd5+3/iRSTmaRzdHCczqB10tk/Smcw6j7YP0p3RusEIGxGfVWEhqooZ7ZUc+krGqivitJYFaGhOkp1LLctlHy7ZH0D+3pH+Mmve3jV+gaal/mcXS5kE+6HgHUZz9uCbS9hZm8E/hK42t0nTn69UPb2DLO5QG2RrRsa+e4vD7Ovd4SNy9zzF5mLu3N8dIrOgVRYp/rfqdZJZ/B4xl8cfxlQF4R3W0MVF7TV0VgVpaEqNQLPdf+7GLz5vFX8qvM4P9nTw++8qq3Q5SxYNuG+E9hkZhtIhfq7gPdk7mBmlwD/CFzr7t05r3KRJqZnONg/ylsuXFOQ479+cytm8N1fHuZP33R2QWqQ8pVMOt1DExzoG+GFvtS0wYMZUweHTlq5tCoaTrVLqqJctamahqpo8DxCfVV0xfaeFysRj3BZeyM79vfxhs2tWV/kVCzmDXd3nzazDwEPkJoK+RV3f9rMPgnscvf7gM8CNcA3g/bDQXe/IY91Z+WFvlFmkl6wUfOaukp+Y1ML/7qrk4++YVPZ/eOQ/HJ3eocnOXR8jEMDYxw6PsqhoP/9QtBKyZw6GAkbdZURGqujnL+2jsag591QHaGxKkqsiPrexeK1m5rZsb+Pn+7p5a0XnVHochYkq567u28Htp+07eMZj9+Y47pyIj1T5qzW5Qv3u3YcfMnzM+or+cmve/hZRy9Xn92ybHVIaZieSdI5MMaB3hH29aZG4OkphF0DYy8Jb0hdvLO2vpJoOMRl7Y00Vkdn54LXVUY0wFig+qool6xrYOeBfl63uZWa2Mq57nPlVLoIL85xX95pkJnOXZ2goSrCPTs7Fe5yWmOTM+wNLtrp6H7x60DfCFMzL/a+45FQMNqOcll74+xJzPqqCPWVUSqjGn3n2tVnt/D4wQEe7ujlN1fQzJmSDveOnmHW1ldSFS3cx6wIh3jbJW189ZED9I9M0li9svp2kjvp9U/2947MfqWvwDx0fGx2v5BBQ1WU1kSMV5/ZTEsiOnv1ZVU0rJlXy6w5EeOVa+t4ZF/fihqglXa4dw+zcRlbMqfzzsvW8ZWH9/Pt3Ye46aoNhS5H8sjd6RmaYF9GgO/rGWF/7zAHgnNAadGKEC01MVoSMc5dk6AlEac1EaOpOrrsU3dlbq/d1MyTh06wu/N4oUvJWsmGezLp7OsZ4fINTYUuhXNWJ7hoXT3f3NXJH1zZrpFXCUj3wvccG6IjaKXs7U6NwjNnoVSEjMbq1Mj7yo1NNC1w/RMpDm0NVbQ1VLJj38q5bqVkw/3wiTHGpmbY2Fq4fnumd2xp4y+//RSP7u/n8jML/wNHsjM8Mc3+nhH29abCe2/PCB3dw+zvHXnJKoS18QqaEzHOX1tLS02M5kSMlpoYtZWRkpv/Xa4u39DItx4/xI79/VyxAv4Nl2y4z86UKZKLh3774rV88UcdfPy7T/NvH76KaIV+7S4W0zNJugbGXlyBsDfVRtnXM/KSKzGNYBGrRGoVwtagjdKSiBXV5fOSHxesrWf7k0f56iMvKNwLaW/PCEBR9NwBqmMVfOrGV3Lzv+zijof28qHXbyp0SWUnmXQ6B0Z59sgQvz6W+tpz7OWj8MbqVMukraGSi9fVp9ooiRjN6oWXtWhFiEtf0cADTx2le2ic1kS80CXNqWTDvaN7mPqqCE1FNDvljeet4rcuWMPnf9TB9ResWfZliMvJwMgkzx8b4vmjQzx3dJBnjwzxzOHBl4R4Q1WEVbXxYBQem22nFHJ2lRS3rRsa+VlHL994tJMPv6G4B2gl+7d4b/cwZ7XUFN2Jj0/ccB4/3dPDrfc+yd23XKEbeSzR8MQ0e46lR+LD/DoI9Mx2Sn1VhM2rE1za3sCa2jir6+K0JGLEKtRKkYVpronx2k3N3PXoQf7omo1F/Ztc6YZ7zzBvPHdVoct4mdZEnP9+/bl87N4n+bsHnucvrj2n6H4AFaOpmST7ekZ47uggzx9NhflzR4foGnhxfngkbLQm4rQ1VHHpKxpYVRtndW2cRFyzUiR3PvDqdm75l138+5NHuPHitYUu57RKMtx7hiboG5ksmpkyJy9JkHTnsvYGbv/JXoYnpvjkDa/UCD4wk3S6BkZ5/ugQe7qHef5oaiS+r3d49krNkKVGUKvr4py7ppZViTiramM0VEc1M0Xy7g2bWzl7VQ1fenAvb73wjKL9t1uS4f79p44A8JqNzQWu5NRCZvz2xWu5ZH0Ddzy0j+HxaT77uxcRKeJf8XJtfGqGF/pGU5fcdw/T0TPMnmOpy+8z10upr0z1xV+zsXl2JN6cWN6784hkCoWM/3LNWfzxN37J/yvim3mUZLjfu/sQ56xKcP4ZtYUu5bTMjFuv20xdZYTPPvA8Tx0e5NbrNgfLBBfnSGChkknnyOD47KJXs/PFe4bpGhgjY7lw6qsitCZiXNaeOrnZWpuaZqgphlKM3nLhGj73g19z24/38qbzVhXlv9mSC/d9PcPsPnicW6/bXJR/4JnMjA++7izOXpXgb7c/y03/vIvXbGzilteeyWvOairqE36T00kGx6foG56kd3iCnqEJDp8Y43Cw/OzB4M49kyctOZteI2VTa4LmmhitidRzzfuXlaQiHOIPr97If//2kzzc0cdVm4qvS1By4f6d3Ycwo6hPdKRl9uL/4MoNPLq/j4f39vH7d+6kJlbB6ze3csWZTZyzOsE5qxMLXm7U3TkxNkXfyCQDI5P0j0wyMDrJwOgUw+PTDI1PMTwxw8T0DBPTSSank8wknelkkmQSppOp51MzzuRMkvGpGcankgxPTDE+lTzlMeurIlRGwjRURbl8QyNN1S/erb5WJzalhPzOpWv5/A/38MUH9yjc8y2ZdO7dfYirzmpmdV1xX2BwsnDIePXGZv7nOy7i5x19fP+po/zg2WPc96vDs/uk74rTVB2jKhamIhSiImQ4zsR0kompJKNTMwyOTTE4NsWJsSmmk6e+Va0BsUiIWEWYSDj1PhVhI2RGyFK/VYRDqcchM+IVIRKxCirCIeIVIeLRMPGKENWxCmriFdTEKqiLR3TDBykbsYowt/zGmXzq/mf40XPHeP3m4pqdV1LhvuuFAboGxlb0Le2+9Vjq9rQXravngrY6jo9OcWxwnKOD4wyOTTEyMU3P8ASTx5Mk3UkGjet0QEfCIRLxCloSMSojYapjFVRHg//GKqiKhqmKhomGQxpFiyzRey9fzzd3dfLn33yC7R95bVENKksq3L+9u4uqaHhFLag/l5ClVhRsrI5y7priPTksUq7ikTC3vfdVvPULP+Mjd+/mrlsuL5oLm4qjihwYmZjm/ieOcO35q6leQbfCEpGVbWNLDf/jba/k0QP9fP6Hewpdzqyswt3MrjWz582sw8w+dorXY2b2jeD1HWbWnutC59I3PMF7v7yDkYlp3nP5+uU8tIgIb7ukjd+9tI0vPNjB7T/Zy/TMqSccLKd5w93MwsBtwHXAecC7zey8k3a7CRhw97OA/w18JteFnk5n/yhvv/0XPHtkkH9436VsaW9crkOLiMz6mxvP583nreLT33uOt33p5zx7ZLCg9WTTv9gKdLj7PgAz+zpwI/BMxj43An8dPP5X4ItmZu5+6qkaS+DudPaPsbtzgN0Hj3P/E4eZmnG+dvPlCnYRKZiqaAW3v+9Stj95lE/c9xRv/cLPuKy9kcvaG9jS3sjahkrqKiPUxiPLcl1HNuG+FujMeN4FXH66fdx92sxOAE1Aby6KzPStxw/x59/8FQBV0TCvWt/AX99wHme1JnJ9KBGRBTEzfuvCNbxmYxO3/2QvD+/t5YsPdnDyjOQ/vHojH7tuc15rWdYzj2a2DdgWPB02s+eX+p7PAl+7ZVHf2kwefvgUiVL9bPpcK0tJfq735uA9bv0M3Lr4b39FNjtlE+6HgHUZz9uCbafap8vMKoA6oO/kN3L3O4A7siks38xsl7tvKXQd+VCqn03Lr4uLAAAGJ0lEQVSfa2Up1c+1UmTT+NkJbDKzDWYWBd4F3HfSPvcB/yl4/HbgR/not4uISHbmHbkHPfQPAQ8AYeAr7v60mX0S2OXu9wH/BHzVzDqAflI/AEREpECy6rm7+3Zg+0nbPp7xeBz43dyWlndF0R7Kk1L9bPpcK0upfq4VwdQ9EREpPSWz/ICIiLyoLMN9vuUUViIz+4qZdZvZU4WuJZfMbJ2ZPWhmz5jZ02b20ULXlAtmFjezR83sV8Hn+ptC15RLZhY2s91mdn+haylXZRfuWS6nsBLdCVxb6CLyYBr4M3c/D7gC+GCJ/P+aAF7v7hcBFwPXmtkVBa4plz5K6jIUKZCyC3cyllNw90kgvZzCiubuD5GaqVRS3P2Iuz8ePB4iFRjFf5uteXjKcPA0EnyVxAkwM2sDfgv4cqFrKWflGO6nWk5hxYdFOQhWG70E2FHYSnIjaF38EugGfuDuJfG5gL8H/htQ+KURy1g5hrusQGZWA3wL+GN3L+xyezni7jPufjGpq763mtkrC13TUpnZW4Bud3+s0LWUu3IM92yWU5AiYmYRUsH+NXe/t9D15Jq7HwcepDTOmVwJ3GBmB0i1PF9vZv+3sCWVp3IM92yWU5AiYakbvf4T8Ky7f67Q9eSKmbWYWX3wuBJ4E/BcYataOne/1d3b3L2d1L+tH7n7+wpcVlkqu3B392kgvZzCs8A97v50YataOjO7G/gFcI6ZdZnZTYWuKUeuBN5PagT4y+Dr+kIXlQNrgAfN7AlSA44fuLumDUrO6ApVEZESVHYjdxGRcqBwFxEpQQp3EZESpHAXESlBCncRkRKkcBcRKUEKd1k2ZjYTzFN/Oljq9s/MLBS8tsXMPj/H97ab2XuWr9qXHXssWAemKJjZO4MlqzU3Xk5J4S7LaczdL3b380ldkXkd8AkAd9/l7h+Z43vbgYKEe2BvsA5M1oLlpfPC3b8B3Jyv95eVT+EuBeHu3cA24EOWck16FGpmV2dcjbrbzBLAp4HXBtv+JBhN/9TMHg++XhN87zVm9mMz+1cze87MvhYsYYCZXWZmPw9+a3jUzBLByoyfNbOdZvaEmf3nbOo3s++Y2WPBbyHbMrYPm9n/MrNfAa8+zTHPDx7/MjjmpuB735ex/R/TPxyCm8s8HrzHD3P4v0FKmbvrS1/L8gUMn2LbcWAVcA1wf7Dt34Arg8c1pG7kPvt6sL0KiAePNwG7gsfXACdILQgXIrUkw1VAFNgHXBbsVxu87zbgr4JtMWAXsOGkGtuBp07a1hj8txJ4CmgKnjvwjuDx6Y75BeC9GftUAucGnzsSbP8S8AGghdQS1Rsyj5vxWe8/1Z+1vvRVscCfBSLL4WHgc2b2NeBed+8KBt+ZIsAXzexiYAY4O+O1R929CyDok7eTCvwj7r4TwINlg83szcCFZvb24HvrSP2w2D9PjR8xs7cFj9cF39MX1PKtYPs5pznmL4C/DG5qca+77zGzNwCXAjuDz1pJap33K4CH3H1/8B4ld0MWyQ+FuxSMmZ1JKgy7SY1cAXD3T5vZvwPXAw+b2W+e4tv/BDgGXERqhD6e8dpExuMZ5v57bsCH3f2BBdR9DfBG4NXuPmpmPwbiwcvj7j4z1/e7+11mtoPU3Yq2B60gA/7Z3W896VhvzbYukUzquUtBmFkLcDvwRXf3k17b6O5PuvtnSK2YuBkYAhIZu9WRGhUnSa0aOd/Jy+eBNWZ2WXCMhJlVkFod9I+CNeMxs7PNrHqe96oDBoJg30xqdJ31MYMfavvc/fPAd4ELgR8Cbzez1mDfRjN7BfAI8BtmtiG9fZ7aRACN3GV5VQZtkgipG19/FTjVGu1/bGavI3WbtqeB7wWPZ4ITlXeS6kl/y8w+AHwfGJnrwO4+aWbvBL4QrJ8+Rmr0/WVSbZvHgxOvPcBvz/M5vg/8oZk9SyrAH1ngMd8BvN/MpoCjwN+6e7+Z/RXwH8H00Cngg+7+SHDC9t5gezepmUYic9KSvyLzsNS9W+9396K6DV7QHvpzd39LoWuR4qO2jMj8ZoC6YruIidRvLwOFrkWKk0buIiIlSCN3EZESpHAXESlBCncRkRKkcBcRKUEKdxGREvT/ATo7m9Xow2WbAAAAAElFTkSuQmCC\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
034115801254177.6772726732231.7616450631313314644301.3793099.01460194.240.78942552.18798436.4045525260.69132851.2825670.9160.38740350.60879260.54540889.3772776.873578e-05False9.151687.144427e-05False8.4889722.706882e-05False12.8938871.6651089e-0512.6086192.012062e-0512.8763962.3067741e-050.05False0False-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
134261700018182.422346281661931.8965490934568279157468.83387121.251043781.032.1118643.18115647.1178915617.51220036.27319982.1230.387728480.42091510.525871910.9070130.00022140892False9.9303860.00012110764False8.85347754.9011807e-05False13.415972.6955051e-0513.1454572.280877e-0513.1483962.8573446e-050.05False0False-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
233973506333181.916104075264171.6016118478755128178285.66408218.72205331.5248.07161376.8005255.193275513.5315918.58645870.1810.142291810.1809760.3048609810.7722130.00029274976False9.8727680.00020426574False10.6188620.00029184672False14.5464252.8020406e-0514.4694553.3199176e-0514.4783715.6386434e-050.05False3True-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
332820301127178.75318876919636-0.555956110553872432473.4772839.6623769.75125.287186263.7307111.667126452.07312900.25610426.5830.173366950.534920450.46005779.8101040.00031453688False9.1797750.00037050634False9.4124370.00019436823False14.37575152.9173685e-0513.6235044.5021006e-0513.8546454.790652e-050.05False1False-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
433973202460181.053849891571451.4622938813032518132598.42136409.02168351.0861.1522537.5280848.986256150.26666587.59335204.450.170611650.168949010.3416316211.0936580.000500724False11.0628930.0002987016False10.8344650.00031592397False14.4277653.01189e-0514.3531832.7845383e-0514.6090627.127012e-050.05False0False-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
532967104790185.94497244047702-0.1301125249351443nan187.33821219.1875nan2.54852373.67581688204.6833514.19973419.55710.243458230.31603750.5458519nannanFalse18.2184370.014770208False18.0479580.018208006False14.1148453.221714e-0515.0354349.764196e-0515.0650750.000173312250.05False3True-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
634261700377182.422285837783141.89864672774414994314.489711055.9341197914.61.53417544.143134153.145529060.3821903.48419293.1290.27122510.420915130.516181814.8126750.0003860734False13.7910120.00040687204False8.7039340.00013880424False14.00713353.2501826e-0513.0487172.0864387e-0513.1864932.9048544e-050.05False0False-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
732679200381186.2487892564654-0.8531669047723471nan325.361241001.0604nan3.76687173.2912778123.1371342.61621935.61390.243269970.416302980.42474237nannanFalse17.6190870.012570118False16.398850.0035696728False14.125693.2515396e-0516.080120.000336652615.6829530.000238249050.05False3True-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
833397604704182.002674319629680.6218080879386891136505.44903814.3837022.872.99592293.3019157.0828112574.3817362.40811040.8640.377419830.61937310.480803411.0621260.0005805945False9.0098040.0003523384False9.0931590.00020375848False13.6512833.2588356e-0513.3009753.8731712e-0513.7924924.7281228e-050.05False0False-1nannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse00.0nannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
933253901087182.856075545849220.19398475747811611060620.11030950.22035309.4373.00092424.2632496.29211135.84411660.25417185.2950.349839060.361898930.942016548.8361020.0003818338False8.8669090.00044680908False8.1284260.00026474704False13.7831923.410904e-0513.733233.369796e-0513.3121075.9514918e-050.05False2False-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": [ "\n", "# 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-12\", dtype='Table length=10\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
idxradecz_specz_sourcez_qualrelspecz_idagn
0174.0060.720930.05054130.987GAMAJ113601.43+004315.30
1174.022790.705940.33119130.987GAMAJ113605.47+004221.30
2174.100710.658910.22979141.0GAMAJ113624.17+003932.00
3174.153120.815430.00375130.987GAMAJ113636.75+004855.50
4174.28050.706080.11397130.987GAMAJ113707.32+004221.80
5174.3035833330.7899166666670.07441740.991TGN376Z2150
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\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "specz[:10].show_in_notebook()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "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'] * u.deg, specz['dec'] * u.deg)\n", ")" ] }, { "cell_type": "code", "execution_count": 29, "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-12 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": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Rename columns for UKIDSS-LAS 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','ks']:\n", " master_catalogue['f_ap_viking_' + band.strip('s')].name = 'f_ap_vista_' + band\n", " master_catalogue['ferr_ap_viking_' + band.strip('s')].name = 'ferr_ap_vista_' + band\n", " master_catalogue['m_ap_viking_' + band.strip('s')].name = 'm_ap_vista_' + band\n", " master_catalogue['merr_ap_viking_' + band.strip('s')].name = 'merr_ap_vista_' + band\n", " master_catalogue['f_viking_' + band.strip('s')].name = 'f_vista_' + band\n", " master_catalogue['ferr_viking_' + band.strip('s')].name = 'ferr_vista_' + band\n", " master_catalogue['m_viking_' + band.strip('s')].name = 'm_vista_' + band\n", " master_catalogue['merr_viking_' + band.strip('s')].name = 'merr_vista_' + band\n", " master_catalogue['flag_viking_' + band.strip('s')].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": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "decals_moc = MOC(filename=\"../../dmu0/dmu0_DECaLS/data/DECaLS_GAMA-12_MOC.fits\")\n", "hsc_moc = MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_wide_GAMA-12_MOC.fits\")\n", "kids_moc = MOC(filename=\"../../dmu0/dmu0_KIDS/data/KIDS-DR3_GAMA-12_MOC.fits\")\n", "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_GAMA-12_MOC.fits\")\n", "las_moc = MOC(filename=\"../../dmu0/dmu0_UKIDSS-LAS/data/UKIDSS-LAS_GAMA-12_MOC.fits\")\n", "viking_moc = MOC(filename=\"../../dmu0/dmu0_VISTA-VIKING/data/VIKING_GAMA-12_MOC.fits\")" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "was_observed_optical = inMoc(\n", " master_catalogue['ra'], master_catalogue['dec'],\n", " decals_moc + hsc_moc + ps1_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": 33, "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": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# SpARCS is a catalogue of sources detected in r (with fluxes measured at \n", "# this prior position in the other bands). Thus, we are only using the r\n", "# CFHT band.\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_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": 35, "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.\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": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "921 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-12.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]" ] }, { "cell_type": "code", "execution_count": 37, "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', '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": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "master_catalogue[id_names].write(\n", " \"{}/master_list_cross_ident_gama-12{}.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": 39, "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": 40, "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_association_flag\", \"zspec_qual\", \"zspec\"]" ] }, { "cell_type": "code", "execution_count": 41, "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": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue[columns].write(\"{}/master_catalogue_gama-12{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)" ] } ], "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 }