{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# XMM-LSS master catalogue - IRAC merging\n", "\n", "This notebook presents the merge of the IRAC pristine catalogues to produce the HELP master catalogue on XMM-LSS." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This notebook was run with herschelhelp_internal version: \n", "0246c5d (Thu Jan 25 17:01:47 2018 +0000)\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": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/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": [ "candels = Table.read(\"{}/CANDELS-UDS.fits\".format(TMP_DIR)) # 1.2\n", "\n", "#cfht_wirds = Table.read(\"{}/CFHT-WIRDS.fits\".format(TMP_DIR)) # 1.3\n", "#cfhtls_wide = Table.read(\"{}/CFHTLS-WIDE.fits\".format(TMP_DIR)) # 1.4a\n", "#cfhtls_deep = Table.read(\"{}/CFHTLS-DEEP.fits\".format(TMP_DIR)) # 1.4b\n", "#We no longer use CFHTLenS as it is the same raw data set as CFHTLS-WIDE\n", "# cfhtlens = Table.read(\"{}/CFHTLENS.fits\".format(TMP_DIR)) # 1.5\n", "#decals = Table.read(\"{}/DECaLS.fits\".format(TMP_DIR)) # 1.6\n", "\n", "servs = Table.read(\"{}/SERVS.fits\".format(TMP_DIR)) # 1.8\n", "swire = Table.read(\"{}/SWIRE.fits\".format(TMP_DIR)) # 1.7\n", "\n", "#hsc_wide = Table.read(\"{}/HSC-WIDE.fits\".format(TMP_DIR)) # 1.9a\n", "#hsc_deep = Table.read(\"{}/HSC-DEEP.fits\".format(TMP_DIR)) # 1.9b\n", "#hsc_udeep = Table.read(\"{}/HSC-UDEEP.fits\".format(TMP_DIR)) # 1.9c\n", "#ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR)) # 1.10\n", "#sxds = Table.read(\"{}/SXDS.fits\".format(TMP_DIR)) # 1.11\n", "#sparcs = Table.read(\"{}/SpARCS.fits\".format(TMP_DIR)) # 1.12\n", "#dxs = Table.read(\"{}/UKIDSS-DXS.fits\".format(TMP_DIR)) # 1.13\n", "#uds = Table.read(\"{}/UKIDSS-UDS.fits\".format(TMP_DIR)) # 1.14\n", "#vipers = Table.read(\"{}/VIPERS.fits\".format(TMP_DIR)) # 1.15\n", "#vhs = Table.read(\"{}/VISTA-VHS.fits\".format(TMP_DIR)) # 1.16\n", "#video = Table.read(\"{}/VISTA-VIDEO.fits\".format(TMP_DIR)) # 1.17\n", "#viking = Table.read(\"{}/VISTA-VIKING.fits\".format(TMP_DIR)) # 1.18" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## II - Merging tables\n", "\n", "We first merge the optical catalogues and then add the infrared ones. We start with PanSTARRS because it coevrs the whole field.\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": [ "### Start with CANDELS" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f_acs_f606w removed.\n", "ferr_acs_f606w removed.\n", "f_ap_acs_f606w removed.\n", "ferr_ap_acs_f606w removed.\n", "f_acs_f814w removed.\n", "ferr_acs_f814w removed.\n", "f_ap_acs_f814w removed.\n", "ferr_ap_acs_f814w removed.\n", "f_wfc3_f125w removed.\n", "ferr_wfc3_f125w removed.\n", "f_ap_wfc3_f125w removed.\n", "ferr_ap_wfc3_f125w removed.\n", "f_wfc3_f160w removed.\n", "ferr_wfc3_f160w removed.\n", "f_ap_wfc3_f160w removed.\n", "ferr_ap_wfc3_f160w removed.\n", "f_candels-megacam_u removed.\n", "ferr_candels-megacam_u removed.\n", "f_suprime_b removed.\n", "ferr_suprime_b removed.\n", "f_suprime_v removed.\n", "ferr_suprime_v removed.\n", "f_suprime_rc removed.\n", "ferr_suprime_rc removed.\n", "f_suprime_ip removed.\n", "ferr_suprime_ip removed.\n", "f_suprime_zp removed.\n", "ferr_suprime_zp removed.\n", "f_hawki_k removed.\n", "ferr_hawki_y removed.\n", "ferr_hawki_k removed.\n", "f_candels-ukidss_j removed.\n", "ferr_candels-ukidss_j removed.\n", "f_candels-ukidss_h removed.\n", "ferr_candels-ukidss_h removed.\n", "f_candels-ukidss_k removed.\n", "ferr_candels-ukidss_k removed.\n", "m_acs_f606w removed.\n", "merr_acs_f606w removed.\n", "flag_acs_f606w removed.\n", "m_ap_acs_f606w removed.\n", "merr_ap_acs_f606w removed.\n", "m_acs_f814w removed.\n", "merr_acs_f814w removed.\n", "flag_acs_f814w removed.\n", "m_ap_acs_f814w removed.\n", "merr_ap_acs_f814w removed.\n", "m_wfc3_f125w removed.\n", "merr_wfc3_f125w removed.\n", "flag_wfc3_f125w removed.\n", "m_ap_wfc3_f125w removed.\n", "merr_ap_wfc3_f125w removed.\n", "m_wfc3_f160w removed.\n", "merr_wfc3_f160w removed.\n", "flag_wfc3_f160w removed.\n", "m_ap_wfc3_f160w removed.\n", "merr_ap_wfc3_f160w removed.\n", "m_candels-megacam_u removed.\n", "merr_candels-megacam_u removed.\n", "flag_candels-megacam_u removed.\n", "m_suprime_b removed.\n", "merr_suprime_b removed.\n", "flag_suprime_b removed.\n", "m_suprime_v removed.\n", "merr_suprime_v removed.\n", "flag_suprime_v removed.\n", "m_suprime_rc removed.\n", "merr_suprime_rc removed.\n", "flag_suprime_rc removed.\n", "m_suprime_ip removed.\n", "merr_suprime_ip removed.\n", "flag_suprime_ip removed.\n", "m_suprime_zp removed.\n", "merr_suprime_zp removed.\n", "flag_suprime_zp removed.\n", "m_hawki_k removed.\n", "merr_hawki_k removed.\n", "flag_hawki_k removed.\n", "m_candels-ukidss_j removed.\n", "merr_candels-ukidss_j removed.\n", "flag_candels-ukidss_j removed.\n", "m_candels-ukidss_h removed.\n", "merr_candels-ukidss_h removed.\n", "flag_candels-ukidss_h removed.\n", "m_candels-ukidss_k removed.\n", "merr_candels-ukidss_k removed.\n", "flag_candels-ukidss_k removed.\n" ] } ], "source": [ "master_catalogue = candels\n", "master_catalogue['candels_ra'].name = 'ra'\n", "master_catalogue['candels_dec'].name = 'dec'\n", "del candels\n", "unused_bands = [ 'candels-megacam', 'candels-ukidss', 'hawki', 'suprime', 'wfc3', 'acs']\n", "for col in master_catalogue.colnames:\n", " \n", " for band in unused_bands:\n", " if band in col:\n", " master_catalogue.remove_column(col)\n", " print(col, ' removed.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add SERVS" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HELP Warning: There weren't any cross matches. The two surveys probably don't overlap.\n" ] } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(servs['servs_ra'], servs['servs_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Given the graph above, we use 1 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, servs, \"servs_ra\", \"servs_dec\", radius=1.*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add SWIRE" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(swire['swire_ra'], swire['swire_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Given the graph above, we use 1 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, swire, \"swire_ra\", \"swire_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": 10, "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] = master_catalogue[col].astype(float)\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": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Since this is not the final merged catalogue. We rename column names to make them unique\n", "master_catalogue['ra'].name = 'irac_ra'\n", "master_catalogue['dec'].name = 'irac_dec'\n", "master_catalogue['flag_merged'].name = 'irac_flag_merged'" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<Table length=10>\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
idxcandels_idirac_rairac_deccandels_stellarityf_candels-irac_i1ferr_candels-irac_i1f_candels-irac_i2ferr_candels-irac_i2f_candels-irac_i3ferr_candels-irac_i3f_candels-irac_i4ferr_candels-irac_i4m_candels-irac_i1merr_candels-irac_i1flag_candels-irac_i1m_candels-irac_i2merr_candels-irac_i2flag_candels-irac_i2m_candels-irac_i3merr_candels-irac_i3flag_candels-irac_i3m_candels-irac_i4merr_candels-irac_i4flag_candels-irac_i4candels_flag_cleanedcandels_flag_gaiairac_flag_mergedservs_intidf_ap_servs_irac_i1ferr_ap_servs_irac_i1f_servs_irac_i1ferr_servs_irac_i1servs_stellarity_irac_i1f_ap_servs_irac_i2ferr_ap_servs_irac_i2f_servs_irac_i2ferr_servs_irac_i2servs_stellarity_irac_i2m_ap_servs_irac_i1merr_ap_servs_irac_i1m_servs_irac_i1merr_servs_irac_i1flag_servs_irac_i1m_ap_servs_irac_i2merr_ap_servs_irac_i2m_servs_irac_i2merr_servs_irac_i2flag_servs_irac_i2servs_flag_cleanedservs_flag_gaiaswire_intidf_ap_swire_irac_i1ferr_ap_swire_irac_i1f_swire_irac_i1ferr_swire_irac_i1swire_stellarity_irac_i1f_ap_swire_irac_i2ferr_ap_swire_irac_i2f_swire_irac_i2ferr_swire_irac_i2swire_stellarity_irac_i2f_ap_irac_i3ferr_ap_irac_i3f_irac_i3ferr_irac_i3swire_stellarity_irac_i3f_ap_irac_i4ferr_ap_irac_i4f_irac_i4ferr_irac_i4swire_stellarity_irac_i4m_ap_swire_irac_i1merr_ap_swire_irac_i1m_swire_irac_i1merr_swire_irac_i1flag_swire_irac_i1m_ap_swire_irac_i2merr_ap_swire_irac_i2m_swire_irac_i2merr_swire_irac_i2flag_swire_irac_i2m_ap_irac_i3merr_ap_irac_i3m_irac_i3merr_irac_i3flag_irac_i3m_ap_irac_i4merr_ap_irac_i4m_irac_i4merr_irac_i4flag_irac_i4swire_flag_cleanedswire_flag_gaia
degdeguJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJy
03548934.4574665693-5.125053159730.810000002384-0.00507590.0137912-0.03299340.01839680.49913090.5398261-0.11589990.6673397nan-2.94994092615Falsenan-0.605395982581False24.65446385791.17425857835Falsenan-6.25155736254FalseFalse0False-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0
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27234.4389173693-5.276554859730.009999999776480.30793840.04544040.30709240.05873670.7691.34641830.08713641.606874925.17884037770.160214794383False25.18182732860.207665711486False24.18518415051.90098191815False26.549500966820.0219684936FalseFalse0False-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0
32973434.3542499693-5.232409259730.8299999833110.00845980.05127510.09982620.0589584-0.45598571.5066038-3.72487021.744061729.08159976026.58067950455False26.40188865120.641247182149Falsenan-3.58733682194Falsenan-0.508364272941FalseFalse0False-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0
42979534.5105653693-5.231299559730.889999985695-0.02453360.05039110.16835090.05895840.43568211.7339267-1.4821651.6043624nan-2.23006169774False25.83446139390.38023716805False24.80207570674.32101065108Falsenan-1.17524995074FalseFalse0False-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0
52979134.4299348693-5.231405959730.730000019073-0.09982620.05145190.0008460.059183.80862261.21581530.43568212.1613164nan-0.559604498955False31.581574092475.9501992879False22.44808014960.346596349428False24.80207570675.38608188268FalseFalse0False-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0
6805734.5051666693-5.230473259730.07999999821190.26225520.04986060.17173490.059180.40015071.7827061-0.39676681.484596425.35318972960.206422822544False25.81285359620.374145666359False24.89444104764.83704902981Falsenan-4.06253764411FalseFalse0False-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0
73421534.4212012693-5.154552959730.910000026226-0.03637730.05056790.11336190.0594017-1.55661171.1297956-0.4949011.7403766nan-1.50927638468False26.26383220930.568926388091Falsenan-0.788032100039Falsenan-3.81811692547FalseFalse0False-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0
82984234.3557057693-5.230302659730.8100000023840.47798130.05162880.44498790.0594017-0.61841481.4218428-0.80876121.721448324.70147273480.117275000859False24.77912949520.144935573111Falsenan-2.49629569899Falsenan-2.31098962701FalseFalse0False-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0
9771334.4335831693-5.232463859730.370000004768-0.15143120.0518056-0.09052040.05940170.48644121.28074982.18602422.1521039nan-0.371437428543Falsenan-0.712486647365False24.68242412212.85863209592False23.05086258641.06888895403FalseFalse0False-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0
\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "master_catalogue[:10].show_in_notebook()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue.add_column(Column(data=(np.char.array(master_catalogue['servs_intid'].astype(str)) \n", " + np.char.array(master_catalogue['swire_intid'].astype(str) )\n", " + np.char.array(master_catalogue['candels_id'].astype(str) )), \n", " name=\"irac_intid\"))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['candels_id', 'servs_intid', 'swire_intid', 'irac_intid']\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": "markdown", "metadata": {}, "source": [ "## VII - Choosing between multiple values for the same filter\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### VII.a SERVS and SWIRE IRAC fluxes\n", "\n", "Both SERVS and SWIRE provide IRAC1 and IRAC2 fluxes. SERVS is deeper but tends to under-estimate flux of bright sources (Mattia said over 2000 µJy) as illustrated by this comparison of SWIRE, SERVS, and Spitzer-EIP fluxes. we include CANDELS fluxes where no other flux is available. CANDELS includes all four IRAC bands." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "seip = Table.read(\"../../dmu0/dmu0_SEIP/data/SEIP_XMM-LSS.fits\")\n", "seip_coords = SkyCoord(seip['ra'], seip['dec'])\n", "idx, d2d, _ = seip_coords.match_to_catalog_sky(SkyCoord(master_catalogue['irac_ra'], master_catalogue['irac_dec']))\n", "mask = d2d <= 2 * u.arcsec" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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aJCuUiUhIBAtnVs3PVT0WEWlwMn4/CvLWkzE6Ga74p9/liEiUCBbOXDU/V/VY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\nQGjOAAAACoTmDAAAoEBozgAAAArkf563ck1Dpzs5AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter(seip['i1_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_servs_irac_i1'], label=\"SERVS\", s=2.)\n", "ax.scatter(seip['i1_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_swire_irac_i1'], label=\"SWIRE\", s=2.)\n", "ax.set_xscale('log')\n", "ax.set_yscale('log')\n", "ax.set_xlabel(\"SEIP flux [μJy]\")\n", "ax.set_ylabel(\"SERVS/SWIRE flux [μJy]\")\n", "ax.set_title(\"IRAC 1\")\n", "ax.legend()\n", "ax.axvline(2000, color=\"black\", linestyle=\"--\", linewidth=1.)\n", "ax.plot(seip['i1_f_ap1'][mask], seip['i1_f_ap1'][mask], linewidth=.1, color=\"black\", alpha=.5);" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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y9mnbuHEjK1asoF+/fvTv3z/ociSKQvFMm5kdZGaLzOyMoGsREZHkUVhYGHQJUVNRUcHL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\nwEWbmZmZWQ78Dcty/EDV7O7iAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter(seip['i2_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_servs_irac_i2'], label=\"SERVS\", s=2.)\n", "ax.scatter(seip['i2_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_swire_irac_i2'], label=\"SWIRE\", s=2.)\n", "ax.set_xscale('log')\n", "ax.set_yscale('log')\n", "ax.set_xlabel(\"SEIP flux [μJy]\")\n", "ax.set_ylabel(\"SERVS/SWIRE flux [μJy]\")\n", "ax.set_title(\"IRAC 2\")\n", "ax.legend()\n", "ax.axvline(2000, color=\"black\", linestyle=\"--\", linewidth=1.)\n", "\n", "ax.plot(seip['i1_f_ap2'][mask], seip['i1_f_ap2'][mask], linewidth=.1, color=\"black\", alpha=.5);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When both SWIRE and SERVS fluxes are provided, we use the SERVS flux below 2000 μJy and the SWIRE flux over.\n", "\n", "We create a table indicating for each source the origin on the IRAC1 and IRAC2 fluxes that will be saved separately." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "irac_origin = Table()\n", "irac_origin.add_column(master_catalogue['irac_intid'])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1776 sources with SERVS flux\n", "735 sources with SWIRE flux\n", "660 sources with SERVS and SWIRE flux\n", "1775 sources for which we use SERVS\n", "76 sources for which we use SWIRE\n" ] } ], "source": [ "# IRAC1 aperture flux and magnitudes\n", "has_servs = ~np.isnan(master_catalogue['f_ap_servs_irac_i1'])\n", "has_swire = ~np.isnan(master_catalogue['f_ap_swire_irac_i1'])\n", "has_both = has_servs & has_swire\n", "\n", "print(\"{} sources with SERVS flux\".format(np.sum(has_servs)))\n", "print(\"{} sources with SWIRE flux\".format(np.sum(has_swire)))\n", "print(\"{} sources with SERVS and SWIRE flux\".format(np.sum(has_both)))\n", "\n", "has_servs_above_limit = has_servs.copy()\n", "has_servs_above_limit[has_servs] = master_catalogue['f_ap_servs_irac_i1'][has_servs] > 2000\n", "\n", "use_swire = (has_swire & ~has_servs) | (has_both & has_servs_above_limit)\n", "use_servs = (has_servs & ~(has_both & has_servs_above_limit))\n", "\n", "print(\"{} sources for which we use SERVS\".format(np.sum(use_servs)))\n", "print(\"{} sources for which we use SWIRE\".format(np.sum(use_swire)))\n", "\n", "f_ap_irac_i = np.full(len(master_catalogue), np.nan)\n", "f_ap_irac_i[use_servs] = master_catalogue['f_ap_servs_irac_i1'][use_servs]\n", "f_ap_irac_i[use_swire] = master_catalogue['f_ap_swire_irac_i1'][use_swire]\n", "\n", "ferr_ap_irac_i = np.full(len(master_catalogue), np.nan)\n", "ferr_ap_irac_i[use_servs] = master_catalogue['ferr_ap_servs_irac_i1'][use_servs]\n", "ferr_ap_irac_i[use_swire] = master_catalogue['ferr_ap_swire_irac_i1'][use_swire]\n", "\n", "m_ap_irac_i = np.full(len(master_catalogue), np.nan)\n", "m_ap_irac_i[use_servs] = master_catalogue['m_ap_servs_irac_i1'][use_servs]\n", "m_ap_irac_i[use_swire] = master_catalogue['m_ap_swire_irac_i1'][use_swire]\n", "\n", "merr_ap_irac_i = np.full(len(master_catalogue), np.nan)\n", "merr_ap_irac_i[use_servs] = master_catalogue['merr_ap_servs_irac_i1'][use_servs]\n", "merr_ap_irac_i[use_swire] = master_catalogue['merr_ap_swire_irac_i1'][use_swire]\n", "\n", "master_catalogue.add_column(Column(data=f_ap_irac_i, name=\"f_ap_irac_i1\"))\n", "master_catalogue.add_column(Column(data=ferr_ap_irac_i, name=\"ferr_ap_irac_i1\"))\n", "master_catalogue.add_column(Column(data=m_ap_irac_i, name=\"m_ap_irac_i1\"))\n", "master_catalogue.add_column(Column(data=merr_ap_irac_i, name=\"merr_ap_irac_i1\"))\n", "\n", "master_catalogue.remove_columns(['f_ap_servs_irac_i1', 'f_ap_swire_irac_i1', 'ferr_ap_servs_irac_i1',\n", " 'ferr_ap_swire_irac_i1', 'm_ap_servs_irac_i1', 'm_ap_swire_irac_i1',\n", " 'merr_ap_servs_irac_i1', 'merr_ap_swire_irac_i1'])\n", "\n", "origin = np.full(len(master_catalogue), ' ', dtype=' 2000\n", "\n", "use_swire = (has_swire & ~has_servs) | (has_both & has_servs_above_limit)\n", "use_servs = (has_servs & ~(has_both & has_servs_above_limit))\n", "use_candels = has_candels & ~has_servs & ~has_swire\n", "\n", "print(\"{} sources for which we use SERVS\".format(np.sum(use_servs)))\n", "print(\"{} sources for which we use SWIRE\".format(np.sum(use_swire)))\n", "print(\"{} sources for which we use CANDELS\".format(np.sum(use_candels)))\n", "\n", "f_irac_i = np.full(len(master_catalogue), np.nan)\n", "f_irac_i[use_servs] = master_catalogue['f_servs_irac_i1'][use_servs]\n", "f_irac_i[use_swire] = master_catalogue['f_swire_irac_i1'][use_swire]\n", "f_irac_i[use_candels] = master_catalogue['f_candels-irac_i1'][use_candels]\n", "\n", "ferr_irac_i = np.full(len(master_catalogue), np.nan)\n", "ferr_irac_i[use_servs] = master_catalogue['ferr_servs_irac_i1'][use_servs]\n", "ferr_irac_i[use_swire] = master_catalogue['ferr_swire_irac_i1'][use_swire]\n", "ferr_irac_i[use_candels] = master_catalogue['ferr_candels-irac_i1'][use_candels]\n", "\n", "flag_irac_i = np.full(len(master_catalogue), False, dtype=bool)\n", "flag_irac_i[use_servs] = master_catalogue['flag_servs_irac_i1'][use_servs]\n", "flag_irac_i[use_swire] = master_catalogue['flag_swire_irac_i1'][use_swire]\n", "flag_irac_i[use_candels] = master_catalogue['flag_candels-irac_i1'][use_candels]\n", "\n", "m_irac_i = np.full(len(master_catalogue), np.nan)\n", "m_irac_i[use_servs] = master_catalogue['m_servs_irac_i1'][use_servs]\n", "m_irac_i[use_swire] = master_catalogue['m_swire_irac_i1'][use_swire]\n", "m_irac_i[use_candels] = master_catalogue['m_candels-irac_i1'][use_candels]\n", "\n", "merr_irac_i = np.full(len(master_catalogue), np.nan)\n", "merr_irac_i[use_servs] = master_catalogue['merr_servs_irac_i1'][use_servs]\n", "merr_irac_i[use_swire] = master_catalogue['merr_swire_irac_i1'][use_swire]\n", "merr_irac_i[use_candels] = master_catalogue['merr_candels-irac_i1'][use_candels]\n", "\n", "master_catalogue.add_column(Column(data=f_irac_i, name=\"f_irac_i1\"))\n", "master_catalogue.add_column(Column(data=ferr_irac_i, name=\"ferr_irac_i1\"))\n", "master_catalogue.add_column(Column(data=m_irac_i, name=\"m_irac_i1\"))\n", "master_catalogue.add_column(Column(data=merr_irac_i, name=\"merr_irac_i1\"))\n", "master_catalogue.add_column(Column(data=flag_irac_i, name=\"flag_irac_i1\"))\n", "\n", "master_catalogue.remove_columns(['f_servs_irac_i1', 'f_swire_irac_i1', 'f_candels-irac_i1',\n", " 'ferr_servs_irac_i1','ferr_swire_irac_i1', 'ferr_candels-irac_i1',\n", " 'm_servs_irac_i1', 'm_swire_irac_i1', 'm_candels-irac_i1',\n", " 'merr_servs_irac_i1', 'merr_swire_irac_i1', 'merr_candels-irac_i1', \n", " 'flag_servs_irac_i1', 'flag_swire_irac_i1', 'flag_candels-irac_i1'])\n", "\n", "origin = np.full(len(master_catalogue), ' ', dtype=' 2000\n", "\n", "use_swire = (has_swire & ~has_servs) | (has_both & has_servs_above_limit)\n", "use_servs = (has_servs & ~(has_both & has_servs_above_limit))\n", "\n", "print(\"{} sources for which we use SERVS\".format(np.sum(use_servs)))\n", "print(\"{} sources for which we use SWIRE\".format(np.sum(use_swire)))\n", "\n", "f_ap_irac_i = np.full(len(master_catalogue), np.nan)\n", "f_ap_irac_i[use_servs] = master_catalogue['f_ap_servs_irac_i2'][use_servs]\n", "f_ap_irac_i[use_swire] = master_catalogue['f_ap_swire_irac_i2'][use_swire]\n", "\n", "ferr_ap_irac_i = np.full(len(master_catalogue), np.nan)\n", "ferr_ap_irac_i[use_servs] = master_catalogue['ferr_ap_servs_irac_i2'][use_servs]\n", "ferr_ap_irac_i[use_swire] = master_catalogue['ferr_ap_swire_irac_i2'][use_swire]\n", "\n", "m_ap_irac_i = np.full(len(master_catalogue), np.nan)\n", "m_ap_irac_i[use_servs] = master_catalogue['m_ap_servs_irac_i2'][use_servs]\n", "m_ap_irac_i[use_swire] = master_catalogue['m_ap_swire_irac_i2'][use_swire]\n", "\n", "merr_ap_irac_i = np.full(len(master_catalogue), np.nan)\n", "merr_ap_irac_i[use_servs] = master_catalogue['merr_ap_servs_irac_i2'][use_servs]\n", "merr_ap_irac_i[use_swire] = master_catalogue['merr_ap_swire_irac_i2'][use_swire]\n", "\n", "master_catalogue.add_column(Column(data=f_ap_irac_i, name=\"f_ap_irac_i2\"))\n", "master_catalogue.add_column(Column(data=ferr_ap_irac_i, name=\"ferr_ap_irac_i2\"))\n", "master_catalogue.add_column(Column(data=m_ap_irac_i, name=\"m_ap_irac_i2\"))\n", "master_catalogue.add_column(Column(data=merr_ap_irac_i, name=\"merr_ap_irac_i2\"))\n", "\n", "master_catalogue.remove_columns(['f_ap_servs_irac_i2', 'f_ap_swire_irac_i2', 'ferr_ap_servs_irac_i2',\n", " 'ferr_ap_swire_irac_i2', 'm_ap_servs_irac_i2', 'm_ap_swire_irac_i2',\n", " 'merr_ap_servs_irac_i2', 'merr_ap_swire_irac_i2'])\n", "\n", "origin = np.full(len(master_catalogue), ' ', dtype=' 2000\n", "\n", "use_swire = (has_swire & ~has_servs) | (has_both & has_servs_above_limit)\n", "use_servs = (has_servs & ~(has_both & has_servs_above_limit))\n", "use_candels = has_candels & ~has_servs & ~has_swire\n", "\n", "print(\"{} sources for which we use SERVS\".format(np.sum(use_servs)))\n", "print(\"{} sources for which we use SWIRE\".format(np.sum(use_swire)))\n", "print(\"{} sources for which we use CANDELS\".format(np.sum(use_candels)))\n", "\n", "f_irac_i = np.full(len(master_catalogue), np.nan)\n", "f_irac_i[use_servs] = master_catalogue['f_servs_irac_i2'][use_servs]\n", "f_irac_i[use_swire] = master_catalogue['f_swire_irac_i2'][use_swire]\n", "f_irac_i[use_candels] = master_catalogue['f_candels-irac_i2'][use_candels]\n", "\n", "ferr_irac_i = np.full(len(master_catalogue), np.nan)\n", "ferr_irac_i[use_servs] = master_catalogue['ferr_servs_irac_i2'][use_servs]\n", "ferr_irac_i[use_swire] = master_catalogue['ferr_swire_irac_i2'][use_swire]\n", "ferr_irac_i[use_candels] = master_catalogue['ferr_candels-irac_i2'][use_candels]\n", "\n", "flag_irac_i = np.full(len(master_catalogue), False, dtype=bool)\n", "flag_irac_i[use_servs] = master_catalogue['flag_servs_irac_i2'][use_servs]\n", "flag_irac_i[use_swire] = master_catalogue['flag_swire_irac_i2'][use_swire]\n", "flag_irac_i[use_candels] = master_catalogue['flag_candels-irac_i2'][use_candels]\n", "\n", "m_irac_i = np.full(len(master_catalogue), np.nan)\n", "m_irac_i[use_servs] = master_catalogue['m_servs_irac_i2'][use_servs]\n", "m_irac_i[use_swire] = master_catalogue['m_swire_irac_i2'][use_swire]\n", "m_irac_i[use_candels] = master_catalogue['m_candels-irac_i2'][use_candels]\n", "\n", "merr_irac_i = np.full(len(master_catalogue), np.nan)\n", "merr_irac_i[use_servs] = master_catalogue['merr_servs_irac_i2'][use_servs]\n", "merr_irac_i[use_swire] = master_catalogue['merr_swire_irac_i2'][use_swire]\n", "merr_irac_i[use_candels] = master_catalogue['merr_candels-irac_i2'][use_candels]\n", "\n", "master_catalogue.add_column(Column(data=f_irac_i, name=\"f_irac_i2\"))\n", "master_catalogue.add_column(Column(data=ferr_irac_i, name=\"ferr_irac_i2\"))\n", "master_catalogue.add_column(Column(data=m_irac_i, name=\"m_irac_i2\"))\n", "master_catalogue.add_column(Column(data=merr_irac_i, name=\"merr_irac_i2\"))\n", "master_catalogue.add_column(Column(data=flag_irac_i, name=\"flag_irac_i2\"))\n", "\n", "master_catalogue.remove_columns(['f_servs_irac_i2', 'f_swire_irac_i2', 'f_candels-irac_i2',\n", " 'ferr_servs_irac_i2','ferr_swire_irac_i2', 'ferr_candels-irac_i2',\n", " 'm_servs_irac_i2', 'm_swire_irac_i2', 'm_candels-irac_i2',\n", " 'merr_servs_irac_i2', 'merr_swire_irac_i2', 'merr_candels-irac_i2', \n", " 'flag_servs_irac_i2', 'flag_swire_irac_i2', 'flag_candels-irac_i2'])\n", "\n", "origin = np.full(len(master_catalogue), ' ', dtype='