{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# xFLS master catalogue\n", "\n", "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on xFLS." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This notebook was run with herschelhelp_internal version: \n", "0246c5d (Thu Jan 25 17:01:47 2018 +0000) [with local modifications]\n", "This notebook was executed on: \n", "2018-05-01 13:44:22.041363\n" ] } ], "source": [ "from herschelhelp_internal import git_version\n", "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))\n", "import datetime\n", "print(\"This notebook was executed on: \\n{}\".format(datetime.datetime.now()))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "#%config InlineBackend.figure_format = 'svg'\n", "\n", "import matplotlib.pyplot as plt\n", "plt.rc('figure', figsize=(10, 6))\n", "\n", "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": [ "spitzer = Table.read(\"{}/Spitzer.fits\".format(TMP_DIR))\n", "wfc = Table.read(\"{}/INT-WFC.fits\".format(TMP_DIR))\n", "kpno = Table.read(\"{}/KPNO.fits\".format(TMP_DIR))\n", "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))\n", "legacy = Table.read(\"{}/LegacySurvey.fits\".format(TMP_DIR))\n", "uhs = Table.read(\"{}/UHS.fits\".format(TMP_DIR)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## II - Merging tables\n", "\n", "At every step, we look at the distribution of the distances to the nearest source in the merged catalogue to determine the best crossmatching radius." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### WFC" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue = wfc\n", "master_catalogue['wfc_ra'].name = 'ra'\n", "master_catalogue['wfc_dec'].name = 'dec'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add PanSTARRS" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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N5W7palA6k9O+3pGyXhcAAEytHKFrn6QlRa8XB9uOt/2Y3P02d1/n7uva2trKUNb01TNc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\njtVzahNqrkuqsTah+lRxX6C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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(ps1['ps1_ra'], ps1['ps1_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, ps1, \"ps1_ra\", \"ps1_dec\", radius=0.8*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add Legacy Survey" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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kcp5vjFqBkS5J6miq5fQiAAARInRVid6RCWVzXvZJ9AXtTTU6PDKhnHtFPh8AAJwYoatK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\nAIA5IRF1AfPNgYE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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(legacy['legacy_ra'], legacy['legacy_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, legacy, \"legacy_ra\", \"legacy_dec\", radius=0.8*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add KPNO" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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AC5gKrKB0NqdkJqeWhnBFztfeFNFMzikxnanI+QAAWOzmFazM7Aoz22Vmu83sU8d4vdXM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\nq9tjsx3D81N2YTVFWRwO1JP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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(kpno['kpno_ra'], kpno['kpno_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, kpno, \"kpno_ra\", \"kpno_dec\", radius=0.8*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add UHS" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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Pz3+wikZMyxfU6EWCFQAAeUWwCsnBziElUk7L5tcU5HjXNtVqV3u/UmlXkOMBADAbEaxC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\nNa11ivDLsDcEq4v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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(uhs['uhs_ra'], uhs['uhs_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, uhs, \"uhs_ra\", \"uhs_dec\", radius=0.8*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add Spitzer" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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D0ZjXJYmIyCyjBqFJpB5W02NyA9Hb1tbw0PZj3PHNF3nPuhrMjDs21ntUnYiI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\np4CPbJzLu9fO0SXoMi3uf/E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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(spitzer['spitzer_ra'], spitzer['spitzer_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, spitzer, \"spitzer_ra\", \"spitzer_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", "
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7465698301564259.05341508659.82522388970.0nannannannan15.1250.01813.5040.01814.950.01613.1380.01614.6660.02212.8870.022nannannannannannannannanFalse3235.9353.647314401.2238.753False3801.8956.026820174.4297.300547361False4938.55100.06925421.4515.107814223FalsenannannannanFalseFalse1False-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannanFalseFalse0-1nannannannannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
8465698300805259.2109437359.82084360170.0nannannannan17.1140.0316.7810.0314.9760.01613.5360.01614.6280.02412.7190.024nannannannannannannannanFalse518.08414.3152704.04419.4535False3711.9354.70113983.0206.060796976False5114.46113.05429675.6655.974403024FalsenannannannanFalseFalse1False-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannanFalseFalse0-1nannannannannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
9465698303358258.6944995259.89706631170.0nannannannan17.3330.0317.0910.0315.0450.01613.8870.01614.8440.02213.7340.022nannannannannannannannanFalse423.44811.7003529.17614.6217False3483.3751.332910120.4149.140462279False4191.7984.937211652.0236.101187766FalsenannannannanFalseFalse2True-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannanFalseFalse0-1nannannannannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0
\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. We keep trace of the origin of the stellarity." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "wfc_stellarity, legacy_stellarity, kpno_stellarity, uhs_stellarity, spitzer_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), \"xFLS\", dtype='" ] }, "metadata": {}, "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": 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", "No overlapping filters on xFLS." ] }, { "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": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "spitzer_moc = MOC(filename=\"../../dmu0/dmu0_DataFusion-Spitzer/data/Datafusion-xFLS_MOC.fits\")\n", "wfc_moc = MOC(filename=\"../../dmu0/dmu0_INTWFC/data/fls_intwfc_v2.1_HELP_coverage_MOC.fits\")\n", "kpno_moc = MOC(filename=\"../../dmu0/dmu0_KPNO-FLS/data/KPNO-FLS_xFLS_MOC.fits\")\n", "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_xFLS_v2_MOC.fits\")\n", "legacy_moc = MOC(filename=\"../../dmu0/dmu0_LegacySurvey/data/LegacySurvey-dr4_xFLS_MOC.fits\")\n", "uhs_moc = MOC(filename=\"../../dmu0/dmu0_UHS/data/UHS-DR1_xFLS_MOC.fits\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "was_observed_optical = inMoc(\n", " master_catalogue['ra'], master_catalogue['dec'],\n", " wfc_moc + legacy_moc + kpno_moc + ps1_moc) \n", "\n", "was_observed_nir = inMoc(\n", " master_catalogue['ra'], master_catalogue['dec'],\n", " uhs_moc\n", ")\n", "\n", "was_observed_mir = inMoc(\n", " master_catalogue['ra'], master_catalogue['dec'],\n", " spitzer_moc\n", ")" ] }, { "cell_type": "code", "execution_count": 30, "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": 31, "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_wfc_u']) +\n", " 1 * ~np.isnan(master_catalogue['f_wfc_g']) +\n", " 1 * ~np.isnan(master_catalogue['f_wfc_r']) +\n", " 1 * ~np.isnan(master_catalogue['f_wfc_i']) +\n", " 1 * ~np.isnan(master_catalogue['f_wfc_z']) +\n", " #Legacy Survey\n", " 1 * ~np.isnan(master_catalogue['f_90prime_g']) +\n", " 1 * ~np.isnan(master_catalogue['f_90prime_r']) +\n", " 1 * ~np.isnan(master_catalogue['f_mosaic_z']) +\n", " #KPNO-FLS\n", " 1 * ~np.isnan(master_catalogue['f_mosaic_r']) +\n", " #PanSTARRS\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", ")\n", "\n", "nb_nir_flux = (\n", " 1 * ~np.isnan(master_catalogue['f_ukidss_j']) \n", ")\n", "\n", "nb_mir_flux = (\n", " 1 * ~np.isnan(master_catalogue['f_irac_i1']) +\n", " 1 * ~np.isnan(master_catalogue['f_irac_i2']) +\n", " 1 * ~np.isnan(master_catalogue['f_irac_i3']) +\n", " 1 * ~np.isnan(master_catalogue['f_irac_i4'])\n", ")" ] }, { "cell_type": "code", "execution_count": 32, "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 catalogues. 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": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "91 master list rows had multiple associations.\n" ] } ], "source": [ "#\n", "# Addind SDSS ids\n", "#\n", "sdss = Table.read(\"../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_xFLS.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": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['wfc_id', 'ps1_id', 'legacy_id', 'kpno_intid', 'uhs_id', 'spitzer_intid', '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)\n", "master_catalogue[id_names].write(\n", " \"{}/master_list_cross_ident_xfls{}.fits\".format(OUT_DIR, SUFFIX))\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": 35, "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": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "columns = [\"help_id\", \"field\", \"ra\", \"dec\", \"hp_idx\"]\n", "\n", "bands = [column[5:] for column in master_catalogue.colnames if 'f_ap' in column]\n", "for band in bands:\n", " columns += [\"f_ap_{}\".format(band), \"ferr_ap_{}\".format(band),\n", " \"m_ap_{}\".format(band), \"merr_ap_{}\".format(band),\n", " \"f_{}\".format(band), \"ferr_{}\".format(band),\n", " \"m_{}\".format(band), \"merr_{}\".format(band),\n", " \"flag_{}\".format(band)] \n", " \n", "columns += [\"stellarity\", \"stellarity_origin\", \"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \"flag_optnir_obs\", \n", " \"flag_optnir_det\", \"zspec\", \"zspec_qual\", \"zspec_association_flag\", \"ebv\"]" ] }, { "cell_type": "code", "execution_count": 37, "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": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue[columns].write(\"{}/master_catalogue_xfls{}.fits\".format(OUT_DIR, SUFFIX))" ] } ], "metadata": { "kernelspec": { "display_name": "Python (herschelhelp_internal)", "language": "python", "name": "helpint" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }