{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# HATLAS-SGP master catalogue\n", "\n", "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on GAMA-09." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This notebook was run with herschelhelp_internal version: \n", "0246c5d (Thu Jan 25 17:01:47 2018 +0000) [with local modifications]\n", "This notebook was executed on: \n", "2018-02-21 21:17:54.577861\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": {}, "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": [ "atlas = Table.read(\"{}/ATLAS.fits\".format(TMP_DIR))\n", "kids = Table.read(\"{}/KIDS.fits\".format(TMP_DIR))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## II - Merging tables\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ATLAS" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue = atlas\n", "master_catalogue['atlas_ra'].name = 'ra'\n", "master_catalogue['atlas_dec'].name = 'dec'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add KIDS" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAF3CAYAAABnvQURAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEIIVAABAQAhWAAAAAfn/CQIFuOhRMYQAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(kids['kids_ra'], kids['kids_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "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": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue.add_column(Column(data=(np.char.array(master_catalogue['kids_id'].astype(str)) \n", " + np.char.array(master_catalogue['atlas_id'].astype(str) )), \n", " name=\"omegacam_intid\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cleaning" ] }, { "cell_type": "code", "execution_count": 9, "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": "markdown", "metadata": {}, "source": [ "## VI - Choosing between multiple values for the same filter\n", "\n", "### ATLAS and KIDS\n", "The ATLAS and KIDS surveys are both on the VLT Survey Telescope (VST). KIDS is significantly deeper so we take KIDS fluxes if available.\n", "\n", "| Survey | Bands observed |\n", "|-------------|------------------|\n", "| ATLAS | u, ul, griz |\n", "| KIDS | ugri |\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "omegacam_origin = Table()\n", "omegacam_origin.add_column(master_catalogue['omegacam_intid'])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(4, 0) will return an array of dtype('int64')\n", " format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n" ] } ], "source": [ "omegacam_stats = Table()\n", "omegacam_stats.add_column(Column(data=['u', 'g','r','i'], name=\"Band\"))\n", "for col in [\"ATLAS\", \"KIDS\"]:\n", " omegacam_stats.add_column(Column(data=np.full(4, 0), name=\"{}\".format(col)))\n", " omegacam_stats.add_column(Column(data=np.full(4, 0), name=\"use {}\".format(col)))\n", " omegacam_stats.add_column(Column(data=np.full(4, 0), name=\"{} ap\".format(col)))\n", " omegacam_stats.add_column(Column(data=np.full(4, 0), name=\"use {} ap\".format(col)))\n", " \n", "#ul and z only in ATLAS so we can just rename those columns\n", "for col in master_catalogue.colnames:\n", " if ('atlas_ul' in col) or ('atlas_z' in col):\n", " master_catalogue[col].name = col.replace('atlas', 'omegacam')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "omegacam_bands = ['u','g','r','i']\n", "for band in omegacam_bands:\n", "\n", " # omegacam total flux \n", " has_kids = ~np.isnan(master_catalogue['f_kids_' + band])\n", " has_atlas = ~np.isnan(master_catalogue['f_atlas_' + band])\n", "\n", " \n", "\n", " use_kids = has_kids\n", " use_atlas = has_atlas & ~has_kids\n", "\n", "\n", " f_omegacam = np.full(len(master_catalogue), np.nan)\n", " f_omegacam[use_kids] = master_catalogue['f_kids_' + band][use_kids]\n", " f_omegacam[use_atlas] = master_catalogue['f_atlas_' + band][use_atlas]\n", " \n", "\n", " ferr_omegacam = np.full(len(master_catalogue), np.nan)\n", " ferr_omegacam[use_kids] = master_catalogue['ferr_kids_' + band][use_kids]\n", " ferr_omegacam[use_atlas] = master_catalogue['ferr_atlas_' + band][use_atlas]\n", "\n", " \n", " m_omegacam = np.full(len(master_catalogue), np.nan)\n", " m_omegacam[use_kids] = master_catalogue['m_kids_' + band][use_kids]\n", " m_omegacam[use_atlas] = master_catalogue['m_atlas_' + band][use_atlas]\n", " \n", "\n", " merr_omegacam = np.full(len(master_catalogue), np.nan)\n", " merr_omegacam[use_kids] = master_catalogue['merr_kids_' + band][use_kids]\n", " merr_omegacam[use_atlas] = master_catalogue['merr_atlas_' + band][use_atlas]\n", "\n", "\n", " flag_omegacam = np.full(len(master_catalogue), False, dtype=bool)\n", " flag_omegacam[use_kids] = master_catalogue['flag_kids_' + band][use_kids]\n", " flag_omegacam[use_atlas] = master_catalogue['flag_atlas_' + band][use_atlas]\n", "\n", "\n", " master_catalogue.add_column(Column(data=f_omegacam, name=\"f_omegacam_\" + band))\n", " master_catalogue.add_column(Column(data=ferr_omegacam, name=\"ferr_omegacam_\" + band))\n", " master_catalogue.add_column(Column(data=m_omegacam, name=\"m_omegacam_\" + band))\n", " master_catalogue.add_column(Column(data=merr_omegacam, name=\"merr_omegacam_\" + band))\n", " master_catalogue.add_column(Column(data=flag_omegacam, name=\"flag_omegacam_\" + band))\n", "\n", " old_kids_columns = ['f_kids_' + band,\n", " 'ferr_kids_' + band,\n", " 'm_kids_' + band, \n", " 'merr_kids_' + band,\n", " 'flag_kids_' + band]\n", " old_atlas_columns = ['f_atlas_' + band,\n", " 'ferr_atlas_' + band,\n", " 'm_atlas_' + band, \n", " 'merr_atlas_' + band,\n", " 'flag_atlas_' + band]\n", "\n", " \n", " old_columns = old_kids_columns + old_atlas_columns \n", " master_catalogue.remove_columns(old_columns)\n", "\n", " origin = np.full(len(master_catalogue), ' ', dtype='\n", "idxBandATLASuse ATLASATLAS apuse ATLAS apKIDSuse KIDSKIDS apuse KIDS ap\n", "0u1050318.0895887.0916026.0761965.06705188.06705188.06705188.06705188.0\n", "1g3618643.02658537.03580843.02620748.08819714.08819714.08819714.08819714.0\n", "2r4487236.03372392.04469703.03354910.09604235.09604235.09604235.09604235.0\n", "3i4155027.03041240.04152537.03038758.08261916.08261916.08261916.08261916.0\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "omegacam_stats.show_in_notebook()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "omegacam_origin.write(\"{}/hatlas-sgp_omegacam_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#band ul is actually the same as u but measured in a different way. we remove it.\n", "for col in master_catalogue.colnames:\n", " if col.endswith('_ul'):\n", " master_catalogue.remove_column(col)\n", " \n", "master_catalogue['flag_merged'].name = 'omegacam_flag_merged'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## X - Saving the catalogue" ] }, { "cell_type": "code", "execution_count": 16, "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\", \"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \"flag_optnir_obs\", \"flag_optnir_det\", \"ebv\"]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing columns: {'atlas_flag_cleaned', 'omegacam_intid', 'atlas_flag_gaia', 'kids_id', 'kids_flag_gaia', 'kids_stellarity', 'kids_flag_cleaned', 'atlas_id', 'omegacam_flag_merged', 'atlas_stellarity'}\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": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue['ra'].name = 'omegacam_ra'\n", "master_catalogue['dec'].name = 'omegacam_dec'\n", "master_catalogue.write(\"{}/omegacam_merged_hatlas-sgp.fits\".format(TMP_DIR), 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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }