{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# HDF-N master catalogue\n", "\n", "This notebook presents the merge of the various pristine catalogues to produce the HELP master catalogue on HDF-N." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This notebook was run with herschelhelp_internal version: \n", "1407877 (Mon Feb 4 12:56:29 2019 +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/miniconda3/envs/herschelhelp_internal/lib/python3.6/_collections_abc.py:841: MatplotlibDeprecationWarning: \n", "The examples.directory rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2. In the future, examples will be found relative to the 'datapath' directory.\n", " self[key] = other[key]\n", "/Users/rs548/miniconda3/envs/herschelhelp_internal/lib/python3.6/_collections_abc.py:841: MatplotlibDeprecationWarning: \n", "The savefig.frameon rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n", " self[key] = other[key]\n", "/Users/rs548/miniconda3/envs/herschelhelp_internal/lib/python3.6/_collections_abc.py:841: MatplotlibDeprecationWarning: \n", "The text.latex.unicode rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2.\n", " self[key] = other[key]\n", "/Users/rs548/miniconda3/envs/herschelhelp_internal/lib/python3.6/_collections_abc.py:841: MatplotlibDeprecationWarning: \n", "The verbose.fileo rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n", " self[key] = other[key]\n", "/Users/rs548/miniconda3/envs/herschelhelp_internal/lib/python3.6/_collections_abc.py:841: MatplotlibDeprecationWarning: \n", "The verbose.level rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n", " self[key] = other[key]\n", "/Users/rs548/miniconda3/envs/herschelhelp_internal/lib/python3.6/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": {}, "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": {}, "outputs": [], "source": [ "#threed = Table.read(\"{}/CANDELS-3D-HST.fits\".format(TMP_DIR)) # 1.1\n", "#acs = Table.read(\"{}/ACS.fits\".format(TMP_DIR)) # 1.2 GOODS-ACS\n", "hawaii = Table.read(\"{}/Hawaii.fits\".format(TMP_DIR)) # 1.3 Hawaii-HDFN\n", "ultra = Table.read(\"{}/Ultradeep.fits\".format(TMP_DIR)) # 1.4 Ultradeep_Ks_GOODS-N\n", "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR)) # 1.5 PanSTARRS\n", "candels_gn = Table.read(\"{}/CANDELS-GOODS-N.fits\".format(TMP_DIR)) # 1.6 CANDELS-GOODS-N" ] }, { "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": [ "### PanSTARRS" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "master_catalogue = ps1\n", "master_catalogue['ps1_ra'].name = 'ra'\n", "master_catalogue['ps1_dec'].name = 'dec'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CANDELS-GOODS-N" ] }, { "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(candels_gn['candels-gn_ra'], candels_gn['candels-gn_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Given the graph above, we use 0.8 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, candels_gn, \"candels-gn_ra\", \"candels-gn_dec\", radius=0.8*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ultradeep" ] }, { "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(ultra['ultradeep_ra'], ultra['ultradeep_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Given the graph above, we use 0.8 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, ultra, \"ultradeep_ra\", \"ultradeep_dec\", radius=0.8*u.arcsec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hawaii" ] }, { "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(hawaii['hawaii_ra'], hawaii['hawaii_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Given the graph above, we use 0.8 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, hawaii, \"hawaii_ra\", \"hawaii_dec\", radius=0.8*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": 12, "metadata": {}, "outputs": [], "source": [ "for col in master_catalogue.colnames:\n", " if col.startswith('m_'):\n", " if col.startswith('m_ap_'):\n", " sn5 = master_catalogue['f_{}'.format(col[5:])] / master_catalogue['ferr_{}'.format(col[5:])] > 5\n", " m = (~sn5 \n", " | (master_catalogue['merr_{}'.format(col[5:])]<0)\n", " | (master_catalogue[col]>40)\n", " | (master_catalogue[col]==np.inf)\n", " )\n", " master_catalogue[col][m] = np.nan\n", " master_catalogue['merr_{}'.format(col[5:])][m] = np.nan\n", " master_catalogue['f_{}'.format(col[5:])][m] = np.nan\n", " master_catalogue['ferr_{}'.format(col[5:])][m] = np.nan\n", " else:\n", " sn5 = master_catalogue['f_{}'.format(col[2:])] / master_catalogue['ferr_{}'.format(col[2:])] > 5\n", " m = (~sn5 \n", " | (master_catalogue['merr_{}'.format(col[2:])]<0)\n", " | (master_catalogue[col]>40)\n", " | (master_catalogue[col]==np.inf)\n", " )\n", " master_catalogue[col][m] = np.nan\n", " master_catalogue['merr_{}'.format(col[2:])][m] = np.nan\n", " master_catalogue['f_{}'.format(col[2:])][m] = np.nan\n", " master_catalogue['ferr_{}'.format(col[2:])][m] = np.nan\n", " \n", " \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": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=10\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
idxps1_idradecm_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_gaiaflag_mergedcandels-gn_idcandels-gn_stellarityf_acs_f435wferr_acs_f435wf_acs_f606wferr_acs_f606wf_acs_f775wferr_acs_f775wf_acs_f814wferr_acs_f814wf_acs_f850lpferr_acs_f850lpf_wfc3_f105wferr_wfc3_f105wf_wfc3_f125wferr_wfc3_f125wf_wfc3_f140wferr_wfc3_f140wf_wfc3_f160wferr_wfc3_f160wf_moircs_kferr_moircs_kf_candels-wircam_kferr_candels-wircam_kf_candels-irac_i1ferr_candels-irac_i1f_candels-irac_i2ferr_candels-irac_i2f_candels-irac_i3ferr_candels-irac_i3f_candels-irac_i4ferr_candels-irac_i4m_acs_f435wmerr_acs_f435wflag_acs_f435wm_acs_f606wmerr_acs_f606wflag_acs_f606wm_acs_f775wmerr_acs_f775wflag_acs_f775wm_acs_f814wmerr_acs_f814wflag_acs_f814wm_acs_f850lpmerr_acs_f850lpflag_acs_f850lpm_wfc3_f105wmerr_wfc3_f105wflag_wfc3_f105wm_wfc3_f125wmerr_wfc3_f125wflag_wfc3_f125wm_wfc3_f140wmerr_wfc3_f140wflag_wfc3_f140wm_wfc3_f160wmerr_wfc3_f160wflag_wfc3_f160wm_moircs_kmerr_moircs_kflag_moircs_km_candels-wircam_kmerr_candels-wircam_kflag_candels-wircam_km_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-gn_flag_cleanedcandels-gn_flag_gaiaultradeep_idf_ultradeep-wircam_kferr_ultradeep-wircam_kf_ultradeep-irac_i1ferr_ultradeep-irac_i1f_ultradeep-irac_i2ferr_ultradeep-irac_i2f_ultradeep-irac_i3ferr_ultradeep-irac_i3f_ultradeep-irac_i4ferr_ultradeep-irac_i4m_ultradeep-wircam_kmerr_ultradeep-wircam_kflag_ultradeep-wircam_km_ultradeep-irac_i1merr_ultradeep-irac_i1flag_ultradeep-irac_i1m_ultradeep-irac_i2merr_ultradeep-irac_i2flag_ultradeep-irac_i2m_ultradeep-irac_i3merr_ultradeep-irac_i3flag_ultradeep-irac_i3m_ultradeep-irac_i4merr_ultradeep-irac_i4flag_ultradeep-irac_i4ultradeep_flag_cleanedultradeep_flag_gaiahawaii_idm_ap_mosaic_umerr_ap_mosaic_um_mosaic_umerr_mosaic_um_ap_suprime_bmerr_ap_suprime_bm_suprime_bmerr_suprime_bm_ap_suprime_vmerr_ap_suprime_vm_suprime_vmerr_suprime_vm_ap_suprime_rmerr_ap_suprime_rm_suprime_rmerr_suprime_rm_ap_suprime_ipmerr_ap_suprime_ipm_suprime_ipmerr_suprime_ipm_ap_suprime_zpmerr_ap_suprime_zpm_suprime_zpmerr_suprime_zpm_ap_quirc_hkmerr_ap_quirc_hkm_quirc_hkmerr_quirc_hkf_ap_mosaic_uferr_ap_mosaic_uf_mosaic_uferr_mosaic_uflag_mosaic_uf_ap_suprime_bferr_ap_suprime_bf_suprime_bferr_suprime_bflag_suprime_bf_ap_suprime_vferr_ap_suprime_vf_suprime_vferr_suprime_vflag_suprime_vf_ap_suprime_rferr_ap_suprime_rf_suprime_rferr_suprime_rflag_suprime_rf_ap_suprime_ipferr_ap_suprime_ipf_suprime_ipferr_suprime_ipflag_suprime_ipf_ap_suprime_zpferr_ap_suprime_zpf_suprime_zpferr_suprime_zpflag_suprime_zpf_ap_quirc_hkferr_ap_quirc_hkf_quirc_hkferr_quirc_hkflag_quirc_hkhawaii_flag_cleanedhawaii_flag_gaia
degdeg0 galaxy, 1 star
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8182711883352854935188.33531290699262.2618624131452513.2623996734619140.001086000003851950213.317799568176270.001086000003851950212.8603000640869140.001086000003851950213.399000167846680.001086000003851950212.8718996047973630.0059580001980066312.9200000762939450.003525000065565109313.845700263977050.001520000048913061613.444800376892090.001400999957695603412.7004995346069340.00192700000479817412.7692003250122070.003051999956369417990.37145958285717.9947425431552217095.43565596006717.099589298819858False26054.3338884954426.060664256442815863.53360240143815.867387932551587False25777.46216438129141.454364334940624660.3916394743880.06353823797694False10512.83611925418914.71767391144623415208.27122812329319.62427637012485False30185.6259530481253.57443281475326628334.78163116331479.6489533305299FalseFalse3False-1nannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannan0.0nannanFalsenannanFalsenannanFalsenannanFalseFalse0-1nannannannannannannannannannannannan0.0nannanFalsenannanFalsenannanFalsenannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0
9183161895594883740189.55952015699262.6358052531452513.0670003890991210.001188999973237514513.1206998825073240.0021599999163299812.95769977569580.001086000003851950212.995300292968750.001086000003851950212.6855001449584960.002143000019714236312.7515001296997070.00541399978101253512.6693000793457030.001086000003851950212.738400459289550.001086000003851950212.6830997467041020.00168600003235042112.7413997650146480.004079000093042850521537.7258380694523.58616700155660720498.40393731241440.78021032683208False23818.81162388136723.82459883157945423008.05372544311523.013643944961217False30605.53263433940460.4084645527364328800.49473398334143.61303556011953False31065.6159000934631.0731638489299429150.08438310455729.157166919185936False30673.27168290185747.6314014610818129069.669486151353109.2117809271649FalseFalse3False-1nannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannan0.0nannanFalsenannanFalsenannanFalsenannanFalseFalse0-1nannannannannannannannannannannannan0.0nannanFalsenannanFalsenannanFalsenannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0
\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 13, "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": 14, "metadata": {}, "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": [ "combining the flag_merged column which contains information regarding multiple associations" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "master_catalogue['flag_merged'].name = 'flag_merged_tmp'\n", "flag_merged_columns = [column for column in master_catalogue.colnames\n", " if 'flag_merged' in column]\n", "\n", "flag_merged_column = np.zeros(len(master_catalogue), dtype=bool)\n", "for column in flag_merged_columns:\n", " flag_merged_column |= master_catalogue[column]\n", " \n", "master_catalogue.add_column(Column(data=flag_merged_column, name=\"flag_merged\"))\n", "master_catalogue.remove_columns(flag_merged_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": 16, "metadata": {}, "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": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "candels-gn_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": 18, "metadata": {}, "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": 19, "metadata": {}, "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": 20, "metadata": {}, "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), \"HDF-N\", dtype='" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(specz['ra'] * u.deg, specz['dec'] * u.deg)\n", ")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "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" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "We have to choose between the various HST catalogues which may contains different objects depending on the prior catalogue. The CANDELS-GOODS-N catalogue is taken as a base and any missing wircam or IRAC fluxes are taken from the Ultradeep Ks selected catalogues" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "bands = [\n", " ['candels-wircam_k', 'ultradeep-wircam_k', 'wircam_ks'],\n", " ['candels-irac_i1', 'ultradeep-irac_i1', 'irac_i1'],\n", " ['candels-irac_i2', 'ultradeep-irac_i2', 'irac_i2'],\n", " ['candels-irac_i3', 'ultradeep-irac_i3', 'irac_i3'],\n", " ['candels-irac_i4', 'ultradeep-irac_i4', 'irac_i4'],\n", " \n", "]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "ir_origin = Table()\n", "ir_origin.add_column(master_catalogue['help_id'])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "ir_stats = Table()\n", "ir_stats.add_column(Column(data=np.array(bands)[:,2], name=\"Band\"))\n", "for col in [\"CANDELS-GOODS-N\", \"Ultradeep\"]:\n", " ir_stats.add_column(Column(data=np.full(5, 0), name=\"{}\".format(col), dtype=str))\n", " ir_stats.add_column(Column(data=np.full(5, 0), name=\"use {}\".format(col), dtype=str))\n", " " ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=5\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
idxBandCANDELS-GOODS-Nuse CANDELS-GOODS-NUltradeepuse Ultradeep
0wircam_ks0000
1irac_i10000
2irac_i20000
3irac_i30000
4irac_i40000
\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ir_stats.show_in_notebook()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "\n", "for band in bands:\n", "\n", " # total flux \n", " has_candels = ~np.isnan(master_catalogue['f_' + band[0]])\n", " has_ultradeep = ~np.isnan(master_catalogue['f_' + band[1]])\n", " \n", "\n", " use_candels = has_candels\n", " use_ultradeep = has_ultradeep & ~has_candels\n", "\n", " f_ir = np.full(len(master_catalogue), np.nan)\n", " f_ir[use_candels] = master_catalogue['f_' + band[0]][use_candels]\n", " f_ir[use_ultradeep] = master_catalogue['f_' + band[1]][use_ultradeep]\n", "\n", " ferr_ir = np.full(len(master_catalogue), np.nan)\n", " ferr_ir[use_candels] = master_catalogue['ferr_' + band[0]][use_candels]\n", " ferr_ir[use_ultradeep] = master_catalogue['ferr_' + band[1]][use_ultradeep]\n", "\n", " m_ir = np.full(len(master_catalogue), np.nan)\n", " m_ir[use_candels] = master_catalogue['m_' + band[0]][use_candels]\n", " m_ir[use_ultradeep] = master_catalogue['m_' + band[1]][use_ultradeep]\n", " \n", " merr_ir = np.full(len(master_catalogue), np.nan)\n", " merr_ir[use_candels] = master_catalogue['merr_' + band[0]][use_candels]\n", " merr_ir[use_ultradeep] = master_catalogue['merr_' + band[1]][use_ultradeep]\n", " \n", " flag_ir = np.full(len(master_catalogue), False, dtype=bool)\n", " flag_ir[use_candels] = master_catalogue['flag_' + band[0]][use_candels]\n", " flag_ir[use_ultradeep] = master_catalogue['flag_' + band[1]][use_ultradeep]\n", "\n", " master_catalogue.add_column(Column(data=f_ir, name=\"f_\" + band[2]))\n", " master_catalogue.add_column(Column(data=ferr_ir, name=\"ferr_\" + band[2]))\n", " master_catalogue.add_column(Column(data=m_ir, name=\"m_\" + band[2]))\n", " master_catalogue.add_column(Column(data=merr_ir, name=\"merr_\" + band[2]))\n", " master_catalogue.add_column(Column(data=flag_ir, name=\"flag_\" + band[2]))\n", " \n", " master_catalogue.remove_columns(['f_' + band[0], 'f_' + band[1],\n", " 'ferr_' + band[0], 'ferr_' + band[1],\n", " 'm_' + band[0], 'm_' + band[1],\n", " 'merr_' + band[0], 'merr_' + band[1],\n", " 'flag_' + band[0], 'flag_' + band[1],])\n", "\n", " origin = np.full(len(master_catalogue), ' ', dtype='Table length=5\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
idxBandCANDELS-GOODS-Nuse CANDELS-GOODS-NUltradeepuse Ultradeep
0wircam_ks0000
1irac_i10000
2irac_i20000
3irac_i30000
4irac_i40000
\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ir_stats.show_in_notebook()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "ir_origin.write(\"{}/hdf-n_wircam_irac_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)" ] }, { "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 the different depths in the catalogue we are using.*" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "candels_gn_moc = MOC(filename=\"../../dmu0/dmu0_CANDELS-3D-HST/data/CANDELS-3D-HST_HDF-N_MOC.fits\")\n", "ultra_moc = MOC(filename=\"../../dmu0/dmu0_Ultradeep-Ks-GOODS-N/data/Ultradeep_Ks_GOODS-N_HELP-coverage_MOC.fits\")\n", "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_HDF-N_v2_MOC.fits\") \n", "hawaii_moc = MOC(filename=\"../../dmu0/dmu0_Hawaii-HDFN/data/R_MOC.fits\")" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "was_observed_optical = inMoc(\n", " master_catalogue['ra'], master_catalogue['dec'],\n", " ps1_moc + hawaii_moc)\n", "\n", "was_observed_nir = inMoc(\n", " master_catalogue['ra'], master_catalogue['dec'],\n", " candels_gn_moc + ultra_moc\n", ")\n", "\n", "was_observed_mir = inMoc(\n", " master_catalogue['ra'], master_catalogue['dec'],\n", " candels_gn_moc + ultra_moc\n", ")" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "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": 37, "metadata": {}, "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", " # 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", " 1 * ~np.isnan(master_catalogue['f_suprime_r']) + \n", " 1 * ~np.isnan(master_catalogue['f_suprime_ip']) +\n", " 1 * ~np.isnan(master_catalogue['f_suprime_zp']) +\n", " 1 * ~np.isnan(master_catalogue['f_mosaic_u']) +\n", " 1 * ~np.isnan(master_catalogue['f_acs_f435w']) +\n", " 1 * ~np.isnan(master_catalogue['f_acs_f606w']) +\n", " 1 * ~np.isnan(master_catalogue['f_acs_f775w']) +\n", " 1 * ~np.isnan(master_catalogue['f_wfc3_f140w']) \n", ")\n", "\n", "nb_nir_flux = (\n", " 1 * ~np.isnan(master_catalogue['f_wircam_ks'])+\n", " 1 * ~np.isnan(master_catalogue['f_quirc_hk'])+\n", " 1 * ~np.isnan(master_catalogue['f_moircs_k'])\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": 38, "metadata": {}, "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": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "130679 60893 10669 14886 8559\n" ] } ], "source": [ "print(\n", " len(master_catalogue),\n", " np.sum(has_optical_flux),\n", " np.sum(has_nir_flux),\n", " np.sum(has_mir_flux),\n", " np.sum(master_catalogue['flag_optnir_det']>5)\n", ")" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13500\n" ] } ], "source": [ "try:\n", " print(np.sum(Table.read('./data/master_catalogue_hdf-n_20180427.fits')['flag_optnir_det']>=5))\n", "except:\n", " print('Old catalogue not present')" ] }, { "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": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8 master list rows had multiple associations.\n" ] } ], "source": [ "#\n", "# Addind SDSS ids\n", "#\n", "sdss = Table.read(\"../../dmu0/dmu0_SDSS/data/SDSS-DR13_HDF-N.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": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ps1_id', 'candels-gn_id', 'ultradeep_id', 'hawaii_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)\n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "master_catalogue[id_names].write(\n", " \"{}/master_list_cross_ident_hdf-n{}.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": 44, "metadata": {}, "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": 45, "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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degdeg
0189.39211408699261.86001443314525nannannannan11.19439983367920.012.9521999359130860.010.4635000228881840.012.4686002731323240.012.6522998809814450.0665509998798370412.4560003280639650.13014699518680573nan0.26212599873542786nannannannannannanFalse120848.16672829370.023939.772866649120.0False236919.054533321640.037373.1660748134450.0False31555.861961508661934.239787164832337809.4072439101154532.206553511177False250726.2638148531360532.08138740447nannanFalsenannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalseFalse3nanNO_INFORMATION0.00892060480490826HELP_J123734.107+615136.052HDF-Nnan-99FalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalse11184308828
1188.74456256699262.6216493931452513.7244997024536130.000668999971821904213.7730998992919920.001510999980382621313.4221000671386720.000211999999010004113.4694995880126950.003282000077888369613.3304996490478520.001086000003851950213.3341999053955080.001086000003851950213.3352003097534180.001567000057548284513.4104003906250.00339600001461803913.3154001235961910.00200799992308020613.379099845886230.002897999947890639311754.3906199100077.24272337888488311239.83770236499715.6422844456594False15529.589398023753.03229543472783814866.20662633142144.93807159759724False16896.63180213441616.90073714203050816839.14504749770316.84323641995519False16823.6364963295324.280888159009715697.83803079392649.10019390384396False17133.2578356456431.68686856468408316156.97532213403243.12549718469189FalsenannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalseFalse3nanNO_INFORMATION0.010016679480973926HELP_J123458.695+623717.938HDF-Nnan-99FalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalse11184359432
2189.41162934699262.61717949314524414.7763004302978520.001665999996475875414.8453998565673830.002091000089421868313.9601001739501950.000735000008717179314.0101003646850590.002115000039339065613.6489000320434570.001925999997183680513.7080001831054690.002390000037848949413.534999847412110.002243000082671642313.5857000350952150.002447000006213784213.4406003952026370.00206599989905953413.5184001922607420.0034880000166594984461.4890136622926.8458992603036174186.3935958924838.062501135178042False9461.4986212935056.4050563467002559035.6594486630717.601347367461177False12602.01485400366822.3548597412082711934.38177941763226.270813093928364False13995.87519268732628.91379054754193613357.34016125877930.104376472258494False15267.21571526836329.05131650619611214211.50012089443245.65539257252542FalsenannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalseFalse3nanNO_INFORMATION0.009391061279668227HELP_J123738.791+623701.846HDF-Nnan-99FalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalse11184358063
3189.61526489699262.6304488331452515.199099540710450.003115999978035688415.2462997436523440.0053349998779594914.647399902343750.000748999998904764714.7025003433227540.002023000037297606514.4289999008178710.001903999946080148214.4886999130249020.00122199999168515214.3299999237060550.001505999942310154414.3955001831054690.00341599993407726314.268300056457520.003629999933764338514.3642997741699220.00190399994608014823022.45736728869368.674277461516012893.87722931502814.21969222133911False5023.8890430270893.4657524279235144775.2912265725578.897570410867228False6143.276173894184510.7731486272539785814.6025550312286.544356025673393False6729.7670357393179.334706462922076335.77763820656519.933954398493377False7123.27937024110923.8156409713908456520.4878549642711.434645422949615FalsenannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalseFalse3nanNO_INFORMATION0.009617077668294387HELP_J123827.664+623749.616HDF-Nnan-99FalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalse11184358005
4189.93911159699262.3108263931452516.0858001708984380.003849999979138374316.141500473022460.00800499971956014615.5888004302978520.000845999980811029715.6396999359130860.000751000014133751415.4131002426147460.001426000031642615815.4758996963500980.003248000051826238615.3423004150390620.001813999959267675915.4091997146606450.003202999942004680615.2957000732421880.00341599993407726315.3903999328613280.0053209997713565831335.61082908449574.7360506553779381268.81940142921289.354849647733708False2110.9591436571281.64484834087727452014.28085820880231.393271170616574False2481.76069975612433.2595310176379522342.2870724825847.0069953451631894False2648.9870426836484.425810216579462490.69250648163467.347722143602428False2765.15812061600678.6998848489553072534.194983325571212.419638276548316FalsenannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalseFalse3nanNO_INFORMATION0.010904169296597516HELP_J123945.387+621838.975HDF-Nnan-99FalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalse11184007074
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\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "master_catalogue[:10].show_in_notebook()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "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", "\n", "\n", "bands_no_ap = (set([column[5:] for column in master_catalogue.colnames if 'flag' in column]) \n", " - set(bands) \n", " - set(['cleaned', 'gaia', 'merged', 'optnir_det', 'optnir_obs'])\n", " )\n", "\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", "\n", "for band in bands_no_ap:\n", " columns += [\"f_{}\".format(band), \"ferr_{}\".format(band),\n", " \"m_{}\".format(band), \"merr_{}\".format(band),\n", " #\"flag_{}\".format(band)\n", " ] \n", "\n", "columns += [\"stellarity\", \"stellarity_origin\", \"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \n", " \"flag_optnir_obs\", \"flag_optnir_det\", \n", " \"zspec\", \"zspec_qual\", \"zspec_association_flag\", \"ebv\"] # " ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing columns: {'flag_suprime_ip', 'flag_gpc1_y', 'flag_wfc3_f105w', 'flag_acs_f814w', 'flag_gpc1_g', 'flag_acs_f850lp', 'flag_irac_i3', 'flag_quirc_hk', 'flag_gpc1_z', 'flag_acs_f606w', 'flag_irac_i2', 'flag_suprime_r', 'flag_wfc3_f160w', 'flag_wfc3_f125w', 'flag_mosaic_u', 'flag_irac_i1', 'flag_suprime_v', 'flag_suprime_zp', 'flag_gpc1_i', 'flag_wfc3_f140w', 'flag_wircam_ks', 'flag_suprime_b', 'flag_irac_i4', 'flag_acs_f435w', 'flag_acs_f775w', 'flag_gpc1_r', 'flag_moircs_k'}\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": 48, "metadata": {}, "outputs": [], "source": [ "#master_catalogue[columns].write(\"{}/master_catalogue_hdf-n{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)\n", "master_catalogue.write(\"{}/master_catalogue_hdf-n{}.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": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 2 }