From 354d48e94bebaa96deb49e94cfcc9f066ba040e7 Mon Sep 17 00:00:00 2001 From: Raphael Shirley Date: Wed, 17 Jan 2018 13:27:24 +0000 Subject: [PATCH] Working on DECAM merge on HS82 Also bug fixes on DES across southern fields #15 #4 #6 #17 #11 #18 #20 --- dmu1/dmu1_ml_AKARI-SEP/1.3_DES.ipynb | 6 +- dmu1/dmu1_ml_CDFS-SWIRE/1.9_DES.ipynb | 38 +- dmu1/dmu1_ml_ELAIS-S1/1.6_DES.ipynb | 6 +- .../1.8.2_DES.ipynb | 6 +- .../2.2_DECAM_merging.ipynb | 619 +++++++ ...ing_low_memory.ipynb => 2.3_Merging.ipynb} | 0 .../2_Merging.ipynb | 1478 ----------------- dmu1/dmu1_ml_SGP/1.5_DES.ipynb | 6 +- dmu1/dmu1_ml_SSDF/1.3_DES.ipynb | 6 +- dmu1/dmu1_ml_XMM-LSS/1.6.2_DES.ipynb | 6 +- 10 files changed, 678 insertions(+), 1493 deletions(-) create mode 100644 dmu1/dmu1_ml_Herschel-Stripe-82/2.2_DECAM_merging.ipynb rename dmu1/dmu1_ml_Herschel-Stripe-82/{2.2_Merging_low_memory.ipynb => 2.3_Merging.ipynb} (100%) delete mode 100644 dmu1/dmu1_ml_Herschel-Stripe-82/2_Merging.ipynb diff --git a/dmu1/dmu1_ml_AKARI-SEP/1.3_DES.ipynb b/dmu1/dmu1_ml_AKARI-SEP/1.3_DES.ipynb index f0dca590..1ab19631 100644 --- a/dmu1/dmu1_ml_AKARI-SEP/1.3_DES.ipynb +++ b/dmu1/dmu1_ml_AKARI-SEP/1.3_DES.ipynb @@ -108,6 +108,7 @@ " 'WAVG_MAG_PSF_G': \"m_ap_decam_g\", \n", " 'WAVG_MAGERR_PSF_G': \"merr_ap_decam_g\",\n", " \n", + " 'FLUX_AUTO_R': \"f_decam_r\", \n", " 'FLUXERR_AUTO_R': \"ferr_decam_r\", \n", " 'WAVG_FLUX_PSF_R': \"f_ap_decam_r\", \n", " 'WAVG_FLUXERR_PSF_R': \"ferr_ap_decam_r\",\n", @@ -116,6 +117,7 @@ " 'WAVG_MAG_PSF_R': \"m_ap_decam_r\", \n", " 'WAVG_MAGERR_PSF_R': \"merr_ap_decam_r\",\n", " \n", + " 'FLUX_AUTO_I': \"f_decam_i\",\n", " 'FLUXERR_AUTO_I': \"ferr_decam_i\", \n", " 'WAVG_FLUX_PSF_I': \"f_ap_decam_i\", \n", " 'WAVG_FLUXERR_PSF_I': \"ferr_ap_decam_i\",\n", @@ -124,6 +126,7 @@ " 'WAVG_MAG_PSF_I': \"m_ap_decam_i\", \n", " 'WAVG_MAGERR_PSF_I': \"merr_ap_decam_i\",\n", " \n", + " 'FLUX_AUTO_Z': \"f_decam_z\",\n", " 'FLUXERR_AUTO_Z': \"ferr_decam_z\", \n", " 'WAVG_FLUX_PSF_Z': \"f_ap_decam_z\", \n", " 'WAVG_FLUXERR_PSF_Z': \"ferr_ap_decam_z\",\n", @@ -132,13 +135,14 @@ " 'WAVG_MAG_PSF_Z': \"m_ap_decam_z\", \n", " 'WAVG_MAGERR_PSF_Z': \"merr_ap_decam_z\",\n", " \n", + " 'FLUX_AUTO_Y': \"f_decam_y\",\n", " 'FLUXERR_AUTO_Y': \"ferr_decam_y\", \n", " 'WAVG_FLUX_PSF_Y': \"f_ap_decam_y\", \n", " 'WAVG_FLUXERR_PSF_Y': \"ferr_ap_decam_y\",\n", " 'MAG_AUTO_Y': \"m_decam_y\", \n", " 'MAGERR_AUTO_Y': \"merr_decam_y\", \n", " 'WAVG_MAG_PSF_Y': \"m_ap_decam_y\", \n", - " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\",\n", + " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\"\n", "\n", " })\n", "\n", diff --git a/dmu1/dmu1_ml_CDFS-SWIRE/1.9_DES.ipynb b/dmu1/dmu1_ml_CDFS-SWIRE/1.9_DES.ipynb index 6a879373..e48305ca 100644 --- a/dmu1/dmu1_ml_CDFS-SWIRE/1.9_DES.ipynb +++ b/dmu1/dmu1_ml_CDFS-SWIRE/1.9_DES.ipynb @@ -24,7 +24,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "from herschelhelp_internal import git_version\n", @@ -34,7 +36,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -104,6 +108,7 @@ " 'WAVG_MAG_PSF_G': \"m_ap_decam_g\", \n", " 'WAVG_MAGERR_PSF_G': \"merr_ap_decam_g\",\n", " \n", + " 'FLUX_AUTO_R': \"f_decam_r\", \n", " 'FLUXERR_AUTO_R': \"ferr_decam_r\", \n", " 'WAVG_FLUX_PSF_R': \"f_ap_decam_r\", \n", " 'WAVG_FLUXERR_PSF_R': \"ferr_ap_decam_r\",\n", @@ -112,6 +117,7 @@ " 'WAVG_MAG_PSF_R': \"m_ap_decam_r\", \n", " 'WAVG_MAGERR_PSF_R': \"merr_ap_decam_r\",\n", " \n", + " 'FLUX_AUTO_I': \"f_decam_i\",\n", " 'FLUXERR_AUTO_I': \"ferr_decam_i\", \n", " 'WAVG_FLUX_PSF_I': \"f_ap_decam_i\", \n", " 'WAVG_FLUXERR_PSF_I': \"ferr_ap_decam_i\",\n", @@ -120,6 +126,7 @@ " 'WAVG_MAG_PSF_I': \"m_ap_decam_i\", \n", " 'WAVG_MAGERR_PSF_I': \"merr_ap_decam_i\",\n", " \n", + " 'FLUX_AUTO_Z': \"f_decam_z\",\n", " 'FLUXERR_AUTO_Z': \"ferr_decam_z\", \n", " 'WAVG_FLUX_PSF_Z': \"f_ap_decam_z\", \n", " 'WAVG_FLUXERR_PSF_Z': \"ferr_ap_decam_z\",\n", @@ -128,13 +135,14 @@ " 'WAVG_MAG_PSF_Z': \"m_ap_decam_z\", \n", " 'WAVG_MAGERR_PSF_Z': \"merr_ap_decam_z\",\n", " \n", + " 'FLUX_AUTO_Y': \"f_decam_y\",\n", " 'FLUXERR_AUTO_Y': \"ferr_decam_y\", \n", " 'WAVG_FLUX_PSF_Y': \"f_ap_decam_y\", \n", " 'WAVG_FLUXERR_PSF_Y': \"ferr_ap_decam_y\",\n", " 'MAG_AUTO_Y': \"m_decam_y\", \n", " 'MAGERR_AUTO_Y': \"merr_decam_y\", \n", " 'WAVG_MAG_PSF_Y': \"m_ap_decam_y\", \n", - " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\",\n", + " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\"\n", "\n", " })\n", "\n", @@ -191,7 +199,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "catalogue[:10].show_in_notebook()" @@ -214,7 +224,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "SORT_COLS = ['merr_ap_decam_g', 'merr_ap_decam_r','merr_ap_decam_i','merr_ap_decam_z','merr_ap_decam_y']\n", @@ -255,7 +267,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL], \n", @@ -265,7 +279,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "delta_ra, delta_dec = astrometric_correction(\n", @@ -292,7 +308,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL], \n", @@ -322,7 +340,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "GAIA_FLAG_NAME = \"des_flag_gaia\"\n", diff --git a/dmu1/dmu1_ml_ELAIS-S1/1.6_DES.ipynb b/dmu1/dmu1_ml_ELAIS-S1/1.6_DES.ipynb index 74ad413e..4e6bdc16 100644 --- a/dmu1/dmu1_ml_ELAIS-S1/1.6_DES.ipynb +++ b/dmu1/dmu1_ml_ELAIS-S1/1.6_DES.ipynb @@ -108,6 +108,7 @@ " 'WAVG_MAG_PSF_G': \"m_ap_decam_g\", \n", " 'WAVG_MAGERR_PSF_G': \"merr_ap_decam_g\",\n", " \n", + " 'FLUX_AUTO_R': \"f_decam_r\", \n", " 'FLUXERR_AUTO_R': \"ferr_decam_r\", \n", " 'WAVG_FLUX_PSF_R': \"f_ap_decam_r\", \n", " 'WAVG_FLUXERR_PSF_R': \"ferr_ap_decam_r\",\n", @@ -116,6 +117,7 @@ " 'WAVG_MAG_PSF_R': \"m_ap_decam_r\", \n", " 'WAVG_MAGERR_PSF_R': \"merr_ap_decam_r\",\n", " \n", + " 'FLUX_AUTO_I': \"f_decam_i\",\n", " 'FLUXERR_AUTO_I': \"ferr_decam_i\", \n", " 'WAVG_FLUX_PSF_I': \"f_ap_decam_i\", \n", " 'WAVG_FLUXERR_PSF_I': \"ferr_ap_decam_i\",\n", @@ -124,6 +126,7 @@ " 'WAVG_MAG_PSF_I': \"m_ap_decam_i\", \n", " 'WAVG_MAGERR_PSF_I': \"merr_ap_decam_i\",\n", " \n", + " 'FLUX_AUTO_Z': \"f_decam_z\",\n", " 'FLUXERR_AUTO_Z': \"ferr_decam_z\", \n", " 'WAVG_FLUX_PSF_Z': \"f_ap_decam_z\", \n", " 'WAVG_FLUXERR_PSF_Z': \"ferr_ap_decam_z\",\n", @@ -132,13 +135,14 @@ " 'WAVG_MAG_PSF_Z': \"m_ap_decam_z\", \n", " 'WAVG_MAGERR_PSF_Z': \"merr_ap_decam_z\",\n", " \n", + " 'FLUX_AUTO_Y': \"f_decam_y\",\n", " 'FLUXERR_AUTO_Y': \"ferr_decam_y\", \n", " 'WAVG_FLUX_PSF_Y': \"f_ap_decam_y\", \n", " 'WAVG_FLUXERR_PSF_Y': \"ferr_ap_decam_y\",\n", " 'MAG_AUTO_Y': \"m_decam_y\", \n", " 'MAGERR_AUTO_Y': \"merr_decam_y\", \n", " 'WAVG_MAG_PSF_Y': \"m_ap_decam_y\", \n", - " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\",\n", + " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\"\n", "\n", " })\n", "\n", diff --git a/dmu1/dmu1_ml_Herschel-Stripe-82/1.8.2_DES.ipynb b/dmu1/dmu1_ml_Herschel-Stripe-82/1.8.2_DES.ipynb index 19b8e39c..fbf562c8 100644 --- a/dmu1/dmu1_ml_Herschel-Stripe-82/1.8.2_DES.ipynb +++ b/dmu1/dmu1_ml_Herschel-Stripe-82/1.8.2_DES.ipynb @@ -108,6 +108,7 @@ " 'WAVG_MAG_PSF_G': \"m_ap_decam_g\", \n", " 'WAVG_MAGERR_PSF_G': \"merr_ap_decam_g\",\n", " \n", + " 'FLUX_AUTO_R': \"f_decam_r\", \n", " 'FLUXERR_AUTO_R': \"ferr_decam_r\", \n", " 'WAVG_FLUX_PSF_R': \"f_ap_decam_r\", \n", " 'WAVG_FLUXERR_PSF_R': \"ferr_ap_decam_r\",\n", @@ -116,6 +117,7 @@ " 'WAVG_MAG_PSF_R': \"m_ap_decam_r\", \n", " 'WAVG_MAGERR_PSF_R': \"merr_ap_decam_r\",\n", " \n", + " 'FLUX_AUTO_I': \"f_decam_i\",\n", " 'FLUXERR_AUTO_I': \"ferr_decam_i\", \n", " 'WAVG_FLUX_PSF_I': \"f_ap_decam_i\", \n", " 'WAVG_FLUXERR_PSF_I': \"ferr_ap_decam_i\",\n", @@ -124,6 +126,7 @@ " 'WAVG_MAG_PSF_I': \"m_ap_decam_i\", \n", " 'WAVG_MAGERR_PSF_I': \"merr_ap_decam_i\",\n", " \n", + " 'FLUX_AUTO_Z': \"f_decam_z\",\n", " 'FLUXERR_AUTO_Z': \"ferr_decam_z\", \n", " 'WAVG_FLUX_PSF_Z': \"f_ap_decam_z\", \n", " 'WAVG_FLUXERR_PSF_Z': \"ferr_ap_decam_z\",\n", @@ -132,13 +135,14 @@ " 'WAVG_MAG_PSF_Z': \"m_ap_decam_z\", \n", " 'WAVG_MAGERR_PSF_Z': \"merr_ap_decam_z\",\n", " \n", + " 'FLUX_AUTO_Y': \"f_decam_y\",\n", " 'FLUXERR_AUTO_Y': \"ferr_decam_y\", \n", " 'WAVG_FLUX_PSF_Y': \"f_ap_decam_y\", \n", " 'WAVG_FLUXERR_PSF_Y': \"ferr_ap_decam_y\",\n", " 'MAG_AUTO_Y': \"m_decam_y\", \n", " 'MAGERR_AUTO_Y': \"merr_decam_y\", \n", " 'WAVG_MAG_PSF_Y': \"m_ap_decam_y\", \n", - " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\",\n", + " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\"\n", "\n", " })\n", "\n", diff --git a/dmu1/dmu1_ml_Herschel-Stripe-82/2.2_DECAM_merging.ipynb b/dmu1/dmu1_ml_Herschel-Stripe-82/2.2_DECAM_merging.ipynb new file mode 100644 index 00000000..10aebc72 --- /dev/null +++ b/dmu1/dmu1_ml_Herschel-Stripe-82/2.2_DECAM_merging.ipynb @@ -0,0 +1,619 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Herschel Stripe 82 DECAM merging\n", + "\n", + "Both DES and DECaLS provide DECam fluxes which have overlapping coverage. We chose which to use DES preferentially. In this notebook we cross match both catalogues and take the DES fluxes where available, using DECaLS otherwise" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "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": null, + "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\n", + "from herschelhelp_internal.utils import coords_to_hpidx, ebv, gen_help_id, inMoc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "des = Table.read(\"{}/des.fits\".format(TMP_DIR))\n", + "decals = Table.read(\"{}/DECALS.fits\".format(TMP_DIR))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for col in des.colnames:\n", + " if '_decam_' in col:\n", + " des[col].name = col.replace('_decam_', '_des-decam_')\n", + " \n", + "for col in decals.colnames:\n", + " if '_decam_' in col:\n", + " decals[col].name = col.replace('_decam_', '_decals-decam_')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## II - Merging tables\n", + "\n", + "We first merge the optical catalogues and then add the infrared ones: HSC, VHS, VICS82, UKIDSS-LAS, PanSTARRS, SHELA, SpIES.\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": [ + "### DES" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "master_catalogue = des\n", + "master_catalogue['des_ra'].name = 'ra'\n", + "master_catalogue['des_dec'].name = 'dec'\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add DECaLS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "nb_merge_dist_plot(\n", + " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", + " SkyCoord(decals['decals_ra'], decals['decals_dec'])\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Given the graph above, we use 0.8 arc-second radius\n", + "master_catalogue = merge_catalogues(master_catalogue, decals, \"decals_ra\", \"decals_dec\", radius=0.8*u.arcsec)\n" + ] + }, + { + "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": null, + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "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=\"decam_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": null, + "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=\"decam_flag_gaia\"\n", + "))\n", + "master_catalogue.remove_columns(flag_gaia_columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each prisitine catalogue may contain one or several stellarity columns indicating the probability (0 to 1) of each source being a star. We merge these columns taking the highest value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stellarity_columns = [column for column in master_catalogue.colnames\n", + " if 'stellarity' in column]\n", + "\n", + "master_catalogue.add_column(Column(\n", + " data=np.nanmax([master_catalogue[column] for column in stellarity_columns], axis=0),\n", + " name=\"decam_stellarity\"\n", + "))\n", + "master_catalogue.remove_columns(stellarity_columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## VIII - Cross-identification table\n", + "\n", + "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogue. This can be used to easily get additional information from them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "master_catalogue.add_column(Column(data=(np.char.array(master_catalogue['des_id'].astype(str)) \n", + " + np.char.array(master_catalogue['decals_id'].astype(str) )), \n", + " name=\"decam_intid\"))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "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": [ + "## VI - Choosing between multiple values for the same filter\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "decam_origin = Table()\n", + "decam_origin.add_column(master_catalogue['decam_intid'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "decam_stats = Table()\n", + "decam_stats.add_column(Column(data=['g','r','i','z','y'], name=\"Band\"))\n", + "for col in [\"DES\", \"DECaLS\"]:\n", + " decam_stats.add_column(Column(data=np.full(5, 0), name=\"{}\".format(col)))\n", + " decam_stats.add_column(Column(data=np.full(5, 0), name=\"use {}\".format(col)))\n", + " decam_stats.add_column(Column(data=np.full(5, 0), name=\"{} ap\".format(col)))\n", + " decam_stats.add_column(Column(data=np.full(5, 0), name=\"use {} ap\".format(col)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "decam_bands = ['g','r','i','z','y'] # Lowercase naming convention (k is Ks)\n", + "for band in decam_bands:\n", + " if (band == 'i') or (band == 'y'):\n", + " master_catalogue[\"f_des-decam_{}\".format(band)].name = \"f_decam_{}\".format(band)\n", + " master_catalogue[\"ferr_des-decam_{}\".format(band)].name = \"ferr_decam_{}\".format(band)\n", + " master_catalogue[\"m_des-decam_{}\".format(band)].name = \"m_decam_{}\".format(band)\n", + " master_catalogue[\"merr_des-decam_{}\".format(band)].name = \"merr_decam_{}\".format(band)\n", + " master_catalogue[\"f_ap_des-decam_{}\".format(band)].name = \"f_ap_decam_{}\".format(band)\n", + " master_catalogue[\"ferr_ap_des-decam_{}\".format(band)].name = \"ferr_ap_decam_{}\".format(band)\n", + " master_catalogue[\"m_ap_des-decam_{}\".format(band)].name = \"m_ap_decam_{}\".format(band)\n", + " master_catalogue[\"merr_ap_des-decam_{}\".format(band)].name = \"merr_ap_decam_{}\".format(band)\n", + " master_catalogue[\"flag_des-decam_{}\".format(band)].name = \"flag_decam_{}\".format(band)\n", + " \n", + " continue\n", + "\n", + " # DECam total flux \n", + " has_des = ~np.isnan(master_catalogue['f_des-decam_' + band])\n", + " has_decals = ~np.isnan(master_catalogue['f_decals-decam_' + band])\n", + "\n", + " use_des = has_des \n", + " use_decals = has_decals & ~has_des\n", + "\n", + "\n", + " f_decam = np.full(len(master_catalogue), np.nan)\n", + " f_decam[use_des] = master_catalogue['f_des-decam_' + band][use_des]\n", + " f_decam[use_decals] = master_catalogue['f_decals-decam_' + band][use_decals]\n", + "\n", + " ferr_decam = np.full(len(master_catalogue), np.nan)\n", + " ferr_decam[use_des] = master_catalogue['ferr_des-decam_' + band][use_des]\n", + " ferr_decam[use_decals] = master_catalogue['ferr_decals-decam_' + band][use_decals]\n", + " \n", + " m_decam = np.full(len(master_catalogue), np.nan)\n", + " m_decam[use_des] = master_catalogue['m_des-decam_' + band][use_des]\n", + " m_decam[use_decals] = master_catalogue['m_decals-decam_' + band][use_decals]\n", + " \n", + " merr_decam = np.full(len(master_catalogue), np.nan)\n", + " merr_decam[use_des] = master_catalogue['merr_des-decam_' + band][use_des]\n", + " merr_decam[use_decals] = master_catalogue['merr_decals-decam_' + band][use_decals]\n", + " \n", + " flag_decam = np.full(len(master_catalogue), False, dtype=bool)\n", + " flag_decam[use_des] = master_catalogue['flag_des-decam_' + band][use_des]\n", + " flag_decam[use_decals] = master_catalogue['flag_decals-decam_' + band][use_decals]\n", + "\n", + " master_catalogue.add_column(Column(data=f_decam, name=\"f_decam_\" + band))\n", + " master_catalogue.add_column(Column(data=ferr_decam, name=\"ferr_decam_\" + band))\n", + " master_catalogue.add_column(Column(data=m_decam, name=\"m_decam_\" + band))\n", + " master_catalogue.add_column(Column(data=merr_decam, name=\"merr_decam_\" + band))\n", + " master_catalogue.add_column(Column(data=flag_decam, name=\"flag_decam_\" + band))\n", + "\n", + "\n", + " old_des_columns = ['f_des-decam_' + band,\n", + " 'ferr_des-decam_' + band,\n", + " 'm_des-decam_' + band, \n", + " 'merr_des-decam_' + band,\n", + " 'flag_des-decam_' + band]\n", + " old_decals_columns = ['f_decals-decam_' + band,\n", + " 'ferr_decals-decam_' + band,\n", + " 'm_decals-decam_' + band, \n", + " 'merr_decals-decam_' + band,\n", + " 'flag_decals-decam_' + band]\n", + " \n", + " old_columns = old_des_columns + old_decals_columns\n", + " master_catalogue.remove_columns(old_columns)\n", + "\n", + " origin = np.full(len(master_catalogue), ' ', dtype='" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nb_merge_dist_plot(\n", - " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", - " SkyCoord(las['las_ra'], las['las_dec'])\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Given the graph above, we use 0.8 arc-second radius\n", - "master_catalogue = merge_catalogues(master_catalogue, las, \"las_ra\", \"las_dec\", radius=0.8*u.arcsec)\n", - "del las" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add PanSTARRS" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "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(ps1['ps1_ra'], ps1['ps1_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, ps1, \"ps1_ra\", \"ps1_dec\", radius=0.8*u.arcsec)\n", - "del ps1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add SDSS\n", - "We are waiting for a new SDSS-82 catalogue, which does not suffer from the issue of multiple sources per object due to including all exposure extractions." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "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(sdss['sdss_ra'], sdss['sdss_dec'])\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Given the graph above, we use 0.8 arc-second radius\n", - "master_catalogue = merge_catalogues(master_catalogue, sdss, \"sdss_ra\", \"sdss_dec\", radius=0.8*u.arcsec)\n", - "del sdss" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add DECaLS" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Z9nMDALDQEawC4JzTsf4xdbUFEKy8qxBfZ6FQAAB8R7AKQN9oSmPpXFkb14uKVwYSrAAA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\nhPiZM0M8EtYdXa2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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(decals['decals_ra'], decals['decals_dec'])\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Given the graph above, we use 0.8 arc-second radius\n", - "master_catalogue = merge_catalogues(master_catalogue, decals, \"decals_ra\", \"decals_dec\", radius=0.8*u.arcsec)\n", - "del decals" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add RCSLenS" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "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(rcs['rcs_ra'], rcs['rcs_dec'])\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Given the graph above, we use 0.8 arc-second radius\n", - "master_catalogue = merge_catalogues(master_catalogue, rcs, \"rcs_ra\", \"rcs_dec\", radius=0.8*u.arcsec)\n", - "del rcs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add SHELA" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "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(shela['shela_ra'], shela['shela_dec'])\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Given the graph above, we use 1 arc-second radius\n", - "master_catalogue = merge_catalogues(master_catalogue, shela, \"shela_ra\", \"shela_dec\", radius=1.*u.arcsec)\n", - "del shela" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add SpIES" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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A3waW5FLck5lCc7aC1att7KonHDR2Hh3zuxQRERFfzSVYrQYOT7vfV3zsFDNb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\nhotW8sPnj/HgngGuu6DT75J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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(spies['spies_ra'], spies['spies_dec'])\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Given the graph above, we use 1 arc-second radius\n", - "master_catalogue = merge_catalogues(master_catalogue, spies, \"spies_ra\", \"spies_dec\", radius=1.*u.arcsec)\n", - "del spies" - ] - }, - { - "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": 24, - "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": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "<Table length=10>\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
idxhsc_idradecm_ap_suprime_gmerr_ap_suprime_gm_suprime_gmerr_suprime_gm_ap_suprime_rmerr_ap_suprime_rm_suprime_rmerr_suprime_rm_ap_suprime_imerr_ap_suprime_im_suprime_imerr_suprime_im_ap_suprime_zmerr_ap_suprime_zm_suprime_zmerr_suprime_zm_ap_suprime_ymerr_ap_suprime_ym_suprime_ymerr_suprime_ym_ap_suprime_n816merr_ap_suprime_n816m_suprime_n816merr_suprime_n816m_ap_suprime_n921merr_ap_suprime_n921m_suprime_n921merr_suprime_n921f_ap_suprime_gferr_ap_suprime_gf_suprime_gferr_suprime_gflag_suprime_gf_ap_suprime_rferr_ap_suprime_rf_suprime_rferr_suprime_rflag_suprime_rf_ap_suprime_iferr_ap_suprime_if_suprime_iferr_suprime_iflag_suprime_if_ap_suprime_zferr_ap_suprime_zf_suprime_zferr_suprime_zflag_suprime_zf_ap_suprime_yferr_ap_suprime_yf_suprime_yferr_suprime_yflag_suprime_yf_ap_suprime_n816ferr_ap_suprime_n816f_suprime_n816ferr_suprime_n816flag_suprime_n816f_ap_suprime_n921ferr_ap_suprime_n921f_suprime_n921ferr_suprime_n921flag_suprime_n921hsc_flag_cleanedhsc_flag_gaiaflag_mergedvhs_idvhs_stellaritym_vhs_ymerr_vhs_ym_ap_vhs_ymerr_ap_vhs_ym_vhs_jmerr_vhs_jm_ap_vhs_jmerr_ap_vhs_jm_vhs_hmerr_vhs_hm_ap_vhs_hmerr_ap_vhs_hm_vhs_kmerr_vhs_km_ap_vhs_kmerr_ap_vhs_kf_vhs_yferr_vhs_yflag_vhs_yf_ap_vhs_yferr_ap_vhs_yf_vhs_jferr_vhs_jflag_vhs_jf_ap_vhs_jferr_ap_vhs_jf_vhs_hferr_vhs_hflag_vhs_hf_ap_vhs_hferr_ap_vhs_hf_vhs_kferr_vhs_kflag_vhs_kf_ap_vhs_kferr_ap_vhs_kvhs_flag_cleanedvhs_flag_gaiavics82_idvics82_stellaritym_vics82_km_ap_vics82_km_vics82_jm_ap_vics82_jmerr_vics82_kf_vics82_kferr_vics82_kflag_vics82_kmerr_ap_vics82_kf_ap_vics82_kferr_ap_vics82_kmerr_vics82_jf_vics82_jferr_vics82_jflag_vics82_jmerr_ap_vics82_jf_ap_vics82_jferr_ap_vics82_jvics82_flag_cleanedvics82_flag_gaialas_idm_ukidss_ymerr_ukidss_ym_ap_ukidss_ymerr_ap_ukidss_ym_ukidss_jmerr_ukidss_jm_ap_ukidss_jmerr_ap_ukidss_jm_ap_ukidss_hmerr_ap_ukidss_hm_ukidss_hmerr_ukidss_hm_ap_ukidss_kmerr_ap_ukidss_km_ukidss_kmerr_ukidss_klas_stellarityf_ukidss_yferr_ukidss_yflag_ukidss_yf_ap_ukidss_yferr_ap_ukidss_yf_ukidss_jferr_ukidss_jflag_ukidss_jf_ap_ukidss_jferr_ap_ukidss_jf_ap_ukidss_hferr_ap_ukidss_hf_ukidss_hferr_ukidss_hflag_ukidss_hf_ap_ukidss_kferr_ap_ukidss_kf_ukidss_kferr_ukidss_kflag_ukidss_klas_flag_cleanedlas_flag_gaiaps1_idm_ap_gpc1_gmerr_ap_gpc1_gm_gpc1_gmerr_gpc1_gm_ap_gpc1_rmerr_ap_gpc1_rm_gpc1_rmerr_gpc1_rm_ap_gpc1_imerr_ap_gpc1_im_gpc1_imerr_gpc1_im_ap_gpc1_zmerr_ap_gpc1_zm_gpc1_zmerr_gpc1_zm_ap_gpc1_ymerr_ap_gpc1_ym_gpc1_ymerr_gpc1_yf_ap_gpc1_gferr_ap_gpc1_gf_gpc1_gferr_gpc1_gflag_gpc1_gf_ap_gpc1_rferr_ap_gpc1_rf_gpc1_rferr_gpc1_rflag_gpc1_rf_ap_gpc1_iferr_ap_gpc1_if_gpc1_iferr_gpc1_iflag_gpc1_if_ap_gpc1_zferr_ap_gpc1_zf_gpc1_zferr_gpc1_zflag_gpc1_zf_ap_gpc1_yferr_ap_gpc1_yf_gpc1_yferr_gpc1_yflag_gpc1_yps1_flag_cleanedps1_flag_gaiasdss_idsdss_stellaritym_sdss_um_sdss_gm_sdss_rm_sdss_im_sdss_zmerr_sdss_umerr_sdss_gmerr_sdss_rmerr_sdss_imerr_sdss_zm_ap_sdss_um_ap_sdss_gm_ap_sdss_rm_ap_sdss_im_ap_sdss_zmerr_ap_sdss_umerr_ap_sdss_gmerr_ap_sdss_rmerr_ap_sdss_imerr_ap_sdss_zf_sdss_uferr_sdss_uflag_sdss_uf_sdss_gferr_sdss_gflag_sdss_gf_sdss_rferr_sdss_rflag_sdss_rf_sdss_iferr_sdss_iflag_sdss_if_sdss_zferr_sdss_zflag_sdss_zf_ap_sdss_uferr_ap_sdss_uf_ap_sdss_gferr_ap_sdss_gf_ap_sdss_rferr_ap_sdss_rf_ap_sdss_iferr_ap_sdss_if_ap_sdss_zferr_ap_sdss_zsdss_flag_cleanedsdss_flag_gaiadecals_idf_decam_gf_decam_rf_decam_zferr_decam_gferr_decam_rferr_decam_zf_ap_decam_gf_ap_decam_rf_ap_decam_zferr_ap_decam_gferr_ap_decam_rferr_ap_decam_zm_decam_gmerr_decam_gflag_decam_gm_decam_rmerr_decam_rflag_decam_rm_decam_zmerr_decam_zflag_decam_zm_ap_decam_gmerr_ap_decam_gm_ap_decam_rmerr_ap_decam_rm_ap_decam_zmerr_ap_decam_zdecals_stellaritydecals_flag_cleaneddecals_flag_gaiarcs_idrcs_stellaritym_rcs_gmerr_rcs_gm_rcs_rmerr_rcs_rm_rcs_imerr_rcs_im_rcs_zmerr_rcs_zm_rcs_ymerr_rcs_yf_rcs_gferr_rcs_gf_ap_rcs_gferr_ap_rcs_gm_ap_rcs_gmerr_ap_rcs_gflag_rcs_gf_rcs_rferr_rcs_rf_ap_rcs_rferr_ap_rcs_rm_ap_rcs_rmerr_ap_rcs_rflag_rcs_rf_rcs_iferr_rcs_if_ap_rcs_iferr_ap_rcs_im_ap_rcs_imerr_ap_rcs_iflag_rcs_if_rcs_zferr_rcs_zf_ap_rcs_zferr_ap_rcs_zm_ap_rcs_zmerr_ap_rcs_zflag_rcs_zf_rcs_yferr_rcs_yf_ap_rcs_yferr_ap_rcs_ym_ap_rcs_ymerr_ap_rcs_yflag_rcs_yrcs_flag_cleanedrcs_flag_gaiashela_intidf_shela_irac1ferr_shela_irac1f_ap_shela_irac1ferr_ap_shela_irac1f_shela_irac2ferr_shela_irac2f_ap_shela_irac2ferr_ap_shela_irac2m_shela_irac1merr_shela_irac1flag_shela_irac1m_ap_shela_irac1merr_ap_shela_irac1m_shela_irac2merr_shela_irac2flag_shela_irac2m_ap_shela_irac2merr_ap_shela_irac2shela_flag_cleanedshela_flag_gaiaspies_intidf_ap_spies_irac1ferr_ap_spies_irac1f_spies_irac1ferr_spies_irac1spies_stellarity_irac1f_ap_spies_irac2ferr_ap_spies_irac2f_spies_irac2ferr_spies_irac2spies_stellarity_irac2m_ap_spies_irac1merr_ap_spies_irac1m_spies_irac1merr_spies_irac1flag_spies_irac1m_ap_spies_irac2merr_ap_spies_irac2m_spies_irac2merr_spies_irac2flag_spies_irac2spies_flag_cleanedspies_flag_gaia
degdeg
042697064458515903353.7500524360.18843248065819.20880.00076160119.38420.00064052718.80570.00072267518.88460.00058783218.69020.00072674718.67340.00049034118.64030.0009998718.73830.00071541218.63430.0018041518.61240.0012006618.65270.0014992618.64290.0011264418.65970.0013920118.65210.00099159575.24530.052781564.02050.0377687False109.0750.072601101.4290.0549153False121.320.0812064123.2060.0556424False127.0260.11698116.0580.0764728False127.7290.212245130.3350.144131False125.5770.173405126.720.131471False124.7690.159965125.6460.114751FalseFalse3False-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannanFalsenannannannannannanFalsenannannanFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannanFalsenannannannannannanFalsenannannannannannanFalsenannannannannannanFalsenannannannannannanFalseFalse0-1nannannannannannannannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0
142697064458508303353.652565850.1161450501121.72160.0034550121.45070.0040667920.55120.0018722120.29970.0022255720.02290.001340419.75510.001641119.74370.0022193419.46260.0026826519.58030.0033218219.28370.0040917819.92730.002971919.67790.0035274919.66730.0025234219.39820.003085237.436020.02366279.544130.035749False21.85430.037684927.54960.056472False35.55040.043888845.49430.0687648False45.97630.093979659.55910.147159False53.44110.16350470.23060.264676False38.82060.1062648.84570.158697False49.32470.11463863.20190.179594FalseFalse0False-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannanFalsenannannannannannanFalsenannannanFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannanFalsenannannannannannanFalsenannannannannannanFalsenannannannannannanFalsenannannannannannanFalseFalse0-1nannannannannannannannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0
242697060163548288353.6761145470.052636049136320.66860.0020112419.24080.0020056220.12980.001756218.71840.0018298919.74150.0013417618.37660.001593319.6070.0022038818.18260.0025435219.38980.0034901517.96250.004216619.75830.0037475118.34850.0040840119.57350.0032165718.18110.0037781219.61360.036332773.06310.134965False32.2170.0521118118.2040.199221False46.06780.0569311161.9460.237654False52.14150.105839193.6190.453585False63.69380.204747237.1390.920962False45.35980.156563166.1810.625091False53.77780.159321193.8880.674687FalseFalse0False-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannanFalsenannannannannannanFalsenannannanFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannanFalsenannannannannannanFalsenannannannannannanFalsenannannannannannanFalsenannannannannannanFalseFalse0-1nannannannannannannannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0
342697068753489548353.6431851020.45944719921121.44320.0029085921.16380.003677320.5160.0017662920.26440.0023179220.10490.0016128719.86350.0021807219.7390.0024353419.47890.0032371619.72180.004162719.42170.0056187520.01060.003755519.77070.0050073219.83710.0031947219.59020.004330249.609520.02574312.43010.0420996False22.57420.03672428.45960.060758False32.96530.048970241.17180.0826942False46.17340.10356858.67210.174933False46.90980.17985261.84970.320076False35.95640.12437144.84360.206815False42.18410.12412552.95650.211206FalseFalse0False-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannanFalsenannannannannannanFalsenannannanFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannanFalsenannannannannannanFalsenannannannannannanFalsenannannannannannanFalsenannannannannannanFalseFalse0-1nannannannannannannannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0
442697073048455227353.689474590.62128561956120.7210.0021243219.7540.0032166220.26990.0019454519.20780.0029275119.90680.0016164418.76350.0025067619.82430.003071518.63610.0044665719.67610.0043087218.39050.0063658319.9220.0036768418.7570.0053178519.8310.0036502118.66610.0055881318.68980.036567945.54020.134918False28.31630.050737875.31250.203068False39.56050.0588975113.3980.261814False42.68670.120759127.5170.524588False48.92660.194164159.8780.937387False39.0110.132111114.0740.558728False42.42240.142623124.0360.638394FalseFalse0False-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannanFalsenannannannannannanFalsenannannanFalse0-1nannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalsenannannannanFalseFalse0-1nannannannannannannannannannannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannanFalsenannanFalsenannannannannannannannannannanFalse0-1nannannannannannannannannannannannannannanFalsenannanFalsenannanFalsenannannannannannannanFalse0-1nannannannannannannannannannannannannannannannannanFalsenannannannannannanFalsenannannannannannanFalsenannannannannannanFalsenannannannannannanFalseFalse0-1nannannannannannannannannannanFalsenannannannanFalsenannanFalse0-1nannannannannannannannannannannannannannanFalsenannannannanFalseFalse0
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\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "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": 26, - "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": 27, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "flag_gaia_columns = [column for column in master_catalogue.colnames\n", - " if 'flag_gaia' in column]\n", - "\n", - "master_catalogue.add_column(Column(\n", - " data=np.max([master_catalogue[column] for column in flag_gaia_columns], axis=0),\n", - " name=\"flag_gaia\"\n", - "))\n", - "master_catalogue.remove_columns(flag_gaia_columns)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each prisitine catalogue may contain one or several stellarity columns indicating the probability (0 to 1) of each source being a star. We merge these columns taking the highest value." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:5: RuntimeWarning: All-NaN axis encountered\n" - ] - } - ], - "source": [ - "stellarity_columns = [column for column in master_catalogue.colnames\n", - " if 'stellarity' in column]\n", - "\n", - "master_catalogue.add_column(Column(\n", - " data=np.nanmax([master_catalogue[column] for column in stellarity_columns], axis=0),\n", - " name=\"stellarity\"\n", - "))\n", - "master_catalogue.remove_columns(stellarity_columns)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IV - Adding E(B-V) column" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "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": 31, - "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), \"Herschel-Stripe-82\", dtype='" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# servs -> shela and swire -> spies\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(seip['i1_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_shela_irac1'], label=\"SHELA\", s=2.)\n", - "ax.scatter(seip['i1_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_spies_irac1'], label=\"SpIES\", s=2.)\n", - "ax.set_xscale('log')\n", - "ax.set_yscale('log')\n", - "ax.set_xlabel(\"SEIP flux [μJy]\")\n", - "ax.set_ylabel(\"SHELA/SpIES 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": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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jY+HRr0OY0eydR/jG9GugeANjxz/OpeeezhFDf1W/hUrg1OdMYlXtyJm7X2Vm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\n+QSoc/AuOCFZXwAAAABJRU5ErkJggg==\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_shela_irac2'], label=\"SHELA\", s=2.)\n", - "ax.scatter(seip['i2_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_spies_irac2'], label=\"SpIES\", s=2.)\n", - "ax.set_xscale('log')\n", - "ax.set_yscale('log')\n", - "ax.set_xlabel(\"SEIP flux [μJy]\")\n", - "ax.set_ylabel(\"SHELA/SpIES 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 SHELA and SpIES fluxes are provided, we use the SpIES flux.\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": 36, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "irac_origin = Table()\n", - "irac_origin.add_column(master_catalogue['help_id'])" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "67868 sources with SHELA flux\n", - "62528 sources with SpIES flux\n", - "21709 sources with SHELA and SpIES flux\n", - "67868 sources for which we use SHELA\n", - "40819 sources for which we use SpIES\n" - ] - } - ], - "source": [ - "# IRAC1 aperture flux and magnitudes\n", - "has_shela = ~np.isnan(master_catalogue['f_ap_shela_irac1'])\n", - "has_spies = ~np.isnan(master_catalogue['f_ap_spies_irac1'])\n", - "has_both = has_shela & has_spies\n", - "\n", - "print(\"{} sources with SHELA flux\".format(np.sum(has_shela)))\n", - "print(\"{} sources with SpIES flux\".format(np.sum(has_spies)))\n", - "print(\"{} sources with SHELA and SpIES flux\".format(np.sum(has_both)))\n", - "\n", - "use_shela = has_shela\n", - "use_spies = (has_spies & ~has_shela)\n", - "\n", - "print(\"{} sources for which we use SHELA\".format(np.sum(use_shela)))\n", - "print(\"{} sources for which we use SpIES\".format(np.sum(use_spies)))\n", - "\n", - "f_ap_irac = np.full(len(master_catalogue), np.nan)\n", - "f_ap_irac[use_shela] = master_catalogue['f_ap_shela_irac1'][use_shela]\n", - "f_ap_irac[use_spies] = master_catalogue['f_ap_spies_irac1'][use_spies]\n", - "\n", - "ferr_ap_irac = np.full(len(master_catalogue), np.nan)\n", - "ferr_ap_irac[use_shela] = master_catalogue['ferr_ap_shela_irac1'][use_shela]\n", - "ferr_ap_irac[use_spies] = master_catalogue['ferr_ap_spies_irac1'][use_spies]\n", - "\n", - "m_ap_irac = np.full(len(master_catalogue), np.nan)\n", - "m_ap_irac[use_shela] = master_catalogue['m_ap_shela_irac1'][use_shela]\n", - "m_ap_irac[use_spies] = master_catalogue['m_ap_spies_irac1'][use_spies]\n", - "\n", - "merr_ap_irac = np.full(len(master_catalogue), np.nan)\n", - "merr_ap_irac[use_shela] = master_catalogue['merr_ap_shela_irac1'][use_shela]\n", - "merr_ap_irac[use_spies] = master_catalogue['merr_ap_spies_irac1'][use_spies]\n", - "\n", - "master_catalogue.add_column(Column(data=f_ap_irac, name=\"f_ap_irac_i1\"))\n", - "master_catalogue.add_column(Column(data=ferr_ap_irac, name=\"ferr_ap_irac_i1\"))\n", - "master_catalogue.add_column(Column(data=m_ap_irac, name=\"m_ap_irac_i1\"))\n", - "master_catalogue.add_column(Column(data=merr_ap_irac, name=\"merr_ap_irac_i1\"))\n", - "\n", - "master_catalogue.remove_columns(['f_ap_shela_irac1', 'f_ap_spies_irac1', \n", - " 'ferr_ap_shela_irac1', 'ferr_ap_spies_irac1', \n", - " 'm_ap_shela_irac1', 'm_ap_spies_irac1',\n", - " 'merr_ap_shela_irac1', 'merr_ap_spies_irac1'])\n", - "\n", - "origin = np.full(len(master_catalogue), ' ', dtype='= 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": [ - "## VIII - Cross-identification table\n", - "\n", - "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogue. This can be used to easily get additional information from them." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#TODO: ADD SDSS normal ids" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['hsc_id', 'vhs_id', 'vics82_id', 'las_id', 'ps1_id', 'sdss_id', 'decals_id', 'rcs_id', 'shela_intid', 'spies_intid', 'help_id']\n" - ] - } - ], - "source": [ - "\n", - "id_names = []\n", - "for col in master_catalogue.colnames:\n", - " if '_id' in col:\n", - " id_names += [col]\n", - " if '_intid' in col:\n", - " id_names += [col]\n", - " \n", - "print(id_names)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "master_catalogue[id_names].write(\n", - " \"{}/master_list_cross_ident_herschel-stripe-82{}.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": [ - "## IX - 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": 50, - "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": [ - "## IX - Saving the catalogue" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "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": 52, - "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": 53, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "master_catalogue[columns].write(\"{}/master_catalogue_herschel-stripe-82{}.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": "3.6.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/dmu1/dmu1_ml_SGP/1.5_DES.ipynb b/dmu1/dmu1_ml_SGP/1.5_DES.ipynb index 1d79fed8..3055e75f 100644 --- a/dmu1/dmu1_ml_SGP/1.5_DES.ipynb +++ b/dmu1/dmu1_ml_SGP/1.5_DES.ipynb @@ -108,6 +108,7 @@ " 'WAVG_MAG_PSF_G': \"m_ap_decam_g\", \n", " 'WAVG_MAGERR_PSF_G': \"merr_ap_decam_g\",\n", " \n", + " 'FLUX_AUTO_R': \"f_decam_r\", \n", " 'FLUXERR_AUTO_R': \"ferr_decam_r\", \n", " 'WAVG_FLUX_PSF_R': \"f_ap_decam_r\", \n", " 'WAVG_FLUXERR_PSF_R': \"ferr_ap_decam_r\",\n", @@ -116,6 +117,7 @@ " 'WAVG_MAG_PSF_R': \"m_ap_decam_r\", \n", " 'WAVG_MAGERR_PSF_R': \"merr_ap_decam_r\",\n", " \n", + " 'FLUX_AUTO_I': \"f_decam_i\",\n", " 'FLUXERR_AUTO_I': \"ferr_decam_i\", \n", " 'WAVG_FLUX_PSF_I': \"f_ap_decam_i\", \n", " 'WAVG_FLUXERR_PSF_I': \"ferr_ap_decam_i\",\n", @@ -124,6 +126,7 @@ " 'WAVG_MAG_PSF_I': \"m_ap_decam_i\", \n", " 'WAVG_MAGERR_PSF_I': \"merr_ap_decam_i\",\n", " \n", + " 'FLUX_AUTO_Z': \"f_decam_z\",\n", " 'FLUXERR_AUTO_Z': \"ferr_decam_z\", \n", " 'WAVG_FLUX_PSF_Z': \"f_ap_decam_z\", \n", " 'WAVG_FLUXERR_PSF_Z': \"ferr_ap_decam_z\",\n", @@ -132,13 +135,14 @@ " 'WAVG_MAG_PSF_Z': \"m_ap_decam_z\", \n", " 'WAVG_MAGERR_PSF_Z': \"merr_ap_decam_z\",\n", " \n", + " 'FLUX_AUTO_Y': \"f_decam_y\",\n", " 'FLUXERR_AUTO_Y': \"ferr_decam_y\", \n", " 'WAVG_FLUX_PSF_Y': \"f_ap_decam_y\", \n", " 'WAVG_FLUXERR_PSF_Y': \"ferr_ap_decam_y\",\n", " 'MAG_AUTO_Y': \"m_decam_y\", \n", " 'MAGERR_AUTO_Y': \"merr_decam_y\", \n", " 'WAVG_MAG_PSF_Y': \"m_ap_decam_y\", \n", - " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\",\n", + " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\"\n", "\n", " })\n", "\n", diff --git a/dmu1/dmu1_ml_SSDF/1.3_DES.ipynb b/dmu1/dmu1_ml_SSDF/1.3_DES.ipynb index 5421e2d3..73f0566a 100644 --- a/dmu1/dmu1_ml_SSDF/1.3_DES.ipynb +++ b/dmu1/dmu1_ml_SSDF/1.3_DES.ipynb @@ -108,6 +108,7 @@ " 'WAVG_MAG_PSF_G': \"m_ap_decam_g\", \n", " 'WAVG_MAGERR_PSF_G': \"merr_ap_decam_g\",\n", " \n", + " 'FLUX_AUTO_R': \"f_decam_r\", \n", " 'FLUXERR_AUTO_R': \"ferr_decam_r\", \n", " 'WAVG_FLUX_PSF_R': \"f_ap_decam_r\", \n", " 'WAVG_FLUXERR_PSF_R': \"ferr_ap_decam_r\",\n", @@ -116,6 +117,7 @@ " 'WAVG_MAG_PSF_R': \"m_ap_decam_r\", \n", " 'WAVG_MAGERR_PSF_R': \"merr_ap_decam_r\",\n", " \n", + " 'FLUX_AUTO_I': \"f_decam_i\",\n", " 'FLUXERR_AUTO_I': \"ferr_decam_i\", \n", " 'WAVG_FLUX_PSF_I': \"f_ap_decam_i\", \n", " 'WAVG_FLUXERR_PSF_I': \"ferr_ap_decam_i\",\n", @@ -124,6 +126,7 @@ " 'WAVG_MAG_PSF_I': \"m_ap_decam_i\", \n", " 'WAVG_MAGERR_PSF_I': \"merr_ap_decam_i\",\n", " \n", + " 'FLUX_AUTO_Z': \"f_decam_z\",\n", " 'FLUXERR_AUTO_Z': \"ferr_decam_z\", \n", " 'WAVG_FLUX_PSF_Z': \"f_ap_decam_z\", \n", " 'WAVG_FLUXERR_PSF_Z': \"ferr_ap_decam_z\",\n", @@ -132,13 +135,14 @@ " 'WAVG_MAG_PSF_Z': \"m_ap_decam_z\", \n", " 'WAVG_MAGERR_PSF_Z': \"merr_ap_decam_z\",\n", " \n", + " 'FLUX_AUTO_Y': \"f_decam_y\",\n", " 'FLUXERR_AUTO_Y': \"ferr_decam_y\", \n", " 'WAVG_FLUX_PSF_Y': \"f_ap_decam_y\", \n", " 'WAVG_FLUXERR_PSF_Y': \"ferr_ap_decam_y\",\n", " 'MAG_AUTO_Y': \"m_decam_y\", \n", " 'MAGERR_AUTO_Y': \"merr_decam_y\", \n", " 'WAVG_MAG_PSF_Y': \"m_ap_decam_y\", \n", - " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\",\n", + " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\"\n", "\n", " })\n", "\n", diff --git a/dmu1/dmu1_ml_XMM-LSS/1.6.2_DES.ipynb b/dmu1/dmu1_ml_XMM-LSS/1.6.2_DES.ipynb index 469b28ae..d85d7c9e 100644 --- a/dmu1/dmu1_ml_XMM-LSS/1.6.2_DES.ipynb +++ b/dmu1/dmu1_ml_XMM-LSS/1.6.2_DES.ipynb @@ -108,6 +108,7 @@ " 'WAVG_MAG_PSF_G': \"m_ap_decam_g\", \n", " 'WAVG_MAGERR_PSF_G': \"merr_ap_decam_g\",\n", " \n", + " 'FLUX_AUTO_R': \"f_decam_r\", \n", " 'FLUXERR_AUTO_R': \"ferr_decam_r\", \n", " 'WAVG_FLUX_PSF_R': \"f_ap_decam_r\", \n", " 'WAVG_FLUXERR_PSF_R': \"ferr_ap_decam_r\",\n", @@ -116,6 +117,7 @@ " 'WAVG_MAG_PSF_R': \"m_ap_decam_r\", \n", " 'WAVG_MAGERR_PSF_R': \"merr_ap_decam_r\",\n", " \n", + " 'FLUX_AUTO_I': \"f_decam_i\",\n", " 'FLUXERR_AUTO_I': \"ferr_decam_i\", \n", " 'WAVG_FLUX_PSF_I': \"f_ap_decam_i\", \n", " 'WAVG_FLUXERR_PSF_I': \"ferr_ap_decam_i\",\n", @@ -124,6 +126,7 @@ " 'WAVG_MAG_PSF_I': \"m_ap_decam_i\", \n", " 'WAVG_MAGERR_PSF_I': \"merr_ap_decam_i\",\n", " \n", + " 'FLUX_AUTO_Z': \"f_decam_z\",\n", " 'FLUXERR_AUTO_Z': \"ferr_decam_z\", \n", " 'WAVG_FLUX_PSF_Z': \"f_ap_decam_z\", \n", " 'WAVG_FLUXERR_PSF_Z': \"ferr_ap_decam_z\",\n", @@ -132,13 +135,14 @@ " 'WAVG_MAG_PSF_Z': \"m_ap_decam_z\", \n", " 'WAVG_MAGERR_PSF_Z': \"merr_ap_decam_z\",\n", " \n", + " 'FLUX_AUTO_Y': \"f_decam_y\",\n", " 'FLUXERR_AUTO_Y': \"ferr_decam_y\", \n", " 'WAVG_FLUX_PSF_Y': \"f_ap_decam_y\", \n", " 'WAVG_FLUXERR_PSF_Y': \"ferr_ap_decam_y\",\n", " 'MAG_AUTO_Y': \"m_decam_y\", \n", " 'MAGERR_AUTO_Y': \"merr_decam_y\", \n", " 'WAVG_MAG_PSF_Y': \"m_ap_decam_y\", \n", - " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\",\n", + " 'WAVG_MAGERR_PSF_Y': \"merr_ap_decam_y\"\n", "\n", " })\n", "\n",