{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# ELAIS-N2 Selection Functions\n", "## Depth maps and selection functions\n", "\n", "The simplest selection function available is the field MOC which specifies the area for which there is Herschel data. Each pristine catalogue also has a MOC defining the area for which that data is available.\n", "\n", "The next stage is to provide mean flux standard deviations which act as a proxy for the catalogue's 5$\\sigma$ depth" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This notebook was run with herschelhelp_internal version: \n", "017bb1e (Mon Jun 18 14:58:59 2018 +0100)\n", "This notebook was executed on: \n", "2018-06-24 20:29:01.221713\n" ] } ], "source": [ "from herschelhelp_internal import git_version\n", "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))\n", "import datetime\n", "print(\"This notebook was executed on: \\n{}\".format(datetime.datetime.now()))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "#%config InlineBackend.figure_format = 'svg'\n", "\n", "import matplotlib.pyplot as plt\n", "plt.rc('figure', figsize=(10, 6))\n", "\n", "import os\n", "import time\n", "\n", "from astropy import units as u\n", "from astropy.coordinates import SkyCoord\n", "from astropy.table import Column, Table, join\n", "import numpy as np\n", "from pymoc import MOC\n", "import healpy as hp\n", "#import pandas as pd #Astropy has group_by function so apandas isn't required.\n", "import seaborn as sns\n", "\n", "import warnings\n", "#We ignore warnings - this is a little dangerous but a huge number of warnings are generated by empty cells later\n", "warnings.filterwarnings('ignore')\n", "\n", "from herschelhelp_internal.utils import inMoc, coords_to_hpidx, flux_to_mag\n", "from herschelhelp_internal.masterlist import find_last_ml_suffix, nb_ccplots\n", "\n", "from astropy.io.votable import parse_single_table" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "FIELD = 'ELAIS-N2'\n", "#FILTERS_DIR = \"/Users/rs548/GitHub/herschelhelp_python/database_builder/filters/\"\n", "FILTERS_DIR = \"/opt/herschelhelp_python/database_builder/filters/\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Depth maps produced using: master_catalogue_elais-n2_20180218.fits\n" ] } ], "source": [ "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n", "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n", "SUFFIX = find_last_ml_suffix()\n", "#SUFFIX = \"20171016\"\n", "\n", "master_catalogue_filename = \"master_catalogue_{}_{}.fits\".format(FIELD.lower(), SUFFIX)\n", "master_catalogue = Table.read(\"{}/{}\".format(OUT_DIR, master_catalogue_filename))\n", "\n", "print(\"Depth maps produced using: {}\".format(master_catalogue_filename))\n", "\n", "ORDER = 10\n", "#TODO write code to decide on appropriate order\n", "\n", "field_moc = MOC(filename=\"../../dmu2/dmu2_field_coverages/{}_MOC.fits\".format(FIELD))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Remove sources whose signal to noise ratio is less than five as these will have been selected using forced \n", "# photometry and so the errors will not refelct the RMS of the map \n", "for n,col in enumerate(master_catalogue.colnames):\n", " if col.startswith(\"f_\"):\n", " err_col = \"ferr{}\".format(col[1:])\n", " errs = master_catalogue[err_col]\n", " fluxes = master_catalogue[col]\n", " mask = fluxes/errs < 5.0\n", " master_catalogue[col][mask] = np.nan\n", " master_catalogue[err_col][mask] = np.nan" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## I - Group masterlist objects by healpix cell and calculate depths\n", "We add a column to the masterlist catalogue for the target order healpix cell per object." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "#Add a column to the catalogue with the order=ORDER hp_idx\n", "master_catalogue.add_column(Column(data=coords_to_hpidx(master_catalogue['ra'],\n", " master_catalogue['dec'],\n", " ORDER), \n", " name=\"hp_idx_O_{}\".format(str(ORDER))\n", " )\n", " )" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Convert catalogue to pandas and group by the order=ORDER pixel\n", "\n", "group = master_catalogue.group_by([\"hp_idx_O_{}\".format(str(ORDER))])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "#Downgrade the groups from order=ORDER to order=13 and then fill out the appropriate cells\n", "#hp.pixelfunc.ud_grade([2599293, 2599294], nside_out=hp.order2nside(13))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## II Create a table of all Order=13 healpix cells in the field and populate it\n", "We create a table with every order=13 healpix cell in the field MOC. We then calculate the healpix cell at lower order that the order=13 cell is in. We then fill in the depth at every order=13 cell as calculated for the lower order cell that that the order=13 cell is inside." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "depths = Table()\n", "depths['hp_idx_O_13'] = list(field_moc.flattened(13))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "Table length=10\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
idxhp_idx_O_13
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\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "depths[:10].show_in_notebook()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "depths.add_column(Column(data=hp.pixelfunc.ang2pix(2**ORDER,\n", " hp.pixelfunc.pix2ang(2**13, depths['hp_idx_O_13'], nest=True)[0],\n", " hp.pixelfunc.pix2ang(2**13, depths['hp_idx_O_13'], nest=True)[1],\n", " nest = True),\n", " name=\"hp_idx_O_{}\".format(str(ORDER))\n", " )\n", " )" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "Table length=10\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
idxhp_idx_O_13hp_idx_O_10
01578259632466030
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21578259672466030
31578259742466030
41578259752466030
51578259762466030
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91578259802466030
\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "depths[:10].show_in_notebook()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "Table masked=True length=10\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
idxhp_idx_O_13hp_idx_O_10ferr_ap_wfc_u_meanf_ap_wfc_u_p90ferr_wfc_u_meanf_wfc_u_p90ferr_ap_wfc_g_meanf_ap_wfc_g_p90ferr_wfc_g_meanf_wfc_g_p90ferr_ap_wfc_r_meanf_ap_wfc_r_p90ferr_wfc_r_meanf_wfc_r_p90ferr_ap_wfc_i_meanf_ap_wfc_i_p90ferr_wfc_i_meanf_wfc_i_p90ferr_ap_wfc_z_meanf_ap_wfc_z_p90ferr_wfc_z_meanf_wfc_z_p90ferr_ap_gpc1_g_meanf_ap_gpc1_g_p90ferr_gpc1_g_meanf_gpc1_g_p90ferr_ap_gpc1_r_meanf_ap_gpc1_r_p90ferr_gpc1_r_meanf_gpc1_r_p90ferr_ap_gpc1_i_meanf_ap_gpc1_i_p90ferr_gpc1_i_meanf_gpc1_i_p90ferr_ap_gpc1_z_meanf_ap_gpc1_z_p90ferr_gpc1_z_meanf_gpc1_z_p90ferr_ap_gpc1_y_meanf_ap_gpc1_y_p90ferr_gpc1_y_meanf_gpc1_y_p90ferr_ap_megacam_u_meanf_ap_megacam_u_p90ferr_megacam_u_meanf_megacam_u_p90ferr_ap_megacam_g_meanf_ap_megacam_g_p90ferr_megacam_g_meanf_megacam_g_p90ferr_ap_megacam_r_meanf_ap_megacam_r_p90ferr_megacam_r_meanf_megacam_r_p90ferr_ap_megacam_z_meanf_ap_megacam_z_p90ferr_megacam_z_meanf_megacam_z_p90ferr_ap_irac_i1_meanf_ap_irac_i1_p90ferr_irac_i1_meanf_irac_i1_p90ferr_ap_irac_i2_meanf_ap_irac_i2_p90ferr_irac_i2_meanf_irac_i2_p90ferr_ap_irac_i3_meanf_ap_irac_i3_p90ferr_irac_i3_meanf_irac_i3_p90ferr_ap_irac_i4_meanf_ap_irac_i4_p90ferr_irac_i4_meanf_irac_i4_p90ferr_megacam_i_meanf_megacam_i_p90ferr_megacam_y_meanf_megacam_y_p90
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\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for col in master_catalogue.colnames:\n", " if col.startswith(\"f_\"):\n", " errcol = \"ferr{}\".format(col[1:])\n", " depths = join(depths, \n", " group[\"hp_idx_O_{}\".format(str(ORDER)), errcol].groups.aggregate(np.nanmean),\n", " join_type='left')\n", " depths[errcol].name = errcol + \"_mean\"\n", " depths = join(depths, \n", " group[\"hp_idx_O_{}\".format(str(ORDER)), col].groups.aggregate(lambda x: np.nanpercentile(x, 90.)),\n", " join_type='left')\n", " depths[col].name = col + \"_p90\"\n", "\n", "depths[:10].show_in_notebook()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## III - Save the depth map table" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "depths.write(\"{}/depths_{}_{}.fits\".format(OUT_DIR, FIELD.lower(), SUFFIX), overwrite=True)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## IV - Overview plots\n", "\n", "### IV.a - Filters\n", "First we simply plot all the filters available on this field to give an overview of coverage." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'gpc1_g',\n", " 'gpc1_i',\n", " 'gpc1_r',\n", " 'gpc1_y',\n", " 'gpc1_z',\n", " 'irac_i1',\n", " 'irac_i2',\n", " 'irac_i3',\n", " 'irac_i4',\n", " 'megacam_g',\n", " 'megacam_i',\n", " 'megacam_r',\n", " 'megacam_u',\n", " 'megacam_y',\n", " 'megacam_z',\n", " 'wfc_g',\n", " 'wfc_i',\n", " 'wfc_r',\n", " 'wfc_u',\n", " 'wfc_z'}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tot_bands = [column[2:] for column in master_catalogue.colnames \n", " if (column.startswith('f_') & ~column.startswith('f_ap_'))]\n", "ap_bands = [column[5:] for column in master_catalogue.colnames \n", " if column.startswith('f_ap_') ]\n", "bands = set(tot_bands) | set(ap_bands)\n", "bands" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Passbands on ELAIS-N2')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for b in bands:\n", " plt.plot(Table(data = parse_single_table(FILTERS_DIR + b + '.xml').array.data)['Wavelength']\n", " ,Table(data = parse_single_table(FILTERS_DIR + b + '.xml').array.data)['Transmission']\n", " , label=b)\n", "plt.xlabel('Wavelength ($\\AA$)')\n", "plt.ylabel('Transmission')\n", "plt.xscale('log')\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.title('Passbands on {}'.format(FIELD))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### IV.a - Depth overview\n", "Then we plot the mean depths available across the area a given band is available" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "wfc_u: mean flux error: 2.9266533851623535, 3sigma in AB mag (Aperture): 21.54126863742477\n", "wfc_g: mean flux error: 1.0299211740493774, 3sigma in AB mag (Aperture): 22.675186896062677\n", "wfc_r: mean flux error: 1.2919201850891113, 3sigma in AB mag (Aperture): 22.42910765383278\n", "wfc_i: mean flux error: 2.170649528503418, 3sigma in AB mag (Aperture): 21.865722592593407\n", "wfc_z: mean flux error: 10.164146136529181, 3sigma in AB mag (Aperture): 20.189519611808343\n", "gpc1_g: mean flux error: 51.87735443111652, 3sigma in AB mag (Aperture): 18.4197523121179\n", "gpc1_r: mean flux error: 10.171353039622048, 3sigma in AB mag (Aperture): 20.188750041720887\n", "gpc1_i: mean flux error: 120.33102429417636, 3sigma in AB mag (Aperture): 17.506252829292343\n", "gpc1_z: mean flux error: 4.9894292762420855, 3sigma in AB mag (Aperture): 20.962069685692605\n", "gpc1_y: mean flux error: 165.25365842992585, 3sigma in AB mag (Aperture): 17.161819156220538\n", "megacam_u: mean flux error: 0.01454273983836174, 3sigma in AB mag (Aperture): 27.300581275953284\n", "megacam_g: mean flux error: 0.012285456992685795, 3sigma in AB mag (Aperture): 27.48371857334815\n", "megacam_r: mean flux error: 0.019595744088292122, 3sigma in AB mag (Aperture): 26.976792465379766\n", "megacam_z: mean flux error: 0.04526262730360031, 3sigma in AB mag (Aperture): 26.06784746494177\n", "irac_i1: mean flux error: 0.9428658125398736, 3sigma in AB mag (Aperture): 22.771072140955134\n", "irac_i2: mean flux error: 1.2073978272421195, 3sigma in AB mag (Aperture): 22.502570888228654\n", "irac_i3: mean flux error: 5.390256035553003, 3sigma in AB mag (Aperture): 20.878173376863437\n", "irac_i4: mean flux error: 5.013002662036264, 3sigma in AB mag (Aperture): 20.956952025113573\n", "wfc_u: mean flux error: 4.577387809753418, 3sigma in AB mag (Total): 21.05565259147219\n", "wfc_g: mean flux error: 3.0452506026270623, 3sigma in AB mag (Total): 21.498139268676987\n", "wfc_r: mean flux error: 3.6446392064770996, 3sigma in AB mag (Total): 21.303060506457136\n", "wfc_i: mean flux error: 5.199480717117017, 3sigma in AB mag (Total): 20.91729693341764\n", "wfc_z: mean flux error: 21.565357208251953, 3sigma in AB mag (Total): 19.372805222728836\n", "gpc1_g: mean flux error: 65.39997040182902, 3sigma in AB mag (Total): 18.16825298376346\n", "gpc1_r: mean flux error: 8.793651083001205, 3sigma in AB mag (Total): 20.346773789288157\n", "gpc1_i: mean flux error: 45.1041461865128, 3sigma in AB mag (Total): 18.5716556979096\n", "gpc1_z: mean flux error: 5.357145476129616, 3sigma in AB mag (Total): 20.88486326312529\n", "gpc1_y: mean flux error: 39.92801720786317, 3sigma in AB mag (Total): 18.704002503133516\n", "megacam_u: mean flux error: 0.0178123377263546, 3sigma in AB mag (Total): 27.080394561029173\n", "megacam_g: mean flux error: 0.022474996849983456, 3sigma in AB mag (Total): 26.827947764360637\n", "megacam_r: mean flux error: 0.029624568004309598, 3sigma in AB mag (Total): 26.528066798120626\n", "megacam_z: mean flux error: 0.11273171323374004, 3sigma in AB mag (Total): 25.07708159515814\n", "irac_i1: mean flux error: 0.9901802967900702, 3sigma in AB mag (Total): 22.717911162636746\n", "irac_i2: mean flux error: 1.3231832384101259, 3sigma in AB mag (Total): 22.403146886292298\n", "irac_i3: mean flux error: 5.267856647227315, 3sigma in AB mag (Total): 20.90311199292315\n", "irac_i4: mean flux error: 5.2573284024092555, 3sigma in AB mag (Total): 20.90528409732419\n", "megacam_i: mean flux error: 0.07957976311445236, 3sigma in AB mag (Total): 25.455190258087434\n", "megacam_y: mean flux error: nan, 3sigma in AB mag (Total): nan\n" ] } ], "source": [ "average_depths = []\n", "for b in ap_bands:\n", " \n", " mean_err = np.nanmean(depths['ferr_ap_{}_mean'.format(b)])\n", " print(\"{}: mean flux error: {}, 3sigma in AB mag (Aperture): {}\".format(b, mean_err, flux_to_mag(3.0*mean_err*1.e-6)[0]))\n", " average_depths += [('ap_' + b, flux_to_mag(1.0*mean_err*1.e-6)[0], \n", " flux_to_mag(3.0*mean_err*1.e-6)[0], \n", " flux_to_mag(5.0*mean_err*1.e-6)[0])]\n", " \n", "for b in tot_bands:\n", " \n", " mean_err = np.nanmean(depths['ferr_{}_mean'.format(b)])\n", " print(\"{}: mean flux error: {}, 3sigma in AB mag (Total): {}\".format(b, mean_err, flux_to_mag(3.0*mean_err*1.e-6)[0]))\n", " average_depths += [(b, flux_to_mag(1.0*mean_err*1.e-6)[0], \n", " flux_to_mag(3.0*mean_err*1.e-6)[0], \n", " flux_to_mag(5.0*mean_err*1.e-6)[0])]\n", " \n", "average_depths = np.array(average_depths, dtype=[('band', \"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for dat in data:\n", " wav_deets = FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission']))\n", " depth = average_depths['5s'][average_depths['band'] == dat[0]]\n", " #print(depth)\n", " plt.plot([wav_deets[0],wav_deets[1]], [depth,depth], label=dat[0])\n", " \n", "plt.xlabel('Wavelength ($\\AA$)')\n", "plt.ylabel('Depth')\n", "plt.xscale('log')\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.title('Depths on {}'.format(FIELD))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### IV.c - Depth vs coverage comparison\n", "\n", "How best to do this? Colour/intensity plot over area? Percentage coverage vs mean depth?" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Depths (5 $\\\\sigma$) vs coverage on ELAIS-N2')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for dat in data:\n", " wav_deets = FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission']))\n", " depth = average_depths['5s'][average_depths['band'] == dat[0]]\n", " #print(depth)\n", " coverage = np.sum(~np.isnan(depths['ferr_{}_mean'.format(dat[0])]))/len(depths)\n", " plt.plot(coverage, depth, 'x', label=dat[0])\n", " \n", "plt.xlabel('Coverage')\n", "plt.ylabel('Depth')\n", "#plt.xscale('log')\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.title('Depths (5 $\\sigma$) vs coverage on {}'.format(FIELD))" ] } ], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }