Match_LOFAR_analysis_final_numbers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of the final catalogue of matched sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will analyse the changes in the classification using the new sigma and the new catalogue without the galaxies that went to LGZ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration\n",
    "\n",
    "### Load libraries and setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from astropy.table import Table, join\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord, search_around_sky\n",
    "from IPython.display import clear_output\n",
    "import pickle\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mltier1 import (get_center, Field, parallel_process, describe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "# mpl.rc('figure', figsize=(6.64, 6.64*0.74), dpi=100)\n",
    "# mpl.rc('figure.subplot', left=0.15, right=0.95, bottom=0.15, top=0.92)\n",
    "# mpl.rc('lines', linewidth=1.75, markersize=8.0, markeredgewidth=0.75)\n",
    "# mpl.rc('font', size=18.0, family=\"serif\", serif=\"CM\")\n",
    "# mpl.rc('xtick', labelsize='small')\n",
    "# mpl.rc('ytick', labelsize='small')\n",
    "# mpl.rc('xtick.major', width=1.0, size=8)\n",
    "# mpl.rc('ytick.major', width=1.0, size=8)\n",
    "# mpl.rc('xtick.minor', width=1.0, size=4)\n",
    "# mpl.rc('ytick.minor', width=1.0, size=4)\n",
    "# mpl.rc('axes', linewidth=1.5)\n",
    "# mpl.rc('legend', fontsize='small', numpoints=1, labelspacing=0.4, frameon=False)\n",
    "# mpl.rc('text', usetex=True)\n",
    "# mpl.rc('savefig', dpi=300) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def most_common(a, n=2):\n",
    "    u, c = np.unique(a, return_counts=True)\n",
    "    order = np.argsort(c)\n",
    "    for i in range(n):\n",
    "        print(c[order][-(i+1)], u[order][-(i+1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "save_intermediate = True\n",
    "plot_intermediate = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "idp = \"idata/final_analysis_pdf_v2.0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if not os.path.isdir(idp):\n",
    "    os.makedirs(idp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pwli = Table.read(\"lofar_pw_pdf.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Source_Name',\n",
       " 'RA',\n",
       " 'E_RA',\n",
       " 'DEC',\n",
       " 'E_DEC',\n",
       " 'Peak_flux',\n",
       " 'E_Peak_flux',\n",
       " 'Total_flux',\n",
       " 'E_Total_flux',\n",
       " 'Maj',\n",
       " 'E_Maj',\n",
       " 'Min',\n",
       " 'E_Min',\n",
       " 'PA',\n",
       " 'E_PA',\n",
       " 'Isl_rms',\n",
       " 'S_Code',\n",
       " 'Mosaic_ID',\n",
       " 'AllWISE',\n",
       " 'objID',\n",
       " 'ra',\n",
       " 'dec',\n",
       " 'raErr',\n",
       " 'decErr',\n",
       " 'W1mag',\n",
       " 'W1magErr',\n",
       " 'i',\n",
       " 'iErr',\n",
       " 'colour',\n",
       " 'category',\n",
       " 'lr',\n",
       " 'lr_dist']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwli.colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lofar_all = Table.read(\"data/LOFAR_HBA_T1_DR1_merge_ID_optical_f_v1.2.fits\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Join data tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: MergeConflictWarning: Cannot merge meta key 'DATE-HDU' types <class 'str'> and <class 'str'>, choosing DATE-HDU='2018-06-01T19:19:01' [astropy.utils.metadata]\n",
      "WARNING: MergeConflictWarning: Cannot merge meta key 'STILVERS' types <class 'str'> and <class 'str'>, choosing STILVERS='3.3' [astropy.utils.metadata]\n"
     ]
    }
   ],
   "source": [
    "pwl = join(pwli, lofar_all[['Source_Name', 'AllWISE', 'objID', 'ML_LR', \n",
    "                            'ID_flag', 'ID_name', 'ID_ra', 'ID_dec', \n",
    "                            'LGZ_Size', 'LGZ_Width', 'LGZ_PA', 'LGZ_Assoc', \n",
    "                            'LGZ_Assoc_Qual', 'LGZ_ID_Qual']], \n",
    "           join_type='left', \n",
    "           keys='Source_Name', \n",
    "           uniq_col_name='{col_name}{table_name}', \n",
    "           table_names=['', '_input'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colour_limits = [0.0, 0.5, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.5, 4.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bin_list, centers, Q_0_colour, n_m, q_m = pickle.load(open(\"lofar_params.pckl\", \"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore and repair data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source_Name N/A <U22\n",
      "RA 1e+20 >f8\n",
      "E_RA 1e+20 >f8\n",
      "DEC 1e+20 >f8\n",
      "E_DEC 1e+20 >f8\n",
      "Peak_flux 1e+20 >f8\n",
      "E_Peak_flux 1e+20 >f8\n",
      "Total_flux 1e+20 >f8\n",
      "E_Total_flux 1e+20 >f8\n",
      "Maj 1e+20 >f8\n",
      "E_Maj 1e+20 >f8\n",
      "Min 1e+20 >f8\n",
      "E_Min 1e+20 >f8\n",
      "PA 1e+20 >f8\n",
      "E_PA 1e+20 >f8\n",
      "Isl_rms 1e+20 >f8\n",
      "S_Code N/A <U1\n",
      "Mosaic_ID N/A <U11\n",
      "AllWISE N/A <U19\n",
      "objID 999999 >i8\n",
      "ra 1e+20 >f8\n",
      "dec 1e+20 >f8\n",
      "raErr 1e+20 >f8\n",
      "decErr 1e+20 >f8\n",
      "W1mag 1e+20 >f8\n",
      "W1magErr 1e+20 >f8\n",
      "i 1e+20 >f8\n",
      "iErr 1e+20 >f8\n",
      "colour 1e+20 >f8\n",
      "category 1e+20 >f8\n",
      "lr 1e+20 >f8\n",
      "lr_dist 1e+20 >f8\n",
      "AllWISE_input N/A <U19\n",
      "objID_input 999999 >i8\n",
      "ML_LR 1e+20 >f8\n",
      "ID_flag 999999 >i8\n",
      "ID_name N/A <U27\n",
      "ID_ra 1e+20 >f8\n",
      "ID_dec 1e+20 >f8\n",
      "LGZ_Size 1e+20 >f8\n",
      "LGZ_Width 1e+20 >f8\n",
      "LGZ_PA 1e+20 >f8\n",
      "LGZ_Assoc 1e+20 >f8\n",
      "LGZ_Assoc_Qual 1e+20 >f8\n",
      "LGZ_ID_Qual 1e+20 >f8\n"
     ]
    }
   ],
   "source": [
    "for col in pwl.colnames:\n",
    "    fv = pwl[col].fill_value\n",
    "    typ = pwl[col].dtype\n",
    "    print(col, fv, typ)\n",
    "    # Restore NaNs\n",
    "    if fv == 1e+20:\n",
    "        pwl[col][(pwl[col] == fv)] = np.nan\n",
    "#     if (isinstance(fv, np.float64) and (fv != 1e+20)):\n",
    "#         print(col, fv)\n",
    "#         pwl[col].fill_value = 1e+20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pwl[\"colour\"][(pwl[\"colour\"] == 1e+20)] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.128 +/- 0.814; median: 2.068; limits: [-1.640, 9.281]; N=318769 (172386 NaN; 0 masked)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jsm/programs/soft/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py:4147: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedColumn.\n",
      "  part.partition(kth)\n"
     ]
    }
   ],
   "source": [
    "describe(pwl[\"colour\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the AllWISE_input that are '' to 'N/A'. That comes from a previous error with the fill value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pwl[\"AllWISE_input\"][pwl[\"AllWISE_input\"] == \"\"] = \"N/A\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots for the paper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### q(m) over n(m) depending on the colour (not used)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f263b34f080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (12,10)\n",
    "\n",
    "from matplotlib import cm\n",
    "from matplotlib.collections import LineCollection\n",
    "\n",
    "cm_subsection = linspace(0., 1., 16) \n",
    "colors = [ cm.viridis(x) for x in cm_subsection ]\n",
    "\n",
    "low = np.nonzero(centers[1] >= 15)[0][0]\n",
    "high = np.nonzero(centers[1] >= 22.2)[0][0]\n",
    "\n",
    "fig, a = plt.subplots()\n",
    "\n",
    "for i, q_m_k in enumerate(q_m):\n",
    "    #plot(centers[i], q_m_old[i]/n_m_old[i])\n",
    "    a = subplot(4,4,i+1)\n",
    "    if i not in [-1]:\n",
    "        q_m_aux = q_m[i]/np.sum(q_m[i])\n",
    "        lwidths = (q_m_aux/np.max(q_m_aux)*10).astype(float) \n",
    "        #print(lwidths)\n",
    "        \n",
    "        y_aux = q_m_k/n_m[i]\n",
    "        factor = np.max(y_aux[low:high])\n",
    "        y = y_aux\n",
    "        #print(y)\n",
    "        x = centers[i]\n",
    "        \n",
    "        points = np.array([x, y]).T.reshape(-1, 1, 2)\n",
    "        segments = np.concatenate([points[:-1], points[1:]], axis=1)\n",
    "        lc = LineCollection(segments, linewidths=lwidths, color=colors[i])\n",
    "        \n",
    "        a.add_collection(lc)\n",
    "        \n",
    "        #plot(centers[i], x/factor, color=colors[i-1])\n",
    "        xlim([12, 30])\n",
    "        if i == 0:\n",
    "            xlim([10, 23])\n",
    "        ylim([0, 1.2*factor])\n",
    "\n",
    "subplots_adjust(left=0.125, \n",
    "                bottom=0.1, \n",
    "                right=0.9, \n",
    "                top=0.9,\n",
    "                wspace=0.4, \n",
    "                hspace=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explanation of the KDE used in the computing of the q(m) and n(m):\n",
    "* https://jakevdp.github.io/blog/2013/12/01/kernel-density-estimation/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 3 of the paper\n",
    "\n",
    "Help from:\n",
    "* https://stackoverflow.com/questions/19877666/add-legends-to-linecollection-plot\n",
    "* https://matplotlib.org/users/mathtext.html#mathtext-tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2639242d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save = True\n",
    "save_pdf = True\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (6.64, 6.64*0.74)\n",
    "plt.rcParams[\"figure.dpi\"] = 100\n",
    "plt.rcParams['lines.linewidth'] = 1.75\n",
    "plt.rcParams['lines.markersize'] = 8.0\n",
    "plt.rcParams['lines.markeredgewidth'] = 0.75\n",
    "plt.rcParams['font.size'] = 15.0 ## not 18.0\n",
    "plt.rcParams['font.family'] = \"serif\"\n",
    "plt.rcParams['font.serif'] = \"CM\"\n",
    "plt.rcParams['xtick.labelsize'] = 'small'\n",
    "plt.rcParams['ytick.labelsize'] = 'small'\n",
    "plt.rcParams['xtick.major.width'] = 1.0\n",
    "plt.rcParams['xtick.major.size'] = 8\n",
    "plt.rcParams['ytick.major.width'] = 1.0\n",
    "plt.rcParams['ytick.major.size'] = 8\n",
    "plt.rcParams['xtick.minor.width'] = 1.0\n",
    "plt.rcParams['xtick.minor.size'] = 4\n",
    "plt.rcParams['ytick.minor.width'] = 1.0\n",
    "plt.rcParams['ytick.minor.size'] = 4\n",
    "plt.rcParams['axes.linewidth'] = 1.5\n",
    "plt.rcParams['legend.fontsize'] = 'small'\n",
    "plt.rcParams['legend.numpoints'] = 1\n",
    "plt.rcParams['legend.labelspacing'] = 0.4\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "\n",
    "from matplotlib import cm\n",
    "from matplotlib.collections import LineCollection\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "cm_subsection = linspace(0., 1., 16) \n",
    "colors = [ cm.viridis(x) for x in cm_subsection ]\n",
    "\n",
    "low = np.nonzero(centers[1] >= 15)[0][0]\n",
    "high = np.nonzero(centers[1] >= 22.2)[0][0]\n",
    "\n",
    "cm_subsection = linspace(0., 1., 14) \n",
    "colors = [ cm.jet(x) for x in cm_subsection ]\n",
    "\n",
    "fig, a = plt.subplots()\n",
    "lcs = []\n",
    "proxies = []\n",
    "\n",
    "def make_proxy(zvalue, scalar_mappable, **kwargs):\n",
    "    color = scalar_mappable.cmap(scalar_mappable.norm(zvalue))\n",
    "    return Line2D([0, 1], [0, 1], color=color, **kwargs)\n",
    "\n",
    "for i, q_m_k in enumerate(q_m):\n",
    "    #plot(centers[i], q_m_old[i]/n_m_old[i])\n",
    "    q_m_aux = q_m[i]/np.sum(q_m[i])\n",
    "    lwidths = (q_m_aux/np.max(q_m_aux)*10).astype(float)\n",
    "    if save_pdf and (i<6): # Solve problems with the line with in pdfs\n",
    "        lwidths[lwidths < 0.005] = 0\n",
    "    #print(lwidths)\n",
    "\n",
    "    y_aux = q_m_k/n_m[i]\n",
    "    factor = np.max(y_aux[low:high])\n",
    "    y = y_aux\n",
    "    #print(y)\n",
    "    x = centers[i]\n",
    "\n",
    "    points = np.array([x, y]).T.reshape(-1, 1, 2)\n",
    "    segments = np.concatenate([points[:-1], points[1:]], axis=1)\n",
    "    #lc = LineCollection(segments, linewidths=lwidths, color=colors[i])\n",
    "    \n",
    "    if i not in [0]:  \n",
    "        if i == 1:\n",
    "            color = \"k\"\n",
    "        else:\n",
    "            color = colors[i-2]\n",
    "            \n",
    "        lcs.append(LineCollection(segments, linewidths=lwidths, color=color))\n",
    "        proxies.append(Line2D([0, 1], [0, 1], color=color, lw=5))\n",
    "        \n",
    "        a.add_collection(lcs[-1])\n",
    "        xlim([16, 26])\n",
    "        ylim([0, 700000])\n",
    "        xlabel(\"$i$ magnitude\")\n",
    "        ylabel(\"$q(m,c)/n(m,c)$\")\n",
    "\n",
    "        \n",
    "        \n",
    "inset = plt.axes([0.285, 0.6, .2, .25])\n",
    "q_m_aux = q_m[0]/np.sum(q_m[0])\n",
    "lwidths = (q_m_aux/np.max(q_m_aux)*10).astype(float)\n",
    "y_aux = q_m[0]/n_m[0]\n",
    "factor = np.max(y_aux[low:high])\n",
    "y = y_aux\n",
    "x = centers[0]\n",
    "points = np.array([x, y]).T.reshape(-1, 1, 2)\n",
    "segments = np.concatenate([points[:-1], points[1:]], axis=1)\n",
    "lc = LineCollection(segments, linewidths=lwidths, color=\"k\")\n",
    "proxy = Line2D([0, 1], [0, 1], color=\"k\", lw=5)\n",
    "inset.add_collection(lc)\n",
    "xlim([15, 23])\n",
    "ylim([0, 10000])\n",
    "xlabel(\"$W1$ magnitude\")\n",
    "ylabel(\"$q(m)/n(m)$\")\n",
    "inset.legend([proxy], [\"only $W1$\"], fontsize=\"xx-small\")\n",
    "        \n",
    "\n",
    "    #ylim([0, 1.2*factor])\n",
    "        \n",
    "#print(lcs)\n",
    "\n",
    "# a.legend(proxies, [\"$i-W1 < 0.0$\", \n",
    "#              \"$0.0 \\leq i-W1 < 0.5$\", \n",
    "#              \"$0.5 \\leq i-W1 < 1.0$\", \n",
    "#              \"$1.0 \\leq i-W1 < 1.25$\", \n",
    "#              \"$1.25 \\leq i-W1 < 1.5$\", \n",
    "#              \"$1.5 \\leq i-W1 < 1.75$\", \n",
    "#              \"$1.75 \\leq i-W1 < 2.0$\", \n",
    "#              \"$2.0 \\leq i-W1 < 2.25$\",\n",
    "#              \"$2.25 \\leq i-W1 < 2.5$\",\n",
    "#              \"$2.5 \\leq i-W1 < 2.75$\",\n",
    "#              \"$2.75 \\leq i-W1 < 3.0$\",\n",
    "#              \"$3.0 \\leq i-W1 < 3.5$\",\n",
    "#              \"$3.5 \\leq i-W1 < 4.0$\",\n",
    "#              \"$i-W1 \\geq 4.0$\"])\n",
    "\n",
    "# a.legend(proxies, [\"only $i$\",\n",
    "#                    \"$i-W1 \\in (-\\infty, 0.0)$\", \n",
    "#                    \"$i-W1 \\in [0.0, 0.5)$\",\n",
    "#                    \"$i-W1 \\in [0.5, 1.0)$\",\n",
    "#                    \"$i-W1 \\in [1.0, 1.25)$\",\n",
    "#                    \"$i-W1 \\in [1.25, 1.5)$\",\n",
    "#                    \"$i-W1 \\in [1.5, 1.75)$\",\n",
    "#                    \"$i-W1 \\in [1.75, 2.0)$\",\n",
    "#                    \"$i-W1 \\in [2.0, 2.25)$\",\n",
    "#                    \"$i-W1 \\in [2.25, 2.5)$\",\n",
    "#                    \"$i-W1 \\in [2.5, 2.75)$\",\n",
    "#                    \"$i-W1 \\in [2.75, 3.0)$\",\n",
    "#                    \"$i-W1 \\in [3.0, 3.5)$\",\n",
    "#                    \"$i-W1 \\in [3.5, 4.0)$\",\n",
    "#                    \"$i-W1 \\in [4.0, \\infty)$\"],\n",
    "#         fontsize=\"xx-small\")\n",
    "\n",
    "a.legend(proxies, [\"only $i$\",\n",
    "                   \"$(-\\infty, 0.0)$\", \n",
    "                   \"$[0.0, 0.5)$\",\n",
    "                   \"$[0.5, 1.0)$\",\n",
    "                   \"$[1.0, 1.25)$\",\n",
    "                   \"$[1.25, 1.5)$\",\n",
    "                   \"$[1.5, 1.75)$\",\n",
    "                   \"$[1.75, 2.0)$\",\n",
    "                   \"$[2.0, 2.25)$\",\n",
    "                   \"$[2.25, 2.5)$\",\n",
    "                   \"$[2.5, 2.75)$\",\n",
    "                   \"$[2.75, 3.0)$\",\n",
    "                   \"$[3.0, 3.5)$\",\n",
    "                   \"$[3.5, 4.0)$\",\n",
    "                   \"$[4.0, \\infty)$\"],\n",
    "        fontsize=\"xx-small\",\n",
    "        title=\"$i-W1$\")\n",
    "\n",
    "# subplots_adjust(left=0.15, \n",
    "#                 bottom=0.1, \n",
    "#                 right=0.9, \n",
    "#                 top=0.9,\n",
    "#                 wspace=0.4, \n",
    "#                 hspace=0.2)\n",
    "\n",
    "subplots_adjust(left=0.15, \n",
    "                bottom=0.15, \n",
    "                right=0.95, \n",
    "                top=0.92,\n",
    "                wspace=0.4, \n",
    "                hspace=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 4 of the paper: completeness and reliability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "q0 = np.sum(Q_0_colour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def completeness(lr, threshold, q0):\n",
    "    n = len(lr)\n",
    "    lrt = lr[lr < threshold]\n",
    "    return 1. - np.sum((q0 * lrt)/(q0 * lrt + (1 - q0)))/float(n)/q0\n",
    "\n",
    "def reliability(lr, threshold, q0):\n",
    "    n = len(lr)\n",
    "    lrt = lr[lr > threshold]\n",
    "    return 1. - np.sum((1. - q0)/(q0 * lrt + (1 - q0)))/float(n)/q0\n",
    "\n",
    "completeness_v = np.vectorize(completeness, excluded=[0])\n",
    "reliability_v = np.vectorize(reliability, excluded=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pwl[\"lrt\"] = pwl[\"lr\"]\n",
    "pwl[\"lrt\"][np.isnan(pwl[\"lr\"])] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_test = 100\n",
    "threshold_mean = np.percentile(pwl[\"lrt\"], 100*(1 - q0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jsm/programs/soft/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py:2834: UserWarning: Warning: converting a masked element to nan.\n",
      "  res = array(outputs, copy=False, subok=True, dtype=otypes[0])\n"
     ]
    }
   ],
   "source": [
    "thresholds = np.arange(0., 10., 0.01)\n",
    "thresholds_fine = np.arange(0.1, 2., 0.001)\n",
    "completeness_t = completeness_v(pwl[\"lrt\"], thresholds, q0)\n",
    "reliability_t = reliability_v(pwl[\"lrt\"], thresholds, q0)\n",
    "average_t = (completeness_t + reliability_t)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "completeness_t_fine = completeness_v(pwl[\"lrt\"], thresholds_fine, q0)\n",
    "reliability_t_fine = reliability_v(pwl[\"lrt\"], thresholds_fine, q0)\n",
    "average_t_fine = (completeness_t_fine + reliability_t_fine)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3580000000000002"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholds_fine[np.argmax(average_t_fine)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6390000000000005"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholds_fine[np.argmin(np.abs(completeness_t_fine-reliability_t_fine))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "226625"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pwl[\"lrt\"] >= 0.358)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "225016"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pwl[\"lrt\"] >= 0.639)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "220267"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum((pwl[\"lrt\"] >= 0.639) & (pwl['ID_flag'] == 1) & ~np.isnan(pwl['ra']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "318769"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pwl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "299759"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pwl[pwl['ID_flag'] == 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "220267 0.7349117006262532\n"
     ]
    }
   ],
   "source": [
    "n0 = np.sum((pwl[\"lrt\"] >= 0.639) & (pwl['ID_flag'] == 1))\n",
    "print(n0, n0/len(pwl[pwl['ID_flag'] == 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "219538 0.7324794223923075\n"
     ]
    }
   ],
   "source": [
    "n0 = np.sum((pwl[\"lrt\"] >= 0.81268) & (pwl['ID_flag'] == 1))\n",
    "print(n0, n0/len(pwl[pwl['ID_flag'] == 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jsm/programs/soft/anaconda3/lib/python3.6/site-packages/astropy/table/column.py:954: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  return getattr(self.data, op)(other)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "220537 0.7358125444166036\n"
     ]
    }
   ],
   "source": [
    "n0 = np.sum((pwl[\"ML_LR\"] >= 0.81268) & (pwl['ID_flag'] == 1))\n",
    "print(n0, n0/len(pwl[pwl['ID_flag'] == 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7058904724110563"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pwl[\"lrt\"] >= 0.639)/len(pwl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "threshold_sel = 0.639"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jsm/programs/soft/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py:2834: UserWarning: Warning: converting a masked element to nan.\n",
      "  res = array(outputs, copy=False, subok=True, dtype=otypes[0])\n"
     ]
    }
   ],
   "source": [
    "thresholds_fine = np.arange(0.0, 2., 0.001)\n",
    "completeness_t_fine = completeness_v(pwl[\"lrt\"], thresholds_fine, q0)\n",
    "reliability_t_fine = reliability_v(pwl[\"lrt\"], thresholds_fine, q0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2639242c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (6.64, 6.64*0.74)\n",
    "plt.rcParams[\"figure.dpi\"] = 100\n",
    "plt.rcParams['lines.linewidth'] = 1.75\n",
    "plt.rcParams['lines.markersize'] = 8.0\n",
    "plt.rcParams['lines.markeredgewidth'] = 0.75\n",
    "plt.rcParams['font.size'] = 18.0\n",
    "plt.rcParams['font.family'] = \"serif\"\n",
    "plt.rcParams['font.serif'] = \"CM\"\n",
    "plt.rcParams['xtick.labelsize'] = 'small'\n",
    "plt.rcParams['ytick.labelsize'] = 'small'\n",
    "plt.rcParams['xtick.major.width'] = 1.0\n",
    "plt.rcParams['xtick.major.size'] = 8\n",
    "plt.rcParams['ytick.major.width'] = 1.0\n",
    "plt.rcParams['ytick.major.size'] = 8\n",
    "plt.rcParams['xtick.minor.width'] = 1.0\n",
    "plt.rcParams['xtick.minor.size'] = 4\n",
    "plt.rcParams['ytick.minor.width'] = 1.0\n",
    "plt.rcParams['ytick.minor.size'] = 4\n",
    "plt.rcParams['axes.linewidth'] = 1.5\n",
    "plt.rcParams['legend.fontsize'] = 'small'\n",
    "plt.rcParams['legend.numpoints'] = 1\n",
    "plt.rcParams['legend.labelspacing'] = 0.4\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "\n",
    "plot(thresholds_fine, completeness_t_fine, \"-\", label=\"Completeness\")\n",
    "plot(thresholds_fine, reliability_t_fine, \"-\", label=\"Reliability\")\n",
    "text(0.66, 0.971, \"0.639\")\n",
    "#plot(thresholds_fine, average_t_fine, \"-\", label=\"average\")\n",
    "vlines(threshold_sel, 0.9, 1., \"k\", linestyles=\"dashed\", label=\"Threshold\\nselected\")\n",
    "#vlines(threshold_mean, 0.9, 1., \"y\", linestyles=\"dashed\")\n",
    "ylim([0.97, 1.])\n",
    "xlim([0.0, 1.5])\n",
    "legend(loc=4)\n",
    "xlabel(\"Threshold\")\n",
    "ylabel(\"Completeness/Reliability\")\n",
    "\n",
    "# subplots_adjust(left=0.2, \n",
    "#                 bottom=0.1, \n",
    "#                 right=0.95, \n",
    "#                 top=0.9,\n",
    "#                 wspace=0.4, \n",
    "#                 hspace=0.2)\n",
    "subplots_adjust(left=0.15, \n",
    "                bottom=0.15, \n",
    "                right=0.95, \n",
    "                top=0.92,\n",
    "                wspace=0.4, \n",
    "                hspace=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### N_LOFAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond_mlr = (pwl['ID_flag'] == 1) & (pwl[\"lrt\"] >= 0.639)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "318769"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pwl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92144"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.isnan(pwl[\"category\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.isnan(pwl[\"category\"][cond_mlr]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 67241 65658\n",
      "1.0 13001 11841\n",
      "2.0 358 299\n",
      "3.0 1754 1675\n",
      "4.0 7076 6878\n",
      "5.0 9692 9459\n",
      "6.0 15021 14655\n",
      "7.0 17435 16977\n",
      "8.0 17334 16885\n",
      "9.0 16266 15867\n",
      "10.0 15046 14690\n",
      "11.0 13090 12813\n",
      "12.0 11203 10959\n",
      "13.0 14636 14336\n",
      "14.0 5569 5429\n",
      "15.0 1903 1846\n"
     ]
    }
   ],
   "source": [
    "n_c, n_c_mlr = [], []\n",
    "for i in np.unique(pwl[\"category\"][~np.isnan(pwl[\"category\"])]):\n",
    "    n_c.append(np.sum((pwl[\"category\"] == i)))\n",
    "    n_c_mlr.append(np.sum((pwl[\"category\"][cond_mlr] == i)))\n",
    "    print(i, n_c[-1], n_c_mlr[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "226625 220267\n",
      "226625 318769\n",
      " 0  67241  65658 29.671 29.808 2.35%\n",
      " 1  13001  11841  5.737  5.376 8.92%\n",
      " 2    358    299  0.158  0.136 16.48%\n",
      " 3   1754   1675  0.774  0.760 4.50%\n",
      " 4   7076   6878  3.122  3.123 2.80%\n",
      " 5   9692   9459  4.277  4.294 2.40%\n",
      " 6  15021  14655  6.628  6.653 2.44%\n",
      " 7  17435  16977  7.693  7.707 2.63%\n",
      " 8  17334  16885  7.649  7.666 2.59%\n",
      " 9  16266  15867  7.177  7.204 2.45%\n",
      "10  15046  14690  6.639  6.669 2.37%\n",
      "11  13090  12813  5.776  5.817 2.12%\n",
      "12  11203  10959  4.943  4.975 2.18%\n",
      "13  14636  14336  6.458  6.508 2.05%\n",
      "14   5569   5429  2.457  2.465 2.51%\n",
      "15   1903   1846  0.840  0.838 3.00%\n"
     ]
    }
   ],
   "source": [
    "total_n_c = np.sum(n_c)\n",
    "total_n_c_mlr = np.sum(n_c_mlr)\n",
    "print(total_n_c, total_n_c_mlr)\n",
    "print(len(pwl)-np.sum(np.isnan(pwl[\"category\"])), len(pwl)-np.sum(np.isnan(pwl[\"category\"][cond_mlr])))\n",
    "for i in range(16):\n",
    "    print(\"{:2d} {:6d} {:6d} {:6.3f} {:6.3f} {:.2%}\".format(i, \n",
    "                                                     n_c[i], \n",
    "                                                     n_c_mlr[i],\n",
    "                                                     n_c[i]/total_n_c*100, \n",
    "                                                     n_c_mlr[i]/total_n_c_mlr*100,\n",
    "                                                     (n_c[i]-n_c_mlr[i])/n_c[i]\n",
    "         ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0  65658\n",
      " 1  11841\n",
      " 2    299\n",
      " 3   1675\n",
      " 4   6878\n",
      " 5   9459\n",
      " 6  14655\n",
      " 7  16977\n",
      " 8  16885\n",
      " 9  15867\n",
      "10  14690\n",
      "11  12813\n",
      "12  10959\n",
      "13  14336\n",
      "14   5429\n",
      "15   1846\n"
     ]
    }
   ],
   "source": [
    "for i in range(16):\n",
    "    print(\"{:2d} {:6d}\".format(i, n_c_mlr[i] ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fractions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the full catalogue of galaxies and work out the fraction of matched LOFAR. It will be necessary to work in the restricted area with both the pw and the lofar matched catalogue.\n",
    "The fractions could be corrected if the relative fractions of sources of each type change between the full area and the restricted area for the LOFAR matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "field = Field(170.0, 190.0, 46.8, 55.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined = Table.read(\"pw.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined = field.filter_catalogue(combined, \n",
    "                               colnames=(\"ra\", \"dec\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"colour\"] = combined[\"i\"] - combined[\"W1mag\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "colour_limits = [0.0, 0.5, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.5, 4.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_panstarrs = (~np.isnan(combined[\"i\"]) & np.isnan(combined[\"W1mag\"])) # Sources with only i-band\n",
    "combined_wise =(np.isnan(combined[\"i\"]) & ~np.isnan(combined[\"W1mag\"])) # Sources with only W1-band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jsm/programs/soft/anaconda3/lib/python3.6/site-packages/astropy/table/column.py:954: RuntimeWarning: invalid value encountered in less\n",
      "  return getattr(self.data, op)(other)\n",
      "/home/jsm/programs/soft/anaconda3/lib/python3.6/site-packages/astropy/table/column.py:954: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  return getattr(self.data, op)(other)\n"
     ]
    }
   ],
   "source": [
    "# Start with the W1-only, i-only and \"less than lower colour\" bins\n",
    "colour_bin_def = [{\"name\":\"only W1\", \"condition\": combined_wise},\n",
    "                  {\"name\":\"only i\", \"condition\": combined_panstarrs},\n",
    "                  {\"name\":\"-inf to {}\".format(colour_limits[0]), \n",
    "                   \"condition\": (combined[\"colour\"] < colour_limits[0])}]\n",
    "\n",
    "# Get the colour bins\n",
    "for i in range(len(colour_limits)-1):\n",
    "    name = \"{} to {}\".format(colour_limits[i], colour_limits[i+1])\n",
    "    condition = ((combined[\"colour\"] >= colour_limits[i]) & \n",
    "                 (combined[\"colour\"] < colour_limits[i+1]))\n",
    "    colour_bin_def.append({\"name\":name, \"condition\":condition})\n",
    "\n",
    "# Add the \"more than higher colour\" bin\n",
    "colour_bin_def.append({\"name\":\"{} to inf\".format(colour_limits[-1]), \n",
    "                       \"condition\": (combined[\"colour\"] >= colour_limits[-1])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"category\"] = np.nan\n",
    "for i in range(len(colour_bin_def)):\n",
    "    combined[\"category\"][colour_bin_def[i][\"condition\"]] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers_combined_bins = np.array([np.sum(a[\"condition\"]) for a in colour_bin_def])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 669839, 1778426,  123221,   86953,  127155,   91489,  106076,\n",
       "        112638,  109949,   98183,   81055,   61913,   44390,   46507,\n",
       "         14347,    5536])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers_combined_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "pwlf = field.filter_catalogue(pwl[cond_mlr], \n",
    "                               colnames=(\"ra\", \"dec\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jsm/programs/soft/anaconda3/lib/python3.6/site-packages/astropy/table/column.py:954: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  return getattr(self.data, op)(other)\n",
      "/home/jsm/programs/soft/anaconda3/lib/python3.6/site-packages/astropy/table/column.py:954: RuntimeWarning: invalid value encountered in less_equal\n",
      "  return getattr(self.data, op)(other)\n"
     ]
    }
   ],
   "source": [
    "pwlf_all = field.filter_catalogue(pwl, \n",
    "                               colnames=(\"ra\", \"dec\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jsm/programs/soft/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py:4033: RuntimeWarning: All-NaN slice encountered\n",
      "  r = func(a, **kwargs)\n",
      "/home/jsm/programs/soft/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:664: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedColumn.\n",
      "  a.partition(kth, axis=axis, kind=kind, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0    nan  20066  669839  0.030 +/-  0.000\n",
      " 1    nan   4091 1778426  0.002 +/-  0.000\n",
      " 2  -0.66    101  123221  0.001 +/-  0.000\n",
      " 3   0.26    543   86953  0.006 +/-  0.000\n",
      " 4   0.78   2358  127155  0.019 +/-  0.000\n",
      " 5   1.13   3351   91489  0.037 +/-  0.001\n",
      " 6   1.38   4803  106076  0.045 +/-  0.001\n",
      " 7   1.63   5375  112638  0.048 +/-  0.001\n",
      " 8   1.87   5174  109949  0.047 +/-  0.001\n",
      " 9   2.12   4659   98183  0.047 +/-  0.001\n",
      "10   2.37   4485   81055  0.055 +/-  0.001\n",
      "11   2.62   3915   61913  0.063 +/-  0.001\n",
      "12   2.86   3382   44390  0.076 +/-  0.001\n",
      "13   3.19   4501   46507  0.097 +/-  0.001\n",
      "14   3.67   1715   14347  0.120 +/-  0.003\n",
      "15   4.31    555    5536  0.100 +/-  0.004\n"
     ]
    }
   ],
   "source": [
    "lofar_c, pw_c, lofar_c_all = [], [], []\n",
    "avg_colour = []\n",
    "for i in range(16):\n",
    "    lofar_c.append(np.sum(pwlf[\"category\"] == i))\n",
    "    lofar_c_all.append(np.sum(pwlf_all[\"category\"] == i))\n",
    "    pw_c.append(np.sum(combined[\"category\"] == i))\n",
    "    avg_colour.append(np.nanmedian(combined[\"colour\"][combined[\"category\"] == i]))\n",
    "    print(\"{:2d} {:6.2f} {:6d} {:7d} {:6.3f} +/- {:6.3f}\".format(i, avg_colour[-1],\n",
    "                                                                 lofar_c_all[-1], pw_c[-1], \n",
    "                                             lofar_c_all[-1]/pw_c[-1], np.sqrt(lofar_c_all[-1])/pw_c[-1]))\n",
    "    #print(\"{:2d} {:6d} {:7d} {:5.3f}\".format(i, lofar_c[-1], pw_c[-1], lofar_c[-1]/pw_c[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7933275842582065"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.array(lofar_c_all)/np.array(pw_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.019415478133624833"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.array(lofar_c_all))/np.sum(np.array(pw_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08960027028857859"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pwl)/len(combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2639294128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lc = np.array(lofar_c_all)\n",
    "pc = np.array(pw_c)\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (6.64, 6.64*0.74)\n",
    "plt.rcParams[\"figure.dpi\"] = 100\n",
    "plt.rcParams['lines.linewidth'] = 1.75\n",
    "plt.rcParams['lines.markersize'] = 8.0\n",
    "plt.rcParams['lines.markeredgewidth'] = 0.75\n",
    "plt.rcParams['font.size'] = 18.0\n",
    "plt.rcParams['font.family'] = \"serif\"\n",
    "plt.rcParams['font.serif'] = \"CM\"\n",
    "plt.rcParams['xtick.labelsize'] = 'small'\n",
    "plt.rcParams['ytick.labelsize'] = 'small'\n",
    "plt.rcParams['xtick.major.width'] = 1.0\n",
    "plt.rcParams['xtick.major.size'] = 8\n",
    "plt.rcParams['ytick.major.width'] = 1.0\n",
    "plt.rcParams['ytick.major.size'] = 8\n",
    "plt.rcParams['xtick.minor.width'] = 1.0\n",
    "plt.rcParams['xtick.minor.size'] = 4\n",
    "plt.rcParams['ytick.minor.width'] = 1.0\n",
    "plt.rcParams['ytick.minor.size'] = 4\n",
    "plt.rcParams['axes.linewidth'] = 1.5\n",
    "plt.rcParams['legend.fontsize'] = 'small'\n",
    "plt.rcParams['legend.numpoints'] = 1\n",
    "plt.rcParams['legend.labelspacing'] = 0.4\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "\n",
    "cm_subsection = linspace(0., 1., 14) \n",
    "colors = [ cm.jet(x) for x in cm_subsection ]\n",
    "\n",
    "# i-only\n",
    "plot([0,0], [0,lc[1]/pc[1]*100], marker=\",\", ls=\"-\", color=\"k\")\n",
    "scatter([0], lc[1]/pc[1]*100, s=lc[1]/10, c=\"k\")\n",
    "plot([0], lc[1]/pc[1]*100, marker=\".\", ls=\"\", color=\"w\", ms=2)\n",
    "\n",
    "# w1-only\n",
    "plot([15,15], [0,lc[0]/pc[0]*100], marker=\",\", ls=\"-\", color=\"k\")\n",
    "scatter([15], lc[0]/pc[0]*100, s=lc[0]/10, c=\"k\")\n",
    "plot([15], lc[0]/pc[0]*100, marker=\".\", ls=\"\", color=\"w\", ms=2)\n",
    "\n",
    "# colours\n",
    "for i in range(14):\n",
    "    plot([i+1,i+1], [0,lc[i+2]/pc[i+2]*100], marker=\",\", ls=\"-\", color=colors[i])\n",
    "    scatter([i+1], lc[i+2]/pc[i+2]*100, s=lc[i+2]/10, c=colors[i])\n",
    "    if i == 0:\n",
    "        plot([i+1], lc[i+2]/pc[i+2]*100, marker=\".\", ls=\"\", color=\"w\", ms=1)\n",
    "    else:\n",
    "        plot([i+1], lc[i+2]/pc[i+2]*100, marker=\".\", ls=\"\", color=\"w\", ms=2)\n",
    "\n",
    "#text(0.66, 0.971, \"0.639\")\n",
    "ylim([0., 14.])\n",
    "xlim([-0.5, 15.5])\n",
    "#legend(fontsize=\"small\")\n",
    "xlabel(\"Colour category\")\n",
    "ylabel(\"Fraction of galaxies detected by LoTSS\\n(per cent)\")\n",
    "\n",
    "\n",
    "xt = [\"$i$ only\",\n",
    "       \"$(-\\infty, 0.0)$\", \n",
    "       \"$[0.0, 0.5)$\",\n",
    "       \"$[0.5, 1.0)$\",\n",
    "       \"$[1.0, 1.25)$\",\n",
    "       \"$[1.25, 1.5)$\",\n",
    "       \"$[1.5, 1.75)$\",\n",
    "       \"$[1.75, 2.0)$\",\n",
    "       \"$[2.0, 2.25)$\",\n",
    "       \"$[2.25, 2.5)$\",\n",
    "       \"$[2.5, 2.75)$\",\n",
    "       \"$[2.75, 3.0)$\",\n",
    "       \"$[3.0, 3.5)$\",\n",
    "       \"$[3.5, 4.0)$\",\n",
    "       \"$[4.0, \\infty)$\", \n",
    "     \"$W1$ only\"]\n",
    "xticks(np.arange(16), xt, rotation=90)\n",
    "\n",
    "subplots_adjust(left=0.2, \n",
    "                bottom=0.3, \n",
    "                right=0.95, \n",
    "                top=0.9,\n",
    "                wspace=0.4, \n",
    "                hspace=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d40bf5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (6.64, 6.64*0.74)\n",
    "plt.rcParams[\"figure.dpi\"] = 100\n",
    "plt.rcParams['lines.linewidth'] = 1.75\n",
    "plt.rcParams['lines.markersize'] = 8.0\n",
    "plt.rcParams['lines.markeredgewidth'] = 0.75\n",
    "plt.rcParams['font.size'] = 18.0\n",
    "plt.rcParams['font.family'] = \"serif\"\n",
    "plt.rcParams['font.serif'] = \"CM\"\n",
    "plt.rcParams['xtick.labelsize'] = 'small'\n",
    "plt.rcParams['ytick.labelsize'] = 'small'\n",
    "plt.rcParams['xtick.major.width'] = 1.0\n",
    "plt.rcParams['xtick.major.size'] = 8\n",
    "plt.rcParams['ytick.major.width'] = 1.0\n",
    "plt.rcParams['ytick.major.size'] = 8\n",
    "plt.rcParams['xtick.minor.width'] = 1.0\n",
    "plt.rcParams['xtick.minor.size'] = 4\n",
    "plt.rcParams['ytick.minor.width'] = 1.0\n",
    "plt.rcParams['ytick.minor.size'] = 4\n",
    "plt.rcParams['axes.linewidth'] = 1.5\n",
    "plt.rcParams['legend.fontsize'] = 'small'\n",
    "plt.rcParams['legend.numpoints'] = 1\n",
    "plt.rcParams['legend.labelspacing'] = 0.4\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "\n",
    "cm_subsection = linspace(0., 1., 14) \n",
    "colors = [ cm.jet(x) for x in cm_subsection ]\n",
    "\n",
    "# # i-only\n",
    "# plot([0,0], [0,lc[1]/pc[1]*100], marker=\",\", ls=\"-\", color=\"k\")\n",
    "# scatter([0], lc[1]/pc[1]*100, s=lc[1]/10, c=\"k\")\n",
    "# plot([0], lc[1]/pc[1]*100, marker=\".\", ls=\"\", color=\"w\", ms=2)\n",
    "\n",
    "# # w1-only\n",
    "# plot([15,15], [0,lc[0]/pc[0]*100], marker=\",\", ls=\"-\", color=\"k\")\n",
    "# scatter([15], lc[0]/pc[0]*100, s=lc[0]/10, c=\"k\")\n",
    "# plot([15], lc[0]/pc[0]*100, marker=\".\", ls=\"\", color=\"w\", ms=2)\n",
    "\n",
    "# colours\n",
    "for i in range(14):\n",
    "    #plot([avg_colour[i+2],avg_colour[i+2]], [0,lc[i+2]/pc[i+2]*100], marker=\",\", ls=\"-\", color=colors[i])\n",
    "    scatter(avg_colour[i+2], lc[i+2]/pc[i+2]*100, s=lc[i+2]/10, c=colors[i])\n",
    "    if i == 0:\n",
    "        plot(avg_colour[i+2], lc[i+2]/pc[i+2]*100, marker=\".\", ls=\"\", color=\"w\", ms=1)\n",
    "    else:\n",
    "        plot(avg_colour[i+2], lc[i+2]/pc[i+2]*100, marker=\".\", ls=\"\", color=\"w\", ms=2)\n",
    "\n",
    "#text(0.66, 0.971, \"0.639\")\n",
    "ylim([0., 14.])\n",
    "#xlim([-0.5, 13.5])\n",
    "#legend(fontsize=\"small\")\n",
    "xlabel(\"$i-W1$ (magnitude)\")\n",
    "ylabel(\"Fraction of galaxies detected\\nby LoTSS (per cent)\")\n",
    "\n",
    "\n",
    "# xt = [\"$(-\\infty, 0.0)$\", \n",
    "#        \"$[0.0, 0.5)$\",\n",
    "#        \"$[0.5, 1.0)$\",\n",
    "#        \"$[1.0, 1.25)$\",\n",
    "#        \"$[1.25, 1.5)$\",\n",
    "#        \"$[1.5, 1.75)$\",\n",
    "#        \"$[1.75, 2.0)$\",\n",
    "#        \"$[2.0, 2.25)$\",\n",
    "#        \"$[2.25, 2.5)$\",\n",
    "#        \"$[2.5, 2.75)$\",\n",
    "#        \"$[2.75, 3.0)$\",\n",
    "#        \"$[3.0, 3.5)$\",\n",
    "#        \"$[3.5, 4.0)$\",\n",
    "#        \"$[4.0, \\infty)$\"]\n",
    "# xticks(np.arange(14), xt, rotation=90)\n",
    "\n",
    "# subplots_adjust(left=0.2, \n",
    "#                 bottom=0.1, \n",
    "#                 right=0.95, \n",
    "#                 top=0.9,\n",
    "#                 wspace=0.4, \n",
    "#                 hspace=0.2)\n",
    "subplots_adjust(left=0.15, \n",
    "                bottom=0.15, \n",
    "                right=0.95, \n",
    "                top=0.92,\n",
    "                wspace=0.4, \n",
    "                hspace=0.2)\n",
    "\n",
    "\n",
    "save = True\n",
    "if save:\n",
    "    plt.savefig(\"idata/fraction.png\")\n",
    "    plt.savefig(\"idata/fraction.svg\")\n",
    "    plt.savefig(\"idata/fraction.pdf\")\n",
    "    plt.savefig(\"idata/fraction_high.png\", dpi=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the additional columns for the types of matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "threshold_sel = 0.639"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jsm/programs/soft/anaconda3/lib/python3.6/site-packages/astropy/table/column.py:954: RuntimeWarning: invalid value encountered in less\n",
      "  return getattr(self.data, op)(other)\n"
     ]
    }
   ],
   "source": [
    "cond_mlr = (pwl['ID_flag'] == 1) & (pwl['Maj'] < 30.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pwlaux = pwl[cond_mlr].filled()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matched sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pwlaux_match = pwlaux[~np.isnan(pwlaux['ML_LR'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "214988"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pwlaux_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "213797\n"
     ]
    }
   ],
   "source": [
    "cond_match = (\n",
    "    ~np.isnan(pwlaux_match['lr']) &\n",
    "    (pwlaux_match['lr'] >= threshold_sel) &\n",
    "    (\n",
    "        (pwlaux_match[\"AllWISE_input\"] != \"N/A\") |\n",
    "         ~np.isnan(pwlaux_match['objID_input'])\n",
    "    ) &\n",
    "    (\n",
    "        ( \n",
    "            (pwlaux_match[\"AllWISE\"] == pwlaux_match[\"AllWISE_input\"]) &\n",
    "            (pwlaux_match[\"objID\"] == pwlaux_match[\"objID_input\"]) &\n",
    "            ~np.isnan(pwlaux_match[\"objID\"]) &\n",
    "            (pwlaux_match[\"AllWISE\"] != \"N/A\")\n",
    "        ) |\n",
    "        ( \n",
    "            (pwlaux_match[\"AllWISE\"] == pwlaux_match[\"AllWISE_input\"]) &\n",
    "            np.isnan(pwlaux_match[\"objID\"])\n",
    "        ) |\n",
    "        ( \n",
    "            (pwlaux_match[\"AllWISE\"] == \"N/A\") &\n",
    "            (pwlaux_match[\"objID\"] == pwlaux_match[\"objID_input\"])\n",
    "        )\n",
    "    )\n",
    "        \n",
    ")\n",
    "m_m = np.sum(cond_match)\n",
    "print(m_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196\n"
     ]
    }
   ],
   "source": [
    "cond_diffmatch = (\n",
    "    ~np.isnan(pwlaux_match['lr']) &\n",
    "    (pwlaux_match['lr'] >= threshold_sel) &\n",
    "    (\n",
    "        (pwlaux_match[\"AllWISE_input\"] != \"N/A\") |\n",
    "         ~np.isnan(pwlaux_match['objID_input'])\n",
    "    ) &\n",
    "    (\n",
    "        ( \n",
    "            (pwlaux_match[\"AllWISE\"] != pwlaux_match[\"AllWISE_input\"]) |\n",
    "            (pwlaux_match[\"objID\"] != pwlaux_match[\"objID_input\"])\n",
    "        ) \n",
    "    )\n",
    "\n",
    ")\n",
    "m_dm = np.sum(cond_diffmatch)\n",
    "print(m_dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "998\n"
     ]
    }
   ],
   "source": [
    "cond_nomatch = (\n",
    "    np.isnan(pwlaux_match['lr']) |\n",
    "    (pwlaux_match['lr'] < threshold_sel)\n",
    ")\n",
    "m_nm = np.sum(cond_nomatch)\n",
    "print(m_nm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "214991"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_nm + m_dm + m_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "218603"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "217070+512+1021"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Non-matched sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pwlaux_nomatch = pwlaux[np.isnan(pwlaux['ML_LR'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84771"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pwlaux_nomatch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6307\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jsm/programs/soft/anaconda3/lib/python3.6/site-packages/astropy/table/column.py:954: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  return getattr(self.data, op)(other)\n"
     ]
    }
   ],
   "source": [
    "cond2_match = (\n",
    "    ~np.isnan(pwlaux_nomatch['lr']) &\n",
    "    (pwlaux_nomatch['lr'] >= threshold_sel)   \n",
    ")\n",
    "m2_m = np.sum(cond2_match)\n",
    "print(m2_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78464\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jsm/programs/soft/anaconda3/lib/python3.6/site-packages/astropy/table/column.py:954: RuntimeWarning: invalid value encountered in less\n",
      "  return getattr(self.data, op)(other)\n"
     ]
    }
   ],
   "source": [
    "cond2_nomatch = (\n",
    "    np.isnan(pwlaux_nomatch['lr']) |\n",
    "    (pwlaux_nomatch['lr'] < threshold_sel)\n",
    ")\n",
    "m2_nm = np.sum(cond2_nomatch)\n",
    "print(m2_nm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84771"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2_nm + m2_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "299762"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2_nm + m2_m + m_nm + m_dm + m_m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diagnostic columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pwl['match_code'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pwl['match_code'][np.isin(pwl[\"Source_Name\"], pwl[cond_mlr & ~np.isnan(pwl['ML_LR'])][cond_diffmatch][\"Source_Name\"])] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pwl['match_code'][np.isin(pwl[\"Source_Name\"], pwl[cond_mlr & ~np.isnan(pwl['ML_LR'])][cond_match][\"Source_Name\"])]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pwl['match_code'][np.isin(pwl[\"Source_Name\"], pwl[cond_mlr & ~np.isnan(pwl['ML_LR'])][cond_nomatch][\"Source_Name\"])] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pwl['match_code'][np.isin(pwl[\"Source_Name\"], pwl[cond_mlr & np.isnan(pwl['ML_LR'])][cond2_match][\"Source_Name\"])] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pwl['match_code'][np.isin(pwl[\"Source_Name\"], pwl[cond_mlr & np.isnan(pwl['ML_LR'])][cond2_nomatch][\"Source_Name\"])] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 19010\n",
      "1 213797\n",
      "2 193\n",
      "3 998\n",
      "4 6307\n",
      "5 78464\n"
     ]
    }
   ],
   "source": [
    "for i in range(6):\n",
    "    print(i, np.sum(pwl['match_code'] == i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Study the 3 repeated sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3 sources that are in group 1 and 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pwl['match_code2'] = 0\n",
    "pwl['match_code2'][np.isin(pwl[\"Source_Name\"], pwl[cond_mlr & ~np.isnan(pwl['ML_LR'])][cond_match][\"Source_Name\"])]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=3&gt;\n",
       "<table id=\"table140279022630392\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Source_Name</th><th>AllWISE</th><th>AllWISE_input</th><th>objID</th><th>objID_input</th></tr></thead>\n",
       "<thead><tr><th>str88</th><th>str19</th><th>str19</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>ILTJ105150.16+472306.9</td><td>N/A</td><td>J105150.11+472306.4</td><td>164861629587942860</td><td>164861629587942860</td></tr>\n",
       "<tr><td>ILTJ124446.75+545233.0</td><td>N/A</td><td>J124446.79+545232.8</td><td>173851911952891597</td><td>173851911952891597</td></tr>\n",
       "<tr><td>ILTJ131700.45+490748.4</td><td>N/A</td><td>J131700.38+490748.1</td><td>166951992516796699</td><td>166951992516796699</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=3>\n",
       "     Source_Name       AllWISE ...       objID           objID_input    \n",
       "        str88           str19  ...       int64              int64       \n",
       "---------------------- ------- ... ------------------ ------------------\n",
       "ILTJ105150.16+472306.9     N/A ... 164861629587942860 164861629587942860\n",
       "ILTJ124446.75+545233.0     N/A ... 173851911952891597 173851911952891597\n",
       "ILTJ131700.45+490748.4     N/A ... 166951992516796699 166951992516796699"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pwl[np.isin(pwl[\"Source_Name\"], pwl[cond_mlr & ~np.isnan(pwl['ML_LR'])][cond_diffmatch][\"Source_Name\"])]\n",
    "t[t['match_code2'] != 0][['Source_Name', \"AllWISE\", \"AllWISE_input\", \"objID\", \"objID_input\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=1&gt;\n",
       "<table id=\"table140279023473496\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Source_Name</th><th>RA</th><th>E_RA</th><th>DEC</th><th>E_DEC</th><th>Peak_flux</th><th>E_Peak_flux</th><th>Total_flux</th><th>E_Total_flux</th><th>Maj</th><th>E_Maj</th><th>Min</th><th>E_Min</th><th>PA</th><th>E_PA</th><th>Isl_rms</th><th>S_Code</th><th>Mosaic_ID</th><th>AllWISE</th><th>objID</th><th>ra</th><th>dec</th><th>raErr</th><th>decErr</th><th>W1mag</th><th>W1magErr</th><th>i</th><th>iErr</th><th>colour</th><th>category</th><th>lr</th><th>lr_dist</th><th>AllWISE_input</th><th>objID_input</th><th>ML_LR</th><th>ID_flag</th><th>ID_name</th><th>ID_ra</th><th>ID_dec</th><th>LGZ_Size</th><th>LGZ_Width</th><th>LGZ_PA</th><th>LGZ_Assoc</th><th>LGZ_Assoc_Qual</th><th>LGZ_ID_Qual</th><th>match_code</th><th>match_code2</th></tr></thead>\n",
       "<thead><tr><th>str88</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str1</th><th>str11</th><th>str19</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str19</th><th>int64</th><th>float64</th><th>int64</th><th>str27</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>ILTJ105150.16+472306.9</td><td>162.958997743</td><td>0.144574540402</td><td>47.3852535063</td><td>0.135700150377</td><td>1.0400645428</td><td>0.0620059670136</td><td>0.88200388277</td><td>0.113680644158</td><td>5.65331936162</td><td>0.345452470339</td><td>5.40066860218</td><td>0.314129798109</td><td>113.261924388</td><td>53.196333999</td><td>0.0649695139145</td><td>S</td><td>P3Hetdex16</td><td>N/A</td><td>164861629587942860</td><td>162.95880637</td><td>47.38512028</td><td>0.0451599992812</td><td>0.0261400006711</td><td>nan</td><td>nan</td><td>18.5082593446</td><td>0.0200500003994</td><td>nan</td><td>1.0</td><td>67.0586944523</td><td>0.669040319887</td><td>J105150.11+472306.4</td><td>164861629587942860</td><td>7.67859626092</td><td>1</td><td>PSO J105150.107+472306.380</td><td>162.95880637</td><td>47.38512028</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1</td><td>1</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=1>\n",
       "     Source_Name             RA           E_RA      ... match_code match_code2\n",
       "        str88             float64       float64     ...   int64       int64   \n",
       "---------------------- ------------- -------------- ... ---------- -----------\n",
       "ILTJ105150.16+472306.9 162.958997743 0.144574540402 ...          1           1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwl[pwl[\"objID\"] == 164861629587942860]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse changes in the matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102267 N/A\n",
      "4 J132633.11+484745.9\n",
      "2 J132959.54+471558.1\n",
      "2 J140250.06+491021.4\n",
      "2 J113321.11+470145.2\n",
      "2 J113234.86+530404.6\n",
      "2 J114607.72+472940.5\n",
      "2 J120601.09+472843.3\n",
      "2 J150913.19+523142.4\n",
      "2 J120534.28+503221.7\n"
     ]
    }
   ],
   "source": [
    "most_common(pwl[\"AllWISE_input\"].filled(), n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save data for tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(~np.isnan(pwl[\"colour\"]) & (pwl[\"match_code\"] == 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f954133b4e0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zcuTIGm1j0KBBLF++PDgV1LRpU/7yl7+QlZW1T7lWrVrxzDPPcOWVVwanHO+77z6OPbby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\nf/mFuKMl/zhtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95414e9dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplot(1,2,1)\n",
    "val, bins, _ = hist(pwl[\"colour\"][~np.isnan(pwl[\"colour\"]) & (pwl[\"match_code\"] != 0)], \n",
    "                    bins=50, normed=True, alpha=0.5, label=\"Total\")\n",
    "val, bins, _ = hist(pwl[\"colour\"][~np.isnan(pwl[\"colour\"]) & (pwl[\"match_code\"] == 4)], \n",
    "                    bins=bins, normed=True, alpha=0.5, label=\"New matches\")\n",
    "xlabel(\"Colour\")\n",
    "ylabel(\"N normed\")\n",
    "legend()\n",
    "subplot(1,2,2)\n",
    "val, bins, _ = hist(pwl[\"colour\"][~np.isnan(pwl[\"colour\"]) & (pwl[\"match_code\"] != 0)], \n",
    "                    bins=bins, normed=True, alpha=0.5, label=\"Total\")\n",
    "val, bins, _ = hist(pwl[\"colour\"][~np.isnan(pwl[\"colour\"]) & (pwl[\"match_code\"] == 2)], \n",
    "                    bins=bins, normed=True, alpha=0.5, label=\"Different match\")\n",
    "xlabel(\"Colour\")\n",
    "ylabel(\"N normed\")\n",
    "legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional description of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "513.417 +/- 678.597; median: 249.663; limits: [1.291, 5209.663]; N=300623 (81490 NaN; 0 masked)\n"
     ]
    }
   ],
   "source": [
    "describe(pwlaux['ML_LR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "559.391 +/- 1009.254; median: 137.720; limits: [0.000, 10404.787]; N=300623 (12224 NaN; 0 masked)\n"
     ]
    }
   ],
   "source": [
    "describe(pwlaux['lr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300623"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pwlaux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6019"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(\n",
    "    (pwlaux[\"AllWISE\"] != pwlaux[\"AllWISE_input\"]) |\n",
    "    (pwlaux[\"objID\"] != pwlaux[\"objID_input\"])  \n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2551"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(\n",
    "    (pwlaux[\"AllWISE\"] != pwlaux[\"AllWISE_input\"]) &\n",
    "    (pwlaux[\"objID\"] != pwlaux[\"objID_input\"])  \n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(\n",
    "    (pwl[cond_mlr].filled()[\"AllWISE\"] != pwl[cond_mlr][\"AllWISE_input\"]) &\n",
    "    (pwl[cond_mlr].filled()[\"objID\"] == pwl[cond_mlr][\"objID_input\"])  \n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "212"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(\n",
    "    (pwl[cond_mlr].filled()[\"AllWISE\"] == pwl[cond_mlr][\"AllWISE_input\"]) &\n",
    "    (pwl[cond_mlr].filled()[\"objID\"] != pwl[cond_mlr][\"objID_input\"])  \n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AllWISE\n",
      "90589 N/A\n",
      "2 J114351.84+534758.6\n",
      "2 J121406.39+462302.4\n",
      "AllWISE_input\n",
      "92739 N/A\n",
      "1 J121404.24+554324.9\n",
      "1 J121404.58+541509.0\n",
      "objID\n",
      "144664 999999\n",
      "2 163352016837856893\n",
      "2 163971633340322080\n",
      "objID_input\n",
      "146362 999999\n",
      "1 167041976433181499\n",
      "1 167041974666874572\n"
     ]
    }
   ],
   "source": [
    "for i in [\"AllWISE\", \"AllWISE_input\", \"objID\", \"objID_input\"]:\n",
    "    print(i)\n",
    "    most_common(pwl[cond_mlr][i].filled(), n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.21493265,  0.04089325,  0.001     ,  0.00569206,  0.02510625,\n",
       "        0.03587855,  0.0513815 ,  0.05742317,  0.05541292,  0.05003769,\n",
       "        0.04789634,  0.04209503,  0.03628279,  0.04824595,  0.01839814,\n",
       "        0.00593242])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q_0_colour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.73660870087729835"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(Q_0_colour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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