AGN statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AGN selections\n",
    "\n",
    "Plots and stats for AGN section of the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle as pcl\n",
    "\n",
    "from scipy.stats import binned_statistic\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.patches as mpatches\n",
    "from matplotlib import rc\n",
    "rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n",
    "rc('text', usetex=True)\n",
    "\n",
    "from palettable.tableau import GreenOrange_12\n",
    "cmap = GreenOrange_12.hex_colors\n",
    "\n",
    "from astropy.cosmology import Planck13, z_at_value\n",
    "import astropy.units as u\n",
    "\n",
    "from methods import normalise_coods\n",
    "from methods import z_distort\n",
    "from methods import factor_h\n",
    "from methods import bhattacharyya\n",
    "from methods import get_protoclusters\n",
    "\n",
    "from overdensity_cylinder import overdensity_cylinder\n",
    "\n",
    "h = 0.673\n",
    "L = 480.279 / h\n",
    "\n",
    "c = 2.97e8 # m s^-1\n",
    "\n",
    "directory = '/lustre/scratch/astro/cl478/protoclusters_data/'\n",
    "# directory = '/cosma/home/dp004/dc-love2/gpfs/protoclusters_data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "redshifts = ['2p07','3p10','3p95','5p03','5p92','6p97','8p22','8p93','9p72']\n",
    "zees = [float(z.replace('p','.')) for z in redshifts]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/pact/cl478/protoclusters/protoclusters/lib/python2.7/site-packages/ipykernel_launcher.py:11: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "gals_sfr = [None] * len(redshifts)\n",
    "gals_mstar = [None] * len(redshifts)\n",
    "\n",
    "for i, (z, zed) in enumerate(zip(redshifts,zees)):  \n",
    "    gals_sfr[i] = z_distort(factor_h(pd.read_csv('%shenriques2015a_z%s_sfr_r200.csv'%(directory,z), \\\n",
    "                              skiprows=122, skipfooter=1, engine='python'), h), zed, L)\n",
    "    \n",
    "    gals_mstar[i] = z_distort(factor_h(pd.read_csv('%shenriques2015a_z%s_stellarMass_r200.csv'%(directory,z), \\\n",
    "                                skiprows=122, skipfooter=1, engine='python'), h), zed, L)\n",
    "    \n",
    "    for gals in [gals_sfr, gals_mstar]:\n",
    "#         print \"Filling in NaN values...\"\n",
    "        gals[i].ix[np.isnan(gals[i]['z0_haloId']), 'z0_haloId'] = -1\n",
    "        gals[i].ix[np.isnan(gals[i]['z0_centralId']), 'z0_centralId'] = -1\n",
    "        gals[i].ix[np.isnan(gals[i]['z0_central_mcrit200']), 'z0_central_mcrit200'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quasar / HzRG accretion limits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quasar limits: 0.00359737971146 0.0179868985573\n",
      "radio limits: 0.001 0.004\n"
     ]
    }
   ],
   "source": [
    "def luminosity_to_accretion(lum, epsilon=0.1):    \n",
    "    acc = lum / epsilon / c**2\n",
    "    acc /= 1.989e30\n",
    "    acc *= (365.25*24*60*60)\n",
    "    acc /= 1e7\n",
    "    return acc\n",
    "\n",
    "quasar_lim = luminosity_to_accretion(2e43)\n",
    "cons_quasar_lim = luminosity_to_accretion(1e44)\n",
    "\n",
    "radio_lim = 0.001\n",
    "cons_radio_lim = 0.004 # .005\n",
    "\n",
    "print \"quasar limits:\", quasar_lim, cons_quasar_lim\n",
    "print \"radio limits:\", radio_lim, cons_radio_lim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def accr_to_lbol(accr, sol=True, epsilon=0.1):\n",
    "    lbol = accr / (365.25*24*60*60) * c**2 * epsilon * 1.989e30 * 1e7\n",
    "    if sol: lbol /= 3.9e33\n",
    "    return lbol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number densities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def H06(x, z=0, hubble_units=0, **kwargs):\n",
    "    \"\"\"\n",
    "    Inputs: xmin, xmax - range along x-axis\n",
    "            z - redshift\n",
    "            *args - optional plotting arguments\n",
    "    \"\"\"\n",
    "\n",
    "    h100=0.7\n",
    "    phi_star=10.**(-4.825) #/h100**3\n",
    "    zref=2.\n",
    "    log10L_star0=13.036  # L_star is in Lsun\n",
    "    kL1=0.632\n",
    "    kL2=-11.76\n",
    "    kL3=-14.25\n",
    "    gamma10=0.417\n",
    "    kg1=-0.623\n",
    "    gamma20=2.174\n",
    "    kg21=1.460\n",
    "    kg22=-0.793\n",
    "    \n",
    "    eps=np.log10((1+z)/(1+zref))\n",
    "    x_star=log10L_star0+kL1*eps+kL2*eps**2+kL3*eps**3\n",
    "    if hubble_units==2: x_star=x_star+2*np.log10(h100)\n",
    "    xx=10.**(x-x_star)\n",
    "    gamma1=gamma10*10.**(eps*kg1)\n",
    "    gamma2=2.*gamma20/(10.**(eps*kg21)+10.**(eps*kg22))\n",
    "    y=phi_star/(xx**gamma1+xx**gamma2)\n",
    "    if hubble_units==1 or hubble_units==2: y=y/h100**3\n",
    "    return y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Wall(z):\n",
    "    return -12.49 + 2.704*z - 1.145*z**2 + 0.1796*z**3 - 0.01019*z**4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.integrate import quad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numbden = np.zeros((2, len(redshifts[:5])))\n",
    "\n",
    "for i, z in enumerate(redshifts[:5]):\n",
    "    numbden[0, i] = np.sum(gals[i]['zn_radioAccretionRate'] >= radio_lim) / L**3  # Mpc^-3\n",
    "    numbden[1, i] = np.sum(gals[i]['zn_quasarAccretionRate'] >= quasar_lim) / L**3  # Mpc^-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/pact/cl478/protoclusters/protoclusters/lib/python2.7/site-packages/ipykernel_launcher.py:27: RuntimeWarning: overflow encountered in double_scalars\n",
      "/mnt/pact/cl478/protoclusters/protoclusters/lib/python2.7/site-packages/ipykernel_launcher.py:24: RuntimeWarning: overflow encountered in double_scalars\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2aab0ede0790>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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yy2rZ3/KT3YSApl2YKWB47ebTOXzsBJOeXczjuRs4LgUMwU/H/nB6Fly63gxA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\nIPgQEVlBEAQf8v8B2prG8X50Yi8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab0efc45d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 4))\n",
    "\n",
    "plt.plot(zees[:5], np.log10(numbden[0]), label='HzRGs');\n",
    "plt.plot(zees[:5], np.log10(numbden[1]), label='QSOs', color='orange');\n",
    "\n",
    "# use cut off equivalent to 1e44 Lbol erg s^-1\n",
    "plt.plot(zees[:5], np.log10([quad(H06, np.log10(1e11 / 3), np.inf, z)[0] for z in zees[:5]]), label='HRH+06', color='orange', linestyle='dashed')\n",
    "\n",
    "plt.legend(frameon=False, prop={'size': 14});\n",
    "plt.xlabel('$z$', size=16)\n",
    "plt.ylabel('$\\mathrm{log_{10}(\\phi \\;\\; Mpc^{-3})}$', size=16)\n",
    "\n",
    "# plt.savefig('output/AGN_number_densities.png', bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average host halo mass\n",
    "\n",
    "must adjust cuts to include lower accretion rate AGN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.001 42.7450437147\n",
      "0.00359737971146 43.3010299957\n"
     ]
    }
   ],
   "source": [
    "print radio_lim, np.log10(accr_to_lbol(radio_lim, sol=False))\n",
    "print quasar_lim, np.log10(accr_to_lbol(quasar_lim, sol=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00018 42.0003162198\n",
      "0.00018 42.0003162198\n"
     ]
    }
   ],
   "source": [
    "radio_lim_all = 1.8e-4\n",
    "quasar_lim_all = 1.8e-4\n",
    "\n",
    "print radio_lim_all, np.log10(accr_to_lbol(radio_lim_all, sol=False))\n",
    "print quasar_lim_all, np.log10(accr_to_lbol(quasar_lim_all, sol=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import binned_statistic\n",
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "my_cmap = plt.cm.viridis\n",
    "my_cmap.set_under('w',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.07\n",
      "3.1\n",
      "3.95\n",
      "5.03\n"
     ]
    },
    {
     "data": {
      "image/png": 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Lxyz3D73nnntw6NAhrFy5MiIhrXV1dbjuuut6vP/cc8+htLQUjz/+OCwWS+BmDxb5B/hG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\nemLA+jOrFQ0YAIAFuTMxKi4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/NWbyafZ39Oq4/BcycwXvhdGVzt6qrnzWdDEh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\nh8NRGVwxczgcjsr4/wYqsFopH+j0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aabb482a4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 8))\n",
    "\n",
    "gs1 = gridspec.GridSpec(15, 2)\n",
    "gs1.update(hspace=0, wspace=0) # bottom = 0.57, top = 1,\n",
    "\n",
    "ax1 = fig.add_subplot(gs1[2:7,0])\n",
    "ax2 = fig.add_subplot(gs1[2:7,1])\n",
    "ax3 = fig.add_subplot(gs1[10:15,0])\n",
    "ax4 = fig.add_subplot(gs1[10:15,1])\n",
    "\n",
    "ax1t = fig.add_subplot(gs1[0:2,0])\n",
    "ax2t = fig.add_subplot(gs1[0:2,1])\n",
    "ax3t = fig.add_subplot(gs1[8:10,0])\n",
    "ax4t = fig.add_subplot(gs1[8:10,1])\n",
    "\n",
    "axes = [ax1,ax2,ax3,ax4]\n",
    "axest = [ax1t,ax2t,ax3t,ax4t]\n",
    "\n",
    "xbins = np.linspace(10,15,51)\n",
    "ybins = np.linspace(42,47,51)\n",
    "\n",
    "for i, (ax, axt, z) in enumerate(zip(axes, axest, zees)):\n",
    "    \n",
    "    print z\n",
    "    \n",
    "    ax.text(0.1, 0.85, '$z = %s$'%z, transform = ax.transAxes, size=15, alpha=0.8)\n",
    "\n",
    "    \n",
    "    # histograms\n",
    "    axt.hist(np.log10(gals[i]['zn_mvir'][gals[i]['zn_radioAccretionRate'] >= radio_lim_all] * 1e10), \n",
    "             alpha=.3, label='HzRG', color='red', bins=xbins)\n",
    "    \n",
    "    axt.hist(np.log10(gals[i]['zn_mvir'][gals[i]['zn_quasarAccretionRate'] >= quasar_lim_all] * 1e10), \n",
    "             alpha=.3, label='Quasar', color='blue', bins=xbins)\n",
    "    \n",
    "    axt.hist(np.log10(gals[i]['zn_mvir'][gals[i]['zn_radioAccretionRate'] >= radio_lim] * 1e10), \n",
    "             histtype='step', color='red', bins=xbins)\n",
    "    \n",
    "    axt.hist(np.log10(gals[i]['zn_mvir'][gals[i]['zn_quasarAccretionRate'] >= quasar_lim] * 1e10), \n",
    "             histtype='step', color='blue', bins=xbins)\n",
    "\n",
    "    axt.set_yscale(\"log\")\n",
    "    \n",
    "    # medians\n",
    "    quasar_stat = binned_statistic(np.log10(accr_to_lbol(gals[i]['zn_quasarAccretionRate'][gals[i]['zn_quasarAccretionRate'] > quasar_lim_all], sol=False)),\n",
    "                                  np.log10(gals[i]['zn_mvir'][gals[i]['zn_quasarAccretionRate'] > quasar_lim_all] * 1e10), statistic='median')\n",
    "\n",
    "    ax.plot(quasar_stat[0], quasar_stat[1][1:] - (quasar_stat[1][1] - quasar_stat[1][0]), \n",
    "            linestyle='dashed', color='white')\n",
    "\n",
    "\n",
    "    radio_stat = binned_statistic(np.log10(accr_to_lbol(gals[i]['zn_radioAccretionRate'][gals[i]['zn_radioAccretionRate'] > radio_lim_all], sol=False)),\n",
    "                                 np.log10(gals[i]['zn_mvir'][gals[i]['zn_radioAccretionRate'] > radio_lim_all] * 1e10), statistic='median')\n",
    "\n",
    "    ax.plot(radio_stat[0], radio_stat[1][1:] - (radio_stat[1][1] - radio_stat[1][0]), \n",
    "            linestyle='-.', color='white')\n",
    "\n",
    "    # 2d hist\n",
    "#     temp_gals = np.array(gals[i][['zn_mvir','zn_quasarAccretionRate']][gals[i]['zn_quasarAccretionRate'] > quasar_lim])\n",
    "#     temp_gals = np.vstack([temp_gals, gals[i][['zn_mvir','zn_radioAccretionRate']][gals[i]['zn_radioAccretionRate'] > radio_lim]])\n",
    "\n",
    "    # combined accretion rate, for all galaxies above either accretion cut\n",
    "    temp_gals = gals[i][['zn_mvir','zn_quasarAccretionRate',\n",
    "         'zn_radioAccretionRate']][(gals[i]['zn_radioAccretionRate'] > radio_lim_all) \\\n",
    "          | (gals[i]['zn_quasarAccretionRate'] > quasar_lim_all)]\n",
    "\n",
    "    temp_gals['accr'] = temp_gals['zn_quasarAccretionRate'] + temp_gals['zn_radioAccretionRate']\n",
    "\n",
    "    \n",
    "    \n",
    "    if i == 0:\n",
    "        p = ax.hist2d(np.log10(temp_gals['zn_mvir'] * 1e10), np.log10(accr_to_lbol(temp_gals['accr'], sol=False)),\n",
    "                   bins=[xbins,ybins], cmap=my_cmap, vmin=1,  norm=LogNorm());\n",
    "    else:\n",
    "        ax.hist2d(np.log10(temp_gals['zn_mvir'] * 1e10), np.log10(accr_to_lbol(temp_gals['accr'], sol=False)),\n",
    "               bins=[xbins,ybins], cmap=my_cmap, vmin=1,  norm=LogNorm());\n",
    "    \n",
    "\n",
    "    ax.set_ylim(42, 46.9);\n",
    "    ax.set_xlim(10.6, 14.3);\n",
    "    axt.set_xlim(10.6, 14.3);\n",
    "    \n",
    "    axt.set_xticklabels([])\n",
    "    \n",
    "    ax.yaxis.set_ticks_position('both')\n",
    "    \n",
    "    ax.tick_params('both', which='both')\n",
    "    axt.set_ylim(1, 5e5)\n",
    "    \n",
    "    ax.hlines(np.log10(accr_to_lbol(radio_lim, sol=False)), 10, 15, color='red', linestyle='dotted', alpha=.5)\n",
    "    ax.hlines(np.log10(accr_to_lbol(quasar_lim, sol=False)), 10, 15, color='blue', linestyle='dotted', alpha=.5)\n",
    "    \n",
    "    \n",
    "# for ax in [ax1,ax2]:\n",
    "#     ax.tick_params(labeltop=True, top=True, labelbottom=False)\n",
    "    \n",
    "for ax in [ax2,ax4,ax2t,ax4t]:\n",
    "    ax.tick_params(labelright=True, labelleft=False)\n",
    "    \n",
    "\n",
    "fig.text(0.41, 0.06, '$\\mathrm{log_{10}} \\, (M \\,/\\, M_{\\odot})$', transform=fig.transFigure, size=15)\n",
    "\n",
    "for ax, axt in zip([ax1,ax3], [ax1t,ax3t]):\n",
    "    ax.set_ylabel('$L_{\\mathrm{bol}} \\,/\\, (\\mathrm{erg \\;\\; s^{-1}})$', size=15)\n",
    "    axt.set_ylabel('$N$', size=15)\n",
    "    \n",
    "    \n",
    "## Legend\n",
    "\n",
    "# Legend 2\n",
    "patchA = mpatches.Patch(color='red',alpha=.3,label='HzRG')\n",
    "patchB = mpatches.Patch(color='blue',alpha=.3,label='Quasar')\n",
    "\n",
    "patch1 = mpatches.Patch(color='lightgrey',label='All')\n",
    "line1 = plt.Line2D((0,1.5),(0,0), color='black', linestyle='solid', linewidth=3, label='Selection')\n",
    "\n",
    "legend = ax4t.legend(handles = [patchB, patchA, patch1, line1], ncol=4, prop={'size': 9}, \n",
    "                     labelspacing=.2, frameon=False, bbox_to_anchor=(1., 0.99)).set_zorder(10)\n",
    "\n",
    "\n",
    "# colorbar\n",
    "cax = fig.add_axes([0.81, 0.14, 0.02, 0.2])\n",
    "fig.colorbar(p[3], cax, orientation = 'vertical') \n",
    "\n",
    "# plt.savefig('output/AGN_luminosities.png', bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Quasar / HzRG distance from protocluster centre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z: 2p07 n(radio): 11541 \n",
      "mean: 4.32 median: 3.77 std: 2.75 \n",
      "\n",
      "z: 3p10 n(radio): 3421 \n",
      "mean: 4.47 median: 3.78 std: 3.02 \n",
      "\n",
      "z: 3p95 n(radio): 690 \n",
      "mean: 4.29 median: 3.54 std: 2.92 \n",
      "\n",
      "z: 5p03 n(radio): 53 \n",
      "mean: 4.44 median: 3.43 std: 3.5 \n",
      "\n",
      "z: 5p92 n(radio): 5 \n",
      "mean: 6.01 median: 5.44 std: 3.26 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "gals = gals_sfr\n",
    "\n",
    "for i, redshift in enumerate(redshifts[:5]):\n",
    "    \n",
    "    agn = gals[i][gals[i]['zn_radioAccretionRate'] >= radio_lim].reset_index(drop=True) # find all agn    \n",
    "    agn = agn[agn['z0_central_mcrit200'] >= 1e4].reset_index(drop=True) # agn in protoclusters\n",
    "    \n",
    "    distances = [None] * len(agn)\n",
    "\n",
    "    for idx, q in agn.iterrows():\n",
    "        \n",
    "        # subset coordinates of descendant halo\n",
    "        subset_gals = gals[i][gals[i]['z0_centralId'] == q['z0_centralId']]\n",
    "        \n",
    "        coods = normalise_coods(np.array(subset_gals[['zn_x','zn_y','zn_z']]), L)  # normalise coordinates\n",
    "        center = np.median(coods,axis=0) # find descendant halo center\n",
    "        quasar_location = coods[subset_gals['zn_galaxyId'] == q['zn_galaxyId']]\n",
    "        \n",
    "        # distance between agn and descendant halo progenitor centre\n",
    "        distances[idx] = float(np.sqrt(np.sum((quasar_location - center)**2, axis=1)))  \n",
    "    \n",
    "    \n",
    "    print \"z:\", redshift, \"n(radio):\", len(agn), \"\\nmean:\", round(np.mean(distances),2),\\\n",
    "    \"median:\", round(np.median(distances),2), \"std:\", round(np.std(distances),2),\"\\n\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z: 2p07 n(quasar): 20287 \n",
      "mean: 5.27 median: 4.74 std: 3.07 \n",
      "\n",
      "z: 3p10 n(quasar): 14324 \n",
      "mean: 5.57 median: 5.01 std: 3.23 \n",
      "\n",
      "z: 3p95 n(quasar): 7183 \n",
      "mean: 5.68 median: 5.05 std: 3.37 \n",
      "\n",
      "z: 5p03 n(quasar): 2033 \n",
      "mean: 5.74 median: 5.1 std: 3.55 \n",
      "\n",
      "z: 5p92 n(quasar): 561 \n",
      "mean: 5.79 median: 4.99 std: 3.66 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "gals = gals_sfr\n",
    "\n",
    "for i, redshift in enumerate(redshifts[:5]):\n",
    "    \n",
    "    agn = gals[i][gals[i]['zn_quasarAccretionRate'] >= radio_lim].reset_index(drop=True) # find all agn    \n",
    "    agn = agn[agn['z0_central_mcrit200'] >= 1e4].reset_index(drop=True) # agn in protoclusters\n",
    "    \n",
    "    distances = [None] * len(agn)\n",
    "\n",
    "    for idx, q in agn.iterrows():\n",
    "        \n",
    "        # subset coordinates of descendant halo\n",
    "        subset_gals = gals[i][gals[i]['z0_centralId'] == q['z0_centralId']]\n",
    "        \n",
    "        coods = normalise_coods(np.array(subset_gals[['zn_x','zn_y','zn_z']]), L)  # normalise coordinates\n",
    "        center = np.median(coods,axis=0) # find descendant halo center\n",
    "        quasar_location = coods[subset_gals['zn_galaxyId'] == q['zn_galaxyId']]\n",
    "        \n",
    "        # distance between agn and descendant halo progenitor centre\n",
    "        distances[idx] = float(np.sqrt(np.sum((quasar_location - center)**2, axis=1)))  \n",
    "    \n",
    "    \n",
    "    print \"z:\", redshift, \"n(quasar):\", len(agn), \"\\nmean:\", round(np.mean(distances),2),\\\n",
    "    \"median:\", round(np.median(distances),2), \"std:\", round(np.std(distances),2),\"\\n\"\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find average galaxy distance from protocluster centre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gals = gals_sfr\n",
    "\n",
    "distances = [None] * len(redshifts)\n",
    "centres = [None] * len(redshifts)\n",
    "\n",
    "for i, redshift in enumerate(redshifts[:1]):\n",
    "    \n",
    "#     clusters = pd.unique(gals[i][gals[i]['z0_central_mcrit200'] > 1e4]['z0_centralId'])\n",
    "    clusters = get_protoclusters(gals[i], L)\n",
    "    \n",
    "#     clusters['coods'] = normalise_coods(np.array(clusters['coods'][[0,1,2]]), L)\n",
    "    \n",
    "    distances[i] = [None] * len(clusters['coods'])\n",
    "    centres[i] = [None] * len(clusters['coods'])\n",
    "\n",
    "    for idx, (clust, c) in enumerate(zip(clusters['clusters'].iterrows(), clusters['coods'].iterrows())):\n",
    "\n",
    "        subset_gals = gals[i][gals[i]['z0_centralId'] == clust[1]['z0_centralId']]  # subset coordinates of descendant halo\n",
    "        coods = normalise_coods(np.array(subset_gals[['zn_x','zn_y','zn_z']]), L)  # normalise coordinates\n",
    "\n",
    "#         center = np.median(coods,axis=0) # find descendant halo center\n",
    "        \n",
    "#         centres[i][idx] = center\n",
    "        distances[i][idx] = np.mean(np.sqrt(np.sum(pow(coods - c[1], 2.),axis=1)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 1009.243234389964,\n",
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       " 1009.242572472225,\n",
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       " 1009.2462714905168,\n",
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       " 1009.2447761424321,\n",
       " 1009.2481993962996,\n",
       " 1009.2509504415262,\n",
       " ...]"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[np.mean(d) for d in distances[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.6087324516083266"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median([np.mean(d) for d in distances[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "!!!!\n",
    "\n",
    "Need to calculate overdensity surrounding quasars in the model..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "R = [3, 6, 8, 10, 14, 20]\n",
    "\n",
    "quasar_stats = {z: {'cstats': {r: None for r in R}, 'clusters': None} for z in zees[:5]}\n",
    "hzrg_stats = {z: {'cstats': {r: None for r in R}, 'clusters': None} for z in zees[:5]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.07\n",
      "3\n",
      "6\n",
      "8\n",
      "10\n",
      "14\n",
      "20\n",
      "3.1\n",
      "3\n",
      "6\n",
      "8\n",
      "10\n",
      "14\n",
      "20\n",
      "3.95\n",
      "3\n",
      "6\n",
      "8\n",
      "10\n",
      "14\n",
      "20\n",
      "5.03\n",
      "3\n",
      "6\n",
      "8\n",
      "10\n",
      "14\n",
      "20\n",
      "5.92\n",
      "3\n",
      "6\n",
      "8\n",
      "10\n",
      "14\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "for i, z in enumerate(zees[:5]):\n",
    "    \n",
    "    print z\n",
    "    \n",
    "    # find all agn\n",
    "    hzrgs = gals[i][gals[i]['zn_radioAccretionRate'] >= radio_lim].reset_index(drop=True) \n",
    "    quasars = gals[i][gals[i]['zn_quasarAccretionRate'] >= quasar_lim].reset_index(drop=True) \n",
    "    \n",
    "    # save cluster mass data\n",
    "    hzrg_stats[z]['clusters'] = hzrgs['z0_central_mcrit200']\n",
    "    quasar_stats[z]['clusters'] = quasars['z0_central_mcrit200']\n",
    "    \n",
    "    for r in R:  # loop through aperture sizes\n",
    "        print r\n",
    "    \n",
    "        quasar_stats[z]['cstats'][r] = overdensity_cylinder(gals_mstar[i], quasars[['zn_x','zn_y','zn_z']],\n",
    "                                                            L=L, R=r, dc=r)\n",
    "        \n",
    "        hzrg_stats[z]['cstats'][r] = overdensity_cylinder(gals_mstar[i], hzrgs[['zn_x','zn_y','zn_z']],\n",
    "                                                          L=L, R=r, dc=r)\n",
    "\n",
    "    \n",
    "pcl.dump([quasar_stats, hzrg_stats], open('agn_stats.p', 'w'))\n",
    "# quasar_stats, hzrg_stats = pcl.load(open('agn_stats.p', 'r'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quasar overdensity / descendant mass plot, figure 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -1.   -0.5   0.    0.5   1.    1.5   2.    2.5   3.    3.5   4.    4.5\n",
      "   5.    5.5   6.    6.5   7.    7.5   8.    8.5   9.    9.5  10.   10.5\n",
      "  11.   11.5  12.   12.5  13.   13.5  14.   14.5  15.   15.5  16.   16.5\n",
      "  17. ]\n",
      "[ -0.75  -0.25   0.25   0.75   1.25   1.75   2.25   2.75   3.25   3.75\n",
      "   4.25   4.75   5.25   5.75   6.25   6.75   7.25   7.75   8.25   8.75\n",
      "   9.25   9.75  10.25  10.75  11.25  11.75  12.25  12.75  13.25  13.75\n",
      "  14.25  14.75  15.25  15.75  16.25  16.75]\n"
     ]
    }
   ],
   "source": [
    "BinLimits = [np.linspace(-1, 39, 21), \n",
    "             np.linspace(-1, 17, 37),\n",
    "             np.linspace(-1, 11, 25)]\n",
    "\n",
    "Bins = [np.linspace(0, 38, 20),\n",
    "        np.linspace(-0.75, 16.75, 36), \n",
    "        np.linspace(-0.75, 10.75, 24)]\n",
    "\n",
    "print BinLimits[1]\n",
    "print Bins[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 14.27765216,  14.41904247,  14.68450769,  14.96018932,\n",
       "        15.20405544,  15.41356672,  15.59442327,  15.75247346,\n",
       "        15.89237027,  16.01763427,  16.13092118,  16.23425669,\n",
       "        16.3292096 ,  16.41701413,  16.49865598,  16.5749334 ,\n",
       "        16.64650099,  16.71390173,  16.7775907 ,  16.83795288])"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def func(x, a, b, c, d, C):\n",
    "    return a * (x[1]+1)**b * (x[0]+1)**c + C\n",
    "\n",
    "\n",
    "def scale(x):\n",
    "    return np.log10(x * 1e3 * 1e10)\n",
    "\n",
    "\n",
    "args=[1.45145241, -1.104172, 2.64782728, 1., 18.53122657]\n",
    "\n",
    "scale(func(np.vstack([Bins[0], [2.07] * len(Bins[0])]), args[0], args[1], args[2], args[3],args[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2.07\n",
      "1 3.1\n",
      "2 3.95\n",
      "3 5.03\n"
     ]
    },
    {
     "data": {
      "image/png": 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VUt1Wy3dWf4np6YWJNgAfboLXvxObV3/7EhSv6fw4rKr86WwTVW4//7R4Rmp+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\nTRT/+h57fv+IsmVfAKBP6oalx0gs3Udi7jGCmIz+SEoVvop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zQ1HYUq3gp7JytjnzMKoU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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aac514bb550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "\n",
    "gs1 = gridspec.GridSpec(32, 26)\n",
    "\n",
    "gs1.update(bottom = 0.57, top = 1, hspace=0, wspace=5.5)\n",
    "\n",
    "ax1 = fig.add_subplot(gs1[0:26,0:8])\n",
    "ax5 = fig.add_subplot(gs1[26:32,0:8])\n",
    "ax2 = fig.add_subplot(gs1[0:26,8:16])\n",
    "ax6 = fig.add_subplot(gs1[26:32,8:16])\n",
    "ax3 = fig.add_subplot(gs1[0:26,16:24])\n",
    "ax7 = fig.add_subplot(gs1[26:32,16:24])\n",
    "ax9 = fig.add_subplot(gs1[16:23,20:24])\n",
    "\n",
    "Bhatta =[[] for i in range(len(zees[:4]))]\n",
    "\n",
    "radii = [6,10,14]\n",
    "\n",
    "for i, z in enumerate(zees[:4]):\n",
    "    print i, z\n",
    "    \n",
    "    for axt, axb, R, binLimits, bins in zip([ax1,ax2,ax3], [ax5,ax6,ax7], radii, BinLimits, Bins):\n",
    "        \n",
    "        cluster_mask = quasar_stats[z]['clusters'] >= 1e4\n",
    "        nan_mask = ~np.isinf(1 / quasar_stats[z]['clusters'])\n",
    "        \n",
    "        # Subplot\n",
    "        phiB, mass = np.histogram(quasar_stats[z]['cstats'][R][~cluster_mask,0] + 1, bins=binLimits, normed=True)\n",
    "        phiA, mass = np.histogram(quasar_stats[z]['cstats'][R][cluster_mask,0] + 1, bins=binLimits, normed=True)\n",
    "        \n",
    "        DB, BC = bhattacharyya(phiA*np.diff(binLimits), phiB*np.diff(binLimits))\n",
    "        \n",
    "        Bhatta[i].append(DB)\n",
    "\n",
    "        # Main Plot\n",
    "        median = binned_statistic(quasar_stats[z]['cstats'][R][nan_mask,0] + 1, \n",
    "                          np.log10(quasar_stats[z]['clusters'][nan_mask] * 1e10),\n",
    "                          bins=binLimits, statistic='median')[0]\n",
    "\n",
    "        count = binned_statistic(quasar_stats[z]['cstats'][R][nan_mask,0] + 1, \n",
    "                                 np.log10(quasar_stats[z]['clusters'][nan_mask] * 1e10),\n",
    "                                 bins=binLimits, statistic='count')[0]\n",
    "\n",
    "        std = binned_statistic(quasar_stats[z]['cstats'][R][nan_mask,0] + 1, \n",
    "                               np.log10(quasar_stats[z]['clusters'][nan_mask] * 1e10),\n",
    "                               bins=binLimits, statistic='std')[0]\n",
    "\n",
    "        cmask = (count > 10)\n",
    "        \n",
    "        if i==0:\n",
    "            axt.fill_between(bins[cmask], median[cmask]+std[cmask], median[cmask]-std[cmask], alpha=0.15, linewidth=0, color=cmap[i*2])\n",
    "\n",
    "        axt.plot(bins[cmask], median[cmask], color=cmap[i*2], label='$z = %.2f$'%z)\n",
    "        \n",
    "        dgal_mask = quasar_stats[z]['cstats'][R][:,0] > max(bins[cmask])\n",
    "        \n",
    "        axt.scatter(quasar_stats[z]['cstats'][R][nan_mask & dgal_mask,0] + 1,\n",
    "            np.log10(quasar_stats[z]['clusters'][nan_mask & dgal_mask] * 1e10),\n",
    "            marker='.', s=8, c=cmap[i], lw=0, label=None)\n",
    "\n",
    "        axb.set_xlim(0, binLimits[-1])\n",
    "        axt.set_xlim(0, binLimits[-1])\n",
    "\n",
    "        axt.hlines(14, -10, 60, linestyle='--', color='grey')\n",
    "        \n",
    "        axt.set_xticklabels([])\n",
    "        \n",
    "        # plot fitted mass\n",
    "        if R == 10:\n",
    "            axt.plot(bins[bins>4.5] + 1, scale(func(np.vstack([bins[bins>4.5], [z] * len(bins[bins>4.5])]), \n",
    "                     args[0],args[1],args[2],args[3],args[4])), color=cmap[i*2], \n",
    "                     linestyle='dashed', alpha=0.6)\n",
    "\n",
    "        axb.step(bins, phiA, color=cmap[i*2], alpha=1)# + i*0.1)\n",
    "        axb.step(bins, phiB, color=cmap[i*2], linestyle='dotted', alpha=1)# + i*0.1)\n",
    "\n",
    "        if(i==0):\n",
    "            axt.text(0.05, 0.92, '$R = D/2 = %s \\; \\mathrm{cMpc}$'%R, transform=axt.transAxes, size=13)\n",
    "\n",
    "\n",
    "# Calculate R = 20 & 3 Bhattacharya distance, but don't plot\n",
    "for i, z in enumerate(zees[:4]):\n",
    "    \n",
    "    for R, switch, binLimits, bins in zip([3,20],[True,False], BinLimits, Bins):\n",
    "        \n",
    "        cluster_mask = quasar_stats[z]['clusters'] >= 1e4\n",
    "        \n",
    "        # Subplot\n",
    "        phiB, mass = np.histogram(quasar_stats[z]['cstats'][R][~cluster_mask,0] + 1, bins=binLimits, normed=True)\n",
    "        phiA, mass = np.histogram(quasar_stats[z]['cstats'][R][cluster_mask,0] + 1, bins=binLimits, normed=True)\n",
    "        \n",
    "        DB, BC = bhattacharyya(phiA*np.diff(binLimits), phiB*np.diff(binLimits))\n",
    "        \n",
    "        if switch:\n",
    "            Bhatta[i] = [DB] + Bhatta[i]\n",
    "        else:\n",
    "            Bhatta[i].append(DB)\n",
    "        \n",
    "\n",
    "for i,z in enumerate(zees[:4]):\n",
    "    ax9.plot([3] + radii + [20], Bhatta[i], color=cmap[i*2])\n",
    "    \n",
    "    \n",
    "ax9.set_xlabel('$R$')\n",
    "ax9.set_ylabel('$D_{B}$')\n",
    "ax9.set_yticks([])\n",
    "\n",
    "# ax9.set_xticklabels([4,6,8,10,12,14,16,18,''])\n",
    "ax9.set_yticklabels(['',0.2,0.3,0.4,0.5,0.6,0.7,''])\n",
    "\n",
    "ax9.vlines(10, 0, 0.7, colors='grey', linestyles='dotted')\n",
    "\n",
    "for ax in [ax1, ax2, ax3]:\n",
    "    ax.set_ylim(12.6,15.8)\n",
    "    \n",
    "\n",
    "## Legend\n",
    "line1 = plt.Line2D((0,1),(0,0), color='black', linestyle='solid')\n",
    "line2 = plt.Line2D((0,1),(0,0), color='black', linestyle='dotted')\n",
    "\n",
    "ax5.legend(handles=[line1, line2], \n",
    "           labels=[r'$M_{200} \\, h^{-1} / M_{\\odot} \\, \\geq \\, 10^{14}$',\n",
    "                   r'$M_{200} \\, h^{-1} / M_{\\odot} \\, < \\, 10^{14}$'], \n",
    "           loc=1, prop={'size':10}, frameon=False)\n",
    "\n",
    "ax1.legend(loc=4, frameon=False, prop={'size':13})\n",
    "\n",
    "# for ax in [ax2,ax3,ax6,ax7]:\n",
    "#     ax.set_yticklabels([])\n",
    "\n",
    "    \n",
    "for ax in [ax5,ax6,ax7]:\n",
    "    ax.set_xlabel('$\\delta + 1$', size=16, ha='center')\n",
    "#     ax.set_ylim(0,0.5)\n",
    "\n",
    "ax1.set_ylabel('$\\mathrm{log_{10}}(M_{200} / M_{\\odot})$',va='center',size=15,rotation='vertical',labelpad=8)\n",
    "ax5.set_ylabel('$\\mathrm{Norm. \\; PDF}$',va='center',size=15,rotation='vertical',labelpad=8)  \n",
    "\n",
    "# plt.savefig('output/quasar_select.png', bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HzRG overdensity / descendant mass plot, figure 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -1.   0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.]\n",
      "[ -0.5   0.5   1.5   2.5   3.5   4.5   5.5   6.5   7.5   8.5   9.5  10.5]\n"
     ]
    }
   ],
   "source": [
    "BinLimits = [np.linspace(-1, 38, 14), \n",
    "             np.linspace(-1, 17, 19),\n",
    "             np.linspace(-1, 11, 13)]\n",
    "\n",
    "Bins = [np.linspace(0.5, 36.5, 13),\n",
    "        np.linspace(-0.5, 16.5, 18), \n",
    "        np.linspace(-0.5, 10.5, 12)]\n",
    "\n",
    "print BinLimits[2]\n",
    "print Bins[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2.07\n",
      "1 3.1\n",
      "2 3.95\n",
      "3 5.03\n"
     ]
    },
    {
     "data": {
      "image/png": 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+npJ5F22b3Svgve/B+Lvgqifb/28iSUngDLnxRPxd1r4mtC69eSqE4LUjq3iu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L6f\nGDRkTRnWYnyUD34+xkxiqivK0W0dMGH4y2qJjqYRG81VH/PfrgAzs3APQe2t/NSVzjqziwBLJ8+4\n0rOLd+Frt/ETV3pOyR4AfILriQ6MwIgR/0+3mjnd0sHOoirKAwtQdhuLWjp4r7CKCr8CfLo6WdjS\nwTunK6nyKcBkaSO3pYMdJyuodhTgZ25ifksHLTFZpPkZM+JL1IwrDGuVkQLHOOHj7HpBWALc8gc4\n7AxXKdMWwrSFo5ixwfnOdf8JwONHi4kP9GfdlCQAHv34DKkhgdw5ORGA7x4uYkp4MF/IcAadMz7h\nxDeeZt+Wa5jbaibYz5d9gf4AzGs1E9AjvaClAz9/P/YF+hFZX48Odb+zan1FI42qxe0KT5S/35AG\nLRiNwQ5aS466HRdGugudGJs8Menm8cIKuu/i+fahQrIiQ7h1kjM+uHMRBGDD3gJWJ8dwzcRYogIj\nCK+s51NvvWRYXqObqrCaAg0d6rrbyqRomWz9Ejfckv2X6H+UtuZhLtdtu1Y555rYtuqxYS1nw6vG\n/XhDErMBDAlg5Se380GfCQQ/uaz3JKT583pPong0d2mv9JEFywCY88bzWOyVxA2z+8mna/1JDZ3B\nnhvPHwLa19Qr7fAL7JW2BYT2SncFRbLnxueprClnUmggP1w+tIkgxwIjuyxINzv3pIYEcnl8JAAx\nAX781NUN8+zHOwCIy145ankbrCPP5NAwZRPxU5y/62M/mUPTtO+SOmUJXQ4bp5+ZS/OMLRC+CLPN\nQumzOdSmriLeVaHJ6DMCVGafdPdkqAAmkx9xJl+34+mxvL+SOIQZ6GMaTxATGud299iRHuxgqN3g\nRroLnRDdFr3qmofr60Pr3nohbXNuwW77O1PDhzYQS3+q2zsonpBj2PK6nW41Q2WDVHguccOu8Fxg\nlLaDw1yu2xx+xrTKeKtanUVOWhb3Tkse9rL+9v7f8DMPfw6BhML3SQjII/f+x4e9rD9t/JLzwfJ/\nDntZ3sqoLgvSzc44Z17/MTA2KjwAt9v3kD3l8+fSd+qPmDP5Rroczjm/7mY/szNuwOzqFpJbuoOQ\n6Ix/654yEHN9+ZDydyh5Jn51J92ff97aBm1DWuWIGurgEUKMls5ZzrkJzZY2bt+5Bmv2LTDpEVrb\n67jr/evpyvkipG+isamcez64AVvu3ZD6derrC7nvX5/Fseg+SHmA2prjPLDrJvTlX4WJ62HWp2gN\n92PBNfcblteCp7OYxkmmGTjUNeCRFiMx9gyrlqC1flcpNRfYDFwGHASe6K8S5GlZRwuI72yCimHe\nNBv6OfA3ZhjH5upjhiwHoKXLuElM31zmHNhh8TBbw3Ix7gbl0/PuoNJsZY9Bgclbm6+N6LIg3eyM\nk/2Ae6OQjaY5X+19HWnWg0fOPfbzMTHja+fjTZApgGlfz8f37Z8PaV1+UUO7sFLgCKcgej5b7vyt\nex88kEJrW5vbx3b42ZPURE8eUoHGW2NEX0PpQgfSCiycslc5p+owW4y9opC5dzP+LTVsiFnBDalx\nXJ0YTVuXnYcPnuamtHiWTYiiyWrjkUOF3DIpgcvjI6m3dPG9w0V8IWMCC+MiqDFb2XLkDHdkJjI/\nNpwaWxingyYbOty3EN2GVeFRSl2Ns1vbCzi7tkUCm5VSv9Bav2dA/gYturQQh9aQlDi8BfmHQIgx\nJ0GTdpxPvP1tsHXCtT90pt/8lvP/6h84///jm2AKhE9vcaZf+yoER8MK57CXG09vhYkL+NmJFVgc\nDr4+MxWAZ4+XAfCVGSkAPP1JKQE+Ptw/3Tmq25PHSgj3M51rGXr8aDFnHdOJ8zlhyDYaZe6S6zhb\n2WDIsqT5WgxWQGTCaGfBo+z5P3M++PSX3frc4y/+hna7na23raPT7uChA6cGVaiZfmSf80bmJPcG\nO1gf7kOoVUOVe/P++IZF0JDhfjfYsRIjhtoNTlqBPSvjzIeklu1n3/E/GbZMT9/nFRQQyrQe3drC\nQmJ7paMiJxLVIx0Tk0lMj3R8wgzie6TD596CRSuwG5fHqrJ2AtQnxs7tAxB9l6tc5/1VKU+N1gdy\nEWS4/cAytNaf6/Pcy0qph3AOWd0vpdR6nIMcZGitt7n7en/MsxewL2IRH6W6N8N3X0bOdhySOMeQ\n5QBQth/CkyF0+Iv61J4/EqSaYJgtPKVlldBSSaoBwWllp4WV068l6Krhj35jdPP1UK+w9rccuXG5\nf0OJCUOJE329+vrjHKzNpyrFvdvsjRzN0ZNsc29zPrB2wB9vhty7YdZa5zDdf74NFn4JZq6B9nr4\n6xdhyQMwbTW5H/8On652uG2dW+tTn/sOH1e6Pw3bP9NS+Y+4BqZFpbv3weqjrA4+Am52gdmwt4DT\nrWavbxka6fmXxiJPlCcGklq2n8jmcogz7oLJWLvPa+q8OwH4aY/nQv18z90fCRDpb+qV7nn/JEBC\nkH+vdO30LNLba43PrLWd034TDC0bLG7pcA62YjBPjdYnF0GGX+Gpv8DzhRf6gFJqBdCgtX5HKbVe\nKZWjtT442NcvZG/U5YbMfGz0bMfFzRXkHf4z97labr794U9IC0/iHlfLzsMf/Iip0ZO4y9Xy89D7\nTzI7dipfXPMMAF/b+Ti5E+Zw2wbn0I59e8t2t+x062756bZxVlqv9ObZ6TzzRidVne4POdvXdfVN\nxNl8SR34rQM6ejAPjhWwwIAKDzDkwkxfqYlLmdVpzChQRp7QjKqEGS0sbTYzbt/q1meGEhOA6It9\nZrB83v4Fy6xtJDe71zL87MRvYvfzd3d1I6599y5sDhsPWFrZYmklEucErI/tfIJHrW1EANXtdTz5\nzx/wPWs74UB5azWmRAtZdg1AoK9Pr0LJxQo1fzmTg09yDtvcrIDc/PaveK3lONMjgunUEdTo2SSo\nowSqZsw6klo9iwT1MYGqlQ4dzVk9kwnqMJsbi5lUeQh/Ny+6rAyaB7FXA+5d3BorLUOXCk+VJwaj\nKWIi/zEGhkP3lPKSj4jY/yvC5t8J6VdA3Sl4/UG4+juQuhBqPoH/ech5YTU5B6qOwFsPw2eegMQ5\nUHEQtj8C1zwFCTOhdC+rLEd5PTya15ONHbxofvk+ukwBYMjYtE5mu4PwinzDz8PdlR2jR+vzxvLC\nSBtuhSfzAs8v4MKTka7E2QUOnFddVuC892ewr/crvWQP6ezhkWt/NtBbx66X10PsZFjmGk//xf+E\nCbNhqauS8MLtMHEBXL7Bmf7TLZCxDBbd50z/4SaY+hmOLd1CUZuVTJy3WlU45hOgmolVpwAodywg\nSDUQo04DUOZYSIg6S7RyDuVb6lhMqKrm+/MvAyA27h7CVQVRqgSAEscVRKhSIlUpWitK9eVEqmIi\nVDkO7UuZXkyUOkO4qsCuTZTrRayb7GBpU54hTdlDLcz057XkVbyWvIq5UQY0reG8Opwy8Nsuaixd\nBRykocSEmAE+Myg1s1aSHzIXW7h7FZ4ivwlM9jewL4eHJC/9P7R3mbE6Sjhy7Y/4VEoumBsxmwLI\nv+7HLEnOgfY62vyCOL7mGRYmZkNrNacmTCc9/XLKyg/Q8dc7MC3bROa8L1JcvAvLK+vxv/oRJmXf\nQlHhTrpefYDAT28hLeuzrKh4DUtDETVznunVN7+yw8ITR4v5zylJXBYdRmlbJ0/ll7B+ajKzo0KJ\nDJlJu04A3JsT6fmIy1jg18BljcVufW5NyS7W8CykuVew2hB9F6ftqSN6E/RYuddolHikPCEGtvP9\nrSyuP03Y/Ds53VjCb3c9w7e6zIQABQ1n+PPu/2ajrZNgIL/uFC9/9HMeslkIAj6uPcEbH/2Cb9it\nBAJ5Nfns2PdLFvlP4WTYUjq0D8GqgU4dRo3OJl4dI0g1uXUxJEC10a5jqNMzWNv0HW4tf4HKM8a1\nyL2UuBTtcNDS0mHYMgGIysQ8/TPGLlMAw6/wbFNKHcDZotOA86prBnCxEllkn3TfStNAr/drVv7r\nmGwW9m05M5i3j4jWkqOkp83mc3NvPffclit6z5T+xKe+0Sv91LLek4P9+KrNAGx49zFuKt/PstjJ\nANy343tsbqsmBWez5/rtj/C99rMkAV0OG/e/830e66gnATDbLHz1vS08bm4kFliZnIb9uc8yKTaC\nRRt3sDmvgOw/30Z6YgILv/E/PLT/Exb85RHSUtJY8OCrPLj3KEv/spnUjKnkbniJB3YfZvkLDxGQ\nksSrcxZRarNxZcVezEFNnIyfjnJ0sbx8D82h7ZyOmYaP3crV5bupD7NS6EqvLP+Q6ggoipqCr92C\nCg3ijCWCHYerCU2MYoX1MCdMEzhRE0B4YgTLrUfINyVzqsZEZGI4V1qPcsSUQlGNDzFJoSy15HPI\nN52SWk1W7Bv89MSz7PSbSnmtnaTEABZYT7LXdzJVtV1MTPJnvuUUu32nUFtrJS3JxGWWQj70mUbd\n2U4ykxRzLMW87zOdnLou9uTejb3FRFjtaaYU/pN9C+7G5KuIqDlJZtEH7F14D34+msiq42QU72LP\n4vUEoImqPMakkj3sWXIvAdqOqaaUyuKd3LPoLkzaQVzpQZKqj3Fw4R34ak18yQESzhbwce4X8NGa\nCWf2EtNYzNF5t+CjNYlFe4horuCTnJvxmXYzyac+JLStluOX3YhCk3LyfYLMTZzMvgHQpBW8h5+1\ng9OzrwMUk45vRznsFGV9BlBk5L+FQ/lQPGMlKMXkY/+gyzeAkulXo5Vi2pHXMAeEUT71ShxKMePj\nv9EaHEPl5KU4lGLmoZdoDkukKnMJDqWYdfAFqoNT+b/u/1SMiAkXjRP1ZZ/w+h0JmIMisASEoRx2\nIlqqCAiJZ0bgSUJNvm5leDHOuWP2feRea9ZId2vsngx43Q+uZ2JwFqTkEuUTwLp9eaSEzYXkHGKU\nH+v25ZEWlQuJ2cTjx7z8MiIyk3B3CsCwHU8TGxIAPEPE8X9Q+9tnKF3/Y0xTryUq/+9U//ZnVGx4\nHlKuIvrIS1T85nniv/kHHs7OZdeL3yVl76+Z9chbxE6cx0e/fpHOj15l7mP/IjJ+Ort/+Se68v6H\neY/vIzR6Erue+zWnThXzwooHec+Uz6z9HxHa0M6+5Stx+DmYs3c3gS1W9i9fjvZ1kP3RLvzaHOxf\nfiVphW8yu8zC2RMdzEtsJdhhpqA2gBqiWZhQT4C2crwmgBrfOK6IrcKEnePV/kwO/CPMuo6OtgBm\nHn0Vq/Ln9KzVAMz6+BXMfqEUznQOAzz70Iu0BUZzZsbVAGQf/AtNIRMomXYlAJcd+CN14amUTXUO\nM56z//fURmVSPnkJAPP3/oaihByeyvo8r+57mwUf/T/KEuZQNSkXgEW7t3EmaT416c5hfJd8+Byn\nUpdwNjWbK8/WEFtfyF/unU5XQAhoTURLJe2BkdgCQkA7iGipoj0wCltAMMphJ7y1mvagKGz+59Nt\nQdHY/YPwcdgIa62hLTgGu18gPvYuwtpqaQ2OxeEX8G9pX5uVkPaztIXE4jAF4GuzENJeR1tIHA6T\nPyabheD2OlpD47FEJ3H3o28N9RA3tDxRX3ac33913oArDW+upCUiaeDcjWMhi+6jPTIV4qdDYwk1\nQVEUr32erNgp0HCGypAYypb9imnRk6DuFOUhcVRc+TCTo9Kg9gQlYQlUr/gu6RHJUJNPUXgiWQcL\nWVi5gwkpAcyafxsl//g1f58Ry6r6nczMvYMz//gVf58ZyzVn32X6gnUU/mMbr2TFcUPtO0xbcC8n\n33iel+fEcXPVdqYs3MCJN57j5ex44h12dpqyaC5pY+YEK1O7qnjXNIvWmlbmJJjJtNWy3XcO7bXN\n5CS0k2qr523fOZhrm1iQ0EKirYm3fOZgOdvE5QkNxNjbiDU1Ag7+mXsnvloTXX6EtPID5C2+Gx+t\niS07THLVEQ4tvAMfrYkvySPh7Ilz5/eEM/uIbSji2PxbQWsSiz4iqrmMF5c5bzU49MQ6wltrOH7Z\nWkCRcvJ9gs2NFGRfDyjSCt7D39LOqdnXgVKkH9+B1LjI8QAADOZJREFUr72LoqzVOJQi85O30UDx\njFU4lGLVmXwCra388Ss5aCCovR6tfLAER6GB4LY67L4mrEGRrvRZ7L7+WIMi0EBIWy1dpkC6AsPQ\nKELaaugyBZ1Lh7bWYPULpisw1JWuxuIfgi3AmQ5rqaIzIAxbQAhaKcKaKzEHRmD3D0aD8/XACGz+\nwTjQWCMnDCcu9Gu4o7Q1A/OVUjcCucBBrfUvDcnZBbj6464HSE0935mqIXkmoc1Vnly124zswnRV\nykJqJs6HaavPPbf/iq+RMuX8cLoHlm1kTeZycA1Rm7f821yTsQxcQ9QeXPl9VqVfwWprOx93lgER\nADyUlUi+pQJwXv3YNCOW45YqwNkd7lvTIjllrQGmAvDw1BAKu84SEprNtlWPUVp9DPOJ3fhnP8ik\nBfdwpjwP64m9BM7bSNq8Oygs2YOt4ADBC79NytxbOVm4E11wiJAljzJx9o2cKHgb2yfH+HtKLhOa\nisiPSie9qYg34+aSVHeaPdGTSG0s5u24bJLrTrI7JoOJDSXsiMtmYt0JPozOJLGhjHfjZpNSf5yD\n4XFMaS41ZL+btI115c8x01LGez4zaHZYuLf0GaZaKnlXzaTFYeGB4h+Saa1hu8qi3WHh60U/INVa\nz3ay6HBY+FbhFhKsjfzGfyU+2vtbBcaLnrEiPa7/1r4Ah5VQ3Upy+MgUXsZKP31l8sPSVE1Kzupe\n83ekp1/eK52ReVWvdMD0eSQtuZWEIH9ujGyh2e68+pkUHMD14U202s0ApIYGcl1oHe0OZzojLIg6\nvzbcvVYaaWviR/OXkBF/C6+cuJ2mhiIeWXw/ydHxvJR/C80tlTx+xTeICQvnxSM309Rex1PLNvFe\nxhIq/vQEvp0+/PeM/8BX2cjZ9QHaqnh25vX4KDvzGt/H5vDhJ1k3oJQmt2EnJoeVB8qfBuCTjgCq\nAxLZWPIEAPlmf2p08rn0sU5/an0c3Nwj7W/y4fPdaYsfJqs/t7rSRy0mfC1B3NadtvrSaqlhavtx\nN/cKlKbkEtFc4fbnLkW94kT84HoFtEQkoTMu7fsx105dde7x5Ki0XnN2TYue1CudFTulVzo7fnqv\n9LyELLateowy06+pM5cRXOBslAs0+XB7aCnBFc4L2UEmX24PLiLE4jy/B5t8uSPwJKEW57Ee4ufL\nnf75hFmrAWf327t8DxPiYyZq8Uba3vw5jTMvp7Lw78TO/TIdb/2cupkrCCp6g5g599C5/XlqZ1yJ\nqWQH0dPuoHrn/6d6+lIcZe8TlXkbZz/4PRXTF2Gu2EcoITiqLTxy6juE2Tt5S8/G6ujk+yf/iwBH\nF2/pOVgdZh49uRl/h503HbOx2tt59ORm/Bx23rTPpstu5tGTD+PrcLjSFu4t/in/illm9NdFU1gi\nEW3ef9+pJymt9eDfrFS61rp4WCtUaiuww9WndgWQo7V+crCv9zR//nx94MCB4WRHCDHClFJ5Wuv5\nPdJuxwScXdoGFSdAYoUQY1HfWNHP64aVJ0DihBBj0UBxopu71T33+m/0bwfObm+4/r8DoJSKvNjr\nQohxaygxQeKEEELKE0KIQXG3hacBeBznzX8K0D3+g3NOnvnAX7XW911kORtx3jiYo7V+0hWc3tVa\nz+vv9YsspxW4VKfQjQXqRjsTo+RS3fbxst1pWuted2EPJSYMNk643utNscKbvkfJS/8kL/0b6bz8\nW6zoy6jyhOu93hQnBuJNx8VAxlJeYWzlV/I6iDgB7ld4HtJaP9XP85cBv8Q1YIHW+l13cjpUSqkD\ng2nGGo9k2y+9bb9Ut9sI3rTvJC/9k7z0T/IycsbS9klePWcs5VfyOnhudWnrW9lRSoUrpX4BHMDZ\nTzZ6pCo7QgghhBBCCDGQIQ/ZoJRaCxQDk4DJWuuHjcqUEEIIIYQQQhjB7WGplVLpwEs4Kzr3aK1f\nNjhP7tg2iusebbLtl55LdbuN4E37TvLSP8lL/yQvI2csbZ/k1XPGUn4lr4Pk7j08PwAeAp68WIuO\nUmqd1vpXBuRP9KCU2qq13tQjvR7nABIZWuuxdNALIcSocjeeejLeXiAvAPO01l+60PuVUutHIC8X\nXZech8Rg9D2uxKXBm8qt7nZp2wg8BRxQSq11/d3Y4/FapdQ9gMcPaqXUeqXUih4nhnHNtZ039Uiv\nABq01t3DcOaMVt48zfVdr1dKPd/nuXH9/SulbnJt4yW13cMx0P4Zyf3X33Hb5/Wt3e8bgbxcdF0j\ntV+UUjlKqUalVKHr70V382pQPtyKp56Mt/3k5SacI51u65Hua71SqhBnwcEwffMy0LrGy3lorMXV\ngWKLt1Hn50/zat3HgOsvcuBPjJ4e5QOvPWa9rdzqboXnSa31t7TWL2utX3H99Xz8itb6l8AhT2S2\n22jvtNHgOvn1POGs7JEuAlaMeKZGQH8n/0vh+3dt40rXNka7CorjfruHYzQLrf3kZdQKrRfgLYXW\naK11lNY6E7iZ/i+OeXy/DCGeeize9pOXDKC7EFPI+XlkerpHa53Z/Z15MC8DrWvMn4fGWlwdZGwR\nbnJVcG527ddInFOseCXXMXrQdcwe8NZj1tvKre5WeAZ7NcHTLTxjPsgaoO/Vh8xRyYXn9XfyH/ff\nvyuQdf+OMnBu57jf7mEatUJrP0at0HoBXlFo7bP+DK11f5Wakdwv3QaKpyMWb7XWT/aYLyaX/ifL\nzHBdBNnoqXwMcl3j4Tw01uLqYGKL11BK5Yzwb3moPgfkwbnfoLfnubs83l0+GAtGNV64Oyz1GSPf\nNwzjIciKQbjAyf+S+f5dTcLPa62buIS2e4ik0HphXlVodV2VvlCBYiT3i9dyXbUt0lof7Pua6/g6\n6HqfRwvoI7muUTKm4uogY4s3iR7tDAxSJpDp6ibm1bHH9XsscrWER7vKB2IAQx6WWoy6JnoHksLR\nyshIuNjJf7zSWje5moRXemuTtbg4KbRe0MoLnaRHKa8DxdPRiLef7+8m7+57DHrky5NdMwda1yV1\nHvImY+GcOIZad8BZ8S105bfJm7sKurrf5eHsBbJVKeXVrXw9jGq8GKsVHgmysIPzTdkZeP9VnuHq\nefIf99+/6ypTd+GiCPg8l8B2D5MUWvvhpYXWfk/QI7lf+ug3nva4cXlE461yjoi2yfV4RZ+8FPUY\n3SgT8GSBt991jdZ+8ZCxGlf7jS1eJsN13+16XPeijnaGLqIQaHA9bsDZeuat1mutt2mtXwKuBv5t\nJEcvNarxYqxWeMZDkHWL62rD/O6rDq6rEJGuk2GkN1/lGa5+Tv6Xwvefw/ltjMQZjC+F7R4OKbT2\nz6sKra5CT1Of50Z0vwwmnrry9O6FXvdUXlz/tyrnKHaNrud65cVVMbwJqDfyCnp/+6XvukZqv4yg\nMRdX+4st3khr/ZKrUD4WvMP54yAa2D+KeRk012/OKyvp3lZudWseHm/i6mN5EMjp0Z9VjDOuH8ov\ncV5xicY5iso74/37dxUquk9kK7VrLo7xvt3D1Xf/dBfOtNbz+nvdg/n4t+MWONAnL+tdr2d4+rvs\nu67R2i+udeUAX+pxTPfNy4jtFyFgbMXVC50TRzdX44PrOOieI8ZrjwNXzOyezyZay/xXgzJmKzxC\nCCGEEEIIMZCx2qVNCCGEEEIIIQYkFR4hhBBCCCHEuCUVHiGEEEIIIcS4JRUeIYQQQgghxLglFR4h\nhBBCCCHEuCUVHiGEEEIIIcS4JRUe4VFKqRWuiet2KKXyRmBdWz25DiGE8SROCCEGQ2KFGCrTaGdA\njHvdE7lt6zGb+oCUUpFa66aB33lupumVQA7OibiEEGOLxAkhxGBIrBBDIi08wtMylVIZAG4Emxxg\nxWBXoLV+R2u9CedM2UKIsUfihBBiMCRWiCGRCo/wtOeBHaOdCSGEV5M4IYQYDIkVYkikwiM8xtXc\nPB9oUkq9ONr5EUJ4H4kTQojBkFghhkPu4REe4Wpy3qq1vlkpdQDIc6cPrRBi/JM4IYQYDIkVYrik\nwiM85UXgZgCt9UGlVBOQQT99Yl2joPS8+TAaiFRKrezz1k0S3IQYVyROCCEGQ2KFGBap8AjDuW4Q\nRGvdd3STfkc7cd0c2PfzGVrrlzyTQyHEaJM4IYQYDIkVwghyD4/whAzgQHfC1e+2Qa6kCCF6kDgh\nhBgMiRVi2KTCIzzhIM4m5G7rgU0XeK8Q4tIkcUIIMRgSK8SwSZc2YTitdZFrFuT1rqeaPNmU3GOM\n/Ztc6Y3AO1prGUNfCC8lcUIIMRgSK4QRlNZ6tPMgRC/S31YIMRCJE0KIwZBYIUAqPEIIIYQQQohx\nTO7hEUIIIYQQQoxbUuERQgghhBBCjFtS4RFCCCGEEEKMW1LhEUIIIYQQQoxbUuERQgghhBBCjFtS\n4RFCCCGEEEKMW1LhEUIIIYQQQoxbUuERQgghhBBCjFv/C5vIoPuXNSgdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aac6bcab110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "\n",
    "gs1 = gridspec.GridSpec(32, 26)\n",
    "\n",
    "gs1.update(bottom = 0.57, top = 1, hspace=0, wspace=5.5)\n",
    "\n",
    "ax1 = fig.add_subplot(gs1[0:26,0:8])\n",
    "ax5 = fig.add_subplot(gs1[26:32,0:8])\n",
    "ax2 = fig.add_subplot(gs1[0:26,8:16])\n",
    "ax6 = fig.add_subplot(gs1[26:32,8:16])\n",
    "ax3 = fig.add_subplot(gs1[0:26,16:24])\n",
    "ax7 = fig.add_subplot(gs1[26:32,16:24])\n",
    "ax9 = fig.add_subplot(gs1[16:23,20:24])\n",
    "\n",
    "Bhatta =[[] for i in range(len(zees[:4]))]\n",
    "\n",
    "radii = [6,10,14]\n",
    "\n",
    "for i, z in enumerate(zees[:4]):\n",
    "    print i, z\n",
    "    \n",
    "    for axt, axb, R, binLimits, bins in zip([ax1,ax2,ax3], [ax5,ax6,ax7], radii, BinLimits, Bins):\n",
    "        \n",
    "        cluster_mask = hzrg_stats[z]['clusters'] >= 1e4\n",
    "        nan_mask = ~np.isinf(1 / hzrg_stats[z]['clusters'])\n",
    "        \n",
    "        # Subplot\n",
    "        phiB, mass = np.histogram(hzrg_stats[z]['cstats'][R][~cluster_mask,0] + 1, bins=binLimits, normed=True)\n",
    "        phiA, mass = np.histogram(hzrg_stats[z]['cstats'][R][cluster_mask,0] + 1, bins=binLimits, normed=True)\n",
    "        \n",
    "        DB, BC = bhattacharyya(phiA*np.diff(binLimits), phiB*np.diff(binLimits))\n",
    "        \n",
    "        Bhatta[i].append(DB)\n",
    "\n",
    "        # Main Plot\n",
    "        median = binned_statistic(hzrg_stats[z]['cstats'][R][nan_mask,0] + 1, \n",
    "                          np.log10(hzrg_stats[z]['clusters'][nan_mask] * 1e10),\n",
    "                          bins=binLimits, statistic='median')[0]\n",
    "\n",
    "        count = binned_statistic(hzrg_stats[z]['cstats'][R][nan_mask,0] + 1, \n",
    "                                 np.log10(hzrg_stats[z]['clusters'][nan_mask] * 1e10),\n",
    "                                 bins=binLimits, statistic='count')[0]\n",
    "\n",
    "        std = binned_statistic(hzrg_stats[z]['cstats'][R][nan_mask,0] + 1, \n",
    "                               np.log10(hzrg_stats[z]['clusters'][nan_mask] * 1e10),\n",
    "                               bins=binLimits, statistic='std')[0]\n",
    "\n",
    "        cmask = (count > 0)\n",
    "        \n",
    "        if np.sum(cmask) == 0:\n",
    "            print 'not enough radio galaxies...'\n",
    "        else:\n",
    "        \n",
    "            if i==0:\n",
    "                axt.fill_between(bins[cmask], median[cmask]+std[cmask], median[cmask]-std[cmask], \n",
    "                                 alpha=0.15, linewidth=0, color=cmap[i*2])\n",
    "\n",
    "            axt.plot(bins[cmask], median[cmask], color=cmap[i*2], label='$z = %.2f$'%z)\n",
    "\n",
    "            dgal_mask = hzrg_stats[z]['cstats'][R][:,0] > max(bins[cmask])\n",
    "\n",
    "            axt.scatter(hzrg_stats[z]['cstats'][R][nan_mask & dgal_mask,0] + 1,\n",
    "                np.log10(hzrg_stats[z]['clusters'][nan_mask & dgal_mask] * 1e10),\n",
    "                marker='.', s=8, c=cmap[i], lw=0, label=None)\n",
    "\n",
    "            \n",
    "        # plot fitted mass\n",
    "        if R == 10:\n",
    "            axt.plot(bins[bins>4.5] + 1, scale(func(np.vstack([bins[bins>4.5], [z] * len(bins[bins>4.5])]), \n",
    "                     args[0],args[1],args[2],args[3],args[4])), color=cmap[i*2], \n",
    "                     linestyle='dashed', alpha=0.6)\n",
    "\n",
    "        \n",
    "        axb.set_xlim(0, binLimits[-1])\n",
    "        axt.set_xlim(0, binLimits[-1])\n",
    "\n",
    "        axt.hlines(14, -10, 60, linestyle='--', color='grey')\n",
    "\n",
    "        axt.set_xticklabels([])\n",
    "\n",
    "        axb.step(bins, phiA, color=cmap[i*2], alpha=1)# + i*0.1)\n",
    "        axb.step(bins, phiB, color=cmap[i*2], linestyle='dotted', alpha=1)# + i*0.1)\n",
    "\n",
    "        if(i==0):\n",
    "            axt.text(0.05, 0.92, '$R = D/2 = %s \\; \\mathrm{cMpc}$'%R, transform=axt.transAxes, size=13)\n",
    "\n",
    "\n",
    "# Calculate R = 20 & 3 Bhattacharya distance, but don't plot\n",
    "for i, z in enumerate(zees[:4]):\n",
    "    \n",
    "    for R, switch, binLimits, bins in zip([3,20],[True,False], BinLimits, Bins):\n",
    "        \n",
    "        cluster_mask = hzrg_stats[z]['clusters'] >= 1e4\n",
    "        \n",
    "        phiB, mass = np.histogram(hzrg_stats[z]['cstats'][R][~cluster_mask,0] + 1, bins=binLimits, normed=True)\n",
    "        phiA, mass = np.histogram(hzrg_stats[z]['cstats'][R][cluster_mask,0] + 1, bins=binLimits, normed=True)\n",
    "        \n",
    "        DB, BC = bhattacharyya(phiA*np.diff(binLimits), phiB*np.diff(binLimits))\n",
    "        \n",
    "        if switch:\n",
    "            Bhatta[i] = [DB] + Bhatta[i]\n",
    "        else:\n",
    "            Bhatta[i].append(DB)\n",
    "        \n",
    "\n",
    "for i,z in enumerate(zees[:3]):\n",
    "    ax9.plot([3] + radii + [20], Bhatta[i], color=cmap[i*2])\n",
    "    \n",
    "    \n",
    "ax9.set_xlabel('$R$')\n",
    "ax9.set_ylabel('$D_{B}$')\n",
    "ax9.set_yticks([])\n",
    "\n",
    "ax9.set_yticklabels(['',0.2,0.3,0.4,0.5,0.6,0.7,''])\n",
    "\n",
    "ax9.vlines(10, 0, 0.7, colors='grey', linestyles='dotted')\n",
    "\n",
    "for ax in [ax1, ax2, ax3]:\n",
    "    ax.set_ylim(12.6,15.8)\n",
    "    \n",
    "\n",
    "## Legend\n",
    "line1 = plt.Line2D((0,1),(0,0), color='black', linestyle='solid')\n",
    "line2 = plt.Line2D((0,1),(0,0), color='black', linestyle='dotted')\n",
    "\n",
    "ax5.legend(handles=[line1, line2], \n",
    "           labels=[r'$M_{200} \\, h^{-1} / M_{\\odot} \\, \\geq \\, 10^{14}$',\n",
    "                   r'$M_{200} \\, h^{-1} / M_{\\odot} \\, < \\, 10^{14}$'], \n",
    "           loc=1, prop={'size':10}, frameon=False)\n",
    "\n",
    "ax1.legend(loc=4, frameon=False, prop={'size':13})\n",
    "\n",
    "# for ax in [ax2,ax3,ax6,ax7]:\n",
    "#     ax.set_yticklabels([])\n",
    "\n",
    "    \n",
    "for ax in [ax5,ax6,ax7]:\n",
    "    ax.set_xlabel('$\\delta + 1$', size=16, ha='center')\n",
    "\n",
    "ax1.set_ylabel('$\\mathrm{log_{10}}(M_{200} / M_{\\odot})$',va='center',size=15,rotation='vertical',labelpad=8)  \n",
    "ax5.set_ylabel('$\\mathrm{Norm. \\; PDF}$',va='center',size=15,rotation='vertical',labelpad=8)  \n",
    "\n",
    "# plt.savefig('output/radio_select.png', bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "AGN fractions, figure 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_agn_frac(gals, L, quasar_lim, radio_lim):\n",
    "\n",
    "    cluster_ids = pd.unique(gals[gals['z0_central_mcrit200'] > 1e4]['z0_centralId'])\n",
    "    \n",
    "    N_quasar = [None] * len(cluster_ids)\n",
    "    N_radio = [None] * len(cluster_ids)\n",
    "\n",
    "    # print \"Looping through clusters...\"    \n",
    "    for i, cluster in enumerate(cluster_ids):\n",
    "        \n",
    "        quasar_mask = gals[gals['z0_centralId']==cluster]['zn_quasarAccretionRate'] > quasar_lim\n",
    "        radio_mask = gals[gals['z0_centralId']==cluster]['zn_radioAccretionRate'] > radio_lim\n",
    "        \n",
    "        N_quasar[i] = np.sum(quasar_mask)\n",
    "        N_radio[i] = np.sum(radio_mask)\n",
    "        \n",
    "    return N_quasar, N_radio, len(cluster_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_quasar = [None] * len(redshifts)\n",
    "n_radio = [None] * len(redshifts)\n",
    "\n",
    "n_quasar_cons = [None] * len(redshifts)\n",
    "n_radio_cons = [None] * len(redshifts)\n",
    "\n",
    "n_clust = [None] * len(redshifts)\n",
    "\n",
    "gals = gals_mstar\n",
    "\n",
    "for i,z in enumerate(redshifts):\n",
    "    n_quasar[i], n_radio[i], n_clust[i] = calc_agn_frac(gals[i], L, quasar_lim, radio_lim)\n",
    "    n_quasar_cons[i], n_radio_cons[i], n_clust[i] = calc_agn_frac(gals[i], L, cons_quasar_lim, cons_radio_lim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "quasar_in_pcs = [None] * len(gals)\n",
    "radio_in_pcs = [None] * len(gals)\n",
    "\n",
    "all_quasar = [None] * len(gals)\n",
    "all_radio = [None] * len(gals)\n",
    "\n",
    "quasar_in_pcs_cons = [None] * len(gals)\n",
    "radio_in_pcs_cons = [None] * len(gals)\n",
    "\n",
    "all_quasar_cons = [None] * len(gals)\n",
    "all_radio_cons = [None] * len(gals)\n",
    "\n",
    "for i in range(len(gals)):\n",
    "    clust = gals[i]['z0_central_mcrit200'] > 1e4\n",
    "\n",
    "    quasar_mask = gals[i]['zn_quasarAccretionRate'] > quasar_lim\n",
    "    radio_mask = gals[i]['zn_radioAccretionRate'] > radio_lim\n",
    "\n",
    "    quasar_in_pcs[i] = float(np.sum((quasar_mask & clust)))\n",
    "    radio_in_pcs[i] = float(np.sum((radio_mask & clust)))\n",
    "    \n",
    "    all_quasar[i] = np.sum(quasar_mask)\n",
    "    all_radio[i] = np.sum(radio_mask)\n",
    "    \n",
    "    quasar_mask = gals[i]['zn_quasarAccretionRate'] > cons_quasar_lim\n",
    "    radio_mask = gals[i]['zn_radioAccretionRate'] > cons_radio_lim\n",
    "\n",
    "    quasar_in_pcs_cons[i] = float(np.sum((quasar_mask & clust)))\n",
    "    radio_in_pcs_cons[i] = float(np.sum((radio_mask & clust)))\n",
    "    \n",
    "    all_quasar_cons[i] = np.sum(quasar_mask)\n",
    "    all_radio_cons[i] = np.sum(radio_mask)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3825, 3825, 3825, 3825, 3779, 3082, 1235,  493,  148])"
      ]
     },
     "execution_count": 538,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_clust = np.array(n_clust)\n",
    "n_clust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ages = np.array([4, 3, 2.5, 2.1, 1.8, 1.6, 1.4, 1.25, 1.1, 1.0, 0.9])*u.Gyr\n",
    "ageticks = [z_at_value(Planck13.age, age) for age in ages]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2aac551474d0>"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hx3K2UVBMBC5jOnQTu5zHQcuO2mM0ig0hYIGkgHOCGv/49mhFonWyw46ddRCt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aac6fbd6e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2,1, figsize=(4.5,4.5))\n",
    "\n",
    "plt.subplots_adjust(hspace=0)\n",
    "\n",
    "z_lim = 5\n",
    "\n",
    "ax1.plot(zees[:z_lim], np.array([float(np.sum([x > 0 for x in y])) for y in n_quasar[:z_lim]]) / n_clust[:z_lim], \n",
    "        c=cmap[1], lw=4, linestyle='dashed')\n",
    "\n",
    "ax1.plot(zees[:z_lim], np.array([float(np.sum([x > 0 for x in y])) for y in n_radio[:z_lim]]) / n_clust[:z_lim], \n",
    "          c=cmap[4], lw=4, linestyle='dashed')\n",
    "\n",
    "# cons\n",
    "ax2.plot(zees[:z_lim], np.array([float(np.sum([x > 0 for x in y])) for y in n_radio_cons[:z_lim]]) / n_clust[:z_lim], \n",
    "         c=cmap[4], lw=4, linestyle='dashed')\n",
    "\n",
    "ax2.plot(zees[:z_lim], np.array([float(np.sum([x > 0 for x in y])) for y in n_quasar_cons[:z_lim]]) / n_clust[:z_lim], \n",
    "         c=cmap[1], lw=4, linestyle='dashed')\n",
    "\n",
    "\n",
    "ax1.plot(zees[:z_lim], np.array(quasar_in_pcs[:z_lim])/np.array(all_quasar[:z_lim]), c=cmap[1], lw=5)\n",
    "ax1.plot(zees[:z_lim], np.array(radio_in_pcs[:z_lim])/np.array(all_radio[:z_lim]), c=cmap[4], lw=5)\n",
    "\n",
    "#cons\n",
    "ax2.plot(zees[:z_lim], np.array(quasar_in_pcs_cons[:z_lim])/np.array(all_quasar_cons[:z_lim]), c=cmap[1], lw=5)\n",
    "ax2.plot(zees[:z_lim], np.array(radio_in_pcs_cons[:z_lim])/np.array(all_radio_cons[:z_lim]), c=cmap[4], lw=5)\n",
    "\n",
    "\n",
    "ax2.set_xlabel('$z$', size=13)\n",
    "\n",
    "for ax in [ax1,ax2]:\n",
    "    ax.set_ylim(0, 1)\n",
    "    ax.grid()\n",
    "    #ax.set_ylabel('$f$', rotation=0, size=20)\n",
    "    \n",
    "axt = ax1.twiny()\n",
    "axt.set_xticks(ageticks)\n",
    "axt.set_xticklabels([\"$%.2f$\"%age for age in ages.value]);\n",
    "\n",
    "axt.set_xlabel('$t (Gyr)$', size=11, labelpad=12)\n",
    "\n",
    "ax1.set_xticklabels([])\n",
    "ax1.set_yticklabels(['','$0.2$','$0.4$','$0.6$','$0.8$','$1.0$'])\n",
    "\n",
    "ax1.set_xlim(2.05, 5.7)\n",
    "ax2.set_xlim(2.05, 5.7)\n",
    "axt.set_xlim(2.05, 5.7)\n",
    "\n",
    "line1 = plt.Line2D((0,1),(0,0), color=cmap[4], linestyle='solid')\n",
    "line2 = plt.Line2D((0,1),(0,0), color=cmap[1], linestyle='solid')\n",
    "\n",
    "line3 = plt.Line2D((0,1),(0,0), color='black', linestyle='solid')\n",
    "line4 = plt.Line2D((0,1),(0,0), color='black', linestyle='dashed')\n",
    "\n",
    "ax2.legend(handles=[line1, line2, line3, line4], \n",
    "           labels=['HzRGs','Quasars',r'$f(AGN)$',r'$f(pcs)$'], \n",
    "           loc=(-0.06, -0.4), prop={'size':9}, frameon=False, ncol=4)\n",
    "\n",
    "\n",
    "# plt.savefig('output/agn_stats.png', bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jff7yZ3d682m6wQZxywgbFGuDPdLr56WJcNkszaoapyYiPNOChjAlccupGq9N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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aac72df2b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1,1, figsize=(4.5,3))\n",
    "\n",
    "plt.subplots_adjust(hspace=0)\n",
    "\n",
    "z_lim = 5\n",
    "\n",
    "ax1.plot(zees[:z_lim], np.array([float(np.sum([x > 0 for x in y])) for y in n_quasar[:z_lim]]) / n_clust[:z_lim], \n",
    "        c=cmap[1], lw=4, linestyle='dashed')\n",
    "\n",
    "ax1.plot(zees[:z_lim], np.array([float(np.sum([x > 0 for x in y])) for y in n_radio[:z_lim]]) / n_clust[:z_lim], \n",
    "          c=cmap[4], lw=4, linestyle='dashed')\n",
    "\n",
    "# cons\n",
    "ax1.plot(zees[:z_lim], np.array([float(np.sum([x > 0 for x in y])) for y in n_quasar_cons[:z_lim]]) / n_clust[:z_lim], \n",
    "         c=cmap[1], lw=4, linestyle='dashed', alpha=0.5)\n",
    "\n",
    "ax1.plot(zees[:z_lim], np.array([float(np.sum([x > 0 for x in y])) for y in n_radio_cons[:z_lim]]) / n_clust[:z_lim], \n",
    "         c=cmap[4], lw=4, linestyle='dashed', alpha=0.5)\n",
    "\n",
    "\n",
    "ax1.plot(zees[:z_lim], np.array(quasar_in_pcs[:z_lim])/np.array(all_quasar[:z_lim]), \n",
    "         c=cmap[1], lw=5)\n",
    "\n",
    "ax1.plot(zees[:z_lim], np.array(radio_in_pcs[:z_lim])/np.array(all_radio[:z_lim]), \n",
    "         c=cmap[4], lw=5)\n",
    "\n",
    "#cons\n",
    "ax1.plot(zees[:z_lim], np.array(quasar_in_pcs_cons[:z_lim])/np.array(all_quasar_cons[:z_lim]), \n",
    "         c=cmap[1], lw=5, alpha=0.5)\n",
    "\n",
    "ax1.plot(zees[:z_lim], np.array(radio_in_pcs_cons[:z_lim])/np.array(all_radio_cons[:z_lim]), \n",
    "         c=cmap[4], lw=5, alpha=0.5)\n",
    "\n",
    "\n",
    "ax1.set_ylim(0, 1)\n",
    "ax1.grid()\n",
    "ax1.set_ylabel('$f$', size=15)\n",
    "ax1.set_xlabel('$z$', size=15)\n",
    "\n",
    "axt = ax1.twiny()\n",
    "axt.set_xticks(ageticks)\n",
    "axt.set_xticklabels([\"$%.2f$\"%age for age in ages.value]);\n",
    "\n",
    "axt.set_xlabel('$t (Gyr)$', size=11, labelpad=12)\n",
    "\n",
    "# ax1.set_xticklabels([])\n",
    "ax1.set_yticklabels(['','$0.2$','$0.4$','$0.6$','$0.8$','$1.0$'])\n",
    "\n",
    "ax1.set_xlim(2.05, 5.7)\n",
    "axt.set_xlim(2.05, 5.7)\n",
    "\n",
    "line1 = plt.Line2D((0,1),(0,0), color=cmap[4], linestyle='solid')\n",
    "line2 = plt.Line2D((0,1),(0,0), color=cmap[1], linestyle='solid')\n",
    "\n",
    "line5 = plt.Line2D((0,1),(0,0), color=cmap[4], alpha=0.5, linestyle='solid')\n",
    "line6 = plt.Line2D((0,1),(0,0), color=cmap[1], alpha=0.5, linestyle='solid')\n",
    "\n",
    "line3 = plt.Line2D((0,1),(0,0), color='black', linestyle='solid')\n",
    "line4 = plt.Line2D((0,1),(0,0), color='black', linestyle='dashed')\n",
    "\n",
    "ax1.legend(handles=[line1, line2, line5, line6, line3, line4], \n",
    "           labels=['HzRGs','Quasars','HzRGs (cons)','Quasars (cons)',r'$P$',r'$C$'], \n",
    "           loc=(-0.01, -0.5), prop={'size':10}, frameon=False, ncol=3)\n",
    "\n",
    "\n",
    "# plt.savefig('output/agn_stats.png', bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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