mcs_resolution_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.18/04\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import uproot\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as col \n",
    "import ROOT as r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "treename1 = 'NuCCanalyzer/Event'\n",
    "treename2 = \"NuCCanalyzer/Daughters\"\n",
    "\n",
    "msc_overlay_run1 = uproot.open(\"~/NuCCanalyser/ntuples/NuCC_overlay.root\")\n",
    "msc_overlay_run2 = uproot.open(\"~/NuCCanalyser/ntuples/NuCC_overlay.root\")\n",
    "msc_overlay_run3 = uproot.open(\"~/NuCCanalyser/ntuples/NuCC_overlay.root\")\n",
    "msc_overlay_run1 = msc_overlay_run1['NuCCanalyzer/Daughters']\n",
    "msc_overlay_run2 = msc_overlay_run2['NuCCanalyzer/Event']\n",
    "msc_overlay_run3 = msc_overlay_run3['trajmcsntuple/tree']\n",
    "\n",
    "f_mc_only1 = uproot.open(\"~/NuCCanalyser/ntuples/NuCCdirt.root\")\n",
    "t_mc_only1 = f_mc_only1[treename1]\n",
    "\n",
    "f_data_run1 = uproot.open(\"~/NuCCanalyser/ntuples/NuCCbnb.root\")\n",
    "t_data_run1 = f_data_run1[treename1]\n",
    "\n",
    "f_ovrl_run1 = uproot.open(\"~/NuCCanalyser/ntuples/NuCC_overlay.root\")\n",
    "t_ovrl_run1 = f_ovrl_run1[treename1]\n",
    "\n",
    "f_mc_only2 = uproot.open(\"~/NuCCanalyser/ntuples/NuCCdirt.root\")\n",
    "t_mc_only2 = f_mc_only2[treename2]\n",
    "\n",
    "f_data_run2 = uproot.open(\"~/NuCCanalyser/ntuples/NuCCbnb.root\")\n",
    "t_data_run2 = f_data_run2[treename2]\n",
    "\n",
    "f_ovrl_run2 = uproot.open(\"~/NuCCanalyser/ntuples/NuCC_overlay.root\")\n",
    "t_ovrl_run2 = f_ovrl_run2[treename2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ovr = msc_overlay_run1.pandas.df([\n",
    "   'track_is_muon_candidate', 'mcs_trackLength_rm10', 'mcs_trackMom_MuFwd_rm10', 'mcs_trackMomErr_MuFwd_rm10', \n",
    "    'track_range_mom_mu', \"mc_neutrino\",\"track_score\",\"vtx_distance\",\"track_chi2_proton\",\n",
    "    \"track_chi2_muon\",\"track_length\",\"generation\",\"mc_pdg\",\n",
    "])\n",
    "\n",
    "df_ovr1 = msc_overlay_run2.pandas.df([\n",
    "   'nu_score', 'nu_flash_chi2','nu_mu_cc_selected',\"mc_nu_pdg\",\"daughters_start_contained\",\"nu_contained\",\"mc_nu_vtx_distance\"\n",
    "])\n",
    "\n",
    "df_ovr2 = msc_overlay_run3.pandas.df([\n",
    "    'simLength', 'simMatchPurity', \n",
    "])\n",
    "\n",
    "muon_candidate_selection = \"track_score>0.90 &\\\n",
    "                            vtx_distance<4.0 &\\\n",
    "                            track_chi2_proton>60.0 &\\\n",
    "                            track_chi2_muon<30.0 &\\\n",
    "                            (track_chi2_proton/track_chi2_muon)>7.0 &\\\n",
    "                            generation==2 &\\\n",
    "                            track_length>5\"\n",
    "event_selection = \"mc_nu_pdg==14 &\\\n",
    "                   (nu_flash_chi2<10.0 | nu_score>0.25) &\\\n",
    "                   daughters_start_contained &\\\n",
    "                   nu_contained &\\\n",
    "                   longest_muon_candidate_length==track_length &\\\n",
    "                   nu_score>0.06\"\n",
    "\n",
    "nuScore_ovrl = msc_overlay_run2.array(\"nu_score\")\n",
    "fmScore_ovrl = msc_overlay_run2.array(\"nu_flash_chi2\")\n",
    "mcnupdg_ovrl = msc_overlay_run2.array(\"mc_nu_pdg\")\n",
    "daughters_start_contained_ovrl = msc_overlay_run2.array(\"daughters_start_contained\")\n",
    "nu_contained = msc_overlay_run2.array(\"nu_contained\")\n",
    "track_length = msc_overlay_run1.array(\"track_length\")\n",
    "\n",
    "cut_ovrl = (mcnupdg_ovrl == 14)&(nuScore_ovrl > 0.06)&((fmScore_ovrl < 10.0)|(nuScore_ovrl>0.25))&\\\n",
    "           (daughters_start_contained_ovrl)&(nu_contained)\n",
    "\n",
    "label_ovrl = \"BNB Overlay Run1\"\n",
    "trkLength_ovrl = msc_overlay_run1.array(\"mcs_trackLength_rm10\")\n",
    "trkMom_MuFwd_ovrl = msc_overlay_run1.array(\"mcs_trackMom_MuFwd_rm10\")\n",
    "trkMomErr_MuFwd_ovrl = msc_overlay_run1.array(\"mcs_trackMomErr_MuFwd_rm10\")\n",
    "trkMom_RangeMu_ovrl = msc_overlay_run1.array(\"track_range_mom_mu\")\n",
    "trkIsContained_ovrl = msc_overlay_run1.array(\"track_is_muon_candidate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_mc = \"MC BNB only\"\n",
    "trkLength_mc = t_mc_only2.array(\"mcs_trackLength_rm10\")\n",
    "trkMom_MuFwd_mc = t_mc_only2.array(\"mcs_trackMom_MuFwd_rm10\")\n",
    "trkMomErr_MuFwd_mc = t_mc_only2.array(\"mcs_trackMomErr_MuFwd_rm10\")\n",
    "trkMom_RangeMu_mc = t_mc_only2.array(\"track_range_mom_mu\")\n",
    "trkIsContained_mc = t_mc_only2.array(\"track_is_muon_candidate\")\n",
    "\n",
    "label_data = \"Data Run1 5E19\"\n",
    "trkLength_data = t_data_run2.array(\"mcs_trackLength_rm10\")\n",
    "trkMom_MuFwd_data = t_data_run2.array(\"mcs_trackMom_MuFwd_rm10\")\n",
    "trkMomErr_MuFwd_data = t_data_run2.array(\"mcs_trackMomErr_MuFwd_rm10\")\n",
    "trkMom_RangeMu_data = t_data_run2.array(\"track_range_mom_mu\")\n",
    "trkIsContained_data = t_data_run2.array(\"track_is_muon_candidate\")\n",
    "\n",
    "label_ovrl = \"BNB Overlay Run1\"\n",
    "trkLength_ovrl = t_ovrl_run2.array(\"mcs_trackLength_rm10\")\n",
    "trkMom_MuFwd_ovrl = t_ovrl_run2.array(\"mcs_trackMom_MuFwd_rm10\")\n",
    "trkMomErr_MuFwd_ovrl = t_ovrl_run2.array(\"mcs_trackMomErr_MuFwd_rm10\")\n",
    "trkMom_RangeMu_ovrl = t_ovrl_run2.array(\"track_range_mom_mu\")\n",
    "trkIsContained_ovrl = t_ovrl_run2.array(\"track_is_muon_candidate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "selection_mc = (trkIsContained_mc==1)&(trkLength_mc>100)&(trkMomErr_MuFwd_mc<7.5)&((trkMom_MuFwd_mc-trkMom_RangeMu_mc)>-0.2)\n",
    "mcscont_mc = trkMom_MuFwd_mc[selection_mc]\n",
    "rngcont_mc = trkMom_RangeMu_mc[selection_mc]\n",
    "\n",
    "selection_data = (trkIsContained_data==1)&(trkLength_data>100)&(trkMomErr_MuFwd_data<7.5)&((trkMom_MuFwd_data-trkMom_RangeMu_data)>-0.2)\n",
    "mcscont_data = trkMom_MuFwd_data[selection_data]\n",
    "rngcont_data = trkMom_RangeMu_data[selection_data]\n",
    "\n",
    "selection_ovrl = (trkIsContained_ovrl==1)&(trkLength_ovrl>100)&(trkMomErr_MuFwd_ovrl<7.5)&((trkMom_MuFwd_ovrl-trkMom_RangeMu_ovrl)>-0.2)\n",
    "mcscont_ovrl = trkMom_MuFwd_ovrl[selection_ovrl]\n",
    "rngcont_ovrl = trkMom_RangeMu_ovrl[selection_ovrl]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Purity: 4.00%\n"
     ]
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "cut = ((df_ovr1.nu_mu_cc_selected)&\\\n",
    "      (df_ovr1.mc_nu_pdg == 14)&\\\n",
    "      (df_ovr1.nu_score > 0.06)&\\\n",
    "      ((df_ovr1.nu_flash_chi2 < 10.0)|(df_ovr1.nu_score>0.25))&\\\n",
    "      (df_ovr1.daughters_start_contained)&\\\n",
    "      (df_ovr1.nu_contained)&\\\n",
    "      (df_ovr.track_length>20.0)&\\\n",
    "                            (df_ovr.track_score>0.90)&\\\n",
    "                            (df_ovr.vtx_distance<4.0)&\\\n",
    "                            (df_ovr.track_chi2_proton>60.0)&\\\n",
    "                            (df_ovr.track_chi2_muon<30.0)&\\\n",
    "                            (df_ovr.generation == 2)&\\\n",
    "                            (df_ovr.track_length>5)&\\\n",
    "                            ((df_ovr.track_chi2_proton/df_ovr.track_chi2_muon)>7.0))\n",
    "\n",
    "query_event = (cut &\\\n",
    "              (df_ovr1.mc_nu_vtx_distance<5) &\\\n",
    "              (abs(df_ovr.mc_pdg)==13))\n",
    "\n",
    "\"\"\"Instead of 0.06 move it to 0.15 for nu_score\"\"\"\n",
    "\n",
    "bins = np.linspace(0, 1, 34)\n",
    "x = bins[:-1] + np.diff(bins) / 2\n",
    "hb, _ = np.histogram(df_ovr1.nu_score[query_event], bins=bins)#~cut\n",
    "ax1.bar(x, hb, width=np.diff(bins), color='blue', label='Background')\n",
    "hs, _ = np.histogram(df_ovr1.nu_score[cut], bins=bins)\n",
    "ax1.bar(x, hs, bottom=hb, width=np.diff(bins), color='green', label='Signal')\n",
    "ax1.set_xlim(0, 1)\n",
    "ax1.legend(loc='upper left')\n",
    "\n",
    "bins = np.linspace(0, 30, 31)\n",
    "x = bins[:-1] + np.diff(bins) / 2\n",
    "hb, _ = np.histogram(df_ovr1.nu_flash_chi2[query_event], bins=bins)\n",
    "ax2.bar(x, hb, width=np.diff(bins), color='blue', label='Background')\n",
    "hs, _ = np.histogram(df_ovr1.nu_flash_chi2[cut], bins=bins)\n",
    "ax2.bar(x, hs, bottom=hb, width=np.diff(bins), color='green', label='Signal')\n",
    "ax2.set_xlim(0, 30)\n",
    "ax2.legend(loc='upper right')\n",
    "\n",
    "_, _, _, h2 = ax3.hist2d(df_ovr1.nu_score[query_event], df_ovr1.nu_flash_chi2[query_event],\n",
    "                         bins=(20,20), range=[[0,1],[0,30]], norm=col.LogNorm())\n",
    "ax3.set_xlabel('nuScore')\n",
    "ax3.set_ylabel('fmScore')\n",
    "fig.colorbar(h2, ax=ax3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print('Purity: %1.2f%%' % (100.0 * np.sum(cut) / len(df_ovr1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xb = np.linspace(0, 1, 21)[:-1]\n",
    "\n",
    "beta = ROOT.TCanvas(\"beta\",\"2dplot\",200,10,500,300)\n",
    "dy = ROOT.TGraph()\n",
    "dy.SetTitle(\"nuScore purity\")\n",
    "for i,nu_cut in enumerate(xb): \n",
    "    pcut = (df_ovr1.nu_score>nu_cut)\n",
    "    alpha = np.sum(pcut&query_event)\n",
    "    alpha = float(alpha)\n",
    "    gamma = np.sum(pcut)\n",
    "    gamma = float(gamma)\n",
    "    purity =  alpha/gamma\n",
    "    #efficiency = np.sum(pcut&cut) / np.sum(cut)\n",
    "    #purity = np.sum(pcut&cut) / np.sum(pcut)\n",
    "    \n",
    "    dy.SetPoint(i,nu_cut,purity)\n",
    "     \n",
    "dy.Draw()\n",
    "beta.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: beta\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xb = np.linspace(0, 1, 21)[:-1]\n",
    "\n",
    "beta = ROOT.TCanvas(\"beta\",\"2dplot\",200,10,500,300)\n",
    "dy = ROOT.TGraph()\n",
    "dy.SetTitle(\"nuScore efficiency\")\n",
    "for i,nu_cut in enumerate(xb): \n",
    "    pcut = (df_ovr1.nu_score>nu_cut)\n",
    "    alpha = np.sum(pcut&cut)#cut\n",
    "    alpha = float(alpha)\n",
    "    gamma = np.sum(cut)#cut\n",
    "    gamma = float(gamma)\n",
    "    efficiency =  alpha/gamma\n",
    "    #efficiency = np.sum(pcut&cut) / np.sum(cut)\n",
    "    #purity = np.sum(pcut&cut) / np.sum(pcut)\n",
    "    \n",
    "    dy.SetPoint(i,nu_cut,efficiency)\n",
    "     \n",
    "dy.Draw()\n",
    "beta.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: beta\n"
     ]
    },
    {
     "data": {
      "image/png": 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gAbJOin52xV3J0uV/Sxm29z6/uk4RI8/HXMqExyvTpcvSFTHVkzEXMdVxzMW9pVciKNA/u+KuZCGEYRjim/7VY5nFe5+ualnKhE/GXMSEhxBOp5O19nQ6xZ9G8qf6dszypzqOM01sEfO8KkEnRb33zrn4Ffrswi/ShBDihWiEv+8/xISvJ4QQ51ZdL30hf6onY1ZKFTHVaYVeyjyvaxTDGOOcG8fROWeMefFo5onv+Pjv5XJ59XBmSUMtaMLzMRc04ZfLJY6zoKnOx1zKVKcoK2ieVyJohV6i9GtdvK/ePn/L21JBE26tHYbhcrkUtE7Mx1zQVI/jmJf+90xQDf32irvyFf02YsLXEwcZg0YVMtW3Y5Y/1WmQcYaLmOd1vfg3hD/JHNUX4u+nZY1ZZb9BlzL4NOZSJjy/xH/83V/+sCdjLmKqbwcpf8yrog8dACohqOQCAHgGgQ4AlSDQAaASBDoAVIJAB4BKEOgAUAkCHQAqQaADQCUIdACoBIEOAJUg0AGgEgQ6AFSCQAeAShDoAFAJAh0AKkGgA0AlCHQAqASBDgCV+D8ro66xjaopagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yb = np.linspace(0, 30, 31)\n",
    "\n",
    "beta = ROOT.TCanvas(\"beta\",\"2dplot\",200,10,500,300)\n",
    "dy = ROOT.TGraph()\n",
    "dy.SetTitle(\"fmScore Purity\")\n",
    "for i,fm_cut in enumerate(yb): \n",
    "    pcut = (df_ovr1.nu_flash_chi2>fm_cut)\n",
    "    alpha = np.sum(pcut&cut)\n",
    "    alpha = float(alpha)\n",
    "    gamma = np.sum(pcut)\n",
    "    gamma = float(gamma)\n",
    "    purity =  alpha/gamma\n",
    "    #efficiency = np.sum(pcut&cut) / np.sum(cut)\n",
    "    #purity = np.sum(pcut&cut) / np.sum(pcut)\n",
    "    \n",
    "    dy.SetPoint(i,fm_cut,purity)\n",
    "     \n",
    "dy.Draw()\n",
    "beta.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: beta\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yb = np.linspace(0, 30, 31)\n",
    "\n",
    "beta = ROOT.TCanvas(\"beta\",\"2dplot\",200,10,500,300)\n",
    "dy = ROOT.TGraph()\n",
    "dy.SetTitle(\"fmScore Efficiency\")\n",
    "for i,fm_cut in enumerate(yb): \n",
    "    pcut = (df_ovr1.nu_flash_chi2>fm_cut)\n",
    "    alpha = np.sum(pcut&cut)\n",
    "    alpha = float(alpha)\n",
    "    gamma = np.sum(cut)\n",
    "    gamma = float(gamma)\n",
    "    efficiency =  alpha/gamma\n",
    "    #efficiency = np.sum(pcut&cut) / np.sum(cut)\n",
    "    #purity = np.sum(pcut&cut) / np.sum(pcut)\n",
    "    \n",
    "    dy.SetPoint(i,fm_cut,efficiency)\n",
    "     \n",
    "dy.Draw()\n",
    "beta.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xb = np.linspace(0, 1, 21)[:-1]\n",
    "yb = np.linspace(0, 30, 31)\n",
    "\n",
    "pgrid = np.empty(shape=(len(xb), len(yb)), dtype=np.float32)\n",
    "\n",
    "for i, nu_cut in enumerate(xb):\n",
    "    for j, fm_cut in enumerate(yb):\n",
    "        pcut = ((df_ovr1.nu_score>nu_cut)&(df_ovr1.nu_flash_chi2>fm_cut))\n",
    "        alpha = np.sum(pcut&cut)\n",
    "        alpha = float(alpha)\n",
    "        beta = np.sum(cut)\n",
    "        beta = float(beta)\n",
    "        gamma = np.sum(pcut)\n",
    "        gamma = float(gamma)\n",
    "        efficiency = alpha/beta\n",
    "        purity =  alpha/gamma\n",
    "        #efficiency = np.sum(pcut&cut) / np.sum(cut)\n",
    "        #purity = np.sum(pcut&cut) / np.sum(pcut)\n",
    "        pgrid[i,j] = efficiency * purity\n",
    "\n",
    "plt.imshow(pgrid.T, aspect='auto', origin='lower', extent=(xb[0], xb[-1], yb[0], yb[-1]))\n",
    "cbar = plt.colorbar()\n",
    "plt.xlabel('nuScore cut')\n",
    "plt.ylabel('fmScore cut')\n",
    "cbar.set_label(r'efficiency $\\times$ purity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sel = ((df_ovr.track_is_muon_candidate)&\n",
    "       (df_ovr.mc_neutrino < 1)&\n",
    "       (df_ovr.mcs_trackLength_rm10 > 100)&\n",
    "       (df_ovr.mcs_trackMomErr_MuFwd_rm10 < 7.5))\n",
    "\n",
    "plt.hist2d(df_ovr.track_range_mom_mu[sel], df_ovr.mcs_trackMom_MuFwd_rm10[sel],\n",
    "           bins=100, range=[[0,2.5],[0,2.5]], norm=col.LogNorm())\n",
    "plt.colorbar()\n",
    "plt.xlabel('Range Momentum [GeV]')\n",
    "plt.ylabel('MCS Momentum [GeV]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xbins = np.linspace(0, 2, 5)\n",
    "y = np.empty(shape=len(xbins)-1)\n",
    "ye = np.empty_like(y)\n",
    "\n",
    "for i in range(len(xbins)-1):\n",
    "    p = df_ovr.track_range_mom_mu\n",
    "    selection = sel & (p >= xbins[i]) & (p < xbins[i+1]) & (df_ovr.mcs_trackMom_MuFwd_rm10>0)\n",
    "    mcs = df_ovr.mcs_trackMom_MuFwd_rm10[selection]\n",
    "    rng = df_ovr.track_range_mom_mu[selection]\n",
    "    delta = (1.0 / mcs - 1.0 / rng) / (1.0 / rng)\n",
    "    y[i] = np.mean(delta)\n",
    "    ye[i] = np.std(delta)\n",
    "\n",
    "x = xbins[:-1] + np.diff(xbins) / 2\n",
    "plt.errorbar(x, y, ye, np.diff(xbins)/2, 'o', c='red', label='Run 1 overlay')\n",
    "plt.xlabel(r'$p_{range}$ (GeV)')\n",
    "plt.ylabel(r'$p^{-1}_\\mathrm{MCS}$ fractional bias')\n",
    "plt.axhline(0, ls='dotted', c='black')\n",
    "plt.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"fractionalbias.png\",format ='png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeBiasAndResolPlots(selection,trkMom_MuFwd,trkMom_RangeMu,xmin,xmax,xstp,label,color):\n",
    "    bins = np.arange(xmin,xmax+xstp,xstp)\n",
    "    loedges = bins[:-1]\n",
    "    hiedges = bins[1:]\n",
    "    #print loedges\n",
    "    #print hiedges\n",
    "    hlist = []\n",
    "    hb = r.TH1F(\"bias\"+label,\"\",len(loedges),xmin,xmax)\n",
    "    hr = r.TH1F(\"resol\"+label,\"\",len(loedges),xmin,xmax)\n",
    "    hb.GetXaxis().SetTitle(\"p_{range} [GeV]\")\n",
    "    hr.GetXaxis().SetTitle(\"p_{range} [GeV]\")\n",
    "    hb.GetYaxis().SetTitle(\"p_{mcs}^{-1} fractional bias\")\n",
    "    hr.GetYaxis().SetTitle(\"p_{mcs}^{-1} fractional resolution\")\n",
    "    hb.GetYaxis().SetTitleOffset(1.6)\n",
    "    hr.GetYaxis().SetTitleOffset(1.6)\n",
    "    hr.SetLineColor(color)\n",
    "    hb.SetLineColor(color)\n",
    "    hr.SetLineColor(color)\n",
    "    for b in range(0,len(loedges)):\n",
    "        #print b, loedges[b], hiedges[b]\n",
    "        abin = selection&(trkMom_RangeMu>=loedges[b])&(trkMom_RangeMu<hiedges[b])\n",
    "        mcscontb = trkMom_MuFwd[abin]\n",
    "        rngcontb = trkMom_RangeMu[abin]\n",
    "        vals = (1./mcscontb - 1./rngcontb)/(1./rngcontb)\n",
    "        h = r.TH1F(label+\"bin\"+str(b),label+\"bin\"+str(b),20,-0.6,0.6)\n",
    "        for v in vals: h.Fill(v)\n",
    "        #c = r.TCanvas(\"c\")\n",
    "        #c.Draw()\n",
    "        #h.Draw()\n",
    "        fr = h.Fit(\"gaus\",\"MESQ\",\"\",-0.2,0.2)\n",
    "        hlist.append(h)\n",
    "        if int(fr)>0:\n",
    "            #print fr.GetParams()[0], fr.GetErrors()[0]\n",
    "            #print fr.GetParams()[1], fr.GetErrors()[1]\n",
    "            #print fr.GetParams()[2], fr.GetErrors()[2]\n",
    "            hb.SetBinContent(b+1,fr.GetParams()[1])\n",
    "            hb.SetBinError(b+1,fr.GetErrors()[1])\n",
    "            hr.SetBinContent(b+1,fr.GetParams()[2])\n",
    "            hr.SetBinError(b+1,fr.GetErrors()[2])\n",
    "    return hb, hr, hlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 4))\n",
    "plt.subplot(131)\n",
    "plt.hist2d(rngcont_mc, mcscont_mc, bins=50, range=[[0,2.5],[0,2.5]], norm=col.LogNorm())\n",
    "plt.colorbar()\n",
    "plt.title(\"Pseudo-exiting Tracks (rm 10cm)\")\n",
    "plt.xlabel('Range Momentum [GeV]')\n",
    "plt.ylabel('MCS Momentum [GeV]')\n",
    "plt.text(1., 2.2, \"dirt\", fontsize=12)\n",
    "plt.subplot(132)\n",
    "plt.hist2d(rngcont_data, mcscont_data, bins=50, range=[[0,2.5],[0,2.5]], norm=col.LogNorm())\n",
    "plt.colorbar()\n",
    "plt.title(\"Pseudo-exiting Tracks (rm 10cm)\")\n",
    "plt.xlabel('Range Momentum [GeV]')\n",
    "plt.ylabel('MCS Momentum [GeV]')\n",
    "plt.text(1., 2.2, \"bnb\", fontsize=12)\n",
    "plt.subplot(133)\n",
    "plt.hist2d(rngcont_ovrl, mcscont_ovrl, bins=50, range=[[0,2.5],[0,2.5]], norm=col.LogNorm())\n",
    "plt.colorbar()\n",
    "plt.title(\"Pseudo-exiting Tracks (rm 10cm) overlay\")\n",
    "plt.xlabel('Range Momentum [GeV]')\n",
    "plt.ylabel('MCS Momentum [GeV]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " NEW MINIMUM FOUND.  GO BACK TO MINIMIZATION STEP.\n",
      " =================================================\n",
      "                                                  V\n",
      "                                                  V\n",
      "                                                  V\n",
      "                                               VVVVVVV\n",
      "                                                VVVVV\n",
      "                                                 VVV\n",
      "                                                  V\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: biasMC BNB only (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: resolMC BNB only (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: MC BNB onlybin0 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: MC BNB onlybin1 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: MC BNB onlybin2 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: MC BNB onlybin3 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: MC BNB onlybin4 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: MC BNB onlybin5 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: MC BNB onlybin6 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: MC BNB onlybin7 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: MC BNB onlybin8 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: biasData Run1 5E19 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: resolData Run1 5E19 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: Data Run1 5E19bin0 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: Data Run1 5E19bin1 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: Data Run1 5E19bin2 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: Data Run1 5E19bin3 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: Data Run1 5E19bin4 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: Data Run1 5E19bin5 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: Data Run1 5E19bin6 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: Data Run1 5E19bin7 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: Data Run1 5E19bin8 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: biasBNB Overlay Run1 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: resolBNB Overlay Run1 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: BNB Overlay Run1bin0 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: BNB Overlay Run1bin1 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: BNB Overlay Run1bin2 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: BNB Overlay Run1bin3 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: BNB Overlay Run1bin4 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: BNB Overlay Run1bin5 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: BNB Overlay Run1bin6 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: BNB Overlay Run1bin7 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: BNB Overlay Run1bin8 (Potential memory leak).\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: cd4\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: co4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cqv5Jfyatj0ZWCcbMKuBy9kpUS9/oDxYhhNxClSA1Yrn97pQ5Dsbh3ynb6xfqjg48LdnwX7z/aF//FJuqqvqXLa7rOt6yNNW7M5ijdwsVxaML/j259Khb0HMgbc/QbSGRS5cyk6voSdZTtXRycrySXrweTFmWIYTJZBILUF3Xs9lsOp3OZrP+1Y0ZsWP9adn1xu/F/RBCG+6H4EbxwEUuPfDXfZf+KkIIIRSHONKKAau7vkv/DqaxQ2s6ncZuqqIo5vN5XK27symjsaESFcWjXdPP4bg63zOzS65ls5yMrypVknLRe5EsauDZyGqXTH/2X+ymikGqu/NDCCHeE7573F8/eRs4qmw+zQB7SztsZxDwvKUPVZPJpH9Lh6VL53W9Vmu/t9gseTt5VqP4pYyikcDgnv3g0OQqkoeqOKh34Qz0Tb1T7WZp20ky2f5qsm0YkI+0AUjZOXuJQ1XTNP1uqrIsu/zUNE13p1I4HkeNwHapw1Ab7qd9QcYicahaLBZLdyTtplUtFovYg1WWZXwQ85YLK4zSGJJKd20FgFUHiT46q85byot/rs6Xquu6aZo4I6rrpqrrejKZxIX9bi3GZyzlw3nOJOXinyflQL9BZecsHeNExO7aCtsXZnVWJNtcdE2XTE4n/lszXFvhsuySa9ksp+NwxUHZOaKsdslj3FB57Twqk6tGL5sPMcDexjCHgdHJ695/OtU5FF3xwKqDlgVl5/wco6dqd66hMAKjO7zzWQKWHLiOOUvmbOUVqhiNMSaV0cVB4JCOEX2UnTMjVLGPkRaIMUZA4ECOU8eUnbMkVLE/xQIYu6PVsZEei3IpeU1UJ09F8Sg+aJ/+ckwEQeDI2laiOjd5hSpn/2Xrqws+xccAYyTlcEh5hSpBagT8jjh7Dv9G78i/NddWOBt5hSqA/AlS7MoI4JkxUZ3dqAvA2KljHJhQxT4coANjN0gdk+fOg1DFxdpwf+gmAIyTY9FzIlSxM6UBGK/B+4oGbwCHl9dEdefUAHBAg/xlMV39bOQVqgSpHKkFALADw3/sRt4FxiuTg8NMmsHB5NVTBZA/ExXGasBflhHA8yBUsZUqACsEKWAtw39crAgPh24CwGVldXCYVWNILa+eKp3qABzE4H9TjACegbxClSCVF/s/AOws8fBfXddVVdV13S1pmqaqqqqqmqbZshr5knSB8crw4DDDJpFIylBVVdVsNuv+DyE0TTOZTOKz3YO6rpdWAzi+/nwDh38nLpODw0yawcEkC1VN0ywWi7Zt67qez+dxYV3XZVk2TdM0TVmWsTbNZrPpdFrXddu2i8WiX8LIiGMpTtpSonL4Bzy7lKEq/h+TU/xysVh0K8Ta1D1e+kYy5biKU7TU+eTw72Q5OOS4EoeqyWQSj/m62LR0hBdX23TYV2yWqp3sxAbndDVNEwNTt8Th34nL8OBQjT1RiSeqt23bNM18Pu8XqVWbylO7Wdp2shObnVM0mUym0+nSoZ3DP45EXT1pyUJVv/p0s9Rjj3pcGL80NQEYUCxB3YT07f1PDv/GTcbl6JKFqv5Uqvg4nkrTdVktFou4vJuy0F+ZjKhEnK5YkSaTSRzjizMWHP6dsmyTrkp7ilJe/HM6nXYnzsQTAOu6bpomdol3daqu68lkEhf2pzWQl2wrETyDrjMpzv6MX/bnUS0Wi1i+4uFfnL0eHP6RkEurn66Uoaqu61iDlmZ3Li2pqipOvXIsCOTA4d8JkloYQpHPJICiyKgx56urRL3fRVE8att7w7TnGYy02fk4t11y7ZHe6sJz2yxjFUvZ1t/U4UrExa+8rtJyOVntknnd+88NlXNh43N+1vad61DnIIwAnqi8QpUgBQCMVOLrVDFujpyAE6CUMZC8eqrIgv5C2MpEhXEYxa+mKMbRTnYjVAHsR5AiAdOqTpHhP56wewMnQCljOHn1VOlUH57NDpyAoUtZUTy6cJ32yaqDt5ZU8gpVghQAY7fz5a/u6Vc7MYb/CCHoMAeAZyVU0aOnEBg1x4cMSqgC4LSM7vhQFjwVQhX2Z4CBjC7/sVVeE9Wd/Xc0/TNT4oYuwsOww+kqAJlyfMjQ8gpVgtQx/e38lCdl6EB3a4fT4/AvZ44PGVBeoYrB+MMAOxOkcjbW40NXqzoJ5lSdN73lAIMqwsOhm0AyQhW6qYDxc4hIBoSqM6YGASdm1IeIavL4CVVnb9Q1COAEqMOnIq+J6s6pAQBGKq9QJUgBsDcDZ+Qh5fBfVVXFE1VVxYVN01RVVVVV0zTdmnVdV1VV13XCd2cvbbg/dBMAUjqF0+ikw5FL2VO1WCzKsuziVAihaZrJZFKWZQhhMpnEjqi6rmez2XQ6nc1mTdP0wxbHpmsQ9meiAum1rUR1AoqEFaEoll8tBqwYm2J/VV3XRVFMp9PYTVUUxXw+j6utfjsHFPfek97gRfForJcBzINdci2bJTtPskgRHo5xl/+qUp1BWT6ErHbJZMN/XXLqj/0tFotuhaqqZrNZ93jpGzkqx0PAicnmzyrnLNnwX787ajabdcmxn5/6q619kWLzH/t8cujpsEkBcuN+NWOWLFTVdd1NPK+qajKZbOmCirPXV5dLTgCcKdOqxi/l8F+XorrAVJZlt7BpmqVp7AzDTgucDAWNnKQMVV3vVOyyikOB3bSqxWIRl5dlGR/0V+bITuHcY4DoZEY52vZ0fpazlHL4L+aq+OV8Pu8WxplSXTdVXdeTySQunE6nqRoAADCglNepij1PS/Ol4rBgf0lVVW3bbppWxWHpKgeAw0h/Q+XVqLQ2PK1dWPQkbxhf0b0MnAB/KciMe/8B7McV1fPiV0A28gpVHJajOkhBkALWSj/8R+78PQCAAxCqABghXe/kR6g6GwoQcHp0vZMToerMKECcvXg9l6qq+lce7hb2769V1/XSagBb5BWqXFIBOLR4jeJ46/d4bZf+hYu7B3VdxxW61ciIvxFkKa9Q1fYM3ZbTogBBCOHJfbGapqnrejqdxvto1XUdb1Qab1Ea15nNZtPptK7rtm0Xi8WWO8QzGH8pyExeoYrDUoA4e3Vdx5todREqhNDdojQ86cHqHnfLhSrgQq5TBZyXOE0qJqcYsMLKPR5ihNo06rdlfoJedjhnQtXpKIpHa5e34f72FeDc1HUdJ6FPJpMtMWjTLUolp4GZz0CuhKqT0rb31iwt7sfn/B2AOF+q+z9OS48TquIKcUzQzPQRUNLIT15zqpz9BxxaN2Wqu1ZCVVXdtKrFYhGXdzPWY95yYQXgQnn1VOlMSU88hZ66rpum6Q7b4pyq/sKumyr2Y8WF0+l0sBYD45FXqOJQpFV4Ip73F1ZO7luaQVVVVdu2m6ZVMRgHimRMqALOztqctPtChudAkSzlNaeKxBzSAcCxCFVnwCEdABxeXsN//ZP+TFoH4Cl638lbXqFKkEpJ9YHDcPg3MNucXOUVqkhP9YHUBClgrYPMqeofxsUTkquq6t+ONN4gwsX0ANiV3neylz5ULSWqeBeIEEL3IN7KNN4K3unKh6L6ACdJNyEZSxyqljqf6rqON9WKt9OKz85ms+l0Wtd127aLxaLfg0Viqg8AHEvKUNU0TQxM3ZLudlohhNg11T3uf1fCNgBwgvS+MwYpQ9VkMplOp0sjektfrt4doq/YLGE7T5/NBZwkve/kLVmoijmpm5C+vf9p07PtZqnaeUZsNOA0OFBkJJJdUiGO9PWnpc/n8zihKi6J06rMTN9dUTwaugkA2XCgSPaShaquMyme8Re/7M+jWiwW8/k8hBBnrHc3indhhS3a9t7e3+OQDg7MxT+BtQ578c8YnmIB6rqp6rqeTCZxYX9WOykp9HAwgtRROVBkPNKHqqqq+hUn9kj1R/3iCksLAWBJUTyKf06K8DCYEUH2jnGbmrXhae1CnerPyiEdcIouMxcCji6ve/+dfJA60tzzU9+MAJChvELVOXC8BbCjNtwfugmwh4PcUJlhGPsDTpLed0ZCqDo5qg8ADEGoAiBLet8Zm7zmVDn77/JUH+Ak+VvAeOQVqgSpZ2UDwuE5/APWyitUAeRPkALWMqfqJBj7A06MssYICVUnxNEzcFqK8HDoJsAehCoAgASEqvHTSQ6cGGWNccprovoYz6k50u38LjSSzQWcuV1qZixnxv4YnbxC1ViC1JIhb+fneA4Y1CUOLC+umcX9nVaDzOQVqjKRS+fT7sYZRoHTIP1AdBah6iAHUozBvr96v3d2McaJCmOiA57ROotQFU71j6XSs9W+v/Tx9VAyEEHqGGxkRsjZf+On9ABABvLqqdKpDgCMVF6hSpDag7E/4PSobIxZyuG/pmmqqqqqqq7r1YVN03QL67peWo1LEkOB06OyMU4pQ9VkMgkhVFU1m82qqgohNE0TF3bPhhDquo4rdKsBHJPDP+Ag2kSm02n3at3jsizLsowLy7KcTqdxgC8+iI/n83n3OFVjloTw8ECvzCnxOVlyuF1yWCGEWI66Gjifz+PCsiyX6lj8v6tj7elulktLueOE8Ld/50TleUZZ7ZLJ5lTFQ7oQQtM0TdPE2rRYLOKD8KQHKx7z9Tuo4gFiqmYAbBFLUOyOqqpqMpk0TVPXdVmW3cK6rmOH+nQ6jY+LojirSjXw5UWM/TFaKSeqx2I0m81CCPHILzydn0Kvlq19hWLzFMXWbgY8s9XiU1WVw79Vp3ltPziwxGf/xaO6ePy3JQZtKk+7JyfXaQQuIU6cCk8md3aDgA7/hue8P8Yv5fBf//84Lb3rUQ8hxDHBhId6DqSAy4mpKI7ubVnt2Q//2Jtty5ilPPsvDvyFJ9EqPOlXj48Xi0VcXpZlf1qDM2uAYyqKIk4874rPQQ//gPORsqeqaZquVzzOqeov7OpU7MfqjhRTNQDgQl1vepei4oBgd0y4WCy6kwHjag7/gB2lnFPVVZ+l2Z1LXehVVbVte26zPoEcxBrVXTYvhDCfzx3+Dc+EKk5Ckc/kgKLYozFF8cicKtLyoVqy1y55AtYe6a0uPIfNMsC+EEPVqW/YtVSeZ5TVLpnXvf/cUBkYytq+cx3qwO7yClWCFMDZMfbHqUh59h8AXJKDasYvr54qgPyZqACsJVQB7EeQSsnYHyfE8B8AQ5NTOQl59VTpVAcARiqvUCVIAQAjZfgPgIGYUMVpEaoAGJQxCk6FUAUAkIBQBcAQjP1xcvKaqO7sP4DzotRzQvIKVYIUkD+Hf8BaeYUqgPwJUgkY++MUCVUAp68oHg3dhHXEU06LUAVwFtr23tBNgBPn7D8AjsvYHydKqAJgCMb+ODl5Df85pwYAGKm8QpUgBXDijP1xuhIP/1VVVVVVXdfdkqZp4sKmabqFdV0vrQbAGXEIzSlKGari4F1VVbPZLD5ummYymcRnuwd1Xc9ms7haVVUJGwBwBEXP0G0ZG1uMk5YsVMVup6Zp6rqez+fd47Ism6ZpmqYsy7jObDabTqd1Xbdtu1gs+j1YAPlre4ZuyzjZbpyoZKGqqqrpdLq0ZLFY9L+czWbd4265UAUAnIBkE9XjxKnwZHSvC1hLA3wxQm0a9dvSl+6IEGDcjP1x6hKf/RdTURzd27JanL2+7pmHm185y3ssALAXR8icrpShqiiKOIOqW9L/Mk6r2j4z3V0UAICRShaqYtdUXdddiooDgt08qsViESewxxnrcfZ6940A7GVk/ffG/jgDyUJVTEjddRNCCPP5PIanOCbYdVPVdT2ZTLqBwlQNADg34+vdN/bHSUscqtYuX5pBVVVV27abp1UBcFp0U3EejnGbmrXhae1C9/4DOFmqOqfOvf8A9uPwD1grr1AFkD9Baj/G/jgbiW+oDABrSKKcAaEKgIPRTcU5EaoAODDdVJwHoQo4R1VV9S8EEy/ysrSwruuqqlygGNhRXqGq6Bm6LcDJqut6sVh0XzZN0124uHsQ7w0fbwvhonqX17a6qTgfRT6nsRRFRo3hDBXFo/Fdn/qQTnWX7I7Z5vN5TEvx/9hHFfur6rouiqK7N3xRFN3K+WwWn4WmcNsAACAASURBVNjT4Pf4jPLZJUNuPVUAh9a27VIJ7vda9e9Y2u+g2nTTCICO61QBLN/joeu1WrvylvkJ+RwxA8enpwpgvU29U+1mx20gkBehCjh3ZVl2+alpmrIszUwHLiGv4T931AKOrz+ParFYzOfzEEJZlnVdN00T89YRLqxQFI8O/RbAQeUVqgQp4PhieIoHdV03VV3Xk8kkLpxOp8dpibPAYNTyClUAx7F0CBd7pPqjflVVtW27tBBgC3OqAELYcK6fRAXsTqgCAEhAqAIASECoAgBIIK+J6i6pAORPpQLWyitUKU9A/lQqYK30w39VVfXv7RBPSF5aWNd1vA988ncHABhE4lBV13X/fu9N00wmk/i4e1DX9Ww2i5cwdroyAHAaioT92N08g/l8HtNS/L+733vsnSqKYjqdxm6qoii6lYsiZWNgX0XxyPWs++ySax1us/gEnie/92eUVaVK2VO1epP2fq9V/+5a/Q6qTfeBBwAYkYNPVF8a4Ot6rdau3D+nZkk+ORQAYNUw16na1DvVbnbcBgIA7Oewoaosyy4/NU3T3f4dAODEHHb4rz+ParFYzOfzEEJZlnVdx3vChxBcWAEYl90v/lkUjw7fHCAXhw1VMTzFAtR1U9V1PZlM4sLpdHrQBgAkt9eEBCd2wflIH6qWyk3skeqP+lVV1bbt0kIAgFE7xm1q1oantQvdUQsAGCn3/gMASGCYSyoAAJwYoQoAIAGhCgAgAaEKACABoQoAIIG8zv5zSQUgf0/f+v3hYO0AMpNXqBKkgPypVMBahv8AABIQqgAAEhCqAAASyGtOFQyrKB7tvnLb3jtcS4DzsVflCYpPxvIKVc7+Y0B71al9iyDAWvsmJMUnZ3mFKkEKABgpc6oAABLIq6cKIH8mKgBr6am62NNXT86Ihu0lz1aFjBvGJm3P0G35SrYfJA3bS56tChk3LCtCFQBAAnkN/+lUBwBGKq9QJUgBACM1suG/Hcd00652/HfUsAOtlvSl7id9tWQvdfwtxqpsf1kati8N21e2DTuOYXqq/v7v/z6EcOPGjX/7t38bpAEAF1KpgL0MEKqKorhx48aNGzd+/vOfhxBUKyBDKhXZihdVd2n1DB07VH300UchhP/zf/5P/PLnP/+5UsVI7VjR4mq734liy8tuesqNwJJTqchW3N+L4v4uO35R7DRRgVSOHar+67/+q3t848aNI787JLRjRWvbe0XxaHsCW3p27StvqqGrL75X2mMtlYqTcaDDP9ZrjyuWp/518z788MPcrqEHREeuD/lQqWBEBqoTaxy7p+rGjRvdIWDsYL9z5063UY7cGIC1VCrgEkZ2SQUAgDwVxz/qKoriww8/vHPnTjxd+Wtf+1pVVXVdr65Z13XTNJuePaiqquL/W966KAbYetsbtkuzD2rwBuzSjAEbOdLP1YB74oBUqmeR7T6YSQN2aYZKtWoEler4I44/+clPuncvy3I6nYYQptPp0mplWcblcbVjtnB7w7p1jr/1tjes/+wgv9ldttvgzRhwK430czXgnjgslerSst0Hd2leJs1Qqda+aZ57Yt9g07s+/PDD7lcSt0L/2fl83i2Zz+fH3ED9t15tWH/5kT9S2xvWXxLXnM/n+TQvk2YMuJVG+rkacE/MhEqVtmEq1S7NUKn2bVg+lWqwOVX//d//3T2OHXp9TdPE/2MnXvzyOPrvtdqwuMJsNus+VUezvWFVVS01aW3jD+fC7ZZDMwbcSiP9XA24J2ZCpdpXtvtgpFJtN9LPVUaVaqg0F3vqui/D00k8PhtCiA+OmTq3NywumU6n/VycScOiofq0d2xeJs04/lYa6edqwD0xE9lun5F+ojoqVZ5baaSfq3wq1ZEuqfDOO+8sLXnw4MFisYiPY6hczZ5xwzVNM5lMMmlYN0uuy8UHOoC43BaL956cTqeDTGy8sHmZNGOQrbS9YUf7XO3bsOgIe2ImVKpDNyxSqVSqtA2LcqhURwpVq3d42P4p6W++uOEO9Mvbt2GxVd0vbDKZzOfzHBoWQiiKoizLMxyg2UueW+lon6t9HW1PzIRKdeiGhVz3wdzkuZVUqosN1UXW9rrvyrKMnXXz+bzr5+yePf5cwu0Ni47f+bm9YXErzXuO3La1zRtEtltpjJ+rdtA9MRPZbp8xfqJUqi3NyGQrjfFz1WZTqYYskf2ZbnFJ/zSH/rNH/khtb1h/yTFbtb1h3YjyUBttbfMGke1WGuPnqh10T8xEtttnjJ8olWpLMzLZSmP8XLXZVKrhjzu3//ADbpps/35k27Aok+Zl0oxVI21Yts0+mmy3T7a/mmwbFmXSvEyasWqkDRu82QNcERUA4PS49x8AQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkIVAEACQhUAQAJCFQBAAkLVMK5du1Y87dq1awd9x5dffrlY8cILL7zxxhup3uIHP/jBz372s03Pvv3229962p07d+JTV65cWW1bURRXrlxZepHbt2//+Mc/Xlr44MGDW7du3bp1q7/w2rVrmzbpN77xjaIodvyhbj2x4/pwSlSqUVSqn/70p6+99tprr73205/+dJf1OaCWIVy9ejWEcPWJ7tdx9+7dC7/3zTffDCG8+eabe73jSy+9FEJ4/mnxTV9//fXL/hxfee+990II77333qYVbt++venj981vfjOE8M0V3/nOd/qv8ODBgxDCgwcP+gtffPHF/gu+9dZbcfmWj/eOn/xXXnnFzsKZU6nyr1Tf/va3Vap82PrDiOWpv+Tu3bs77g+xVH3/+9/f6x1jqVqqSq+//nqsX3u91JL33nvvtddei43fUqquX7++6aeLpWrLWzx48CB++1KpirnnlVdeiV/GshUfP3tx6b/CW2+9FUJ48cUXn+UFYXRUqr4MK9W7774bQvj2t78dv3z11VdDCK+++uqlX5BnJFQNY7VUtW178+bNEMLNmzfjl/fu3eu6ha9duxYXvvnmm90RT1etYhmKRWfTwdzaUtW2bb9ULX37Sy+99NJLL7Vte/fu3atXr969eze28OrVq10j2ydF4cJStaV2XFiq+m/RL1VLrxmjT6xc8anY8tjmbrXnnnvuueeei4+vXLnywx/+cDKZxA78yWTSf6nuaLJ9ug7CmVCp+jKsVDFFvfvuu/336jIWx+ePxDDWlqq2bfs7VVekuoLVPjn465eqrm98eyf5luO/WI/6j/sNaHvHprF5XRXov9SFnerxu27fvn39+vXbt2/3n7qwVEVxLsJSqVrqPeqW9BvctTmu8/Wvfz08fZgYQuhmRcRq9dZbb62+8i6NhFOiUvWfyrBSvfvuu/1EFTuu9FQNyB+JYWwpVXH5d7/73f4K/S+XOtVX60v/y86mmQrdwd8upar/VP+Iqt25VPX9+Mc/jk/FUrVqaabC2lIVVo7/+qWqO06NP0L8crVUbXrBTuy9N/zHuVGpxlKpYpdV0E01tK+t/YgwuD/84Q/9L7/44osQwv379x8+fLi0ZturAi+//PL2l/3888+XlnRHjReKPerP4vr163/5y1/i46Io/uM//uPf//3fu2dXC9amEtZ56623fvGLXxRFEUPP48ePl1b44x//GB/85je/+e1vf/vpp5+uvsjqmTt9Dx48+N3vfvfXv/41hPDZZ59tbw+cG5Vq7ZIlR6hUf/7zn5ceMAiXVMhO1wP8ve99rztlN5aqtd54440XXnghrvbJJ59sf/GlTvXnn3/+k08+2fFc5f65P5fQtm1Xp0II8RSbt99+u1vy/1b853/+5/bX/OCDD+Ix3+PHjx8/frx6vt4u7d/yc926desXv/jFX//61/7sByCoVDlVqhDC//2//zduqxDC7peMITmhKiM/+tGPQghxXsLzzz//xz/+8dq1a/fu3WufdKqveuONN379619//vnnL730UixDe71j7GlfPSgckQ8++KCrvL/73e9CCN3ZN0u+/PLLvV75+vXrjx8/jl308ZWBoFJdyoEqVbw8VX/J6hUWOCbDfxn57W9/G570A8cDvguLSDzgy78T5c6dOx9//PHt27c/+uijuCQeC/Y71S/h1q1bjx8/7n78OJVhU6kK+xzCPnjwIHZQiVOwRKXa1+Eq1Z///Oel8T7Df8MSqobUnS/zxRdfxKOT/tkr4cnUhPv373cj7kuef/75zz///OWXX/79738fjwXDPsdzv/rVr+LhY/c49rH/6le/euGFF57lR1vy0UcfFUXx8ccfx170zz777LPPPluqKVeuXFmdmrBYLLa8bOxMun79+q1btz777LPYsfTBBx90KxRFETdpnKOw+2SLmKVWG2laFWdIpeqvk1WlevXVV//1X//17/7u7+JE9ZiouknrDCDlrHd2tnogEi+v0q1w7969/rNdp3rsY4+Pl87IDb25nKun1Wy5+kv3MeiuIhOenH0TeufU9JsX9j+nZummDdevX++e2jLNs/8Kq+fUtE9fp7h/dt7qS3Xn1yydU9Nd8aW/NZYuf7y2PXDyVKrMK1XbO+8vcvbfsIo2+/7Yc3b//v0QQjyPpv84hPBP//RPIYRf/vKXIYQ4f/NXv/pVfCoewF36TZdeLa14/PeMfelL4k0hQgj9I79OnP/xm9/8JuE7An0q1S4OWqniXf/+5V/+5bKtIw2hCgAgAWf/AQAkIFQBACQgVAEAJCBUAQAkIFQBACQgVAEAJCBUAQAkIFQBACQgVAEAJCBUAQAkIFQBACQgVAEAJCBUAQAkIFQBACRwkFBVFEX3uGmaqqqqqmqapltY13VVVXVdH+LdAQCOL32oWkpUk8kkPu4e1HU9m82qqor/J28AAMAA2qSm02n/ZcuyLMuyezydTtu2DSHEB/HxfD7vHgNZSVsfTsPQvxNg2dBV4StfS/hTNU0zm82m0+lsNotLFotFWZbxceyaikN+/Q6qOD7YbZeE7QGeRb/XmT6VCvKRVaVKOfw3mUym0+nSiN7Sl3Fm1aZRv2KzhO0EAEguWaiKOambkN6flr5q07NbutRStRMA4BCSDf8tFovQm40+mUzm83lZll1+apqmLEsz04Gx6/edO+QDOkXyihDP+IsvG0/0i4+LopjP5/HyCnG1/ppxBeUJ8mGXXMtmgaxktUumnKi+qq7rpmniUV3XTVXX9WQyiQu7swUBAEbtGPmuf37floVZhU3ALrmWzQJZyWqXzKkpOW0XwC65ls0CWclqlzzs8N++TP8EAEYqr1AlSAEAI3WQGyoDAJwboQoAIAGhCgAggbzmVHGGiuLRXuu37b0DtQR25JSaM6RSsQuhiuHtXn32rWtwCILUeVKpuFBeocrxHwAwUnmFKkEKABgpE9UBABIQqgAAEhCqAAASEKoAABIQqgAAEhCqAAASyOuSCq5TBeRPpQLWyitUKU9A/lQqYC3DfwAACQhVAAAJCFUAAAkIVQAACQhVAAAJ5HX2nxOVAYCRyitUCVIAwEgZ/gMASCBlqGqapqqqqqrquu4WFj3d8rqul1YDGIt+WRu6LUBGUg7/TSaTsiyrqprNZs0TIYTpdBpXqKoqhFDX9Ww2m06n3WoJ2wBwaCYqAGslC1Wx26lLSLPZrPtyqUcqJqq6ruu6Looi9m+lagYAwCCSDf/VdT2fz0MIsfOpLMvwJFRVVdUf+wtPuqwiPVUAwAlIOfwXp0nFPqoYsLrlcUwwPIlTm7qmtkxQ0N8OAOQs8SUV4qBeVVWTyaRt26VeqNlsFuPUpiE/yQkAGKmUw3/9k/vig9WhPdOngMFtP0+5O7fGecrAXlJeUiEO8IVeqJpMJl2Kms1mcaJVWZb9We0KFnBMcZpBnJMQH//pT38KIfzzE7FSxckMcTVHg8BO2nRiJYrm83nbtv2ZVd179RdOp9Pu29M2hrEI4eGBVuYZneQuGXuh4uNYi+bz+T//8z+v/rD9AtXVtPZENwsXUqmyldUumfjin/P5PJaebkJ627bdwrhat7BtW91UwDFVVdWN7nVLFotFeHKecv9Z5ykDe0k8UX1tJ/nuCwEOKs6mCr2rEHdPlWVZluX7778fQphMJsF5ysCe8rqhcr9UqU3AgcRSE69CHFZ6od5///0YqpynDOwlr1ClVAGHVhRFWZb9IPWnP/3pH/7hH/rr6EoHLiHlnCqAzMWuqbqu+/cnvXnzZpei3n//fecpA5eTV08VwEHFhBRH96L5fP7pp5/evHmzm37QpajJZNINFB6/qcDoCFXAGdl0El/btvFqVd04YDxP2R3fgd0JVQAh9OJUn0QF7M6cKgCABPLqqXJJBQBgpPIKVYIUADBSeYUqgPzpUwfWEqoA9iNIAWuZqA4AkIBQBQCQgFAFAJBAXnOqTP8EAEYqr1AlSAEAI2X4DwAgAaEKACCBvIb/APJn9iewllAFsB9BCljL8B8AQAJCFQBAAnkN/5mpAACMVF6hSpACAEbK8B8AQAIpQ1XTNFVVVVVV1/XqwqZpuoV1XS+tBgAwailD1WQyCSFUVTWbzaqqCiE0TRMXds+GEOq6jit0qwEAjF2yUBW7nZqmqet6Op0uFou4sCzLpmmapinLMq4zm82m02ld123bLhaLfg8WAMBIpQxV8/k8hNBFqBBCjFZR7JrqHnfLhSpgXIqeodsCZCTl2X9xmlRMTjFghafzU3gSoTaN+m2pUE4MBDKhHAFrJT77Lw7qlWXZzaBaa1PvVLtZ2nYygKL46h8AnJyUw3/d2XzdgzihKj6OY4Jmpp+j1SAlVwFwclL2VHVTprpQVVVVN61qsVjE5d2M9Zi3XFjhxPXzU9uGrtNRrgLgtCSbU1XXddM03aSoOKeqv7DrpqrrejKZxIXT6TRVA8hRl5z6A7ht+7flRREM7AJwKlJOVI/n/YWVk/vi9T+7JVVVtW27tJATtDZRdUv0VAFwWhLf+29tTtp9ISdoe19UUYTw8FhNAYADyuuGyv1LKjjjb7yK4lEb7nePN6z1MK7Thvsh3DtW0wDgUPIKVYLUqbngN3rPICAAJyOvUMVp6LqpdmXGOqOiTx1YS6jiYHb5Y2PGOiMkSAFrJb6iOgDAeRKqSO1yPU/6qwAYOaGKw9h9fMRICgAnIa85VaZ/jt6zdDiZrg7AmOXVU9X2DN0WLq/Y93qeft0cV1VVVVX1bzwa7/FQVVV3D/gQQl3XS6sBbJFXqAI4tNgjXlXVbDaLj5ummUwm8dnuQV3Xs9ksruYOEMAuhCrSefbJ5qarc2Cx26lpmrqu433f4+OyLOONSsuyjOvMZrPpdFrXddu2i8Wi34MFsJZQRWqXG8szAshRVFU1nU6XliwWi/6Xs9mse9wtF6qAC+U1UR3goOLEqfBkdK8LWEsDfDFCbRr1KzZ3qZoPCudMqCKRVCN3zgHk8GIqiqN7W1aLs9dXl0tOwFp5hSqXVBi9Z/mtuWUNR1EURZxB1S3pfxmnVZmZDlxCXqFKkAIOKnZN1XXdpag4INjNo1osFnECe5yxHmevd98IsEVeoQpCMALIAcWE1F03IYQwn89jeIo95V03VV3Xk8mkGygcpLXAuAhVpJBq2M4IIAe26SS+2CPVH/Wrqqpt203TqgBWuaQC6eheYszWhieJCtidUAUAkIBQxTM7xICdQUAAxkaoIpFUY3/GEAEYp7wmqrtOFZA/lQpYK69QpTwB+VOpgLUM//FsDjf5ybQqAEYlcaiK1ybuX3q46OkuoFfX9dJqjFvaA3fdAACMUMpQFecZxBs+xMd/+tOfQgj//ERZluHJzeHjaq4BAwCchmRzqmK3U7xacVVVk8mkaZrFYhFC6G6qFc1ms3hz+Lqui6JwwWIA4AQk66mqqmrp9lhVVcVQVVVVf+wvPH2R4k13jWAEDj3tybQqAMYjWU9VnE0Vnozu9SNUWZZlWb7//vvhyX1MN3VNFZv/iDrdJl+H+NW4CSAAY5P4kgrdHd37o4Gd999/P4aqTUN+khMAMFKJJ6qXZdm2bXdaX5yo3mf6FABwkpKFqhik6rpunggh3Lx5s0tR77//fjz7ryzLfj+WCyuM1XGG5wwCkp/+lWKGbguQkWTDfzEhxdG9aD6ff/rppzdv3uzqTpeiJpNJN1CYqgEM43AjtqZVkSsTFYC1EoeqVW3bxkHAf/iHf4hLqqpq29aVFACAU3KMe/91capPomJXReEa6wDkL68bKrv3+2gcZ2DOCCBwBEt1xl8fLiuvUCVIjczRfl86q4ADceRGOolvqAyJyVLA4XSJqm2/qjZiFpclVAFwlvqJaukBXIpQxUg4dgQOYW2QUnC4lLzmVDEORy43pquTGafUnIJNVUXB4RkIVVyWvyWcK0HqZBThYSgeLS3822+3KIrw8OgtYtzyClWO/wA4mra9t27xvdhZteFZ2CivUCVIAXBwOw7wuZgLezJRnT0NONvARAcgnW2je7IUlyJUcSlHrjgKHADZE6oAOCd79XnrIGcfQhUA5+fC/m8d5OxPqGJM2nB/6CYAwHp5nf3nkgq5G7An3BX5gGd3iTLiHEB2lleoEqTGwa+J8+bwb/R2/K05lmNPeYUqgPwJUsBa5lQBACQgVDE2euOBy7l09VB22I1Qxc4GLyvGXIBnt1clUXbYh1DFnpQYAFhHqALOUVVVTdN0XxY9dV3HhXVdV1XVfQmwXV5n/zlROU9F8SiE0PYew6jVdb1YLLovY7qaTqfxy6qq4jqz2Ww6nc5ms6Zp+gmMUXrGCQyuVsUO8gpVglS22vZeKO7/7cHgVDeeQbHyxzUGpqUeqZio6rqu67ooiqZpYthi3C5ROlytip0Z/mM3edSUIjwcugmMXtu2S8dvMVRVVdUf+wtPuqz66wBskbinKtag/iyEpmni4zg7IS6s6zoe9pmsMDL6hzhdVVVVVTWbzUKvlK1dc7Wvq6O7Hc5Zyp6qWGhiVYqPm6aZTCbx2e5BnKkQV9OdDgyuaZq2beNIX5xE1S1fu3672fEaDeQnWaiKfU6xX2o+n3ePy7KMczzLsozrdDMV2rZdLBY61YFhrVYhx3unJskEhjxmQZCzZKGqqqru3JluSf/8mq5fPZipwLNT3UhnMpl0RWk2m5VlGULojgPXTmNnlC7dlagPkt0km1MVpyOE3nnI3fL+at2E0LUvYqZCntpwf+gm9DgTh9Tm8/lkMunqT5eiuoVLR4wAayWeqN4VoO1HdZtOTpacsua3wwnpV5uqqtq2XTrk6xYaCgR2lDJUFUURZ1B1S/pfxmlVyhOQp7XVScniKS6Sx1aJJ6rHayV0Vx/uT6taLBZxHTMVSMMgILCLJLVClmIHyXqqYkLqrpsQQpjP5zFjxTHBrpvKTAWelWlVwL6kIg4vcahau3xpUoKZCiMjvgDADo5x7z8zFU6Bgzx4wq3fgbXyuqGyUgXkT3U6a+aqs1leoUqpYj+qG7BdwgkMZnNykZT3/oPjkaWA3akYHIVQxVYOywBgN0IVFyvCw6GbAAC5E6oAABIQqhg5A5TAJgeqD8oOG+R19p9LKrAHZ+IAu0j410TZYau8QpUglRe1AwB2ZviPi0i68LSiZ+i2ABnJq6cKIH/61IG19FQxfnoLgCNTdlhHqGLMdBgAmxwo9yg7bCZUsYHjMOAEyEAcUV5zqlxSITt+CwCwm7xClSAFAIyU4T9OgsFKAIYmVDFyejeBVUc40HIsxwqhinUUC+AEHOigy7EcG+Q1p4q8KBywjlNqgLWEKoD9CFLAWob/OBWGLAEYVF49VTrVuYy2lagAGFxeoUqQyoKAAoza0YpYUZh7Sp/hPzZQKYBRO2gRUyFZJ32oqqqqaZruy6Knruu4sK7rqqq6LwEAxi7x8F9d14vFovsypqvpdBq/rKoqrjObzabT6Ww2a5qmn8DgmeiKB2A4KUNVsTKMHQPTUo9UTFR1Xdd1XRRF0zQxbJGFkU6oMlcdgKGlHP5r23ZppnkMVVVV9cf+wpMuq/465EV/D2zWn9UwdFsYms8APcc4+6+qqqqqZrNZeBKnNnVNbalQTgwEMqEcZe1oKUcHOSsOG6qWeqFms1mMU5uG/JQqABLw14QhHPaSCqtDe6ZPcVgOHAEYyGFD1WQy6VLUbDYryzKEUJZlnF+1dho7Qxp1InFgCsCgDjv8N5/PJ5NJN1OqS1Hdwu5qC2REOgGA/aXvqWrbtuudqqqqbdv5fD6fz7v5Ut3Ctm11UwGDWLpMcZzoubTQZYrHZ5Du9lH38ZPUMW5TE0vV6sIjvDXAqtXLFE8mk/i4exAvUxzPXFavRuZo3e369XlaXvf+c/UXEvDhYauiKOIVXjp1XZdlGW/w0E367C5T3LbtYrFwRT3gQnmFqrZn6LacnxPIIj427GC1wvR7rbqL6gWXKQb2dIyLfzImcglnaWmAr7sbxNqVXaYYWEuoAljPZYrHpSgetU8eDNwUzpVQBZy7OKEqPo7TqsxMH6M23P/bg/besd+7KHTzE3KbUwVpnMD8MI6oqqpuWtVisYgT1V2meKyOHG5kKXr0VBFCOKEU4han7K+u66Zp4kyprpvKZYqBfeUVqvrTP81aGIBtztlYqjDxegr9Ub94meJN06oAVuUVqgQpYChrw5NExa5MqyK3UAUXuvC8nvbJekV4OMB8VeDcmHXAE0IVY5pQtVtIfRSE5gAAIABJREFUujeinwhIw15PBoSqM9Xv7/nblV3Cw+DiLsCoDTsAZwTw7AlV5+urXp/ifhjkyi4wTk6pYZkRQEIIQhXAvgQpYK28Lv5Z9AzdlrNxupu6u7wyABxBXj1Vjv8Gc2JbXlc8MAjTqs5bXj1VHJvkAZyAHEqZLIVQRQhqAXAKivBw6CZw7oQqAIAEhKozlkOH+aGdw88IQB6EqrN3qmN/p/pzAZAroQqAMcutQzq39nBEeYUq16kC8qdS5SiHzukc2sCgXKfqTJ3RhTFdNobUVCpgrbx6qji2k/7b4PxqAI4pfaiqqqppmu7LpmmqqlpaWNd1VVV1XSd/dwCAQSQOVXVdLxaL7sumaSaTSXzcPajrejabVVUV/0/bAAAY1hnNr+BpRcLJAd2czfl8HtNS/D/2UcX+qrqui6KYTqexm6ooim7lokjZGLbpZtee9AYvikdflbaT/kkPxC65ls2Sl141K4pHbXtv0NaEEJ40yYfkWLLaJVP2VLVtu/SD9XutYtdU97hb3h8W5Kiy+RQe0Dn8jIA9nTwc/Oy/pQG+rtdq7cpbzk/OJ4cCAKwa5uy/Tb1T7WbHbSAAwH4O21NVlmWXn5qmKcvSzPTDKYpHu6x2pjMoXa0KOCY15ywdNlT151EtFov5fB5CKMuyruumaWLecmGFhHaapFncj6seujG5aFt3jSCt/kQF/ehDynPXVnPO2GFDVQxPsQB13VR1XU8mk7hwOp0etAEAyQlSR7O9Az7+GorwMOzWTw+HdowTEeP1Py9cmNVZkWO06+nEZ3O671cb5Gx+5LTskmvZLMd0QVnLdtfOtmGnKKtd8hgT1dfOozK5ahjn3Cl9zj87AIeX1w2VzVTo23Hi+WWc27Y1xQE4PnPVz09eoUqQWpLF1YEBMpTxkVIRHp7pedZnL69QBYcir8NJsmuTk2Eu/skwMj6wA4CxE6rOjwM7ADgAoQoADsP4wJkxpwpgP85THl7+YcVJx2cpr1ClVB2Q3RsSUZ1y4RdBZvIKVUrVwdnCAHAY5lQBACQgVJ0HY3/AyRhXQRtXa3k2QtU5MfYHG1RVVTzR3Zk03ve9qqqmaYZsHGvlX9DybyGp5TWnCmAQi8WiLMv+jd6bpplMJmVZhhAmk4kZn8CFhCqAEEJY6o6q67osy7iwqqq6ruu6HqJdwGjkNfxX9AzdlhNiY8JWXXLqj/0tFotuhaqqZrPZEE0DxiSvniod7Adk28IGXaiK4akoiliL+qOBfVuO+hSxgxvjUWJRqMBnIq9QBXB8/aG9qqomk8n2memS0/DG8itwXfUzk9fwH+nZn+EiTdN0KarrneomVMUV4ox1gC2EqvMwlqM6GEI80S9GqNhlFYcCu2lVi8XCLHXgQob/gHNX13XMVfHL+XzeLYzTp5autsBgxtv1blrVeRCqTtp4CxAcV+ymilf77C9cWkIWxpVOTKs6J3mFqv45NaaCJmNLwm5W85NEBewur1AlSAEAI2Wi+unS4QyckrHXtLG3nx0IVadO5x9wSsZY08bYZi7l4KHKvd+BE+OGWsBaB59T5d7vw1Dr4WBUrQGoaYxBcejq0N1FqxMDVv9mW/Gqeqtrnp6ieLTX+m1779LvFL//kt9+Eori0eU3IOexS16CzXJMX+3Fo6ppa4pPFwpH8iOMSFa75GF7qrrkFPur4pfxcVwh3r70rC5VfIw/8w7pgFzte2wZv+cADTkuV6s6D0cKVe79PgBbDMjSJY8t1TSyd9hQ5d7vADyTE+vgcb+ak3bYs//c+30AJ1aAAMJJdFOdwI/ARQ4eqtz7fRj2XgA4roMP/7n3+1HppgLInBHA03Xw61S59/sA7K7AaTixA0XnAJ66I91Q2b3fj8G+CkfRP0/Z6TXHYCMzEkcKVTtSqhKw3eDAVCeelRHAE5VXqFKqAPibk+x9NwJ40vIKVVyevRQYwmWukL4XB9uMh1B1WlQf4OgOcvetkz9QNAJ4ig57nSqO5OSrD3CeTjJ2nOQPRQhBT9VaB+/NTsvNz4GkRlYDIRtC1XoH6c0+KIkKSGfgGngmve9GAE9OXqHKJRX2dialBzhDJ/xXwDmAJyqvUCVIXZLtBpyMs0obOqtOS16hiv2cVemBbOhTP4aT37A6q07RWYSqE590efKlBzIjSB3QqeSMXf7utE/WHN8sXjY4i1AVBp90eQinUnoAlo08tu76F6e4H0Jow/0QTu4v1LlynaqRG3npAfjKuR0rKuAnR6garba1QwIn6Awr27mlydOV1/Cf6Z8AZ+o8g0U3Xd1pgCchr56qtmfotgBwdOdX/IvwcOgmkExeoQqAc3Se3VRLbITxE6oAGJQbmHY/uFw1ckIVABk420QVnfmPfyrymqgOkD+n1KSkb2aJGetjpqfqYkWu+7yG7SXPVoWMG8YmeZ5Sk+0HaVvDBh34y26LtW0IIbM2fSW7zZWlvHqqHP8B7Gj1RigjuyWXqVSr3BBw5PIKVYIUwO76t0Mpivtjuh+XRMUpyitU7W5kB2QARP2eGImK0zKyUFUURdebteWYrL/ajq+WsGFHW20XZ96wpfB9YRZv23tnvsVIIttf1sANWxrY6n2vLbYvDcvTMKHq7//+70MIN27c+Ld/+7dBGsA5WIrdFw6O6P5kiUqVzOY4BadkgFBVFMWNGzdu3Ljx85//PITQr1a7/FXzl4/DiZ8unzHC1kp1sHfc+ME7nc+kOMVJO3bH2kcfffSP//iP8U3feeedn//8510DdunlG6RfUcNOo2E2xYEadpIGqVRF8WhtZ+opfNjCBXFq7HvBs6y29Hs/501x0IYdx7F7qv7rv/6re3zjxo0jvzvALrZXqoR96qfTBbVdNn/z8nSJ2Z+HbA7PoD2uWJ66L0MIH374YfcYyMqR60M+VCoYkYHqxBrH7qm6ceNGdwj40UcfhRDu3LnTbZQjNwZgLZUKuAS3qQEASGCA6V1FUXz44Yd37tyJpyt/7Wtfq6qqruvVNeu6bppm07MHVVVV/H/LWw8yOW57w3Zp9kEN3oBdmjFgI0f6uRpwTxyQSvUsst0HM2nALs1QqVaNoFIdf8TxJz/5SffuZVlOp9MQwnQ6XVqtLMu4PK52zBZub1i3zvG33vaG9Z8d5De7y3YbvBkDbqWRfq4G3BOHpVJdWrb74C7Ny6QZKtXaN81zT+wbbHrXhx9+2P1K4lboPzufz7sl8/n8mBuo/9arDesvP/JHanvD+kvimvP5PJ/mZdKMAbfSSD9XA+6JmVCp0jZMpdqlGSrVvg3Lp1INNqfqv//7v7vHsUOvr2ma+H/sxItfHkf/vVYbFleYzWbdp+potjesqqqlJq1t/OFcuN1yaMaAW2mkn6sB98RMqFT7ynYfjFSq7Ub6ucqoUg2V5mJPXfdleDqJx2dDCPHBMVPn9obFJdPptJ+LM2lYNFSf9o7Ny6QZx99KI/1cDbgnZiLb7TPST1RHpcpzK430c5VPpTrSJRXeeeedpSUPHjxYLBbxcQyVq9kzbrimaSaTSSYN62bJdbn4QAcQl9tiRVGEEKbT6SATGy9sXibNGGQrbW/Y0T5X+zYsOsKemAmV6tANi1QqlSptw6IcKtWRQtXqbbO2f0r6my9uuAP98vZtWGxV9wubTCbz+TyHhoUQiqIoy/IMB2j2kudWOtrnal9H2xMzoVIdumEh130wN3luJZXqYkN1kbW97ruyLGNn3Xw+7/o5u2ePP5dwe8Oi43d+bm9Y3ErzniO3bW3zBpHtVhrj56oddE/MRLbbZ4yfKJVqSzMy2Upj/Fy12VSqIUtkf6ZbXNI/zaH/7JE/Utsb1l9yzFZtb1g3ojzURlvbvEFku5XG+LlqB90TM5Ht9hnjJ0ql2tKMTLbSGD9XbTaVavjjzu0//ICbJtu/H9k2LMqkeZk0Y9VIG5Zts48m2+2T7a8m24ZFmTQvk2asGmnDBm/2AFdEBQA4Pe79BwCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUAUAkIBQBQCQgFAFAJCAUDWMa9euFU+7du3aQd/x5ZdfLla88MILb7zxRqq3+MEPfvCzn/1s07Nvv/32t552586d+NSVK1dW21YUxZUrV5Ze5Pbt2z/+8Y+XFj548ODWrVu3bt3qL7x27dqmTfqNb3yjKIq9frRXX3313Xff3etb4ASoVPlXqp/85CffftpPfvKTC7+LQ2kZwtWrV0MIV5/ofh1379698HvffPPNEMKbb7651zu+9NJLIYTnnxbf9PXXX7/sz/GV9957L4Tw3nvvbVrh9u3bmz5+3/zmN0MI31zxne98p/8KDx48CCE8ePCgv/DFF1/sv+Bbb70Vl2/5eO/7yf/Hf/zHEMK3vvWt3b8FToNKlX+l+ta3vrXU2jt37lz4XRyIUDWMWJ76S+7evbvjLhRL1fe///293jGWqqWq9Prrr8f6tddLLXnvvfdee+212Pgtper69eubfrpYqra8xYMHD+K3L5WqV155JYTwyiuvxC9j2YqP901Om7zzzjvxpYQqzpBK1ZdnpVKdsiJUDWO1VLVte/PmzRDCzZs345f37t3ruoWvXbsWF7755pvdEU9XrWIZikVn08Hc2lLVtm2/VC19+0svvfTSSy+1bXv37t2rV6/evXs3tvDq1atdI9snReHCUrWldlxYqvpv0S9VS6/51ltvdZUrPhVbHtvcrfbcc88999xz8fGVK1d++MMfTiaT2IE/mUw2vbWyxRlSqfryrFSqU1aEqmGsLVVt2/Z3qq5IdQWrfXLw1y9VXd/49k7yLcd/sR71H/cb0PaOTWPzuirQf6kLO9Xjd92+ffv69eu3b9/uP3VhqYriXISlUvXiiy8uvUtc0m9w1+a4zte//vXw9GFiCKGbFdGvVrFfPXZWKVucIZWq/1SelSpWp27614XN46BMVM/Ol19+GUL43ve+F0Jo2/bzzz///PPPv/vd78Znf/nLX3ad6o8fPw4hfP7557G+/M///E/btnHJphf/5JNPXuj59a9//fzzz//+97/fsW1t237xxRfxXT799NNL/HQff/zxZ5999vHHHxdF8fbbb/efWp3+WZblhS/417/+tXscpzL03bx584svvvjiiy9ite0245K2bb/88sv4c83n87jw1Vdf/ctf/vLOO+/867/+6z4/IpwFlSqHShXnpP/liY8++mjfs3BIS6jK1B/+8Ie2d3Ty/7d3d7uSXPXdgFdJnCY3wEGMZzxwD2OlquOATWyPktxBJDMjrCCMQ0AiUrqKMztgWwoyGsMRdzA2owjH0NUSnHIcj+3hvZN6DxZT1PTX7r13fazqfh5Zo+7a7d1r+uM/v1pf9fjx4xDCvXv3th/ZNE1ba1588cXDv/aLp4XOWeOFYo/6ddy6dauN8yGEn/70p92fbk//jOeFB8Re9CzL4pqad999d+MBn3zySbzxi1/8Iuwpr9srd6IHDx589atflajgAJVq8koVOjPT//3f/z2E8LWvfe2ivyJDEaqS0/YAf/3rX2/PhGKp2un111+/efNmfNjHH398+JdvdKrfuHHj448/PnKtcnftzxU0TfPpp5+2d+MSm+4p4P/b8qtf/erw73znnXditXr48OHDhw/jbNDLtn/n8Xi2F3dSaDdTsKsCtFSqFCrV22+/3TTNgwcP2rtf/epXu+1nZEJVQr71rW+FEOK8hBs3bnzyySfPPvvs3bt3m6bZ1xv8+uuv//znP4/96rEMXeoZ4/SFA53w6XvnnXfayvvrX/86hNCuvtkQRysu5a0nQgiffvrpW2+9JVeBSnUFg1Yq0vGlqRvAX/zyl78MT/qB4wnfhUUknvBdtkKN786dOx999NGrr7764YcfxiPxXOonP/nJdX7tK6+88vDhw/avH+eH7itV4TKnsO1OCiGE//u//4tDgf/4j/9oNBBUqssarlJ97Wtf+/TTT7svrG6qaQlVU2rXyzx+/DienXRXr4QQ7t27d//+/Xv37rUj7htu3LjxxRdfvPjii7/5zW/iuWC4zPnc+++/H08f29uxj/3999+/efPmdf5qG+L0yY8++ij2oj969OjRo0cbNeWZZ57ZnpqwXq8P/Npbt249fPjw1q1br7zyyqNHjx4+fPjcc8+988477QOyLIsvaZyjcPxki43wFEcDJSrOk0rVfUxSlerOnTv/9V//9bWvfS1u+x6zYLsFPBO4zFJBerN9IhK3V2kfcPfu3e5P20712Mceb2+syA2duZzd9cbRgd1f2o9Bu4tMeLKjcegsVO42Lzy9n0pzxELljYs2dKeCHpjm2f0N2wuVm6f3Ke4uWt7+Ve2ONRsLlTd2fNn5pQi2VOAsqVTpV6qNCGVXhWllTfL9secsLqK5f//+xu0QwhtvvBFCeO+990IIcf7m+++/H38UT+Cu/KQbv61f8fzvmn3pG9r1yd0zv1ac/xHX1ABDUKmOMWilinsrvP3221dtHf0QqgAAemD1HwBAD4QqAIAeCFUAAD0QqgAAeiBUAQD0QKgCAOiBUAUA0AOhCgCgB0IVAEAPhCoAgB4IVQAAPRCqAAB6IFQBAPRAqAIA6IFQBQDQA6GK0WVZyLKpGwFwEcWKS8qappm6DX+W+eyeh/iBO/BmX/gARpNOfUiHSnU+DtcilSod6VSqL03dgKek87owoCwLh9/rCx/AKKSHfXw4z8XhWqRSpSGpSmX4DwCgB2n1VHXzpvgPAMxIWqFKkAIAZsrwHwBAD9LqqQJIn4kKwE5CFcDlCFLATob/AAB6IFQBwJaUdj9iLoQqANjDUC+XkdacKtM/AYCZSitUCVIAwEwZ/gMA6EFaPVWcvq25n1n2wcaRZs/xP/+0udt/qwAOUqk4hlDFFJ4e592sPtm9HQfjT/bUL4ChqVRcyPAfAEAPhCoAgB4IVQAAPUhrTpV9qoD0qVTATmmFKuUJSJ9KBexk+A8AoAdCFQBAD/oPVUVR1HW9caQoirIs2yNlWW4cAQCYtZ5DVVmW6/W6eyTO6CyKoqqqGLbKsqyqKh4piqLfBgAATKLPierZ1hVIyrLM87ztuKrrOmap5XJZlmVZllmWxYM9NgMAYHx9hqq4IqYbrWJgisN8cRAwHu+mKKEKADgBw26psF6v42hgnudVVa1Wq3h8X4ra7utqWcMMAKRs8NV/eZ43TVPXdZ7ni8UiHtyYyd5q9hu6ncBZ2VhSE7vMNw5aUgNcynhbKsTeKSN9wOQ2ltTUdd2e8rU3LKkBLmvYULVcLtvKVVVVnuchhDzP45lfuxhw0DYAdGVZVlVV90i7pCb2qcei1C6paZpmvV7v618HaA0bqmKpyrIsTpZqU9R6vc6ybLFYLJfLQRsAsGF7RkG31yp2TbW32+NCFXCh/ieqb1SrWIm6S/yKooizrPSoA4nYKEexcFlSw8WyLHjTeWKkOVXbtUmiAhJnSc352p+b/8LbzRbX/iNVxxQ16MPGHsV5njvrIwSxiUtLK1RlHVO3hekoZIyrKIp2WtV6vY4T1S2pAS5r2M0/L0vnOTC+sizruo7ncm03VVmWi8UiHrSkBjhGWqEKYBzbS2o2Vs9YUgNcVlrDfwBT2RmeJCrgeEIVAEAPhCoAgB4IVQAAPRCqAAB6IFQBAPQgrS0Vunt+2rMKAJiRtEKVIAWkz+kfsFNaoYoT5+pDnARBCtjJnCpG5x8kAE6RUAUA0AOhCgCgB0IVAEAPhCoAgB6ktfrPQmUAYKbSClWCFAAwU4b/AAB6IFQBAPSg/1BVFEVd1xsHy7IsimLjblmWvT87AMAkeg5VZVmu1+vt41VVtcfLsqyqqiiK+Ge/DQAAmESfoSrLsqqqdh7v3q2qarlclmXZNM16vd7u1gJIWdYxdVuAhPQZqpqm2V6+F8f4lstl92C3g0qoAual6Zi6LUBChp2oXtd1VVXduhMj1L5Rv2y/QdsJAHBNw4aqxWKR53ld1zFLtZ1S+3qnmv0GbScAwDUNvvnner1eLBbx9mKxEI8AOClZFvzTRghh6J6qtp8pzqmKiSrP8zjRKvZX2VgBgFmSpXjaBJepKctysVjEaVIbE9gBYGJm8XJV2VTjcXVdb0xXz7LJGsNIYql6+l3Osg+a5u4xj9z7YIbhK7mTl+X0HV+pLvtgBpDUV3Kyy9TY9hMAOCWu/QcA0IMJ5lQd0N2PKp3ePCZjTQ0A85FWqBKk+LOmMVcUgHkx/AcA0IO0eqoA0meiArCTUAVwOYIUsJPhPwCAHghVAAA9EKoAAHqQ1pwq0z8BgJlKK1QJUgDATBn+AwDogVAFANADoYqxuOwMaSuKoiiKsizbI3Vdx4N1XU/WLGA+hCrGZdocSYqrZIqiqKqqKIoQQl3Xi8Ui/rS9AXBAWhPVAcYXO6La7qiqqkIIZVnmeR4Pxh6sbicWwDY9VcC5i11TZVnWdV1VVZ7nIYT1et19QExaAAcIVQAhz/OqquIwX9sjFcPWtmy/sdoLpCitUKU2AeMry3K9Xq9Wq6Zp8jy/cAZVs984DQbSlNacKiUJGF9d13met/PTsyyLR9pZVvHuhC0EZiGtniqA8RVFsV6vY4SKY39xJ4V2WtV6vTZLHbhQWj1VAOOLU9TbUb/VatUejFMR2n4sgAP676na2Civ3T2ve55XluXGEYAJ1XXdNE2cVtXmp7qu4xGbfwLH6DlUxfme7d148hcrVFVVMUWVZRm312s32QNIwXZFUqOA42U9zg1vl+ytVqtYidqJn+HJrIWmabIsWy6XMWBlWdY+OMv6bAzJiR+Prbc4yz5omrtHPn7vgxmAr+ROXpbTd6nio1JNLamvZJ89VdsrijfG+NrlM92TP/3qAMAJGHaiencPvbgNTNtrtfPxB7anSieHAsBTssyFTQkjbKkQl8/ERNWd/rnzwbbUA2BO/PNEx7ChKk5UXy6X7YIasz4BSJfreXANw4aqxWIR93epnwgh5HkehwW7W+0BQCr0P3Elg2/+uV6vuxfSapqmLMvFYhGnTy2Xy6EbANCv7uxPkxOAVv+hqltidpaboijiZnqGAoE5EqSAnSa79p9EBQCcEhdUBgDoQVoXVDZTgU12fwFgJtIKVYIUf9E01jYDMCOG/wAAeiBUAQD0QKgCAOiBUAUA0AOhCgCgB0IVAEAP0tpSwT5VQPpUKmCntEKV8gSkT6UCdjL8BwDQA6EKAKAHQhWjcMEZAE6dUMWIzEQB4HQJVQAAPRCqAODaTHIgtS0V7P4CwMw0jURFlFaoEqQAgJky/AcA0AOhCgCgB0IVAEAP+g9VRVHUdd3ereu6KIqNg2VZFkVRlmXvzw4wtKxj6rYACek5VJVluV6v27t1XS8Wi3i7vVGWZVVVRVHEP/ttAMDQmo6p2wIkpM9QlWVZVVXdI2VZ5nle13Vd13mex66pqqqWy2VZlk3TrNfrbg8WAMBM9Rmqts/bur1WsWuqvd0eF6q4gBEWYByqDdcz+D5VGwN8MULtG/U7MEFBN/s5sqUeMD7/3HBV06z+29c71ew3bgMBAC5n2FAVJ1TF23FalZnpAMBJGjZUFUXRTqtar9dxono7Yz3mLRsrAAAnYNg5VWVZ1nUdZ0q13VRlWS4Wi3hwuVwO2gAAGEmWmY915vrvqWqaZmNx32q1apqmHQcsiqJpmnhQNxWQiO1NiXfuXQw7yFKEEEZY/Rf2rPUzueqEZdkHG0eaPcchEXGuwnK5rKqqfmKxWOR5HkJYLBaWywAXGiNUcYaa5u5T97N7Ow5CGuq6Xq/XMTa1nVXt3sXtQT3rwGFpXVDZFbWA8cXkVNd1dw3Nvr2LAfZJK1TZmAoYX0xRi8UiDvm1kxMObFO8z0gtBpJk+A8ghCeXbeheBv7wIwE2pNVTBTC+bo9UvB03K97Yu3iClgGzIlQB5257O+K4k8L23sUABxj+AwjL5bId9VutVmHP3sUAB+ipAghlWbabErf5aXvvYoADhCrmwKIqRrHdHaWDCjheWsN/3QXJ1tcQQghNI1EBMAtphSpBCoBxuKAWvUsrVHGadDUBSXJBLfplThVj0Q0JwEkTqgAAeiBUAQD0wJwqgMuxThnYSagCuBxBCtgprVDl/A8AmKm0QpUgBcCMZZmVzufMRHUAuDZZCqEKAEKwTTE9GCNUFUVRFEVZlu2Ruq7jQdd+ByAhOpy4hsFDVZx7XhRFVVXxeu91XS8Wi/jT9gYAwKwNO1E9dkS13VFVVYUQyrLM8zwejD1Y3U4sAIA5GranKnZNlWVZ13VVVXmehxDW63X3ATFpAQDM2uDDf3meV1UVh/naHqkYtrZl+w3dTgCA6xg2VJVluV6vV6tV0zR5nl84g6rZb9B2AgBc07Chqq7rPM/b+entkXaWVbw7aBsAAEYw+Jyq9XodI1Qc+4s7KbTTqtbrtVnqwLyYmQDsNOzqvzhFvR31W61W7cFYjNp+LIC5MCEB2Gnwa/+1o37d8FTX9cYRuIAragGQtpEuU7OdnyQqjiVLATAHrv0HANADoQoAoAeDz6m6lO5SGlNBAYAZSStUCVIAwEwZ/gMA6IFQBQDQA6EKAKAHQhUDcx0PAM6DUMUoLEEA4NSltfoPIH02fwF2SitUKVVA+lQnYKe0QpVSBQDMlDlVANAfq3POmFAFwNnrJQlsg7ZhAAAgAElEQVQZbDl7QhUAhBCkIq5LqAIA6IFQBQDQA6EKAKAHaYWqrGPqtgDnqFt86rouiqIoirqup2sRMBtphaqmY+q2AGdnI1EtFot4u70BcEBaoQpgKmVZbtzN87yu67qu8zzf+CnANqEKINR1XVXVcrlsj6zX6/Z2URRVVU3RLmBOxghVZVkWRdE9zzNTAUjKYrFYLpdFUXQPbtxtZfuN0FQgWYOHqniG1/4ZzFQAEhNLU3uad+HJXrPf4G0FEjbsBZXrul6v17HQtJ1V7UyF9qDJChwly0K4P3UjOEFxpK97srdardoyFUKI06qmah4wF8P2VLWnfTE2xbtmKnBpOgAYUtvPtFqt4t04P6EtVuv12rkfcKExQtVisYhDfu0EBTMVgMTFPvVYf/I831e1AFrDDv9FcfivO5Xq8CMBJlEURbcKxf0UxCngSMP2VHWLUTtL3UwFYC4kKuB4w4aq7lSqeNtMBQDgJA0+/LdcLttRvzgJtCzLuq7jNCkzFQCA0zD4PlVlWcY1NXFBTTxY13U8YvNPAOA0jHSZmu3uKB1UZ8GyTQDOhmv/MTyLOgE4A2mFKhtTATB3Tbg3dROYxhj7VB3PPlVA+rpnfaoWT2ka0x7OWVqhCiB9gtSpEYPoSVrDfwAwDVmZaxOqAAB6IFQBAPRAqAIA6IFQBQDQA6EKAKAHaW2pYPcXAGCm0gpVghQAMFOG/wAAeiBUMSeuqAVAstIa/oO9XFGLZJj9CewkVAFcjiAF7GT4DwCgB0IVAEAP0hr+M1MBAJiptEKVIAUAzJThPwZjsR4A50SoYmB6H4Ez5KzyLI0Xqrrzpeq6LoqiKIq6rkdrAABs6j39OJM8YyOFqo1EtVgs4u32BgBMRhKiD2NMVC/LcuNunuexj6ooirIsNx5AarLsg6mbAACpy4ZecBf7pZbLZVVV8bmyLGtDVVmW3eNW/6Upyz5omrtX+N9C6PX8r/dfyEG+kjt5WU7GnyvbEIVFsRpRUl/JwYf/YqIqiqJ7cONuK9tv6HYCAFzHsKEqhqd2QvqF09Kb/QZtJ8DxnO8BOw07p2q9XofObPTFYrFardqxvxBCXdd5ng/aBoB+Oc0Ddho2VLWlJ86sineLoqiqKh5fr9er1WrQNgAAjGCCzT/j6r/Yc57n+b75VQAAMzLStf+Kouh2mNd1Hff/HOfZOSlZZk0NAAma7DI1EhWXJksBkDDX/gMA6MFIw39H6q5Ptr4GAJiRtEKVIAUAzJThP4AQQiiKIl6NtD0S19O02xcDHCZUMQw7TTMrce5B3EUv3o6768WftjfgEpTB8yNUMSTjucxB7J2q67osy7gdcbwdL/8QL/zQ7cHidAyTe7Jwf4hfS/qEKuDcFUWxXC43jsSrbLV32+tAcIKc/tGTtCaqA4wvTpwKIZRlWVVVG7D2bad34DrKVtvAOROqAEJ4EpWWy+WFI32SE7BTWqHKPlXAJOKlSLur/Lp347SqSRoGzEhaoUqQAsYXu6bKsmxTVBwQbOdRrdfrOIEd4IC0QhXA+GKW6u6bsFqtYsaK3ed5nrtcKXAhoQo4d/v29oz7KYhTwJFsqQCwl0QFHE+oYobsUwxAeoQq5sQ+xQAkS6gCAOiBUAUA0IO0Vv/Z/PNEmPMEJK8J9wZ/jixzYcGzklaoEqROincTSN9AlappnF6eobRCFUD69KmnKcs+uNTjvXP0Tqg6U5etPkBLkEpW09y9xKOz4Yf/ODNjhKq4e15RFO213+u6bi+2ZW+9qVyu+gAABw2++i/2k8dLk8bbdV2319jqXmwLAGC+hg1VsTsq9kvFa7zH23mex4tq5Xnedl8BAMzXsKGqKIrlcrlxZL1ed+9WVTVoGwAARjDsnKqiKOKUqbIsq6pqA9a+eVTZ/gWoZobyFLu/AJCYMSaqx6i0XC4vHOmTnLiY3V8ASNLgoSrLsjiDqj3SvRunVQ3dBgB4ymjnZrrVz8mwoardN6FNUXFAsJ1HtV6v4wR2ToduJGAuBo07utXPz7ChKmap7r4Jq9UqZqw4JpjnuX2qTpMzM+DabFPMvIwRqnYer+tanALgMNsUMyODb/65j0QFAJwS1/4DuBwXVAZ2SitUKVXXYfIBjEN1AnZKK1QpVdd0XpMPLFQGICWTzamCq5OlgBmxscLZEKroldoBpG+0SuUM8MwIVQxAHQHSp1LRN6EKAKAHQhUAQA+EKgCAHqS1pYJ9qrrsO3UxuyoAkIy0QpUgteG89p26FJd/B65mktLhDPA8GP6jP1IOMBejRRxZ6pwIVfRNBQHgLKU1/AeQPrM/gZ2EKmbOTAVGJ0jNmFkKDMnwHz0Zv1T5hw24mkmqhzx3BoQqeiXoAGxQGM9GWqEq65i6LX+RVGO6NOxS0mxVSLhhzEuyHyQNu5Q0WxUSblhS0ppTZabCXE37ZTOtCnp12Z2HZ7Oj3uSxQLE6dWmFKuZt/GJhC1AYxvE5aX7Xfpgk1ihW50Go4tpUCjhvF+aqjQdM07M1bqXafk2aPcfDjLr6uEiWzohbll3cmGMeM/LDnnxD7oVw/8JfddmHHf6mpfKKxVLVOT5cw7Lsg83XJMtCCNkRY8cJfnhOo2Hn5hze00EftuNbvP9XbTx4LpVq74NitHr6kX3+HYd82NwbNo5peqqef/75EMLt27ffeuutSRrQr6a5m2X3jjnV6PdhSRi9m2rrPO9+E+7FH2RbgXU2LyNJOrFKddZS6FBvRwDNrDpdE4SqLMtu3759+/btt99+O4SgWs1YW6fGKhB7QtLd2JIm3Hv6NHRuUz1IyYlVqvbrkML3YmcbBmzY6JXqYnLViRo7VH344YchhN///vfx7ttvv51gqdr+bqdQhlKRwgnfAU8175iRVthhFpXqsi7Vpz5oM3Y+Y8/9ytuVKpEQ0+2vigdCCNm9VJrH9Ywdqv7whz+0t2/fvj3ysx/v6RHuhAfjjok4R8agq6WldArB1uKaJtwLQ/7bwAmbS6WaDZWqa+dKwMTPVzlSM65Yntq7IYQHDx60t4GkjFwf0qFSwYxMVCd2GLun6vbt2+0pYOxgv3PnTvuijNwYgJ1UKuAK0rpMDQDATE2wu0OWZQ8ePLhz505crvylL32pKIqyLLcfWZZlXdf7fjqooijinweeepK9MQ437JhmD2ryBhzTjAkbOdPP1YTfxAmpVNeR7HcwkQYc0wyVatsMKtX4I44/+MEP2mfP83y5XIYQlsvlxsPyPI/H48PGbOHhhrWPGf/VO9yw7k8neWePed0mb8aEr9JMP1cTfhOnpVJdWbLfwWOal0gzVKqdT5rmN7FrsuldDx48aN+S+Cp0f7pardojq9VqzBeo+9TbDeseH/kjdbhh3SPxkavVKp3mJdKMCV+lmX6uJvwmJkKl6rdhKtUxzVCpLtuwdCrVZHOq/vjHP7a3Y4deV13X8c/YiRfvjqP7XNsNiw+oqqr9VI3mcMOKotho0s7GD+fC1y2FZkz4Ks30czXhNzERKtVlJfsdjFSqw2b6uUqoUk2V5mJPXXs3PJ3E409DCPHGmKnzcMPikeVy2c3FiTQsmqpP+8jmJdKM8V+lmX6uJvwmJiLZ12emn6iWSpXmqzTTz1U6lWqkLRV++MMfbhx588031+t1vB1D5Xb2jC9cXdeLxSKRhrWz5NpcPNAJxNVesSzLQgjL5XKSiY0XNi+RZkzyKh1u2Gifq8s2LBrhm5gIlWrohkUqlUrVb8OiFCrVSKFq+woPhz8l3ZcvvnADvXmXbVhsVfuGLRaL1WqVQsNCCFmW5Xl+hgM0l5LmqzTa5+qyRvsmJkKlGrphIdXvYGrSfJVUqotN1UXWdLrv8jyPnXWr1art52x/Ov5cwsMNi8bv/DzcsPgqrTpGbtvO5k0i2Vdpjp+rZtJvYiKSfX3m+IlSqQ40I5FXaY6fqyaZSjVliezOdItHusscuj8d+SN1uGHdI2O26nDD2hHlqV60nc2bRLKv0hw/V82k38REJPv6zPETpVIdaEYir9IcP1dNMpVq+vPOw3/5CV+aZP/9SLZhUSLNS6QZ22basGSbPZpkX59k35pkGxYl0rxEmrFtpg2bvNkT7IgKAHB6XPsPAKAHQhUAQA+EKgCAHghVAAA9EKoAAHogVAEA9ECoAgDogVAFANADoQoAoAdCFQBAD4QqAIAeCFUAAD0QqgAAeiBUAQD0QKgCAOiBUAUA0AOhCgCgB0IVAEAPhCoAgB4IVQAAPRCqAAB6IFQBAPRAqAIA6IFQBQDQA6EKAKAHQhUAQA+EKgCAHghVAAA9EKoAAHogVAEA9ECoAgDogVAFANADoQoAoAdCFQBAD4QqAIAeCFUAAD0QqgAAeiBUAQD0QKgCAOiBUAUA0AOhCgCgB0IVAEAPhCoAgB4IVQAAPRCqAAB6IFQBAPRAqAIA6IFQBQDQA6EKAKAHQhUAQA+EKgCAHghVAAA9EKoAAHogVAEA9ECoAgDogVAFANADoQoAoAdCFQBAD4QqAIAeCFXTePbZZ7OnPfvss4M+44svvphtuXnz5uuvv97XU/zzP//zf/zHf+z76fe///2vPu3OnTvxR88888x227Ise+aZZzZ+yauvvvpv//ZvGwfffPPNV1555ZVXXukefPbZZ/e9pH/913+dZdmRf6lXnjjy8XBKVKpZVKrvfOc73/zmN7/5zW9+5zvfOebxDKhhCl/5yldCCF95on07XnvttQv/3+9+97shhO9+97uXesZvfOMbIYQbT4tP+u1vf/uqf4+/+NGPfhRC+NGPfrTvAa+++uq+j9/f/M3fhBD+Zsvf/u3fdn/Dm2++GUJ48803uwefe+657i/83ve+F48f+Hgf/8n3ZeHMqVTpV6qXXnpJpUqHV38asTx1j7z22mtHfh9iqfqHf/iHSz1jLFUbVenb3/52rF+X+lUbfvSjH/3TP/1TbPyBUnXr1q19f7tYqg48xZtvvhn/941S9fLLL4cQXn755Xg3lq14+/rFJf62N954Y/uJ4EyoVF1pVqoQws2bN+PtGLBeeuml6/xCrkOomsZ2qWqa5oUXXgghvPDCC/Hu3bt3227hZ599Nh787ne/257xtNUqlqFYdPadzO0sVU3TdEvVxv/+jW984xvf+EbTNK+99tpXvvKV1157LbbwK1/5StvI5ununAOl6kDtuLBUdZ+iW6o2fuf3vve9tnLFH8WWxza3D/vyl7/85S9/Od5+5pln/uVf/mWxWMQO/MViEY+/8cYbGynq5ZdfFqo4NypVV4KV6l//9V83UlQ3YzE+oWoaO0tV0zTdL1VbpNqC1Tw5+euWqrZv/HAn+YHzv1iPure7DWg656axeW0V6P6qCzvV4//16quv3rp169VXX+3+6MJSFcW5CBul6rnnntt4lnik2+C2zfExf/VXfxWePk0MIbSzImK1imeWsZsq/glnSKXq/ijBStU8naK2MxYjM1E9OX/6059CCF//+tdDCE3TfPHFF1988cXf//3fx5++9957baf6w4cPQwhffPFFrC+ff/550zTxyL5f/vHHH9/s+PnPf37jxo3f/OY3R7ataZrHjx/HZ/ntb397hb/dRx999OjRo48++ijLsu9///vdH21P/8zz/MJf+Nlnn7W341SGrhdeeOHx48ePHz+O1bZ9GTc0TfOnP/0p/r1Wq1UI4dGjRyGEhw8fZln23nvvZVnW9uoDQaVKo1KFEF566aXPP/88tuRnP/vZzZs3/+d//ucyf1f69KWpG8Bu//u//9u9+/jx4xDCvXv37t+/v/HIpnMS8+KLLx7+tdtVrD1rvFDsUb+OW7duffrpp/F2lmU//elPf/KTn7Q/jWeBXdtHNnzve9979913syyLHUuxdnd98skn8cYvfvGLX/7ylzvL6/bKndZnn3328ssvP/fcc5999tnDhw9v3boVwxYQqVQ7j2wYtFJ9/vnn8cbNmzc///zz9i6T0FOVnLYH+Otf/3p7JhRL1U6vv/76zZs348M+/vjjw798o1P9xo0bH3/88ZFrlbtrf66gaZq2ToUQ4hKb7ing/9vyq1/96vDvfOedd+LshIcPHz58+DAWrMu2/8Df64033vj1r3/97rvv/vrXvw5Pn2vCmVOpUqhU3/zmNz///PM43vfZZ5/F5Hr8ljH0TqhKyLe+9a0QQpyXcOPGjU8++eTZZ5+9e/du0zT7eoNff/31n//857FfPZahSz1jnL5woBM+fe+8805beWP02TdOF0crjrT9SzZWRMPZUqmuYKBKFfuluuN9N2/evF5LuRbDfwn55S9/GZ70A8cTvguLSDzhu2yFGt+dO3c++uijV1999cMPP4xH4rlgt1P9Cl555ZWHDx+2f/04P/TA5KfjT2G3I5RuKohUqssarlKRGqFqSu16mcePH8ezk+7qlfBkasK9e/faEfcNN27c+OKLL1588cXf/OY38VwwXOZ87v3334+nj+3t2Mf+/vvv93u68+GHH2ZZ9tFHH8Ve9EePHj169GijpjzzzDPbUxPW6/WBX3vr1q041emVV1559OjRw4cPn3vuuXfeead9QJZl8SWNcxSOn2zx7rvvvvfee++99168GxPV4U57OFUqVfcxSVWql1566Wc/+9lzzz0Xd6iKc6p0Vk3pussHuZLtE5G4vUr7gLt373Z/2naqxz72eHtjRW7ozOXsrjeODuz+0n4M2l1kwpMdjUNnoXK3eeHp/VSaIxYqb1y04datW+2PDkzz7P6G7YXKzdP7FHcXLW//qnbHmo2Fyu3K5I1XY+OXbKyIhnOgUqVfqTZ2VLdJ1bSyJvn+2HN27969EEJcR9O9HUKIu1PGrpQ4f/P999+PP4oncFd+0o3f1q94/nfNvvQN7frk7plfK87/+MUvfnG1Xx6nl7777rtXbR2cPpXqGINWqnjVv//+7/++auvoh1AFANADq/8AAHogVAEA9ECoAgDogVAFANADoQoAoAdCFQBAD4QqAIAeCFUAAD0QqgAAeiBUAQD0QKgCAOiBUAUA0AOhCgCgB0IVAEAPhCoAgB4IVYwoy0KW7b0LkIJuaVKmuIysaZqp2/BnmQ/uqYsftWzPXRKUTn1Ih0p18rqlSZmahXQq1ZembsBT0nld6N+Tf4r+8i5nWfCmJ0x62MeH9sR1S5MylbykKpXhP8alNgFwotLqqeIcZZmkxbx0z4z1YZyand0eyhTHEaqYTtOYAcocCVKnr32LlSkuI61Q5fwPAJiptEKVIAUAzJSJ6gAAPRCqGIVJCQCcusFDVV3XRVEURVGWZXsw6+ge58QZ3gXgdA0eqhaLRQihKIqqqoqiCCHUdR1CWD4RDwJMriiKWKCineeEAPsMO1E9VqK2SFVV1d5VpICklGW5Xq/bu3VdLxaL5XJZ13WsXarW6TswUcFWVRxh8Gv/xVO9uq7bgFUUxXq9zvN8vV4vl8u2TmVZQhcipGexVG2/v20J89an56y+ku1+LqvVKnafd3vWY9WKr8ZZvSxn53Cl8r4nKamv5OBbKsSe83iet1qtusfjmGDonP8duIJPOi8ZfbKxHmloA1N7JNao9m6e5+O3CpiX8fJd91SvFfOW87/Td+A8zylgqs7wK5llWdtT1Yq1qz1++Oqt5/aKzVqWfbBxpAn3QghZuL/zuDKVpqQq1Rhzqto/46T1OAI46PMCXF+cVhU6Y4JROhWca2qau0/dz+7tOPjkOFxo8NV/cYAvdMb4FotFW56qqtKpDiSonajeNI3zQOAYg/dU1XXdnQEa/1wsFu3B7gJmgEQsFos8z7ubLIhWwGFjjES2y2cOH0xqWJSemVM1Q2f4lezOqdqeO2X254nJsg+2hv+s/pufpL6SKTUlpdeFPh3eN0G1SpWv5E5elpMhVJ2GpL6Srv3HWJL50APAEAbfp+pSuv3t6QRPAIALpRWqBCkApnHhRsSuVMNFDP+RBvuqAynYGZtkKY4jVDE11QqAkyBUAQD0IK05VQDps6QG2EmoArgcQQrYyfAfAEAP0uqp0qkOAMxUWqFKkAIAZsrwHwBAD4QqBmZXTwDOg1DFKAzsAnDqhCoAgB4IVQCcvSMnKpjPwEFCFclQrYBp7Z+okIX7YzaEmUprSwX7VJ2pppGomBGVCtgprVClPAHpU6mAnQz/AQD0QKgCAOiBUAUA0AOhCgCgB0IVAEAP0lr9Z6HyGcqyD0IITef2YU1zd+AWAcBVpBWqBKnz1DR3Q3YvHBGYjkldADAJw38MyZaeAJwNoYrh6YAE4AwMHqrqui6KoiiKsiy3D9Z1PXQDAABGMHioWiwWIYSiKKqqKooihFDXdTzY/hRgRrKOqdsCJGTYieqxd6rtjqqqKh7M8zwejD1Y3U4szlqWGSskfZbUnJpLhWNliv2G7akqy3K1WoUQ6rqu6zrP8xDCer1uHxB7sAZtA/OgSAHTurAKKVNcZPAtFWJfVExOMWDFgzsffKAv3anhjLQbHxy/+xQAzN0Y+1TFAb6iKBaLxeFsJDmdjD/vOHXc7lMAcAIGH/5r50u1N9oJVSGEdkwQAGDWBl/9106ZakNVURTttKr1em2WOgBwAgbvqcrzPC48Xq/XcU5V92Ce5/vmVwGMbGPzPDvqAZcy+JyquO4vPD05PR4Up4B0lGXZXZscd9SL8xMunA8KEMaZqL4zPElUQDq2lx7bUQ+4LNf+AwhN02z0RdlRD7isMXqqjtc9WdTZDkzLjnrApaQVqtSjk+KyaJwolQrYyfAfA/PPD/NkRz3gsoQqgB3sqAdcllAFsIMd9YDLSmtOFcCENiZL2VEPuBQ9VSTG9HZSIlGdvivUHGWKPYQqkmFKOzCVI+uPMsVBaQ3/2acKAJiptEKVIAUAzJThPwCAHghVAAA9SGv4DyB9Zn8COwlVAJcjSAE7Gf4DAOiBUAUA0IO0hv/MVAAAZiqtUCVIAQAzZfiPYbg2FgBnRqhiSLoeATgbQhUAQA+EKgCAHghVAAA9SGv1H4QQQpaZjEXKbP5yOq68pEaZYpe0QpVSde6axrJB0qc6nZpLvaHKFPulFaqUKgBgpsypAgDogVAFANCDMUJVURRFUZRl2R7JOpbL5QhtAAAY1OBzqrIsy/O8KIqqqqqqaprmd7/7XQjhP//zP+MD8jwfug0AAEMbNlTF3qm6rkMIRVEsFou6rtfrdQihqqpBnxoAYEzDDv8VRbExulcURQxVRVEY+wMATsawPVVxNlUIoSzLqqq6ESrP8zzPf/zjH4dOr1W2f/MPuy0AACkbY5+qGJWWy2V3NLD14x//uA1VkhMAMFMjTVTvBqnf/e53f/d3fzf08wIMxLUfgJ3GmKhelmUbqoqieOGFF9qY9eMf/9jqv9PThHtTNwEGJEgBOw0bqmJyWiwW7ZHVavXb3/72hRdeaE/1NkYDOR3+4QHgnIwRqra1u1UZBwQATsNkF1QWpwCAU+LafwAAPZisp2ona2r4sywzJQuAeUkrVAlShKYJ+/eABejLddcpO/dji+E/gN3iNSHi1jCcrCsEI1mKPYQqgB3ibISiKKqqipfbAjgsreE/gBTE7WDaTWHaS2kBHKCnCmBTeyX4uq6rqnLhB+AYQhXADnmeV1UVLwixMa0q22+atgJpEKoANpVluV6vV6tV0zR5nnevtRVCaPabqsFACtIKVU74gBTUdZ3neRwE3JhfBbBPWqHKCR+QgqIo1ut1DFJx7M8CQOBCVv8BbIpT1NtRv9VqNW17gFkQqgB2aEf99FEBR0pr+I9TYD4cJ0SiAo4nVDEMs+IAODNCFQBAD4QqAIAepDVRvbs9lV0VAIAZSStUCVL8RZaZmEWanP4BO6UVqiCEEJrGEkJSJkgBO5lTBcD56eXMzekfTxOqADhXV+501FvJLkIVAEAPhCoAgB4IVQAAPUhr9Z+FygDATKUVqgQpAGCmDP8BAPRgjFBVFEVRFGVZtkfquo4H67oeoQEAAEMbPFTFaVJFUVRVFW/Xdb1YLOJP2xsAALM27Jyq2DsVu6OKolgsFnVdl2WZ53l7sCzLbicW82Z/Yc6AJTXnKcs+6N5tdh38y0+bu8O3iOQMG6qKotg+sl6v8zxv71ZVJVSdmCzc9+8MJ0yQOkM7QlJ2b/fx/UmLkzd4qIq5qizLqqqWy2V7fOfjs/39HKoYAJCyMbZUiFFpuVxe2CMlOQEAMzV4qMqyrJ1BFXXv1nXdDgUCAMzXGBPVy7JsU1QcEKyqKt5dr9er1WrQNjBjWeZS8ADMxbChKmap7r4Jq9UqZqw4Jpjn+b75VZy1prGQEIB5GSNU7Twe9/8c9NkBYAfnbAxjssvUSFQATCgL9/v4LfIZf+HafwBweWZ8smWMLRWOZ59iAGCm0gpVghSQPqd/wE5phSqA9AlSwE7mVAEA9ECoAgDogVAFANADoQoAoAdCFf2xCR4AZyytUJV1TN0WrsrCKADOUlqhqumYui3AuSvLsiiKsiynbggwD2mFKoBEFEVRVVX759TNAWbA5p8Am+q6Xq/XsctcZxVwJD1VpM3sOqZQ13X8M8apeBfgMKGKVJlXx3RiilosFnVdLxaLjeG/bL9JWgskwvAfwG5x+C/mqu3jABv0VAFs6nZNxdtGAE9H7x2Keih5Iq1QpRcdSEF3KlW8bQHgqemlu1GfJU9La/hPpzqQiOVy2Y76rVaraRsDzEJaPVUAiSjLsmma1WrVNI1uKuAYQhXAXuIUcDyhCgCgB0IVAEAPhCoAgB4IVfRkuF0w7K8BwByktaVCd3sq2yvMUr/vWtNIVCRIpQJ2SitUKU9A+lQqYCfDfwAAPRCqAAB6MFKoKoqieznS7jX+4nW1AABmbYw5VWVZrtfr9m5MV8vlMt61YTEAcAIGD1XZ1uqt7oXfAQBOw+DDf03TbKyUiaGqKApjfxzLxgpALxQThjTZlgpFURRFUVVV6PRabXdrtaxhPlO2qgJ61/s/KFnW/+9khiYIVd0Z6yGEqtqCJ7UAAAuoSURBVKraUCU5ATAnTvzomGBLhY1QBQBwAiYIVYvFol3xV1VVnufjtwEAoF8TDP+tVqvFYtFOn9JxBcyLa/8BO40Uqrp1pyiKpmnaNYDjNACgL4IUsNOUq/+memr6Z54mAGfPtf/oj9N3AM7YZD1VO5mpAADMVFqhSpACAGbK8B8zYdoWAGkTqkie/ksgfU78EKoA4Fqc+PGEUAUA0AOhCgCgB2mt/gNIn81f5sq0JwaWVqhSqoD0qU7z5u1jMGmFKqUKAJgpc6oAAHogVAFAH8zZOntCFdemjgBnztwVQghCFb1RUwA4b0IVAEAPhCqAQzID3MBx0gpVWcfUbSE9PhWMTi0CjmefKuagaSQqxleW5dRNAOYkrZ4qgETUdV1V1XK5nLohwGwIVQA7LBaL5XJZFMX2j7L9Rm8mkBChCmBTzFJFUdR1HUKIf7aa/SZoK5CMtOZUAaRgvV6HEBaLRby7WCxWq9XOXitmY5x+xCyzad85E6oANrV9TnVdLxYLXVCnY7i30noaDP8BAPQirZ6q7jRPp4bA5IqiUIuAI6UVqhQvAGCmDP9xPeYQAEAIQaiiH7oYATh7I4WqdruXqK7roig2DgIAzNcYoaosy7jpSxSXKMfb7Q04RhPuTd0EANht8Inq29dtKMsyz/PYR1UURVmWrlrKxewBQzKsUwZ2GrynavvSDd1eq6Ioqqoaug0APXJdGmCnabZU2He1hwOXI1W8AICU2acKAKAHE2yp0E6oCiHUdZ3n+fhtAOCMjDkj0+zPMzZBqCqKop1WtV6vzVIHYAxDD4YYbDl7E4SquPovy7Isy/I83ze/CgBgRkaaU7UxWaqu67j/5zjPDgAwtMkuUyNRnQJTBwDgibRW/9lSL01Z9sHO4/EdysL9sOcBAHA+0gpVglSymubujqPZvb0/AoAzM9nwH1yRMUcAkpRWTxUc4vJ/pMFEBWAnoQrgcgQpYCfDfwAAPRCqAAB6IFQBcNJGn4vZhHsjPyOJSGtOlemfAAxinH9TrKc5b2mFKkFqThQOAOgw/Mf1yMEAEEIQqgAAeiFUAQD0QKhihkznAiA9QhVzkoX7UzcBQtYxdVuAhKS1+g8gfdYpAzulFarsUwUAzFRaoUqQAqBPhmgZkTlVAJy68c/YhbmzJFRxJeoFwE6GXM6YUMU1qB0A8IRQBQDQA6GKeTL+yPCKoiiKoizLqRsCzINQxdwYc2QUcYeXoiiqqrLJJ3CMtLZUsE8VkILYO1XXdQihKIrFYlHXdVEUkzYKSF1aoUqQAlKwnZ8kKuBCaYUqgBTE2VQhhLIsq6paLpfdnx4YDXRmOKEs+2D7YLP/R9A7oQpgtxielsvlxlx1ySlZTXP3qftP4u/m8XFkmTmg52aCiepFUbQXeNejPksm7XIGsizL87xpGqv/Zm/0ZJOF+yM/I4mYoKdqvV7neS5OzZ4zME5XDFJlWca56sGcKuAI0wz/tXUKrk7XOoOJNWqxWLRHVquVXAUcNvbwX7tE2dgfVydLMbC6rpunqVfAhcbuqWpDVbulXnfKpzU1o7EWBgD6NXaoKsuynfW5vaWe5DSmaZbDAMCJmmD4z8RPAOD0TBCqYu9UeLK+RrSaGfspALOQQrFKoQ2MaILhv5ir4t3VajVyA+iHgVpgFqYqVk0jUZ2hCbZUiN1Urk5KD+yqwBRc+h3YabLL1EhUXIuzQKYjSAE7TXCZGgCA05PWBZV1qgMAM5VWqBKkUmfEDQD2MPzH5cm+QOLSOQNMpyUMT6gC4ERNewbo/PP8CFXMnLNAANIgVDFbzgIBSIlQBQDQA6GKoxloA4D90tpSwT5VM+B9ARKX2hmgC2qdjbRClSDFVShYjMvp32iy7IOr/88pvDUuqHVm0gpVcDkKFlMQpMbUNHenbgIcy5wqAIAeCFUcR4cQMAuKFdMRqrgMox7ALKRWrES98yBUcRIULCBNqcU7hiRUMXMKFgBpSGv1n4XKidIPBMyCYsWk0gpVglTSEn937FYFRGmWAjXqDBj+Y/7UKSBlatTZEKq4iO50ADhCWsN/pGsWZ1p61xmF2Z+JSv8MUI06dUIVB6VfpCLXq2FEglTS0nx31KjzYPiPI6RZpABaIgsJEKoulqX6XR28YVf9/VO+Yvuf+nzfR85Dsh+kURt2mTPAaV6xi57U+zhraQ3/malwHVn2wSC/N7E3Yv9f834T7m08wPXt4SzM4t97I4BnIK1QJUhdU58ZIskv/wV/wexeCKEJ92IQHCplAmmay78gpqufLsN/HDSvb37b2iQTITCIGX3f1ahTN7NQdeSYbr8PG/8Zp2/Ynv9r+oZd+LBr16xTeyuv9zCuJtk36wQbtv3dT6Rh+x7WU646wbfyGg9LxzSh6vnnn3/++ed/+MMfTvLsXGBXkZqTJ82OU6zgylSq1M20WOmvOl0ThKoYPG/fvv3222+rVgnJsj//F82rSG3o1ixliytRqZI292KlRp2osSeqf/jhhyGE3//+9/Hu22+//dZbb43cBi7+Ds+xSG3oLrRRs7gklSohhycnzLdYqVGnaOxQ9Yc//KG9ffv27ZGf/QR1N6EIf17+duAxF5hvedolC/eb5q5qxRWoVD075mu46zGHStIJ1Kv4V1CjTkkzrlie2rshhAcPHrS3gaSMXB/SoVLBjExUJ3YYu6fq9u3b7Slg7GC/c+dO+6KM3BiAnVQq4ApmtqUCAECasvHPurIse/DgwZ07d55//vkQwpe+9KWiKMqy3H5kWZZ1Xe/76aCKooh/HnjqLJvg1TvcsGOaPajJG3BMMyZs5Ew/VxN+EyekUl1Hst/BRBpwTDNUqm0zqFTjjzj+4Ac/aJ89z/PlchlCWC6XGw/L8zwejw8bs4WHG9Y+ZvxX73DDuj+d5J095nWbvBkTvkoz/VxN+E2clkp1Zcl+B49pXiLNUKl2Pmma38SuyaZ3PXjwoH1L4qvQ/elqtWqPrFarMV+g7lNvN6x7fOSP1OGGdY/ER65Wq3Sal0gzJnyVZvq5mvCbmAiVqt+GqVTHNEOlumzD0qlUk82p+uMf/9jejh16XXVdxz9jJ168O47uc203LD6gqqr2UzWaww0rimKjSTsbP5wLX7cUmjHhqzTTz9WE38REqFSXlex3MFKpDpvp5yqhSjVVmos9de3d8HQSjz8NIcQbY6bOww2LR5bLZTcXJ9KwaKo+7SObl0gzxn+VZvq5mvCbmIhkX5+ZfqJaKlWar9JMP1fpVKqRtlTYvsjDm2++uV6v4+0YKrezZ3zh6rpeLBaJNKydJdfm4oFOIK72isULayyXy0kmNl7YvESaMcmrdLhho32uLtuwaIRvYiJUqqEbFqlUKlW/DYtSqFQjhartKzwc/pR0X774wg305l22YbFV7Ru2WCxWq1UKDQshZFmW5/kZDtBcSpqv0mifq8sa7ZuYCJVq6IaFVL+DqUnzVVKpLjZVF1nT6b7L8zx21q1Wq7afs/3p+HMJDzcsGr/z83DD4qu06hi5bTubN4lkX6U5fq6aSb+JiUj29ZnjJ0qlOtCMRF6lOX6ummQq1ZQlsjvTLR7pLnPo/nTkj9ThhnWPjNmqww1rR5SnetF2Nm8Syb5Kc/xcNZN+ExOR7Oszx0+USnWgGYm8SnP8XDXJVKrpzzsP/+UnfGmS/fcj2YZFiTQvkWZsm2nDkm32aJJ9fZJ9a5JtWJRI8xJpxraZNmzyZk+wIyoAwOlx7T8AgB4IVQAAPRCqAAB6IFQBAPRAqAIA6IFQBQDQA6EKAKAHQhUAQA+EKgCAHghVAAA9EKoAAHogVAEA9ECoAgDogVAFANADoQoAoAdCFQBAD4QqAIAeCFUAAD0QqgAAeiBUAQD0QKgCAOiBUAUA0AOhCgCgB0IVAEAPhCoAgB4IVQAAPRCqAAB6IFQBAPRAqAIA6IFQBQDQA6EKAKAHQhUAQA+EKgCAHghVAAA9EKoAAHogVAEA9ECoAgDogVAFANCD/w9hCIcTuZvEtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xmin = 0.4\n",
    "xmax = 1.8\n",
    "xstp = 0.175\n",
    "\n",
    "hb_mc, hr_mc, hlist_mc = makeBiasAndResolPlots(selection_mc,trkMom_MuFwd_mc,trkMom_RangeMu_mc,xmin,xmax,xstp,label_mc,r.kBlue)\n",
    "hb_data, hr_data, hlist_data = makeBiasAndResolPlots(selection_data,trkMom_MuFwd_data,trkMom_RangeMu_data,xmin,xmax,xstp,label_data,r.kBlack)\n",
    "hb_ovrl, hr_ovrl, hlist_ovrl = makeBiasAndResolPlots(selection_ovrl,trkMom_MuFwd_ovrl,trkMom_RangeMu_ovrl,xmin,xmax,xstp,label_ovrl,r.kRed)\n",
    "\n",
    "#hb_mc.GetYaxis().SetRangeUser(-0.1,0.3)\n",
    "#hr_mc.GetYaxis().SetRangeUser(0.,0.35)\n",
    "hb_mc.GetYaxis().SetRangeUser(-0.2,0.2)\n",
    "hr_mc.GetYaxis().SetRangeUser(0.,0.4)\n",
    "\n",
    "hr_data.SetMarkerStyle(20)\n",
    "hb_data.SetMarkerStyle(20)\n",
    "\n",
    "r.gStyle.SetOptStat(0)\n",
    "\n",
    "leg = r.TLegend(.25,.70,.6,.88)\n",
    "leg.SetNColumns(1)\n",
    "leg.SetBorderSize(0)\n",
    "leg.SetFillColor(0)\n",
    "leg.SetFillStyle(0)\n",
    "leg.SetTextFont(42)\n",
    "leg.SetTextSize(0.035)\n",
    "leg.AddEntry(hb_mc,label_mc,\"L\")\n",
    "leg.AddEntry(hb_data,label_data,\"LP\")\n",
    "leg.AddEntry(hb_ovrl,label_ovrl,\"L\")\n",
    "\n",
    "ttext = r.TPaveText(0.20,0.90,0.9,0.96, \"NDC\")#NDC=relative to pad dimensions\n",
    "ttext.SetFillColor(0)\n",
    "ttext.SetTextSize(0.04) \n",
    "ttext.SetBorderSize(0)\n",
    "ttext.AddText(\"Pseudo-exiting Tracks (rm 10cm)\")\n",
    "c = r.TCanvas(\"c\",\"c\",1200,600)\n",
    "c.Divide(2,1)\n",
    "c.Draw()\n",
    "c.cd(1)\n",
    "r.gPad.SetLeftMargin(0.2)\n",
    "hb_mc.Draw()\n",
    "hb_ovrl.Draw(\"same\")\n",
    "hb_data.Draw(\"same,P\")\n",
    "leg.Draw()\n",
    "ttext.Draw()\n",
    "c.cd(2)\n",
    "r.gPad.SetLeftMargin(0.2)\n",
    "hr_mc.Draw()\n",
    "hr_ovrl.Draw(\"same\")\n",
    "hr_data.Draw(\"same,P\")\n",
    "ttext.Draw()\n",
    "leg.Draw()\n",
    "\n",
    "cd4 = r.TCanvas(\"cd4\",\"cd4\",800,1600)\n",
    "cd4.Divide(2,4)\n",
    "cd4.Draw()\n",
    "for h in range(0,len(hlist_data)):\n",
    "    cd4.cd(h+1)\n",
    "    hlist_data[h].Draw()\n",
    "\n",
    "co4 = r.TCanvas(\"co4\",\"co4\",800,1600)\n",
    "co4.Divide(2,4)\n",
    "co4.Draw()\n",
    "for h in range(0,len(hlist_ovrl)):\n",
    "    co4.cd(h+1)\n",
    "    hlist_ovrl[h].Draw()\n",
    "#print len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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