examples/Example_4_fastbnb.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "chinese-isaac",
   "metadata": {},
   "source": [
    "# Example 4\n",
    "\n",
    "This notebook shows predictions for nueCCQE-like events at MiniBooNE using the \"fastbnb\" module -- installed separately from:\n",
    "\n",
    "[github.com/mhostert/MiniBooNE-DarkNews](https://github.com/mhostert/MiniBooNE-DarkNews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "compact-danger",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "professional-latitude",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import interpolate\n",
    "\n",
    "from importlib import resources\n",
    "\n",
    "import DarkNews as dn\n",
    "from DarkNews import GenLauncher\n",
    "from DarkNews import Cfourvec as Cfv\n",
    "from DarkNews import plot_tools as pt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a54bb01c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fastbnb # (https://github.com/mhostert/MiniBooNE-DarkNews)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1d2ee0e8",
   "metadata": {},
   "source": [
    "\n",
    "### Generate events for MiniBooNE FHC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5becc5d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "common_kwargs={'loglevel': 'WARNING', 'HNLtype': 'dirac', 'neval': 1e4, 'experiment': 'miniboone_fhc'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fc5e1894",
   "metadata": {},
   "outputs": [],
   "source": [
    "kwargs_1={'m5': 0.150, 'm4': 0.001, 'mzprime': 0.03,'Umu5': 3.5e-5, 'Umu4': 2.5e-5, 'epsilon': 1e-3, 'path': 'data/example_1'}\n",
    "kwargs_2={'m5': 0.200, 'm4': 0.001, 'mzprime': 0.150,'Umu5': 3.5e-5, 'Umu4': 2.5e-5, 'epsilon': 1e-3, 'path': 'data/example_2'}\n",
    "kwargs_3={'m5': 0.400, 'm4': 0.001, 'mzprime': 0.200,'Umu5': 3.5e-5, 'Umu4': 2.5e-5, 'epsilon': 1e-3, 'path': 'data/example_3'}\n",
    "\n",
    "df_1 = GenLauncher(**kwargs_1, **common_kwargs).run()\n",
    "df_2 = GenLauncher(**kwargs_2, **common_kwargs).run()\n",
    "df_3 = GenLauncher(**kwargs_3, **common_kwargs).run()\n",
    "\n",
    "kwargs_4={'m5': 0.150, 'm4': 0.001, 'mzprime': 1.25,'Umu5': 3.5e-5, 'Umu4': 2.5e-5, 'epsilon': 1e-3, 'path': 'data/example_4'}\n",
    "kwargs_5={'m5': 0.200, 'm4': 0.001, 'mzprime': 1.25,'Umu5': 3.5e-5, 'Umu4': 2.5e-5, 'epsilon': 1e-3, 'path': 'data/example_5'}\n",
    "kwargs_6={'m5': 0.400, 'm4': 0.001, 'mzprime': 1.25,'Umu5': 3.5e-5, 'Umu4': 2.5e-5, 'epsilon': 1e-3, 'path': 'data/example_6'}\n",
    "\n",
    "df_4 = GenLauncher(**kwargs_4, **common_kwargs).run()\n",
    "df_5 = GenLauncher(**kwargs_5, **common_kwargs).run()\n",
    "df_6 = GenLauncher(**kwargs_6, **common_kwargs).run()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9aed5aac",
   "metadata": {},
   "source": [
    "# Decay selection\n",
    "\n",
    "We take the DarkNews events, pass them through a decay selection code that reweights the \"w_event_rate\" weights based on the probability that N5 decays inside the fiducial volume of the detector. This only affects \"w_event_rate\". The original weights can be accessed in \"w_pre_decay\".\n",
    "\n",
    "* df_decay_i['w_event_rate'] --- reweighted.\n",
    "* df_decay_1['w_pre_decay'] --- original weights.\n",
    "\n",
    "**NOTE**:  Since we do not monte-carlo the decay probability, \"pos_decay\" is not physical as the decay could have happened anywhere inside the fiducial volume. This method is faster and more efficient for long-lifetimes, but it does not determine a fixed decay position. We assign a dummy value for pos_decay based on the average position between the start and the end of the fiducial volume, but this is only a placeholder and does not constitute the actual position of the decay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "140ec642",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_decay_1 = fastbnb.decayer.decay_selection(df_1, l_decay_proper_cm=df_1.attrs['N5_ctau0'], experiment='miniboone')\n",
    "df_decay_2 = fastbnb.decayer.decay_selection(df_2, l_decay_proper_cm=df_2.attrs['N5_ctau0'], experiment='miniboone')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c5af01a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%prun\n",
    "df_decay_3 = fastbnb.decayer.decay_selection(df_3, l_decay_proper_cm=df_3.attrs['N5_ctau0'], experiment='miniboone')\n",
    "df_decay_4 = fastbnb.decayer.decay_selection(df_4, l_decay_proper_cm=df_4.attrs['N5_ctau0'], experiment='miniboone')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6481ea04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%prun\n",
    "df_decay_5 = fastbnb.decayer.decay_selection(df_5, l_decay_proper_cm=df_5.attrs['N5_ctau0'], experiment='miniboone')\n",
    "df_decay_6 = fastbnb.decayer.decay_selection(df_6, l_decay_proper_cm=df_6.attrs['N5_ctau0'], experiment='miniboone')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4c234c81",
   "metadata": {},
   "source": [
    "# Signal selection\n",
    "\n",
    "The fiducialized events are then put through our simplified MiniBooNE signal selection. Only events compatible with the MiniBooNE nueCCQE events remain.\n",
    "\n",
    "The signal selection follows one of the following methods:\n",
    "        \n",
    "        *   'circ1' picks events based on Kelly&Kopp criteria: \n",
    "                r = sqrt( (1 - CosTheta)^2/4 + (1 - Emax/Etot)^2 )\n",
    "        *   'circ0' picks events based on Kelly&Kopp criteria: \n",
    "                r = sqrt( (1 + CosTheta)^2/4 + (Emax/Etot)^2 )\n",
    "        *   'diag' picks events based on Kelly&Kopp criteria: \n",
    "                r = 1 - 1/2*((1 + CosTheta)/2 + Emax/Etot)\n",
    "        *   'invmass' picks events based on invmass criteria from Patterson, assuming m_ee is correctly reconstructed.\n",
    "\n",
    "\n",
    "The resulting dataframe (df_analysis_i) contains a new column: \"reco_w\", which is the weight of the evets after signal selection. If a given event does not pass the criteria, its weight is 0. For events that do pass the criteria, the weights are multiplied by the MiniBooNE efficiency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "084f25cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_analysis_1   = fastbnb.analysis.reco_nueCCQElike_Enu(df_decay_1, cut='circ1')\n",
    "df_analysis_2   = fastbnb.analysis.reco_nueCCQElike_Enu(df_decay_2, cut='circ1')\n",
    "df_analysis_3   = fastbnb.analysis.reco_nueCCQElike_Enu(df_decay_3, cut='circ1')\n",
    "df_analysis_4   = fastbnb.analysis.reco_nueCCQElike_Enu(df_decay_4, cut='circ1')\n",
    "df_analysis_5   = fastbnb.analysis.reco_nueCCQElike_Enu(df_decay_5, cut='circ1')\n",
    "df_analysis_6   = fastbnb.analysis.reco_nueCCQElike_Enu(df_decay_6, cut='circ1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f32f0f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_analysis_1_circ0   = fastbnb.analysis.reco_nueCCQElike_Enu(df_decay_1, cut='circ0')\n",
    "df_analysis_1_diag   = fastbnb.analysis.reco_nueCCQElike_Enu(df_decay_1, cut='diag')\n",
    "df_analysis_1_invmass   = fastbnb.analysis.reco_nueCCQElike_Enu(df_decay_1, cut='invmass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6aaa639e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/darknews/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 444x377.872 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pt.std_fig()\n",
    "\n",
    "VAR_1 = np.sqrt((df_analysis_1['P_decay_ell_plus','0'] + df_analysis_1['P_decay_ell_minus','0'])**2 - 0.135**2)\n",
    "VAR_2 = np.sqrt((df_analysis_2['P_decay_ell_plus','0'] + df_analysis_2['P_decay_ell_minus','0'])**2 - 0.135**2)\n",
    "VAR_3 = np.sqrt((df_analysis_3['P_decay_ell_plus','0'] + df_analysis_3['P_decay_ell_minus','0'])**2 - 0.135**2)\n",
    "VAR_4 = np.sqrt((df_analysis_4['P_decay_ell_plus','0'] + df_analysis_4['P_decay_ell_minus','0'])**2 - 0.135**2)\n",
    "VAR_5 = np.sqrt((df_analysis_5['P_decay_ell_plus','0'] + df_analysis_5['P_decay_ell_minus','0'])**2 - 0.135**2)\n",
    "VAR_6 = np.sqrt((df_analysis_6['P_decay_ell_plus','0'] + df_analysis_6['P_decay_ell_minus','0'])**2 - 0.135**2)\n",
    "\n",
    "ALPHA = 0.3\n",
    "LW = 1.5\n",
    "\n",
    "bins = np.linspace(0.0, 2.0, 11, endpoint=True)\n",
    "binc = (bins[:-1]+bins[1:])/2\n",
    "patterson_eff = np.genfromtxt(resources.files('fastbnb.include.Patterson_tools').joinpath('pi0_nueCCQE_eff.dat').open())\n",
    "ax.hist(binc, bins= bins, weights=patterson_eff, histtype='step', color='black', ls=(1,(4,1)), label=fr'NC$\\pi^0$ (Patterson 2007)')\n",
    "\n",
    "hist_1_t, _ = np.histogram(VAR_1, bins = bins, weights = df_analysis_1['w_event_rate']/df_analysis_1['w_event_rate'].sum())\n",
    "hist_2_t, _ = np.histogram(VAR_2, bins = bins, weights = df_analysis_2['w_event_rate']/df_analysis_2['w_event_rate'].sum())\n",
    "hist_3_t, _ = np.histogram(VAR_3, bins = bins, weights = df_analysis_3['w_event_rate']/df_analysis_3['w_event_rate'].sum())\n",
    "\n",
    "hist_1, _ = np.histogram(VAR_1, bins = bins, weights = df_analysis_1['reco_w']/df_analysis_1['w_event_rate'].sum())\n",
    "hist_2, _ = np.histogram(VAR_2, bins = bins, weights = df_analysis_2['reco_w']/df_analysis_2['w_event_rate'].sum())\n",
    "hist_3, _ = np.histogram(VAR_3, bins = bins, weights = df_analysis_3['reco_w']/df_analysis_3['w_event_rate'].sum())\n",
    "\n",
    "\n",
    "eps_1 = df_analysis_1['reco_w'].sum()/df_analysis_1['w_event_rate'].sum()\n",
    "eps_2 = df_analysis_2['reco_w'].sum()/df_analysis_2['w_event_rate'].sum()\n",
    "eps_3 = df_analysis_3['reco_w'].sum()/df_analysis_3['w_event_rate'].sum()\n",
    "\n",
    "_ = ax.hist(binc, bins=bins, weights=hist_1/(hist_1_t+1e-6), histtype='step', edgecolor='royalblue', lw=LW, label=r'$m_4 = 150$~MeV, $m_{Z^\\prime} = 30$~MeV')\n",
    "_ = ax.hist(binc, bins=bins, weights=hist_2/(hist_2_t+1e-6), histtype='step', edgecolor='orange', lw=LW, label=r'$m_4 = 200$~MeV, $m_{Z^\\prime} = 150$~MeV')\n",
    "_ = ax.hist(binc, bins=bins, weights=hist_3/(hist_3_t+1e-6), histtype='step', edgecolor='green', lw=LW, label=r'$m_4 = 400$~MeV, $m_{Z^\\prime} = 200$~MeV')\n",
    "\n",
    "ax.legend(loc='upper left', ncol=1, fontsize=10)\n",
    "ax.set_xlabel(r'True $\\pi^0$ momentum $\\sqrt{E_{\\rm tot}^2 - m_{\\pi^0}^2}$ (GeV)')\n",
    "ax.set_ylabel(r'Efficiency')\n",
    "ax.set_xlim(0, max(bins))\n",
    "ax.set_ylim(1e-3, 2)\n",
    "ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "62984c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 444x377.872 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pt.std_fig()\n",
    "\n",
    "bins = np.linspace(0.0, 2.0, 11, endpoint=True)\n",
    "binc = (bins[:-1]+bins[1:])/2\n",
    "patterson_eff = np.genfromtxt(resources.files('fastbnb.include.Patterson_tools').joinpath('pi0_nueCCQE_eff.dat').open())\n",
    "ax.hist(binc, bins= bins, weights=patterson_eff, histtype='step', color='black', ls=(1,(4,1)), label=fr'NC$\\pi^0$ (Patterson 2007)')\n",
    "\n",
    "hist_4_t, _ = np.histogram(VAR_4, bins = bins, weights = df_analysis_4['w_event_rate']/np.sum(df_analysis_4['w_event_rate']))\n",
    "hist_5_t, _ = np.histogram(VAR_5, bins = bins, weights = df_analysis_5['w_event_rate']/np.sum(df_analysis_5['w_event_rate']))\n",
    "hist_6_t, _ = np.histogram(VAR_6, bins = bins, weights = df_analysis_6['w_event_rate']/np.sum(df_analysis_6['w_event_rate']))\n",
    "hist_4, _ = np.histogram(VAR_4, bins = bins, weights = df_analysis_4['reco_w']/np.sum(df_analysis_4['w_event_rate']))\n",
    "hist_5, _ = np.histogram(VAR_5, bins = bins, weights = df_analysis_5['reco_w']/np.sum(df_analysis_5['w_event_rate']))\n",
    "hist_6, _ = np.histogram(VAR_6, bins = bins, weights = df_analysis_6['reco_w']/np.sum(df_analysis_6['w_event_rate']))\n",
    "\n",
    "eps_4 = df_analysis_4['reco_w'].sum()/df_analysis_4['w_event_rate'].sum()\n",
    "eps_5 = df_analysis_5['reco_w'].sum()/df_analysis_5['w_event_rate'].sum()\n",
    "eps_6 = df_analysis_6['reco_w'].sum()/df_analysis_6['w_event_rate'].sum()\n",
    "\n",
    "_ = ax.hist(binc, bins = bins, weights=hist_4/hist_4_t, histtype='step', edgecolor='royalblue', lw=LW, label=r'$m_4 = 150$~MeV $m_{Z^\\prime} = 1.25$~GeV')\n",
    "_ = ax.hist(binc, bins = bins, weights=hist_5/hist_5_t, histtype='step', edgecolor='orange', lw=LW, label=r'$m_4 = 200$~MeV $m_{Z^\\prime} = 1.25$~GeV')\n",
    "_ = ax.hist(binc, bins = bins, weights=hist_6/hist_6_t, histtype='step', edgecolor='green', lw=LW, label=r'$m_4 = 400$~MeV $m_{Z^\\prime} = 1.25$~GeV')\n",
    "\n",
    "ax.legend(loc='upper left', ncol=1, fontsize=10)\n",
    "ax.set_xlabel(r'True $\\pi^0$ momentum $\\sqrt{E_{\\rm tot}^2 - m_{\\pi^0}^2}$ (GeV)')\n",
    "ax.set_ylabel(r'Efficiency')\n",
    "ax.set_xlim(0, max(bins))\n",
    "ax.set_ylim(1e-3, 2)\n",
    "ax.set_yscale('log')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9763eaae",
   "metadata": {},
   "source": [
    "#### In reco variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6f6799c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 444x377.872 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pt.std_fig()\n",
    "\n",
    "VAR_1 = df_analysis_1['reco_Evis']\n",
    "VAR_2 = df_analysis_2['reco_Evis']\n",
    "VAR_3 = df_analysis_3['reco_Evis']\n",
    "VAR_4 = df_analysis_4['reco_Evis']\n",
    "VAR_5 = df_analysis_5['reco_Evis']\n",
    "VAR_6 = df_analysis_6['reco_Evis']\n",
    "\n",
    "bins = np.linspace(0.0, 1.0, 21, endpoint=True)\n",
    "binc = (bins[:-1]+bins[1:])/2\n",
    "\n",
    "kelly_bins = np.linspace(0.0, 1.0, 21, endpoint=True)\n",
    "kelly_circ1_eff = np.genfromtxt(resources.files('fastbnb.include.pi0_tools').joinpath('pi0_efficiency_circ1.dat').open())\n",
    "ax.hist(kelly_circ1_eff[:,0], bins= kelly_bins, weights=kelly_circ1_eff[:,1], histtype='step', color='black', ls=(1,(2,1)), label=fr'NC$\\pi^0$ Kelly and Kopp 2022')\n",
    "\n",
    "hist_1_t, _ = np.histogram(VAR_1, bins = bins, weights = df_analysis_1['w_event_rate']/df_analysis_1['w_event_rate'].sum())\n",
    "hist_2_t, _ = np.histogram(VAR_2, bins = bins, weights = df_analysis_2['w_event_rate']/df_analysis_2['w_event_rate'].sum())\n",
    "hist_3_t, _ = np.histogram(VAR_3, bins = bins, weights = df_analysis_3['w_event_rate']/df_analysis_3['w_event_rate'].sum())\n",
    "\n",
    "hist_1, _ = np.histogram(VAR_1, bins = bins, weights = df_analysis_1['reco_w']/df_analysis_1['w_event_rate'].sum())\n",
    "hist_2, _ = np.histogram(VAR_2, bins = bins, weights = df_analysis_2['reco_w']/df_analysis_2['w_event_rate'].sum())\n",
    "hist_3, _ = np.histogram(VAR_3, bins = bins, weights = df_analysis_3['reco_w']/df_analysis_3['w_event_rate'].sum())\n",
    "\n",
    "eps_1 = df_analysis_1['reco_w'].sum()/df_analysis_1['w_event_rate'].sum()\n",
    "eps_2 = df_analysis_2['reco_w'].sum()/df_analysis_2['w_event_rate'].sum()\n",
    "eps_3 = df_analysis_3['reco_w'].sum()/df_analysis_3['w_event_rate'].sum()\n",
    "\n",
    "_ = ax.hist(binc, bins=bins, weights=hist_1/(hist_1_t+1e-6), histtype='step', edgecolor='royalblue', lw=LW, label=r'$m_4 = 150$~MeV, $m_{Z^\\prime} = 30$~MeV')\n",
    "_ = ax.hist(binc, bins=bins, weights=hist_2/(hist_2_t+1e-6), histtype='step', edgecolor='orange', lw=LW, label=r'$m_4 = 200$~MeV, $m_{Z^\\prime} = 150$~MeV')\n",
    "_ = ax.hist(binc, bins=bins, weights=hist_3/(hist_3_t+1e-6), histtype='step', edgecolor='green', lw=LW, label=r'$m_4 = 400$~MeV, $m_{Z^\\prime} = 200$~MeV')\n",
    "\n",
    "ax.legend(loc='upper left', ncol=1, fontsize=10)\n",
    "ax.set_xlabel(r'$E_{\\rm vis}^{\\rm reco}$ (GeV)')\n",
    "ax.set_ylabel(r'Efficiency')\n",
    "ax.set_xlim(0, max(bins))\n",
    "ax.set_ylim(0.5e-3, 1)\n",
    "ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "be0bd270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 444x377.872 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pt.std_fig()\n",
    "\n",
    "bins = np.linspace(0.0, 1.0, 21, endpoint=True)\n",
    "binc = (bins[:-1]+bins[1:])/2\n",
    "\n",
    "kelly_bins = np.linspace(0.0, 1.0, 21, endpoint=True)\n",
    "kelly_circ1_eff = np.genfromtxt(resources.files('fastbnb.include.pi0_tools').joinpath('pi0_efficiency_circ1.dat').open())\n",
    "ax.hist(kelly_circ1_eff[:,0], bins= kelly_bins, weights=kelly_circ1_eff[:,1], histtype='step', color='black', ls=(1,(2,1)), label=fr'NC$\\pi^0$ Kelly and Kopp 2022')\n",
    "\n",
    "hist_4_t, _ = np.histogram(VAR_4, bins = bins, weights = df_analysis_4['w_event_rate']/np.sum(df_analysis_4['w_event_rate']))\n",
    "hist_5_t, _ = np.histogram(VAR_5, bins = bins, weights = df_analysis_5['w_event_rate']/np.sum(df_analysis_5['w_event_rate']))\n",
    "hist_6_t, _ = np.histogram(VAR_6, bins = bins, weights = df_analysis_6['w_event_rate']/np.sum(df_analysis_6['w_event_rate']))\n",
    "hist_4, _ = np.histogram(VAR_4, bins = bins, weights = df_analysis_4['reco_w']/np.sum(df_analysis_4['w_event_rate']))\n",
    "hist_5, _ = np.histogram(VAR_5, bins = bins, weights = df_analysis_5['reco_w']/np.sum(df_analysis_5['w_event_rate']))\n",
    "hist_6, _ = np.histogram(VAR_6, bins = bins, weights = df_analysis_6['reco_w']/np.sum(df_analysis_6['w_event_rate']))\n",
    "\n",
    "eps_4 = df_analysis_4['reco_w'].sum()/df_analysis_4['w_event_rate'].sum()\n",
    "eps_5 = df_analysis_5['reco_w'].sum()/df_analysis_5['w_event_rate'].sum()\n",
    "eps_6 = df_analysis_6['reco_w'].sum()/df_analysis_6['w_event_rate'].sum()\n",
    "\n",
    "_ = ax.hist(binc, bins = bins, weights=hist_4/hist_4_t, histtype='step', edgecolor='royalblue', lw=LW, label=r'$m_4 = 150$~MeV $m_{Z^\\prime} = 1.25$~GeV')\n",
    "_ = ax.hist(binc, bins = bins, weights=hist_5/hist_5_t, histtype='step', edgecolor='orange', lw=LW, label=r'$m_4 = 200$~MeV $m_{Z^\\prime} = 1.25$~GeV')\n",
    "_ = ax.hist(binc, bins = bins, weights=hist_6/hist_6_t, histtype='step', edgecolor='green', lw=LW, label=r'$m_4 = 400$~MeV $m_{Z^\\prime} = 1.25$~GeV')\n",
    "\n",
    "ax.legend(loc='upper left', ncol=1, fontsize=10)\n",
    "ax.set_xlabel(r'$E_{\\rm vis}^{\\rm reco}$ (GeV)')\n",
    "ax.set_ylabel(r'Efficiency')\n",
    "ax.set_xlim(0, max(bins))\n",
    "ax.set_ylim(0.5e-3, 1)\n",
    "ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f884989c",
   "metadata": {},
   "source": [
    "## Plotting a few cut flow distributions\n",
    "\n",
    "We pick a specific model df_1 and plot a few kinematical variables before and after signal selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7f17679a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_decay_1\n",
    "\n",
    "# already selected for fiducial volume\n",
    "w = df['w_event_rate'].to_numpy() \n",
    "\n",
    "# Smear e+ and e-\n",
    "pep = fastbnb.fastmc.smear_samples(df['P_decay_ell_plus'], dn.const.m_e, exp='miniboone')\n",
    "pem = fastbnb.fastmc.smear_samples(df['P_decay_ell_minus'], dn.const.m_e, exp='miniboone')\n",
    "\n",
    "# Kinematics of interest\n",
    "emax_r = np.where(pep[:,0] >= pem[:,0], pep[:,0], pem[:,0])\n",
    "etot_r = pep[:,0] + pem[:,0]\n",
    "\n",
    "# Get rid of invisible events (smearing brough E <= 0)\n",
    "w[pep[:,0] < 1e-3] *= 0\n",
    "w[pem[:,0] < 1e-3] *= 0\n",
    "\n",
    "costhetaep_r = Cfv.get_cosTheta(pep)\n",
    "costhetaem_r = Cfv.get_cosTheta(pem)\n",
    "costhetaemax_r = np.where(pep[:,0] >= pem[:,0], costhetaep_r, costhetaem_r)\n",
    "\n",
    "Delta_costheta_r = Cfv.get_cos_opening_angle(pem,pep)\n",
    "\n",
    "mee_r = Cfv.inv_mass(pep+pem, pep+pem)\n",
    "\n",
    "r_circ1 = np.sqrt( (1 - Delta_costheta_r)**2/4 + (1 - emax_r/etot_r)**2 )\n",
    "r_circ0 = np.sqrt( (1 + Delta_costheta_r)**2/4 + (emax_r/etot_r)**2 )\n",
    "r_diag  = 1 - 1/2*((1 + Delta_costheta_r)/2 + emax_r/etot_r)\n",
    "\n",
    "ovl = (Delta_costheta_r > np.cos(13*np.pi/180))\n",
    "asy = ((pep[:,0] < 0.03) & (pem[:,0] > 0.03)) | ((pem[:,0] < 0.03) & (pep[:,0] > 0.03))\n",
    "OldCut = ovl | asy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6cfd3dd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "f_cut_diag    = fastbnb.fastmc.get_r_cut_func(cut='diag')\n",
    "f_cut_circle1 = fastbnb.fastmc.get_r_cut_func(cut='circ1')\n",
    "f_cut_circle0 = fastbnb.fastmc.get_r_cut_func(cut='circ0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "393d0b55",
   "metadata": {},
   "outputs": [],
   "source": [
    "NBINS = 50\n",
    "HistWeight = w/np.sum(w)*NBINS\n",
    "\n",
    "HistWeight_CutCirc1 = HistWeight * (r_circ1 < f_cut_circle1(etot_r))\n",
    "HistWeight_CutCirc0 = HistWeight * (r_circ0 > f_cut_circle0(etot_r))\n",
    "HistWeight_CutDiag  = HistWeight * (r_diag < f_cut_diag(etot_r))\n",
    "HistWeight_CutMee   = HistWeight * (mee_r < fastbnb.fastmc.mee_cut_func(etot_r)) * OldCut\n",
    "\n",
    "\n",
    "eff_CutCirc1 = np.sum(HistWeight_CutCirc1) / np.sum(HistWeight)\n",
    "eff_CutCirc0 = np.sum(HistWeight_CutCirc0) / np.sum(HistWeight)\n",
    "eff_CutDiag = np.sum(HistWeight_CutDiag) / np.sum(HistWeight)\n",
    "eff_CutMee = np.sum(HistWeight_CutMee) / np.sum(HistWeight)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5dbfcc40",
   "metadata": {},
   "source": [
    "### Invariant mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "15aa9e7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Events (a.u.)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 444x377.872 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pt.std_fig()\n",
    "\n",
    "VAR = mee_r\n",
    "bins = np.linspace(0, np.max(VAR), NBINS)\n",
    "ALPHA = 0.3\n",
    "LW = 1.\n",
    "\n",
    "_ = ax.hist(VAR, bins=bins, histtype='step', edgecolor='black', weights=HistWeight, zorder=2, density=False, label=r'Total')\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'grey', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutMee, label=r'$m_{ee}$ cut'+fr' $(\\epsilon = {eff_CutMee*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='grey', lw=LW, weights = HistWeight_CutMee, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'navajowhite', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutCirc0, label=r'$r_{\\rm Circle 0}$'+fr' $(\\epsilon = {eff_CutCirc0*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='navajowhite', lw=LW, weights = HistWeight_CutCirc0, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'violet', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutDiag, label=r'$r_{\\rm Diagonal}$'+fr' $(\\epsilon = {eff_CutDiag*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='violet', lw=LW, weights=HistWeight_CutDiag, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'steelblue', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutCirc1, label=r'$r_{\\rm Circle 1}$'+fr' $(\\epsilon = {eff_CutCirc1*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='steelblue', lw=LW, weights = HistWeight_CutCirc1, zorder=3)\n",
    "\n",
    "ax.set_title(rf'$m_{{Z^\\prime}} = {df.attrs[\"model\"].mzprime}$ GeV, $m_5 = {df.attrs[\"model\"].m5}$ GeV, $m_4 = {df.attrs[\"model\"].m4}$ GeV', fontsize=11)\n",
    "ax.legend(loc='upper right')\n",
    "\n",
    "ax.set_xlim(0,np.max(bins))\n",
    "ax.set_ylim(1e-3,10)\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(r'$m_{ee}/$GeV')\n",
    "ax.set_ylabel(r'Events (a.u.)')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d978df38",
   "metadata": {},
   "source": [
    "### Total energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ed2301f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Events (a.u.)')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 444x377.872 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pt.std_fig()\n",
    "\n",
    "VAR = etot_r\n",
    "bins = np.linspace(0, 3, NBINS)\n",
    "ALPHA = 0.3\n",
    "LW = 1.\n",
    "\n",
    "_ = ax.hist(VAR, bins=bins, histtype='step', edgecolor='black', weights=HistWeight, zorder=2, density=False, label=r'Total')\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'grey', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutMee, label=r'$m_{ee}$ cut'+fr' $(\\epsilon = {eff_CutMee*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='grey', lw=LW, weights = HistWeight_CutMee, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'navajowhite', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutCirc0, label=r'$r_{\\rm Circle 0}$'+fr' $(\\epsilon = {eff_CutCirc0*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='navajowhite', lw=LW, weights = HistWeight_CutCirc0, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'violet', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutDiag, label=r'$r_{\\rm Diagonal}$'+fr' $(\\epsilon = {eff_CutDiag*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='violet', lw=LW, weights=HistWeight_CutDiag, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'steelblue', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutCirc1, label=r'$r_{\\rm Circle 1}$'+fr' $(\\epsilon = {eff_CutCirc1*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='steelblue', lw=LW, weights = HistWeight_CutCirc1, zorder=3)\n",
    "\n",
    "ax.set_title(rf'$m_{{Z^\\prime}} = {df.attrs[\"model\"].mzprime}$ GeV, $m_5 = {df.attrs[\"model\"].m5}$ GeV, $m_4 = {df.attrs[\"model\"].m4}$ GeV', fontsize=11)\n",
    "\n",
    "ax.legend(loc='upper right')\n",
    "ax.set_xlim(0, max(bins))\n",
    "ax.set_ylim(1e-3,30)\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(r'$E_{e^+} + E_{e^-}$ (GeV)')\n",
    "ax.set_ylabel(r'Events (a.u.)')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "59109495",
   "metadata": {},
   "source": [
    "### Emax/Etot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3faf4bd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Events (a.u.)')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 444x377.872 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pt.std_fig()\n",
    "VAR = emax_r/etot_r\n",
    "bins = np.linspace(0.5, np.max(VAR), NBINS)\n",
    "LW = 1.\n",
    "\n",
    "_ = ax.hist(VAR, bins=bins, histtype='step', edgecolor='black', weights=HistWeight, zorder=2, density=False, label=r'Total')\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'grey', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutMee, label=r'$m_{ee}$ cut'+fr' $(\\epsilon = {eff_CutMee*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='grey', lw=LW, weights = HistWeight_CutMee, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'navajowhite', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutCirc0, label=r'$r_{\\rm Circle 0}$'+fr' $(\\epsilon = {eff_CutCirc0*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='navajowhite', lw=LW, weights = HistWeight_CutCirc0, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'violet', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutDiag, label=r'$r_{\\rm Diagonal}$'+fr' $(\\epsilon = {eff_CutDiag*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='violet', lw=LW, weights=HistWeight_CutDiag, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'steelblue', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutCirc1, label=r'$r_{\\rm Circle 1}$'+fr' $(\\epsilon = {eff_CutCirc1*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='steelblue', lw=LW, weights = HistWeight_CutCirc1, zorder=3)\n",
    "\n",
    "ax.set_title(rf'$m_{{Z^\\prime}} = {df.attrs[\"model\"].mzprime}$ GeV, $m_5 = {df.attrs[\"model\"].m5}$ GeV, $m_4 = {df.attrs[\"model\"].m4}$ GeV', fontsize=11)\n",
    "\n",
    "ax.legend(loc='upper right', ncol=1, fontsize=10) \n",
    "ax.set_xlim(0.5, 1)\n",
    "ax.set_ylim(1e-3, 30)\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(r'$\\frac{E_{e_{\\rm leading}}}{E_{e^+} + E_{e^-}}$')\n",
    "ax.set_ylabel(r'Events (a.u.)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9f68d3d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dl/ccbsfs591r1fhgcws28m8xy40000gn/T/ipykernel_50070/2401580745.py:2: RuntimeWarning: invalid value encountered in divide\n",
      "  VAR = (np.sqrt(pep[:,0]**2 - dn.const.m_e**2) - np.sqrt(pem[:,0]**2 - dn.const.m_e**2))/(np.sqrt(pep[:,0]**2 - dn.const.m_e**2) + np.sqrt(pem[:,0]**2 - dn.const.m_e**2))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Events (a.u.)')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 444x377.872 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pt.std_fig()\n",
    "VAR = (np.sqrt(pep[:,0]**2 - dn.const.m_e**2) - np.sqrt(pem[:,0]**2 - dn.const.m_e**2))/(np.sqrt(pep[:,0]**2 - dn.const.m_e**2) + np.sqrt(pem[:,0]**2 - dn.const.m_e**2))\n",
    "bins = np.linspace(-1, 1, NBINS)\n",
    "ALPHA = 0.3\n",
    "LW = 1.\n",
    "\n",
    "_ = ax.hist(VAR, bins=bins, histtype='step', edgecolor='black', weights=HistWeight, zorder=2, density=False, label=r'Total')\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'grey', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutMee, label=r'$m_{ee}$ cut'+fr' $(\\epsilon = {eff_CutMee*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='grey', lw=LW, weights = HistWeight_CutMee, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'navajowhite', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutCirc0, label=r'$r_{\\rm Circle 0}$'+fr' $(\\epsilon = {eff_CutCirc0*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='navajowhite', lw=LW, weights = HistWeight_CutCirc0, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'violet', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutDiag, label=r'$r_{\\rm Diagonal}$'+fr' $(\\epsilon = {eff_CutDiag*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='violet', lw=LW, weights=HistWeight_CutDiag, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'steelblue', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutCirc1, label=r'$r_{\\rm Circle 1}$'+fr' $(\\epsilon = {eff_CutCirc1*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='steelblue', lw=LW, weights = HistWeight_CutCirc1, zorder=3)\n",
    "\n",
    "ax.set_title(rf'$m_{{Z^\\prime}} = {df.attrs[\"model\"].mzprime}$ GeV, $m_5 = {df.attrs[\"model\"].m5}$ GeV, $m_4 = {df.attrs[\"model\"].m4}$ GeV', fontsize=11)\n",
    "\n",
    "ax.legend(loc='upper right', ncol=2, fontsize=10) \n",
    "\n",
    "ax.set_xlim(-1,1)\n",
    "ax.set_ylim(1e-3,30)\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(r'$\\frac{|\\vec{p}_{e^+}| - |\\vec{p}_{e^-}|}{|\\vec{p}_{e^+}| + |\\vec{p}_{e^-}|}$')\n",
    "ax.set_ylabel(r'Events (a.u.)')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8a2cb76f",
   "metadata": {},
   "source": [
    "### Opening angle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "971ba3fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Events (a.u.)')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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crEuAU1UVPp8PsVgMgiAgFAohEong7NmzmJqa0m/n9/v1jVGBD7O5i9XTzRePxzE5OWk4FgwGIQhC0ekXVVUxNzdX8nHHxsaK9pqmp6f1XpoWhOPxeNlzhUE4FArh/PnzkGUZKysr+v0LVbNWuRoMcAXqMQd38+ZN9Pb24pvf/CaOHj1qOHf16lX86q/+Km7evMkAR1Qjv9+vD5NpAWxlZcXQQ5JlGW63uy7Pd/HiRYiiqH/gz87O1uVx8xWm1quqikgkYth1vLD3Vyr4VUJRFP3LgFa4XpsnK3UuXyQS0QNxKBTSe2jRaNQUCJuFAa4BUqkUZmZm8Oabb+LNN980nZ+ZmSnauyOi/bl48aLhA3RpaQk+n0//+cKFC3VL2CgMPtWm8JdaH1ZYrEIL2KXmrGrpwcmyjJMnT+qJJBqXy1XyXOHzx2IxvbfWrB5aOQxwBeoxRLm9vY2+vj488MADePzxxw3nLl++jDfffBPb29u1NpWIsBfQ8pMaJEnSf9YChSiKek9CGz7TeiBer9fymBW/32+aX9KyuAVB0IfutOP5w5aF9ynWq3G73Za9plJq6cGJomi4r9Y2QRBKnssXCoX29fzVrFWuRkevg2uEmZkZXL58GcvLyzU/Vn9/P1wul+Fff39/HVpJRBqrta9aoNI+pPOHyUKhkGleyOqYFVEUEQ6HEQqFEI1GEY1G9fmyU6dO6evAtKAXDof14BQMBhGLxRCJREpmenu9XsPnjyiKCIVCWFhY0J+zngRBwOTkJBYWFhCJRLC8vKz3eEud00SjUfh8PkPQKxXAZFk29LAbiVmURdSSRfnCCy/gxRdfxMMPP2yaLF5ZWcGVK1fwqU99Cp/+9Kfr2WQiKkOSJITDYQSDQUxOTkIQBMtjrVZsHVk7suq9aT1iq2xQrfpJLUshmEXZBJcvX8a7775rOv7aa6+Vve/Vq1f1EjiFuIyAqDFEUcSJEyf0Hp+WLGF1rJW08letSs7YD6uhSY/HY9lLzR8ybgYGuCpdvnwZ3/rWt9DX12d5fnt727KbfvjwYQDAv/k3/wbvvPOO5X25jICoMURRxK1btyBJkl5T0epYq3m9XsO6t24xNzdXU7bnfnGIskB+kslrr71WtAusDUM6nU7LXQeK1ZFLJBL48z//c3zqU5+y/MNdXV3F6dOnDWt4iOhg6rYAVy8coqzSftfBPfjgg6Z5tkocO3YM9957bzVNJKIDgsGtNsyiJCKirsQeXJtaXV21PM4EFCKiyjDAtZnx8XEMDg7i9OnTlueZgEJEVBkGuAL1qGRSiyNHjmB1ddVyJwItAYV1LImIymOAK9CsDU9v3LhR9NzIyAgDGNEB0SmZktq2Q1ba9TUwwDWZ0+mEw+HAt7/97aK36e3txczMDHcUJ+py2u4AzQwO2ubRVnU3ZVnGmTNnEIvFDPfRqo9Eo1EkEgl9RwNZlhEKhRAKhQyPs7Cw0JQNTcthgGuyoaEhfPazn0U6nbY8n0wm8dJLLyGVSjHAUXtIWRckaJjBg7F8RpbloutlG0WSJCwuLuo1MvP3dotGo3rQyqeqKmRZhiiKEEVRX4AuSRKAvcXzhbVAp6en912AuREY4FpgaGgIQ0NDrW4GEbXQ3Nxc0+tNagWfgb3AlL+rdrGyYIqimHqY2lBlsZqZ2u1bXfaMAa4DcQkBUWdTVbXpH/yKoiCRSEAQBEOPrByPx6PXkFRVVS9BuLCwYNimqNDU1BSi0WhLhyoZ4Aq0OouyFC4hIDKLRqNYXl7G1NQUVlZWoKpqW8z/lHLx4kWcOHHCdDy//mQl2/fshzYkGo1G9VqXoihWVNBZm38D9np6iqIgHo+X/D17PB6EQiEGuHbSrCzKanAJAZGRtgHn3NycviOAz+dr+wAXj8dNJf6CwWDZjUtr2bk7kUhAURQ9qWV6ehqjo6OopByx1ZY358+f17fFAWAZkFu9szcDXIc5cuQIAxjRB7QhNlVV9Q/h/F21I5EIgA834FQUBX6/H6Iowu12G25biizLCAQCJW+v9YhkWdafo5jCtHpVVRGJRDA/P69nVlrdv9aduwVB0J9X+2+p9H8rkUhED8ahUEifx2vH7X0Y4NrQ8FAvenbvAOk180mHE+gZbH6jiNqQtlt3YRYfsNcj8vl8hg/d/DmjSoMbAH1bnWK0Ibvp6Wl4vd6yG5YKgqDPawF7GyF7vd6yPc9aenD1mPNTVRWxWEzvrbW6h1YOA1yb6bNn8YWTP4Oed64i885V03n7wC7sj/09BjmiDywtLcHn8xmOqaqKixcvIhwOG46HQiG4XC5Diry29mt+fh7hcBiLi4uIRCJwuVxYXl7G7OysKYtwYWEBoihCURScPXsWkiTB7Xbr5xVFKdlmt9sNRVH0npP2WOXU2oObnJzUe49ahmOxjUmt1ubtN/Xfak/MZmKAazP9aQc2L/0CkHVY38CRxcj/fgv2MeM6ugFHGg/eN96EFhK1F23YMd/Kyoplj0U7JgiC/v/aB/zk5CQmJychyzJisRjC4bAeCPKDQCQSgSAI8Pv9kGUZ0WjUcsixFK/Xi3A4rLdbFEWEQiE9cALF0/Zrsbi4iFAohImJCcRiMcMyAUmS9J+1Oc38NkSjUfh8PsPrLBXAZFk2ffFoNga4BunBNhy2/Wdi9u4CyDqw/VPvYfgu4x9P7k4G6ZUB7PzoGhzDG4ZzjwC4/MdhXLVlamg1UefJ/5DWTE5OWvaItMBj1TvRjl24cAETExMAYDn0GYvF4Ha79YXOXq8XFy9eLBvU8ln12OqdNWlFEARTr1bj9Xrh9XqL9tCWl5dN57Q5Q5fLZQrIFy5cKLmMoBkY4BqgB9s42nsFdpt1dtLuhhO7W72W57K7Q0gByA5m4RCM99/tcwCOHFJ/9bj1Ezuy6Pv75mFNopp0YGURQRBw6tQpU+KDlnVZKL8n4na7DcGqsHfm8/n0bERgrwc5OTmJCxcu6LepJGlDS71vt8SMYqwCn8fjKTrECdRn3q8WDHAF6rEOzmHLwm7L4VZmDJlcQSDb6EHvd9yw7Rbfa3YHO8jazeftg8DAyR3ktm2mc28pP8HYtYdh3y4ytEl0wITDYUQiEb2HAXwYeBRFgaIokCRJn5PTgo1WZkq7n9frNdze7/djYWFBr8t46tQpiKKIlZUVvc7j+fPny7ZPW4vWroWKazE3N9fyMl0AYMtVsgjiANLWwa2vr2N4eNh0/oUXXsCLL76Ihx9+2LSexWnbxJHtt3Dzzr3IFHyHsCX70P/yPdh+dA25QfNworpxG9Effxe/NPmLGBseNZ0f7uvB3YNO03Hl8o9xz2s/g+8//Fc4/Nh9lq+JlU6I2k83BrhGK/f5rGEPrgFsGw6s/+kn0Jt1wGogMmffxe7wNtC/azr3fjqFv7IdxYvyuwDeNZ13Ouz4/ZM/ZQpyh4cOAQB+//d/Hxe/+6eW7WKlE6L2w+DWOAxwDWBLO4CsA9nH30emzzycmOvZtQxuALCR2cWuzYFfvn8ID44ae3A3N3fwx/H3cPnWHSS3jb2/nGMAKaTxG7/xGwgd/g3T47LSCREdNAxwDZQb2kHOWXyurRSX04F7h/oNxwZ7HOi12/DbMetEEifseGb8OB75qY9U9ZxERN2EAa6DjDh78YWfPYJUxpwAs3k7jW/95AaSqSTuS5vn6LhOjogOGga4DjPi7MWI0zyzl/xg3ffuO+8gY3vDdF5bJ7cU/5Hl4zIBhYi6DQNcl0jb9oZCt74n4vb3DlnfyJFF+I8/hz9b/mvTKSagEFG3YYBrgVtbGdy2GGYEgPfT1skn5WT69u73/rE76LNYfpBLbaN/dQRf/63fw+3DxnNMQCFqb52ylKDUzgSteA0McE12ayuDUOw6MiVWH9pzWfQ7zNmXpWgJKL+zWrxgqxN2PDP6GB55zJidqc3PcadwovajbZ/TzOCgLVjXKpHkV22JRqN6qbHp6Wm9XaFQSK/Okkgk9C1/ZFlGKBRCKBQylD5bWFho+L59DHBNdjuTRSYH/PJ9w3D1mX/9m+k7uP7WCob7/qd9PW6pBBQA2Ehu4w+vvQ/1jeu4e/uK4dze/FwEj//DaVx/27yZKocvD7bt17ab+nx9j/Y19fnambYLdzNLXkmShMXFRYTDYcOuCwAQCAQQi8UA7AW7M2fOYHFxEaqqQpZliKIIURT1Ci1avU5RFE11PbWKMY2seMIAV6AepboA4CbSWNtII7tjXCbwdmoHAODq68HdA+ZkkQ3Y8T6q+0AploACADe2y8zRObL4q//0Z9gq+It4++238aVf/384fEnUAnNzcyX3lWuEYDCoBzFRFPVi1oXFoUVR1AOYoiimHqY2VFlsbzzt9vk9xXpjgCswMzODmZkZvRRMNW5tZvAVvIL0Zev5tB4b0N9T3fq4apWao8vtZLDzt33A/7jLdL/DuAvf+qffwc7W+01pJxHtUVW16cWKFUVBIpGAIAiGHhkAvW5nPpfLpc+7aQWWVVXVb7ewsFByR4GpqSlEo9GGDVUywJUwMjKCF198EQMDA6Zzr732GoaHejE6ZIPTtmk4l8hsI41dBO4V0GOx0Lu/x47h3uYWRa5kjq4Y564dv3aba+ioPUWjUSwvL2NqagorKytQVbXhczvNcPHiRZw4ccJ0PL9Ac7232NGGRLVd0iORCERRhN/vL7odkLartzb/BuztZaftcl7qWng8HoRCIQa4VpiZmcHKyorlueGhXnzh5E+jJ9sPJI29m9Tm3kLrUUcPBgbao7p/uTm6Yt558x38FzWLjZ3qsjuJGkmr7q9t0On1euHz+boiwMXjcVMh92AwWHZXb1VVMTc3V/Kxx8bGLH9HiURC3wpIC6Cjo6MoVZNfC3yF2/6EQiGcP38esizrn6NWAVkLkI3AAFdCX18fbDabYSt6zT29A9j8079jufP2Fu4A+D62YIO579c6peboitnu2V82J1EzacNnqqrqH7DxeByKoujBYGpqColEAvF4HMFgUL+P2+3WkydaQZZlBAKBom2w2iU8Eonom4xqWYqFygXAUkRRhCAI+vNq/5VlGYIgmIKRNpxZKBKJ6L//UCikz+M1e/87Brgy7r//ftO3KAAYWMsCP7YuqLyd2gJeA3Z6sgCaO9dGdJCIoqgPpxUe9/l8EATB8IHq8/n0D9tWBjdgb3iu1BybIAiGYcGVlRV4vd6yvdNaenCl2uP1ei13Ay/8fFRVFbFYTO+tNbKHVg4DXI2sCirnUFsGJhFVbmlpCT6fr6LbejwevfeTn/6+sLAAURSxvLxs6P0sLCzA4/FgaWkJbrcbp06dgiAI+tyUNpynrfcKBAIIh8NQVRWKouhBpNjjl+J2u6Eoip6NqD1fObX24CYnJ/Xeo5bhaLV4W9vJvLAHt9/U/8LElXpi94KIOpqiKBUPe7ndbsRiMUN2ILAX+LTHyE99v3Xrlr6jt7aoORqN6jt9T09P61mCHo8HgiDA5XLB7/frPcVij1+O1+vF8vKy/rMoigiFQvpu4lpCR70tLi7qO5rPz88bXod2LhqNIhwOm9L/o9Go3nPWlApgsixX/OWkGuzBEVFHy/8ALkdVVcs5dVmW9fMarceUn/auPV8gENB/LhyC03o7+cetHr8cqx5bvbMmrQiCYDkUqbVJ651Zfamw6qFqc4Za4M934cKFkssIasUARxW5ceOG/iYtxFJe3a2bKossLS1Z9jqAvR6TLMt6VQ5tjkxRFMMH/sTEBGRZNs37FVPq8cvRUu+bmZhRC6uhSY/HY/latWDfyLV+DHBUUn9/P4A7WH35B3j+P/y+5W3eWb+JZ/8kyiBHbUNRFCwtLelDhloWZTgc1ueWFEWBJEn6HJskSRAEAUtLSwiFQnog0npxHo8H8/PzmJ6exsLCgl6v8fz58/pzauWptMBY6vHz21AsWGpr0Tql2PJ+zM3NNbRMFwDYcqUWOBxgyWQSv/M7v4MHHngAv/iLv2g6P7CWxeGle5E58RYyBUkmb9xJ49dffRv/+CGXZTmuUjY213FZ+S5O/MxnMHyocZOvlVKTKXx99W38n3gE9xdZ9HDYbsPO338fj/+d8t9IiTpFYbJEMBgsOnTXaN0Y4GqhVZpaX1/H8PBw0dt1fQ9Oq2ytjaHXs6tfrt5kN7A5e9Frt+H3dl8vehvnrh2fv5LFVr/5HIcvqVMFg0E9W1JVVUxMTLSsLQxu1enqAKeqKi5cuKCPuQcCgbptO9GO9SYboVwFlFvvJ/Dt91N4cfkKlD+6aDp/89bb+IPF/8AgRx1HFMWmJHVQ43REgNMmed1ut+UfnLbGRFuzofXSVlZWDNlPWvXrevTi7uxk267eZKOUqoAyfs8YnO9v4ft3PwLc/Yj5vrDj+pX3GOCIqOnaPsBJkqRP9o6NjZnOB4NBvWcG7PXStIWJhVs4jI2NVbRQcj/ucvagt03qTbZC76AT/+pn7sf2trmH9/YNFX+USCGdYh1LImq+th9D83q98Pv9RYcVtaoEGp/Pp08EF645uXXrVqOaeaD1DjoxJAya/vXZm7tRJhFRvrYPcKVoabdWxwHok8OaVuyvRERErdHRAc6qKoC25gXY6/3lVxNIJBIVL84kImqG/VQ3aaVihR6A9n0NHR3g8tP/NfkVuLWtMrS6bVNTU0WHOtPpNJLJpOFfO9pKbyB5J2H5byu90ermEdE+RCKRplbbl2VZD1SKohiCliRJkCQJ0WgUoVDIcC4UCul1OCORiJ7LoNWSLNw3c2FhoQmvpry2TzIpxaqIZ+GCyEozJufm5vDVr37VcOzXf/3Xa2le3W2lN/DXr3wHuznrlH273YFf+Phn0e8canLLqJu99Np7TX2+Tz56T1Ofr1W03bObOW0SDocRiUQA7I1w5ZctCwQCuHTpkj7ype1VpyX5aQWqtcoq+VNBhSNj09PT+95VoBE6OsAV24Cvmu0XZmdn8dRTT+k/J5NJ/Pt//+9rbmM9be+ksZvLQrz/4+h3HjKc20rfgfLWq9jeSTPAEXWAubk5U13MRpuYmMDa2hoA8+LxxcVFQ81I7XxhNjrwYUHpQCBg+Rry79vKvIeODnBer9c09quqalXzbE6nE06nE+fOncO5c+eQzWbxT/7JP6lTS+ur33kIQwMjrW5GxYoVanZsOuDYLr7EYuSeETz4+IONbBpRS7Qy4a3YNE3+5+bi4qJhGyDtczZ/Z4WFhYWSOwFo00PlNmhtpI4OcMDeEGR+sdKlpaWausUzMzOYmZnRa1FS9bRCzSvyFVxcNFY5OdQ3jODP/Uv09ziL3l/dyQL/6DqDHJUUjUaxvLyMqakprKysQFXVln6oVuLixYs4ceKE6Xh+YeVGVFFRVVXf3WB5eRnBYNAQaGVZxoULF+Dz+QzPr+1qAOx95iqKgng8XvL37PF4EAqFGOBKkSQJsizrlbuBvV+wdlG0Dfi0St7BYLCibSio8Vyjo3C+mbKscnIHwFfx45L3d/ba8b+9nMWNrRumc6V6f+z5HRxaZaK5uTmcOHFC36m73QNcPB7H5OSk4VgwGCy7G7eqqpibmyv52GNjY0Vfv7ZpK7A3d5a/qzkAfYsgbVNTLYehMJchFArh/PnzkGVZTzCxCsjNTKCx0vYBzuv1wuv1lvyDbfVEJlkrVeUEALI9u9jtsz534/3b+KO3tvCSfAX/eZ+9P/b8Dg7ti66qqvqHcP4HtpZQoQ2rabt/i6IIt9ttuG0psizrSRfFaMFnamqq7JfswmQ4VVX1HbS14hVWQ5jlAmA5iqLobdPKGxbOkwmCgEAgAJ/Ph7W1NdOQZiQS0YNxKBTSN5xtx33r2j7ANVsnzMF1kt5BJ3oH93+/IVc/nG+9VVXvz9lrx5ev3c0AdwCIoohoNGo57x4MBuHz+QwfuvlzRpUGN+DDnk0p2vBoJfKXM2n3LfdFHqitByfLMk6ePKknmWhcLhckSUIgENDPaa81PyBqzx+LxfTeWqt7aOXUFOBeffVVJBIJ/duIKIp46KGH6tS01uj0ObjUZvH1e329zo7JsKy29/f2Wwk8934WW5lMo5tIbWJpaQk+n89wTFVVXLx40bR/WygUgsvlgqIo+vCcLMs4c+YM5ufnEQ6Hsbi4iEgkApfLheXlZczOzpp6MfkF3rVg4vV69d5MOW6327I3VU4tPThRFA331YZ3tU1h878kyLIMQRBMPdH9pv5Xk9FeT/sOcJcuXUI4HMbVq1chiiKOHj0KYO8PKpFI4OrVq5icnEQoFOr4YNdJehx9sNsc+OGVl4reptPWyVXT+1vf3fsGWixzE+Aedd1GG3bMt7KyYtnj0o5pX8iBD1PeJycnMTk5CVmWEYvFDLt/53/QRyIRCIIAv98PWZarGprzer0Ih8P6/bR5Ly1wAvXduxLYe82Tk5NYWFiAIAiIx+N6ir/H48HU1JQ+pLu0tIRYLGa4fzQahc/nMwT7UgFMWwTeShUHuPX1dYRCIUxOTuLiRfO+X/muXr2KZ555BuPj4/jiF79YcyObqV2GKK16YqV6Z86+Afz0w7+ETNa6wHG5dXJb6Q1s76Qt79tJPT8tc3PpL76Hhd85Z3mbjc2b+PNLzzHIdQmrXtPk5KRljyi/ylEh7diFCxf0zU2thj5jsRjcbre+0LmaZUlWPbZm7D3n8XiKzg/mB1SrtiwvL5t6b9qcocvlMgXkCxculFxG0AwVBbj19XWcP38ezzzzTEUPevToUfzmb/4mrl69iqeffrqjglyrhyjL9cTsNgd6HH2W55x9A3BiYN/P2U0VUsbHhuF8M4Xrj3wc44983PI29+XsUF6/wQBXoU6sLCIIAk6dOmXqXRXbDzK/J+J2u01F2vMDos/ng6IoemCzWghdCS31vt0SM4qxGposFjC131+ri9tXFOBGRkaqClJHjx7tqODWDsr1xHocfXD27T+IldJNFVKGR2yI/PIuvnf9IWxlzFmWb1x/B9/ZTGIr1YLGUVNpZam0Hgbw4XCklj0oSZI+J6cFG63MlHY/r9druL3f78fCwgKi0SgSiQROnToF4MMlTZpymZRer9ew7q2bzM3NtUV2O7Mo21C1PbFadVqFlGI+clca68ggtWPehfyOugNstqBR1BLFhv1EUTRkURZmVBZ+OGtzVhqrLEVtSdN+29eulfhr0Q7BDahzgHvuuecQiUTwZ3/2Z/V8WKqj/c7tdZpM1oHsrg2P3WWdAj6wNYT/9D7gsHGXcWoP3dZ7ayd1DXCiKOL48eP1fMima5ckk3qrZW6vk6QzfZB/8jB6HNbziW9c26uMn7jxnmWWJTMsibpHXQPc8ePHOz7AtTrJpFFaMbfXKulMH9JFlsHtYi/L8o++I+HSr/2J6XxPbhuvvvxdBjmiLlD3ObgvfOEL+MY3vlHvh6U6aNXcXju5Z2SvPmbqZ334hZ81r9HZ3UnjyvX3GOCIukDVAc7lcsFmsxmOaZOl3RLghod6MXbIAafNnJXQY9tpQYuoVsNDNvy2/Wdxe9c8B/cWNvF7va9j83YLGkZEdVd1gPN6vZifn9fTbxOJhF7epVvMfO5R9PXaAZgXjG7Z91LmMxnAnKvXfbqlBFi2Pwv3//oqrr57FFuZfsO57Ss3gDXAsWNvUeuIqJ6qDnDz8/N6mS5gb63c0aNH8cILL9SlYa2Sn2Sy+tzXsPLqFo4cOWq6XTKZA/AuMtniG3Z2g24rAZbJOpAb2Ibb/SPTOUfvEPA/mGFJ1C2qDnD5wS1fp6/pyE8yWf+9bbizDlgVru/BHQDvIuPYBdC93/hrLQHWbkplWWoZlg4wwBF1g6oD3C//8i+bjiUSCUxOTuKJJ56oqVFtI+vA68IyjlgUjd5ObQGvATs9WXRzgAMqS04pNoTZjsOXxbIsU1vNbwsRNU7VAe7WrVuYnZ01HOuGdXCFNntuI3fI/GmYg/U6q4Om7Pq6Dhq+JKLuUtMc3MmTJ+vZFupApYYwa9nBAGhd76/YVjtcBE7UWaoOcAxupKlmfV25HQyA5vf+tK12Li5exPP/7A9M5wcHB7G6usogR9QhWIuyQH4W5Uuni2cOUm1K7WAAtCZ55dDQMIA7mPk/ZvBrv/EvDedWV1dx+vRpvPjiizh27Jjl/dnDI2ovrEVZID+LMvu7nGdrtHbcwWAnN4rB3nHDsYfuP4THHp7E6dOni96PPTyi9sJalEQf6O8HnLDja6++Drz6uun8o//03+KZv3M3hkdtpnPlenjs3RE1X90C3Pr6OiRJwsTEBB6ySKvvRDeRxrsZG/rumBMh3k6xVFe3GTzUg2/4dnF7w/y2+MmNPnzt9RR2egQcvnfcdP6IbQijH3mwaA+PvTui5qtbgBsZGcGTTz6J2dlZzM3N1ethW+pX8QrSqhN49W3L8z02oL+nu9fAtatSGZjVZl+mM314O3UEPT3moWnXYAZObOHp//4j4L9b3//v/t/P4F/9vbsgDBir27B3R9QaVQe4q1evIhAIQJZl2Gw25HI52Gw2y51uO1Uau/i0cwv33Huf5fn+HjuGe7u7VFc7KpeBWUv2ZbFF4KP2LH4bx3H1E2u4M+w0nb+Z3MJzL1/Fg+KjeORe45zi+Pg4BgcH2bsjarKqA9zCwgLOnz+P48eP4/z58zhz5gxkWcba2lo929dygn0Xdw8chHLKjVPvXcRLZWA2MvtyHE5khgew7trfkogjR45gdXUVN2/eNJ3Tenc3b95seoC7du2aZZsA9iqpO1Qd4Dwej55Qom2b4/F4Or7YMtVPPXYRLxUc2zEDs5gjR460VcC4du0ajh07hlQqZXmevUrqBlUHOFEU8fzzz+PkyZO4cuUKvve97+Hnfu7noCgKPv3pT9ezjU2Vvw7uodNfa3VzOlotu4jXIzhScTdv3kQqlcKzzz5rmhdsZa+SqJ5q2vA0GAwiGo1idnYWR48ehc1mw6lTp/D5z3++nm1sqvx1cIHffbHVzel41e4iXktwbKTepANOmJcJ9CX3jl2/ecfyfiODfbh7pP12Uz927Bg8Hk+rm0HUEFUHuOPHj+PKlSv6z1evXsXKygpLeFHdVBscGyHbmwUcWYy9fAhjFucPYQBO2DH/x69a3t/Z48D/e2oCI4PGXudb6zvoH7mr/g0movouE2Bwo3ZR7x3Is/1ZjHzuZay+eQybO/2m844dG37r8sexkTOnYCaxg69lfoxf+89/Y/nYf/f/+gbUzeJVc5gMQlSdigLc+vo6zp8/jy9+8Yv7evBkMolIJLLv+xFVq5E7kNuH0tg+nMHWds7ibA7bEzY4MuZlI2OpXvz2a8fx409sYnvYeN/vr76Gv36zHxvb1pusMhmEqHoVBThtEfe/+Bf/AqdOnaooieT8+fOQZRnf+MY3am4kUaUavQP5YO9m0XMZRw+2suY1csAuHsAA7gzbkXYZA9xPnHtzd1evXoU8Yl6Osrq6ymQQoipVPER59OhRPPPMMzh//jx+8zd/E6Ojozhx4gQEQQAAqKqKW7duIRaLYXR0FMFgEGfOnGlUu4mKasTcXSbrQHbXhsfuihe9TXbXDvmtn0XaMshZOzQ8AuAW/vW//te4/Y71Yw8ODuJTn/oUgxjRPu17Du7MmTM4c+aMXnsykUhAVVUIgoDjx493TZkuonzpTB/knzyMHof1XNlgXxqPfeQt9Dgy+wpwY+N3A7iFb33rW7jfogcHALb+YaR7R/D6O+uG40xQISqt6iQTbdiS6KAoVsarUtpSAqtjH/vYx0wlvgDg/fVN/LNzf4md7I8tH7NcggrRQVbX7XKIDjqrOTrHoB279n7c97L57Zb74C2Yu2OdZLKe2sZOdhdPfuIoxoeN2ZvlElSIDjoGOKI6KDdHt3PXAJR33MjkjG+5tZvbwFVgd8sqM/ND48P9uM9lTIopl6DCJQR00DHAFWCpLqpGqTm6XkcWx+69hsce/VvTOUfvEHAVuJ5Yg+Md8xIDrTJKX9JmqqByd884gHeLJqhwCQEddAxwBViqi6pVao6uWPDLbGTgxI2S+8w5YcdjLw9gHMZeWg53A3gXf/gf/hD33m29Bx2XENBBxgBH1ATV7jPXl7ThsZcHsPUoEB80zu/dSOwA1wD3/W587GdcjWq6SanKKgCHRql91BTgkskkhoeHAQDPP/88AOCJJ56ovVVEB0ipfeacsGEcvYgPbmLrkHGebjvV/OSScpVVAA6NUvuoOsCdOnUKP//zP48zZ87g1KlTEAQBXq8XTz/9NEtzUVfaSm9geydd9Hw1NS47TaltdgAOjVJ7qTrATU1N4cknn8Qrr7wCSZKQze7NL5w/f75ujSNqlGLFmIsFqa30Bv76le9gN1d8zVm1NS5rdV29Y0pQqcci8NXV1aLHuM0OdYKqA9zo6CgAQJIkwy4CY2NWm4kQtYeyG6kWCVLbO2ns5rIQ7/84+p2HTPertcZlOQO9W3D0FUzi9efghB1P/+UPgL8036faReDj4+MYHBzE6dOnLc8PDg5ifHx8349L1GxVB7hYLAZVVREOh7GwsAAAeO6553D16tW6NY6o3koVY64kSPU7D2FowFxxpFF6sQ2gF4+NX0HPmHEz1YzzEH77+8cR/0QSqWHjW1lbBP63P45DGDAvP7DqnWmOHDmC1dVVbtFDHa/qAPelL30J58+fRzgcxsmTJ3Hp0iUoiqIXXyZqV+20kWo5Duz12n5y624kN+42nBve0hJU+kwJKrfuEoA3yxdxLtYTO3LkCIMYdbyasijzdws4efIkNzwlqlJv0mFayK0dB4CtnT5s2Iw9sb6d4sOPtRRxBoCRwT7cPdIZXwKIiqk6wH3zm9/E5z//ef3n5557DgBgs9m4VICoQtneLODIYuzlQ7Ceve4FHNm928E81FhOtUWcnb0OfPMLv8QgRx2t6gCnqqrhZ21ngaeffrqmBhEdJNn+LEY+9zJW3zyGzZ1+0/mB3i0ce2AVb9y6HyiyQsGq92e1c0G+UkWcbya38NzLV7Ge2maAo462rwD3W7/1W1AUBQCwsrKCeNw4tt+uc3DRaBTLy8uYn59vdVOoA1gtISi2rKAe7ENp9Lhuw7FjLnXS07sJ+1AauGW+X6neX7ldCjRWRZyJusW+AtyXvvQlrK+vIxQKYXR01LQOxu/3t908nCRJelAmKqXsEgKbAz2Ovro+Z2U7hduQyZqHJ0v1/rQyXuV2KbAq4lyu90fUKfY9RDkyMoJnnnkGzz33XF02PFUUBeFwGG63G9PT06bzCwsLEEURiqJAFEX4/f59Pb7X6wUALC0t1dxW6m6llhAAewHQ2VffIbtyO4UDe0EwnbEOrPahNLYPZ7C1vb8yXlrP7r6Xe/CQqYhzZb0/onZX9RxcseA2OzuLubm5ih5DkiSoqgpZli0XiAeDQQQCAT1IBQIBiKLICgrUMK1YQlDrTuHV0Hp27x3Zwa7Luohzud4fUbuzV3vH5557Dg8//DDGxsb0fy6XS1/0XQmv1wu/31903i4SiejBDQB8Ph/C4XC1TSaiAtv9u9g6lDP82+5nz426Q9U9uKWlJSwuLkIURf1YLpfDl7/85bo0TJIky8AnSZL+/5FIxJToopmdnW3LhBciImqOqgNcIBDA8ePHTcdDoVBNDdIULkMAAJfLhUQiof9sNWdH1Er7LeJ80JTaS44lwKjeqg5wNpvNsB+c5tKlS4YF4NVKJBJwuYybOAqCYBn4SpEkCYuLi1hZWUEkEikaFNPpNNLpDxcaJZONSwun7lNtEed6GOzdNB3b6Nmp+/Psh1Wtyxs3buCJJ54oupfc4OAgnn/+edx1l/UuCAyAtF9VB7iLFy8iGAxCEAQ9EOVyOcRisboEuMLgBuz16vY77Oj1eg3zeMXMzc3hq1/9quHYZ/7tn+zruejgqrWIczVKLTH40dYHz5Mtvn9dObtqFpl3zNkv9kE77CPW0/eV7ETwX//rfzUFMS34/cqv/ErR9pQKgAx+ZKXqALeysoKzZ8+aAlG9kkAEQTAMRwLWvbp6mZ2dxVNPPaX/nEwm8c//4/ca8lzUnZqdgVlqiYG6vg3gBrC7//TM3Q+W3G395RZu/+Vt8w16gZEvjFgGuVp2Iih1v3IBkLuIk5WqA9z8/Lzlou78pJNaeL1e03CkqqoV9caq4XQ64XQ6G/LYRI1SbInBdqb6FP+Mc+++7/90Bn1DxqHO3EYO/X+bw+HUbtFeXLU7EZS7X7EAyF3EqZiqA5wW3F599VWIoojh4WG88cYblokn1fL7/ZAkybBYu9Hlts6dO4dz584hm83iodNfa+hzEbVaf08aQ33GZQHjgzvotdvwO3972fI+TtjxzJ1fxH043Iwm6riFD+1X1QHulVdeQSAQwOjoKGZnZ/HEE08gFotBURR8+tOfrugxJEmCLMuGclp+v1/vBS4uLiIUCkFRFKiqimAw2PBF3jMzM5iZmUEymUTgd19s6HMRtcwHc3MfFd7Ex+7bMJ3+2F12/M31R7C9a6ygsp5IY/HaT5Dc2sF9TWko1dNBy2KtKcnkypUrAD7cKufJJ5/E888/X/FjaAkgZ8+eLXobFkimg24rvYHtHetkkaqXH3wwN/fO+ig2r91jODXYl8ZjH3kLH7srg9SOsYzX+9usU9mprl27hmPHjpXMYu22ecyqA9zk5KTl8cLEkE7DIUpqJ1vpDfz1K9/Bbs66VmWtyw+2M73YSBsTYxqdndkoVksTNNX2Tkr1eGp53Fa4efMmUqkUnn32WRw7dsxwrlvnMasOcMvLy/jMZz6Dw4cPw2bb+1b3xhtvYGVlpS7LBFqFQ5TUTrZ30tjNZSHe/3H0Ow8ZzjVq+UGjsjMbpdzSBKC63km5Hk+1j9tqx44dOzD1fKsOcMFgEMePH8fo6CiAvXVkqqoiFovVrXFEtKffeQhDA+aducu5vr4JxzvrlsdLaUR2ZqOUW5qg9U5efPFFU88FKN4LK9XjyX/cbuv1dJOqA9zRo0dx5coVnD9/HvF4HG63G2fOnKln24ioSkM9Djhhx9PffR347uuWt3HCjqEe8z5znahUhmUli89L9cIOUo+n21Qd4J5++ml88Ytf7Lqgxjk46gajfT34bRzH1U+s4c6weX3noWQaR18exXZfAmstaF8zlerh1aMXVmzur9T8XDfN7bWzqgPcv/t3/w6iKOKJJ56oZ3tajnNw1C3G4URmeADrLnN1lRHYMQ4n3m5Bu1qhEWvoqu0ZduLcXqcG5KoD3OzsLE6ePInz58/DZrPB6/XioYceqmPTiIjaVyU9Q6t5v9XV1Y6a2+vEgKypOsB96UtfAgB9iPKFF15AKBSCz+fr6CxKIiq+9m4nzc1Q8xXrGVbSu/vUpz7VdgHBSicn21Qd4F599VV8/OMfxxtvvIFnnnkGkUgEoihiamqqnu1rOs7B0UGyuXUHyW3jcoCdTBrf/9F3LdfejQ99FMBd2EpvNamFnamWotPltKoaSScm21Qd4Px+P0ZHR6EoCqanpxGLxXD06NF6tq0lOAdHB8HO9iaAEcSvfx8/Sr5lOm+3OfDoR38ePQ5jqa7sVhbY3MH2tnlbIDJqxLzfQaxGUouqAxwAvQYlEXWWzM7eLgH3uB6Cfdz8xbTH0Qdnnzk5JbWzAaD0Zqq7tzawe8c6ANoP9cE+xp3Nq3UQq5HUoqbtchjciNpbb9IBJ8z1I/vu7PXMenv6MeTsr9vz7d7awHp4E8gWWV/n2MRIEAxyNerE4cJWqDjAJZNJKIoCRVEgiiKefPJJAMD6+jouXryI9fW9agnduHSAqJFKFVNObSaresxsbxZwZDH28iGMWd7iHsCRRcZR35Jbu3e2gawD/T9/C7Yh48dLbiODrb8Zw+6dbQY4aoqKA9zo6CjOnj2L2dlZDA8P68dHRkb0TMpoNIpAIIBs1rowbCdgkgk1U7liysDefFjhXFg52f4sRj73Mq6+exRbGXMPzZZN4GMffQ/br/UCDUiMtA31wD5iHOLcRenyYET1VnGAO3nyJObm5krexu/36z27TsUkE2oUq95YajNZtJiypth8WCmZrAO5gW243T8qepvtnV2kM32wW2/MTdTxKg5w+eO9zz33HC5cuICrV69iamoKfr9fX+StbVZKRHt6HH2w2xz44ZWXLM/bbQ4cGnTtO4iVUmpHAADYSCWx/MPv4oF7PoGh+j0tUVupOMCNjX04kv/kk0/i5MmT+PKXv4wvfvGLRW9HRICzbwA//fAvIZO1ziyspodWiWI7AgBAcnMTyY3S2ZBEna7iAKft+aYRBAEPP/xw2dsR0V6Qc4JdJaJmqjjALS0tYXR0FLlcznBMEATD7S5cuGDq1RERtTOrHQFK7RDerbrt97CvAGcV0JaXl/X/V1W143twzKIkOjgqqRk5Pj7e5FY1X7f+HioOcNPT03jmmWfK3u7LX/5yTQ1qNWZREtVO3Uhie/eO4Vjf5i6c2P+u5I3UyJqRtWpmb6qdfw+1qDjABYPBim7X6cWWiah6qc0UgEH8t8t/Y6px+bHh+/EZPIjUZgrDGG1NAy00omZkLVrVm2q330M9VBzgjh8/XtfbEVH3SafT6MEgfureh/G421gr0ZbaAV7fuw0V1629qVaoqdgyER1MPbdzyPxkzXxc3SuLMugcwMDhw4Zzm9nbTWlbN+jG3lQrMMARUcU2sbdw/K1X+oBX7ljcwonD9hRyNm6MSq3HAEdEFcvYduHALn4Prxe9jWN3F1/OCDhc9BZEzcEAR0QVO9Rjw8d3/xaPPvyLGBocNp2/vraGP3trAxsZ9uBapVimZSevZ6sWA1wBroMjKs2Jbdw90IPhIfMuBRupIvvAUcOVy74EOnc9W7UY4ApwHRwRdaJy2ZfAwcvAZIAjorp7P72LN+4YlwOkNzPIIg2hNU06EJh9acQAR9Thiu363dfrRL+zuTtn9ztssOey+M/XUsC1lOm8E69gPiXiLov7rq+vI5Uy30czODiIkZH2qoRC7Y0BjqhDld1nzu7AL3z8s00NcsN9DhzP/RAP3jeJgYINXLc2NvHtG5u4vW3ew2d9fR1f//rXkc0W39n8wcPj8H/2H2JwYNB0zn6oD/ax5gZzan8McEQdqtQ+c1vpO1DeehXbO+mm9+Kc2Ma4046hgV7D8dSO9X54AJBKpZDNZvHJT34Sw8Pm7MzcWgrHXv05ZBcdsFwu7tjESBAMcmTAAEfUwbptn7nh4WG4XC7T8d0NB5B1YPdn38LQmLFnmNvIYOtvxrB7Z5sBjgwY4IioY+z078I+Ygzou9gse79S83uc2+teDHBEXaxYAkqx461+3EYoN7/X29uLmZkZBrkuxABH1IXKJaAAgN3mQI+jry0et5FKze8lk0m89NJLSKVSDHBdiAGOqAuVSkDR9Dj64Ozb3/xdox4XAIaHejHUswOnzTzkuNtTPLuy4scvMr9H3YsBjqhLNSoBpRGPa8+lMfO5R9HXexOAuRJH5tAh3Aawm2MpMKocA1wB1qIkaj57bgd9vXb8ZH0A/YPmocI767twAdjNNvcji4vPOxsDXAHWoiRqrFQqhXfeecdw7M7aGu4aArYyDjhgnr9LlxgSbZRKFp8zQaW9McARUVPYbHYAwA9/+EP8t7/5juHcR1z9eOQfPIK+vho+kjIbQNp8uGf3DoaHes0nyii3+JwJKu2PAY6ImqKnZ69n9sjDj8Bzz2OGc0M9OwBuYrC/Hzv7fNyc9jG2dhmwm3cZvwvAzOcexQ/WqktUabfklGTiPaSLLMdwDgxj2HVPk1vUvhjgiKipBgYH4XIZ60nuZU4W3+alpNwHQ5ojDwMucxBbS7yP0d730Gvv/E1Yk4n30H/rRQz32C3Pb6d2kcSnGOQ+wABHRN2hZxDozZkOZ6A2vy0Nkt5MYrjHjsvv2uDoNZYly+5s4PGP2LG+mQTQPgHu2rVrRfeoa/T+dAxwRNQx1FQSu2vGQUz77V4M4e4Wtag1HL1DGBo2DptuJAHAPETbSteuXcOxY8dKlklbXV1tWJBjgCOittfXt5ck8t8vL+PG5RuGc3fhLvxj+z/GnY0UhC4qPN0Nbt68iVQqhWeffRbHjh0znFtdXcXp06dx8+ZNBjgi6g59KWCgIOGj1wbsDjlhsUIAADDY7wQAfPqxT2BncMtwbjORAa4DW1vbAANcWzp27Bg8Hk/Tn5cBjoiaIo29JI/eH4zj8A8Omc6vOx7A7v/8BlBixxth6DByh4xBbD1VfjcB6kzF5u/u3KlsKJYBjoiaQpuFyTz+PjJD64Zzto1eOC7fjVy6Dxhq/qJuaj/l5u8q0fUBLhqNIpFIIBaLwefzwe/3t7pJRAdats+OjNOY5m7bsYFVJhuvk/bFKzV/F4vFMD09XfYxujrAKYoCAPovYnR0FF6vF4IgtLBVRNQItmQPMu/ZzCduObHb78RATwa9BTsVDPXsVFXlpBN16r54VvN3HTNEqSgKwuEw3G63ZUReWFiAKIpQFAWiKO6rB6aqKi5cuKDfZ3JyEisrK/B6vXVrPxG11m5PBju5HfT91Rhu/5X5fA8exLrjPrg/9zLsvYrx5Djwsc89irWcRY2vLnMQ98VraYCTJAmqqkKWZYyNjZnOB4NBBAIBPSAFAgGIolhxNo7H48Hi4qL+88rKCiYnJ+vTeCJqC9m+DP4g9weYmvgHGBseNZ1fe/c2Bn7kwtX3RnDoQePQ6FZqHR8d2YQ9t98CYZ2r3UqPNZJ1vZcm8Xq98Pv9RYcMI5GIobfl8/kQDoereq6FhQXMz89zeJKojdmSfbAnzP9sydI7hN/BHewe3oFDyJn+7Q5mAADprB076DP828rUNvM3PNSLnt07QHrN/C9TfXIE1UfLhyiLkSTJMhhJkqT/fyQSQTwet7z/7Oysfn/tsSqZlCSi5sv17CJn30X/y8VLTOXsu8j1tE89yT57dm+T1u0fAG9b3MDmAB7w7ZUQo5Zo2wCnqqrpmMvlQiKR0H+uJGDJsgxVVTE9PQ1VVTkHR9SO+neR9tyALVN8UCnXswv0t0+A67Xvoq/XjjXcg1FXQamwzCaQvAJk0wxwLdS2AS6RSJjGiQVBsAx8xSiKgpMnTwIAzpw5A1VVsba2ZnnbdDqNdPrDieZk0no7CiJqkP5d5NA+AaxSGfQBveaF66Vwp/DmaNsAZzUJqqrqvubQRFEsGtAKzc3N4atf/arh2Gf+7Z9U/FxERJXo1p3CV1dXKzrWTG0b4ARBMAxHAta9unqZnZ3FU089pf+cTCbxz//j9xryXER0cHXbTuHj4+MYHBzE6dOnLc8PDg5ifHy8ya3a07YBzuv1moYjVVVt2PyZ0+mE0+lsyGMTERXqlnT9I0eOYHV1teo934r18urR+2vbAAcAfr8fkiTpQW1paQnz8/MNfc5z587h3LlzyGazeOj01xr6XETU3oaHejHUs/PBjuNG9p5MC1rUno4cObLvLW/K9fyA2nt/LV/oLcsyJEnSy2r5/X6IoggAWFxcRCgUgqIoUFUVwWCw4VsuzMzMYGZmBslkEoHffbGhz0VE7cueS+8tA+i9CcCidyIA2zu72O1lFc1qlOv5AbXv+N3SAOf1euH1enH27Nmit2l0j42IusPN29YJZcnUZlU7fttzO+jrteMn6wPoHzTPhanJO/jOX67gHz352L4fm/ZU0/Pbj7YeomwFDlESdZb+Xid67A78fysvWJ7f2/H7YX1X8P3ayjjgsNiJdTPjQHLj4JT46kQMcAU4REnUWQ71D+LU5K9ga8e6YHJvqh94fW9X8F1wr7mDhAGOiDreof5BHOq3rhhis7Xfx1yp5JWDtIVPo7XflSci6mJlk1cO0BY+jcYAV4BzcES0HzdumZNberCFu4rcvlzyykHcwqdRGOAKcA6OiCrR7+xDj8OBb/+XvzCd+4irH8F/8Ahu37mDw07zHnVA8eSVWrfwoQ8xwBERVeHQ0ACe/F9+CVtpc+JKJn0bwC62trZwuPlNow8wwBERVenQ0AAODQ2Yjm8kswDuNL9BZMAAV4BzcERUL5vr7+OGxTGYYyI1QPHdBQ+omZkZXL58GcvLy61uChF1KEdPH7Yzuzgy8B7u2v6B4d+RgfewndmFo8c8/0b1xR4cEVGd9Q8ewrUNIJuxXlju6OlD/+D+Nkml/WOAIyJqAAaw1uMQJRERdSUGuALnzp3D448/jhMnTrS6KUREVAMGuAJMMiEi6g4McERE1JUY4IiIqCsxwBERUVdigCMioq7EAFeAWZRERN2BAa4AsyiJiLoDAxwREXUlBjgiIupKDHBERNSVWGyZiIja0vr6OlKplOn47du3K7o/AxwREbWd9fV1fP3rX0c2m636MRjgiIio7aRSKWSzWXzyk5/E8PCw4dyrr75a0WNwDq4A18EREbWP4eFhuFwuw7++vsp2Q2eAK8B1cERE3YEBjoiIuhIDHBERdSUGOCIi6koMcERE1JUY4IiIqCsxwBERUVdigCMioq7EAEdERF2JAY6IiLoSAxwREXUlBrgCrEVJRNQduJtAgZmZGczMzCCZTCLwuy+2ujlERG2v2L5tADA4OIiRkZEmt2gPAxwREVWt3L5tvb29mJmZaUmQY4AjIqKqldq3LZlM4qWXXkIqlWKAIyKi2qhra8jY3zEdv3HjRkOfV9u3rZ0wwBERdYG+vr2P8xf+4i/wbmLL8jYOhwNOp7OZzWopBjgioi4w2N8PAPiFT/4CNjK9lrdxOp0YGhpqZrNaigGOiKiLDI8Mw5kbaHUz2gLXwRERUVdigCMioq7EAEdERF2p6+fgotEoBEGAoihQVRVnz55tdZOIiKgJuj7AhUIhxONxAIDNZmOAIyI6IFoe4BRFQTgchtvtxvT0tOn8wsICRFGEoigQRRF+v39fj68FN+3+RERkrdhi8FbWk6xFSwOcJElQVRWyLGNsbMx0PhgMIhAIwOv1AgACgQBEUYTH49n384TDYcRisbq0m4iomzidTjgcDnz729+2PN/KepK1aGmA0wLXhQsXLM9HIhGEw2H9Z5/Ph3A4bDhW6fO4XC6EQqF935eIqNsNDQ3hs5/9LNLptOlcq+tJ1qLlQ5TFSJIEQRAsj2sikYg+BFlodnbWcH+PxwNJkiBJkh5YiYhoz9DQUNdVOWnbAKeqqumYy+VCIpHQf7aas8sXjUaxtLSk99oEQbB8XCI62Hpym0B6zXyMOlrbBrhEImGqTL3fAOX3+5FIJCBJEmRZhtfrLZqkkk6nDd3z9fV1AEAmm0UqdWf/L4CIDLbSe/uFpTZTuG3badrz9mw64QSwsbmJzB1j0NrY2ML28C5GcQV4+4rh3CiA7Z1d3N7Ywq69eZ8B21t7hZI3U5tIJpOGc5upTaAH2Noyt6nfkQF697av2SyyN1s1Njf3fmd37twxtUc7rt1O+/9K71tKqcfd+uB3lMvlSj9Irg34/f7c/Py84dji4mJOFEXTMUEQGtKGr3zlKzkA/Md//Md//Nch/65fv17yc71te3CCIBiGIwHrXl29zM7O4qmnntJ/VlUVH/3oR3Ht2rWOm1itRDKZxIMPPojr16+bNinsFt3+Gvn6OhtfX/VyuRxu376N++67r+Tt2jbAeb1e03CkqqoNSxBxOp2W+ySNjIx05R+fZnh4uKtfH9D9r5Gvr7Px9VWnko5HW9ei9Pv9hqzJpaUlBIPBFraIiIg6RcsXesuyDEmSoCgKgL2gplUcWVxcRCgU0utIBoPBfS/yJiKig6nlC729Xm/J+pDz8/NNbNGHnE4nvvKVr3Tt9u7d/vqA7n+NfH2dja+v8Wy5XLk8SyIios7T1nNwRERE1WKAIyKirtS2ywRaqdYtetqNqqqIRCIAPsxEzX9NWhKPx+OBqqpYWVnB5OSkZS3QdlRJ+zv5mkYiEcvroSVjdeL1q3WbrHa/nqVeXze8H0u9vrZ6PzakLEgHm56ezi0tLek/+/3+XCwWa2GLajc9Pa3//9raWg6A4TXNz88bqgMUVpVpd+Xa3+nXVBRFyyoO8Xg8l8t13vVbWlrKLS4u5rxer2Vby12vdr+elbw+TSe+H8u9vnZ6PzLAFSiM+eFw2PAH2Wni8XjO6/Xm1tbW9GNerzfn9/v1n+fn53PxeLytPiT2o1z7O/2aFn5AxOPxXDgcNpzvxOtnVaIvlyt/vTrlelq9vm56Pxa7fu30fuQQZZ5KtujpRCsrK0gkEvprE0URKysrhtt0+m7nxdrfDde0cPgmHA6bls90+vXTlLte3XA9+X4ULI83AgNcnkq26Ok0oihibW3NcMxqT7xoNAoAWF5exokTJ9puTqOcYu3vhmua/2GxsLCA2dlZ0206/fppyl2vTr+efD+qpts28voxwOWpxxY97U6WZSQSCUMPwOPx6JPAfr8fbrcboih2TNWYUu3vpmuqqiri8bjpG3CnX7985a5XN11PgO9HoLHXj8sE8ljtVKCqaltlL9XqzJkzuHTpkuE1eb1e0+7n2iaxnaBU+7vpms7NzSEQCJiOd/r1y1fuenXT9QT4fgQae/0Y4PI0e4ueZguFQjh//rzpm6CWsqyxmhNoZ6Xa303XNBKJWLa7069fvnLXq5uuJ9+Pexp5/Rjg8jR7i55mikQimJqa0t9M2qSuoigIBoN6sWtg7zV3yiR3ufZ3yzVVVdXyunT69StU7np1y/Xk+xGG8426fgxwBbpxix7t9QiCAEVRIMsyZFkGsPftan5+3vAGkiTJMpGhHVXS/m64pvkfGPk6/fpZKXe9Ov168v3YvOvHYssWQqEQ3G63/s2j0zKY8qmqitHRUdPx+fl5fRcHrSrB2NgY4vE4AoFAR30jrqT9nX5NZVlGIBBAPB43neu066dtkzU3NwdRFDE1NWXYJgsof73a+XqWen3d8H4sd/3a6f3IAEdERF2JQ5RERNSVGOCIiKgrMcAREVFXYoAjIqKuxABHRERdiQGOiIi6EgMcERF1JQY4IqpapVXgO7XaP3U2BjiiJlEUBT6fr6LbSpKEUCiEhYWFomW6Wk1RFFNh3WIikQiDHDUdK5kQNUkwGEQkEsHS0lLJ0kuKoiAUCmFxcdHw/+0mGAwatnGRJAlLS0sAALfbjenp6ZK3J2o09uCImkDrvXm9XoRCoZK39fl8hg0wtUK87SQSiZj2pltaWsL8/Lze9sJ2T0xMGIrsEjUaAxxRE4TDYfj9fszPz0OW5aIf9NFoFKIoGgrXtqPFxUVDL1RRFLjdbv3nyclJU9unp6fZg6OmYoAjajBFUXDixAkAe7sbl+rFFe7aLcuyaUPMVrPagVkURcRiMSiKAlVVEQ6HLYdh2zVgU3digCNqMK33pinVi5NlGfF4HAsLC1hYWNC3JKmEJElNSeSQJEkP2PnC4TAkSUIkEsH8/LwpCAJ7gbAdh1ypO/W0ugFE3Sy/96bJ78XFYjH9uCzLEATBMP8WCoXabjPPRCJhGbwAmBJLComiiEQi0YBWEZmxB0fUQIW9N41VL05RFExOTuo/S5IEj8dTcQ+uWWrtJXK5ADULe3BEDWLVe9NY9eJEUTT0jMLhsKE3V4y2Fi0Wi+m9QKB8b6pagiBUHaSs5u+IGoUBjqhBwuEwgsFg0cSKQCCAYDCoJ5J4PB49cMiyDJfLZUjUUFUVkUhEH+bTApj2X0mSMDk5aQogxe4nSZKpbS6Xy7LHmU8URX29234lEom265FS92KAI2oAVVX1RJFyQqGQHjB8Ph+i0SgURTGl1J88eRKXLl1CIpFANBqtuC3F7ldqsXkpXq+3op6lFUVRGOCoaRjgiBpAEARUUyTo7Nmzlse1zMOVlRW4XK6it6vX/cqpZpixcI6RqNFYqouoA6iqijNnzuglu7ShzHKBptr7laMNb+5nnk/LCGUPjpqFPTiiDiAIAk6cOIFoNKoneZSbK6vlfuV4vV4Eg8GKk0YURcHY2BiDGzUVe3BEVLWFhYWKhj0rvR1RPTHAERFRV+JCbyIi6koMcERE1JUY4IiIqCsxwBERUVdigCMioq7EAEdERF2JAY6IiLoSAxwREXUlBjgiIupK/z9zh1Q81Ox6nQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 444x377.872 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pt.std_fig()\n",
    "\n",
    "VAR = np.arccos(Delta_costheta_r)*180/np.pi\n",
    "bins = np.linspace(0, 160, NBINS)\n",
    "ALPHA = 0.3\n",
    "LW = 1.\n",
    "\n",
    "_ = ax.hist(VAR, bins=bins, histtype='step', edgecolor='black', weights=HistWeight, zorder=2, density=False, label=r'Total')\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'grey', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutMee, label=r'$m_{ee}$ cut'+fr' $(\\epsilon = {eff_CutMee*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='grey', lw=LW, weights = HistWeight_CutMee, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'navajowhite', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutCirc0, label=r'$r_{\\rm Circle 0}$'+fr' $(\\epsilon = {eff_CutCirc0*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='navajowhite', lw=LW, weights = HistWeight_CutCirc0, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'violet', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutDiag, label=r'$r_{\\rm Diagonal}$'+fr' $(\\epsilon = {eff_CutDiag*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='violet', lw=LW, weights=HistWeight_CutDiag, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'steelblue', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutCirc1, label=r'$r_{\\rm Circle 1}$'+fr' $(\\epsilon = {eff_CutCirc1*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='steelblue', lw=LW, weights = HistWeight_CutCirc1, zorder=3)\n",
    "\n",
    "ax.set_title(rf'$m_{{Z^\\prime}} = {df.attrs[\"model\"].mzprime}$ GeV, $m_5 = {df.attrs[\"model\"].m5}$ GeV, $m_4 = {df.attrs[\"model\"].m4}$ GeV', fontsize=11)\n",
    "\n",
    "ax.legend(loc='upper right')\n",
    "ax.set_xlim(0, max(bins))\n",
    "ax.set_ylim(1e-3,30)\n",
    "ax.set_yscale('log')\n",
    "\n",
    "ax.set_xlabel(r'$\\Delta \\theta_{e^+e^-}$ $({}^\\circ)$')\n",
    "ax.set_ylabel(r'Events (a.u.)')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "707b6bc0",
   "metadata": {},
   "source": [
    "### Angle of the leading electron "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d80b873a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Events (a.u.)')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 444x377.872 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pt.std_fig()\n",
    "\n",
    "VAR = np.arccos(costhetaemax_r)*180/np.pi\n",
    "bins = np.linspace(0, 50, NBINS)\n",
    "\n",
    "ALPHA = 0.3\n",
    "LW = 1.\n",
    "\n",
    "_ = ax.hist(VAR, bins=bins, histtype='step', edgecolor='black', weights=HistWeight, zorder=2, density=False, label=r'Total')\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'grey', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutMee, label=r'$m_{ee}$ cut'+fr' $(\\epsilon = {eff_CutMee*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='grey', lw=LW, weights = HistWeight_CutMee, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'navajowhite', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutCirc0, label=r'$r_{\\rm Circle 0}$'+fr' $(\\epsilon = {eff_CutCirc0*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='navajowhite', lw=LW, weights = HistWeight_CutCirc0, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'violet', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutDiag, label=r'$r_{\\rm Diagonal}$'+fr' $(\\epsilon = {eff_CutDiag*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='violet', lw=LW, weights=HistWeight_CutDiag, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'steelblue', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = HistWeight_CutCirc1, label=r'$r_{\\rm Circle 1}$'+fr' $(\\epsilon = {eff_CutCirc1*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='steelblue', lw=LW, weights = HistWeight_CutCirc1, zorder=3)\n",
    "\n",
    "ax.set_title(rf'$m_{{Z^\\prime}} = {df.attrs[\"model\"].mzprime}$ GeV, $m_5 = {df.attrs[\"model\"].m5}$ GeV, $m_4 = {df.attrs[\"model\"].m4}$ GeV', fontsize=11)\n",
    "\n",
    "ax.legend(loc='upper right')\n",
    "ax.set_ylim(1e-3,30)\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlim(0, max(bins))\n",
    "ax.set_xlabel(r'$\\theta_{e_{\\rm leading}}$ $({}^\\circ)$')\n",
    "ax.set_ylabel(r'Events (a.u.)')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8e96d7c4",
   "metadata": {},
   "source": [
    "## Exploring a π0-like sample\n",
    "\n",
    "Instead of single shower CCQE events, we can plot the invariant mass for events that do not pass the CCQE criteria due to the large e+e- separation angles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a97bd7f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pi0_ovl = (Delta_costheta_r > np.cos(13*np.pi/180))\n",
    "pi0_asy = (pep[:,0] > 0.03) & (pem[:,0] > 0.03)\n",
    "pi0_OldCut = pi0_ovl | pi0_asy\n",
    "\n",
    "pi0_HistWeight_CutCirc1 = HistWeight * (r_circ1 > f_cut_circle1(etot_r))\n",
    "pi0_HistWeight_CutCirc0 = HistWeight * (r_circ0 < f_cut_circle0(etot_r))\n",
    "pi0_HistWeight_CutDiag = HistWeight * (r_diag > f_cut_diag(etot_r))\n",
    "pi0_HistWeight_CutMee = HistWeight * pi0_OldCut\n",
    "\n",
    "\n",
    "pi0_eff_CutCirc1 = np.sum(pi0_HistWeight_CutCirc1) / np.sum(HistWeight)\n",
    "pi0_eff_CutCirc0 = np.sum(pi0_HistWeight_CutCirc0) / np.sum(HistWeight)\n",
    "pi0_eff_CutDiag = np.sum(pi0_HistWeight_CutDiag) / np.sum(HistWeight)\n",
    "pi0_eff_CutMee = np.sum(pi0_HistWeight_CutMee) / np.sum(HistWeight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "729c0f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Events (a.u.)')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 444x377.872 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pt.std_fig()\n",
    "\n",
    "VAR = mee_r\n",
    "bins = np.linspace(0, np.max(mee_r), NBINS)\n",
    "ALPHA = 0.3\n",
    "LW = 1.\n",
    "\n",
    "_ = ax.hist(VAR, bins=bins, histtype='step', edgecolor='black', weights=HistWeight, zorder=2, density=False, label=r'Total')\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'grey', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = pi0_HistWeight_CutMee, label=r'$m_{ee}$ cut'+fr' $(\\epsilon = {pi0_eff_CutMee*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='grey', lw=LW, weights = pi0_HistWeight_CutMee, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'navajowhite', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = pi0_HistWeight_CutCirc0, label=r'$r_{\\rm Circle 0}$'+fr' $(\\epsilon = {pi0_eff_CutCirc0*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='navajowhite', lw=LW, weights = pi0_HistWeight_CutCirc0, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'violet', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = pi0_HistWeight_CutDiag, label=r'$r_{\\rm Diagonal}$'+fr' $(\\epsilon = {pi0_eff_CutDiag*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='violet', lw=LW, weights=pi0_HistWeight_CutDiag, zorder=3)\n",
    "\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'stepfilled', facecolor = 'steelblue', edgecolor = 'black', lw = 0.0, alpha = ALPHA, zorder = 1, weights = pi0_HistWeight_CutCirc1, label=r'$r_{\\rm Circle 1}$'+fr' $(\\epsilon = {pi0_eff_CutCirc1*100:.0f}\\%$)')\n",
    "_ = ax.hist(VAR, bins = bins, histtype = 'step', facecolor = 'None', edgecolor='steelblue', lw=LW, weights = pi0_HistWeight_CutCirc1, zorder=3)\n",
    "\n",
    "ax.set_title(rf'$m_{{Z^\\prime}} = {df.attrs[\"model\"].mzprime}$ GeV, $m_5 = {df.attrs[\"model\"].m5}$ GeV, $m_4 = {df.attrs[\"model\"].m4}$ GeV', fontsize=11)\n",
    "\n",
    "ax.legend(loc='upper right')\n",
    "ax.set_xlim(0, max(bins))\n",
    "\n",
    "ax.set_xlabel(r'$m_{e^+e^-}$/GeV')\n",
    "ax.set_ylabel(r'Events (a.u.)')"
   ]
  }
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