{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8109cc2b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-12T22:42:48.072732Z",
     "start_time": "2023-09-12T22:42:47.716626Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import awkward as ak\n",
    "import numpy as np\n",
    "\n",
    "import h5py as h5\n",
    "\n",
    "import numba as nb\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import mplhep as hep\n",
    "plt.style.use(hep.style.CMS)\n",
    "hep.style.use(\"CMS\")\n",
    "plt.rcParams.update({\"font.size\": 16})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f5491f26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-12T22:52:43.225350Z",
     "start_time": "2023-09-12T22:52:43.222740Z"
    }
   },
   "outputs": [],
   "source": [
    "proj_dir = Path.cwd().parent.parent\n",
    "\n",
    "test_file = proj_dir.joinpath('reports/bv2/hhh_test.h5')\n",
    "pred_file = proj_dir.joinpath('reports/bv2/dp_on/pred_v53.h5')\n",
    "\n",
    "plot_dir = proj_dir.joinpath('reports/bv2/figures')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f51df5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-12T22:52:58.120492Z",
     "start_time": "2023-09-12T22:52:58.004623Z"
    }
   },
   "outputs": [],
   "source": [
    "testfile = h5.File(test_file)\n",
    "predfile = h5.File(pred_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e073d10c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-12T22:53:15.289153Z",
     "start_time": "2023-09-12T22:53:15.285287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KeysViewHDF5 ['b1', 'b2', 'mask', 'pt']>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testfile['TARGETS']['h1'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d6876c5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-12T22:54:56.866346Z",
     "start_time": "2023-09-12T22:54:56.861623Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KeysViewHDF5 ['assignment_probability', 'bb', 'detection_probability', 'marginal_probability']>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predfile['TARGETS']['bh2'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "799e22ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KeysViewHDF5 ['MASK', 'fj_charge', 'fj_chargedenergyfrac', 'fj_cosphi', 'fj_ehadovereem', 'fj_eta', 'fj_mass', 'fj_ncharged', 'fj_neutralenergyfrac', 'fj_nneutral', 'fj_phi', 'fj_pt', 'fj_sdmass', 'fj_sinphi', 'fj_tau21', 'fj_tau32']>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testfile['INPUTS']['BoostedJets'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2c80bac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  6, 11,  5,  4,  3,  0, 11, 10, 11,  5, 10,  0,  0,  3, 10,  6,\n",
       "        1, 10,  6,  5,  0,  2,  3, 11,  1, 10,  2, 11,  8, 11, 10, 10,  2,\n",
       "       10, 10,  6,  7, 10,  5,  1,  1,  0,  0,  0,  6, 10,  7,  3,  1,  1,\n",
       "        6,  1,  3,  1, 10, 10,  9,  6,  1,  5,  1, 11,  2, 10, 12,  3, 10,\n",
       "       10,  1, 10,  1,  1,  4, 11,  2, 10,  2,  0, 10,  2, 11,  0, 11, 10,\n",
       "        0,  7, 10,  0, 11,  6,  4,  3,  0, 11,  4,  1,  1,  3, 10])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predfile[\"TARGETS\"][\"bh1\"]['bb'][0:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd50151f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False, False, False, False, False,  True,\n",
       "        True, False, False, False, False, False, False,  True, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False,  True,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "        True, False, False, False, False,  True, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testfile[\"TARGETS\"][\"bh1\"]['mask'][0:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e96fcc84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1,  0, -1, -1, -1, -1, -1,  0,  1, -1, -1, -1, -1, -1, -1,  0,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1,  0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1,\n",
       "       -1,  0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testfile[\"TARGETS\"][\"bh1\"]['bb'][0:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed6afa9d",
   "metadata": {},
   "source": [
    "### Test if indices in pred bb are out of boundary for target mask =True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5b380738",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12]\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(predfile[\"TARGETS\"][\"bh3\"]['bb'][:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "25bd40f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1  0  1  2]\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(testfile[\"TARGETS\"][\"bh3\"]['bb'][:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e63c807",
   "metadata": {},
   "source": [
    "### OptionL: examaine pt order"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96c44c80",
   "metadata": {},
   "source": [
    "### Obtain python arrays for calculating purity and efficiency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dc664b0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collect H pt, mask, target and predicted jet and fjets for 3 Hs in each event\n",
    "# h pt\n",
    "h1_pt = np.array(testfile['TARGETS']['h1']['pt'])\n",
    "h2_pt = np.array(testfile['TARGETS']['h2']['pt'])\n",
    "h3_pt = np.array(testfile['TARGETS']['h3']['pt'])\n",
    "\n",
    "bh1_pt = np.array(testfile['TARGETS']['bh1']['pt'])\n",
    "bh2_pt = np.array(testfile['TARGETS']['bh2']['pt'])\n",
    "bh3_pt = np.array(testfile['TARGETS']['bh3']['pt'])\n",
    "\n",
    "# mask\n",
    "h1_mask = np.array(testfile['TARGETS']['h1']['mask'])\n",
    "h2_mask = np.array(testfile['TARGETS']['h2']['mask'])\n",
    "h3_mask = np.array(testfile['TARGETS']['h3']['mask'])\n",
    "\n",
    "bh1_mask = np.array(testfile['TARGETS']['bh1']['mask'])\n",
    "bh2_mask = np.array(testfile['TARGETS']['bh2']['mask'])\n",
    "bh3_mask = np.array(testfile['TARGETS']['bh3']['mask'])\n",
    "\n",
    "# target jet/fjets\n",
    "b1_h1_t = np.array(testfile[\"TARGETS\"][\"h1\"]['b1'])\n",
    "b1_h2_t = np.array(testfile[\"TARGETS\"][\"h2\"]['b1'])\n",
    "b1_h3_t = np.array(testfile[\"TARGETS\"][\"h3\"]['b1'])\n",
    "\n",
    "b2_h1_t = np.array(testfile[\"TARGETS\"][\"h1\"]['b2'])\n",
    "b2_h2_t = np.array(testfile[\"TARGETS\"][\"h2\"]['b2'])\n",
    "b2_h3_t = np.array(testfile[\"TARGETS\"][\"h3\"]['b2'])\n",
    "\n",
    "bb_bh1_t = np.array(testfile[\"TARGETS\"][\"bh1\"]['bb'])\n",
    "bb_bh2_t = np.array(testfile[\"TARGETS\"][\"bh2\"]['bb'])\n",
    "bb_bh3_t = np.array(testfile[\"TARGETS\"][\"bh3\"]['bb'])\n",
    "\n",
    "# pred jet/fjets\n",
    "b1_h1_p = np.array(predfile[\"TARGETS\"][\"h1\"]['b1'])\n",
    "b1_h2_p = np.array(predfile[\"TARGETS\"][\"h2\"]['b1'])\n",
    "b1_h3_p = np.array(predfile[\"TARGETS\"][\"h3\"]['b1'])\n",
    "\n",
    "b2_h1_p = np.array(predfile[\"TARGETS\"][\"h1\"]['b2'])\n",
    "b2_h2_p = np.array(predfile[\"TARGETS\"][\"h2\"]['b2'])\n",
    "b2_h3_p = np.array(predfile[\"TARGETS\"][\"h3\"]['b2'])\n",
    "\n",
    "bb_bh1_p = np.array(predfile[\"TARGETS\"][\"bh1\"]['bb'])\n",
    "bb_bh2_p = np.array(predfile[\"TARGETS\"][\"bh2\"]['bb'])\n",
    "bb_bh3_p = np.array(predfile[\"TARGETS\"][\"bh3\"]['bb'])\n",
    "\n",
    "\n",
    "# fatjet assignment probability\n",
    "ap_bh1 = np.array(predfile[\"TARGETS\"][\"bh1\"]['assignment_probability'])\n",
    "ap_bh2 = np.array(predfile[\"TARGETS\"][\"bh2\"]['assignment_probability'])\n",
    "ap_bh3 = np.array(predfile[\"TARGETS\"][\"bh3\"]['assignment_probability'])\n",
    "\n",
    "# detection probability\n",
    "dp_bh1 = np.array(predfile[\"TARGETS\"][\"bh1\"]['detection_probability'])\n",
    "dp_bh2 = np.array(predfile[\"TARGETS\"][\"bh2\"]['detection_probability'])\n",
    "dp_bh3 = np.array(predfile[\"TARGETS\"][\"bh3\"]['detection_probability'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a7385080",
   "metadata": {},
   "outputs": [],
   "source": [
    "fj_pts = np.array(predfile[\"INPUTS\"][\"BoostedJets\"][\"fj_pt\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "188d9d1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dp_bh1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bcaf5554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XF+vlAgAAAEBEtmAwGIzlhGHDhqmurk7Hjx/XNddcI0n66KOPNHjwYE2fPl0lJSWSJIfDoaFDh+qhhx5qNcfEiROVmpoqv9+v/v37KycnR2VlZUpOTlZjY6MmTJggr9er6upqpaSkSJKmTZumAwcOqLKyUv369ZMkLVmyRKtWrVJVVZWGDRsmSXrqqae0aNEilZeXKy8vT5K0Z88eTZw4UcXFxZo/f36rfhwOh3w+n7KysnTq1KlY3o5OMejRXfFuIWGdXDs53i0AAADgKhZLNohpZ8rv98vr9WrKlCmhICVJN910k4YMGaIjR45Ikurr6+Xz+ZSXl6fJkye3+klNTZUk7d69W2fPntWCBQuUnJwsSUpJSdG8efN05swZlZWVSfritr3XX39dM2fODAUpSVqwYIGSkpJUWloaOlZSUqLc3NxQkJKkgoICZWdnh9UBAAAAQHvEFKYaGxv1zDPPaM6cOa3GPvvsM33rW9+SJH344YeSpO985zuSFHZbX0v79++XpLDgI0n5+fmSpAMHDkiSDh48KL/fHzrerE+fPsrOzg7VNTQ0qLKyslVd85zvvfeeAoFAdBcLAAAAAJcRU5i69tprNWfOHI0YMULSF7f3vf3225ozZ458Pl/o80vNYWrbtm1KT09X79691bt3b/3rv/5r2OeWLMtS9+7d1aNHj7DXycjIUFpammpqakJ1ksJ2pZr1798/6rpgMMiDKAAAAAB0iJT2nJyTk6OLFy9Kkn75y1/qjjvukCQdP35ckvTmm29q8eLFuuGGG7Rv3z5t3LhRhw4dUkVFhZKTk2VZljIyMiLOnZGR0SokRarNyMhQRUVFVHWSVFNTI7vdHvE1g8GgLly4EN3FR5CWlqa0tDTj8wEAAAC0T0NDgxoaGozPj+WREu0KU6+88oosy9If//hHLVu2TJ988omeeeYZ3XzzzZo/f74efvhhORwOSdLMmTN1yy236JFHHlFJSYlmzZolm83WZrNNTU3y+/2SJJvN1uaFxVInKVQbSXV1tXr16hXt5beyfPlyrVixwvh8AAAAAO2zZs0arVy5skteq11h6oc//KGkLx5l3qtXL23YsEHz5s3TjBkzNGPGjFb1Dz30kH72s59p//79mjVrlvr27dvm48rPnz8ful2vb9++khSxNpY6KfItgM0GDBigo0ePtjn+VdiVAgAAAOJr8eLFoa9cMjF48GBVV1dHVRtTmPrP//xP7du3T5MnTw7tODWbMmWKNmzYIK/Xq+zs7IjnX3vtterTp4/OnDkjSbLb7aqvr9eFCxfUs2fPUN25c+fk9/tDwaf5trxIF3X69Omo62w2WyhwRWKz2cL6AAAAAHBlae9Hb5rvdotGTA+gOH36tB588EHt27ev1VjzblBWVpZmzpwZcWutvr5eZ86c0eDBgyVJo0ePliTt3bs3rK75z6NGjZIkjRw5Uqmpqa3qampqdPjw4VBdWlqacnNzW9U1zzl8+PDQ91YBAAAAQHvEFKaGDx+ub3zjG3rhhRdCn0GSvnhk+oYNG9SrVy8NGzZMSUlJWrt2rbxeb9j5v/71ryV9sYslSZMmTVJmZqaKi4tDjyxvbGzU+vXrZbfbQw+0yMzM1KRJk7RlyxadPn06NN+6desUDAY1a9as0DGXy6WqqiqVl5eHjpWVlenIkSNhdQAAAADQHjFt0/Ts2VMrVqzQI488ottuu02TJ09WY2OjXn31Vf3Hf/yHfvvb36pHjx5auXKldu/erbFjx2r27NnKzMzUW2+9pV27dmn+/PmhHanU1FStXbtWDzzwgCZMmKC8vDzt2bNHb7/9tjZv3hy2i7Rs2TLl5+dr/Pjxmj59uk6cOKGtW7dq9uzZYbcVFhUVadOmTbrvvvvkcrkUCARCX+RbWFjYQW8bAAAAgK87WzCWZ//9Py+99JKKi4t1/PhxXXvttRo6dKiWLFmicePGhWqOHDmiZcuW6d1339XFixc1bNgw/fjHP44YaEpLS/Xss8/qgw8+UE5OjubOnRvxARYVFRV67LHH9O677yorK0t33XWXli9frqSk8A222tpaLVq0SPv27ZPNZlNeXp6eeOIJ9e7dO+L1OBwO+Xw+ZWVl6dSpU7G+HR1u0KO74t1Cwjq5dnK8WwAAAMBVLJZsYBSmrjaEqSsHYQoAAACdKZZsENNnpgAAAAAAXyBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAICBlHg3kEgsy5LT6Yw45na75Xa7u7gjAAAAAB3N4/HI4/FEHLMsK+p5CFMt2O12eb3eeLcBAAAAoBNdbqPE4XDI5/NFNQ+3+QEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAgZR4N5BILMuS0+mMOOZ2u+V2u7u4IwAAAAAdzePxyOPxRByzLCvqeQhTLdjtdnm93ni3AQAAAKATXW6jxOFwyOfzRTUPt/kBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYSIl3A4nEsiw5nc6IY263W263u4s7AgAAANDRPB6PPB5PxDHLsqKehzDVgt1ul9frjXcbAAAAADrR5TZKHA6HfD5fVPNwmx8AAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAICBlHg3kEgsy5LT6Yw45na75Xa7u7gjAAAAAB3N4/HI4/FEHLMsK+p5CFMt2O12eb3eeLcBAAAAoBNdbqPE4XDI5/NFNQ+3+QEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAaMwtWvXLo0ZM0YZGRnq37+/Jk+erEOHDoXVBAIBLVmyRLm5ucrMzNTUqVNVUVERcb7S0lKNHz9e6enpGjdunF588cWIdRUVFZo6daoyMzOVk5OjJUuWqKmpqVVdbW2tXC6XBg4cqEGDBsnlcqm2ttbkUgEAAAAgopjD1Kuvvqo777xTFy5c0L/9279p9uzZOnTokG677TZ5vd5QXVFRkVavXq3s7GzNnTtXR48eVUFBQavQtXHjRhUWFkqSFi5cKElyuVx6/vnnw+oOHTqkgoICHTt2THPnztWQIUO0evVqFRUVhdXV1dUpPz9fO3bs0F133aVp06bptddeU35+vurq6mK9XAAAAACIyBYMBoOxnDBs2DDV1dXp+PHjuuaaayRJH330kQYPHqzp06erpKRE77//vkaMGKHly5drxYoVkqTq6mrl5uZq7Nix2r59uyTJ7/erf//+ysnJUVlZmZKTk9XY2KgJEybI6/WqurpaKSkpkqRp06bpwIEDqqysVL9+/SRJS5Ys0apVq1RVVaVhw4ZJkp566iktWrRI5eXlysvLkyTt2bNHEydOVHFxsebPn9/qmhwOh3w+n7KysnTq1CmDt7FjDXp0V7xbSFgn106OdwsAAAC4isWSDWLamfL7/fJ6vZoyZUooSEnSTTfdpCFDhujIkSOSpJKSEiUnJ4cFlwEDBmj69OnavXu3zp8/L0navXu3zp49qwULFig5OVmSlJKSonnz5unMmTMqKyuT9MVte6+//rpmzpwZClKStGDBAiUlJam0tDR0rKSkRLm5uaEgJUkFBQXKzs4OqwMAAACA9ogpTDU2NuqZZ57RnDlzWo199tln+ta3viVJ2r9/v3Jzc9W7d++wmvz8fDU0NKiysjJUJyks+DTXSdKBAwckSQcPHpTf7w8db9anTx9lZ2eH6prn/nJd85zvvfeeAoFALJcMAAAAABGlxFJ87bXXhgWpjz76SJ9++qk2bdokn8+n3/zmN5Iky7LkdDpbnd+/f39JUk1NTaiue/fu6tGjR1hdRkaG0tLSwuokhe1KtZzz448/jqouGAyqtrZWdrs9lssGAAAAgFZiClNflpOTo4sXL0qSfvnLX+qOO+6Q9EWoGTNmTKv6jIwMSeFhqvlYpNovh6lItRkZGaGnBH5VXfNrtxWmgsGgLly4EHEsGmlpaUpLSzM+HwAAAED7NDQ0qKGhwfj8WB4p0a4w9corr8iyLP3xj3/UsmXL9Mknn+iZZ56RzWaL2ETzY8z9fr8ktVnXXNuyTop8YbHUtXztSKqrq9WrV682x79KywduAAAAAOh6a9as0cqVK7vktdoVpn74wx9K+uJR5r169dKGDRs0b9489e3bN+JjyJsfPNF8G15bdc21LesktTlntHUtXzuSAQMG6OjRo22OfxV2pQAAAID4Wrx4cegrl0wMHjxY1dXVUdXGFKb+8z//U/v27dPkyZPlcDjCxqZMmaINGzbI6/XKbrdHbOD06dOS/jvQ2O121dfX68KFC+rZs2eo7ty5c/L7/WF1ktqcM9o6m80WClyR2Gy2sD4AAAAAXFna+9Gb5rvdohHT0/xOnz6tBx98UPv27Ws11rwblJWVpdGjR6uqqkpnz54Nq9m7d6+6deumnJwcSdLo0aNDx79cJ0mjRo2SJI0cOVKpqamt6mpqanT48OFQXVpamnJzc1vVNc85fPjw0PdWAQAAAEB7xBSmhg8frm984xt64YUXQp9Bkr54ZPqGDRvUq1cvDRs2TC6XS01NTSouLg7V+Hw+bdu2TVOmTNH1118vSZo0aZIyMzNVXFwcemR5Y2Oj1q9fL7vdHnqgRWZmpiZNmqQtW7aEdrckad26dQoGg5o1a1bomMvlUlVVlcrLy0PHysrKdOTIkbA6AAAAAGiPmLZpevbsqRUrVuiRRx7RbbfdpsmTJ6uxsVGvvvqq/uM//kO//e1v1aNHD+Xm5ur+++/XqlWrdPLkSd14443aunWrGhsbtXTp0tB8qampWrt2rR544AFNmDBBeXl52rNnj95++21t3rw5bBdp2bJlys/P1/jx4zV9+nSdOHFCW7du1ezZs5WdnR2qKyoq0qZNm3TffffJ5XIpEAiEvsi3sLCwA94yAAAAAIhxZ0qSHn74YZWWlqqpqUlPPPGEnn32WfXr109vvPFGWFh54YUXtHjxYlVWVmrDhg1yOp0qLy8P3eLXbM6cOSopKVEgENCTTz6ppKQkvfzyy3K5XGF1I0aM0J49e3Trrbfq6aef1pEjR7R06VI999xzYXXp6enau3evpkyZou3bt2vHjh26++67VV5ezuehAAAAAHQYWzCWB6lfpRwOh3w+n7KysnTq1Kl4t6NBj+6KdwsJ6+TayfFuAQAAAFexWLJBzDtTAAAAAADCFAAAAAAYIUwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYSIl3A4nEsiw5nc6IY263W263u4s7AgAAANDRPB6PPB5PxDHLsqKehzDVgt1ul9frjXcbAAAAADrR5TZKHA6HfD5fVPNwmx8AAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAICBlHg3kEgsy5LT6Yw45na75Xa7u7gjAAAAAB3N4/HI4/FEHLMsK+p5CFMt2O12eb3eeLcBAAAAoBNdbqPE4XDI5/NFNQ+3+QEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABhIiXcDicSyLDmdzohjbrdbbre7izsCAAAA0NE8Ho88Hk/EMcuyop6HMNWC3W6X1+uNdxsAAAAAOtHlNkocDod8Pl9U83CbHwAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgIGUeDeQSCzLktPpjDjmdrvldru7uCMAAAAAHc3j8cjj8UQcsywr6nkIUy3Y7XZ5vd54twEAAACgE11uo8ThcMjn80U1D7f5AQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABozD10Ucf6d5779WgQYPUq1cvjRkzRtu2bQurOXfunHw+X8SfpqamsNrS0lKNHz9e6enpGjdunF588cWIr1tRUaGpU6cqMzNTOTk5WrJkSau5JKm2tlYul0sDBw7UoEGD5HK5VFtba3KpAAAAABBRSqwn+Hw+5eTkKDk5WXPmzFFGRob+8Ic/aPr06Tp27JiWLVsmSbrnnnu0b9++iHPU1tbq+uuvlyRt3LhRP/7xj/WDH/xACxcu1J/+9Ce5XC794x//0Jw5c0LnHDp0SAUFBerbt6/mzp2r//qv/9Lq1av117/+VSUlJaG6uro65efn65NPPlFhYaEk6be//a3ef/99vfnmm0pPT4/1kgEAAACgFVswGAzGcsKDDz6oZ599VgcPHtTw4cMlSU1NTbr33nu1c+dOHT9+XIMGDZLD4dDQoUP10EMPtZpj4sSJSk1Nld/vV//+/ZWTk6OysjIlJyersbFREyZMkNfrVXV1tVJSvsh706ZN04EDB1RZWal+/fpJkpYsWaJVq1apqqpKw4YNkyQ99dRTWrRokcrLy5WXlydJ2rNnjyZOnKji4mLNnz+/VT8Oh0M+n09ZWVk6depULG9Hpxj06K54t5CwTq6dHO8WAAAAcBWLJRvEfJvfO++8o+9973uhICVJSUlJWrhwofx+v9555x3V19fL5/MpLy9PkydPbvWTmpoqSdq9e7fOnj2rBQsWKDk5WZKUkpKiefPm6cyZMyorK5P0xU7W66+/rpkzZ4aClCQtWLBASUlJKi0tDR0rKSlRbm5uKEhJUkFBgbKzs8PqAAAAAKA9Yg5Tfr9fI0aMaHW8sbFR0hfB58MPP5Qkfec735EknT9/PuJc+/fvl6Sw4CNJ+fn5kqQDBw5Ikg4ePCi/3x863qxPnz7Kzs4O1TU0NKiysrJVXfOc7733ngKBQHQXCgAAAACXEfNnpo4ePRrx+MsvvyxJGjp0aChMbdu2TYWFhfrss8+UkZGhH/3oR3r88cdDn1uyLEvdu3dXjx49wubKyMhQWlqaampqQnWSwnalmvXv318ff/xxVHXBYFC1tbWy2+0RryEYDOrChQuXvf7LSUtLU1pamvH5AAAAANqnoaFBDQ0NxufH8imomMNUpBf7xS9+oeeee04jRozQ7bffrtWrV0uS3nzzTS1evFg33HCD9u3bp40bN+rQoUOqqKhQcnKyLMtSRkZGxHkzMjJahalItRkZGaqoqIiqTpJqamraDFPV1dXq1atXLJcfZvny5VqxYoXx+QAAAADaZ82aNVq5cmWXvFa7wtT777+vn/zkJ9q/f78GDhyo7du3y2az6eabb9b8+fP18MMPy+FwSJJmzpypW265RY888ohKSko0a9Ys2Wy2NpNfU1OT/H6/JMlms0mKnBJjqZMUqo1kwIABbe68RYNdKQAAACC+Fi9erIULFxqfP3jwYFVXV0dVaxSmAoFAKPEFAgEVFhaquLg4dPvejBkzNGPGjFbnPfTQQ/rZz36m/fv3a9asWerbt6/q6uoivsb58+dDt+v17dtXkiLWxlInRb4FsJnNZlPPnj3bHAcAAACQ2Nr70ZvmDZpoxBymgsGgZsyYod/97nfKzs7WM888o9tuuy2qc6+99lr16dNHZ86ckSTZ7XbV19frwoULYSHm3Llz8vv9oeDTfFtepIR4+vTpqOtsNlsocAEAAABAe8T8NL/HH39cv/vd7zRjxgxVVFREDFIzZ86MeJ9ifX29zpw5o8GDB0uSRo8eLUnau3dvWF3zn0eNGiVJGjlypFJTU1vV1dTU6PDhw6G6tLQ05ebmtqprnnP48OGh760CAAAAgPaIKUw1NTXpN7/5jfr376+SkhJdc801kSdNStLatWvl9XrDjv/617+WJE2ZMkWSNGnSJGVmZqq4uDj0yPLGxkatX79edrtdd9xxhyQpMzNTkyZN0pYtW3T69OnQfOvWrVMwGNSsWbNCx1wul6qqqlReXh46VlZWpiNHjoTVAQAAAEB7xLRN89FHH+mvf/2rxo4dq2effTZiTV5enlauXKndu3dr7Nixmj17tjIzM/XWW29p165dmj9/fmhHKjU1VWvXrtUDDzygCRMmKC8vT3v27NHbb7+tzZs3h+0iLVu2TPn5+Ro/frymT5+uEydOaOvWrZo9e7ays7NDdUVFRdq0aZPuu+8+uVwuBQKB0Bf5FhYWmrxHAAAAANCKLRjDg9T/9Kc/qaCg4LI1Gzdu1Jw5c3TkyBEtW7ZM7777ri5evKhhw4bpxz/+ccRAU1paqmeffVYffPCBcnJyNHfu3IgPsKioqNBjjz2md999V1lZWbrrrru0fPlyJSWFb7DV1tZq0aJF2rdvn2w2m/Ly8vTEE0+od+/eEXt2OBzy+XzKysrSqVOnon07Os2gR3fFu4WEdXLt5Hi3AAAAgKtYLNkgpjB1tSJMXTkIUwAAAOhMsWSDmB9AAQAAAAAgTAEAAACAEcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABhIiXcDicSyLDmdzohjbrdbbre7izsCAAAA0NE8Ho88Hk/EMcuyop6HMNWC3W6X1+uNdxsAAAAAOtHlNkocDod8Pl9U83CbHwAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgIGUeDeQSCzLktPpjDjmdrvldru7uCMAAAAAHc3j8cjj8UQcsywr6nkIUy3Y7XZ5vd54twEAAACgE11uo8ThcMjn80U1D7f5AQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGEiJdwOJxLIsOZ3OiGNut1tut7uLOwIAAADQ0TwejzweT8Qxy7Kinocw1YLdbpfX6413GwAAAAA60eU2ShwOh3w+X1TzcJsfAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAgZR4N5BILMuS0+mMOOZ2u+V2u7u4IwAAAAAdzePxyOPxRByzLCvqeQhTLdjtdnm93ni3AQAAAKATXW6jxOFwyOfzRTUPt/kBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAGjMPXRRx/p3nvv1aBBg9SrVy+NGTNG27ZtC6sJBAJasmSJcnNzlZmZqalTp6qioiLifKWlpRo/frzS09M1btw4vfjiixHrKioqNHXqVGVmZionJ0dLlixRU1NTq7ra2lq5XC4NHDhQgwYNksvlUm1trcmlAgAAAEBEMYcpn8+nnJwc/elPf9I999yjhx9+WI2NjZo+fboee+yxUF1RUZFWr16t7OxszZ07V0ePHlVBQYEOHToUNt/GjRtVWFgoSVq4cKEkyeVy6fnnnw+rO3TokAoKCnTs2DHNnTtXQ4YM0erVq1VUVBRWV1dXp/z8fO3YsUN33XWXpk2bptdee035+fmqq6uL9XIBAAAAICJbMBgMxnLCgw8+qGeffVYHDx7U8OHDJUlNTU269957tXPnTh0/flznz5/XiBEjtHz5cq1YsUKSVF1drdzcXI0dO1bbt2+XJPn9fvXv3185OTkqKytTcnKyGhsbNWHCBHm9XlVXVyslJUWSNG3aNB04cECVlZXq16+fJGnJkiVatWqVqqqqNGzYMEnSU089pUWLFqm8vFx5eXmSpD179mjixIkqLi7W/PnzW12Tw+GQz+dTVlaWTp06ZfA2dqxBj+6KdwsJ6+TayfFuAQAAAFexWLJBzDtT77zzjr73ve+FgpQkJSUlaeHChfL7/XrnnXdUUlKi5OTksOAyYMAATZ8+Xbt379b58+clSbt379bZs2e1YMECJScnS5JSUlI0b948nTlzRmVlZZK+uG3v9ddf18yZM0NBSpIWLFigpKQklZaWho6VlJQoNzc3FKQkqaCgQNnZ2WF1AAAAANAeMYcpv9+vESNGtDre2Ngo6Yvgs3//fuXm5qp3795hNfn5+WpoaFBlZaUkaf/+/ZIUFnya6yTpwIEDkqSDBw/K7/eHjjfr06ePsrOzQ3XNc3+5rnnO9957T4FAINZLBgAAAIBWYg5TR48elcfjaXX85ZdfliQNHTpUlmWF7SA169+/vySppqZGkmRZlrp3764ePXqE1WVkZCgtLS2sTlKbc0ZbFwwGeRAFAAAAgA6R0t4JgsGgfvGLX+i5557TiBEjdPvtt8uyLI0ZM6ZVbUZGhqTwMNV8LFLtl0NSpNqMjIzQUwK/qq75te12e5vXcuHChbYv9iukpaUpLS3N+HwAAAAA7dPQ0KCGhgbj82N5pES7wtT777+vn/zkJ9q/f78GDhyo7du3y2azyWazRWyi+THmfr9fktqsa65tWSdFvrBY6lq+diTV1dXq1atXm+NfpeUDNwAAAAB0vTVr1mjlypVd8lpGYSoQCISaDAQCKiwsVHFxsdLT0yVJffv2jfgY8uYHTzTfhtdWXXNtyzpJbc4ZbV3L145kwIABOnr0aJvjX4VdKQAAACC+Fi9eHPrKJRODBw9WdXV1VLUxh6lgMKgZM2bod7/7nbKzs/XMM8/otttuC6ux2+0RGzh9+rSk/w40drtd9fX1unDhgnr27BmqO3funPx+f1idpDbnjLbOZrOFAlckNpstrA8AAAAAV5b2fvSm+W63aMT8AIrHH39cv/vd7zRjxgxVVFS0ClKSNHr0aFVVVens2bNhx/fu3atu3bopJycnVNd8/Mt1kjRq1ChJ0siRI5WamtqqrqamRocPHw7VpaWlKTc3t1Vd85zDhw8PfW8VAAAAALRHTGGqqalJv/nNb9S/f3+VlJTommuuiVjncrnU1NSk4uLi0DGfz6dt27ZpypQpuv766yVJkyZNUmZmpoqLi0OPLG9sbNT69etlt9t1xx13SJIyMzM1adIkbdmyJbS7JUnr1q1TMBjUrFmzwl67qqpK5eXloWNlZWU6cuRIWB0AAAAAtEdM2zQfffSR/vrXv2rs2LF69tlnI9bk5eUpNzdX999/v1atWqWTJ0/qxhtv1NatW9XY2KilS5eGalNTU7V27Vo98MADmjBhgvLy8rRnzx69/fbb2rx5c9gu0rJly5Sfn6/x48dr+vTpOnHihLZu3arZs2crOzs7VFdUVKRNmzbpvvvuk8vlUiAQCH2Rb2FhYazvDwAAAABEFFOYOnnypCTprbfe0ltvvRWxZuPGjXI6nXrhhRd04403aseOHdq9e7duu+02rVy5MnSLX7M5c+YoLS1Nzz77rJ588knl5OTo5Zdf1owZM8LqRowYoT179uixxx7T008/raysLC1dulTLly8Pq0tPT9fevXu1aNGi0NMF7777bj3xxBN8HgoAAABAh7EFY3mQ+lXK4XDI5/MpKytLp06dinc7GvTorni3kLBOrp0c7xYAAABwFYslG8T8AAoAAAAAAGEKAAAAAIwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAykxLuBRGJZlpxOZ8Qxt9stt9vdxR0BAAAA6Ggej0cejyfimGVZUc9DmGrBbrfL6/XGuw0AAAAAnehyGyUOh0M+ny+qebjNDwAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwEBKvBtIJJZlyel0Rhxzu91yu91d3BEAAACAjubxeOTxeCKOWZYV9TyEqRbsdru8Xm+82wAAAADQiS63UeJwOOTz+aKah9v8AAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADKTEu4FEYlmWnE5nxDG32y23293FHQEAAADoaB6PRx6PJ+KYZVlRz0OYasFut8vr9ca7DQAAAACd6HIbJQ6HQz6fL6p5uM0PAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAykxLuBRGJZlpxOZ8Qxt9stt9vdxR0BAAAA6Ggej0cejyfimGVZUc9DmGrBbrfL6/XGuw0AAAAAnehyGyUOh0M+ny+qebjNDwAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwEC7w9RNN92kl156qdXxc+fOyefzRfxpamoKqy0tLdX48eOVnp6ucePG6cUXX4z4WhUVFZo6daoyMzOVk5OjJUuWtJpLkmpra+VyuTRw4EANGjRILpdLtbW17b1UAAAAAAhpV5jauXOn/vKXv0Qcu+eee+RwOCL+nD9/PlS3ceNGFRYWSpIWLlwoSXK5XHr++efD5jt06JAKCgp07NgxzZ07V0OGDNHq1atVVFQUVldXV6f8/Hzt2LFDd911l6ZNm6bXXntN+fn5qqura8/lAgAAAEBISqwn/P3vf9fOnTv15z//WZs2bWqz7sMPP9QPf/hDPfTQQ63GevbsKUny+/169NFHlZeXp7KyMiUnJ+vnP/+5JkyYoMWLF8vlcikl5YsWV65cqWuuuUZvvvmm+vXrJ+mLXbFVq1bppz/9qYYNGyZJ2rx5sw4fPqzy8nLl5eVJku68805NnDhRJSUlmj9/fqyXDAAAAACtxLwz9de//lX//M//rPXr16u+vj5iTX19vXw+n/Ly8jR58uRWP6mpqZKk3bt36+zZs1qwYIGSk5MlSSkpKZo3b57OnDmjsrIySV/ctvf6669r5syZoSAlSQsWLFBSUpJKS0tDx0pKSpSbmxsKUpJUUFCg7OzssDoAAAAAaI+Yw9Stt96qS5cu6dKlS6Gw82UffvihJOk73/mOJIXd1tfS/v37JSks+EhSfn6+JOnAgQOSpIMHD8rv94eON+vTp4+ys7NDdQ0NDaqsrGxV1zzne++9p0AgENV1AgAAAMDlxHybn81mU1pamiSpW7duEWuaw9S2bdtUWFiozz77TBkZGfrRj36kxx9/XOnp6ZIky7LUvXt39ejRI+z8jIwMpaWlqaamJlQnKWxXqln//v318ccfR1UXDAZVW1sru90ese9gMKgLFy5c9vovJy0tLfTeAAAAAOh6DQ0NamhoMD4/GAxGXRtzmIrG8ePHJUlvvvmmFi9erBtuuEH79u3Txo0bdejQIVVUVCg5OVmWZSkjIyPiHBkZGa3CVKTajIwMVVRURFUnSTU1NW2GqerqavXq1SuWSw2zfPlyrVixwvh8AAAAAO2zZs0arVy5skteq1PC1M0336z58+fr4YcflsPhkCTNnDlTt9xyix555BGVlJRo1qxZstlsbSa/pqYm+f1+SV/shkmRU2IsdZJCtZEMGDBAR48ejfYyW2FXCgAAAIivxYsXh54SbmLw4MGqrq6OqrZTwtSMGTM0Y8aMVscfeugh/exnP9P+/fs1a9Ys9e3bt83HlZ8/fz50u17fvn0lKWJtLHVS5FsAm9lsttCTBgEAAABcedr70ZvmDZpotPtLe2Nx7bXXqk+fPjpz5owkyW63q76+vtXnlM6dOye/3x8KPs235UVKiKdPn466zmazhQIXAAAAALRHp4SpmTNnRrxPsb6+XmfOnNHgwYMlSaNHj5Yk7d27N6yu+c+jRo2SJI0cOVKpqamt6mpqanT48OFQXVpamnJzc1vVNc85fPjw0PdWAQAAAEB7dEqYSkpK0tq1a+X1esOO//rXv5YkTZkyRZI0adIkZWZmqri4OPTI8sbGRq1fv152u1133HGHJCkzM1OTJk3Sli1bdPr06dB869atUzAY1KxZs0LHXC6XqqqqVF5eHjpWVlamI0eOhNUBAAAAQHt0yjbNypUrtXv3bo0dO1azZ89WZmam3nrrLe3atUvz588P7UilpqZq7dq1euCBBzRhwgTl5eVpz549evvtt7V58+awXaRly5YpPz9f48eP1/Tp03XixAlt3bpVs2fPVnZ2dqiuqKhImzZt0n333SeXy6VAIBD6It/CwsLOuFwAAAAAX0OdsjN18803689//rPy8vL08ssva/Xq1aqrq9Nvf/tbFRcXh9XOmTNHJSUlCgQCevLJJ5WUlKSXX35ZLpcrrG7EiBHas2ePbr31Vj399NM6cuSIli5dqueeey6sLj09XXv37tWUKVO0fft27dixQ3fffbfKy8t5uAQAAACADmMLxvKtVFcph8Mhn8+nrKwsnTp1Kt7taNCju+LdQsI6uXZyvFsAAADAVSyWbNClT/MDAAAAgKsFYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMBASrwbSCSWZcnpdEYcc7vdcrvdXdwRAAAAgI7m8Xjk8XgijlmWFfU8hKkW7Ha7vF5vvNsAAAAA0Ikut1HicDjk8/mimofb/AAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAykxLuBRGJZlpxOZ8Qxt9stt9vdxR0BAAAA6Ggej0cejyfimGVZUc9DmGrBbrfL6/XGuw0AAAAAnehyGyUOh0M+ny+qebjNDwAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwABhCgAAAAAMEKYAAAAAwEBKvBtIJJZlyel0Rhxzu91yu91d3BEAAACAjubxeOTxeCKOWZYV9TyEqRbsdru8Xm+82wAAAADQiS63UeJwOOTz+aKah9v8AAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADBCmAAAAAMAAYQoAAAAADKTEu4FEYlmWnE5nxDG32y23293FHQEAAADoaB6PRx6PJ+KYZVlRz0OYasFut8vr9ca7DQAAAACd6HIbJQ6HQz6fL6p5uM0PAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAwQpgAAAADAAGEKAAAAAAy0O0zddNNNeumll1odDwQCWrJkiXJzc5WZmampU6eqoqIi4hylpaUaP3680tPTNW7cOL344osR6yoqKjR16lRlZmYqJydHS5YsUVNTU6u62tpauVwuDRw4UIMGDZLL5VJtbW37LhQAAAAAWmhXmNq5c6f+8pe/RBwrKirS6tWrlZ2drblz5+ro0aMqKCjQoUOHwuo2btyowsJCSdLChQslSS6XS88//3xY3aFDh1RQUKBjx45p7ty5GjJkiFavXq2ioqKwurq6OuXn52vHjh266667NG3aNL322mvKz89XXV1dey4XAAAAAEJSYj3h73//u3bu3Kk///nP2rRpU8Sa999/X6WlpVq+fLlWrFghSZo7d65yc3P1q1/9Stu3b5ck+f1+Pfroo8rLy1NZWZmSk5P185//XBMmTNDixYvlcrmUkvJFiytXrtQ111yjN998U/369ZP0xa7YqlWr9NOf/lTDhg2TJG3evFmHDx9WeXm58vLyJEl33nmnJk6cqJKSEs2fPz/WSwYAAACAVmLemfrrX/+qf/7nf9b69etVX18fsaakpETJyclhwWXAgAGaPn26du/erfPnz0uSdu/erbNnz2rBggVKTk6WJKWkpGjevHk6c+aMysrKJH1x297rr7+umTNnhoKUJC1YsEBJSUkqLS0Ne+3c3NxQkJKkgoICZWdnh9UBAAAAQHvEHKZuvfVWXbp0SZcuXQqFnS/bv3+/cnNz1bt377Dj+fn5amhoUGVlZahOUljwaa6TpAMHDkiSDh48KL/fHzrerE+fPsrOzg7VNc/95brmOd977z0FAoEYrxgAAAAAWos5TNlsNqWlpSktLU3dunWLWGNZVtgOUrP+/ftLkmpqakJ13bt3V48ePcLqMjIylJaWFlYnqc05o60LBoM8iAIAAABAh4j5M1PRsCxLY8aMaXU8IyNDUniYaj4WqfbLISlSbUZGRugpgV9V1/zadrs94msGg0FduHCh7Qv7Cs0hEwAAAEB8NDQ0qKGhwfj8YDAYdW2nhCmbzRaxiebHmPv9/svWNde2rJMiX1gsdS1fO5Lq6mr16tWrzfGv0vKBGwAAAAC63po1a7Ry5couea1OCVN9+/aN+Bjy5gdPNN+G11Zdc23LOkltzhltXcvXjmTAgAE6evRom+NfhV0pAAAAIL4WL14c+solE4MHD1Z1dXVUtZ0Spux2e8QGTp8+Lem/A43dbld9fb0uXLignj17hurOnTsnv98fViepzTmjrbPZbKHAFYnNZgvrAwAAAMCVpb0fvWm+2y0a7frS3raMHj1aVVVVOnv2bNjxvXv3qlu3bsrJyQnVNR//cp0kjRo1SpI0cuRIpaamtqqrqanR4cOHQ3VpaWnKzc1tVdc85/Dhw0PfWwUAAAAA7dEpYcrlcqmpqUnFxcWhYz6fT9u2bdOUKVN0/fXXS5ImTZqkzMxMFRcXhx5Z3tjYqPXr18tut+uOO+6QJGVmZmrSpEnasmVLaHdLktatW6dgMKhZs2aFvXZVVZXKy8tDx8rKynTkyJGwOgAAAABoj07ZpsnNzdX999+vVatW6eTJk7rxxhu1detWNTY2aunSpaG61NRUrV27Vg888IAmTJigvLw87dmzR2+//bY2b94ctou0bNky5efna/z48Zo+fbpOnDihrVu3avbs2crOzg7VFRUVadOmTbrvvvvkcrkUCARCX+RbWFjYGZcLAAAA4GuoU3amJOmFF17Q4sWLVVlZqQ0bNsjpdKq8vDx0i1+zOXPmqKSkRIFAQE8++aSSkpL08ssvy+VyhdWNGDFCe/bs0a233qqnn35aR44c0dKlS/Xcc8+F1aWnp2vv3r2aMmWKtm/frh07dujuu+9WeXk5n4cCAAAA0GFswVgepH6Vcjgc8vl8ysrK0qlTp+LdjgY9uiveLSSsk2snx7sFAAAAXMViyQadtjMFAAAAAFczwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGEiJdwOJxLIsOZ3OiGNut1tut7uLOwIAAADQ0TwejzweT8Qxy7Kinocw1YLdbpfX6413GwAAAAA60eU2ShwOh3w+X1TzcJsfAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAgZR4N5BILMuS0+mMOOZ2u+V2u7u4IwAAAAAdzePxyOPxRByzLCvqeQhTLdjtdnm93ni3AQAAAKATXW6jxOFwyOfzRTUPt/kBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgAHCFAAAAAAYIEwBAAAAgIGUeDeQSCzLktPpjDjmdrvldru7uCMAAAAAHc3j8cjj8UQcsywr6nkIUy3Y7XZ5vd54twEAAACgE11uo8ThcMjn80U1D7f5AQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGCBMAQAAAIABwhQAAAAAGEiJdwOJxLIsOZ3OiGNut1tut7uLOwIAAADQ0TwejzweT8Qxy7Kinocw1YLdbpfX6413GwAAAAA60eU2ShwOh3w+X1TzcJsfAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAgU4NU59++ql8Pl+rH8uyQjWBQEBLlixRbm6uMjMzNXXqVFVUVEScr7S0VOPHj1d6errGjRunF198MWJdRUWFpk6dqszMTOXk5GjJkiVqamrqlGsEAAAA8PXUaWGqsbFRAwcOlMPhaPUzYcKEUF1RUZFWr16t7OxszZ07V0ePHlVBQYEOHToUNt/GjRtVWFgoSVq4cKEkyeVy6fnnnw+rO3TokAoKCnTs2DHNnTtXQ4YM0erVq1VUVNRZlwoAAADga8gWDAaDnTHxiRMndMstt+jhhx/W+PHjw8Z69OihcePG6f3339eIESO0fPlyrVixQpJUXV2t3NxcjR07Vtu3b5ck+f1+9e/fXzk5OSorK1NycrIaGxs1YcIEeb1eVVdXKyUlRZI0bdo0HThwQJWVlerXr58kacmSJVq1apWqqqo0bNiwVr06HA75fD5lZWXp1KlTnfF2xGTQo7vi3ULCOrl2crxbAAAAwFUslmzQaTtTx48flyTde++9mjx5ctjPuHHjJEklJSVKTk7W/PnzQ+cNGDBA06dP1+7du3X+/HlJ0u7du3X27FktWLBAycnJkqSUlBTNmzdPZ86cUVlZmSSptrZWr7/+umbOnBkKUpK0YMECJSUlqbS0tLMuFwAAAMDXTKeHqW9/+9sKBAK6cOFCq5r9+/crNzdXvXv3Djuen5+vhoYGVVZWhuokKS8vr1WdJB04cECSdPDgQfn9/tDxZn369FF2dnaoDgAAAADaK6WzJv7www/VvXt3/eQnP9Err7wiv9+vG264QfPnz9fChQuVlJQky7LkdDpbndu/f39JUk1NjSTJsix1795dPXr0CKvLyMhQWlpaWJ2ksF2plnN+/PHHl+05GAxGDH3RSktLU1pamvH5AAAAANqnoaFBDQ0NxufH8imoTgtTx48fV319vf7yl7+ouLhY3bp109atW/Xwww/rk08+UXFxsSzL0pgxY1qdm5GRISk8TDUfi1T75TAVqTYjI6PNpwQ2q66uVq9evaK/yC9p+dkvAAAAAF1vzZo1WrlyZZe8VqeFqXHjxun73/++fvGLX4R2a2bPnq0pU6boN7/5jebNmyebzRYx+TU/xtzv90tSm3XNtS3rpMhpsmVdWwYMGKCjR49GeYWtsSsFAAAAxNfixYtDT/82MXjwYFVXV0dV22lhaunSpa2O2Ww2PfTQQ9q1a5cqKirUt29f1dXVtaprfvBE8+16bdU117ask9TmnJFu//tyfz179rxsDQAAAIDE1d6P3jRv0ESjU7+0N5KBAwdKks6cOSO73R4x9Z0+fVrSf4cpu92u+vr6Vp9nOnfunPx+f1idpDbn/KowBQAAAADR6pQwdfLkSU2ZMiX0PVFfHpO+2D4bPXq0qqqqdPbs2bCavXv3qlu3bsrJyZEkjR49OnT8y3WSNGrUKEnSyJEjlZqa2qqupqZGhw8fDtUBAAAAQHt1SpgaOHCgKisr9Ytf/EL19fWh442NjXriiSdkt9s1evRouVwuNTU1qbi4OFTj8/m0bds2TZkyRddff70kadKkScrMzFRxcbECgUBorvXr18tut+uOO+6QJGVmZmrSpEnasmVLaHdLktatW6dgMKhZs2Z1xuUCAAAA+BrqlM9M2Ww2rVu3Tvfee69GjhypmTNnqqmpSb///e91+PBhbdmyRT169FBubq7uv/9+rVq1SidPntSNN96orVu3qrGxMewzV6mpqVq7dq0eeOABTZgwQXl5edqzZ4/efvttbd68WSkp/30Zy5YtU35+vsaPH6/p06frxIkT2rp1q2bPnq3s7OzOuFwAAAAAX0Od9pmpe+65Rzt37lSfPn20bt06bdiwQQMGDNA777yj++67L1T3wgsvaPHixaqsrNSGDRvkdDpVXl4eusWv2Zw5c1RSUqJAIKAnn3xSSUlJevnll+VyucLqRowYoT179ujWW2/V008/rSNHjmjp0qV67rnnOutSAQAAAHwN2YKxfCvVVcrhcMjn8ykrK0unTp2Kdzsa9OiueLeQsE6unRzvFgAAAHAViyUbdPnT/AAAAADgakCYAgAAAAADhCkAAAAAMECYAgAAAAADhCkAAAAAMECYAgAAAAADhCkAAAAAMECYAgAAAAADhCkAAAAAMJAS7wYSiWVZcjqdEcfcbrfcbncXdwQAAACgo3k8Hnk8nohjlmVFPQ9hqgW73S6v1xvvNgAAAAB0osttlDgcDvl8vqjm4TY/AAAAADBAmAIAAAAAA4QpAAAAADBAmAIAAAAAA4QpAAAAADBAmAIAAAAAA4QpAAAAADBAmAIAAAAAA4QpAAAAADBAmAIAAAAAA4QpAAAAADBAmAIAAAAAA4QpAAAAADBAmAIAAAAAA4QpAAAAADBAmAIAAAAAAynxbiCRWJYlp9MZccztdsvtdndxRwAAAAA6msfjkcfjiThmWVbU8xCmWrDb7fJ6vfFuAwAAAEAnutxGicPhkM/ni2oebvMDAAAAAAOEKQAAAAAwQJgCAAAAAAOEKQAAAAAwQJgCAAAAAAOEKQAAAAAwQJgCAAAAAAOEKQAAAAAwQJgCAAAAAAOEKQAAAAAwQJgCAAAAAAOEKQAAAAAwQJgCAAAAAAOEKQAAAAAwQJgCAAAAAAOEKQAAAAAwkBLvBhKJZVlyOp0Rx9xut9xudxd3BAAAAKCjeTweeTyeiGOWZUU9D2GqBbvdLq/XG+82AAAAAHSiy22UOBwO+Xy+qObhNj8AAAAAMECYAgAAAAADhCkAAAAAMECYAgAAAAADhCkAAAAAMECYAgAAAAADhCkAAAAAMECYAgAAAAADhCkAAAAAMECYAgAAAAADhCkAAAAAMECYAgAAAAADhCkAAAAAMECYAgAAAAADhCkAAAAAMECYAgAAAAADKfFuIJFYliWn0xlxzO12y+12d3FHAAAAADqax+ORx+OJOGZZVtTzsDPVgt1ul9frjfjTVUGqoaFBdW+9pGCjv0teD1e+hoYGrVixQg0NDfFuBVcQ1g1ixZpBrFgziFVXrhm3293m7/12uz3qeWzBYDDYiX1eERwOh3w+n7KysnTq1Km49nLhwgX16tVLN/yvbUpK+0Zce0lEJ9dOjncLCad5zXz22Wfq2bNnvNvBFYJ1g1ixZhAr1gxilShrJpZswM4UAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTAEAAACAAcIUAAAAABggTOErfX5oZ7xbCGnrm6rjIZF6STSJ9N7Qy5Uhkd4berkyJNJ7k0i9SInXT6JIpPclkXqREq+fK8lV+6W9tbW1WrRokfbt2yebzabx48friSeeUGZmZqtavrT38qo3ztWAf/3/4t2GJHqJpKnhb/pk3T/H/QvuWnI6nfJ6vfFuQxK9tCVRvhixWSK9N/QSGWumbYnUi5Q4/bBmIhv06K6E+R2iWaL088HPf5AQayaWbJDSRT11qbq6OuXn5+uTTz5RYWGhJOm3v/2t3n//fb355ptKT0+Pb4MAAAAArnhXZZjavHmzDh8+rPLycuXl5UmS7rzzTk2cOFElJSWaP39+nDsEAAAAcKW7KsNUSUmJcnNzQ0FKkgoKCpSdna3S0lLCFK5K2cv/mDi3htZc1KBHd8W7DUlSYrwjAPD1k738j6H/JsK/T4n0bxOuHlddmGpoaFBlZaUWLVrUaiw/P1/r169XIBBQcnJyHLoD0NVOJNA/non0D3lTw98k8UtOJPQSGWumbYnUi5R4/QBXs6vuaX6WZUmS+vXr12qsf//+CgaDqq2t7eq2AAAAAFxlrrqn+VVUVOi73/2uNm7cqDlz5oSNPfPMM3rwwQf1wQcfaOjQoaHj3bp1k9/vV1JSUsQQFi2bzWZ8brNgMKjq6molX9dbUvvn6wiBv9Up+Rvp8W5DEr1EFlTg4jnWTBvopS2JtW4S6b2hl7awZtqSSL1IidQPa6YtidSLlDj92Humqbq6WgMGDGj379XtiTinT59WU1OTUlNT9Y9//OOytVfdbX7Nb3ykN7CpqUmS5Pf7w44HAoHQeHV1dSd3GJ3AxXPxbiFM4OLZeLcQQi+RsWbaRi9tS6R1k0jvDb20jTUTWSL1IiVWP6yZyBKpFykx+qm++P/+myi/j/+/jHA5V12Y6tu3r6QvHo/+ZefPn5fU+hbAa665RpcuXVJycrL69Olj/NodsTMFAAAAoH3aszN15swZBQIBXXPNNV9Ze9WFKbvdLilyoj19+rRsNlsocDWrr6/vkt4AAAAAXD2uus9MSdLw4cMVDAZVWVkZdjw7O1tpaWk6ePBgfBoDAAAAcNW46p7mJ0kul0tVVVUqLy8PHSsrK9ORI0c0a9asOHYGAAAA4GpxVe5M1dXVady4cTp16pRcLpcCgYBKSkr0zW9+U2+88YZ69uwZ7xYBAAAAXOGuyjAlSbW1tVq0aJH27dsnm82mvLw8PfHEE+rdu3e8WwMAAABwFbhqwxQAAAAAdKar8jNTiaq2tlYul0sDBw7UoEGD5HK5VFtbG9W5Tz31lL73ve8pIyNDEydO1P/9v/+3k7tFIjBdM36/X4899phycnJ03XXXyel0atGiRbp48WIXdI14a8/fNS0tXbpUqampndAhEk171sybb76pe+65R3369NGtt96qNWvWRPXdLLiyma6Zv//971q2bJmGDBmi6667Trm5uVq3bh1r5mvmpptu0ksvvRR1fUL/HhxElzh//nxw6NChwfT09OD8+fOD8+fPD/bq1Ss4dOjQ4Pnz5y977pIlS4KSglOmTAkuX748mJOTE+zWrVvw9ddf75rmERftWTN33XVXUFLw7rvvDq5ZsyZYWFgYTE5ODjqdzuDf//73rrkAxEV71k1Lx44dC3br1i2YkpLSec0iIbRnzezZsyd47bXXBocOHRpctmxZ8J577glKCj788MNd0zziwnTNNDU1Bf/H//gfweTk5GBhYWFw7dq1wbvvvjsoKfjAAw903QUgrv7whz8EJQVLS0ujqk/034MJU13kySefDNpstuDevXtDx8rKyoKSgsXFxW2eZ1lWMCUlJehyuULHPv/88+C3v/3t4PDhwzuzZcSZ6Zp59913g5KC/+t//a+w49u3bw9KCv7qV7/qtJ4Rf6br5svy8vKCkghTXwOmayYQCARvuOGGYE5OTrC+vj50/P777w+mpKQEP/30007tG/FjumbKy8uDkoKPP/542PGHHnooKCn4X//1X53WM+Lrb3/7W3Dbtm3BefPmBbt37x51mLoSfg8mTHWR3NzciP+jZ2dnB0eNGtXmeevWrQtKClZVVYUdf/zxx4OSgkeOHOnwXpEYTNfME088EZQUPHbsWKuxm2++OVhQUNChfSKxmK6blkpKSoJJSUnBoUOHEqa+BkzXzB//+MegpOC///u/hx1/5513gunp6a2O4+phumaefPLJoKTg0aNHw46//vrrQUnBV199taNbRYI4duxYUFLYTzRh6kr4PZjPTHWBhoYGVVZWKj8/v9VYfn6+3nvvvTbvFd6/f7+uv/56DRs2rNV5knTgwIGObxhx154189lnn8nhcGjQoEGtxhobG40+O4MrQ3vWTbPz58/rpz/9qf7n//yfGjlyZGe1igTRnjXz2muvqVu3bpo4cWLY8e9///s6f/68fvSjH3VKz4iv9qyZm266SZJ04sSJsOMffvihJOnmm2/u4G6RKG699VZdunRJly5dUllZWdTnXQm/BxOmuoBlWZKkfv36tRrr37+/gsFgm7/gWpbV5nmSVFNT04GdIlG0Z82sXLlSn3zyidLS0sKOv/POO/r44481dOjQjm8YCaE966bZo48+KklatWpVxzeIhNOeNePz+ZSVlaXrrrtOkvT555+rqamp85pFQmjPmsnPz9c3v/lNud1u/f73v9fx48dVWlqq5cuX67vf/a6GDBnSqb0jfmw2m9LS0pSWlqZu3bpFfd6V8HswYaoLNP/Fk5GR0Wqs+Vhbi8GyLKPzcGVrz5qJ5N1339Wdd96plJQULViwoGOaRMJp77p599139dxzz+nxxx+POAeuPu1ZM6dPn9b111+vf//3f9e3v/1t9erVS9ddd52mTJmijz76qPOaRly1Z8306NFDO3fuVE1Nje6++259+9vf1v33369u3bpp165dSkri11KEuxJ+D2bVdgGbzSZJCkb4Sq/m/xfP7/e3ea7JebiytWfNtPT5559r4cKFGjdunC5cuKANGzZoxIgRHdssEkZ71k1jY6MefPBBjRs3Tvfff3/nNYmE0p41c+7cOR07dkyzZ8/Wfffdp61bt2rx4sV666239IMf/EB1dXWd1jfipz1r5qOPPtLEiRPVp08f/Z//83+0detWPfLII/r88881ZcoU/e1vf+u8xnFFuhJ+D06JdwNfB3379pWkiP+wnD9/XlLk7fLmcyOl7q86D1e29qyZZm+99Zbuv/9+nTx5UoMHD9bzzz+v73//+x3eKxJHe9ZNcXGxjh49qsrKys5qDwmoPWvm+uuv14cffqg33nhD48aNCx0fM2aM/umf/klPP/20fv7zn3d804ir9qyZ+fPn6+LFi/J6vRowYIAkafr06Ro3bpzuvPNO/eY3v9EjjzzSOY3jinQl/B7MzlQXsNvtkqTq6upWY6dPn5bNZgv95RTp3E8//bRVKj99+rSkxFhE6HjtWTOS9Pvf/1633367LMvSE088oQ8++IAg9TVgum7q6uq0YsUK3X///erVq5d8Pp98Pp/q6+sVDAbl8/n06aefdnr/6Hrt+bumX79+GjBgQFiQkr74XEzPnj31/vvvd3zDiLv2rJk33nhDeXl5oSDVbPLkyUpPT9dbb73V8Q3jinYl/B5MmOoCaWlpys3N1d69e1uN7d27V8OHD1dKSuRNwtGjR+vcuXOqqqpqdZ4kjRo1quMbRty1Z818/PHH+pd/+Rf169dPFRUVWrhwYZu1uLqYrpvPPvtMFy9e1KZNm+RwOEI/r7zyigKBgBwOR6snKeHq0J6/a4YMGaKLFy+2+iUnEAjoH//4h3r37t0pPSO+2rNmevfurX/84x+tjjc1Ncnv9/NZTbRyJfweTJjqIi6XS1VVVSovLw8dKysr05EjRzRr1qw2z5s5c6ZSUlL05JNPho59/vnn2rhxo0aOHMmT2a5ipmvmueee09/+9jc988wzPBnpa8hk3fTt21c7d+5s9fNP//RPSkpK0s6dO/XSSy911SWgi5n+XVNUVKTPP/9czz33XNjx559/XpcuXdIPfvCDTusZ8WW6ZsaOHau9e/fqyJEjYcc3b96s+vp61gxauRJ+D7YFI32qCx2urq5O48aN06lTp+RyuRQIBFRSUqJvfvObeuONN9SzZ099/vnnKikpUVZWlu66667QucuWLdMvf/lLTZs2Tbm5ufr973+vY8eO6Q9/+IMKCgrid1HoVKZrZuzYsXrvvff061//WsnJya3m7d+/P9//chVrz981XzZr1iyVlpYmxAd80Xnas2bmzp2rZ555RtOnT1dubq4++OADbdmyRbfffrvKy8tDDyvA1cV0zVRXV2vYsGFqaGjQrFmz5HA4dPDgQb3yyisaM2aM9u3bF/HfLVxd3njjDd1+++0qLS3Vv/zLv4SOX7G/B3f1twR/nZ05cyZYWFgYvPHGG4MDBw4MFhUVBc+ePRsaP3nyZFBS8Pbbb2917pNPPhkcNWpUMD09PVhQUBDcs2dPV7aOODFZM1lZWa2+Zbzlz9ixY+NxKehC7fm7pqWioqJgSkpKZ7eLBNCeNbN27drgd7/73eB1110XHDZsWPCxxx4LNjQ0dGX7iAPTNfPpp58G58yZE/zWt74V/MY3vhHMyckJPvbYY8FLly519SUgTvbt2xeUFCwtLQ07fqX+HszOFAAAAAAY4DNTAAAAAGCAMAUAAAAABghTAAAAAGCAMAUAAAAABghTAAAAAGCAMAUAAAAABghTAAAAAGCAMAUAAAAABghTAAAAAGCAMAUAAAAABghTAAAAAGCAMAUAAAAABghTAAAAAGDg/wfWdPL/zXXGCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dp_bh2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea3fc91f",
   "metadata": {},
   "source": [
    "# Calculating efficiency (denominator should be pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "93177b60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dp_bh3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72173996",
   "metadata": {},
   "source": [
    "### As prediction h1 can be matched to target h1, 2, 3,  I will do things in the event level first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a8871464",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert some arrays to ak array\n",
    "dps = np.concatenate((dp_bh1.reshape(-1,1), dp_bh2.reshape(-1,1), dp_bh3.reshape(-1,1)), axis=1)\n",
    "dps = ak.Array(dps)\n",
    "bb_ps = np.concatenate((bb_bh1_p.reshape(-1,1), bb_bh2_p.reshape(-1,1), bb_bh3_p.reshape(-1,1)), axis=1)\n",
    "bb_ps = ak.Array(bb_ps)\n",
    "bb_ts = np.concatenate((bb_bh1_t.reshape(-1,1), bb_bh2_t.reshape(-1,1), bb_bh3_t.reshape(-1,1)), axis=1)\n",
    "bb_ts = ak.Array(bb_ts)\n",
    "fj_pts = ak.Array(fj_pts)\n",
    "\n",
    "# p: prediction\n",
    "DP_threshold = 0.5\n",
    "dp_filter = dps > DP_threshold\n",
    "bb_ps_passed = bb_ps.mask[dp_filter]\n",
    "bb_ps_passed = ak.drop_none(bb_ps_passed)\n",
    "\n",
    "dps_passed = dps.mask[dp_filter]\n",
    "dps_passed = ak.drop_none(dps_passed)\n",
    "\n",
    "sort_by_dp = ak.argsort(dps_passed, axis=-1, ascending=False)\n",
    "bb_ps_passed = bb_ps_passed[sort_by_dp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "147f9f15",
   "metadata": {},
   "outputs": [],
   "source": [
    "bh_effs = []\n",
    "# for each event\n",
    "for bb_p_event, bb_t_event, fj_pt_event in zip(bb_ps_passed, bb_ts, fj_pts):\n",
    "    # for each predicted fatjet, check if the targets have a t fatject same with the p fatjet\n",
    "    for bb_p in bb_p_event:\n",
    "        match = 0\n",
    "        for bb_t in bb_t_event:\n",
    "            if bb_p == bb_t+10:\n",
    "                match = 1\n",
    "        bh_effs.append([fj_pt_event[bb_t], match])\n",
    "bh_effs = np.array(bh_effs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0767ca2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set x axis (pT) of the scattered points\n",
    "bins = np.arange(200, 1000, 100)\n",
    "bin_centers = [(bins[i]+bins[i+1])/2 for i in range(bins.size-1)]\n",
    "\n",
    "# group points into bins by fatjet pT\n",
    "eff_inds = np.digitize(bh_effs[:,0], bins)\n",
    "\n",
    "#np.concatenate((ap_bh1.reshape(-1,1), ap_bh2.reshape(-1,1), ap_bh3.reshape(-1,1)), axis=1)\n",
    "effs_per_bin = []\n",
    "for bin_i in range(1, len(bins)):\n",
    "    effs_per_bin.append(bh_effs[:,1][eff_inds==bin_i])\n",
    "effs_per_bin = ak.Array(effs_per_bin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "26627862",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1434.0 out of 2900 assignment are same with target in bin centered at 250.0 GeV\n",
      "1532.0 out of 2130 assignment are same with target in bin centered at 350.0 GeV\n",
      "816.0 out of 1075 assignment are same with target in bin centered at 450.0 GeV\n",
      "365.0 out of 472 assignment are same with target in bin centered at 550.0 GeV\n",
      "186.0 out of 243 assignment are same with target in bin centered at 650.0 GeV\n",
      "88.0 out of 124 assignment are same with target in bin centered at 750.0 GeV\n",
      "49.0 out of 61 assignment are same with target in bin centered at 850.0 GeV\n"
     ]
    }
   ],
   "source": [
    "for effs, bin_c in zip(effs_per_bin, bin_centers):\n",
    "    print(f\"{ak.sum(effs)} out of {ak.count(effs)} assignment are same with target in bin centered at {bin_c} GeV\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "63c11585",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "ax.error_bar(x=bin_centers, y=ak.mean(effs_per_bin, axis=-1))\n",
    "ax.set(xlabel=r\"reco H pT\", ylabel=r\"Matching efficiency\", title=\"SPANet Boosted H Matching Efficiency vs. Reco H pT\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"{str(plot_dir)}/test_efficiency.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "39798763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>[0.5,\n",
       " 0.449,\n",
       " 0.428,\n",
       " 0.419,\n",
       " 0.424,\n",
       " 0.454,\n",
       " 0.398]\n",
       "-----------------\n",
       "type: 7 * float64</pre>"
      ],
      "text/plain": [
       "<Array [0.5, 0.449, 0.428, 0.419, 0.424, 0.454, 0.398] type='7 * float64'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.std(effs_per_bin, axis=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e904e58c",
   "metadata": {},
   "source": [
    "# Calculating purity (denominator should be target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cc951b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the truth level boosted higgs mask\n",
    "bh_masks = np.concatenate((bh1_mask.reshape(-1,1), bh2_mask.reshape(-1,1), bh3_mask.reshape(-1,1)), axis=1)\n",
    "bh_masks = ak.Array(bh_masks)\n",
    "\n",
    "# applying masks to the target bh's bb indices:\n",
    "bb_ts_selected = bb_ts.mask[bh_masks]\n",
    "bb_ts_selected = ak.drop_none(bb_ts_selected)\n",
    "\n",
    "bh_pts = np.concatenate((bh1_pt.reshape(-1,1), bh2_pt.reshape(-1,1), bh3_pt.reshape(-1,1)), axis=1)\n",
    "bh_pts = ak.Array(bh_pts)\n",
    "bh_selected_pts = bh_pts.mask[bh_masks]\n",
    "bh_selected_pts = ak.drop_none(bh_selected_pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d81a1e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "bh_purs = []\n",
    "# for each event\n",
    "for bb_t_event, bb_p_event, bh_pt_event in zip(bb_ts_selected, bb_ps_passed, bh_selected_pts):\n",
    "    # for each target fatjet, check if the predictions have a p fatject same with the t fatjet\n",
    "    for i, bb_t in enumerate(bb_t_event):\n",
    "        match = 0\n",
    "        for bb_p in bb_p_event:\n",
    "            if bb_p == bb_t+10:\n",
    "                match = 1\n",
    "        bh_purs.append([bh_pt_event[i], match])\n",
    "bh_purs = np.array(bh_purs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "71545610",
   "metadata": {},
   "outputs": [],
   "source": [
    "# group points into bins by fatjet pT\n",
    "pur_inds = np.digitize(bh_purs[:,0], bins)\n",
    "\n",
    "# diepense (gen_H_pT, purity) points into bins\n",
    "purs_per_bin = []\n",
    "for bin_i in range(1, len(bins)):\n",
    "    purs_per_bin.append(bh_purs[:,1][pur_inds==bin_i])\n",
    "purs_per_bin = ak.Array(purs_per_bin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b2d9e543",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1249.0 out of 1530 target assignments are matched to >=1 predicted assignment in bin centered at 250.0 GeV\n",
      "3588.0 out of 3885 target assignments are matched to >=1 predicted assignment in bin centered at 350.0 GeV\n",
      "1770.0 out of 1849 target assignments are matched to >=1 predicted assignment in bin centered at 450.0 GeV\n",
      "665.0 out of 721 target assignments are matched to >=1 predicted assignment in bin centered at 550.0 GeV\n",
      "286.0 out of 310 target assignments are matched to >=1 predicted assignment in bin centered at 650.0 GeV\n",
      "131.0 out of 140 target assignments are matched to >=1 predicted assignment in bin centered at 750.0 GeV\n",
      "55.0 out of 56 target assignments are matched to >=1 predicted assignment in bin centered at 850.0 GeV\n"
     ]
    }
   ],
   "source": [
    "for purs, bin_c in zip(purs_per_bin, bin_centers):\n",
    "    print(f\"{ak.sum(purs)} out of {ak.count(purs)} target assignments are matched to >=1 predicted assignment in bin centered at {bin_c} GeV\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c7e7275f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "ax.scatter(x=bin_centers, y=ak.mean(purs_per_bin, axis=-1))\n",
    "ax.set(xlabel=r\"gen H pT\", ylabel=r\"Matching purity\", title=\"SPANet Boosted H Matching purity vs. gen H pT\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"{str(plot_dir)}/test_purity.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62ec74c5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}