{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\t\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "import awkward as ak\n",
    "from scipy.optimize import curve_fit\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31618"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "file = uproot.open(\"tracking_losses_ntuple_B_match_default_weights.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "\n",
    "\n",
    "#selektiere nur elektronen von B->K*ee\n",
    "allcolumns = file.arrays()\n",
    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal)] #B: 9056\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal)] #B: 1466\n",
    "\n",
    "ak.num(found, axis=0) + ak.num(lost, axis=0)\n",
    "#ak.count(found, axis=None)\t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#found\n",
    "\n",
    "brem_e_f = found[\"brem_photons_pe\"]\n",
    "brem_z_f = found[\"brem_vtx_z\"]\n",
    "brem_x_f = found[\"brem_vtx_x\"]\n",
    "e_f = found[\"energy\"]\n",
    "length_f = found[\"brem_vtx_z_length\"]\n",
    "\n",
    "brem_f = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(found,axis=0)):\n",
    "    brem_f.begin_record()\n",
    "    #[:,\"energy\"] energy\n",
    "    brem_f.field(\"energy\").append(e_f[itr])\n",
    "    #[:,\"photon_length\"] number of vertices\n",
    "    brem_f.field(\"photon_length\").integer(length_f[itr])\n",
    "    #[:,\"brem_photons_pe\",:] photon energy \n",
    "    brem_f.field(\"brem_photons_pe\").append(brem_e_f[itr])\n",
    "    #[:,\"brem_vtx_z\",:] brem vtx z\n",
    "    brem_f.field(\"brem_vtx_x\").append(brem_x_f[itr])\n",
    "    brem_f.field(\"brem_vtx_z\").append(brem_z_f[itr])\n",
    "    brem_f.end_record()\n",
    "\n",
    "brem_f = ak.Array(brem_f)\n",
    "\n",
    "#lost\n",
    "\n",
    "brem_e_l = lost[\"brem_photons_pe\"]\n",
    "brem_z_l = lost[\"brem_vtx_z\"]\n",
    "brem_x_l = lost[\"brem_vtx_x\"]\n",
    "e_l = lost[\"energy\"]\n",
    "length_l = lost[\"brem_vtx_z_length\"]\n",
    "\n",
    "brem_l = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(lost,axis=0)):\n",
    "    brem_l.begin_record()\n",
    "    #[:,\"energy\"] energy\n",
    "    brem_l.field(\"energy\").append(e_l[itr])\n",
    "    #[:,\"photon_length\"] number of vertices\n",
    "    brem_l.field(\"photon_length\").integer(length_l[itr])\n",
    "    #[:,\"brem_photons_pe\",:] photon energy \n",
    "    brem_l.field(\"brem_photons_pe\").append(brem_e_l[itr])\n",
    "    #[:,\"brem_vtx_z\",:] brem vtx z\n",
    "    brem_l.field(\"brem_vtx_x\").append(brem_x_l[itr])\n",
    "    brem_l.field(\"brem_vtx_z\").append(brem_z_l[itr])\n",
    "    brem_l.end_record()\n",
    "\n",
    "brem_l = ak.Array(brem_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "photon_cut = 0\n",
    "\n",
    "cut_brem_found = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(brem_f, axis=0)):\n",
    "    cut_brem_found.begin_record()\n",
    "    cut_brem_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
    "    \n",
    "    cut_brem_found.field(\"brem_photons_pe\")\n",
    "    cut_brem_found.begin_list()\n",
    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
    "        if brem_f[itr, \"brem_vtx_z\", jentry]>5000 or brem_f[itr, \"brem_photons_pe\", jentry]<photon_cut:\n",
    "            continue\n",
    "        else:\n",
    "            cut_brem_found.real(brem_f[itr,\"brem_photons_pe\", jentry])\n",
    "            \n",
    "            #cut_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
    "    cut_brem_found.end_list()\n",
    "\n",
    "    cut_brem_found.field(\"brem_vtx_x\")\n",
    "    cut_brem_found.begin_list()\n",
    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
    "        if brem_f[itr, \"brem_vtx_z\", jentry]>5000 or brem_f[itr, \"brem_photons_pe\", jentry]<photon_cut:\n",
    "            continue\n",
    "        else:\n",
    "            cut_brem_found.real(brem_f[itr, \"brem_vtx_x\",jentry])\n",
    "    cut_brem_found.end_list()\n",
    "    \n",
    "    cut_brem_found.field(\"brem_vtx_z\")\n",
    "    cut_brem_found.begin_list()\n",
    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
    "        if brem_f[itr, \"brem_vtx_z\", jentry]>5000 or brem_f[itr, \"brem_photons_pe\", jentry]<photon_cut:\n",
    "            continue\n",
    "        else:\n",
    "            cut_brem_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
    "    cut_brem_found.end_list()\n",
    "    \n",
    "\n",
    "    \n",
    "    cut_brem_found.end_record()\n",
    "\n",
    "tuple_found = ak.Array(cut_brem_found)\n",
    "\n",
    "\n",
    "\n",
    "cut_brem_lost = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(brem_l, axis=0)):\n",
    "    cut_brem_lost.begin_record()\n",
    "    cut_brem_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n",
    "    \n",
    "    \n",
    "    cut_brem_lost.field(\"brem_photons_pe\")\n",
    "    cut_brem_lost.begin_list()\n",
    "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
    "        if brem_l[itr, \"brem_vtx_z\", jentry]>5000 or brem_l[itr, \"brem_photons_pe\", jentry]<photon_cut:\n",
    "            continue\n",
    "        else:\n",
    "            cut_brem_lost.real(brem_l[itr,\"brem_photons_pe\", jentry])\n",
    "            \n",
    "            #cut_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
    "    cut_brem_lost.end_list()\n",
    "\n",
    "    cut_brem_lost.field(\"brem_vtx_x\")\n",
    "    cut_brem_lost.begin_list()\n",
    "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
    "        if brem_l[itr, \"brem_vtx_z\", jentry]>5000 or brem_l[itr, \"brem_photons_pe\", jentry]<photon_cut:\n",
    "            continue\n",
    "        else:\n",
    "            cut_brem_lost.real(brem_l[itr, \"brem_vtx_x\",jentry])\n",
    "    cut_brem_lost.end_list()\n",
    "    \n",
    "    cut_brem_lost.field(\"brem_vtx_z\")\n",
    "    cut_brem_lost.begin_list()\n",
    "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
    "        if brem_l[itr, \"brem_vtx_z\", jentry]>5000 or brem_l[itr, \"brem_photons_pe\", jentry]<photon_cut:\n",
    "            continue\n",
    "        else:\n",
    "            cut_brem_lost.real(brem_l[itr, \"brem_vtx_z\",jentry])\n",
    "    cut_brem_lost.end_list()\n",
    "        \n",
    "    cut_brem_lost.end_record()\n",
    "\n",
    "tuple_lost = ak.Array(cut_brem_lost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>{energy: 3.26e+04,\n",
       " brem_photons_pe: [824, 287, 1.26e+04, 4.49e+03],\n",
       " brem_vtx_x: [-8.5, -8.52, -33.8, -133],\n",
       " brem_vtx_z: [157, 158, 601, 2.33e+03]}\n",
       "-------------------------------------------------\n",
       "type: {\n",
       "    energy: float64,\n",
       "    brem_photons_pe: var * float64,\n",
       "    brem_vtx_x: var * float64,\n",
       "    brem_vtx_z: var * float64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Record {energy: 3.26e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data in cut_brem_found and cut_brem_lost\n",
    "\n",
    "length_found = ak.num(tuple_found[\"brem_photons_pe\"],axis=-1)\n",
    "length_lost = ak.num(tuple_lost[\"brem_photons_pe\"], axis=-1)\n",
    "\n",
    "tuple_found[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>{energy: 2.12e+04,\n",
       " brem_photons_pe: [460, 1.71e+03, 249, 401, 979],\n",
       " brem_vtx_x: [5.01, 26.4, 26.4, 26.6, 64.9],\n",
       " brem_vtx_z: [199, 956, 956, 962, 2.29e+03]}\n",
       "-------------------------------------------------\n",
       "type: {\n",
       "    energy: float64,\n",
       "    brem_photons_pe: var * float64,\n",
       "    brem_vtx_x: var * float64,\n",
       "    brem_vtx_z: var * float64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Record {energy: 2.12e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple_found[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z_found = ak.to_numpy(ak.sum(tuple_found[\"brem_photons_pe\"],axis=-1,keepdims=False)) / ak.to_numpy(tuple_found[\"energy\"])\n",
    "Z_lost = ak.to_numpy(ak.sum(tuple_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)) / ak.to_numpy(tuple_lost[\"energy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Z_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
    "plt.hist(Z_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"a.u.\")\n",
    "plt.title(r'$E_{ph}/E_0$')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found:  79481 , lost:  7087\n",
      "0.08916596419270015\n"
     ]
    }
   ],
   "source": [
    "brem_x_found = ak.to_numpy(ak.flatten(tuple_found[\"brem_vtx_x\"]))\n",
    "brem_z_found = ak.to_numpy(ak.flatten(tuple_found[\"brem_vtx_z\"]))\n",
    "\n",
    "brem_x_lost = ak.to_numpy(ak.flatten(tuple_lost[\"brem_vtx_x\"]))\n",
    "brem_z_lost = ak.to_numpy(ak.flatten(tuple_lost[\"brem_vtx_z\"]))\n",
    "\n",
    "n_found = len(brem_x_found)\n",
    "n_lost = len(brem_x_lost)\n",
    "print(\"found: \", n_found, \", lost: \", n_lost)\n",
    "stretch_factor = n_lost/n_found\n",
    "print(stretch_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmax=300\n",
    "nbins=100\n",
    "\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,8))\n",
    "\n",
    "\n",
    "a0 = ax0.hist2d(brem_z_found, brem_x_found, density=False, bins=nbins,  cmap=plt.cm.jet, cmin=1, vmax=vmax, range=[[-200,4000],[-1000,1000]])\n",
    "ax0.set_ylim(-1000,1000)\n",
    "ax0.set_xlim(-200,4000)\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(r\"$e^\\pm$ found brem vertices\")\n",
    "\n",
    "a1 = ax1.hist2d(brem_z_lost, brem_x_lost, density=False, bins=nbins, cmap=plt.cm.jet, cmin=1,vmax=vmax*stretch_factor, range=[[-200,4000],[-1000,1000]])\n",
    "ax1.set_ylim(-1000,1000)\n",
    "ax1.set_xlim(-200,4000)\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(r\"$e^\\pm$ lost brem vertices\")\n",
    "#ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
    "\n",
    "plt.colorbar(a0[3], ax=ax1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>{energy: 3.26e+04,\n",
       " brem_photons_pe: [824, 287, 1.26e+04, 4.49e+03],\n",
       " brem_vtx_x: [-8.5, -8.52, -33.8, -133],\n",
       " brem_vtx_z: [157, 158, 601, 2.33e+03]}\n",
       "-------------------------------------------------\n",
       "type: {\n",
       "    energy: float64,\n",
       "    brem_photons_pe: var * float64,\n",
       "    brem_vtx_x: var * float64,\n",
       "    brem_vtx_z: var * float64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Record {energy: 3.26e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple_found[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "for jelec in range(ak.num(tuple_lost,axis=0)):\n",
    "    for jphoton in range(ak.num(tuple_lost[jelec][\"brem_photons_pe\"],axis=0)):\n",
    "        print(jphoton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "cut = 4000\n",
    "\n",
    "tf = tuple_found[ak.sum(tuple_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>cut]\n",
    "tl = tuple_lost[ak.sum(tuple_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>cut]\n",
    "\n",
    "cut_x_found = ak.to_numpy(ak.flatten(tf[\"brem_vtx_x\"]))\n",
    "cut_z_found = ak.to_numpy(ak.flatten(tf[\"brem_vtx_z\"]))\n",
    "\n",
    "cut_x_lost = ak.to_numpy(ak.flatten(tf[\"brem_vtx_x\"]))\n",
    "cut_z_lost = ak.to_numpy(ak.flatten(tf[\"brem_vtx_z\"]))\n",
    "\n",
    "# how many tracks of all are included?\n",
    "ratio_f = tuple_found[ak.sum(tuple_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>cut]\n",
    "ratio_l = tuple_lost[ak.sum(tuple_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>cut]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABkgAAALACAYAAADYJP6YAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC4WUlEQVR4nOz9f5Aj6XnYeT7onmGrFM0ZdI1MHU3ZnkGRXkmxVIwTLZpaez20WJDt0DFssap6ztbtyV5rCtZhV7c3DjY4N7uembvllqqk1UacF5ZQTe/yD8uOmaribpxDJ8kA5RvLP2SxgRv9OPG8ngKbskgd5WF1sdmrUnO6GvdHEWAj83kL75t4M5GZ+H4iKshJvHjzzTcTieftF++TpeFwOBQAAAAAAAAAAIAFcmHeDQAAAAAAAAAAAEgbEyQAAAAAAAAAAGDhMEECAAAAAAAAAAAWDhMkAAAAAAAAAABg4TBBAgAAAAAAAAAAFg4TJACQYcfHx/NuAgAAAICcYPwAAIAbJkgAIMO63a50u915NwMAAABADjB+AADADRMkAAAAAAAAAABg4TBBAgCYq36/L7u7u1PLDQYD2d3dJW0AAAAAUGDE/QCANDFBAgAF1u/3pdlsysbGhqysrMjOzs68mzQ2GAxkY2NDqtWqtNvtc8vu7OzIysqK1Ot1OTo6SqmFxTIYDOTKlStWk1EAAABYHKMxQ7ValWq1Ote2EPfPjrgfANw8Mu8GAAC+qV6vT/z3YDAQEZG9vb2J7dMmFETOBjof/vCH5fbt2yIi0mw25fDw0FNLZ1epVGRvb09KpdLUstevX5fPfvazsr+/n0LL8m8wGMjy8rKUy+XxtuPjYzk+Ps7UNQAAAIDZ+Bg/BEEgImeTE6P/nwQtRg0j7ndD3A8As2OCBAAyJDxw2d/fl3K5LKurq851bW1tyfLy8vi/t7e3Z27fPD18LDjfxsaG7O3tTQyUgiCQ4XA4v0YBAADAO1/jhyQnRka0GFVD3G+PuB8AZkeKLQAoqH6/P+8mYA42NjY49wAAAMgUYlT/6FMA8IMJEgAomN3dXdnY2JDBYDB+zsfGxoZ0u91xmePjY6nX69JsNqVWq0mtVpt4fX9/X65cuSKlUmkcdHe7XdnY2JBSqSQbGxvjenZ3d6Varcr+/r50u12pVqsTZR422u/oL84zUUZ1XLlyRa5cuRJJK7C/vz8+nt3d3UiZfr8vGxsbUqvVZGVlRZrN5kTdu7u7UqvVZHd3VwaDgdRqNbly5YrUarXxgyJHuZGvXLky8X6Tfr8/7s9qtTpOfSBylvqsVCpN9MV5bTzvGPf398fnq16vTwya9vf3x9eCqU/r9fr4eggPtqa16eFrql6vy8rKCnmPAQAACmDa2CFcJhwLnhej2uyXuJ+4HwASNQQApG5vb2+4ubk5vH79+jAIguHh4aGxXKfTibWPSqUyrFQqke29Xm9YLpeHvV5vvK3dbg9FZLi9vT3etrm5ORSRiXKHh4dDERmur6+P/3t9fX0oIsPV1dXh9evXh71eb/zeh+s7PDwclsvliePZ3t4eisgwCIKpxzOqc3V1dbi5uTlst9vDIAjG24bDs/6qVCpDEZno31H9vV5vXHZUflR21MaH9zM6nk6nM7HvTqczcewP95HJaF+jvnu4Xx5u07Q2TjvG69evD0Vk4po6PDwc9/XDdY9eq1QqE+XL5fKwXC5bt2k4HA7X19eH169fH/93u92eOP8AAACIL43xgxaX244dpsWCWoxqQtxP3A8AaWKCBABStrm5OREsl8vlYbvdHr/28N/q6uo4OH/4z4ZpgiQIgkiwPNr+cIA9CrgfHgTcvn07EuyPguZwUBwOytfX19X9uk6Q7O3tRY5TRMYDwdGA4OGg/eFjDA9qyuXyUESGt2/fHg6HZ4OC8CBg9N7wAGRU1nZAMGrrw7a3tyeOyaaN5x3jeYNPbaAUBEGk/aP6R/uzaVO5XI60h4ESAADA7NIaP2hxue3YYVosGGeChLifuB8A0sBD2gEgRbVaTW7evCmf//znReRs+fPx8fH4IYrhhyz6NhgMpN/vy/Xr1yOvjZZat9vtWA901x62eHR0NN7v/v6+lwfFhx8gOVre3el0ZHV1ddyO7/3e750oNzr2ra0ttd6bN29OPMwyfDyVSkX6/f7EQyMrlYqIiBweHlq1fdTW/f19WV9fFxGRV199VXq9nlMbTcfoarS/GzduTGy/fv36+BqxbVOlUpGdnR154oknxu/VrjMAAADYm+f4wWXskEQsSNxP3A8AaWCCBABSsru7K91uV/b29qRcLsvu7q5sb29Lp9MZB9xJOy/X79WrV0VEJvLk+jKqM4njHA1uwu0OD3RGx763t+e9DbY2Nzel2WxKu92W9fV16ff7434XcW+jNinlYrS/8+qxbdPe3p5Uq9Xx8e3t7UUGtQAAALA37/GDy9ghjViQuD8+4n4AMOMh7QCQktEDAweDwfhBd4eHhxO/XkrL6KGDDxsFyw//UsqX0SBmtKLEp1G7pw0SR21IYgLIVrlclvX1del2uzIYDOTVV1+deJBk2m202Z9tmyqVinz+85+X1dVVGQwGUq1WeVgjAADADLIyfrAZO6QRCxL3x0fcDwBmTJAAQAq63a6InP2S6Pr167K9vS2bm5upt2P0y55Rex42GvisrKx43+9oEDNaUu7TqN3Tlp2P2rC/v6++rvVJEl544QUROUuH0O/3J35tlXYbR/s2/UpsMBhYt2kwGEi5XJZOpzOu7+FBIAAAAOxlYfzgMnZIIxYk7o+PuB8AzJggAYAUaZMPSf1q6OjoKLJio1KpSBAEMhgMIvu9efOmlMvl8cDriSeeiLRv9P+1X5GdZ7ScfHd3V32va30P63a7UqlUxrl9TUa/tGs2m5F0AWn+4ikIgnHe3o2NjcTaaNOnD5+X8CCs2WzK8vKydZsefr7M+vr6OB/2PH+5BwAAkHdpjh/CXMYOtrEgcb//NhL3A8BsmCABgBSMAtLRr4dEznK87uzsJJLS6jyjHMYP/8rn+PhYtre35caNG+Ol66NfGTWbTel2u7K7uzsOfrvdrtRqNRGxS5tVLpfHD+6rVqvjpeajVAGDwUB2dnam1jEqOzIYDMZ5bx8+lof/19SGjY0N2dnZkVqtNpGqwHQ8o/oefn30/11Th436/tq1a7HaaDpGkW8OotvttgwGg/EvwLT2l8vl8QCnVqvJxsaGNJtNqVarsrKyIuVy2bpNr7322sS5OT4+lkqlktrzdQAAAIokK+MH27HDtFjQFKNqiPuJ+wEgVUMAQCo6nc6wUqkMRWQYBMGw3W4nsp9erzfc3NwcishQRIabm5vDTqczUeb27dvD9fX14erq6nBzc3O4ubk57PV6kbq2t7eH5XJ5WC6Xh9evXx8Oh8NhpVIZXr9+fdjr9Ya9Xm8YBMFQRIaVSmXY6XSGt2/fntj/9vb2uL52uz3RB4eHh+P6Dg8Pzz2u27dvD69fvz5cXV2daPft27fHZfb29sb1VyoVtY+3t7eNZXq93nB1dXUoIsNyuTzc29sbt3t0POvr68Nerzc8PDwcrq+vj8u6nM9RH5mc10abYwyCYFgul8f76PV647aOzkm430bnMQiCyPUyrU3D4XC4uro6PpfXr18frq+vT+wDAAAAbuY1fgjHijZjB5tYMByjmhD32x8jcT8AzK40HA6HSU/CAAAAAAAAAAAAZAkptgAAAAAAAAAAwNzM61lGj8xlrwk4Pj6Wra0tEZl8YNRIv9+Xra0tqVQqcnx8LLVaLfJgL19lAAAAACBrGDMBAAAgK0ql0sR/B0EgvV5v/N9pxZ2FmCDpdrvSbrdlf39fNjc3I68PBgOpVqvS6/XGDx1eWVmRo6OjcXlfZQAAAAAgaxgzAQAAICt2d3dlc3NTVlZWxttWV1fH/z/NuLNQzyAplUqyubkp7XZ7YnutVhMRkU6nM962u7sr9XpdRofvqwwAAAAAZBVjJgAAAMxbrVabiBe110XSiTsL/wyS4+Nj6Xa74w4buXr1qoicdZqvMgAAAACQN4yZAAAAkJb9/X25efOmbGxsqPFh2nFn4SdIbt68KSIilUplYvto2U2n0/FWBgAAAADyhjETAAAA0tLpdOT4+Fj29/elXq/LlStXpNvtjl9PO+4sxDNIzjMYDEREpFwuG1/3VeY8b731lvz8z/+8vPvd75Zv+ZZvmd5wg3e84x3yjne8I/b7AQAAABtf//rX5etf/3rs9//hH/6h/N7v/Z784A/+oHzbt32bx5bBN8ZMAAAAZ4oWA//O7/yOvPXWW87vm7UfRETe/e53y/ve977I9na7Le12W/r9vrTbbdnd3ZVarSaHh4dSqVRSiTsfVvgJksPDQxERWV5eVl8/Pj72VuY8v/RLvyR//a//dYsWAwAAAMXxqU99Sn7kR35k3s3AORgzAQAA+JWFGPh3fud35L1/4k/I23Pa/6OPPipvvvmm/PE//sfV14MgkHa7LbVaTTY2NqTZbMre3l4qcefDCj9BsrKyIiIiR0dH6uuVSsVbmfM8+eSTIiJy48YN+ZN/8k9ObbeJ9muotbU1OTg4iF2n5u7du/LMM8/I66+/LpcvX/ZadxLtpd4znLd81st5y2e9nLd81st5y2e9nLf51Dvrr8Z+/dd/XX78x39c3v3ud8/aPCSsiGOmv/pX/+rMnxVf9x5fn1sf9WSpLfRvsvUUsX991UP/JlsP/ZtsPfRvsvWsra3JP/pH/6gwMfBbb70lb4vIR0XEdS3L6Tf+4vqKiPzjt9+Wt956yzhBMrK+vi7r6+vS7/dFxF9sast6guTOnTvWlZo89thjM9fhatQZplmjSqXircx5lpaWRORsZmyUC82XpaUl73WOzvfTTz/t/bwl0V7qPcN5y2e9nLd81st5y2e9nLd81st5y2+9IjJTqqQ8yuO4qYhjJh/XtK97j6/Pl496stQW+jfZeorYv77qoX+TrYf+TbYe+jfZepaWluSDH/zgzG0RyVYM/G0i8kdT3qdrstNarTZ+DkkacefDrCdIyuWylEol64rDSqWS3L9/P/b74xo9uT6cd2z039Vq1VsZAAAAAIstj+MmxkwAAADF9Yikn0Yqzv5GsWTacafTBMnm5qY88cQT1pWPvPXWW3Ljxg3n9/lQLpclCALpdDpy/fr18fbRjNS1a9e8lQEAAACw2PI4bmLMBAAAgHnqdDpSr9dFxF9sast6guTatWvyEz/xE9YVh83yKyob5z145caNG1KtVmUwGIyX12xvb8v29vb4Sfe+ygAAAABYXFkeNzFmAgAAWDyPiMijc9inpt/vy3PPPSfPPvvseGJjf39flpeXZX19fVwuzbjTaQVJVvX7fWm32yIi8tprr0mtVpPV1dVxm4MgkF6vJ81mUyqVigwGA2k2m7K5uTmuw1cZAAAAAIsrq+MmxkwAAACYt0qlIsvLy7K1tSWdTkeCIJBarTaOU0fSjDutJ0hGS1zimvX95wmCQNrtdqQjw2X29vam1uOjDAAAAIDFlNVxE2MmAAAAzFu5XJZOp2NVNq2484Jtwaeeesq58k9+8pMzvR92Go3GvJvgJKn2Um+y8tYPeas3KXnrh7zVm5S89UPe6k1K3vohb/UmJW/9kLd6FxHjpmzI0jXtqy0+6slSW3zJ0jFlqS2+ZO2YsnSefMhSv/iqh/5Nth76N9l6stS/Pl2Ubz6oPa2/i6kcmR+l4XA4jPvmN954Q7rdrhweHkZeOzo6km63K1/5yldmamBR9Pt9qVar0uv1JAiCeTdnqjt37sjjjz8uX/3qV+Wxxx6bd3NgifOWT5y3fOK85RPnLZ84b/n0z/7ZP5NnnnlGXn/9dflzf+7Pzbs5c8W4yU7Wxkzce5JF/yaL/k0W/Zss+jdZ9G+yshQDj2Kr/7OIfEfK+/5dEfnvRDIT153HOsVW2Mc//nH5yZ/8STlvfiXpB7MDAAAAQJYxbgIAAMA8Zekh7VlknWIrbHd3V9bW1qTX68nt27cjf2+++aasra35bCsAAAAA5ArjJgAAACC7Yk/mLC8vy87Ojjz55JPq648//rhsb2/HrR4AAAAAco9xEwAAAOZp9AyStPeZF7FXkNTrdRkMBueW+epXvxq3egAAAADIPcZNAAAAQHbFniD52Mc+Jnt7e/KFL3xB7ty5E/m7deuWbG1t+WwrAAAAAOQK4yYAAAAgu2Kvrrlz544cHh5KpVLx2R4AAAAAKAzGTQAAAJgnHtJ+vthtXV9fl263K0EQqMH+7du35Zd/+ZdnalwRra2tydLSkvpao9GQRqORcosAAACAeFqtlrRaLfW1P/iDP0i5NdnEuMkdYyYAAJBlxMDFEnuC5ObNm9LtduX7v//7jWVu3LgRt/rCOjg4kCAI5t2MqS5duiQvvfSSXLp0ad5NgQPOWz5x3vKJ85ZPnLd84rxl13n/WP2rv/qr8n3f933yjne8I+VWZQvjJndZGTNx70kW/Zss+jdZ9G+y6N9k0b+zy1sMzEPaz1caDofDOG/8gR/4AdnZ2ZGnn37ac5OKqd/vS7ValV6vl4lgHwAAAEgS8e8Zxk32uGYAAEDeZSmeGbXlJRF5MuV93xKRV0Qy0Q/TxH5Ie7vdlldfffXcMp/+9KfjVg8AAAAAuce4CQAAAMiu2KtrPvOZz0i/35cf+7Efk3K5rJbZ3d2Vj370o3F3AQAAAAC5xrgJAAAA88RD2s8Xu62vvfaadLvdc8uUSqW41QMAAABA7jFuAgAAALIr9gTJxsaGVCoVqdfr6utf+cpXZGdnJ3bDAAAAACDvGDcBAABgnh6R9Fd0LMQKkmvXrsnq6qo89dRTxjJPPPFE3OoBAAAAIPcYNwEAAADZFfsh7Y8//vi5Qb7I2VPqAQAAAGBRMW4CAADAPI2eQZLm30KsIBEReeONN6Tb7crh4WHktaOjI+l2u/KjP/qjs+wCAAAAAHKNcRMAAACK5hdE5BcNr309zYbMKPYEycc//nH5yZ/8SRkOh8YyPGwQAAAAwCJj3AQAAIAi+kvf+NMMRORjKbZlFrFTbO3u7sra2pr0ej25fft25O/NN9+UtbU1n20FAAAAgFxh3AQAAIB5IsXW+WK3dXl5WXZ2duTJJ59UX3/88cdle3s7bvUAAAAAkHuMmwAAAIDsir2CpF6vy2AwOLfMV7/61bjVAwAAAEDuMW4CAADAPF2Us1USaf5dTOXI/Ig9QfKxj31M9vb25Atf+ILcuXMn8nfr1i3Z2try2VYAAAAAyBXGTQAAAEB2xU6xdefOHTk8PJRKpeKzPYW3trYmS0tL6muNRkMajUbKLQIAAADiabVa0mq11NdOTk5Sbk02MW5yx5gJAABkGTFwscSeIFlfX5dutytBEKjB/u3bt+WXf/mXZ2pcER0cHEgQBPNuBgAAADCz8/6xut/vS7VaTblF2cO4yR1jJgAAkGV5i4FHD2lPe595EbutN2/elG63K9///d9vLHPjxo241QMAAABA7jFuAgAAALIr9gTJ1atXZXl5+dwyzz33XNzqAQAAACD3GDcBAABgnkYPaU97n3kR+yHt7XZbXn311XPLfPrTn45bPQAAAADkHuMmAAAAILtiTx595jOfkX6/Lz/2Yz8m5XJZLbO7uysf/ehH4+4CAAAAAHKNcRMAAADmiWeQnC92W1977TXpdrvnlimVSnGrBwAAAIDcY9wEAAAAZFfsCZKNjQ2pVCpSr9fV17/yla/Izs5O7IYBAAAAQN4xbgIAAACyK/YEybVr12R1dVWeeuopY5knnngibvUAAAAAkHuMmwAAADBPPKT9fNYPab9z587Efz/++OPnBvkiIn/qT/0p4/sBAAAAoGgYNwEAAAD5YT1BsrW1NdOOZn0/AAAAAGQd4yYAAABkyegh7Wn+5ekh7dYTJAAAAAAAAAAAAEVhPZmzv78v733ve2U4HDrv5Pj4WHZ3d/k1FAAAAIBCY9wEAAAA5If1BMnh4aE899xzsXdUKpVivxcAAAAA8oBxEwAAALLkEUk/5VWeUmw5TZAAAAAAAMwYNwEAAAD5YT1B8tRTTyXZDgAAAADIPcZNAAAAyJLRQ9rT3mde5KmthbC2tiZLS0vqa41GQxqNRsotAgAAAOJptVrSarXU105OTlJuDYqCMRMAAMgyYuBiYYIkZQcHBxIEwbybAQAAAMzsvH+s7vf7Uq1WU24RioAxEwAAyLK8xcAXJf1JgIsp728WF+bdAAAAAAAAAAAAgLSxggQAAAAAAAAAAFj7ByLyc4bX7qXZkBkxQQIAAAAAAAAAQAEl9ZD2v/GNP81vichHEthnEhJNsfXJT34yyeoBAAAAIPcYNwEAAADzEXuC5Kd+6qfOff3g4EDq9Xrc6gEAAAAg9xg3AQAAYJ5GK0jS/MtT2qrYEyRbW1vy67/+65Htt27dkr/wF/6CXLt2baaGAQAAAEDeMW4CAAAAsiv2BEkQBPKxj31MXnjhhfG2n/qpn5KVlRXpdDry4Q9/WNbW1rw0EgAAAADyiHETAAAAkF2xV7vs7+/L448/Ljdu3JD3ve99IiIyGAzkqaeekna7LR/+8Iflq1/9qreGAgAAAEDeMG4CAADAPF2U9FNeXUx5f7OI3Tef+cxnJAgC6XQ6cnh4KCIi5XJZut2uPPnkkyIi8vjjj3tpJAAAAADkEeMmAAAAILtip9haX1+XlZUV2d/fl83NTbl9+7Z0u11ZX18fP4jwzp073hoKAAAAAHnDuAkAAADz9MhFkUcfSffvkRwtIYk9QSIi8tRTT0mv15Of/dmflccff1yCIJCbN2/KcDiU9773vTxwEAAAAMDCY9wEAAAAZFPsFFurq6vyT/7JP1Ff+9jHPiZra2vyAz/wA7EbBgAAAAB5x7gJAAAA83TxosgjMy2TiLHPByJymu4+44rdNe12+9zXK5XK1DIAAAAAUGSMmwAAAIDsir2C5Kmnnppa5sMf/nDc6gtrbW1NlpaW1NcajYY0Go2UWwQAAADE02q1pNVqqa+dnJyk3JpsYtzkjjETAADIMmLgYok9QYJ4Dg4OJAiCeTcDAAAAmNl5/1jd7/elWq2m3CIUAWMmAACQZXmLgR+5IPJoyg9Nz9OkQ8rZxwAAAAAAAAAAAOYvT5M5AAAAAAAAAADA0iOPiDyS9gqSUrr7mwUrSAAAAAAAAAAAwMJhggQAAAAAAAAAACwcUmwBAAAAAAAAAFBAj1wUeTTlWYA8TTqwggQAAAAAAAAAACwcJkgsDQaDeTcBAAAAADKLMRMAAEAGXRCRiyn/5WjWIUdNnV2tVpNSqaT+dbvdibLh1zc2NiZe7/f7srGxIc1mU+r1uuzv76d5KAAAAADgHWMmAAAALJI8pQObyWAwkMFgINvb21Iul8fbDw8PZWdnR1ZXV8fbdnd3ZXNzU1ZWVsbbHn59MBhItVqVXq8nQRCIiMjKyoocHR3J5uZm8gcDAAAAAJ4xZgIAACigi5L+LMCDlPc3g4WZIOl2u9Lr9SYCfRGJBPoiInt7e9LpdIx11et1WV1dHQf6IjL+VRTBPgAAAIA8YswEAACARbMwKbY2Nzcjgb6IyKuvvjqxFHx/f19u3rwpGxsbsru7Gyl/fHws3W5XarXaxParV6+KiKjvAQAAAICsY8wEAAAAW63/VeS7f1//W7s979bZW5gJEs3x8bH0+325du3aeFun05Hj42PZ39+Xer0uV65cmci1e/PmTRERqVQqE3WNfhl13q+oAAAAACBPGDMBAADk3CPJ/DUeF/ntP6r/HfyRVI9wJguTYkvz2muvSRAEE7+Sarfb0m63pd/vS7vdlt3dXanVanJ4eCiVSkUGg4GIiPrLKhEZv25y9+5duXPnTuw2X7p0SS5duhT7/QAAAICNe/fuyb1792K//+7dux5bg3lhzAQAABYJMfDiWegJkr29PXn22WfV14IgkHa7LbVaTTY2NqTZbMre3p4cHh6KiMjy8rL6vuPj43P3+cwzz8zU5pdeeklefvnlmeoAAAAAptna2pJXXnll3s3AnDFmAgAAi6SQMfA8HtJ+mvL+ZrCwEySjvLjtdvvccuvr67K+vi79fl9ERFZWVkRE5OjoSC0fXkYe9vrrr8vTTz/t3uBv4JdQAAAASMMLL7wgzz//fOz3v/HGGzP/QzfmizETAABYNMTAi2dhJ0i63a5UKpWpwbmISK1WG+fUHZU3/eppWn2XL1+Wxx57zK2xAAAAQMpmTVN0+fJlj63BPDBmAgAAi4YYePEs7ATJq6++Kuvr69blr169OvG/4by5o/+uVqueWggAAAAA88OYCQAAoAAuyFmarbT3mRM5aqpf+/v7xly6YZ1OR+r1uoicPWgwCALpdDoTZUa/lrp27ZrfhgIAAADAHDBmAgAAQNEt5ATJ/v7+OGh/WL/fl2q1Kjs7OxNll5eXJ345dePGDel2uxO/iNre3pbt7W0pl8uJtx8AAAAAksSYCQAAoCBGD2lP8y/tFSszWMgUW6+++qr6q6VKpSLLy8uytbUlnU5HgiCQWq0WeShhEATS6/Wk2WxKpVKRwWAgzWZTNjc30zoEAAAAAEgMYyYAAAAsgoWcINnb21O3l8vlyDJwkyAIjPUAAAAAQJ4xZgIAAMAiWMgJEgAAAAAAAAAACm+U9irtfebEQj6DBAAAAAAAAAAALLYczeUAAAAAAAAAAABrFyT9h6bnaFlGjpoKAAAAAAAAAADgBytIAAAAgHOUSq9Etg2HL82hJQAAAED2ET9nzEVJfxYg7RUrM2AFCQAAAAAAAAAAWDhMkAAAAAAAAAAAgIVDii0AAAAsFG3JvwjL/gEAAIBpSJ+VQ49I+rMAOZp1yFFTi2FtbU2WlpbU1xqNhjQajZRbBAAAAMTTarWk1Wqpr52cnKTcGhQFYyYAAJBlxMDFwgRJyg4ODiQIgnk3AwAAAJjZef9Y3e/3pVqtptwiFAFjJgAAkGW5i4EvSPoPTc/Rgz2YIAEAAMBCcU0BoJUnTRcAAAAWkU28axM/EzcjK3I0lwMAAAAAAAAAAOAHK0gAAAAAAAAAACiii5L+LEDaKb1mwAQJAAAA4IiUAAAAAIA94mdkFRMkAAAAAAAAAAAUUUIrSFr/TqT1u/prJw/87y8pTJAAAAAAAAAAAABrjT929qfp3xGpfjbd9sTFBAkAAAAAAAAAAEX0iKQ/C5CjWYcL824AAAAAAAAAAABA2pggAQAAAAAAAAAACydHi10AAACAbCiVXlG3D4cvpdwSAAAAIPvC8TNxc4ouyNmD2tPeZ07kqKkAAAAAAAAAAAB+sIIEAAAAAAAAAIAiuijpzwKkvWJlBkyQAAAAYCpSSk1a1OMGAABAcrSYuyhxZ1GOA8XDBEnK1tbWZGlpSX2t0WhIo9FIuUUAAABAPK1WS1qtlvrayclJyq1BUTBmAgAAWUYMXCxMkKTs4OBAgiCYdzMAAACAmZ33j9X9fl+q1WrKLUIRMGYCAABZlrsYmBRb52KCBAAAAFMt8pL4Iqc6AAAAQHYUJcYkfkaeMEECAAAAAAAAAEARXZT0V3SwggQAAAAi/HqqqEwPrTfhnAMAAMxfOIYjRkuPTfzM+cA8MEECAAAAAAAAAEAR8QySc12YdwMAAAAAAAAAAADSxgoSAACABLFMPP84hwAAAMVAXJcO+hl5wgQJAAAAAAAAAABFRIqtczFBAgAAAAAAAAAAUtftdmVjY0Nu3749sb3f78vW1pZUKhU5Pj6WWq0m6+vrzmWmYYIEAAAAU5VKr6jb87h8vkjHAgAAgOLQ4tSsxah5aCNCLkr6Kzoc9lev1yPbBoOBVKtV6fV6EgSBiIisrKzI0dGRbG5uWpexwUPaAQAAAAAAAABAqprNplQqlcj2er0uq6ur44mPUdmHJ1NsythgggQAAAAAAAAAAKSm2+3KE088MTHBISJyfHws3W5XarXaxParV6+KiMju7q5VGVuk2AIAAMBURVo2X6RjAQAAQHHkIU7NQxsRktGHtLfbbdnb25Nmszmx/ebNmyIikZUlo4mUTqczfu28MrZptlhBAgAAAAAAAAAAUtFsNmV7e1t9bTAYiIhIuVw2vm5TxhYrSAAAAAAAAAAAKKIYK0junZ79xXX3gfm1fr8vTzzxhPrsERGRw8NDERFZXl5WXz8+PrYqY4sJkpStra3J0tKS+lqj0ZBGo5FyiwAAAHCeUukV67KLlnKg1WpJq9VSXzs5OUm5NSgKxkwAAOSbTfyc57h5EWLgrb7IK72E6t7akr29PePrKysrIiJydHSkvl6pVKzK2GKCJGUHBweRB88AAAAAeXTeP1b3+32pVqsptwhFwJgJAABkWe5i4BgrSF74gMjzMxzGG2+JPPPp6PZmsym1Wm0iBdbo/4/+dzS5YVoFUqlUrMrYYoIEAAAAAAAAAACIiMili2d/cV02zDp0u13Z2dlRX1tZWZEgCOQzn/mMiESfIzL672q1KlevXp1axhYTJAAAAAWiLWfP8/L1JJiW/Jv6if4DAADAeRYpBrc51qIeO2bX60XzdjWbTdnd3ZXbt2+PtwVBIJ1OR65fvz7e1u12RUTk2rVrUi6Xp5axdcH5KAAAAAAAAAAAQPZdnNPfDG7cuCHdbndihcj29rZsb29LuVy2LmODFSQAAAAAAAAAACATgiCQXq8nzWZTKpWKDAYDaTabsrm56VTGBhMkAAAABeKynN0l1ZRrWqosy2ObAQAAkF028aVNaqo8pOrKWntgIcZD2r3s09Jo1UdYEASyt7d37nttykxDii0AAAAAAAAAALBwmCABAAAAAAAAAAALhxRbAAAADrKeasrUPk1W2pw213NInwIAAGRfOGZLMy6ziReLEifGTRU27T1IUMZTbM0bK0gAAAAAAAAAAMDCYQUJAAAAAAAAAABFdFHSX9GRoxUkTJAAAAA4yPpS8KTal/XjThv9AQAAkC3zjM987bvIMWaRjw35xgQJAAAAAAAAAABFxDNIzsUzSAAAAAAAAAAAwMJhBUnK1tbWZGlpSX2t0WhIo9FIuUUAAGBRlUqvqNuLvvzdx/Flpe/m3Y5WqyWtVkt97eTkJJU2oHgYMwEAikiL2/IYd8dtc/j4kzz2pPdFDFwsTJCk7ODgQIIgmHczAAAAgJmd94/V/X5fqtVqyi1CETBmAgAAWZa7GJgUW+cixRYAAAAAAAAAAFg4rCABAABYUHlc0j8PWe6nLLcNAAAA37RIcdu8j3Xe+88cVpCcixUkAAAAAAAAAABg4TBBAgAAAAAAAAAAFg4ptgAAwMIrlV5Rtxd9abbLcS9qH4nox74Ixw0AAJAV4Xgsj7GYTUxZlLizKMdRGBcl/ZRXpNgCAAAAAAAAAADILiZIRGQwGMy7CQAAAACQWYyZAAAAcmr0kPY0/3K0gmQhU2yVSqWJ/w6CQHq93vi/+/2+bG1tSaVSkePjY6nVarK+vj7xHpsyAAAgWxY5TZTG5bgXtY/OLGTIjAXHmAkAMC9FTc9kcwxFOM4zxM+LoPXPRVr/Un/t5O102zKLhbtad3d3ZXNzU1ZWVsbbVldXx/9/MBhItVqVXq8nQRCIiMjKyoocHR3J5uamdRkAAAAAyCPGTAAAAAUyWkHiWeNDZ3+a/r8Tqf6U/30mYeEmSPb29qTT6Rhfr9frsrq6Og7iRUSazabU6/VxIG9TBgAAAADyiDETAAAAFsVCTZDs7+/LzZs3ZWNjQ2q1WiQwPz4+lm63K9vb2xPbr169KiJnv6S6du3a1DIE/AAAZFNxlqybmdKIaRahP3wYDl+cdxOA1DBmAgDMWx5jVJsYPI/HFRfxM/JkoR7S3ul05Pj4WPb396Ver8uVK1ek2+2OX79586aIiFQqlYn3jX711Ol0rMoAAAAAQB4xZgIAACiYi3P6y4mFWkHSbrel3W5Lv9+Xdrstu7u7UqvV5PDwUCqVigwGAxERKZfL6vsHg4FVmfPcvXtX7ty5E/sYLl26JJcuXYr9fgAAAMDGvXv35N69e7Hff/fuXY+tQVoYMwEAgEVGDLx4FmqCZCQIAmm321Kr1WRjY0Oazabs7e3J4eGhiIgsLy+r7zs+PrYqc55nnnkmfsNF5KWXXpKXX355pjoAAEBxLdLS/bhMKRBc+s5HHVm3tbUlr7xin7INxcKYCQAAe0WKAafR4mCb4w+/L6t9VsgYOKGHtE/dZ04s5ATJyPr6uqyvr0u/3xcRkZWVFREROTo6UstXKhWrMud5/fXX5emnn47ZYuGXUAAAAEjFCy+8IM8//3zs97/xxhsz/0M35o8xEwAAWCTEwItnoSdIRERqtdo4p+4oUDf9oqlSqViVOc/ly5flsccei9dYAAAAj1xWQSzCigmTuL+Sy7tZ0xRdvnzZY2swT4yZAACYnU1MWZS4M8/HQQy8eBZ+gkRE5OrVqxP/G86JO/rvarVqVQYAAAAAioQxEwAAQE6RYutcF+bdgHnrdDpSr9dF5OwhgkEQSKfTmSgz+rXUtWvXrMoAAAAAQFEwZgIAAEBRLcwKkn6/L88995w8++yzcv36dRER2d/fl+XlZVlfXx+Xu3HjhlSrVRkMBuOl39vb27K9vS3lctm6DAAAacvzMmYka5HTY+lmD4FNfWqyuH2NPGHMBADZRayfP3l5KLm7eLG0TfxcnD7KmAuS/oqOHC3LWJgJkkqlIsvLy7K1tSWdTkeCIJBarSbtdnuiXBAE0uv1pNlsSqVSkcFgIM1mUzY3N53KAAAAAECeMGYCAADAolmYCZJyuRxZ4m0SBIHs7e3NXAYAAAAA8oIxEwAAABbNwkyQAABQdCxHhiuX1FvFur7uz1xDsfoDAABkHbFH/tmkScvHeY4XS+fj2ArqEUl/FiBHsw45ygYGAAAAAAAAAADgR47mcgAAAAAAAAAAgLWLkv4sQNoPhZ8BEyQAAAAFYrN0H260/nNJTwYAAIBiIwafZBM/L3L/IFuYIAEAAAAAAAAAoIhYQXIunkECAAAAAAAAAAAWDitIAAAACsRlqbpLmqhFTilVKn1i3k0AAABAhtnExDZpuIqSqov4GXnCBAkAAAAAAAAAAEV0QdJPeZWjvFVMkKRsbW1NlpaW1NcajYY0Go2UWwQAAADE02q1pNVqqa+dnJyk3BoUBWMmAACQZcTAxcIEScoODg4kCIJ5NwMAgNxxTfFUlOXprlyO26U/XPrZVD47abrcQuDh8MXINtOxZOcY03HeP1b3+32pVqsptwhFwJgJAIrBNi4Nl8tj3GRzrDbHFbd/0u3D6bG0Tfyc5/Fa7mLgRyT9WYAczTrkaLELAAAAAAAAAACAHzmaywEAAAAAAAAAAPPW+nmR1v9Tf+3k6+m2ZRZMkAAAsIDymBbJdX9JtS/J/vCxzNylvI9jSapssu47lTb1EwAAQJbYxJJppjWyrdfX/n2lmYpTj02ZuH3vq4w/02Np4ueMuSiJzAI0/vLZn6b/pkj1x/3vMwmk2AIAAAAAAAAAAAuHFSQAAAAAAAAAABTRBTlbRZL2PnOCCRIAABZQPtMi6bKe8spFltOWuS6Tz/p1k5RFPW4AADB/2UvFFOUrvk4yTp9nyjGbmHve59CXohwH8o8JEgAAAAAAAAAAiiihZ5BM3WdO5GixCwAAAAAAAAAAgB+sIAEAoCDSTgeVFUVKeZWUJNOQ5bOPkguBk+zrWduRz3MFAABEivPdnvU0WD6Fz1ncNmfvWP3E0mle00X5/CAZTJAAAAAAAAAAAFBEj0j6swA5mnUgxRYAAAAAAAAAAFg4OZrLAQAA52GJcHxZSYvkwqXNpuPI43HPRzRkHg5fTLUFrueKcwgAQLEs+ne7r3RVvti0J7xtsdI8pRc/2/RrcfvZ0gVJ/6HpOVqWkaOmAgAAAAAAAAAA+MEKkpStra3J0tKS+lqj0ZBGo5FyiwAAAIB4Wq2WtFot9bWTk5OUW4OiYMwEAACyjBi4WJggSdnBwYEEQTDvZgAAFgQplOwk2R9JLaV3TZvlUkfxPZpY+aQ+c1k9V+f9Y3W/35dqtZpyi1AEjJkAwE7W0k4lydex+eozm/RZ096TXzax8fQyvsZJ8+jX3MXAFyX9WYC0U3rNgBRbAAAAAAAAAABg4bCCBAAAAAAAAACAInpE0p8FyNGsQ46aCgAARNxS+LguN04qHVRe+egPrXySqc9c9uejHflM4/a2Y/loyOySygwAAMCVTSoom3irqGm44sbpNqmxfKV5Ctcdd1/ZO4c2sTTxM/KDCRIAAAAAAAAAAIqIZ5Cci2eQAAAAAAAAAACAhcMKEgAAcsbHkup8pkVKTtr9kfY5NO2vVPqEwx71sHE4fNGhjqy471j+a5EtSX5WSHUHAADifPcXOYZIKs1U3HripECzi72jMXf24m2bWDq5+Dl7KceQd0yQAAAAAAAAAABQRBck/ZRXOcpblaOmAgAAAAAAAAAA+MEKEgAACsJlST3LkCflsT9c2mxKx+VGX0qfz3RtriFwuiFztvsOAADMg02sX+QYImvHZtOeeDF4NObOXkopm9g4ufh5/sefQzyk/VysIAEAAAAAAAAAAAuHCRIAAAAAAAAAALBwSLEFAPDGJcVT1uXxWLLSvjz2XR65pLYy9b/bsn89bBwOX3SoIyuWEqs5nynH7BX9+AAA6chDvJi1Nqa576wdexbZpL0Kb7OLvaMxd/bibT+xdB6vszy2WUTOLqsEZgFa/+DsT3Nyz//+ksIECQAAAAAAAAAAsNb435/9afr/H5HqX0m1ObExQQIA8CYXv5ywVKRjSVuW+y4rv353aYf5l2Z6GOdjVYgme79cm4X+wHl/5Ysry59vAEB+5OH7JA9tTMq8jz3NX+nb7EuPrx+xKHP+ezT5iLltYuNixs/z/mzEdkHSf2h6jh7swQRJytbW1mRpSV+K1mg0pNFopNwiAAAAIJ5WqyWtVkt97eTkJOXWoCgYMwEAgCwjBi4WJkhSdnBwIEEQzLsZAAAAwMzO+8fqfr8v1Wo15RahCBgzAQCALMtdDHxR0p8FSHvFygyYIAEAAEiZy9LsJB+w7qJU+oS6PR9pAMJcf9Xl0n/J9HU++xkAACDbbOJytzS4D3OPC/MRB9rE0jbHHi9uDvdR9voHeZOjbGAAAAAAAAAAAAB+sIIEAAAAAAAAAIAiIsXWuZggAQCgwExLv11SPM2D1m4fbc7jcZuZwrhHlW2mZfD6Q5Dt610U9yNbzOdq9vCaNAEAAMBGOB6Zd6zrqz1ZOw5dOOazicFtYu+ixNw28XO8uJlYGb4xQQIAAAAAAAAAQBE9IunPAuRo1oFnkAAAAAAAAAAAgIWTo7kcAJifvKYpgl9ZuQ5c2pH1a9QlpVSS/e9St492JFmHtpzdzJR6a9ayWecaAmvpEEz94dL/AABMylrKJExK8/zY7Gue14dNHO8zba5Nf8Q5P3HbGH2fTQxoE0/nIea2iaVt4mfiZmQDEyQAAAAAAAAAABTQ8ILIMOWHpg9zlLcqR00FAAAAAAAAAADwgxUkAGCBpe12fC6hzqKsHIupHXns/zTTEiS9PxdZaQdsvT3vBiQqK+kDAQCLc+/Ncpx2njTbGN5X1vrM577nmVouD9ddPuUvfs7aZ8yn04sipynPApymvGJlFqwgAQAAAAAAAAAAC4cVJAAAAAAAAAAAFNCDOawgeZCjFSRMkABYWKQU8Y++S0dWrl0f7TDV4cK0P5d2uKQtM0n/vCwZtp9Yt8Gt/9/pVEdSx+1nf4867lXr66851pFd3LsBIB3zTCOUNYt87LbSvF7i7CtuHK/VbbM/m5RjYcmmTArHh9NjcLs+i8bcSV0L8fvHJpbOX/zMfWlxMUGSsrW1NVla0v9Bo9FoSKPRSLlFAAAAQDytVktarZb62slJ9B8KABuMmQAAQJYRAxcLEyQpOzg4kCAI5t0MAAAAYGbn/WN1v9+XarWacotQBIyZAABAluUtBj69WJL7F0sp73MoIsNU9xkXEyQACsVlmS/LJ5FXPq7drKTpMslKujBNVvpIW8Z/JhremY9v2WF/piXx6YaTfvr/bcfy/AoMAHA+m1giOzEE8iDO9ZJsSqnp0kwDlvb+o8LxoU0MbhN7azF3MvF2/P6yiaWJn5EfTJAAAAAAAAAAAFBApxcvyukjF1Le5wMRuZ/qPuNKt2cAAAAAAAAAAAAygBUkAApFWyLqkjpnHua9DBrJcTm3Wb4OXNNxuRxLVo4xK+1wYwrjXH6lcyfl/WXFo47ltYdFZyPlGAAgG8KxxLzHIOH95zPWySebvp/n+bGJ1W3KpH1NZe8aDsd8NjGxTeytxZJZi7dtYmmb+Jm4GdnAlQgAAAAAAAAAQAE9uHhRTi+mm0jqwcWSZG9yT8cECQAAAAAAAAAAsPbJ1n35ZEufBPnDk2HKrYmPCRJLg8FAKpXKvJsBYE5cUwy5yN5S4WJK8hz6SKHgIz2cSx0ux+3aR2lf0y7H6KNPs8P0a5zZl+Xr/WQKGxchnNT6T0sbICLydpINATKNMRMwO1+pl7Idw8yfr/S2cccBcVKy+UrV5atMXHFTfE17T/rC8WG8GDx6rFo9eYy3beLnYsbN1Wp73k2IOJULcioXvdf7NxoX5W80Lqmv/Xr/VFarJ973mYSFe0j7/v6+VKtVKZVKUq1WpdvtquVKpdLE38bGxsTr/X5fNjY2pNlsSr1el/39/TSaDwAAAACJYswEAACARZHHKcjYdnZ2pNPpSL1el8PDQ9nZ2ZFarSadTkdWV1fH5XZ3d2Vzc1NWVlbG2x5+fTAYSLValV6vJ0EQiIjIysqKHB0dyebmZnoHBAAAAAAeMWYCAAAollO5KPcTWEFy/j7zY6EmSD772c9Kp9MZ//ezzz4r1WpVtre3J4L5vb29iXJh9XpdVldXx4G+iIx/FUWwD2TP/JfeFo/LUnLX/ve13D1NSbVvHincit7/SaZUM9U96+elVPqE9fvPzJ4Oyq0d+Xjw3iyGw+uRbT5S6yWZ+g+IizET4Af38km+Uib5SkHmi6/9J5lKLY99FreNNsca51q0i8fjpZSy21f+4m1f8XMex6e9Xl2q1d15NwMOFibFVrfble3t7YltQRBIEAQyGAzG2/b39+XmzZuysbEhu7vRi/n4+Fi63a7UarWJ7VevXhURUd8DAAAAAFnHmAkAAACLZmEmSFZXV40PDHx4e6fTkePjY9nf35d6vS5XrlyZyLl78+bNyHtEZPzLqPN+RQUAAAAAWcWYCQAAoHgeyEU5lUdS/XuQckqvWSxUii3NYDCQer0+/u92uy3tdlv6/b60223Z3d2VWq0mh4eHUqlUxr+cKpfLxvrOc/fuXblz507s9l66dEkuXboU+/0ApsvjEk5NkqlbkuwPl2XQaadWKhKXPnVdCp1cGis9bBkOX5x5fy7tSPta8nF85rQAjzrUYlran2446efe5tZmvf9MddinQMj6fenevXty79692O+/e/eux9ZgnhgzAcnIQpwxja/0TEmloorbh1nrZ19s+sNXujO7/UfjJV+xe5qfn7htjsaQNrG3FksmE2/H78Pp7bGLn6fHzfP4rBIDL56FniDZ39+XSqWi5sANgkDa7bbUajXZ2NiQZrMpe3t7cnh4KCIiy8vLap3Hx8fn7vOZZ56Zqc0vvfSSvPzyyzPVAQAAAEyztbUlr7wy+7NWkG+MmQAAwCIpYgx8KhfkNPWHtD9IdX+zWOgJkq2tLdnb2zu3zPr6uqyvr0u/3xcRkZWVFREROTo6UsublqSPvP766/L000+7N/Yb+CUUAAAA0vDCCy/I888/H/v9b7zxxsz/0I35Y8wEAAAWCTHw4lnYCZJmsyk3btyYGpyLiNRqtXFO3VF506+eptV3+fJleeyxx9waCwAxJLkU1UeKmyRTgGE6Uz9nJdVDcmm6kmtH2te0OW2WxrR8fcmhDlPZE4c6Zjefe4SWDuFtQ1mXPs22b/mWn4hsc+n/y5cv+2wO5oAxEwBf37txUnVlJS7NE5t0uWmnS7YRJ26f9zVkF4uHY3CbOFErk0y8neznySZ+zmbc/HCqzjifH2Lg/FnICZJRjtzRQwJtXL16deJ/w3lzR/9drVY9tRIAAAAA5oMxEwAAQDGcPaQ93RRbD3KUYuvCvBuQtv39fRERWV1dndg+Wg6u6XQ644cSlstlCYJAOp3ORJnRr6WuXbvms7kAAAAAkCrGTAAAAFgUC7WCpNvtytbWltTrddnd3R1v7/V6418xPffcc/Lss8/K9evXReRscLC8vCzr6+vj8jdu3JBqtSqDwWC8PHx7e1u2t7elXC6nd0AAEsGy7el8pGfKSj8nmS7MRVb6Q2NaPj4cvuhQRzZSqmUldVd2fM2hrCmlVHLhZLZTa2hpA0TM/WRP+8y5fN58yU5fI02MmYD0LNJ9NnysNt/xafePrzRgNuZ57uPGGUVN1ZUum9hbiyX9xNtpnkNdOH6OFzeHr+Ek4+Si3KcfzOEh7Q/kNNX9zWJhJkj6/b7UajURkfEvmx52+/ZtERFZXl6Wra0t6XQ6EgSB1Go1abfbE2WDIJBeryfNZlMqlYoMBgNpNpuyubmZ/IEAAAAAQAIYMwEAAGDRLMwESRAEMhwOp5YLLwM/r769vb1ZmwUAAAAAmcCYCQAAAItmYSZIAMCWnk7ovlo2K8sts5KGJql9utbrsrTaR5vTTseVJB/HotXh4xy6pHYzlU8yHVpS59a1XrdzqKdP05lSSiUnufuYj2M5MWz3EV7r3zkAgGKxGXekGdOnOabwVa9tPTbxVJw2JZmGy5c4bYwbz9vUY3Od+Upd5qvvfV0/drF3cjG3v89z3DaG4+e4cTOxsqv7ckHup5xi636OHn2en5YCAAAAAAAAAAB4wgQJAAAAAAAAAAAF9EAekdOU/x5MWSG0v78v1WpVSqWSrKysSLfbjZTp9/uysbEhzWZT6vW67O/vxyozDSm2ACBkOHxx3k1wlnY6LZeURj7qdq03qTRRrsftsqTbR1qwpNIizDstwDR+rn9TSDT78u1Zz4vr9aUt3U/2vmZKNZVlriHwUiKtMPFx/0mb3r4vpd4OAMiTrI07kvxOsUm9lGQ9SaWHipvSNU7KJl+x/rxj+/jXWTh+ixenx7mGbM5zsjF41uJtm1g6ufg5qc9GXNH9EwNPs7u7K71eT7a3t0VEpNlsSq1Wk8PDQ6lUKiIiMhgMpFqtSq/XkyAIRERkZWVFjo6OZHNz07qMDVaQAAAAAAAAAABQQA/kgpzKxVT/Hpwz7XB8fCztdltWV1dldXVVbty4ISJnq0FG6vW6rK6ujic+RGS8SsSljA0mSAAAAAAAAAAAQOKuX78+8d/lcllEZDzRcXx8LN1uV2q12kS5q1evisjZChSbMrZIsQUAIemnp8mfIqVtykp6Gpfl+T7Sgrlw7aO09+nnWpo9lZaZfbjltjzctGz9bat6z7zHql3ne6eHOrJOS2sQ7eczj868N/17KBv3Kpf7T7/fl2rVfmACAItmkcYdeUgFleZ3rU2KIF/pvKbtWxO375Os21+8Pj02t0vDFY7FbWLwuLF3HuNtm/g5XtwcvnfON51WdP/EwO729/dle3t7nF7r5s2bIiLj/x4ZTaB0Op3xa+eVsU2zxQQJAAAAAAAAAAAFdPqNFFsuvn7vgXz93jD2Pr92165cs9mU3d3dcZotkbNni4h8c2VJ2GAwsCpjiwmSlK2trcnSkv6Lz0ajIY1GI+UWAenL+oNmtV8xZKXNST5QfNb9uUrywebZuZaikn04d/4e0p7kShEfv7Lzc61HfwnqZxWQjwcf+njgY5Krb2an99+7HGtZVrZ9MUZr8i98jbZaLWm1WvLd3/1qpOzJSdYeKIq8YMyEtMz7wb424455Pzg9Tnt89avPB6DPc3zg7yHg2XpIu8/VIjYrauLsX3/fZGxu0696e+LE4nFjo/nG29Hjt4mlixk/j66NUQwsIpE4uCgx8P+w9RXZfeWtRPexs7Mjg8FAjo+PZWNjQ9rttmxubsrh4aGIiCwva9fRWQoumzK2mCBJ2cHBwcSDYwAAAIC8Ou8fq8/SC1RTbhGKgDETAADIsrzFwKdyUe47riD5P7zwLvnfPf9HYu/zf3njRP7WM7fOLTN6Fkm325WNjQ3Z3t6Wzc1NWVlZERGRo6Mj9X2VSsWqjC0mSAAAAAAAAAAAgIiIvOPSBXnHpfjv/9bL9hMyq6ursrm5KTs7OyLyzckN0yqQSqViVcYWEyQAUpfsMnEfDzqM3hqznErLxMeDtZOsI+vpsZJKK+VSr6kvstJHbumxop/N88sncz26SrsOt8/QjqEW7WGHpgcguqQGMD0c8vcd6kifnrLik4616L9K0s2eAsHleyvr91IAyDKf98roQ4Nt7uXpjTvixlZx0ij5SsM171RZvsYDcdNFzfOh8RqbeD5uirg4fR33/MRJ06bHk+FY3CYGt4m9tZh7vvF29LNpE0vbxM/x4mab+2ua6Qrhx/d+7/eOJzWuXr0qItHniIz+u1qtWpWxdSFekwEAAAAAAAAAQJY9kItyKo+k+vfAMaXXYDCQ1dVVETl78HoQBNLpdCbKdLtdERG5du2aVRlbTJAAAAAAAAAAAIBEjR7Ivr+/P942GAyk0+lIu90eb7tx44Z0u92JFSLb29uyvb0t5XLZuowNUmwBKBT3dFqak5lrmPdy8Djc0iXNvtw8yf0l1T5TvT7SZrnU6+Nacql7Hqmt9Hr1Zf0un3vXlG9J1ZEsl/DOlDZLoy+Bz8q9ze3z4hoCv0fZ9kVDWfu655FOcdZ2kNILAHTxxiHxxh1ZSx0TN6VWmvX62n+4nrjps6bVa/u+OPUkdb70euKlwHaJQc57X7rxuU0MaBN7R2PueaeWi+7f5lht4ufp9SSZHjDOvvL4bz0iZw9pP3Vc0eFjn5pyuSzHx8fy3HPPSbvdllqtJpVKJbISJAgC6fV60mw2pVKpyGAwkGazKZubm05lbDBBAgAAAAAAAAAAEheeDDEJgkD29vZmLjMNEyQAAAAAAAAAABTQA7mQ+gqSBzl6sgcTJAAybT6pPB6duYYspCUxpSMypcnRmNqWdkoXkySvg7TTJSWXlsqtn12WDPtos1tqKx8p9Ex1m44x+jlKMsWcVrf5s2y6V7mk6zClidIkFzamfz/Q+858bpcd6p79Hpv20v08pAUAgHlJ9p4cb9yRZsqXaBwy/XvOR8rWs32nl1rHdv+++EthNT3dWpwycfat8Rm/R697P3G6XSo1LR4Pf35tYnCb2NtPzJ3s5yd674ruzyZ+jnc/SSrNIDHx4srPVA4AAAAAAAAAAIAnrCABAAAAAAAAAKCATueQYus0R+symCABkLokU02lnZYk/bRZLumIZk+L5LpEOdvpsUxfecmkw3GV9nLetK9d177zsfTfT/oAl1BJLztragHT+0ulnza8Q1ve/05D2cfiNMm79JezHzm1o1TaSbIx1u3Igiy3DQBc+Rw7JJXyJdn0WTYpkqan8/JRb9wytqJt0uI293Q/cccBaY4Xk0o/ZLMv2/6J9z6bOD1aJk5srr0nGovbxODpxd7JxmzRWDp6DpOLn+cZj9rsu1ptp9AS+MQECQAAAAAAAAAABXQqF+V+6itI0t3fLPKz1gUAAAAAAAAAAMATVpCkbG1tTZaWltTXGo2GNBqNlFsE+OGylDf76TmWU92bj7RZLlxSHbnur1T6hFLHbOmFzrh9Xbm020eKJx/7c2OfLsw1tZXbObRPKZVkSq9kU5/ZH4vJrJ9x7ZycMS3Rd7mHmVJvafT4JUku9yW3a8zluEVE3qVs+5pjHdk1233w10Tk1+S7vuuPRMqenGipJoDpGDPBB5vvkPmPS5Ibd/hKc2XznjjpxfyNG+zGCXZtmn4c8Y41bkwaPrbpsb62r3Bf6/38yNQyvtLI+UmDGz9Oj5daTovHw7G4zefZJgZNLuaOc43r5WyOI3/xs1vauF/7xp9E4uAnnzyRW7cSaCASwwRJyg4ODiQIgnk3AwAAAPDgAyLyAfnt344OsPv9vlSr1fSbhNxjzAQAALLtA9/4k0gcnMUY+IFclNOUpwEekGILAAAAAAAAAAAgu1hBAuScj7RIer16Shcf6ZKSTLWT1G3Ntc0+0sLoZWc/L8mmlIou+/aT5ii5FEo+JJUmzVxvtJ/PRJdju/eRqe4o03VnTgllL6nUSu7psVyOxb7vTPcqt+M2tU1blm9KdeSS2uMJQzvST582+/5czpWIyKPKtrcdyhbH/NPRAMgzX2MXX+mZ/I6l/IxDfKV5sks1ZJN6aXq9Nmmeom30M27wla4pLl+pzPR6wn0UN9afHveEz33ceN5XCqc416/OJuaLfnbtjiO8f5sY3Cb2jsbcSaY3i1PGrl9t4uf8xc15jYVP5ULqD00/zdG6jPy0FAAAAAAAAAAAwBNWkAAAAAAAAAAAUEBnzyBJdwVJnp5BwgQJkHNJLe/zkUrLLBu3HvOy3XfNXMes6apcU2n5WEaeZCqsWffn2jY/aeaSSpNmKqt9LkyfFdOSZi3lj16Hj7RsZummunNJV2XinsbKvh06+xRPrsetL903pdgybdd82aGszkc6Lj8pvVxSi4m4pS2bXVLpM03ymioAQP74ut/4G6ukOy6J3t+njzt8pbSySUsWdzzgKzVWnHpt9x0vlZuvVFBamfC1p12L4XhxeqwfN03atHrjsjv2eKnK7OJ0m+OYHpfbHYdNDG4TP8aLueOk4Yofc9rE0snFz75SjoUREy8uUmwBAAAAAAAAAICFk42fcQMAAAAAAAAAAK9O5YLc5yHtRkyQADmXVBoO1xRP2fGoQ1nTkvavWdeQVCqnJLmkiXI9Ppc6/KQ00pdD+0it5INLqjWXlEtm2vWvL2MulXbU7cPh9Zlbod0nTOfVTworU99Fy7unqfOR+syljihzm3/a8I7fd9jfY9btEPl2w/YvWtfg4/Pm5zOrpahwLW/qu2yE12mn6QIAG77uTTbpotJnMw4Jjz+mjzvi3ruTGmPYxLe+Uvu4pCKeFI3J4qRwissuDVec2H96rK/F+HHie5t4Pn6KK+3Yp6cKi/ajTYw9fV82sZvennAsbhOD28TeWsw9Pd6Oc/3Gv+ZtYmmb+Dm9uJnYGOfJxggOAAAAAAAAAAB4dSoX5TSBaYBfbH1efql1S33t6yen3veXFCZIAAAAAAAAAACAtb/YeEr+YuMp9bVB/1g+Xv2VlFsUDxMkQApMy3mzvZzPdcnv0sx71PvJ9TblmjZFo6XlsU9LZeJyvl2XxLulcjKJHrePpfnu6dqi1557/9su5T5vafjs/aHX7XJN62126w/TZ9OUestHqjXtuH0ci+t9KV56hWl1uF+PLu12uT5MaTmWlW2me6O+XT+W91q0aX7cvmtnv5ZE7hjKuqQtM5k9RHf5Xsh2PAIgC7KXmsTmPh5vfGKXxkcTZxwyfdzhKzVU3HrsYqfJ44g7hgjHkPp44X6ojG2bp6deisaw8cYDNvVETY857WL9aIwfLwXa9Hjeru9tY67JcnbXUNw+s2mTzTkLx+I2MXj0PhFt43xjbrv7vU0f2sTPceNm91jZV3pAFBMTJAAAAAAAAAAAFNADuSinKT+k/UHK+5tFfh4nDwAAAAAAAAAA4AkrSIApfKSjYJlemL7cfTh8PrLNfXm2y1J6bQmsiMgXPbQjyiUdlI+UXqZbvI9jMX992KetcflsuX4O3dK1uabambUds3OrV0+l5Wd/Pq4D+xRnrufKT+q5KHObk2Fu87sM27Wl6keGst+jbh0O/7rSjn9sqGP2FIs+uH3Xfrtj7R9QtkW/K86YvltcJHMv9RGPuH2GvjTz/gD4FyelSHHHM9HvsPCYxP6+Z/N9GP6O8DPusIl9550+yybFVZhNqh/b9G92qdPcxwVJxZv29bjH+nFTkEXrsUknbNencVLL2bdpdnp7wrG4TQwejb3Dcbcec6cXb9vd721iaZv4OW7c7H7/8BUb21ybvd5HnOtN2qlcSH0FyWmO1mUwQZKytbU1WVrSb2yNRkMajUbKLQIAAADi+rVv/Glmn4DGYmLMBAAAss0cA6+tfSrVlmB2TJCk7ODgQIIgmHczAAAAAA8+IPovBEXOVpDsptgWFAVjJgAAkG3mGPjg4CNSrVbTbc4Up3JR7qe+giQ/zyBhggSF4ZZSxz7Fk8g74zZprFT6pGF/Pzpz3SLfOXMNet+5Dkrf71D2uxza8UOO7XBZemo6xjcd9+lfUqmZRNzSQflJHaX/gthct81S7Wlcyutlh8MXI9tMS7bdUlCZflE9a1m9vOs51NMEmJaqu7bPpY7ZuX0vPGrYrqUtMN1nvja1TfHaYeojU5u1uk1tdvmO+wPDdr3upO4pPq5p9zQFX1C2mVIC3LGu1T193fy5pCLo9/tSrTJBAvhilyJo8v7ma3yjjWfijWXijV2ix24zVrEZn0THJdF92Y5HbL5bwu1Ob9zha3xhk1IqftqpyZjHLi61/c60KRdOSzZ9PBA/XZWfMnFSjumxfbhum/7yF8fb3d/Csa9NnG4Tn9vsSzvWcBmbGNzm/qvF3JP1xE0/N+09tnXb3e9s4ufpcbPbmC55NrFwv99PoSXwKT/JwAAAAAAAAAAAADzJ7s/TAAAAAAAAAABAbA/kopymPA3wgBRbQPpcUj6USjsONZtSmLiYPU2Xmbas1I2+rFJPC2YW7Sfzks73OrRDX5porvs9hu2aI8N2bbmoaWmuaWnp2w7tsE+LZGK3THpU1v76N6VWMqe40erW+8Jtmb/+dWWuQzsvpnNi6if7PjVzSXllSmNlV++ZaD+Z67VPi+TOpZ9cPkOm+7HLuTW1zbTd5f7tkrbJ1GaXlF6mJenavc1Uh+k+qHnCsP2L6laX+5jrPW/2Ot7lWLt92qykUo65pRBz2x+AfLD5DNvFeXHGN77GM/HGLtHUMTZjFZvxSXRcEt1XdDyi35NtxiHh712bcYdLXPuw6emqwmziX5trTKsn2q/Txw12cakW/2rvC/ej1ofhY407HoiTKmx6PXaxfvR6iRff2xynzbWp3W+m971dHG9zX7KJz7X2hO9VWj3h+NAmBreJvbWYezLetvk8x4377N5nE0vbxM/Tr1ebNGBxU44RG2OECRIAAAAAAAAAAAroVC6k/tD00xw92SM/LQUAAAAAAAAAAPCEFSQoNLeUNaalpz6Wk/++hzpMZk+xlRRz2g99Sbze/+93rPvvWrXtzPcYtmtpvUwptmZLpWViTm31okPdpuXEpmvG/ivB9bM16/78MKWTcEkHZWqza+onlzqiTNfBrCl83PlIMZc20zk0XQdZuMea2mD6fvoTDnXo151+fXynoY5lw3Z7rumjZvemoR3/0FD+Aw5165+LWY/F9f026Qji1g0gG2ziMX/jG1/jmfl+r9qk6or2WXQ8ot9jbcYh4fGHzbjDPZ2WRk97FY4pbcYU2jmcHtfHGz8kOV7QYiCbcUG4TbYppGz2Pyl8fuKmEbJjk4IsTdq5D5+fNO8n2r7C91KbGNwm9Z8Wc7vH28mmlIrG0tE42iZ+jsbNcdoYN+WYTTovLAYmSAAAAAAAAAAAKKCzh7Snm2IrTw9pJ8UWAAAAAAAAAABYOKwgQe6Yl4xql7Np2appeeIXHcpmxXclVO+3J1SviMh7Ddu1/tdTlZivg8ChHa4pqNJkOm7T0nTt+teXRZtTLml1mz5DSfaRzXLqOEzLxF3SZpnqMG2Pttt8DqP7M6fS+ml1u75sOMl0aKbj1q5fU5o607nV9jl7ejiXVGbm8i7HLWI+do3LsTxm2K71k6ltOv1a+sdOdcy6v7N9zp42Ti+vf3cOh3/JUPe/ULbeUcuKfKth+/yRNgDID/2eZpPaJzx2ydr4xtfYxddYRRuXhPss+h2qnx+bcYhNuqqkaMcRjhOnjyns4k3t2vR1rOE2xh0naPFcnPRZWj2TbbIZZ2lxfzjm9xfr24wdbeJ5re/DdcdLyWYXs9ucH5dxycNs2hiOxePF4NE0aX5i7rgppezi7+i9PBxH28XP842bFyk2fjCHh7Q/yNG6jPy0FAAAAAAAAAAAwBNWkCARrg8BdXuQmMtlqz0kS8T8q9svK9t8/DrpGQ91mNj/GtrtvPwZpzpE/pZ1O0Q+ZNj+urLN9Ve+r9o344cM2/+n9ygb9Qf6JveLcf39PlZ/uK3CMv1S3vRwTx8PPJ/91+96u308SN2Vy8q2aFnzL8JMK4y0lSWuK6VcVljYr5wxM/2KVft1vo+wxbUO7RhNfWo6bq28y3XnuupF+44z7c/lV8RunxUfD4Z0Ke9Wt+t1oN3zTOflD6xrdVkN6SO2cu3/2VfwfMlpf0ARxP11bpTNfUob64S/A3yNb3yNZ6Z/p9nde6Jjlej7bMYnH1K2hcck038pfbZ/i3FIePxhNe7w9Qv46Huiv1yfPqawW92kxQzh79I4D0QXicawtuOEcJviPlzdhs1KlMkyet8vhcrYxPpajG8TS4aP3eb8aHFkOIaPG7/bvM8mTteOI84D6W360CYGt4m9p7cnbpznq4zd+bGJn6fHzTYr9mz6I26fxfle7/U+MrXetN2Xi3I/5RUkae9vFqwgAQAAAAAAAAAAC4cVJClbW1uTpSX9Fw6NRkMajUbKLQIAAADiabVa0mq1ROTfK6/6eH4UFhFjJgAAkGXfjIFFwnHw2tqnUm8PZsMEScoODg4kCFweIp1PbmmARPRL0bRU1iXVjukhgKZl5b+pbHu/WtJ0LPpDlb/DUIfN8uQpHpn+MLdvcnnwt/5waPO5ta3XXLe+BNP1IZJ6X6t+xbD9l56NbvtBQ1lTpqnbP6NsPDIU1s6Lnv7IfG5dloibGu2S2sr0j14uqa1cmD4Xpnb4WDLvknLMlLbPpT+0lFI++l9nfgC8/YMefdRhbvP0VATf5PLwdlP/m/rU5R94XVKOuXwOTfW6XNOm4/ARCtqnBHRNE6VxS8tpqts1BeFfVLa5XHcmhs/4qrIsv+u6VD96jZVK2neTiFt6uEmjf6w2p9janVoHEJbnMZNdyqLwfUL7rNmMebSxTnicM318o7Ux+h0fjbGjn3uL+5/V2MXmYeLReqJ9H929TT3R+6fteMRiHBIef9iMO7SQITLW0MYZ01MvRfveJobWGmST9ir8XeNr3KBdd9r3WrhumzjKZjxgk1ZJO65w7B+3zybpD3ufHpuH32cXz9ucQ5s0aVo5rc/C/WETp2ttDL/P5prW6rG5hsL7iht7T8bbcR/APu09JtG6oucnWpdN/GzTH8pnIxwrW8XJNrGxTUq2Mw//YCN87LduZS8GfiAX5TTlaYAHpNgCAAAAAAAAAADILlaQAAAAAAAAAABQQKdyQU5TXtFxmqN1GUyQwJpbSgvTpWVKQ6MtaTQtr/2AYfvvK9sMy5yfMiyT/ryydO69hjre1JdzqktR/5IhpdQvaEv1HD+W36dsM6WOMnpPdNOqXrJU+jn9has/HN1209DP64Zm7CvnfFW/ZszX4980bFeYMrAphyKm3X2nvvnvf/SfR7b9zccMffe1HWWjKQ2cj5Q6pjq08+WS0s60T1NqMRPts2+6H7gsw3f9yrNZ5j7iklrJJWWTnrrIfCxaf5jStZku6uhxD4fXZ67DfNyux+jCJp1B8lxSTelc+8LlM6eXLZX+vrLVlO5GTxfmcowuqbfMqR71OvTt+jJ8c919ZasppvkDw3YH3c9Ft73b9KVlorXPlCbGdK+/Zb03re/6/b5Uq9lKLwDMwu6+Fr5na5/F8H1T+77Sxjzh8Y4yTgmPc2zGN8q4JjKm0cYzkbGMxfdV7LFLaLyijFUi4xSb8Yk2LgmPSZTxiH4tWIxDwrdym3GHMt4IjzXUcUZkjKGNL+KkCNLeE/5+sRlDxB03hD8vLmOHh8VM7WPVH3FSQWkxsU3aqclYX782J49Di+2j77M5dps22wofW3IxezQdX5x7u8bm+o2WicbdWsw9+b1h02atTPjYbccp0W3RWDrarzbxc8y4ORwrW8XJNrGxdt3fmlpz+NgXKQb+l63fkH/Z+g31tbdP8vM8QiZIAAAAAAAAAACAtf+o8T3yHzW+R33td/u/L//36qsptygeJkgAAAAAAAAAACigs4e0p5tiK08PaWeCZMHZLLn7JtMyWSU9k7GsadmbQ9qIK4btH3xfdNt/phd9xwf15Zpff0I5lv/KsL8f0VOKqOkr/pah6C9odejpcIxeVrZ92NT/pu3KPrcNRatf1rf/98q2DxqO5YcMde8ry5N/1FC2a9iupBowLj/9Y4ZrXVv6/5ZbO377o8o1/bKhjr+tXf8eUrQYl5ablv5q16OprI+lkqY6tOvU8XPhxPRVqC0Zd0lxZipvOm6tHS6ptEz7c6tDX3KvpA48t26XfjLV4XKNmcq6pGBzOS9u17+5/2ZlaodLaiVTmigXpjRp9lzTZs1ad6mk37zN5+pPK9tuGcqa4gON6d6mxCO/92/VkqXSP3RohyldRZL3WCAfwvcb/b4UjlFsxkFanGcx9tHGPOHxjjLWCY9zrMY36rgm9L2hjWciYxmLe8nLyrbI2MUiFtTGKuFxis34RBuXhMck2nhE/RqZHIeo32Hh8YfNuEPZV2Ss8bJST2SMEXd8EY6ntPFB+FrQysQZP2jvCV8fPr/DwnFf3PFAuIx2HOF9aTFnuO9tYv1o/BuO7/V4J1yPzXHGjZO1MjZxe/h98fbvLzYP78smBo8be7vH27On+TXXpcXS0X61iZ9t4mbtMx76blPi5GiMbBMb+7mfVKttL/UgPUyQAAAAAAAAAABQQKdyQe7zkHYjJkhm0O/3ZWtrSyqVihwfH0utVpP1ddPTpwEAAABgsTBmAgAAQJYxQRLTYDCQarUqvV5PgiAQEZGVlRU5OjqSzc3NVNrgkh6rVPoZQy3fqZS9ZSgbTbsiIiJ/Vdn2l/Wi3/fsP1W3f5v8VmTbihyqZf+C/JK6/f3ym5Ft7/n4V9Sy//YH9fb9yZ8YRrYN31lSy5b+q2hZEVE/VcMnDHV8m1LHk3q1oh+K/Nb3vzey7T985E298H1Dao1Hoss8/y/B31GL/jfyn6jbv+9PR8/tv5Ln1bIf+Gv/TN3+az+slDeutDWki3m/su03n9HLfruhau3OeNlQ1pBi67/95f8yutH0MVRPrqlxt0yVeOCynNT09aHVYUrtY9qfS5ooE5tl7OeVNXFJ2WQqb1pWrR23qY9czpVp2bJeh7bkfDh80VB2x1C3diymFH9J0o7RdB24pjPTuKSN83HduaQyM6XLc1nmH40Zzm/HFx3q1pnTfka5pQwIDPtbNdT9LxzqdmH6XHzOQ93aNWY6V/Yp5kz97HKusDjmMWayGSPpY6PvDJW5pZQJjYcsxkHa+Cc89tHGPdqYJzze0cY64XGOzfhGHdeEPv7aeCYylnkyWk04zLUau2hjltB4RRurhMcpNuMTbVwSGZOoX8NKfBseh2hjkHCIbzPuUMYbkbGGOs4IXx/a+OKW9sYYbGLT8MHajBu0MuHvMttxgp/0THaxYbiMFm+Fj0M7Vpt+nZ7qLhzfa7F9NK5PO54Pt9sm/XCctFwi0eOIe95tYvFwDG4Te2sxd3hf8eJsm3jNLqaOxtLhONpf/Kxdd3FiZZvYePq/I9jEGb1eXarVXbfmJexULsppytMAaT/zZBb5WeuSMfV6XVZXV8eBvohIs9mUer0+x1YBAAAAQDYwZgIAAEDWMUESw/HxsXS7XanVahPbr169KiIiu7vZmiUEAAAAgDQxZgIAAEAekGIrhps3b4qISKVSmdg++mVUp9PxumRcS3cioi+NK5X+rqGWH9Y3v1NZGvyzetEP/bVfVLf/H+XvRbZt/JN/rJb9sp5pyrRL1b923D6z/4dh+5cN299Qtv1ZQ9nvVbb9gmEZ7Tv15axfkndHN943pVIxpDpSVhE+YcrpZVjK+UflS8pWvZO+LO/Sq/6PlW23DM0wLWdVs4sZlrF+l6HqN5RtphRbd/XNT3wo2k9f+SPv0Qu/qS3XNS2n/g7D9luG7RrT0miXZd2mpcFfU7aZlh67pnrRuKRL0tom4paCyjXFlkvarKS+kl3ScdkuuR9xSfHketwuadJM17RL+0zXnY/r0aUdWh2m9xvuKWpfG8q+3/C9EM1aaahXxHSvz3bKJVPqP5MPKNt+w1DWdK/RGM5t6U9Htxkyew6HSllxSznm41zp+9NiAyyKtMZM0dQxNmMkZWwUHhcpA5TweMhmHKSNf2zGPtrYxtt4Jzy+0W7jb4T+WxvPhMcy2jgmNIaxG7so9+jQ17E+VpmMv23GJ+q4JDwmuaVUo92/I+MQJV4Ojz/eUKoOjzuU8UZ4rKGOMyJjDO17PDy2uKWUCdPirnDd2r7C/WEzbtC+J21iM5v0TNr+p6ershsP2KTP8hX7T4/1w/G9HtvbxKs2abBsUplp15DN/sN1x70WbPYVfp/2nvDnziIG12LvSNyt1TN575p/nG0TS9vEzzZxs9L34VhZiZPDMbJNbBy3X8N193ofiVVPkh7IxdRTXj3IUYotJkhiGAwGIiJSLpfPfV1z9+5duXPH8BwIoz986P8/Ipw2AAAAJO3evXty7949y9J/qGz7us/mIGfSGzNNXnt37tyRS5cuyaVLlyzfDwAA8E1uMbBIOBa5e9fwS1pkFv/SHsPh4dlD9JaX9RnT4+Nj43ufecbwsGhrf15EPjxjHQAAAMD5tra25JVX7FeiAA+b15jp8cf/a3nppZfk5Zdfjl0HAABYXLPGwM888xMeW+PHA7kwhxUk+XmyBxMkMaysrIiIyNGRnsYovIz8Ya+//ro8/fTTsfc9/ddQpnQbn9M3f01Z8vZP9SWjv/3Xvlvd/u/kj0Wr+IHvU8v++b/zr9TtP/J/jW570pCF6ZXf17e7+DOmF34ouqn/HxjK/pxhu7Yi71OGsk8q21YNqWL+V31z7c1/Ht24blju+G8M7Xgruukr8oRe9pEPqZvfqSYL+Ha17J+Sf6pu/8J3fGd04y/ozTAubb6qbPsVw8X0XkPVv6dsM2W2+g/1zV/pKp/F/5+hDrWfTEul/61huynF0KxlTcuWTe3T0lWZls2aluVqvxg1tdmUTk67Pkz7Mx2Ltk9Tf7ikeHLpf9Pyb1NaMK2vTW3WQwBTWsfZuaR2E7Fbsj+Na3lN3BQA05hCMJeUey7XruGzoqbSMnFLS6UtY59/OoDzmZfe/zWHWjyE10PtfJnu/3qKLZ3+OTQfd0lE/suJLV/96ses9/bGG294+HEQ8mreY6ZJ4bhMGRuFx0XKmCg8HrIZB2njn/DYRxv3xB3zRMY5NuMbbVwTvl1/SinzZOi/tXFMaAxjNXbRxiyh8Yo6VgmNU2zGJ9q4JDImUccjyvd1eByijUHC4w+bcYcy3oiMNdRxRniMocUN4e8WmzjVpowWg4X3bzNu0GKP8FjBNu4JnzOt7nAbbcYDNrGiTZ9p8WW4j2xi/Wgc4i++Dx+HTeow23g8TtzuK07XYjebFHU210voWrSKvafH3FmMs6NtsomfY8bNkVhZi5NtYuTJc/bNYzgVkY9/4/9PttEmFiYGzh8mSGIYBfOmXz2dF+xfvnxZHnvMlLsfAAAAyIpoaleXOPbyZdMDvLAIGDMBAIB8ejgGdo+FiYHzhwmSGK5ePftpSDhv7ui/q9Vq6m0CAAAAgKxgzAQAAJANp3NIsXVKiq1iK5fLEgSBdDoduX79+nh7t9sVEZFr167Nq2kyHH5U3W5eUtmPbvqknqbr9z+pz5L+bfm/RTcuGZYEPm1oxn+sbPsretHLf0vJByUiT37r5yPbvtWQfmTftMS1dCuy6b8Z/gNDWaXvRETky5Etzw4/Zaijq2w0fSz1Nv/4e3eiG/f/X4Y6TEs1o2v42w/qetH7r6qb/41oucj0duhlDb5oesGQLuZXtI1v6mVvGfJmfUTZ9i2GZihZwURE5H+jbPvPDGX/trbRdB2Y0uhpy61Ny4tNaXm08qY67K8l9/RYLmm6DKnWVKaHvbos53b92nRJl+TCR5td+s6Vdoxuqb788JGmS2PqOx/nVmPqI5cUW6br/zcc9mn6zNoftzmVk+kYZz9f+j61G/15XD7LPtIY3lK2me6DJlqfmtpm/zk0ncN5p3VA9mRpzBQeI+ljo1Bsr4yJwuMhq3HQ08quwmOfvxItoo15wuMdbawTGefYjG/Ucc3kmEYdz0TGMtPvO3ZjFy3OnIwx1bFKaJxiMz6xGpeo4xHlOzEyDlHGIOHxh824QxtvhMca2jgjMsbQzk/4OrcZU2jff+Ey2hgifF5txg1a7GGTdsomztViI5u4w+Y70yY9k4047fEV49vE8z7j+LipucJ89X2Ydqw2KbbC15kWe4fr1q776ccRjdH8jYWiddvE0jbxc9yUfbdC/20TJ9vExtOv6SymN8PsmCCJ6caNG1KtVmUwGIyXh29vb8v29raUy+X5Ng4AAAAA5owxEwAAwPydykW5n/oKknT3NwsmSGIKgkB6vZ40m02pVCoyGAyk2WzK5ubmvJsGAAAAAHPHmAkAAABZxwTJDIIgkL29vXk3w8pw+KK6XU+TYEqhYUqpoyzJPtFS5IjIv/ouQx3fHt30K3odd/+23r7feuR7oxvfZ9hd2bBdfiay5eA//TFDWVPasqjX/ge9/7X9mZdO6ktl/+7f+5i21VCHaflidJ9f+YlVQ1m9///V33lW2fpJtezn/s6P6lX/orLttl7UtISxVIoulR4OP6SX/R69bjlWtpkmvv+Gvnn4hVJ0f7eGhkp+V9lmunhNtPNiSCEmf2DYri1dNT2AzPT1od0nTGm6TNej1g7Xryvtc2RKT6Yt7xfR2+eaUkcrb7qXmlIguXBJqWNaXq212XBPNy5lTiq1lY+wxdQfpuvUpaxL+1z6w/Q5fJdD3aZ7iks6qCcN231cB/b9Yb7/238vm/rOXPctZasphYVLagvTZ+tJ67LmtGXauTXtz9T/2ncqKQTgJotjJm1sFP0sabFV+DvcYhykjn9CYx9l3KONeSLjHe3WXg5vsBnfTL9/6uOZcN1aDDF5T7Qbu0yPwfSxymSf2YxP1HFJeEyijEe0e2F4HKKNQSLjj+No3ZFxhzLeCI819HFGeIxhM76wGVNoY4nw94gWu4TjAptxg3YtxE3zFL4+tbFBeFxgMx6wKaMda5zY3ybW177Tw+2xSWnsK56PG79r/WoTr4fL2Ozf5ri0azocT9rE4Dax95PKtjjnZ/px6fcym5g6GkuH67KLn23iZu16fXJqmej3uk1srPVZ+N5OLFxETJAAAAAAAAAAAFBAD+SinKY8DfAgRym28vM4eQAAAAAAAAAAAE9YQYII1+Vi5tQOmn9h2K6k2DKmoTFsv6/U/TnXVBLKkuL/8cuOdSgfq//CUNSYzkxjSGHy32sbTct1TUtSlWP5H03tMLT5f9Y2GvruvzZUPdTKm47FtERcq8OQnkbLbCUi8sPRTe9ufl4t+nt/7yl1e+m3lGXuP/PThh1q15JL2icRPaXBbxjKunBJv2Mq75Y2zm05tal92nbXPtWY7ikuqbdcj2XW/bkuhXdZTm8KI7Rz69oOrQ6XNFgibn1qqlvra9fwSTsW0/WvtcPUti8atmvpXEz3UpcUbPr9P+1l5qa4w6UdpdLnfDVH4XKdGj7L36bEOu/U45/hwJQWTPvO0eItEae4CFgQNvcUu3GQNv4Jfxa1z7eyLTzeUcc64c+zzfjGIr3pf6EUsbpHhMYwVmMX7T4aaqM6Vgm153/WyoSOXRuXRMYk2neoNhYJv08Zg4THHxbjDm28ERlrqOOM8HnVYuFwHOBrTGETg9mMG7R4Kfw+7XrR6g5vs+kPTfhzp/VZ3DaG2aTzsomxw7GrTUrjuPF8+H22cZFNf4Trihun26R5Cu9LO45wLG4Tg9ucw+i9Nal4W/ses/v+8xVL21wfynkOx8pKnByOke1iY/sxUN6cyoXUH5p+mqN1GflpKQAAAAAAAAAAgCesIAEAAAAAAAAAoIDOnkGS7gqSPD2DhAmSBZdcWgxT+hfTJaelRXJdxqYtVXyPoawhTcXwxci2UulfG8q6pPLQczlp+zPXYVjC/7nokm+Xes37M71iWFb7m9FzbmpHybh2TVu+/v/V6yj9Q0Mdf9FUedT/1rD956Obfu/jv2gobEoF96ayzZDqy4npc6GlWHBNR6SdW9Nn2cSlvCFtnFNaKtP+bJaNT6tb45IWSURvn0uqqXcZtrvU4ZoWzEdooJ1DU72m68AmpcKIqT9sUh6ct7/zytvuz8THdWdyS9nmdv/RvuNKJVOazCed6p6Vn1SgDt8VRj7S5RnO7VeUbW992lDHR9Wtw+HzDu0w+YiHOoB88DcuCn8XaN9/4bGP7bgnfH/RxjqTsanN+MYunUp0PGMz5ojcgz2NXfSxSigesBif6OOScBuj4xF9LGLx3RIef1iNO7TxRnisEXecEb724o4pwrGYTUxkU0aLE23SV9nE4Npn0+ZYbVJIhfdvE7trcb/N+2xSfsWJ723iee38hPvHNi2YTToxm7qn1auJc941t5Rt0z+b4XuwHnM/abF/d7bffdF4Om4sHe5rm3Oo9H04Vlbj5MkYOX5sTDx8nt9s/XP5rda/VF+7f+L6b1LzwwQJAAAAAAAAAACw9v7Gn5X3N/6s+tq/7/+u7FX/u5RbFA8TJAAAAAAAAAAAFNCpXJD7PKTdiAmSlK2trcnSkr6ErNFoSKPRSLlFs0sqTZcxpZRxCZ62dOs3DWVNqbc+pGz7oqGsi9cN23/YugZTP5dK05cSxuOWWkxfBvpn9KqHfcM+teWvenoyM4cUN58zbL/5KWWjKfWBabu2bNhUVrsVu6bUcUklZOojbVm3a6o7Fy4pr0z94ZK2yTU9lsY15ZjL16xW1rX/tWMxpbAytU3rJ9elsVp5l7RgJqY6TO1zSRtnuj60tBam82L6zNmkhxjRzouWflBE5LsM212ug281bNe8V91q+r7W03TZl/VFb4fpC8DE5R5r/7kfDq+r20ulHeuyPsxyXlqtlrRaLfnc5/698qqPzz0WUV7GTL7uXfpn0CblqDbWCY9zPqSUiTO+0cYz08cy0dQxvsYu0bGKXZqa0PhEHZeE7/m24xGLODH89WM17tDijvD9VSsT/j6yGV/YfN9pZcLjCF9jCJs0WNpxae8Lx0JxY16b8YBNLBAuY9NnNjGezdjS5jh9xfPae2zq1vo5fBw2cbp2Lw33mU06MS0eD8fiNufHJvaOxtzh7wmbONtnfB29v9rE0jb3k+mfFS0GDsfKvuJk7fvY1I+jGFhElDiYGDhvmCBJ2cHBgQRBMO9mAAAAADMb/WO1/g+8XxKR3bSbhAJgzAQAALLs4R9sROPg7MXAp/KInKY8DZD2/maRn7UuAAAAAAAAAAAAnuRnKgcLx5zKyZR6S2O6xF2Wld9yKGvimp7GhY8UYJo3Ddv/tGH7rciWUqlrKPt+w3Zt2aq+hNl8fXxS2fohfXc3TcujbZaxj5iuMZe0Wdr14ZrKSWNKqWPy+8o2lzRMJqY6TMfoshzVpZ9cP4daO0zHYmqztt2UlkeTZJtN5zDap+bP2ycc2uGajki7fl3DlllTnIm43Q9c6jZduy5tNt3/n3TY31cM27WUBT7uSzqX73bX+EAvrx+LuR0/pGwz3WMfM2x3kUyI7ppKyyXNAID4XFISTtLuFTZjg1sWZcJ8jWd8jV20sUp4nHIrUiI6PtHGJeExiV1sFB2HfChadWT8YRNnaOfZJn1W+JzF/R63GVOExxFxUwiH36e12SYOszlWm2vaJrbWyoS32cT/vtqj9fNkf+jXbziut4nnbVKZ2cY0vtKS2VwfNteZTXvC97MnlTLhurXYOxx3+4m5bb5HbL9/ouWibYy+zyZ+jhs3+4mVk0pd1uvVpVrN1goSnI8JEgAAAAAAAAAACuiBXJDTlB/S/iBHiavy01IAAAAAAAAAAABPWEGC3HFJ9TIcvuhhj65pijRa6iI35uWR75y5bp22XNWtvPlc/Zx1rcPh847tcFmO+mXHul1El/aarkdzmiJ7Wt2l0o6htEsfuaSOEknua2X2lEbm/jf1k02qglHdLil/kky5py3fn32Jtvn+Y0oXoG033Uv1a8Zu6f+Ij+sxufRR+ufT/nNvvnZ/xvAOLV2hKa2F6fvpfco2/XshKymX3NrxHqc6SqXfULZqachEXK4ll8+Wa3qsWcv6qLvf75NeAIjJ5nsw/lgnzvgm3ngmeu/yNXaxGatMH5/YjEvsxyM2939f44/J2GfWWOP8esKxss1x2qSd8jVusB0nTO7P7linjwfsUhT5iv/9xPp6TBGu2yae18Zc4c+Ydh26pAV+mJ94PXzubT4r+vUSjsVtYnDtXhqOu+3/bSUJdvuKxtLRc28TP08/pzbXa9y0sEmVKZXqU9+TttM5rCA5zdG6jPy0FAAAAAAAAAAAwBNWkAAAAAAAAAAAUEAP5OIcnkGS7v5mwQQJcsecjsKlrL60W18+/Z3W+zMzpeGwZ077MXPVBtcM+3vVUP69SlltSeV5PqrUoS93NacUcFm+nGSKrSjz9ehyK9bLuqXrcUkHZWqbS+qtJNNx2bfPvf/t03q53JfcuKYWcy0fZZcmYCS5tFR66jP9+MzXtFaH3maX9I3mdFV63bNeH+bP97c7tUOnpQIwtfkZh7J6n7qmB0jus9U3bP9Lhu1aKpcvGsq+S93qsvQ/ueN249Zm7Tr9kucWAYvD5j6gl5kc5/gb38Qbz0TTjsSqRhEdq0THKXHHJ5NjEvtUzjbjkGTGH25xvLmMXaohm1hR23c4jksyDdf0/dv12fTY2t93tk0cHy/Wj34OtTb7ieuj8beWelq7hqancrNL3xWO06P1xDln+r7CsbhNH0Zj72h7ojF3uEzcONvf9arF0uE42iZ+jsbNNseaZqxs157J66PXq5NmNmdIsQUAAAAAAAAAABYOK0iQO2k/DHY4fNZDLaZfmfqg/UL6ulMNPh4S7vKgNfMDh3/auqwfyf363Y32a3TXh6Pbc/u1xez7MzP9Ct9+n24PvjfVO3s7kmP6mnZbwaDTj8/tfuDSPlPbTKtC7B826v4Q+VmZjsVlNZLb/VFjXtGnMT2Q1/5h5aVS11BHUv3s5ztfvz7e71iLdg71lSKmvnY7Fm2lq16vj4e3m7g9pD167Z49pP3vzdwOYBElOeaJN77xNZ6JN3aJN16Z/v2rxxs/PbVMfGmOP8LxmMtKbzO7sYSvONplnPRNdnGkVo9NnyVFiw2nr4aI0lbS23x+wvu3iee11SHTH4Bu95D4uMJttFnNpD1sfvrn3i4WD8dvNg8312JuP/0T97sles5sYmmb+Dka39q1MRwrR+uxWflhw+4h7ZPXS79vWq0+P6dyQe7zkHaj/LQUAAAAAAAAAADAE1aQAAAAAAAAAABQQKdyUU5TngZI+6Hws2CCBMi54dAtnZbOZemwvrRTX8asLaF3a4d76hCXY3nToazOnOLJJY2VtlR69geHuz1I11fd9g+IdLl2zamVXI7FtCzZ5oGaI65fmzZL989rR3Kp1sxc6nYp63ZN+3nwnn0aC/P+XM65j/RwLg9ZNV1L2oN09Yexm1OmfI+y7XOGstnmli7MROs/l/53FTfdgF9u9/QkU2ICKIr4Yxeb79jJWC/++GT6w7z1e7JNG93HH/px2MRJcVINafu3eTB2nHqmf8fYXC96CiebNmpjA5txgU1sGN6XFjOE9+UnBZrO1wPgpz/EPn4MPz1uj9Yd51xE2X3GbFKg2cTgWuwdjruzF3PbpQELs4mf48bNk7FyknGyzbVAHJx/TJCkbG1tTZaW9H+kazQa0mg0Um4RAAAAENevisi/lu/+7p+LvHJykpVnfSFvGDMBAIBsO4uBRSQSBxMD5w8TJCk7ODiQIAjm3QwAAADAgw+KyAflt3/b9JD2avpNQu4xZgIAANn2wW/8SSQOzmIM/EAupp7y6gEptgA8bB5pMVzo6UdMqa30Zc72S8/PE13S7KOOJLmkAHNbbmxaCmyfpshHiiK3VFomphRKs6f6cuFy7ZrrcE2ppp0v069JtLJuqWzczsvsadxMfKTc0+twu2b0z6GP62728Mncjijz+f7kzO1wSUMm8k6nmn18L/j5btGYUo65lE/u+ybLcQMpBIDFNO/7kk16pnCsF/87ZPL+bn/syXwv2ByHXQyoxTrJpEyKPxYKpzeLl87LhjY2sGlj+HvQLh2lTUrl6Wl77PowXiq1afs+2//0eD7aZrtz6CuVW5w43SYu1/sjTixuE3tPj7nj3t/C74t/b7eJpZOLn+f5nUQsXExMkAAAAAAAAAAAUECnciH1FSSnciHV/c0iPy0FAAAAAAAAAADwhBUkADyJ3k7cU9n4uCW5LB/+smH7Yx7aobFPaeQjDY2JOW1W9Hy5pBAz1eG6BNUl9ZDL+TbXm1yqqaTShflIn+bWNv1BuSZuS+L1/vdxjMmlZXjUsN0lfZrLtWvqC9Pye5fz5ZJq6muG7fpx+1j67lKH2/3KNcWW9r2Q3MMXXe6lrvdpF7P3/5dmbgMAxGczPrFJO2XL5rs9PP7wNe6YHmvESb+jsUlhapcWLN64wW6sMP1c6PVMTzlmx88YIF48bLPv6bGiXRytfcbixfB2n00b4TjdJr2ZzfWiHVc4FreJwW1iUC3mnjyOuHFe3PtA9H02x5Fc/By+XvQUaH7SicXps17vI7H2laRTuSj3U19Bkp9nkLCCBAAAAAAAAAAALBxWkAAAAAAAAAAAAGtfaP2CfKH1i+prpydfT7k18TFBAuSc3fLH5NksafxmWVN6pmh599QhLre1ZNIfmbimmtIkmUrFR3+4XAdJ0pf+z953yR6Lfao1PynV7PvDvL/Z08b5aYdJUp/xtxOq1/W4TcfnspxdTxem79N0vvV0Aml/P7nV/YRj7VpKRlPKMfu0ccmlEEuf1o5+vy/V6u4cWgMgq9L8boibciVcxr7NNuOQZGKTuOMMXylo4hxX2uOGaIqveNeivzZOT4EWJ+WZXXozmzhvejwff18aX58NP3G63XGE22wTg0dj72jd2r1kMt5O8l5qV49NLG0TP09P0+YvLVhywvvq9/up7dvWA7kopwlMA3xH4yPyHQ09pdid/qH8avV57/tMAim2AAAAAAAAAADAwmEFCQAAAAAAAAAABXQqF1J/aPppjtZlMEECpCDraTE0rm1zWWrsIz2Qmf3SXHPKn08pZf9zQ1n9WEqlTyh16EvfXcomyUdKI5elrT6Wp7vU7ePzZt5f9ByesU81pdebXJo6133q7bC/Tk195N6nLnX4SIEQTR81HOrLhOeRTk53R9mmp9Iy0e8HP20ord93s/wdJ/K7juW1lAmmPtX6360/3D5b+YsxAOTPvNOXxNm3zfdynBRGZtPHIdG0PZ9SyvznoTJafDcZJ+npqqaX8SVOWiW/6ZnOr0erK+71G22jFrNOT58VFrc//F3n06+P6DWl9fP0GN5PKl0Rmzg93fg8XgwePc9azO1+TSXLJpa2iZ+jfWZzbHbXq68UglgE+ZnKAQAAAAAAAAAA8IQVJAAAAAAAAAAAFNDZQ9rTTbH1IOX9zYIJEiAFSS7ly8oyway0w4+TyBb39EDJpCnKCh8p1ZK9ZqJfb67Lq12Wfvu4DpLq0+ykfdLTTiR5LG7L/k1pMaL3A3f2qf/cjtEUxmn709M+aakJzEwpAnz0UdpcjltE77+vGcrOHl67pLLJyvevfu1+KfV2AEiGr3tNmvesrNwfzzd93BE3JsraGCNOKii/53Dy+zluejWbNvpKe+VrX/5Mxpg+06TFSV0WPwadHpvbtTsc89nE4DYxqBZzZy3etjkOm/g5Xtxs89lI8zsger0QA+cNEyQAAAAAAAAAABTQqVyQ+zyk3YgJkpStra3J0pI+09poNKTRaKTcIgAAACCeVqslrVZLRP698qr96ingYYyZAABAln0zBhaJxsHEwHnDBEnKDg4OJAiCeTcDyCSXlCLJii5pNS1RT2pJs2tKL72O2VNb+Ugd5crHdaCdL9c2u7QjqTRRflK4Jddmlzpcz6GP/teY6zCl3pptfz6Y2/x3De/QwrvHDGXfjtOkAnBNsaWVP/JUd3bZfA7P+8fqfr8v1Wo1kbah2BgzYZ6STfMUNn3c4Svu0GIdm2OL0x9JpoKKe37i9GvcFFJx0kzFT6Xmpz2+Uhb5SgvmlirXre449PaEY3GbGLwosbdNvGsTP2c/bjZd03mLgU/lopymPA2Q9jNPZpGftS4AAAAAAAAAAACeMEECAAAAAAAAAAAWDim2ACTGNcWTj+XrfpbAR9Om+EhXZZJUajEfqZV81T1rO1zbnFQKqrTbkZXjdk+PpS1/d8vD6rYc3xTORPc5jz514XLdmZeka9vvGMouW7Rq2v7S5ed+/DXHvWrptEz9YeprF8mE6PP4XgaALPGVRkgT733Txx1x2+OrnjgpnOLWE+YrxZXt/nylokozNda092jvs0uVFS+et2uzFudM1p1kf9gI163vKxwL2sTgNrF3ejF3/LGfTSxtEz/HjZv9xMpJfidkzQO5mHrKqwek2AIAAAAAAAAAAMguVpAAAAAAAAAAAFBAD+TCHFaQ5GddBhMkABIzj6WIftJVve2hjtlkPbVS2inHXMu6tCOpvvNRxzz6P26aAN/tcNunW/qupOipCHTD4YsJtuQkoXpNS+Bd01XNxs/9+D2O5b9d2XbLUPYxx7qjkro+ipIiAADiSvI+GC+lVXrjjnmncEoqdZhtGbu0Un5SY9nsO80+m2dqKl16sbtNfO4v7vIVg2sxdzLxdvz7jU0sbRM/x4ubfZ0zYmOM5GcqBwAAAAAAAAAAwBMmSAAAAAAAAAAAKKDTbzykPe2/8+zv70u1WpVSqSTValW63W6kTL/fl42NDWk2m1Kv12V/fz9WmWlIsQUAEfNP1+O61DPt1Eo+0kS5tCMr0k5tZWb/9e2aHkur2/X4in4dmNnfO/ykZbNP6eVKb987E9tf+kzpwkyWLLeJJJfiLNv0a+ZLqbcDAPIlvXGHTWyVZAqnOGVs9pW2pNJe6abH/HYx5WQ9cdON5eH8RE3/jMVPk+YnFo/uPw8xt00sbRM/FyNuDp/DXu8jc2pJfuzs7Ein05F6vS6Hh4eys7MjtVpNOp2OrK6uiojIYDCQarUqvV5PgiAQEZGVlRU5OjqSzc1N6zI2WEECAAAAAAAAAEABncoFuS8XU/07PWfa4bOf/ax0Oh3Z3NyU7e1t6fV6IiKyvb09LlOv12V1dXU88SEi41UiLmVsMEECAAAAAAAAAAAS1e12JyZCRESCIJAgCGQwGIiIyPHxsXS7XanVahPlrl69KiIiu7u7VmVskWILQKFkZUmvy5LkrLTZhUuqqSTTdPlIU5SV/ndbxj57OgYffZr2dWCS5LXkciwmPupIjr6sPak0AkmmqXPzRcfyR8q2YqQE8EU7h/1+X6pV+4EJAORZmt9lNmmeshLjjti0Oakymnn3mV2qLveYP25/zLvv57n/dOPyaPwYbU+8mNtX+jc7NrH04sTP4b7u9/tzaonZ2TNB0p0GMD2DZJRCS1OpVERE5ObNmxP/PTJaKdLpdMavnVfGNs0WK0gAAAAAAAAAAMBcDAYD2djYGP9/EZFyuWwsa1PGFitIUra2tiZLS/pDPRuNhjQajZRbBAAAAMTTarWk1Wqpr52cFPNXgkgeYyYAAJBlixADP7j3dRne+3r899/9A+uy+/v7UqlUxis+Dg8PRURkeXlZLX98fGxVxhYTJCk7ODiYeHAMUGTZSaXiKnprdD2W7B9jlFuKp+Tq8NF3PlI8ZZlLm5M8viTbkdR14FrWR5quWT8X7sv+tX9UfNuxDtt6RVyWymfn8/aYY/k7yjZT2otkwusk72Gz1H3eP1afpdiqztQ2LCbGTEhKuilobEwfd8w7FZSNOP0aNz6Ke+w2aZWy1q9J9kdS+/J1fmzK2JxDrYyv6zXKVwyu1TM93k73+rWJpW3iZz9xs6/Ps0s9eYuBH8hFY8ork7e2PiVHr/xsQi2atLW1JXt7e+P/XllZERGRoyMtVdtZWi2bMraYIAEAAAAAAAAAACIisvzC35Qrz/8nsd9/741/I7/7zH86tVyz2ZQbN25MTGiM/r9pFUilUrEqY4sJEgAAAAAAAAAACuiBXHBeQSKXls7+4rp8eWqR3d1dqdVqkZXDV69eFZHoc0RG/12tVq3K2GKCBEBisrZE2d6jkS3D4YuptsA1pU7a6YiSrEPjI7VMktejS/t8HIvL9eGaDirL6cmS7Dsf6fJc+zo52lJ+Uzqo6P3OzBQcf82hjqxwzQvs0k8uZe1lJV0eAORZ9u536Y07bOKRJNMj+dpXWNqpuuLsP24b45yzJNsTh89jD78v7nlOLja3icFt4kQt5s5avG0TS9scq5+4Oc1Uc/Bnf39fRERWV1cntvf7fQmCQIIgkE6nI9evXx+/1u12RUTk2rVrUi6Xp5axxQQJAAAAAAAAAABIXLfbla2tLanX67K7uzve3uv1pFqtShAEcuPGDalWqzIYDMbpsra3t2V7e1vK5bKIiFUZG0yQAAAAAAAAAABQQPflglx0TbHlYZ+afr8vtVpNRETq9Xrk9du3b4uISBAE0uv1pNlsSqVSkcFgIM1mUzY3N8dlbcrYYIIEQKH4WC48HF6fXsijNJc4m/bnus+s1OFSd1aWy7qkYfKR9sm1HUmZR9o4l336WGrvcr6STVumLVU3pdi641CvvrQ/K5+tZLmk5NLSK7jJ8j0MAKAL37vt0v8kN+6I05449drWHac9Pr8Pk+oPGzaxoFZmnqnLNEmmabOpO068btOv/q4zmxjcJvaOxtz5jANt4ud4cfM8P8+YXRAEMhwOrcvu7e3NXGYafSoHE8IPewEAAAAATGLcBAAAkD0P5BE5TfnvQY7WZeSnpR7s7+/L1tbW+GEv29vbkQfBiIiUSqWJ/x4t1xnp9/uytbUllUpFjo+PpVaryfr6euLtBxbRPB4OnfYvd9P+tUOSqzySeuCd+yqg7PZpVqS9Csj1mkn/web2XM/3rCtZzH33CYdWmEI+Hw9pz6PZV3n4OS9udWeZfp1/KfV2wA/GTcA3JTk2SLLupL5LfK388BXr2R7nPPsjTUmu8LE5h/OO4eNen9PK6KuAbGK+cAwe9yHtWRMvlo5eQ/Hi5qx/7nq9j8ypJYhrYSZIdnZ2pNPpSL1el8PDQ9nZ2ZFarSadTmci2N/d3ZXNzU1ZWVkZb3v49cFgINVqVXq9ngRBICIiKysrcnR05JzfDAAAAACyhHETAABAsTyQC3Ka8jNIHuQocdXCTJB89rOflU6nM/7vZ599VqrVauTXUHt7exPlwur1uqyuro6DfBGRZrMp9XqdQB8AAABArjFuAgAAwCJZiAmSbrcr29vbE9uCIJAgCCby5O7v78vNmzdlY2NDarVaJHA/Pj5W67p69aqIfPNXVAD8yUpqpXmk+sqCrPR/XiX14HWX6zEradJcuRyLjzqS7I/0H9KuMT2k/TGHOlweVO4mK/dY8/l+l1LWlBLA1NdA9jFuAqLSTuFUhIcP2z2gPn/HZcvXA9jt0jy5l0lS3FRqcR6cHve4kntIuyYcF9rE3n5i7rRTh0f3ZxM/EzcjG/Kz1mUGq6urUqlU1Nce3t7pdOT4+Fj29/elXq/LlStXpNvtjl+/efNm5D0iMv5V1Hm/oAIAAACALGPcBAAAUDyn30ixle5ffqYdFmIFiclgMJB6vT7+73a7Le12W/r9vrTbbdnd3ZVarSaHh4dSqVTGv5oql8vG+qa5e/eu3LlzJ3abL126JJcuXYr9fgAAAMDGvXv35N69e5al/1DZ9nWfzcEcpT1uYswEAADmxS0GFgnHwXfv3vXbICRuYSdI9vf3pVKpqEu7gyCQdrsttVpNNjY2pNlsyt7enhweHoqIyPLyslrn8fHx1P0+88wzM7X7pZdekpdffnmmOoAi87GMdN7Lkn3JSsqaRZZUaqWkyrqWd/lcJHk9utTtmkorC58X9/uPy1J1l3+AfFvdmvY1nSTz9bHjUEtxwuutrS155ZX8ff/Br3mMmxgzIY/ipMbKw7ijCCm/0uYrhVOaZWyuO1/Xgk1/xE0nlhS7z6VNDG4Te0djbl/3lyRFr3ub+DmbcfOsMfAzz/yEx9b48eDBRTl9kPJD2lPe3yyyeSWmYGtrS/b29s4ts76+Luvr69Lv90VEZGVlRUREjo6O1PKm5egPe/311+Xpp592a+xD+CUUAAAA0vDCCy/I888/H/v9b7zxxsz/0I35m8e4iTETAACYl1lj4Mcf/z+JyKe8tQfJy9UESb/fl2azaVW2UqlIu91WX2s2m3Ljxg2rCY1arTbOpzsqb/rFk019ly9flscec3kgKgAAAJC+WdMUXb582WNr4CLv4ybGTAAAYF5mT9X5Dm9tQTpyNUESBMHMD/Qb5ccdPSDQxtWrVyf+N5wzd/Tf1Wp1prYBKA4fqb6KwjVNgI8UT1nua5cUT0keR5JpkZJKDeHaHy596uO8+Ej15cItHZSeHsstHZdeNsufN7Mlx/JayKz36XD4onNr5i2P91Kcj3ETUGxFTXtlE0PGTRc1zz5KM12Ur3RevuJ52+Oy6Y84qcvipvOyYZdSKhwv2sTe0TLZ+4zbxNLT4+esxc2+7h29Xl2q1V0fTfLm9PSCyP10U16dnubnIe35aakH+/v7IiKyuro6sX20FFzT6XTGDyQsl8vqYGP0S6lr1675bC4AAAAApI5xEwAAABZFrlaQzKLb7crW1pbU63XZ3f3mLF6v1xv/gum5556TZ599Vq5fvy4iZwOD5eVlWV9fH5e/ceOGVKtVGQwG46Xh29vbsr29LeVyOb0DAgAAAADPGDcBAAAUy+n9iyL3050GOE15xcosFmKCpN/vS61WExEZ/6rpYbdv3xYRkeXlZdna2pJOpyNBEEitVovk4w2CQHq9njSbTalUKjIYDKTZbMrm5mbyBwJgKpvly+eVdanXZ/kkZKENIsm2IyvH6CIrqWyyfl6SSm3lysc9Zdal2n5SmZlCPpdQ0DUtVZa5pBZzq6NU+oS6PWspBB5W9NSGsMe4CYgvTmofm3rilvElj/vK2ndSmim/5t2HNqmyfPVH3NRlcdKbuYwJJoVjbZvYOw8xd9xYevJ9Wtw8z5g5jyn84MdCTJAEQSDD4XBqOds8vUEQyN7e3qzNAgAAAIDMYNwEAACARbMQEyQAAAAAAAAAACyaB6cXU39I+4NTUmwBwFzoy1y51eVd2imNfLVj1rKLLO0+dUlXlXaqL3PbTPc2bcm7j5RSJ+rWfKZcuuNYXj923ex9neWl+1lpBwBkTfTezRgkDbbfmXHSKtnsL2sp0eYtyf6widd9pbqz23f4M+4rBo/Gnb6uX39sYmmb+Dle3DzP/ph/3yMJfGMDAAAAAAAAAFBAp6cXZJjACpLTT96Q079/Q3/xD//Q+/6SwgQJAAAAAAAAAACwdvFHn5OLP/qc+tqDX39D7n//Mym3KB4mSAAUCssdiyntlEau7VhUPvo/K+mZfOzPR8q3Wcue0cI70/L1JcN2zTsN213ST2XFo/NuwLm41wBA/nDvng+bdFqmcr72VwRxUhalnRI0Tt1x063FKWMXg9vE3lrMnbV4e76xdFE/h0k6vX9RHryd7jNBklixkpQL824AAAAAAAAAAABA2pggAQAAAAAAAAAAC4cUWwCA3Ep7aW2S6aDM6ZLsJdWOrC2Vn4VbWiqR4fDFmffpcow+ypZKn7Cuw83XEqp3HpJLU8CSfwAA5ivJ7+I4qahs6rHha195SDem989kvB43Trdpd5wy/mLwPMTcfmJp4ub0DB9clOFpytMAD/KTYosJkpStra3J0pKec7DRaEij0Ui5RQAAAEA8rVZLWq2W+trJSdbyZSMvGDMBAIAsIwYuFiZIUnZwcCBBEMy7GQAAAMDMzvvH6n6/L9VqNeUWoQgYMwEAgCzLXQx8/4JI2g9Nv5+fJ3swQQIAgKWsp5pyWapv2p+fFE/2abqSTFvmwkcqrawci8h9h7JvJ9aKbCMEBgAA7uaZnsom1tfqjZcuano8n35qXvd4Pd022sTgRYm9iaVRLPmZygEAAAAAAAAAAPCEKT8AAAAAAAAAAIro9GL6KbZOeUg7AABIWVJpulzr9ZGmKylJpvrykXLM9v2udZj4SC2WTy5pyNyUSp9Qty9uXwMAAB/ixn7hWDJOyi2f7bGh1e3rOOLU4ytVV3HiQT+xtBY3F6ePkCdMkAAAAAAAAAAAUESnJZH7pfT3mRM8gwQAAAAAAAAAACwcVpAAABZCkmmK0pZsyqX89YcPSaUn81G3a1ownR7y+WlzHsNJ1zZH0wi4nhdfqRkAAMDs8vC9HCcVlCZrxxVXnOPwlxpreqou3WTMGb89WYu3bdozPX7W+sPXdY+QU0kyy7B5nznBChIAAAAAAAAAALBwmCABAAAAAAAAAAALJ2trtAAASESRluamfSw+0jAVKcWZST6PxT4U9JPqKyuWHcsvWZfM53UAAMBiycP3dVJtjJvmKY+pj7LXxumxd/x0XmmyiaWnx8/ZOz8FRoqtc7GCBAAAAAAAAAAALBxWkAAAAAAAAAAAUET3Jf0VJGnvbwZMkAAAkDMuS6x9LFvOSh2u4qYPKCZTdGofCmZvaf8sjhzLn0S2uPbH4l57AAAgDptYI058ETcmSTKWyWP6LjvhGHx67J2PmNsmlo4XPxfn3CNPmCBJ2dramiwt6Xn4Go2GNBqNlFsEAAAAxNNqtaTVaqmvnZxEB8aADcZMAAAgy4iBi4UJkpQdHBxIEATzbgYAAAAws/P+sbrf70u1Wk25RSgCxkwAACDLchcD3xeRt+ewz5xgggQAgJzJyrJj0xJpl/b5qMMkqXYk2WYXfo7vRV/NyRm3aD0rnzkAALA40ow/4qS48pnONs7+bOL0JPswXpuLEntPj6WJn5EnTJAAAAAAAAAAAFBED0TkdA77zAkmSAAAAAAAAAAAgL2Dlsin9WexyL38PIuFCRIAQOZlJaVRknwuUU+LqX0ux5KVY3RpR9ptdr3+Xfrfx2crK+fQjf7wZ5NS6RPKVj21QD77AwCAfMpjDC2SbiooG+H92/RrFtPLJtUmm/7wVUYz7+sjanosbRM/Z++4CuxUknkmyF9unP1p/pe+yHMZexaLwYV5NwAAAAAAAAAAACBtTJAAAAAAAAAAAICFQ4otAEDmLcLSW5djJOVYcaWdHstFVtrhx9uO5bU0Al9TS+rpBESGwxcd9wkAAKbJdrxhZtPurKXhspHHNmtsjiOpY01zX/HZxNLT42ctbiZmTsh9SSbF1rR95gQrSAAAAAAAAAAAwMJhBQkAILfy+cv12WX9+Hy0L+vHqMnjA89ZuWTL5edPOfqp1Dcs9rkFABRNUVYiz7PNcfedtX7O2kPR46wcsn1f9tjExNmKm4vT94qkHtI+bZ85wQoSAAAAAAAAAACwcJggAQAAAAAAAAAAC4cUWwCA3Mrjcte0U9lkJXVOVtqRJB/HYuqnWevw84D1IoWNruvLXR7qnr9+KtLnEACArH2vJZm2J82Hd2fvQeHTxW1jnJjc5jzbXQt5iCVtYmmb+Dlbx+rrmq5W217q8YoUW+diBQkAAAAAAAAAAFg42ZqqAwAAAAAAAAAAfrCC5FxMkKRsbW1NlpaW1NcajYY0Go2UWwQAwPl8pMfKwxL8LHDpp1LpE4ZXopGvj9Rdpoh6EdKnacfumrYsy2Y5h61WS1qtlvraycnJTO3C4mLMBADJiZvyq1ix3flsjjUai/uKwafXk49zMT1+zlrc7PLZOC8GfvLJE7l1y2fLkDQmSFJ2cHAgQRDMuxkAAADAzM77x+p+vy/VajXlFqEIGDMBAIAsy10MzAqSc/EMEgAAAAAAAAAAsHBYQQIAQIrSXg7tY3/5WMK9eIbDF9XtSS1V5zqwk8d+ymObAQDIiyS/Z+PUzfe+H+FY3FcMvkjnJ2vHmrX2ID1MkAAAAAAAAAAAUET3ReTtOewzJ0ixBQAAAAAAAAAAFg4rSAAAAByYls+blmRr5X0s33Zth0sdSe0vO9xCYB99ms9+AgAAyI9wHKbFXzZlfO1r2ntsy2QvjpweS8ftj+wda0GcSvoPTech7QAAAAAAAAAAANnFBAkAAAAAAAAAAFg4pNgCAACZk+Xl1q7tyEq7NVluW5KGwxcTrHsx+xQAAMBX+qq4bPY3z1itKHGir1i6KP2RC6eS/kPTSbEFAAAAAAAAAACQXawgAQAAAAAAAACgiFhBci4mSAAASJGWOkqE5cVhafeHy3nJyjl02Z+pzaZQUFs2X6Rr1PUclko7StnrXuoGAADFluXUsb4leVw26bvSTPFlU3f03Edj73DcnYdrw+aatomfF+mzgWxjggQAAAAAAAAAgCJKagXJP22JvN7SX3v7JIEdJoMJEgAAAAAAAAAAYO/PN87+NL/TF/lENd32xMQEScrW1tZkaWlJfa3RaEijYbioAACFwJLhbHI5L1k5h+a0WVHm1FH2dRQpdZR7m6O/fnLpuyJrtVrSaum/Gjs5yc+vxpAtjJkAFEkeY6UssunHNPvaJhaMpp2a/p48pJ2ya0+x42di4GJhgiRlBwcHEgTBvJsBAAAAzOy8f6zu9/tSrebjV2PIFsZMAAAgy3IXA9+X9B/Snvb+ZnBh3g0AAAAAAAAAAABIGytIAAAoiDwsx05T0fsj/WMpTthYKn1C3T4cvmhdR5GuJQAAkH1Fjm3Dx5aH40qujdmPubVY2iaOzsN5LaykHtI+bZ85wQoSAAAAAAAAAACwcJggAQAAAAAAAAAACyf767YAAICVpJYsa8v5k9yfL1lvX1KSS7+Qo6fsTTX7seT1cwEAAPLJV4yRxVRd896/D/76NQ8xd7w25jGVWmGQYutcrCABAAAAAAAAAAALZ+EnSAaDwbybAAAAAACZxrgJAAAgp+6LyNsp/+VhMdQ3LFyKrVKpNPHfQRBIr9cb/3e/35etrS2pVCpyfHwstVpN1tfXJ95jUwYAgKJg6XN6fCzN18qb0kG5hIKmdixqqqmiHx/AuAkAiokYZro4qaDsYvDpsbdNPXk9h3ltN4pvoSZIdnd3ZXNzU1ZWVsbbVldXx/9/MBhItVqVXq8nQRCIiMjKyoocHR3J5uamdRkAAAAAyCvGTQAAAAVyKuk/EyRHzyBZqAmSvb096XQ6xtfr9bqsrq6OA3gRkWazKfV6fRzE25QBAAAAgLxi3AQAAIBFsTATJPv7+3Lz5k3Z2NiQWq0WCcqPj4+l2+3K9vb2xParV6+KyNmvqK5duza1DME+AADZlfV0UD7a4SNNl4us9J2bhQmBAWeMmwAA85Z2POtjX0m2OXvxNrE0imVhHtLe6XTk+PhY9vf3pV6vy5UrV6Tb7Y5fv3nzpoiIVCqVifeNfvHU6XSsygAAAABAXjFuAgAAKJhTOXtoepp/pNjKnna7Le12W/r9vrTbbdnd3ZVarSaHh4dSqVRkMBiIiEi5XFbfPxgMrMpMc/fuXblz506sYxARuXTpkly6dCn2+wEAAAAb9+7dk3v37sV+/927dz22BmnJwriJMRMAAJgXYuDFszATJCNBEEi73ZZarSYbGxvSbDZlb29PDg8PRURkeXlZfd/x8bFVmWmeeeaZeA3/hpdeeklefvnlmeoAAGBRZW95ejqSTC2W9bRlmuHwRcfy0WPJ43G72traklde0Y8TxTfPcRNjJgBYbEWJp8LxYtzj8lWPLzaxtE38PO/jMClkDDxaQZL2PnNi4SZIRtbX12V9fV36/b6IiKysrIiIyNHRkVq+UqlYlZnm9ddfl6effjpGi8/wSygAAACk4YUXXpDnn38+9vvfeOONmf+hG/M3j3ETYyYAADAvxMCLJ1cTJP1+X5rNplXZSqUi7Xb73DK1Wm2cT3cUpJt+zVSpVKzKTHP58mV57LHHppYDAAAA5mnWNEWXL1/22Bq4yPu4iTETAACYF2LgxZOrCZIgCLw/0O/q1asT/xvOhzv672q1alUGAABgntJeqp7VpfFJW9TjRj4wbgIAIF1JxoZFiTuLchy5RIqtc12YdwPmqdPpSL1eF5GzBwhqA4nRL6WuXbtmVQYAAAAAioRxEwAAAIpqISZI+v2+VKtV2dnZGW/b39+X5eVlWV9fH2+7ceOGdLvdiV86bW9vy/b2tpTLZesyAAAAAJA3jJsAAAAK6L6IvJ3yX9orVmaQqxRbcVUqFVleXpatrS3pdDoSBIHUarVIrt0gCKTX60mz2ZRKpSKDwUCazaZsbm46lQEAAJiXUumVyDaWs6dH638RzgHygXETAADxEIO7o8+QFQsxQVIul61z8AZBIHt7ezOXAQAAAIA8YdwEAAAAa//v1tmf5v5Jum2ZwUJMkAAAgPzjl/l2tP5Isu/yeF5c22wq71IHAADAolmkFQI2MXjcY/dVjy8259Umfp73cSyUU0nmoenf0zj703y5L/IPqwns1L+FeAYJAAAAAAAAAADAw1hBAgAAAAAAAABAEZ1K+g9NT2LFSkKYIAEAALnAEuz4XFNHufR1Hs+La5tdyucx5RgAAEASFj3+sUk7ZdNHWetHX21epBRsyDYmSAAAAAAAAAAAKCJWkJyLZ5AAAAAAAAAAAICFwwoSAACw8BY1LVLRj8/E9Xy7LP9f1D4FAABIUjgey2PMlcc2a2xiY5syRekP5B8TJAAAAAAAAAAAFNF9EXl7DvvMCSZIUra2tiZLS0vqa41GQxqNRsotAgAAAOJptVrSarXU105OTlJuDYqCMRMAAMgyYuBiYYIkZQcHBxIEwbybAQAAHuIjtVJWpN3mRUhPVqRj8e28f6zu9/tSrVZTbhGKgDETACAsTgqnNCXZnjymF8tDG2eRuxj4VNJ/aDoPaQcAAAAAAAAAAMguJkgAAAAAAAAAAMDCIcUWAABAgaS9nD2Py+fz2GYAAABkV5LxZdZi16y1BxZOJf2HppNiCwAAAAAAAAAAILtYQQIAAAAAAAAAQBGxguRcTJAAAAAYsHwcIiKl0iuRbVwbAAAA80Mslm3Ez8gTJkgAAAAAAAAAACii+yLy9hz2mRM8gwQAAAAAAAAAACwcVpAAAAAgNm35vEi2l9An2eY89gcAAACyKxxfzjuu9JU+izRci+v4+Fi2trZERGR7ezvyer/fl62tLalUKnJ8fCy1Wk3W19edy9higgQAAAAAAAAAgCJ6IOk/NP2Bvrnb7Uq73Zb9/X3Z3NyMvD4YDKRarUqv15MgCEREZGVlRY6Ojsblbcq4IMUWAAAAAAAAAABI1Orqquzt7Rlfr9frsrq6Op74EBFpNptSr9edyrhgBQkAAABiW4Rl8C7HuAj9AQAAgPTkMb60aXMejyu37kv6D02Psb/j42PpdruRtFtXr14VEZHd3V25du3a1DKuq0hYQQIAAAAAAAAAAObm5s2bIiJSqVQmto9WinQ6HasyrlhBkrK1tTVZWlpSX2s0GtJoNFJuEQAAABBPq9WSVqulvnZycpJya1AUjJkAAECWLUQMfHpP5MG9+O+/f9f5LYPBQEREyuWy8XWbMq6YIEnZwcHBRH40AACAeSmVXlG3F325u+vxaf1U9D6ydd4/Vvf7falWqym3CEXAmAkAUERFiSlt2lyUYzXJXQx8Ku4pr/7tlsib+ngxKYeHhyIisry8rL5+fHxsVcYVEyQAAAAAAAAAAOBM5QWRJ5+P//47b4j82jNOb1lZWRERkaOjI71JlYpVGVdMkAAAAAAAAAAAUET3ReRt1zdd+sZfXJed3zGa3DCtAqlUKlZlXDFBAgAAUHCLmkrLF/oJAAAArsIx+CLFlIt0rPDn6tWrIhJ9jsjov6vVqlUZVxec3wEAAAAAAAAAALLvgZw9hyTNvwfuzSyXyxIEgXQ6nYnt3W5XRESuXbtmVcYVEyQAAAAAAAAAACBx5z1I/caNG9LtdidWiGxvb8v29raUy2XrMi5IsQUAAFBwLHEHAAAA0kUMDkT1+31pt9siIvLaa69JrVaT1dXV8cRGEATS6/Wk2WxKpVKRwWAgzWZTNjc3x3XYlHHBBAkAAAAAAAAAAEV0KmcPak97n4ogCKTdbo8nSUxl9vb2zq3epowtUmwBAAAAAAAAAICFwwoSAACAgiuVXlG3L+qyfx/9QZ8CAADgPOF4sShxohYH2xxbUfsjF+5L+itI0t7fDFhBAgAAAAAAAAAAFg4TJAAAAAAAAAAAYOGQYgsAAKBA4i55XyQ++oM+BQAAwMgixeBxj6uo/ZEL90Xk7TnsMydYQQIAAAAAAAAAABYOK0gAAAAAAAAAACiiByJyOod95gQTJClbW1uTpaUl9bVGoyGNRiPlFgEAgCJJaum6ljYgyf1lnak/TIraT61WS1qtlvrayclJyq1BUTBmAgDkja9Yr8ipumzi57wcKzFwsTBBkrKDgwMJgmDezQAAAABmdt4/Vvf7falWqym3CEXAmAkAAGRZ7mLgU0n/mSBpr1iZAc8gAQAAAAAAAAAAC4cVJAAAAAvKJW1WXpa7J6HIqQ4AAACQDpuYsigxJvEz8oQJEgAAAAAAAAAAiui+pJ9iK+39zYAJEgAAAEy1yA9pX4RjBAAAwPwVZeVFHtuMxcUECQAAAAAAAAAARXRfRN6ewz5zgoe0AwAAAAAAAACAhcMKEgAAgAXlsvS9SMvkFzldGAAAAObDJtbMQzxalDRgwAgTJAAAAAAAAAAAFNEDETmdwz5zggkSAAAAAAAAAABg707r7E8zPEm3LTNgggQAAAA4B2kEAAAAAHvEzxlzKsk8NP1bG2d/mq/3Rb5cTWCn/vGQdgAAAAAAAAAAsHBYQQIAAAAAAAAAQBEltYJk2j5zggkSAAAALBTX5f2kAwAAAADO2MTGxM/IEyZIUra2tiZLS0vqa41GQxoNQ942AAAAIGNarZa0WvqDGU9O8vNgRmQLYyYAAJBlxMDFwgRJyg4ODiQIgnk3AwAAAJjZef9Y3e/3pVrNx4MZkS2MmQAAQJblLgZOO73WvPYZExMkAAAABVIqvRLZZlrirpU9rzwAAACAKJsY3CVOB5AeJkgAAAAAAAAAACiiUxEpzWGfOXFh3g0AAAAAAAAAAABIGytIAAAACsRlmT5L+u2QDgEAAADnsYkNFyl+JH5GnjBBAgAAAAAAAABAEc0j3RUptgAAAAAAAAAAALKLFSQWBoOBVCqVeTcDAAAAc0A6AMAO4yYAACBC/Jw5pyIyTHmfD1Le3wwWZgVJrVaTUqmk/nW73Ymy4dc3NjYmXu/3+7KxsSHNZlPq9brs7++neSgAAAAAkAjGTQAAAFgkC7GCZDAYyGAwkO3tbSmXy+Pth4eHsrOzI6urq+Ntu7u7srm5KSsrK+NtD78+GAykWq1Kr9eTIAhERGRlZUWOjo5kc3Mz+YMBAAAAgAQwbgIAACig+yJSSnmfaa9YmcFCTJB0u13p9XoTQb6IRIJ8Efn/t3f/vm1k96LAv7642I0Xi1za2SaNC6p7VTBy6m1IBBcpTXmB16QKVahLYcJVdis9qldBudkieIBX9B9wL9lsqhQWkTQ3lfiwVV6RyESwiNe+MeYWjpiVRdH6McOZ4Xw+wMASOTo6mu+cwff48JwTh4eHMRqNLixre3s7Wq3WPMmPiPknoiT6AABAVek3AQBQN7VYYqvb7Z5L8iMinj59emYa+HA4jOfPn8fW1lYcHBycO382m8V4PI52u33m9fv370dELPwZAACAKtBvAgCgbmoxQLLIbDaLyWQSDx8+nL82Go1iNpvFcDiM7e3tuHPnzpl1dp8/fx4RcW7jwdNPRS37BBUAAEDV6DcBAFTcm4KOiqjFEluLfPXVV5EkyZlPSA0GgxgMBjGZTGIwGMTBwUG02+04Pj6OZrMZ0+k0ImLhp6oiYv7+Mt9++2389a9/vXa9P/zww/jwww+v/fMAAHAZr169ilevXl3q3H/7t90Fr/7/bCtEIYroN+kzAQBFuUoOHHE+D/7663/PukrkrLYDJIeHh/HZZ58tfC9JkhgMBtFut2Nrayt6vV4cHh7G8fFxRETcvXt34c/NZrP3/t5PP/302nWOiPj1r38dn3/++Y3KAACA99nd3Y0vvvii6GpQsCL6TfpMAEBRbpoDf/rp/8mwNhmq0Kbpq1bLAZLTNXEHg8HS8zqdTnQ6nZhMJhERsbGxERERJycnC89/dwr5Il9//XX85Cc/uVqFv8cnoQAAWIXHjx/Hr371q0ude/EMki+zrBIrVlS/SZ8JACjKVXLgiMUzSG76YQ9Wq1IDJJPJJHq93qXObTabFyby4/E4ms3mpQY02u32fD3d0/Mv+sTTZcr7+OOP44c//OF7zwMAgCJdZZmiND0/QDKZTGJz88uMa8VlVL3fpM8EABTlqkt1vpsHn35ghOqo1ABJkiSZbOj39OnT6HQ6lz7//v37Z/59d83c0+83NzdvXDcAAICb0G8CAIDL+ZeiK1CE4XB44Tq67xqNRrG9vR0RbzcZXNTZOP2k1MOHD7OtKAAAQEH0mwAAuNh+RPyvC44HBdbramo3QDIcDucJ+/e9XQJgM/b29s6ce/fu3TOfmnry5EmMx+Mzn4bq9/vR7/ej0WjkXn8AAIC86TcBALDcTkT81wXHswLrdTWVWmIrC0+fPl34iaVmsxl3796N3d3dGI1GkSRJtNvtc+vxJkkSR0dH0ev1otlsxnQ6jV6vF91ud1V/AgAAQK70mwAAqIPaDZAcHh4ufL3RaFx6nd4kSS4sBwAAoOr0mwAAqIPaLbEFAAAAAABggAQAAAAAAKid2i2xBQAAAAAA9fD3iPjvAn5nNZhBAgAAAAAA1I4ZJAAAAAAAsJb+Hquf0WEGCQAAAAAAQGkZIAEAAAAAAGrHElsAAAAAALCWbNK+jAGSFXvw4EHcvn174Xs7Ozuxs7Oz4hoBAMD17O/vx/7+/sL3Xr58ueLasC70mQCAMpMDrxcDJCv27NmzSJKk6GoAAMCNLfvP6slkEpubmyuuEetAnwkAKLPq5cBvYvUzOt6s+Pddnz1IAAAAAACA2jFAAgAAAAAA1I4ltgAAAAAAYC3ZpH0ZM0gAAAAAAIDaMYMEAACWuHXri3OvpemvC6gJAACUn/y5bMwgWcYMEgAAAAAAoHbMIAEAAAAAgLX0JlY/o+PNin/f9RkgAQCAJSwHAAAAlyd/pkossQUAAAAAANSOGSQAAAAAALCWbNK+jBkkAAAAAABA7ZhBAgAAAAAAaymvTdr/7z+ORV7l8PvyYYAEAAAAAAC4gv/9j2OR/4qIzgrrcn0GSFbswYMHcfv27YXv7ezsxM7OzoprBAAA17O/vx/7+/sL33v58uWKa8O60GcCAMpMDrxeDJCs2LNnzyJJkqKrAQAAN7bsP6snk0lsbm6uuEasA30mAKDMqpcD26R9GZu0AwAAAAAAtWMGCQAAAAAArKW/x+pndJhBAgAAAAAAUFpmkAAAAAAAwFqyB8kyZpAAAAAAAAC1Y4AEAAAAAACoHUtsAQAAAADAWnoTq1/y6s2Kf9/1mUECAAAAAADUjhkkAAAAAACwlmzSvowZJAAAAAAAQO0YIAEAAAAAAGrHElsAAAAAALCWbNK+jBkkAAAAAABA7ZhBAgAAAAAAa8km7csYIFmxBw8exO3btxe+t7OzEzs7OyuuEQAAXM/+/n7s7+8vfO/ly5crrg3rQp8JACgzOfB6MUCyYs+ePYskSYquBgAA3Niy/6yeTCaxubm54hqxDvSZAIAyq14ObA+SZexBAgAAAAAA1I4BEgAAAAAAoHYssQUAAAAAAGvJJu3LmEECAAAAAADUjhkkAAAAAACwlswgWcYACQAAAAAAcAX/ERH/ecF7r1dZkRsxQAIAAAAAAFzBz/5xLPL/IuLxCutyfQZIAAAAAABgLf09Vr/kVXWW2LJJOwAAAAAAUDtmkAAAAAAAwFqySfsyZpAAAAAAAAC1Y4CEhV69ehWff/55vHr1quiqcAXiVk3iVk3iVk3iVk3iVk2vX78+8y9UjWdPvlzffLm++XJ98+X65sv1zVc5c+A38c99SFZ1vFnJX5YFAyQs9OrVq/jiiy88LCtG3KpJ3KpJ3KpJ3KpJ3KqpnJ1DuDzPnny5vvlyffPl+ubL9c2X65svOXD12INkxR48eBC3b99e+N7Ozk7s7OysuEYAAHA9+/v7sb+/v/C9v/3tbyuuDetCnwkAKDM58HoxQLJiz549iyRJiq4GAADc2LL/rP7tb38bn3766YprxDrQZwIAyqx6ObBN2pexxBYAAAAAAFA7ZpAAAAAAAMBaOt2kfdW/sxrMIAEAAAAAAGrHAAkAAAAAAFA7ltgCAAAAAIC1ZJP2ZcwgWQP7+/tFV+FK8qqvcvNVtetQtXLzUrXrULVy81K161C1cvNStetQtXLzUrXrULVyoShluqezqksW5ZSpLlkp099UprpkpWx/U5nilIUyXZesynF98y3H9c23nDJdX1bHAMkaqFrjrVrHvmrl5qVq16Fq5ealatehauXmpWrXoWrl5qVq16Fq5ealatehauVCUcp0T6/jf/y4vvmVkWU5WSjb31SmOGWhTNclq3Jc33zLcX3zLadM1zdbp5u0r/Kozibta7XE1mw2i93d3YiI6Pf7596fTCaxu7sbzWYzZrNZtNvt6HQ6uZwDAABQRvpNAADw1toMkIzH4xgMBjEcDqPb7Z57fzqdxubmZhwdHUWSJBERsbGxEScnJ/PzszoHAACgjPSbAADqxh4ky6zNElutVisODw8vfH97eztardY8OY+I6PV6sb29nfk5AAAAZaTfBAAA/7Q2AyTLzGazGI/H0W63z7x+//79iIg4ODjI7BwAAIAq0m8CAKBuajFA8vz584iIaDabZ14//TTTaDTK7BwAAIAq0m8CAFhHq96g/fSohrXZg2SZ6XQaERGNRuPC97M65yIvX76MiLcbFX777beXqPViH3zwQXzwwQfnyp5MJtcuc5HTOv7+97+Pjz/+ONOy86ivct8St2qWK27VLFfcqlmuuFWzXHErptzXr1/H69evr13mH/7wh4iI+O67725UN1an6H5Tln2mLNpKVs+erNptFuWUqS6ub77lrOP1zaoc1zffclzffMtxffMt5+XLl/G73/1uDXPgP9fkd15TumYiIu12u2dee/ToURoR6dHR0cLzm81mZudc5De/+U0aEQ6Hw+FwOBwOR62OL7/88hpZPXmLKF+/SZ/J4XA4HA7HuhxlyIG/+eab9KOPPirsGnz00UfpN998U/RleK9azCDZ2NiIiIiTk5OF7zebzczOucjPfvaz+PLLL+PHP/5x/OAHP7h03d+1aAYJAABk7aYzSL777rv405/+FD//+c8zrBV5KrrfpM8EABRtnXLge/fuxR//+Mf485+Lmc3xySefxL179wr53VdRmgGSyWQSvV7vUuc2m80YDAaXLvs0AZ/NZhe+n9U5F/nkk0/iF7/4xeUqDAAAsMA695v0mQAAsnXv3r1KDFIUqTQDJEmS5LZZ3/379yPi/Fq3p99vbm5mdg4AAEBe9JsAACA7/1J0BVah0Wgs7EiMx+OIiHj48GFm58AqXLSxJZAf7Q6Koe3B6ug3kQfPcdaB+5h14D6GxUozgyQLF03hjoh48uRJbG5uxnQ6nU/p7vf70e/3o9FoZHpOlU0mk9jd3Y1msxmz2Sza7XZ0Op2iq1V7t27dOvN9kiRxdHQ0//4ycRPbfMxms9jd3Y2It8+Cd2UVG/HL1vviFqHdldlwOIzd3d2YTCaRJEn0+/1otVpnztH2yucycYvQ9srm+3E7Xa5Je6s+/abl3Is34zl+c/oY+dIXyJ98PV/y6nzJf2uo6F3is3J0dJR2u900ItJGo5EeHh6mL168OHdOp9NJHz16lHY6nXQwGCwsJ4tzquj4+DiNiPTo6Gj+WrPZXJu/r6oGg0Ha7XbTfr8/P74fo8vETWzzMRqN0k6nk0ZE2u12z72fVWzEL1vvi1uaandl1u/301arlQ4Gg/TRo0dpRKQRkY5Go/k52l75XCZuaartlc1pPEajUToajdIkSdKISI+Pj+fnaG/Vo9+0nHvxZjzHb04fI1/6AvmTr+dLXp0v+W89rc0ACTfXarXSVqt15rXBYJCu0ThaJb0bk0Xvvy9uYpuvi5LrrGIjfvlY1inS7sqr0+mc+f7o6CiNiDPXWtsrn8vELU21vbLp9/tnvj+N2+Hh4fw17Y114168Gc/x7Ohj5EtfID/y9XzJq/Ml/62nWuxBwvvNZrMYj8fRbrfPvH66weLBwUER1aq94XAYz58/j62trYUxuEzcxLYYWcVG/FZPuyuv8Xh8bhmEJEkiSZL5erraXvlcJm4R2l4ZPXr06Mz3p8siJUkSEdob68e9eDOe4/nz3M2f+/hm5Ov5klfnT/5bTwZIiIiI58+fR0TM1wc+dfoAeHeDRVZjNBrFbDaL4XAY29vbcefOnfnmlhGXi5vYFiOr2Ijf6ml35dVqtc5d01Onr2t75XOZuEVoe1UwHA6j3+9rb6wt9+LNeI7nz3M3f+7jm5Gv50tevXry33owQEJExHyk+aINE78/Es3qDAaDSNM0jo6OotvtzjdsOo3HZeImtsXIKjbit3raXfVMp9PY2tqafx2h7VXB9+MWoe2VXa/Xm28geUp7Y924F2/Gczx/nrv5cx/nQ76eL3l1PuS/9WGAhIiIOD4+joiIu3fvLnx/NputsDa8K0mSGAwGcXh4GBFvH9IRl4ub2BYjq9iIX3G0u2oYDofRbDaj2+1GhLZXFe/G7fu0vfLZ29uL6XQas9nszFIN2hvrxr2YDc/x/Hjuro77ODvy9XzJq/Mh/60XAyRERMTGxkZERJycnCx8/6IpfKxWp9OJTqcTk8kkIi4XN7EtRlaxEb/iaXfltru7O0/4I7S9qng3botoe+Xx6NGjODw8jNFoFI1GY772tfbGunEvZstzPHueu6vnPr45+Xq+5NX5kP/WiwESIuKfDe+iEUoNszza7fb8AXqZuIltMbKKjfiVg3ZXTr1eL548eXLmemp75bcobhfR9sql1WpFt9udT/nX3lg37sXseY5ny3O3GO7j65Ov50tenT/5bz0YICEiIu7fvx8R59e4O/1+c3Nz5XXiYqfxukzcxLYYWcVG/MpDuyuXg4ODaLfb803sTml75XZR3JbR9srlpz/96bzDpr2xbtyL+fAcz47nbnHcx1cnX8+XvHp15L/rzwAJEfF2U6AkSWI0Gp15fTweR0TEw4cPi6gWC4xGo9je3o6Iy8VNbIuRVWzErxy0u3IZDocR8fbTPN83mUy0vRJbFreLaHvlM51O5zHU3lg37sXseY5ny3O3GO7jq5Ov50tevVry3xpI4R+Ojo7SiEiPj4/nrzWbzbTf7xdYq/o6OjpKkyQ5c/0PDw/Tbrd77rz3xU1s8/PixYs0Is7FJU2zi434Ze+iuGl35TcajdIkSdLBYHDm6Ha76WAwSNNU2yuj98VN2yufFy9epJ1OJz08PJy/dnx8nLZarTPnaW+sG/fi9XiOZ0sfI1/6AvmSr+dLXp0f+W993UrTNM13CIYqmUwmsbu7G81mM6bTabTb7eh2u0VXq5Zms1lsbW3F8+fP4/79+5EkSbTb7XOfEIi4XNzENnuTySQGg0EcHBxEo9GIJ0+eRKvVikajceacLGIjftlZFjftrtwmk8nSqcYvXryYtz9trzwuE7eI0PZKqN1uz2PSbrej2WxGp9M5d572xrpxL16dHCo7+hj50hfIl3w9X/Lq/Ml/68kACQAAAAAAUDv2IAEAAAAAAGrHAAkAAAAAAFA7BkgAAAAAAIDaMUACAAAAAADUjgESAAAAAACgdgyQAAAAAAAAtWOABAAAAAAAqB0DJAAAAAAAQO0YIAEAAAAAAGrnX4uuAAAA8H7T6TQ2Njai2WxGp9OJiIjHjx9Ho9EovF6DwSBms1l89dVXMZvN4sWLF4XXCwCAapP/sgoGSAAAoEJ6vV50u92iqzHXbDaj3+9HRESj0Yi9vb2CawQAwDqR/5InS2wBAACZ+NGPflR0FQAAYGXkv9VngAQAAAAAAKgdAyQAALBCGxsb0W63Y2trK7a3t2Nraytu3boVt27dislkcuXyZrNZHBwcRLvdjoODg5hOp9Fut+POnTvRbrdjNptFRMTe3l5sbGzEnTt3otfrZfbzAACwjPyXMrMHCQAArFCr1YrBYDD/fm9vL4bDYXS73UiS5MrlnZycxNHRUYzH44iIOD4+jn6/HycnJ/OOaLPZjK2trRiNRtHr9WJvby8+++yzSJLkxj8PAADLyH8pMwMkAACwQltbW/OvZ7NZ9Hq9aDQa840er6rZbMb29nYcHByc2TAyIiJJkhiPx3F8fBzNZjMiIh4/fhzD4TDG43EkSXLjnwcAgGXkv5SZJbYAAGCFWq3W/Otf/vKXERHR7/ej0WjcuOx3yzjt1N29e/fca8fHx5n/PAAAvEv+S5kZIAEAgAKMx+MYDoeRJEl0u92iqwMAALmS/1JGBkgAAKAA29vbERFxeHhYcE0AACB/8l/KyAAJAACsWK/Xi+l0Go8ePZpP2QcAgHUl/6WsDJAAAMAKTafT2NvbW7gx5d7eXkG1AgCAfMh/KTMDJAAAsEIXLS0wmUziL3/5y7XKPDk5Wfj6bDY79/7p14teu+7PAwDAReS/lJkBEgAAWJHxeBzj8Tgi3nYQt7e3Y3t7O9rtdmxubsbGxsaVy5xMJvNP4h0cHMRwOJx/ffq7er1eTCaTmE6n0ev15nU5ODi48c8DAMBF5L+U3a00TdOiKwEAACw3nU5jY2MjBoNBdLvdoquz0N7eXvR6vXjx4kU0Go2iqwMAQIXJf1kFM0gAAKBCTqf9l9F1l0gAAICLyH/J078WXQEAAODydnd35x2xx48fF/5Jtel0GoPBICLCkgMAAGRO/kueLLEFAAAAAADUjiW2AAAAAACA2jFAAgAAAAAA1I4BEgAAAAAAoHYMkAAAAAAAALVjgAQAAAAAAKgdAyQAAAAAAEDtGCABAAAAAABqxwAJAAAAAABQOwZIAAAAAACA2jFAAgAAAAAA1M7/ANEOndZwvXZlAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmax=500\n",
    "\n",
    "\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,8))\n",
    "\n",
    "\n",
    "a0 = ax0.hist2d(cut_z_found, cut_x_found, density=False, bins=200,  cmap=plt.cm.jet, cmin=1, vmax =vmax)\n",
    "ax0.set_ylim(-1000,1000)\n",
    "ax0.set_xlim(-200,4000)\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(r\"$e^\\pm$ found brem vertices\")\n",
    "\n",
    "a1 = ax1.hist2d(cut_z_lost, cut_x_lost, density=False, bins=200, cmap=plt.cm.jet, cmin=1, vmax=vmax)\n",
    "ax1.set_ylim(-1000,1000)\n",
    "ax1.set_xlim(-200,4000)\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(r\"$e^\\pm$ lost brem vertices\")\n",
    "#ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
    "\n",
    "plt.colorbar(a0[3], ax=ax1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tuner",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}