{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d5cf2b3d-0f34-4d13-9ba3-dd2e3fb66337",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot 2-D histograms as functions of hours with all stations combined\n",
    "\n",
    "from docx import Document\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "from itertools import chain\n",
    "from matplotlib.colors import LinearSegmentedColormap, Normalize\n",
    "import matplotlib.colors as mcolors\n",
    "from matplotlib.colors import BoundaryNorm\n",
    "\n",
    "# Load the Word document\n",
    "file_path = \"/Users/cdz/Desktop/Khafid_etal/BMKG_SurfaceStationData/CombinedStations.docx\"\n",
    "doc = Document(file_path)\n",
    "\n",
    "len(doc.tables)\n",
    "\n",
    "table = doc.tables[0]\n",
    "\n",
    "data = []\n",
    "for row in table.rows:\n",
    "    data.append([cell.text.strip() for cell in row.cells])\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df.head()\n",
    "\n",
    "# Define column names\n",
    "df.columns = [\n",
    "    \"station_id\",\n",
    "    \"station_name\",\n",
    "    \"region\",\n",
    "    \"longitude\",\n",
    "    \"latitude\",\n",
    "    \"elevation_m\",\n",
    "    \"value\"\n",
    "]\n",
    "\n",
    "id = pd.to_numeric(df[\"station_id\"], errors=\"coerce\")\n",
    "nid = id.shape[0]\n",
    "\n",
    "y_labs = ['Time', 'T (˚C)', 'p (hPa)', 'V (m/s)', 'q (g/kg)', 'WDA (degree)', 'Rain (mm/3hrs)'] \n",
    "x_labs = \"Local Time\"\n",
    "labs = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)']\n",
    "\n",
    "cmap = LinearSegmentedColormap.from_list(\"white_to_dark\", [\"white\", \"black\"])\n",
    "norm = BoundaryNorm([0, 1, np.inf], ncolors=cmap.N, clip=True)\n",
    "\n",
    "count_std = np.zeros(6) \n",
    "\n",
    "y1 = [2017, 20, 1000,  0, 13,   0,  0]\n",
    "y2 = [2023, 25, 1021, 10, 25, 360, 30]\n",
    "\n",
    "#var = ['Time', 'rainfall3h', 'Temp', 'Specific humidity', 'Pres', 'Windspeed', 'Windir', 'RH'] \n",
    "vsh = ['T', 'p', 'V', 'q', 'WDA', 'R3h']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51ce5ba3-dcbb-4870-a512-b2c338c0e4f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r9zbLsjR37lw99dRTuvPOOyVJb775psLDw7V06VKNHj1akjRhwgRJ0vr16y86VlBQkCIiIiqVV0FBQalOzbm5uZKkvLw8l18j/v7+Lq0vmb9XTJh2sD979qzLMadOnXI5Ji8vz+UY6edC2F2CgoJcjqlfv77RWAEBAS7HeHt7uxzjzq73pq9Bd3WVl8w6t5u83s+cOeNyjGT2XJi8Ls7/ouhysWcFAFCjpaWlKSMjQ/Hx8c5lvr6+uummm7Rp0yaX72/27Nlq0KCBrr32Wj3//PMX/bCdlJSkkJAQ56VJkyZGjwEAUD72rAAAarSMjAxJUnh4eKnl4eHhLu8d/e1vf6tOnTqpfv362rJli6ZOnaq0tDS9/vrr5a4/depUJSYmOq/n5uZSsABAFaJYAQBcES782ZVlWS7/FGvixInO/3fo0EH169fXXXfd5dzbciFfX1/5+vqaJQwAuCR+BgYAqNHOHV9ybg/LOZmZmWX2trjq+uuvlyT98MMPl3U/AAAzFCsAgBqtWbNmioiIUHJysnPZ2bNnlZKSoh49elzWfX/11VeSpMjIyMu6HwCAGX4GBgCwvby8vFJ7N9LS0pSamqrQ0FDFxMRowoQJmjVrllq0aKEWLVpo1qxZCggI0JAhQ5wxGRkZysjIcN7P119/raCgIMXExCg0NFRffPGFNm/erJtvvlkhISHaunWrJk6cqIEDByomJsbtjxkAQLECAKgBtm3bpptvvtl5/dxB7cOGDdOSJUs0efJknT59Wo8++qiys7N13XXXae3ataVOFfvaa69p5syZzus33nijJGnx4sUaPny4fH199c4772jmzJkqKChQbGysRo0apcmTJ7vpUQIALuSwTE9afQXJzc1VSEiIcnJyjPpPwJzJy8/0XPoX/p69Muiz8j9RUVEux0hS8+bNXY6pbI+LC4WEhLgcY3pw9JXWZ4XtYNU49zz++OOPLvfUoM/K/9Bn5X/os/I/9Fn5n5rQZ6Vt27ZVMqdwzAoAAAAAW+JnYPAok28uTDupu7OD/ZEjR1yOKSwsNBrL5BsPk29xjh8/7nKMJB06dMjlmHr16hmN5efn53KMyfMnSXXr1jWKQ+2QmZnp8vvMZM+gyWteMvs2vLi42Ggsk+3NiRMnXI7JyspyOUYy27aZfsNv8jc2PV4qLCzM5ZjAwECXY0z37pnsnTbZKyCZfdYw3XNh8hklNzfXLeNIZo/LZK9vVe6xZM8KAAAAAFuiWAEAAABgSxQrAAAAAGyJYgUAAACALVGsAAAAALAlihUAAAAAtkSxAgAAAMCWKFYAAAAA2BLFCgAAAABbooM9qoxJR9+TJ0+6HGPSiV6Sdu/e7XLMnj17jMY6ffq0yzHBwcFGY5l0Dy4sLHQ55tixYy7HSGadg027cps8F6ad6E06+tL1vvZIS0tTQECASzEmHcdNtxve3t4ux5hsNySzTts//fSTyzFHjhxxOUYy62Bv0n1dMutgb7INNWXyGjTZFkpmz2FxcbHRWCZd20+cOGE0VlZWlssxR48edTnG9LNQQUGByzEmf2OTz0EVYc8KAAAAAFuiWAEAAABgSxQrAAAAAGyJYgUAAACALVGsAAAAALAlWxcrSUlJ6tq1q4KCghQWFqbBgweXOaPTjBkz1Lp1awUGBqp+/fq69dZb9eWXX3ooYwAAAABVxdbFSkpKihISErR582YlJyerqKhI8fHxys/Pd67TsmVLzZs3T19//bU+//xzNW3aVPHx8canWQUAAABgD7bus7J69epS1xcvXqywsDBt375dN954oyRpyJAhpdaZM2eO3njjDe3cuVO33HKL23IFAAAAULVsXaxc6FxjqdDQ0HJvP3v2rBYuXKiQkBB17NixwvspKCgo1RQnNze3ahMFAAAAcNlqTLFiWZYSExPVq1cvtWvXrtRtH330ke69916dOnVKkZGRSk5OVsOGDSu8r6SkJM2cObO6U651TLrs5uXluRxjWlyadG1t0KCB0VhBQUEux9SvX99oLB8fH5djTDpRnzx50uUYSapTx/Vfm5p0vJak7Oxsl2N8fX2NxjLpRm8ylsnzB8/7/vvv5efn51KMyc+XLzbXXYyruUnmndRNHpdJR2/Tn3+bdNr28jL7+GSybTOZuySzbu8lJSUux5jOkybPocncJZk976avp8OHD7sck5aW5nJMVlaWyzGS2fs4ICDA5RjT1215aswsOHbsWO3cuVPLli0rc9vNN9+s1NRUbdq0SX379tXdd9+tzMzMCu9r6tSpysnJcV7S09OrM3UAAAAABmpEsTJu3DitWrVK69atU3R0dJnbAwMD1bx5c11//fV644035OXlpTfeeKPC+/P19VVwcHCpCwAAAAB7sfXPwCzL0rhx47RixQqtX79ezZo1q3RcVe5+AgAAAOB+ti5WEhIStHTpUn3wwQcKCgpSRkaGJCkkJET+/v7Kz8/X888/r4EDByoyMlJZWVmaP3++Dh06pN/85jcezh4AAADA5bB1sbJgwQJJUu/evUstX7x4sYYPH666devq+++/15tvvqn//Oc/atCggbp27aqNGzeqbdu2HsgYAAAAQFWxdbFiWdZFb/fz89P777/vpmwAAAAAuJOtixUAQM1WWFiojIwMnTp1So0aNaqwTxYAAOWpEWcDAwDUHHl5efrzn/+s3r17KyQkRE2bNlWbNm3UqFEjxcbGatSoUdq6daun0wQA1AAUKwCAKvPSSy+padOmWrRokX75y1/q/fffV2pqqnbv3q0vvvhC06dPV1FRkW677Tb17dtXe/fu9XTKAAAb42dg57Es65LHyVQFh8NR7WNcDtPn4NSpUy7HmHRgNekOK/38ba+rvL29jcYy6egbEhJiNJZJh/OzZ8+6JUYy6yqfm5trNJbJ39jkdStJjRs3djnmqquucjnGpNO45Hp3aJNu1+XZtGmT1q1bp/bt25d7e7du3TRy5Ei99tpreuONN5SSkqIWLVpUydh2kpmZKV9fX5diTF6/P/30k8sxktm2zaS7uWS2nTd5Lky5+neSpLp16xqNZfIcmv6NTT5rmMSYdpU3ed5N5yGT1+ChQ4eMxjLpRn/06FGXY0yfC5PX7pkzZ1yOMc2vPBQrAIAq8/e//71S6/n6+urRRx+t5mwAADVdlRcrBQUF2rJli/bv3+88oDIuLq7SDR0BAAAAQKrCYmXTpk165ZVXtHLlSp09e1ZXXXWV/P39dfz4cRUUFOjqq6/WI488ojFjxigoKKiqhgUA2NjWrVv197//XQcPHizzswBOPQ8AuJQqOcB+0KBBuuuuu9S4cWOtWbNGJ0+eVFZWlg4dOqRTp05p7969evrpp/Wvf/1LLVu2VHJyclUMCwCwseXLl6tnz5769ttvtWLFChUWFurbb7/VZ599ZnyMFgCgdqmSPSvx8fH6+9//Lh8fn3Jvv/rqq3X11Vdr2LBh2rVrl44cOVIVwwIAbGzWrFl66aWXlJCQoKCgIL388stq1qyZRo8ercjISE+nBwCoAapkz0pCQkKFhcqF2rZtq9tuu60qhgUA2Ni+fft0xx13SPr5gPr8/Hw5HA5NnDhRCxcu9HB2AICaoMr6rGRnZ+uVV14p95SjOTk5Fd4GALgyhYaG6uTJk5J+PtXzN998I0k6ceKE8SmjAQC1S5UVK/PmzdOGDRsUHBxc5raQkBBt3LhRr7zySlUNBwCwuRtuuMF5jOLdd9+t3/72txo1apTuu+8+3XLLLR7ODgBQE1RZsfLee+9pzJgxFd4+evRo/eMf/6iq4QAANjdv3jzde++9kqSpU6fq8ccf108//aQ777xTb7zxhoezAwDUBFV26uJ9+/ZdtAtxixYttG/fvqoarlqcPn3apa6+rnaIPseke6g7u96bdinOyclxS8yJEydcjpHM/l6mBwFHRUW5HOPv7280lgmTbrSWZRmNZdKJ+tixY0ZjmXQBPnz4sNFYLVu2dDkmJibG5ZhGjRq5HCOp3L3cF1PVP9M9cOCA1q5dq8LCQt10001q27atJk+erMmTJ1fpOHZVUlLi8rY0Ozvb5XFMt4cmioqK3Bbn5+fncoxpW4SrrrrK5RhXPiucLz8/3+WYcz+ldFVmZqZRnKtM5hPJ9W3U5Yz1008/uRxjOjf85z//cTmmTh3X9x3Ur1/f5RhJlT7G/HzFxcUuxxQUFLgcU5EqK1bq1q2rI0eOVDgZHzlyxOiPAQCoWTZs2KDbb7/deVyKl5eX3nzzTd13330ezgwAUNNUWfUQFxenlStXVnj7ihUrFBcXV1XDAQBsatq0abr55pt16NAhZWVlaeTIkbVmjwoAoGpV2Z6VsWPH6t5771V0dLT+7//+z/lTp+LiYs2fP18vvfSSli5dWlXDAQBs6uuvv9aGDRucP4d88cUXtWjRImVnZxv/dAEAUDtVWbHy61//WpMnT9b48eP11FNP6eqrr5bD4dC+ffuUl5enSZMm6a677qqq4QAANnXixAmFhYU5rwcGBiogIEAnTpygWAEAuKTKihVJev755zVo0CD97W9/0w8//CDLsnTjjTdqyJAh6tatW1UOBQCwsW+//VYZGRnO65Zl6bvvvit1sHCHDh08kRoAoAap0mJFkrp160ZhAgC13C233FLmDHL9+/eXw+GQZVlyOBxGZ5gBANQunJ4LAFCl0tLS9OOPPyotLa3M5dzyH3/80aX73LBhgwYMGKCoqCg5HI4yJ3SxLEszZsxQVFSU/P391bt3b+3atavUOgsXLlTv3r0VHBwsh8NR7ml/s7OzNXToUIWEhCgkJERDhw516+mBAQClXVaxkpqaqptvvlndunXTRx99VFU5AQBqsNjY2EpdXJGfn6+OHTtq3rx55d7+wgsvaM6cOZo3b562bt2qiIgI3XbbbaV+dnbq1Cn17dtXTz75ZIXjDBkyRKmpqVq9erVWr16t1NRUDR061KVcAQBV57J+Bvbggw9qypQpateunXr06KHMzEwFBARUVW5ul5WVpbNnz1Z6fdMmfr6+vi7HmDagNOltU1hYaDSWSbMrkwZPJo2kJLMmXq5+oDqnYcOGLseY/o1NmjWaPO8mjaQks4ampj2Zjh8/7nKMSdNKSdq7d6/LMef6jrjC9Fv98PBwl9Y3fR4u5cSJE9qyZYsyMzPLNEl88MEHK30//fr1U79+/cq9zbIszZ07V0899ZTuvPNOSdKbb76p8PBwLV26VKNHj5YkTZgwQZK0fv36cu/nu+++0+rVq7V582Zdd911kqRFixape/fu2r17t1q1alUmpqCgoFTzs3PNNSMjI11ubJiVleXS+pJ5w0CT7bxJQ2PJrOligwYNXI4xacZrOpZpk1yTefLQoUNGY51/zFhlHTlyxOUY00aNgYGBLseYfj4xadRo+t4ymcsjIiJcjnF1G3+OyWdQk7nr9OnTLsdU5LKKFcuyVKdOHXl5eRm/cQEAV6YPP/xQ999/v/Lz8xUUFFSqcHU4HC4VKxeTlpamjIwMxcfHO5f5+vrqpptu0qZNm5zFyqV88cUXCgkJcRYqknT99dcrJCREmzZtKrdYSUpK0syZMy//QQAAynVZPwN766239Nprr2n48OFatmxZjd6rAgCoWo899phGjhypkydP6sSJE8rOznZeTPaCVeTcN8gXftMYHh7u0rfLGRkZpU65fE5YWFiF9zN16lTl5OQ4L+np6S5kDgC4lMvasxIXF1fh7nQAQO12+PBhjR8/3m1fZF34k8NzZx27nPu41P34+voa/awCAFA5nA0MAFAt+vTpo23btlX7OOd+733h3o/MzEyXftcdERGhn376qczyY8eOGf8+HABweaqkz8rBgwcVExNT6fUPHz6sxo0bV8XQAAAbWbVqlfP/d9xxhyZNmqRvv/1W7du3L3OSi4EDB1bJmM2aNVNERISSk5MVFxcnSTp79qxSUlI0e/bsSt9P9+7dlZOToy1btjj7hX355ZfKyclRjx49qiRXAIBrqqRY6dq1qwYOHKhRo0ZV2BAyJydH7777rl5++WWNHj1a48aNq4qhAQA2Mnjw4DLLnn322TLLXG0KmZeXpx9++MF5PS0tTampqQoNDVVMTIwmTJigWbNmqUWLFmrRooVmzZqlgIAADRkyxBmTkZGhjIwM5/18/fXXCgoKUkxMjEJDQ3XNNdeob9++GjVqlP785z9Lkh555BH179+/3IPrAQDVr0qKle+++06zZs1S37595e3trS5duigqKkp+fn7Kzs7Wt99+q127dqlLly76wx/+UOHpJwEANduFpyeuKtu2bdPNN9/svJ6YmChJGjZsmJYsWaLJkyfr9OnTevTRR5Wdna3rrrtOa9euVVBQkDPmtddeK3XmrhtvvFGStHjxYg0fPlyS9Le//U3jx493nlls4MCBFfZ2AQBUvyopVkJDQ/XHP/5Rv/vd7/TJJ59o48aN2r9/v06fPq2GDRvq/vvvV58+fdSuXbuqGA4AUMv07t37oqfIdzgcmjFjhmbMmFHhOpe6Xfp5Pnv77bcNswQAVLUqKVbO8fPz05133ulsygUAqF2WL1+ue++9t1Lrpqen6+DBg+rZs2c1ZwUAqKmqtFip6b7++muXTrFp0qVckurXr+9yjOmpP026jpt2HTXplmvSsfn8btGuMOmibPK3ksw685p2bTdpyGoylunPe0xeuxceiF1ZRUVFLseYdm436fSclpbmckx5fT8qw9WTmFRVt+EFCxZoxowZGjFihAYOHKhrrrmm1O05OTn697//rbffflv//Oc/9cYbb1TJuHbTsmVLl1/7x44dc3kckxjJrOu4aQd7k7kyOjra5RjTDvYm22uTbY308+vfVaanxjbZzh8+fNjlmMzMTJdjJLPXk+nz7srxceeYfu4yee02bdrU5RjTz6AmnwtNOtjn5+e7HFMRihUAQJVJSUnRRx99pFdeeUVPPvmkAgMDFR4e7jyGMSMjQ40aNdKIESP0zTffGBdjAIDagWIFAFCl+vfvr/79+ysrK0uff/55qWMY4+LiFBcXZ7wnEQBQu1CsAACqRYMGDTRo0CBPpwEAqMGq7KutkSNH6uTJk1V1dwAAAABquSorVt58880qO0ATAAAAAKqsWDE5IxEAAAAAVKRKj3B0OBxVeXdKSkpS165dFRQUpLCwMA0ePFi7d+923l5YWKgnnnhC7du3V2BgoKKiovTggw8anVIUAAAAgL1UabHSsmVLhYaGXvTiipSUFCUkJGjz5s1KTk5WUVGR4uPjneduPnXqlHbs2KFp06Zpx44dev/997Vnzx4NHDiwKh8WAOAyWZbFHngAgMuq9GxgM2fOVEhISJXd3+rVq0tdX7x4scLCwrR9+3bdeOONCgkJUXJycql1XnnlFXXr1k0HDx5UTExMufdbUFBQqrFgbm5uleUMAPifN954Qy+99JL27t0rSWrRooUmTJighx9+2MOZAQBqgiotVu69995qbfB1rvPrxfbQ5OTkyOFwXLRbeVJSkmbOnFlm+d69e+Xn51fpfEy6r0tmXUdNuq9Lkr+/v8sxZ8+eNRrLpCOyK8/3OUFBQS7HSGYdYl3dG3iOScdh074TJp15TTrEm3aV9/Jy3xnS3fl6Mnn/m3wxYtLxWnK90/P5X+BUlWnTpumll17SuHHj1L17d0nSF198oYkTJ2r//v363e9+V+Vj2kVMTIzq1avnUozJl32NGjVyOUYy+3ubbgNMPheYPK7g4GCXYySz7XVhYaHRWIGBgS7HmHQcl8yOJTbpEH/o0CGXYySzzwym84nJe6tJkyZGY/3iF79wy1imr3eTQzZM/lZVeYbgKvsUUdXHq1zIsiwlJiaqV69eateuXbnrnDlzRlOmTNGQIUMu+kecOnWqEhMTnddzc3ONX5QAgPItWLBAixYt0n333edcNnDgQHXo0EHjxo27oosVAEDVqLJipbp/izx27Fjt3LlTn3/+ebm3FxYW6t5771VJSYnmz59/0fvy9fU1+iYFAFB5xcXF6tKlS5nlnTt3NvoGFwBQ+1TZAfYlJSXV9hOwcePGadWqVVq3bp2io6PL3F5YWKi7775baWlpSk5ONt41BgCoOg888IAWLFhQZvnChQt1//33eyAjAEBN474fkxuwLEvjxo3TihUrtH79ejVr1qzMOucKlb1792rdunVq0KCBBzIFAJTnjTfe0Nq1a3X99ddLkjZv3qz09HQ9+OCDpX6OO2fOHE+lCACwMVsXKwkJCVq6dKk++OADBQUFKSMjQ9LPB0r5+/urqKhId911l3bs2KGPPvpIxcXFznVCQ0OND0oDAFy+b775Rp06dZIk7du3T9LPB043atRI33zzjXO96j7mEQBQc9m6WDn384HevXuXWr548WINHz5chw4d0qpVqyRJ1157bal11q1bVyYOAOA+69at83QKAIAaztbFyqUO2m/atClNxgAAAIArVJV2sAcAAACAqkKxAgAAAMCWbP0zMHfbuHGjSx16TTrRSu7tUnyVQed705/WmfSuCQgIcDnG9BTZJt3NTbvlmnSjNz3I2GSsunXruhxj2r3aXa8Lyew9afK6kMzeJ6dPn3Y55vjx4y7HSFJ+fr5L69P3pGo1bNhQQUFBLsWYvH5N54bi4mKXY0y3Aa4+D5J738sm20OT508ym1NMtvGm3PnTepMO56Y98ho3buxyjEknetOxQkNDXY5xZ79Af39/l2NM3lcVYc8KAAAAAFuiWAEAAABgSxQrAAAAAGyJYgUAAACALVGsAAAAALAlihUAAAAAtkSxAgAAAMCWKFYAAAAA2BLFCgAAAABbooP9eTIyMlzquJmbm2s0ztmzZ12OMe2kbtJ11KSTqiQ1adLE5ZhrrrnG5ZiYmBiXYySzjsimHZtN/14mTDrfm8SYMulGb/oaNO0qbcLkOTTpRG3aUTovL88t46B8wcHBCg4OdinGZHtt+pp353bDpNO2j4+PyzFV2TG7usayezd6k8dl2knd5DOUyXtEkqKiolyOCQ8PNxrLZP4yeVzufC2ZfKYpKiqqsvHZswIAAADAlihWAAAAANgSxQoAAAAAW6JYAQAAAGBLFCsAAAAAbIliBQAAAIAtUawAAAAAsCWKFQAAAAC2RLECAAAAwJboYH+efv36udSJ9aeffjIaxyQuKyvLaKwzZ864HGPatT0oKMjlmIiICJdjmjZt6nKMJJe7SUvmz4U7O8uaMOlsbPpcmHSiNu2IXK9ePZdjGjVqZDRWdHS0yzEtWrRwOcb0vZ+ZmenS+qdPn9a//vUvo7FQlq+vr8uvY5P3mMl72ZTpds0kziTG4XC4HGPK9Hk36QRu2rXdhEl+fn5+RmMVFBS4HGM6N1x11VUux4SEhBiNZfJ81K1b1+UYd77eTcYyeS1VxN6fqAAAAADUWhQrAAAAAGyJYgUAAACALVGsAAAAALAlihUAAAAAtkSxAgAAAMCWKFYAALa3YcMGDRgwQFFRUXI4HFq5cmWp2y3L0owZMxQVFSV/f3/17t1bu3btKrVOQUGBxo0bp4YNGyowMFADBw7UoUOHSq3TtGlTORyOUpcpU6ZU98MDAFSAYgUAYHv5+fnq2LGj5s2bV+7tL7zwgubMmaN58+Zp69atioiI0G233aaTJ08615kwYYJWrFih5cuX6/PPP1deXp769++v4uLiUvf17LPP6ujRo87L008/Xa2PDQBQMZpCAgBsr1+/furXr1+5t1mWpblz5+qpp57SnXfeKUl68803FR4erqVLl2r06NHKycnRG2+8ob/+9a+69dZbJUlvv/22mjRpon/+85/q06eP8/6CgoIq3bC2oKCgVHO73Nxc04cIACgHxcp5evXqpcDAwEqvn52dbTTOsWPH3BIjSSdOnDCKMxEZGelyTExMjMsxYWFhLsdIZp1vTTs2u7OzrAmT7ssmHXYls67cJjGS2d/Ylff8+erXr+9yzJkzZ1yOOX36tMsxkusfmvPy8ozGsYO0tDRlZGQoPj7euczX11c33XSTNm3apNGjR2v79u0qLCwstU5UVJTatWunTZs2lSpWZs+ereeee05NmjTRb37zG02aNEk+Pj7ljp2UlKSZM2eWWV63bl2X3zMm2w27d7G+nDg7M31MJnNKRa+9S3FX1/GAgACXYySppKTE5RjTecikq7zp8273bvQmTF63pp+fykOxAgCo0TIyMiRJ4eHhpZaHh4frwIEDznV8fHzKFJnh4eHOeEn67W9/q06dOql+/frasmWLpk6dqrS0NL3++uvljj116lQlJiY6r+fm5qpJkyZV8rgAABQrAIArxIXfTlqWdclvLC9cZ+LEic7/d+jQQfXr19ddd92l2bNnq0GDBmXifX19jfboAQAqhwPsAQA12rnjS87fQyJJmZmZzr0tEREROnv2bJmf756/Tnmuv/56SdIPP/xQlSkDACqJYgUAUKM1a9ZMERERSk5Odi47e/asUlJS1KNHD0lS586d5e3tXWqdo0eP6ptvvnGuU56vvvpKktkxeQCAy8fPwAAAtpeXl1dq70ZaWppSU1MVGhqqmJgYTZgwQbNmzVKLFi3UokULzZo1SwEBARoyZIgkKSQkRA899JAee+wxNWjQQKGhoXr88cfVvn1759nBvvjiC23evFk333yzQkJCtHXrVk2cOFEDBw40OhkIAODy2XrPSlJSkrp27aqgoCCFhYVp8ODB2r17d6l13n//ffXp00cNGzaUw+FQamqqZ5IFAFSbbdu2KS4uTnFxcZKkxMRExcXF6ZlnnpEkTZ48WRMmTNCjjz6qLl266PDhw1q7dq2CgoKc9/HSSy9p8ODBuvvuu9WzZ08FBAToww8/dJ69x9fXV++884569+6tNm3a6JlnntGoUaO0bNky9z9gAIAkm+9ZSUlJUUJCgrp27aqioiI99dRTio+P17fffus83Wh+fr569uyp3/zmNxo1apSHMwYAVIfevXtf9JTbDodDM2bM0IwZMypcx8/PT6+88opeeeWVcm/v1KmTNm/efLmpAgCqkK2LldWrV5e6vnjxYoWFhWn79u268cYbJUlDhw6VJO3fv9/d6QEAAACoRrYuVi6Uk5MjSQoNDb2s+6HjMAAAAGB/NaZYsSxLiYmJ6tWrl9q1a3dZ91VRx+EOHTooODi40vdzfsHjCpM4k87Xkln36+LiYqOxTDqBl9e34FJc+Rud70rsKlsTXOynOxUx7Xzr7e3tcoxpjwyT7svuipGkoqIil9bnS5uq5XA4XN5+VGXH50th2+YZJs+7O7eHJmOZdno3eS5MX7cm87/p8857q+rZ+gD7840dO1Y7d+6skgMdp06dqpycHOclPT29CjIEAAAAUJVqxJ6VcePGadWqVdqwYYOio6Mv+/7oOAwAAADYn62LFcuyNG7cOK1YsULr169Xs2bNPJ0SAAAAADexdbGSkJCgpUuX6oMPPlBQUJAyMjIk/dzcy9/fX5J0/PhxHTx4UEeOHJEkZx+WiIgIRUREeCZxAAAAAJfN1sesLFiwQDk5Oerdu7ciIyOdl3feece5zqpVqxQXF6c77rhDknTvvfcqLi5Or732mqfSBgAAAFAFbL1npTJnERo+fLiGDx9e/ckAAAAAcCtb71kBAAAAUHtRrAAAAACwJYoVAAAAALZk62NW3C0wMNClLuznzkjmKlc7S5vGmMaZdsw26V3j5+fncoxJJ1qJrrKeYvfn3TQ/kziT125ljt0rj5eXa5v3s2fPGo2DqmP39wo8w/R1YdKB3e6vQXdur2Ef7FkBAAAAYEsUKwAAAABsiWIFAAAAgC1RrAAAAACwJYoVAAAAALZEsQIAAADAlihWAAAAANgSxQoAAAAAW6JYAQAAAGBLdLA/j5eXl0tdn93VWVoy7ypfXFxsFGfCpDu3yXNBJ1rYgd1fh66+H03evwDsy2QbZRJj+lnI7ttQ2Ad7VgAAAADYEsUKAAAAAFuiWAEAAABgSxQrAAAAAGyJYgUAAACALVGsAAAAALAlihUAAAAAtkSxAgAAAMCWKFYAAAAA2BId7M9Tp04d1alT+frNtGurSZwreZ3PpCu16eMyydH0cQG1hbu6PNNNGoAJth2obnxSBAAAAGBLFCsAAAAAbIliBQAAAIAtUawAAAAAsCWKFQAAAAC2RLECAAAAwJYoVgAAAADYEsUKAAAAAFuiWAEAAABgS3SwvwymXVtN4ky7yptw5+MCAAAAKsKeFQAAAAC2RLECAAAAwJYoVgAAAADYEsUKAAAAAFuiWAEAAABgS7YuVpKSktS1a1cFBQUpLCxMgwcP1u7du0utY1mWZsyYoaioKPn7+6t3797atWuXhzIGAFSHDRs2aMCAAYqKipLD4dDKlStL3V6ZuaCgoEDjxo1Tw4YNFRgYqIEDB+rQoUOl1snOztbQoUMVEhKikJAQDR06VCdOnKjmRwcAqIiti5WUlBQlJCRo8+bNSk5OVlFRkeLj45Wfn+9c54UXXtCcOXM0b948bd26VREREbrtttt08uRJD2YOAKhK+fn56tixo+bNm1fu7ZWZCyZMmKAVK1Zo+fLl+vzzz5WXl6f+/furuLjYuc6QIUOUmpqq1atXa/Xq1UpNTdXQoUOr/fEBACpg1SCZmZmWJCslJcWyLMsqKSmxIiIirN///vfOdc6cOWOFhIRYr732WqXvNycnx5Jk5eTkVHnOVaWkpMToUlxc7LaLSX4A7KEmbAfPkWStWLHCeb0yc8GJEycsb29va/ny5c51Dh8+bNWpU8davXq1ZVmW9e2331qSrM2bNzvX+eKLLyxJ1vfff1+p3GrS8wgA1aUqt4U1qilkTk6OJCk0NFSSlJaWpoyMDMXHxzvX8fX11U033aRNmzZp9OjR5d5PQUGBCgoKytxvbm5udaV+2SzDppCmcSZMmkLSSBKwh3PbP3duM6pKZeaC7du3q7CwsNQ6UVFRateunTZt2qQ+ffroiy++UEhIiK677jrnOtdff71CQkK0adMmtWrVqszYNXE+AYDqVpVzSo0pVizLUmJionr16qV27dpJkjIyMiRJ4eHhpdYNDw/XgQMHKryvpKQkzZw5s8zyJk2aVGHGAFDzZGVlKSQkxNNpuKQyc0FGRoZ8fHxUv379Muuci8/IyFBYWFiZ+w8LC3OucyHmEwCoWFXMKTWmWBk7dqx27typzz//vMxtF347b1nWRb+xnzp1qhITE53XT5w4odjYWB08eNCWk3Rubq6aNGmi9PR0BQcHezqdctk9R/K7PHbPT7J/jnbPLycnRzExMc491zWRq3NBeeuUt/7F7qemzSeS/V+L5Hf57J4j+V0eu+cnVe2cUiOKlXHjxmnVqlXasGGDoqOjncsjIiIk/fxtWGRkpHN5ZmZmmW/Yzufr6ytfX98yy0NCQmz7R5ek4OBgW+cn2T9H8rs8ds9Psn+Ods+vTh1bn3elXJWZCyIiInT27FllZ2eX2ruSmZmpHj16ONf56aefytz/sWPHKpxTaup8Itn/tUh+l8/uOZLf5bF7flLVzCm2npUsy9LYsWP1/vvv67PPPlOzZs1K3d6sWTNFREQoOTnZuezs2bNKSUlxTj4AgCtbZeaCzp07y9vbu9Q6R48e1TfffONcp3v37srJydGWLVuc63z55ZfKyclhTgEAD7H1npWEhAQtXbpUH3zwgYKCgpy/GQ4JCZG/v78cDocmTJigWbNmqUWLFmrRooVmzZqlgIAADRkyxMPZAwCqSl5enn744Qfn9bS0NKWmpio0NFQxMTGXnAtCQkL00EMP6bHHHlODBg0UGhqqxx9/XO3bt9ett94qSbrmmmvUt29fjRo1Sn/+858lSY888oj69+9f7sH1AAA3uOzziVUjSeVeFi9e7FynpKTEmj59uhUREWH5+vpaN954o/X111+7NM6ZM2es6dOnW2fOnKniR1A17J6fZdk/R/K7PHbPz7LsnyP5XZ5169aVOx8MGzbMsqzKzQWnT5+2xo4da4WGhlr+/v5W//79rYMHD5ZaJysry7r//vutoKAgKygoyLr//vut7OzsSudp9+fRsuyfI/ldPrvnSH6Xx+75WVbV5uiwrBp4nkoAAAAAVzxbH7MCAAAAoPaiWAEAAABgSxQrAAAAAGyJYgUAAACALdX6YmX+/Plq1qyZ/Pz81LlzZ23cuNHTKTktWLBAHTp0cDb96d69uz799FNPp1XK4cOH9cADD6hBgwYKCAjQtddeq+3bt3s6LaeTJ09qwoQJio2Nlb+/v3r06KGtW7d6LJ8NGzZowIABioqKksPh0MqVK523FRYW6oknnlD79u0VGBioqKgoPfjggzpy5Igt8pOk4cOHy+FwlLpcf/31tskvLy9PY8eOVXR0tPz9/XXNNddowYIFbssvKSlJXbt2VVBQkMLCwjR48GDt3r271Drvv/+++vTpo4YNG8rhcCg1NdVW+Z1v9OjRcjgcmjt3rttyrOnsOqfUhPlEYk5xhd3nk0vlKDGnXApzys9qdbHyzjvvaMKECXrqqaf01Vdf6YYbblC/fv108OBBT6cmSYqOjtbvf/97bdu2Tdu2bdMvf/lLDRo0SLt27fJ0apKk7Oxs9ezZU97e3vr000/17bff6sUXX9RVV13l6dScHn74YSUnJ+uvf/2rvv76a8XHx+vWW2/V4cOHPZJPfn6+OnbsqHnz5pW57dSpU9qxY4emTZumHTt26P3339eePXs0cOBAW+R3Tt++fXX06FHn5ZNPPrFNfhMnTtTq1av19ttv67vvvtPEiRM1btw4ffDBB27JLyUlRQkJCdq8ebOSk5NVVFSk+Ph45efnl3oMPXv21O9//3u35ORqfuesXLlSX375paKiotyeZ01l5znF7vOJxJziKrvPJ5fK8RzmlIoxp/zXZZ/8uAbr1q2bNWbMmFLLWrdubU2ZMsVDGV1a/fr1rddff93TaViWZVlPPPGE1atXL0+nUaFTp05ZdevWtT766KNSyzt27Gg99dRTHsrqfyRZK1asuOg6W7ZssSRZBw4ccE9S5ykvv2HDhlmDBg1yey7lKS+/tm3bWs8++2ypZZ06dbKefvppN2b2P5mZmZYkKyUlpcxtaWlpliTrq6++cn9i/1VRfocOHbIaN25sffPNN1ZsbKz10ksveSbBGqamzSl2mk8siznlcth9PrEs5pSqUFvnlFq7Z+Xs2bPavn274uPjSy2Pj4/Xpk2bPJRVxYqLi7V8+XLl5+ere/funk5HkrRq1Sp16dJFv/nNbxQWFqa4uDgtWrTI02k5FRUVqbi4WH5+fqWW+/v76/PPP/dQVq7JycmRw+Gw1TeL69evV1hYmFq2bKlRo0YpMzPT0yk59erVS6tWrdLhw4dlWZbWrVunPXv2qE+fPh7JJycnR5IUGhrqkfEvpbz8SkpKNHToUE2aNElt27b1VGo1Tk2aU+w4n0jMKdXNjvOJxJziito6p9TaYuU///mPiouLFR4eXmp5eHi4MjIyPJRVWV9//bXq1asnX19fjRkzRitWrFCbNm08nZYk6ccff9SCBQvUokULrVmzRmPGjNH48eP11ltveTo1SVJQUJC6d++u5557TkeOHFFxcbHefvttffnllzp69Kin07ukM2fOaMqUKRoyZIiCg4M9nY4kqV+/fvrb3/6mzz77TC+++KK2bt2qX/7ylyooKPB0apKkP/3pT2rTpo2io6Pl4+Ojvn37av78+erVq5fbc7EsS4mJierVq5fatWvn9vEvpaL8Zs+eLS8vL40fP96D2dU8NWFOsfN8IjGnVCc7zicSc4oravOc4lUVCdZkDoej1HXLssos86RWrVopNTVVJ06c0Hvvvadhw4YpJSXFFhNMSUmJunTpolmzZkmS4uLitGvXLi1YsEAPPvigh7P72V//+leNHDlSjRs3Vt26ddWpUycNGTJEO3bs8HRqF1VYWKh7771XJSUlmj9/vqfTcbrnnnuc/2/Xrp26dOmi2NhYffzxx7rzzjs9mNnP/vSnP2nz5s1atWqVYmNjtWHDBj366KOKjIzUrbfe6tZcxo4dq507d9r2G9fy8tu+fbtefvll7dixw1bbwZrEznOKnecTiTmluth1PpGYU1xRm+eUWrtnpWHDhqpbt26Zb7wyMzPLfDPmST4+PmrevLm6dOmipKQkdezYUS+//LKn05IkRUZGlpnkrrnmGlscTHrOL37xC6WkpCgvL0/p6enasmWLCgsL1axZM0+nVqHCwkLdfffdSktLU3Jysq2+BbtQZGSkYmNjtXfvXk+notOnT+vJJ5/UnDlzNGDAAHXo0EFjx47VPffcoz/+8Y9uzWXcuHFatWqV1q1bp+joaLeOXRkV5bdx40ZlZmYqJiZGXl5e8vLy0oEDB/TYY4+padOmnku4BqgJc4qd5xOJOaU61KT5RGJOqUhtn1NqbbHi4+Ojzp07Kzk5udTy5ORk9ejRw0NZXZplWbbZPdqzZ88yp6jbs2ePYmNjPZRRxQIDAxUZGans7GytWbNGgwYN8nRK5To3sezdu1f//Oc/1aBBA0+ndFFZWVlKT09XZGSkp1NRYWGhCgsLVadO6c1a3bp1VVJS4pYcLMvS2LFj9f777+uzzz6z3QeYS+U3dOhQ7dy5U6mpqc5LVFSUJk2apDVr1ngo65qhJs4pdppPJOaUqlbT5hOJOeVCzCk/q9U/A0tMTNTQoUPVpUsXde/eXQsXLtTBgwc1ZswYT6cmSXryySfVr18/NWnSRCdPntTy5cu1fv16rV692tOpSfr5lH49evTQrFmzdPfdd2vLli1auHChFi5c6OnUnNasWSPLstSqVSv98MMPmjRpklq1aqURI0Z4JJ+8vDz98MMPzutpaWlKTU1VaGiooqKidNddd2nHjh366KOPVFxc7PyWNjQ0VD4+Ph7NLzQ0VDNmzNCvf/1rRUZGav/+/XryySfVsGFD/epXv6r23C6VX0xMjG666SZNmjRJ/v7+io2NVUpKit566y3NmTPHLfklJCRo6dKl+uCDDxQUFOT8+4WEhMjf31+SdPz4cR08eNDZ7+Dch7OIiAhFRER4NL8GDRqU+UDj7e2tiIgItWrVqlpzuxLYeU6x+3wiMae4yu7zyaVyZE65NOaU/7qMM5RdEV599VUrNjbW8vHxsTp16lTu6eA8ZeTIkc7cGjVqZN1yyy3W2rVrPZ1WKR9++KHVrl07y9fX12rdurW1cOFCT6dUyjvvvGNdffXVlo+PjxUREWElJCRYJ06c8Fg+69atsySVuQwbNsx52sHyLuvWrfN4fqdOnbLi4+OtRo0aWd7e3lZMTIw1bNgw6+DBg27J7VL5WZZlHT161Bo+fLgVFRVl+fn5Wa1atbJefPFFq6SkxC35VfT3W7x4sXOdxYsXl7vO9OnTbZHfhTh1sWvsOqfUhPnEsphTXGH3+eRSOTKnXBpzys8c/x0MAAAAAGyl1h6zAgAAAMDeKFYAAAAA2BLFCgAAAABbolgBAAAAYEsUKwAAAABsiWIFAAAAgC1RrAAAAACwJYoVAAAAALZEsQJ4yIwZM3Tttdde1n04HA6tXLmySvIBANRMzCe4klGsoNYaPny4Bg8e7Ok0yjVjxgw5HI6LXvbv36+jR4+qX79+nk4XAGo15hOg+lCsADb0+OOP6+jRo85LdHS0nn322VLLmjRpooiICPn6+no6XQCATTGfoKajWAEqkJKSom7dusnX11eRkZGaMmWKioqKnLeXlJRo9uzZat68uXx9fRUTE6Pnn3/eefsTTzyhli1bKiAgQFdffbWmTZumwsLCSo1dr149RUREOC9169ZVUFBQmWXn77bfv3+/HA6H3n33Xd1www3y9/dX165dtWfPHm3dulVdunRRvXr11LdvXx07dqzUeIsXL9Y111wjPz8/tW7dWvPnz7/8JxAAIIn5BLgcXp5OALCjw4cP6/bbb9fw4cP11ltv6fvvv9eoUaPk5+enGTNmSJKmTp2qRYsW6aWXXlKvXr109OhRff/99877CAoK0pIlSxQVFaWvv/5ao0aNUlBQkCZPnlytuU+fPl1z585VTEyMRo4cqfvuu0/BwcF6+eWXFRAQoLvvvlvPPPOMFixYIElatGiRpk+frnnz5ikuLk5fffWVRo0apcDAQA0bNqxacwWAKx3zCfMJLpMF1FLDhg2zBg0aVO5tTz75pNWqVSurpKTEuezVV1+16tWrZxUXF1u5ubmWr6+vtWjRokqP98ILL1idO3d2Xp8+fbrVsWPHSsXGxsZaL730UpnlkqwVK1ZYlmVZaWlpliTr9ddfd96+bNkyS5L1r3/9y7ksKSnJatWqlfN6kyZNrKVLl5a63+eee87q3r17pXIDgNqO+eRnzCeoDuxZAcrx3XffqXv37nI4HM5lPXv2VF5eng4dOqSMjAwVFBTolltuqfA+/vGPf2ju3Ln64YcflJeXp6KiIgUHB1d77h06dHD+Pzw8XJLUvn37UssyMzMlSceOHVN6eroeeughjRo1yrlOUVGRQkJCqj1XALjSMZ8wn+DyUKwA5bAsq9TEcm6Z9PPpHf39/S8av3nzZt17772aOXOm+vTpo5CQEC1fvlwvvvhiteV8jre3t/P/5x7DhctKSkokyfnvokWLdN1115W6n7p161Z3qgBwxWM+YT7B5aFYAcrRpk0bvffee6UmmU2bNikoKEiNGzdWo0aN5O/vr3/96196+OGHy8T/+9//VmxsrJ566innsgMHDrgt/8oKDw9X48aN9eOPP+r+++/3dDoAcMVhPgEuD8UKarWcnBylpqaWWhYaGqpHH31Uc+fO1bhx4zR27Fjt3r1b06dPV2JiourUqSM/Pz898cQTmjx5snx8fNSzZ08dO3ZMu3bt0kMPPaTmzZvr4MGDWr58ubp27aqPP/5YK1as8MyDvIQZM2Zo/PjxCg4OVr9+/VRQUKBt27YpOztbiYmJnk4PAGoE5hPmE1QPihXUauvXr1dcXFypZcOGDdOSJUv0ySefaNKkSerYsaNCQ0P10EMP6emnn3auN23aNHl5eemZZ57RkSNHFBkZqTFjxkiSBg0apIkTJ2rs2LEqKCjQHXfcoWnTpjnP/GInDz/8sAICAvSHP/xBkydPVmBgoNq3b68JEyZ4OjUAqDGYT5hPUD0c1rkfTgIAAACAjdAUEgAAAIAtUawAAAAAsCWKFQAAAAC2RLECAAAAwJYoVgAAAADYEsUKAAAAAFuiWAEAAABgSxQrAAAAAGyJYgUAAACALVGsAAAAALAlihUAAAAAtkSxcoGsrCyFhYVp//79l1z38ccf1/jx46s/KQBAjXSpOWX9+vVyOBw6ceKEJOmjjz5SXFycSkpK3JckANgYxcoFkpKSNGDAADVt2vSS606ePFmLFy9WWlpa9ScGAKhxXJlTJKl///5yOBxaunRp9SYGADUExcp5Tp8+rTfeeEMPP/xwpdYPCwtTfHy8XnvttWrODABQ07g6p5wzYsQIvfLKK9WUFQDULBQr5/n000/l5eWl7t27O5ft2rVLd9xxh4KDgxUUFKQbbrhB+/btc94+cOBALVu2zBPpAgBsrLw55ZNPPlHLli3l7++vm2++udyfhw0cOFBbtmzRjz/+6MZsAcCeKFbOs2HDBnXp0sV5/fDhw7rxxhvl5+enzz77TNu3b9fIkSNVVFTkXKdbt25KT0/XgQMHPJEyAMCmLpxT0tPTdeedd+r2229XamqqHn74YU2ZMqVMXGxsrMLCwrRx40Z3pgsAtuTl6QTsZP/+/YqKinJef/XVVxUSEqLly5fL29tbktSyZctSMY0bN3bGxsbGui9ZAICtXTinLFiwQFdffbVeeuklORwOtWrVSl9//bVmz55dJrZx48aVOtELAFzp2LNyntOnT8vPz895PTU1VTfccIOzUCmPv7+/JOnUqVPVnh8AoOa4cE757rvvdP3118vhcDiXnf8TsfP5+/szrwCAKFZKadiwobKzs53XzxUiF3P8+HFJUqNGjaotLwBAzXPhnGJZVqVjjx8/zrwCAKJYKSUuLk7ffvut83qHDh20ceNGFRYWVhjzzTffyNvbW23btnVHigCAGuLCOaVNmzbavHlzqXUuvC5JZ86c0b59+xQXF1ftOQKA3VGsnKdPnz7atWuX85uwsWPHKjc3V/fee6+2bdumvXv36q9//at2797tjNm4caNuuOGGSu2FAQDUHhfOKWPGjNG+ffuUmJio3bt3a+nSpVqyZEmZuM2bN8vX17fCn4gBQG1CsXKe9u3bq0uXLnr33XclSQ0aNNBnn32mvLw83XTTTercubMWLVpU6hiWZcuWadSoUZ5KGQBgUxfOKTExMXrvvff04YcfqmPHjnrttdc0a9asMnHLli3T/fffr4CAAHenDAC247Bc+RFtLfDJJ5/o8ccf1zfffKM6dS5ey3388ceaNGmSdu7cKS8vTqwGACjNlTlFko4dO6bWrVtr27ZtatasmRsyBAB74xP2BW6//Xbt3btXhw8fVpMmTS66bn5+vhYvXkyhAgAolytziiSlpaVp/vz5FCoA8F/sWQEAAABgSxyzAgAAAMCWKFYAAAAA2BLFCgAAAABb4shwSSUlJTpy5IiCgoLkcDg8nQ4AuJ1lWTp58qSioqIqddYqlI/5BACqdk6hWJF05MiRSp2lBQCudOnp6YqOjvZ0GjUW8wkA/E9VzCkUK5KCgoIk/fyEBgcHezgbAHC/3NxcNWnSxLk9hBnmE9Rk7jxBLHser2xVOadQrOh/b5jg4GAmFwC1Gh8gLg/zCWoyihVUtar4O/PDZAAAAAC2RLECAAAAwJYoVgAAAADYEsUKAAAAAFuiWAEA1ApJSUnq2rWrgoKCFBYWpsGDB2v37t0Vrj969Gg5HA7NnTvXfUkCAEqhWAEA1AopKSlKSEjQ5s2blZycrKKiIsXHxys/P7/MuitXrtSXX36pqKgoD2QKADiHUxcDAGqF1atXl7q+ePFihYWFafv27brxxhudyw8fPqyxY8dqzZo1uuOOOy56nwUFBSooKHBez83NrdqkYUvuPMWvKXflaHpqWrs/h5xa2T7YswIAqJVycnIkSaGhoc5lJSUlGjp0qCZNmqS2bdte8j6SkpIUEhLivNC9HgCqFsUKAKDWsSxLiYmJ6tWrl9q1a+dcPnv2bHl5eWn8+PGVup+pU6cqJyfHeUlPT6+ulAGgVuJnYACAWmfs2LHauXOnPv/8c+ey7du36+WXX9aOHTsq/RMQX19f+fr6VleaAFDrsWcFAFCrjBs3TqtWrdK6desUHR3tXL5x40ZlZmYqJiZGXl5e8vLy0oEDB/TYY4+padOmnksYAGox9qwAAGoFy7I0btw4rVixQuvXr1ezZs1K3T506FDdeuutpZb16dNHQ4cO1YgRI9yZKgDgvyhWAAC1QkJCgpYuXaoPPvhAQUFBysjIkCSFhITI399fDRo0UIMGDUrFeHt7KyIiQq1atfJEygBQ6/EzMABArbBgwQLl5OSod+/eioyMdF7eeecdT6cGAKgAe1YAALWCSV+H/fv3V30iAIBKY88KAAAAAFtizwpgE3bv5muKLsAA3MF0G2oS586xTNl9TjGZG0znE5M4dz5/7nwuaiL2rAAAAACwJYoVAAAAALZEsQIAAADAlihWAAAAANgSxQoAAAAAW6JYAQAAAGBLFCsAAAAAbIliBQAAAIAtUawAAAAAsCU62APVwO6dgwFUL8uy3LIduFK7WJs8d8XFxUZjlZSUuCVGMntcpq8jd81D7uwqz1iXF3M5ca4yfY+Uhz0rAAAAAGyJYgUAAACALVGsAAAAALAlihUAAAAAtkSxAgAAAMCWKFYAAAAA2FKNL1aKior09NNPq1mzZvL399fVV1+tZ599tkpPmQYAAADA/Wp8n5XZs2frtdde05tvvqm2bdtq27ZtGjFihEJCQvTb3/7W0+kBAAAAMFTji5UvvvhCgwYN0h133CFJatq0qZYtW6Zt27ZVGFNQUKCCggLn9dzc3GrPEwAAAIBranyx0qtXL7322mvas2ePWrZsqf/3//6fPv/8c82dO7fCmKSkJM2cOdN9SaLGcmcn+iu1S7HdxwKqQ3Fxscsd1a/ELtaS+7rRFxYWuhxjGmeSn2mc3eehOnXMjigwiTN93bpzLBNeXq5/HHfn824SY/oeKU+NL1aeeOIJ5eTkqHXr1qpbt66Ki4v1/PPP67777qswZurUqUpMTHRez83NVZMmTdyRLgAAAIBKqvHFyjvvvKO3335bS5cuVdu2bZWamqoJEyYoKipKw4YNKzfG19dXvr6+bs4UAAAAgCtqfLEyadIkTZkyRffee68kqX379jpw4ICSkpIqLFYAAAAA2F+NP3XxqVOnyvyWrm7dupy6GAAAAKjhavyelQEDBuj5559XTEyM2rZtq6+++kpz5szRyJEjPZ0aAAAAgMtQ44uVV155RdOmTdOjjz6qzMxMRUVFafTo0XrmmWc8nRoAAACAy1Dji5WgoCDNnTv3oqcqBgAAAFDz1PhjVgAAAABcmShWAAC1QlJSkrp27aqgoCCFhYVp8ODB2r17t/P2wsJCPfHEE2rfvr0CAwMVFRWlBx98UEeOHPFg1gBQu9X4n4EBlWXSmdedXeXd2aXYhGl+pl12Tbiz4zBqnpSUFCUkJKhr164qKirSU089pfj4eH377bcKDAzUqVOntGPHDk2bNk0dO3ZUdna2JkyYoIEDB2rbtm0ujXXmzBn5+Pi4FFO3bl2X1pfM318m7xXT95fJ2TkLCgpcjjl9+rTLMaZxZ8+eNRrLpKu3O89uarKdN+m+Lrmvk7ppnMn70ZS3t7fLMab5mcSZ5Hfq1CmXYypCsQIAqBVWr15d6vrixYsVFham7du368Ybb1RISIiSk5NLrfPKK6+oW7duOnjwoGJiYtyZLgBAFCsAgFoqJydHkhQaGnrRdRwOh6666qpyby8oKCi1FyA3N7dKcwSA2o5jVgAAtY5lWUpMTFSvXr3Url27ctc5c+aMpkyZoiFDhig4OLjcdZKSkhQSEuK8NGnSpDrTBoBah2IFAFDrjB07Vjt37tSyZcvKvb2wsFD33nuvSkpKNH/+/ArvZ+rUqcrJyXFe0tPTqytlAKiV+BkYAKBWGTdunFatWqUNGzYoOjq6zO2FhYW6++67lZaWps8++6zCvSqS5OvrK19f3+pMFwBqNYoVAECtYFmWxo0bpxUrVmj9+vVq1qxZmXXOFSp79+7VunXr1KBBAw9kCgA4h2IFAFArJCQkaOnSpfrggw8UFBSkjIwMSVJISIj8/f1VVFSku+66Szt27NBHH32k4uJi5zqhoaEun4oYAHD5KFYAALXCggULJEm9e/cutXzx4sUaPny4Dh06pFWrVkmSrr322lLrrFu3rkwcAKD6UawAAGqFSzW8a9q0qe2bswJAbUOxghqnJnSVN4kz7VLsrg9X7uwO787OwUB1OHHihMvdyk26RJvESGbvMdPu4UVFRS7HmHS/zs7OdjlGkvLy8lyOOXPmjNFYdu9Gb8J0bjCJc2fXdlMmz7vJSTpMf5ZqEmcSc/LkSZdjKsKpiwEAAADYEsUKAAAAAFuiWAEAAABgSxQrAAAAAGyJYgUAAACALVGsAAAAALAlihUAAAAAtkSxAgAAAMCWKFYAAAAA2BId7OFR7uwqb9I52J1d5V3tdn05Y5nEmHYp9vJy32bGpMO26eMy4c6x4FkHDx5UYGCgSzEmXaIDAgJcjjEdy7QLuMm2LScnxy0xpnEFBQVGY7mzg727xjKdk01eT6bb0KKiIqM4EybPh7u6ypvGmWxn8vPzXY6pCHtWAAAAANgSxQoAAAAAW6JYAQAAAGBLFCsAAAAAbIliBQAAAIAtUawAAAAAsCWKFQAAAAC2RLECAAAAwJYoVgAAAADYEh3sz2NZlnEnVlfYvYu1O56DyxnLND93jmXSsdm0g71JnMnjMu1ebcKkE31NYPK82317gfLt27dP/v7+LsX4+fm5PI6rY1zOWN7e3kZjmbyGc3NzXY7Jy8tzOcY0znR7bfJcuHMbYNL13iTGNM70eTfpYG/6uEzmL3fOySbvY5Ou96dPn3Y5piJX5icCAAAAADUexQoAAAAAW6JYAQAAAGBLFCsAAAAAbIliBQAAAIAtXRHFyuHDh/XAAw+oQYMGCggI0LXXXqvt27d7Oi0AAAAAl6HGn7o4OztbPXv21M0336xPP/1UYWFh2rdvn6666ipPpwYAAADgMtT4YmX27Nlq0qSJFi9e7FzWtGlTzyUEAAAAoErU+J+BrVq1Sl26dNFvfvMbhYWFKS4uTosWLbpoTEFBgXJzc0tdAAAAANhLjd+z8uOPP2rBggVKTEzUk08+qS1btmj8+PHy9fXVgw8+WG5MUlKSZs6cWWa5w+GgW7Tc2y3XnWO5swM7APtJSkrS+++/r++//17+/v7q0aOHZs+erVatWjnXsSxLM2fO1MKFC5Wdna3rrrtOr776qtq2bevSWGlpafL19XUpxqSztGlXeZMO9iZdrCXJy8v1jxom3cMLCwtdjpGks2fPuhxj+rybdDc3nbtMO7C7yp1d5U06vUtmf2OT/CSz58P0OTRh8rnL5HkvKChwOaYiNX7PSklJiTp16qRZs2YpLi5Oo0eP1qhRo7RgwYIKY6ZOnaqcnBznJT093Y0ZAwA8ISUlRQkJCdq8ebOSk5NVVFSk+Ph45efnO9d54YUXNGfOHM2bN09bt25VRESEbrvtNp08edKDmQNA7VXj96xERkaqTZs2pZZdc801eu+99yqM8fX1dfkbLwBAzbZ69epS1xcvXqywsDBt375dN954oyzL0ty5c/XUU0/pzjvvlCS9+eabCg8P19KlSzV69GhPpA0AtVqN37PSs2dP7d69u9SyPXv2KDY21kMZAQBqgpycHElSaGiopJ9/upWRkaH4+HjnOr6+vrrpppu0adOmcu+DYyABoHrV+GJl4sSJ2rx5s2bNmqUffvhBS5cu1cKFC5WQkODp1AAANmVZlhITE9WrVy+1a9dOkpSRkSFJCg8PL7VueHi487YLJSUlKSQkxHlp0qRJ9SYOALVMjf8ZWNeuXbVixQpNnTpVzz77rJo1a6a5c+fq/vvv93RqAIDLVFBQoC1btmj//v06deqUGjVqpLi4ODVr1uyy7nfs2LHauXOnPv/88zK3XXgAqmVZFR6UOnXqVCUmJjqv5+bmUrAAQBWq8cWKJPXv31/9+/f3dBoAgCqyadMmvfLKK1q5cqXOnj2rq666Sv7+/jp+/LgKCgp09dVX65FHHtGYMWMUFBTk0n2PGzdOq1at0oYNGxQdHe1cHhERIennPSyRkZHO5ZmZmWX2tpzDMZAAUL1q/M/AAABXlkGDBumuu+5S48aNtWbNGp08eVJZWVk6dOiQTp06pb179+rpp5/Wv/71L7Vs2VLJycmVul/LsjR27Fi9//77+uyzz8rsnWnWrJkiIiJK3d/Zs2eVkpKiHj16VOljBABUjkf2rOTk5GjFihXauHFjmV37ffr0YVIAgFosPj5ef//73yvs63H11Vfr6quv1rBhw7Rr1y4dOXKkUvebkJCgpUuX6oMPPlBQUJDzOJSQkBD5+/vL4XBowoQJmjVrllq0aKEWLVpo1qxZCggI0JAhQ6rs8QEAKs+te1aOHj2qUaNGKTIyUs8++6zy8/N17bXX6pZbblF0dLTWrVun2267TW3atNE777zjztQAADaRkJBQ6QaEbdu21W233VapdRcsWKCcnBz17t1bkZGRzsv5883kyZM1YcIEPfroo+rSpYsOHz6stWvXuvxTMwBA1XDrnpWOHTvqwQcf1JYtW5xnX7nQ6dOntXLlSs2ZM0fp6el6/PHH3ZafZVnG3VGrm2mnd5PH486xTDrsmnbldWdXWZPOt6ZjmcSZ/K1MuyibdHo2PQbApFO26evdNM6u7Lrtq0qVeYwOh0MzZszQjBkzLmuswsJCl7uVnzlzxuVxTLeHJq9fk/eXZPZ+rmyxerkxktnjMu1gbxLnzm2USYw75y7TsUxeg6Yd7E2ew7Nnz7ocY5qfSWd5k+e9KudItxYru3btUqNGjS66jr+/v+677z7dd999OnbsmJsyAwDYUf369cud9BwOh/z8/NS8eXMNHz5cI0aM8EB2AIDq5tZi5VKFyuWuDwC4sjzzzDN6/vnn1a9fP3Xr1k2WZWnr1q1avXq1EhISlJaWpv/7v/9TUVGRRo0a5el0AQBVzGOnLn7zzTfVsGFD3XHHHZJ+/p3wwoUL1aZNGy1btowO9AAAff755/rd736nMWPGlFr+5z//WWvXrtV7772nDh066E9/+hPFCgBcgTx26uJZs2bJ399fkvTFF19o3rx5euGFF9SwYUNNnDjRU2kBAGxkzZo1uvXWW8ssv+WWW7RmzRpJ0u23364ff/zR3akBANzAY8VKenq6mjdvLklauXKl7rrrLj3yyCNKSkrSxo0bPZUWAMBGQkND9eGHH5ZZ/uGHHyo0NFSSlJ+fz9m6AOAK5bGfgdWrV09ZWVmKiYnR2rVrnXtT/Pz8dPr0aU+lBQCwkWnTpun//u//tG7dOnXr1k0Oh0NbtmzRJ598otdee02SlJycrJtuusnDmQIAqoPHipXbbrtNDz/8sOLi4rRnzx7nsSu7du1S06ZNPZUWAMBGRo0apTZt2mjevHl6//33ZVmWWrduXaqr/GOPPebhLAEA1cVjxcqrr76qp59+Wunp6XrvvffUoEEDSdL27dt13333eSotAIDN9OzZUz179iyz/PTp085jHwEAVya3FysLFy7UwIEDFRERoXnz5pW5febMme5OCQBgUwkJCXr11VfLLM/Pz9cdd9yh9evXuz8pAIDbuL1YWbZsmcaPH6+OHTtq0KBBGjx4sNq0aePuNMpl5w72pnmZdBA17YhskqM7O9gWFha6HGPS6dV0LJMOtqZx7uxgb/LNt+nr3c/Pz+UYV7uMX26cu7j63rfrtm/t2rV6+umn9bvf/c65LC8vT/369fNgVpd21VVXufx6NNlumHS9l8y6X5u+Rky2USYdx01ipJ//Vq4y3aNnsh013da4q4O96evCZC43Hcv0c427mLxH3PmZwWQ7Y7ptKo/bZ9t169bp6NGjGjdunFJTU9W9e3f94he/UGJiotavX2/7FxQAwH3Wrl2rxYsX66WXXpIknTx5UvHx8XI4HFq9erWHswMAVDePHLNSv359PfDAA3rggQd09uxZffbZZ1q1apWGDh2qU6dO6Y477tDAgQPVr18/BQYGeiJFAIANNGvWTGvWrFHv3r1Vp04dLV++XL6+vvr444+ZHwCgFvD47xh8fHzUt29fzZ8/X+np6VqzZo2aNm2q5557TnPmzPF0egAAD2vXrp0++ugjPfXUUwoICNCnn35KoQIAtYTHzgZWkS5duqhLly569tlnjX6/CwCo2eLi4sr97byvr6+OHDlS6sxgO3bscGdqAAA381ixYlmW/vGPf2jdunXKzMwsdayKw+HQe++9J29vb0+lBwDwkMGDB3s6BQCATXisWPntb3+rhQsX6uabb1Z4eLjRGSgAAFee6dOna8+ePWrZsqWnUwEAeJjHipW3335b77//vm6//XZPpQAAsKm4uDjFxMRo4MCBGjx4sLp37+7plAAAHuCxA+xDQkJ09dVXe2p4AICNZWVlafbs2crKytKvfvUrhYeH66GHHtKqVauq9Pz9AAB781ixMmPGDM2cOVOnT5/2VAoAAJvy8/PTwIED9frrr+vo0aNasWKFGjVqpClTpqhBgwYaNGiQ/vKXvygzM9PTqQIAqpHHfgb2m9/8RsuWLVNYWJiaNm1a5mB6T5zhxV0d7N15fI7J43Fnh1iTGHd2sDfp8izJqAg3PfudyfPhztegyeMy/Ru787lwV8d3d+Vn1w720s/PQY8ePdSjRw/9/ve/1969e7Vq1SotWbJE//d//6c5c+YoISHB02mWEhsbq4CAAJdiTLY3pl2sCwoK3DaWyeNy9bmTzDrRm8aZngDI19fX5Rh3bq/tfvyw6dzgzsdl8rnG5D1i+pnBZCyT7cWpU6dcjqmIx4qV4cOHa/v27XrggQc4wB4AUGktWrTQY489pscee0xZWVk6fvy4p1MCAFQTjxUrH3/8sdasWaNevXp5KgUAgM2tWrWq3OUOh0N+fn5q0aKFWrRo4easAADu4rFipUmTJgoODvbU8ACAGmDw4MFyOBxlfqZ2bpnD4VCvXr20cuVK1a9f30NZAgCqi8cOsH/xxRc1efJk7d+/31MpAABsLjk5WV27dlVycrJycnKUk5Oj5ORkdevWTR999JE2bNigrKwsPf74455OFQBQDTy2Z+WBBx7QqVOn9Itf/EIBAQFlDlbjN8gAgHMNhHv06OFcdsstt8jPz0+PPPKIdu3apblz52rkyJEezBIAUF08VqzMnTvXU0MDAGqIffv2lfuT4eDgYP3444+Sfj7g/j//+Y+7UwMAuIHHipVhw4Z5amgAQA3RuXNnTZo0SW+99ZYaNWokSTp27JgmT56srl27SpL27t2r6OhoT6YJAKgmbj1mJT8/v1rXBwBcWd544w2lpaUpOjpazZs3V4sWLRQdHa39+/fr9ddflyTl5eVp2rRpHs4UAFAd3LpnpXnz5ho3bpyGDx+uqKioctexLEv//Oc/NWfOHN14442aOnWqO1MEANhIq1at9N1332nNmjXas2ePLMtS69atddttt6lOnZ+/bxs8eLBnkwQAVBu3Fivr16/X008/rZkzZ+raa69Vly5dFBUVJT8/P2VnZ+vbb7/VF198IW9vb02dOlWPPPKIO9NTUVGRccdyV1ypDTDd1YH1zJkzLsdIZnvqTp48aTRWTk6O28Yyed7PfchzhY+Pj8sxkoxOUW76Nw4NDXU5xs/Pz2isunXruhxj8rybbi9cjXPHts+Uw+FQ37591bdvX0+nUmnR0dEKDAx0KcZke2jSWVoy60Zv+hox6eht8r4MCgpyOUaS6tWr53KM6fbQhLu2ATWBOx/ThadLryyTHE3Gcuf70WTblJeX53JMRdxarLRq1Up///vfdejQIf3973/Xhg0btGnTJp0+fVoNGzZUXFycFi1apNtvv91oUgcA1HzLly/XvffeW6l109PTdfDgQfXs2bOaswIAeIJHKoLo6GhNnDhRK1as0FdffaXvv/9en3/+uV555RX179+fQgUAarEFCxaodevWmj17tr777rsyt+fk5OiTTz7RkCFD1Llz50qf6n7Dhg0aMGCAoqKi5HA4tHLlylK35+XlaezYsYqOjpa/v7+uueYaLViwoCoeEgDAkMfOBgYAQHlSUlL00Ucf6ZVXXtGTTz6pwMBAhYeHO38ynJGRoUaNGmnEiBH65ptvFBYWVqn7zc/PV8eOHTVixAj9+te/LnP7xIkTtW7dOr399ttq2rSp1q5dq0cffVRRUVEaNGhQVT9MAEAlUKwAAGynf//+6t+/v7KysvT5559r//79pX4yHBcX5/Je+H79+qlfv34V3v7FF19o2LBh6t27tyTpkUce0Z///Gdt27atwmKloKCg1HEjubm5LuUEALg4ihUAgG01aNDAbXs1evXqpVWrVmnkyJGKiorS+vXrtWfPHr388ssVxiQlJWnmzJluyQ8AaqMr7uCQpKQkORwOTZgwwdOpAABqkD/96U9q06aNoqOj5ePjo759+2r+/Pnq1atXhTFTp05VTk6O85Kenu7GjAHgyuf2PSupqam69tprq+W+t27dqoULF6pDhw7Vcv8AgCvXn/70J23evFmrVq1SbGysNmzYoEcffVSRkZG69dZby43x9fWVr6+vmzMFgNrD7XtWOnXqpM6dO2vBggVGvSgqkpeXp/vvv1+LFi1S/fr1L7puQUGBcnNzS10AALXX6dOn9eSTT2rOnDkaMGCAOnTooLFjx+qee+7RH//4R0+nBwC1ltv3rPz73//WX/7yF02ZMkWPPfaY7rzzTj300EO6+eabL+t+ExISdMcdd+jWW2/V7373u4uuW9FvjEtKSlxqluPO5kSmY5k0GjJthGTSaMgkxrQRkkkTNJNGSJJUXFxsFGfC5LVh0tDM9Ntjk1ORm7wuTONMx3LX4zJ977uan+n7/kpRWFiowsLCMs9b3bp1jf5ujRo1crnZoMm2zXRbY7JtMx3L5DXs7e3tcozpNspke2jSFFZy7/vM5Hm3eyNJdzZqdGczTpNtjDs/q5m8903ewxVx+56V7t27a9GiRcrIyNCCBQt06NAh3XrrrfrFL36h559/XocOHXL5PpcvX64dO3YoKSmpUuvzG2MAqH3y8vKUmpqq1NRUSVJaWppSU1N18OBBBQcH66abbtKkSZO0fv16paWlacmSJXrrrbf0q1/9yrOJA0At5rGzgfn7+2vYsGEaNmyY9u3bp8WLF+vPf/6zZsyYodtuu02ffPJJpe4nPT1dv/3tb7V27Vr5+flVKobfGANAzZCYmFjpdefMmXPR27dt21ZqL/65+x42bJiWLFmi5cuXa+rUqbr//vt1/PhxxcbG6vnnn9eYMWPMkgcAXDZbnLr4F7/4haZMmaImTZroySef1Jo1ayodu337dmVmZqpz587OZcXFxdqwYYPmzZungoIC4921AADP+uqrr7Rjxw4VFRWpVatWkqQ9e/aobt266tSpk3O9yvz0onfv3hf96URERIQWL158+UkDAKqMx4uVlJQU/eUvf9F7772nunXr6u6779ZDDz1U6fhbbrlFX3/9dallI0aMUOvWrfXEE09QqABADTZgwAAFBQXpzTffdJ48JTs7WyNGjNANN9ygxx57zMMZAgCqk0eKlfT0dC1ZskRLlixRWlqaevTooVdeeUV33323AgMDXbqvoKAgtWvXrtSywMBANWjQoMxyAEDN8uKLL2rt2rWlzvJYv359/e53v1N8fDzFCgBc4dxerNx2221at26dGjVqpAcffFAjR4507toHAOB8ubm5+umnn9S2bdtSyzMzM3Xy5EkPZQUAcBe3Fyv+/v5677331L9//2r7idb69eur5X4BAO71q1/9SiNGjNCLL76o66+/XpK0efNmTZo0SXfeeaeHswMAVDe3FyurVq1y95AAgBrqtdde0+OPP64HHnjA2RvEy8tLDz30kP7whz94ODsAQHXz+AH2AABUJCAgQPPnz9cf/vAH7du3T5ZlqXnz5i4f3wgAqJkoVs5z5swZlzrZuquztLuZdkU16YhcUFDgckx2drbLMZL0n//8x+WYrKwso7GOHz/ucozJcyGZdb026TNU2T5GF3K1i7f084kzTJi8BoODg43GMnkOvbxc3+Sabi9cjTt16pTROO4SGBioDh06eDqNSqtXr57Lr2OTba9pV3l3dbGWzOZKk/eKSYxk3o3ehN072F+p3NnB3oTJWKavJZM4k+1FVbL3p2YAAAAAtRbFCgAAAABbolgBAAAAYEsUKwAAAABsiWIFAAAAgC1RrAAAAACwJYoVAAAAALZEsQIAAADAlihWAAAAANgSHezPU1hYqLNnz1Z6fdPupibdck3HMuk6atoV1ZXn7hyTrtm5ubkux0hSTk6OW2IkKT8/3+UYk+dPMns9mXR6N+0Offr0aZdj/P39jcYyed69vb2NxjJ5n5iM5a7tjOnrD+Xz9/dXQECASzEmrynT7bVJN3rTsUxew3XquP5dqkmMZLYNdWf3cLsz3Ua587mwewd7E+58/kzGqso5hT0rAAAAAGyJYgUAAACALVGsAAAAALAlihUAAAAAtkSxAgAAAMCWKFYAAAAA2BLFCgAAAABbolgBAAAAYEsUKwAAAABsiQ725zl58qRLXTpNu5uadgI3YdJ11KSzsWTWFd2kG/3hw4ddjpGkn376yeUYk/wks67tpt2X3dVJ3fS58PPzcznm1KlTRmOZdLA3+VtJUr169VyOMXneTbcXro6Vl5dnNA7K5+Xl5fLfzp0dqU1eV+7sYG/CpBO9u9m967jdu7a7Mz+7Pxfu5K7PGRVhzwoAAAAAW6JYAQAAAGBLFCsAAAAAbIliBQBQK2zYsEEDBgxQVFSUHA6HVq5cWWad7777TgMHDlRISIiCgoJ0/fXX6+DBg+5PFgAgiWIFAFBL5Ofnq2PHjpo3b165t+/bt0+9evVS69attX79ev2///f/NG3aNKOTRAAAqgZnAwMA1Ar9+vVTv379Krz9qaee0u23364XXnjBuezqq692R2oAgAqwZwUAUOuVlJTo448/VsuWLdWnTx+FhYXpuuuuK/enYucrKChQbm5uqQsAoOpQrAAAar3MzEzl5eXp97//vfr27au1a9fqV7/6le68806lpKRUGJeUlKSQkBDnpUmTJm7MGgCufBQrAIBar6SkRJI0aNAgTZw4Uddee62mTJmi/v3767XXXqswburUqcrJyXFe0tPT3ZUyANQKHLNynoMHDyowMLDS65t2N3XnwZrnJmBXmHawLygocDkmIyPD5Zh9+/a5HCNJOTk5LscUFhYajRUQEOByjOnrwsfHx+WYoqIil2POnDnjcowknTx50uWY48ePG42VmZnplhhJql+/vssxJq8L0w72vr6+Lq2fn59vNM6VomHDhvLy8lKbNm1KLb/mmmv0+eefVxjn6+tb7nPtrg72du+IbspkfjWdk93Zqdydz6HdXand6E3GsvvrwiS/unXrVtn47FkBANR6Pj4+6tq1q3bv3l1q+Z49exQbG+uhrAAA7FkBANQKeXl5+uGHH5zX09LSlJqaqtDQUMXExGjSpEm65557dOONN+rmm2/W6tWr9eGHH2r9+vWeSxoAajmKFQBArbBt2zbdfPPNzuuJiYmSpGHDhmnJkiX61a9+pddee01JSUkaP368WrVqpffee0+9evXyVMoAUOtRrAAAaoXevXtf8rfXI0eO1MiRI92UEQDgUjhmBQAAAIAt1fhiJSkpSV27dlVQUJDCwsI0ePDgMgdIAgAAAKh5anyxkpKSooSEBG3evFnJyckqKipSfHx8rT8NJwAAAFDT1fhjVlavXl3q+uLFixUWFqbt27frxhtv9FBWAAAAAC5XjS9WLnSu8V9oaGiF6xQUFJRqYJibm1vteQEAAABwzRVVrFiWpcTERPXq1Uvt2rWrcL2kpCTNnDmzzPK9e/fK39+/0uOZdIeX5NIY59SpY/aLPZNO5aYd7E06nJt0sDeJkcye93r16hmNFRwc7HJMgwYNjMYyeVznF+uVZfrTSpMvA06dOmU01rkvK1xx/Phxo7ECAwPdEuPt7e1yjPRzk0NXmLx/UbE6deoYb7ddYdr52u4ds0244/muSUz+xu7svm73rvLuZPf8TP7GVfl+vKLe2WPHjtXOnTu1bNmyi643depU5eTkOC/p6eluyhAAAABAZV0xe1bGjRunVatWacOGDYqOjr7our6+vvL19XVTZgAAAABM1PhixbIsjRs3TitWrND69evVrFkzT6cEAAAAoArU+GIlISFBS5cu1QcffKCgoCDn8QwhISFGv+UHAAAAYA81/piVBQsWKCcnR71791ZkZKTz8s4773g6NQAAAACXocbvWbkSz2gCAAAA4ArYswIAAADgykSxAgAAAMCWKFYAAAAA2FKNP2alKq1evVpeXpV/Sky6w0uud5aWzLubmnQdN+luLpl1vjfJLzQ01OUYSUantTYdKzw83G1jmbyeTF67J0+edDlGkk6fPu1yjEnXe0k6cOCAyzGmHezr1q3rcozJe8Tk7ytJAQEBLq1vuj1D+Uw62Lur47gp02NE3dX9uiY8F+7krufD7t3XJffmaDKW3V9PJo+JDvYAAAAArngUKwAAAABsiWIFAAAAgC1RrAAAAACwJYoVAAAAALZEsQIAAADAlihWAAAAANgSxQoAAAAAW6JYAQAAAGBLdLA/T25urksd7M+cOWM0jklXz8LCQqOxTDrEm3TmllzvmC1JUVFRLsd06dLF5RhJatmypcsxsbGxRmOZdLD39/c3Gsvk73X27FmXY06dOuVyjCTFxMS4HLNr1y6jserVq+dyzMGDB43GysrKcjnGZJth8reSpOLiYpfWp4N91XI4HDWis7c7mDwP7oqB5/D3+p8r8bmoysfEnhUAAAAAtkSxAgAAAMCWKFYAAAAA2BLFCgAAAABbolgBANQKGzZs0IABAxQVFSWHw6GVK1dWuO7o0aPlcDg0d+5ct+UHACiLYgUAUCvk5+erY8eOmjdv3kXXW7lypb788kujsxUCAKoWpy4GANQK/fr1U79+/S66zuHDhzV27FitWbNGd9xxh5syAwBUhGIFAABJJSUlGjp0qCZNmqS2bdtWKqagoEAFBQXO67m5udWVHgDUSvwMDAAASbNnz5aXl5fGjx9f6ZikpCSFhIQ4L02aNKnGDAGg9mHPynnmzZvnUgds0+6cJSUlLsdYlmX7sby8XH85BQYGuiVGknx9fV2O8fHxMRrLpKt8nTru++7A5G9s+rpwtZO6JHXq1MlorNOnT7scU1hYaDSWSZzJ+9EkxiQuLy9P3bp1MxrrSrB9+3a9/PLL2rFjh0vb9qlTpyoxMdF5PTc317hgsXsXa7vn507ufC5Mt738vS6P3f/Gdv/70sEeAIAqtHHjRmVmZiomJkZeXl7y8vLSgQMH9Nhjj6lp06YVxvn6+io4OLjUBQBQddizAgCo9YYOHapbb7211LI+ffpo6NChGjFihIeyAgBQrAAAaoW8vDz98MMPzutpaWlKTU1VaGioYmJi1KBBg1Lre3t7KyIiQq1atXJ3qgCA/6JYAQDUCtu2bdPNN9/svH7uWJNhw4ZpyZIlHsoKAHAxFCsAgFqhd+/eLh3Iun///upLBgBQKRxgDwAAAMCWKFYAAAAA2BLFCgAAAABbolgBAAAAYEscYH+e1q1b09ALqGLe3t4ux/j5+RmNddVVVxnF4efO66g6DofD9h2mXeXOTup27+ht+lyYuNJeR55g9+fQ7vl5GntWAAAAANgSxQoAAAAAW6JYAQAAAGBLFCsAAAAAbIliBQAAAIAtXTHFyvz589WsWTP5+fmpc+fO2rhxo6dTAgAAAHAZrohi5Z133tGECRP01FNP6auvvtINN9ygfv366eDBg55ODQAAAIChK6JYmTNnjh566CE9/PDDuuaaazR37lw1adJECxYs8HRqAAAAAAzV+KaQZ8+e1fbt2zVlypRSy+Pj47Vp06ZyYwoKClRQUOC8npOTI4mmaABqr3PbP3c2u7sSnXv+rsT5hKaQ/8P7pGah6aL7VeWcUuOLlf/85z8qLi5WeHh4qeXh4eHKyMgoNyYpKUkzZ84ss7xJkybVkiMA1BRZWVkKCQnxdBo1VlZWliTmEwCQqmZOqfHFyjkXVs2WZVVYSU+dOlWJiYnO6ydOnFBsbKwOHjxoy0k6NzdXTZo0UXp6uoKDgz2dTrnsniP5XR675yfZP0e755eTk6OYmBiFhoZ6OpUa7dzzZ9f5RLL/a5H8Lp/dcyS/y2P3/KSqnVNqfLHSsGFD1a1bt8xelMzMzDJ7W87x9fWVr69vmeUhISG2/aNLUnBwsK3zk+yfI/ldHrvnJ9k/R7vnV6fOFXEoo8ece/7sPp9I9n8tkt/ls3uO5Hd57J6fVDVzSo2flXx8fNS5c2clJyeXWp6cnKwePXp4KCsAAAAAl6vG71mRpMTERA0dOlRdunRR9+7dtXDhQh08eFBjxozxdGoAAAAADF0Rxco999yjrKwsPfvsszp69KjatWunTz75RLGxsZWK9/X11fTp08v9aZgd2D0/yf45kt/lsXt+kv1zJL/aoSY8j3bPkfwun91zJL/LY/f8pKrN0WFx/j0AAAAANlTjj1kBAAAAcGWiWAEAAABgSxQrAAAAAGyJYgUAAACALdX6YmX+/Plq1qyZ/Pz81LlzZ23cuNHTKTktWLBAHTp0cDb96d69uz799FNPp1XK4cOH9cADD6hBgwYKCAjQtddeq+3bt3s6LaeTJ09qwoQJio2Nlb+/v3r06KGtW7d6LJ8NGzZowIABioqKksPh0MqVK523FRYW6oknnlD79u0VGBioqKgoPfjggzpy5Igt8pOk4cOHy+FwlLpcf/31tskvLy9PY8eOVXR0tPz9/XXNNddowYIFbssvKSlJXbt2VVBQkMLCwjR48GDt3r271Drvv/+++vTpo4YNG8rhcCg1NdVW+Z1v9OjRcjgcmjt3rttyrOnsOqfUhPlEYk5xhd3nk0vlKDGnXApzys9qdbHyzjvvaMKECXrqqaf01Vdf6YYbblC/fv108OBBT6cmSYqOjtbvf/97bdu2Tdu2bdMvf/lLDRo0SLt27fJ0apKk7Oxs9ezZU97e3vr000/17bff6sUXX9RVV13l6dScHn74YSUnJ+uvf/2rvv76a8XHx+vWW2/V4cOHPZJPfn6+OnbsqHnz5pW57dSpU9qxY4emTZumHTt26P3339eePXs0cOBAW+R3Tt++fXX06FHn5ZNPPrFNfhMnTtTq1av19ttv67vvvtPEiRM1btw4ffDBB27JLyUlRQkJCdq8ebOSk5NVVFSk+Ph45efnl3oMPXv21O9//3u35ORqfuesXLlSX375paKiotyeZ01l5znF7vOJxJziKrvPJ5fK8RzmlIoxp/yXVYt169bNGjNmTKllrVu3tqZMmeKhjC6tfv361uuvv+7pNCzLsqwnnnjC6tWrl6fTqNCpU6esunXrWh999FGp5R07drSeeuopD2X1P5KsFStWXHSdLVu2WJKsAwcOuCep85SX37Bhw6xBgwa5PZfylJdf27ZtrWeffbbUsk6dOllPP/20GzP7n8zMTEuSlZKSUua2tLQ0S5L11VdfuT+x/6oov0OHDlmNGze2vvnmGys2NtZ66aWXPJNgDVPT5hQ7zSeWxZxyOew+n1gWc0pVqK1zSq3ds3L27Flt375d8fHxpZbHx8dr06ZNHsqqYsXFxVq+fLny8/PVvXt3T6cjSVq1apW6dOmi3/zmNwoLC1NcXJwWLVrk6bScioqKVFxcLD8/v1LL/f399fnnn3soK9fk5OTI4XDY6pvF9evXKywsTC1bttSoUaOUmZnp6ZScevXqpVWrVunw4cOyLEvr1q3Tnj171KdPH4/kk5OTI0kKDQ31yPiXUl5+JSUlGjp0qCZNmqS2bdt6KrUapybNKXacTyTmlOpmx/lEYk5xRW2dU2ptsfKf//xHxcXFCg8PL7U8PDxcGRkZHsqqrK+//lr16tWTr6+vxowZoxUrVqhNmzaeTkuS9OOPP2rBggVq0aKF1qxZozFjxmj8+PF66623PJ2aJCkoKEjdu3fXc889pyNHjqi4uFhvv/22vvzySx09etTT6V3SmTNnNGXKFA0ZMkTBwcGeTkeS1K9fP/3tb3/TZ599phdffFFbt27VL3/5SxUUFHg6NUnSn/70J7Vp00bR0dHy8fFR3759NX/+fPXq1cvtuViWpcTERPXq1Uvt2rVz+/iXUlF+s2fPlpeXl8aPH+/B7GqemjCn2Hk+kZhTqpMd5xOJOcUVtXlO8aqKBGsyh8NR6rplWWWWeVKrVq2UmpqqEydO6L333tOwYcOUkpJiiwmmpKREXbp00axZsyRJcXFx2rVrlxYsWKAHH3zQw9n97K9//atGjhypxo0bq27duurUqZOGDBmiHTt2eDq1iyosLNS9996rkpISzZ8/39PpON1zzz3O/7dr105dunRRbGysPv74Y915550ezOxnf/rTn7R582atWrVKsbGx2rBhgx599FFFRkbq1ltvdWsuY8eO1c6dO237jWt5+W3fvl0vv/yyduzYYavtYE1i5znFzvOJxJxSXew6n0jMKa6ozXNKrd2z0rBhQ9WtW7fMN16ZmZllvhnzJB8fHzVv3lxdunRRUlKSOnbsqJdfftnTaUmSIiMjy0xy11xzjS0OJj3nF7/4hVJSUpSXl6f09HRt2bJFhYWFatasmadTq1BhYaHuvvtupaWlKTk52Vbfgl0oMjJSsbGx2rt3r6dT0enTp/Xkk09qzpw5GjBggDp06KCxY8fqnnvu0R//+Ee35jJu3DitWrVK69atU3R0tFvHroyK8tu4caMyMzMVExMjLy8veXl56cCBA3rsscfUtGlTzyVcA9SEOcXO84nEnFIdatJ8IjGnVKS2zym1tljx8fFR586dlZycXGp5cnKyevTo4aGsLs2yLNvsHu3Zs2eZU9Tt2bNHsbGxHsqoYoGBgYqMjFR2drbWrFmjQYMGeTqlcp2bWPbu3at//vOfatCggadTuqisrCylp6crMjLS06mosLBQhYWFqlOn9Gatbt26KikpcUsOlmVp7Nixev/99/XZZ5/Z7gPMpfIbOnSodu7cqdTUVOclKipKkyZN0po1azyUdc1QE+cUO80nEnNKVatp84nEnHIh5pSf1eqfgSUmJmro0KHq0qWLunfvroULF+rgwYMaM2aMp1OTJD355JPq16+fmjRpopMnT2r58uVav369Vq9e7enUJP18Sr8ePXpo1qxZuvvuu7VlyxYtXLhQCxcu9HRqTmvWrJFlWWrVqpV++OEHTZo0Sa1atdKIESM8kk9eXp5++OEH5/W0tDSlpqYqNDRUUVFRuuuuu7Rjxw599NFHKi4udn5LGxoaKh8fH4/mFxoaqhkzZujXv/61IiMjtX//fj355JNq2LChfvWrX1V7bpfKLyYmRjfddJMmTZokf39/xcbGKiUlRW+99ZbmzJnjlvwSEhK0dOlSffDBBwoKCnL+/UJCQuTv7y9JOn78uA4ePOjsd3Duw1lERIQiIiI8ml+DBg3KfKDx9vZWRESEWrVqVa25XQnsPKfYfT6RmFNcZff55FI5MqdcGnPKf13GGcquCK+++qoVGxtr+fj4WJ06dSr3dHCeMnLkSGdujRo1sm655RZr7dq1nk6rlA8//NBq166d5evra7Vu3dpauHChp1Mq5Z133rGuvvpqy8fHx4qIiLASEhKsEydOeCyfdevWWZLKXIYNG+Y87WB5l3Xr1nk8v1OnTlnx8fFWo0aNLG9vbysmJsYaNmyYdfDgQbfkdqn8LMuyjh49ag0fPtyKioqy/Pz8rFatWlkvvviiVVJS4pb8Kvr7LV682LnO4sWLy11n+vTptsjvQpy62DV2nVNqwnxiWcwprrD7fHKpHJlTLo055WeO/w4GAAAAALZSa49ZAQAAAGBvFCsAAAAAbIliBQAAAIAtUawAAAAAsCWKFQAAAAC2RLECAAAAwJYoVgAAAADYEsUKAAAAAFuiWAE8ZMaMGbr22msv6z4cDodWrlxZJfkAAGom5hNcyShWUGsNHz5cgwcP9nQa5ZoxY4YcDsdFL/v379fRo0fVr18/T6cLALUa8wlQfShWABt6/PHHdfToUeclOjpazz77bKllTZo0UUREhHx9fT2dLgDApphPUNNRrAAVSElJUbdu3eTr66vIyEhNmTJFRUVFzttLSko0e/ZsNW/eXL6+voqJidHzzz/vvP2JJ55Qy5YtFRAQoKuvvlrTpk1TYWFhpcauV6+eIiIinJe6desqKCiozLLzd9vv379fDodD7777rm644Qb5+/ura9eu2rNnj7Zu3aouXbqoXr166tu3r44dO1ZqvMWLF+uaa66Rn5+fWrdurfnz51/+EwgAkMR8AlwOL08nANjR4cOHdfvtt2v48OF666239P3332vUqFHy8/PTjBkzJElTp07VokWL9NJLL6lXr146evSovv/+e+d9BAUFacmSJYqKitLXX3+tUaNGKSgoSJMnT67W3KdPn665c+cqJiZGI0eO1H333afg4GC9/PLLCggI0N13361nnnlGCxYskCQtWrRI06dP17x58xQXF6evvvpKo0aNUmBgoIYNG1atuQLAlY75hPkEl8kCaqlhw4ZZgwYNKve2J5980mrVqpVVUlLiXPbqq69a9erVs4qLi63c3FzL19fXWrRoUaXHe+GFF6zOnTs7r0+fPt3q2LFjpWJjY2Otl156qcxySdaKFSssy7KstLQ0S5L1+uuvO29ftmyZJcn617/+5VyWlJRktWrVynm9SZMm1tKlS0vd73PPPWd17969UrkBQG3HfPIz5hNUB/asAOX47rvv1L17dzkcDueynj17Ki8vT4cOHVJGRoYKCgp0yy23VHgf//jHPzR37lz98MMPysvLU1FRkYKDg6s99w4dOjj/Hx4eLklq3759qWWZmZmSpGPHjik9PV0PPfSQRo0a5VynqKhIISEh1Z4rAFzpmE+YT3B5KFaAcliWVWpiObdM+vn0jv7+/heN37x5s+69917NnDlTffr0UUhIiJYvX64XX3yx2nI+x9vb2/n/c4/hwmUlJSWS5Px30aJFuu6660rdT926das7VQC44jGfMJ/g8lCsAOVo06aN3nvvvVKTzKZNmxQUFKTGjRurUaNG8vf317/+9S89/PDDZeL//e9/KzY2Vk899ZRz2YEDB9yWf2WFh4ercePG+vHHH3X//fd7Oh0AuOIwnwCXh2IFtVpOTo5SU1NLLQsNDdWjjz6quXPnaty4cRo7dqx2796t6dOnKzExUXXq1JGfn5+eeOIJTZ48WT4+PurZs6eOHTumXbt26aGHHlLz5s118OBBLV++XF27dtXHH3+sFStWeOZBXsKMGTM0fvx4BQcHq1+/fiooKNC2bduUnZ2txMRET6cHADUC8wnzCaoHxQpqtfXr1ysuLq7UsmHDhmnJkiX65JNPNGnSJHXs2FGhoaF66KGH9PTTTzvXmzZtmry8vPTMM8/oyJEjioyM1JgxYyRJgwYN0sSJEzV27FgVFBTojjvu0LRp05xnfrGThx9+WAEBAfrDH/6gyZMnKzAwUO3bt9eECRM8nRoA1BjMJ8wnqB4O69wPJwEAAADARmgKCQAAAMCWKFYAAAAA2BLFCgAAAABbolgBAAAAYEsUKwAAAABsiWIFAAAAgC1RrAAAAACwJYoVAAAAALZEsQIAAADAlihWAAAAANgSxQoAAAAAW6JYuUBWVpbCwsK0f//+Sq1/1113ac6cOdWbFACgRrpwTvn3v/+t9u3by9vbW4MHD9bXX3+t6Oho5efnezZRALAph2VZlqeTsJPHH39c2dnZeuONNyq1/s6dO3XzzTcrLS1NwcHB1ZwdAKAmuXBOue6669SyZUslJSWpXr16uuqqq3TnnXeqU6dOevrppz2cLQDYD3tWznP69Gm98cYbevjhhysd06FDBzVt2lR/+9vfqjEzAEBNU96csm/fPv3yl79UdHS0rrrqKknSiBEjtGDBAhUXF3soUwCwL4qV83z66afy8vJS9+7dncu+/fZb3X777apXr57Cw8M1dOhQ/ec//ykVN3DgQC1btszd6QIAbOz8OWX//v1yOBzKysrSyJEj5XA4tGTJEklSnz59lJWVpZSUFM8mDAA2RLFyng0bNqhLly7O60ePHtVNN92ka6+9Vtu2bdPq1av1008/6e677y4V161bN23ZskUFBQXuThkAYFPnzylNmjTR0aNHFRwcrLlz5+ro0aO65557JEk+Pj7q2LGjNm7c6Ml0AcCWvDydgJ3s379fUVFRzusLFixQp06dNGvWLOeyv/zlL2rSpIn27Nmjli1bSpIaN26sgoICZWRkKDY21u15AwDs5/w5pW7duoqIiJDD4VBISIgiIiJKrdu4ceNKn9gFAGoTipXznD59Wn5+fs7r27dv17p161SvXr0y6+7bt89ZrPj7+0uSTp065Z5EAQC2d+GccjH+/v7MIQBQDoqV8zRs2FDZ2dnO6yUlJRowYIBmz55dZt3IyEjn/48fPy5JatSoUfUnCQCoES6cUy7m+PHj+sUvflHNGQFAzcMxK+eJi4vTt99+67zeqVMn7dq1S02bNlXz5s1LXQIDA53rffPNN4qOjlbDhg09kTYAwIYunFMu5ptvvlFcXFw1ZwQANQ/Fynn69OmjXbt2Ob8JS0hI0PHjx3Xfffdpy5Yt+vHHH7V27VqNHDmy1CkmN27cqPj4eE+lDQCwoQvnlIrs379fhw8f1q233uqmzACg5qBYOU/79u3VpUsXvfvuu5KkqKgo/fvf/1ZxcbH69Pn/7d17XBV1/sfx91G5iYCiclMkTLQUc0vNtDYvKUmbedvVsi0su/jwUoTmtRTLJG29dNPSWu1m2m5e2jKVUjGXLmq6mZVZ4SWFH2YKggoI8/vD9WwkImc4nBnk9Xw8zkPPnJn5fmbOnPPlc2bm+7lZsbGxevjhhxUUFKRatc7uutOnT2vlypW6//77rQwdAGAzv+9TLuTtt99WXFwcA7QAQBmoYP87a9as0dixY/X11187E5LyvPjii1q9erXWr1/vgegAANXJxfqUgoICxcTE6O2339b1119vQYQAYG/cYP87t9xyi/bu3atDhw4pMjLyovN7eXnp+eef90BkAIDq5mJ9yv79+zV58mQSFQC4AM6sAAAAALAl7lkBAAAAYEskKwAAAABsiWQFAAAAgC1xg73OVqo/fPiwAgIC5HA4rA4HADzOMAydOHFCERERFRoJEWWjPwEA9/YpJCuSDh8+XKGRvwDgUnfw4EE1bdrU6jCqLfoTAPgfd/QpJCuSAgICJEnvvfee/P39K7xcSUmJqfa8vLxcXsbsoG1mYjTblplfEc20ZfbXSl9fX48sI0l16rj+0apdu7aptsz8YmH3X3yLiopMLVdQUODyMmfOnPFYW2a2y+z3jKvHU35+vvr27ev8PoQ55/bf/v37FRgYaHE0F2b3gUDt/h1l9/gk898dnsIxWDl2jy83N1dRUVFu6VNIVvS/N9zf398jyYq3t7fLy5j9UBcXF3usLTN/NJvZh2Y/oH5+fi4vYzZZMZOQkqz8j9lkxcx+N5usmGmrsLDQ5WU8laycY/djw+7O7b/AwECSlUqw+3Fo9/gkkpXKsvt7bPf4znFHnFyYDAAAAMCWSFYAAAAA2BLJCgAAAABbIlkBAAAAYEskKwAAAABsiWQFAAAAgC2RrAAAAACwJeqs/EatWrVcqk1gduxoM8uZbctMnRVPjs1uZpx1M9skmatxYaa4o3Rp1j4xu989WevHzLFr9ng38x6bPZ48wcz24MIMw7B9HQnASnavA2O2VpWn2P37xZ3x0TsBAAAAsCWSFQAAAAC2RLICAAAAwJZIVgAAAADYEskKAAAAAFsiWQEAAABgSyQrAAAAAGyJZAUAAACALdm3QpkFmjZtqoCAgArPb7aIn5nia54skme2LTPF/zy1jFlmC+V5eXm5vIwnjyczbZk9LswsVx2KQpqJ0cx+N3sMurrciRMnTLUD1ER2L8gn2b/QsN2LLsI+OLMCAAAAwJZIVgAAAADYEskKAAAAAFsiWQEAAABgSyQrAAAAAGyJZAUAAACALZGsAAAAALAlS5OVBQsW6KqrrlJgYKACAwPVuXNnffjhh87XDcNQcnKyIiIi5Ofnp27dumn37t2l1lFQUKDRo0erUaNG8vf312233aaff/7Z05sCALC5lJQUdezYUQEBAQoJCVG/fv20Z8+eUvMMHTpUDoej1OO6666zKGIAgKXJStOmTfX0009r27Zt2rZtm3r06KG+ffs6E5JZs2Zpzpw5euGFF7R161aFhYWpV69epYqXJSYmauXKlVq2bJm2bNmivLw83XrrrR4tHAgAsL+0tDSNHDlSn332mVJTU3XmzBnFxcUpPz+/1Hy9e/dWZmam87FmzRqLIgYAOAyblWENDg7WM888o3vvvVcRERFKTEzU+PHjJZ09ixIaGqqZM2fqwQcfVE5Ojho3bqw33nhDgwcPliQdPnxYkZGRWrNmjW6++eYKtZmbm6ugoCB9//33LlWw9yRPVpX3ZEVvM/GZ3RdmK4F7qq06depUQSTuY3b/mXm/zLZl9ti1M7NVqM1UsI+JiVFOTo4CAwNNtVndHDlyRCEhIUpLS9ONN94o6eyZlePHj2vVqlWm1nmuP/nll19svR/tXt2c+CrP7t+Hdo/Pk38zXIpyc3PVsGFDt/QptnkniouLtWzZMuXn56tz587KyMhQVlaW4uLinPP4+Pioa9euSk9PlyRt375dRUVFpeaJiIhQbGysc56yFBQUKDc3t9QDAFCz5OTkSDr7I9lvbdq0SSEhIWrZsqXuv/9+ZWdnX3Ad9CcAULUsT1Z27dqlevXqycfHR8OHD9fKlSvVunVrZWVlSZJCQ0NLzR8aGup8LSsrS97e3mrQoMEF5ylLSkqKgoKCnI/IyEg3bxUAwM4Mw1BSUpJuuOEGxcbGOqfHx8frrbfe0oYNGzR79mxt3bpVPXr0UEFBQZnroT8BgKpl+XUnrVq10s6dO3X8+HG9++67SkhIUFpamvP1359qNQzjoqdfLzbPxIkTlZSU5Hyem5tLBwMANcioUaP01VdfacuWLaWmn7ukWJJiY2PVoUMHRUVF6YMPPtCAAQPOWw/9CQBULcuTFW9vb7Vo0UKS1KFDB23dulXPPvus8z6VrKwshYeHO+fPzs52nm0JCwtTYWGhjh07VursSnZ2trp06XLBNn18fOTj41MVmwMAsLnRo0frvffe0+bNm9W0adNy5w0PD1dUVJT27t1b5uv0JwBQtSy/DOz3DMNQQUGBoqOjFRYWptTUVOdrhYWFSktLcyYi7du3l5eXV6l5MjMz9fXXX5ebrAAAah7DMDRq1CitWLFCGzZsUHR09EWXOXr0qA4ePFjqRzMAgOdYemZl0qRJio+PV2RkpE6cOKFly5Zp06ZNWrt2rRwOhxITEzVjxgzFxMQoJiZGM2bMUN26dTVkyBBJUlBQkIYNG6YxY8aoYcOGCg4O1tixY9W2bVv17NnTyk0DANjMyJEjtXTpUq1evVoBAQHOexuDgoLk5+envLw8JScna+DAgQoPD9e+ffs0adIkNWrUSP3797c4egComSxNVv7v//5Pd911lzIzMxUUFKSrrrpKa9euVa9evSRJ48aN06lTpzRixAgdO3ZMnTp10vr160sNLzx37lzVqVNHgwYN0qlTp3TTTTdpyZIlql27tlWbBQCwoQULFkiSunXrVmr64sWLNXToUNWuXVu7du3S66+/ruPHjys8PFzdu3fX8uXLbTusPQBc6mxXZ8UK1FkpjTorlUedlf+hzkrlUGeleqHOinsQX+XZ/fvQ7vFRZ6VyLsk6KwAAAADwW/b+KdfD6tatK39//wrPb/YXfjO/yHjyFwiz22UmRk+eWTGznCf3hSfb8uQxaKYts/vCzOWfZn89M7Ocnc/uFRYWVlEkNZPD4bD1r+92v6jCzvtOsn98kv3fY1SO3Y9Bd8bHmRUAAAAAtkSyAgAAAMCWSFYAAAAA2BLJCgAAAABbIlkBAAAAYEskKwAAAABsiWQFAAAAgC2RrAAAAACwJZIVAAAAALZEBfvfMAzDpYqvZqtzmlnObOVrT1awNdOWmarZZqreS1TztYonK72beY/Nfo49dTx56nvG7tWQqxtX+xNP4/2uHDu/t+fY/T0225fjLLu/vyUlJW5bF2dWAAAAANgSyQoAAAAAWyJZAQAAAGBLJCsAAAAAbIlkBQAAAIAtkawAAAAAsCWSFQAAAAC2RLICAAAAwJZIVgAAAADYEhXsf6NWrVqmK2e72o6nmKmya7Yyr5nlvLy8XF7GbNXbwsJCjywjmavcana/mzmezFS+rV27tsvLmF3O29vbVFtmlzPDzD408x5Twb56snsFe7uz+77zZD9ult33od3Zff/VpO9s+3/aAAAAANRIJCsAAAAAbMnSZCUlJUUdO3ZUQECAQkJC1K9fP+3Zs6fUPEOHDpXD4Sj1uO6660rNU1BQoNGjR6tRo0by9/fXbbfdpp9//tmTmwIAAADAzSxNVtLS0jRy5Eh99tlnSk1N1ZkzZxQXF6f8/PxS8/Xu3VuZmZnOx5o1a0q9npiYqJUrV2rZsmXasmWL8vLydOutt5q+twEAAACA9Sy9wX7t2rWlni9evFghISHavn27brzxRud0Hx8fhYWFlbmOnJwcvfrqq3rjjTfUs2dPSdKbb76pyMhIffTRR7r55purbgMAAAAAVBlb3bOSk5MjSQoODi41fdOmTQoJCVHLli11//33Kzs72/na9u3bVVRUpLi4OOe0iIgIxcbGKj09vcx2CgoKlJubW+oBAAAAwF5sk6wYhqGkpCTdcMMNio2NdU6Pj4/XW2+9pQ0bNmj27NnaunWrevTooYKCAklSVlaWvL291aBBg1LrCw0NVVZWVpltpaSkKCgoyPmIjIysug0DAAAAYIpt6qyMGjVKX331lbZs2VJq+uDBg53/j42NVYcOHRQVFaUPPvhAAwYMuOD6DMO44BjUEydOVFJSkvN5bm4uCQsAAABgM7ZIVkaPHq333ntPmzdvVtOmTcudNzw8XFFRUdq7d68kKSwsTIWFhTp27FipsyvZ2dnq0qVLmevw8fGRj4+P+zYAAFAl9uzZo7fffluffPKJ9u3bp5MnT6px48a6+uqrdfPNN2vgwIF8nwPAJczSZMUwDI0ePVorV67Upk2bFB0dfdFljh49qoMHDyo8PFyS1L59e3l5eSk1NVWDBg2SJGVmZurrr7/WrFmzqjT+6sCTFU7NVPQ1U928Th1zh21RUZFHlpGk06dPu7yM2Wq5Xl5eLi9jptJ7dajYbOZ4N3MMSub2R3XYh3axY8cOjRs3Tp988om6dOmia6+9Vv369ZOfn59+/fVXff3115o8ebJGjx6tcePGKTEx8aJJS0pKilasWKHvvvtOfn5+6tKli2bOnKlWrVo55zEMQ9OmTdPChQt17NgxderUSS+++KLatGlT1ZvsUXavzs1n5dLHMVg5dt9/7ozP0mRl5MiRWrp0qVavXq2AgADnPSZBQUHy8/NTXl6ekpOTNXDgQIWHh2vfvn2aNGmSGjVqpP79+zvnHTZsmMaMGaOGDRsqODhYY8eOVdu2bZ2jgwEAqpd+/frp0Ucf1fLly88bdOW3Pv30U82dO1ezZ8/WpEmTyl3nueHyO3bsqDNnzmjy5MmKi4vTN998I39/f0nSrFmzNGfOHC1ZskQtW7bU9OnT1atXL+3Zs0cBAQFu3UYAwMU5DAtTswv9Crp48WINHTpUp06dUr9+/bRjxw4dP35c4eHh6t69u5588slS95icPn1ajz76qJYuXapTp07ppptu0vz58yt8H0pubq6CgoJ06NAhBQYGumXbynOpZutm6tqYOXNxbnAFV506dcrlZU6cOGGqrUvxzIqZdiRzZ8LMxCfJ1OVAZrfL7mdWXG0rNzdXTZs2VU5Ojke+By+msLDQpePA1fkl6ciRIwoJCVFaWppuvPFGGYahiIgIJSYmavz48ZLOft+EhoZq5syZevDBB89bR0FBQanvpHP3QB45csQW+7G6sns/6cmrFswqKSmxOoRynTlzxuoQymX2rDvOys3NVePGjd3Sp1h+GVh5/Pz8tG7duouux9fXV88//7yef/55d4UGALDQxRKP48ePq379+hWevyy/Hy4/IyNDWVlZpYbC9/HxUdeuXZWenl5mspKSkqJp06a53DYAoGJcTlb27dtX5o2OnTt3lq+vb1XECACowWbOnKnLLrvMOTrkoEGD9O677yosLExr1qxRu3btXF5nWcPln7sUOTQ0tNS8oaGh2r9/f5nrYXRJAKhaFU5Wli5dqueee05ffPGFQkJC1KRJE+eNjj/++KN8fX115513avz48YqKiqrKmAEANcjLL7+sN998U5KUmpqq1NRUffjhh3rnnXf06KOPav369S6v80LD5UvnX+JT3lD4jC4JAFWrQsnKNddco1q1amno0KF655131KxZs1KvFxQU6NNPP9WyZcvUoUMHzZ8/X3/5y1+qJGAAQM2SmZnpPFvx/vvva9CgQYqLi9Nll12mTp06uby+Cw2XHxYWJunsGZZzI05KZ4fC//3ZFgCAZ1ToDrYnn3xS27Zt06hRo85LVKSzvyx169ZNL730kr799ltddtll7o4TAFBDNWjQQAcPHpQkrV271jnSo2EYLg3sYRiGRo0apRUrVmjDhg3nDZcfHR2tsLAwpaamOqcVFhYqLS3tgnW7AABVq0JnVv70pz9VeIWNGjVSo0aNTAcEAMBvDRgwQEOGDFFMTIyOHj2q+Ph4SdLOnTvVokWLCq/nYsPlOxwOJSYmasaMGYqJiVFMTIxmzJihunXrasiQIVWybQCA8pkaDezHH3/U4sWL9eOPP+rZZ59VSEiI1q5dq8jIyEuucBYAwFpz585VdHS0Dhw4oFmzZqlevXqSzl4eNmLEiAqvZ8GCBZKkbt26lZp+brh8SRo3bpxOnTqlESNGOItCrl+/nhorAGARl+uspKWlKT4+Xtdff702b96sb7/9Vs2bN9esWbP0xRdf6J///GdVxVplztVZ+fnnn10aC9rsOOtmxo/35JjuZsdmN1MzxUztEzPLmF3OTL0Uydy+MMvMWPBmljFTL0UyV8fE7A3LZpYz+9kysw89WZvF1fhyc3MVFRVlmzor5xQVFemBBx7Q448/rubNm1sdzkWd60+ys7NttR+rG7vXMbF7HRjJ/nVM7F4HhjorlZObm6uQkBC39Ckuf9omTJig6dOnKzU1tdS49t27d9enn35aqWAAAPgtLy8vrVy50uowAAAWcTlZ2bVrl/r373/e9MaNG+vo0aNuCQoAgHP69++vVatWWR0GAMACLl/XUb9+fWVmZp43isqOHTvUpEkTtwUGAIAktWjRQk8++aTS09PVvn17+fv7l3r9oYcesigyAEBVczlZGTJkiMaPH69//OMfcjgcKikp0b///W+NHTtWd999d1XECACowV555RXVr19f27dv1/bt20u95nA4SFYA4BLmcrLy1FNPaejQoWrSpIkMw1Dr1q1VXFysIUOG6LHHHquKGAEANVhGRobVIQAALOJysuLl5aW33npLTzzxhHbs2KGSkhJdffXViomJqYr4AAAAANRQ5sYilRQZGakzZ87o8ssvNz2kKQAAF1NcXKwlS5bo448/VnZ29nlDnm7YsMGiyAAAVc3lLOPkyZMaPXq0XnvtNUnS999/r+bNm+uhhx5SRESEJkyY4PYgAQA118MPP6wlS5boT3/6k2JjY21fgwMA4D4uJysTJ07Uf/7zH23atEm9e/d2Tu/Zs6emTp1KsgIAcKtly5bpnXfe0S233GJ1KAAAD3M5WVm1apWWL1+u6667rtSvW61bt9aPP/7o1uA8rVatWtWiKq0rDMNweRmz+8BT1bntHp9kbr+bZaZKsZnKwZ6sNmym6r1ZZi9jNROjmYrIZs8iuHrsenKfu8rb21stWrSwOgyXlJSU2LpCN9W5L312PwPpyX7SDDt/fiXzf59URy5v6ZEjRxQSEnLe9Pz8fNt/MAAA1c+YMWP07LPP2v6PGwCA+7n8k2LHjh31wQcfaPTo0ZL+l7kvWrRInTt3dm90AIAaacCAAaWeb9iwQR9++KHatGlz3lmgFStWeDI0AIAHuZyspKSkqHfv3vrmm2905swZPfvss9q9e7c+/fRTpaWlVUWMAIAaJigoqNTz/v37WxQJAMBKLicrXbp0UXp6up555hldfvnlWr9+va655hp9+umnatu2bVXECACoYRYvXmx1CAAAG3ApWSkqKtIDDzygxx9/3Dl0MQAAAABUBZdusPfy8tLKlSurKhYAAEp55ZVXlJCQ4DzTsnz5cl155ZVq3ry5pk6danF0AICq5vJoYP3799eqVavc0nhKSoo6duyogIAAhYSEqF+/ftqzZ0+peQzDUHJysiIiIuTn56du3bpp9+7dpeYpKCjQ6NGj1ahRI/n7++u2227Tzz//7JYYAQDWmDdvnhITE5WXl6fJkyfrqaee0siRI/XXv/5V99xzj5599lktXLjQ6jABAFXI5XtWWrRooSeffFLp6elq3769/P39S73+0EMPVXhdaWlpGjlypDp27KgzZ85o8uTJiouL0zfffONc76xZszRnzhwtWbJELVu21PTp09WrVy/t2bNHAQEBkqTExET961//0rJly9SwYUONGTNGt956q7Zv385Y8gBQTb388stauHChhgwZoh07dujaa6/VSy+9pGHDhkmSmjZtqhdffFEPPPCAxZECAKqKw3Bx4Pro6OgLr8zh0E8//WQ6mHM1XNLS0nTjjTfKMAxFREQoMTFR48ePl3T2LEpoaKhmzpypBx98UDk5OWrcuLHeeOMNDR48WJJ0+PBhRUZGas2aNbr55psv2m5ubq6CgoJ0+PBhBQYGmo6/oszUozFbw8ZMXQKzbRUVFbm8zKlTpzyyjNnlTp8+baots8t5ipliUmYTf29vb5eXqVu3rqm2/Pz8XF7GbDHES60oZG5uriIiIpSTk+OR78GKqFu3rr777js1a9ZMkuTr66vt27erTZs2kqQffvhBHTt21LFjx6wMs5Rz/UlWVpZt9mNZ+CGvcqpDQb7i4mKrQyiXmYLGnmT399ju8eXm5iokJMQtfYrLZ1YyMjIq1WB5cnJyJEnBwcHOtrKyshQXF+ecx8fHR127dlV6eroefPBBbd++XUVFRaXmiYiIUGxsrNLT08tMVgoKClRQUOB8npubK+nsH/au/HFvtrqpJ4tnmklWzG5XYWGhy8vk5eW5vMyJEydcXsZsW2YTo98eXxXlyS9uM3+o+Pr6mmrLTLJiZv9J5hLm358drkpmPo9m/6h0tS07/mFTt25d5efnO583btxY9erVKzWPXf/gqVWrlq3/mLDj+/1bdk+mKFBaeexDVJRtvkkNw1BSUpJuuOEGxcbGSpKysrIkSaGhoaXmDQ0Ndb6WlZUlb29vNWjQ4ILz/F5KSoqCgoKcj8jISHdvDgCgkq644gp99dVXzucHDx5UVFSU8/l3332nyy67zILIAACe4vKZlaSkpDKnOxwO+fr6qkWLFurbt6/z7EhFjRo1Sl999ZW2bNlS5rp/yzCMi56dKG+eiRMnltqO3NxcEhYAsJmZM2eWe+brwIEDevDBBz0YEQDA01xOVnbs2KEvv/xSxcXFatWqlQzD0N69e1W7dm1dccUVmj9/vsaMGaMtW7aodevWFVrn6NGj9d5772nz5s1q2rSpc3pYWJiks2dPwsPDndOzs7OdZ1vCwsJUWFioY8eOlTq7kp2drS5dupTZno+Pj3x8fFzddACABzVu3FgtW7a84OsjRozwYDQAACu4fBlY37591bNnTx0+fFjbt2/Xl19+qUOHDqlXr1664447dOjQId1444165JFHLrouwzA0atQorVixQhs2bDjv5v3o6GiFhYUpNTXVOa2wsFBpaWnORKR9+/by8vIqNU9mZqa+/vrrCyYrAAD7u/rqq3XllVdq/PjxSk9PtzocAIAFXD6z8swzzyg1NbXUnf2BgYFKTk5WXFycHn74YU2ZMqXUDe8XMnLkSC1dulSrV69WQECA8x6ToKAg+fn5yeFwKDExUTNmzFBMTIxiYmI0Y8YM1a1bV0OGDHHOO2zYMI0ZM0YNGzZUcHCwxo4dq7Zt26pnz56ubh4AwCaOHj2q1NRUrV69WgMGDJBhGLr11lvVt29fxcXFmR70AQBQfbh8ZiUnJ0fZ2dnnTT9y5IhzVK369etXaGSoBQsWKCcnR926dVN4eLjzsXz5cuc848aNU2JiokaMGKEOHTro0KFDWr9+vbPGiiTNnTtX/fr106BBg3T99derbt26+te//mX70UQAABfm6+urPn366JVXXlFmZqZWrlypxo0ba8KECWrYsKH69u2rv//972X2SQCAS4PLdVbuvPNOffrpp5o9e7Y6duwoh8OhL774QmPHjlWXLl30xhtvaNmyZfrb3/6mbdu2VVXcbnVuXPxDhw65NBY0QxeXZmboYjPDEDN0ceXZfehis3VWfj+sbUWYHbrYzP6oU8flk9mmf3Rx9Xvm3EAjdqqzUp69e/fqvffe0+rVq/X5559rzpw5GjlypNVhOfuT7OxsW+9Hs9/znmL3Hxs92Y+bZff32MxQ855k92PQzkOjSxbXWXn55Zf1yCOP6Pbbb3f+cVWnTh0lJCRo7ty5ks4ON/nKK69UKjAAAC4kJiZGY8aM0ZgxY3T06FH9+uuvVocEAKgCLicr9erV06JFizR37lz99NNPMgxDl19+ealfNP/whz+4M0YAQA136NAh/fvf/1Z2dnapX4wdDodGjx6thg0bWhgdAKCquH5Nwn9lZWUpMzNTN954o/z8/CpU+8TuXK04bHZ77X4ZmNlTi2aWM7MvzFxKI5m7HMnMpW2SudPbnrwMzMx75eXlZaotM8OEm23Lz8/P5WXMXt5mJkYzlxV46lS/nS95WLx4sYYPHy5vb281bNiw1PfGuWTFboqKimx/mYud2f3vCbtfgiPZ/zIwT/Z5Zth9/5n9W8hTiouL3bYulz9tR48e1U033aSWLVvqlltuUWZmpiTpvvvu05gxY9wWGAAAkjRlyhRNmTJFOTk52rdvnzIyMpyPn376qcLr2bx5s/r06aOIiAg5HA6tWrWq1OtDhw6Vw+Eo9bjuuuvcvDUAAFe4nKw88sgj8vLy0oEDB0rdBDt48GCtXbvWrcEBAHDy5Endfvvtlf41Oz8/X+3atdMLL7xwwXl69+6tzMxM52PNmjWVahMAUDkun0Nav3691q1bV6rSvHT2Zsf9+/e7LTAAACRp2LBh+sc//qEJEyZUaj3x8fGKj48vdx4fHx+FhYVVqh0AgPu4nKzk5+eXOazoL7/8YuradAAAypOSkqJbb71Va9euVdu2bc+7X2jOnDlua2vTpk0KCQlR/fr11bVrVz311FMKCQm54PwFBQWlhio/V28MAOAeLicrN954o15//XU9+eSTks7eBFdSUqJnnnlG3bt3d3uAAICabcaMGVq3bp1atWolSefdYO8u8fHx+stf/qKoqChlZGTo8ccfV48ePbR9+/YL/hiXkpKiadOmuS0GAEBpLicrzzzzjLp166Zt27apsLBQ48aN0+7du/Xrr7/q3//+d1XECACowebMmaO///3vGjp0aJW2M3jwYOf/Y2Nj1aFDB0VFRemDDz7QgAEDylxm4sSJSkpKcj4/V1wTAOAeLicrrVu31ldffaUFCxaodu3ays/P14ABAzRy5EiFh4dXRYwAgBrMx8dH119/vcfbDQ8PV1RUlPbu3XvBeXx8fLgEGgCqkKlBmsPCwjjtDQDwiIcffljPP/+8nnvuOY+2e/ToUR08eJAf4gDAQhVKVr766qsKr/Cqq64yHQwAAL/3xRdfaMOGDXr//ffVpk2b826wX7FiRYXWk5eXpx9++MH5PCMjQzt37lRwcLCCg4OVnJysgQMHKjw8XPv27dOkSZPUqFEj9e/f363bAwCouAolK3/4wx/kcDjOq1J/rjr6b6e5s2KlpxUXF1fr+MtipgKr2RtWzew7MxVsfzvyTlW3ZbaCvZl9YbYtM1WyzRwXZmtcmKn0XtaIgxVx+vRpl5fx9/c31ZaZ5TxV9d7Mcnau1ly/fv0L3jPiim3btpUaCObcvSYJCQlasGCBdu3apddff13Hjx9XeHi4unfvruXLlysgIMDltmrVqmXrKufn+m+YUx32n50/05L5PsVT3Dl4ByqnQslKRkaG8/87duzQ2LFj9eijj6pz586SpE8//VSzZ8/WrFmzqiZKAECNtXjxYresp1u3buX+kblu3Tq3tAMAcJ8KJStRUVHO///lL3/Rc889p1tuucU57aqrrlJkZKQef/xx9evXz+1BAgAAAKh5XL7BfteuXYqOjj5venR0tL755hu3BAUAwDlHjx7VlClTtHHjRmVnZ593ecuvv/5qUWQAgKrmcrJy5ZVXavr06Xr11Vfl6+sr6ew9BNOnT9eVV17p9gABADXbX//6V/34448aNmyYQkNDuZYcAGoQl5OVl156SX369FFkZKTatWsnSfrPf/4jh8Oh999/3+0BAgBqti1btmjLli3OPgcAUHO4nKxce+21ysjI0JtvvqnvvvtOhmFo8ODBGjJkiOlRdQAAuJArrrhCp06dsjoMAIAFTBWFrFu3rh544AF3xwIAwHnmz5+vCRMmaMqUKYqNjT1vCOjAwECLIgMAVLUKDXL96aefVniF+fn52r17t+mAAAD4rfr16ysnJ0c9evRQSEiIGjRooAYNGqh+/fpq0KCB1eEBAKpQhc6s3H333brssst0//3365ZbblG9evXOm+ebb77Rm2++qcWLF2vWrFlq06aN24MFANQ8d955p7y9vbV06VJusAeAGqZCyco333yjl19+WVOmTNGdd96pli1bKiIiQr6+vjp27Ji+++475efna8CAAUpNTVVsbGxVx10lvL295e3tXeH5zVaw9WRH68kqu56qim6mCrhkbl+YqXovmdsXZqrem13OzDFo9lgy8x7XqWPqClVTy5lty5XvinPMVKP3VJVns58rT/j666+1Y8cOtWrVyupQKuzMmTOmvz88we4Jn93jqw7M9imeUlhYaHUI5TLzfe1Jdv+MmPk76EIq1Et7eXlp1KhRGjVqlL788kt98skn2rdvn06dOqV27drpkUceUffu3RUcHOy2wAAAkKQOHTro4MGD1SpZAQC4h8s/KV5zzTW65ppr3NL45s2b9cwzz2j79u3KzMzUypUr1a9fP+frQ4cO1WuvvVZqmU6dOumzzz5zPi8oKNDYsWP19ttv69SpU7rppps0f/58NW3a1C0xAgCsNXr0aD388MN69NFH1bZt2/POAl111VUWRQYAqGrmrn9wk/z8fLVr10733HOPBg4cWOY8vXv31uLFi53Pf3/pRWJiov71r39p2bJlatiwocaMGaNbb71V27dvt/0pPADAxQ0ePFiSdO+99zqnORwOGYYhh8Nh+8tdAADmWZqsxMfHKz4+vtx5fHx8FBYWVuZrOTk5evXVV/XGG2+oZ8+ekqQ333xTkZGR+uijj3TzzTeXuVxBQYEKCgqcz3Nzc01uAQCgqmVkZFgdAgDAIpYmKxWxadMmhYSEqH79+urataueeuophYSESJK2b9+uoqIixcXFOeePiIhQbGys0tPTL5ispKSkaNq0aR6JHwBQOVFRUVaHAACwiGeGmTEpPj5eb731ljZs2KDZs2dr69at6tGjh/OsSFZWlry9vc8bZz80NFRZWVkXXO/EiROVk5PjfBw8eLBKtwMA4BrqewEAJDcmK0ePHtW8efPctTpJZ69T/tOf/qTY2Fj16dNHH374ob7//nt98MEH5S537jrmC/Hx8VFgYGCpBwDAPu6++2716tVL77zzjvLy8sqc55tvvtGkSZPUokULffnllx6OEADgCZVKVgzD0Lp16zRo0CBFREToqaeecldcZQoPD1dUVJT27t0rSQoLC1NhYaGOHTtWar7s7GyFhoZWaSwAgKrzzTffqG/fvpoyZYoaNGigNm3aqFevXurTp49uuOEGNWrUSO3bt9f+/fuVmpqqu+66y+qQAQBVwFSysm/fPk2ZMkVRUVG65ZZb5Ovrqw8++KDcS6/c4ejRozp48KDCw8MlSe3bt5eXl5dSU1Od82RmZurrr79Wly5dqjQWAEDVOVff67vvvtPnn3+uBx54QLGxsWrSpIm6deuml19+WYcOHdJbb71VbQsRAwAursI32BcUFGjFihV65ZVXlJ6ervj4eM2ZM0d33HGHJkyYoNatW7vceF5enn744Qfn84yMDO3cuVPBwcEKDg5WcnKyBg4cqPDwcO3bt0+TJk1So0aN1L9/f0lSUFCQhg0bpjFjxqhhw4YKDg7W2LFj1bZtW+foYK74/ShhcI2ZaqVmKtiafY/MVGA3OySqmX1RVFRkqi0zMZqprO3JCvZmqsNL5ioO+/r6mmrLzH6vU8f1MU3MDsHu6n43e/x5gjvre+Esd1aXrgpmvjc8ycxn2dPMfM97kt0rsNs9PrN9sqe4M74Kf9qaNGmi1q1b669//av++c9/Om9qv+OOO0w3vm3bNnXv3t35PCkpSZKUkJCgBQsWaNeuXXr99dd1/PhxhYeHq3v37lq+fLkCAgKcy8ydO1d16tTRoEGDnEUhlyxZQo0VAAAAoJqrcLJSXFwsh8Mhh8PhtkSgW7du5WZe69atu+g6fH199fzzz+v55593S0wAAAAA7KHC51kzMzP1wAMP6O2331ZYWJgGDhyolStX2v40GQAAAIDqqcLJiq+vr+68805t2LBBu3bt0pVXXqmHHnpIZ86c0VNPPaXU1FTT1/cDAAAAwO+ZuoPt8ssv1/Tp07V//369//77Kigo0K233spwwQAAAADcplLDWdSqVUu33HKLbrnlFh05ckRvvPGGu+ICAMDp448/1scff6zs7OzzRrL6+9//blFUAICq5nKysnfvXq1evVr79u2Tw+FQ8+bN1a9fP0VHRztH8wIAwF2mTZumJ554Qh06dFB4eDj3SgJADeJSspKSkqIpU6aopKREISEhMgxDR44c0fjx4zVjxgyNHTu2quIEANRQL730kpYsWUKVegCogSqcrGzcuFGPPfaYHn/8cT388MPOOiu//vqr5s2bpwkTJujaa6/VjTfeWGXBVrXatWu7NCyz2YI3Zn4VNPtLoieLBpmJ0cww2HXr1nV5GclcETSz+8/MYBNm2/LkdpnhyV/BzRxPZovPmSlc6anPiGT/gmGuKCwsVJcuXawOwyVnzpyxdVE+uxddZMCeyrN74U/e48qx+3e8O7//Kvxt9dJLL+m+++5TcnKyM1GRpODgYD3xxBO69957tWDBArcFBgCAJN13331aunSp1WEAACxQ4TMrX3zxRbk30N911126++673RIUAADnnD59WgsXLtRHH32kq666Sl5eXqVenzNnjkWRAQCqWoWTlf/7v//TZZdddsHXo6OjlZWV5Y6YAABw+uqrr/SHP/xBkvT111+Xeo2b7QHg0lbhZOX06dPlXqPt5eWlwsJCtwQFAMA5GzdudMt6Nm/erGeeeUbbt29XZmamVq5cqX79+jlfNwxD06ZN08KFC3Xs2DF16tRJL774otq0aeOW9gEArnNpNLBXXnlF9erVK/O1EydOuCUgAACqQn5+vtq1a6d77rlHAwcOPO/1WbNmac6cOVqyZIlatmyp6dOnq1evXtqzZ48CAgIsiBgAUOFkpVmzZlq0aNFF5wEAoLIGDBigJUuWKDAwUAMGDCh33hUrVlRonfHx8YqPjy/zNcMwNG/ePE2ePNnZ3muvvabQ0FAtXbpUDz74YJnLFRQUqKCgwPk8Nze3QrEAACqmwsnKvn37qjAMAAD+JygoyHk/SlBQUJW3l5GRoaysLMXFxTmn+fj4qGvXrkpPT79gspKSkqJp06ZVeXwAUFNVOFn54Ycf1KJFi6qMBQAASdLixYvL/H9VOTdATGhoaKnpoaGh2r9//wWXmzhxopKSkpzPc3NzFRkZWTVBAkANVOFkpWXLlmrSpIm6d+/ufJQ3OhgAANXN70cXMwyj3BHHfHx85OPjU9VhAUCNVeFkJS0tTWlpadq0aZNGjRql06dPq1mzZurRo4czeWnSpElVxlrl8vPzXarqa7Z6qJnKwZ4cntOTldSLioo8soxkrlqu2QrAdq/Ma+Y99mQ1ZLNV2+vUcWnMkEoxE6OZ+Dy1L06ePGmqHU/55z//qXfeeUcHDhw4b+TJL7/8stLrDwsLk3T2DEt4eLhzenZ29nlnWyrKzhWmzX6Peordh6T+fa0fO7L7CK12/86x+48Q5Y3Qawfu/P6r8F/Nf/zjH/XYY4/po48+0vHjx7Vx40bdc889ysjI0AMPPKBmzZqpVatWbgsMAABJeu6553TPPfcoJCREO3bs0LXXXquGDRvqp59+uuAN866Kjo5WWFiYUlNTndMKCwuVlpamLl26uKUNAIDrTP0M6eXlpRtvvFEdO3ZU586dtW7dOi1atEg//PCDu+MDANRw8+fP18KFC3XHHXfotdde07hx49S8eXNNmTJFv/76a4XXk5eXV6qfysjI0M6dOxUcHKxmzZopMTFRM2bMUExMjGJiYjRjxgzVrVtXQ4YMqYrNAgBUgEvJyunTp5Wenq6NGzdq06ZN2rp1q6Kjo9W1a1ctWLBAXbt2rao4AQA11IEDB5xnN/z8/Jx1ve666y5dd911euGFFyq0nm3btql79+7O5+dujE9ISNCSJUs0btw4nTp1SiNGjHAWhVy/fj01VgDAQhVOVrp27aqtW7fq8ssv14033qjRo0era9eupq/lBQCgIsLCwnT06FFFRUUpKipKn332mdq1a6eMjAyXrovu1q1bufM7HA4lJycrOTnZDVEDANyhwvespKenq1GjRurevbtuuukm9ejRg0QFAFDlevTooX/961+SpGHDhumRRx5Rr169NHjwYPXv39/i6AAAVanCZ1aOHz+uTz75RJs2bdLMmTN1xx13qGXLluratau6deumrl27qnHjxlUZKwCgBlq4cKFzNLrhw4crODhYW7ZsUZ8+fTR8+HCLowMAVKUKn1nx9/dX79699fTTT+vzzz/XL7/8olmzZqlu3bqaNWuWmjZtqtjYWJca37x5s/r06aOIiAg5HA6tWrWq1OuGYSg5OVkRERHy8/NTt27dtHv37lLzFBQUaPTo0WrUqJH8/f1122236eeff3YpDgCAfdWqVavUUMyDBg3Sc889p4ceekhHjhyxMDIAQFVzveDHf/n7+ys4OFjBwcFq0KCB6tSpo2+//daldeTn56tdu3YXvDly1qxZmjNnjl544QVt3bpVYWFh6tWrl/PmSklKTEzUypUrtWzZMm3ZskV5eXm69dZbbV/nAgBgXlZWlkaPHq0WLVpYHQoAoApVOFkpKSnRF198oVmzZik+Pl7169dXly5dNH/+fIWFhenFF1/UTz/95FLj8fHxmj59ugYMGHDea4ZhaN68eZo8ebIGDBig2NhYvfbaazp58qSWLl0qScrJydGrr76q2bNnq2fPnrr66qv15ptvateuXfroo49cigUAYC/Hjx/XnXfeqcaNGysiIkLPPfecSkpKNGXKFDVv3lyfffaZ/v73v1sdJgCgClX4npX69esrPz9f4eHh6tatm+bMmaPu3bvr8ssvr5LAMjIylJWVpbi4OOc0Hx8fde3aVenp6XrwwQe1fft2FRUVlZonIiJCsbGxSk9P180331zmugsKClRQUOB8npubK+lsxWdXqj57soK9J6vKm4nPLDPVuc1W9DbD7H43s5zZis1m2vJkZW1P7gtPHhtmYjSzjNnPo6vLuVrx3hMmTZqkzZs3KyEhQWvXrtUjjzyitWvX6vTp0/rwww9tPVx+QUGBrStMUyG+cjz5HWrWmTNnrA6hXHY/Bs38/eRJdn9/3RlfhXunZ555Rt27d1fLli3d1nh5srKyJOm8EcdCQ0O1f/9+5zze3t5q0KDBefOcW74sKSkpmjZtmpsjBgC40wcffKDFixerZ8+eGjFihFq0aKGWLVtq3rx5VocGAPCQCv/09uCDD3osUfmt32fehmFcNBu/2DwTJ05UTk6O83Hw4EG3xAoAcJ/Dhw+rdevWkqTmzZvL19dX9913n8VRAQA8yXPX+7goLCxMks47Q5Kdne082xIWFqbCwkIdO3bsgvOUxcfHR4GBgaUeAAB7KSkpKXU5UO3ateXv729hRAAAT7PfRcr/FR0drbCwMKWmpurqq6+WJBUWFiotLU0zZ86UJLVv315eXl5KTU3VoEGDJEmZmZn6+uuvNWvWLMtiBwBUnmEYGjp0qHx8fCRJp0+f1vDhw89LWFasWGFFeAAAD7A0WcnLy9MPP/zgfJ6RkaGdO3cqODhYzZo1U2JiombMmKGYmBjFxMRoxowZqlu3roYMGSJJCgoK0rBhwzRmzBg1bNhQwcHBGjt2rNq2bauePXtatVkAADdISEgo9fyvf/2rRZEAAKxiabKybds2de/e3fk8KSlJ0tkOasmSJRo3bpxOnTqlESNG6NixY+rUqZPWr1+vgIAA5zJz585VnTp1NGjQIJ06dUo33XSTlixZ4tFRgQAA7rd48WKrQwAAWMxhVIfx96pYbm6ugoKClJGRUSoRuphLdehis8MJmmnL7kPvMXRx5TF0ceWW8dTQxSdOnFDz5s2Vk5PDfXyVcK4/+e6771zqTzzN7sPG2n3oYjsO9f17J0+etDqEchUVFVkdQrns/h7bPb4TJ04oJibGLX2KbW+wBwAAAFCzkawAAAAAsCV7n0PysDNnznjksiRPXrZzKV4GZraqrJnlzO53M5cjmd3vZpa7FC8dkzxbcdhT77HZbXI1vuLiYlPtoGxFRUW2vsylsLDQ6hDK5evra3UI5bL7ZWqS/d/jvLw8q0Mol93fY29vb6tDKJc7L0PkzAoAAAAAWyJZAQAAAGBLJCsAAAAAbIlkBQAAAIAtkawAAAAAsCWSFQAAAAC2RLICAAAAwJZIVgAAAADYEskKAAAAAFuigv1vFBQUuFQR1FOVpSvDTIV4s5XUPVUV3ez+M1ON1uy+qFXL9d8BzLZl5jg0s9/NVkM2U8XbzHFrlpn3SjL3fplpy2x8rh7vp0+fNtUOynb69GnVqWPfLtZs/+UpZr8P8T9mvns9ye7fOXY/Bu0enzv7cc6sAAAAALAlkhUAAAAAtkSyAgAAAMCWSFYAAAAA2BLJCgAA/5WcnCyHw1HqERYWZnVYAFBj2XeoEgAALNCmTRt99NFHzueeHMERAFAayQoAAL9Rp06dCp9NKSgoUEFBgfN5bm5uVYUFADUSl4EBAPAbe/fuVUREhKKjo3X77bfrp59+uuC8KSkpCgoKcj4iIyM9GCkAXPpIVgAA+K9OnTrp9ddf17p167Ro0SJlZWWpS5cuOnr0aJnzT5w4UTk5Oc7HwYMHPRwxAFzauAzsN/Lz812qCGqmCrhkrpK62baKi4tNLWeGmRjNVGA1WxXaTCVws22ZeY/NMlOJ2kxlY7MV7M0s58nj1mwVYDPLearqveT6fj958qSpdi418fHxzv+3bdtWnTt31uWXX67XXntNSUlJ583v4+MjHx+f86afOnXK1ve62L26ube3t9UhlMud1bmryqlTp6wOoVx2/86x+3ts9u8TT8nLy3PbujizAgDABfj7+6tt27bau3ev1aEAQI1k62TlYkNIGoah5ORkRUREyM/PT926ddPu3bstjBgAcCkpKCjQt99+q/DwcKtDAYAaydbJinR2CMnMzEznY9euXc7XZs2apTlz5uiFF17Q1q1bFRYWpl69eunEiRMWRgwAqK7Gjh2rtLQ0ZWRk6PPPP9ef//xn5ebmKiEhwerQAKBGsvcFb7rwEJKGYWjevHmaPHmyBgwYIEl67bXXFBoaqqVLl+rBBx+84DoZahIAUJaff/5Zd9xxh3755Rc1btxY1113nT777DNFRUVZHRoA1Ei2P7NyoSEkMzIylJWVpbi4OOe8Pj4+6tq1q9LT08tdJ0NNAgDKsmzZMh0+fFiFhYU6dOiQ3n33XbVu3drqsACgxrJ1slLeEJJZWVmSpNDQ0FLLhIaGOl+7EIaaBAAAAOzP1peBlTeE5HXXXSfp/KFADcO46PCgFxpqEgAAAIB92PrMyu/9dgjJc/ex/P4sSnZ29nlnWwAAAABUP9UqWfntEJLR0dEKCwtTamqq8/XCwkKlpaWpS5cuFkYJAAAAwB1sfRnY2LFj1adPHzVr1kzZ2dmaPn26cwhJh8OhxMREzZgxQzExMYqJidGMGTNUt25dDRkyxFR72dnZys/Pd/NWnM9sxWwzPFmB1UwFezOV3s1WhzdTEdlsW2Yqy5o9LsxUezezjNmK12aWM9tWSUmJqeXMMHO8e5Krle898d1Xk+zdu1d169a1OowLMvvd5ime7CfNaNCggdUhXNSRI0esDqFcv/zyi9UhlMvOn19JCgoKsjqEcp08edJt67J1snKxISTHjRunU6dOacSIETp27Jg6deqk9evXKyAgwOLIAQAAAFSWrZOVZcuWlfu6w+FQcnKykpOTPRMQAAAAAI+pVvesAAAAAKg5SFYAAAAA2BLJCgAAAABbIlkBAAAAYEskKwAAAABsiWQFAAAAgC2RrAAAAACwJVvXWfG0OnXqmKo87ipPVr728fFxeRmzlYPNLGemqryrlbkrw2xbnqy+bGYfmuHn52dqOTP7oqCgwFRbZ86ccXmZoqIiU20VFxd7ZBkz2yS5fux68nNVE+zYscPU96+nmD3uPcXu1bmDg4OtDuGisrOzrQ6hXP/3f/9ndQjl8uTfambUrVvX6hDKVVhY6LZ10TsBAAAAsCWSFQAAAAC2RLICAAAAwJZIVgAAAADYEskKAAAAAFsiWQEAAABgSyQrAAAAAGyJZAUAAACALZGsAAAAALAlKtj/xrp161yqOOzJSu+ebMtsZWMzFc79/f1dXsZsxXYzFZEDAwNNtWVmX/j6+ppqy8z+MHNclJSUuLyMZO54ysnJMdXWqVOnXF4mLy/PVFtmYiwoKHB5GbNVgF1ty0xsuLAXXnjB6hAA4JLAmRUAAAAAtkSyAgAAAMCWSFYAAAAA2BLJCgAAAABbIlkBAAAAYEuXTLIyf/58RUdHy9fXV+3bt9cnn3xidUgAgGqKPgUA7OGSSFaWL1+uxMRETZ48WTt27NAf//hHxcfH68CBA1aHBgCoZuhTAMA+LolkZc6cORo2bJjuu+8+XXnllZo3b54iIyO1YMECq0MDAFQz9CkAYB/VvihkYWGhtm/frgkTJpSaHhcXp/T09DKXKSgoKFUA7VxxN1eLolEUsrRatVzPfWvXru3yMmaLE5opnlinjrmPiGEYLi9TXFxsqi0z75fdi0KePHnSVFtmikKaWUaSTp8+7fIyZgovmv08mi0KaebYvZS42qdcqD8BALinT6n2ycovv/yi4uJihYaGlpoeGhqqrKysMpdJSUnRtGnTzpvOr2YAarqjR48qKCjI6jAs42qfcqH+BADgnj6l2icr5/z+l2LDMC746/HEiROVlJTkfH78+HFFRUXpwIEDtuykc3NzFRkZqYMHDyowMNDqcMpk9xiJr3LsHp9k/xjtHl9OTo6aNWum4OBgq0OxhYr2KdWtP5HsfywSX+XZPUbiqxy7xye5t0+p9slKo0aNVLt27fN+8crOzj7vl7FzfHx85OPjc970oKAg277pkhQYGGjr+CT7x0h8lWP3+CT7x2j3+MxcznkpcbVPqa79iWT/Y5H4Ks/uMRJf5dg9Psk9fUq175W8vb3Vvn17paamlpqempqqLl26WBQVAKA6ok8BAHup9mdWJCkpKUl33XWXOnTooM6dO2vhwoU6cOCAhg8fbnVoAIBqhj4FAOzjkkhWBg8erKNHj+qJJ55QZmamYmNjtWbNGkVFRVVoeR8fH02dOrXMU/l2YPf4JPvHSHyVY/f4JPvHSHzVR2X6lOqwH+0eI/FVnt1jJL7KsXt8kntjdBg1fZxKAAAAALZU7e9ZAQAAAHBpIlkBAAAAYEskKwAAAABsiWQFAAAAgC3V+GRl/vz5io6Olq+vr9q3b69PPvnE6pCcFixYoKuuuspZ9Kdz58768MMPrQ6rlEOHDumvf/2rGjZsqLp16+oPf/iDtm/fbnVYTidOnFBiYqKioqLk5+enLl26aOvWrZbFs3nzZvXp00cRERFyOBxatWqV87WioiKNHz9ebdu2lb+/vyIiInT33Xfr8OHDtohPkoYOHSqHw1Hqcd1119kmvry8PI0aNUpNmzaVn5+frrzySi1YsMBj8aWkpKhjx44KCAhQSEiI+vXrpz179pSaZ8WKFbr55pvVqFEjORwO7dy501bx/daDDz4oh8OhefPmeSzG6s6ufUp16E8k+hRX2L0/uViMEn3KxdCnnFWjk5Xly5crMTFRkydP1o4dO/THP/5R8fHxOnDggNWhSZKaNm2qp59+Wtu2bdO2bdvUo0cP9e3bV7t377Y6NEnSsWPHdP3118vLy0sffvihvvnmG82ePVv169e3OjSn++67T6mpqXrjjTe0a9cuxcXFqWfPnjp06JAl8eTn56tdu3Z64YUXznvt5MmT+vLLL/X444/ryy+/1IoVK/T999/rtttus0V85/Tu3VuZmZnOx5o1a2wT3yOPPKK1a9fqzTff1LfffqtHHnlEo0eP1urVqz0SX1pamkaOHKnPPvtMqampOnPmjOLi4pSfn19qG66//no9/fTTHonJ1fjOWbVqlT7//HNFRER4PM7qys59it37E4k+xVV2708uFuM59CkXRp/yX0YNdu211xrDhw8vNe2KK64wJkyYYFFEF9egQQPjlVdesToMwzAMY/z48cYNN9xgdRgXdPLkSaN27drG+++/X2p6u3btjMmTJ1sU1f9IMlauXFnuPF988YUhydi/f79ngvqNsuJLSEgw+vbt6/FYylJWfG3atDGeeOKJUtOuueYa47HHHvNgZP+TnZ1tSDLS0tLOey0jI8OQZOzYscPzgf3XheL7+eefjSZNmhhff/21ERUVZcydO9eaAKuZ6tan2Kk/MQz6lMqwe39iGPQp7lBT+5Qae2alsLBQ27dvV1xcXKnpcXFxSk9PtyiqCysuLtayZcuUn5+vzp07Wx2OJOm9995Thw4d9Je//EUhISG6+uqrtWjRIqvDcjpz5oyKi4vl6+tbarqfn5+2bNliUVSuycnJkcPhsNUvi5s2bVJISIhatmyp+++/X9nZ2VaH5HTDDTfovffe06FDh2QYhjZu3Kjvv/9eN998syXx5OTkSJKCg4Mtaf9iyoqvpKREd911lx599FG1adPGqtCqnerUp9ixP5HoU6qaHfsTiT7FFTW1T6mxycovv/yi4uJihYaGlpoeGhqqrKwsi6I6365du1SvXj35+Pho+PDhWrlypVq3bm11WJKkn376SQsWLFBMTIzWrVun4cOH66GHHtLrr79udWiSpICAAHXu3FlPPvmkDh8+rOLiYr355pv6/PPPlZmZaXV4F3X69GlNmDBBQ4YMUWBgoNXhSJLi4+P11ltvacOGDZo9e7a2bt2qHj16qKCgwOrQJEnPPfecWrduraZNm8rb21u9e/fW/PnzdcMNN3g8FsMwlJSUpBtuuEGxsbEeb/9iLhTfzJkzVadOHT300EMWRlf9VIc+xc79iUSfUpXs2J9I9CmuqMl9Sh13BFidORyOUs8NwzhvmpVatWqlnTt36vjx43r33XeVkJCgtLQ0W3QwJSUl6tChg2bMmCFJuvrqq7V7924tWLBAd999t8XRnfXGG2/o3nvvVZMmTVS7dm1dc801GjJkiL788kurQytXUVGRbr/9dpWUlGj+/PlWh+M0ePBg5/9jY2PVoUMHRUVF6YMPPtCAAQMsjOys5557Tp999pnee+89RUVFafPmzRoxYoTCw8PVs2dPj8YyatQoffXVV7b9xbWs+LZv365nn31WX375pa2+B6sTO/cpdu5PJPqUqmLX/kSiT3FFTe5TauyZlUaNGql27drn/eKVnZ193i9jVvL29laLFi3UoUMHpaSkqF27dnr22WetDkuSFB4efl4nd+WVV9riZtJzLr/8cqWlpSkvL08HDx7UF198oaKiIkVHR1sd2gUVFRVp0KBBysjIUGpqqq1+Bfu98PBwRUVFae/evVaHolOnTmnSpEmaM2eO+vTpo6uuukqjRo3S4MGD9be//c2jsYwePVrvvfeeNm7cqKZNm3q07Yq4UHyffPKJsrOz1axZM9WpU0d16tTR/v37NWbMGF122WXWBVwNVIc+xc79iUSfUhWqU38i0adcSE3vU2pssuLt7a327dsrNTW11PTU1FR16dLFoqguzjAM25wevf76688bou77779XVFSURRFdmL+/v8LDw3Xs2DGtW7dOffv2tTqkMp3rWPbu3auPPvpIDRs2tDqkch09elQHDx5UeHi41aGoqKhIRUVFqlWr9Nda7dq1VVJS4pEYDMPQqFGjtGLFCm3YsMF2f8BcLL677rpLX331lXbu3Ol8RERE6NFHH9W6dessirp6qI59ip36E4k+xd2qW38i0af8Hn3KWTX6MrCkpCTddddd6tChgzp37qyFCxfqwIEDGj58uNWhSZImTZqk+Ph4RUZG6sSJE1q2bJk2bdqktWvXWh2apLND+nXp0kUzZszQoEGD9MUXX2jhwoVauHCh1aE5rVu3ToZhqFWrVvrhhx/06KOPqlWrVrrnnnssiScvL08//PCD83lGRoZ27typ4OBgRURE6M9//rO+/PJLvf/++youLnb+ShscHCxvb29L4wsODlZycrIGDhyo8PBw7du3T5MmTVKjRo3Uv3//Ko/tYvE1a9ZMXbt21aOPPio/Pz9FRUUpLS1Nr7/+uubMmeOR+EaOHKmlS5dq9erVCggIcL5/QUFB8vPzkyT9+uuvOnDggLPewbk/zsLCwhQWFmZpfA0bNjzvDxovLy+FhYWpVatWVRrbpcDOfYrd+xOJPsVVdu9PLhYjfcrF0af8VyVGKLskvPjii0ZUVJTh7e1tXHPNNWUOB2eVe++91xlb48aNjZtuuslYv3691WGV8q9//cuIjY01fHx8jCuuuMJYuHCh1SGVsnz5cqN58+aGt7e3ERYWZowcOdI4fvy4ZfFs3LjRkHTeIyEhwTnsYFmPjRs3Wh7fyZMnjbi4OKNx48aGl5eX0axZMyMhIcE4cOCAR2K7WHyGYRiZmZnG0KFDjYiICMPX19do1aqVMXv2bKOkpMQj8V3o/Vu8eLFznsWLF5c5z9SpU20R3+8xdLFr7NqnVIf+xDDoU1xh9/7kYjHSp1wcfcpZjv82BgAAAAC2UmPvWQEAAABgbyQrAAAAAGyJZAUAAACALZGsAAAAALAlkhUAAAAAtkSyAgAAAMCWSFYAAAAA2BLJCgAAAABbIlkBLJKcnKw//OEPlVqHw+HQqlWr3BIPAKB6oj/BpYxkBTXW0KFD1a9fP6vDKFNycrIcDke5j3379ikzM1Px8fFWhwsANRr9CVB1SFYAGxo7dqwyMzOdj6ZNm+qJJ54oNS0yMlJhYWHy8fGxOlwAgE3Rn6C6I1kBLiAtLU3XXnutfHx8FB4ergkTJujMmTPO10tKSjRz5ky1aNFCPj4+atasmZ566inn6+PHj1fLli1Vt25dNW/eXI8//riKiooq1Ha9evUUFhbmfNSuXVsBAQHnTfvtaft9+/bJ4XDonXfe0R//+Ef5+fmpY8eO+v7777V161Z16NBB9erVU+/evXXkyJFS7S1evFhXXnmlfH19dcUVV2j+/PmV34EAAEn0J0Bl1LE6AMCODh06pFtuuUVDhw7V66+/ru+++07333+/fH19lZycLEmaOHGiFi1apLlz5+qGG25QZmamvvvuO+c6AgICtGTJEkVERGjXrl26//77FRAQoHHjxlVp7FOnTtW8efPUrFkz3XvvvbrjjjsUGBioZ599VnXr1tWgQYM0ZcoULViwQJK0aNEiTZ06VS+88IKuvvpq7dixQ/fff7/8/f2VkJBQpbECwKWO/oT+BJVkADVUQkKC0bdv3zJfmzRpktGqVSujpKTEOe3FF1806tWrZxQXFxu5ubmGj4+PsWjRogq3N2vWLKN9+/bO51OnTjXatWtXoWWjoqKMuXPnnjddkrFy5UrDMAwjIyPDkGS88sorztfffvttQ5Lx8ccfO6elpKQYrVq1cj6PjIw0li5dWmq9Tz75pNG5c+cKxQYANR39yVn0J6gKnFkByvDtt9+qc+fOcjgczmnXX3+98vLy9PPPPysrK0sFBQW66aabLriOf/7zn5o3b55++OEH5eXl6cyZMwoMDKzy2K+66irn/0NDQyVJbdu2LTUtOztbknTkyBEdPHhQw4YN0/333++c58yZMwoKCqryWAHgUkd/Qn+CyiFZAcpgGEapjuXcNOns8I5+fn7lLv/ZZ5/p9ttv17Rp03TzzTcrKChIy5Yt0+zZs6ss5nO8vLyc/z+3Db+fVlJSIknOfxctWqROnTqVWk/t2rWrOlQAuOTRn9CfoHJIVoAytG7dWu+++26pTiY9PV0BAQFq0qSJGjduLD8/P3388ce67777zlv+3//+t6KiojR58mTntP3793ss/ooKDQ1VkyZN9NNPP+nOO++0OhwAuOTQnwCVQ7KCGi0nJ0c7d+4sNS04OFgjRozQvHnzNHr0aI0aNUp79uzR1KlTlZSUpFq1asnX11fjx4/XuHHj5O3treuvv15HjhzR7t27NWzYMLVo0UIHDhzQsmXL1LFjR33wwQdauXKlNRt5EcnJyXrooYcUGBio+Ph4FRQUaNu2bTp27JiSkpKsDg8AqgX6E/oTVA2SFdRomzZt0tVXX11qWkJCgpYsWaI1a9bo0UcfVbt27RQcHKxhw4bpsccec873+OOPq06dOpoyZYoOHz6s8PBwDR8+XJLUt29fPfLIIxo1apQKCgr0pz/9SY8//rhz5Bc7ue+++1S3bl0988wzGjdunPz9/dW2bVslJiZaHRoAVBv0J/QnqBoO49yFkwAAAABgIxSFBAAAAGBLJCsAAAAAbIlkBQAAAIAtkawAAAAAsCWSFQAAAAC2RLICAAAAwJZIVgAAAADYEskKAAAAAFsiWQEAAABgSyQrAAAAAGyJZAUAAACALZGsAAAAALAlkhUAAAAAtkSyAgAAAMCWSFYAAAAA2BLJCgAAAABbIlkBAAAAYEskKwAAAABsiWQFAAAAgC2RrAAAAACwpTpWBwBcKk6fPq3CwkKrwwAAoFK8vb3l6+trdRiAJJIVwC1Onz6t6OhoZWVlWR0KAACVEhYWpoyMDBIW2ILDMAzD6iCA6i43N1dBQUE6ePCgAgICJEmGYejcx6usfysz7UKvudKWu9q8UNue2vbftu3pba/K/e2Oba+KNjnWatb+tvO2V2WbVXmsVYf9LUk5OTkKDAwUYDXOrABuFBgYaGmyUpXrqK5x1+RtJ27itmubxF2x+a1gZdtAWbjBHgAAAIAtkawAAAAAsCWSFQAAAAC2RLICAAAAwJZIVgAAAADYEskKAAAAAFsiWQEAAABgS9RZAdwoNzfX0nH9XWnLXW1eqG1Pbftv2/b0tlfl/nbHtldFmxxrNWt/23nbq7LNqjzWqsP+BuyEZAVwA8MwVK9ePUVGRlodCgAAlVKvXj2SF9gGyQrgBg6HQ3l5eTp48KACAwOtDseWcnNzFRkZyT4qB/uofOyfi2MfXRz7qHzn9o/D4bA6FEASyQrgVoGBgXR+F8E+ujj2UfnYPxfHPro49hFQPXCDPQAAAABbIlkBAAAAYEskK4Ab+Pj4aOrUqfLx8bE6FNtiH10c+6h87J+LYx9dHPuofOwf2I3DYLgHAAAAADbEmRUAAAAAtkSyAgAAAMCWSFYAAAAA2BLJCgAAAABbIlkB3GD+/PmKjo6Wr6+v2rdvr08++cTqkGwjOTlZDoej1CMsLMzqsCyzefNm9enTRxEREXI4HFq1alWp1w3DUHJysiIiIuTn56du3bpp9+7d1gRrkYvto6FDh553TF133XXWBGuBlJQUdezYUQEBAQoJCVG/fv20Z8+eUvPU9OOoIvuoph9HCxYs0FVXXeUsjtm5c2d9+OGHztdr+jEE+yBZASpp+fLlSkxM1OTJk7Vjxw798Y9/VHx8vA4cOGB1aLbRpk0bZWZmOh+7du2yOiTL5Ofnq127dnrhhRfKfH3WrFmaM2eOXnjhBW3dulVhYWHq1auXTpw44eFIrXOxfSRJvXv3LnVMrVmzxoMRWistLU0jR47UZ599ptTUVJ05c0ZxcXHKz893zlPTj6OK7COpZh9HTZs21dNPP61t27Zp27Zt6tGjh/r27etMSGr6MQQbMQBUyrXXXmsMHz681LQrrrjCmDBhgkUR2cvUqVONdu3aWR2GLUkyVq5c6XxeUlJihIWFGU8//bRz2unTp42goCDjpZdesiBC6/1+HxmGYSQkJBh9+/a1JB47ys7ONiQZaWlphmFwHJXl9/vIMDiOytKgQQPjlVde4RiCrXBmBaiEwsJCbd++XXFxcaWmx8XFKT093aKo7Gfv3r2KiIhQdHS0br/9dv30009Wh2RLGRkZysrKKnU8+fj4qGvXrhxPv7Np0yaFhISoZcuWuv/++5WdnW11SJbJycmRJAUHB0viOCrL7/fRORxHZxUXF2vZsmXKz89X586dOYZgKyQrQCX88ssvKi4uVmhoaKnpoaGhysrKsigqe+nUqZNef/11rVu3TosWLVJWVpa6dOmio0ePWh2a7Zw7ZjieyhcfH6+33npLGzZs0OzZs7V161b16NFDBQUFVofmcYZhKCkpSTfccINiY2MlcRz9Xln7SOI4kqRdu3apXr168vHx0fDhw7Vy5Uq1bt2aYwi2UsfqAIBLgcPhKPXcMIzzptVU8fHxzv+3bdtWnTt31uWXX67XXntNSUlJFkZmXxxP5Rs8eLDz/7GxserQoYOioqL0wQcfaMCAARZG5nmjRo3SV199pS1btpz3GsfRWRfaRxxHUqtWrbRz504dP35c7777rhISEpSWluZ8nWMIdsCZFaASGjVqpNq1a5/3S1N2dvZ5v0jhLH9/f7Vt21Z79+61OhTbOTdKGseTa8LDwxUVFVXjjqnRo0frvffe08aNG9W0aVPndI6j/7nQPipLTTyOvL291aJFC3Xo0EEpKSlq166dnn32WY4h2ArJClAJ3t7eat++vVJTU0tNT01NVZcuXSyKyt4KCgr07bffKjw83OpQbCc6OlphYWGljqfCwkKlpaVxPJXj6NGjOnjwYI05pgzD0KhRo7RixQpt2LBB0dHRpV7nOLr4PipLTTuOymIYhgoKCjiGYCtcBgZUUlJSku666y516NBBnTt31sKFC3XgwAENHz7c6tBsYezYserTp4+aNWum7OxsTZ8+Xbm5uUpISLA6NEvk5eXphx9+cD7PyMjQzp07FRwcrGbNmikxMVEzZsxQTEyMYmJiNGPGDNWtW1dDhgyxMGrPKm8fBQcHKzk5WQMHDlR4eLj27dunSZMmqVGjRurfv7+FUXvOyJEjtXTpUq1evVoBAQHOX7+DgoLk5+cnh8NR44+ji+2jvLy8Gn8cTZo0SfHx8YqMjNSJEye0bNkybdq0SWvXruUYgr1YNg4ZcAl58cUXjaioKMPb29u45pprSg2PWdMNHjzYCA8PN7y8vIyIiAhjwIABxu7du60OyzIbN240JJ33SEhIMAzj7LCzU6dONcLCwgwfHx/jxhtvNHbt2mVt0B5W3j46efKkERcXZzRu3Njw8vIymjVrZiQkJBgHDhywOmyPKWvfSDIWL17snKemH0cX20ccR4Zx7733Ovutxo0bGzfddJOxfv165+s1/RiCfTgMwzA8mRwBAAAAQEVwzwoAAAAAWyJZAQAAAGBLJCsAAAAAbIlkBQAAAIAtkawAAAAAsCWSFQAAAAC2RLICAAAAwJZIVgAAAADYEskKAMB2Dh48qFtuuUWBgYG68sortXr16vPm+cc//qGGDRvqyJEjFkQIAPAEkhUAgO0kJCQoJydH//znP9W/f38NGjRIP/74o/P1nJwcPfzww/rb3/6mxo0bWxgpAKAqOQzDMKwOAgCAc06ePKl69erp3//+tzp37ixJatmypZKSkjR8+HBJ0vDhw7Vnzx5t3LjRylABAFWsjtUBAADwW4WFhTIMQ/7+/s5p9erV0+nTpyVJ6enpev3117Vz506LIgQAeAqXgQEAbKV+/fq64oorNHv2bB07dkyrVq3Sf/7zH3Xp0kVFRUV64IEHNHHiRLVs2dLqUAEAVYwzKwAA23n11Vc1cOBABQcHq1atWnrsscd07bXXavr06TIMQ+PHj7c6RACAB3DPCgDAloqKivTTTz8pLCxMQUFB2rt3r9q1a6f169erS5cumjZtmhYvXqzCwkL9+c9/1t/+9jf5+vpaHTYAwI1IVgAA1cJNN92kFi1a6OWXX9arr76qadOm6eOPP1a9evXUu3dv9evXT9OmTbM6TACAG3HPCgDA9pYsWaJvvvlGM2fOlCR9+OGH+stf/qKYmBiFh4dr2LBhWrNmjcVRAgDcjXtWAAC29ssvv2js2LGaP3++6tevL0kyDEP5+fnOefLy8sSFAgBw6SFZAQDYWlJSkjp16qRBgwY5p918880aM2aMOnfurHr16um5557TfffdZ2GUAICqwD0rAADb+vjjj9W3b1/t3r1bUVFRzunFxcWaPHmyXnvtNRUWFmrgwIF69tln5efnZ2G0AAB3I1kBAAAAYEvcYA8AAADAlkhWAAAAANgSyQoAAAAAWyJZAQAAAGBLJCsAAAAAbIlkBQAAAIAtkawAAAAAsCWSFQAAAAC2RLICAAAAwJZIVgAAAADYEskKAAAAAFv6f8zlZl/oITizAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x1000 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot histograms\n",
    "\n",
    "vmin = 0\n",
    "vmax = 30\n",
    "\n",
    "fig, axs = plt.subplots(nrows=3, ncols=2, figsize=(8, 10), layout=\"constrained\")\n",
    "axs_flat = axs.flatten()\n",
    "axs[0, 0].set_xlim(0, 24)\n",
    "axs[0, 0].set_xticks(np.arange(0, 25, 3))\n",
    "\n",
    "HC = np.zeros((24, 24))\n",
    "HCR = np.zeros((8, 24))\n",
    "WDA = np.zeros((24, 24))\n",
    "\n",
    "HCR[:] = 0\n",
    "WDA[:] = 0\n",
    "\n",
    "# loop through variables\n",
    "nv = 1\n",
    "for v in vsh:\n",
    "\n",
    "    HC[:] = 0\n",
    "    \n",
    "# loop through stations\n",
    "    for n in range(nid): \n",
    "#    for n in range(2): \n",
    "        sid = id[n]\n",
    "\n",
    "        if sid == 96147 or sid == 96607 or sid == 97686:\n",
    "            continue\n",
    "        \n",
    "        data_in_path = f\"/Users/cdz/Desktop/Khafid_etal/Manuscript/2Dhist_stations_Data/hist2d_data_{sid}_{v}.npz\"\n",
    "#        print(data_in_path)\n",
    "        \n",
    "        data = np.load(data_in_path)\n",
    "    \n",
    "        if v == 'R3h':\n",
    "            HCR = HCR + data['H']\n",
    "        if v!= 'R3h':\n",
    "            HC = HC + data['H']\n",
    "\n",
    "    data_out_path = f\"/Users/cdz/Desktop/Khafid_etal/Manuscript/2Dhist_stations_combined/hist2d_data_combined_{v}.npz\"\n",
    "      \n",
    "    ylimit1 = y1[nv]\n",
    "    ylimit2 = y2[nv]\n",
    "    dy = (ylimit2-ylimit1)/25\n",
    "\n",
    "    ybins = np.arange(ylimit1, ylimit2, dy)\n",
    "    xbins = np.arange(0, 25, 1)     # hourly bins \n",
    "\n",
    "    mv = nv - 1\n",
    "    row = mv // 2  # number of columns = 2\n",
    "    col = mv % 2    \n",
    "    axs[row, col].set_xlim(0, 24)\n",
    "    axs[row, col].set_xticks(np.arange(0, 25, 3))\n",
    "\n",
    "    xedges = data[\"xedges\"]\n",
    "    yedges = data[\"yedges\"]\n",
    "    \n",
    "    if v == 'R3h':\n",
    "#        HCR[:,0] = 0\n",
    "        PR = 8*100*HCR/HCR.sum()\n",
    "        xbins = np.arange(0, 25, 3)     # 3 hourly bins \n",
    "        np.savez(data_out_path, H=PR, xedges=xedges, yedges=yedges)\n",
    "        HCR[:,0] = 0\n",
    "#        axs[row, col].imshow(HCR.T, origin=\"lower\", aspect=\"auto\", cmap=cmap, extent=[0, 24, 0, HCR.shape[0]])  \n",
    "        axs[row, col].pcolormesh(xbins, ybins, PR.T, cmap=cmap, vmin=vmin, vmax=vmax)\n",
    "    if v!= 'R3h':\n",
    "        P = 24*100*HC/HC.sum()\n",
    "#        axs[row, col].imshow(HC.T, origin=\"lower\", aspect=\"auto\", cmap=cmap)  \n",
    "        im = axs[row, col].pcolormesh(xbins, ybins, P.T, cmap=cmap, vmin=vmin, vmax=vmax)\n",
    "        np.savez(data_out_path, H=HC, xedges=xedges, yedges=yedges)\n",
    "\n",
    "        \n",
    "#    plt.colorbar(label=\"Counts\")\n",
    "    axs[row, col].set_ylabel(y_labs[nv])\n",
    "    axs[row, col].set_xlabel(x_labs)\n",
    "    axs[row,col].set_title(labs[nv-1], fontsize=10, loc='left')\n",
    "\n",
    "#    print(nv, mv, xlimit1, xlimit2, x_labs[nv])\n",
    "\n",
    "    H = 0\n",
    "    nv = nv + 1\n",
    "\n",
    "cbar = fig.colorbar(im, ax=axs, orientation='horizontal', location='bottom',\n",
    "                    pad=0.02, shrink=0.6, aspect=40)\n",
    "cbar.set_label('%', fontsize=12)\n",
    "\n",
    "fig_path = f\"/Users/cdz/Desktop/Khafid_etal/Manuscript/2Dhist_stations_combined/2Dhist_combined.png\"\n",
    "fig.savefig(fig_path)   \n",
    "plt.show()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffe99be5-daad-4bb0-a337-fc7e447f5f86",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}