insertion-throughput-real-order.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a6f404dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import common\n",
    "import importlib\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.patches import Patch\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "import socket\n",
    "import plotting\n",
    "import matplotlib.style\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# Show all columns and rows in a dataframe\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbdfab77",
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffled_experiments = \"(hostname == 'scyper21')\" ## Scyper21 was used for experiments that loaded the graph in shuffled order\n",
    "ordered_experiments = \"(hostname == 'scyper22')\" ## Scyper22 was used for experiments that loaded the graph in sorted order\n",
    "\n",
    "libraries = [\"sortledton.4\", \"g1_v6-ref-ignore-build\", \"livegraph3_ro\", \"llama8-ref\", \"stinger7-ref\", \"teseo-lcc.12\"]\n",
    "libraries_filter = \" | \".join(map(lambda l: \"library == '\" + l + \"'\", libraries))\n",
    "\n",
    "edges_per_graph = pd.DataFrame({\n",
    "    \"graph\": [\"com-friendster\", \"dota-league\", \"graph500-22\", \n",
    "              \"graph500-24\", \"graph500-26\", \"uniform-22\",\n",
    "              \"uniform-24\", \"uniform-26\", \"edit-enwiki.el\", \"yahoo-song.el\"\n",
    "              ],\n",
    "    \"edges\": [1806067135, 50870313, 64155735, 260379520, 1051922853, 64155735, 260379520, 1051922853, 255689125, 256771317]\n",
    "})\n",
    "\n",
    "data_ordered = common.import_gfe_dbms(\"View_Inserts\", \"data/new.sqlite3\").query(ordered_experiments).copy() # data from the experiments\n",
    "data_ordered['ordering'] = 'timestamp'\n",
    "data_shuffled = common.import_gfe_dbms(\"View_Inserts\", \"data/new.sqlite3\").query(shuffled_experiments).copy()\n",
    "data_shuffled['ordering'] = 'random'\n",
    "\n",
    "data = pd.concat([data_ordered, data_shuffled])\n",
    "\n",
    "data[\"build_frequency\"].fillna(pd.Timedelta(0), inplace=True) # replace NaT with 0, otherwise the records are ignored in the group by\n",
    "data[\"block_size\"].fillna(-1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "427673aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.query(\"((block_size == 512) and library == 'sortledton.4') or library != 'sortledton.4'\") \n",
    "data = data.merge(edges_per_graph, on=\"graph\")\n",
    "data[\"edges_per_second\"] = data[\"edges\"] / data[\"completion_time_secs\"]\n",
    "\n",
    "data = data.query(libraries_filter)\n",
    "data = data.query(\"graph == 'yahoo-song.el' or graph == 'edit-enwiki.el'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba445343",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter library by threads\n",
    "threading_query = \"(num_threads == 56 and library in ['sortledton.4', 'g1_v6-ref-ignore-build', 'stinger7-ref'])\"\n",
    "threading_query += \" or (num_threads == 20 or num_threads == 16 and library in ['livegraph3_ro'])\"\n",
    "threading_query += \" or (num_threads == 16 and library in ['llama8-ref'])\"\n",
    "data = data.query(threading_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "64fa3f6e",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>edges_per_second</th>\n",
       "      <th>count</th>\n",
       "      <th>cov</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>graph</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">edit-enwiki.el</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">random</th>\n",
       "      <th>g1_v6-ref-ignore-build</th>\n",
       "      <td>2.166164e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>livegraph3_ro</th>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
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       "      <th rowspan=\"2\" valign=\"top\">timestamp</th>\n",
       "      <th>g1_v6-ref-ignore-build</th>\n",
       "      <td>3.752407e+05</td>\n",
       "      <td>2</td>\n",
       "      <td>0.033908</td>\n",
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       "    <tr>\n",
       "      <th>sortledton.4</th>\n",
       "      <td>5.977732e+05</td>\n",
       "      <td>5</td>\n",
       "      <td>0.004744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">yahoo-song.el</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">random</th>\n",
       "      <th>g1_v6-ref-ignore-build</th>\n",
       "      <td>3.383912e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>livegraph3_ro</th>\n",
       "      <td>3.516672e+05</td>\n",
       "      <td>2</td>\n",
       "      <td>0.035394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>llama8-ref</th>\n",
       "      <td>2.053117e+05</td>\n",
       "      <td>2</td>\n",
       "      <td>0.002073</td>\n",
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       "    <tr>\n",
       "      <th>sortledton.4</th>\n",
       "      <td>3.537807e+06</td>\n",
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       "      <th>stinger7-ref</th>\n",
       "      <td>3.364155e+04</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">timestamp</th>\n",
       "      <th>g1_v6-ref-ignore-build</th>\n",
       "      <td>2.561384e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>0.034691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>livegraph3_ro</th>\n",
       "      <td>9.729530e+04</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>llama8-ref</th>\n",
       "      <td>7.753096e+04</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sortledton.4</th>\n",
       "      <td>6.819061e+05</td>\n",
       "      <td>2</td>\n",
       "      <td>0.003226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stinger7-ref</th>\n",
       "      <td>3.559977e+04</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000462</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                                                 edges_per_second  count  \\\n",
       "graph          ordering  library                                           \n",
       "edit-enwiki.el random    g1_v6-ref-ignore-build      2.166164e+06      1   \n",
       "                         livegraph3_ro               3.033236e+05      3   \n",
       "                         sortledton.4                3.436918e+06      1   \n",
       "               timestamp g1_v6-ref-ignore-build      3.752407e+05      2   \n",
       "                         sortledton.4                5.977732e+05      5   \n",
       "yahoo-song.el  random    g1_v6-ref-ignore-build      3.383912e+06      1   \n",
       "                         livegraph3_ro               3.516672e+05      2   \n",
       "                         llama8-ref                  2.053117e+05      2   \n",
       "                         sortledton.4                3.537807e+06      1   \n",
       "                         stinger7-ref                3.364155e+04      1   \n",
       "               timestamp g1_v6-ref-ignore-build      2.561384e+06      2   \n",
       "                         livegraph3_ro               9.729530e+04      1   \n",
       "                         llama8-ref                  7.753096e+04      1   \n",
       "                         sortledton.4                6.819061e+05      2   \n",
       "                         stinger7-ref                3.559977e+04      2   \n",
       "\n",
       "                                                      cov  \n",
       "graph          ordering  library                           \n",
       "edit-enwiki.el random    g1_v6-ref-ignore-build       NaN  \n",
       "                         livegraph3_ro           0.041264  \n",
       "                         sortledton.4                 NaN  \n",
       "               timestamp g1_v6-ref-ignore-build  0.033908  \n",
       "                         sortledton.4            0.004744  \n",
       "yahoo-song.el  random    g1_v6-ref-ignore-build       NaN  \n",
       "                         livegraph3_ro           0.035394  \n",
       "                         llama8-ref              0.002073  \n",
       "                         sortledton.4                 NaN  \n",
       "                         stinger7-ref                 NaN  \n",
       "               timestamp g1_v6-ref-ignore-build  0.034691  \n",
       "                         livegraph3_ro                NaN  \n",
       "                         llama8-ref                   NaN  \n",
       "                         sortledton.4            0.003226  \n",
       "                         stinger7-ref            0.000462  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_grouped = data.groupby([\"graph\", \"ordering\", \"library\"]) \\\n",
    "    .agg(std_eps=(\"edges_per_second\", \"std\"), edges_per_second=(\"edges_per_second\", \"median\"), count=(\"edges_per_second\", \"count\"))\n",
    "data_grouped[\"cov\"] = data_grouped[\"std_eps\"] / data_grouped[\"edges_per_second\"]\n",
    "data_grouped.drop([\"std_eps\"], axis=1, inplace=True)\n",
    "data_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d9e71d45",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">edges_per_second</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>library</th>\n",
       "      <th>sortledton.4</th>\n",
       "      <th>g1_v6-ref-ignore-build</th>\n",
       "      <th>stinger7-ref</th>\n",
       "      <th>llama8-ref</th>\n",
       "      <th>livegraph3_ro</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>graph</th>\n",
       "      <th>ordering</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">edit-enwiki.el</th>\n",
       "      <th>random</th>\n",
       "      <td>3.436918e+06</td>\n",
       "      <td>2.166164e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>303323.609747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <td>5.977732e+05</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">yahoo-song.el</th>\n",
       "      <th>random</th>\n",
       "      <td>3.537807e+06</td>\n",
       "      <td>3.383912e+06</td>\n",
       "      <td>33641.548950</td>\n",
       "      <td>205311.709167</td>\n",
       "      <td>351667.215347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <td>6.819061e+05</td>\n",
       "      <td>2.561384e+06</td>\n",
       "      <td>35599.773559</td>\n",
       "      <td>77530.961805</td>\n",
       "      <td>97295.297144</td>\n",
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      ],
      "text/plain": [
       "                         edges_per_second                         \\\n",
       "library                      sortledton.4 g1_v6-ref-ignore-build   \n",
       "graph          ordering                                            \n",
       "edit-enwiki.el random        3.436918e+06           2.166164e+06   \n",
       "               timestamp     5.977732e+05           3.752407e+05   \n",
       "yahoo-song.el  random        3.537807e+06           3.383912e+06   \n",
       "               timestamp     6.819061e+05           2.561384e+06   \n",
       "\n",
       "                                                                      \n",
       "library                   stinger7-ref     llama8-ref  livegraph3_ro  \n",
       "graph          ordering                                               \n",
       "edit-enwiki.el random              NaN            NaN  303323.609747  \n",
       "               timestamp           NaN            NaN            NaN  \n",
       "yahoo-song.el  random     33641.548950  205311.709167  351667.215347  \n",
       "               timestamp  35599.773559   77530.961805   97295.297144  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pivot = data_grouped.reset_index().pivot(index=[\"graph\", \"ordering\"], values=[\"edges_per_second\"], columns=\"library\")\n",
    "data_pivot = data_pivot[[(\"edges_per_second\", \"sortledton.4\"), \n",
    "                         (\"edges_per_second\", \"g1_v6-ref-ignore-build\"),\n",
    "                         (\"edges_per_second\", \"stinger7-ref\"),\n",
    "                         (\"edges_per_second\", \"llama8-ref\"),\n",
    "                         (\"edges_per_second\", \"livegraph3_ro\"),\n",
    "                        ]]\n",
    "data_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "031c66e5",
   "metadata": {},
   "outputs": [
    {
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UiVxA6qHjs4A/BT49zZ//XmAnwbyhqfwSsA14+zTnI16+jabYoc25XKRiF96pdAOjFDes2N8/156bxa7SXs3fuSRp+q1bty5uSpPx8fFpndOz2Adiud4/Mx3nyANJpy2DnXmJDXomduaJ3CvT9aos9b2mFPe/fPX19dHQ0EBraytdXV309PREp2iLTC2XaXGhWIUEPNNNjWZbNOAq7SqDEHA/QQ/LP5mG9K8Avk3QyzSTduDvgWuA16YhH9lFFguKrEAeaTRFLoKxF/1cLlKxF+tKN05K/fm5XqSLvWFN13duD05Jmlm2bNkSfdDY2dmZcvhdOi0tLaxevTraeHvqqadybsCUW67zQmc6LvYetmHDhpLN9eViRZJmNIOdeevq6oreG2NHNsZO/ZVuFF2p7zWluP/FynbPiqz6HhEZ4n7llVfS3NxMc3Mz9fX1OT8ozdSZJlZsG9T7amYGPFUmIeCPCebS/MMSpvvzBEHMc3M8vo0gOPrLwNGCP3W6Gk2xF/3R0dGsF/3Yi3WmAFs5LoSluGElrnifi2IX/Jmu71ySNLPkMgQsk5l+zY80qmBqwcJMMn0Hsec2Nzc796ak2mewsyDt7e0pg3r9/f3Rn9NNU1aqe00p73+5GhgYiP6+9fX17Nixo+iAba75ip0Ozp6cmTmkXWUUAu4GPl+i9D5AELzMNdgZ8QvAQAHnTZmuRlO+Ab/YYyp9sUt1w8rnlSjXQOZTTz1VsnxX23cuScpdbENkYGAg7xXXY4fqFTudynSIvZdGVsVNZ3R0NOM9L3Yu7kyrrsem19XVRVdX17Qv6iRJJVctwc7zz5/ezy9QbEAzslBR5F6QaU2GUt1rSnn/y1XsWg7ZOvvkOhJydHQ0a90kdqHg+vp626NZGPBUBfwewbDyYswCvgWcU+D57wO+WPCnT1ejKd+LfmzP0kqvxDYdN6xMq6BHFNuwqubvXJKUn9iGV0dHR84P13p6euIaSDNxOHvskMFsK8Jm29/e3h5tREXmIsv22X19ffT19c34nrCSFKdagp23r4NzC++wM51uuOGG6M+PPvpoTsPZoXT3mlLe/wqR7b6XuI5HJtnyt2bNmujP2Rb4lQFPVcz+Is8/BRysaB6mo9F0/fXXR3scZrvoj46Oxt1MKt34KtUNK/Yi393dnfF7zbY/F9X8nUuS8rNhw4boNX90dJR3vetd9PX1pb2XRB7Oxd6bsk1jU+x9qVCxIybGx8dpbW1N2Ysldr61TGJ/z9bW1pQPIcfHx+no6Ig+fGxpaSnZfJ+SNO2qKdh5083Tm4cixC5KtGXLluj9o7m5Oes9oRT3mlLf/3IR22npnnvuSXlMJN+xv1O2OsLAwAArV65MOi7ye0XaqvX19XkFUk9XBjxVASeBPSVI55mKnj8djabEC9eKFStS9mAcGBiILnwEwWJIpe7OXkiDbbpuWKmCkN3d3fT05De/TarfaSZ955Kk6VVfX8/g4GD0/j0+Pk5XVxcNDQ0sWrSI1tZWVq5cyaJFi6LbYu9lnZ2dWXtUPPbYY/T09NDT05P3CJBi9ff3R3+3oaGh6O/U1dXFypUraWhooK+vj+bm5riHdqnmO2tvb487JvI9rVy5Mi692DnMYudsk6QZ7TQKdj799NOsXLky51e+bSyI72UZCTbm0puyVPeaUt7/crF69eq4z2toaIi2T7u7u+PyHdtmHBgYoKenJ2V7M3LcwMBAdOX3jo4OWltbaWhoiKtTbNq0qaB8n25ctEgVMAq8UYJ0ngFWFXl+4SKNptbWVsbHx6ONpq6uruiKbOeddx6jo6McOnQoKdiWrtEUWQRpaGiI8fFxVq5cGfd0LHbeDggujNPxdOexxx5j0aJFQHAjyqXHRuSGFbkhRQK8V1xxBc3NzYyOjsZd3NPdsHp7e6OfPTo6Smtra/Q7GB8f5+mnn45+n52dnTkNfc/0O82U71ySNP2am5v50Y9+xJo1a+J6emRbxGfDhg1pg52R1VhHR0cZHx+PNvJ6e3vL2uOxvr6eH/3oR3G9W4aGhpIWONi+fXtODdH+/v7oiAwgbl62WC0tLfT39/sgUFJ1OI2CnZD+2p1OIUHAzs7OpPvK9ddfn9O5pbjXlPr+l4sdO3awYsWKaCwgVaA40s5cuXJl9Hfq7u5OuY7F6tWrufLKK6OLIyfmP6K3t9eRhjky4KkSeQFYluOxha+OHm8D8GAR5z9XdA6mo9EEMDg4GHdRTDe5cnt7e0l7U5SiwVaKG1ZzczODg4PRGwik/g4iN5BMAc9cf6dKfeeSpPKLPHAbHR1ly5YtbN++Pe66H7l3NDc3s3LlypwaFtu3b6erqyv6UC7y4LPc6uvrGRkZoa+vj97e3uj9r7m5ma6uLtatWwfEj3rIlM/e3l66u7vZsGEDAwMD0e8oMoTxhhtusOElaeb76U9h9f8Nk5Nw4AC8/nrq42bNgrqz4TOln+sxzquvwiuvpN9//vnwzS3BK+KnP4XFi6c3XwWqr6+npaUlGqCL7QGZi1Lca0p9/8umpaWFw4cP093dHc1z5PPa29vp6uqKtjd7e3vj1q5I94Bw9erVjIyMJH0PkZ6pd955Z8G9Uk9HoZ07d05WOhOauZYvX571mOuuu46RkZHpz0wZLFq0iMcff7ygc0vZaIoYGBigv78/6WLX0tJCV1dXytXNI2KDd5OTuRfzyND72Abbpk2bovnONd3R0dGSNI56enp49NFHozeQ+vr66A0k8vtHbqyRnrX5/k6xivnOOzo6ooHvw4cPZ7wZxQaFR0ZG7BUjqay8d2vRokWMjo5SX1/P4cOHK50dSZo23vMUaybd/4aGhqLTpq1bt67qRhHu2rWr0lnIyICnMsol4ClJkqTKGhgYiI5i6OrqyjiSJLaB1d7ezvbt28uSR0mSSq2a738GPKeXixZJkiRJVe6KK66IzveVrcEUO39Z7EITkiRVG+9/SseApyRJklTl6uvro1O1jI6OsmjRIrZs2RKdniUy9c6iRYuiU9O0tLQ4/6Ykqap5/1M6LlokSZIk1YD+/n5aW1sZGhpidHQ0utJrKs3NzezYsaOMuZMkaXp4/1Mq9vCUJEmSasTg4CC9vb1pF8Nrbm5mw4YNjIyMuNKrJKlmeP9TIhctUkYuWiRJklSdxsfHGR0d5dChQ5x33nk0NzfbyJMk1bxquf+Njo5G5xVduXJlxgWXZqKZvmiRAU9lZMBTkiRJkiRJsWZ6wNMh7ZIkSZIkSZJqhgFPSZIkSZIkSTXDVdpVtOuuu46RkZFKZ6MkFi1axOOPP17pbEiSNK28d0uSJKmWGfBU0UZGRnhhzx4uyXDMfuBwhv0XA2dn2H8KGAHeSrP/bcAiMndZfg34cYb9peruPDQ0xKOPPsrAwACjo6OMj48DUF9fT3NzM+3t7XR1daVdPa6WdHR0sGXLFgAmJ0s/XXDsdx2ZmBqCFfiam5tZuXIlq1evPi2+a0nKx8jICM8NP8eZF51Z6awU5fhPj+d87JYtW+jo6ACgvb2d7du3F/SZK1euZGBgAJiee5skSZKKZ8BTJXEJ8GyG/W8A1wGJTYtZwAPAJ3L4jB8CHwZeTth+AfAPwHtySON/AJ8CEpsnK4EXczg/k9HRUbq6uqKNoETj4+MMDQ0xNDRET08P7e3tbNiwgZaWliI/+fQzNDTEmjVrGBoaSrl/dHSU0dFRBgYG6O7upr29nd7eXgOfkhTjzIvO5PL+yyudjaLs6dhT6SxIkiRpBnIOT5XFWcDfA/cCvwr8AnAT8B1yC3ZCEND8V+DjwIrw6+PAD8gt2En4s7aHP/sXwnm5L5y3UI5ppLJlyxYWLVqUFOysr6+npaWFlpYW6uvr4/YNDAzQ2toa7QGp3PT09NDa2poU7Gxubs74XS9atIi+vr4y5lSSJEmSJFWCPTxVNrOB28OvQl0A/FmR+YgES0sldogcBEHODRs2cP311ycF3sbHx+nr66O3tzc6/Lqjo4P+/n5Wr15dwlzVpq6urrigZXNzM93d3Rm/63vuuSc6rUBXVxfj4+OsW7eujLmWpJlp8sQkb778ZqWzUZTJEw4plyRJUjIDnlIRhoaG4oKdq1evpr+/P+3x9fX1rFu3jnXr1sXNAdbR0cHIyIhDrjPo6+uLC3Z2dnbS29ub9vh033V3dzctLS20t7dPe54laSY7/pPjPPMrz1Q6G8Wr7lH5kiRJmgYOaZeKkE+wM9H27dvj5u/csGFDSfNWS8bHx+nq6oq+X7duXcZgZ6Lt27fH9aCN/X+TJEmSJEm1xYCnVKC+vr64VcHzCXZG3HnnnXHpFSIyXLuWdXd3R39uaWkpKDi8adOm6LD3yHB3SZIkSZJUewx4SgWK7WEYG5DLx+rVq9m+fXv0lcrKlSsJhUIsWrQoum3Lli20trYSCoVYs2ZNyvMic4suWrSIUCgUTWPlypX09PSkDZSOj4/T09NDT09PNKA7Pj5Od3d3NK2GhgZaW1vp7u7OO+A6MDBAR0cHDQ0NcXnKtHhTbHCy0J6w9fX1cQHmVOl0dHQkfdeRaQsiv3su+Y0V+e5aW1vjfueOjo6kRa4kSbWj0PswpL4f9fX1Jd1LUt2HI4v7RY5rbW2NzmE93fmWJEmaKUI7d+50tneltXz58qzHLFu2DPbs4dnpz860WgZw+eU8+2z232R8fJyGhobo+8nJ6StGkfknm5ubGRkZSVq4J3Eo/ejoKCtXrowGKzPZvn170lyWQ0NDtLa2AtDf3099fT0dHR0ZGzjpFl3q6OiIBgYPHz5Md3d3xp6VLS0tDA4Oxm0bGBhg5cqVQBC0PHz4cNbfK5NQKBT9+fDhw3GLHUXyG/muu7u76enpySu/sXp6erIGw5ubm9m+fbvzt0oqq2XLlrFnz55KZ6MkLs/x3h27yGB7e3vaB43ZxM4Lner+X+x9GOLvR4ODg6xYsYKhoaGUadTX1zM4OMh5551Ha2trxs/NNF94KfItSZJOH7t27ap0FjJy0SKpAE8//XT059h5OKdb7MI9nZ2dLFq0KOnzW1tb44KT7e3t0cbN6OhoXK/ClStXZmz8bN++PS5A2dLSQnNzM0NDQ3ENolxWml+zZk00+Ll69WquvPJKXnnlFbZs2RJNa2hoiO7u7rjel7EN0lI0rlpaWqKNxoGBgbR5jvRyTczvwMBA9PxU+Y1ItaJ8S0sL5513Hk8//XQ0jdHRUVpbWxkcHDToKams5pw3hwW/uaDS2SjKy19/udJZSFLK+/ChQ4fo6OhgaGiI5uZm2tvbWbRoEU899VT0njo+Ph59MDk6Opr2OAju1+ke1JUy35IkSZVmwFMlMwnsAvaF3y8F2oBQuhNSeBV4AvgJcD7wPmBJnvnYB3wfeAV4J3A1cG6eaWQTG+wrV2X/0KFDdHV10d7eHu15mSh2aFtLSws7duxIeVxs75SBgQE6OztTfmYkYJfqM8fHx+N6nKxZsyZjwHPLli20tLTQ398f951t2LAhridkX19fXAAxtvF15ZVXpk0/V1dccUVcsDGV0dHR6GruifkFMuYXgt818t3V19ezadOmpO9mdHSUrq4uBgYGoo3VTL1FJanU5tTPYcEt1R3wfOVvX6l0FuKU+j48Pj7OwMAA69atS3mvifRYjdzXUi2gGDtSYmhoiPHx8aQ8lTrfkiRJleYcniqJU8D1BMHFW8Ovq4AbgIkc0/gh0AL8OvAp4P8Bfhb4Sh75+HL4nP8nnMavh9P8YR5p5GJkZCT6cy4Bz66urug8WJlemYY/Rxoo27dvT9kIAeKGu6ULigJpe1CmEhn2l5hWZAhdpIdpZO7PTHbs2JHy+1q3bl10+/j4eFyQMzYome73yUdsGq+8krmhXEh+IX5O13Q9XyND2SPf39DQUM7zgkqSZqbpuA+vXr065UiC1atXx43ySLeAYnt7e9wIiVQP+6Yj35IkSZVkwFMlMQakCtX0A79F0Pszk/3Ah4HEKvibwMfC6WTzKPDx8DmxRsNpH8ghjUKUc/L+2EV3UtmwYUN0AaRMgdh8AofZFgnatGlT9Od77rkn7XHr1q3L+LnlnBogItP/3YYNG3LO76FDh6I/xw73X716ddZh+LHf36OPPpolx5KkmWw67sNdXV1p98V+RqaHpunuWRHTkW9JkqRKcki7SuJIhn39BIHI5RmO+TyQaRaujwMdGfZPAr+bYf/LwOeAP8twzGvA2Rn2x4pdNTVVwyFRa2tr2sDXoUOH0i5EkChb8CyXoGFk1fBcROadzPaZzc3NjI6Oxs0fligynC5ficPoixWbRuz/Y6Jsv/d5552XcnviHGfZxH5Orn8HkqSZqdT3YQimYkkn9l6U6bhspiPfkiRJlWTAU2Wxl8wBz70xP88GTibsPwgcAlKHmOBw+JhEsWntTbE/1pvkHvCMbWDkEqTq7OxMO89V7Kro2eQzX+jQ0BBPP/00IyMjjI6ORl/5BA1z/bxIwBNIG/AsdK7T2PNipxIoVOyCU5nyVGh+Y/PY1dWVsWdOolxWxpUkVYdS3IfzUarel+XOtyRJ0nQw4CkVIHEurFQLAOSq1EGuvr6+uMUHEtXX19Pe3p7TfJH5BDwj0n1uoQHElStXRucGfeyxx+jt7S0onYjYAHWmHi3penBmk0uPX0lS7SrlfbicqjXfkiRJqRjwVFkszWH/jvDPib07AeaTvncnQEP4mMRenrFpZcvDGVn2x6qvr6elpSUaPLvnnnuyznWZTj6T/mcLqnZ0dMQ1RFpaWmhvb+fKK6+kubmZ5uZm6uvrGR8fz6nBkmswNjbIV+r5vWKDy5HVarMN7U8nsnI6TA3FL7XYQOmGDRsqMjepJKkySn0fLpdqzbckSVI6BjxVEvNIP49nB9CW5fzfB/6K9PN4finL+SGC+TlvSLP/AuCzWdLIdTh7ROxw5b6+Pu688868g33j4+M89thjeX5yagMDA9FGSH19PTt27Cg62JZrwDO21+R0BBE7Ozujwcru7m4GBwcLSid27rF8hprnI/ZvoLm5ueDgrCSpukzHfbgcqjXfkiRJmbhKu0qiidSLCnUAXyMISGbSCHwbSFxC5gzgy2nSTnR9+NjEnpqLgH8gCHqWUmdnZzS4NT4+TkdHLrmMt2LFipLNiRXbUzRbz8Jch12Pjo5mnaM0dlXy+vr6aQl4xvaeHRoaKmjRhI6Ojuh33dzcnHZO1WLFLlSUy6rro6Oj0eB57IJHkqTqMh334XKo1nxLkiRlYsBTJTELeBTYCXwl/NoZ3laXYxrvAQaBvwa+ADwI/B/g1jzycWv4nAfDafx1OM335JFGPvr7+6M/DwwM5Bz0jARIp2tV7mzzT+Yz/D5bcHHNmjXRn6criFhfXx83d2dPT09ePTRXrlwZNwSv2HlAM2lvb48Gfbds2ZL1/7irq4u+vj76+voKnjdUkjSzlPI+XE7Vmm9JkqREDmlXyYQIVmJfXkQa5wK/VmQ+loRf5dDe3s66deuii+ps2bKFhoYG7rzzzrgeoLESFwWIHa5djCuvvDL68z333MPq1auTjhkfH2fNmjVxwb9sPUwHBgZYuXIl/f39cb/P+Pg4K1asiAb06uvrp7Uh1NnZyeDgYPS76uvrY2BggO7ubq6//vqk73p8fJy+vj7uueeeuN+xv79/2oeZ9/b2Rnt6tra20tvbmxQMjvxfRHp1trS0OIRQUlmdGD/Byw+mm0ymOpwYP1H6IRwFmq778HSr1nxLkiRlYsBTKtKGDRs4//zzoz0hx8fH6e7upru7OzrJ/3nnncfo6GjSnJiRQNhjjz1WdMNh9erV0QUFhoaGaGhooLOzk/PPP59XXnmFoaGhaHCtubk5mpeBgQF6enqiCxTEihw3MDBAQ0NDdKGfVEPdN23aVFT+c9Hb20t9fX00wBw7HDzyXUPwf5Bq/tH+/v6UDblSa29vZ/Xq1dGGYVdXF93d3VxxxRVx32lEfX19XG9hSSqHE4dO8NIDL1U6G8UrIOD59NNPx01Bks3KlStZt25dxmOm4z5cDtWab0mSpEwMeEolsG7dOtrb21mzZk1cIDDdoj8tLS1s2rQp2qOvvb29JKue7tixIzov6Pj4eDQwGKuzszPaAzHSgOnu7qa9vT2pwbJ69WquvPLK6FD9oaGhlEO0e3t7yxJIhCDAfMMNN+T8XUPw/fb29k7L/KLp9Pf3R4erw9QK84laWlro7+8va94k6XSX7pqcTq6LEpb6Plwu1ZpvSZKkdAx4qiReAJZVOhNFegG4pIjzW1paGBwcZGBggP7+fp5++mlGR0cZHx+PLuZzxRVX0NXVlTR0ORLEKzbo1dLSwuHDh+nu7mZgYCD6+ZHVwmM/u7e3N26hnHSfvXr1akZGRtiwYUM0zcjxq1evLmh1+mJFvuuhoSEeffRRBgYG4np11tfXc8UVV7By5UpWr15dsWBib28v3d3dSd9dpKfsDTfcULZAsSTFWrQocZnA6jWTfpfpuA+bb0mSpPyFdu7cOVnpTGjmWr58edZjrrvuOkZGRqY/M2WwaNEiHn/88Upno6KGhoZobW0Fgp6rLlAgSZIkSZJi7dq1q9JZyMgenira6R4glCRJkiRJ0swxq9IZkCRJkiRJkqRSsYfnDLRv3z72798PQGtrK3V1dRXOkSRJkiRJklQdDHjOMGNjY3R1dUXfr1+/nra2tgrmSJIkSZIkSaoeDmmfYe64445KZ0GSJEmSJEmqWvbwnEHuv/9+xsbGKp0Nnebq6+tZvXo1EKxaL0mSJEmSVE0MeM4Qg4ODbNu2rdLZkGhubqa/v7/S2ZAkSZIkSSqIQ9pngImJCdavX1/pbEiSJEmSJElVz4DnDLB+/XomJiYqnQ1JkiRJkiSp6hnwrLBHHnmEwcHB6PumpqYK5kaSJEmSJEmqbgY8K2jfvn309fVF31977bV88IMfrGCOJEmSJEmSpOpmwLOC7r777ujPTU1N3HbbbRXMjSRJkiRJklT9DHhWyPr16xkbG4u+v/feeyuYG0mSJEmSJKk2GPCsgN27d7N79+7o+87OTufulCRJkiRJkkrAgGeZjY2NsX79+uj71tZWbrzxxsplSJIkSZIkSaohBjzLLDbYWVdXF/dekiRJkiRJUnHmVDoDp5Pe3l6Gh4ej79evX09dXV0Fc5Tdrl27Kp0FSZIkSZIkKWf28CyTffv2sXnz5uj7a6+9ltbW1grmSJIkSZIkSao9BjzLYGJigttuuy36vqmpKe69JEmSJEmSpNIw4FkG9913HxMTE9H39957bwVzI0mSJEmSJNUuA57TbOvWrezevTv6fu3atTQ1NVUwR5IkSZIkSVLtctGiaTQ2NsbGjRuj71tbW1m1atW0fNZjjz1Gf39/SdMcHx/nzDPPZOHChfT29pY0bUmSJEmSJGk6GPCcRrHBzrq6OtavXz9tn3Xs2DEOHjxY8nRPnDjBvn37uOqqq+K2v/rqq8ybN6/gdL/97W/zS7/0S0nb/+Ef/oEPf/jDBad75MgRzj333KTtd911V9Hf/7Jly9izZ0/ctg996EN8+9vfLirdj370o3z1q1+N23bOOedw5MiRotL98z//c9asWZO0/ZlnnmHZsmUFp/vss8/y7ne/O2n7pk2b+OhHP1pwugDz5s3j6NGjcdt++7d/mz//8z8vKt0Pf/jDfOc734nbdvnll/Pss88Wle769eu5++67k7ZbPgKWjymWjymWj4DlY4rlY4rlI2D5mGL5mGL5CFg+plg+plg+ApaPKeUoH7t27Soq/elmwHMaxRaIiYmJgnp3proI7Ny5M2nb3LlzmT9/ft7pZ3Lo0CFOnTrFrFmzaGxsjNsXCoWKSjvSczTV9mKEQqGU6RZz8Y9YsGABr776aty2UnznDQ0NSXmuq6srOt25c+em/C7mzCmu2M+ZMydlunPnzi0qXQgW9Iqd7xaC76dY8+fPT8rzggULik533rx5Kb8Ly0fA8hH/ecWyfAQsH1MsH1MsHwHLxxTLxxTLR8DyMcXyMcXyEbB8TLF8TJmu8lEuoZ07d05WOhO1qrOzk+Hh4ZKnmyrgOR06Ojo4ePAgCxcu5MUXXyzLZ0qSJEmSJGlmm+k9PF20SJIkSZIkSVLNcEj7NFq1alVS999sdu7cGdcrtK2tjaVLl5Y6a5IkSZIkSVJNMuA5jQqZs/PIkSNxAc+rrrqKtra2UmZLkiRJkiRJqlkOaZckSZIkSZJUMwx4SpIkSZIkSaoZBjwlSZIkSZIk1QwDnpIkSZIkSZJqhgFPSZIkSZIkSTXDgKckSZIkSZKkmjGn0hlQvK6uLrq6uiqdDUmSJEmSJKkq2cNTkiRJkiRJUs0w4ClJkiRJkiSpZhjwlCRJkiRJklQzDHhKkiRJkiRJqhkGPCVJkiRJkiTVDAOekiRJkiRJkmqGAU9JkiRJkiRJNcOApyRJkiRJkqSaYcBTkiRJkiRJUs0w4ClJkiRJkiSpZhjwlCRJkiRJklQzDHhKkiRJkiRJqhkGPCVJkiRJkiTVDAOekiRJkiRJkmqGAU9JkiRJkiRJNcOApyRJkiRJkqSaYcBTkiRJkiRJUs0w4ClJkiRJkiSpZhjwlCRJkiRJklQzDHhKkiRJkiRJqhkGPCVJkiRJkiTVDAOekiRJkiRJkmqGAU9JkiRJkiRJNcOApyRJkiRJkqSaMafSGZAkSZJUGtdddx0jIyOVzkZKixYt4vHHH690NiRJ0mnAgKckSZJUI0ZGRtiz5wXgkgLOfgsYLjIHZwHNKba/UGS6kiRJuTPgKUmSJNWUS4BnCzjvceBXi/zsEPC/gdkJ25cVma4kSVLuDHjWiMcee4z+/v6Spnno0CEA9u/fz4UXXhi377nnnuOcc84pOO1du3Zxyy23JG1/8MEHWb58ecHpHj16lMsuuyxp+9q1a1m7dm3B6QJcffXV7Nu3L25bW1sbDz30UFHp3nHHHTzyyCNx2+rq6nj++eeLSvfhhx9m3bp1Sdt37NjB0qVLC0537969rFixIml7T08PN910U8HpAlx66aVMTEzEbbvxxhu59957i0r35ptvZvfu3XHblixZwhNPPFFUuhs3bmTjxo1J2y0fAcvHFMvHFMtHwPIxxfIxpRTlY+pvLbbu9iCwPIcUnin4s6e8DvyIXHuYWj4Clo8p3j+mWD4Clo8plo8plo9ANZaPcjHgWSOOHTvGwYMHpyXtU6dO8dJLL8Vtm5ycLCrN48ePJ6UZ2V6MycnJlOkeOXKkqHQBXn755aS0S/GdHz58OCndYm5WEceOHUv5XZw4caKodE+cOJEy3WPHjhWVLsDY2BhHjx6N23b48OGi0z148GBSns8999yi0z1y5EjK78LyEbB8xH9esSwfAcvHFMvHFMtH4Pjx4zH/T7Hp51o+ShHwjKSTW8DT8jF1vuUj4P1jiuVj6nzLR8DyMcXyMXV+tZWPcjHgWSPmzp3L/PnzS5rmoUOHOHXqFLNmzaKxsTFuXygUKirtM888k4ULF6bcXoxQKJQy3Xnz5hWVLsCCBQt49dVX47aV4jtvaGhIynNdXV3R6c6dOzfldzFnTnHFfs6cOSnTnTt3blHpAjQ1NSU9QWpoaCg63fnz5yflecGCBUWnO2/evJTfheUjYPmI/7xiWT4Clo8plo8plo/AmWeeyZw5cwjaTrH5zLV8lDLg+Ws5HWn5mDrf8hHw/jHF8jF1vuUjYPmYYvmYOr/ayke5hHbu3FncowDVrI6ODg4ePMjChQt58cUXK50dSZIkZbFs2TL27IH85/B8Czg7/G86VwC/DHwJeCXDcTcAmxNzxuWXw7PPFjK3qCRJmml27dpV6SxkNKvSGZAkSZJUacOkD3ZeBvwV8H3gD4FR4C4gXY+XUvUUlSRJKoxD2qU8XHfddYyMjFQ6G2ktWrSIxx9/vNLZkCRJVSdVz8uLgbuBW4hfdX0esB74HeBPgD8jfp7QvcCbwBnTkVFJkqSsDHhKeRgZGWHPnheAi4DYwGc98A5yK1KTwGHgP4CT4W0/E37l6wWCBgVAcfO2SJKk01lsr8x3AL8PdJJ5/s+fAe4HPgV8DvgaQd3mBEGP0WXTkVFJkqSsDHhKebsE+CxwI/ARgqFdiwtIZwL4InAv8EFgS57nHyeYa+vdwB8BdxaQB0mSVHsKmaL/GeBcYB3wCdIPV0/lIqAPuJ1gqPvmcHoGPCVJUmU4h6dUkHrgB8AjFBbshKAh8XsE82B9qIDzDwJfB/4VuK7APEiSpNpzMvshST5MUCf5DPkFO2MtIagb/YAgeCpJklQZ9vCUCvJLJUzrPIIhY/laCNxcwnxIkqTaUEgVv5C6SDrvTbHtJ4yMvMWyZTOz16fzoEuSVFsMeEqSJEmaZm8xefw47NkTt3USeC7LmecQDJpP51XgpSxpLCR9n9MXspwrSZKqjwFPSZIkSdPuEpLXgh8jCEYmmgWcCv/8n4HvZUj3iwTLJmVyB/DJNPtmZp9TSZJUDOfwlCRJklQRC4C3xbyfTXywE+DiLGm8M+H97PArVrY0JElSbTHgKc1YPwZ+C/gC8DsUtuKqJEnSJEFd4gsEdYsfVzQ3sWYDH415f5L4YGeIIMeZXA00J6QRu2zTIuCqIvIoSZKqj0PapRlpEvh5YH/Mtv8MdFUmO5IkqYr1Al+Kef8d4KcE4cRySf/gdiPBPJrbE7bPAh4AVmRJ+VzgmwTrzL+csO8C4K9wzXhJkk43BjylGWcf8H7glYTttwL/BPxPyttAkSRJ1WkS+E3gGwnbXyIYTP49YEkZ8vEN4M20e88C/p4guPk94CDBEPTfInuwM+I9wL8CnwP2hrctBT5LEPSUJEmnFwOe0oyzkORgZ8T3MNgpSZJyEwK+m2bff5B6uaDpUJf1iNnA7eFXoS4A/qyI8yVJUu1wDk9pxhnPsO9EuTIhSZJqwskM+14tUx4SB5pLkiRNLwOe0oxzb4Z9PwH+sVwZkSRJVe17BHN1ppOpzlEqbwL3lOFzJEmSphjwlGaU/QQLC2TyuXJkRJIkVb1sdYavAAemOQ9/SfDAVpIkqXwMeEp5S7/KaPHp3gu8keW4bwP/PE15kCRJteGfCVZjz+QNoIfpq9u8CfzxNKUtSZKUnosWSXl7HrgcWBx+LYn5uYnszxHGgeGY176Yn8dzzMP7gQsTPvsocE7Ov4UkSapV+wjqCrl4APgLkus0kVd9lvNPAWMk12mGgREyrc4uSZI0XQx4VsC+ffvYuXMng4OD7N+/n4mJCQDq6upYunQpixcvZtWqVTQ1NVU4p0ptEngu/Er0duASphoNNwF/Fz420hA4WKJ8vBh+PRGz7fISpS1JkqpXvoscjgNPhV+J5jMVCL0M+BXgYaaCmy8ArxeaUUmSpGlhwLOMxsbGWL9+PcPDwyn3T0xMMDg4yODgIJs3b6atrY3bb7+durq6MudUhXsd+D/h1/+PIAC5F/h9XGFdkiRVn4Ph1/eBzQR1m5eAb+ad0mtMzVS+FGgDQnmc/yrBY96fAOcD7yMIxeZjH8Fv8grwTuDqPM+XJEnVwYBnmQwODnL77bfndc7u3bsZHBykt7fX3p5V56MEVfpZwGrgbUAH8FYlMyVJklSAtwFbgOvC7/8COAP4al6p/Bi4NeZ9B/A1IJdH+z8Efh0Yjdl2BvDFhDQz+TLwKeIH2TeH/z0rxzQkSVJ1MOBZBpGenYmamppoaWmJBjP37t3L4OBgdIg7BL0+u7q6eOSRR+zpWTU+Dvwp8XN5/irwNwRV9eMVyJMkSVIhzgT+GvjlmG2zgT6CkOOXC065P/zvo2Tu6bkf+DDwcsL2N4GPEfT27MjyWY8S1NASjRL8NkuzZVaSJFUVA55l0NfXFxfErKur4/bbb6etrS3l8b29vWzevDn6fmJigoceeoiurq5pz6uK9SlgI6mr7b8CbCXoHZFtJXZJkqRKezvwONCeYt8s4M+Y6mdZmH6CQOTyDMd8nuRgZ6yPkzngOQn8bob9JzPskyRJ1SnbctIq0tjYGLt3747bdv/996cNdgJ0dXXxkY98JG7btm3bpiV/KqV1pA92RqwkWMRobllyJEmSVJizCeosqYKdESGCVd7vKOqT9uaxf3aK/QeBQxnOP0zqJSNTpSVJkmqDAc9pNjg4GPf+2muvZcmS7NOrd3V1xQ1hn5iYYN++fSXPn0rls8CfkNvU+1cB3ya3Gavy8fYSpydJkk5P5xDUVZbncGwI2ECwQKMkSdLMYMBzmiUGPFetWpXzuUuXxs8mtH///pLkSaX2h+FXPuuM/iKwHTi3RHn4BeDiEqUlSZJOX+cS1FF+IY9zQsDnCOpD+cs2f2bs/lTDz+cD52U4vyF8TCKHskuSVLsMeE6zsbGxuPe59O6MaGxsjHt/5MiRkuRJpfQZgt6dhfgvwA6KL4bvBf6hBOlIkqTT2yyCusn/VeD5nwXuzOuMDiD9RE+B3wcWZNj/pSznhwhmG03HRQ0kSao9RkimWSl7Zc6bN69kaalULiry/IuBU0WmcR6lHx4vSZJOP6eA/1RkGrnXjTqAr5F9jEwjwQD7RQnbI2vEZ1uhHeD68LFnJGxfBLwzh/MlSVJ18YHmNNu6dWvB5w4NDcW9X7x4cbHZUck9U+T5z86APEiSJEU8C3ywiPPT10suZqr/51KCnp25Tgj0HmAQ2An8mGCI+pVA7mOn4FbgauApgkWMLiaYWf39eaQhSZKqgwHPGWr37t1xw+EXL15MU1NTBXOk1IoNNpYiWPnv4ZckSVKxnmG6Ap5nA11FpHwu8GtFnA9BgDSfIKkkSapOBjxnoEceeYS+vr64bV1dxVQPNX2eASbJb8GixPNLoRQ9RSVJkoqpm0wWeb4kSVJpGPCsoImJCR566KHoz3v37mV4eDjpuLVr19La2lru7CknrwAvAxcUeH6pGgU2LiRJUikUU6c4ABwqVUYkSZIKZsCzgsbGxti8eXPa/a2traxdu9ah7DPesxQW8MylF8RsYDHwfJbjDHhKkqRsLgWGgZMZjilm9IojTiRJ0szgKu0z2P79+1P2+NRMU2iwcQwYz7D/BmBP+PU3wLJpyIMkSap9ywjqEnsIgpLXZzj2MLC/wM+xPiJJkmYGA54VVFdXx+LFi9MuSDQ2Nsb69eu5+eabmZiYqEAOlZtCK/fpzvsVYAjYTDCtfgj4VeCHwDeAd5UwD5IkqXa9i6Du8EOCukSIYH30RwnqGr+c5rxS1m3eDnQDZxSYpiRJUv4MeFZQU1MTfX199PX18dBDD7Fz5062bt3KRz7ykbjjxsbG6OrqMug5YzQQPxtEqRoFvwg8CXwL+LkUx88GbiEY3v4loDFm3xHgrQLzIUmSasscgrrC8wR1h9kpjvk54O8I6h6/mLCvFHWbOcDHgBeAP6HwBR4lSZLyZ8Bzhqmrq6Orq4utW7dSV1cX3T42NhZd4EiV1shUAyLE1FxX+Yo0Ct7LVIPjAzmcdwZTDYgNBAFYgOMF5EGSJNWexQR1hVx6VX6AoA7ydwR1Eigs4HmKYLh8iKCOtJcg6JrbXPQngfsI+qH+InATsCPPHOwHfgdoD79+h2AZJUmSdPpx0aIZqq6ujttvv53169dHt23bto2urq6Uxz/22GP09/eXNA+HDgWrbO7fv58LL7wwbt9zzz3HOeecU3Dau3bt4pZbbkna/uCDD7J8+fKC0z169CiXXXZZ0va1a9eydu3agtMFuPrqq9m3b1/4XVv4318G3gb8BLg4zxTfIhhS9n1gTcK+OrIvVDQXWAd0AvcDX43b+/DDD7Nu3bqks3bs2MHSpUvzzOuUvXv3smLFiqTtPT093HTTTQWnC3DppZcm9WS+8cYbuffee4tK9+abb2b37t1x25YsWcITTzxRVLobN25k48aNSdstH4G2traiH9TccccdPPLII3Hb6urqeP75bOUjM8vHFMtHwPIxxfIxpRrLx9TfWmzd7UFgeYYzQwR1mg8D/cDWhP1HgeTyAWvDLwjqQiuAzwPvzjnPVxOERl8h/tHtPxJM7vMF4BM5pPNDgty/HLNtB/AVYD5BbS2X2lU2lo8p1Vg+vH8EvH9MsXwELB9TLB9Tpqt8lIsBzxmsra2Nurq66B/YxMQEY2NjKef7PHbsGAcPHpyWfJw6dYqXXnopbtvkZCE9GqccP348Kc3I9mJMTk6mTPfIkSNFpQvw8ssvc+LEifC7yGcsIxiCXsj38Q2CBsZ3YtKLyOdmVQ98Dvhm3NZjx46l/C6mfofCnDhxImW6x44dKypdCHoyHz16NG7b4cOHi0734MGDSXk+99xzi073yJEjKb+L07V8JKZdimvS4cOHk9ItpjIXYfmYYvkIWD6mWD6mVGP5SK6rQO6jQGYRLJqYuKjRJMl1FQim1Im4mGBhpPy8TLCMYyqTwKcIalvJTbkprwK/TnywM+IU8O/hn4svHZaPWNVYPrx/BLx/TLF8BCwfUywfU6arfJSLAc8ZrrGxMW6l9v3796cMeM6dO5f58+eX9LMPHTrEqVOnmDVrFo2NjXH7QqHi5mE688wzWbhwYcrtxQiFQinTnTdvXlHpAixYsIB9+/YRXI8WhLdGvvNCvo/IOQ1AYp7rKNbcuXNTfhdz5hRX7OfMmZMy3blz5xaVLgTz2iY+QWpoaEhzdO7mz5+flOcFCxakOTp38+bNS/ldnK7l49VXX43bVoprUkNDQ1KeY6f7KJTlY4rlI2D5mGL5mFKN5WPOnDkJdRWAfMtHYj5CJNdVAOYlHJO/dxCsHZ/OJPA1Mgc8nwBGY97PJgh0xjb/z2eq1lYMy8eUaiwf3j8C3j+mWD4Clo8plo8p01U+yiW0c+fO4h4FKK3e3l42b94cfb927VpWrVqVVxrr16+P6wq/fv162traMpxROh0dHRw8eJCFCxfy4osvluUzZ7ply5axZw8Ec1TNNMu4/HJ49tmZmDdJklQOM7euchaXczwpV2OkDqXOIghaAvwC8L0MKX+RoCdoJl8APplm3zKAyy+3DiVJUh527dpV6Sxk5KJF0yixJ2bi/BK5GBuLH+RTiicLkiRJ0kyQ2E8ksp58bF+lbP11zs/hc0o7DkqSJM10BjynUWLAc+/evXmnETucHUgaWq6Z7DvABPD3wOsVzoskSVIxXieo00wQ1HFK4+3AlTHvTyb8C8E68pm8j8zr0Z+R8BmSJKn2GfCcRq2trXHvh4eH8+rluXVr/AqZTU1NKefv1Ez0Y4K1Qs8BfoVgIJUkSVK1eoCgTnMOQR3nJyVL+WGCJRhT+RDZh6svIRjWns4Xw8dIkqTThwHPaXbttdfGvb/vvvtyOm9iYoKNGzfGbfvgBz9YsnxpukwCfw38p4TtnwH+gPjVTCVJkma6I8Bngd9L2H4xQZ2n+OUALgGeIr4X5hnAx4DHmRrmnsmtwKPED12fDzwW3idJkk4vBjyn2Y033hj3fnh4mM7Ozow9Pbdu3Zp0Xl1dHV1dXdOSR5Xa76TZ/jngP8qZEUmSpCL9O/D5NPv+e8k+5RLg+wQD518CjgFfIr/15a8nqGm9En79B9BRshxKkqRqMqfSGah1TU1NdHZ20tfXF902PDxMV1cXixcvpqmpiaVLl3LkyBH279/P8PBw0kJFEKzOrmoQAs7KsP+icmVEkiSpBDLVXc4ifnmh4p0FFDuB03mlyIgkSapqBjzL4MYbb2RsbIxt27bFbR8eHmZ4eJjdu3dnPH/9+vVJ84Fqphoj6JeQzteBNWXKiyRJUrG+nmHfi8B+wEU1JUnSzOKQ9jK57bbbWL9+PXV1dTmf09raykMPPURbW9s05kyldS/wZob9fwy8Vaa8SJIkFeMtgrpLOscJ6j6SJEkziz08y6itrY22tjYGBwfZtWsXe/fuZf/+/UxMTADB8PfGxkZaW1tpa2tzRfaqcwD4SpZj/g34BvBb054bSZKk4vwl8OMsx3wF6AYWTH92JEmScmTAswJaW1sdol71ThEMXR8Ov/YBTwJv5HDuncC/AIvDryVAM/lNyx9xOCYP/wH8TAFpSJIkHQdGCeo0kbrF3+Rw3uvAKuADBHWaSP1mIQ4mkyRJlWLAU8rbCFBHUMEvxL8DfQnbZgHvJD4IGvl5QfgzYxsgkdfBhHQMeEqSpL3A+5mqS0TqFouAl4l/YBv5+ScED3QL8VT4Fevt4c+L1GlOFpi2JElS/gx4Snk7Pg1pniIY7v5vwPaY7bcAdwO/AfxoGj5XkiTVnpPAP4dfEc0EdYy7gYfKkIfXgWfCrymHwv82UNj67icJQrYNBCHVQnN2mOCR8uwC05AkSTOb40ykGeu/Af+ToIHyJEHvCEmSpHwtAXYT1Cm+DvzXiuRiL3B++PUO4LE8zn0D+Dgwl2Cw/FzgfcALeaQxHD4nNo2PA5N5pCFJkqqDAU9pRroV+HOm+h1cSNBQubRiOZIkSdXoMmAXQV0CgrrFV4GuMubhDGBO3KD2g8ANZF/uEYJendcBXwbejNn+FHAluQU9XyAIdsYOvH8znOZPcjhfkiRVFwOe0ozzCeBLJBfPRoIGy7vLnSFJklSVfpag7tCYsH0WQajvv5chD2cBW0k3ePyTBDOJZvIA8RP+xBoHbs4hFzeFj03ltRzOlyRJ1cWAp5S3QmacytXtwBcyfMYCYCfw3mnMgyRJqn4/R1BneEea/SHgi8Bt05iHucC3gA+lPeJNkpc7SvTdmJ9nJ/wL8H2CIe/pvJ7wGanSkCRJtcWAp5S3iwlWaS+13wN6yB5QnQ88QTCIS5IkKdFZwA6C2TIzCQH3Ap+ZhjzUAf8AXJ31yINZ9r8S83Nkvs3ENd8Pkd7hhPeRc527U5Kk2mXAU8rbXOA7wLwSpvmHwOfJvfdoA8Hgrp8vYR4kSVJt+E8EdYVchAjqIHeX8PPnEdSVPpC0ZzbJPSsvzpLaO2N+PkXQgIlN4wyCMTDpLADelpCHWeG0JElSbTLgKRXk54EBoL4Ead0DfLaA884Fvk2qxoQkSTqd5VvFDwF/APxxCT67nqB3aeqHsieJ7525CLgqS4q/Tfwj4VMJafw2mYenzwY+mpAHg52SJNU2A55Swa4E2opMYxbwqSLOP4fyrrIqSZJq16covnnQBlyR05EXAH9F8Ag3kxWkn+F8JcGiRtlsDB+baFY4H5IkqbbMqXQGpOr2TJHnnwKep7hFiJ4tMg+SJEkQ1EmK7fuYvl7SALSEf15KML4l12DjJ4BlwNeAHxPMaP4BghBtLosPnQX8PUFw9HsE84ZeDPxWOG1JklRbDHhKBTsGjJYgnWcoLuBZbNBVkiQJSlOnGCGoI81N2tNIMCFQoVaEX4WaDdwefkmSpNrmkHapYM9RmvU9i+2hacBTkiSVQilGjUwS9BSVJEmqHAOeUsFKFWgsJp0J4EclyockSTq9zYS6jSRJUvEMeEoFmwmNgj0lyoMkSdJMqNtIkiQVz4CnVLBMlfkQcDPwv4E/IZimP51/A45OQx4kSZJydYRgOaB0GgjqNP+boI6Tas30COsnkiSpsgx4SgVLV5n/VeCHwIPAzwLdBIsb/R6pJvAPFNpT0waFJEkqhXR1kbMJ6jCjBHWanyWo4/wrcF2ac6yfSJKkyjqtA56vvfYaQ0NDvPDCC5XOiqrOOPBiwrargH8G/oagMRCrHvg8QWPhE8AZCfsLbRiUYnEBSZKkxLrIGQR1lhGCOkx9wv7/DPwt8E/A8oR9PwVeLXkOJUmScjWn0hkotQMHDjA2NkZLS0vaY771rW/R19fHxMRE3Pa2tjY6Ozu54IILpjubqnqxgcYrgT8GVpB5eBfAAuCLwFpgPfCXwCkKD3hGzpsPzC4wDUmSpEidYhbwm8BdwMU5nPfzwBPAAPAZ4Onw9meB9+eVg33A94FXgHcCVwPn5nH+JLArnA7AUqCN7LUzSZJUe2om4Pnoo4/y8MMPR4OYDz30UMrA5d13382TTz7J5ORk0r5du3axe/du7rvvPn7u535u2vOsavYMcDlBj4dfI/+q9MXAXwDrgM9SWE/NQwRzf94NfIqgwSFJknSygHOeBVYDfwhclue5IWAl0A78NfD7BHWl3AOeXyaozbwZs60Z+CbwnhzOnwD+G7AlYXsH8DWgLuecSJKkWlATAc877riDoaGhaBAzFEodfNq9eze7d++OOybxnMnJSW6//fa0AVMp8CHgoxTfq/Iygqp5IdMqzCIYIj+/yDxIkqTaUkifxi8Dl5Tgc3+dYD7zn+R81qPAx1NsHwU+TDBbaKZa+SSpg50A/TGfYU9PSZJOH1Uf8Ny8eTODg4NAELRM1XMz4v77748LbNbV1dHS0sJrr73G4OAgoVAomsbGjRvp6ekpy++gavSuEqdXSAOjvsR5kCRJtaGQafqLDXbGmk2udaVJ4Hcz7H8Z+BzwZxmO2U3qYGdEP0FAdXlOOZJULa677jpGRkYqnY2UFi1axOOPP17pbEintaoOeE5MTNDX15cyiHnOOefEHTs4OMjExEQ0oNnU1MRXvvIV6urqoml1dXVx4MCB6PEHDhywl6ckSZI0TQ4DB1Nsn83UwPy9WdLItj9yzPLcsyWpCoyMjLBnz57o+0agIcPxPwZey5LmUtKP4TtJ9uvN2cBbWY6RVB5VHfDctm1b3Pu2tjbuuuuulMdGhrJPTk4SCoVYu3ZtNNgJUFdXx1133cWtt94aDaBu3bqVNWvWTFPuJUmSJElSKdwFdGXY3w7sCP8c+1Al1j8B56U5/xBwfortsWn9F2B/1pxKKodCxrvMGM8//zwwNQ/n7bffnvbYXbt2RQOZkV6giZYsWcLixYuj6e3bty/pGKl8JoHXwz+/nulASZKkLGLrFOmngCq3BlLPRh4biFiaJY1s+3M9RlJ1y+dakSrYOZ/0wU4ozfVKUvlUdcBz//7g2UkoFOLaa6/l7LPPTntcZPX2UCjE8uXL06a5dGlwiZqcnDTgqQp7AphLMMX+RcAblc2OJEmqUm8AFxLUKeYCOyubnRghMs/PeQHw2SxptBGsxp5OR/gYSbUrl3L++8CCDPu/lOX8UlyvJJVPVQ9p379/f7TXZlNTU9rjIosaRYazt7a2pj02Np1IkLQaPPbYY/T392c/MA+HDh0Cgu/5wgsvjNv33HPPJc2Tmo9du3Zxyy23JG1/8MEHMwakszl69CiXXXZZ0va1a9eydu3agtMFuPrqq2OC4JHvow14qKh04Q7gkYRtZwA/inn/CrAYeBaYl1OqDz/8MOvWrUvavmPHjmhgvxB79+5lxYoVSdt7enq46aabCk4X4NJLL00qdzfeeCP33ntvUenefPPN0WktIpYsWcITTzxRVLobN25k48aNSdstH4G2tjYeeqi48nHHHXfwyCPx5aOuri7aw79Qlo8plo+A5WOK5WNKNZaP5LrKJHCcYDBmxArgOeDSHFM+CiSXD1gbfmUzSer+VHA1sI9gKcbxhH2LgL8i8wrtEAQhvhb+ObE2/HbgHwkeHdcBxZUOy0esaiwf3j8CtXL/iOgguAaEsqTTCHwb+A0gcamjeuDT4deNQLrScT3B1fSTwJsx2xOvVy+++GJSG9ryMcXyMeV0vH+US1UHPCOLEGXz9NNPx73PFPCsVseOHePgwVRTvhfv1KlTvPTSS3HbIsP+C3X8+PGkNCPbizE5OZky3SNHjhSVLsDLL7/MiRMnwu8in1GK7/xwTHqZvAi8l+D2nP3v/tixYym/i6nfoTAnTpxIme6xY8eKShdgbGyMo0ePxm07fPhw0ekePHgwKc/nnntu0ekeOXIk5XdxupaPxLRLcU06fPhwUrrFVOYiLB9TLB8By8cUy8eUaiwfyXWVdC4DfkBQt8hmMk16uZaPPwBS//28nCLlpcCfAFcBuX7bdcCjBKux7wW+QRDofJ2pwfzFlw7LR6xqLB/ePwK1cv9obGzkYoKyn711FHgPMEjQz/3HwP9L0Ad+nKmHLtlKx60ED2ueImgNXkzy9erkyZOWDywfsbx/lF9VBzzr6up47bVgnbWxsbG0xw0NDUVXZ29sbEw79B2Ii9rHLmo0082dO5f581PNKFK4Q4cOcerUKWbNmkVjY2PcvlwCzZmceeaZLFy4MOX2YoRCoZTpzpuXW6/ITBYsWMC+ffsIrkeRwRCl+M4bgMQ8p2ukzCLX2/ncuXNTfhdz5hRX7OfMmZMy3blz5xaVLgQ9rBOfIDU0ZFprMTfz589PyvOCBZkGtORm3rx5Kb+L07V8vPrqq3HbSnFNamhoSMpzKa7Nlo8plo+A5WOK5WNKNZaPOXPmJNRVDpCud2XQ9M9FiOS6CuQ66iRYKuTzKfcsAF5N2NYK/FqOKccKEazEvhx4Afi3hP2lqNlbPqZUY/nw/hGolfsHBKui5/uXcS5T15gvA4njOnMpHUvCr3Rmz55t+cDyEcv7R/mFdu7cOXNmLc/THXfcweDgIKFQiMWLF/OVr3wl6Zjh4WG6urqiF4Brr72WT3/602nT7OrqYnh4GAj+cx988MHpyXwV6Ojo4ODBgyxcuJAXX3yx0tmZEZYtW8aePRAMLZ8u3wU+mGbf+cAYwZD3RMu4/HJ49tnpzJskSZrJ4usqbxIM4jyU5ujvAr9Yhlx9EvgfXM701qAKtQzg8sutQ0lVZtmyZbBnz4y7rnhN0eli165dlc5CRlW9aNHixYuBoJv08PAwP/jBD5KO6evrix4DwZwO6QwPDzM8PEwoFIoGUaXy+1yGfa8Af1mujEiSpKr2ddIHOyFznaNU9gN9ZfgcSZKkKVUd8IxMwBoZrn777bfz3e9+N7p/06ZN0R6gEHRDbmlpSZnWgQMHWL9+PTAVHL3iiiumMfdSKv8MbM9yzB8Db5UhL5IkqXq9RVBnyOQ7wP+a5nzcSzBDniRJUvlU/Rye11xzDd/61reiQc9I0DJWZHX2a6+9Nmnfo48+yvPPP8+TTz4JTAVPAa655pppzb+q1RGCAVmLgLNKkN4p4KfAMHB3Dsf/iGDy/18lmDnmvBLkQZIk1Y7Xgc+SPItlKusI6hWLCdYyL0V/iDcIFll8HkieckqSJGm6VXXAE4I5N3ft2sWxY8figpURkW11dXXRHqGxHnroIV577bVoUDRyzpo1a8qSf1WjF4F3E0yPfRFB0HFxwutdxM+zOUkwpGsfQWAz9vUCkO/qd38SfkEQ8FxMsNBR6sm8JUnS6eRHwIYcj/0usDL881kED3QT6zVLCOYCjV0U483w58TWaSL1nJ8S1H0kSZIqo+oDnnV1dfT29nLHHXewf/9+YGqFssnJyejK7OvXr8+4OntssHTVqlV85CMfmf7Mq8pNAj8JvwYS9s0GLgEeJei1+R3gtWnKxyHgX8I/G/CUJEmFeoNgFEuqhTbOBj5E0Bv0IwQPbNOt/i5JklRZVR/whGA19YceeoitW7fy5JNPMjY2Ft3e2tqaNXgZCXQ2NjbS2dmZcWEjKTezCObNeg/wReBqgoaBJElSNWoiqNNcBPwRQdAzP/uB9vDPSwkG3V+Qx/k7gK8SPGo+H/gA8GmCx8y5OAk8QNCn9RXgncBv5/H5kma+dOV8RR5p7Ac+D+wNvy/keiWp8moi4BmxatUqVq1aldc5nZ2dnHPOOSxZsoTGxsZpyplOL2cAW4DI3+JFwG6C2+zzlcqUJElSgS4FniAY1g7wG8A3gdUEQ9tzc5ggaEn4378Cvk3weDib/wF8iviB8o8TjKF5nOyzqr8BXEf80pD/CGwGFuCM6FItyFTOvwB8Ioc0fgh8GHg5Zlu+16tDeE2RZoKqXqW9FK699lra2toMdqpEzgL+lqlgZ0QTsAtYVu4MSZIkFeHdBHWYxLryKoI6z5kFp/wyQej01SzH7SA52BmxHVibw2etJT4IEjEJHMjhfEkzX6Zy/immHrik8yrw68QHOyPyuV55TZFmhtM+4CmVztuBbcAvpdm/ANhJbs8FJUmSKu29BHWXBWn2/xJB3eftOac4m/gh6CPhT8jkq8QHO2clpPFVMs8mehL484Q82AiSaku2cj4JfC1LGk8AowlpFHK9kjQznFb3+tdee43XXpuuhWN0ejsb+Huyzw7zMwS30iumPUeSJEmFu4Kgr9L8LMe1E9SB0i8OGuskycHJH2c55ycxP88CTiWk8Sape2RFvAy8lZCHU5xmDSGpxuVSzvO51kTSKOZ6JamyamoOz4jXXnuNXbt28fTTTzM8PBxdvT1WXV0dS5Ys4YorrqCtrY0LLnAKYhVqHkFF//05Hn8ewaruvwT8rxLm42dKmJYkSTp9/TxB3ebcHI9vI5jd7peBo3l/WraQ6vkxP4fC/84mPhCRab68hoT3kXNDKY6VVJ1yKef5XGvSKUUaksqjpgKeBw4coLe3lyeffDK6LbICe6KjR48yNDTE0NAQfX19tLa2snbtWgOfylM9QQX/fXmedy7BNPu/AnyvBPn4I+ChEqQjSZJObx8AvgWck+d5v0DwQPdDZJ/lbsoZwJU55Ojx8M8nE/6FoBaWadGit4c/46kMaUiqbrmU8w9kSeN9BNekdEux5Xu9klRZNTOSY/Pmzdx88808+eSTTE5ORgOdoVAo7QuIHvv0009z880389hjj1Xy11DV+QL5BzsjzgH+juKL4TXAZ4pMQ5IkaRaFBTsj3gd8Ma8zvggsyXLMp4GVafbVk9sj34fDx6aS22B8STNdpnL+IYKFizJZQuYrWK7XK68p0sxQEwHPTZs2sWnTpmjwMhLQjLyfnJzkggsu4JJLLuGCCy6I2w7EHd/b28umTZsq/BupehS7Bt9/EMwuU8k8SJIkQVAnOVhkGslTSUXELv4xH3gMuDWHFGcT9Jj6GEEPq4j3EfTmuiSHNC4JHxvbO+uMcJrvzOF8STNfpnL+OPHXoHRuBR4lfuh6vtcrrynSzFD1Q9p3797NI488ktRrs7Gxkc7OTlpbW6mrq0s6b2JigsHBQR5++GGGh4fjgp6bN2+mqamJa665pty/jqrOMxU+H2APDsqSJEml8QzwriLPT20p8N3wz5nm3EzlLOBLwJ8SLE5yHpmHsadyCfB94A3gEMHa87OBZXmmI2nmSlfO83F9+HUo/D7f65XzA0szQ1UHPCcmJrj//vuTAp1r166ltbU147l1dXW0tbXR1tbG4OAgGzduZP/+/dGgZ19fH8uXL+fss+2QrkxmQsDzdeBHJUhHkiTpGWBVkeenl2/gINFsoKnINM4qQRqSZrZSlPNir1eSKquqh7Tv2rWLiYkJIAh2NjU10dvbmzXYmai1tZXe3l4aGxuj2yYmJti1a1cps6ua9BzF9a4sRcCzlOlIkqTTWzF1ihPA86XKiCRJUsGqOuC5e/dugOi8nevXr085fD0XdXV13HXXXdG0AAOeysFxYKSI8w14SpKkmaSYOsUIQd1IkiSpsqp6SPvY2Fg0OLl48WIuuSSXKcvTW7JkCYsXL2Z4eBiA/fvTT7perN27d7Nz507GxsbYv38/ExMT1NXVMW/ePBYvXsxVV11FW1vbtH2+SukZsq/Xl8pbZO8FcQXwywSzVr2SJQ+SJEnpnA98HPg7YDDDcc8T1FHeVsBnWB+RJEkzQ1UHPCNzbgJ5D2NPp7W1dVoDnlu3bqWvry86FD/WxMQEExMTjI2NsXv3burq6ujs7GTVqmLmUdL0ewb49QLOGyZoUKRyGfB54P8mmPb6dmAjcD+Q/LdjA0OSJKVWB9wGrAXmAXcD3wR+n9QPXt8EXiCoi+QrU31kFsEq8JIkSdOvqoe0xw5fnzdvXknSjE2n0OHx6dx+++1s3LgxZbAzlYmJCTZu3Mj9999f0nyo1AoNNj6bYtvFwP8E/g9BEDWyxt88YD0wStBgOTPhvL3AZIH5kCRJtedMgjrDjwjqEJE6bgj4DYK6xl8A70xxbqo6Si5S1Yk+BDxFYT1GJUmSClPVAc/GxkYmJ4Mgz9jYWEnSjE3nnHPOKUmaAPfffz+Dg8nDh1pbW/nIRz5CZ2cn1157LYsXL046Ztu2bQY9Z5SLgf8r5n2hAc/Y894B/A+CwOVvEqxBmsrPEPTyHAbWxBx3gqBHhiRJUj1BL837gflpjpkD/FdgH0Ed5B0x+0pRt/kvwE7g2wRT9OTmdWCMwpeEnAQOhV8+CpaUzkmCa83rRaRR7PVK0vSq6oBnZBj75ORkymBiISLphEKhkg2THxwcZNu2bXHbWltb2bp1K/fddx9dXV3ceOON3HbbbfT19dHb20tTU1Pc8du2bSvZ76hinQ38M/C3wLsJgo+FTND/DHAu8EcEk/z/d5J7bqZzEdAH7AE+Et7mIgGSJAmgCbgwx2PPJKiDjBBMp3MuhQU8jxPUid5NUEf6J2B5zmcPA+8D5gILw/9+HHgjjxw8RhC2PT/8ekd4m6TatZf8yvkbBNeW2GvN+wgeEeWqFNcrSdOvqufwXLVqFZs3byYUCrF//36+9a1vcc011xSc3u7du+Pm7SzVokGPPPJI3PvW1lbuu+++tMcvWbKEhx56iJtvvjmux2lvby99fX0lyZOKFQKuA64BNhMsKNSU8YxkHwY2AecVkY8lwCNAN/B+Rkb+g2XLlhWR3vRZtGgRjz/+eKWzIUmSUqoDfg/4GLClgPMPAl8neBCbbqRKai8QBA/GY7a9CXw5vO/vc0jxKwQ5T8zRDQS9PW/NK0eSqsVJci/nJwlacNsTtj8FXBn+N9syyLlcryTNDFUd8GxsbOSaa67hW9/6FgAbN26kqamJn/u5n8s7rcHBQe6+++7oIkgtLS20tLQUncexsbGknpnr16/P6dx7772Xm2++Ofp+eHiYsbGxpN6fqqTZwM1Zj0qts4T5eC9wiuPHj/OjPXt4V5ajR8n8BHIBQc+IdN4k6AeSbqhYCFgEnBF+n88TU0mSVEnnUVgdZSGF1oluIj54EGs78AWCZZfS2Qd8MsP+TwJXEzwmllSbcinnD5Ac7IwYJ7iC/UuWz8nleiVpZqjqIe0At912G5dcEjyHmZyc5Pbbb+eBBx7gwIEDOZ1/4MABHnjgAdatWxdN44ILLsg5KJnN7t274963tbXlvBhSU1NT0rB6h7Urm9eBQYLlBlK9niY+2Dk74V8IZt1Kd/6zwF1knhdrkmB5hMjx2Z6USpKk09PrBL2qIlLVS76bJY3vk3km8zcTPkNS7cmlnMdeS1Jda75P5k4hpbheSSqfqu7hGdHX18f69et58sknmZycZNu2bWzbto0PfvCDXHrppTQ2NtLU1ERjYyP79+9nbGyM/fv38/zzz/Pkk08CRBc/Ouecc7j77rsBeO2113L6/LPPPjvtvsTFlK666qq8frfFixfHBTlLtTiTatsh0g+wP5zwPjLJdmwA82CW9F/JIQ/Z0pAkSbJeIqlU8rlWRK4xiQsOTXc7SlL5VHXAc3BwkE2bNsVtC4VC0eDlk08+GQ1ophM5NnLexMQEXV1dOeehrq6Ov/3bv027P3ZOUAiG4edj3rx5eR0vnUEwJD2dBcDbgLfC72cT3KRPxRxzcZbPeGfC+8hTzdgKQ7Y0JEmSrJdIKpVcrhX/GP75FMFw1xBT14pStaMSA6OSKqOqh7RPTEywb98+hoeHGR4ejs6/GQqFoj9PTk6mfSUem8s5qdLIpLGxkcWLF0df+c6/mdij0/k7q8Uk8DsEs7j8FvDjsn3yb5N5Yv/ZwEdj3p8k/iYdIshxJlcDzQlpxDYqFgH59WWWJEkz248JaghfIKjjZK8H58J6iaRSyKWc/zbBNSXiFPHXinK0oySVT1X38IzIJfBYyvPycdttmaZYz27Xrl1x75cuXVpUeiqXXuBLMe+/A/yU+Fts6Z1NMBl3NhsJFhJKnLR7Vvj8FVnOPxf4JsE68y8n7LsA+KvwMZIkqRZMAj8PxI5c+s9A7qOiMrFeIqkYuZbzFQSPbD5F8iOblZSnHSWpfKo64NnU1MS1115b0TzkugBRIR555BEmJiai75uamliyxPUlZ7ZJ4DeBbyRsf4lgEMT3mM41Qi8GzszhuLOAvye4KX+PYK6ZiwmeSOZ6k34P8K/A54C94W1Lgc8SVDokSVIt2Ae8n+SZMm8F/gn4nxT7QNd6iaRCNQA/IPdy/glgGfA1gn7r84EPEARBM/XujCjF9UpSeVR1wHPx4sWsXbu20tmYFoODg/T19cVt6+zsrFBulLsQ6dfm+w9gYRnzktls4Pbwq1AXAH9WmuxIkqQZaSHplwX6HqUavWK9RFIhGsn/ocYKigtOluJ6JWn6VXXAs1Y98sgjScHO1tZW2traKpQj5Sdxrb9YrxIMPJckSaoG4xn2nShXJiRJkvJS1YsW1Zp9+/bR2dmZFOxcvHgx9913X4Vypfx8j2CuznTuLVdGJEmSSiBT3eUnTK15LEmSNHPYw3MGmJiY4L777mP37t1J+xYvXszGjRsrkCsV5nNZ9n8F6MbZpCRJ0sy3n2Ahxkw+B/xDGfIiSZKUu5oNeL722mvs3buX4eFhjhw5wsTEBEeOHCEUCvEHf/AHlc5eVG9vL5s3b065b+3ataxatarMOVLh/plgNfZM3gB6gPuZ7hXbJUmSCjdJ0LvzjSzHfZugDvTz054jSZKkXNVcwHNoaIi+vj6Gh4eT9k1OThIKxQeZbrnlFpqamli1ahUf+MAHypVNdu/ezX333Re3CntEa2sra9eupampqWz5UT6OA48DwzGvfcCLOZ7/APAXwGKCFdsXJ7zqs5x/ChiL+dxIHt7M43eQJEm16zWCekkT2WewGie5ThP5eTzHz3s/cCGp6zXNwJn5ZF6SJKloNRPwPHDgAPfffz9DQ0NAENyMCIVCce9jTU5OMjg4yODgIE1NTdx7771ccMH0DTfONnz99ttvZ8mSJXmn+9hjj9Hf31+KLEYdOnQIgP3793PhhRfG7Xvuuec455xzCk57165d3HLLLUnbH3zwQZYvX15wukePHuWyyy5L2r527VrWrl1bcLoAV199Nfv27SOYoP9Xi0oraEA8FX4lmkXQMLgeuAz4FeBhphogLwCvZ/2Eh4F1KbbvAJYWkOOIvaRe1bAHuKmIdAEuvfTSpIcAN954I/feW9zcpzfffHNSmVuyZAlPPPFEUelu3Lgx5ZQTp3f5mNLW1sZDDz1UVLp33HEHjzzySNy2uro6nn/++aLSffjhh1m3LrmE7Nixg6VLCy8he/fuZcWK5BLS09PDTTcVV0IsHwHLxxTLxxTLR2DXrl0xdZWLwlvnEPS+/AWCO/XfAc8xFdw8WPDnxXsx/Er8bmYBF5PuwezV4VzEagOKKx1wB/BIwrY6oLjSYfmIVY3lw/tHoFbuHwDnFpxq4FIgsQvSjRS/8sKLL76Y1Ia2fEyxfEw5He8f5VITAc/9+/dz6623MjExEe3FGRvkjGxLF/SMHPPSSy/R1dXF/fffzyWXXFLyfI6NjdHV1ZX0B9PU1MTatWtpbW0tOO1jx45x8GCpKqzxTp06xUsvvRS3LdN3mYvjx48npRnZXozI/2OiI0eOFJUuwMsvv8yJE+VYjfQUQUDzIWAzcDnwEvDNvFJ5LXxWonx+g1cJmi0/Ac4H3hc+P1W6x9KksS+cTi6VkbGxMY4ePRq37fDhw7lmN62DBw8m/V2ce26x1aPg7yrV39vpWj4S0y7FNenw4cNJ6RZTmYs4duxYyu+i2DJ+4sSJlOkeO5auhOTO8hGwfEyxfEyxfASOHz+e4v/pBPBd4F0EdYq9wO9TvhXWTwE/AoK6SWRG0KUEgc2XSa5XpCsdqeol6boJHE6R7jkE9ZLvA68A7yQIuObD8jGlGsuH949Ardw/GhsbUx6fqpyn+8scA44mbDsE7GTqYUzkepXPhGQnT560fGD5iOX9o/yqPuA5MTERF0SMBDbr6upoaWnh0ksvZfHixdxxxx0pz29paeFb3/pWdKj70aNH6erq4qGHHippT899+/Zx2223JQU7Ozs7ufHGG4tOf+7cucyfP7/odGIdOnSIU6dOMWvWrKSbSeLUAPk688wzWbhwYcrtxQiFQinTnTdvXlHpAixYsIB9+/aVKegJ8FfAdeGf/wI4A/hqTmdOAH+eYvvbCQbk5+KHwK8DozHbzgDuBJK/YZibYtuXgU8R9OnI5fbe1NSUVEYaGhpyODOz+fPnJ/1dLFiwoOh0582bl/Lv7XQtH6+++mrctlJckxoaGpLyXFdXV3S6c+fOTfldzJlT3G1xzpw5KdOdOzdVCcmP5SNg+Zhi+Zhi+QiceeaZzJkzJ0Vd5VcI6hKzgNXA24AO4K2iPi8/IX7MJLfGbOkA5pNcr0hVOtLVS74IcWlGNKRI9wTws8T3NW0O/3tWxrxPsXxMqcby4f0jUCv3j1Ri2x8RzQRdR96T4vgm4nt4TgLfA76WcFxHeFsu39IbwOzZsy0fWD5ief8ov9DOnTuLexRQYevXr+fJJ5+MFvDJyUluvPFG1qxZE3fc1VcHz29DoRA7duyI2zc2Nsb69et54YUXogHT5cuXl2xxo4mJCW688ca4P5TI8PmZPE9nR0cHBw8eZOHChbz4Yq7zU9a2ZcuWsWfPnmn+lDOBvwZ+OWH7KeB3CW7jqV0OPEMwGH5LmmM6gEfJ/IRyP/BzBL0uUnksnE4mjwIfic3b5Zfz7LPPZjlLkiQVI7mu8nHgT0mey/PvCEKIxfXeyc3lBFPyJA9rr1S9JNZsYKn1FKnqLFu2DPbsIVJyM5XzBcC/Apm6NE1SmnbURXhN0elh165dlc5CRtlmMZ/RJiYmosHOSBfu3t7epGBnNk1NTWzcuDFuGPvu3bs5cOBASfKZuDjR4sWL6e3tndHBTlXK24FtJAc7ISiufwZ8MmMKu0l/kwboDx+TyedJ36iAoOmUySRBaFaSJFXSp4D/j9RV/l8BtpJ738ZC/WdgF+lCBDOhXnIyy/mSZr5s5fxl4HNZ0ihVO8prijQzVHXA8+GHHwam5uhcs2YNixcvLiituro61q9fH7eS+9atW4vO49jYWNxk3nV1dWzcuLEkXaJVKdPVMDiboLdFe4ZjQgSrvKeeogGCmbmyyXZM7P7ZKfYfJJjbJp3DlG4JBEmSVIh1wEYy90VaSVD3KH7YW2otBLNu/kzGoypVL0mVlqTqlEs5z+daU+gxuaQhqTyqOuA5PDwc/bmxsZGPfCRdB/bcNDU10draGu0tOjg4WFR6kBw07ezsNNhZ9f4TwTT5pXQO8G1geQ7HhoANBAsOSJIkJZoP/Am5LbFxFUEdpNT10/8L2EGwvJAkSVJ5VXXAc2xsDAjm5WxraytJmpEeopOTk+zfv7/o9LZt2xb3/qqrrio6TVXaLGA78P4SpXduOL1fyOOcEMGgjD9M2rM0h7OzHRO7P9WQjPnAeRnObyD1ggOSJKkc3kF+6wn/IkFdpPgVhAO/AHwHqM/p6ErVSxx2KtWOXMp5PteaQo/JJQ1J5VHVq7Tv378/Ovy8FKt8AXHzaiauRpWviYmJpDS6urqKSrOzs7NkwV0VYx5Bb4gPEEx/XahZBL0fWgs8/7PA68A90S1tBBNq96c5oyN8TCa/T7BGfLr5sr6U5fwQwWyjN2Q5TpIkzRT/haBO8j6ChRIL9V7gH8i1x+hMqJdUdYNIEpC9nF9A0HLKpFTtqN4sx0gqj6q+v9fV1fHaa6+VNM3YAGWxQ8/37k2ewSPSK1W1oI7gWWIxThEMkS/GRXHvQsDXwj8n3qw7wvuy9floJAjn/gYwErP9DOCLZF8JFYIVDg8RLLGUvCarJEmaeS6muGAnBH0tcw92VrpesohgsRNJ1S9TOf8rMq/QDqVrR12cS2YlTbuqDng2NjYyPDxMKBTi6aef5oYbiu9P9vzzz0d/Puecc4pKq9geoqoGz5QgjWeBD5Y0D3XAowSrlkbC7ksJnkjmOsDtPcAgsBP4McEQkSuBJXnk7Fbg6vBLkiTNdM+WII30daOLgTvDP1eyXvIUweImFxPMYFqqSYokVV66cp7rhB2laEdN1xK3kvJT1QHPpUuXMjw8zOTkJENDQ7zwwgtccsklBac3MTHBk08+GR0mX+iK7xH25qx1/w78RwnSeYZSBzwhuCEvJ7dlkNI5F/i1Is6HoCFSqhnBJEnSdCrFg9x/D7/ekbTnbKCYyZ1KVS/JJ0gqqfoUW85L0Y6SVHlVHfBctWoV27ZtIxQKMTk5yd133803vvGNgtPr6+sDggWLQqFQ0QsM3Xjjjdx4441FpaGZrBSNgmLTmSxhPiRJ0umtVHWKZ0kV8JQkSSqXql6lffHixdEenaFQiJdeeomPfexjHDhwIO+0Nm3aFA2eQjB/5wc/WEyvO9W+mRDwPEAwU40kSVKxZkLdRpIkqXhVHfAEWL9+PZOTwVTjoVCIvXv3cvPNN/PAAw/wwgsvZD1/aGiIO+64g82bN0d7ioZCITo7O6c766p6uVTmLwVm55BOodPll2KuLUmSpFxGjcwmqNtkY8BTkiRVVlUPaQdoamrirrvu4u677yYUCkWDltu2bWPbtm1A8lycd999NxMTE+zbty+6sFAk0Alw7bXXcs0115T3F1EVylSZXwb8EXAdsA/4A+CxNMceBvYDTSXOgyRJUq7GgPEM+28A/hBYDDwO/B7pH7xaP5EkSZVV9T08Adra2rj33ns5++yz4wKXk5OTTE5Osm/fvuixk5OTPPnkkwwNDXH06NFo79DIvlWrVvHpT3+67L+Dqk26XhDvAr4B/BD4VYIpr5cSrPU3BPxymvQKbRjYoJAkSaWQrk7xKwR1mM0Ey4CECOo4PySo87wrTVqFjl6RJEkqXk0EPAFaW1t5+OGHueaaa6KBTiDa6zP2FREbGG1sbOSuu+4y2Kkc/RQ4GvO+EfgS8DxwC6mHsf8c8HfAk8AvJuwz4ClJkiopsU7xiwR1lm8R1GESzSao8zxPUAdqjNl3BHhxGvIoSZKUm6of0h6rrq6O2267jdtuu41t27bx9NNPMzQ0FB22nqixsZHFixezatUqWltby5xbVbdIo6AB+H+B3wXm5njuBwgaEP8AfAb4VwoLXJ4iGEoWAm4mGDL/Zs5nnwQeAL4LvAK8E/htYEUeOdgPfB7YG36/FPgscEEeaUiSpJkgUhd5L/DHwC8R1DGyOQP4GPCbwP8H/AnBdD3PABfl/OnWSyQVYj/BMq75lPMdwFeBnwDnE7TOPk32lRciSnG9kjT9airgGevaa6/l2muvBWBiYoKjR49GA591dXU0NjZmOl3K4t+A3wduB84t4PwQwfD2DwP9wNYC0vgJwW3188C7gX5+DLwBnJXlzDcIZhfdHrPtHwkGq30B+EQOn/5Dgty/HLNtB/BXwLeB9+SQhiRJmg5vFXjOo8BqChsENhdYB3QC9xPUlXJjvURSoQ4TPKbJtZz/D+BTxE+68TjwnfC/5WhHSSqPmg14xqqrq6Ourq7S2VBN+Ri59XrIZhbBIgDXF3DuxcDfxG15DVhLMLAsk7XE36QjJgkqAMvI/ITyVeDXiW9URLwM/AYwSGGhYEmSVKy3FXDONyhN3aYe+Bz5zOFpvURSMXIt5ztIDnZGbKd07ShJM0PNzOEplVcpGgTFppf6nK8SDLNI5yTw5zHvZxN/IZgEvpblk58ARhPSiB0CMgLszJKGJEmaSSpTt7FeIqkUcinnXyU+2DmL+GtFOdpRksrHgKdUY94kdQ+HiJeJH+h2kmA20NiLwY+zfMZPEt6fJLlykC0NSZIk6yWSSiWfa8UsgmtN7LWiHO0oSeVjwFOqQedl2NeQ8D7yVDO2H8b8LOmfn0MesqUhSZJkvURSqeRzrYhcYxIXKprudpSk8qmZOTwPHDjA4OAgY2NjjI2NpV2ZPVehUIienp4S5U4qxOvALoJ1A/8J+FBOZ72PzJNtvx24Engq/P5kwr+EPzHbZ5xB+jXhzwh/hiRJmim+A7yfYF3h5QQ1gsqzXiKpFHIp5x8gWJgIUl9rStWOGs6WWUllURMBz82bN7Np06aSpTc5OUkoVOp5jKR8PQD8Xsz7HwPvzHjGLOChHFJ+mOBmPZ5i34cIJtzOZAnwRYKlm1L5YvgYSZI0E/yYYA3ziD8G7qxQXpJZL5FUrFzK+acJHv2kWnSontK1o5zHU5oZqn5I+wMPPMCmTZuYnJyMvqTqdgT4LPHBTghWZf9rMq162gxcksMnXELwZDL2KegZBA2Fx0ke2pHKrcCjxA/bmA88Ft4nSZIqbZKg7vCfErZ/BvgDgjpHOTwHHE+713qJpELNJvdyPpvgmvIxgmtMxPsIrkHlakdJKo+q7uE5PDzM1q1bCYVC0R6ZBj1V/f4d+Hyaff8d+LW0Z56Rdk+yS4DvA28Ah4AF5H+Dvj78OhR+n2nOG0mSVAm/k2b754DfBOaVIQ/bsh5hvURSIZYCHXkcfxbwJeBPCRYhOo/Mw9hTKcX1StL0q+qA59atW6M/R4Kc1157LatWraKxsZG6urpKZU0qwkUZ9p1F/LTYxTsLaCoyDRsUkiTNRCEyN+Uz1TlK6WdzPtJ6iaRymE3x15pSXK8kTZ+qDnju3buXUCgUnXOzp6eH1tbWSmdLKtLXM+x7EdgPNJYpL5IkqXqNAS9l2P91YE0Z8nF/GT5DkiRpSlXP4bl//34gWFH92muvNdipGvAWwUIC6RwH7i1TXiRJUnW7l/Rrl0NQ53hrmvPwT8DANH+GJElSvKoOeE5MTER/XrLEtRdVC/6SYCXVTL5CMOOMJElSOgcI6gyZ/BvwjWnOxx9Oc/qSJEnJqnpIe2NjI/v37ycUCtHY6BBflcuLwHpgccyroYB0jgOjwD5gOPz6mxzOex1YBXwAWBKTB0mSJAgejK4iWFIjmzuBf2GqPrEEaAbOLOBzDzNVpxkGnge+XUA6kiRJxanqgGdTU1N0WPvg4CAtLS0VzpFOD0eAuxO2zSc+ABppMCwiaHREKv6xwc2fAKcKzMNT4ZckSVKiV8KvXPw70JewbRbwTuLrNJGfFwAjxNdpIq+DxWZckiSpJKo64HnttdcyODgIBAHPNWvKMem6lMrB8OufY7Y1A9sJgqMPVSJTTBL0tYCgD2oh67ufJAjZNgBvLyIfpV1bXpIkTZ9TBMPd/42gLhNxC0G95jeAHxWU8qHwv5Wsl7xOUD9aQLBSs6TaU2w5L0U7SlJlVfUcnm1tbVxyySUADA8Ps2nTpgrnSIpYAuwmCHp+HfivZc/BY8A7gPPDr3eEt+XqDeDjwFxgYfjf9wEv5JHGcPic5/I4R5IkzUT/DfifBHWbJylkOp29zIx6SWwaHycIbEiqDenKeS4TfEQU247ymiLNDFUd8ARYv349k5OTTE5OsnnzZoOemgEuA3YBF4bfzwa+CnSVLQdfAW4gfmDZwfC2bMsXQNB74jrgy8Sv7foUcCW5NS5eIKhsOPBekqRqdyvw50z1k7qQ4MHupXmlcjLm55lQL3kznOZPcjhf0syXqZxfR/w1KJ1StKO8pkgzQ1UPaYdgHs+vfOUr3H777UxMTLB582a2bdvG8uXLWbJkCY2NjcybN6+gtCO9R6Xc/SwwQPAcMNYsglvtGcCfTmsO9gGfzLD/k8DVBH1Q03mA+AFsscaBmwmWN8jkpvCxkiSpmn0C+ALJAzobCR7wtgPPFJz6TKiXvJblXEnVIVM5305wJbstw/mlakd5TZFmhqoPeALMmzePc845h9dee43JyUmOHj3Ktm3bik53x44dJcidTh8/R3ArPT/N/hDwRYKg5/3TlovvE9/7IdGbBE89M92ovxvz82yCJ5WRfyOf8QZwVprzX8eenZIkVb/bgR7Sz163ANgJrAT+taBPqES9JFUakqpbLuX8u2QOeJa6HSWpsqp+SPvg4CA333wzBw4cYHJyklAoRCgUVMoiQ90LeUn5uRLYQfpgZ0QIuBf4zLTlJJc1WbOtoRqbRqQ0JDYIDpHe4Qz7JElSNfg9Mgc7I+YDTxDUhQpT7npJ5Fxr/FLtyKWc53OtSacUaUgqj6ru4TkxMcHdd98dDVDGBjpPN4899hj9/f0lTfPQoaDquH//fi688MK4fc899xznnHNOwWnv2rWLW265JWn7gw8+yPLlywtO9+jRo1x22WVJ29euXcvatWsLThfg6quvZt++fSn2vB/4O+DcHFMKAZ8HzgTuKipPU+YRPNd8i3fmcPTFWfa/E/jH8M+nCJ6MhIhvXLwv4ZwegmEkEPT3eBvwVg55iXXppZcyMTERt+3GG2/k3nvvzTOleDfffDO7d++O27ZkyRKeeOKJotLduHEjGzduTNpu+Qi0tbXx0EMPFZXuHXfcwSOPPBK3ra6ujueff76odB9++GHWrVuXtH3Hjh0sXbq04HT37t3LihUrkrb39PRw0003pTgjd5aPgOVjiuVjiuUjsGvXrjR1lXz9IfDZmPdHCeYoT7Q2/GogGOXyy8A/Z0w5MgtobJ3icwSPgyPagNjSka1ecgZB3SPWHcAjpDabIAhyKmNOU7N8TKnG8uH9I1Ar9w+YaoEltj9SlfNUbaBLgUjpeD1hX6rrVa7tqBdffDGpDW35mGL5mHI63j/KpaoDntu2bWNiYiIu0FlXV0dLSwtNTU0Fz91ZjY4dO8bBg9meNxXm1KlTvPTSS3Hbig0qHz9+PCnNyPZiTE5Opkz3yJEjRaUL8PLLL3PixImErQ3APwD53lxCwB8Q3JaL7e1ZT9DI+EUgmFemGRhNc/RFwFVZUvxtYDNTT0RTNQgSv+VjMT/PBj5KMGtpPsbGxjh69GjctsOHi+8vevDgwaS/i3PPzTVAnd6RI0dS/r2druUjMe1SXJMOHz6clG4xlbmIY8eOpfwukst4fk6cOJEy3WPHjqU4Oj+Wj4DlY4rlY4rlI3D8+PGi/5/gHuD/Tdg2SfKdHyC2fJwLfBu4hkyDOhN7Zr6N5B5RiaUjW73kt5kKTEQcTpPjVHnIh+VjSjWWD+8fgVq5fzQ2NkZ/Tmx/JJbzEPBbKdIeI3ikk0piGovIrR31CHDy5EnLB5aPWN4/yq+qA547d+4kFApFh7J/8IMf5Pbbb6eurq7SWSu7uXPnMn/+/JKmeejQIU6dOsWsWbPibiYw1Zu2UGeeeSYLFy5Mub0YoVAoZbqlCH4vWLCAffv2JVyQLiP/YGesT1F8wLMNuCL67lzgm8AHiW+GQNAj4k/J3hd1BcGk3p8iebjX+4EfpzhnbsL7jQQrJaZbZCCVpqampCdIDQ0NeaSQ2vz585P+LhYsSOwLkr958+al/Hs7XcvHq6++GretFNekhoaGpDyX4ho/d+7clN/FnDnF3RbnzJmTMt25cxNLSP4sHwHLxxTLxxTLR+DMM89kzpw5RTSeZhHc/ROFgOT8BiNMYp0DdJHrLHYXEDyIHUvYnlg6MtVLVhIsEpKogfgcTxIsZpLY/JtF8lKTmVg+plRj+fD+EaiV+0eidO2PWQTXiVR9RJuY6uEJQQ/RCZKvFRcAf0Vu7agLgGOzZ1s+sHzE8v5RfqGdO3dW7fjv6667LrpQUVNTEw8++GCls1RTOjo6OHjwIAsXLuTFF1+sdHZmhGXLlrFnz56YLZ8B/qiIFH8AtBSXKS4BhoGzuJzjPBveeoBgiNje8PulBIPTLsgj5R3A1wgCnPOBDxA0NhJ7UaRzkqBy8YfARZdfzrPPPpvlDEmSVIzkukq+fgC8t4jzP0PQSzRZA1O1nkrWS75H0Iv0YoIeX58AsJ4iVZ1ly5bBnj3Eltx05Tz1gPjUim1HLQOvKTot7Nq1q9JZyKiqe3hGhrOHQiFWrVpV6ezotPRMhc8HGCH5GWRwQ/6zIlNeQX6Vg0SzCdZ3/Ysi8yFJksrlGYoLeKav2zQCA0WkXKp6ye1FpCFpZitFOS9FO0pS5VX1Ku11dXXRuSoWL15c4dzo9FRswLIUT/0mgeImUJYkSQoUWzcpxcNcSZKk4lR1wDN2Jav9+/dXMCc6fY0CrxVxfqkaBTYuJElSKRRTp5gAflSqjEiSJBWsqgOeH/zgB6M/P/300xXMiU5vxcyTZcBTkiTNJMXUKYqpE0mSJJVOVQc8V61axdlnnw3Ak08+yQsvvFDhHOn0VGjD4Aip1zuPaAD+BPjfwM0EK6SWOg+SJKn2hQjqEv+boG6RaYXVfwOOFvg51kckSdLMUNUBT4D777+fyclJJicnue222zhw4ECls6TTTqGV+3S9IM4Gfo9guHw38LPAg8C/AteVOA+SJKm2/SrwQ4K6xM8S1C1GCeoac9OcU2hPzXT1kcVU+VqpkiSpylR9wHPJkiXce++9ABw9epSbb76ZTZs2GfjUNEqssBc6uX9io+AM4BMEq65/HqhP2P+fgb8F/glYnrDvpwSLF0mSJAFcBfwz8DcEgc5Y9QR1jVGCuscZCfsLfZCaWCe6ENgU3j67wDQlSZLyV/WPWh999FEgmM/zySefZHJyks2bN7N582YAGhsbqauryzvdUCjEl7/85ZLmVbViMfBR4I+BVyi8URA5bxbwm8BdwMU5nPfzwBPAAPAZIDJ/bf4Bz33A9wl+i3cCVwPn5nH+JLArnA7AUqCNzIPvJUnSdLsY2EH2O/IC4IvAWmA98JfAKYqv28wnqKN8DDgr57Otl0jK12sEZT+fcv4qQWvqJ8D5wPuAJXl+brHXK0nTr+oDnr29vYRCweUt8u/k5FTgZ2xsLK/0QqEQk5OT0bSkZCGChsFHgQeA+4HDZJ4PK5VngdXAHwKXFZCHlUA78NfA7wPDHM4jhS8DnwLejNnWDHwTeE8O508A/w3YkrC9A/gakP9jBkmSVBpnk1/z/2LgL4B1wGcpbPTKIYK5P+8mqGHMy+ts6yWSCvFj4AZyL+c/BH6doH97xBkEj35uzfEzi71eSSqPqg94pmKwUuUxj6BX5u9QWN+BLwOXFJmHEMEt+1eBuewH+gkq95k8Cnw8xfZR4MMEs4VekOH8SVI3Kgh/fuQzLImSJFWTywju7oUsBDqLoCYxP+8zrZdIKkau5Xw/wTXl5YTtbxL0Rz+f0rSjJM0MVR/wPPvssw1wqsLyr9gHig12xppN5Pb+cTLfqCeB382w/2Xgc8CfZThmN6kbFRH94Xwsz3CMJEmaqQqpo9QX9EnWSySVQi7l/PMkBztjlaodJWlmqPqA59atWyudBWlGOUgwqOy8NPsPh49JNBs4Gf55b5bPyLY/cszyHI6TJEmnL+slkkolWzmPvVbEXmMiytGOklQ+Vb9KuyRJkiRJkiRFGPCUps3rMf/mv4J6oeaT/qkkBEsrpRqEH/uEc2mWz8i2P9djJElStZgkvm5TGtZLJJVKPteKxN6dUJ52lKTyMeApTYs3gAsJ5tWcC+ws2yd/Kcv+EJnnwbqAYH3WTNrIPL9NR/gYSZJUK54gqNOEgIsI6jrFs14iqRRyKee/DyzIsL8c7ShJ5WPAUyq5I8AighlgIlYAz0/7JzeSfWVBgOsJ1og/I2H7IuAfyLwSKgQ3+6+l+ayO8D6XEpMkqVY8B7THvH8FWExQ5yme9RJJxci1nDcC3ya4tsQ6g+AaVI52lKTyqfpFi6SZZRJ4LzCWYt9lwA/C+6dHQx7H3gpcDTxFMPn2xcBVwLk5nl8HPEqwmmFkcu6lBE9WbVRIklQrfgC0pNj+IkGdZoRS3Pmtl0gqxMUEZT/Xcv4eYJBg/N2PCYaoXwksyeMzi71eSSoPA55SSWW71b6nLLnI1RLyu7knChGshLi8FJmRJEkz0Hsz7JtFKcOJ1ksk5ets8r8KnQv8WpGfW+z1StL0M+BZQRMTEwwODgLQ2tpKXV1dhXOk4r0JvJph/z8Cv1imvEiSJBXrexn2jRPUfRIHd0qSJFWWc3hW0NatW1m/fj3r16+PBj5V7b5O/NydiT5XroxIkiSVQKa6yyvAX5YrI5IkSTkz4FlB27Ztq3QWVFJvAX+c5ZjvAP+rDHmRJEkq1j8D27Mc88cEdSBJkqSZw4BnhQwODjI2lmphG1WfQwRBzM8C/5bD8euAAYJpsk+VKA9vlDAtSZJ0+jpFUEcZIKizZPMj4A8I6kKZRrlIkiSVj3N4VsjGjRsrnQUV7D+A/wcYDr/yrdx/F1gZ/vksYBGwOOG1BGgkfgruNwkaFcMxr33hf39KsEK8JEnSfuABpuoV7yJ+ns3J8DGRekTs6wXgeJ6f9yfhF8B5JNdpFmM9RZIklZMBzzLbt28f9913n707q9p/AA+WKK03gGfDr0RnAx8i6DXxEYIGyMkSfa4kSapdh4G1Me9nA5cAjwJ3E0yx89o0ffYh4F/CL0mSpMow4DmNJiYm2Lt3L2NjY+zbt4+hoSEDncpDE/BF4CLgjwiCnrnbD3we2Bt+v5Rg0P0FeaSxA/gq8BPgfOADwKcJmk25OEnQv+SnBL+FJEmqhFkEc22+h6BucTXBg9Ty2g+0h3+uZL3kuwTLLb0T+O08Pl/SzJeunK/II41StKMkVZ4Bz2m0detW+vr6Kp0NVaVLgScIhrUD/AbwTWA1wdD2zH4IfBh4OWbbDuCvgG8TNHey+R/Ap4gfgPY4QZ+QxwkG42fyBnAd2Zc6kCRJ0+kMYAuwKvz+ImA3QfP/+TLmYxaHOcWO8LuZUC/5R2AzsIBgIL6k6papnH8B+EQOaZSiHXUIrynSTOCiRdKM825gF1PBzohVwN8CZ2Y8+1Xg14m/SUe8TBA6fTVLDnaQ3KiI2E78ILl01mKwU5KkyjqLoO6wKmF7E0FdY1mZ8vG7wNuSts6EeskkcCCH8yXNfJnK+acg+sAlnVK1o7ymSDODPTynUVNTE21tbSn3DQ8PO7xdKbyX4DY9P83+XwK2ETy7fD3lEU8AozHvI8O8IrN/jgA7gV/LkIuvEt+omEWwfNLJmP1/SvohZCeBP8+QviRJmm5vB7aSfiDnAoIawUqCPk3TZS1wH7AJmBn1ktnh9E7lkHtJ1SFbOZ8Evkbmoe2lakdJmhkMeE6jtra2tAHP3t5eNm/eXOYcqTTmAwenId0rCAZKZBsA0Q78PXANqRYc+EnC+1TLHP04yyfEpjGL5AbBmwRPOZvSnP8y8FaWz5AkSdMlRFBXSF0PnfIzBE38DwNPT0M+7iSYhzwU3TIT6iWRPKRKS1J1yqWc53OtiU0jVr5pSKoch7RLeXsHwcrppfTzwAC5z/bSRhAcPSdpz/k5nJ2u/2iqNCJNlMReE5ly2pBDHiRJ0nS5mOzBzojzCOog/6XEebiLxGBnOuWul0TOzZ4zSdUil3Kez7UmnVKkIak8DHhKBbmbYO2+UvgAQfDy3DzP+wWCBkr8ee8jWJ4gnTOAK3PIUcTJhH8jn5FpcYC35/AZkiRpuszN8/hzCZb/+cUSff4fAevJJaRYiXpJqjQkVbdcynnstSSVUrejJFWWAU+pYL9HMKy8GLOAb5Gqp2Zu3gd8MW7LkqQt8b4YPiaTTxPM6JVKPfBQDjl7OHysJEmqBucAf0fxzYNrgM/kfPRMqJecncP5kma+TOX8QwQLF2VSqnaU1xRpZjDgKRVlf5Hnn6L4+UCT83Ar8CjxQy7mA4+F92UzG3gc+BjxTznfBzwFXJJDGpeEj7WnpyRJ1eI/KH5Wy/TrE8cOQ58J9ZIzwmm+M4fzJc18mcr546Rf2CxWKdpRXlOkmcFFi6SCnQT2lCCdZ4B3FXl+suvDr0Ph97nODhpxFvAlglVPXw6fn2m4WCqXAN8HLs/zPEmSVAmp6xT52UNQR0oOLSwFvhv+uZL1kjcI6kcLwrlclmc6kmaudOU8H8W2o5wfWJoZ7OEpFWyU4FZarGIbF5nPP4/8b9KxZhOseppvoyKWN31JkqpBKQKerwM/Srt3JtRLzgqnkW8QRFL1KEU5L/Z6JamyDHhKBStFo6DYdE4Az5coH5Ik6fQ2E+o2kiRJxTPgKRVsJjQKRoDjJcqHJEk6vc2Euo0kSVLxnMOzRjz22GP09/eXNM1Dh4JZS/bv38+FF14Yt++5557jnHMKXVkcdu3axS233JK0/cEHH2T58uUFp3v06FEuu+yypO1r165l7dq1BacLcPXVV7Nv377wuwuZmtUlnfOBjxOseDqY4bhnwunFqiO3npvpGxQPA+tSbN9BMIdWofYCK1Js7wFuKiJdgEsvvZSJiYm4bTfeeCP33ntvUenefPPN7N69O27bkiVLeOKJJ4pKd+PGjWzcuDFpu+Uj0NbWxkMP5bJ2bnp33HEHjzzySNy2uro6nn++uJ7NDz/8MOvWJZeQHTt2sHRp4SVk7969rFiRXEJ6enq46abiSojlI2D5mGL5mGL5COzatSuhrhLxILA8y9lvkb3u8TaCgaKvkXlxo9wDnlcD+xK2tZHbyuuZ3AE8krAt19pVJpaPKdVYPrx/BGrl/gFwbsGpBi4FJhK23QgUVzrgxRdfTGpDWz6mWD6mnI73j3Ix4Fkjjh07xsGDxa72ndqpU6d46aWX4rZNTk4Wlebx48eT0oxsL8bk5GTKdI8cOVJUugAvv/wyJ06cCL9L/owpdcBtwFpgHnA38E3g90ldzT6VIr1cb1bpGxTH0uTyRIpt+TiRJt1jRaYLMDY2xtGjR+O2HT58uOh0Dx48mPR3ce65xVaPgr+rVH9vp2v5SEy7FNekw4cPJ6VbTGUu4tixYym/i6kyXpgTJ06kTPfYseJLiOUjYPmYYvmYYvkIHD9+PE1dJZfyMUwQ9ExlCUFY8q2EY96W5pzcA54vk1yvKEWN9nCKdIsvHZaPWNVYPrx/BGrl/tHY2FhUugBjwNGEbcWXDjh58qTlA8tHLO8f5WfAs0bMnTuX+fPnlzTNQ4cOcerUKWbNmpV0MwmFiluG5swzz2ThwoUptxcjFAqlTHfevHlFpQuwYMEC9u3bR3A9WkByNfpM4HeAO4HY/4sQ8BvArxL0sLgL+EnCuecBb495X5djrtI3KOYCyd9E8YV+Tpp05xaZLkBTU1PSE6SGhoai050/f37S38WCBQuKTnfevHkp/95O1/Lx6quvxm0rxTWpoaEhKc91dbmWj/Tmzp2b8ruYM6e4EjJnzpyU6c6dW3wJsXwELB9TLB9TLB+BM888kzlz5sTUVaJ7cjj72RTbLiZ4cPurwLtT7O8EXgX+jPig6l7gTeCMrJ+6IJxCrFLUaBtIrq8UXzosH7GqsXx4/wjUyv2jFJpI7uFZfOmA2bNnWz6wfMTy/lF+oZ07dxb3KEAF6e3tZfPmzdH369evp62trYI5StbR0cHBgwdZuHAhL774YqWzMyMsW7aMPXsAHgV+Nrx1NvBbwB+QPDQ9leNAH/B54N/D2+4C1heQo8uY6jUa4nImUzZXMnmd4CnmAgpbxXCSqaegDaRekX0ZwOWX8+yz+eZOkiTlY6quku899y7gD8M/v4NgZEonuQVLfwp8DvgacDK87RnCNYCws7ic41lzVY56SSLrKVJ1WrZsGezZk/fVDoIr1csE14m3Zzk2nXTXK68pOl3s2rWr0lnIyEWLpIJEelZ+BHiOIICZS7ATgobDfydYcOjzBDPPFDK5/3GC4WfvBv4WOIM38zh7GHgfUz1B5xLMOPpGHmk8RtAkOj/8ekd4myRJqqRC+jM8Q1An+SOCOsp/J7dgJ8BFBHWhPQR1o0h6ubNeIqkQe8mvnL9BcG2Jvda8D3ghjzRKcb2SNP0MeEoFqQd+QDAd/uIC06gDfg8YBT5UwPkHga8D/wpcB+GUcrlZv0Bwk34qZtubwJfDKZ1MdVKCrwA3ED/P1sHwtq/kcL4kSZouudzJE32YoCbxGQof/L2EoG70A/JZSsR6iaRCnST3cn6S4JryZYjrKPIUcCXla0dJKg8DnlJBfgl4b4nSOo9gyFi+FgI3EzuA4lR4SzY3AeNp9m0HvpDl/H3AJzPs/yTJK65KkqRyKWQ+sE6COkkpvJegrpQb6yWSipVLOX+A4JqSyjjlaUdJKh8DnlKN+T6Zh1O8TvwTydkJ/wJ8N4fPyDR8/s2Ez5AkSUrFeomkUsilnMdeS1Jda8rRjpJUPgY8pRp0KMO+wwnvI8MuYmf7Okhmr+SQh2xpSJL+/+3de3wkZ33n+69mxh4zLo9vkx2rDTjBtGTwJhC3IQkn0CM7JJscybnakUbk5CTkSBA2CTsjs69cYOSQQzZYmmyyCUE6JyQnIEvghGQtLZub0chJNpvgdsI5YFstBBjsFgPGF7mwPcYzOn883Zru6uququ6q6urqz/v10mumq6uqH5W6fvXUr54LAOolAMISJFZUYoyzC3rU91EA4kPCE0iZC2VmCmzksKQLql7vlQkE56qWXePxGS93vN6r+plUvfYBAABAvQRAWILEinMysaY6VsRxHwUgPiQ8gcR6RNLPyowE8w75nXH1raqv5FfbK+nnql6fVe1Fuq/8qc3cJOkVjn1UPx29VtKQZ0kBAEA8dmTqEv9Z5ir/SEdLU416CYAw+DnP3yoTUyrOqTZWxHEfBSA+rYxoDiByO5K+R9JW1bLvkDTZdKuLZQbj9nJSZoZB56Dde8rb3+yx/aWSPi4zn+tpx3tXSfozBZmbFQAARGtO0geqXv+1pC+r9tY/ao0f3FIvAdAOv+f5zTKPfd6p+oj0ZsVzHwUgPiQ8gcQpSnqD6kekepuk/yHpj9XoBuUaSft9fMJFkv67zEX572XGmrlG5omk34v0ayT9q6T3SlovLxuU9G6ZSgcAAOi0HUk/LenDjuWPyXTO/HtJAzGU48NqNq0Q9RIArbpc0r/I/3n+i5Kul/QhmbbuhyS9USYJ2qx1Z0UY8QpAPEh4dsjk5KQmJ5u31kOvulqNh9//e4XVGmOvpKnyT6uukvT7oZQGAACEr0+N5wz+mkydIw6W5xrUSwC0ol/BH2rcrPaSk2HEKwDRYwxPIHGeavLei3EVAgAApIJzDuJqT8dUBmdHcwAAgGiR8AQS584m731J0j/EVRAAANDV/l5mrM5GmtU5wvKCpN+M4XMAAADOI+EJJMqWzMQCzbw3joIAAICu51Vn+KCkr0Rchj+ReWALAAAQHxKeQGCNZxltf793SnreY72/kvSPEZUBAACkwz/KzMbezPOS3q/o6jYvSHpfRPsGAABojEmLgMAelvRqSdnyz0DV/zPyfo7wlKSNqp9i1f+f8lmGN0h6qeOzm43RBQAAekdRpq7gx29L+iPV12kqP5d5bH9OUkn1dZoNSZtqNjs7AABAVEh4AoHtSHqo/OP0Ekmv1PmbhqOSPlFet3Ij8HhI5Xi0/PPJkPYHAADSIegkh09J+lT5x+mQzidCXyXphyTdpfPJzc9Jeq7VggIAAESChCcQquck/X/ln/9NpiXouqRfU9wzrO9IOiVzOyJJg5LykvoC7ONpmXTqlyRdKen1Mrc8QRTL+7k04HYAACAJHi///LOkJZm6zWOSPh54T9/Q+ZHKO1kv+WdJX5f0ckk3BdweQPK5nedB7kXCuI8C0HkkPIFI/JxMlX6PpJ+QdIGkWyV9M5ZPtyX9jKQ/dSy/VdKHJFk+9vFpST8m6fNVyy6U9DuS3uazHH8g6Z0yndlIeAIA0K0ukKlV3FJ+/UcytYI/DLSXR1Rbh+hkvaTiFeV/L/K5DwDJ1ug8/7ik1/jYPoz7qOdFTAGSgEmLgND9vM4nOyt+WNJfSNof+afvyP0iLUl3S/pZeU9NsCXpB1R7UyGZisPby/vx8lGZI8HIXQAAdLP9kv6rzic7JWmvpHmZWkHrklAv+bxMIhZA92t2nv+ApK94bB/WfRQxBUgGEp5AqN4p6ffkfmr9kKRlRf28b03uF+mKu8vrNPMbkk43ef/nPbbfkfTvPdYBAABJ9xJJK5J+0OW9PZJ+X9IvtfUJSaiXMO0j0P28zvPTkt7rsY+w7qOIKUAykPAEQvMuSSfVfHSXN8tMYnQgslKsh7BO9ft7Xd5/XNITTbZ/UuFNzQQAADrhYpk6y/c1WadPZpb329v6pE7VS9z2BaA7+TnPg8SaVtfxsw8A8SDhCYTi3ZL+k/wNZT0k6a/kbwSYIBhGGwAAhOESmbrKER/r9kn6LZkJGgEAAJKBSYuAtv26TMIziO+V9DeS/p3MnKPt+l8kfUrSCxr0sbbXOoOS7i3/361LxiFJVzTZ/vLyOrTyBACg21wqk+z8rgDb9Ml0Fr1Q0nsCf2Kn6iV0OwW83XLLLdrc3Ox0MVxtbm7q2vL//ZznfmKNlyDxCkBnkfAE2vIrCp7srPhumcvh6yWda6MMr5X0lzKXeCkvM4tgowH8by2v08yvSfozNR4v6wMe2/fJjOr1kx7rAQCAJNkjUzfJtbj9uyU9J+k3fW+RhHoJN0RAY5ubm3rwwQcdSy+S9K1qvcPoOUlflJnPPBxe5/lV8r5rC+s+as5jHQDx4PoOtOVlbW5/jdpLdkqmTcP57vF9kj5U/r/zYn1r+T2vzu/9Mm07flxS9fPcCyX9Tnk/Xm6TGU/rl8RM7QAAdIdzMkmMdvivGyWhXnKtvGddBuD0BrXfjvEmSatt7uNCVZ/Rzc7zP5NJejYT1n3UNR7rAIgHCU+gLZ9pc/vPRlIGS9JHZWYtrQycPSjzRNLvSJ+vkVSQqYY8ItN+9HWSBgKU7G0yVZmbAmwDAAA66bOS3tTG9o3rRtdI+uXy/ztZL/mUTLfXa2RGVn9DgH0AkNq/BwprH/WPKxqd55f63GMY91EX+VwPQLRIeAJtafdCHcaF/qvln1p9MlMNHGljz5dK+pE2tpfMjYjfCgYAAOi0zyiqhOfFkibb2HNY9ZIgSVIAbir3H/+mje2/FkI53Ntnt3ueh3EfBaDzmKUdaMtn1F5HqDASnlI4LUUBAADaqZvstLk9gO7Rzv1HWHGi3aHBAKQZCU+gLV9X4yH0/QjrYs/NBQAACEM7dYqvyIygByD92okVYd27MAIvgMbo0g607bPyHgLbjZ9WEHslZSU97LEeCU+gW91yyy3a3Nz0XrEDrr32Wt1zzz2dLgaA0FwnaUPS2SbrVHqv+B2trho9ToDeEXXC00+8IuEJoDESnkDbPiPp5ha2K0l6qsn7Pynp12USnvdI+lU1vpEg4Ql0q83NTT344OckvbLBGs1aku+Rmbrjiqp1v67GXbyuqlrXy+d8rgcg+a6X9H9KukVSUdJ7JH2swbpPStqSlGnhc6iPAL0jqoRnkHgFAI3RpR1oW6sX+0bb/ZCkByQtyQy33SfphyV9WtKHJX1biGUAkAyvlHmg4fYz7LL+SyT9R5kB/09Leqj8U5kE4F1ynyN0uMnnOH8aJWABdI9vk6k7fFqmLtEnM9/wR2XqGj/YYLsw6zaVeHVhi/sE0HkXuCxrdS6DRr3cWo1XAOCOhCcQ2OWqbRwd1k3B90q6T9J/k/SdLuvvlfQWme7tH5DUX/XetujSAaRVdazYJ+ntMq0v/5PcW2teIem3JG2W1w0jXgHoLvtk6goPy9Qd9rqs852SPiFT9/hex3th1G2c8aqVLvIAkuGVcr//eLSFfX1Z0jNVr/vVXrwCAHckPIHA+nX+gtyn1p9uVm4KXqvzF/A3+tjuQp2/gfgtmQSsApfhrKQZmeen3yvpqKR7A+3BdHh7h6TvK/+8Q2a6AgBhOSfT2rJPJuasy9wU+OlqmlHtDUQ78QpAd8nK1BX8tKp8o0wd5BMydRKptYRnO/GKegmQbH1yv/9oJVZUtrm8vK/PqZ14taXg5/m9MjHme2VizoyajxTqFEa8AhA9Ep5AS67V+S4XN0v6Ugv7+KZMF42CTBeNoC0fDsh0W/28pF+TtKNHJD3vY8vny594u8zooP8gaVHSmyX9rs9P/7TMs9YPyFzg7y3//7Xl9wCE4UsyMeb/lYk5r2hhH2HEKwDdJWidok+mZlCQGVLnhRY+s/V4Rb0E6BbO+48vtrCPL5a3/UJ5XwcCbl8dr/bpSQU7z39XJrYsysSae2Rizw8qvvsoAPEg4Qm05dsl/YWkl7ew7Ycl3ab2T8PLJL1X0l59Q9IxH1sck/Q3Lst3JL1T3k8on5b0Y3KfRuW0pB8vrwOgXdfIxJh/G8K+2olXAHrDHplJEz/cwratxyvqJUC3uUzm/uNtLWz79vK2l7ZZhj2qdH/3e57fKxNT3Pq6/I3iuY8CEB8SnkAoWhmXKpqxrP5QzbtknJX0f1e93qvaQLAj6UMen/FJmee61fuoHm1nU9KqZ0kBeIsiTjCOHgAv8dVrqJcA3Sw590B+zvM/VG2y83zK9Pz7Ud9HAYjPPu9V0A0+9rGP6e677w51n0888YQkaWtrSy996Utr3nvooYd0ySWXtLzvU6dO6S1veUvd8o985CM6cuRIy/t95pln9KpXvapu+bFjx3TsmJ9ndo3ddNNNKhaL5VeV45GXtNDWfk2HiEXHMktm3L3gXpB5ynlKppOI010ynekrKhf1PTKjb0nSI032vy7pZxzL3CoGzfbRyHXXXSfbtmuWjY2N6c4772xhb+eNj49rbW2tZtnAwIA++clPtrXfkydP6uTJk3XLOT+MfD6vhYX2zo/bb79di4u154dlWXr44dbOj4q77rpL73pX/Rly7733anBwsOX9rq+v6+abb65b/v73v19Hjx5teb/GdZJsx7IxSe2dH9K4pDXHsoE298n5UY3zw4jy/OD6YZw6dcqlriJJH5F0pOX9mglG6s8P09apvfPjTWqvXiLVD9ThVi/53yVdpVZrV+dxfpzXjecH1w8jquuHudNwuwO5V2bG9VatywyZ4VR7tgeJFdUxpuIFmcj5FrnXrk7Lf7x69NFH6+6hOT/O68Xzg+tH/Eh4psSzzz6rxx9/PJJ9nzt3To899ljNsp2d9ia9OHPmTN0+K8vbsbOz47rf7e3ttvYrSadPn9aLL75YflX5jDCO+ZNV+6to/WIlmTman3XZqyRd7Hi9V+ZiXf2s9VCTfb8of93Cmu2jkVKppGeeeaZm2ZNPPtnCnmo9/vjjdd+LSy9ttxuN+V65fd969fxw7juMmPTkk0/W7bedylzFs88+63oszp/jrXnxxRdd9/vss8+2tV+jpNpZTSUTP9r1uOqjBedHBefHeUk+P7h+GGfOnHGpq0hSe+eHabfkVqto//xo9FfyWy+RpCt9fM7Tqk9utILz47xuPD+4fhhRXT8a34G0d36Y7d32W5vOCBIrGrUz/Yoax6XLHa8b3Uc9Kens2bOcH+L8qMb1I34kPFPiwIEDOnSolRRTY0888YTOnTunPXv2qL+/v+a9vr72uiLs379fV199tevydvT19bnu9+DBg23tV5IOHz6sYrEoE48Ol5c2O+Z/LekNkv5OplXFSxqsd7kkZ5mtlsv5ekkXyQz/XX8kzJ5fJ+lT5ddnHf9KzeeK3yfz27uNk1VxYfkzgspkMnVPkC6/3Fm1CO7QoUN134vDhw83WNu/gwcPun7fevX8ePrp2lR4GDHp8ssvryuzZbV+flQcOHDA9Vjs29feZXHfvn2u+z1wwGtA/nMyMeP7m6yTUX0Lz/bPDxPHDsmcuWck7Zf3We6N8+M8zo/z27d2fnjj+mHs379f+/btc9RVJHNet6NPJgZVYsQL5X/bPz+ukpmj+ZuO5X7rJZKp+1yo5lMtHZYZdbBdnB/ndeP5wfXDiOr6Ye5A/o3OpwB3JF2g9tMO++R+Z3O+ruLn/uONMhMNSe4twS+QKX2js+Ml8ncftSFp7969nB/i/KjG9SN+faurq+09CkBq3XrrrXr88cd19dVX69FHH+10cRLh+uuv14MPStJnPdZ8RNK3Vr1+n6RfjqhUFRdpj85oXdIrPdb8nMzF+imX975f0idUO56Nmw/KDDnu5g9UO4T59ZL06lfrs5/1Om5A7zFx5cHyq0cU/6RCjeLV9Xr1q8V5C3QZ/3WVoN4n6VerXgeNVxfp1TrjWqq46yVO1FOAxoLFlB2ZxxhfLb++StJWRCWTTDMP0wLR6zyXTGLyB+U+6dBlMonMMO6jvkMipqAnnDp1qtNFaIpJi4BQ7Uj6c9UmDyTpVyS9R2F0/WrmFfK+SKu8zqdU+xT0QpkbhXvkfVMhmQrFR1XbxvWQpI+ptfkaAUhmluM/l/v8oWHzildhdP4E0P22Jb1btclOKcx4Rb0ESIOHZEbA/GrVsq+Ul7U7Nmhje+X/PN8rE1PeLhNjKl4vf8lOKZx4BSAedGkHQveOBsvfK+mnFUb3r0Yu9F5l1ysl/bOk5yU9IdPVK+gF+rbyzxPl11cE3B6Am1+Q9CMxfVazePVKBYsqANLpq5J+o8F74cUr6iVAt1uRGW/c6bHye9dF8qmDkm4NsP5Fkj4g6b/IdIi/orwsiDDiFYDokfAEQtWn5pfMl8VVEN8ukhmVqx3cUABhukiNh9IPk1e8uiCGMgBIvmZ1l/DjFfUSoFt9e4vvdcZetR9rwohXAKJDl3YgVCW5zyBY8f/EVRAAXetRRTveVYVXvHoqhjIASL5mdZe44hWA5Jtt8T0AiAYJTyBUd6r5HKHvU/08pABQ7YxMLImaV7x6PIYyAEi2b8rUXRqJK14BSLb/Ielvm7z/N5L+MaayAIBBwhMIzVdk5ght5ouSPhx9UQB0uQ/KjCwVFT/xioczAP5EZjb2ZqKOVwCS79dDWgcAwsMYnkBLzsl0Bd0o/xQl3SczdLWXX5b0T5Ky5Z8BmfnV97dQjieryvBiC9sDSKbnJI1IeqNMjKjEi6sV/Fll6/Fqc3NT119/fcDPi8e1116re+65p9PFAFLkjKTPy8SISrz4Cx/bhRmvACRf9f3HhswM7H/lY7u/lDQqM3lRturn8hbKUIlXZ1vYFkCvIOEJBLYpyZKp4Lfiq5LmHcv2SHq5apOglf8fLn9m9Q1I5Ycup0B6far8U+0lkq5VbYzIysxRKknrqo0RRZn40Vq82jlzRnrwwZa2jdLnOl0AIPHWJb1BtXFiQCZ+nFZtjKj8/0syD0ha4SdekZgAutdjMjGl3fuPj7osO6TaWBV1vALQK0h4AoGdiWCf52S6u39RZoybirdIukPSj0v6QuC97sg8g5XMs9NW5lE9K1PVuFzm1qUVOy1+NgCn5yR9pvxTsVfSXTJn2rjCTCq8UtJnA5TsSZlHNHtb+Kwg8SqZbU6BJDkrM15e9Zh5r5CpY9whaSGGMrjFK+mJ8r+drJe0G69g3HLLLdrc3Ox0MRqiJ0CYnlZ0Y3A+Xv4JN17FWS8BkEwkPIHE+hlJ/5fMJfo+STfJPNH052OS3qHzz2APSfp9Sbf53P55Scck/aHOT2vyOpm0yit97mNDJv3ykKRX+9wGQBAXyLSW+NGq16OKc/zNynleadt1oaS3Sjop6SKf+2g3XgHwMiDpXkkvlZl1/QJJfxx7KdYlXVn+fyfrJc54xYPZ1mxuburBjQ3pZS9rb0c7O9JXviI916A3wp49UiYjXXih/31++cvtlQkd1F68SkK9hJgCJAMJTyCR3iZzWa2MffVSSWsySc+HPbf+oKS3O5Y9LuknZVpWvM1j+7OSblFtW1PJVBxeV/7X6+bic5JeL+kpz9ICaM2Fkv5M0nDVsh+T9HGZVuHNZmAPh9t5/oKkPyi/99/l3aqi3XgFwMurZJIH/eXXe2XShvslzcVUhgslndPZqvHGk1AvqcSriyVd47E9GnjZy6Q//fPWt3/+eek//JL0hQY9mS67XPrgvDQwEGy/P/Gj3usggdqLV0mol5yV6XBPTAE6j5HEgcT5RUkfUP3p2S/plKR/23TroqRfavL+L5XXaea3VX9TUfGUzFNTL0dFshOIzkWSllWb7KwYlnSP/LdjaF2z8/xvJP1nj+3DiFcAmvl2mbpDv2P5HpkUwC/EUIZKvHJPMyShXvINH9sjApVk5z/9T/f3W012IgKtTK4aVPvxKgn1kt8WMQVIClp4AoH1yXRUiMKUpPercSeIw5JWJb1Z0r+6rvHPat6u6wWZlhDNqo5/V/X/vTJPKiv/Vj7jeTVOpzyn+qkLEFySx8ZiXKxOOiCTPLipyTo/IOm/ycyc/GzLn9Qs0jnPc7dY8XeSjjfZRxjxCkAj3ylzi39lg/f7JP2OTOvL2YjK4B2vOlEvcdsHYkays8t8q8youf8a0f7bj1dJqZf8XZP3AMSLhCcQ2DUyHRvskPf7q5LeK+8RXw5J+qRMQqM+rfh1H5/kNbdi9T4qCQ/nDcETkjINtn+ywXIEszs21sGD0teb/GWvvFK69NJoC/PCC1KpJJ1jRszOsiR9QtIbfax7k0znrf9VrcarZokA53leWbc6SRok1jTSzlywQO+6SKZb6OUe6/VJulOm9db7Qi6D/3gVd73ELV4hRiQ7u9BeNbv/aM/rJP2V2o1XSamX+NkHgHiQ8AQCOyDpryX9O0nbIe3z1yW9O8D6l8s8Bf1BOWdMfLljzUoHsuobA68xZV4u6R/K/z8n05Gkr2ofF8q0NW3ksMzw4vFNm5JiXsnOqXdJR/105mtDsSi9bYJkZ8cdlPSXkr4nwDZvUjvxqlklwXme75W5qaj+lviJNdVaiVcA3HyrvJMHFX2SfkMmiXAipM9vHK+SUC9xi1eICcnOLtb4/qN1b5B5MOL3wX3jeJWUekl1vALQWYzhCbTkeyT9raTLQtjXbypYsrPiUpmnobUtJ26S9Iqq12dVe5G+VtKQx57fqtp2pucc+3irmg/4vVfSz3l8BnxKSrLzKdrtdtZlMq21giQ7K8KMV+c5z/Ozqr2p6JP0sx77CCNeAXATtIrfJ+k9CqeV52VqFq+SUC9xxivEhGRnCrjff7TmTTIPRoL2UnKPV0mpl7zV430A8SHhCbTsdZLybe5jj6R3trH9JZIma5ZcKjNHs1tLh6tk5nT2qlbcLDOot1vn+jfLDMbt5WR5XUSEZGePyUu6sY3tw4hX9Rqd53tkYsjNHtuHEa8AhOmdav/2wH+8SkK9ZE+5HIgYyc4Uqb//aM1keV+temfdkiTUS24WMQVICrq0A235TJvbn5P0sKTXtrGPz9YteY3MkOLvlbReXjYo047U7wX4FyVdL+lDkh6RGTn0jTJVi2atKCoukhk98LdlOuwjRCQ7e1D9eR5cu/GqXvV5/vcy41pdI9OCwuumoiKMeAUgLA+r/baPjePV5ZJuKP+/k/USZ7z6RZ9lQItIdqZQGPWSdvfxcN2SpNRLrvC5HoBokfDskGKxqOXlZT3wwAPa3t6WbdvKZDLq7+/XyMiI8vnwW+IgbM9K+nwI+/mM2kt4uicxrpL0+23sVTIVA7+VAzd7Zead/6M2y4EqSUh2vuQl0X4+XGzKxJwDLW4fVryqVznPp9rYRxjxCkAYwngw0jhe9csMsNGqsOol7cQrBESyM6XCiBXt7sN9e+olACpIeMasVCrp5MmTKhQKru+VSiUVCgVZlqXp6WnlcrkOlBL+PKRw5vds9+lm+K22kFBJSHZ+13dLXz0dbRngYkemJcMNXis2EFa8ApBuYbTaajdeITVIdqZYEhKeYcQrAGlGwjNGxWJRx48fl23bnuvatq2pqSlNTExobGwshtIhuLASje3sx5b0hZDKgURLSrLzt39HegsxqTM+o9YTCDwYAeBHmHUbEp5huOWWW7S5udnpYrja3NyUrr7a/U2SnSkW1v3H5yV9Q9LFLW5P3QZAcyQ8Y2LbtmuyM5fLKZvNSpK2tra0trZW8/78/LwymQxd3BMpCQnPB0MqAxItScnOiy6Kthxoop1YwU0BAD+SULdBtc3NTT34YJfV90h2plyY38cHZSZWbAVxBkBzJDxjMjMzU5PstCxLs7OzGnBc6G3b1vT0dE2X95mZGeVyOVmWFVt54Uezi2yfpKOS/qOkT0j6LUmNJn/5oqRn1NoshVzoU+/KK0l29oRrJX23pLvUuOt5VAnPIPEKQHpty0wH1MjlMnHih2TiRFTxCl0tKcnOp5+m3hIZr/P7NZLeV/7/r0j6tMe+Wkl4esUrAJD2dLoAvaBYLNa13Jybm6tLdkomETozM6NMJrO7zLZtLSwsRF5OBNXoYv/DMhf2j0j6dpmbg89L+lU1nnCk1Sel3FCk3qWXRrt/kp0JsV8mZvyrpFsarBNFwrNZvALQWxrVRS6WiQmfl4kR365o4xW6VlKSnXctSF//erSf0dMand9ZSUuSHpB5MPJD5f8vSnplwH156bJWzwA6ghaeMbjrrrtqXo+OjtYkNN2cOHFCk5OTu69XVlZqXqPTnpL0qGPZkMzTzO92Wf8ySb8h6RfK63xQ0gtV739G0ne1UA4G60YbSHYm0HdI+q+S/lGmVcSpqve+LOlpSUGT4E+ptXh1p2rjFIB0cyYeLpT0NplYdNhl/SjiFdzsu2KfDv+029+gsx773cfOv0hSsnPm/dF+Rs9z3n+8VNIJST8t6QLHe3skjUr6cUl/LOkOSVXfm5bvZXioAsAbCc8YOFt3jo97d08dGBhQNpvVxsaGJNPKs1AoMGt7YlRfnF8nkzi4WaZraDOHJf2OpGOSpiX9iaRzav2iXdnukEwX1LOBti5K+mdJX5f0ckk3KdityY7M7U2x/HpQUl7eRwEJQLIz4b5H0icl/a1MIuH+8vLPSnpDwH21Gq/M+2Gc50/L/DZfknSlpNdLCnrLWx2vWh0EBEAzlTrFHpnExQlJ1/jYLrx4Rb3E3b7L9unwW5KX8Cz9XskMakCys8dU33/8iqS3S/KqL14g6f+Q9FOSPiBTF/m62r8HMtMe7aiz9ZJW4hWA6NGlPWLVY3FKCjQW58jISM3rU6dOhVUstO0zkl4t6eOS/knS9ynYZfYaSX9U3s+Pq7Wnm0/I3PbfIWlT0r5Ao+79gUyntJ+S9E5JPyYzn2qzUXaq2ZJuk7m4v638MyTpJ8vvIcFIdnaJPklvlqlO/5mkV6m1G4PW49U5tX+ef1omtvyYTKz5KZnY80Gf20v18erLAbYFelOwB6DGZyX9hEzM+JD8JTsr2o9X1Eu61M4Oyc6e4rz/+A/yTnZWu0im4cfnZRp/bKu1ccMr8eoCPaLO10uCxisA8aCFZ8SKxWLN6yAtNJ3rrq+vh1ImhOH7Jf2cpL1t7udVkv5U0uda2HaPTGXh0O6SLUl3S7rVY8uPSvp5l+Wfl/QDMqNyXdVk+x1JPyNTcqe7qz6j21tUpBLJzi7UJ1OV/mGZtghBtR6vSpIedlnu9zzfkokppx3LX5BpD3KlWo9XAJpp5Qr8B2o8zl6Qzw0er6iXdLGvfEX6whfc3yPZmUL19x+tOSjTivwdai9emfpqEuol1fEKQDLQwjNiziSl20RFjTjH+ax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      "text/plain": [
       "<Figure size 1356x629.921 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('classic')\n",
    "\n",
    "color_dict = {('edges_per_second', 'g1_v6-ref-ignore-build'):'red',\n",
    "                  ('edges_per_second', 'sortledton.4'):'blue',\n",
    "                  ('edges_per_second', 'llama8-ref'):'limegreen',\n",
    "                  ('edges_per_second', 'teseo-lcc.12'):'mediumorchid',\n",
    "                  ('edges_per_second', 'stinger7-ref'):'black',\n",
    "                  ('edges_per_second', 'livegraph3_ro'):'darkturquoise',\n",
    "                  }\n",
    "\n",
    "scale_y = 1e6\n",
    "ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_y) if x / scale_y == round(x/scale_y) else None)\n",
    "\n",
    "plotting.latexify(columns=2, fig_height=plotting.cm2inch(4))\n",
    "\n",
    "p = data_pivot.plot.bar(width=0.7, linewidth=0.3, color=color_dict)\n",
    "\n",
    "plt.ylabel(\"meps\")\n",
    "plt.xlabel(\"Dataset\")\n",
    "\n",
    "positions = (0, 1, 2, 3)\n",
    "x_labels = (\"wi random\", \"wi ordered\", \"ya random\", \"ya ordered\")\n",
    "plt.xticks(positions, x_labels)\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "p.yaxis.set_major_formatter(ticks_y)\n",
    "plt.yticks()\n",
    "\n",
    "plotting.apply_hatches(['sortledton', 'graphone', 'stinger', 'llama', 'livegraph'], 4, 0, p.patches)\n",
    "\n",
    "legend_labels = [\"Sortledton\", \"GraphOne\", \"Stinger\", \"Llama\", \"Livegraph\"]\n",
    "plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n",
    "       ncol=3, mode=\"expand\", borderaxespad=0., labels=legend_labels).get_frame().set_linewidth(0.0)\n",
    "\n",
    "plotting.format_axes(p, categorical_x=True)\n",
    "p.yaxis.grid(which=\"minor\",b=None)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(plotting.base_path + \"insertions-real-workload.png\", dpi=400)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}