plotting_jmh/python/plotting_ij_clij_radii_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "data": {
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       "<p>228 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Filename  \\\n",
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       "..                                                ...   \n",
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       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
       "\n",
       "                                            Benchmark Mode  Threads  Samples  \\\n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
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       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "..                                                ...  ...      ...      ...   \n",
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       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
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       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "\n",
       "           Score  Score Error (99.9%)   Unit  Param: radius  Param: size  \n",
       "0   5.156150e+07         1.465257e+05  ns/op             10           11  \n",
       "0   6.540087e+07         1.748913e+06  ns/op             10           11  \n",
       "0   6.077356e+07         2.015307e+05  ns/op             12           11  \n",
       "0   5.344673e+07         1.550336e+06  ns/op             12           11  \n",
       "0   6.975714e+07         1.027469e+05  ns/op             14           11  \n",
       "0   5.685088e+07         2.930542e+06  ns/op             14           11  \n",
       "0   7.890142e+07         1.034565e+05  ns/op             16           11  \n",
       "0   5.006100e+07         2.355385e+06  ns/op             16           11  \n",
       "0   8.812778e+07         1.384649e+05  ns/op             18           11  \n",
       "0   5.075102e+07         1.288828e+06  ns/op             18           11  \n",
       "0   1.489889e+07         8.250413e+04  ns/op              2           11  \n",
       "0   5.307933e+07         5.175365e+06  ns/op              2           11  \n",
       "0   9.738208e+07         2.021528e+05  ns/op             20           11  \n",
       "0   4.774114e+07         2.282750e+06  ns/op             20           11  \n",
       "0   1.072598e+08         3.445648e+05  ns/op             22           11  \n",
       "0   4.915389e+07         3.246477e+06  ns/op             22           11  \n",
       "0   1.161671e+08         2.378000e+05  ns/op             24           11  \n",
       "0   4.656734e+07         2.900648e+06  ns/op             24           11  \n",
       "0   1.277179e+08         3.954523e+05  ns/op             26           11  \n",
       "0   4.731835e+07         3.004216e+06  ns/op             26           11  \n",
       "0   1.348678e+08         7.267847e+05  ns/op             28           11  \n",
       "0   4.884338e+07         2.259547e+06  ns/op             28           11  \n",
       "0   1.521739e+08         4.455981e+05  ns/op             30           11  \n",
       "0   4.498564e+07         2.332621e+06  ns/op             30           11  \n",
       "0   2.366875e+07         1.349032e+05  ns/op              4           11  \n",
       "0   5.863501e+07         1.670107e+06  ns/op              4           11  \n",
       "0   3.306796e+07         1.353218e+05  ns/op              6           11  \n",
       "0   7.190767e+07         7.488590e+05  ns/op              6           11  \n",
       "0   4.244309e+07         1.753742e+05  ns/op              8           11  \n",
       "0   8.673512e+07         6.996970e+05  ns/op              8           11  \n",
       "..           ...                  ...    ...            ...          ...  \n",
       "0   7.191452e+06         4.346580e+04  ns/op              7           11  \n",
       "0   6.910091e+07         3.580585e+06  ns/op              7           11  \n",
       "0   8.704720e+06         4.104128e+04  ns/op              8           11  \n",
       "0   8.001683e+07         2.415939e+06  ns/op              8           11  \n",
       "0   1.043844e+07         4.279961e+04  ns/op              9           11  \n",
       "0   8.241872e+07         2.454343e+06  ns/op              9           11  \n",
       "0   7.512526e+06         1.043917e+05  ns/op              1            7  \n",
       "0   3.786292e+08         6.625573e+06  ns/op              1            7  \n",
       "0   1.389446e+09         1.539842e+06  ns/op             10            7  \n",
       "0   8.040349e+10         7.614458e+08  ns/op             10            7  \n",
       "0   1.821127e+09         1.757874e+06  ns/op             11            7  \n",
       "0   1.036271e+11         1.028245e+09  ns/op             11            7  \n",
       "0   2.343645e+09         1.730117e+06  ns/op             12            7  \n",
       "0   1.221406e+11         1.073200e+09  ns/op             12            7  \n",
       "0   2.302897e+07         1.549775e+05  ns/op              2            7  \n",
       "0   9.858032e+08         6.649608e+06  ns/op              2            7  \n",
       "0   5.460141e+07         3.762793e+05  ns/op              3            7  \n",
       "0   2.775411e+09         1.011815e+07  ns/op              3            7  \n",
       "0   1.089852e+08         1.896059e+05  ns/op              4            7  \n",
       "0   5.666679e+09         2.405659e+07  ns/op              4            7  \n",
       "0   1.960778e+08         4.255997e+05  ns/op              5            7  \n",
       "0   1.051779e+10         4.485428e+07  ns/op              5            7  \n",
       "0   3.324433e+08         6.375740e+05  ns/op              6            7  \n",
       "0   1.742598e+10         3.269739e+07  ns/op              6            7  \n",
       "0   4.974485e+08         7.020596e+05  ns/op              7            7  \n",
       "0   2.753612e+10         3.023573e+07  ns/op              7            7  \n",
       "0   7.308070e+08         9.374714e+05  ns/op              8            7  \n",
       "0   4.286251e+10         4.129948e+08  ns/op              8            7  \n",
       "0   1.024753e+09         1.278390e+06  ns/op              9            7  \n",
       "0   5.439770e+10         5.669702e+08  ns/op              9            7  \n",
       "\n",
       "[228 rows x 10 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# source / target dir\n",
    "data_dir = \"../../data/benchmarking-jmh/kernelsize/*\" \n",
    "save_dir = \"../images/kernelsize/\"\n",
    "\n",
    "# activating autoreload\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys\n",
    "from script import create_master_dataframe\n",
    "\n",
    "# creating a cumulative dataframe from all the files\n",
    "mdf = create_master_dataframe(data_dir)\n",
    "\n",
    "# mdf = mdf.sort_values(['Filename', 'Param: radius'], ascending=[1, 1])\n",
    "mdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a sub-dataframe based on conditions\n",
    "\n",
    "from script import cond_and\n",
    "\n",
    "# creating a cumulative dataframe from all the files\n",
    "mdf = create_master_dataframe(data_dir)\n",
    "\n",
    "name = \"clij_ij_comparison\"\n",
    "\n",
    "cond_pc = mdf[\"Filename\"].str.contains(name) # cond computer name\n",
    "cond_list = [cond_pc] # combine them in a list\n",
    "cond = cond_and(cond_list) # generate their intersection\n",
    "\n",
    "# creating a sub-dataframe that satisfies the respective conditions\n",
    "#mdf = mdf[cond]\n",
    "mdf = mdf.drop([\"Param: size\"], axis=1)\n",
    "\n",
    "mdf = mdf.sort_values(['Param: radius', 'Filename'], ascending=[1, 1])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>1.209758e+09</td>\n",
       "      <td>4.747381e+07</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>7.519775e+06</td>\n",
       "      <td>8.552277e+04</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>4.880923e+07</td>\n",
       "      <td>4.940264e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.770126e+08</td>\n",
       "      <td>2.001365e+05</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>3.999245e+09</td>\n",
       "      <td>3.990688e+07</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>5.536881e+06</td>\n",
       "      <td>9.073481e+04</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>5.879001e+07</td>\n",
       "      <td>2.071232e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>4.050919e+07</td>\n",
       "      <td>5.771196e+05</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>4.053622e+09</td>\n",
       "      <td>9.748495e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>2.426535e+06</td>\n",
       "      <td>4.121902e+04</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>5.433479e+07</td>\n",
       "      <td>2.383152e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>2.302897e+07</td>\n",
       "      <td>1.549775e+05</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>9.858032e+08</td>\n",
       "      <td>6.649608e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.241414e+07</td>\n",
       "      <td>6.111005e+04</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>6.037679e+07</td>\n",
       "      <td>4.544095e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>5.261079e+08</td>\n",
       "      <td>1.975277e+07</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.152899e+10</td>\n",
       "      <td>8.216994e+07</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>2.950647e+06</td>\n",
       "      <td>5.114841e+04</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>6.218651e+07</td>\n",
       "      <td>6.577452e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.984919e+09</td>\n",
       "      <td>3.148889e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.313101e+09</td>\n",
       "      <td>1.071698e+07</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.400526e+07</td>\n",
       "      <td>1.170081e+05</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>6.025946e+07</td>\n",
       "      <td>2.168449e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.664182e+08</td>\n",
       "      <td>6.699812e+05</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>4.342283e+09</td>\n",
       "      <td>1.410435e+07</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.277179e+08</td>\n",
       "      <td>3.954523e+05</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>4.731835e+07</td>\n",
       "      <td>3.004216e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>2.175170e+09</td>\n",
       "      <td>6.304698e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.328854e+09</td>\n",
       "      <td>8.032779e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.515629e+07</td>\n",
       "      <td>8.123831e+04</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>6.098710e+07</td>\n",
       "      <td>2.057739e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.788393e+08</td>\n",
       "      <td>6.015367e+05</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>4.100514e+09</td>\n",
       "      <td>1.780705e+07</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.348678e+08</td>\n",
       "      <td>7.267847e+05</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>4.884338e+07</td>\n",
       "      <td>2.259547e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>2.357443e+09</td>\n",
       "      <td>6.358486e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.288134e+09</td>\n",
       "      <td>7.783845e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.579197e+07</td>\n",
       "      <td>1.022070e+05</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>5.945867e+07</td>\n",
       "      <td>1.903072e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.909429e+08</td>\n",
       "      <td>6.821204e+05</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>4.303480e+09</td>\n",
       "      <td>1.197796e+07</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.521739e+08</td>\n",
       "      <td>4.455981e+05</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>4.498564e+07</td>\n",
       "      <td>2.332621e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>2.537331e+09</td>\n",
       "      <td>1.921188e+07</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.360594e+09</td>\n",
       "      <td>1.251117e+07</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.668367e+07</td>\n",
       "      <td>1.108890e+05</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>6.046139e+07</td>\n",
       "      <td>1.935665e+06</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>2.024459e+08</td>\n",
       "      <td>6.604433e+05</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...</td>\n",
       "      <td>net.haesleinhuepf.clij.benchmark.jmh.GaussianB...</td>\n",
       "      <td>ss</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>4.331530e+09</td>\n",
       "      <td>1.407257e+07</td>\n",
       "      <td>ns/op</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>228 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Filename  \\\n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
       "..                                                ...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "0   kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
       "\n",
       "                                            Benchmark Mode  Threads  Samples  \\\n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
       "..                                                ...  ...      ...      ...   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "0   net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
       "\n",
       "           Score  Score Error (99.9%)   Unit  Param: radius  \n",
       "0   4.522321e+06         4.855797e+04  ns/op              1  \n",
       "0   3.241969e+07         4.004848e+06  ns/op              1  \n",
       "0   5.272933e+07         1.611809e+05  ns/op              1  \n",
       "0   1.131476e+09         1.322050e+07  ns/op              1  \n",
       "0   2.058476e+06         4.407013e+04  ns/op              1  \n",
       "0   5.112204e+07         3.390388e+06  ns/op              1  \n",
       "0   7.512526e+06         1.043917e+05  ns/op              1  \n",
       "0   3.786292e+08         6.625573e+06  ns/op              1  \n",
       "0   1.489889e+07         8.250413e+04  ns/op              2  \n",
       "0   5.307933e+07         5.175365e+06  ns/op              2  \n",
       "0   2.336786e+08         1.676508e+06  ns/op              2  \n",
       "0   1.209758e+09         4.747381e+07  ns/op              2  \n",
       "0   7.519775e+06         8.552277e+04  ns/op              2  \n",
       "0   4.880923e+07         4.940264e+06  ns/op              2  \n",
       "0   1.770126e+08         2.001365e+05  ns/op              2  \n",
       "0   3.999245e+09         3.990688e+07  ns/op              2  \n",
       "0   5.536881e+06         9.073481e+04  ns/op              2  \n",
       "0   5.879001e+07         2.071232e+06  ns/op              2  \n",
       "0   4.050919e+07         5.771196e+05  ns/op              2  \n",
       "0   4.053622e+09         9.748495e+06  ns/op              2  \n",
       "0   2.426535e+06         4.121902e+04  ns/op              2  \n",
       "0   5.433479e+07         2.383152e+06  ns/op              2  \n",
       "0   2.302897e+07         1.549775e+05  ns/op              2  \n",
       "0   9.858032e+08         6.649608e+06  ns/op              2  \n",
       "0   1.241414e+07         6.111005e+04  ns/op              3  \n",
       "0   6.037679e+07         4.544095e+06  ns/op              3  \n",
       "0   5.261079e+08         1.975277e+07  ns/op              3  \n",
       "0   1.152899e+10         8.216994e+07  ns/op              3  \n",
       "0   2.950647e+06         5.114841e+04  ns/op              3  \n",
       "0   6.218651e+07         6.577452e+06  ns/op              3  \n",
       "..           ...                  ...    ...            ...  \n",
       "0   1.984919e+09         3.148889e+06  ns/op             24  \n",
       "0   1.313101e+09         1.071698e+07  ns/op             24  \n",
       "0   1.400526e+07         1.170081e+05  ns/op             24  \n",
       "0   6.025946e+07         2.168449e+06  ns/op             24  \n",
       "0   1.664182e+08         6.699812e+05  ns/op             24  \n",
       "0   4.342283e+09         1.410435e+07  ns/op             24  \n",
       "0   1.277179e+08         3.954523e+05  ns/op             26  \n",
       "0   4.731835e+07         3.004216e+06  ns/op             26  \n",
       "0   2.175170e+09         6.304698e+06  ns/op             26  \n",
       "0   1.328854e+09         8.032779e+06  ns/op             26  \n",
       "0   1.515629e+07         8.123831e+04  ns/op             26  \n",
       "0   6.098710e+07         2.057739e+06  ns/op             26  \n",
       "0   1.788393e+08         6.015367e+05  ns/op             26  \n",
       "0   4.100514e+09         1.780705e+07  ns/op             26  \n",
       "0   1.348678e+08         7.267847e+05  ns/op             28  \n",
       "0   4.884338e+07         2.259547e+06  ns/op             28  \n",
       "0   2.357443e+09         6.358486e+06  ns/op             28  \n",
       "0   1.288134e+09         7.783845e+06  ns/op             28  \n",
       "0   1.579197e+07         1.022070e+05  ns/op             28  \n",
       "0   5.945867e+07         1.903072e+06  ns/op             28  \n",
       "0   1.909429e+08         6.821204e+05  ns/op             28  \n",
       "0   4.303480e+09         1.197796e+07  ns/op             28  \n",
       "0   1.521739e+08         4.455981e+05  ns/op             30  \n",
       "0   4.498564e+07         2.332621e+06  ns/op             30  \n",
       "0   2.537331e+09         1.921188e+07  ns/op             30  \n",
       "0   1.360594e+09         1.251117e+07  ns/op             30  \n",
       "0   1.668367e+07         1.108890e+05  ns/op             30  \n",
       "0   6.046139e+07         1.935665e+06  ns/op             30  \n",
       "0   2.024459e+08         6.604433e+05  ns/op             30  \n",
       "0   4.331530e+09         1.407257e+07  ns/op             30  \n",
       "\n",
       "[228 rows x 9 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- processing a new frame -----\n",
      "----- detected operations:\n",
      "Minimum2D\n",
      "Minimum3D\n",
      "GaussianBlur2D\n",
      "GaussianBlur3D\n",
      "----- detected machines:\n",
      "MYERS-PC-21\n",
      "MYERS-PC-22\n",
      "----- detected benchmarks:\n",
      "clij\n",
      "ijrun\n",
      "----- detected params:\n",
      "Param: radius\n",
      "----- parameter Param: radius has the following values:\n",
      "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 18 20 22 24 26 28 30]\n",
      "----- processing operation: Minimum2D -----\n",
      "------> processing param Param: radius...\n",
      "-----> other param values ()\n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "\n",
      "          Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0  4.522321e+06         4.855797e+04  ns/op              1  \n",
      "0  3.241969e+07         4.004848e+06  ns/op              1  \n",
      "0  7.519775e+06         8.552277e+04  ns/op              2  \n",
      "0  4.880923e+07         4.940264e+06  ns/op              2  \n",
      "0  1.241414e+07         6.111005e+04  ns/op              3  \n",
      "0  6.037679e+07         4.544095e+06  ns/op              3  \n",
      "0  1.891985e+07         1.718284e+05  ns/op              4  \n",
      "0  6.796848e+07         3.628285e+06  ns/op              4  \n",
      "0  2.761269e+07         1.172400e+05  ns/op              5  \n",
      "0  7.802098e+07         3.631851e+06  ns/op              5  \n",
      "0  3.726205e+07         9.664554e+04  ns/op              6  \n",
      "0  8.683488e+07         4.121157e+06  ns/op              6  \n",
      "0  4.833610e+07         1.214236e+05  ns/op              7  \n",
      "0  1.007904e+08         4.675880e+06  ns/op              7  \n",
      "0  6.168165e+07         1.345048e+05  ns/op              8  \n",
      "0  1.068080e+08         4.077304e+06  ns/op              8  \n",
      "0  7.718390e+07         1.169017e+05  ns/op              9  \n",
      "0  1.168357e+08         3.744315e+06  ns/op              9  \n",
      "0  9.552774e+07         1.875779e+05  ns/op             10  \n",
      "0  1.235263e+08         4.161401e+06  ns/op             10  \n",
      "0  1.157670e+08         4.806787e+05  ns/op             11  \n",
      "0  1.322315e+08         5.439748e+06  ns/op             11  \n",
      "0  1.356636e+08         3.848377e+05  ns/op             12  \n",
      "0  1.418015e+08         4.993288e+06  ns/op             12  \n",
      "0  1.637318e+08         6.492769e+05  ns/op             13  \n",
      "0  1.617020e+08         3.813555e+06  ns/op             13  \n",
      "0  1.935413e+08         6.729157e+05  ns/op             14  \n",
      "0  1.598987e+08         3.420342e+06  ns/op             14  \n",
      "0  2.271414e+08         8.197795e+05  ns/op             15  \n",
      "0  1.589232e+08         3.761610e+06  ns/op             15  \n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum2D_r...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "\n",
      "          Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0  4.522321e+06         4.855797e+04  ns/op              1  \n",
      "0  3.241969e+07         4.004848e+06  ns/op              1  \n",
      "0  7.519775e+06         8.552277e+04  ns/op              2  \n",
      "0  4.880923e+07         4.940264e+06  ns/op              2  \n",
      "0  1.241414e+07         6.111005e+04  ns/op              3  \n",
      "0  6.037679e+07         4.544095e+06  ns/op              3  \n",
      "0  1.891985e+07         1.718284e+05  ns/op              4  \n",
      "0  6.796848e+07         3.628285e+06  ns/op              4  \n",
      "0  2.761269e+07         1.172400e+05  ns/op              5  \n",
      "0  7.802098e+07         3.631851e+06  ns/op              5  \n",
      "0  3.726205e+07         9.664554e+04  ns/op              6  \n",
      "0  8.683488e+07         4.121157e+06  ns/op              6  \n",
      "0  4.833610e+07         1.214236e+05  ns/op              7  \n",
      "0  1.007904e+08         4.675880e+06  ns/op              7  \n",
      "0  6.168165e+07         1.345048e+05  ns/op              8  \n",
      "0  1.068080e+08         4.077304e+06  ns/op              8  \n",
      "0  7.718390e+07         1.169017e+05  ns/op              9  \n",
      "0  1.168357e+08         3.744315e+06  ns/op              9  \n",
      "0  9.552774e+07         1.875779e+05  ns/op             10  \n",
      "0  1.235263e+08         4.161401e+06  ns/op             10  \n",
      "0  1.157670e+08         4.806787e+05  ns/op             11  \n",
      "0  1.322315e+08         5.439748e+06  ns/op             11  \n",
      "0  1.356636e+08         3.848377e+05  ns/op             12  \n",
      "0  1.418015e+08         4.993288e+06  ns/op             12  \n",
      "0  1.637318e+08         6.492769e+05  ns/op             13  \n",
      "0  1.617020e+08         3.813555e+06  ns/op             13  \n",
      "0  1.935413e+08         6.729157e+05  ns/op             14  \n",
      "0  1.598987e+08         3.420342e+06  ns/op             14  \n",
      "0  2.271414e+08         8.197795e+05  ns/op             15  \n",
      "0  1.589232e+08         3.761610e+06  ns/op             15  \n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "\n",
      "          Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0  2.058476e+06         4.407013e+04  ns/op              1  \n",
      "0  5.112204e+07         3.390388e+06  ns/op              1  \n",
      "0  2.426535e+06         4.121902e+04  ns/op              2  \n",
      "0  5.433479e+07         2.383152e+06  ns/op              2  \n",
      "0  2.950647e+06         5.114841e+04  ns/op              3  \n",
      "0  6.218651e+07         6.577452e+06  ns/op              3  \n",
      "0  3.717587e+06         6.227578e+04  ns/op              4  \n",
      "0  6.150815e+07         4.897647e+06  ns/op              4  \n",
      "0  4.733440e+06         6.120642e+04  ns/op              5  \n",
      "0  6.623746e+07         4.532118e+06  ns/op              5  \n",
      "0  5.871269e+06         5.959652e+04  ns/op              6  \n",
      "0  6.759584e+07         5.429500e+06  ns/op              6  \n",
      "0  7.191452e+06         4.346580e+04  ns/op              7  \n",
      "0  6.910091e+07         3.580585e+06  ns/op              7  \n",
      "0  8.704720e+06         4.104128e+04  ns/op              8  \n",
      "0  8.001683e+07         2.415939e+06  ns/op              8  \n",
      "0  1.043844e+07         4.279961e+04  ns/op              9  \n",
      "0  8.241872e+07         2.454343e+06  ns/op              9  \n",
      "0  1.258776e+07         4.790522e+04  ns/op             10  \n",
      "0  8.681736e+07         1.759562e+06  ns/op             10  \n",
      "0  1.463303e+07         5.481608e+04  ns/op             11  \n",
      "0  8.573723e+07         6.508944e+06  ns/op             11  \n",
      "0  1.683710e+07         5.885983e+04  ns/op             12  \n",
      "0  9.209666e+07         1.899117e+06  ns/op             12  \n",
      "0  1.954415e+07         5.981092e+04  ns/op             13  \n",
      "0  8.512694e+07         2.502839e+06  ns/op             13  \n",
      "0  2.237718e+07         1.221072e+05  ns/op             14  \n",
      "0  9.728320e+07         4.131874e+06  ns/op             14  \n",
      "0  2.539698e+07         6.408213e+04  ns/op             15  \n",
      "0  1.023369e+08         3.218125e+06  ns/op             15  \n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum2D_r...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum2D...   ss        1      100   \n",
      "\n",
      "          Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0  2.058476e+06         4.407013e+04  ns/op              1  \n",
      "0  5.112204e+07         3.390388e+06  ns/op              1  \n",
      "0  2.426535e+06         4.121902e+04  ns/op              2  \n",
      "0  5.433479e+07         2.383152e+06  ns/op              2  \n",
      "0  2.950647e+06         5.114841e+04  ns/op              3  \n",
      "0  6.218651e+07         6.577452e+06  ns/op              3  \n",
      "0  3.717587e+06         6.227578e+04  ns/op              4  \n",
      "0  6.150815e+07         4.897647e+06  ns/op              4  \n",
      "0  4.733440e+06         6.120642e+04  ns/op              5  \n",
      "0  6.623746e+07         4.532118e+06  ns/op              5  \n",
      "0  5.871269e+06         5.959652e+04  ns/op              6  \n",
      "0  6.759584e+07         5.429500e+06  ns/op              6  \n",
      "0  7.191452e+06         4.346580e+04  ns/op              7  \n",
      "0  6.910091e+07         3.580585e+06  ns/op              7  \n",
      "0  8.704720e+06         4.104128e+04  ns/op              8  \n",
      "0  8.001683e+07         2.415939e+06  ns/op              8  \n",
      "0  1.043844e+07         4.279961e+04  ns/op              9  \n",
      "0  8.241872e+07         2.454343e+06  ns/op              9  \n",
      "0  1.258776e+07         4.790522e+04  ns/op             10  \n",
      "0  8.681736e+07         1.759562e+06  ns/op             10  \n",
      "0  1.463303e+07         5.481608e+04  ns/op             11  \n",
      "0  8.573723e+07         6.508944e+06  ns/op             11  \n",
      "0  1.683710e+07         5.885983e+04  ns/op             12  \n",
      "0  9.209666e+07         1.899117e+06  ns/op             12  \n",
      "0  1.954415e+07         5.981092e+04  ns/op             13  \n",
      "0  8.512694e+07         2.502839e+06  ns/op             13  \n",
      "0  2.237718e+07         1.221072e+05  ns/op             14  \n",
      "0  9.728320e+07         4.131874e+06  ns/op             14  \n",
      "0  2.539698e+07         6.408213e+04  ns/op             15  \n",
      "0  1.023369e+08         3.218125e+06  ns/op             15  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- processing operation: Minimum3D -----\n",
      "------> processing param Param: radius...\n",
      "-----> other param values ()\n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "\n",
      "          Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0  5.272933e+07         1.611809e+05  ns/op              1  \n",
      "0  1.131476e+09         1.322050e+07  ns/op              1  \n",
      "0  1.770126e+08         2.001365e+05  ns/op              2  \n",
      "0  3.999245e+09         3.990688e+07  ns/op              2  \n",
      "0  5.261079e+08         1.975277e+07  ns/op              3  \n",
      "0  1.152899e+10         8.216994e+07  ns/op              3  \n",
      "0  1.050380e+09         1.467726e+06  ns/op              4  \n",
      "0  2.264527e+10         2.011742e+08  ns/op              4  \n",
      "0  1.981834e+09         4.101093e+06  ns/op              5  \n",
      "0  3.826708e+10         3.697927e+08  ns/op              5  \n",
      "0  3.356271e+09         4.498377e+06  ns/op              6  \n",
      "0  6.531598e+10         4.621663e+08  ns/op              6  \n",
      "0  5.123903e+09         4.299180e+06  ns/op              7  \n",
      "0  9.990482e+10         5.366115e+08  ns/op              7  \n",
      "0  7.532945e+09         1.573083e+07  ns/op              8  \n",
      "0  1.470287e+11         6.603022e+08  ns/op              8  \n",
      "0  1.088772e+10         3.644917e+07  ns/op              9  \n",
      "0  2.086267e+11         6.245256e+08  ns/op              9  \n",
      "0  1.485262e+10         3.296501e+07  ns/op             10  \n",
      "0  2.750507e+11         7.449734e+08  ns/op             10  \n",
      "0  1.988336e+10         2.517928e+07  ns/op             11  \n",
      "0  3.617747e+11         7.948510e+08  ns/op             11  \n",
      "0  2.591520e+10         3.885873e+07  ns/op             12  \n",
      "0  4.577287e+11         1.124546e+09  ns/op             12  \n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.Minimum3D_r...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "\n",
      "          Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0  5.272933e+07         1.611809e+05  ns/op              1  \n",
      "0  1.131476e+09         1.322050e+07  ns/op              1  \n",
      "0  1.770126e+08         2.001365e+05  ns/op              2  \n",
      "0  3.999245e+09         3.990688e+07  ns/op              2  \n",
      "0  5.261079e+08         1.975277e+07  ns/op              3  \n",
      "0  1.152899e+10         8.216994e+07  ns/op              3  \n",
      "0  1.050380e+09         1.467726e+06  ns/op              4  \n",
      "0  2.264527e+10         2.011742e+08  ns/op              4  \n",
      "0  1.981834e+09         4.101093e+06  ns/op              5  \n",
      "0  3.826708e+10         3.697927e+08  ns/op              5  \n",
      "0  3.356271e+09         4.498377e+06  ns/op              6  \n",
      "0  6.531598e+10         4.621663e+08  ns/op              6  \n",
      "0  5.123903e+09         4.299180e+06  ns/op              7  \n",
      "0  9.990482e+10         5.366115e+08  ns/op              7  \n",
      "0  7.532945e+09         1.573083e+07  ns/op              8  \n",
      "0  1.470287e+11         6.603022e+08  ns/op              8  \n",
      "0  1.088772e+10         3.644917e+07  ns/op              9  \n",
      "0  2.086267e+11         6.245256e+08  ns/op              9  \n",
      "0  1.485262e+10         3.296501e+07  ns/op             10  \n",
      "0  2.750507e+11         7.449734e+08  ns/op             10  \n",
      "0  1.988336e+10         2.517928e+07  ns/op             11  \n",
      "0  3.617747e+11         7.948510e+08  ns/op             11  \n",
      "0  2.591520e+10         3.885873e+07  ns/op             12  \n",
      "0  4.577287e+11         1.124546e+09  ns/op             12  \n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "\n",
      "          Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0  7.512526e+06         1.043917e+05  ns/op              1  \n",
      "0  3.786292e+08         6.625573e+06  ns/op              1  \n",
      "0  2.302897e+07         1.549775e+05  ns/op              2  \n",
      "0  9.858032e+08         6.649608e+06  ns/op              2  \n",
      "0  5.460141e+07         3.762793e+05  ns/op              3  \n",
      "0  2.775411e+09         1.011815e+07  ns/op              3  \n",
      "0  1.089852e+08         1.896059e+05  ns/op              4  \n",
      "0  5.666679e+09         2.405659e+07  ns/op              4  \n",
      "0  1.960778e+08         4.255997e+05  ns/op              5  \n",
      "0  1.051779e+10         4.485428e+07  ns/op              5  \n",
      "0  3.324433e+08         6.375740e+05  ns/op              6  \n",
      "0  1.742598e+10         3.269739e+07  ns/op              6  \n",
      "0  4.974485e+08         7.020596e+05  ns/op              7  \n",
      "0  2.753612e+10         3.023573e+07  ns/op              7  \n",
      "0  7.308070e+08         9.374714e+05  ns/op              8  \n",
      "0  4.286251e+10         4.129948e+08  ns/op              8  \n",
      "0  1.024753e+09         1.278390e+06  ns/op              9  \n",
      "0  5.439770e+10         5.669702e+08  ns/op              9  \n",
      "0  1.389446e+09         1.539842e+06  ns/op             10  \n",
      "0  8.040349e+10         7.614458e+08  ns/op             10  \n",
      "0  1.821127e+09         1.757874e+06  ns/op             11  \n",
      "0  1.036271e+11         1.028245e+09  ns/op             11  \n",
      "0  2.343645e+09         1.730117e+06  ns/op             12  \n",
      "0  1.221406e+11         1.073200e+09  ns/op             12  \n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.Minimum3D_r...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.Minimum3D...   ss        1      100   \n",
      "\n",
      "          Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0  7.512526e+06         1.043917e+05  ns/op              1  \n",
      "0  3.786292e+08         6.625573e+06  ns/op              1  \n",
      "0  2.302897e+07         1.549775e+05  ns/op              2  \n",
      "0  9.858032e+08         6.649608e+06  ns/op              2  \n",
      "0  5.460141e+07         3.762793e+05  ns/op              3  \n",
      "0  2.775411e+09         1.011815e+07  ns/op              3  \n",
      "0  1.089852e+08         1.896059e+05  ns/op              4  \n",
      "0  5.666679e+09         2.405659e+07  ns/op              4  \n",
      "0  1.960778e+08         4.255997e+05  ns/op              5  \n",
      "0  1.051779e+10         4.485428e+07  ns/op              5  \n",
      "0  3.324433e+08         6.375740e+05  ns/op              6  \n",
      "0  1.742598e+10         3.269739e+07  ns/op              6  \n",
      "0  4.974485e+08         7.020596e+05  ns/op              7  \n",
      "0  2.753612e+10         3.023573e+07  ns/op              7  \n",
      "0  7.308070e+08         9.374714e+05  ns/op              8  \n",
      "0  4.286251e+10         4.129948e+08  ns/op              8  \n",
      "0  1.024753e+09         1.278390e+06  ns/op              9  \n",
      "0  5.439770e+10         5.669702e+08  ns/op              9  \n",
      "0  1.389446e+09         1.539842e+06  ns/op             10  \n",
      "0  8.040349e+10         7.614458e+08  ns/op             10  \n",
      "0  1.821127e+09         1.757874e+06  ns/op             11  \n",
      "0  1.036271e+11         1.028245e+09  ns/op             11  \n",
      "0  2.343645e+09         1.730117e+06  ns/op             12  \n",
      "0  1.221406e+11         1.073200e+09  ns/op             12  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- processing operation: GaussianBlur2D -----\n",
      "------> processing param Param: radius...\n",
      "-----> other param values ()\n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "\n",
      "         Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0   14898886.0         8.250413e+04  ns/op              2  \n",
      "0   53079329.0         5.175365e+06  ns/op              2  \n",
      "0   23668746.0         1.349032e+05  ns/op              4  \n",
      "0   58635012.0         1.670107e+06  ns/op              4  \n",
      "0   33067957.0         1.353218e+05  ns/op              6  \n",
      "0   71907667.0         7.488590e+05  ns/op              6  \n",
      "0   42443087.0         1.753742e+05  ns/op              8  \n",
      "0   86735117.0         6.996970e+05  ns/op              8  \n",
      "0   51561502.0         1.465257e+05  ns/op             10  \n",
      "0   65400871.0         1.748913e+06  ns/op             10  \n",
      "0   60773559.0         2.015307e+05  ns/op             12  \n",
      "0   53446734.0         1.550336e+06  ns/op             12  \n",
      "0   69757139.0         1.027469e+05  ns/op             14  \n",
      "0   56850876.0         2.930542e+06  ns/op             14  \n",
      "0   78901419.0         1.034565e+05  ns/op             16  \n",
      "0   50060996.0         2.355385e+06  ns/op             16  \n",
      "0   88127777.0         1.384649e+05  ns/op             18  \n",
      "0   50751022.0         1.288828e+06  ns/op             18  \n",
      "0   97382076.0         2.021528e+05  ns/op             20  \n",
      "0   47741141.0         2.282750e+06  ns/op             20  \n",
      "0  107259806.0         3.445648e+05  ns/op             22  \n",
      "0   49153893.0         3.246477e+06  ns/op             22  \n",
      "0  116167074.0         2.378000e+05  ns/op             24  \n",
      "0   46567345.0         2.900648e+06  ns/op             24  \n",
      "0  127717918.0         3.954523e+05  ns/op             26  \n",
      "0   47318349.0         3.004216e+06  ns/op             26  \n",
      "0  134867779.0         7.267847e+05  ns/op             28  \n",
      "0   48843376.0         2.259547e+06  ns/op             28  \n",
      "0  152173926.0         4.455981e+05  ns/op             30  \n",
      "0   44985640.0         2.332621e+06  ns/op             30  \n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "\n",
      "         Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0   14898886.0         8.250413e+04  ns/op              2  \n",
      "0   53079329.0         5.175365e+06  ns/op              2  \n",
      "0   23668746.0         1.349032e+05  ns/op              4  \n",
      "0   58635012.0         1.670107e+06  ns/op              4  \n",
      "0   33067957.0         1.353218e+05  ns/op              6  \n",
      "0   71907667.0         7.488590e+05  ns/op              6  \n",
      "0   42443087.0         1.753742e+05  ns/op              8  \n",
      "0   86735117.0         6.996970e+05  ns/op              8  \n",
      "0   51561502.0         1.465257e+05  ns/op             10  \n",
      "0   65400871.0         1.748913e+06  ns/op             10  \n",
      "0   60773559.0         2.015307e+05  ns/op             12  \n",
      "0   53446734.0         1.550336e+06  ns/op             12  \n",
      "0   69757139.0         1.027469e+05  ns/op             14  \n",
      "0   56850876.0         2.930542e+06  ns/op             14  \n",
      "0   78901419.0         1.034565e+05  ns/op             16  \n",
      "0   50060996.0         2.355385e+06  ns/op             16  \n",
      "0   88127777.0         1.384649e+05  ns/op             18  \n",
      "0   50751022.0         1.288828e+06  ns/op             18  \n",
      "0   97382076.0         2.021528e+05  ns/op             20  \n",
      "0   47741141.0         2.282750e+06  ns/op             20  \n",
      "0  107259806.0         3.445648e+05  ns/op             22  \n",
      "0   49153893.0         3.246477e+06  ns/op             22  \n",
      "0  116167074.0         2.378000e+05  ns/op             24  \n",
      "0   46567345.0         2.900648e+06  ns/op             24  \n",
      "0  127717918.0         3.954523e+05  ns/op             26  \n",
      "0   47318349.0         3.004216e+06  ns/op             26  \n",
      "0  134867779.0         7.267847e+05  ns/op             28  \n",
      "0   48843376.0         2.259547e+06  ns/op             28  \n",
      "0  152173926.0         4.455981e+05  ns/op             30  \n",
      "0   44985640.0         2.332621e+06  ns/op             30  \n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "\n",
      "         Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0   5536880.91         9.073481e+04  ns/op              2  \n",
      "0  58790014.44         2.071232e+06  ns/op              2  \n",
      "0   6216977.26         1.025495e+05  ns/op              4  \n",
      "0  66164166.31         2.106798e+06  ns/op              4  \n",
      "0   7038844.61         7.663981e+04  ns/op              6  \n",
      "0  72704348.75         2.421137e+06  ns/op              6  \n",
      "0   7868229.09         6.913490e+04  ns/op              8  \n",
      "0  81071723.81         2.620053e+06  ns/op              8  \n",
      "0   8605554.87         1.039524e+05  ns/op             10  \n",
      "0  71829430.95         2.068533e+06  ns/op             10  \n",
      "0   9285484.89         1.327238e+05  ns/op             12  \n",
      "0  66406354.00         1.955045e+06  ns/op             12  \n",
      "0  10190188.11         6.060185e+04  ns/op             14  \n",
      "0  66662930.89         2.131443e+06  ns/op             14  \n",
      "0  10835357.23         9.873804e+04  ns/op             16  \n",
      "0  63571490.38         2.150787e+06  ns/op             16  \n",
      "0  11912139.67         6.898747e+04  ns/op             18  \n",
      "0  63418233.34         2.193743e+06  ns/op             18  \n",
      "0  12644523.96         1.399646e+05  ns/op             20  \n",
      "0  61296555.36         2.433137e+06  ns/op             20  \n",
      "0  13458923.07         1.125840e+05  ns/op             22  \n",
      "0  61260514.00         1.998525e+06  ns/op             22  \n",
      "0  14005263.92         1.170081e+05  ns/op             24  \n",
      "0  60259455.73         2.168449e+06  ns/op             24  \n",
      "0  15156294.74         8.123831e+04  ns/op             26  \n",
      "0  60987099.07         2.057739e+06  ns/op             26  \n",
      "0  15791972.07         1.022070e+05  ns/op             28  \n",
      "0  59458673.73         1.903072e+06  ns/op             28  \n",
      "0  16683674.03         1.108890e+05  ns/op             30  \n",
      "0  60461393.73         1.935665e+06  ns/op             30  \n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "\n",
      "         Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0   5536880.91         9.073481e+04  ns/op              2  \n",
      "0  58790014.44         2.071232e+06  ns/op              2  \n",
      "0   6216977.26         1.025495e+05  ns/op              4  \n",
      "0  66164166.31         2.106798e+06  ns/op              4  \n",
      "0   7038844.61         7.663981e+04  ns/op              6  \n",
      "0  72704348.75         2.421137e+06  ns/op              6  \n",
      "0   7868229.09         6.913490e+04  ns/op              8  \n",
      "0  81071723.81         2.620053e+06  ns/op              8  \n",
      "0   8605554.87         1.039524e+05  ns/op             10  \n",
      "0  71829430.95         2.068533e+06  ns/op             10  \n",
      "0   9285484.89         1.327238e+05  ns/op             12  \n",
      "0  66406354.00         1.955045e+06  ns/op             12  \n",
      "0  10190188.11         6.060185e+04  ns/op             14  \n",
      "0  66662930.89         2.131443e+06  ns/op             14  \n",
      "0  10835357.23         9.873804e+04  ns/op             16  \n",
      "0  63571490.38         2.150787e+06  ns/op             16  \n",
      "0  11912139.67         6.898747e+04  ns/op             18  \n",
      "0  63418233.34         2.193743e+06  ns/op             18  \n",
      "0  12644523.96         1.399646e+05  ns/op             20  \n",
      "0  61296555.36         2.433137e+06  ns/op             20  \n",
      "0  13458923.07         1.125840e+05  ns/op             22  \n",
      "0  61260514.00         1.998525e+06  ns/op             22  \n",
      "0  14005263.92         1.170081e+05  ns/op             24  \n",
      "0  60259455.73         2.168449e+06  ns/op             24  \n",
      "0  15156294.74         8.123831e+04  ns/op             26  \n",
      "0  60987099.07         2.057739e+06  ns/op             26  \n",
      "0  15791972.07         1.022070e+05  ns/op             28  \n",
      "0  59458673.73         1.903072e+06  ns/op             28  \n",
      "0  16683674.03         1.108890e+05  ns/op             30  \n",
      "0  60461393.73         1.935665e+06  ns/op             30  \n"
     ]
    },
    {
     "data": {
      "image/png": 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dISkyMrJE2bIU9fQWX2arLNVZTaBp06Z8/vnn2t6z0NDQSl9ra2vLzJkzGTZsGKBMfvrjLlnluXfvnvZzeW2Vl5dX6fG/xlA8iazKLmiG+DmXJz8/n9dee02741eXLl344osvDLLKQfF/HD399NMmubKCEJUlyaoQosb17t1bu35nTEwM7777brVftRZPVorWKC1LZYYAeHt7A0qSkpWVVWaZ9PR0fv75Zz2jLMne3l4bd0XjQcvi5uam/azP9cXb6rffftNZ7vvvv68wsTeWO3fuaHf+srS0rNKavYb6OZelsLCQN954g4MHDwLKa/gNGzZUe33WytiyZQt79uwBlF77v/zlL7V+TyFqkySrQoha8fHHH+Pi4gLATz/9xJw5c8pNMgFSUlLK3JoUoF27dtrPq1evLjXDXaPRsHbtWvbv319hbEWv57Ozs0utRgDKtq6LFy8mJSVFZx0nTpxgy5Yt5a44EBAQoD3/xy1D33777XLXI01MTOTw4cOAknAUJV6VUbytvvnmmzJXZTh+/DgrVqyodJ2GlJWVxf/93/9pf8b9+/ev0i5WNfFzrgqNRsPSpUu1CaOPjw8bN27EwcGhRu/zR3fv3uWtt97ivffe0x5bsmRJuZtOCFEXyAQrIUStaNSoEZs2bWLu3LncvXuXX3/9laCgILp370737t1p3LgxDg4O5OTkEB8fT1hYGEFBQdohA9bW1iWW2xk2bBgrVqzQzrZ/9tlnefrpp2nUqBEJCQkEBgZy+fJlWrRogZ2dHeHh4Tpje/bZZ9mwYQPp6els3LiRmzdvMmTIEGxtbYmOjmbnzp3cvXuXkSNH6hxzm5SUxHvvvceKFSt44okn8Pf3x8PDAysrK5KTkzl16pT29a+ZmRkzZ84scf327dvZvn07fn5+9OzZUzsxLT09nRs3bhAQEKB9Df7ss8/qNZu7cePGDBw4kKNHj3Lnzh2eeuopJk6ciJeXF1lZWZw4cYKDBw9ibW3N0KFDtb1/hlTWqg8ZGRnadVbj4+MB5VX+4sWLq3SPmvg5V8XKlSu12+laW1szefJkzp8/X+41NjY22uRal5s3b5Zot7y8PB48eEBcXBwXL14kJCRE2wNvaWnJokWLmDRpUjWfRgjjk2RVCFFrfH19+fHHH1mxYgW7d+8mPz+fM2fOlJgs9UeWlpaMGDGCV199tcTEIFtbWz799FPmzJlDZmYm4eHhpRLS5s2bs3btWt59991y43J1dWX58uXaNVKPHDnCkSNHtOdVKhVz5szhiSee0JnEFC3jlJuby9GjRzl69GiZ5ezs7Fi2bFmpxeRVKhUajYbIyEgiIyN1xjp69OgqLYq/bNkyXnjhBWJiYkhKSmL16tWl4lqxYgWRkZFGSVbnz59fYZlmzZrxySef0KJFiyrdoyZ+zlVx4cIF7efc3FzefvvtCq/x9PQsEVtZ9u7dW2GcKpWKPn368Oqrr5a73a4QdYkkq0KIWtWgQQOWL1/O/PnzOXDgAKdPn+bWrVvadUUdHBxwdXWlbdu2dOnSheHDh+vsRezRowc///wzGzZs4OTJkyQmJmJvb0+TJk0YPnw4kyZNqvSr1iFDhrBjxw7Wr1/P2bNnSU1NpX79+nTs2JEpU6bQq1evcrd7HTt2LD4+Ppw6dYrg4GCioqJITk4mPz8fBwcHWrRoQZ8+fXjuuedKrSULEBQUxKlTpzh9+jTh4eHcvXuXrKwsrK2tcXd3p1OnTowfP1479ldfbm5u7Nixg02bNnHgwAF+++03zMzMcHd3p1+/frzwwgs0a9as3ETZ0GxtbXFxccHPz4/BgwczatQorK2tq1VndX/OpkqlUmFra4ujoyPOzs74+vrSvn17BgwYUKXxvUKYMpWmslubCCGEEEIIYWAywUoIIYQQQpgsSVaFEEIIIYTJkmRVCCGEEEKYLElWhRBCCCGEyZJkVQghhBBCmCxZuuoRERoaioWF/DirKj8/X9rPAKSdDUfa2jCknQ1H2towDNXO+fn5pdaf1kV+6o8ICwsL/P39jR1GnRUWFibtZwDSzoYjbW0Y0s6GI21tGIZq57CwsEqXlWEAQgghhBDCZEmyKoQQQgghTJYkq0IIIYQQwmRJsiqEEEIIIUyWJKtCCCGEEMJkSbIqhBBCCCFMliSrQgghhBDCZEmyKoQQQgghTJYkq0IIIYQQwmRJsiqEEEIIIUyWbLcqhBBCCPE402ggLhQiA/CMuwkJLcFvDDTpauzIAOlZFUIIIYR4fCVEwIaB8N1YyLxHvnV9yLynfF8/ABIjjR2hJKtCCCGEEI+lhAjYNAK8B8HrkTBuLQnt/gLj1irfvQfBN8ONnrDWiWEAKSkprF+/niNHjhAfH4+trS3t2rVj0qRJDBkyRO/68vPzOXfuHOHh4YSHhxMREcHt27fRaDRMnTqVJUuWlHv9m2++yU8//VRumdatW7Nnzx6d59VqNVu3bmX37t3ExMQA4OXlxZgxY5g8eTKWlpZ6P5cQQgghRKVoNLBrHnSfBYPfLn3e2vHh8Z/nwkvHDBldCSafrN64cYNp06aRnJwMgL29Penp6Zw8eZKTJ08yZcoUli5dqled8fHxTJ8+vdqxWVtb4+joWOY5Z2dnnddlZmYyY8YMLl26pK0H0CbP+/bt45tvvsHOzq7aMQohhBBClBIXCveiYJrujjUA+r4KwesgLgQ8jTOG1aST1by8PObOnUtycjJt2rRhxYoV+Pr6kpOTw+bNm1m5ciVbtmzBz8+PZ599Vq+67e3t8fX1pV27drRr145vv/2WyEj9urlHjhzJ8uXL9boG4O233+bSpUs4OTnxwQcfMHjwYAAOHz7M3//+dy5cuMA//vEPPvzwQ73rFkIIIYSoUGQAtB0L1g4Pj61sT/v0BGh4FNzbK8esHZVyEQGSrJZl+/btxMbGYmdnx/r162ncuDEANjY2zJ49m6SkJLZs2cJnn33GmDFjKv3q3MPDg5CQEFQqlfbYjh07auUZ/ujq1asEBgYC8P7775cYxjBkyBAKCwtZuHAhu3bt4sUXX6RNmzYGiUsIIYQQj5GcNHB0f/g99hzcv4MKDWQnlyzr6A5ZKYaNrxiTnmAVEBAAwOjRo7WJanGzZs1CpVKRmJhIcHBwpes1MzMrkaga0u7du9FoNHh5eTF06NBS54cNG4aXlxcajabcMa9CCCGEEFVmUx/S45XPSddhyzhQqchw7aycKy49HmzqGT7G/zHZZDUzM5PLly8D0Ldv3zLLuLu706pVKwBOnz5tsNiq48yZM4DuZwLo06cPUHeeSQghhBB1jN8YiNiljFvd9BTkZULTHjzw6AcN2z4sl5uulGs7xmihmuwwgOjoaDQaDYA2IS1Lq1atuHHjBtHR0YYKTev06dMMGzaMu3fvYm1tTfPmzXnyySd54YUXcHV1LVVeo9Fw8+ZNoOJnAozyTEIIIYR4DHh2AZcWyhqruQ/Aowt4DyHFsT+e5sWGVQatBNfWRhuvCibcs5qUlKT93KhRI53lis4lJibWekx/FB8fT1xcHHZ2dmRlZREeHs6XX37JqFGjyuwVzczMJCsrC4CGDRvqrLfomTIzM8nMzKyd4IUQQgjx+MrPBXWWkqjau0HT3tDjL2gsbJTzuelw+J9w7msY96VRQzXZntWipA6UCVW6FJ0zZFLXtm1b/P39GTBgAO7u7piZmZGRkcGRI0f46KOPSEpKYt68eezcuZMWLVporyseo62trc76i5/LzMzE3t6+wpjy8/MJCwur4hMJtVot7WcA0s6GI21tGNLOhiNtXYM0BbQ88SoOyVHk2rpjps7BLOQb7v9+EzcrF1LOp1Dv7jFyHZoT23cVufFqiDde25tssmrKpk6dWuqYg4MDY8aMoWvXrowfP5779++zZs0aPvnkE73rLxr+oA8LCwv8/f31vk4owsLCpP0MQNrZcKStDUPa2XCkrWuIRgM/TId7F6B+M6y9+kP/xZCVjEtEAMl3b+HS1AeG/xU7z6741FIY+vzDw2ST1eIL4ufk5ODg4FBmuezsbIBK9T4agqenJ5MnT+aLL77g2LFjFBYWYmamjLYoHmNR3GUpfs5UnksIIYQQj4Bf/gYRP4ODOzTpCb3ngbOX8suzK3FhYTQwsX8UmOyYVTc3N+3nhIQEneWKxqqWNwbU0Dp27AhARkYGaWlp2uP29vbaJLy8MbZF5+zt7SVZFUIIIUTNOL4Czq4HWxftGFUata34OiMz2WTV29tbuxZqVFSUznJF57y9vQ0SV3WoVCpatmwJPDrPJIQQQog6IPQ7OPKesiNV8z7QeRI062nsqCrFZJNVe3t7OnToAMCJEyfKLBMfH69N7Hr16mWw2Cpy6dIlQHmG+vVLLqxbFGdQUJDO60+ePFmirBBCCCFElV3dC7tfAQsbaN5XWTO1demNiUyVySaroOxcBRAYGMjvv/9e6vzGjRvRaDS4ubkZLLGraPLT3bt32bZtGwD9+/fXjlctUvRMMTExHDx4sNT1Bw4cICYmBpVKpS0rhBBCCFElv52GH6aBygy8+kLrIdDuGTDSTp5VYdLJ6sSJE/H09CQrK4vZs2dz9epVQJlwtX79erZu3QrAokWLsLS0LHHtoEGD8PHx4c033yyz7vT0dFJSUrS/1Go1ALm5uSWO/3Ei1K5du1i4cCGHDh0iJeXhPrmZmZns2bOHiRMnkpaWhp2dHQsWLCh1Xz8/P0aOHAnAkiVLOHToEBqNBo1Gw6FDh1i6dCkATz/9ND4+tTUHTwghhBCPvMRI2PosFOYriapXX+j0Apib7Pz6Mpl0tNbW1nz55ZdMmzaNa9euMXbsWBwcHMjJySE/Px+AyZMnM2HCBL3rnjdvHmfPni11fPv27Wzfvl37fcGCBSxcuFD7vbCwkAMHDnDgwAFAWbXAysqKBw8eUFhYCICzszOffvqpzjGny5YtIzY2lsuXLzN//nxsbGzQaDTk5uYCygStd999V+9nEkIIIYQA4H4cfDtK2UbVq5+yA1X3WWCpe+16U2XSySqAj48PgYGBrFu3jqNHjxIfH4+DgwN+fn5MmjSJYcOGGTSenj178sorrxAaGsqtW7dITU0lIyMDJycnWrVqRf/+/ZkwYQLOzs4663BwcGDbtm1s3bqV3bt3ExMTAyibDTz99NNMmTKlVE+xEEIIIUSlZKfBN8MgKxma9gL3DtBrrjK5qg5SaaqyAr0wObJYcvVI+xmGtLPhSFsbhrSz4UhbV5I6G9YPhKRIaNwZPDpBv9ehftNKXW6odtbnPiY9ZlUIIYQQQlRSYQFsGackqg3bQkNf6DWv0omqqZJkVQghhBCirtNoYPsU+O0MOLeEhu2gx0vg1sbYkVWbJKtCCCGEEHXdnlfhWiA4eULjjtB5Mnh2MXZUNUKSVSGEEEKIuuzoBxCyCexclVn/7cZBywHGjqrGSLIqhBBCCFFXnd0Iv34I1k7QrBe0GgJ+T9epRf8rIsmqEEIIIURdFP4z/PJXZRtVrz7Q7Ano+GcwMzd2ZDVKklUhhBBCiLrm5gnY+RdQmUPL/tC4E3SbDhZWxo6sxkmyKoQQQghRl8SHwX+eV7ZRbTkQGrRRZv5b2Rs7slohyaoQQgghRF2Rehs2jwF1JrQYAM7NoPc8sHMxdmS1RpJVIYQQQoi6IDMZNo2A7FTw6gdOHsqi/06NjR1ZrZJkVQghhBDC1OVmwjfD4cFdaNJdSVR7zoYG3saOrNZJsiqEEEIIYcoK8uG7pyH5BjTqAPWbQ+ep0Njf2JEZhCSrQgghhBCmqrBQmUwVFwIurcC1NbR/Rlmq6jEhyaoQQgghhKnaNQ+iDoFTE3DvAK2HQpunHqlF/ysiyaoQQgghhCk68DZc+g/Yu4JnN2jeGzpMALPHK317vJ5WCCGEEKIuOLUaTq363zaqT0DjDtBlKphbGjsyg7MwdgBCCCGEEKKYS/+Fg+8o26i2HKjM+O/xF7C0NXZkRiE9q0IIIYQQpuLGQQhYACozaDUEnNyh11ywqWfsyIxGelaFEEIIIQxJo4G4UIgMgJw0sKkPfmNAUwjfT4XCAmUSlZ0L9JoPDg2NHbFRSbIqhBBCCGEoCRHKDP97UdB2LDi6Q3o8bH4a8nNAUwCth4GNk9Kj6twZ7+vsAAAgAElEQVTc2BEbnSSrQgghhBCGkBChbJfafRZM2w3WjsrxB3fhxgFQZ4LZ/1KzbjOhoZ/xYjUhkqwKIYQQQtQ2jUbpUe0+Cwa//fB4dhpsGgWZidC0J+Tch+RoaNrDeLGaGJlgJYQQQghR2+JClVf/fV9Tvms08Hkn+LgNpN4Ed39w8oTeCyEjQdmxSgDSsyqEEEIIUfsiA5Qxqlb2kHgVzn8D92OhMB8atFZ+efWBTn+G305BRAB4djV21CZBklUhhBBCiNqWnapskbrzJbh5FDKTlOPmVkqvauOO0PkFMDNXJl1lpRg3XhMiyaoQQgghRG0pUMOd83DzV3gQB4VqZQ3VRu3g/h1lOECDltB9JlhYK9ekx4O9m3HjNiGSrAohhBBC1DR1Dtz6FYI+hbsXlWWpADy6gkcnUGdBw/bQyA86T324MkBuOkTsgmkBxovdxEiyKoQQQghRU/IyIXIPnFoFSZHKAv9WDuDVHxLDlSQ1Pwe8+kGLflCvqTI8oEjQSnBtLeNVi5FkVQghhBCiunLuw4V/w9n1kBoDaJRX+U16gF0DyM9WdqK6thdaDYK2Yx72poLSoxq0Es59DTP3GespTJIkq0IIIYQQVZWZDGfWKolqRrxyrL4XeHYBcxvQ5EM9T2g9FBp1gJRo+HkufOKnJKxFO1hF7FJ6VGfuk80A/kCSVSGEEEIIfaXGwokVEPGz0quqMoOG7aBRe0CjzOpv2h1a9AeXlg9f9Tf0g5eOKeuoRgQos/7t3ZQxqvLqv0ySrAohhBBCVFZ8OPz6Adw4pLzaN7MEz+7QwBs0hWBtD96DoVlvsG+gux7PrpKcVpIkq0IIIYQQ5dFoICYIfv0QfjutLORvZQ9eT4JjY6Dwf6/6hynrpVraGjviR4okq0IIIYQQZdFolFf1Jz6G+MuABuxcoXFnsK0HZhbg0Rm8Byo7UJnJLva1QZJVIYQQQjzaNBqIC1W2PM1JA5v64DcGmuh4DV+QD+c3wek1kBajHKvfTBmTauUAVnbQcgA0f0KZICVqlSSrQgghhHh0JUTArnlwLwrajn04+/67seDaCsZ9+XD2fV4mBH0O5zdCVjKgAjdfcPFWElTHxtBqGHh2BmsHoz7W40SSVSGEEEI8mhIiYNMI6D4Lpu0uua7pU8uVdU2/GQ7P/wfCf4RL20GdqUyaatxZWbDfylbZGtV7sJLUmpkb73keU5KsCiGEEOLRo9EoPardZ8Hgt0uft3aEjhOVMambRwEasLSDpj2VHlRrB2WXKa++4ORZcpcpYVCSrAohhBDi0RMXqrz6n7an9Lnbp+Do+8oMfzTKMXd/aNAKHBopC/g36QY29QwasihbjSerUVFRnDt3jry8PPr27Yu3t3e160xJSWH9+vUcOXKE+Ph4bG1tadeuHZMmTWLIkCF615efn8+5c+cIDw8nPDyciIgIbt++jUajYerUqSxZsqTc6yMjIzl8+DDnzp0jKiqKtLQ0bGxsaN68OQMGDOCFF17AxcVF5/U+Pj4Vxvi3v/2NF198Ue9nE0IIIQTKZKq2Y0uOLY06DAf/DxLCle+OHsquUWm3laWo+rysLOpvbmmcmEWZ9E5WT548yRdffEHnzp1ZvHhxiXObNm3ik08+oaCgAAAzMzMWL17MjBkzqhzgjRs3mDZtGsnJyQDY29uTnp7OyZMnOXnyJFOmTGHp0qV61RkfH8/06dOrFE9AQAB//etftd9VKhWOjo5kZGRok99t27axZs0aunXrVm5d9erVw9Ky7D8QtrayRpsQQghRZTlpD2fqF6jhlzfg/NfKd+cW4NIKHBsqr/1/v6Qs6O/R2XjxCp30Tlb37dtHaGgozz77bInj169f56OPPkKj0VCvXj0sLCxITk5mxYoVdOnShY4dO+odXF5eHnPnziU5OZk2bdqwYsUKfH19ycnJYfPmzaxcuZItW7bg5+dXKp6K2Nvb4+vrS7t27WjXrh3ffvstkZGRFV6Xn5+PtbU1o0aNYuTIkXTt2hU7Oztyc3M5ceIE//rXv7h79y5z5szhl19+wc3NTWddq1evpmfPnnrFLYQQQohKsKmvzPpPjYFtz0PSVbB1VpLTek2g1RDls50L/DxP2fJUmCS9k9WLFy8C0Ldv3xLHv//+ezQaDf369WPt2rVYWVmxZMkSduzYwbZt26qUrG7fvp3Y2Fjs7OxYv349jRs3BsDGxobZs2eTlJTEli1b+OyzzxgzZozOXso/8vDwICQkBFWxwdI7duyo1LWdOnXi0KFDNGzYsMRxa2trhgwZgo+PD6NGjSI9PZ3t27ezYMGCSj6tEEIIIWqM3xj4diRc/gEKcqFBG2XJqY5/VrZCtbRRyuWmQ8QumBZg3HiFTnpvtZCcnIyFhUWpZO3EiROoVCrmzZuHlZUVAAsXLgQgJCSkSsEFBCi/cUaPHq1NVIubNWsWKpWKxMREgoODK12vmZlZiURVHy1btiz17MU1bdpU21saHh5epXsIIYQQohrU2XDmC8jPgUI1NHsC2j4Ng5Yqu00VJaqgLF/l2ho8dWwQIIxO72T1wYMH2NnZlTiWkpLC7du3cXJyonPnh+M93N3dsbW1JTExUe/AMjMzuXz5MlC6F7d4/a1atQLg9OnTet+jthSNNy0sLDRyJEIIIcRjJiEC1vaEKz8qQwFU5mDjBD3nKrtQFclNh8P/hHNfKxsDCJOl9zAAOzs7Hjx4QF5enrYH9fz58wAlEtUilX01/0fR0dFoNMpyEkUJaVlatWrFjRs3iI6OrtJ9alp+fj6hoaEAtGnTptyyH3zwAfHx8WRkZFCvXj3atm3LmDFjGDlyJObmsuiwEEIIUWkajZJ47nsTCvOVnaea9oIW/eH0KljVGdqOebiDVcQupUd15r6HO1gJk6R3z2pR4rhv3z7tsV27dqFSqejRo0eJspmZmaSnp5f72lyXpKQk7edGjRrpLFd0riq9t7Vhy5YtJCUlYW5uzvjx48stGxkZSW5uLpaWlty7d4/jx4+zePFipk+fzoMHDwwUsRBCCFHH5TyA/0yEva+DmYWykL//RBj8f9DhGXjpGEzbpUyiykpR/jstQDkuiarJ07tndeTIkYSGhvLOO+8QGhpKSkoKhw8fxsLCgqeeeqpE2UuXLqHRaGjWrJmO2nTLysrSfraxsdFZruhcZmam3veoaWFhYaxcuRKAqVOn0rJlyzLLjR8/npEjR9KpUyecnJwAiI2NZcuWLWzZsoWzZ8/yyiuv8M0331T63vn5+YSFhVX/IR5TarVa2s8ApJ0NR9raMKSdDUdXW9umROB15i0sc5JRW7uQ2aAjae5DeeDYG6LvAnf/V9ISGhVbOSgZSJaf3R+Z4u9pvZPViRMncuDAAc6ePcv27du1r+oXLFhQahLUL7/8UmaP66MoNjaWefPmkZubS6dOnXjttdd0ll2+fHmpY02bNuWtt96iadOmvPfee5w8eZKgoCCd43X/yMLCAn9//yrH/7gLCwuT9jMAaWfDkbY2DGlnwynV1oWFEPQp/Po+oIFG7bBs0Z/63WZS37W10eKs6wz1e1qfhFjvZNXCwoJNmzaxZ88eLl68iIODA08++STdu3cvUU6tVpOQkEC3bt3o37+/vrcpMYkrJycHBweHMstlZ2cDyrqpxnL37l2mTZtGUlISfn5+rF+/XjueV1+TJ09m06ZNxMXFcfTo0Uonq0IIIcRjI/MefD8NbgeBpT007Q6+T4P/c8pkKvFIqdJ2q+bm5owdO5axY8fqLGNpacn69eurHFjxxfQTEhJ0JqtFY1WrMi62JsTHxzN16lTi4uJo06YN33zzDfXqVX0vYTMzM9q3b09cXByxsbE1GKkQQgjxCLj5K/wwHbJTlO1SvfpBl6nQ/Akw03sqjqgDqpSsGoK3tzcqlQqNRkNUVBTe3t5llouKitKWN7SEhASmTp1KbGwsLVq0YNOmTbi4uNRY/VVdC1YIIYR45BTmw8G34eQqUJmBe0doMwy6zoB6nsaOTtQik/0niL29PR06dACUDQfKEh8fr01We/XqZbDY4GGievv2bZo1a8bmzZtxdXWtdr0ajYYrV64A4Okpf/iEEEII7sfR6thcOPk5WDuA9yDovQD6LZZE9TFgsskqKDtXAQQGBvL777+XOr9x40Y0Gg1ubm4GTVaTkpKYNm0aMTExNGnShO+++67c5bWKK5qQpsu2bduIi4sDYMCAAdUNVQghhKjbIgNhbQ/s0q5CvabQ7lkY/Db4Tyi5E5V4ZJl0sjpx4kQ8PT3Jyspi9uzZXL16FVAmXK1fv56tW7cCsGjRolKbDwwaNAgfHx/efPPNMutOT08nJSVF+0utVgOQm5tb4njRBK4iKSkpTJ8+nVu3buHh4cF3331X5lawuixatIhPP/2UsLAw8vLytMfv3LnDhx9+yHvvvQcoPcVPPvlkpesVQgghHin5ubDnNdg+CfJzyazvBz1egiHvQGN/kKFyjw2THbMKYG1tzZdffsm0adO4du0aY8eOxcHBgZycHPLz8wFl9vyECRP0rnvevHmcPXu21PHt27ezfft27fcFCxawcOFC7fdt27Zphx6kpaXx3HPP6byHu7s7O3bsKHEsNTWV/fv3s27dOszNzXF0dEStVpdYJ7Znz56sWrVK72cSQgghHgnJ0fDfSZB0FWzqQfN+JLj0o2WvWWBu0qmLqAUm/xP38fEhMDCQdevWcfToUeLj43FwcMDPz49JkyYxbNgwg8ZT/DV+VlZWic0L/sja2rrUsdmzZ+Pj48OlS5eIj48nLS0NlUqFp6cn7du35+mnn2bIkCEyuUoIIcTj6eJ/YM8rkJ8D9ZuD72jo/iIZcZmSqD6mVJqKBlGKOkEWpq4eaT/DkHY2HGlrw5B2rkF5mRDwMlz5EcytwKMzdPwzdJgA1g7S1gZiyE0BKnufCv+JUlhYiJmsWyaEEEKI2hJ/Gf47GdJug60ztBwEPV+Cpj1lbKqoOFktmugzcOBAnnzySRwdHQ0RlxBCCCEedRoNnF0P+5co66g2aKXM9u82A5wqP3lZPNoqTFbT09PZs2cPgYGBmJub061bNwYOHMigQYNo2rSpIWIUQgghxKMmOxV+mgPX94GFLTTvDV1mgN8osCg950M8vipMVk+ePMmvv/7K4cOHOXXqFGfOnCE4OJjly5fj7e3NoEGDGDhwIJ07dzZEvEIIIYQwdRoNxIVCZADkpIFNffAbA026Kud/C4bvp0BGAti7QesR0GsOuLc3btzCJFWYrLq4uDB+/HjGjx9PXl4eZ86c4ciRI/z6669ERUURFRXFhg0bcHZ2ZsCAAQwcOJC+fftia2triPiFEEIIYUoSImDXPLgXBW3HgqM7pMfDd2PBtRU06QHnNgAqaNgWOk6GzpPArua2KxePFr3WgLCysuLJJ5/ULlYfGRnJ4cOHOXLkCJGRkezcuZOffvoJS0tLevXqxYABAxg0aBDu7u61ErwQQgghTEhCBGwaAd1nwbTdYF1snku/12DzGDi7Tnnt36If9JgN3gPBzNx4MQuTV60Fy/z8/PDz82PBggUkJiZy9OhRDh8+THBwMMePH+fEiRMsW7YMX19fBg8ezIABA2jfXrr4hRBCiEeORqP0qHafpWyHWlzUYdjxojJO1coBLG3hqY/ApYVxYhV1So2tSdWwYUOef/551q9fT3BwMGvXrmX8+PE0aNCAyMhI1qxZw4QJE/jqq69q6pZCCCGEMBVxocqr/76vPTxWoIYDS2HrM5CbDh5dlO1S1VmQnWK8WEWdUitbQdjY2DB48GAGDx4MwKVLlzhy5AhHjhyhoKCgNm4phBBCCGOKDFDGqFo7KN/vXoCAhcoaqtZO0HIgPLEQmnSDuxchIgA8uxo3ZlEnGGTfso4dO9KxY0deffVV1Gq1IW4phBBCCEPKSVMmUz34XelNvfKjctzMEnrOhe4vgmMj5ZijO2RJz6qoHINvsmtpaWnoWwohhBCitlk5wPX9cGo1FOQqO1E16QFdpkDr4WBh9bBseryyZJUQlWDwZFUIIYQQj5DCQgjbDhe2Kr2rFrbQpCe0fwbajVN6UYvLTYeIXTAtwDjxijpHklUhhBBCVM1vZyDwdUi4AioLsLAB9w4wZhW4+YBKVfqaoJXg2lrGq4pKq7HVAIQQQgjxmEj7Df4zCb4ZDgnh4NQEOj4Po1bCvWtw+XvIyyh5TW46HP4nnPsaxn1pnLhFnSQ9q0IIIYSonNx0OLYcgtdBoVrZdapJD/B/HloPVTYB8OwMP8+F4PXQdszDHawidik9qjP3QUM/Yz+JqEMkWRVCCCFE+QoLIHQzHPqHMi7Vyh7cu0HbccpyVU6NH5Zt6AcvHYO4EGV5qqwUZTLVNFmqSlSNJKtCCCGE0O3mMWVcanIUmFuBmx/4jIAOzymJaVnjUkFJTCU5FTVAklUhhBBClJYcDYGvKcmqygzqNYUW/cF/IjTvBeayFKUwjConq5mZmfzwww+cOnWKu3fvkpOTw6FDh7Tn09PTOXbsGCqVitGjR9dIsEIIIYSoZdmpcHgZhHwLmgLlFX6T7tD+WWg9DGycjB2heMxUKVm9fPky8+fPJykpCY1GA4DqD68BHB0dWbduHdHR0Xh6etK5c+fqRyuEEEKI2lGgViZOHVsOeenKZCl3f/AdpYxNredp7AjFY0rvpavu3bvH7NmzSUxMpE2bNrz88ss4ODiUWXbChAloNBoOHDhQ7UCFEEIIUQs0Grj6C6zqDAeWQGGekqR2mwVPfQS95kmiKoxK72R106ZNpKSk0LdvX3bs2MG8efOwsbEps+yAAQMAOHv2bLWCFEIIIUQtSIhQ1kr970R4EAcuLaH9BBi6DAa+Be7tdU+gEsJA9B4GUDQO9W9/+xsWFuVf3rx5cywtLblz506VAxRCCCFEDcu8B/vfgrDvAQ04uCvjUv3GQJthYFvf2BEKoaV3shoXF4e1tTVt2rSpVHl7e3syMjIqLiiEEEKI2pWfCyc/hxOfQn422NSHxp2g1RBoNw7qNzV2hEKUoneyqlKpKCwsrFRZtVpNRkYG9vb2egcmhBBCiErSaCAuFCIDlEX7beorvaRNuj48H74T9r0FGfFgaQseXaB5X/CfAO4d5HW/MFl6J6seHh7cvHmTu3fv4uHhUW7Z06dPk5+fj5eXV1XjE0IIIUR5EiJg1zy4F6XsJlW0vel3Y8G1FfR+GU59Br9fAjMLcPV5uBSVVx+wsDb2EwhRLr2T1T59+nDz5k22bdvG4sWLdZbLzs7m448/RqVS0a9fv2oFKYQQQogyJETAphHQfRZM260sN1Wk7yvwn4mwY4byvV5T8Oiq7D7lMwJsnY0TsxB60ns1gJkzZ2Jtbc2mTZvYsmUL+fn5Jc5rNBqOHz/Oc889x/Xr13F0dGTy5Mk1FrAQQgghUF7t75qnJKqD336YqKqz4eA78OUTyhapFrZg7aSUG/ZP6PRnSVRFnaJ3suru7s7y5ctRqVS8//779OzZk7S0NACee+45evTowezZs7lx4waWlpasWLECZ2f5QyGEEELUqLhQ5dV/39eU7xoNhG6Ble3g5GfKdqhNe0LnKVCoVsanOjc3bsxCVEGVdrAaMWIEDRs2ZNmyZURGRmqPh4WFaT/7+Pjw7rvvys5VQgghRG2IDFDGqFo7wL0bsGMW/H5ROdeoHbh3gvbjocWToM6Eq7uhaTfjxixEFVQpWQXo0qULP/30E5GRkYSGhpKYmEhhYSENGjSgS5cu+Pv712ScQgghhCguJw3s3eDgu3B6FRQWQP1myiz/1sPA5ymwc1HKOrpDVopRwxWiqqqcrBbx8/PDz8+vJmIRQgghRGXlZkDYD0qvqZUDeHQGr37g/xy4tChZNj1eSWyFqIOqnawKIYQQwoCyUyFwMVz5UfneoBU07Q3+z0Pz3mD+h7/ac9MhYhdMCzB8rELUAElWhRBCiLpAo4FL2+GXv0HufbBxBk0B1PdSZvkXvfL/o6CV4NoaPLsaNFwhakqVktWCggJ+/PFH9u3bx/Xr17l//z4FBQU6y6tUKiIiIqocpBBCCPFYS42Bn+bAb6eVWf7u/tCiPzTrCbvmw+k10PfVkuus5qYrieq5r2HmPqOFLkR16Z2spqenM3PmTK5cuYJGo6mNmIQQQggBUKBWEs7jK6AgDxwbQ7Peyu5TrYaApY0yDODnuRC8HtqOebiDVcQupUd15j5oKHNLRN2ld7K6cuVKLl++jIWFBWPHjqV37940aNAAc3Pz2ohPCCGEeDzdOQ87X4KUaLC0BY/eys5THSZAPc+H5Rr6wUvHIC4EIgKUWf/2bsoYVXn1Lx4BeierBw4cQKVS8e677/KnP/2pNmISQgghHl85D+DAEmWBfwBnL+WVf6dJ0KQHmOnYz8ezqySn4pFUpWEAlpaWjBs3rjbiKVNKSgrr16/nyJEjxMfHY2trS7t27Zg0aRJDhgzRu778/HzOnTtHeHg44eHhREREcPv2bTQaDVOnTmXJkiWVqicyMpKNGzdy9uxZUlNTcXFxoUePHsyaNQtfX99yr1Wr1WzdupXdu3cTExMDgJeXF2PGjGHy5MlYWlrq/VxCCCHquIgA2PMqZN1Ttkht0l155e83GmzqGTs6IYxC72S1cePGJCYmYmFhmIUEbty4wbRp00hOTgbA3t6e9PR0Tp48ycmTJ5kyZQpLly7Vq874+HimT59erbh2797N3//+d9RqNQCOjo4kJCSwe/du9u3bx4cffsioUaPKvDYzM5MZM2Zw6dIlAKytrQG0yfO+ffv45ptvsLOzq1aMQggh6oj7dyDgZYg+DGYW4OYLPiOh45/BrY2xoxPCqHS8S9DtqaeeIjs7mwsXLtRGPCXk5eUxd+5ckpOTadOmDbt27SI0NJTQ0FBee+01VCoVW7ZsYceOHXrXbW9vT9euXZk6dSoffvihXhsb3LhxQ5uojhw5kqCgIM6fP09QUBAjR45ErVbz5ptvEh0dXeb1b7/9NpcuXcLJyYm1a9dy6dIlLl26xNq1a3FycuLChQv84x//0PuZhBBC1DGFBXB6LazuqiSqdm7gOxqGvgcD/i6JqhBUIVl98cUXad26NUuWLCE2NrY2YtLavn07sbGx2NnZsX79eu2rdRsbG2bPns0LL7wAwGeffabt4awMDw8PQkJC2LZtG0uWLGHcuHE4OjpWfOH/rFq1CrVaTbt27VixYgVubsquIG5ubqxYsYK2bduSl5fHqlWrSl179epVAgMDAXj//fcZMmQIKpUKlUrFkCFD+Ne//gXArl27uH79eqVjEkIIUcf8Hgbr+sH+t5Tvnt2g32vw1EfQZihYWBk3PiFMhN7v8h0cHNi8eTPvvPMOI0eOZPjw4bRp0wY3NzdUKpXO66oyxjUgQNltY/To0TRu3LjU+VmzZrF161YSExMJDg6mb9++larXTNfg9EpIT0/n6NGjAMyYMaPUcAgLCwtmzJjBX//6V44cOUJGRgYODg7a87t370aj0eDl5cXQoUNL1T9s2DC8vLyIiYlhz549vPbaa1WOVQghhAnKy4TDy+DsOmWh/3pNlWWoukxVtkwt5+9SIR5HVRp4GhsbS3x8PGq1msDAQG1PoS4qlUrvZDUzM5PLly8D6ExC3d3dadWqFTdu3OD06dOVTlarIyQkRNuL26dPnzLLFMWRl5dHSEgI/fv31547c+ZMiTJl6dOnDzExMZw+fbqmwhZCCGEKrh+AgIWQEa8s4O/ZAzr/Gdo8BdYOFV8vxGNI72T1ypUrTJs2jZycHACsrKxwdnauVm9lWaKjo7WbDrRq1UpnuaJkVdf40JoWFRUFgKurKy4uZW9t5+LiQoMGDUhOTiY6OlqbrGo0Gm7evKmNW5eic4Z6JiGEELUsPQECX4eru8HMHFx9oO1YZTkqlxbGjk4Ik6Z3svr555+TnZ1NixYtWLZsGV27di339X9VJSUlaT83atRIZ7mic4mJiTUeQ1mK4mrYsGG55Ro1akRycnKJ58jMzCQrK6vC64ueKTMzk8zMTOzt7asbthBCCGMoLISQb+Hg/0FeBti5gldf6DpD+a+5YVbWEaIu0/tPSVhYGCqVilWrVtG6devaiAlAm9SBMqFKl6JzmZmZtRZLcUVxlRdT8fPF4yr+2dbWVue1xc9VNlnNz88nLCyswnKibGq1WtrPAKSdDUfauhZpNNimRlLv7nEa59wn8Uo97ns8SbZL2xLFrB/comnIB9ilRlJoZkW2c1tSm43gfpOBFGTWh/AIIz1A3SS/pw3DFNtZ72S1sLAQOzu7Wk1UHwVFQxgMda2FhQX+/v5VvufjLiwsTNrPAKSdDUfaupYkRMCueXAvCtqOJUHlQiPbQhqeWgyurWDcl+DcAn79CE59DoX54NQEs9bDse8+A/tG7WkiE6iqRH5PG4ah2lmfhFjvZLV169aEhYWRm5urXcy+NhRfED8nJ6fEjPrisrOzAQz2qrworqIxu7oUnS8eV/HPRXGXpfg5GQIghBAmIiECNo2A7rNg2m6wdiQhLIxG/v7w1HIIWgkbB4OVozKBysoBmvZQXvm3HgqWut+oCSF003tW1NSpU8nPz2f79u21EY9W0dqlAAkJCTrLFY1VrWgMaU0piquiMbJF54s/h729vTbZLe/6onP29vaSrAohhCnQaJQe1e6zYPDbykz+4vLzIO03ZVmqjHhwbQNPvAxPr4K2YyRRFaIa9E5WR4wYwZw5c/joo49Ys2ZNibGlNcnb21s7catoBn5Zis55e3vXShx/VDRT/969e6SkpJRZJiUlRbs9bPG4VCoVLVu2BEzrmYQQQlQgLlR59d/3D2tfazRw4d+wujNc/gFsXZTtUnvNhycXQ/2mxolXiEeI3sMApk6dCiiTgNauXcu6deto0qQJrq6uOlcFUKlUbN68Wa/72Nvb06FDB8LCwjhx4gTDhw8vVSY+Pl6b2PXq1SxgiNgAACAASURBVEvPJ6marl27YmlpiVqtJigoiDFjxpQqExQUBIClpSVdu3Ytca5Xr15cuXJFW6YsJ0+e1JYVQghhAiIDlKWmiq+Fei+KlicWwr1LYG4Fnl2hyzS4dQJSbylLVAkhqk3vZPXs2bMlvqvVam7dusWtW7d0XlPVpa1Gjx5NWFgYgYGBzJ8/v9QuVhs3bkSj0eDm5mawxM7R0ZH+/ftz6NAhvv32W0aNGoW5+cP/IeXn5/Ptt98CMGjQoFJjbUePHs3GjRuJiYnh4MGDpXaxOnDgADExMahUKkaPHl3rzyOEEKISctLA0V35nJsOxz6E06txAHDyhDbDoecc5fV/2m3IKvvNmxBCf3onqwsWLKiNOMo0ceJENm/eTFxcHLNnz+ajjz7C19eXnJwcvvt/9u48PqryXvz458yemUky2ReyJyQhCWtkR1ATEDfcqm21ltZSe1Vab/e6/LxttdJrF9vaXqu12grSa12uZRcIIIsQ1oRsQPZ9sk9mksns5/fHSCANS4JCWJ638iI5c+aZ50wgfPOc5/v9vvUWq1atAuCJJ55ArVYPee5NN91Ec3Mzd999N7/85S+HjW2z2QY7UQGDHzudziG39wMCAoaVmfrOd77Dxx9/TFlZGT/4wQ946qmniIiIoKOjgxdeeIGysjLUajXf+c53hr3uhAkTuPXWW9mwYQNPP/00kiSRl5cHQEFBAc888wwAd9xxBxkZGRfytgmCIAifN50JbK1Q/A5sfgr6O0EbhDU4k6C870PqjaD6NOnYZgZDxLnHEwRhxC7rYFWr1fLKK6+wdOlSjh8/zp133onRaMThcODxeAB48MEHue+++0Y99mOPPTZslRjgnXfeGZI8tnz5cr797W8POScjI4Pnn3+ep59+mg0bNrBx40YCAwOx2WzIsoxarWbFihVn7VL13HPP0djYSElJCY8//jg6nQ5ZlnE6nQBMnjyZn/70p6O+JkEQBOEiicyED/8ARW/7b/lHT4IpD9CkmEBWxg2nznPaoPxfsHTNmE1VEK42l33rjIyMDNavX8+rr77K9u3bMZvNGI1GJkyYwAMPPMCiRYvGZF533XUX6enpvP766+zfvx+LxUJ4eDgzZ85k2bJlTJgw4azPNRqNrF69mlWrVrF27Vrq6uoAyMrK4o477uChhx4atlIsCIIgjAF7NxT8zN+FCkAbBBPvgxmPQEQGnpKSoefvfgnCx/v3rwqC8Lm47INVgLCwMJ566imeeuqpET9n27Zt53x85cqVn3VaZGVl8dvf/vaCnqvRaHj44Yd5+OGHP/M8BEEQhM+ZzwsH34CCn4PTCgEhEDURWg75A9bgcXB6PobT5g9UD/wVHt40dvMWhKvQFRGsCoIgCMIlU/8JrPsedFSASgdxMyD3a5B5m3/f6oePwoHXIWsJUQMKqPH5b/2Hj/cHqpFnv7MmCMLonTNYPXkrOyUlhfXr1w85NhqSJFFeLnogC4IgCJcxawt89DSUfQCSAkJT/QX9c78GIUn+cwJM8MgOaD4E5WtQ9dZCeLJ/j6q49S8IF8U5g9WTPepP71X/WXreC4IgCMJlx+OEvX+Cj18Ez4A/kz9pPsx+DGKngeIM/XPG5cK4XJqPHiVM9KsXhIvqnMHqW2+9BYBOpxt2TBAEQRCueCc2w8YfQk8dqA2QdD3M+Bak5YFGP9azEwSB8wSrM2bMGNExQRAEQbiidFXDxh9B1VZ/e9SICTDpizDly6eK/wuCcFkQCVaCIAjCtcPZB7t+A5+8DD43BMb6C/rPfhwis4Zm+AuCcFkYdbCal5dHWFgY//znP0d0/gMPPEB7eztbt24d9eQEQRAE4XMhy1D6Pnz0FPS1+ctPxd8Isx6FpHmg0oz1DAVBOItRB6vNzc2DnZZGwmw209raOtqXEQRBEITPh7kU1n8fGvf5u0/FTIGpX4Wcu0EfOtazEwThPC76NgCPx4PiTJmUgiAIgnAx2bth+y/8xf1lH5gSYfwif5Z/aMpYz04QhBG6qMFqX18f3d3dGI3Gi/kygiAIgnCKzwuH34KtPwWHBQJCIWkuzP42xF0HCuVYz1AQhFE4b7B67Ngxjh07NuSY0+nkww8/POtzZFnGarWyZcsWvF7vBTUSEARBEIRRayiEDd8Hc8mn3admwoxHIGMxaMXCiSBcic4brG7dupU//elPQ4719fXx5JNPnndwWZaRJImvfe1rFzxBQRAE4Rony9B8GCrW+FdKdSaYsATiTusYZTPDlmfh6Dv+7lNhaTDhTpixDIJix27ugiB8ZucNVgMDA4mJiRn8vKWlBYVCQVRU1Fmfo1AoMBqNjB8/nvvuu0/UZhUEQRAuTFs5/Osx6KyCrDv9NVBtZnjrTghPgzv+ADU7YMcvwd0PhkhIXgDznoCoHFGKShCuAucNVpcuXcrSpUsHP8/MzCQkJIRt27Zd1IkJgiAI17i2cnhzMUxfBkvXgjbw1GO3/BLWfgdeW+BPnlIb/EHqrMch9QZQacds2oIgfL5GnWC1fPly9HrRgk4QLleyLFPc1MumUjO9A26CA9QszolmSrxprKcmCCMny/4V1enLIO/ZoY9118KmJ+HERkDy70294Scw9SuiFJUgXIUuKFgVBOHydNxs44fvFVPT0c8tOdFEBeloszr4yuuFpEQY+PV9k0mPCjz/QIIw1poP+2/9L1136pjLDrt/C3t+D14XBI2DxHlwbK0/218EqoJwVRLtVgXhKnHcbOO+P3/CQ7MTWf3NWRi1p/56/9eSbF7ZUcUXXvmE9x6dIwJW4fJXsca/R1Vr9K+yln8I7y8Dn8fffSrlBpj7nxA/E9aqoHwNjMs936iCIFyBRLAqCFcBWZb54XvFPDQ7kR/enDnscaNWNXj8B+8Ws2b5vEs9RUEYHYfFn0zVVg4bfgj1u0GhhtipkPt1yLnn1B7WwGh/AwBBEK5KorWUIFwFipt6qeno57Eb0gaP5f1mB7NeKKB3wD147NEb0qhu76O40TIW0xSEkVPp/Kurf57nD1RDkiB3KXxxlf/305OtbGbQBY/ZVAVBuLhEsCoIV4FNpWZuyYnG8Omt/3arg8aeATpsDv5RWI/H6wP8K6y3TIxhY6l5LKcrCGfn88Khv0HR29B5wn/Lf8JdcN/f4dZfQ3Dc0POdNij/F2QtGZPpCoJw8YltAIJwFegdcBMVpAOgobufL79WiMvjD1DfPdREcriRm3OiAYgK0tJjd591LEEYMw2FsOEHYD4KSi1oDJA0D+55FdS6Mz9n90sQPl7sVxWEq5hYWRWEq0BwgJo2q4MTZhv3vrKXZssAWpUCpQTVHf387ZNaylusALRZnQTp1GM8Y0E4jc0MHzwCbyyCtlIIz/AnTz34HtTthJ0v+ldQT+e0QcHP4cBf4a5XxmbegiBcEmJlVRCuAotzovnya/vYUm7GMuBhWoKJOFMAapWCNcUtHG6w8Nquap7IG8/GklZWf3PWWE9ZEMDjgsJXPu0+ZQdjFKTcCNd/D8LT/d2nHv4IPnwUCl/z3+o/2cGq/F/+FdWHN0HkhLG+EkEQLiIRrArCVWDA5cHp8TLghlnJoUQG6chNDOGWidF02px8XNnJnqoumnoGSI4wMFk0CBDGWuUW2Pgj6K7x3+5PuRHmPAHJ80B52sp/5AR4ZAc0H/KXp7J3gyEClopSVYJwrRDBqiBc4bZVtLP8H4fxyaBSSHT1O1mcE80DMxNQKxX86JZMarsO0dA9QFefk+8tTEeWZSTRM10YC13V/u5TlR+BQgXRE2HKV2DKl8+d0T8uVwSngnCNGnWwmpeXN6rztVotQUFBpKamMnfuXBYtWoRKJWJkQfg8rC1q5gfvHcXl8XFTRiQ+fFS39/PrzScobbESFaSlzerAbHWikMAnw97qLqYnhTIzJWyspy9cS5x9sOs38MnL4HNDcAKk3gTzvw+mhLGenSAIl7FRR43Nzc2DH0uShCzLZzzv3x8rKirigw8+IDExkd/97ndkZg4vXC4Iwsit3lfPf60tw+uTWZQVhVatZFFWFLdOjKGkuZeNpWa6+pyEG3Ws+sZMPjjUxLuHmyhqtPB2YT2xpgDiQ/VjfRnC1U6WofR9+Ohp6DODzgSJc+D6H/gL/CtEnq8gCOc26mB1xYoV9Pb28j//8z/YbDZmzJjB9OnTiYyMBKCjo4P9+/dz4MABgoODefTRRwEoKytj8+bN1NXVsWzZMtasWUNoqOjjLAijJcsyf9lVy39vOgbA4uxomljHDQk53DZpCpIkkRylYMu+xxnwDPDU1KfIiUsgKiiVEx02jnbt4ZOuLv7+yVKeyB9PoKgMIFws5hJ/KaqGfaDUQNx0uG4ZZN8J6oCxnp0gCFeIUQerCxcu5P7770ej0bB69WqmTp16xvOKiopYvnw577//Pu+88w56vZ7ly5fzta99DbPZzMqVK3niiSc+8wUIwrVElmVe2nKCP26vQqmQuDkrGqVSYmHSRN6qXkFWrAGD2kBBQwF2tx2lQsnu5t2UdJSwJHUJcye2Uln5DpbmL7K/tov3DgXw1dlJKBVi/6rwObJ3w7bn4dCb/pXVsFRIvwXmPgHGyLGenSAIV5hR33959dVXqa2t5Re/+MVZA1WAKVOm8Itf/ILKykpeffVVABITE3nyySeRZZmPP/74wmctCNcgn0/muXXlvLytCo1KwS050ahVEl+ensCjM5bwyMRH+Mmun/B6yesY1UbUCjUKFCQE+vcDvnjgRVbV/oK5Id9E555MaYuVbcfa2XG8fYyvTLhq+Lxw4HX4w1Q4+FcICIXse+D+VbDoeRGoCoJwQUYdrG7evBmtVsuCBQvOe+78+fPR6XRs2rRp8NiCBQtQKpU0NDSM9qUF4Zrl8fr4yQdHeWNPHXqNksXZ0aiUCh6alUhISCd/KvoTVZYq5sTO4WjnUdrsbUOe3+3o5mjHUaZGTiXQ1EhmYhcAxY0WPjjczHGz7UwvKwgjV7cH/nw9rP8+eJ2QdD3c+Sd/96moLH/NVEEQhAsw6m0Ara2tqNUj2+MmSRJKpRKz+VQfcq1Wi9FoZGBgYLQvLQjXJLfXxxP/e4QNJWaCdCryJkSiVCi4ZaqaIuuHnKg/gUFtICEwAUmSMKgNbG3YilahRaPUUNtby9aGreQn5pMcnIzdbacnej+R1jTM5mTKW628tbeW7y3MIMyoHevLFa40vc2w5VkofQ8kBURmQc4XYPrDEBAy1rMTBOEqMOpgNSgoiK6uLsrLy8nKyjrnueXl5fT39xMeHj54zOv10tfXR1iYKJsjCOcz4PLw6KrD7DjRQYhezQ0ZEfgkK6lJdWxtPYZaoSYuMA6FdOomSXJwMjOjZ7KnZQ92j50t9VtYmLiQ5OBkAPRqPVOi07A76rD1B1DbGY1JD6sLG3hkQQpalXKsLle4XMgyNB+GijXgsPgz+CcsgbjT6px6nLD3j/Dxr8AzAIGxkHojzP8hhCaP3dwFQbjqjHobwKxZs5BlmWeeeYaenp6zntfT08MzzzyDJEnMmnWqtWNDQwNer5eYmJgLm7EgXCP6nW6WvrGfHSc6iAjUMm+8gW72Q9gGWp0niDHEEG2IHhKo2lw2tjVs45OWTwCQkfHKXhptjTg8jsHzFAoF0xMSSUtsRKUeoLjJyse1xawrbjlrOTrhGtFWDn+5Ed66E/o7QR/u//2tO+G1G/yPH98Ef5oBBT8HpQrSFsIX/gpLXhaBqiAIn7tRr6wuX76cgoICKioqWLx4Mffeey+5ublDSlcdPHiQDz74AIvFQkBAAMuXLx98/oYNGwCYMWPG53QJgnD1sdhdPPTXQkqarUQHaxif0EItB0kN05FkikGrHHq7fsAzwOG2w5R2leKTfYTqQul2dKOSVHhlL2VdZVRbqpkRM4MJoRNQSAq0KiXTEoOx9ddQU5tFaZObv2tWEhJ4Hzeljx+jKxfGVFs5vLkYpi+DpWtBG3jqsVt+CZufhVevB5/H330qZirkLoVJ9/tbpgqCIFwEow5Wk5KS+POf/8x3v/tduru7efPNN3nzzTeHnSfLMiaTiZdeeomkpKTB47GxsTzxxBPcfPPNn2nignC16rA5eOAvhVS29xFt8hE6bjPdij5mxCcSYQwacq7L6+Jox1GKOopw+9yEB4STFJREUUcRBpUBjVLDjOgZbGnYgk/2sbNpJ+Vd5cwbN48YQwzhRg05CU76+ppo74inqc3Eiwd+h9V3N7eNX4BaKWqwXjNkGf71mD9QzXt26GNOG+z8FRx5yx+oKjUw9SH/Lf8gcZdMEISL64L6ns6cOZMNGzawatUqNm/eTHV1NV6vFwClUklqaiqLFi3iwQcfJCRk6Ab7u++++7PPWhCuUk09dh74yz4augcIC+nGGL2bAE0Qs5ImYNCe+uvq9Xkp7yrnYPtBHB4HwZpgZsbMREKioLGA/IR8ClsLAUgxpbBIWsTW+q0kBSXRYGvgw6oPSQ9JZ1bMLNIijXT2tWMfCKa7K5FAQz9/LXqPKmsp92bcObjXVbjKNR+GzipYuu7UMZ8PSv4Jm5+B/g5/wlT8bKjdDlMeFIGqIAiXxAUFqwAmk4nly5ezfPly3G43vb29g6upI60WIAjCKdXtfXzp9V10WH2EhDYSHn2M8IBYpiWGEqDxJz35ZB+VPZUcaDuAzWXDoDKwIG4BGaEZNFgb/Fn/Cf6s/5PBKviTrvIT89nasJW5sXOpt9ZzoucEtb215EblkjVuApb+Kk5UTaSpOZ1UrYMjzU10Ol5lduxs8hLyMGqMY/XWCJdCxRrIuhO0n36dmw/Dxh9B0wFQaiFhNsz4D8i8BdZ913/+6QlXgiAIF8kFB6unU6vVQzL+BUEYnd01NfzHqsP02QOIiKgnKqKZUGMYUxNMaFVKZFmm3lZPYWsh3Y5uNEoNs2JmkROeg1qhPlWe6tNA9UySg5PJT/AHrHnxeWSHZ7OneQ/7WvdRoa0gLTwXu72auoZMWlvSUavLiDLo2Ne6j9KuUm5Pvp2JEROHJHQJVxGHBQKjoa8dtv4Mit72Hw/PgMxbYc53QP9pi+zAaH+XKkEQhEvgcwlWL7bu7m5ee+01tm3bhtlsJiAggOzsbB544AHy8/MveFy3282qVatYu3YtdXV1gH9P7pIlS3jwwQfPuEJcWFjIV7/61RG/xsqVK4clk2VkZJz3eT/60Y/4xje+MeLXEa5MVpeVvx/eyh83OXE6goiLbsYU1kpEYACT40xoVApa+1rZZ96Hud+MSlIxNXIqUyOmolWdSrLqdnQPCVRdXhfzxs0brAQQpY9Co9QMBqzdjm5yo3KJM8ZR3FHM4fbDHOraRnBwLGGRPrras+jpSqBa0cD05CiUOHj72NtktWdxa8qtROpFJ6KrjjYQqrdB4Z/B1QfGKEi5Aeb/CMLThp5rM4MhYixmKQjCNegzBattbW0cP34cq9WKx+M557l33XXXBb1GZWUlS5cupavL33HHYDBgs9nYs2cPe/bs4aGHHuKZZ54Z9bj9/f18/etfp7i4GPA3KwAoKyujrKyMTZs28cYbb6DX64c8bySryFarFZfLhVqtJj09/aznBQcHn3XLREBAwGguR7jCODwO9pv3s6poB9v3Z+B2BZE0rolAUxNRQTomxQXT6+qhsLGQems9EhJZYVlcF3UdBvXwrOvcKP/tWLfPTbu9HVmWmRkzk2lR0zjacZQ9zXtQSAqi9FEkBycPBrUqhYrcqFwyQjLY27qXKksVhK3CqJpNS+vNGAy9FDdZmJUcRkKgkSpLFX84/AfyE/OZEzsHjVJzSd834SKp3AKl/wfWJlAbIPUmmPMEJF8Pin+ru+u0Qfm/YOmasZmrIAjXnAsKVsvKynjuuecGA73zkSTpgoJVl8vFo48+SldXF+np6fzqV78iMzMTh8PB3//+d1566SVWrlzJhAkTuPfee0c19rPPPktxcTFBQUGsWLGCvLw8AAoKCnjyySc5cuQIP/vZz/jv//7vIc+bNm0ae/bsOeu4siyTl5dHc3MzeXl5mEyms5778ssvM3PmzFHNW7iyeXwejnYcZXPdZkqbbRQemYbbrSUloR5DYCuxpgDiI+Dj5u2c6DkBQJopjenR0zFpz/5nyevz0mZvwyt7mRY5jQXxCwZXP+MD47ku6joKGgo42nkUnVJHeED4kNv5Ro2RhYkLyQ7LZnvDTqzBezDoS2jovBmVRkFpSy/TEkKINcYy4BlgY+1GituLuSPtDlKCUy7umyZcPF3VsOknULkZJCWoAyBhJty/8tTe1X+3+yUIHw/jxH5VQRAujVEHq8eOHeMrX/kKDocDWZbRaDSEhISgVH7+XW/eeecdGhsb0ev1vPbaa4ONBHQ6Hd/61rfo6Ohg5cqV/O53v2PJkiUjTuw6duwY69evB+CFF14YspUgPz8fn8/Ht7/9bf71r3/xjW9845yro/9u3759NDc3A4w6gBauXicTozbVbaKlr4X2zhD2HZqOx6tifHIdOn0bUSYFVqmEd0+U45N9xBvjmRkzkwj92W+3emUvHfYOXD4XE8MmclPCTcQYh2doRxmi+HLml5ltnc1HdR9R21tLkCYIk9aEdFrP9lhjLF/KvI+tNYeo7StBEfMuTc54vL1pBLWrSY8yEqAKICEwgS5HF68Vv8bMmJnkJ+YTqAkc9rrCZcph9Zei2vc//lJUpkT/amr2PfDPr8Du38K87w6ts+q0+QPVA3+FhzeN3dwFQbjmjDpYffnllxkYGCAmJoaf//znzJkz56IEqgBr1vhvM91+++1n7Hi1bNkyVq1aRXt7O4WFhcybN29E465duxZZlklKSmLhwoXDHl+0aBFJSUnU1dWxbt06vve97414zu+//z4AUVFRzJ07d8TPE65OsizTaGtkS/0WTvScwKAy0NkRx8eHEpBlBZmp1Si0rci6Bg5ZKvD4PEQGRDIzZiZxgXFnHdcn++gY6MDpcZIRmkF+Qj5xgXFDAs9/J0kSycHJfHPiN6normBT7SYabA2EB4QP2VqgVCi5ISkXZVUM1T0NeANKaJWb6GtPIkAzi4RQf4AbHhCOW+tmv3k/ZV1l3J5yO5MiJokErMuZzwfF/4Ct/3WqFFXS9TD/BxA9CSQJHv4IPnwUCl+DrCX+ZCqb2X/rP3y8P1CNnDDWVyIIwjVk1MHqoUOHkCSJX//61+TmXrzbQP39/ZSUlACcNQiNjo4mLS2NyspK9u7dO+Jgdd++feccF2Du3LnU1dWxd+/eEc+5r6+PLVu2AP49uhcriBeuDB32DrY1bqOovQiNQkOcMY79lQr2FMcDkJlWQb/6CH1U4rY7MWlNzIyeSXJw8lmDTlmW6RzoZMAzQIophfzEfJKDzn7+mSgVSnLCc0gPSeeg+SBbG7bSNdBFlCFqsDOWVqVkalwkLpeamqZZyCEfYdPXsrGxmemuGUyJykYhKVAr1MQHxmN1WVldsZrDbYe5LeU2ogxRn/0NFD5fTQdhww+h5TCoPi1FNfNRyLgFVKftPY6cAI/sgOZDUL7Gn/VviPDvURW3/gVBGAOjDlYHBgbQ6XQXNVAFqK6uHuxRnpaWdtbzTgar1dXVIxpXlmVqampGNO7JeYzUunXrcDj8/dfvueee856/YsUKzGYzfX19BAcHk5WVxZIlS7j11ltFoHsFs7qs7G7azZ6WPUhIxBhiUEpKdpZJ7C+LR6HwEJ/2Ea2Kw3jlAQxqA3OjZpMRmnHWVUlZlul2dNPn7iMhKIGFiQtJM6V9plVMjVLDnHFzmBgxkU9aPmFX0y5kZKL0UagUKsKMWlIjDTidNqprHwHTYbRR6yls201V7zHmx11PtCEagCBNEEa1kRprDS8feZm8xDzmxM4Z1hZWGAM2s38ltfh/QVJARCZk3gFzHvevrJ7NuFwRnAqCcFkYdbAaFxdHU1MTsiyPajVntDo6OgY/joo6+yrNycfa29tHNG5/fz92ux2AyMizl985OW5/fz/9/f0YDOfve/3BBx8AkJubO6TF7NlUVFSg1+tRq9V0dnayc+dOdu7cyT//+U/+9Kc/ERQUdN4xhLHx2tHXSDWlkpeQN3is393PobZDbGvYhtPrJFIfiVappbqnhoP1PdQfuwtNcCnGmPV0Sb2oJQ3To2cxMXwiKsWZ/yrKsozFacHqshJjiOGe9HvIDMlE+e8Z2p9BoCaQm5NuJjcql4KGAo60H0Gr0BKhjyA1wkBXfzdOZz0NTdMJkOMxRK+n21HN/1X932AXLIPagEJSEGuIxeFxsKl2E0VtRSxJW0JKcMpF/V4hnJnkdfn3mH78K3D3Q2CMvxTVgh9BqEiKEwThyjHqYPW2227j5ZdfZu/evcyZM+dizAlgMKAEf0LV2Zx8rL+/f0Tjnn7eucpDnf7YSILV6urqweoI50usuvvuu7n11luZMmXKYEDa2NjIypUrWblyJfv37+c///M/eeONN857PSd5PB6OHj064vOFodxu96jeP5VFxY+Lf8xjiY+RakilwlbBm01vIssyN4bdiE6pw2K30DTQzH7LIZwdN2FIegVFQCMeWUm8ZjyTTRloZA1dHV1nfI1+Tz9Wr5UQVQgzTDNIUaXgbfZS1lz2eV32MJlyJiHaEPZZ9lHSVYJeqSdKbaBd14ApyEB3TzTjNAsIDYzCqzvOiZ4T1FhqyDRmkqY/tdKrlbXUW+t5seVFso3ZTDdNx6AyjPp9Fi6ALBNo/oTxxX8AewtelZ7+yJl0pD9Af8RkaOqDJvE1+LyIP9OXjnivL43L8X0edbC6bNkyCgoKePbZZ3nzzTeJj4+/GPMacye3IIzUycQqvV7P4sWLz3nuL3/5y2HH4uPjeeqpp4iPj+f5559nz5497N69e8T7cFUqFZMmTRrVnIVTjh49Oqr3L0fOQRuh5TcHf8Pk8MmoPeOxty9E9gZQ50smPc5Ov6KC/a0H8Tqi0EZuBiSCpGTmxk0nKSzsrGP3rP5sYwAAIABJREFUufrocnQRrg3nS4lfYnLEZNTKS9vCOF/O51j3MTbWbqTd3k6GxoBS1YDDGUxLewo5IU4kwpkY009xVyGltlIaXY3Mi51HQlACh9oOERoYSkpgCuZ+M1udW7lt3G0ozcph73NBQwHVlmoemfTIJb3Gq1LHCX8pquoCZEkJMVNRTn2IoClfIkhz/rtDwuiN9nuHcOHEe31pXKr3eVQLRKMdfMOGDdx99928/PLLLFmyhEWLFjFx4kSMxnP3DR9tndXTi/E7HI6zjj8wMAAwotv0/37eyeeea9yRjO31egcrFyxevHjEczmTBx98kDfffJPm5ma2b98+4mBVuDQcHgcVXRXsbN5Ja18rGYGzOWjei9uciVJpQKnuw+5Q8u7hWlQRW5AkGWVAC0YSCVWmMyMxnojAM+/jtLvtdA50YtQYuSv1LqZFTxuzPZ8KSUFWWBbjTeM53H6Yj+o209LXxri4CmpqJlNVl0pGWildPUHcmfIFjltKOdx+mPW160kMSiQhMGFI+1eby8bjBY+jkBX8LeFvJAUnoVaoKWgo4MldT7Li+hVjcp1XDUcv7Phv2P+qvxRVSDJdpsmE3/MiBIpkN0EQrmyjDlZ/8pOfDO4/k2WZNWvWDAZqZ3MhTQEiIk7VlmxraztrsHpyr+q59p+ezmAwoNfrsdvt59znevIxg8Fw3uBz586dg3tsP2ttVYVCQU5ODs3NzTQ2Nn6msYTPT4+jhyPtR/ik5RP6XH0EagLReVM4fCia1LQIWsf9L1qlFpVChVt9GLW2CSTw9KUTo00nWK8hN9FEqGF48DngGaBjoIMAZQC3ptzKdVHXoVfrzzCLS0+tVDMzZiY54Tl8FPIxfzm0nshoLW2tWbS0JhMTW8Vxcz9T46cN6YLVaGskKSiJrfVb/RULgpNRK9Q4PU7+WvJXjBojWoWWd068w4p5K4bs/RVGweeFI6ug4Gdg74KAUEieD/N/SEubl3ARqAqCcBUYdbAaGxt7MeYxTGpqKpIkIcsyVVVVpKamnvG8qqqqwfNHQpIkUlJSKC0tHXzuZx335BaAxMRErrvuuhHNYyREUsrYkmWZJlsTheZCjrQfwSf7CNWFEhoUiizDyp3hTB3fx/xJkZR3zeXjpo8B6HX14nWFo+xdTJAiDHNnIPkLWzHph7YmdXqdtNvbUSvULExYyMyYmRg1575DMVYMagP3ZN5KmCqTP+z9F/b+Ntq7oggO7KXN1051Zx/jIwMHu2Dtat5FTW8NWqWWzfWbWZjgr2eslJTEB8ZzvPs4O5t3MiViCkfaj6BUKMkKyyJKHyX+3I9Uwz5/KSrzUVDpIHEuzHoM0m8GpRraL689Z4IgCBdq1MHqtm3bLsY8hjEYDEycOJGjR4+ya9cubr755mHnmM3mwaBy1qxZIx571qxZlJaWsnv37rOec7Kl6vnG7e7uZseOHcDIylWdjyzLlJaWAjBu3LjPPJ4wem6fmxM9J9jdtJtaay0qSUVkQOSQfaPmbg3dVhXXTy9lU20pdda6wcc8/Sloe+8nMf4ESslJSflUBgaMmPQuAFxeF232NpSSkvlx85kTO4dgbfClvswLMj81meaue9jCMT45MUB1QwKZGW1UttkI1qmJDNIRa4zlvvT7KO8qZ795Pz7Zx+b6zWiVWtSSmjprHbtadrEwcSFJQUnY3Da21G9hS/0WYgwxTI+eTnpIOmEBZ9/Xe03rbfaXoip599NSVBMg+26Y+S0IOHtLXkEQhCvVqIPVS+n222/n6NGjrF+/nscff3xYF6vXX38dWZaJiIgYVbB6++238/rrr1NXV8eWLVuGdbHavHkzdXV1SJLE7bfffs6x1qxZg9vtRqFQcPfdd5/3tc9X8mv16tWD7VpvuOGG81+M8Lnpc/VR3FHMruZdWBwW9Go9cca4YbVMnV4ne5sqMaSuZFNDNwDB6jAsLgue3smog0oIM+4gQBPHtEQTrj47J5r0hIf0097fjozMrJhZzBs374oLyCRJYsmUcdR09KNI7WPbsQ7q6iYRn7qdw01Ork+Nx6BVoZAU5ITnkGpKZb95P+Vd5Ti8Dpw42VK/ZXAvK/hrtAZpgvDJPnqdvaypXoOERFJQErlRuYwPHU+Q5iov4ybL0HwYKtaAwwI6E0xYAnGn1Tl1O2Dvy7DzN+AZgKBYSLnRX4oqJGnMpi4IgnCxXdbB6pe+9CX+/ve/09zczLe+9S1efPFFMjMzcTgcvPXWW6xatQqAJ554ArV6aLb0TTfdRHNzM3ffffew7PsJEyZw6623smHDBp5++mkkSSIvz79nrqCggGeeeQaAO+64g4yMjHPO8WRt1blz556zHuxJTzzxBElJSeTn55OZmYlG47813NTUxNtvv83f/vY3wL+iO3/+/POOJ3x25n4ze7r38N6B93D73ARrg4kPjB/2Q0XnQCelnaVUWirx4EGp0DEpfDK2vgBq7IdwND8Ako9xoWo6fAfIDgvEqI3AoHPT3e/E3G9mWuQ0FsQvIFI/sj3WlyOjVsWXZsTzx21V5CaGcLAevF234A3fzu76Y8xNTMao9e/zDlAFsCBuAVmhWbxf+T4yMl7Zy77WfXQ6OkkzpRGqCwX8SV0huhBCdCF4fV7a7G28W/kuKklFRkgGU6OmkmpKJUB19pJzV6S2cvjXY9BZBVl3nmpv+tadEJ4Gd/4PdFfDR0+BpQG0QZA0H+Z/D+JmgEK0txUE4ep2WQerWq2WV155haVLl3L8+HHuvPNOjEYjDocDj8cD+LPn77vvvlGP/dxzz9HY2EhJSQmPP/44Op0OWZZxOp0ATJ48mZ/+9KfnHKOsrIzjx48DI0+s6unp4aOPPuLVV19FqVQSGBiI2+0eUv915syZ/OEPfxj1NQkj5/V5qe6tZm/LXo51H8Nis5Aek45WpT3jeWWdZZjtZgAiAiJQD8xAsmfT2X2QZs8uHM1fQqHpRmXaT4z+BnJMwexs3obdY6XdNpv4YBPfmfodYowxZ5rOFSctMpCF2VFsKjEzzqSjqtXBzaH3Y3OVcdRcTnyolmh99ODWiT53HzIySkmJT/Yx4BngUNshf4krXSjjTeNJM6URpPWvoCoVSsICwggjDLfXTVVvFWVdZWiVWiZFTGJixESSg5IveUmvz11bOby5GKYvg6VrQRt46rFbfgkfPQ2vXu/P8FeoITYXcr8GE78AmssjCU8QBOFiO2ew+uSTTwL+TPvvfve7Q46NhiRJvPDCCxcwPcjIyGD9+vW8+uqrbN++HbPZjNFoZMKECTzwwAMsWrTogsY1Go2sXr2aVatWsXbtWurq6gDIysrijjvu4KGHHhq2WvvvTq6qmkymwZXZ8/nWt75FRkYGxcXFmM1mLBYLkiQxbtw4cnJyuOOOO8jPzxdJJhfJgGeAss4ydjXvoq2/Da1SS6whFo1dMyRQtblslHeVU9FdwYBnAKWkJCMkg5zwHCL1kVQ2K1hX1IJWtwtn6xdJi9LRrtmJUiExNSEYq9vL5IjJHGo7hLMvg5eW3EOM8RytLa9A+ROiOG62IQPd/e3sOG7h/utm0tE/nrDwBsz2clSSCrvHzrbGbRhUBhQomBs3l631W5kePZ1+Vz/VvdUUmgspNBcSqY8kzZRGqikVo9qfbKZWqonS++9aOD1OitqLOGA+gEFtYFrUNLLDsokPjP9cu3pdErLsX1Gdvgzynh362EAPbF/hz/SXvaDUwrSHYMGPwXjlrsoLgiBcCEk+R/X7zMxMJEkiOTmZDRs2DDk2kqL5J8+TJImKiorPb9bCMKJY8rl1DXQNlp6yu+0EagIxaU2DPxS0tbURGRlJU18TpZ2l1FvrkZEJ1ASSHZbNhNAJ6FT+1femngE2l7XjNG7F2Xo/WeNU+BS9tLENlVLm+rjrSTWlkp+Yz2+372an5fe8dNOLV2V5phbLAC9tOUHvgJvN5W3EBOu4JTsaq8PDF2cb+LD+TdbXrGdmzEwquirweD18Jfsr1PbWDtZhTQhMoKmviSpLFbW9tbh9bgBiDbGkhaSREpwy7Na/LMsMeAbocfbglb2EakPJjcplQtgEYgwxV8YPe02H/Lf6v38MtJ9WgfB54dDfYNtz/oBVHw4Js6G6AJauG7qH9TzE94RLQ7zPl454ry+NS9kUYKSvc86V1bvuugtJkobUPD15TBAud7IsU2+tp7C1kKMdR5GRCdWFEh4QPuQ8p9dJVX8VBccL6HX2ApAYmEh2eDYJgQmDf949Ph8VLX3sLYnGrmpFNXAPvr4JNHW0kJrcwg3h15McnEheYh7RAUm8+nENu4qj+eHdP6PaUn1VBquxpgDunDKOfx5sYEq8iaJGC0ebexkfaeSNA3s4aN/M96/7PhaHxf81+PSH3OTgZPIT8oc0DkgMSsTj81BvrafKUkW9tZ6W/hZ2N+0mLjCONFMaycHJaJQaJElCr9ajV+uRZZk+dx8FDQVsadhCtD6aGdEzSA9NJ0wXdvl+v6pY49+jejJQrd0Fm34MbWWgDoCk62HW4zA+H9Y+4T9/FMGqIAjC1eKcweqZ2oKe6ZggXE7cXjfHuo+xq3kXDbYG1JKaSH3ksP2NgwlTPZV4ZA9apZYpEVPIDsse3Dt5kt3l4VBtP8XHkrEPBBJqmkpcQjE9pp1Y2q+juOgmwieEYhsI5bXKATaWbCM10si7/zGb9KhArmazU8OoaLWilCRaLAMcrO9BG1ROqecVbo3+Pg9kfhkZL3PHzWX1wdWDX5MYQ8ywgFWlUJFqSiXVlIrL66LOWkdlTyVNtiYabA0om5QkBCUw3jSehKAE1Ao1kiQRqAkkUBOIT/ZhdVlZW7MWahisKJAemn75VRRwWPzJVN21sPn/wbG1/lJUkdmQcy/MWAa6T0uaBUaDvXts5ysIgjBGLusEK0EYDavLSnF7Mbubd9Pr7MWgNgwrPXUyYaq0s5Q2exsAkfpI4tXxTEuYhkox/K9EV5+TwkonJ2rScbl1REbVEB5VjuyL4OvTbuSLk2ZQ1eZmY6kZy4CbcKOW1d+cxeT4a6PmpVIh8YXcOH7d1c+88eGsK26lqO048xK/TX93JtuPtZOfFcW0qGnI42SMCUZKO0sp6SxBqVAyLXIa7fb2wVJWJ2mUGtJD0kkPScfhcVDTW0OlpZLa3lpqe2tRKVQkByWTFpJGvNG/Z1UhKTBpTZi0Jrw+L+32dt6rfA+lpCQ9JJ1pkdNINaVeFh3CXnM2k9ryCXmf/B68bgiO/7QU1Q/BlDDk3ILuUqqVEo+M0VwFQRDGkghWhSvCa0dfI9WUOuxWuizLtPa3csB8gIPmg3hkDzaXDbvbznXRp7qJ2Vw2yrrKqOiuwOFxoJSUZIZkkh2eTaQ+kra2tmGBqizL1HfZ2V8JDY2Z+HxKEhPLSQ0PRuu9l2VzppKb5K+TOjmeayY4PZMQg4b7r4vjjT113JARwebyG6jw6FmcrWPd0RbiQgPIjA5CrVCTEZpBRmgGt6feTm1vLSWdJZR2ltJoa0StUBOiC0GrHFqVQafSkRWWRVZYFv3ufqot1VRZqqi0VFJpqUSr1JISnEKaKY1YYywKSTGkosD+1v2029up6KpAo9QwMWIik8InDW4rOF1BQwHVlmoemfQIPtmH1+fFK5/2y3fqd5/swyN7znqeT/bh8XkGj3l8HtweB1EnCkhsOMCTIUaeMRjQh6bSkp7HXxs3I330VZ6b+xyR+kgi9BEcbtrDU33lrJjynUv5JRUEQbhsjDpYdblctLW1odFohtUVdTqd/PGPf6SwsBCXy8WCBQv4j//4DwICrrK6iMIll2pK5cldT7Lien8feZ/s47b/uw27286cmDkoFArCA8Jp6WthT8se8hPy/clQ/5YwFaQJYmrMVDJDM9GpdGd9PbfXR1FzGyU1BjrNOaiUXm6YKBGhuwlZVvPwDUlkRl9mt5XH2KQ4E3NSw9hb3UV2TBBlrVZKW6ykRRpZtbee/1yYPuR8rVJLZmgmmaGZ3J7yaeDaUUJZVxlunxu1Qk2oLnRYMGlQG5gUMYlJEZOwOq1UWaqoslRR0V1BRXcFAaoA0kxppJnSBtu3RugjBrcbhOhCKGkv4bcHf4tCUvBQ1kN4fB48Pg/Vlmp2Ne9iVswsfvrJT/HJPnyyD1mW8eFDRkaSJfh0G6x06gMkWfI/foYEVPnT/5K7W7ijcjexfV0kKZQ8I3v5WVgIuTEpxEv+wBZgQ+0GJCRa+1s52naI/zcAivDxVHRVEKGPIEQbcuVVPxAEQbhAow5W33nnHV544QXuvfdenn/++cHjsiyzdOlSiouLB79RHz9+nP379/P222+jEIWrhc8gLyGPFdev4Cc7f8JjUx6jz92HxeEv+xVrjEWpUA5mmC8YtwCry8o/jv2DXtenCVNBiWSHDU2YOhOvz0uTtYNycxeNDdlYuhMJClBw1+REHB4fGqWSZdcnkxhmuFSXfsWQJInbJ8VS1d6HRqmg1epgb00X40w6uvrdPLrqECaFi9TmYyzOiWbKaSvRAaqAwZVTu9tOrbWW4vZiKroqcPvcaJVaQnQhwwLXIG0Q06KmMS1qGj2OHiotlVT1VFHSWUJJZwlGtXEwcM2LzxuyP1atUOPDR7ejGwUKmvqa2N28m/nj5pMYnIgCBZIkIUnSqY+RLihhK6i/m9mlG0htLcMrSRwOHcehuEkYgxP4RdE/eFraT6jKiFLyB6DxgfE0dJ3gaNshnu/soX7SPRTWbUZCwoePAGUA44zjSApOIsYQQ3hAOGEBYWfcxiIIgnClG/V3tl27dgGwZMmSIcc3btxIUVERKpWKL37xi+h0OlavXk1RURHvv//+BRXuF4STXF4XJo2J2bGz+f3h3zMrZhZqhT9h6mSguqV+C9GGaD5u+hiP7EGn1DE1YipZYVnDEqZOJ8syA94BGm2NWOxuWttDaW1YjMWmIC4kgFtzounqdxFq0LDs+hSigs6+InutM2hVfGlGAn8sqCQ/M5L3Djfx/uFmlAqJ6CAdkl6mq8/JV14vJCXCwK/vmzwsAU2v1pMdlk12WDZ2t52a3hqK2os41n0Mj8+DTqUjRBsyLGEuRBfCjOgZTI+aTudA5+CKa1FHEUUdRZi0JhIDE9lSv4WFif4Wywr8e1xre2vZ07KHhYkLh+2d/SzUbge5J3YwuWo3StlLtTGUvTEZWJPngT6UHoDpD/N0ybusaNjK/f0OomQJ2f42BZKFp+0ylukP4wuKIv7TMWVZxul1YrabqemtQca/OKBRaIgxxpAcnEyMIYZOVydur/vKb5wgCMI1b9TBam1tLeAvnn+69evXI0kSjz/+OI8++igA6enp/PjHP2bdunUiWBUuiN1tp6i9iI+bPsbitBBtiCY/IZ+CxgK0Ci1qhZo9zXs42nkUgOa+ZiL1keSE+fvSn2ulyePz0O3oxuF14PK5iZGvo7k1lLp6H5YBN9mxQSxIj6DFMkBCmJ6vz0kmxKA563iCX2qEkUU50byzvxGFJOHxySSaAuixu+jq8/K1GyJ5+rYJ/GVXDV945RPee3TOWSsm6NV6csJzyAnPGdyrWtRexImeE3hkDwHKAEw60+APLsDgbf8IfQSzYmZhtpup6qmiurea6t5qADbVbUKj0KBT6jjWdYydzTuZGzuXEF0IPY6ewdv/Pj7dAnByOwCnPj59e8Cw7QI+L+E9jcS2HWe/z01BWAh1hhCspjh8Gj1yTzm+7tOeEz+ByP4OVkrdRHp9dCmt3BwyidYp84at5EqShE6lQ6fSDbaqlWUZl89F10AXTbYmfLKP7p5utju3E6OPITE4kXHGcYQHhBOhjxi2J1gQBOFyds6mAGeSm+uv83fo0KEhx6dPn05fXx87duwY3MvqcrmYPHkyJpOJvXv3fk5TFs7kaiuWbHVZOdR2iF1Nu7C77YToQgjUnApoKroq2NG0Y/BzBQrSQ9PJCcshQh9xhhH9ZFmm391Pj7MHCYnxIePJDp3G+p3dVPUFcKi+B4fbx/Vp4UyKC6axZ4CsmCAemp2IQStusY6U0+1l/q+2E6LX4PXJVLb3odcoUSJzw4QoJo0L5v7pCby2s5pdlZ2sWT5vVOPbXLbBwLWypxKf7POvuOpCzvoDik/20dLX4t8qYKka3B96OdMoNUQERPiDzAB/AB6sCR7RVoS2tjZCwkOwe+zY3XZ8sg/wB7tR+igSgxKJC4wbHP9yqJBwJbravvdezsR7fWlccU0BzsTpdA5rQ1pdXY3NZiMpKWlI0pVGoyEoKAibzTbalxGuUV0DXRS2FrKvdR8un4twXfiQIv69zl7/LeGeY4PHIgIiuD3l9nMmTHl8HroGunB4HQRrg1mYuJBJEZOw2/W8tbeO/U0eTnR3IwG3T4ohIVRPQ4+dGUmh3D89Hq1KJLOMRoXZhs3hYU5KGIE6NW1WB1aHB4NaIinUQIXZxu+3nuDe3Dj+tqeO4kbLqKopBGoCmRI5hSmRU7C6rFRbqjncdpgaSw1e2YtercekNQ0JXBWSgrjAOOIC45g/bj5vlL6BR/Zg0ppICExAIfn3pSokhf8Xp33Ovz0mKZAY+rnB0UdO/UHiu2qRgFpjGCci0/BF5SBpDYPjDHmd08att9azuX4zakmNFy9xxjhcPhdt9jaa+5oHr0OtUA8GrydXSk1a05ASbSdplBo0Sg0m7an31u1zY3fbOWg+yL7WfYA/Uay5r5n0kHQWJS0aDIz1Kv0ZA+PTKyYIgiBcbKMOVsPDw2lra8NsNhMdHQ3A7t27gVOrrqdzOp0EBYmsaeHczP1m9rbs5aD5IDIy4QHhQ4LPDnsHR9qPDO7RC9YE0+vqRa1Q0+PsobW/ddhew5OdjU4mYmWGZjIjZgapwf7tAQfqunnnwAmOm21UdLowalXcMTmGYJ2axh47N2VEccfkGFRKkRw4WptKzdw2MYZ7po7jHwcauTk7mncPNTHgkelzeYgP0dNhc/LG7lqmJYawoaT1gkt/BWmCmBo5lamRU+l19lLVU8WRjiPUWGrwyT4MagMmrWlI9nyDrQGP7EGj0NDn7iPWGHvBe1XVbie5J7YP7kutMYTwSWwG1qS5hOjDYASroLW9tWxr3IZBZUCj1DAzZuZgMlhiUCK9zl46BjroHOikw+7/vbW/dfD5KoVq8Ae7CH0EEQERgyupw+arUKPWqofs4/b4PPS6enn3xLtU9VQRY4xBRmZX8y5UChWPTvJv7VJICo73HOeDyg+4d/y9fFj14WDQDgwmoJ2elPbviWrAkMD/07Q1f0WFk89HYl3NOsYZx5EblTtkbIWkQK/SY9QYCdQEYlAZ2NG0QwTPgnAVG3WwOnnyZDZv3szLL7/Mc889R29vL2+//TaSJDF37twh57a2tuJwOEhMTPzcJixcPWRZptHWOLjnVCEpiNBHDGZ8y7JMS38LR9qP0GhrBCAhMIFoQzSH2w6f8R/25OBk3D43XQNd/qQsnYnFyYvJCc8hLMBfE9Xp8fJ/R5opqGinvNVKbWc/oQEK7s6NRwJaex3cOXkceRMiL99WnZe53gE3UUE6ZqaEUW62UtZsRa9RYnd5+UdhAzdnR5MUbqDf6aG4yYLT7WXA5SVA89lWsIO1weRG55IbnYvFYaHSUsmRtiPUWeuQkdGr9FgcFrY1nTkwHFXAKvvIbDjMrLJN6F39dGr07I5KpSVlLgTHwQhLS52sYpGfkE9hayFw5na0IboQ0kP85b9kWcbqstIx0DEYvHYMdGC2m6HLP64CBeG28FNbCAIiCNWFnrHklUqhIjssG71Kz9aGrYTrw0kITAD5VEc4GZnmvmYOmA8wPWo6Tq+Tw22HT70dyPj/l4ceO/mxPPz4kF1oJ6uAfVoazNxvZm31WqZGTiXa4F8Y2dG4A4Ab428cHKfD3sGhtkPclnIb66rXERYQRpAmCKPGiFFtJEgTJJLMBOEKN+pg9aGHHmLz5s188MEHbNy4EY/Hg8vlIiYmhvz8/CHn7tmzBxiejCVc23yyjxpLDTubd3Ki+wRqpb/15slbtrIsU2et43D7Ydrt7UhIpJnSmBo5FZvL5v8HPHHoP+x58Xlsqd/ClMgpxBpiyQ7L5rro60gxpQxJvunud7G6sJ6jTRaKG3tpszlJizAyJULC55OxDLj58owEZqaE/n/23jtcjvq8+/7M7Gyvp+3pRdLRUe9IqAsjgYEAsQDH2IYQAk6MS/LYj329JvHr2FyJwXFNnmAcktd+bLAxhphqwKiAEKig3rtOP2dP3d6nvH/M2dXZU6RzVFDxfnTttauZ2ZnZObM737l/9/2980L1AnBb9aF/URS4e34Vjb3HMBpErJKKCryyr4OFdQUsnlCEJIr0x9I8+c5J7ltcS5n74rgteCweFpYtZGHZQvoT/Zz0n+SF4y+woXUD87zzOO4/DowsDM9FeW8jy/e/SknIR8wg8Y53IkcnLEIrmQrS2IuXBgvVCe4J2XP6XPslCAJusxu32U29px7QvzfhdJjemC5c2wPtBFNBumPd2feJgkihpTAnhaDIcsbyaug2MxHTElsJjcFGdnbtvOiOCaNR7aym2Fqs74v1jN0YQJWzCoDTgdPs6trFssplmA1mtnVuQ9GUrP+tqqlIooTD5KDQUkiRpYgSWwkukwunyYnDqEdnrZI1/33Pk+cKZtxi9brrruOxxx7j8ccfJxaLAVBbW8sPf/hDTKbcSumXX34ZgOuvv/4i7Gqeqx1ZlTnuP86m1k00hZqwGCxUOiuz3pKKquiRsO49BJIBDIKB6UXTmVsyF7fZPeKFXUPDF/EhiRLLK5eztWMrd9XfxZ31dw7b/snuML/a2kyHP872xn7CSZmFdQUsmVjEiZZOSMo8tHwCMyvdH/Whuea4ZWYZ9/33dr595ww8NhP3Lqzhlb0dGA0Cdy2o4Y0Dnexo8tPuj9MTSXLXvEq6wwl+vO44n15UzZxqz0UVD4WWQsLpMO+2vcu3l34+IGicAAAgAElEQVSbMnsZu7p20RZuoy3chlE0MrdkLuua1zHPO49yR7n+xiHlpwXxELec+IBZPaeREXjPU8I7ZZNJeKeA0Qpx/eYqEzkc/HoovoiPPT17mOedhyRItIZbWVapj061hdsAPVK8snIl65v1G7SziURBEHCZXLhMLiZ6JtIlduH1eommo3oEdlAawZH4kTPvQ6DQUphTxHVD1Q2sb1mPWTRjMpiGffc+KoaK58E0BhvZ0LrhrMdF0zRkTSalpOiJ9dAWbiOtpLMpB5m/jVWyUmgppNBSiNfqxW1x54hZh9GRb8KQJ89l5LzKmz/5yU9yxx13cPz4cRwOB3V1dcNM/9PpNPfccw933303N95440XZ2TxXJ2klzaG+Q2xs2UhXrAu70U61szobtUkraY70H2Ffzz4i6Qgm0cQ87zxmF8/OVigPvVjG0jGWVizFIBqYXjydBaULmOCewKa2TTy6+VHsJnu2Nauqamw+0cPLezsIxFK8f7IXRdW4eXop08pddIUSaMAXbpjExBLH5TpM1xRzqtxMLLHz03dP8vWPT2VGhQubyUA4ruCySHzyuio2n+hlf1sQUQBZ1agqsBGKp/nFliZWT/Vyy8xyTNLFyRfe0LKBRzc/yhMrnsieF9eXXU9foo9QMpQdgt7esZ0f7vohN9fezNLKpdkInZiK4frwv3Du/C2Cmibu8BKsWUL9dQ8wuWQaCLnNAjLvy+RfZl4Pnv/skWf5xORPsLJqZc4yCTmh56RGOmkMNaKoCnO9c2kMNiKJEjbJht1ox2wwn1PQC4KgD4ebHDmCLpqOZlMHMpHYY/5jHPMfy+6fTbIRlaPElThvN71NnbsuK3izDRIG5ZiO+jzCtMx3f6Tnoe+1Slau817HuuZ1GEUjJoOJU/5TbGzbeE7xLAgCRsGIUTRiN47cyEPRFFJKinAqTF+8j4O9B9HQEBFRUUEDo8GI2+TW2/daiyiyFOEyu3AYHQTTQcKpMAbBgCRKGARDtoguT548F4dxW1fluTK5Ei094nKcfT37eLf1XfwJPy6TC4/5TMQsISeyfeETSgKrZGV28WxmFM8Y5gO5q2sXBeYCSmwl9Cf6sUpWVlSuYEHpAjyW3MKcwZXK8ZTC/+xuY9vpXvoiKbad7sdsFLl9VgUVHgsdwQQui8Sy4iSrl8z/yI7NnwLHu8Lc89QW7l9SyyM31GMQBP7xtx+A2UWJ08zuFj97WwNomoaiwuJJRSysLSCtaLQH4kwtc/LpRTUXxdv26f1PM8kzKStUz0ZOpbuqwr7fwLp/glgvmF1QtRAWPwITVo5ryP98yfgB98R78EV9NAWbaAu3kVAS2eigTbJhM9qwGCzZ71dXV9ewlthnIy7Hs5HXjCgNpUKX6mNdMBaDJZuf6jK5cJlduE1uXGbXRfWR1TSNtJpmZ9dOHEYHJdYS0moaURB5p/UdFEXhxtobz7g8IOKL+YimoswqmYUkShhFY/bZaDAiCRJGgy6iM9NMou7cIAkSBtGQI34z/8+8zizz26O/ZaJnIquqVuVMzzwP5lpwcBjvde68v/d/4lyJ1lV5sXqNcCWJ1XAqzO6u3Wxu30wkFcFj9uA0ObMX0Ugqwr6efRzuP4ysyrhMLuaWzGVK4ZQRPTI1TSOQDBBKhfCYPayqWsVc79xz+kJ2hRI8s7WZ5v4op3uiHOoIUWAzcuecCtxWI63+OOVuCw8tn0D76WNXzPG7ljjeFeZrL+zjdE+UW2aWkYoEOB0ROdIZptBm4uMzShEFgT8c6KQvmqKuyMbNM8owSyIdgTh2s8RfLqml3jty04DzQtOgfTcceRUSAbB4YNqdUDXEzaR5C7zxdeg6CAYzeKfDgr+E6Z8AW+HF25/zQFEV/Ek/3bFuuqJdNIWaaA23Ek/HEQRdwMaCMWrKanIE7HhoDDbyVtNbeltaTWVpxVIqHZVoaGialvOccR4Y6VlDG/E9oz6fZd4O3w4UTcFhdGAxWAimgqTV9LB9NxvMOSLWZXLhNrtxmVzYjfYRbb7GcjyGpkL89uhvkRWZz07/bLaBRGOwkXdb32VF1QpqnDXDmklkXmcbSAxpOqGhnYm0MyRaL+QWpXVGO9nTraeSlNpKEQSBTa2bQIBb6m7BYrBglsx0hDtY17KOtfVrmeudi1WyYpNsWcFsMpiyYtlkMCGJUnaaUTReMSkQQ69zqqaiqAppNY2sychq7uP99vf50a4f8aV5X2Ked152uqIpuqOEUR912Nm1k+9s+Q5PrHxiTML2WmE0MT+SnrgUYv6S+qwGg0EOHz6Mw+Fg1qxZOfN6e3v57ne/y/bt20mlUqxcuZJvfOMblJSMbtKe59rBn/Czw7eDDzo+ICWnKLQWUugqzJm/t3svxwPHUTWVIksR87zzmOSZNOLFQ9X0vu3RdJRSWym3TriVmcUzh/WHH4mD7UF+va2ZeFplb0uAVn+c6kIrfzZTt6Jq7ovRUObkL5fU4rQYaT/nGvOcDw2lTl790nL2tQZ486CPpqDG0knF3DGrgiO+EKIgUGA38amF1bxzrJsjnWGe+7CF22aWU1Vgoy+S5MmNJ7lzbgWrGryI4gUOrXYdhle+AL0nYfqfg7MMwj741Z9DcT184ikw2uDtb+piVhChcCJMuQ0WPqS/vgIwiAaKrXql//QivYA1833J2FptO7otGy3NRGAzIuVcBUUZYXbBjgkXkcZgI4qmYBJNJJQEyyuXU+eqI6EkCKVChJKh7HMwFcy6JQxFFEScJqcuYAcisVlhexbngHMVvBkw0Bhq5N22d8+ZX3yxqHJWZYvQVlevps5dhyRKaGg4jA4UTeG4/zgftH/A4vLFKJrCh74PUVQFFZVBiRpnHBoGOTWIgohBNGASTZgN5mznNIvBglWy6g+j/mwUjbzZ+Ca1rlqWVy7PimCjaMQgGIaJyffb36c51Mydk+7MzkuradJKWu8qqKRIKkmSSpKUkiKlpGjrbGNdah1pNU1KSWWFZ0b8D7ZAA/15VvEsfrLrJ1lXiaGOEhnBf13pdVmv5gJLQTbVw260YzfacRgd2I32MV1/rhYmeSbx6OZHeXzF41nBqmkaiqbkLJdJo3p8xeOXYzeB8xCrv/vd7/jRj37E/fffnyNWU6kU9913H83Nzdk7vzfeeIMjR47w0ksvYTbn2/tdq3THutnWsU3/EdQUiq3FWG3W7PyuWBd7uvfQGNRb9Zbby5nnnUeNs2bEC6aiKvTEe0ipKWqdtdzTcA8NBQ1nbZ2aQVZU1h/p4s2DPgyiwKbj3fhjaWZWurihwYumaTT3RVlQW8C9i2qwGK+MiMG1zpxqD3OqPezfn2L27GkAHPOF+fX2Ztr8MSo8Vm6eXkalx8o7x3p4YVcry+uLmVvtwWaW+P2edlr649yzoOr8O4l1HYZf3AILH4YHXgPzoGjtrU/Apu/Bf64CTQFVBnsp1K2AJY9AxXwQr2y/XVEQswJ2auFUivxFzJw1E3/CT2+8NycC25fo0wWKpmGRLNiNdiySBVEQx2yl9VFyLvFslayU2oanPMiqTDgVJpQKEUzqAjYjaDsiHbRqrcPeY5NsORHZwVHZOlfdWQu+LnsRmqDvk4Dekrcx2MiWji3n5eCQifgqmpIVhLF0jHAqfGaaqs/PCF9f1MfvT/yed9vepdyme/VmxOEN1Tdk921wRPi5o8/lCOWhzTYyjTREQSSuxHGqTiRBwmQ0DWvSMdL1pNJZmeMqMdhRojHYyN6evaypWUOVs4qUkqI71k1buI2UmgJ0C7hMNNwgGrK+zQXmAgqthXjMHhxGBzajLStoL7e7RCZ1JSP2sw/5zOuEnCAhJ7h9wu18bdPXWF29mlJ7KS+ffBlZkXmm5hkaChpyhOrljDqP+1c/Y0d1++2350x/5ZVXaGpqwm6388UvfhGLxcKTTz5JY2Mjv/nNb3jwwQcvzh7nuSLQNN1z8f3299nfsx8gp+e4pmm0RdrY070n232nzlWX45k4lLSapifWg6zJTCmYwsqqlUxwTxjzkF0okeZ3O1rZ3xZAAN444COZVlk5WRc9KUWlPRBnRUMJa+dVYsyb/V9WppQ5+cqaBp7f0cqhziCVbiszKtx4nRbeONDJeyd66QgmWDPNS22hnT0tAToCce5fUktVwThbg2qaHlFd+DCs/lbuPFWFw6/A3udASerR1Ikf05eddCOYrt42pKIgZouCphROYSUrUTWVYFK3tOqOddMcaqYl3EJfpA9f1Mee7j0sKluE1+bNWdflEqwXIp4lUaLAUkCBpWDYPE3TiMmxYSI2lAoRSAV0z9oR1ucyuSgwF/B209tIooSIyPbO7ezr2ceckjnZbmQZcobyhwiYwfNGWmbE+VlD2jP/t0pWFngXnClCE00c7TvK5vbN5x3lFQRBz5Fl7Df0g+3GimuKc+zGqp3VAFmBeD4COiWlcJjGXwQ7+Hw5m8PF2aKmmYhjSkkRSAboifWQ6kuhamr2b5IR22aDGbfZTYGlgEJLIQXmAhwmB282vklDQQM31d2EXbKfNb1iQ/MGjvmPcd/0+3JE5uBHLB0jmo4STUeJpWPEZP3/CTlBSk2hqEpOcWMmkJi5OcjkRV9fpt/8La9cjiiIaJpGSkldMUIVzkOstrbqd6KTJ0/Omf7WW28hCAJf/epX+exnPwtAeXk5jzzyCG+//XZerF5FnC0pPZOTtbltM0f9R+mOdaNoCovKFgH6UGRjsJHd3buzw48NBQ3M886j0DJynl9SSdIT04fr5nrnsqRiCVWOqnHdmbb2x/jV1iZ6IyliSYWNx7oxiAJ3zKlgQrGdWEqmK5Tk1pnl3DKj7MKHk/NcFArsJh5eMYENR7t580AnTouREqeZexdVs/5INye7I/SEk/zZrHJqCq10h5P82/oT3Luwmvm1BWM/R9p360P/D7yeO715K7zxtYG8VBOUzoTe47DkSzB5zcjrusoRBTEr4KYUTmEFK9A0jddOvcZj2x7j4VkPU2ApoCXUwrLKZQiCQHu4HQS9+9U877wzFl923eIrcyHMXKwHP2fQNE0XWNoZQTY4GjaSg0JntJMdvh0sKluUHdYGPe+9yll1QeJZEITsEG8FFcPmp5RUjoANpoKEk2GCqSCBVAAVNRt92929O+f5cqIoCgklwTtt7wCwrnkdZoMu0MwG81lfjzRvvPm957Ibu9g3O6qm6ukDAykE2XSCgf9nX6tpqp3VNAYbdYeL5reZWTQTs8FMIBnAJtnOKlYFQUASJN2Rg9FvYBVNyaYytIRaOOk/iazJekQ51snzx57ntdOvUeOswWly8vrp1zEIBh6e9bAuPuVoNnVjful8WkOtOd8JICe3eaTCOpfJlS3KG8tvpNfmxWlyZsW8gMAO3w6e2vfUFSFU4TzEan9/P06nE6v1zDCvqqrs3r0bQRC47bbbstNXrlyJKIqcPn364uxtno+EkfJYQO8o88rJV2gMNmI2mEnJKXZ372ZNzRoUVeGY/xh7u/cSTAWRBIlZxbOYUzIHp2nk4phYOkZfog+DYGBJxRKuL79+WDRnJDRNY19bkLcO+gjGU8RSCpGETInTRGt/jJ3NfpwWiTtmV1DiNBNOpOmPpviL66pZVl+Ut5S5wpAMIh+fUcaEYjvPbjuTFnDbzDL2tQXZfKKH53e2csOUEmaUu4gkZX65tZmm/hi3zy7HLI0h8nPkVT1H1TwQlQm2wZOLIRXWI6kFddBwK1z31/DBj6Fp8zUrVkdCEAR8MR/fW/m9nNy1UCqEP+EfVhz0YeeHtIRb+MTkT4BGbuHQQEFUzuuBQqKMoFU0JZs3qakaCmeGlTPzD/UeYlfXLu6qv4uGwgZUTWVSwSRkRaYv3kdPrAdJlLL+uBn7ukxRUca/+XwxGUzZtIqhqJpKJBXh+WPPI2syZbYyphVNy1lmaO3ySH67ma5f450/4rID28ukY3nMHkqsJdmcz6SS1O25lD5kTT77hx+EUTSOW+wWW4tZVbWK9c3rs9NOB06zoWUDKypXUGQpoi/eN6qoTKsDonPI9FgyBgGQlTOCdGh+5VjIFADu793P/t792ekZazib0YZdsmMz2s7832gf0XVjKAbBgEEyYGF4c5MqZxXFlmLWN6/Ppg/IqkyaNCf8J5BEiY5oB1vat3BD9Q1Mck+6pDZoqqaSVvR0AYfRwdySuezs2omIyJN7n8z5PbjcjFusKor+gzKYo0ePEo/HaWhooKDgzHCLwWDA6XQSjUYvfE/zfGSsrlnN4ysezxGsn3j5E0TSEeaWzKXOXUdzqJmNbRtZVbWKYDLIs23PEpNjmAwmFpQuYFbxLKySdcT1h1Nh/Ak/FsnCmpo1XFd2HW7z2Iz4j/nCfP3FfZzqibB6qpd4SuWoL4QvlEAUBJKySpnLwu2zy7GbJfyxFNGkzF8trWNuzfChwDxXDg2lTr5600BaQEeQcreVudUeylwW3jjYyYYj3XT443xsqpeqAiubjvXQ7o/xmetrKXacIyc+EdCLqdIJ2PLvsPmHICfAXgK1y2HR56D6ejBI4CyHWP9H86GvIIZW+Q7ukjWUKYVTLvn+dMe6+f6q7496scwMxwaSAd5peYfj/uOU2krpjnVnc3IBVFTM4pniIJNouuCLvyiI9CV00ScJEr2JXswG82UrPsswuAgtko6wuHzxiPukqEpO8dJYX/vTflJKSvefHSOyLBOVo/yx+Y8A2YjveBAQMBqMGDRD9m/pFJ1nrMAytmBirh1YjmWYaKQr1sXm9s1Zh4tZxbOwG+3E5Jg+jD4Q2QwkAviU4WkgGURBxCpZzwjaAVE7WNDajHoh49CbpgnuCaypPRN5zszPdInb0rFlzKkbmqYhq3JOasB4/p6ZkYGhqKjM8867YoQqnIdY9Xq9tLe309TURF1dHQDvvfceoHe3Gko8Hsfj8QybnufKZqhgzWCWzDSHmlnfvJ4aVw2b2zeTUlLYJTtLypcwvWj6iEMpmqYRTAUJJoO4zW7umHQHc71zRzXqVlWNSEomGEsTSqQJxtPsaw3wb+tPMLXcxU3TStlwVG8jede8Sv5woJPeSApRgBumlGA3S3SHE2gafH7VJCaXXkTrozyXDI/NxEPLJ7DxaDdvHPDhtEiUuS18elENbx/yccQXpjuc5LZZ5dQV2Wjpj/Pjdce5b3Et08pdo6/Y4oG2HfAfC/SoqtGuF08teBAabs4ttgr7dBF7mdA0LR/9Z7h4HorJYMJr8+K1eWkoaMhOz+SiZoRsf7wfX9RHV6yL3ngvSTmZtffKFCJlorGDWzOfjcEFXyIiy6qWXRFuCWN1cDCIBmyi7Zz2fyMxVCCdVRCpSZqCTVl3ggpHxTkFZY7/7MAjE10cr3fw0OOztXPriMdndslw+yRFVYjLcaJyNCtkM6I2My0q693hRutSB+gFjEOjtJKdmUUzWde8LmsRdqDnAFs7tzK3ZC6yKnOw9+C5xeY4bhwyubQmgwmLZMFtdg+LkIdTYY70H0FEZE/3Hja0bLhiBOu4xerChQtpa2vjiSee4Lvf/S69vb08++yzCILAypUrc5Ztbm4mlUrh9Z57aDfPlcdgweo26dGVpmATB3oPICBwOngat9nN0vKlNBQ0jJgsPth+ymvz8hcNf8HMkpmYRBNJWaUrlCAYTxOK60P1vmCCnkiS3kiKeFpBQE9zUzSV9473MqnEwfwaD2bJgNEgIisq/7O7nUhSZtGEQlRVY+PRblY1lGAzGXh4xUSqC6/eApk/RSSDyM0DaQHPbGumtT9GpcfKnXMq2NnsZ+upPn67o4XVU0uZUubEH0vxn5tOcduscm6c6kUaWjjXcxwaN0HHHhAMUDINZt0Dcz4N7srcZZNhvdjqgVc/ss+rqhodwTgnuyN89Xf70DSNn923gDnVnrxbxXkwOBe10pH791VURS+gSgYIJoP0xnvpiHTQFeuiO9adLZbR0DAIBqySNWvZlMndHFrwJSvyFeOW8FE4OAiCHuU0Gow4OHuxU2OwkcZgY9ZubKJ74mUR8+dzfAyiIdv97WxomkZcjp+Jzg4UOQ0WtzE5Rmekc8T0i7gcJ06c9zveB2BX965Rt5VJyTAbzNiMtjGlZWSmGUXjOe3qdnfvxi7Z0VSNz8/9/BVTXAXnIVYffPBBXn/9dTZt2sSyZXofa03TmDx5MqtWrcpZdvPmzQDD/FjzXD1kBOtX3vmKfmcb6wKg2FrMPO+8Uav1FVWhO9ZDJJWg2FLB4qKP4zbUcKpNZsuhFnqjScIJ+Yw33gAmScQsibgsEsUOE+LAl8sXTBBNKqxsKMm24EzJKpGkjEEQ+PiMUqaWuUimFfa2BpBVlS/eOAWvc3jeUJ6rg8kjpAUsrCuk3G3hzYM+3jrkoz0QZ2VDMTajgdf3d9Dqj/HJ66pxWYyQCMG7T8D2n+l2VKIRihtg7VNQNhtG+uF+/8dQPBkqFwyfdxFJygotfTGO+sLsbfXTG04SiKeJJmU0Df7xpYM0lDr41MJqFtQV4jhfu648ORhEw6juAEklSTAZ1COyiQBdsS58UV82rQDIuiUsLFtIoaUwJ6c0K36a13NjzY3UumpzmiaMlL87uCnA0CYLI713qNgQBZHOaCe7u3YzzzsPk2hiRZVeMNcebscoGplTMod1zetYWLqQGlcNkqgXCWWKhS5VTuSV4tU7tKgrI1Y1Tbsogl4QhOywPyNnvmW3l1JTwwTtdt92VE2l0FLIlIIpo4rN8yl2GytDxXxSTbKwTD9frhTBOu5fwIaGBp588kkee+wx2traEEWRRYsW8c///M/DTvgXX3wRgCVLllycvc1zWVhdsxqLZCEuxzEbzNxce3M2YpFSVJLpNAlZIZlWCSeTdMd6iKVTmNVKClgIYiXvCSKa5hsQowbMkki524I0Ru/Kkz0R6r0OkrLCgfYgR30hIkkZAbhrfiUVHiuqqtEZStBQ6sBjNeWF6jVAJi3g3WM9/OFAJ3aTgaoCG59ZVMNbh3wcaA/SFUpw26xyagvtHGwP0ek/xucLPqR4yz9DrA8sbqi6Xi+g2vhtPXJaODF36D8Z1oXqjv8P/vqtS/JZwok0p3uiHGwPcqAjSCKtIgKqprH1dD+BWBoE3c7VJImsO9LFllN9LJ1UxB1zK1gysQiP7doxJL/SMBvM2bSCwWiaRjQd5Y3GN/jXHf/KQ7MeotRaii/m48bqG+nq66I1rLvkGAQDc0vmsqFlA/NL51Nhr9CN9TNtUgfap5oMpqxt0LDHQCvWzHNmeUmQEEV9XZl17u7azR+b/shXF3yVJRVLsvNUTSWhJLJemls7t/LrI7+m1F5KoaWQqBzNdv8abL001NUhU2E+dP8kURpTU4mLFenNCPq0miYux1FUJcf/dfD/M6JeFEQ6I53s7taFvCRKtIZaWVa5DBGRtkgboEcrM0V687zzqHZW56QlZD73hQpFQRCy4rOAguxxUjUVk2gilArhNrsva1S+zlXH9s7t2bSGkepXLhfndbu+cuVK1q9fT39/P3a7fUTD/3Q6zT/8wz8A+cjq1c6Glg0k5AQm0UxaSXO8O0BLt4VYSkFWMt1O0iTwIwgCRdJkplnmUmQqxyRd+F17Iq3QGYwTjssc7tR7lZsHIrAWo4EKjxVZVWnpjzG70kOpy0IkOfZq1zxXNpJBZM30UmqLbDy7vZnW/iiVHhtr51Wy7XQfO5r8PPdhCzdPL2WJpZnlh5+gOH6UtGhBKp+HMP9+vUWqvQjqlsDLj8D2p2H6nWc6WB1+RY+o/vVb4J127p0aA5qm0RNOcqonwp7WAKe6I6iqhmQQKbCbKHUa6I0keXFXG7Or3Nw9v5Dnd+qi56bppaxqKGFncz/rj3aTlFU2HulmyaQiltcX43Xlb8Q+KgRBwGFyEEgGhlVHK6rCtr3bmDJtSo6I3Ny+maZgE5+b/bkx2wedDx/6PhxTxfbSyqXM9c7NaZeZifRlBG1W3A48x9IxwukwkVSESDqS9fNMyklkVc52ihoscEEvjNvVtYvF5YspshRlGwZE01GKLEUsLlusR3rLFlJmK8tpKpD5h0A2mqwHrzUMooGQHMIu27PD4FbJikWyZJ9tki1bRGcymHj15KusqV3DyqqVOS1lRUEkqSSzw/fxdJz329+nKdTEgtIFWcuycDpMMBFEVgZ93oE0ERU1K9wHC9vBObZn41JEnjNesBnxLmvymdeZbl+qmv0svqiP3d27mV86Xxfz4VZWVK4gEoxgk/T0uStFsAraUH+NPFcl4+mxOx4ypsDJpBlVNeE0FhDhJBXSQgpMVWhCioQSQBQMlJlmUmmZjc1w4X3TZUXldG+UY74wTX1R1AGLxkleB1PLnNQW2XjuQ/3C/qnrqmkPxFg8sYh7FlTzDy8doNhh5hu3Th3z9i7V8cuTy4Ue52Asze92tnCgXU8LsBgNNPVGeftQJ3FZ428Nr/FV6UX2mObzknUt8+Yu5BMr5g3P/WzfBYdfhURQj7xOv/OiDP3LikqbP87xrjC7W/z4QklAw2aU8NiMOY0oNE3jtztaqS2ysXSSbo/0zDbdTP7+xbXZ5bac6qW5L8Zd8yrpDicBmF9bwKqGEqoKRu+Ukz+nPxr+1I6zrMo5ojbbDUlJsLVjKz8/+HPW1q+l0llJLB0jko4gKzImyYTFoDsytIXbeP3069w75V6uK7suR2BmRGWm2GqwyDxy8Ahz5sz5SD+vouq+tXE5Tiwd03NMB0RuKJnbGS2SiuhFT5qqR2IFciLVGTHri/rY0qHbU+3q0nNU7516L6cCp9jYspGVVSupclahqLmCM/MQOSOGBxv9a5ou6o2iMSvibZINq9GadSuwS3bMkh7hff3U69S561hVvSon3/XowaPDjvOGlg05NzsXg/F8dy44EUrTNPx+P4lEgoqK4cbKea5eBnev+Nq6J1BRcYglFJmKaU5sQxaDuKVKaq3XU2aegUW8sIp7VdVo9cc45gtzsupi3HIAACAASURBVCdCWtEQgKpCK+UuK3ta/dw0rTSbswoDnbT8cdZML+XPZlcQTyu8eaCT33xu8QV++jxXIm6bkQeXTWDT8R5e39eB0wRrky/zFekFvqL8Df+p3MGb4ipumlBCcUEFW7uTtL9zkvsW11I6OBpZueCi5aXGUwqNvVGOdIbY1xoglEwjAk6LkWqPddQGFF2hJIFYmrvnn7m5Gykv8braQva2BvDH01QX2kgrKntbAuxq7md6uZuPTS1hUokj7yCQ5yNBEiW98GiE4qpdXbvOajc2mFXVqzgVOMWNNTeOeduX4xw3iAbsol6wd7acVMjNS82I2swjnAoTTAbZ1bWLLR1bWFKxBIfRoTfeQKA13IrJYGJ+6Xw2tW1ieeVyphRMGdHrNSMqRyusOleaRoaRXBBg5OO8umb11ZcGALoi/tnPfsa2bduIx+MIgsDhw4ez84PBID/84Q8B+OY3v4nJlM+1upoY3mbtCQAUEoiClUrTHDpSB5hq+zh11vMXhpqm0RVKcswX5nh3mFhKN3gudZmZUuqkodSJ3SyhaRpNfVF2Nvdno1CqqqFoGmvnV3DDFC+CIPDUuyeZ5HUwpzpvl3atIhlEVk8rZWpsJ6a3v0FJsoWg4Ob+wsNoTGRvn4vfHJG5ZWaK2kIbvlCSn6w7zmcX1zKzcmx+vufCH01xujfCvlY9fzqtaBhE8FhN1NhsY7pQnOwOU11o5UR3GF8wgS+UwB9LYxAFjvnC1HsdGEQBkyRS73VwsjtCmcuC0SBSWWBFUTVOdIc51BFkQomD1VO9TCt3Ych3Z8tzmRhP1O1yi59LwUh5qYPZ0LKBt5vf5gc3/IDVNauRVZm4HCchJzCIhqzofK/1PR59/1HWTl57zR2j8+W8xOoLL7zAd77zHWR59LxAt9tNe3s7W7ZsYdWqVaxenT/gVwsj9QMuDn6T3lgIa91mptiWUGKaTHtiD5sC/4YkmKi1Xj+ubfijKY76whzrChOMpwHwWI3MmuBmSpmTgiGFJIIgcPP0Ul7Y1YasaJR7LNw5t4K75lUyt6aASFLmqXdP8uy2Fl74fL6g75qmvxHeepTK42+iCQZ2WpfxnPlTFNdOY5XDQ0V3mPWHu3lpTzvXTyhk0YRCwgmZ/9p8mptnlHLz9LKc4fixkCneO9kVZndLgJb+GGhgNooU2k1j6qIVS8n4QomsMG33x1E1ONWjN01xmCUkUUBWNd465MN+0sDsSg8zK104zBLxdG6nHoMoUO62omoavmCc/3rvNBUeC2umlTKrKn+zlifPlcapwKmc66okSjhNzmFdHlfXruZx4XFOBU7lxeoA4xarR48ezQrVT33qU9xxxx18+ctfJhAIDFt27dq1fPDBB6xfvz4vVq8ihn6hMphFKw3GeykfaFlZa72eVfw9frmVWs4tViMJmePdYY4NGLsD2EwG5lZ7mFrmxOs0nzUi5bGZWDlZLzo50hnilpllrDvSxbPbW3jzQCeTvA5e+PwSGvINAK5NUlHY/CO9A5WSAmc5woQbmHf9IwSDpbx+wIctkmSy10mJw8wbB3xsb+ynM5jg4zNKKXdb+ONBH619ce5dVH3OyvqUrNLSH+VoZ5g9rX76IikEARxmIxWesztZyKpKbzhFZzCOL5SgK5TM3pQBSKKAzWTALBm4fmIhZS4LTouRZ7Y1o2oa08td7G8LsvV0Hx829eMwS1R6Rh6DFAUBr9OC5tDwx9L8alszhfZOJprjTJ6qYDXlvVrz5LkS+FOPPF8I4xarv/jFL5BlmQceeIBHH30U0NuqjsTixfrw8P79+0ecn+fKZPAXStM09rUF6Y0kScgqBzvCGA16VyHQBevZhGoyrXCiJ8IxX5g2fxwAk0FkWrmTqWUuqgqsWS/V0dA0jd5IimhKZml9Ef+8dia+YII3D/rwx9IUO8z85nOL80P/1yqaBodegj/+A4Q7dcupupWw+PMwYSUGyczqSqgtdvDrbc20+mNUuq38xXVVbDrew8GOEM992MotM8uoLbJzvCvEP750EKdFz+tyW43cMrOMudUewok0jb1RDraH2N8eIJFWMQjgshqpKRx5eF/TNEIJWY+YDkRNe8JJlEG1qwU2I9PKnZS5LJS5LRTZzfSEk7y0p53aQntOHrYoCCysK2R+TQGneiLsbvFnxW4wnmZutYeJxfZhubCCIFBoN1FgMxJKyKw/FeNo9DAfm+Jl0cRC3Xs2T548ea5Cxi1Wd+zYgSAIPPzww+dctri4GKvVSmdn53ntXJ7LyzFfmK+/uI/TPVFEAUQBkrLKS3va8diM3Dy9lKIRerLLikpj30Alf28MRdMwCAKTSuxMKXUyodg+vMvQKIQTaXojSaoLbDywtI6GUr2QpNRlyYvTPwV8B+GNr0HLVt3Uv2wOzLsfZt0NtlzXiXqvg/91UwMv7GxlX1uQcreF1dNKqfBY2Xi0m//Z3cbcKg/tgTj+aIqKAitzqtxEEmk+/fRW3FYTswaG3CWDSIFNt5caSlJW6Aols8LUF0zkDNFbJJHqQmtWmJa6LCN2oip1mfHYjDl52IMxiAINpU56I0nSsorXZeF4V5j2QBynRWJOlYcZFa5h684I8HKnhHWgWcLbh32smFzCkklFFI/wnc2TJ0+eK5lxi9Wenh5sNhslJWPrnW0ymYhGo+PesTyXl2O+MJ/82RbuX1LLbz63mLVPfkA4KXNdbQHemRZ2Nvfzwq42PrmgiiKHGVXTaPPH9Ur+7ggpRe9XXFVgZUqZk/oSx7haRyZlBV8wic1k4N6FNSycUDjuPMM8VzGxfnjnX2Dnz0FTwVML9WtgyRehaNKob3NbM24B3by+rxOrycC0chdep5lX93WwpzWAx2rk/sW1/H5vO+8c7WH19FJunOalqSfGeyd6s+c06LmqfdFUTq5pfzSV3Z4oQInTTEOpIytO3daztzXMMDgPG/Sq/8GkZJWdzf3sbwtm92l5fTEH2oMcaA/y/sletp3uY2q5k7lVnhFvHO1mCftAvuvGo928e6yb6yfqXq0Vo6QV5MmTJ8+VxrjFqtVqJRaLoaoq4jm6D0UiEcLhMIWFF+67meejQ9M0vv7iPu5fUsvXPz7cq9QkiSydVIymwRsHOqkpsnO860wlv9d5ppLfYRnfKaaoGl2hBIqm8bEpJXxsmjc/fPmnhKrArv8LGx6DRACshVC7DJZ+CaoWgnjuGx6DKHDj1FJqi+x6WkB/jHK3BbPRQKFBpD+a4sXdA91rJJHqgVSU+hIn0jGB1/Z3Uu914Asm6A4nSCtnhvOdFokGr4NSt4Vyt4USh3nMowQjUeQw88kFVbx9uIt9rUG9g5UAbx/2cbI7QoHNlCOe7WaJxROLuK6ugJNderOBg+0hDraHqCm0MafazYQi+zCxbDUaqCm0kZJVtp/uY+upPuZUuVk1xUtd0djcC/LkyZPncjFusTpx4kT27dvH0aNHmT59+lmXffPNN1FVlSlTppz3Dub56NnXFuR0T5TnRvEq9cdSHPOFOdEdJhiX6Y8FcFuNLKrTK/kL7eO3KdM0jf5oinBCZkaliz+bXTFqQUmeqwxNg/bdcORVKttPQ9dEmHYnVA3xOW3eCn/439B9CCQLVF8PC/8GptwC5uGejudiUomDv1/TwIu7WnnnWDeBWIqHlk3gWFeY9473omgaVqOBvS0BOgcip5nOZ7ua/RgNerpJJmJa5rJgN1+wNfUw3DYjd86poD0QZ8ORLhQNLJKBu+ZXUTZKpypJFJla7mJKmZPOYIK9rQFO9kRo6Y/hthqZU+WmRBre78UkiVQV2JAVlUMdIfa2BphS5uRjU700eJ2jesLmyZMnz+Vk3L+8q1evZu/evfz0pz/lP/7jP0Zd7uTJk/zgBz9AEARuueWWC9rJPB8tbx30cevMspwLs6ZpRJMyG49244/pVc1Wo4ECm5FSl4Wbp5eed3QmmpTpDicpc1u4d1ENMypc+UjPtULXYXjlC9B7Eqb/ObLZA9Fe+NWfQ3E9fOIpvYPU2/8vHHwRBBGKGvT2qAsfBNeFNRpxW4381dIJ7GryU+GxEk7KzK7SW/L+bmcr8bTC5pO9ABTZTdQWueiPpiiwmVg9zXvO4r+xoKoaSVklKSskZJWUrMBAN3YNvQmAwywxvdzFqiklxJIyBztCJGWFYDyNyzK6wbcgCFR4rPpnS6TZ3xbkYEeQ9070IokwM2xgdrV7mBWcZBCp8OherU19MZ569xQ1hTbWTCtleoUrn3KTJ0+eK4pxi9X77ruP5557jg0bNvB3f/d3PPTQQ9l2X6lUiuPHj/PWW2/x61//mng8Tn19PWvXrr2gnezv7+fpp59m48aN+Hw+rFYrM2bM4DOf+Qxr1qw57/Wm02meffZZXnvtNZqamgCoq6vjzjvv5LOf/SxG48jDz9/4xjd46aWXzrruyZMn8/rrr1/0bX8UBOPp3G4/QDSlEE7ISKLAtDInU8qcVBfY2NbYRzytnJe4TCsqncEEZknk7vmVLJ5UNCa/yjxXCV2H4Re3wMKH4YHXwOyka/9+SmfPhlufgE3fh6dvADSQk+AohQmrYNnfQelMuEg3LAZRwG0zsby+GLNkoKU/RqXHistqJC2rfHxGGV6XOXvubTnVSzytjFmoappGSlZJyioJWSGZVtFgQIwCaNhMEkV2E/VOC2UuMwV2E26rEZfViNtqxCzl9hLvj6bY3xZg84lemvtjmCSRYrs5xzVgKE6LkWX1xVw/oZCjXWF2nu5lb1uAvW0B6opszK32DHM0MIgCZS4Lmqbn5v78g0a8TjNrppUyp9ozrjzzS0nGleStgz6C8XSOg0OePHmufc4rZ/Xpp5/moYce4u2332bdunXZeYN7yWqaRkVFBT/96U+RpPMfOjtx4gQPPPAAfX19ANjtdsLhMB988AEffPAB999/P9/85jfHvd5oNMqDDz7Ivn37ADCb9ZywQ4cOcejQId566y1+/vOfY7PZRl2H2WzG6RzZ07OgYHj3iou57UuJ22qkK5TImWY3GSi0m1gxuYi6ojNDspGkjM00vr+vqul5qWlZZWl9MTdNLz2n52WeqwxN0yOqCx+G1d8aPq/pfTj8MsgJPZo68QZY8mWYsAKki1+t7rYa6Ysk+X9uncqLO9vY0+pHAMxGA9WFud+zoee0pmmkFY2krOgR0rSKomlZMaqhYZb070d1oY1St5kiuzlHjNpNhnHd0BXaTdwwxcvy+mJO9UT5sLGPva1BFE3FbTGetYhLMojMrHBTLMaRzS72tgY41ROlqS9Goc3EnGo308pzo6eCIFDsMFNkNxGIp/nN9mZ+uaUJQQCrSaLQbuLWmWXMqxn9d+1SMdiV5NaZZZS6LHSFEtz339uZWGLnB5+ck/dWHkQmeJQfncpzLXFeKrK+vp5XXnmFp556ipdffplgMJgz3263s3btWr74xS+eVbSdi1QqxSOPPEJfXx8NDQ18//vfZ+rUqSQSCX75y1/y4x//mGeeeYZp06Zx9913j2vd3/rWt9i3bx8ul4vHH38827Rgw4YNPProo+zZs4fvfOc7fO973xt1HbfddhtPPPHEuD/Xxdj2peSWmWXc99/b+fadM7KpAIKgt30cbISeklVOdke4a37VmNftj6UIxFJMKXNxx+wKaooujyDPc4lp360P/T+QO7pgDrfAs/8EpzaCKEHJdOg/Ccv+F0z62CXbncHn9ANL65h4ws67x3rIpGjKih4ZjSRlTnRFWFZfRGt/DNBvrowGEbdN91otdZkpcepV/64B4eiwSJekzalkEJkyMJJxx5wU+9uCvH+yh5b+GJJBpNgxevcsQRCoKrBRVWAjGE+zvy3AwY4Q7xzrYcupPmZUuJhT5cFlNea8R1E1tp7uxx9LoQEiUOGx8vP3G3FZJZZOKqbIYUISRSRRwGAQMA68lgwCkigOTDvzfymzjEHAaBCRDPryoiBgEAUMItnXZ6YJNPfF+Mrze1k7r5If3DMbl9WEKILTbORbd0znZ5tOcc9TW3jxkaV/coI1JasEYin8sTT90RRdoTgdwQT/uek0AH+3up66IjulLgtep5lCu+mCigHz5LmcnHfI0+Px8Oijj/KNb3yDkydP0tPTg6IoFBcX09DQMGqjgPHw/PPP09rais1m4+mnn6a8vBwAi8XC3/7t39LT08MzzzzDT37yE+68884xD50fPXqUP/zhDwB897vfzUklWLNmDaqq8uUvf5lXXnmFhx56iIaGhgv+LFfCtsfKnCo3E0vs/PTdkyO6AWTY2dxPgc00ahHIYGIpme5QkiKHiYeWT2RWpTtfzHEtc+RVmP7nZwqjEkHY9H0atv0UNAXc1TDpRlj297D5h3D63UsqVoee0zdM8fLKF5fx6+0tNPfFMIjgsEg09kUpc1v462UTcNsGhuotEi6r8bLncXpsJlY2lLCsvpjG3ggfNvrZ3eJHVlRcViOes0Rb3VYjKyaXcP2EIo74QuxrDbC7JcCelgATS+zMrfZQ6bHSF03x4q42Zle5uXt+Fc/vbAUNbptVTiKtsKclwNuHu7hjVjkeu4m0opKSIarJaJou7DU481rTI8+qpkf8MtNBT5PIpHpkotTZvRdAUzXeOd5DpcdCLCXzs02nWXekC4CbppXiMEsUO80sqC3gb361kx99am725sFlla6JlCJV1QgnZPpjKfyxFH3hJO2BOJ3BBH3RFIp6JsJvEAQsRjGbPXOyO8LB9lB2vkkSqfRYmVBsp8JjpcRhpsRpznc4y3NVcMGlrYIgMHnyZCZPnnwx9ieHV199FYDbb789K1QH8/DDD/Pss8/S3d3N9u3bWb58+ZjW+9prr6FpGnV1ddx0003D5t98883U1dXR1NTE66+/zle/+tUL+yBXyLbHiiAI/OCTc7jnqS0APHJDfc78of6PZ0NWVHyhBKIgcMecCpZPLr5i8uDyXEISAXCWgarC3l/D+n+CWB+K0Yk0cYUuUjNWVM4y3Vf1EjLSOT2xxMH/vrmBSEJGFAV+8UEjRzvDV3zLXoMoUO91Uu91cvuccg62Bdl8YiDaKg5EW0f5jpkkkTlVHmZXumnpj2VTBE71RCmyG0krGjMrXLlNCgQwGkSMBpGVDSVIBoHNJ3v59KKaS/o5fcEEsaTCjQtLs7m6mRuGCo+VpKzQG05SYDOx2d/Lv68/QYHdBJqGKOqNEYqdZrwDD7f1TJ6wyyJdUVHGeErBPyBI+6MpOoMJOvxxusJJ4mkZURDQNb6GRTJgMRrwOs1IopC9QdE0ja5QkkRaQdPgeFeE+hIHZW5LNq+6J5ykuS965oZBEPA6zdQW2qkpsuJ1WihxmsfsFZzn/MnJw46lcNtM+Tzss3DxfVguEtFolAMHDgCMKkLLysqor6/nxIkTbN26dcxiddu2bWddL8CyZctoampi69at49zzK3fb46Gh1MmLjyzlay/s45dbmhEEPSKys8lPRzA+zP9xKJqm0R3WfzgXTijklhlloy6b5xrE4gHfAXh6Ffj263moVYtoL/s4tTd9PteKKuwD+9iajFwIQ8/pW2aWUeoy0xVK8uaBTiZ5HVe8UB2Ky2JkaX0xiycW0dQXZWdTPzub/SRllVRCoUTTRiwUEwSB2iI7tUV2/LEU+1oDHOwIoqhwqCOEKArMrhz5onldbSF7WwP4QolRR1Uyeb5pRR14aMiqSkpWkVUtOy1nvqKSUlTkgem9kRSiAL/d0ZKdn5BVBODlve2UOM14B6KDk70OwkmZOdUeNE1DUTUSskqHP05jTxRZUUHQP7eq6mkdBXYTJQ4zXpeZEoc5m1/sshpxmqWLPvIjKyrBeHpAlKbpDido9yfwBeME4mk0bSDCrGlIBhGryYDTIlHsMJ2z2K83kmTd4S4CsXTWqzeeUoZ1GzQbDTnWgrKiEksp7Gn1s72pDxEBDXCaJaqLbEwoslHqslLiNFPkMF320YUrgcz5lVb0G4DUwHmblvVzOSmfOadTAy4gsZRCPK2QGHhu88d565CPQExvMCIA9V4n/3dLI9UFNr53z2zmX4b88CuZcYvVPXv28NhjjzF9+nT+5V/+5azLfvOb3+TQoUN85zvfYfbs2ePazqlTp7KJ4vX19aMulxGrp06dGtN6NU3j9OnTY1pvZj9GY+vWrdx88810dHRgNpupra1l5cqV3HfffRQXD2+feDG3/VHQUOrk1S8tZ19rgL/6xYckZA2zUTyr/yPobgL90SQTSxzcOaeCCcXDTcrzXMME26FzP5zeCAhQNBmm3QGL/oZgU0+uUE2G4fAr8MCrH8muDT6n3zzowx9LU+ww85vPLb6q2/eKosDEEgcTSxzcNruCg+1B/ueDQ7T6YxgGiqdGG9EosOnFXIIAncEEibTKjiY/u5r92XzTD5v6kRWVtKyRVlWsRgNvHfThtEikBwnMjACV1eEer+NBEoWBoW1dYNrNIkaDXvipadAVStDmj2eXFwCLUSQpK3gdenSw2GHCMYIv7uCCucZevS20omkIAtnopVkyUGA36VFZl5lie66YHa1gTtM0IkmZwEAeaX80RUcgTnswTl84SVJWB96nISBgMRqwmgyUuyznHentjSQHpW4U6qkbwE3TS1nVUDKs22DOcTaIuKxiTt6yqmnEUwqNPVEOd4SyFmsmg0i5W08jqCzQBWyJ0zzuAtvLgTJwg5SU1TMCc0BUZv6vi02NRFoXl7G0TCKtkEirJFIKCVl/nZQVZEUXrMqgm0FBGOQAog0UXw5omEwOtkEQCCXSrDvczdQyJ7fOKOO1/R0ALKgtYJLXznFfmE/951bunl/F0klFTCh2UFlgpcD2px3tHvdZ9uqrr3L06FE+85nPnHPZefPm8eKLL/Lqq6+OW6z29PRkX5eWlo66XGZed3f3mNYbjUaJxfTCCa/Xe871RqNRotEodrt92DI+nw9JkrIOBZlq/ueee46f/OQnLFmy5JJt+6NkTrWHYoeZcFJmVqV7VKGaSCt0hRK4rEb+ckkd82oKLknRSZ4rlHQCtvwf2PwDvcpflHShevd/Q+mMgfzEntz3vP9jKJ4MlQtGXOWlYk6156oWp2fDMdDlyhp246mqZ2eTnw+b+kmGEtjNEoU204hRQ1nRqCm06VHa3ih7WwO0+uOkFNh6qm/Y8gL6d15PEdCFl9Oii1uT4UwxVWa+0SBiFAdeS3qB1bD5A+8TBYH3T/YSTyncNP3M7/8z25oBuO/6GgLxND3hJN3hJEc7QyRllYPtISCk758AhTZTVlSVOPR0ALPRgEnSC0adI/yUZYbM4ymF411hDrQFB1mR6eLDajQgqxqdgQThSJjC3R9S7rZgEETCSZmMGM3kilqNuvg1GcSLKjg0TWPd4S5mV7lzUzcGyHQbBHj7cNeYUjdEQci26R28nZSi0h9L0XYijjIo77jYaaamwEZtsS2bRuCxGi84Mq1pGrI6EL0cEJS+iIytO5IjOFOySiKtEE3JxFMKsZRMPK3//XSBqdvJZcTlxqO6VrhpWmn2BkUbtE3Qm26IIlmBKQ6ITUkUMFuM2UJAURif64KmaWw42s2c6jN/r8z7C+0mCu0mJhY7eP9kL+sOd5FWVN45pv9mFjlMTCtzMbHETqXHSrHD/CdV9zFusbpz506AEfMth7Jq1SoAduzYMd7NZEUd6AVVo5GZF41Gx7TewctZraN3SBo8b6hgnD59OrNnz+aGG26grKwMURSJRCJs3LiRf/3Xf6Wnp4cvfOEL/P73v2fChAkXddujIcsy+/fvP+dy50simSCd1ujv92NMhXPmKapGX1xvtbqo0sq8cgFTqI1DB9su2f5cbNLp9CU9ftc0moar4z0q9v87png3imQj5r2OQOUNVBz8Gb3v/IyehvtQjbbscRbTMUqOP0vR6Zc5tepJkvljf9GRZZlg+ykmG/n/2Xvz+KjKs///fWbfMtkXCDsIBBFRVFBRKSJWFNS2tn5FRfv4uLauWG3lae1TrXtdWqvyo27wWG0fHwVKCxjEBeoKStglSDAEEiDr7JOZOb8/zsxkhixMQpYBrvfrNa9M5tznPvecifiZ676uz8XAoRG+rWvm62o3G2tCKApkmXWYE3xbw0E/B70qB/aHsQNnFxtZ6vajAucMsqHXaRFPg05h/T4fVoOOk4sOV1ypAuHWL0eij5D2IxB9JJKjD7O6xkNJlopRrxBRVZqbtQ5j+6prNP9cwGZT2RAKM2WIDZtRR70vTL0/QoM/TL2vmW3VQbZVt/ybZTcqZFv1ZFv0ZFt1ZFv0WAxKu6LjUFO9el+I1VV+3MFIvCos313Hh9vDZJh1TCy2kG1N+F9rM/h94Kf7qfWGqfMEOLO/kfLv9hEIR/AFmtEpsHdfdTxYMMiq8pUnwJZde8m1HVnNgIEW4RCOqOzzqOzaoxKMaNFpVLAaFAodBvpnGMi1GbAbFUIRaI5un4ciKs0R8DVH8IdVArGfIZVA9GcwOi4WpFeAiBpBV/ZFoolx9AuEJhz1UQEZc5fQXos+R6XJHyHQrBUDbq08wACnscP7oQKhI7pbycQ+r3MGmKip0YoFQyHtCrHfAYbYVDb4gjQ1NJBr02t2jzUq3+6pid8Pu0nH4Cwjg5xG8u16cqz6bgsOpeP/DzstVqurq8nIyCAr6/BRiby8PCwWC9XV1V1aXF8S+4bVFtdee22r1xwOB7NmzWLChAlcfvnlNDY28qc//YmnnnqqW6/dHgaDodPR685gKa2nmRA5OdkUZmt2U6qqctAdxBcMce6ILGaM7UdBCs4A6UhZWVmP3r9jlprN8K9faL6pOiMUjUN/ytVknHQFGbYcOPNHFL57C4UrfgRjZlHj01FojWhb/3knwH++x6iCkr5+F8ckh/5NnwH8RFXZU+9j/e56raFHMIwt6qM6zhLgna+qyM7Njxc0mXdpEmv0kJZOYsFQhKpvvuUHp/Zr1Tykq0RUleZQhEBCtMymj+CwuhuuvwAAIABJREFUBNlUpzKmvxNVVbn45CzsZj313mZC0RzPHfUucuxmThxaDMDQQ+b2N4c54ApwwB3QfroCVDUF2dPUIkOs0YKl/IRHW+4KB90BPtimbbmfNljbcg+FQvx44rB44ekHuxs7zOdPhVgk0xsM4w1o0UJvMLo9nfC8zhMgHIHF292t5nh7q4ssq4kchxaxK3RaqfLpGTk4P8mCsLtRVRVfcxhPIMS/a/z8e+d+VNDSB7KsZNtMqKioakxYRrfITQoGi4I5wbos9ohttdfU1HS409oeyTm9OvQ6UIwWPtztTsrp7UlUVWXbjgMMzrWD1UlDQIsGByPaca/eQf8sS9zF4oQGqAsbGFPYdkqhJxjmoL+ZfbUqHFSxmXWcUOBgZGEGA7JtFGVaOmwi0hG99f/DzgjiTovV5uZmdJ34Q9fpdPj9nf9OmWiI7/f7cTja7g3u82l5S6lulSeOi53b0bydmRuguLiY2bNn8+c//5kPPviASCQSv189fe3exOVvptYdYECOjevOHsIJBY7jOp/muMNbB6sfhi9fBjUCWYNh2FSYfDvkDGsZV1ACN34AVetgyxIMjbsgb6iWo9rLW/+CtuU4MMfGwBwb008sYmt1E2t2HOTbAx4UVJxWA1/urmtzSzlGZyzroEV4xbp8BUNajqtW8NSyDWs26MiyGilyWsjL0BoUTBldwH+9u4lJw3O55bzhFGRY0Om0vL9vql3M/+hbvqlxM3lEHt/VedEpWievxAIpS7TxQ2Lzh+ZwhFp3kP0uf1zI7mnwsbuuZUfPFPWxTUwjWLVtf5e33FVVa7t7qOBs73m4g7xfRQGbUY9OUciw6CnOsmIzGbCZ9Kz7rp6IqtI/U7Mi27nfTXn0vKoGH1v3NZFpM5Ib3XbOsZvItZvJthm7xSFBURS8wTClW/dHxaGCXtG21teW1/aaOIxxJDm9qdAizjXx6Y3+9ARCeIKaaPdGf8ZyuXceaL0TvGTDXhQgP8NMcZaV5nAEb7DtuG6sRXMsJzu2hm3VLr7e0wAomA06hufbGVWYwYAcG8VZ1qPaiafTYrWgoIDKykp2797N4MGDOxy7e/duvF4vxcXFnV5Yfn5LdXBNTU27YjWWq9pRDmgidrsdm82G1+vtMM81dsxut3daMMY6ebndbhoaGsjJyem1a/c0wVCE3bVe7GY9V54+iNOG5kiF6PFEOATrXoH3f6d5p1pzYPDZWovU4gmaFVVbFE+A4glUlZWRKxHstMBq0nPqoGxOGZjFvkY/63fXEwxHWLmlBk8gxOQRyQ4NbVnWJQrRxMpoXdQxVY0+zNGmCgVOC7l2E3nR6vsMi/Y/3AyLAbup7Qr8E/s7mfv3DUz7w0dtOjgs+dnZFDmtVNZ72XnAzZa9Texp0L7w6xRwWo04zIakinqjXkdRpoWizBbBHYmo1HmD8ejrAVeA/e4AexuTgy0RVcUTCFOQYaY5HNFa1boDcZFp1CscdAdY8vVeIqjRPEpNiHZUd6ZTiAvOXLsp/lx7JD/X/FTbzuvdvE/L2Z15shYND4Uj1Hub+fCbA0QiKjaznjpPkG8PelqJpkyrMUHAtojZzvwb39PisDMcSU5vOKLiDYbiIjQmPr2HiNDDfa5Wox67WftC4Q5o4nNscSZ2kx6b2cB7W2pQVZVTBmWzp97L3gY/X1U2xM8/4ApQnGWlONsa/1JyKIqiRP9GWsRrMBSh4qCXrftcgIpep2Nwro2SogwG5Wpeu20VIKYrnV7paaedRmVlJS+++CKPPPJIh2NffPFFFEXh9NNP7/TChg8fjqIoqKpKeXk5w4cPb3NceXl5fHwqKIrCsGHD2LRpU/zc7pg33a99pLx393ks27iXlZtqmDo6n++NLiDDkloTBuEY4dsP4Z/3wsHtYLDCwDNh4o0w8kIwpdeXKiF1FEWhf5aV/llWpo0p5L3NNTz8z628vHYXRPMAl27Yy3d1XhwWA+eMyMPbHMZT54VolbjTaqI420yuI2YDZcBhjorRqBDtaj5dqg4OmbZMxhZnMuvk/tR7m9lT76V8f1S81ntBBb1ehzMqkA/dCdLpNNeEPIeZkqitt6qqNPlDHHAFWLe7DndAK+LZsq+JLftazl302Xet1r2r1oNep2AzaWJFq5xvW3zaTHrMhs4XX43Id/DOV1WcNzK/3S1fg15HptXIfpc/ycklFIkkuRbEHrtrPew6mCxinRZDXLjGI7F2Y6vGCz1R8HUk1DQFaPA288NTtYBRJKLGm1VU1fviIrQ5rHLQHeDNL74jFFHxBjSLqfaIfbGwm7X0EZtZj92kFaXFRKg9+tkm/t1XN/p556sqxvRzxj8v7bjC+IFZjI9ar9U0+fnfdVUUZ1k46AmyYU8jG/ZonUJz7CZNvGZZGZBtTSqEi6EoCmajnvyESGowFKGm0c+uAx4ty1eBAVlWRvdzMjjXxoAsG05r6/8u0oVOi9Vrr72Wd955h3fffZeMjAzuvPPOVj3svV4vTz/9NO+88w6KojB79uxOL8xut3PSSSdRVlbGxx9/zIUXXthqTHV1dVzYTZo0KeW5J02axKZNm1izZk27Y9auXdvpeWNs2LAB0N7Dobm9PX3tnmTi0FwmDMpJikYIxwH1FbD8V7B9GSg6yBsFY38AE67TDP2FYwaLUc/M8f255OR+fLD9AH/94jv21PnIsBiYcVI/ThmUFW8vm2Exxrcie8P1I1UHB0VR4qJq3IAsLj9FpdYTpLIuKl73NVFZ50VVtJawmVYjtnasqBRFO55pNbK71sPAHBtnDc/DEwhxwB1g5eYaQuEwpwzKSRKf22uaCEdUppUU9uj//AudZrJsxi6lbmgNJDRxnkg4otLoa6bWE0gSsZV1PipqvUljHWZDUgQWtJbaPzxMC+5UvHojEc0mLRhqsUSrcYfw6Nzx34Mxn96Q2vI87nuq0uALoqLy6r8rCIYjSakV/7u+dQHwAVeADIuRbLuRASZrXHzazYbolw7ti1csst1ZUvm8FEXh24Mech0mLj91AKqq0uBrpqrex54GH1X1PjZWNbKxShOvWVYjxdlWBmRZ6Z9txdlOEMlk0JHrMJMb/T0UjtDga6Z0i1bYFYnatzX5QnjcTZxUuYWLx/VPmyYFnRaro0eP5tZbb+X5559n4cKFvP3225x22mn0769tOezdu5cvv/wyXs1/4403ctJJJ3VpcZdccgllZWUsW7aM2267rVUXqwULFqCqKvn5+Z0SdpdccgkLFiygoqKC9957r5WzwcqVK6moqEBRFC655JKkY6qqdvhHunfvXt544w1Ac0M4NL/3SK7d1xz6j5pwjBNww8d/gE/+COEgZPSDYVPgrNu1XNQ0/QYuHDmKovC90QV8b3Rq6VXpjKK0RExPGZSNqqoccAWorPfxTY2LrfuaqK0PokWJNSP+tsSr2ajHE9CibTFrJ6tJTyikcubw3KSxm/c1YjP1fJRKURSmjynk7+s04XXa4Jyk453pNhhDr1OSxGeMSESl0a9FYmsTROyhub4Ar39SQY7DRK7NjD8aoVz/XX2LF28ogs2kZ8WmapxWY4LAjJrpHyIsk/G283r0nqCleZgMOiKq9jzbZsJk0CzSdtd6URQ4Y0hOXHzazHo2VTUSDEc4f3TnC7hSpSufl6IoZNtMZNtMjC3OjEf7NfHqparex+a9TWzeq6V/OC2GeMrAgGwbTkvbf4eG6H3JtpniBWj13mDUJ1bFnuHh6gWfMSzfzpNXnNznzVK6lLDw85//HKfTyTPPPIPH4+HDDz9MavkGmv3SHXfcwXXXXdflxV155ZW89tprVFVVcdNNN/H4448zevRo/H4/r7/+OosWLQLgjjvuwGhM/jYxdepUqqqquPzyy3n00UeTjpWUlDBjxgz++c9/8sADD6AoCueffz4Aq1atYt68eQDMnDmTUaNGJZ27ePFiVq1axaWXXsqpp54az0f1eDysXr2axx9/nIaGBmw2Gz/72c9avacjubYg9AqqChv/DiseAM9+MDth6Hlw5m1afqrhUDMfQTh6UBSFAqeFAqeFCYOziUS0bnuV9V6273OxrcbFQXcAFDAb9GRajFiMupS23EETHOX73fzgMNHF7iLXYeaKCQNYuaWGDZWN8Q5WK7dUU77ffdhug6mi07WIpuEJ6cwRVaXJp4lYrXmEil6nsK/BT2VdS7HwxzsOtjlvLM/XaNDFvXqNUa9eo6HFh9ek1+H3esjJzmw5Hn3EhKhJr0Of0IK2I6/eUw7pEOVrDvdKg4Nch5kfnlrMyi01fF3ZQHGWFZNBx+INVVTVH747ZGK0f0x/J6AVPFdFo657Gnxs3eeK5qpq0e9YzuuALCtZhzQXSM4xHsBbX1biDzZz+9QTGH6lgxc+KOdHL/yb/73lrD4VrF3+ZObMmcNll13G8uXL+frrrzl4UPtDzMvL45RTTuHCCy8kMzPziBZnNpt54YUXmDNnDtu3b+fSSy/F4XDg9/vj3mSzZ8/miiuu6PTcv/vd76isrGTjxo3cdtttWCxa/+RAQHP7O/nkk3nwwQdbnReJRFi5ciUrV64ENNcCk8lEU1MTkYjmQZGdnc0f/vCHdnNOu3ptQehxqtbDP+dq1ft6E/Q7BSbMgRMvB2t6bAcJQnei0ynxYqvTh+QQjqhUN/nZU+dl674mvtnv5oA7RERVcZj1fPptLeeckNdu1LSzbgndQa7DzP87YxDVjT4Wf70XVdVyKg/XbbA70CkKWTYTWTYTexv9cXGoqioufygeRbxobFFSA4g15Qexm/RMPiH1Vss1Nc0UFjpTHt+XXzAiatQ/tllrThAMRdApSrwBwffH9iMSUdnX6ENVtRzi0YUZWIwGPMEQzU0qTosBq7HtFJVEMixGRhcZGV2k3RtPIERVg4899T6qGnxsr3GxvUYTrzaTvqVgK9PCe1vbd7hwmA3ce+FoAOb+fQNLfpZaS/ue4Ii+RmRmZvKTn/yEn/zkJ921nlaMGjWKZcuW8dJLL7F69Wqqq6txOByUlJRw1VVXMX369C7N63A4eOONN1i0aBFLly6loqIC0Az/Z86cyTXXXNMqWgswceJE7rzzTtavX8+uXbuor6/H7XbjdDoZMWIE5513HldccQXZ2e339e3qtQWhx3DVQOmDsOGv2u/ZQ2Hk92HSzZA9pC9XJgi9il6nxAtYJg7LJRSOsK/RT2Wdl6JMKy+v2YUnGOKEQgfhiFasE46oBJrDrPuuns17m5g5rj+egGY+H1GjRT3ECnw0I/uIqu1EHvoT0KKjUUcFEtp4KkRbwkYPJVpyx86dcVI/IqjoUOLuLWaDDotRi1x2pZArVQ4Vh06rMS4S+2e1NLsJhiLsPNDz0ecjyelNhUjUjkxry5osSFXAbtJTkGGhKNNMv0wL2TZtPVk2Y5tFfolflLZVN/FNjZsDriCKomIy6FMWr3azgZGFGfFIqDcYYm+DP546sGO/mx373Qnj9Xz1XT0Dsm1t+rzfMmUEr66tYENlQ591/lPUrjjQC2mHmNofGcft/QsF4bMX4YNHodkDtjwYcg6c9TPofyp0s3n4cXuf+wC51z3Dlr2N3PW3Deyu9TCyIAOXx0tYZ2Rvg1aINmlYLrkOEwZdrOWsgkHXsp1tiG1r63QYDDpMCa1mtTa1WqtPg04XN8U3HGKSr48a5xv0Cc91LecFQxHqvUEafM3Ue4Lsa/RR3ehnvyuAPxSOC+GISrcKWVVVefOLSgbn2uLiMLbtfs2kFqvLf+88yO5ab6fdALrSFKDWHeDv65IbOcTWc2iOaFtb72qSINW8gnVKrIGWisVooCDDRL9MK0VOCzkOE1lWE1l2ze/3SO5nW+LVEwh3Wrweir85zN4GH59V1NHkbSYQisQjvgpaGsmbN57J6UNbcmrn/n0DeQ4z9180usvv51A682/UESdofPPNN2zevJna2lqtCjMnhxNPPJGRI0ce6dSCIHQFVdW287cuAX8DWLKgZBYMOMSE/5sVWvep+gow2mDwZDjjJjhhGphsbU4tCMc7Y/pnsuLOc+NWWt/uqWZQvwK+P7aIUwdlp0W/dqtJT6at9e5cJKJtzTf4gtR7k4XsAXcQX3MIBSUeyTXpOydke6Lg60hJJaf3R6cW4zAbaPQ144tGSLX3o/1zajboyM8wM7LIQv9MK9l2E9k2I1lWExmWtv2Bu4O2ovw1rsARR14tRj3D8h3sOuhhcI6NCYOz2dfgZ0+Dj68rG4hE1Pg9iFHoNFPvbe6R95kKXRarq1ev5qmnnmLnzp1tHh8xYgT33HMPU6ZM6eolBEHoLDVbYPGtcLAcxlyqWUu5quH1SyFvBFz2Aih6WH4f7HwfdAbIL4GTfgynzIaMnquEFYRjiZiVVllZkHHjxvT1clJCp1PItBnJtBkZnGxgkCRkY/6riULW3xyCQ4Ss2ajDatRjMujiTRd6q+CrPVRVJRxNt4hEn5sMOmac1I/9TX5WbdtPOKISCqucPTyPbLsJb3OYUATyMkwMz3fQL0trXpFl00Sp02JMiy8hBr2uW8VrzOHCbNAzJM/OkDw7uw568AebcViS5WFNU6BPHYG6JFYXLFjAU089Fc9tsNvt8Q5S+/fvx+PxsGPHDm655Rbmzp3Lf/zHf3TfigVBaJuaLfDK9+H0G2DOUjAnVG5e9Ki21f/SeRBuBiLgLIZh39O2/PNHixWVIBzHdFbIVjf62dfoSxKyscxak17H98cW0eRvZvmm6paCr1OKKciwRHM9w0QiUUGpqvF83nDUuD+iqkQiEFbVuGVkvTtEwOht2YZPzNlFywVG1SKSOkVLuTAZFMwGPSa9jtx+TsYPysZq1FPkNJPj0NrMZtlMZFqNveIX3J0cqXhNtQDNHQjxr437eOM/+877vdNitaysLC5UJ0+ezM9//vN4e9HEMX/605/46KOPePLJJzn99NMld0oQehJV1SKqp98A5/86+VgkDJv+D77+HwgHtMjqCd+HSbfB4DNBL8V8giC0T2eEbL0nSHWTn70NPprDKheNLQK0/fRAOMKeBh86RROUMdsps0HL1zUZdVgMWsqB2ajXnkfTEIw6he8qmhk1cjBGfSwfWIdRl/A8mhNsiM59tInPIyVV8QoqZqOeDIs+pQK0Fz4oZ3iBo8+Kq6ALYvXVV19FVVUuvfRSHnvssTbHjBs3jvnz53PfffexePFiXnvtNZ566qkjXqwgCO1QtV7b+p/zj+TXd38C/7wHajaDwaxZUe3fonmmDj23b9YqCMIxw2GFbLRFrUGvtBKWnS0Mcnj3Mm5Q+047QjKpiNex/TP5uPwA7kCIM4Yk5xi7AyFe+KCcRZ9+x99vPrOP3oVGp8XqunXrUBSFuXPnHnbs3LlzWbJkCV988UWXFicIQopsXaLlqJod2u9N+2DFr2Dz/2ktUnOGQ8lMOO2n8OFjUL5KxKogCD2KTtdiYC/0Pe2J10/KD/KH0m/4n8++07I5IirPrtrBZ9/WMrzAwd9vPvPo62BVV1eH0+kkP//wRr75+fk4nU7q6+u7tDhBEFLE36AVU4WC8OkL8OGj0OwFewEMPQfO/Bn0G69ZUWUUgbeur1csCIIg9CEx8fqj0wbyo9MGsn53HW+vr2LHnv0MzrFxx/kn9OnWfyKdFqt2ux23243f78di6dhA1+fz4fF4cDgcXV6gIAgpYMmCvV/BnydB3U7NimrIOTDxZhhxPhhbDLlxVYM99a4xgiAIwrHPqYNzOHVwTtT/9MS+Xk4SnXb8HjNmDOFwmDfffPOwY9966y1CoRAlJSVdWpwgCCnQuEdrj7pzFdTtgrzRcM5c+OFfoOSSZKEacMGWxTBmVt+tVxAEQRA6Qacjq5dddhn//ve/efLJJ4lEIm22Bg2FQixatIgnn3wSRVH4wQ9+0G0LFgQhSigA//4TfPQ4hPyaZ2reSPjB/weFJ7ZtRbXmacg7AYontD4mCIIgCGlIp8XqrFmz+Ne//sXq1at54okneOmll5gwYQKFhYUoikJ1dTXr1q2jqakJVVWZOnUqM2fO7Im1C8LxS3kpLLtH6z5lssPQKVqXqvd/qxVVZQ9O9lkNuDSh+sVf4KfL+2rVgiAIgtBputQU4Nlnn+Xxxx/nr3/9K42Njbz//vtxC4pYowC9Xs9VV13Fvffe232rFYTjnfrdsPx+2P5P0OmhYAyc/P9g/FVgz4MhZ8G7t8Bn87Wt/lgHqy2LtYjqT5dDgaTlCIIgCEcPXRKrJpOJefPmccMNN7BixQo2b95MXZ1WXZyTk8PYsWOZPn06RUVF3bpYQThuafbD2mfh46c0Y/+Mflr3qbPvgPxRLVv+BSVw4wdaDuuWJVrVvz0f5iyRrX9BEAThqKRLYjVGUVERc+bM6a61CILQFt+sgH/OhYbvwOSAIWfDmT+HIZPBYGr7nOIJIk4FQRCEY4JOi9XLL78cRVF49tlnGThwYE+sSRAE0Cr7//UL2LFSK54qHAvjZ8PJV4It5/DnC4IgCMIxQKfFanl5OUajUYSqIPQUzT74+A+w9hkIB8FZDMO/B2ffCbkj2q7yFwRBEIRjlE6L1cLCQmpra3tiLYJwfKOqsG0ZLL9P8041Z8DQ8+CsO2DwJNBLy0JBEATh+KPTTQEmT56M3+9nw4YNPbEeQTg+qd0Ji34Ab83WqveLxsHU38APF8Cwc0SoCoIgCMctnRart9xyC1lZWfzmN7+JOwAIgtBFgh4o/W94fiLsfB8yB8D4q+GKV2Hif4I1PfoyC4IgCEJf0ek0gO+++4677rqLRx99lIsuuohLL72U8ePHk5OTg16vb/e8008//YgWKgjHFKqqeZ8uvx9c+8CcCYPP1KyoBk7UPFQFQRAEQei8WL3mmmviDQB8Ph8LFy5k4cKFHZ6jKApbtmzp2goF4VjjwDfwz3tg10egM0K/U+C06+HEy8Hi7OvVCYIgCEJa0SWf1ViXqp4aLwjHJAEXfPg4fPo8RMKQNQiGnw+T79LaowqCIAiC0IpOi9Vt27b1xDoE4ehFVaFqPWxdAv4GsGRBySwYMKHl+Ka3YcWvwF2jHR98Nky+UzPuly1/QRAEQWiXI+pgJQjHPTVbYPGtcLAcxlwKGUVaNf/rl0LeCDj3Xvjkz7B7DehNmjg9/QZNzJodfb16QRAEQUh7RKwKQlep2QKvfF8Tn3OWar6oMb73gGZD9eZV2u/ZQ+CE6VoBVeaAPlmuIAiCIByNpCxWt23bxptvvsmmTZtwu91kZWVx8sknc9VVVzF4sOTbCccZqqpFVE+/Ac7/dfLrZX+DlQ+A5wAYLGAww49egX7jQddptzhBEARBOK5JSaz+9a9/5aGHHiISiSQVS23YsIG//vWvPPnkk0yfPr3HFikIaUfVem3rf84/Wl6r3gTL7obKz0BvhgFnwCnXwor7AFWEqiAIgiB0gcOK1W3btvHwww8TDocxGAyMHTuWoqIiqqqq2Lx5M8FgkPvuu49x48ZRVFTUG2sWhL5n6xItR9XsAF8DrH4YPp+vHcsZBiMvgrN+Ds5+UPkJbFmi5asKgiAIgtApDitW/+d//odQKMSgQYP485//zIgRI+LHysrKuPXWW6mtreVvf/sbt99+e48uVhDSBn8DOArh6zdg5Tzw1oI1B4aeC+fcrbVLjfoRk1EEXun2JgiCIAhd4bD7kl9++SWKovCb3/wmSagCjBs3jrlz56KqKl988UWPLVIQ0o5wM6x/Dd69BYJurevUxX+Ay1+Cfie3CFXQ3AEsmX23VkEQBEE4ijlsZLW6uhq9Xs8ZZ5zR5vGzzjoLgJqamu5dmSCkI756eP9hLaKKCtlDNRuqM2+DjMLW4wMura3qnCW9vlRBEARBOBY4rFj1+Xzk5eVhNBrbPF5QUBAfJwjHLJEIbHgDVv4X+Oq0LX81AkMmwwW/TY6kJrLmacg7QfJVBUEQBKGLdJvPqrRUFY5Z9n6tVflXrdOsqAadCRNv1rxTX58FjgKtZWqiz2rApQnVL/4CP13eZ0sXBEEQhKMdaQogCO3hrYP3H4IvX9Z+zx0Boy/Rtvwd2o4CP12h5a1+Nh/GzGrpYLVlsRZR/elyKCjpu/cgCIIgCEc5KYnVxsZGrr322i6PURSF1157rfOrE4S+IBKBrxZC6W+0HFVbLgw9D865BwpPTN7yLyiBGz/Qoq5blmgC156v5ajK1r8gCIIgHDEpidXm5mY+//zzLo9R2svnE4R0Y+9X8I+7Ye/6li3/ST+DkRdonajao3iCiFNBEARB6AEOK1Yvv/zy3liHIPQp+kAjLL0T1r2qvZB7gratP/EWcOT36doEQRAE4XjmsGL1kUce6Y11CELfEInAV68zauU8aHaBLQ+GToFz74aCMe1X+QuCIAiC0CscFQVWdXV1zJ8/n/fff5/q6mqsVisnnngiV111FdOmTevyvM3NzSxatIilS5dSUVEBwJAhQ5g1axazZ89u165r69atrFq1ii+++ILy8nIaGhqwWCwMHjyYKVOmcPXVV5OTk9PudUeNGnXYtf3iF7/gP/7jP7r0voQUqVqnbfnv+xqd3gyDz9K2/E+Y1vGWvyAIgiAIvUbai9UdO3YwZ84camtrAbDb7bhcLtauXcvatWu55pprmDdvXqfn9Xg8XH/99WzYsAEAs1kTJ5s3b2bz5s0sX76cl19+GZvNlnTekiVLuPfee+O/K4pCRkYGbrc7fu4bb7zBn/70J0477bQO15CZmdmuILZarZ1+T0KKeGph1W9h/eva77kj2Z83iaJZvwF7Xt+uTRAEQRCEJNJarAaDQW655RZqa2sZOXIkTzzxBKNHj8bv9/Paa6/x9NNPs3DhQkpKSvjhD3/Yqbl//etfs2HDBpxOJ4888gjnn38+AKtWreKXv/wlX331Fb/97W957LHHks4LhUKYzWYuvvhiZsyYwYQJE7DZbAQCAT7++GMefvhh9u7dy80338y//vUv8vPbz3eX9oafAAAgAElEQVT84x//yMSJEzt/Y4SuEQlrLVJLHwR/o1a1H63y31/dTJEIVUEQBEFIO3R9vYCOeOutt6isrMRmszF//nxGjx4NgMVi4aabbuLqq68G4JlnnqG5uTnlebdt28ayZcsA+P3vf8+0adNQFAVFUZg2bRoPP/wwAIsXL+abb75JOnf8+PGUlpbyyCOPcM4558Qjr2azmWnTpvH6669jNptxuVy89dZbR3wPhG5iz5cwfwr84y4IBWHwZJj5R7jsBSiU3FRBEARBSFfSWqwuWaL1U7/kkkvo169fq+M33HADiqKwf/9+Pvvss5TnXbp0KaqqMmTIEC644IJWx6dPn86QIUNQVZV//OMfSceGDRsWbzHbFgMHDoxHSzdv3pzymoQewnMQFv8MFpwPNRshbxScfTv8+HUYfREYTH29QkEQBEEQOiBt0wA8Hg8bN24EYPLkyW2OKSoqYsSIEezYsYNPPvmk3XGH8umnn3Y4L8DZZ59NRUUFn3zySSdX3pJvGolEOn2ukAKqClXrYesS8DeAJQtKZsGABJ/TSFjrPLXqdxCIbvkPmwLnzIX8URJJFQRBEISjhLQVqzt37kRVVQBGjBjR7riYWN25c2dK86qqyrfffpvSvLF1dIZQKMT69esBGDlyZIdjH3nkEaqrq3G73WRmZjJmzBhmzZrFjBkz0Ov1nbrucUPNFlh8KxwshzGXtrQ3ff1SyBuhbesHXLDsbqjeCEYrDD4HzvoZDJ8qkVRBEARBOMpIW7F64MCB+PPCwsJ2x8WO7d+/P6V5PR4PXq8XoMPt/Ni8Ho8Hj8eD3W5Paf6FCxdy4MAB9Hr9YRsqbN26FZvNhtFo5ODBg3z00Ud89NFH/O1vf+P555/H6XSmdM3jhpot8Mr34fQbYM5SMGe0HLvoUXj/YXjpXAgHQdFpEdQTfwCn/yfYc/tu3YIgCIIgdJm0FasxQQlaQVV7xI55PJ6U5k0c15E9VOKxVMVqWVkZTz/9NADXXnstw4YNa3Pc5ZdfzowZMxg/fnxckFZWVrJw4UIWLlzI559/zp133snLL7+c0nsCLaJbVlaW8vijDlVlxOobcQ2eRU3+ZbB9V8KxMLnfLqZo8+vow0Eiip7G4qnsH30tgYzBsLMKqOpw+ubm5mP7/qUJcp97D7nXvYPc595D7nXvkI73OW3Fal8TS0FIlcrKSm699VYCgQDjx4/n7rvvbnfso48+2uq1gQMH8qtf/YqBAwfy0EMPsXbtWtasWZNyHq7BYGDcuHGdWvNRxZ514K3C9oNVFJodLa9/9xksuwtqNoPRBoPORLf3K7IvvJfsganbgpWVlR3b9y9NkPvce8i97h3kPvcecq97h966z50RxGnrBpBoxu/3+9sd5/P5AFLepk8cFzu3o3lTmXvv3r3MmTOHAwcOUFJSwvz58zGZupYbOXv2bIqLiwFYvXp1l+Y4Jtm6RMtRjQnVpn3wzs3w8nTYvxXyR8M598CVb8DYH8K2f/btegVBEARB6BbSNrKaaKZfU1ODw+Foc1wsV7Wj/NNE7HY7NpsNr9fbYZ5r7Jjdbu9QrFZXV3PttddSVVXFyJEjefnll8nMzExpLW2h0+kYO3YsVVVVVFZWdnmeYw5/g1ZMFfTAv/8Ia56GkB8UPYz9EZw7F/JO0Kr8M4rAW9fXKxYEQRAEoRtIW7E6fPhwFEVBVVXKy8sZPnx4m+PKy8vj41NBURSGDRvGpk2b4ud2dd6amhquvfZaKisrGTp0KK+88go5OTkprSPVtQpRzJmwew18tRDcNVpx1aCz4MzbYNh5oE9oW+uq1qyqBEEQBEE46knbNAC73c5JJ50EwMcff9zmmOrq6rionDRpUspzx8auWbOm3TFr167tcN6YUN29ezeDBg3itddeIy/vyNt1qqrKpk2bAOLpAMc9O1draQDffQq+eig+DS58BH78GpwwLVmoBlywZTGMmdV36xUEQRAEodtIW7EKWucqgGXLlrFv375WxxcsWICqquTn53dKrMbmraio4L333mt1fOXKlVRUVKAoSnxsIgcOHGDOnDlUVFQwYMAAXn/99Q7ttRI5XOHWG2+8QVWVVrk+ZcqUlOY8Ztm/DRb9CBZeBvW7wWCB/qdoeamnXgOWNqy91jytpQMUT2h9TBAEQRCEo460FqtXXnklxcXFeL1ebrrpJrZt2wZoBVfz589n0aJFANxxxx0Yjcakc6dOncqoUaO4//77W81bUlLCjBkzAHjggQcoLS1FVVVUVaW0tJR58+YBMHPmTEaNGpV0bl1dHddddx27du2if//+vP766222gm2PO+64gz/84Q+UlZURDAbjr+/Zs4fHHnuMhx56CNAiuueee27K8x5TuA/A0rvghbOg/D3IHASnzIYfL4QD2+Dzl7QIaiIBF6z6b/jiL1pjAEEQBEEQjgnSNmcVwGw288ILLzBnzhy2b9/OpZdeisPhwO/3EwqFAK16/oorruj03L/73e+orKxk48aN3HbbbVgsFlRVJRAIAHDyySfz4IMPtjrvjTfeiKceNDQ08OMf/7jdaxQVFfH2228nvVZfX8+KFSt46aWX0Ov1ZGRk0NzcnOT/OnHiRJ577rlOv6ejnmYffPpn+PgprZDKlgeDz4bJd0K/8aDTwU9XwLu3wGfzta3+WAerLYu1iOpPl0NBSV+/E0EQBEEQuom0FqsAo0aNYtmyZbz00kusXr2a6upqHA4HJSUlXHXVVUyfPr1L8zocDt544w0WLVrE0qVLqaioAGDMmDHMnDmTa665plW0FpK38b1eb1LzgkMxm82tXrvpppsYNWoUGzZsoLq6moaGBhRFobi4mLFjxzJz5kymTZt2fBVXRSKw8e9Q+iC49oLJAUOnwKTbYPiU5BapBSVw4wdQtQ62LNGq/u35MGeJbP0LgiAIwjGIonbW/V5IS45as+SKtbDiV7Dva9CboOgkOOUazSu1rZzUHuKovX9HGXKfew+5172D3OfeQ+5179CbTQFSvU7aR1aFY5TanbDyv2D7MlB0kD8KRl6kWVE5UvPMFQRBEATh2EfEqtC7eOvgw8fgiwUQCUHmQBh6HpxzN+Sm5pUrCIIgCMLxg4hVoXcIBeDz+fDh4xBoAluuZup/zl3Q7xSteEoQBEEQhF7h4IsvYh4xgoxp0w471lVaSqC8nLybb+6FlbVGFILQs6gqbH4H/nQarJwHahiGnguz/gw/elkrihKhKgiCIAi9innECKru/QWu0tIOx7lKS6m69xeYR4zopZW1RlSC0HNUfg5/uQD+fh007YP+p8K0/9ZM/Ud/P7nKXxAEQRCOYQ6++OJhhWEMV2kpB198sUfXkzFtGsVPPN6hYI0J1eInHk8pAttTiFgVup/6Ck2g/uUC2PMl5I2EM26Eq96CM24Ac0Zfr1AQBEEQepV0jGR2JFjTRaiC5KwKh0NVoWo9bF0C/gawZEHJLBjQhqeprwE+fhI+exHCzeAcoG35nztXiqcEQRCEXiXdcjIThWF7ArAvBOKh66KgIK2EKohYFTqiZgssvhUOlsOYS1u6Rb1+KeSN0NqaFpRowvTLl+GDR8BXD9YcGHwWTL4b+kvxlCAIgtD7xCKZhxNcicKsp+lIsPaGQFRVFTUYJOJyEXG7CbvcRDxuVFUl64c/ZM9dd6MfOpSqysq0EaogYlVoj5ot8Mr34fQbYM7S5K37ix6FNU/DyxfC9x6Az16Cup1gtMGQc2DizXDCdMlJFQRBEPqMoyWSmTFtWkrrUEMhIh5PXGBGXC7CbjcRl5uIu+V52OUi0tRIuMmlPXe7tYfXS8TjgXC4w/XpvvkG27nnpo1QBRGrQluoqhZRPf0GOP/XrY+bM6BkJmx4C/71C9AZoN94GPcTOPUayUkVBEE4Dkm3bXfou0hmPILp9aL6/UR8fiK+lueqqpL14yuouutuzKNG4d+2DfvkybhWraLh/96JClEXEbeHiMcTn6dTKAqK0QgGA4rRiGI0YsjPRzEYwGhEZzSC2YTOZEYxmQi7XQS2bCXSvz/ezz/HVVqaNoJVxKrQmqr12tb/nH+0Pta4B1b9N5S9FX1BgVGXwMVPgiO/V5cpCIJwPJNu4jAdt92hbcHatHwFe++/n8J58zCfcAL+bduI+HxRMenTnvt8msj0Jzz3+Yh4o+LR5yPi1cZG/H5Unw814CfiD6AGAlrgJwX8mzYB4Pngg5YXYwIz+lOfmYmSmwtGA4rBiGIyoTObwWxGZzajmM3oM50YcvMw5OdjKCxAn5mpHTOZUEyxn0Z0JlP0uUkTrrR8JgOefYZdBQUM2r9fclaFNGfrEi1H1exoeS3cDB88Cp/8UTP4dxZrxVNBN+QMEaEqCILQy6SbOOztbXdVVYl4vEQaGwg1NBCOPRobW57X1xOuqyfcUI/OamXPz34OihIXktXz5nV9AXo9il6vCUu9HsWgB4MRfaZFO2aIHtcbNFFoMGjRzGiUM9zQQGDrVozFxTTv20fmJRdjnzwZfU4uerutRVAmPGJCE4MBRVGO6P7FaPWZlJWl9Fn2JiJWhdb4G7RiqkSeHQ9NezQ3gOHna8VTxRNg9UNaC1VBEIRjmHSLYkJ65mR2ddtdDQYJNTQQiQrNmPiM/15Xj353BbtCYe21xkbCLheEQodflF7XElk0m1EDARS7HWNBQYLQjG6NGwxgMsW3zXUWCzqHA70zA11mJvrMLO15RgZ6my0e/UyKhBpi4tSIYoz+rtcnLSkeyXzu2aSc1Yxp03CcdWa3fBap0NFnkk6CVcSq0BpLllb1n8idG2HT22DOhOFTWoqnXNVgl6iqIAjdS7qJw3SLYsbo8+ry5mYigYC2fe4PoAb8GPr1I+/mm6m6+x5yrr8O87BheD7/nMbFS7BNmkjj4iXUvvoakYYGwk1NhJuaUsrHVIBALMpoNmHIy0Mxt4hQndWqPWw2DPl5GPv1w1BcjDG/AJ3Djm/demoeewznxRfjWrWK/DvuIOPC6d0WoUyVtj6XvhCGqfx9pItgFbEqtKZklmZPddFjLakAOh2MuyJ5XMAFWxbDnCW9v0ZBEI5p0k0cpmMUs6219Xvod9jPPBNXaSk1D/+e/LvuRJ+bi+fTzxLyKf1ajqU/QCQQ/en3o/pb8jI18elrORbQ8jAjgQBqMBh/EIl0uLbal+Yn/e5ds1Z7YjBoItNoROd0ouTmatvcFjM6iwXFakVntaHLdGLs1x/jgGL2+HwMPWkcOqsFxWJFZzG3/DR0LGdcpaXUPP44xU8+kVx9b9D36meVTpHMQHl5SteJrStQXi5iVUgjik/VfFTX/KFtN4AYa56GvBO0dABBEIRuJB3FYU9EMdVwWKv2jj3cbsLx372a5ZDHQ9jjJpJoRRQdG/F64w/V52PvPXOT5t//6GNdf8M6XUtepk4Xzb/UtswVsxlFHz2uS3g9lsNpMKAYDQQrdhM+eBBDcTEZF1yAsbgY04AB6DOdmii1WFrEqdmMYrGgtOPNXVlWhvWksZ1+GxLJbJvO7ERkTJsmaQBCmqEomuH/yxdqv0++K9mOKuDShOoXf4GfLu+bNQqC0K2k27Y79P0Wd1s4zj+ffg8/TNXceyn85f1gs3Hwk085+PzzZF89m3BDA3WvvUY45ncZN193xS2IEq2I1ECgawtJyI1UjEZ0djtKVibhxiZUrxddRgbGfv2ShCP6aIFP7ByLGcVuR2+3o7NH8zIznOicTm073WSMik9jS7FQYk6mXq/lacYEbeJzWj6jzMsuo2nFCuynTeh1wSORzGMDEatC2xSUwE9XwLu3wGfzYcyslg5WWxZrEdWfLtfGCYLQadJNHKbbtnuMrhqoQ9RE3eeLCsRYBDIqFL1e7Vji626PZqzucbdENqPj1Jg9kd/fUkn+mwcxAgei16t7+ZX234iitBTixB5OZ1QEauIRkwmd0RjPwVSitkT6rEz0OTnoc/Mw5Oehd0YtiWJjo3ZEnk8/o/p3v8N5ySW4SkvJ+9ltOKdP774PoxMc+hllTDu/179cSCTz2EHEqtA+BSVw4wdQtQ62LNGq/u35Wo6qbP0LwhGRbuIwnbbdI4EA4cZGreq7qQl0ungrSPPw4QTKy7FNnEjT8hU0vP22JipjUcuYuPT7tZzKrnKILREGgxa9dDohKjDD9fVEXC50WVmYR4xosRey2TBkZaHPzkafl4chJxt9RkabVkRxwWk0oTNFbY26UPDjKi2l+qGHWuVkKjpdWkQz+2LbXSKZxw4iVoXDUzxBxKlwVJNuUUxIL3GYypo6uxY1HNa68EQFZ7ihkXCTZjkUSfy9vp5wfUN8XMTtQg20LzID27YB4F0bLdRRlORoZTSfUme3oeiTI5nxrj1GYzwyqbNa0Tmd6LOy0GdnYcjN0/Ipo0Iybj+UaEtkNOJeu5bqB39L5JzJKF+uI/e6OX0mdNJFHLa3lhi9vSaJZB47iFgVBOGYJ92imDHSMSczcU39H38Mx9ln07hsGTUPPUzuTTehqioN//u/hBubNIHZGBWcMTP2xkYtT9PrTa2Dj6IkRBqN6HNykyOPUUuiiMuFf/NmzMOGEaioIOvHP8Zx9tnoMp0tfpcxz8uE5/HX9PpuNVGv/u1/U/zkE33e7SedxGG6bbsLxw4iVgVB6HbSLZKZjlHMjtaWck6mqsbbQ6rRFpGxHuTK1q007d13SKvIaG9yr4+ILzGP06uN87bkZarhMFU/vz3pegefe679NxKzIjKZUOx2DNnZWuTSZEKxmNFZrFrlt82KPjcPY2Ehxn5FGAqibSFjleGxinCTKV4VntgKMvH+OM6chH1C7+76pFO3n3QTh7LtLvQUIlYFQeh20jGS2ddRTFVVUX0+rYDH7da2yF3R524Xzu9/nz133oVpyBCCu3ZhmzCBxiVLqX/zzbi4VGO9yWOCsoNKcgNQlcrCYvZEsdzM6HNDVhZhlwvV59MqywcOjFoMWTT/y4wMjIWFGPr1w1hUqInTmA2R2RL1wIyKziOIaMoWd/ukmziUbXehpxCxKgjHABLJ7Pq6UllH3AvT5YqLTc2WSBOaYbebSNyqSOvIE2lqGRvz0DycgTpAsLwcAO/nn2svxFpBxgRlNIKps9m0XuR6Q5v+lh5/gIycnHgBj85uR+90ost0YsjMRJfh1KrM7XZt2zyxNaTBgPuTT6j53UPxbj95t9yM84ILuu2zSIV0EofpFsUEEYfC8YOIVUE4BpBIZjKJ2+MRrw/V540/R68n+8qfUHXX3ZhHj8a/dSuOc87Btep9Gt55N0mQxkRmKq0gk1CUeA4lpmh/cWcGirGl57hiMmlb3mYzitVCpLEJ75dfYh49muD27WTPmYPjnMnobHatY4/J1OJ5aUwuKMJgaGWkXlZWxqBx47p0/1ylpdQ89HDryvInlLSIYsaQLW5BOD4QsSoIXUAimV1bFwUF8XX0f+wx7GedRai2Nu6FqcY8MaPCUtv69iXnWMa6+3i92mteX9xgPb49HgymVNzj37gRAPfq1S0vxiKYRk0U6jIyUHKyo5FHY3Ivcou23a3LzMSQl4ehoBBjfh66rGz0Nmt0rBmducU3s61t8XhO5jNPJ4lD2/iTsU+c2K2fyeFIl233dBOHEsUUhL5DxKpwVJBu4vB4i2SqwWBSW8ckT0vvIYU6sePxtpFu9Lm57Pn57RiMRvYEg2AwUHX77Ye/cEfELItifpgxkel0tryWsC2uGKKWRSYjoaYmgtu/wdCviFB1DRkXXoj9zDPR5+eht9laIp6HCs2YEXs3VZVD+ojD9tYSQ7a4BUHoK0SsCkcF6SYO0zmS2e+R32utIO+/D+spp+D+4EOtFeS11xB2u6l74w2te09UZIa9XlSPm7DbkyRC4x1+/H4Ihbq+qGgLRvR6lGAQJdqRB72hVfvG+BZ5Ymcei0VrAZmZiSErUzNaz8xCZ7XGo59JFkXt+GJiMKAoSksU87lnk6KYmTMuImPKlG77LFIhncRhum27C4IgxBCxKrQi3aKYkJ7isFsN1IPBaBTS05IrGf2ptX/0JuRQurXXmqI9x93upGhnTFhWP/jbpGvULfhLx4vQ6Vq3g7TZMDidybmRMYsioxHFZNSijyYTOoddawOZnY0hJwdDTja6jAx0ZjPe9es58PQzhE89BX3ZRvJ+fjsZU7+XJChjYrInOVqimDEkJ1MQBEHEqtAG6RbFjNHX1kOHogaDWCdMoPD++6i6Zy55t96CpaQEz6efUr9wEc5LLsG/bTveL9dFRWe0etzlahGmXq3/eJcil0m5lUYUiwWDwxH/PXRgP5HGJnQ5OVhGjozmWWo+mHpnJvqsTAzZ2Vrnnqws9E4nSixaeWiUMxad7MIWuKu0lAPPPkfxU08mGagbsjJ7VeykUxQT0k8cyra7IAjpiqKqqbQYEdKdsrIyxnWx8rctDif++spAva1rd2YtkWCwVeQy4vGwa8sWBuTmEolthccimm4X4SZXS1FPLIrp7QaB2Vannei2NiYTOlNLnqTebseQk4MuN0eLWubmajZENltLHmVC1x/3hx+y9xf34bzwQppWrOizLdtDP5vY32lv//2ker2+/Lvubrr73wShbeQ+9x5yr3uH3rrPnbmORFaFNkmHKKYaiSR05WmxINLZbORcc7VmPTRmDP4tW8g4/3w8n3+B6/33k83WEwuBfL52BaYBqG5vIXpdSyW4wYBiNmGw2+NFOzGRGdy7l0hdHYbCQiwnj0PvcKDPykafl4chO0sTmJmZmsBMbCeZKDQPsR/qCq7SUvb+4r7455Mx7fw+EWDpFMlMtyimIAiCkDoiVoV2ScVAXY1Ekn0sEy2IEi2HvO1YECUIyYjPpxX1JNoPHQb/hg0AuJYvTz6QYKBO1G5IE5jRyKWhpYhHZzbjCgTIzM9Hb7drBTx5udG8yxz0mZkoFqtW7GMyRSvKE8SmwdBSpHPZZTStWEHWrFlpEcmEvtniTrd8TNniFgRBOHoRsSp0SFxQ3DMXncNOuK4endPJ3l89gHr3PSkJyg7R6VoqwmM/jUZ0VmtyC8gE30vFaCLschHcsQNDYSGh/ftxTJmCbcKpGPLy0Ofmorfb29weT4pmJuRelpWVMfAIDNQTBZdEMiWSKQiCIHQfIlaFw5IxbRr2s87SjNNjnXL0+mi7x1gbyGjbxyTrIaPWscdkQm+zavZDWZkYsrK07fGs6Ja40ZRsNZRY0JNoQxTdIm/Peijr8sv6PH82dr8kkimRTEEQBKF7ELEqHBZXaSmeTz/VeoSXlpJ/5504pkxpw99SrIdiSCRTEARBELqHI6/mEI5pEgVZ8VNPUvzUk9Q8+ij+jWVaPqfT2WLO3gdCNUaiOHSVlvboOg63lr5YU97NN6csPjOmTetxX1xBEARB6C5ErArtcrgoZm+Iwo7Wcii9ubauRDIFQRAEQeg8IlaFNkmnKCaknziUSKYgCIIg9A5HRc5qXV0d8+fP5/3336e6uhqr1cqJJ57IVVddxbQjyLtrbm5m0aJFLF26lIqKCgCGDBnCrFmzmD17NkajscPzt27dyoIFC/j888+pr68nJyeHM844gxtuuIHRo0f36LV7knQr1gEp2BEEQRCE45W0F6s7duxgzpw51NbWAmC323G5XKxdu5a1a9dyzTXXMG/evE7P6/F4uP7669kQ9ek0m80AbN68mc2bN7N8+XJefvllbDZbm+cvXbqUX/7ylzQ3NwOQkZFBTU0NS5cuZfny5Tz22GNcfPHFPXLtnkaKdQRBEARBSBfSOg0gGAxyyy23UFtby8iRI1m8eDHr169n/fr13H333SiKwsKFC3n77bc7Pfevf/1rNmzYgNPp5Pnnn2fDhg1s2LCB559/HqfTyVdffcVvf/vbNs/dsWNHXKjOmDGDNWvW8OWXX7JmzRpmzJhBc3Mz999/Pzt37uz2a/cGssUtCIIgCEK6kNZi9a233qKyshKbzcb8+fPjW+sWi4WbbrqJq6++GoBnnnkmHuFMhW3btrFs2TIAfv/73zNt2jQURUFRFKZNm8bDDz8MwOLFi/nmm29anf/cc8/R3NzMiSeeyBNPPEF+fj4A+fn5PPHEE4wZM4ZgMMhzzz3X7dcWBEEQBEE4nkhrsbpkyRIALrnkEvr169fq+A033ICiKOzfv5/PPvss5XmXLl2KqqoMGTKECy64oNXx6dOnM2TIEFRV5R//+EfSMZfLxerVqwG4/vrrMRiSMykMBgPXX389AO+//z5ut7vbri0IgiAIgnC8kbZi1ePxsHHjRgAmT57c5piioiJGjBgBwCeffJLy3J9++mmH8wKcffbZbc67bt26eBQ3NuZQYvMGg0HWrVvXbdcWBEEQBEE43khbsbpz505UVQWIC9K2iB1rLz/0UFRV5dtvv+3yvOVRS6S8vDxycnLaPDcnJ4fc3NxW5x/ptQVBEARBEI430lasHjhwIP68sLCw3XGxY/v3709pXo/Hg9frBaCgoOCw83o8HjweT6t1dXRu4vmJ7+NIry0IgiAIgnC8kbbWVTFRB1pBVXvEjqUq6hLHWa3WdsclHvN4PNjt9qR1dbSm9tZ1pNfuiFAoRFlZ2WHHCW3T3Nws968XkPvce8i97h3kPvcecq97h3S8z2krVvuaWApCX5zflXMNBgPjxo3r8jWPd8rKyuT+9QJyn3sPude9g9zn3kPude/QW/e5M4I4bdMAEg3x/X5/u+N8Ph9AStHHQ8fFzu1o3kPPia2rozUlHk8890ivLQiCIAiCcLyRtmI15l0KUFNT0+64WK7q4XJIY9jt9rjg7CjPNXbMbrcnCcbYug6XIxs7nvg+jvTagiAIgiAIxxtpmwYwfPhwFEVBVVXKy8sZPnx4m+Ni1fntHT8URVEYNmwYmzZtip/bmXljlfoHDx6krq6uTUeAurq6eHvYxPOP9NodITmrR47cv95B7nPvIfe6d5D73HvIve4deuM+h0KhlMemrQpWXa8AACAASURBVFi12+2cdNJJlJWV8fHHH3PhhRe2GlNdXR0XdpMmTUp57kmTJrFp0ybWrFnT7pi1a9e2Oe+ECRMwGo00NzezZs0aZs2a1erc2LxGo5EJEyZ027U74tRTT015rCAIgiAIwtFC2qYBgNa5CmDZsmXs27ev1fEFCxagqir5+fmdEnaxeSsqKnjvvfdaHV+5ciUVFRUoihIfGyMjI4PzzjsPgFdffZVwOJx0PBQK8eqrrwIwdepUHA5Ht11bEARBEATheCOtxeqVV15JcXExXq+Xm266iW3btgFa8dL8+fNZtGgRAHfccQdGozHp3KlTpzJq1Cjuv//+VvOWlJQwY8YMAB544AFKS0tRVRVVVSktLWXevHkAzJw5k1GjRrU6//bbb8doNLJ582bmzp0b91I9cOAA9957L5s3b8ZoNHL77bd3+7UFQRAEQRCOJxT1SD2aepjt27czZ84c6uvrAXA4HPj9/niuw+zZs/n1r3/d6rypU6dSVVXF5ZdfzqOPPtrquNvt5rrrrou3dLVYLKiqSiAQAODkk0/mlVdeabfA6d133+WBBx4gFAqhKAoZGRm4XC5UVcVoNPLII48wc+bMNs890msLgiAIgiAcL6S9WAWora3lpZdeYvXq1VRXV2Oz2SgpKeGqq65i+vTpbZ5zOLEKEAwGWbRoEUuXLqWiogKAIUOGMHPmTK655ppW0dpD2bJlCwsWLODzzz+noaGBrKwsJk6cyA033EBJSUmH5x7ptQVBEARBEI4HjgqxKgiCIAiCIByfpHXOqiAIgiAIgnB8I2JVEIT/v737jqrieBs4/r10EQUVxUiwiyWxxIgmtthiwULTFEssiRqNvZKgRgwaNJbYIqImKqhJLBEVY5RYYu/tJ6JRFDSKIoh0uMC+f/Cy4dLBRuT5nOM53t3Z2bnDzO7cndkZIYQQotiSxqoQQgghhCi2iu2iAEI8L9u2bePLL7/MN9zx48dzXKFMpIuOjubUqVNcuXKFwMBArly5ok7jtnz5cjp16pRvHPv27WPTpk1cuXKFhIQEXnvtNdq3b8+wYcMk7zN5mrzOeNk0L7nNqlIShYWFERAQwPHjx7l69Srh4eHo6+tjZWVFixYt6N+/P7a2tnnGcfLkSdatW8fFixeJjo6mYsWKtG7dmqFDh2JjY/OCvknx9jT5PGDAAE6dOpVn/O3atWPlypXPI+n/ORcuXODAgQP873//486dO0RERJCUlISFhQUNGjSgZ8+e9OjRA41Gk2scL/taLY1VUWLp6enlWcn09KTjIS8BAQEFavTnZtasWWzYsAEAfX19TExMuH37Nj/99BM7d+5k/fr1hVpy+FX2tHkN6dP+mZiY5LpPwP3792nfvj2Z3zs2NTUlJSWF27dvc/v2bbZu3YqrqysDBgzIMY5Vq1axYMECFEVBo9FQunRp/vnnH3755Rd27dqFl5cXzZs3f1FfqVh6FvmccYypqWmO+8qWLfvM0/1flTHzUAZTU1MMDAwIDw/n0KFDHDp0iM2bN/PDDz/keC0oDtdqaayKEuu1115j//79LzsZ/2kVK1akfv36NGjQgDfeeIPRo0cX6Lhff/2VDRs2oNFoGD9+PAMHDsTExISgoCAmT57M9evXGTFiBLt27cLIyOg5f4v/hqLmdQY3NzecnZ2fU+peDampqSiKQuvWrXF0dOTdd9/F0tKS1NRUAgMD8fT05MyZM3h4eFCtWjXatm2rc/yRI0fUhuqAAQMYM2YMZcuWJTQ0lGnTpnHy5ElGjx7Nnj17KFeu3Ev6li/f0+ZzhiFDhhS6HpREdnZ2NGrUiGbNmlGtWjV1Dvfw8HA2b97MsmXLOHnyJJ6ennh4eOgcW2yu1YoQJczWrVsVW1tbpX379i87Kf9pWq022zZbW1vF1tZW2bdvX67HJSUlKa1atVJsbW2Vb775Jtv+e/fuKU2aNFFsbW0VX1/fZ5rm/6qi5rWiKEr79u0VW1tbZevWrc8rea+M6Oho5cqVK7nuT0pKUnr06KHY2toqAwYMyLbfyclJsbW1VT7//PNs+2JiYpQ2bdootra2yty5c59puv9rnjaf+/fvr9ja2ipLlix5nsksMRYsWKDY2toqDRs2VJKSktTtxelaLf2cQogiMTAoWsfM8ePHCQ8PR6PR8Nlnn2Xb/9prr9G9e3cAna6rkqyoeS0Kp0yZMjRo0CDX/UZGRvTq1QuAK1eu6Oy7efOmum3o0KHZjjUzM+Pjjz8GYNeuXTpd4CXN0+SzePYaN24MQFJSEtHR0er24nStlsaqEOKFOnHiBAB16tShcuXKOYZp3bo1kP5iQHx8/AtLmxD5KVWqFABpaWk62zPKtZmZGU2aNMnx2Ixy/eDBA4KDg59jKv/7cstn8eydPXsWSB/LWqFCBXV7cbpWy891UWJFRkbi5OTErVu3ALCysqJ58+b079+funXrvuTUvbpu3rwJQO3atXMNU6dOHQAURSE4OJg333zzhaTtVfbjjz+ycOFCoqKiMDMzo27dunTp0gUXFxeMjY1fdvL+MzLeQs/6pvqNGzcAqFWrVq4vZ2aU64zw8gJh7nLL58x27tzJtm3bCA8Px9TUlJo1a9KxY0c+/vhjeWkwH/Hx8dy/f5/t27fz008/AfDJJ5/ozAhQnK7V0lgVJVZCQgJXr16lbNmyxMfH67yFOnHiRD799NOXncRXUsaUS5UqVco1jJWVVbbw4un8/fffmJiYYGxszOPHjzlx4gQnTpxg06ZNrFy5kipVqrzsJBZ7ly5dYt++fQD07t1bZ19ByrWJiQnm5uY8efJEynUe8srnzEJCQjA0NKRUqVJER0dz/vx5zp8/z8aNG1mxYgX16tV7UUn+TwgPD1efhGZmaGjIoEGDGDNmTLbwUDyu1dJYFSVOpUqVGD16NJ07d6Z69eoYGRmh1Wo5e/YsCxcu5OLFi8ybN49KlSrRs2fPl53cV05GV1Fu0yhl3RcXF/fc0/Qq69ixI82aNcPOzk6dqu3hw4ds2bKFFStWcP36dYYNG8a2bdtk5oU8REVFMXHiRNLS0mjUqFG2mRUKUq4z9j958kTKdS7yy2eA5s2b4+LiQqtWrbC0tESj0RAVFYW/vz8LFy7k3r17fPbZZ+zcubNEz7qQlb6+PpaWlkD63M3JycloNBr69+/P4MGD0dfX1wlfnK7VMmZVlDitW7dm1KhR2NraqjdnQ0ND3nnnHXx9fdXxZvPnz5fxUuI/z83NjS5duujMKVypUiVGjhzJ999/D6Q/df3tt99eVhKLvcTEREaNGkVoaCjlypVj4cKF2W7sBVWSX6zKT0HzefTo0Tg6OlKxYkW129rCwoJ+/fqxbt06DA0NCQ8PV7u3Rbry5ctz9OhRjh49ysWLFwkICKB///6sX7+enj17cubMmZedxFxJY1WITIyMjBg3bhyQvsJKYGDgS07RqydjEu/ExMRcwyQkJKj/z5gTUDx7GU9dAQ4cOPCSU1M8JScnM2rUKE6fPk3ZsmX58ccfc1yFqiDlOvN+Kde6CprP+XnzzText7cHpEznRU9PDxsbG6ZNm8bUqVOJiopiwoQJOtfe4nStlsaqEFlkTOMBcOfOnZeYkldTxYoVgfSu6Nxk3pcRXjwfjRo1AqSs5yQ5OZkxY8Zw+PBhzMzMWLNmTa5TLhWkXCcmJqpTA0m5/ldh8rkgMq7hUqYL5qOPPsLIyIgHDx7w119/qduL07VaGqtC5CGvtZJF0WS8AZ3x9nRO/v77byA9/2vWrPlC0lXSSVnXpdVqGTt2LAcOHMDU1BRvb2+1YZ+TjDemb968mevwoYxynTl8SVfYfC4MKdMFY2xsjIWFBQChoaHq9uJ0rZbGqhBZXLx4Uf2/tbX1S0zJq+mdd94B0i9yYWFhOYY5cuQIkP6EJLe1v8WzcenSJUDKemZarZZx48axf/9+TExM8PLy4u23387zmIxyHRsby/nz53MMk1GuK1WqJD/CKFo+F0TGNVzKdMHExcXx+PFjQLcrvzhdq6WxKkqU/F5u0Gq1LF68GEi/obzxxhsvIlklSsY64IqisHr16mz779+/j7+/P4C6io0omvzK+8GDB9WXKtq1a/cCUlT8paSkMHHiRAICAjA2NmbFihW0aNEi3+Nq1apF/fr1AXIs17GxsWzatAmAHj16lPinfkXN5/zKdGBgILt37wakTEN6Pudn7dq1aLVaAOzs7NTtxelaLY1VUaL8888/9OnTh19//ZW7d++q21NSUjh16hQDBgxQn4pMnDgx18m9RbrIyEidfxliY2N1tmdcCCH9JbZRo0YB4Ovri7e3tzqAPygoiOHDhxMfH0/VqlXp06fPi/1CxVhR8trDwwMPDw9Onz6t8yJEeHg4K1euVOdVrFWrFi4uLi/uyxRTqampTJ48mT/++AMjIyOWLVtGy5YtC3z8hAkTANi/fz+zZ88mJiYGSB87OXLkSB48eIC5uXmOy7GWJE+Tz97e3nz55ZccPnxYzV+AJ0+esGnTJgYOHIhWq8XS0pIhQ4Y8r6/wn/H333/Tp08ftmzZwj///KNuVxSFGzduMGvWLJYuXQpAly5ddBauKE7Xao0i82iIEuTu3bt07NhR/WxsbIypqSmxsbHqTd7AwIAJEybIogAFUNCVvtavX5/tqcmMGTP45ZdfgPQ8NzExITY2FoAKFSqwbt06nQtnSVeUvHZ1dVWnpNJoNJQpUwZAZ/3vevXq8cMPP0iXKXD69Gn69+8PpE9nZ25unmf4LVu28Nprr+lsW7lyJYsWLUJRFPT09ChdurTaqDI1NWXFihVq92pJ9TT5vHTpUpYtW6buMzMzQ19fn+joaPWpq7W1NcuXL1efdJdkV69exdHRUf2ccc+Lj48nKSlJ3d6hQwcWLlyoLnObWXG4VsuiAKJEsbS0ZNq0aZw/f56rV68SGRlJTEwMJiYm1K5dm+bNm/Phhx/KMogvwKxZs2jdujUbN27k6tWr6i/0Dh06MGzYMJ01qkXRfPTRR5QvX54LFy5w7949Hj9+TFpamjrEpUuXLnTv3l0WA/h/mV+M0mq1PHr0KM/wqamp2bYNHz6cxo0bs27dOi5cuEBMTAxVqlShVatWDB8+vEjTMb1qniafu3btSmpqKufPn+fOnTs8fvyYpKQkKlSogK2tLR07dsTJyUmmBvt/NWrUYMGCBZw4cYLLly/z6NEjoqKiMDIyombNmjRq1IhevXrRqlWrXOMoDtdqebIqhBBCCCGKLRmQJ4QQQgghii1prAohhBBCiGJLGqtCCCGEEKLYksaqEEIIIYQotqSxKoQQQgghii1prAohhBBCiGJLGqtCCCGEEKLYksaqEEIIIYQotqSxKoQQ/3EdOnSgbt26bNu27WUnRQghnjlprAohhBBCiGLL4GUnQAghxNOxsbHByMiIMmXKvOykCCHEM6dRFEV52YkQQgghhBAiJzIMQAghhBBCFFsyDEAIIYoZRVHw8/Nj+/btBAUFERMTg5mZGeXKlePNN9+kY8eOdOvWTQ3foUMH/vnnH7799lucnZ2zxffw4UOWLVvGoUOHiIyMxNLSknbt2jFixAhu3brFJ598AsC1a9d0jlu6dCnLli2jefPm+Pj4sGfPHtavX8/169fR09OjUaNGjB07loYNGwIQGxvLmjVr2L17N/fv36ds2bK8//77jB8/nrJly2ZLV2JiIkePHuXgwYP873//4/79+8TGxlK+fHneeust+vfvj52d3bPMWiHEf5A0VoUQopiZOnUqfn5+6mczMzPi4+OJiori1q1bnDp1Sqexmpe///6bTz75hMjISABKlSpFVFQUGzduZN++fYwfP75A8SxatAgvLy8MDAwwMjIiJiaGw4cPc+bMGdauXUvVqlUZNGgQ165dw9TUlLS0NMLDw9m4cSOXLl3i559/xtDQUCfO3bt38+WXX6qfTU1N0dPT48GDB+zZs4c//viDSZMm8dlnnxUojUKIV5M0VoUQohg5c+YMfn5+6OnpMWXKFHr37k2ZMmVQFIWIiAhOnTrFsWPHChRXcnIyY8aMITIyEmtra2bPns0777yDRqPhzJkzuLm54enpmW88V69e5dy5c3z55Zd8+OGHlCpVimvXrjF27Fhu3brF7NmzqVy5MsnJyWzYsIFmzZqRnJyMn58fM2fO5H//+x9btmzh448/1om3bNmyfPTRR/Ts2ZPatWtjYWGBoijcu3ePtWvXsn79ehYsWEDz5s1p1KhRkfJTCPHfJ2NWhRCiGDl//jwALVu2ZPDgweob/hqNBktLS+zt7fHw8ChQXP7+/gQHB2NoaIi3tzfvvvsuGo0GgGbNmrF69WqSk5PzjScmJoZhw4YxaNAgSpUqBUDdunXVdFy6dIkDBw6wcuVKmjVrBoCRkRF9+vTBwcEBgD179mSLt1OnTri7u9OsWTMsLCzU72ltbY2bmxsuLi6kpaWxadOmAn1fIcSrSRqrQghRjJiZmQHw+PFj0tLSniquP/74A4D27dtTu3btbPttbGywt7fPNx5DQ0M+/fTTbNubNm2KsbExAF26dKFatWrZwrz77rsAXL9+vVBph/R0A5w7d67QxwohXh0yDEAIIYqRli1bYmhoyJUrVxg4cCC9e/emRYsWVK5cudBxXb16FYC333471zDNmjXLd+Ura2trtRGdmZ6eHuXKlSMsLAxbW9scj7W0tAQgOjo6x/0PHz5kw4YNHD16lJCQEGJjY7M10h8+fJhn+oQQrzZprAohRDFSrVo13N3d+eabbzh16hSnTp0CoHLlyrRq1YrevXvTtGnTAsWV8VJVpUqVcg2T174MFStWzHWfvr4+8G+jNCs9vfQOvJSUlGz7zp49y7Bhw4iNjVW3lS5dGhMTEzQaDVqtlidPnhAfH59vGoUQry5prAohRDHj4uJCu3bt+P333zl58iRnz54lLCyMrVu3snXrVvr378/06dNfWHoyxrk+bZjMUlJSmDRpErGxsbzxxhuMHz+epk2bUrp0aTXM8ePHGTRoUGGTK4R4xciYVSGEKIYqVKhA//79Wbp0KceOHWP79u307NkTAF9fXw4dOpRvHOXLlwcgPDw81zB57XueLly4wL1799DX12fFihW0adNGp6EK8OjRo5eSNiFE8SKNVSGE+A+oX78+3333nTo29OTJkwU6BtK723Nz+vTpZ5PAQrp//z6Q3qC2srLKMUxBp+gSQrzapLEqhBDFSF5TSWk0GvXt+4JMOdW5c2cA9u/fz82bN7Ptv3v3Lrt37y5iSp9OxpRcjx49IiIiItv+a9eusWvXrhedLCFEMSSNVSGEKEZmzZrFhAkT+PPPP4mKilK3R0ZGsmjRIi5fvgxA27Zt842rR48eVK9eHa1Wy7Bhwzhx4gSKogDp00F99tlnauP3RWvatCmmpqYoisK4ceMICQkBQKvVsnfvXoYMGYKpqelLSZsQoniRF6yEEKIYSUlJwd/fH39/f+DfeVczvzHfr1+/AjVWjYyMWLJkCZ988gl3795l4MCBlCpVCo1GQ3x8PBUrVmTKlCm4ublhZGT0fL5QLsqWLcukSZOYNWsWp06donPnzpQuXZrk5GS0Wi3W1tZMnTqVyZMnv9B0CSGKH2msCiFEMTJy5Ejq16/PiRMnCA4OJjw8nOTkZKysrGjcuDG9e/fmvffeK3B8devWZceOHSxfvpyDBw8SGRlJxYoV6dWrF6NGjeLChQtAeuPxRevXrx+VK1fmxx9/JDAwkNTUVF5//XU6duzIsGHDCAoKeuFpEkIUPxolo09ICCFEibNo0SK8vLxo0aIF69evf9nJEUKIbGTMqhBClFCPHz9my5YtALRu3folp0YIIXImjVUhhHiFHTp0iHnz5hEUFKTOIKDVajl69CiffPIJjx49wsLCgt69e7/klAohRM5kzKoQQrzCoqKiWLNmDWvWrEGj0WBubk5cXBxarRYAU1NTFixYoC4gIIQQxY2MWRVCiFfY/fv32bx5MydOnOCff/4hMjISAwMDXnvtNVq2bMmgQYN4/fXXX3YyhRAiV9JYFSVSWloaUvSFEEKI50Oj0aCn92xGm8owAFGiJCcnExoaqnaBCiGEEOL5MDQ0pGrVqk89j7M8WRUlyo0bNyhTpgwVKlRAo9G87OQIIYQQryRFUYiIiCAmJobatWs/VVzyZFWUGGlpaWi1WipUqICBQQGLvqLAP+fg6g5IjAITC6jfC15/+/kmVghRJI+8vDCuXZsynTrlGzYmIICkGzew/PzzF5AyIUqeChUqEBkZSVpa2lMNCZCpq0SJkdGJUOAnqg8CYVV7WO8AZ3+Cqzsh7lH6Z+928PDq80usEKJIjGvX5p/JU4gJCFC33ezRg5s9euiEiwkI4J/JUzB+yic+QojcZdxvn7YTXxqrQuTkQSD81BVqdYCJV6HMa1C6IjguT/9cqwP82EUarEIUM2U6dcL6u3nZGqyZZTRUrb+bV6AnsEKIl0saq0JkpSjgNxLsPoOOM8C4jO5+4zLp2+0+g+0jXk4ahRC5yqvBKg1VIf57ZMyqEFn9cw4e3YCBu/IO13o8nFwJ/5wF66KNYe3QoQMJCQn89ddfGBoaAnD8+HEGDRpE7969+euvv1izZg22trYAhISE0LdvX37++We2b9/Oxo0bqVSpkhpf9+7dGTZsGEuXLlX3abVaqlatioeHB5aWlgCcPn2a+fPnk5iYiFarpWzZsixbtkzdn5mrqyvHjh2jXLlyJCcnY2dnx/Tp0zE0NCQlJQUvLy927dqFvr4+qamp2NnZMXnyZMqWLasTz7Vr15g1axYREREYGhrSpEkTpk+frr4lOmbMGM6dO0d4eDjnzp2jdOnS+ebf0aNH+eGHHwgLC8Pc3Bw9PT369OnDhx9+WKS/R1EsXryYOnXqYG9vX+Q4Mv+9AAwMDHBzc6Np06YA1K1bt8B5ktmDBw/46quvuHv3LkZGRtSsWRN3d3csLCyA9PJnZGSEkZERiYmJ1KpVi6FDh6rnLagOHTrg5eWFra0tbm5uODk50axZs2eSN0WVucGqb26Onlnp59JQlTqcXofzK2u5uXz5MosXLyY4OBgLCwvS0tLo3LkzI0eOfKq/S2Fs2rSJpKQkBg0aVOQ4tm3bxpw5c7C2tgbSu73HjBlDp/8vZ5nrSGEkJSUxfvx4bt68iYmJCZaWlri7u6tzIw8YMIB79+5hZmZGQkIC1tbWDBw4kHbt2hXqPAMGDGDIkCG0b99ep94+i7x5JhQhSoiUlBQlMDBQSUlJSd9wwFNRljXP/s+zuqJ8a6O7bZZl+r+sYefYpIfPuv2AZ4HS1L59e8XJyUnZs2ePum3ixImKs7Oz4unpqfj7+ytOTk6KVqtVUlNTlb59+yq//vqroiiKsmTJEsXTM+fzZN6XmpqqjBs3Tvn6668VRVEUrVarNG/eXLly5Yoa/ubNm0psbGyOcU2dOlXx8fFRFEVREhMTlQ8++EBZv369oiiKMmXKFGX48OFKVFSUeq7du3croaGh2eK5deuWcvXqVfVvMXbsWGXFihXq/qNHjyqPHj1SbG1tc01LZocPH1ZatWqlnD17Vt129+5dZe7cufkeW9xk/Vv6+/srzs7O6ueC5klmWq1WCQ8PV06fPq1u8/T0VKZNm6Z+bt++vXLt2jX18759+5S3335buXDhQqHOlTWeF+HhsmXKje7d8/13/b12SmDdekpg/fpKYL36yvX32hXouIfLlhUoHVKH0+twfmUtJ0FBQUrz5s2V/fv3q9siIiKUmTNn5nlccbR161Zl9OjR6ueLFy8qLVq0UD8XpY5otVolMTFROXjwoJKWlqYoiqL4+PgogwcPVsP0799fJ/9OnTqltGrVSqc8FkTWeJ6VbPfdIpJhAEJkpaSBXgE7HfQN0sM/BRcXF7Zu3QpATEwMFy9epE2bNgDY29tTvXp1vLy8WL9+PWZmZvTp06dQ8evp6WFnZ8e9e/cAiIuLIy4ujooVK6phatasWaCndsbGxrz99tvcunWLkJAQ9uzZw5w5czA3N1fP1a1bN2xsbLIdW716derVqweAvr4+DRs25M6dO+r+li1bUqFChQJ/r+XLl/PFF1/oPAW0trZmypQp6udJkybh7OxMz549GT58OBEREQCcPHkSZ2dnNdz169fp0KEDAJGRkQwZMoSePXvSs2dPvvzySwAuXLiAs7MzDg4O9OjRg40bNwLpT618fX2B9CdqH374IY6OjvTo0UP9u0L6k4vvvvuOvn370qlTJ2bMmJHrd4uOjlbzNKsOHTpw/fp19bOzszMnT55Uz7Fo0SIGDhzIp59+iqWlJc2aNVPDNm7cWCfPs+rUqRN9+/ZlzZo1Oe4/f/48ffv2pVevXvTs2ZOAHMaEDhgwgAMHDgC6efOy6JmVBj0NpCnomZmlf37GpA5T6LIGsGrVKvr06UP79u3VbeXLl+frr79WP8+dOxcXFxccHBzo378/t2/fBuDu3bu0aNFCDRcXF0fdunUBSExMZNy4cdjb29OrVy+GDBkCwO3bt/noo4/U8rto0SIgvWdj7ty5QPrT4759++Lk5IS9vT0rV65Uz+Hq6srMmTMZNGgQXbp0YdSoUSQnJ+f43aKionKtw5nrCKT3Km3btk09h4eHB59++ikODg4YGxvz3nvvqS8q5ZevdnZ2jB49Gm9v7xz337x5k08//VS9vm3atClbmMz1NnPevEwyDECUXO2mpv/Lat/X6W/9Oy7/d9vy/78ofnFSN+z2kekvXr3vXuRk2NnZ4evry4MHD9i/fz9du3ZFX19f3T9jxgycnZ1JS0tj8+bNuqffvp1jx46pn0eMGEHXrl11wiQnJ3Pw4EG1K9bc3JyPP/6Yzp0706xZM5o0aYK9vT01atTIN61PnjzhyJEjDBw4kCtXrlCtWrUirSkfHx/P5s2bmTRpUqGPzRAYGMj06dPzDPPVV1+p6fP29mb5PX9tsAAAHo1JREFU8uV5NhIBduzYgbW1NT/++COQftMBWLlyJYMHD6Znz55Ael5k1aBBAzZu3Ii+vj5RUVE4OzvTpk0btZv3zp07+Pj4oNVqsbe35/z587z11lvAv3/LmJgY4uLiWL16dSFy419Xr15l9erVapd0htTUVDZs2KB2S+amYcOGOTZCo6KiGDVqFEuXLqVp06akpaURHR1dpDQ+CxW/+IKKX3yRb7iYgADujh6DXpkyKCkpVBo37pmPVZU6rKugZS0wMJAuXbrkGWbo0KFMnZp+nfb39+fbb7/VaUDm5PDhw0RHR7N7927g3zrs6+tLu3bt+Pz/pyrL2J6ZtbU1a9euVYfGfPTRR7Rs2ZKGDRsCEBQUxNq1azE0NKRfv37s3buXHv8/08SxY8dwcHAgMTGRBw8eqI3hwjp79iy+vr45/vjw8fHRadznpGHDhsyZMyfb9pSUFEaOHMnYsWPVshQZGVmkNL5o0lgVIqv6vdKnp+o2F4zNcg+XFAOBfjBwx1Of0sHBgd9++42AgADmz5/Pzp071X0WFhb06tWL+Ph4nScpAI6OjuqFPKuMm2BoaCi2trZ069ZN3efm5sagQYM4efIkx48fx8nJidWrV+s8GcnM29ubLVu2oNFo6Ny5M87Ozvz+++9F+q5arZbx48fTunXrfG9m+ck8DdnEiRO5ceMGjx49IiAggFKlSrFz5078/PzQarUkJibmOJ4vq8aNG7N27Vo8PT2xs7NTn5C1aNGCFStWEBISwjvvvJNjXkVFReHm5sbt27fR19fn8ePH3LhxQ22s2tvbo6+vj76+PvXr1yc0NFRtrGb+Wx4/fpzRo0ezZ88eTExMCpUnDg4O2RqqiqLg7u5O2bJlGTBgQJ7HK7lMMXPhwgVq1aqlPsnW09PLdzziy5YxRtXAygo9s9JUGjfuub1cJXU4XWHKGujW4blz53Ls2DEiIyNZt24dNWvW5OjRo/j6+hIXF0daWhqxsbH5xlmvXj2Cg4OZOXMmzZs3p23btkD6j4p58+aRkJCAnZ0dLVu2zHZsUlIS7u7uBAUFodFoCAsLIygoSG2svv/++2qdbNSoEaGhoeqxLVu2ZMmSJUB6b83gwYPZtm0bVlZW+aY5s27duuXYUPXy8iIkJAR397wfjuRWh2/dukVKSorOGPKi/FB5GWQYgBBZWTcFy9pwZGHe4Y4sAss6RX65KjMnJyd8fHwwNjamevXq2fZnNHAKw9HRET8/Pw4cOIBWq1Uvohmsra1xdnbmu+++o1evXuzZs4cbN27g4OCAg4ODzgVx2LBhbN++nd9++41Ro0ah0Wh44403CAkJ4fHjxzme38PDQ43r2rVrQPpNbty4cVSsWBE3N7dCfZ+s6tevz8WLF9XPCxYswM/Pj0ePHpGWlsaZM2fYsGEDq1evZufOnbi6upKUlASk52da2r/DNzK2A7z11lts376dRo0asXfvXlxcXEhNTWXQoEGsXLmSSpUqsWjRImbOnJktTV9//TV2dnZqI7l69eo6cRsbG6v/z3iZJSfvvvsuSUlJOt39mY/LnPas3ZCmpqbZjvHw8OD+/fssWrQo34m5L1++TJ06dfIM81+Q+WWqjK7/gkxrVVRSh/89pqBlLWsdnjp1Kn5+fuqLX/fu3cPDw4P58+eza9cuFi5cqNYnAwODXOuwjY0N/v7+tGnThnPnztGzZ0+ePHlCly5d2LRpEzVq1MDX15fhw4dnS9PChQuxtLTkt99+Y8eOHTRv3rxIddjW1pbXXnuNc+fOZduX9bjM8UPOdXjNmjXs3buXVatWUapUqRzPmeFVqcOZSWNViKw0GnBcAadXw5+z0p+gZpYUk7799Jr0cM+AlZUVEyZMeKpu8dxYWFjg4eHBhg0bePjwIXFxcRw6dEj99Z2YmEhwcDA2NjbUrl0bPz8//Pz8dMaN5aRatWp07twZNzc3tTtYURS2b99OaGgo06ZNU+OqW7cuKSkpTJgwAXNzc7755punXu525MiRLF++nAsXLqjbEhIS1P9HR0djZmaGubk5ycnJ/PLLL+o+Gxsb7t69q96k/fz81H137tzB1NQUe3t7pk+fzu3bt4mPj1fz6IMPPmD48OE6N9nM57S2tkaj0XD69GmCgoKK9N2CgoKIj49X3/jNrGrVquq5L126xK1bt/KMy8PDg5CQEJYvX57v+twBAQFs2rSJwYMHZ9v31ltvERwcrN5809LScuxGLQ7yeuv/eTVYpQ4XrqxBehf/r7/+yqFDh9RtycnJpKSkAOnjfw0NDbG0tERRFDZs2KCGs7S0JCUlheDgYEC3DoeFhaHRaOjYsSNTpkxBURTu37/P7du3qVChAo6OjkyePDnXOmxlZYWBgQHBwcEcPXo03++Rk7CwMEJCQnIcmlG1alUuXboEpF9vzp49m2dcP/30E/7+/vz000/ZZmjI6syZMyxbtoyhQ4dm21ejRg0MDQ11nqjLMAAh/ssq1Ychf6TPo3rSGzR66S9TbR+Z3vVvWQeG7EkP94y4uLgU+pis491atmyZY5digwYN6NatGytXrmT8+PH8/PPPzJkzB2NjY1JSUmjTpg39+vUr9PnnzJnDihUr6NOnDwYGBiiKQrNmzdSXlTLbvXs3e/fupW7dujg6OgLQtGlT9Yb6+eefExgYCEDXrl2pXr06Pj4+uZ67bdu2zJ49m3nz5vHw4UPKly+PoaEhM2bMoFSpUrRt25YdO3bQrVs3rKysaNq0KYcPHwbSGxZDhgzBxcUFa2tr7Ozs1HhPnTrF2rVr0dPTIy0tjSlTplCmTBkWLVrEyZMnMTQ0RE9PL8d8njhxIu7u7nh7e1OrVi0aN25c4LzM+FsqioJGo2HevHk5dtGNGzcOV1dXtmzZQoMGDfJcc/vs2bP4+PhQs2ZN9aWe119/neXL/x2PPWbMGIyMjEhISKBWrVp4e3vTpEkTAP7880/279/P7NmzMTc3Z9myZXh6ehIXF4dGo2Hs2LF07Ngxz+/1tD9KCqsg01NlbrA+yyEBJbkOF6SsZVWvXj1WrVrF4sWLcXd3p1y5cupY0KpVq2JiYkLXrl3p3r07VapU0em2NzAwYNq0aQwdOpTKlSurXf2Q/pLUggULUBQFRVFwcHCgXr16eHl5sXPnTgwNDVEUJcfekREjRjBlyhR27tyJtbU177zzToHzMmPMqqIopKamMn78ePWFtMyGDh3KuHHjOHLkCDVq1MjzOhEWFoanpyc2NjZ88sknABgZGemMffbw8OD7778nISGBKlWq8M0336jjWi9fvsySJUtYtWoVBgYG/PDDD3zzzTf88MMPAPTr14+PPvqowN/xZdEouQ1uEOIVk5qayvXr17G1tS1cd9w/ZyFwByQ+ARNzaNDrmXT9C/GqGzp0KI6OjnTv3v2FnfORlxfGtWsXqAEaExBA0o0bWP7/CzdCCF0zZ86kSpUqDBs2rEjHF/m+m4U8WRUiP9ZvS+NUiEIaNGgQycnJvPfeey/0vIVpeJbp1ElWsRIiF66urly7di3Hsb0vmjxZFSXGs/qFJ16cq1ev4urqmm27k5PTy19RRQiRr4iICHWe08xyG+4gXi3P6r4rjVVRYkhjVQghhHhxntV9V2YDEEIIIYQQxZY0VoXIg6IoXLgTxVuz9tJo5h94/h7EhTvFc7oeIQR4X/Lmz9A/dbY5bnfEcbtjtrB/hv6J96Wcl6UUQhQf0lgVIhfXwmJwWH6U/qtPkpqmoK+nISI2if6rT9Jr2RGuP4jJPxIhxAtVy6IWXx7+MluDNas/Q//ky8NfUsui1gtKmRCiqKSxKkQOroXF0MfrGG3qWHLiq45YlTXB0syY7/o05sRXHWlTx5LeK45Jg1WIYqZj1Y582+bbPBusGQ3Vb9t8S8eqec8TK4R4+WTqKiGyUBSFyVsuMuDdakzukn1CZzNjA3X7pM0X2TGqdZHP1aFDBxISEvjrr7/U9dyPHz/OoEGD6N27N3/99Rdr1qzB1tYWgJCQEPr27cvPP//M9u3b2bhxo7ruPED37t0ZNmwYS5cuVfdptVqqVq2Kh4cHlpaWAJw+fZr58+eTmJiIVqulbNmyLFu2TN2fmaurK8eOHaNcuXIkJydjZ2fH9OnT1SURvby82LVrl7qEoJ2dHZMnT8620sq1a9eYNWsWERERGBoa0qRJE6ZPn66udDNmzBjOnTtHeHg4586dy3Ft7KyOHj3KDz/8QFhYGObm5ujp6dGnTx8+/PDDov1BimDx4sXUqVNHZ73twsr894L0Cc/d3Nxo2rQpAHXr1i1wnmT24MEDvvrqK+7evYuRkRE1a9bE3d0dCwsLIL38GRkZYWRkRGJiIrVq1WLo0KHqebNycHDgl19+wcTEhKFDhzJ9+nSqVq1a5O/9vGRusH7b5ludfc+6oSp1OL0O51fWcnP58mUWL15McHAwFhYWpKWl0blzZ0aOHPlUf5fC2LRpE0lJSU81w8i2bduYM2cO1tbWQPp9ZMyYMXT6/6nROnTogJeXl1oOCiopKYnx48dz8+ZNTExMsLS0xN3dXV3dbsCAAdy7dw8zMzMSEhKwtrZm4MCBtGvXLsf4MtdbNzc3nJycaNasWZG/9wujCFFCpKSkKIGBgUpKSkqe4c6HPlbemLFHiU3Uqts6LTiodFpwUCdcTKJWaTD9d+VC6OMip6l9+/aKk5OTsmfPHnXbxIkTFWdnZ8XT01Px9/dXnJycFK1Wq6Smpip9+/ZVfv31V0VRFGXJkiWKp6dnjvFm3peamqqMGzdO+frrrxVFURStVqs0b95cuXLlihr+5s2bSmxsbI5xTZ06VfHx8VEURVESExOVDz74QFm/fr2iKIoyZcoUZfjw4UpUVJR6rt27dyuhoaHZ4rl165Zy9epVRVHS/xZjx45VVqxYoe4/evSo8ujRI8XW1jbXtGR2+PBhpVWrVsrZs2fVbXfv3lXmzp2b77HFTda/pb+/v+Ls7Kx+LmieZKbVapXw8HDl9OnT6jZPT09l2rRp6uf27dsr165dUz/v27dPefvtt5ULFy4U5Wuo5y0uAkICFDtfO6XTr50Uh98c1M8BIQHP7BxSh9PrcH5lLSdBQUFK8+bNlf3796vbIiIilJkzZ+Z5XHG0detWZfTo0ernixcvKi1atFA/Z61rBaHVapXExETl4MGDSlpamqIoiuLj46MMHjxYDdO/f3+d/Dt16pTSqlUrnfJYWM+yDhf0vpsfebIqSqzFAX+z69K9bNsfxSahpwHH5f+uCR0SEQ/A+wsP6YTV09Mw6KdTWJoZ62zv0agKYzvVKVA6XFxc2Lp1K126dCEmJoaLFy/SvXt3kpKSsLe3JyAgAC8vL8zMzDAzM1OXMiwoPT097OzsOHjwIABxcXHExcVRsWJFNUzNmjULFJexsTFvv/02t27dIiQkhD179nDgwAHMzc3Vc3Xr1i3HY6tXr67+X19fn4YNG6rregM6SykWxPLly/niiy90ngJaW1szZcoU9fOkSZMIDg5Gq9VSpUoV5syZQ4UKFTh58iRz585l27ZtAFy/fp3PP/+c/fv3ExkZyaRJkwgPDwfgzTff5Ntvv+XChQvMmjWL1NRUUlNT6du3L3379sXV1ZU333yT/v37c/z4cb7//nuSkpJISUlh8ODB6hKcAwYMoFGjRpw/f56HDx/SsmVLZs2aleN3i46OVvM0q6xPaJydnZk6dSotWrRgwIABNG3alAsXLgCwbt06nSdtjRs35ueff841Tzt16kTfvn1Zs2YNS5YsybY/8xPezOnIet7Zs2fj4uLCyZMngfQy17RpU65du6bGM3HiRPbt20dERARffPFFoZcqXXFxBX/c+iPfcOZG5oTFh6FBw/gD47EytWLpuaUsPbc0z+O61OjCiMYjCpQWqcNgaWlZqLIGsGrVKvr06aMuDQpQvnx5dQlmgLlz53Lq1ClSUlIoU6YMHh4eVK9enbt37+ZaxhITE3F1deX69esYGBhgaWnJjz/+yO3bt3F1dSU+Ph5FUejQoQPjx49n6dKlxMfHM3XqVK5du4a7uzsJCQkkJSXh4OCgTorv6uqKiYkJt2/f5v79+9SpU4eFCxeqvUOZRUVF5VqHBwwYwJAhQ9TvPWbMGNq1a4ezszOurq6YmZlx69YtwsLC8Pf311lYo3Hjxqxbty7XPLWzs2P06NF4e3vTpUuXbPuz1tuMdOR03qw9Oi1atGDr1q28/vrrdOjQAWdnZ44cOcLDhw/p3bv3c30aLo1VIbLIeJmqIAz0NKSmPd1UxXZ2dvj6+vLgwQP2799P165ddeajmzFjBs7OzqSlpemsBw3Z1xUfMWIEXbt21QmTnJzMwYMH1W5qc3NzPv74Yzp37kyzZs1o0qQJ9vb21KhRI9+0PnnyhCNHjjBw4ECuXLlCtWrVcly/Pj/x8fFs3ryZSZMmFfrYDIGBgUyfPj3PMF999ZWaPm9vb5YvX86MGTPyPGbHjh1YW1vz448/Auk3HYCVK1cyePBgevbsCaTnRVYNGjRg48aN6OvrExUVhbOzM23atFG7ee/cuYOPjw9arRZ7e3vOnz/PW2+9Bfz7t4yJiSEuLo7Vq1cXIjf+dfXqVVavXq12SWdITU1lw4YNardkbho2bEhAQMBTnffu3bv5hjc2Nmbz5s3cvHmT3r174+DggIHBs78llTYsjQYNCgplDMtQ2rBwQykKQuqwroKWtcDAwBwbVJkNHTpUXTzA39+fb7/9lpUrV+Z5zOHDh4mOjmb37t3Av3XY19eXdu3a8fn/r3KWsT0za2tr1q5dqw6N+eijj2jZsiUNGzYEICgoiLVr12JoaEi/fv3Yu3cvPXr0AODYsWM4ODiQmJjIgwcPWLRoUZ7pzM3Zs2fx9fXNcdiPj4+PTuM+Jw0bNmTOnDnP9Lw5iY6O5ueffyYyMpL3338fFxcXrKysCn3egpDGqiixxnaqk+PTT8/fg4iITeK7Po3VbRlPVPdN0F06ctLmi1iaGePaLfvY1sJwcHDgt99+IyAggPnz57Nz5051n4WFBb169SI+Pl7nSQqAo6NjrqvAZNwEQ0NDsbW11Xla4ubmxqBBgzh58iTHjx/HycmJ1atX5zp2ydvbmy1btqDRaOjcuTPOzs78/vvvRfquWq2W8ePH07p163xvZvnRaP79UTFx4kRu3LjBo0ePCAgIoFSpUuzcuRM/Pz+0Wi2JiYk5jufLqnHjxqxduxZPT0/s7Oxo06YNkP5UYcWKFYSEhPDOO+/kmFdRUVG4ublx+/Zt9PX1efz4MTdu3FAbq/b29ujr66Ovr0/9+vUJDQ1VG6uZ/5bHjx9n9OjR7NmzBxMTk0LliYODQ7aGqqIouLu7U7ZsWQYMGJDn8UoR14nJ6bx5yWj016pVCwMDAx49ekTlypULfPyIxiMK9OTzz9A/GX9gPGUMy5CipDC66ejn8lKV1OF0hSlroFuH586dy7Fjx4iMjGTdunXUrFmTo0eP4uvrS1xcHGlpacTGxuYbZ7169QgODmbmzJk0b96ctm3bAuk/KubNm0dCQgJ2dnY59uYkJSXh7u5OUFAQGo2GsLAwgoKC1Mbq+++/r9bJRo0aERoaqh7bsmVLtUfi+vXrDB48mG3bthW6AdetW7ccG4xeXl6EhITg7u6e5/FFrcO5nTc3GXW4fPny2NjYcOfOnefWWJXZAITIouublfn9f2HEJaXkGS42KYXfL9+n25sFv8HmxsnJCR8fH4yNjXW62jJkNHAKw9HRET8/Pw4cOIBWq83WrWttbY2zszPfffcdvXr1Ys+ePdy4cQMHBwccHBx0LojDhg1j+/bt/Pbbb4waNQqNRsMbb7xBSEgIjx8/zvH8Hh4ealwZ3b9arZZx48ZRsWJF3NzcCvV9sqpfvz4XL15UPy9YsAA/Pz8ePXpEWloaZ86cYcOGDaxevZqdO3fi6upKUlISkJ6faWlp6rEZ2wHeeusttm/fTqNGjdi7dy8uLi6kpqYyaNAgVq5cSaVKlVi0aBEzZ87Mlqavv/4aOzs7tZFcvXp1nbiNjf8dLpLxMktO3n33XZKSkrh+/Xq2fVnTnpycrLPf1NQ02zEeHh7cv3+fRYsWoaeX92X/8uXL1KlTsCEsuZ3XwMAg1/zNkDkv9PT0SEnJu74VRcbLVFamVlQyrZTvLAFPQ+rwv8cUtKxlrcNTp07Fz89PffHr3r17eHh4MH/+fHbt2sXChQvVspRXGbOxscHf3582bdpw7tw5evbsyZMnT+jSpQubNm2iRo0a+Pr65rjm/cKFC7G0tOS3335jx44dNG/evEh12NbWltdee41z585l25f1uKz1I6c6vGbNGvbu3cuqVasoVapUjufM8CzqcEY687rWZK3DueXFsyCNVSGyaPy6OTUrluaHgzfyDLfi4A1qVTKjsU3eb7sWhJWVFRMmTHiqbvHcWFhY4OHhwYYNG3j48CFxcXEcOnRI/fWdmJhIcHAwNjY21K5dGz8/P/z8/HTGjeWkWrVqdO7cGTc3N6Kjo4H0X/Tbt28nNDSUadOmqXHVrVuXlJQUJkyYgLm5Od98843OE5WiGDlyJMuXL1fHSQIkJCSo/4+OjsbMzAxzc3OSk5P55Zdf1H02NjbcvXtXvUn7+fmp++7cuYOpqSn29vZMnz6d27dvEx8fr+bRBx98wPDhw3VuspnPaW1tjUaj4fTp0wQFBRXpuwUFBREfH6++8ZtZ1apV1XNfunSJW7du5RmXh4cHISEhLF++PMexdZkFBASwadMmBg8eXKR0Z7C0tCQlJUUdz5g5f1+UzG/9Z3T9F2Raq6KSOly4sgbpXfy//vorhw79+y5AcnKy+sMlJiYGQ0NDLC0tURSFDRs2qOHyKmNhYWFoNBo6duzIlClTUBSF+/fvc/v2bSpUqICjoyOTJ0/OtQ5bWVlhYGBAcHAwR48ezRamIMLCwggJCclxaEbVqlW5dOkSkH69OXv2bJ5x/fTTT/j7+/PTTz9lm6EhqzNnzrBs2TKGDh1apHRnZmNjo+bR3r17iY+Pf+o4i0qGAQiRhUajYX6fxvRekT6ObES72jr7Y5NSWHHwBr4nQtn8+bvP7LyFfcEEso93a9myZY5dig0aNKBbt26sXLmS8ePH8/PPPzNnzhyMjY1JSUmhTZs29OvXr9DnnzNnDitWrKBPnz4YGBigKArNmjWjQ4cO2cLu3r2bvXv3UrduXRwd01cTatq0qXpD/fzzzwkMDASga9euVK9eHR8fn1zP3bZtW2bPns28efN4+PAh5cuXx9DQkBkzZlCqVCnatm3Ljh076NatG1ZWVjRt2pTDhw8D6Q2LIUOG4OLigrW1NXZ2dmq8p06dYu3atejp6ZGWlsaUKVMoU6YMixYt4uTJkxgaGqKnp5djPk+cOBF3d3e8vb2pVasWjRs3zhYmNxl/S0VR0Gg0zJs3L8exhOPGjcPV1ZUtW7bQoEEDateunUNs6c6ePYuPjw81a9ZUX+p5/fXXWb58uRpmzJgxGBkZkZCQQK1atfD29qZJkyYA/Pnnn+zfv5/Zs2fnGH9uPzgMDAyYNm0aQ4cOpXLlymo37IuSdXqqzC9TZZ3W6lkOCSjJdbggZS2revXqsWrVKhYvXoy7uzvlypVTx4JWrVoVExMTunbtSvfu3alSpYpOt31eZezatWssWLAARVFQFAUHBwfq1auHl5cXO3fuxNDQEEVRcuwdGTFiBFOmTGHnzp1YW1vzzjvvFDgvM8asKopCamoq48ePp1697EPEhg4dyrhx4zhy5Ag1atTI8zoRFhaGp6cnNjY2fPLJJwAYGRnpjH328PDg+++/JyEhgSpVqvDNN9+o41ovX77MkiVLWLVqVY7x5/XQ4KuvvmLWrFmUL1+eFi1a5DsN2fOkUYo6uEGI/5jU1FSuX7+Ora1tgbrjrj+IYdLmiwSHx9H1zcpYlTXmQXQSv1++T61KZszv0xhbqzIvIOVCFC8pKSm0aNGCPXv2ZBuD+bIVdB5VWRhAlHTdu3dn9uzZ6o/T56Gw993cyJNVIXJha1WGHaNac/FOFL//L4zH8VoszYzZOPSdZ9L1L8R/0YMHD+jfvz+9evUqdg1VgJtRNwvUAM14wnoz6qY0VkWJ06VLF2rVqkWjRo1edlIKRJ6sihLjWf3CEy/O1atXcXV1zbbdycnpqVabEUK8GBEREQwZMiTb9tyGO4hXy7O670pjVZQY0lgVQgghXpxndd+V2QBEiZExkFx+nwkhhBDPX8b99mlnf5Exq6LE0NPTw9DQkIiICCpUqPDUlUcIIYQQOVMUhYiICHUGlachwwBEiZKcnExoaCharfZlJ0UIIYR4pRkaGlK1atUCzbubF2msihIpLS1NhgMIIYQQz4lGo3nqJ6pqXNJYFUIIIYQQxZW8YCWEEEIIIYotaawKIYQQQohiSxqrQgghhBCi2JLGqhBCCCGEKLaksSqEEEIIIYqt/wM7XXUNP1GfUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- processing operation: GaussianBlur3D -----\n",
      "------> processing param Param: radius...\n",
      "-----> other param values ()\n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "\n",
      "          Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0  2.336786e+08         1.676508e+06  ns/op              2  \n",
      "0  1.209758e+09         4.747381e+07  ns/op              2  \n",
      "0  4.220158e+08         1.838007e+07  ns/op              4  \n",
      "0  1.648362e+09         4.324802e+07  ns/op              4  \n",
      "0  5.783233e+08         2.671036e+07  ns/op              6  \n",
      "0  1.848573e+09         1.946695e+07  ns/op              6  \n",
      "0  6.989796e+08         1.287248e+06  ns/op              8  \n",
      "0  2.182192e+09         7.172907e+07  ns/op              8  \n",
      "0  8.492435e+08         3.971863e+06  ns/op             10  \n",
      "0  1.649076e+09         1.780221e+07  ns/op             10  \n",
      "0  1.020954e+09         1.439591e+06  ns/op             12  \n",
      "0  1.441712e+09         8.182756e+06  ns/op             12  \n",
      "0  1.174058e+09         1.436492e+06  ns/op             14  \n",
      "0  1.477515e+09         1.264294e+07  ns/op             14  \n",
      "0  1.333669e+09         1.625344e+06  ns/op             16  \n",
      "0  1.378768e+09         1.129117e+07  ns/op             16  \n",
      "0  1.500427e+09         2.708196e+06  ns/op             18  \n",
      "0  1.382361e+09         1.187071e+07  ns/op             18  \n",
      "0  1.661789e+09         2.842191e+06  ns/op             20  \n",
      "0  1.318835e+09         1.075418e+07  ns/op             20  \n",
      "0  1.831307e+09         2.010276e+06  ns/op             22  \n",
      "0  1.335889e+09         1.160717e+07  ns/op             22  \n",
      "0  1.984919e+09         3.148889e+06  ns/op             24  \n",
      "0  1.313101e+09         1.071698e+07  ns/op             24  \n",
      "0  2.175170e+09         6.304698e+06  ns/op             26  \n",
      "0  1.328854e+09         8.032779e+06  ns/op             26  \n",
      "0  2.357443e+09         6.358486e+06  ns/op             28  \n",
      "0  1.288134e+09         7.783845e+06  ns/op             28  \n",
      "0  2.537331e+09         1.921188e+07  ns/op             30  \n",
      "0  1.360594e+09         1.251117e+07  ns/op             30  \n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-21_.GaussianBlu...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "\n",
      "          Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0  2.336786e+08         1.676508e+06  ns/op              2  \n",
      "0  1.209758e+09         4.747381e+07  ns/op              2  \n",
      "0  4.220158e+08         1.838007e+07  ns/op              4  \n",
      "0  1.648362e+09         4.324802e+07  ns/op              4  \n",
      "0  5.783233e+08         2.671036e+07  ns/op              6  \n",
      "0  1.848573e+09         1.946695e+07  ns/op              6  \n",
      "0  6.989796e+08         1.287248e+06  ns/op              8  \n",
      "0  2.182192e+09         7.172907e+07  ns/op              8  \n",
      "0  8.492435e+08         3.971863e+06  ns/op             10  \n",
      "0  1.649076e+09         1.780221e+07  ns/op             10  \n",
      "0  1.020954e+09         1.439591e+06  ns/op             12  \n",
      "0  1.441712e+09         8.182756e+06  ns/op             12  \n",
      "0  1.174058e+09         1.436492e+06  ns/op             14  \n",
      "0  1.477515e+09         1.264294e+07  ns/op             14  \n",
      "0  1.333669e+09         1.625344e+06  ns/op             16  \n",
      "0  1.378768e+09         1.129117e+07  ns/op             16  \n",
      "0  1.500427e+09         2.708196e+06  ns/op             18  \n",
      "0  1.382361e+09         1.187071e+07  ns/op             18  \n",
      "0  1.661789e+09         2.842191e+06  ns/op             20  \n",
      "0  1.318835e+09         1.075418e+07  ns/op             20  \n",
      "0  1.831307e+09         2.010276e+06  ns/op             22  \n",
      "0  1.335889e+09         1.160717e+07  ns/op             22  \n",
      "0  1.984919e+09         3.148889e+06  ns/op             24  \n",
      "0  1.313101e+09         1.071698e+07  ns/op             24  \n",
      "0  2.175170e+09         6.304698e+06  ns/op             26  \n",
      "0  1.328854e+09         8.032779e+06  ns/op             26  \n",
      "0  2.357443e+09         6.358486e+06  ns/op             28  \n",
      "0  1.288134e+09         7.783845e+06  ns/op             28  \n",
      "0  2.537331e+09         1.921188e+07  ns/op             30  \n",
      "0  1.360594e+09         1.251117e+07  ns/op             30  \n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "\n",
      "          Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0  4.050919e+07         5.771196e+05  ns/op              2  \n",
      "0  4.053622e+09         9.748495e+06  ns/op              2  \n",
      "0  5.162598e+07         7.138822e+05  ns/op              4  \n",
      "0  4.209621e+09         9.874136e+06  ns/op              4  \n",
      "0  6.450536e+07         5.370668e+05  ns/op              6  \n",
      "0  4.355972e+09         1.292392e+07  ns/op              6  \n",
      "0  7.247316e+07         3.457563e+05  ns/op              8  \n",
      "0  4.404737e+09         3.322994e+07  ns/op              8  \n",
      "0  8.501982e+07         7.169958e+05  ns/op             10  \n",
      "0  4.314930e+09         2.634163e+07  ns/op             10  \n",
      "0  9.613651e+07         6.966724e+05  ns/op             12  \n",
      "0  4.175890e+09         2.305375e+07  ns/op             12  \n",
      "0  1.067649e+08         5.704501e+05  ns/op             14  \n",
      "0  4.189684e+09         9.945189e+06  ns/op             14  \n",
      "0  1.186592e+08         3.190294e+05  ns/op             16  \n",
      "0  4.403803e+09         1.315782e+07  ns/op             16  \n",
      "0  1.307524e+08         5.407848e+05  ns/op             18  \n",
      "0  4.439855e+09         7.871172e+07  ns/op             18  \n",
      "0  1.429348e+08         4.530492e+05  ns/op             20  \n",
      "0  4.403099e+09         2.104337e+07  ns/op             20  \n",
      "0  1.555917e+08         5.022813e+05  ns/op             22  \n",
      "0  4.197585e+09         7.479986e+07  ns/op             22  \n",
      "0  1.664182e+08         6.699812e+05  ns/op             24  \n",
      "0  4.342283e+09         1.410435e+07  ns/op             24  \n",
      "0  1.788393e+08         6.015367e+05  ns/op             26  \n",
      "0  4.100514e+09         1.780705e+07  ns/op             26  \n",
      "0  1.909429e+08         6.821204e+05  ns/op             28  \n",
      "0  4.303480e+09         1.197796e+07  ns/op             28  \n",
      "0  2.024459e+08         6.604433e+05  ns/op             30  \n",
      "0  4.331530e+09         1.407257e+07  ns/op             30  \n",
      "                                            Filename  \\\n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "0  kernelsize\\jmh-result_MYERS-PC-22_.GaussianBlu...   \n",
      "\n",
      "                                           Benchmark Mode  Threads  Samples  \\\n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "0  net.haesleinhuepf.clij.benchmark.jmh.GaussianB...   ss        1      100   \n",
      "\n",
      "          Score  Score Error (99.9%)   Unit  Param: radius  \n",
      "0  4.050919e+07         5.771196e+05  ns/op              2  \n",
      "0  4.053622e+09         9.748495e+06  ns/op              2  \n",
      "0  5.162598e+07         7.138822e+05  ns/op              4  \n",
      "0  4.209621e+09         9.874136e+06  ns/op              4  \n",
      "0  6.450536e+07         5.370668e+05  ns/op              6  \n",
      "0  4.355972e+09         1.292392e+07  ns/op              6  \n",
      "0  7.247316e+07         3.457563e+05  ns/op              8  \n",
      "0  4.404737e+09         3.322994e+07  ns/op              8  \n",
      "0  8.501982e+07         7.169958e+05  ns/op             10  \n",
      "0  4.314930e+09         2.634163e+07  ns/op             10  \n",
      "0  9.613651e+07         6.966724e+05  ns/op             12  \n",
      "0  4.175890e+09         2.305375e+07  ns/op             12  \n",
      "0  1.067649e+08         5.704501e+05  ns/op             14  \n",
      "0  4.189684e+09         9.945189e+06  ns/op             14  \n",
      "0  1.186592e+08         3.190294e+05  ns/op             16  \n",
      "0  4.403803e+09         1.315782e+07  ns/op             16  \n",
      "0  1.307524e+08         5.407848e+05  ns/op             18  \n",
      "0  4.439855e+09         7.871172e+07  ns/op             18  \n",
      "0  1.429348e+08         4.530492e+05  ns/op             20  \n",
      "0  4.403099e+09         2.104337e+07  ns/op             20  \n",
      "0  1.555917e+08         5.022813e+05  ns/op             22  \n",
      "0  4.197585e+09         7.479986e+07  ns/op             22  \n",
      "0  1.664182e+08         6.699812e+05  ns/op             24  \n",
      "0  4.342283e+09         1.410435e+07  ns/op             24  \n",
      "0  1.788393e+08         6.015367e+05  ns/op             26  \n",
      "0  4.100514e+09         1.780705e+07  ns/op             26  \n",
      "0  1.909429e+08         6.821204e+05  ns/op             28  \n",
      "0  4.303480e+09         1.197796e+07  ns/op             28  \n",
      "0  2.024459e+08         6.604433e+05  ns/op             30  \n",
      "0  4.331530e+09         1.407257e+07  ns/op             30  \n"
     ]
    },
    {
     "data": {
      "image/png": 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F2M/DrXNWgYH09R2ogoICNQewyWSSYFWIAZJgVYgx4p//+Z/V+6+88ooaOF2IztNR9jVDUl5eXr9TV/prejun2erJhWQTSE5O5ve//73a3H748OEBv9ZisfDDH/6Qr33ta4Bv8NP5s2T1pba2Vr3f17ZyOp0D7v87GjoHkUOZBW0k9vOF6jyL1XA21Xe+OPrmN78ZkJkVhAhEEqwKMUbMnz+fOXPmAL6pOH/1q19dcFNr52DFn6O0JwPpAuCfk/348eO0tbX1+JyWlhbWrVs3yFJ2ZbPZ1HL31x+0J9HR0er9wby+87bqHAyd78033xzWOemHU2lpqTrzl8FgGFLO3pHaz0NVXV3Njh07AF/t+nDVfq5du5YNGzao6/3Xf/3XYVmvEGOBBKtCjCG/+c1viIiIAOC9997j9ttv7zPIBKivr+9xalKAKVOmqPefe+65biPcFUXhhRde4KOPPuq3bP7m+fb29m7ZCMA3res999xDfX19r+vYuXMna9eu7TPjwPvvv68uP39Cg4cffrjPfKTV1dXk5OQAvoDDH3gNROdt9b//+789ZmXYsWMHzz777IDXOZLa2tr4j//4D3UfL168eEizWA3Hfh6KV199lWPHjvX5nLKyMm6//XY1iL7uuusueMR+eXk5DzzwAI8//rj62IMPPtjnpBNCiK5kgJUQY0hsbCyvvPIKP/3pTykvL2f79u3s2rWL2bNnM3v2bOLj4wkKCsLhcFBZWcmxY8fYtWuX2mXAZDJ1OXl/7Wtf49lnn1VH299www1885vfJDY2lqqqKjZu3Mjx48dJT0/HarVy4sSJXst2ww038PLLL9PS0sKf//xnvvzyS1asWIHFYqGgoIB3332X8vJyVq5c2Wuf25qaGh5//HGeffZZrrrqKrKzs0lISMBoNFJXV8fu3bvZvn074OvH+8Mf/rDL69944w3eeOMNsrKymDt3rjowraWlhTNnzvD++++rzeA33HDDoJqI4+PjWbp0Kdu2baO0tJSvf/3rrF69mrS0NNra2ti5cydbtmzBZDJxzTXXsGXLlgGve7j0lPWhtbVVzbNaWVkJ+Jry77nnniG9x3Ds56HYt28fTz75JBkZGcyfP5/x48er/8vV1dUcOXKEbdu20dHRAUBSUhL33Xdfv+v98ssvu2w3p9NJc3MzZWVl5ObmcujQIbUG3mAwsGbNGr73ve8N2+cSYiyQYFWIMSYzM5O3336bZ599lg8++AC3283evXu7DJY6n8Fg4LrrruMXv/hFl4FBFouF//qv/+L222/Hbrdz4sSJbgFpamoqL7zwAr/61a/6LFdUVBRPP/20miN169atbN26VV2u0Wi4/fbbueqqq3oNYvxpnDo6Oti2bRvbtm3r8XlWq5XHHnusWzJ5jUaDoijk5eWRl5fXa1m/8Y1vDCkp/mOPPcYtt9xCYWEhNTU1PPfcc93K9eyzz5KXlzcqwWpvU5B2lpKSwm9/+1vS09OH9B7DsZ8vREFBQb+zeS1YsIAnnniiS5eP3mzatKnfcmo0Gq6++mp+8YtfMHXq1EGVVwghwaoQY1JkZCRPP/00P/vZz/j444/Zs2cPZ8+eVfOKBgUFERUVxeTJk7niiiu49tpre61FnDNnDuvWrePll1/ms88+o7q6GpvNRlJSEtdeey3f+973CAoKGlC5VqxYwTvvvMNLL73E/v37aWhoICwsjOnTp3Prrbcyb968Pqd7/da3vsWkSZPYvXs3+/btIz8/n7q6OtxuN0FBQaSnp3P11Vdz0003dcslC7Br1y52797Nnj17OHHiBOXl5bS1tWEymYiLi2PGjBmsWrVK7fs7WNHR0bzzzju88sorfPzxxxQXF6PVaomLi2PhwoXccsstpKSk9BkojzSLxUJERARZWVksX76c66+/HpPJdEHrvND9PBS//vWv2b9/P/v27eP48ePU1NRQV1eHy+UiODiY5ORksrOzuf7665k5c+aQ3kOj0WCxWAgODiY8PJzMzEymTp3KkiVLhtS/Vwjho1EGOo2KEEIIIYQQI0wGWAkhhBBCiIAlwaoQQgghhAhYEqwKIYQQQoiAJcGqEEIIIYQIWBKsCiGEEEKIgDXmUlcdPnwYvX7MfewR4Xa7ZdsGENkfgUP2ReCQfRFYZH8EjpHeF263u1uu696Muf8QvV5Pdnb2aBfjsnTs2DHZtgFE9kfgkH0ROGRfBBbZH4FjpPdFf9MfdybdAIQQQgghRMCSYFUIIYQQQgQsCVaFEEIIIUTAkmBVCCGEEEIELAlWhRBCCCFEwJJgVQghhBBCBCwJVoUQQgghRMCSYFUIIYQQQgQsCVaFEEIIIUTAkmBVCCGEEEIELAlWhRBCCCFEwJJgVQghhBBCBCwJVoUQQgghRMCSYFUIIYQQQgQsCVaFEEIIIUTA0o92AYQQQlya/nT0T8Tb4pkcOZmathqKWop4/sjzANw+43ZSglKItkYTaYkktzqXwuZCfpL9k1EutRDiUiPBqhBCiH55FS/1jnrq2uuosldR1FLE0Zqj/OHoH5gZM5M4Wxwmncn3ZA0UNBRwovYEGjRU2Cs4Un2EFSkreL/gfRJsCURaIom0RBJsCEaj0YzuhxNCBDQJVoUQQnTh9rqpa6+j1lFLpb2SoqYiSltLaXe1o9FoUFAw68yMCx2HRW9ha8lWVlhWkBycjFbj610WbY0G4GzTWXKrc1mSvIQISwQHKg7gVtxo8K0n2BhMYlAiKcEpxFhjiLREEmGOwKgzjuYmEEIEEAlWhejHS8deIiMsg+Upy/t9bk5xDgWNBdLUKYbFSPzvOT1OattrqWmvodJeSWFTIeWt5Ti9TjWgtOqt2Aw2Is2R3WpBM8Iy0Gq0fFL8CStSVnRZdrbprO/x1BWkh6YDEGYKU5d7FS8Ot4Oi5iJO1Z9S30+n0RFjjSEpOImkoCSiLFFEWiIJMYYMuBZWjlshLh8SrArRj4ywDO7feT9PLXyqzxNfTnGO+jwhhsNw/++1u9upbfMFpmWtZRQ1F1HVVoXb66vp1KDBarASZg7DqDUOODBMD01nRcoKPin+BJPWhFFnPBeoppwLVM+n1WixGqxYDdYuj7s8LuwuO7nVueyv3I8WLQoKNoONhKAEUoJTiLXGqrWwZr2527rluBXi8iHBqhD9WJ6ynKcWPtXnia/zCW8gNTlCDMRQ//cURcHuslPTXkNtey0lLSUUNRdR21aLFy8AOo0Oq8FKlDkKg87QaxkURcHpceLwOOjwdNDh6cDhPne/w9NBh9v3O9gQTENHA23uNj4q/IiEoAQq7BU0OBow6U2YdWZMOhMmnQmz3nffoDV0C4oNOgOhulBCTaHqY/5a2LKWMvIb8n1lQ0Gr0RJliSIpKImk4HO1sEuTl8pxK8RlQoJVMeoCubnO4/XQ6mplYvhE7phxB7/c/ktunHQjm89uxqt4WXPFGmrba/nz8T/z8LyHWZa8bETKJcaOvgLWnOIc7tt5Hw/Pe5jU4FT2lO2huKWY4pZi6h31arO6QWvAqDUSZAzC7XWrQWazs7nHwLNzYNrh6RhQOTVo1AFWCgoAZa1llLWW9fk6LVpM+q8CWJ1ZvX/+3/77Zp2ZKEsURp0RrUaLy+ui3d3O57Wfc7j6sPqZLXoL8bZ4bp50M7/c8Ut+OfuXfHPcN7tsOwlUhbg0SLAqRt1oNtcpioLD46C5o5lmp+/W0N5AZXsltW21NHY00uHuUGt+sqOz+cepf6gn/w/PfsjBqoPMjJnJnoo9HK89TmJQIskhycRZ43wjns2RfdZcCdGfzgHrQ3MfIsQYwtbirbz/5fuMDxvP63mv4/K6cHldeLwePIrHF5R6z9V6ehTPgN7LoDWowWKQIahbjWhPf5t0JoxaI4XNhXxY+CFGrRGv4mVx8mLirHG+2liPo1sg7A+U/Y81O5txtDvwKt4BlbXXoFZnQqfVUdRcBMC4kHE8ue9Jntn/DAYMrHKs4q0v3uKxqx6TQLUXgVyJIMYeCVbFqLvYzewur4sWZwstzhaaO5pp7Gikpr2G6rZq6trrsLvseBSP2tTp9DjxeH0ne5fXhcPjoN3dTpurjTZ3Gx7Fg8fjweFxsK9yH5HmSFxeF63OVhRF4WzTWU43nFZrePRaPbHWWJKDk0kMSiTSEkmUJYogQ5Ck7BEDoigKWrTE2+J58LMHuyw73XC62/O1aDHqjZh1ZoKNwUTpotRm9863nh7TaXVDKqO/j6pNb8OoMzI3fm6/fVZ7+6xuxa0G2d2C3E73HR4HTreTNncbDR0NuLyuPtftVJw4cfK3vL8xKXwSu8p2UWGvYGrUVFJDUomxxsgx+RXp83v5uhQvRCRYFQGhv6bOvr40/f3zmp3NvoDU2Uxtey1V9ipKW0qpbqvG7rbj9DjVGh2nx4nL66LD00G7u73fpk6z3oxVbyXWGovVYCW/MR+v4sWkM9HY0Uido67L80OMIUSYI4gwRxBqDMXpdlLRWoEXLxp8J8MQUwhJQUmkBPsSp0dZogg3h6PXDu6wvBS/eMTANDga+OvJv7Iufx217bUA6kVQuCmcSRGTsOgtXW699QO9mDoPptpXsQ/oOuhqMAGrRqPBoDFgMBoIImhQ5fB4PedqbXsIco9UH8GLFwWFUw2nqGqrorilmBN1JzDqjESYI5gWNY2MsAySg5N7HLg1Vkhf/cvXpXghIsGqCBg9fTn6D5bHrn6M6dHTKWgooLKtkpKWEoqbi6mwV1DbXkuLq0WtiekckPr7zvXEqDViNViJNEdi0Vt8o5L1vpHJFr1FvW/Wm9FpztU2nW06yxcNX2DUGvEoHpanLCfSEkm9o556Rz0NjgbqHHUUNxdT2Fyovk6LllBTKBHmCMLMYb6guq2Wz2s/R6vxjXY2ao3E2+JJCUkhPiieKLNvsMj5o6U7uxS/eETfDlYe5C/H/8Leir24FTd6rZ4JYRMw6Aycrj/N+LDxFDQWYNKZ1Jp9f0CGBnVkv///X/nqR4sWnVaHTvPVrZf7gwl0zx/17w9WYegBa18URcGrePEoHryKt+uNc/d1Gh1mvRmDzoBNsVFhr0Cj0RBjiKHWVUtiUCJV9ipya3LRaXSkh6aTFpJGvaOe7aXbMWgNjA8bz9SoqaSEpPSYtutydyGVCGNZoFcgXIoXIhKsioCyPGU5982+j59v+zk6jQ6v4iXCHMHjex9Xg9C++rPpNXosBgthprBuQadV77tZDL7HdBqdr0lf8eDxetQToP9vj+KhsaMRT7sHjUbTZSae2XGzibPGUWGvIKc4h7nxc8kIzSAlOEWtGfV4fa/vHMTWO+opaCqApnNl1ml0hJvCCTeHE2oKpba91hcMfzWABCDCHEFycDLJwclEWaOINEcSbg5Hq9Fekl88ors2Vxuvn3qdt794Wx2UFG4KJy0kjRhrDA6Pgz3le3j0qkf5p/H/dG6fLniKRcmLcHlcOD1OnF5fC4LL48Lpdfoe++rW5m5Tu7S0u9tpd7fj8DhwuB20uFpwe914vV6+qvz3tQJoQKNo1IAXBbRaLTqNjkp7Jfsr93NVwlVEW6Jpd7erAbLD7et7Gm2J5qr4q9hStIV58fOIt8V3CSq9iq+m0x9g96bzhadWo0Wr0aLX6DHqjBi0Bsx6M0ad0dd/9qvf/vun6k/xceHH/HT6TwltDeWA8wA5JTnMip2FzWAjrz6P/MZ88hvzCTWGkhmRSXpoOmebzpJXnwdAjDWGaVHTGBc2jqSgpDEzaUFflQjyfdKzS6EC4VK7ENEoitJ71dNl6NixY2RnZ492MS5LQ922Lo+LXWW7+KT4Ew5UHqDSXtnlxKRB063GUx3k0Wlwhz8vpFrr8lWTu3oS1KCebBXFd+L11yb512fRWzDrzVh0FiwGy7n305k4VnuMPx79I2uuWMPCxIVoNBoaOxrZWryVtSfXcnXC1YQYQ9T3VRQFo86ortPfNOvyumh0+LoOdA5iW12tXbaLQWsg3BxOuCmcYGMwZr0Zs853QwMWvYUEWwIpISnE2eI4XX+aZw48w9OLnmZ5ynJ1fwTiF89Y09exkVeXx5+P/5ntpdvp8HSg0+hICUkh0ZZIuDmcrMgsUOB/jvyPum/9hnPfKoqiZgrwB7n+gLdL1xm3r+uM3W0bqAafAAAgAElEQVTnk6JP1O4s7e52dcAUgF57LpA0ao2UtpTS5GxiYdLCbv1mDVoDeq1e/a3X6tFr9Ofud1rm/+2vDe7P+dvo2LFjTJk6hdfyXuO/D/032dHZZIRloEPHqYZTnGo4hcPtQIOGtJA0siKzSAxKpM3VRrOzGQUFk87EpPBJZEVmkRqSSpgp7LKsdfUqXlqcLTQ4GnjnzDv834n/w6Q1odFouv0vDtXlek7u79gMlO/lzuWIboymJqxmxMo1mH0vwaoYNoPZtkXNRWz6chO7ynZxqv4UTq8T8J3gQo2hNDgaiLZGU9tey8zomSQEJ3SpUdVpdGptij+I69J376tA06g1YtD5TpZGnfHc31/d959M+zvRDPSL59GrHmVGzAw1AC1vLVe7Krg8LrQaLV58TZT+INakM6HVaOnwdKiv63xrd7d3eS+TzqT2hbUZbJh1ZoKMQRh0BqraqjhSdQSrwYoRI3ddeRdPHXiKpxcOz4lFDM35x4bT4+S9M+/x91N/99W0A8GGYNJD04mzxhFtjWZewjymRU0jtyb3kjjp+fmPU3+rwGjqadt03hdbCrdw/677WZK8xJd3Vm8lzBRGcUsxefV5lLSUAGAz2MiMyCQzIpMQYwgOt4OmjiacHidoIN4Wz7ToaYwLHUdCUAIG7aWV/cPtddPY0Uijo5GGjgZ1JrMTdScobS2ltq0Wu9uuPt+sM7N60mpumnQTScFJFxSoX87n5N6OzYt1zCqKgst7roWl3d1Ok6OJpo4mmlxNtHS0qOM6/L9bXa2Ut5bzZdOX6NFj0BtG7LtEgtU+XM4Hxmjra9s63A62Fm9la8lWDlUeotZRqy4LN4UTa4sl3BROh6eDozVHWTV+Fd/M+CYnak/w+yO/597Z97Ikecm5mhqdcdADkYZqoF8sfT3v/JNBlb2Kcnu5b/CXy96l+bNzAK7X6mlz+UY617d3DWL9Ab6fVW8l3BwOCpTZy9CiRaPRcHXi1Vybei0Z4b5BIyHGkOHdQKJf/mOjuLmYPx//Mx8VfkSbuw0NGnVK0UhzJFmRWcyNn8u4sHEYtIZh+d8bq3rbJud/T/lz1f77jH+n1dVKYXMhVr2VSEskdpedU/WnOFV/Sm35SApKYnLkZNJC0tBpfV2J/NlG/FPTZkVkkRmRSUpISpeJDUab0+Ok3lHv657UXk+53XcxXdNWg9PtpNnVTHVbNbXttdQ76tUWrmBDMKGmUMpbyzHqjTjcDsD33b0gcQG3Tr6VieETh5RJ4nI/J5//f9jb/6VX8ar9zlucLTR0NNDU0eRLqdjRTIurhVZnqy/AdLbS6mrF7rLT5m7rkivZ5XXh9rpxe90DTlfX2T9l/BNPLHhiODdBryRY7cPlfmCMpvO3bV5dHpvPbmZ3+W4KGgtwK27AVzMYZ4sjwhRBlCUKs8FManAqTq+TV0+8ypMLnuRraV9T1zPaJ+KL2VleURRaXC00Onx9W2vbaylvLaeyrZImR9O5LgV81aVA56uN1Wv0tLnbutXCNjga1O0Mvj68qSGpap9Ys95MYlAiWZFZpIWmjam+d6PF4/Xw8o6X2W7fzonaE2pAkxaSRkJQAjHWGLUWNcoS1eW1gT5QI5D1tu16Ogf4t92Pp/2Y/MZ8thZv5WzTWSx6i7pPSltKOVl/kqKmIrx4MevMTIqYRFZElu8ikXN5m/21rlqNlqTgJKZFTSMtNI0EW8KQU4MNlKIotLvb1aDU/51SYa+g3lGPVzn3neL2uml0NFLdXk15azkOjy8I1Wv1JNoSfX3kLVGUtpRypOYI16RewxUxV7C+YD0n60763g+FIEMQM2JmcNuU25gRM0OdHGIgxsI5+ePCj7l7+93oNXq8eEkOSsagM+BwO9R0if48yW6vu/8VnkeDBoPWoLYiGnSGc11qOnWn0Wl0aDXaLgMqDVoDjY5GPq//nDhjHA2eBqlZDQRj4cAYLXsO76EsqIxPSz7lSPURmp3NgO9AirJEEWOJIcIcQYgxhEhrJFMip5ARlkFScBJ7K/ZeUk2dI6Hd3a7WxNY76qlorTjXpcDrQqPx9Yvt3KXAoDVwqv4UO8t2dhkN7hdhjiDaEq1mJbDqrYwLG0dWRBYpISnEWGMCovn2clBtr+aVE6/wQcEHNDl9I+rirHEkhyQTa4klMzKTOXFzGB82XiaNGEEDOQd4FS8FjQVsK9lGQWMBZr2ZaEs0Wo2WNlcbpxtOk1efR1PHuf2aFZlFRmhGl33p8XpodjZjd9nx4iXIEMSUyClMjJhIanAqQcbBpeY6v4z+/qSNHY1Ut/kCzkp7JU3Opi7Hv1nna63Ra/Xq9LslLSXUO+rV9UVZokgOSiY5JJloczRNzibfd1BHI/sr9/PAnAe4YeINaDQaqtuqeeXzV3j91OvEWGKoc9Th8row6UxkRmRy6+RbuSrhKoKNwf1+jsv5nJxXl8f/nfg/Pi35lDZ3W7fl/j7YXW46A3qNHp1Wp/72B5l6jd63vFM/bv8yjUYDii/1m06jQ6/V9zj7m7/lzl/xcbzmOC8df4m7Zt5FUlsSxCJ9VgPB5Xxg9OZi1c54FS+51bl8WPghe8v3UthcqH45WvVWYm2xRJojibREEmwMZmLYRDIjM0kOTu6SBkaaOgfH5XXR1NGknqQq7ZVU2CuoslfxZdOXHKk+wsyYmVicFrQ2LbvKdpEVkUWHp4PS1lK1D6wWLdHWaCLNkYSZwwgz+W6ZEZlMCJ+gdhm4HAeOXCyKorCzdCf/d/L/OFR1SM3FG2uIZXz0eGJtscyLn0d2dHa3WlQxMgZzDlAUhS+bvmRbyTbyG/Ix6UxEWaPQaXQoikKFvYK8+jwKGgvwKB6MWiPjw8czOWIy0dbobuvyB38erwetRktKSIpa6xpni+vxQtHtdavHu78/qf94b3O1+S5av8qm4L9oNevM6LS+MjZ0NKjBaXlrudo0bNFb1AwjSUFJWPQW7C47DR0NaNAwPnw8WrS8kPtCr4Op3s9/n//c859Mj55Oi7OFopYi2t3t6DV60kPTuWniTSxPXd5tWwx1f1wKmp3NvHHqDdblr6O4pRjwpUl0eV3EWGOoaa8hOyqbxKBE0KCmk/PPHNd5oo7O+9NqsHYZsOivPfXf7zyg0V+r2p+eBh+O5KBcCVb7cLkdGAMxnMFgTVsNHxZ+yPaS7RyvPa5eLeo0OkL1oSSEJPhmZzIGkRqSypTIKaSGpBIfFN/roANp6hweOUW+vnd3z7qbzIhMdp/YjT3Izsm6kxypPsKVsVcyKXySGrSWtZRRZi9Tm50MWgOxVl/f4QhLBMGGYOKD4pkcOZn00HQSgxLHdJL0vjR1NLH25FrePfMuNe01gK+mKiU4hXhbPMEdway6YpWaK1WMnqGcA/wz020r2caZhjNdglaADk8HZxrOcLLupDpBSJQliqyILCaET+ixWfxA5QFMOhPh5nAUFEKNoepMWnannTJ7GZX2SmraaihuLqbV1cr4sPHotN0HZ3bmcDsobSmlpNUXoNpdvoFRWo1vBjR/gOqvMHB5XdS11+HyuAgxhTA3fi7ToqZxrPbYgM8b9+24j3+Z8i/UO+o5VX+KouYimp3Nap/slekr+aeMfyI5OLnbxe/lcE72er3sLNvJ66de50DlAVxeF1p8XUDMOjMFTQV8Pe3rfC/re5ysP8lvDvyGh+Y9xLKUZWpwebG7h5yvv8GHIxGwSrDah8vhwBiKoabRcHld7KvYx8eFH7O/Yj9l9jJ1WbAxmFhLLJGWSCLMEWCHxZmL1dlfbAbbiHw20fsXz7Rp06h31LMufx0v5L7AvPh5hBpDQeO72rcZbDQ7mylrLaO0tZRqezVefKO5LXoLsdZYwkxhau14ekg6kyMnkxKSQqw1dsS/YIfbhV4oHa46zP9+/r98VvYZbsWNQWtQg4E4Wxzz4ucxPXo6FfkVY/J7JxBdyDlAURQKmwv5tORTTjecxqg1Em2JVo8DRVGoba/lZP1JzjScweV1odPoyAjLICsii3hbvBqsdZ5MIS0kDbvbzroz61BQWJK8BIPWgEVvoaqtiu0l21mR2vOkCh7FQ7W9Wq09rW6vVpeFmcLU/8cEW4J6oaQoCi3OFhqdjejQkRWZxey42erAPhjasfEvU/6F4zXH2Va8jRP1JyhqLlJnXou1xrIgcQGrM1czIWyCus0u5XNyWUsZa0+u5cPCD9WLlDBTmG/qXksMbe42DlQe4MF5D7Jq/KpBtyZeLIMZfHgxyynBah8u5QPjQg00jUZJcwmbCzezs3QnJ+tOqqPODVoD0ZZodWrQMFMYE8Mnkhnha9ovP1PO9OnTR+vjjVmDHfV8/+z7SQtN44uGL8hvyKfV3YoGjS/9l85CXUcdpS2llLaWdunTFmoM9U0Jawon0uKblGBSxCQmhk8kOTi5x1yTgV5rPpRWh/nx83nri7f4x6l/UNpaCnx1ggpOJSEogUkRk5iXMI8JYRPUwWtj+Xsn0AzHvlAUhaLmIraXbCevPs8XtFqju1y8uTwuCpoKyKvLo7KtEoBQUyhZEVlMCp+E1WDtNvvXP079A4DVmauB7rOD+TV3NFPcUkxpSyllrWXqd7RRZyQpKEkNUM/vM+r0OH21qF4XkZZI5sfPZ0rUFHWA2HBxeV2crj/NtpJtHK85TlFzEeX2csCXQWBmzExuybqF7JhsTp84fUkdG06Pk/X563nnzDvk1eXhxYtBayAtJI1YWyyhxlAmRkxEq9HywpHeu1CMZsA6lMGHF+N7WYLVPoz1k0ZPaTTu23EfqzNXU2Wv4kDVAfVKGHwn4RhLDNHWaCJMEaSEpDAl6qumfVt8lybNsb5tR8uFfPG4vW6q23y1MqfrT1PQWKCe+PyzfFW3V1PW4qt59afv0aAh0hKpzqQVbg4n3hbPlMgppIemkxTs6wN3KfRHHmirw10z7uJ47XFySnJwepzoNDoSghJIDU4lMSiRufFzmR4znRhrTLd1yLEROIZzXyiKQklLCZ+WfEpefR56jZ4Ya0y3Fgd/8/jp+tM4PA60aEkLTSMrIguX18XWkq2sSFmhTlW7OnN1l0A1MSiRstYySlpK1MkVwHccxlpjSQr2Bag9DZBUFIXGjkZanC0YtAayo7O5MvZKUkNSL3rLiH+g2o7SHRypPkJhUyElrSV4FS82g43JEZO52no1N867MaBT6imKwvHa46w9uZadpTvVnLOx1lg1Y0KoKZT58fOZFj2N47XHA/57rycj/T0lwWof5KTh69t47857SQlOIb8xHw0atenXpDMRbYkmxhpDpCWSOGsckyMnMz58PElBSX2OXpVtG1iGsj9cHhcV9gpKWko4VX+KwuZCtU+rTW/Di5dKe6Wvz2trmS8pOr4UWdHWaMJMYURbfL/Tw9KZHDGZ0tZSnj3wbEDWMPRXho8KP+L+nfcTaY5Ua8eCDEGkhKSQHJxMZngm8xLmMTF8Yp8pwOTYCBwXY18oikJpSynbS7dzou4EOq2OGEtMt0EuHq+Hs81nyavLU2vlgwxBxFpjKWwuxKzzTRk7J24OnxR/wrjQcbS6WqmyV6nf0cGGYLXmNDE4sdc0UQ63gzpHHR7FQ5w1jvkJ85kcOXlAI/SHmz+o31W2iwOVByhsLqS4uRin14lBY2BCxARunHgji5MW9zkYa6Q1OBp44/QbvF/wvjpBhEVvISM0g2hrNDaDjYnhE5kbP7dLf/RAb1HqjQSrAWSsnjRcHhe7y3ez+exm9lbsVfvXAESaI4m2RhNjiSHKEsXEiIlkRWSpV4wDHQ0+VrdtoBqO/eFwOyi3l1PcXKyeYP0zFNkMNjrcHVTYKyhtLaXCXqEuM+lMxFhiCDOHEWWJwulxsr9yP3fMuINV41cRYY5Ao9EERKDq17ksE8Im8Njex9hbsRfwjdiNs8WRGpJKUlAScxPmMj16OrHW2AEdH3JsBI6LuS8URaGstYztJdv5vO5zdBodMdbuQSv4mvL9Ew50nh3q/JRzeq2exKBENa1UqDG01/85r+KlwdGA3WXHpDNxZeyVzIiZQVJwUsCkpKu0V7K3fC+7K3ZT1FTEl41f4vA61JzQK9NXcm36taQEp4xKJhK31832ku38/dTfOVJ9BKfXiQYNKcEpJAQlEGoKJcwcxvy4+WTHXF5ZPSRYDSBj6aRR21bLh0UfklOUw7GaY136nrq9biLMETR2NHJN6jV8Y9w31KTVQx2tPJa27aXgYuyPNlcbpa2lFDcVc7L+JJV2X22jVqPFrDfT6vRN3VfaWqqOigdf7ZFFb6Guvc43cMRgYUHCAj4s+pBrU69lXNg4wHeyV1DweD14FI86C4v/b6/i7fK4/2//cvW3f1mnv3taT5fne73qnPd+Rq2R9NB0UkNSmRQxifnx85kYMXFQSc9Bjo1AMlL7oqy1jJ2lOzlacxStRkusNbbHoNWreClpKSGvLo+zzWcB38Xe5MjJvoF61rh+m+vbXG3qjFMpwSnMT5jPxPCJWA3Wi/LZhkO9o54DlQd4//j7NGmbKGwppKnDlx82MSiRhYkLWTVhFRPCJ1z02Qr9A+f+lvc3Pin6RK3MCTWGkhGWQaQ5ErPeNwnE5ZwbOZCD1ZGZr1KMCEVRyKvPY+OXG9lRuoPC5kJ1WagplFRLKnqtni8avuCmSTexasIqzjad5dE9j3Jd+nWkhqSOXuHFJcFqsDIxfCITwyeyIm0FTR1N6rzSp+pPYXfZSQxOJCUkBZPWRENHA2X2MkpbzgWvHd4OOjo62HB2A0atkU+KP8Fb5FUDyJHin8lFq9H6bl9NT+sXZY5icfJi5sXPY0bMjAHXogoBkBiUyOrM1SxKWsSO0h0cqzmGRqMhxhrTJY2fVqMlNSQVr+LlbPNZDFoDHsVDrDXWl4uzFx6vh3pHPe3udmwGG4uSFpEdnd0l40AgizBHcG3atYQ2hOKMdrK9ZDtfNn1JcXMxpa2l/P3038kpzuHK2Cu5ceKNTIueNuyp8+xOOxu+3MDbX7zNmcYzeBQPOo2OieETibPGEWQIIswc1usMc2LkSLB6iXO4Hews3cnmws3sr9ivdrzXaXTEWmN9N1ssKcEpALyW9xrPLHpGnc50SuQULHpLwDTFiktLqCnUN8I5MouV6St9wWlLGQVNBZyqP4XJY2JcyDgmhU9Ci5ZaRy07SnegoGDSmgg1h/qm/vvqptVqu/6t0aozuHSeqUWD5txvvjox+38pGvV+59dpNVq0Wl9Q6ud/3KA1YNKbqLRXsr10O5nhmZxpPMO8+Hlcl37dCG9VcTlJCEpgdeZqliQvYWfpTo5UH/EFrZYYtXbOP5jKprdh1BmZGz+3xywAiqJgd9lp7GhEQWF82Hhff8lecrleCqw6K/OS5jE7bjbHa47zacmn5Dfm+waTtZayuXAzeyr2MCVyCjdNuolZcbP4x6l/DLlPqNvrJrc6l7/l/Y29FXvVQaMxlhjSQ9MJM4dh1BrJjMhkTvwcMsIyes0RLkaOBKuXoCp7FZvObmJr8VY+r/tcHQBj0VvUfjUxlhgmRkxUk0wfrT7K/bvu59eLft3tAF+espynFj4lAau4IBqNhghzBBHmCKZFT8OreKlrr6OstYwzDWc43XAau8uOgoJeq8etuMkIzSDOFqc2zQNq8Nm57576W1HU4NWoM6pTCfqnEDTrzGrCdKveem6WF//ML+f99t/XaXRsLdnK/Tvv5zeLf6Nmyrh/5/0YdAY5JsQFi7PFceOkG1mYtJBdZbs4XH0YjaKhzd3Gp6WfdskGkB6azoqUFWrAmhycTF17HR3eDkKMIaxIWcG06GkBNRjpQln0FubEz2FmzEzy6vPYVrKNLxq+oKS5hOKWYj4r/4zcmlwmhE0gMyKTl4+93OugTT+1H/qCp6hsreTNL95k89nNlLWWoaBg1pmZGjmVWGssZr2ZMFMY8xPmMzVqKpGWyBH89KI/EqxeAryKl2PVx9hwdgOflX2mjiLVoPGllrLGEG+LJ84WR3Z0tpr30t9fKac4h/t39R2ISsAqhptW45vONdoazYyYGWwp3MK6/HXcOeNOgo3BnKg7weazm/nuxO9yZeyVWPQWjDojRq3xXDB53pSCnacZHM4BIz0N9JJjQlwMcbY4vjvxuyxKWsTLx15m49mNzIyZ2a3JPy0kjavir2JL0RauiLmCpSlLmR03m4zQjMuyv6SfQedLrzUlcgr5jflsL93OybqTlLSUUNhUSG5NLnn1eUSZo7hn+z38ctYvWZ25ulvXB39axpsn3cxfT/6VE3Un6PB0AJAcnExaSBrBhmB0Wh2TIyd3mxRBBBYJVofJcKeqaHO1kVOcw8eFH3Og6oA6ZZ5BayDOFke8NZ4YawxpoWlMj55Oemg68bb4HjviFzQWDOhk6z85FzQWyIlZDKuc4hwe/OzBbjX7y1KWcf/O+5kbP5dFSYtGrWy9BaQSsIqL5XjtcdYXrOeR+Y+gQcOBKt80nTqtjorWClyKi7TQNDIjM/nrib/yz5P/mcyIzNEu9ojRaXXqpCOFzYXsKtvFsZpjatBaZi9Di5an9j/FnvI93D7jdiaGT0SDhtdOvsbvDv8Oq97KqydfBXwpv6ZGTiXKEoVeqyfMHMZVCVcxNWqqbwZGEdAkWB0mGWEZg04CfL6SlhI2frlRTTDdOUVQWkia2ryfFZnF5MjJpIWkEWYO67dsg8nftjxluZyQxbAK5GBwIKmzRruM4vJ0fiXCgqQFzI2by5HqI2RFZnFl7JWkhaSpNX9jtRJBo9GQHppOemg6Fa0V7C7fzYHKA5S1llHUXERDRwPbSrexvXQ7Fr2FEGMIFW0VALS52xgfNp7U4FRMehN6jZ4pUVOYFTeLcaHjLnqWATF8LsnUVT/5yU/Yvn07AKtWreLpp58e8GsvZmqGgc6E41/u9ro5VHWIjV9uZHf5bqraqgBf836EOUJt3k8MSmRa9DQmhE0gOSQ5YDvSS3qewBII+yPQZ7AaqeTdgbAvhE+g7wuP13PRZ5YKJEPZH7Xtteyv2M/u8t2U28s5U3+GRmejujzYENylxjTCHKH2RR3uqWUvJ5K6ahht2LBBDVQDTV81MP6T8cPzH6apo4nbt9zO4arDtHvaAV8+xwRbAvE2X/N+RlgG2dHZpIem9ziFnhCXgkDvgiKtDiLQjKVAdaiiLFGsHLeSBUkLOFR1iJ2lO3n3zLt4FA+JtkRmxs5Eq9EyNXIqs+JmkR6aLrWol7hLau81Njby5JNPEhwcTExMDAUFBaNdpG7OD1iXJS/j9bzX+c3B3xBtieaBnQ+oI5uDDcGMCxpHgi2BGGsMU6KmkBWRRWpoakDPkyzEQEkwKIS4WEKMISxNXorD5WB9/npSg1MpbikmMSiRW7JuGVA3OXFpuKSC1aeffpq6ujoeeeQRNm/eHJDBKvhOuk9c/QT3fHoPOo2ODq9vBGJVexWRlkhirbHE2eJIDk4mOyqbjPAMkoOSL+sRnkIIIcRwyynO4ZE9j/DM4mdYnLSYbcXbePCzB8mKzJKL38vIJROs7t69m/fee4/p06ezevVqNm/ePNpF6tPsuNm4FTduxY1Vb2VK1BRiLbGMDx/PtKhppIWmEW2JviRmGhFCCCECTU993a9JuwatVisDIi8zl0Sw6nA4ePjhh9Hr9Tz66KNotYHff/NQ9SFMOhOJtkRKWktYnLSY7078LjaDbbSLJoQQQlzSAjnLiBh+gR/1Ab///e8pKSnh+9//PpmZgZ9nzn8Q/XrRr1n37XU8u/hZXsh9gb0Ve0e7aEIIIcQlbbAp53KKc0a4hGK4BXywevLkSV599VUSEhK46667Rrs4/Tr/INJoNHLQCCGEEMNkKFlGxKUtoPOsejwebrzxRk6cOMEf/vAHli1bpi679dZb2b9//6DzrB4+fBi9/uL0fjjYeJAXi17kjtQ7mBU2a9DLL3UulwuDQQaJBQrZH4FD9kXgkH0RWGR/BI7R2BeXRZ7VV155hRMnTnDNNdd0CVQvhF6vvyhJb3OKc/jj8T/y68W/7vVqL5tsUotTfTWvaZdfP5pAT7Y91sj+CByyLwKH7IvAIvsjcIzGpAADFbDdAEpKSnj++eex2Ww89NBDo12cfkmzhBBCCCHE8AvYmtWnnnqK9vZ2fvGLXxAcHIzdbu+y3OPxAOB2u9VlFotl1DIFSPJzIYQQQojhF7DBallZGQC/+93v+N3vftfr8z744AM++OADANatW0dWVtaIlE8IIYQQQlx8AdsNQAghhBBCiICtWV2/fn2fy4eaDUAIIYQQQlw6pGZVCCGEEEIELAlWhRBCCCFEwJJgVQghhBBCBKyA7bPan7Vr1452EYQQQgghxEUmNatCCCGEECJgSbAqhBBCCCEClgSrQgghhBAiYEmwKoQQQgghApYEq0IIIYQQImBJsCqEEEIIIQLWsKeuys/P58CBAzidThYsWEBGRsZwv4UQQgghhBgjBh2sfvbZZ7z44ovMnDmTe+65p8uyV155hd/+9rd4PB4AtFot99xzDz/4wQ+Gp7RCCCGEEGJMGXQ3gA8//JDDhw8zbty4Lo9/8cUXPPPMM7jdbkJCQoiIiMDj8fDss89y9OjRYSuwEEIIIYQYOwYdrObm5gKwYMGCLo+/+eabKIrCwoUL2blzJ5999hk33HADXq+X119/fXhKK4QQQgghxpRBB6t1dXXo9XpiYmK6PL5z5040Gg133HEHRqMRgLvuuguAQ4cODUNRhRBCCCHEWDPoYLW5uRmr1drlsfr6eoqKiggJCWHmzJnq43FxcVgsFqqrqy+8pEIIIYQQYswZdLBqtVppbm7G6XSqjx08eBCgS6DqZzAY0Ol0F1BEIYQQQggxVg06WB0/fjzgG2jlt379ejQaDXPmzOnyXLvdTktLS7cuA0IIIYQQQgzEoFNXrVy5ksOHD/PII49w+PBh6uvrycnJQa/X8/Wvf73Lc48ePYqiKKSkpPSk3/EAACAASURBVAxbgYUQQgghxNgx6GB19erVfPzxx+zfv5833ngDRVEAuPPOO4mPj+/y3M2bN/dY4yqEEEIIIcRADDpY1ev1vPLKK2zYsIHc3FyCgoJYtGgRs2fP7vI8l8tFVVUVs2bNYvHixcNWYCGEEEIIMXYMabpVnU7Ht771Lb71rW/1+hyDwcBLL7005IIJIYQQQggx6AFWQgghhBBCjBQJVoUQQgghRMCSYFUIIYQQQgQsCVaFEEIIIUTAkmBVCCGEEEIELAlWhRBCCCFEwJJgVQghhBBCBKx+g1Wv1zsS5RBCCCGEEKKbficFmDdvHosWLWLp0qUsWrSI4ODgkSiXEEIIIYQQ/QerLS0tbNiwgY0bN6LT6Zg1axZLly5l2bJlJCcnj0QZhRBCCCHEGNVvsPrZZ5+xfft2cnJy2L17N3v37mXfvn08/fTTZGRksGzZMpYuXcrMmTNHorxCCCGEEGIM6TdYjYiIYNWqVaxatQqn08nevXvZunUr27dvJz8/n/z8fF5++WXCw8NZsmQJS5cuZcGCBVgslpEovxBCCCGEuIz1G6x2ZjQaWbRoEYsWLQIgLy+PnJwctm7dSl5eHu+++y7vvfceBoOBefPmsWTJEpYtW0ZcXNxFKbwQQgghhLi8DSpYPV9WVhZZWVnceeedVFdXs23bNnJycti3bx87duxg586dPPbYY2RmZrJ8+XKWLFnC1KlTh6vsQgghhBDiMndBwWpnMTEx3Hzzzdx88804HA4+++wzcnJy2LFjB3l5eeTl5fHCCy+wZs0abr/99uF6WyGEEEIIcRkbtmC1M7PZzPLly1m+fDkAR48eZevWrWzduhWPx3Mx3lIIIYQQQlyGLkqwer7p06czffp0fvGLX+ByuUbiLYUQQgghxGVgxKdbNRgMI/2WQgghhBDiEjXiwaoQQgghhBADJcGqEEIIIYQIWBKsCiGEEEKIgCXBqhBCCCGECFgSrAohhBBCiIAlwaoQQgghhAhYEqwKIYQQQoiANeRJAex2O2+99Ra7d++mvLwch8PBJ598oi5vaWnh008/RaPR8I1vfGNYCiuEEP+fvTsPj6rOEv//vrUnqez7RgIJAQKETZBNQLEFbFlsRbvbBaXtblHGHnvaGZ1ff50Z27adGW2nxwV1VBQVtUGFiLIIuLEvgQRICEsSspCEJJWlklRqvb8/LgYjawWyAOf1PD6ST92qOsWFcPJZzhFCCHF16VSyum/fPh5++GFqampQVRUARVE6XBMcHMxrr73G0aNHSUxMZMSIERcfrRBCCCGEuKr4vQ2gtraW3/72t5w4cYKMjAweeeQRrFbrGa+dO3cuqqqybt26iw5UCCGEEEJcffxOVhcvXozNZmPixIl8/PHHPPTQQ1gsljNeO2XKFAB27NhxUUEKIYQQQoirk9/J6vf7UP/5n/8Zg+HcuwhSUlIwGo2Ul5d3OkAhhBBCCHH18jtZraiowGw2k5GRcUHXBwUF0dLS4ndgQgghhBBC+J2sKorSfqjqfNxuN83NzQQFBfkdmBBCCCGEEH4nqwkJCbhcLo4fP37ea7du3YrH4yE1NbUzsQkhhBBCiKuc38nqhAkTAFi6dOk5r3M4HDz33HMoisJ1113XueiEEEIIIcRVze9kdf78+ZjNZhYvXsy7776Lx+Pp8Liqqnz77bfccccdHDp0iODgYO66665LFrAQQgghhLh6+J2sxsXF8eyzz6IoCs888wzXXnstDQ0NANxxxx2MGTOG3/72txw+fBij0ch///d/Ex4efskDF0IIIYQQVz6/k1WA6dOns2TJEgYOHEhLSwsejwdVVcnLy8Nut6OqKgMGDGDJkiVMnjz5UscshBBCCCGuEp1qtwowcuRIPv30UwoKCsjJyeHEiRP4fD4iIyMZOXIkWVlZlzJOIYQQQghxFep0svq9QYMGMWjQoEsRixBCCCGEEB1cdLLalfbu3ctXX33F/v37KSsro66uDqfTSVhYGJmZmcycOZNbbrkFRVF6OlQhhBBCCNEFenWy+t577/HZZ5+1fx0YGIjBYKCmpoZvvvmGb775hmXLlvHKK69gtVp7MFIhhBBCCNEVOpWser1eli9fzpo1azh06BCNjY14vd6zXq8oCvn5+X6/z+jRo8nKyuKaa64hJSWlvRNWTU0Ny5Yt46WXXmL79u08++yzPP300535KEIIIYQQohfzO1m12+3Mnz+f/fv3X3Db1c668847zzgeHR3NQw89RFtbG6+99hrZ2dk8+eSTmEymLo1HCCGEEEJ0L7+T1RdeeIF9+/ZhMBiYPXs248aNIzIyEr1e3xXxndOwYcMAcDqdNDU1ERUV1e0xCCGEEEKIruN3srpu3ToUReHf//3fuf3227sipgu2e/duQNvLGhkZ2aOxCCGEEEKIS69T2wCMRiNz5szpinjOq7W1lcrKSlasWMHixYsBuPfee6UigBBCCCHEFcjvZDU+Pp4TJ05gMHRfIYGamhomTpx42rjRaOS+++7jkUce6bZYhBBCCCFE91FUP09J/e1vf+PVV19l6dKljBgxoqvi6sBmszFz5kwAmpqacLlcKIrCfffdx/z584mJibng18rJyenWRPtq4na7MRqNPR2GOEnuR+8h96L3kHvRu8j96D164l5caLdTv5PV5uZmfvnLX+LxeHjttddITk7uVICd5fP5qKio4J133mHp0qUEBwfz8ssvc80111zQ8/Py8qQVbBeR39veRe5H7yH3oveQe9G7yP3oPbr7Xvjzfn5PMVqtVt555x3+7d/+jZtvvplp06aRkZFBdHT0OfeNXqo9rjqdjuTkZP74xz+SnJzMM888w+9//3vWrl1LQEDAJXkPIYQQQgjRO3RqPbysrIyqqircbjeff/45n3/++TmvVxSlSw5k/fznP+e5556jurqab7/9lmnTpl3y9xBCCCGEED3H72R1//79zJs3j7a2NgBMJhPh4eHodLpLHtz5mM1mwsLCOHHiBKWlpd3+/kIIIYQQomv5naz+7W9/w+Fw0LdvX/70pz8xatSoHisb1dLSQn19PUB7K1YhhBBCCHHl8DtZzcvLQ1EU/vd//5f+/ft3RUwAeDye857af/vtt3G73QCMHj26y2IRQgghhBA9w++1e5/PR2BgYJcmqgCHDx9m7ty5LF++nIqKivZxVVU5cuQITz31FC+++CIA06ZN6/J4hBBCCCFE9/N7ZrV///7k5eXhdDoxm81dEVO7vLw88vLyAG1/amBgIK2trTidzvZrbrjhBv7zP/+zS+MQQgghhBA9w++Z1XvvvRePx8NHH33UFfG069u3L88//zxz585l4MCBBAcHY7fb0ev19OvXjzlz5vDWW2+xaNEiKVklhBBCCHGF8ntmdfr06Tz44IP813/9F01NTcyfP5/AwMBLHpjFYuGWW27hlltuueSvLYQQQgghLg9+J6v33nsvAAEBAbz88su89tprJCUlERUVddaqAIqi8M4771xcpEIIIYQQ4qrjd7K6Y8eODl+73W6Ki4spLi4+63N6qrSVEEIIIYS4vPmdrC5cuLAr4hBCCCGEEOI0kqwKIYQQQoheq/t7pAohhBBCCHGBJFkVQgghhBC9liSrQgghhBCi1zrnntVBgwYB0K9fPz7//PMOY/5QFIX8/PxOhCeEEEIIIa5m50xWVVXt8P8f/1oIIYQQQoiudM5kdcmSJYDWTerHY0IIIYQQQnS1cyarY8aMuaAxIYQQQgghuoIcsBJCCCGEEL2W38nq1KlTueOOOy74+l/+8pfceOON/r6NEEIIIYQQ/newqqiowOl0XvD1VVVVVFZW+vs2QgghhBBCdP02AI/Hg04nuw2EEEIIIYT/ujSLbG5uxmazYbVau/JthBBCCCHEFeq82wAOHjzIwYMHO4w5nU5WrFhx1ueoqkpTUxNffvklXq+3U40EhBBCCCGEOG+yun79el5++eUOY83NzTzxxBPnfXFVVVEUhfvuu6/TAQohhBBCiKvXeZPV4OBg4uPj278+fvw4Op2O2NjYsz5Hp9NhtVrp378/c+fOldqsQgghhBC9japCRQ4UZJNYUQTV/WDQLEga1dORdXDeZHXevHnMmzev/euBAwcSHh7Oxo0buzQwIYQQQgjRRarzYeVDUHsEMmfjMYdBSy0smQ1R6TBnEcT0jm2cfpeuWrhwIYGBgV0RixBCCCGE6GrV+bB4Oox+AOZ9BuZgqvPyiM3KghnPwqYX4K1pMH9tr0hYO5WsCiGEEEKIy5CqajOqox+AqU+e/rg5+NT4igXwm6+7M7ozkgKoQgghhBBXi4ocbel/4u9PjTkaMLUc73jdxEeh9jBU7O7e+M7A75lVIYQQQghxmSrIhszZoDdC+U7IeQ/2vk9/nQHGVZ26zhysXZefDYk9e+BKklUhhBBCiKuBzwf1x8BeAYsmQH0J+NwnH1ROvz44Dlpt3RnhGUmyKoQQQghxJbNXQe5HsG8ZnMgH1auNW2MgehBU5KCqZ3leUHS3hnomkqwKIYQQQlxp2uyQ/ynseQ+q8sDt0MbNIeBq0eqpGkyACiPvpUzXh74/fL7TDvkrYV52DwTfkSSrQgghhBBXAo8bDq2GnHegdBu4mrVxSyjEZUFwAigGKFoPTRVw3T9ppalCk7Hv39/xtTa9AFH9e3y/KkiyKoQQQghx+VJVKP4Wdi2Goq+grUEbN1khYRSExIMhAHR6iEiFpLEw6l74+91QvgNSJ4DuB8WhnHYtUd35Jsxf0yMf6cckWRVCCCGEuNwcz4Vdb0Dhamip0caMARA3TDsYZQnVEtTogZA0GqIHQFDUqefPX6vVUd3+OmTOItahgyKftvQf1V9LVHtBQwCQZFUIIYQQ4vJQVww7/w8KVkJjuTamN0FMJgTHQ2AE6M0QP1SbVY3qDwFhZ36tmEFawf+K3ZCfjaGxGKL6antUe8HS/w/5naxOnTrVr+vNZjMhISGkpaUxYcIEbrrpJgwGyZGFEEIIIc7LXgW73oL9n0DdYW1MZ4DI/hCSoJ3WN1khYTgkjNASVFPQhb9+4ihIHEVFXh6RWVld8xkukt9ZY0VFRfuvFUVBPWOtg9Mf27t3L5988gkpKSn8z//8DwMHDuxEuEIIIYQQlwlV1TpGFWRre0ktYdop/KTzzFy21sPe9yHvI6jaB6ig6CEsBUKTtGX+gHBteT8uCyLTwGDulo/UE/xOVv/yl7/Q2NjIK6+8gt1uZ8yYMYwePZqYmBgAampq2LFjBzt37iQ0NJQFCxYAcODAAdatW0dJSQkPPPAA2dnZREREXNpPI4QQQgjRG1Tnw8qHtNammbO1BNNeBUtmQ1Q6zFnUcU+o067Nnu5dqh18Un2gKNryfliydpI/JB6Sr4XYIRCeCvqrY6Xa70/5k5/8hDvuuAOTycTSpUsZMWLEGa/bu3cvCxcu5OOPP+ajjz4iMDCQhQsXct9991FVVcW7777L7373u4v+AEIIIYQQvUp1PiyeDqMfgHmfaa1LvzfjWe20/VvT4N6VUFcEe96Fku/A5wEU7SBUWB8I7QPhKdBnXHuJqQ4n968Sfn/i1157jeLiYv785z+fNVEFGD58OH/+8585fPgwr732GgApKSk88cQTqKrKN9980/mohRBCCCF6I1XVZlRHPwBTn+yYqIJ2ACppNARGwevXw8fztZJTpmBtST9zNoz+FVz//2D6M3Djv8OA6VrSehUmqtCJmdV169ZhNpuZPHnyea+dNGkSFouFNWvW8OijjwIwefJk9Ho9paWl/kcrhBBCCNGbVeRoS//zVp0a83q0mdO9S6Hwc62D1PfCUrTDUfEjIHm0VmrqhyWmhP/JamVlJUaj8YKuVRQFvV5PVVVV+5jZbMZqteJwOPx9ayGEEEKI3q0gW5sdNQZqXaT2LtWW+VWf9rjJqp3kjx6kdZGKSocZ/3X2ElPC/2Q1JCSEuro68vPzyczMPOe1+fn5tLS0EBV16icEr9dLc3MzkZGR/kcrhBBCCNFbqSrYirQk9K8DoblaGzcGagekYjMh7YZTJaa+ex5abZKonoffyerYsWNZtWoVf/zjH3nzzTcJDw8/43X19fX88Y9/RFEUxo4d2z5eWlqK1+slPj6+81ELIYQQQvQWdUch9yPI+xAajmljBot2Yj86EzKmQdI1EJkORsup59mrtDqp4pz8TlYXLlzIhg0bKCgoYPr06dx2222MGjWqQ+mqXbt28cknn9DQ0EBAQAALFy5sf/4XX3wBwJgxYy7RRxBCCCGE6GaNFbBvOeR+ADUF2pjeBEEx0FoHU56AfpMhagCYAk9/vtOutTadl929cV+G/E5WU1NTefXVV3n00Uex2WwsXryYxYsXn3adqqqEhYXxwgsvkJqa2j6ekJDA7373O6ZNm3ZRgQshhBBCdKuWOjjwqbYP9XgOoGrdpILjtX2oaTdAynj44g/gbNKW+89m0wvaVoBe1tq0N+pUNdlrr72WL774gvfee49169Zx9OhRvF4vAHq9nrS0NG666Sbuuuuu07YJ3HrrrRcftRBCCCFEd3DaoWCV1lHq2BZQvaDotBP7EenQ9zptBjUmEwJPNjv62etaHVWAiY92LF/ltGuJ6s43Yf6a7v88l6FOtz4ICwtj4cKFLFy4ELfbTWNjY/ts6oVWCxBCCCGE6HXcbXBojTaDWvQVeF2AAgEREJEKyWMh/UaIG6LtOVWUjs+PGQTz18KKBbD9dcicdaqDVf5KbUZ1/pqOHazEWV2SPl1Go7HDiX8hhBBCiMuK1w1F32gzqIdWg/tkiU1LmFZmKmkU9J+mJaghiacnqD8WMwh+8zVU7Ib8bO3Uf1C0tkdVlv79cnU0lRXiElFVldzyRj7LPc7xBgcpkYFMHxLP8GQpOyKEEJcdnw/KTtZCLciGtkZt3BysLevHZcGAGRA/TCve35kOUomjJDm9SBeVrFZXV1NYWEhTUxMej+ec186ZM+di3kqIHldYZeex5bkcOdGMx+tDBSakRfHetlL6RQfx3NxhZMQGn/d1hBBCXCKqqnWMKsiGtgZtFnTQLG0W9FzPqcyF3A9h/8fQckIbNwZph6Rih8CAmyFxBIT3Bb3M6/W0Tt2BAwcO8Kc//Ync3NwLul5RFElWxWWtsMrO3Fe3MDkjmtTIQL4urEGnUwgPMrH4/mv4urCG2xdtYfmC8ZKwCiFEd6jOh5UPaa1NM2ef2hO6ZLbWFWrOoo57QmsPawlq3kfQWKaNGSxaQhrzw1qo/cFg6pnPJM7I72T14MGD3H333bS1taGqKiaTifDwcPR6fVfEJ0SPU1WV3/99L1lJYaBAbIgFg15bCgoNMLJsZzkLrk8H4A/LcsleOLEnwxVCiCtfdT4sng6jH4B5n3U8bT/jWe20/VvTYO4SOL5Hq4VaW6g9rjdBaDJED4T+N0HyGIgeAMaAnvks4rz8TlZffPFFHA4H8fHxPPXUU4wfP14SVXFF+zzvOIeq7cwYHEefiCD0ulOb6kMDjLi9Pt78roj7J/Tl7c0l5JY1MEz2sAohRNdQVW1GdfQDMPXJ0x/3OMEap82avjtbG/u+FmpUBvS7AVInQMzAjkmu6LX8TlZ3796Noig899xzjBolG4bFlcvj9bHx4An+tuEIfSIC6RttBcDW4sLe5sag06GqKlFWM5WNDj7cWcrUQbGs3l8lyaoQQnSVihxt6X/eqlNjbU1Q8Jl2kr90K6g+QIe2HDYEBv0UUidBbCYEnLlNvOi9/E5WHQ4HFotFElVxRbO1uPhoZyn5lU0YdApWixGvT2XXMRs7i+vxqipur5dtxTbG9YskPjSA8vpWKhsdWIydOC0qhBDiwhRka3tUDRY4tA5ylsDBVYAKKFph/vB+kDIOqvZr+1enPNHTUYuL4HeympSURHl5OaqqopyvxpgQl6EDxxtZur2UNreX1Mggjje0UWN3snRHKbYWFxFBJlweHw63lx3FNnTAtf0iSQwLYGeJDYNOweXxYTJI0iqEEJeUqkLdEWgsh+fSwVGvjVtCITwVEsdA/59A/FBt2X/jn7T6puKy5ney+tOf/pQXX3yRrVu3Mn78+K6ISYge4fR4WXegmvUF1YQFGEkKD8Tp8dLY6qKkrhUdMLZvBKNSw/lgRxkGvYJRr2NbsQ1FpzA8KYzKxjZSIgNZvruMO0f36bC/VQghRCc1lELuR7D3Pagv0caMgRCZAfFZMGjmmWuh2qu0QvzisuZ3svrAAw+wYcMGnnzySRYvXkxycnJXxCVEtzrR1MYHO0o5WtNCYlgAJoOOozXNfF1YQ7PTg1GvMCA2mGv7RbY/R6co3DoikU9yytl6tI4yWyvhgSayksLYWmQjxGLkp1nxsgIhhBCd0dYIB1bAnvegfIc2pjeBNRZaa+Enf4LUiRCZBvoztHl32rXWpvOyuzduccn5nax+8cUX3Hrrrbz44ovMmjWLm266iaFDh2K1Ws/5PKmzKnojVVXZU9rA33eV4fWppEYG0uLysi6/iqM1LZgMOm4YGEN8iJnlORUEmPRckxLR/vwAo55bhibw0a5SyusdjOwThkGnIyksgHX51YQEGJmUIT/VCyHEBfG64ch6LUE9vFb7WtFpCWpUBmRMh5TxsOofwX5cO9F/NptegKj+0j3qCuB3svr444+3zxSpqkp2djbZ2ef+qUWaAojeyOHy8lnecTYdriXSaiLYbGB/RRObjtbi8vhIj7YyZUA0QWbtr8ncUUmsy68mt6yRPhGB6HWw7kAVR2qaCQ0wYjT4yCltINhiZHhyGHEhFj7JKSfEYmB4Hzl9KoQQZ/R9F6rcpbBvudaJCrRT++H9IPU6GDBNK9wfcLLSyq2vaXVUASY+2rEEldOuJao734T5a7r3s4gu4XeympCQ0BVxnFFVVRXr169n69atFBQUUFNTg16vJzY2lmuvvZa7776bjIyMbotHXDnK61tZur2UigYHyeEBNLV5WL67nOONbVjNBm7KjCUtuuNqQaTVzKxhCRyqtlPX4sKgU2hxepg9PIHEsEBanB4+zinnm0M1KAoMSwojIsjEu9uOYbUYSI+Ren5CCNGuvoSYgrfhqw2n9qGarBA9SNt/OnQuxA0Fawz8eDtVzCCYvxZWLIDtr0PmrFMdrPJXajOq89d07GAlLlt+J6sbN27sijhOU1lZyfXXX4+qqu1jgYGBeDweSkpKKCkp4eOPP+bxxx/nnnvu6ZaYxOXP51PZXlTHJ3sq0OsUEsMC2HWsnl0lWjmqrKRQxqdFYjZ0bHThdHupsrdhMeiZNz6VcWmRGHQ6lu8uY1tRHW6vjyCzgZ+NTOLj3eVaO1ZFYWhiKB6vylubinn4hv4khkmHFCHEVcxRr+1D3fselO8iDrR9qOF9tXqoQ2+HhBEQ1uf0BPXHYgbBb76Git2Qn62d+g+K1vaoytL/FcXvZLW7eL1eVFVl4sSJzJkzh3HjxhEVFYXX6yU/P59nn32WXbt28fTTT5OSksKkSZN6OmTRyzU7PXyaU86OEhuxwRYaHW4+3FGGrdVFZJCJGwbGkPCjZNLt9VHd2AYKTB0Yy6SMaEIDTm3kv3N0HwJNBjYerCY+NACr2cBtI5NYnlPOxoMn0CkwOCGUE/Y23vyuiIevTyfSau7ujy6EED3H44LD67SWp4fWgs8Nih5CErFbEggecxf0GQeR6aDvRFqSOEqS0ytcr01WQ0ND+fTTT8nMzOwwrtfrGTp0KIsXL+a2227j0KFDvPHGG5KsinMqrm3h/W3HqG1xEhdiYVuRjX0VjegVhXH9IhmVEt6hzJTXp1Ld1IbH62NMv0huHBRLdPDpSaZepzB7eAJBZj2rciuJDjZjtRi4bWQiy3eXs77gBIqikBkfQkWDg8WbS/jt5H4EW85wclUIIXqj7/eUFmRr+0ktYTBoFiSdI0FUVSjfqSWo+z/WTvYDBEZpp/fTboCM6ZRUuxk6YnT3fA5x2eq1yWpwcPBpieoPmUwmZs2axXPPPceBAwe6MTJxOfH6VL49dILPciuxGPW4PSpLd5TS4vSSGBbA1IExhAeZ2q/3qSon7E7a3F6GJoYybXAcyRGB53wPRVG4cVAswWYjH+4sxauqhFiM7TOsX+ZXowMGxAVTVu/g3a3HmD+xLxaj/pyvK4QQPa46H1Y+pLU3zZx9al/oktlaZ6g5izruC7UVQd7fYe9SaDimjZlDtL2nCSNg+F3a9ZZQANTavB74UOJyc85k9YkntPZkMTExPProox3G/KEoCs8880wnwju3gABtydbn813y1xaXv8ZWN3/fVUZeRSMhFgPbiuray1FNHRjD4ISQDpUt6lpc2Ns8pMdYuXloHGnR1guukaooCmPTIgkw6Viy9Rher0p4kElLWHeXsy6/GkVRyIi1Ulhl5++7yvjlmD4Y9NLlSgjRS1Xnw+LpMPoBmPdZxxP3M57VTty/NQ1++Xeo3q8lqBW7tccNFojsD7FDYfgvIWGYdlBKiE44Z7L66aefoigKffv2bU9Wvx/74cGns/n+uq5KVnfs0IoES0UA8WOFVXbe334Mu8ON3eFm7f4qXF4f6TFWpmScKkcF0OhwY2t1kRhq4Rdj+pAZH4Kuk52nhiWH86DJwJubiqmxO4kONnPbyEQ+zqlgbX4VOiWOftFWdpbYCDYbmTMiQZoGCCF6H1XVZlRHPwBTnzz9cb0J4odry/rfl5BS9BDaB6IHwNA7oM+1F3ZQSojzOGeyOmfOHBRFITo6+rSxnpaXl8eXX34JwO23397D0Yjewu31sb6gmrX7q/CpsKvE1l6OatrgWPr9oBxVi9NDTbOT8EAT91zbh+F9wjFegpnO/rHBPHx9Ov/3XRGVjW3Eh1r42chEPt5dzuoDVdw8JJ6UyEC+KqwmJMDADQNjesXfKSGEaFeRoy39z1t1akxVoXQb5H2k7UN1Np18QNFmULNuh4wZENGvcwelhDgLRb2QKdJeXhwsCgAAIABJREFUpqGhgblz51JaWkpWVhYffvghev2F7f/LycnBYJC/RF3B7XZjNPbcwaGGNi/rjrRQ0uCkttXHoToXPhX6RxjJirVg1GsJodOjYmvzYjEojE0KYEiMGbPh0i/H2xxeVhbYaXR6iQ7UY3f52FjcitOjMrFPANFBBmpbPdzc30pmjOWSv39P3w9xityL3kPuxYWJ2/8qBmc9FcN/T1DNXkKqthBSuQmT4wQAHqMVV1ASLRFDMLcep82aTFXWQr/fR+5H79ET9yIrK+uCrrvssra2tjYWLlxIaWkp4eHh/PWvf73gRBXAYDBc8G+O8E9eXl6P/N6qqsr+iibW7CjluNPEgdpW6lvdRAaZmDoohvhQbW+zy+Oj2t6Gwaxw56gYrusf1eWn8ocNcbF4czFl9a2kxQYSEeHi45wKNpc5uCUrgYxIMzvqXAwbnMqg+JBL+t49dT/E6eRe9B5yLy5AYznsKgJ7NRGf3wKeNm3cGKQV7I8biuGa+zHEDiHQEgIbniKk1UZMJ35f5X70Ht19L/LyLvxw3WWVrLpcLhYuXMjOnTsJCQnhrbfeIjk5uafDEj2oze1l9b4qviyo4uiJFgqr7aeVo/L4fFQ3OfGpKuPTIrl+QEy31ToNDzLxm8lpLNlaQmFlE8kRQdqWgJxyPs+rZOaweEIDjCzeXMLC69PpE3nuygNCCHHJ+bxamalDa6FwNdQUnHosKEZrcxo9EIb9AmIzISiq4/PtVVoxfiG6iN/Jqsvlorq6GpPJRGxsbIfHnE4nL730Etu3b8flcjF58mQefPDB9lP7F8PlcvHII4/w3XffYbVaefPNN89Z2kpc+aoa21i64xhbj9aRf7yJFlfHclQ+n0pVkwOn28fwPmFMGxzXPsvanaxmA/Mn9OXDHWXklNpIDg/kZyOS+CSnnM/yKpk1LIEAo543NhWx8Pp0YkIu/ZYAIYTooNUGRzbA4bVawf7v66AazBDeDwLCtRP+v/gAYgeD8SzfO512rb3pvOzui11cdfxOVj/66COeeeYZbrvtNp5++un2cVVVmTdvHrm5ue2VAgoLC9mxYwfvv/8+Ol3n9wS63W5+97vf8dVXXxEYGMjrr78uywZXMVVV2X2snre3lLCntIGKBgfmH5SjAqixO2lxehgQF8yMofGkRgb26CEmi1HPXWP7EGjW892hGpLCA7l1RCKf7KkgO/c4s4clYPDpeHNTMQ9NSSc0UPZwCSEuIVXVks/D66BwDVTsAvVk2ceACO2AVFQGDJ17ssxUHLxxAxR+AUnXnP11N70AUf2lg5ToUn4nq9999x0As2bN6jC+evVq9u7di8Fg4M4778RisbB06VL27t3Lxx9/zNy5czsVoNvt5h//8R/ZuHEjFouFV199lVGj5C/FlUpVVXLLG1mzv4pGh5vQACPTh8QxPDkMgFaXh09yyvloZxkHq+y4vSr9Y6xMzogm0KSnweGmodVNn4hA7h6bwsC44E6XobrUjHodt49MIsikZ+2BKuJCAzokrLOGJVBjd/LO1mJ+fV0aASZpGiCEuAiuFij6RlveP7wW7JXauM4AIYlaWamk0TD4Vq3Vqdna8flzFp0qSzXx0Y51Vp12LVHd+SbMX9M9n0dctfxOVouLiwFOW4L//PPPURSFhx9+mAULFgBa/dN/+Zd/YdWqVZ1KVj0eD//0T//E+vXrMZvNLFq0iGuvvdbv1xGXh8IqO48tz6WopoUZQ+KIDbFQ3dTG3W9sp190EP/0kwF8nnec9QersbW4sZoNTB8cTb9oK/Y2NyV1rUQHm7lvfApZSWG9suC+Tqdw89B4gswGPs2pICrYzK3DE/l0TwWf5WkzrEdrWli64xj3jku9JKW0hBBXEVuxNnt6aA2UbAKvSxs3h2idoyLStFapKeO0hFV3jh+KYwbB/LWwYgFsfx0yZ53qYJW/UptRnb+mYwcrIbqA38mqzWYjMDAQq7XjT2DfF+j/2c9+1j42Y8YMHn/8cQ4dOuR3YF6vl8cee4y1a9diMpl46aWXGD9+vN+vIy4PhVV25r66hXvGpbD012Ox/qBo//+7JZMnPs3j/rd3oKqgAsOSQhmfFoXH56OkroVgs4E7rklmdN9wzIbePSOpKApTBsQQZDLwwY5SQgKMzB6ewIq9Faw8uSUgt6yRT8zlzB2V3GtmhoUQXURVtbqmBdnQ1gCWMC2hTLqAVUSvG0q3arOnh9ZA3RFtXNGBNVYr0h8zUGtzGj0AAsL8iy1mEPzma60zVX62ttc1KFrboypL/6Kb+J2sOp3O0+pwHT16FLvdTmpqaodDVyaTiZCQEOx2u9+B5eTk8MUXXwDa0vD52rwuX76c+Ph4v99H9DxVVXlseS73jEvhsWkDOzzW1ObmhS8L+aawFp8Kep3CbSMTiQgyUdXUhlGv8NOh8UxIj+rQlepyMLpvBEFmA4s3F2M26Jg9PJGVeyvIzq1k1rB4Nh2pJSTAyPTBcdI0QIgrVXW+1imq9ghkzj41c7lkNkSla0vxP565bD4Bh7/UlvaPbABXszZuDNBanIb3hf43QvqNEJZyaQr0J46S5FT0GL//BEdFRVFdXU1VVRVxcXEAbNq0CeCMe0mdTichIf7Xj/T5fO2/drvd1NbWnvN6r9fr93uI3iG3vJGimhY++PXY9jGfT2VvWQNPfJJHYXUzep3CmL4R7Cmtp8buxOuDyRnRTBkQTVigqQejvziZCSEsmJLGG5uKMegUZg1LYOXe42TnVTIzK57V+6oIsRiZkB51/hcTQlxeqvNh8XStpem8zzruCZ3xrLYn9K1pcN9q8DpPLe8f33PquqBo7WBUVAYM+znEZUFghLQ4FVcUv5PVYcOGsW7dOl588UX+9Kc/0djYyPvvv4+iKEyYMKHDtZWVlbS1tZGSkuJ3YNdeey2FhYV+P09cftbsr2LGkDgCTXrK61vZU1rPv3y8D4fLiwokhQUweUA0Lq+PqsY2PD6Vf5k+4Iop8dQv2srD16fzxndFeNtUZmbFk51Xyaq8Sm4eGseyXWUEWwxkJfm5fCeE6L1UVZtRHf0ATH3yzI/HZUFoErw+CXwebVxv0mZOw1Ogz3htH2lEP63klBBXKL+T1XvuuYd169bxySefsHr1ajweDy6Xi/j4eG688cYO127evBk4/TCWEN9TVZXKRgcej4/ffbiHvWUNVDS04fWpKMDUgTFEWU00Oz1kxodgMejxquoVk6h+LzEsgIevT+fN74qptrcxMyuez/IqWb2vimmD41iy9RgLphhIi7ae/8WEEL1fRY629D9v1amxuqNaqahDa7V9qN8nqACRaRCbBUNuheSxYI2R2VNx1fA7Wb3mmmt46qmn+Mtf/kJraysAKSkpPP/885hMHZdjV6xYASAn+EUHWoLaRkFlE18VnmDT4VoaHW48Pq0+b2yIGXubB4NOwWrWExcawE+z4ukfY+Wx5XlEdVP3qe4WZTXz4JQ03tlSTFFNCz8dEsfn+6pYl1/N1IHRvLWpmIU3pPdIYwMhxCVWkK3NitaXaL/Ozz7VOUrRQ0iCVloqbqh2TWQ6TPtzT0YsRI/p1K7ruXPnMnPmTA4dOoTVaiU1NfW0ov9ut5vbb7+d2267jRtuuOGSBCsuX6qqUtXUxsFKO1uL6thT2kCprYUTTU7Uk9cMSwplUHwIPlXF5fERHxbAjCFxDE4IRa9TaHZ6WL2vkqU/2Nt6pQkNMPLAdf14f9sx9lU0Mn1ILKv3V7HhYA1TBkTz5nfFPHR9OhFBl+8+XSGuat+f/D+yHhrLYO/72rjBDFEDtBnUjBmQfgMEJ4BOBxue0k7hC3GV6vQRQYvFcs4uUkajkTlz5nT25cUVQFVVTtidHKxsYkeJjYLjdo7ZWiird+Dy+FAU6BsVxKD4YLYX23C4vbg8PoYmhTIhPYq0aCv6H5RtWvT1EdJirAxLvrL3bgaaDNw7PpVlu8rZUVzHtMFxrD1QxTeHapiYHsnizcX8dnJah/JeQohezOeFsu3a7GnBSmg6ro0reojJPNU5KnWC1ub0x+xV2kEqIa5S8q+duKRUVaXG7uRglZ0dxTaKa5opa3BQXu/A1qIVpw4PNDImNYIBsVZcXpWmNjdjUiP46lAN49OimHtNcodErNnpYdHXR3hvWynLHhzXUx+tW5kNen4+Opkgk4GNhdXcOCiWLwuq2XykDp8K7207xv0TUnt9TVkhrlpeN5R8pyWoBz+DlpMVbUxWrbVpULS2L/Xuj7Ul/7Nx2rUC/POyuyduIXohv5PVxsZG8vPzsVqtDB06tMNjtbW1PPPMM2zfvh2Xy8WkSZN4/PHHiY6WnwivZKqqUtPsZE+lgy+rDlFqa6XG7qSi3kFZfSs+FUx6HUMSQshMCCEyyERti4vaFheJYQHMHBbP0MQwyupb+cOyXMY+s4HpQ+KIDTFT3eRk9b5K0mKsLHtwHBmxwecP6Aph0OuYPTyBQLOez/OOc8OAGDYWnmBrUR2qClazgV+M6dNh9lkI0YPcbVD0lZZcFn4BbY3auCUU4odBdCaMuAsSRoApCP7vetj5xpmrAXxv0wtapyipcSquYn4nq3//+9/561//yj333NMhWXW5XNx9990cO3YMVdV2IX7xxRcUFBTw6aefYjZfmYdirlaqqlLb7KKwqomdJfWU2lo5fqKZFlyU1LbS7NROsSaFBZCZEEJ6jBWXx0ddi4tqu5NhSWFMSI+kX5S1vUNTRmww2QsnklvWwOr9VdS3uomymln667FX/NL/2eh0CjdlxmI1G1i2q4xJ/aP49lAt24ttgEqIxcDMYQnSNECInuJshiNfQsFnULga3NrBYwIiIGEkxA6GkfdC7BAwBXZ87pxFWh1VgImPdqyz6rRrierON7WWpkJcxfxOVr8vR3XLLbd0GF+5ciUlJSUEBQXx8MMPY7FYePnllykuLmbp0qXcf//9lyZi0aNqm50cqrKzo8TGsboW3B4fthYXJbZWjje0AW1YzQbGpEaQmRBCiMWArcXF8QYHQWYDNw2O5ZqUCKKDz/7Dy7DksKs2OT0TRVG0Dl0mPe9uO8aE9Eg2HaljR0k9CgohAUamDIjp6TCFuHo4GrTyUvkrtYNSXqc2HhStJaexQ2DUPIgeBMZzlNmLGQTz18KKBbD9da06wPcdrPJXajOq89ec3sFKiKuM38lqWVkZAP379+8wvmbNGhRF4fe//z133XUXAPHx8SxYsIB169ZJsnoZq2t2cqj6ZIJa24pX9dHm8lFW7+DIiWZcXh96nUKfUAMj+8aQHBGI16ftXW1odZEYHsCcEYkMSQzFYpQ9lp01vE84gWYDb35XzNi+KtuKbWwvsaFTFEIsBkamRPR0iEL0Tt+fwC/IJrGiCKr7waBZkOTH0npLLRz8XEsii785WQNVAWsshKdqy/TDf6kdljL4Ua0jZhD85muo2K3tb221aUnvvGxZ+hfiJL+TVZvNRnBwMAEBp2o9+nw+cnJyUBSFm2++uX180qRJ6HQ6ioqKLk204qKpqkpueSNr9lfR6HATGmBk+pA4hv9oJrO+xUVhtZ1dJTaO1rSAqtWfrmpso6CqifpWNwAxwWYy40MYEBdMo60Wq9VMeX0riqIwIjmMcWlR9IsKal/qFxcnIzb4VLcrn8rOY/VsK67DoFf4R4uR/jHW9vtbXGGjb8XBM95fIa4a1flap6jaI5A5G485TEs8l8yGqHRtKf5sM5dNx6FglXaC/9gWUH3aN8LgBK2LVMo47RR/RBroL/K8cuIoSU6FOAu//3Z5vV58Pl+HsYMHD+JwOMjIyCA8/FTZDb1eT3BwMC0tLRcfqbhohVV2HlueS1FNCzOGxBEbYqG6qY2739hOv+ggnrwlE69PZdexeo6eaEZFxWzQ0+b2cLCqmZK6FlQVLEYdw5PDyIwPITrYjE9VsbW4qGx2kxTkZfrgeEalhl+xxft7Wp/IQB6+QUtYParKntIGNh2pxeHyUtnURnm9gxlD4gg166lrdrbf3+fmDruqDqgJQXU+LJ6utTSd9xmYg6nOyyM2KwtmPKvtCX1rmrYU/33CWl+i7T/NXwHlu7QxRQ8hiVpSmjZFm5UNTwWdrBQJ0R38TlZjYmKoqKigpKSE1NRUAL799ltA6271Yw6Hg7AwmdXpaYVVdua+uoV7xqWw9Ndj20tDNba6mTksgVe+PsIv/m8b16VHERcagEmvUHiihYOVdhxuLwqQEhlIZkIIfaOCMOh0uL0+KhocuL0++kQEMqx/MLMmZcpSfzeIDbHw0JR03tpcjNenklfeyFeHahgUF8yqf5hIalQQeXl5ZGVl8W+zBrPo6yPcvmgLyxeMl4RVXB1UVZtRHf3AmU/bm4NPjS+/H4bcBgdWQPV+bUxn0BLSiH5akf6MaRCarBXpF0J0K7+T1dGjR1NeXs6zzz7LM888Q21tLe+99x6KojBp0qQO1x47dgyXy0VMjBz+6EmqqvLY8lzuGZfCY9MGAnDj89/Q1OZmYv8ofCr0i7LicHnZeayeQFMT1U3agYGwACMj+oQxKC4Eq0X749Lc5qGu1YFeURjRJ5zxaZGkRgaxf/8+SVS7UXiQid9OTsNi0HG42o7Drf3w8PbmEn4/LaP9OqvZ0H7f/7Asl+yFE3sqZCG6T0WOtvQ/b9Xpj6kqVO3T2pweWAG2o7DxadCbtNnTyDQYNFubRQ1J1Jb+hRA9xu9k9f7772fVqlV88803TJgwAdCSof79+zN58uQO13733XcAp9VjFd0rt7yRopoWPjjZprS+xUV9qwuXx0dssJmqJie7j9Vz+IQdnwqtLg+Z8VpN1IRQC4qi4FNVapudNDs9hFiM/HRoPKNSIqTtZw+zmg2M7huBdyMMjAvmYJWdT/aUE2jWc31Mx+06C6ak8/bmEnLLGqTagrjyFWRD5mwwW7WvfV4o30X8vrdg4xZoKNXGDRZtljUoBq7//yBlvHYiXxJUIXoNv5PVjIwMXn75ZZ566inKy8vR6XSMGTOGp59++rRaj8uXLwdg3Liro+tQb7VmfxUzhsQRZDZwrK6FtzeX4PJ48fjgve2l2Nu0mqgJoRZUIC7EwqQMrZGDy+Ojxt6Gx+cjJTKI20clMSg+RGZQe5GNB2uYmRXPmL4RvL2lhIJKO69+c5R3DApvhiUzNDGUILMBq9nAjKHxrN5fJcmquPK1NUBgJBz8QivQX/gFtNYRDWAMhOiBEDUAsu7UOk152mDobT0dtRDiDDp1fHHSpEmsX78em81GUFDQGQv+u91u/vVf/xWQmdWe1uhwExNiJudYPR/sKMXh9tLi8uJTIUin55qUcDITQggPNLHlaC0Otxd7mxtbiwu9TmFkSjjj06JIjQyU4vO9UKPDTXxoALeP0tqzvvTVEQ5W2Wlxqzz8fg7pMVZuG5nE+PRIYoLNNDjcPR2yEF2nuQYOr4Xib6H+GGz5mzZuCYPYodQbYwmfskAr2B94stxb4edauSghRK90UbU2IiLOXtfRaDQyZsyYi3l5cYmEWAzsKW2gvN6By+1lY2ENPhUCTXrmj+/bXlbK51Opa3ah0yl4fCq3ZCUwKiWccFnq79VCA4xUN7Wh1yn8NCueILOeh5buwe3xUt/qYnuxjZzSepIjAlHQmi44PV7MBpkdF1eI2sPazGnBKijfCagnH1AgfiTED4UR90D0AMoOlRCennXquU67Vjt1XnZPRC6EuAAXWRhO269aX19PW1sbCQkJlyImcQk5XF58qsqOYhvX9o1ga1EdBp0Oq9mAyaBDp1NOLvU7cbg9lNW38sytQ5k1PEGSmcvE9CFx3P3Gdv591mCCzAauHxjLu/PHsHjDPlSzlYoGB6W2VopqtBJyJ5raqGxo4xdjkhmXFnXObmJC9Eon959S+LmWoNqOauM6A4QlQ1gqpEyE/E+h3yT4yX+c/bU2vaB1ipIap0L0Wp1OVvPy8nj11VfZtm0bDocDRVHIz89vf7yxsZHnn38egD/+8Y+YTDI7191qm50s2VpCZaMDo17H5qN1hAYYmZkVzxf7q/CpKsfqWjDodYxKDefA8UYGxYUw95rkng5d+GFYUij9ooN45esj7af+r0mNQBkZRkBsX3aW2Mgrb2BHsY0TdicOt4+tRXXsLLGRFB7ATZmx3DYqmfQYK3pp3iB6K1crFH2tdZE6tBpa67RxQwBE9j95gn8WpE48VWJq8CytjqpODxMf1Q5Sfc9p1xLVnW9qLU2FEL1Wp5LVZcuW8R//8R94PJ6zXhMaGkpFRQVbtmxh8uTJTJ06tdNBCv8V1TTz9pYSmhxu9lU0abVSFegbFUSwxciMIXE4PT5mDIljYFwwS3eU8lluJcselMNwlxtFUXhu7jBuX7QF0E79W80GjHqFzIQQ+kQGUmZrxdbq4raRiTQ63FTUOyird1BS18rr3xXz0a5yhiSGMG9cKmPTIgmxGHv4UwmBtv/00BotQS36SjsEBe37T4keAMPuhPgREBR1+gn+mEFawf8VC2D765A5i1iHDop82tJ/VH8tUT1bByshRK/gd7J68ODB9kT1zjvvZObMmfzDP/wDDQ0Np1176623snnzZtavXy/JajdRVZWdJTY+2lmGx+vju8N12FpdZMaHMCw5lA0FJ/i/74pIiQxkXL9IVu+v4pEP9pAWY2XZg+OkYPxlKiM2mOULxvOHZbm8s+UY04fEobQ1ohbmsnpfJWkxVj59aAL9Y6yU1zvILWtga1Et5fUOymwOyupb2Xykjm1FNpLDA/jp0DjmXpNMSmSQHKoT3av2sJacHlx1soOUCihgjYHQoRA7BEberSWq5gv4fhUzCH7zNVTshvxsDI3FENVX26MqS/9CXBb8TlYXL16Mx+Nh3rx5PPHEE4DWVvVMxo7V6nrm5eVdRIjiQnm8Plbvr+LL/Go8Ph8bD56gze3juv5RjEgOw6fCuLRIzAYdASY9bq9KlNXM0l+PlVJGV4CM2GCyF04kt6yB1furKGn0kRp1+v1NjggkOSKQnwyO5XB1MzuLbewtq+eYrZVymzbb+vLXRSzZVsqI5HDmT0xhbL8oKVcmzk1VtUL8Bdla2ShLmLYsn3SehNDn1Q5FHfxc++/H+0/DUyFlEmTO1LpJGTq5pSxxFCSOoiIvj8isrPNfL4ToNfxOVnfu3ImiKDzwwAPnvTYqKoqAgAAqKys7FZy4cC1ODx/uLGVvWQN2h4dvD9eg1ynMHBZPvygrLo+P8vpWRvQJ5+djkgk0XfTZOtFLDUsOY1hyGHl5LrKyBp71OrNBz5DEUIYkhtLQmsiBiiY2H62loLKJY3WtlNc7+PZwDZuO1JAcEcic4Qn8fHQf4sMCuvHTdB9VVcktb2TN/ioaHW5CA4xMHxLHcPlB7vyq87XWprVHtEL8wXFgr4IlsyEqHeYs6rjU7mrVlvW/r4HqsGnjxgCIzDi5/3Rmx/2nQoirlt8ZS01NDYGBgURHX1hNOpPJREtLi9+BiQt3oqmNd7aUUF7voNzmYE9ZAyEWAzOHJRBlNdPi9HDC7uQnmbHcPDQeg16+8YuOwgJNTOgfxfj0SMpsDvaW1bP5SC2HT7RQamvhWF0rf9twhDc3lTAmNZxfXdePMX0jMF4hf5YKq+w8tjyXopoWZgyJIzbEQnVTG3e/sZ1+0UE8N3eYbJE5m+p8WDwdRj8A8z7ruDQ/41ntENNb0+DnH4Ct6Mz7T+OGakX6s+6E+OFn3n8qhLhq+Z2sBgQE0Nrais/nQ3een3abm5ux2+3nrMcqLs7hajtvbymh1eUlt7yBkrpW4kMt3JIVT6DJQH2rC3ubh5+PTmZcWqTsPxTnpCgKfSID6RMZyE2D4zhc3cz24jq2FdVRXNtCeb2DjYU1fHWohpSIQG4blcgvx6QQab18y18VVtmZ++oW7hmXwtJfj8VqPvVt8d9mDWbR10e4fdEWli8YLwnrj6mqNqM6+gGY+uTpjzdVgjlE6xj19s0nB0/uPw07uf90hB/7T4UQVyW/k9V+/fqRm5vLwYMHyczMPOe1q1evxufzMWDAgE4HKM5MVVW2Ha1jWU45qg++PVxDXYuLQXHB3DAoBr2iUNXkQK8oLJiSJv/ICr9ZjHqGJoUyNCmU20Ymsb+ikW8P17CntIFjtlZK6lp5ft1hFn1dxPi0SH59XT9Gp0a0N5n4sd64zK6qKo8tz+WecSntZb9+yGo2tI//YVku2QsndneIvVtFjrb0P2+V9nWH/aertJlUAEUPKJAwHAbcAoNuubj9p0KIq4rfyerUqVPZu3cvr7zyCi+99NJZrzty5AjPPfcciqIwffr0iwpSdOT2+liVW8lXhdX4VNhQcAKH28uE9EhG9QlHBcrrHUQHm7l/Ql/iQi09HbK4zIUHmbguI5oJ6VGU2lrZU1bPhoJqCquaKbW1sr7gBBsKTpASGcgdo5O5+9oUQgJOlb/qrcvsueWNFNW08MGvx57zugVT0nl7cwm5ZQ1yGPGHCrK1xLN0GxR8Bgc/O1X/1BgAURkQcXL/6eG1WrH+yX/o0ZCFEJcfv5PVu+++mw8++IANGzbwyCOP8Ktf/QpV1VrbuVwuDh06xJo1a3j//fdxOBykp6dz6623XvLAr1b2Njcf7iglr7yRFpeXbwpr0Onglqx40qKteLw+yupbGRgfwt1jU6ReprikdDqF1KggUqOCmDEknsIqO1uO1PLVoRpKalsoqWvlv9YU8uKGI1yXEcVvJvXDajJwx2tbe9Uyu9enYm9z88H2UkalhLO+oBpbi4u6ZhfvbisB4ImbBzGyTxhJ4YFYzQZmDI1n9f4qSVYBnM1wZL2WrDaWQ+4H2rglVKt/GnNy/2nCCAiM1Paf2o5Cq61n4xZCXJY6tWf19ddf51e/+hXr1q3jyy+/bH9s2LBh7b9WVZWEhAReeeUVDAY5eX4pVDY6eHtzCVVNDo43Oth9rAGr2cCsYQlEB5tpc3s53uhgYnrxJytkAAAgAElEQVQUc0YkSrtU0aUsRn175YG5o5M5UNHImgOV5BzTtgmsO1DNugPVmAw6RqWEs2BKWodEFS5+mV1VVRpaXZywuzjR1EZti5O6Zhe2Fhf1rS4aW93a/x0emp0eWl0eHC4vTo8Pj09tf51vDtWc9tqPf7wPBQi2GIgNseBTVUIsRnKO2UiLDiYkwHB17QFvtUHham0G9ehG8Dq1cb0ZEkZBTCaMulc79X+m/af2Kgi6sIO5QgjxQ53KItPT01m5ciWLFi1ixYoVNDY2dng8KCiIW2+9lYcffpjw8PBLEujVrqCyiSVbS3B6vOwrb6KotoW4EO0gVZDZQJND+0f51hGJTMmIOeu+QSG6QpTVzOQBMVzXP5qSuhZ2H6vns9zj7D/eiK3FzdajdVzz9HqmZMTw28n9GNGn4/eF+yf0ZfHmElbsKSc8yExNUxt1LS7qWlw0tLpoaHXT6HDT1ObG7vDQ8oOkUz1LTD9m1CuYDDrMRj3BFiNmg45GhxsVlT4RQeh1YDLoMet1uL0+Ghxu7G0eGlpdHD7R3P46P1u0lQCjniirib5RQWQlhTEqJZzBCSFEWs1XVsvapuPa/tP8lXBsC6he4P9n77zD26zO/v95tC3Le2cPx4mdxAmQMENZKYEWQlhtgYRQoLRwNeXtS3gZ5df2bYFSZulbGqCMskopO6yEhD0zISbOdOIsJ97bsvbz++NI3ntJdu7PdemSfJ6jR7d0LOmr+9xDg5g0taUfO0p1mLriZYhO6vw87jp1jqUrh8pyQRBGEH12ecbHx3Pbbbdx6623UlBQQFlZGX6/n+TkZLKysjptFCD0Dl3X+Wx3Oa9/U4RBg093lVNe72Fqegzzp6ViMhooq3Pj8we4et5EcsfIFqUQPgwGjUkpDialODgvdxS3v/4d24/UYtBgb3kDq/KLWZVfjNGgkRJjpcHlo9Hrb/Jy/tdLW7o8v1HTMJs0zEYDdquJeLsBi0nDYjRgMRowmwxYTMHbob8NGiajhslowGQ0YDMbsFtM2M1GqpweXlh3gCtPHE+iw0KU2YjVbMCgaVTUu9lX4eRApZOSGhdrt5cyIdmOL6BT2+jlULVqWfvp7nIATEaNRLuFsYl2po+K5dhxCRwzLp6MuIGpSztkCWrlBSr2dNtKOLxZjWlGJUwTJqrap9MvhMSJqnD/P86Arx9BP/P/dW7f5w+p1qbSMUoQhD7Q7/15TdOYMmUKU6ZMGQh7hBa4fX7e2nKYT3eVoaGxensJTo+fkyYlMXeC8kwVVTmJsZn5xWmTGZdkD7PFgtBMlMWIzWxkfnYaS0+ewJZDVbyyqYg120oIBHRqnF7MRo3YKDNmo0ajxw/A+KRoJT7NGmaDAbNJw2oyEmU2Em0xEWUxEmUxYreYiLYYsVvVuN1iwmpWQrX52og1KGCtQRHbcute13U27q9iX2UDl84d2+Hz0HWdO9/ZzuTUaH6/cDoHKlQlhOKaRmoavVQ3eqkNemGrnB5K69xs2l/Fs1/tR9MgzmYmwQqztweYPTaOORMSmZTsIMrS8x/0g5qgputwZIvK3t/2JpTvUuMGMyRMgqRJMOVsyDqn4wL9i1aw8x8/5eZ109jrjuXcGRkt7PuaSdZa7tffJetnT/fNPkEQBo2WP4ILiyqZWLQj7FVaOkKCSSOUmkYvL6zbz44jtTR6/Hy4swwN+MHMdKakxuAP6ByscjIxOZorT5pAYrSUgBEij7goMyW1LlJirMzPTueMqWkUljfw7cFqGtw+ooNCM9pq4snPC0mMtvCzUyc1iUur2YDVaGwSnwMd3qJpGvdfOotLVnwJqKz/lnG19W4fKz4u4JVNh3j5FyeRlRbDyZPVMZfXT1mdm7J6N4erGtlX2cChSicVDUq8VgdrHFc3eims9lP4TRGvf1MEgN1iJC3WyuQUB7PGqjCCnIxY4qLM7eJgB6UObMCvMvh3vN2cJAVgsqkM/qRMyFkEk04DR1qXBfp3BsZwqed3LIn6gn9ZXsRhXgCmdDAX8zvLKlYYLueSxt/xij6GrJ5ZJwjCEND2R3Cc1UhFvTvsVVo6QtNDqfw95JtvvuEPf/gDOTk53HXXXV3OveOOO8jPz+d///d/yY2QXsx5eXkRY0tnHKpy8syX+6iod3OwqpEN+6pwWE2cn5tBaqwNjy9AUbWT48Yn8qM5Y3vloRlMhsNrezQRCevx7cFqFj+xjnW3n0W0tfPfxvVuHyfctZZ//ezEsGTb7yqpY/nL6kP7nBnppMVaKal18953R5ic6ujxh7Y/oFPZ4FEits5FYUUDByudbN9fgmaxB+Nufeq60dsq3tZs1Eh2WJmQZGfG6DiOHZfA7LFxXPfcZr6XlczNZ09VdU23rwRXter8lL0QxhzHfat38Nnu8q4T1Hxu2PuJ2uLf8U5ziSmLQ8WfpmTBzB/BuBPA3rNGLrquc8EjX3DqlGSVKFe0SYUPuGpUZYCchTC6h/YNEZHwvmhLJNYgHioicT2OBlr+CA79SA+tRehH+nNf7R/UKi29Wftee1ZXrlzJjh07uPzyy7ude8wxx/DKK6+wcuVK+WfsIVuLanjuq/34AwHyDtVSUFZPWqyV83NHEW01NbVOXTA9nXNmZIysZA5hxDFrTByTUqL5+8cFHRbdD7Hi4wImpzrCVhYqKy2Glb+cx5aD1by3tZgqp5dkh7XX4jkUi5sSYwViOQ0lRNZt9pI6LpPyeg+HqpzsK2/gUHUjlfUealxeapwqeayszs2RGhdf7a0ECtEAHTB7anh00985xruFSVOnk5CUDnVHMD67EH9iJkvm/5Wnv6jns12lTMuIxa+DHtAJuOsxFH6MseB9TPs+Aq+LABpeawLupBk0xk/BM+WH+JOnopui8AOB4gD+QDno4Nd1ArpOIKATAAIBHb+uo+sQ0HUKy+rZWVzHZXPH8sqmgwT0VIi/lvQ4G5NToklyWLHqetjr1EbyVmek1iAeLhzNQr+vDMdmKL0Wqxs3bgTg+9//frdzTzvtNAA2bNjQ24c56ggEdD7eVcrKbw9jNhr4dHc5pXVuslIdfD8nDZPRQGWDB6fHx+ITxzF3QuLRVTZHGJb0dJv9+a8P8PIvTgqXmU2ESnENJJqmYTcbgolncPxE5bX0+AJUNLibBOq+8gYOVDoprXU1xcIWVTXi9vrYXKaxmQXAAvi25dnPg1rgCbWNv+Spjj5r7cCi4CWIG3W/IiC/kTYn7TW3vb61w3Gb2UCM1UxitAWHzcRtr33HomNGMTEpmsw0B2mxNqLMxkH9LIvkrc7h0uo3UgWhCP2+MRybofRarBYXFxMTE0N8fPeGJycnY7PZKC4u7pNxRwsur5/Xvyniyz0VmAzw7ndHaPD4OWFiIicEv9iO1LgwG1Xr1MxUefMJw4estBheuf5klr+8hWe+3N/hNnsoHvRowmIykBEXRUZcFLlj1FggoFPT6KWs3k1prYuHP9jN9JKVzIx386r9R9Q0etlZrMpopcfaQAMNGN24i1K3GbdmYS7bMBBA06Bec1BjiKfamIzXlghGC2gamgYazQKxpVbUQkeC5249R2s1/3B1IyaDRlqTLRq6Di6fH7fXj9Pjp86tng9ASa2bbUdqm18Do0a01UxitJnUGBtjE6IYl2RnQlI0k1OjGZtgJ9ra93q2HYnB0NZjuMXgcPFuRaogHC5CPxJZtbWY+dmpbD9Sy47iWvaUNvDSxoOY0MlrsQseSc1Qei1WvV4vhrbZoF1gMBhwuVy9fZijhqoGD89/vZ+C0nrcXj/v7igF4NwZ6WSlxRDQVSJVRmwUPz1lAqmx0jpVGH4M1Db7SMdg0EiItpAQbSErLYbPv8mnosjAD675PXPdZpXMVd1Ica2LuIZCJpZ9yITSj0ghn+WGn5NILYtjNnMwKptDiSfiGz0XzZ4MmoZBKVQlRoNCVNNQ44BBU17gtsfVWPNtA+oaDV7ddIh6l49rTp3YNA/A6fEF43bdVDq91DV6Wb+vEoMGqTE2Gjx+3D4/jR4/DW4fe8s87Clr4Ks2r4fRoBFtMRJvt5AaY2VMQhRjE+2MT7IzOcXBxORoYm3mDhPvIl0MDgfvVqQKwkhf25aE0ytd0+hh2+E6dpXUUlDaQGFFA0VVjRyqdOIN6Lzx7eFW880dSLu0WCtVTu+g29odvRarqampHDx4kP379zN+/Pgu5+7fvx+n08no0aP7bOBI5kCFk39+WUi100NxrYt1hZVEW4ycN2sU6bE2vP4Ah6oamT4qlstPGEeMtE4VhjmDsc0+kjnHsJ7FgeP5vTmacXYD4xq3cVz5u6qLVKjElNFCfUIO7xWfwL8mvse4n7zJuJj0LjP4B4pkh5XFT6xj7oTEThPodF2not7D9+77iHsvyWVUfBS1TuVtLa93U16vGj/Uu700egI4Pf6mpg8ub4AGj4+iqkYOVDrZuL+q1bk1wG41EhdlJsVhZVRCFOMS7IxLshMI6OwpreeFa07o8jn0VgwGAjpev7LL6fHjdAft9arbTq8fl9dHoydAo9ePq+kSwO1T141eP9sO1xBlMXLlk+tw+wN4fAEKyxsAmJQcjc1sbCrTlhBt4ZZX8zh5chJ2qylYqs1IjM2Ew2oixmYixmYmxmYi1mYiymLCbDT0K6chUgWhruts2l/FnrJ6Hrn8GI7UNOL2BnAF6zXH280kRVuJshjDvo09FF7pino324/Usv1IHXvL69lf4eRQlZPyeg/OYDnAlhgNGiaDRrTJwNhEOzazUZUAtJiI1hvazS+pdZPssPbLxoGg12J1zpw5HDx4kEcffZQ//elPXc599NFH0TSNuXPn9tnAkcq3B6p4Yd0B0HS2FtWyq7Se1BiVSOWwmWj0+CmubeS0rBTOnzUai6nn3mxBEEYGs4z7mWTN4O+P/52bXX+FhmBbWFOUamuaOAWmX8CK/ZlMbtjFrAwHxGYMnX09SKDTNI2nvywkM1U1iugIrz9AvctHnctHrUslm1XWeygNCtrKeg+1jR6c3gCNbj9Oj4+GoAh0ulUJscPVLrYcqml37rl3ryUuSgmYUfE2jN4G4nblKeHo8ePyBbBbjPzs2Y2kOKy4/QG8QfHo8+t4A+raH9DxBQL4AzqBXtXQ6ZqyOhUiEUqmA9hZUt/h3B3FdT06p0FTosRsNGAyaliNBtWdLVgOLsrULISjQ+LXqkRLqJRcRb1bVcn4/lR2HKklJspErM0MwTAPp0ddTpmUzJOfF/Lk53vJiLPhCgpHlzeAx6deX5fXj8cXwB28eJqu/VTW1GL58sumMa8/gNevB6+b18Af0PH59VZtkk+99+NOXwOTQcNuMaIDV/9zA1PTY4izm0myW0hyqCTI1BgrabE2UmOsJERbsJkHrrLOQHmlAwGdkloX24tr2VVcT0FZPQcqnBRVN1Je78btC7S7j8VowGFVXfZCtaljrCbsVhMWk0aDy88nu8tYNHs0Y5OiSIq2EhdlpuzgnlbnqXf7eO+7I/yrG+//UNBrsXrllVfy+uuv88YbbxATE8N//dd/Ybe3LkbvdDp56KGHeP3119E0jSuuuGLADB7u+AM6a7eX8O53R7CZDXy8s5ySWjeZKQ7Onp6G2ahaQNY0erj42DF8LytFEqkE4WiirgR2r4ad76Ltep/7/R9xSdHvwXou16fvwJE6AWZcAhNOpp4olaC26QAvT9moykUNIQOVQGc2GprCHzpC13UaPH7qXKr5Qm0wAa08GGpQ3uCmqsFDnduPK+jxLCxvQNfBbDJQ4/S2iZdt70ECKK1zY9Q0DAYl9owGTXVNM2rYzAYMmgmTQcMQHDcaVHiFQVMhHIZgWIXJaMAYHDNqGkajFjyvhknT0AxQUFqP1xdg1rgENTcYqqFpSqB4/AECurq9pagGs0FjfJLqoOYPend9AR2/H/x6SETrwTF17Qvo+PwBGjx+al2+pvv2hiufXt+jeX98e3uvztsaNxqtX0NDcB0MmobJYMBiBM2ijje4fRgNGvF2S/M6BO/bJIq9ATz+AG6vCjf5ck9Ft1aYjRp2i/JUx0WZibdbSLCrBMHkaCvJsRbSYmykx9pIibESb7d06EjqrVf61etPpqjKya6SenaW1LGntJ4DlUqQVtR7Wgn0EDazAYfVRFqsiWhrsxi1m43B7n5qtyEt1kpKjI24KDOxNjNxUWairUYuWvEldW4vP5w5uemc3rLWYj3cVVpa0muxOm3aNG644QYeeeQRnnvuOV599VXmzJnDqFHqF/Phw4fZuHEjTqcTgOuuu46ZM2cOrNXDlEaPn1c2H2R9YSVWo4F38oqpd/s4fkIiJ05S2f2ldS4CAbj21ElMHzW0XzyCIIQBXYfS7bDzXXUp2hQ8oIE9iazGYl6Z9Q3Lj5zPM8U/5JzkDNL2WSnJ29ucoHZ1LlnP/RROWznk5g9FAp2maTisass7o5OPRbfPT13QO1vn8vL4p3sprXPx/Zx0KurdlNd5qGn0UFFVQ3x8LEaDis81GuC7olpsJiO5Y2IxGgxomkqAC3knzcZgN7VQC19jaLxFe1+TAWuwpa8x2OI3tOWqrg2t/t5ZXMeN//6GW8+ZSqzNgtGojmkaQTGqhGa108t5//c5D/5oFpNTHU2eR18LD6Tb68ftU+EFIY+lq0mw+ZuEm8cXwO8P4PErMazrBEVtgEDQc+nX1fX+igY0g2oh7A+WLztU1QjA6IQoJb410AwalfUeArrO2AR7k4A3GprFuhb8W0MJUJNBeXzNBgP1tdVkpCar19EUfE1Dr6up9W1r8PLq5iLqXD6WnZnZtEbqWsMX0Gn0+FVYhsfPY5/swWIyMGd8AhX1HiqdHuqDbZ49vgAunx+XR4VoKM+uHvTUN/bIg24xGpRYDArBeLsZDdh+pJbTs1J4acMB0mJtpMXaMGgaBaV17CqpZ09ZPYVlDeQfqWXKb95r/z+P6gIYZzcTbVHhHQ6b8oI7bCbio8wkO6ykxlpJi7ERH20hNiiyVVfArndjh1OVFuhjB6tly5YRGxvLX/7yFxoaGvjkk0+avH+hHgNRUVHceOONXHXVVQNm7HCmot7Nc1/vp7CsAZ9f573vDqMDC6anMS09Fl3XOVTlJMFu4apTJjAmQVqnCsKIxe+F/V/Czvdg5ztQfUCNG8yQOBkSJ8KE70H2+fDq1WSlRrHysrM7T1D74A+QPAVGHxeWpxMJCXRWkxGrw9gUX3ftqZNY/MQ6nr7qeKKtJvwBnXq3jw3ffse0adOaxKPb6+fsv3zK0z+dy+yx8ZgMhqZks8FkTEIUk1Md/HvDwS5rED/1RSFT0hycO7P/4R26HhTBgWah62u55R7QVfhDQOefXxRS5fRyzbyJuL3N2/n+gN5COBqxmAw88dleEqItXHnSBMxBkd4kIA0GjEYNc0i0Gw2YgmLeZDCQv/U7cnN759BKiFax0tkZsd02G7nxxW9a/R+GXoNGr6pY0Ri6hASu20+NSzXtqGrwUN7goao+GFPtDTQLXG9zeIPHF6C4xsWhKmcrgfvXDwu6fB4GjabXIi3GRpzdTIxVhWYkOaykOJRXND1WhSmEhGhclBmrydCv/9GOfmRqrhr0nVsiskpLn9utLl26lEWLFrFq1Sq+/fZbysvLAVWu6phjjmHBggXExR2dnsG22X+g+qBHWYyU1qmC33aLkfNyM8iIi2pqnZqZ4mDJSeOJt0vrVEEYcTRWQ8FaJVB3rwZ3MP7Q4oC0GarFafb5MPnM1h2kFq2ApxYAMGver5l1bgth465TQnXDk3D1qiF8Mh0TSQl0beNpjQaNuCgzqdGmVs6A+1bvIDPVwQkTk4bUvnDUINY0DYtJw0L3ORBXnTKRxU+s4+9XHNutINz0fNWQ/jDpT7ORptfAZIConiUttxS4rhbC1hUUug0uH7VuHzVOD1VOL+99V0xA1xkVb2vy4DZ6/QQCEBulkuESoiyMSrCxtUjFWf/yzCmtxGi0ZXDrD0P7H5n7agJMSI7MKi19FqsAcXFx/PjHP+bHP/7xQNkz7Gmb/RcIZi4eqlL1CF2+AMkOC+fPGkWszYzb56eo2sUJExO55LgxAxrgLQhCmKnaBzvfY+Km/8AbeRDwqfHoVEjJhpSpMOsyGH0smKM6PkdqNly9Gt64HtY9rlqYxqRDXTFse1N5VK9epeYJTQyHhhSRXIM4krvPDfXathS4cT0QuF6/TkW9m7sunNlK4Lp8fuxmtVXvsJkwGjSWv7yFZIeVEycN7Y+lloR+ZOblecjN7Xytw0m/xKrQmrbZfz/862dUNng4f+Yo1u4oobTOjUGDs6alEWszU+/yUd7g5ryZGczPSZPWqYIQTnQdijbD9pXgqgZbPGQvhDG92FoPBODwZhV7uuNdKFNJJw7NAHFjIWECjJkDM3+kRKahhz9OU7Phuo9VPOu2leCshOgUWLoybFv/w4HhsNUZCSEUHRHpYj+Shf45M9JZ/MQ6fr9wOnFBT2lHRFK2faTTb7G6a9cu8vPzqaioQNM0EhMTmT59OllZWQNh37Cho+y/gK4C5VfmHabe7WPO+AQAPtpZytk5abh8AZaeNJ5jxiVIxr8ghJOSbfDmDVBeADkXNHsun70AkjPVVnxnnkuPE/Z+DLveU1v8TeWlbJAyDRIncSgqh7FnXqvO25/3+ujjRJz2kuGy1RlJIRQhIlkQhuyLRKEfyV7p4UqfxepHH33EAw88wJ49ezo8npmZyU033cTpp5/e14cYVnTUjaQxWELFqGmcnZNGdkYsbq+fbw9WU9ngYfmCqUxKcYTRakEQKNkGT58Dc6+FpW+BtcUX77n3wOcPqZjRq1c3C9a6Eti1SnlQ934EPlUrk6gEGHUMJE1pKi+FNYaqvDzGDmH9U6E9w2GrMxKJVEHYkkgT+pHulR6O9EmsPvHEEzzwwANNmf/R0dGkpqYCUFpaSkNDA7t37+b6669n+fLlXHPNNQNncYSyamsx585IbwpEr2rwUF7vQQMuOnY0o+KjCAR0imtdZKY6SI6xilAVhHCj68qjOvdaOOu37Y9bY9S4Drz8U5h5SfvyUrEZkDBReVGPXQKp08EkSZLCyCLSBGGkE+le6eFGr8VqXl5ek1CdN28ey5YtY9asWe3m/O1vf+PTTz/l/vvvZ+7cueTm5g6Y0ZFITaOXtFhb09/xwULCbp+fUfFReP0BDlY5mT02ntQYG/UeXxitFQQBUDGq5QWw9O32x/xe2P+F2trf8TbUHIIP/whGi8rcT5gIE06F6RdA/PghaW8qCMLwYTh4pYcLvRar//znP9F1nQsuuIA///nPHc7Jzc3l8ccf55ZbbuHNN9/kmWee4YEHHui3sZFMXJSZklpX09+aphFtNeH1B3B6fJTUujhrWho/zM3gtte+i4heu4Jw1LN9pYpRtQZ3OZyVUPCBij/d/X6L8lIxaovfngyn3waTz2hdXkoQBKETxCvdf3otVjdt2oSmaSxfvrzbucuXL2flypVs2LChT8YNJ1pm/7WsSRfQdcrrPfx47jhOnpxEg8cv2X+CECk0VoPBAJ/eD7tWQ9FG0IO9ttuWl9q1Gty1MPPi8NosCIJwlNFrsVpZWUlsbCwpKSndzk1JSSE2Npaqqqo+GTec6Cj7b8UVx/LypoNcdvw4pqXHqjHJ/hOE8OKuh8JPlPjc+gp4gn3iDWa1nZ8wQdU9zf2x2u4PlZf69gVVLkoQBEEYUnotVqOjo6mvr8flcmGz2bqc29jYSENDAw7HyE8k6ij7Lysthv+eP5U4u1my/wQhnFTsUdv6u1bD/s9VPCqo7lGaAab+UIUDTD4Tojsozu2uUwX4l64cWrsFQRCE3ovVnJwcvvrqK/79739z1VVXdTn3pZdewufzkZ19dHRWkew/QYgQfG7Y/2VQoK6Cyr1qXDNA7GjlQU2dprb337kJUrIg99LOz/f5Q6qIv9Q4FQRBGHJ6LVYXLVrEl19+yf33308gEGDJkiWYza27M/h8Pp5//nnuv/9+NE3joosuGjCDIx3J/hOEMFF7RInT3e/Dno/AG9zet0RDao7K3s88C6aeCzEZzdn7Fz6q6qgCzPt16zqr7jolVDc8qVqaCoIgCENOr8XqwoULee+99/joo4+47777eOyxxzjuuONIS0tD0zSKi4vZtGkTtbW16LrOmWeeyfnnnz8Ytkc0kv0nCINMwK/qne5arS4l3wUPaBCTBukzIHkqzLxUtTi12Ds+T2q2Kvj/xvWw7nHIWdjcwWrbm8qjevWqzjtYCYIgCINKn5oCPPzww9x77728+OKL1NTU8OGHHza1Cw01CjAajVx++eXcfPPNfTautraW9evXk5+fz7Zt28jPz6esTLUyfOSRR5g/f36fzy0IwhCj66qu6faV4KoGWzxkL4Qxvdhad1bCng+VOC1YA43B5E2TTYnKhIkw7hSYvhDiJ6hM/56Qmg3XfazE77aV6nGiU1SMqmz9C4IghJU+iVWLxcIdd9zBtddey+rVq8nPz6eyshKAxMREZsyYwdlnn016enq/jFu7di233XZbv84hCEIEULJNdYoqL1CJTCHP5bMXQHImLFrRsedS16EkH3YHvaeHNjSXlrInKyGZmAnTL1StTW1x/bNz9HEiTgVBECKMPonVEOnp6SxdunSgbOmQlJQUsrOzycnJYfr06SxbtmxQH08QhAGmZBs8fY5qabr0rdYxoefeo2JCn1qgtuJTs1Upqb2fBAXq+1B3WM01mJpLS2UcoxKikrPA2K+PMUEQBCHC6fWn/IUXXoimaTz88MOMHTt2MGxqYuHChUdVcpYgjDh0XXlU514LZ/22/XFrjBpvrIbnL1FZ+fs+ay4tZYuD9FxInAzTfqBKS9mTpLWpIAjCUUSvxWpBQQFms3nQhSqAySQeE0EY1hRtVlv/S99uPe7zwIEvled01yqo3KPGa4sgLlhaKmUazL5CJUqZpD2xIAjC0Uqv1WBaWhoVFRWDYUuZsucAACAASURBVIsgCCON7StVjKrVobb3d69RGfa73wdPvZpjDpaWctUoz+rCRyB2lHhPBUEQBKAPYnXevHm89NJLbNmyhVmzZg2GTYIgjBQaylTm/0uLlVD1udS4Iw3SciApS7U1HTMHPrtfZeHHjQ6vzYIgCEJE0Wuxev3117N69Wp+97vf8dRTT5GYmDgYdgmCMFxprIad78H2N1UGfyh7PyZDxZ6OOQ5mL4akzNalpeqKVbkoQRAEQWiBpocKo/aQDRs2UFhYyD333IPZbOaCCy5g9uzZJCYmYjQaO73f3Llz+20swNSpU4G+11ndvHmzxMIOEl6vt103MyF8DOV6GN01xB75nLiij4gp3Yim+9EBnzUek6eW0sk/pmrieXgcY1TL0zYYvE6y31vE3nkP05g48orvy3sjcpC1iCxkPSKHcKxFbm5uj+b1WrUtWbKkqQFAY2Mjzz33HM8991yX99E0jW3btvX2oQYFk8nU4xdH6B15eXny2kYQg74e9WWw4y3IfwP2fQ66X8WZxo6GhEloE+ZhnnkJvHoNaRmjSDvlvM7P9cEfIHUaU07/8eDZG0bkvRE5yFpEFrIekcNQr0VeXl6P5/bJxdhLZ2yv5wuCEKHUHoEdbyuBeuBLtcWvGZRATZwMk05TBfrjxzdv8S9aoeqoAsz7des6q+46VWd1w5OqpakgCIIgtKHXYnXHjh2DYYcgCJFKzSHVgjT/ddVBCh00I8SNUd2jMs+C7PMhbmzH7U1Ts1XB/zeuh3WPQ87C5g5W295UbVKvXtVxBytBEAThqEeCNwVBaE/VvmaBenizGjOYIH4cJE2GrHNh6jlKoPakxFRqNlz3MRRtUud1VqpkqqUrpb2pIAiC0CUiVgVhJKHrqhD/9pWMLtoLJZMge6HKwO+O8gKVwZ//OhR/p8YMZkiYqATq1PNgynzlUe1rDdTRx4k4FQRBEHpFj8Xqjh07+Pe//83WrVupr68nPj6eWbNmcfnllzN+/PjBtFEQhJ5Qsk21Ni0vgJwL8FnjoaEcnr0AkjNV7GjbrfbSHapw/9bXoGy7GjNaIGGSuk/2BSoOtT8CVRAEQRD6QY/E6osvvsidd95JIBBolSy1ZcsWXnzxRe6//37OPvvsQTGwsrKyw/H6+vpWx2JiYqT8hXD0UrINnj4H5l4LS98CawwleXmk5ebCufeoJKanFsBPV6mkqG1vQv5rUFGg7m+0qgSp5Ckw/SKYcIpKmhKBKgiCIISZbsXqjh07uOuuu/D7/ZhMJmbMmEF6ejpFRUXk5+fj8Xi45ZZbyM3NJT09fcANPOmkkzocv+WWW1r9/eyzz3LCCScM+OMLQsSj68qjOvdaOOu37Y9bHCoBqvAzePw08HvUuMmqCvMnZ8H0i2H8iSJQBUEQhIijW7H6wgsv4PP5GDduHH//+9/JzMxsOpaXl8cNN9xARUUF//nPf/jVr341qMYKgtABRZvV1v/St5vHdJ2oynxY/R/lQa0taj4WNxYyZsPMH8GYY0WgCoIgCBFNt2J148aNaJrG7373u1ZCFVTngeXLl3PrrbeyYcOGQTFw586dg3JeQRgxbF8JOReo2zveUS1Od61iSn2JGjPbISkLUqaBq1p5Un94vwhUQRAEYVjQrVgtLi7GaDRy/PHHd3j85JNPBqCkpGRgLRMEoWt0Hcp2wv7PobYY/jweAj51zOLA5ZiAbdxsyP0JZORC7Cj48I+qbJQIVUEQBGGY0K1YbWxsJDk5udPkpdTU1KZ5giAMMu56KPwUdq+BXe9B3ZHgAQ0caRA3GlJzYOYl7C3zk3P8ma2FaV2xqm8qCIIgCMOEAauzKi1VBWEQ0HUo3xUUp6vgwNcQ8KpjFgckTQF7Ihz5Fn7yPKROB4sdAF99Xmuh6q5TVQCWrgzDExEEQRCEviFNAQQh0vA0tPCermpOjtKM4EhVCVEp2TD7JyqbPzoVnjgTdr4HY+Z2ft7PH1KlqaQovyAIgjCM6JFYramp4corr+zzHE3TeOaZZ3pvnSAcDeg6lO+GgjWw+33Y90Vr72nyVEiYoFqcTjgZ4seD2db6HItWqDqqAPN+DdaY5mPuOiVUNzwJV68akqckCIIgCANFj8Sq1+tl/fr1fZ6jSTKHMFJo0c4UVzXY4nvezrQlngZV97RgjcrerzmoxjUDONJVx6iUaTDrJ5AyFexJXSdFpWbD1avhjeth3eOQs5C0RgPsDTYASJ6ihGrbDlaCIAiCEOF0K1YvvPDCobBDECKfNu1MiUlXCUtdtTMNoeuqW9TuoPd0/xfNxflD3tPEiTBlgWpvGj8OjL3syJaaDdd9DEWbYNtKTDWFkDxRxajK1r8gCIIwTOlWrP7pT38aCjsEIbLpoJ1pEy3bmV69ulmwepyw77NmgVq9X41rBiV0Y8cqr+msyyAtG6ISBsbW0cfB6OMoyssjKTd3YM4pCIIgCGFCEqwEoTu6a2dqjWkef+VqOPZKJVD3fdbae5qSrWJPpyyAyaerTlJGeQsKgiAIQlfIN6UgdEdH7UxDeJyw7/Ng7OkqqD4Aq24Nek8zlCBNmaoK86fPAFvs0NsvCIIgCMMYEauC0B2hdqZWB/i9UJwHB9bBng+U99TnVvMsDrDFgT0ZTroBppwNsWPAYAiv/YIgCIIwjBGxKghd0VgFJfmq/NNTC+Dwt+BzqWMh72n8OJUglftj1VXKXadCBgRBEARB6DciVgUhhK5DVaGqc1r4CRzaAFX7gWB3Ns0I0cngmKpqnU46HaYuUEX6Q2Wlvn1e2pkKgiAIwgAiYlU4evG5Yf+XsPdjOLQeireCu7b5uNGqOkZZY5WIXfh/MHoOJIwHk7X9+aSdqSAIgiAMOCJWhchioIrut8XngYrdSpge+FrFnVYfBN3fPMcao4rxO9IgcTKMPxnSZ0LCRHjhYlUndfblnT+GtDMVBEEQhAFHxKoQOfSn6H4IXVcit64Uir9VXaKObFHb+e6a5nkGk/KaRiWqhChHiuoYNepYSJ6sEqNalpWSdqaCIAiCEBZErAqRQV+K7vs80FAG9SXKS7r/cyVMaw5BQzn43c3nsMaoJChbvCofFRUH0amQMVudL3Fi68dsSwftTJvEtLQzFQRBEIRBQ8SqEH66K7pvccDJy8BZCS8tgXk3KnFbkq+EakM5NFaCHgjeQVNe09jRqiuUORosUWC0qML8GbmQNBkc6b0rK9WmnSnOSpVMJe1MBUEQBGHQELEqhJ+2RfcDfvjbHOU5PfF6qCyE2iIlTCsK4N1bwNvQfH+jVWXnx45W4tFoBU0HNIjNgFHHKc9nwgQw2/pvb7CdqSAIgiAIg4+IVSH8bF8J2edBzUEo/g4Oroe6Egj4YN2jaqs/VNsU1HjqdIgbHfSOmsBTr8pHme0qKSo9V23t25Oay0oJgiAIgjDsELEqhA93vfKUHlwHrlp4+yaoP6LiQL1ONae2SGXnx45WmfrVB5RwHTVbidaAV42POkYlYcWNa50YJQiCIAjCsEa+1YWhxVkJ5buhaKMSqdUH1N+eBpqK79uTVRyr0Qxzf6ZiURurwNeoYlNt8TDh1GBi1CSVMCUIgiAIwohExKowuOi6ijUt2wkHvoKDG5S3tK4YXFXBScFt+omnQ8pUsMXBhn+o2NXaQ0q0Jk+FlCzY/hZc9CSMnROuZyQIgiAIwhAiYlUYeAIBqDkApTug8FM4/E0wQapYdY0CFVuaNlNl5cdPgC0vgrNC1Uh1VcMxV8LoY5V4TZgA5ij44A/qbxGqgiAIgnDUIGJVGBj8Xqx1+2FrAexeA6XboO6wSo7Sg9v70akwKhOSMtVtd6261B+BcSeo+6VNh9NvUV2jQolR7jolVKXoviAIgiAcdYhYFfqOx6kSpA5/A7veY9zhbeCpVOISQDNCwiTlPU2YDCYzOKuUd7X2kEqamnw6JGdB/Dio3KuK7j/6PSm6LwiCIAgCIGL16EPXVV3T7SvVdrstHrIXwpge1g1trFYCtfBzKHhfdY6qLwa/hyhQBfwzZkFipiot5W1U3lNXpdr6H31MsKzUJLAnti4rJUX3BUEQBEFog4jVo4mSbapTVHkB5FzQ7Ll89gJV9mnRivaeS11XW/mlO2DXe7D/C6g5DM5ymrL3Y9IhMZMKEkjKGKdEcMAH9aUq3jTz++r88eNUslR3SNF9QRAEQRCCiFg9WijZBk+fo1qaLn0LrDHNx869Bz5/CJ5aAFevVpn3tUWqQH/+a3D4WxV/6gl2jTKYVdxpUlCABnzgqUerqgK/G8adCGkzVFH+qITwPF9BEARBEEYEIlaPBnRdeVTnXgtn/bb9cWsMnH67iif9148gaQqU71Re14AvOCcWRh0LiZPV9r2nXh1z16qxUcdSUu4l8YTvg8E4tM9PEARBEIQRi4jVo4GizWrrf+nbrce9LhV/uus92PkeVBaqovvVB9Tx2NHN3lPNqOJP0VVZ1AmnQlqOytoPFuX3NuaJUBUEQRAEYUARsXo0sH2lilG1OpSXtXo/PH4meOrAYGpubWq0gDUOopOVGPW7VfeogB+SJ6vkqMTJSsQaDOF9ToIgCIIgHBWIWD0acFWrrfst/4aNT0NxXrNANdshNQfix6vbRRvB7wFLNIw+FVKmqdhTS3R4n4MgCIIgCEclIlZHMoEAHFynMvir9isRCir+1BSlMvOnLFDb+kYzpGSrAv0JE2HBXa3LSgmCIAiCIIQBEasjkeqDsP4fKpO/5mDzeOp0tY1vtimBGp0KY+Y2tzQN+ODju+GM20WoCoIgCIIQEYhYHSl4G+Gb59Wl+DvQ/aAZVGZ//Hg4vEnFnyZODJaWmg5xY1vHnn7wB9UpSmqcCoIgCIIQIYhYHc7oOuxeAxuegMJPwOdS47Gjgx2iksFkgdhRMPUH8MHvVULVxFNb11l116k6qxueVC1NBUEQBEEQIgQRq8ORku2w7lGV5d9YqcZs8aoQvyNdJUPFj4VxJ6nyUjEZalt/wsnwxvWw/nHIWdjcwWrbm8qjevWq9h2sBEEQBEEQwoiI1eFCfSls+qfK6K/co8ZMUZCSA3GjVaeoxIkw9iRIywZHWvu409RsuO5jKNoE21aCsxKiU2DpStn6FwRBEAQhIhGxGsl4GiD/DSVSizaobX+DScWgJkxQgjR5iopBTclW9VF7khg1+jgRp4IgCIIgDAtErEYafp+KP934lIpH9bsBTQnRhElqez81B8adoGqgRieH22JBEARBEIRBQ8TqYKDrqsXp9pWqIL8tHrIXwphOvJm6Dke2qEz+ra9AY5Uat8aodqcpUyDjGBh7giozZU8cuuciCIIgCIIQRkSsDjQl2+DNG6C8QLU4DSUxPXsBJGfCohXNSUxV+yHvP/DtC1BVqMbMUSqTPyUbxp8MY4+H5CyIig/fcxIEQRAEQQgTIlYHkpJt8PQ5MPdaWPpW6/JQ596jykM9eTac+AvY8zEcWq+OGS2q5mnKNJh0JowLClRbbFiehiAIgiAII5PyRx/FmplJzPz53c6tW7sWd0EByb/4xRBY1jkiVgcKXVce1bnXwlm/bX3M64I9H0LpdlXT9JN7QTNCzCiVIJV1jopBTZ4KVkd47BcEQRAEod9Euhi0ZmZSdPP/MPq+e7u0sW7t2qZ54cbQ/RShRxRtVlv/8/67eezgBli5DO7PhP9cCTvfC2bsG+C4pXDZi+py0g0qO1+EqiAIgiB0Sfmjj1K3dm2P5tatXUv5o48OskWtCYnB7mwMiUFrZuYQWaaImT+f0ffd26WNLYVqT0T3YCNidaDYvlLFqIYEZ30ZPDkfNj+rtvnHzFXb/4tfg5mXgjUWRs1WBfwFQRCEo4pIF1yRbJ+Iwf7TlY3htq0jRKwOFK5qlUwVIjoZFj4Cp9wIl78MS9+Gc+6BjFyIGwOumvDZKgiCMMKJZLEFkS+4Itk+EYMDQ0c2RoptbZGY1YHCFq+y/kNoGhy7uOO5dcWqc5QgCMIwJpJj8yI9Lq+lUOjMxnAKh+FsX6QIrpY2jrr3z8Scdhq1a9Zw5Dd3kP7b/4dtxgw8Bw+ie33oXi+61ws+da37msdaHtd9oXkdHPd1Mc/T4rjHo44Hxwx2O4eW/QrjhPEUFZeE/XXrCBGrA0X2QlWe6tw/dx176q6DbW+qFqeCIAhdEMliECJbEEa62OrOxnDbNhzsc5x5Jhl/upui5TeT/vvfEX3yydR/9DEld99N6k03YR41isYtW9oLOY+3hdBre/F0IAa7uI/H0/XfPh+6x0PRsl+1sv3I7b8Z2hfLaASDAc1gUNfBv0NjmsWCoXAfsYsWRZxQBRGrA8foY1Ud1c8fbF8NoCWfP6QqAEi7U0EIOyIG+0ekC8JIF1ud2RhO2/RAICiwlJcvavZs0m6/naLly0ldfjP2uXOp/+ILyh9+mKRf/BwtKoq6jz9WAq9TARgSe552os9YVsah6OgOBF+LuR5Pswj0NXsR8fub7D5y622tnkfJ3XcP3ovUUvgZjc0CMPg3QfGnRUU1jfkqKtDr6zHExGAeNaq9eGx535Yi0mhU40YjmkldG8xmdX6LBc1qxWCxolmtaDYrBqsVzWZrum66bbagmU1oZjOaKXgdvF3/+RccueMOAt/7HrWrVxMz/6yIE6wiVgcKTVMF/59aoP6e9+vWdVbddUqobngSrl4VHhsFYYgRMdg/Il0MdmdjuG3ryD5SU4fELt3vb+9l68j75vGiWW0kLllC0X/fhG3mTFx5ecRdcgne0lIqn31WbfM2ibSQ16/F1q/P12bb19d+S7jFNjAt/w5e8PnQ/X4IBDp9TiV33tnq7/KH/9rv18kA1EEr0dd022hAMxibx2y2ZgHX6tqAt7iEQE0NhoQErOPHB+9jAIOxlTdRayn8DEYl3CzmoOALir8mwWcDmxWDzYZmtWGIigqKQwuaxdws+FpeQudvIT7rP/iAopv/h7hFi6hdvZqUZb+MGDFYt3YtR/7f/2P0/fdRmJrKuNLSsL9nO0LTdV0PtxFDSV5eHrm5uYP3AKXb4Y3rgx2sFjZ3sNr2pvKotuxgNcIY9NdW6JDOBGFH6zHUgrCnoiCcoqa7xx4I2/r73ujMhkgQg53ZMli26X5/F5671lu5LbdznZs3U/Xc8/gnTsRYWEjswoVYJ03s0uvX4bau14Pu9hBoJzpbbB/7fF2KvkGnI69dm9tak5hrOda8NdzR/TwHDxKoqsKYlIQlM7OVaFReP5MaMxnRTGYMQfGnWawYbFYl+Gw2JfiiotBsNvYXH2FSdrYSgCHB1/J2SACaTM0CUNPQNK3p6Yb+12IXLKB29eqIeD+EGKr3xUDYFvqcGiobe/O5KGJ1sCjaBNtWqqx/W5wSriN867+vr22ke98i3b7OPljarke4PiSHQgz2l8EWgwPxuTNYX3q6rkNnArClEPS1H2+77du4NZ+alW9izZyCe/duYs48E/Po0d3GB7ba7g2Nt0wICbcIbOMpa+nRa/Lchbx2rYSgsf3cTsb9lZW4tm3DlJ6Or6SEqGOOwTxmDJrZrDx9QW+ewWoFiwWD1abGgp4/g9UKIa+gydx6y9dkghbbvk3eP01rfk4txF9HDJYgHOgfcpHwedKZbd2Nh9u2lmsxFDaKWO2Co9H7N1Riq6+vbaR73yLdvs4ee6g/eHprXyTY1ZKB/NJr5f3z+di2ZQvTMqd0nrzRMmO3TTZvy4t7127q1q7FMn48nn37sJ9wAubU1PbnaSn4QtcdJH40CcBwfA0EhVKruL+2275txkLCrr1QDM5pcd3kKTQqgegrr8C1dSuBpCQMFRXYTzwR66RJzTF91uB2r83W5PVT18GYwI62fS2WVuKvJ6KvIyJZcA22ff35To7kz5VI/pEeKQ4OEatdMFhiNZK9b0MltgbjQ2egbOsvkW5fRzYM9ZZOb+3ryi5d15XXrkVMXShGD5+39ZivtedNbxXP12K8VYxfB7F9Pi+e/ftxrt+AOSMD7+HD2HJyMMbHdyAAO76EPJRhEX/QRuS1jNVrKfI68vS13gJuHxPYOnMYY2hMbf9qRhOYTPjKynB+/TWWSZPwFBYSe+652GbOaI4BjIpqFoRWazvx1+T9M1vUeVt6M/sgAkO0/F8rTE1lYgTF5UWy4BoK+wbLyRGJYrCv8waaSAkdG3FitbKykscff5wPP/yQ4uJioqKimD59Opdffjnze7nAgyVWI/2fU+Ly+k+k29fWlsLkZMYfOcKR225vKuvSLNxCJVW8tKvH11bYeT3K+9bhsTb3b5m00Taxw+PBV1mJr6gIg91OwOnEEBuLZjS2T/JokeUbVtqJt9YJGx16/zrYLm50u7E7ojvw+rW5NhjAZMRgNKlzm00qli+4xes9dIi699/Hlp2De/t24q+4AvucOUoARtlax/213PZtKQZbis4BJFK9g5H8Iy6SBddQ2deX741I/76NZOdVVwz1zvOIEqu7d+9m6dKlVFRUABAdHY3L5cIf/DJbsmQJd9xxR4/PN5iLMVw/eML9C7krG8P9mrWl9v01HL7lf8i4624cp86jbu0HFP/v/5J2263Y5x7fiQeu44xgvF4CLbZm25V98bS4b/BcAa8XPG2SOzqo84fPF+6XqvU2bwuBFHC7wetVdf2CYrWzhI7OxtrdNrZJEjE2b/+qayXYQlm8WCzB2yoDOBQH6N61m+p//YuoOXNo3LSJ5GXLcJxycodZv4TiAnsQ9zfS3xuR+kMuksNjIl1wRfKO3HAVg5GOiNU+4vF4+MEPfsDBgwfJysrivvvuY9q0abhcLp555hkeeughdF3n7rvv5uKLL+7ROQd7MSL1Q7szOwYj9kgPBDrOpPV4Woswj7fDeY1btlD92mvYpk7FtWMHseecg2XiBLXF6g+A34fu8wc9cKHbPvD50QNtjgc9dXpwDH+LuaGxkCcwND8QaHVf/KHz+sO3vWtsn8TRyuPXxpvnq6hAb2jAEBODKSOj3bZwe7HXfJ6mLeNQ9m0os7dFXT/NYmnK9DVYrKq8i9WKZotS11E2VQsw6NXDZEIzW2j4+itK7ryLmLPOpO6DD8m4+y5i5s9vEnwD7e3rDZEal9eRbd2NDyWR+iM9UuLyOiPSBVek5zoIA4+I1T7y3HPPceedd2K323n33XfJyMhodfzOO+/kueeeIzU1lQ8//BCz2dztOYdiMQb6S0/X9TZbtB1kybYaa7EF22r7Vh1r3LqVmjffxJo1BffOXcScdSbmMWM69OQ1ewlbJGm0zdj1qG1iv8eN5uu6Tt+QYjAo717I4xX8W2sx3vUxDU0ztJ4XPOYrKSFQV4chLg7LuHEdZvu2T/owNidjmEwYrKqcS1MGr9WmyrvYbBjs9uYYv6aCzqGkjq7r+tV/+CFFN/8P/uOPx7h+fdh/IIU4mj2DIzWeO5K9g5ESlyd0jYjVyEHEah+59NJLycvL40c/+hF//OMf2x0vLi7m9NNPR9d1nnzySebNm9ftOYdqMWrff5+i/74Jc0Y63sNHsM2Yjik+oX22bihBxNNaiDYJzKCHL2x0mLHbtoyLEmdevx+LzdZFiZYOrkPnMhmbtmt9ZWU0bt6MZdw4PAcP4jjtNKxTMoNeOtWFA7MFg0XF4BmCQg6LuamzByYzBrOptUBsSgYJeg2NbQRkqJRLyOPYppZfiOFS0y+SEkmOds/gSK2UEenewY4QcRRZyHpEDpEsVk2DbEufaWho4LvvvgPoVISmp6eTmZnJ7t27+eqrr3okVoeK6BNOAMB78BAArrzvutx6bRV7Z7ahRdubs3fbZOF2Vby5bcu2JhFmNDQVbPaVleJcvwHLhAl49u8nZsECbNNzMNqi1HZuVFTzJVTfr225llDMXouM3e+2biVn1qx+vW6hL90xD/+llfct/sLI6FfcVhTEzD8r7GKrM9vIyyNmfvcdkIbcrhaE276eiLxw2uguKOjRY4ZsdBcUDKl9vRGe6v0S/vewIAjDED1C2bJli56VlaVnZWXpBQUFnc678cYb9aysLP3nP/95j887FNSuWaNvnzVb33/Ntfr2mbl62eOP687vtuqNO3bqrj17dPf+/brn8GHdW1qq+6qqdF9dve53OnW/260HPB494PcPnl2zj9Fr16zp8O/+0N/XtjNbBtLG/hDJ9nVkQ8v1CJeNPX3ccNlXtmJFjx+zds0avWzFij49zlB97gjdI2sRWch6RA5DvRa9ebyIFatr165tEqt1dXWdzrv77rv1rKws/cILL+zReYdiMQZTEA6kXd2N95b+vLbd2RDu1zCS7evssduuRzhsHCoxGOnIF3LkIGsRWch6RA6RLFYjNmb1rbfeYvny5QDk5+djMnUcsfDQQw/x6KOPMmHCBFavXt3teTdv3tzpuQYCbf0GjP/3f/iXLUM/fm6340NFd48/EPZ5vd4eJbn19bHD9RpGun2G115DHzO23WN2tB7a+g1ohw4SuOiiIbNP6Pt7Qxh4ZC0iC1mPyCEcazHsY1YHC5PJNLh1Vh95hNEP3N8+Nis3l7oJ41Xc24QwlHDpzK4BtK+vwdnlX36JtSvb2tjoLiggeQiDwCPdPjp5rA7XQxIZwoIkkUQOshaRhaxH5BCOBKueErFi1W63N912uVw4HI4O5zU2NgKqWUA4ieREDUnS6B+Rbp8gCIIgjGQiVqympKQ03S4pKelUrJaWlgKQmpo6JHZ1RiQLQhFbgiAIgiAMVyJWrE6ePBlN09B1nYKCAiZPntzhvIKCgqb54UQEoSAIgiAIwsATvt6G3RAdHc3MmTMB+OyzzzqcU1xc3CRWTzzxxCGzTRAEQRAEQRgaIlasApx33nkAvPPOOxw5cqTd8SeeeAJd10lJSRGxKgiCIAiCMAKJaLH6k5/8hNGjR+N0Ovn5z3/Ojh07AJVw9fjjj/P8888DcOONN0rpC0EQBEEQhBFIxMasAlitVlasWMHSpUvZuXMnF1xwAQ6HA5fLhc/nA+CKLJoELAAAIABJREFUK67g0ksvDbOlgiAIgiAIwmAQ0WIVYOrUqbzzzjs89thjfPTRRxQXF+NwOMjOzubyyy/n7LPPDreJgiAIgiAIwiAR8WIVICkpidtvv53bb7893KYIgiAIgiAIQ0hEx6wKgiAIgiAIRzciVgVBEARBEISIRcSqIAiCIAiCELFouq7r4TZiKNm8eTMm07AI1RUEQRAEQRiR+Hw+jj322B7NPerEqiAIgiAIgjB8kDAAQRAEQRAEIWIRsSoIgiAIgiBELCJWBUEQBEEQhIhFxKogCIIgCIIQsYhYFQRBEARBECIWEauCIAiCIAhCxCJiVRAEQRAEQYhYRKwKgiAIgiAIEYu0chL6xGuvvcZtt93W7byvvvqKxMTEIbBoZFNbW8v69evJz89n27Zt5OfnU1ZWBsAjjzzC/Pnzuz3HmjVrePHFF8nPz6exsZGMjAzOOOMMrrvuOlmjXtCftTjzzDMpKirq8vxXXHEFv/3tbwfU5pFKcXExa9eu5auvvmL79u2UlZVhNBpJS0vjhBNOYPHixWRlZXV5jnXr1vHMM8+wZcsWamtrSUlJYd68efzsZz9j7NixQ/RMRgb9WY8lS5awfv36Ls9/+umn89hjjw2G6SOOb7/9lo8++oitW7dy8OBBKioqcLvdxMfHk5OTw/nnn895552HpmmdniOSvjNErAr9wmAwdPlPazCI834gWLt2bY9+HHTGH/7wB1544QUAjEYjNpuNffv28fTTT/PWW2/x7LPPMnny5IEyd0TT37UAcDgc2Gy2To8J3XPkyBHOOOMMWjZhtNvt+Hw+9u3bx759+3j11Ve59dZbWbJkSYfn+Mc//sEDDzyArutomkZ0dDRFRUW89NJLvP322zz66KMcf/zxQ/WUhjUDsR6h+9jt9g6PxcbGDrjdI5Xnn3+et956q+lvu92OyWSirKyMTz75hE8++YSXX36Zv//97x1+5kTad4aIVaFfZGRk8OGHH4bbjKOClJQUsrOzycnJYfr06SxbtqxH9/vPf/7DCy+8gKZp/PrXv2bp0qXYbDZ27NjBzTffzK5du7j++ut5++23sVgsg/wsRgZ9XYsQv/nNb7jooosGybqjA7/fj67rzJs3j0WLFnHSSSeRnJyM3+9n27Zt3HPPPWzcuJE777yT8ePH873vfa/V/T///PMmobpkyRJ+9atfERsby4EDB7jjjjtYt24dy5YtY9WqVSQkJITpWQ4f+rseIa6++upev5+E9sydO5fc3FzmzJnD+PHjiY6OBqCsrIyXX36Zv/3tb6xbt4577rmHO++8s9V9I/I7QxeEPvDqq6/qWVlZ+hlnnBFuU44KvF5vu7GsrCw9KytLX7NmTaf3c7vd+imnnKJnZWXpf/zjH9sdP3z4sD579mw9KytLf/755wfU5pFKX9dC13X9jDPO0LOysvRXX311sMw7aqitrdXz8/M7Pe52u/XzzjtPz8rK0pcsWdLu+IUXXqhnZWXpv/jFL9odq6ur00899VQ9KytL//Of/zygdo9U+rseixcv1rOysvS//vWvg2mmEOSBBx7Qs7Ky9JkzZ+put7tpPFK/M2SPVhCGASZT3zZBvvrqK8rKytA0jWuvvbbd8YyMDH74wx8CtNoyEjqnr2shDCwxMTHk5OR0etxisbBw4UIA8vPzWx3bs2dP09jPfvazdvd1OBxcdtllALz99tuttraFjunPeghDz6xZswBwu93U1tY2jUfqd4aIVUEYwXz99dcATJkyhfT09A7nzJs3D1AB+U6nc8hsE4TBJioqCoBAINBqPPS+cDgczJ49u8P7ht4XJSUl7N27dxCtPHrobD2EoWfTpk2AimVNSkpqGo/U7wxxEQj9orKykgsvvJDCwkIA0tLSOP7441m8eDFTp04Ns3XCnj17AMjMzOx0zpQpUwDQdZ29e/cyY8aMIbHtaOapp57iwQcfpLq6GofDwdSpU1mwYAEXX3wxVqs13OaNGELZ5W0z0AsKCgCYPHlyp0mgofdFaL4kIPafztajJW+99RavvfYaZWVl2O12Jk2axFlnncVll10myYf9xOl0cuTIEd544w2efvppAK688spWFQEi9TtDxKrQLxobG9m+fTuxsbE4nc5WWZ833XQT11xzTbhNPKoJlVRKTU3tdE5aWlq7+cLgsnv3bmw2G1arlaqqKr7++mu+/vprXnzxRR577DFGjRoVbhOHPXl5eaxZswaASy65pNWxnrwvbDYbcXFx1NTUyPtiAOhqPVqyf/9+zGYzUVFR1NbW8s033/DNN9/wr3/9ixUrVjBt2rShMnlEUFZW1uQJbYnZbOaqq67iV7/6Vbv5EHnfGSJWhT6RmprKsmXLOPvss5kwYQIWiwWv18umTZt48MEH2bJlC/feey+pqamcf/754Tb3qCW0RdNZmaS2xxoaGgbdpqOZs846izlz5jB37tymkm+lpaW88sorrFixgl27dnHdddfx2muvSWWGflBdXc1NN91EIBAgNze3XeWFnrwvQsdramrkfdFPulsPgOOPP56LL76YU045heTkZDRNo7q6mnfeeYcHH3yQw4cPc+211/LWW29JdYZeYDQaSU5OBlSNaI/Hg6ZpLF68mJ/+9KcYjcZW8yP1O0NiVoU+MW/ePH75y1+SlZXV9KVqNps58cQTef7555viwO6//36JTxKEIL/5zW9YsGBBq9rEqamp3HDDDfzlL38BlNf19ddfD5eJwx6Xy8Uvf/lLDhw4QEJCAg8++GC7L+SeIolV/aen67Fs2TIWLVpESkpK07Z0fHw8V1xxBc888wxms5mysrKm7WuhZyQmJvLFF1/wxRdfsGXLFtauXcvixYt59tlnOf/889m4cWO4TewRIlaFAcfy/9u787gqqv6B45972RRRSFEsEhcU3C0TLUofQ3PBBUQxM030edBcchcpxMTU3PmZEkLaY4paaSqaZkouuYXm/qRIhoKaoKAoi8CFO78/7sPEvrnR4/f9evXKO3PmzLkznJnvnXPmHFNTJk6cCBhmNLlw4cJTLtGzK3dw7YyMjGLTPHjwQP137lh84snLfeoKsH///qdcmr+nrKwsxo0bx4kTJ6hRowZffvllkbNQlaVe5F0v9aJiyno+StOyZUtcXV0BqRsPQ6vVUq9ePWbMmMH06dNJTk5m8uTJ+e4BlfWeIcGqeCxyh8UAuHbt2lMsybOtdu3agKGpuTh51+WmF09H69atAakzFZGVlcX48eM5dOgQFhYWrF69utihlMpSLzIyMtQhfaRelF95zkdZ5N5TpG48GoMGDcLU1JSEhAR+/vlndXllvWdIsCoeu5LmHhaPV+4bzLlvPxfl999/BwznqVGjRk+kXKJkUmfKR6fTMWHCBPbv34+5uTmhoaFq4F+U3Ded//jjj2K7KeXWi7zpRdmU93yUh9SNR8PMzAwrKysA4uLi1OWV9Z4hwap4LM6ePav+29bW9imW5Nn26quvAoaLS3x8fJFpDh8+DBieXBQ3J7d4Ms6dOwdInSkPnU7HxIkT2bdvH1WqVGHlypW88sorJW6TWy9SU1M5ffp0kWly60WdOnXkR1w5VOR8lEXuPUXqxqORlpbG3bt3gfxN+ZX1niHBqii30l460Ol0LFu2DDBc6Fu0aPEkiiWKkDs/t6IorFq1qtD6mzdvsnPnTgB1dhnxeJRWbw4cOKC+7NC5c+cnUKK/v+zsbKZMmUJERARmZmYEBwfToUOHUrezt7enWbNmAEXWi9TUVDZu3AhA79695WleGVX0fJRWNy5cuMCuXbsAqRtlkZ2dXWqaNWvWoNPpAHByclKXV9Z7hgSrotxu3LiBp6cn3377LdevX1eXZ2dnc/z4cYYOHao+rZgyZUqxg26L8rlz506+/3KlpqbmW557AQLDy27jxo0DICwsjNDQULXjfFRUFKNGjSI9PR07Ozs8PT2f7Bf6G6vIuZgzZw5z5szhxIkT+V5QuH37NiEhIep4h/b29vTv3//JfZm/qZycHKZNm8aPP/6IqakpK1aswNnZuczbT548GYB9+/Yxd+5cUlJSAEOfyDFjxpCQkIClpWWR07GKwh7mfISGhvLhhx9y6NAh9TwA3Lt3j40bNzJs2DB0Oh3W1taMGDHicX2F/xm///47np6ebN68mRs3bqjLFUXh8uXLzJ49m+XLlwPQvXv3fBNgVNZ7hkaRsTlEOV2/fp0uXbqon83MzDA3Nyc1NVW9ORsbGzN58mSZFOARKuuMYGvXri30NGPmzJl88803gOHcVKlShdTUVABq1arFV199le+CJUpWkXPh6+urDkml0WioXr06QL55uZs2bcrnn38uTZ1lcOLECYYMGQIYhs2ztLQsMf3mzZt5/vnn8y0LCQkhMDAQRVHQarVUq1ZNDZbMzc0JDg5Wm0VFyR7mfCxfvpwVK1ao6ywsLDAyMuL+/fvqU1dbW1uCgoLUJ+KieBcvXsTd3V39nHuPTk9PJzMzU13u4uLC0qVL1Wlw86ps9wyZFECUm7W1NTNmzOD06dNcvHiRO3fukJKSQpUqVWjcuDHt27fn7bfflukJK5HZs2fzxhtvsGHDBi5evKj+MnZxcWHkyJH55oYWj8egQYOoWbMmZ86c4c8//+Tu3bvo9Xq1q0z37t3p1auXTAZQRnlfjNLpdCQmJpaYPicnp9CyUaNG0aZNG7766ivOnDlDSkoKL7zwAq+//jqjRo2q0DBLz6qHOR89evQgJyeH06dPc+3aNe7evUtmZia1atXCwcGBLl260K9fPxlCrIwaNmzIkiVL+OWXXzh//jyJiYkkJydjampKo0aNaN26NX379uX1118vNo/Kds+QJ6tCCCGEEKLSks6EQgghhBCi0pJgVQghhBBCVFoSrAohhBBCiEpLglUhhBBCCFFpSbAqhBBCCCEqLQlWhRBCCCFEpSXBqhBCCCGEqLQkWBVCCCGEEJWWBKtCCPEYuLi44OjoyJYtW552UYQQ4m9NglUhhBBCCFFpGT/tAgghxP+ievXqYWpqSvXq1Z92UYQQ4m9NoyiK8rQLIYQQQgghRFGkG4AQQgghhKi0pBuAEEKUgaIohIeHs23bNqKiokhJScHCwoLnnnuOli1b0qVLF3r27Kmmd3Fx4caNG3z66ad4eHgUyu/WrVusWLGCgwcPcufOHaytrencuTOjR4/mypUrvPfeewBcunQp33bLly9nxYoVtG/fnnXr1rF7927Wrl1LdHQ0Wq2W1q1bM2HCBFq1agVAamoqq1evZteuXdy8eZMaNWrw1ltvMWnSJGrUqFGoXBkZGRw5coQDBw7wn//8h5s3b5KamkrNmjV5+eWXGTJkCE5OTo/y0AohRIkkWBVCiDKYPn064eHh6mcLCwvS09NJTk7mypUrHD9+PF+wWpLff/+d9957jzt37gBQtWpVkpOT2bBhA3v37mXSpEllyicwMJCVK1dibGyMqakpKSkpHDp0iF9//ZU1a9ZgZ2eHl5cXly5dwtzcHL1ez+3bt9mwYQPnzp3j66+/xsTEJF+eu3bt4sMPP1Q/m5ubo9VqSUhIYPfu3fz4449MnTqVf/3rX2UqoxBCPCwJVoUQohS//vor4eHhaLVafHx8GDBgANWrV0dRFJKSkjh+/DhHjx4tU15ZWVmMHz+eO3fuYGtry9y5c3n11VfRaDT8+uuv+Pn5MX/+/FLzuXjxIqdOneLDDz/k7bffpmrVqly6dIkJEyZw5coV5s6dS926dcnKymL9+vW0a9eOrKwswsPDmTVrFv/5z3/YvHkz77zzTr58a9SowaBBg+jTpw+NGzfGysoKRVH4888/WbNmDWvXrmXJkiW0b9+e1q1bV+h4CiFEeUifVSGEKMXp06cBcHZ2Zvjw4eob/hqNBmtra1xdXZkzZ06Z8tq5cycxMTGYmJgQGhrKa6+9hkajAaBdu3asWrWKrKysUvNJSUlh5MiReHl5UbVqVQAcHR3Vcpw7d479+/cTEhJCu3btADA1NcXT0xM3NzcAdu/eXSjfrl27EhAQQLt27bCyslK/p62tLX5+fvTv3x+9Xs/GjRvL9H2FEOJhSbAqhBClsLCwAODu3bvo9fqHyuvHH38E4M0336Rx48aF1terVw9XV9dS8zExMeGf//xnoeVt27bFzMwMgO7du1O/fv1CaV577TUAoqOjy1V2MJQb4NSpU+XeVgghKkK6AQghRCmcnZ0xMTHht99+Y9iwYQwYMIAOHTpQt27dcud18eJFAF555ZVi07Rr167Uma9sbW3VIDovrVbLc889R3x8PA4ODkVua21tDcD9+/eLXH/r1i3Wr1/PkSNHiI2NJTU1tVCQfuvWrRLLJ4QQj4oEq0IIUYr69esTEBDAJ598wvHjxzl+/DgAdevW5fXXX2fAgAG0bdu2THnlvlRVp06dYtOUtC5X7dq1i11nZGQE/BWUFqTVGhrVsrOzC607efIkI0eOJDU1VV1WrVo1qlSpgkajQafTce/ePdLT00stoxBCPAoSrAohRBn079+fzp0788MPPxAZGcnJkyeJj4/nu+++47vvvmPIkCH4+/s/sfLk9nN92DR5ZWdnM3XqVFJTU2nRogWTJk2ibdu2VKtWTU1z7NgxvLy8yltcIYSoMOmzKoQQZVSrVi2GDBnC8uXLOXr0KNu2baNPnz4AhIWFcfDgwVLzqFmzJgC3b98uNk1J6x6nM2fO8Oeff2JkZERwcDAdO3bMF6gCJCYmPpWyCSGeXRKsCiFEBTVr1oxFixapfUMjIyPLtA0YmtuLc+LEiUdTwHK6efMmYAiobWxsikxT1iG6hBDiUZFgVQghSlHSUFIajUZ9+74sQ05169YNgH379vHHH38UWn/9+nV27dpVwZI+nNwhuRITE0lKSiq0/tKlS3z//fdPulhCiGecBKtCCFGK2bNnM3nyZH766SeSk5PV5Xfu3CEwMJDz588D0KlTp1Lz6t27Nw0aNECn0zFy5Eh++eUXFEUBDMNB/etf/1KD3yetbdu2mJuboygKEydOJDY2FgCdTseePXsYMWIE5ubmT6VsQohnl7xgJYQQpcjOzmbnzp3s3LkT+Gvc1bxvzL/77rtlClZNTU357LPPeO+997h+/TrDhg2jatWqaDQa0tPTqV27Nj4+Pvj5+WFqavp4vlAxatSowdSpU5k9ezbHjx+nW7duVKtWjaysLHQ6Hba2tkyfPp1p06Y90XIJIZ5tEqwKIUQpxowZQ7Nmzfjll1+IiYnh9u3bZGVlYWNjQ5s2bRgwYAD/+Mc/ypyfo6Mj27dvJygoiAMHDnDnzh1q165N3759GTduHGfOnAEMweOT9u6771K3bl2+/PJLLly4QE5ODi+++CJdunRh5MiRREVFPfEyCSGebRolt/1JCCFEpRAYGMjKlSvp0KEDa9eufdrFEUKIp0r6rAohRCVy9+5dNm/eDMAbb7zxlEsjhBBPnwSrQgjxhB08eJCFCxcSFRWljiCg0+k4cuQI7733HomJiVhZWTFgwICnXFIhhHj6pM+qEEI8YcnJyaxevZrVq1ej0WiwtLQkLS0NnU4HgLm5OUuWLFEnEBBCiGeZ9FkVQogn7ObNm2zatIlffvmFGzducOfOHYyNjXn++edxdnbGy8uLF1988WkXUwghKgUJVsUzSa/XI3/6QgghxOOh0WjQah9Nb1PpBiCeKVlZWcTFxanNrUIIIYR4PExMTLCzs3voMaPlyap4ply+fJnq1atTq1YtNBrN0y6OEEII8T9JURSSkpJISUmhcePGD5WXPFkVzwy9Xo9Op6NWrVoYG5fxT19R4MYpuLgdMpKhihU06wsvvvJ4CyuEqLDElSsxa9yY6l27lpo2JSKCzMuXsX7//SdQMiGeLbVq1eLOnTvo9fqH6hIgQ1eJZ0ZuI0KZn6gmXIAv3oS1bnDy33BxB6QlGj6HdoZbFx9fYYUQFWbWuDE3pvmQEhGhLvujd2/+6N07X7qUiAhuTPPB7CGf+gghipZ7v33YRnwJVoUoSsIF+HcPsHeBKReh+vNQrTa4Bxk+27vAl90lYBWiEqretSu2ixYWCljzyg1UbRctLNMTWCHE0yPBqhAFKQqEjwGnf0GXmWBWPf96s+qG5U7/gm2jn04ZhRAlKilglUBViL8X6bMqREE3TkHiZRj2fcnp3pgEkSFw4yTYVqwPq4uLCw8ePODnn3/GxMQEgGPHjuHl5cWAAQP4+eefWb16NQ4ODgDExsYyePBgvv76a7Zt28aGDRuoU6eOml+vXr0YOXIky5cvV9fpdDrs7OyYM2cO1tbWAJw4cYLFixeTkZGBTqejRo0arFixQl2fl6+vL0ePHuW5554jKysLJycn/P39MTExITs7m5UrV/L9999jZGRETk4OTk5OTJs2jRo1auTL59KlS8yePZukpCRMTEx46aWX8Pf3V98SHT9+PKdOneL27ducOnWKatWqlXr8jhw5wueff058fDyWlpZotVo8PT15++23K3Q+KmLZsmU0adIEV1fXCueR93wBGBsb4+fnR9u2bQFwdHQs8zHJKz09nWHDhpGZmQlA7dq1CQgIUMdwdXR0xMHBAY1GQ2ZmJi1atGDMmDHlfhkib/m8vb3x9/fHzs4OPz8/+vXrR7t27cqV36OSN2A1srREa1HtkQeqUocNdTghIYGPPvqI69evY2pqSqNGjQgICMDKyqrE43f+/HmWLVtGTEwMVlZW6PV6unXrxpgxYx7qvJTHxo0byczMxMvLq8J5bNmyhXnz5mFrawsYmr3Hjx9P1//+jbm4uLBy5Ur176A8RowYwe3bt9FqtVSrVg1/f3+aNWum5mtqaoqpqSkZGRnY29vj7e2tXjvKKm/58tbbR3F9exRkNADxzMjJySE6OhoHBweMjIzgwAL4bUvhhKm3QMkxNP3nuhNj+H/NRvnT3r8JWiOwqJN/eQsP6Dy91DK5uLhgZWXFqFGj6N69OwBTp07lypUrtG/fnlatWrFq1Sq+/fZbtFotQ4cOxd3dHU9PT5YvX056ejrTpxfeT951er2eKVOmYGlpyaxZs8jOzub111/n3//+N82bNwcgJiYGGxubIoMhX19fWrZsyZAhQ8jMzOS9996jd+/eDB06lOnTp3Pv3j0WLFiApaUler2eH3/8kZYtW1KvXr18+Vy9epWMjAyaNm1KTk4OU6ZMoWnTprz/3xdbjh49iqOjI87OzmUKzA4fPoyvry+fffaZemG+ceMG69evx8fHp9RjX5kUPJe7du1i9erVfPfdd0DFgtXs7Gy0Wi3p6elYWFgAsGbNGn799VdWrFhRKF+9Xs/mzZtZvHgx3333XaHzV5KKBtMP43ZQEPd/+KFMafWpaWTHx4NWAwoY29igtSi5rDV69qT22LGl5i112FCHExMTuXr1qvrDZMGCBaSmpvLJJ58Ue+wuXbrEe++9x/z583nzzTcBuHPnDsuXL+fjjz8u9dhXJlu2bOHAgQN89tlnAJw7d46RI0fyyy+/ABULVrOzszE2Nub+/fvqD4eIiAiCgoLYunVrkflGRETg6+vL6tWradOmTZn39TDBdEkK3XcrSLoBCFGQogdtGRsdjIwN6R9C//791aAkJSWFs2fP0rFjRwBcXV1p0KABK1euZO3atVhYWODp6Vmu/LVaLU5OTvz5558ApKWlkZaWRu3atdU0jRo1KlOgYWZmxiuvvMKVK1eIjY1l9+7dzJs3D0tLS3VfPXv2LDLQadCgAU2bNgXAyMiIVq1ace3aNXW9s7MztWrVKvP3CgoKYuzYsfmeINja2uYLVKdOnYqHhwd9+vRh1KhRJCUlARAZGYmHh4eaLjo6GhcXF8BwsxwxYgR9+vShT58+fPjhhwCcOXMGDw8P3Nzc6N27Nxs2bAAMgUBYWBhgeKL29ttv4+7uTu/evdXzCjB06FAWLVrE4MGD6dq1KzNnziz2u92/f189pgW5uLgQHR2tfvbw8CAyMlLdR2BgIMOGDeOf//wnWq1WDVQVRSE1NbXYN3K1Wi0DBw6kY8eO6ncr6MCBA/Tv35++ffvi5ubG2bNnSyzf0KFD2b9/f7Hf80nRWlQzBKp6Ba2FRamBanlJHQZra+t8T9DbtGmTr34X5YsvvsDT01MNVAFq1qyZL1BdsGAB/fv3x83NjSFDhnD16lUArl+/TocOHdR0aWlpODo6ApCRkcHEiRNxdXWlb9++jBgxAjAE24MGDaJv37706dOHwMBAwPDDYMGCBYAhgB48eDD9+vXD1dWVkJAQdR++vr7MmjULLy8vunfvzrhx48jKyiryuyUnJxdbhwvWi/Hjx7NlyxZ1H3PmzOGf//wnbm5uAPmecKekpJT4knDXrl0ZPHgwq1evLnL96dOnGTx4sHoMIoro0523fHmvb0+TdAMQz67O04t++rn3Y8Nb/+5Bfy0L+u9FcWxk/rTbxhhevHoroMLFcHJyIiwsjISEBPbt20ePHj3y/QKdOXMmHh4e6PV6Nm3alH/327Zx9OhR9fPo0aPp0aNHvjRZWVkcOHBAbcaxtLTknXfeoVu3brRr146XXnoJV1dXGjZsWGpZ7927x+HDhxk2bBi//fYb9evXr9D89enp6WzatImpU6eWe9tcFy5cwN/fv8Q0H330kVq+0NBQgoKCSgwSAbZv346trS1ffvklYLjpAISEhDB8+HD69OkDGI5FQc2bN2fDhg0YGRmRnJyMh4cHHTt2VJt5r127xrp169DpdLi6unL69Glefvll4K9zmZKSQlpaGqtWrSrH0fjLxYsXWbVqldokDeDl5UV0dDQ1a9Ys9iaWq1WrVhw5cqTQ8itXruDn50dYWBgNGzZEp9ORkZFRoTI+CrXHji3Tk08w9FG9/sF4tNWro2RnU2fixEfaV1XqcH45OTmsX79ebQIvzoULF9Sn0cXx9vZWnzzv3LmTTz/9NF8AWZRDhw5x//59du3aBfxVh8PCwuiNpA8DAAAd6UlEQVTcubPampO7PC9bW1vWrFmjNqsPGjQIZ2dnWrVqBUBUVBRr1qzBxMSEd999lz179tD7v6NMHD16FDc3NzIyMkhISFCD4fI6efIkYWFh+X58+Pj4qD9KS7s2tGrVqsggNDk5mXHjxrF8+XLatm2LXq/n/v37FSrjkybBqhAFNetrGJ6q5wIwsyg+XWYKXAiHYdsfepdubm5s3bqViIgIFi9ezI4dO9R1VlZW9O3bl/T09HxPUgDc3d2LbEKEv26CcXFxODg40LNnT3Wdn58fXl5eREZGcuzYMfr168eqVauK7VsYGhrK5s2b0Wg0dOvWDQ8PD34oYxNsQTqdjkmTJvHGG2+UejMrTd4nDFOmTOHy5cskJiYSERFB1apV2bFjB+Hh4WpgVVR/voLatGnDmjVrmD9/Pk5OTuoTsg4dOhAcHExsbCyvvvpqkccqOTkZPz8/rl69ipGREXfv3uXy5ctqsOrq6oqRkRFGRkY0a9aMuLg4NVjNey6PHTvGBx98wO7du6lSpUq5jombm1u+QBUMzf96vZ7g4GCCg4OZNWtWsdsX1zPs6NGjdOrUSQ2ITExMCu2nMsrto5rb9F9n4sTH8nKV1GEDRVEICAigRo0aDB06tNS88tbhBQsWcPToUe7cucNXX31Fo0aNOHLkCGFhYaSlpaHX60lNTS01z6ZNmxITE8OsWbNo3749nTp1Agw/KhYuXMiDBw9wcnLC2dm50LaZmZkEBAQQFRWFRqMhPj6eqKgoNVh966231DrZunVr4uLi1G2dnZ3VbgDR0dEMHz6cLVu2YGNjU2qZ8+rZs2ehp+QLFy4EYOvWrSxcuJAvvvii2O2Lq8NnzpzB3t5ebY3SarWl9imuLKQbgBAF2bYF68ZweGnJ6Q4HgnWTCr9clVe/fv1Yt24dZmZmNGjQoND63ACnPNzd3QkPD2f//v3odDr1IprL1tYWDw8PFi1aRN++fdm9ezeXL1/Gzc0NNzc3AgL+elo8cuRItm3bxtatWxk3bhwajYYWLVoQGxvL3bt3i9z/nDlz1LwuXboEGG5yEydOpHbt2vj5+ZXr+xTUrFmzfM3QS5YsITw8nMTERPR6Pb/++ivr169n1apV7NixA19fX/VFIyMjI/T6v7pv5C4HePnll9m2bRutW7dmz5499O/fn5ycHLy8vAgJCaFOnToEBgYWGfB9/PHHODk5qUFygwYN8uVtZmam/jv3ZZaivPbaa2RmZuZr7s+7Xd6yF2yGNDc3LzLP3Gb+8PDwItfnOn/+PE2aNCkxzd9F3pepcpv+yzKsVUVIHf5rm5s3bxIYGFjqIPAF6/D06dMJDw9XX/z6888/mTNnDosXL+b7779n6dKlan0yNjYutg7Xq1ePnTt30rFjR06dOkWfPn24d+8e3bt3Z+PGjTRs2JCwsDBGjRpVqExLly7F2tqarVu3sn37dtq3b1+hOuzg4MDzzz/PqVOnCq0ruF3e/KH4OgyGv7PIyMhizxn8b9XhXBKsClGQRgPuwXBiFfw02/AENa/MFMPyE6sN6R4BGxsbJk+e/FDN4sWxsrJizpw5rF+/nlu3bpGWlsbBgwfVX98ZGRnExMRQr149GjduTHh4OOHh4aW+4FC/fn26deuGn5+f2pSkKArbtm0jLi6OGTNmqHk5OjqSnZ3N5MmTsbS05JNPPnno6W7HjBlDUFAQZ86cUZc9ePBA/ff9+/exsLDA0tKSrKwsvvnmG3VdvXr1uH79unrBzxvAXbt2DXNzc1xdXfH39+fq1aukp6erx2jgwIGMGjWqyP6a9+/fx9bWFo1Gw4kTJ4iKiqrQd4uKiiI9PV19az8vOzs7dd/nzp3jypUrxeaTmJiYr6lz586dar++gnKbqA8dOsQ777xTaP0bb7zBoUOH1P3pdDpSUlIKpassSnrr/3EErFKHDYFqbGwsQUFBZZoL3tvbm2+//ZaDBw+qy7KyssjOzgYM/TNNTEywtrZGURTWr1+vprO2tiY7O5uYGMPLr3nrcHx8PBqNhi5duuDj44OiKNy8eZOrV69Sq1Yt3N3dmTZtWrF12MbGBmNjY2JiYorsElMW8fHxxMbGFtk1w87OjnPnzgGG683JkyeLzSc1NZWEhAT18969e7Gysir2iWhERAQbN25k+PDhhda9/PLLxMTEqAG0Xq8vsitEZSTdAIQoSp1mMOJHwziqkaGg0Rpepto2xtD0b90ERuw2pHtE+vfvX+5tCvZ3c3Z2LrJJsXnz5vTs2ZOQkBAmTZrE119/zbx58zAzMyM7O5uOHTvy7rvvlnv/8+bNIzg4GE9PT4yNjVEUhXbt2qkvK+W1a9cu9uzZg6OjI+7u7gC0bdtWvaG+//77XLhwAYAePXrQoEED1q1bV+y+O3XqxNy5c1m4cCG3bt2iZs2amJiYMHPmTKpWrUqnTp3Yvn07PXv2xMbGhrZt23Lo0CHAEFiMGDGC/v37Y2tri5OTk5rv8ePHWbNmDVqtFr1ej4+PD9WrVycwMJDIyEhMTEzQarVFHucpU6YQEBBAaGgo9vb25XobN/dcKoqCRqNh4cKFRfYlnDhxIr6+vmzevJnmzZuXOMxUfHw8/v7+6s2/Xr16LFq0KF+aQYMGAYYgoXnz5mzcuFF9uWbjxo3cunWLCRMmUL9+febOncuUKVPQ6XQYGRkxe/ZsWrduXeL3etgfJRVRluGp8gasj6pLwLNch0+ePMm6deto1KiR+gLZiy++SFBQUKF8cjVt2pQvvviCZcuWERAQwHPPPaf2BbWzs6NKlSr06NGDXr168cILL+Rrtjc2NmbGjBl4e3tTt25dtakfDC9JLVmyBEVRUBQFNzc3mjZtysqVK9mxYwcmJiYoilJk68jo0aPx8fFhx44d2Nra8uqrr5b5WOb2WVUUhZycHCZNmqS+kJaXt7c3EydO5PDhwzRs2LDE60RKSgoffPABmZmZaDQaatasSUhISL56NX78eExNTXnw4AH29vaEhoby0ksvAfDTTz+xb98+5s6di6WlJStWrGD+/PmkpaWh0WiYMGECXbp0KfF7PY06XKgMMnSVeFZUeAiNGyfhwnbIuAdVLKF530fS9C/E/7pevXoxd+5c9cb5pCSuXIlZ48ZlCkBTIiLIvHwZ6/++dCOE+Iu3tzfu7u706tWrQts/qqGr5MmqEKWxfUWCUyHKqXv37tjb25f65PVxKE/gWb1rV5nFSogieHl5kZWVxT/+8Y+nXRR5siqeHY/qF554ci5evIivr2+h5f369Xuo2WaEEE9GUlKSOs5pXsV1dxD/Wx7VfVeCVfHMkGBVCCGEeHJkBishhBBCCPE/T4JVIUqgKApnriXz8uw9tJ71I/N/iOLMtb/HUB9CPKtCz4XyU9xP+Za5b3PHfZt7obQ/xf1E6LnQJ1U0IUQFSLAqRDEuxafgFnSEIasiydErGGk1JKVmMmRVJH1XHCY6ofKOMSnEs8zeyp4PD31YKGAt6Ke4n/jw0IfYW9k/oZIJISpCglUhinApPgXPlUfp2MSaXz7qgk2NKlhbmLHIsw2/fNSFjk2sGRB8VAJWISqhLnZd+LTjpyUGrLmB6qcdP6WLXcnjTAohni4ZukqIAhRFYdrmswx9rT7Tuhce0NnCzFhdPnXTWbaPe6PC+3JxceHBgwf8/PPP6jzrx44dw8vLiwEDBvDzzz+zevVqHBwcAIiNjWXw4MF8/fXXbNu2jQ0bNqjzzoNhXMuRI0eyfPlydZ1Op8POzo45c+ZgbW0NwIkTJ1i8eDEZGRnodDpq1KjBihUr1PV5+fr6cvToUZ577jmysrJwcnLC399fnRJx5cqVfP/99+oUgk5OTkybNo0aNWrky+fSpUvMnj2bpKQkTExMeOmll/D391dnuhk/fjynTp3i9u3bnDp1qtDc2EU5cuQIn3/+OfHx8VhaWqLVavH09OTtt9+u2AmpgGXLltGkSRNcXV0rnEfe8wWGAc/9/PzUObwdHR3LfEzySk9PZ9iwYep0jrVr1yYgIECdGcvR0REHBwc0Gg2ZmZm0aNGCMWPGFDnRQEJCAlOnTlUnanBzc+Obb75R50mvbPIGrJ92/DTfukcZqEodNtThhIQEPvroI65fv46pqSmNGjUiICCg1Lnnz58/z7Jly4iJicHKygq9Xk+3bt0YM2bMQ52X8ti4cSOZmZkPNcLIli1bmDdvHra2toDhPjJ+/Hi6/ndYNBcXF1auXKn+HZTHiBEjuH37NlqtlmrVquHv70+zZs3UfE1NTTE1NSUjIwN7e3u8vb3Va0dBeeutt7c3/v7+2NnZVfBbP0GKEM+I7Oxs5cKFC0p2dnaJ6U7H3VVazNytpGbo1GVdlxxQui45kC9dSoZOae7/g3Im7m6Fy/Tmm28q/fr1U3bv3q0umzJliuLh4aHMnz9f2blzp9KvXz9Fp9MpOTk5yuDBg5Vvv/1WURRF+eyzz5T58+cXmW/edTk5OcrEiROVjz/+WFEURdHpdEr79u2V3377TU3/xx9/KKmpqUXmNX36dGXdunWKoihKRkaGMnDgQGXt2rWKoiiKj4+PMmrUKCU5OVnd165du5S4uLhC+Vy5ckW5ePGioiiGczFhwgQlODhYXX/kyBElMTFRcXBwKLYseR06dEh5/fXXlZMnT6rLrl+/rixYsKDUbSubgudy586dioeHh/q5rMckr9y/mZSUFHXZv//9b2Xs2LFF5puTk6N88803ipOTU5Hnrzz7rUwiYiMUpzAnpeu3XRW3rW7q54jYiEeSv9RhQx2+ffu2cuLECTXt/PnzlRkzZpR47KKiopT27dsr+/btU5clJSUps2bNKnG7yui7775TPvjgA/Xz2bNnlQ4dOqif33zzTeXSpUvlyjO3Lt27d09dtnfvXsXd3b3YfPfu3au88sorypkzZ8r9HQru91Eo6323NPJkVTyzlkX8zvfn/iy0PDE1E60G3IP+mhM6NikdgLeWHsyXVqvV4PXv41hbmOVb3rv1C0zo2qRM5ejfvz/fffcd3bt3JyUlhbNnz9KrVy8yMzNxdXUlIiKClStXYmFhgYWFhTqVYVlptVqcnJw4cOAAAGlpaaSlpVG7dm01TaNGjcqUl5mZGa+88gpXrlwhNjaW3bt3s3//fiwtLdV99ezZs8htGzRooP7byMiIVq1aqfN6A/mmUiyLoKAgxo4dm+8Jgq2tLT4+PurnqVOnEhMTg06n44UXXmDevHnUqlWLyMhIFixYwJYtWwCIjo7m/fffZ9++fdy5c4epU6dy+/ZtAFq2bMmnn37KmTNnmD17Njk5OeTk5DB48GAGDx6Mr68vLVu2ZMiQIRw7doz/+7//IzMzk+zsbIYPH65OwTl06FBat27N6dOnuXXrFs7OzsyePbvI73b//n31mBZU8AmNh4cH06dPp0OHDgwdOpS2bdty5swZAL766issLCwAw5Oe1NRUtNqie39ptVoGDhxIZGQkGzZsKDQG5vXr1+nfvz+RkZFA/qe9Li4ueHp6cvToUerUqcPAgQOLPb65+QwePJiDBw+SkpLCjBkzyj3wePDZYH688mOZ0lqaWhKfHo8GDZP2T8LG3Iblp5az/NTyYrfp3rA7o9uMLlP+UofB2to631PdNm3a8PXXX5dYli+++AJPT0/efPNNdVnNmjXVKZgBFixYwPHjx8nOzqZ69erMmTOHBg0aFPp7TEtLo23btly6dImMjAx8fX2Jjo7G2NgYa2trvvzyS65evYqvry/p6ekoioKLiwuTJk1i+fLlpKenM336dC5dukRAQAAPHjwgMzMTNzc3Ro0aBRieUFepUoWrV69y8+ZNmjRpwtKlS9XWobySk5OLrcNDhw5lxIgR6vceP348nTt3xsPDA19fXywsLLhy5Qrx8fHs3Lkz3xPulJSUEqc/7dq1K4MHD2b16tV89tlnhdYXrLe515KC1465c+cWe3xz85kyZQp79+4lKSmJsWPHVmi64bKSYFWIAnJfpioLY62GHP3DDVXs5OREWFgYCQkJ7Nu3jx49euQbj27mzJl4eHig1+vZtGlTvm0Lzis+evRoevTokS9NVlYWBw4cUJupLS0teeedd+jWrRvt2rXjpZdewtXVlYYNG5Za1nv37nH48GGGDRvGb7/9Rv369Yucv7406enpbNq0ialTp5Z721wXLlzA39+/xDQfffSRWr7Q0FCCgoKYOXNmidts374dW1tbvvzyS8Bw0wEICQlh+PDh9OnTBzAci4KaN2/Ohg0bMDIyIjk5GQ8PDzp27Kg28167do1169ah0+lwdXXl9OnTvPzyy8Bf5zIlJYW0tDRWrVpVjqPxl4sXL7Jq1Sq1SRoMM9FER0dTs2ZNVq9eXeL2rVq14siRIyWmKcrNmzdZu3YtGo1GvcEVJzk5mRYtWjBhwgR+/vln5s6d+1hnyalmUg0NGhQUqptUp5pJ+bpTlEbqcH45OTmsX79ebQIvzoULF+jevXuJaby9vdUfTjt37uTTTz8lJCSkxG0OHTrE/fv32bVrF/BXHQ4LC6Nz5868/98ZznKX52Vra8uaNWvUZvVBgwbh7OxMq1atAIiKimLNmjWYmJjw7rvvsmfPHnr37g3A0aNHcXNzIyMjg4SEBAIDA0ssZ3FOnjxJWFhYvm4/Pj4+ar0q7drQqlUrIiIiyr3fvNeO69evl5rezMyMTZs28ccffzBgwADc3NwwNn48YaUEq+KZNaFrkyKffs7/IYqk1EwWebZRl+U+Ud07Of8Ndeqms1hbmOHbs3Df1vJwc3Nj69atREREsHjxYnbs2KGus7Kyom/fvqSnp+d7kgLg7u5e7CwwuTfBuLg4HBwc8j0t8fPzw8vLi8jISI4dO0a/fv1YtWoV7dq1KzKv0NBQNm/ejEajoVu3bnh4ePDDDz9U6LvqdDomTZrEG2+8UerNrDR5nzBMmTKFy5cvk5iYSEREBFWrVmXHjh2Eh4ej0+nIyMgosj9fQW3atGHNmjXMnz8fJycnOnbsCECHDh0IDg4mNjaWV199tchjlZycjJ+fH1evXsXIyIi7d+9y+fJlNVh1dXXFyMgIIyMjmjVrRlxcnBqs5j2Xx44d44MPPmD37t3l7hPq5uaWL1AFWLNmDXq9nuDgYIKDg5k1a1ax2ysVnCfGw8OjxCc+eZmbm6vn/uWXX+batWvl3t/oNqPL/OTzp7ifmLR/EtVNqpOtZPNB2w8e+UtVUocNFEUhICCAGjVqMHTo0FLzyvs3s2DBAo4ePcqdO3f46quvaNSoEUeOHCEsLIy0tDT0ej2pqaml5tm0aVNiYmKYNWsW7du3p1OnToDhR8XChQt58OABTk5ORbbmZGZmEhAQQFRUFBqNhvj4eKKiotRg9a233lLrZOvWrYmLi1O3dXZ2Vp9mRkdHM3z4cLZs2YKNjU2pZc6rZ8+ehfqnL1y4EICtW7eycOFCvvjii2K3r2gdLuraUZLcH+729vYYGxuTmJhI3bp1K7Tv0shoAEIU0KNlXX74TzxpmdklpkvNzOaH8zfp2fLhK2e/fv1Yt24dZmZm+ZracuUGOOXh7u5OeHg4+/fvR6fTFWoSsrW1xcPDg0WLFtG3b192797N5cuXcXNzw83NjYCAADXtyJEj2bZtG1u3bmXcuHFoNBpatGhBbGwsd+/eLXL/c+bMUfPKbTrS6XRMnDiR2rVr4+fnV67vU1CzZs04e/as+nnJkiWEh4eTmJiIXq/n119/Zf369axatYodO3bg6+urvmhkZGSEXq9Xt81dDobgadu2bbRu3Zo9e/bQv39/cnJy8PLyIiQkhDp16hAYGFhkwPfxxx/j5OSkBskNGjTIl7eZ2V/dRXJfZinKa6+9RmZmJtHR0YXWFSx7VlZWvvXm5uZF5pnbzB8eHl7k+lznz5+nSZOydWEpbr8lHV8gX7OpVqst9jg8CrkvU9mY21DHvE6powRUlNThv7a5efMmgYGBxXY5yVWwDk+fPp3w8HD1xa8///yTOXPmsHjxYr7//nuWLl2q/i0ZGxsX+zdWr149du7cSceOHTl16hR9+vTh3r17dO/enY0bN9KwYUPCwsLU5v28li5dirW1NVu3bmX79u20b9++QnXYwcGB559/nlOnThVaV3C7gvWjuDoMhr+zyMjIYs8ZPJo6XNLxzZX3WGi1WrKzS75nPgwJVoUooM2LljSqXY3PD1wuMV3wgcvY17GgTb2S33YtCxsbGyZPnvxQzeLFsbKyYs6cOaxfv55bt26RlpbGwYMH1V/fGRkZxMTEUK9ePRo3bkx4eDjh4eH5+o0VpX79+nTr1g0/Pz/u378PGH7Rb9u2jbi4OGbMmKHm5ejoSHZ2NpMnT8bS0pJPPvmkzE/hijNmzBiCgoLUPlYADx48UP99//59LCwssLS0JCsri2+++UZdV69ePa5fv65e8PMGcNeuXcPc3BxXV1f8/f25evUq6enp6jEaOHAgo0aNyneTzbtPW1tbNBoNJ06cICoqqkLfLSoqivT0dPWt/bzs7OzUfZ87d44rV64Um09iYmK+ps6dO3fi6OhYZNrcJupDhw7xzjvvVKjcuUo6vk9S3rf+c5v+yzKsVUVIHTYEqrGxsQQFBRXZj7Mgb29vvv32Ww4e/OtdgKysLDXoSUlJwcTEBGtraxRFYf369Wo6a2trsrOz1T6zef/G4uPj0Wg0dOnSBR8fHxRF4ebNm1y9epVatWrh7u7OtGnTiq3DNjY2GBsbExMTU6EuMblliI2NLbJrhp2dHefOnQMM15uTJ08Wm09qaioJCQnq571792JlZVXsKAsRERFs3LiR4cOHV6jcuUo6vk+DdAMQogCNRsNizzYMCDb0IxvdOf8wPqmZ2QQfuEzYL3Fsev+1R7bfinROL9jfzdnZucgmxebNm9OzZ09CQkKYNGkSX3/9NfPmzcPMzIzs7Gw6duzIu+++W+79z5s3j+DgYDw9PTE2NkZRFNq1a4eLi0uhtLt27WLPnj04Ojri7m6YSaht27bqDfX999/nwoULAPTo0YMGDRqowyQVpVOnTsydO5eFCxdy69YtatasiYmJCTNnzqRq1ap06tSJ7du307NnT2xsbGjbti2HDh0CDIHFiBEj6N+/P7a2tjg5Oan5Hj9+nDVr1qDVatHr9fj4+FC9enUCAwOJjIzExMQErVZb5HGeMmUKAQEBhIaGYm9vT5s2bQqlKU7uuVQUBY1Gw8KFC4vsSzhx4kR8fX3ZvHkzzZs3L3KYqVzx8fH4+/urN/969eqxaNGifGkGDRoEGIKE5s2bs3HjRurVqwcYhvS5desWEyZMKDL/4n5wlHR8n5SCw1PlfZmq4LBWj6pLwLNch0+ePMm6deto1KiR+gLZiy++SFBQULH7btq0KV988QXLli0jICCA5557Tu0LamdnR5UqVejRowe9evXihRdeyNdsb2xszIwZM/D29qZu3bpqUz8YhthasmQJiqKgKApubm40bdqUlStXsmPHDkxMTFAUpcjWkdGjR+Pj48OOHTuwtbXl1VdfLfOxzO2zqigKOTk5TJo0iaZNC3cR8/b2ZuLEiRw+fJiGDRuWeJ1ISUnhgw8+IDMzE41GQ82aNQkJCclX98aPH4+pqSkPHjzA3t6e0NBQXnrpJQB++ukn9u3bx9y5c4vMv7g6XNLxfRo0SkU7NwjxN5OTk0N0dDQODg5lao6LTkhh6qazxNxOo0fLutjUMCPhfiY/nL+JfR0LFnu2wcGm+hMouRCVS1JSEm+++SZnz5596Cfkj0NZx1GViQHEsyo7O5sOHTqwe/fuQv2oH6Xy3neLI90AhCiGg011to97g/X/6oC1hRl303VYW5ixwftVto97QwJV8Uw6d+4cAwcOZOzYsZUyUAX4I/mPMgWguU9Y/0j+4wmVTIinLyEhgZ49e9K3b9/HGqg+SvJkVTwzHtUvPPHkXLx4EV9f30LL+/Xr91CzzQghnoykpCRGjBhRaHlx3R3E/5ZHdd+VYFU8MyRYFUIIIZ4c6QYgRDnlNlnK7zMhhBDi8cu93z5slyEZDUA8M7RaLSYmJiQlJVGrVq1K299OCCGE+LtTFIWkpCR1BJWHId0AxDMlKyuLuLg4dDrd0y6KEEII8T/NxMQEOzu7Mo27WxIJVsUzSa/XS3cAIYQQ4jHRaDQP/URVzUuCVSGEEEIIUVnJC1ZCCCGEEKLSkmBVCCGEEEJUWhKsCiGEEEKISkuCVSGEEEIIUWlJsCqEEEIIISqt/wfQCTFT1n233gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from script import process_frame\n",
    "f = process_frame(mdf, verbose=1, save_dir=save_dir, name=name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": false,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}