JSON convert Pandas DF 33 peaks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert JSON to Pandas DF 33 peaks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Date: August 2019 | Author: Hilary Goh | Team: Woodside, Intelligent Assets & Robotics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only using data from one pump\n",
    "* cleanest signal\n",
    "* Ground truth available\n",
    "* ON & OFF state\n",
    "* assume UTC time for both time series?\n",
    "\n",
    "###### OPAM:1473 | FLOC: AU21.A6424AP7 | Thing ID: 00:80:00:00:04:01:21:C0 \n",
    "###### PHD tag: PGP.64FI491.DACA.PV | Group: Membrane Biological Reactor | GTm: m3/hr\n",
    "\n",
    "---\n",
    "\n",
    "###### Data source:\n",
    "* saved json from motorjul31_110839C0ON.ipynb\n",
    "* var timestart=\"2019-07-31 00:06:00\"\n",
    "* var timeend=\"2019-07-31 11:30:00\"\n",
    "* no filtering - full raw dataset\n",
    "* V2 at 30min sampling resolution\n",
    "* GT at 1min sampling resolution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Import libraries and modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import pandas_profiling\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import seaborn as sb\n",
    "sb.set()\n",
    "#sb.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Convert JSON into pandas dataframe\n",
    "* check timestamp conversion\n",
    "* check array (correct columns, correct index)\n",
    "* split pandas dataframe\n",
    "* check keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('motorjul31_110839C0ON.json','r') as fp: #open the file for viewing, not using pandas just yet\n",
    "     ON = json.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('motorjul31_083838C0OFF.json','r') as fp: \n",
    "     OFF = json.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['id', 'start', 'end', 'V1', 'V2', 'V3', 'T', 'gotData', 'groundtruth', 'sensorId', 'sensorType', 'QUUUUuery'])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ON.keys() #check for dictionaries and lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2019-7-31 11:08:39',\n",
       " '2019-7-31 11:08:39',\n",
       " '2019-7-31 11:08:39',\n",
       " '2019-7-31 11:08:39',\n",
       " '2019-7-31 11:08:39',\n",
       " '2019-7-31 11:08:39',\n",
       " '2019-7-31 11:08:39',\n",
       " '2019-7-31 11:08:39',\n",
       " '2019-7-31 11:08:39',\n",
       " '2019-7-31 11:08:39']"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ON['V3']['data']['ts'][0:10] #check timestamp format for vib 3\n",
    "# readings every half hour, all 33 peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['id', 'start', 'end', 'V1', 'V2', 'V3', 'T', 'gotData', 'groundtruth', 'sensorId', 'sensorType', 'QUUUUuery'])"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OFF.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2019-7-31 08:38:38',\n",
       " '2019-7-31 08:38:38',\n",
       " '2019-7-31 08:38:38',\n",
       " '2019-7-31 08:38:38',\n",
       " '2019-7-31 08:38:38',\n",
       " '2019-7-31 08:38:38',\n",
       " '2019-7-31 08:38:38',\n",
       " '2019-7-31 08:38:38',\n",
       " '2019-7-31 08:38:38',\n",
       " '2019-7-31 08:38:38']"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OFF['V3']['data']['ts'][0:10] \n",
    "# readings every half hour, all 33 peaks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Transform each feature from dict/list to table\n",
    "* need to do each feature\n",
    "* so we can merge into one table\n",
    "* match and fill timestamps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsV3ON =pd.to_datetime(ON['V3']['data']['ts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-07-31 11:08:39', '2019-07-31 11:08:39',\n",
       "               '2019-07-31 11:08:39', '2019-07-31 11:08:39',\n",
       "               '2019-07-31 11:08:39'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsV3ON[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsV3OFF =pd.to_datetime(OFF['V3']['data']['ts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-07-31 08:38:38', '2019-07-31 08:38:38',\n",
       "               '2019-07-31 08:38:38', '2019-07-31 08:38:38',\n",
       "               '2019-07-31 08:38:38'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsV3OFF[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Need to get amps and freq for V3 (probe on motor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "ampV3ON = np.array(ON['V3']['data']['amp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "ampV3OFF = np.array(OFF['V3']['data']['amp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02741, 0.02742, 0.02745, 0.02745, 0.02873, 0.02924, 0.02944,\n",
       "       0.02993, 0.03014, 0.0302 ])"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ampV3ON[0:10] #check values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02318, 0.02349, 0.0235 , 0.02355, 0.02372, 0.0238 , 0.02431,\n",
       "       0.02466, 0.02478, 0.02498])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ampV3OFF[0:10] #check values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "freqV3ON = np.array(ON['V3']['data']['freq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "freqV3OFF = np.array(OFF['V3']['data']['freq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 892,  356,  692,  456,  967,  293,    1, 1129, 1330,  888])"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqV3ON[0:10] #check values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 430, 1530,    2,  157,    1,  168,  779,  443,  842, 1418])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqV3OFF[0:10] #check values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33, 2)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afV3ON  = np.vstack([ampV3ON, freqV3ON])\n",
    "afV3ON.T.shape  # T for matrix transpose\n",
    "# should be 33 rows and 2 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33, 2)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afV3OFF  = np.vstack([ampV3OFF, freqV3OFF])\n",
    "afV3OFF.T.shape  # T for matrix transpose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Check pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ampV3ON</th>\n",
       "      <th>freqV3ON</th>\n",
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       "    <tr>\n",
       "      <th>2019-07-31 11:08:39</th>\n",
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       "      <th>2019-07-31 11:08:39</th>\n",
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       "    <tr>\n",
       "      <th>2019-07-31 11:08:39</th>\n",
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       "      <th>2019-07-31 11:08:39</th>\n",
       "      <td>0.09052</td>\n",
       "      <td>16.0</td>\n",
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       "    <tr>\n",
       "      <th>2019-07-31 11:08:39</th>\n",
       "      <td>0.07617</td>\n",
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       "      <th>2019-07-31 11:08:39</th>\n",
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       "      <th>2019-07-31 11:08:39</th>\n",
       "      <td>0.10930</td>\n",
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       "    <tr>\n",
       "      <th>2019-07-31 11:08:39</th>\n",
       "      <td>0.03126</td>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <th>2019-07-31 11:08:39</th>\n",
       "      <td>0.11757</td>\n",
       "      <td>107.0</td>\n",
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       "    <tr>\n",
       "      <th>2019-07-31 11:08:39</th>\n",
       "      <td>0.03075</td>\n",
       "      <td>268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 11:08:39</th>\n",
       "      <td>0.02924</td>\n",
       "      <td>293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 11:08:39</th>\n",
       "      <td>0.02742</td>\n",
       "      <td>356.0</td>\n",
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       "    <tr>\n",
       "      <th>2019-07-31 11:08:39</th>\n",
       "      <td>0.02745</td>\n",
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       "      <th>2019-07-31 11:08:39</th>\n",
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       "      <td>0.02745</td>\n",
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       "      <th>2019-07-31 11:08:39</th>\n",
       "      <td>0.03042</td>\n",
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       "      <th>2019-07-31 11:08:39</th>\n",
       "      <td>0.03024</td>\n",
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       "      <th>2019-07-31 11:08:39</th>\n",
       "      <td>0.03251</td>\n",
       "      <td>817.0</td>\n",
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       "      <th>2019-07-31 11:08:39</th>\n",
       "      <td>0.03020</td>\n",
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       "      <td>0.03024</td>\n",
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      "text/plain": [
       "                     ampV3ON  freqV3ON\n",
       "2019-07-31 11:08:39  0.02944       1.0\n",
       "2019-07-31 11:08:39  0.03157       2.0\n",
       "2019-07-31 11:08:39  0.09325       5.0\n",
       "2019-07-31 11:08:39  0.10050       7.0\n",
       "2019-07-31 11:08:39  0.05106       8.0\n",
       "2019-07-31 11:08:39  0.05547       9.0\n",
       "2019-07-31 11:08:39  0.04832      10.0\n",
       "2019-07-31 11:08:39  0.03125      11.0\n",
       "2019-07-31 11:08:39  0.03806      14.0\n",
       "2019-07-31 11:08:39  0.04121      15.0\n",
       "2019-07-31 11:08:39  0.09052      16.0\n",
       "2019-07-31 11:08:39  0.07617      18.0\n",
       "2019-07-31 11:08:39  0.04217      20.0\n",
       "2019-07-31 11:08:39  0.10930      30.0\n",
       "2019-07-31 11:08:39  0.03126      35.0\n",
       "2019-07-31 11:08:39  0.11757     107.0\n",
       "2019-07-31 11:08:39  0.03075     268.0\n",
       "2019-07-31 11:08:39  0.02924     293.0\n",
       "2019-07-31 11:08:39  0.02742     356.0\n",
       "2019-07-31 11:08:39  0.02745     456.0\n",
       "2019-07-31 11:08:39  0.06322     534.0\n",
       "2019-07-31 11:08:39  0.03142     667.0\n",
       "2019-07-31 11:08:39  0.02745     692.0\n",
       "2019-07-31 11:08:39  0.03042     731.0\n",
       "2019-07-31 11:08:39  0.03024     742.0\n",
       "2019-07-31 11:08:39  0.03251     817.0\n",
       "2019-07-31 11:08:39  0.03020     888.0\n",
       "2019-07-31 11:08:39  0.02741     892.0\n",
       "2019-07-31 11:08:39  0.03426     956.0\n",
       "2019-07-31 11:08:39  0.02873     967.0\n",
       "2019-07-31 11:08:39  0.02993    1129.0\n",
       "2019-07-31 11:08:39  0.03024    1230.0\n",
       "2019-07-31 11:08:39  0.03014    1330.0"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V3ON_df = pd.DataFrame(afV3ON.T, columns = ['ampV3ON','freqV3ON'], index=tsV3ON)\n",
    "V3ON_df = V3ON_df.sort_values(by = 'freqV3ON')\n",
    "V3ON_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create index, will use this to join two df's later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timeON</th>\n",
       "      <th>ampV3ON</th>\n",
       "      <th>freqV3ON</th>\n",
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       "      <th>0</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.09325</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.10050</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.05106</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.05547</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.04832</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03806</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.04121</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.09052</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.07617</td>\n",
       "      <td>18.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.04217</td>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.10930</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03126</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.11757</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03075</td>\n",
       "      <td>268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02924</td>\n",
       "      <td>293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02742</td>\n",
       "      <td>356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02745</td>\n",
       "      <td>456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.06322</td>\n",
       "      <td>534.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03142</td>\n",
       "      <td>667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02745</td>\n",
       "      <td>692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03042</td>\n",
       "      <td>731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03024</td>\n",
       "      <td>742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03251</td>\n",
       "      <td>817.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03020</td>\n",
       "      <td>888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02741</td>\n",
       "      <td>892.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03426</td>\n",
       "      <td>956.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02873</td>\n",
       "      <td>967.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02993</td>\n",
       "      <td>1129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03024</td>\n",
       "      <td>1230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03014</td>\n",
       "      <td>1330.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timeON  ampV3ON  freqV3ON\n",
       "0  2019-07-31 11:08:39  0.02944       1.0\n",
       "1  2019-07-31 11:08:39  0.03157       2.0\n",
       "2  2019-07-31 11:08:39  0.09325       5.0\n",
       "3  2019-07-31 11:08:39  0.10050       7.0\n",
       "4  2019-07-31 11:08:39  0.05106       8.0\n",
       "5  2019-07-31 11:08:39  0.05547       9.0\n",
       "6  2019-07-31 11:08:39  0.04832      10.0\n",
       "7  2019-07-31 11:08:39  0.03125      11.0\n",
       "8  2019-07-31 11:08:39  0.03806      14.0\n",
       "9  2019-07-31 11:08:39  0.04121      15.0\n",
       "10 2019-07-31 11:08:39  0.09052      16.0\n",
       "11 2019-07-31 11:08:39  0.07617      18.0\n",
       "12 2019-07-31 11:08:39  0.04217      20.0\n",
       "13 2019-07-31 11:08:39  0.10930      30.0\n",
       "14 2019-07-31 11:08:39  0.03126      35.0\n",
       "15 2019-07-31 11:08:39  0.11757     107.0\n",
       "16 2019-07-31 11:08:39  0.03075     268.0\n",
       "17 2019-07-31 11:08:39  0.02924     293.0\n",
       "18 2019-07-31 11:08:39  0.02742     356.0\n",
       "19 2019-07-31 11:08:39  0.02745     456.0\n",
       "20 2019-07-31 11:08:39  0.06322     534.0\n",
       "21 2019-07-31 11:08:39  0.03142     667.0\n",
       "22 2019-07-31 11:08:39  0.02745     692.0\n",
       "23 2019-07-31 11:08:39  0.03042     731.0\n",
       "24 2019-07-31 11:08:39  0.03024     742.0\n",
       "25 2019-07-31 11:08:39  0.03251     817.0\n",
       "26 2019-07-31 11:08:39  0.03020     888.0\n",
       "27 2019-07-31 11:08:39  0.02741     892.0\n",
       "28 2019-07-31 11:08:39  0.03426     956.0\n",
       "29 2019-07-31 11:08:39  0.02873     967.0\n",
       "30 2019-07-31 11:08:39  0.02993    1129.0\n",
       "31 2019-07-31 11:08:39  0.03024    1230.0\n",
       "32 2019-07-31 11:08:39  0.03014    1330.0"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V3ON_df = V3ON_df.reset_index().rename(columns={'index':'timeON'})\n",
    "V3ON_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ampV3OFF</th>\n",
       "      <th>freqV3OFF</th>\n",
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       "  <tbody>\n",
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       "      <th>2019-07-31 08:38:38</th>\n",
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       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
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       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.14007</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.05014</td>\n",
       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.03454</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.03233</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.04130</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.09480</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.05719</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.05777</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.03141</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.07105</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.02355</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.02380</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.02512</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.03800</td>\n",
       "      <td>207.0</td>\n",
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       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.02988</td>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.03085</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.03628</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.02602</td>\n",
       "      <td>393.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.02318</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.02466</td>\n",
       "      <td>443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.04402</td>\n",
       "      <td>534.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.02431</td>\n",
       "      <td>779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.02478</td>\n",
       "      <td>842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.03382</td>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.03000</td>\n",
       "      <td>1142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.02498</td>\n",
       "      <td>1418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.02732</td>\n",
       "      <td>1516.0</td>\n",
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       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.02602</td>\n",
       "      <td>1529.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.02349</td>\n",
       "      <td>1530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 08:38:38</th>\n",
       "      <td>0.03165</td>\n",
       "      <td>1566.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ampV3OFF  freqV3OFF\n",
       "2019-07-31 08:38:38   0.02372        1.0\n",
       "2019-07-31 08:38:38   0.02350        2.0\n",
       "2019-07-31 08:38:38   0.07258        5.0\n",
       "2019-07-31 08:38:38   0.14007        7.0\n",
       "2019-07-31 08:38:38   0.05014        9.0\n",
       "2019-07-31 08:38:38   0.03454       11.0\n",
       "2019-07-31 08:38:38   0.03233       14.0\n",
       "2019-07-31 08:38:38   0.04130       15.0\n",
       "2019-07-31 08:38:38   0.09480       18.0\n",
       "2019-07-31 08:38:38   0.05719       20.0\n",
       "2019-07-31 08:38:38   0.05777       30.0\n",
       "2019-07-31 08:38:38   0.03141       32.0\n",
       "2019-07-31 08:38:38   0.07105      107.0\n",
       "2019-07-31 08:38:38   0.02355      157.0\n",
       "2019-07-31 08:38:38   0.02380      168.0\n",
       "2019-07-31 08:38:38   0.02512      182.0\n",
       "2019-07-31 08:38:38   0.03800      207.0\n",
       "2019-07-31 08:38:38   0.02988      213.0\n",
       "2019-07-31 08:38:38   0.03085      306.0\n",
       "2019-07-31 08:38:38   0.03628      343.0\n",
       "2019-07-31 08:38:38   0.02602      393.0\n",
       "2019-07-31 08:38:38   0.02318      430.0\n",
       "2019-07-31 08:38:38   0.02466      443.0\n",
       "2019-07-31 08:38:38   0.04402      534.0\n",
       "2019-07-31 08:38:38   0.02431      779.0\n",
       "2019-07-31 08:38:38   0.02478      842.0\n",
       "2019-07-31 08:38:38   0.03382     1131.0\n",
       "2019-07-31 08:38:38   0.03000     1142.0\n",
       "2019-07-31 08:38:38   0.02498     1418.0\n",
       "2019-07-31 08:38:38   0.02732     1516.0\n",
       "2019-07-31 08:38:38   0.02602     1529.0\n",
       "2019-07-31 08:38:38   0.02349     1530.0\n",
       "2019-07-31 08:38:38   0.03165     1566.0"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V3OFF_df = pd.DataFrame(afV3OFF.T, columns = ['ampV3OFF','freqV3OFF'], index=tsV3OFF)\n",
    "V3OFF_df = V3OFF_df.sort_values(by = 'freqV3OFF')\n",
    "V3OFF_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timeOFF</th>\n",
       "      <th>ampV3OFF</th>\n",
       "      <th>freqV3OFF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02372</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02350</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.07258</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.14007</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.05014</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03454</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03233</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.04130</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.09480</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.05719</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.05777</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03141</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.07105</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02355</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02380</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02512</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03800</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02988</td>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03085</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03628</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02602</td>\n",
       "      <td>393.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02318</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02466</td>\n",
       "      <td>443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.04402</td>\n",
       "      <td>534.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02431</td>\n",
       "      <td>779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02478</td>\n",
       "      <td>842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03382</td>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>1142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02498</td>\n",
       "      <td>1418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02732</td>\n",
       "      <td>1516.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02602</td>\n",
       "      <td>1529.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02349</td>\n",
       "      <td>1530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03165</td>\n",
       "      <td>1566.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               timeOFF  ampV3OFF  freqV3OFF\n",
       "0  2019-07-31 08:38:38   0.02372        1.0\n",
       "1  2019-07-31 08:38:38   0.02350        2.0\n",
       "2  2019-07-31 08:38:38   0.07258        5.0\n",
       "3  2019-07-31 08:38:38   0.14007        7.0\n",
       "4  2019-07-31 08:38:38   0.05014        9.0\n",
       "5  2019-07-31 08:38:38   0.03454       11.0\n",
       "6  2019-07-31 08:38:38   0.03233       14.0\n",
       "7  2019-07-31 08:38:38   0.04130       15.0\n",
       "8  2019-07-31 08:38:38   0.09480       18.0\n",
       "9  2019-07-31 08:38:38   0.05719       20.0\n",
       "10 2019-07-31 08:38:38   0.05777       30.0\n",
       "11 2019-07-31 08:38:38   0.03141       32.0\n",
       "12 2019-07-31 08:38:38   0.07105      107.0\n",
       "13 2019-07-31 08:38:38   0.02355      157.0\n",
       "14 2019-07-31 08:38:38   0.02380      168.0\n",
       "15 2019-07-31 08:38:38   0.02512      182.0\n",
       "16 2019-07-31 08:38:38   0.03800      207.0\n",
       "17 2019-07-31 08:38:38   0.02988      213.0\n",
       "18 2019-07-31 08:38:38   0.03085      306.0\n",
       "19 2019-07-31 08:38:38   0.03628      343.0\n",
       "20 2019-07-31 08:38:38   0.02602      393.0\n",
       "21 2019-07-31 08:38:38   0.02318      430.0\n",
       "22 2019-07-31 08:38:38   0.02466      443.0\n",
       "23 2019-07-31 08:38:38   0.04402      534.0\n",
       "24 2019-07-31 08:38:38   0.02431      779.0\n",
       "25 2019-07-31 08:38:38   0.02478      842.0\n",
       "26 2019-07-31 08:38:38   0.03382     1131.0\n",
       "27 2019-07-31 08:38:38   0.03000     1142.0\n",
       "28 2019-07-31 08:38:38   0.02498     1418.0\n",
       "29 2019-07-31 08:38:38   0.02732     1516.0\n",
       "30 2019-07-31 08:38:38   0.02602     1529.0\n",
       "31 2019-07-31 08:38:38   0.02349     1530.0\n",
       "32 2019-07-31 08:38:38   0.03165     1566.0"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V3OFF_df = V3OFF_df.reset_index().rename(columns={'index':'timeOFF'})\n",
    "V3OFF_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Combine into one dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timeON</th>\n",
       "      <th>ampV3ON</th>\n",
       "      <th>freqV3ON</th>\n",
       "      <th>timeOFF</th>\n",
       "      <th>ampV3OFF</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02944</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02372</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03157</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02350</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.09325</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.07258</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.10050</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.14007</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.05106</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.05014</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.05547</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03454</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.04832</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03233</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.04130</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03806</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.09480</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.04121</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.05719</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.09052</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.05777</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.07617</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03141</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.04217</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.07105</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.10930</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02355</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03126</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02380</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.11757</td>\n",
       "      <td>107.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02512</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03075</td>\n",
       "      <td>268.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03800</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02924</td>\n",
       "      <td>293.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02988</td>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02742</td>\n",
       "      <td>356.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03085</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02745</td>\n",
       "      <td>456.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03628</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.06322</td>\n",
       "      <td>534.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02602</td>\n",
       "      <td>393.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03142</td>\n",
       "      <td>667.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02318</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02745</td>\n",
       "      <td>692.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02466</td>\n",
       "      <td>443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03042</td>\n",
       "      <td>731.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.04402</td>\n",
       "      <td>534.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03024</td>\n",
       "      <td>742.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02431</td>\n",
       "      <td>779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03251</td>\n",
       "      <td>817.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02478</td>\n",
       "      <td>842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03020</td>\n",
       "      <td>888.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03382</td>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02741</td>\n",
       "      <td>892.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>1142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03426</td>\n",
       "      <td>956.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02498</td>\n",
       "      <td>1418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02873</td>\n",
       "      <td>967.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02732</td>\n",
       "      <td>1516.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02993</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02602</td>\n",
       "      <td>1529.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03024</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02349</td>\n",
       "      <td>1530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03014</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03165</td>\n",
       "      <td>1566.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timeON  ampV3ON  freqV3ON             timeOFF  ampV3OFF  \\\n",
       "0  2019-07-31 11:08:39  0.02944       1.0 2019-07-31 08:38:38   0.02372   \n",
       "1  2019-07-31 11:08:39  0.03157       2.0 2019-07-31 08:38:38   0.02350   \n",
       "2  2019-07-31 11:08:39  0.09325       5.0 2019-07-31 08:38:38   0.07258   \n",
       "3  2019-07-31 11:08:39  0.10050       7.0 2019-07-31 08:38:38   0.14007   \n",
       "4  2019-07-31 11:08:39  0.05106       8.0 2019-07-31 08:38:38   0.05014   \n",
       "5  2019-07-31 11:08:39  0.05547       9.0 2019-07-31 08:38:38   0.03454   \n",
       "6  2019-07-31 11:08:39  0.04832      10.0 2019-07-31 08:38:38   0.03233   \n",
       "7  2019-07-31 11:08:39  0.03125      11.0 2019-07-31 08:38:38   0.04130   \n",
       "8  2019-07-31 11:08:39  0.03806      14.0 2019-07-31 08:38:38   0.09480   \n",
       "9  2019-07-31 11:08:39  0.04121      15.0 2019-07-31 08:38:38   0.05719   \n",
       "10 2019-07-31 11:08:39  0.09052      16.0 2019-07-31 08:38:38   0.05777   \n",
       "11 2019-07-31 11:08:39  0.07617      18.0 2019-07-31 08:38:38   0.03141   \n",
       "12 2019-07-31 11:08:39  0.04217      20.0 2019-07-31 08:38:38   0.07105   \n",
       "13 2019-07-31 11:08:39  0.10930      30.0 2019-07-31 08:38:38   0.02355   \n",
       "14 2019-07-31 11:08:39  0.03126      35.0 2019-07-31 08:38:38   0.02380   \n",
       "15 2019-07-31 11:08:39  0.11757     107.0 2019-07-31 08:38:38   0.02512   \n",
       "16 2019-07-31 11:08:39  0.03075     268.0 2019-07-31 08:38:38   0.03800   \n",
       "17 2019-07-31 11:08:39  0.02924     293.0 2019-07-31 08:38:38   0.02988   \n",
       "18 2019-07-31 11:08:39  0.02742     356.0 2019-07-31 08:38:38   0.03085   \n",
       "19 2019-07-31 11:08:39  0.02745     456.0 2019-07-31 08:38:38   0.03628   \n",
       "20 2019-07-31 11:08:39  0.06322     534.0 2019-07-31 08:38:38   0.02602   \n",
       "21 2019-07-31 11:08:39  0.03142     667.0 2019-07-31 08:38:38   0.02318   \n",
       "22 2019-07-31 11:08:39  0.02745     692.0 2019-07-31 08:38:38   0.02466   \n",
       "23 2019-07-31 11:08:39  0.03042     731.0 2019-07-31 08:38:38   0.04402   \n",
       "24 2019-07-31 11:08:39  0.03024     742.0 2019-07-31 08:38:38   0.02431   \n",
       "25 2019-07-31 11:08:39  0.03251     817.0 2019-07-31 08:38:38   0.02478   \n",
       "26 2019-07-31 11:08:39  0.03020     888.0 2019-07-31 08:38:38   0.03382   \n",
       "27 2019-07-31 11:08:39  0.02741     892.0 2019-07-31 08:38:38   0.03000   \n",
       "28 2019-07-31 11:08:39  0.03426     956.0 2019-07-31 08:38:38   0.02498   \n",
       "29 2019-07-31 11:08:39  0.02873     967.0 2019-07-31 08:38:38   0.02732   \n",
       "30 2019-07-31 11:08:39  0.02993    1129.0 2019-07-31 08:38:38   0.02602   \n",
       "31 2019-07-31 11:08:39  0.03024    1230.0 2019-07-31 08:38:38   0.02349   \n",
       "32 2019-07-31 11:08:39  0.03014    1330.0 2019-07-31 08:38:38   0.03165   \n",
       "\n",
       "    freqV3OFF  \n",
       "0         1.0  \n",
       "1         2.0  \n",
       "2         5.0  \n",
       "3         7.0  \n",
       "4         9.0  \n",
       "5        11.0  \n",
       "6        14.0  \n",
       "7        15.0  \n",
       "8        18.0  \n",
       "9        20.0  \n",
       "10       30.0  \n",
       "11       32.0  \n",
       "12      107.0  \n",
       "13      157.0  \n",
       "14      168.0  \n",
       "15      182.0  \n",
       "16      207.0  \n",
       "17      213.0  \n",
       "18      306.0  \n",
       "19      343.0  \n",
       "20      393.0  \n",
       "21      430.0  \n",
       "22      443.0  \n",
       "23      534.0  \n",
       "24      779.0  \n",
       "25      842.0  \n",
       "26     1131.0  \n",
       "27     1142.0  \n",
       "28     1418.0  \n",
       "29     1516.0  \n",
       "30     1529.0  \n",
       "31     1530.0  \n",
       "32     1566.0  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C0V3 = V3ON_df.join(V3OFF_df, how = 'left')\n",
    "C0V3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timeON       datetime64[ns]\n",
       "ampV3ON             float64\n",
       "freqV3ON            float64\n",
       "timeOFF      datetime64[ns]\n",
       "ampV3OFF            float64\n",
       "freqV3OFF           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C0V3.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Calculate delta values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "want to see change in particular frequency\n",
    "\n",
    "new columns --> calculation\n",
    "* freqV3ON - freqV3OFF\n",
    "* ampV3On - ampV3OFF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timeON</th>\n",
       "      <th>ampV3ON</th>\n",
       "      <th>freqV3ON</th>\n",
       "      <th>timeOFF</th>\n",
       "      <th>ampV3OFF</th>\n",
       "      <th>freqV3OFF</th>\n",
       "      <th>freq_delta</th>\n",
       "      <th>amp_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02944</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02372</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03157</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02350</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.09325</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.07258</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.10050</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.14007</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.05106</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.05014</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.05547</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03454</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.02093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.04832</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03233</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.01599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.04130</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.01005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03806</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.09480</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.05674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.04121</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.05719</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.01598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.09052</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.05777</td>\n",
       "      <td>30.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.03275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.07617</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03141</td>\n",
       "      <td>32.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.04476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.04217</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.07105</td>\n",
       "      <td>107.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.02888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.10930</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02355</td>\n",
       "      <td>157.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>0.08575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03126</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02380</td>\n",
       "      <td>168.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.00746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.11757</td>\n",
       "      <td>107.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02512</td>\n",
       "      <td>182.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.09245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03075</td>\n",
       "      <td>268.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03800</td>\n",
       "      <td>207.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.00725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02924</td>\n",
       "      <td>293.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02988</td>\n",
       "      <td>213.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.00064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02742</td>\n",
       "      <td>356.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03085</td>\n",
       "      <td>306.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.00343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02745</td>\n",
       "      <td>456.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03628</td>\n",
       "      <td>343.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>0.00883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.06322</td>\n",
       "      <td>534.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02602</td>\n",
       "      <td>393.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0.03720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03142</td>\n",
       "      <td>667.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02318</td>\n",
       "      <td>430.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>0.00824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02745</td>\n",
       "      <td>692.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02466</td>\n",
       "      <td>443.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.00279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03042</td>\n",
       "      <td>731.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.04402</td>\n",
       "      <td>534.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.01360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03024</td>\n",
       "      <td>742.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02431</td>\n",
       "      <td>779.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.00593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03251</td>\n",
       "      <td>817.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02478</td>\n",
       "      <td>842.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.00773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03020</td>\n",
       "      <td>888.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03382</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>0.00362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02741</td>\n",
       "      <td>892.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>1142.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.00259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03426</td>\n",
       "      <td>956.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02498</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>0.00928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02873</td>\n",
       "      <td>967.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02732</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>0.00141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02993</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02602</td>\n",
       "      <td>1529.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.00391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03024</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02349</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.00675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03014</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.03165</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.00151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timeON  ampV3ON  freqV3ON             timeOFF  ampV3OFF  \\\n",
       "0  2019-07-31 11:08:39  0.02944       1.0 2019-07-31 08:38:38   0.02372   \n",
       "1  2019-07-31 11:08:39  0.03157       2.0 2019-07-31 08:38:38   0.02350   \n",
       "2  2019-07-31 11:08:39  0.09325       5.0 2019-07-31 08:38:38   0.07258   \n",
       "3  2019-07-31 11:08:39  0.10050       7.0 2019-07-31 08:38:38   0.14007   \n",
       "4  2019-07-31 11:08:39  0.05106       8.0 2019-07-31 08:38:38   0.05014   \n",
       "5  2019-07-31 11:08:39  0.05547       9.0 2019-07-31 08:38:38   0.03454   \n",
       "6  2019-07-31 11:08:39  0.04832      10.0 2019-07-31 08:38:38   0.03233   \n",
       "7  2019-07-31 11:08:39  0.03125      11.0 2019-07-31 08:38:38   0.04130   \n",
       "8  2019-07-31 11:08:39  0.03806      14.0 2019-07-31 08:38:38   0.09480   \n",
       "9  2019-07-31 11:08:39  0.04121      15.0 2019-07-31 08:38:38   0.05719   \n",
       "10 2019-07-31 11:08:39  0.09052      16.0 2019-07-31 08:38:38   0.05777   \n",
       "11 2019-07-31 11:08:39  0.07617      18.0 2019-07-31 08:38:38   0.03141   \n",
       "12 2019-07-31 11:08:39  0.04217      20.0 2019-07-31 08:38:38   0.07105   \n",
       "13 2019-07-31 11:08:39  0.10930      30.0 2019-07-31 08:38:38   0.02355   \n",
       "14 2019-07-31 11:08:39  0.03126      35.0 2019-07-31 08:38:38   0.02380   \n",
       "15 2019-07-31 11:08:39  0.11757     107.0 2019-07-31 08:38:38   0.02512   \n",
       "16 2019-07-31 11:08:39  0.03075     268.0 2019-07-31 08:38:38   0.03800   \n",
       "17 2019-07-31 11:08:39  0.02924     293.0 2019-07-31 08:38:38   0.02988   \n",
       "18 2019-07-31 11:08:39  0.02742     356.0 2019-07-31 08:38:38   0.03085   \n",
       "19 2019-07-31 11:08:39  0.02745     456.0 2019-07-31 08:38:38   0.03628   \n",
       "20 2019-07-31 11:08:39  0.06322     534.0 2019-07-31 08:38:38   0.02602   \n",
       "21 2019-07-31 11:08:39  0.03142     667.0 2019-07-31 08:38:38   0.02318   \n",
       "22 2019-07-31 11:08:39  0.02745     692.0 2019-07-31 08:38:38   0.02466   \n",
       "23 2019-07-31 11:08:39  0.03042     731.0 2019-07-31 08:38:38   0.04402   \n",
       "24 2019-07-31 11:08:39  0.03024     742.0 2019-07-31 08:38:38   0.02431   \n",
       "25 2019-07-31 11:08:39  0.03251     817.0 2019-07-31 08:38:38   0.02478   \n",
       "26 2019-07-31 11:08:39  0.03020     888.0 2019-07-31 08:38:38   0.03382   \n",
       "27 2019-07-31 11:08:39  0.02741     892.0 2019-07-31 08:38:38   0.03000   \n",
       "28 2019-07-31 11:08:39  0.03426     956.0 2019-07-31 08:38:38   0.02498   \n",
       "29 2019-07-31 11:08:39  0.02873     967.0 2019-07-31 08:38:38   0.02732   \n",
       "30 2019-07-31 11:08:39  0.02993    1129.0 2019-07-31 08:38:38   0.02602   \n",
       "31 2019-07-31 11:08:39  0.03024    1230.0 2019-07-31 08:38:38   0.02349   \n",
       "32 2019-07-31 11:08:39  0.03014    1330.0 2019-07-31 08:38:38   0.03165   \n",
       "\n",
       "    freqV3OFF  freq_delta  amp_delta  \n",
       "0         1.0         0.0    0.00572  \n",
       "1         2.0         0.0    0.00807  \n",
       "2         5.0         0.0    0.02067  \n",
       "3         7.0         0.0    0.03957  \n",
       "4         9.0         1.0    0.00092  \n",
       "5        11.0         2.0    0.02093  \n",
       "6        14.0         4.0    0.01599  \n",
       "7        15.0         4.0    0.01005  \n",
       "8        18.0         4.0    0.05674  \n",
       "9        20.0         5.0    0.01598  \n",
       "10       30.0        14.0    0.03275  \n",
       "11       32.0        14.0    0.04476  \n",
       "12      107.0        87.0    0.02888  \n",
       "13      157.0       127.0    0.08575  \n",
       "14      168.0       133.0    0.00746  \n",
       "15      182.0        75.0    0.09245  \n",
       "16      207.0        61.0    0.00725  \n",
       "17      213.0        80.0    0.00064  \n",
       "18      306.0        50.0    0.00343  \n",
       "19      343.0       113.0    0.00883  \n",
       "20      393.0       141.0    0.03720  \n",
       "21      430.0       237.0    0.00824  \n",
       "22      443.0       249.0    0.00279  \n",
       "23      534.0       197.0    0.01360  \n",
       "24      779.0        37.0    0.00593  \n",
       "25      842.0        25.0    0.00773  \n",
       "26     1131.0       243.0    0.00362  \n",
       "27     1142.0       250.0    0.00259  \n",
       "28     1418.0       462.0    0.00928  \n",
       "29     1516.0       549.0    0.00141  \n",
       "30     1529.0       400.0    0.00391  \n",
       "31     1530.0       300.0    0.00675  \n",
       "32     1566.0       236.0    0.00151  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C0V3['freq_delta'] = C0V3.apply(lambda row: row.freqV3ON - row.freqV3OFF, axis = 1).abs()\n",
    "C0V3['amp_delta'] = C0V3.apply(lambda row: row.ampV3ON - row.ampV3OFF, axis = 1).abs()\n",
    "C0V3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "<div class=\"container pandas-profiling\">\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Overview</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Dataset info</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Number of variables</th>\n",
       "                <td>8 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Number of observations</th>\n",
       "                <td>33 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total Missing (%)</th>\n",
       "                <td>0.0% </td>\n",
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       "            <tr>\n",
       "                <th>Total size in memory</th>\n",
       "                <td>2.1 KiB </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Average record size in memory</th>\n",
       "                <td>66.4 B </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
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       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Variables types</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Numeric</th>\n",
       "                <td>5 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Categorical</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Boolean</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Date</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Text (Unique)</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Rejected</th>\n",
       "                <td>3 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Unsupported</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n",
       "        \n",
       "        <p class=\"h4\">Warnings</p>\n",
       "        <ul class=\"list-unstyled\"><li><a href=\"#pp_var_freqV3OFF\"><code>freqV3OFF</code></a> is highly correlated with <a href=\"#pp_var_freqV3ON\"><code>freqV3ON</code></a> (ρ = 0.9549) <span class=\"label label-primary\">Rejected</span></li><li><a href=\"#pp_var_freq_delta\"><code>freq_delta</code></a> has 4 / 12.1% zeros <span class=\"label label-info\">Zeros</span></li><li><a href=\"#pp_var_timeOFF\"><code>timeOFF</code></a> has constant value 2019-07-31 08:38:38 <span class=\"label label-primary\">Rejected</span></li><li><a href=\"#pp_var_timeON\"><code>timeON</code></a> has constant value 2019-07-31 11:08:39 <span class=\"label label-primary\">Rejected</span></li> </ul>\n",
       "    </div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Variables</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_ampV3OFF\">ampV3OFF<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>32</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>97.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.039458</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0.02318</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>0.14007</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram2937059919368536635\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAABw0lEQVR4nO3dsa3TYBSG4eMrWmeAyN6CIdiJmp0Ygi1sZQBHFBTEFBCaiz7dROE3Ds9TJlLOb8mvjpMm3bquazU2z3ON49h6LDs3TVMNw9B05rum037p%2B76qfl7w4XDY4gjsyLIsNY7j7/umpU0C6bquqqoOh8OrQN5//Hzz53359OEh5%2BLfdr1vWnppPhF2RCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAg2%2BZfbR/PPuPwtNggET7FB7mHr8BY2CAQCgeC/fcRqwWPc/tkgENggT%2BDWTdVqSz3DBt0kkHVdq6pqWZZX733/9rX1cd7sT%2BdN7rmWW2fcM%2BeeGfd41PVfX7veNy116wZT53mucRxbj2XnpmmqYRiaztwkkMvlUqfTqfq%2Br67rWo9nZ9Z1rfP5XMfjsV5e2n5t3iQQ2Au/YkEgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQ/AAa6WPjdFLAagAAAABJRU5ErkJggg%3D%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives2937059919368536635,#minihistogram2937059919368536635\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives2937059919368536635\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles2937059919368536635\"\n",
       "                                                  aria-controls=\"quantiles2937059919368536635\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram2937059919368536635\" aria-controls=\"histogram2937059919368536635\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common2937059919368536635\" aria-controls=\"common2937059919368536635\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme2937059919368536635\" aria-controls=\"extreme2937059919368536635\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles2937059919368536635\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0.02318</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0.023496</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>0.02478</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>0.03085</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>0.0413</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>0.081468</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>0.14007</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>0.11689</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>0.01652</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>0.024797</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.62844</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>8.165</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>0.039458</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>0.016594</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>2.6575</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>1.3021</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>0.0006149</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>344.0 B</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram2937059919368536635\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAAgAElEQVR4nO3df3TV9X348VdMIDgGsYCQRJAyB5UfyiowkWHBH%2BPHbF3FHnVUCp06rUJFRAuydayrhDqtVCmuOkXbrhU9VcamR4FJgQ6pyo9RLVNUrChgoFN%2BWI0In%2B8ffslMAxL0fXNzyeNxzj2n93M/uXl93g3ps5/7yb1FWZZlAQBAMkflewAAgCONwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDESvI9QHOwb9%2B%2B2Lx5c7Rp0yaKioryPQ4ANAtZlsWuXbuisrIyjjqqcc8pCaxGsHnz5ujSpUu%2BxwCAZmnTpk3RuXPnRv2eAqsRtGnTJiI%2B%2BC%2B4bdu2eZ4GAJqHnTt3RpcuXWr/d7gxCaxGsP9lwbZt2wosAGhk%2Bbg8x0XuAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxH/Zc4PpPeyzfIzTYMzeOyPcIANAonMECAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBizT6wqqqqYsCAAdGmTZvo2LFjfPGLX4znn3%2B%2Bzj41NTUxYcKE6NChQ7Ru3TrOPffceO211/I0MQDQ1DX7wFq6dGlcddVVsXLlyli0aFG8//77MWzYsHj77bdr95k4cWI8/PDDcf/998cvfvGL2L17d3z%2B85%2BPvXv35nFyAKCpKsn3APn22GOP1bk/d%2B7c6NixY6xatSo%2B97nPxY4dO%2BLuu%2B%2BOH/3oR3H22WdHRMSPf/zj6NKlSyxevDiGDx%2Bej7EBgCas2Z/B%2Bn07duyIiIh27dpFRMSqVatiz549MWzYsNp9Kisro0%2BfPrFixYq8zAgANG3N/gzWh2VZFpMmTYrBgwdHnz59IiJi69at0bJly/jUpz5VZ99OnTrF1q1bD/g8NTU1UVNTU3t/586duRsaAGhynMH6kPHjx8e6devipz/96SH3zbIsioqKDvhYVVVVlJWV1d66dOmSelQAoAkTWP/fhAkTYsGCBbFkyZLo3Llz7fby8vJ477334s0336yzf3V1dXTq1OmAzzV16tTYsWNH7W3Tpk05nR0AaFqafWBlWRbjx4%2BPhx56KJ544ono1q1bncf79esXLVq0iEWLFtVu27JlSzz77LMxaNCgAz5naWlptG3bts4NAGg%2Bmv01WFdddVX85Cc/iX/7t3%2BLNm3a1F5XVVZWFkcffXSUlZXFJZdcEtdee220b98%2B2rVrF5MnT46TTjqp9q8KAQA%2BrNkH1h133BEREUOHDq2zfe7cuTFu3LiIiLj11lujpKQkLrjggnjnnXfirLPOinvvvTeKi4sbeVoAoBA0%2B8DKsuyQ%2B7Rq1Spuv/32uP322xthIgCg0DX7a7AAAFITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEmn1gLVu2LL7whS9EZWVlFBUVxfz58%2Bs8Pm7cuCgqKqpzGzhwYJ6mBQAKQbMPrLfffjv69u0bs2fPPug%2BI0aMiC1bttTeHn300UacEAAoNCX5HiDfRo4cGSNHjvzIfUpLS6O8vLyRJgIACl2zP4PVED//%2Bc%2BjY8eO0aNHj7jsssuiurr6I/evqamJnTt31rkBAM2HwDqEkSNHxr/%2B67/GE088Ebfccks8/fTTceaZZ0ZNTc1Bv6aqqirKyspqb126dGnEiQGAfGv2LxEeyoUXXlj7n/v06RP9%2B/ePrl27xiOPPBKjRo064NdMnTo1Jk2aVHt/586dIgsAmhGBdZgqKiqia9eusWHDhoPuU1paGqWlpY04FQDQlHiJ8DD99re/jU2bNkVFRUW%2BRwEAmqhmfwZr9%2B7d8eKLL9be37hxY6xduzbatWsX7dq1i%2BnTp8f5558fFRUV8corr8QNN9wQHTp0iPPOOy%2BPUwMATVmzD6xnnnkmzjjjjNr7%2B6%2BdGjt2bNxxxx3xq1/9Kn74wx/GW2%2B9FRUVFXHGGWfEvHnzok2bNvkaGQBo4pp9YA0dOjSyLDvo448//ngjTgMAHAlcgwUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgUbWD/%2B8Y/j3XffzfcYAAD1FGxgTZo0KcrLy%2BPyyy%2BPp556Kt/jAADUKtjA2rx5c9xzzz2xZcuWGDx4cPTu3TtuueWW2LZtW75HAwCauYINrJKSkhg1alQsWLAgXn311Rg7dmzcc8890blz5xg1alQ88sgjH/kROAAAuVKwgfVh5eXlcdZZZ8XQoUOjqKgonnnmmRg9enR07949li9fnu/xAIBmpqADa/v27TFr1qzo27dv/Nmf/VlUV1fH/Pnz4ze/%2BU28/vrr8fnPfz6%2B8pWv5HtMAKCZKcn3AB/XeeedF48%2B%2Bmh069YtLr300hg7dmwce%2ByxtY//4R/%2BYVx//fVx22235XFKAKA5KtjAatu2bSxevDhOP/30g%2B5TUVERGzZsaMSpAAAKOLDuu%2B%2B%2BQ%2B5TVFQUJ5xwQiNMAwDwfwr2GqxrrrkmZs%2BeXW/797///bj22mvzMBEAwAcKNrAefPDBGDhwYL3tp512WsybNy8PEwEAfKBgA2v79u3xqU99qt72tm3bxvbt2/MwEQDABwo2sE444YR4/PHH621//PHHo1u3bnmYCADgAwV7kfvEiRNj4sSJ8dvf/jbOPPPMiIj4z//8z7jpppvi5ptvzvN0AEBzVrCBddlll8W7774bM2bMiL//%2B7%2BPiIjOnTvHbbfdFn/913%2Bd5%2BkAgOasYAMrImLChAkxYcKE2LJlSxx99NFxzDHH5HskAIDCDqz9Kioq8j0CAECtgr3Ifdu2bfHVr341jj/%2B%2BGjVqlW0bNmyzg0AIF8K9gzWuHHj4qWXXorrrrsuKioqoqioKN8jAQBERAEH1rJly2LZsmXx2c9%2BNt%2BjAADUUbAvEXbu3NlZKwCgSSrYwLr11ltj6tSp8dprr%2BV7FACAOgr2JcIxY8bErl27omvXrtG2bdto0aJFncerq6vzNBkA0NwVbGDNnDkz3yMAABxQwQbWJZdcku8RAAAOqGCvwYqIeOWVV2L69OkxZsyY2pcEFy5cGOvXr8/zZABAc1awgbV8%2BfLo3bt3LF26NB544IHYvXt3RESsXr06vvnNb%2BZ5OgCgOSvYwPrGN74R06dPjyVLltR55/YzzzwzVq5cmcfJAIDmrmADa926dfGlL32p3vaOHTvGtm3b8jARAMAHCjawjjnmmNi6dWu97WvXro3jjjsuDxMBAHygYAProosuiilTpsS2bdtq39H9l7/8ZUyePDkuvvjiPE8HADRnBRtYM2bMiPLy8qioqIjdu3dHr169YtCgQTFgwID4u7/7u3yPBwA0YwX7PlgtW7aMefPmxQsvvBCrV6%2BOffv2xSmnnBInnnhivkcDAJq5gg2s/Xr06BE9evTI9xgAALUKNrD%2B5m/%2B5iMfv/POOxtpEgCAugo2sLZs2VLn/p49e%2BK5556LXbt2xec%2B97k8TQUAUMCB9e///u/1tr3//vvxta99LXr27JmHiQAAPlCwf0V4ICUlJTF58uT4p3/6p3yPAgA0Y0dUYEVEvPzyy7Fnz558jwEANGMF%2BxLh9ddfX%2Bd%2BlmWxZcuWWLBgQXz5y1/O01QAAAUcWE8%2B%2BWSd%2B0cddVQce%2ByxMXPmzLjsssvyNBUAQAEH1vLly/M9AgDAAR1x12ABAORbwZ7BGjBgQO2HPB/KU089leNpAAD%2BT8EG1hlnnBE/%2BMEPokePHnHaaadFRMTKlSvj%2Beefj8svvzxKS0vzPCEA0FwVbGC99dZbcdVVV8WMGTPqbJ82bVq88cYb8S//8i95mgwAaO4K9hqsBx54IL761a/W2z5u3Lh48MEH8zARAMAHCjawSktLY8WKFfW2r1ixwsuDAEBeFexLhF//%2BtfjiiuuiDVr1sTAgQMj4oNrsO6666644YYb8jwdANCcFWxgTZs2Lbp16xbf%2B9734p577omIiJ49e8Zdd90Vo0ePzvN0AEBzVrCBFRExevRoMQUANDkFew1WRMTOnTvj3nvvjW9%2B85vx5ptvRkTEf//3f8eWLVsa/BzLli2LL3zhC1FZWRlFRUUxf/78Oo9nWRbTp0%2BPysrKOProo2Po0KHx3HPPJT0OAODIUrCB9eyzz0aPHj3iW9/6VlRVVdUG1gMPPBBTpkxp8PO8/fbb0bdv35g9e/YBH7/pppviu9/9bsyePTuefvrpKC8vjz//8z%2BPXbt2JTkOAODIU7CBdc0118To0aPjpZdeilatWtVuP%2Becc2LZsmUNfp6RI0fGt7/97Rg1alS9x7Isi1mzZsW0adNi1KhR0adPn7jvvvvid7/7XfzkJz9JchwAwJGnYAPr6aefjiuvvLLex%2BUcd9xxh/US4UfZuHFjbN26NYYNG1a7rbS0NIYMGXLAt4jYr6amJnbu3FnnBgA0HwUbWC1btozdu3fX275hw4bo0KFDku%2BxdevWiIjo1KlTne2dOnWqfexAqqqqoqysrPbWpUuXJPMAAIWhYAPr3HPPjX/8x3%2BM999/PyIiioqK4vXXX48pU6Yc8OW%2BT%2BL3z5JlWfaRHzQ9derU2LFjR%2B1t06ZNSecBAJq2gg2sW265JTZv3hzl5eXxzjvvxJlnnhl/9Ed/FK1atar3%2BYQfV3l5eUREvbNV1dXV9c5qfVhpaWm0bdu2zg0AaD4K9n2wysrKYsWKFbFo0aJYvXp17Nu3L0455ZQYPnz4R55dOhzdunWL8vLyWLRoUXz2s5%2BNiIj33nsvli5dGt/5zneSfA8A4MhTkIG1Z8%2Be%2BIu/%2BIuYM2dODBs2rM5F6Idr9%2B7d8eKLL9be37hxY6xduzbatWsXxx9/fEycODFmzJgR3bt3j%2B7du8eMGTPiD/7gD7zBKQBwUAUZWC1atIg1a9YkOVP1zDPPxBlnnFF7f9KkSRERMXbs2Lj33nvj%2Buuvj3feeSeuvPLKePPNN%2BPUU0%2BNhQsXRps2bT7x9wYAjkxFWZZl%2BR7i45g4cWK0bt06brzxxnyPckg7d%2B6MsrKy2LFjR/LrsfpPeyzp8%2BXSMzeOyPcIADQjufzf30MpyDNY%2B82ePTsWL14c/fv3j9atW9d57KabbsrTVABAc1ewgbVq1ao4%2BeSTIyJi3bp1dR5LdZE7AMDHUXCB9fLLL0e3bt1i%2BfLl%2BR4FAOCACu59sLp37x7btm2rvX/hhRfGG2%2B8kceJAADqKrjA%2Bv1r8h999NF4%2B%2B238zQNAEB9BRdYAABNXcEFVlFRUb2L2F3UDgA0JQV3kXuWZTFu3LgoLS2NiIh33303rrjiinpv0/DQQw/lYzwAgMILrLFjx9a5f/HFF%2BdpEgCAAyu4wJo7d26%2BRwAA%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%2BeYlwsNUU1MT69evj9NPP/2g%2B5SWlkZpaWkjTgUANCXOYB3C5MmTY%2BnSpbFx48b45S9/GV/60pdi586dMXbs2HyPBgA0Uc5gHcJrr70Wf/VXfxXbt2%2BPY489NgYOHBgrV66Mrl275ns0AKCJEliHcP/99%2Bd7BACgwHiJEAAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGLeaJRG03/aY/ke4bA8c%2BOIfI8AQIFyBgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJObDnuEgfDg1AB%2BXM1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQWEm%2BBwCan/7THsv3CIflmRtH5HsEoMA4gwUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJleR7AADS6j/tsXyP0GDP3Dgi3yMc0fws5I8zWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQmsBpozZ05069YtWrVqFf369Yvly5fneyQAoIkSWA0wb968mDhxYkybNi3WrFkTp59%2BeowcOTJeffXVfI8GADRBAqsBvvvd78Yll1wSl156afTs2TNmzZoVXbp0iTvuuCPfowEATZAPez6E9957L1atWhVTpkyps33YsGGxYsWKA35NTU1N1NTU1N7fsWNHRETs3Lkz%2BXx7a95O/pwUplz8fOVKof3cFtLaRhTW%2Bhba2haa5v6zsP85syxL/tyHIrAOYfv27bF3797o1KlTne2dOnWKrVu3HvBrqqqq4h/%2B4R/qbe/SpUtOZoSIiLJb8j3Bkcva5o61Zb9c/izs2rUrysrKcvcNDkBgNVBRUVGd%2B1mW1du239SpU2PSpEm19/ft2xf/%2B7//G%2B3btz/o10R8UNpdunSJTZs2Rdu2bdMMfoSyVg1jnRrOWjWctWoY69RwuVqrLMti165dUVlZmew5G0pgHUKHDh2iuLi43tmq6urqeme19istLY3S0tI624455pgGf8%2B2bdv6x9hA1qphrFPDWauGs1YNY50aLhdr1dhnrvZzkfshtGzZMvr16xeLFi2qs33RokUxaNCgPE0FADRlzmA1wKRJk2LMmDHRv3//OO200%2BLOO%2B%2BMV199Na644op8jwYANEHF06dPn57vIZq6Pn36RPv27WPGjBlx8803xzvvvBM/%2BtGPom/fvsm/V3FxcQwdOjRKSrTvoVirhrFODWetGs5aNYx1argjba2Ksnz87SIAwBHMNVgAAIkJLACAxAQWAEBiAgsAIDGBlWNz5syJbt26RatWraJfv36xfPnyj9z/Zz/7WfTq1StKS0ujV69e8fDDD9c%2BtmfPnvjGN74RJ510UrRu3ToqKyvjK1/5SmzevDnXh5FzKdfp911%2B%2BeVRVFQUs2bNSj12XuRirdavXx/nnntulJWVRZs2bWLgwIHx6quv5uoQGkXqddq9e3eMHz8%2BOnfuHEcffXT07NnziPnA98NZq%2Beeey7OP//8%2BPSnP/2R/64Od/0LReq1qqqqigEDBkSbNm2iY8eO8cUvfjGef/75XB5Co8jFz9R%2BVVVVUVRUFBMnTkw9dloZOXP//fdnLVq0yO66667s17/%2BdXb11VdnrVu3zn7zm98ccP8VK1ZkxcXF2YwZM7L169dnM2bMyEpKSrKVK1dmWZZlb731Vnb22Wdn8%2BbNy/7nf/4ne/LJJ7NTTz0169evX2MeVnKp1%2BnDHn744axv375ZZWVlduutt%2Bb6UHIuF2v14osvZu3atcuuu%2B66bPXq1dlLL72U/cd//Ef2xhtvNNZhJZeLdbr00kuzE044IVuyZEm2cePG7Ac/%2BEFWXFyczZ8/v7EOKycOd62eeuqpbPLkydlPf/rTrLy8/ID/rg73OQtFLtZq%2BPDh2dy5c7Nnn302W7t2bXbOOedkxx9/fLZ79%2B5cH07O5GKdPrzvpz/96ezkk0/Orr766lwdQhICK4f%2B9E//NLviiivqbDvxxBOzKVOmHHD/Cy64IBsxYkSdbcOHD88uuuiig36Pp556KouIgv7Flat1eu2117Ljjjsue/bZZ7OuXbseEYGVi7W68MILs4svvjj9sHmUi3Xq3bt39q1vfavOPqecckr2t3/7t4mmzo/DXasPO9i/q0/ynE1ZLtbq91VXV2cRkS1duvRjz5lvuVqnXbt2Zd27d88WLVqUDRkypMkHlpcIc%2BS9996LVatWxbBhw%2BpsHzZsWKxYseKAX/Pkk0/W23/48OEH3T8iYseOHVFUVHRYn3XYlORqnfbt2xdjxoyJ6667Lnr37p1%2B8DzIxVrt27cvHnnkkejRo0cMHz48OnbsGKeeemrMnz8/NwfRCHL1MzV48OBYsGBBvP7665FlWSxZsiReeOGFGD58ePqDaCQfZ63y8ZxNQWMd144dOyIiol27dsmeszHlcp2uuuqqOOecc%2BLss8/%2BRM/TWARWjmzfvj327t1b7wOhO3XqVO%2BDo/fbunXrYe3/7rvvxpQpU2L06NEF%2B0GiuVqn73znO1FSUhJf//rX0w%2BdJ7lYq%2Brq6ti9e3fMnDkzRowYEQsXLozzzjsvRo0aFUuXLs3NgeRYrn6mbrvttujVq1d07tw5WrZsGSNGjIg5c%2BbE4MGD0x9EI/k4a5WP52wKGuO4siyLSZMmxeDBg6NPnz5JnrOx5Wqd7r///li1alVUVVV90hEbzZHxfvRNWFFRUZ37WZbV2/Zx9t%2BzZ09cdNFFsW/fvpgzZ06aYfMo5TqtWrUqvve978Xq1as/8jkKVcq12rdvX0RE/OVf/mVcc801ERHxJ3/yJ7FixYr453/%2B5xgyZEjK0RtV6n97t912W6xcuTIWLFgQXbt2jWXLlsWVV14ZFRUVBfP/qA/mcNcqX8/ZFOTyuMaPHx/r1q2LX/ziF0meL59SrtOmTZvi6quvjoULF0arVq1SjNcoBFaOdOjQIYqLi%2BsVe3V1db2y36%2B8vLxB%2B%2B/ZsycuuOCC2LhxYzzxxBMFe/YqIjfrtHz58qiuro7jjz%2B%2B9vG9e/fGtddeG7NmzYpXXnkl7UE0klysVYcOHaKkpCR69epVZ5%2BePXsW7C/5XKzTO%2B%2B8EzfccEM8/PDDcc4550RExMknnxxr166Nm2%2B%2BuWAD6%2BOsVT6esynI9XFNmDAhFixYEMuWLYvOnTt/4ufLl1ys06pVq6K6ujr69etXu23v3r2xbNmymD17dtTU1ERxcfEnmjsXvESYIy1btox%2B/frFokWL6mxftGhRDBo06IBfc9ppp9Xbf%2BHChXX23x9XGzZsiMWLF0f79u3TD9%2BIcrFOY8aMiXXr1sXatWtrb5WVlXHdddfF448/npsDaQS5WKuWLVvGgAED6v1Z%2BAsvvBBdu3ZNOH3jycU67dmzJ/bs2RNHHVX3V2ZxcXHtWcBC9HHWKh/P2RTk6riyLIvx48fHQw89FE888UR069btk46aV7lYp7POOit%2B9atf1fmd3r9///jyl78ca9eubZJxFRHepiGX9v%2Bp6t133539%2Bte/ziZOnJi1bt06e%2BWVV7Isy7IxY8bU%2BauK//qv/8qKi4uzmTNnZuvXr89mzpxZ50/F9%2BzZk5177rlZ586ds7Vr12ZbtmypvdXU1OTlGFNIvU4HcqT8FWEu1uqhhx7KWrRokd15553Zhg0bsttvvz0rLi7Oli9f3ujHl0ou1mnIkCFZ7969syVLlmQvv/xyNnfu3KxVq1bZnDlzGv34UjrctaqpqcnWrFmTrVmzJquoqMgmT56crVmzJtuwYUODn7NQ5WKtvva1r2VlZWXZz3/%2B8zq/03/3u981%2BvGlkot1%2Bn2F8FeEAivHvv/972ddu3bNWrZsmZ1yyil1/vR2yJAh2dixY%2Bvs/%2BCDD2af%2BcxnshYtWmQnnnhi9rOf/az2sY0bN2YRccDbkiVLGumIciPlOh3IkRJYWZabtbr77ruzP/7jP85atWqV9e3bt%2BDf2ynL0q/Tli1bsnHjxmWVlZVZq1atss985jPZLbfcku3bt68xDienDmetDvZ7aMiQIQ1%2BzkKWeq0O9jt97ty5jXdQOZCLn6kPK4TAKsqyLGukk2UAAM2Ca7AAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAif0/4F0euw9ZbLUAAAAASUVORK5CYII%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common2937059919368536635\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02602</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">6.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02318</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0235</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.03454</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.038</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.03165</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.03</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02372</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02466</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02478</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (22)</td>\n",
       "        <td class=\"number\">22</td>\n",
       "        <td class=\"number\">66.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme2937059919368536635\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02318</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02349</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0235</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02355</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02372</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.05777</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.07105</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.07258</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0948</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.14007</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_ampV3ON\">ampV3ON<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>31</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>93.9%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.046308</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0.02741</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>0.11757</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram6344650535864952975\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAABvElEQVR4nO3dsW3bUBSG0UsjLTmAQG6RIbJT6uyUIbIFCQ1AIUWKiCkSpbHxJxbsR0s%2Bp5QA3UeBHy5Ysdu2bavGlmWpaZpaj%2BXGzfNc4zg2nfmh6bQ/%2Br6vqt8XPAzDHkfghqzrWtM0/b1vWtolkK7rqqpqGIZHgXz8/PXZv/fty6cXORdv2%2BW%2Baemh%2BUS4IQKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAsEu70l/ad6tzmuxQSC4iw1yjWu2znO99y11D5vdBoHg3W6Qt6rFZuP/2SAQ7LJBtm2rqqp1XR999/PH99bHeTVPXd%2B/3NP1X%2BOp/%2Bzy2eW%2Baanbdpi6LEtN09R6LDdunucax7HpzF0COZ/PdTweq%2B/76rqu9XhuzLZtdTqd6nA41MND26eCXQKBW%2BEhHQKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoHgF746U8Ym9rPpAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives6344650535864952975,#minihistogram6344650535864952975\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives6344650535864952975\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles6344650535864952975\"\n",
       "                                                  aria-controls=\"quantiles6344650535864952975\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram6344650535864952975\" aria-controls=\"histogram6344650535864952975\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common6344650535864952975\" aria-controls=\"common6344650535864952975\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme6344650535864952975\" aria-controls=\"extreme6344650535864952975\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles6344650535864952975\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0.02741</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0.027438</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>0.03014</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>0.03142</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>0.05106</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>0.10402</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>0.11757</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>0.09016</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>0.02092</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>0.026738</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.57741</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>1.2119</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>0.046308</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>0.020746</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>1.5728</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>1.5282</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>0.00071495</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>344.0 B</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram6344650535864952975\">\n",
       "            <img 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t2rM4x1dXVcrlcHhsAAAgdfhGwvi82NlZDhgzRoEGDZLPZtG3bNo0ZM0bt2rXTpk2bjLzG2bNnNXr0aF24cEGLFi2qt296erpGjhypLl26aOjQoVqzZo0k6bXXXqtzTG5urhwOh3tLTEw0UjcAAAgMfhOwKioqNH/%2BfHXt2lW33HKLysvLtWrVKh08eFBff/21br/9do0bN%2B6qX%2Bfs2bO67777VFJSory8vHpXr2rTrFkzdenSRcXFxXX2yc7OVmVlpXs7dOjQ1ZYNAAACiF885H7PPfdo7dq1Sk5O1sSJE5WZmamWLVu6j//bv/2bHnvsMS1YsOCqXudiuCouLtbGjRt1ww03NHiO6upq7dmzR/3796%2Bzj91ul91uv5pSAQBAAPOLgBUdHa0NGzbUG1ri4uLqXTWSpKqqKn311Vfu/ZKSEhUVFal58%2BaKj4/Xvffeqx07dugvf/mLzp8/L6fTKUlq3ry5IiIiJElDhgzRPffco6lTp0qSZs6cqTvuuENt2rRReXm5/ud//kcul0uZmZlXe9oAACBI%2BUXAqu95potsNpvatm1bb59t27Zp8ODB7v2srCxJUmZmpnJycrR69WpJ0k9/%2BlOPcRs3btSgQYMkSfv27VNFRYX72OHDh3X//feroqJCLVu2VEpKirZs2aKkpKTLOjcAABB6/CJgzZgxQ23btnWvGl300ksvaf/%2B/Zo3b95lzTNo0KB6/9rwcv4S8cCBAx77b7/99mW9NgAAwEV%2B8ZD7u%2B%2B%2Bq5SUlBrtffr00YoVK3xQEQAAwJXzi4BVUVGh66%2B/vkZ7dHS0x%2B06AACAQOAXAatt27Zat25djfZ169YpOTnZBxUBAABcOb94Bmv69OmaPn26jh07pltvvVWS9Ne//lXPPPOMnn32WR9XBwAA0DB%2BEbAefPBBnT59WnPmzNF//dd/SZISEhK0YMECPfDAAz6uDgAAoGH8ImBJ0rRp0zRt2jSVlZXpmmuuueQXMAMAAPgrvwlYF8XFxfm6BAAAgKviFw%2B5Hz16VP/5n/%2BpNm3aKDIyUhERER4bAABAIPGLFazx48dr3759evTRRxUXFyebzebrkgAAAK6YXwSsgoICFRQU6Oabb/Z1KQAAAFfNL24RJiQksGoFAACChl8ErOeff17Z2dk6fPiwr0sBAAC4an5xizAjI0MnTpxQUlKSoqOj1bRpU4/j5eXlPqoMAACg4fwiYM2dO9fXJQAAABjjFwFrwoQJvi4BAADAGL94BkuSDhw4oJycHGVkZLhvCa5fv1579uzxcWUAAAAN4xcBa9OmTerUqZPy8/P1zjvvqKqqSpK0Y8cO/eY3v/FxdQAAAA3jFwHr8ccfV05OjjZu3Ojxye233nqrtmzZ4sPKAAAAGs4vAtauXbt077331mhv1aqVjh496oOKAAAArpxfBKzrrrtOTqezRntRUZFat27tg4oAAACunF8ErNGjR2vWrFk6evSo%2BxPdP/30U82cOVNjx471cXUAAAAN4xcBa86cOYqNjVVcXJyqqqrUsWNH9e3bVz179tSTTz7p6/IAAAAaxC8%2BBysiIkIrVqzQl19%2BqR07dujChQvq1q2b2rdv7%2BvSAAAAGswvAtZFN910k2666SZflwEAAHBV/CJgPfTQQ/Uef%2BWVVxqpEgAAgKvnFwGrrKzMY//s2bP64osvdOLECQ0YMMBHVQEAAFwZvwhYf/7zn2u0nTt3Tr/4xS/UoUMHH1QEAABw5fzirwhrEx4erpkzZ%2Br3v//9ZY8pKCjQHXfcofj4eNlsNq1atcrjuGVZysnJUXx8vK655hoNGjRIX3zxxSXnXbRokZKTkxUZGanu3btr06ZNDT4fAAAQOvw2YEnS/v37dfbs2cvuf/LkSXXt2lULFy6s9fgzzzyj5557TgsXLtTWrVsVGxurYcOG6cSJE3XOuWLFCk2fPl2zZ8/Wzp071b9/f6Wnp6u0tLTB5wMAAEKDX9wifOyxxzz2LctSWVmZVq9erZ///OeXPU96errS09NrPWZZlubPn6/Zs2drxIgRkqTXXntNMTExevPNN/Xwww/XOu65557ThAkTNHHiREnS/PnztW7dOi1evFi5ubmXXRsAAAgdfhGwNm/e7LHfpEkTtWzZUnPnztWDDz5o5DVKSkrkdDqVmprqbrPb7Ro4cKAKCwtrDVhnzpzR9u3bNWvWLI/21NRUFRYWGqkLAAAEH78IWI3xTNPF7zqMiYnxaI%2BJidHBgwdrHVNRUaHz58/XOqa27068qLq6WtXV1e59l8t1pWUDAIAA5NfPYHnDxe86vMiyrBptVzsmNzdXDofDvSUmJl55wQAAIOD4xQpWz549LxlyLvr73/9%2BRa8RGxsr6buVrLi4OHd7eXl5jRWqi1q0aKGwsLAaq1X1jZGk7OxsZWVlufddLhchCwCAEOIXK1iDBw/W3r17ZVmWUlJSlJKSIknau3evBg0apLS0NPd2pZKTkxUbG6u8vDx325kzZ5Sfn6%2B%2BffvWOiYiIkLdu3f3GCNJeXl5dY6Rvnu2Kzo62mMDAAChwy9WsI4fP64pU6Zozpw5Hu2zZ8/WN998oz/%2B8Y%2BXNU9VVZW%2B%2Buor935JSYmKiorUvHlztWnTRtOnT9ecOXPUrl07tWvXTnPmzNG1116rMWPGuMcMGTJE99xzj6ZOnSpJysrKUkZGhnr06KE%2BffrolVdeUWlpqSZNmmTgzAEAQDDyi4D1zjvvaOvWrTXax48frx49elx2wNq2bZsGDx7s3r94my4zM1PLly/XY489plOnTmny5Mn69ttv1bt3b61fv15RUVHuMfv27VNFRYV7f9SoUTp27Jh%2B%2B9vfqqysTJ07d9batWuVlJR0pacLAACCnF8ELLvdrsLCQrVr186jvbCwUHa7/bLnGTRokCzLqvO4zWZTTk6OcnJy6uxz4MCBGm2TJ0/W5MmTL7sOAAAQ2vwiYP3yl7/UpEmTtHPnTvfzV1u2bNGSJUv0xBNP%2BLg6AACAhvGLgDV79mwlJyfrhRde0NKlSyVJHTp00JIlSzyejwIAAAgEfhGwJGnMmDGEKQAAEBT84mMapO8%2BK2r58uX6zW9%2Bo2%2B//VaS9Nlnn6msrMzHlQEAADSMX6xg7d69W0OHDtW1116rQ4cOafz48br%2B%2Buv1zjvv6PDhw3rttdd8XSIAAMBl84sVrBkzZmjMmDHat2%2BfIiMj3e233XabCgoKfFgZAABAw/nFCtbWrVu1ePHiGl%2BX07p1a24RAgCAgOMXK1gRERGqqqqq0V5cXKwWLVr4oCIAAIAr5xcB684779R///d/69y5c5K%2B%2B0DQr7/%2BWrNmzdKIESN8XB0AAEDD%2BEXAmjdvno4cOaLY2FidOnVKt956q2688UZFRkbW%2BH5CAAAAf%2BcXz2A5HA4VFhYqLy9PO3bs0IULF9StWzelpaXVeC4LAADA3/k8YJ09e1Y/%2B9nPtGjRIqWmpio1NdXXJQEAAFwVn98ibNq0qXbu3MlKFQAACBo%2BD1iSNHbsWC1btszXZQAAABjh81uEFy1cuFAbNmxQjx491KxZM49jzzzzjI%2BqAgAAaDi/CFjbt2/XT37yE0nSrl27PI5x6xAAAAQanwas/fv3Kzk5WZs2bfJlGQAAAEb59Bmsdu3a6ejRo%2B79UaNG6ZtvvvFhRQAAAFfPpwHLsiyP/bVr1%2BrkyZM%2BqgYAAMAMv/grQgAAgGDi04Bls9lqPMTOQ%2B0AACDQ%2BfQhd8uyNH78eNntdknS6dOnNWnSpBof0/D%2B%2B%2B/7ojwAAIAr4tOAlZmZ6bE/duxYH1UCAABgjk8DFp/eDgAAghEPuQMAABhGwAIAADCMgAUAAGBYSAWsH/3oR%2B6Phvj%2BNmXKlFr7L1%2B%2BvNb%2Bp0%2BfbuTKAQBAIPGLL3tuLFu3btX58%2Bfd%2B7t379awYcP0H//xH3WOiY6O1t69ez3aIiMjvVYjAAAIfCEVsFq2bOmxP3fuXLVt21YDBw6sc4zNZlNsbKy3SwMAAEEkpG4Rft%2BZM2f0xhtv6IEHHqj30%2BOrqqqUlJSkhIQE3X777dq5c2cjVgkAAAJRyAasVatW6fjx4xo/fnydfdq3b6/ly5dr9erVeuuttxQZGalbbrlFxcXF9c5dXV0tl8vlsQEAgNARsgHr1VdfVXp6uuLj4%2Bvsk5KSorFjx6pr167q37%2B/3nnnHd1000168cUX6507NzdXDofDvSUmJpouHwAA%2BLGQDFgHDx7Uhg0bNHHixAaNa9KkiXr27HnJFazs7GxVVla6t0OHDl1NuQAAIMCE1EPuFy1btkytWrXSbbfd1qBxlmWpqKhIXbp0qbef3W53f4E1AAAIPSEXsC5cuKBly5YpMzNT4eGepz9u3Di1bt1aubm5kqSnnnpKKSkpateunVwulxYsWKCioiK99NJLvigdAAAEiJALWBs2bFBpaakeeOCBGsdKS0vVpMm/7poeP35cDz30kJxOpxwOh26%2B%2BWYVFBSoV69ejVkyAAAIMDbLsixfFxHsXC6XHA6HKisrFR0dbXTuHrM/NDqfN2373XBflwAACCHe/P17KSH5kDsAAIA3EbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYFlIBKycnRzabzWOLjY2td0x%2Bfr66d%2B%2BuyMhI3XjjjXr55ZcbqVoAABCown1dQGPr1KmTNmzY4N4PCwurs29JSYl%2B9rOf6cEHH9Qbb7yhv/3tb5o8ebJatmypkSNHNka5AAAgAIVcwAoPD7/kqtVFL7/8stq0aaP58%2BdLkjp06KBt27bp2WefJWABAIA6hdQtQkkqLi5WfHy8kpOTNXr0aO3fv7/Ovps3b1ZqaqpHW1pamrZt26azZ896u1QAABCgQipg9e7dW6%2B//rrWrVunJUuWyOl0qm/fvjp27Fit/Z1Op2JiYjzaYmJidO7cOVVUVNT5OtXV1XK5XB4bAAAIHSEVsNLT0zVy5Eh16dJFQ4cO1Zo1ayRJr732Wp1jbDabx75lWbW2f19ubq4cDod7S0xMNFA9AAAIFCEVsH6oWbNm6tKli4qLi2s9HhsbK6fT6dFWXl6u8PBw3XDDDXXOm52drcrKSvd26NAho3UDAAD/FnIPuX9fdXW19uzZo/79%2B9d6vE%2BfPvrzn//s0bZ%2B/Xr16NFDTZs2rXNeu90uu91utFYAABA4QmoFa%2BbMmcrPz1dJSYk%2B/fRT3XvvvXK5XMrMzJT03crTuHHj3P0nTZqkgwcPKisrS3v27NHSpUv16quvaubMmb46BQAAEABCagXr8OHDuv/%2B%2B1VRUaGWLVsqJSVFW7ZsUVJSkiSprKxMpaWl7v7Jyclau3atZsyYoZdeeknx8fFasGABH9EAAADqZbMuPrUNr3G5XHI4HKqsrFR0dLTRuXvM/tDofN607XfDfV0CACCEePP376WE1C1CAACAxkDAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADAv3dQEIHT1mf%2BjrEhpk2%2B%2BG%2B7oEAECAYgULAADAMAIWAACAYSEVsHJzc9WzZ09FRUWpVatWuvvuu7V37956xyxfvlw2m63Gdvr06UaqGgAABJqQClj5%2BfmaMmWKtmzZory8PJ07d06pqak6efJkveOio6NVVlbmsUVGRjZS1QAAINCE1EPuH37o%2BZD1smXL1KpVK23fvl0DBgyoc5zNZlNsbKy3ywMAAEEipFawfqiyslKS1Lx583r7VVVVKSkpSQkJCbr99tu1c%2BfOxigPAAAEqJANWJZlKSsrS/369VPnzp3r7Ne%2BfXstX75cq1ev1ltvvaXIyEjdcsstKi4urnNMdXW1XC6XxwYAAEJHSN0i/L6pU6dq165d%2BuSTT%2Brtl5KSopSUFPf%2BLbfcom7duunFF1/UggULah2Tm5urp556ymi9AAAgcITkCta0adO0evVqbdy4UQkJCQ0a26RJE/Xs2bPeFazs7GxVVla6t0OHDl1tyQAAIICE1AqWZdDYjDQAAAstSURBVFmaNm2aVq5cqY8//ljJyclXNEdRUZG6dOlSZx%2B73S673X41pQIAgAAWUgFrypQpevPNN/XBBx8oKipKTqdTkuRwOHTNNddIksaNG6fWrVsrNzdXkvTUU08pJSVF7dq1k8vl0oIFC1RUVKSXXnrJZ%2BcBAAD8W0gFrMWLF0uSBg0a5NG%2BbNkyjR8/XpJUWlqqJk3%2Bdef0%2BPHjeuihh%2BR0OuVwOHTzzTeroKBAvXr1aqyyAQBAgLFZlmX5uohg53K55HA4VFlZqejoaKNzB9oXKAcSvuwZAAKbN3//XkpIPuQOAADgTQQsAAAAw0LqGSygIbj96j3cfgUaRyD9HAu2nwusYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw8J9XQAA%2BLsesz/0dQkAAgwrWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMC8mAtWjRIiUnJysyMlLdu3fXpk2b6u3/3nvvqWPHjrLb7erYsaNWrlzZSJUCAIBAFHIBa8WKFZo%2Bfbpmz56tnTt3qn///kpPT1dpaWmt/Tdv3qxRo0YpIyNDn332mTIyMnTffffp008/beTKAQBAoAi5gPXcc89pwoQJmjhxojp06KD58%2BcrMTFRixcvrrX//PnzNWzYMGVnZ6t9%2B/bKzs7WkCFDNH/%2B/EauHAAABIpwXxfQmM6cOaPt27dr1qxZHu2pqakqLCysdczmzZs1Y8YMj7a0tLR6A1Z1dbWqq6vd%2B5WVlZIkl8t1paXX6Xz1SeNzAt7mjX8L3sS/M8D7vPFz4eKclmUZn/tSQipgVVRU6Pz584qJifFoj4mJkdPprHWM0%2BlsUH9Jys3N1VNPPVWjPTEx8QqqBoKPY56vKwDgb7z5c%2BHEiRNyOBzee4FahFTAushms3nsW5ZVo%2B1q%2BmdnZysrK8u9f%2BHCBf3f//2fbrjhhnrH4dJcLpcSExN16NAhRUdH%2B7qckMQ18C3ef9/jGvje5V4Dy7J04sQJxcfHN2J13wmpgNWiRQuFhYXVWH0qLy%2BvsUp1UWxsbIP6S5Ldbpfdbvdou%2B66666watQmOjqaH2w%2BxjXwLd5/3%2BMa%2BN7lXIPGXrm6KKQeco%2BIiFD37t2Vl5fn0Z6Xl6e%2BffvWOqZPnz41%2Bq9fv77O/gAAACG1giVJWVlZysjIUI8ePdSnTx%2B98sorKi0t1aRJkyRJ48aNU%2BvWrZWbmytJeuSRRzRgwAA9/fTTuuuuu/TBBx9ow4YN%2BuSTT3x5GgAAwI%2BF5eTk5Pi6iMbUuXNn3XDDDZozZ46effZZnTp1Sn/605/UtWtXSdILL7yg8PBw3X333ZK%2BezC9Y8eOeu655zRnzhyVlpZq8eLFGjZsmC9PI6SFhYVp0KBBCg8Puf8/8BtcA9/i/fc9roHv%2Bfs1sFm%2B%2BNtFAACAIBZSz2ABAAA0BgIWAACAYQQsAAAAwwhYAAAAhhGw4FOLFi1ScnKyIiMj1b17d23atKne/u%2B99546duwou92ujh07auXKlXX2ffjhh2Wz2fhi7kvwxjXYs2eP7rzzTjkcDkVFRSklJUWlpaXeOoWAZ/oaVFVVaerUqUpISNA111yjDh061PmF9vhOQ67BF198oZEjR%2BpHP/pRvT9jGnpdQ5np9z83N1c9e/ZUVFSUWrVqpbvvvlt79%2B715inUZAE%2B8vbbb1tNmza1lixZYv3jH/%2BwHnnkEatZs2bWwYMHa%2B1fWFhohYWFWXPmzLH27NljzZkzxwoPD7e2bNlSo%2B/KlSutrl27WvHx8dbzzz/v7VMJWN64Bl999ZXVvHlz69FHH7V27Nhh7du3z/rLX/5iffPNN411WgHFG9dg4sSJVtu2ba2NGzdaJSUl1h/%2B8AcrLCzMWrVqVWOdVkBp6DX4%2B9//bs2cOdN66623rNjY2Fp/xjR0zlDmjfc/LS3NWrZsmbV7926rqKjIuu2226w2bdpYVVVV3j4dNwIWfKZXr17WpEmTPNrat29vzZo1q9b%2B9913nzV8%2BHCPtrS0NGv06NEebYcPH7Zat25t7d6920pKSiJg1cMb12DUqFHW2LFjzRcbpLxxDTp16mT99re/9ejTrVs369e//rWhqoNLQ6/B99X1M%2BZq5gw13nj/f6i8vNySZOXn519xnQ3FLUL4xJkzZ7R9%2B3alpqZ6tKempqqwsLDWMZs3b67RPy0tzaP/hQsXlJGRoUcffVSdOnUyX3gQ8cY1uHDhgtasWaObbrpJaWlpatWqlXr37q1Vq1Z55yQCnLf%2BHfTr10%2BrV6/W119/LcuytHHjRn355ZdKS0szfxIB7kqugS/mDFaN9V5VVlZKkpo3b25szkshYMEnKioqdP78%2BRpfmh0TE1Pjy7Uvcjqdl%2Bz/9NNPKzw8XL/85S/NFx1kvHENysvLVVVVpblz52r48OFav3697rnnHo0YMUL5%2BfneOZEA5q1/BwsWLFDHjh2VkJCgiIgIDR8%2BXIsWLVK/fv3Mn0SAu5Jr4Is5g1VjvFeWZSkrK0v9%2BvVT586djcx5Ofzz8%2BURMmw2m8e%2BZVk12i63//bt2/XCCy9ox44d9c4BTyavwYULFyRJd911l2bMmCFJ%2BulPf6rCwkK9/PLLGjhwoMnSg4bJayB9F7C2bNmi1atXKykpSQUFBZo8ebLi4uI0dOhQs8UHiYZeA1/NGay8%2BV5NnTpVu3btavTvECZgwSdatGihsLCwGv%2BHUl5eXuP/ZC6KjY2tt/%2BmTZtUXl6uNm3auI%2BfP39ev/rVrzR//nwdOHDA7EkEOG9cgxYtWig8PFwdO3b06NOhQwe%2BIL0W3rgGp06d0hNPPKGVK1fqtttukyT95Cc/UVFRkZ599lkC1g9cyTXwxZzBytvv1bRp07R69WoVFBQoISHhqudrCG4RwiciIiLUvXt35eXlebTn5eWpb9%2B%2BtY7p06dPjf7r169398/IyNCuXbtUVFTk3uLj4/Xoo49q3bp13jmRAOaNaxAREaGePXvW%2BHPoL7/8UklJSQarDw7euAZnz57V2bNn1aSJ54/3sLAw9woj/uVKroEv5gxW3nqvLMvS1KlT9f777%2Bujjz5ScnLy1ZZ6RUUAPnHxT3NfffVV6x//%2BIc1ffp0q1mzZtaBAwcsy7KsjIwMj78i%2Bdvf/maFhYVZc%2BfOtfbs2WPNnTu3zo9puIi/IqyfN67B%2B%2B%2B/bzVt2tR65ZVXrOLiYuvFF1%2B0wsLCrE2bNjX6%2BQUCb1yDgQMHWp06dbI2btxo7d%2B/31q2bJkVGRlpLVq0qNHPLxA09BpUV1dbO3futHbu3GnFxcVZM2fOtHbu3GkVFxdf9pz4F2%2B8/7/4xS8sh8Nhffzxx1ZZWZl7%2B%2Bc//9lo50XAgk%2B99NJLVlJSkhUREWF169bN409oBw4caGVmZnr0f/fdd60f//jHVtOmTa327dtb7733Xr3zE7AuzRvX4NVXX7X%2B/d//3YqMjLS6du3K5y9dgulrUFZWZo0fP96Kj4%2B3IiMjrR//%2BMfWvHnzrAsXLjTG6QSkhlyDkpISS1KNbeDAgZc9JzyZfv9rOy7JWrZsWaOdk%2B3/FwIAAABDeAYLAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBh/w9PeTQeHNr2AwAAAABJRU5ErkJggg%3D%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common6344650535864952975\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02745</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">6.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.03024</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">6.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.04832</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.05547</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.1005</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.03075</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.03251</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.03142</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.11757</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.03042</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (21)</td>\n",
       "        <td class=\"number\">21</td>\n",
       "        <td class=\"number\">63.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme6344650535864952975\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02741</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02742</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02745</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">6.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02873</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.02924</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.09052</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.09325</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.1005</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.1093</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.11757</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_amp_delta\">amp_delta<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>33</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>100.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.018527</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0.00064</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>0.09245</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram2756420526863610987\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAAByklEQVR4nO3dMY7aYBSF0WeU1l4AwrvIIrKn1NlTFpFd2GIBoBQpglMkpJnRHYFmfo%2BZc0qQeMb408M0dMuyLNXYPM81jmPrsWzcNE11OByazvzUdNo/fd9X1d83PAzDGofAhpxOpxrH8f9109IqgXRdV1VVwzA8CeTz1%2B83v96Pb19e5bh4367XTUu75hNhQwQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEq/yJ52vzx5%2B8FRsEgofYIK3cuqlsqe2zQSAQCAQf9ivWPTf2LWY80teyR3j/NggEH3aDPBI/HrwdGwSCVTbIsixVVXU6nZ489/vXz9aH8648d05ecus5u2fGPe75LJ87tutj1%2BumpW5ZYeo8zzWOY%2BuxbNw0TXU4HJrOXCWQy%2BVSx%2BOx%2Br6vrutaj2djlmWp8/lc%2B/2%2Bdru2dwWrBAJb4SYdAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgeAPJ3ZfiRolZRIAAAAASUVORK5CYII%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives2756420526863610987,#minihistogram2756420526863610987\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives2756420526863610987\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles2756420526863610987\"\n",
       "                                                  aria-controls=\"quantiles2756420526863610987\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram2756420526863610987\" aria-controls=\"histogram2756420526863610987\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common2756420526863610987\" aria-controls=\"common2756420526863610987\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme2756420526863610987\" aria-controls=\"extreme2756420526863610987\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles2756420526863610987\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0.00064</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0.001214</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>0.00391</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>0.00824</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>0.02093</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>0.068344</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>0.09245</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>0.09181</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>0.01702</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>0.023017</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>1.2423</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>3.9589</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>0.018527</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>0.016632</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>2.0406</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>0.6114</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>0.00052977</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>344.0 B</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram2756420526863610987\">\n",
       "            <img 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msYvApYkPf7443r88cdVXFysNm3a6LrrrvN1SQAAAFfFbwJWjaioKF%2BXAAAAcE384iH3kydP6r777tMNN9yg8PBwhYWFeWwAAACBxC9WsKZNm6bDhw/rySefVFRUlGw2m69LAgAAuGp%2BEbBycnKUk5OjW265xdelAAAAXDO/uEUYHR3NqhUAAGgx/CJgvfTSS0pPT9fx48d9XQoAAMA184tbhCkpKTpz5oxiY2MVERGh1q1bexwvLS31UWUAAABN5xcBa8GCBb4uAQAAwBi/CFgPPPCAr0sAAAAwxi%2BewZKkL7/8UhkZGUpJSam9Jbht2zYdPHjQx5UBAAA0jV8ErJ07d6pnz57Kzs7W22%2B/rYqKCknSvn379Otf/9rH1QEAADSNXwSsp59%2BWhkZGdqxY4fHm9vvuOMO7d6924eVAQAANJ1fBKxPP/1U48aNq9PeqVMnnTx50gcVAQAAXD2/CFjXXXedSkpK6rTn5%2Berc%2BfOVzxOTk6ORo8eLZfLJZvNpo0bN3octyxLGRkZcrlcatOmjYYMGaLPP//8suMuW7ZMcXFxCg8PV%2B/evbVz584rrgkAAAQfvwhYEydO1Jw5c3Ty5MnaN7rv2bNHs2fP1pQpU654nLNnzyohIUFLliyp9/hzzz2nF198UUuWLNHHH38sp9OpESNG6MyZMw2OuW7dOs2cOVNz587V/v37NXDgQCUnJ6uoqKhpFwkAAIKGzbIsy9dFXLhwQSkpKVq/fr2qq6sVFhamqqoqjR8/Xn/84x8VGtr0t0nYbDZt2LBB99xzj6RvVq9cLpdmzpypp59%2BWpJUWVmpyMhILVy4UA8//HC94/Tr10%2B9evXS8uXLa9u6d%2B%2Bue%2B65R5mZmVdUi9vtlsPhUHl5uSIiIpp8LY3pM/d9o%2BN5U96zo3xdAgAgiHjz5%2B/l%2BMV7sMLCwrRu3Tp98cUX2rdvn6qrq9WrVy9169bN2NcoLCxUSUmJkpKSatvsdrsGDx6s3NzcegPWhQsXtHfvXs2ZM8ejPSkpSbm5ucZqAwAALYtfBKwaXbt2VdeuXb0yds0zXpGRkR7tkZGROnr0aL3nlJWV6dKlS/WeU98zYzUqKytVWVlZu%2B92u6%2B2bAAAEID8ImA99NBDjR5/7bXXjH2tmme8aliWVaftWs/JzMzUvHnzrr5IAAAQ0PwiYBUXF3vsV1VV6fPPP9eZM2c0aNAgI1/D6XRK%2BmYlKyoqqra9tLS0zgpVjQ4dOigkJKTOalVj50hSenq60tLSavfdbrdiYmKupXwAABBA/CJg/fnPf67TdvHiRf37v/%2B7unfvbuRrxMXFyel0KisrS7fccoukb56xys7O1sKFC%2Bs9JywsTL1791ZWVpbuvffe2vasrCzdfffdDX4tu90uu91upG4AABB4/OI1DfUJDQ3V7Nmz9bvf/e6Kz6moqFB%2Bfr7y8/MlffNge35%2BvoqKimSz2TRz5kzNnz9fGzZs0GeffaZp06bpe9/7niZPnlw7xrBhwzxe85CWlqY//OEPWrVqlQ4ePKhZs2apqKhIM2bMMHexAACgRfGLFayGHDlyRFVVVVfcPy8vT0OHDq3dr7lNl5qaqjVr1uipp57SuXPn9Mgjj%2Bgf//iH%2BvXrp23btqldu3a15xw%2BfFhlZWW1%2BxMmTNCpU6f029/%2BVsXFxbr55pu1ZcsWxcbGGrhCAADQEvnFe7Ceeuopj33LslRcXKxNmzbpZz/7mcc7qAIR78H6Bu/BAgA0p6B/D9auXbs89lu1aqWOHTtqwYIFevDBB31UFQAAwNXxi4DFZ/sBAICWxG8fcgcAAAhUfrGC1bdv38u%2B7LPGRx995OVqAAAAro1fBKyhQ4fq97//vbp27ar%2B/ftLknbv3q1Dhw7p4Ycf5p1SAAAgoPhFwDp9%2BrQeffRRzZ8/36N97ty5%2Bvrrr/WHP/zBR5UBAAA0nV88g/X222/rvvvuq9M%2Bbdo0vfPOOz6oCAAA4Or5RcCy2%2B3Kzc2t056bm8vtQQAAEHD84hbhz3/%2Bc82YMUP79%2B9XYmKipG%2BewVqxYoWeeeYZH1cHAADQNH4RsObOnau4uDi9/PLLWrVqlSSpe/fuWrFihcfnBAIAAAQCvwhYkjR58mTCFAAAaBH84hks6ZvPC1qzZo1%2B/etf6x//%2BIck6ZNPPlFxcbGPKwMAAGgav1jB%2BuyzzzR8%2BHB973vf07FjxzRt2jR9//vf19tvv63jx49r7dq1vi4RAADgivnFCtasWbM0efJkHT58WOHh4bXtP/3pT5WTk%2BPDygAAAJrOL1awPv74Yy1fvrzOx%2BV07tyZW4QAACDg%2BMUKVlhYmCoqKuq0FxQUqEOHDj6oCAAA4Or5RcC666679B//8R%2B6ePGiJMlms%2Bmrr77SnDlzNGbMGB9XBwAA0DR%2BEbBeeOEFnThxQk6nU%2BfOndMdd9yhG2%2B8UeHh4XU%2BnxAAAMDf%2BcUzWA6HQ7m5ucrKytK%2BfftUXV2tXr16aeTIkXWeywIAAPB3Pg9YVVVV%2BslPfqJly5YpKSlJSUlJvi4JAADgmvj8FmHr1q21f/9%2BVqoAAECL4fOAJUlTpkzR6tWrfV0GAACAET6/RVhjyZIl2r59u/r06aO2bdt6HHvuued8VBUAAEDT%2BUXA2rt3r370ox9Jkj799FOPY9w6BAAAgcanAevIkSOKi4vTzp07fVkGAACAUT59Buumm27SyZMna/cnTJigr7/%2B2ocVAQAAXDufBizLsjz2t2zZorNnz/qoGgAAADP84rcIAQAAWhKfBiybzVbnIXYeagcAAIHOpw%2B5W5aladOmyW63S5LOnz%2BvGTNm1HlNw7vvvmvk6/3gBz/Q0aNH67Q/8sgjWrp0aZ32NWvW6L777qvTfu7cOYWHhxupCQAAtDw%2BDVipqake%2B1OmTPHq1/v444916dKl2v3PPvtMI0aM0L/92781eE5ERIQOHTrk0Ua4AgAAjfFpwGrut7d37NjRY3/BggXq0qWLBg8e3OA5NptNTqfT26UBAIAWJGgfcr9w4YLeeOMN3X///Y0%2B91VRUaHY2FhFR0frzjvv1P79%2B5uxSgAAEIiCNmBt3LhRp0%2Bf1rRp0xrs061bN61Zs0abNm3Sm2%2B%2BqfDwcN12220qKChodOzKykq53W6PDQAABI%2BgDVgrV65UcnKyXC5Xg30SExM1ZcoUJSQkaODAgXr77bfVtWtXvfLKK42OnZmZKYfDUbvFxMSYLh8AAPixoAxYR48e1fbt2zV9%2BvQmndeqVSv17dv3sitY6enpKi8vr92OHTt2LeUCAIAA4xcf9tzcVq9erU6dOumnP/1pk86zLEv5%2BfmKj49vtJ/dbq999QQAAAg%2BQRewqqurtXr1aqWmpio01PPyp06dqs6dOyszM1OSNG/ePCUmJuqmm26S2%2B3W4sWLlZ%2BfX%2B87swAAAGoEXcDavn27ioqKdP/999c5VlRUpFat/nnX9PTp03rooYdUUlIih8OhW265RTk5Obr11lubs2QAABBgbNZ3P3EZxrndbjkcDpWXlysiIsLo2H3mvm90PG/Ke3aUr0sAAAQRb/78vZygfMgdAADAmwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMCyoAlZGRoZsNpvH5nQ6Gz0nOztbvXv3Vnh4uG688Ua9%2BuqrzVQtAAAIVKG%2BLqC59ezZU9u3b6/dDwkJabBvYWGhfvKTn%2BjBBx/UG2%2B8ob/%2B9a965JFH1LFjR40dO7Y5ygUAAAEo6AJWaGjoZVetarz66qu64YYbtGjRIklS9%2B7dlZeXp%2Beff56ABQAAGhRUtwglqaCgQC6XS3FxcZo4caKOHDnSYN9du3YpKSnJo23kyJHKy8tTVVWVt0sFAAABKqgCVr9%2B/fT6669r69atWrFihUpKSjRgwACdOnWq3v4lJSWKjIz0aIuMjNTFixdVVlbW4NeprKyU2%2B322AAAQPAIqoCVnJyssWPHKj4%2BXsOHD9fmzZslSWvXrm3wHJvN5rFvWVa97d%2BWmZkph8NRu8XExBioHgAABIqgCljf1bZtW8XHx6ugoKDe406nUyUlJR5tpaWlCg0NVfv27RscNz09XeXl5bXbsWPHjNYNAAD8W9A95P5tlZWVOnjwoAYOHFjv8f79%2B%2BvPf/6zR9u2bdvUp08ftW7dusFx7Xa77Ha70VoBAEDgCKoVrNmzZys7O1uFhYXas2ePxo0bJ7fbrdTUVEnfrDxNnTq1tv%2BMGTN09OhRpaWl6eDBg1q1apVWrlyp2bNn%2B%2BoSAABAAAiqFazjx49r0qRJKisrU8eOHZWYmKjdu3crNjZWklRcXKyioqLa/nFxcdqyZYtmzZqlpUuXyuVyafHixbyiAQAANMpm1Ty1Da9xu91yOBwqLy9XRESE0bH7zH3f6HjelPfsKF%2BXAAAIIt78%2BXs5QXWLEAAAoDkQsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMC6rPIoRvBdLH%2Bkh8tA8A4OqxggUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGBbq6wIAmNFn7vu%2BLuGK5T07ytclAIBXsYIFAABgGAELAADAsKAKWJmZmerbt6/atWunTp066Z577tGhQ4caPWfNmjWy2Wx1tvPnzzdT1QAAINAEVcDKzs7Wo48%2Bqt27dysrK0sXL15UUlKSzp492%2Bh5ERERKi4u9tjCw8ObqWoAABBoguoh9/ff93wIePXq1erUqZP27t2rQYMGNXiezWaT0%2Bn0dnkAAKCFCKoVrO8qLy%2BXJF1//fWN9quoqFBsbKyio6N15513av/%2B/c1RHgAACFBBG7Asy1JaWppuv/123XzzzQ3269atm9asWaNNmzbpzTffVHh4uG677TYVFBQ0eE5lZaXcbrfHBgAAgkdQ3SL8tscee0yffvqpPvzww0b7JSYmKjExsXb/tttuU69evfTKK69o8eLF9Z6TmZmpefPmGa0XAAAEjqBcwXr88ce1adMm7dixQ9HR0U06t1WrVurbt2%2BjK1jp6ekqLy%2Bv3Y4dO3atJQMAgAASVCtYlmXp8ccf14YNG/TBBx8oLi7uqsbIz89XfHx8g33sdrvsdvu1lAoAAAJYUAWsRx99VP/5n/%2Bp9957T%2B3atVNJSYkkyeFwqE2bNpKkqVOnqnPnzsrMzJQkzZs3T4mJibrpppvkdru1ePFi5efna%2BnSpT67DgAA4N%2BCKmAtX75ckjRkyBCP9tWrV2vatGmSpKKiIrVq9c87p6dPn9ZDDz2kkpISORwO3XLLLcrJydGtt97aXGUDAIAAE1QBy7Ksy/b54IMPPPZfeuklvfTSS16qCAAAtERB%2BZA7AACANxGwAAAADAuqW4RAU/SZ%2B/7lO%2BGqBNrc5j07ytclAFclkP6stbQ/Z6xgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGhfq6AACAWX3mvu/rEq5Y3rOjfF0C4BWsYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgWFAGrGXLlikuLk7h4eHq3bu3du7c2Wj/9evXq0ePHrLb7erRo4c2bNjQTJUCAIBAFHQBa926dZo5c6bmzp2r/fv3a%2BDAgUpOTlZRUVG9/Xft2qUJEyYoJSVFn3zyiVJSUjR%2B/Hjt2bOnmSsHAACBIugC1osvvqgHHnhA06dPV/fu3bVo0SLFxMRo%2BfLl9fZftGiRRowYofT0dHXr1k3p6ekaNmyYFi1a1MyVAwCAQBHq6wKa04ULF7R3717NmTPHoz0pKUm5ubn1nrNr1y7NmjXLo23kyJGNBqzKykpVVlbW7peXl0uS3G731ZbeoEuVZ42PCcCTN/7selMg/b0QaHMbaIL9e6FmTMuyjI99OUEVsMrKyi3ouMoAAAjZSURBVHTp0iVFRkZ6tEdGRqqkpKTec0pKSprUX5IyMzM1b968Ou0xMTFXUTUAX3O84OsKWi7mFjW8%2Bb1w5swZORwO732BegRVwKphs9k89i3LqtN2Lf3T09OVlpZWu19dXa3//d//Vfv27Rs970q43W7FxMTo2LFjioiIuKax0DTMve8w977D3PsOc3/tLMvSmTNn5HK5mv1rB1XA6tChg0JCQuqsPpWWltZZparhdDqb1F%2BS7Ha77Ha7R9t11113lVXXLyIigj9wPsLc%2Bw5z7zvMve8w99emuVeuagTVQ%2B5hYWHq3bu3srKyPNqzsrI0YMCAes/p379/nf7btm1rsD8AAEBQrWBJUlpamlJSUtSnTx/1799fr732moqKijRjxgxJ0tSpU9W5c2dlZmZKkp544gkNGjRICxcu1N1336333ntP27dv14cffujLywAAAH4sJCMjI8PXRTSnm2%2B%2BWe3bt9f8%2BfP1/PPP69y5c/rjH/%2BohIQESdLLL7%2Bs0NBQ3XPPPZK%2BeTC9R48eevHFFzV//nwVFRVp%2BfLlGjFihM%2BuISQkREOGDFFoaNDlY59j7n2Hufcd5t53mPvAZbN88buLAAAALVhQPYMFAADQHAhYAAAAhhGwAAAADCNgAQAAGEbA8rFly5YpLi5O4eHh6t27t3bu3Nlo//Xr16tHjx6y2%2B3q0aOHNmzY4HHcsixlZGTI5XKpTZs2GjJkiD7//HNvXkLAMjn3VVVVevrppxUfH6%2B2bdvK5XJp6tSpOnHihLcvIyCZ/r7/tocfflg2m40PZG%2BAN%2Bb%2B4MGDuuuuu%2BRwONSuXTslJiaqqKjIW5cQ0EzPf0VFhR577DFFR0erTZs26t69u5YvX%2B7NS8CVsuAzb731ltW6dWtrxYoV1t/%2B9jfriSeesNq2bWsdPXq03v65ublWSEiINX/%2BfOvgwYPW/PnzrdDQUGv37t21fRYsWGC1a9fOWr9%2BvXXgwAFrwoQJVlRUlOV2u5vrsgKC6bk/ffq0NXz4cGvdunXW//zP/1i7du2y%2BvXrZ/Xu3bs5LysgeOP7vsaGDRushIQEy%2BVyWS%2B99JK3LyXgeGPu//73v1vXX3%2B99eSTT1r79u2zDh8%2BbP3Xf/2X9fXXXzfXZQUMb8z/9OnTrS5dulg7duywCgsLrd///vdWSEiItXHjxua6LDSAgOVDt956qzVjxgyPtm7dullz5sypt//48eOtUaNGebSNHDnSmjhxomVZllVdXW05nU5rwYIFtcfPnz9vORwO69VXXzVcfWAzPff1%2BeijjyxJDf7lGay8NffHjx%2B3OnfubH322WdWbGwsAase3pj7CRMmWFOmTDFfbAvkjfnv2bOn9dvf/tajT69evaxf/vKXhqrG1eIWoY9cuHBBe/fuVVJSkkd7UlKScnNz6z1n165ddfqPHDmytn9hYaFKSko8%2Btjtdg0ePLjBMYORN%2Ba%2BPuXl5bLZbMY/hzKQeWvuq6urlZKSoieffFI9e/Y0X3gL4I25r66u1ubNm9W1a1eNHDlSnTp1Ur9%2B/bRx40bvXEQA89b3/u23365Nmzbpq6%2B%2BkmVZ2rFjh7744guNHDnS/EWgSQhYPlJWVqZLly7V%2BdDoyMjIOh8uXaOkpKTR/jX/bMqYwcgbc/9d58%2Bf15w5czR58mQ%2BpPVbvDX3CxcuVGhoqH7%2B85%2BbL7qF8Mbcl5aWqqKiQgsWLNCoUaO0bds23XvvvRozZoyys7O9cyEBylvf%2B4sXL1aPHj0UHR2tsLAwjRo1SsuWLdPtt99u/iLQJLx738dsNpvHvmVZddqa2r%2BpYwYrb8y99M0D7xMnTlR1dbWWLVtmptgWxuTc7927Vy%2B//LL27dvH9/kVMDn31dXVkqS7775bs2bNkiT9%2BMc/Vm5url599VUNHjzYZOktgum/dxYvXqzdu3dr06ZNio2NVU5Ojh555BFFRUVp%2BPDhZotHk7CC5SMdOnRQSEhInf9zKS0trfN/LDWcTmej/Z1OpyQ1acxg5I25r1FVVaXx48ersLBQWVlZrF59hzfmfufOnSotLdUNN9yg0NBQhYaG6ujRo/rFL36hH/zgB165jkDkjbnv0KGDQkND1aNHD48%2B3bt357cIv8Mb83/u3Dk988wzevHFFzV69Gj96Ec/0mOPPaYJEybo%2Beef986F4IoRsHwkLCxMvXv3VlZWlkd7VlaWBgwYUO85/fv3r9N/27Zttf3j4uLkdDo9%2Bly4cEHZ2dkNjhmMvDH30j/DVUFBgbZv36727dubLz7AeWPuU1JS9Omnnyo/P792c7lcevLJJ7V161bvXEgA8sbch4WFqW/fvjp06JBHny%2B%2B%2BEKxsbEGqw983pj/qqoqVVVVqVUrzx/lISEhtauL8CHfPFsPy/rnr%2ByuXLnS%2Btvf/mbNnDnTatu2rfXll19almVZKSkpHr9d8te//tUKCQmxFixYYB08eNBasGBBva9pcDgc1rvvvmsdOHDAmjRpEq9pqIfpua%2BqqrLuuusuKzo62srPz7eKi4trt8rKSp9co7/yxvf9d/FbhPXzxty/%2B%2B67VuvWra3XXnvNKigosF555RUrJCTE2rlzZ7Nfn7/zxvwPHjzY6tmzp7Vjxw7ryJEj1urVq63w8HBr2bJlzX598ETA8rGlS5dasbGxVlhYmNWrVy8rOzu79tjgwYOt1NRUj/7vvPOO9cMf/tBq3bq11a1bN2v9%2BvUex6urq63f/OY3ltPptOx2uzVo0CDrwIEDzXEpAcfk3BcWFlqS6t127NjRTFcUOEx/338XAath3pj7lStXWv/6r/9qhYeHWwkJCbyDqRGm57%2B4uNiaNm2a5XK5rPDwcOuHP/yh9cILL1jV1dXNcTlohM2yLMuXK2gAAAAtDc9gAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAw7P8BCeJeRd7msCkAAAAASUVORK5CYII%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common2756420526863610987\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0372</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0006400000000000017</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.09244999999999999</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.003429999999999999</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.015990000000000004</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.020929999999999997</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.04476</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.020669999999999994</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.005930000000000001</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.010050000000000003</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (23)</td>\n",
       "        <td class=\"number\">23</td>\n",
       "        <td class=\"number\">69.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme2756420526863610987\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0006400000000000017</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0009200000000000041</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.001409999999999998</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0015099999999999975</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0025899999999999986</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.039569999999999994</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.04476</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.05674</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.08574999999999999</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.09244999999999999</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow ignore\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_freqV3OFF\"><s>freqV3OFF</s><br/>\n",
       "            <small>Highly correlated</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <p><em>This variable is highly correlated with <a href=\"#pp_var_freqV3ON\"><code>freqV3ON</code></a> and should be ignored for analysis</em></p>\n",
       "</div>\n",
       "<div class=\"col-md-6\">\n",
       "    <table class=\"stats \">\n",
       "        <tr>\n",
       "            <th>Correlation</th>\n",
       "            <td>0.9549</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_freqV3ON\">freqV3ON<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>33</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>100.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>401.7</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>1</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>1330</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram1335574268028209523\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAABxElEQVR4nO3dPa7TQBSG4eOI1llAZO%2BCRbAnavbEItiFrSzAEQUFMQWE5l4%2BlCsY5%2Bd5ykTy8Sh%2BNZPK3bquazU2z3ON49h6LHdumqYahqHpzHdNp/3S931V/Vzwfr/f4ha4I8uy1DiOv5%2BbljYJpOu6qqra7/cvAnn/8fPV1/vy6cM/uS9u2%2BW5aWnXfCLcEYFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAsMkr2Pgzr6C7LXYQCAQCwdMesR7pKHPtWm51HbfIDgKBQCB42iPWM2t1vHyEY6wdBAKBQOCIdYW3HBkexbOu3Q4CwSY7yLquVVW1LMuL775/%2B3r19V67zt%2B8ZQ7/32u/5eWzy3PTUrduMHWe5xrHsfVY7tw0TTUMQ9OZmwRyPp/reDxW3/fVdV3r8dyZdV3rdDrV4XCo3a7tv4JNAoF74U86BAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAsEP%2BEdbLwfXHpMAAAAASUVORK5CYII%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives1335574268028209523,#minihistogram1335574268028209523\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives1335574268028209523\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles1335574268028209523\"\n",
       "                                                  aria-controls=\"quantiles1335574268028209523\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram1335574268028209523\" aria-controls=\"histogram1335574268028209523\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common1335574268028209523\" aria-controls=\"common1335574268028209523\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme1335574268028209523\" aria-controls=\"extreme1335574268028209523\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles1335574268028209523\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>3.8</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>14</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>268</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>742</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>1169.4</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>1330</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>1329</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>728</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>437.82</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>1.0899</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-1.0244</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>401.7</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>388.32</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>0.64222</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>13256</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>191680</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>344.0 B</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram1335574268028209523\">\n",
       "            <img 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EMa1twizsrJ05swZtW7dWgkJCWrQoEHY9tLSUocqAwAA0c61AWvOnDlOlwAAAFAt1wasH//4x06XAAAAUC3XPoMlSZ9%2B%2BqlmzJihrKys0C3B9evX669//avDlQEAgGjm2oC1detWdezYUZs3b9bvf/97lZeXS5IKCgr03HPPOVwdAACIZq4NWFOmTNGMGTO0cePGsDe3P/zww9q5c6eDlQEAgGjn2oC1Z88e/eAHP6jSnpycrJMnTzpQEQAAwGWuDVhNmjRRSUlJlfbCwkK1bNnSgYoAAAAuc23AGjJkiKZOnaqTJ0%2BG3uj%2B3nvv6Wc/%2B5lGjBjhcHUAACCauTZgzZ49WykpKWrRooXKy8t1xx136P7771fXrl317LPPOl0eAACIYq59D1ZsbKxWrFihAwcOqKCgQJWVlbrnnnvUvn17p0sDAABRzrUB64p27dqpXbt2TpcBAAAQ4tqA9c///M/X3L5o0aJaqgQAACCcawPWiRMnwtYrKiq0b98%2BnTlzRg899JBDVQEAALg4YL377rtV2i5evKif/vSn6tChgwMVAQAAXObaTxFWJyYmRj/72c/0q1/9ytoxL168qJ///OdKT09Xw4YNddttt%2Bn5559XZWWltXMAAIC6xbUzWFdz%2BPBhVVRUWDveiy%2B%2BqIULF2rp0qXq2LGj8vLy9OSTT8rn82nChAnWzgMAAOoO1wasZ555JmzdGKMTJ05ozZo1Gj58uLXz7NixQ9/73vfUv39/SVKbNm305ptvKi8vz9o5AABA3eLagLVjx46w9Xr16ukf/uEfNGfOHP3kJz%2Bxdp7u3btr4cKFOnDggNq1a6cPPvhA27Zt09y5c62dAwAA1C2uDVhbt26tlfNMmTJFgUBA7du3V/369XXp0iXNmjVLQ4cOveo%2BwWBQwWAwtF5WVlYbpQIAgAhRpx5yvxVWrFihZcuWafny5SooKNDSpUv18ssva%2BnSpVfdJycnRz6fL7SkpqbWYsUAAMBpHmOMcbqIG9G1a9fQlzzXZNeuXTd8ntTUVE2dOlVjxowJtb3wwgtatmyZPv7442r3qW4GKzU1VYFAQAkJCTdcS3Uyp6%2B1erxbKW/Wo06XAACIImVlZfL5fLfk729NXHuLsFevXvrP//xPtWvXTt26dZMk7dy5U/v379dTTz0lr9dr5Tznzp1TvXrhE33169e/5msavF6vtfMDAAD3cW3A%2BuKLLzRmzBjNnj07rH369On6/PPP9V//9V9WzjNw4EDNmjVLaWlp6tixo3bv3q1XX31Vo0aNsnJ8AABQ97j2FmGTJk30/vvvq23btmHtBw8eVGZmpgKBgJXznDlzRs8%2B%2B6xWrVql0tJS%2Bf1%2BDR06VM8995xiY2Ov6xi3coqSW4QAAFSPW4Q3wOv1avv27VUC1vbt263enouPj9fcuXN5LQMAALhurg1Y48eP1%2BjRo7V7927dd999ki4/g7V48WL927/9m8PVAQCAaObagDV9%2BnSlp6fr17/%2BtV5//XVJUocOHbR48WINGzbM4eoAAEA0c23AkqRhw4YRpgAAQMRx9YtGy8rKtGTJEj333HM6ffq0JOmDDz7QiRMnHK4MAABEM9fOYH344Yfq3bu3GjVqpOLiYj3xxBNq2rSpfv/73%2Buzzz675pvWAQAAbiXXzmBNmjRJw4YN0yeffKK4uLhQe//%2B/bVlyxYHKwMAANHOtTNY77//vhYsWFDl63JatmzJLUIAAOAo185gxcbGqry8vEr7wYMH1axZMwcqAgAAuMy1AWvQoEH65S9/qYsXL0qSPB6Pjh07pqlTp2rw4MEOVwcAAKKZawPWK6%2B8ouPHjyslJUVffvmlHn74Yd12222Ki4ur8v2EAAAAtcm1z2D5fD5t375dubm5KigoUGVlpe655x717du3ynNZAAAAtcmVAauiokLf/e53NX/%2BfPXp00d9%2BvRxuiQAAIAQV94ibNCggXbv3s1MFQAAiEiuDFiSNGLECL3xxhtOlwEAAFCFK28RXjFv3jxt2LBBmZmZaty4cdi2l156yaGqAABAtHNtwMrPz9ddd90lSdqzZ0/YNm4dAgAAJ7kuYB0%2BfFjp6enaunWr06UAAABUy3XPYLVt21YnT54MrT/%2B%2BOP6/PPPHawIAAAgnOsCljEmbP1Pf/qTzp4961A1AAAAVbkuYAEAAEQ61wUsj8dT5SF2HmoHAACRxHUPuRtj9MQTT8jr9UqSzp8/r9GjR1d5TcPKlSudKA8AAMB9ASs7OztsfcSIEQ5VAgAAUD3XBSze3g4AACKd657BAgAAiHQELAAAAMsIWAAAAJYRsK7DsWPHNGLECCUlJalRo0a6%2B%2B67lZ%2Bf73RZAAAgQrnuIffadvr0aT3wwAPq1auX/u///k/Jycn65JNP1KRJE6dLAwAAEYqAVYMXX3xRqampYZ9ebNOmjXMFAQCAiMctwhqsWbNGmZmZ%2BuEPf6jk5GR17txZixcvdrosAAAQwQhYNTh8%2BLAWLFigtm3bat26dRo9erTGjx%2Bv3/72t1fdJxgMqqysLGwBAADRg1uENaisrFRmZqZmz54tSercubP27dunBQsWaOTIkdXuk5OTo5kzZ9ZmmQAAIIIwg1WDFi1a6I477ghr69Chg44ePXrVfaZNm6ZAIBBaiouLb3WZAAAggjCDVYMHHnhA%2B/fvD2s7cOCAWrdufdV9vF5v6MuoAQBA9GEGqwaTJk3Szp07NXv2bB06dEjLly/XokWLNGbMGKdLAwAAEYqAVYOuXbtq1apVevPNN5WRkaFf/vKXmjt3roYPH%2B50aQAAIEJxi/A6DBgwQAMGDHC6DAAA4BLMYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAesbysnJkcfj0cSJE50uBQAARCgC1jfw/vvva9GiRbrrrrucLgUAAEQwAtZ1Ki8v1/Dhw7V48WI1bdrU6XIAAEAEI2BdpzFjxqh///7q3bu306UAAIAIF%2BN0AW7w1ltvKT8/X3l5edfVPxgMKhgMhtbLyspuVWkAACACEbBqUFxcrAkTJmj9%2BvWKi4u7rn1ycnI0c%2BbMW1wZgNqSOX2t0yV8I3mzHnW6BCDqcYuwBvn5%2BSotLVWXLl0UExOjmJgYbd68Wa%2B99ppiYmJ06dKlKvtMmzZNgUAgtBQXFztQOQAAcAozWDV45JFHtHfv3rC2J598Uu3bt9eUKVNUv379Kvt4vV55vd7aKhEAAEQYAlYN4uPjlZGREdbWuHFjJSUlVWkHAACQuEUIAABgHTNYN2DTpk1OlwAAACIYM1gAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYFmM0wUAkSpz%2BlqnS/hG8mY96nQJiBBu%2Bm%2BX/25RVzGDBQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbCuQ05Ojrp27ar4%2BHglJyfrscce0/79%2B50uCwAARCgC1nXYvHmzxowZo507dyo3N1cXL15Unz59dPbsWadLAwAAESjG6QLcYO3atWHrb7zxhpKTk5Wfn6%2BHHnrIoaoAAECkYgbrBgQCAUlSYmKiw5UAAIBIxAzWN2SM0eTJk9W9e3dlZGRU2ycYDCoYDIbWy8rKaqs8AAAQAQhY39DYsWO1Z88ebdu27ap9cnJyNHPmzFqsCgDcKXP62po7RZC8WY86XcI34qbxddvY1oRbhN/AuHHjtGbNGm3cuFGtWrW6ar9p06YpEAiEluLi4lqsEgAAOI0ZrOtgjNG4ceO0atUqbdq0Senp6dfs7/V65fV6a6k6AAAQaQhY12HMmDFavny5/vd//1fx8fEqKSmRJPl8PjVs2NDh6gAAQKThFuF1WLBggQKBgHr27KkWLVqElhUrVjhdGgAAiEDMYF0HY4zTJQAAABdhBgsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALItxugAAdmROX%2Bt0CUCdx88ZrhczWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgLWdZo/f77S09MVFxenLl26aOvWrU6XBAAAIhQB6zqsWLFCEydO1PTp07V79249%2BOCD6tevn44ePep0aQAAIAIRsK7Dq6%2B%2Bqh//%2BMf6p3/6J3Xo0EFz585VamqqFixY4HRpAAAgAsU4XUCku3DhgvLz8zV16tSw9j59%2Bmj79u3V7hMMBhUMBkPrgUBAklRWVma9vkvBs9aPeavciuu/ldw0tgDgdrfib8SVYxpjrB%2B7JgSsGpw6dUqXLl1S8%2BbNw9qbN2%2BukpKSavfJycnRzJkzq7SnpqbekhrdwveK0xUAACLVrfwbcebMGfl8vlt3gmoQsK6Tx%2BMJWzfGVGm7Ytq0aZo8eXJovbKyUn//%2B9%2BVlJR01X2%2BqbKyMqWmpqq4uFgJCQlWjulGjMNljANjcAXjcBnjwBhIl/9WnzlzRn6/v9bPTcCqQbNmzVS/fv0qs1WlpaVVZrWu8Hq98nq9YW1NmjS5JfUlJCRE7Q/OVzEOlzEOjMEVjMNljANjUNszV1fwkHsNYmNj1aVLF%2BXm5oa15%2Bbm6v7773eoKgAAEMmYwboOkydPVlZWljIzM9WtWzctWrRIR48e1ejRo50uDQAARKD6M2bMmOF0EZEuIyNDSUlJmj17tl5%2B%2BWV9%2BeWX%2Bt3vfqdOnTo5Wlf9%2BvXVs2dPxcREd05mHC5jHBiDKxiHyxgHxsBJHuPEZxcBAADqMJ7BAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELJeaP3%2B%2B0tPTFRcXpy5dumjr1q1Ol2RNTk6Ounbtqvj4eCUnJ%2Buxxx7T/v37w/oEg0GNGzdOzZo1U%2BPGjTVo0CB99tlnYX2OHj2qgQMHqnHjxmrWrJnGjx%2BvCxcu1OalWJOTkyOPx6OJEyeG2qJlDI4dO6YRI0YoKSlJjRo10t133638/PzQdmOMZsyYIb/fr4YNG6pnz57at29f2DFOnz6trKws%2BXw%2B%2BXw%2BZWVl6YsvvqjtS7lhFy9e1M9//nOlp6erYcOGuu222/T888%2BrsrIy1KcujsOWLVs0cOBA%2Bf1%2BeTwerV69Omy7rWveu3evevTooYYNG6ply5Z6/vnnHfnuuupcawwqKio0ZcoU3XnnnWrcuLH8fr9Gjhyp48ePhx3D7WPgWgau89Zbb5kGDRqYxYsXm48%2B%2BshMmDDBNG7c2Bw5csTp0qzo27eveeONN8yHH35oCgsLTf/%2B/U1aWpopLy8P9Rk9erRp2bKlyc3NNQUFBaZXr16mU6dO5uLFi8YYYy5evGgyMjJMr169TEFBgcnNzTV%2Bv9%2BMHTvWqcu6Ybt27TJt2rQxd911l5kwYUKoPRrG4O9//7tp3bq1eeKJJ8x7771nioqKzIYNG8yhQ4dCfebMmWPi4%2BPNO%2B%2B8Y/bu3Wsef/xx06JFC1NWVhbq8%2Bijj5qMjAyzfft2s337dpORkWEGDBjgxCXdkBdeeMEkJSWZP/zhD6aoqMi8/fbb5lvf%2BpaZO3duqE9dHIc//elPZvr06eadd94xksyqVavCttu45kAgYJo3b26GDBli9u7da9555x0THx9vXn755Vq7zmu51hh88cUXpnfv3mbFihXm448/Njt27DD33nuv6dKlS9gx3D4GbkXAcqHvfOc7ZvTo0WFt7du3N1OnTnWoolurtLTUSDKbN282xlz%2BpdKgQQPz1ltvhfocO3bM1KtXz6xdu9YYc/mXUr169cyxY8dCfd58803j9XpNIBCo3Qu4CWfOnDFt27Y1ubm5pkePHqGAFS1jMGXKFNO9e/erbq%2BsrDQpKSlmzpw5obbz588bn89nFi5caIwx5qOPPjKSzM6dO0N9duzYYSSZjz/%2B%2BNYVb1H//v3NqFGjwqqg5tgAAAbiSURBVNoGDx5sRowYYYyJjnH4eriwdc3z5883Pp/PnD9/PtQnJyfH%2BP1%2BU1lZeasv6xupLmR%2B3a5du4yk0D%2B469oYuAm3CF3mwoULys/PV58%2BfcLa%2B/Tpo%2B3btztU1a0VCAQkSYmJiZKk/Px8VVRUhI2B3%2B9XRkZGaAx27NihjIyMsC/47Nu3r4LBYNjtpUg3ZswY9e/fX7179w5rj5YxWLNmjTIzM/XDH/5QycnJ6ty5sxYvXhzaXlRUpJKSkrBx8Hq96tGjR9g4%2BHw%2B3XvvvaE%2B9913n3w%2Bn2t%2BZrp3764///nPOnDggCTpgw8%2B0LZt2/Td735XUvSMw1fZuuYdO3aoR48eYd8f27dvXx0/flyffvpp7VyMRYFAQB6PJ/T9t9E4BpGCgOUyp06d0qVLl6p80XTz5s2rfCF1XWCM0eTJk9W9e3dlZGRIkkpKShQbG6umTZuG9f3qGJSUlFQZo6ZNmyo2NtY14/TWW28pPz9fOTk5VbZFyxgcPnxYCxYsUNu2bbVu3TqNHj1a48eP129/%2B1tJCl3HtX4eSkpKlJycXOXYycnJrhmHKVOmaOjQoWrfvr0aNGigzp07a%2BLEiRo6dKik6BmHr7J1zdX9nFxZd9u4nD9/XlOnTtWwYcNCX%2B4cbWMQSXh3vkt5PJ6wdWNMlba6YOzYsdqzZ4%2B2bdtWY9%2Bvj0F14%2BGWcSouLtaECRO0fv16xcXFXfd%2BdWkMJKmyslKZmZmaPXu2JKlz587at2%2BfFixYoJEjR4b61fTz4PZxWLFihZYtW6bly5erY8eOKiws1MSJE%2BX3%2B5WdnR3qV9fHoTo2rrm6Y1xt30hVUVGhIUOGqLKyUvPnzw/bFi1jEGmYwXKZZs2aqX79%2BlX%2BVVFaWlrlXyBuN27cOK1Zs0YbN25Uq1atQu0pKSm6cOGCTp8%2BHdb/q2OQkpJSZYxOnz6tiooKV4xTfn6%2BSktL1aVLF8XExCgmJkabN2/Wa6%2B9ppiYGDVv3rzOj4EktWjRQnfccUdYW4cOHXT06FFJl69Rqvqv7K%2BPw%2Beff17l2CdPnnTNODz99NOaOnWqhgwZojvvvFNZWVmaNGlSaHYzWsbhq2xdc3U/J6WlpZKqzo5FqoqKCv3oRz9SUVGRcnNzQ7NXUvSMQSQiYLlMbGysunTpotzc3LD23Nxc3X///Q5VZZcxRmPHjtXKlSv1l7/8Renp6WHbu3TpogYNGoSNwYkTJ/Thhx%2BGxqBbt2768MMPdeLEiVCf9evXy%2Bv1qkuXLrVzITfhkUce0d69e1VYWBhaMjMzNXz48ND/rutjIEkPPPBAlVd0HDhwQK1bt5YkpaenKyUlJWwcLly4oM2bN4eNQyAQ0K5du0J93nvvPQUCAdf8zJw7d0716oX/uq5fv37oNQ3RMg5fZeuau3Xrpi1btoS9vmT9%2BvXy%2B/1q06ZN7VzMTbgSrg4ePKgNGzYoKSkpbHs0jEHEqv3n6nGzrrym4b//%2B7/NRx99ZCZOnGgaN25sPv30U6dLs%2BKnP/2p8fl8ZtOmTebEiROh5dy5c6E%2Bo0ePNq1atTIbNmwwBQUF5uGHH672FQWPPPKIKSgoMBs2bDCtWrVy1SsKvu6rnyI0JjrGYNeuXSYmJsbMmjXLHDx40PzP//yPadSokVm2bFmoz5w5c4zP5zMrV640e/fuNUOHDq32o/p33XWX2bFjh9mxY4e58847I/r1BF%2BXnZ1tWrZsGXpNw8qVK02zZs3MM888E%2BpTF8fhzJkzZvfu3Wb37t1Gknn11VfN7t27Q5%2BQs3HNX3zxhWnevLkZOnSo2bt3r1m5cqVJSEiImFcUXGsMKioqzKBBg0yrVq1MYWFh2O/LYDAYOobbx8CtCFgu9Zvf/Ma0bt3axMbGmnvuuSf0CoO6QFK1yxtvvBHq8%2BWXX5qxY8eaxMRE07BhQzNgwABz9OjRsOMcOXLE9O/f3zRs2NAkJiaasWPHhn0M2W2%2BHrCiZQzeffddk5GRYbxer2nfvr1ZtGhR2PbKykrzi1/8wqSkpBiv12seeughs3fv3rA%2Bf/vb38zw4cNNfHy8iY%2BPN8OHDzenT5%2Buzcu4KWVlZWbChAkmLS3NxMXFmdtuu81Mnz497I9oXRyHjRs3Vvu7IDs72xhj75r37NljHnzwQeP1ek1KSoqZMWNGxLye4FpjUFRUdNXflxs3bgwdw%2B1j4FYeY3hVKwAAgE08gwUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwLL/ByzUahtqsRjiAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common1335574268028209523\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">1330.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">107.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">8.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">10.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">11.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (23)</td>\n",
       "        <td class=\"number\">23</td>\n",
       "        <td class=\"number\">69.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme1335574268028209523\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">1.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">8.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">956.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">967.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1129.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1230.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1330.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_freq_delta\">freq_delta<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>27</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>81.8%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>124.24</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>549</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>12.1%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-6376204844084339736\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAABwUlEQVR4nO3dwY2bUBSG0YuVLRRgQRcpIj1lnZ5SRLoAuQCsLLKIySJxNjP6pVjRw8ycs7QlX4z49EAg0W3btlVjy7LUNE2tx3Jw8zzXOI5NZ35oOu2Pvu%2Br6vcfHoZhj03gQNZ1rWma/h43Le0SSNd1VVU1DMOLQD5%2B/vrPv/fty6f/sl08t/tx09Kp%2BUQ4EIFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAS7vP7gvfAqh%2BOzgkAgEAjexCnWI6cyj3D68/5YQSAQCAQCgUAgEAgEAoFAIBAIBAKBQCB4E3fSW2lxx77FA46tnjx4xLM9rWAFgWCXFWTbtqqqWtf1xXc/f3xvvTmH99p%2BTJ55H7/2X%2B6f3Y%2Bblrpth6nLstQ0Ta3HcnDzPNc4jk1n7hLI7Xary%2BVSfd9X13Wtx3Mw27bV9Xqt8/lcp1Pbq4JdAoGjcJEOgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQPAL3EZWxmDyPdEAAAAASUVORK5CYII%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-6376204844084339736,#minihistogram-6376204844084339736\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-6376204844084339736\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-6376204844084339736\"\n",
       "                                                  aria-controls=\"quantiles-6376204844084339736\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-6376204844084339736\" aria-controls=\"histogram-6376204844084339736\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-6376204844084339736\" aria-controls=\"common-6376204844084339736\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-6376204844084339736\" aria-controls=\"extreme-6376204844084339736\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-6376204844084339736\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>4</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>75</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>236</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>424.8</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>549</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>549</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>232</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>146.25</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>1.1771</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>1.319</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>124.24</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>115.69</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>1.3492</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>4100</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>21388</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>344.0 B</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-6376204844084339736\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAAgAElEQVR4nO3de3BU9f3/8ddCyIKYLDchiQkaLd64xApe8BpA0QioZVpFIEa8FJSLMVQl8rViLQQZpYgIFq2KtQo6AqWtAlExwQGUBCKIFlEQIgQCFjchyhKSz%2B8PfmzdBkjQT/bsYZ%2BPmZ3xnD27%2B85HTJ6ePWw8xhgjAAAAWNPE6QEAAABONAQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZTFODxANamtrtWPHDsXFxcnj8Tg9DgAAUcEYo8rKSiUlJalJk/CeUyKwwmDHjh1KSUlxegwAAKJSaWmpkpOTw/qaBFYYxMXFSTr0Lzg%2BPt7haQAAiA4VFRVKSUkJ/hwOJwIrDA6/LRgfH09gAQAQZk5cnsNF7gAAAJYRWAAAAJYRWAAAAJYRWAAAAJYRWAAAAJYRWAAAAJYRWAAAAJYRWAAAAJYRWAAAAJYRWAAAAJYRWAAAAJYRWAAAAJbxy55drsf4xU6P0GBFE69zegQAAMKCM1gAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWRX1gFRYWasCAAUpKSpLH49HChQuPeuzw4cPl8Xg0bdq0ME4IAADcJuoDq6qqSmlpaZoxY8Yxj1u4cKE%2B%2BugjJSUlhWkyAADgVjFOD%2BC0jIwMZWRkHPOY7du3a9SoUVqyZIn69esXpskAAIBbRf0ZrPrU1tYqMzNTDzzwgDp37uz0OAAAwAWi/gxWfZ544gnFxMRozJgxDX5MIBBQIBAIbldUVDTGaAAAIEJxBusYiouL9fTTT%2Bvll1%2BWx%2BNp8OPy8vLk8/mCt5SUlEacEgAARBoC6xiWL1%2Bu8vJydezYUTExMYqJidHWrVs1duxYnX766Ud9XG5urvx%2Bf/BWWloavqEBAIDjeIvwGDIzM3X11VeH7Lv22muVmZmpYcOGHfVxXq9XXq%2B3sccDAAARKuoDa9%2B%2Bffryyy%2BD21u2bFFJSYnatGmjjh07qm3btiHHN2vWTAkJCTr77LPDPSoAAHCJqA%2BsoqIi9erVK7idk5MjScrKytLLL7/s0FQAAMDNoj6w0tPTZYxp8PFff/114w0DAABOCFzkDgAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYBmBBQAAYFnUB1ZhYaEGDBigpKQkeTweLVy4MHhfdXW1HnroIXXt2lUtW7ZUUlKSbrvtNu3YscPBiQEAQKSL%2BsCqqqpSWlqaZsyYUee%2B77//XmvWrNEjjzyiNWvWaP78%2Bfriiy90ww03ODApAABwixinB3BaRkaGMjIyjnifz%2BdTfn5%2ByL5nnnlGF110kbZt26aOHTuGY0QAAOAyUR9Yx8vv98vj8ahVq1ZHPSYQCCgQCAS3KyoqwjEaAACIEFH/FuHx2L9/v8aNG6fBgwcrPj7%2BqMfl5eXJ5/MFbykpKWGcEgAAOI3AaqDq6moNGjRItbW1mjlz5jGPzc3Nld/vD95KS0vDNCUAAIgEvEXYANXV1br55pu1ZcsWvf/%2B%2B8c8eyVJXq9XXq83TNMBAIBIQ2DV43Bcbdq0ScuWLVPbtm2dHgkAAES4qA%2Bsffv26csvvwxub9myRSUlJWrTpo2SkpL061//WmvWrNE///lP1dTUaOfOnZKkNm3aKDY21qmxAQBABIv6wCoqKlKvXr2C2zk5OZKkrKwsTZgwQYsWLZIknX/%2B%2BSGPW7ZsmdLT08M2JwAAcI%2BoD6z09HQZY456/7HuAwAAOBL%2BFiEAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlUR9YhYWFGjBggJKSkuTxeLRw4cKQ%2B40xmjBhgpKSktSiRQulp6drw4YNDk0LAADcIOoDq6qqSmlpaZoxY8YR758yZYqmTp2qGTNmaPXq1UpISNA111yjysrKME8KAADcIsbpAZyWkZGhjIyMI95njNG0adM0fvx4DRw4UJI0Z84cdejQQa%2B99pqGDx8ezlEBAIBLRP0ZrGPZsmWLdu7cqb59%2Bwb3eb1eXXXVVVqxYoWDkwEAgEgW9WewjmXnzp2SpA4dOoTs79Chg7Zu3XrUxwUCAQUCgeB2RUVF4wwIAAAiEmewGsDj8YRsG2Pq7PuxvLw8%2BXy%2B4C0lJaWxRwQAABGEwDqGhIQESf89k3VYeXl5nbNaP5abmyu/3x%2B8lZaWNuqcAAAgshBYx5CamqqEhATl5%2BcH9x04cEAFBQW69NJLj/o4r9er%2BPj4kBsAAIgeUX8N1r59%2B/Tll18Gt7ds2aKSkhK1adNGHTt2VHZ2tiZNmqROnTqpU6dOmjRpkk466SQNHjzYwakBAEAki/rAKioqUq9evYLbOTk5kqSsrCy9/PLLevDBB/XDDz/o3nvv1d69e3XxxRdr6dKliouLc2pkAAAQ4TzGGOP0ECe6iooK%2BXw%2B%2Bf1%2B628X9hi/2OrzNaaiidc5PQIAIIo05s/f%2BnANFgAAgGUEFgAAgGUEFgAAgGWuDaxXX31V%2B/fvd3oMAACAOlwbWDk5OUpISNDw4cP18ccfOz0OAABAkGsDa8eOHXrxxRdVVlamyy%2B/XJ07d9ZTTz2l3bt3Oz0aAACIcq4NrJiYGA0cOFCLFi3Stm3blJWVpRdffFHJyckaOHCg/vWvf4lPoAAAAE5wbWD9WEJCgvr06aP09HR5PB4VFRVp8ODB6tSpk5YvX%2B70eAAAIMq4OrD27NmjadOmKS0tTZdddpnKy8u1cOFCbd26Vdu3b1f//v112223OT0mAACIMq79VTm/%2BtWv9Pbbbys1NVV33XWXsrKydMoppwTvP/nkk/Xggw9q%2BvTpDk4JAACikWsDKz4%2BXu%2B%2B%2B66uuOKKox6TmJioTZs2hXEqAAAAFwfWnDlz6j3G4/HozDPPDMM0AAAA/%2BXaa7Duv/9%2BzZgxo87%2BZ599VmPHjnVgIgAAgENcG1hvvvmmLrnkkjr7e/bsqXnz5jkwEQAAwCGuDaw9e/aodevWdfbHx8drz549DkwEAABwiGsD68wzz9SSJUvq7F%2ByZIlSU1MdmAgAAOAQ117knp2drezsbH377bfq3bu3JOm9997TlClT9OSTTzo8HQAAiGauDay7775b%2B/fv16RJk/Too49KkpKTkzV9%2BnTdcccdDk8HAACimWsDS5JGjx6t0aNHq6ysTC1atFCrVq2cHgkAAMDdgXVYYmKi0yMAAAAEufYi9927d2vYsGHq2LGjmjdvrtjY2JAbAACAU1x7Buv222/XV199pQceeECJiYnyeDxOjwQAACDJxYFVWFiowsJC/fKXv3R6FAAAgBCufYswOTmZs1YAACAiuTaw/vSnPyk3N1fffPON06MAAACEcO1bhJmZmaqsrNRpp52m%2BPh4NWvWLOT%2B8vJyhyYDAADRzrWBNXnyZKdHAAAAOCLXBtadd97p9AgAAABH5NprsCTp66%2B/1oQJE5SZmRl8S3Dp0qX6/PPPHZ4MAABEM9cG1vLly9W5c2cVFBTojTfe0L59%2ByRJa9as0e9//3uHpwMAANHMtYH10EMPacKECVq2bFnIJ7f37t1bq1atsvY6Bw8e1P/93/8pNTVVLVq00BlnnKE//OEPqq2ttfYaAADgxOLaa7DWrVunv/3tb3X2t2/fXrt377b2Ok888YSee%2B45zZkzR507d1ZRUZGGDRsmn8%2Bn%2B%2B67z9rrAACAE4drA6tVq1bauXOnUlNTQ/aXlJTo1FNPtfY6K1eu1I033qh%2B/fpJkk4//XS9/vrrKioqsvYaAADgxOLatwgHDRqkcePGaffu3cFPdP/oo4/0u9/9TkOHDrX2Opdffrnee%2B89ffHFF5KkTz75RB9%2B%2BKGuv/76oz4mEAiooqIi5AYAAKKHa89gTZo0SZmZmUpMTFRtba3OO%2B88VVdX6%2Babb9Yjjzxi7XUeeugh%2Bf1%2BnXPOOWratKlqamo0ceJE3XrrrUd9TF5enh577DFrMwAAAHdxbWDFxsZq3rx5%2BuKLL7RmzRrV1tbqggsu0DnnnGP1debNm6dXX31Vr732mjp37qySkhJlZ2crKSlJWVlZR3xMbm6ucnJygtsVFRVKSUmxOhcAAIhcrg2sw8466yydddZZjfb8DzzwgMaNG6dBgwZJkrp27aqtW7cqLy/vqIHl9Xrl9XobbSYAABDZXBtYv/3tb495/%2BzZs628zvfff68mTUIvVWvatCkf0wAAAI7KtYFVVlYWsl1dXa0NGzaosrJSV155pbXXGTBggCZOnKiOHTuqc%2BfOWrt2raZOnao77rjD2msAAIATi2sD6x//%2BEedfQcPHtQ999yjc88919rrPPPMM3rkkUd07733qry8XElJSRo%2BfDifFg8AAI7KY4wxTg9h08aNG5Wenl7nDJeTKioq5PP55Pf7FR8fb/W5e4xfbPX5GlPRxOucHgEAEEUa8%2BdvfVz7OVhHs3nzZlVXVzs9BgAAiGKufYvwwQcfDNk2xqisrEyLFi3SkCFDHJoKAADAxYG1cuXKkO0mTZrolFNO0eTJk3X33Xc7NBUAAICLA2v58uVOjwAAAHBEJ9w1WAAAAE5z7RmsCy%2B8MPhLnuvz8ccfN/I0AAAA/%2BXawOrVq5f%2B/Oc/66yzzlLPnj0lSatWrdLGjRs1fPhwflUNAABwjGsD67vvvtPIkSM1adKkkP3jx4/Xrl279MILLzg0GQAAiHauvQbrjTfe0LBhw%2Brsv/322/Xmm286MBEAAMAhrg0sr9erFStW1Nm/YsUK3h4EAACOcu1bhGPGjNGIESO0du1aXXLJJZIOXYP1/PPP6%2BGHH3Z4OgAAEM1cG1jjx49Xamqqnn76ab344ouSpHPPPVfPP/%2B8Bg8e7PB0AAAgmrk2sCRp8ODBxBQAAIg4rr0GSzr0W7Jffvll/f73v9fevXslSZ988onKysocngwAAEQz157B%2BvTTT3X11VfrpJNOUmlpqW6//Xa1bt1ab7zxhr755hvNmTPH6REBAECUcu0ZrPvvv1%2BDBw/WV199pebNmwf39%2BvXT4WFhQ5OBgAAop1rz2CtXr1as2bNqvPrck499VTeIgQAAI5y7Rms2NhY7du3r87%2BTZs2qV27dg5MBAAAcIhrA%2BuGG27Q448/roMHD0qSPB6Ptm/frnHjxmngwIEOTwcAAKKZawPrqaee0o4dO5SQkKAffvhBvXv31hlnnKHmzZvX%2Bf2EAAAA4eTaa7B8Pp9WrFih/Px8rVmzRrW1tbrgggt07bXX1rkuCwAAIJxcGVjV1dW6/vrrNXPmTPXt21d9%2B/Z1eiQAAIAgV75F2KxZM61du5YzVQAAICK5MrAkaejQoXrppZecHgMAAKAOV75FeNiMGTP07rvvqkePHmrZsmXIfVOmTHFoKgAAEO1cG1jFxcXq1q2bJGndunUh9/HWIQAAcJLrAmvz5s1KTU3V8uXLnR4FAADgiFx3DVanTp20e/fu4PYtt9yiXbt2OTgRAABAKNcFljEmZPvtt99WVVWVQ9MAAADU5brAAgAAiHSuCyyPx1PnIvbGvqh9%2B/btGjp0qNq2bauTTjpJ559/voqLixv1NQEAgHu57iJ3Y4xuv/12eb1eSdL%2B/fs1YsSIOh/TMH/%2BfCuvt3fvXl122WXq1auX3nnnHbVv315fffWVWrVqZeX5AQDAicd1gZWVlRWyPXTo0EZ9vSeeeEIpKSkhH2p6%2BumnN%2BprAgAAd3NdYIX709sXLVqka6%2B9Vr/5zW9UUFCgU089Vffee6/uvvvuoz4mEAgoEAgEtysqKsIxKgAAiBCuuwYr3DZv3qxZs2apU6dOWrJkiUaMGKExY8bolVdeOepj8vLy5PP5greUlJQwTgwAAJzmMf/7uQcIERsbqx49emjFihXBfWPGjNHq1au1cuXKIz7mSGewUlJS5Pf7FR8fb3W%2BHuMXW32%2BxlQ08TqnRwAARJGKigr5fL5G%2BflbH85g1SMxMVHnnXdeyL5zzz1X27ZtO%2BpjvF6v4uPjQ24AACB6EFj1uOyyy7Rx48aQfV988YVOO%2B00hyYCAACRjsCqx/33369Vq1Zp0qRJ%2BvLLL/Xaa69p9uzZGjlypNOjAQCACEVg1ePCCy/UggUL9Prrr6tLly56/PHHNW3aNA0ZMsTp0QAAQIRy3cc0OKF///7q37%2B/02MAAACX4AwWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZTFODwAg%2BvQYv9jpEY5L0cTrnB4BgMtwBgsAAMAyAgsAAMAyAgsAAMAyAgsAAMAyAgsAAMAyAgsAAMAyAgsAAMAyAgsAAMAyAgsAAMAyAgsAAMAyAgsAAMAyAgsAAMAyAgsAAMAyAgsAAMAyAgsAAMAyAus45eXlyePxKDs72%2BlRAABAhCKwjsPq1as1e/ZsdevWzelRAABABCOwGmjfvn0aMmSInn/%2BebVu3drpcQAAQAQjsBpo5MiR6tevn66%2B%2BmqnRwEAABEuxukB3GDu3LkqLi5WUVFRg44PBAIKBALB7YqKisYaDQAARCACqx6lpaW67777tHTpUjVv3rxBj8nLy9Njjz3WyJO5T4/xi50e4YRWNPE6p0cAAPx/vEVYj%2BLiYpWXl6t79%2B6KiYlRTEyMCgoKNH36dMXExKimpqbOY3Jzc%2BX3%2B4O30tJSByYHAABO4QxWPfr06aP169eH7Bs2bJjOOeccPfTQQ2ratGmdx3i9Xnm93nCNCAAAIgyBVY%2B4uDh16dIlZF/Lli3Vtm3bOvsBAAAk3iIEAACwjjNYP8EHH3zg9AgAACCCcQYLAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgILAADAMgKrAfLy8nThhRcqLi5O7du310033aSNGzc6PRYAAIhQBFYDFBQUaOTIkVq1apXy8/N18OBB9e3bV1VVVU6PBgAAIlCM0wO4weLFi0O2X3rpJbVv317FxcW68sorHZoKAABEKgLrJ/D7/ZKkNm3aHPH%2BQCCgQCAQ3K6oqAjLXAAAIDLwFuFxMsYoJydHl19%2Bubp06XLEY/Ly8uTz%2BYK3lJSUME8JAACcRGAdp1GjRmndunV6/fXXj3pMbm6u/H5/8FZaWhrGCQEAgNN4i/A4jB49WosWLVJhYaGSk5OPepzX65XX6w3jZAAAIJIQWA1gjNHo0aO1YMECffDBB0pNTXV6JAAAEMEIrAYYOXKkXnvtNf39739XXFycdu7cKUny%2BXxq0aKFw9MBAIBIwzVYDTBr1iz5/X6lp6crMTExeJs3b57TowEAgAjEGawGMMY4PQIAAHARzmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYFuP0AADs6DF%2BsdMjnLDctrZFE69zeoQGc9vaovG46c9tQ3AGCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICq4Fmzpyp1NRUNW/eXN27d9fy5cudHgkAAEQoAqsB5s2bp%2BzsbI0fP15r167VFVdcoYyMDG3bts3p0QAAQAQisBpg6tSpuvPOO3XXXXfp3HPP1bRp05SSkqJZs2Y5PRoAAIhAMU4PEOkOHDig4uJijRs3LmR/3759tWLFiiM%2BJhAIKBAIBLf9fr8kqaKiwvp8NYEq688JwN0a43tNY%2BF7GA5rjD%2B3h5/TGGP9uetDYNVjz549qqmpUYcOHUL2d%2BjQQTt37jziY/Ly8vTYY4/V2Z%2BSktIoMwLAj/mecnoC4Pg15p/byspK%2BXy%2BxnuBIyCwGsjj8YRsG2Pq7DssNzdXOTk5we3a2lr95z//Udu2bY/6mJ%2BioqJCKSkpKi0tVXx8vLXnjQas3c/D%2Bv10rN3Pw/r9dNG4dsYYVVZWKikpKeyvTWDVo127dmratGmds1Xl5eV1zmod5vV65fV6Q/a1atWq0WaMj4%2BPmv9YbGPtfh7W76dj7X4e1u%2Bni7a1C/eZq8O4yL0esbGx6t69u/Lz80P25%2Bfn69JLL3VoKgAAEMk4g9UAOTk5yszMVI8ePdSzZ0/Nnj1b27Zt04gRI5weDQAARKCmEyZMmOD0EJGuS5cuatu2rSZNmqQnn3xSP/zwg/76178qLS3N6dHUtGlTpaenKyaGVj5erN3Pw/r9dKzdz8P6/XSsXfh4jBN/dxEAAOAExjVYAAAAlhFYAAAAlhFYAAAAlhFYAAAAlhFYLjVz5kylpqaqefPm6t69u5YvX%2B70SBGhsLBQAwYMUFJSkjwejxYuXBhyvzFGEyZMUFJSklq0aKH09HRt2LAh5Ji9e/cqMzNTPp9PPp9PmZmZ%2Bu6778L5ZTgiLy9PF154oeLi4tS%2BfXvddNNN2rhxY8gxgUBAo0ePVrt27dSyZUvdcMMN%2Buabb0KO2bZtmwYMGKCWLVuqXbt2GjNmjA4cOBDOLyXsZs2apW7dugU/wLFnz5565513gvezbg2Xl5cnj8ej7Ozs4D7W7%2BgmTJggj8cTcktISAjez/c85xBYLjRv3jxlZ2dr/PjxWrt2ra644gplZGRo27ZtTo/muKqqKqWlpWnGjBlHvH/KlCmaOnWqZsyYodWrVyshIUHXXHONKisrg8cMHjxYJSUlWrx4sRYvXqySkhJlZmaG60twTEFBgUaOHKlVq1YpPz9fBw8eVN%2B%2BfVVV9d9fxpudna0FCxZo7ty5%2BvDDD7Vv3z71799fNTU1kqSamhr169dPVVVV%2BvDDDzV37ly99dZbGjt2rFNfVlgkJydr8uTJKioqUlFRkXr37q0bb7wx%2BIOMdWuY1atXa/bs2erWrVvIftbv2Dp37qyysrLgbf369cH7%2BJ7nIAPXueiii8yIESNC9p1zzjlm3LhxDk0UmSSZBQsWBLdra2tNQkKCmTx5cnDf/v37jc/nM88995wxxpjPPvvMSDKrVq0KHrNy5Uojyfz73/8O3/ARoLy83EgyBQUFxhhjvvvuO9OsWTMzd%2B7c4DHbt283TZo0MYsXLzbGGPP222%2BbJk2amO3btwePef31143X6zV%2Bvz%2B8X4DDWrdubV544QXWrYEqKytNp06dTH5%2BvrnqqqvMfffdZ4zhz119Hn30UZOWlnbE%2B/ie5yzOYLnMgQMHVFxcrL59%2B4bs79u3r1asWOHQVO6wZcsW7dy5M2TtvF6vrrrqquDarVy5Uj6fTxdffHHwmEsuuUQ%2Bny/q1tfv90uS2rRpI0kqLi5WdXV1yPolJSWpS5cuIevXpUuXkF%2Bseu211yoQCKi4uDiM0zunpqZGc%2BfOVVVVlXr27Mm6NdDIkSPVr18/XX311SH7Wb/6bdq0SUlJSUpNTdWgQYO0efNmSXzPcxof5eoye/bsUU1NTZ1fNN2hQ4c6v5AaoQ6vz5HWbuvWrcFj2rdvX%2Bex7du3j6r1NcYoJydHl19%2Bubp06SLp0NrExsaqdevWIcf%2B%2BM/ezp0766xv69atFRsbe8Kv3/r169WzZ0/t379fJ598shYsWKDzzjtPJSUlrFs95s6dq%2BLiYhUVFdW5jz93x3bxxRfrlVde0VlnnaVdu3bpj3/8oy699FJt2LCB73kOI7BcyuPxhGwbY%2Brsw5HVt3ZHWsdoW99Ro0Zp3bp1%2BvDDD%2Bs9lvU75Oyzz1ZJSYm%2B%2B%2B47vfXWW8rKylJBQcFRj2fdDiktLdV9992npUuXqnnz5g1%2BHOt3SEZGRvCfu3btqp49e%2BrMM8/UnDlzdMkll0jie55TeIvQZdq1a6emTZvW%2BT%2BL8vLyOv%2BXglCH/2bNsdYuISFBu3btqvPY3bt3R836jh49WosWLdKyZcuUnJwc3J%2BQkKADBw5o7969Icf/7/r97/ru3btX1dXVJ/z6xcbG6he/%2BIV69OihvLw8paWl6emnn2bd6lFcXKzy8nJ1795dMTExiomJUUFBgaZPn66YmBh16NCB9TsOLVu2VNeuXbVp0ya%2B5zmMwIYc7CoAAAMXSURBVHKZ2NhYde/eXfn5%2BSH78/Pzdemllzo0lTukpqYqISEhZO0OHDiggoKC4Nr17NlTfr9fH3/8cfCYjz76SH6//4RfX2OMRo0apfnz5%2Bv9999XampqyP3du3dXs2bNQtavrKxMn376acj6ffrppyorKwses3TpUnm9XnXv3j08X0iEMMYoEAiwbvXo06eP1q9fr5KSkuCtR48eGjJkSPCfWb%2BGCwQC%2Bvzzz5WYmMj3PKc5cWU9fp65c%2BeaZs2amb/85S/ms88%2BM9nZ2aZly5bm66%2B/dno0x1VWVpq1a9eatWvXGklm6tSpZu3atWbr1q3GGGMmT55sfD6fmT9/vlm/fr259dZbTWJioqmoqAg%2Bx3XXXWe6detmVq5caVauXGm6du1q%2Bvfv79SXFDb33HOP8fl85oMPPjBlZWXB2/fffx88ZsSIESY5Odm8%2B%2B67Zs2aNaZ3794mLS3NHDx40BhjzMGDB02XLl1Mnz59zJo1a8y7775rkpOTzahRo5z6ssIiNzfXFBYWmi1btph169aZhx9%2B2DRp0sQsXbrUGMO6Ha8f/y1CY1i/Yxk7dqz54IMPzObNm82qVatM//79TVxcXPDnAd/znENgudSzzz5rTjvtNBMbG2suuOCC4F%2Blj3bLli0zkurcsrKyjDGH/tryo48%2BahISEozX6zVXXnmlWb9%2BfchzfPvtt2bIkCEmLi7OxMXFmSFDhpi9e/c68NWE15HWTZJ56aWXgsf88MMPZtSoUaZNmzamRYsWpn///mbbtm0hz7N161bTr18/06JFC9OmTRszatQos3///jB/NeF1xx13BP97POWUU0yfPn2CcWUM63a8/jewWL%2Bju%2BWWW0xiYqJp1qyZSUpKMgMHDjQbNmwI3s/3POd4jDHGmXNnAAAAJyauwQIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALDs/wFJfaVBIHkg6wAAAABJRU5ErkJggg%3D%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-6376204844084339736\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">12.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:24%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4.0</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">9.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:18%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14.0</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">6.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">50.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">87.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">127.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">133.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (17)</td>\n",
       "        <td class=\"number\">17</td>\n",
       "        <td class=\"number\">51.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-6376204844084339736\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">12.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4.0</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">9.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:75%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">250.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">300.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">400.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">462.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">549.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow ignore\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_timeOFF\"><s>timeOFF</s><br/>\n",
       "            <small>Constant</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <p><em>This variable is constant and should be ignored for analysis</em></p>\n",
       "</div>\n",
       "<div class=\"col-md-6\">\n",
       "    <table class=\"stats \">\n",
       "        <tr>\n",
       "            <th>Constant value</th>\n",
       "            <td>2019-07-31 08:38:38</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow ignore\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_timeON\"><s>timeON</s><br/>\n",
       "            <small>Constant</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <p><em>This variable is constant and should be ignored for analysis</em></p>\n",
       "</div>\n",
       "<div class=\"col-md-6\">\n",
       "    <table class=\"stats \">\n",
       "        <tr>\n",
       "            <th>Constant value</th>\n",
       "            <td>2019-07-31 11:08:39</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Correlations</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAncAAAIrCAYAAACNhr/uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAAgAElEQVR4nOzdeVhUZf/H8TcIyKICCribK9qjGSiC%2B5NoKSpuqeSCuFYuuJSllktGbpX1uGSZj09WmuKeJaZmpv5MJbcyt1zSUFQQV3ABhN8f5OQIpjnQOdHndV1cw9znPme%2Bcyfx4XvOzNhlZmZmIiIiIiL5gr3RBYiIiIhI7lG4ExEREclHFO5ERERE8hGFOxEREZF8ROFOREREJB9RuBMRERHJRxTuRERERPIRhTsRERGRfEThTkRERCQfcTC6ABExp1OnTtG0adN7bnd0dKRQoUKUL1%2BeJ554gu7du1OoUKG/sEIREcmJnT5%2BTERycme48/X1zRbc0tLSuHDhAqdPnwagVKlSzJs3j0ceeeQvr1VERH6ncCciOboz3H3yyScEBQXlOG/Hjh0MGDCA5ORk/P39WbRo0V9ZpoiI3EXX3ImITYKCgnjhhRcA2LNnDz/99JPBFYmI/LMp3ImIzZ588knL9z/88IOBlYiIiF5QISI2K1y4sOX7lJQUq23ff/89n376Kbt37%2BbSpUsUKVIEPz8/wsPDqVevXo7Hu3LlCosWLWLTpk0cPXqU5ORkXFxcKFeuHE2aNKFHjx64u7tb7VO1alUAtm7dyuTJk9mwYQP29vZUr16d//3vfzg4OPDDDz8wb948Dhw4wJkzZyhYsCAVKlSgWbNmdO3aNccXhNy4cYNFixYRExPD0aNHSUtLo3jx4tSvX5/evXtTvnx5q/k7duygR48ePP744yxYsIBPP/2UlStXcvLkSRwdHalevTrh4eE0a9bsYZZaROS%2BFO5ExGYnT560fF%2BiRAnL92%2B//TZz5swBwN3dHV9fXxISEtiwYQMbNmygb9%2B%2BvPTSS1bHOnHiBD179uTMmTM4ODhQrlw5SpcuzenTp9m/fz/79%2B9n9erVLFu2DDc3t2y1REZGsmfPHnx9fblw4QLe3t44ODiwbt06hg0bRnp6Op6enlSuXJmUlBR%2B/PFHfvjhB1atWsWiRYusAt7Zs2fp1asXx48fB6B8%2BfK4ublx7NgxoqOjWblyJZMnT6Zly5bZ6khLS6Nfv35s27YNT09PKlWqxC%2B//ML27dvZvn07r732Gl26dLFt4UVEcpIpIpKDuLi4TF9f30xfX9/M7du3/%2BHcl19%2BOdPX1zezevXqmYmJiZmZmZmZCxcuzPT19c0MCAjI/Pzzzy1zMzIyMlevXp3p5%2BeX6evrm7l48WKrY3Xv3j3T19c3s3Pnzpnnzp2z2m/FihWZ1apVy/T19c2cP3%2B%2B1X63a61Ro0ZmbGxsZmZmZuatW7cyL168mHnr1q3MBg0aZPr6%2BmbOmTMnMz093bLfTz/9lFm3bt1MX1/fzNmzZ1vG09PTM9u2bZvp6%2Bub2bx588yDBw9atl29ejXz1VdftTznvXv3WrZt377dUoufn1/mqlWrLNuuXLmSGRERkenr65sZGBiYmZaW9ofrKiLyMHTNnYg8lBs3bnDgwAHGjRvHypUrAejZsydeXl6kpqYyY8YMACZOnEibNm0s%2B9nZ2dGyZUtLx27GjBmkp6cDkJSUxJEjRwCIiorCx8fHar927doRGBgIwOHDh3OsKyQkhDp16gBgb2%2BPh4cHFy5cIDExEYDOnTtToEABy/zq1aszbNgwmjVrhoeHh2X8q6%2B%2B4uDBgxQsWJA5c%2BZQrVo1y7ZChQrxxhtv0KhRI9LS0nj33XdzrGXw4MGEhoZa7hcuXNjyvC9dusQvv/xyj9UVEXl4Oi0rIvfVo0eP%2B87p1KkTQ4YMAbJeNXv%2B/Hnc3Nzu%2BUbIbdq0ISoqinPnznHgwAFq1qxJsWLF2L59Ozdu3MDZ2TnbPrdu3bKcNr1x40aOx61du3a2MU9PT9zd3bl8%2BTLDhw%2Bnf//%2BPP7449jbZ/1927lzZzp37my1zzfffANAcHAwZcuWzfGxevXqxZYtW4iNjeXq1atW1x4CNGnSJNs%2BlSpVsnx/5cqVHI8rImILhTsRua%2B738TYzs6OggUL4uHhQdWqVWnWrBmVK1e2bL/dfUtLS6Nbt273PG6BAgXIyMjg%2BPHj1KxZ0zLu7OzMmTNn%2BOGHH/j111%2BJi4vj2LFjHDx4kGvXrgGQkZGR4zG9vb1zfJzhw4czZswYNm3axKZNm3B3dycoKIgGDRrwxBNPWF0rCFi6atWrV79n/be33bp1i5MnT1KjRg2r7cWLF8%2B2z52h9datW/c8tojIw1K4E5H7Gj169D3fxDgnV69eBSA1NZXdu3ffd/6dHazjx4/z5ptvsmnTJqsAV6hQIQICAkhISODQoUP3PFZOHT/I6s498sgjfPTRR3z33XdcvnyZdevWsW7dOuzs7HjiiSd47bXXLCEvOTkZIFs37k53Bt67XyUMWR/R9kcy9R7yIpIHFO5EJNe5uLgAWZ2t5cuXP/B%2BSUlJdO/enaSkJEqVKkXnzp3517/%2BRcWKFSlTpgx2dna8%2BOKLfxju/khQUBBBQUHcuHGDnTt38v3337Nlyxb279/Pxo0bOXPmDCtXrsTOzs7yStzbQTUnd4bSnF65KyJiBIU7Ecl1FSpUALLe1iQ9PR0Hh%2Bz/q8nMzGTHjh2UKFGCUqVK4eTkxLJly0hKSsLDw4Nly5ZRtGjRbPudO3fuT9eTmppKXFwcycnJPP744zg7O9OwYUMaNmzIsGHDWL16NS%2B88AKHDh3i8OHDVKtWjYoVK3LgwAH2799/z%2BPu27cPyDpNXa5cuT9dl4hIXtCrZUUk19WpU4fChQuTkpJyz87dF198QUREBCEhIZw9exbI%2BjxbgFKlSuUY7I4ePcrevXuBP3e92ubNm2nZsiXPPvssqamp2bbXr1/f8v3t495%2BMcQ333xDXFxcjsf95JNPAPDz86NIkSIPXI%2BISF5SuBORXOfq6sqzzz4LwIQJE1i2bJnV9XNff/0148aNA7LeuuR216tixYoAHDp0iLVr11rmZ2ZmsnnzZvr27UtaWhoA169ff%2BB6GjdujKenJ5cuXWLEiBFcunTJsi0lJYUpU6YAULJkSapUqQJAixYtqFq1Kjdv3qRfv35Wp4KTk5MZM2YM//d//4eDgwPDhw9/8MUREcljOi0rInmiX79%2BxMXFsXjxYl555RXeeustypQpw7lz50hISACgVq1avPHGG5Z9OnbsyGeffcbJkycZPHgwpUuXxtPTkzNnzpCUlISjoyOBgYHExsb%2BqdOzTk5OTJs2jT59%2BhATE8OGDRsoV64c9vb2xMXFce3aNVxcXJg8eTJOTk4AODg4MGvWLPr168fx48dp27at1SdU3H67lvHjxxMQEJC7iyciYgOFOxHJE3Z2dkRFRdG8eXMWLVrE3r17LW8K7OfnR%2BvWrQkLC7OEKch69enSpUuZM2cOGzdu5NSpU5w/f54SJUrwxBNPEBERgaurK82aNePQoUPEx8dTqlSpB6onKCiIJUuW8NFHH7Fr1y5OnDiBg4MDJUqUoGHDhvTu3TvbscqUKcOyZctYuHAhX331FceOHePs2bOULFmSRo0a0a1bt2yfLSsiYjS7TL0WX0RERCTf0DV3IiIiIvmIwp2IiIhIPqJwJyIiIpKPKNyJiIiIPKALFy7w5JNPsmPHjnvO2bRpE6Ghofj5%2BRESEsLGjRutts%2BZM4fGjRvj5%2BdHeHg4x48fz9UaFe5EREREHsCuXbsICwvj119/veecEydOEBkZyZAhQ9i5cyeRkZEMHTrU8vZNK1as4NNPP2Xu3Lns2LGD6tWrM3jw4Fz9rGmFOxEREZH7WLFiBcOHD2fYsGH3nRcQEECzZs1wcHCgZcuW1KlTh%2BjoaAAWL15M165dqVKlCgULFuTFF18kPj7%2BDzuBf5bCnYiIiOR7CQkJ7N%2B/3%2Brr9huqP4iGDRuyfv16WrZs%2BYfzjh49iq%2Bvr9VY5cqVLZ9yc/d2R0dHypcvb/UpOLbSmxgbzc7O6AqyVKgAR45AlSrwyy9GV0PU6%2BZ5%2B0UPDxg4EN57D%2B741CrDjCn3sdElZClUCNq3hxUrIDnZ6GoAmHk1wugSAChSBLp3h/nz4coVo6sBb2%2BjK/idmxu0bAkxMZCSYnQ1EPbGY0aXkKVMGfjyS2jdGn77jGXD7dtnzOPm0e/F6OnTmTlzptXYoEGDiIyMfKD9vR/wByklJQUXFxerMWdnZ65du/ZA23ODwp1k8fCAAgWybsWKszPY22fdyh2cnLIW5o5PmJAsBQtmLU3BgkZXYj53/rMxQ7gzjcKFs/4fXLiw0ZXkW2FhYQQHB1uNPWhg%2BzNcXFy4ceOG1diNGzdwc3N7oO25QeFOREREzMM%2Bb64Y8/HxwcfHJ0%2BOfSdfX1/2799vNXb06FFq1KgBQJUqVThy5AhNmjQBIC0tjRMnTmQ7lWsLXXMnIiIi5mFvnzdff5E2bdoQGxtLTEwM6enpxMTEEBsbS9u2bQF4%2BumnmT9/PocOHeLmzZtMnToVLy8vAgICcq0GhTsRERERG/j7%2B7Nq1SoAKlWqxHvvvcfs2bOpU6cOs2bNYsaMGVSoUAGAjh070rNnTwYOHEjdunU5cOAAs2fPxtHRMdfq0WlZERERMY%2B/sMv2sA4fPmx1f8%2BePVb3GzVqRKNGjXLc187Ojt69e9O7d%2B88q8/8KygiIiIiD0ydOxERETGPv0HnzuwU7kRERMQ8FO5sphUUERERyUfUuRMRERHzUOfOZlpBERERkXxEnTsRERExD3XubKZwJyIiIuahcGczraCIiIhIPqLOnYiIiJiHOnc20wqKiIiI5CPq3ImIiIh5qHNnM4U7ERERMQ%2BFO5tpBUVERETyEXXuRERExDzUubOZVlBEREQkH1HnTkRERMxDnTubKdyJiIiIeSjc2UwrKCIiIpKPqHMnIiIi5qHOnc20giIiIiL5iDp3IiIiYh7q3NlM4U5ERETMQ%2BHOZlpBERERkXxEnTsRERExD3XubPa3WcHMzExatGjBpEmTctz%2B7rvv0rZtWwAOHjxIjx49qF27NkFBQbz00ktcvHjRav62bdvo27cvderUwd/fn1atWjFz5kxu3LhhmbN8%2BXKqVq1KTExMtscLDw9nxowZufgMRUREBHv7vPn6B/nbPFs7Ozu6d%2B/OypUrSU1NtdqWlpbG0qVL6dGjB6mpqfTr14%2BgoCB27NjB%2BvXrSUxMZPLkyZb5CxcuZMCAATRo0IC1a9eya9cupkyZwrZt2wgLCyMlJcXq%2BKNHj%2BbkyZN/yfMUERERsUWuhbtvvvmGZ555hnr16vH444/TvXt3Tpw4wfLly%2BnatStTpkwhMDCQunXr8umnn7J48WKaNGlC7dq1GTt2rOU4wcHBzJw5k%2BbNm%2BPv70%2B3bt04evQoAO3atSM9PZ3169dbPfa6devIyMigdevWODk5sW7dOvr374%2BDgwOXL1/m%2BvXrFC1aFIDExEQmTZrEa6%2B9Rq9evShatCj29vbUqFGD//73v6SkpDBr1izLsUuXLk1QUBBDhw7NFipFREQkl6lzZ7Ncuebu7NmzDBkyhGnTphEcHMzFixcZNGgQ7733HvXq1WPXrl089dRTbN%2B%2BnUWLFvHGG28QEhJCTEwMR48eJSwsjNDQUOrUqQNAdHQ0H374IRUqVGDixIk8//zzrFmzhkKFCtG%2BfXsWL15Mq1atLI%2B/cOFCOnfuTMGCBQFwdXUF4JlnnmHPnj1UrlyZPn36ALBlyxYyMzMJCQnJ9jxcXFwIDQ1l1apVvPTSS5bxyZMn065dOyZNmsS4ceMeep0SEhJITEy0GvOuUAEfD4%2BHPmauqVbN%2BtZgJUoYXcHvihWzvjXcb3%2BoGM7d3frWBLydja4gi6en9a3RzFIHQOHC1reGe/RRoyvIUqGC9a3RDh40ugKxQa6Eu6JFi7J69WrKlStHcnIyZ8%2BexdPTk3PnzgFZYSsiIgI7OzsaNmzIrVu36NOnDy4uLjz22GP4%2BPhw%2BvRpS7jr06cPj/72Azdq1CgCAgLYvXs3QUFBdO/enZCQEH799VfKlSvH0aNH2bt3L1OnTs1W17x587h586alS7dy5UoSEhJwd3fHyckpx%2Bfi4%2BNDQkKC1Zi7uzvvvPMO4eHhBAYG5hgMH0R0dDQzZ860Ghs0ZAiRQ4Y81PHyxGefGV0BAP2MLiAHHToYXcFtoUYXYK1xY6MrsAgzuoC7PPWU0RWYV716Rlfwm6cWG12BtSlTjK4gy2OPGffY/7AuW17IlXDn6OjIl19%2ByaJFi7Czs8PX15fk5GQcHLIO7%2BHhgZ2dHQD2v/1HK1KkiGV/e3t7MjIyLPcfeeQRy/cuLi54eHhYOl7ly5enQYMGLFmyhBdffJHPPvuM5s2bU7x48Wx1OTs74%2BzszOjRo6lfvz6HDx/G29ubpKQkbt68aen03enUqVN4e3tnG/f392fo0KGMHj2a6tWrP8wyERYWRnBwsNWYd2gofPzxQx0vV1WrlhXsunaFQ4eMroY5/XcbXYJFsWJZwW75ckhKMroa6FfiC6NLyOLunhXsNm%2BGy5eNrgaA6GvmCL6enlnBbt06uOu1XIbVYxaFC2cFu23b4OpVo6uBp/7b2egSslSokBXsRoyAX34xuhpjKdzZLFfC3Zo1a5g/fz4LFy60BLOoqCh%2B/vlnAEuwe1C3O34AKSkpXLx4kZIlS1rGwsPDefXVV3nuuef4/PPPmTt3rmXbqVOn6NGjB4sWLcLHxwfAcq2cu7s7TZo0wdHRkeXLl9OlSxerx01JSSEmJoYWLVrkWFefPn2IjY1l6NChODo6/qnnBFldwds1WZjth/jQIdizx%2BgqOHvW6AqyS0oySV1OF4yuwNrly3DBHDUlmiAs3OniRbjrSgz5zdWr5gi%2Bpjv9%2BMsv5qtJ/nZyJR5fvXoVe3t7nJ2dyczMZPPmzaxcuZK0tLSHOt5HH33EyZMnuX79OpMmTaJixYr4%2B/tbtjdu3Bg3NzeioqKoWLEifn5%2Blm2lS5fGw8ODSZMmkZKSwoULFxg/fjyNGzemdOnSFC1alLFjx/Lmm28yb948Lly4QFpaGj/%2B%2BCN9%2B/bFzc2NgQMH5liXnZ0dU6ZMISkpib179z7UcxMREZE/oBdU2CxXnm379u2pX78%2BrVq1om7durz//vtERETwyy%2B/PFTAq127NgMHDqRBgwYkJiby4YcfWk7nQlbI6tq1KytXriQ8PNxqXzs7O2bNmkV6ejrBwcG0bduWkiVL8s4771jmdOzYkdmzZ7N9%2B3ZatmxJYGAgI0eOpF69eixevJhChQrdszZPT0%2BmTp1qOeUsIiIiYia5klCcnJx48803s40PHjwYyLrW7LYyZcpw%2BPBhq3nffPON1f2AgAAmTpz4h48ZERFBREREjttKlChx3zcYDgwMJDAw8A/ndOjQgQ45XEUfEBDA/v37/3BfEREReQj/sC5bXlD7SURERMxD4c5mWkERERGRfMR0nbu7T9GKiIjIP4g6dzbTCoqIiIjkI6br3ImIiMg/mDp3NlO4ExEREfNQuLOZVlBEREQkH1HnTkRERMxDnTubKdyJiIiI3EdSUhJjxowhNjaWAgUK0KZNG0aMGJHtE6v69u3Lrl27rMauXbtGWFgYr7/%2BOufPn6dBgwa4urpatnt6eubqu4Uo3ImIiIh5mLRzN3ToUIoXL86WLVs4f/48/fv3Z968efTt29dq3n//%2B1%2Br%2B0uXLmXmzJkMGjQIgH379lG6dOk8fes3hTsRERExjzwKdwkJCSQmJlqNeXt74%2BPjc999T548SWxsLJs3b8bFxYWyZcsyYMAA3nrrrWzh7k7Hjx8nKiqKuXPnWh5n37591KhRw7Yncx8KdyIiIpLvRUdHM3PmTKuxQYMGERkZed99jxw5goeHB8WLF7eMVapUifj4eK5cuUKRIkVy3G/8%2BPG0a9eOgIAAy9i%2Bffu4fPkyrVu35vz58zz22GOMGDGCypUrP%2BQzy07hTkRERMwjjzp3YWFhBAcHW415e3s/0L4pKSm4uLhYjd2%2Bf%2B3atRzD3c6dO/nhhx94%2B%2B23rcaLFClC5cqV6devH05OTkybNo1evXoRExND4cKF/8xTuieFOxEREcn3fHx8HugUbE5cXV25fv261djt%2B25ubjnuEx0dTUhISLYAOXXqVKv7o0aNYtmyZezcuZMmTZo8VH13M%2BdViyIiIvLPZG%2BfN182qFKlCpcuXeL8%2BfOWsWPHjlGiRIkcu23p6els2LCBNm3aWI0nJyczZcoUTp8%2BbRm7desW6enpODs721TjnRTuRERExDxMGO7Kly9P7dq1mThxIsnJycTFxTFr1iw6duyY4/zDhw9z8%2BZNatWqZTVeqFAhvvvuO6ZMmcLVq1dJSUkhKiqKMmXKWF2XZyuFOxEREZH7mD59Ounp6TRt2pTOnTvTqFEjBgwYAIC/vz%2BrVq2yzI2Li8Pd3Z2CBQtmO86sWbPIyMigWbNmNGrUiMTERObMmYOjo2Ou1apr7kRERMQ8TPo%2Bd15eXkyfPj3HbXv27LG636JFC1q0aJHj3NKlS2d71W5uM%2BcKioiIiMhDUedOREREzMOknbu/E4U7ERERMQ%2BFO5tpBUVERETyEXXuRERExDzUubOZVlBEREQkH1HnTkRERMxDnTubKdyJiIiIeSjc2UwrKCIiIpKPqHMnIiIi5qHOnc20giIiIiL5iDp3Bot6PdPoEgAoUQL6AXP67%2BbsWaOrgTFj7Ywu4Xf%2B/tBvN/3erwV3fX6gEWZ/YI5/M16p8DSwLDWU8zeMribLoJddjS4hi58fhH1H2LT6sHev0dVA9epGV/C7qlXhqfk89Ul3OHzY6Gp4t/c%2Bo0sAwMcHugEL2i4moZ7R1WQZZtQDq3NnM4U7ERERMQ%2BFO5tpBUVERETyEXXuRERExDzUubOZwp2IiIiYh8KdzbSCIiIiIvmIOnciIiJiHurc2UwrKCIiIpKPqHMnIiIi5qHOnc0U7kRERMQ8FO5sphUUERERyUfUuRMRERHzUOfOZlpBERERkXxEnTsRERExD3XubKZwJyIiIuahcGczraCIiIhIPqLOnYiIiJiHOnc20wqKiIiI5CPq3ImIiIh5qHNnM4U7ERERMQ%2BFO5tpBUVERETyEXXuRERExDzUubOZVlBEREQkH1HnTkRERMxDnTubKdyJiIiIeSjc2UwrKCIiIpKPqHMnIiIi5qHOnc20giIiIiL5iDp3IiIiYh7q3NlM4U5ERETMQ%2BHOZlpBERERkXxEnTsRERExD3XubJYnKxgTE0O9evWoXbs2GzduzLXjjhkzhg4dOuS4bevWrdSoUYPz589z%2BfJlhg8fTlBQELVq1SIiIoKDBw9azT958iQjRoygYcOG%2BPn58cQTTzB27Fji4%2BMtc06dOkXVqlUZN25ctsebMWMG4eHhufbcRERExLySkpIYMGAAAQEBBAUFMWHCBNLT03Oc27dvXx577DH8/f0tX5s3bwbg1q1bTJkyhfr16%2BPv70///v1JSEjI1VrzJNwtWbKEVq1asWvXLpo0aZJrxw0PD2f//v389NNP2bYtXLiQVq1a4eXlxejRo0lOTmb9%2BvXs2LGDmjVrMmDAAMvcffv20b59ewoWLMjChQvZs2cPCxYsAKBt27YcPnzY6tiLFi0iJiYm156HiIiI3IO9fd582Wjo0KG4urqyZcsWli5dyrZt25g3b16Oc3/66Sfmzp3Lnj17LF%2BNGzcG4P3332fr1q0sW7aMLVu24OzszOjRo22u7065flq2Y8eO7N%2B/n%2B%2B//54FCxaQkZFBr169WLZsGa1bt2bcuHGsXr2aDz74gPj4eB555BFeeOEFGjZsCEBycjJvvPEG69evx8XFhcGDBzNmzBg2bNiAr68vgYGBLF68mBo1alge89y5c2zcuJHo6GgA3nnnHTIyMihYsCCXL1/mypUreHp6WuaPGTOGkJAQXn/9dctY6dKlef3110lOTmb06NEsWbLEsi08PJwxY8ZQvXp1HnnkkYdem4SEBBITE63GHB29KVbM56GPmVuKFbO%2BNZy/v9EV/K5aNetbg3l5GV1BFg8P61tT8PMzuoIsvr7Wt0arWNHoCn5Xvrz1rcF8jP/fLwC3f0Xd8avKULncSPpz8ui0bE6/g729vfF5gH8EJ0%2BeJDY2ls2bN%2BPi4kLZsmUZMGAAb731Fn379rWaGxcXx%2BXLl/nXv/6V47GWLFnC8OHDKVmyJACvvvoqDRs2JC4ujrJlyz7ks7OW6%2BFu6dKlhIeHExgYSPv27WnatCkpKSls3bqVGzdusGnTJsaNG8f7779PrVq12Lx5M5GRkSxevJgqVaowfvx44uLiWLt2Lfb29gwbNszq%2BOHh4YwcOZKRI0fi6uoKQHR0NDVr1rQEPkdHRwDeffddZs%2BejZubG7NnzwayTrUePHiQV199Ncf6O3XqRM%2BePa1Oz/bs2ZNTp04xdOhQoqOjcXJyeqi1iY6OZubMmVZjAwcOol%2B/yIc6Xl64x1nvv16/3UZXkN1nnxldAQBPG13AXZo2NbqCOzz9ndEVWLvHX/UCvPGG0RUA0M3oAu7SsqXRFWR5912jK8h9Of0OHjRoEJGR9/8dfOTIETw8PChevLhlrFKlSsTHx3PlyhWKFCliGd%2B3bx9ubm4MGzaMffv24eXlRc%2BePenYsSNXr17l7Nmz%2BN7xh5%2BXlxfu7u4cPnzYvOEuJ%2B3atcPJyQknJyfmz59Ply5dqFOnDgBNmjQhODiYRYsWMWLECNasWcPs2bPx%2Bq09MWrUKNq2bWs5VtOmTXF3dycmJoaOHTuSnp7OkiVLeOWVV7I9bv/%2B/Rk4cCALFiygX79%2BrFq1ypLave7R/rid4BMSEqzmTJ48mfbt2zNp0qQcr8F7EGFhYQQHB1uNffGFN3PmPNThclWxYlnBbvlySEoyuhro934to0v4XbVqWcGua1c4dMjoalj2qjmCr4dHVrDbsAEuXTK6mixPT61vdAlZfH2zgl3PnvDzz0ZXY77O3RtvwOjRcOKE0dWwIGS%2B0SUAWR27li0hJgYuXjS6GsgkPgcAACAASURBVIPlUecup9/B3t7eD7RvSkoKLi4uVmO371%2B7ds0q3KWmpuLn58ewYcOoUqUKO3bsIDIyEjc3N/x/Oyt1uzl1m7OzMykpKX/6Od3LXxLu7mx5nj59mtjYWBYuXGgZu3XrFnXr1uXKlSukpaVZWpUAFSpUsDpWgQIF6NKlC0uWLKFjx458/fXXFChQgCeffDLb4zo7OwPQq1cvlixZwoYNG2j6W5shPj4%2B27Ehq7MHWf/BMzMzLeMeHh688847dO/enaCgoIdZBnx8fLK1f5cvh7NnH%2BpweSIpyST17NljdAXZHTpkirrOnze6AmuXLpmopr17ja7A2s8/m6OmtDSjK8juxAm46/pmIyTUNroCaxcvGnxKNB/L6Xfwg3J1deX69etWY7fvu7m5WY23a9eOdu3aWe43bNiQdu3asWbNGurXr2%2B17203btzIdhxb/CWvN7azs7N8X6JECQYOHMjOnTstX6tXr2bChAkUK1YMV1dX4uLiLPPP5pA0OnXqxKFDhzhy5AjR0dF07doVB4ffc%2BozzzzDV199ZbVPamoq7u7ulC1blurVq7N06dIca126dCnVq1endOnS2bb5%2B/szdOhQXn31VX799dc/vQ4iIiJyHyZ8QUWVKlW4dOkS5%2B/4S/bYsWOUKFGCwoULW81dunQpa9assRpLTU2lYMGCuLu7U7x4cY4ePWrZlpiYyKVLl6xO1drqL38zmc6dO/PJJ5/w448/Alnnpjt06MCXX36JnZ0dXbp0Ydq0aZw5c4bk5GTefPPNbMfw9PSkdevWfPjhh%2Bzdu5dOnTpZba9ZsyYzZszg9OnTpKamMn36dFJTUy3t2IkTJ7JlyxbGjh3LqVOnyMjIIC4ujtGjR7N161YmTJhwz/r79u1L7dq1WbVqVS6uioiIiACmDHfly5endu3aTJw4keTkZOLi4pg1axYdO3bMNjc5OZmoqCgOHDhARkYG3377LV9%2B%2BSVhYWEAdOjQgffff5%2B4uDiSk5OZOHEigYGBlCtXzqYa7/SXv4lxixYtuHbtGq%2B88grx8fF4eHjQs2dPy3vGDR06lOnTp9OpUyccHBxoeY%2BrS8PDw2nbti2dOnWyeiUswPDhwylQoABhYWGkpaXh5%2BfHxx9/jLu7OwDVqlVjxYoVvP/%2B%2B3Tr1o1Lly7h4eFBo0aNWLVqFWXKlLln/XZ2dpbr70REROSfYfr06bz%2B%2Bus0bdoUe3t72rVrZ3mbNX9/f8aPH0%2BbNm2IiIjg2rVrDBo0iKSkJMqWLcuUKVMICAgAYODAgaSnp9OtWzdSUlIICgriP//5T67Wapd554VlJnTq1CmaNm3Khg0b/jB0/V1FRRldQZYSJaBfP5gzxxzX3I0Za3f/SX8Vf3/YvRtq1TLFNXezPzDHj6yXFzz9NCxbZp5r7p4b5nr/SX8FPz/47juoX98c19xVr250Bb%2BrWhXmz4fu3U1xzd27Xb83ugQg6y1ZunWDBQvMc83dXW9W8deZNClvjjtqVN4c14T0GR8iIiIi%2BYg%2BW1ZERETMQ58tazPTh7syZcpk%2BzgwERERyacU7mymFRQRERHJR0zfuRMREZF/EHXubKZwJyIiIuahcGczraCIiIhIPqLOnYiIiJiHOnc20wqKiIiI5CPq3ImIiIh5qHNnM4U7ERERMQ%2BFO5tpBUVERETyEXXuRERExDzUubOZVlBEREQkH1HnTkRERMxDnTubKdyJiIiIeSjc2UwrKCIiIpKPqHMnIiIi5qHOnc20giIiIiL5iDp3IiIiYh7q3NlM4U5ERETMQ%2BHOZlpBERERkXxEnTsRERExD3XubKYVFBEREclH1LkTERER81DnzmYKdyIiImIeCnc20wqKiIiI5CPq3ImIiIh5qHNnM4U7g40p97HRJWQpWhQIpV%2BJL8DpgtHVMPuDTKNLsPDygqeBZa/u5vx5o6uB5563M7qELP7%2B8PRunp5QC/bsMboaAD6eZ45/N0WLQijwxajvuGD8j5OpflcWLQqtgNVd5ptibYaV2mB0CVkKFwYC6VYlFkpcNbqa3zQ1ugB5SAp3IiIiYh5m%2Bmvkb0rhTkRERMxD4c5mWkERERGRfESdOxERETEPde5sphUUERERyUfUuRMRERHzUOfOZgp3IiIiYh4KdzbTCoqIiIjkI%2BrciYiIiHmoc2czraCIiIhIPqLOnYiIiJiHOnc2U7gTERER81C4s5lWUERERCQfUedOREREzEOdO5sp3ImIiIjcR1JSEmPGjCE2NpYCBQrQpk0bRowYgYND9ii1cOFC5s2bR0JCAj4%2BPvTo0YNu3boBkJGRQe3atcnMzMTOzs6yz9atW3F1dc2VWhXuRERExDxM2rkbOnQoxYsXZ8uWLZw/f57%2B/fszb948%2BvbtazXv66%2B/5p133mHOnDk8/vjj7N27l2effRYvLy%2BaN2/O0aNHSUtLY/fu3Tg5OeVJrQp3IiIiYh55FO4SEhJITEy0GvP29sbHx%2Be%2B%2B548eZLY2Fg2b96Mi4sLZcuWZcCAAbz11lvZwt25c%2Bfo168ffn5%2BAPj7%2BxMUFMT3339P8%2BbN2bdvH1WrVs2zYAcKdyIiIvIPEB0dzcyZM63GBg0aRGRk5H33PXLkCB4eHhQvXtwyVqlSJeLj47ly5QpFihSxjN8%2B/XpbUlIS33//PaNGjQJg37593Lx5k6effprTp09TqVIlXnzxRWrVqmXL07OicCciIiLmkUedu7CwMIKDg63GvL29H2jflJQUXFxcrMZu37927ZpVuLtTYmIizz33HDVq1KB169YAODs7U7NmTYYMGYK7uzsLFiygT58%2BrFq1irJly/7Zp5UjhTsRERHJ93x8fB7oFGxOXF1duX79utXY7ftubm457rN3716GDBlCQEAAkyZNsrzwYuTIkVbz%2BvTpw/Lly9m0aRPdu3d/qPruZs6rFkVEROSfyd4%2Bb75sUKVKFS5dusT58%2BctY8eOHaNEiRIULlw42/ylS5fSs2dPIiIimDp1qtX1de%2B%2B%2By4HDhywmp%2BamkrBggVtqvFOCnciIiJiHiYMd%2BXLl6d27dpMnDiR5ORk4uLimDVrFh07dsw2d%2B3atbz22mvMmDGD3r17Z9v%2B888/M2HCBBITE0lNTWXmzJkkJyfz5JNP2lTjnRTuRERERO5j%2BvTppKen07RpUzp37kyjRo0YMGAAkPWK2FWrVgEwc%2BZMbt26xeDBg/H397d8jR07FoBJkyZRrlw52rZtS1BQELGxsXz00Ud4eHjkWq265k5ERETMw6Tvc%2Bfl5cX06dNz3LZnzx7L91988cUfHsfDw4NJkyblam13U7gTERER8zBpuPs70QqKiIiI5CPq3ImIiIh5qHNnM62giIiISD6izp2IiIiYhzp3NlO4ExEREfNQuLOZVlBEREQkHzFd5y4zM5OQkBD%2B/e9/M2rUqGzb3333Xb799ls%2B//xzDh48yKRJk9i/fz8ODg40btyYV155BU9PT8v8bdu2MXfuXH744QfS09MpVaoUISEh9O3bF2dnZwB27NhBjx49cHV1zfZ4Y8eOpX379gQHB5OYmGj5bLjb/P39%2Bd///pfLqyAiIvIPpc6dzUy3gnZ2dnTv3p2VK1eSmppqtS0tLY2lS5fSo0cPUlNT6devH0FBQezYsYP169eTmJjI5MmTLfMXLlzIgAEDaNCgAWvXrmXXrl1MmTKFbdu2ERYWRkpKitXx9%2BzZk%2B2rffv2lu3jx4/Ptl3BTkRERMzkT4e7b775hmeeeYZ69erx%2BOOP0717d06cOMHy5cvp2rUrU6ZMITAwkLp16/Lpp5%2ByePFimjRpQu3atS0fvQEQHBzMzJkzad68Of7%2B/nTr1o2jR48C0K5dO9LT01m/fr3VY69bt46MjAxat26Nk5MT69ato3///jg4OHD58mWuX79O0aJFAUhMTGTSpEm89tpr9OrVi6JFi2Jvb0%2BNGjX473//S0pKCrNmzbJl7URERCS3mfCzZf9u/tRp2bNnzzJkyBCmTZtGcHAwFy9eZNCgQbz33nvUq1ePXbt28dRTT7F9%2B3YWLVrEG2%2B8QUhICDExMRw9epSwsDBCQ0OpU6cOANHR0Xz44YdUqFCBiRMn8vzzz7NmzRoKFSpE%2B/btWbx4Ma1atbI8/sKFC%2BncuTMFCxYEsJxGfeaZZ9izZw%2BVK1emT58%2BAGzZssVyivduLi4uhIaGsmrVKl566aWHW7mHkJCQQGJiotWYd0YGPl5ef1kN9%2BTubn1rMK/U%2B8/5q9z%2BuL9c/Ng/2/j7G11BlmrVrG9N4Le/7Qxnsh8nU/1eK1LE%2BtZwhQsbXUGW25cF5XB5kCGuXjXusc30D/Zv6k%2BFu6JFi7J69WrKlStHcnIyZ8%2BexdPTk3PnzgFZYSsiIgI7OzsaNmzIrVu36NOnDy4uLjz22GP4%2BPhw%2BvRpS7jr06cPjz76KACjRo0iICCA3bt3ExQURPfu3QkJCeHXX3%2BlXLlyHD16lL179zJ16tRsdc2bN4%2BbN29aunQrV64kISEBd3d3nJyccnwuPj4%2BJCQkWI0FBARY3W/atClTpkyx3B8/fjwTJ060mrN58%2BYcr9XLSXR0NDNnzrQaGzRwIJG9ej3Q/n%2BJxo2NrgCAp40uIAdNmxpdwW%2Be3m10BdY%2B%2B8zoCixCjS7gLib5cTKlRo2MruC2QKMLsFajhtEVZNmwwegKxAZ/Ktw5Ojry5ZdfsmjRIuzs7PD19SU5OdnyIgMPDw/s7OwAsP8teRe5488ze3t7MjIyLPcfeeQRy/cuLi54eHhYOlvly5enQYMGLFmyhBdffJHPPvuM5s2bU7x48Wx1OTs74%2BzszOjRo6lfvz6HDx/G29ubpKQkbt68aen03enUqVN4e3tbje3cufMPn/%2B4cePo0KHDH875I2FhYQQHB1uNecfGwn0%2BZPgv4e6e9Zto82a4fNnoaliWap5f0x4eWcFuwwa4dMnoauDpCbWMLiFLtWpZwa5rVzh0yOhqAPhivDmCr8l%2BnEzVCClSJCvYbdkCV64YXQ208o41uoQsrq5Zwe6nn%2BDaNaOrMZaZ/sH%2BTf2pcLdmzRrmz5/PwoULLcEsKiqKn3/%2BGcAS7B7U7Y4fQEpKChcvXqRkyZKWsfDwcF599VWee%2B45Pv/8c%2BbOnWvZdurUKXr06MGiRYvw8fEBsLwAw93dnSZNmuDo6Mjy5cvp0qWL1eOmpKQQExNDixYt/lS9tvLx8bHUarFzJ1y48JfW8YcuXzZFPedvGF1BdpcuwfnzRlcB7NljdAXWDh0yTU0m%2BKdrxSQ/Tqb8XXnlijnWBmcDTz/m5No1Y0%2BJSr7wp37kr169ir29Pc7OzmRmZrJ582ZWrlxJWlraQz34Rx99xMmTJ7l%2B/TqTJk2iYsWK%2BN9xPVHjxo1xc3MjKiqKihUr4ufnZ9lWunRpPDw8mDRpEikpKVy4cIHx48fTuHFjSpcuTdGiRRk7dixvvvkm8%2BbN48KFC6SlpfHjjz/St29f3NzcGDhw4EPVLSIiInlEL6iw2Z/q3LVv355du3bRqlUrChQoQMWKFYmIiGDBggUPFfBq167NwIEDiY%2BPp06dOnz44YeW07mQ1Qns2rUrEydO5K233rLa187OjlmzZjFhwgSCg4NxcnKiWbNmvPDCC5Y5HTt2pFy5cvzvf//jgw8%2B4ObNm5QsWZIWLVrQt2/fB75WTkRERP4i/7Aglhf%2BVLhzcnLizTffzDY%2BePBgIOuastvKlCnD4cOHreZ98803VvcDAgKyvUDhbhEREUREROS4rUSJEsyYMeMP9w8MDCQw8I8vmA0KCspW693url1ERETEjEz3CRUiIiLyD6bOnc20giIiIiL5iGGdO53mFBERkWzUubOZTsuKiIiIeSjc2UwrKCIiIpKPqHMnIiIi5qHOnc20giIiIiL5iDp3IiIiYh7q3NlM4U5ERETMQ%2BHOZlpBERERkXxEnTsRERExD3XubKYVFBEREclH1LkTERER81DnzmYKdyIiImIeCnc20wqKiIiI5CPq3ImIiIh5qHNnM62giIiISD6izp2IiIiYhzp3NlO4ExEREfNQuLOZVlBEREQkH1HnTkRERMxDnTubaQVFRERE8hF17kRERMQ81LmzmcKdiIiImIfCnc20giIiIiL5iDp3IiIiYh7q3NlMKygiIiLmYW%2BfN182SkpKYsCAAQQEBBAUFMSECRNIT0/Pce6mTZsIDQ3Fz8%2BPkJAQNm7caLV9zpw5NG7cGD8/P8LDwzl%2B/LjN9d1J4U5ERETkPoYOHYqrqytbtmxh6dKlbNu2jXnz5mWbd%2BLECSIjIxkyZAg7d%2B4kMjKSoUOHcu7cOQBWrFjBp59%2Byty5c9mxYwfVq1dn8ODBZGZm5lqtCnciIiJiHnnUuUtISGD//v1WXwkJCQ9U0smTJ4mNjeWll17CxcWFsmXLMmDAABYsWJBt7ooVKwgICKBZs2Y4ODjQsmVL6tSpQ3R0NACLFy%2Bma9euVKlShYIFC/Liiy8SHx/Pjh07cm0Jdc2dwWZejTC6BAC8nSEMiL4WSuJVo6uBQS%2B7Gl3C7/z84OnveHpqfdi71%2Bhq%2BHhe7v11Z4uiRSEU%2BGL8bi5cMLqaLBE97YwuIYu/P4TuJnRcLdizx%2BhqwNfX6Ap%2B969/QasVtPpvezhwwOhqmPvyYaNLAKBYMWgHrIwPJCnJ6Gqy9DG6gFwWHR3NzJkzrcYGDRpEZGTkffc9cuQIHh4eFC9e3DJWqVIl4uPjuXLlCkWKFLGMHz16FN%2B7fuYqV67MoUOHLNv79etn2ebo6Ej58uU5dOgQdevWfajndjeFOxERETGNTPLmj7SwsDCCg4Otxry9vR9o35SUFFxcXKzGbt%2B/du2aVbjLaa6zszPXrl17oO25QeFORERETCMjI2%2BO6%2BPjg4%2BPz0Pt6%2BrqyvXr163Gbt93c3OzGndxceHGjRtWYzdu3LDMu9/23KBr7kRERET%2BQJUqVbh06RLnz5%2B3jB07dowSJUpQuHBhq7m%2Bvr4cOXLEauzo0aNUqVLFcqw7t6elpXHixIlsp3JtoXAnIiIippGRkTdftihfvjy1a9dm4sSJJCcnExcXx6xZs%2BjYsWO2uW3atCE2NpaYmBjS09OJiYkhNjaWtm3bAvD0008zf/58Dh06xM2bN5k6dSpeXl4EBATYVuQdFO5ERERE7mP69Omkp6fTtGlTOnfuTKNGjRgwYAAA/v7%2BrFq1Csh6ocV7773H7NmzqVOnDrNmzWLGjBlUqFABgI4dO9KzZ08GDhxI3bp1OXDgALNnz8bR0THXatU1dyIiImIaeXXNna28vLyYPn16jtv23PWq%2BEaNGtGoUaMc59rZ2dG7d2969%2B6d6zXepnAnIiIipmHWcPd3otOyIiIiIvmIOnciIiJiGurc2U6dOxEREZF8RJ07ERERMQ117myncCciIiKmoXBnO52WFREREclH1LkTERER01Dnznbq3ImIiIjkI%2BrciYiIiGmoc2c7hTsRERExDYU72%2Bm0rIiIiEg%2Bos6diIiImIY6d7ZT505EREQkH1HnTkRERExDnTvbKdyJiIiIaSjc2U6nZUVERETyEXXuRERExDTUubOdOnciIiIi%2BYg6dyIiImIa6tzZTuFORERETEPhznY6LSsiIiKSj6hzJyIiIqahzp3t1LkTERERyUfUuRMRERHTUOfOdg8d7mJiYoiKiiI1NZW3336bJk2a5EpBY8aMYf/%2B/Sxfvjzbtq1bt/Lcc8/x7bff4ujoSFRUFFu2bCEtLY3HHnuMkSNH8uijj1rmnzx5klmzZrF161aSk5Px8PCgcePGPP/885QqVcoyr2rVqhQsWJACBQpYPV7Lli2ZMGECI0eO5IsvvsDJySlbTTt37sy2n4iIiDwchTvbPXS4W7JkCa1atWL06NG5WQ/h4eGEhoby008/UaNGDattCxcupFWrVnh5eREZGUlaWhrr16/HxcWF6dOnM2DAADZu3AjAvn37iIiIoHXr1ixcuJAyZcoQHx/P7Nmzadu2LfPnz6dq1aqWY8%2BZM4egoKB71hUaGsrkyZNz9bmKiIiI5LaHuuauY8eObN%2B%2BnUWLFvHoo49StWpVJk%2BeTJ06dRg/fjwAq1evJjQ0lNq1a9OhQwf%2B7//%2Bz7J/cnIyI0eOpHbt2jRs2JDFixdTtWpVTp06ha%2BvL4GBgSxevNjqMc%2BdO8fGjRsJDw8H4J133mHatGkUKVKEa9euceXKFTw9PS3zx4wZQ0hICK%2B//jply5bFzs6O0qVL8/rrr9OoUaNcD6UiIiJiu4yMvPn6J3mozt3SpUsJDw8nMDCQ9u3b07RpU1JSUti6dSs3btxg06ZNjBs3jvfff59atWqxefNmIiMjWbx4MVWqVGH8%2BPHExcWxdu1a7O3tGTZsmNXxw8PDGTlyJCNHjsTV1RWA6OhoatasaenmOTo6AvDuu%2B8ye/Zs3NzcmD17NgCnTp3i4MGDvPrqqznW36lTJ3r27El8fLzV6dm8lpCQQGJiotWYnZ03Xl4%2Bf1kN93I7F9%2BRj43l52d0Bb/z9bW%2BNVjRokZXkMXd3frWFPz9ja4gS7Vq1rdGe%2BQRoyv4XcWK1rcGK1bM6AqymO3nKSnJ6ArEFrn2gop27drh5OSEk5MT8%2BfPp0uXLtSpUweAJk2aEBwczKJFixgxYgRr1qxh9uzZeHl5ATBq1Cjatm1rOVbTpk1xd3cnJiaGjh07kp6ezpIlS3jllVeyPW7//v0ZOHAgCxYsoF%2B/fqxatcoSoG4f/24%2BPllhKiEhwRLunn/%2Beatr5zw8PPj6668t97/88kur%2B5AVLBs1avTAaxQdHc3MmTOtxgYOHERYWOQDHyOvPfWU0RX8Juw7oyvIbt48oysAINToAu7SuLHRFdwhdLfRFVj77DOjKzCvqVONrgCAdkYXcJdcunzdZnPnGvfY/7QuW17ItXB3OzABnD59mtjYWBYuXGgZu3XrFnXr1uXKlSukpaVRsmRJy7YKFSpYHatAgQJ06dKFJUuW0LFjR77%2B%2BmsKFCjAk08%2Bme1xnZ2dAejVqxdLlixhw4YNNG3aFID4%2BPhsx4aszh6At7e3ZeyDDz74w2vuWrdubfM1d2FhYQQHB1uNffutN9HRNh02V3h6ZgW7devg4kWjq4GwafWNLuF3vr5Zwa5nT/j5Z6Or4YtR5gi%2B7u5ZwW7zZrh82ehqsoSOq2V0CVmqVcsKdl27wqFDRldjvs7d1Knw4otw/LjR1bAyYoXRJQBZP09NmsDGjeb5eTKKwp3tci3c2dnZWb4vUaIE7dq149lnn7WMxcfH4%2BzsjKenJ66ursTFxVHxt7b82bNnsx2vU6dOvPfeexw5coTo6Gi6du2Kg8Pv5T7zzDP07NmTFi1aWMZSU1Nxd3enbNmyVK9enaVLl9KgQYNsx166dCnVq1endOnSufLcH5SPj49VCIasH%2BS7ztQa6uJFk9Szd6/RFWT388%2BmqOvCBaMrsHb5solq2rPH6AqsHTpkjppSUoyuILvjx%2BHAAaOrMN3px8uXzVeT/P3kyZsYd%2B7cmU8%2B%2BYQff/wRyHrlaocOHfjyyy%2Bxs7OjS5cuTJs2jTNnzpCcnMybb76Z7Rienp60bt2aDz/8kL1799KpUyer7TVr1mTGjBmcPn2a1NRUpk%2BfTmpqqqUzNnHiRLZs2cLYsWM5deoUGRkZxMXFMXr0aLZu3cqECRPy4qmLiIiIDfSCCtvlyZsYt2jRgmvXrvHKK68QHx%2BPh4cHPXv2tLzSdejQoUyfPp1OnTrh4OBAy5YtczxOeHg4bdu2pVOnTlavhAUYPnw4BQoUICwsjLS0NPz8/Pj4449x/%2B1q1GrVqrFixQref/99unXrxqVLl/Dw8KBRo0asWrWKMmXK5MVTFxERERv804JYXnjocPfpp59avj98%2BHC27R06dKBDhw457uvk5MTw4cMZPnw4kHUN3Nwcrt6sVq1ajse%2BfYwRI0YwYsSIe9ZYtmxZJk6c%2BIfP417130nvbyciIiJ/F/r4MRERETENde5slyfX3ImIiIiIMUzRuStTpsx9T42KiIhI/qfOne1MEe5EREREQOEuN%2Bi0rIiIiEg%2Bos6diIiImIY6d7ZT505EREQkH1HnTkRERExDnTvbKdyJiIiIafwdw921a9eIiorim2%2B%2BIT09naZNmzJu3Djc3NxynL927VpmzZpFXFwcHh4edOjQgQEDBmBvn3VCNSQkhPj4eMt9gKVLl1KpUqUHqkfhTkRERMQGUVFRnDlzhrVr13Lr1i2GDh3K22%2B/zbhx47LN/emnn3j55Zf5z3/%2Bw7///W9%2B%2BeUX%2BvXrh6urK7179yY5OZlffvmFDRs2ULp06YeqR9fciYiIiGlkZOTNV0JCAvv377f6SkhIsLne69ev88UXXzB48GA8PDwoVqwYw4cPZ/ny5Vy/fj3b/NOnT/PMM8/QpEkT7O3tqVSpEk8%2B%2BSTff/89kBX%2BPDw8HjrYgTp3IiIi8g8QHR3NzJkzrcYGDRpEZGTkffe9ceMG586dy3Hb9evXSUtLw9fX1zJWqVIlbty4wYkTJ3j00Uet5jdv3pzmzZtbHfvbb78lNDQUgH379uHi4kL37t05cuQIpUuXJjIykiZNmjzwc1W4ExEREdPIq2vuwsLCCA4Othrz9vZ%2BoH1/%2BOEHevTokeO2IUOGAODq6moZc3FxASAlJeUPj5ucnMyQIUNwdnamZ8%2BeANjZ2fHYY4/xwgsvUKpUKb766isiIyOZP38%2Bfn5%2BD1Svwp2IiIiYRl6FOx8fH3x8fB5q36CgoHt%2BTOqBAweYNm0a169ft7yA4vbp2EKFCt3zmMePH2fw4MEUK1aMTz75xDK3b9%2B%2BVvPatGnDl19%2Bydq1ax843OmaOxEREZGHVKFCBRwdHTl69Khl7NixYzg6OlK%2BfPkc99m0aROdOnWiUaNGzJ07F3d3d8u2uXPnsm3bNqv5qampFCxY8IFrUrgTERER08irF1TkFRcXF0JCZ2LBdQAAIABJREFUQnj77be5cOECFy5c4O2336Z169Y4Oztnm793714GDhzIqFGjGDFiBA4O1idRz5w5w/jx44mLiyM9PZ2lS5eyZ88e2rdv/8A1KdyJiIiI2GDcuHGUL1%2Be0NBQWrRoQZkyZRg7dqxle6tWrfjggw8A%2BOCDD0hPT2fChAn4%2B/tbvm6fjn355Zdp3LgxXbt2JSAggEWLFvHhhx/yyCOPPHA9uuZORERETOPv%2BCbGhQoVIioqiqioqBy3r1692vL97ZB3L05OTrzyyiu88sorD12Pwp2IiIiYxt8x3JmNTsuKiIiI5CPq3ImIiIhpqHNnO3XuRERERPIRde5ERETENNS5s53CnYiIiJiGwp3tdFpWREREJB9R505ERERMQ50726lzJyIiIpKPqHMn/9/enYdFVfb/A3%2Bzg7hABCk/zaVQ3EUExI1H3BV9FEVCXErScgNRn9REXNBQs0ylRK00lEcEt0TI5XGrNJfcsK%2Ba4haCLAqpLCODzO8PYnJkEGyE%2Bzjn/bour3HOmeXtEc58zuc%2B5x4iIiLJYOdOdyzuiIiISDJY3OmOxZ1gtraiE5Swtta8Fa5lS9EJ/takyd%2B3SqXYLAAMJXIyRWkOQ0PpZELTpqITlCj9gu%2BGDYG8PLFZAODqVdEJ/mZpWXJ7%2B7YkctWvLzpBiVq1Sm7t7AALC7FZ6NXH4o6IiIgkg5073UnleJuIiIiIXgJ27oiIiEgy2LnTHYs7IiIikgwWd7rjsCwRERGRHmHnjoiIiCSDnTvdsXNHREREpEfYuSMiIiLJYOdOdyzuiIiISDJY3OmOw7JEREREeoSdOyIiIpIMdu50x84dERERkR5h546IiIgkg5073bG4IyIiIslgcac7DssSERER6RF27oiIiEgy2LnTHYs7IiIikgwWd7rjsCwRERGRHmHnjoiIiCSDnTvdsXNHREREpEfYuSMiIiLJYOdOdyzuiIiISDJY3OmOw7JEREREeoSdOyIiIpIMdu50x84dERERkR5h546IiIgkg5073bG4IyIiIslgcac7DssSERER6RF27oiIiEgy2LnTHTt3RERERHqExV05bt26JToCERGR7BQXV82fqpSfn4/Zs2fDzc0Nzs7O%2BOijj5CXl1fu4%2BfNm4dWrVrByclJ/Wfr1q3q9evXr0e3bt3Qrl07jBo1Cjdu3HihPC%2B9uEtMTIS7uzucnZ1x%2BPDhl/3yL2TWrFmYNWtWpR47atQorF69GgBw6NAhBAQEVGU0IiIi0uJVLO7CwsJw9%2B5d7Nu3D/v378fdu3exfPnych9/8eJFhIWF4dy5c%2Bo/vr6%2BAICdO3di06ZN%2BOabb3Dy5Em0bNkSgYGBUKlUlc7z0ou7uLg4DBgwAGfOnEH37t1f9stXiz///POFNiIRERHJU0FBAeLj4xEYGAgrKyvY2NhgxowZ2LFjBwoKCso8vrCwEFevXkWrVq20vl5sbCxGjBgBBwcHmJmZYfr06UhLS8PJkycrnemlXlAxbNgw/N///R9Onz6N6OhoFBcX47333sP27dvh5eWFefPmISEhAZGRkUhLS0PDhg0xbdo0dOnSBQCQm5uLRYsW4cCBA7CwsEBgYCDmzp2LgwcPon79%2BhW%2B/8GDB/H5558jNTUVbm5uAABra2v1%2Bue9d6mTJ09i3rx5UCqVcHJywt69ewEA4eHhSEpKwv379/H6669jwoQJGDZs2Attn8zMTGRlZWkse/LEFq%2B/bvdCr1MVatXSvBWuWTPRCf7WqJHmrWCvvSY6QYnatTVvJaFFC9EJSjRponkrmqWl6AR/c3TUvBVMKvu80v8iqfxXPXok7r2rqsum7TPY1tYWdnYVfwYrFApkZGRoXVdQUAClUommTZuql7311ltQKBS4desWmjdvrvH4K1euoKioCKtWrcKZM2dQq1YtDB06FO%2B//z4MDQ2RnJyMcePGqR9vYmKCRo0a4cqVK%2BjYsWOl/q0vtbjbtm0bRo0aBVdXVwwZMgQ9evRAXl4ejh07BoVCgaNHj2LevHlYs2YN2rdvjx9//BFTpkxBbGwsHBwcsGDBAqSkpGDfvn0wNDREcHBwpd/7xo0bCAoKwieffIL%2B/fvjyJEjCAwMxKBBgwCgwvcu5ebmhgULFiAiIgKHDh0CAIwbNw5WVlZISEiAqakpoqKiEBYWhn79%2BsHyBX4Tt27dioiICI1lkyZNxogRUyr9GlXN3V10gr/03iw6QVmLFolOAAAYIDrAM7p2FZ3gKQN2ik6g6bPPRCeQrv/%2BV3QCAEAn0QGe0bat6AQl9u0TneDl0/YZPHnyZEyZUvFn8IULFzB69Git64KCggAANWrUUC%2BzsLAAAK3n3T169Aiurq4YNWoUPv/8c1y%2BfBmTJk2CoaEh3n//feTl5amfX8rc3Bz5%2BfkV5ixV5VOhDB48GKampjA1NcXmzZvh5%2BcHFxcXAED37t3h6emJmJgYzJw5Ez/88APWrl2L119/HQAwe/Zs/Pvf/67U%2ByQmJqJVq1bqYq5nz54aw8LPe%2B%2B5c%2Bc%2B97UXLVoES0tLmJiYIC0tDZaWllAoFHjw4MELFXe%2Bvr7w9PTUWHbhgi3276/0S1SZWrVKCrtffhF7xFaqd9RI0RH%2B1qhRSWEXEgJI4EKbBD9pFL61a5cUdj/9BDx8KDpNiQFfDxEdoUSTJiWF3fTpwAueCF0lbt8WneBvjo4lhd2IEcCVK6LT4HjEWdERAJR07Nq2BS5cAJ5zHr4sVFXnTttnsK2tbaWe6%2Bbmht9//13rukuXLmHlypUoKChQ1wSlw7E1a9Ys8/jOnTujc%2BfO6vtt2rTBmDFjkJiYiPfffx8WFhZQKBQaz1EoFC9Ub1R5cfd0uzM1NRWnTp3Cli1b1MuePHmCjh074uHDh1AqlahXr556XePGjSv9PhkZGbC3t9dY9uabbyInJ6fC965ISkoKli1bhlu3bqFRo0Zo2LAhAKD4BX8C7ezsyrR/f/sN%2BCuiJDx6JJE85fwSCXXrliRyZWeLTqDp4UMJZbp0SXQCTTduSCPT1auiE5R15Qpw7pzoFJI4mH1aXp70MlW3qirutH0GvwyNGzeGiYkJkpOT0fav1uv169fVw6nP%2Bt///od79%2B7hnXfeUS8rLCyEubk5AMDBwQHXrl1TN6iUSiVu3bqlMexbkSov7gwMDNR/r1u3LgYPHozx48erl6WlpcHc3BzW1taoUaMGUlJS0OSv81TS09Mr/T5169bFkSNHNJalp6fDzMyswvd%2BHqVSiQ8%2B%2BADTpk3DiBEjYGBggN9%2B%2Bw27d%2B%2BudDYiIiLSTxYWFujXrx%2BWL1%2BOlStXAgCWL18OLy8vrTWGSqVCeHg4GjZsiI4dO%2BL8%2BfOIiorC7NmzAQBDhw7F6tWr0a1bNzRu3BgrVqzA66%2B/jg4dOlQ6U7XOczd8%2BHBERUUhKSkJQMmlwN7e3tizZw8MDAzg5%2BeHlStX4u7du8jNzcWyZcsq/dqDBg3C1atXERsbi6KiIvz88884cOBApd77WWZmZigoKEBRURGUSiUUCgXMzc1hYGCAtLQ0fPrppwBKCj8iIiJ6eV7FqVDmzZuHRo0aYeDAgejbty/q16%2BP0NBQ9foBAwYgMjISANCrVy/Mnj0b8%2BfPh5OTE/7zn/9gypQp6tPQhg0bhnfffReTJk1Cx44dcenSJaxduxYmJiaVzlOtXz/Wt29f5Ofn4%2BOPP0ZaWhqsrKzw7rvvYtSoUQCAqVOnYtWqVfDx8YGxsTH69%2B9f6ddu0KABIiMjsWTJEixevBgtW7ZEr169Kv3eT3NxcYGNjQ1cXFwQExODTz75BCtXrsSiRYtgY2OD4cOHIzk5GVevXn2hoWMiIiLSPzVr1kRYWBjCwsK0rk9ISNC4/84772gMyz7NwMAAY8eOxdixY/9xnpde3G3atEn9d20nH3p7e8Pb21vrc01NTTFjxgzMmDEDAHDnzh188803lX5vd3d3fP/99%2BWuf957P53bzs5Oo6PXrFmzMhd2PD28S0RERC8Hv1tWd9XauSMiIiJ6HhZ3unslirvs7GwMHDjwuY85J4GrroiIiIhEk3RxV79%2BffXQLos3IiIi/cfOne6q9WpZIiIiIqpaku7cERERkbywc6c7FndEREQkGSzudMdhWSIiIiI9ws4dERERSQY7d7pj546IiIhIj7BzR0RERJLBzp3uWNwRERGRZLC40x2HZYmIiIj0CDt3REREJBns3OmOnTsiIiIiPcLOHREREUkGO3e6Y3FHREREksHiTnccliUiIiLSI%2BzcERERkWSwc6c7FndEREQkGSzudMdhWSIiIiI9ws4dERERSQY7d7pj546IiIhIj7BzR0RERJLBzp3uWNwRERGRZLC40x2HZYmIiIj0CDt3REREJBns3OmOnTsiIiIiPcLOHREREUkGO3e6M1CpVCrRIWStdWvRCUo0bw7ExgLDhwOXL4tOgxVjL4qOoGZnB/j7A9HRQGam6DRAcJuDoiOUqFULcHUFTp0CHj0SnQYA8M2tHqIjAABsbIDBg4Fdu4D790WnAerXF53gb7VqAZ06AcePS%2BPHpk9fA9ERSjg5AWfPAu3bA%2BfOiU5TQlB54ONTNa8bF1c1rytFHJYlIiIi0iMcliUiIiLJ4LCs7ti5IyIiItIj7NwRERGRZLBzpzsWd0RERCQZLO50x2FZIiIiIj3Czh0RERFJBjt3umPnjoiIiEiPsHNHREREksHOne5Y3BEREZFksLjTHYdliYiIiPQIO3dEREQkGezc6Y6dOyIiIiI9ws4dERERScar2LnLz89HWFgYDh06hKKiIvTo0QPz5s2DpaVlmceGhoYiPj5eY5lCoUCnTp3wzTffoLi4GM7OzlCpVDAwMFA/5tixY6hRo0al8rC4IyIiIsl4FYu7sLAw3L17F/v27cOTJ08wdepULF%2B%2BHPPmzSvz2IULF2LhwoXq%2Bz///DOmT5%2BOWbNmAQCSk5OhVCpx9uxZmJqa/qM8HJYlIiIi%2BocKCgoQHx%2BPwMBAWFlZwcbGBjNmzMCOHTtQUFDw3OdmZ2djxowZmDNnDhwcHAAAFy9eRLNmzf5xYQewc0dEREQSUlWdu8zMTGRlZWkss7W1hZ2dXYXPVSgUyMjI0LquoKAASqUSTZs2VS976623oFAocOvWLTRv3rzc112%2BfDlatWqFQYMGqZddvHgRjx8/xtChQ5Gamoq33noL06dPR/v27SvMWYrFHREREem9rVu3IiIiQmPZ5MmTMWXKlAqfe%2BHCBYwePVrruqCgIADQOB/OwsICAJCXl1fua6akpGD37t2Ii4vTWG5ubo42bdogKCgIderUQXR0NAICArB79240aNCgwqwAizsiIiKSkKrq3Pn6%2BsLT01Njma2tbaWe6%2Bbmht9//13rukuXLmHlypUoKChQX0BROhxbs2bNcl9z%2B/btcHJyKtPZKz33rlRAQAB27NiBo0ePYuTIkZXKy%2BKOiIiIJKOqijs7O7tKDcG%2BqMaNG8PExATJyclo27YtAOD69eswMTFBo0aNyn3e/v37MXbs2DLLV6xYgT59%2BqBFixbqZYWFhTAzM6t0Jl5QQURERPQPWVhYoF%2B/fli%2BfDmys7ORnZ2N5cuXw8vLC%2Bbm5lqfk5OTg%2BvXr8PFxaXMuqtXr2Lx4sXIyspCYWEhIiIikJubi169elU6E4s7IiIikozi4qr5U5XmzZuHRo0aYeDAgejbty/q16%2BP0NBQ9foBAwYgMjJSff/OnTsAgDfeeKPMa4WHh%2BPNN9/Ev//9b7i5ueHUqVPYsGEDrKysKp2Hw7JEREREOqhZsybCwsIQFhamdX1CQoLG/datW5d7Dp%2BVlRXCw8N1ysPijoiIiCTjVZzEWGpY3BEREZFksLjTHc%2B5IyIiItIj7NwRERGRZLBzpzt27oiIiIj0CDt3REREJBns3OmOxR0RERFJBos73XFY9jlu374tOgIRERHRC9H74s7T0xM7duyo1GObNWuGkydPAgCWLl2KNWvWVGU0IiIiesar%2BA0VUsNh2XLk5OSIjkBERCQ7civEqsJLK%2B4OHTqEdevW4fbt28jPz0fr1q2xaNEinD17Ftu2bUPbtm2xfft2GBoaYtKkSTAzM8OaNWvw8OFDDBgwAAsXLgRQ0mnz9vZGfHw8MjMz0aJFCyxYsABvv/12hRlUKhXWrl2LzZs3Q6FQwMfHB0%2BePFGvLywsxJo1a7B79248evQIbdu2RUhICBo2bKjxOl9%2B%2BSXi4%2BMBAJcuXcLu3btx9uxZfPHFF7hx4wYePHgABwcHhIaGol27di9rExIRERHp7KUUd%2Bnp6QgKCsLKlSvh6emJnJwcTJ48GV9%2B%2BSXc3d1x5swZ9O7dGydOnEBMTAwWLVqEfv36ITExEcnJyfD19cXAgQPh4uICANi6dSvWrVuHxo0b45NPPsGHH36IH374ASYmJs/NsX37dnz33Xf4%2Buuv4eDggIiICKSnp6vXr1ixAidOnMDGjRthZ2eH9evXY%2BzYsUhMTISZmZn6cZMmTUJKSgoAYMmSJVAoFJgwYQICAwPh5%2BcHhUKBjz/%2BGMuWLcN///vfSm%2BnzMxMZGVlaSyzrVsXdtbWlX6NKtO4seatYHZ2ohP8rfS/Rwr/TQCAWrVEJyhRo4bmrQTY2IhOUKJOHc1b0aTyIwMAlpaat8I5OYlOUMLRUfNWtHPnhL01O3e6eynF3WuvvYaEhAS8%2BeabyM3NRXp6OqytrZGRkQEAqFGjBsaMGQMDAwN06dIFT548QUBAACwsLNC6dWvY2dkhNTVVXdwFBASgefPmAIDZs2ejQ4cOOHv2LNzc3J6b4/vvv8fw4cPRsmVLAEBQUBBiY2MBlHT1YmJisGrVKjRo0ABASREXGxuLI0eOoE%2BfPuW%2BromJCbZu3YqGDRvi8ePHSE1NhZWVFS5evPhC22nr1q2IiIjQWDZ54kRMCQp6odepUkuXik4AAPAXHUCL/v1FJyjlKjqAplatRCdQGyw6wDO6dxedQLrathWd4C9nz4pOoOkFGgZVysBAdALSwUsp7kxMTLBnzx7ExMTAwMAATZs2RW5uLoyNS17eysoKBn/9oBgallzDUbt2bfXzDQ0NUfxUqf70MKmFhQWsrKzKdLy0yczMRL169dT3jYyMYG9vDwDIzs5Gfn4%2BgoKC1BkAQKlUIjU19bmva2RkhJMnT2LcuHHIz8/H22%2B/DWNjY6hUqgozPc3X1xeenp4ay2ynTgWOHXuh16kSjRuXFHYzZwI3b4pOg%2Bh/x4qOoGZtXVLYJSYCUjgV09/hlOgIJWrUKCnsfvsNyM8XnQYAsCtNGoVvnTolhd3hw8CDB6LTSKsTbmlZUthduADk5YlOA3Sa3F50hBKOjiWF3YgRwJUrotMIxc6d7l5KcffDDz9g8%2BbN2LJli7owCwsLw9WrVwFAXdhVVmnHDwDy8vKQk5OjUbSVp27duurhVKCkW5eZmQkAsLa2hpmZGb799luN8%2BRu3LiBN95447mve%2BHCBYSFhSEmJgat/upSfPvtt7j5gkWQnZ0d7J7dy6anl/yRips3gcuXRadAprvoBGXl5AB//TiJVfeR6ASa8vOBR9LIdP%2B%2B6ASaHjyQRiYLC9EJysrLk8iPjcDhR62uXJFepmrG4k53L2UqlEePHsHQ0BDm5uZQqVT48ccfsWvXLiiVyn/0ehs2bMDt27dRUFCA8PBwNGnSBE6VOC/Cx8cHsbGxOHfuHJRKJdasWaPu%2BBkaGmLYsGH47LPPkJ6ejuLiYuzcuRNeXl5a57MzNTXFo7/2PE//%2BwDg/PnziIqKQmFh4T/69xERERFVlZfSuRsyZAjOnDmDAQMGwMjICE2aNMGYMWMQHR39jwo8Z2dnTJo0CWlpaXBxccG6des0hlLL4%2BXlhZycHAQHB%2BPBgwfo27cvmjVrpl4/c%2BZMrF69GiNGjMCff/6JBg0aYNWqVWjRokWZ1%2Brfvz%2BCg4Pxr3/9C4cPH8aIESPg7%2B%2BP4uJi1K9fH6NGjcJnn32Ge/fu4fXXX3/hfyMRERGVxc6d7gxUL3riWBXz9PTE5MmT4e3tLTpK9WjdWnSCEs2bA7GxwPDhkhiWXTH2xS5WqUp2doC/PxAdLY1h2eA2B0VHKFGrFuDqCpw6JZHxNeCbWz1ERwBQctXu4MHArl3SGJatX190gr/VqgV06gQcPy6NH5s%2BfSVy4YCTU8nFHe3bS2dYVlB54OBQNa977VrVvK4UcRJjIiIikgx27nT3yhR3%2B/btw6xZs8pd7%2BzsjK%2B//roaExEREdHLxuJOd5Ir7g4dOqR1eZ8%2BfZ47Fx0RERERSbC4IyIiIvli5053L2UqFCIiIiKSBnbuiIiISDLYudMdizsiIiKSDBZ3uuOwLBEREZEeYeeOiIiIJIOdO92xc0dERESkR9i5IyIiIslg5053LO6IiIhIMljc6Y7DskRERER6hJ07IiIikgx27nTHzh0RERGRHmHnjoiIiCSDnTvdsbgjIiIiyWBxpzsOyxIRERHpEXbuiIiISDLYudMdO3dEREREeoSdOyIiIpIMdu50x%2BKOiIiIJIPFne44LEtERESkR9i5IyIiIslg50537NwRERER6REWd0RERCQZxcVV86c6FBQUwNfXFzt27Hju4y5cuAAfHx84OTnB09MTcXFxGut37tyJXr16oV27dvD29sa5c%2BdeKAeLOyIiIpKMV7W4u3btGvz9/XH%2B/PnnPu7BgwcYP348Bg8ejNOnT2Px4sUIDw9HUlISAODkyZMICwvDkiVLcPr0aQwaNAgTJkxAQUFBpbOwuCMiIiLSwS%2B//IIxY8ZgyJAhsLe3f%2B5j9%2B/fDysrK/j7%2B8PY2Bju7u4YOHAgoqOjAQBxcXEYMGAAnJ2dYWJignfffRfW1tZITEysdB5eUEFERESSUVVdtszMTGRlZWkss7W1hZ2dXYXPVSgUyMjI0LrO1tYWjo6OOHz4MMzMzLBhw4bnvta1a9fQtGlTjWVvv/02tm3bBgBITk7G0KFDy6y/cuVKhTlLsbgT7eJF0QkAlPzQb129Gr4REZX6Qa9qwaIDPCUzMxOrV2%2BFr6%2BvJLYN0EN0AABP/cxIZrsAAaID/EV6PzPSIblto1KJTgDgqd%2BnvXulsV0Eqqr/ktWrtyIiIkJj2eTJkzFlypQKn3vhwgWMHj1a67ovv/wSPXv2rHSOvLw8WFhYaCwzNzdHfn5%2BpdZXBos7AgBkZWUhIiICnp6est%2BxPIvbRjtul/Jx25SP20Y7bpeq5%2BvrC09PT41ltra2lXqum5sbfv/995eSw8LCAo8ePdJYplAoYGlpqV6vUCjKrLe2tq70e7C4IyIiIr1nZ2cnicK5adOmOHbsmMay5ORkODg4AAAcHBxw7dq1Muu7detW6ffgBRVERERE1aRXr164d%2B8eNm7cCKVSiRMnTiA%2BPl59nt2wYcMQHx%2BPEydOQKlUYuPGjbh//z569epV6fdgcUdERERUhQYMGIDIyEgAgLW1Nb799lvs3bsXbm5uCAkJQUhICDp27AgAcHd3x7x58zB//ny4uroiISEB69evh5WVVaXfz0ClksjZpCRUZmYmtm6V0EnOEsJtox23S/m4bcrHbaMdtwu9TCzuiIiIiPQIh2WJiIiI9AiLOyIiIiI9wuKOiIiISI%2BwuCMiIiLSIyzuiIiIiPQIizsiIiIiPcLijoiIiEiPsLgjIiIi0iMs7oiIiIj0CIs7IiIiIj3C4o6I6B/itzdSVcjOzhYdgV5xxqIDUPWLiIh47noDAwNMmjSpmtJIx6hRo2BgYFDuegMDA3z33XfVmEg6AgIC8M0336jvKxQKmJubC0wkDc7Ozjh79qz6/rFjx9C5c2eBiaSpoKAADx48QHFxMQBAqVTi6tWr6NWrl%2BBkYiUlJWHZsmXIyMjQ2DbZ2dn47bffBKejVxmLOxk6efKk1uVKpRLnz5%2BHsbGxLIs7Nzc3rcv/%2BOMP7N69G/Xq1avmRNJx7tw5jfvdunXDqVOnBKWRjmc7d8HBwdwuz9i%2BfTvCwsLw%2BPFjjeU2NjayL%2B4WLlyIBg0awMHBASkpKejcuTOioqIwffp00dHoFcfiToY2bdpUZllKSgqmTp2KunXr4rPPPhOQSrzJkyeXWbZv3z5s3LgRHh4eWLJkiYBU0sThyBLPdnq5XcqKjIzE1KlTYWlpidOnT2PMmDH49NNP2eEEcO3aNWzevBl37tzB4sWL8d5778HJyQkLFy7Ee%2B%2B9JzoevcJ4zh1h37598Pb2hq2tLXbt2gVnZ2fRkYRTKpUICwvD9OnTMWHCBKxduxbW1taiY0nG84av5YzbpaysrCyMGTMG7u7u%2BOOPP9CyZUt88skniIuLEx1NuNq1a8Pc3BwNGjTAtWvXAADt2rVDamqq4GT0qmPnTsaUSiXCw8MRGxuL4OBgBAQEiI4kCSkpKQgKCsKff/6JzZs3o127dqIjEb2ybGxsoFQqUa9ePdy8eRMAYG9vj/v37wtOJl6TJk2wZcsW%2BPn5oUaNGrh8%2BTJMTU15kEA6Y3EnUyxgtNu7dy9CQkLg4uKCDRs2oE6dOqIjSUJRURF27dqlvq9UKjXuA8DgwYOrO5ZwhYWFmD17tvp%2Bfn6%2Bxn0ACA8Pr%2B5YktKmTRuEhoZi7ty5aNSoEbZs2QJzc3NYWVmJjiZcUFAQJkyYgM6dOyMgIADDhw%2BHkZER/Pz8REejV5yBiieJyM4PP/yAuXPnws3NDUuWLEGtWrVER5KEBQsWICYmBiNHjiz3fBd7e/tqTiUNnp6ez11vYGCAgwcPVlMa6Zg1a1aFXRa5F3eZmZkICQnBokWL8Mcff%2BDDDz%2BEQqFAeHg4Bg4cKDqecI8fP4aJiQkMDQ1x4cIF5Obm8nxE0hmLOxlydHQEUFKolPfBJMcP6tLtApQUK6W/GqV/NzAwwOXLl0XFI3olZWVlwdbWVn2/qKgISqUSd%2B7cgYODg8Bk4g0ePLhMBxwoOZg6dOiQgETeepVPAAAV8ElEQVSkLzgsK0Ny7ySUR44FbWXt2rVLlsOuFZk7dy7CwsJEx5C0Pn36aMwFaGxsDENDQ/j6%2Bmosl4s//vgDa9asAQAkJyeXGcbPzc2FQqEQEY30CDt3RFqkp6cjMzMTtra2sp7frlT79u01Pohnz57NgwSU3S69e/fG/v37BSaShtu3byMgIAAqlQppaWllTmdQKBR47bXXEB8fLyihWMuWLUNOTg7i4%2BPLDE2bmpqif//%2B5c67SVQZ7NzJVEFBAb766ivs3btXXcT06dMHEydOhKWlpeh4wly7dg0hISFISkpSD8W2bNkSixcvRrNmzUTHE%2BbZY0B2OUs8u11ycnIEJZGWhg0bYs6cOcjJycH8%2BfPLzCFpZmYGFxcXQenE%2B%2BijjwAADRo0wMSJEwWnIX3E4k6GHj9%2BDD8/P%2BTl5WHgwIGws7PDnTt3kJiYiJ9%2B%2BgmxsbGy/Gqpu3fvwt/fH127dkVwcDDs7OyQkpKCbdu2wd/fH7t375btBRWcrFe7Z7cLp7D4W/fu3QEA9evXh6urq%2BA00nL69GkAgIuLi/rvz5Jz8Uu647CsDH355Zf45Zdf8PXXX2sUcXl5eRg3bhw6deqk9dsa9F1oaCgMDAywYMGCMutCQkJgaGiIhQsXCkgm3rPDj66urvyaLXC7PE9F32ENaP9WGDl4%2BuItbXjxFumKnTsZ2rt3L5YuXVqmO2dpaYlZs2bh448/luVO9%2Beff0Z0dLTWdR988AFGjx5dzYlI6oqLi/Hrr7%2BqO5lFRUUa9wH5dmDK%2Bw7rUnLucl65ckV0BNJz7NzJkLOzM86cOaN1XVFREVxdXWV5FZuTkxPOnTundZ1KpUL79u3LXa/vHB0dNT6MS89HfJocOw3swJCuCgsLcfToUaSmpsLX1xe3b9%2Bu8OeKqCLs3MmQkZER/vzzT60zxD969AhmZmYCUolXs2ZNpKam4v/9v/9XZl1aWpqsv60iKipKdARJYgemcq5fv44tW7YgPT0dYWFhSEhIwMiRI0XHEu6PP/7A2LFjoVQq8fDhQ3h4eGDo0KGIiIhQn7NI9E8Yig5A1a9t27b4/vvvta6Lj49H27ZtqzmRNHTu3Bnr16/Xum79%2BvXo0qVLNSeSDldX1wr/yF16ejqSkpKQnp4uOoqkHDt2DD4%2BPsjJycHx48ehUCjw5ZdfYt26daKjCbd48WJ4e3vjyJEjMDY2RuPGjbFo0SKsWrVKdDR61alIdo4fP65ycnJSxcfHq5RKpUqlUqkeP36siomJUbVv3151%2BvRpwQnFuHXrlqp9%2B/aquXPnqk6fPq26deuW6vjx46pp06apXFxcVHfu3BEdUaj8/HzV8uXLVT179lS1adNG1aNHD9WyZctUubm5oqMJdfXqVZWPj4/K0dFR1axZM5Wjo6Nq6NChqitXroiOJgne3t6qI0eOqFQqlapDhw4qlUqlSkpKUnl6eoqMJQmurq6qx48fq1QqlcrFxUWlUqlUT548UTk7O4uMRXqAw7Iy5O7ujv/85z%2BYM2cO5syZgzp16uD%2B/fswMTHB3Llz0aFDB9ERhWjYsCE2btyI0NBQxMbGqr92rGXLloiKitI6XCsXnD5Hu6enz5k2bRqnz9Hi9u3b6NatG4C/L6Jo3bo1Hjx4IDKWJNSqVQv37t3T%2BBnJysqS9Skg9HKwuJMpPz8/9OrVCz/%2B%2BKP6ux//9a9/4bXXXhMdTajWrVtj586dSE1NVU/uXL9%2BfdGxhPv6669Rs2ZNxMTEaBRxEyZMwLhx4/D111/L8grrNWvWoF%2B/fhrT5zRp0gQeHh4ICQlBZGSkbKfPKWVvb4%2BzZ8/C2dlZvezixYv85hcAAwcOxOTJkzF9%2BnQUFxcjKSkJn376KQYMGCA6Gr3ieLUsQaVS4ffff0edOnW4w33KnTt3cPDgQVhZWcHT0xO1atUSHUmYgQMHYunSpWjRokWZdUlJSfj444%2BxZ88eAcnE8vT0RHR0tNbfm5SUFIwePRqHDx8WkEw6EhISsGDBAvj5%2BSEqKgoTJ07Epk2bMG3aNNl/X7FSqcTnn3%2BOmJgYFBQUwMzMDMOGDcPMmTNhamoqOh69wljcydCff/6JpUuX4rXXXkNAQADGjBmDa9euwcDAAB4eHli%2BfDlq1qwpOma1u3LlCiZOnAgbGxvMnj0bAQEBsLe3R2FhIZRKJb777js0bNhQdEwhOH2Odpw%2Bp3KOHj2K6OhopKamom7duhg%2BfDj69OkjOpakZGdnw9raWtbz/9HLw2FZGQoPD0dmZiZu3ryJH3/8EQ4ODvjuu%2B/w%2BPFjLFiwAF988QVCQkJEx6x2S5cuhZeXFx4%2BfIgPPvgAQUFBePfddwEAq1atQnh4OCIjI8WGFITT52jH6XMqx8PDAx4eHqJjSMauXbsqfIzcu5qkG3buZMjd3R0HDhxATk4OevfujVOnTqmHHLOzszFkyBAcPXpUcMrq16FDB5w8eRK5ubno2LEjkpKSYGJiAqBkolEPDw/88ssvglOKMW7cOHTp0gVjxowpsy4qKgrHjx%2BXZeE7a9YsmJubY/78%2BWXWzZ8/H0VFRVi0aFH1B5OA2bNnV/iY8PDwakgiPZ6engBKvuEkIyMDVlZWsLe3R2ZmJrKysuDo6FipApCoPOzcyZBSqYSlpSVq1qyJdu3aaXRdzM3NoVAoBKYTx9jYGAUFBahTpw7Gjx%2Bv8RVS9%2B7dg7GxfH9dxo4di0mTJsHGxgZ9%2B/aFsbExCgsLsXPnTqxcuRJr164VHVGICRMmwNvbG8XFxRg0aBBsbW2RlpaGbdu24aeffsLOnTtFRxQuJycHP/30E7p3744GDRogIyMDBw4cQO/evUVHE%2BbQoUMASkYLTE1NERQUBEPDkmlnv/rqK9y5c0dkPNID7NzJ0JgxY%2BDu7o4PP/xQY/m9e/cQHh4OpVIpy0k0Z8yYgfz8fHzxxRcaJzPv378fq1evRteuXfHRRx8JTCjWli1bsGTJEgAoM33O0KFDBacT5%2BLFiwgNDcXly5c1ps9ZvHgxv0YKwIcffggfHx/06NFDveznn39GZGQkNm/eLDCZeK6urjh27Jh6hAAoOYfVzc2t3HNciSqDxZ0MXb58GePHj8eBAwc0prXo0aMHrKys8NVXX%2BGNN94QmFCMhw8fIiQkBCtWrICRkZF6%2BTvvvIO2bdtixowZGjthObp37x6nzykHp8/RzsnJCWfOnFF3pgDgyZMn6NChg%2BwvNunatSvWr1%2BvcRBw/vx5BAcHy/4qa9INizuZUiqVZQqV8k4Ml4vs7GwWKpXE6XO04/Q5ZXl5eWHChAkac7fFxcUhOjpa9ueVRUZGYtOmTfDx8YG9vT1SUlIQGxuLwMBA%2BPv7i45HrzAWdzI0ZMgQjBkzBv379%2BdcSk9p3bo1vLy8MHr0aDRv3lx0HEnh9Dnacfqcih08eBBBQUFo06YN6tWrhzt37uDq1auIjIyEm5ub6HjCbdu2Dbt370ZGRgbq1asHHx8fTmJMOmNxJ0MrVqzA9u3boVKp4OvrCz8/P9ja2oqOJdyxY8cQExODw4cPo23bthgzZgx69uypMZwkVzNnzkRmZiYKCgqQl5cHBwcHhISEqKfPqV%2B/viynz3nvvffQunVrPHz4EAkJCZg0aZLG9DmXLl2S5VXEz7px4wYSExORmZmJunXrYuDAgWjQoIHoWK%2BE8ePHY926daJj0CuGxZ1MFRUVYd%2B%2BfYiJicH58%2BfRp08fjB49Gm3atBEdTbiMjAxs3boV27Ztg7GxMUaMGAEfHx9Zz1nG6XO04/Q5L0f79u1lOQl2ZXDb0D/BloRMGRsbY8CAAdi0aRN27tyJ2rVrY%2BzYsfD19UVCQoLoeEK98cYbCAwMxOHDh/HRRx/h%2BPHj6N69O0JDQ0VHE6Z0%2BpwGDRpw%2BpyncPqcl4M9BqKXi8Ud4e2330ZoaCiOHj2Kxo0bY8aMGaIjSYKRkRH69u2Ljz/%2BGD179kRcXJzoSMK0bNlSPZfdli1b1Odq3rt3D3PnzpXtuVNdunTBRx99hMLCQgQHB6u3y/79%2B/HBBx9g4MCBghO%2BGviVW0QvF4s7QkpKClasWIG%2Bffvi3LlzlZpZXt8pFArs2LED77zzDnx8fGBmZibrCWlnzZqF6OjoMh06X19f3Lp1C3PmzBGUTKzQ0FAYGxtrTJ0DAN9%2B%2By06deqE4OBgQcmISM54zp1MKZVKHDhwAHFxcTh16hQ6deqEkSNHyv77Hy9duoTY2Fjs2bMHtWvXhp%2BfH3x8fLR%2Bp6rccPqcsjh9zsvB88rKx21D/wRPCJGhpUuXYteuXVAqlRg8eDDmz58v%2B%2BkaAMDb2xuXL1%2BGi4sLwsPD0aNHD14p%2B5fyps%2BRc2EHAB4eHpw%2Bh4gkh59cMnTkyBFMnjwZR48eRUhICAu7v7Rq1Qq7d%2B9GVFQUevXqxcLuKd26dcPy5cvRvXt3rFq1CllZWaIjSUJkZCRyc3Ph4%2BMDf39/7N%2B/H8XFxaJjkR7h4Br9ExyWlbknT57g7NmzyMrKQr169eDk5CQ6knBpaWmVepy9vX0VJ5EWTp9TPk6foxsnJyfZfhVZRfvg/fv3o3fv3oLS0auKxZ2MXb9%2BHR9%2B%2BCHu3r0LKysr5OTkoEmTJli/fj3q1q0rOp4wjo6O6qv3VCpVmSv5SpddvnxZRDxJSE5Oxn//%2B1/s3r0bb731FkaPHs1Z9VHyQX3gwAHExsbi/Pnz8PLywsKFC0XHEqoyB0v3799H69atqyGNtHAfTFWFxZ2MjRo1Ck2aNMHs2bNhbm6OvLw8hIeH4969e7KeVT8uLg4JCQmYNWsWmjRpgrt372LZsmVo3Lgx/Pz81I%2BT%2B/lmAJCXl4ewsDB8//33si52n5WcnIx169YhPj5e9tuFB0vl4z6YqgqLOxlr3749Tpw4oXGCfEFBAbp27Ypff/1VYDKxunXrhu%2B//x7W1tbqZbm5uRg0aBAOHTokMJl0pKSkYNu2bdixYwdq1KgBf39/jB49WnQsoRQKBRITExEbG4vff/8dXl5e8Pf3h6Ojo%2BhoQvFgqXzcB1NV4dWyMmZnZ4ebN2%2BiWbNm6mWl533IWX5%2BfpmTmHNzc6FUKgUlkgZt0%2BcsWrSI0%2BdomT4nMjKS0%2Bf8ZfXq1RoHSw0bNsTSpUsxaNAg2U%2BYzn0wVRUWdzLm5eWF8ePHIyAgAA0bNkRGRga%2B/fZbdOjQAbt27VI/bvDgwQJTVr%2BePXti0qRJCAwMRL169ZCSkoIvvvgCQ4YMER1NGE6fox2nz6kYD5bKx30wVRUOy8qYp6dnhY8xMDDAwYMHqyGNdOTm5mLBggXYu3cvlEolLCwsMGLECAQHB8v2u0L79euHkSNHYvDgwbC0tBQdRzJCQ0MxatQoODg4iI4iWbNmzcLt27fLHCx17twZ06ZNEx1PKO6DqaqwuCMqR2FhIR49egQbGxvRUSSF0%2BeUxelzyseDJaLqx%2BJO5n799VekpqaWGTaR%2BzDA9evXsWXLFqSnpyMsLAwJCQkYOXKk6FjCceoG7XhFaMV4sKQd98FUFVjcydi8efOwbds22NnZaXwYyX0Y4NixY5gyZQq6d%2B%2BOw4cPIyEhAd7e3njvvfcwfvx40fGE4tQN2vGK0OfjwZJ23AdTlVGRbHXo0EF18eJF0TEkx9vbW3XkyBGVSlWyjVQqlSopKUnl6ekpMpYkODk5qR4/fqyxLD8/X%2BXs7CwokTR07dpVlZ2drbHs0aNHqu7duwtKJB0///yzysnJSTVt2jSVk5OTKi0tTdWxY0fV2rVrRUcTjvtgqiq8rEvGatWqhaZNm4qOITm3b99Gt27dAEB9NN26dWs8ePBAZCxJKJ264WmcuoFXhD7P559/jhUrVuCzzz6DkZER6tWrh3Xr1mHr1q2iownHfTBVFZ7NKmMTJkzAnDlzEBAQgNq1a2usk%2BOJ36Xs7e1x9uxZODs7q5ddvHhR9gUMwKkbysPpc8rHg6XycR9MVYXFnYw9fvwYiYmJ2LNnj3qZiid%2B44MPPsCECRPg5%2BcHpVKJ9evXY9OmTbKftgEAduzYASMjI2zcuFFj%2BfHjx3H8%2BHEAJR/gcivuQkJCsGDBAowfP17jitDAwEDR0YTjwVL5uA%2BmqsILKmSsU6dOmDJlCrp06VJm4lW5nvhd6ujRo4iOjkZqairq1q2L4cOHo0%2BfPqJjkcTxitCyEhISsGDBAvj5%2BSEqKgoTJ05UHyzJ7SDgWdwHU1VhcSdjbm5uOHnypOgYkhMWFobg4GDUrFlTdBRJ4tQN2vGK0PLxYEk77oOpqrC4k7GlS5eiXr16sv/C92e5urri%2BPHjnGBVC07doB2nzykfD5bKx30wVRUWdzLm7%2B%2BPM2fOwNLSEnXq1NH4sJbzB/XSpUuRl5eHIUOGlCli5H6Ss4uLCzZs2IBWrVqJjiIpQ4cORWBgIDw8PODi4oLTp0/j4sWLmDp1qqx/lwAeLD0P98FUVfjbJmPDhg3DsGHDyiwvKioSkEa8o0ePwsPDAxs2bAAAxMbGaqznSc6cuqE8vCK0fEOHDsXChQt5sKQF98FUVVjcyVj79u3x1VdfISMjA8XFxQAApVKJmzdvwsfHR3C66jdjxgycPn0ahoaG%2BN///lfmnDLi1A3l4RWhZfFgqWLcB1NVYXEnYyEhIVCpVLC2tkZ2djaaN2%2BOXbt24d133xUdTQgTExMsXrwYRkZG2LFjh9bHTJ48uZpTSQunbtCO0%2BeUxYOlinEfTFWFxZ2MXbx4EUePHkVaWhq%2B%2BOILhISEoFu3bli7dq0si5i5c%2BciLi4OxcXFWq9ge/bL4OXoq6%2B%2BQkhIiNapG%2BRswIABqFmzJqKjo2Fvb48TJ05gzpw5sr4ilAdLFeM%2BmKoKizsZq1GjBurUqQNjY2NcvXoVANCtWzfMnDlTcDIx%2BvXrh379%2BsHHxwebNm0SHUeSnjx5Aj8/P9ExJKf0ilAPDw/RUSSDB0sV4z6YqgqLOxl788031efFFBcXIyUlBaamprI/mTcuLk50BMny9vZGVFQUp254Rnx8PGbPni06hqTwYKli3AdTVeFUKDJ26NAhBAcHY8%2BePYiPj0d0dDSMjIzQqVMnLFmyRHQ8kiBO3aAdp8%2Bhf4L7YKoqLO5kLiMjAzY2NjA2NkZiYiJyc3MxePBgmJqaio5GErRz506ty4uKimR5dV9p18XR0VHrerlfaEIV4z6YqgKLOyKqtNu3b5c7dcOJEycEp6t%2BpRMWt2jRotwrQvkdoURU3XjOHRFVGqdu0MQrQolIiljcEVGlceoGTbwilIikiMOyRFRpnTp1wvHjx5GXlwcvLy8cPnwYAODu7o5ffvlFcDpxfHx8eJU1EUkGZyElokornbrB0tJSPXVDRkaG7KduYGFHRFLCYVkiqrTx48cjMDAQe/bsga%2BvL9555x0YGRmhR48eoqMREdFfOCxLRC%2BEUzcQEUkbizsiIiIiPcJz7oiIiIj0CIs7IiIiIj3C4o6IiIhIj7C4IyIiItIjLO6IiIiI9AiLOyIiIiI9wuKOiIiISI/8f0GD/7enO%2BTWAAAAAElFTkSuQmCC\" class=\"center-img\">\n",
       "    <img 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E1CrFvz0k62j4ZWRxrn8YpkyMGoUzJgBly7ZOhp48%2BlN9%2B70Z3B2hpYtYedOMMhttV7Z2tbWIQDG22bc3Gwdwe/c3SE8HKKi4Le7stnUuNU%2Btg4hh5cXfPopdO4MZ8/aOpocBw7Y5n0L6XsxevZs5s6da9E2dOhQIiIi7mt%2BDw%2BP%2B%2BqXmpqKs7OzRZuTkxNpaWn3Nb0gKLmTHO7uUKxYzrNYcHYGO7ucZ7mFvX3Oh7C9PkZup23mzpyccsbGycnWkRhMyZI5n8G/3ZdZCl5ISAgBAQEWbfebsP0Rzs7OXL9%2B3aLt%2BvXr5vt032t6QdCnsoiIiBiHXeEcMebp6Ymnp2ehLPtW3t7eHDlyxKItLi6O%2BvXrA1CrVi1OnDhB69atAcjIyODUqVN5duVaQ8fciYiIiHHY2RXO40/SqVMnYmNjiYmJITMzk5iYGGJjY%2BncuTMAzz77LEuWLOHYsWPcuHGDWbNmUa5cOXx9fQssBiV3IiIiIlbw8fFh/fr1ANSoUYP33nuP%2BfPn06RJE6KiopgzZw7Vq1cHIDg4mH79%2BjFkyBCaNm3K999/z/z583FwcCiweLRbVkRERIzjT6yyPajjx49bvD5w28knLVu2pGXLlvnOazKZeP7553n%2B%2BecLLT7jj6CIiIiI3DdV7kRERMQ4/gKVO6NTciciIiLGoeTOahpBERERkSJElTsRERExDlXurKYRFBERESlCVLkTERER41DlzmpK7kRERMQ4lNxZTSMoIiIiUoSociciIiLGocqd1TSCIiIiIkWIKnciIiJiHKrcWU3JnYiIiBiHkjuraQRFREREihBV7kRERMQ4VLmzmkZQREREpAhR5U5ERESMQ5U7qym5ExEREeNQcmc1jaCIiIhIEaLKnYiIiBiHKndW0wiKiIiIFCGq3ImIiIhxqHJnNSV3IiIiYhxK7qymERQREREpQlS5ExEREeNQ5c5qf5kRzM7Opl27dkybNi3f6W%2B//TadO3cG4OjRo/Tt25fGjRvj7%2B/PK6%2B8wi%2B//GLRf/fu3QwcOJAmTZrg4%2BNDhw4dmDt3LtevXzf3WbNmDbVr1yYmJibP%2B4WGhjJnzpwCXEMRERHBzq5wHg%2BRv8zamkwm%2BvTpw7p160hPT7eYlpGRwapVq%2Bjbty/p6ekMGjQIf39/9uzZw5YtW0hKSmL69Onm/suWLSM8PJzmzZuzadMm9u3bx4wZM9i9ezchISGkpqZaLH/cuHGcPn36T1lPEREREWsUWHL35Zdf8txzz/Hkk0/yxBNP0KdPH06dOsWaNWvo1asXM2bMwM/Pj6ZNm7J48WJWrFhB69atady4MRMmTDAvJyAggLlz59K2bVt8fHzo3bs3cXFxAHTp0oXMzEy2bNli8d6bN28mKyuLjh074ujoyObNmxk8eDD29vZcuXKFa9euUaZMGQCSkpKYNm0a//znP%2Bnfvz9lypTBzs6O%2BvXr8%2BGHH5KamkpUVJR52V5eXvj7%2BxMZGZknqRQREZECpsqd1QrkmLvz588zfPhw3n33XQICAvjll18YOnQo7733Hk8%2B%2BST79u3j73//O19//TXLly/n9ddfJzAwkJiYGOLi4ggJCSEoKIgmTZoAEB0dzQcffED16tWZOnUqL730Ehs3bqREiRJ07dqVFStW0KFDB/P7L1u2jB49elC8eHEAXFxcAHjuuec4cOAANWvWZMCAAQDs3LmT7OxsAgMD86yHs7MzQUFBrF%2B/nldeecXcPn36dLp06cK0adOYOHHiA49TYmIiSUlJFm0e1avj6e7%2BwMssMHXqWD7bmJeXrSP4nYeH5bPNlSxp6whyuLpaPhuAUbYbo20zJUrYOoLflS1r%2BWxzBvnMo1o1y2dbO3bM1hGIFQokuStTpgwbNmygatWqpKSkcP78eUqXLs2FCxeAnGQrLCwMk8lEixYtuHnzJgMGDMDZ2ZkGDRrg6enJ2bNnzcndgAEDqFu3LgBjxozB19eX/fv34%2B/vT58%2BfQgMDOTnn3%2BmatWqxMXFcfDgQWbNmpUnrkWLFnHjxg1zlW7dunUkJibi5uaGo6Njvuvi6elJYmKiRZubmxtvvfUWoaGh%2BPn55ZsY3o/o6Gjmzp1r0TZ0%2BHAihg9/oOUVik8%2BsXUEAETaOoB89O5t6whyNbN1AJaeeMLWEZhFGmxojLPNGE/XrraO4DcDl9k6Akt3OK78T%2BfjY7v3fsiqbIWhQJI7BwcHPv/8c5YvX47JZMLb25uUlBTs7XMW7%2B7ujslkAsDut19aqVKlzPPb2dmRlZVlfv3II4%2BYf3Z2dsbd3d1c8apWrRrNmzdn5cqVjBgxgk8%2B%2BYS2bdtSvnz5PHE5OTnh5OTEuHHjaNasGcePH8fDw4Pk5GRu3LhhrvTd6syZM3jk8%2B%2B2j48PkZGRjBs3jnr16j3IMBESEkJAQIBFm0dQEHz00QMtr0DVqZOT2PXqZYj/2N7pu9/WIZh5eOR8SS9dCrcVXm0i0u9/tg4hh6trTmJ36BDcdpyqrbwTa4zszmjbjNEqd127wtq1kJxs62hg4Bc9bR1CjmrVchK7MWPg1ClbR2NbSu6sViDJ3caNG1myZAnLli0zJ2aTJ0/mhx9%2BADAndvcrt%2BIHkJqayi%2B//ELFihXNbaGhobz66qu8%2BOKLfPrppyxcuNA87cyZM/Tt25fly5fj6ekJYD5Wzs3NjdatW%2BPg4MCaNWvo2dPyjzo1NZWYmBjatWuXb1wDBgwgNjaWyMhIHBwc/tA6QU5VMDcms59%2B%2BsPLKVTHjsGBA7aOgrNtbB1BXklJcPasraMAfv3V1hFYSk01TEyG%2BP3cwijbjJubrSPIKzkZzp%2B3dRQY4p9ZC6dOGS8m%2BcspkPT4119/xc7ODicnJ7Kzs9mxYwfr1q0jIyPjgZb373//m9OnT3Pt2jWmTZvGo48%2Bis8tJeJWrVrh6urK5MmTefTRR2nYsKF5mpeXF%2B7u7kybNo3U1FQuXbrEpEmTaNWqFV5eXpQpU4YJEybwxhtvsGjRIi5dukRGRgbffvstAwcOxNXVlSFDhuQbl8lkYsaMGSQnJ3Pw4MEHWjcRERG5C51QYbUCWduuXbvSrFkzOnToQNOmTZk3bx5hYWH89NNPD5TgNW7cmCFDhtC8eXOSkpL44IMPzLtzISfJ6tWrF%2BvWrSM0NNRiXpPJRFRUFJmZmQQEBNC5c2cqVqzIW2%2B9Ze4THBzM/Pnz%2Bfrrr2nfvj1%2Bfn6MHj2aJ598khUrVlDiLvswSpcuzaxZs8y7nEVERESMpEAyFEdHR95444087cOGDQNyjjXLVblyZY4fP27R78svv7R47evry9SpU%2B/6nmFhYYSFheU7rUKFCve8wLCfnx9%2Bfn537dOtWze6deuWp93X15cjR47cdV4RERF5AA9Zla0wqPwkIiIixqHkzmoaQREREZEixHCVu9t30YqIiMhDRJU7q2kERURERIoQw1XuRERE5CGmyp3VlNyJiIiIcSi5s5pGUERERKQIUeVOREREjEOVO6spuRMRERG5h%2BTkZMaPH09sbCzFihWjU6dOjBo1Ks8dqwYOHMi%2Bffss2tLS0ggJCeG1117j4sWLNG/eHBcXF/P00qVLF%2BjVQpTciYiIiHEYtHIXGRlJ%2BfLl2blzJxcvXmTw4MEsWrSIgQMHWvT78MMPLV6vWrWKuXPnMnToUAAOHz6Ml5dXoV76TcmdiIiIGEchJXeJiYkkJSVZtHl4eODp6XnPeU%2BfPk1sbCw7duzA2dmZKlWqEB4ezptvvpknubvVjz/%2ByOTJk1m4cKH5fQ4fPkz9%2BvWtW5l7UHInIiIiRV50dDRz5861aBs6dCgRERH3nPfEiRO4u7tTvnx5c1uNGjVISEjg6tWrlCpVKt/5Jk2aRJcuXfD19TW3HT58mCtXrtCxY0cuXrxIgwYNGDVqFDVr1nzANctLyZ2IiIgYRyFV7kJCQggICLBo8/DwuK95U1NTcXZ2tmjLfZ2WlpZvcrd3714OHTrEzJkzLdpLlSpFzZo1GTRoEI6Ojrz77rv079%2BfmJgYSpYs%2BUdW6Y6U3ImIiEiR5%2BnpeV%2B7YPPj4uLCtWvXLNpyX7u6uuY7T3R0NIGBgXkSyFmzZlm8HjNmDKtXr2bv3r20bt36geK7nTGPWhQREZGHk51d4TysUKtWLS5fvszFixfNbSdPnqRChQr5VtsyMzP54osv6NSpk0V7SkoKM2bM4OzZs%2Ba2mzdvkpmZiZOTk1Ux3krJnYiIiBiHAZO7atWq0bhxY6ZOnUpKSgrx8fFERUURHBycb//jx49z48YNGjVqZNFeokQJ/ve//zFjxgx%2B/fVXUlNTmTx5MpUrV7Y4Ls9aSu5ERERE7mH27NlkZmbSpk0bevToQcuWLQkPDwfAx8eH9evXm/vGx8fj5uZG8eLF8ywnKiqKrKwsnn76aVq2bElSUhILFizAwcGhwGLVMXciIiJiHAa9zl25cuWYPXt2vtMOHDhg8bpdu3a0a9cu375eXl55ztotaMYcQRERERF5IKrciYiIiHEYtHL3V6LkTkRERIxDyZ3VNIIiIiIiRYgqdyIiImIcqtxZTSMoIiIiUoSociciIiLGocqd1ZTciYiIiHEoubOaRlBERESkCFHlTkRERIxDlTuraQRFREREihBV7mzslZHZtg4BAC8viATe6bufs21sHQ28OdNk6xB%2B5%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%2BeSTNG7cmG3bthXYcsePH0%2B3bt3ynbZr1y7q16/PxYsXuXLlCiNHjsTf359GjRoRFhbG0aNHLfqfPn2aUaNG0aJFCxo2bMhTTz3FhAkTSEhIMPc5c%2BYMtWvXZuLEiXneb86cOYSGhhbYuomIiIhxJScnEx4ejq%2BvL/7%2B/kyZMoXMzMx8%2Bw4cOJAGDRrg4%2BNjfuzYsQOAmzdvMmPGDJo1a4aPjw%2BDBw8mMTGxQGMtlORu5cqVdOjQgX379tG6desCW25oaChHjhzhu%2B%2B%2ByzNt2bJldOjQgXLlyjFu3DhSUlLYsmULe/bs4fHHHyc8PNzc9/Dhw3Tt2pXixYuzbNkyDhw4wNKlSwHo3Lkzx48ft1j28uXLiYmJKbD1EBERkTuwsyuch5UiIyNxcXFh586drFq1it27d7No0aJ8%2B3733XcsXLiQAwcOmB%2BtWrUCYN68eezatYvVq1ezc%2BdOnJycGDdunNXx3arAd8sGBwdz5MgRvvnmG5YuXUpWVhb9%2B/dn9erVdOzYkYkTJ7Jhwwbef/99EhISeOSRR/jHP/5BixYtAEhJSeH1119ny5YtODs7M2zYMMaPH88XX3yBt7c3fn5%2BrFixgvr165vf88KFC2zbto3o6GgA3nrrLbKysihevDhXrlzh6tWrlC5d2tx//PjxBAYG8tprr5nbvLy8eO2110hJSWHcuHGsXLnSPC00NJTx48dTr149HnnkkQcem8TERJKSkizanJw8KFvW84GXWVA8PCyfbc7Hx9YR/K5OHctnGytTxtYR5HBzs3w2BKNsNwbbZihXztYR/K5mTctnG/O0/ccv8PvftVH%2Bvgu4kPTHFNJu2fy%2Bgz08PPC8j43g9OnTxMbGsmPHDpydnalSpQrh4eG8%2BeabDBw40KJvfHw8V65c4bHHHst3WStXrmTkyJFUrFgRgFdffZUWLVoQHx9PlSpVHnDtLBV4crdq1SpCQ0Px8/Oja9eutGnThtTUVHbt2sX169fZvn07EydOZN68eTRq1IgdO3YQERHBihUrqFWrFpMmTSI%2BPp5NmzZhZ2fHyy%2B/bLH80NBQRo8ezejRo3FxcQEgOjqaxx9/3JzwOTg4APD2228zf/58XF1dmRpbgQkAACAASURBVD9/PpCzq/Xo0aO8%2Buqr%2BcbfvXt3%2BvXrZ7F7tl%2B/fpw5c4bIyEiio6NxdHR8oLGJjo5m7ty5Fm1Dhgxl2LCIB1peYejd29YR/CZyv60jyOuTT2wdAQBBtg7gNr/9M2oMQQbbbgyyzRhSVJStIwDAaAfXdOhg6whyzJpl6wgKXn7fwUOHDiUi4t7fwSdOnMDd3Z3y5cub22rUqEFCQgJXr16lVKlS5vbDhw/j6urKyy%2B/zOHDhylXrhz9%2BvUjODiYX3/9lfPnz%2BPt7W3uX65cOdzc3Dh%2B/Lhxk7v8dOnSBUdHRxwdHVmyZAk9e/akSZMmALRu3ZqAgACWL1/OqFGj2LhxI/Pnz6fcb/9pjhkzhs6dO5uX1aZNG9zc3IiJiSE4OJjMzExWrlzJ2LFj87zv4MGDGTJkCEuXLmXQoEGsX7/enLWXu8N/srkZfGJiokWf6dOn07VrV6ZNm5bvMXj3IyQkhICAAIu25cs9eOedB1pcgfLwyEnsli6F2/6xsYnIjxvZOoTf1amT8yXdqxccO2braPhskjESGDe3nMRuxw64csXW0eQImmiQ7cZg24zhKndRURAeDnFxto6GxaGbbR0CkFOx69ABNmyAS5dsHY2NFVLlLr/vYI/73F2VmpqKs7OzRVvu67S0NIvkLj09nYYNG/Lyyy9Tq1Yt9uzZQ0REBK6urvj8tnchtziVy8nJidTU1D%2B8TnfypyR3t5Y8z549S2xsLMuWLTO33bx5k6ZNm3L16lUyMjLMpUqA6tWrWyyrWLFi9OzZk5UrVxIcHMzWrVspVqwYzzzzTJ73dXJyAqB///6sXLmSL774gjZt2gCQkJCQZ9mQU9mDnF94dna2ud3d3Z233nqLPn364O/v/yDDgKenZ57y7/XrcPbsAy2uUCQlGSSeAwdsHUFex44ZIi6jffBfuWKgmAzw%2B7FgkG2GChVsHUFecXFw%2BLCto7Dt7sd8XLpkvJiKivy%2Bg%2B%2BXi4sL165ds2jLfe3q6mrR3qVLF7p06WJ%2B3aJFC7p06cLGjRtp1qyZxby5rl%2B/nmc51vhTzjc2mUzmnytUqMCQIUPYu3ev%2BbFhwwamTJlC2bJlcXFxIT4%2B3tz//PnzeZbXvXt3jh07xokTJ4iOjqZXr17Y2/%2Bepz733HP85z//sZgnPT0dNzc3qlSpQr169Vi1alW%2Bsa5atYp69erh5eWVZ5qPjw%2BRkZG8%2Buqr/Pzzz394HEREROQeDHhCRa1atbh8%2BTIXL140t508eZIKFSpQsmRJi76rVq1i48aNFm3p6ekUL14cNzc3ypcvT9wtVeukpCQuX75ssavWWn/6xWR69OjBxx9/zLfffgvk7Jvu1q0bn3/%2BOSaTiZ49e/Luu%2B9y7tw5UlJSeOONN/Iso3Tp0nTs2JEPPviAgwcP0r17d4vpjz/%2BOHPmzOHs2bOkp6cze/Zs0tPTzeXYqVOnsnPnTiZMmMCZM2fIysoiPj6ecePGsWvXLqZMmXLH%2BAcOHEjjxo1Zv359AY6KiIiIAIZM7qpVq0bjxo2ZOnUqKSkpxMfHExUVRXBwcJ6%2BKSkpTJ48me%2B//56srCy%2B%2BuorPv/8c0JCQgDo1q0b8%2BbNIz4%2BnpSUFKZOnYqfnx9Vq1a1KsZb/ekXMW7Xrh1paWmMHTuWhIQE3N3d6devn/macZGRkcyePZvu3btjb29P%2B/bt811OaGgonTt3pnv37hZnwgKMHDmSYsWKERISQkZGBg0bNuSjjz7C7bfT%2BurUqcPatWuZN28evXv35vLly7i7u9OyZUvWr19P5cqV7xi/yWQyH38nIiIiD4fZs2fz2muv0aZNG%2Bzs7OjSpYv5Mms%2BPj5MmjSJTp06ERYWRlpaGkOHDiU5OZkqVaowY8YMfH19ARgyZAiZmZn07t2b1NRU/P39eaeAD743Zd96YJkBnTlzhjZt2vDFF1/cNen6q3rlFVtHkMPLCyIj4Z13jHHM3ZszTffu9Gfx8YH9%2B6FRI0McP/XRImP8yZYpA0FB8NlnxjnmLqyfQbYbg20zhjrmrkED2LwZ/v53QxxzN2vkOVuHAORckiU0FBYvNs4xdyNG2OiNp00rnOWOGVM4yzUg3eNDREREpAjRvWVFRETEOHRvWasZPrmrXLlyntuBiYiISBGl5M5qGkERERGRIsTwlTsRERF5iKhyZzUldyIiImIcSu6sphEUERERKUJUuRMRERHjUOXOahpBERERkSJElTsRERExDlXurKbkTkRERIxDyZ3VNIIiIiIiRYgqdyIiImIcqtxZTSMoIiIiUoSociciIiLGocqd1ZTciYiIiHEoubOaRlBERESkCFHlTkRERIxDlTuraQRFREREihBV7kRERMQ4VLmzmpI7ERERMQ4ld1bTCIqIiIgUIarciYiIiHGocmc1jaCIiIhIEaLKnYiIiBiHKndWU3InIiIixqHkzmoaQREREZEiRJU7ERERMQ5V7qym5M7G3nx6k61DyFGyJNCMSL//wa%2B/2joaPqqfbesQzMqUgSDgs0n7uXTJ1tFAWD%2BTrUPI4eMDQfsJmtgIDhywdTQAfLTIGNuN0baZ69dtHcHvypWDZ4HVL27m4kVbRwMjqhnrMzi0xv/A0/afwTna2joAeUBK7kRERMQ4VLmzmpI7ERERMQ4ld1bTCIqIiIgUIarciYiIiHGocmc1jaCIiIhIEaLKnYiIiBiHKndWU3InIiIixqHkzmoaQREREZEiRJU7ERERMQ5V7qymERQREREpQlS5ExEREeNQ5c5qSu5ERETEOJTcWU0jKCIiIlKEqHInIiIixqHKndWU3ImIiIjcQ3JyMuPHjyc2NpZixYrRqVMnRo0ahb193lRq2bJlLFq0iMTERDw9Penbty%2B9e/cGICsri8aNG5OdnY3JZDLPs2vXLlxcXAokViV3IiIiYhwGrdxFRkZSvnx5du7cycWLFxk8eDCLFi1i4MCBFv22bt3KW2%2B9xYIFC3jiiSc4ePAgL7zwAuXKlaNt27bExcWRkZHB/v37cXR0LJRYldyJiIiIcRRScpeYmEhSUpJFm4eHB56envec9/Tp08TGxrJjxw6cnZ2pUqUK4eHhvPnmm3mSuwsXLjBo0CAaNmwIgI%2BPD/7%2B/nzzzTe0bduWw4cPU7t27UJL7EDJnYiIiDwEoqOjmTt3rkXb0KFDiYiIuOe8J06cwN3dnfLly5vbatSoQUJCAlevXqVUqVLm9tzdr7mSk5P55ptvGDNmDACHDx/mxo0bPPvss5w9e5YaNWowYsQIGjVqZM3qWVByJyIiIsZRSJW7kJAQAgICLNo8PDzua97U1FScnZ0t2nJfp6WlWSR3t0pKSuLFF1%2Bkfv36dOzYEQAnJycef/xxhg8fjpubG0uXLmXAgAGsX7%2BeKlWq/NHVypeSOxERESnyPD0972sXbH5cXFy4du2aRVvua1dX13znOXjwIMOHD8fX15dp06aZT7wYPXq0Rb8BAwawZs0atm/fTp8%2BfR4ovtsZ86hFEREReTjZ2RXOwwq1atXi8uXLXLx40dx28uRJKlSoQMmSJfP0X7VqFf369SMsLIxZs2ZZHF/39ttv8/3331v0T09Pp3jx4lbFeCsldyIiImIcBkzuqlWrRuPGjZk6dSopKSnEx8cTFRVFcHBwnr6bNm3in//8J3PmzOH555/PM/2HH35gypQpJCUlkZ6ezty5c0lJSeGZZ56xKsZbKbkTERERuYfZs2eTmZlJmzZt6NGjBy1btiQ8PBzIOSN2/fr1AMydO5ebN28ybNgwfHx8zI8JEyYAMG3aNKpWrUrnzp3x9/cnNjaWf//737i7uxdYrDrmTkRERIzDoNe5K1euHLNnz8532oEDB8w/f/bZZ3ddjru7O9OmTSvQ2G6n5E5ERESMw6DJ3V%2BJRlBERESkCFHlTkRERIxDlTuraQRFREREihBV7kRERMQ4VLmzmpI7ERERMQ4ld1bTCIqIiIgUIYar3GVnZxMYGMjf/vY3xowZk2f622%2B/zVdffcWnn37K0aNHmTZtGkeOHMHe3p5WrVoxduxYSpcube6/e/duFi5cyKFDh8jMzKRSpUoEBgYycOBAnJycANizZw99%2B/bFxcUlz/tNmDCBrl27EhAQQFJSkvnecLl8fHz417/%2BVcCjICIi8pBS5c5qhhtBk8lEnz59WLduHenp6RbTMjIyWLVqFX379iU9PZ1Bgwbh7%2B/Pnj172LJlC0lJSUyfPt3cf9myZYSHh9O8eXM2bdrEvn37mDFjBrt37yYkJITU1FSL5R84cCDPo2vXrubpkyZNyjNdiZ2IiIgYyR9O7r788kuee%2B45nnzySZ544gn69OnDqVOnWLNmDb169WLGjBn4%2BfnRtGlTFi9ezIoVK2jdujWNGzc233oDICAggLlz59K2bVt8fHzo3bs3cXFxAHTp0oXMzEy2bNli8d6bN28mKyuLjh074ujoyObNmxk8eDD29vZcuXKFa9euUaZMGQCSkpKYNm0a//znP%2Bnfvz9lypTBzs6O%2BvXr8%2BGHH5KamkpUVJQ1YyciIiIFzYD3lv2r%2BUO7Zc%2BfP8/w4cN59913CQgI4JdffmHo0KG89957PPnkk%2Bzbt4%2B///3vfP311yxfvpzXX3%2BdwMBAYmJiiIuLIyQkhKCgIJo0aQJAdHQ0H3zwAdWrV2fq1Km89NJLbNy4kRIlStC1a1dWrFhBhw4dzO%2B/bNkyevToQfHixQHMu1Gfe%2B45Dhw4QM2aNRkwYAAAO3fuNO/ivZ2zszNBQUGsX7%2BeV1555cFG7gEkJiaSlJRk0eZx4wae5cr9aTHckaur5bONlUm/d58/i5ub5bPN%2BfjYOoIcdepYPhvAb//b2ZzRtpl0A/095d4%2BswBvo2mdkiVtHUEOg30G8%2BuvtnvvhywRKwx/KLkrU6YMGzZsoGrVqqSkpHD%2B/HlKly7NhQsXgJxkKywsDJPJRIsWLbh58yYDBgzA2dmZBg0a4OnpydmzZ83J3YABA6hbty4AY8aMwdfXl/379%2BPv70%2BfPn0IDAzk559/pmrVqsTFxXHw4EFmzZqVJ65FixZx48YNc5Vu3bp1JCYm4ubmhqOjY77r4unpSWJiokWbr6%2Bvxes2bdowY8YM8%2BtJkyYxdepUiz47duzI91i9/ERHRzN37lyLtqFDhhDRqdN9zf%2BneOIJW0cAQJCtA8hHq1a2juA3QfttHYGlTz6xdQRmRttuDLPNGFCbNraOIFczWwdgySCfwWzaZOsIxAp/KLlzcHDg888/Z/ny5ZhMJry9vUlJSTGfZODu7o7JZALA7rfMu1SpUub57ezsyMrKMr9%2B5JFHzD87Ozvj7u5urmxVq1aN5s2bs3LlSkaMGMEnn3xC27ZtKV%2B%2BfJ64nJyccHJyYty4cTRr1ozjx4/j4eFBcnIyN27cMFf6bnXmzBk8PDws2vbu3XvX9Z84cSLdunW7a5%2B7CQkJISAgwKLN4%2BRJ%2BN//HniZBcbVNedD5dAhuO1YRFv4LNk4H7hubjlf0jt2wJUrto4GgiY2snUIOerUyUnsevWCY8dsHQ0An00yRuJrtG3GaJW7Nm3giy/g8mVbRwPPVjTA5y8Y7jPYplS5s9ofSu42btzIkiVLWLZsmTkxmzx5Mj/88AOAObG7X7kVP4DU1FR%2B%2BeUXKlasaG4LDQ3l1Vdf5cUXX%2BTTTz9l4cKF5mlnzpyhb9%2B%2BLF%2B%2BHE9PTwDzCRhubm60bt0aBwcH1qxZQ8%2BePS3eNzU1lZiYGNq1a/eH4rWWp6enOVazM2dsW/6%2BXWqqIeK5dMnWEeR15YpB4jpwwNYRWDp2zDAxGeL3cwujbDPXr9s6grwuX4aLF20dBVDC9p93FgzyGSx/bX8oPf7111%2Bxs7PDycmJ7OxsduzYwbp168jIyHigN//3v//N6dOnuXbtGtOmTePRRx/F55bjiVq1aoWrqyuTJ0/m0UcfpWHDhuZpXl5euLu7M23aNFJTU7l06RKTJk2iVatWeHl5UaZMGSZMmMAbb7zBokWLuHTpEhkZGXz77bcMHDgQV1dXhgwZ8kBxi4iISCHRCRVW%2B0OVu65du7Jv3z46dOhAsWLFePTRRwkLC2Pp0qUPlOA1btyYIUOGkJCQQJMmTfjggw/Mu3MhpxLYq1cvpk6dyptvvmkxr8lkIioqiilTphAQEICjoyNPP/00//jHP8x9goODqVq1Kv/61794//33uXHjBhUrVqRdu3YMHDjwvo%2BVExERkT/JQ5aIFYY/lNw5Ojryxhtv5GkfNmwYkHNMWa7KlStz/Phxi35ffvmlxWtfX988JyjcLiwsjLCwsHynVahQgTlz5tx1fj8/P/z8/O7ax9/fP0%2Bst7s9dhEREREjMtwdKkREROQhpsqd1TSCIiIiIkWIzSp32s0pIiIieahyZzXtlhURERHjUHJnNY2giIiISBGiyp2IiIgYhyp3VtMIioiIiBQhqtyJiIiIcahyZzUldyIiImIcSu6sphEUERERKUJUuRMRERHjUOXOahpBERERkSJElTsRERExDlXurKbkTkRERIxDyZ3VNIIiIiIiRYgqdyIiImIcqtxZTSMoIiIiUoSociciIiLGocqd1ZTciYiIiHEoubOaRlBERESkCFHlTkRERIxDlTuraQRFREREihBV7kRERMQ4VLmzmpI7ERERMQ4ld1bTCIqIiIgUIarciYiIiHGocmc1jaCIiIgYh51d4TyslJycTHh4OL6%2Bvvj7%2BzNlyhQyMzPz7bt9%2B3aCgoJo2LAhgYGBbNu2zWL6ggULaNWqFQ0bNiQ0NJQff/zR6vhupeRORERE5B4iIyNxcXFh586drFq1it27d7No0aI8/U6dOkVERATDhw9n7969REREEBkZyYULFwBYu3YtixcvZuHChezZs4d69eoxbNgwsrOzCyxWJXciIiJiHIVUuUtMTOTIkSMWj8TExPsK6fTp08TGxvLKK6/g7OxMlSpVCA8PZ%2BnSpXn6rl27Fl9fX55%2B%2Bmns7e1p3749TZo0ITo6GoAVK1bQq1cvatWqRfHixRkxYgQJCQns2bOnwIZQx9zZ2Ctb29o6BAC8vCCyGbwT24yzZ20dDbw502TrEH7n4wNB%2Bwma2AgOHLB1NHy0qOD%2Bu7NGmTIQBHw2aT%2BXLtk6mhxh/Qyy3Rhsm6FCBVtH8LsGDeDZzTw7/%2B9w%2BLCto2HWyHO2DgEAT08IbQaLTzbjPvONQjfCGF9PBSY6Opq5c%2BdatA0dOpSIiIh7znvixAnc3d0pX768ua1GjRokJCRw9epVSpUqZW6Pi4vD29vbYv6aNWty7Ngx8/RBgwaZpzk4OFCtWjWOHTtG06ZNH2jdbqfkTkRERAwjm8L5Jy0kJISAgACLNg8Pj/uaNzU1FWdnZ4u23NdpaWkWyV1%2BfZ2cnEhLS7uv6QVByZ2IiIgYRlZW4SzX09MTT0/PB5rXxcWFa9euWbTlvnZ1dbVod3Z25vr16xZt169fN/e71/SCoGPuRERERO6iVq1aXL58mYsXL5rbTp48SYUKFShZsqRFX29vb06cOGHRFhcXR61atczLunV6RkYGp06dyrMr1xpK7kRERMQwsrIK52GNatWq0bhxY6ZOnUpKSgrx8fFERUURHBycp2%2BnTp2IjY0lJiaGzMxMYmJiiI2NpXPnzgA8%2B%2ByzLFmyhGPHjnHjxg1mzZpFuXLl8PX1tS7IWyi5ExEREbmH2bNnk5mZSZs2bejRowctW7YkPDwcAB8fH9avXw/knGjx3nvvMX/%2BfJo0aUJUVBRz5syhevXqAAQHB9OvXz%2BGDBlC06ZN%2Bf7775k/fz4ODg4FFquOuRMRERHDKKxj7qxVrlw5Zs%2Bene%2B0A7edFd%2ByZUtatmyZb1%2BTycTzzz/P888/X%2BAx5lJyJyIiIoZh1OTur0S7ZUVERESKEFXuRERExDBUubOeKnciIiIiRYgqdyIiImIYqtxZT8mdiIiIGIaSO%2Btpt6yIiIhIEaLKnYiIiBiGKnfWU%2BVOREREpAhR5U5EREQMQ5U76ym5ExEREcNQcmc97ZYVERERKUJUuRMRERHDUOXOeqrciYiIiBQhqtyJiIiIYahyZz0ldyIiImIYSu6sp92yIiIiIkWIKnciIiJiGKrcWU%2BVOxEREZEiRJU7ERERMQxV7qyn5E5EREQMQ8md9bRbVkRERKQIUeVOREREDEOVO%2BupciciIiJShKhyJyIiIoahyp31Hji5i4mJYfLkyaSnpzNz5kxat25dIAGNHz%2BeI0eOsGbNmjzTdu3axYsvvshXX32Fg4MDkydPZufOnWRkZNCgQQNGjx5N3bp1zf1Pnz5NVFQUu3btIiUlBXd3d1q1asVLL71EpUqVzP1q165N8eLFKVasmMX7tW/fnilTpjB69Gg%2B%2B%2BwzHB0d88S0d%2B/ePPOJiIjIg1FyZ70HTu5WrlxJhw4dGDduXEHGQ2hoKEFBQXz33XfUr1/fYtqyZcvo0KED5cqVIyIigoyMDLZs2YKzszOzZ88mPDycbdu2AXD48GHCwsLo2LEjy5Yto3LlyiQkJDB//nw6d%2B7MkiVLqF27tnnZCxYswN/f/45xBQUFMX369AJdVxEREZGC9kDH3AUHB/P111%2BzfPly6tatS%2B3atZk%2BfTpNmjRh0qRJAGzYsIGgoCAaN25Mt27d%2BO9//2uePyUlhdGjR9O4cWNatGjBihUrqF27NmfOnMHb2xs/Pz9WrFhh8Z4XLlxg27ZthIaGAvDWW2/x7rvvUqpUKdLS0rh69SqlS5c29x8/fjyBgYG89tprVKlSBZPJhJeXF6%2B99hotW7Ys8KRURERErJeVVTiPh8kDVe5WrVpFaGgofn5%2BdO3alTZt2pCamsquXbu4fv0627dvZ%2BLEicybN49GjRqxY8cOIiIiWLFiBbVq1WLSpEnEx8ezadMm7OzsePnlly2WHxoayujRoxk9ejQuLi4AREdH8/jjj5ureQ4ODgC8/fbbzJ8/H1dXV%2BbPnw/AmTNnOHr0KK%2B%2B%2Bmq%2B8Xfv3p1%2B/fqRkJBgsXu2sCUmJpKUlGTR5uTkQdmynn9aDHfi4WH5bHM%2BPraO4Hd16lg%2B21iZMraOIIebm%2BWzIRhluzHYNkO5craO4Hc1a1o%2B25in7T9%2Bgd//ro3y952YaOsIxBoFdkJFly5dcHR0xNHRkSVLltCzZ0%2BaNGkCQOvWrQkICGD58uWMGjWKjRs3Mn/%2BfMr99oEzZswYOnfubF5WmzZtcHNzIyYmhuDgYDIzM1m5ciVjx47N876DBw9myJAhLF26lEGDBrF%2B/XpzAlXuDh9onr/9NScmJpqTu5deesni2Dl3d3e2bt1qfv35559bvIacxLJly5b3PUbR0dHMnTvXom3IkKEMGxZx38sobL172zqC30Tut3UEeX3yia0jACDI1gHcplUrW0dwiyCDbTcG2WYMKSrK1hEAEGrrAG7ToYOtI8gxa5bt3vthq7IVhgJL7jxv%2Bffn7NmzxMbGsmzZMnPbzZs3adq0KVevXiUjI4OKFSuap1WvXt1iWcWKFaNnz56sXLmS4OBgtm7dSrFixXjmmWfyvK%2BTkxMA/fv3Z%2BXKlXzxxRe0adMGgISEhDzLhpzKHoDHLWWq999//67H3HXs2NHqY%2B5CQkIICAiwaFu%2B3IN33rFqsQXCwyMnsVu6FG4rLtpE5MeNbB3C7%2BrUyfmS7tULjh2zdTR8NskYCYybW05it2MHXLli62hyBE00yHZjsG3GcJW7qCgID4e4OFtHw%2BLQzbYOAcip2HXoABs2wKVLto7GtpTcWa/AkjuTyWT%2BuUKFCnTp0oUXXnjB3JaQkICTkxOlS5fGxcWF%2BPh4Hn30UQDOnz%2BfZ3ndu3fnvffe48SJE0RHR9OrVy/s7X8P97nnnqNfv360a9fO3Jaeno6bmxtVqlShXr16rFq1iubNm%2BdZ9qpVq6hXrx5eXl4Fsu73y9PT0yIJBrh%2BHc6e/VPDuKukJIPEc%2BCArSPI69gxQ8RltA/%2BK1cMFJMBfj8WDLLNUKGCrSPIKy4ODh%2B2dRSG2/146ZLxYpK/nkK5iHGPHj34%2BOOP%2Bfbbb4GcM1e7devG559/jslkomfPnrz77rucO3eOlJQU3njjjTzLKF26NB07duSDDz7g4MGDdO/e3WL6448/zpw5czh79izp6enMnj2b9PR0c2Vs6tSp7Ny5kwkTJnDmzBmysrKIj49n3Lhx7Nq1iylTphTGqouIiIgVdEKF9QrlIsbt2rUjLS2NsWPHkpCQgLu7O/369TOf6RoZGcns2bPp3r079vb2tG/fPt/lhIaG0rlzZ7p3725xJizAyJEjKVasGCEhIWRkZNCwYUM%2B%2Bugj3H47urtOnTqsXbuWefPm0bt3by5fvoy7uzstW7Zk/fr1VK5cuTBWXURERKzwsCViheGBk7vFixebfz5%2B/Hie6d26daNbt275zuvo6MjIkSMZOXIkkHMM3MKFC/P0q1OnTr7Lzl3GqFGjGDVq1B1jrFKlClOnTr3retwp/lvp%2BnYiIiLyV6Hbj4mIiIhhqHJnvUI55k5EREREbMMQlbvKlSvfc9eoiIiIFH2q3FnPEMmdiIiICCi5KwjaLSsiIiJShKhyJyIiIoahyp31VLkTERERKUJUuRMRERHDUOXOekruRERExDD%2BisldWloakydP5ssvvyQzM5M2bdowceJEXF1d8%2B2/adMmoqKiiI%2BPx93dnW7duhEeHo6dXc4O1cDAQBISEsyvAVatWkWNGjXuKx4ldyIiIiJWmDx5MufOnWPTpk3cvHmTyMhIZs6cycSJE/P0/e677/i///s/3nnnHf72t7/x008/MWjQIFxcXHj%2B%2BedJSUnhp59%2B4osvvsDLy%2BuB4tExdyIiImIYWVmF80hMTOTIkSMWj8TERKvjvXbtGp999hnDhg3D3d2dsmXLMnLkSNasWcO1a9fy9D979izPPfccrVu3xs7Ojho1avDMM8/wzTffADnJn7u7%2BwMndqDKnYiIP9ML%2BwAAIABJREFUiDwEoqOjmTt3rkXb0KFDiYiIuOe8169f58KFC/lOu3btGhkZGXh7e5vbatSowfXr1zl16hR169a16N%2B2bVvatm1rseyvvvqKoKAgAA4fPoyzszN9%2BvThxIkTeHl5ERERQevWre97XZXciYiIiGEU1jF3ISEhBAQEWLR5eHjc17yHDh2ib9%2B%2B%2BU4bPnw4AC4uLuY2Z2dnAFJTU%2B%2B63JSUFIYPH46TkxP9%2BvUDwGQy0aBBA/7xj39QqVIl/vOf/xAREcGSJUto2LDhfcWr5E5EREQMo7CSO09PTzw9PR9oXn9//zveJvX777/n3Xff5dq1a%2BYTKHJ3x5YoUeKOy/zxxx8ZNmwYZcuW5eOPPzb3HThwoEW/Tp068fnnn7Np06b7Tu50zJ2IiIjIA6pevToODg7ExcWZ206ePImDgwPVqlXLd57t27fTvXt3WrZsycKFC3FzczNPW7hwIbt377bon56eTvHixe87JiV3IiIiYhiFdUJFYXF2diYwMJCZM2dy6dIlLl26xMyZM%2BnYsSP/397dx9V8//8Df3QdhdIK/ZiLLdpcJsnV9BHmovIh0kLaNDZXtfBBJBexMJu5GLnYWDQpV5Oai4%2BrDWPmKp8vRjaW0gXlooujk87vj%2BbM0UnZUa%2B3837cb7dux3m/T%2Bc8vB3v83w/X%2B/365ibm5d5/Pnz5zF%2B/HiEhoZi2rRpMDbWHES9ffs25s6di9TUVBQXF2Pbtm04d%2B4cBg0aVOlMLO6IiIiIdDB79mw0adIEXl5e6Nu3Lxo2bIjw8HD1eg8PD0RFRQEAoqKiUFxcjAULFsDJyUn982Q4durUqejevTuGDRuGDh06IDY2FmvXrkXjxo0rnYfn3BEREZFkvIqTGFtaWiIiIgIRERFa1ycmJqr//KTIK4%2BpqSlmzJiBGTNm/OM8LO6IiIhIMl7F4k5qOCxLREREpEfYuSMiIiLJYOdOd%2BzcEREREekRdu6IiIhIMti50x2LOyIiIpIMFne647AsERERkR5h546IiIgkg5073bFzR0RERKRH2LkjIiIiyWDnTncs7oiIiEgyWNzpjsWdYHXqiE5QytLy71tJZKpfX3SCv7322t%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%2B2bt2qXr9u3Tp0794d7dq1g7%2B/P37//fcXyvPSi7ukpCR07twZzs7OOHz48Mt%2B%2Bhcyffp0TJ8%2BvVKP9ff3x4oVKwAAhw4dQmBgYFVGIyIiIi1exeIuIiICt2/fxr59%2B7B//37cvn0bS5YsKffxFy9eREREBM6dO6f%2B8fX1BQDs3LkTmzZtwtdff41Tp06hZcuWCAoKgkqlqnSel17cxcfHw8PDA2fOnEGPHj1e9tNXi3v37r3QRiQiIiJ5KiwsREJCAoKCgmBlZQUbGxtMmTIFO3bsQGFhYZnHFxUV4erVq2jVqpXW54uLi8OwYcPg4OAAMzMzTJ48Genp6Th16lSlM73UCyqGDBmC//u//8Pp06cRExODkpISfPDBB9i%2BfTs8PT0xe/ZsJCYmIioqCunp6WjcuDEmTZqEbt26AQDy8vIwf/58HDhwADVq1EBQUBBmzZqFgwcPomHDhhW%2B/sGDB/HFF18gLS0Nrq6uAABra2v1%2Bue99hOnTp3C7NmzoVQq4eTkhL179wIAIiMjkZycjLt37%2BK1117D2LFjMWTIkBfaPllZWcjOztZYZmJiCxsbuxd6nqpgY6N5K1zr1qIT/O3NNzVvBXvtNdEJSllZad5KglTeNxJ7z6BBA9EJ/uboqHkrWN26ohOUql1b81a0nBxxr11VXTZtn8G2traws6v4M1ihUCAzM1PrusLCQiiVSjRv3ly97I033oBCocCNGzfw1ltvaTz%2BypUrKC4uxvLly3HmzBnUqlULgwcPxocffghDQ0OkpKRg9OjR6sebmJigSZMmuHLlCjp16lSpv%2BtLLe62bdsGf39/dOzYEYMGDULPnj2Rn5%2BP48ePQ6FQ4OjRo5g9ezZWr16N9u3b48cff8TEiRMRFxcHBwcHzJ07F6mpqdi3bx8MDQ0REhJS6df%2B/fffERwcjE8//RT9%2B/fHkSNHEBQUhAEDBgBAha/9hKurK%2BbOnYuVK1fi0KFDAIDRo0fDysoKiYmJMDU1RXR0NCIiItCvXz9YWFhUOuPWrVuxcuVKjWXjx0/Ahx9OrPRzVLVBg0Qn%2BMuH%2B0UnKGvVKtEJAACDRQd4Rs%2BeohM8ZbDE3jcSec9I0nffiU4AAOgvOsAznuk3CLN5s%2BgEL5%2B2z%2BAJEyZg4sSKP4MvXLiAkSNHal0XHBwMAKhZs6Z6WY0aNQBA63l3Dx8%2BRMeOHeHv748vvvgCly9fxvjx42FoaIgPP/wQ%2Bfn56t9/wtzcHAUFBRXmfKLKp0IZOHAgTE1NYWpqis2bN8PPzw8uLi4AgB49esDd3R2xsbGYNm0afvjhB6xZswav/dWaCA0Nxb///e9KvU5SUhJatWqlLuZ69eqlMSz8vNeeNWvWc597/vz5sLCwgImJCdLT02FhYQGFQoH79%2B%2B/UHHn6%2BsLd3d3jWV79thi/fpKP0WVsbEpLex27gTu3hWdBvgw7l3REf725pulH9LjxgEpKaLTYPtH0ihgrKxKC7uDB4F790SnKTV4jUTeNxJ7z%2BDOHdEJ/uboWFrYDRsGXLkiOg2S5p8VHQFAaceuWzfg2DHgwQPRacSqqs6dts9gW1vbSv2uq6srfvvtN63rLl26hGXLlqGwsFBdEzwZjrW0tCzz%2BK5du6Jr167q%2B23atEFAQACSkpLw4YcfokaNGlAoFBq/o1AoXqjeqPLi7ul2Z1paGn755Rds2bJFvezx48fo1KkTHjx4AKVSiQZPDR80bdq00q%2BTmZkJe3t7jWWvv/46cnNzK3ztiqSmpmLx4sW4ceMGmjRpgsaNGwMASl7wHWhnZ1em/btzJ5CR8UJPU6Xu3pVInosXRScoKyVFErmk9DkNlBZ2kskkgX8fDRJ5z0jjP/UzrlwBzp0TnULo8KM2Dx5IL1N1q6riTttn8MvQtGlTmJiYICUlBW3btgUAXL9%2BXT2c%2Bqz//ve/uHPnDt577z31sqKiIpibmwMAHBwccO3aNXWDSqlU4saNGxrDvhWp8uLOwMBA/ef69etj4MCBGDNmjHpZeno6zM3NYW1tjZo1ayI1NRXNmjUDAGS8wA6pfv36OHLkiMayjIwMmJmZVfjaz6NUKvHRRx9h0qRJGDZsGAwMDPC///0Pu3fvrnQ2IiIi0k81atRAv379sGTJEixbtgwAsGTJEnh6emqtMVQqFSIjI9G4cWN06tQJ58%2BfR3R0NEJDQwEAgwcPxooVK9C9e3c0bdoUS5cuxWuvvYYOHTpUOlO1znM3dOhQREdHIzk5GUDppcDe3t7Ys2cPDAwM4Ofnh2XLluH27dvIy8vD4sWLK/3cAwYMwNWrVxEXF4fi4mIcO3YMBw4cqNRrP8vMzAyFhYUoLi6GUqmEQqGAubk5DAwMkJ6ejs8%2B%2BwxAaeFHREREL8%2BrOBXK7Nmz0aRJE3h5eaFv375o2LAhwsPD1es9PDwQFRUFAOjduzdCQ0MxZ84cODk54T//%2BQ8mTpyoPg1tyJAheP/99zF%2B/Hh06tQJly5dwpo1a2BiYlLpPNX69WN9%2B/ZFQUEBZsyYgfT0dFhZWeH999%2BHv78/AOCTTz7B8uXL4ePjA2NjY/TvX/lTXRs1aoSoqCgsXLgQCxYsQMuWLdG7d%2B9Kv/bTXFxcYGNjAxcXF8TGxuLTTz/FsmXLMH/%2BfNjY2GDo0KFISUnB1atXX2jomIiIiPSPpaUlIiIiEBERoXV9YmKixv333ntPY1j2aQYGBhg1ahRGjRr1j/O89OJu06ZN6j9rO/nQ29sb3t7eWn/X1NQUU6ZMwZQpUwAAt27dwtdff13p1%2B7cuTO%2B//77ctc/77Wfzm1nZ6fR0WvRokWZCzueHt4lIiKil4PfLau7au3cERERET0PizvdvRLFXU5ODry8vJ77mHMSuOqKiIiISDRJF3cNGzZUD%2B2yeCMiItJ/7NzprlqvliUiIiKiqiXpzh0RERHJCzt3umNxR0RERJLB4k53HJYlIiIi0iPs3BEREZFksHOnO3buiIiIiPQIO3dEREQkGezc6Y7FHREREUkGizvdcViWiIiISI%2Bwc0dERESSwc6d7ti5IyIiItIj7NwRERGRZLBzpzsWd0RERCQZLO50x2FZIiIiIj3Czh0RERFJBjt3umNxR0RERJLB4k53HJYlIiIi0iPs3BEREZFksHOnO3buiIiIiPQIO3dEREQkGezc6Y7FHREREUkGizvdcViWiIiISI%2Bwc0dERESSwc6d7ti5IyIiItIj7NwRERGRZLBzpzsDlUqlEh1C1pycRCco5egIbNkC%2BPkBV66IToPPR5wTHUHNzg7w9wc2bQKyskSnASa32ic6QqlatYAuXYATJ4CHD0WnAQB8/r8%2BoiMAkN57pl490Qn%2BVrcu0L8/kJQE5OSITgOM8DcQHaGUkxNw9izQvj1wTiL7P0HlgY9P1TxvfHzVPK8UcViWiIiISI9wWJaIiIgkg8OyumPnjoiIiEiPsHNHREREksHOne5Y3BEREZFksLjTHYdliYiIiPQIO3dEREQkGezc6Y6dOyIiIiI9ws4dERERSQY7d7pjcUdERESSweJOdxyWJSIiItIj7NwRERGRZLBzpzt27oiIiIj0CDt3REREJBmvYueuoKAAEREROHToEIqLi9GzZ0/Mnj0bFhYWZR4bHh6OhIQEjWUKhQJdunTB119/jZKSEjg7O0OlUsHAwED9mOPHj6NmzZqVysPijoiIiCTjVSzuIiIicPv2bezbtw%2BPHz/GJ598giVLlmD27NllHjtv3jzMmzdPff/YsWOYPHkypk%2BfDgBISUmBUqnE2bNnYWpq%2Bo/ycFiWiIiI6B8qLCxEQkICgoKCYGVlBRsbG0yZMgU7duxAYWHhc383JycHU6ZMwcyZM%2BHg4AAAuHjxIlq0aPGPCzuAnTsiIiKSkKrq3GVlZSE7O1tjma2tLezs7Cr8XYVCgczMTK3rCgsLoVQq0bx5c/WyN954AwqFAjdu3MBbb71V7vMuWbIErVq1woABA9TLLl68iEePHmHw4MFIS0vDG2%2B8gcmTJ6N9%2B/YV5nyCxR0RERHpva1bt2LlypUayyZMmICJEydW%2BLsXLlzAyJEjta4LDg4GAI3z4WrUqAEAyM/PL/c5U1NTsXv3bsTHx2ssNzc3R5s2bRAcHIw6deogJiYGgYGB2L17Nxo1alRhVoDFHREREUlIVXXufH194e7urrHM1ta2Ur/r6uqK3377Teu6S5cuYdmyZSgsLFRfQPFkONbS0rLc59y%2BfTucnJzKdPaenHv3RGBgIHbs2IGjR49ixIgRlcrL4o6IiIgko6qKOzs7u0oNwb6opk2bwsTEBCkpKWjbti0A4Pr16zAxMUGTJk3K/b39%2B/dj1KhRZZYvXboUffr0wdtvv61eVlRUBDMzs0pn4gUVRERERP9QjRo10K9fPyxZsgQ5OTnIycnBkiVL4OnpCXNzc62/k5ubi%2BvXr8PFxaXMuqtXr2LBggXIzs5GUVERVq5ciby8PPTu3bvSmVjcERERkWSUlFTNT1WaPXs2mjRpAi8vL/Tt2xcNGzZEeHi4er2HhweioqLU92/dugUAqFevXpnnioyMxOuvv45///vfcHV1xS%2B//IINGzbAysqq0nk4LEtERESkA0tLS0RERCAiIkLr%2BsTERI37rVu3LvccPisrK0RGRuqUh8UdERERScarOImx1LC4IyIiIslgcac7nnNHREREpEfYuSMiIiLJYOdOd%2BzcEREREekRdu6IiIhIMti50x2LOyIiIpIMFne647Dsc9y8eVN0BCIiIqIXovfFnbu7O3bs2FGpx7Zo0QKnTp0CACxatAirV6%2BuymhERET0jFfxGyqkhsOy5cjNzRUdgYiISHbkVohVhZdW3B06dAhr167FzZs3UVBQgNatW2P%2B/Pk4e/Ystm3bhrZt22L79u0wNDTE%2BPHjYWZmhtWrV%2BPBgwfw8PDAvHnzAJR22ry9vZGQkICsrCy8/fbbmDt3Lt58880KM6hUKqxZswabN2%2BGQqGAj48PHj9%2BrF5fVFSE1atXY/fu3Xj48CHatm2LsLAwNG7cWON5vvrqKyQkJAAALl26hN27d%2BPs2bP48ssv8fvvv%2BP%2B/ftwcHBAeHg42rVr97I2IREREZHOXkpxl5GRgeDgYCxbtgzu7u7Izc3FhAkT8NVXX6Fz5844c%2BYM3n33XZw8eRKxsbGYP38%2B%2BvXrh6SkJKSkpMDX1xdeXl5wcXEBAGzduhVr165F06ZN8emnn%2BLjjz/GDz/8ABMTk%2Bfm2L59O7799lusX78eDg4OWLlyJTIyMtTrly5dipMnT2Ljxo2ws7PDunXrMGrUKCQlJcHMzEz9uPHjxyM1NRUAsHDhQigUCowdOxZBQUHw8/ODQqHAjBkzsHjxYnz33XeV3k5ZWVnIzs7WWGZbrx7srK0r/RxVpkkTzVvB7OxEJ/hb3bqat8LVqiU6QSkLC81bCZDK%2B0Zq7xmp5ACA2rU1b4VzchKdoJSjo%2BataOfOCXtpdu5091KKu7p16yIxMRGvv/468vLykJGRAWtra2RmZgIAatasiYCAABgYGKBbt254/PgxAgMDUaNGDbRu3Rp2dnZIS0tTF3eBgYF46623AAChoaHo0KEDzp49C1dX1%2Bfm%2BP777zF06FC0bNkSABAcHIy4uDgApV292NhYLF%2B%2BHI0aNQJQWsTFxcXhyJEj6NOnT7nPa2Jigq1bt6Jx48Z49OgR0tLSYGVlhYsXL77Qdtq6dStWrlypsWzCuHGYGBz8Qs9TpXT8suKXxV90AC08PEQneKKL6ACa2rYVnUDNX2KbRjrvGenp1k10gr/0Pys6gaYXaBhUKQMD0QlIBy%2BluDMxMcGePXsQGxsLAwMDNG/eHHl5eTA2Ln16KysrGPz1RjE0LL2Go/ZTh22GhoYoeapUf3qYtEaNGrCysirT8dImKysLDRo0UN83MjKCvb09ACAnJwcFBQUIDg5WZwAApVKJtLS05z6vkZERTp06hdGjR6OgoABvvvkmjI2NoVKpKsz0NF9fX7i7u2sss508GTh58oWep0o0aVJa2IWGAjduiE6DTf23iI6gVrdu6Yd0YiKQkyM6DeD/xgnREUpZWJQWdhcuAPn5otMAADZdl0Z1J7X3jI2N6AR/q127tLA7dgx48EB0GqB/WHvREUo5OpYWdsOGAVeuiE4jFDt3unspxd0PP/yAzZs3Y8uWLerCLCIiAlevXgUAdWFXWU86fgCQn5%2BP3NxcjaKtPPXr11cPpwKl3bqsrCwAgLW1NczMzPDNN99onCf3%2B%2B%2B/o169es993gsXLiAiIgKxsbFo1aoVAOCbb77BH3/88UJ/Lzs7O9g9O26UmVn6IxU3bkhix5LVQXSCsnJygL/eTmLZPRSdQFN%2BPvBQGpkk8e/zFKm8Z6TYhHnwQBqFr8jhR62uXJFepmrG4k53L2UqlIcPH8LQ0BDm5uZQqVT48ccfsWvXLiiVyn/0fBs2bMDNmzdRWFiIyMhINGvWDE6VOC/Cx8cHcXFxOHfuHJRKJVavXq3u%2BBkaGmLIkCH4/PPPkZGRgZKSEuzcuROenp5a57MzNTXFw78%2BsJ7%2B%2BwHA%2BfPnER0djaKion/09yMiIiKqKi%2Blczdo0CCcOXMGHh4eMDIyQrNmzRAQEICYmJh/VOA5Oztj/PjxSE9Ph4uLC9auXasxlFoeT09P5ObmIiQkBPfv30ffvn3RokUL9fpp06ZhxYoVGDZsGO7du4dGjRph%2BfLlePvtt8s8V//%2B/RESEoJ//etfOHz4MIYNG4bhw4ejpKQEDRs2hL%2B/Pz7//HPcuXMHr7322gv/HYmIiKgsdu50Z6B60RPHqpi7uzsmTJgAb29v0VGqh5Su1NqyBfDzk8Sw7OcjpDMsYWcH%2BPsDmzZJY4htcqt9oiOUqlUL6NIFOHFCMsOyn/%2Bv/AujqpPU3jMVnHlSrerWBfr3B5KSpDEsO8JfImPWTk7A2bNA%2B/bSGZYVVB44OFTN8167VjXPK0WcxJiIiIgkg5073b0yxd2%2Bffswffr0ctc7Oztj/fr11ZiIiIiIXjYWd7qTXHF36NAhrcv79Onz3LnoiIiIiEiCxR0RERHJFzt3unspU6EQERERkTSwc0dERESSwc6d7ljcERERkWSwuNMdh2WJiIiI9Ag7d0RERCQZ7Nzpjp07IiIiIj3Czh0RERFJBjt3umNxR0RERJLB4k53HJYlIiIi0iPs3BEREZFksHOnO3buiIiIiPQIO3dEREQkGezc6Y7FHREREUkGizvdcViWiIiISI%2Bwc0dERESSwc6d7ti5IyIiItIj7NwRERGRZLBzpzsWd0RERCQZLO50x2FZIiIiIj3Czh0RERFJBjt3umPnjoiIiEiPsLgjIiIiySgpqZqf6lBYWAhfX1/s2LHjuY%2B7cOECfHx84OTkBHd3d8THx2us37lzJ3r37o127drB29sb586de6EcLO6IiIhIMl7V4u7atWsYPnw4zp8//9zH3b9/H2PGjMHAgQNx%2BvRpLFiwAJGRkUhOTgYAnDp1ChEREVi4cCFOnz6NAQMGYOzYsSgsLKx0FhZ3RERERDr4%2BeefERAQgEGDBsHe3v65j92/fz%2BsrKwwfPhwGBsbo3PnzvDy8kJMTAwAID4%2BHh4eHnB2doaJiQnef/99WFtbIykpqdJ5eEEFERERSUZVddmysrKQnZ2tsczW1hZ2dnYV/q5CoUBmZqbWdba2tnB0dMThw4dhZmaGDRs2PPe5rl27hubNm2sse/PNN7Ft2zYAQEpKCgYPHlxm/ZUrVyrM%2BQSLO9FecBy9qmRlZWHrihXwXbasUm/0qjZZdICnZGVlYcWKrfD19ZXEtgH6iA4A4Kn3jGS2CzBZGptGgu8Z6ZDcthmhEp0AwFP/n/bulcZ2EUhVRf8kK1ZsxcqVKzWWTZgwARMnTqzwdy9cuICRI0dqXffVV1%2BhV69elc6Rn5%2BPGjVqaCwzNzdHQUFBpdZXBos7AgBkZ2dj5cqVcHd3l/2O5VncNtpxu5SP26Z83DbacbtUPV9fX7i7u2sss7W1rdTvurq64rfffnspOWrUqIGHDx9qLFMoFLCwsFCvVygUZdZbW1tX%2BjVY3BEREZHes7Ozk0Th3Lx5cxw/flxjWUpKChwcHAAADg4OuHbtWpn13bt3r/Rr8IIKIiIiomrSu3dv3LlzBxs3boRSqcTJkyeRkJCgPs9uyJAhSEhIwMmTJ6FUKrFx40bcvXsXvXv3rvRrsLgjIiIiqkIeHh6IiooCAFhbW%2BObb77B3r174erqirCwMISFhaFTp04AgM6dO2P27NmYM2cOOnbsiMTERKxbtw5WVlaVfj0DlaqqTl2kV0lWVha2bpXQSc4Swm2jHbdL%2Bbhtysdtox23C71MLO6IiIiI9AiHZYmIiIj0CIs7IiIiIj3C4o6IiIhIj7C4IyIiItIjLO6IiIiI9AiLOyIiIiI9wuKOiIiISI%2BwuCMiIiLSIyzuiIiIiPQIizsiIiIiPcLijojoH%2BK3N1JVyMnJER2BXnHGogNQ9Vu5cuVz1xsYGGD8%2BPHVlEY6/P39YWBgUO56AwMDfPvtt9WYSDoCAwPx9ddfq%2B8rFAqYm5sLTCQNzs7OOHv2rPr%2B8ePH0bVrV4GJpKmwsBD3799HSUkJAECpVOLq1avo3bu34GRiJScnY/HixcjMzNTYNjk5Ofjf//4nOB29yljcydCpU6e0LlcqlTh//jyMjY1lWdy5urpqXf7nn39i9%2B7daNCgQTUnko5z585p3O/evTt%2B%2BeUXQWmk49nOXUhICLfLM7Zv346IiAg8evRIY7mNjY3si7t58%2BahUaNGcHBwQGpqKrp27Yro6GhMnjxZdDR6xbG4k6FNmzaVWZaamopPPvkE9evXx%2Beffy4glXgTJkwos2zfvn3YuHEj3NzcsHDhQgGppInDkaWe7fRyu5QVFRWFTz75BBYWFjh9%2BjQCAgLw2WefscMJ4Nq1a9i8eTNu3bqFBQsW4IMPPoCTkxPmzZuHDz74QHQ8eoXxnDvCvn374O3tDVtbW%2BzatQvOzs6iIwmnVCoRERGByZMnY%2BzYsVizZg2sra1Fx5KM5w1fyxm3S1nZ2dkICAhA586d8eeff6Jly5b49NNPER8fLzqacLVr14a5uTkaNWqEa9euAQDatWuHtLQ0wcnoVcfOnYwplUpERkYiLi4OISEhCAwMFB1JElJTUxEcHIx79%2B5h8%2BbNaNeunehIRK8sGxsbKJVKNGjQAH/88QcAwN7eHnfv3hWcTLxmzZphy5Yt8PPzQ82aNXH58mWYmpryIIF0xuJOpljAaLd3716EhYXBxcUFGzZsQJ06dURHkoTi4mJVjE/7AAAXI0lEQVTs2rVLfV%2BpVGrcB4CBAwdWdyzhioqKEBoaqr5fUFCgcR8AIiMjqzuWpLRp0wbh4eGYNWsWmjRpgi1btsDc3BxWVlaiowkXHByMsWPHomvXrggMDMTQoUNhZGQEPz8/0dHoFWeg4kkisvPDDz9g1qxZcHV1xcKFC1GrVi3RkSRh7ty5iI2NxYgRI8o938Xe3r6aU0mDu7v7c9cbGBjg4MGD1ZRGOqZPn15hl0XuxV1WVhbCwsIwf/58/Pnnn/j444%2BhUCgQGRkJLy8v0fGEe/ToEUxMTGBoaIgLFy4gLy%2BP5yOSzljcyZCjoyOA0kKlvA8mOX5QP9kuQGmx8uS/xpM/GxgY4PLly6LiEb2SsrOzYWtrq75fXFwMpVKJW7duwcHBQWAy8QYOHFimAw6UHkwdOnRIQCLSFxyWlSG5dxLKI8eCtrJ27doly2HXisyaNQsRERGiY0hanz59NOYCNDY2hqGhIXx9fTWWy8Wff/6J1atXAwBSUlLKDOPn5eVBoVCIiEZ6hJ07Ii0yMjKQlZUFW1tbWc9v90T79u01PohDQ0N5kICy2%2BXdd9/F/v37BSaShps3byIwMBAqlQrp6ellTmdQKBSoW7cuEhISBCUUa/HixcjNzUVCQkKZoWlTU1P079%2B/3Hk3iSqDnTuZKiwsxKpVq7B37151EdOnTx%2BMGzcOFhYWouMJc%2B3aNYSFhSE5OVk9FNuyZUssWLAALVq0EB1PmGePAdnlLPXsdsnNzRWURFoaN26MmTNnIjc3F3PmzCkzh6SZmRlcXFwEpRNv6tSpAIBGjRph3LhxgtOQPmJxJ0OPHj2Cn58f8vPz4eXlBTs7O9y6dQtJSUn46aefEBcXJ8uvlrp9%2BzaGDx%2BOd955ByEhIbCzs0Nqaiq2bduG4cOHY/fu3bK9oIKT9Wr37HbhFBZ/69GjBwCgYcOG6Nixo%2BA00nL69GkAgIuLi/rPz5Jz8Uu647CsDH311Vf4%2BeefsX79eo0iLj8/H6NHj0aXLl20fluDvgsPD4eBgQHmzp1bZl1YWBgMDQ0xb948AcnEe3b4sWPHjvyaLXC7PE9F32ENaP9WGDl4%2BuItbXjxFumKnTsZ2rt3LxYtWlSmO2dhYYHp06djxowZstzpHjt2DDExMVrXffTRRxg5cmQ1JyKpKykpwa%2B//qruZBYXF2vcB%2BTbgSnvO6yfkHOX88qVK6IjkJ5j506GnJ2dcebMGa3riouL0bFjR1lexebk5IRz585pXadSqdC%2Bffty1%2Bs7R0dHjQ/jJ%2BcjPk2OnQZ2YEhXRUVFOHr0KNLS0uDr64ubN29W%2BL4iqgg7dzJkZGSEe/fuaZ0h/uHDhzAzMxOQSjxLS0ukpaXh//2//1dmXXp6uqy/rSI6Olp0BEliB6Zyrl%2B/ji1btiAjIwMRERFITEzEiBEjRMcS7s8//8SoUaOgVCrx4MEDuLm5YfDgwVi5cqX6nEWif8JQdACqfm3btsX333%2BvdV1CQgLatm1bzYmkoWvXrli3bp3WdevWrUO3bt2qOZF0dOzYscIfucvIyEBycjIyMjJER5GU48ePw8fHB7m5uThx4gQUCgW%2B%2BuorrF27VnQ04RYsWABvb28cOXIExsbGaNq0KebPn4/ly5eLjkavOhXJzokTJ1ROTk6qhIQElVKpVKlUKtWjR49UsbGxqvbt26tOnz4tOKEYN27cULVv3141a9Ys1enTp1U3btxQnThxQjVp0iSVi4uL6tatW6IjClVQUKBasmSJqlevXqo2bdqoevbsqVq8eLEqLy9PdDShrl69qvLx8VE5OjqqWrRooXJ0dFQNHjxYdeXKFdHRJMHb21t15MgRlUqlUnXo0EGlUqlUycnJKnd3d5GxJKFjx46qR48eqVQqlcrFxUWlUqlUjx8/Vjk7O4uMRXqAw7Iy1LlzZ/znP//BzJkzMXPmTNSpUwd3796FiYkJZs2ahQ4dOoiOKETjxo2xceNGhIeHIy4uTv21Yy1btkR0dLTW4Vq54PQ52j09fc6kSZM4fY4WN2/eRPfu3QH8fRFF69atcf/%2BfZGxJKFWrVq4c%2BeOxnskOztb1qeA0MvB4k6m/Pz80Lt3b/z444/q737817/%2Bhbp164qOJlTr1q2xc%2BdOpKWlqSd3btiwoehYwq1fvx6WlpaIjY3VKOLGjh2L0aNHY/369bK8wnr16tXo16%2BfxvQ5zZo1g5ubG8LCwhAVFSXb6XOesLe3x9mzZ%2BHs7KxedvHiRX7zCwAvLy9MmDABkydPRklJCZKTk/HZZ5/Bw8NDdDR6xfFqWYJKpcJvv/2GOnXqcIf7lFu3buHgwYOwsrKCu7s7atWqJTqSMF5eXli0aBHefvvtMuuSk5MxY8YM7NmzR0Aysdzd3RETE6P1/01qaipGjhyJw4cPC0gmHYmJiZg7dy78/PwQHR2NcePGYdOmTZg0aZLsv69YqVTiiy%2B%2BQGxsLAoLC2FmZoYhQ4Zg2rRpMDU1FR2PXmEs7mTo3r17WLRoEerWrYvAwEAEBATg2rVrMDAwgJubG5YsWQJLS0vRMavdlStXMG7cONjY2CA0NBSBgYGwt7dHUVERlEolvv32WzRu3Fh0TCE4fY52nD6nco4ePYqYmBikpaWhfv36GDp0KPr06SM6lqTk5OTA2tpa1vP/0cvDYVkZioyMRFZWFv744w/8%2BOOPcHBwwLfffotHjx5h7ty5%2BPLLLxEWFiY6ZrVbtGgRPD098eDBA3z00UcIDg7G%2B%2B%2B/DwBYvnw5IiMjERUVJTakIJw%2BRztOn1M5bm5ucHNzEx1DMnbt2lXhY%2BTe1STdsHMnQ507d8aBAweQm5uLd999F7/88ot6yDEnJweDBg3C0aNHBaesfh06dMCpU6eQl5eHTp06ITk5GSYmJgBKJxp1c3PDzz//LDilGKNHj0a3bt0QEBBQZl10dDROnDghy8J3%2BvTpMDc3x5w5c8qsmzNnDoqLizF//vzqDyYBoaGhFT4mMjKyGpJIj7u7O4DSbzjJzMyElZUV7O3tkZWVhezsbDg6OlaqACQqDzt3MqRUKmFhYQFLS0u0a9dOo%2Btibm4OhUIhMJ04xsbGKCwsRJ06dTBmzBiNr5C6c%2BcOjI3l%2B99l1KhRGD9%2BPGxsbNC3b18YGxujqKgIO3fuxLJly7BmzRrREYUYO3YsvL29UVJSggEDBsDW1hbp6enYtm0bfvrpJ%2BzcuVN0ROFyc3Px008/oUePHmjUqBEyMzNx4MABvPvuu6KjCXPo0CEApaMFpqamCA4OhqFh6bSzq1atwq1bt0TGIz3Azp0MBQQEoHPnzvj44481lt%2B5cweRkZFQKpWynERzypQpKCgowJdffqlxMvP%2B/fuxYsUKvPPOO5g6darAhGJt2bIFCxcuBIAy0%2BcMHjxYcDpxLl68iPDwcFy%2BfFlj%2BpwFCxbwa6QAfPzxx/Dx8UHPnj3Vy44dO4aoqChs3rxZYDLxOnbsiOPHj6tHCIDSc1hdXV3LPceVqDJY3MnQ5cuXMWbMGBw4cEBjWouePXvCysoKq1atQr169QQmFOPBgwcICwvD0qVLYWRkpF7%2B3nvvoW3btpgyZYrGTliO7ty5w%2BlzysHpc7RzcnLCmTNn1J0pAHj8%2BDE6dOgg%2B4tN3nnnHaxbt07jIOD8%2BfMICQmR/VXWpBsWdzKlVCrLFCrlnRguFzk5OSxUKonT52jH6XPK8vT0xNixYzXmbouPj0dMTIzszyuLiorCpk2b4OPjA3t7e6SmpiIuLg5BQUEYPny46Hj0CmNxJ0ODBg1CQEAA%2Bvfvz7mUntK6dWt4enpi5MiReOutt0THkRROn6Mdp8%2Bp2MGDBxEcHIw2bdqgQYMGuHXrFq5evYqoqCi4urqKjifctm3bsHv3bmRmZqJBgwbw8fHhJMakMxZ3MrR06VJs374dKpUKvr6%2B8PPzg62trehYwh0/fhyxsbE4fPgw2rZti4CAAPTq1UtjOEmupk2bhqysLBQWFiI/Px8ODg4ICwtTT5/TsGFDWU6f88EHH6B169Z48OABEhMTMX78eI3pcy5duiTLq4if9fvvvyMpKQlZWVmoX78%2BvLy80KhRI9GxXgljxozB2rVrRcegVwyLO5kqLi7Gvn37EBsbi/Pnz6NPnz4YOXIk2rRpIzqacJmZmdi6dSu2bdsGY2NjDBs2DD4%2BPrKes4zT52jH6XNejvbt28tyEuzK4Lahf4ItCZkyNjaGh4cHNm3ahJ07d6J27doYNWoUfH19kZiYKDqeUPXq1UNQUBAOHz6MqVOn4sSJE%2BjRowfCw8NFRxPmyfQ5jRo14vQ5T%2BH0OS8HewxELxeLO8Kbb76J8PBwHD16FE2bNsWUKVNER5IEIyMj9O3bFzNmzECvXr0QHx8vOpIwLVu2VM9lt2XLFvW5mnfu3MGsWbNke%2B5Ut27dMHXqVBQVFSEkJES9Xfbv34%2BPPvoIXl5eghO%2BGviVW0QvF4s7QmpqKpYuXYq%2Bffvi3LlzlZpZXt8pFArs2LED7733Hnx8fGBmZibrCWmnT5%2BOmJiYMh06X19f3LhxAzNnzhSUTKzw8HAYGxtrTJ0DAN988w26dOmCkJAQQcmISM54zp1MKZVKHDhwAPHx8fjll1/QpUsXjBgxQvbf/3jp0iXExcVhz549qF27Nvz8/ODj46P1O1XlhtPnlMXpc14OnldWPm4b%2Bid4QogMLVq0CLt27YJSqcTAgQMxZ84c2U/XAADe3t64fPkyXFxcEBkZiZ49e/JK2b%2BUN32OnAs7AHBzc%2BP0OUQkOfzkkqEjR45gwoQJOHr0KMLCwljY/aVVq1bYvXs3oqOj0bt3bxZ2T%2BnevTuWLFmCHj16YPny5cjOzhYdSRKioqKQl5cHHx8fDB8%2BHPv370dJSYnoWKRHOLhG/wSHZWXu8ePHOHv2LLKzs9GgQQM4OTmJjiRcenp6pR5nb29fxUmkhdPnlI/T5%2BjGyclJtl9FVtE%2BeP/%2B/Xj33XcFpaNXFYs7Gbt%2B/To%2B/vhj3L59G1ZWVsjNzUWzZs2wbt061K9fX3Q8YRwdHdVX76lUqjJX8j1ZdvnyZRHxJCElJQXfffcddu/ejTfeeAMjR47krPoo/aA%2BcOAA4uLicP78eXh6emLevHmiYwlVmYOlu3fvonXr1tWQRlq4D6aqwuJOxvz9/dGsWTOEhobC3Nwc%2Bfn5iIyMxJ07d2Q9q358fDwSExMxffp0NGvWDLdv38bixYvRtGlT%2BPn5qR8n9/PNACA/Px8RERH4/vvvZV3sPislJQVr165FQkKC7LcLD5bKx30wVRUWdzLWvn17nDx5UuME%2BcLCQrzzzjv49ddfBSYTq3v37vj%2B%2B%2B9hbW2tXpaXl4cBAwbg0KFDApNJR2pqKrZt24YdO3agZs2aGD58OEaOHCk6llAKhQJJSUmIi4vDb7/9Bk9PTwwfPhyOjo6iownFg6XycR9MVYVXy8qYnZ0d/vjjD7Ro0UK97Ml5H3JWUFBQ5iTmvLw8KJVKQYmkQdv0OfPnz%2Bf0OVqmz4mKiuL0OX9ZsWKFxsFS48aNsWjRIgwYMED2E6ZzH0xVhcWdjHl6emLMmDEIDAxE48aNkZmZiW%2B%2B%2BQYdOnTArl271I8bOHCgwJTVr1evXhg/fjyCgoLQoEEDpKam4ssvv8SgQYNERxOG0%2Bdox%2BlzKsaDpfJxH0xVhcOyMubu7l7hYwwMDHDw4MFqSCMdeXl5mDt3Lvbu3QulUokaNWpg2LBhCAkJke13hfbr1w8jRozAwIEDYWFhITqOZISHh8Pf3x8ODg6io0jW9OnTcfPmzTIHS127dsWkSZNExxOK%2B2CqKizuiMpRVFSEhw8fwsbGRnQUSeH0OWVx%2Bpzy8WCJqPqxuJO5X3/9FWlpaWWGTeQ%2BDHD9%2BnVs2bIFGRkZiIiIQGJiIkaMGCE6lnCcukE7XhFaMR4sacd9MFUFFncyNnv2bGzbtg12dnYaH0ZyHwY4fvw4Jk6ciB49euDw4cNITEyEt7c3PvjgA4wZM0Z0PKE4dYN2vCL0%2BXiwpB33wVRlVCRbHTp0UF28eFF0DMnx9vZWHTlyRKVSlW4jlUqlSk5OVrm7u4uMJQlOTk6qR48eaSwrKChQOTs7C0okDe%2B8844qJydHY9nDhw9VPXr0EJRIOo4dO6ZycnJSTZo0SeXk5KRKT09XderUSbVmzRrR0YTjPpiqCi/rkrFatWqhefPmomNIzs2bN9G9e3cAUB9Nt27dGvfv3xcZSxKeTN3wNE7dwCtCn%2BeLL77A0qVL8fnnn8PIyAgNGjTA2rVrsXXrVtHRhOM%2BmKoKz2aVsbFjx2LmzJkIDAxE7dq1NdbJ8cTvJ%2Bzt7XH27Fk4Ozurl128eFH2BQzAqRvKw%2BlzyseDpfJxH0xVhcWdjD169AhJSUnYs2ePepmKJ37jo48%2BwtixY%2BHn5welUol169Zh06ZNsp%2B2AQB27NgBIyMjbNy4UWP5iRMncOLECQClH%2BByK%2B7CwsIwd%2B5cjBkzRuOK0KCgINHRhOPBUvm4D6aqwgsqZKxLly6YOHEiunXrVmbiVbme%2BP3E0aNHERMTg7S0NNSvXx9Dhw5Fnz59RMciieMVoWUlJiZi7ty58PPzQ3R0NMaNG6c%2BWJLbQcCzuA%2BmqsLiTsZcXV1x6tQp0TEkJyIiAiEhIbC0tBQdRZI4dYN2vCK0fDxY0o77YKoqLO5kbNGiRWjQoIHsv/D9WR07dsSJEyc4waoWnLpBO06fUz4eLJWP%2B2CqKizuZGz48OE4c%2BYMLCwsUKdOHY0Pazl/UC9atAj5%2BfkYNGhQmSJG7ic5u7i4YMOGDWjVqpXoKJIyePBgBAUFwc3NDS4uLjh9%2BjQuXryITz75RNb/lwAeLD0P98FUVfi/TcaGDBmCIUOGlFleXFwsII14R48ehZubGzZs2AAAiIuL01jPk5w5dUN5eEVo%2BQYPHox58%2BbxYEkL7oOpqrC4k7H27dtj1apVyMzMRElJCQBAqVTijz/%2BgI%2BPj%2BB01W/KlCk4ffo0DA0N8d///rfMOWXEqRvKwytCy%2BLBUsW4D6aqwuJOxsLCwqBSqWBtbY2cnBy89dZb2LVrF95//33R0YQwMTHBggULYGRkhB07dmh9zIQJE6o5lbRw6gbtOH1OWTxYqhj3wVRVWNzJ2MWLF3H06FGkp6fjyy%2B/RFhYGLp37441a9bIsoiZNWsW4uPjUVJSovUKtme/DF6OVq1ahbCwMK1TN8iZh4cHLC0tERMTA3t7e5w8eRIzZ86U9RWhPFiqGPfBVFVY3MlYzZo1UadOHRgbG%2BPq1asAgO7du2PatGmCk4nRr18/9OvXDz4%2BPti0aZPoOJL0%2BPFj%2BPn5iY4hOU%2BuCHVzcxMdRTJ4sFQx7oOpqrC4k7HXX39dfV5MSUkJUlNTYWpqKvuTeePj40VHkCxvb29ER0dz6oZnJCQkIDQ0VHQMSeHBUsW4D6aqwqlQZOzQoUMICQnBnj17kJCQgJiYGBgZGaFLly5YuHCh6HgkQZy6QTtOn0P/BPfBVFVY3MlcZmYmbGxsYGxsjKSkJOTl5WHgwIEwNTUVHY0kaOfOnVqXFxcXy/LqviddF0dHR63r5X6hCVWM%2B2CqCizuiKjSbt68We7UDSdPnhScrvo9mbD47bffLveKUH5HKBFVN55zR0SVxqkbNPGKUCKSIhZ3RFRpnLpBE68IJSIp4rAsEVValy5dcOLECeTn58PT0xOHDx8GAHTu3Bk///yz4HTi%2BPj48CprIpIMzkJKRJX2ZOoGCwsL9dQNmZmZsp%2B6gYUdEUkJh2WJqNLGjBmDoKAg7NmzB76%2BvnjvvfdgZGSEnj17io5GRER/4bAsEb0QTt1ARCRtLO6IiIiI9AjPuSMiIiLSIyzuiIiIiPQIizsiIiIiPcLijoiIiEiPsLgjIiIi0iMs7oiIiIj0CIs7IiIiIj3y/wFMH8rvva2W6QAAAABJRU5ErkJggg%3D%3D\" class=\"center-img\">\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Sample</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n",
       "        <table border=\"1\" class=\"dataframe sample\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timeON</th>\n",
       "      <th>ampV3ON</th>\n",
       "      <th>freqV3ON</th>\n",
       "      <th>timeOFF</th>\n",
       "      <th>ampV3OFF</th>\n",
       "      <th>freqV3OFF</th>\n",
       "      <th>freq_delta</th>\n",
       "      <th>amp_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.02944</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02372</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.03157</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.02350</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.09325</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.07258</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.10050</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.14007</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-07-31 11:08:39</td>\n",
       "      <td>0.05106</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2019-07-31 08:38:38</td>\n",
       "      <td>0.05014</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "    </div>\n",
       "</div>\n",
       "</div>"
      ],
      "text/plain": [
       "<pandas_profiling.ProfileReport at 0x24d17353828>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_profiling.ProfileReport(C0V3) # see if this works, might provide warnings & labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C0V3[['freqV3ON', 'freqV3OFF', 'freq_delta']].plot(figsize=(30,10))\n",
    "plt.legend(fontsize = 20)\n",
    "plt.tick_params(labelsize = 20)\n",
    "# double click image to zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wmD79byxYMJ8lSxaxePH7hIWF07lzF66//uZa9+C64457SE5uy9tvv8GiRe+RmJjEH/4wjc8+W0lq6qp6zzNy5I/45z9nUlpaypgx4+vt17VrN1566TVefvkF1q79mqKiVBITk7nttru4+eafER4eXuuY8eMncNFFF/Puu++wdu03bNy4AafTSdu27bj33l8ybtyPiYmJ/d6fzc9+dgcrVy7nwIH0732siIiIiIiIiIiINB+WZWJmpeHL2II/Ywv+I7sD9+9yhuNI7oW931hscUnBEMwIj8aw2Rt72iJNhmFZltXYkzjVcnI8mGaTe1tNwsMPP0hq6irmz19EUlJyY09HmoGEBDdZuvGliFSj64KI1KTrgohUp2uCiNSk64L8UGZxHv6MLfgObMafsRWrzAOArVUnHO36YG/fF3ubrhg21bCcC3RNOLvZbAbx8dH1tutPmYiIiIiIiIiIiIjIKWL5vfgzd1WEYFswcw8AYETEYO9wfiAIa9cHW0RMI89UpPlRKCYiIiIiIiIiIiIicpIsy8LKP4IvY3NgWcRD28FXDjY79jbdcV1yHY52fbDFt8cwbI09XZFzmtfnJ89TTr6nnDxPGcc8ZcHtPE8ZDoeNx38+pN7jFYqJiIiIiIiIiIiIiHwPVnkJvoPb8FcEYVZhNgBGTBucKUNxtO+DPbkXhjO8kWcqcm7w+kzyPWXkFZWTV1hWEXKVB/ZVbOd5yigq9dU61m4ziIt2ERcdRmLLqOOeR6GYnFFPPPF0Y09BRERERERERERE5HuxLBMze39wSUT/kd1gmeAMx5HcC/v5Y3C074stpnVjT1XkrOL1meQXVQ+4qqq6KrfzPeV4Sry1jrXbDGIrwq42LSNJ6RBHXHRYMACr3I6KcGIzDCBwT7HjUSgmIiIiIiIiIiIiIlKDWZyHP2MLvgNb8B/cilVaCICtVUdc/cZib9cHe5tuGHb9ml2aH5/frLZsYVXQlV8j9Kor7LIZVWFX67gIUtrFBYOu2MrQyx1GdLWw61TRn1YRERERERERERERafYsvxd/5q5AEJaxGTPnAABGRAz2dn1wtO+LvW1vbJGxjTxTkdPH5zcpKCqvda+uvMJy8ooqnj1lJwi7XCTERdCtWthVvborOvLUh10NpVBMRERERERERERERJody7KwCo7gOxAIwfyHvgNfGRh27IndcV0yCUe7vtji22MYtsaersgPYlkWJWU+cgvLOFbxyC0o5VhhaKVXYXHdYVdMlJO46DBaxYbTrW1MIOByhxEb5QpuuyOcJ1y+sLEpFBMRERERERERERGRZsEqL8F3aBv+A1vwZWzBKswCwIhpjTNlMI52fbEn98RwRTTyTEUazrIsikp9FWFXKbkFZRXhV2lVAFZYRlm5P+Q4A4iJCixVGB8TTpfkmGBVV2x0GC0qtt2RrrM+7GqoRg3F9uzZw2OPPcbGjRuJi4vj5ptv5s4772zMKYmIiIiIiIiIiIhIE2FZJmZ2eqAS7MBm/Ef2gOUHRxj25F44zh+No31fbDGtG3uqInWyLAtPiZfcgrKq0KuOaq9ynxlynGFAXHQYLdxhtG0VRZ/O8bRwh9EyJrCvhTuwlKHD3ryqIBstFPN6vdx1110MHDiQRx55hL179/Lggw/SunVrfvzjHzfWtERERERERERERETkHGYW5+HP2BoIwjK2YpUWAmCL74ir32js7fpgb9Mdw66F1KRxmZZFYbE3UNFVUd2VW1ndVVBV4eXzhwZeNsOghdtFC3c4Hdq46detFS3dYbSMCQ8GXrHRLuy25hV4NUSj/ak/cuQI559/PtOmTSM8PJyOHTty2WWXsWbNGoViIiIiIiIiIiIiItIglt+H/8gu/Ac248vYgpmTDoAR7sberg+O9n2xt+2NLTK2kWcqzYlpWhQUlweruapXdx2reJ3nKcPnt0KOs9uMYLDVKcnNRT0SAhVe7jBauAOhV2xU01nO8ExrtFCsXbt2zJ49GwiU/61bt441a9Ywbdq0xpqSiIiIiIiIiIiISJNnWRZYZtXDDDxb1barHhaY/kBbsN0KLEFomlX7K8c0/TXGquob3F+jL5aJZVbOyV9tDla1sfzV5lM1llVSgP/wDvCVgWHHntgN14BJONr3wRbfAcNQpYyceqZpkecpC7lfV+X9uwLLHJaS5ynHb4YGXg67URFwhdOtXWxwuzIEaxkTjjvSic1Q4HW6nBX1ocOGDePo0aOMGDGCUaNGNfZ0RERERERERERERJoMs6QA364v8O5cjXnsYCBYOhcYdrAZYNgCD5s9EHIZVfsMVzjOlMGBirDkXhiuiMaetTQhlmWRU1DK/sxC0jIL2Z9ZSGZuMbkFZZg1/hw5HbaKaq4wUtq3oGVMaHVXi5gw3BFODAVejeqsCMWee+45jh49yl/+8heeeOIJ/vSnPzX2lJqNJUsW89prr3LkSCbR0W6ee+5l2rfvcEbOvWXLJqZOvZ3u3VOYO/et4/adP/9t/vGPv3P77Xdz++13A5CZmcnLLz/PunVryc/Po2PHzlxzzSTGjftJvReWtLR9zJ//X779dg1ZWUeJiIggObkdV1wxknHjJhAdHV3rmPvuu5sNG9Ydd37R0dF89NGnALzyyr+YO/elE77/uXPfpHv3HifsJyIiIiIiIiIi8n1Zpg9f+iZ8O1LxpW8Cy48toQuufleDzV4RMlUES8HQqTJwsoOt2n7DqDqmsn8wrLIHxwm0hfaloq9Rq2/geCOkrxE6J5EzyLIscvJLA+HXkaoQzFPiBQL38UpuFUXfbq2ICnMEA7DKCq+ocIcCr3PAWRGK9e3bF4DS0lJ+//vf87vf/Q6Xy9XIs2r60tL2MWPGY0RGRnHNNZMwDBuJiUln7Px9+pxP+/Yd2LVrJ+np++nQoWO9fZct+wjDMBg9+moAjh49wt13/4z8/DyGD7+ShITWfPPNl/ztb9PZuXMHDz74+1pjvPXW6/zrX88AcPHFAxk2bDjFxSVs3ryRZ56ZzX//+waPP/4Uffr0rXMO1113Y52hGYDLFVZr39Chl9OtW0q976lly/h620RERERERERERE6GPzcD745UfLu/xCopwIiIwdn3KpwpQ7G3bNvY0xM5K1iWRVZ+oAIs8CggLbOQolIfELivV3KrKC7o3opOiW46JrppnxCNy2knIcFNVlZhI78DOVmNFoodOXKELVu2cOWVVwb3de3aFa/Xi8fjoWXLlo01tWZj164dmKbJxInXcffd9zbKHEaNGsvLL7/AJ598zG233VVnn4MHM9i2bQsXXHARycmBv7ife24Oubk5zJjxNEOGXA6Az3cfDzwwlffem88111xLly7dgmO8++48nnvuH3Tq1IXHHptB585dQs6xbNlHPPHEYzzwwFRefvk1unTpWmsekyffSFJScoPf29Chwxk7dnyD+4uIiIiIiIiIiJwMq6wI7+6vAssjZu0Dw46j4wU4ewzB3r4vhu2sqI0QaRSWZZGVVxKs/ErLLCT9SGgA1jYhiv49EuiYGEOnRDftEqJwOuyNPHM5HRrtarhnzx7uv/9+UlNTiY8PVMxs3bqVli1bKhA7Q7zeQNlnXFxco81h9OhxvPLKv/jkk2X1hmIff/w/gGDAZFkW2dlZ9Ox5XjAQA3A4HIwYMZJNmzawdeuWYCiWnZ3Ns8/OJjY2ltmzn6VVq4Ra57jqqtGYpsljj/1/PPHEI7z00mun+q2KiIiIiIiIiIicMpZp4j+4NVAVtn8d+H3YWrYnbNCNOLoNwhYR09hTFDnjTMsi61hJyPKH+zMLKS6rCsDaJUTTv0frYAVYu4RonA4t19lcnFQoduTIEcaOHcv999/PlClTarX7fD7eeOMN5s2bR0ZGBgkJCUycOJG7774bp9MJwIABA+jatSsPPfQQDz30EOnp6Tz99NNMnTr1B70haZhJk8aTmXkYgDlzZjJnzkzGjBnH//73AVOm3InHU8gHHywkLCyMBx98mCuuGIllWSxc+C6LFr1HWloaLpeLfv0u4I477iElpWfI+KZp8s47b7F48XtkZmbSrl07br/9bvbs2c3cuS8xf/4ikpKSSUxM5MIL+7Nu3Vr27NlN167das11+fKlREREMHx4oKrQMAyeeebFOt/X/v1pALRoURWsLlz4LuXl5dx00611BmKVRo0ay1tvvc727dv47rvt9OzZ63t9piIiIiIiIiIiIqebmZ+Jd8dqvLu+wCrKhbAonD0vx9ljKLb4jrqnkTQbpmVx9FgJaZkFVcsgHvFQUhGAOeyBAOySXq3pkOimU6Kbtq0UgDV33zsUKyoq4v7778fj8dTb59FHH+Wdd96hf//+XHHFFaxbt445c+awY8cO5syZA4DT6eRf//oXjz76KNdddx1RUVH87Gc/49Zbbz35dyMNNnnyjaxf/y2pqau45JJB9O7dh6SkZP73vw9YtOg9ACZMmER6ehq9e/cBYPr0aSxduoTOnbswYcJESkpKWblyGVOn3sFTT82mf/8BwfEfffTPLF++lE6dOnPNNddy8GAGf/7zQ3UGTaNHX826dWv55JOPa4Vi3323nf370xg9+moiIyPrfC+maZKdncWSJYtZtGgBKSk9uPTSy4Lta9d+AxCyrz7Dhg1nz55dpKZ+qlBMRERERERERETOClZ5Cb69a/DuXI0/cycYBvZ2fXEOugFHxwsx7M7GnqLIaWVaFkdyi4PLH+7PLCT9aCElZX4AHHYb7VtHMfC8NoEKsDZu2iZE4bArAJNQ3ysUO3jwIPfffz9bt26tt8+6det45513GDVqFP/4xz8wDAPLsnjooYd4//33WblyJSNGjAAgOTmZF1544Ye9AzkpkyffRHS0m9TUVVx66SAmT76Jw4cPAXDsWC6vvvom3bunBPuvWLGcpUuXcNVVo/njH/+CwxH40bnllinceeetTJ8+jXnzFuJ0Ovn66y9ZvnwpAwYMZMaMmYSFhQEwb95bzJkzs9Zchg+/klmznmTFimW17m22bFno0ol1efzxaSxdGujXoUNH/v73OcH5ARw4sD/YdiKdOnUG4NChg7Xa5s37L9HR0bX2u91uJk++qdb+1NRPg59pTZMn34Tb7T7hfEREREREREREpHmyLAv/4R14d6bi27sWfGUYsYm4LpmEs/tgbFEtGnuKIqeFaVpkhgRgBew/6qGsPBCAOR022reO5tLzEulYUQGW3EoBmDRMg0Oxf//738yZM4fS0lIuvfRSvvrqqzr7vfnmmwDcd999wVJdwzD49a9/zcKFC5k/f34wFDtbeXd+jnfHZ409jeNy9hiGM2XwaRm7Xbv2IYEYwAcfLATggQceDAmckpPbMmHCtbz22qusWfM1l102hE8++RiAu+76eTAQA7juuht5//13SU/fHzJ2ZGQkw4aNYOnSJSHLFpqmyfLlH5OYmMSFF/avd76dO3flxhtvYc+eXXzzzVfce++dzJr1LElJyQDBqsbIyKgTvveYmMBay/n5ebXa5s//b53HJCYm1ROKrSI1dVWdx4wdO16hmIiIiIiIiIiI1GJ6cvDuXI13x2qswixwhuPsNhBnylBsbbppeURpUkzT4nBuMfszC6oqwI54KPNWBWAdWkczuE9lABZDUnykAjA5aQ0OxV577TXatm3LI488QlpaWr2h2Nq1a2nRogUpKaGhSps2bejUqRNr1qz5YTNugPj42tU830fh4XAKnSd1u7Uzxu0Ox53ww0IVtzscgKioMBIS3JSVBUKjjh07kFBj7F27viMsLIylSxfWGufIkUBV1aFDaSQkjGH//r3Y7XYGDx4QEqABDBp0Kenp+2nZMirkHDfccB1Lly7hyy8/ZejQSwD48ssvycnJ5t5776V16/pvDPqrX90f3H7zzTd59NFHeeaZp3nxxcB9x2JjY8nJycHtdtZZ6VWdyxX4UtGmTUJwfi5X4D188skntGvX7rjHQ+DzBHjiiSeYOHHiCfvL6Vfz51lERNcFEalJ1wURqU7XBBGp6XRfF0xvGcU7vqFw0wpK9m0GLMI79sE94gaielyKzRV+Ws8vcib4/SYZRz3sOZjH7ox8dh/IY9+hfEorKsBcTjtdkmO46pIOdG0XR7f2cbRvHY39LAzA9F3h3NXg5O2CKHMAACAASURBVOeRRx7hsssuw263k5aWVmef8vJyMjMz6devX53tbdu2Zd++feTm5tKyZcuTmnBD5OR4ME3r5AdI6o8zqf7KpLNBKVCaVfiDxigsLAWgqKiMrKxCcnOLADAMO1k1xi4oKMDv9/PMM8/UO97hw1lkZRWSn1+A0+nk2LGSWn1crsB9wXJziwgLqzpH1669ad26DR9+uIQpU6ZiGAbz5r0LwOWX/6jWfOrzox/9mFdfnUtqaiqHDuXidDpJSmpLTk4O69dvO+F9wjZuDCwNGhfXKnjO8nJfnXOuT1FRGRD4fBs6bzl9EhLc+u8gIiF0XRCRmnRdEJHqdE0QkZpO13XBsizMrL14d6Ti3fM1lJdgRMfjuujHOFOGYItJCPwOMN8LeE/5+aX5sCwLywrcl8tvWphmxbMV2K75urJP9f7BfiF9Ksc0K9qpdYzftMjJLyXtSAEHjngo95kAuJw2OrRxM6RvEh0T3XRMdJMUH4ndFhqAVf7O+myi7wpnN5vNOG7hVINDsaFDh56wT15eYMm5+paFq9xfWFh4WkMxOfUiIiKJjIxkwYIPT9jX7XaTkXGAoiIPUVGhP3yVPyM12Ww2Ro0ay+uvz2Xr1s2kpPTks89W0q/fhbRtG1qdVVJSwoYN64iOjqZv39oBbJs2SWRkHKCgIJ/4+FYMHXo5W7ZsIjX10xOGYqtXB5bNHDZs+Anfp4iIiIiIiIiIyPdlFufh2/UF3p2rMY8dArsLR+f+OHsMxZ7cE8M4+6pi5NSxLIvScj8FReXkF5VTWFwe3C4o9lJYXI7XZwZDqfpCqkCIFbgFTejr0P6VrxtTmNNOhzbRDLsgmU6JbjomxpDUMhKbTUuBypl3StcI9PkCFTUul6vO9sr9ZWVlp/K0cgZ069adjRvXk5OTTXx8q5C2zz9PZevWzYwYMZLu3VPo0eM8tm/fxpYtmxk4cFBI3x07ttd7jjFjrub11+eyatVK8vLy8Hg8jBkzrlY/j6eQ3/72l6Sk9OTVV98IafP5fKSl7SUqKorY2DgARo8OjPvuu+8wfvwEEhOT6jz/qlUr2LZtCykpPejVq3eDPhcREREREREREZETsfw+fOkb8O5IxX9gM1gmtjbdCBs6BWfXSzAqVleSc5NpWRSX+gLBVvVHcR3bxV68FdVSNUVHOHFHOnE57NhsBnabEXx2OWzYbDbsNgPDIKTNZjOwGdW2a7y2V7wO6d+QPrVeg91mq7WvvnkE5moQ5rQrAJOzxikNxcLDA2vber11l/OWl5cDEBERcSpPK2fAmDHj2LBhHbNmPcm0aY/jdDoByM7O5umnZ5CdncXVV/8YgHHjfsLChe/y4ovP0adP32C12NKlS9i587t6z9GhQyd69erN6tWryM/PIzw8nCuuGFmrX0JCa/r2PZ/NmzexfPlSRo4cBQT+lcVLLz1PTk421147OXg/s/j4Vtx//6954olH+b//u5e//vXvdOnSNWTMVatWMn36NFwuF3/84yM//AMTEREREREREZFmz5+TjndHKr7dX2GVFmJExuE6fzSOHkOwxyU39vTkOPymSWGxt0a45a1d4VVcjqfYW2c1ls0wcEc5iYl0ERPlIrFlFLFRgW13pDO4Xfm65tKBInLqndJQLDo6GpvNhsfjqbO9sDCwzmZ9yyvK2Wvs2PGsXv0Zn366gj17rmfgwEH4fH5WrlxGfn4+U6feF1zmsGfPXkyZcidz577ElCk3MXjwULKyjvLZZ5/idsdQWFhQ73nGjBnHzJl/IyvrKJdfPoLIyKg6+/3mN3/gF7+4k0cf/TMrVy4nMTGZzZs3sm3bFnr2PI977rkvpP/VV/8Y0zR5+ukZ3HbbTQwYMJCuXbvj9ZazceMGduzYTnx8PNOmPU7Xrt1O3QcnIiIiIiIiIiLNilXqwbv7S7w7VmPm7AebA0fHCwLLI7brg2GzN/YUmy2vzx8Itoorgq2KwKt6hVdhsZf8onKKSrzUteigw24jNspJTJSLFu4wOiS6A+FWZFXAFRMZaI+KcGIzVCElcjY5paGYy+UiOTmZjIyMOtszMjJo0aIFcXFxp/K0cgYYhsH06X9jwYL5LFmyiMWL3ycsLJzOnbtw/fU317oH1x133ENyclvefvsNFi16j8TEJP7wh2l89tlKUlNX1XuekSN/xD//OZPS0lLGjBlfb7+uXbvx0kuv8fLLL7B27dcUFaWSmJjMbbfdxc03/yxYtVjd+PETuOiii3n33XdYu/YbNm7cgNPppG3bdtx77y8ZN+7HxMTEnvRnJCIiIiIiIiIizZNl+vFnbAlUhe3fAKYPW3xHwi67GWe3QRjh0Y09xXOeZVmBkMoKLFdYuc/ntyqqtrwV9+WqY+nCitclZf46xw532YOhVpuWkXRvHxcMtir3V1Z1hbvsGAq6RM5ZhmVZ3/suewsWLODhhx/m4YcfZsqUKSFtv/vd71i4cCEfffQRnTt3Du4/cuQIl19+OcOHD+eFF174wRM/npwcD2Yj3zxQ6vbwww+SmrqK+fMXkZSkEnE5/RIS3GRlFTb2NETkLKLrgojUpOuCiFSna4KI1HS864KZdxjvjlS8u77AKs7DCHfj6HZpoCosvsMZnmmVMq+fvMIyjlU+PFXbJWU+Kn8lbFqAZWECWFXBk2VZWBaBB6HbFf+r1qf2MWBR+etZ07JqjE3o+Sv7nuD831dUuCMYZrmrVXHFVixVGBPlIjbShTvKRZhT1XvScPqucHaz2Qzi4+v/hwintFIMYMKECSxcuJBZs2Yxe/ZsbDYblmUxc+ZMLMvi+uuvP9WnFBERERERERERETkjrPISvHu+xrtzNeaR3WDYsLfvi7PHLTg69MOwn/JfuVad27IoKvVVBFylVaFXteArr7CMolJfrWMjwuy0cIcTGebAMAIrQxmAYTOwG0ZgH4H9GASX/avcF3JMxf/ZKgqmbBXHGFT2q7FdcRw1zhM6dtUxIeevds66zm+32YIhV2VVlzvSicOu+3OJSG2n/Ap92WWXMXbsWJYsWcL111/PwIEDWb9+PWvXrmXUqFEMHz78VJ9SRERERERERERE5LSxLBP/oe8CyyPu+xb85djikgkbOBlH98uwRf7w28X4TZN8T3mtoCuvsIzcirDrmKcMr88MOc4AYqJcxLnDaB0XQUr7OFpEh9HCHfoId52+sE5E5FxxWq6ETz75JN26deO9997jP//5D8nJyTzwwAPcddddWm9VREREREREREREznqWtwzTk03u9o0UbViBVZgNrgicKYNx9hiCLaFLg3/XWVbur7aEYWlFRVc5uYWl5HkCoVdBUXmtZQIddoO46DBausPolOTmQncrWrjDq8Ku6DBio12qihIRaaCTuqfY2U73FBORSlrjV0Rq0nVBRGrSdUFEqtM1QaTps7xlWMV5mMV5WMV5WEXVtiseZlEeeEsqjjCwtz0PZ4+hODpdhOFwVY1lWXhKvKFLGBZUVXhV7i8uq2s5Qwct3WHEVYRbNSu74txhuCOcKjIQOcvou8LZ7YzfU0xERERERERERETkTAsJu4qOYRXnYxYHnusOu6qxOzAi4zAi47C1aIu9bW+IjKPEFoWZ1It9uTZyj5WRl55eEXyVVlR+lePz17GcYbSLFtFhtG4RQY8OcSGVXXFazlBEpNHoyisiIiIiIiIiIiJnrUDYdQyzOL8i7Kpe2RXYZxbnHyfsaoERGRsMu4yoOGyRcVjhsRQSSU55GFnFNnIKysjOLyU7q4ScglJyC8rwmxawLTicw26jhTsQeHVJjg2p8IpzB5Y5jInScoYiImcrhWIiIiIiIiIiIiJyxlne0mD1VrCSq9qShpWv8ZbWPrgi7LJFxmFr2Q57+76B4KsiADMiW2CGx3CszE5OfinZBaWB52Ol5KSVkp1fyrHCbMwad5aJjXbRKjaczkkxDOgZQXxsOJ3bxWEzTVq4w4jWcoYiIuc0hWIiIiIiIiIiIiJyyvywsMsZWMIwJOwKvA4ubxgVB65IfH6L3IJAwJVTUEp2fgnZByrCr/w08grLqB55GUCcO4xWseF0bx9Lq9hwWsVGEB8TTqvYcFrGhOF02GtNSfcPEhFpOhSKiYiIiIiIiIiIyA9iFh2jfOMSvDs/h/Li2h2qh13x7WuHXRVLGuKKDFZilXv95FRWeOWXkn2olJyCPLLzD5OdX0q+pzzkFIYBLd3hxMeG06tjC1rFhgcDr/i4CFq6w7SsoYhIM6dQTERERERERERERE6KWZBF+cYP8e5YDZaFo+sAbC3bHzfsqlRW7udoQSk5+SVkHy0lpyLsqqz8KigKDb3sNoMWFZVefTvHEx8bXlHtFQi/4hR6iYjICSgUExERERERERERke/FzMukbMNifLu+BMOGs+cwXP3GYHMnBPuUlPkCVV7pJeQUHAssb5hfVfnlKfGGjGm3GcGgq1/X+KrlDSv2xUWHYbPpfl4iInLyFIqJiIiIiIiIiIicwyzLwrQs/H4Lv1nx8Jv4TQtfte2qdhMz2BZ4Xb2t1jjVxo4oPkyn7NUkerbhNxykRfdne/QAio5F419xBK/vcHDJw6JSX8g8HXZboKorNpyOie5qyxsGgq/YaBc2Q6GXiIicPgrFRERERERERERETjHLsigp81NU6g08SnwVz148pT6KSgL7S8v99QdSZvUwq3awVb3v6dbens2PIjZzvusApZaDld6+fOnrQ2lJJPZcLzYjD7vdwOmw0dIdTtfk2GCFV3xsOK1iwnFHKfQSEZHGpVCsmVuyZDGvvfYqR45kEh3t5rnnXqZ9+w6NPa3v7R//eJr58//LnDkvcNFFF3/v4ydNGo/HU8hHH30a3OfxeFi69EOuvfb6UzhTERERERERETmXmKZFcZmvdrBV4qWoWrhVuV0ZeBWX+jCt+sOqMKedqAgHYU47DrsNu80IPpwOG+GVryvb7JXttmrbFa/raHdUP7Zi22YYVW21xq5jHJuBLWcPbP4Q6+AWcEXi6juB6N4j+Ul4ND85g/8dRERETgWFYs1YWto+Zsx4jMjIKK65ZhKGYSMxMamxp3XWuPHGicTHt1IoJiIiIiIiItIE+PwmxaVV4ZanItyqK9iqHoAVl/o4Xh1WRJidqHBn4BHhoGVMOFERTqLCHcF90RGV7U6iwx1EhjtxOmxn7L1/X5Zl4T+0nfJ1i/Af/g4j3I3rkutwnXcFhiuisacnIiJy0hSKNWO7du3ANE0mTryOu+++t7Gnc9Y5diyX+PhWjT0NEREREREREanG6/PjqVaxVRVk1b08YWW4VVrur3dMA4gMd1SEWYHwqk2LyGCoFfpcEXhFOIkMc+Cwn73h1vdlWRb+A5soW78Y88hujMg4wgbdiLPXcAxHWGNPT0RE5AdTKNaMeb1eAOLi4hp5JiIiIiIiIiLSFPn8JmVeP6Vlfkq9fsrK/ZSV+4LbVfsC26WV7eV+ymr0Ka14+PxmveezGUZIiBUXHUa7hOha4VZ0jXArIszRrO91ZVkmvrR1lK9bjJmzHyM6nrAht+JMGYLhcDX29ERERE4ZhWLN1KRJ48nMPAzAnDkzmTNnJmPGjON///uAKVPuxOMp5IMPFhIWFsaDDz7MFVeMxLIsFi58l0WL3iMtLQ2Xy0W/fhdwxx33kJLSM2R80zR55523WLz4PTIzM2nXrh233343e/bsZu7cl5g/fxFJScknNfcPPljI/Plvk5GRTkJCa2644af19s3IOMCrr77ImjVf4/EUkpzcltGjr+bGG2/B4aj7x3/durU88MBUAHbv3smQIRdz2213cccd9wCwadMG3n77TbZu3UR+fj7h4RH07NmLW2+9/aTuZyYiIiIiIiLS2CzLwue3AgFWua8qhKoRWgX2+6oCq4pHmddfLciqCrV8/uMtPBjK6bAR7rIT5rQHnl2B59josKp9Tnu1ii5HxXKEVdvhLjtGMw63vi/L9OPb+w3l6xdjHjuEEduG8MvvwNF9EIZNvzYUEZGmR3+7NVOTJ9/I+vXfkpq6iksuGUTv3n1ISkrmf//7gEWL3gNgwoRJpKen0bt3HwCmT5/G0qVL6Ny5CxMmTKSkpJSVK5cxdeodPPXUbPr3HxAc/9FH/8zy5Uvp1Kkz11xzLQcPZvDnPz9Ez569ftC8X3rpef7zn1dITExi3LifkJOTw6xZT9ZZ7bZjx3f88pdTKSsrY9iwESQmJrFp03r+9a9n2bBhPU8+OQu73V7ruKSkZG677S7mzn2Jli3j+clPJnLhhf0BSE39lD/96ffExbVg6NARREZGsm/fHr766gvWr/+Wl19+je7de/yg9ygiIiIiIiLyQ/n8Jrsy8tl1II/ishqVV9VCq9KKwKvM68dvNjzACnNWhFYVz2EuO1HhDlrGhAX3hbscIX1qBl6BbUfFWDbstqazDOHZzjJ9+HZ+QdmGD7EKjmBr0ZbwK6bi6HIJhv47iIhIE6ZQrJmaPPkmoqPdpKau4tJLBzF58k0cPnwICNxL69VX36R795Rg/xUrlrN06RKuumo0f/zjX4JVVrfcMoU777yV6dOnMW/eQpxOJ19//SXLly9lwICBzJgxk7CwwJrT8+a9xZw5M096zgcOpPPGG/+me/cU5sz5F263G4DPP0/loYd+HdLXsiwef3wa5eVenn/+1ZAw7p//nMk777zFwoULmDjxulrnSUpK5o477gmGYpUVYgDPP/9PoqOjmTv3TVq2jA/uf/PN//D88/9kxYrlCsVERERERESkUeQWlLJpbw6b9+Swbf8xysr9GBAMrcKrBVnuSBcJNfdXBFXBQMsZGmRVhlkup71ZLzV4LrN85Xh3rqZ8w4dYnhxsrToSdtX9ODpdiGEoDBMRkaZPoVgdvj78LV8eXtPY0ziuQUkDGJjU/7SM3a5d+5BADAJLFgI88MCDIcsOJie3ZcKEa3nttVdZs+ZrLrtsCJ988jEAd93182AgBnDddTfy/vvvkp6+/6TmtXLlcvx+P7feenswEAMYPHgol1wyiK+//iK4b+vWLezdu4eJE6+rVZ12550/Z8GC+SxZsrjOUKw+pmlyzz334XI5QwIxIFhJduxY7sm8NREREREREZHvrbIabPPeHDbvzeFgVhEA8TFhDDqvDX27xNOzYwsiwvTrn+bO8pbh3f4p5Zv+h1Wch61NN8KH/Ax7+75ablJERJoVfSuSWuq619fOndtxucJYsGBerbb09DQAdu3awWWXDWHPnt3Y7fZa9xkzDIMLLrjopEOx3bt3AdCz53m12vr2PT8kFNuxYzsABw8e5JVX/lWrf2RkJLt378SyrAZ/+bPZbFx++QgAMjMPs3fvHg4ezCAtbS/r1q0FAsGZiIiIiIiIyOlSVzWY3WaQ0j6OwSOS6Ns1nuT4SAUdAoBVXkL51k/wbl6KVVqIPbkXrivuwZ7UUz8jIiLSLCkUq8PApP6nrQrrXFC9uqtSYWEhfr+fuXNfqve4goICAIqKPDidzpCKskqxsbXv/dVQhYWB8SMjI2u1ud0xIa89nkIAvv76i5CwrKaSkmIiI6MaPIc9e3Yze/ZTrF//LQAOh4NOnbrQs+d5HDiQjmU1fP11ERERERERkRMJVoPtqagGy1Y1mJyYVeqhfMsyyrcsg/Ji7O3PJ+zC8dgTuzf21ERERBqVvjFJg0RERBIZGcmCBR+esK/b7SYj4wBFRR6ioqJD2vLy8k56DpXBl8fjqRWulZQU15ovwEMP/Zlx435y0uesrri4iF/96hd4PB5+8Yv/Y8CAgXTs2Amn08nWrVtYtuyjU3IeERERERERad6OWw3WV9VgUj+zpADvpo8o37YCvKU4OvXHdeF47AmdGntqIiIiZwWFYtIg3bp1Z+PG9eTkZBMf3yqk7fPPU9m6dTMjRoyke/cUevQ4j+3bt7Fly2YGDhwU0rdyWcOT0aNHT1asWMbmzRtp27ZdSNt334WO261b9+D+mqGYz+fj+ef/SVJSEpMm3dDg83/77Rpyc3O48cZbuPHGn4a07d+/D0CVYiIiIiIiIvK9nbAarGs8vTq2INylX+NI3cyiY5RvXIJ3+yowvTi6DMR14TjsLdud+GAREZFmRN+mpEHGjBnHhg3rmDXrSaZNexyn0wlAdnY2Tz89g+zsLK6++scAjBv3ExYufJcXX3yOPn36BqvFli5dws6d3530HK688ke88sqL/PvfrzBgwMBgOLdx43pWr14V0rdfvwtJSmrLhx8uZPTosfTpc36w7Y03/s0777zJmDHjjns+h8OBz+cNvna5AstK5ubmhPTLzMwMLivp8/lO+v2JiIiIiIhI86FqMDkVzMIsyjcswbsjFSwTR/fLCLtgHLa4xMaemoiIyFlJoZg0yNix41m9+jM+/XQFe/Zcz8CBg/D5/KxcuYz8/HymTr0vWL3Vs2cvpky5k7lzX2LKlJsYPHgoWVlH+eyzT3G7Y4L3Bvu+EhOT+MUvfsmsWU9y++03M2zYFRQVeVi5cjlt2iRy8GBGsK/dbudPf3qE3/zmfn7xi7sYOvRykpPbsWPHdr79dg1JSW255577jnu+hITW7N+fxt///gSXXjqY/v0HkJSUzNKlS8jPz6NbtxSOHj1CauoqwsJcGIZBQUH+Sb03ERERERERadp8fpNdB/LYvDe3djVY70T6dmmpajBpMDMvk7INH+Db9QUYNpw9huK6YCw2d0JjT01EROSspm9a0iCGYTB9+t9YsGA+S5YsYvHi9wkLC6dz5y5cf/3NDBs2PKT/HXfcQ3JyW95++w0WLXqPxMQk/vCHaXz22UpSU1fVfZIGuPbaySQktOb11+eyZMkiYmPjuOOOe3A6nfzzn7NC+vbrdwEvvvgf/vOfV/j22zV88cVqEhJaM2nSDdx66220bBl/3HP96le/Y/bsp/jww0X4fD6GDBnGrFnP8vzzc9i0aSMbN66nTZtERo0aw5Qpd/Hb3z7Axo3rKS4uJjIy8qTfo4iIiIiIiDQNqgaTU82fe4Dy9R/g2/sN2Jw4e4/E1W8MtqgWjT01ERGRc4JhNcGbIOXkeDDNJve2moSHH36Q1NRVzJ+/iKSk5MaejjQDCQlusrIKG3saInIW0XVBRGrSdUFEqvsh14T6q8HC6ds1XtVgctL8WWmUr1+EL20dOMNx9b4SZ99R2CJiGntqzYK+K4hIdbomnN1sNoP4+Oh62/UtTERERERERETkJOXkl7J5bw6b99ZdDXZ+13iSVA0mJ8mfuYuy9YvxH9gErkhcF/0EV5+rMMLr/2WfiIiI1E+hmDSaJUsWc/jwoQb1dbvdTJ5802mekYiIiIiIiMjxHa8aTPcGk1PBsiz8h7ZTvn4x/kPbMcLduC6ZhOu8KzFcEY09PRERkXOavqFJo1myZDEbNqxrUN/ExCSFYiIiIiIiItIo6qoGc9gD1WBDzk+ibxdVg8kPZ1kW/gObKFu/GPPIbozIOMIG3Yiz53AMZ1hjT09E5Hs7VprHvoJ0Ihzh9GjRDZtha+wpiSgUkzPriSeeDm4/88yLjTgTERERERERkbpVrwbbtDeHQ6oGk9PIskx8aesoX7cYM2c/RnQ8YUNuxZkyBMPhauzpiYg0iNfv5YDnIHvz97MvP520gnTyyvKD7fHhLRiSfCmXJl9MjMvdiDOV5k7f3kRERERERESkWSst95F+xEPa4QL2HfGwYVdWSDXYUFWDySlmmX7MnAP4D+/Au+MzzGMHMWLaEH75HTi6D8Kw6Vd2InL2siyL3NI80goCAdjegv1kFB7Cb/kBiA9vSbe4znSO7UjnmA5kl+Sy+uBXLNz7Pz7Y9zEXJPRhSNtL6R7XRX+vyhmnv2FFREREREREpNko8/o5cMTDvswC0g4XkpZZQGZOMVZFe+uWkaoGk1PO8pXjz9qH//AO/Jk78R/ZDd5SAGwt2xN+xVQcXS7BsGlpMRE5+5T7vaQXZrAvfz9pBensy99PfnkhAC6bk44x7bmi/dBACBbboVYlWMeY9vRv04/MoqN8fuhrvjq8lm+PbqRNZAJD2l7KwMT+RDkjG+OtSTNkWJZlnbjbuSUnx4NpNrm3JSInISHBTVZWYWNPQ0TOIrouiEhNui6INF1en58DR4tIqxaAHcwuovI3IbFRLjonxdAp0U2nJDcdE2Po1ile1wT5wazyEvxHduE/vDMQgh3dC6YPAFuLdtiTUrAnpmBP6oEtqkUjz1ZORN8VpDkJVIEdY1/+fvYWpJOWn84Bz0FMywSgVUQ8nWMC4Vfn2A60jUrCbrN/r3OU+72sP7qJ1INfsa9gP06bg4ta92No20vpFNPhrK8e0zXh7GazGcTHR9fbrn/uJCIiIiIiIiLnPJ/fJCPLEwy/0jILOZhVhL/iH826I510Sozhwu4JdEpy0ykxhv+fvfuObus+84T/vfeiA0QHCPbei0RSEtW73GM7jtPrpE123pnJ5N3d2Zk5Z9+/diY57zn77mSyUzbZZDaJnTixEsd2LBfJRZZsS7IoiV0kVVjETrCTIFHuff8AxGbZlmWRFyC/n3N4QFxcAA9VLoH7xfP8HEl6laum9UIOTEbDr1gnmOzvBhQFEESI7mxoyw9D4yuC5CuAYHj/E3VERGstGAmie6oX1ye6ol+T3Zhc0QV2OHMfcm1ZyLZmIkn38Y9hOkmL2pQa1KbU4MZUH071ncG7AxdwdqAOaZYU7Enbjq3JVTBoDB/7uYhWYqcYEa1r/OQGEa3E4wIRrcTjAlHiCUdk9I3MoHNgKvrVP4kbw9MIR6LnAswGDbJvdoD5rMhJSYIjSX9bnzznMYFuhzw1sjwEG++P3iBpISXnL3SBSd48CFqGr4mOxwVaLxRFgX9uFNeWjEG8Md2/0AXmMbpi64BlIceWhVRz8kfuArtTc+E5vDt4Cad630HvdD/0kg5bfdXYk7od6Umpa1LD7eIxIb6xU4yIiIiIiIiIEpYsK+jzzyx0gHUNTKF7aBqhUC5T2AAAIABJREFUcPQEnlGvQbYvCUe2ZCwEYW6bIe5HL1HiUBQF8nj/4npg/W1QZkajN+qMkHyF0BXuhialCKI7G4LE021EFB/mI0F0T/bg+kQ3rsdCsKnQNABAL+mQZc3Ekcz90VGI1ixYdGbVajVoDNiTth27U2vROdmD071ncLb/PE73nkGONRO707aj2rsJOkmrWo20PrBTjIjWNX5yg4hW4nGBiFbicYEofsiKgsHRWXT2T+F6bARi9+AUgqFoAKbXSchOTloYf5idkgSP3QjxLgZgPCaQIkcg+7tj64G1ITLQAWUu+m9CMNpi64EVQUophOhIhyCKKldMq43HBUoEiqJgJDCK65OLYxB7l3SBeU3uxbXArFlItfggCvF9/JoNzeLswAWc6j2DwdkhmDRG1KbUYHfqdvjMXtXq4jEhvrFTjIiIiIiIiIjijqwoGB4L4Hqs+6uzfwpdg1OYC0YAADqtiMzkJOzdlIqcWACW7DTd1QCMCACUcBCR4euLnWCDV4DQHABASPJAytwETWwcomD1sguRiOLCXHge3VM3YgFYF65PdGM6NAMg2gWWbc3EPVkHkGPNRLYtExatel1gd8qkNeFAxm7sT9+FK+PXcKr3DN688Q5e7zmNAnsu9qRtxyZPOTQiYw66ffzXQkRERERERESrSlEUjEzMLaz/dXMtsMB8GACg1YjI9FqwqzwF2SlJyPIlIcVlgsQOHFoFSnAWkYEr0QBsoB2RoWuAHP23KDrToS3YubAmmGh2qFwtEVH09+hwYGTZGMTe6X4oiE5LSzZ5UO4qiXaB2bKQYk6O+y6wj0IQBBQ48lDgyMNUcBrv9L2L031n8LPmXyFJa8GO1K3YlVoLt9GpdqmUABiKEREREREREdFdoygKRifnY8HX5EIQNjMXDR0kUUCG14La0mRk+5KQ7UtCqtsMjbR+Tt5RfJEDk0vWA2uHPNoNKAogSBA9WdCWH4YmpQhScgEEw/uPWyIiWitz4Xl0TfYsG4U4E5oFABgkA7KtGbgv+yBybFnItmbCrDWpXPHaSdJZcE/2ARzO2ofW0Q6c7j2D411v4HjXGyhxFWJP6naUuYohiZLapVKcYihGRERERERERHdsbGo+Gn71Ty0EYVOzIQDRACzNY0ZNkWdhDbA0twVaDQMwWh2KokCZHllcD6y/HfLEQPRGSQcpOQ+6qochpRRB8uZB0OrVLZiIaIn+mUE82XoUnZPdC11gPpMXle4y5FijXWA+s3dddYHdKVEQUeYqQpmrCGNz43i77xze6juH/9X4c9j1NuxK3Yadqdtg19vULpXiDEMxIiIiIiIiIrptsqyg48Y46tqHcaljBCMTsbWXBCDNbcamPDeyU5KQ7bMiw2uGVsNPatPqURQF8njfsk4wZWY0eqPOBMlXAF3RXmhSCiG6syFIPBVGRPHp4lAjftn6G+hEHe7LPoQcWxZyrBkwbaAusDvlMNjxYO49uC/7EJr8rTjVewYvXD+OFztfRYW7FHtSt6PImc8wkQAwFCMiIiIiIiKiDxEKy2jtGkVd2zAuXRnB1GwIGklEWbYDR7ZkICfFioxkC/RaBmC0uhQ5DHmke3E9sP52KPPTAADBaIt2gN1cD8yZBoEnQIkozkXkCJ6/9jKOd7+BHGsmvlH+JTgMdrXLSkiSKGGTpxybPOUYnvXjrb6zeKf/XdQPN8FtcGJ32nZsT9mCJB1H5W5kDMWIiIiIiIiI6D0C82E0XvPjQvswGq76MReMwKCTUJnnQk2RF+U5Thj1PK1AUYosA+F5KJFQ9DIcBMLBFZdLtkdW3j4PhEOxy+D77DcPRMILzylYvZCyNkfXA/MVQrB6IQiCin8KREQfzXRwBj9rfhJtY1ewO207Hi94GFqRv1vvBo/JhUfzH8CDufegfqgRp/rO4A9Xj+GP117GZm8F9qTtQJ4tm783NiD+DyMiIiIiIiIiAMDUbBCXOkZwoX0YzZ1jCEdkJJm02FbiRXWhFyVZDq4HlmAUObIkdApCCa8MreaBSOi9odWtwqzILUKr2CXk8IcX8x4CoNFB0OhWXOohaA0QjFZAo49t10LQ6AGNDqI9BZKvEKLZcdf/vIiI1kr35A38uPEXmApN44vFn8bO1K1ql7QuaUUNtviqsMVXhf6ZQZzuPYOzA3U4P3gJPnMy9qRuxzZfNUxao9ql0hoRFEVR1C7ibvP7pyHL6+7HIqI74PEkYXh4Su0yiCiO8LhARCvxuEAb3ejkHOrah3GxfRhtPeNQFMBlNaC60IOaIg/y02wQxY3zKepEOiYoigwlMAllehTytB/KtB/y9Gj0ciZ6qQQmP/oDC8KSMEoHQXpvaBUNqXSL+0krwy09BI12+eOs2A+Slp/Qp4SQSMcFSgzv9L2Lp9qfQZLWgm9VfBlZ1gy1S9pQgpEg6gbrcarvDLome6AVtdiSvBl70rbf1t8FjwnxTRQFuFzvPyKTnWJEREREREREG0y/fwZ1bcO40D6MzoHoSZ00txkP7shGTaEHmckWhhVxQAnOLoZcC5d+KDOj0eszo4AcWX4njR6ixQXB4oTkyoBgckDQGW/RkaWHIN0itNLoAFHDv38iolUQlsN4uuM5nO49gyJHPv6k7Atc30oFOkmHHalbsSN1K7qnbuB07xm8O3gJ7/S/i8ykNOxO244a72YYNHq1S6VVwE4xIlrX+MkNIlqJxwUiWonHBdoIFEVB58AULrRHg7B+/ywAIDfViupCD6oLPfA5TSpXGR/W6pigRMKxcMu/2Ol1M+yKXUcosPxOggjB7FgIvUSzE4LFtXjd4gJ0JgZaRHcZXyvQ3TA+P4H/3fhLXJ/sxpHM/fhE7r2QREntsigmEA7g3YGLONV7Bn0zAzBIBmzzVWN3Wi3SLCnL9uUxIb6xU4yIiIiIiIhoA4rIMjp6JqJBWMcwRifnIQoCijLtOFidjqoCN5xWg9plrkvvGWu4EHYtdnxFxxou/0CvYEiKhls2L6TU4mVhl2BxQTDaIIhc042IKNF0jF3DT5ueQFAO4hvlX0K1t1LtkmgFo8aIvek7sSdtB65PduFU7xm83X8Ob/a+jVxbNvakbUeVpwJaSat2qfQxsVOMiNY1fnKDiFbicYGIVuJxgdaTUDiC5s4xXGgfxqWOEUwHQtBqRJRlO1Fd6MHmAjcsRp7M+SC3c0xQgoH3rN21fLzhGCCHl99Jo4uGW+bFkEu0xDq9zE4IFmd0fCERxR2+VqA7pSgK3rjxFn5/5Y9wG534dsVXkWJOVrssuk3ToRmc7a/D6d4zGAqMwKwxYXvKFnyi4iC0c+ywj1fsFCMiIiIiIiJaxwLzYTRe86OubRgN1/yYD0Zg1EvYlOdGdaEH5blOGHR8+3+7lEgI8uTwkrBr5XhDPxB8n7GGZickb14s7HIuC8GgN3OsIRHRBjIfCeJXl4/i/OAlVLrL8JXSz8CoMapdFn0EFq0ZhzL34kDGbrSPXcXp3jN4/cZpvNrzJnJtWaj11aDauwkmLf9eEwk7xYhoXeOnuYhoJR4XiGglHhcoEU3OBnGpYwQX2ofR0jmKcESB1aRFVWx9sJIsBzQSx+x9EEUOQ54YhDx6Y+ErMnoDytQI3jPWUG9Z0tnlXNHx5YRgskPgujBE6xZfK9BHNTzrx48bf47+mUE8lHsv7snaD1Hg7+X1YGJ+Ck1TjXjtytsYmB2CRtSg0l2KWl8NSpyFXCcuDrBTjIiIiIiIiGgd8E/MRdcHax9G+41xKArgthlwsDod1YUe5KfZIIrsRFpJUZRoh1cs9FoIwcb7F0ccCiJEmw+SJweWTfsxJyYtC8EEjV7dH4KIiBJG00gr/k/LUxAA/Nmmr6PUVaR2SXQX2fRJeDT9Xux07UD31A2cHbiA84MXcWGoAUk6C7YmV6HWV4P0pFS1S6X3wVCMiIiIiIiIKE71jcygLhaEdQ1EuxTSPGZ8Ymc2qgs9yPBaOJJvCWV+JhZ89UAe7Y0GYWM3lo07FMxOiM506DIqIDrTo18238J6Xk52hBAR0R2QFRkvdb6KY9dPIM2Sgm9VfAVuo1PtsmiVCIKALGsGsqwZeCz/QTT7L+PswAWcvPE2Xus5hTRLCrb7arDFVwWrLkntcmkJhmJEREREREREcUJRFHQOTKGuLRqEDYzOAgDyUq349IE8VBd6kOzgwu5KOAh5vA/yaC8ioz0L3V/K7PjiTjoTJGc6tPk7FsIvyZEGQW9Wr3AiIlqXZkMB/LzlKTT5W7HNV43PFz0GnaRTuyxaIxpRg02ecmzylGM6OIO6oXqc7a/D7678Ec9cPYZSZyG2+WpQ6S6FVtKqXe6Gx1CMiIiIiIiISEURWUZ7z8TCaMSxqXmIgoDiLDsOb0lHVYEHjqSNOb5PkWUoU0PLxx6O3oA8OQjcXCJd0kC0p0JKK4V0s/PLkQ7B7GAXHRERrbq+6QH8uPHn8M+N4dOFj2Bf2k7+/tnALDoz9qXvxL70nRiYGcTZgQs4N3ABTc1PwqgxoNq7CdtTapBjzeK/E5UwFCMiIiIiIiJaY6FwBM3Xx3ChfRiXroxgOhCCTiOiLMeJx/bmYlO+GxbjxvkksaIoUAITC6HXQgg21gdEgrG9BAhWLyRnOjR52xZHH1qTIXBReyIiUkHd4CU80fo0DBoD/qrqO8izZ6tdEsURnzkZj+Tdj0/k3ov2sas401+Hdwcu4K2+s/AYXaj11WCbrxoujtlcUwzFiIiIiIiIiNbAxEwQrZ2juNAxgsZrfswHIzDqNdic70J1oQflOS7odes/3FGCAchjve/p/lLmpxf2EYxWiM4MaEsPRLu/HGkQHWkQtBuzY46IiOJLRI7g2asv4tWeN5Fry8Y3y78Em96qdlkUp0RBRLGzAMXOAsyFH8XF4Sac66/DH6+/gj9efwUF9lzU+mpQ5a2AQWNQu9x1j6EYERERERER0SqYmAmirXsMbd3juNw9hn5/dH0wm1mHHWU+VBe6UZzpgEYSVa50dSiRMOSJfsijvbHurx7IY71QpkYWd9IaIDrSoMmphujMiI0+TINo5IlFIiKKT1PBafy06Ql0jF/DvvSdeCz/IWhEnman22PQGLAjZQt2pGyBPzCGcwMXcG6gDk9cfhq/af8DNnvKUZtSgyJHPkRhfb5GVBv/txIRERERERHdBe8Xgul1EgrT7dhdkYKiTAeyU5IgrqM1JBRFgTI9smTsYTQEkyf6ATkS3UmQINp9kLx5EIv3xbq/0iEkuSDwhA8RESWIzslu/KTxl5gJzeArJZ9FbUqN2iVRAnMZHbg/5xDuyz6I65PdONt/HnVDDXh38CLsehu2JlehNqUGKeZktUtdVxiKEREREREREd2B2w3BsnwWSGLiBz9KMAB5chDy+MDyy7E+IDS3sJ9gcUF0pkOXtWlx3S9bCgSJpyCIiChxvdV7Fr9t/wNseiv+Y83/hYykNLVLonVCEATk2rKQa8vC4wUPo9HfirP9dXi1500c734DmUnpqE2pwRbvZlh0ZrXLTXh8RUpERERERER0GzZCCKaEg5AnhyBPDEKeGIASu5QnBqEEJpbtK5idEG3J0BbugujMgORIg+hMh6AzqlQ9ERHR3ReKhPDb9mfxdv85lDgL8bWyz8OiZTBBq0MraVHtrUS1txKTwSmcH7yEs/11eLr9Wfy+448odxVjW0oNyl3FHNt5h/inRkRERERERHQL6zUEU+QwlKmRhbBr8WsAyvQoAGVhX8FohWjzQZNZCcGWDNHmg2hLhmj1QtDo1fshiIiI1sDY3Dh+0vhLdE314L6sg3gw9x6u80RrxqpLwsGMPTiYsQe90/0421+Hc4MXUD/SDLPWhC3Jm1Hrq0FmUjqEdTSae7UxFCMiIiIiIiLC+grBFEWGMjMWHXG4EH5Fxx0qkyOAElncWWeCaPNB8hUuhl6xS3Z9ERHRRtU+dgU/bXoSYTmMb1d8BZs85WqXRBtYmiUFjxU8hEfy7sflsQ6c7a/DW33ncPLG2/CZvKj11WCrrwoOg13tUuMeQzEiIiIiIiLakBI9BFMUBUpgYsWow9jX5CAQCS3urNFBtCVDcmVCzNkK0e6DaE2GYPdB0Fv46WIiIqIYRVHwas+b+MOVY0g2efDtiq8g2exVuywiAIAkSihzFaPMVYzZUAAXhxpwZqAOz157Ec9dewlFjnzUptRgk6ccekmndrlxiaEYERERERERbQiTM0G09UQDsLbucfSNzACI/xBMmZ9Z3u215BKhucUdRQmi1Rvt+sooX9b1JZjsDL6IiIg+xFx4Hk9efhoXhhqw2VOBL5d8GgaNQe2yiG7JpDViV1otdqXVYnjWj7MDdTg3UIeftzwFvaRDlacStSk1yLfncOznEgzFiIiIiIiIaF36sBBsV7kvbkIwJTS3bG2vm91eyvgAlPnpxR0FAYLFDdGWDG1ywfJRhxYXBFFS74cgIiJKYIOzw/hJ4y8wMDOER/MewOHMffxACSUMj8mFh3LvwQM5h3F1vBNnB+piXWTn4TQ4sM1XjVpfNbwmj9qlqo6hGBEREREREa0LiRCCyRODiIz1QlnR8aXMji/bTzA7oh1fOVsg2pMhWn0Q7MkQkzwQJK0qtRMREa1XDcPN+HnLbyCJIv588zdR7CxQuySiOyIKIgocuShw5OIzhY+gfrgZZwfq8HLna3ip81XkWLNQm1KDGm8lTFqT2uWqgqEYERERERERJaQPCsEK0m3YWe5DUaYdWclJ0EjqdYIpioJITyOC9S8g0t+2sF0wJEGwJUNKL1/W8SVakyFo9arVS0REtFHIioxj14/jxc5XkZmUhm+WfwUuo0PtsojuCp2kw1ZfFbb6qjA+P4F3By7izEAdnmr7PY52PIcKdym2+2pQ4iyEtIGmDTAUIyIiIiIiooSQKCHYTYocQfjaOQTrj0H290AwO6Cv/SyklKLouEO9We0SiYiINqzZ0Cz+veXXaPG3YXvKFnyu8JPQshub1im73oYjWftxOHMfeqZ6cXagDucHL+HiUAOStBZUespQ6S5FkSN/3f8/YChGREREREREcScwH8bo5Bx6R2bQ1jOeECHYTUp4HqHLpxBsfAnK1AhEeyoM+74BTf4OCBLfhhMREantxlQfftL4C4zNT+BzRY9hd2ot1w+jDUEQBGRa05FpTccn8x9Ei78N5wYv4vzgRbzVdxZ6SYcSZxEq3aUod5fAvA5HLPLVOBEREREREa2pUFjG2NQcRifn4Z+cw+jUPMYm5+CfnMdobHtgPrywf7yHYDcpc9MINr+KUPMJKHNTEJPzYdjxRUhZmyAI8VcvERHRRnRu4AJ+dfl3MGmM+F71d5Bjy1K7JCJVaERNtEPMU4ZQJIT28atoGG5G40gLLg03QhRE5NmyY11kZXAbnWqXfFcwFCMiIiIiIqK7RpYVTMwEMTo5Fw28lgRdo7EAbHIm+J77WYxaOK16eGxGFGXY4bQa4LTqkewwIcNricsQ7CZ52o9gw0sIXT4JhIOQMjdBt/lBaHyFapdGREREMRE5gmeuvIDXb5xGvj0H3yj/Eqy6JLXLIooLWkmLMlcxylzF+KzySXRP3UDDcAsaRprxu47n8buO55Fq9qHSXYpKTxkyktIgJuiHvhiKERERERER0W1RFAUzc+H3D7wm5zE+PY+IrCy7n14rwWnVw2k1IMNrgctqgCN23WU1wJGkh16beIt7R0ZvIFh/DOErZwEAmvxa6DY9AMmZrnJlREREtNTE/BR+2vQErk5cx4GM3fhk3oOQxMR77UG0FkRBRLY1E9nWTDycdx+GZ/1oHGlGw0gLXu56HS91vQabzooKTykq3WUodORBKyZO1JQ4lRIREREREdGqmg9GMDq1JPBaGXxNzSEYkpfdRxIFOJKiAVdBhg0uqwHOJD0cscDLadXDpNesq3U6wgPtCF56AZHuekCjg7bsIHSV90G0uNQujYiIiFa4NtGF/934S8yGA/iT0s9ji69K7ZKIEorH5MLBzL04mLkX06EZNI9cRsNIM84NXMDp3jMwSHqUuGLrkLmKYYrzdcgYihEREREREW0A4YiM8anFNbxGJ5ePNBydnMPMXHjZfQQAVosOziQD0j1mVOa54IwFYDfHG1rNOojrKPB6P4oiI9JVj/n6FyAPXoGgt0BX80noyg5BMFjULo+IiIhWUBQFp3rP4GjHc3AY7PjPm7+BNEuK2mURJTSL1ozalBrUptQgFAmhbewKGkZa0DjSgotDDRAFEfm2nNg6ZKVwxeE6ZAzFiIiIiIiI1onx6XlcuTGxJPSKBl7+yTlMTgehrNjfbNDAkWSAy6pHfpptYcThzeDLkaSP67W81oISCSN85R0EG16EPNYHweKCfueXoC3eA0GjV7s8IiIiuoVgJITftD2DMwPnUeYqxtdKPxf33StEiUYraVHuLkG5uwSy8kl0Td5AQ2zM4tGO53C04zmkWVKi65C5o+uQxcP0CIZiRERERERE68DQ2Cz+2y/qMB0IAQB0GnGhm6six7UYeFn1cCZFLw06viV8P0owgNDlkwg2vgJlZhSiMwOGA9+GJm8bhARaM4GIiGij8QfG8JOmX6BnqhcPZB/G/TmHIQob+0M+RKtNFETk2DKRY8vEI3n3Y2h2JBqQDbfgpc7X8GLnq7Drbah0l6LCXYpCRx40Kr2m5it5IiIiIiKiBDc7F8IPjzZAURT8ly9UIc1jgdmwvtbxWityYBKhpuMItrwGzM9ASimCbs/XIGVU8M+TiIgozrWOtuPfm38FWZHxncqvocJdqnZJRBuS1+TG4cx9OJy5D9PBGTT5W9Ew0oIz/efxZu87MEh6lLqKUOkuQ5mrGCatcc1qYyhGRERERESUwMIRGf/8TBOGxgL4T5/bjKJMh9olJSR5cgjBhpcQajsFRMLQZFdDt/kBSN48tUsjIiKiD6EoCo53v4Hnrr6EFHMyvlXxFXhNbrXLIiIAFp0Z21O2YHvKFgQjIbSNdaBhuAWN/hZciK1DVmDPRaW7DBXuUriMq/t+hqEYERERERFRglIUBU8eb0dr1xi+/kAJA7E7EBnpQrD+GMLXzgGCCG3BLug23Q/RnqJ2aURERHQb5sJz+GXr07g03Iga7yZ8seTT0Es6tcsiolvQSVpUxEYoyoqMzskeNI60oGG4GU93PIunO55FuiU1OmbRU4oMy91fh4yhGBERERERUYJ65d0enLzUhwe2Z2F3JUOc26UoCiJ9rQjWH0PkRhOgNUBbcR90FfdANDNYJIoXiqIgJIcwGw5gNhRAIDyHQDgAr8nDDhCidURWZITkMEKREEJyCMFIEEE5vPB9SA4hJIcXv4+EEFxy2exvw3BgBJ/KfwgHMvZw3DFRghAFEbm2LOTasmLrkA2jIRaQvdj5Ko51noBDb0eFuxSVnlIU2HPvyjpkDMWIiIiIiIgS0MWOYfz2tSuoKfLgsX25apeTEBRZRrizDsH6Y5CHr0MwWqHb9jh0JQcg6M1ql0e0LoXkMALhAAKhQDTcigVb0W1zsW2x6+G5WPh1c9scIkrklo+bbPKiwl2CCncpcqyZkERpjX8yovUtIkcQlIMIRj48nAquCKqi+4di+y9+v3K/YOz2sBy+oxoFCNBJWth0VvzF5m+h0MGRx0SJzGvyLKxDNhWcRtNIdB2yd/rfxZu9b8MgGVDmKkKlpwxlriIYNXe2DhlDMSIiIiIiogTTNTCF//VcM7J8SfjmQ6UQ+YnoD6SEgwh1vI1gw4tQJgYhWL3Q7/4qtIW7IGg4Xonog0TkCAKRxbAqEI4GWTdDrpvdWwvhVmgx9JoNBxD6kJPdGkGCUWuESRP9MmtNcBudy7YZNQYYNUaYtEboJT26JnvQNNKK13tO40T3SZg1JpS6ilHhLkGpq/COT5IRbQQXhhpwbuDCslBqIcCKhBCUgwjJYciKfEePrxEkaCUtdKIWWlELnaSDVtRCK2lg1pig1WuhFTXQibrF/Zbtr43t/95tKy8lQWJXGNE6laSzYEfqVuxI3YpgJIjLox1oHGlB40gr6obqIQoiCu15qPCUYpO7DA6D/bYfW1AURVnF2lXh909Dltfdj0VEd8DjScLw8JTaZRBRHOFxgYhWSrTjwtjUPP7bL84DAP7rV7fAbtGrXFH8UoKzCLa8jlDjK1ACExDd2dBtfgCa7C0QRFHt8ihOJdox4cPIioy58HwspFoMsAILHVlzSzq1ApgNLdknHMB8JPiBjy8KIowaQyy8WgyxTNro9ei22O3axes399VK2jv+2QLhObSOtqNxpAXN/suYCc1CFEQU2HNj65WUwG103fHjE920Xo4LiqLgv779fYTkEDxGdyx40kAr6WIBlGYxxLpFQLUylLrV96LA36+0/q2XY0Iiiq5D1o2G4RY0jDRjcHYYAJBhSUWFpwyV7jJkWlPhdie972MwFCOidY2/pIhoJR4XiGilRDouzAcj+MGTFzAwNou//WI1MpPf/83eRibPjiPU+AqCLa8DoQCktDLoNj8IKbWEnyinD5VIx4QPEpEjON79Bl7qfA0hOfS++wkQYNAYFoKqaKBlWhJ0LQZYi0HXYgiml3Rx8f9KVmRcm+hC00grGkdaMDA7BADwmZNR4YqNWbRl8oQ93ZH1cly4NtGF/173z/hKyWdRm1KjdjlECWu9HBPWg8GZoeg6ZCMtuD7RBQUKChw5+Pt7/vp978PxiURERERERAlAVhT8+PlmdA9N4S8+VclA7Bbk8QEEG44h1P42oESgydkK3eYHILmz1S6NaE31Tvfjl62/Rc9ULzZ7ypFny46GWdqbXVqmhRDMoNGvi6BIFETk23OQb8/Bo/kPYHjWj0Z/dMzSqz1v4nj3G7BozShzFaPcXYISZyGMGoPaZROtqbrBS9CIGlR6ytQuhYjorkg2e3HE7MWRrP2YCk6jcaQVN2Z6P/A+DMWIiIiIiIgSwO/euIqLHSP43KECbM53q12anvQ1AAAgAElEQVROXIkMXUOw/hjC1+sASYK2aA90m+6HaPWqXRrRmrrZHXbs+gkYNQZ8s/zLqPJWqF2WKjwmFw6a9uBgxh7MhgJoHW1DY6yL7OxAHSRBWjZm0WV0ql0y0aqSFRkXhxpQ5ipmIExE61KSzoKdqVshits+cD+GYkRERERERHHuzfo+vHi2G/ur0nBkS7ra5cQFRVEQudGEYP0xRPpaAZ0Jus0PQlt+BKLJpnZ5RGtuaXdYjXcTPlP4KCw6s9plxQWT1oia5M2oSd6MiBzBtYkuNPpb0DTSiqc7nsXTHc8i1exDuTs6ZjHbmrEuuueIlroyfh0TwSnUeDepXQoRkaoYihEREREREcWx1q4x/PLlNpRlO/CFwwVxsXaPmhQ5gvC1dxGsPwbZ3w3BZId++2ehLd4PQWdUuzyiNcfusI9GEiUUOHJR4MjFY/kPYWh2eKGD7ET3SbzS9TosWjPKXSWocJeg2FkIg0avdtlEH1vd4CXoJB3K3SVql0JEpCqGYkRERERERHFqYHQW//JMI7wOI/7Do+XQSBu3c0EJzyPUdgrBhpehTA1DtKfAsPfr0BTsgCBp1S6PSBXsDvv4vCYPDmV6cChzL2ZDs2jxt6HR34r6kWacGTgPjSChwJG3MGbRaXCoXTLRRxaRI7g03IQKVwn0kk7tcoiIVMVQjIiIiIiIKA5NB0L4x6frIQgCvvvpTTAZNmbwo8xNI9jyGkJNx6HMTUH05kG/43PQZFVB4Hgz2qDYHbY6TFoTtviqsMVXhYgcwdWJTjSORMcs/rb9D/ht+x+QZklBhasE5e5SZFnTOWaREkLb2BVMh2ZQk7xZ7VKIiFTHUIyIiIiIiCjOhMIy/ufvGzE6OY+//nwVvPb4HguoKAogh4FwEEo4CERCUMLzQHjJZSS4eHs4GLu+eDsiS25beIwg5PF+IDwPKaMSus0PQvIVbvgRkrSxsTtsbUiihEJHHgodefhUwScwODOERn90zOLLXa/jpa7XkKSzLBuzyA4cild1Q/UwagwodRWpXQoRkeoYihEREREREcURRVHwi5cuo71nHN/+RCny0213+DgyEAnFgqelQdTN0OlmCPU+wdXKYCuy+DgrgyuEgwCUO/uBJS2g0UHQ6AApdqnRQtDoIRit0LqzoS0/BMmZcWePT7ROsDtMXclmL5LNXhzO3IeZ0Cya/ZfRNNKKi0ONeKf/XWhEDQodeahwRccsOgx2tUsmAgCE5DDqh5tQ6S6DVuSpYCIiHgmJiIiIiIjiyLEzXXiraQAP78rG9jLf++6nhIOQJwYhj/XGvvogj/dBDkxGQ6pI6A4rEJaEVNpYSKVb3KY3Q9Doo8GVtGS7Rhe7rl28fVnQpVuxvzb6+Bw9RvSh+qYH8MvW36Cb3WFxwaw1YZuvGtt81YjIEVwZv45GfwsaR1rxG/8z+E37M0i3pKLCXYIKdykyktI4ZpFU0+pvQyA8x9GJREQxDMWIiIiIiIjixPnLQ/jdyWuoLU3GI7tzAGBhhODS4Csy1gtlcghQYt1ZggDBmgzJkQYptSQWPN1ucKVfHn6JGo4nJIoT7A6Lf5IoociZjyJnPj6V/wkMzA6hcSQakL3U+Rpe7HwVNl0SyhbGLBZAxzGLtIbqhuph1ppQ7MhXuxQiorjAUIyIiIiIiCgOXO8ZwbFjb+LB5Dnc7x1B4OXjkMf7oEwOY2E0oSBBtCVDcmZAzKuF6EiD6EiFaPNBkLSq1k9Ed9fS7rBqbyU+U/goknQWtcuiDyAIAlLMyUgxJ+OerAOYDs6g2X8ZjSMtuDBUj7f7z0EralDkyEe5Ozpm0a6/sxG5RLcjGAmiYaQFW5OrIImS2uUQEcUFhmJERERERERrSAnNLY46HIt2fYX9vXBOj+B7FgAhINIkQbT5ILmzIObvgOhMg2hPg2hLhiDxbRzRerayO+wb5V9CtbdS7bLoDlh0ZtSm1KA2pQZhOYyO8WtoHGlF00gLmvyX8VQbkJGUhgPpu1GbUqN2ubQONfkvIxgJYkvyJrVLISKKG3w3RUREREREtAqUYGBZ8CWP9UEe64Uy7V/cSdQAtmRcnrWjJ5SB3bur4c7Kg2jzQhD5do1oo2F32PqlETUocRaixFmITxc8jP6ZQTSNtOLdwYt44vLTKHDkwmlwqF0mrTN1g/Ww6pKQb89VuxQiorjBd1lEREREREQfgzI/A3m8f1nwJY/1QZkZXdxJ0kC0p0DyFUC074PoSIPkSIVi8eBHzzSjaXQUf/XpSiTnutT7QYhINewO21gEQUCqxYdUiw9bfVX4f975AV7rPoXHCx9WuzRaRwLhOTT7W7EztRaiIKpdDhFR3GAoRkREREREdBuU+RlEFkKvJZ1fs+OLO0laiPZUSClFC+t9SY40CEkeCOJ7T0j9+kQHGq768eV7ClHOQIxoQ2J32MbmMNixNbkKb/WdxX05h2DRmtUuidaJxpEWhOQwRycSEa3AUIyIiIiIiGgJZW461vXVu7j212gvlMDE4k4aXbTbK60sFnylQnSkQbC4bxl+3crrF27g+PkeHN6SjgPV6av00xBRvGJ3GN10JGs/zg7U4eSNt/FgzhG1y6F1om6wHg69HdnWTLVLISKKKwzFiIiIiIhow1FC81BmxiDPjGKiawxzPdeiIdh4H5TA5OKOWkO08yujYiH4Eh2pECwuCB9jFFHTNT+ePN6ByjwXPnew4C78RESUSNgdRkulmJNR4S7FyRtv4XDmPuglndolUYKbCc2idbQd+zN2cXQiEdEKDMWIiIiIiGjdUCJhKLPjUGbHIc+MRb+fGYMc2xYNwsaBUGDhPgEA0BohOlKhydwEcSH8SoNgdkIQhLtaY+/wNP712Sakus3404fLIIp39/GJKH5Fu8NO4sXrx2FgdxgtcU/Wfvz3un/BO33vYn/GLrXLoQRXP9yEiBLBFu9mtUshIoo7DMWIiIiIiCjuKYoMJTAFZXYMysx4NOSaGYMyGw25bm5X5qbee2dRgmCyQzA7oiMP08shmOwQY9s8ufkYndPe9fDrViZngvjh0QZoNRK++3gljHq+JSPaKNgdRh8k15aNPFs2TnSfxJ607ZBESe2SKIHVDdbDbXQhIylN7VKIiOIO34EREREREZFqFEUBgrOLwdbNDq+ZFd1esxOAEllxbwGC0QrBbIdgdkLy5kEwOSCYFwMvwWSHYLB84KhDjTUJwvwtwrS7LBSO4Ee/b8DETBD/5QvVcNkMq/6cRKQ+dofR7TqStR//1vB/UDdUj22+arXLoQQ1FZxG29gV3Jt1YE0+8ENElGgYihERERER0apQwvNQZidiIdfYipBrMfxCJPjeO+vN0WDLZIfoSIEYC7sEkwPizbDLZIUgJsZbGkVR8LNjl3G1dxJ/9mg5clOtapdERGuA3WH0UZS5ipFiTsbxrjewNbmKgQbdkYtDDVCgoCaZoxOJiG4lMd5BEhERERFR3FDkMJTZyfeMLpRnl3d4ITj73jtLuugYQ7MdkicHQrZjIfyKbndAMNkgaPRr/nOtpufe6sTZlkE8tjcXW4q9apdDRKuM3WF0J0RBxJHM/fhF62/Q7L+McneJ2iVRAqobqofPnIxUi0/tUoiI4hJDMSIiIiIighIOQpmbghKYhBKYgBKYghyYjF2Pfc1NQpmN3gYoyx9AkKJhltkO0eaDlFoc6+paEniZ7IDOtOE++X6mZQDPnr6OXeU+PLgjS+1yiGiVsTuMPo4tyZvx/LWXcbz7DYZi9JGNzY3j6ngnHsw5onYpRERxi6EYEREREdE6pCgyMD9762Ar9n30tmgQhlDg1g+k0UfX7TImQbS4IXhyYut2OZav22VM+sB1uzaqKzcm8LMXLqMww46v3l+84QJBoo2E3WF0N0iihEOZe3G04zlcm+hCro0fpqDbd3N0YnXyJrVLISKKWwzFiIiIiIgSxPJurqXB1srQayrazaVE3vsgggDBkATBYIVgskL05EQDLaMVgtEKMXYpGGKX2vU1xnAtDY8H8KPfN8Bp1ePPH6uARmJoSLResTuM7qadqdvw4vUTON71Bv608qtql0MJpG6oARmWVCSbPGqXQkQUtxiKERERERGp5D3dXHOT0bW6Frq5piDHRhl+5G6um6HWyi+9BYLIcGa1zc6F8cOjDYhEFHz38UpYjFq1SyKiVcDuMFoNekmHvek78WLnCQzMDMJnTla7JEoAI4FRdE5249G8B9QuhYgorjEUIyIiIiJaJZGxXshD15Z3c81NLazZdVvdXMYkdnMlmIgs41+fbcLg6Cz+789uRorLrHZJRLQKot1hv0X31A1UeSvxWXaH0V20P30XTnSfxPHuk/hyyWfULocSwIXBegBgME9E9CEYihERERER3WWRkS4ELzyHcGfd4sYl3VyC2QWJ3VzrkqIo+NXxDjRfH8XX7i9GSZZD7ZKI6C6LyBGc6D6JY+wOo1Vk0ZmxM3UbTveewUM598BhsKtdEsW5uqF65Fgz4TI61S6FiCiuMRQjIiIiIrpLIsPXo2FY10VAZ4Su+hFoC3ZCMNnZzbVBnDh/A69f7MV9tZnYuylV7XKI6C5jdxitpUMZe3Cq9x281nMKnyr4hNrlUBwbmBnCjek+PF7wsNqlEBHFPYZiREREREQfU2TwCuYvPIdITwOgN0O35THoyg9D0JnULo3WUP2VETz1WgeqCtx4fH+e2uUQ0V3E7jBSg8voRI13E97qO4v7sw/BpOXrCrq1uqF6CBBQ5a1QuxQiorjHUIyIiIiI6A6FBzoQvPAsIjeaIOgt0G17HLrSQxB0RrVLozXWMzSNf3uuGZneJHz7E2UQBUHtkojoLmF3GKnpSNZ+vDt4EW/2voP7sg+pXQ7FIUVRUDdYj3x7Dux6m9rlEBHFPYZiREREREQfUbjvcjQM62uFYLRCX/tZaEsPQNAa1C6NVDA+PY8fHq2HSa/BXz5eCb1OUrskIroL2B1G8SDNkoIyVzFe7zmNgxl7oZO0apdEcaZvZgCDs0M4kLFL7VKIiBICQzEiIiIiotugKAoifa3RMKy/DYLRBv2Oz0Nbsh+ChuuFbVTzoQh+9LsGTAdC+Nsv1sCRxH8LROsBu8MonhzJ3I9/vPhvONN/HnvTd6hdDsWZ84OXIAoiNns4OpGI6HYwFCMiIiIi+gCKoiByownzF56FPHgFgtkB/c4vQVu8F4JGp3Z5pCJZUfDTP7ags38Kf/6pCmT5ktQuiYg+pogcwTMtL+Hppj+yO4ziRr49BznWTJzoPoldqdsgiexIpihFUXBhsB5FjnwG90REt4mhGBERERHRLSiKgkhPPebrnoM8fA2CxQX97q9AW7QHAkcXEYBn3ryG823D+MyBfFQVeNQuh4ju0MT8JFr8bWgebcPl0Q4EwgF2h1FcEQQBR7L248eNv8DF4UZsSd6sdkkUJ7qnbmBkbpTrzRERfQQMxYiIiIiIllAUBeGuiwheeBbySBeEJDf0e/8E2oJdECS+fKao0w39eOGdLuzdlIp7t2WoXQ4RfQQROYJrE11oGW1Di78NN6b7AAA2XRI2ecpwoKAW6ZoslaskWq7CXYpkkxfHu95AjXcTBEFQuySKA+cHL0ESJGzylKtdChFRwuC7eiIiIiIiAIoiI3y9DsGLz0H290CwemHY9w1oCnZAEPmymRa1dY/h5y9dRkmWA1+6p5AnJokSwPj8RLQbzB/tBpuLzEEUROTasvBI7v0odRUhzZICQRDg8SRheHhK7ZKJlhEFEUcy9+GJy0/j8mgHSlyFapdEKpMVGReGGlDqKoRJa1S7HCKihMF390RERES0oSmyjPD1dxG88BzksV4INh8M+78FTf52CFyzAwAQlsMIhOcQCM9BJ2lh19vULkk1g6Oz+J+/b4THbsSffbIcGklUuyQiuoWwHI52g/nb0Oy/jL6ZAQCAXW9DtbcSZa4iFDnzYdTwRDIljq2+Kvzx+it4pfsNhmKEaxNdGJ+fwKN5D6hdChFRQmEoRkREREQbkiJHEL56FsGLz0Me74doT4Xh4Hegyd0GQVw/QUdEjiAQmcNcLNQKhAMLAVcgHN0+Gw4suX1uYf+b20NyeOHxBAh4IOcw7ss+BFFYP39Ot2M6EMI/Hm2AIAj4q09Xwmzg2nJE8WRsbhzN/sto8behbewK5iLzkAQJebZsPJr3AEpdRUg1+9jdSQlLI2pwIGM3nrnyArome5Bl5fjejaxusB5aUYsKd6napRARJRSGYkRERES0oShyBOEr72D+4vNQJgYhOtNhOPxn0ORsgRBnIY+syMvDqqWBVuTWgdbK8Csohz70eXSiFkaNAQaNEUaNASaNEU6DAyaNAQaNAUYput2oMaB1tB0vXD+O9rGr+FrZ5zdM11g4IuNfnmmEfyKA//S5KngdJrVLItrwQnIYV8evo8XfhpbRNvTPDAIAHHo7tiRvRqmrCEWOfBg0BpUrJbp7dqfW4qXO1/BK1xv4VsWX1S6HVBKRI7g41IByVzEMGr3a5RARJRSGYkRERES0IShyGOH2t6Nh2NQwRFcm9Ef+AprsqlULwyJyBBPByfeEWrcMuiLv3T4fCX7oc2hFTTS4WhJe2fW2WMgV264xwqAxwBS7vjLokj7CmMhtvmoUOwvwm7Zn8P1z/4gvl3wG5e6Sj/PHFPcURcEvX27D5e5xfOuhUhRm2NUuiWjD8gdG0TIaXRusbewKgpEgNIKEPHsOtqdsQZmrGD6Tl91gtG4ZNAbsTduBV7pex+DsMJJNHrVLIhV0jF/DVGgaNcmb1S6FiCjhMBQjIiIionVNiYQQan8LwYvPQ5n2Q/TkwLDzC5AyN6/aSdP5SBBv9Z7Bie6TmAhOve9+GkFaElxFwyurKWlJyLW4fXnItRh0acW1fUkvCAK2p2xBtjUTP2t+Ev/a8O84lLEXD+fdB80a17JWXjrXjVMN/XhoZzZ2lPvULodoQwlFQrgycT22NlgbBmeHAAAugwO1vhqUuYpQYM9jpwRtKPszduG1njfxavdJfKH4cbXLIRXUDdZDL+lQ5ipWuxQiooSzPt+1EhEREdGGp4SDCLWdQvDSC1BmRiF6c2HY/VVIGRWrFoYFwnN488bbeK3nFKZDMyi05+HBnHtg1C52ZRklQ/S6ZIBWStw1qXxmL/5zzZ/j91dewKs9b6Jj/Bq+XvZFeEwutUu7q+rahnH09avYWuzFo3ty1C6HaEMYCfjR7G9Di/8y2seuIiiHoBE1KLDnYndaLcqcRfCaPOwGow3LqkvC9pSteKfvHB7MuQc2vVXtkmgNheUw6oebUOkugy6BX0sSEamFoRgRERERrStKOIjQ5ZPRMGx2HFJyAXT7vg4prWzVTqDOhmbxes9pvH7jLQTCAZS6inBf1iHk2bNX5fnihVbS4rNFj6LIkYcnLh/FD979R3yh+FPrZpRP58AkfvJ8M3JSrfjGgyUQeQKeaFUEIyF0jF9Di/8yWkbbMDQ7AgBwG13YkboVpc4iFDjyoJd0KldKFD8OZ+7F6d4zeL3nNB7Nf0DtcmgNXR7twEx4FjXJm9QuhYgoITEUIyIiIqJ1QQnNI9T6OoL1x6AEJiGlFEN38E8hpRSvWhg2FZzGaz2n8OaNtzEXmccmdxnuzT6ILGvGqjxfvNrsrUBGUjr+vflX+Fnzr3B59Ao+XfgwdAl8Ant0cg4/PNqAJJMWf/GpSui0t7/uGhF9uKHZ4Wg32GgbOsauIiSHoRU1KLDnYW/aTpS5ot1gRHRrbqML1d5KnOo9g3uzD8CoMapdEq2RuqF6GDVGlDgL1S6FiCghqRqKdXd34x/+4R9QV1cHo9GIBx54AN/73veg13MWOBERERHdHiU0h2Dzawg1vAhlbgpSWhl01Q9Dk1K0as85Pj+BV7vfxKneMwjLYVR7K3Fv9kGkWVJW7TnjncvowPeqv4M/Xn8Fx7vewLXJLnyj7ItItSTeGlxzwTD+6WgD5oMR/Mcv1cBmTtxwjyheBCNBtI9dRctodG2wkYAfAOA1urErtRalrmIU2HM5CozoIziStR91Q/U41XsG92QdULscWgOhSAgNw82o8lau27VciYhWm2pHz2AwiO985zvIz8/HU089Bb/fj7/7u78DAPzN3/yNWmURERERUYJQggEEm08g1PAylPlpSBkV0Fc9DMlXsGrP6Q+M4UT3G3i7/13IioytyVW4N+sAks3eVXvORCKJEh7Jux+Fjjz8vOUp/L/n/wmPFzyMXam1CbP2jywr+PFzLegZnsZ3H9+EdK9F7ZKIEpKiKNFusNE2tPjb0DF+DWE5DK2oRZEjDwcz9qDUWbTu1iEkWksZSWkocRbi9Z7TOJC+O6HXKqXb0zzahrnIPEcnEhF9DKqFYg0NDeju7sbTTz8Ns9mMvLw8fPe738UPfvADhmJERERE9L6U+RkEm04g2PgyEJyFlLkJ+upHIHlzV+05h2ZH8ErX6zg7UAcBAranbME9WfvhNvJk7q2UOAvxd9u+h1+0/Aa/bvs9Lo9dwReKPgWTNv5HOz39xhVcujKCLx4pRGUe/36JPor5SBDtY1eiYxH9bfDPjQIAkk1e7E3bgVJXEfJtOTxxT3QXHcncj3+69GOcHajD7rTtapdDq+zCYD0sWjMK7Xlql0JElLBUC8Vyc3Px4x//GGazeWGbIAgIBoNqlUREREREcUyZm0aw6RUEG48DoQA02dXQVT0MyZO9as/ZPzOIlztfw/nBS9CIEvak7cCRzH1wGOyr9pzrhVWXhD/b9HWc6D6J56+9jO7JHvxJ2ReRY8tUu7T3dfJSL14+14ND1ek4VJOudjlECcEfGMOl4Ua0+NtwZfwawkoEOkmHIkc+DmfuQ5mrCC6jU+0yidatQkceMpPScaL7JHamboMoiGqXRKtkPhJE40gLalO2QBK51ikR0Z1SLRRzOp3YuXPnwnVZlvHEE0+gpqZGrZKIiIiIKA7Jc1MINbyMYPMJIDQHTc4W6KofhuRavXClZ6oPL3e+ikvDTdBKWhzM3INDGftg0yet2nOuR6Ig4p6sA8i35+Lfm3+F/+/Cv+Dh3PtwKHNv3J20a+kcxROvtKMi14XPHc5XuxyihNA52Y0fXfwJ5iLzSDEnY1/6LpS6ipBnz4GWa90QrQlBEHAkaz9+2vQELg03odpbqXZJtEoaR1oQlEOo8XJ0IhHRxxE3r1K///3vo7W1FUePHlW7FCIiIiKKA/LsBIINLyHU8hoQDkKTtw26qk9Acq5eB0/nZDde6nwVjSOtMEgG3Jt9EAfSd8OiM3/4nel95dqy8Ldb/wpPXj6KP1w9hvaxq/hK6WeRpIuP9bp6Bqfwz880wecy4TuPlEES4yuwI4pHvdP9+OdLP4VFa8Zfb/1LJJs8apdEtGFt9pTDa3TjeNcbqPJUJMw6nvTRXBish01nRZ49W+1SiIgSmuqhmKIo+Pu//3v8+te/xg9/+EMUFKzewuhEREREFP/k2XEE619EqOV1QA5Bk7c9GoY5UlftOa+MX8eL10/g8lgHzBoTHsq5F/vSdybEGliJwqQ14pvlX8LpvjM42vE8/uHc/8BXSz+HYqe6r/+nZoP4/pMXoJUEfPfxShj1qr9FIop7AzND+NHFn0An6fCXVd/meEQilYmCiMOZ+/D/s3ff4XGVB9rw7zNdZSSN2qj3LqvYcpWLZFvuNhgIEAKhk80GQrJJ9iVvdr/3+5JvSzabhARIskuHLCSEjsEYG9uyZMtFxZJtyZLVu2bUu6ad8/5hICFxke2Rzszo/l1XLl94znnOPVfk0czc53me1xvexoXhZqQGcsazp5m2T6N2sB5ro1a53Gx7IiJ3I+snPlEU8U//9E/Ys2cPnnzySRQVFckZh4iIiIhkJEkiLCf/BFvtp4AoQpW8CtrcXVAEhM3R9STUDzdiX9tBNI20Qq/2xe7E7VgbuQo6lXZOrrnQCYKAtZGrkOAfhxfOvYZnqp/Hltj12B6/SZa9MewOEc+8cxaDozP4X3ctRrA/S1CiqxmYHsLT1c8BAliIEbmQ5WFL8GHrfuxvP8xSzAPV9NfCLjmQF5ordxQiIrcnayn205/+FHv27MHTTz+N9evXyxmFiIiIiGRmqzsE25l9UCXnQ5u3Gwq/0Dm5jiRJODd4HvvaDqFtrAMBWn/cnnwz8iOWQ6NUz8k16csifcPxxLLH8eaF97Gv/RAujLTggcy7EKgzzGuO94+2orFrFD+4Ow+Jkf7zem0idzQ8M4KnTj8Lm8OG7yz5Oy6ZSORC1Eo11kevwfvNH6NjvAsx+rlbbprmX6W5BkE6A+L8ouWOQkTk9q5rvq3JZEJeXh5efvnlSz5ut9vx8ssvY/v27cjOzsbGjRvxm9/8Bjab7Ytjqqur8corr+Dxxx/HokWL0N/f/8X/iIiIiGhhEScGYTn1FpRRi6ArfGROCjFREnHafBY/Lf81/uvMyxi3juOu1Fvx/616AoXRq1mIzTOtUoN70m/H/Rl3oXuiB/9+6leo6T83b9dv7BrB3hPtWJMVjoIl/OKQ6GrGrRN4uvo5TNom8WjuQ4j0DZc7EhH9lbWRK6FT6vBp+xG5o5ATTVgnUT/UiCWhOdwvjojICa55ptjk5CS+/e1vY2Ji4rLH/OQnP8Ebb7yBvLw8bNiwAVVVVXjqqafQ0NCAp556CgDwySefAAB+8Ytf4Be/+MWXzq+trYVKxbX8iYiIiBYCSZIwU/oKIEnQrb3P6R/2HaIDleYafNJ+GH2TJoR6B+Pr6XdgmXGxLEv20ZctC1uMWL9ovFj7Gp49+yoKolbjlsTtUM9hSTltseP5D+sQ5KfDXUXc05joaiZtU3i6+jkMz4zg0dyHEcuZCkQuyUvlhbWRK/FpxxHsmtqKEO8guSORE1T3n4UoicgzculEIiJnuKbmqbu7G9/+9rdRW1t72WOqqqrwxhtvYMuWLfj1r38NQRAgSRJ++MMf4r333sPhw1FP/icAACAASURBVIexfv16PPHEE3jiiSdu+AkQERERkXuzN5+Ao/MMtKu+BoXeeUtxOUQHTvZVYX/7IfRPDyLCJwwPZH4NS0KzuUG5iwn1Dsb38x7FB80f41BnKZpHWvHgorvnbGm2Px5sxMDoDJ742hJ4aXkzHtGVTNtn8JuaF2CaNOPvcx5EUkC83JGI6ArWR6/B4c5SHOwswVdTb5E7DjlBpfkMQr2DEcUZukRETjHrbwNefvll7Nq1C/X19Vi5cuVlj3vttdcAAI899tgXd/kKgoDvfe97EAQBb7755g1GJiIiIiJPIU6PwXLsNShCE6DOLHLKmDaHDSVdx/H/Hv8PvFb/JnQqHb6RdS/+9/LvYqkxl4WYi1IrVLgteRe+mX0/hi0j+Gn5r3Gyt9Lp1zl9oR+lZ3qxfWUsUqIDnD4+kSexOqz4Xc1L6BzvxsNZX0daIGdWErk6f60fVoTn4XhvOcas43LHoRs0ahlD43Az8kJzuXQiEZGTzPq2yFdffRWRkZH48Y9/jLa2Npw4ceKSx1VUVMBgMCAlJeVLf280GhEXF4fy8vIbSzwLQUG+c34NInIfISF6uSMQkYvh64LrML/3AiTbDCJ3fxuaEP8bGstit+LT5lJ80HAAw9OjSAlKwDdX3I3csEx+ieBGNoSsQE5sCp468RJePf8G2qbb8PCSr0Kn1t3w2MPjM3h1fwMSIvzx0O5sqFV/Lkj5ukD0ZTaHDT87+hJaxtrwnZUPIj9mqdyR5hVfE8id3a7bjrK95Tg1WI67sm+WO47HkON1oeJCBSRI2JSWjxB/vi4RuRK+V3Bfsy7FfvzjHyM/Px9KpRJtbW2XPMZqtaKvrw85OTmXfDwyMhKtra0YGhpCYGDgdQWejcHBCYiiNGfjE5H7CAnRo7+fd8cR0Z/xdcF12DuqMV1bCs2SmzEKA3Cd/79M22dQ2nUcBztLMGGbRHJAAr6eeidSDIkQBAEDA5ffC5dclQrfWvQQPm79FB+3HUS9uRkPZt6DaH3EdY8oSRKeeusMJqft+MGdqRgZnvziMb4uEH2ZQ3Tg+XP/gzMD53FP+h1I9kpdUP9G+JpA7k4Nb+SELMK+xiNYE5IPnerGbyxZ6OR6XTjScgoRPmHQWn35ukTkQvhewbUpFMIVJ07NuhRbu3btVY8ZGRkBAOj1l25JP//78fHxOS3FiIiIiMi1SdZpzJS+CoUhAprFO69rjCnbFA53HUNx51FM2aeREZiKLXEbuN+Nh1AICuxI2IxkQyJerv0Dfl7xNG5J3omCyPzrmvlXUtODmuZB3LUxGZEhXFmC6HJEScSr59/AmYFa3JGyG6vCF9YMMSJPsTm2ENX9Z3G05ySKYgrkjkPXYWhmGC2jbdiVsFXuKEREHsWpu0rb7XYAgEajueTjn/+9xWJx5mWJiIiIyM1YTr0FaXIYXkX/BEGpvqZzx60TONRZipKuMsw4LMgOzsTWuA2I9Yueo7QkpxRDIn60/B/w+/Nv4M0L7+PCUBPuTr8dPmrvWY9hGp7CHw82IT3WgI1Lo+YwLZF7EyURf6h/GxWmauxO3I6CqHy5IxHRdYr1i0aKIQmHOkpRELUaaoVTvwKkeVBlPgMAyAu99IpcRER0fZz6G1Gnuzgd22azXfJxq9UKAPDy8nLmZYmIiIjIjdj7GmGrOwT1oiIojUmzPm/UMoZPO47gaPcJ2EQ7FodmYWvcRkT6hs9hWnIFvhoffDP7ARzuLMV7zR/j30/9Cg9kfg2JAXFXPdchinh+Tx2UCgEP7UiHgvvLEV2SJEl4u3EPynrLsS2uCJtiC+WOREQ3aHNMIZ6peR7lfaeRH7FM7jh0jSpNNYjRRyHEO0juKEREHsWppZivry8UCgUmJi69b8P4+MV1Ni+3vCIREREReTbJboWl5EUIvoHQLrttVucMzQzjQHsxynrLIUoilhkXY3PseoT5hM5xWnIlgiBgQ8w6JAbE48Xa1/Gr0/+FHfGbsDl2PRSC4rLnfXS8Hc09Y/i7mzIR6Mc9VYgu54OWfSjuOoYN0WuxI36T3HGIyAnSApMR7RuBTzuKsTI874q/L8m1mKcG0DHehVuSdsgdhYjI4zi1FNNoNIiIiEBXV9clH+/q6oLBYEBAQIAzL0tEREREbsJ6eg/EkV54bf8BBPWVC4r+qUHsbz+EE32VECBgZXgeNsWs592yC1ysXzR+uOw7+EP929jT8gkuDDfjvoyvwl/r9zfHtvaO4YOjbViRYcSKDKMMaYncw762Q9jffhhrIlfi1qSd17VvHxG5HkEQUBRbiJdqX8fZgTrkhCySOxLNUpW5BgCXTiQimgtOX1A4Ly8P77//PlpbWxEf/+dNzk0mE9rb21FYWOjsSxIRERGRG3AMdsJavReq5NVQRV3+S5m+SRP2tR1Ghek0lAol1kauRFFMAQJ1hnlMS67MS6XDA5lfQ1pgCv504T3826kncW/GV5EZlPrFMRabA8/tqYO/rwb3bE6RMS2RazvUWYo9LfuwPGwJ7kzZzUKMyMMsDsnCHl0g9rcXIzs4k//G3USlqQYJ/nEw6DixgIjI2Zw+b3r37t0AgCeffBKiKAK4uDb5L3/5S0iShDvvvNPZlyQiIiIiFyeJDsyUvAhB6w3dqrsueYwoiXi17g38y8lfoqb/LDZEr8VPVv0Qd6TsZiFGf0MQBORHLMMTyx6Hn0aP39a8gPea9sIhOgAAbx5uQt/QFB7akQ4fnVrmtESu6Vj3SbzduAe5IVm4J+12Lq1G5IGUCiU2xhSgbawDTSMtcsehWeiZ6EPPZB/yjJwlRkQ0F5w+Uyw/Px/bt2/H3r17ceedd2LFihU4ffo0KioqsGXLFs4UIyIiIlqAbOf2Q+xvhW7j30PQ+V7ymLMDdTjZV4mCqNXYHlcEX43PPKckdxTuY8Q/Lv023m7agwMdxWgcaUG+fhsOVXVj87JoZMQFyh2RyCWd6qvCHxreQWZQGh7IvAtKhVLuSEQ0R1aGL8Xe1gPY31GMZEOi3HHoKqrMNRAgYHFIttxRiIg80pzcBvazn/0Mjz/+OIaHh/HKK69gYGAAjz/+OH7+859zmjYRERHRAiOOmWEpfxeq2MVQJSy/7HHFXWUwaANwW9JOFmJ0TTRKNe5KvRUPLboHfZNmvN7xAoJjh3FbQYLc0YhcUnX/Ofz+/J+QHJCAhxd9HSqF0++XJSIXolGqURi9GnWDDeie6JU7Dl2BJEmoNNcg2ZAIf61e7jhERB7put753nrrrbj11lsv+7harcajjz6KRx999LqDEREREZH7kyQJMyUvAQoltGvuvewNUj0Tfbgw3ISbE7ZxtgJdt8UhWThSNoELisOYNJ7EW03Abck3QaPk8olEn6sdbMCL515DrD4af5d9P/99EC0Q6yJXYX/7YRxoL8b9mZdeyprk1zXRA/PUAIqiC+SOQkTksbhgOBERERHNGVtDCRw956FdcQcUPpffF+xIdxlUChXyIy4/k4zoao7X9uFs/Qx2hNyFTTGFONpzEv9Z8TR6J01yRyNyCY3DzXju7CuI8DHiWzkPQqfSyh2JiOaJt9obayJWotJcg8HpIbnj0GVUmmqgEBTICV0kdxQiIo/FUoyIiIiI5oQ4NQLLiT9CGZ4Kdfrl73adsk3jVG8llhpzuWwiXbeB0Wm8duACUqL8sX1FPHYnbcejOQ9hzDqO/yh/CmU9pyBJktwxiWTTOtqO3515CUFeQXgs9xF4q73kjkRE82xDzFoIEHCws0TuKHQJny+dmBaYDF813xMTEc0VlmJERERENCcsR38POGzQrX0AgnD5t53He8thFW0ojFo9j+nIk4iihOc/PA9JAh7emQGF4uIynRlBqfjR8n9Agn8sXqt/Cy/Vvo5p+4zMaWk2BqYHsa/tEP715C/xROmPUdJ1HKIkyh3LbXWO9+A3NS9Cr9Hj8dxHeAMC0QIVoPXHsrDFKOspx7h1Qu449FfaxjowNDOMpaG5ckchIvJo3E2XiIiIiJzO1loBe1slNMu/AkVA2GWPEyURJV1lSPCPQ7Q+ch4Tkif5pLwDFzpH8OD2dAQHfHn2i7/WD4/lPoz97cX4qHU/2sc68eCiuxHrFy1TWrqcUcsYqsxnUGGqRttYBwAgwT8WXiovvHHhXRzvPYU7U29BnF+MzEndS9+kCc9UPwedUovHc78Bf62f3JGISEabYgpworcCR7rKsDNhs9xx6C9UmmqgUqiQHZIhdxQiIo/GUoyIiIiInEqyTMJy9PdQBMVAk731isfWDtZjYGYINyVe+Tiiy+kwjeOdIy1YkhKC1VmXLmAVggJb4zYgOSABL9W+jl9U/hY3J27D+ug1UFxhFiPNvSnbFKr7z6HcVI3G4WZIkBDpG46bE7chLzQXQV6Gi8tJmarxTtOH+HnFb5AfsQw3JW7j0lKz0D81iKdOPwuFoMDjix9BkNfl93YkooUhzMeI7OBMlHSVYVNsIbRKjdyRCBdvFKsy1yAzMBVeKi5vS0Q0l1iKEREREZFTWU68AWlmHF7b/gGC4spvN490lcFf44fckKx5SkeexGZ34PkP6+DjpcZ9W1MhCMIVj08MiMP/Xv5dvHb+TbzT9CEahpvw9fQ7EAL9PCUmALA4rDg7UIcKUzXqBhvgkBwI8QrC1rgNWGrMRZiP8UvHC4KApWGLkRmcjr2tB1DcdQzV/edwc+I2rApfxmLzMoZnRvBU9bOwSw58d/E3EeodInckInIRm2ML8fOBWpT1nML66DVyxyEAzSOtGLWOI8+YI3cUIiKPx1KMiIiIiJzG3l0HW0MJNDnboQyOu+Kxpkkzzg9dwM74LVAqlPMTkDzKOyUt6OqfxHdvz4bee3Z3uvuovfFI1r0o6T6Odxr34N9P/Qqbk9ciWhuLOL9o/izOEbtox/mhC6gwVePMQB2sDiv8NX4oiMrHUmMuYvRRVy01vVQ63Ja8CyvDl+KNhnfxev3bKOspx52puxGjj5qnZ+IeRi3jeOr0s5iyTeM7S76BCN/LL2NLRAtPvH8skgLicbCjBOsiV/F3nwuoMNdAo1BjUTCXTiQimmssxYiIiIjIKSS7BTMlL0HwN0KTt/uqxx/pLoNKUGJN5Ip5SEee5nz7MPaf6sT6xZHITgy+pnMFQUBBVD4S/OPwpwvv4q3avZAgQavUIDkgEamBSUgzJCPcx3jVooYuT5RENI20oMJUjdPms5iyT8NH5Y1lxsVYasxFUkD8dc3yivQNxz8s+Xuc6qvCu00f4WflT2Nt5ErsStgCb7X3HDwT9zJhm8Qz1c9hxDqGb+c+zMKQiC5pU0whfnfmJVSYqrEiPE/uOAuaQ3Sg2nwWWcEZXM6SiGgesBQjIiIiIqewVLwLabwfXjt/CEF15Q/00/YZnOitwBJjDvQa33lKSJ5iasaGFz6qQ6jBC3esT7rucaL1Efh+3qPw8lPgeFMN6oeb0DDUiHOD5wEAfho9Ug1JSA1MRpohCQZdgLOegseSJAnt452oMFWjylSDUes4NEoNsoMzsMy4GGmByVBdZVnV2RAEASvC85AVnIEPWz9BSddxVJnPYHfSDqwIW7Jgl1Sctk/jN9XPwzw9gG9lP4gE/zi5IxGRi8oMSkOETxgOdBRjWdjiBfu66QouDDdjwjbJpROJiOYJSzEiIiIiumEOcwtsZz+BOq0Qqoi0qx5/srcSFocVhVGr5yEdeZrXDlzAyLgVP/p6HrSaG1/yyVfrg9zQLOSGXtzbbnB6GA3DTWgYbsT5oQsoN50GABi9Q5BqSEZaYBKSAxLhrfa64Wt7it5JEyr6TqPCXIOB6UGoBCUygtKw1JiDrOAMaObozndvtRfuSNmNVeHL8EbDu/if839CWc8p3JmyG1H6iDm5pquyOKz4bc1L6J7owzey7kVq4PUXxkTk+QRBwKbYQrxS90fUDtYji8v2yabCXA2dUoeMwFS5oxARLQgsxYiIiIjohkii/eKyiV7+0K6846rHi5KII93HEOcXg1i/6HlISJ7k1HkTjteacPOaeCRE+M3JNYK8DMj3Wob8iGUQJRG9kybUDzWifrgRJ3rLUdJdBgECYv2ikfbZTLJ4/1ionTADyp0MTg+h0lSDCnM1uid6IUBAqiEJW2I3IDdk0byWhtH6SHwv71s40VuJ95v34qflv0Zh1GrsSNgEL5Xnl5c2hw3PnnkFraPteHDR3VgUnC53JCJyA3mhOdjT8gkOtBezFJOJTbSjpr8WOSGZUCvVcschIloQFtanNiIiIiJyOmv1XohDndBtfhyC5ur7+ZwfaoR5agD3ZXx1HtKRJxket+D3nzQgPtwPO1bFzss1FYICkb7hiPQNx8aYdbCLdrSOdqBhuBENw03Y31GMfe2HoFaokRQQj1RDEtICkxHpG+6RS1GNWcdRZTqDClM1WsfaAQDxfjH4SvJNWBKaA3+tXrZsCkGB/IhlyAnJxAct+1DcdQyV5hrckrQDy4yLPXZ/OIfowAu1/4P64Ubcm34nloRmyx2JiNyEUqHExuh1eLPxfTSPtCExIE7uSAtO/dAFTNunuXQiEdE8YilGRERERNfNMdIDa9UHUCUshzpuyazOOdJ1DHqNL7+4pWsiShJe/KgONoeIR3ZlQKWUp3BSKVRINiQg2ZCAndiCafsMmkZaPptJ1oT3mvcCzYCP2vtiQWZIRmpgMoK9AmXJ6wxTtmnU9J9DhakaDcNNkCAhwicMNyVsRZ4x1+Wem4/aG3el3or88GX4Y8O7eKXujyjrOYU7UnYjwjdM7nhOJUoiXq77A84OnMdXU2/BivA8uSMRkZtZFbEMe9sO4EDHYSQGPCB3nAWnwlQNH5U30gzJckchIlowWIoRERER0XWRJBGWIy8Bai20+XfP6hzz1ADqBhuwLW4jVAtsqTm6MYcqu1DbNoyvb0lFWODVZyTOFy+VDlnBGV8sOzViGUXDUBMahptQP9SIKvMZAECwLhCpgclIC0xGSkAifDU+csa+KqvDirMD51FpqkbtYD3skgPBukBsjl2PpcZctyiXYv2i8Y9LH0NZzym83/wx/r38V1gftQbb44ugU+nkjnfDREnEa+ffQpX5DG5J2oG1kavkjkREbkir1KAgajX2th5Az0SfW7y+e4qLv2vrsNSYC6XixvdIJSKi2eE3EURERER0XWx1h+AwNUJX+DAU3v6zOqekuwyCIGBN5Mo5TkeepGdgEm8WNyM7MQiFuRFyx7miAK0/VoTnYUV4HiRJgmnKjPqhJtQPN6LSVI1jPSchQECUPuLiLDJDEhID4qBRauSODofowPmhC6gw1eDMwDlYHFb4afRYG7kKecZcxPlFu90ShApBgTWRK5EbkoX3m/fiYGcJKkzVuC15J5aE5rjd8/mcJEl488IHONFXgR3xm1AUUyB3JCJyYwVR+fi0vRifdhzBvRl3yh1nwTg3WA+Lw4q80Fy5oxARLSgsxYiIiIjomokTg7CcegvKqEVQJa+e1TkzdguO91RgSWg2/LV+c5yQPIXdIeK5PXXQqpV4YFuaW5UYgiAgzMeIMB8jCqNXwyE60D7ehYahRtQPN+JQZykOdBRDJSiR4B/32UyyJMToo+ZtPzJREtE80ooKUzVO95/FpG0KXiov5IXmYqkxF8mGBI/YG81X44O7029HfsRyvNHwLl6sfR1lPeW4PeVmhPmEyh3vmkiShPebP0ZJdxmKYgqwLa5I7khE5OZ81T5YHbECR7rLsDNhMwJ1BrkjLQiVphroNb5INiTIHYWIaEFhKUZERERE10SSJMyUvgJIInRr75t1SXGqrwozjhkURM2uRCMCgA+OtaLdNI5Hb8mCv69W7jg3RKlQIsE/Fgn+sdgWXwSLw4qmkdYvSrI9LfuwpwXwUnkhxZCINEMSUgOTEeoV7NQyUJIkdI53o9x0GlXmMxixjEKjUCMrOANLjblID0qF2kOXN433j8X/WvY4SrtPYE/LPvzbqSexMWYdtsZthNYFZuvNxr62gzjQUYx1kauwO3G7WxXFROS6NsSsxZHuMhzqLMVXkm+SO47Hm7HPoHbwPPIjlnvEzSdERO7EMz/pEBEREdGcsTefgKPzDLSr7oJCHzKrcyRJwpHuMsToIxHvFzPHCclTNHWN4qPj7ViTFY681Nn9rLkTrVKDzKBUZAalAgDGrRNoGG5Cw1Ajzg81oqb/HADAoA1AamDSxeUWA5Pgp9Ff1/X6Js2oMFWj0lQN8/QAlIISGUEpuCVxO7JCMt2mFLpRCkGBgqh8LA7NwntNe7G//TDK+07jK8m7kBOyyKVLpoMdJfiwdT9Whi3F7Sk3u3RWInIvgToDlhpzcaznFLbFFcFH7Tr7d3qiMwN1sIl2Lp1IRCQDlmJERERENGvi9BgsZa9DEZoAdeamWZ/XMNyEvkkTvp5+B7/EpVmZttjx3Ie1CPLT4a6iZLnjzAu9xhdLjReXLZQkCf3Tg2gYbkT9UBPO9NfiRG8FACDCJwxpgRf3I0sKiIdOpbvsmEMzw6g01aDCVI2uiR4IEJBsSERRbAFyQ7IW9Jeefho97s2484slFZ8793tkBKbi9pSbEOrteiVsafdxvNP0IZaEZuPu9K9wZgEROd2mmEKc6qtCSVcZtsVzada5VGWuQYDWH/H+vFmMiGi+sRQjIiIiolmzHH8dknUKXusehKCY/ReyxV3H4Kv2QV5ozhymI0/yxqFGDIzM4Im7l8BLu/A+tgiCgFDvYIR6B2Nt5CqIkojO8W40DDWhfrgRJd3HcaizFApBgXi/mIv7kRmSEecXjSn7NE6bz6DCVI3m0TYAQKxfNG5L3oUlodkI0PrL++RcTFJAPH647Dso6T6OD1s+wb+e/CWKYguxJXY9NC4ye+5kbyXeaHgPi4LScV/GV1mIEdGciPANw6KgNBR3HcPGmHUu8xroaaZsU6gbvIDCqNV8PSciksHC+3RJRERERNfF3lENe9MJaJbcDGVg1KzPG5gewrmB89gcux5qpXoOE5KnON3Yj5KaXmxfGYuU6AC547gEhaBArF80Yv2isTluPawOG1pG29Aw3IT6oUZ83Pop9rYegFapgU20Q5REhPkYsSthC/JCcxHiHST3U3BpSoUS66PXYEloNt5p+hD72g6ivK8Kt6fcjKzgDFmznTafxe/P/wkphkQ8vOgeqDx0vzcicg2bYtfjyarfoay3HIXcB3ZOVPfXwiE5kGfkzWJERHLgu2kiIiIiuirJOo2Z0lehMERAs3jnNZ1b0l0GQRCwNnLlHKUjTzI6acXLH9cjJtQXu9fGyx3HZWmUaqQFJiMtMBk3J27DpG0KF4abcWG4CTqVDkuNuYjwCeNypdfIX+uHBzK/htURK/DGhffwX2dexqKgdNyecjOCvQLnPc+5gfN4qfZ1xPvH4u+y7+eNBUQ055IC4pHgH4tDHSVYG7ESSoVS7kgep8pcg2BdIGL0s7/JjIiInIdzdImIiIjoqiyn3oI0OQzdugchXMOXslaHFcd7ypETsggGHWf80JVJkoRXPq7HtMWBR3ZlQKXkx5XZ8lF7Y3FoFu5MvQU3J25DpG84C7EbkGJIxI+WfRe3JO3AhZFm/MvJn2Nv6wHYHLZ5y9Aw1ITnzv0ekb5h+FbOA9ByGTMimiebYgoxODOMKvMZuaN4nHHrBBqGm5BnzOXvaSIimfBTJhERERFdkb2vEba6Q1AvKoLSmHRN55b3ncaUfZrL79CslNT0oLppAF8pTERkiK/ccWiBUyqUKIopwP9Z8QNkBWfgo9YD+JdTv0TtYP2cX7tltA3/dfZlhHoF49Hch+Gl8przaxIRfW5RcDrCfIw40FEMSZLkjuNRTpvPQpRELp1IRCQjlmJEREREdFmS3QpLyYsQfAOhXXbbtZ0rSSjuOoZI33Ak+sfNTUDyGKbhKfzxYBPSYw0oWsrlhMh1GHQBeGjRPXgs92EoBAG/rXkRz559FUMzw3NyvY7xLvy25kUEaPzwWO4j8FX7zMl1iIguRyEoUBRTgO6JXtQNXZA7jkepMtcgzDsUET5hckchIlqwWIoRERER0WVZT++BONIL3dr7Iah113Ru00gLeib7UBi1msvD0BU5RBHPf1gHpULAQzvSoeDPC7mg9MAU/Gj593BTwlbUDTbgJyd+jk/aDsEu2p12jZ6JPjxT/Ty8VF54fPE34K/VO21sIqJrscyYiwCtPw60H5Y7iscYsYyiaaQVecYcvjcmIpIRSzEiIiIiuiTHYCes1XuhSl4NVXTWNZ9f3FUGH5U3lhoXz0E68iR7j7ejuXsM92xJQaDftZWvRPNJrVBhS9wG/J+VP0BGUCo+aNmHfzv1JOqHGm94bPPUAJ6ufg4qQYnHc7/BfRiJSFYqhQobo9eicaQFraMdcsfxCFXmM5AgIS+USycSEcmJpRgRERER/Q1JdGCm5EUIWm/oVt11zecPzQyjpv8c8iOWQ6NUz0FC8hStvWP44FgbVmQYsTKDSwmRewjUGfCNrHvxrZwH4ZBEPF39HF449z8Ynhm5rvEGp4fx1OlnIUoivr34GwjxDnJyYiKia5cfsQLeKi8c6CiWO4pHqDLVIMo3AkafULmjEBEtaCzFiIiIiOhv2M7th9jfCu3qeyDofK/5/NLuEwCAtZGrnB2NPIjF5sBze+rg56PBPZtT5I5DdM0yg9Lwz8u/h53xm3F2oA4/OflzfNpxBA7RMesxRi1jeLr6Wcw4LHgs9xGE+xjnMDER0ezpVFqsi8rHmf5amCbNcsdxawPTQ2gd60CekbPEiIjkxlKMiIiIiL5EHDPDUv4ulDG5UCUsv+bzrQ4bjvWcRHZIJoK8DHOQkDzFW4eb0Tc0hYd2pMNHxxmF5J7USjW2xRfhn1d8HykBiXi36SP8W/mvcGG4+arnTlgn8VT1cxizjuPRnAcRrY+Yh8RERLNXGLUaKoUSn3YckTuKW6sy1wAAl04kInIBLMWIiIiI6AuSJGGm0nKzaQAAIABJREFU5CVAoYBuzb3XtQl4pakak7YpFEblz0FC8hTnWgZxsKoLm5ZGIyMuUO44RDcs2CsIf5/zAL6ZfT9sDit+ffq/8VLt6xi1jF3y+CnbNJ6pfg6D04P4ZvYDiPePnefERERXp9f4YlX4cpzsq8KIZVTuOG6rylSDOL8YBHnxPQ8RkdxYihERERHRF+wNpXD0nId2xR1Q+F77h3ZJknCk6xgifMKQHJA4BwnJE0xM2/DC3vOICPbBbQUJcschcqqs4Az884ofYFvcRlSbz+InJ/4ThzpLv7Sk4ozdgt/WvIieSRMeyboPKQa+XhKR69oYsw4SJBzqLJU7ilsyTZrROdHDpROJiFwESzEiIiIiAgCIUyOYOfEHKMNToU4vvK4xWkbb0TnRg3VR+dc1y4w8nyRJeHVfPSambHhkZwY0aqXckYicTqNUY2fCFvzTiu8jwT8ObzfuwU/Lf42mkVZYHTb895mX0T7eiQcX3Y3MoFS54xIRXVGwVyCWhGbjWPdJTNmm5Y7jdirNNRAgYElottxRiIgILMWIiIiI6DOWo78HHDbo1j4AQbi+t4lHuo7BS+WF5WFLnJyOPMXx2j5UNPRj99p4xIbp5Y5DNKdCvYPxrZwH8UjWvZi2z+DJqt/hX0/9Eo0jLfh6+h3IDVkkd0QiolkpiinEjMOC0u7jckdxK5IkodJUg8SAOARo/eWOQ0REYClGRERERABsrRWwt1VCk7cbioCw6xpjxDKK0/1nsSp8KbRKjZMTkicYGJ3GawcuIDnKH9tWcP8kWhgEQUBuyCL8Pyt/gM2x6zFuHcddqbfy5gEicivR+gikB6bgcOdRWB02ueO4jZ7JPvRNmZEXmit3FCIi+gxLMSIiIqIFTrJMwnL091AExUCTvfW6xznafQKSJKEgKt+J6chTiJKEFz48D1ECHt6ZAYWCy2vSwqJVanBz4jb8Yt3/j9WRK+SOQ0R0zTbHrse4bQIn+yrkjuI2Kk01UAgKLA7NkjsKERF9hqUYERER0QJnOfEGpJlx6AoehKBQXdcYNtGOo90nkRmUhmCvICcnJE+w/1QnGjpH8LWiZIQEeMkdh0g23G+RiNxVckACYv2i8WlHCURJlDuOy5MkCZXmGqQEJEKv8ZU7DhERfYalGBEREdECZu+ug62hBJrsrVAGx133OFWmGozbJlAYvdp54chjdJon8E5JM5akhGBNVrjccYiIiOg6CIKAzTGFGJgexGnzWbnjuLyO8S4MTA8iz8ilE4mIXAlLMSIiIqIFSrJbMFPyEgQ/IzR5u29orCNdZTB6hyLNkOykdOQpbHYHnttTC2+dGvduTeUsGSIiIjeWHZKJUO9gHOgohiRJcsdxaZWmGigFJXJDMuWOQkREf4GlGBEREdECZal4F9J4P3Tr7oeg0lz3OK2jHWgf70RBVD4LD/ob75a0oqt/Eg9sS4Of9/X/nBEREZH8FIICRTEF6BzvRv1wo9xxXJYoiagyn0F6YAq81d5yxyEior/AUoyIiIhoAXKYW2A7+wnUaYVQRaTf0FhHuo5Bp9RiRdgSJ6UjT1HfPoxPTnWgcHEkcpKC5Y5DRERETrA8LA/+Gj0OtBfLHcVltY52YNgygjxjjtxRiIjor7AUIyIiIlpgJNF+cdlEL39oV95xQ2ONWsZRZT6DleFLoVPpnJSQPMHUjB0vfFSHUIMX7lyfJHccIiIichK1QoX10WvRMNyEjrEuueO4pEpzNdQKFbKDM+SOQkREf4WlGBEREdECY63eC3GoE9o190LQ3NhyLsd6TsAhObAuKt9J6chTvHbgAobHrXh4Vwa0GqXccYiIiMiJ1kSuhJdKh/0dxXJHcTmfL52YGZTOm8aIiFwQSzEiIiKiBcQx0gNr1QdQJSyDOu7Glju0i3Yc7T6BjMBUGL1DnJSQPEF5vRnHa/uwMz8WiRH+cschIiIiJ/NS6bA2chWqzWdhnuqXO45LaRxuwbh1gksnEhG5KJZiRERERAuEJImwHHkJUGuhzb/nhserNp/FqHUcBZwlRn9heNyCV/fVIz5cj535cXLHISIiojlSGLUGSoUSn3aUyB3FpVSaq6FVarAoKE3uKEREdAksxYiIiIgWCFvdIThMjdCt/CoU3jc+e6e4qwwhXkHICEp1QjryBJIk4cW952Gzi3hkVyZUSn7cICIi8lT+Wj1WhuXhZF8lRi3jcsdxCQ7RgWrzOWQFZ0Cj1Mgdh4iILoGfUomIiIgWAHFiEJZTb0EZmQlVypobHq9jrAutY+0oiFoNhcC3lHTRoapu1LYO4c4NSQgLvLH96oiIiMj1bYwpgEN0oLjrqNxRXEL9cCMm7VNYasyVOwoREV0Gv8EgIiIi8nCSJGGm9BVAEqFbez8EQbjhMYu7jkGj1GBleJ4TEpIn6B2cxJ8ONyErIQiFiyPljkNERETzINQ7GLmhWSjtPo5p+4zccWRXaaqBl8oLaYEpckchIqLLYClGRERE5OHszSfg6DwD7bLboPALueHxxq0TqDTXYGVYHrxUXk5ISO7O7hDx7J46aNVKPLA9zSnFKxEREbmHzTGFmLbP4Gj3CbmjyMrmsKGmvxY5IZlQK1RyxyEiostgKUZERETkwcSZcVjKXociNAHqzE1OGfNYzynYRTsKovKdMh65vw+OtaG9bxz3bU1FgK9W7jhEREQ0j2L8opBmSMbhzlLYRLvccWRTN9SAGccMloZy6UQiIlfGUoyIiIjIg1nKXodknYJu3YMQFDf+1s8hOlDafRxphmSE+RidkJDcXVP3KD463obVWWHISw2VOw4RERHJYFNsIUat4zjVVyl3FNlUmmrgq/ZBiiFR7ihERHQFLMWIiIiIPJS9oxr2puPQ5O6EMjDKKWPWDNRixDLKWWIEAJix2vH8njoE+enwtSLunUFERLRQpRqSEK2PxKcdRyBKotxx5p3FYcXZgTrkhmZBqVDKHYeIiK6ApRgRERGRB5Ks05gpfRWKgAhoFu902rjFnccQpDNgUXC608Yk9/XHg03oH5nGwzsz4KXl3hlEREQLlSAI2BRTCPPUAM7018odZ96dG6iDVbRhaWiO3FGIiOgqWIoREREReSDLqbcgTQ5DV/AgBKXaKWN2jfegebQV66LyoRD4NnKhO93Yj5KaHmxdGYOU6AC54xAREZHMFodmIdgrCPvbiyFJktxx5lWlqQb+Gj8kBsTLHYWIiK6C32YQEREReRh7XyNsdYegXlQEpTHJaeMe6ToGjUKN/PBlThuT3NPYpBUvf1yP6FBf7F6TIHccIiIicgEKQYFNMQVoH+/Ef1Y+g9rB+gVRjk3bp1E71IAlodm8cYyIyA1wjRMiIiIiDyLZrbCUvAjBNxDaZbc5bdwJ2yTKTaexPCwP3mpvp41L7keSJLz8cT2mLQ78410ZUKv45Q8RERFdtDpiBQRBwL62Q/htzYuI84vBjvhNSA9MgSAIcsebE2f662AX7cgzculEIiJ3wFKMiIiIyINYT++BONILr23fh6DWOW3c4z3lsIl2FETlO21Mck+lZ3pR3TSAr25IQlSIr9xxiIiIyIUIgoDVESuwIiwPJ3orsK/tEH5T8wLi/WKxI2ET0gzJHleOVZirEagzIM4vRu4oREQ0CyzFiIiIiDyEY7AT1uq9UCXnQxWd5bRxRUlESfdxJAckINI33GnjkvsQRQm9Q1No6x3DHz5tRHqsAUXLouWORURERC5KpVBhTeRKrAxfiuO9Ffik7RCeqX4eCf5x2BG/CamGJI8oxyZsk6gfasTG6HUe8XyIiBYClmJEREREHkASHZgpeRGC1hu6VV9z6thnB+owNDOM25J2OnVcck0OUUTv4BTa+8bR1jeOdtM4Ok0TsNgcAIAAXw0e2pEOBb/4ISIioqtQKVRY+3k51lOOT9oP4enq55DoH48d8ZuQYkh06zKpxnwOoiRy6UQiIjfCUoyIiIjIA9jO7YfY3wrdhm9C0Dl3SbvirjIYtAHICs5w6rgkP7tDRM/AJNo/K7/a+8bRaZ6A1S4CADRqBWKMeqzJDkdcmB6xYXqEB3lDqeA+YkRERDR7aoUK66JWYVXEMpT1nMInbYfwVPWzSAqIx474zUgxJMod8bpUmmsQ6hWMKN8IuaMQEdEssRQjIiIicnPimBmW8nehjMmBKnGFU8fumejDheEm3JywDUqF0qlj0/yyO0R090+i3fTZDLC+MXSaJ2F3XCzAtBolYkN9UZAbibgwPWLC9AgP9IZC4b53bxMREZFrUStUKIjKR374MhzrOYX97Yfw69P/jeSABOyI34xkQ4LcEWdt1DKOC8PN2Bq3wa1nuxERLTQsxYiIiIjcmCRJmCl5CVAooFtzn9M/kB/pOga1QoX8iOVOHZfmls0uoqt/4osZYG194+jun4DdIQEAvLRKxBr12JgXiVjjxRlgxkBvLolIRERE80KtVKMwejXyI5bjWM9J7G8/jF+d/i+kGJKwI34TkgLi5Y54Vaf7z0CChDxjrtxRiIjoGrAUIyIiInJj9oZSOHrOQ7vmXih8A5069pRtCqf6qrDUuBi+Gh+njk3OY7U50Nk/gY7P9wDrG0f3wCQc4sUCzFurQmyYHkVLo79YAjEkwIsFGBEREclOo1RjffQarI5YgaM9J7C//TCerPodUg1J2BG/GYkBcXJHvKwqUw0ifMIQ7mOUOwoREV0DlmJEREREbkASRUiWCUjT45BmxiDNjEOaHoOl/B0ow1OhTi90+jWP91bAKtpQELXa6WPT9bHYHOg0X5wB1tY3hva+CfQMTEKULhZgPjoV4sL02LI8BrGfF2D+Oi7pQ0RERC5No1RjQ/RarIlYgdLuEzjQXoxfVv0WaYZk7EjYjAT/WLkjfsnwzAiaR9uwK2GL3FGIiOgasRQjIiIikoEkOiDNTHxRbl38c/zin5f8uwkA0t+MI+j00K29H4KgcGo+URJR0lWGRP84ROu5cbgcZqx2dJj+vARie984egYn8Vn/Bb23GrFheuQkBX0xAyzIjwUYERERuS+NUoONMeuwJnIlSruP40B7MX5R+RukB6ZgR/xmxPvHyB0RAFBprgEALAnNkTkJERFdK5ZiRERERE4gifaLJdf0X5daf1t4idNjgGUKlyq5AAGC1geClx6Clx8UhggIOv3F/9Zd/Lsv/bdOD0GhdPrzqR2sx8DMEG5K3Ob0selvTVvs6Pis+Gr77M++wakvfkL8fDSIC9NjSUrIFwWYQa9lAUZEREQeSavUoCimAGsjV6GkqwyfdhzBzyufQUZQKnbEb0Kcn7zlWJXpDGL0kQj1DpY1BxERXTuWYkRERESXIDnsfzFr67MlC6fHv1xwTY9B/OwYWCYvM5IAQef7RYmlMERCGZH+F8WW35cLL63PnJRc1+pIVxkCtP7IDVkkdxSPMzVjQ7vpL5ZANE3ANDT1xeMBvhrEhflhWVoo4sL8vijAiIiIiBYarVKDTbGFF8ux7ovl2H9WPIPMoDTsiN+EWL/oec/UPzWI9vFO3JK0Y96vTUREN46lGBERES0Ykt0KS18r7L19fzWD62LhJf5FCQbr1KUHEYQ/z9DS6aEMiv6rYuuvii6tLwSFc5c2nGumSTPOD13AzvgtULpAQefOZqx2NPeMoaNvHG19F2eAmUemv3g80E+LWKMeqzKNF2eAGfXw92UBRkRERPSXdCotNseux7rIVTjSVYaDHSX4WcXTWBSUjh3xmxDjFzVvWf68dGL2vF2TiIich6UYEREReTzJboXtfDGs1R9iYnrsyw8KioszuT4rshRBsZeewfV54aX1cfr+Xa7mSHcZVIISayJXyB3FbVmsDnxa2Yl9JzswOWMHAAT56RAXpsea7HDEflaA+floZE5KRERE5D50Kh22xG3Auqh8HOk6hoMdJfiPiqeQFZyBHfGbEK2PnPMMVeYaJPjHIlBnmPNrERGR87EUIyIiIo8liXbYGo7CWvUBpMkhKCPSEbT1YYzbtRcLMJ0foPX2+JLrWkzbZ3CitwJ5xlzoNb5yx3E7NruII9Xd+PB4O8YmrchKCELR0ijEh/vB10stdzwiIiIij+Cl0mFr3EYUROWjuPMYDnaW4qflv0ZOcCa2xW9CtD5iTq7bO2lC90Qvbk++eU7GJyKiucdSjIiIiDyOJImwN5+EpeI9SGMmKEIToCt8GKrIDPiG6DHdPy53RJd1orcCFocVBVH5ckdxKw5RRNnZPnxwrBWDYxakRAfg0VsWITkqQO5oRERERB7LS+WFbfFFKIxejcOdR3GosxQ15bXICVmEHfGbEOkb7tTrVZpqIEDAYi6dSETktliKERERkceQJAn29tOwlr8DcbgLisAo6LZ8B8qYXAiCIHc8lydKIkq6yhDvFyPLpuXuSJQklJ83472jrTANTSEuTI/7tqUhMy6QP3NERERE88RL5YXt8ZtQGLUGhztLcajzKGr6zyE3JAvb44ucUo5JkoQqcw2SAxLgr9U7ITUREcmBpRgRERG5PUmS4OiuhaX8HYj9LRD8jdBt+CZUicu5NOI1OD/UCPP0AO6Pv0vuKC5PkiTUNA3inZIWdPVPIDLEB4/dmoXFycEsw4iIiIhk4q32wo6EzVgfvQaHOktxuPMoqvvPYnFoNrbHFSHCN+y6x+6a6IVpqh8botc6MTEREc03lmJERETk1hx9jbCUvw1Hbz0En0Bo1z0AdcoaCAql3NHczpGuY/DT6LE4NEvuKC7tfNsQ3ilpQXPPGEIDvPCNXRlYnm6EQsEyjIiIiMgVeKu9sTNhC9ZHr8WhjhIc7jqKavNZLAnNxrb4IoT7GK95zEpTNRSCArkhfK9MROTOWIoRERGRW3IMtF8swzrPQPDygzb/bqjTCyEo1XJHc0vmqQHUDTZgW9xGqBR8i3gpzd2jeKekBefbh2HQa3Hf1lSszgqHSsnZiERERESuyEftjV2JW7E+Zi0OdpTgSNcxVJnPYEloNrbHFyFsluXY50snphmS4avxmePUREQ0l/iNBxEREbkVx0gPrBXvwt5SDmh9oFn+FWgyN0FQa+WO5tZKussgCALWRK6UO4rL6TCN492SFtQ0D0LvrcZXNyZj/eIIqFWcjUhERETkDnzVPrg5cRs2Rq/Dwc4SFH9WjuUZc7A9rghGn9Arnt801IbBmWFsj980T4mJiGiusBQjIiIityCO9cNS9R7sjWWASgvN4l3QZG+FoOWdmjdqxm7B8Z4KLAnNhr/WT+44LqN3cBLvH23FqfNmeGtVuHVdAoqWRkGn4VtoIiIiInfkq7lYjm2I/vPMsUpTDZYaF2Nb/EYYvUMued6xjgqoBCVyQjLnOTERETkbP9ETERGRSxMnh2E9vQe2+iOAIEC9aDM0uTug8GJ54yyn+iox45hBYdRquaO4hIHRaXxwtA3HzvVCo1Jix6pYbF0RAx8dl+YkIiIi8gR6jS92J23Hxph1ONBRjJKu46gwncbysCXYGrcBoX9RjomSiOOdlcgISoOXykvG1ERE5AwsxYiIiMglSTMTsFR/BFvtp4AoQp22DpolN0HhY5A7mkeRJAlHusoQo49CnF+M3HFkNTphwYdl7Siu7oYgABvzorBjVRz8fTRyRyMiIiKiOaDX+OLWpJ0oiinAgfZilHafQLnpNJYZF2NbXBFCvIPQPNKG4elR7E7YIXdcIiJyApZiRERE5FIk6zSsZ/bBevYTwGaBKnkVtHm7ofC78jr/dH0ahpvQN2XGvel3QhAEuePIYmLaho9PtuNgRRfsDglrssNx0+o4BPrp5I5GRERERPPAT6PHbcm7UBRTiE87ilHafRzln80cs9gt0Co1yArOkDsmERE5AUsxIiIicgmS3QJb7UFYqj8CLJNQxeVBs/RWKAMj5Y7m0Yq7jsFX7YMlxhy5o8y7aYsdB8o78Ul5B2YsDqzIMOLmtfEwGrzljkZEREREMvDXfl6OfTZzrOcE7KIdq6LzoFVy9QAiIk/AUoyIiIhkJTnssNUXw1q1B9L0KJTRWdAuvQ3KkDi5o3m8gekhnBs4jy2x66FWLJy3hVabA4equrH3RDsmpm1YnByMW9YmICrUV+5oREREROQC/LV++ErKTSiKLcDxnnJsTFsFWORORUREzrBwvv0gIiIilyKJDtgby2Cpeh/S+ACUYSnQFH0LqvBUuaMtGCXdZRAEAWsiV8odZV7YHSJKz/Riz7FWjExYkRlnwC3rEpEQ4Sd3NCIiIiJyQQFaf2yLL0KInx79/eNyxyEiIidgKUZERETzSpJE2FsqYK14B+JoHxTBcdCtuQ/KqEULdk8rOVgdVhzvKUdOyCIYdAFyx5lToijheG0f3j/aioHRGSRF+ePvbspEaoxB7mhERERERERENI9YihEREdG8kCQJjs4aWMrfgTjYAYUhArpNj0EVl8cyTAblfacxZZ9GYdRquaPMGUmSUNnQj3dLW9A7OIUYoy++uzkbWQlB/JkjIiIiIiIiWoBYihEREdGcs/ech6X8bYimJgj6EOgKH4EqaRUEhULuaAuSJEko7jqGKN8IJPrHyR3H6SRJwtmWIbxb0oJ20zjCg7zxrd2LsCQ1BAqWYUREREREREQLFksxIiIimjMOcwss5W/D0V0LwccA7Zr7oE5bC0HBtyByahxpQc9kH+5Ou93jZkw1dAzjnZIWNHaNIthfh4d2pGNVZhgUCs96nkRERERERER07fiNFBERETmdY6gT1vJ3YG8/DUGnh3blXVBnrMf/Ze++o+Oq7/z/v+6doplR16h3yZZlWe6m2fSwhBI2ECeB1A0bElLJbkJ+37Nn/8v+ud9skmW/KXs2QAokAVIgAZJsSKFDMC4gucm2JFuS1UZtpBlNu/f3x8iyREwxSL4a6fk4Z46mXI1e9yCPfe6L9+djuL1OR4OkJ3ueVbYnoPPKNjsdZcF0npzQL586pvbOEeXnePXxd6/RpZsq5XYxjQgAAAAAANIoxQAAwIKxxvsV2/WwkkdflLw+ec/bKe/6q2V4/U5Hw4yR6VHtG2rX39VeLq/L43Scd6x3aFK/erpTuw8PKcfv0c1XrtaVW6uU5XE5HQ0AAAAAACwxlGIAAOAdsyZDiu9+RIlDz0gut7ybr5d343UyfDlOR8NrPN37giTp0qrtDid5ZwZGI3rkmU692D6gLK9LN13SoKvPr5E/i3/eAgAAAACAM+OqAQAAeNusyLjiex9VYv+fJUme1qvk3fwemYECh5PhTOKphJ7te1EbS1oV9Bc6HedtGZmY1m+e69Izr5yUyzR07YW1uu6iOuX4M3/qDQAAAAAALC5KMQAAcNbs2JTi+36reNv/SqmkPGsukXfbjTJzgk5Hwxt4eWCvphIRXVG9w+koZ21iKq7HX+jWn3b3yrZtXb65UjfsqFdBTpbT0QAAAAAAQIagFAMAAG+ZHY8q3vYHxV/5rRSPyr3qImVtu0lmQbnT0fAmbNvWkz3PqjK7XE0Fq5yO85ZFphP63V+P6w8v9SieTOni9RV678X1Ki5gnzoAAAAAAHB2KMUAAMCbspNxJfb/WfG9j8qeDstdt0Xe83bKFaxxOhreomPj3Tox2acPN++UYRhOx3lTsXhKT7x8Qr994bgisaTOX1uqmy5tUEUw2+loAAAAAAAgQ1GKAQCA12VbSSUOPaP47kdkT43KVbVOWeftlKtstdPRcJb+0vOM/G6/zi/f6nSUN5RIpvSXPX167PkuTUQS2rQqqPdd1qjaslynowEAAAAAgAxHKQYAwApn27YUm5I1MZi+hYdkn7o/dlJ2dEJm2Wr5rrxd7soWp+PibRiLjWvvUJuurL5EWS6v03Fk27bC0YSGxqIaGo1qcCw6e78vFNFkNKG1tQX64uWrtLoq3+m4AAAAAABgmaAUAwBgBbCtlOypEVkTQ7ImBk+XXuH0Y8Wj8443/Pky80rlql4vT+MFctVuyogl93BmT/e+INu2dVn19nP2M1OWpdBETEOj6cJrcKb0OnV/Op6ad3xBjlelBX5tWh3URa3laq0vOmdZAQAAAADAykApBgDAMmEnptOlV/hU6TU0M/01JDs8LNlzSgjTJSO3WGZeqTylq2XmlcjIK5WZVyozt0SGJ8u5E8GCSlhJPdv7otYXr1WxP7ig7x2NJdMTXmcovULjMVm2PXus22WqpMCnkgK/mmoKVFrgV0mBXyWFfhXn+5TlcS1oNgAAAAAAgNeiFAMAIEPYti07Op4uuU5Nes1Z7tCOTsz/Bm8gPe1VXCez8bz5pVd2kQzTdOZEcE7tHtincGJSl1dffNbfa9m2xifj6aJrpvCaLcDGogpHEvOOz/a5VVroV0NFni5o8au0wK/SwnT5VZCbJZNpQwAAAAAA4CBKMQAAlhA7lZAdDp1hf6/0BJiS8TlHGzJyimTmlshVu3mm9CpJF195pTKysh07DywdT/Y8p7JAqdYWNp3x9UTS0vD46dJrduJrfFpDY1ElktbssYYhBfPS015bmkpmC6/01JdPAZ/nXJ0WAAAAAADAWaMUAwDgHLNjU7PLGs7u7zWzt5c9OSLp9JJzcnlniy5XdavM3FOlV4mM3GIZLkoIvL7O8ePqDp/QjQ1/r67+sAZHT095Dc3cHwvH5v7GKcvjUkmBT2WFfm1oLDpdehX6Fczzye1iwhAAAAAAAGQmSjEAABaYbVmyp0ZOF13z9vcalOKReccb/jwZeaVyla85PeU1U4QZ/nwZLDmHN5GyLI1OxDQ4Nr/0OuJ6UvK79bOHopK1a/b4/GyvSgr9aqkrnFd6lRT4lRfw8DsHAAAAAACWJUoxAADeAdu2ZI2dVKq/Q6n+w7IGj8kKD0lW6vRBhktGbrHMvBJ5ShtnprxKT098eXzOnQAyhm3bGpuMq38kkr6FIhoYTd8PjU8rZZ2e93KZhoqChmL1PSq3W3ThFWtVcqr4yvcry+ty8EwAAAAAAACcQSkGAMBZsFMJWUNdSs6UYKlIwgpSAAAgAElEQVSBDik2JSk98eUqXSV3/daZ/b1KZeaWyMgpkmFSQuCtmY4nNTAS1cmRqZniK6r+UET9oxHF4qfLVq/bVFlRQLVluTp/bWm69JrZ26so16ffdj+hxzstfWb79SoNlDh4RgAAAAAAAEsDpRgAAG/Ajk0pNXBkdhIsNXRMSiUlSWZ+uTz12+Qqb5KrvElGXhnLzuEtsSxbw+PRmamvma+hKfWPRDQ2GZ89zpAUzPepvCigpuoKlRUFVB4MqKIooILcLJmv8/uWtJJ6pvcFrQs2U4gBAAAAAADMoBQDAGCGbduyJ0Pp8qu/Q6n+DlmjPekXDZfMkjp5Wv8uXYKVNcn05zkbGEteODJnucOZJQ/7RyIaGosqmTq93GG2z63yooBa64vSxddM+VVW6JfHffZThnsHX9VEPKwrqi9eyNMBAAAAAADIaJRiAIAVy7YsWaM980owe2ok/aLHJ1fZanlXXZAuwUobZbiznA2MJSmRTGlgNKqBucXXaPrr1HRy9jiXaai00K/yooA2ry6eLb7KiwLK8XsWdMrwLz3PqdRfrJaiNQv2ngAAAAAAAJmOUgwAsGLYyZhSg52ze4Gl+o9IiagkycgulKt8zekpsKIaGabpcGIsFbZtazQcmz/xNVN8hcanZc85tiDHq/KigM5fWzqv+Arm++Q6B79Txyd61DnRrQ80vVemwe8wAAAAAADAKZRiAIBly4pOzJRf6f3ArKFuyU5JkszCanlWXzSzH9gaGTlB9gODorHkvOJrYPR0ARZPWLPHZXlcKi8KqLEyTzvWl88WX2WFAfmznP3n1V96npXX5dVFFdsczQEAAAAAALDUUIoBAJYF27ZlTwzOWQrxsKzx/vSLpluu0kZ5N107sxTiahm+HGcDwzGplKWBkYhOjkTmL3k4EtH4VHz2OMOQSvL9KisKqLm2cLb4Ki8KqCDHuyRL1HB8Ui8P7tOOigvkd/udjgMAAAAAALCkUIoBADKSbaVkhY7PK8Hs6ET6xazs9H5gzZeml0QsrpPh9jobGIvGtm3FEimFI4mZW1wTkbgm5zwORxOamIorHElobDKmlHV6wcMcv0flRQFtaAyenvgqCqi0wC+PO7OWH3y2769KWkldXr3D6SgAAAAAAABLDqUYACAj2PGoUoNH0wXYQIdSA0elZEySZOSWyFW9fnZPMLOgQgZ7KWUs27Y1HU+ly6zXKbbC0fTXyUhcE5GEEknrjO/lcZvKC3iUE/AqN+BRRTBbVWW5yvO5ZwuwHL/nHJ/h4khZKT3d+7zWFjapPLvU6TgAAAAAAABLDqUYAGBJsiJj85dCDB2XbFsyDJlFtfKcmgIrb5KZXeh0XLwB27YVjaVmi6zTZdf8rxNzSrBk6swll9djKtefLrjyAl5VF2crd6bwypl57tTj3IBHWR7X3yxzWFKSq6Gh8Lk49XNq33C7xmLj+lDz+5yOAgAAAAAAsCRRigEAHGfbtqyxk/OXQgwPpV90eeUqWyXvlr9Pl2Clq2R42SvJSemSK6mJ1ym2Juc+H03fT6bsM75Xlsc1W2AV5GSppjRnttTKmy23vMr1p79meV3n+Gwzx19OPKugr0itwbVORwEAAAAAAFiSKMUAAIvOtizZ02HZ0YmZ2/js/XQZ1iE7NilJMvx5cpU1ydV6lVzla2QW18ow+etqscUTKU1E4pqYSi9RmL4fnzO9lX4tHE2XXnP35Jory+tS3kyRVZibpdqy3NPF1pyvpwovr4eSayH0hPt0dLxT71v9HpksHQoAAAAAAHBGXGUEALwtdirxmpIrLCs6Ljsy89z0xMz9cdnTk5LOUKKYbhm5xXLXb5ldCtHIK/ub5e5w9k4tWThbbs0rul5TfE3FNR1PnfF9fF7XbIFVnO9TfUXunAmudMk197HHTcnlhCd7npXX9GhHxflORwEAAAAAAFiyKMUAAJLSJYoS06cnuF4z0fXa5xWPnvmNPD4Z/jwZ/jyZ+aUyylfPPjb8+ennZx7LG6AAOwuWZWtyOvGakuu1j08/f6Z9uQxJ2X6P8rK9ygt4VF+eLrnysmdus/fT01xMci19k4kpvTSwRxeUb1PAE3A6DgAAAAAAwJJFKQYAy5htW1Iskp7gmltuRcbnFFynJ72Uip/5jbKyZc4UWq5g7emSK5Av05cnI5B3+jl31rk9yQyXTFl/W3BFXlNyTSVmljGMyz7DwJ3LNNJLEs4UW5XF2XMKLs+8sis34JHLZHm95eT5vpeUsJK6ovpip6MAAAAAAAAsaZRiAJBhbCspOxp+zQTX+JyCa+4tLNlnWBbPMOdMb+XJLCifM8GVP+81w5/Lnl5naTqePL1E4WuWKZz7fDgS19R08ozv4fWYygt4lZ/tVXG+T42VebPTXXnZ6edzZ4qubJ+bibsVKmWl9GTPc1pTsEqVOeVOxwEAAAAAAFjSuMoJAEucnUoqdfKgkl27lTy+T/Zk6MwHujynlyjMLpKruH52msvw5aa/niq6srJlGEwLLZSJqbj2HhnW3o5hHTw++rr7c2X73DPTWl5Vl+YoP+BV7swkV/q+d/Z+lpdlCxeabdt68PDDeumpPenlQpcBS7biqbg+sOa9TkcBAAAAAABY8ijFAGAJsuMRJY+/omT3HiWPvyIlopLLK3fNepnNl86WX+aciS55fEwLnUP9IxHt6RjSnsPDOto7LltSMC9LF7WWqyTfN1t+5WefXrbQ7aKIdNLTvS/oqd7ndUH1ZuUYuU7HWTDZnmxtLF7ndAwAAAAAAIAlj1IMAJYIa3IkXYJ171Gq74BkpWT4cuVpPE/uuq1yVa9jvy4HWbatzr4J7ekY1p6OIZ0MRSRJtWU5eu8lDdrSVKya0hyKySXq+ESPftHxa60LNusrOz6t0PCU05EAAAAAAABwjlGKAYBDbNuWNdqjZFe6CLOGOiVJRn6ZPOuvlrt+m1ylq2SYTBc5JZFM6UD3qPZ0pJdGHJ+KyzQMNdcW6MotVdrcVKzifL/TMfEmIomo7m67TzneHH2i5UMyWToUAAAAAABgRaIUA4BzyLZSSvV3pCfCunbLDg9JkszSRnnP/4Dc9VtlFlQwbeSgqemEXjkS0u6OIbUdG1EskVKW16UNjUFtaSrWxlVBZfs8TsfEW2Tbtu47+JBGYmP68tbPKseb7XQkAAAAAAAAOIRSDAAWmZ2IKdnTpmT3bqW698mOTUqmW66qdXJvfo/cdZtlBgqcjrmiDY9H08siHh7S4RPjsmxb+TlebW8t0+amErXUFcrjZrooE/2l51ntG2rT+1a/R4359U7HAQAAAAAAgIMoxQBgEVjRiZlpsD1K9bZLqYTkDchdu0nu+q1yV6+X4WXZPafYtq3jA5Pa0zGkPR3DOjE4KUmqLM7WdRfVanNTsRoq8mQysZfROse79csjj2pjcauuqrnM6TgAAAAAAABwGKUYACwQa6xfye7dSnTtljVwVJItIycoT8sVctdtkatijQyTj12nJFOWDp8Y057Dw9p7ZEihiZgMSaur83Xzlau1palYZUUBp2NigUwlIrq77X4VZuXr4y0fZElSAAAAAAAAUIoBwNtl25aswWOz+4NZYyclSWawVt5tN8pdt0VmsJaL8Q6KxpJq6xzRnsNDeuVoSJFYUh63qdb6Ir334gZtWl2svGyv0zGxwCzb0o/2P6CJeFh3bvu8Ah7KTgAAAAAAAFCKAcBZsZNxpfoOKNm1R8nuPbKj45JhylW5Vlnr3pUuwnKLnY65oo2GY9p7ZFh7OoZ0sHtUyZStHL9HW9YUa2tTidY1FCnL43I6JhbRH48/pbbQAX1wzY2qy6txOg4AAAAAAACWCEoxAHgT9vSkksf3pSfCTrwqJWOSxyd3zQa567bIXbtJRla20zFXLNu21ReKaM/h9P5gnScnJEmlBX69a2u1tq4p0eqqfJkmE3srwZGxTv362O+0pXSjLq/a4XQcAAAAAAAALCFLqhSLx+PauXOn/vVf/1U7dnAhC4BzrPDQ7DRY6uQhybZkBArkadoud91WuapaZLg8TsdcsSzL1pHece3pSBdhg6NRSVJDRa52XtaoLU3FqizOZunKFSYcn9Q9bfcr6CvUR9e+n//+AAAAAAAAmGfJlGKxWEx33nmnOjo6nI4CYAWybVtW6LiSXbuV7N4tK3RCkmQWVsq76Xq567fILGmQYZgOJ12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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C0V3[['freqV3ON', 'freqV3OFF', 'freq_delta']].plot(figsize=(30,10))\n",
    "plt.legend(fontsize = 20)\n",
    "plt.tick_params(labelsize = 20)\n",
    "plt.yscale('log')\n",
    "# double click image to zoom in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Amplitude plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C0V3[['ampV3ON', 'ampV3OFF','amp_delta']].plot(figsize=(30,10))\n",
    "plt.legend(fontsize = 20)\n",
    "plt.tick_params(labelsize = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Export to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_csv = C0V3.to_csv('C0V3jul31.csv')"
   ]
  },
  {
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}