Sync.ipynb

{
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reset css and font defaults in:\n",
      "C:\\Users\\User\\.jupyter\\custom &\n",
      "C:\\Users\\User\\AppData\\Roaming\\jupyter\\nbextensions\n"
     ]
    }
   ],
   "source": [
    "# theme switcher\n",
    "#!jt -t oceans16\n",
    "!jt -r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#How-To-Use\" data-toc-modified-id=\"How-To-Use-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>How To Use</a></span></li><li><span><a href=\"#Synchronize-Behavior-Events-from-Phenosys-with-Recorded-Events-from-Neuron\" data-toc-modified-id=\"Synchronize-Behavior-Events-from-Phenosys-with-Recorded-Events-from-Neuron-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Synchronize Behavior Events from Phenosys with Recorded Events from Neuron</a></span><ul class=\"toc-item\"><li><span><a href=\"#Load-&amp;-Read-Files\" data-toc-modified-id=\"Load-&amp;-Read-Files-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Load &amp; Read Files</a></span><ul class=\"toc-item\"><li><span><a href=\"#Load-Neuron-Files-&amp;-get-TTL-Signals-->-moved-to-sync-class-✔\" data-toc-modified-id=\"Load-Neuron-Files-&amp;-get-TTL-Signals-->-moved-to-sync-class-✔-2.1.1\"><span class=\"toc-item-num\">2.1.1&nbsp;&nbsp;</span>Load Neuron Files &amp; get TTL Signals -&gt; moved to sync class ✔</a></span></li><li><span><a href=\"#Load-CSV-File-->-moved-to-sync-class-✔\" data-toc-modified-id=\"Load-CSV-File-->-moved-to-sync-class-✔-2.1.2\"><span class=\"toc-item-num\">2.1.2&nbsp;&nbsp;</span>Load CSV File -&gt; moved to sync class ✔</a></span></li></ul></li><li><span><a href=\"#convert-ttl-durration-to-events\" data-toc-modified-id=\"convert-ttl-durration-to-events-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>convert ttl durration to events</a></span></li><li><span><a href=\"#Align-Neuron-TTL-and-Phenosys\" data-toc-modified-id=\"Align-Neuron-TTL-and-Phenosys-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Align Neuron TTL and Phenosys</a></span></li><li><span><a href=\"#Combine-DataFrames\" data-toc-modified-id=\"Combine-DataFrames-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Combine DataFrames</a></span><ul class=\"toc-item\"><li><span><a href=\"#experiment-with-multi-level-columns-(not-working-with-qgrid)\" data-toc-modified-id=\"experiment-with-multi-level-columns-(not-working-with-qgrid)-2.4.1\"><span class=\"toc-item-num\">2.4.1&nbsp;&nbsp;</span>experiment with multi level columns (not working with qgrid)</a></span></li></ul></li><li><span><a href=\"#Separate-in-Trials\" data-toc-modified-id=\"Separate-in-Trials-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>Separate in Trials</a></span></li></ul></li></ul></div>"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How To Use\n",
    "\n",
    "**1. define session & folder -> must be name of folder in parent folder with all behavior data**\n",
    "    - folder/session/electrophysiology/digitalin.dat\n",
    "    \n",
    "**2. prepare csv from Phenosys**\n",
    "    - partent/session/behavior/output.csv\n",
    "    - example included in repository\n",
    "    - headers: DateTime,SystemMsg,probability,gambl side\n",
    "    - all other columns must be deleted\n",
    "\n",
    "**3. create sync object** \n",
    "    - sync_obj = Sync(session, folder, number_of_ttl_channels_in_ditialin.dat, channel_with_information(default=1))\n",
    "\n",
    "**4. investigate ttl signal and how it is overlapping with csv output (1.3)**\n",
    "\n",
    "**5. create combined dataframe & separated trials**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synchronize Behavior Events from Phenosys with Recorded Events from Neuron\n",
    "There are two methods available,\n",
    "first finiding the best fit of consequitive trials in the Neuron Event list by comparing timestamps to the Phenosys Event list\n",
    "second (an also to varify the accuracy of the first method each single event withouth paying attantion to consequitive trials is compared from Neuron list to Phenosys list and then complete trials are recronstructed from these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "import scipy.stats as st\n",
    "import importlib\n",
    "import os\n",
    "import sys\n",
    "import platform\n",
    "import qgrid\n",
    "import datetime\n",
    "\n",
    "from sync_class import Sync\n",
    "\n",
    "\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.max_rows', 100)\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "session = 'JG14_190621'\n",
    "\n",
    "# load calss and set folder depending on platform\n",
    "if platform.system() == 'Linux':\n",
    "    # Linux\n",
    "    os.chdir(\"/home/max/ExpanDrive/Google Drive/3.1 Code Repository/1.4-klausbergerlab-sync/\")\n",
    "    from sync_class import Sync\n",
    "    folder = \"/home/max/ExpanDrive/Google Drive/3 Projekte/Masterarbeit Laborarbeit Neuroscience/1 Data Analysis\"+'/'+session\n",
    "elif platform.system() == 'Windows':\n",
    "    # windows\n",
    "    os.chdir(r\"C:\\Users\\User\\Google Drive\\3.1 Code Repository\\1.4-klausbergerlab-sync\")\n",
    "    from sync_class import Sync\n",
    "    folder = r\"C:/Users/User/Google Drive/3 Projekte/Masterarbeit Laborarbeit Neuroscience/1 Data Analysis\"+ r\"/\" + session\n",
    "    \n",
    "    # create sync object \n",
    "sync_obj = Sync(session, folder, 7, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cell_style": "split",
    "scrolled": false
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   "outputs": [
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      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ttl1 = sync_obj.ttl_signals['channel 1']\n",
    "qgrid.show_grid(ttl1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cell_style": "split",
    "scrolled": false
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   "outputs": [
    {
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      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "csv = sync_obj.csv\n",
    "qgrid.show_grid(csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b3312a3bc7a4e88a00b283bce3f463d",
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       "version_minor": 0
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      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first find missing rows\n",
    "missing_rows = (1900,1931,1996,2058,2127)\n",
    "combined = sync_obj.combine_dataframes(missing_rows, 'channel 1')\n",
    "\n",
    "qgrid.show_grid(combined)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Load & Read Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "if platform.system() == 'Linux':\n",
    "    # Linux\n",
    "    folder = \"/home/max/ExpanDrive/Google Drive/3 Projekte/Masterarbeit Laborarbeit Neuroscience/1 Data Analysis\"+'/'+session\n",
    "elif platform.system() == 'Windows':\n",
    "    # windows\n",
    "    folder = r\"C:/Users/User/Google Drive/3 Projekte/Masterarbeit Laborarbeit Neuroscience/1 Data Analysis\"+ r\"/\" + session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# load neuron binary files to array\n",
    "with open(folder+'/electrophysiology/digitalin.dat', 'r') as f:\n",
    "    #a = np.fromfile(f, dtype=np.uint32)\n",
    "    binary = np.fromfile(f, dtype=np.uint16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Load Neuron Files & get TTL Signals -> moved to sync class ✔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# find length of ttl signal\n",
    "def ttl_find_lenght(data_frame, column, zeros=False):\n",
    "    # calculate length of ttl signlas for each frame\n",
    "    df = data_frame\n",
    "    frame = column\n",
    "    change = np.where(df[frame].values[:-1] != df[frame].values[1:])[0]+1\n",
    "    change = np.insert(change, 0, 0)\n",
    "    values = np.empty((change.shape[0],),int)\n",
    "    values[::2] = 0\n",
    "    values[1::2] = 1\n",
    "    output = pd.Series(values, index=change)\n",
    "    diff = np.diff(output.index)\n",
    "    last = df.shape[0] - output.index[-1]\n",
    "    diff= np.append(diff, last)\n",
    "    output_df = pd.DataFrame({'start':output.index, 'value':output.values, 'length':diff})\n",
    "    if zeros:\n",
    "        return output_df\n",
    "    else:\n",
    "        return output_df.loc[output_df['value']>0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# get individual channels ttl signals from binary array\n",
    "ttl_signal = pd.DataFrame(columns=['frame 0', 'frame 1', 'frame 2', 'frame 3', 'frame 4', 'frame 5'])\n",
    "for i in range(7):\n",
    "    key = 'frame '+str(i)\n",
    "    ttl_signal[key]=np.bitwise_and(binary, 2^i)\n",
    "ttl_signal.index.name = 'sampling rate 20kHz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# calculate length of ttl signals 0 and 1 for each channel\n",
    "ttl0=ttl_find_lenght(ttl_signal,'frame 0')\n",
    "ttl1=ttl_find_lenght(ttl_signal, 'frame 1')\n",
    "ttl2=ttl_find_lenght(ttl_signal, 'frame 2')\n",
    "ttl3=ttl_find_lenght(ttl_signal, 'frame 3')\n",
    "ttl4=ttl_find_lenght(ttl_signal, 'frame 4')\n",
    "ttl5=ttl_find_lenght(ttl_signal, 'frame 5')\n",
    "ttl6=ttl_find_lenght(ttl_signal, 'frame 6')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2410.5"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ttl singal with behavior data should be in channel 1\n",
    "ttl1.size/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Sync' object has no attribute 'ttl1'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-32-d23050801e90>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msync\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mttl1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'Sync' object has no attribute 'ttl1'"
     ]
    }
   ],
   "source": [
    "sync.ttl1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Load CSV File -> moved to sync class ✔"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "- load the csv file from Phenosys\n",
    "- convert timestamps to ms and sampling rate\n",
    "- align timestamps with Neuron Sampling Rate \n",
    "    - \n",
    "- clena up event column\n",
    "- cleanup probability column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#load csv file\n",
    "csv_file = folder+'/behavior/output.csv'\n",
    "csv_file\n",
    "csv = pd.read_csv(csv_file, delimiter=',', encoding='utf-16', header=0, skiprows=[1])\n",
    "csv.columns=['Event Time', 'Event', 'Probability', 'side']\n",
    "\n",
    "# get gamble side\n",
    "gamble_string = csv.loc[ csv['side'].notnull(), 'side'].values[0]\n",
    "if 'RIGHT' in gamble_string:\n",
    "    gamble_side = 'right'\n",
    "if 'LEFT' in gamble_string:\n",
    "    gamble_side = 'left'\n",
    "    \n",
    "# drop side column\n",
    "csv.drop('side', axis=1, inplace=True)\n",
    "\n",
    "# Cleanup DateTime ===========================================\n",
    "# convert to datetime format with ms\n",
    "def convert_to_datetime(excel_string):\n",
    "    second = (excel_string-25569)*86400.0\n",
    "    return datetime.datetime.utcfromtimestamp(second)\n",
    "csv['Event Time'] = csv['Event Time'].apply(convert_to_datetime)\n",
    "start_dateteime = csv.loc[0, 'Event Time']\n",
    "#csv['Event Time'] = csv['Event Time'].dt.time\n",
    "\n",
    "# convert ms to sampling rate time delta\n",
    "delta = csv['Event Time'] - csv.loc[0, 'Event Time']\n",
    "csv.insert (1, 'Delta [20kHz]', (delta.dt.total_seconds()*20000).astype('uint64') )\n",
    "\n",
    "\n",
    "# clean up proabability column ===============================\n",
    "\n",
    "# calculate where prob changes\n",
    "prob = csv.loc[csv['Probability'].notnull(),'Probability']\n",
    "prob_change = np.where(prob.values[:-1] != prob.values[1:])[0]\n",
    "prob_change_idx = prob.iloc[prob_change].index.values\n",
    "prob_change_idx = np.append(prob_change_idx, prob.index[-1])\n",
    "\n",
    "# find probability function\n",
    "def match_probability(df, start, stop):\n",
    "    if \"prob75\" in (df.loc[stop]['Probability']):\n",
    "        df.loc[ start:stop, 'Probability' ] =0.75\n",
    "    elif \"prob25\" in (df.loc[stop]['Probability']):\n",
    "        df.loc[ start:stop, 'Probability' ] =0.25\n",
    "    elif \"prob12\" in (df.loc[stop]['Probability']):\n",
    "        df.loc[ start:stop, 'Probability' ] =0.125\n",
    "        \n",
    "# change 3 bins probability to number\n",
    "# change first bin\n",
    "start = 0\n",
    "stop = prob_change_idx[0]\n",
    "match_probability(csv, start, stop)\n",
    "# change second bin\n",
    "start = prob_change_idx[0]+1\n",
    "stop = prob_change_idx[1]\n",
    "match_probability(csv, start, stop)\n",
    "# change third bin\n",
    "start = prob_change_idx[1]+1\n",
    "stop = stop = prob_change_idx[2]\n",
    "match_probability(csv, start, stop)\n",
    "# add probability to last rows\n",
    "nan = np.where(csv['Probability'].isnull())[0]\n",
    "csv.loc[nan[0]:, 'Probability'] = csv.loc[nan[0]-1, 'Probability']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
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       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qgrid.show_grid(csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": 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>Event Time</th>\n",
       "      <th>Start</th>\n",
       "      <th>Event</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-21 19:14:48.311002</td>\n",
       "      <td>0</td>\n",
       "      <td>start</td>\n",
       "      <td>0.75</td>\n",
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       "      <th>1</th>\n",
       "      <td>2019-06-21 19:14:49.125002</td>\n",
       "      <td>16280</td>\n",
       "      <td>TIstarts</td>\n",
       "      <td>0.75</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-21 19:14:50.163003</td>\n",
       "      <td>37040</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "      <td>0.75</td>\n",
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       "    <tr>\n",
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       "      <td>2019-06-21 19:14:50.194997</td>\n",
       "      <td>37679</td>\n",
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       "      <td>0.75</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-21 19:14:50.230999</td>\n",
       "      <td>38399</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "      <td>0.75</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2476</th>\n",
       "      <td>2019-06-21 19:33:22.424003</td>\n",
       "      <td>22282260</td>\n",
       "      <td>TIstarts</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2477</th>\n",
       "      <td>2019-06-21 19:33:23.427003</td>\n",
       "      <td>22302320</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2478</th>\n",
       "      <td>2019-06-21 19:33:23.458997</td>\n",
       "      <td>22302959</td>\n",
       "      <td>SOUND_start</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2479</th>\n",
       "      <td>2019-06-21 19:33:23.493997</td>\n",
       "      <td>22303659</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2480</th>\n",
       "      <td>2019-06-21 19:33:24.021003</td>\n",
       "      <td>22314200</td>\n",
       "      <td>end</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2481 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Event Time     Start                   Event Probability\n",
       "0    2019-06-21 19:14:48.311002         0                   start        0.75\n",
       "1    2019-06-21 19:14:49.125002     16280                TIstarts        0.75\n",
       "2    2019-06-21 19:14:50.163003     37040      IND-CUE_pres_start        0.75\n",
       "3    2019-06-21 19:14:50.194997     37679             SOUND_start        0.75\n",
       "4    2019-06-21 19:14:50.230999     38399  resp-time-window_start        0.75\n",
       "...                         ...       ...                     ...         ...\n",
       "2476 2019-06-21 19:33:22.424003  22282260                TIstarts       0.125\n",
       "2477 2019-06-21 19:33:23.427003  22302320      IND-CUE_pres_start       0.125\n",
       "2478 2019-06-21 19:33:23.458997  22302959             SOUND_start       0.125\n",
       "2479 2019-06-21 19:33:23.493997  22303659  resp-time-window_start       0.125\n",
       "2480 2019-06-21 19:33:24.021003  22314200                     end       0.125\n",
       "\n",
       "[2481 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv = sync_obj.csv.copy()\n",
    "csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['start', 'cue', 'sound', 'openloop', 'right_rw', 'right_norw', 'left_rw', 'left_norw', 'no response in time', 'iti', 'end'])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sync_obj.ttl_event_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "replace = dict()\n",
    "replace['TIstarts']='start'\n",
    "replace['IND-CUE_pres_start']='cue'\n",
    "replace['SOUND_start']='sound'\n",
    "replace['resp-time-window_start']='openloop'\n",
    "replace['right_rewarded']='right_rw'\n",
    "replace['right_NOreward']='right_norw'\n",
    "replace['left_rewarded']='left_rw'\n",
    "replace['left_NOreward']='left_norw'\n",
    "replace['no response in time']='no response in time'\n",
    "replace['ITIstarts']='iti'\n",
    "replace['ITIends']='end'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'csv' has no attribute 'loc'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-fc210a41cf26>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcsv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Event'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mreplace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'csv' has no attribute 'loc'"
     ]
    }
   ],
   "source": [
    "csv.loc[0, 'Event']\n",
    "replace.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>Event Time</th>\n",
       "      <th>Start</th>\n",
       "      <th>Event</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-21 19:14:48.311002</td>\n",
       "      <td>0</td>\n",
       "      <td>start</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-21 19:14:49.125002</td>\n",
       "      <td>16280</td>\n",
       "      <td>start</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-21 19:14:50.163003</td>\n",
       "      <td>37040</td>\n",
       "      <td>cue</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-06-21 19:14:50.194997</td>\n",
       "      <td>37679</td>\n",
       "      <td>sound</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-21 19:14:50.230999</td>\n",
       "      <td>38399</td>\n",
       "      <td>openloop</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2476</th>\n",
       "      <td>2019-06-21 19:33:22.424003</td>\n",
       "      <td>22282260</td>\n",
       "      <td>start</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2477</th>\n",
       "      <td>2019-06-21 19:33:23.427003</td>\n",
       "      <td>22302320</td>\n",
       "      <td>cue</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2478</th>\n",
       "      <td>2019-06-21 19:33:23.458997</td>\n",
       "      <td>22302959</td>\n",
       "      <td>sound</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2479</th>\n",
       "      <td>2019-06-21 19:33:23.493997</td>\n",
       "      <td>22303659</td>\n",
       "      <td>openloop</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2480</th>\n",
       "      <td>2019-06-21 19:33:24.021003</td>\n",
       "      <td>22314200</td>\n",
       "      <td>end</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2481 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Event Time     Start     Event Probability\n",
       "0    2019-06-21 19:14:48.311002         0     start        0.75\n",
       "1    2019-06-21 19:14:49.125002     16280     start        0.75\n",
       "2    2019-06-21 19:14:50.163003     37040       cue        0.75\n",
       "3    2019-06-21 19:14:50.194997     37679     sound        0.75\n",
       "4    2019-06-21 19:14:50.230999     38399  openloop        0.75\n",
       "...                         ...       ...       ...         ...\n",
       "2476 2019-06-21 19:33:22.424003  22282260     start       0.125\n",
       "2477 2019-06-21 19:33:23.427003  22302320       cue       0.125\n",
       "2478 2019-06-21 19:33:23.458997  22302959     sound       0.125\n",
       "2479 2019-06-21 19:33:23.493997  22303659  openloop       0.125\n",
       "2480 2019-06-21 19:33:24.021003  22314200       end       0.125\n",
       "\n",
       "[2481 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cleanup csv envent names\n",
    "csv['Event'] = csv['Event'].apply(lambda event: replace[event] if event in replace.keys() else event)\n",
    "\n",
    "csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## convert ttl durration to events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "ttl1 = sync_obj.ttl_signals['channel 1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start\n",
      "TIstarts\n",
      "IND-CUE_pres_start\n",
      "SOUND_start\n",
      "resp-time-window_start\n",
      "no response in time\n",
      "ITIstarts\n",
      "ITIends\n",
      "right_rewarded\n",
      "right_NOreward\n",
      "wheel is not stopping\n",
      "left_rewarded\n",
      "left_NOreward\n",
      "end\n"
     ]
    }
   ],
   "source": [
    "for value in sync_obj.csv['Event'].unique():\n",
    "    print(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#convert ttl durration to event\n",
    "# durration dict\n",
    "durr_range = dict()\n",
    "durr_range['TIstart']=(11,29)\n",
    "durr_range['IND-CUE_pres_start']=(31,49)\n",
    "durr_range['SOUND_start']=(51,69)\n",
    "durr_range['resp-time-window_start']=(71, 89)\n",
    "durr_range['right_rewarded']=(91,110)\n",
    "durr_range['right_Noreward']=(111,129)\n",
    "durr_range['left_rewarded']=(131,149)\n",
    "durr_range['left_NOreward']=(151,169)\n",
    "durr_range['no response in time']=(173,186)\n",
    "durr_range['ITIstarts']=(190,213)\n",
    "durr_range['ITIends']=(215,245)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def convert_durration_to_event(durr):\n",
    "    for key, (start,stop) in durr_range.items():\n",
    "        if durr>=start and durr<=stop:\n",
    "            return key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def convert_ttl_to_event(channel):\n",
    "    sync_obj.ttl_signals[channel]['Event'] = sync_obj.ttl_signals[channel]['length'].apply(convert_durration_to_event)\n",
    "    return sync_obj.ttl_signals[channel]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>184261</th>\n",
       "      <td>184261</td>\n",
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       "      <th>346501</th>\n",
       "      <td>346501</td>\n",
       "      <td>178</td>\n",
       "      <td>no response in time</td>\n",
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       "      <th>...</th>\n",
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       "      <th>22450560</th>\n",
       "      <td>22450560</td>\n",
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       "      <td>ITIends</td>\n",
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       "    <tr>\n",
       "      <th>22450817</th>\n",
       "      <td>22450817</td>\n",
       "      <td>21</td>\n",
       "      <td>TIstart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22470860</th>\n",
       "      <td>22470860</td>\n",
       "      <td>37</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22471517</th>\n",
       "      <td>22471517</td>\n",
       "      <td>61</td>\n",
       "      <td>SOUND_start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22472202</th>\n",
       "      <td>22472202</td>\n",
       "      <td>76</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2171 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        start  length                   Event\n",
       "sampling rate 20kHz                                          \n",
       "184261                 184261      17                 TIstart\n",
       "204966                 204966      39      IND-CUE_pres_start\n",
       "205627                 205627      61             SOUND_start\n",
       "206346                 206346      80  resp-time-window_start\n",
       "346501                 346501     178     no response in time\n",
       "...                       ...     ...                     ...\n",
       "22450560             22450560     216                 ITIends\n",
       "22450817             22450817      21                 TIstart\n",
       "22470860             22470860      37      IND-CUE_pres_start\n",
       "22471517             22471517      61             SOUND_start\n",
       "22472202             22472202      76  resp-time-window_start\n",
       "\n",
       "[2171 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_channel = 1\n",
    "ttl_info_channel = convert_ttl_to_event('channel '+str(info_channel))\n",
    "ttl_info_channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "      <td>22450560</td>\n",
       "      <td>216</td>\n",
       "      <td>ITIends</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22450817</th>\n",
       "      <td>22450817</td>\n",
       "      <td>21</td>\n",
       "      <td>TIstart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22470860</th>\n",
       "      <td>22470860</td>\n",
       "      <td>37</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22471517</th>\n",
       "      <td>22471517</td>\n",
       "      <td>61</td>\n",
       "      <td>SOUND_start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22472202</th>\n",
       "      <td>22472202</td>\n",
       "      <td>76</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2171 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        start  length                   Event\n",
       "sampling rate 20kHz                                          \n",
       "184261                 184261      17                 TIstart\n",
       "204966                 204966      39      IND-CUE_pres_start\n",
       "205627                 205627      61             SOUND_start\n",
       "206346                 206346      80  resp-time-window_start\n",
       "346501                 346501     178     no response in time\n",
       "...                       ...     ...                     ...\n",
       "22450560             22450560     216                 ITIends\n",
       "22450817             22450817      21                 TIstart\n",
       "22470860             22470860      37      IND-CUE_pres_start\n",
       "22471517             22471517      61             SOUND_start\n",
       "22472202             22472202      76  resp-time-window_start\n",
       "\n",
       "[2171 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sync_obj.ttl_info_channel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Align Neuron TTL and Phenosys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['start', 'wheel is not stopping', 'end'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find events not encoded in ttl but in csv\n",
    "not_in_ttl = csv['Event'].unique()[~np.isin(csv['Event'].unique(), ttl1['Event'].unique())]\n",
    "not_in_ttl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# get only rows of csv which are encoding events also in ttl\n",
    "csv_combined = csv.loc[ (csv['Event']!=not_in_ttl[0]) & (csv['Event']!=not_in_ttl[1]) & (csv['Event']!=not_in_ttl[2]) ]\n",
    "\n",
    "# build data frame showing both ttl and csv events\n",
    "a = {'ttl':ttl1['Event'].values,'csv':csv_combined['Event'].values}\n",
    "diff = pd.DataFrame.from_dict(a, orient='index')\n",
    "diff = diff.transpose()\n",
    "diff['compare'] = diff['ttl']==diff['csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "006686e71cd647c58b4f4ac8233d8f4d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shift\n",
    "#inser 1902 NaN\n",
    "ttl = ttl1['Event'].values\n",
    "ttl = np.insert(ttl, 1900, np.nan)\n",
    "ttl = np.insert(ttl, 1931, np.nan)\n",
    "ttl = np.insert(ttl, 1996, np.nan)\n",
    "ttl = np.insert(ttl, 2058, np.nan)\n",
    "ttl = np.insert(ttl, 2127, np.nan)\n",
    "a = {'ttl':ttl,'csv':csv_combined['Event'].values}\n",
    "diff = pd.DataFrame.from_dict(a, orient='index')\n",
    "diff = diff.transpose()\n",
    "diff['compare'] = diff['ttl']==diff['csv']\n",
    "qgrid.show_grid(diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Combine DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Function to insert row in the dataframe \n",
    "def Insert_row(row_number, df, row_value): \n",
    "    # Starting value of upper half \n",
    "    start_upper = 0\n",
    "    # End value of upper half \n",
    "    end_upper = row_number \n",
    "    # Start value of lower half \n",
    "    start_lower = row_number \n",
    "    # End value of lower half \n",
    "    end_lower = df.shape[0] \n",
    "    # Create a list of upper_half index \n",
    "    upper_half = [*range(start_upper, end_upper, 1)] \n",
    "    # Create a list of lower_half index \n",
    "    lower_half = [*range(start_lower, end_lower, 1)] \n",
    "    # Increment the value of lower half by 1 \n",
    "    lower_half = [x.__add__(1) for x in lower_half] \n",
    "    # Combine the two lists \n",
    "    index_ = upper_half + lower_half \n",
    "    # Update the index of the dataframe \n",
    "    df.index = index_ \n",
    "    # Insert a row at the end \n",
    "    df.loc[row_number] = row_value \n",
    "    # Sort the index labels \n",
    "    df = df.sort_index() \n",
    "    # return the dataframe \n",
    "    return df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# create combined dataframe\n",
    "# create ttl dataframe\n",
    "ttl_combined = sync_obj.ttl_signals['channel 1'].copy()\n",
    "ttl_combined.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8572f0fa5bd84c39bee5015e81f60001",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# insert missing rows (from align section)\n",
    "missing_rows = (1900,1931,1996,2058,2127)\n",
    "for row in missing_rows:\n",
    "    ttl_combined = Insert_row(row, ttl_combined, np.nan)\n",
    "qgrid.show_grid(ttl_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# create one big DataFrame with second level column indexing\n",
    "ttl_combined.columns=(['TTL Start', 'TTL Length', 'TTL Event'])\n",
    "csv=sync_obj.csv\n",
    "ttl1=sync_obj.ttl_info_channel\n",
    "# create sub dataframe of csv with only events that are encoded in ttl \n",
    "not_in_ttl = csv['Event'].unique()[~np.isin(csv['Event'].unique(), ttl1['Event'].unique())]\n",
    "csv_combined = csv.loc[ (csv['Event']!=not_in_ttl[0]) & (csv['Event']!=not_in_ttl[1]) & (csv['Event']!=not_in_ttl[2]) ].copy()\n",
    "csv_combined.drop('Event Time', axis=1, inplace=True)\n",
    "csv_combined.columns=(['CSV Start', 'CSV Event', 'CSV Probability'])\n",
    "csv_combined.reset_index(inplace=True, drop=True)\n",
    "#csv_combined.drop('index', axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "cell_style": "split",
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>TTL Start</th>\n",
       "      <th>TTL Length</th>\n",
       "      <th>TTL Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>184261.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>204966.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>cue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>205627.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>sound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>206346.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>openloop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>346501.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>no response in time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2171</th>\n",
       "      <td>22450560.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>end</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>22450817.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>22470860.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>cue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174</th>\n",
       "      <td>22471517.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>sound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2175</th>\n",
       "      <td>22472202.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>openloop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2176 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       TTL Start  TTL Length            TTL Event\n",
       "0       184261.0        17.0                start\n",
       "1       204966.0        39.0                  cue\n",
       "2       205627.0        61.0                sound\n",
       "3       206346.0        80.0             openloop\n",
       "4       346501.0       178.0  no response in time\n",
       "...          ...         ...                  ...\n",
       "2171  22450560.0       216.0                  end\n",
       "2172  22450817.0        21.0                start\n",
       "2173  22470860.0        37.0                  cue\n",
       "2174  22471517.0        61.0                sound\n",
       "2175  22472202.0        76.0             openloop\n",
       "\n",
       "[2176 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttl_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "cell_style": "split",
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>CSV Start</th>\n",
       "      <th>CSV Event</th>\n",
       "      <th>CSV Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16280</td>\n",
       "      <td>start</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37040</td>\n",
       "      <td>cue</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37679</td>\n",
       "      <td>sound</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38399</td>\n",
       "      <td>openloop</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>178559</td>\n",
       "      <td>no response in time</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2171</th>\n",
       "      <td>22281999</td>\n",
       "      <td>end</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>22282260</td>\n",
       "      <td>start</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>22302320</td>\n",
       "      <td>cue</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174</th>\n",
       "      <td>22302959</td>\n",
       "      <td>sound</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2175</th>\n",
       "      <td>22303659</td>\n",
       "      <td>openloop</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2176 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CSV Start            CSV Event CSV Probability\n",
       "0         16280                start            0.75\n",
       "1         37040                  cue            0.75\n",
       "2         37679                sound            0.75\n",
       "3         38399             openloop            0.75\n",
       "4        178559  no response in time            0.75\n",
       "...         ...                  ...             ...\n",
       "2171   22281999                  end           0.125\n",
       "2172   22282260                start           0.125\n",
       "2173   22302320                  cue           0.125\n",
       "2174   22302959                sound           0.125\n",
       "2175   22303659             openloop           0.125\n",
       "\n",
       "[2176 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "646706158e06411d8dd1fd137cb41f22",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create combined dataframe\n",
    "combined = pd.merge(ttl_combined, csv_combined, how='outer', left_index=True, right_index=True)\n",
    "combined['Compare'] = combined['TTL Event']==combined['CSV Event']\n",
    "combined['TTL Start norm'] = combined['TTL Start']-combined.loc[0, 'TTL Start']\n",
    "combined['CSV Start norm'] = combined['CSV Start']-combined.loc[0, 'CSV Start']\n",
    "combined['Delta (TTL-CSV)'] = combined['TTL Start norm']-combined['CSV Start norm']\n",
    "\n",
    "qgrid.show_grid(combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#ttl = np.insert(ttl, 1900, np.nan) tistart\n",
    "#ttl = np.insert(ttl, 1931, np.nan) tistart\n",
    "#ttl = np.insert(ttl, 1996, np.nan) tistart\n",
    "#ttl = np.insert(ttl, 2058, np.nan) no response in time\n",
    "#ttl = np.insert(ttl, 2127, np.nan) no response in time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "cell_style": "center",
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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sTEIBKwmwP2Y1Sri/3gIYPw3c+0ZXPCuLT0TRwABBBBAAAEEDBH478Y/5OVvNmW0XTBvuGx9p60hfdEoAnYX4H7M7hUUIYCxfw1NmwEXvGnUdIQAAggggAACCLhCYPbGI9Lnm80EMK6oNpMMVoD7sWAFQ38+AUzoa2CbEXDB26ZUDBQBBBBAAAEEELCFAAGMLcrEIC0iwP2YRQoRxDAIYILAc9upXPBuqzjzRQABBBBAAAEEjBXgESRjfWndWQLcj9m/ngQw9q+haTPggjeNmo4QQAABBBBAAAFXCBDAuKLMTFInAe7HdIIMYTMEMCHEt1vXXPB2qxjjRQABBBBAAAEErC1AAGPt+jA6awlwP2ategQyGgKYQNRceg4XvEsLz7QRQAABBBBAAAGDBHgHjEGwNOtIAe7H7F9WAhj719C0GXDBm0ZNRwgggAACCCCAgCsECGBcUWYmqZMA92M6QYawGQKYAPHDwsJ8PnP69Onyz3/+0+vxs2fPlkmTJsmGDRvk/PnzUrZsWbnvvvukX79+Urx48ev2ceLECRk5cqT8+OOPcuTIEYmKipJ69epJr169pHPnzj6Pz9cDueB9leI4BBBAAAEEEEAAAV8ECGB8UeIYBK4IcD9m/5VAABNgDUuWLHndMxMSErRARf22bt0qtWrVuub4F154QSZOnKj977lz55bIyEg5d+6c9t+jo6Nl2bJlUr16da/9bN++XVq0aCFxcXHa3wsWLCiJiYmSkpKi/ffevXvLuHHjApyd99O44HXlpDEEEEAAAQQQQMD1AtcEMPnCZevbbV3vAgAC3gS4H7P/uiCAMaiGbdq0kUWLFkmDBg1k3bp11/QyefJk6dmzp6idNEOGDJE+ffpoAczmzZvliSeekG3btknlypVFBS158uTxOD8pKUlq1KghBw4c0IKdmTNnSp06dbQAZsyYMTJo0CBJS0uTadOmSffu3XWbIRe8bpQ0hAACCCCAAAIIICAi7IBhGSDguwD3Y75bWfVIAhgDKqMeBypfvrykpqbKRx99JM8//7xHL5cuXdL+fuzYMVG7YCZMmODx94MHD2oBiwpU1A4Z9UhR5p86Xu1wUYHNzp07pVy5ch5/T99ZU6pUKYmNjZWIiAhdZskFrwsjjSCAAAIIIIAAAgj8LUAAw1JAwHcB7sd8t7LqkQQwBlRm+PDhMmDAAO0ZvT///FOKFSvm0ctPP/0kHTp00Ha/qLBFvfcl669Hjx4ydepUadKkiaxcudLjz40bN5a1a9fK008/LVOmTLnm3EOHDkmFChW0XTALFiwQtRtHjx8XvB6KtIEAAggggAACCCCQLsAjSKwFBHwX4H7MdyurHkkAY0BlqlatKnv27JGHH35YZs2adU0Pr7/+unzwwQdSs2ZN7VEjb79vv/1WunTpooU06r0w6gW76qfeLVOoUCEtXFHHPPTQQ17PV23v2LFD3njjDXnvvfd0mSUXvC6MNIIAAggggAACCCDwtwABDEsBAd8FuB/z3cqqRxLA6FwZtVvlzjvv1FqdN2+etG/f/poeOnbsqP3tkUceka+//trrCNSjReoxJPWLiYmR+vXra/9W75Np1KiR9m91TLVq1byer9pW4c+9994rc+bM0WWWXPC6MNIIAggggAACCCCAAAEMawABvwW4H/ObzHInEMDoXBL1Yl31gt2bbrpJDh8+rH3dKOtPfSp648aN2ot3R48e7XUE8fHxUqRIEe1vKkBRQYr6qU9Od+rUSfu3OkbthvH2U22PHTtWC25UgKPHjwteD0XaQAABBBBAAAEEEEgXYAcMawEB3wW4H/PdyqpHEsDoWJkLFy6I+jz12bNn5c0335SRI0d6bb1KlSqyd+9e7T0xQ4cO9XrM5cuXM75+9NVXX0nXrl2149S/H3/8ce3f6pjw8HCv56u21btoVF+7d+/2aZbpF3R2B6uXB6svMqmvMPFDAAEEEEAAAQQQQCBYAQKYYAU5300CBDD2rzYBjI41/PLLL7VPSKvf9R4P8iWASU5Ozvh6EQGMjkWiKQQQQAABBBBAAAHLCBDAWKYUDMQGAgQwNihSDkMkgNGxhq1bt5bFixfL7bffLqtWrcq2ZR5B0hGdphBAAAEEEEAAAQRsK/DDhiPyyqzNGeMvmC9ctr7d1rbzYeAIGClAAGOkrjltE8Do5Kze96I+/ZyamiqffPKJPPvss9m2rN7nMnfu3IBewqve59KwYUOtbV7Cq1PxaAYBBBBAAAEEEEAgJAIEMCFhp1ObChDA2LRwmYZNAKNTDYcNGyYDBw6UfPnyybFjx6Rw4cLZtqw+DT1q1CipVauWbN261etx3333nfYZ6+t9hlod8+CDD3o9X7W9fft2PkOtU31pBgEEEEAAAQQQQEB/AQIY/U1p0bkCBDD2ry0BjE41TH+vi3pZrnpny/V+8+fP1z5PrcKVQ4cOSZkyZa45/JlnnpEpU6ZIkyZNRH3aOvNPPeK0Zs0a6dGjh/bFpay/I0eOSLly5SQtLU0WLFggbdq00WWWXPC6MNIIAggggAACCCCAwN8CWQOYQvnCZQuPILE+EPAqwP2Y/RcGAYwONVy+fLncfffdWksLFy4U9S6Y6/3U14TKly+v7ZTp3bu3jBs3zuNw9ThT9erV5fz58zJx4kTp1auXx98nTJignRcVFaU9hlS2bFmPv6u/qWNKlSolsbGxGS/zDXaqXPDBCnI+AggggAACCCCAQGYBdsCwHhDwXYD7Md+trHokAYwOlVE7UaZOnaoFISrwyJUrV46tqp0rPXv21HbBqM9Fv/TSS5I/f37ZsmWLdOvWTfvPypUra48RqU8/Z/6pz0DXqFFDDhw4ILVr15YZM2Zo/6k+g63CnP79+2u7X6ZNmybdu3fPcSy+HsAF76sUxyGAAAIIIIAAAgj4IkAA44uSe4/Z/me8DPzvNgnPFSYjHqgtlW8s4F4MEeF+zP7lJ4AJsoYq9ChZsqScPXtWBgwYIEOHDvW5xeeff14+/vhj7fjw8HCJjIzU2lG/6OhoWbZsmbYTxttPBTMtWrSQuLg47c+FChWSxMREUZ+vVr8XX3xRxo8f7/NYfDmQC94XJY5BAAEEEEAAAQQQ8FWAR5B8lXLncR3G/SY7jl65P7q9UjH5umcTd0L8PWvux+xffgKYIGs4c+ZMbceK+u3du1fbteLPb/bs2VoIs2HDBu2RI7WL5v7775e+fftKiRIlrtuUCl9Gjhwpc+bMEfXYknokSX3iWgU7nTt39mcYPh3LBe8TEwchgAACCCCAAAII+ChAAOMjlEsPq9B3rsfMY0d2dKnElWlzP2b/8hPA2L+Gps2AC940ajpCAAEEEEAAAQRcIUAA44oyBzxJAhhPOu7HAl5KljmRAMYypbD+QLjgrV8jRogAAggggAACCNhJgADGTtUyf6wEMAQw5q86Y3skgDHW11GtE8A4qpxMBgEEEEAAAQQQCLkAAUzIS2DpARDAEMBYeoEGMDgCmADQ3HoKAYxbK8+8EUAAAQQQQAABYwQIYIxxdUqrBDAEME5Zy+nzIIBxWkUNnA8BjIG4NI0AAggggAACCLhQgADGhUX3Y8oEMAQwfiwXWxxKAGOLMlljkAQw1qgDo0AAAQQQQAABBJwiQADjlEoaMw8CGAIYY1ZW6FolgAmdve16JoCxXckYMAIIIIAAAgggYGkBAhhLlyfkgyOAIYAJ+SLUeQAEMDqDOrk5AhgnV5e5IYAAAggggAAC5gsQwJhvbqceCWAIYOy0Xn0ZKwGML0ocowkQwLAQEEAAAQQQQAABBPQUIIDRU9N5bRHAEMA4bVUTwDitogbOhwDGQFyaRgABBBBAAAEEXCjw/foj8uq3mzNmXihfuGx5u60LJZiyNwECGAIYp10ZBDBOq6iB8yGAMRCXphFAAAEEEEAAARcKZA1gCuePkM1vtXGhBFMmgMl5DXA/lrOR1Y8ggLF6hSw0Pi54CxWDoSCAAAIIIIAAAg4QIIBxQBENnAI7YDxxuR8zcLGZ1DQBjEnQTuiGC94JVWQOCCCAAAIIIICAdQQIYKxTCyuOhACGAMaK6zKYMRHABKPnsnMJYFxWcKaLAAIIIIAAAggYLEAAYzCwzZsngCGAsfkSvmb4BDBOq6iB8yGAMRCXphFAAAEEEEAAARcKEMC4sOh+TJkAhgDGj+Vii0MJYGxRJmsMkgDGGnVgFAgggAACCCCAgFMECGCcUklj5kEAQwBjzMoKXasEMKGzt13PBDC2KxkDRgABBBBAAAEELC1AAGPp8oR8cAQwBDAhX4Q6D4AARmdQJzdHAOPk6jI3BBBAAAEEEEDAfAECGPPN7dQjAQwBjJ3Wqy9jJYDxRYljNAECGBYCAggggAACCCCAgJ4CBDB6ajqvLQIYAhinrWoCGKdV1MD5EMAYiEvTCCCAAAIIIICACwUIYFxYdD+mTABDAOPHcrHFoQQwtiiTNQZJAGONOjAKBBBAAAEEEEDAKQIEME6ppDHzIIAhgDFmZYWuVQKY0NnbrmcCGNuVjAEjgAACCCCAAAKWFvhu/RF57dvNGWMsnD9CNr/VxtJjZnDmCRDAEMCYt9rM6YkAxhxnR/RCAOOIMjIJBBBAAAEEEEDAMgIEMJYphSUHQgBDAGPJhRnEoAhggsBz26kEMG6rOPNFAAEEEEAAAQSMFeARJGN97d46AQwBjN3XcNbxE8A4raIGzocAxkBcmkYAAQQQQAABBFwowA4YFxbdjylnDWAeqFda2tSIlna1bvKjFeccyv2Y/WtJAGP/Gpo2Ay5406jpCAEEEEAAAQQQcIUAAYwryhzwJLMGMOkN/fxaM6lQPCrgdu16Ivdjdq3c1XETwNi/hqbNgAveNGo6QgABBBBAAAEEXCFAAOOKMgc8yewCmAfqlpbRj9wWcLt2PZH7MbtWjgDG/pULwQy44EOATpcIIIAAAggggICDBbIGMEUiI2TTYL6C5OCS+zW17AKY9rVKyqQn6vvVlhMO5n7M/lVkB4z9a2jaDLjgTaOmIwQQQAABBBBAwBUCBDCuKHPAkySA8aTjfizgpWSZEwlgLFMK6w+EC976NWKECCCAAAIIIICAnQQIYOxULfPHSgBDAGP+qjO2RwIYY30d1ToBjKPKyWQQQAABBBBAAIGQCxDAhLwElh4AAQwBjKUXaACDI4AJAM2tpxDAuLXyzBsBBBBAAAEEEDBGgADGGFentEoAQwDjlLWcPg8CGKdV1MD5EMAYiEvTCCCAAAIIIICACwUIYFxYdD+mTABDAOPHcrHFoQQwtiiTNQZJAGONOjAKBBBAAAEEEEDAKQIEME6ppDHzyC6A6XBrSfn4cb6CZIw6rRopQABjpK7D2iaAcVhBmQ4CCCCAAAIIIBBiAQKYEBfA4t0TwHgWiPsxiy9YH4ZHAOMDEodcEeCCZyUggAACCCCAAAII6ClAAKOnpvPaIoAhgHHaqiaAcVpFDZwPAYyBuDSNAAIIIIAAAgi4UIAAxoVF92PKBDAEMH4sF1scSgBjizJZY5AEMNaoA6NAAAEEEEAAAQScIkAA45RKGjMPAhgCGGNWVuhaJYAJnb3teiaAsV3JGDACCCCAAAIIIGBpgW9jDsvr323JGGORyAjZNLiNpcfM4MwTIIAhgDFvtZnTEwGMOc6O6IUAxhFlZBIIIIAAAggggIBlBLIGMEUjI2QjAYxl6hPqgRDAEMCEeg3q3T8BjN6iDm6PAMbBxWVqCCCAAAIIIIBACAR4BCkE6DbqkgCGAMZGy9WnoRLA+MTEQUqAAIZ1gAACCCCAAAIIIKCnAI8g6anpvLYIYAhgnLaqCWCcVlED50MAYyAuTSOAAAIIIIAAAi4U4BEkFxbdxymnpaVJxX7zvB7d4daS8vHj9X1syTmHcT9m/1oSwNi/hqbNgAveNGo6QgABBBBAAAEEXCFAAOOKMgc0ydTUNKnUnwAmMx73YwEtJUudRABjqXJYezBc8NauD6NDAAEEEEAAAQTsJkAAY7eKmTdeAphrrbkfM2/9GdUTAatEAJAAACAASURBVIxRsg5slwvegUVlSggggAACCCCAQAgFCGBCiG/xrlNS0+RmdsB4VIn7MYsvWh+GRwDjAxKHXBHggmclIIAAAggggAACCOgpQACjp6az2kpOSZXKA37yOineAZPkrGK7aDYEMC4qdrBTJYAJVpDzEUAAAQQQQAABBDILEMCwHrITuJySKrdkE8C0r1VSJj3BS3hZPfYTIICxX81CNmICmJDR0zECCCCAAAIIIOBIAQIYR5ZVl0ldSk6VKgO974AhgGEHjC6LLASNEMCEAN2uXRLA2LVyjBsBBBBAAAEEELCmAAGMNetihVElJadI1YHzvQ6FAIYAxgprNJAxEMAEoubScwhgXFp4po0AAggggAACCBgkMCvmsLzx3ZaM1otGRsjGwW0M6o1m7SRw8XKKVBtEAJO5ZtyP2WkFex8rAYz9a2jaDLjgTaOmIwQQQAABBBBAwBUCBDCuKHNAkySAuZaN+7GAlpKlTiKAsVQ5rD0YLnhr14fRIYAAAggggAACdhPgESS7Vcy88RLAEMCYt9rM64kAxjxr2/dEAGP7EjIBBBBAAAEEEEDAUgIEMJYqh6UGc+FSilQfzCNImYvC/ZillmhAgyGACYjNnSdxwbuz7swaAQQQQAABBBAwSoAAxihZ+7ebeClZagxe4HUivISXl/DadYUTwNi1ciEYNwFMCNDpEgEEEEAAAQQQcLAA74BxcHGDnNr5pGSp+RYBTGZG7seCXFQWOJ0AxgJFsMsQuODtUinGiQACCCCAAAII2EMgawBTLCqPbBjU2h6DZ5SGCiQkJUstAhgPY+7HDF1ypjROAGMKszM64YJ3Rh2ZBQIIIIAAAgggYBUBAhirVMJ64zh38bLc+vZCrwPjESQeQbLeivVtRAQwvjnleNShQ4dk/PjxMn/+fFH/Tk1NlZtuukkaNmwojzzyiHTq1MlrG7Nnz5ZJkybJhg0b5Pz581K2bFm57777pF+/flK8ePHr9nvixAkZOXKk/Pjjj3LkyBGJioqSevXqSa9evaRz5845jtnfAwhg/BXjeAQQQAABBBBAAIHrCRDAsD6yEzh78bLUJoDx4OF+zP7XCwGMDjWcMWOGPPfcc5KYmKi1FhkZKWFhYVqgon4tW7aUxYsXX9PTCy+8IBMnTtT+99y5c2vnnTt3Tvvv0dHRsmzZMqlevbrXEW7fvl1atGghcXFx2t8LFiyo9Z+SkqL99969e8u4ceN0mN3VJrjgdeWkMQQQQAABBBBAwPUCBDCuXwLZAhDAXEvD/Zj9rxcCmCBr+N1332k7XNSOl3/961/yxhtvSNWqVbVWT506Jb/88ovExsbKK6+84tHT5MmTpWfPnlpQM2TIEOnTp48WwGzevFmeeOIJ2bZtm1SuXFlU0JInTx6Pc5OSkqRGjRpy4MABqVWrlsycOVPq1KmjBTBjxoyRQYMGSVpamkybNk26d+8e5AwJYHQDpCEEEEAAAQQQQAABDwECGBZEdgLxFy5LnXd4BCmzDwGM/a8XApggaqgeAapWrZqcPn1a+vfvL8OGDfOptUuXLkn58uXl2LFjonbBTJgwweO8gwcPagGLClTUDhn1SFHmnzpe7XBRgc3OnTulXLlyHn9P31lTqlQpLfyJiIjwaVw5HcQFn5MQf0cAAQQQQAABBBDwR4AAxh8tdx0bn3hZ6rxLAEMA46x1TwATRD3fffddeeutt6RChQqyZ88en4OOn376STp06KDtflFhi3rvS9Zfjx49ZOrUqdKkSRNZuXKlx58bN24sa9eulaefflqmTJlyzbnqHTRqTGoXzIIFC6RNmzZBzPLqqQQwujDSCAIIIIAAAggggMDfAgQwLIXsBP5KvCS3vbvI6595CS8v4bXrlUMAE0Tl1KNGKngZOHCg9hiRr7/XX39dPvjgA6lZs6b2qJG337fffitdunTRQhr1Xhj1gl31S0hIkEKFCmnhijrmoYce8nq+anvHjh3aI1Hvvfeer0O77nEEMLow0ggCCCCAAAIIIIAAAQxrIAeBM+cvSd0hBDCZmbgfs/9lQwATYA3V+13Sv1Kkdpmox4HUF4lWrVqlPTqkdrW0a9dOVNiSdYdLx44dZd68edq7Y77++muvI1CPFqnHkNQvJiZG6tevr/173bp10qhRI+3f6hj1CJS3n2p71qxZcu+998qcOXMCnKXnaVzwujDSCAIIIIAAAggggAABDGsgB4HT5y9JPQIYDyXux+x/2RDABFjD1atXa48HqZ96/4sKX9SLeNN3qqR/Aalo0aJaAHLnnXdm9KQ+Fb1x40btxbujR4/2OoL4+HgpUqSI9jd1vgpS1E99cjr9k9bqGLUbxttPtT127FgtuFEBji+/9As6u2PVu2vUC4HVS4D5IYAAAggggAACCCAQrACPIAUr6NzzTyUkSf2h135JVs2YR5C4H7PryieACbBy8+fPl/bt22tnq8eEKlWqJJ9//rncdddd2v+2fPly7QtE+/fv1z4pvWvXroxApUqVKrJ3714ZMGCADB061OsILl++nPH1o6+++kq6du2qHaf+/fjjj2v/VseEh4d7PV+1PXz4cFF97d6926dZEsD4xMRBCCCAAAIIIIAAAjoJEMDoBOnAZk4mJEmDbAKYdjVLyifdrjwh4KYfO2DsX20CmABrqB4hUo8SpQcwGzZskNtuu82jtU2bNmk7UNTOmFGjRslrr72m/d2XACY5OTnjpb5mBTA5UXDB5yTE3xFAAAEEEEAAAQT8EZi17rC88f2WjFOKReWRDYNa+9MExzpUgADm2sJyP2b/xU4AE2ANf/31V2natKl2dosWLWTJkiVeW1JfIFq0aJG0bdtW1K4Z9bPqI0g5UXDB5yTE3xFAAAEEEEAAAQT8EcgawNwQlUfWE8D4Q+jYY0+cS5KGw7w/gsQOGB5BsuvCJ4AJsHL79u2TW265RTu7V69eMnHiRK8t9e7dWyZMmCDVq1fXvkqkfup9LnPnzg3oJbzqfS4NGzbU2uElvAEWj9MQQAABBBBAAAEELCHAI0iWKIMlBxF37qI0Gub9/+QmgCGAseSi9WFQBDA+IHk7RD1WVLBgQe2LR9cLYF588UX56KOPtC8abd++XWtKfRpaPZJUq1Yt2bp1q9cRfPfdd/Lwww9f9zPU6pgHH3zQ6/mqbdUfn6EOsMCchgACCCCAAAIIIGC4AI8gGU5s2w7izl6URsMJYDIXkCcSbLucMwZOABNEDdN3sjRv3lyWLl3qtaXWrVvL4sWLpUOHDtquF/VLf4GvennvoUOHpEyZMtec+8wzz8iUKVO0Ly2tXLnS4++33367rFmzRnr06CGTJ0++5twjR45IuXLlJC0tTdQnstVjUHr8uOD1UKQNBBBAAAEEEEAAgXQBHkFiLWQncPzsRWlMAOPBw/2Y/a8XApggajhr1iztMSIVpKhHg9S7XTL/1Kem1Ut4VRCidsE8//zz2p/V55zLly8vx44dE/WI0rhx4zzOO3z4sPbIkvqUtXq0Se2wyfxTjzSp89Qnr9VjSGXLlvX4e/pjT6VKlZLY2NiMl/kGMVXtVC74YAU5HwEEEEAAAQQQQCCzAAEM6yE7gWPxF+X2EeyAyezD/Zj9rxcCmCBqqIIVtUNF7UZRn6GePn263H333VqLv/32mzz11FNy4MABqVixomzbtk0iIyMzelM7V3r27KmFN+pz0S+99JLkz59ftmzZIt26ddP+s3LlytpjRHny5PEYZVJSkvZIk2q7du3aMmPGDO0/L1y4oIU5/fv310KfadOmaZ/C1uvHBa+XJO0ggAACCCCAAAIIKAECGNZBdgJH4y9IkxHenzLgHTC8A8auVw4BTJCVO3r0qDRr1kz27NmjtVSgQAHtPxMSErT/LF26tPbIkXonS9af2hHz8ccfa/9zeHi4FtCcPXtW++/R0dGybNkybSeMt58KZtTXl+Li4rQ/FypUSHsfjfp8tfqpd8+MHz8+yNl5nk4AoysnjSGAAAIIIIAAAq4XIIBx/RLIFoAA5loa7sfsf70QwOhQQxV8jB49WtRLcffv3y8pKSly8803S6dOnaRPnz5yww03ZNvL7NmztRBmw4YN2iNH6nGi+++/X/r27SslSpS47uhU+DJy5EiZM2eOqMeW1CNJ6jEoFex07txZh5kRwOiOSIMIIIAAAggggAACGQIEMNZeDDGxp+W5meslKTlVxj9aV5pXu9G0Af/51wW5YyQ7YDKDE8CYtvwM64gAxjBa5zXMBe+8mjIjBBBAAAEEEEAglAIEMKHUz7nvJiOWyNH4ixkHxo7smPNJOh3xx18X5E4CGA9N7sd0WlwhbIYAJoT4duuaC95uFWO8CCCAAAIIIICAtQUIYKxTn8spqTJ+yV7ZfCReut9RQdvtUqHvla+4pv/MDGCOnEmUu95b5hWId8DwDhjrXDn+jYQAxj8vVx9NAOPq8jN5BBBAAAEEEEBAdwECGN1JA25w3taj0uvLDdr5kXlyy4ZBraXaoPkhC2AOn06Uu98ngMlcAO7HAl7eljmRAMYypbD+QLjgrV8jRogAAggggAACCNhJgADGOtWq3H+eJKemZQxoypMNpMcXMa4IYFJS0+SnbUcl/sJl6XxbaYnKG26dwmQaCfdjliyLX4MigPGLy90Hc8G7u/7MHgEEEEAAAQQQ0FsgawBTvEAeiRnYWu9uaM8HgUr95kqm/EVCHcAcOpUo94wyZwfMlN8OyNC5OzWlh+qXkQ8eruODmPmHcD9mvrnePRLA6C3q4Pa44B1cXKaGAAIIIIAAAgiEQIAdMCFAz6ZLqwUwB0+dl6ajfvY6Wj3eAXM+KVl+P3leKt9Y4JpHrX4f0UHCwsKsU5y/R8L9mOVK4veACGD8JnPvCVzw7q09M0cAAQQQQAABBIwQIIAxQjWwNt0UwGT+wlKpwvnkz0xfelJ6D9YrIx92sd4uGO7HAlvbVjqLAMZK1bD4WLjgLV4ghocAAggggAACCNhMgEeQrFMwqwUwsSfPS7MP9N8Bs3LfSXlsypoc4Q8M7yC5cllrFwz3YzmWzfIHEMBYvkTWGSAXvHVqwUgQQAABBBBAAAEnCHyz7pC8+f3WjKnwDpjQVdVqAYx6PKi5zgHM9b6slFV+15B2ki8id+gK4qVn7scsVY6ABkMAExCbO0/igndn3Zk1AggggAACCCBglAABjFGy/rdrtQDmwIkEafHhL14n0rZmtHzarYHfk+z7/Rb5et1hn87bPLiNFI6M8OlYsw7ifswsaeP6IYAxztZxLXPBO66kTAgBBBBAAAEEEAipAO+ACSm/R+dWC2D2n0iQljoFMNv+iJdPfz0gczb/6TP42v4t5cZC+Xw+3owDuR8zQ9nYPghgjPV1VOtc8I4qJ5NBAAEEEEAAAQRCLpB1B8wNUXlk/SA+Qx2KwlgtgNkXlyCtRge/AyY1NU2ajFwix88m+cX66+vNpdwNkX6dY/TB3I8ZLWx8+wQwxhs7pgcueMeUkokggAACCCCAAAKWEOARJEuUQRuE9QKYc9Jq9K9egbJ7BCnu7EVZuitO6pcvKrdEF9TOPZmQJA2GLvYbelGfezLa8Ptkg07gfswgWBObJYAxEdvuXXHB272CjB8BBBBAAAEEELCWAAGMdeph9wAmKTlF7npvmZw4lyR5w3PJ4leaStlikXL87EVpPHyJ39D/78W7pFbpwn6fZ+QJ3I8ZqWtO2wQw5jg7ohcueEeUkUkggAACCCCAAAKWESCAsUwpLLcDZu/xc9J6jO87YLK+T6jzbaVk7KN15ciZRC2Y8ff3/b+bSP3yxfw9zdDjuR8zlNeUxglgTGF2Ridc8M6oI7NAAAEEEEAAAQSsIkAAY5VKWO8RpD3Hz0kbPwKYcYv3ypjFezJAa9xUSOa9dLfEnjwvzbL5nPX19L/q0VjuqFzcOgUSEe7HLFWOgAZDABMQmztP4oJ3Z92ZNQIIIIAAAgggYJQAAYxRsv63a7VHkHYfOydtx3rfAdOuZkn5pFt9j0lmDWBqlioknz/VUNqN+01On7/kN8i0pxpIi2rRfp9n5Ancjxmpa07bBDDmODuiFy54R5SRSSCAAAIIIIAAApYRIICxTCks9wjSrmNnpd3Y37wC+RLAqBMbVigq62LPBIQ86fF60v7WmwI616iTuB8zSta8dglgzLO2fU9c8LYvIRNAAAEEEEAAAQQsJUAAY51yWG0HzM6jZ6X9ON8DmLGL98jYxXsDAl36alNp8aHnJ6/HPnKbdK5bOqD2jDqJ+zGjZM1rlwDGPGvb98QFb/sSMgEEEEAAAQQQQMBSAgQw1imH1QKYHX+elQ7jfQ9gxizaI+OWBBbAbHm7jdR+e6FHMd578FZ5pGE56xSId8BYqhaBDoYAJlA5F55HAOPCojNlBBBAAAEEEEDAQIGv1x6Svj9szeiheIE8EjOwtYE90nR2AlYLYLb/GS8dxy/3OlxvjyAFE8DsHtpOqg6c79HXjKcbyd23lLDUguF+zFLlCGgwBDABsbnzJC54d9adWSOAAAIIIIAAAkYJXBvA5JWYga2M6o52ryPg5gDm9xEdpFL/eZKWdgXoscblZPg/brXceuF+zHIl8XtABDB+k7n3BC5499aemSOAAAIIIIAAAkYI8AiSEaqBtWm1AGbbH/Fy7wTfd8CMXrRHxgf4CFLsyI6yYPsxGTZ3p9xUOJ+Me7SulCycLzBIA8/ifsxAXJOaJoAxCdoJ3XDBO6GKzAEBBBBAAAEEELCOAI8gWacWbg1gCuePkM1vtbFOIa4zEu7HbFGm6w6SAMb+NTRtBlzwplHTEQIIIIAAAggg4AoBHkGyTpmtFsBsPRIv932U8w6Y1NQ0+XLNQflo2T45fjbJb9DlbzaXMkUj/T4vFCdwPxYKdX37JIDR19PRrXHBO7q8TA4BBBBAAAEEEDBdgADGdPJsO7RaALPlyF9y/0crvI43/SW855OSZeKyffLxz/sDgny51S3ycqsqAZ0bipO4HwuFur59EsDo6+no1rjgHV1eJocAAggggAACCJguQABjOrltApjNh/+SThO9BzCta0TLhK515ZkvYuS3vScDQlTvfbHbj/sxu1Xs2vESwNi/hqbNgAveNGo6QgABBBBAAAEEXCFAAGOdMvuyA0Z9LSgsLMyUQW86/Jd0ziaACXYAXz3TWO64uXiwzZh+PvdjppPr3iEBjO6kzm2QC965tWVmCCCAAAIIIIBAKAQIYEKh7r3PrAFMtZIFZdexcx4H7x/eQXLnsncAE5E7TPYMbW9akKRnhbkf01MzNG0RwITG3Za9csHbsmwMGgEEEEAAAQQQsKwAAYx1SpM1gPE2sn3D2kt47lymDHrjoTPyj49X6t7XiAdula6NyunerhkNcj9mhrKxfRDAGOvrqNa54B1VTiaDAAIIIIAAAgiEXOD/1h6Sfj9szRhH8QJ5JWZgq5CPy0kDSEtLk/UHz0iJgnml/A1R2U7NlwBm77D2EmFSALP+4Gl5cNIq3Usx7B+15PHG5XVv14wGuR8zQ9nYPghgjPV1VOtc8I4qJ5NBAAEEEEAAAQRCLkAAY3wJ3vxui3wTc1jy5M4l0//VMNt3n/gSwOwe2k7yhuc2ftAisvrAKXn0s9W69zWkU03p1qSC7u2a0SD3Y2YoG9sHAYyxvo5qnQveUeVkMggggAACCCCAQMgFvlpzSPrPZgeMUYW4nJIqtwz4KaP5G6LyyPpBrb1250sAs2tIO8kXYU4As3zvSXli6hrdaWY+3VjuusV+L+BVENyP6b4cTG+QAMZ0cvt2yAVv39oxcgQQQAABBBBAwIoCWQMY9ZjMugE8gqRXrS5eTpFqg+Z7NJfd55etFsAs2xUn3aev04tCa6duuSLyw7/vsOULeAlgdF0KIWuMACZk9PbrmADGfjVjxAgggAACCCCAgJUF2AFjbHX0DmB2vNtWIvOEGzvov1tvM+YX2XM8Qbe+fuh1h9QqVVjyhJvzEmHdBp6pIe7HjFA1t00CGHO9bd0bF7yty8fgEUAAAQQQQAABywl8ueagDJi9LWNcvIRX3xJduJQi1QfrtwNm+zttJSqvOQFMhb5zdcXIbuePrp0Y3Bj3YwYDm9A8AYwJyE7pggveKZVkHggggAACCCCAgDUEsgYwPIKkb10SLyVLjcELPBoN5hGkbe+0lQImBDAHTiRIiw9/CRijUYVisjb2tE/zDriTEJzI/VgI0HXukgBGZ1AnN8cF7+TqMjcEEEAAAQQQQMB8gZmrD8rA/17dAUMAo28NziclS8239AtgtrzdRgrli9B3kFlaO5WQJPWHLg6qj7sqF5fl+04SwASlyMlGCBDAGKHq0DYJYBxaWKaFAAIIIIAAApYQSElNk09+2S8bD/0lT91RwbZfavEHkwDGHy3/jz138bLc+vZCn4IIX17Cu/mtNlI4v3EBTFpamrQe86vsiwvu3S/Nq5aQZbtP+DRv/1VDdwb3Y6Gz16tnAhi9JF3QDhe8C4rMFBFAAAEEEEAgZAILth+TZ2es1/qPypNb+1ywWZ/8DdWkCWCMlT978bLU1jOAGdxGCkfqH8Acjb8gR+MvypNT10pCUnLQKG1rRsuC7ccJYIKWpAG9BQhg9BZ1cHsEMA4uLlNDAAEEEEAAgZALVB80Xy5cTskYx+QnG0jrGtEhH5eRA5ix+qAM4hEkw4jjEy9LnXf12wGzcVBrKRqVR9fxLtpxXHp9uV4up6Tp1m7HW2+SuVuPEsDoJkpDegkQwOgl6YJ2CGBcUGSmiAACCCCAAAIhE6jcf54kp169Cf2sW31pU7NkyMZjRscEMMYq/5V4SW57d5FPQYQvjyBtGNRaiukYwJw4lyQNhwX3vhdvgiqAOXU+SVYfuPIi3kH31pCn76poLLYJrXM/ZgKywV0QwBgM7KTmueCdVE3mggACCCCAAAJWE3BlALMqVgb9b3tGKXgJr76r8vT5S1JviH4BzPqBreSGAnkDHqTakXPyfJJUKh4lYWFh8vxXG2TuFs+dKgE3nunEjrVvkiGdasn0lbFyY8G80rVROcmdK0yPpkPaBvdjIeXXpXMCGF0Y3dEIF7w76swsEUAAAQQQQCA0AgQwIgQw+q49b18UCuYz1DEDW0nxAAMY9WLdLp+uEhUKdWlQRlpWj85455G+sxZRAczEx+rp3WzI2+N+LOQlCHoABDBBE7qnAS5499SamSKAAAIIIICA+QI3958n6ktI6T83PIL0xapYGcwOGMMW28mEJGmQ5ZPOwQQwawe0lBsL5gtovD3+EyOLd3q+GDeghrKc9J9/NZJ65Yp4fO3p3to3yUcEMHrw0obOAgQwOoM6uTkCGCdXl7khgAACCCCAQKgFCGDYAaP3Gow7d1EaDVvi0WxQAUz/lnJjocACmAp95wY9vaZVSsgve65+Xlo9VrR/eAet3Yr95kra3/nlfXVKyYSudYPuz2oNcD9mtYr4Px4CGP/NXHsGF7xrS8/EEUAAAQQQQMAEATc+gsQOGGMXVtzZi9JouH4BzJr+LSU6RAHM/Jfvlj/OXJCn/xPjNVDKHPAQwBi7rmg9cAECmMDtXHcmAYzrSs6EEUAAAQQQQMBEATfugPnPylh560dewmvUMjsWf1FuH6FfALOqXwu5qXD+gIYb7A6Y30d0kJ93n5Du09flGMDcX6eUjGcHTEB14iRjBQhgjPV1VOsEMI4qJ5NBAAEEEEAAAYsJEMDwCJLeS/Jo/AVpMmKp18Aia1++fIZ6Zd8WUqqI+QFMjZsKybyX7pafd8fJU5/nHMB0uq2UjHuUR5D0Xk+0F7wAAUzwhq5pgQDGNaVmoggggAACCCAQAgE3BjDTV/wub8/ZkaHNV5D0XXh//HVB7hypXwCzom8LKe1HAJOUnCJPTFkj4blyyaoDpwKeXLuaJeWTbvXl7MXLUvvthRntvHN/TfnnHRUkLS1NKvabl/G/E8AETM2JBgsQwBgM7KTmCWCcVE3mggACCCCAAAJWE8gawEx+soG0rhFttWHqOp6sAcyNBfPK2gGtdO3DzY0dOZMod723zIMgmJfw/vZGcylbLNInUhWK1Bi8QC5cTvHp+Osd1LZmtHzarYF2yLcxh2XSL/ulVqnCMuKBWyUqb/g1AUzn20rJWHbABO1OA/oLEMDob+rYFglgHFtaJoYAAggggAACFhAggOERJL2X4eHTiXL3+6EJYGauPigD/7tNlym1qREtnz15JYDx9ktNTZNK/a/ugCGA0YWdRgwQIIAxANWpTRLAOLWyzAsBBBBAAAEErCDgxgDm8xW/yzs8gmTY8jt0KlHuGaVfAPPr682l3A0574C5lJwqVQb+pNu8vurRWO6oXDzb9lJS00RdP+m/f9QtLWMeuU23/q3SEPdjVqlE4OMggAncznVncsG7ruRMGAEEEEAAAQRMFCCAEeERJH0XXOzJ89Lsg589Gg3mEaSfX2smFYpH5TjI57/cIHO3Hs3xOF8OyB+RW7a/01Zy5QrzOYB5oG5pGU0A4wsvx5gsQABjMriduyOAsXP1GDsCCCCAAAIIWF3AjQHMtOW/y7v/7+pLeAlg9F2lv588L811DGCWvdZMKuYQwMTEnpaHPlml20QGdqwuPe6udN32klNSpfKAqztuCGB046chnQUIYHQGdXJzBDBOri5zQwABBBBAAIFQCxDAsANG7zW4/0SCtPzwF49mg9kBs/TVplKpRIFsh7lox3F55ouYHKdRNbqg7D5+Lsfj1AGLX7lHKt9Y8LrHXk5JlVsyBzD1SsvoLjyC5BMwB5kqQABjKre9OyOAsXf9GD0CCCCAAAIIWFugUr+5kpp2dYxu+AoSO2CMXZP74hKk1Wj9ApjFrzSVyjdmH8A8NGmlxBw8k+OkyhTNL0fOXLjucYXzR0jXRuWkb/tqObZHAJMjEQdYRIAAJohCTJ8+Xbp3755jCydOnJDixb2/NGr27NkyadIk2bBhg5w/f17K99nFngAAIABJREFUli0r9913n/Tr1y/bc9I7VO2OHDlSfvzxRzly5IhERUVJvXr1pFevXtK5c+ccx+XvAQQw/opxPAIIIIAAAggg4LuAGwOYqct/lyE8guT7IvHzyL3Hz0nrMb96nBXMDpj03SjqMaMRP+2SEgXyypDOtaREwbxaH3eOXCp//HX9YEUdVzBfuJy7mHzNbB6sV0aKRUXIy62qaJ+X9vWX9aW/qp0Pu9Tx9XTbHMf9mG1Kle1ACWCCqGF6AJMrVy4pUaJEti3t2LFDihUrds3fX3jhBZk4caL2v+fOnVsiIyPl3LkrW/Gio6Nl2bJlUr16da/tbt++XVq0aCFxcXHa3wsWLCiJiYmSkpKi/ffevXvLuHHjgpjdtadywevKSWMIIIAAAggggICHAAEMjyDpfUnsOX5O2ugYwCzqc4/2DpjM71t55u6KMqBjDW3oTUctk4OnEgOaxv89c7s0ufmGgM7NGsA8VL+MfPAwAUxAmJxkqAABTBC86QFM+fLlJTY21q+WJk+eLD179pSwsDAZMmSI9OnTRwtgNm/eLE888YRs27ZNKleuLCpoyZMnj0fbSUlJUqNGDTlw4IDUqlVLZs6cKXXq1NECmDFjxsigQYMkLS1Npk2b5tMOHV8HTgDjqxTHIYAAAggggAAC/gtkDWCmPNlAWtWI9r8hG50x5bcDMnTuzowR8xJefYu369hZaTf2N49Gg9kBs+Dle+TDhbtl4Y7jHm0eGN5BZsUclr4/bPVpAg3KF73mUaWFfe6RKtHXf9dLdo0nJadI1YHzM/5MAONTGTgoBAIEMEGgBxrAXLp0SVRoc+zYMVG7YCZMmOAxioMHD2oBiwpU1A4Z9UhR5p86Xu1wUYHNzp07pVy5ch5/T99ZU6pUKS0YioiICGKWV08lgNGFkUYQQAABBBBAAAGvAm7cAUMAY+zFsPPoWWk/Tr8A5uuet8ujn62+ZtAjHrhV+vkYvqiTVdgy+H/bZPWB0xltrRvQKuNRJn9VLl5OkWqDrgYwD9cvI6PYAeMvI8ebIEAAEwRyoAHMTz/9JB06dNB2v6iwRb33JeuvR48eMnXqVGnSpImsXLnS48+NGzeWtWvXytNPPy1Tpky55txDhw5JhQoVtF0wCxYskDZt2gQxSwIYXfBoBAEEEEAAAQQQyEGAAIZHkPS+SHb8eVY6jNcvgFG7snp4+cpRwbzhci7p2ne6ZDcftQtn97Fz0uXTVRJ/4bI8EORXi7IGMF0alJH3H+IRJL3XE+0FL0AAE4RhoAHM66+/Lh988IHUrFlTe9TI2+/bb7+VLl26aCGNei+MesGu+iUkJEihQoW0cEUd89BDD3k9X7Wt3j3zxhtvyHvvvRfELAlgdMGjEQQQQAABBBBAwM8Axo2PIEUXyitr+rdiregksO2PeLl3wnKP1oJ5BOmzbvWl54z1QY0uLEzk9xEdtTZOJSTJiYQkUZ+lVvc9gf4IYAKV4zyzBQhgghBPD2DUo0BVq1aV3bt3a62VLl1amjVrJi+++KLceuut1/TQsWNHmTdvnjzyyCPy9ddfex2BerRIPYakfjExMVK/fn3t3+vWrZNGjRpp/1bHVKvm/bNsqu1Zs2bJvffeK3PmzAlilldP5REkXRhpBAEEEEAAAQQQ8CrAO2DYAaP3paF3ADPp8Xry7y83BDXMzAFMUA1lOjlrAPNIg7Ly3kO19WreMu1wP2aZUgQ8EAKYgOlEMn+GWiW2RYoU0XaoXL58WWtVfdlIfSb6tdde8+hFfSp648aN2ot3R48e7XUE8fHxWnvqpwIUFaSon/rkdKdOnbR/q2PUbhhvP9X22LFjteBGBTh6/Ljg9VCkDQQQQAABBBBAwLuAGwOYyb8ekGHzeAmvUdfEliN/yf0frfBoPpgdMO1rlZSfth0Lari5wkQO/L0DJqiGMp184VKKVB989R0wBDB6ydKO3gIEMEGILly4UFasWCEPPPCAtgMmX758WviyfPly6devn6xZs0Zr/csvv5THHnsso6cqVarI3r17ZcCAATJ06FCvI1DtpH/96KuvvpKuXbtqx6l/P/7449q/1THh4eFez1dtDx8+XFRf6TtzcppqesCS3XHq5cFqTOorTPwQQAABBBBAAAEE9BWo2G+upKVdbdMNjyBlDWB4BEnfNbX58F/SaaJ+AYweozMigEm8lCw1Bi/IGN6jDcvKyAfZAaNHvWhDXwECGH09M1pTIUXTpk21EKZMmTLay3Zz5cql/d2XACY5OTnj60UEMAYViWYRQAABBBBAAAELCbgxgPns1/0yfN6ujCoQwOi7IDceOiP/+Njzgx7B7IDRY3S5c4XJ/uEd9Ggqo42sAUzXRmVlxAMEMLoi05guAgQwujB6b2TJkiXSqtWVl4hlfo8LjyAZiE7TCCCAAAIIIICATQXc+AgSAYyxi3XDoTPygMUCmPBcYbJP5wDmfFKy1Hzr6g4YAhhj1xWtBy5AABO4XY5nnj9/XgoUKKAdp16I+/DDD2v/Vu9zmTt3bkAv4VVBTsOGDbV2eAlvjiXgAAQQQAABBBBAwDYC7IARYQeMvst1/cHT8uCkVR6NetsB89PWo0G/XNfXkRsRwCQkJUstjwCmnIx44NqPofg6Rqsexzs5rVoZ38dFAOO7ld9HZg5gMn8yWn0aetSoUVKrVi3ZunWr13a/++47LbC53meo1TEPPvig1/NV29u3b+cz1H5XjRMQQAABBBBAAIHQCLgxgPn0l/0y4iceQTJqxcXEnpaHPsk5gHn7x+0yfWVswMNoVrWENK1SQt6Zs8NrG62qR8vince1v0XkDpO9w/R9BIkAJuDScaLJAgQwBoIvXbpUWrZsqfWgPh/doEED7d/z58+X9u3ba+HKoUOHtHfEZP0988wzMmXKFGnSpImsXOn53Obtt9+uvVumR48eMnny5GvOPXLkiJQrV07S0tJkwYIF0qZNG11mSeKqCyONIIAAAggggAACXgUIYNgBo/elsfb309Ll05wDmAGzt8qXaw4F3P0D9UpL/fJFZcDsbde00aVBGTl3MTnj60lGBDDnLl6WW99emNH3Y43LyfB/sAMm4IJyomECBDAB0qpwQwUo2f3UF4qaNWumhSelS5fWgpb0l/CqrwmVL19ejh07Jr1795Zx48Z5NHP48GGpXr26qB00EydOlF69enn8fcKECdp5UVFR2mNIZcuW9fi7+ps6plSpUhIbG5vxMt8Ap5pxGgFMsIKcjwACCCCAAAIIZC+QNYCZ+s8G0rJ6tKPJPvllv4xkB4xhNV5z4JQ88tlqj/a9PYL0xnebZVbMkYDH8WST8lL9pkLS7wfP3f3fPtdEGpQvKr2+3JARwOTJnUv2DGsfcF/eTswawDzeuJwMI4DR1ZjG9BEggAnQUQUbjzzyiLYLpXXr1lKhQgWtJfX1IvVp6v79+2fsXJk5c2bGp6PTu1M7V3r27KmFOOpz0S+99JLkz59ftmzZIt26ddP+s3LlytpjROmfo04/V31hqUaNGnLgwAGpXbu2zJgxQ/vPCxcuaGGO6lsFRNOmTZPu3bsHOMNrTyOA0Y2ShhBAAAEEEEAAgWsE3LgDhgDG2Ath1f5T0nVyzgHMK99skh82/hHwYP7d7GapcEOkvPn91QCmeIE8EjOwtdbmczPWy/ztx7R/GxHAnL14WWpn2gFDABNwKTnRYAECmACBVQBTsWLFjLPz5cunvXD37Nmzona4qF9ERIS8//778vLLL3vt5fnnn5ePP/5Y+1t4eLhERkZq56tfdHS0LFu2TNsJ4+2ngpkWLVpIXFyc9udChQpJYmKiFgCp34svvijjx48PcHbeTyOA0ZWTxhBAAAEEEEAAAQ8BNwYwk37eL+/N5x0wRl0KK/eflMcmr/Fo3tsOmBe+2iD/b8vRgIfxbNNKcnOJAvLGd1sy2igWlUc2DPISwITnkj1D9d0BE3/hstR55+ojSE/cXk6GduYRpIALyomGCRDABEirdpuod7SoR4w2bdokJ06ckPj4eC1EqVSpkjRv3lyee+45qVKlynV7mD17thbCbNiwQXvkSD1OdP/990vfvn2lRIkS1z1XhS8jR46UOXPmiHpsST2SpD5xrYKdzp07Bziz7E8jgNGdlAYRQAABBBBAAIEMATc+gpQ1gClZKJ+s7n/lHYr8ghdYse+kPD4l+wDmr8RLMmfLURm3eK+cTEgKuMO37qshBfNFyGvfbs5oo0hkhGwafOVdlM/OiJEF26+8hDePCQFMt9vLy5DOtQKej1VP5H7MqpXxfVwEML5buf5ILnjXLwEAEEAAAQQQQMBAAQIYXsKr9/JavvekPDHVewCjXlnQduyvsud4QtDdLn7lHtlyJF5emXU1gCmUL1y2vN32mgAmb3gu2a33DpjEy1Ln3as7YAhggi4pDRgkQABjEKwTmyWAcWJVmRMCCCCAAAIIWEXAjQHMxz/vk/fn784oQXShvLKmfyurlMT24/h1zwl5ctpaj3mUKJhXetxVUe6sXFzunbA86DnOeLqR3H1LCfnvxj/k5W82ZbRXMG+4bH3nSgDT84sYWbjjyg4YMwIY9VLgdzuxAybo4tKA7gIEMLqTOrdBAhjn1paZIYAAAggggEDoBSr0nesxCDd8BSlrAMMjSPquw1/2nJB/Zglg0nsY9+ht8tLXVwMTf3tW73iJGdBKcuW68mXYhduPSc8Z6zOaqVQiSpa+2kz77898ESOL/g5g8kXkkl1D9H0HjHqU6rZ3F2X0TQDjbzU53iwBAhizpB3QDwGMA4rIFBBAAAEEEEDAsgJuDGAmLtsnoxZc3QFDAKPv8vx5d5w89fk6r422q1ky48tEgfT6+4gO2hdd03+XklOl4bDFol6Iq35f9mis7bLJGsDkj8gtO4e0C6TLbM85c/6S1B1yNYD5Z5Py8g47YHQ1pjF9BAhg9HF0RSsEMK4oM5NEAAEEEEAAgRAJuPERJAIYYxfbsl1x0n269wAm2J69fU1p17Gz8vXaw1K3XBHpdFvpjC56/CdGFu+88giSGQHMU3dUkLfvrxnsFC13PvdjliuJ3wMigPGbzL0ncMG7t/bMHAEEEEAAAQSMF2AHDC/h1XuVNRmxRI7GX9S7WWlbM1o+7dbA53YzBzCReXLLjnf13QFz+vwlqZdpBwwBjM+l4UCTBQhgTAa3c3cEMHauHmNHAAEEEEAAAasLEMAQwOi5RuPOXpRGw5fo2WRGW8teayYVi0f53HaP/6yTxTvjtOONCGBOJSRJ/aGLM8ZDAONzaTjQZAECGJPB7dwdAYydq8fYEUAAAQQQQMDqAgQwIrwDRr9VuvnwX9Jp4gr9Gvy7pUcblpWRD9b2q93MAUxUntyyXecdMAQwfpWDg0MoQAATQny7dU0AY7eKMV4EEEAAAQQQsJNA1gBm2lMNpEW1aDtNwe+xfrR0r3ywcE/GeQQwfhNme4JRAUyXBmXk/Yfq+DXQp6evkyW7ruyAMSKAOZmQJA0y7YDpfmcFees+3gHjV5E42BQBAhhTmJ3RCQGMM+rILBBAAAEEEEDAmgIEMO7eAbPz6Fn5cs1BqV26iDzcoIzHF4YCWbFGBTAP1isjH3YJPIApkDdctr3TNpApZXtO1gDmX3dWlMH31dC1Dys0xv2YFaoQ3BgIYILzc9XZXPCuKjeTRQABBBBAAAGTBQhg3BvAXE5JlcbDl4h6maz6ff5UQ2le7cagVuC62NPy8CerAm5D7UZSwUZyappHGw/ULS2jH7nNr3b/NX2dLP17B4wRAcyJc0naJ7DTfwQwfpWHg00UIIAxEdvuXRHA2L2CjB8BBBBAAAEErCzgxgBmwpK98uEiHkFavvekPDF1TcbyvCEqj6wf1Drg5XroVKLcM2pZwOerE1f2bSFnEi/J9BWx8u36IxltfftcE2lYoZhfbXf/fK0s231CO6dg3nDZqvMOmLhzF6XRsKsvHH76rooy6F52wPhVJA42RYAAxhRmZ3RCAOOMOjILBBBAAAEEELCmAAGMe3fALN11XP41PcZjYcaO7BjQQj18OlHufj+48GV2rzukbrmiWv9paWnyzpwdsnD7MWlbq6QMvreG349HmR3A9LirogwkgAlo/XCSsQIEMMb6Oqp1AhhHlZPJIIAAAggggIDFBNwYwIxfsldGswNGlu2Kk+7T1+kSwHQY95vsOHo24NVdJbqALOzTNODzvZ341Odr5ef0HTD5wmXr2/q+AybrJ7cJYHQtH43pKEAAoyOm05sigHF6hZkfAggggAACCIRSgADGvTtglu2Ok+6fBx/AJCWnSNWB84Naxg0rFJVvn7sjqDaynpw5gCmUL1y2GBzAPHN3RRnQkUeQdC0ijekiQACjC6M7GiGAcUedmSUCCCCAAAIIhEbAjQHMuMV7Zcxi3gGjVwBzKiFJ6mf6HHMgK/mpOyrI2/fr+wnnf05bK7/sufIOGCMCmONnL2ovMU7/EcAEUnnOMUOAAMYMZYf0QQDjkEIyDQQQQAABBBCwpAABjMhNhfPJqn4tLVkfIwf18+44ecrHHTB/JV6Swf/bLn/8dUFebV1F7qhcPGNoB0+dl6ajfg54qHnDc8nyN1tIiYJ5A27D24mZA5jC+SNk81ttdG3/WPxFuX3E1QCm5z2VpH+H6rr2YYXGuB+zQhWCGwMBTHB+rjqbC95V5WayCCCAAAIIIGCyAAGMex9B8ieAGbt4j4xdvFdbnaUK55MVfVtkvBR32x/xcu+E5QGt3Ao3RMrUpxrKzSUKBHT+9U56ctpa+fXvHTBmBDDP3lNJ+hHA6F5HGgxegAAmeEPXtEAA45pSM1EEEEAAAQQQCIGAGwOYzGGCIi9ZKJ+s7s8OGGWR3VeQsq6TXUPaSb6I3NqKHfTfbTJj9UGfVm/xAnlk6WvNZOOhv6RcsUipWDzKp/MCOShzAFMkMkI2DdZ3B8zR+AvSZMTSjKERwARSJc4xQ4AAxgxlh/RBAOOQQjINBBBAAAEEEPAq8OdfF+SdOdvlUnKq9gLPyjfqvxPgevQEMO59BEm9H0U9ppP5lzmAUZ+WfvSz1RKeO0wOnkr0OE49zlMwb7j8b/Mf0uebzT5f3V/1aOzx+JLPJwZwYLepa+S3vSe1M40IYNS1e8fITAFM00rSrz2PIAVQKk4xWIAAxmBgJzVPAOOkajIXBBBAAAEEEMgq8NLXG+V/m/7U/uc7br5BvnrmdlORsgYwnz/VUJpXu9HUMZjd2ZhFe2TckiuP06ifW98Bk1MAsy8uQVqN/sVreWY920S6fLrKr9JN6FpX7qtTyq9zgjk4cwBTNDJCNuq8A4YAJpjqcK6ZAgQwZmrbvC8CGJsXkOEjgAACCCCAwHUFsgYg2T0CYhQjAYx7Axj1fhT1mE7mX+b1t+vYWWk39jddlt7NJaJkyavNdGnL10aMDmDUC4nvzLQD5rmmN0vf9tV8HZ5tjuN+zDalynagBDD2r6FpM+CCN42ajhBAAAEEEEAgBAIEMOajswPminlOn6EO5uW6masakTtMlr7aTMoWizS12E9MWSPL9115BKlYVB7ZMKi1rv1nDWD+3exmebMdAYyuyDSmiwABjC6M7miEAMYddWaWCCCAAAIIuFWAAMb8yo9etEfG8wiSLNl5XJ7+T4xHATLvgNl46Iz84+OVQRWoV7ObpV2tklK7TJGg2gnk5MwBzA1ReWS9zgHMkTOJctd7yzKGRgATSJU4xwwBAhgzlB3SBwGMQwrJNBBAAAEEEEDAqwABjPkLgwDmivnC7cek54z12QYwMbGn5aFP/HvPS9Zqmv1IXeb+H5+yWlbsO6X9T0YEMOolxXe/fzWAUWHTG+yAMf+CpsccBQhgciTigHQBAhjWAgIIIIAAAgg4WcBqAcy0pxpIi2rRTiaX0Qt3y/il+zLm6NaX8M7fdlSem7kh2wBm1f5T0nXy6qDWglUCGPX565iB+j6ClDWAeb75zfJ6Wx5BCmrBcLIhAgQwhrA6s1ECGGfWlVkhgAACCCCAwBUBAhjzVwIBzBXzuVuOyvNfZR/ALNpxXJ75wvMRJX+rFcoA5rHJq2Xl/is7YAhg/K3c1eO5HwvczipnEsBYpRI2GAcXvA2KxBARQAABBBBAIGABqwUwU//ZQFpWZwdMwAW10Yk/bv5Tev/fRo8Rpwcml5JTpcrAn3yejfrM85nEyx7H5w3PJbuHtve5Db0P9Axg8krMwFa6dpF1B8wLzSvLa22r6tqHFRrjfswKVQhuDAQwwfm56mwueFeVm8kigAACCCDgOgECGPNL/uHC3TKBR5Dkvxv/kJe/2eQ1gPlm3SF58/utPhfnxRaVPUzViYXyhcuWt9v63IbeB3b9bLWsOpC+A0b/AObQqUS5Z9TVd8AQwOhdQdrTS4AARi9JF7RDAOOCIjNFBBBAAAEEXCwQygAm7uxFaTR8iYf+lCcbSKsazt4BQwBzpeTfrz8ir367+ZoAJi0tTSr2m+fzVTn43hrSrUl5uWWA546Zl1vdIi+3quJzO3ofmDmAKVEwr6wboO8OmIOnzkvTUT9nDFuFUK+2YQeM3nWkveAFCGCCN3RNCwQwrik1E0UAAQQQQMCVAqEMYAbM3ipfrjnk4T75yQbS2uEBzAcLdstHy3gJ76x1h+WN77d41H9N/5by0dJ9MmP1wRyvx4jcYfLL682lVJH82rGZH2kKCxPZNLiNFM4fkWM7Rh3w6GerZPWB01rzZgQwvVtUllcIYIwqJ+0GIUAAEwSe204lgHFbxZkvAggggAAC7hIIZQCTtW8l78YAplThfLKyX0t3LTwR+b+1h6TfD74/ZpQVaP/wDpI7V5jH/5yckirJqWmSJ3cuyZXlb2YDZw5gbiyYV9bqvAMm9uR5afbB1R0wBDBmV5j+fBUggPFViuOEAIZFgAACCCCAAAJOFrBaAPNZt/rSpmZJJ5PLqAW7ZOKy/RlzdGsAM3P1QRn4320B1XrG043k7ltKBHSuWSc98ukqWfP7lR0wRgQwv588L80zBzAtb5FXWofukSujXLkfM0rWvHYJYMyztn1PXPC2LyETQAABBBBAAIHrCBDAmL88CGCumH+xKlYG/2+73wUI9btdfB1wl09Xydq/A5joQnllTX993wFDAONrJTgu1AIEMKGugI36J4CxUbEYKgIIIIAAAgj4LWC1AObTbvWlLTtg/K6jHU/4fMXv8s6cHX4PvW/7avJc05v9Ps/sEzIHMCUL5ZPV/fV9zOzAiQRp8eEvGdN6qeUt0ocdMGaXmf58ECCA8QGJQ64IEMCwEhBAAAEEEEDAyQIEMOZX9/35u+Tjn3kEacpvB2To3J1+F+D55jfL622r+X2e2Sd0+WSVrI298giSGQGMXXYG+VsH7sf8FbPe8QQw1quJZUfEBW/Z0jAwBBBAAAEEENBBwGoBzCdP1Jd2tZz9DhgCmCsL97Nf98vwebv8XsXqPSe9W97i93lmn5A5gLmpcD5ZpfOLlvefSJCWmXbAEMCYXWH681WAAMZXKY5jBwxrAAEEEEAAAQQcLWC9AKaetKt1k6PN35u/Sya5cAdMamqazFxzUDYfjpcnm5SXOZv/lCnLf/e71mv7t5QbC+Xz+zyzT3j4k5WyLvaM1q0RAcy+uARpNfrqI0h9WlWRl1pZP5jytw78H+L+ilnveAIY69XEsiPigrdsaRgYAggggAACCOggQACjA6KfTQz+3zb5YtXBjLPc8BWkFftOyuNT1mTMuXD+CIm/cNlPOZHp3RtKs6o3+n1eKE7IHMAYUeOsAYxddgb5Wwvux/wVs97xBDDWq4llR8QFb9nSMDAEEEAAAQQQ0EHAagHMpMfrSftbnbsDZvuf8dJx/HKPyhlxc67D0tC1ibZjfpXdx88F1eb3/75D6pcvGlQbZp780KSVEnPwyg4YI2q8L+6ctBr9a8aUCGDMrC59+SNAAOOPlsuPJYBx+QJg+ggggAACCDhcgADG3ALf/9Fy2XIk3hIBTEJSskxctk9SUtPk+WaVpXBkhGEYWddZIB0t7HOPVIkuGMipITkncwBTukh+WdG3ha7jyBrAvNq6irxog3fj+IvA/Zi/YtY7ngDGejWx7Ii44C1bGgaGAAIIIIAAAjoIWC2A+fjxetLBwTtgvAURRuyO8GVp9J+9Vb5ac0g7tPNtpWTso3V9OS2gY4INYIx4h0pAE/HjpAcnrZT1f++AMSKA2Xv8nLQec3UHDAGMH8XhUFMFCGBM5bZ3ZwQw9q4fo0cAAQQQQACB6wtYLYCZ+Fg96VjbuY8gWSmAMav26uW7lfrPy/FSDAsTGd2ljvT5ZvM1xy5/s7mUKRqZYxtWOsDoAGbP8XPSJlMA81qbKvJCC17Ca6U1wFiuCBDAsBJ8FiCA8ZmKAxFAAAEEEEDAhgJm3YR7o/EWRhDAmLeIzKp9UnKKVB04P8eJDexYXe66pbi0G/vbNcdufquNqBf32un3wMcrZMOhv7QhG7EDJmsA83rbqvJ888p2IvJprNyP+cRk6YMIYCxdHmsNjgveWvVgNAgggAACCCCgr4BZN+G+BjAfPVZX7q1dSt9JWqg1N+6AUe+aqfXWgutW4YXmleW1tlVl/4kEafnh1U8rp5+0e2g7yRue20KVzHko//h4hWz8O4ApUzS/LH9T33fA7D52TtqOvfoIEgFMzjXhiNAIEMCExt2WvRLA2LJsDBoBBBBAAAEEfBQggPERSqfD3BjAfLPukLz5/dbrCr7Rrqr0alZZDp1KlHtGLfM49rmmN0vf9tV0qoB5zWQOYMoWyy+/vUEAE4g+92OBqFnrHAIYa9XD0qPhgrd0eRgcAggggAACCAQpYLUAZkLXunJfHXbABFlWn07Xq/bqK0pfrIqVY2cvSvc7KkrJwvlEvfflm5jDsnRXnCzacTzH8bzbqaY82aSCHDmTKHe95xnAxI7smOP5Vjyg88QVsunwlUeQjAhgdh076/G4FjtgrLgKGJMSIIA5SfyYAAAgAElEQVRhHfgsQADjMxUHIoAAAgggYGuB79Yfkekrf5f65YpK/47Vbfe4Q6D4et2EB9K/t90gBDCBSAZ2jh61/+OvC3LnyKUZA6hXroj80OtOmbXusLzx/RafB/b/XrxLapUu7HUHjBMCmHLFIuXXN5r77OHLgTuPnpX2466+Lyd9F5Ev59rpGO7H7FQt72MlgLF/DU2bARe8adR0hAACCCCAQMgEzpy/JHWHLMrof9yjt0mn20qHbDxmdqzHTXig4/UWwIzvWlfud9kOGCPeD+JLTfSo/SuzNskPG/7w6O7rnrfLo5+t9mUI2jHdbi8vQzrX0v598XKKVBt09YW9T99VUQbdW8Pntqx0YKeJK2Tz3ztgzAhg3mxXTf7d7GYrEegyFu7HdGEMaSMEMCHlt1fnXPD2qhejRQABBBBAIBCBHzYckVdmXf30bZHICNk0uE0gTdnuHD1uwgOdtLcAxunhl7c5G/F4ii810aP23uZT4YZIiT2V6MsQZNlrzUQdH6a+Qf33T+2eGbVwt1QqHiUfPVZPShTM61NbVjsocwBT/oZI+eV1fXfA7PjzrHQYf3UHDAGM1VYA40kXIIBhLfgsQADjMxUHIoDA/2fvPMCjKro3/pJGCYROIHQIISQQeu+9C1KlqSgiUkUF6aAgRRAERETKh4KIFFGR3nsHQ68hQOi9hJryPXMhm93NJtnd3Hv33t13nuf/fHzuzJkzv3Pn+zsvZ86QAAmQgG4JUICJD52a1z0owLzmrsTh3JrNqJQAY83ccX3U/N5s8UuOvi1+2InQiIeKxfjEtYdoOm2nwVVRqFgULHa2xvOY/iNKAUb/MVRtBdzwqqHmRCRAAiRAAiTgMAIUYCjAqPXxWRKdCmbzljJB1G72CjB3nrzAltO3IJ6X/mrlyRS57SoCjMjy2SpzBoy5ADO4cSA+pgCTou+Rg5UhQAFGGa5OaZUCjFOGlYsiARIgARIgARMCFGAowKi1JSwJMOKqzWYNCTCxsbFYfvgqrt5/hi6V8yOLt5cBj63FdZPimtMnDfYOqasWetXneeuHnTj6JgNGCQHm+NWHaDY9PgOGAozqIeaEVhKgAGMlKHYDKMDwKyABEiABEiAB5yew/FAEPl/KGjAi0mpmJFgSI75vXwotSztvAWRLay6c3RubPtdOBswfBy7jy+XHpI1fNn9mLP+kivTnQ5fuofXMPbL9D4Io1lupUFbZ7GnNkLEAo0SWk7kAM6RJILrX4BUkrX0H9IfPUPMbsIEABRgbYLErCZAACZAACeiUAAWY+MBRgFH2I7YkwBTJkR4bPqup7MQWrCd2Bcn8n58Z00h6lr3OpK0IuxMpm59qfmuyOW2DoebTd+LY1dc1YCjA2ADOrCvPY/az08pIZsBoJRI68IMbXgdBooskQAIkQAIkkEICvIKkHQFmSvuSeLt0nhRGVLvDLQkwAb7psb6/dgWYY6MaIEMaT1jy3V7SbcrmwaS2Je0drotxzabvwPGrjyRflbhmdiziIZr/EH8FaWiTYvioRiFdsLHFSZ7HbKGlzb4UYLQZF016xQ2vybDQKRIgARIgARKQlQAFGO0IMJPblUSrMq4lwBT1zYB1/WvI+k1bY8xcUDn1dSOk9XJPILQcGFpPegpaTgFm/5C6yOGTxho3ddvHRIDJ7o3NMl8zMxdghjUthm7VKcDo9oNxYscpwDhxcOVeGgUYuYnSHgmQAAmQAAlojwAFGAowan2VlkSMwJwZsPZTxwswI5sHoWvVggmElp1f1kYaT3eUG7NRFkw/dymLBsE5ZbGlZSNNp+3AiWtvMmAUEGCORjzAWz/sMiCgAKPlr8G1faMAI3P8o6OjUaFCBRw+fFiyPHLkSIwaNSrRWVasWIGZM2dK/SMjI5E3b140b94cgwcPRrZs2ZL07vbt2xg/fjz++ecfREREwNvbG2XKlEHPnj3RsmVLmVfGIryyA6VBEiABEiABEtAgAQow2hFgvmtbEq3LulYGTLFcPljTr7rqO8OSGCTqspj/c3FVaPCfR/EqOlYWHy+MbQJ3t1Sy2NKyEWMBRolCyxRgtBx9+mZMgAKMzN/DpEmTMGDAAIPVpASY3r17Y8aMGVJfd3d3pEuXDo8fP5b+u6+vL7Zs2YJixYpZ9PDEiROoU6cObt26Jf2eIUMGPH36FEIAEq1v376YOnWqrKtjBoysOGmMBEiABEiABDRJgAIMBRi1PkxLokewnw9W9VVXgHkZFYOAYWsSLNuSAJM7U1pcffAsxYjENaYtX9RC+tQeKbalBwNNpu7AyeuvM2CUEGBCrzxAixnMgNHDt+DqPlKAkfELCA8PR3BwMHLkyIFnz57h5s2biWbAzJ49G927d0eqVKkwevRo9O/fXxJgQkND0blzZxw/fhz+/v4QQouXl5eJly9evEBQUBDCwsJQvHhxLFy4ECVLlpQEmClTpmD48OGIjY3FvHnz0LVrV9lWSAFGNpQ0RAIkQAIkQAKaJUABJj40F8c1kf5dTY1mSYwQ2RaiQKuzNktrLpE7I1b2qabqklcciUD/P+KfXo+b3JIAI5djs7qURUMXuHoUx8tYgPHPkR4bZX7p6r8rD9DSSIAZ3iwIH1YrKFe4NGOH5zHNhMJuRyjA2I0u4cBGjRph3bp1+Pfff9GrVy9cunTJogDz8uVL5M+fHzdu3IDIgpk+fbqJMTFOCCxCUBEZMuJKkXET/UWGixBsTp06hXz58pn8HpdZ4+fnByEKeXp6yrJKbnhZMNIICZAACZAACWiaAAWY+PCEjW0CN5Wuh1gSIya2CUHbcnk1/b2kxDlLaw7JkxH/9FZPgHn6MgpBI9ZZXIaSAsyCDyugepHsKcGnq7GNp+7AqTcZMGoIMCOaBeEDCjC6+kZcxVkKMDJF+rfffpMyV1q1aoXly5ejQIECiQowa9asQZMmr/9GRYgtou6LeevWrRvmzp2LypUrY/fu3SY/V6xYEfv378eHH36IOXPmJBh7+fJlaX6RBSMEoQYNGsiySgowsmCkERIgARIgARLQNAFzASZjWk+EjpTn3yU0vXAgQb0PNetzUIB5/XWoIcBERcdg5tYLOH7tIXafv4vHL6IUF2BERsbof09K84jrR3sG1YGHu5vWt4Rs/hkLMEVypMcGmTNgjly+j7d/jD8zUYCRLXQ0JDMBCjAyAL137x4CAwOla0ciIyVPnjxJCjCiRoyoFSOuK4mrRpba0qVL0a5dO0mkEXVhRIFd0Z48eQIfHx9JXBF92rRpY3G8sH3y5EkMHDgQEyZMkGGVLMIrC0QaIQESIAESIAGNE6AAEx+g8980Vu2QbEmA+bZNCNoxA0b2HbMy9Br6/H5EdruJGexRszAGNQ7E+hM3cDTiIdqWy4P8WV//u72rtEbfb8fpG69rXSohwBy+fB+tjASYuFesnI0v/0Jc/xGlACNDDN9//3388ssvmDx5slTLRbSkMmCaNm2K1atXo3379li8eLFFD4SQI64hiXbw4EGULVtW+vOBAwekV5ZEE32E8GOpCdtLlixBs2bNsHLlShlWSQFGFog0QgIkQAIkQAIaJ8ArSPEBOvdNY3iqlKVAAeY1dzVqwBQavAox8jxiZNVuDh3RABnTyVMSwKoJNdjJWIAJ8E2P9f1ryuqluQAzqnkQ3q/KGjCyQqYxWQhQgEkhxk2bNqFevXooVaqUJJSI14ySE2DEU9FHjhyRxBoh2lhqDx8+RKZMmaSfhIAihBTRxJPTLVq0kP4s+ohsGEtN2P7+++8l4Ub4ZU2LU1QT6ytq14iCwKIIMBsJkAAJkAAJkIBzEqAAEx/Xs2Maw8tDnWsiFgWY1iFoV961asAoKcDM23kRX7+5BqTW7j0yvD4ye5s+qKHW3FqaR2kB5tCl+2g9M/4KEgUYLUWfvhgToACTgu/h+fPnKFGihPQakajTImqzxLWkMmACAgJw7tw5DB06FGPGjLHowatXrwyvHy1atAgdOnSQ+ok/d+rUSfqz6OPhYfnpOmF77NixEHOdOXPGqlVSgLEKEzuRAAmQAAmQgFMToAATH94zYxohtcfrv1xTulGAeU1YCQFGXN2ftT0M49ecVjqMJvZ/+aACaga4TqHdpOA2nLIdZ26+voJU1DcD1vWvIWsszAWYr94KxntVCsg6hxaM8QqSFqKQMh8owKSA3+DBgzF+/Hj06NEDM2fONLGUUgEmKirK8HqRWgJMcii44ZMjxN9JgARIgARIQP8Elh+KwOdL45/kzZTOE/+NcM0ivI4WYCa0LoH25U1fu9T/Fxa/AkuiU/HcPvi3T/UULfPWo+dYf/ImyhXIjELZ0iNg2JoU2bNnsFjHyt7VVHvG3B4f1RxjLMAE5syAtZ/KLcDcQ+uZewxLogCjZnQ5ly0EKMDYQsuo79GjR6XrPVmzZsXp06cN14XiuiQlwGj1ClJyKCjAJEeIv5MACZAACZCA/glQgImP4enRjZDG03EZMONblcA7FSjA2LKrXkRFo8a3W3Dz0Qt4uKVClJrFXt44Kmq++KT1oPhiFLgGU7bh7M0n0j9RQoA5GH4PbX6KF2C+bhGMdyszA8aWvcO+6hCgAGMn51q1amHbtm2YNWsWOnbsmMCKeIVIPActsmSGDBki/Z4+fXrpP0U9l1WrVtlVhFfUcylfvrxkh0V47Qweh5EACZAACZAACSRKgFeQKMCotT2UyIBZe/w6eiw8rNgSvmtb0iRDTEwkrk0du/pQmnNimxC0deKXq+wFq7YAM7pFMLpQgLE3XBynIAEKMHbCjctwsWX4/fv3pUwZ8TT0xIkTUbx4cRw7dsyiiWXLlqFt27ZJPkMt+rRu3drieGH7xIkTfIbalgCxLwmQAAmQAAmQACjAxH8Ep75uhLRejsuAGdeqBDq4WAZMsJ8PVvW1/wrS3J0XMVqBQrvdaxRC8xA/iKtFBQevNvlfihU9q+Dhs1fIkMYTZfNn5v+KWCBQf/I2nLulXAbMgfB7aGuUAUMBhp+hVglQgLEzMikRYNauXYvGjRtL4orIksmTJ08CLz766CPMmTMHlStXlgr8GrdKlSph37596NatG2bPnp1gbEREBPLlywdRcGzdunVo0ECee9u8gmTnx8JhJEACJEACJKAjAuYCTMa0nggdKc+/S2gdg3lGxsmvGyKdl+UHD+Rei6VsEAowtlOesyMMY1adsn1gMiP+6lUVpfK+fqHUPFai1kuJPBlln9OZDBoLMMVy+WBNP/tFNktcEggwLYujS6X8zoRQWgvPY/oPKQUYhWKYVA0Y8Zxz/vz5cePGDfTt2xdTp0418eLKlSsoVqwYIiMjMWPGDPTs2dPk9+nTp0vjvL29pWtIefOaPk8ofhN9/Pz8EB4ebijmm9KlcsOnlCDHkwAJkAAJkID2CZjXgHFlAebEVw3hndpxAszYt0ugY0XXqgFjnAGz6dRNjF19Cn6Z0kJc/cnhkybRDfT8VTTuP32JyuM2K7LJjo1qIGW4iNb39yP4J/SaYZ6Dw+ohW/rUiszrLEbrTd6G828yYJQQYPZfvId2s+JrwIymAOMsn47TrYMCjEIhTUqAEVOKzJXu3btLWTDiueh+/fohbdq0EMV9u3TpIv2nv7+/dI3Iy8vLxMsXL14gKChIev46JCQECxYskP7z2bNnkpgjas6I7Jd58+aha9eusq2QAoxsKGmIBEiABEiABDRLgEV440Nz/KuGSE8BRrFv1VLWj5hMXPcJ8M2AL4xe4/qgakGMaB5k0Zfbj1/g7R93IeL+M0V8LV8gM5b2qGKwfeluJDr8vBfXHj5H16oFMLJ5sCLzOpNRYwEmKJcPVsucAbMv7C7a/7zXgGxMy+LozAwYZ/qEnGYtFGAUCmVyAoyYtlevXvjxxx8lDzw8PJAuXTo8evRI+u++vr7YsmWLlAljqQlhpk6dOrh165b0s4+PD54+fQrxfLVoffr0wbRp02RdHQUYWXHSGAmQAAmQAAlokgBrwMSHxTjrQelgWRIjvnm7ODpVdL5rFHEsExNgEmMdPr4pYmJiMW/XRRyNeIiGwTnRNCQXBiwNxdJDEYqEqEy+TBjTsgSC/HxM7IuMm0fPXyFHhsSzchRxSKdG6363FRduR0reqyHAOOve4XlMpxvAyG0KMArF0BoBRky9YsUKSYQ5fPiwdOVIXCd66623MGjQIGTPnj1J74T4Mn78eKxcuRLi2pK4kiSeuBbCTsuWLWVfGTe87EhpkARIgARIgAQ0R4ACTHxIjo5qAJ83106UDpQlMSIlf4v/9GUUXkXHQlwh02qzVYA5/01jzNoehonrzhiW9MsHFfDevP2yL3HqO6XQolRu2e26qsE6321F2BsBJqWFli0x3Bt2F+8YZcBQgHHVL03766YAo/0YacZDCjCaCQUdIQESIAESIAHFCLAGjP4FmIPh9/DB/AOIfBmNr1sEazaLxlYBxvi5Z8U2ACC9dCQK64pSAWzyEFBagNlz4S46zI6/guSs9ZN4HpPne3SkFQowjqSvs7m54XUWMLpLAiRAAiRAAnYQoAATD028/qRWBoklMcLeQqI1vt2Cy/eeGhYiru5ood189BzilSJxbef9qgVQZOgaLbhl4oN4eap9ubxwc6P4Imdw6kzairA7r68gCYHr3z7yvoJkLsA46wtiPI/J+VU6xhYFGMdw1+Ws3PC6DBudJgESIAESIAGbCPAKkpEAM6IBMqZT5wqPnAKMuS2tCDCtZ+7GoUv3JcBDmgRi7OrTNn2bSnee0LoE2pd33lenlOaXlH1jAUZkMq3sU01Wd3ZfuIOOs/cZbFKAkRUvjclIgAKMjDCd3RQFGGePMNdHAiRAAiRAAgAFmPiv4L8R9ZEpnelrlEp9IxYFmBbB6FK5gM1TalGAiYqOgb8GM17i4Pau7Y8vGha1mTUHWEeg9qStuPgmA0YNAWZ8qxJ4p4LziWk8j1n3vWm5FwUYLUdHY75xw2ssIHSHBEiABEiABBQgQAEmHuqR4fWR2Vv/AszFcU0SrWfy7GU05u4Mk34XTz2n9XJX4KsC7kW+RJnRGxSxnVKjpfNlwux3yyFb+tQpNcXxiRAwFmBC8mTEP71lzoA5fwcd58RnwFCA4aeoVQIUYLQaGQ36RQFGg0GhSyRAAiRAAiQgMwHzGjCZ0nnivxENZJ5Fm+bMM0cOD6+PLA4UYEQB3XdlyIA5901jeLq7WYQ++M9j+H3/Zem3ThXz4Zu3SyQbnOiYWExafwa7zt9B23J50aVS8k9ln735GA2mbE/Wtlwdkrri5OuTGjcfvYCHWyps+aIW8mZJJ9e0tJMIgVoTtyD87uu6REoIMOJb7GQkwDjrdTKex/S/xSjA6D+Gqq2AG1411JyIBEiABEiABBxGgAJMPPpDw+ohq0pZEZauIH31VjDeq5LyK0gnv26IdF4eFr8pe64r7Tx3B53nxmcb7B5UB36Z0ib6zZr3V+PjFuxG/nMiwVQzOpaRBIBtZ2+jUqEs8M+RQQ13XH4OYwGmZJ6M+FvmDBhzAebb1iFoVz6v03HneUz/IaUAo/8YqrYCbnjVUHMiEiABEiABEnAYAV5Bikd/cFg91a6lKCnAhCZRTNgeAabJ1B04ef2RAdTn9QPQp24Ri9/syWuP0GTaDtm/ZyGOlR2zMVG70zqURt/fj5j83rKUH75/p7TsvtBg8gRqTtyCS28yYJQQYMxFPgowyceEPRxDgAKMY7jrclYKMLoMG50mARIgARIgAZsIUICJx3VgaD1kz6BOXRBLAsyo5kF4v2pBm+InOpvbSmodtggwc3dexL9Hr+HI5QcmPvWvF4B+9RIKMHeevEC9ydvw4Okrm9eQ1IA4IcUSs7hxf/WqipYzdpmYUTOjSdYFO4ExEwEmbyb83auqrKtKIMC0CUG7csyAkRUyjclCgAKMLBhdwwgFGNeIM1dJAiRAAiTg2gTMBZiMaT0ROtL5a8DExsai4ODVJsF3FgEmqStC1ggwr6JjsPjAFQz/67jFzdGvbhH0rx9g+O3ag2cY9tdxbD59S/bNVL1INvzcpZxULDgpAebU141QbMRaw/yNi+fEzM5lZfeHBq0jUOPbLbh873UNmJIKCDA7zt1Gl7n7Dc58SwHGusCwl+oEKMCojly/E1KA0W/s6DkJkAAJkAAJWEvAvAaMqwgwMTGxKDTEVIDZP7QucmRIYy26FPWzJCaMbB6ErjJkwGwbUAv5s3pb9M8aAebdefux/eztRNfXt24RfFY/AELE+m79Wfyw5XyKWCQ1eN2nNVA05+u6LYkJMHmzpMWOgXWk4sJjV59Cgaze+PndssiVMfE6NYo5TMMSgerfbsaVe8+kP5fKmwkiQ0nOJr5P8Z3GtYltQqQC0c7WeB7Tf0QpwOg/hqqtgBteNdSciARIgARIgAQcRsBVi/CKl30KmwswQ+oih4/jBJgRzYLwQbWUX0Ha+FmNRIvNJiXALNh7CdM2ncPtxy+S/B771PHH5w2KYtmhCHyxNFSxb7d4bh/806sa3NxSJSnALOpWEVX8synmBw3bTsBYgBHPfq/oqawAM6ltSbQpm8d2RzU+gucxjQfICvcowFgBiV1eE+CG55dAAiRAAiRAAs5PwFVrwERFx8B/6BqTAO8bUhe+TiDArOlXHcVy+Vj8eBMTYGx5NvrjGoVw7tYTRa4cGTttnpFkKQNmfKsSeKdCPuffqDpbYbUJmxFx/3UGjBICjHjV6j2jDBgKMDr7QFzIXQowLhTslC6VAkxKCXI8CZAACZAACWifgKsKMKLOSREzAWbv4LrImdFxGTDDmwXhQxkyYP7pXRUheTJZLcCIjJfy3yT+wpDaX3GHCnkx9u0SSJXqdeZLXJu84ayUoWMi0qiYtaQ2Bz3PZyzAlMmXCX/KnAGz9cwtvP+/AwZE37UtidbMgNHzJ+O0vlOAcdrQyr8wCjDyM6VFEiABEiABEtAaAVe9gmRJgNkzuA7UqhtiKZtDLgFm+SeVUTZ/FqsEmJ86l0GPhYc19VmeHt0IaTzdE/j06PkrhIxab/LPj41qgAxpPDXlP50Bqo7fjKsPXmfAqCHATG5XEq3K8AoSvz3tEaAAo72YaNYjCjCaDQ0dIwESIAESIAHZCLiqAPMyKgYBw0yvICX1epBswN8YsiTADGtaDN2qF7J5KnNb41qVQIdEruUk9ZKQzRPLPCBzOk+s7FMNeTKnS9Ry7UlbcfFOpPR77kxpsWtQHZm9oDk5CBgLMGXzZ8byT6rIYdZgY8uZW+hqlAFDAUZWvDQmIwEKMDLCdHZTFGCcPcJcHwmQAAmQAAkArirAvIiKRtFh8c8Wi29BHObFoV6NpqQA458jPTZ+VtPiMpQUYLzc3TChTQn0/8O2wryiXs1XbwWjYDZvZM+QOkn8/x69hkHLj0G81vW/ruUR4Pv6hSQ2bRFQW4CZ0r4k3i7NDBhtfQX0RhCgAMPvwGoCFGCsRsWOJEACJEACJKBbAq5aA+b5q2gEDjcVYHZ+WTvJ7As5gyyHACOuUU3ffD5BXRThZ/j4pqoLMEK8GtioKPot/s9qVI2L50T/+gE2CSniBSv3Ny8jWT0RO6pKwFiAKZc/M5bJnQFz+ha6zo+vAUMBRtXwcjIbCFCAsQGWq3elAOPqXwDXTwIkQAIk4AoEzAUYkVkQOrKB0y/dkgCzY2Bt5M2S+PUXOaFYEmCGNimGj2pYfwXpryNX8ekfCcUO85obkS+icPneUxTOnj7BtSs515Qnc1oMaGi9AFM4uzc2fV5LThdoSyMEqozbhGsPn0veKCHAbD59Ex/MP2hY7fftS6Fl6dwaWb18bvA8Jh9LR1miAOMo8jqclxteh0GjyyRAAiRAAiRgIwHzK0iuIsA8exmNYiNMM2D0JsAkdp2omn82LOxWUfoSxAtHTabtkP5T6ZYvSzp80bAo+v5+JNmpsnp7YfZ75VAmX+Zk+7KD/ggYCzDlC2TG0h7y1oAxF2CmvlMKLUpRgNHfl+L8HlOAcf4Yy7ZCCjCyoaQhEiABEiABEtAsAVetAfP0ZRSCRqwziYujBZghTQLRvUZhq7+VxASYCgWzYMnHlSU783ZexNf/nrTaZko65s+aTsqA6b0ocQFGPBfcopQf3FKlghuvEaUEt6bHVh63CdffZMAoIcBsOnUTH/4SnwFDAUbTn4NLO0cBxqXDb9viKcDYxou9SYAESIAESECPBFy1Boy4lhM80lSA2T6gNvJlddwVpOQEmCkbzuK3fZdRMyA7vm0TgsJDVlv85ErlzYS/elWVfvt8SSiWH45Q5dMsIAkwgei1yPRZ6/71ApA+jQferZwfnu5uqvjCSRxLoNLYTbjx6PUVpAoFsmBJj9eCoFyNAoxcJGlHaQIUYJQm7ET2KcA4UTC5FBIgARIgARJIhICrCjBPXkShuJkAs21ALeTP6q3Kt2Ipe2Vw40B8XNNyBsyhS/fReuZug2/iBSDjZ3iNnQ7288GqvtWlf/TevP3Ydva2KmsSrxgNbFgUn/xmKsAkVhBYFac4iUMImAgwRhlZcjmz8eRNdPuVGTBy8aQd5QhQgFGOrdNZpgDjdCHlgkiABEiABEggAQFXLcJrSYDZ+kUtFMimHQFGCCfLDkWgSuGs2HTqFjaeummIX+syeRLNbCmSIz02fFYT4XciUWvSVlm/+m9bh2Dg8qMWbRbK7o1JbUui1Y/xQlGNgOz49YMKsvpAY9onUHHsRtx89LrukPGVOLk833DyJj4yEmCmdSiNt0r6yWVeM3Z4HtNMKOx2hAKM3ehcbyA3vOvFnCsmARIgARJwPQKuWoT38fNXKDFqvUnAt3xRCyKLQ41mKQNmUONA9HiTAfPg6UuUG7MRUTGxFt3pXdsfP2w5b/E3cRVIXEEq9fUG2ZcislnKjN6Ae5EvE9gWrxpt/KwmWszYhaMRD5HG0w3/9K5m0xPTsjtMgw4hYCzAVCyYBX+8qUkklzPmAsz0DqXRnAKMXExKNtkAACAASURBVHhpR0YCFGBkhOnspijAOHuEuT4SIAESIAESAFy1CO+j568QYibAbP68JgplT6/KZ2FJgPmyUSA+qfX6CtLcnRcxOoniueLFIfG0dGLNwy1VouKNvQv8oWNpNAvxw45zt/HJwsMQWUTGLS7zRrwwJfoUzZlBtStd9q6J45QhUOGbjbj15uUtJQSY9SduoPuCQwbnKcAoE0daTTkBCjApZ+gyFijAuEyouVASIAESIAEXJuBqNWDO33qCn7dfQGZvL8zaFmYS+U2f10RhGQWY2NhYqWjuVytPIE/mdBj1VrBUQFe05ASYwX8exe/7r2jiy8ybJS0aF88FUaMmVapUkk9CZPlx63lM3xyfhRPgmx7r+9fUhM90wrEEjAWYSoWyYHF3eYvwrjtxAx8bCTBx4qBjVy3/7DyPyc9UbYsUYNQmruP5uOF1HDy6TgIkQAIkQAJWEnClGjBCEKn73TaE3Ym0SEdcn/HPIV8GjKjhIorgGreDw+ohW/rUFgWYgY2Komctf1x98AxVx2+2MoLKdlvRswpK58tscZIZW85j4rozht+K+mbAuv41lHWI1nVBoPw3G3H7TQaMGgLMjI5l0DQkly7Y2OIkz2O20NJmXwow2oyLJr3ihtdkWOgUCZAACZAACchKwJVqwNx98gJlx2xMlJ/cAowQUYSYYtw+qx+AvnWLJCnANJm6AyevP5I1zvYYm9mpDBqXSPxQe+XeU1T/dovB9KwuZdEwOKc9U3GMkxEwFmAqF8qK37tXknWFa4/fQI+F8VeQKMDIipfGZCRAAUZGmM5uigKMs0eY6yMBEiABEiAB16oBI/5GXhwME2sbP6sB/xwZZPssxDPX5nVSRI2XoFw+6PP7kQTzDGhYFC9eRWOa0bUe2Zyx0lD3GoUgCpyKuh1ftygOLw+3JEfO2nYBfxy8gqqFs2FE8yB4uifd30o32E3nBEQB6TtPXr+CpIYA82OnMmiShFioV5w8j+k1cvF+U4DRfwxVWwE3vGqoOREJkAAJkAAJOIyAKxXhFQdCcTBMrG3oXwNFfK0TYJ6+jMLzVzHI4u1l0Vx0TCwKD1md4DdRA0ZcTbLUCmXzTvR6lFofyIWxTeDu9rrOCxsJ2EvAWIARz6gv+kjuDJjr6LHwsME9CjD2RorjlCZAAUZpwk5knwKMEwWTSyEBEiABEiCBRAi4UhHe5DJg1vevYdWTyaeuP0KXuftw58lLfF4/AH3qFjGhK8QX8fvuC3d1992JZ6bZSCClBMqN2SDtD9GUEGDWHLuOT36LF2CSuy6X0vU4ajzPY44iL9+8FGDkY+n0lrjhnT7EXCAJkAAJkAAJJHiGOmNaT4SObOCUZG49eo4KYzclurZ1n9aQnk5Orr394y4cufzA0C0ua0QU+f37v2sYv+Y0bjx6npwZzf3epmweTGpbUnN+0SH9ESg7egPuRr4WYKr6Z8Vv3eTNgDEXYH7qXAaNirMIr/6+FOf3mAKM88dYthVSgJENJQ2RAAmQAAmQgGYJLDsUgS+Whhr8c2YB5tqDZ6iSxOtCaz+tjsCcPsnGyvwJ6dOjGyGNpzt2X7iDjrP3JTteax083VPhvcoF0Ku2v/Q8NxsJpJSAsQBTzT8bFnarmFKTJuNXH7uOnkYZMBRgZMVLYzISoAAjI0xnN0UBxtkjzPWRAAmQAAmQALD04BUMWHbUgCJTOk/8N8I5M2DMX+0xj/+n9Yqgbbm8yJ0pbZKfhrkA822bEOnJXeMnmR3xbW0fUBs1Jsa/SmStDyObB6Fr1YLWdmc/EkiWQJnRG3DvTQaMOgJMWTQq7nwvcPE8luynpvkOFGA0HyLtOMgNr51Y0BMSIAESIAESUIqAKwkwl+5GoubErUmizJDaA+v614BfEiKMuQCjVGysteuTxgMLPqyIkDwZUXBwwsK/ydnZP7QucmRIk1w3/k4CVhMwFmCqF8kmfZ9ytlVHr6PXovgaMD91pgAjJ1/ako8ABRj5WDq9JQowTh9iLpAESIAESIAEsOTgFQw0yoBx5itIYbefoM5325KNeocK+TCuVYlE+2lJgGlRyg9T3ylt8NVW35z16kayQWYHRQmU/no97j99Jc2hhACzL+wupr95rj0WsehfLwDlCmRRdE2OMM7zmCOoyzsnBRh5eTq1NW54pw4vF0cCJEACJEACEoElB65g4PL4K0jOLMCcv/UY9SZvTzbyxXP74N8+1S32i4qOgf/QNcnaUKPDxDYhaFEqN7w83JIVYPJnTYdPahZGtSLZcD/yFY5efYBGwTmRNX1qNVzlHC5GQGkBxlVw8jym/0hTgNF/DFVbATe8aqg5EQmQAAmQAAk4jMAfBy7jy+XHDPM7swBz5sZjNPw+eQGmZJ6M+Lt3NYsxmbfzIr7+96TD4hU38cbPasI/R/oEfiSWAVOpUBYs7l7Z4X7TAdcgUOrr9XigYAaMa1AEeB7Tf6QpwOg/hqqtgBteNdSciARIgARIgAQcRmDx/ssY9Ge8AOPMRXhPXnuEJtN2JMu6dL5MWNGzqsV+wSPWIvJldLI2lOzwbesQtCuf1+IUFGCUJE/b1hIwFmBqBGTHrx9UsHYo+xkR4HlM/58DBRj9x1C1FXDDq4aaE5EACZAACZCAwwi4kgBz/OpDNJu+M1nW5fJnxrJPqpj0C78TiVErT2DrmdvJjle6w5kxjZDaw90mAeaHjqXRLMRPaddonwQkAiW/Wo+Hz17XgKEAY/9HwfOY/ey0MpICjFYioQM/uOF1ECS6SAIkQAIkQAIpJPD7/ssYbJQB48xXkEKvPECLGbuSJVahYBYs+Tj+us7esLt45+e9yY5TukORHOmxsk81pPG0LL6I+c0zYDKk8UDNgOyY0r4UPN3ja8Uo7SvtuzYBYwFGfH+/MAPGrg+C5zG7sGlqEAUYTYVD285ww2s7PvSOBEiABEiABOQgsGjfZQxZ4Ro1YA5fvo9WP+5OFlvlQlnxe/dKUr87T16g3JiNyY5RuoMotHvq60Zwd0uV5FTj15zGT9suSH26Vi2Akc2DlXaN9kkgAYGQUevw6HmU9M8pwNj/gfA8Zj87rYykAKOVSOjAD254HQSJLpIACZAACZBACgn8tu8Shq44brDizDVgOs7ei90X7iZLrJp/NizsVlHq99mS//Dn4avJjpG7Q4ncGXHs6kOD2S1f1ELBbN7JThMdE4s/D0cgJjYWrcvkgQezXpJlxg7yEoi4/xTVJmwxGA3288GqvpZfFZN3ZuezxvOY/mNKAUb/MVRtBdzwqqHmRCRAAiRAAiTgMAKuIsDcfPQcFcdusoqzqFkxqnkQZm69gKWHIqwaI3en5Z9Uwah/TkDUnvmycSA6V8ov9xS0RwKKEFh68AoGLIt/2t7DLRXOj22iyFzObpTnMf1HmAKM/mOo2gq44VVDzYlIgARIgARIwGEEFu69hGF/OW8GTExMLNzcUmHrmVt4/38HrOacxtMNz1/FWN1fzo4+aTwQOrIBUqVK+rqRnHPSFgnIRUBkYH22JNRgTtyaCxvXVC7zLmWH5zH9h5sCjP5jqNoKuOFVQ82JSIAESIAESMBhBBbsvYThRgKMkkV4L9x+AnFFJsA3gyrrnb7pHKZvPo/0aTzQtEQuiLU6oo1oFoS25fKgxKj1Vk2/vn8N1RhZ5RA7kYANBP7+7yr6Lf7PZET4eAowNiA0dOV5zB5q2hpDAUZb8dC0N9zwmg4PnSMBEiABEiABWQgs2BOO4X+fMNhSSoBZfigCXywLRWwsMLxZED6sVlAW/xMzcvnuU9SYGF+HQtHJkjBeIGs6bB1QG7GxsSg4eLVVbhz/qiHSp/awqi87kYDWCKwMvYY+vx+hACNDYHgekwGig01QgHFwAPQ0PTe8nqJFX0mABEiABEjAPgK/7gnHCCMBRqkivObPIyv9N+LiJSDxIpCj29+9qqJk3kySG+YMEvPtwtgmyb525Oh1cX4SSIzA6mPX0fO3wxRgZPhEeB6TAaKDTVCAcXAA9DQ9N7yeokVfSYAESIAESMA+As4qwIz4+zh+3eOYK0ciEr4+qbHhs5rwSeNpCIy1AozS4pR9XwpHkYB1BNYev4EeCw9RgLEOV5K9eB6TAaKDTVCAcXAA9DQ9N7yeokVfSYAESIAESMA+Ar/sDsfIf5S/gqR2BoyjBZhjoxogg5H4IqJjjQAzsU0I2pbLa18wOYoENEBgw8mb+OjXgxRgZIgFz2MyQHSwCQowDg6AnqbnhtdTtOgrCZAACZAACdhHYP6uixi18qRhsFI1YJQWYK49eIb2P+/BlXvP7AMh06gBDYuiZ63CFl8wSk6AaVnKD9+/U1omT2iGBBxDYMvpW+g63/TFMWZ12RcLnsfs46alURRgtBQNjfvCDa/xANE9EiABEiABmwlMXn8G83eHo0LBLJjWoTTSebHQ6f92XcRXRgKMXmvAdPvlADaeumXzNyH3gKQOmrO2XcA4C3VpRjYPwouoGHSqmC9B1ozc/tEeCShNYNvZ23hv3n6TaSjA2Eed5zH7uGlpFAUYLUVD475ww2s8QHSPBEiABEjAJgLhdyJRa9JWw5iv3grGe1UK2GTDGTvP23kRX/8bnwGjVwEmuewSNWI39Z1SaFEqd5JTHbl8H+duPcGkdWfw4OkrjHwrCJ0q5lfDPc5BAqoQ2HnuDjrP3UcBRgbaPI/JANHBJijAODgAepqeG15P0aKvJEACJEACyRFYuPcShv113NDNL2Ma7B5cN7lhTv+7VgSYe5Ev8d36M4iJjUX/+gHIkSGN1ew3nbqJD38xrTlh9WAZOpbLnxm/fVQRqT3crbYWExOLl9ExSONp/RirjbMjCTiQwO4Ld9BxNgUYOULA85gcFB1rgwKMY/nranZueF2Fi86SAAmQAAkkQ2DBnnAMN3puOXemtNg1qI7Lc5u78yJGG2XAOKoGzBdLQ7HsUIQUj6YhuTCjY5lkYxMbG4szNx+j85x9uPPkZbL9leiQI0Nq/NmzCvJkTqeEedokAd0R2Bd2F+1/3mviN68g2RdGnsfs46alURRgtBQNjfvCDa/xANE9EiABEiABmwiYP7dMAeY1vjk7wjBm1SkDS0cJMLYW6RUZJIWGrLbpG0is8/ftS+HTP/6zyZYQXia0DkGwnw9y+FifrWPTJOxMAjokcDD8Htr8tIcCjAyx43lMBogONkEBJgUBOHToEP755x8cOHAA58+fx61bt/D06VNkzZoVpUqVQqdOndCxY0e4ubklOsuKFSswc+ZMHD58GJGRkcibNy+aN2+OwYMHI1u2bEl6d/v2bYwfP17yISIiAt7e3ihTpgx69uyJli1bpmBllodyw8uOlAZJgARIgAQcSMD8ueW8WdJix0BmwJgLMErUgBGZKgUHm4olcX8jfuluJH7YfB5L32S/xH0ilv7G/PjVh5i36yICc2bA6RuP8efhq7J8UUeG10fp0RtssrXww4qoViTpf3ezySA7k4CTEDh8+T5a/bibAowM8eR5TAaIDjZBASYFAejRowdmzZplsCAEENGEkBLXatasKQkkPj4+CWbq3bs3ZsyYIf1zd3d3pEuXDo8fP5b+u6+vL7Zs2YJixYpZ9PDEiROoU6eOJPqIliFDBkn8iY6Olv573759MXXq1BSsLuFQbnhZcdIYCZAACZCAgwmYv/aTL0s6bB9Y28FeOX762dvD8M3q+AwYJQSYl1ExCBi2JsGBTIgvNSfGF0Y27mAuwERFx6DGt1tw7eFzWaGl9XTHfyPro+iwtVbbtabYrtXG2JEEnIxA6JUHaDFjV4L97mTLVGU5PI+pglnRSSjApADv/Pnzcf/+fVSvXh1FixaVRBDRhCgyd+5cDB8+XBJEPvjgA+m/G7fZs2eje/fuSJUqFUaPHo3+/ftLAkxoaCg6d+6M48ePw9/fH0Jo8fLyMhn74sULBAUFISwsDMWLF8fChQtRsmRJSYCZMmWKNK/4m6V58+aha9euKVih6VBueNlQ0hAJkAAJkIAGCJgXmy2QNR22DqAAYy7AKHEFKfJFFIJHrktwIOu96DD+PXrd4tdhLsBcvBOJ2kavWMn1SX1WPwB96xaBLa8osZ6FXPRpxxkJiEy1ZtN3UoCRIbg8j8kA0cEmKMAoGIChQ4di7NixSJMmDR4+fGgQUl6+fIn8+fPjxo0bEFkw06dPN/Hi0qVLksAiBBWRISOuFBk30V9kuAjB5tSpU8iXL5/J73GZNX5+fggPD4enp6csq+SGlwUjjZAACZAACWiEgPlVm4LZvLHli1oa8c5xbvy8/QLGrj5tcEAJAeZ+5MsEV3yyeHtBvHyUWDMXOWZuvYAJa+P9TCmxesVy4Lt2pSDWK5q1AsyQJoHoXqNwSqfneBJwWgKnrj9C46k7KMDIEGGex2SA6GATFGAUDIC4etSiRQtpBiG2iGtFoq1ZswZNmjSRsl+E2CLqvpi3bt26SVkzlStXxu7dpncmK1asiP379+PDDz/EnDlzEoy9fPkyChQoIGXBrFu3Dg0aNJBlldzwsmCkERIgARIgAY0QMBdgCmXzxmYKMJi17QLGrVFWgLn16DkqjN1k05dgLMA8fv4KJUatt2l8cp0PDquHbOlTG7olJ8BUKZwVNQOy44NqBeHpnni9v+Tm5e8k4OwEzt58jAZTtlOAkSHQPI/JANHBJijAKBiAQYMGYcKECUifPj0ePXokCS6iDRgwAJMmTUJwcLB01chSW7p0Kdq1ayeNEXVh4urLPHnyRKonI8QV0adNmzYWxwvbJ0+exMCBAyUf5Gjc8HJQpA0SIAESIAGtEDC/alMouzc2f84MmJ+2XcB4IwFGiRowEfefotqELTZ9CuNalYC7WyqUzpsJyw5HYNa2MJvGJ9V5VpeyaBic06RLYgLMtA6lUSxnBhTxfX31nI0ESCBpAudvPUG9ydsowMjwofA8JgNEB5ugACNzAMS1IZGBsmDBAumFopiYGAghZty4cYaZmjZtitWrV6N9+/ZYvHixRQ/E1SJxDUm0gwcPomzZstKfxYtLFSpUkP4s+gQGBlocL2wvWbIEzZo1w8qVK2VZJTe8LBhphARIgARIQCMEzDM9/HOkx8bPamrEO8e5oYYAs+TgFQxcdtRxizSaeUP/GhbFlB4LDmHtiRsmPk5oXQLty5te/dbEIugECWiYgKV6TaybZF/AeB6zj5uWRlGAkSEaDx48QObMmRNY8vDwQK9evaRsF/HnuCaeij5y5IhUeHfy5MkWPRA1YzJlyiT9JgQUIaSIZnytSfSx9LqS6Cdsf//995JwIwQcORo3vBwUaYMESIAESEArBMyFhgDf9FjfnwKMeW0VuWvAjPrnBObvDtfEZ2Ap8yXOMVGPZsaW87h6/xlSe7qhuF9GvF+1AK8baSJydEJPBC7ffYoaE00z3ijA2BdBnsfs46alURRgZIiGuF4UEBAgWRKiyPPnz6WrQ59++ql0BShnTtOUVtH33LlzEEV6x4wZY9GDV69eGYr2Llq0CB06dJD6iT936tRJ+rPoYyzsGBuKKwAs5jpz5oxVq4zb0Il1FsWDxYtM4hUmNhIgARIgARLQO4Eft57Ht2vj/39kUd8MWNe/ht6XlWL/zbnIKcBsPXML7//vQIp9lMNA+3J5MaFNiBymaIMESCAJApauHFKAse+ToQBjHzctjaIAI3M0RG0WUVh36tSp+OGHH5AxY0b89ddfqFatmmEmawSYqKgow+tFFGBkDhLNkQAJkAAJkAAgZTdMXBcvwATmzIC1n1KAMeciVw0Y8e9IBQev1sS317h4Tszs/Pp6NxsJkICyBK4/fIbK4zabTEIBxj7mFGDs46alURRgFIyGEGFEFkzu3Llx9uxZ6dlo0XgFSUHoNE0CJEACJEACVhKgAGMZVFICzJ0nLzDkz2O48eg5vmwUiKr+2ZKlfTD8nvSqUuiVB4iKiU22v9Id3FIBR0Y0MDw3rfR8tE8Crk7g1uPnqPCN6atnFGDs+yoowNjHTUujKMAoGA1xFUnUcRFXdpYtW4bWrVtLs4l6LqtWrbKrCK+o51K+fHnJDovwKhg8miYBEiABEnB6Aj9sPodJ688a1hmUywer+1V3+nUnt0BzASbuCtKLqGg0nbYT4kUT0bJnSI0DQ+slaU5kvdScuBWX7z1NblrVfj89uhHSeLqrNh8nIgFXJ3D3yQuUHbPRBAMFGPu+Cgow9nHT0igKMApHQ2S/XLt2TXoKWtSDEU3858SJE1G8eHEcO3bMogdCsGnbtm2Sz1AbizrmRoTtEydO8BlqheNL8yRAAiRAAvolMG3TOUzeEC/AFM/tg3/7UIAxF6biBJhOc/Zi1/m7JgG/MLaJ9DR0Ym1v2F288/NezXwkuwbVQe5MaTXjDx0hAVcg8ODpS5T6egMFGBmCTQFGBogONkEBRsEAPHnyBFmyZJGK5c6YMQM9e/aUZlu7di0aN24siSviyeo8efIk8OKjjz7CnDlzULlyZezevdvk90qVKmHfvn3o1q0bZs+enWBsREQE8uXLB/G3TuvWrUODBg1kWSU3vCwYaYQESIAESEAjBL7feBbfbzxn8KZE7oxY2Se+ZptG3FTdjembzuE7I2FKCDD/9qmG6t+avmIiHDv1dSOk9UqYTfL0ZRTSeXmgwKBVqvtvPuG3rUOw7dxttCqdG3WL+TrcHzpAAq5G4NHzVwgZtZ4CjAyB53lMBogONkEBxs4AREdHw83NTRJREmujR4/GiBEjpJ9FpovIShFNvCaUP39+3LhxA3379pUK9hq3K1euoFixYoiMjDQRbuL6TJ8+XRrn7e0tXUPKmzevyXjxm+jj5+eH8PBwQzFfO5dqGMYNn1KCHE8CJEACJKAlAlM2nMXUTfECTEiejPinNwUY88wgUYT35y7l0G7WngThCxW1VNJ5Gv65+Mufnr8dxprjNzQTal510Ewo6IiLEoh8EYXgkesowMgQf57HZIDoYBMUYOwMgBA2WrVqJWW1iAwTkXEimvgXj9OnT0vCyY8//ij9d9Fv+fLlJjOJzJXu3btLAs7YsWPRr18/pE2bFkePHkWXLl2k//T395euEYmnn42bqCkTFBSEsLAwhISEYMGCBdJ/Pnv2TBJzhgwZIs07b948dO3a1c4VJhzGDS8bShoiARIgARLQAAFx/UiIDXGtZJ6M+JsCDKZuPIcpG+OvZiUVqv1D6yJHhjSGLuPWnMKsbWGyR7dTxXz4bd9lu+xSgLELGweRgGwEnr+KRuDwtRRgZCDK85gMEB1sggKMnQEQAkzBggUNo9OkSYP06dNDXDsSxXfjWtOmTfHHH39I2SrmrVevXpJII5qHh4f0StKjR4+k/+7r64stW7ZImTCWmhBm6tSpg1u3bkk/+/j44OnTpxDPV4vWp08fTJs2zc7VWR7GDS8rThojARIgARJwMIHJ689g2ubzBi9K5c2Ev3pVdbBXjp/eFgFm55e1kSdzOryMisGV+09R97ttsi+gbx1/fNagqNXXmdJ6uuPZq2jJj6nvlEKLUrll94kGSYAErCcg/vchYNgakwEURq3nZ9yT5zH7uGlpFAUYO6MhrhH99ddf2Lx5M/bv34/r16/jzp07EJtC1HSpUKECOnXqhIYNGyY5w4oVKyQR5vDhw9KVI3Gd6K233sKgQYOQPXv2JMcK8WX8+PFYuXIlxLUlIfKIJ66FsNOyZUs7V5b4MG542ZHSIAmQAAmQgAMJTFp3Bj9siRdgyuTLhD97UoAxr42TVIjmvlcOI/4+gasPnikSyQmtS6BdubxSxrC19WSOf9UQKw5HIHfmtKgTyJovigSGRknABgIxMbEoNGQ1BRgbmCXWlecxGSA62AQFGAcHQE/Tc8PrKVr0lQRIgARIIDkC3649jR+3XjB0K5s/M5Z/UiW5YU7/u3ltnKQWbJxtIjeYr1sE493KBQxmrRVg+DfrckeC9kgg5QQaTNmGszdfP2E/oGFR9Krtn3KjLmiB5zH9B50CjP5jqNoKuOFVQ82JSIAESIAEVCAwYe1pzDQSYMrlz4xlLizARMfEYvrmcyYvQ6kQhkSnOD26EdJ4xr+wZI0A813bkmhdNuHrko5cB+cmARIATl1/JNWXypkxjSTAeKf2IBY7CPA8Zgc0jQ2hAKOxgGjZHW54LUeHvpEACZAACdhKYPya0/hpW3wGTPkCmbG0h2tmwOy5cBcdZu+1FaFi/Td/XhOFsqc3sZ+YADOmZXGpBo1fprRoGOyb5AuVijlMwyRAAiSgAgGex1SArPAUFGAUBuxM5rnhnSmaXAsJkAAJkID5iz0VCmbBko8ruxwYkflSZfwm3Hz0wuFrL50vE+a9Vx6ZvU1fgBSONZyyHWduPjbxsX6QL2Z0LAMvDzeH+04HSIAESEBpAjyPKU1YefsUYJRn7DQzcMM7TSi5EBIgARIgAQBjV5/Cz9vjn0yuWDAL/tC5APPbvktYe/wGGgTnRJdK+ZOMc2xsrPT7xTuRqCPz60VDmgRi7OrTNn1nTUrkxI+dyiY6JvTKA7SYscvwuxBrVrBosk2M2ZkESEDfBHge03f8hPcUYPQfQ9VWwA2vGmpORAIkQAIkoAKBb1adxOwdFw0zVS6UFb93r6TCzPJP8fxVNBpM2Y7L954ajG/oXwNFfDNYnEy8dDR7exj8fTPAyz0VDoTfl9UpUUBXvI5kTcvq7YXedfzRqnQeZEznmeSQnr8dwupjN+Dt5S6JZcVzZ7RmCvYhARIgAacgwPOY/sNIAUb/MVRtBdzwqqHmRCRAAiRAAioQGP3vSczdGS/AVCmcFYs+0qcA8/v+yxj85zETaq3K5MbkdqUSkFSj3su3rUMwcPnRJKO4pl91pPZwS1DrJalB4rrUkcv3pSemc2VMq8JXwilIgARIQDsEeB7TTizs9YQCjL3kXHAcN7wLBp1LJgESIAEnJvD1ypOYtytegKnqnxW/ddOfABNx/ymqTdiSIFItXy8yKwAAIABJREFUSvlh6julE/zzPr8fwcrQa4pGduo7pdBv8X+JzvFtmxC0K5dXUR9onARIgAScjQDPY/qPKAUY/cdQtRVww6uGmhORAAmQAAmoQOCrlSfwv13hhpmqF8mGBR9WVGFm+aYQV4/qTNqKaw+fJzDaspQfvjcTYP7+72qSwog9nn31VjC+WXUKL6NjDMNFYdxeiw4nam7tp9URmNPHnuk4hgRIgARclgDPY/oPPQUY/cdQtRVww6uGmhORAAmQAAmoQGDUPycwf7d+BZgXUdFoPn0nzt58YpHW26VzY0r711eQDl26h5Wh103WKwfiVX2rIdgvIwKGrrFagPFyd8Oh4fWQIU3S9V7k8I82SIAESMCZCPA8pv9oUoDRfwxVWwE3vGqoOREJkAAJkIAKBEb+fRy/7LlkmKlGQHb8+kEFFWa2fYoD4fcwd8dFBPn5oGetwvBwd8OifZcxZIVp3Rdjy3ECzKFL99Hmp9148+iR7ZMnMkL4MbBRoPRrwLA1eBkVnwHzY6cy6PmbaQZMUC4fuLkBH1UvhBalcsvmBw2RAAmQgKsQ4HlM/5GmAKP/GKq2Am541VBzIhIgARIgARUIDP/rOBbsjRdgahXNjvldtSfAiEyXyuM2417kS4nK9+1LoWXp3Oix4BDWnriRKKlWpXNjcvtSKP31etx/+kp2ogs/rIhqRbJZLcCEjW0CN7dUsvtBgyRAAiTgKgR4HtN/pCnA6D+Gqq2AG1411JyIBEiABEhABQLD/jqGhXsvG2aqXTQ7/qdBAWb3hTvoOHufCZGxb5dIMvtFdBavIH3XtiQKDl4tO82mJXLhh46lkSrVa0GlzndbEXY70jDP/94vj67zD5jMGz6+qex+0CAJkAAJuBIBnsf0H20KMPqPoWor4IZXDTUnIgESIAESUIHA0BXH8Nu+eAGmbmAOzH2/vAozWz/F2uPX0XvREUTFxFo/SKGeaT3dsX1gbaT1ckf61B4ms+w+fwcd57wWibJ4e2HfkLqYuO4Mft4eJv2zZiFCsCmjkGc0SwIkQAKuQYDnMf3HmQKM/mOo2gq44VVDzYlIgARIwKkJiJd7bjx8jjyZ00q1TBzVBv95DL/vjxdg6hXLgTnvaUOAiY6JRZe5+7D7wl1H4TGZ18vDDdsG1EKujGkT9WfV0ev478p9tC+fF/45MkBcnZq3Mxwi3h9WLwgfFt3VRCzpBAmQgH4J8Dym39jFeU4BRv8xVG0F3PCqoeZEJEACJOC0BB4+e4VWP+7ChduRqFAwCxZ/VMlhdUEGLT+KxQeuGFjXK+aLOe+V0wR7IWYk9Yyz2k6u7F0NJfJkVHtazkcCJEACJGBEgOcx/X8OFGD0H0PVVsANrxpqTkQCJEACTktg6sZzmLLxrGF9c94th3pBvg5Z78BloVhyMMIwd/0gX8x+17ECzKvoGMzdeRHj15x2CJPEJj02qgGfjdZUROgMCZCAKxLgeUz/UacAo/8YqrYCbnjVUHMiEiABEnBaAi1m7ELolQeG9fWtWwSf1Q9wyHo/XxKK5YfjBZiGwb6Y1cWxAszSg1cwYNlRh/BIbNJP6xXBp/UcEyNNgaAzJEACJOBgAjyPOTgAMkxPAUYGiK5ighveVSLNdZIACZCAcgS0JMD0/+M/rDhy1bDYxsVzYmbnssotPhHLsbGx2Hz6lvRrz98O40VUjKw+ZErniQd2PkP9ZaNA9KhZyPDakayO0RgJkAAJkIBNBHgeswmXJjtTgNFkWLTpFDe8NuNCr0iABEhATwS0JMD0/f0I/gm9JrsAcyziIVr/tBsvo2KwtEdllC+QJckQfb/xLL7feE6RMIqXi2Z0Ko0P5h+02v7yT6pIBZKzens5tEiy1Q6zIwmQAAm4CAGex/QfaAow+o+haivghlcNNSciARIgAacloCUBRhS5FcVu41qj4Jz4qYv9GTB3n7xAv8X/Yef5OybxS65+SoFBqxSLd66MaTC6RXF0+9U6AaZ0vkxY0bOqYv7QMAmQAAmQgP0EeB6zn51WRlKA0UokdOAHN7wOgkQXSYAESEDjBLQkwPT87RBWH7thIJbSGjCJZbKMfbsEOlbMZzEyouhukaFrFIuaX8Y0GN86BO/O229xjupFsqFpiVwQfmRM5wXxFHc6Lw/F/KFhEiABEiAB+wnwPGY/O62MpACjlUjowA9ueB0EiS6SAAmQgMYJaEmA+XjBQaw7cdNALKWvIAUMXYOX0QnrtwxrWgzdqheyGJlvVp3E7B0XFYta7kxpsfmLmqjwzSaIJ8CN2/tVCmBk8yDWd1GMPg2TAAmQgLwEeB6Tl6cjrFGAcQR1nc7JDa/TwNFtEiABEtAQAS0JMN1+OYiNp+IFmHrFfDHnPftfQQoZtQ6PnkcloD24cSA+rllY+uc/bj2PPw9fRZZ0Xrj28Bki7j9TNDp5s6TFjoF1pJenBHvjFj6+qaJz0zgJkAAJkIC8BHgek5enI6xRgHEEdZ3OyQ2v08DRbRIgARLQEAEtCTAfzD9geH1IIBLXb+a8V95uWpXHbcL1h88TjB/YqCh61vJH2O0nqPPdNrvt2zMwX5Z02D6wtjTUvNYMBRh7iHIMCZAACTiOAM9jjmMv18wUYOQi6QJ2uOFdIMhcIgmQAAkoTEBLAsx78/Zj29nbhhXXCcyBee/bLsCcv/UYi/Zdwbxdlq8S9axVGGk83TF5w1lF6dYIyI4aRbJhzKpThnkKZE2HrQNeCzCT1p3BD1vOS3+uUDALlnxcWVF/aJwESIAESEBeAjyPycvTEdYowDiCuk7n5IbXaeDoNgmQAAloiICWBJguc/dhx7n4F4tqF82O/3WtYBMtUby20thNuBv50qZxcncO8E2Pf/tUx697wk0EmILZvLHli1rSdE9fRmHapvO4H/kSfer6I0/mdHK7QXskQAIkQAIKEuB5TEG4KpmmAKMSaGeYhhveGaLINZAACZCAYwloSYDpNGcvdp2/awBSMyA7fvnAOgHm6oNnOHDxniS8jP73pEOhGl8z+nn7BYxdfdrgT6Hs3tj8+WsBho0ESIAESEDfBHge03f8hPcUYPQfQ9VWwA2vGmpORAIkQAJOS0BLAsw7P+/B3rB7BtbiCs+vSQgwMTGxWHYoAjvO38HK0GuaidHuQXXglymt5M9P2y5g/Jp4AaZwdm9sogCjmVjRERIgARJICQGex1JCTxtjKcBoIw668IIbXhdhopMkQAIkoGkCWhJgWs7Yhf+uPDDwql4kGxZ8WDFRfssPReDzpaGa4ju6ZXF0qZTf4NPMrRcwYW28AFMkR3ps+KympnymMyRAAiRAAvYR4HnMPm5aGkUBRkvR0Lgv3PAaDxDdIwESIAEdENCKADN/10WMWml6daiafzYs7Ja4AGP+ipCjcf/RvRIqFspq4sbZm4/RYMp2wz/7tk0I2pXL62hXOT8JkAAJkIAMBHgekwGig01QgHFwAPQ0PTe8nqJFX0mABEhAmwS0IMBEx8Si8JDVCQBV9c+K37pVShScVgQYUavmp85lkdbL3aKv41afwu/7L6NK4Wz4/p1S0gtMbCRAAiRAAvonwPOY/mNIAUb/MVRtBdzwqqHmRCRAAk5MIDY2FrGxQKpU4v9SOfFKLS9NbQHm36PX8OvuSyidPxO+aFAUnu5uePIiCsVHrkvgYOVCWfF7d1MBRrxyNHHdGczeESbFTa7WKDgn1p64YbO5T2oVxpeNAm0exwEkQAIkQAL6J8DzmP5jSAFG/zFUbQXc8Kqh5kQkQAJOSiAqOgY9Fh7GxlM30TDYFz92Kgt3N9cSYRIIMHX88VmDoopE/OGzVyg/ZiNeRsdI9rvXKISXUTGYvzvc4nyVCmXB4u6VTX4zr6kih6P/9qmGVceuQ9hOqg1rWgzNS/ohq7cX/jx8FW5uqfB26dwu983IwZw2SIAESMAZCPA8pv8oUoDRfwxVWwE3vGqoOREJkICTEhAv5/T5/YhhdXPfK4e6xXyddLWWl6WmALPiSAT6/2F90dwKBbNgycfxAsyCvZcw/K/jssWnaYlcmNy+JFJ7uGPy+jOYtvl8krbDxzeVbW4aIgESIAES0D8Bnsf0H0MKMPqPoWor4IZXDTUnIgEScFIC3X89iPUnbxpW175cXkxoE+Kkq7W8LPOXh/oqmAFjswBTIAuW9HgtwNx69BwVxm6SLTYbP6sB/xwZDPambzqH7zacpQAjG2EaIgESIAHnJ8DzmP5jTAFG/zFUbQXc8Kqh5kQkQAJOSuCD+Qew+fQtw+o6VsyHsW+XcNLVQrruc+zqAxTMlh5ZvL2kdaohwMTExCI04gFWHLmKX/dcsomvuOIT+SLKRCizyYCFzj1rFcZAs7otP227gPFr4p+LtjQHM2BSSp7jSYAESMC5CPA8pv94UoDRfwxVWwE3vGqoOREJkICTEnh33n5sP3vbsLr3KufHVy2KO+VqRbHhNj/twaFL95EtvRf+6V0NfpnSqiLA9F50GP8eva4Jrmv6VUdgzgwJCi7P2RGGMatOJerjZ/UD0LduEU2sgU6QAAmQAAlogwDPY9qIQ0q8oACTEnouNpYb3sUCzuWSAAnITqDj7L3YfeGuwe6H1QpieLMg2efRgsHdF+6g4+x9BlfirlspmQHz939XpcK2p2881gIC9Knjj88TKTA8cd1pzNiSeBHeo6MawCeNpybWQSdIgARIgAS0QYDnMW3EISVeUIBJCT0XG8sN72IB53JJgARkJ9Bu1h7sv3jPYPfjmoUwuHEx2efRgsGFey9hmFEB21wZ02DP4LqKZMCIbJt1J26ix8JDWli6wYdz3zSWnr221AYuC8WSgxGGn6oXyYbGxXPhyYtX6FQxP7xTe2hqLXSGBEiABEjA8QR4HnN8DFLqAQWYlBJ0ofHc8C4UbC6VBEhAEQKtZ+6WruTEtV61C2NAw0BF5nK00V/3hGPE3ycMbuTOlBa7BtVRRIAZtPwoFh+44uglm8y/9lNx9cgnUZ/Grj6Fn7eHGX7vUbMwBjV2zm9BU4GhMyRAAiSgYwI8j+k4eG9cpwCj/xiqtgJueNVQcyISIAEnJdDih50IjXhoWJ2o8SFqfThj+9+ui/hq5UnD0vJmSYsdAxMKMOKaTlZvL+w8fxdtyuZGo+K5bMJx58kLlBuz0aYxSnc+8VXDZDNYLt2NRO1JWxETC6T2cMPuQXWQNX1qpV2jfRIgARIgAR0T4HlMx8GjAKP/4Km9Am54tYlzPhIgAWcj0GTqDpy8/siwrM/rB6CPkxZanbvzIkb/Gy/A5M+aDtsG1E6QAVOlcFZDXRx3t1TYN6QuslkQIq4/fIYeCw/j8t1IDG5SDM1D/BB+NxJ/HLiC+bvDNfWpWPt60b6wu9h+7rZ09ah47oyaWgOdIQESIAES0B4Bnse0FxNbPWIGjK3EXLg/N7wLB59LJwESkIVAgynbcPbmE4OtgY2Komctf1lsa83I7O1h+GZ1/Cs/hbJ5Y/MXtRIIMOZ+f9koEJ/UKpxgOeY1UwpkTYfwu0+1tmz83KUsGgTn1JxfdIgESIAESED/BHge038MKcDoP4aqrYAbXjXUnIgESMBJCdT5bivCbkcaVjekSSC610goNii1/EOX7mHernAE+/ng4xqFITJOlGqztl3AuDWnDeb9c6THxs9qJivAfNEgAL3rJHx+ucCgVUq5Kovdg8PqSa8WeXlYLroryyQ0QgIkQAIk4NIEeB7Tf/gpwOg/hqqtgBteNdSciARIwEkJ1Jy4BZeMsjZGNAvCB9UKqrLal1ExKP/NRjx89kqa78dOZdCkhG31Vmxx9Met5/Ht2jOGIQG+6bG+f/ICTGJZQVoWYDpXyocxLUvYgod9SYAESIAESMBmAjyP2YxMcwMowGguJNp1iBteu7GhZyRAAvogUHX8Zlx98Mzg7OgWwehSuYAqzu8+fwcd5+wzzCWSX8LGNVVs7h82n8Ok9WcN9gNzZsDaT2skmwEzuHEgPq6ZMCtIqwLM1HdK4a2SfkiVSrlsIsWCRMMkQAIkQAK6IsDzmK7CZdFZCjD6j6FqK+CGVw01JyIBEnBSAhXHbsTNRy8Mqxv7dgl0rJhPldXuOHcbXebuN5nL2mKx9jg4bdM5TN4QL8AU9c2At0r5YeK6+KwYS3aHNS2GbtULJfhJSQGmWC4fCIFoxZGrVi81fWoPiGtHaTzdrR7DjiRAAiRAAiSQEgI8j6WEnjbGUoDRRhx04QU3vC7CRCdJgAQ0TKDcmA248+SlQwSYnefuoPPc+AwY4YSSAsyUDWcxddM5m6MxvFkQPjS6lnXu5mP0XfwfThm9HmWz0WQG7BpUB5PXn8XywxFWm/7lgwqoGZDd6v7sSAIkQAIkQAIpJcDzWEoJOn48BRjHx0A3HnDD6yZUdJQESECjBEp+td5Qg0W4OKZlcXSulF8Vb82vICktwExefwbTNp+3eW2jmgfh/aoF8So6BmuO30Df34/YbMPaAR0q5IXIQhLXhwYsDcXSQ9YJMGXzZ8Zv3Soy+8Va0OxHAiRAAiQgCwGex2TB6FAjFGAcil9fk3PD6yte9JYESEB7BEqMWofHz6MMjo1uWRxd1BJgLtxBx9nqZcBMXHcaM7ZcsDkII5sHoWvVguj/x382XQmydaJJbUuiTdk8hmFfLjuKPw5escrMxXFNWPPFKlLsRAIkQAIkICcBnsfkpOkYWxRgHMNdl7Nyw+sybHSaBEhAQwRKjFyHxy/iBZivWwTjXZWK8O65cBcdZu81oaHkFaQJa09j5lbbBZhu1QpiaNNiKDh4teyRaxaSS6q5U6VwtgS2Vx29jl6LDic758c1C2Fw42LJ9mMHEiABEiABEpCbAM9jchNV3x4FGPWZ63ZGbnjdho6OkwAJaISAuQATd91GDffUFmDGrT6FWdvD1Fia1XMkJThFRcfAf+iaJG3xxSOrUbMjCZAACZCAAgR4HlMAqsomKcCoDFzP03HD6zl69J0ESEALBMwFmLjrNmr4tjfsLt75Wb0MmMZTdyhaONdWZmv6VYd47SipZumlpQENi6JtuTzIlNYLXh5utk7L/iRAAiRAAiQgGwGex2RD6TBDFGAchl5/E3PD6y9m9JgESEBbBIqPXIcnRleQzF/8UdLbfWF30d4GAebZy2jM3x2OZYeuoGTeTPimZQmk9Ur+yeW//7uKQcuP4dmraCWXY5PtGR3LoGlIrmTHmAswb5fOjSntSyU7jh1IgARIgARIQA0CPI+pQVnZOSjAKMvXqaxzwztVOLkYEiABBxAwF2CGNS2GbtULqeLJ/ov30G7WHpO5LF3JEULNyeuPMHb1KbyKjjX0H9+qBN6pkC9JX4W4JAoNx8YPk21tgTkz4PSNxzbb2zekLnx90lg17rv1ZzDd6OWm06Mb8aUjq8ixEwmQAAmQgBoEeB5Tg7Kyc1CAUZavU1nnhneqcHIxJEACDiBgLsAMbVIMH9WwT4ARYsesbRek13h61CyEdF4eSa7oQPg9tP0paQHmf7su4quVJy3a8XJ3w9lvGic5x/azt/HuvP2KkC2ZJyNCIx5abTtP5rTSa0ofVito9Zj7kS/x+dJQnL/1BJ83CECLUrmtHsuOJEACJEACJKA0AZ7HlCasvH0KMMozdpoZuOGdJpRcCAmQgIMIBI9Yi8iX8VdzBjcOxMc1C9vlzcBloVhyMEIa26FCPoxrVSJJO9ZkwLSeuRuHLt23S4B59PwVQkatt2st1gwqlz8zDibimxhfvUg27Dh3RzIliuVSPLGGKvuQAAmQAAnoiQDPY3qKlmVfKcCkIIYRERH4888/sWnTJoSGhuL69evw9PRE/vz5Ua9ePfTr1w+FCiX9N5srVqzAzJkzcfjwYURGRiJv3rxo3rw5Bg8ejGzZEj6Taezu7du3MX78ePzzzz8Qvnh7e6NMmTLo2bMnWrZsmYKVWR7KDS87UhokARJwMQLmAsygxoHoYacAY16vJLknpa2pAWOpCG1ciDzdU+HcN00sRmznuTvoPHefotGsXCgr9oTdtThHtvSpsfPL2lhz/Dp8M6RBFf+k//+noo7SOAmQAAmQAAkoRIDnMYXAqmiWAoydsK9cuSIJLbFGF919fHzw7NkzvHr1SrKaNm1azJ8/H+3atbM4S+/evTFjxgzpN3d3d6RLlw6PH7++3+7r64stW7agWLFiFseeOHECderUwa1bt6TfM2TIgKdPnyI6+vXfrPbt2xdTp061c3WWh3HDy4qTxkiABFyQQNCItXhqlAEzsFFR9KzlbxcJWwWYpJ6hvvvkBebtuogZWy4k6ouHWyqcH5tQgJHz2lGujGmwuHsljFt9GmtP3DDxxTjDxdxJa4vs2gWag0iABEiABEhAIwR4HtNIIFLgBgUYO+GFh4dL2S1NmjTB+++/j9q1ayNr1qyIiorC7t270adPHxw9ehQeHh44dOgQQkJCTGaaPXs2unfvLt3dHz16NPr37y8JMCKTpnPnzjh+/Dj8/f0hhBYvLy+TsS9evEBQUBDCwsJQvHhxLFy4ECVLlpQEmClTpmD48OGSMDRv3jx07drVzhUmHMYNLxtKGiIBEnBRAuYCjHjiuFdtdQSY3efvoOMc0yyVuKyZDj/vTTS7JC5U7m6pcOGNABMVHYOomFjcfvwC1b/dIks0awRkx68fVJBsfbLwENYcNxVg6gbmwKbTr//Swbi1KOWHqe+UlsUHGiEBEiABEiABLRPgeUzL0bHONwow1nFK0Ovhw4cQIowQPiy1mzdvokSJEhDXhIQIIsSQuPby5Uspe+bGjRsQWTDTp083MXHp0iVJYBGCisiQEVeKjJvoLzJchGBz6tQp5Mtn+ipFXGaNn5+f5KO4FiVH44aXgyJtkAAJaJHApbuRuHT3KSoXzgpPdzfFXDQXYL5oEIDedYrYNZ+tGTA7zt1Gl7mmBXLjBJikrh7FOeeWCggb1xTHrz7EB/MP4NbjF3b5ndig/UPqIseb14p6/nYIq4+ZCjANg32x7sRNk+ET24RItV68PJSLmayLpDESIAESIAESSAEBnsdSAE8jQynAKBgIkRnzyy+/SNkvIrMlrq1Zs0bKnBHZL0JsEXVfzFu3bt0wd+5cVK5cWcqoMW4VK1bE/v378eGHH2LOnDkJxl6+fBkFChSQsmDWrVuHBg0ayLJKbnhZMNIICZCAxggci3iItrN24/mrGBhnYSjhZrHha/HsVXwR3s/qB6BvXXUEmG1nb+M9sxeKhAAj/n9FwcGrk11unADz8YKDCYSQZAcn0+GvXlVRKm8mQy9LAkzTkFxYdfS6oU+xXD5Y0696SqfmeBIgARIgARLQDQGex3QTqkQdpQCjYAy/+OILfPfdd1I2i7hKFNcGDBiASZMmITg4WLpqZKktXbpUqh0jRBpRF0YU2BXtyZMnELVmxL8wiz5t2rSxOF7YPnnyJAYOHIgJEybIskpueFkw0ggJkIDGCDSdtgMnrj0yeLVjYG3kzZJOES8Dh6+RhJ641r9eAPrVU0eA2XLmFrr+74DJuoQA8yo6BkWGrrFqvaK/NdkyVhkz6mReQLjXb4ex6li82CK6tizlh7/+u2YYFZTLB6spwNiKmv1JgARIgAR0TIDnMR0H743rFGAUjGH58uVx8OBBSUj5448/DDM1bdoUq1evRvv27bF48WKLHoirRUK4EU3YKFu2rPTnAwcOoEKF13fkRZ/AwECL44XtJUuWoFmzZli5cqUsq+SGlwUjjZAACTiQwLmbj/HoeRTK5MskCdyimQsKK3tXQ4k8GRXxsuiwNXgRFS/A9KtbBP3rB9g1l61XkDafvokP5h80mWtV32qYu/Mi/jx81SofxPPO/Rb/Z1VfazuZZ7+Icb0WHTbJdhH/bFaXsvh4wSGDWXH9qG25hBmk1s7LfiRAAiRAAiSgNwI8j+ktYgn9pQCjUAz/+usvvP3225J1ceWoUaNGhpnEU9FHjhyRCu9OnjzZogeixkymTK/TsYWAIoQU0cST0y1atJD+LPqIbBhLTdj+/vvvJeFGCDjWtLgNnVhfUbtGFAQWRYDZSIAESEBvBNYcuy4d7GNigY9rFsLgxq9fmXOkANO3jj8+a1DUZpSWrg0l9wz1xpM30e1X6/7/gc0O2TlgcruSaFUmT4LRvRcdxr9G141Ehz2D62DOjotYcuAKqvhnlQrvpvF0t3NmDiMBEiABEiAB/RGgAKO/mJl7TAFGgRiKui7lypXDnTt3JLFEiDHGLSAgAOfOncPQoUMxZswYix6Ip6zjXj9atGgROnToIPUTf+7UqZP0Z9FHvLJkqQnbY8eOhZjrzJkzVq2SAoxVmNiJBEhApwQSyxhxpADTp44/PrdDgBGvEPmbXRuKE2AePn2F0atO4uaj5/iiQVGUfFNbZd2JGyYZJI4KY4MgX/z8brkkp+/7+xH8Exp/3Uh03jekLnzfFOl1lO+clwRIgARIgAQcSYACjCPpyzM3BRh5OBqsPHjwADVq1MCxY8ekZ6rFlaEsWbLYLMCI56zjXi9SS4BJDgU3fHKE+DsJkICWCWhBgAkYtgYvja4g9apdGAMaWr5KmhTL56+iETh8rUmXOAFm8vozmLb5vPRbkRzpseGzmtKf1x6/jh4LDzs8RCe/boh0Xpb/8iDOOUvXpc6MaYTUHsx4cXgA6QAJkAAJkIDDCPA85jD0sk1MAUY2lK8L5NavXx979+6FeAJ6x44dkghj3rR6BSk5FNzwyRHi7yRAAlomoEUB5pNahfFlI9sFmEfPXyFk1HoT3GfHNMbiA5fxf/buAzyKam3g+JtGCRB670Vq6DXSEakWVJBPRa8IogKCetVLsYICioUiNhALXEVFUZESOoogXToIhhBAeicBEpJ8zxluYjbZZGezs7Mzu/95nvt4JWdO+b3nIOdlZs5LP/7z0XdV4OD4G6fuLdxxTAb/17cJmGn3NxF1mpGrKyUlVfp+vE42xp7Tio65s548FFXF1W0jhAZCAAAgAElEQVT8HAEEEEAAAb8WYD9m//CSgDEohgkJCdK9e3f55ZdfpGTJkrJ69WqpU+fG9wUyX+p7LgsWLMjVR3jV91zUx33VxUd4DQoe1SCAQEAIWDEB83j76jKiu/sJmLPxidJk7FKHuD3YqrLM+v1QllimPTny8/a/ZeiXW30W648fbCpd6pXxWfs0jAACCCCAgN0FSMDYPYIiJGAMiOGVK1e0j+SuWLFCihYtqv2zUaNG2dasjoaeOHGiREZGaq8qObvmzp0rffr0yfEYalXmnnvucXq/qlsdfc0x1AYEmCoQQMAvBCyRgBm9SBKT/zkF6bF21WRkD+fJ+pzQ1fddWo5brisuO17pIoXyhWnfVFHfVvHF9WG/JtIt0vWTL77oG20igAACCCBgFwESMHaJVPb9JAHjYQzViUDqQ7vR0dHaiURLly5NPyY6u6oXL16sPS2jHgmPi4uTChWyngDx6KOPyowZMyQqKkrWrl3rUFWrVq1k/fr1MnDgQJk+fXqWZo4cOSKVKlUSdUqG6leXLl08HOWN21nwhjBSCQII+EjAigmYQe2qyahcJGAOn02Qtm+u1CW56YXOUqJgXvlh61F56uvcHSEdWT5Cdh69qKu9zIXublJe3rk3+7+UyFWl3IQAAggggEAACrAfs3/QScB4EEN1CtHdd98tP//8sxQoUEBLdrRu3dpljeo458qVK8vx48dl2LBhMnnyZId7Dh8+rL2+FB8fL9OmTZPBgwc7/Hzq1KnafapN9RpSxYoVHX6ufqbKqO/QxMbGpn/M12XHXBRgwXsqyP0IIOBLgcwJmLRvo5h5CtJNoxdKUnJqOsPANlXlhdvqus1y8HS8dHxrla771o7oJPHXrsut7/6iq3zGQoXyhopKEg3tVEOqjlzo9v3qhrQncHJ1MzchgAACCCCAQLoA+zH7TwYSMLmMYXJysnY09Lfffiv58+fXvunSsWNH3bWpJ1cGDRqkPQWjjosePny4Vs/27dvlwQcf1P5Zo0YN7TWitOOo0ypXT93UrVtXYmJipEGDBjJr1iztn+pVKJXMGTVqlPb0y8yZM6V///66++SqIAvelRA/RwABKwtkTrT8Na6HhAQHSeZf/2loa2lQoYhXhpI5AfNI66ry0u3uJWBUMqXF68skPjFZVx9XPdtBhn/9h2w7fF5X+bRCpSPyyvpRndPvyeykp7JhnWrIM7k4ZltP3ZRBAAEEEEAg0ATYj9k/4iRgchlD9bHd9u1vHO2pFkKRIjn/YV097ZL5GjJkiLz//vvaL4eGhkp4eLhcvHjjEe/SpUvLypUrs/2Qr0rMdOrUSU6ePKmVV68/qQ8Bq+Or1fXkk0/KlClTcjk657ex4A3lpDIEEDBZIHMCQZ0alCc02KcJmP6tq8jLt9fTLbH3+EXpNulX3eVVwcVPtXX7HnXf5P9rJHc2Kp/e1pwNcTLie+ffLXPWocEdqsvzuTjhya3BURgBBBBAAIEAEmA/Zv9gk4DJZQxXrVrl1hMv6okUZ9e8efO0JMyWLVu0V47U60R33HGHjBgxQjtNKadLJV8mTJgg8+fPF/XaknolSR1xrRI7vXr1yuXIsr+NBW84KRUigICJApkTMHvHdpN8YSGmJmBqjFoo11P++e/BwzdXkVfu0JeAOXnpqrR4Xd+HdzOyzh7QUvp9st4t6TplI+SHITdL3tCQ9PuuJiVL7RcXZ1vPpL6NJChI5It1h6RZ5aLybNdaEhYS7Fa7FEYAAQQQQAAB9mP+PAdIwPhzdA0eGwkYg0GpDgEETBXInIDZPaarhOcJzZKA+XFIa2lY0TuvIGXuw0NRlWXMnZG6HO6f/rus/euMrrIZC03s3UCem7vdrfvSvo+T+absXkNqUbWYfPNYlFttUBgBBBBAAAEE3BNgP+aelxVLk4CxYlQs2icWvEUDQ7cQQECXQObkQdrHYTP/+rzBN0vjSkV11elOofMJidJozFKHWx5sVVnG9tKXgMnNN1jc6V9a2X9FVZZXs0kKZdeHnvXLyrQHmuSmOe5BAAEEEEAAAZ0C7Md0Qlm4GAkYCwfHal1jwVstIvQHAQTcEcicPNj2chcpnD8syxMw3z1xszStbHwCpueUX2XX345HOT/QspK8flf9bIdx4UqSTF62Xy5eTZK5m4+4M9xcl/195C1SpnA+p/dnl4BZ9kx7qVGqYK7b5EYEEEAAAQQQcC3Afsy1kdVLkICxeoQs1D8WvIWCQVcQQMBtgczJg60v3ipFC+TJkoCZ+3iUNKtSzO36c7rh2vVkqfVC1u+n3N+ykozLJgHj6psrhnbwf5Wp14jU60TZXZkN65cvLM91rSXtaub8zTJv9JU6EUAAAQQQCDQB9mP2jzgJGPvH0LQRsOBNo6YhBBDwgkDm5MGmFzpLiYJ5syRgXCUhctO1AycvSed3fsly630tKsr4uxs4rXLK8v3yztI/c9Ncru+JndAzx3szG375aEu5uXqJXLfHjQgggAACCCCgX4D9mH4rq5YkAWPVyFiwXyx4CwaFLiGAgG6BzMmDDaNukVIR+bIkYOYMaiWtqhXXXa+egjuPXpDbpq7JUrRvs4ryRu+sCZjE6ylS84VFeqo2rEyTSkXk+8Gtc6zv/z5eJ7/HnE0v88OQ1tLISx8sNmxgVIQAAggggICfCLAfs38gScDYP4amjYAFbxo1DSGAgBcEMidg0r51YsZTHdG7jstjszZnGdW9zSrIm70byomLV+Xg6XgZ+/PuLN+J8QKF0yoXDmsrdctF5Nhc7Ol46TLpF1EJoqolCoj69ktIcJBZXaQdBBBAAAEEAlqA/Zj9w08Cxv4xNG0ELHjTqGkIAQS8IJA50fLbiE5Svkj+LE/A/HdgS2ldw7jXaj5a/ZeMX7TX6Yh6N62gJTImRu/zwohvVNmzQVlZsP1YjvXPeKiZdK5bWlcfDp9NkG1HzkubGiWkSHgeXfdQCAEEEEAAAQQ8F2A/5rmhr2sgAePrCNiofRa8jYJFVxFAIItA5gTMr893lIrFwrMkYGYNaCFtbzLuo7I5HR99d5Py8v2Wo16L1gs968jAttWyjDFzg2v+01EqFA33Wj+oGAEEEEAAAQQ8F2A/5rmhr2sgAePrCNiofRa8jYJFVxFAwGUCZvVzHaRy8QJZkhOf9W8uHWqV8ljw202H5eNfYmT/ycvZ1tW5TmlZtueEx205q2BE99ryaNtq2itCOSWB1L1rR3SSckXye6UfVIoAAggggAACxgiwHzPG0Ze1kIDxpb7N2mbB2yxgdBcBBBwEMichVvy7vVQrWTBLcuLTh5tLx9qeJWD+PHFJuryb9dSjzCFpXKmIbI07b3ik0pJLaRW7SsDsf727hIUEG94PKkQAAQQQQAAB4wTYjxln6auaSMD4St6G7bLgbRg0uowAAukCmZMQy55pJzVKFcqSgPnkX83kljr6voeSkfeHrUdla9w56dOsoszZGCezf49zqV+pWLjEnU1wWc6dAkufbic3lS6UY/Ip4w/ff6CJ9Khf1p0mKIsAAggggAACPhBgP+YDdIObJAFjMKg/V8eC9+foMjYE/F8gcwIm+ql2UqtM1gTM9Ieaya06P0ibprZm/2np98l6nyPuGdNN8ucJydKPnJ6AiZ3Q0+f9pgMIIIAAAggg4FqA/ZhrI6uXIAFj9QhZqH8seAsFg64ggIDbApmTEIuGt5U6ZSOyPAHz0YNNpWu9Mm7V333yr7Ln2EW37jG68JxBraRVteJOq80uAfOfbrXliQ7Vje4K9SGAAAIIIICAFwTYj3kB1eQqScCYDG7n5ljwdo4efUcAgcxJiJ+fbCOR5QtnScB82K+JdIvU90rO0t0n5Nf9p+SLdYd8Bjz1vsaiPubr7MmXtE5lHvv8oW2kVEReKR2Rz2f9pmEEEEAAAQQQcE+A/Zh7XlYsTQLGilGxaJ9Y8BYNDN1CAAFdApmTED8NbS0NKhTJkoDR+02U3X9flB5TftXVtrcKdahVUj7r38Jl9dkdwe3yRgoggAACCCCAgGUE2I9ZJhS57ggJmFzTBd6NLPjAizkjRsCfBDInIX4Y0loalC8s1UYtdBjme/c3ltsalHM59Hs/WicbDp51WU5vgYh8oXLx6nW9xeXJTjXksfbVpWDeUJf3jP15t3yy5mB6ub1ju0m+sKzfinFZEQUQQAABBBBAwGcC7Md8Rm9YwyRgDKP0/4pY8P4fY0aIgD8LZE7AfPfEzfLl+jj5bssRh2FPua+x3NEw5wTMnA1xMuL7HYZxqVeI9h6/KEfOXdFV53Nda8mQjjV0lVWFTl26JsO+2ioxpy/Ls11qaSc1cSGAAAIIIICAvQTYj9krXs56SwLG/jE0bQQseNOoaQgBBLwgkNNJQBmbm/x/jeTORuWz9CA1NVVOX06UjbFnZfB/txjWw4YVCsuHDzaV3h+sk6PnXSdgfhvRScoXyW9Y+1SEAAIIIIAAAvYQYD9mjzjl1EsSMPaPoWkjYMGbRk1DCCDgBQG9CZh3+zaUuxpXcOjBmcvX5IEZ62Xv8UuG9iztQ8Cq0tYTVmSbgFGvG/154pL8u0stqVm6kKF9oDIEEEAAAQQQsIcA+zF7xIkEjP3jZIkRsOAtEQY6gQACuRTQm4B5q09D6d3UMQEza12svPjjrly2nPW2ZpWLivrYb6kMpxD9a+YGWf3nKadtxE7oaVjbVIQAAggggAAC9hRgP2bPuGXsNU/A2D+Gpo2ABW8aNQ0hgIAXBPQmYN7s3UDuzfSNlA4TV0rsmQRDeqVeH1KvEWW+1Ddguk1yfqoSCRhD6KkEAQQQQAABWwuwH7N1+LTOk4CxfwxNGwEL3jRqGkIAAS8I6E3AvHFPfenbvJLWg/MJibJ8z0n597fbDOtRjVIFZdkz7Z3WN37RHvlodYzDzz54oIl0r1/WsPapCAEEEEAAAQTsKcB+zJ5xy9hrEjD2j6FpI2DBm0ZNQwgg4AUBvQmY8XfXl/taVJIf/zgqw+f8YXhP7mpcXt7t28hpvdeTU+Spr/+QFXtPSt2yEVq5isXCDe8DFSKAAAIIIICA/QTYj9kvZpl7TALG/jE0bQQseNOoaQgBBAwU+HbTYfl+y1FZF3NGV62v3xUptcsUkns+WKervDuFutYrLa/1qi8lC+V15zbKIoAAAggggAACwn7M/pOABIz9Y2jaCFjwplHTEAIIGCCQkpIq9360TjYdOudWbWN7RcrKvSe1p1CMvHJ69cjIdqgLAQQQQAABBPxTgP2Y/eNKAsb+MTRtBCx406hpCAEEDBCYtOxPmbRsv9s1jepRW8Yt3Ov2fa5u+H3kLVKmcD5Xxfg5AggggAACCCDgVID9mP0nBgkY+8fQtBGw4E2jpiEEEPBQYOfRC3Lb1DUe1mLc7VPuayx3NCxnXIXUhAACCCCAAAIBJ8B+zP4hJwFj/xiaNgIWvGnUNIQAAm4IJF5PkV1/X5BqJQtK4fxhci4+URqPXepGDd4t+mjbqjK6Z13vNkLtCCCAAAIIIOD3AuzH7B9iEjD2j6FpI2DBm0ZNQwgErEDs6XhZtPO4nLh4VeqVi5C7m1SQkOCgbD3Ud17uev832XbkgpSOyCsLh7WVz9bGytQVByxh2KZGCfnowaZSIG+oJfpDJxBAAAEEEEDAvgLsx+wbu7Sek4CxfwxNGwEL3jRqGkIgIAUuXk2SVuOWS0Jicvr4n+hQXf7TrXa2Hupjuf0/25j+8yc71fBp8mVE99ryaNtqWtJIHSkdGhIckLFk0AgggAACCCBgvAD7MeNNza6RBIzZ4jZujwVv4+DRdQRsIJA5mZLW5dgJPbPtfW4/tOstjr/G9cjxiR1vtUu9CCCAAAIIIOD/AuzH7B9jEjD2j6FpI2DBm0ZNQwgEnMDWuHPywg87ZdffF7OMPacEzFvR++S9ldZ43Uh1PKe+BlxQGTACCCCAAAIIGCrAfsxQTp9URgLGJ+z2bJQFb8+40WsErC6gTiy64701kpLqvKfOkhpn4xNl6or98ulvsZYaHgkYS4WDziCAAAIIIOBXAuzH7B9OEjD2j6FpI2DBm0ZNQwgElEDvD9bKpkPnsh1zWlLj0Jl4eXz2FlEf6r2S9M93YozCiqpWXMb2ipTO76zOVZXta5aUzx9pkat7uQkBBBBAAAEEEHAlwH7MlZD1f04CxvoxskwPWfCWCQUdQcAvBA6cvCTfbTkqH6z6K8fxqARMamqqNHttmZyJT/Ta2NX3W85cviYtxi132Ua5wvnkiwEtZNmek/LG4r1SrnB+7d+rlyzo8l4KIIAAAggggAACuRFgP5YbNWvdQwLGWvGwdG9Y8JYOD51DwFYCxy5ckajxK3T1WSVg3l91QN5cvE9XeXcLda1XWt7t20jC84TKhStJ0vDVJdlWUaNUQZnyf41F/TNP6I0Tjq4kJmsf3k37d3fbpzwCCCCAAAIIIKBHgP2YHiVrlyEBY+34WKp3LHhLhYPOIGA7gehdx2Xsz7u1p0VUsmLNgdO6xqASMFVGLNBV1t1CJQrmlU0vdE6/LfF6itR8YVG21fCNF3eFKY8AAggggAACRgmwHzNK0nf1kIDxnb3tWmbB2y5kdBgBrwqoZMWincekZMG8cnONEjm2pV4hqjpyYa76c2vd0rJ094lc3evqpq8ebSVR1YunF8upn090qC7/6VbbVZX8HAEEEEAAAQQQ8IoA+zGvsJpaKQkYU7nt3RgL3t7xo/cIGC0w9Mst8vP2Y1q17/ZtKHc1rpBtE66eLDG6b3rqW/1cB6lcvECWopmftqlXLkLKF8kv4++uL8UL5tVTNWUQQAABBBBAAAHDBdiPGU5qeoUkYEwnt2+DLHj7xo6eI+ANgcyJipxez7l0NUnqv5L9t1W80b+c6jzwencJDbnxDZfMV8ZxqY/trh15i9ndoz0EEEAAAQQQQCCLAPsx+08KEjD2j6FpI2DBm0ZNQwhYVkCdEvTL/lPSqGJR6fjWKod+OkvAnLx4VfvWy+zfD8mWuPM+H5f6WO6WF2+VwvnDsu3LVxvi5IUfdkp4WIh8/FAzh1eUfD4AOoAAAggggAACASvAfsz+oScBY/8YmjYCFrxp1DSEgCUF1GtEHSaulL8vXJXQ4CC5npKaYwJGnQ7UbuJKOXXpmmXGs3tMV+20I1fXhYQkCQkJkoJ5XZd1VRc/RwABBBBAAAEEjBBgP2aEom/rIAHjW39btc6Ct1W46CwChgvM23pEnv56W7b1Zn4C5sc/jsrwOX8Y3g93Kpz5cDN55LNN2i2dapeSmQ83d+d2yiKAAAIIIIAAApYRYD9mmVDkuiMkYHJNF3g3suADL+aMGIGMAlOW75d3lv6pOwHzwaq/5I3Few1FfLx9dflw9V+661RJoS1x5+TIuSvStV5pyRsaovteCiKAAAIIIIAAAlYSYD9mpWjkri8kYHLnFpB3seADMuwMOsAF1LHMczcfkUU7j8uKvSdz1FDJjo2xZ0U9+XIuIUkW/O+EJKMIv3ksSqt/YvQ+3VXm9GFg3ZVQEAEEEEAAAQQQsIAA+zELBMHDLpCA8RAwkG5nwQdStBkrAjcEVu49Kf0/26iLQ33cttX45aK+FWP0NeW+xnJHw3Ly/qoD8uZifQmYF3rWkYFtqxndFepDAAEEEEAAAQR8IsB+zCfshjZKAsZQTv+ujAXv3/FldAg4E8h81HROSp3rlJJle3J+SsZd5e6RZeSDfk3Tb3PntaaYcT0kODjI3SYpjwACCCCAAAIIWFKA/Zglw+JWp0jAuMUV2IVZ8IEdf0YfeAKTlv0pk5bt99nAVz3bQSoXD5egoH+SKB+t/kvGL3L9XZmmlYvKd0/c7LO+0zACCCCAAAIIIGC0APsxo0XNr48EjPnmtm2RBW/b0NFxBHQJbD50VsYt3Cvx165rR0efiU/UdZ83ClUrWUBW/LtDlqp/O3BaHpixPsuvh+cJkaXPtJcv1sXK2cuJMrRTDalcvIA3ukadCCCAAAIIIICATwTYj/mE3dBGScAYyunflbHg/Tu+jM5RQH18dmPsOVEb+8jyhf2eR42309ur5eDpeEuMdeerXaVg3tAsfVH9vH/6elkXc0YqFssvk/+vsez++6K0qVFCqpQg4WKJ4NEJBBBAAAEEEPCKAPsxr7CaWikJGFO57d0YC97e8aP37gm8Fb1P3lt5QLtpYu8G0qdZRfcqsFnpQ2fipf3EVZbodbuaJeWLR1pk25fklFSJOXVZyhXJLwWcJGksMQg6gQACCCCAAAIIGCzAfsxgUB9URwLGB+h2bZIFb9fI0e/cCGT++Ky/H2e8bPcJGfjFptxQGXqP+pDvy7fXk4rFwg2tl8oQQAABBBBAAAG7C7Afs3sERUjA2D+Gpo2ABW8aNQ1ZQCDQEjA/b/9bhn651WfyeUKC5YN+TeSWOqV91gcaRgABBBBAAAEErCzAfszK0dHXNxIw+pwoJSIseKZBbgX2Hb8kC7b/LVHVS0hU9eK5rcbU+wIpAZOUnCL3frROtsadN9VYNfZY+2rSt1lFyRcWor1SxIUAAggggAACCCDgXID9mP1nBgkY+8fQtBGw4E2j9quG1Ik6zV9fJgmJyaJOE17yVDu5qXQhy4/RTgkY9WHa1FSR4OB/jmt2BZyQeF1e/nGXbIg9K4fOJLgq7rWfz3y4mXSqzVMvXgOmYgQQQAABBBDwGwH2Y/YPJQkY+8fQtBGw4E2j9quGPv3toLw6f3f6mDrVLiUzH25u+THaJQGjElwDPt8omw+dk36tKstLt9WVIJXpcnE9++02mbv5iKtiXv15/rAQ2T2mq67+erUjVI4AAggggAACCNhAgP2YDYLkooskYOwfQ9NGwII3jdqvGpq07E+ZtGx/+pgiy0fIz0+2tfwY7ZKAmbf1iDz99bZ0z48ebCpd65XJ0Vc9/VL3pWifxqBC0fwS/VQ7TjHyaRRoHAEEEEAAAQTsJMB+zE7Rct5XEjAexPDSpUuycuVK2bRpk/a/zZs3y8mTJ7Ua1a936NDBZe3z5s2TDz74QLZs2SLx8fFSsWJFuf3222XkyJFSokSJHO8/deqUTJgwQX766Sc5cuSIFChQQJo0aSKDBw+WXr16uWzb3QIseHfFKK8ESMB4dx688tMu+WxtbHojbW8qIbMGtHTa6JwNcTL91xj561S8oZ1SSbXKxQvIgu3HdNW7YFgbqVeusK6yFEIAAQQQQAABBBC4IcB+zP4zgQSMBzH84Ycf5K677nJag54EzNChQ2XatGna/SEhIRIeHi4qqaOu0qVLa0mcOnXqOK1/165d0qlTp/SET6FChSQhIUGSk5O18sOGDZPJkyd7MLqst7LgDeUMmMpIwBgf6uvJKbLvxCUpWSivfLw6RmasOZjeSKtqxWTOoKj0f1/712kZ9tVWOX050fiO/K/GbS93kZHfb5eFO467bKNQvlDZ8UpXl+UogAACCCCAAAIIIOAowH7M/jOCBIwHMVQJmEGDBknTpk2lWbNmUrduXbn//vu1Gl0lYKZPn67dq77VMHbsWHn66ae1BMy2bdukX79+snPnTqlRo4aoREuePHkcennt2jWtrZiYGImMjJTZs2dLw4YNtQTMu+++Ky+++KKoj3LOnDlT+vfv78EIWfCG4QVwRSRgjA2+WtvD5/whP237WwrmDZVGFYvImgOn0xtpWbWYfP3YPwkY9QHkU5euGduJDLV9/kgLaV+zpDwxe7Ms2uk8AZMnNFgSr6dod33Yr6l0i8z5FSmvdZaKEUAAAQQQQAABGwuQgLFx8P7XdRIwHsRQPW2inlxJuy5fvizqSRRXCZjExESpXLmyHD9+XNRTMFOnTnXoxaFDh7QEi0qoqCdk1CtFGS9VXj3hohI2e/bskUqVKjn8PO3JmnLlyklsbKyEhYV5MMp/bmXBG8IYcJWQgDE25Gv2n5Z+n6zPttLMCZjM37Ixqjc9G5SVkd1rS4Wi4VqVj83aJNG7Tjit/sN+TeTztYekSeUi8lTnmhIWEmxUN6gHAQQQQAABBBAIGAH2Y/YPNQkYA2OoNwGzaNEi6dGjh/b0i0q2qO++ZL4GDhwon3zyiURFRcnatWsdftyyZUvZsGGDDBgwQGbMmJHl3ri4OKlSpYr2FEx0dLR06dLFkFGy4A1hDLhKrJ6AuXg1SeLOJEitMoUcEgNW+ghvckqqPPPNH7J09wntOO+crvJF8sv3g2+W0hH5tGJGJmA61yklTSoXlQdaVpbC+R0Tu49+sUnrn7MrdkLPgJv3DBgBBBBAAAEEEDBagP2Y0aLm10cCxkBzvQmY5557Tt566y2pV6+e9qqRs+vbb7+Ve++9V0vSqO/CqA/sqku1ERERoSVXVJnevXs7vV/VvXv3bnn++efljTfeMGSULHhDGAOuEisnYNSrOXe8t0aOXbgqzSoXlW8fj0o/EtlKCZglu47LoFmbdc+dsJAgUachNa1UTBqOWaL7PlcFc0qkjJq3Q75cH5elis51SsuMfzVzVTU/RwABBBBAAAEEEHAhwH7M/lOEBIyBMdSbgOnZs6csXLhQ+vbtK3PmzHHaA/VqkXoNSV3qhCX1nRl1bdy4UVq0aKH9f1Wmdu3aTu9XdX/zzTdy2223yfz58w0ZJQveEMaAq8TKCZjMJwilfc9EJTirjlzoECtfPsUx8PONsmzPjRPWfHnlZHDkXIK0e3OlpKQ69nDlsx2kaokbCWQuBBBAAAEEEEAAgdwLsB/LvZ1V7iQBY2Ak9CZg1FHRW7du1T68+8477zjtwYULF6RIkSLaz1QCRSVS1KWOnL7zzju1/6/KqKdhnF2q7kmTJmmJG5XA0XOlLejsyqpv16gPAquPAHMhoFcgcwKmXrkIWTCsrd7bvVquw8SVEnsmIb2NF3rWkYFtq4l65af6KOskYB75bKOs2OvbBIyej+dujD0rfT5cl+6pXof6bUQnr8aQyhFAAAEEEEAAgUARIAFj/0iTgDEwhnoTMJi7UrcAACAASURBVDVr1pT9+/fL6NGj5bXXXnPag6SkpPTTj7788ku57777tHLq/z/wwAPa/1dlQkNDnd6v6h43bpyotvbt26drlCRgdDFRyE0BOyZgkpJT5KbRixxG6ssnYB7+dIOs2nfKTXnjikc/1U77Ro6ea8H2Y9r3asLzhMjHDzWT5lWK6bmNMggggAACCCCAAAIuBEjA2H+KkIAxMIZGJmCuX7+efnqRWQkYVxQseFdC/NyZgB0TMNeuJ0utFxb7NAGjnsKZsny/xJ1NkLV/nZYTF33z5NnTnWvK8M43uTW51ZHTQUHCaUduqVEYAQQQQAABBBDIWYD9mP1nCAkYA2OoNwFj1VeQXFGw4F0J8XN/ScBcSUyWOi+Zn4BRSZeDpy/Ltesp8t3mozLzt4M+nVR5Q4Plp6FtdD/94tPO0jgCCCCAAAIIIODnAuzH7B9gEjAGxlBvAkZ9z2XBggW5+giv+p5L8+bNtV7zEV4Dg0dVXhOw8hMwHd9aJQdPx6ePPe0bMPHXrku9l6MdTLz9CpL68O9903+X32POei0Weise3KG6tKxWXKqVKCAVi4XrvY1yCCCAAAIIIIAAAl4UIAHjRVyTqiYBYyC03gSMOhp64sSJEhkZKTt27HDag7lz50qfPn1yPIZalbnnnnuc3q/q3rVrF8dQGxhfqsqdgB0TMJeuJkn9VxyPb85tAmbHkQvy6dqDUrdshPRvXVVCgoOcQqrXjO6fvj53yDncVTQ8TM4lJOmqV7021KtReXn1znoSkS9M1z0UQgABBBBAAAEEEDBHgASMOc7ebIUEjIG6ehMwixcvlu7du2vJlbi4OKlQoUKWXjz66KMyY8YMiYqKkrVr1zr8vFWrVrJ+/XoZOHCgTJ8+Pcu9R44ckUqVKon6G/Xo6Gjp0qWLIaNkwRvCGHCV2DEBc+FKkjR81bMEzLn4RBk2Z6v8uv90eszfu7+x3NagnMMcSElJldE/7JCvNhw2dG70b11Fnrm1pnR6e7WcuqTv+zHLnmkvNUoVNLQfVIYAAggggAACCCBgjAD7MWMcfVkLCRgD9fUmYNRxzpUrV5bjx4/LsGHDZPLkyQ69OHz4sNSpU0fi4+Nl2rRpMnjwYIefT506VbuvQIEC2mtIFStWdPi5+pkqU65cOYmNjU3/mK+nQ2XBeyoYmPfbMQGjkieNxy51CJi7T8C8vWSfTF1xwKGO0OAgOTCuh8OvRe86Lo/N2mz45Dg4voeW5G06dqmciU/UVf+a/3SUCkV55UgXFoUQQAABBBBAAAGTBdiPmQzuheZIwHiIevr0P3+7rRImVapU0Wr84YcfpHXr1um1Fy5c2CERop5cGTRokLZBUsdFDx8+XPLnzy/bt2+XBx98UPtnjRo1tNeI8uTJ49DLa9euSd26dSUmJkYaNGggs2bN0v555coVLZkzatQo7emXmTNnSv/+/T0c4T+3s+ANowyoiqycgOn01iqJyfANmNE96sij7arJmcvXpOlryzxKwFQZscBpnNMSOUfPX5EPVh2Q2b/HGT4fXr8rUh5oWVmrt9GYJXJexytIFYvll9XPdpTgbF6RMryTVIgAAggggAACCCDglgD7Mbe4LFmYBIyHYVEJFD3XypUrpUOHDg5FhwwZIu+//772a6GhoRIeHi4XL17U/r106dKi7lFPwji7VGKmU6dOcvLkSe3HERERkpCQIOr4anU9+eSTMmXKFD1d012GBa+bioIZBNxNwJy8dFWe/Xa7/H3+iozsXltuqVPaa57ZJWDUKzvNX/duAqbPh2tlY+w5w8f2Wf/m0qFWqfR6xy3cIx//EpNtO9VKFpCbqxeXR1pXlWolef3I8IBQIQIIIIAAAgggYJAA+zGDIH1YDQkYD/E9ScCopufNm6clYbZs2aK9cqReJ7rjjjtkxIgRUrJkyRx7p5IvEyZMkPnz54t6bUm9kqSOuFaJnV69enk4sqy3s+ANJw2ICjMnYCLLR8jPT7bNduwv/bhTvlh3SPu5ym/GjLvxKo03ruwSMCcvXpUW45Y7NKn6oefpEHWE9bI9J+TJr7Y67bJ6AkZ996XaqIWGD2nDqFukVEQ+h3pVMqnvR+u0J31ub1hOioWHyef/81UFN73QWUoUzGt4X6gQAQQQQAABBBBAwFgB9mPGevqiNhIwvlC3aZsseJsGzsfddjcBk/nVnT9f6y55QoO9MorsEjDHL1yVVuMdEzD7X+8uYSHZ90MlVT5bGytjft6dY19VAuZ8QqI0GuP4jRlPB3jg9e4Smk3/riYla68hlY7IK1eTUmTUvB2yJe6cDGxTVR6MuvHaJBcCCCCAAAIIIICAtQXYj1k7Pnp6RwJGjxJlNAEWPBMhNwKeJmD2ju0m+cJCctO0y3uyS8Co77O0nrDC4X5X/fhm02F5fu52l22qBEzMqcva6URGXTklX4xqg3oQQAABBBBAAAEEfCvAfsy3/ka0TgLGCMUAqYMFHyCBNniYk5ftl3eX/Zlea71yEbJgWPavIGV+Amb3mK4SnifU4F7dqK7T26sk5lR8et3qI7zdIsvI4p3H5fWFexzazK4f6umSVftOyavzd8mxC1dd9nPtiE7y0MwNcuDkZZdlXRXo16qSvHJ7vWyffHF1Pz9HAAEEEEAAAQQQsI8A+zH7xCq7npKAsX8MTRsBC940alMaSryeIp+sOSgXrybJY+2qSZFwx9O2jOqEpwmYHa90kUL5wozqjkM9mRMwD7aqLN9tOSIJiclZ2tv5alcpmDdrIuj/Pl4nv8ec9Ur/cqq0eIE8sm7kLV57Pcv0AdEgAggggAACCCCAQI4C7MfsP0FIwNg/hqaNgAVvGrUpDb29ZJ9MXXFAa6tznVIy41/NvdKupwmYP1661WvJocwJGFcA6oTmNjeVlOe71pIZv8bIL/tPy9n4RFe3eeXnPw5pLQ0rFvFK3VSKAAIIIIAAAgggYD0B9mPWi4m7PSIB465YAJdnwftX8DO/6qO+TeKNy9MEzOYXOktxL53S424Cxhs+7tb58u11pX/rqu7eRnkEEEAAAQQQQAABmwuwH7N5ANUpr6mpqan2HwYjMEOABW+Gsnlt2CUBs2H0LVKqkOPRykYp2SUBs/zf7SUkKEiKhueRwuHeeR3LKFPqQQABBBBAAAEEEPCOAPsx77iaWSsJGDO1bd4WC97mAczUfbskYH4feYuUKeydBMwtb6+SvzJ8hNeqEfbma1hWHTP9QgABBBBAAAEEEHAUYD9m/xlBAsb+MTRtBCx406hNacguCZjfRnSS8kXye8XELgmYmHE9JFh9gIYLAQQQQAABBBBAIGAF2I/ZP/QkYOwfQ9NGwII3jdqUhuySgPn1+Y5SsVi4V0x8lYB5tktN+dfNVaT+K0tcjqtRxSLyw5DWLstRAAEEEEAAAQQQQMC/BdiP2T++JGDsH0PTRsCCN43alIbskoBZ+WwHqVqigFdMfJGAaV2juMwe0FKCgoIkcwwyDvLhm6vImfhEGX7LTVKjVEGvjJ9KEUAAAQQQQAABBOwjwH7MPrHKrqckYOwfQ9NGwII3jdqUhuySgFn2THuvJSDMTsBUK1FAfnqyjRTMG6rFeNrKAzIxep/TeHvrVCpTJheNIIAAAggggAACCBguwH7McFLTKyQBYzq5fRtkwds3ds56bpcEzJKn20nN0oUMx993/JJ0nfSL4fU6qzA0OEje7N1A7m5SweHHFxKS5LHZm2THkQsSn5js8DMSMKaEhkYQQAABBBBAAAHbCLAfs02osu0oCRj7x9C0EbDgTaP2ekNHziVImzdWmrLhn7xsv7y77M/0tuqVi5AFw9pmO8bMiaFFw9tKnbIRhpocu3BFosavMLTO7CpTpziVjsirvXKU0zXw802ybM8JrciYO+vJQ1FVTOkfjSCAAAIIIIAAAgjYQ4D9mD3ilFMvScDYP4amjYAFbxq11xsa8uUWWbD9mC0SMD8/2UYiyxc2zGRL3Dm5+/21htXnqqJ9r3WTvKEhropJQuJ1+XrjYSleMK/c3qCsy4SNywopgAACCCCAAAIIIOBXAuzH7B9OEjD2j6FpI2DBm0bt9YacffzVW6+8ePoEzE9DW0uDCkU8Nrl2PVk+XBXj8DSOx5XqqMBbrjqapggCCCCAAAIIIICAHwmwH7N/MEnA2D+Gpo2ABW8atdcbcpaAOTi+h1eeuvA0ATNv8M3SuFLRLCaLdx6XLzfESYsqReWJDjUkJDjnV3xGfLdd5mw87HXbzA2QgDGdnAYRQAABBBBAAAG/FGA/Zv+wkoCxfwxNGwEL3jRqrzfkLAETM66HBLtIYuSmY54mYOY+HiXNqhRzaFp9vLbZ60slKTlV+/XPH2kh7WuWzLZ7qampUnXkwtx036N7oqoVl68GtfKoDm5GAAEEEEAAAQQQQEAJsB+z/zwgAWP/GJo2Aha8adReb8hZAubA690lNCTY8LY9TcB8PaiVtKxWXOtXSkqq/LTtb5n+a4zs+vtiel8rFw+X1c91dNr305evyc0TVkji9RTDx+aqwu2vdJGIfGGuivFzBBBAAAEEEEAAAQRcCrAfc0lk+QIkYCwfIut0kAVvnVh42hNnCZg/X+sueUKtl4D58tGWcnP1EtqQ3191QN5cvC/L8MsWzifrRt4i6kmXT3+LlRV7T8rB0/Fy9PwVT6ncvr98kfxSvmh++VdUFenZoKzb93MDAggggAACCCCAAALOBNiP2X9ekICxfwxNGwEL3jRqrzfkLAGzd2w3yRfm+rQedzvn6RMwswe0lDY33UjAOOu3+vW0BMygLzbJkt03jnL21TVnUCtp9b8ndnzVB9pFAAEEEEAAAQQQ8D8B9mP2jykJGPvH0LQRsOBNo/Z6Q84SGXvGdJP8eayXgMn4fZfsEjBlIvLJ4qfaSqMxSw23U0+0uPMkzc5Xu0rBvKGG94MKEUAAAQQQQAABBAJbgP2Y/eNPAsb+MTRtBCx406i93pCzRMbuMV0lPI/xiYNJy/6UScv2p4+pXrkIWTCsbbZjzNy3kd1ra997KVckv3y4+i+n95WOyCvv3ttI7p+x3jC72xqUldd71Zdhc7bK6j9POa03T0iw3NO0gny1IU77eec6pWXGv5oZ1gcqQgABBBBAAAEEEEAgTYD9mP3nAgkY+8fQtBGw4E2j9npDzhIw3npy452lf8qU5blPwOjBKFUor7zbt5E8YFACplnlojL3iZu1pv81c4PTBMwjravKPU3LS83SheSLdYfk0tUkeSiqihQrkEdPlymDAAIIIIAAAggggIBbAuzH3OKyZGESMJYMizU7xYK3Zlxy0ytnCRhvndjzzpJ9MmXFgfRuuvsEjJ7xqQTM2F6R8tiszXqKuyzzRIfq8p9utbVyz3z9h3y/9ajDPd76Xo7LjlEAAQQQQAABBBBAIGAF2I/ZP/QkYOwfQ9NGwII3jdrrDTlLwGx7qYsUDjf+yOS3ovfJeyu9m4AxGizjh3RjT8dLh7dWpTexcXRnKVkor9FNUh8CCCCAAAIIIIAAAjkKsB+z/wQhAWP/GJo2Aha8adReb8hZAmbri7dKUS+8PjMxeq9MW/nPt1u88QSMkWDVShaQ5c+0l6CgoPRq1+w/LSv3nZTukWWkWZViRjZHXQgggAACCCCAAAII6BJgP6aLydKFSMBYOjzW6hwL3lrx8KQ3zhIwm1/oLMULGv9kxxuL98oHq+yRgAkLCZJNL9wqhfMb/ySQJ/HiXgQQQAABBBBAAAEE2I/Zfw6QgLF/DE0bAQveNGqvN+QsAeOtV2smLNrrcHpRTk/AnLx0VVq8vtzr43fWgDpues1/Ojo8+eKTjtAoAggggAACCCCAAAJOBNiP2X9akICxfwxNGwEL3jRqrzfkLAGzYdQtUioin+Ftj1+4Rz76JSa9XmcJGHWC0Pur/nJ4UsbwjriocNHwtlKnbITZzdIeAggggAACCCCAAAK6BNiP6WKydCESMJYOj7U6x4K3Vjw86Y2zBMzvI2+RMoWNT8CMW7hHPs4mAXPterJMXrZfS7748nrl9rrycOuqvuwCbSOAAAIIIIAAAgggkKMA+zH7TxASMPaPoWkjYMGbRu31hpwlYNaN7CRlC+c3vO3Xft4tM9YcTK834xMwD36yXn7df9rwNt2pkCdf3NGiLAIIIIAAAggggICvBNiP+UreuHZJwBhn6fc1seD9J8TOEjC/jegk6jsoRl9jf94tnzhJwJxPSJRGY5Ya3Zzu+spE5JPxd9eXjrVL6b6HgggggAACCCCAAAII+EqA/Ziv5I1rlwSMcZZ+XxML3n9C7CwB8+vzHaVisfBcDTI1NVXWHDgtYSHB0qpacYc6Xp2/Sz79LTb919QTMLMHtJTGY32XfFGd+e/AltK6RolcjZebEEAAAQQQQAABBBAwW4D9mNnixrdHAsZ4U7+tkQXvP6F1loD55bmOUql47hIw4xftkY9W3/jQbubvqbzy0y75bO0/CZhqJQpIzOl40zHf6tNQnv12m9ZukfAwUac+qYQRFwIIIIAAAggggAACdhBgP2aHKOXcRxIw9o+haSNgwZtG7dWG1NMqVUcuzNLGqmc7SJUSBXS3nZKSKmN+3i3zt/0tZ+IT0+9rVrmozH3i5vR/f/nHnfL5ukO66/VWwZhxPWT2+kOy/cgFeSiqsjSoUMRbTVEvAggggAACCCCAAAKGC7AfM5zU9ApJwJhObt8GWfD2jV3GnienpEr1UVkTMCv+3V6qlSyoe5DRu47LY7M2Oy0fO6Fn+q+/9ONO+cJLCZghHavLtJWuT1DqVLuUzHy4ue6xURABBBBAAAEEEEAAAasJsB+zWkTc7w8JGPfNAvYOFrx/hD4pOUVuGr0oy2CWPdNeapTSn4BpOnapw5MvGStMS8DEnUmQdhNXegUuPE+I7B7TTZy9TpW5wQXD2ki9coW90g8qRQABBBBAAAEEEEDADAH2Y2Yoe7cNEjDe9fWr2lnw/hHOa9eTpdYLi7MMZunT7eSm0oV0DzKnxIdKwKhXlG6bukZ2H7uou053Cv40tLX2GpGeBEzGJ3LcaYOyCCCAAAIIIIAAAghYRYD9mFUikft+kIDJvV3A3cmC94+QX0lMljovZU3ARD/VTmqVMSYB82n/5vLXycvy2oI9XkHrHllGPujXVKubBIxXiKkUAQQQQAABBBBAwGIC7McsFpBcdIcETC7QAvUWFrx/RD7+2nWp93J0lsEsfqqt1C4ToXuQehIfuivTWTAsJEim3d9Ebq1bWoKCgrJNwKgfpabeqFR9J+a5rrV1tkAxBBBAAAEEEEAAAQSsKcB+zJpxcadXJGDc0Qrwsix4/5gAl64mSf1XlmQZzMJhbaVuuZwTMInXUyQ0OEiCg4N0PXlipNg9TSrI2/c2zFKls0TQD0NayxdrY6VIeB55pktNKZg31MiuUBcCCCCAAAIIIIAAAqYLsB8zndzwBknAGE7qvxWy4P0jtheuJEnDV7MmYH5+so1Els/+Q7UTo/dqJw7VKl1IZg1oIS3GLTcN5NkuNWVop5uctpc5AdO7aQV5q0/WRI1pnaUhBBBAAAEEEEAAAQS8IMB+zAuoJldJAsZkcDs3x4K3c/Ru9P1cfKJMXXFAZv52MMtg5g9tI/UrZE3ApKamyidrDjp8z+VfUZXlcy8dLe1M+dfnO0rFYuG6EjAHx/dIfz3J/hFjBAgggAACCCCAAAII3BBgP2b/mUACxv4xNG0ELHjTqL3W0L0frZMNB886rf/HIa2lYcUiDj87n5CoPemiXj3y1VW7TCFZNLxttkmVrzfGyX++26F1r2OtkvJp/xa+6irtIoAAAggggAACCCDgNQH2Y16jNa1iEjCmUdu/IRa8fWO48+gFyRsaLLe++0u2g5g3+GZpXKlo+s/PXL4mt09dI39fuOqTgT/evrqULJRX7mpcXooVyJNtH9QTOtG7jsuJi9ekT7MKEp6H7734JGA0igACCCCAAAIIIOBVAfZjXuU1pXISMKYw+0cjLHh7xnHK8v3yztI/XXb+uydulqaVi4pKaHyx7pC8/NMul/d4s8CGUbdIqYh83myCuhFAAAEEEEAAAQQQsI0A+zHbhCrbjpKAsX8MTRsBC940akMb0ntc9NzHo6RZlWKyfM8JGfD5Jo/7oJI5RcPDZNmek27XdW+zCvJmbz6k6zYcNyCAAAIIIIAAAgj4rQD7MfuHlgSM/WNo2ghY8KZRG9qQ3gTMN49FSdnC+aTfJ+vl0JkEj/uw5Ol28ubifbJszwnddXWPLCNtbyqpvUoUFhKs+z4KIoAAAggggAACCCDg7wLsx+wfYRIw9o+haSNgwZtGbWhDehMwTSoVkS1x5w1pu0rxcFn1XEd59ItNsnS3vgTMnY3KyaS+jTjByJAIUAkCCCCAAAIIIICAvwmwH7N/REnA2D+Gpo2ABW8atUcNzd/2t3y35Yi0qVFCBrSpKlVHLvSoPndvLpw/TDa90Fl7gmXQF5tkiY4EzOrnOkjl4gXcbYryCCCAAAIIIIAAAggEjAD7MfuHmgSM/WNo2ghY8KZR56qh5JRU6TdjvayLOZN+/7ePR0mfD9flqr7c3vTr8x2lYrFw7XY9CZh37m0odzepkNvmuA8BBBBAAAEEEEAAgYAQYD9m/zCTgLF/DE0bAQveNOpcNbRk13EZNGuzw731yxeWHUcv5Kq+3Ny069WuUiDvP8dAPzZrk0Tv+ucVpHuaVJBukWXkXEKilC+SX0oUzCu1yhTKTVPcgwACCCCAAAIIIIBAQAmwH7N/uEnA2D+Gpo2ABe996mvXk2Xq8gNy4ORlebxDdWlUsYiuRlNSUqXaqKyvGlUqFi5xZz3/oK6eTuwd203yhYU4FM2cgOnTtIJM7MPpRno8KYMAAggggAACCCCAQEYB9mP2nw8kYOwfQ9NGwIL3PvV/1x+S0fN2ag2pE4l++08nCQ4Octnw2r9Oy/3T12cpV7JQXjl16ZrL+z0t8OJtdbXvzWS+Hp+1WRbvOp7+y72bVpC3SMB4ys39CCCAAAIIIIAAAgEowH7M/kEnAWP/GJo2Aha896kzn1jk6uO0l69dl2W7T8hTX//htHOF8obKpWvXvdrxumUj5IsBLbTXiTJfL/ywQ2b/Hpf+y4+2rSqje9b1an+oHAEEEEAAAQQQQAABfxRgP2b/qJKAsX8MTRsBC9771JkTMKue7SBVSmR/OtCTX20VdepRdldIcJCoj/N66xrYpqqM7FFHVDvOrqPnr0ibN1ZIaqpoZdaN6CSlIvJ5qzvUiwACCCCAAAIIIICA3wqwH7N/aEnA2DyGp06dkgkTJshPP/0kR44ckQIFCkiTJk1k8ODB0qtXL0NHx4I3lNNpZe4kYM7FJ0rjsUu936kcWnD23ZfMxX+POSMr9p6UW+uWluZVivm0vzSOAAIIIIAAAggggIBdBdiP2TVy//SbBIyNY7hr1y7p1KmTnDx5UhtFoUKFJCEhQZKTk7V/HzZsmEyePNmwEbLgDaPMtqLMCZjl/24v1UsWzFL+5KWrMur7HbJsz43Y++Ia3aOOPNqumi+apk0EEEAAAQQQQAABBAJOgP2Y/UNOAsamMbx27ZrUrVtXYmJiJDIyUmbPni0NGzbUEjDvvvuuvPjii5KamiozZ86U/v37GzJKFrwhjOmVXE1KlinL98v7q/6SZpWLythekdJ98q8OjSx+qq3ULhPh8Gvqvh6Tf5WY0/HGdsiN2r55LEpaVOVpFjfIKIoAAggggAACCCCAgEcC7Mc84rPEzSRgLBEG9zsxdepU7QmX8PBw2bNnj1SqVMmhkqFDh8q0adOkXLlyEhsbK2FhYe43kukOFrzHhA4VvL/qgLy5eF/6r5UrnE/+vnDVocz8oW2kfoXCDr/23eYj8u9vt3ncmc8faSH/mrnBrXqqlSwgPw5pLYXyeT6f3GqYwggggAACCCCAAAIIBLgA+zH7TwASMDaNYcuWLWXDhg0yYMAAmTFjRpZRxMXFSZUqVbSnYKKjo6VLly4ej5QF7zGhQwWZXzdyVvt3T0RJ08qOT5q8sXivfLDqL486s++1bnLs/FXp8NYq3fW0vamEzBrQUnd5CiKAAAIIIIAAAggggIBxAuzHjLP0VU0kYHwl70G7ly9floiICC258u2330rv3r2d1lavXj3ZvXu3PP/88/LGG2940OKNW1nwHhNqFWw+dFbu+WCdrsq+erSVRFUvrpVNSk6R91f+Je8u+1PXvdkV+mtcD+1EomMXrkjU+BW66soTGiy//aeTlCyU9ahpXRVQCAEEEEAAAQQQQAABBDwSYD/mEZ8lbiYBY4kwuNeJjRs3SosWLbSb1OtHtWvXdlpB37595ZtvvpHbbrtN5s+f714jTkpbdcGfT0gUddyxOuo4JTVVqpYoYNlXZBISr0vTscvkStKNDyW7uib2biB3NiovF68macmXmb8ddHVLtj9XyZMvB7aUm0oX0sqcjU+UJjpPUZozqJW0qnYjEcSFAAIIIIAAAggggAAC5gtYdT9mvoR9WyQBY8PYqSOn77zzTq3nFy5c0J6GcXY9/fTTMmnSJGnatKls2rTJ45FadcF/s+mwPD93e/r4Zg9oKW1uKuHxeL1RwbytR+Tprz3/fou7fVMf+Z37xM0Ot6lkUN2XonOsqmDeUNnxShcJCgpyt0nKI4AAAggggAACCCCAgIECVt2PGThEv6+KBIwNQ/zll1/KAw88oPU8KSlJQkNDnY5i9OjRMm7cOKlZs6bs2/fPx16zG3Lags7u54mJiZInTx5RJzBZ6Zq7+Yg8m+GjtF880kLa1SxppS5qfVm+54QM+NzzRFhuBja0Yw15tmsth1uTU1Kl+qiFDr92a93SsnT3ifRfW/ZMO6lR6sYTM1wIIIAAAggggAACCCDgOwESML6zN6plEjBGSZpYDwkYR+zvtxyRZ77556mST/s3l461SpkWkf0nLsmoeTvkekqqvN6r2YSz7QAAIABJREFUvtQtl/WJpN1/X5QeUxyPmDatgyKy/ZUuEuHk5KLHZm2S6F03Ei4f9msi3SLLyuo/T8mC7X9rht3rlzWzm7SFAAIIIIAAAggggAAC2QiQgLH/1CABY8MY8gqSY9B+/OOoDJ/zR/ovzny4mXSqXdq0yKqjnFXSQl0tqxaTrx+Lcmj7zOVr0vS1Zab1J3NDI7rXlsfbV3fafuL1FFm085iUK5JfmldxPG3JZx2mYQQQQAABBBBAAAEEEMgiQALG/pOCBIwNY6i+59K8eXOt53yEV+SnbX/LsK+2pkdyxkPNpHNd8xIwmY+Tjp3Q02FWjVu4Rz7+JcYnM+32huXkzXsaSP48IT5pn0YRQAABBBBAAAEEEEDAGAESMMY4+rIWEjC+1M9l2xmPoZ47d67cc889TmuKjIyUXbt2+f0x1D9v/1uGfvlPAubjB5tKl3plcqnr/m3ZJWDU0yXqOystXl8ml65dd79iD+/Y/3p3CQsJ9rAWbkcAAQQQQAABBBBAAAErCJCAsUIUPOsDCRjP/Hx2d6tWrWT9+vUycOBAmT59epZ+HDlyRCpVqiSpqakSHR0tXbp08bivVl3wC3cck8H/3ZI+vrRvmXg8YJ0VOEvA/HH4vKhXky5cSdJZi7HFop9qJ7XK8PFcY1WpDQEEEEAAAQQQQAAB3wlYdT/mOxH7tUwCxn4x03o8depUGTZsmBQoUEB7DalixYoOI1E/U2XKlSsnsbGxEhYW5vFIrbrgF+88Lo/P3pw+vvcfaCI9TPx4bOYEzPBbbpLJy/d77K0qyBMaLOpJGneu8kXyy28jOrlzC2URQAABBBBAAAEEEEDA4gJW3Y9ZnM1S3SMBY6lw6O+MOgq6bt26EhMTIw0aNJBZs2Zp/7xy5YpMnjxZRo0apT39MnPmTOnfv7/+inMoadUFv2TXcRk0658EzHv3N5bbGpQzZMx6KsmcgNFzj54y6gjokgXzScMxS/QUTy/zw5DW0qhiEbfuoTACCCCAAAIIIIAAAghYW8Cq+zFrq1mrdyRgrBUPt3qjvu/SqVMnOXnypHZfRESEJCQkyPXrN7438uSTT8qUKVPcqjOnwlZd8Mt2n5CBX2xK7/qU+xrLHQ09S8CcvHhV5m09KmfjE+WuJuWldpmsR0urBlNSUqXaqIWGGadVVKxAHtn8Qme5dj1Far+4WHf9j7atKqN71tVdnoIIIIAAAggggAACCCBgDwGr7sfsoWeNXpKAsUYcct0LlXyZMGGCzJ8/Xw4fPqy9ktSkSRMZMmSI9OrVK9f1OrvRqgt+xd4T8shn/yRgJvVtJL0al8/12I9fuCqtxi93uP+jB5tKVycf9r2alOxWgkRvpz7s11S6RZZxK8HTo34ZebdvI8kbyolHep0phwACCCCAAAIIIICAXQSsuh+zi58V+kkCxgpRsEkfrLrgV+47Kf0/3Ziu+M69DeXuJhVyrTpq3g75cn2cw/3BQSIx4x2Pl1YFzickSqMxS3PdlrMb725SXt65t1H6j/S84pSWsDG0I1SGAAIIIIAAAggggAAClhGw6n7MMkA26AgJGBsEySpdtOqC/+XPU/LQzA3pTG/1aSi9m+pPwFxISJK9xy9K08pFZduRC9Lnw7WSkppVPXZC1gRM5uSPp7G6p0kFGX93fe3ju2mXqwTMZ/2bS4dapTxtmvsRQAABBBBAAAEEEEDAwgJW3Y9ZmMxyXSMBY7mQWLdDVl3wa/afln6frE+He7N3A6lWooD8sv+0dI8sI3XKOv9+i7rh6Pkr0vnt1XIlKdklfMYETFJyirw6f5fM/t3xSRmXleRQYMcrXaRQvqynVeWUgKlWsoCs+HcHT5rlXgQQQAABBBBAAAEEELCBgFX3Yzags0wXScBYJhTW74hVF/zaA6fl/hn/JGAySuYLC5YNoztLhJPEhio34rvtMmfjYV34aQkYlbRpPWGFrnv0Fnr1jnryr5urOC2eUwKmf+sq8vLt9fQ2QzkEEEAAAQQQQAABBBCwqYBV92M25fRJt0nA+ITdno1adcGv++uM3Df992xR/9OttjzRobrTn1cftVCSnb1v5KR0WgLm6a//0E5IMuoKChLZN7a7w2tHGetu+OoSuXAlKUtzpQrllYXD20qJgnmN6gr1IIAAAggggAACCCCAgEUFrLofsyiXJbtFAsaSYbFmp6y64DccPCv3frQuW7ScjmaOfDlaLl+7cWy3q+vg+B6ycMdxGfLlFldFdf+8Z/2y8k7fhjmeXJT5Gzeq8u8H3yzVSxSUwuFZX1nS3TgFEUAAAQQQQAABBBBAwDYCVt2P2QbQAh0lAWOBINilC1Zd8Jtiz0rvD7NPwAxoU1VevK1uFuY5G+JkxPc7dPMXyhsql3Qma/RWuu3lLlI4v+skyttL9snUFQe0avs0rSAT+zTU2wTlEEAAAQQQQAABBBBAwA8ErLof8wNa04ZAAsY0avs3ZNUFv/nQObnng7XZAjv7TkrMqcvS6e3VPg1Kx1ol5dP+LXT14Xpyinyz6Yhcu54s97WoJPnCQnTdRyEEEEAAAQQQQAABBBDwDwGr7sf8Q9ecUZCAMcfZL1qx6oLfGndO7no/+wTMwzdXkVfucPxQrTsf380peCqJcmej8vLU13+4FeM7GpYTdVx2xuOm3aqAwggggAACCCCAAAIIIBBQAlbdjwVUEDwcLAkYDwED6XarLvjtR87LHe/9lm0oHoqqLGPujNSeHpm0bL9sjj0nG2LPGhK6Xa92FfUEzkMzN+iur2GFwvLj0Da6y1MQAQQQQAABBBBAAAEEELDqfozI6BcgAaPfKuBLWnXB7zx6QW6buibb+PRrVUle61VfPv3toLw6f7dhcXysXTUZ2aOOuDqFKWOD+cNC5NvHoySyfGHD+kFFCCCAAAIIIIAAAggg4P8CVt2P+b+8cSMkAWOcpd/XZNUFv+vvC9JzSvYJmCaVishXg1pJrRcWGxaj8kXyy6rnOkhYSLD2BExO36DJ2Oivz3eUisXCDesHFSGAAAIIIIAAAggggEBgCFh1PxYY+saMkgSMMY4BUYtVF/yeYxel++Rfc4zB6B515PWFezyO0yu315X/y/QR3B1HLsjt72WfAEprNO1JHI87QQUIIIAAAggggAACCCAQcAJW3Y8FXCA8GDAJGA/wAu1Wqy74P09cki7v/mJKOH55rqNUKu74BMve4xel26ScE0Cqc7vHdJXwPKGm9JNGEEAAAQQQQAABBBBAwL8ErLof8y9l746GBIx3ff2qdqsu+AMnL0nnd7yfgHF2nLUKsKsEUEhwkIzqUUcGtKnqV/OBwSCAAAIIIIAAAggggIB5Albdj5knYP+WSMDYP4amjcCqC/6vU5fllrdXe9Vh2v1NpGeDsk7b2H/iktya6QmceuUipEh4mLx0Wz2pVCxc8ucJ8Wr/qBwBBBBAAAEEEEAAAQT8W8Cq+zH/Vjd2dCRgjPX069qsuuAPno6Xjm+t8pr9wfE9JCgoKNv6nSVgYif09Fp/qBgBBBBAAAEEEEAAAQQCT8Cq+7HAi0TuR0wCJvd2AXenVRf8oTPx0n6idxIwr/WKlH6tKucY68TrKRL5SrSof6prZPfa8lj76gE3PxgwAggggAACCCCAAAIIeE/Aqvsx743Y/2omAeN/MfXaiKy64A+fTZC2b640fNzqqOnVz3WQ0JBgl3Uv3nlcpizfLzVKFZTX7oqUiHxhLu+hAAIIIIAAAggggAACCCCgV8Cq+zG9/aecCAkYZoFuAasu+KPnr0jrCSt0j0NvQVevHumth3IIIIAAAggggAACCCCAgKcCVt2PeTquQLqfBEwgRdvDsVp1wR+7cEWixhubgJn7eJQ0q1LMQzFuRwABBBBAAAEEEEAAAQSMEbDqfsyY0QVGLSRgAiPOhozSqgv+xMWr0nLcco/HOOyWm2TB9r+le2RZ+XeXmjl+eNfjxqgAAQQQQAABBBBAAAEEEHBDwKr7MTeGEPBFScAE/BTQD2DVBX/q0jVp/voy/QPJpiQnF3lMSAUIIIAAAggggAACCCDgJQGr7se8NFy/rJYEjF+G1TuDsuqCP3P5mjR9zbMETM8GZWXa/U28A0etCCCAAAIIIIAAAggggICHAlbdj3k4rIC6nQRMQIXbs8FadcGfi0+UxmOXejS4X5/vKBWLhXtUBzcjgAACCCCAAAIIIIAAAt4SsOp+zFvj9cd6ScD4Y1S9NCarLvgLCUnScMwS3aMuGh4m5xKSHMrz+pFuPgoigAACCCCAAAIIIICADwSsuh/zAYVtmyQBY9vQmd9xqy74S1eTpP4rjgmYfGHBcjUpxSnS4qfaSvy16/LwzI1y6dp1ea5rLRnSsYb5oLSIAAIIIIAAAggggAACCOgUsOp+TGf3KSYiJGCYBroFrLrgVTKl3svRDuPY/EJnp9+FGdqxhjzbtZZW9nxColxJSpayhfPrNqAgAggggAACCCCAAAIIIOALAavux3xhYdc2ScDYNXI+6LdVF/yVxGSp89JiBxH1StGz326TuZuPZPl1H9DRJAIIIIAAAggggAACCCDgkYBV92MeDSrAbiYBE2AB92S4Vl3wV5OSpfaLWRMwSckpctPoRSRgPAk69yKAAAIIIIAAAggggIAlBKy6H7MEjk06QQLGJoGyQjetuuBzSrQM/XKL/Lz9mMZ3T5MK8va9Da1ASR8QQAABBBBAAAEEEEAAAbcErLofc2sQAV6YBEyATwB3hm/VBX89OUVqZPOky8lLV+WdJX9KcHCQ/PvWmlK8YF53hkxZBBBAAAEEEEAAAQQQQMASAlbdj1kCxyadIAFjk0BZoZtWXfCpqalSdeTCdKLbGpSV9+5vYgUy+oAAAggggAACCCCAAAIIGCJg1f2YIYMLkEpIwARIoI0YppUX/I9/HJUx83dL+aL5Zdr9TaRisXAjhkwdCCCAAAIIIIAAAggggIAlBKy8H7MEkA06QQLGBkGyShdZ8FaJBP1AAAEEEEAAAQQQQACBQBNgP2b/iJOAsX8MTRsBC940ahpCAAEEEEAAAQQQQAABBBwE2I/Zf0KQgLF/DE0bAQveNGoaQgABBBBAAAEEEEAAAQRIwPjZHCAB42cB9eZwSMB4U5e6EUAAAQQQQAABBBBAAIHsBdiP2X92kICxfwxNGwEL3jRqGkIAAQQQQAABBBBAAAEEHATYj9l/QpCAsX8MTRsBC940ahpCAAEEEEAAAQQQQAABBEjA+NkcIAHjZwH15nBIwHhTl7oRQAABBBBAAAEEEEAAgewF2I/Zf3aQgLF/DE0bAQveNGoaQgABBBBAAAEEEEAAAQQcBNiP2X9CkICxfwxNGwEL3jRqGkIAAQQQQAABBBBAAAEESMD42RwgAeNnAfXmcEjAeFOXuhFAAAEEEEAAAQQQQACB7AXYj9l/dpCAsX8MTRsBC940ahpCAAEEEEAAAQQQQAABBBwE2I/Zf0KQgLF/DE0bAQveNGoaQgABBBBAAAEEEEAAAQRIwPjZHCAB42cB9eZwSMB4U5e6EUAAAQQQQAABBBBAAIHsBdiP2X92kICxfwxNGwEL3jRqGkIAAQQQQAABBBBAAAEEHATYj9l/QpCAsX8MTRsBC940ahpCAAEEEEAAAQQQQAABBEjA+NkcIAHjZwH15nBIwHhTl7oRQAABBBBAAAEEEEAAgewF2I/Zf3aQgLF/DE0bAQveNGoaQgABBBBAAAEEEEAAAQQcBNiP2X9CkICxfwxNGwEL3jRqGkIAAQQQQAABBBBAAAEESMD42RwgAeNnAfXmcEjAeFOXuhFAAAEEEEAAAQQQQACB7AXYj9l/dpCAsX8MTRsBC940ahpCAAEEEEAAAQQQQAABBBwE2I/Zf0KQgLF/DE0bAQveNGoaQgABBBBAAAEEEEAAAQRIwPjZHCAB42cB9eZwSMB4U5e6EUAAAQQQQAABBBBAAIHsBdiP2X92kICxfwxNGwEL3jRqGkIAAQQQQAABBBBAAAEEHATYj9l/QpCAsX8MTRsBC940ahpCAAEEEEAAAQQQQAABBEjA+NkcIAHjZwH15nBIwHhTl7oRQAABBBBAAAEEEEAAgewF2I/Zf3aQgLF/DE0bAQveNGoaQgABBBBAAAEEEEAAAQQcBNiP2X9CkICxfwxNGwEL3jRqGkIAAQQQQAABBBBAAAEESMD42RwgAeNnAfXmcIKCgrTq8+TJ481mqBsBBBBAAAEEEEAAAQQQQCCTQGJiovYrqamp2NhUgASMTQPni25bOQGT9psRySFfzAza9JYA89pbstTrKwHmtK/kadebAsxrb+pSty8EmNO+UNfXJgkYfU5WLkUCxsrRoW+6BXg9SjcVBW0kwLy2UbDoqi4B5rQuJgrZTIB5bbOA0V2XAsxpl0QUQCDXAiRgck3HjVYS4D8UVooGfTFKgHltlCT1WEWAOW2VSNAPIwWY10ZqUpcVBJjTVogCffBXARIw/hrZABsX/6EIsIAHyHCZ1wES6AAaJnM6gIIdQENlXgdQsANkqMzpAAk0w/SJAAkYn7DTqNEC/IfCaFHqs4IA89oKUaAPRgowp43UpC6rCDCvrRIJ+mGUAHPaKEnqQSCrAAkYZoVfCPAfCr8II4PIJMC8Zkr4mwBz2t8iyniUAPOaeeBvAsxpf4so47GSAAkYK0WDvuRagP9Q5JqOGy0swLy2cHDoWq4EmNO5YuMmiwswry0eILrntgBz2m0ybkBAtwAJGN1UFLSyAP+hsHJ06FtuBZjXuZXjPqsKMKetGhn65YkA89oTPe61ogBz2opRoU/+IkACxl8iGeDj4D8UAT4B/HT4zGs/DWwAD4s5HcDB9+OhM6/9OLgBOjTmdIAGnmGbIkACxhRmGkEAAQQQQAABBBBAAAEEEEAAgUAWIAETyNFn7AgggAACCCCAAAIIIIAAAgggYIoACRhTmGkEAQQQQAABBBBAAAEEEEAAAQQCWYAETCBHn7EjgAACCCCAAAIIIIAAAggggIApAiRgTGGmEQQQQAABBBBAAAEEEEAAAQQQCGQBEjCBHH3GjgACCCCAAAIIIIAAAggggAACpgiQgDGFmUYQQAABBBBAAAEEEEAAAQQQQCCQBUjABHL0GTsCCCCAAAIIIIAAAggggAACCJgiQALGFGYaQQABBBBAAAEEEEAAAQQQQACBQBYgARPI0feDsZ86dUomTJggP/30kxw5ckQKFCggTZo0kcGDB0uvXr38YIQMwU4Cn332mfTv399ll9W8LVGihNNy8+bNkw8++EC2bNki8fHxUrFiRbn99ttl5MiR2d6TVhHrwSU9BZwIXLp0SVauXCmbNm3S/rd582Y5efKkVlL9eocOHVy6+XLeetK2y4FRwJYCnszpKlWqyKFDh3Ic95AhQ+S9997Ltoynvxczp2057bzeafXn3O+//16WL18u27Ztk2PHjklYWJhUrlxZOnfuLMOHD5dq1arl2A9P5hbz2ushpoEAESABEyCB9sdh7tq1Szp16pS+UShUqJAkJCRIcnKyNtxhw4bJ5MmT/XHojMmiAmkJmODgYClZsmS2vdy9e7cUK1Ysy8+HDh0q06ZN0349JCREwsPDRW0k1FW6dGltM1ynTh2n9bIeLDopbNCtH374Qe666y6nPdWTgPHlvPWkbRuEhi7mUsCTOZ2WgImIiJD8+fM77cEjjzwi48aN88rvxczpXAbdz287fPiwlmhJTU1NH6mao1euXJGkpCTt19R8VX8Ouffee51qeDK3PP0zhidt+3loGV4ACpCACcCg+8OQr127JnXr1pWYmBiJjIyU2bNnS8OGDbUEzLvvvisvvvii9h+pmTNn6noiwR9MGIPvBdISMOoPSbGxsW51aPr06TJo0CAJCgqSsWPHytNPP60lYNTfcvXr10927twpNWrUEPWHoDx58jjUzXpwi5rCmQTUZlXNvaZNm0qzZs2031vvv/9+rZSrBIwv560nbTMJ/FvAkzmdloD59NNP5eGHH3YLytPfi5nTbnEHVGH1Zwr1dEuPHj20edmxY0cpXry4XL9+XdauXStPPvmkbN++XUJDQ7WnGBs0aODg48ncYl4H1FRjsCYIkIAxAZkmjBeYOnWq9oSL2qDu2bNHKlWq5NBIWqa9XLly2kZYPaLJhYC3BXKbgElMTNT+Zuv48eOi5q6a3xkv9Ti82hSrBKN6Qka9YpfxYj14O7L+Xb96alA9cZV2Xb58WdQTha4SML6ct5627d8RZXS5ndNKzpMEjCe/FzOnmbc5CVy4cEH786z6y0Zn14kTJ6R+/fqiXhNSr0Krv4BMuzydW8xr5iYCxgqQgDHWk9pMEmjZsqVs2LBBBgwYIDNmzMjSalxcnPaHKPUUTHR0tHTp0sWkntFMIAvkNgGzaNEi7W+11NMvKtmivvuS+Ro4cKB88sknEhUVpf1tV8aL9RDIs874setNwPhy3nratvFq1GhlAb1z2tMEjCe/FzOnrTyD7NE39WTM559/rj39op6eTbs8nVvMa3vEn17aR4AEjH1iRU//J6D+IKXee1XJlW+//VZ69+7t1KZevXqivrXx/PPPyxtvvIEfAl4XyG0C5rnnnpO33npL1JxVrxo5u9RcV+91qySN+i6M+uC0ulgPXg9rwDWgd7Pqy3nrSdsBF1AGrP0+qeepLk8SMJ7+XsycZqJ6KvDss8/K22+/rT0xq15XTrs8mVvMa0+jwv0IZBUgAcOssJ3Axo0bpUWLFlq/1etHtWvXdjqGvn37yjfffCO33XabzJ8/33bjpMP2E0hLwKhX42rVqiX79u3TBlG+fHntJBn1jrZ6RDjz1bNnT1m4cKGoOTtnzhynA1dzXf2hSl3qpBr1vQ51sR7sN0+s3mO9m1VfzltP2ra6P/0zXkDvnM6YgFEJ8bNnz8rp06elcOHC2lMF6i981Osd+fLly9JJT38vZk4bH/dAq7F58+banw/UX9Z8/fXX6cP3ZG4xrwNtFjFeMwRIwJihTBuGCqgjp++8806tTvVOrHoaxtmlPmI6adIkbaOq/oPEhYC3BTIeQ62eVClSpIj2N69pJxSo72yoY9PV31JlvNTR6Vu3btU+vPvOO+847aaa66o+damEokosqov14O2oBl79ejervpy3nrQdeBFlxHrndMYEjPr/KtGiPnp+8eLFdESVRP/555+zfHvO09+LmdPMU08EMp78pV456tatW3p1nswt5rUnUeFeBJwLkIBhZthO4Msvv5QHHnhA67fa2Kovvju7Ro8erR0TWbNmzfQnEWw3WDpsK4ElS5bIb7/9Jnfffbf2BIz6w7uao2vWrJGRI0fK+vXrtfH897//TT9lRv27mqP79+8XNWdfe+01p2NW9aSdfqTWwH333aeVYz3YaorYorN6N6u+nLeetG2LINBJQwX0zmnV6PDhw6V169bSvn17KV26tNaPY8eOad/gUr8/qxNh1NMxW7ZscTiRztPfi5nThoY8oCpT345TJ9ipp7XUX1CqZEzGy5O5xbwOqKnEYE0SIAFjEjTNGCfg6X8MjOsJNSGgX0D9oV39gV4lYSpUqKB9bDc4OFirQM8fjtRRk2mneZGA0e9OSfcF9G5WfTlvPWnbfRHusLuA3jntapwZnwb46KOPtOPb0y5P/2zCnHalz8+dCZw/f17atWsnO3bs0I6pVq8MFStWzO0EjLf+jMG8Zt4ikFWABAyzwnYCnj4OabsB02G/EVi+fLl07txZG0/G77j48vFgv8FlIIYJ6N2s+nLeetK2YVBUZBsBvXNaz4Datm2rPdWY+ftynv7ZhDmtR58yGQXUvL711lvl999/l3Llysmvv/6qJWEyX57MLeY1cw4B4wVIwBhvSo1eFlAbV/WhMXXxEV4vY1O9oQLx8fFSsGBBrU71geg+ffpo/1/9QX7BggW5+ggv68HQEFHZ/07W0nNijC/nrSdtE+TAEzAyAZN2okzmk2Y8/b2YOR1489KTESckJEj37t3ll19+kZIlS8rq1aulTp06Tqv0ZG4xrz2JEvci4FyABAwzw3YCGY/Emzt3rtxzzz1OxxAZGakdw8cx1LYLsd92OGMCJuMR6mqOTpw4UdScVY8RO7vUXFcJm5yOoWY9+O3UMXVgejervpy3nrRtKiaNWUJA75zW09nsjvT19M8mzGk9+pRRAleuXNH+4mbFihVStGhR7Z+NGjXKFseTucW8Zs4hYLwACRjjTanRBIFWrVpp39IYOHCgTJ8+PUuLR44c0U4oSE1NlejoaOnSpYsJvaIJBHIWUH9IuuWWW7RC6j1t9dE8dS1evFj7myyVXImLi9O+EZP5evTRR2XGjBkSFRUla9eudfgx64GZZ6SA3s2qL+etp20b6UVd1hfQO6f1jER9b0O96qGO9lWnIWW8PPm9mDmtR58y6nty6kO76s+26hTQpUuXSosWLXKE8XRuMa+ZdwgYK0ACxlhPajNJYOrUqTJs2DApUKCA9hpSxYoVHVpWP1Nl1DuxsbGx6R8vNal7NBOAAirZpxIo2V3qFKMOHTpoyZPy5ctriZa0j/AmJiZK5cqV5fjx49q8njx5skM1hw8f1h4tVk/QTJs2TQYPHuzwc9ZDAE44Lw5Z72bVl/PW07a9yEfVFhTQO6dd/T6uXhVVTx6o68MPP5THHnvMsN+LmdMWnDgW65L6c4Q6ZVEl/tSff1USRp3Y5erydG558mcMT9t2NTZ+joAdBUjA2DFq9Fk7BlK9fx0TEyMNGjSQWbNmaf9Uj2WqzeuoUaO0p19mzpwp/fv3RwwBrwuoRF/fvn21p7LUR/GqVKmitalOFlBHU6s5mfbkyuzZs9OPUk/rmHqSS52ooZI46vh0dRRq/vz5Zfv27fLggw9q/6xRo4b2Wl3acdT/394d61IWRWEA3oWEaEgUao3eEwgPIAqNTikRiVKtEqVCK16AiAeg04lX0FJRachkneSKmTFinHCXle+0mLOizA35AAAHSElEQVT292937v2de8/gZ/0+fHm85U8Qty8dHFH0DfZv3M709RP8iYmJ3wrtYe7bPucuH6gFdrfk/d89vbm52T0Gr6ysdFcojo+Pd/9ElOOHh4dtZ2enPT4+vnkb6vi+vo/F9rSN+y+Bp6entrq62uLty/HcIMrAhYWFD4P12Vv29YeZfSOBDwkoYD7E5JsyCsQL0cXFxXZ7e9uNF5dixoeSxQveOOKJ1P7+fsbRzVRQIAqYmZmZl5WNjY11H7j78PDQ4i9AccRtpPf29trW1tabAhsbG+3g4KD72sjISPfkP34+junp6XZ+fv7PD9nz+1BwU33jkt67euv1GLEH40qu18cw922fc38jr1MNQeAze3ptba0dHR1108bPR+EYR9zqd3DMzc2109PTv668HXy972OxPT2EzfIDThkftjs/P99NOjo62iYnJ9+dOkrDP48+e8u+/gGbxIg/RkAB82OiMuhbAlG+7O7utrOzsxZv04hLMuN2e/GfzPLyMjQC3yYQV1/FZ7TEVS7X19ft7u6u3d/fdyVK3BYy/lK1vr7eZmdn353p5OSkK2Gurq66txzF2+uWlpba9vZ2d6eD9w6/D98Wd7kTfebF6muEYe7bPucuF6QFvQh8Zk/H7XyPj4/b5eVlu7m56a6ieX5+blNTU91zi/gg9LgKIcr0r3wstqdt5D8FLi4u/uuKl7gK/K2jz97q+xyjz7ntCAKVBBQwldK0FgIECBAgQIAAAQIECBAgQCClgAImZSyGIkCAAAECBAgQIECAAAECBCoJKGAqpWktBAgQIECAAAECBAgQIECAQEoBBUzKWAxFgAABAgQIECBAgAABAgQIVBJQwFRK01oIECBAgAABAgQIECBAgACBlAIKmJSxGIoAAQIECBAgQIAAAQIECBCoJKCAqZSmtRAgQIAAAQIECBAgQIAAAQIpBRQwKWMxFAECBAgQIECAAAECBAgQIFBJQAFTKU1rIUCAAAECBAgQIECAAAECBFIKKGBSxmIoAgQIECBAgAABAgQIECBAoJKAAqZSmtZCgAABAgQIECBAgAABAgQIpBRQwKSMxVAECBAgQIAAAQIECBAgQIBAJQEFTKU0rYUAAQIECBAgQIAAAQIECBBIKaCASRmLoQgQIECAAAECBAgQIECAAIFKAgqYSmlaCwECBAgQIECAAAECBAgQIJBSQAGTMhZDESBAgAABAgQIECBAgAABApUEFDCV0rQWAgQIECBAgAABAgQIECBAIKWAAiZlLIYiQIAAAQIECBAgQIAAAQIEKgkoYCqlaS0ECBAgQIAAAQIECBAgQIBASgEFTMpYDEWAAAECBAgQIECAAAECBAhUElDAVErTWggQIECAAAECBAgQIECAAIGUAgqYlLEYigABAgQIECBAgAABAgQIEKgkoICplKa1ECBAgAABAgQIECBAgAABAikFFDApYzEUAQIECBAgQIAAAQIECBAgUElAAVMpTWshQIAAAQIECBAgQIAAAQIEUgooYFLGYigCBAgQIECAAAECBAgQIECgkoACplKa1kKAAAECBAgQIECAAAECBAikFFDApIzFUAQIECBAgAABAgQIECBAgEAlAQVMpTSthQABAgQIECBAgAABAgQIEEgpoIBJGYuhCBAgQIAAAQIECBAgQIAAgUoCCphKaVoLAQIECBAgQIAAAQIECBAgkFJAAZMyFkMRIECAAAECBAgQIECAAAEClQQUMJXStBYCBAgQIECAAAECBAgQIEAgpYACJmUshiJAgAABAgQIECBAgAABAgQqCShgKqVpLQQIECBAgAABAgQIECBAgEBKAQVMylgMRYAAAQIECBAgQIAAAQIECFQSUMBUStNaCBAgQIAAAQIECBAgQIAAgZQCCpiUsRiKAAECBAgQIECAAAECBAgQqCSggKmUprUQIECAAAECBAgQIECAAAECKQUUMCljMRQBAgQIECBAgAABAgQIECBQSUABUylNayFAgAABAgQIECBAgAABAgRSCihgUsZiKAIECBAgQIAAAQIECBAgQKCSgAKmUprWQoAAAQIECBAgQIAAAQIECKQUUMCkjMVQBAgQIECAAAECBAgQIECAQCUBBUylNK2FAAECBAgQIECAAAECBAgQSCmggEkZi6EIECBAgAABAgQIECBAgACBSgIKmEppWgsBAgQIECBAgAABAgQIECCQUkABkzIWQxEgQIAAAQIECBAgQIAAAQKVBBQwldK0FgIECBAgQIAAAQIECBAgQCClgAImZSyGIkCAAAECBAgQIECAAAECBCoJKGAqpWktBAgQIECAAAECBAgQIECAQEoBBUzKWAxFgAABAgQIECBAgAABAgQIVBJQwFRK01oIECBAgAABAgQIECBAgACBlAIKmJSxGIoAAQIECBAgQIAAAQIECBCoJKCAqZSmtRAgQIAAAQIECBAgQIAAAQIpBRQwKWMxFAECBAgQIECAAAECBAgQIFBJ4BcSKJcxKksqzAAAAABJRU5ErkJggg==\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1659dd65108>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined['Delta (TTL-CSV)'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "cell_style": "center",
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>TTL Start</th>\n",
       "      <th>TTL Length</th>\n",
       "      <th>TTL Event</th>\n",
       "      <th>CSV Start</th>\n",
       "      <th>CSV Event</th>\n",
       "      <th>CSV Probability</th>\n",
       "      <th>Compare</th>\n",
       "      <th>TTL Start norm</th>\n",
       "      <th>CSV Start norm</th>\n",
       "      <th>Delta (CSV-TTL)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1720</th>\n",
       "      <td>15282526.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "      <td>15114179</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "      <td>0.125</td>\n",
       "      <td>True</td>\n",
       "      <td>15098265.0</td>\n",
       "      <td>15097899</td>\n",
       "      <td>-366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1721</th>\n",
       "      <td>15296141.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>left_rewarded</td>\n",
       "      <td>15127779</td>\n",
       "      <td>left_rewarded</td>\n",
       "      <td>0.125</td>\n",
       "      <td>True</td>\n",
       "      <td>15111880.0</td>\n",
       "      <td>15111499</td>\n",
       "      <td>-381.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1722</th>\n",
       "      <td>15299954.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>ITIstarts</td>\n",
       "      <td>15131539</td>\n",
       "      <td>ITIstarts</td>\n",
       "      <td>0.125</td>\n",
       "      <td>True</td>\n",
       "      <td>15115693.0</td>\n",
       "      <td>15115259</td>\n",
       "      <td>-434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>15320163.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>ITIends</td>\n",
       "      <td>15151800</td>\n",
       "      <td>ITIends</td>\n",
       "      <td>0.125</td>\n",
       "      <td>True</td>\n",
       "      <td>15135902.0</td>\n",
       "      <td>15135520</td>\n",
       "      <td>-382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1724</th>\n",
       "      <td>15320423.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>TIstarts</td>\n",
       "      <td>15152079</td>\n",
       "      <td>TIstarts</td>\n",
       "      <td>0.125</td>\n",
       "      <td>True</td>\n",
       "      <td>15136162.0</td>\n",
       "      <td>15135799</td>\n",
       "      <td>-363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>15340525.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "      <td>15172119</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "      <td>0.125</td>\n",
       "      <td>True</td>\n",
       "      <td>15156264.0</td>\n",
       "      <td>15155839</td>\n",
       "      <td>-425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>15341564.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>SOUND_start</td>\n",
       "      <td>15172860</td>\n",
       "      <td>SOUND_start</td>\n",
       "      <td>0.125</td>\n",
       "      <td>True</td>\n",
       "      <td>15157303.0</td>\n",
       "      <td>15156580</td>\n",
       "      <td>-723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>15342248.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "      <td>15173899</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "      <td>0.125</td>\n",
       "      <td>True</td>\n",
       "      <td>15157987.0</td>\n",
       "      <td>15157619</td>\n",
       "      <td>-368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>15351670.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>left_NOreward</td>\n",
       "      <td>15183319</td>\n",
       "      <td>left_rewarded</td>\n",
       "      <td>0.125</td>\n",
       "      <td>False</td>\n",
       "      <td>15167409.0</td>\n",
       "      <td>15167039</td>\n",
       "      <td>-370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1729</th>\n",
       "      <td>15355445.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>ITIstarts</td>\n",
       "      <td>15187079</td>\n",
       "      <td>ITIstarts</td>\n",
       "      <td>0.125</td>\n",
       "      <td>True</td>\n",
       "      <td>15171184.0</td>\n",
       "      <td>15170799</td>\n",
       "      <td>-385.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TTL Start  TTL Length               TTL Event  CSV Start  \\\n",
       "1720  15282526.0        75.0  resp-time-window_start   15114179   \n",
       "1721  15296141.0       141.0           left_rewarded   15127779   \n",
       "1722  15299954.0       208.0               ITIstarts   15131539   \n",
       "1723  15320163.0       220.0                 ITIends   15151800   \n",
       "1724  15320423.0        20.0                TIstarts   15152079   \n",
       "1725  15340525.0        40.0      IND-CUE_pres_start   15172119   \n",
       "1726  15341564.0        61.0             SOUND_start   15172860   \n",
       "1727  15342248.0        76.0  resp-time-window_start   15173899   \n",
       "1728  15351670.0       155.0           left_NOreward   15183319   \n",
       "1729  15355445.0       200.0               ITIstarts   15187079   \n",
       "\n",
       "                   CSV Event CSV Probability  Compare  TTL Start norm  \\\n",
       "1720  resp-time-window_start           0.125     True      15098265.0   \n",
       "1721           left_rewarded           0.125     True      15111880.0   \n",
       "1722               ITIstarts           0.125     True      15115693.0   \n",
       "1723                 ITIends           0.125     True      15135902.0   \n",
       "1724                TIstarts           0.125     True      15136162.0   \n",
       "1725      IND-CUE_pres_start           0.125     True      15156264.0   \n",
       "1726             SOUND_start           0.125     True      15157303.0   \n",
       "1727  resp-time-window_start           0.125     True      15157987.0   \n",
       "1728           left_rewarded           0.125    False      15167409.0   \n",
       "1729               ITIstarts           0.125     True      15171184.0   \n",
       "\n",
       "      CSV Start norm  Delta (CSV-TTL)  \n",
       "1720        15097899           -366.0  \n",
       "1721        15111499           -381.0  \n",
       "1722        15115259           -434.0  \n",
       "1723        15135520           -382.0  \n",
       "1724        15135799           -363.0  \n",
       "1725        15155839           -425.0  \n",
       "1726        15156580           -723.0  \n",
       "1727        15157619           -368.0  \n",
       "1728        15167039           -370.0  \n",
       "1729        15170799           -385.0  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.iloc[1720:1730]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### experiment with multi level columns (not working with qgrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th colspan=\"3\" halign=\"left\">ttl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>Start</th>\n",
       "      <th>Length</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>184261</td>\n",
       "      <td>17</td>\n",
       "      <td>TIstarts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>204966</td>\n",
       "      <td>39</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>205627</td>\n",
       "      <td>61</td>\n",
       "      <td>SOUND_start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>206346</td>\n",
       "      <td>80</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>346501</td>\n",
       "      <td>178</td>\n",
       "      <td>no response in time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2171</th>\n",
       "      <td>2.24506e+07</td>\n",
       "      <td>216</td>\n",
       "      <td>ITIends</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>2.24508e+07</td>\n",
       "      <td>21</td>\n",
       "      <td>TIstarts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>2.24709e+07</td>\n",
       "      <td>37</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174</th>\n",
       "      <td>2.24715e+07</td>\n",
       "      <td>61</td>\n",
       "      <td>SOUND_start</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2175</th>\n",
       "      <td>2.24722e+07</td>\n",
       "      <td>76</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2176 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "source          ttl                               \n",
       "type          Start Length                   Event\n",
       "0            184261     17                TIstarts\n",
       "1            204966     39      IND-CUE_pres_start\n",
       "2            205627     61             SOUND_start\n",
       "3            206346     80  resp-time-window_start\n",
       "4            346501    178     no response in time\n",
       "...             ...    ...                     ...\n",
       "2171    2.24506e+07    216                 ITIends\n",
       "2172    2.24508e+07     21                TIstarts\n",
       "2173    2.24709e+07     37      IND-CUE_pres_start\n",
       "2174    2.24715e+07     61             SOUND_start\n",
       "2175    2.24722e+07     76  resp-time-window_start\n",
       "\n",
       "[2176 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create one big DataFrame with second level column indexing\n",
    "# create colum index\n",
    "\n",
    "ttl_combined_columns = ttl_combined.columns\n",
    "columns = pd.MultiIndex.from_product([['ttl'], ttl_combined_columns],\n",
    "                                     names=['source', 'type'])\n",
    "ttl_combined = pd.DataFrame(ttl_combined.values, columns=columns)\n",
    "ttl_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th colspan=\"3\" halign=\"left\">csv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>Start</th>\n",
       "      <th>Event</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16280</td>\n",
       "      <td>TIstarts</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37040</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37679</td>\n",
       "      <td>SOUND_start</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38399</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>178559</td>\n",
       "      <td>no response in time</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2171</th>\n",
       "      <td>22281999</td>\n",
       "      <td>ITIends</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>22282260</td>\n",
       "      <td>TIstarts</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>22302320</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174</th>\n",
       "      <td>22302959</td>\n",
       "      <td>SOUND_start</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2175</th>\n",
       "      <td>22303659</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2176 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "source       csv                                    \n",
       "type       Start                   Event Probability\n",
       "0          16280                TIstarts        0.75\n",
       "1          37040      IND-CUE_pres_start        0.75\n",
       "2          37679             SOUND_start        0.75\n",
       "3          38399  resp-time-window_start        0.75\n",
       "4         178559     no response in time        0.75\n",
       "...          ...                     ...         ...\n",
       "2171    22281999                 ITIends       0.125\n",
       "2172    22282260                TIstarts       0.125\n",
       "2173    22302320      IND-CUE_pres_start       0.125\n",
       "2174    22302959             SOUND_start       0.125\n",
       "2175    22303659  resp-time-window_start       0.125\n",
       "\n",
       "[2176 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create sub dataframe of csv with only events that are encoded in ttl \n",
    "not_in_ttl = csv['Event'].unique()[~np.isin(csv['Event'].unique(), ttl1['Event'].unique())]\n",
    "csv_combined = csv.loc[ (csv['Event']!=not_in_ttl[0]) & (csv['Event']!=not_in_ttl[1]) & (csv['Event']!=not_in_ttl[2]) ].copy()\n",
    "csv_combined.drop('Event Time', axis=1, inplace=True)\n",
    "# prepare csv df\n",
    "csv_combined_columns = csv_combined.columns\n",
    "columns = pd.MultiIndex.from_product([['csv'], csv_combined_columns],\n",
    "                                     names=['source', 'type'])\n",
    "csv_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# merge both dfs\n",
    "#combined = pd.merge(ttl_combined, csv_combined, how='outer', right_index=True, left_index=True)\n",
    "combined = pd.concat([ttl_combined, csv_combined], axis=1)\n",
    "#qgrid.show_grid(combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th colspan=\"3\" halign=\"left\">ttl</th>\n",
       "      <th colspan=\"3\" halign=\"left\">csv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>Start</th>\n",
       "      <th>Length</th>\n",
       "      <th>Event</th>\n",
       "      <th>Start</th>\n",
       "      <th>Event</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>184261</td>\n",
       "      <td>17</td>\n",
       "      <td>TIstarts</td>\n",
       "      <td>16280</td>\n",
       "      <td>TIstarts</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>204966</td>\n",
       "      <td>39</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "      <td>37040</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>205627</td>\n",
       "      <td>61</td>\n",
       "      <td>SOUND_start</td>\n",
       "      <td>37679</td>\n",
       "      <td>SOUND_start</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>206346</td>\n",
       "      <td>80</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "      <td>38399</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>346501</td>\n",
       "      <td>178</td>\n",
       "      <td>no response in time</td>\n",
       "      <td>178559</td>\n",
       "      <td>no response in time</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2171</th>\n",
       "      <td>2.24506e+07</td>\n",
       "      <td>216</td>\n",
       "      <td>ITIends</td>\n",
       "      <td>22281999</td>\n",
       "      <td>ITIends</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>2.24508e+07</td>\n",
       "      <td>21</td>\n",
       "      <td>TIstarts</td>\n",
       "      <td>22282260</td>\n",
       "      <td>TIstarts</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>2.24709e+07</td>\n",
       "      <td>37</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "      <td>22302320</td>\n",
       "      <td>IND-CUE_pres_start</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174</th>\n",
       "      <td>2.24715e+07</td>\n",
       "      <td>61</td>\n",
       "      <td>SOUND_start</td>\n",
       "      <td>22302959</td>\n",
       "      <td>SOUND_start</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2175</th>\n",
       "      <td>2.24722e+07</td>\n",
       "      <td>76</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "      <td>22303659</td>\n",
       "      <td>resp-time-window_start</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2176 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "source          ttl                                      csv  \\\n",
       "type          Start Length                   Event     Start   \n",
       "0            184261     17                TIstarts     16280   \n",
       "1            204966     39      IND-CUE_pres_start     37040   \n",
       "2            205627     61             SOUND_start     37679   \n",
       "3            206346     80  resp-time-window_start     38399   \n",
       "4            346501    178     no response in time    178559   \n",
       "...             ...    ...                     ...       ...   \n",
       "2171    2.24506e+07    216                 ITIends  22281999   \n",
       "2172    2.24508e+07     21                TIstarts  22282260   \n",
       "2173    2.24709e+07     37      IND-CUE_pres_start  22302320   \n",
       "2174    2.24715e+07     61             SOUND_start  22302959   \n",
       "2175    2.24722e+07     76  resp-time-window_start  22303659   \n",
       "\n",
       "source                                      \n",
       "type                     Event Probability  \n",
       "0                     TIstarts        0.75  \n",
       "1           IND-CUE_pres_start        0.75  \n",
       "2                  SOUND_start        0.75  \n",
       "3       resp-time-window_start        0.75  \n",
       "4          no response in time        0.75  \n",
       "...                        ...         ...  \n",
       "2171                   ITIends       0.125  \n",
       "2172                  TIstarts       0.125  \n",
       "2173        IND-CUE_pres_start       0.125  \n",
       "2174               SOUND_start       0.125  \n",
       "2175    resp-time-window_start       0.125  \n",
       "\n",
       "[2176 rows x 6 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separate in Trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create trial index\n",
    "combined = sync_obj.combine_dataframes(missing_rows, 'channel 1')\n",
    "\n",
    "combined['index']=combined.index\n",
    "combined['Trial']=np.nan\n",
    "trial=1\n",
    "for index, row in combined.iterrows():\n",
    "    l1 = ['start', 'cue', 'sound', 'openloop']\n",
    "    l2 = ['iti', 'end']\n",
    "    if (row['CSV Event'] == 'start') & ( list(combined.loc[index:(index+3)]['CSV Event'].values)==l1 )  & ( list(combined.loc[(index+5):(index+6)]['CSV Event'].values)==l2 ):\n",
    "        combined.loc[index:(index+6),'Trial']=trial\n",
    "        trial+=1\n",
    "    else:\n",
    "        row['Trial']=np.nan\n",
    "combined.set_index(['Trial', 'index'], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25be034816434744bb59b6d8712d6291",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "qgrid.show_grid(combined.loc[~np.isnan(combined.index.get_level_values('Trial')),:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group by trial & transform & than append to big matrix\n"
   ]
  }
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