{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Pandas and XArray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates how Pandas and XArray can be used to work with the [QCoDeS DataSet](DataSet-class-walkthrough.ipynb). It is not meant as a general introduction to Pandas and XArray. We refer to the official documentation for [Pandas](https://pandas.pydata.org/) and [XArray](http://xarray.pydata.org/en/stable/) for this. This notebook requires that both Pandas and XArray are installed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we borrow an example from the measurement notebook to have some data to work with. We split the measurement in two so we can try merging it with Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logging hadn't been started.\n",
      "Activating auto-logging. Current session state plus future input saved.\n",
      "Filename       : C:\\Users\\jenielse\\.qcodes\\logs\\command_history.log\n",
      "Mode           : append\n",
      "Output logging : True\n",
      "Raw input log  : False\n",
      "Timestamping   : True\n",
      "State          : active\n",
      "Qcodes Logfile : C:\\Users\\jenielse\\.qcodes\\logs\\240206-23912-qcodes.log\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import qcodes as qc\n",
    "from qcodes.dataset import (\n",
    "    Measurement,\n",
    "    initialise_or_create_database_at,\n",
    "    load_or_create_experiment,\n",
    ")\n",
    "from qcodes.instrument_drivers.mock_instruments import (\n",
    "    DummyInstrument,\n",
    "    DummyInstrumentWithMeasurement,\n",
    ")\n",
    "\n",
    "qc.logger.start_all_logging()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparatory mocking of physical setup\n",
    "dac = DummyInstrument('dac', gates=['ch1', 'ch2'])\n",
    "dmm = DummyInstrumentWithMeasurement('dmm', setter_instr=dac)\n",
    "station = qc.Station(dmm, dac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "initialise_or_create_database_at(Path.cwd() / \"working_with_pandas\")\n",
    "exp = load_or_create_experiment(experiment_name='working_with_pandas',\n",
    "                          sample_name=\"no sample\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qcodes.dataset.measurements.Measurement at 0x23faeb7e090>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas = Measurement(exp)\n",
    "meas.register_parameter(dac.ch1)  # register the first independent parameter\n",
    "meas.register_parameter(dac.ch2)  # register the second independent parameter\n",
    "meas.register_parameter(dmm.v2, setpoints=(dac.ch1, dac.ch2))  # register the dependent one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then perform a very basic experiment. To be able to demonstrate merging of datasets in Pandas we will perform the measurement in two parts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 1. \n"
     ]
    }
   ],
   "source": [
    "# run a 2D sweep\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "\n",
    "    for v1 in np.linspace(-1, 0, 200, endpoint=False):\n",
    "        for v2 in np.linspace(-1, 1, 201):\n",
    "            dac.ch1(v1)\n",
    "            dac.ch2(v2)\n",
    "            val = dmm.v2.get()\n",
    "            datasaver.add_result((dac.ch1, v1),\n",
    "                                 (dac.ch2, v2),\n",
    "                                 (dmm.v2, val))\n",
    "\n",
    "dataset1 = datasaver.dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 2. \n"
     ]
    }
   ],
   "source": [
    "# run a 2D sweep\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "\n",
    "    for v1 in np.linspace(0, 1, 201):\n",
    "        for v2 in np.linspace(-1, 1, 201):\n",
    "            dac.ch1(v1)\n",
    "            dac.ch2(v2)\n",
    "            val = dmm.v2.get()\n",
    "            datasaver.add_result((dac.ch1, v1),\n",
    "                                 (dac.ch2, v2),\n",
    "                                 (dmm.v2, val))\n",
    "\n",
    "dataset2 = datasaver.dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two methods exists for extracting data to pandas dataframes. `to_pandas_dataframe` exports all the data from the dataset into a single dataframe. \n",
    "`to_pandas_dataframe_dict` returns the data as a dict from measured (dependent) parameters to DataFrames. \n",
    "\n",
    "Please note that the `to_pandas_dataframe` is only intended to be used when all dependent parameters have the same setpoint. If this is not the case for the DataSet then `to_pandas_dataframe_dict` should be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = dataset1.to_pandas_dataframe()\n",
    "df2 = dataset2.to_pandas_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets first inspect the Pandas DataFrame. Note how both dependent variables are used for the index. Pandas refers to this as a [MultiIndex](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html). For visual clarity, we just look at the first N points of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>dmm_v2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dac_ch1</th>\n",
       "      <th>dac_ch2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"10\" valign=\"top\">-1.0</th>\n",
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       "      <td>0.000095</td>\n",
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       "      <th>-0.97</th>\n",
       "      <td>-0.000016</td>\n",
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       "      <th>-0.96</th>\n",
       "      <td>-0.000118</td>\n",
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       "      <td>0.000312</td>\n",
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       "      <td>-0.000255</td>\n",
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       "    <tr>\n",
       "      <th>-0.93</th>\n",
       "      <td>-0.000073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.92</th>\n",
       "      <td>-0.000832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.91</th>\n",
       "      <td>0.000499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   dmm_v2\n",
       "dac_ch1 dac_ch2          \n",
       "-1.0    -1.00    0.000755\n",
       "        -0.99   -0.000913\n",
       "        -0.98    0.000095\n",
       "        -0.97   -0.000016\n",
       "        -0.96   -0.000118\n",
       "        -0.95    0.000312\n",
       "        -0.94   -0.000255\n",
       "        -0.93   -0.000073\n",
       "        -0.92   -0.000832\n",
       "        -0.91    0.000499"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[:N]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also reset the index to return a simpler view where all data points are simply indexed by a running counter. As we shall see below this can be needed in some situations. Note that calling `reset_index` leaves the original dataframe untouched."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dac_ch1</th>\n",
       "      <th>dac_ch2</th>\n",
       "      <th>dmm_v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.000755</td>\n",
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       "      <th>1</th>\n",
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       "      <td>-0.99</td>\n",
       "      <td>-0.000913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.000118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>0.000312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.000255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.000073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.000832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>0.000499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dac_ch1  dac_ch2    dmm_v2\n",
       "0     -1.0    -1.00  0.000755\n",
       "1     -1.0    -0.99 -0.000913\n",
       "2     -1.0    -0.98  0.000095\n",
       "3     -1.0    -0.97 -0.000016\n",
       "4     -1.0    -0.96 -0.000118\n",
       "5     -1.0    -0.95  0.000312\n",
       "6     -1.0    -0.94 -0.000255\n",
       "7     -1.0    -0.93 -0.000073\n",
       "8     -1.0    -0.92 -0.000832\n",
       "9     -1.0    -0.91  0.000499"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.reset_index()[0:N]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has built-in support for various forms of plotting. This does not, however, support MultiIndex at the moment so we use `reset_index` to make the data available for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='dac_ch1', ylabel='dac_ch2'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FjpNa3bvcabXFCLJ35zOqB0fbbOOKkPXW286zCWmybK50qSGi/CFUe1jFKfiFUNZqSLbSpsApJUjatLC6qnmp+Iz73rWtM578x2SxBBpJqO5UYH6Vi3SYZYNqqa5qX4pORH+jEdKWuxijMl753gkmx9vBA8styA6ao839m9js8TvVenC03dbjot6OdMuATJEKyR76kxglVMATRh92W2hVYTHGs0NzA6OqOLoCgub5cTUyzDQqjjaSnfJ9YuiSFapdt2n6l4FergCZ8t6YiFzz8RrRXpVibe4eYyLsXfKp/H4By4f0guzetsUWU86/t94Ol3U9gCaIOSwQqj4ox6UzZk93zGUlO1GmaYo02WJmk014oGfsRrGt9Jm2qTINWMRJ54fkDJh+ltfaMjt6rSqskA2VnU/er7hx5Hh5d2PShrXvJcAhYShtdeuSRVKfWkxzzKFDw/q+3na89eBom61Pq/W2s6wEQJVf9HE7KkCIKzOLKuxCafMyJJuxEkQslJjqGjfZ15kWm48lKksEdA9lFxsjIDXzQ37daimysstVsbP53JUatcxOGcf66PHej4IbcwwRAU0DFoCk5QOiSRybKhxtjhZ7S2yB9Zqj+a0HR9tsPTDqbedZqRVSs09CeaCOPOyqNMF07Mei02blg7vo+tYVYCw+l+mzsc12sRjTHZOzQtbFAFQuD5sAWie1PylOTfMzRUqrul6bmnN1/660WtZNqbNkfLvuhZU9y/0I9XB9naM5bbmv7C6wnjnqbXltWqaieKBFqwAk+/Bdgqn3NRJMbfMVqjvGj2j03MeyZvUwecj8+CLpNUXAShprUn+yBqjWUNnJ2dKj9p7RYyWlFpmeAvBkBSTlWGaWyW0U3yMXJrGza62fS6A2Uq6A8OCB9Qkn3dtOtR4cbbf1uKi3HW/2cUTFdsbIArA1cHA4LcMk+Qr2VgO4WOZoDqbM9COrKm3ScV0/rmpXI2u7AxCO7XZ23cr3zs7XgeA4Vkh9jG8EPfJuP4/42m16CkQgTZWVYmsU19q5elqu7B8RjlpyQXbLhJY3dx9v9vidaj042mbrBdm9La+NE16XroWINR5+GITU46ykT7If/5T7oHhIThV4zD4iROYsBJ8c0vbR9svlAMkCgJEK0pl1pD3HaX8yrNsB6mrv0wAdywLZ/TWwXD1PgFSQbTU/tX7UmKeu8xjXZxujsPX1jfrxS2LtAmartbv0GdWDo222Pq3W284y8+SMDxy5f6upou351algYmTqfZUEoNl/pGQPW/PgtOt1Wd/pgkr/UoqnZLLqHJFpU9mcGnc0Ds+5gvGbBHYq7Ms07FDJ9mS1kUy6i7vidIEz62vA08j9Wjumdn6Qw/wE3dPSS7J7m9d6cLTN1gOj3pbOyCFfywzIaY0uhmK5HhRj1/Tq8J0iKkaonYx5mcBI1MKZzyMpoSJO5Eric98AsehL2VsdqFlQ1SFsLtmYkpWpgYyuNFoWfjRedo1U82PZyBozaeOXbUZB9oRz6Nqn2yPpUmsXWNmzMrp9icyzg9/kbDW/S59RPTjaZuuZo962x8bQCCw6IcsK1Y6LDyTDIEX24jDY2FNY5Kr2tk1lC8p01axxINjEMCfmveqcNUfJsUyRjYChrusyBsjV0mkFmLBi5sgEIQmfa3Hj+VXiTWzT6oBq/dVxcZQPaAboxCcb7AJ0OQqMEaw/V9JyhIMHDtX7syTWp9Xmtx4cbbftzvuut22zGmNQoRjYI59iXknVjKQ00P3w2gIblw5bzHpmgBH9FAyLecDqFtUFjcNNBlhkKTP5b3I3ALVM002cWl9jgioaH/u5iz2Jx6f3Mr2XzRCjuuA5+o6kvYr4JVtk++CcORc5xrY1EhcGBNn+aBtZz3IfZ6qEy3vpT0TY+7A19HZkWg+Ottl6QXZvh9emSX9ZZmjM0h+lbRUwqhE18dm+6DbHNlZxN3DGgp6MlSifqwlkVNuYRThsu1npU+e2rpRYV3tUF0dnLJKN18UKTXqPrE/ZvvyHgJDrUmanAEvK7FHJ/NgxNrGy/tsLpdfNvJf/bghYf3C5K2R7bH62WZlg3y3Wg6Nttj6t1tvhteJh0rlfbNKU68NlBSDa0h8VkbGRhic1RTlIy0Tgs7SZAiA+qLsCWOZjmmnxk0onZIxWzgax9dH3aaa6196BNK72u7cAqjhOQTqkzQieDOgxY5D6TtGVzZgRh+OYffDN+m8YJACRZapdCCJQs0U/CBZkiykCubnjd6r14GibrQdGvW2d1f6oj2FFIkOk2+0v8jHrWW2VZWREeLDZmkTzByzTibqtwqaUnyt9q7akDMu4NrvAD1U+a/ciWMgaC+82rTRJQG2PLYBTpzjaWlmBeky8jA2y+Sln4upsuZgiI4ALoKIAMhJHMrrOJd2T94Irg9PIOTgnQAwgajKfkb6PE4FLX5uV5X6ELmb5kN0JjnbcWb/lLW/BKaecgj179uCcc87Bhz/84U7fJz/5ySCikdeznvWs6POiF71oZP8znvGMw3EqAOb9I9/bkWVbeQ/UYld+zZeiCnIYeXpT8bDaKtMMhmWKDCja/A+KAvgQhfMd9yAcOVTW3ZK/GVx3Ml8rbap4uAu02Jf1m5apsS9g5O9cBBfmmHgerhiP2ufaK+5H6reep92nA5ONvW1TxzbFISJQ9m4gp55j7DcSgCv75whomuTTdS5NI227UT/Ztv5gXyH7SLXlhr2Fvetd78Kll16KG264Aeeccw6uv/56nH/++fjEJz6B4447bsT/3e9+N9bX08375S9/GY9//OPxH//jf8z8nvGMZ+C//tf/Gr+vrR0+kV3PHPW2OCupiEg3dGxHeo+/ls1n+/TJmlk8mMvEzCzfkbQsZdpq/oYq5zlD/2JKT4cI+XtsIxs3SpegbDJjRoo+ju1QwXbY7SUDUsbTMdVCil11hGpT6ct9diZcZGZ82kdmoAijPkbwHEGmAMHYP5vasudS9J1q/Sy32bYV8Iw7zygCr4w/EfY+dLkrZHsQ/CZ/fG32+J1qOwocXXfddXjpS1+Kiy++GABwww034D3veQ9uvPFGXH755SP+X/M1X5N9/93f/V0cddRRI+BobW0NJ5xwwtZ1vLfextqiAHIZZ8qH7Czft8hGxMzYJAiKVqCSSamScZEqQG2M8/htFq+64qHcFa+rflDBDo3sq/WnHIOaX00cHZkffXfIxM8Z8yN+Jg0WT9o5ZIJn7b+NU+trrc/6WYGPTS92gUd7fOnfNSaVMXzwgeVmjvq02vy2Y856fX0dt912G/bt2xe3Oeewb98+3HrrrVPFePvb347nPe95eMhDHpJtv+WWW3DcccfhMY95DF72spfhy1/+8kL7Ps76tFpvm7eOeyi7t6ocx+GxcU1SYGXi1/jw3WybkhY0KRppYMrjkYDAxEO0DTfZOV6G4jzHgakuYEAU00jV2WL2ZbeXPjZlJKCl2nZMjVV8DDAiTUdp6hGpf6RtdKa/Kn0D6umv2K+y/5QzPnafM+2X5xf9KuPXMXaDJdcc9Ta/7Zgr+6UvfQlt2+L444/Pth9//PG4/fbbJx7/4Q9/GH//93+Pt7/97dn2ZzzjGXjOc56DU089Ff/0T/+E17zmNXjmM5+JW2+9FU3TVGMdOnQIhw6l4l/33XffHGcUrE+r7RYzv5oPa7PmoZmmHR3ePkxBaG0ubab+HaLqzl/+RR9HiYF6Z0cO7mijdqhlT7rO07Idlv0oPiecJcBDGZBan6adyUZAYIPk3dbEIko/pyvi6CSk1hSXhgygJ+tvV0Xr8lxrPuXYjZxDxz6d9eY4j6eXJJ6DsGGeE1j0GI3tCG6w3PzCYopALvc5bpXtGHC0WXv729+O008/HU984hOz7c973vPi59NPPx1nnHEGvuEbvgG33HILnvrUp1ZjXXPNNbj66qsX0q9+Kn9v09k4cNWxvZaGmZS+OQwWq0LLfT9/NesCaGSyqunOM1t8Vg81kUcj6FN+TGz7sK03Wv9c+pABGsZPp55n/SsLIE5qZ4Rpko5nn2EVz+kYRzGTZs+ZZUNWIXvS+U3T91IbVIs17l36DI90bloFW8/Xc/LRYxsyAImr6blDB5d74VnPBL/ZOkebPH6n2o6BhMceeyyapsFdd92Vbb/rrrsm6oUOHDiA3/3d38UP/dAPTWznUY96FI499lh86lOf6vS54oorcO+998bXnXfeOd1JVKwHRrvFNnmdOx/0M24/nMCI6t2wM8/izKmZ4hYps7id8teEfnW1S8X7aJsd5uRBGwFGLbh5cE8ADiUoyrpPozPNRlJL1TaRf+7oc1agUs9d0lEx9WnTUM6lmWRd52nBkL7X0mVlSqw8v5F4NLrfnp+Tc9B37Xvj0rHWx7JYjZNjarPa3NILsnub33YMOFpdXcVZZ52Fm2++OW7z3uPmm2/GueeeO/bY3//938ehQ4fwwhe+cGI7//Iv/4Ivf/nLOPHEEzt91tbWcPTRR2ev3nrbUsum2k8DiLafIYpWdHu2RV67dk0BMsoQFkvNDDaRHrhRNDyhX2PjjH/oj4i/u/wLRqcKDjPwQ8avpstKwCJOmTegZ2J/dFsJeuy0+HHn3wV0YlyN54x2qDjWxugCYCj2O5diliAtxm3SS4DUwSUXZHtJq23m1ReB3AF26aWX4qKLLsLZZ5+NJz7xibj++utx4MCBOHvtwgsvxCMe8Qhcc8012XFvf/vbccEFF+Df/tt/m22///77cfXVV+M//If/gBNOOAH/9E//hMsuuwyPfvSjcf755x+WcwoTVXv2aHcaoWOufMd2rvhw+k609TWIZrS0sOroemLTBXAAx3zIDMfBTBNPY0TUlS4r2ixnuMV9MXDS2JQ+ma8x49NZibpkfMZpaUbeK77lemJABHZaQTqCPY9UUqHsuzP3VzZdv+gD0ehUebtPTcFSNQ4MkDMz0NQ3hqEE/BgClGo+8p21TYzOxNP7VBiwcM522r9eoHxm3sracj9CPTv4Tc422+zxO9WW+8oW9tznPhd33303rrzySuzfvx9nnnkmbrrppijS/uxnPwtXVG79xCc+gb/4i7/An/7pn47Ea5oGf/d3f4d3vvOduOeee3DSSSfh6U9/Ot7whjcctlpHPTDazTaLhsg+0kVYmj2QdX8XsFqwlc3Y75UuzDQNPgYpgxcP2GkjuVzXNxrBxK95xYer+W7fy89d34ttafq6+d5Vc8jG6ZzSb0GdxpEWIruEDBRkbbqwnWzc6oKuBfApx6QLENXixRSbBTgl+JRUWGPScfE0tQ3O+1VOzacE+kjjCcBhQpAUiQ6JyQmpRtKmAW21+6K3I9J2FDgCgEsuuQSXXHJJdd8tt9wysu0xj3lMp65n7969eN/73rfI7s1svSC7t+lMHwDyYOExSARAtlbXJpucZn9ZoyiRLrOKrQuWYxP/NkZKBKCDNdI0y9hgUzwIJ51nAYaqx4wDF/Fgux9VBiQ+/Qm5gJpTDNZT6mqzBDZj+1UAiBGAZ/st/1EBtIqja1XAlQ3KmCMbR0FN8CUu7h1h5my/2Elc9iDRILEVZzuA4WK/IoAcEWQDG+stltlaENpNIrjNHr9TbceBoyPNemDU26hVUElkAcr9Y2aq6XFbZCWwt2mzuUTWkTXJGgnvk3JhZD90ATIqgAm6wR9N42T62JVWMyCBrG+tfzVQUnZDH+BZHAY5l8CpZV08ok8CUxxxU2eKLK55ZpiY2tpt5RiQYX6yc1IfFMyRbI8LuAqos7PJKrWLAmGU+kXwaQFZMqlKbcsRyLMBUgEF6Qw7CGAiEh85B26kTwyQWZ6eZSz3PvTwraYwj/VptfmtB0e99ba0VjyYpwEcEVzog2oTNg1uH+ninGBs3HIXndsgp1nuqwOjahxKcfKsGhVOY/qh2wvQ0DXbrOY7Ett+1vXJyPoruiFzauW2tG8i62P3MwdNUOkzLk4EPNKuBSE6trEOE6XvZUVrOTfW62LaI3NOBAoARYAPS5scgQ8Q03FCkXHsgwGD2j9t0xvw6cKx7AjUAtyY85VjH3hguafyt9g887Pc3NjWWQ+Ottl6QXZvozaBFQIwlkYp2ZdF2shzdbP37+x/uEfWOeuMWwLLDl/LrHQ2St0pPgOGWN9rxRi7CjSWXR5JSVHabvcJMGTDaOTgibrjqdX0RCP90m1IdX8swNB92j/vAzB3Y8TRhr0ZAWdqDQnjE/rJ7OMY5wCQwn5hkBTwKLsT2Cq5BNJmuIfkWpkhCwCKJE4aG24CNo3f5XxXVuuFgnvb+daDo222Pq22G63rQW0fzuPuiylYoUWm06rkB1U/zxV0luMtS1Jtu/adO/ZZt459FhSV4KRMmdl+2WrV1hcwKasKeFGQkaVQlcWQTbpkiNF7jWiRaqxUbc2xyPQU/S3fNa0GPcYlRisCSwUqY9qM46HC5xwwRgIv6pAkXSj91DRZADVh4Vku++5MWo1c+PmpAFhYJXYBCSlDFQXZHNpmq+OKqUczxuY6LLP1abX5rQdH22zhl18PkHaXzXK9qf4H+DBoimIPpI0tA/LTVu2mKYGYZU+y98JG0nHl8ea7AASbKmPjq4xR9K8tgVGmpEYYG8uGaL+NfooMGwKYStRFn2ugqBQzj7RZHhf/k3/WYoks7JBiTwXrFJgZFo1PBFQCNtjGE9bLMoEAEjCR8yUHE0/iMwdhtaf4jobALUuaVdtESLkRTIFHwwrJcidsxjOAJCPIbgIIGmEDCdgYLnfSqV94dn7rwdE2W88cHQmmT4jNmuox5KHVCRoo+R5Gm1tPFGsVjQQccwwqhM+49mlql2o/pkyZVcNZZqVzmr25nvGhbFiXyOKor2mzhnvKeLqjBGV2ynzZryjarsQx4CwBCSve1nSYgIrsvJAYJAp6IEJgeALTI+Jo8VHAx6B8LEn7JwCKzJgwjI8CHgFXpHqk8NnZNk0qT6sAcHl+sc6RAC+9B6wgWyqL7z1qtX7f9LbjrQdH22yLeqz2tp222StY3AVlGmLEnYwsaUrWZdrmuz5vxnQWEzMmpgQjXpAHphU4j+046uOQ4UiqBek+VrYnPFIBOuWxlB6sWcpM940wMi7fJimkuFhrrONj4mcppMpssy5wFpsx4ElwuGVzEvCA0jjINEXZjC/EFFfZD0by0RjMMnWeUPRNwJR0JoqjixQXCxNFTODGmSn4xrfRcwht+lizwKTOGnMOMqYscWx6DT6QSATZbs0BDyz52moMgt/kjyje5PE71Xpw1Ftvh93MgxAomAvNBUwAPeUDehFdYhRYY0Fi6wxUTDiiaNNWkK44JwBSBnZ2fDrGaQrGaMR3HCDKvhe+2bZ03bVKNSlgKKfeZ8wSFWM5hhUCwqDFBVUr/YmAyNQRqgm0SbJo2papRM0W2DkXBdTcOKBNYIlNrEwcHdNqOgYWxCWNEBoH9irIlj4pUHMAwQlwMv1SdogDwNE2AyZNfQ++Tnyc8XFg8gKMOGq8gATuVleX+xHap9Xmt+W+srvA+qzabrL8wRjzAyWzMm3KbFHAaGwTs7ZRAX55wJnbpBEKqxhH2649tAZk9HuXyLo4lsrjus5hZJsBH0ZQnG/Xr8GH9GFv49lFWmsps1q/7LvTY1BMqyeTBpMq0BZkGZYkMCopLZb2h36EWzmlzlgBXVzjLLhanzgmLjE1mqpScXRkl5z6uhHGKfrK/cCqiQJDp/cryGJ9N8AwaI3kGhgfD5ZmggicWhVkJ+PGoe3/gB+x1oOjbbZekH2kWvlAt9thULH6FQ889TkMACjrWuzKnO3Gc1hkv028EgzV2h/XN/tZ+joisq4dU16bLqammsKjtNORpMrU15wPUX5aNbaqdg6d/THvTeX+0nSWAhmborLxTDFJBsKSGlo5mpQVApgppaAaAqjJyT3xieBM22xytioAFmm/oVBoxzBH0CLxEpybAC99IyBG4wGJHZIq4SzptJAK1IRRYK7YQdJ1YatvCNQy0BCIGngO7FGc+u9o6cGRZwppxU3G2I3Wg6Nttl6QvctM/+hnaTQgLq5q10zbCmDUgdnKBWLnC4zZAV18fteW0dE4qlUaB3zK7wUQKPdpmyO7DCCZpjDlCNjQa2d9kAGMeC5UxCnfu6bXl+Jv62NmYI2WChABs/anbDsKqOU2MfojIo5gRH3Z9CufnaaLtbIRRgcfx8mXyQVWyFEATeA0Ay8TUEs7jQsgBkhrn7HxcRRiSuotir9luj+VDJICHTumVMYLH70DSAtEIgApJmBtz3ILsls4tNhkWm2Tx+9U68HRNtu4v+G97WSrIpACPFiGpfIg3grmqGyyEJluijGa6GPaz7JkCayMOBEhzuoqEVDcTCO7xo6d2V4934mMTPxPalvpE7Lv6TMBZup9EbvGCI20OWabTWGx3ScNZ3V6XGJuVIRci2lBCSHNJIMwLJ6MgDr4aXaO7floCk3TVlH4jDQFX8dQhPos3QxtKUBDODdTpTpptKT9BkBLQKNNhjQci6iamQLIYQqskAiyw+0lffCGZZK2iQK7zw75feUIDz64jt6OTOvB0XZb/MPf2863CdeyZBns7K1qBm6BwMjiMQFF84mtS4AyPbrPxNYUN47qeti8R2RhfEbE1pX+dIEeexZdvm7ML2XLDhX9COyXOU+Jo4uhkmUpSmBUA8RVX+Qia8sORZChgIISW2X1RLZNQhBQqziaVIdjgJ60xSK2JhKBMhlBtjJEzsGB4QWQqYBa3eI0e0JW0VpnpCXWSobDCqhlLBT/xfdGCj06cx10HLRNB3iwpMzCuAUiKE3p9w5w0QehblJk++Q6IsUEEdb2LvcjtE+rzW/LfWV3gfVptSPJMjqk8t26Ftd90QxRxeJDm9P3GY4ug9U/dx06S5vj2JJxPtP0rQbGJrVhv4cnNRLTlwOXOAW/1Eh1MT+TWKoofEZMccW2HcXmY0FGy25F1sa2FXayAWtJQI0sdRbrABEFNoa07pICBBebYk2NOYLXIcrAU86cMbOk5igCvGyqvAuaIDhnxNFIC8FGgBJAkdd+6TmIGxvwGACrYaaY4K3w3AGeguYosVcBWLmypAIAPwCGvlK/a4nMw8FvMi222eN3qvXgaJutF2TvRCuBT81F/ijbh1MNCG92cdhq2+jsXlpq4vDa6OKwIx6TQda4GDUgw8rm6Dps433HAyUkhiZuSGNJQAIc4/pWW9m+bNcCoZHCjECsUq0FEGP169RWPFdTx4dkJlmamSUxXQAE5BWoaBoskXeRQYIwNbFaNZI4WvvvkJb5EDZH03LMAeBQq6Lt1K8Ivjil7nwTgGYURzsBREwZeAEknp6DjID2yw8AN5R4ZH30B4OKyRHjwbTJ8EGvLsOqa7BttobQVlvLhHaTzM9mj9+ptjsh4RJZzxztRBt3zfQnLhu/ginQzwCqlaMX1T0yr01ZB9NRc6u86mzRiNPkeNU2K0CHpGKzpkPMO1V8szhdbFF8oOoxFD+STkGv9SsyQC5vk2g0faepLxEVx+Uu5EGcvjvTB+OjwMg5aE0iMm3qqvQsgCnsc6Y/kKn1UvPHpfik/XKyaGsT4nn5jIZADYWZYi71P/iENtUPzsVFZTl7IcRtkPs0oS9+YOMhxuTGpRiNMEANSd8QWC9H4IHEkXa8/dyktrmheP5+ICm5xsVrwC6cBzeE1bV+4dmaveUtb8Epp5yCPXv24JxzzsGHP/zhsf7XX389HvOYx2Dv3r04+eST8apXvQoHDx48TL2tW88cbbPVZ+n0tvOtuKZVHQyN+i3YMuaEZ52JJv2LQG6CQFwlGTW2ZiQuugGRFW2PY7rGpaNgQNkIM0NjzqVkkPQ/oUPKgGltnFh8udZGF/CyKS6uHRsblqYbc6tQAku+jJn2szI/kuqCrGyvMVP6DInViXE4thmZI7uGWQSMSWyd6YAkNRWm4Ou5yfGcmBlNefkGgbVqpO9WHK1tNYhiadZ4lOKRF/arRdQMMQQoeeTskrbZyhiZOF76GVkrb9KLqoOScWIHPHhouStkb4fm6F3vehcuvfRS3HDDDTjnnHNw/fXX4/zzz8cnPvEJHHfccSP+v/3bv43LL78cN954I8477zx88pOfxIte9CIQEa677rpN9X0z1jNH2209LtrhRsULqKbKqkDAHLMVxsgA0fTAyD7M68Cj88iSrZm2r7YNsu/Fvko/yBn2Qw8ZB4y6UlmGEcr0PPF8TBtyTNaWZYf0e2RRzGfYtkxftE3LBMX98u5MX5XJiMyQfHbKcriwaKqyTnE/ZawNWZaI8ngaR5mayC7F9pwInin66EKxPMjbQfQJrFBkk0iYG9OmbxKTFPvbILFDwtxEtmkgDNDAtpP65YW1YkeA+PgB4n5lgmJsYZnaJjFLcayd9NcR9uxd7qn8zA5+ky+esUL2ddddh5e+9KW4+OKLcdppp+GGG27AUUcdhRtvvLHq/8EPfhDf/u3fjhe84AU45ZRT8PSnPx3Pf/7zJ7JNW209ONpm29zyDL3NZ9M8smd4rFeZC/s+5hrPxOSUx07Yrr/mZ2Ymi1l10/RhqtMwwGBc3K4UV7nPAiKi2dJmJTtEJSCR9JVhcEaYqHHAq9bP2r6YuqoBIQV9yABOqBRt/BV8EMUUnwKkvNI0svZIAFuoWE1xVldcSsNJelJSbRnQoQA20suFJUOcCzPVYopOgJaACQhY8wbkReAUwY+8Bi5LkSlbYwGPl7Z9kwCcF2CDQfBRkOb1OEmnKcjxg1HAZX1aFaML+IqAbgAcGrbd9/ERZvfdd1/2OnTo0IjP+vo6brvtNuzbty9uc85h3759uPXWW6txzzvvPNx2220RDH3605/Ge9/7XnzXd33X1pzIlNan1bbZ+rTaTrQa4NEchRHZZlPSt8Bs+sl0S2elzX9vTQAv1jOmc8YB/dix+cai7AtRJrae6Ftuj9uQX6OOYykyPB0x7XvTjBzfCZSyFJ4Zc0eiWq6Du5jWGQFbKUUGObXEGAXhc6ptRFKYESF1NKBQI0gF2aSpssSWxBXqndV0BbG1phnDdHgVR7MpGZGn0VwrIKZFYHLYTPfX1KALwxDSasJyEVKbLm/Ty5AFQbgIzymk6XwT8JofkKT75JJz8GWmOBPOD+RSD0jSkUCLUB2bOGQWVZC9lcTvIqwFod1kJ/X4k08+Odt+1VVX4fWvf3227Utf+hLatsXxxx+fbT/++ONx++23V+O/4AUvwJe+9CU86UlPAjNjOBziR37kR/Ca17xmU/3erPXgaJutB0bbYdOM+Tif2r74pDWbKg/mRVvZldgkZe/jrQNQzGijbZnzjwCOsl1Zm5P+LVQATuf5jasuncVQH/mPbnIU6xONrbQdgXDhmwEdeY91iXTak4hwnDP6qhpwshqnVPk5q/Wk4AMhNslsNzKFHoMGJ4i0Q/0gFhaKgVit2qW/SZpXcKnKtC4x4mXpjbhOmjlPTxzZotD3EEhng8G54KPASfodlvCQKtpNGi8WrVcoIIlYEiAwSKEukcaL/TI+YPURsCX9lDl0sRSAdwRHDDSytpoAs7j2msydUC0TOyz9wrOeN1+nSCdT33nnnTj66KPj9rW1tU3FVbvlllvwpje9CW9961txzjnn4FOf+hRe8YpX4A1veANe97rXLaSNeWy5r+wusJ452unGFRJpgnB5C22uCte1h/tYf8RznijyHgsQi1/eNXanFgvmsA5WKW879TcyQBlYI/m/MA16CJmK1lXmacw2BVtJmJQzT5rGs4xR8R7aF+CiXXYA+3RMrFbdCPsiaTQtDxL1Q4CIoxNo0yn4JHEAQqxEHZkjjkJs1n2yPlla2kPScLKNPGK1aj8wIFPwThQ8C/OjcUlnwBnRtm8gFa0h0/Qh5wURWwuDJD7Uiq8SuTJeaf01hP4NhDETQbaWGGBhoqDxgCAGbxO4U5E6N8CB9d1TIfvoo4/OwFHNjj32WDRNg7vuuivbftddd+GEE06oHvO6170OP/iDP4iXvOQlAIDTTz8dBw4cwA//8A/jp37qp+DGFWXdQus1R9ttPS7aRls0gBkHBBbcjH3p5nnbnbSGmG0TSOiButqMO8e3Wz20cpzZRjBtdgGoLtASx4zSOyRFlJCLbB6z1EfZTvWdkg8h1wxZH6srsmwThX5ZAbVqicjECtWq5VinYEi0RI1hT0rNjvat0e0CPKyPCq91+r+jNC1e0lxehdUqgrZT9EWr413NBzKdXuMh7TdT+oOwG5kI2lttkqTeVDPkS1+J7dXHAX4FIVU2UJ/UF53SjwZAQ8EnxtV0GmL8o5ZckL1ZMba+prXV1VWcddZZuPnmm1MfvMfNN9+Mc889t3rMAw88MAKAGklPbydx0DNH22y9IHs7bbNjr78jTTrtcAAj5KCEY5pm1kDm/Kdgu+Iq7PIHKwdGGm8ccLEuk4DTKMChCfu7v6cp5zmLQwnHZMfNCoSKd00xKShSqkHATVrnTMGQaIEcRdYmgVFK2iG2omnTL1JQJP6xfYrC6jSVX9NYCs5SIcOoo1E2R3y4Kdow7aSlNwhO+6IicAVcoFgTyWnqDYgslU6Z1xlrQZdE4ZQdYlkCyNpoOpbsUr+8S2wTG19dNy1M01cfYbY8gthaz99pPwHiNAZtAzQsM+LSVRNB9hDLbB6bL1Q56/GXXnopLrroIpx99tl44hOfiOuvvx4HDhzAxRdfDAC48MIL8YhHPALXXHMNAODZz342rrvuOjzhCU+IabXXve51ePaznx1B0nZYD4622fq02rJbASK22eoiZOR/tcdHCM7TptAm9sHGAzIdz0j/jG8esN4GitG3AGREO2TaUiDiu3wMIoqCbK74VPpYA0xqqiuKQAWFr7w3zWibtk+G0QH7ok0kAKZgS3zsUh2RDSJJKekxAlSiDkkZJ0mdoUnnkC394dIsreDjEatVy3mxMCqOQtoq1TmSKykpLiugBiGIo0VAzapXElDkBwCpaDtWq04+Cq5ci1yQzeEe8o7hfEqRqQicGgSNFaX7i0Wk7QcSR8/BEdrBDhVkb0OF7Oc+97m4++67ceWVV2L//v0488wzcdNNN0WR9mc/+9mMKXrta18LIsJrX/tafO5zn8PXfu3X4tnPfjZ+5md+ZlP93qz14GibrQdGy27jxNclCDg8fynLVFa2oOvkg1O3qSJarh4zqQ8FcIjfTZuYop1ifxGt06/aWVLWQjdZMKSbKv2yoIUZncLumm/WpoAd1+ELJHE0VRaltT4CZggwImthgTSNRqZfTleVd0F7REG4HNkf8VG2Ki0FIj4x1YYYh7gQUEubTGHpDyt09uLDMgQsDJ0ngisE1Kxtap+lSGOowG3E0ZryM2MRi0o2opVukNqU2zyl8sJkPJ2lpuBL4zhGBtY8ECp+k7CkoHgbRM2RA1ZX+wrZNbvkkktwySWXVPfdcsst2ffBYICrrroKV1111WHo2fTWg6Nttp452oGmD75siRBMlZqav828qdEu0XQVsCcxJJU2NWYnOTU2BnWPSwdjNFXsCFC0Y8X36EPyf2VKFEwagDSpzVpaze7TtBWJKnmM2Jr1HBuXBpQoPnAhaSGt+BxnuuntJmAjMj3yHusbQRkfE1vapsYAKiCm6EIKyYAmc16ekyC7FFBD03swgmwVUIswG3HGWniPFagl7UVxCj2iyJq9YY6M2Fqn1Suj5V1gjBTEBfE20vR/ielZ+4Uo1uaBtE3qw7LgLIfaSczwja5DF9qBAUckfb1/fdkrZM+mGeqKsRutB0fbbT0u2gFmKAd7wSwLo98X3bJLoGcSOzQeGHU83CfEs21ODYyo2DdFm2SnkHd3aHI8yq+RnkOYuo7AgGgXx6XTagvEwrwraLGMkeZ0bPqrkj6LIFZvH+2ymSoPJ9dc2R592DtZ9FV8QtosT4MF8CKslzI08kRnILI6JOeg4xOugzA1CrJiWk59ApsTGSniCMyYArBoZIabAlFP6VzCtHphb5rEROl0/eArKS4BIJA+axxPHCRIwvBoyowdwCthaLxce+84zp5TxggDCCNl/kULSGOEdwVFXoa+BeA84p+BVlKRR+1ZckE2FrB8yLLnDrfIenC0zdYLsrfLJlAx8Q8Cm+8V7mSLU2kWMMw3G82c5xzH12sXjaOw4oHjgo5sioxKzW8c+wQYgbE9TwUziTXqZK9s/BoAs9uEKQHZ/igYks9GEK0v1uOFZbJMkU1xaTtctMuElFpyoz5pKr4AlbToWz4VX/vq9DgnACXs89J3L0LlwJCIgJoKtmkARLGzMkWEWImafIpjBdkswEQZKJ1WXwqyNXXFMuTwMmuMTc0i8dF4sU1GnHUHl4BUFGQbtokB6VfyYc7juWFqR+80HgAH2+UWZPc2v/XgaJutT6ttl5VMEFW2ldtRfD4MxoiLnc4dYNr+0rT3owEUI7s6tnftI4pprsynBDK1WicjQCb0K9Yr0oVZO8Xbo6xOFRSJ3ibvV3mMvivISvtydkiOywCUaUunzOuCsYaZinFIZ6RRSM95I8hWFouRdDyOQPDC0MgpxdRauL+SmNmwTBG8IRdHGxaHHY/EcSSi5pZBjWGiMkF2ElCrvihW0yaKoCgJsiH0jf7rFLE1ksg6+gojpTiWGVmbsUI2B8DJcok1vdcOEIuUcxt8/CCAKTBiKg4OcJv6t7n1xtj8bDXumaPetsN6YLSdVht7zt+jaJmRCZi32gwWY89zAqT4G9cEHNOWfq0yNB1MStlUuX0kDFXfyYKYDp8qbk1U1Ug/yVXiTBJb236OpNyc8aVRsbWJk6bTS7dtms5MQQ9EiQiyo49qgxCE1wJ6yMaNwAlhTTNz+gEUaTqNBcwkYXfon/Qrm53G8fjoo/G0AjU4xsnE0U1iW8I0/aTn0eVFdDYbS7pKgQ4TgAHJZ2m7CYBGxdEghKeVxCRwVudISwNE4GLajKwQJQA0UkxSwRGZNp1pjxDkZEgg1TfAYLDcwMHzAtJqmzx+p9qOU1q95S1vwSmnnII9e/bgnHPOGbty7zve8Q7EBSnltWfPnsyHmXHllVfixBNPxN69e7Fv3z784z/+41afRrS5C/f1dnjMgqFtrHw9m1H98wRglzE4I/EmpOb0oV/bbl/F9gzv1NJaVJxLGc9iogg2KrHKz7Xv1X3I2yLXfYzL95FOpTfxOPMF4gKxpq2g16EAqFzuM/JyGo/SCvYKXswMLxDJwrCIZQJ82aaNIQvK+qw/CABJCz/qorOauqI03T8eEws0wmyT9mVBWT8QkNIkUKSaJa+xpWAjD+SlhSQV8Ai7402cWPTRJeATwdkg94sFIMkcN5B+iK8fpBfL64Elr3PU2/y2o8DRu971Llx66aW46qqr8JGPfASPf/zjcf755+OLX/xi5zFHH300vvCFL8TXZz7zmWz/z//8z+MXf/EXccMNN+BDH/oQHvKQh+D888/HwYMHt/p0APTM0dJYJ+hR1oXG+GyNEdlZSDMdKB9syhBj+x/XYuuMV4IbeY3rX9FeXKzUtkWUH14FKUXckTZDZ2K8DE8VB3eBtbivaMNOk4/HGNBUxGQTVz+TxpOupvSXSxWwgaiPCfuQxVNwwo4CseRMmwpslK2R1FyM55JPrFbdWECkxwdfrRgdq1UrQIk+MPEEWEicBGRSFemsWrUea4AJFCBp2wpmpEq2FwBmWScFM7pN2+QVpIrYhqniJgc5aBJA8nb/IH3GIFTTjscIGIvVtRvgqLUlF2Qf5grZR5LtqLO+7rrr8NKXvhQXX3wxTjvtNNxwww046qijcOONN3YeQ0Q44YQT4suuFszMuP766/Ha174W3/M934MzzjgDv/7rv47Pf/7z+KM/+qPDcEa9LY3pPOnMDi8YKpusV6Ke8mByxfeOGJS31R2vcnyV3RnfXmRxx/mOHNsBZFRjRLpK/Jh4ltUp+14e68p9BhxGQJh8lKmKDI0BLrCgxbSVBNnKprjI+FgwpEApABQn4MUCItO2MD4WiPiaDyn4sbGDT1zOQ7crs9Kkc9B6QGl5D0qAhFKqCtE3MUjKxsSK1i75sswiU2YoAhuXahApsBlZEsSyTRakDUybsh0mXgRDKwA7hh+EKfzswjssMFoB/Cri8iPcAA+0yz6Vnxby2o22Y8DR+vo6brvtNuzbty9uc85h3759uPXWWzuPu//++/H1X//1OPnkk/E93/M9+PjHPx733XHHHdi/f38W85hjjsE555wzNuYirU+rLYl1XocxAGCrLDY5Y5v6EM++TzpkjE8Zb9o+lIyRApNx1uVjt0XGiGSXPdei7RoYqjA9o98p/04EXausts8yRRGgKcAqwVRjtxPYxovgx5lUmYupqJgqUwCm7FIBfrjoGzeuyvhov7JUmYIN01Z8d2ER2eivgMGwTnGttfhCBCLKuEQmqTHMj7I3ZMCQAUWxX6I5sqApS5ENlHGqpM0GADcMv8LRN6bNmrytkDpTgMaxjQi6TJuDgYqYejvSbMcIsr/0pS+hbduM+QGA448/Hrfffnv1mMc85jG48cYbccYZZ+Dee+/Fm9/8Zpx33nn4+Mc/jq/7uq/D/v37Y4wypu6r2aFDh3Do0KH4/b777pv3tPq02mExAqoz0YyVBR0PJyiy3cM8gHkMIBrHGEUZEZmNpV8Zb5ZukfnYEZum6LuKjys+ed8rgu4yflGBOrFGLhxPEkfF90ZYnbNMAlCi8Bmpr0ASWytLVgqyncYIsQkKbDjXbhlwQhTisG3TACJlhuIsfvGJYEhOhWXKvJP6RnF2mqaN4hT5cIBvSGoiATwwQE39AVOw0kyZV7CDBHzsVP5sBp4KsHUsGiPa5uTDNqayTlZAreenl1tBYcNpmr4AO01xsgtT0HRc0HCqyu0AJg5FIL1cWzkfP+CqzG6ZbDvWVjtSbMeAo3ns3HPPzVYCPu+88/DN3/zN+JVf+RW84Q1vmDvuNddcg6uvvnoRXUQ/lf9wGeUPRd1Wzk4DEGclATgcIuwsJQRMV+l6NMhMfS3brMbr3jl67BjWJ+K+TqA2BiBl2xW4pOn6sS/xswFGtfGw+x2loosAAJfilL7Vz6Z9kKw5hpw1orxMQaxW7fTeo+hjZ1nFKf0a3yWfWKXaF206QBdptXV6EH3NGFDwVR+tgWSXAQlT71H4CLiQ6twsmDL0R2oCwU6ZN+Pvwr2dlgqx8RBTXrHvEofY+OiMMg6f2cs0fZb01xDxqRbHVApPpjY5tk0K8lotTimVsdmWEQjXzTNLVW359+mAB5e8zlE/W21+2zFptWOPPRZN0+Cuu+7Ktt9111044YQTpoqxsrKCJzzhCfjUpz4FAPG4WWNeccUVuPfee+PrzjvvnOVUMuuB0WEwfZiNjHUXi+Q7mYrDYZOBERWveODk2JME3pW0WNYkkB8/gREqeji+3WocE0UAUjyHcQCvTJ3ZfpcAqUynQbcXvgbsBADj4oy0mMqStFPSFSVBdmRJJFXGMvNNxc9wRrytbRGMZsnsM+m3mNoSH2/SZpEhsekvEvbG6I0iC0NG6yPHpfQZYj9LcbRNT40Is02KzKvQe5D7llqkKNYuBdTqY1Jy0JSb6IKCj6bDOKXmBpTHbQIQ8o5Df1ZYdE+2TdUfeRmnUO7ANx7cMB6ytjLx1t5O6zVH89uOAUerq6s466yzcPPNN8dt3nvcfPPNGTs0ztq2xf/5P/8HJ554IgDg1FNPxQknnJDFvO+++/ChD31obMy1tTUcffTR2au37bYx/4C5ZIfGogN5W/AfhK34+1IDABOszkqRdai0U7RZ60f5maj7lG2/LXC1YCe+J+1QwAuV61OLV/QlfxX7IhiiKHoeOT7GQQAxyr5YYCQ+sZSAABjvklYoFYGUc4xT392o1seZY1wAhVGILfG86o0UlEUgUsxQU18RWXsTJ0zXJyO2pgh0cl2RiUNGg2P1Q9Ket/EKMKSgKk3XRw7A7Cwy2WcF2WlmWRJPswsgB8RgEVIr0InxGgFKIrwGIZudpj7cMKA+ApDCOXAAUCtsBNnrXXd5bzvcdlRa7dJLL8VFF12Es88+G0984hNx/fXX48CBA7j44osBABdeeCEe8YhH4JprrgEA/PRP/zS+7du+DY9+9KNxzz334Bd+4Rfwmc98Bi95yUsAhD+0r3zlK/HGN74R3/iN34hTTz0Vr3vd63DSSSfhggsuOCznRKB+CZHDYlOOMdHUrnNZiRjmbWvmtJse1nGcamy6wM+UKbtqmrhMZ44DWEXKKqu95CpxutZGq7ZpgRF3n1eRRrNr26U10ajoK8L6cFKtOmO2NJ5jsAt5KAuuYh2kCHoQ8kROxpJCKkfZLQURpMeoBslWnSRNz+nCsWGbJ04pSSLwgIGW4vpkSeOT2mRKYICgDJP2V3wc54vIavrLxaZTCk6BjrbZSJtWwyQ/2zVNZ+ON+AwANwznEitaE4t2iRGWHQn6IB6Ef3LUIJ0XsfQ5nIsfMBwoapwCKGOQT3W52IVjV1eWW5Ddp9Xmtx0Fjp773Ofi7rvvxpVXXon9+/fjzDPPxE033RQF1Z/97GfhzDIDX/nKV/DSl74U+/fvx8Mf/nCcddZZ+OAHP4jTTjst+lx22WU4cOAAfviHfxj33HMPnvSkJ+Gmm24aKRa5VdYDo0XZuHGspdPIvCP/vAWptLiIa3zGHQatmT01jAFG0X/M/mkYo1rpgZK9mcYEGCXQYYBOGUP/vbsKuKsCrkq/s/YMcDDxoh7IHJME1C71T1Nderqkla3NwrqqHzIsErGkwwqxtU2tkR6jgEk1QERRb0SUx9U11mKqi0zb4svqq2MUWR09VoXdoekE4JCE59ZH4sQ2ZfxifxVPNAo0gKghouQTKmcnlikKrp0AskbGWwTUmQicAuhhJ5dHRdsNgu5Jp/UTi0qcwA2Hf6MN4OGBhlJFjKEBqDpOA8ay44YeHM1vxL3oZdN233334ZhjjsG99947c4rt6YPvDytc97aFpkCo3GZNH75blGm2XYjP7Hn/6EwJ4qp4pgvk1LYDcamKanyKoIjGrX2mZn0o/qdoS5mV4MvA6NIptdSaxq6m3eQ/ZbrMOcB7oGnSdguG9NwZIf2l3y04qrVpZoyx9ItNHGZZR4yQjb1vFACFtnlAUWxtZ4XpgqnwANRnQLHPKqr2DcH5ULCQvLSt4EfiqeDZi8g6CbJTP3xjBM8twCuIAm89T9+ENlTE7EsBtTkvPwBcC7SrAIYhHtk2fWBxyIeaQtQK48OQcQsslR9waGuV4TYI7SqDhjImcfkVPT8GtQS/4kFDAq8E0bUyevAILNqQgFUPbBCwErZTQ8DQIENddLjxuOq8f4+LTzsbs9pmnhmzxH/ae/9/WHnI5gpVbhxYx599169sWV+X1XYUc3QkWo9ND4OVKZZsu6yzvdXrplmSCocBGJk2pxN4GzPsRJWRM/G6lxyx8WpjWwM8nMejwsumwWrrsI1LuZHdZlimsr6ShnEOxCwASI7RtcdMGySzr+L6ZEjr4CljQhFkcQRUyuDYdJMd97gAayMpn8jWSFsupfl8o+udIbJBYW01xHXOwkKxZnq8nI6+RyaKgtbHgjSdFaYz2iCaISpTZvJOQEp/2TSYmTEW94mQOsWRMR4A1CqbgzTTTdJhoe+JQWMpzOhXAHLCABFCWtGTsDwC6IQVCulSAC4syouBgh4OJ94gpjcBBlq9bwA0LeAYD13yCtmMzU/F361PqB4c9XbkW1b92nyuaWO2yorQs0/X14f0FMcopomn2SXC5jpTVq5UP6m5SSk3+11ZHNtHzn1Gp+lTJQ6N9rFM3ZUps4kpN4pT4QHEtFLqb4qjX1n2xaKMbFJvQEqRmf7YNqCaIwU3SNP0ObZt+wSjTRJxNMs7wrXwDjLl3AAXL4UbM1YotOVlOnxgbBDZp6xsAAyjJb6+Sd+9AVseaSo/62r2ZGI7UwuJA0ukfUltMjyzYdEQGCQDFr2wS7GGUcOhzViyQLb5AJjAPgAfBtB4Yb8E7IAFFHlJswEkeiwMpM5A0yKzhvHA8BCW2fq02vzWg6Nttl6QfbisoG669EaLsuyhj1xjJOmB2eJZymKSa2IVRplJA7K42DxNH2bxGetvAEK1Px2AqybsrumZIvgqQZDLmbESCCogqjBPIZwBOOKvwufAkPm0DSmeFozMWaai747AXnyUOTLpoQic5J3AFQE1EtDTmj6qmyGCdyrI5jRVH4gAKBOBO077qMIKkT0OEVQBGBVkmzZSrSE5T0eh6GIcN5kxJswZgYIOiMLli+epxSob/a6sDgV2SEElIaTRgMAO6bGuFebQA0xwjQeTgxt4tHBwAwaTBzmCHwiYUirQhSKRq4MtSsP3tu3Wg6Nttj6tdjiMx7xbFLMFTZpmlGFhZrDn+QDS2P2p3biQ7Lhj7D4dilnYrFnBUtxWfBHdjZHfjI/XlSbV/VVG0ICQCoBTYbEliaA6JQ3nLCtESUdUxhMAo2ks0mN1jMs6RtqmS+dnAQ2bYfLapjPpEomhQuz0HnZaIKOFH0OFbN2GQpCdygAAwiJ5RKATGUkDmCwrpH2K9ZO0He2Hsj/6LiDMywyw2CalwowqnvZGHoZGBNHiQwrIGh86I1P7AQdqAkSjhoW5YwGXbSqGPvDwLeAaBrMHOR9uAcfgoU6tSwC0GbRwbrn/fvfM0fzWg6NtNpJfi71t1uYFOcr5T5mymtUMKLLbwJgytUZT92ti1evoOObX7riq0tacKzBODQSNgpDRz/E/9Thl+izWQyq22zgZA+TSvpGUm3y3IEfBiOkLE4Lg3LZtgElKlQWUQnabxIvpIgFEaV1gisBJmSkLzOJzyWk8SJ8oTq/Pqktrmk/aTD4UWRQASUckfQmgg+IMsnQOxscyPyK2jnoiMvuQxNuxKjcUOHHUOWlaDgMgztITAKKMUWKFxFfZIhbwA4S0FzvwwIsg2/w1cBxiS+pOQZIbIPxAERYrMkdMcIMWzA6DgYf3KksLjBozgaRCtnOMIfcVso9U68HRNlvPHC3KajmiWkpJt9vPPB4wbLJbmja1bE6smVO1MWChy33a22gSk1MCiHl0WdOAq9ioqQo9qa0SJNXidabMijilDxnmhJEDJhVSC2hJDIsLSfEi9ZbSajCpKHm4GnBjZ44RmTZjqsywOtJ8qfuJYmsjulbhc5Zy00rUNo6AJw0+MvVe+mXF2zD90Xc2bVrmKFatRgI6pFPmiYJYepDaJCngyFIvSdkhAFFAHaffg4BG2FcHYMULc8QCyAJiJCeAR6b76zs1CDokApzzAQw1IRU3EOZpINWznSO0LaNtG5Ck9Jxr4ZzHQ1eWu0J2b/NbD4622WZ5rvU2qxWjGx+25kkZdUBbxxzVJE1T1RyaFjhX4nf7FufZBYwmbJvYXNf5FeCLxuwbC85qKbLMhyKrkz7n8SNDA+MHmPRZ2S+KU/kDKApMjQVH6ZXazJgoPSUzQy0TdkNj5j4RKCGl4kKqjNMUfNmnJJEuesvSphVWR1DjKAixVZBtZqhZwKgC6giAVLwd+xGkVMknsDGpFlOIqSLrUJco0GVWBB7H3HFMxxEjpMpaEl1RADVJZC3UmfPh2IZl6D2cY3gOQAaMyA41Aw9uQx2jxjE8ezSND0SWa+GFORoOCauDFod4AOdylmh10OKgPzh6/y2R9czR/NaDo96OMCsemubX/1T+i+wGT8MS1YxTvye1Ie6TAVLHeZYMS+fhFFMLI4DGjm2t3tG4HlEFwNZE0l19VlBTY4zsGJbxbKkC+27ATZymr+wLOEuZxVpIETsFZBFZIZf6nhif1DaTPYewj3xe0Zq1fIACLROHYFJRxieIrYV9aRGLN9o2WYTZ2Uw0IGettF+SRrOC6mwmnAA5BTMcj2FhthDApEuMD8eUGUV2SatVExDBUQQ+TQA73JCgJcSZZ67x8HBBTySgiYRdosbDsUMzaAECBoOQVguskAeB0LgWxA6rTcjJrQ5aMAgrjQ8z/B2DVjcwbJuYVmuaFg0BexqTN1xCY6ZQCHSTMXaj9eBom63Pqi3K9Emlf9n1wbNF4KfWtDXz0JweGNUCbfYws3MaNqe2zXzunLY/LnYJIgxLQgpuam2PY4ksm0Plu/lciRsBTxmblG1R8EIprsRLjJJpp5yur4JspCnukUEiBEbFxouVsYVBsmwLaYVsZGm1qiBbAYsUrIxT5qmIZ8XWNg0WReCJXSoBUBRkR50SombJMQDH8Ib5Us2QrpPmYpsc3+OsRccShMKsMtEsMXzSaDU+pN+AAIZ8eHdoIygiZY6cl5QZMJBp+s55DJrAFjUcxNcOoS5SSKsNsdJ4MIZYcS0aIjSOgbYBDXy6lQCsNkM4Kqb3L5l50KbrHG32+J1qPTjaZusF2Zu1Mcika8mJLt9FACgbYpZ0V81mWM9sKtao3DTlbZfEqBUGrIt5suAkAytFoUcbp974uI4hS2HVAFK5z4IYjR3ZIUBZoQSaitMbaUv9EUXO2XR/mwYD4rIdKa2nqSspCulohI2xaTWbKssF2dbHxC0E2bGYpPVVoKPC7tJHZ7vbafqSetOUXfBleIcANgYAWpZp9TDjw5G10tlmFKf0pzi6pErSHAWWiBoEVogBOA+CQ9N4tExoBi18S2gaZY6AQePRemDQtPAcUmTrcFgd+HhdB65Fyw6rgxYYElaaIRgN1gYtWh9YJiKC5yDUJgoz3VYaj4DgejsSrQdH22y9IHuTFtM6Y6gb3d8puh63bwpb5CW0lbo7AYP5WPXRbVPEmXJ22tSgqNbJ4rixGcMuZmqkvhEXTVDxXtlnAVOWgkMUPMOCD0ZkdVKlbCvIVnCFVOJZtTLqY7VFWfVr02ZkhRJIU1AUffX2tMt7KCNVVNFOOh3EAvBWbB0XkWXTP21DARRytopsH7LUG6f4HACPJwCkYmutVi11kxylJUWUQWoAcKgnFEAQh3eHmCoL5x3aIudBRGicR+uC2BrUwgkoGjQc0BwITdPCOQgr1GKl8SBlieQkVpohGkmrEQFrUuxxtWnhHdAIGFpvG0n7MVaawCwdNVjuR2ivOZrflvvK7gIr/+73No9ZSqBm4/bJsYtgjgiRZZmPNcpycd194rytKnDRDgFTA6CR7V03J43p45gxjP20zMvUTBQV2/S7xjExa5c7pslsvNgZc06J8Ykgxfoq85P5JkARABLJ4ZLSUMxDQX+T9ZmKFBsBaYkR06ZqeaIPCh9pQwBMrFdIyEXXQMYgWSA0Ish2qb1MkK19dwqgtABlPk0/sk0KwqLIWv592GrV8p0cywovLJWttUAjg1vADYQVIpll5inONltpWmzAoRkMg2bbEQYuTMcfuBbMhBXXiu8Q7EPKbMW1aJmx6oLWaNUN0XpgrdnARttgz6AFWmClabNbc08zxLp/sHKzLY/1mqP5rQdH223xJ2pvW2L2AV9jZCg+uTbfFoeUwKYA0jRLd1ACGlPpmaxPbcp8zU9iT0x/TUp7ISwiyyPMT8W3du6T4sew1p9H4sWV6tXVXpsIRiwIQrovZD22KMiWY1jaz0TWClYprQk2wvTA+Bjgoz4WaGUVsin3Iemn19M17JLOTouiawuGlCHSNi1wMj5ZRWupiaSgybOpaE2B6WFdjgOBJWK5T1mm30eNUVwvjUEDBVCSVpNUGYmPk6VBXMOBtXEBFAEUxNWtTr0P70Q+CKjhQRQAU0thZhmEJQIarDZhmRACsOKGcGiCIJsCc8QgrDUtBtRi4AAijw0/gGeCIxY9ksde1z9Cj1Trr+w2W59WW4DFh6R94tlx1adhB1pZlNao1uTMQTCexRrb1SkbneF8Z5plZxiVNNQGxE0K1QWMRgCZaczqhTriJJ0OpeMzFku+l+xRXI4jgaaMSSrblLisbZIBIAUgj4u4KsMDZGm10O/kG1kgbVdYKAVSsUCjk1pGFOJlFa0NAIrT/c22DBCRaTNqoDikWATw+Sal/CwAihWtWViiCMjsDDnOhNghxcdSe8jLUh4EckEwHcTWgHMMopRWI4JofxC+AxEcNY3HgDwaB6y4FhgAq64FwFhzLTwDA+fRkAcobCMKjBADWHMbGMJhxXnAr8CZtdUIHmtuA6Hq5PJan1ab33pwtM3WC7IXYFY/EjZgFGhQ+Ik8X75rij7kYaeuVm1tihzrRHF0V6MKCDaTPhxJSxXfLTAz+xJOKs5vHDs0btu4lFvtnVBARgUlBrhZ3ziMFGei5YyRvlM8rwiaADPbjOLMtXg7GgG1pqwUjEWiyiF0wqS1dCabTnX3EXwgMVykxSrD2UZgpqyVBUIyJlr92lbazliq6C/aoJazVJkyRynlxlE7FGaSyX2h8Zjj9HxqwvIcqVo4AjgCQjoNTRBbA2ikwnUAREHH1EjKbeBaeBBWmhZDIqw0DCcz3FbdEENusOaGIWU2aIGWsdq0cgqMVRqihfo4rDUbaAFhjjwGwlR5IAqyHTxWXLv0s9X6tNr81oOjbbaeOVqQZcBI36nYt8X/yDeLu7pmfRW3yGw1kwrrZKQSwKgvWDvh+HIfxf/kQ9IFYnR5jtK3EE5n2/UpH9vi+jEZY4SROBbY6FR+XSjWCrJtnSOCZXEQr72CCVsJO7Ai8iNIwY85PoIsK3QuvxMAhDTcSPVrFUU7SgxUcXwEQ0ZkXfpEQbZuN1PuiSkBJO2PQ1wXLQq8dUYaIa19JpW74xIeLqTIVJBN5j2AowB8PBEaSZk1UvDROUZDgVUaNB4NC3NEQ6y4VJfIkQ/pr8JnzQ1BaERfxHAIAMrDYy0KsocAGHtcSMlFQbZfAVP4gbXqhlh1Q+xxy13nqLf5rQdH22xjH0S9zWDjxlD++utDcquqYWtrNVZnrJkH+0gwpIf5pNtk0nnNIMzuFnmPOS4yGBP8pgBoI/2tMVUmlZWl2izQsftqbF4WB/E43ZcxQurrpPZLTKElQBFnrRlQFvcr6xRTS0hFHM3wRQG19skZbY+CnAjw5AbRafYWCEk3vNxebH1U4C3T9L1O17caKQvikIpTcsOytpoZN0o6Ip21hhZh3TNBkyq2hmOQZ1naAyCZodbIkh6MkD4D+wCSvAtT8VtgtQnAhymIrVvWitaiJ2od1gYtCIyGGCtuiJYbrLohmAlrzRCeBRBxAFJrbogWLviAsEYbaB2wp9nAettgj2iRVlwL/bdKBOx1G2h5+QXZm02L9cxRb9tjPS7aWossg0EZWwiMQvg544+ZnUY6rbveojY8qWP5Z+aRNjuLPE6KU3RFPwdGpPCZxkZYqBl8GahNwbf+nLFL4qMz0djEKXyUQYrsifgo2xQ0NsknMjOaBsuAlzJbBlwJogkannwqv+p12NzKCcAZYGQATp56G/VJa79hNE5kfBBF23o9A1vFmaYoS925dG4gL6CS5V5GYG1cEFuHtYGT2JohjJEPxRox8GgcwxGHd9cCCGJr8mEWGUBYdS0ceaw4BiEUcFx1LYas0/MdVlwLGgRwBDAcKXPksNdtAAD2KEhyQwxoAysUdE4bfoBWBNmrbogBeex1y722GmP2f3q1GLvRenC0zdb9wOutbvpUkHGzS0XEsRyhB7Al/8TLZuZuomC2upqbNqU1rVWO6WS9pmk7CXOQU14dx5ds0MS4cYdheiptG+CQ2tHvqssp9lVYpxqTlDFVpls1n8DaaDwgLryit6vuk7SVAp9ymr6dbp8XcbR9RC7adpaxSkMDs89RYIxiFW0bW1NsCvgARME1AZFJ0vHQdc0kLZZm6aWp96on0plnOlsssEQBLDUuAKlQyNGjEbZmQGHB2EHj4aTSdUijBf0QEJgdYsKq8yAO9Y4G5DFAYJAYwCoNAfLY2wwxZMKaC7oikAqsgxDbg7GHNrABF7RIfg2NpNsCTPU4ym0AOITejkzrwdE2W59WG2clqKk9SA2wGKeT2cIhjtdwJnxSgLxNC5a2wEZYoRn61wWGusopTBO/9Ovwzy51ZI4wCoL0OxWjb9Nv2kYhZrbio5G6RBa8xCbtduRiZyQgE8GNpO+ijwVGQM4AmX5l4ElPQ0AXZQJrwxQJyOFGfOL6a2E0o2jbzkDTuCL+jpfCrJ8W6hTJebnANIWh8CDn0Aw8fAs0MtPNUQBIADAgj6FzQQxNhJWBh5e6RAOSafmuBbjBCrXgJky9J++w2rQgDqxQmHFmxdZDgAZYc0OsgNDIrLMWDfbSBuAc9jYbYAL2uA00HPRJhENo4eDZBQYLAVQ5SkuKLKOF1eM293elXz6kt22xHhiNs3JsSqCk7JH8FbbbptTWbLp78Vk7a3z7tNJNlbRaiQ9ntWm6NS6tNu15Uf6BLIMzLt4kYESdX+pWLkA7bZuantJmYjoNKSUlu+Nlj4BAU11pjTU2++I7I9VEiu0kxicTR9s2DXjLij6WPnHKPWJarSbItnqijCmSNJsHQsHG2C+hs3S2GZljBdio2Fq/EyFokwgBQDhhjBCYIuYgnoYLgIjg4ZyInx2jaTzIB00QmEMRRwIccZiqTzJbjMN0fHCLFedB1GKFGE6m9a+4Fg0QhNgErFFYgy2ApTA4IcXmseqGICas0RDsAjha4SEa8iC3gnUehHMFY4WGWKUh9tByC7L72WrzWw+Ottl65mhemzBmWyy6tt2IRf1mPnaKPk5LKE2KlWlnulyou7mJS5rkACsBiBnBVjWFRaP7R3yFvanFkM880hfjH9mdlMZSwBSZHOPLZTwLGGQ7OcSK2Hl6iwzDQ0kcLV9j2koBpjI7MPsUzFl9UXZO5rsAPLbLdhQpPBVU21lsYdFXNm0axog5TNPX89Cq1yoQJwZxWARWiV3nGF7SZ14rWwNomhZghAVhSbZRmOY/cC3YuyS6FuaICAEMcQBA7AkDNwR8WAbEIYCvNWox5KAR8p6w5tbh/YoItAPQWqEWjBZrNARAgUnyIQU3BGFvMwQ8YYWDFokggmxah8cBLLN5pjDTcJMxdqP14Gi7rcdFi7Fsur6wMofDaB7WKB44netYgDSBebH7JgCjGK2Woqzpg4hGxNZZHNu3WrzOzox8yL9OI/C2zE8tPhe+4AIYwdA1HfECBgi6ooJliqCkbFO7TmkKPoBUuTqyQZSzQmTbpFQ4UvfZNgtBdpluK311thp0HTbIPx9T5TuKrX2Kq7ohZZ6IObJMYWaalj4I66aR83BSqwiQ6fkIk95AgTlyFJb3GEhBxoFomVaEbWoiOKJQ2ZqbOIts1bVoqMWqpLsCOFqH44FM4Q8aI0KobcTEWHEeKxiCyWGNNkDMASS5wDYNmLFGQeN00K8i1O9mrLkNrFCLNdpbv/962/HWg6Nttl6QPY2VT7Oa6S9WefroiptbyR5tKjSnU9pUH8cgJ908tuI2iYthv0r/Lv1Q134LEsb5jwNtVH4pAnYdE7dzPb7Zlt1RpQZp3LGGpVHFSdQdmenvZMCWMlSZ2FpZI71tSZimTMOUAJDGi4AIhk2iFCcWiFQf+936IvhmAMyNtumdNyBLAR2nlJojkM5IIwbgBSQF5ogYoCZoiuCkoCO1sgwI5J0xIBFkAxg0DPItGteC4TGgFh7AqlS0dhSYo4YRmB9HWKEhCC4IqwFhhTwGGGKFWsARVt0GGE1ghySFFpijILwmBlZJBdnr2IDDKm2AsYqHNOuwiDkwTQ9gmU0npG42xm60Hhxtsx3ZabVpQM1m46PSxla3uyibguGieZkpdDM2m5lWP/UxY+JMBEo1YNMF0DgBGxs3LsVh4tRSdvLdFnhM2wtAwXotOIEnl0BI0jsVx8Y2dDt1Vr8GDPgxWqZQ/drGG2WbSoYpgi6rQYLx1UMdF3qlNKYxlUbIpu3HBWgdQLGgY4gFRlj+wxOcA4Cw3Eesfk0ejYitWwoghu1sNbCAmQYr5EFNqGnEHITajTBHq9Rig2TqPRNWmxbOBzaI0MIJG9SiCUCGSOKFRWbBoRaS+uzBOpgIe8kIsr3DGg0DkAzJOhCCIHuFWgxouf/O9Jqj+a0HR9tsRy4wAhYHUGzKzH4fF/8w/IMem+6qWeksDFcNIMXn9rgGOvbZzZNYn1ob0+qCyka72i0BWXz4drXT0edaOm0K9ont95i2Kvo1wiQlX3v7BaYmrJvmCYhrhUWAQplGKKUCR+OVxR9Df4o2I0slYEWACQwblLNRyGsXFWk1yxIFHw4PP0ocdhBty0KvRKHAZGyTkzA7MkeIBR7jOyHOUHMUpvM7mbrfOIZnL5Wng2CbXFgwdiC+dp00MAQcqU8rlbA9HAIYAoc0lyMv7y0cgkg7TOVvhRVq4ZiwiiEg+1apBdCKzzrW3AZY0muNExE3gHUM0AikXEGLPbSOAZa7zlFv81sPjrbZjmzm6HCYfZKYVBUwH+MxS7OxyRkF2TOwN1NV246alwIo1NJpHSm2alptHPNU+Oqx4Vk8oc1x/bOXMyLEov1a/4tDSlORuA0ZAZJhdyKAQL49ix/BRtoe6yJxiqMACjCskYkRmRphaBgU02shpjg706Zsj+k0NVfEQwJAsVhlOUNNxNZkRNfZ7DrSwqOy37HokmSEGvlMOlxS0wiiOfKMRpglR2GmGSAVrtEEQbYHBk0ALgPHMvMMGBCjlfXL2Ifija0P7NBAwMqAWrRwGFALBmGVhlh3DVbcEI0IslcoTOFfpQ0AhBXagMdKqGUkNZFWIQvNIsxoW8UQLQFrtAEHYY4IWOMNMBxImLAVtADfX7/hlsR65mh+68HRdluPi2YwyyClX/yjQGOKdNUCuqJVq2cGRvF9PEiaKu5Y5mSKbbK2GNXi1I43KZdRf0TZThajC6DV2CWy19YELs/B+paAR5GJAV8JGIV9cUq6Zf9KEGQ+kAIFTavZBiPjYypaj4xDaivbpykzBUEFs2WZHgAZYxRTWxovgrbEGOk+282YIrNjEmebyWhR2haLVGr6LKbeAJ22n5ijsI8IQW9EAo4c0Gj1awpT9xtlkqQCtSOgoRagsJgscRMF2QPyaBqPASXmaJU2MOQBBq4FOBSKdFjHitQhci6wQsweq9SCsIFVatEgfAfJEiMYAgg1kYghKbfAMg3QhlQcM4bUIJwBsIINDIix4o7CMls/W21+68HRNlsvyJ7HihRNbd9hmso/PTDSn/0dLIp1i6fQBbwyJwOQgIm3U8kKZYzPFP3LulGCHoTU15RtjsSpAj3Kz6vmK6+RUx/pO2XAo+ozAlAsKyXHC1OWsTcWVMTvlB0fRdgW1ABZSQBfgB2JmvRDMNspsUh2qDzlPll6kZBYJgFAXv4N5b4hJRYoHxYwqYAqMbWK8WMVbGWVpFeqIdIaRtD0GksBRZJp+5Iey9JqjLiAbEMtGIEJaiik5AaO4XiIAQKgWqEWQwTGB2CsUhByg7RgZNh3CI2IrCkCIFAQdhMBA7TwxFjFEEPRLTFW0aBNNwdYtEjLvbZab/NbD4622fq02laZ/hLegb964nN7XN8r+yzrUj2EzEeKjFF2/80ChmI/OtocC3omtJeeuuG7o+mupWW1xrVjQWE5bkUKDvLZpsS4+B5jGsamFEcDZI4za6tlPiadVgFDI/617+ov2qORH/7xHJLIO69hZOOltJllithsj8AI4TMR0DShojURRHOEWP06LBzrQnHFhmKhxwCCwqy3ATxaopBCaxxWRJDdGOZohYZoqcGaMEcrbgjyLrBMaNEQY5U20GIg0/Wl4COzsEEBjK1iAy25AI4ArCFM919zLRoffPZiPVTIRkgaD9DKurvL/feln602v/XgaJutB0azWO1BzMX+DtsMk2TACvt5r5c8cSb1QdJ1UzjVd015jva+m3s2XIowg2sHUKLKFws6JjWrt0YXfrM7KY+fMUlZXMpvuQrIIJTT9eWztMlEEaBw2T8FaCWeI/NAKgDPyDvy/Qq+sqytAVksmqCaHim2qakziVMuZAvVFum4kla9JnmHgCEPIh9TZkShhGIUZDuGEwaJKIAQJ6muptECjyGl1aAFHMtsNU2veTgAKwJ4BlKPaEBtWN4DPkzl52EUZAcAFKb3E3mswmNAHisIL4cwk42ZMYBH44AGDKKgNdIbwoGxAo9myR+hARxt7t/3bn1EHaZKeYuzt7zlLTjllFOwZ88enHPOOfjwhz/c6ftrv/Zr+I7v+A48/OEPx8Mf/nDs27dvxP9FL3oRSGhyfT3jGc/Y6tOItvkH0061RZ23fVoo7b81YzqydMJ8QaZqR19j45Q/C8f5l9qsrpTdpDaroSVOV3HGaf+6xv50oZzCt0ynFYew8YnAtEwZ1pq0YCPzMzHM/phei0xM7qdMkKacRsBOZGnsdh7tD+WptJG0npPTc8V2sz++Yr+KdJllihoFU+ojoEin5DtEXZHu0+rRAfAEQXZ4ydppFCbEEwENgs+AAlBqHMtish4NBARBZqA5j0ZSbyuaVqNWWCL9PgwaIgrT7EOBxyDgXpG10FYQ0nKrFLREK+SxSsPgQy0aAUoD8pKW81ijwDStocUq2vBOjFViuD6tdsTajgJH73rXu3DppZfiqquuwkc+8hE8/vGPx/nnn48vfvGLVf9bbrkFz3/+8/GBD3wAt956K04++WQ8/elPx+c+97nM7xnPeAa+8IUvxNfv/M7vHI7TAdAzR7OZTX0Ut26ZhikfhMD8HDMjA89lV2ayaYBbxHjjfCtAcGyKanQ/ez96/4Wf/PVYCgoUAJR9rM2YG9evzj5aQDPGFwV4qbTZBZxG4llQk+8Mb86ce2zXutBomzaGC2DNzjSz0/BL9kfPvQRDsTmXjqn5oPDJxkAAUKyTRJTqHFnApD8GxEfjBQCWwFCobh3WRHMyO40oaIlIQFXQCIXCj41TloeFLYLMLhPwRMmnEVF1AD1DYY0CwBroNhcATgA6GxiI1igAniHWKEzzX8UQA2LsoWEASlIAcgXyikwSYy+GWCFgFcAKAWtgNBTSeg0Bq/BYIaBxD6nfU0tiOltts6/daDsKHF133XV46UtfiosvvhinnXYabrjhBhx11FG48cYbq/6/9Vu/hf/0n/4TzjzzTDz2sY/F2972NnjvcfPNN2d+a2trOOGEE+Lr4Q9/+OE4nV1ui0qEV9Ili2oze+5zdfvMNnHZi442M4cCkGyiTaqBmK4U5FhmqsN3Gp1RyWqVD/Nxx2Zu3AFwkECEHjDi0gW4iuNg0mA1fRONfmZdt2wEpIz2LZYIKNq0uH8EUNmXy30y3VNxDohtcj2eg2nfCxBig101pRZGTdmi9IKscaZaowAuEoMU4jnDBDXCEg2EOXLQ/SE1F7Q+HqsuAKRGmCNnWKZGfeR9IKLrEF+YKAwxACJbtEoeKwisU2gzaE4aAlbkfQ8x9hJjDzEGApzAy80c8YJeu9F2DDhaX1/Hbbfdhn379sVtzjns27cPt95661QxHnjgAWxsbOBrvuZrsu233HILjjvuODzmMY/By172Mnz5y19eaN/H2e5Nq23WbB5kin++NINvdlw6TMEKdYl+F2UR640BFltRqmAicBtz0luxr9zf4Tu21x1CbCCf3ZYxTGPaiZDc6pY6AFICYglo1YBRFqd8mX3ZbLLKcZFBMrWOKAIvznxG20oAicvtRX8UGBEB5BIQCi+OKbSQXlNBthSCVF2RY0mbsQFMwigZwBTAjzI26hOKPDbCKA2ETRoIIBqQgqtWfFlAVwBJmp4bSIptBRwZqQExVgnSpgIjwhrkMwJoWpPtjpa7CGTPHM1vy60mM/alL30Jbdvi+OOPz7Yff/zxuP3226eK8ZM/+ZM46aSTMoD1jGc8A895znNw6qmn4p/+6Z/wmte8Bs985jNx6623ommaapxDhw7h0KFD8ft99903xxkF69Nq0xih/gicdexm/EfOCLWMLCjSSDOB2tnaHRu7K3U1q5VszazH1s5pHJCZhkGa2OacPva6wQALu48S+Om620obTa2l7couearEKwXhmU++rzbM2m4CaoUvIdZAypgyC3TAeT+yMQlgiqDLpPjCJ9UvIvF3lIpYBhZJK2wnUETCyDiEfc4BxIGRIuKoQVIgxGhDKQAmkLJERrwdlu4I28Cic4KAJwRRdqhoHYBPC8SaRQ08QCK6lvNoEPo20HNC+LfYIFwg9dEha0Ag3jGP0N5mtF1zZX/2Z38Wv/u7v4tbbrkFe/bsiduf97znxc+nn346zjjjDHzDN3wDbrnlFjz1qU+txrrmmmtw9dVXL6Rf/VR+tWkfSZs4blLaqGL22kxVrbqzfzQ1IJmqnRmqX1d71ZV+6opBoz56745dfsTWOFo0S5rFy2NnqbH4ubLY7Oih4Hi9dB/l78UrpqaIwMYnLkYr8ZTFyTrp0scIWiRGrcAjl6dc+IwwS6j4F2AptcNpuwi3yQCqmFbTRWXtZZX9VnROYCn0qBWyBVC5MGvNwcMTpKYRj6TdPFwEUgNqAYTK2cmnBeDQwIOJpD6Ri5okZZ4YYR8ADMR3RWssgbGCIKRvKBQAb+SaDkLrWKGwnhpzvDNARBiAll+QvYi82C59PO2YtNqxxx6Lpmlw1113ZdvvuusunHDCCWOPffOb34yf/dmfxZ/+6Z/ijDPOGOv7qEc9Csceeyw+9alPdfpcccUVuPfee+PrzjvvnP5ECuuBkdq4cRindTGAZ+ZZUVOYlcJs9uE+JXiZKMTuBDAdx83K0FT9KfeJHyfE7tQA6TZ9+E5o05qKo61b9t0I5ztCdNX+iV8UDHX2RbQ3CnqzvJPxkf5YZkrbH9EVZW0jAy5c7ragSN/t7W/Bm41XuI2LF32LfUGQrZcseOkSISTBAraixBxBGCJwmBqPXIPUuFaYKB91SoPsHRhgmGayQVJnwhytSF0j9Qlrq6mf1DyK70HLpLWQSACSpswakOiNCAM4DMhhBQ6OKL5WQBiQA+hhWGpbREptl6bVdgw4Wl1dxVlnnZWJqVVcfe6553Ye9/M///N4wxvegJtuuglnn332xHb+5V/+BV/+8pdx4okndvqsra3h6KOPzl69baeZJ8NWgU2aF8hy/j4lczS2I1PGMQGn86OO2KXGalLJgLLtseUBZuiX/ZyBn7xN/YWfjpnQR+sXH/iTOkfiUVUuJcBXAreuqHoeFTBUNDlW2F36W2G2TbFVZ7hV4lkAFLbZ5B/nciqpdK0z1EJdICPOtuk1GPE2GA4QAbbolACk0gDqTyK6TtqkRkCQgib1jTolSamFOFpGwKc2tT1SDVEAdQ4hnUZEIFDQHVGDPdRgjRo05EAgED9Qu5q9HQG2Y8ARAFx66aX4tV/7Nbzzne/EP/zDP+BlL3sZDhw4gIsvvhgAcOGFF+KKK66I/j/3cz+H173udbjxxhtxyimnYP/+/di/fz/uvz8sFnj//ffj1a9+Nf7qr/4K//zP/4ybb74Z3/M934NHP/rROP/88w/LOS17hdXZ7HCeS+WpA8zGkkzbDGCwzdYzfWPZmJreqGMoamNB46bpT7KJxSk7+tCVdtN95ZBOIcKe2OYs+0YAV2VsqfSdrd0qAKnFrfhWWa5xpgBIXbuOj33g6N8JuGJfRVcU6zBpQcgkwCbRHKlvAE46mw2yzpoAHz11I8qOgIlCsUUCxbhEdtq/pNiQ9EoDhO8KvhoVeKvwGxAReOpnY07PAqMGTlJnFP8nIxD6BIJzqxMuxvaaLYe2mddutB2lOXruc5+Lu+++G1deeSX279+PM888EzfddFMUaX/2s5+Fcwnv/fIv/zLW19fxfd/3fVmcq666Cq9//evRNA3+7u/+Du985ztxzz334KSTTsLTn/50vOENb8Da2tphOacja221rTyXjLqQTeZhtlVNl3E5XbPp0mzKai0QtG0ipbZpcLdoEXXnsVvkO3JsCYi6QUraVRv/CX0hCBQY4zsuxrTbgHS7kXmwVdoKd2bOdo2m+czuAjPabanrHL84l8TWca01pM+hZmjuQwJgFDg5UtbK1lVS0MTCNDFUQE7KICGl5Vw8Vn0Ds4TIUGVFweFkcJwMYgMHwKtyLJ5vA7f0wGERs8362Wo7xC655BJccskl1X233HJL9v2f//mfx8bau3cv3ve+9y2oZ/NZL8geZwosrHUAjTnE1jObybZMXuJDLOpR9Njp0mpbVuKha+HXacateiwmA9NxdZOqLBFNvJ7ZXdAJSCqxOT3iRsTR5nMsxDjpHLLji3uTyq/hEZ6xkdqtcQCaOj7b7wXDkwG7DtDVyWZlx3KxLYmuRwTZBiilddSEaSFlidgMo9ZCYjiWdwuKKNxcygxpmwqe1De2KcDMRYAVtOMkAElPJLFEiKm1BIbSZ4KuDRe+lYLs4LqB3o5M23Hg6EizHhjNYPoQ1z9RXQ/6w2DseXqAZG3TguyuDlXGYlpQ0gVS4ngX2/POIkMWdnZa13HZDLaajwEKtVSX8ZqrTSDVKYr+o58zQpKKmW6ctqdjbF81J0FmRlq9zbl+mNvLV3mvYtZaO9pvM8ssBZZ10rLNKbJ+ogKkWIIsAAkv73mz+aX3Mt6JQQKCbsjrpeMWVv/EyAuXR9BDgOOgd2LiCLrY8D6OGIbkCuCMCEyMBgQPhosAKJ1QOk/ddlRlUJfIFiGo7pmj3npbNrO/phnVpxOj/iBEZfuymz4sZ2WOukiHmWIYgKSfJ4GoWjs1xqcLfFV9CkBW68u4eJpWH+cDAjHnAKlEEiNgqIxnD1QUZS9EeJJydgjnAInRrfrsBDLT2VTAqOYtpxAJT8o2j0S2LEoZLqbsmGT6fnGs1CkK7FCAWmHGG2VtqcAbRDJcJMLusFkrcCM7JjBJZC6bzpYDBdDn4nYkICRFp5QpIgOQqvrQJRdkL0IztFt/v+8oQfaRaEeWIHtBllV/njLdMxJjC8d1ltBz/HWqA6NJYzBl7FkF2ZOYo4kNUvdxVQH0BAZItk3syYQ2udSpUfE+TZtUfJ4wPlyCq8y9dp5jw0WfkTAZLVOJOe5cCUDU+VR2E1UwYgIk+p6dngEvo2QrVbaZ9BVgZrBJas70jRQhGaCjEUjOJfXHMFzFuEUGybRJ09xnvNyC7O2yWRaIB4B77rkHP/qjP4oTTzwRa2tr+KZv+ia8973vPUy9rVvPHG2z9Wm1inWOSW17B22yKO2RDVHmLaay6Z0nLzS7GMB3WJesmes6LMEPhqk0WKiDj3msdtyk230SoJvGavHKdovzzIg9EUVvrgPKDiH9FqpkVsedZ8lKpe2jn+02l+03uqRp79llpxe6ce5sMWYwXSD+hhtuwDnnnIPrr78e559/Pj7xiU/guOOOG/FfX1/H0572NBx33HH4gz/4AzziEY/AZz7zGfybf/NvNtnxzVkPjrbZyBHY9wApt2I8qmCJ0I1WNNWxIIDU1fSCAZIFyvU/zpU4dhhGA46cf2fKbl5B9jQ2Sfs0Lu6YfdVTnxSTO+6NaZm3Llce31cAo6m8Wr+n6UDWJkaF5eU/jbKfwIR7uDjLDBhx0dXka4fW27QVB584RDGWZZKmbzM7XQ6MnJPAnAVIbepBHmkJEEbOICVNkrRJU/y75fXx+7fZtmO2ml0gHgBuuOEGvOc978GNN96Iyy+/fMT/xhtvxL/+67/igx/8IFZWwlp1p5xyyqb6vAhbdtx7xFvPHM1qQraPfZCMQwwzGptX/CVb5jImGU2XWmMTf5b+xc+TU3idsbsE2V0+XW3W9F6TNGGzpt5qfdhsm5Y1oQ59SRavOJDkP4X8JWaUGKOC7MwqT/6uNsvbu4vp6fpNYX0yFNLdZho2GrmHfGVokRQ+AjISGOHSh9PfQdZ4AoZ8ttpu8rGXk0HwhXhafTyHNKAHxTY90m2bXlIOgEgm7U9pyy7IBvK/YfO8xO67777sZdcXVZtngfg//uM/xrnnnosf/dEfxfHHH4/HPe5xeNOb3oS2bTd/7puwHhxtsy1BAmGJbQzPPxYEbAHgHKH6Z7lys/VnbsA8jkHqil0DJuqTiaBRH/Mp2hw5jvKH3VhQZ3264tV8J7RJI6DO+Hrj03kOZZvdPmkiWEd7doNlfEqf2j8Hw8Rk7zXAVMaMbVHeboxCJS6JLE4MyenfQnmpmCWcKdPtg/4688kAEGTNPoTLELrM8JzAFgzQSXHC8R7aP4IX9VDrU7zYpvTfa6uxTY7D5AUwja1Fxwe69x1hdvLJJ+OYY46Jr2uuuWbEZ9wC8fv376/G/fSnP40/+IM/QNu2eO9734vXve51uPbaa/HGN75xS85jWuvTar3tQJvEDG0B5LS/xmcOP4npWqCV/Sse2KMzi4qf/SPxON9XS//YNmu+k8593P4u0XbuNHpM7TzN9slT+Xl8mzXgYgEYIYACQwj5sp8WrNicESMpg2v+I301tyZnzQo4KY7h/NgyhgUvxBTPJ0wGDF4lbtThjQu3sk4cpARYKIx7y4QBMZgp+TAl0MKAhwPBC+PjQJRm+rHE8xSGyTMwcGF8m3gOoUMeYSaaMkh6Dgp5GMAQQMMML+PWqPrIXHNbCEC/Bzs8xYLntUWm1e68885sqaxFFUr23uO4447Dr/7qr6JpGpx11ln43Oc+h1/4hV/AVVddtZA25rEeHG2z9Vm1cdZJS0x3+KIBSfZAmGW6/ZSIam7wVRw/rifKnMylHcLWUp0sj6tZNFEKJKqxAEP1me00Gi/RIEUMw5xZK69Vjalxxhc5+KiFykCLBU6T2rHvxVgQ12+Jal/GOoqLR5xMWr8kBM8s1a/Vx8Gzj6CNQWgZGBDgPcE34QQ9wpIcLD5heVgfgRPkOCIHRhse/I4lniwJwoCnsLgHw0e2SdN3Xs5HgZCXIRsSsAKGB0z/WdglD0ejN5lnD8L2pn4mWskWzhsDmGod0XkWiD/xxBOxsrKCpmnitm/+5m/G/v37sb6+jtXV7ZkR2KfVttnmKiS4q22af+kFv78ZK36lz329aumhse4z9l2ZC9u/TkHymP5NY7P2bZI/V57Qs4xXBWyMHNeVQqsF69pfY1/s55E2K/s7YtsUXpZkKkFRjU0qv9sXKp9jm6NxRu6YwkdZG0s7jeh3IhNEpmmK+BMIbERKlekZB4DkmbQedlgIhFPqTVZNk5fEQWir5QE8GvFxMXXGHJfAjccyjCYKgX1qpW8tGMyMIXuwQCwGo+UWHj68sxdGbFiO2K62eRaI//Zv/3Z86lOfgvc+bvvkJz+JE088cSpg9IUvfAG/+Zu/ife+971YX88F8gcOHMBP//RPz3UuM4Ojt771rdi3bx++//u/PxsAIOQbH/WoR83Vkd1qvSB7FhsDTDINyRQUyrRWsCX2egX9xYygYqywuHifxWrHdKW1an0em7Ya41trc5L/JP0Q+zqT0+Wv4hTjW00flvEyNsmiBrN/BGREBBB9EpihAkzwyDWNrY+cxmjKjUo/7QsVn4vjYjgu4pQvKgESjfiEoaF4OvlnQJ9nMeVlWJ4wVAT24RJ57xCzdJTASgA24sMW9IT3VgBRK/1pfQBQLTcCphJgGnITYjDQskPLDp4QfCmk68KxhKG0qe8e4dUGXgqeWD57DDlniFp4tPBg2lNeyCUzWtBrept1gfiXvexl+Nd//Ve84hWvwCc/+Um85z3vwZve9Cb86I/+6MS2/vqv/xqnnXYafvRHfxTf933fh2/5lm/Bxz/+8bj//vvvx9VXXz1T/9VmAke/+Iu/iFe/+tV47GMfi7W1NXzXd31XJspq2xaf+cxn5urIbrXDWXJm55nmI8bdpsVP660Cm+ZhEwFRbHKaNmfv38zAeTPnPo3gedZ2VOTRJY4GxgNBSzXYdiexX+Pa1LSipTpsP3J6I2d9RjqH/PjONuWrbrYAZKTNYjsqj6cSMNnPZUpOtlPxboFcVvPRI7fYl9SDdJrC5rABQrJdGaTwHgRU3oij49AKkGE4tCzL0vIogzTkwAa1wgy1BuS0wgJ5aVN9Q0zxk3hDVhAV3vXYFggv5vBCYI6EH4IHYwMt1nmIjQCLwvZlF2TXQPE8rxnsuc99Lt785jfjyiuvxJlnnomPfexjIwvEf+ELX4j+J598Mt73vvfhr//6r3HGGWfg5S9/OV7xildUp/2X9prXvAbf+73fi6985Su466678LSnPQ3f+Z3fiY9+9KOzdbpiM2mOfuVXfgW/9mu/hhe84AUAAuK74IIL8OCDD85NXe164wWyHDvSphLK6IdRf30A2+/AYoeUQzqNYR+WtslpEa70fxKDNC7uOAH1rP6Tjp3XN1IDPJ65yq5VZUzI+M/KbI3cBxxnJMW2bNyUJUIEDjbGSDpVFq/VW5LS9Uq3q+zMTpMj26Sn3Xmeut8OF+fDxQjgJvaj/CcSwxHkDk79ZkMdxX4QyAPsCkQX/RPAYma5nQPwcMzwnkCNypUVtLAAHoZnh5YDZ9SyAzkPRtAkeSYM4dAQo2UHhxYtGjhq4eEwZA7CaRAcOwyBUN9ILw8DjBYDcthgH4TVFJglpsAUpUvMGDLQwKOVe6MVrREhsQYb7OHgAtQyP9I8hwn/DsvOHG2PzbJAPACce+65+Ku/+quZ27ntttvwlre8Bc45POxhD8Nb3/pWPPKRj8RTn/pUvO9978MjH/nImWOqzQSO7rjjDpx33nnx+3nnnYf3v//92LdvHzY2NvDKV75y7o7sVuvTauPOv4JERlw4MEvj2IkFWFao0z7QprZxuZCZerLJ47vCTgfYrO/sa8DN0Ma4Y0twZUEMIb8XVAWsdXDkQR2+2PjmXZ+BFuRZBkWpHANQcvaHYtqIALCofnUmmbZTwzI5m4TcqTgOHllf1HQIst8NJQNgv9t29HOY5hVUOQZEsQdYZtIJNgrjxQ7eezRNSLU5mTbmPaFxgZlpJH3mJFVG3KL14YdHQ8CQCQM4tIIAhwAILYbegVw4oSE7NGAMAQwYGKIBCBh6ABRGfMgOAwrgqfFA2wDEQe4NkkGT8xxymOE2pLTIrpMBHFBY822dW6xRAzulnwGsc4sGG1hqm4P5qcZYYjt48GD2/fLLL8dgMMDTn/503HjjjXPHnQkcHXvssbjzzjuz6pWPe9zj8P73vx///t//e3z+85+fuyO71foK2bNaOVacfuVPSuEswuxDylHUHW3Fkhxj424GaCzEJoC0EsyMm4E27ly62BTL5MSnNOUx4yK0ZX8QH6SWYEnsS+4bGRplntg4Z0gEAii0iVARO48tb4rPOI0kKw3kKeD98pzNkMcp+/puhgIoWCWvvx8oTImvgaGCwdaFV1m3U1isFwSw9C8NvwAnAcwAwXsH5zyYg9aIyIcZYpIKcxS2wwX2xnODhlrRHgXE54jQckCVjsNMpkbOzTNjCEIjjJIjgNSHgJYHaDAMpQUEnToAGzwIyTTyAJOwUj4MO4AGLDPighZpgLA22zqCcMqRQ0inpZlsS21MsCnRuWMsqT3ucY/DBz/4QZxxxhnZ9p/4iZ+A9x7Pf/7z5449k+boSU96Et797nePbD/ttNNw880340/+5E/m7shutSObOTpc/6gUFJmH2lYBB/s0RXhgTgeM9Ck0fb/Gxu0CG7X7aZr+TSN8HvG1DE3RdlcazPrZa9XVx67r6UOcrFCjdcnAMlcAku1TepEG4jwkad9R+KifdiNWGjSxlXjh4urbX/XRh5I+KDx90+fK74LQpnyO07OQsUlU+vjU36xfKsgufGP7SA9a9oEh8h5GX2TqFTGFafocQEbY5mSIXNQKeS+aIBFtt95h6F0UU7fsgg9EX8SEDU8Y+gYtNxjCwXODITsMRWM0VB92GHKDdR5gyA2GFDRL62iwwQ02GNhgJ37Bf130SOscNEgbCDqkofcyJAEYeehstj6ttp124YUX4i//8i+r+y677DJcffXVc6fWZgJHl19++QhCU/uWb/kWvP/978eVV145V0d2qx3WRUB3nM0yNvpQ6nhfoE0PiDoDTNWvbuDcAYI63acYi5rweQrfUpxeHf9OoXLFJ+vHdPFI016FD9k4JbVSOd8ESmx8ztqMYMmWW2b1Q8VGjw19Rt6/CGQ421YeU/onYCPQ2wImGx/d4IuMTzhO4V8CgDoEjACMFCixMhMMjMxUQ2B+mI0wWgTYnpPgetgG8fSQRWzNDq2CIQAtCEMftEMtGng0QZytYElmq21w2L7uBwgJrwGGPBAgJMAJIc4GN2jRYKgvbjAUYDQEJG4AbBsIoGiDfXy1zAKclluQzbyY17LaS17yEvzGb/xG5/6f/MmfxB133DFX7JnSameccUYnOAICxfW4xz1uro7sWlviG2/ztsiTs7R/IbKotUlO/oovEHyOa3bmWJP7NRGAdaWcaofNmoabRuNT9pExKlyupdbKvljhTgSOpk2bIivaz9o2qa2oMdJIpQjHOQEUshis1RXZVJ/GLZgimysLqS0aGa7sXtEVT7PzTGm1vO1iSNTHptE4dcn6aBot6IDSAJBpOqbQNKgBbxxyVlHhTAp4ODQcz9Gn8WRJL7smpdAIDvAe1IQUmE7jd8SACLBJfps78tjwoRJ2HHYPOMdgNHAUWBz24fiWCQ05eQ8geIMBh5DGW3NtSCUK6iNu4ODEJ5yDA8NzEzRNcCHNx1qWgeBoKNP+PVbk8g0oAGeGTP8HwHjI6L24TGbv2c3EWHJ74xvfiB/4gR/AqaeeurCYc1fIvueee/DhD38YX/ziF7PiTUSEH/zBH1xI53aDjV23Z9cbF5/1T3xtzCj34S3QAmz6UnUhF2NTA7BKrBnA26Z0UiVYmtV30uy1sBHx6U+FjwU62cy0AgDZmEaETQDY+yBO6bgewSeBBBUopza1W4JW9F36ofqkCFrIgBZzelCfCApYlo23554PSwZ0LBAKuCPpjCptxmGDefcE6Ow09W+LvnIATRErqlaSXHz3nuEckt4IIs52Yaaa8wRPDeBatN4Brg1d5qAvGnqgEZ1RACANHFqQJ3hyWCHGum8wIGCDCQ4eh/wKBq4F+aA7Anmsc4OBxCX4oEECi08btVMrBKyLz7oMjJMLQtwAzFgVHdUqBYG4XieP8H2IvOjg0tkRrjlS+/3f/31cddVVOOecc/DCF74Q3//9349jjz12UzHnAkf/43/8D/zAD/wA7r//fhx99NHZH9keHM1mFEWMvc1mes9NGrspAMk8zW7GJrE48Rk7BYCxsWg2amsk/iTQI0zJ2DHoYoi6mKhsO7pjd41ZFsscX7JNteOFzcnAhvHNAIRhbSIQouJY5iTIZmFdLIgCwlpl5lTjkGYACQHkmGHJPhhQBRPDBo59NyQRjXmPbJKcLKtC2TJQAOAJopGOjahomzgAJnZBb8SyJpr3PrI4QYvk4BqP1jcAEVqZQeapBXsHRx6ew5T+ICInsPMhzUdtEITTEIEHCuPVOA6Aiwjk2sDkcQuA0HAQoTeOw8w3CuwQJB6Y4KTUQANG6wlELZyc+IA8wD4ud8cs+igpTtnb9tvf/u3f4uMf/zh+67d+C29+85vxyle+Ek972tPwAz/wA7jgggtw1FFHzRxzruVDfvzHfxwvfvGLcf/99+Oee+7BV77ylfj613/913lC7lrrgdEsVmEERvbpX237WmwXmHkTMwwnHMeIgGUqZmdE7DxtN1gebMUB04jADYgYqRI+7vhxAoZJ51pjm7j4XvYvqoV59Fg1Xek0ghcgW6U+HoNYiZvsNr1WDACUi6JtH1mAkd2n8eytq/G16vdYAbX6IsXxCLPVY59tf2SfhywyVvpQEoGrj7f91nEJVa/Vj31gn8Iwkgx7WlbEexVnu6g3GraqGQoC7I0WGPokjG7ZYcM3UVTdssM6U/BlwoZv0PogyB5yg0PyeZ0dNthh3Tsc8kFzdJBXsM4DHPIDbGCAQ7yCDW5w0A+wzgOsY4CDGGBDPg/RYJ2DcHudHQ6xw7pW3kbQSm3A4SA7LLsgm3gxr51g3/It34I3velN+PSnP40PfOADOOWUU/DKV76yc023STYXc/S5z30OL3/5y+dCY73l1jNHc5rVhFTzD0jMxKx6m4lt227IQ3XW+GP6FKdGz8ocTbqNKm1Wl9kYxxzFtJXSE2Yl0tLHprhKlmgkXs1HqY2OePGczT6GsB2J4UrsTjo2u1ssMwJEXYmlXQKhQoZtMVol00hMpwFgpONTLNHtSL0g7YfVFcXT8ZzW8mNzjGI5iRPLfBl2i0w9Ijs86gMEQBOV4coSKWsFkYZ7AI5BXtgkR2ERsqgtk3pX4JieIwqiaweHtuWgHWKW4o9harx3APtQBBI+DETLHg6EoQ+LgTgKgIocA5JOG7qwOG0QazOc56hlan2Ifcg7DChM1/fsQJIOHJCXvgbB9wAEsAv8jx+CEab9DyXVxiG/iRYNiIbYYFOWAYR1brDhH8RSmwLjzcbYYfaQhzwEe/fuxerqKr761a/OFWMu5uj888/H3/zN38zVYG+F7cAbb2ksPkgpvavZVNMigVHW/DwXz/SrMzAyBml8uIpPF0NT8R3LHIWf/aOxszRYAUq58ClTabW+dfnod8+jfY/7BZxm+xIwUr84ow3I/83FNpHi2QeKIhbbR41lj4WyLpzYHbafxQdIM8R8Hi/zZfH26XtkeaD7ZfjUp5jSTyWjBORMkgfIU+YTUEoeZ4RBij4kzBFF5sgrg+RJlgohDFtlj2S9M9/E9NpQpvJveELrHdZbWffMN4FJ8jKTjBus+zAFf10YpQ3fYN0PsCEM0AYPcNCH2WeH/AAb7AKjxMHvECd2aAMNDnGI96AfRJ+DfgUbvIJDWMEGBniQB1hXnziTzuFBH7aDHorelsPuuOMO/MzP/Ay+5Vu+BWeffTY++tGP4uqrr8b+/fvnijc1c/THf/zH8fOznvUsvPrVr8b//b//F6effjpWVlYy3+/+7u+eqzO70XpBNlBlfSaa+blvmQb7INtqU9JgZGmJSQdVWJSa5zSsVC1OwYSM860yR2FHHWB1tdnlU/Md22bZd9ngfR3oMqJQWtmdlM4ytEmpayrazEhIZXSsIFvAatQgQVga9YmslaFoJF7oV8FI2eGJ+xHZoWwMZdIlSTy2LI/+JvBm+AyuVAZJtUzWJ6UfCew4EYEUtEOhf4nxCjWc0piyVEvUKtoa3xNAUmSz9YAjJ6xOYHGIgvbHkQd8WDLEOWDDB0H00Avv5gEiD2odvAsVq7klNEQ45AkOLOfq4agBwWMVLmiQpCMNPA7ppZG2lfVbIQoicng8KMDPccB9xB5MDqsU6hyF4pkuuySHMMAGL3uFbMJuEGR/27d9W1yb7eKLL8bzn/98POIRj9hUzKnB0QUXXDCyrbaeGhGhbduR7b3VrU+rAeaJVWyfBJo6UMCi02illWTJzKm1LvQyn9ummpg2dQcsbkwz9mkCuLPp067mmfPPJA8ECzKij6CQCEoMiMlSZVoPiEN6TABVBEicql9HH0cm5cXpoWLBGHKhd8y6SQwyxwUf7RMSGFdWhwBIJe0ItGI8M5ZetrliOK2vF4AW9+nyMAbraVopdjzsDGUMBCBJZ5hCOg2MWHGamSVVBhAFxgjko/ibHId0nAvrrDltkwLIYQcM4MPxYWRkan+DhoYgOMARBuzRhlwbNtjBEYdlRyS9B1JsSZJqc4DUTGrYY4MbOGIE2AQRhkOWvA0i8VAPKRSiXGqzIHgzMZbcnvrUp+LGG2/EaaedtrCYU4MjX9LrvS3EemCkVhuHMWBJ6xhpKiTqXjaDKKY085yNf3xmbnL6g7oBzJgYmxmCWXRI0cUARGVQRp3q8cv9QHrix7QdjfpE3wRC4h3CnKbfy/ds+Q/tQ5hrbuIwMhGPgqjYrG5wmS+ZMYCXtdV0iRI9BBRBQpr2r/HJ6HZMm4Jekg9MvASC4jZlh+x3l2J1+eg6ahnAItUT6bR9ieEpASQKjJLOTGMAaDkuraMXpiUErY8ySggz2IbOofEeHi7dQz70ZcNrXSTBty3AjhRegsBYV2KtDUCJmUXCRGCEE2/E56AwR+GyhdICK05yhxx8H+RVtMLsEsJacA0FALiGIbyM2wYaPOhXgCUXZO8W+5mf+Zmp/I4++mh87GMfw6Me9aiJvnPXOeptMdYzR3Na+jnbvX+r2KOYWZB1zuMDKvVlKhZpqvQUJgOdGuhQFDAF0BlhvkqfqhBaT1q3hbRJBHLjtExln8exR9bPprCspqnoS0xfVQZOGRpldmI6rdamsiOR3iGJav7Nxv5xyCc522q4P8LzWNY18whpWM6bC0yUHKFlArKp9ApCjCicEvjRro5M05fhccIeZT7KFjWQdB6J2Nr6yDnova46JidAk9JJRBaugUzpd3A+oC02bYbLRrKGWsj7ORcE24ADta1AG0YLAjWMjbYJLJkPrBOFKXJoiMOito0LU/Ado/UDDNAGVoqDaNsz0AwYbUugxsO3DVbdMLBQ7EEYogXBcWCZHHkwEzwFxgggHOIBHIVyBEQUNE6cL3q6dKYAe7MxjhCb5Vk7Fzh6+ctfjkc/+tF4+ctfnm3/pV/6JXzqU5/C9ddfP0/Y3WlH0I23ORsDdKKZ/eUDeNLDeIHWBWjjL+1p257WbyxAkp3j2JhxAKkLoNUAUmlFjGxMtM+1dixb0xVbj4n7E5Mzsuis+hhAmLFEMB+iJilNFbNaoAAK1NcVYEP7oAAi1fyJDI0BNKSsR2SCLECSBWDtbDQG0NBIvMg2aVtmxlqmJQrYIp/5pkNlfCM2jNoqc3s51RIhaZ1YNEnyDgBoEbVJCajK+OilbQMQ1X8T9jZu7QxH5+FbneUWZpMBoaAkmLFBgb3xLbDaeOmvi6k3JQgJgG+BQcMYMrCqKTwEcNp4oJUSBo4YbQusuKAlIk29UaiXRDI2Q3ZYcQEQrbg2MFMANtow5Z+w5ILsHhzNbXPNVvvDP/xDfPu3f/vI9vPOOw9/8Ad/sOlO7SbrBdlq48ah4+E97vgtZONUtxCbJbPdvI+32cDbXOxiJEAmHDvN/mo6i4th5wgQR5gVaxnbw3n8EmCVfSz7UgK9ss++sk0/+1qbQbBLcbt89nattpByITCo9cknOy71385ei4hNbyEzG03bSfGST/gcWDFqJY5PbdhaSKEt5DWL5LMD8llsbL5LjSQCBfDDKS61BE2pxRlzrfjGmkjhM7dh9hqYwC0BcLItLCIbaiGl2W3DNiweuzF08L4Ji8p6h2Eri8K2DhuyGO16m2auDX2D9XYQah/5JvocahsMeYBD7Urw8Ss45Ac41MqMNa+z2VZwsA01kA76VRzy4ftBHmDdh7TZBg9woF2V2kcreNCvYp1X0cLhAb+GdT9Eb0emzcUcffnLX8Yxxxwzsv3oo4/Gl770pU13ajdZn1bbjCky2YbxswxD0ZXpZq/Z39FTNjkLKzWHzSzONmLmEiCW20dijGvHxrBpt0inaPBKn0KBnYI9ko5EgiOfNRbYEQPo9DqCFaaY5tnETOAlpKdMjSMFQkyyphpDdUYESrPTWHQ+KvQ2tYbiECrjI6xNBC2EJF0SpscOTyxhZIeM05DGfzpNDqyUimHmmLIjAtAKK+Qgs/SQGCRmSXkiMlY6Sw6tNh1Sb4npCmkwAod12BzghYlidjEcHMExwG4YmCsOnfSsa6I5OGoTc+ZCyYAVpz5DbPgmsEJClzWO0bLHgDy8b0BEsjRJWKLEOYA19UeAbwmr1Ma02roPC9e2WHZBtrlJNhNjF9pc4OjRj340brrpJlxyySXZ9j/5kz+ZSujUW7IeGE1j48aoY98Wggjw+PeseN8i2xwXclaNVU1DNE4XZH1qx1hgYT/Yfpc6I/t5kvbIbrCXvHbOnsO6aTEukKojymGqkTGgJTE+Zs0M0u2V/ily0XgCPuISIh6SRqMC23GqUK1D4JH7SJsax8k+Jjt+lGatyRu3slsXudWUmV2HWbbFtJpdR82bU5S0WToXxCULowZJwA9B0nGas5NxD5o8SvHEp211Wr2OiQiyicNcejCGJNfNh7RXyw0GHMCU57B0yLqEXlddEw9AzLIIbRingxSYqgfbUHKGycMx0FCLAbnAAHqHIQMP+FW0WjiSCAOEtmXhtng7DNnhoF8BkJexWTZbRIXrnVIhexqb5cflXGm1Sy+9FJdddhmuuuoq/Pmf/zn+/M//HFdeeSUuv/xyvOpVr5on5NT2lre8Baeccgr27NmDc845Bx/+8IfH+v/+7/8+HvvYx2LPnj04/fTT8d73vjfbz8y48sorceKJJ2Lv3r3Yt28f/vEf/3ErTyGzrWQCjmwr/8V2PNi3ybpBr324ztDHsQSZPJXHxetKjY24VZYUqcUqP/PovhirZILssToLtqvNarrNx1OO+zrScwaC5L5l3zkAnOQjO8y2PNVlxlx9Ws7uwogxJB6JvzI6+opte2FRGJLi4ljgkeI20z+WxVR9IJU0bkyVqWm6TLdz8qE2sV+w7XAQcWsaLUuxeQKxvKSIpKbcSItFapHI1gl74dIyI7LPDwnMhLZ18OzA7MC+QesJbRuKSA6HYZmRYRuWC9mQl+eQaht6h/WhptxC4cehb7DhQ/psnQfY8JJq85qOG+Bgu4p1v4KDfiWk1NompNh8gwdbKfzYrmKDGxzikHK7v13DA+0q7m/XwjZewYPtkqfVeEGvI8RmISPmAkcvfvGLce211+Ltb387nvKUp+ApT3kKfvM3fxO//Mu/jJe+9KXzhJzK3vWud+HSSy/FVVddhY985CN4/OMfj/PPPx9f/OIXq/4f/OAH8fznPx8/9EM/hI9+9KO44IILcMEFF+Dv//7vo8/P//zP4xd/8Rdxww034EMf+hAe8pCH4Pzzz8fBg4dnFkLPHE1r8VEyhVUYiK02yl/doLe43ou+/uyL70hj0SW8tgBA0lHd8YuxLWeLqY98yWbAle1NYq3UT0W/ui0KtGVf1xjqPtUXFX5k21EAk30nkC/Gx8bS01CgAgSAZH2k6nSskB1BBsdq2nllbEoaIDkHYgjY4Ay8SG4wrafmxd9oiGL/rIZIX20CQ85qkzjFC355G+QBDAU4tXJcrKodtpGnsGy9p5CO8wR4J5W0XZiW7x38MOxrh0F/1MZq2g6taJSGwzATbaPVddhEj9QmDdKhoWiJhkEvtN4GHdKhtsEhATuH/ADrbVhjbd03eHAYPj/YhrXZDrUDPOBXse4HODBcwzoPcP9wDQf9AA+I7mgoFbIfaIOOaeD6JbR2kv3Jn/zJ1MUhiTf5dL777ruxd+9ePPSho6r9v/zLv8TZZ5+NtbW1zTQR7ZxzzsH/8//8P/ilX/olAKH20sknn4wf+7Efw+WXXz7i/9znPhcHDhzA//yf/zNu+7Zv+zaceeaZuOGGG8DMOOmkk/DjP/7j+Imf+AkAwL333ovjjz8e73jHO/C85z1vqn7dd999OOaYY3Dvvffi6KOPnumcntb8xyMKmW+tjaVPRn0PJ0CK+GPWNmfrZ14/pqNNqvzmMamS7uA5iKIuUFXbnn3O+0Ek6Sl7XAmQdPYaVcZDt5UxtIBPU5n5VhwTp5lrDEo+nPlz8o99o5TO0mn4Mj2KCaYNeXf6Hs5Tv6d9kPgSB8EnbsviWB+K7fkYJz8nTkRNOK8mYSgmAC7/bN81FjeI/9S0xmGIyyPHBH+OaToGB40VOPMhx3JcQF80MMhS9pPQXm4QgGfTeDADg8YHaVTj5VQZg4bjPjBjrWnRIswoAwgrTQsnfysGzsMzY0/j0QLY02zAc4MVamOba02L1hPWmiFaJuxxG/AgrFJo+yGD9VT8XOyBdhXf84iLse/4p2FW28wzY5b4j/y5N8Lt3VwtJv/gQXz2J1+7ZX1dhDEz/uAP/gAf+MAH8MUvfnGkLuO73/3umWPOxRxZ+9qv/doqMAKAZz7zmfjc5z632SYAAOvr67jtttuwb9++uM05h3379uHWW2+tHnPrrbdm/kBYF07977jjDuzfvz/zOeaYY3DOOed0xly09Wm1rbIxjMIWNQdgurRU7cBpvaeJXUuhzdSlQphdxqv1oZq249GPXem9aeJXZ55xHrOa8mOTMhvtS84ghV2kx8SUWN5mSoOlmCS6pLTGmrBnbHy8iacMEiNjhEbWRBMGSt8BhmuNj56PtmniUJlyMwwSjE+WvhsiZ7NMOi1sozzVpmyR+JCZuaa+yiiFVFuYucatE12QizPcvKTefBvYo7T+WmCQPDsM2zDDbWMY2KWNNqyvNmwbbEj67KAySW1ge9bblZCSawc4NAyfD7bJZ92HtNqGH+BQG9ijkHZrcIgH+OpwDQ+2K1jnBgf9APcN13DIN1h6zRHMdZ33td0nMYW98pWvxA/+4A/ijjvuwEMf+lAcc8wx2Wse29IikItMGX3pS19C27Y4/vjjs+3HH388br/99uox+/fvr/rrQnT6Ps6nZocOHcKhQ4fi9/vuu2/6EymsT6tNY8oYTTNWs7BLCzZG/Esy05IiJYsyqQ0gMEi+6y+X6cgUm2PbJr3F3oPchN9OtXTYSDvZoOT7SjF22RcgMEq2nbjemcbWNx49FkjTzgcSR+NJwUarTUpHUYwZavkElXQQJHNkaogBbjnMUjMgKqxLZipjU14/iXz4HHy1anaqkB2yhoWQW3wcOAqo44L2JjbUxxyry4LYMYkCbzPkgRGTtlrtO5LQ2wEYErgJQJCZkw8HsXXsLyhi1wgaVZDNDhjq4LjAPJFokaiBH4ZetW3QYLVSJDIUGQ3/pjak7eEwCMPXW5fdbQSAm3BS1AbQdbBtwNovx2hAUniSQa3DkAnUAq0P9wgTsOJCm4ENIxA1YA6C7AeHK8CyLx+yS+w3fuM38O53vxvf9V3ftbCYfYXsOeyaa67B1VdfvZBY/VT+LbZZgMdC28UMP7nE2QKFCf2198zoFHx90mE0Tieg4fH7rU9pJctU6Iy0wnLG2GRapdFjRr6PO4dY2RDdYxirAZo2i/GJuyPoNHFE95SW4lBQVS6xQemyx4rWUj6SKB5rrxczjI9M7feUfrbHseO0/pksDBumuFOcjKfgTWfPhan4UqzSFIuMwyNYRcsA2NlvoPCdGaBBAmnUCojSZUSUDVNAuCL3pFRo1CLjMKBR5uCHWYXDAGS0QKbcNGnYG6BtHZzzMSPqmjZK0TwTnEOsnUREIIQTDv9OPFpPcI4xbBtQw/DeYUBt0IsDAIbY8A5uwFhvwyK2LRw8D8M19YRDbVgvzoeVdbHBzQ4QZBN2w1T+Y445ZuEz5TedVjtcduyxx6JpGtx1113Z9rvuugsnnHBC9ZgTTjhhrL++zxITAK644grce++98XXnnXfOfD5qPTCaxuLv2yl99bej3cz1z1tgSYQ8y1FTgJOKe7UNqpy/NV85qKIjYubAmnT5JsfR75z7shGKd85gG5caK79bsXVMr0FelTje57PRdIzUX1Nsmr4zpx2P80K1GOFy2A7EdJqeu2cRRwtwMOmz2AfrE4XUVBSFRNwPLn1CKUpqkQTWURxNmW8UcVtRtS+OUx8bpxRiyznQ0Bwrr+ijgmzTrxCLin0EUtH2BlIRSR+KR3otHDkMKbd2SFI0MrA7bSuibU2xtQ7DYYNhS1hvg4B7fdhgfRhmuK23YebawZhWE8H2MKTY1r0Va69E0fZ6O8DBdoAWIcXXwuGgpOJWll2QzQt6Lbm9/vWvx9VXX40HH3xwYTF3DDhaXV3FWWedhZtvvjlu897j5ptvxrnnnls95txzz838AeDP/uzPov+pp56KE044IfO577778KEPfagzJgCsra3h6KOPzl69HQ6b519p8a97WvCxCZsf8OpD3j70N2FdKS+gG3RMAyJt38b5RF8vH8M2sqBkXDzm0Rcwqj2KdIgfjVPEI1s6oJh1Fn1KAAaYkgOIU/LzPsJokOx5KvhBnPkWZ59BQYtsQwGmvAFIDJk+X/pw4SMao5YzYGVnrNnSAKo7yoAN0vcYz2iTsn6J5oiYslltYSYcjWiRqJVt8g4mYCjsRkth6n8bvitIAhN4SAA7tEMngKkJM9raMJMtgCZCy4SNYQP2Dhsy/X/YhurbG63MdPOE9Y1UYXujte8BLG2wi7qlg6JbOtiu4MF2FQfbFbQcANUDw/XKzd/b4bbv//7vx1e+8hUcd9xxOP300/Gt3/qt2Wse29K02qLFxpdeeikuuuginH322XjiE5+I66+/HgcOHMDFF18MALjwwgvxiEc8Atdccw0A4BWveAW+8zu/E9deey2e9axn4Xd/93fxN3/zN/jVX/3V2L9XvvKVeOMb34hv/MZvxKmnnorXve51OOmkk3DBBRcstO9dJkqDw9LW7jEGoopE3/UztgYgaTNl85uKOaGPXbstkzIuTTfl9on6qc44yNdBA4RBMuUCasfWgOFIKo3NL1vOfcp2YyxKKae4k2L6SCtQp2M0lg5jAmkxK6gFHzWrpxokqwcTMMLE6a4kSlWv9dRYv1PS/khB0VhQksN79AFEv0Tp9E282C9InHbUJ8vCOgSfRnwEDMVq1TJkTIjVuMnbeKI9UmdZq42alJqEGVpI+o2HSJXEicICuESS5gvT3jxLFW2GLG4r4VoXqgV4F0/He4emCZiWVoC2JWw4imurAYGJalxYlBYUvhMB661U3tb12xzDbxBWGh/0XAisVMsOjpZbkL0Q5mcHPJ4uuugi3HbbbXjhC1+I448/fiHYY8cIsoEwNf/uu+/GlVdeif379+PMM8/ETTfdFAXVn/3sZ+GMiPS8887Db//2b+O1r30tXvOa1+Abv/Eb8Ud/9Ed43OMeF30uu+wyHDhwAD/8wz+Me+65B0960pNw0003Yc+ezU1/nNZ6YDSrTYs6uHhHAg061X2RIMlgL9vsfEt+6NNjcv/qmjXWnaMgo3CZLlM5BYhi06Y1CxJi5i7pdPIxq8SzguyyjZo2yKbXrGmcuISHqZAddS7aQQOgYrsqcNYnsgqoGXChonIUxAgTFEXWIrohr4AmLmEb1iATcJaqaTPg0+Ilqi9iT0kcLYu8Rg0SFT6G3WEKTE6M1xaAzjMcKAm0CZHZ0ncFVapRigDLXNoo4obpuzBewVeAUmyboh6JJGAYsRCRNjj/WeNEr6QjyBTE2wy0w1CzwENLg3O8XTaGFLVMDIIoyuHAaOO94gI5uBGWHjk0DLfFQLRaGAyBluKtN2TCweEg6I+W2HZLhez3vOc9eN/73ocnPelJC4s5V52jO+64A8PhEN/4jd+Ybf/Hf/xHrKys4JRTTllU/3aEbaZmxdMH3x/+wPY2p81K0dT+mMmf380CpY6uzL+USEQTY3cHl5pPZVvUFiE9dWrHltuK79UaSLVt9nscB7PfUdpClMdgzusflT5FjLx98avtY061kcjUQHJmbKQdLo9VAa/2o/xOUp8oxs7jsrST6iSRPpuDNRQBSqx3FD8jxtNjajWRmAFIHaCAFWR/rIGkxwsAFJ+sdpH+flAB+gApnmxP/UGMwz74wiPWSwptyj8MYaSwAqDlNNnLASBOY8EMrEi8hpOPAwBOjFsTRNxuRYClC8uSgADnwhg0Aw/fEgYrHm1LaBovl9vDyXg1TRBt7xkMsdE6rDQeHoSBa0EOWJV6Spkgu3V4xTd9D15warcEo8sOV52jU37mZ+A2+UPfHzyIf/6pn1rqOkePfexj8Xu/93s444wzFhZzLs3Ri170Inzwgx8c2f6hD30IL3rRizbbp11lvSB7q0z+yo+YoRXiQ7ZgK+a1LpKGZ619ZAKOA2z6ixtd95EyZRNAVu3YSlpL2+qsgVSLU/PlIHZJbIHxK/VFZYqt3A9EYfNofyTf4zuOYTkv3WeEzxqP7HmJIDsu4eEDLZPXGvKSigoxqE3taHot1iNqQ1tRcF2KpTn1M9MHxTiQOkJGx+QZjgNLFbRHRpDtjY8IwHWJEFvPKArOVYPEpl8qxDaCbFhBtokTheJD+Tw0vlJhm7TStn5X8bbqlTwBQ0oVt9vwPb5r5W3RKgUhdxBxt21IlQ2HQbTdtqFC9zDWR2qk0jaJoDtoj4a+kSrcoj8aNji00aBlCgJwUNjeOux1iylwvGXGC3otuV177bW47LLL8M///M8LizlXWu2jH/0ovv3bv31k+7d927eNLEbbW29ba/ZfbkndKDViP5tt8WGp71tAkWvaYF72qCtdFXdzfprjYthY9tTJ7LNtldojSSmN1SCVcco2DVOlccLmjvO0wETZmTL9NpJmA6IgxRe+I0AuXCBSBkdTb8SxzeyuCk4p/RN9OE7lT4BP4rU+sFOS8SEgpJdUyCNtMnFM8UV9kEttgkk0PnZMOcQdctA/+dRRhjArjNBf1Q754l+DMDZZmyoYV+ZInWUfNQAPETPU4Zxy36hfogByWAATaz6OEfRZrfz7aE2q1UtizgM0EPAkbUZmkOW8WUsmpGsUmC+pV9SyLOgbZsC5xsO3Dq7xJq3m4T2ByKFtCWjC4rZNAwxbAq0O0Q5zLmF9o8FXNzaw1LYIcLMDwNELX/hCPPDAA/iGb/gGHHXUUVhZybVg//qv/zpzzLnAERHhq1/96sj2e++9F23bVo7orct6QfaiTf+Km7+U8S+87Cd5QmR/xfWYLQBISEAA6AAVmdnHMacnUDWwee8Mqw/TKYjiGjgp2Z9xoKj01bSYFUtHNkuBij6kqTteF+jKYkugpgBO7JHyRDwaQ0CUCozj7ePS/oBzcnClsInlPKNPY/oCxCn+tlyMFkSMWE/aZKc6IwUrxkeLGVmgAgRGxUFEzAhaJ/GJ7ireNvhRtU7RxwKaJg0ffGW4hjn4UR8w4ow3b/5pkY4rIbA9FNiu6OMRi2kSCWukl641gnMXxjiJ3oMvRKelab+Q1iPo2nbwjJYc4Em03oEFcg3De2BADu0w/G3wQx2QUDzSOcKDhwYYDDwcBQ1ZqNZNWFnyUoG7RXN0/fXXLzzmXFf23/27f4drrrkGv/M7v4OmCXdj27a45pprFiqI2g3Wp9UWaSUQquzPJaSy2Zvti+9S2fRkkXbR/y5AUh41jtEB8vPMgAgQF44aB34KtqbzPDI2x2ME4JRVr/UpHjtT9s+AHzX73ZlrHtkipKdrFPuMnkPoo2EyACPUpgSQ2FbLJgFbabxjhezMJ7VJ0g4rW+RVHK1NJyE2CZJQUbUCJHgnDBWgD/wgyCakmVtaTFG4LSnUSFqQEjq3LQy549CHoDUyaAqGWZJ+qhRIBdlRyG5F2+Y2UNbLgjKmkMrjNniGmGn8nTJyTOAhh7Shmtyjuo2k0jaJb/7PRu8PF0CjVyBL6fI7QivXYSiXqt0IYydnBHIO3hMGA0bbNlGn7hkYDpsYr7fttYsuumjhMecCRz/3cz+Hf/fv/h0e85jH4Du+4zsAAP/7f/9v3HfffXj/+9+/0A4e6RaXgehtMdYJjCyj1PEHbRpmZVus0ucaiJmK+Opwsum5GQDSVD6lb1mtGjDHFN2rXc9xrFJ5Lk4pFAVJBXOk7JZpN5vqr3hSkEECQhpHtltcEUtCKz4S4CSpoyBgDs5xuFnSX00YHxKxdwoCSTVp1zUtx+Y8jQ/StH/yCFW0S2aMFNAhsS5yjArF0zT9cIJaRVvxIQFR2pWzQwkMpeti9mk1biCmNFnE1hF4kWF+pLswaT4LEkl95SJoqjAxR3INW72YSGUSNL3bAm4FoeikqMyp8SAQWiIMh0mQHQApYX0Y85jLabukQrbaF7/4xerCs/MItecCR6eddhr+7u/+Dr/0S7+Ev/3bv8XevXtx4YUX4pJLLsHXfM3XzBNy11rPHM1rhvnJrOthqtvNw5qN/xTMzFxmu8kJDMclNaY1PQcqWZfSD1matt6GQQLZWMiuUh/VpXuq9CM8czn3KZmiDgYpMlH2uriCCSxZJJu6q7Wjl15nOXGDkWsewY7EaWTJj2y/tKlrmWmuITJIwsew1jJioHHiq0yUYXlcWiojACSztpocTxGDRVpFTt2F/tk13GINJAUlwjINYcCmjoc9/9QmWoQZbDL+xAAaBUDhusTUVux37FrChENzqdTXXgcfgEi2LV5yBa3ptNVHU28QICkloQI7pgySvZ8bBCE3TDw5jxhbZ+oJU8guXCceSl/asJYcs5e6SWEw241QePKoZhVLbfZP32ZiLLnddtttuOiii/AP//API89UIppL7jN3wvSkk07Cm970pnkP702s6xHf2yQrR23cSHLhYx6uum3K1NWsVtYhGr8m2jjj/LNSCONcpwk9rX7Ibjf7Rs6hlgKrsj8oGKRwXDjcgLWYojJ9qgGksk3bju1H1zps1kdF1jUGSU9f9+kvVF0rTEsTKLBhlpRWik9AFCFHHwTRdhBbcwTRdiziMEHAtbAeuoZZ7EegosItEvEiS8pN2CBoHDNIkdlKwm6O9ZY4gYkWQVdlCkpmgEOZH/VVtokD6GDjQw2iCFxn4rGmQWUGHAYQwXnwSeOmcWTcW0CrrzNRwFzexCME4DagBMpsOreVtGcsSCmL7A4BHjiMkEQt4cD6kguyd4m9+MUvxjd90zfh7W9/+3IUgXzggQfw2c9+FuvreQn1RdYa6K232WxGuKkPny1ijkaYQU7vc81gq7EoU7Q/8sfCPl1mTc+VbJH3IOfqLGiXoFq3MULNoQw0epBNcU6TqqvFt9vZfmdEBomR661qLJFJWVk2I7vTeNSHvAIoTjIl6YsVW8df98ypSGQEAZxEyAUJErvechKB65uk6pkIjhmeKQARL9WqSzCjYyMgA5Ed4tSm/XfC1gdR86QgkrzJiClQKn04Zc9YgZL66Gw5BryAHq9psJbDQrgqBm8JcAxqCT6CdRFxG2G39ouHqh9DArWgUBqAWATeCBfMewFRcg6SitQlTtZIFeDLabtFkP3pT38af/iHf4hHP/rRC4s5Fzi6++67cfHFF+NP/uRPqvv7GWvTW59VW5Rx8T6Nj/yh36qUWld3IlnVAVymCih/0Cek2FL2rGSpCiZGP4/EqMQvWRyJDw4C1qpveZw1yw5l5J6Z2t0FkEojykXgUVtkoAyRPHHNFK6Rfuhx8kAl2SYzwuKMK9MmE+U++iYAnFoWgERRJ8NtuhdJD4AyT5yDKI8QVFksATHhQW91T0F47BCAU9RC6cy3obwLOxaIFZ3hxlGbE3Q7qeJ2GD4VgcOMqfHRWkp6KuLLunh9q+OJPDUmuIQgvsIcERLBQwiMFlFYboTk/KgFeCNV4SYFn57hpEQCiTA91IRyUlFcgCBRrN/EUm+JLXBuCVjx6YTkMmA9zFhbalPgvdkYS25PfepT8bd/+7fbD45e+cpX4p577sGHPvQhPPnJT8Z/+2//DXfddRfe+MY34tprr11Y53aD9YLsbbbyATuFrmYhVv5q34zN0MdqGqz8HNm0Ce1U2q2WLJh0HJttFDZwBroQUj01gNT1uYtZskBLwVCswm38mDO9U6x2RLqEBSc2wdRDCuyPUCrCIgWxdYhHIshGEU99okBYwEBMGSkTBMuwsJmKL+caRdsC7ORYckgz5eLwKEOFwHJJCQQLXhRoaVsQEBUBDSFo6NTHptFAKWul7IuyRPKKl0srbsvIJAF1OF8lcuJ1gsRBAKOaltN+JFYICQQp0NE2KfUHgIBHAUracaY06JLaS0UyCW275ILsXWJve9vbcNFFF+Hv//7v8bjHPW6kztF3f/d3zxxzLnD0/ve/H//9v/93nH322XDO4eu//uvxtKc9DUcffTSuueYaPOtZz5on7K60XpC9nVYiADakUuUBezi6MOvBY0sXID4gtUr3KEuloKEi9FZgYhmXkfg8+p1otJ0pgFVsjwGlRzRdRzpOJRDSftVAbg34lQxSoPDkXeLGOkVcHAuTapO4dm0t7Z++4iw4YXUsM8UpIEmajximrpEAGi+NCmMU9qkeygAy7aAyRnI90/psCnSQCDMSYXeRTmIAaDgKpglBrxT7HZcOSQDRsQjYPQMbIZ62ldKGAuRUkJ0uM9AC3AiTpJr5IWIWK7Kt0tUgttZ4BN5IPlp5XTVPurYcNtLljrL3RkoLMCK7pOvQAcLs6S0zoASMpJL3Uc3yLzy76bTYDnhE3XrrrfjLv/zLajZrXkH2XHOXDxw4gOOOOw4A8PCHPxx33303AOD000/HRz7ykXlC7lrb6mfv7rVZBjY+kTHyl2ArwOs4LMOJNZkuVvEA74gZH05dfrU4dkgY+YN/Yre4+1xsW2W8rE0Gy896ZqlAXTuHWrxx58ko/H0CK8yRpekESECakRfHlqM4m5gjaEnxwjZiWS5EU3YMWS4kPKFJ/AmQitHJJ+yDLDeC5Kv7RfQcly2x+zhso9gmZ3GozX2c16VFUptZHA+4NoAZJ+k03Z7FkxRZjCfLh+jSI3GZEdb2Qty43IldrqRN+1ybYukSJ7qkiTI/riU4T3BDCsuoeMCZpUzI7HOt+LTCHLUE2hCfNixfQusEHAJoPR1z4FCut1064wW9ltx+7Md+DC984QvxhS98Ad777DWvzGcu5ugxj3kMPvGJT+CUU07B4x//ePzKr/wKTjnlFNxwww048cQT5+rIrjX7S7a3BdosY1peAzbbtohBYqRp/Zq6masNfVpPUUVbv05ikCbZOAapYNw6U3g2Ttd5xzQXg1yTs19mZliMXdMylRqouA8JP1vFrsZi5D8dNQ2nqbbG5bPZ7LFa/FDrCmloU5cozmIUoBQnpfnUnVRFOy1Nom0GVYyT4U6C+EiG+TDDLOqK2LSPNP0/sDHqQ6nSNijFU4aG5TwJcVabMlKx2CQj9jOSXkOtaJ2WL4lrtnEaunhdzDt5FWQjptqYARogCac9wC4AJq/ib51xpkU2RflNPrBcURvWaMqSQtoMARh54rRunaQmuVFWSgEdYe+yM0e7xL785S/jVa96FY4//viFxZwLHL3iFa/AF77wBQDAVVddhWc84xn4zd/8TayuruKd73znwjq3G6xPqy2D2WtggZIBSFvRqmUjtIl5m5sWwJn4VZAUp/XUABAMYMH49qQ/1Ta6hNVlKtNznMkWU1Lg9KAr+1Gm0WxsBTV2OyP3iccVoLiangv/yWC1xtNUDEwfQeDWCMV9KC4YC0rayth6nFwGHnojHhZAg8DUgASE1UTboaHQD03VKWgVLBSAQwJcUZujom0RirP++NZ7QEAUoeIjTirwJkIosMhSTkAAnnjJ9UGsjxT7J1P5nW1ThdiQFJwwUyznTgqM1ImNb6vY1AD3jQAOdeFbrbTthhQBJbUAVuy6dSEubWD5NUeLYH52wCPqOc95Dj7wgQ/gG77hGxYWcy5w9MIXvjB+Puuss/CZz3wGt99+Ox75yEfi2GOPXVjndoP1guwls/jgHKFbtoQ9sn/ELSiarQaSCTZNPysAqbNeUXacHDgufAVQBMzQwSAxV9ZfM21HEGOATUQHSrd09RXjgZONb2EO6wWxC4MVsfTVSG0iMmBD+yXpNGWdyLkE8qRyd6w5xFJ8saEo2hYeQ5gkZYcQQUfqcmiYgSCsFmYl1UmSg7QQptfrrrvCObALcXSpEdYlTrRNjzBlngFuCOyKNtVHY7jUr4BOkITdlF9SJa7iJfAAOY6arojJhejRtoiQZu2ZhW9ZwJQuwaI1mUi0TeE6mRs0lhqQBlTXxQFK+aGCbPH3gTla9r/du2Uq/zd90zfhiiuuwF/8xV/g9NNPHxFkv/zlL5855tTg6NJLL5066HXXXTdzR3ar9czRdlj2ez83ez3iwxeLB0axPdu0YSU209w0zI5ppzPNFoFHWXkayJ6adl+HkN22ET9bIFoTVWtbSg1QvoOFJSHXFP0zn2ORRp2RRqPjY0EMkATZNvem/XCmjXhs+J7Sfbpf25SxVLYoMjthbBXvEQK7EcAUpIp2aDOkrjiCL9Jx1B9XWlbAkbAuaSo/SNuUhmKVb2GLlGnRVJnxgbJjrTJvMkxDTpXEm9QmmCO7A0ao26TIh5EKXaqvMwBO0n4MZYXk35/FxM4IqM0tquJtvVVU2B3BpwqzfXGbuRQnCrxbgm+EYZJbwElByDRTLcRcc8td52i32Nve9jY89KEPxZ//+Z/jz//8z7N9RLS14OijH/1o9v0jH/kIhsMhHvOYxwAAPvnJT6JpGpx11lkzd2I3W1lBubfDbeXYc3q3wGgrmCM1fTgW98LM7BGbvs/IIuVtcfFu4luA1OWT9cUcZ3RILNtJ93exRrajCj4I6WkJZLPACKgDoK4UWY1x0jZCcKS56UpJQEAXIdMlRdYEcUzjdoaALp9SSQDY3meAaHOkLSnFpOwJyyK4pMvP6FR3LQ1gr6WKwxvRLZlVUwIVomgMgC0Oqam6ePoSVGmeWBqAUtVrHVbLCnmEGW8GlIA5psqir4x5XiFb+qU+CtQEhMXLoiDdtqm+eg5eSlKJaFw1SDCnFdvUOALuSPRL8U5TrKy3YAs8eMjkAnvbNrvjjjsWHnNqcPSBD3wgfr7uuuvwsIc9DO985zvx8Ic/HADwla98BRdffHFciLa3Ka3HRYfBSqZojkHfKmBkrLYm0BxRZm+rCpAyZ+tUARSm3S59kbZZskz6Pi7tZVkmfWxbQTbDMA/JJfXdfLbrr2l/auu3RTCjNAGlcWAWLZQoge252ArZVpANpGn/ko5TMBeGzzApXiplk0wld6HNkF4LAcNxwiAJ+AmgSKfwI7FMmvZqksg6iMGlcWd8yppKDvnsvQALQpsy7hZswIfzpFYpHNnXKBiSsfWhTS+MYKiQzSkNZgBPhJMbYdzCuXAaTwFFUZBtQJ9eOgWs1CZsBjm/mDnVqtwyGy4yRFrbqEn3ls64O2qwqUUmtt70n+5mYyyhTZvNIqK56i/OdWWvvfZa/Omf/mkERkCY0v/GN74RT3/60/HjP/7j84TdlcbLeucdUTbvGBeAoItp2Cxw4uI9Cz8Le1QyLdPPYBufyuPcqcYgdR1fYUVDRWefDmeTWim1SaWQ2uLcyA4hPgQY6UFYZY00zWaraHcBM+/NOGkMn853hGUy7QCALCarnWTbpj7FCalIJAC0IqDWVJkZK3iK0/nTcKdK2xRbgqTgAloJvhRiy/IgilkAgFuf4klBRR4KM6W6K6/0itImSMLzFiFZKGmwjA3TIWl1fGSWX8tRV0QI+qWQjlNBOoyAOgTTdF4gDcNFV/CZBNkUwZJW41Z9Eks/CJAq2sgE3lp/SSt8B+AIEWQnZiuC3yGwsbHcq0EcyZqjrc5mzQWO7rvvvljbyNrdd9+Nr371q3N1ZLdan1ZbJisZJmM2XbQVAKna5Lz3hZxHefwUfZy6rEAXg1QChTFtlNuqou0SuITO5W3qNdOUl7Jg1rcWp2SxRtqk/HN80svYatuJUMlj6auhoDdqjCBb1ceqm1KA5FRFTFGDQ6IZij6ss+CkYUmrMTil3GSKegKdQFxYVqgexXpJkC2IQdOCCno0js5OI/m7FfGsFWSnMSIObXqkNJymygKbo5W1w/Vip0uAsMze41j1GkgCbxWGswdoBVVBtjeMj9ZCCqxciEdIvlXRtt4K+p8NxOKXkVGq/BPr7fDZVmez5gJH3/u934uLL74Y1157LZ74xCcCAD70oQ/h1a9+NZ7znOfM1ZHdaj0wWiarXQu7TUGHftUH5ly1VLvNYjR90FPHtPiqFXROl9i51vTY+PE39oTmO4BRCUoM2KEaiLHfa8DJ5q+8/kQ242TBS1faj80TribaLvutVa/jmhPqUxxn42paqvXFjyFz70gVbWWUAtARboScAD2Oa66FuEgMjlMw4mM6DczxgR9BL6T2EbQuEYf1xBR0kAAw1fcQpYrWjMBQOVmvTI93JFPlKRtrraDtwHG4QlFIqdutzIxeG6nKzcKc2arcBIrliBT3R9G2ZyPIDv1pOACvhJY4VO5mljYRAJOItnU8eWi+czyVkUrdCrr2NEueVgM6f+8dSbYV2ay5ruwNN9yAn/iJn8ALXvACbGxshECDAX7oh34Iv/ALvzBPyF1rPXO0k6wAHQAiu7AVou0CJM1tJSMypo8zFaQcB1zsPV3TIRXHdq7JVsZQ5sWm9TJNTLgGKeOW/FjoAir7Xl4/m8YbSZUVwMRaLZ1XOe/A2CRGKB7LyETbERQXPqQnpH87KFRwVnV0AFaSR1KxtepmgDibLvZDWSaX4pLUVGKt5i2CapIaSGjC6Ia0oEz7t3PzzSw7JsRlROJMPcei8UkME7V6miyp0eRLrPWO4tnLP0OOxSBJ25T7IYqttf/CRKX4uvyK6ZcwRxEYKbNlr69cA/LAgwc3sNRmAN6mYiy5bUU2ay5wdNRRR+Gtb30r/r/23j1OrqLM//9U99wySSZDzGUSDIbbEpBLMNmMQRG+ZF4kwqJRlIuRQIyJq0TUABJcIEAWghKFDeZnXroCiwvLZRWMiFlCAoKaDTEQBYQIETYsMBMxGybXufSp3x+nqs5z6tTp23TP6Z553q9Xp6dP16nb6fT59PM89dStt96K7du3AwCOPPJIDB06tKhODGqq4IPHuKACSQaWg1JVTZ+hvuD1lzjyFC+6EqFdNTksXGRIUq9Iytox4R6zutnltCDR10SIGJdYtnNo9uuQJSl4AlTuID0wZanwyxABY4sfGudkiygtMLRw1UHbuk19U3eN0fRXXT/tapOqY1LCLCvTZUj261AZqcUHYDJke76CCOSUhL9nm79KTO+xp91fZm81KUMuLj/zYgo6VaMg4lNHMulNWKWUEDUSyCgRpN1qVEzpAG9tPfIAZFIQngdPt+lJP9jakxA1SqAod58JyO6FUkhqikl9nieCgGyzn5t/vWSNalMFihuR5Ukj7FJpmCSWvuXIt5x5ZGUe9Kp9CbNVy5A6zpBdCZTDm9Unm+DQoUNx4okn9qWKQQ8HZFcy1HQTV0QERUptOaJQXVJoEyaImJg74vppCbJIbiK7MBWFknTOrNrKMScuV1s2QeWy4tiWGi1QtNtTamHgKQuSCCxILheaqccRtB0RSyQ6WreZUb4s+/PjHAPMfmh+/Iu+46vyKZj8SLpvkgaVQ4uLcMZtv2XtK9MxQ54/D2Y7DjU3IAHZRktLZQSTgWUumFy/TR2DpASHzKjYo4wMhKjO3K32WpPGTef5ZU1ySN8i5den3WrS6E6h50RvGaKTNOos2jqWKSUhelWAt9kvzhdCQX6pwIKn+yeFQMojqQaURUsIIJWR8HSGbD1R0t+nrae7spfyD+SAbEo5vFlV4DAd2LBbrdIhYsKOMzFFrBt2uQQSofA4JHMmClFX1IJE2wS09cohkMx8EEtOIfPiKBvcvK33Yl172v/hP4cSbGoLFWKCtm2xJER0jza7vmCgyirhkWXs2s0UM1YqlHTWaSkhU0FwNPQGYjIIzNZB0dRsJTzf4iOk/rzqjd78ccqMb2UK3Iu0334HdDy4WfZPArt1DiFPkC1JdN91bI+yBAk1YD8gW0+lEjFCZ6D2++UJ8nkGTDC5STEAwDP5nGTgLhTCxFVBW7/gW3ygczEJaRJd+sHWUrWpRu4BAlJZq3xxJ/VKQ5VUM9Xj/232j9Ob+lb6V7dEzt93edVR4ZTDm8XiKGFYGFUy5me0umOlsgskIHxjLWu3ihFGpAJzP7dERei1PiwjbYY/t7o+PV8OASNJOf2WKzjaikGK3dqE4rI42QIntGeFPg/hflFXIK2Putj0MVeZ0FgQWJ60dcmTvsiiy/9t65QHCLtdxzyFsleH+qUFnwpuNikDfLeqv6+Zp8KThLEKKWOMOi/Ieg1t6dH1CV9spKADu4P5860vvgWJuoBTyr2ZkoBUwfPSxBr55VIifHnMsnolPgVU0Le+26splBmQ4G1oZenv46bEi+xVIqsXQWwXJNIAZEqv/NNlBInt8sdDL6pZ/acCsutrOEN2JVFKbxaLo4Rhy1Gloq0ithVBJwx0WBfMa31DKoMFyXRLqqaDfhRtQXJZSuyiiLYZsSDRukJ1AiH3o77BuKw+Vj8i4zRBzeQckiE7VH/EQkaEkYpBIlE04bbt+aBt0P7TceixpvR82BbFtO9yS6VglvKb4GWrrZTeY02Yeo07zbSDyFyYnugbfFpbkJRbESRg2fPMJr5+bJPuiwhiqzLEbSiD+BsdYB3MpVIiZBf7oJOBDhUZT+lUYdxyQiqRogp56v+diUXSAkaSz7jQohD+CjgdmJ3WwdaBJvetQv7cioyKYdIuPqO11HnKguRp8SbUdzSUWBPSXDbhAfsPdKOSGSxutXLA4ihpBukHr/KR0WdqFXGJgFB5uIVGibtXMguSfp1H4shgPy+XIDOF4scf3LXc7wFEJCgXkApK1m6wSNC2lEA6HSR5jHOVmSzaMnQqICH0TqogfbD7o4/RrNqxVibVgAkQVn3LZACzH5wubwWBGwHhqWzcWmyRflG/kDVPkeBtSBN3JaCCrWWKiASdqyhwvUmlAgRp0++Fv3u9ycqt21LZKWVGBHFUKUlcb8pdJQFkggzZJmYqnVKxTsJsfmuyVgPBKjmpxKQReVAB3tIPoFYqy7ecScgatRJObVGSIuMRafiWLi0aRcoP2lYfEQ8wcVFUqAqVIXxoXb3jQ1xBaI3a1zoGISyOEoYDsqsIbTGSQHDnsywEQWG47/597YPdJWmeXQIp59J8E0tliYksYovG70jECDNdpysInOpOkaNNPT4lTEJZplVfdAyNEUb0Wrhcd4C17D48npAA0b23rUn6tQm+RtiNFhJLqgm6/F/PiwmuVoWoANOiK+P5QlR/zjKeSUvgx0MhSO4opbKyqHgez1NiQpjs1/BUN/RNX7n9guBof771fmZ+/JAXrEbTFqTeQKQI8zkEhND1wcrKTfIbIdqmDsgW+vroVOD6WMovQ0OkgiX4nvYcqiX9UAHZCFxvvdLf8DYtg/71KOHWq6+/Z4K2dRZt0QuSIVtPkH+8q5uX8g9UWBwlDLvVqgkqeGQeXxoOgZSH+Ci4SwiaKX6zWlphjvNl+O9YgRRRQa62RXYLksvFpW/E6pi2TESsSbb1KOTas4/L8A8Vap1IOepy9cvet02/J4U1z7qXIbXkCyZt/aJpCozrLRBlgkyp1BYkEgzt5/SRxoKnM2HTvdr81V8SQifRhAi8fdoiKXWGbF2v35bMKMGdkWH3HhFpUqb88zJqPCo4Wuo2M14QtC20TtViWNXnSWK5gmpbWXxMGWXJ8eDPtRJa0KvuBMyGszrAG0KY4ZhUAxkVBN4r1fJ/fd2EypAdjE1nyE6X4wcQUxGwOEoYFkaVDP3ik9az65j+3eu4ceq/ywWxeoRyIeXUOq5xIXyTdwkfOkyHQIqKNC0mSYeoJYlaQnK1rftHu2K3FyeqqHCh49ZuIQjlmtIWBCK6JKJB23ZOJNc1DgkqNXHU7SbhWzOAwBKlLS4SMGvKqTVJBnULqK01JG1P16esTqoeoa0jgK9IlCUK0EJHKxAtNJVVyFM5ldR1FFD1IRiKhAqE1tuNqY3NfM+hCgLvVVoq46kM4B4khArI1qJXLatXwsd8npXX09d20hi+aIqAlJRG4PlWNCXkMsGebRCAFDKyeFD3T3jwxZWECsiWkGm/X+Zz7wGpXol0JBdYZcExR8VT4n0PyseuXbswZ84cNDU1obm5GfPnz8fevXuzlv/qV7+KY445BkOGDMFhhx2Gyy67DO+9916onBAi8rj//vvLPZxQ+0ylY387ZLtmdlmHpcH1uhRYOocKbykl8hfi5OYaOkxFgLtt2k60TXVncsXnxDQZERwOAWI2sVXtSfW3U6xE6oI1b9IXRiHLj8oUHaom/D4AX9jYx+x+eKRus5mrOu5ZZTwVVS3JMfMIvxZKJJmboc5sLeGLHi32pG9R8h+e/0zq91/7QkZ4UgVQe0ZE+cesMmqcvohS9WVIO1L6IkOVR0aaPuo2/LKkTX2+ek55nh8LpMuQ89EbbjOl3Gh+fb4w0m2a+jLS1JfSlifaZsbPb+S3DaQyQKpbItXjP6cz/nldXZWd58h8vvv6KJBVq1Zh4sSJaGhoQGtrK5599tm8zrv//vshhMDs2bMLb7TEVI04mjNnDl566SWsW7cOjz76KJ5++mksXLgwtvzbb7+Nt99+GytWrMCLL76Iu+++G2vXrsX8+fMjZe+66y6888475tGfF4YtR5WMLND9Ra+ly4qh6+uHa25rEhQhxLUFwhYZMfW4Vs65E0eqes3KI2uuJHnO1T8qEHSbtF0jbtQN3rWqjbZJRYV1Z9CiSVrthiw6VNi4RBIQFTpaGIXKakGSCffLPhek314gZIQSOub9jCTlovUI8wy/rBc8/OzSnjlfwC7jBYkfjUCSRqwEokoLMF+o+e8pMafKgAgSX4RJCGXNEb1eIGIygcAxAknX5wVl9DkpT0L0avETiCb/XCL6iKASEuQcGYhBqfriSQyt5wzZNg888AAWL16MpUuX4rnnnsNJJ52EmTNnYufOnVnPe+ONN3DFFVcUvVFsqakKt9rLL7+MtWvXYvPmzZg6dSoA4I477sBZZ52FFStWYPz48ZFzjj/+ePz0pz81r4888kjcdNNN+PznP4/e3l7U1ARDb25uRktLS/kHwlQfzrxGtghy3cVl+H29eisuT1I5kAh1r7iEkfSZCA5n8HQQq+Je7m/Va4K2qVhxNG3HB9nomCFH0HZIpBlrDNzL//XYtCbSIUEqhsf0T/orqiTpl7aegD7HtWWXpcd1TqSQa1Yd84hg0r4yIYjIA4kn8o8LLbzsYHaz1D7421iAIMJpA6CsL6ZNqeKXMqoNvw6RCeoybla12S4N2vZjjzyY/dw8BIHddAUa1Aq6lPRXtwkAvV6wf5qOsVJL7IWUqh41TCXAtGbT2b5NGgIVtB245wDZ65n+CHVhRa8EanSG7OC6iAxw8AAHZNt873vfw4IFCzBv3jwAfvbqX/7yl7jzzjuxZMkS5zmZTAZz5szBDTfcgGeeeQa7d+/uY6f7TlVYjjZu3Ijm5mYjjACgra0NqVQKmzZtyrue9957D01NTSFhBACXXnopRo0ahWnTpuHOO+/sV2sOu9UGAvleQ1cMU5mxBFJpKkT4hk7fpRYf9cWcfdFBHh3LVcQlNEh7IfdeTBlQMWD6LwNXlwT8DMz++CR9j1p97H5R61Ccxcm2AIWsZ/qRxa3mOcrrMXnBMe0+MzmDqNXKI/XoMqFn+KKMWpQkfPGjrEjCC+rzj3lhq5C2uihLk/CU+0wSC5CuJxO47fzgZ6tNY2Xy60sRi1LIKqQsU8Zlps6jliSErE8wbrXALagsSMo65lux/Pdq7MzpFYZ2s/b1kS/d3d3YsmUL2trazLFUKoW2tjZs3Lgx9rwbb7wRY8aMcXp2kqIqLEft7e0YM2ZM6FhNTQ1GjhyJ9vb2vOp49913sWzZsogr7sYbb8QZZ5yBxsZGPP744/jKV76CvXv34rLLLoutq6urC11dXeZ1Z2dnAaMJw261akcGVqEQJioU+YunMkHFSsiD1RdLUlygtPWMQDBlX9WWKwAbgQUpW5/p+1KF8qpxhpb9h6xVlnCKBIOT8Zps6QisS2qMghSVINYqV0C+fdwl2KTVP8+efzoOZQ7xyCo3JXRoe0KNQQBqSb/eq01bhVTVKkGjWfWms2pr65OKbBbQosX/rAsgSAOg5960qYKiPZWlG4AJ2tZdlKpeKSEzHlLSz80khd4UV5rEjf5eaOqz5XkQUpmLdES2p+a3169X9qpZU50z10u36Um1r5sS1EDQZtqyQEoAvR7Sg+i3rX2fq6+vR319OM/Tu+++i0wmg7Fjx4aOjx07Fq+88oqz3t/85jf48Y9/jK1bt5a0v30lUdm7ZMkSZ0A0fcRNaCF0dnbi7LPPxnHHHYfrr78+9N61116Lj3zkIzj55JNx1VVX4Zvf/GbOjeqWL1+OESNGmMeECROK7htbjgYAOQUuuYk5Y5H6D1uM91mc53O+QzDptgOrjowW0PWbB8jNzFHG9dpy7zmDtu1x2GJJWn8DYbeUjkXyVDoA1zg9zwi12DYj7UrLokXKOC1RMefGWam0NUbVF7IkUauUsSjpWCYdyK0DwEn5jKon4wVWHC2QlJUnsGLZAdmBJSgI2vZXtaVo0LaEEUg0wFtkPGLxIZYfGgRO4otSnn6QNmncUyjAG0CP57v1eoLg8K6Dgycge8KECaH73vLly/vcvT179uCiiy7Cj370I4waNarP9ZWSRC1Hl19+OS655JKsZY444gi0tLREgrl6e3uxa9eunLFCe/bswaxZszB8+HA8/PDDqK3NHkDX2tqKZcuWoaurK6KKNVdffTUWL15sXnd2dhYtkNhyNBCwhU/cNZUw65GBqAUkl1WkBIT2KvN/MBdhQSJlcy25t9CZtSN9MQX0Xc+yxtn1ezJYVu/sottqo7eCELROKjjo63Q6uOlHUgD43dQJBf0++7E1OhZJ6qzVIsgZFB5nljbpGCIWrbTfnqfHIAGdMNEs/08H4sZlaaL90JYfD37MWMYzsWOA3gtOiyXhV+V5fuZudR30/mu+QJIqP5JqVlue1LJ8f482qbJga/uTF2T3liLIr+RJvyxUsDst40lIpFT6gCD2yU/YTfqVRrg+qT8HUk2phJCqrHIRSgnojXSFANAjgw2EtRDzgCFD6tyfvwqhlEv533zzTTQ1NZnjrvvjqFGjkE6n0dHRETre0dHhvFdv374db7zxBs455xxzzFP/J2pqarBt2zYceeSRfRtAkSQqjkaPHo3Ro0fnLDd9+nTs3r0bW7ZswZQpUwAAGzZsgOd5aG1tjT2vs7MTM2fORH19PdasWYOGhoacbW3duhWHHHJIrDAC3OZEZqBRbKCOtJ6t43GuFSB8Ay6TSIrsVVa0QAJC2a8LQAgRWFlgt03nzcgYS+hYRVJERPgNhC0k1M1mB22r951jt4KSgzZJnTQmSUdw623tdYoBFWRttlwJJiKoT7+mS/1pe85+qTHrvcyUV8kvo1e5qXIqCWMgVmSQo4mOUyVC9C1KRJhpy5ceKwmO9lME+FYbv69+fb5GDEQjpFSJFQGhM2QLf75EWgTuOJ1UUu3DJlKqTEpA9npBlm8vEIUCOsBbqASVIP0K9kP0911TYjAtVJ+UcNbzk9Hv6WByf2sT0WtfB4mD+yt7b7VS0tTUFBJHLurq6jBlyhSsX7/erPr2PA/r16/HokWLIuUnTZqEF154IXTsmmuuwZ49e/Av//IvffLK9JWqiDk69thjMWvWLCxYsACrV69GT08PFi1ahAsuuMCsVHvrrbcwY8YM3HPPPZg2bRo6Oztx5plnYv/+/fj3f/93dHZ2Gp/p6NGjkU6n8Ytf/AIdHR348Ic/jIaGBqxbtw4333wzrrjiin4bm1CeeGYgkM91JFYmat0o1oJYqLXJNnI5q8wllBzCz7aGZelXyFoaJ85C82EKhesMuYgQPyaX+8qac+n5lgu7nyEhY7vDImkBVF+1AAlOVkIypUaj9xOT4Q1npYpziRN59n5uur8eESBUY3rST8ajhZsQgUCSyqRA+5BKBQLNtK3FEVSyyRRJ/OhbmXzhRcpqYZLS1kHPCGIjgpAKhJMQvrsrrcqIlJ+tWo1NCrUHXEavklPGM23V0WWoVYiuhEtLsmpOxzgJI+6CLNp+234Zv02zfZ1Oyql/NKlA7bqadMyHrkJQl6XPdRTA4sWLcfHFF2Pq1KmYNm0abr/9duzbt8+sXps7dy4OPfRQLF++HA0NDTj++OND5zc3NwNA5Hh/UxXiCADuvfdeLFq0CDNmzEAqlcK5556LlStXmvd7enqwbds27N+/HwDw3HPPmZVsRx11VKiu119/HRMnTkRtbS1WrVqFb3zjG5BS4qijjjLLEPsLFkaVCr0uxVqRbGg9MriZmptqSlkdsmzaGupigcIodG7471wb2Ma/Z1vKYqw22U6xhI3flquw7YaMKeJq1xaiDpcbTTsgqUABLMGAHH+rzgjikgKIQPIgpV+vkLRblohz7RWX7e9QgLdl2fJ0Ywg2UNVpA7R1yQgAUje1yMF3J0EHRyvxaNxpWqSSGCX98w8pQWKI/Ea1e830O+MLMO2WM5vO9npmab3vAgOAFITnQYq0Em6+6hPSz7jtB4qrn55aOKkVcDpgKdCIqp9SmszdOuWG0C497VIj4lNkVMLQSiYBcXT++efjr3/9K6677jq0t7dj8uTJWLt2rQnS3rFjB1IVvsoPAITkoJc+09nZiREjRphUAYVwZs15key7zEDF5Tqy37dEQK6VWbnKFNo1eojUm3MD29iK1HhynSeCNuOFmCkUb0XSx/RbTnFGzrXdmq5WaZ9o267ytIy91Ygpr8wREEFsjwiPzXwSaDum3lQg1uxx2GWotUnfkHSckimb9t1wQikQQSwkuoze883VnnKNSW1BSqt901LC3wIECM+FJ4GaVLAPm35fC0stFENtkjnXAs/z/DIZD6hJkZV0pG+e9IWNKuNvkRL0RZr6QPoVdrlCCCBNLF36s+B5uPRrZ2L2+fGhHXH05Z5RSP3HfeVmpOtzh5NkI9N1EH/6/75Vtr5WKlVjORqosDYdTGjrUNw1d1hh8qmvRF0zqHuR2UdLBfmKvPeRIlYwc8gL+prFouTcHy5aKPwM64YdGpMM681IGSIA9GsdSK2Df2k/iJVJ0Bu4rpsKEtuoIK0/pB9QTMci0unA8CQQcntJoZfik77aY7FTFVBBY1vMtCAQXlDWCAdixaSxR5KOMzxeE6NEV7epzV/9zXdJmxmrPrqXnAnw9ozlzXeVhYO2TXyTlGY1mhTKhWfqIekDdKB4RtuWrCBwRIPA/TaVOE6T/7u9fkzXkMbKDshmiofFEcP0J05hpO0ERdTRF9eaC9oVSzBJHfCa1YrkunGTY3EWF7tNakEghYRwdJC6ymh9xsWFwIpg988V76UFkBW8rcerrUgSCJbAR2J1SF3mGtE2SUeNu0YGwdsCKkxImGqM640EUAvbkkSzctvjo58VT7n4dH1aQGrLil79ZuKIPDI+EQgagUAQmXN0ULkIBXj7Yw3alOa6kHgo01eQ1We+ZcrPuJ0JPneqTUnaNAHeOlBcxTf58UleMKc6wJsGgXsyCOwWpM1eD6Im5QsiiidxYG8XKhryke9THYMQFkcJwwHZg418rrW+kZJf1tnKZS3T9+7RrNdGGOS0eMa8r1e4xVlwikHqO4BlQXLEFZmuOXVdjEXGdD0I2o6M3xYgrvddcUpagOgnnZDRLwR/N3vfeuMPM6jXiIRMJiyQqEVLW5JM8LYX/K2Fm9Fauj61TF8LHF3GJcrpMn9lLTKxVilERWNKi7uUiVcSepyplC/AzfymAhGn/z/o+nT/JJSlJxVYfIROR6DrIxsHU4GuXX+eThWg+p7xINOqPiXIpV6Zp8eh+llbW9kB2aVcyj/YYHGUMOxWY4h5A/n91JPERVW+XtHm7IBp/WzHJeW1yo3Gc2QTRRLm5hWs6He1YZubzJIsMj/6tQgVzdkPS9ToxJH+jdcLqqLn2hYkumTednGFXG/SrPIyAdRCjR9aRMhgmLTL9veInQCS9kmnC6DxSDbaKgQRFm06TQGdX215MpmpdZt67tRYzHPQH6HEoRB+mwIIVsaZgHFtBUMgJrVbTvVPqNd+13QW7Yyq3zNJJ/2PAOmXVKviaBoCbUHS8yvhZ8i2xCky/N09kGFxlDDmy58ZoBToMnPhunFncwv1B1TP0cPEshQrlGwLTZb+R5b9qzZd9Wdd5SaldZx0PqK1HHNrCahIpnF7s1vbykKtOFqY0P7T8loM6rialKNNnexQG+JoH2XYFi1om/Y4dVAzLWMEoZovI0K0wCICSekJIzxN3JLnv06RCZYe4IkgSDokFhFO6ul5EDVpMq/KumOManqlmGorFKRO21SCU8KsODPaTrer//Y8QOgYsmAefBcnHX8gmHu7eOPZgQqLo4Rhy9FAp4/XNxRPoy1GWnBZbqRyQqw4NO5IWzJ00HaccIlUBiActO2wJkkyxpDWUBYA41JUNcS2KbXdBc40CbZ7K0sckr1Xmauc1O+rnECRc+k1FSJ8LJWK9sfs56YGa1IDBMJP6vJKYBiBSvtG27eP0z7ZLkLXnNgB9gBUCmx/5RtUf/T+ZiIVrj+UF0lE2/RksBWJKRNeLSekv0ebsfhIv186t5LJv2SW4Ytgbm2roSmj6tEr1khAdsjSllEB2Q1VEJDNt5iiYHGUMCWwKzADBvOtTQ7l+OmXzS1VYrSF02nNAUKajZbJaUGiYw4NVd/IbPOUo21kb1MEQVNW2wjakPSAoyyt0xYL9g1ej8rx4yd8w3eIsIh4U/9I67NBXG/6uJD+jVwq15ZMaauLA9u6pQKeQzFJ1BUXOYcsnTfuOgTiRscH6QBvTwKwMnebMiSRpG7HXgVoAs6VRcrTbjglekmbJuGjEjr+MnyoVXEphKxAuj80qFwgCAbP+BmykXEEZO+v8IBspmhYHDFMRWMJppCgQL8Jo0iX7EPG2oE8rTm0IrtCGT6UjwDM0mZ4dZ0WOsGWEoHLiLid6LQLRNxqsa5Al+uNtB/s9p5bXEUtKuqZ7Glm4mCUxSjY+Fb6VhTpL/iQpH3hapOSyYRdbXZgN+2fnbpASph93Ux94QDqUObuFGkzlQ6uAw3EpjFbWthIaolS7joqLENB4CphpJ7HTCZsBdVB4NSdllJZuXul2bbEBGSrMQkAtXWVfQvlgOziqewrOwhgr9pgJJu9UMa8r49ZN/d+tBxlxTYCac2R08UWV5GltLKNM1LU3ooEoRxNvkCgq8JcwlOLDG1RIqIDCAsLV4wRQefSoXFJtI86Z5IRcbQO291kLCrKOkPbl0CQcludZrWpUzL4CwZF4I4Twt+Z3tz8M4HIoFYcbT2xA8vp9dHn0GMZnWIAQV8lSOoAKHecsjKFrFWkTdMvLdyksQ4Fl5PMGZkTY7GD365ICf9Z9UtQ65fJkK3KplOBENVkPH9bkUqGfGX0qY5BCIujhOGA7MFIoddbEkuHPmTVUSkiiaLvUShiU1tnfZblxlUnaRNASICEq/LvGs5+hcpqUWK3YV2LLPWY3ESWiNLWHFNGl0dwn3f2y9U+3WdNCzm6b5oOZiaCT1IBk/FUkklrjtWzJH0XkTatlXH0XI1tBdPB26G5tSyk2rVlloyJQBillJsuJWG2ZbEzZOtp0iJKyCCWSGmwUL+0NY5aw4zgU+1Y1zBj5z5iBgwsjhKGA7KZKPQzoa0Y9udEWw2oKyjGqlEB5JdAMvbsqDgMWXJEjDgJRFJQTIQsJ5ETAET2uDNvq0qMFQfhFVa6TCRGyhIMRHxI9UxX+ElyXsTCFOeOoy5CChUXZh6k0hppX7hK1abUe76RzNFqnMLqi1+3SmUgVOZukpzSOSdSht11+j0df0RdxVpA2fXoDNsmL5Fui1h8IgHn2uKmhaOqG5YIogJWZ+ymsU4h0eXXUz+kFpUMu9WKh8VRwsS5/RnGR1rP9tv2+zE3Zk25xZKrm5EuRJNI5hRL5mbqcIfR94ManW3Tdl1CTaobbdi1Rduk7dk36Cz9dh0j//lNziT9OiZYO3SV7UBlYxHxwsv2jRVGnagCu/02dZySftOy5ijXlFk1T61Hqo/0eoYydtuJKY3bSgYP0z+r77QsxaQNACmDqJDR8UE0P5NU9QuTSIoEkZP+6T5kiKgzFjjaF4mDnCF7wMLiKGnsFShMFZLkNdS/uHUfJPnbuqEnbEUKLcEn5GdFKmB+nfUpa4FDVznzHAnXNSXWqtD/W0HiZmRQzo6BcVmUsvw60vuJSbusw+0VCo622zSxN0TMATBpFFJEhChXlkzJaP+kDGUIF9DX1BI6+uHK3K0Fj22N0aKKiig7+Ds0XvVsgsCJAFNuQpg4IS+wankSEBkierSbTiDkHgyJTr1hbSr4r6XK1jew5WigwuIoYditNhCohGuYzWRDbkDZYmxyxfP0lRgjV+GuNodoCVcYdYGYsTlqo+4iiRj3n9150qa+SdtFI2IkRiTpY5bIkJbQ0WJJNyHsMepn/bAzYNO/7eSPRvTpvpAl7cZlB8hMBtqVa+K2PL0eTpVSfQxcbQI6EFykrT3pdL9CwdZ2QLZDENpjplYmI76kEkZKJOkgboAs09evST004SUEIPVKOj1Peg4z6OWYowELi6OE4YBspu9QcRD3WVJ3unIJn2KhFhZ9yI5tKbhCICRQ/EqjlhdyCg0at11vefWFWmfoMfq3qy/ZylrH3EHlephh8WBEirLcxAaeS/K5AKw5IqLJlFHCIaWtlcR6pBMpKmuMb6jzUwkI5Q4LBZ5LGWzaa1uTlAsxsrGu3X87VoiOAQiCtkMZykGEK7VI6Xp1fdSy5QX5lEzcEiArfQuRQNf2rY5BCIujhGHLEVNyIp8pYu2gAdymLDlu//ouJ9QII8JCpOjAbfu1fXOPq08JpFA1cUVDgsmYV8JtmJswEN3Pze4b3MItEuPihcuRZ6nLq/NNgDd1y9nQAGpaRkqYHENxX09mw1kEz2YvtKA+LdWkckOGNnM1w1fB3mqjWB10bfYzs+Oq6DzRFAMuayG1pFHLnDVfkePmfGJJSmnBqB6eRG1Dhd9CWRwVTYVf2YFPfjucM0w+WGrD7UMK30Rsd07o/H7E6BIZee2y6OQWTdLx7IjvCd1QY2pyrG6T9nwJEWThtldd0T5oq4XdPTNg6zmXJcoqbxJNUguSQxSG+i9IjiN7v7NYF6Yep4RZVWZci0RU6INCqlgm5RZUQkqSIiZeS1uZjFvNH4NZqacFlTUXoTHY8Uv0bzs+y7USLTQXqqVMdM679lV4QDZTNCyOkoZ1EVM2HALJebMllg9z405AIJl+kW456JvLzRIuOaxJoZcqHYFrakIvlSCSEkowqfm1E3dGLEUIAoQFwmWy/YCiFiSrfiqW9PJ8YZ0Tiv3Rz3Y/zUBFtKywXqeJKKTWF9sSZf4O3HCh+pTnzohjz0PwYzL8ORb29dTB4K4xaWgmcFtUaaxM24G7zhduDY2VvbcaB2QXD4ujhLFX7jBMaSA340LKSC++eH9g6TUaHK3ft61JhbveHDfsbPFAxNITbAlC+hnphxJG5q6iz4kTCURA6Rw+1GqjrSV2TiXYdeiygUiQxHUWnsfAtaXL2DmMTK+0VcrVpmu+aNZoI3YQnrPQPJJrIqPva6+kye4NvY1wMGYzBmsuTBC4RBAobgkkqa1OLvebyxJFPivdB3vsWaks6Lz3pY5BCIujhGG3GlM2st1II7EWtpWpj1ajbBaZgqoJCyP97BJI+ccpyeizLZAK6qR+ou1rgWT//5bm/XAXLJMKdb0JVxmEb+D2sSzjCJoM3+zpPGpLjCT1GOuT7pJrvuwcSyKL0DMr+bQSVuXs8qBxcsJcf2Ol0ivvQkHg2mqnrWl6Sb7KBC4QSl4phQi2cNEWKiAIGLfTDtjWNmbAweIoYVgYMeXDIXy0u0NaZbJloLaDuHM2WwJhZOsXuzpzgwysS337oWGNlyZ+DIlJ/Q997e4XDTKP7x+d/yzJJqkJRVuQqJUlzv0VqctRnrrl1Gv9HMncrd1ydjnaju2eovOn2zVB2/ofaiIKpiW4JlCxSeQ6aWuQhNrzTZBqrCBwKQOLFskELlU/9T5zQo/Jk8EUuvqvHrX1lZ7nSIYtZUXWMRhhcZQwbDli+hdbdeiXrs9gHp9LWwi5bojlwjQlI8/FxSW5rAHEwmMLJKf1DeEpFtH+OfsoLQe79CBgiwXSF2kq95+N64ncvOlrO8bJZYWyV4QB0czdxIKirUtUOBm09UWnEbAtWq44Kjt2iZY3QpAIRS2yTH0k63UqZYSU1BvZhuKfTMpvGLecnjYd/2RSNOk2bVEJHNx7EBWN0p19rmMQwuIoaQbpB4/pT/L5kNEylovN5b4xRYXjZleEtamQ7sW8p3OGiVS4n8UnmbQFhN56gggTalFw1aOzclNjE9lnLpQSQEpHnDaxlFDxZV4SKxK1qNjd0fmHbKEkZTieBnCLF32MiKuQlYkQCgJ3xm9ZAsh1zGUFo1u12EJKWu47vYUIFZGCjFHAv56eNeFeKjhHtan1mCAxXzo33ZCh9WAGJiyOEoYDsplksYQQEPPasji4bp7mOIJf6OW2HtFeemELTe6s1wXVrp4iZqHwcTuwm5wa1ptuK1KwBJ+mBrCezUsS4G3tm2YsXVo4hfqO0CU1uYJcFkBbuGQTNNYYXGMzeZcyfmZpQefKlXfJIcoifbT/pmOkU6ddcFRAhhLwqhPoPnU6UFyklH4NC9rursoOyObVasXD4ihh2K3GVA2Rm5F+Ta0fIMcTgoghV44k/ToUfNzXBoGotczlzrJOMWkSQ64jPQh3/7TFRghSkW7PxlhaBClKxQ7I5XP102E9ssdm2iIuLeJ+04HcZpzWWMx1sNo0ny5lnRK6DSq+AF9wpVJBNm3a79AzOZfOBy2TloEo0xZIZWXSPaRZvise+t+0L3UMQlgcJQwLIyY7LstOKcm3bonYoOFQHdLxPn3bsrCUicgmt0a/RAVHYRYla6wiFb0Rm9VoMcLIUZ2xbFmGKTOGkOBzXbW4frnEDInbsdoyAtflgos0Kd2vHRYgLSj8LNjhlV9a7EkSyyStsjQXkyRtmZk2G8taW4nEuQjt90ygOBWOlhUOVl2eRG1dpQdks+WoWEocGMAUSt9/tTIDmyS/mUT4OauQj/kcu35l2+6bcvxAkNazaS6w1ERyFlllIuWc7djC0FFWjzHLWF37K4b6J8J9B2RM33zxIKkAcPVLWv2iosA1PiIIYsdBj9tldN/pBrFU0NAy9rPnWdcrSGSpRZcZs6SZta3zSVt2mVAWbfuYLueRhzp8YO+B6DwwAwK2HCXNIFXlTDUSozgA4qYg1hOXGwbBjSV7MHOZsC019DgRIJTsP2BiRELoPTJOffN1BbLnsjCRwGAaeO5OE6Dqjli2JLlOVr90X7UFy/QPVl4iBHWQatz9ziKk7GfqlqNjigsCp2JJrZgzc0GzaetVdZa4Mivp6GzQ6wOE8zaFrEa+uGocVuEB2TF6veA6BiEsjhKGA7KZyiWLGIorL+yVT66s0EaJqJeyfwUSYPZLiwzNIZJcbrjCBJOuNCZDtj0fgBUjFK7WBJ6TMdAElPq4PwaPaBf7etrXgbhM9T5noczdgDMXkbCFU8x1pWLQdc0tq5Ok1hwzJw5RBQSuOmmnRLAygdsxS5LMG+lXKM6JCiRLrHVVeIZsdqsVD7vVEobdasyAIOLGgePZeWL4/P7EMuxkLWMfLri/rrmIsar4DWSpyrJ4haoLn+fHY5ONaGPccOH+0fplcEhbkyJ9IH+73HPZzst2PJuLz/WgsU72mOPOtcrSdkLa2fMCNxv5m7+/By4sjhKGA7KZgUE+gsg6RnP8JIm5R8rgtUN0mLggWYofNa75cginOPdTntXZFjAzFiIcRNZrpSqy8jEF/fMCK5NU75N5gj2ntkVMlzHPpDwdN8hr28IW6bollmxhZB8DTD4musmt3krExCvZ1iopUVNT4bdQWaLHIKTCr2zArl27MGfOHDQ1NaG5uRnz58/H3r17s55z+umnQwgRevzjP/5jqMyOHTtw9tlno7GxEWPGjMGVV16J3t7ecg4lBP/yYAYt+fww6M8fD9maityjZejZ/rs0HbHuTNmsH+HORd+jVVliUIsl6UWDvM17+gE6TvVscltZ7dFnjUc6EhEqjnHqc+IsSS6rUFwZV3n1rF14WhD57XqhOiR9VscP7u9CpaNda8U+BitVE3M0Z84cvPPOO1i3bh16enowb948LFy4EPfdd1/W8xYsWIAbb7zRvG5sbDR/ZzIZnH322WhpacHvfvc7vPPOO5g7dy5qa2tx8803l20sFLYcMQOfmOAeGixsByjby6+TwKk7ghs53fwWCP/QKT7hZIzYofE/et5y5RzSFhlXF0LV6cDl4D26PxwACEh/vHSrEjJWEbqe6kSTPDHmepr6yTXXmbxdUyFN58LPrvlyfZas/EsRkeQ6Rp9TqUBQqiSWjcOGRNtnBgRVIY5efvllrF27Fps3b8bUqVMBAHfccQfOOussrFixAuPHj489t7GxES0tLc73Hn/8cfzpT3/CE088gbFjx2Ly5MlYtmwZrrrqKlx//fWoq6sry3gYZnARI3JkOEYkuOHR8xwBvc66iMgqJzEWJIoWRHEJXosXTPpGrfcFcwR4C3WcWmEksrrinCkNzHQGW4H4TQciyowTUl0+upusY+sQej2lNRZaRrrG5JoKMjZqZbKqi1iqXGUEwqLKhcMydXBfhVuO4qyLhdYxCKkKt9rGjRvR3NxshBEAtLW1IZVKYdOmTVnPvffeezFq1Cgcf/zxuPrqq7F///5QvSeccALGjh1rjs2cOROdnZ146aWXSj8QB+xWYxggcmd2/ZqvdCJDCPc7Llt3qIq8x2qrBdNoTIeyQPL2OPvjFCbB+zrvUkgUReoI90uCjlULYyJiXP2JuBXVqc4gcF0fEX4hlxo5ZrcZ99lzXJt0hccc9dWlNphda1VhOWpvb8eYMWNCx2pqajBy5Ei0t7fHnve5z30OH/jABzB+/Hj88Y9/xFVXXYVt27bhZz/7mamXCiMA5nW2eru6utDVFfxi6OzsLHhMGnarMQzFdSfOx3pklesLLi+gC9ok6W5cVu7I0nuJ0Ca5fdvKJE7BqE7abqag0ezWNm158WCdFx4D3Uw3FOytLDJ6YbwQUjXpSmIS7rty4jksZDJ4rd12dFNaXcYuG5vcXRJrlXIbhlI96AsZTT1Q6eKIKZ5ExdGSJUvw7W9/O2uZl19+uej6Fy5caP4+4YQTMG7cOMyYMQPbt2/HkUceWXS9y5cvxw033FD0+RTeW41h8iAUyxJzszLlYt4rF/Z/X+t1dN+0mDLkuXT7vtHGsuQVokREQPZqw0vmER6nCJ8QF9LjN2vFbAkSCyUde8mZCvTnwn7fZVGyytjJS0HGQDfzdW2nIoGD+7vdc1MpEONZn+oYhCQqji6//HJccsklWcscccQRaGlpwc6dO0PHe3t7sWvXrth4Ihetra0AgNdeew1HHnkkWlpa8Oyzz4bKdHR0AEDWeq+++mosXrzYvO7s7MSECRPy7geFhRFT3eRrZsl1rqsecszElWhrgWcV12VF34VRIcPJ18IUU9bO0k0zd1OBVHyAd6gxBBaVUKOWgPCsY6pM1nodcUEg47Mus7Y20WPuIHAasx3eSDgslqgZL9JBVaHeGzDUKPyWBQkq158tIsQzMhBIgMpzBDQOa4iflwpAeP6jr3UMRhIVR6NHj8bo0aNzlps+fTp2796NLVu2YMqUKQCADRs2wPM8I3jyYevWrQCAcePGmXpvuukm7Ny507jt1q1bh6amJhx33HGx9dTX16O+vsLTxjNMv9AXce/4lR953wrgDf1Nbnr0eCHWo1xlc7md+oplYXEFPutytgsOKFQs2erFnluHCczOeK4JtetYiWZWhLm7ENm6hTxHg8ARElB+WhbXD8uYeCeDp3sbalS7+dQJZHYs4RXZ/07iwL6DqGjYclQ0VeEwPfbYYzFr1iwsWLAAzz77LH77299i0aJFuOCCC8xKtbfeeguTJk0ylqDt27dj2bJl2LJlC9544w2sWbMGc+fOxcc+9jGceOKJAIAzzzwTxx13HC666CL84Q9/wH/913/hmmuuwaWXXtpv4keUKk6CYQYrkt7slZgpRBjZfyeJfTOTYbcVFQ2lW8zhuING/F5ERNrbeLiEUa5VUo6NdoOmJWxdErJGedENd+1gd6dF3uoTDQgPzlOZrx1lwp3y/66tq4qwXaYIqkIcAf6qs0mTJmHGjBk466yz8NGPfhQ//OEPzfs9PT3Ytm2bWY1WV1eHJ554AmeeeSYmTZqEyy+/HOeeey5+8YtfmHPS6TQeffRRpNNpTJ8+HZ///Ocxd+7cUF6kcsN7qzGDj0KtHZalw/4/Yy8Zl9aeXNkCXez6XIKpP4RTnOHM9axfWv2K7Gafs98ytun4RvVO9TJ4FkSU+g3TTgWurJzXIaZph4FRqJifyApALSbJszBxRQiXRbhMXELPyIa+zuOVCa9WK56qkb0jR47MmvBx4sSJoQ/rhAkT8Otf/zpnvR/4wAfw2GOPlaSPxcAB2QxDIbFDcT4ZG+f/H12HVcZ5Yxbh8pGboWOVV9IYLeC2IrkCvCNVUAEQU8bZqP0cmlM677aoRfRahSOy48tZ70f2TXPlQrLKhrpsxV7lLENinmjsU3eFbzyb04KXbx2DkKqxHA1UWBgxg498PvM5rEWRsi7x4riZaytH3J0Urpu1jN64k/p/Kx0iJg9riSnqsCpli9PJ7/spm6nHdBDR62Cdo61MdhyY7SaVkmwpEtNkqGoZX4aWdc2tftsLz5tUuaEah3OG7IFK1ViOGIYZDORpLQIQDUZxqoFwPXZ8UmybtqvOuqHT1VuFBICXgGzBzGaYVkyPK8A7EgTumL++512K6agtluizHbOkI7JpX+xM4Ii5Bq5gc71NSTZrmhZeVm4nm/17DkQPVhClcIuxW41JBGEWrDIM0zfoDZAu23aVySak9PvWzTi2rOP92C66b8oFUcjXhcuIExKUCHkVhfCX2NNl9aXNuRTXMfqWFk6Wi1VqUSqt00NKLxBXuVYgusqY9AJWfTHU1Vf4LZRq0L7UMQhht1rCsFuNYYol1w3WVUYgPiA8i9Uq4kqzysYGF9sWqwSJ836FPFthFxSNiXR9V+Uf/F3Id10WN13kujoHEX42rlS4y7iuUSTw3DmgPMfDVCMVLnsHPqFkaAzDlIhS/5+yLEmu9+KsDPkIpEoJ+I5ovnj3XPacQrnL5LJEua1V8QItKJpNuLpElZ2nKUsZy+LU09WbdQxJw2614mFxlDBsOWKY/qSQ/2+uoGGrLpNJmvwdSi2gj5PMzPZNtlKEkQttQbJ/xGn3HHnW2b0j1ib1nl0m1IwMZ78Gsosne8rcZbNYoCLldDoCK1u4KaLinkJxUMCQYRWeDJhXqxUNi6OEcS1YZhim3Lj+5+WTPiCLVcIVhxS6OZH6bZElUbkCCdaPuBjNEXGxOTWGNMvi47AzZNPjwd/hOuIEVf4JM/Ow7jkGtH9vhWfIZoqGxRHDMBVEf/1cyNaGvdy/kHq0dYlameIsF5YlymVBqhSrUgFT4QoViIgrYf1NLFB22qnwfmqOvHDEIpW1e3kFlRf22atrqC2ofH/DbrXi4YDshBmkFkuGiSHJ/xC53C8U4fg7mxsnjxxMQIxLJ89jFYIzhjKbAc6aAiNgjAdLxuZxMsLJykNE684VVG6KOqxVufAylXsdAAQav6+PQQhbjhKGA7IZppopg6UrlDdJmVCyBXtXgmWpGGKmrSgXHi1DsllHyiBsgYrNHK6mPZelKdNb2VvWs+WoeNhylDAckM0wlYrrxhhnJYorr8nTL+XcQkP9fLdXVNnB3/kE31bD900h3kynUY1YmXQZGbZI0TLUMmXKWAky42gYWuEB2UzRsOUoYew9MxmGqWCy/oclpgvnHT4PK1Nsfib7fVXGLubqX7ValgrBlfDc9rBJmV8ZejzH1B2o8AzZ8GSQ2LIvdQxCWBwljeT1agxTmZQ61qcQk0gh3wv6ph8jliLFZfx71UohU5tHGRPuoNxrcdQ31uVRaYKUImZokN6e2K2WMOxWY5hqolR34SLqiGR6puUsF5x52+Vqy/X+IMS2IBFrSdx3tJQSme5MOXvFJAhbjhKGA7IZhnHj2FvM+X4cOcweoaIk3mkgBn/3BRMTHw3y9pz791UOAiUIyC5JT6oPFkcJw5YjhmGyC524QBmJ/DbYzZaF2yWgiJvOX7IV3+2B6KJzQLdNoXFJdQ0VHpDNGbKLht1qCVPana4Zhqlu6PdBHjclpzUpUgixq91CxTw4A8LtzVntG262uKYksLOWF9sXR84kE4ek3jq4t8IDspmiYctR0gxOUc4wTIgcq9SKwk47QP8uUITpckL9njaigwSBx2X3zscdVyqXXSldf3nUM2RYQ2naKhOc56h42HKUMJLVEcMwRZMl2Dq2rN5kNdtmrfQ1tcR4gShyCbqQpcay2mSzPtH3I/UUiG0hK0YsxfXRoutgT+F19yeyRI8CWbVqFSZOnIiGhga0trbi2WefjS37ox/9CKeeeioOOeQQHHLIIWhra8tavr9gcZQw7FZjGKY85LirFe36ign0tkWJebbccrF1Znk/l1Bxtm2dm+94qbjL8f3M395RHnjgASxevBhLly7Fc889h5NOOgkzZ87Ezp07neWfeuopXHjhhXjyySexceNGTJgwAWeeeSbeeuutfu55GBZHCcMB2QzD5Ecxt2LX90shLjx7h9gs9TpTDdDyMlomVtRYwoSupAvFRsHtSgsFqctwHZGuxY3FridKbcVvPCtL8iiE733ve1iwYAHmzZuH4447DqtXr0ZjYyPuvPNOZ/l7770XX/nKVzB58mRMmjQJ//qv/wrP87B+/fpSTEHRsDhKGLYcMQyTLPl+B5XAEhURQ9qiQ0RYxP3mcOXpWKacW6dY27Fkc+nZ9eXBgb0H8yqXGF6JHnnS3d2NLVu2oK2tzRxLpVJoa2vDxo0b86pj//796OnpwciRI/NvuAxwQHbSsOGIYRhDPkv6S02h9cYlo3QFgMe1lU+slK4zDxeeSJFjNC+UjJbJlc/JBJ7n3tup8gOyC7f8uOoAgM7OztDx+vp61NeHUxm8++67yGQyGDt2bOj42LFj8corr+TV3lVXXYXx48eHBFYSsOUoYTggm2GYgEr7Psjmlsv3eIFtRASLKyrYOhZKQRAjwLSLjLrlQhYpGS6bh6jorvSA7BIyYcIEjBgxwjyWL19e8jZuueUW3H///Xj44YfR0JCs8GTLUcIIITjuiGEYxpkJPN/vRmo1ynaeLZyyuRRjAs8JFR8UUeRqs0gdAN588000NTWZw7bVCABGjRqFdDqNjo6O0PGOjg60tLRkbWbFihW45ZZb8MQTT+DEE0/sY6f7DluOEoaFEcMwDFCYcLHPoa/tY7kEEC3jCDyPi2eSsuIDsiNxVsU+ADQ1NYUeLnFUV1eHKVOmhIKpdXD19OnTY7v5ne98B8uWLcPatWsxderU0s9DEbDlKGHYcsQwzOAj175wSRITTxVJailxcA9nyLZZvHgxLr74YkydOhXTpk3D7bffjn379mHevHkAgLlz5+LQQw81brlvf/vbuO6663Dfffdh4sSJaG9vBwAMGzYMw4YNS2wcLI6SplK/HxiGYcoG/eKLE0ql+nLMp55cZYhAAkz8UkNTpQdk93+G7PPPPx9//etfcd1116G9vR2TJ0/G2rVrTZD2jh07kEoFTqsf/OAH6O7uxmc+85lQPUuXLsX111/ft873ARZHCcMB2QzDDG6q5Tsw2s/u/RUekN2XTOO0jgJZtGgRFi1a5HzvqaeeCr1+4403iuhU+eGYo4ThPEcMwzD5Uqrvy9LUk0rx9/dAhS1HCcPxRgzDMPnSn6623KRrK/sWKjz/0dc6BiNVYznatWsX5syZg6amJjQ3N2P+/PnYu3dvbPk33ngDQgjn46GHHjLlXO/ff//9/TEk0z7DMAxTfXTtr/AM2SVcrTbYqGzZS5gzZw7eeecdrFu3Dj09PZg3bx4WLlyI++67z1l+woQJeOedd0LHfvjDH+LWW2/Fxz/+8dDxu+66C7NmzTKvm5ubS97/ONhyxDAMU50MGT4k6S4wZaIqxNHLL7+MtWvXYvPmzSYHwh133IGzzjoLK1aswPjx4yPnpNPpSNKphx9+GOedd15keWBzc3POBFUMwzAMQzm4tyvpLmSnhEkgBxtV4VbbuHEjmpubQ8mh2trakEqlsGnTprzq2LJlC7Zu3Yr58+dH3rv00ksxatQoTJs2DXfeeWe/WnPYrcYwzOBjYHzvpWvTSXchK3pvtb4+BiNVYTlqb2/HmDFjQsdqamowcuRIkzAqFz/+8Y9x7LHH4pRTTgkdv/HGG3HGGWegsbERjz/+OL7yla9g7969uOyyy2Lr6urqQldX8IvB3pCvENitxjDM4KNSv/cKS06ZTle4fSGhpfwDgUSv7JIlS2KDpvUj3518s3HgwAHcd999TqvRtddei4985CM4+eSTcdVVV+Gb3/wmbr311qz1LV++PLQB34QJE4ruG1uOGIZhqpOuAxXuVmOKJlHL0eWXX45LLrkka5kjjjgCLS0t2LlzZ+h4b28vdu3alVes0H/+539i//79mDt3bs6yra2tWLZsGbq6upx7xwDA1VdfjcWLF5vXnZ2dRQskthwxDMNUCoV9H1d8QLYE0Nel+IP0FpWoOBo9ejRGjx6ds9z06dOxe/dubNmyBVOmTAEAbNiwAZ7nobW1Nef5P/7xj/GJT3wir7a2bt2KQw45JFYYAf5uxNneZxiGYQY+B/dW9lL+UsQMccxRBXPsscdi1qxZWLBgAVavXo2enh4sWrQIF1xwgVmp9tZbb2HGjBm45557MG3aNHPua6+9hqeffhqPPfZYpN5f/OIX6OjowIc//GE0NDRg3bp1uPnmm3HFFVf029gEBG8hwjAMU4XU1Ncm3QWmTFSFOAKAe++9F4sWLcKMGTOQSqVw7rnnYuXKleb9np4ebNu2Dfv37w+dd+edd+L9738/zjzzzEidtbW1WLVqFb7xjW9ASomjjjoK3/ve97BgwYKyj0fDwohhGKZKqXSrikQJArJL0pOqo2rE0ciRI2MTPgLAxIkTnfE7N998M26++WbnObNmzQolf0wCIQTHHTEMw1QhPV29SXchO7xarWgqfB3iwIeFEcMwTHXS2NSQdBeYMlE1liOGYRiGqST2d1Z2QDY89D3f5iDdeJbFUcJwQDbDMEx1UttQ2bdQXq1WPJV9ZQcB7FZjGIapUmSFJ/HlmKOi4ZijhBGpCv/PxTAMwzjp6e5JugtMmWDLUcKw5YhhGKY6GTKswgOy2XJU3fRuhAAAGOJJREFUNCyOEqawbQ4ZhmGYSuFAhWfIZnFUPOxWYxiGYZgiqGvgDNkDFbYcJcwgFeUMwzBVj5ep8C9wXspfNCyOEkakBKRX4f/BGIZhmAiZ3kzSXcgKL+UvHnarJQwHZDMMw1QnDUMrPCCbKRq2HCWMEOxaYxiGqUYO7DmQdBeywwHZRcPiKGkkr1djGIapRuob65LuQnY8CYg+3l8GadgHu9USht1qDMMw1Ummp7JjjpjiYctRwnBANsMwTHXiVfp3N7vViobFUcKw5YhhGKY6qRtS4W41lEAcDdKwDxZHCSOEYIHEMAxThRzkDNkDFo45SprB+bljGIapeoYMq0+6C0yZYMtRwkhWRwzDMFVJ18HepLuQHU+iz7/AKz2uqkywOEoYdqsxDMNUJ6LSf9xKz3/0tY5BCLvVEoaFEcMwTHVSyxvPDljYcpQwbDliGIapTg7s4YDsgQqLo6QZnJ87hmGYqmfIsCFJdyE7HHNUNOxWSxgOyGYYhqlOuru6k+4CUybYcpQw7FZjGIapTkTSHcgFu9WKhsVRwrAwYhiGqU5qGyo8Q7ZECcRRSXpSdbBbLWGEqPjfHgzDMIyDg3sOJN0Fpkyw5ShpBqkqZxiGqXYamhqS7kJ22K1WNCyOEoYDshmGYaqT7v09SXchO54HoI9JHL3BmQSSxVHCcEA2wzBMdZJKVXhYBFuOioZjjhKGhRHDMEx1kq5l+8JApWrE0U033YRTTjkFjY2NaG5uzuscKSWuu+46jBs3DkOGDEFbWxteffXVUJldu3Zhzpw5aGpqQnNzM+bPn4+9e/eWYQRuOCCbYRimOunaXyUZsvv6GIRUjTjq7u7GZz/7WXz5y1/O+5zvfOc7WLlyJVavXo1NmzZh6NChmDlzJg4eDD7Qc+bMwUsvvYR169bh0UcfxdNPP42FCxeWYwhO2HLEMAxTnQwZXgUZskvxGIRUjU3whhtuAADcfffdeZWXUuL222/HNddcg09+8pMAgHvuuQdjx47FI488ggsuuAAvv/wy1q5di82bN2Pq1KkAgDvuuANnnXUWVqxYgfHjx5dlLAzDMEz1c3BvV9JdYMpE1ViOCuX1119He3s72trazLERI0agtbUVGzduBABs3LgRzc3NRhgBQFtbG1KpFDZt2tQv/WS3GsMwTHWSrk0n3YWsSOmV5DEYqRrLUaG0t7cDAMaOHRs6PnbsWPNee3s7xowZE3q/pqYGI0eONGVcdHV1oasr+MXQ2dlZqm4zDMMwVUJNTWWLI8gSuMUGaehHopajJUuWQAiR9fHKK68k2UUny5cvx4gRI8xjwoQJRdc1/JChJewZwzAM01+MmvC+pLvAlIlELUeXX345LrnkkqxljjjiiKLqbmlpAQB0dHRg3Lhx5nhHRwcmT55syuzcuTN0Xm9vL3bt2mXOd3H11Vdj8eLF5nVnZ2fRAumsL7bhgVt/DjlIg94YhmGqDgE0jx6Bk884PumeZEdK9HkbhkFqOUpUHI0ePRqjR48uS92HH344WlpasH79eiOGOjs7sWnTJrPibfr06di9eze2bNmCKVOmAAA2bNgAz/PQ2toaW3d9fT3q6+tL0s/PXXMuXvjNy3jpt9tKUh/DMAxTPoQA6hvrcd1Dl6Om0vMceR4g+hgzxDFHlc2OHTuwa9cu7NixA5lMBlu3bgUAHHXUURg2bBgAYNKkSVi+fDk+9alPQQiBr3/96/jnf/5nHH300Tj88MNx7bXXYvz48Zg9ezYA4Nhjj8WsWbOwYMECrF69Gj09PVi0aBEuuOCCflupNmRoA7771A149rHnseXxP6Djf/6KuoZadB3oQd2QWvQc7EFNXQ0yPb0Qad8L6mU81NTVoKerF/VW2XRtGl7GA6REuqYGvT29qG2oRfeBbtQNqUP3gW7U1NfC680AQiCVFsh0ZyJt1tb554p0CpCA53moratB98Fe1A+JaRMS6TRtswf1jXU4uL8LtfW1yPRmICJt+v3Sbfb09CKdTvmuck+itjaN7q5e1A0JxhDfZh26D3SFxpnpzUAASKVTyPRmUFfvt1k/pA7dWdrs6Q7Pm25TZiQkvEib9Y316AqNExCpFHp7MuoaRdtMqespMx7StTXopW021KH7YDdq6mpUmxLpmjR6u3vNvNUPqffL1KaRyXjZ26yvQU937jZ7uujcAul0Cj2kPj0XNXVpZHo9QACplDW3DXXoOtiNuoZav81UChCqzboa9BwMX8/uA92orauBp8dJ2uw+0INa8/8gaFOkUvB6M6itq0X3wR7UD6lD14EufwzdPUW12dsT/UzW1KXR25OBSAkzTrvN2vo69Hb3IF2T8jdBz0ika1Po6cr4/0fUGPJv0/9spVIpCCGQyXiorUur/3v6c1GbR5s16D7Qg5q6WniZDKD/H8S0af5/pgR6ewtps9d8/uuH1KHrYA9qamuibQ6pRdf+4DMZabMnE/mu0tcznU5DQmZvs64GXk/G/0yq/++19X59/rXy/x+Ex0muZ2MduvZ3hduUHqTnB173dvei6X3DccSJH0DbRR/DiFFN/XKPYJKhasTRddddh3/7t38zr08++WQAwJNPPonTTz8dALBt2za89957psw3v/lN7Nu3DwsXLsTu3bvx0Y9+FGvXrkVDQ7BZ4L333otFixZhxowZSKVSOPfcc7Fy5cr+GZQinU5j+jlTMf2cqbkLMwzDMEw+sFutaITkLIR9prOzEyNGjMB7772Hpib+NcEwDMPEU+57hq7/jMYLUCPq+lRXr+zGhv33D7r7W9VYjhiGYRiGKQC2HBXNgE0CyTAMwzAMUwxsOWIYhmGYgYgnAcGWo2JgccQwDMMwAxEpAfR1Kf/gFEfsVmMYhmEYhiGwOGIYhmGYAYj0ZEkehbJq1SpMnDgRDQ0NaG1txbPPPpu1/EMPPYRJkyahoaEBJ5xwAh577LFih1wyWBwxDMMwzEBEeqV5FMADDzyAxYsXY+nSpXjuuedw0kknYebMmZGtujS/+93vcOGFF2L+/Pl4/vnnMXv2bMyePRsvvvhiKWagaDjPUQngPEcMwzBMvvRXnqP/l/40akRtn+rqlT14MvOzvPva2tqKv//7v8f3v/99AP7uChMmTMBXv/pVLFmyJFL+/PPPx759+/Doo4+aYx/+8IcxefJkrF69uk997wtsOWIYhmGYAUh/u9W6u7uxZcsWtLW1mWOpVAptbW3YuHGj85yNGzeGygPAzJkzY8v3F7xajWEYhmEGItJD31er+ed3dnaGDrs2YH/33XeRyWQwduzY0PGxY8filVdecVbf3t7uLN/e3t63fvcRFkclQHsm7Q8PwzAMw9joe0W5o1p60dPnBNm96AEATJgwIXR86dKluP766/tWeQXD4qgE7NmzB0D0w8MwDMMwcezZswcjRowoeb11dXVoaWnBb9pLs+qrpaUFf/jDH0KbtttWIwAYNWoU0uk0Ojo6Qsc7OjrQ0tISW3ch5fsLFkclYPz48XjzzTcxfPhwCCGKrqezsxMTJkzAm2++yYHdDnh+csNzlB2en9zwHGWnFPMjpcSePXswfvz4EvfOp6GhAa+//jq6u7tLUl9dXV1IGGUrN2XKFKxfvx6zZ88G4Adkr1+/HosWLXKeM336dKxfvx5f//rXzbF169Zh+vTppeh60bA4KgGpVArvf//7S1ZfU1MTfyllgecnNzxH2eH5yQ3PUXb6Oj/lsBhRGhoa8hI0pWbx4sW4+OKLMXXqVEybNg2333479u3bh3nz5gEA5s6di0MPPRTLly8HAHzta1/Daaedhu9+97s4++yzcf/99+P3v/89fvjDH/Z73yksjhiGYRiGKQnnn38+/vrXv+K6665De3s7Jk+ejLVr15qg6x07diCVChbKn3LKKbjvvvtwzTXX4Fvf+haOPvpoPPLIIzj++OOTGgIAznNUUXC+pOzw/OSG5yg7PD+54TnKDs/P4IDzHFUQ9fX1WLp0qTPQjeH5yQeeo+zw/OSG5yg7PD+DA7YcMQzDMAzDENhyxDAMwzAMQ2BxxDAMwzAMQ2BxxDAMwzAMQ2BxlCA33XQTTjnlFDQ2NqK5uTmvc6SUuO666zBu3DgMGTIEbW1tePXVV8vb0QTZtWsX5syZg6amJjQ3N2P+/PnYu3dv1nPa29tx0UUXoaWlBUOHDsWHPvQh/PSnP+2nHvc/xcwR4G/4eMYZZ2Do0KFoamrCxz72MRw4cKAfety/FDs/gP//7eMf/ziEEHjkkUfK29EEKXSOdu3aha9+9as45phjMGTIEBx22GG47LLL8N577/Vjr8vHqlWrMHHiRDQ0NKC1tRXPPvts1vIPPfQQJk2ahIaGBpxwwgl47LHSZKZmkoPFUYJ0d3fjs5/9LL785S/nfc53vvMdrFy5EqtXr8amTZswdOhQzJw5EwcPHixjT5Njzpw5eOmll7Bu3To8+uijePrpp7Fw4cKs58ydOxfbtm3DmjVr8MILL+DTn/40zjvvPDz//PP91Ov+pZg52rhxI2bNmoUzzzwTzz77LDZv3oxFixaF8o8MFIqZH83tt9/ep6z31UKhc/T222/j7bffxooVK/Diiy/i7rvvxtq1azF//vx+7HV5eOCBB7B48WIsXboUzz33HE466STMnDkTO3fudJb/3e9+hwsvvBDz58/H888/j9mzZ2P27Nl48cUX+7nnTEmRTOLcddddcsSIETnLeZ4nW1pa5K233mqO7d69W9bX18v/+I//KGMPk+FPf/qTBCA3b95sjv3qV7+SQgj51ltvxZ43dOhQec8994SOjRw5Uv7oRz8qW1+Totg5am1tlddcc01/dDFRip0fKaV8/vnn5aGHHirfeecdCUA+/PDDZe5tMvRljigPPvigrKurkz09PeXoZr8xbdo0eemll5rXmUxGjh8/Xi5fvtxZ/rzzzpNnn3126Fhra6v80pe+VNZ+MuVl4P1MHMC8/vrraG9vR1tbmzk2YsQItLa2YuPGjQn2rDxs3LgRzc3NmDp1qjnW1taGVCqFTZs2xZ53yimn4IEHHsCuXbvgeR7uv/9+HDx4EKeffno/9Lp/KWaOdu7ciU2bNmHMmDE45ZRTMHbsWJx22mn4zW9+01/d7jeK/Qzt378fn/vc57Bq1arEN8AsN8XOkY1OilhTU70bL3R3d2PLli2h79hUKoW2trbY79iNGzeGygPAzJkzB+R38mCCxVEV0d7eDgAmDbtm7Nix5r2BRHt7O8aMGRM6VlNTg5EjR2Yd74MPPoienh68733vQ319Pb70pS/h4YcfxlFHHVXuLvc7xczRX/7yFwDA9ddfjwULFmDt2rX40Ic+hBkzZgy4+LViP0Pf+MY3cMopp+CTn/xkubuYOMXOEeXdd9/FsmXL8nZXVirvvvsuMplMQd+x7e3tg+Y7eTDB4qjELFmyBEKIrI9XXnkl6W4mSrnn6Nprr8Xu3bvxxBNP4Pe//z0WL16M8847Dy+88EIJR1FeyjlHnucBAL70pS9h3rx5OPnkk3HbbbfhmGOOwZ133lnKYZSNcs7PmjVrsGHDBtx+++2l7XQ/01/fRZ2dnTj77LNx3HHH4frrr+97xxmmAqhe+2eFcvnll+OSSy7JWuaII44oqm5t3u/o6MC4cePM8Y6ODkyePLmoOpMg3zlqaWmJBEH29vZi165dsa6O7du34/vf/z5efPFFfPCDHwQAnHTSSXjmmWewatUqrF69uiRjKDflnCP92TnuuONCx4899ljs2LGj+E73I+Wcnw0bNmD79u2RFaTnnnsuTj31VDz11FN96Hn/Uc450uzZswezZs3C8OHD8fDDD6O2trav3U6UUaNGIZ1Oo6OjI3S8o6Mjdi5aWloKKs9UByyOSszo0aMxevTostR9+OGHo6WlBevXrzdiqLOzE5s2bSpoxVvS5DtH06dPx+7du7FlyxZMmTIFgH/j8jwPra2tznP2798PAJFVV+l02lhMqoFyztHEiRMxfvx4bNu2LXT8z3/+Mz7+8Y/3vfP9QDnnZ8mSJfjiF78YOnbCCSfgtttuwznnnNP3zvcT5ZwjwP/umTlzJurr67FmzRo0NDSUrO9JUVdXhylTpmD9+vWYPXs2AN/Sun79eixatMh5zvTp07F+/Xp8/etfN8fWrVuH6dOn90OPmbKRdET4YOZ//ud/5PPPPy9vuOEGOWzYMPn888/L559/Xu7Zs8eUOeaYY+TPfvYz8/qWW26Rzc3N8uc//7n84x//KD/5yU/Kww8/XB44cCCJIZSdWbNmyZNPPllu2rRJ/uY3v5FHH320vPDCC837//u//yuPOeYYuWnTJimllN3d3fKoo46Sp556qty0aZN87bXX5IoVK6QQQv7yl79MahhlpdA5klLK2267TTY1NcmHHnpIvvrqq/Kaa66RDQ0N8rXXXktiCGWlmPmxwQBerSZl4XP03nvvydbWVnnCCSfI1157Tb7zzjvm0dvbm9QwSsL9998v6+vr5d133y3/9Kc/yYULF8rm5mbZ3t4upZTyoosukkuWLDHlf/vb38qamhq5YsUK+fLLL8ulS5fK2tpa+cILLyQ1BKYEsDhKkIsvvlgCiDyefPJJUwaAvOuuu8xrz/PktddeK8eOHSvr6+vljBkz5LZt2/q/8/3E3/72N3nhhRfKYcOGyaamJjlv3ryQeHz99dcjc/bnP/9ZfvrTn5ZjxoyRjY2N8sQTT4ws7R9IFDNHUkq5fPly+f73v182NjbK6dOny2eeeaafe94/FDs/lIEujgqdoyeffNL53QVAvv7668kMooTccccd8rDDDpN1dXVy2rRp8r//+7/Ne6eddpq8+OKLQ+UffPBB+Xd/93eyrq5OfvCDHxywP8QGE0JKKfvbWsUwDMMwDFOp8Go1hmEYhmEYAosjhmEYhmEYAosjhmEYhmEYAosjhmEYhmEYAosjhmEYhmEYAosjhmEYhmEYAosjhmEYhmEYAosjhmEYhmEYAosjhmFw+umnh/aGSgohBB555JGku8EwzCCHxRHDMFXFZZddhilTpqC+vt5swMwwDFNKWBwxDFN1fOELX8D555+fdDcYhhmgsDhimEHGvn37MHfuXAwbNgzjxo3Dd7/73dD7P/nJTzB16lQMHz4cLS0t+NznPoedO3eGyrz00kv4h3/4BzQ1NWH48OE49dRTsX379rzav/POO/HBD34Q9fX1GDduHBYtWhR6/91338WnPvUpNDY24uijj8aaNWtC769cuRKXXnopjjjiiCJGzzAMkxsWRwwzyLjyyivx61//Gj//+c/x+OOP46mnnsJzzz1n3u/p6cGyZcvwhz/8AY888gjeeOMNXHLJJeb9t956Cx/72MdQX1+PDRs2YMuWLfjCF76A3t7enG3/4Ac/wKWXXoqFCxfihRdewJo1a3DUUUeFytxwww0477zz8Mc//hFnnXUW5syZg127dpVs/AzDMDmRDMMMGvbs2SPr6urkgw8+aI797W9/k0OGDJFf+9rXnOds3rxZApB79uyRUkp59dVXy8MPP1x2d3cX3P748ePlP/3TP8W+D0Bec8015vXevXslAPmrX/0qUnbp0qXypJNOKrgPDMMwuWDLEcMMIrZv347u7m60traaYyNHjsQxxxxjXm/ZsgXnnHMODjvsMAwfPhynnXYaAGDHjh0AgK1bt+LUU09FbW1tQW3v3LkTb7/9NmbMmJG13Iknnmj+Hjp0KJqamiJuPYZhmHLC4ohhGMO+ffswc+ZMNDU14d5778XmzZvx8MMPAwC6u7sBAEOGDCmq7nzPs0WXEAKe5xXVJsMwTDGwOGKYQcSRRx6J2tpabNq0yRz7v//7P/z5z38GALzyyiv429/+hltuuQWnnnoqJk2aFLHanHjiiXjmmWfQ09NTUNvDhw/HxIkTsX79+r4PhGEYpoywOGKYQcSwYcMwf/58XHnlldiwYQNefPFFXHLJJUil/K+Cww47DHV1dbjjjjvwl7/8BWvWrMGyZctCdSxatAidnZ244IIL8Pvf/x6vvvoqfvKTn2Dbtm0527/++uvx3e9+FytXrsSrr76K5557DnfccUdBY3jttdewdetWtLe348CBA9i6dSu2bt1qLFsMwzB9pSbpDjAM07/ceuut2Lt3L8455xwMHz4cl19+Od577z0AwOjRo3H33XfjW9/6FlauXIkPfehDWLFiBT7xiU+Y89/3vvdhw4YNuPLKK3HaaachnU5j8uTJ+MhHPpKz7YsvvhgHDx7EbbfdhiuuuAKjRo3CZz7zmYL6/8UvfhG//vWvzeuTTz4ZAPD6669j4sSJBdXFMAzjQkgpZdKdYBiGYRiGqRTYrcYwDMMwDENgccQwTMkYNmxY7OOZZ55JunsMwzB5wW41hmFKxmuvvRb73qGHHlp0GgCGYZj+hMURwzAMwzAMgd1qDMMwDMMwBBZHDMMwDMMwBBZHDMMwDMMwBBZHDMMwDMMwBBZHDMMwDMMwBBZHDMMwDMMwBBZHDMMwDMMwBBZHDMMwDMMwhP8fwq2JMn5/9FkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.reset_index().plot.scatter('dac_ch1', 'dac_ch2', c='dmm_v2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, for the other dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='dac_ch1', ylabel='dac_ch2'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YHZhfSr8qIbZIEF6/ru89MU12j6t22tclDb7uTVfWnxHgGGGB6v77GKWF2+D46ze9qWMzGKMdsnXTUXN0rtoIjrZsFAK4i4vnsDkwU7Ex9bn0Wa5SDZ6JGvLx7mmiqyaXRe0MxeWvn1Nto9+3z0IPm1L8Xd9Ljf6qvxeJpA0gLfjtNCRGn9trLfkMtLQJudArAlfGAyg/K4dkfeUbUt2l45h267NegDQwTiuP38gOnY8Wz5bP/Ghr2wiOtmzCHKGa6PTvvpSUTysB/efMelmHJT59FnwcS9pZJS7fHtf3NRBDkY6rzi0Elcvur4dlmgvZS9qHvwznh3u+7/lSBdLZxpvRLtKSzbFM7F5vvnJuEAxRdd6XEfA+Z3I+qVO0fcfTsZ69A+WEPveBJxvLcZI8H23n5PFOqx2OIPv8/GwfbNRGO7BRAkb6bEzEKummopGec+ukaZYFuey6ZXERASH0n/M+Q4+FPtZm/UwCxBZZULap6Ku63/pc0QkGXg8bEcGE+P79p0Bl7CvgujU6zRckdmnoXlZts+6CUJeXIKLivtLrM2n23g3t89bn23usx39uLEc7n+z0Ma+QLZqjgz/Wsd/93d/Fs571LDz0oQ8FEeE3f/M3l15z880346u+6quwu7uLRz7ykXjrW9+62Q0foo3gaOu2APysApCW+dTg4jj4LIt9XfOA0j+n8+5hE3Wor1mjrwSeKE+M5DrxfwPIm+pWTVXP0jw5rEZF7AV4QnV+7djd6xRF0dnShgfrb63iWw3RGbF1Pl+DYL8HOC1sewRN57KdfMDutkNYaFG3DznIY11B97333ovHPe5xeOMb37iS/8c+9jE885nPxNd//dfjtttuw8te9jK88IUvxHve855NbvnQbEyrbdkYdboKGAQQfceDpgIsPTXUxjKfRf2u4zOXfhu4xszi9759q93qGPpW763a5yBw0/j7hNQ+H0TULzgv+kQlQuqrmg30pWPm9lGrziURuDtfCMPXEX0vAgx2nwsaXqWGUp/vnMi77mLxELlGl5w/bCvGpD7n03IHFXGPdjbY3unZtkM4dvaMZzwDz3jGM1b2v+GGG/CIRzwC/+7f/TsAwJd/+Zfj937v9/Dv//2/x5VXXnlUYS61ERxt2axCdi8IyE6l6qEPLJDTxaQJ080dq/ikU3QwHwUslmIRnCDgrFweT0VcHGPR5hww7C1nMADihoDVMptjQzz4qsCP9TOs1hbHBLIqUOUbGlwtVTSIpE+aW2kn/aT6RIclFPUxD47lwRBKIWqvgN+xLCFQv4994H65kG20c8CG9w04HnaYmqO77767OL67u4vd3YMzZ7fccguuuOKK4tiVV16Jl73sZQdu+yA2ptW2bAm42LN/hDA36VN9HhjMTqRaNbVPzy/45NMDNnzNm6U+IRRxF30aWLLz1l7l0zcGRbHMPr8hZm0VFmsIYA0dm/OxB5WD7Y8va4/8BUWjvdcOMTbF6rj6g6Hxra35SW1aqs1AX1/qbejTuGpXVN3yvG6psG3NTX1gfG5VZ+GwrMHDiGq0M2yTY77xbNS02EEfAHDxxRfjQQ96UHpcd911hxLjHXfcgQsvvLA4duGFF+Luu+/Gffdtr8jmyBxt2SjIapci2aK/mAkAG6vizhtQ8L9Pqaj308OsAPNf3gsn6gGfGqT4djVFRhpDX8zmm9NBlY+zdH8OnNVWjIHGZCxVMRZ9QIh5OK03NCZLRb0DY+o3B17INPW04dM4C2vy+KZyG2UtJgOxbkm+DeAq1vd5sJiLtOE6jfbHXicgjUmaqyOFA3V3SKbRpvd40cD2HT/eDMRo/Xbf54+3IPsw7fbbb8epU6fS68NgjY6zjeDomNictkQOCuhxz34ir33mJns/kdu56JZw10DKX1+DLVtpNgS87LVrk2Ls79O103dfybXPp4q9D06kdB6RAKuemAsfVEYkMQ8xSX2aqCFQVV8rN9/f3uBeZlTnNHtSXcN9+zTc/DjZ2FTXbzJX+7hSGwdIt/V24d8/Bza3iS3S+1BB9VU+E9tHdaMdwE4+4OS2Q1hoHRO6peVMlrcBAKdOnSrA0WHZRRddhDvvvLM4duedd+LUqVM4eXJ74zuCoy0b+5RQPYn3AYI55oOAumTNMvDijwc3oXnNDxFSLZxQTXp9fVB1vPYZZByWxNvnU1SGRu8ERJ7FGmiLqmcz9mxTD2gbfD3E0s0FN3C9Z5fkBJBYHyrn0Tn2DignWTdJg+fuMXfdP/ZzIulV5/B6nFNc6za0Slf5s9dbkfyMmnbq75nrYOzvEQydK7a/t7/tEBaarTg7WBtH+3m9/PLL8a53vas49ju/8zu4/PLLj7TfZTZqjrZsRJQ1OvVDHOSRfFBNjO5cj0Yp+dm5ptG/3fVpcnbXBwIozPv4+ELPMf/a+hq6PxqIfZnPEOirz/c9+7b8ueo9oRCQaxGVz/agofGeG6eqr6Frah97j4I+7NhgfwFzpQVqsFLohhaFITqfXFpgsf/C++n7HPU7b9YHkOO0mM+00cCY+uNzcVH1PNrZZuM7N2/33HMPbrvtNtx2220AZKn+bbfdhk984hMAgFe96lW46qqrkv+LX/xifPSjH8UrXvEKfPCDH8Sb3vQm/Mqv/Ape/vKXbyP8ZCNztGUrMLlnaIZSNhTKf5HpnPlC/rZnn3oomIlQ+hR9uXjg29Fnz17Mwese36H76bu/VcYgEFLl6wVpuTKsHh8PRAb6SgyUu7ty3DHfb+/71gPUFpUqWHQfhYZIrU67Fc+mU6qOz7E6Q12SNlNV8V73B+VcPAud1+6kAEWLb+lorHhvq+Pp34zXjPWl37YR+Gib2uTE8a6QHTkgHnC1Wlxzxe8f/dEf4eu//uvT62uuuQYA8PznPx9vfetb8alPfSoBJQB4xCMegXe+8514+ctfjp/5mZ/Bwx/+cPzCL/zCVpfxAyM42rpRE8Ds9lYLKFNYiS1QBoKj/vrvSbdFPUf2pevc2B0D9fukX/fqgx4fuL6K9lB9wVe+TePASR1L/Vylkub6cePkx8CeA/Xrd4YASl86058PbjKv0pwFjKEeIb0BC3LAoi5lsMx6SzfMOS3OXvX141mNubRcciqOe7F0vo+BPhdZXzzMGK4HtZ7V9aDOuCUAyvPvcwJL9nmoNXmExdu7DL7J2N4Nn592+vPbW021im0jrfaUpzwFi7ZC6qt+/ZSnPAV/8id/sm5oR2pjWm3bZt9xRRpGJ7/0HJBSG6TpnsQCuDRKyOeyj1yT01ZU+SD7uHTMUp+5vvra0ZjNN+Rr/X0VaT57nqtg7Z/dOAXqT1v5R32ufu5L2/U9/HVm1XHSv/1zGp0FbVNY8E9xKPbap3e8vE/1eq4Pcs/l+zjnDqTl9UXqzX3s1sY2xf25WDa03njS7fWN4YG6GwpiwXH378Z3zDUwpPJ5EFCPwOhM24lTJ7YdwmhHZCNztG0jNwkkNoeQUl1wvzwJINhEFPJqncRguEkKeaIuaf7aR5mmPp/0vIaPvjZmgQsfsqDAwcVWjAFcP46Z8OxRYtfSyQxsoheRe4aqasM/F8xZ9Te59mvmp68EgAdqfULxQQYHoBD6f3H1rRC09n0/gwCwamOIZfL+xfV1GmyeSRoUt9d9rWr1feYTazW0SH80V87gTJtnl+ogBu/bBzqCoW3b/hem2w5hoUXgwKvVNt+i+uy2ERxt2YT+J61qbK/1OU3g5JifPMkRqmXoBiz6lqH7iW/Ox01EftLd1AcK1ppGX/NcPALuAGoauYcQXDbNlmm7+yQAMZ/L46RThLEESUtVD4wLz8dD+rr+VZ5WAvYAoCEQ5cFa3ZYfpz5AYxoncvWrXEomva5XL/rndHsVaKp9+sD3SmUINLI1NAjl+7VhmsuDsxo4H9CK+NbUVhyaFfdUfh4W+uSTGIHSdiyc6U2U1zRfxPEgbZyPNoKjLRuTVf8lUCMTT/ErvJ6MjcHwS9Vjtf2IT9EMaWbqNM5QX6v41McWMRruHFF5v57koSpmScuZm1BHVh6zGKfgyS2dUOaAjo0B3FzDVXx+DBz1QWEefM4BSGBuruoby9j/m2xuXOrr+mwINA1N+HPMWA+YmtNt2TW+ltby35W9tYmK2Jc2MT/eBUCyc5sBhJr1GgRJR4lB/GdOohjw4Z73dHNmbbSDWTM53lPo4Wwfcn6Co7Purt/4xjfikksuwYkTJ3DZZZfhfe9736DvU57yFKTtB9zjmc98ZvL57u/+7rnzT3/608/ErQCAACJyj0XL0Y2J6dPY1LqZahuPpT71Y12foWNNM+zbd8+LlvSneOx1NQb6XL6fSMyLXZOWqSMDU3Usn21lYHqm+eeko3LvWf3Lvoi/557sPe17/4tm3H2pjovqdvqe+/rr/TDWMdHcrRS+0LFZIyflY6Y6ntWbye/TUVjd7BF1M99v9d55m1vN1hfUqsdGOyzb+8L5UyH7fLPjDXsre8c73oFrrrkGN9xwAy677DJcf/31uPLKK/GhD30IF1xwwZz/b/zGb2B/Pxfp+sxnPoPHPe5x+Gf/7J8Vfk9/+tPxi7/4i+n1mSyLzhiYtIzx8VWma/BQLz+vgQWQjxl7gh6fwp8kybzIZ+i5jqN+LlIjPbHXzIUfg6GJo/ap26WqyriOurTDaZJObIHdO8tELtOQpD5zrCxzTorL+kLW7lhXsYrH9+/ZJH9/3neo9IEzA345JZmfUxvFBVV7i8zurxjzHp8+kmlB+quAkar9Kd4HN1QLY2P3d0r5HYw5qVOABePVMxxHYz0A254H71GZzWLcRxbpKO3kA493hewIQjwgQD7o9WernVXg6A1veANe9KIX4QUveAEA4IYbbsA73/lO3Hjjjbj22mvn/L/4i7+4eP3Lv/zLuN/97jcHjnZ3d3HRRRcdXeCLzDMifZPissKG9bG5X+P62i+9L9oDRDXtwYv5IF9T+6DyWRYb0J+Cq2P3oma/DHoZILOxSoLsBX31xJPOEBxAhKmnnJhcXrGNUZ/IO42rA01Dfn1i67rWU99Y+XSp0yv1PYNWLCOwLAWXHeczOX1foAVAouq57kJjrmspLQMjdWwJMA33tYr1ASSzM1IioI89yieRgBBQgqVDAIejrW6n79nbdggLbUyrbW5nzV3v7+/j1ltvxRVXXJGOhRBwxRVX4JZbblmpjbe85S147nOfi/vf//7F8ZtvvhkXXHABHvWoR+ElL3kJPvOZzxxq7IuMyIEf/6jTWn2sUO2fjlXnCGU6qq9Ctr3uTe/UPtRzboVHHbuPp74fH0Oq6r1gnOr03aJxqvv0Pk0oY+sbU6vC3FM6Iae5AhAaLEzLWSkCID8vin3RmCZgVd1X+pzla6yy95ymqa9tf87/nap2z/c1f131WVliHtxZCnQtW6OvpXH0NLGVCtxlACgCK+JxoKn/4iMK6vy0ZtIsdxrtrLSzhjm666670HUdLrzwwuL4hRdeiA9+8INLr3/f+96HP//zP8db3vKW4vjTn/50fPM3fzMe8YhH4CMf+Qhe/epX4xnPeAZuueUWNE3/B39vbw97e/kXw913373BHYlxIMCQeb1prH/dN5n3pabsnP2CrtsB5wlbnDEnRrYVX9QgsQRU9QnOqTrfV+GDKmaUjIi14++9HgN7Np+5Aezpsy8G79uXzjIbFFvP9yVn5gX0XiDOphBPE3Y9Bsg/UdJk7ETgi5g2P25+nAY+I+TOZaZmSfmAvlSntxRHzzm4ey3iqksD1BfMA5CCUVqFFOrta302aXBPOl8GoDixVvOb2yKAxnFBICOrdJjWNMebXzicIpDH+x6Pys4acHRQe8tb3oLHPvaxeNKTnlQcf+5zn5v+fuxjH4tLL70UX/ZlX4abb74ZT33qU3vbuu666/C6173uUOKiJoAjl6mUdNJNPBFl7R77ovfnko+6EHKRCt8WBWjdADlWTGABCNH5VCkus0ACnrouMwmRc59FXP5ano/ZbsdiH7p3v9qsSOvUk2+PT+1b/+2PDQHQvv76QKrzoaAlC2xsWDOUgFTPtvcKhPmq5e51cdyGsgcM+bj7Unb+unQ7BvMqq1c6+rb7rHfCduC67z2zftJrwtAKOL+qUVpeMb3lP+epAe75ez2b00htFXdUARgY36jS9mir2t59xzutFpkQD1rn6IDXn6121kDCBz/4wWiaBnfeeWdx/M4771yqF7r33nvxy7/8y/jn//yfL+3nS7/0S/HgBz8YH/7whwd9XvWqV+Fzn/tcetx+++2r3USPMVCteEJOWfgNR30KKix49r4p/dGzyi0EpI1KUwVtayOUPklH43ytHUtDWfs0EE/f/fjYCe619w3lcyAXT8/9JnBTgYI+nz5QUafv7F59erO+xvv2tFtWB9cVdPpMej+91cb7KofX1gd+huLqu8YdJ//QcwvLCSxiL4o4/POAT3lgebspRgy32xuLPfRzvco9LGrSVwkHKgC4YlyHYYmZrFcPUs9zHdQIjDa14y7IHm1zO2vA0c7ODp7whCfgpptuSsdijLjppptw+eWXL7z2V3/1V7G3t4fv/M7vXNrPX/7lX+Izn/kMHvKQhwz67O7u4tSpU8VjY0uaIwMkbid7+/IeWoK/aFl849pJwKPS0/jtN4ol8pVPE7Q9mvcJwfWlvsuW9texp8nbYunx8ZNa4x5FO+aj99v0jBMwP171FiQ1eFq1/ABV7dTPfaDMAIhNsPosu8tXnwNjCuv3cQg8DZUNWHR//qNp4K6+/777W/gZr/pdVCIg+dWT/JBrLm+wNhBJca3W16IYpBl7H2qHjZteN5DyOf1NqCjH6nm0Te30Pcd7KX/UtNpBHmMRyLPArrnmGjz/+c/HE5/4RDzpSU/C9ddfj3vvvTetXrvqqqvwsIc9DNddd11x3Vve8hY8+9nPxt/9u3+3OH7PPffgda97Hb7lW74FF110ET7ykY/gFa94BR75yEeesR2BpTK2TtBGX5L+J5Cm3EKhF6knG/a/8OvJMGlf8pf4/PJ4eeIkMI49PvJc9FX7qF9uB/Ogw/klfU1anWbn3BgQABufGIp7konWYtMUYPBpBB876TkaqCCufnXpBLvep37qelNzKUenAxoqMeDN7qH2BUCkmqBonxV5H30F8dye9en1ZUMTJubvz//tV/7B+oRLB5r+p0cfZu0uKhdg73+fbqeOc2l6yMe4Yqqtr6/e69ZrLKXaKL8GMFxY8ozbcYnj3LB2d7LtEBZa5IB4wNVmB73+bLWzChw95znPwac//Wm85jWvwR133IHHP/7xePe7351E2p/4xCcQKtHuhz70Ifze7/0efvu3f3uuvaZp8Kd/+qd429vehs9+9rN46EMfiqc97Wl4/etff8ZqHTEFnRydTpf8ju6ViNiDFGawTsQMlJuXGmhiTpOZVON2aSKo/oVxcJ9AyUf6BMh+cWjqyLdDqR3O7TAXEzCxm+RNqwQgCcVtPOwRGVloXul3SMEnx8ymOXwhE7lLYyGUmps+4bO3GnwsE1LXmiA3BjVoKaqDK6gghDwJJ80YZy2ZAc+5ZfQD8QwJu2tBt7WSQEANMgf6WKRTIvRU404O+hRcG8M6mQIg+dDW0SalazbXIs0fdDH0h350Ngf8ig9+7TxwfLReOzagd7TDtrMKHAHA1Vdfjauvvrr33M033zx37FGPetTgr7aTJ0/iPe95z2GGt7ZRI4I58pOm/kr3+iM25sBNUuzFuoHAnU78HEEkEyNXom12wm7bfiPpnmqfyGkyTqJx80EofVI7AEdK2AJNALpYtWM+VV8myC7GQIEBBZBnc/T7nRMwANAGUOcKM/o5II1TU7JCdU0d020Yy1RMsm6yJ5RC8cLcRTW75IHHEDvVJ/6uQgABpGRK0kuSu785QORe15PzOsJu508VEOKB7VCKNocmE1tssGiu8UwS7B76ARLmwt1gEkug4uAToAdtB2K4Ng/AjT2VY7kCKzdav033ZtsOYaF1IHQHzOse9Pqz1c46cHSuGSvrwcYIAQIobLm4MT/BfB0gUEYj+Zg4moMCC2M9FAb5Wjok4IqhDE2PDzcAoOmUgHmfkNtJwSsbJD46qTYhzwOpnXxf8PcXM1ijqIyUgRCt7m0AURg2Tj5pDOQGesCGY+HSxI8MpooVccq6UL6vtFLQ2BmLnVD2RcbYuONmHgT1lRQw0DSUhvPH9X2gypcpZPzjJ2ByY9C3JH8ZOBtglIrnoXaG2La5vgYQQ9/hOUak35kI6XOTj2nK+gyagaJUDXwbAKn/BAo2bmGphdG83e/UiW2HsNDGtNrmNoKjbVsIAHEGRAhgki+oNFXYl1pDgLEyaRJCOpaeWdoUbogSo5OWyjeUWQ8FEYn10fSY70uoCZ5vpygfoO345fnkrs83o0DQnNz9EUm/rM2RgSBkEJJi1iddIs96zn4Ek7aXxslN7HYug0QIk0YWt7bgACjUJ1F6FJDqJNh754FW0kvZ+5xjtrHqTUHZZ6IPoPhrzKdnuT55ZtDhDcN2gL4vCUCmEct/+/59mYkh4GTj6o+78zbGS0sDDE7gA0Ciz38Ro1QccG5DuMyzRwk0bA4cfAxbAUh9lt4D/0HZqCFs/2bOrH3h7uMtyO5wcOanO5xQzjobwdG2LRC4EQGzYIg0O0ttyA5FaoqD38VeJnA2ATViKupHsHNIZEfyIRL8ZGxVYq1IU2aaBmsyFjB2yFJ5BGnPfNkYCW1b2CGJi2NM92fGyvCQ3h93dTvIoApRQJvds7FOxvYYdglB+kQ958t9URfBPp7UnAcdyOyNASgzm0SiishT9srFDO0v5vckB+QAiO8j3VcanTwR9/XvX/tjQ2yOpXN8enYTYfcQoOtjlPxxwI13tT1I3331mbuPpeYBzUK3HEtvXHPtWRx1vAcDS2nftmODK45NIMfeJifGKfRctfGd3bIlxkPQRgIHxKwAQmf5JiQGRRgcFUD75dpATr0ZiDEfl1IyNsqAD6DAzATNwX096pc3FT4C0qBxGKjyom0Eyum/IKkegEtBdjAwBKCxjVJDSrkl0baluCyNxwFEDPa+NnGmseDUv7WT7ikEI8P0uRSug7kUtyOTPtI+p3bSSNkEmlJmGkdApmzSD2sDU6GchxLwsDEcSG/0gaQ+9oXm2zG9+qCwO103IOxexHgtiXOuKrdn9WpQN9Reep/d8YWMzyInVPedb3POfY51qgFg30WrWW8xyc2bO6At6tiB5147D0HVMS+QOKbVNrcRHG3bmgbgzn3xZ3DDpL/yGwI6TukSMdLUExITBITKR+dr82lImKiUdoEAHUaa/6wdY0SsOGHh49sxITUAZl2lFilvixIAxEbnkgDmKN8n6uPBUopZQU0CcSrINsanGCfdEJeV8eFO02MuZcRRmTIEee5KRgVB3oKUmvRMFnP/lJBiduPtMAUUMArLhLwsPs09CjwIVQVwlBXFhybl2oyJ8mUIFqWrHA6jmEPKpuneudl6IC5vRUquAnieqfHteBDX17Znp+rTc0yUhUrDPuba1x70313M7+swXrA3UR03FDcXcaQh2QbYqJFZ9b4Xtgyhnvs23Z9uO4SFNm48u7mN4GjLJsxRSGAgsSUmuCWdLBoBEcxZzEw2oSQmQ9kYIwLqdrzY2ph87wPoM6/go+2Q9xGWinxfgLBM6f6sLhPntFwS/pgGgxzjgyzIpiCgUfURHAghep0T5VQgkIGXjSFi8gGXdXuoYcUrWdyu03hOvcRK/O3eQzIWSsclM0h2zE/8niHhQpTuhdYAqq1XgMycVBO/soLpuRZI16CjYGt0ck5veP6MkPscyFzoGR93v3VcfgysP6A4V+5B5wBT7V+3V59LPwjqsfXX1yzc/GRepLiAcj+3le1sBQweEPVRV3osMXJn070dnZ18wPEWZI+2uY3gaNtmIuImA4TMiNghS6XZpBf0R2qefNiYAxNbU9kOAGVzkMXW9qOXHchZJNpe5ONj9z4pRWO0E+Uf2Dp5S+zWjtV4Mh+d07z4OzoAh1gKhgEUEynpEnPrJ42lthPkSz+LlfU5idyRQGAhELf3zQ4kEMQOTFCaYsgE0LAG3GRjS+8Jbpwca0F5bOE/K30Awj/X5oGK/d2XujPAZKlZNzAMF6tPcdkY+KbqeAzM2zEHdorNe/U8M2MOVPlr++7NYu0rDVCAw/Sfqhnqfb32Pm4L+ljZanxylNYLetznFHpuzqdAzjhzAR8Pu++YV8hmEGIv67deG+ejjeBo2xZ0gi5YGGUiAkAdwG0AZjHreHzKTUXb3AbQDE6/lAXHGdSoYDmUfVl7QBRxeOFTpbcAcKPTpfcp2nM+FDTToNokgrbDQKN6okBgEgapGAPoGADK5ES5Z/3CZgMTJOJvJhWuu1VrMk4hFaVkFWYbQ5b+4bu0hq2mS8J1+743Bs7+ltuS8gMFEOCUdrQ0G0fHKdiYxVhqmwxgetbH2K7oKoDXrIyLY45hKXxcjH01luzvWpRe6IZCnh99fOjpsy+OPgZogBUqmCRrz9/LKhqlpSJriyFgpZTYOoDlkFmWItV3FJbi9a8Z8t56EO2BkA1I7XN+2M6J410he0yrbW4jONqyMak+R5kFAxuZGXE1jEzbY7/clVExUTM3iiR0UskgS/8g2WuHlI1gbZ9Yr1WdTFpk79txwAUBiMx5DiQXD5GWIlDRdgJP5UTOIFDwZYVs2T3SPVjMhWg7pYrg+gTYqBvvU6VYDHsUe8RxHicATmgekn+aEpiTsBzgci+tqlwB7N7t/bEUjb0ngi6VVbJJxdpocux2r8Y8pdpVvpYS8v32AZE+diiNEeaP138bENSJnmzc0puXgWVibCzuhNCruHSM51J+PQAJgBMsD4C+oUm50uDNT/7u4IKl+kXK7cAAabUG6jTfnHDemqqbP4jNfSZ8h+4fXI7qkDo+Oy125/f9n8s2gqNtW2MTpH2JU1pBFRtdu9US0JFmXAwgiQmzYT6cmBXT0jJnn9gSaMYiqGarkK0+JDHEBiAE2cYsZnFzagcQHwZiQyAnFGeLJ6r4G0hsS6qiHRe0Yz4GHpSZIijAi1U7BCAKlgiMOeF6aieqzgmylB+UywakKtNym1p4Uu5BOuYCgyRwBRVra/rH2hGwpO+jdOXeiKoda8szPkBGTwYiCbqvHBzirSepHFcSdvexADb5Nc1iVsX853Q6GcSkz6PGxanIlAdBnEB8/tBWDJBnmgbYIPJ+NWDyfdbtD51L87y9Ec53gXibE4hlrIwL0q8RY1gWgLnBPt3rVVN8h2H1+ACYB0hA3ranOIh5YHVuWTc73lWAIssODAdt43y0ERxt2URATcpUcBY163YdnATZuhydXXVoL8gmEsKBMpDxEyJ7n2DiaACUl1VnH+nb8AFg7JT6BAK30r7Jdtgqdasg29clSqkyOLG2+kDbsR/XWdidgQqAvIxfgRoFOGE3IbaaIAs2pigF2SDYlifGMrEyPEwQYbePVetJcSwF8NkXAsxsotbUqLFLZOmmkIFOblx9yQFKc2ONS8c7JZbS3GMTk763xSo3V7nbWIbE4PAAELN2HLu0LO1VAC3v60BVQTIMtFfbKn16dysJMCTe7rMw3J5rOAOZuRBtolBw7cHLSnO/G6sVwULvXm3V+TMCmAoWrOos6QqBfG/nHhiq7cT9j7cgu0NAhz7gul4b56ON4Gjb1hIQlbHRFVSswIR1aXlajWXMAORcNPSiS9WlHa1wrRM9R51xrR0g/4q32kK1j4l+1ZchTAqbj6XRtJ2kJWLxiaAsyPbsgk4MflWXMUTpPIQRYAhZwpY9CqQFMSl/7xJlJsqJytP9ManuJwowM+DJrl3rE+oDBVQxajozx18KxJHBnKKytNJOwVQCfcG2QNFrbG6OedJLGu0UO+c+nRngBKB7umljBoQsxeWF3QrkSrbKtdMLelACJqLhveDsHt14Fvdk762v+QSgF5wMpQB9n8UpA9YD1yw61yfa9j4JCPQDmVpEvhSgLIppDUv7FFq6+EzqfHrThLX1nTs3gdJ9n79v2yGMdkQ2gqMtWyRNd1FmaqywowmXLUWVlhYbOwBJTYUZS1ptpiwKQzRClolp5By3JKLthtKGr6nAY+1TCLKVeVERc2wJwbdjjBZlnxwzkjaFHWgDgBi0vGMT0ootYc/Ux5bpAyiqe0tYxeo5Y3fSOEXno3HZ2HrMpiMMqNCYOh0v1WbZXM7QlKUDdtAyBKzjbZaKUjpAkd4LY+AAICgcc/vEetZI7lu7c8CMPGuU0JqNETLw4aodjTmzS4BDL7nNIWF3XUOpfl0xHKRgL1XjnvPp6WOB/mhRyo29/xAYGmKr0nvVx0D1xLHM7BaXAaWlIGNJN+7fHXAGQdJc7P3g8Xyw3fvtbDuEhTam1Ta3ERxt2VhnadsOQybvPLkakwSWSVvIGzJSRPxbZWMapFk/JgFvBipMOvEHJDAmq72cT1AfAxVQn+DSTiQ6oSQi1z4JyMJrE3Y7AGPfn5H0128gRLATGwOp6rVNSpYyICQ2K/lYmo60HeuLAPKbv+ovbZP/ICer1DIKSu0EVADNJl4RmttmryYil/ZynzZ3JK1M6qKsxs1sRTQ5jwEb+2dvGuf0IrImjdjJdbXdnE4DQCrszsG4lJshcTcGNlbB0Vt2LKVwK6AxxCQlsMGJ6Siqcc/pdgYA0VB/FVAqSgD0nO9tu+5j0fL/YiVWPxCoBdRLzZi+ot31gEaRcjuTGIX64j7TQWzfuukx1xwhIB4wLXbQ689WG8HRtq3VCUMBAkEBSsyCbGOWEHTiFFf5Hm+EbYgNQB3Bf45zJWp5wS2AGUCN/opnZHbI+0B87Je0/SoX9iO3AxVSp1gYyUeE3QLSSFNPrOCiYL2sb5uswZJushIF2o6lDZOPskvcUelDVltIgYzeQGJu/HL89MRpnIRFk3YDoEABjj2TcS7E3woC2CitNE9QqtjNrBqjpM0xX9ORZT2ZhZZ+oJtAHCgmpMQyOWCRNFbJrwReQEBZsdsmuSqudF+V1SDIhN19gCbVn8qhFNW40zkDSpxv3E56UFQArzp+33327d3HzVvveQesaqC0RK+0kWg7tWu1Ms4WcOHfRP9GL/I9tyweZWmFQ7COCd0BmZ+DXn+22giOtmxRmR//Zc6avuHErOgXrqWsGOlXbtqfTH3TKiybKEMWScsmt9ZeJiZIRcxzPpR9BGCUPmTskMYRU+zCRFkqKhLnFIvFTlXMNpE4dgpke6JZSsvAGiUBsz1sQjPdhwm7ZUJ26TkYyHT+CMpAIaW9EAR8yDhxZg4sHiCLv9M8SLmKtmeygDxh6oRvviBkYTdHp9V1InBQJeyGm6Azg8TR3v88TYlr9SufCHOiY19OwFJvGkcCMAAKcODbNUCS2KsBIEHpJwCAnGotmaGE2l07OdTs52wo5Waibe+3zIbSer3n5tvrFW0vm0TrdOPcu7hK2ISt6I8Siq/+nvPtPYGzGTTtnDzeabXRNrcRHG3bWlkRBVteT1KwMCa2I7M6aAFEYWEiASFSyRwxIbZIe5sxiWiblXGKtqS/UR8FFtGAxgIfm6+isSfmY6k/EscsyJbro2d1GK7Gj2OrlPVK5X2Q50YBHi4uY4AsHivYzLbanZK+HNBi2kApxO50vCNLqocA7pBXjVk9KbIx0EZijqdX/G3zAul4JDE5ck6PoBOlsF/eh1yxNY4MB8OUQSJQBxWJ54f0qeDKi/cZZepNjSxuC5ijm4sNmfp7s3vIID43NvCrsk5/zTtA4aQFpEA1DUZehZeYMVQgaqDfPiYJbgpelLarbWXR9lA40n7SC67MJK3g13st8nvpn4/KPFisj80B1vXA3tlgp495hexRc7S5jeBoy8YEEUEbq5JSSCwrz2B6IM+eKOBBZmjMB8bUQCd5guqP+nxMz0Mr+4RBH42dtJ1AQDvfTjBgQayC7MqHVVFkeIRyn/YrPLKxTbkvuV+AtaZSjNJOmv+D6bfkmnS9Ajpm0VE1Nm5AAqH+q0Ew0ID4G1Dhs8QcrZwBMiAEkBhAa0f4N5Qsg5V2MEBiwu7G5jvOk2iE6pZCJniAeWBCsgoPxTnHiiWRmLaTVqcZm4Z5gNQnpLbBS4PYI9ou+s9ASbmwrE1KKTY4lmIZm1PFZW7qk8ambmMIIC09Z8EttrWYnYJ5W6391Id9jux5W2m6PuA0Z2c3UDr5gN1th7DQmAPiAStc81ghe7RtmLE6iR0y1oGQRM2JYTEBNOCW+2cfYyKITWztgRQVPjDNDpHWKVrdx1icoi/9kotEaSUYawqLYhaIM6uPEhTRmAN2YEXboeSDQrSdWZpynBIwSDWRnA/JKj4Q0ua0tuEtI8BqPEVjL0IGqTbmpACn9imqh3sfgksRaixsHAkllik7SDkBNDmdZvPisLBb75mQhd02GWmRTTYHA62chqqcwCotVnoDTA/kNzlmLGaSahC0jElyAElGxJgkPZcKTOp/+tJ6/tm328MkFeDRt7GMQfLjU/RhJxIc7m1mbQZpWVy9l1H5vE6fR24+CMIxCWpj2zs923YIox2RjeBoy8ZtAHNMaTVGBglZkJ1/0JsuxIBS4QPIxM/QVW3C1Fgl6k59bBETIAwEs2iImGkln+h8bM8wKeND4OB8ZhpPIg4Y0QAXTESuE7v5QATZSdity905wPalheXUBCM50EgGIpBSWUwANGUXDGBGE4ibA1JKUVgrWwVncdNcXBxIBNlEYFvSb4CJSqE5EbK4nbOY3HCeAB2AuVFQ5CZZZaK403ZSjjOzV8lH0RaRskCMCrTBUCtE/M3p8+Q1ThlcmdjafWA9o2GiffsAACX4WcQc9LFN/npjB9MJA2bWWU/bNVBaAHoGRdt97c3FDAcg9W8DSKmv4Yl/MzbHgG/AXPGrVa7eFoNUfx7mYji7ARId89g7EDr0/Ptbs43z0UZwtGWLAbIaDSi0LrYdhgmybS+zxIoQIajYOhIDjYmb8+/XSEH3TVPtTUBKLWWdrTAq0Qoxmo8DPtDEj/eJkO/pTOaQapcIPFEWprH9x1RNYiCAZJWbT5Wx+VgqTJfSm+CcAqEjVgaLkgCayUBCZn4sHquMTeojVbSdsJtDBgNakdxKQapHjlnZJS/+ljYAbgKCKM3TwKaSkq4Egiy194Mv488KMPKvexEiJdE2AKvkjWir33I7lBAQp/+SCsoNSMp9mvALAhz1vbUJLFVcr1NTiT3SY4F6qnFrO8sqbdc25KNMTdr0lyjPqynVZkDRHV8GkPqA0jqVttNEvyD2dJ92bn4CTWxOGoc5l/5YLIe7yaRcDdt2rOp8TVbsuNnkmG88G/ngmqFjviDvyGwER9s2FVnHRl/rXBRtGXtiftTH5mBSsbVfMm8+Vo0ZWdjNrIJlBrjNjE1aaaWgyFimpG3R46R7mDFyOyLIduyEMhXclTHbPmWFsJudaLsFaGYTrc7BpORHw7kdu0+d2BlZcN54H9NcKQKJ0cCJXm/xRf+9nIXmxkRlIbW9J5Rwgom/M9DQvztHIAhC1fdUDrC+t2led+lUu3Gm3C+ndJfCVIsNzgcAiEU8bmctpcJSPiCVR1C1OtkHDXYdZ0LCrs1DgyJtZPdjfyefCoAs1Jo4W6ZJSd06TVL6BCwBSETlsv/aJ122oRYJ2Fi0XRRvXAm45B8Bi4DXSub7OWqwNMcmEtKHbQnLdtztvs8fb0H2aJvbCI62bDEQupaFLUeeYwgKPoDEsBQVrXX3+F4fymwMRQFD7HzIMyTKCKR2VEhtS/K9IJsDq75Jv8waJ6Qmq2yt7EWfaFuXqjExYotckykQ4oTzj3EdgwARSdei7Qgv2mZhxjSuQrQNS3HlkgeFT8gsUVCBeANO9ycrBznPz8HeA5TCbn3nGCzESZAq44WPThDyHkGYmvRe6/VJ2K2TicYeba8285G7BjXCeORUmN6PS3HZNia2PJ+CfsqsNEACaPn9oB7RNZOpgfRcMIoReW6zdjwbV6epPKPTl3brZWrc/SXc45kkjcueapZoQerOg5/BStursBvLQNQAQLJ+5ewSAXVqp+/1cnDhi1T6fo425ZaBfRWNBZXdVgJIxwtInXzAyW2HsNDiIQiyD3r92WojONqyxcBA41gdEsAUtF6OpLOU0bD910IWNbOmuAofE0DrBMtBwI8XdqcK2dAJ0gAGyRRr7ZgmBlHjIiMISBc1aV9KH0Xrk5BF2z0+1o6FIDHnX9+RUmiSznPL9A2gRAKCpqzySnlNM5pomykJuqN9ITdZ16VTUrrPpOZI9YuyD3kfctohpzexFFpskEkNXYKfWSIdB+0qMXhwcZH0mcTflK/zsQNBlv07cbaBaGjaUI6HuWmFdHzIEJICAl+NO2ETDzocGJNBd8wJucY97baMURoCUXM+JZNk7cv9VqLtul1ruz5eASQ7xkP+y0TbazJI2cX6XuBW30+dPlxiCytq26kzgT3qz9IK43Pmglvd9vf2tx3CQoug/L13gDbORxvB0ZaNG1LdCpAqJduqsMYxSIQkaTHwwciMjxdtWzXolElp8rNvJ/lYyswYJN3glTU9FaFEQeP6JIjYuiu/11J6zsVDlkbq85l5H5v8TIOkgnNlYRR3IQnEzScwgtUp6twvYQOC1g5nn6SV0dVxtgrPGCkBQpxRl/qw+sRAGg+LZkjbkZSnMkEKnByMyCwTcRZtd+bjhN1Ot8KBwB0nYsa3Iy6l0BxdLGKGAUqfeosJmsGSbAyStF1Vjds+c4m31PEti0v2WPAfNLjo3Xu96It3EEyVjI+4ErgWbXPVjv3dB3Q2SbX13f9SBumoJvdFiGrgCsckHbCpRb1g6ftcAKTjBX6W2XGHDWOF7M1tBEdbttjk7yFfr8d2pO8UYET/40rBEbxPk8vUpOnB2Ci9PumHnA/rcdM8JZ8A2AaqJiXJImn1aZThUKImOB9om76yNZDjGfbJom0mAWQd6zxrcWhKRUTX8pyWztt9pXb0fknvMaRu1MeBMcpjmGSvaTk8IZiwW98xS0fZnnd2f4nJsMkn+QjTY8+mjULgStgtN8KpQUG5rKLxtIGruz8hkUycrkJ8n4biXGlbGL3g2uEkGBeApA1DAHuqxq6VxAVcuQ9xmtz0A+XF2+SeDailcwb3UIKU2paIttNnC32i7Xw2x4OynVUBUl8MfRP6wmX/ofKfBwNzoGUVvODYy3VWs/WyVYc5F9apwMKWgKazwCYnxgrZ56qN4Gjb1spqpVzcD1m4rBNqtOdWTxqw0BVfhY+l3ggAAVGLSTa1j9JKrD6mxYnB+mLdq01TWCznAN+OcA72/ZfE3z0+fnf55BP627HJmiG+FIBOmR+rDm3ft1bjKYu2kb5vbQ4SH04i8sQgKegBRGxtx0yUbuJvs6ji6qStivoeOZJCxNachd1MErMurQc5YXfMOqpMdujEqkyLxZdqKnUKJoFcHkDvmXQbkow5DE06AJ58KWuEvLB7yIcNIClootxnCRAyPIWl3GLE4BL0PnZoiEHoS5EVx6pCksoJlnFZeA7kWIx1Hz7VRlSAyd576It10X0sYEl6xdrLLI2lAbBVLjoDtiidapZW4ZnPMYl9iZ3+/H3bDmGhjZqjzW0ER1u2SJpeCoROfptLfSK3NYg9U+MYDdIJoPYJyrTA2CZSPVHpk7bhMB9liiylR01eaMVA0jNZagktZUZLfUK0NJ/4GuPhfQDkeGzllfYV2BbaqWhbfQEAtpQfmtJhE54jpRGN0Qr649kYI/ExFkwE8AFOtA1NWZnGOInjKdWKMliQ+lJmohB2cxZkNzARs2eQdOJWYbfFkyZ1FUtzFIE4B0LTsRTE1GKUscljYMLuYD4wUGAfEuUfSAGcgRkoWEztBLnKtmIxDGPibebE0pnIOu2Rapksb16TRJTF2wU4qXxNvO0nUf+3ByBDTI+NZTrthMaeSWKUfdUT90D7xTYkq6bTAg2vhV5BZ+PF2isDpEXxLOgn9XEUuKQXGPWhvj42aVV0eObtxKkT2w5hoUUcwvYhZzm7t6mN4GjLJvuJ2TJ90b2Y/iWvwsqMBtySepiPAgRhm6Di6fy9a6Lt5GPt2Q8C81EwkXwoy0/g/I3VSVtgqY+lAnPFbkiVZj0HvQdbiWZpMtutoiMkmQ+TF3+bjwqybZWVpXIMSJFrx/oj5GrcJqCG7b+W9UYmio76S53IidwBES4TUo2nvDpP2gNbOzLRGlFkQm5brSfzsAQaHfBgF09KUVEWZCcGyeZIpW4c8aObAKNgOVKbdrGbpBgoqnGng57E6fGxtKeBiqRaYgarzoh6QEshdoNHsD0gpW8yrQHMEKBxQIIolADJs1dDfdWxr6tFmosbB57bC/Cy+lVrdbxZHwewOXBYP2Pg9fGx/S9Mtx3CaEdkIzjasnGjWYcGqdiwiGuhAuEsukaDgiUpfAJkUg9IKSyrl5ME2b6KtmNWCh8v2o7l91cSbTshda9P3Q4qnzDs48eg9sGsnH8iacaqIRVkM3gGKVXAto+brkYLnNk0IGUejFFhIAuyg8KOIClPqazNzgeJ8YExRtXKt1RqIM276otcJiE4lkfmbVuKbz5y0tgvAnIKLO2FYqUHMlNCXY49gYXg+mLkAp/simgGQ7kKeFIpGgdeko92r30kgTaQwEaquO2ZJM8epZwd3Dl9rgHTkCZp0McDJEux6RtCct/FnFtfP9Q/cpsJhC4BU3U88+cMmC6mbc4EeDHgzmeq8t+aDFfxGTkGoCkEWu60RZMVrweLkQ94/dlqIzjaspkgO2ls9O9AyJWtlW0BMmiY81FAZIwRmY9jc7xo2wAJ7O+Q+0rVr31fDtT0teN9LJ7ISFW0mbOPpfSyILv0MQ1y0k1pO9y6r0adI4ztigwZkNa+MrVid2KFStG2MWQpg2Pt6JhwqyLrJrcDE21rjGljWTamSKuWw6US9fvbC7KZbZk+YOvEjMUxlChshzJRCs6g8aQ5nOW+QlT2KWrqr8lfZonl0UGUSc/AEucPHvJuZpZyA8MJsiUu0lRdqpcEfUMahy0sDefrHBnISHOavcm5mfJFBUxs5duQdmUIIKVxMFCm2/XAdWVufSk2VOdWMX/PxbElwGbFNNtaKbYl7Q1eauB67sRGzS3rDKuLyLl63i5IaibHewqNfAhptfN0tdpZp7R64xvfiEsuuQQnTpzAZZddhve9732Dvm9961tBRMXjxIkyR8zMeM1rXoOHPOQhOHnyJK644gr8n//zf476NrIp8ImNAB1ukOoesdbKiQqA4mSBT6vgpdU2WwCt+ugxW60VW22nlUc0/1D5BNeOxbeuT1PFpY+4wCf5UY+PnkfIY8eVDypfW2GHQOpDAjLSI6cEbby57jMIGJLtWzQNau01Qz6CYNM4BdVtEeW+0nHS4/p3qHRg2h6TouI2qA8VPnbMfKKdT30SuAnqE0T7pHWkzA/m3xDQaH7VbVuTztvxJrc156NjIZvWUgZI/tmos9DM+6B6XvRY6JP/yRGF8lwoTpbtWNx128mdQN5nkdV9DflQQBJ19broe7aKrRLX0KXBjdtRz4/F/Z49k/HeF8YK2eeqnVXg6B3veAeuueYavPa1r8Uf//Ef43GPexyuvPJK/PVf//XgNadOncKnPvWp9Pj4xz9enP+pn/op/OzP/ixuuOEG/MEf/AHuf//748orr8Tp02fmQx+DgJ4COChoSQDGPccFPtZO9L4tlz5t5RMgG8ouasd82uXtRAeC4gQCmlw8Buh87HU7BuhsPAqfkH1igwJYFYAmOF83bgiU2w4WF+W+FOQIsKKso/KAqYHse2cASQFO4esBX49PAkqNAy2OwbPYSX2h4M6uTbouB6xg96cAxfugUcBiPh40GRDRPjIYymBLAGYGFfZjgz34SKBHfVwbw0AGJUCqAU0NAmqg4v8OYd7HX2cxQgFS0W5PH0Ove47RIp+59lDEMmzLgNSy69d2nL9Sxy3d31GSNGmzxuLNwOL4t5taO/nAs6NC9kEf56OdVXf9hje8AS960Yvwghe8AI9+9KNxww034H73ux9uvPHGwWuICBdddFF6XHjhhekcM+P666/HD/7gD+KbvumbcOmll+I//af/hE9+8pP4zd/8zTNwR46xaTPr4YGPn+yNRZFznJ5jw6KJCUDXQn69Jx8Ct+rTcOor+bTqU7UTG3axOJ9W2uEAxLbfp2utHWmzYKsmJagpGCS9b2OhajCUGDLHerHzYU0FejYnOsBjfaWxdH7GIkUHHKICpAI4kfal39mlDyX2JIEgsvdVQVkgcEsOoCiQMWBizJexTsbqKDAy8GPMV/JJ7WjbGheakO9LgUh6bqxPAoeAGEJqJwEmBUqkzBCrdokpi8ftGSGzWVAfoOzTXs8xNwm8uAnS/I0JM6tBT/1cA6e+62AAyfkVTEk1IRfMVz/o6WWQBoGau+chM9A4eHrJ9Su2s/zyza/doDN9du/N4guOMpqldvqeva32v8wsrXbQx/loZw042t/fx6233oorrrgiHQsh4IorrsAtt9wyeN0999yDv//3/z4uvvhifNM3fRPe//73p3Mf+9jHcMcddxRtPuhBD8Jll122sM3DNGNL0OS0WdIAKeiIyv549kSYCU6AIioYkna49Gmyj7UVW3YMC5eMVMPKjHCKZc6nVZ+29ElsTOsAh/k4tsZYJnbxl2DF95XBYu7LMT+WbjSA6YCJpdgKps3Akku5JcanrQBVk4EJuzRabB2oC3U7et8ONCWgRK4vY32sTQMmyIApteeYI6S28vFoW6K4tJ75JHbGJn8DYT0+KeUYQma0QBnoeBbKAIsHSJ7x8Wmkwif7JjaqAEgesIQMTMwHlc+iR68vHCbqATNDAGklwLVgIhlilxZhl1WByTL8c1gAh6rno7Q5BglYlG7saeAwo+m1ZtIceR+jbceOt5rM2V133YWu6wrmBwAuvPBCfPCDH+y95lGPehRuvPFGXHrppfjc5z6Hn/7pn8aTn/xkvP/978fDH/5w3HHHHamNuk0712d7e3vY28u/GO6+++5Nb0smySR2ZnSkK5kaEeFKfSIVEhNgy6Y5yEoJbqp6QKRMDVgF2dkHDXSCUrCiAmUT/EYgC5rDCj7WDjsf2/S2YYlVv7RZxb5Ww4j1PtOWFVAww4wO9lomjlRnsBZk6zydGJyAcs2TvdCxswVe/voUn8YQ3fVG2EdVcJMCiRwPpwVGtjtGEnbrnBehwu4kgEZaKJX7otRbVHG0VdqOvqAkgLr6suqk7cOh7536kB0jkAm79ToEFVdrdW9miLAbEqfErMvgOYu3rYZQEognsbaGRW5DXa5E2/r+yFhqWYPeukbmbJ8rL5J2nXmw0ldI0qyvREASbkOk6IV42p934+2vrwXivh5SqNpbZTVbKnGA/MHLDWJIsDy4BUivc3B9b5COyh/TjS7fyIr3cd1Ajj7Ipjne/MK4t9rmdtaAo03s8ssvx+WXX55eP/nJT8aXf/mX4+d//ufx+te/fuN2r7vuOrzuda87jBB1WT1LUUU/mVvVbBaGhaYMniCt0so+MnnGVie0Fm7GhFS/rny4Ra5sTAB3DG51x/tGlnD3+jSVT4O0D1uKJzBoJmm5EM03/xqPel88Y61WzXm5vsZMUYAYd5y2QIkNQDNCrtgMBU4ywQfz0XnVfPwqP9u6BDp+yQcy91idpBRu9R2cfFj1QOaQOmFE70MlUCMFMFFBg/SlC2WJiv6ioq20+S/kmaKLyVW2tlVtCAA6OUiwOdVKDOjWIY34GA4xoGv71aU+PVbJeCwNH4i0IKd9IAUsWJVvq4VEBqBS0apUUjP5zjMuDljZ3561YBiS1GMeEDpgk5rrATiuPXJjmUoWxKqdRXWNKp9UgHIRGKqP+Xufm9f9h7G+VPtaBQukcQrpvTgrLP0gOF4x7913dqTVDtrG+WjHG/Y6e/CDH4ymaXDnnXcWx++8805cdNFFK7UxmUzwlV/5lfjwhz8MAOm6ddt81atehc997nPpcfvtt69zK4WZIJsbRtcwmESnk3QzLaWUm6w446T9iU5sjUbAExc+UdNfTp+UxNbqE2qfnr7Mp+3xmahPE4XharMoO05YRM2WyjPht2mhrJ3W9UWqV0o6IBsDSunCPD7aTgDYRO0TFDqlnMZDKfJunE+TdVCwtJXTRBWpsB7RdpFOS5ohpONWJTxro8id9yJp5PRagGqKcn8gO0dFeyDXDrkUYPJRYKFpvOST0mr6t0shemG3T72RtuPTXKkmkwFcPZdAH5CBSHGMClA8r8vRvpKWh+Z9lj0vSrF5c2EmsfYqK8JMBN5jtukwLfBZ2ZakxTZawbZmqi1pnOxxpo1oQczbmcCPvyB71BxtamcNONrZ2cETnvAE3HTTTelYjBE33XRTwQ4tsq7r8Gd/9md4yEMeAgB4xCMegYsuuqho8+6778Yf/MEfLGxzd3cXp06dKh6bGqs42gTCcYIslG4MKDFg4uY0yTugYqAjCIBJPoWwm3XSyyLpQticfHr6GvJRLZEJs5PYuo0q2lYfylqnDOAi4EXbCtgwkTGIBrgCEAMjhqqdBGi0DX2YvshiTwJuA0xOS4QGxWq6JNo2QNS4dgzMUOmT0nkOzKYl7IWwuwJW5PqhLMhmjSnH7FaMKfAS0bYXZMtnKAmpHeCyRwxZO2Q+VmoAro+kBVLQYroj2WImFOLvQrRNVAmy80q2tKJN405xGejSccqgK+QHrA+UAIlcO+Yz9LwKQLI+8gF58iLtULVj1rfcv7I5sfYiYDIXSxXTIiCwgkv/BSt6U16peMbxCEdljo7PZH36nnEp/7lqZw04AoBrrrkGb37zm/G2t70NH/jAB/CSl7wE9957L17wghcAAK666iq86lWvSv4/8iM/gt/+7d/GRz/6UfzxH/8xvvM7vxMf//jH8cIXvhCA/EN/2ctehh/90R/Fb/3Wb+HP/uzPcNVVV+GhD30onv3sZ5+Re/LL4hPoSUvruWB1PJuTl8KzgoDsk65JvgqaJpwnX/WBgR177X3s3JBPk4FRWunmgE7NVrHzSaJwJ3BG8O0YQ8XFfYrYOreRV/rZajB27WTmKIEkKzcQMuBJ27B4YbcBGgeUChbKAZ0k7KaSYfLlA6Jnalw7xWpE9TF2KJcaMP/MCqUslbFExjJpvLB2nI+ttmOb8MnAknsdMrArRdv6gU3Az7FMBQhCZnyoBlyUJ37PCHlgVIOY4oF5BslP0jVQMnPgbSlA0jbI+vF9LAM0i16nNhf7LDzXx5719UE9fa1kxwd0zJuBoqGU2nZSbe3uZCv9rmrbYo7WqUcIANdffz0e9ahH4eTJk7j44ovx8pe//IyV0xmys0pz9JznPAef/vSn8ZrXvAZ33HEHHv/4x+Pd7353ElR/4hOfQHAU9t/+7d/iRS96Ee644w580Rd9EZ7whCfg93//9/HoRz86+bziFa/Avffei3/xL/4FPvvZz+Jrv/Zr8e53v3uuWORRWWwYjKgTnm6kSsgTM5SVoZi+FJk4gQDZ1kJ9OLoVRCyPQMLQAKAG0F1N9ctefVh8AACNikwX+rh2kmhbfVRkLeJv1i9qPaa6lqSU9oJvu2+WOBkMpiCxq1iVTZ/DSCLUqJNBqn7dQiTFLM8iCZI+Y6MqoQZJmGxCauvftljJOiik712VDOn4Imto3U8M0+jOCbuJkrCbuZTQpj3RrJ0krs6ds+moAmkV71ytOvWtQuwkJ2k0SFgwqhFKvsjCbNhWHwp4IudzmiLjKBqxtPmwjZt2QKplIXAWu6vuhqtnssELeVCtGncSctdbeABlp9a+iaPhjpG7dx2vXk1Sn6/XG1mjlP9Mmr5aN+RF2uj3SWLtAa1S2Z5eX4Ri/75DdQ07lzUmM7Lx31DL04dXFmGYA1n1nhQKuC3pkNba+uTM2zY0R1aP8IYbbsBll12G66+/HldeeSU+9KEP4YILLpjzf/vb345rr70WN954I5785CfjL/7iL/Dd3/3dICK84Q1vOFDsBzFiPubv7llgd999Nx70oAfhc5/73Nopti/9uTcgzmKCqTKHMRBJRNIzAloGZgAmsJlWwFGEnJsSsBPluTWQoh1EAtoITAPI+0TvY+0EYKcD9kPpQxCRb8PALACT3A6bSLr2adWngYp/ad6nYfAU2cd+tUeosDuICHym96s+xBIzE0CdgJ0wVfG37q0GRto3LXTCyoSZ+NJM4zFMB4A6YZpkjzaSveP0Ozckn9weB/T7RMUcLj7zMZyZ29FUZycOlPY7Q1oVFrr8eSBFc/aW2LK+7KPHFcSk9xbWHmchdoSCIagYmfNYRAjIYgc0WY9FBakqDJd4OZ1DUrgnxKMxueOFuDbHnF6bsJuRN7Ctn6O1476+vE/dV6/PQNtm1fYZbGmdApP0fH3W/fb4zO3Jtqi9ZfucDYiUVxZpL2hj8SU2btWJo8AqdXyFoNwD50V2+IH9y+tfgH/6fd+49nUHmTPWaf8b3vX/w+T+Owdqa3rvPn7nG39+5Vgvu+wyfPVXfzV+7ud+DoDIXy6++GJ87/d+L6699to5/6uvvhof+MAHCnnL93//9+MP/uAP8Hu/93sHiv0gdlal1c5F4yYCE9bUVRTdjBVsbFhAk6bE0DDQxPxo9dgE8k5Oom6/IXoe8YnqY77q03a5vVafk0+n7ahPUJ82Vu245+DiSXGx9qXHlvhInaSoGiTTT2UdFTRt1oWYx6hVZswLshvVIFkqr1Emq1W2StsRnRJ0rLP4m5NPKdouHuajAuokojZBtkvX5bpEWfeU02SUKlsnsXbIbZlIerhCdo8PoRBks9MkwUTb6uPTbonB03aKukvQ6y3VZvcHay/HZnGQnQMcEymUkp2zZ2sXvi/Kx+afMSzWXqTtWeQzpyWqfzFrH/7wUPprib6Iap9F7S0TWm+UQutr4xDaAQ4fGAHuPdYpqwBLvOIYHH5g9zt1ZjIMmxoDaTn/po91Rm2TeoRPfvKTceutt6bU20c/+lG8613vwjd+4/qg8zDtrEqrnZPW6K87eyYIsIlyjDmC7FyrxwNAFMEclKKAgBsWlsgYEwoRHMM8ndFEXa4dQYHFJ8g/IwFVBDQdqFMfYunLtnNP7TAIQWsb6S99kjRholIaBmak8SHHAbkPiwczyu1C2QmjQlh8JVUkr+VHKyPaGmzISj9AJnfqrB1kHxZQRCxAiFxI6b+6uWxaFs+ZLLBMktVLSgfT1eVxyyCymwd9FicGd40HB0yJaYmUfYQQtPvSjV05Ay+wLvd3dYlSyspSjKDUjibTYGSPET+k4wBd2m+pF2YJy9JeUhogA6T8vlhDNhjyBUs2gEyJOTF3ItLPfTnJcUqj6XL/IsWWu8rpLh3kVD7AfNxXvLXTV6uoL8VmDBuR0wMzXPDlNXbM99uXYjPguAp5P5fqW25FDaRVLi1SbCuYjUUx9muFuJ6l9913qIFsKQHyhbuPtyD7MNNqdT2/3d1d7O7uFsc2qUf4Hd/xHbjrrrvwtV/7tWBmzGYzvPjFL8arX/3qA8V9UBuZo22bMTINlMlxj9YxNok9MQDDoIYR9FxQv6A+1DCoEfDT7Ai707TCOIWGgbYTnxBBIaKZdKA29vqgEZ9Q+Fif3odB2nfySUwUK+Nl9ybxUmKQYnF/1Lj7Nf8JKyvBeWVeEJ8kBk+MjR4LXvztxOkBurrNCcS1DIAJyQXglMLueqVgUfnbhNW+irmJtimDGPbXeJYpeB9aINrWeT+4jW+VscoC8SzITsv/tR6V9VWIra1PS5Eao2XMkWNyjGViCqk0ACofY4P8fmwCovRzX4ut07H6WZf7L9w3zbXZxyDVfQy203Osei0rtYKLcd6n77ohn/X0QeuftPbXW+a/qi8SaJRhP9gkvLIV+jP0/H3mbHLi/OEXLr74YjzoQQ9Kj+uuu+5Q2r355pvx4z/+43jTm96EP/7jP8Zv/MZv4J3vfOeBahEehp0/7+wxNVsFhsBZ/KoTcmgY0VgYndAJDFCXmJrQyG/+0IoEmAInn0AMDh1CE4EJENoIq0hNDFDokqbE2mkaawfKZmQfChJPaKKwDgEIRAA6+Z5sIgJFRAJCMAZJfSjqj3phoiIEmLExDPprXNrRn/xagVsEMjZxuV/sRg0E/ZuQ2CP5oW6/2tW7lddSvRtZqJ5IFl3JBmWWrG3Vv1iRRDTavQm7KYdiYZqgO7H/ITMzXgqSLmVhVqK7dXaTlDE/5pOJCEqC7hg4ad25gaIi6SlamybaNpKgUdE1K9nHJO+NMSWeOTLdE0y0DQVLMoAELn3sfklF1iZGtjjTG8POx5gaSm+MFYdOPjXLY5+fJORGfp0qig+Alz4GKQ26m3CHxNF9jJS3EKAVPQd9KATwEp/yAsxjgQWsjwdIK+mQlvVfx+L6GIzv0C0hs62xRgCAA7IyR22HyRzdfvvtheaoZo2AzeoR/tAP/RC+67u+K60if+xjH5sWSf2bf/NvikVWZ9JGcLRla1pGN4ugVv+xM0AhAjEI88MNmrZDnBFCm791AjG6qECFCU0jKuGmhUwmBE29EZqmfrZ5gECIiAw0TZfaYQ5oGkaMpPMOJ59p1U6M+YsxRkITBICFVtqhBogdEILEzp0AOGZCaCMiBwGBs7yNCKIwVZzScQSECOqCfhdRnu1hszrkeUY5pRIVVHQKQkHi02lyKq0KMx8DEOIDwXRJ1MyR3ZwtE3ow8KMhcJfndamibfeUwy2qcSP7cMxY0Pso3JO/YgYzSWxtKUetLG7pJwpA7PRvTaNJB5QF2RDQw5pksyraaeWg4hRJ5wr4gVbRzip0SmOQfDRlZ6m3cqkfcrrHgLjDBuZRTICApHtNiE3IKNFbQpsa9BCYqtNdfWmwFErl6zvywIyxuI8UY8+EvihG72Nh2f33tbHAUjXtpeYRTj/a8YCILe15prBC+sxtERgBmO5Pt9r/MjtMcLRKTT9fj9DK4Vg9wquvvrr3mi984QtzAKhp5BfoNteLjeBo29bIsh8ytoQJIciS/KZhgDTFRZxADQA0CqDaVliVpmEQOoRGJnfmgCZ0iBzQKhtkrFAT5EPHEDDTRcakiQA69WU0lsLh7NOqTxOkHavTxywgi6iRPsj6YgQiwRn6xc0k9wcihMAAOk1VALA9rtQnKhMWofu1yREAEUwBlJZ/2SSU/yGlL2tAvkgNHClDJUSBbqdB5svgNgljIKonVnaMYaKZYom9zdXaddQJ0zCc/Zg37FYQHRYrSgaJTWOcIrHbk1SZRZgyTQ5IQTVFFhspGKKIpGUiyJ5tgQgcWbCk6WmYZIWg4gqk903bCXr/QbcZMRDkgYCCX5m8dcx0Midtk2Ag0t4XbZfddio2WMpMQZlVA22ZLaosgQQHXDyDtA5AmmOCHNi0N7ev/954hs30RwV42YhBChjahy27rMAgFUzUahNU0WZC/CtdegBbDOCO2k4+4HgLsrdh11xzDZ7//OfjiU98Ip70pCfh+uuvn6tH+LCHPSyl5Z71rGfhDW94A77yK78Sl112GT784Q/jh37oh/CsZz0rgaRt2AiOtmxNo8xNG9Mv/4bkWAgdWH1gjE2UX9pNEOF1G4TxmSir07YRsQsIoUOgiI5LH+aAySSimxFCQwiICNpeZMKkkee2ZXSdACmA1UfiaYMwPhP1CTpbR5Y+4XzaIApyS/exFshh7iT1xgKSOhBC6LSdoO0ocGGZlIF6w9KoaS1DFA6FxAQ3wJ34cGBlrSCpMV2ODlLRttIlaQ8yrcpt8xNTlOPgNLFzgLAoUDZEJwUv2jawYyk2dv0C7jnkviy2LLZ2foq62Iu23dxggEjYM07tyL1oIzZBNi5tpUyEbGzMCVckITQjsUIwn6ggQUELW+wp5YEyZUZy/wwdP2O5KoCSAaEDLf7NIEWJrgRAYUmQbW+CpWY3AEhmlW8SaPcxSHPxuNTdgBVgfhWANHhuOVBIYu2VAdIK4GNFt0OzuVTimQdI9x3zCtnbqHO0bj3CH/zBHwQR4Qd/8AfxV3/1V/iSL/kSPOtZz8KP/diPHSjug9oIjrZsoYmiow2sq7iCMDMMTFopkjNpOxBJqotDBEFYISII4FEfIKBtGEwzMALaMANxix312W2lMM5OE3VCFXBEDOw0MznXzsBoMWliYnwCImYMTILGY302UQsbE4gjZtyITwdMmqhAihEoghFAyGBtH+Kzj0ZYL43HJpdAAn6IGDMEhAB0yiARs8x1iV2S50CeZcqMhDTIyIWA8s9aIRNyCggzaAoOORVl8xQgjIvVXYLUXIpgBLYl60htA+R6QvoRXnyFk4vGQArnudQW9CnhldklwLEhrh2WVGdI7QgIsnaMdUKHzPjYQQVQtuEwg9MCRWF7tC6T6YsUbFACcpndIUvDcW7PmMNE+yiblECL96mBSgEM3QRIrp2hIozkEOamAGnO15qrGKRV02lr6XrW8F3Df6UUmwGQNWJIzBRwtHiliMd/gN0vkSO2nRPHu0I2M+UfRAdoY127+uqrB9NoN998c/G6bVu89rWvxWtf+9pNwjsyG8HRlk00OgKOAjFIAQGxAAu0nbIvLMCHASIVPrOAI4DQkqzqagNL6kV9GszQBgFFk2YGBmMSGB3ZD3lGA04+O00n7QaGLUQiYjTMaEkAmbBCwCREBAoCZMBS7ohEczQJHbgBGhX0NiSAqgEjKEJoSCBMUHDiZQQExgxAoxWy03YOZIyGzsg6XprQkq9HYv2CBpKahmSFWfFLuAIXAES0zUhibZlDFSyl1Js0wSHPG1GRS1pGD70RSwOF/H2dfvBaKEkD5r7T9WF1OE10bThA+tIvLsUb0SqIW/sJ9Eg6LumpNJ4ICOvFJlCnLOxWLZkJ3APgxNZSO8VYndSmDpgtLEjMVbrfzCBZVW4AoKiCbEuf6Y0nZgnIFbXljUjpV6rfyzlgo2OQgNOGDFJt7po5gOTF2zWoWEH83bvEfwicDIIPd48HBQl1mrDXxX3uYf9W8r+dIzP7XqiZvjlW6WgsdkcPwA5iVqvooG2cjzaCoy3bZBLBU6BtbfqQ9NWsE6aGAey0HbgLmDTiw6w+0cBMwO5kBp412GmjfvcS2jDDjFvshpn4tNpew6rlJDSImDKwG2ZgDthpZuhY2KUuBs1eRMyYxQeE3db7CHsE5sQuMRN22g4Rwg6FWdCVbYxpbNBSB6CVVJ6CtWlH6Qu1i0CjYvK27RC5QRMiZs4nulVRFFQgHiI6hDT3RUMUkJVxgBQ3oi6vpMrpN/OV9BxrJWmOlJmR4L7+iTP7YiJmRiXaBrhzWiPN3CWxdZ+PAp/kYymoVHAROR5Tc6fyUHkLmsRQddUUmUBE7t8qdAPWp+iOhGgT8MVzgmwow5NjtnSaaYdy0cjKx7NDCcTauFooJYNk9wCwCLM7146Ni01UQ8DGJlLfl6Xl+sAH6a8Dv5LMm6s9tDGDNGC9rM4ywDbnu4L/ITI7fTGvLgDf0Ooxnhtn4CgBUjfrjqzt0bZrIzjasrWaomo17WOsSyBlhYiUHRJWyNJFkzBDEzStRoSdILWEJqphYpZjDUvKjImwm3wYHQNgQkMRgaOk3miK3UaW7jcB6HTvi4YiGg7YsXhCB24ZbdB5FSLIDsyYhCjpviAxN1qwEhwSK9YQg8j0SdKOpHhImaAGje4lFyiCJ5KUM/ZImCRJ04FU4A39nm9FtB1jFB9dVSblA+SLjGGsi/v1ntI9OfXGSt/YvnDotAKlWSNtMKB7jiGlmtgERI0ACo4sKS4Yw+MAWqNgKDqyx690MxICCmZCns+NTUrggTPbw4xUtgF2bbSx9myGnKRoDBJ0xRrn29UxMZ2YiaTZA4v0WdBo3LlSN1UtyVeQkfRHQAYe5Bik4poyZrL3sBdQMObmxyrmou0+MJMGtGaCFgCkRYUbN9mHrc+G7u/IrR9V9dY68q6HCMbEMmyeO55/yTi/wx2oE/c/3oLsbWiOzhUbwdGWbSdExEhom05rD4kmZ9ZJGixaiopZWKGoPhQReIbdRtmcZobIDU40HWaRpIYfRTTM2NFVa7vNFJFb7DbCwoQGCGD1mSEyYSfM0HGD3YYxg65IU59JmCnoMp+IKUISbTfG+ADiE4U5IjSablNpCITBmlCHLraYmGhbJ5cGHYIuchdxeoM2iG5JiA9ZuQZEYCbC7sik6T0BYSEQIkuaMnaCCogbJNF248TEnjlKlA5gEgbbvoGhNI6d05SR/4Vq6as08UbI6i839xWC7OTj5kfu9wFQpufUz1fRFqLHJnOJkTlfbykuCm5SNSYq578EEhqro/cjQmoDf6TxKFhVRkr6Eh8yBsk2kzXhU7o/t0qQHGDSHmpgVIi33b3AQJ3de52yssEwJz9BDqXYauYnNePatvN9VbTTZ8qaHwBuSxilQQZpDrwtAkjDoGAlYfYK7axqh8ok+fx0atO3re/rupqtNey+z993JO0elm1Lc3Qu2AiOtmxt6LDTqr4I8sW/E2YgAk6onmi3mQFosBOiriISn31usavM046yS5MQ0SpIsLTaCQVQO6EDWgFbEwIYQYFIixMhgyyg9pkhYIITutOqpdd2mg4NdSAEBHSYEjAhEXbvOkasEXUuwBEzbgVkzSQeAX9R2CUEMCI6bjX1RmhNqxTSwnowMyIaBERwQ6rJoqQ9ArSoHqkGSlNuMwVOHUi5LgbpKjdJR6lwnIyhUx7H0IhaMREDifUwpoazV/JhRgJdpU+PaDt6H2g72pUTW1u1bXOYa4dR6qqsHQMnBghs1ZeVBugAkLBdlGInHSf5Oxgw1HbmGCTKzI+vWlnoov05W2FYpNyQC1l6YFSzTgmMLgA6NjJEeTVdbXXbywDNwN9zq9gWsEODMSw71nfdhgzSZoBldf8jTa0V75X9w/FpUPuFYX8fru3e72Cbuo52fG0ER1u2nWYmLEpjX6yMnRDR8EzTV8LCMCJ2VSvUKOBoWLRCgIKSELATGJEZIcyE8YHqiVpgh0SQvRsiOiYQzRCI0SpzxObLwE7oZJUYzAeYhBnQAS11iJA4AxoEkgrZDaYpHWbtCcMlPpHlA9dII2iVTWpJ2CUkdmmmqZmZMlEGfASs2Abvlr4JIYK1Srd8EXayFQY6oxYknddoWo1lnIX1kII+xoQwOgdy7Mu0qsioou30tWtf/gpI0nVWKFEBkIbi9j7Tg4rBEitk5JXNqZKVzHMAkJf9E8qik+Zj5AoDqS6R+UTfDqVKCODaJxeOLKprMxCD7u1moMyYI2OJzD9ymhyNXSoYpIDso6UBxCeosFtQICmISuL8PvCjwKovrTefGtM3z4MqA4F9AGlRas3MEStlvaJ8fBMWYyX2yI71ASTPsAwxSGvVPhpuZ/ja9S9ZvW3P7JkQj7Fep5sF2E2Pt+ZoTKttbiM42rLtNMKe7DQsTIiyQlNuEuNzop0BnaTDTGGxQ7MkpI4IONFMgW6CE22Xsj0tZthj4ESYJp/YCfAxqrRBh30wToZ9RA440U4RAU3PBQU9HabEOBGmYEvPdcCJMFOWCgCiir+nknprZogAWmJlGeSX3UzTfKatigAmFHXpvsQ0jYwWEacxUR9CQ6xpM6GJZyxxMUtKki2tRgHGZETOE2UI3keYI2ZhYjjlqSy9BxFkQ1mMCNHfqCg5oYUOMI1PWvVlP1oDUqVtApIPB5SibbCuzNJ515bzw+ExZWhS1i+WP47J+xq40jmCnVcqXxmcD3p8gu9TQUzSzvh2KKcQGSLWNubH4KbfANaaMAYJChv9d28fg0SuHThQakAlVYR0ZqxWH8CxebCvinbRxgImqQ80zZl2VIOWZWzSQY8NAqTFgGH1pf3rAg9pu36LDt88OlUadGmIB0Ntcc3NgM+0jWm1zW0ER1u2iS2zT2k1SbU1LEwNA0kkbak3A1At6woyIpxohG3Z1WX2BKClWRJkgyixTDvK4oBktVoLa2cfJ8IM1EhcneQ+EBDRImJCHUByPcDYIcaUOoAbECnwIelroizVBMC+zrYURAM1IUl5NTo7N1rCAAoPG9IikB2pzwxSRVvQQWSWbVUo6pcug9tZBhscwMToOCBAxN+A+RAY+QsjaAXLqJNKhEcVWmQSQQGIfUmYj05CkWWDVTiWJGqxy4YzMNC6P9YOg/RaKLhAKqhZAAHLSrJ0HS2VZvOAgRxlfNLc5dpKy/6LVFn5WcyQhjMoY3eyz8fNDZRADM/5+N/ySFqi3HbhQ/6gO+mci7Cqc+U1AwCnb5Kvq2j3Wb0Uf24AMjCZ0x/1WQJsNLwqLrV1wIl4IZDbQjuHbYkRpPKY/IH+94Gr5/Vs5+SYVjtXbQRHW7ZdXV0mgmqpSN3STJbON1LMcSeIkPpE0yXhcUsdOmbshik6DjgR9tHxThJWNySC530GTpACqLCPyMCJZoZZFCF1QIcZR+yGqTBHYR8z9ZnGRoALGDMOmFAHjqQ+O9htIkKUFWVgxgyMCUS0vUtTzJhwIkQgNmgIYGa0YDQQYbmItlUvxa2kx1iKPQboUn6KiNzqKqmg+7kRZkwq2u605tMk14wiSRtKgWtOS9WZgm5/ISiCo4AkVrAjRQ8F6EVFGg0Roq2zTyLpvJKLAXiUYOkytsk5KpxtMoPCOS8oJFQ0oKBf5AZwbJ4OmQkSxgSOGckghszXPlwWb3Rf/ZTZoYLR8T6OQcr9UBY+Vz7MmUFKWiYNsvDRsuGc2uS0+o5U7G2wUXycHovZzXl58mPmEiDZ0HqWaGi5frrQ6VQWpdU82wNgDhjVKTo4gGRvXN8c7AHSuoxSnx1Af3RG7GBkzZK2zyzLcfqYV8jmQ0irjczRaFuxSZgBLWOniUlPsUNTUJS0FSBL8AlSD8gmiR2aYp8nONHMAAT1mQrjw0AgwoSmKqTeT0v5CVO0FDFppgAaTDDDHgEnw1T78oJsabvBDHu8g12STRZNkD0JAh4IUiF7HxPxiaZvIkmroRNgwhEzMBrq0r1zqykzrWcEYnRoEXTZfaN7boXACoZC2n2+cQyUrGSTkmeMBoEZHbcJZNl2JGTsGwvoYdsOhF01bi0xwCoQD6oREt0Mg9GAVJxMUYGSfeGnCtmWfqIMiBKToL4KINK8qfdqlawBJFaJ/GXR9eXaS6k0X/EayILrBK4op8nMz/3AJhNZpxnezuVZjYp7NMJFWTw2cFIxSB7AEPRzk6tm53GomB+zml6aS3EhlwUAsjYpEOCL9S0DHnUfy9JqS1JbifVZF/xUr1fWHi06vsxq4HIUQMY+c2cSvCW91OF2evIB8zvTHyfT3ywHbuN8tBEcbdl2dHm8CaEDSQXqhqaqzYGmqoKsVmMVZCOiwVSFz8LUgCJOhCisCgENdZiAsaurzE7QFEyM3dCh0yKRgLA5O+qzQ1NwYOxSxBTCQBEYLfZFSA1gl2aIAdilTgXZ8s+n4bxarUWHGCiJrSkdF7aK1IcD0IJBQTY7Y6ZcjYhmaGEqKwaaBgEdOg5oeZZYmwYMhqbVQgDRDF0UUMcM7KCTSdtE21o/RjixLmUzZBoWQXYIgG5GonEB1EYFUzGTBEFSe6IdQvoSZsgaeyZjPGyCFJ9EVLCrJk2QlCEgAEZZHRNke3YIgie0OCNSgcnkA2Qhde0DYF5srdeyE2QbQFJQxbp6jCzOLrdjTBQH3f/OmCAdKCsNYAyctW0+aZm+S22RMUhMoM4t93eVv4vftAkgoWDCZPgXgBjk8V86k6yiTRryAxZXz+67btnrRX1tAEDmlvYfxczohv2MzbxHlALcOz07knZH276N4GjLJixMkK04dDLepSn2goqbQSK2pha7CpYCCDu0L8wR7SOGIAxSB+yGDjvKerSYYgbnE6ZgEkaq4wDZOqTD1Ngl84kCfFpdJy4+LXYxTT4xAieoQ8MSc4Dsl7ZL4rND0s5EhdQy4UdMAUm9hSAgK5IIsqOVqY+YRk29QdKEHYRdoihVtmOMmMYWDXU4HSdSC0knUxPnNsSY2fItUNrWJKiPCRU7JgVCkq40pqMjAnMo9zUNAtJi0iBBAIFVyg6cax+BgCC6qaS+7igxRLl2EjJbw5BJXUXbNnv4neiZACXeEgCya3yqLbMwlQ8jTUhJRO7JGetVryGQVsZWB0fSZFbLb5mCDDq0b1YfzySVqEaPBS0fQBaX68ynS5wGyReHpHTOkGMVUtlpPyvk03FD+qNVmaRi8ncvFoGoJYzPWtqjwbYWo5LV+zggutkGg3TIRsc8ePlepOWOS9o4H20ER1u2lMZCRAjyMdyhGQKQ6h3tBhFbT3QGJRKGJi3l5z2coCkoROyQrGgjElDTGXPE+1KnKMg1nVutJsxRB/C++AYRXc+YAAU+LUxPtC8slfoEnZUDIiaIaCA717c0A0hYoRA1pxMiWtb0GGfRdiCrOtSoIFvBVJyiDZ1CPdMYEbqABLiYpGRBNk6ghmwDWRNkYyaMjOqXOgaIg84fSo/oCjghOFh1UOITudEvQ2OQND0VhHohMsDkZmUVYaOD0/noH7aTfTDaSH0i0rL5EMnjGW0Tuagj3DX6N9vqYpd2K3ygcRKKOkh+nvLgLx1AvgfbZ22QjEmpvNx+qgvuql8XCEYbGFyuz5UQu6qiXWiBPGjyWiBU7RY3bsBtRSZoWepq0fmDgIJ1UmZ9/WyacjtIHMVlVSmLM2KMXCtjVVv+Jk3GjWfPWRvB0ZZtN8zQoRF2iGWz150ww37S7ZggGzjZTFWQLef2o6a4IExNF3TZPgtz0lDEfhTRNseA3bAn+7HRFLKLPXRvNU7HdmkfM0i7ASLItr3VdmiGGFW0HQm7YQqKrfxQR0SHRsXWwbUjOqCgk96MBAIhEiZhik7F4whBhdWEGTNMzTKhKTqCCrobBCKRYTNksowmwA66uW2bUm8zhqTPdCKPpDWRugYUOtESRZOjKLNBLZgiGmWZYpQv1MiQTd8gX+5JtJ0qW6vWJuXobBsPm0AoTQYcdW8yGCihVNwzzd+RUwmBtHwflAGWsUSWPkqiZoBslxMHoDz+MxCVmSYqxdbs2JZUk4jLVBW5djj7FCUGEsbI2iSg1N+QA0MG6nM8Li2nY5m+pj2j5Fkmva0M/DROdmm5+QAXgxj/y3ltgATFWyusXFuxzd6NaRe1ZYNQHK+LJdbnVwt1U1uvMvea5gGtf702i7Y8uPs+f7wF2ZFJynEcsI3z0UZwtGWbhBnuhxkmupw9ELBL+6DAIsiOATvUoWkiJsSwzbR2MVWffYBFbB1ixS5hBqIJdmkGCoQTNEtbj0SS9FyDGQgTaScGWcofpTJ1SzOQCrKnNMEuMmPEYR8TimhDB4ESwlK16NRnBo4K0sIMrEUeAyZoWXxa6oBmKqxQJIBEAE3YEX1TkBV3OguCmg7MokKaESPol3sSWwMg7IPRCFtFIhQHrIikPQtzJKyN7IuWz+VaUswBRCYm9yyJfaEKQJLJ3cCMnoYBPMD2FEmrtAhAA6DT6txROZUEpuSJGuQ0FNwGuJ6dQW6T9O8iTG+26g09Po7F8SxTcrZUlV1CNuI855NgTGAH0rzmynVesTq24CDHZz52Wc0oyaWphYI9qsLzwc+lw+y4ei8TOtdpuKHUmAdtPl3lQctBGKhVbAOAtDp42RxFHRlAsvFio6bsQ3f4SOzkA04eepujHQ8bwdGWbTfVDpKZrw0CcBpMsUtZbL2PRrVCsgKsIUl17VIH8BQ7mIKpwS51utqJ0VJEAxV0swCqCNEKzSBiacALsvd1ZZuItqcc0OiE2CrwYSZMMEOExCmruyT11bGwVcyEHUwRSQo87qFBQyICn0ABHgtrFbVt0ATQ2BvsJzaiQUyVn4EGFPbRxQYtWaVsEZVHq6hMLUAzBApoeCasEHXIVZMBpiDHKCBy1JpPJvrpECPpfmUR1Oh2JSyFKaMinBgFTAXVO5WTIWCCbBFQ8/wkGSFibp0cTbBsk2fQ1J5UjQbShG1jYYyNbjNirFHBDtm8rZ81I2WA9BZkKY8tt+84AbyCZWKIJohFiM0BUgRTvajolLVfFVvDARPfDrs+k4+Ok1XgtshVG1X4JG1THrfUDvKldiQt+0/nqvctYd4K6NQgpk8nZNaXhivmZfdiEQDzbfX4rsUeLeprAWg4KvCSmMLDSO3NN25/5GPFP4S6z4PFsL+3f6Drj9oSRjxgG+ejjeBoyzbRIo4CcmSS2aUpZmgE8AQSBolFbC2CVWBCU8zQYhf7WsNIltCf0ArVgOiSpmCcwD4iBVmtxllsTWSCbPUJAScwRWxUbB1lM9iATvuSStu7JCnACTH2Y0AgQuAOe8TYxQxMIrYWoMNpifkEETNM0ELThSQpwBZRv7tF7iw8kyzPn2CWJkVbYh5DxAwtCBEcBECxFnwU/ENoosCmlgykRBVSqw8TKHaYQcoF2GapHDTNR0F0SV1EJE3nBNa0jIi3u5jlmKyCZ3KCbKuqTWnpPGl8rKwP5Tk+ShuZwFD/4K6LSMyPMUgGlCyOwEjptJoNZyBvVQLkekrWDkGBi91rn4+uwGO531Av++fMkIgGS8owGHvkgUb2SWU29b7m2SFyf8MBAysCmiDREJsDOBBjQHkI6GTQtTDt1scQratN8qByARjqvbRmojaZxVZJ4S1ltYB1QcaR7bUmrZes2EHHaIHRcpet2qg52txGcLRl26UZCMiMDzEm1KFRNofYfDpMEGXSgqSbWiAJqSVlJuAkkmh0Gupk3zSaAXxahN3GgOiEmypkUwfiPfVlEVeHAFvhZczR/WgPE11sP6HsQyTtBHQA76n+R7JHIajihKRCdiDG/VhKA9i0RjoZRQATjmAC7sf7AoBktgbpajLZssyBHNjqM2itoCCibWadk2XVH7RAJZNsXEu23xgAUmaLo6TxIul83gCBgzBGpj1ChyzShqTUQtAskvvyjbqCTcXY+Uet+5KObiK21XIBum2HbktCNn+6YpGUdOAO2CBtLZVBlvsCzzhLgJVd43BYAqIuxZVwi2G1kIGcVgnIRrmfOvtlIXigZU5FMcciaCeo7mViSt95YOI7tmNLgEjfhD9EsCwEPvMxz2mPBlJwK71eJ5aFNnRzR2QMYUmj+2AclqWUmuWvGeUHqu8DsblNTowVss9VG8HRlm2HZohodNWaFnjEDDMEPacMCyZp37JAjAk6zBRAMYIIlyGsU9R0WEvCChmbs6PAaReyNUgDASpTZuxAGCzz2UGHRn/Pg4COAyaQPdF2McUMAswIUv0aYMxYmKgIwg516ABJo6HJgmwgLaufYIYORmawVKNmQqdIgjHTFXeyUo1DAwIhMjDTL0FR6wgrlOr2EGMaZZk+22wM2yIE4NCAIeUMQpRq2jNdsCppOdsA1zZuZCdYJhH2Kh0WmNBF8WHzBeRLWvcJ44Zztee0TF/DCiIYhxdks/fhLLYOuWCkMUmmc7L5nIOCFfvur0XULjxbUeaBFFD5IL1BQIe5WkqmT/KsUEp/pVgNeQFJbK0AScZSgW4CQsjtmI8HUYRU2DFNe46BSavd7GbcBOzHokBwxY1X5xaxR96WASUdn4VszBGwG0ttKejS5yEXch+eVUAHHTVz5D6k6R8KlozrZgDx9Ofv2yC+M2cjc7S5jeBoyzYJMyDOMNFVXVYhu+FGK1JrHSDszYmtAzj57KquZoc6sO5FNtGtNWQpv/gElqXykWV+CegQEDWtR8pEMVpidNzBKh3P0CqrpD6QdFQD8SEWAXKrAupWAUaA7JiWxdbmIyk3YKYTnPgI5Gh12b59Zck9EjrILm/Q1y7HpKAuxAgmWUKWa+uoafFLRGWOuENMqSEldFL5aaCLyo3Yd0OkvPmtHYq5+nZkBgXNcRkY0OtYtVuOKkNGJMYQZR+rIO33KvNFHKUdeRODpvTSfSD3UchhyFXetoPVD2qCwwoO8BgzhYj0mUhgL/04l3tIgAacb1HBRi8TRdk/YRkDv87HgsyMlvk4UFX4uEm/b9n/HCNVAyKjyOx1vo/VGaOBv/1kPDQvD6UHh/rqe30Y5omXw2zTPx+mFWANyB9uBUq2VLMQo28WyIlTJzYM8szYuFptcxvB0ZZtB5JOmyi4aQi6UkyBDk/TNh67utzeQEkDSBVtnirz02AHM3REUnwRecNYWRYvap4JRWFjiFW4Lak2EDBBhwipkD1D0O07gJakJhIoC7JbMIhMx0NoaJpgQ4uISOqjq9kiEVqawn7Vkaa9grJLQAQToQEkPcbmw7CSlcA+OmrS5u3EnQqzOyUmGhBmoNCkcxNlgKRCNoFJKm1T0LIAINU7WcVsYYaaACAGBER0gQASIXYTIjoVW4egq+F0QiJmTcFFcEeZoUFOp6Sd7q1IJCinuYC8fD5KDWlY6pBU4A2rSE1pI9oCOCUQk/+2+a2emlOfGafJNZ2bD5OzgDDB0R54oGS0kBkfA/Pi55kaxzL54GoggrxKcO4r2oGHuWX63sfa81uL+Psq/FYAPYs0SBrPaloeG7QFtko71udR2krNrxZDEnqvfsn65n8VUPXJSSm3g9v+F6aH0s5ox89GcLRlm4ROK0l3IOZUw6gzQTaRbv/RFszRDqaYIqfDdjFLzFFUhqPliEhBAZgwUCDGDmyhEaFhERxPlIWRrTYiWpCCIdHu7BPUp8OOqIylFlC0ialDRwFBJypLwTUKOaDpsI5CKubYKBgKYFCUytNWGzCwLKFvXN6Ho+iJAkedZxn7mh4EZTaHOYB4psdY4zAGQDgP1grhHezLWlalIUDSb11AJAbQoeMm3RcpKqMgFbhZUYGMuRaOhNxLaLTuT2KQkPzBtpLNgs4+hi5SeqrHh9WHQIklSqSUvdYDac5WoOQ1QrVPAgw+XcZcgBif7rICnOyuT0DJt1egMU4sE+vn0AB2RlicGSYFZABQABB7XWuRhtgUql8fkGVZxD71+c1d7mo+GUO3ap997Szp70htzT7zvePoAFJ/z9ph/XOh8jGWaYmFcLxZFeaDfxTO9EfpuNgIjrZsLXWgoBWqjclRkfQkRBDva62fTpkaKQjfEqPBVNJxPMUkyIy5Y0vfAbS6Ua2c01pKzJiAdcNYqymjy/qZMSEAzGjAupxdvickfSZf4BNdri9ia0oMQAMo69FpezMFfDKFikKpVZpjBiLGrk1mgcEcRQulIOUET1W7LLM1B1lSH0BosY8ZNWCepe84EZmzcmoEwlS0W8pagUUPNVHwKT6MWQiYsKXc5IvbdNKBCECUPb+ijknogCisWmfzPEWEIOxFFyWVmHRQQAIw6Zd+8ACJk54np50MZcibkBmb0iel0+w7mksfv5DLUmNWQbtgT5wP5+bTC/LPmm0KqEge+7DAg6l5fU2aTnw6zM1VPpwcpxNt+xPs2vHHhmwOOA34r3t8lb4Oy/fYmn93lt+L6cLmygUcJWBKKTcDSHMO8lQIuoetmRzvKVTA0cEA3Fn/sdzQwnKX42VvfOMbcckll+DEiRO47LLL8L73vW/Q981vfjP+n//n/8EXfdEX4Yu+6ItwxRVXzPl/93d/t2hm3OPpT3/6Ud9Gsgk6NMSY0Aw7NMNO6GS1GnWYoNOU1yz5TCgmn5YidiDPLTo0FOU8ScFIAUEREwAtyQq0QBE7JDWKJsTYIdEg7UBSZxOISHuHJN2263x2Icd27JmixECMEyTpu4nFAbkH8ZHn3dChrXxa6rCDmfp0UmbAnZtgqmOgx6nDJOiYYab3ruODDk3Q8TEfEt1VQIegVcMnQR6ywa+k/gJFNCQlBQIYbRMxadQnyJhIO7o1S4h6DaMJshkwaSFPeQYIjBBktR6RpMJIBeMAFDDp38FSP/owkTJpag2s9ZKyD1tBS+L0VZ+3BTGf8jgDeR829zCfxL7BtUOU54w5HyrbAjSdltstwJm/1vum8XB91bG4vutUie8z02fDlv1pia/z6z295PxAc4t9ljit0tdgvCv0v+k1q45BbXNE2xGzMUX7fX1x9Txse1843hWyR9vcjjfsrewd73gHrrnmGtxwww247LLLcP311+PKK6/Ehz70IVxwwQVz/jfffDO+/du/HU9+8pNx4sQJ/ORP/iSe9rSn4f3vfz8e9rCHJb+nP/3p+MVf/MX0end394zcDyC1iIitsnVMQKDjIDvacyeCbPNh1SWhkxpBKjaZECNwhwnpjuma8ggsoAf6TPpsdQllDmb1kbZPqE/jfs2bzwmO6mOF/zgxSoS8i/qEGFBBt6RubKm7gBXoqjvJpkgl644a0Q8psMj5m6kUbkw+og9qVJckMc6SDiZSqwxKQKNpNSnUGF3tH4uLQCRjKUvyO3TRKisKCGSrvGhL7LVIZNtEzDpJx5kOO0agCbLTSAiMqKmpVJnbgxddpk+2Yi2lmTSuRNtoG4C2IX9ZWs6W+9utWapMbzO9zyWAQUp/GXmXikvGfK4AKn3tWCeDrI57oa85pexczI45IVINlqacErtUxexfUxoQdveo49TDFPVOvz5Nlgayx8en/ha1s5JZe4tc1mnvYF312irX+PTgugisiutoV7JZnyHHXPS3Xt8nH3i8K2SPq9U2t7MKHL3hDW/Ai170IrzgBS8AANxwww145zvfiRtvvBHXXnvtnP8v/dIvFa9/4Rd+Ab/+67+Om266CVdddVU6vru7i4suuuhogx+wVjeKneiqrQZW2Vo3aCWoWFp1PCTpq1a/4eU5ptcT2JzLSdc7AQBiTNzrjkwnLJqdiU7G/poZ2TwtPikVpz4NqrkC0O/FmNJyja76YcS0AkuWwYvGylJ3DQUEnqGjAFJxtdQTMh8Go0HADDM0AmgATFR0HTWyTlehcQiqgxKfaQxaV4nQwVKAArgoECbcqYZIaknFGNAowGpNkM0M7qB7wAnp2oQOHBuhr3X1WKc+nVNZZ00PS79R/f0s7wFNAhAyuPaVnY47gBFD3qDWYxEPhKxrbwaK7G/zSek0155f+ed95gCRiyth27m4FJ13PVNp8XmixXuiWR/oa8dNej1gZ9C/1jClDzb3+wy1UfVX+NRppD47Tmm2hGBXdT5g3Ed52/4zcQgM1el79g7cxlEa4+DDeUw+hWfczpq02v7+Pm699VZcccUV6VgIAVdccQVuueWWldr4whe+gOl0ii/+4i8ujt9888244IIL8KhHPQoveclL8JnPfOZQY19klgqzFFDjUmQ79jqYj4iiW3DaWLYl0QC1JEi3UWDT6nNDwCQ9SI/NPzcAWiJMKGg7QdshtPpsPjv6t+/L+hZfLvtH1HOsKTN5bi0FRlGKUWpqzKfexEdScxNdxTehWRqn1lKJmrKzMWuUoWoRk88k5L9t/CRVpuk1TbMFkm1cQvIRLVgglhRbYE3BaYqNupQqCyTnQxBf03XJgwHixCKRpcnse9rSXUBe+k8DD2TQI2wM9/r4Z+5phwd8itVzVV/pqY4HuT3Wmy62/Cj6oqIvY8s8YqE6zrmTrm2i+S/xevJLKUDSe1oyOa6bMlv1GndupRQSkQDqVfpd2s7BAcGg8WZT8ZGn0eY7PLSmmklzaG0dhRlzdNDH+WhnDXN01113oes6XHjhhcXxCy+8EB/84AdXauOVr3wlHvrQhxYA6+lPfzq++Zu/GY94xCPwkY98BK9+9avxjGc8A7fccguapv+Dv7e3h729/Ivh7rvv3uCOxGTrjE5XddlkLuLkVlNTIsQWkTTpsnv5qtStLjizOME/BxE3BwTsQFaXgaBL5YOtMBdhtf7iayAAKQAIpF/I6iMmPjskPEKwH8KUJ8cdzDMYAVGZJGGQdlirdEMKQsrS/ZA0Px0F7KiA2qiSQEY1EMAzxJRqk1V3xlCJCXMkQmxSYTcBFCQGkK7SA2ZBxDkMWR0XOYCgK+JAOm6uHcgqwMBSQMDqPDUEdJoulKrbwpLl1J8cS/uo2dI8zlM/EyD7ilGuRE1Igyy3r+kmvaheRZbMjX96tr8X+XDlRpkBss9MYpB63uu63Xq6pLk+8xgU1/ZeVB0rBN1VO6taX39n1FYMoGbD3L0efK+yJTGs1LT/AKwZS/35OWr2qBcg+WOrBdA0Zw2/MNqadtaAo4PaT/zET+CXf/mXcfPNN+PEiVy467nPfW76+7GPfSwuvfRSfNmXfRluvvlmPPWpT+1t67rrrsPrXve6Q4mrgdURkjXcgeCWuEc0upydAdHxwMCMsAuWBmsBTKH6IgjYkXP6DEKjv64nCOhksXmCJ42mbvyzQAgqfCRlZrVVxCfohC+LoOR+bDutlgDbmpGBlDAzTQm72V3LYEo7igpIU2ZEUks7pogIoiEy0CLlBFjvubXa2wwwRdUnC2BiKwInm52gYRYflmsbkrvNUUtqJ4KdDyu7E9EhyBYpnWiLWFehQccGunLNVorZNigSOhdowQBSSrmZGIh1TzNH3XjwIk+WgkLBuFibRRavnht42KdvyvBtZjaGczv1ef+MHh9UfuWfLpDcD/S9Hkq5+ZjnpsJVUmCr2iqgbKHPCn0vSuOt+nru+tW6XmkDWrIPZO9or26MYgPmwzcunjI9yfM+K9jefcc7rTbm1Ta3swb2PvjBD0bTNLjzzjuL43feeedSvdBP//RP4yd+4ifw27/927j00ksX+n7pl34pHvzgB+PDH/7woM+rXvUqfO5zn0uP22+/ffUbqUxWpcWUCiJN30x06X6rgElSUTl91ZBpj+yYpsiU6ZBUWUivieRcq68naKROkfkj6LmQfOx67zPRY6320YJk41loqg1UxBPsWR8G7FL6Te9LtjIRaNRomsueJ6phapFXnjVa3LKBpL2EgZPq2wGi15IVap2m7CQ1F0g1XcQKtYyVk3YaZYxyqoyzD5mPpdPEl/RaSqvZWPGCrWCTL9+UWoNjfwiCDgokwrDVaj61lFJH5mMUD/J5A0T+O7GoCuBAk2f7QK6pqt/sU6Ilqs/X7bhjfr6sCabyj7o9d6Lua6D98hrqOVY3MHBskSVA2NPn4DVDPgcAE4tsHYBB9SD2+SxxsTY2TVslnHoIs/FgGxXUL1KBQ4zSsB13QTYOI6V2nqbVzhpwtLOzgyc84Qm46aab0rEYI2666SZcfvnlg9f91E/9FF7/+tfj3e9+N574xCcu7ecv//Iv8ZnPfAYPechDBn12d3dx6tSp4rGpyWQai0k+sC0dh4ImY5SQng0YBbJtQASINMqsCEACWgrpeEhARxijJgGdoAxVKHwExAStKxTc8QyYiBxQIt+OxEOAgiiNWYFdUAAREjCSFGOjZQMCbOm8LpnXBFurpQ9aBSoChiKk/hMnn0BQEGR9GfjKOqHGAR7zIXsGElCy88EBG1dyMsk4zNd8suWVXER+vq5+rfYBHXJf3B4WFD7m4Va7JR95mPC9uN7FBLh2eB6EJCCU9Dp9ICWfs4iLP5ymiKtT1qcHMwnkWbtz95aPz31/F/fXA5AGYl/gsOT8QYyXd7FxyrCnUTtUN7dgDHypk7X7W8UqjHIotnC8BjpbofCjt+MuyB5tczur0mrXXHMNnv/85+OJT3winvSkJ+H666/Hvffem1avXXXVVXjYwx6G6667DgDwkz/5k3jNa16Dt7/97bjkkktwxx13AAAe8IAH4AEPeADuuecevO51r8O3fMu34KKLLsJHPvIRvOIVr8AjH/lIXHnllWfkngJFkFZ8tgrZjU6UwrYwchLIrrFl9qSlDKGASNI4pIUSddtY3byD1UcLG3KQLzptJ4AA0kQbiyBbfkhR6kuOG+VtRdKKRJdMz45iN8+gX+4+dSN6JeERWjBmJBqpDqzpxkbTVB1ADVpmzEC6ik82qZXNaiUB2CJiqoAsaFouqO6oAQNaMdxKHci4B9kXjqQswpQlrRY1iRgMclh5BF1uH0g2spX7FrAkw5R9SJkhmcelDTuOBKKQGSHPqTgQIOJoO85JIEnOB+qTpiYqm8vtuPNqPquVfvhzfl1YFZ5/USRU8i3mWFLQnPvi4nAeH1R7o2Eong1BQ5/5FFtKu2Fu/iz6XZq6WiU+G/DDaKuyVdJxx8GGxvmgVuuL0ncTDqXDdvd4T6FjhezN7Xi/s5U95znPwac//Wm85jWvwR133IHHP/7xePe7351E2p/4xCcQ3KqO//Af/gP29/fxrd/6rUU7r33ta/HDP/zDaJoGf/qnf4q3ve1t+OxnP4uHPvSheNrTnobXv/71Z6zWUYDoXXJ6x5gLSvWVTVdE6Zo8WSbAg7wyiNKkbcvz5ZX4yCc92PUkvRjbRHqMdJZk72PaAyLhhtiikWnR2pGJTbbnmHHMcev1UcGdaZJmWiPHSgsQcxJZdwgCRFjTVmxTa4TskCZME7jDDI3qs0i1SBFEjWyjokzalCWdxspuMaICN2FXWr2+4S7xPbq5iII8zlucwETlOs4kW6QYnyFDnLdPyROgzU9LaPx1JwxbY++BkT73ApWBPgrtETJwMffeuBy4Cf5Yb5wWV0ZhrjxRr/synxT7gm6lsQ2BgQP8RWCbThyDoAvlwJ+PM9OhAaUC9g+cW6XzRW/W8baxztHmdlaBIwC4+uqrcfXVV/eeu/nmm4vX//f//t+FbZ08eRLvec97Dimyzcw+dsIAcUqR2T9GJRbEJ/1qzwyBTc1SXkZXVrljRgXkVeIKkmBTuomufZuk/TWI9S/3YsY1Abdu2coZIgEC2FoSgXVkY5F0Py73vW8r7hKzTkgsmm0X0tgWteRSMDpmNm4EEVdHZVdSLR5iFe1a4UjxTUAHVshSWB9h3mTcG2O7tC9A2aBUaFNADhsQIhtZa1eCkH3XdIKl3E66X7iClj0fEAOnxRtfgxptm/yvY7veMFMNclx/6RLDWMVEnX3I2uubQ7wPkNmfKt4EwMiKXc7fU4Xvyn5WIWO0kaWAabCNuhEq+111Al8Kcjzgqnz962MAls5EgcaDr7xDCWaXrk5b0lfKKVfvC4Dp3rjx7LlqZ43m6Fy1QDlt5h8plUb+OLnzBnhI8Y+8bjQ1lv3La6RN1Q0pA2TH54KA6YZIfbJuydqDxZBiVpG23Z96mf7Ig7mqKz0mEMnGIOuFANLdysgJqW07DwOUpgdi5XQaTf8RZSDVqNDaJs2Q2LoMWoKCHUseJi2Sez+CAYjkg+Tj20n7qc2Zu9h9UZsQPJ3UNvR0MlaQaZdL/1SggQLbmI+fJ3jeJ7Wprz1o9ZHbnNF3S1y1MXDnC33s+JxPz2BK7CtCoGVudTt1WmbVdpb1sfTmj9AGuxyO5ciB0WEPg3+veoBNonAL67lHdgDLHnrsfg88Me9/nMwE1Qd9nIc2gqMtm3yp58nQAyPSU34OsjSapd7K72r5xSW6F60ZRCUvZJMn2XHSekYDn3/vk0SZrr0MlELh4xvMoCyDJs9UASUITEAnPVA+k+qyAPjVX6b7SduTpb5i4SMx2YqyNHLqnb8c50BrLbZ2P+izD0ofu5f0vdpDPdTpGjgwpMeLDWwrHy+ANsCUfPQ//u4KoEMLfHw81R99PumuKmDWN6fOjfaCeZep8ukBQb2XL/pOP4zv+4NiBapv7JBtCCx66nX+5NHEsoptTPEtMkL+d7bpvbnrCxEP4wt3H++91XzIB3mcjzaCoy2b6VlqM7AgPuUbZeCjZGNscnYgqIIgxvxkwmL4fyFxRXXSzYAVinb9cz4+7xPctQYqEgihfJ2P2sanADxzv7odO1R9weaxq30MeOT+i5jda567u3nL8La26l0gD6LmbqjXlvdu8fb7+f4Kj54f1HM+vfEMvchgK3/Ohq9fBb/0+qzKEi1rfNttHG5DPU2v23b5r6+wtSbJ9frtY0aP1Gho6qOBv/3Pgfz35MRZp0wZbUUb39mtG899jaTvh4IlKH362P0hcFJO+AOTZ8+X2dBEy8wwvdJa5n4ZGjYhpK240nEDGe43H/xd1LrjMubae9jqr76+n66lD/X6rGJc/NWDJhj9N3RINihmPow+h653893aI+Z/8G9y/XwYC/tY2fzP6GXXLtIInYmf48sE/xu1uaJfn4B96TXrua8Xh7PiPZw7OfD3wLHjnnJaRPGu08Z5aCM42rrZL7WS8WAFAPXvm4KIX/Ll5xMyjWvJUm/Zz4EdN0nXPnYtAK3Zna8tfeTv3q8WZt8FItx3qL9/8+kBUPX9+bjMvLw8p260enXlk89Tit81BBuWdGDhl0X/yfp7fy7lxAfPKbBuVLuIZSoE2T3nqM+nZ9Kai7YPzCxKlblLeqPlRe2sN1Z2+RmfxlYGQIsG6ghmpqWf30MYqQ3iTqthD6PfWifGff/66r/Xt+n+8RZkj6vVNrcxrbZli2nypvTvl5H/bRvwt3MRCjDYcx0mY5bjsjLM6vPYdaztcFoNYv7gEuRoOIhaEM18DDDl9Whw/UbnU339aF8xHeV0X9ZOhIEqBxw0EFsu36X7lLES+Y604FdoMXvw5if5Egxx6iH3YxqkHLu7kUR7kX+qfoiW3NW8j77PFfisQVeeJKrcVNEXF6dEOla3g/SeWDw14Bj0cQ71FDKXfOHyXBkY5mzuVJ9POdwVjVf+JK7f46V2GL+oDzJn+H/k6Pkg2etF91P/IOjz3QhcLbhmnXvehLXi+R86G1ndRrES4fAm+5MPOOaCbMBNIhs+zlMbwdGWjXUyq3/LREYSwxlrklkbD0oy+GC4vcsU+BhAYmZEhUgZWOn/KPsYgIquUiwDsjGsga8EbjIAqX3sSg/iPNCTdhxzVNyXPRQMcQaOBibzOfeA1BmSPd6MBZJjSGPZww7Za9Zxd3HOsXlVXzwHcnJfzIQYMUgKlayRB0NO42Xg1QIrfhBTcTlH/YyUGLfsoo6HKx+oj7uX1I4Lc0ioaWsL/IK7uSX/qS/3WRny8f3UPvYm+VM8zD2wNeo/dP2BDd+g9zvoxNF3Y70fzIFzh50yW8XWuedNQc6BJ+RlDRzejH/fPcdbkD3a5jaCoy2bTOZuqwT3iEAiFCIApswAxeQjICYmeCQAyQMmnwKL7lgCOgqcoO0ZE9UhavXpmHytnZxCi7ld5hKcacweqElrUJ/8nOZlBiJTBjGwekCUQQmgm9QGjVl92PgMckBL/GIBluT+Iuc2rR0DNTb3JbDEOR5px7Fecz7IPg6E1POtZ5MKtgqYnwdtjxJ/3IBI8plnjjxAIQAIDujUPuqHnsWLg/PyIiQyN3dn5xRznyak9hmIt8+3/rvv9VLiYAiQ1Gj5oNikeP9XsD6kvshnq7YhADlo+CutADy8Mdo5MTm0to7CDrqv2mGk5c5WGzVHWzb5J0xSbJEIHdsSfXmOnLdasOrLkVh2qyeBMR1kdo621By2XQhAWvTQOmPSZe2Au04DoZjZJb1+hk4mYIIWhDTWSCoAyUQeM6DjLFs2LwFaxlhQAlkRMhl7HzigEzUKY5kSW6Pr1xJ7BBm3BBjtHzU51k1Bg/gERKYUQ9S4xEeBk/YFZPAD5PnIAIydK3xsrAFlczLgSu97uf5+jkSYk0nUoqt6zq597Lzrpiif1OPjj9fYpq6NtNLcF6tA/fUEZajcgoSBNudwFs37ik/PQXdJ3/HSfygAQ6EERDfIy8ZgkSB7yNaJrWr/TBRoXM8OGE/P+7z2hX2pyWLcNu4EABC74zbmla36b3VZG+ehjczRlk23nEWXK++o/saAgDEl+XPO9pqlKnU0RsbSYgl8yLHOsUKmx0kTv7I6M/PRlJo8i48xSFFhjf01c3yVAapOX9u5GWIGENp+hwxKEgvEBCvvGA10MKGzMWAr1RgSEyRMD6FTMDRDA+ag4ClomyH7aDvSb3AlJYNjlOSZEgiz0pQKuJB9fBUmGLBiOwYFGfraEG71S4y5v0BkkWkrnj17gPJvQ2xzx3r8UB2r/k57nlmPVSzsfBbHU/twfuoDar2HVvh2XqG9jdI8RZvcc3xBmz5F12d9qHDRj/RFKbehY8eGSVpshwrsiFAs1V80br2VTFe3btZtfO1oh2Of+tSn8J//83/Gu971Luzv7xfn7r33XvzIj/zIRu2uDY7e9KY34YorrsC3fdu34aabbirO3XXXXfjSL/3SjQI5X61jpMl6GgUIdUyIpGwLMovRAZip/wwAUwZO9uhY9jOLYHROQzTTxFlERMcRM3QCgBToRET1yam3Tn0E9EQBPim1J/+bpr4UUIExI2FxOsjzTAFYxyVLFB3wky1G5N5nTOgQEEmAi1xHmBkoUiDTcdAxCJqaJEwVFHWc/SKJmFvGUwBTxwEzOx9zqk7iUrDKhC5mbRGTBzNZD9XFrBGDHmNNBdrraHr1BBpqnRLJarMaVBR+VJ7X3F9ZK6n2SVeWf1Q+vcyKO+/Jk6K95JcnHMKQD+Z93A/4Minp26kAYdWOMKQorQ+Y9QZv/otADjCP5uooh65d0m7dxiFihKX9H7mtBzrSwo904HCjKe1wQOOJ+x93QTYd0uN42h/+4R/i0Y9+NF760pfiW7/1W/EVX/EVeP/735/O2+bym9ha4Ohnf/Zn8QM/8AP4h//wH2J3dxff+I3fiOuuuy6d77oOH//4xzcK5Hy1Trea7RQgzYxJYp3QGQkcdOyABQOzyGnfspmCmJmmuBIw4eh8YjpmgEcAVQY4nbFC3Gk70QEaA1as7E4WgXeQtJ61N1MwNNOYZ3Agjg0ISQpsplqhzkAOZYDTKWM0QwAxYcZB/ZoMpNTH0mnZJ2SAVPhYGiyzUF0MOo8YGLK41M/aATKr54ASjPWKlW4pgab8RZOFxuTAkvm414KwcjqshxUwVoccWKotMT+MXjEykTs/B86cj6IkGvAt+vFW91eDLnDBVKWLdPDSqryetrmnz1qUzuUb4S7qj29lWybaXqkJF/yippb0c7gptYHJcJU58iBjcmRgyMbXoXwwDmPSv+/z9x24jSO1vn+rmzyOqb361a/GP/2n/xR/+7d/izvvvBPf8A3fgK/7uq/Dn/zJnxy47bU0Rz//8z+PN7/5zfiO7/gOAMBLXvISPPvZz8Z99923MXV1vpukexpMSJiboB9G3dIURLq7F0cQiQapQf6nbSl1+76XdLr4yE72snN90L3oRbMky/QbCuiY0YIw09VpndII5pP7JMwUCnWQ7WM7ncAYhJn2ac8dkqxEQJ0yYYGBKRNaYnSa7xcwKK9nHBAAzBDQcMQsia6DAj4BLZTYHUan+7d1HFSfJIDJAFdgRseNSEb0GoYwPkwCkKBsFSEq+JERNuAWWVaQdawxs8nW/Yq67BugG8nqN4sXeJMBJADFDq41tmEDJaT/p6Qv6gMFBpa8Tw0oKADUlT4VlpCshJ/jfB/2N6HsqwYc5rYMKNmHpDjPxXXktD61QiQBMgvCNHpc+cwFIf+m5CX3Oa1uy9JnS20F3csm+qWNuytP1nXRVrfV4z2UzWYXhsJuDP2vgRXGfoHt3m/ncOIbbSO79dZb8cY3vhEhBDzwgQ/Em970Jvy9v/f38NSnPhXvec978Pf+3t/buO21wNHHPvYxPPnJT06vn/zkJ+O9730vrrjiCkynU7zsZS/bOJDz1URrFEEcEAjYZ9ks1aTGoUQ9sP3TAAIF3WHeJlWd1GSeYG1fWCVC1LSQFWEksDJJJrbuwCkFZhuuQv9m9ZkpS8SaZrN2LGVlqcBOw06pQRYgGMgADhK4mUGCF8ZHgI2xQR0CwMAMpM+NpsqCsEEkoEj6otSOpOzEl0naQ0Rig2bGSCkjBAJiDJK2hOmK9J5s9RwTmBWkpRVvMjhdzCsOwU7Q7VbEJcYIQCHIjv2pMB3+gmkpmCI4X0ZihRb69LRDqEBM5WNGVTs2tcA/s30OqwmnuC93ZZ+IvDYTv7Hraw50cRFHcdwmxUIz1BPfYVhfX0vtkDo/CLiwcVoGFlYBVhQ2i+VgOGWgTcpt9qL9g3XeTY+55qj6Ptm4jWNsp0+X5RSuvfZatG2Lpz3tabjxxhs3bnctcPTgBz8Yt99+Oy655JJ07DGPeQze+9734h//43+MT37ykxsHcr6aAIQGIEbggIY6RBYGowOhIVa2I4J1F/r0m0cZjkB5YZAV8EsTt4IZYTL034qucvOr4aYsu8ELgySaJ5loZCUYICmyCYnvBJIqs8TSjGUzWPOZ6fwwg4IvA05M2GfCDglAaREx5QYBnNJqYMI+AnbAmLEwRx03ei9B2CQmTDkoWyWb7M64QWBG1JQbI/t0xjZxA+KY0mmWPks+FDCLDRpEEW/rSHVs7BTQkL5W1iqny5xImwHmkICRvCfZD/p+GXiiRMWQ0kvuS32uPLgBZfeeD33v14Ci78uyBkA1iKrb7QNUtZ/hH0GCSOyXm6hIHcn7+Bjm+lCfIhaH1OYCoexTpNQ2tFXaGAJGCXygOn8GgdGy+FPKaR4orMXqbACMUnHa3vfygFawbofbeIyHHewhG8v36YHbOKb2mMc8Br//+7+PSy+9tDj+r//1v0aMEd/+7d++cdtraY6+9mu/Fr/xG78xd/zRj340brrpJvz3//7fNw7kfLUZN5ghYMoB+1FYDzvWcYM9/Tv7BEwRVLQMPSfMygwyec8iMGV9QJ5nJH+LqJsxjQKEZsyYKoAyVmgKxpQZ+95HtUbSF7CvbVk/HQvrZednCoY6mC5IGJ59Fr3QPgJmLPc0Q4N9bsQv5nvfR4OOG2WUGhkXJsxiSOPUJXG1+ExVrzWzY8nH2pFVbR0TptG0TQ06FiZqGgUQTfXYrFNWiE1TFDCLbmVdDJipIDtG+RKxZzNW0TdHgKNOPXM+cEsSybFAGUiR+eSiV706nNyOuw4Vq9K3BBL5uQBTSZFenYO7pqeNORCTi3O5+kZU+VT9eEv5O87+1k5RA8ge1cA4n5W+7pnhajGUcSy7bqha9bYn08H0mI+Z506lz9JK6bXN7nFOkH2YVlTILjq1P2A/ctaxnZNjWm2bdtVVV+F//s//2XvuFa94BV73utdtnFpbizm69tprceutt/ae+4qv+Aq8973vxa//+q9vFMj5alNuMUGHiKDKGdUa6VwSwIgR2CHGDMK0QNkjYlkm34AwBWEHAkwa5Sg6kLIbQMPC5pD6BNiKMYAjY6p9TVk+FNKO8+HMFO0zXF9IIKgBY6paHHmGMjeMGQXMIDWMplHqFO1zwA4xptygQYcZ2rTqTDRDrD4R09ggoMOUW021iUgbUa4nYkxjo0xSk9qZcQOOUdJpROrToYvGLoUk5O6Y0BDQRWHuZirSjgjolEqT90TADgEJOLEyURQNT5CCJC5qHfml/MwunVYDGgDGkpACpkIaU4MTK1blfuXVabAaxCSQQ87fXeOnCdLPUrqe3AejAkGJVbL0mgNoyS2ynBuYjwF298sJGNZjkOokKYAx8Fm0xN7Hrqv8CnapBjFc+vnnIVuUWus7vKi9Oja/5+EQS7WorXXPufdoOYO0fnpqDhQdRXptMO6DdXT6mFfIXjvDO9DGcbUXvvCFeOELXzh4/pWvfCVe+cpXbtT2WuDo0ksvnaOvvD3mMY/BYx7zmI0COV9tygEdt5gETUmxTAqWZgqUv9MbEoZmh2UlGEHSToGkHSLJf3cKcKYsk9pMEmgiZCZOeqAABUwkrE+AiKWJlCFyPhMC9lVgLGksSftN9R/OjIEdEPa5AaHDjBsQdcp4yRRmKa59NAK2uFGwJoyQpcMAwh5a7HCHGbcCstAoIBIGiEDY5xasaTliKFtE6ChgFqXM81RTYzO9XvRMujKOAVvWDwCz2ICaTsGSrmLTVNksBoTAml4TsXWqQ6XPXUcIgRHVlznv1WbgiE3MJQIwAUiGJJhBbKkFRWJ6vQAN88kggSLAbs8oI1dSyk2fYc+WvauBmANM6buwAj3FMUbZV0Re9VZbusbu0wOTnPJIlxug8kFzFmSr0xyA05E2L42PK5/c10KgE6if5ZlDVQO27oziUz991y47v4oxkL5Q0jEPthaAuSUupfMG1vfZOQhIGmLvFl+EdZmjkw/YXbOPM2z+B89B2jjm9qM/+qN43vOeh0c84hGH1ubGFbI/+9nP4n3vex/++q//GtFVjSUifNd3fdehBHc+2JQbtJDtOQJMyBiVIyJQtC9zFkEzAmIECBGBRLO0H4Gp/rJnQlqpZqvBOmNxAIBZ15oBYCSfGQj7zNgXF2GdnA+DMUUAccQ+lGlRH9vuI+r9AKIZYmYBbeAESixlJj4NEIF9EtZsxiKgJpL02j4R9tECEdI3JN0lguwGMTbYp4ApGpD6BDD2Y6PsTaPC7izanrGM8cyOQ5bxUxAf6rKwW2ocUSrQKcv9dWUdS6HIuWKVUQtKGoNk6TQFRgxKDI1pk2RpIBIgoiz4yl9u8rFAkU5TIwVVCez4a+DSaqmNedBE9d8eUFWAyJcWKNqJmHtdtNNjpBvfJTBX++mPgMEveY0D8IxSBhE05+vP98cksS84WYA997yGcQ1QaqtXpw2yUGuCgE3Tekc9QXLP3wee1GvgV3Khi5H8arZ3erZZbGfKznHNkdmv/uqv4rWvfS0uu+wyfOd3fie+7du+DQ9+8IMP1OZG4Oi//bf/huc973m45557cOrUqSIPPYKj9WzGLRhai5kiWMXCATNhhZStAEP3zopooRM5CxhpiDFTwEEAOoopxQVE7KuPAJcOTJI2IgjjA0iKK1DElAOAiI6S2kVXeEXscUAgxjS2IJphhkZhE1TUzNjX5OB+bJWRaou0WmBgjxvsELDPE2GrYouGYtIMNczYR4MdAPuxSX02FHPKjAP2IcByFhuEAMxii4CoGiIpxGhs1dQAUGzk/rjREgOEKZOmHUW0PYuNthO0LlMuMdBFAoI8BxjDJF+8bACJNfXWGU1joJcSKOIIZSd0lNMXEBWTN0FSdfY6GWffBEjsQfnvInVWgabcR+ljjFRitPRP8u3U8SC3k59zTaTMMnEiftIdV7FbahJchJBZIbYtarh/UgXmgUXfnDc4P24KIHquW0kovaCtw8iLLO3/qJHPsv6Psu1F4PHg+bv++vajnWn7X//rf+H9738/fumXfgk//dM/jZe97GX4hm/4Bjzvec/Ds5/9bNzvfvdbu82Ntg/5/u//fnzP93wP7rnnHnz2s5/F3/7t36bH3/zN32zS5Hlr+9xgyiJI3ost9rnFlFvsQ/7eiy2maLCHFvvcYD822OeAKVqc5gn2ucUXeCL+EAH36SjP+9ziNOT60yzP+xzknPpMzYdFJL2PBlMO8jqqLzfqY+3JcXucVp99bW+fG+xDYt9Di9PcYC82mMY2+6DFjBuc5oncH7dyX7HB6TjBlFvs8Y74xFbuN7YSW2wwjSLQntp4Rb0HbjFlqnxE2L0fmyTknjJhFhvMYkBEg1lsEKMIsjs91zFh1oneKEZKLNOsC6lQpwixgxZ/VIAUVaPk0mnGIOVl+zTP1ljdA88goccnCbFZQRWVQKPwyY/Cp0/j5IESkCp2zjFJQA8o46qP6pc653iS1qgGM07rUwCxAdAjPjmF1muFaLs+N3RR5bMqQFkLZLjaQUPBr8oKHQjc1AO8aTPHESTovRWia7VD2lZlcsw3nrUfNQd9rGtvfOMbcckll+DEiRO47LLL8L73vW+h/2c/+1m89KUvxUMe8hDs7u7iH/yDf4B3vetda/X5FV/xFfjxH/9xfPSjH8X/+B//A5dccgle9rKX4aKLLlr/BrAhc/RXf/VX+L7v+76N0NhopU15AkYH0hpAtmd9iJKSaimiiwEg0afIUn/AdpmfJsangSz/l3KPJpLmGIWxAYseSMXNjU4YHQLa2Ek7zMrUROzHBg3JcvYZAmIUBoqYsafL6vchS95nWpmpZfWB+ARE7MUWLUVdIRbQKJOFiNTOHreYUIcp2lQJexobMKkvGPvcosUM+9hJG8Umn9ggNIz92KKlGfbiBLasfsZBC0+GJNqeUCdgKOmJSLVVhJaAWZQ4Z12jGEIqbCOq1oucILvTTXBZAJIRP0QEttpHHSAnqBBhs60Yg/xdgCVkP/HJ6TRiSsxOusZSWvrsQU7626W9rL5RMg+MarDDpU8CNxaPS595vVMqwNXH3Ngx4qL9OZ/U8KJ2ODVVg7TcTvZN/j3t5DiG+yraHLL6fA/Q4aK/FdrZFKAtvG5YTLRZUcbV2ZgjLfroK+P2dz58bg277/PHW5B9KNh3zevf8Y534JprrsENN9yAyy67DNdffz2uvPJKfOhDH8IFF1ww57+/v49v+IZvwAUXXIBf+7Vfw8Me9jB8/OMfx9/5O39n45Dvf//74+TJk9jZ2cHnP//5jdrYCBxdeeWV+KM/+qNxH7VDsP0YMMMETQAQI3aCiIApyEQu+kklb4kwjYQJaf2fgORjGhr5UpDU24wJTJ3UEWokxQUIIKIY0JCsBjtBAj4CQ0XOIpKmGBGIsM8BTAF73IKipsMIogdiSQrOmLBDEXssv6T2WdJqU0wwixFMQVfCBezxBAxK7YhPAyYpVRAI6jPDlCdSwwgBs7iDGTWYRaAJAq5ka5EGe1EF2XEiKTwOCEzY61pMQleM0yyKaFsqZDeY6ljMYoNAHWKUJf9RdUcgBVAkmqJOgarMxXkrkdgRqGFwFFF7rmckhTvJi6wjSblwFbmb2NqE2oCml0iuh2Xo7JecgqQiVWY+0fkCuU/vw/lZYnTPJOAsr/IyUOQ+uB4wGdDzYE3v20CG9KWNs29EPq8o4qmBCoG8T6oDlX0oULpRKtgCa8ONdTreA3SsZHcRYw41+SzSAPW1qzGUqUB2bwTmAVT9uuq3F1ysXUl7DiZuaJzHeQ070srYvlbYIaTQ+uzkA04eeptnu73hDW/Ai170IrzgBS8AANxwww145zvfiRtvvBHXXnvtnP+NN96Iv/mbv8Hv//7vYzKR+cPXUlzVPvaxj+Htb3873v72t+NDH/oQvu7rvg6ve93r8K3f+q0b3cfK4Oi3fuu30t/PfOYz8QM/8AP43//7f+Oxj31suiGzf/JP/slGwZyPtsctJmAFPR1OMys7I4AgRFaNDcBEacsPZgKpwJmiLPNniD6moYippmSYRKMTlKlhjuioQQPWH0+E+0jSaBSB02jAzNjDBBN0qnciROLECp1mAUV7UK0QtwIQSJgnBAViyjIFAro4SRvmTmMLBBKwlXwIXdRCBRww5VYBVAC4xZ6yVHtRwNs0Sn2iPYSkJ5qysGZTZYWmWieJo6TiCFowUhkyZhaBdQxgFWRPO9MeIS3lZw6JZYriiM4t5QfnqtpsNZE6TaMp42MpNjjAZLWGyHwU7CRBNgPUUVHfqEiJIQOWQmzN7hmZnCkqbVc+BSvE+XUpqq6Yjlhea5NjpuI5V8qOVazIxR/7GJ9Ca+QWfABVX+Zk9ZHmGBsklqsAOkM2t0Nw1Zb5oKevIfPAk2VLH54DTvZ33WcP0FKbAxaDgA1L8M+C+1gLT6wPPor4jwK/1PWvBmnKzQLY39tf7rRNO8OC7P39fdx666141atelY6FEHDFFVfglltu6b3mt37rt3D55ZfjpS99Kf7rf/2v+JIv+RJ8x3d8B175yleiaZqV+v1H/+gf4Q//8A9x6aWX4gUveAG+/du/HQ972MNWjrvPVgZHz372s+eO9e2nRkToumNeUv0Y2ZTbNHvNQJhAahcRi6i5Cbrii2QqCBxVgC2T5p767HOj83CDwB2IpOLzCVbWJDD24yT7YAZSMfIuR5zmCRqK2Is7mc1BmPMJlNuZoZVNbEmKO+5yh9OYaOzKIGGChgM6Ep1PC8Z93AIau4i2J2i40/aAQITTscGJAOzFCYgI+3GCNsww1UKQDQGnY4udELEfWwTqMI0TNCRlBLpIYCLsxSBlEpQ5msZGUpWcaxdNY0BLjFkkIGRBduSQV6PFgBCyIDtGW9Kve7UZcxQgBR4DAE2rUWJQ7JesgqFAWVfkWQ22tJq+to3qOB8rWKGAAqjMaXU8eLFj5qNL8MnQUzV3+L5y7SI9F61hcuDKfbhrfMHIrI5jsmqf/OzAVR9osMkutdMDFApqbBnjMxT4GjaUTitCcyCtBp6rtKltbLbfWRXXAjuyitUpBhSgLYG9oyF5VrT1Oj8Mzu1I7QAf5aINAHfffXdxeHd3F7u7ZSmDu+66C13X4cILLyyOX3jhhfjgBz/Y2/xHP/pRvPe978Xznvc8vOtd78KHP/xh/Mt/+S8xnU7x2te+dqUQn/rUp+LGG2/Eox/96BVvarmtDI5i9etttMOx/djIxrCBgUiIQZaCB5K6QCFOEnNEASCOsmmrLnmfcoNgzBEDHQUEXVUWOQCaYkJk7KMFR0ZHDYgDmqBL2jmqsHsXp3kCZmBGDQiiMUo+sQWBsIeJLK8nXa3GEmsXRfRMQRgxBrCHFgGNsEbcaHVuBTxogUjYxwQBIS3TBwloJGWQwNLetFOfGDALUpcIeh06W9mmtY2iLr/nBhwFFDGEDUpVuyOpsLpB7DitZJtFKRvQmZAa9gxdqs+5GjYDrAwTbLl+1C95BTzMyKvSjBWKrqgioECF8t+sDFJw8775cAlUfIqMYvbtK+qYMJABI/PRlBfB7deX2vEzOzmWhyTFlVJJCgLq1I4Xf1v/NVCZAwmc+6Ien+IL38YamAMSBkYLJojdPdvAkTBCoZru+sDVotRV37k5Xw/o+mJebTZbyhp5ANTX5FFqflaxCoespMFa1/z71fu+VUGsmZacnDh/KmRffPHFxevXvva1+OEf/uEDtxtjxAUXXID/+B//I5qmwROe8AT81V/9Ff7tv/23K4OjH/uxH1vJ79SpU7jttttWkgRtXOdotMOxfW4xoYgYA1rqBOCwCLL3YoumEeYIxOCuQRM6dCzAh+IUe9wo49MihIj9OEGgiMAq2o7QpfwRp/Wc+RBLKinGmZwDy3MQFkY2qw3q0+E0tyAWxiZoey06zKgVNodZU2XA6TgBEeM0S3puhlYqapOk5SgqcwTG6djKnm2YYMaU4gBm2IuN3pcIsqc8EWCo98WxE5+GlRWS8gimFRI9kbBDIZiPsGms7JLpiWZRAOcsihB+FhuZx1h1YFGqlTeNCrJJBNkAUIitI4DGpdW6lCxCWqVmIEL1QCZqthRbYoX0mQBQl1mIIkUWUVxjDA7589TjAz2f2CCe88mgxlgrV9nat+OfufYpKQKufZK2iXM7Fg/7e86gKLVWMEish+r6RnmyS2ByUfpqWcrLfIqUEJXnq+ulDKqPqgZ4la0ySR8Y3Axfv7IO6DAB1lFhtQWpyWL7mfo9XcFOf/6+g8d3lOZ/ExykDQC33347Tp06lQ7XrBEg+682TYM777yzOH7nnXcOrhp7yEMegslkUqTQvvzLvxx33HEH9vf3sbNzeAB0HX3bRuDo+77v+/DIRz4S3/d931cc/7mf+zl8+MMfxvXXX79Js+el7cUWtphpioCdELUCdBZby87yMg1RR2iDaG4YAhCELWnAHaSGEUc9JrV+9rkVkXOcaAo6ACq2njIhEknKjFUI3QFR2wGF5HOfptz24kTE4Lpsn9WnJVZQBJxWYfYeT0Q3RA2mkdCGiNNxB4CkyiiIsHvaBXAI4kOSUhTNkYnAW91PrlEQA+x1LTgQprFNYzCL2Sfoiraoq9Soc4JsBAU4hFknacdZbNJKtH2S80KYBnSdiKxjlFVvHAOYorB9EWl1GpAF2cISKasRdWNNS5Ep8CEn1uaoKTMHmKyAdtq8tnPAyXxC7sau98Amnev14bTibNhHAE4CWQ6gZHG0OHoghkYqsxtAEtBigNAmLBVQW4rOMVCUkUUJZqyQYUrrITEkWePkWRNOIFBGEvk4kWuHMaivWPSdOjTpxpjHi3mO7JqrVr2ovXRK02mrfMlz+s9mVmLaQ7cjT9sVxpCcdx8odgB7zRs+cerEIcV3RHaI4OjUqVMFOOqznZ0dPOEJT8BNN92UpDgxRtx00024+uqre6/5mq/5Grz97W9HjBEhyI/Nv/iLv8BDHvKQQwVG69pGdY5+/dd/HV/zNV8zd/zJT34yfu3Xfu3AQZ1PJrWNAva6CaY8wek4wX5scTruYF/r95zW+kCn4wT76jNl8dmLE+x1E/Vp1aeVNFSc4D4Wn9PdBPfxBHvc4D69/r44ST527X0KeL4QdzBF6bMXJ/hCtyPtRDm3jwZfiBPsx4kAIe3rdGyxF7VGEbf4QjfRPnJfp7sW+53WQEKD+zoXFzfY6ybY61qpydRpPahozy2mUWpDTbsG+530N+OAva5FxwF7ndQxknpHwiJNOxFXTzspLbDf+XpGsu2I1TWadUH1RqRAScTdXWfASdkhJrAJsFXAzl3QyteUxdadYoJIunJNQUA0tkh8grJPBCB06l8Jsv0S+nTO1zfSvhILZL7dfDtJCO6ZJocX6npJJg5PPkUtJGNVkEXScL5QoKWAoUiRufSbr4qdReI9rI6l3nzan/2zgAnycfUCEpTn5vrq+dv71LIDlyLKV1ZAIHJ/LHV/c6cU1K3CdvSdWocdWQkUrjf72q/3I13KX/aoT33SkPoe1otp/wvTTYM6Z+2aa67Bm9/8ZrztbW/DBz7wAbzkJS/Bvffem1avXXXVVYVg+yUveQn+5m/+Bv/qX/0r/MVf/AXe+c534sd//Mfx0pe+dFu3AGBD5ugzn/kMHvSgB80dP3XqFO66664DB3U+2X6cCANBMnFPQpcE2NNOKlvvR/nFE1UrNAmM/UhAmOF0bNEQYy82mrVRPRFpOo4Jp+NEfSYgiJ6oIdkkY6pC6vu6iSz373Zk6Ty1WnXb+Ri7pD5T9Ykg7McGu5jhPm4RmEXYHURs3amuZ8qESWDcF1tAxdZBx6AJsqnsLAItAfd1LU6EiP1O9lbbjy0iCSs07YQV2osNmKRvY4A4SG0jE1vvdw0mDWPWNSCQlgyQVWddp6k3ZeOEHQrougZN00FqF8lEFCOBAqksxdJqARxlg1wBPwRuoMv09RmkqRxWoGRshfyQJU29ee0QlIkStgmOZULSAxnYIBVkk/eBb2c+vSY+ecuRBFYA92y1gCj3ZWCjAhKZXcrXJxbJmCT18QAhpfJcO+yBERwT5H38PyADBwkoID88I8Cug/r69KxOc5qmqi9/3Vwb3h8pnTYIkOr2a5G0a5Prez1QIcM0ID1hLIixtmX1hIZ6Pwpg9P9v793j7aqqe/HvXPu8EpMQKJAQGo2AihQQS0qMinolVyhUi7UVKvIS4XolPgg+QJHwUKICXi6UyvVVbQvFasVai1Hk1aopaCDWKlBFKP6UBChCAknO2Wut8ftjzjHnmHPNtc/e++xz9jk58/v57LP2XmusMceaa581v3uMMceccJ90jizMUZtumOLZagBwwgkn4PHHH8eFF16IzZs347DDDsP69ettkvYjjzxiPUSAzmX69re/jXPOOQeHHnoo9t13X7znPe/pesHYXqErcnTAAQdg/fr1FTfZt771rVT7qEOMFg0UalAnZJPJASJgB/S6XqoYNPWK9EtRidw8I3eYaezbAStTKoXMJFvrRVN15WpV6FpGpHRidAN6WY7ck+H6Qno5ElM5yclwQjYNANAz5BrQa7VpQjKAUZPMPFryVP4BZCrTZIT0IrhNQ1TGaMAUgxxAVmRokvbUNKHMEiWFTuwulAurmWKLBO3lIehK2ErpmlEZaVs4kbooG/p6SlMTqcysvXrWtiZSBOhimwWhKDP7o55KU+SRE7DLTIcCzDkoAVfPCKY6NXszYPcr6yXSZQM4cdrWMhLJ0V4dIzjC43l1ZIirFDIyFCZ/DHOER55P5HuFuC1u2wzGTo+hS0RWhgdSWxHbzsLTbMmG3ghidpuwi+DII+th4sUDr9e+T8BYoSNRwT9YSS58FehxoTzDVMuyqqA2T6VmcA+PGYLkXXNdOC1sTxIhwM3mCslYx7WNpDFh89RZ7aEO2nUhQUBlJpl/srlFMFOwnsTFieJ4aAxO77RdV1ZjYjo6xerVq2vDaHfccUdl38qVK/Fv//ZvnTfUITqZ4dlVWG3NmjX4wAc+gLVr1+LOO+/EnXfeiQsvvBDnnXcezjnnnG5Uto1Oy5J/5StfwYEHHoiRkREccsghlZLkRIQLL7wQ++yzD+bMmYNVq1bh5z//+WRegoemWfZCh9V4+Q0dStpZmDBROYi81KE3G2qzMvp8HWrTsqM0iJ3lkA5hlSaMBhPiKhsmDOdkdhYDVmZ7MaiX8CiG9PIfNIhRI6NDdlqPbnsYY6Ve5mO0HNRhtGJQh8Fs2GvQyAya92apECvD4a+GDs2ZkNpOM/NttBhAThmaRcMsCaL15bwcSKk9SDpEphOxm+WAWRqkYWbRKRtOy00YrSgbKMySIaWR4YVmdcjMhc+ozEAmjKaX+DD5RaSAUobPzMvIKA6fFVqWi0AqE2bT+Tc6ZKYKvZ/DbaokF2biMFrhHnYcVrMPv7K6HxUZnQStTG6TPQ9VGQ6VScKmBHnhMJUN3cmtF04LZKw+HuRNWwg9T4LwsB7wKUygWL9brw2BJ0qf0+LpHhKmiJ7qORGCEh6zOUaS3VVlWu4T+nW5iDYIVSvIPm8pNo4+T097bYc6Jy2kRmW1PybRmzS6fYZUyJ7oaxfBpCdkv+1tb8Po6Cg+9rGP4dJLLwWgK1p++tOfximnnNKNyrbQaVnyH/zgB/jzP/9zrFu3Dn/0R3+EG264AccffzzuueceHHzwwQCAT37yk7j66qvxpS99Cc9//vPxkY98BEcffTR+9rOfYWRk8pPt9ACvPUcKCo0MyE0hwZwyZCVMuAj6n7xQGGiQ9po0tEelUWqvCRFcQnamZ2gRNHnI1LCWUUDB1a9NYneRZXq2m9Lhq2doRJcEKDOoTFflzlWGHYWuS7TT1Dkag15/TWVKV61WmTmmnAwNAKSTrfMSGCDCzmIAgMLOYhBo6PBaE3rh2aKErttUmGn6pnjjWNlABp1InRdK21qwN6iBnSbZusgHUZhClkCGsVz3U24SsgtSKPMBsy6aJigy2booTPVrgpmSDwAZysIM1mUGKkh7jhRsvpC+N9DeklKB2JVDcMnWxkNEhugQnOfIJmYD4KKRXpI0+VsbaisAU+LKLkEiw3PgNjhpGxB28XvSNtcldtsEaGivEJGTIRM2EkSFiZKz2YWWdKiNDdTXqQmS+cgkSCZJs7DcxeSM94chM/FelfH9Hrx8FJ/c1Z4nj4XFBjMFm1nPRCucVi89PnWz3eq8Qi0JX2CPxHheJjJenfEGkQ69VeyNqkzX7+nAK++z8QbKYyoT/wChp6g7Q+bMTxWyZxK+9a1vtV0cUtEEKfzjjz+OOXPmYN68eZVj3//+97F8+fLolL9usGLFCvzBH/wB/uIv/gKAzoJfunQp3vWud0XLkp9wwgl49tln8c1vftPue9nLXobDDjsM1113HYgIS5Yswbnnnov3ve99AICnn34aixYtwhe/+EWceOKJbdm1detW7Lbbbnj66afHzeYPceyd78FA1gRRhoGs0NWkSWEoK5CXCnMG9HT2kYaWyTieQsBgo8BYkWFOQ8vMaTRREOcT6enng1mJJmUYyZrYWQxgTjaGMRrEQKZH8sLMIBsrBzCn0cT2YgBzsqYpMaArZBNpwjJaDmAky7G9GMDcbAw7aRADIC2jzDT9YgDDRmZO1jRT+fXab3oCk8Jo0cBQVuDZfBAjWRM7yiEMKj1NvyRClinszBsYzErszBsYbhTYXgzq9dfKAUMCtRdoICuwIx/AUKPAjnxQr4lW6rAfoDBW6OKNzWaGgUaJsUIXeGwWA2aJjwxloYCMUDQzZA1CkTcAVeqkagAghbIws9RyBTQA5JodqFwPQsReIQWgqbRPNoclS9b7UgIEhawwhKVpZAoXQlOS+Cggyx2RgtlKGd2GvmRPhsfdnHReUu6OEYBMyhRk7HAyzvOk+1LLkC8D8hK4VUHOVivry6Aonc0KQE4iAZsLXxphXojWxjnFP09Zwob9lDlHEBvFMlAuYdrz+gg9Ck4mlijNhMPKlP4xKVpKOwLCEyNdQfgs/OyF0kJ7ajxN0bYqB+Rpwr7xIkxWX3tDR2018F6jknCtBCGNJWNPHGf/37fh+Hf9YcfnTWTM6ET/cz/xUWRzJvYjv9yxE4988IJJs7UXICJ89atfxe23347HHnusUpfxa1/7Wsc6uwqrSey1115RYgQAf/iHf4hf//rXE20CgCtLvmrVKrtvvLLkGzZs8OQBvS4cyz/00EPYvHmzJ7PbbrthxYoVtTp7jbGioUNG1MCOfNCG09xsrUE0iwGM5mYWW6HDVdsLFyKzM9aKATOjzIS4TGhqB59butlw23OecabDeDsK/Vnq2VHomWXbS9f2aMnHzMw0amgZa5+ecTZm2mqWg9hJesbaTjPzbIcJBY6VAxgth9A0OptmplmzaNhQW7McwFgxgGYxYMJpbhbaaKGrWeeFlsuLhik0qUlROAstLxs6VGZym/Jch82KQoGIQ2eZWfqDZ52ZEFoBEIkQWqH8WWil0kSpVIbgKJFnZIiTnLHGYbRwFlrNrDQV7PPCaGRIDTlvUFRGHHMz2UyoTZA3vdXCLhwWyJl9zpvD5MZ9VhQWunSDPIfwpLcJpi3vvZy2D6eHw3JK6pX2SDIV8ygR4EIwEeIVygf2V455xwNiFLytoJ3CkTFPUK0XrK6hNpnJuGLtM5xJKew4LqQXL2y4tyG2xmB7y1v0CzJk3vWr3xfRBt773vfi5JNPxkMPPYR58+Zht912817dYFKzyXoZV+6mLPnmzZuj8ps3b7bHeV+dTAyjo6MYHR21n8Oy6p1grBhAYeIgXI0aAHaYgVsVXKEaJpSRmdpHwI5C1wVSBVeA1ovANlSGJsg87/VMNmUqSIP0wrMNEMZKoIRCSXpGnMqH7NplOmm7BEF5MoCyy4c0qWGTrUtSKGHWViuUDouRqbGETFeiLhUK0uG+HbmQKTM9A8x4sppmGv1O6Kn2ZM4pCLqtEnZK/ZhJzEauq1+XpfaYUalQlJxADZRFhhwm2dokUVOpNL8pM5Q5dH+DdMiME61NiItJEMzsNGWn8cNNyScFKswx9oyIY5YokfTwiJARkwvj7MhE0jZMSC5M2ublQ2TytfXS8JfG2Oitmya3PENLJFvDFL3UITc5wCmfiEib4eTcTDTAS7KGIV6ZcvbDHfMIDSfvlpFjgPMYeYTKHONQXczD4pGx0nlLAJ+EefLkCEroNZIEINPeJbt+WsVrIhsLrr0VaomQsKvWQ0NwNX58PZUk6baf2V0+2/l+jeedmhDEP0N0aO9d443GhP0LCT3A3/zN3+BrX/sajj322J7pnN6p9tMU69atw8UXX9wTXXoWFcdjSjRKLg9Tollkuvp1MaArQJtK11lmIgiqwGjR0CGvQi/14WQIhdGzszDT/YsBqIzQpAE0TCZuThmGsgI7igEzrb6BjEqM0iAaKKCXH1EYzAptB4DRvAE0dKXthioM8clQUIGd+QAUCuwsRPVrlMihPTkNIuwsGgC0PgVt10BWoCCdLJ0pHcKDKvRUfkVoFgNoZLlenNaMB2OlnvbVLPRsNQ6zFUXDyhRlhgxmodhMV7TOMoAKzktSdiYaFbq4JBUmLGZKKOhVaaH3FdCKc0A1lAuv2bCa8Q4NQBMSGaryyIQClCua6IWmJBky66axHn7ch0TKyegLd8nWpg1dMt3oZ3LBxE3K6NwoWRoAOnXchNfcemeWELEexdeg9aChXFjMiNpxiRO/xRR8b1aMIRKyirbVE8hIAuRynKSclHG6Wm7rPDkx8kBVOTsdnqSQ8m0O9UmiY3eFBKsqUwnZxdAG6WmLGE3kB2/HBKxTxEhPSJB62/bojtHxhfqJPkzl7wd22223ns+UnzG0t5uy5IsXL24pz9tOdALA+eefj6efftq+fvWrX3V8PYxmmWEs16GkZt7Qs7fyBsZMWGlHoWeqPZsPmjCafu1sDmBnzmEqfWyHDVvpWV47mwO20OIOU1Bxh5lNtoNDZHnmZMxssh1m5tkODs/lOtw1ama1cfht1ITstF1Chu0wYcLtpkDjmLG9WTRseFCfb66laGDMXHvT6MvLDKP5IPIiw1hzUK+vlmfIiwx5boo4Fno2W1EqjOUNTdQKLVMYWSr0PhQKRVOHzsrChNCKDGWuTLK1Dqeh4FemPRCFIT1mq2simBloOeyMMx0aUzZsxp9V4fa78BmH4Nz5tjaQ1GeJjpP1CkF6oTYFFK7qte9J4q0hcqWoRcTT7QnadsDNWCsN2eFZaDKcb3KS7Ay3QrM1mTTuhb14n/BGefWOSrd10/NVVU+ozyIWaqMqSQnB9oTEo+TQG5zHqCbZmYhMvhG3Q64pScrYvhahMoLw6tTIuMsxMqGoR0JqSAEJUjQeb+iG1DBBtLewzbba1i8UmVUE6mXksd4M+NM+IZt69JrmuOiii3DxxRdjx47eLecyY8iRLEvO4LLkK1eujJ6zcuVKTx4AbrnlFiv//Oc/H4sXL/Zktm7dirvuuqtWJ6DXlOFS6u2UVG8FvZiqHtRzapjp6A1DmPTAPsY5NrkmFGOFnqo+akjNWNHATkOsmFRp8jHoyezINenYmWvSoXOAuAr1gCZduWsrL7Ws1WPI2Y68gbHCyFCGHfmAJjiFJjqj+QBGLdEb8G0vdf6QrmqdGZtNiMzIjhU6f6hpSFLOla1LhdyQn2beQFk2kOcNTYLMqyxMLlGhUBZmqn7Bn5XJJ1KGDGlCpPOHtHeICuMNEkQGBU+3zwxpMflElgQpV5Ha5CKpQlmyw8TIbm2IDX7uERxBUiH5CQhRKKOJDllCIwmSzUkKZSp5S0yQyE6vl1P6HVGCI0vsJRIyKDQbUyKfyVbUJoh9kgjB8w5Z75MgOJb0WATHmbxFvTuC3FU8QyQ8WDXnhuEr+RJQYYir1cASSxJnPWa/IxM1tgEe0ewKbZ83gVGSgm0vQMF9i5IgCBLaBlnsEDufmeZT+WcJ3vzmN+O3v/0t9t57bxxyyCH4/d//fe/VDSY1rNZJwaV2sGbNGpx66qlYvnw5jjjiCFx11VWVsuT77rsv1q1bBwB4z3veg1e/+tW48sorcdxxx+HGG2/Ej370I3zmM5+x9r33ve/FRz/6UbzgBS+wU/mXLFli14WZbORFA5kJ76BUunhjqTCEUi+WqvSyFwBQIkOea5mcF03NjUzRMNGVDDB5SU2T+zKaD0AphWYxgB0ASjQwZmTyQmFoQJMzBZ0grqCn9DfBC7EqDEFh1Mg0iwHsVDD1hBSgFHJTYmDUzPBiPXptM6VtL6GrexeZCYPp6tdcoLFAhqKAnl2W69lhed6w/aSjXGbmWAY0c4WBAf05VzqviBT0FHxjV5krZAM6sbpUJVBkoKyETraGzhEpzJKgBfTobcJsnFfEoTJkyoXITJjNrYnGoTZUZ3wBYlq8ON/IcFFI62iQoTKW0VE4Tw+Hv1zozEbGHPmx4Tny9UB4hziMpjT5qcgwMeEwH0iv8UZkZey6aeT0unCYsm/D6tdkyYrTA92EQ0gMxHpuLuImSERsy+3a6fXSxuD8Vvk9lXCb/8FL67GdE+gIdVaa6WLgjp5C0f2sv9fP57p2JrkR/3M0/2ry7BgYHpw03T2B/D5ORMc0x6mnnoqNGzfirW99KxYtWtST7/aMScgGOi9L/vKXvxw33HADLrjgAnzoQx/CC17wAnz961+3NY4A4AMf+ACeffZZnHXWWXjqqafwyle+EuvXr5+SGkcAUOQZykbD/ggqS/2AbUKTklHoJGJqaAKhABSkFzcdMwuiArqyNkEv1qprOCuUBIyaKtrINVEBdEXrTA+p+nmf60VhUeglS4h0EnimdL0lPSYYmVwXWeQE6CxzMiWZle2hyRoA7dUxMqWRy4sMetkObXNeKOSqATLx8ZJ0rtBYTigKnVBdFAqlSRZHaQiQSbIuC13PiQpdOZuMZ8YmXecAUaZzikqYXCJlw1eOsJie43XSTNhLe3mYMMHVNzJhnUyuTcahMQCZIacgnVwdk7FhLT4mtzLvx7zsumt2GRJysnzM6hAzxkqFjNzg7belnNeGhB649zDV2V2fmHZJtA8uyKiCGWy+rKtaDT/ZGnxd3LByxIx38RsRTvNmuwld9hxe4oGnc0sGw94vBINq14MrMQsU+gPbssgCqIGMyiIJ3XUICVhoT20z3Tyf2zvH6m5pWw+glE94rYdPNNx1BfE2MBUEcAKwz4IJ6pju+Od//md8+9vfxitf+cqe6eyKHD300EPI8xwveMELvP0///nPMTg4iGXLlgEAtm3bNmEDQ3RalvzP/uzP8Gd/9me1+pRSuOSSS3DJJZf0ysSOUBT8oNQ/1xsZmdIspfEK6XXBAD3YK0XIzEoHZJKRs4zQzBtAQxMUpXSuQmEe/GO5XiqkmTegGrouUaZKI6MLM44Vuq2xfABqgDBWDAgZhQEqtcyAITekK1Y3ylIvW1IqvQ5ckUENwKxlZtaHowIEM/uOCE3jOWoar1Azb6CRFShJE6RMaVIElMiLDAMKKIoGsqzUJKeEKdqoi0GWudJJ6nkG1SAdJrMdbJb5yAEMagKlMjK5RXoQ4xpDOv9c2a0yRSDZe8P6VAYhw2qUq+0TeH5CYmMTsJXzQGWcbC0eZlaPzou3MpklS6atBgJPkvAcWa8SeQnZpDR5s3psXo04B3DeIEtASHibBKFiEFxIjD1tyq3jZmVsLSFDgAIdti0is2wOhLwQJVMjCUFYzA6SgqDxfvkZfltOZhySZPSQeV8Jp8Xeh2E5eclSBwLi0skAbElJ9Zdzx/WGpM3dgtvpFUkKbfL6RnwP0GX/dYjmaD5puhPax9KlS3teg6mrnKPTTjsNP/jBDyr777rrLpx22mkTtWlWQdfZ0XkxRdMlETfzBoqigWY+gKJQaJrcmmZuXk2db+Pl6uSasIzlenHWZt7AqDlvtBjQ21zn82gZnQQ+VuhaQaP5oMlJGrT6xooGmibxuZk3bB2ipj3HJJLnOncoLxo2R2os1/lPozbhXF9Xnuv2yzLDWFNvm80BnUSdm6U+cr0ALBUZmk3TP7nez+/JvEeZoWzq3CFqao8S5ZlLqObk6twQoVyZxGr93iZbl0aGc4gKXawRBbRcbgonmgRs1qcKTXJUDk2eCh0i9QophknWRi/nA8lcIj9pW/Mal79Evj7p4bKJ2X5Ctn4Je4jzn8jkGXGRRuV7lMRSIjZfifw2rNfIJmwLGUumAJA7XpnaL0iXr8ctK2IHO3luwZ6gyD8Wy3C7gBhUAxIkycp4FamDsB/bRrKdmE0xwiUG+cp6ZuMN6J4+sT9G0rzDHZIdInRVQJG6aKsdVHRSdVsr03vMXTA1EYauQT16TXNceeWV+MAHPoCHH364Zzq78hzde++9eMUrXlHZ/7KXvazWq5MQBxUZSJH2+GQmBGQ8EGUBFMokETe010R7TxTKAgC0V0cpHXZCg1CUDTSyUi+makIdhfF25CZsVBYNNBo6LKbHGkJu8m6aua7jkxdajy6GCJRGT1MBY80MGNAeIB0hyOzYVpicozzPQA1CXmRoZEBJmR0Uy6Kh+UiukDU0OcxUiZJ02E+ZdcwoJ5SFDtNRrkCZDpMpQLtSSuiK1Ty9vlTG02IejkoTFeIaRcYLo5Ot3MPb8xwp4+0QMpxvAyjnoSlYv9BjiQIsYUIGfyo/jzNSlj8TXFuZlBE5P+JcO3WeZ50ZV4bUBzgPD3t8XL0kXvdMORJEoi0FU71a2mxklNAXtqXIeaDI2eVIk4lNEqDKMshPYvvlwG8IFn/JoNz4J6tZ1xAYj0gEYTwLWf0aiMuI/cQNWJHA3ihho+hnSYo6Ikix+k3SlqgJNbZF25zYqNjzytghqeTSDKFy9jBOwai+fes0T8juBbmZAeTorW99K7Zv3479998fc+fOxeCgnwv25JNPdqyzK3KklIqGzJ5++mkURdGNylmLMlegLNNRGJPwSmbgLc2ATgUHDnRScyMDilzLFIUmBCWHj8wiqpkCChPSyE0tnjxvmHHCyGSagNGATuwGlCU3ZFa2VwooC6AxoDRxUjoBuqlg1iLTSdtlCU24mgqEBso8M3aZdcmUJjaZtb2hl+1QMLlDepAmU4+IcqW/naZ6NQyRs4nUDUNejAwUae9OpmVsXaJcGRnzP15wGMcUbLShH30OcZiNQz2FeRYbMmqXx7AJ2TBhPuM5MgnZVoaEDOAnWfNWVWUAWMJTXX+NvDAayyBTJiHbyBQElQkZMy7aWkAEL0TGhIcy5dlDVsZcs0za5genIEpQmhgR52Vxm5IkyXGdvU0QA6od38wbQZyoLKsr24ehKs5DCUkWy/I2FuqKEQ6hh5SUIWcr3K5atCBc+gLH8bawzTFSMF7joZ11aKdidzuYrEFV3jfbFwLjeM96icGR6V0qcLbkHF111VU919nVnX3Vq16FdevW4e/+7u/QaOi8kaIosG7dup4mRM0KFAqgzP4SLU0eSJmTnmEFpQcs6CTiTGXITZ2dwnhWYJKRQcZLokiPr6VCbnJz8lwZWZ2wrMmY9vhQrslVDk1mAGiSonRyLcxATqVCs2lmhZnPevwxekjvK5o6SboETGFFXUFb69V2Fbk+VpAvYxcxLTV5YTLDScE2kZq3helD9hiJ0BGHrYhDZoANQ4F0IrVerFV7qTImmHbKPesyekzIjclNeDyabG1kMiFrp+/DVMFGKEOmfAA5PQBA5MuYKtZMeGCn4gvbCiFjPJKVStuA8VYpV9PItgFkJYGyTN88sU6cJjRMeIw9xKRIHgNsOM1cqyZrVRKipBeJE84DL5ECbLkAe9B6RHhrvmw2+Vvsl2BSFE7lr+QEwRUw9GoZISrfcr8gH5Wk6JhHq06GIvuiJsicqFqxcXS1N0LW1mbqGWIML9hn7Z8C79EMKJA4G3Dqqaf2XGdX5OgTn/gEXvWqV+FFL3oRjjzySADAv/7rv2Lr1q247bbbemrgLo8is79yieMqPGAWAGUKKDI9UwgKpSIzQUM/EKjQq6mXZqYWSp1wDCNDuQ5JKePxIUDP1lKlno1WAqpBKHNNUApLfDIozgg2YR09LV7n/RDbnpWa3JnBlMzU+TJXet5ckUGZqfMwicFkFmtFnoEaMNPr9eBKHFLKtScJuQIGtaxqCG8Eh7+gZXXyrzIDPD8SlZ1ejxxQg0qEupR7pHK1a5P4rEy/M3FR5l4opWeyyURq9p4wYbIJ1LwNZXihWBHmU+yNIaWTAKU3SLSVlZqs2sRuDqOZzwr6HmhblA1tsaeMvTv6unifvi7rKfPCYYIElSJEVvKSE6Z9sCx/Ju0GzTLwGm3SY+RIjOndsvQHVEGoJHFRFc+P1MP7a4iDJEAhWhEU4aXwZo/ZGVJUbStst6Y9r4aRbKsutNaK4Ok3kYMs0gZJsP3XOamQpGjSpvDbvokRJGuJkB9nVmAP0BxrTqr+CWOWVMhmPPbYY9GFZw899NCOdXVFjg466CD8+7//O/7iL/4CP/7xjzFnzhyccsopWL16NfbYY49uVM5emORd+8/OS1TwMUVm2jivEA9QAzpXyXgYSJFZGBVw9XugSUdDn180jQeIzDR2aqDIyHgCtJ4C+nNpZIgaxtOQmURZvX4azFpkOjTVcITO1AwqjR7KYTxAOhzHBMCGwQoFZRZ8td4gHvhz84AuFTAGS64Ue7KUk1FlBhrjKfg8M4oMsdD9k5mtlTHeE1L+LDDKhXeHYENaWQFQQ5MrG1IqHHkAYI9lpT6WsQzg5yplZv016dmy0+kdudPhOZMPVPC1OxmAbVcu1MX3k9yaaEwiM+YITIKYtXFiN+CH3jgUZ3Qpc49lvhKJrf5+GvsIOvxlvrPaoyQHfwUbjwT8vCMmRmI2nfMGCcIhvUKZSdISBM7TJ2HPl+3Hk46JyR7nSYGgVGbz+WSuVPTkyHsyn3Wouf08p9rPdjp7KMIkFuMv2+ERsy4Ihfm+tNXWRBASnlbEcArCanPmzZCE7InqmObYuHEjTj31VNx3332V755Sqqt0n64DpkuWLMFll13W7ekJjEIB3gMMjhywd4HzbwhW1iYIFzpHBCbfBaVyeTciidjqaZTGc0SacPFDvlAglNoe1qMAQMjkRiZXQMOQnsy4evifkHN7cqOH1ynjfCIjA5YZ8GWVMd0utcH2GO8Qk6hMwRAv+InU3Jek+zUroEOTPPU+LOZo+idjQgTnqdHeK9gkZuux4WRr4/nhMjowqTBe8UXlEw9lyYghb4G3yc5aU0xiyA+HwcnwwKykPkHQ9I0Qydac2E1Ct5UpzUK6nK8EU7KAZ4wpnTwNWOLprtMlbetFZc0+swgrn8Ntu4RssiTLhk4Z9pjZX5aIEhFe3qMkRxAqniVfpy9TQzq8Adh/Q5KIxAaOcUJl7JcZl0BEbZCKDClqY/Aav+5QhER2Cu66sLZTr2CvQXgyWtYwmvxRfUeqkD0t8La3vQ0vfOEL8fnPf356FIHcvn07HnnkEYyNjXn7u3FhzVZoz4b79ayYAAA2Qdir2wPza9kOzuyF4cPK5o/o2j7Q3hCrhz1HcHqggCb0sdyM2AVPlxIyOfSIaXKEqFQmtGZkBmD0sF2w0+R1/gg00QlkbCVqDuGZZGtAQeUm/FWKStSFk1Ewy35kHPJyMppwARnnbeXa01KSIV9mIFeWWBpiJhKpLcFhmUqFbCHDpEp6fmpkqpW24cJgNrynH+42EdoQUC/ZutC/2D0Z20/QXioi3b+cbM1hNf7OGaKjPVqckE2gXIRwFfcJiaRtsuFdLyFbkBOuol3Zb76CSq5dBlgSJaJZjtSwmCwUafVSRQ+35X9u4WGQW3NZoBKO+snzWaBGT82gTaFMu5B9K+s3tQIZbw6bK7c9wuTnGEUbHYcUTR2GRqZ3hezZkpD9y1/+Ev/wD/+AAw44oGc6uyJHjz/+OE4//XR861vfih5PM9Y6QKHJjuJnrfGM6AGPvSfQB1mGPSw5WfLghaUkocoBxSGqIiLDX3w544uMnBLy3EaujAw5jxS31dQDOZk6QsRtmmn21ptSAtTkafZmn62Y7LZkag1p+4x3iJxNLMOhJU+G843Ya+MlW8OQDCNDEHrgL/hKzlNDcAnU3qKuqJdhHX5Ctg6DKbh2/GrYZBKp4XKZWBeEXYUmKG7BWgJHepzXRfkeLPZcQdhDsPZo+0qnh2sd8ehsvX+aSbqp+XxfShuGVUq2ZRqy7/l6eJBnoqPf2zbrvDDsKcoa5n/IhfBaEqFwf8tQk3gjusB5XyK2cfXrmjAdKFL9OnaNlj2Gtkj5NhjPuARKkrTuRsEpWSbEtQavU7wwW61bbNJQFtOcOYh/uwnpmOY46qij8OMf/7j/5Oi9730vnnrqKdx11114zWteg5tuuglbtmzBRz/6UVx55ZU9M242oFFkNixBJpZi1/PiWjw2RAJAmSUphHeAw0WcT6L1wHlquG6P9S4B2mUD56XKpUzmvE+Aa5+Lwdpp7TCjrmiLvVQmJ8l5T/TDnjhcZabXKxJVpslvS0Fpj43SW2oY4mGej4rtyZ1Xhsx0dk7stkUOjZcqE94cjg55SdtGD0+Bt4/iQmxZxvDJiozJ5/HWXyN9v2zFaEMsda6QsZllTBhTyljbleszLUMm3BgkW0NZO1CSLn3AdtlxW/kJ2ASrR8nQiNDj1TcyBMCGxex3y9jVcORGiWPExIJDcFJGPomZQIjkb0iZkAiVwbnhti7ZOZBh+5TnGSJEB4nQA9TCWxRNWG6LwMk3cflYnkVLdOq5atFWTwfPVnZ5JQyUk58UQ9pDkSdHwHTA5z73OZx66qn4j//4Dxx88MGVOkdveMMbOtbZFTm67bbb8I//+I9Yvnw5sizD8573PPzP//k/sWDBAqxbtw7HHXdcN2pnJRQPyOYzZWZNLpBNJkapdNiIB9mG8YQwIRBeAZvYm0GfB+Nd4mRkI0PGNaBKmJCLAjUDmULIZEYmD2RMW0xwvMRn6GRp/csejiyYafpWD4e44OzJbDgNxiOlLCF0ycew1aqpqbyiilYPz0qTYSz21jDJ4oRs9hwJssbkR3qDbCK1JHRKzCYreFaZC2exl0RO8+fEbuuBIkNUSndP7b1lWa5+Ta7eEqGabA3l8nUsySOyhNAO2Ew6ONGbZQgiJGOIkU2A5u+aIXTE+kx7bIwl40aPzbMSxKkMBrnSXGx0kDS2ytwi+z5oS56vlJ98HSFFdokSZnF8/bxPjMfeAB3THRAxTry2Q7eUj6HSFqO1Z0SSr45mp1nR9j0vXNG7Utm7F2B9sT6q1DXiD1PvNQKAkedM/4TsCYfFpr5bO8aGDRvw/e9/PxrN6jYhu6vlQ5599lnsvffeAIDdd98djz/+OADgkEMOwT333NONylkLxTV1TJJvIzfvc+0tUYWCapptrolTZsJWvFXe0hisTycjZ7kJzxRCn9lmhXLbPCJTmmOlQmZtjMjkTg8vuwFpOxmZMtJWWaOnhFuWw5IgIVv616+ILFHS+jXZUfJFwbb0t5Y0cZiK9RiC4b0KQ1JyQlY6maxgQkKunpHJYXJ6yL0KmNAYuWTsUhMtkJAp4fQZucwQYa5NxPlAUo+SMoZUsUxWkCVuytMDp4ftMnlFKEtH9myCN8sAimsLCbLjqm/DEYugsjXrs96g8MXy4awszlmqC1PJEFdMVshp3kXOxljYzL6EvrBNT18JCmXCmkpSv30ftltW5QNYUhQxPd5OuG0ffE2Tk3xdcx9bkaY+Yce2Hf02oTWoR69pjne9611461vfikcffRRlWXqvbtN8uvIcvehFL8IDDzyAZcuW4SUveQn+3//7f1i2bBmuu+467LPPPl0ZMlthB24zJVlxiIQHIzNA69/T5tdaprQ3RkEkCCtdGZnzlXj5C8CEw0TCcinaKgh6ZVfoY1KmCGSagZ5C/1pTSsjk+hd3VpiaTOzNYVsbCsj1f1tWaMdBViqU4pqVCb0RwYTOdM0hnrBkk79NUjoXdswKIWPDaqZ/BLlARIZJEc/0knqUSdrmUCDfN/bU8S9om5Ad8UDZe5qTrqJdCj0QMor7HSJUSi7XhwBe2NWGEiETqWES1+FVyCZFUGWpq7GbQdTK2nvDeuAdI/b8kRkUifS30XjGbAa1/nq4LZHzTMnQmCVAcMSH3SURr04lATlMyGbEwlWBF6ciw2E0qc9rU1xQ2FY4SMfIzngy3jH7J4JxPCM0vsj4aPNk0s8rS4x66bSR5QQUly9phxT1ZwQfnjvUl3YTfPz3f/83zjnnHCxatKhnOrsiR+95z3vw6KOPAgDWrl2LY445Bn/7t3+LoaEhfOlLX+qZcbMBNr/F5NbAeAT04CqSiGEGcDN7TIFDXLAymUmQJTvDDJZckckv4SRaZWWUL1O0kClh1zLjAoIIE6BlPpLNsYFz7ZK5LjNzDHx9tn6Odpt7Yawc3lR3EFzydwFkuesDJdtirw1cyKyipxREhWAT4KUelRuCIqtNF3w+afcrkUjSFhWpoaxXBcSkhUNvcEnSxEnb+r1OyC5d/yjOCxL5QRw1Kkx+k0jItqvecziSw5BUeknStgSACYN5ydaA80gV+ubZxG8TV7RhPBBsQjYyp5sRvOcfA74HSXiFeD0aQtxDw+dlWTX5WcrGCFNFVoSIeaSXpIjVhx6fQLfmgAotk62j7cvPdcfaGPzHE+lUX+V0cqE7L7+rY1XjQLCtCJmdTiia0zzniJ8VE9UxzfEnf/InuP3227H//vv3TGdX5Oitb32rfX/44Yfjv/7rv3D//ffjuc99Lvbcc8+eGTcbkBWwtfAI0AnLZnC2yc0l0OBnhQI4AdpOa7deBv3gUCzHuR2F8DJYAmUMYJm8CxnjrWCSgIbSCdBKE4qMDIEReSoIri+D9JCYZ6GQUTmAAdjEbplsLT1jrrK1y79hzxp7m5ggkBIyYAJFeh22QU0AeNab5xViAmGJBryE7LBmkU6gJkt2bVt6ATSTSE+R5G/jPSu5LdOn0P2oCQyB1zmDudeW0Ck4zxh77xoQYTBuCLChNhKeOwWb+2PlmSSwPaaWEexxQMElW9u8oooMQOC8JmNIHYGxYbTMkkZPmZWhKnEB3EAaI0nGY6SM14vEfufBiXh9WpEtqSd2LXVepHEHn9YC7ecXtadvwm11p9j1TyWvCPDuxzQiSeVk1XTqEWbLVP4XvvCFOP/88/G9730PhxxySCUh+93vfnfHOtsmR2vWrGlb6ac+9amODZmtsLOeeIccKAv3nLb1cliGQzciMdsLaXDic0TG6lFCxnhzamUoJqNEaMvIcFvSG2OTkPVSJzaJ2HiFpAyTHy8cxlPyWaZ0MlZPk70w+pUxaSmDtoSTwXqnCv3gVSWBmkonVisxM854TAjmWG6OGTKiCYoSW+j14tgLxB4SJZK1xbUTe6+MZ0kReaUFXLK1yylhGSafGcgtFMshV/beEEw1cnMt5JJorY2xZGsu/CQrXfOgZIpDusVkWR9/AbWM+wLDDmoVUiQHXJmQbT1GfNP8tlqHqMgnToDeluJcKu0PCqubv/j2OiK28nT9WJtma6frx2xiSHtieji0FGlL6h43KdqSPmbvnaG20nYvw2lsJ2AeiJK9cx8wQaJIu702pj0MzUlhtemAz33uc5g3bx7uvPNO3Hnnnd4xpdTkkqN7773X+3zPPfcgz3O86EUvAgD853/+JxqNBg4//PCOjZjNsNWQ+Ve5JBM8uysHyoZ7bluZAiYc55OJUulC2PZZwzK8arx4Btn6OUUgw/k4gCNLpdAnZcoWegrRppTh6yOhh2WyQIbb5ILcwna+ZtsHLMNjIrchPFpoaJIC4xmxRRFN3pUO85EJVWrddlFXHl9KuOn68r7ZafrkvEQcFrX5TmS9OLKyNYxqHYYT7bEnKfRasccIcut0uNwqR5YAAEVp60p5dYhMsrX0RNkwH+thWTMDy+YUsfE8E0t4c7yp/pbglPCIS+gZYnIiShVYOSsW7JNtC1WAnL1F9W1ZO4LzJZ+QpCsGs98tnRKRq0vIbkNGhraYFI3vzYmRifER00uVvp0AJBmyClX1eNRb12si1B252pkqZE8LPPTQQz3X2TY5uv322+37T33qU5g/fz6+9KUvYffddwcA/Pa3v8Xpp59uF6JNaA82GZj/NyNVmDMzk8rmmEiCwSRJJPZm5AgGh5S8Adj8COPQDaRMKbaFkOElPICqV4fbMjKEQE8piIqokF1JlmbCQ0IP5wyV1WRrSAJmwlgVGdmXSnt+ZPK3t5RICZPYTVpPhayRrwdCRhGQk1l/Td9Qu7YZwZGXgqzHTRMy0uFIlmFiYkJuZBOfZbK1EgSOc5dEQrbxQHFla5tkzTIAKC/NFHOy/U6ZJkjIMuuB0mM0Exoy94evSwy6tmaR+MzT9e14JvTYz5Js1HiT6sJZcssDbEiQpDeHPSwyOZwJIP8KCNKXatvwRBxhqdgegolYSLq8NgLyFpzP3jqq01PRVzGgPdj+MW11eHpb8EJp5BrtOAwoZabOizRn3vCUtNM1uEsnqmMaot1ollKqq/qLXeUcXXnllfjOd75jiRGgp/R/9KMfxete9zqce+653aidlfCqICt4FZttWM0M0IrgcpIAGyYiwHpIpMckYxmhh71AlqzA7BMJy3ZKe51M7vQAjsDw9ViPFhMM5V9fRtAL13ICcBnIcJgp9+3ykq0L10+c3MyJ3dLTZYssGjJiSRh7NEpxDwqyHiSVcaFFZyeVJqxmEqCtxw+mvzifqYSV8RO+zWBWimKNTDb4voj+th4v423yEruJTKhMtqNJhDKDi1cgkr9HJsSnP5ciCb4EVyfncJbzPpmbyMSjJNhFa+XD116LeRWlSMQnN1jLEF5ZCkLDcU74ZCnqOQj0SsIiZAjuxwCVRj+FsgSvEnzYToSwhYSoo1wcCrbRNsYjWDV6xj2pPbjyA8HpkxlK85QT4iHF8HPMoG4M7O6iRnfm4wv1EbtyztFkR7O6Ikdbt261tY0kHn/8cWzbtq0rQ2YrlCExNkpkQzbwqjHzj9uSPSLsIZGJxjyesB7hzeHkZjIhLpdsDa+ytZcczZ6AmAyEHiEjw3wq1GM8YUzcXN5UVcZWkhZeL2oom29kk7YbFPSTIwdgD4uSMs7Dwr/iLSHhRGpDImxZBNYDX09DAXIqvxcyUybZWgG6BAPfL7LJzJxsrcmaCZWA+0CQGw4JcpITL5/C5MokeOv7Sfpc4jwxMbvNkBAbOiO4ddPYQyaTrZWxiKf0G25TkeHxDfA9M0BAWvgPufdctNS25/R5hKqOIEnyVENQyCNAzMTh/qmsXRGdkTZtjR++72F4UJ4jbY8lEVc8JCo43ObIJOXa9WL1HcH1WoLEh6ez7RqqS1KVMHFMdjSrK3L0xje+EaeffjquvPJKHHHEEQCAu+66C+9///vxJ3/yJ10ZMlvBFbIB6IrKvDBoQEL4f5BXWwiTtjlvR/7CszOpSrhkZDEGeQNbKMNEZTwZJY6Rn/hsbS8DGWkzb+17EjImRMVreBmZrDRhppKTm8mzC8bLwsQnJuPyYDj8pfzrM4NnZnOC/OrXXDVch8lc/hCRIXBErv89bw1fA3RVZpP8bZOtTRgNpXLJ5ATrZdLkj6ztrrK1lskIOiGbvUtWxnwfqHSExJAnKFGPiMh5Jq1XpkaGoCu4875YsjXg3hM57xJP5ZeeAhmtihGOunBahCA54lJCKU6OjpzvcRPRZmC7ri+WWb3cNyTl6gbzdnKMXIOiaddWdOmRWDs1unqGnqsU35eQaLbVmHjg9YGoDE7zhWcB9KNbphyTEc3qihxdd911eN/73oe3vOUtaDZ1AsnAwADOOOMMXH755d2onLXQgyTZAdk+wzksI5fdILhlDsTgHxY75LXVVAldGJJ/LAs9UG6aNnFoxniXbLK1Ui4fRcqwnkCGPSPsxQExaZMFDGFnOlkZzq1ibweUkXFhJyZ/GXtNjGfM9oEhMMiUXcbDJW2Lej7GK8NJ2+xhA4V6AjJZKqNHn6/MgpOW6ClYW1EQFOcesR7Azmwjgp9fpISMArykaO6vDDpMBTG9nglLCZdkzeewDISMN00fNkRnv3gin4jzlpweBOQmAxWFCy0Rf5kAL9laEipJTkohEyZbe0naAQEKCYzYZ3OjArtJVpdmO0MiFuoL98ORRZLH6pYCsccpPm5XPEZxeJWoI83U6vS8MO2PjpVw2pQg1mZ/yE6n2LFtmidky98fE9ExzTEZ0ayuyNHcuXPxl3/5l7j88svx4IMPAgD2339/POc5z+nKiNkMZWr1cB6maugp2oAZMAvhPQH0A9DULnLJyBEZMVDqStQm0djIKCFjCQonGiuzVAjIhnWUCZmVrA/K5s1AORk/kdokOxccdiIbevNkeOBmj4/xTFiZhkzIJksMXIK4tsHpUbbOjipMTR2+viJMyHZeIb18hnI5PIAjFNy/JQFFZqfI28KMAaniBGi3cr0jOlyfiJTwEgo99t6yDImEbDKJ1LYWkiF9RC6VhsNggoBZGfYy2ZCS6U85o005m52MciTHkBvdP5yHJPKGEMiG3iQmSpboCFLTjneoknQNa6f5DwAxiZHXlWWweU2hbt7yNP2KB8rZY6fp15Coil0VhMTIJwJUua46PaFOQYYo3Nc+JmW9tLYhbe/wvD5gzrw5fWk3wcdkRLO6IkeM5zznOTj00EMnomLWQ/GUbvEg1InGPFuJ7K91JZ/lJWzYKSbj6qcY4mQ8LIpM6IUHcwg9imw1aFvHpxB6SrIz45RpiwddAK6CdAHnjeEf1oYc2BAXy0jPTyn6oHRJ1nwNXJjR9h2RTVz38pPYHl5rzZAjQglbkdzaDJGQDRu+slPyjUk2xGX0IPQK6UvRa9CVJSgnN4uN+8Dk//C6Z4CcOm/a4cHbrHsGlC4hW3qUiGx/2xw0myStnIwy9mSZJaDOG2ZIinnvyBY5z4r0WHDOkiQ+vB9mnyQjJVCbbE1CZ0iMeD8TlViFbCJN/jKT9WH0kDdICpJBoq3QU8TbsC0r4rdNERl7XKkKT4nKyOmnoRDB1ROiuEjF/orQeCeGatoM3fUCoffPHQi20997NDY61m8TWmJXTsiWmIxo1oTIUcLEwVWUFSmUIBMagR6cbXKzzmWxj1RbO8cPBXky/KZBeoaYkVGBDEk9mZ75pYy3o1Q8iNboIaAU9YlsTR9pT0koOaxWIkigJkfMWIYTlgufsGQ8dV1U5lZGxlWkJpRiWrwOE5JZ842MjCA7RNZzBMB6uLKCrDdHj3NkQ4HWC8YLstpEahNqaxgZmesDkzfCpIerVZucI+k8sdP1mQQpQAWkREEFXiGz1AjrYcPFNH1N0ghyvFFwbXnT9RHI2Cn9vCMgKgBCz5Nf2dqcGA66kgAxWhGXCDwPju1Etp/cPvsx0BXkNUl9lWTr0L64QfX7Y0nb8jq4TfF5slApP8BtSmLXu8aqZKij65u+JKnXXdVz8A+DieqY5piMaFYiR30Gz1YjRWjwwEiwYR7KlfcFz0w4jJORYas7w8plJtRlwyicjFzEZZC7tlxSs7YLgB3crAznCpErysgeC0d4WEaH35SVYcLjZNzCs4AtN8CJ1BwWYwJmPCguidt5q7SXSCRHszfKrgsHb+p8BnIlEGBkCpejBTL9zXaBE845OdrYI/sHnEhNNp+MPWHsQUNhvGfGsyZl7GwymTtVAmTib8qEpNjzpb1njtjIfDQmSBym5PvE3El79nTnchK41MPPRLu8CHuDmEB44SaR6B1Wtmb5MIwF+F4n+dn77vnvvWRrXRtc3zB53ZIQhbPdQt3GHlsfyh6OkEC00geXaB4bUIgNq3qMKgUdxxuQJCGsqaI9Hmy1dS5/EXqregm2z+YPtGGz7edeGtRbojU4kipkTyf0MpqVyFGfwUtYZKRzfTI7dpAXLiobJDwZEEQBKDMyeUAuHAbF0ZEgkRo8IItHNHEIiT1Isi33S9YLX8GRCF7zi4TN0p5SARm0R4wyZQfkLAfKAX1Oxvbwc4vbYsKQk6l+rdx4Zzw+3npucsV7dpFxSCqrJklDwYb35BR8twq9398sI9cyk/dEL+wqvEuZ9vxwro8lOiVBNZTO3eFZa4Cd2WY9PyArw/k8NiTG3qWMtOcIzi4AkEtxKPsZYO+TTyLMBzM4+gnZTFxITJdUeoFa6UmyXhpzDXJttVBXSJZi3iHxWc7+46Z4vyNbzn5tvPLbC3T6Telznf56W6K21s2s811ZToUnGxWJGem31dEsOKkmToAm11slSSHGsVncy0mxo3fYuW1HT/X1GrMlrDYZSOSoz1A5aSIEpb0UDeWFm5BlUEWpZ2kBAFx4Bg09qGYqgyq1jCUYHL5qiITsklwKCUGHqUr4id2caMwyqiqTFa46tM47cjI6JKUTljNoMpAhSDTODe0qTZiLCQwPZsSeI5/Q2ZwdMuQl52n/hNK06RZXFTINzpcSSdtKmYrdJLxvnCTNfSBqAokwYctE6iDB23q9TCK1J8OkSBF0AUZXtZqJD3t1uH4TkYl/inpJPouDnrkW6pGkxtijyRQnVJubZb0yiCQ+G6YrBy0mXIZ8VkJtXlhLEIcwETp2rPJZGS7GM8YAzbjh+kC2HYYAY2uied4ak2wdFpSM5sdAeL8I9iKpanMILivgKnbH1UfbswnlEabYJSZ9hprnAap6zeqZIY1zvP8YWTDSbxNaQ3w1J6RjFiKRoz7DFgok7WWwi5FydWhVusTlEl5hRqWYRJR2aQ6bkJ3DDSgmIVsX+lOejA29cWJ3ATf1XsGSETvY24rNLjmaawox4aGi1O3bsgGwNXIyYzvl7IGCKA3AA5qyierVpG0mK6afCvY8uYRlGxazydYk+pCcHqrqsf2k4BKQS3ghM0CsKcaJ1PxL19Rf0onULp+ICZsiJkIEVZReW+zdkX2PohRVvY0MaebIRI+Ttr1f26wH5EhNKGMGWn0tzvMEzk8yevRNUp7uCoGBscXOVhP7JAEKz4slWwP+1HX2ZoWuIzKErCydty0GaXdNeI1noFWIUWBXlIAZc9zgX09cbO0iE3qNiMTbihKLHo1aUzL4dUJwQrnpOzqPbW/224TWSOSoayRy1GcoU8FZKaUJA1dPBmzyrypIT50noCzJJv2yR4Nl9IM306E09owMaGKQmbCTDs8plMrJKJBX2Zq9FFKPJ5MDJPVAeGFsFW3jGSlc0rZfv8f3kHGJAelZAZSr7VOIxG6btK1DU6rUxMaXgatHxCEuY5cmbQp+aErImHylrARK6/FhkqWLClZCb8JzZBOgi9ISTTu0yURqwIa+9NBBfkJ2UQKNzCZkI5CxnhEyCdkGdpy2pAD6O2MTuzmEJ7wG7PkJPS5so/W6yDAYX5SQp/AzIUoygOrxmIz1SDHjBTz3p+QJ0stTo5PMZ6VUYH4LG2KI8ROK7qie6rU1flManXtPWiZdTzVahgB53/T1ENUhy9p1+yXMNCRy1Gfo8BM5T4EZNAnwEoQz89DIiGzSMoE9R5wzpM/MyOnTCdRk9WUAQHrpCx5o7QywJqxuX4YAykTStiuE17ADNAHIzDR242Ex09k5adt6RGSFbNI2cOKzDXuV4vqEjE045n7gfjJT7zPpASlhE7FlIjXbaz1fpfSMwYb5IPpdT503snnpJXbLRGoy+vh6iUN07MXje8MJ2QTt+SMFO82eXJv+emBVGed94W8NgqRsMyjZZHzjLTLnWopg7yEF3h5zXlGY49LTY5uME6BwXysiZN7r7iF4ydb83Q49NbFxVBZlrPNWgUmCJvC19ktYPaiGD+17gks0rtOD8ROfO9HXCgTrnZKVtvtXw8gY1dWx6YnG4PQeQlPOUffIxheZHnjyySdx0kknYcGCBVi4cCHOOOMMPPPMMy3l3/Wud+FFL3oR5syZg+c+97l497vfjaefftqTU0pVXjfeeONkX45FZgbczNS1yUrjTSr1Z12jR3tAMiZKZtDn/fJ9ZgbuzBAMq6cM9OSiLaGH7WgUBBRmCr2puRS+pB4tV1ZlSpY17XBekHzJazfXDHmM3Ln2OrgPhAzMtSvRT6zH5isVZXAMmkQVpZPJS+vpUoXflwjaytg+S8ZM6IyvoSCosnTn2y17B02IrijAtYmsrAnD2bCRlCGjl2UKQ5AKcqTAkCiry4SViBd7lQM0GVJkZC0Z8jw/Qo7PiXkD5L5w9pmQ4dwhPUsLgtCxSEDEmLjJQVTaUNMmibYq9tURt9g+7xyCrQQumVqEeHiz0Mznuj6x8Ehb+6NT2FbYZk+JUStd4X3pKyGbPIxunyEVsif6moWY3rRX4KSTTsKjjz6KW265Bc1mE6effjrOOuss3HDDDVH53/zmN/jNb36DK664AgcddBD+67/+C+94xzvwm9/8Bl/96lc92b/6q7/CMcccYz8vXLhwMi/FBw/IJmFWmXAROFfIJggrOxVccUIvzwYqhYxWCqUyPUhwMrIKZMQATpnfFid220TqiIynh20m+DPj7K8WMpERI5O7kJtdqkSGm6QMQdcoMgM/hx2JXB+oknQ5AgKoyTO7ZLgRLszHeiDaZNtNTpUlP8YDxTLUMCEpQ1B07STlZpOZpG3tvWjY8JwOX7E+E6Lie0LGW0XQpIVzaJSRQRAGszIEDqtigL1DOlxJpUvI1vWjdAhUesuqdYl4wDefTQjRJXSzjAhtmftvt4ptV0IP4jlKxgvDzhiACZLuM68mExsds531BZWtPQ+RgQo+V7xKgP/ZzEq0hK3itaqGVLR3xt9n7wWC66ieXSGInY5MtW2ZvrReq6lA+D3YBQnSnPmpQvauihlBju677z6sX78eP/zhD7F8+XIAwDXXXINjjz0WV1xxBZYsWVI55+CDD8Y//MM/2M/7778/Pvaxj+Gtb30r8jzHwIC79IULF2Lx4sWTfyER6MRjMdAS+RWkzUwwxbVw+CFH0OEGOb0+M16HDHZQs7lHEDLmQa+XyTA5Q+w9CWXMgNNSJjcyts4R2TAOy4DIWzQXeekIVCnaghbShKcU0+FFW4CXG6Tzk0oXzuJ8LO5fQwJ1cjNVk6R5yntBUJxIDZ3HQwqaCFIJFJnLv7IDdWn1gMgSPYBtN6REX5g95le/hpOxNYtKZxuTGr7/7B1iMmLuI/ezYruU+Y5wW+HgZL0wTLZK2HpFPKqWYjuep2i8ZGvTFFkCYJY8qXhHJMkx1xAbWEMCViNjk63HG5xj18E2cIiyBXGJhq1CUtWqbU+ocyLRMpeJIjK9gLwH4Wd7D0W+2C6Gnc+M9tuE1gj/tbrVMQsxI8JqGzZswMKFCy0xAoBVq1YhyzLcddddbet5+umnsWDBAo8YAcDZZ5+NPffcE0cccQS+8IUvTN0vKzBB4TBZKcJOsKGrjGeIEcyCtIZM2ZAVh7FMuMgeM+Qg5/BWWQllQchYGwohYwYnP4QnZEohI20vXWhLSdtj1xeGEDl8FYYEg/cQIcXM2suhMQ49mW1R2pAhYjoJXt+AShGG4/7nEJkhWtb7JvpV9K0y4St9fumHykw9JFjyY2TYA0TkCIkkJoKsuBlzTMwC8hISGu+4eVGwrxS2yVBRxCtT2Re+p0g4iVrorehpQ0bsk+NAvN3IuXWkK8LX7H67I3ZqsL/dHOO6cgGdYry2evpoq+lHj+x2T/ZmAhqDjX6b0BKcczTR12zEjPAcbd68GXvvvbe3b2BgAHvssQc2b97clo4nnngCl156Kc466yxv/yWXXILXvva1mDt3Lr7zne/gne98J5555hm8+93vrtU1OjqK0VH3i2Hr1q0dXI0PVRAyJX6tG6IBEFz1Y2UTjW1CL5Fef610ScR2CQLAeBzMD9eSXDKy2cmejIxgKkeTmF4fyMD8IAxlbO6r0ZPxVHe2R19Xxu4jDhOVfiVpmEiRG5yNrEns1knJHB7iQdz8uudq1Uwa2uEvlAAAVqhJREFUzSDGlb85FMTFF6199gGuNBkwi8mSsd16gogAFCa0xTKa6JBsi+9XZpLSuQ8Ld0804RKExhSD5KePrYcEiFCp+T7Iaey2D8x1lHK/eE+kw00g+52yWysDlzEvvURCLEogZKiMXwXXZgg8B9arVYLM91zXbBKDKDsXPAIStB1LtpYyZkvmPUkdrUiR9RahhYODtEu2lReEoMNWJbVfbbrLH2JeuLCvSdZ8jWI7kSTyGYZGY0b4FxK6QF/J0XnnnYdPfOITLWXuu+++CbezdetWHHfccTjooINw0UUXecc+8pGP2PcvfelL8eyzz+Lyyy9vSY7WrVuHiy++eMJ2AXoGFskBSxIkM00fOecOlTpvRpk8GkX+Wl9mHFSAHxrg6f452RwfUrBhPJCQEYM5QcgoaCIywOEnoQduwPLWKePq0JnLm7GhLZCbpl9ao921M4EoS12bqTB6YI6bKfMwU+ZVlllypAdeuLCSIV12un5YIduSD5ObU5RmcVq4AbMEuJaRIpdnpAzhczLGE8T5ReI+yPCbNTDM/QFEngvLuNwfpZR9r6+Lc5gEwZEEgpOvOQdN3nMrA0FkgvwgiVCmKFDxeJAhvOL6dN6VqsowbB8E4ZlY28F+v0J2cLzmHO9z7Xnix0qLnJlwuryX+BySvDD85D5U9MYQttXxkiO9QqUfxLMLNd+dXRSjO1JYbVdFX8nRueeei9NOO62lzH777YfFixfjscce8/bneY4nn3xy3Fyhbdu24ZhjjsH8+fNx0003YXBwsKX8ihUrcOmll2J0dBTDw8NRmfPPPx9r1qyxn7du3YqlS5e21FuLwhX902EI4+VoGE9NwUnJpUv+5bXVWLYgl6QMBQK53BSph5OdzaClx0By+TuF8UzlcLkyGcwvYV+GciZF5BKpTdK2rfxMcGEnJgJWxnhacudF4IRzXoaEBuC8OdwH5pe5JmJGhj1IluTBDuCun4TtnABNbE9plzWxdhXs7NAynOfjZGA9Y1rYkA/2erEniYSXhO+JJHZmEOUp/TqhthTEiQmZ+b7YY0xiUPWKlIX79U76u6EX7oPzfEjiwO8rHp8gubosgUYjKkPe9ymzAzkZWQLE9QD2iSvHVUmqQsIjri+W21OJXkk9dbBtgpmruZ/82TBea6rIHeN2xRT56vWE7QU7lfFatgmvLUtCnbeq7RBepwiJnTbGv49OuEPlk2X01GC6J2Snqfzdo6/kaK+99sJee+01rtzKlSvx1FNPYePGjTj88MMBALfddhvKssSKFStqz9u6dSuOPvpoDA8P4xvf+AZGRsYv9b5p0ybsvvvutcQIAIaHh1se7wS2ErQZV7MCsDVeiKB4HS45K8ok2fJ5lpwokzisuB6OCTmVQk8m9JBJ/jYECkokERsiglzXYdJEh0wCsJExM7B0m8YbU5Y20ZsHJ7ZP28v5QqUlaOxpUWZ2lL3WwpASwPOisWdMlaVZEsT8PCoFgYTRWxo3EycoF8IeJiBsm03IFjPeCCa5vNShvJJlyIbKwFsy3qWSzA11HiS7JAfxAEyex4bYHuIwnTkWJjVLz48lP6h6fEqeNmjkCqkn4h2q8xiFFaNtvSNDUng/3HVxbSbi/CXrnoOzm98D1TblPm4LkWCWGLQreUV1+mS79r0gRRB9JDKpXdcHeix/a9GmUv4JTIraHHSsF8o6s/z+ozAMOhXgez7LsfOZaT6VP6FrzIicoxe/+MU45phjcOaZZ+K6665Ds9nE6tWrceKJJ9qZar/+9a9x1FFH4a//+q9xxBFHYOvWrXjd616H7du342//9m+xdetWmxu01157odFo4J/+6Z+wZcsWvOxlL8PIyAhuueUWXHbZZXjf+943dRfHXhBDehR7FXiQNQM6ZezJ0CEkmLFPT7fOXDVmORjzQ1PIEEylbThyEJIGAkw1bqdHmarcyugjlem12jh/pDAy7B2Q1aGV8WgZD5Q3PT4zbSkdrpIy1lvBuT6mGrevx3lRVEGgjGUMuSuhw4+lnkVnQ5MAqHCeKE3c9HWxHtjQWGa9XvBklfUu6fGO/FXZbT6R8PSY86lwpE2TWuE14fte+jJaZ2GmrZdOr1wzTA6kPO3fhmpFzk7t9PpWRMX8EecQXIkJz5FAxnbeyZ4ZyyYibVZmiTlipD2KrnyEZ2Wd7aFHrQ7WcMFA3AH7PqqK94XMrUKMxAndcIp+OFhC7x1gPkepqkFABMc1emYTrIHh1pGIvqMXHHZm36KuMSPIEQBcf/31WL16NY466ihkWYY3velNuPrqq+3xZrOJBx54ANu3bwcA3HPPPXYm2wEHHODpeuihh7Bs2TIMDg7i2muvxTnnnAMiwgEHHIBPfepTOPPMM6fsung2FWC8FMT5OZmXbM2uJR6YdUiKZTjR2BAtwOjTerLSVXW2rngFO9AAItxUcg6DbsuuGQZHtnTV6dLqsW3y+SCR1MzPV3F9IiGbZTIIGbAerkRdBnrI1DWSbUlvjslpysnrS9j1zsi2gdIQEwivEEn7SV8Y6Xtgp/1LPUa19hoJUhqEzmwSk02kLh1JiIW5pOdIPqAKYw9nypfmM3sjrKOGnHcqljfDuplc1axzxp/DUBn3CYlFkXVb/LF0uqw6qhK6sA1DfvRXS7RpCKlnH/dXHQki+HWh6oTarWxdp8fjUQHBi4akxh+1ouG68H2vELOT90cZYB35aaMvdyW0Q777iUSOusaMIUd77LFHbcFHAFi2bJnn2n7Na14Td3ULHHPMMV7xx37AkSMDUnag1Z6NhslLEh6AzMnYEFcpZGw4hQmACMuJ2UI25ILSrBlm2uJ8Ix2/8mSgGjV6oAd/Jg1BnpG+NiEDconPXhKx6QfrXSqhuNhlJSdGtJWJnB8JS1qcPmsP6zLEQHtvhJfHnG/7yYQzK4MtwXh8YNxgZL1eNg+HZZg8lK4v3TWJe8JkpdHwPUdK2F0yGRE2h0SKPZCZIBEhWRI2eCvUA8GAabwnYvaZtrlKcvzCkRGI/RRrS3iHakNmMU+RvK4oISR4Y3pEJnxuMEHpfE00EpsWdiMgQsH+KRucogRJkqDId8sDP6Nmz2jaHM37bUJLKLT4TdCBjtmIGUOOdlkUzitiH4SlHqS1h0T/mqeCk1BL6HXOShCJtcxIhJ0gkpFlorEMTZFMblbCAyWSiGHajMnE9Mhka9smTMXmTNuueKp7kBxtk5GZoJAjBKZatSejyLTF5MoMyLYzxQwtsGfEhPkKqQdwC7MSG+MGfkmYuLK1LBkAc99ECFB7nnwZW1zRkgYEnhrAJnbzr27OAzN6OMRqF2IlYZ+VrwmVhd4VQaY8cmI9j460kNDD34tK+QA7sAp7mKhWZIJ9ELrYOyTbNEUcPbnxIAkJIdiKAd+KkDcIWEJURqpN1zVPQkBFPGO+cR6ia58RprCitbHZN8p9F/WO8XV4210fcxeMn8eaMDORijT0G+w5Emt02eKFJblCgkRAURgZQ6i4gGBpPD8l7FaZGWzWc8QJ0CxD8AotWpmCPD1ckLBWhnwZLrjo2maZwhRbFNfFBRZZT156x23RSkJEBgCVti07Zd30E5MPq8u8uLgj7PppsG05PWbQLAQxYQ8Le5nsPRHrsHFbVtbJW68T72Pvig2bsfem9L1JlkAx+YOzz7OLv0/MWuATkFK0K/cTO4GM16gkYw4Zc/RWmfYoJHTSLh5HKbAv4p2SL7mvYrdsMyR4MY9SSVX7vHMrTMnyTmmD7Q8pGkNIXGLeqjbCZ+HaZ1Fv1aSCCXfYz2EobRwdswzbt07zhGzq0atDXHvttVi2bBlGRkawYsUK3H333W2dd+ONN0IpheOPP77zRnuMRI76DLdoKNkZXDDExlZhLiTJYHJgtnxMkgwmFbbSdSkIki/jZo+FZEzICD2arDApo4qMbZsJnCQ2TFAkWeHBqzCDpyUp3AdusVW3SKsrpuiIliBwop/sYGn6CYUgIpbsmMFc9n1ED5M9r3J1qEcSHpZj8hMlRfwegbx5KlXaDAawCPmoJ0JVMuEGZtFmhIy58E54XbAyLdursUHJ/RIx4iOPxT57u2P76hAsLdLJYOAlgpP/vhPIQYgEKepiYJowZGgtlofUznmzBIMj0zv40o8K2V/+8pexZs0arF27Fvfccw9e8pKX4Oijj66U4wnx8MMP433vex+OPPLICVxx75DIUZ9hFzHlAbIo/MG1CIgIkQ61sTeCBCkIvDG68jM5YmTIhEcySoJLEKZ6GV7Pq9DtulXsAxluuyCo3MgAnofMkRVomQpJcF4cJT7bJTZKsWWSJLeC8Nk+IPikxvSV8zZxnzrvlfPYMRljD1loM5MT1s37nU4bqrNbuT84X+oJyQYgvEliP5GbZi/JkrCVKNBjSBB7hexDUK42770nMYDXERTy94n2KvlMcl9EPsxBqrRHcKFX75g8h1y4D1VR3qla6YmeI20VNnfIZiqeoe7UdA+vr1uEPN0J4+uaTQhzHBPspKbTTz8dBx10EK677jrMnTsXX/jCF2rPKYoCJ510Ei6++GLst99+U2htPRI56jMy4aWw64uRCKuRCfvIcBsg9pe+DA/uRH4YK5QRpEUO4BUZEq+YnkK2V4prcd4hK1NKGeGJkmu+yTCfJCYy3MVEx3qFZPiK/IHLEEhHPku3Bhtgbfc8IZKgWkIiCAvgE6uwTSZBluSRT4T4HAT7BdmrkJhSEq2gPaC6L3gRtD3aPOEJIgIZe0meI21h/ahpE1KGdZoXIgQAoq2YHqmvQubgj89sV6gnsMteClVDWOH7yvXU7hdtd8Fm+uodqkXs2hLq0Bxr9tuE1qAevdrE2NgYNm7ciFWrVtl9WZZh1apV2LBhQ+15l1xyCfbee2+cccYZHVzc5GJ6+wRnAWw+BScPZyIhl8gVQZQJwlk4M0oMsApwdXLgzrGDodFTljqBuuRp9QTy1vpyMv4MLfJliFwSc8ZLgZSuPQafZ5K27RT3glwCNZSQKQHV0DJcRVuVtoYRsszNniMyiev6+myVaXASMSdkG4NEMrLuW530WyEVnGRt6woJPW60FbKynzgPSN4bkWgMVO2RdsUSqYPBmqe9e3lGgKhWDU6mMdPhye0syeRNiwFazuLiffx09JLJybeRbQvss8ncvCVy6mtITLSfvFl4wl57gdJu8t8LG0wPGBFyW3m+951V1eu0+/0+bxeVNdFKaiE9FRD9FSsxUEcQ63TNMsyZNwMSsnt0W8I1RGPFkJ944gkURYFFixZ5+xctWoT7778/qvd73/sePv/5z2PTpk29MbRHSJ6jfsOMo8hdErF7kQv/ENxW5qGEOSkctioBt/K70FMGeigIy5kwm2uT9ZELmYUy1jsjZcN9wWdZsVmG00IZmw9kwmh5Ibxs5CVt22RoliGXQ+W9INri90UgQwS7kKq1q4ALh4lwnOhLzyMkvUJAlXxJYhMJg1U+e/vJSyL2EpzN9woiZMZLrjiCbLY8FlZIn7RXfF/DfpNbYU+FCLXqgxgq/Sb2SyF7TXJQd0JucVuZNxUVDewqq7ZFk63HR1ulASYTYT9X2jV9FLVn9hGeTrBjFlXIXrp0KXbbbTf7Wrdu3YR1btu2DSeffDI++9nPYs899+yBlb1D8hz1Gaos7dIReso7r1NlZulwdWYi6zWxVZ3lL0/2uMBVkNarehg9yKzHp6Ing5DRnhJF6I1MaQo12vW2YnrIn+5vvBQKpasIzoO39LDIwZl/9ZYEz+sVIyFykMuCqfPhIMF1kaw3jvQq9tZ7FrTP94K9MjEPC58jvVXyesQ5ep06Iws9oFqPjCQQJen6VACIdKkEfT7bbOTt2mZV75nXdmgHIIqGuq39zhrvlD3G3qpwUK4tNAiRPyTP4YZ9e8L6S+FMqUrdoKpImyBnk7WtM0V1a6LNwslduxyGRqZ3hexerq32q1/9CgsWLLD7Y0to7bnnnmg0GtiyZYu3f8uWLdF1UB988EE8/PDDeP3rX2/3leY5ODAwgAceeAD777//xC6gSyRy1Ge4X/Ru5o7OtZFJ0m5AUqbWjS6iCNi1wwQxUGAZ6DGwJLjwDgBIPREZM3OsIsMkoyTrffDsKf1K1CDtDdPFIAtTS0fokW0Bngy4nhPBDxux5yIMg1kPiznX2ON7r0pUSEDhql9bjwEfZxRGH/ezJCXWA+HfJ3eDI7/aLZErq+cIWRuSAt9nuZ9M9JKsDTaUCO4/99HrH2lL7L0kkGXpIm6C8MRCZmFdIm/afwxe+/aPICMmzlexWe6zN6DCmbhvbK2gOlPsiSFjoXG2oRqfkIUz4NqulzQRRAkowdZdihXJlHIJHaEspnmf2Wf+BHUAWLBggUeOYhgaGsLhhx+OW2+91U7HL8sSt956K1avXl2RP/DAA/GTn/zE23fBBRdg27Zt+L//9/92v6B7D5DIUZ+hvSYknsnC21EEuSiAeX4r4Q0QA79SwbNdkogaPV7oIpL3EnpEMpmHEz5sTShJNap6JKQeawv89/K6wkrLkqQpce1FEXgfSr+fANenNt9G5jsZT10R60vzh/spHGxjxRfZnlBGeIhsAcYQnkeJIIuf23O9/ip9giH1SDIh+zFaQVpfO5Wlb5exh2TbwbkVstTqurx9wda+D3VUdVIgp7tMeX1VG74Kv78t2vFPC7xSkWPRZOvJGkfrPHKhjN2yIQrVRLOETlDkRb9NmHZYs2YNTj31VCxfvhxHHHEErrrqKjz77LM4/fTTAQCnnHIK9t13X6xbtw4jIyM4+OCDvfMXLlwIAJX9U41EjvoMsoO8TP40gzUTJB6UZYKwJB/yJfVUEn0BuyJ7SHCYaEiZQhATT4bcMRt2CtsS9hRF0FYkpCOrKVf6gDxS4Ntj9lkPENXLQOqA31+8pAjXW4olR8v2Y96gstTLfQQEyL+usCK1sRnBwMpemNIREsD0tyVKJMgRX7v37XL9KsN2gV1yQVfe5w21pata7Q38vpsmTohi3gpCsASLx2ScF8fGhmPVpsVab+RCV515aMj1m8fO6xEjRjGP0ZQlW8cIXvgc0DvNNrhnPsPHeNef4GPkOdM7IbuXYbV2ccIJJ+Dxxx/HhRdeiM2bN+Owww7D+vXrbZL2I488giyb/unOiRz1GXoGDQWkAiLpF3GyYwmB+eZGyQmcjCRhpRlgpecmKqNcHg6kjCFWPNMsCP1ZmVh+UCsZuKZgw3zSDilL+lgJ3xMliQwTJb6u2Fpf0kMVEk8vl0kck/0dkidxT7yZSbYtbZc9UsrlMsS9sOZxaErp/rB9ZIgPEWyyO8RWek9CIhe8VxQWQawhOYAJ3Tnb/f4O7LChOSZBVX3uekRIMKbLEkh/vCeIvm5FhiqeoZA0tD8CtEykNqqnrrJ1hBCLzbjnAqiSokSS2sWObTv6bUJrhPy3Wx0dYvXq1dEwGgDccccdLc/94he/2HmDk4BEjvqNQhASkoONEgQBiE+vh++hoIiMDNMBMEu5w04jJwIyXgBVTFm3hE38evfymySBEPk/MufIqImWBIAgIzEZYvLDesQ1SE9YpcaQICuW9AHeWmRA1RsUbmNeLtm+HPxinjCx1WvQ8fR6QSgzBHpkAnWgT3rO5L4YGam7LuEtjK2bFr2GaNhJQBJbBvd9OMYS3A5LAiNC5J1gt17Ok+e1Qpx8hXaFRK4biLbkrLMKOQvtmQoEnsgqcZLHYuicKM52DM8d6rcJLdEPz9GugkSO+gw71VgSByKxkr186JILd4GAMnOeHTlYsox1NohvNycr86DEJMQWLiSYhcu0jEmqRlY6UkRwRM6TYU+GsZmTFZV4z0SlNA9yDmMBvs11enhQDQfyWHKzN+ijXkaGw6Q3ThKkoA2C9rhYlLrAok7+FX1O0N4fG2YhS5a8MKD7Qlh9gLA95vEKB/vQOxeQPQJcYje5cF10VlmoM0YsKJCXITNLlENZ23vVtoSQuzy/zUoBx1bjuUfwQqLQ2RPfn42nzbe5glKV/Y52pH6CYINkw50QnVk6+vUARTPlHO2qSOSoz9DT4QVxkF6KMMwD+IO9JTf8gj+IyXybhgoSjfmBCjeQyeRmT4YcqVEy30kSJHJEjIKBNLSLD8WStj3CEHpPpLcgGPzlPtlXrfpSnMsFFS1hAALPkbOLDPkkpbzoFQ/qjqAWvi2mv20xRi6sqBszXwhuR9y30IsT2m76Us4kCz0+fF1xDhGQoSgRCshTSDhUKBvc65CoWgahglvGJISqTp6QhFQvxI+5IbCxoiQOufirJEXR9ddEM/0FG6LE/aszaloYvEug7HsRz3HQC6I+zS9xspDIUZ/hckpQHcBDj4g7qfo5NnjJbaw6tH2ItqggXZENZGKyWTCgx4gMI6ztI/uAvTlSttYu+P0YtsF9GeoxV6TKEi7UxHvhak4RPPKhRUqQmUbGe20IjZQhLeTGIi+BWtgjyYKclibvSYQkcv6Ps8eFynjLqCRSh4RSotXnujCb6DP3MBWDMH93WM4mWVf1cB0grp8VJ0KRfXVC45KFmrPND45KXaKQW9S97xZ1ZLgW0l0VIbYJk4ahOdM7rJbIUfeY/injuzjsul7h7CZOEJYESQ5ksWO8n/WExyU56FRGHm9HJkZUxpOv6wN5bsx2s48i/USG9Nh1vqSssIsMESHizwAVgryUpa1JxcTIKHQDPsGSXTsLEeS2LCcJj/eCa0+Sl9Bm4dkAiYrUFCRWswx8olQhjzHCE5KmqIyvxguzyWvwntBmnTMqjYg+Ztd7E3qinhqvvaD/AEQrW3dDFnyT622YLMgfFeE1tkQ7o+EsHe0mATtnUYXs2YbkOeo3eGmPOi8Mv+R0+LAAIuA/OKUsUCUdvM8LwUVkYnqybHw9oYzQQ0Su4rOUFXpIKU0aQ+8Z76skQMPKyGnUihPCzbk8K8vJlJZgsGfBztwSbZGUKcW6ZbbKdAn3iz30ZCl3n9lj5NxTrh3U7BP3RQFeReqKB0jooWDbclZZeN+VCc2EpI4vJ7Q55sWz3iFHxGTIzDl1Wkx5ryMlsn1pc+16Z3H9tfWKSHit2lfXW4REKNbfnkEpVNYPzJlXrRI9nZASsrtHIkd9hqvJQmKfGD9F6MSSArHmFxnipMIBI8x3CckUkwzbaM0v1G70MJkJ7OGlMEjIqKJwEQtJXOSgzZ4S208mlOUt4SH6zxAdzpNSZabVcDI5y9hoBAlyxcnoflueTCVp228/SkLktcQIsOhLd//jepi86cVk4dvCCO+JhOBs3rjK+2QoyxIj5foUcIQwmtPDIcXSXlbYPletHtc7JK8hrqj6ne2AJIT5RXZ/X3NJZNv8vxf2t7j2cXUkTBZGd+b9NqE1zKNwwjpmIRI56jNUSSjZGwEe60XoRj4AY/k1ZmtXaJcDSDB1m5N27XmB96UiU9ETkYnpMWRCsRdI6KHgGnhwpyxzucg8Y4+9F9KjZHpHybbDitsylGTtKeFKFajKOAMOd2UIkqQj5Eb2v00wDu6J9HDZm+2TTDKfLQ8hd10k9YSDfxgqqx0ga2zma64Lp0kvmOwf7+TIVm48MschQPvNtvemUgSy7hr8HUGfj3O+p6o660zaFcVUOGWi5M8eFFshY/sleY36BZX6fZdFIkd9Bpl1yiqJvY2G8Qrp6e0m1Vd4VsyztPLLm7wxVYeJ4GZimYFfy5hjGZMrRGSknqqMbtfk6piDzqMRPDxEOM1eH9tdlKDMhEZMX/CitW7ALm3/EOnPiowenv5v7Kl4RLTR7jhfQxYQgVLYxKRKidS8mDcn9AbxtQoZ3T3km2W8RLJ/wkrUti9jbcZIc1QW4l7J/jDEwjol5OAs34uTxvFWyO+VbLO2anWF9wR9GRJEbzZavaJWS3wEJuurtaHYoKyBVD2ZHGRcYhT7XNeJCVOFwWm/8Cz5JUe61DEbkchR32HZBrRHg8yYLEJThlRIzzpA+rMotqgLCMINdFSCSvYcadmKDOuZgAz7cxRxqKROBpa4ECdS24FdidCUHgiJF4OFVMTEJ9DTaAQzw5ztljhZTxREeIjcPQi9YHB9XYGx3ctbgrhv3i32SQyZffZq5aAf6LEhyDoCJPVHvEt+7pqUFx6vymwuvvlCD1wbZHWgSkAo8MG0Gr+962lF7oRtXTynK7WSIjPgKvWTeomYV6hy7aoFQQqRPEXTBTu2TfOEbPlvPREdsxCJHPUbxN4J4zWxU7hFnM16kPTgTnadMj4f4gusBDEhu+gqE5eKjNDjZJwXJZRxHpqqjG6Tw1dCpii1zXL9Ni7waAc9ce1hsrUKZLwq2uZlZ7MFJMjrJ+X6q5IAj2rbYa4VrwckBm0FRJOkVZY54mRIFIXnGpnodyJ8H+6LETl73Pyx/UuODCoV7+eKq03qkHvN90mE9qJVq60d48BLpFaB3e14q2pAqITKukr+jsl0itCrWBUw+lvJJExHzJk3p98mJEwSEjnqM2zuhR2g4f/ClyRCepKIgIaoT2RzX9grIKa+K5hBmKoydgwiISNIVCBjDGstYwd8MZpwYjcJPfJamcx5fUBxGYhtLMwUIxfcF+H+8DxR70gOVpqjjbMGGbkwmFcyICRZ5r3nZYoRM9mXMeIR4TLu3pr3tiAn2xBLpA6ZAYlL1/v0rXXXK6tDV/J15MfxvCZR71Gw9WahCRvIT96vI2cdJ39PFOE95AYJgScu8Mq1mTc1ucYndIKx0bF+m9ASabZa90jkqM9QlTEqHOQjAyYfs4OtlIHbShISSzTmwU+Ss1YybGlMhoSM1cehGWNTGEbzCIHydbeSqXiXgm3Mo1Ky9ypIJrd9T2aj+1SPY6LiNLVOkrZDIBOasJinRHgPQ3vqSF7ogfCIn7gnVlZa5t7zOm8hfO+PX/jSFwy2sn9i1xleg6doYp4Sj7DVkCQ/+bsfiHx3vWMRuYQZgWl/twgT59KJHCX0A5WcknDrBN3WehUoMgBB/9IOq0zbgoQQhEf5+vmzHFSjP+zlYCPOK8mFvDyba64BiNd4Cq9TesykjM01KivyBHiJhAS4BGgmQnAekUoCtCBDYZJ0BdJGaZu8ZinbKoGaSQ17hWz4kPuaLed+l/dNsBYrF3on5NjMS3Q4UhF6V7zq0IT6hGVi+4EKqbX3W9hVVyG7DVgSx/YEOURtkblJhbk+QNxrbjzwJlVOnaUj0QzF4Mj0rpCdPEfdI5GjPkPBVW6OJvYqXRsolvzrkYIYwWIZwA9psYznXSI/7GQNbOGhkd4qo5OC45XyAuBf8uQRIxW0Y2XE9VVk5PVxe+K9DYkxYZIJ0fL6WEbaFrnOSpJ8HYGt+yzzfSRiS2SE3iWgOqvMG2TDAdcnhh5RZYeeISxMiiqLuUbURcOKMlRklz/h44IU+IowHtpJjq4lQr1A5LsblQFahw7DaZOpPtEug53bdvTbhIRJQiJH/Ybx/iggntgriYbZVhAbzMN9sXXKwkrW8n2EiMSIjifDNgM2LBV6VTxiJMhMtPJzCxm/sjVcro84RoGdsfXFLHGQidStCE9df8c8f9455o8YI11tpgjZsZyPw2OhPeMPtrYYJveP9eywKeRtwzG8cgGszoZLFbzlU1rZVbGvNQmoXRdO2h4222vEvuvhPq8PPGFAzkAb7ztVbRyJKE1/jCwY6bcJrSF/n0xExyxEIkd9BqG6HpYdyAGX0CtIgBYRg31wzKkRMqaqtidRU43ZkjDjIfFkBGEhIUuArX7NhMMrcsgyQVvRnJE2ZLifQs+P9z74VV/r+ZEELzYVX7YlyV5FBq6Da70wBFdigKqkicOflcrPFUUAh8XIVaIOixxy2KlCJnw1EftCKFjXlSVdYciOvxUxQtThE1aob+nRmiyEHiF5/+MnGPkWPzggdI5LkGbpiDTDMLa92W8TWiKF1bpHWni23wgHZ/O+Et4xLzn1u1K/RZ4byvD5LCPaD9uq6KEgzGU+q3H0AAHRoYAIBjqjMnJdtVYyYV/KNsJj7e6T+2Pv5aDtnS/lQ/1ChuwO/aq0RdYbVg93fmXplfDcccfjwPa6PgHQnmcjQvwrTcYXzK3YPJUP6CghRaQPqIWMIIotQ24JMxlZFvshkbArIJGjPiMMM8WIQHCCPUeFv0DFsXFleI0zEuG6kHxEZFQrPTLJ2myj9tRcl+2DMJlZ9omUkTbLz+GxOoIQI0aVY9w+XN6W1MUEiImOtw1l5GBZt2CFO5FI56RFpYR90lNkl0yR9ofX7StyL6tHlGKQBM9dUI3Czgb9sDYSEfl1iPpBjBixvuCk8lZ5SCr0pqGGVCXsCmgMTvPgC/XoNQsxY8jRk08+iZNOOgkLFizAwoULccYZZ+CZZ55pec5rXvMaKKW81zve8Q5P5pFHHsFxxx2HuXPnYu+998b73/9+5PnULSZY+5wNCQHvE1svBCQIyWTKkLSnTk8L20MPVNQrEYa75LV7fQRfLkaIQpnQQxPql16dkGDI2YF29l8wmNvzBVEKR3mS1bER9ZxwvpDdllUPi+9l9JuokED7ioQNPcLaSlF3iHmFqt7JnjTVOxChkiXvfbdqjI19t6bVhSX0EqPbp3mFbLjQWrev2YppTnsdTjrpJDz66KO45ZZb0Gw2cfrpp+Oss87CDTfc0PK8M888E5dccon9PHfuXPu+KAocd9xxWLx4MX7wgx/g0UcfxSmnnILBwUFcdtllk3YtEn59u2BgB9yU/JhM6IWJnV+neyIydtCHy92RUMqfgRXOpIvJ8DHZtq2qjXjCsxxYvR/sFGzrZILzuU1bKFEc9MiVErqDY14ycmT2Hxzx0Y6oeLVpVmenqtuT9TnV6/EaqOa12M/CI6J43RnbWM3gHmukAwibvVIA4Wdrfh+eyPK7yZ1bIentEMZZPJrMQsyZnypk76qYEeTovvvuw/r16/HDH/4Qy5cvBwBcc801OPbYY3HFFVdgyZIltefOnTsXixcvjh77zne+g5/97Gf47ne/i0WLFuGwww7DpZdeig9+8IO46KKLMDQ0RTUswsHAG/hqiIoc0FvpHS/5U8p4A0QLGUAYKGywhEeQjlAGaE+GIDxUECkuYT+Qk5HnQsjz51IIhDqJ22T5UpMgJZXGQoKuMd1NXDvIJEn71goCJLwn4VR+QRrqw0whSas0pLeV2VRUlbEerTiZq0MYFuN9/o5qs35XRuyfan4x7v+J3D9erlWaZTabsPOZ0X6b0Boxz3w3OmYhZkRYbcOGDVi4cKElRgCwatUqZFmGu+66q+W5119/Pfbcc08cfPDBOP/887F9+3ZP7yGHHIJFixbZfUcffTS2bt2Kn/70p72/kAhUGBIJCYINh0RkwnBSjEh1IiPlWspALN/RynbxmohMrH0ObVAgU+kb+YokjZPUQU4m1h92Vzy8BREG4/Nqk+RD0mBPq8pHYb8bwX4lB+fQ9vYfcu16b1rZW1nOIyY3lc/dSts132+9o04JqpQ3YbaiMdjotwktMdGQ2mwOrc0Iz9HmzZux9957e/sGBgawxx57YPPmzbXnveUtb8Hznvc8LFmyBP/+7/+OD37wg3jggQfwta99zeqVxAiA/dxK7+joKEZH3S+GrVu3dnxNDD9xluBXPeYtIjJwz+9WIaUsDF/1SEYbH9jB3qXYhdZcHyLX5xU7ZBlV1UOE6JT30H4Vhsqk0WYbrdjse4Pctso5vLBR0JdhPSEbShq3n3gfRTx64rqk7eQpimwrjfl7jDeojti0qqId9kE05byVOb1E2Gd1ZC/0sMU8otWTWjXcgZEJMx2NxozwLyR0gb6So/POOw+f+MQnWsrcd999Xes/66yz7PtDDjkE++yzD4466ig8+OCD2H///bvWu27dOlx88cVdny+hn8k84iISPoqMJmHITebvhMSihBsobDumIUlGuHEZprJ64AiIPd8M4EoY1MqFq1TNdYkByfaBaKNy7aKfbB94HefLA46IKBgyUePREPbpS5XkjOylua1PEqr7ZD+HMiGRCfogHGQp6CffkNpr7waS3NWGzQJyGJWZsCExUtgDnZXvS11ILRGdhNYY3THdw2qY+Nd4lv4b9JUcnXvuuTjttNNayuy3335YvHgxHnvsMW9/nud48skna/OJYlixYgUA4Be/+AX2339/LF68GHfffbcns2XLFgBoqff888/HmjVr7OetW7di6dKlbdshQUxMQm9Hpcqw2CfGJ8Vv7P4wQZiPiXODpGbJNfzTYucHORUxGbZZToeOXleQKGRkiKCnsFc8PmE/BdcotRHZooh8jC2XHhDnCTEyynl16ipIe56falIRLCGKHWcZjwRGrs8jS7F+qr/2dhB6gWQ5AL+pyLXbxYFDpRM2Sxo4gZNN4xR8tv9LES9c9H1CQmtM94RsVerXRHXMRvSVHO21117Ya6+9xpVbuXIlnnrqKWzcuBGHH344AOC2225DWZaW8LSDTZs2AQD22Wcfq/djH/sYHnvsMRu2u+WWW7BgwQIcdNBBtXqGh4cxPDzcdrutUR1RyPMclOJRHsgot5ZZTMbbWhLE5MMdVcJrZGXAMuZ8Sxr8gTqUgZEh6EFdVaZUiarOKq5HVWQcuanvJzYhGPjEWChDWV7Ip0amvisjMvpAdWcsvMMeqZi8pzgSv6vIt4+QCMnP4yVS2/XXwlyzSUNAYlp5kaTtngwb36rauD2xZn9CQj12PjPNp/K7330T0zELMSMCpi9+8YtxzDHH4Mwzz8Tdd9+N73//+1i9ejVOPPFEO1Pt17/+NQ488EDrCXrwwQdx6aWXYuPGjXj44YfxjW98A6eccgpe9apX4dBDDwUAvO51r8NBBx2Ek08+GT/+8Y/x7W9/GxdccAHOPvvsHpKf1tBEQH/7yHt4mxf59W0ofMBHEoPjdWVcnSIldOuN/qy5RhkOS64dgh1kVJ0MW0ZlQGY8Qds+65G2O0+Ok0GrPkB1wHfXVRH19sVlxnkayHpHxrY4amTaDoP17qlU8RCJJtoJg/Vler1uuEt55baV/6uEhN5gYHiw3yYkTBJmBDkC9KyzAw88EEcddRSOPfZYvPKVr8RnPvMZe7zZbOKBBx6ws9GGhobw3e9+F6973etw4IEH4txzz8Wb3vQm/NM//ZM9p9Fo4Jvf/CYajQZWrlyJt771rTjllFO8ukiTDZt+IokKmUEs/DEfyliCU/p5MCRJRpUAELHfxJCP0CtUxgiZTwjCooSuaGE1Jcqz3zRryUxJtdcuZaTtFdlAd2116IoThmq2EXj5PXXHyXmFZENe/5HwbrQzUE98MJdT7qNk0JqlIoy3Z2Z0iIgnp/Ye1PQ3f24rbymRpoQu0K8fDW0izVbrHjNithoA7LHHHi0LPi5btsx78C9duhR33nnnuHqf97zn4eabb+6JjRNCHZGoOV6VocoYAdVqMCQ9Ay3UExIJGW5qU8aNTS54FQ7Obkp9XcJyKBPIyj7gkEuNTOUBJvNrWiXAj7cmlgyRRWsgWcHwAj37YzPD7LUFaFlXSJgRlbOc0byppI+1IIf9hP1CRfqpZdgxDF8mJPQWzdGpW02hK1APvv+z9P9nxniOdlXU53oE3o9QhqhaaygmZ7Yta87QODKx8adOJqaH2tATC1N5MpFcoqDdeF8JkhObDh96fKRXR3rToqRJPnjqtjGTWj9spEeO+5Kn11fDj6K/PbPI88pFc7EqDbc0a2oh7a1bryxKjBDZN50uLGFXwtwFI/02IWGSMGM8R7s0eEZWOEXb/dSPD+wynyImw54UhB4G9rT4stECybHxhXWFM5wCz49s35eRRCliVyUROTKNnc8N9UgyFMpYu8LlQXgfy9QNqtSGTBVhWCtMgI7NGLMVspkMhTWmIJKkY2SSamSk3GRyBtlex4nUfCzijauQ0XZCZgkJk4PtW6d3QnYvwmKzNayWPEd9hvIe/C3mTI6X89LOeTIE1Uq27lyJVnkc7NWJDXieB8nIyDydysKokZG8NuekQ5la48NdFHxs/Vnua0WGZHNRmZgzityxikw06hQhTlOF8cKSbclMdxdXwmzG4Mg09y9Qj16zEIkc9RkUjoBhmEdI+om9fM44hKBStVqQJO+cGvLl2RPuj3yO6g7skfo8MmSZQrxN2QdyX7iN9hNqzmsHIbGhCiGKJcD7uVNuW0m2DwlPncmhOa0ur2r61CAaHg3uTTsyYfjSnRA22AurExK6AyXP5a6KaU57d31Ea8wAAbFox+vBxwwZqoToYnpqkogrpKJGj1XToq06mVb5Ii29ZK2OxfVpUTJm1Cc/h6Gv2Pva5mKJ61VT6vW0c4unA8JQWJ2H0HZK8F3rWKYOqS5RQv/RHGv224SWSGG17pE8R31GdaDsxh0QkpjQaxLzwghZT0+4L2ijhTeH+HOtjLS1/j+OKknQUakWx3wPj8vHHp8YxZphr1ClxEAYHhOXFtXXfhdMP4Qh2bCwJXkX795LGbuvHZngWG1V6/TLPaF/mDNvmidky3SKibxmIZLnqM/o/e/fmpG3lRemk3hMC0+Uinh1iEqxRlmoynlz5PvoshZRU6o6ozPlmLYF8jIBOlwk1SNM1gkm9AT7ZHuhzC4BXsbE3vLwuxOQpQrCfmqV/R9D3bFdqZMTZhp2TPcK2QldI5GjGYaWXo7WZ/aiddT9UvfJjGtrPFsr9Xgis7JkG3X65KysWs+O8t9TSVCZquppYYM9fzzsSmN2NHdMrlPGbrNYjlA79D+FyBJmJoZGpneF7BRW6x4prNZnxNONnIeiMg0biHpM2tnXm2UiqNYxECs8WJeoHHvvIncufNXq3GiIi4TXplWURnp4aj63kz8048f0dl3mYRisrdBnQsKujbKY5t9/6tFrFiJ5jvoMlSmURRklFuFnKVM3RVyuNh+Ti8mEkDrr7GKR0CZfmJXUXFd43BY0rhIi75xQn3RgSJlOyUzoGJuKh0IsoXkiOtyNqcpIhDlDtsI44nItQ2X2xrWQaYVZ+vRNmPEo8qLfJrRE8hx1j+Q56jOoJEsI5BpiYfJv1CNiEJOR08bD/BkpY6soe0bBk5XrqNlp6NaBUCU7sYrN4TZasTnm6anZP+6U93b/obs9b7LRrkcnJFeSzHCZhDCRui45PyZD4U0A6mOO06XzEhKmBiPPmeYJ2QldI3mOpgtCfiK9JiEB4B/qkQThcMv5M61kZHt1Mi0rLYeXEuqKXGc7emLndXTOTMF43qM6b1B4fixZ3ta5sl8IACFBMi6zUKbt3KFd6WYkJLSPHdt29NuE1ijJLTM1ER2zEMlz1G/UkKJaj8g453ctM56KFrOvapemqAunTcSmXs3cnuoZ4PHkMhg32viyteTJss3qPtmG3S+Kb8b0tIxJIpBJSJjdGJ471G8TWoN69JqFSOSoz+iKNLTwAI17Tt2xdklYC29QS9le/YNNNz0dtRmSlsh7vSPi8RnvBsbey89yG+qsi3/WtVEnk5Awu1A0p3fOUUL3SGG1PkNlys/bSegv2kmQnohMdH/EE1PxBHXqqenV9Pg0zT4hoQ7lNH92K/QgIbsnlsw8JHLUZ7QzvX7KMI1M6Qu6JT3hvlo9kuSQqRVk3tsQWyZkIeoJibygygyyoIBT277w8PxWMgkJCSGG5kz3sBqhtde5TR2zECms1md0VNBxln5Je4J2HxIyjFUnz8da6bMypSA0wZT3Sj5QC3vC90AkqbqFnpZI36uEhG6wM1XI3mWRPEf9RrvjUi/q4cx2jNt/oedGRfqdnDcn9BbZNoSMXNyXyyBoQURvfiz/SNpvl/AIvFAJCQlTjjnzhvttQkukOkfdI3mO+ozK2lwthfv0Le2m3amwNfTe1M4I68Am9vToDzV6yoi+MLFeVpAO5FqStHG8UVImkeWEhL5idGfebxNag3r0moVI5KjPGDes1moAjsq0o6cLdHJ+ZQZel223E75qp00Zvmqpqy5PKNZG7KnR6nNMTzvkpkXJ7hRmTUjoK9RsZQ6zACms1me0TMgOqxZzDosM67RKEO5EZjx0QuLkNPTxlrVoR09tAjRV+0Ie4/BTmNQc6vHaaFFPyOoJ7W2T7FQ8PxF97uIx/s+2fjyY0+y1hATG4LRfeJagJvgjaqLnz1QkctRn6FXgS/5QFYiFeMJj4xGsdmWqxo0vI+W82jyGiLQiDyERku/ravzUFUystAnXbjuerHEfADVrj4XeIRLy43mWZulDJyFhV8GObdM8Ibs0r4nqmIVI5KjfYO8GlPMKyYNhYq83KyniPQGqBCUqE0nsbelJCrwuMT2hvoq3xcjUrQUWenyk7eF1WV4RemFiHp4YSWnRVlg92iN47ULK13lb2vEKTTdPzXSyJSGhv5gzb06/TWiJ5DnqHokc9RkuIVt8AUMS4w2QkRwUlrGyUl8nMvBlAdhFSEPi00pPbZ4OHAGzx8X1hYQntL0uhydsp61/5hqbK8tqSJl2iEqnJKgOndQrSkhI6AfGRsf6bULCJCElZPcZ1WLJMhQUko92Fv+s8/wEMrWokem6pk6rUFKnerotVtgiqXnCSDPGEhJmK6b9fz/16NUhrr32WixbtgwjIyNYsWIF7r777lrZz372szjyyCOx++67Y/fdd8eqVatayk8VEjnqM6qOCg4NxRKEJXEaT2k3/7aR8BMngdfKdINWobtxzmtXf8fntdNfvfIKtYPkMUpImO4YHJkhFbIn+uoAX/7yl7FmzRqsXbsW99xzD17ykpfg6KOPxmOPPRaVv+OOO/Dnf/7nuP3227FhwwYsXboUr3vd6/DrX/+6Fz3QNRI56jPqnTzteHeslhrFqrVMW2iTkHWsNg3+CQkJMxs7t+3otwnTDp/61Kdw5pln4vTTT8dBBx2E6667DnPnzsUXvvCFqPz111+Pd77znTjssMNw4IEH4nOf+xzKssStt946xZb7SOSo36glCaEHqaUS8V5OnW+RozMuJsthHAulTQfndCJrCQkJnWFkwUi/TWgJrpA90Ve7GBsbw8aNG7Fq1Sq7L8syrFq1Chs2bGhLx/bt29FsNrHHHnt0erk9RUrI7jPGrZAdTRBurdHfdotuQlO9aqtfmG4zwxISEqYzxrY3+21Ca3QRFovqALB161Zv9/DwMIaH/eVTnnjiCRRFgUWLFnn7Fy1ahPvvv7+t5j74wQ9iyZIlHsHqB5LnqM/oaOHZhEnGeEnfCQkJCQ5ZNnueF0uXLsVuu+1mX+vWret5Gx//+Mdx44034qabbsLISH+9cslz1Ge0rJCd0Aek+5GQkNAeGoPTewhVpX5NVAcA/OpXv8KCBQvs/tBrBAB77rknGo0GtmzZ4u3fsmULFi9e3LKdK664Ah//+Mfx3e9+F4ceeujEjO4BZozn6Mknn8RJJ52EBQsWYOHChTjjjDPwzDPP1Mo//PDDUEpFX1/5ylesXOz4jTfeOBWXZNtPSEhISJh5GN0+zStk93C22oIFC7xXjBwNDQ3h8MMP95KpObl65cqVtWZ+8pOfxKWXXor169dj+fLlve+HLjC9aa/ASSedhEcffRS33HILms0mTj/9dJx11lm44YYbovJLly7Fo48+6u37zGc+g8svvxx/+Id/6O3/q7/6KxxzzDH288KFC3tufx2S5yghISFhZmLO/OldIbsfWLNmDU499VQsX74cRxxxBK666io8++yzOP300wEAp5xyCvbdd18blvvEJz6BCy+8EDfccAOWLVuGzZs3AwDmzZuHefPm9e06ZgQ5uu+++7B+/Xr88Ic/tKzymmuuwbHHHosrrrgCS5YsqZzTaDQqbrybbroJb37zmysdvnDhwnFdfgkJCQkJCRI7nxnttwmt0WURx4qODnDCCSfg8ccfx4UXXojNmzfjsMMOw/r1622S9iOPPIIsc0GrT3/60xgbG8Of/umfenrWrl2Liy66aILGd48ZQY42bNiAhQsXeu62VatWIcsy3HXXXXjjG984ro6NGzdi06ZNuPbaayvHzj77bLz97W/Hfvvth3e84x04/fTTpyzcpZRK3qOEhISEGYjGYKPfJrREv9ZWW716NVavXh09dscdd3ifH3744S6smnzMCHK0efNm7L333t6+gYEB7LHHHtYFNx4+//nP48UvfjFe/vKXe/svueQSvPa1r8XcuXPxne98B+985zvxzDPP4N3vfnetrtHRUYyOul8M4RTHTpCIUUJCQsLMRKMxzdN2eziVf7ahr3f2vPPOq02a5le7tRFaYceOHbjhhhtwxhlnVI595CMfwSte8Qq89KUvxQc/+EF84AMfwOWXX95S37p167wpjUuXLu3atpSQnZCQkDAzMbpjmofVErpGXz1H5557Lk477bSWMvvttx8WL15cWZclz3M8+eSTbeUKffWrX8X27dtxyimnjCu7YsUKXHrppRgdHY1m4wPA+eefjzVr1tjPW7du7ZogJc9RQkJCwszEtE/IJgATnMo/W6ub9JUc7bXXXthrr73GlVu5ciWeeuopbNy4EYcffjgA4LbbbkNZllixYsW453/+85/HG97whrba2rRpE3bfffdaYgTEK4MmJCQkJMwu7Hxmek/l71fO0a6AGZFz9OIXvxjHHHMMzjzzTFx33XVoNptYvXo1TjzxRDtT7de//jWOOuoo/PVf/zWOOOIIe+4vfvEL/Mu//Atuvvnmit5/+qd/wpYtW/Cyl70MIyMjuOWWW3DZZZfhfe9735Rdm4IafwmRhISEhIRph4HhwX6bkDBJmBHkCNAr965evRpHHXUUsizDm970Jlx99dX2eLPZxAMPPIDt27d7533hC1/A7/7u7+J1r3tdRefg4CCuvfZanHPOOSAiHHDAAXZF4alCIkYJCQkJMxTT3atC6EFCdk8smXGYMeRojz32qC34CADLli2L5u9cdtlluOyyy6LnHHPMMV7xx34gTeVPSEhImJlojub9NqE10my1rjHN5yHu+kjEKCEhIWFmYu6C/i6OmjB5mDGeo4SEhISEhOmE7Vund0I2SgATrRYz0dluMxSJHPUZKSE7ISEhYWZicGR6D6Fptlr3mN53dhYghdUSEhISZihomhfxTTlHXSPlHPUZKpvm/1wJCQkJCVE0x5r9NiFhkpA8R31G8hwlJCQkzEzMmTfNE7KT56hrJHLUZyjM2jISCQkJCTMaO6Z5hexEjrpHCqslJCQkJCR0gaGRVCF7V0XyHPUZs5SUJyQkJMx4lMU0f4CnqfxdI5GjPkNlClRO83+whISEhIQKirzotwktkabyd48UVuszUkJ2QkJCwszEyHOmeUJ2QtdInqM+Q6kUWktISEiYidixbUe/TWiNlJDdNRI56jcozVdLSEhImIkYnjvUbxNaoyRATXB8maVpHyms1meksFpCQkLCzETRnN45RwndI3mO+oyUkJ2QkJAwM1FO92d3Cqt1jUSO+ozkOUpISEiYmRiaM83DaugBOZqlaR+JHPUZSqlEkBISEhJmIHamCtm7LFLOUb8xO793CQkJCTMec+YN99uEhElC8hz1GZTYUUJCQsKMxOjOvN8mtEZJmPAv8OmeVzVJSOSoz0hhtYSEhISZCTXdf9xSqV8T1TELkcJqfUYiRgkJCQkzE4Np4dldFslz1Gckz1FCQkLCzMSObSkhe1dFIkf9xuz83iUkJCTMeMyZN6ffJrRGyjnqGims1mekhOyEhISEmYmx0bF+m5AwSUieoz4jhdUSEhISZiZUvw0YDyms1jUSOeozEjFKSEhImJkYHJnmFbIJPSBHPbFkxiGF1foMpab9b4+EhISEhAh2btvRbxMSJgnJc9RvzFJWnpCQkDDTMbJgpN8mtEYKq3WNRI76jJSQnZCQkDAzMba92W8TWqMsAUywiGM5O4tAJnLUZ6SE7ISEhISZiSyb5mkRyXPUNVLOUZ+RiFFCQkLCzERjMPkXdlXMGHL0sY99DC9/+csxd+5cLFy4sK1ziAgXXngh9tlnH8yZMwerVq3Cz3/+c0/mySefxEknnYQFCxZg4cKFOOOMM/DMM89MwhXEkRKyExISEmYmRrfPkArZE33NQswYcjQ2NoY/+7M/w//+3/+77XM++clP4uqrr8Z1112Hu+66C895znNw9NFHY+dO94U+6aST8NOf/hS33HILvvnNb+Jf/uVfcNZZZ03GJUSRPEcJCQkJMxNz5s+ACtm9eM1CzBif4MUXXwwA+OIXv9iWPBHhqquuwgUXXIA//uM/BgD89V//NRYtWoSvf/3rOPHEE3Hfffdh/fr1+OEPf4jly5cDAK655hoce+yxuOKKK7BkyZJJuZaEhISEhJmPnc+M9tuEhEnCjPEcdYqHHnoImzdvxqpVq+y+3XbbDStWrMCGDRsAABs2bMDChQstMQKAVatWIcsy3HXXXVNiZwqrJSQkJMxMNAYb/TahJYjKnrxmI2aM56hTbN68GQCwaNEib/+iRYvssc2bN2Pvvff2jg8MDGCPPfawMjGMjo5idNT9Yti6dWuvzE5ISEhImCEYGJje5AjUg7DYLE396Kvn6LzzzoNSquXr/vvv76eJUaxbtw677babfS1durRrXfN3f04PLUtISEhImCrsufR3+m1CwiShr56jc889F6eddlpLmf32268r3YsXLwYAbNmyBfvss4/dv2XLFhx22GFW5rHHHvPOy/McTz75pD0/hvPPPx9r1qyxn7du3do1QTr27avw5cv/ETRLk94SEhISZhwUsHCv3fDS1x7cb0tagwgTXoZhlnqO+kqO9tprL+y1116Tovv5z38+Fi9ejFtvvdWSoa1bt+Kuu+6yM95WrlyJp556Chs3bsThhx8OALjttttQliVWrFhRq3t4eBjDw8M9sfMtF7wJP/neffjp9x/oib6EhISEhMmDUsDw3GFc+JVzMTDd6xyVJaAmmDOUco6mNx555BE8+eSTeOSRR1AUBTZt2gQAOOCAAzBv3jwAwIEHHoh169bhjW98I5RSeO9734uPfvSjeMELXoDnP//5+MhHPoIlS5bg+OOPBwC8+MUvxjHHHIMzzzwT1113HZrNJlavXo0TTzxxymaqzXnOCK6842LcffO9+NG3f4zHHnkcg8MDGNvZxPCcIYzuaGJweAB5M9ehxkaGollgeGQQozvGMDRnCGM7xjA4NIAiLwClkGUZirxwekaGMDbaxMCQ1pM1dDS1LEoMDA2iubOJoTmDWs/wIPIxIVOWGBgcQHOU7RnD0MggmqM5GgOZXvS5KNEYaKA5ljt75gwh39lEY7CBsigBAhoDmSczNDKIsZ1NDAwOoCxLgAjZwACKsRyDcwYxtn0Mg3O0fQNDAyiLEkSEgaCtIWPX4NAAylz/I2cDGfJmodvYMYahkSGM7jQyhZExfTk4MojR7WPm+kYxNDyIgmUyhTwvMDQ8hLGdY9V7kikopft7aHgQYzvFPRkeQN4soDKFTCkUeYnBoQGMjeb6/u0cw9DwIJp8T8j099AAmjtdf1fuSVGiMTSAfDTH8JzB6j0hApXk7smItn1wZAj5aBPZQKY9leE92T6mvwd192REf5+G+PvE9xZAo5Gh2TRtxe6Jcv09xP09dxg7t+/EoOlv5d2TAYztaGJozpC+/4MN5HkBpRSyzPSl6e/hOUMYZZuLAlCAUhnKosDgkLsn+nvUqLknTft9DPu7KEoMDof3ZAj5WDN6T4bmaNsHRwbRHG1iYKCBkggoRX8H96QxkGkZviejkf+ToQEURQFFiN+TnWP6/8T0t70nw0bPXL1lGTIy/D8wtn0Mw3P19em2SigF/TwJ7snYjiYGh01fmudS2Sxq74lSClDK3BNps9ZTNAuoLAOUeS6ZZ071nigAyvs/cf9v4r4poMyNTPSeDKCkElQCA4MZmqOFfQbavhxsoDQe/cZAA/lojvm/Mw/7v2QZVp38Kuy254JJHx8S+ocZQ44uvPBCfOlLX7KfX/rSlwIAbr/9drzmNa8BADzwwAN4+umnrcwHPvABPPvsszjrrLPw1FNP4ZWvfCXWr1+PkRG3WOD111+P1atX46ijjkKWZXjTm96Eq6++emouyqDRaGDl65dj5euXjy+ckJCQkJDQDlJYrWsoSlUIJ4ytW7dit912w9NPP40FC9KviYSEhISEekz2mMH6Xzv3RAyooQnpymkMt22/cdaNbzPGc5SQkJCQkJDQAZLnqGvsskUgExISEhISEhK6QfIcJSQkJCQk7IooCVDJc9QNEjlKSEhISEjYFUEEYKJT+WcnOUphtYSEhISEhIQEgUSOEhISEhISdkFQST15dYprr70Wy5Ytw8jICFasWIG77767pfxXvvIVHHjggRgZGcEhhxyCm2++udtL7hkSOUpISEhISNgVQWVvXh3gy1/+MtasWYO1a9finnvuwUte8hIcffTRlaW6GD/4wQ/w53/+5zjjjDNw77334vjjj8fxxx+P//iP/+hFD3SNVOeoB0h1jhISEhIS2sVU1Tn6H40/wYAanJCunJq4vfha27auWLECf/AHf4C/+Iu/AKCr/i9duhTvete7cN5551XkTzjhBDz77LP45je/afe97GUvw2GHHYbrrrtuQrZPBMlzlJCQkJCQsAtiqsNqY2Nj2LhxI1atWmX3ZVmGVatWYcOGDdFzNmzY4MkDwNFHH10rP1VIs9USEhISEhJ2RVCJic9W0+dv3brV2x1bgP2JJ55AURRYtGiRt3/RokW4//77o+o3b94cld+8efPE7J4gEjnqATgyGX55EhISEhISQvBYMdlZLTmaEy6QnaMJAFi6dKm3f+3atbjooosmpnwaI5GjHmDbtm0Aql+ehISEhISEOmzbtg277bZbz/UODQ1h8eLF+N7m3sz6Wrx4MX784x97i7aHXiMA2HPPPdFoNLBlyxZv/5YtW7B48eJa3Z3ITxUSOeoBlixZgl/96leYP38+lFJd69m6dSuWLl2KX/3qVymxO4LUP62R+qc1Uv+0Ruqf8dGrPiIibNu2DUuWLOmhdQ4jIyN46KGHMDY21hN9Q0NDHjFqJXf44Yfj1ltvxfHHHw9AJ2TfeuutWL16dfSclStX4tZbb8V73/teu++WW27BypUre2F610jkqAfIsgy/+7u/2zN9CxYsSA+nFkj90xqpf1oj9U9rpP4ZH73oo8nwGEmMjIy0RWh6jTVr1uDUU0/F8uXLccQRR+Cqq67Cs88+i9NPPx0AcMopp2DffffFunXrAADvec978OpXvxpXXnkljjvuONx444340Y9+hM985jNTbrtEIkcJCQkJCQkJPcEJJ5yAxx9/HBdeeCE2b96Mww47DOvXr7dJ14888giyzE2Uf/nLX44bbrgBF1xwAT70oQ/hBS94Ab7+9a/j4IMP7tclAEh1jqYVUr2k1kj90xqpf1oj9U9rpP4ZH6mPZg9SnaNphOHhYaxduzaa6JaQ+mc8pP5pjdQ/rZH6Z3ykPpo9SJ6jhISEhISEhASB5DlKSEhISEhISBBI5CghISEhISEhQSCRo4SEhISEhIQEgUSOphjXXnstli1bhpGREaxYsQJ33313S/mvfOUrOPDAAzEyMoJDDjkEN9/cm4qn0xWd9M9nP/tZHHnkkdh9992x++67Y9WqVeP250xHp98fxo033gillC3Mtqui0/556qmncPbZZ2OfffbB8PAwXvjCF+7S/2Od9s9VV12FF73oRZgzZw6WLl2Kc845Bzt37pwia6cW//Iv/4LXv/71WLJkCZRS+PrXvz7uOXfccQd+//d/H8PDwzjggAPwxS9+cdLtTJgiUMKU4cYbb6ShoSH6whe+QD/96U/pzDPPpIULF9KWLVui8t///vep0WjQJz/5SfrZz35GF1xwAQ0ODtJPfvKTKbZ8atBp/7zlLW+ha6+9lu69916677776LTTTqPddtuN/r//7/+bYsunBp32D+Ohhx6ifffdl4488kj64z/+46kxtg/otH9GR0dp+fLldOyxx9L3vvc9euihh+iOO+6gTZs2TbHlU4NO++f666+n4eFhuv766+mhhx6ib3/727TPPvvQOeecM8WWTw1uvvlm+vCHP0xf+9rXCADddNNNLeV/+ctf0ty5c2nNmjX0s5/9jK655hpqNBq0fv36qTE4YVKRyNEU4ogjjqCzzz7bfi6KgpYsWULr1q2Lyr/5zW+m4447ztu3YsUK+l//639Nqp39Qqf9EyLPc5o/fz596UtfmiwT+4pu+ifPc3r5y19On/vc5+jUU0/dpclRp/3z6U9/mvbbbz8aGxubKhP7ik775+yzz6bXvva13r41a9bQK17xikm1czqgHXL0gQ98gH7v937P23fCCSfQ0UcfPYmWJUwVUlhtijA2NoaNGzdi1apVdl+WZVi1ahU2bNgQPWfDhg2ePAAcffTRtfIzGd30T4jt27ej2Wxijz32mCwz+4Zu++eSSy7B3nvvjTPOOGMqzOwbuumfb3zjG1i5ciXOPvtsLFq0CAcffDAuu+wyFEUxVWZPGbrpn5e//OXYuHGjDb398pe/xM0334xjjz12Smye7phNz+fZiLR8yBThiSeeQFEUtoQ6Y9GiRbj//vuj52zevDkqv3nz5kmzs1/opn9CfPCDH8SSJUsqD6xdAd30z/e+9z18/vOfx6ZNm6bAwv6im/755S9/idtuuw0nnXQSbr75ZvziF7/AO9/5TjSbTaxdu3YqzJ4ydNM/b3nLW/DEE0/gla98JYgIeZ7jHe94Bz70oQ9NhcnTHnXP561bt2LHjh2YM2dOnyxL6AWS5yhhl8DHP/5x3Hjjjbjpppv6stjidMO2bdtw8skn47Of/Sz23HPPfpszLVGWJfbee2985jOfweGHH44TTjgBH/7wh3Hdddf127RpgTvuuAOXXXYZ/vIv/xL33HMPvva1r+Gf//mfcemll/bbtISESUfyHE0R9txzTzQaDWzZssXbv2XLFixevDh6zuLFizuSn8nopn8YV1xxBT7+8Y/ju9/9Lg499NDJNLNv6LR/HnzwQTz88MN4/etfb/eVZQkAGBgYwAMPPID9999/co2eQnTz/dlnn30wODiIRqNh9734xS/G5s2bMTY2hqGhoUm1eSrRTf985CMfwcknn4y3v/3tAIBDDjkEzz77LM466yx8+MMf9hYPnY2oez4vWLAgeY12Aczub/cUYmhoCIcffjhuvfVWu68sS9x6661YuXJl9JyVK1d68gBwyy231MrPZHTTPwDwyU9+EpdeeinWr1+P5cuXT4WpfUGn/XPggQfiJz/5CTZt2mRfb3jDG/A//sf/wKZNm7B06dKpNH/S0c335xWveAV+8YtfWNIIAP/5n/+JffbZZ5ciRkB3/bN9+/YKAWIiSWnVqVn1fJ6V6HdG+GzCjTfeSMPDw/TFL36Rfvazn9FZZ51FCxcupM2bNxMR0cknn0znnXeelf/+979PAwMDdMUVV9B9991Ha9eu3eWn8nfSPx//+MdpaGiIvvrVr9Kjjz5qX9u2bevXJUwqOu2fELv6bLVO++eRRx6h+fPn0+rVq+mBBx6gb37zm7T33nvTRz/60X5dwqSi0/5Zu3YtzZ8/n/7u7/6OfvnLX9J3vvMd2n///enNb35zvy5hUrFt2za699576d577yUA9KlPfYruvfde+q//+i8iIjrvvPPo5JNPtvI8lf/9738/3XfffXTttdemqfy7EBI5mmJcc8019NznPpeGhoboiCOOoH/7t3+zx1796lfTqaee6sn//d//Pb3whS+koaEh+r3f+z3653/+5ym2eGrRSf8873nPIwCV19q1a6fe8ClCp98fiV2dHBF13j8/+MEPaMWKFTQ8PEz77bcffexjH6M8z6fY6qlDJ/3TbDbpoosuov33359GRkZo6dKl9M53vpN++9vfTr3hU4Dbb789+jzhPjn11FPp1a9+deWcww47jIaGhmi//fajv/qrv5pyuxMmB4oo+UcTEhISEhISEhgp5yghISEhISEhQSCRo4SEhISEhIQEgUSOEhISEhISEhIEEjlKSEhISEhISBBI5CghISEhISEhQSCRo4SEhISEhIQEgUSOEhISEhISEhIEEjlKSEhISEhISBBI5CghIQGvec1r8N73vrffZkApha9//ev9NiMhIWGWI5GjhISEGYV3v/vdOPzwwzE8PIzDDjus3+YkJCTsgkjkKCEhYcbhbW97G0444YR+m5GQkLCLIpGjhIRZhmeffRannHIK5s2bh3322QdXXnmld/xv/uZvsHz5csyfPx+LFy/GW97yFjz22GOezE9/+lP80R/9ERYsWID58+fjyCOPxIMPPthW+1/4whfwe7/3exgeHsY+++yD1atXe8efeOIJvPGNb8TcuXPxghe8AN/4xje841dffTXOPvts7Lfffl1cfUJCQsL4SOQoIWGW4f3vfz/uvPNO/OM//iO+853v4I477sA999xjjzebTVx66aX48Y9/jK9//et4+OGHcdppp9njv/71r/GqV70Kw8PDuO2227Bx40a87W1vQ57n47b96U9/GmeffTbOOuss/OQnP8E3vvENHHDAAZ7MxRdfjDe/+c3493//dxx77LE46aST8OSTT/bs+hMSEhLGBSUkJMwabNu2jYaGhujv//7v7b7//u//pjlz5tB73vOe6Dk//OEPCQBt27aNiIjOP/98ev7zn09jY2Mdt79kyRL68Ic/XHscAF1wwQX28zPPPEMA6Fvf+lZFdu3atfSSl7ykYxsSEhISxkPyHCUkzCI8+OCDGBsbw4oVK+y+PfbYAy960Yvs540bN+L1r389nvvc52L+/Pl49atfDQB45JFHAACbNm3CkUceicHBwY7afuyxx/Cb3/wGRx11VEu5Qw891L5/znOegwULFlTCegkJCQmTiUSOEhISLJ599lkcffTRWLBgAa6//nr88Ic/xE033QQAGBsbAwDMmTOnK93tnheSLqUUyrLsqs2EhISEbpDIUULCLML++++PwcFB3HXXXXbfb3/7W/znf/4nAOD+++/Hf//3f+PjH/84jjzySBx44IEVr82hhx6Kf/3Xf0Wz2eyo7fnz52PZsmW49dZbJ34hCQkJCZOIRI4SEmYR5s2bhzPOOAPvf//7cdttt+E//uM/cNpppyHL9KPguc99LoaGhnDNNdfgl7/8Jb7xjW/g0ksv9XSsXr0aW7duxYknnogf/ehH+PnPf46/+Zu/wQMPPDBu+xdddBGuvPJKXH311fj5z3+Oe+65B9dcc01H1/CLX/wCmzZtwubNm7Fjxw5s2rQJmzZtsp6thISEhIlioN8GJCQkTC0uv/xyPPPMM3j961+P+fPn49xzz8XTTz8NANhrr73wxS9+ER/60Idw9dVX4/d///dxxRVX4A1veIM9/3d+53dw22234f3vfz9e/epXo9Fo4LDDDsMrXvGKcds+9dRTsXPnTvyf//N/8L73vQ977rkn/vRP/7Qj+9/+9rfjzjvvtJ9f+tKXAgAeeughLFu2rCNdCQkJCTEoIqJ+G5GQkJCQkJCQMF2QwmoJCQkJCQkJCQKJHCUkJPQM8+bNq33967/+a7/NS0hISGgLKayWkJDQM/ziF7+oPbbvvvt2XQYgISEhYSqRyFFCQkJCQkJCgkAKqyUkJCQkJCQkCCRylJCQkJCQkJAgkMhRQkJCQkJCQoJAIkcJCQkJCQkJCQKJHCUkJCQkJCQkCCRylJCQkJCQkJAgkMhRQkJCQkJCQoJAIkcJCQkJCQkJCQL/P82Tb42fPkEAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.reset_index().plot.scatter('dac_ch1', 'dac_ch2', c='dmm_v2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging two dataframes with the same labels is fairly simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df1, df2], sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='dac_ch1', ylabel='dac_ch2'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.reset_index().plot.scatter('dac_ch1', 'dac_ch2', c='dmm_v2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to select a subset of data from the datframe based on the x and y values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": 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></th>\n",
       "      <th>dmm_v2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dac_ch1</th>\n",
       "      <th>dac_ch2</th>\n",
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       "      <td>-0.000225</td>\n",
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       "      <td>-0.000700</td>\n",
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       "      <th>-0.97</th>\n",
       "      <td>0.000253</td>\n",
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       "      <th rowspan=\"4\" valign=\"top\">-0.985</th>\n",
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       "      <td>0.000029</td>\n",
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       "      <td>-0.000440</td>\n",
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       "      <td>0.000234</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                   dmm_v2\n",
       "dac_ch1 dac_ch2          \n",
       "-1.000  -1.00    0.000755\n",
       "        -0.99   -0.000913\n",
       "        -0.98    0.000095\n",
       "        -0.97   -0.000016\n",
       "-0.995  -1.00   -0.000247\n",
       "        -0.99   -0.000049\n",
       "        -0.98    0.000027\n",
       "        -0.97   -0.000225\n",
       "-0.990  -1.00   -0.000283\n",
       "        -0.99   -0.000406\n",
       "        -0.98   -0.000700\n",
       "        -0.97    0.000253\n",
       "-0.985  -1.00   -0.000026\n",
       "        -0.99    0.000029\n",
       "        -0.98   -0.000756\n",
       "        -0.97    0.000221\n",
       "-0.980  -1.00    0.000508\n",
       "        -0.99   -0.000321\n",
       "        -0.98   -0.000459\n",
       "        -0.97    0.000495\n",
       "-0.975  -1.00    0.000607\n",
       "        -0.99    0.000219\n",
       "        -0.98    0.000537\n",
       "        -0.97   -0.000084\n",
       "-0.970  -1.00   -0.000377\n",
       "        -0.99   -0.000071\n",
       "        -0.98   -0.000347\n",
       "        -0.97    0.000299\n",
       "-0.965  -1.00   -0.000216\n",
       "        -0.99   -0.000362\n",
       "        -0.98    0.000693\n",
       "        -0.97    0.000094\n",
       "-0.960  -1.00   -0.000233\n",
       "        -0.99   -0.000446\n",
       "        -0.98   -0.000654\n",
       "        -0.97   -0.000272\n",
       "-0.955  -1.00   -0.000464\n",
       "        -0.99    0.000536\n",
       "        -0.98   -0.000440\n",
       "        -0.97   -0.000430\n",
       "-0.950  -1.00   -0.000537\n",
       "        -0.99    0.000709\n",
       "        -0.98   -0.000559\n",
       "        -0.97    0.000234"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(slice(-1, -0.95), slice(-1, -0.97)), :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with XArray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In many cases when working with data on rectangular grids it may be more convenient to export the data to a [XArray](http://xarray.pydata.org) Dataset or DataArray. This is especially true when working in multi-dimentional parameter space. \n",
    "\n",
    "Let's setup and rerun the above measurment with the added dependent parameter `dmm.v1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qcodes.dataset.measurements.Measurement at 0x23faeb7e090>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1, dac.ch2))  # register the 2nd dependent parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 3. \n"
     ]
    }
   ],
   "source": [
    "# run a 2D sweep\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "\n",
    "    for v1 in np.linspace(-1, 1, 200):\n",
    "        for v2 in np.linspace(-1, 1, 201):\n",
    "            dac.ch1(v1)\n",
    "            dac.ch2(v2)\n",
    "            val1 = dmm.v1.get()\n",
    "            val2 = dmm.v2.get()\n",
    "            datasaver.add_result((dac.ch1, v1),\n",
    "                                 (dac.ch2, v2),\n",
    "                                 (dmm.v1, val1),\n",
    "                                 (dmm.v2, val2))\n",
    "\n",
    "dataset3 = datasaver.dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The QCoDeS DataSet can be directly converted to a XArray [Dataset](http://xarray.pydata.org/en/stable/data-structures.html?#dataset) from the `to_xarray_dataset` method. This method returns the data from measured (dependent) parameters to an XArray Dataset. It's also possible to return a dictionary of XArray DataArray's if you were only interested in a single parameter using the `to_xarray_dataarray` method. For convenience we will access the DataArray's from XArray's Dataset directly.\n",
    "\n",
    "Please note that the `to_xarray_dataset` is only intended to be used when all dependent parameters have the same setpoint. If this is not the case for the DataSet then `to_xarray_dataarray` should be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "xaDataSet = dataset3.to_xarray_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (dac_ch1: 200, dac_ch2: 201)\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
       "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
       "Data variables:\n",
       "    dmm_v1   (dac_ch1, dac_ch2) float64 6.136 6.12 5.98 ... 4.114 4.169 4.038\n",
       "    dmm_v2   (dac_ch1, dac_ch2) float64 0.0002199 4.968e-05 ... -0.0001338\n",
       "Attributes: (12/14)\n",
       "    ds_name:                  results\n",
       "    sample_name:              no sample\n",
       "    exp_name:                 working_with_pandas\n",
       "    snapshot:                 {&quot;station&quot;: {&quot;instruments&quot;: {&quot;dmm&quot;: {&quot;functions...\n",
       "    guid:                     a77a0239-0000-0000-0000-018d7de50aa3\n",
       "    run_timestamp:            2024-02-06 11:09:38\n",
       "    ...                       ...\n",
       "    captured_counter:         3\n",
       "    run_id:                   3\n",
       "    run_description:          {&quot;version&quot;: 3, &quot;interdependencies&quot;: {&quot;paramspec...\n",
       "    parent_dataset_links:     []\n",
       "    run_timestamp_raw:        1707214178.9969792\n",
       "    completed_timestamp_raw:  1707214200.4039166</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d409c6b0-9cec-4121-9e9b-5df7b3b50902' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d409c6b0-9cec-4121-9e9b-5df7b3b50902' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>dac_ch1</span>: 200</li><li><span class='xr-has-index'>dac_ch2</span>: 201</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cdcd5e68-82f0-400a-898c-14ed07afcfed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cdcd5e68-82f0-400a-898c-14ed07afcfed' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch1</span></div><div class='xr-var-dims'>(dac_ch1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.9899 -0.9799 ... 0.9899 1.0</div><input id='attrs-6fbd8f87-853c-4bf3-b28e-2db14b9371c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fbd8f87-853c-4bf3-b28e-2db14b9371c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30078f89-dd6a-451f-9739-0a4d358286c2' class='xr-var-data-in' type='checkbox'><label for='data-30078f89-dd6a-451f-9739-0a4d358286c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dac_ch1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate ch1</dd></dl></div><div class='xr-var-data'><pre>array([-1.      , -0.98995 , -0.979899, -0.969849, -0.959799, -0.949749,\n",
       "       -0.939698, -0.929648, -0.919598, -0.909548, -0.899497, -0.889447,\n",
       "       -0.879397, -0.869347, -0.859296, -0.849246, -0.839196, -0.829146,\n",
       "       -0.819095, -0.809045, -0.798995, -0.788945, -0.778894, -0.768844,\n",
       "       -0.758794, -0.748744, -0.738693, -0.728643, -0.718593, -0.708543,\n",
       "       -0.698492, -0.688442, -0.678392, -0.668342, -0.658291, -0.648241,\n",
       "       -0.638191, -0.628141, -0.61809 , -0.60804 , -0.59799 , -0.58794 ,\n",
       "       -0.577889, -0.567839, -0.557789, -0.547739, -0.537688, -0.527638,\n",
       "       -0.517588, -0.507538, -0.497487, -0.487437, -0.477387, -0.467337,\n",
       "       -0.457286, -0.447236, -0.437186, -0.427136, -0.417085, -0.407035,\n",
       "       -0.396985, -0.386935, -0.376884, -0.366834, -0.356784, -0.346734,\n",
       "       -0.336683, -0.326633, -0.316583, -0.306533, -0.296482, -0.286432,\n",
       "       -0.276382, -0.266332, -0.256281, -0.246231, -0.236181, -0.226131,\n",
       "       -0.21608 , -0.20603 , -0.19598 , -0.18593 , -0.175879, -0.165829,\n",
       "       -0.155779, -0.145729, -0.135678, -0.125628, -0.115578, -0.105528,\n",
       "       -0.095477, -0.085427, -0.075377, -0.065327, -0.055276, -0.045226,\n",
       "       -0.035176, -0.025126, -0.015075, -0.005025,  0.005025,  0.015075,\n",
       "        0.025126,  0.035176,  0.045226,  0.055276,  0.065327,  0.075377,\n",
       "        0.085427,  0.095477,  0.105528,  0.115578,  0.125628,  0.135678,\n",
       "        0.145729,  0.155779,  0.165829,  0.175879,  0.18593 ,  0.19598 ,\n",
       "        0.20603 ,  0.21608 ,  0.226131,  0.236181,  0.246231,  0.256281,\n",
       "        0.266332,  0.276382,  0.286432,  0.296482,  0.306533,  0.316583,\n",
       "        0.326633,  0.336683,  0.346734,  0.356784,  0.366834,  0.376884,\n",
       "        0.386935,  0.396985,  0.407035,  0.417085,  0.427136,  0.437186,\n",
       "        0.447236,  0.457286,  0.467337,  0.477387,  0.487437,  0.497487,\n",
       "        0.507538,  0.517588,  0.527638,  0.537688,  0.547739,  0.557789,\n",
       "        0.567839,  0.577889,  0.58794 ,  0.59799 ,  0.60804 ,  0.61809 ,\n",
       "        0.628141,  0.638191,  0.648241,  0.658291,  0.668342,  0.678392,\n",
       "        0.688442,  0.698492,  0.708543,  0.718593,  0.728643,  0.738693,\n",
       "        0.748744,  0.758794,  0.768844,  0.778894,  0.788945,  0.798995,\n",
       "        0.809045,  0.819095,  0.829146,  0.839196,  0.849246,  0.859296,\n",
       "        0.869347,  0.879397,  0.889447,  0.899497,  0.909548,  0.919598,\n",
       "        0.929648,  0.939698,  0.949749,  0.959799,  0.969849,  0.979899,\n",
       "        0.98995 ,  1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch2</span></div><div class='xr-var-dims'>(dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.99 -0.98 ... 0.98 0.99 1.0</div><input id='attrs-b40fff93-3d36-4ecf-8181-0793bd30f609' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b40fff93-3d36-4ecf-8181-0793bd30f609' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-351cf229-ff2c-485f-be40-842f7e377fed' class='xr-var-data-in' type='checkbox'><label for='data-351cf229-ff2c-485f-be40-842f7e377fed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dac_ch2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate ch2</dd></dl></div><div class='xr-var-data'><pre>array([-1.  , -0.99, -0.98, ...,  0.98,  0.99,  1.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c88d62b-67bc-47bd-adbf-4fa4b2e793c6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4c88d62b-67bc-47bd-adbf-4fa4b2e793c6' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dmm_v1</span></div><div class='xr-var-dims'>(dac_ch1, dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.136 6.12 5.98 ... 4.169 4.038</div><input id='attrs-4911ea5f-e10c-4aa3-8f74-b5a6d8bdc279' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4911ea5f-e10c-4aa3-8f74-b5a6d8bdc279' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e02336e-32ac-4a30-a4af-2fc577565fa9' class='xr-var-data-in' type='checkbox'><label for='data-2e02336e-32ac-4a30-a4af-2fc577565fa9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dmm_v1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate v1</dd></dl></div><div class='xr-var-data'><pre>array([[6.13647031, 6.12018506, 5.98009347, ..., 6.1398718 , 6.04234238,\n",
       "        6.04096824],\n",
       "       [6.1049449 , 6.25411474, 6.0509296 , ..., 6.16030622, 6.10979634,\n",
       "        6.03786531],\n",
       "       [6.13365479, 5.98968415, 6.04970077, ..., 6.09769913, 6.1307883 ,\n",
       "        6.19542996],\n",
       "       ...,\n",
       "       [3.98431332, 4.11382164, 4.06046086, ..., 4.00053436, 4.05640689,\n",
       "        3.99324708],\n",
       "       [4.09840845, 4.00191547, 4.13654302, ..., 4.04186306, 4.01852355,\n",
       "        3.98854675],\n",
       "       [3.99892548, 4.22445888, 4.1866655 , ..., 4.11417074, 4.16928479,\n",
       "        4.03776873]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dmm_v2</span></div><div class='xr-var-dims'>(dac_ch1, dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0002199 4.968e-05 ... -0.0001338</div><input id='attrs-6be270fe-a23c-49e6-9081-4fb6fee9c48d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6be270fe-a23c-49e6-9081-4fb6fee9c48d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49768631-2a67-4465-938b-8997b28956d6' class='xr-var-data-in' type='checkbox'><label for='data-49768631-2a67-4465-938b-8997b28956d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dmm_v2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate v2</dd></dl></div><div class='xr-var-data'><pre>array([[ 2.19904729e-04,  4.96785813e-05,  8.90286736e-05, ...,\n",
       "         1.25409489e-03,  5.85288737e-04, -4.15646598e-04],\n",
       "       [-2.02935808e-05, -9.16893860e-05, -9.86480204e-04, ...,\n",
       "         3.41653433e-04,  9.54207179e-05, -7.88635203e-06],\n",
       "       [ 5.41070373e-04,  4.33830815e-04,  2.15425823e-04, ...,\n",
       "         4.05248243e-04, -4.77157801e-04, -1.05358649e-04],\n",
       "       ...,\n",
       "       [ 1.49097876e-04,  7.26271029e-05,  6.83419233e-04, ...,\n",
       "         1.76560721e-04, -6.28917901e-04,  6.08809325e-04],\n",
       "       [-1.29142891e-04, -4.61976869e-04,  1.09154540e-03, ...,\n",
       "        -4.91235853e-04, -4.44436629e-04, -1.02008974e-03],\n",
       "       [-6.89514695e-05, -6.19619120e-04,  5.39175210e-04, ...,\n",
       "        -3.25640802e-04,  8.10100592e-05, -1.33791114e-04]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7d64db31-429a-45f6-8f3a-a65257e4ac73' class='xr-section-summary-in' type='checkbox'  ><label for='section-7d64db31-429a-45f6-8f3a-a65257e4ac73' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>dac_ch1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f35dff9b-2ab1-42a5-9cf6-cb4095888ef7' class='xr-index-data-in' type='checkbox'/><label for='index-f35dff9b-2ab1-42a5-9cf6-cb4095888ef7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              -1.0, -0.989949748743719, -0.979899497487437,\n",
       "       -0.969849246231156, -0.959798994974874, -0.949748743718593,\n",
       "       -0.939698492462312,  -0.92964824120603, -0.919597989949749,\n",
       "       -0.909547738693467,\n",
       "       ...\n",
       "        0.909547738693467,  0.919597989949749,   0.92964824120603,\n",
       "        0.939698492462312,  0.949748743718593,  0.959798994974874,\n",
       "        0.969849246231156,  0.979899497487437,  0.989949748743719,\n",
       "                      1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;dac_ch1&#x27;, length=200))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dac_ch2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0445d42a-9557-42cc-84c7-95b5b6d0b0c7' class='xr-index-data-in' type='checkbox'/><label for='index-0445d42a-9557-42cc-84c7-95b5b6d0b0c7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -1.0, -0.99, -0.98, -0.97, -0.96, -0.95, -0.94, -0.93, -0.92, -0.91,\n",
       "       ...\n",
       "        0.91,  0.92,  0.93,  0.94,  0.95,  0.96,  0.97,  0.98,  0.99,   1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;dac_ch2&#x27;, length=201))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d1d73fa-de44-4e75-8a1d-bbe969bad4b3' class='xr-section-summary-in' type='checkbox'  ><label for='section-0d1d73fa-de44-4e75-8a1d-bbe969bad4b3' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ds_name :</span></dt><dd>results</dd><dt><span>sample_name :</span></dt><dd>no sample</dd><dt><span>exp_name :</span></dt><dd>working_with_pandas</dd><dt><span>snapshot :</span></dt><dd>{&quot;station&quot;: {&quot;instruments&quot;: {&quot;dmm&quot;: {&quot;functions&quot;: {}, &quot;submodules&quot;: {}, &quot;parameters&quot;: {&quot;IDN&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dmm_IDN&quot;, &quot;value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;raw_value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;ts&quot;: &quot;2024-02-06 11:09:01&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;unit&quot;: &quot;&quot;, &quot;vals&quot;: &quot;&lt;Anything&gt;&quot;, &quot;inter_delay&quot;: 0, &quot;label&quot;: &quot;IDN&quot;, &quot;post_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Anything&gt;&quot;], &quot;name&quot;: &quot;IDN&quot;}, &quot;v1&quot;: {&quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DmmExponentialParameter&quot;, &quot;full_name&quot;: &quot;dmm_v1&quot;, &quot;value&quot;: 4.9783740548731785, &quot;raw_value&quot;: 4.9783740548731785, &quot;ts&quot;: &quot;2024-02-06 11:09:01&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;inter_delay&quot;: 0, &quot;label&quot;: &quot;Gate v1&quot;, &quot;post_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;name&quot;: &quot;v1&quot;}, &quot;v2&quot;: {&quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DmmGaussParameter&quot;, &quot;full_name&quot;: &quot;dmm_v2&quot;, &quot;value&quot;: 0.0002165108170893373, &quot;raw_value&quot;: 0.0002165108170893373, &quot;ts&quot;: &quot;2024-02-06 11:09:30&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;inter_delay&quot;: 0, &quot;label&quot;: &quot;Gate v2&quot;, &quot;post_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;name&quot;: &quot;v2&quot;}}, &quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;label&quot;: &quot;dmm&quot;, &quot;name&quot;: &quot;dmm&quot;}, &quot;dac&quot;: {&quot;functions&quot;: {}, &quot;submodules&quot;: {}, &quot;parameters&quot;: {&quot;IDN&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_IDN&quot;, &quot;value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrument&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;raw_value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrument&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;ts&quot;: &quot;2024-02-06 11:09:01&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;unit&quot;: &quot;&quot;, &quot;vals&quot;: &quot;&lt;Anything&gt;&quot;, &quot;inter_delay&quot;: 0, &quot;label&quot;: &quot;IDN&quot;, &quot;post_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Anything&gt;&quot;], &quot;name&quot;: &quot;IDN&quot;}, &quot;ch1&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_ch1&quot;, &quot;value&quot;: 1.0, &quot;raw_value&quot;: 1.0, &quot;ts&quot;: &quot;2024-02-06 11:09:30&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;inter_delay&quot;: 0, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;post_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;name&quot;: &quot;ch1&quot;}, &quot;ch2&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_ch2&quot;, &quot;value&quot;: 1.0, &quot;raw_value&quot;: 1.0, &quot;ts&quot;: &quot;2024-02-06 11:09:30&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;inter_delay&quot;: 0, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;post_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;name&quot;: &quot;ch2&quot;}}, &quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;label&quot;: &quot;dac&quot;, &quot;name&quot;: &quot;dac&quot;}}, &quot;parameters&quot;: {}, &quot;components&quot;: {}, &quot;config&quot;: null}}</dd><dt><span>guid :</span></dt><dd>a77a0239-0000-0000-0000-018d7de50aa3</dd><dt><span>run_timestamp :</span></dt><dd>2024-02-06 11:09:38</dd><dt><span>completed_timestamp :</span></dt><dd>2024-02-06 11:10:00</dd><dt><span>captured_run_id :</span></dt><dd>3</dd><dt><span>captured_counter :</span></dt><dd>3</dd><dt><span>run_id :</span></dt><dd>3</dd><dt><span>run_description :</span></dt><dd>{&quot;version&quot;: 3, &quot;interdependencies&quot;: {&quot;paramspecs&quot;: [{&quot;name&quot;: &quot;dac_ch1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: []}, {&quot;name&quot;: &quot;dac_ch2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: []}, {&quot;name&quot;: &quot;dmm_v2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;]}, {&quot;name&quot;: &quot;dmm_v1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;]}]}, &quot;interdependencies_&quot;: {&quot;parameters&quot;: {&quot;dmm_v2&quot;: {&quot;name&quot;: &quot;dmm_v2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dac_ch1&quot;: {&quot;name&quot;: &quot;dac_ch1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dac_ch2&quot;: {&quot;name&quot;: &quot;dac_ch2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dmm_v1&quot;: {&quot;name&quot;: &quot;dmm_v1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;unit&quot;: &quot;V&quot;}}, &quot;dependencies&quot;: {&quot;dmm_v2&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;], &quot;dmm_v1&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;]}, &quot;inferences&quot;: {}, &quot;standalones&quot;: []}, &quot;shapes&quot;: null}</dd><dt><span>parent_dataset_links :</span></dt><dd>[]</dd><dt><span>run_timestamp_raw :</span></dt><dd>1707214178.9969792</dd><dt><span>completed_timestamp_raw :</span></dt><dd>1707214200.4039166</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (dac_ch1: 200, dac_ch2: 201)\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
       "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
       "Data variables:\n",
       "    dmm_v1   (dac_ch1, dac_ch2) float64 6.136 6.12 5.98 ... 4.114 4.169 4.038\n",
       "    dmm_v2   (dac_ch1, dac_ch2) float64 0.0002199 4.968e-05 ... -0.0001338\n",
       "Attributes: (12/14)\n",
       "    ds_name:                  results\n",
       "    sample_name:              no sample\n",
       "    exp_name:                 working_with_pandas\n",
       "    snapshot:                 {\"station\": {\"instruments\": {\"dmm\": {\"functions...\n",
       "    guid:                     a77a0239-0000-0000-0000-018d7de50aa3\n",
       "    run_timestamp:            2024-02-06 11:09:38\n",
       "    ...                       ...\n",
       "    captured_counter:         3\n",
       "    run_id:                   3\n",
       "    run_description:          {\"version\": 3, \"interdependencies\": {\"paramspec...\n",
       "    parent_dataset_links:     []\n",
       "    run_timestamp_raw:        1707214178.9969792\n",
       "    completed_timestamp_raw:  1707214200.4039166"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xaDataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned above it's also possible to work with a XArray [DataArray](http://xarray.pydata.org/en/stable/data-structures.html?#dataarray) directly from the DataSet. The DataArray can only contain a single dependent variable and can be obtained from the Dataset by indexing using the parameter name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "xaDataArray = xaDataSet['dmm_v2']# or xaDataSet.dmm_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;dmm_v2&#x27; (dac_ch1: 200, dac_ch2: 201)&gt;\n",
       "array([[ 2.19904729e-04,  4.96785813e-05,  8.90286736e-05, ...,\n",
       "         1.25409489e-03,  5.85288737e-04, -4.15646598e-04],\n",
       "       [-2.02935808e-05, -9.16893860e-05, -9.86480204e-04, ...,\n",
       "         3.41653433e-04,  9.54207179e-05, -7.88635203e-06],\n",
       "       [ 5.41070373e-04,  4.33830815e-04,  2.15425823e-04, ...,\n",
       "         4.05248243e-04, -4.77157801e-04, -1.05358649e-04],\n",
       "       ...,\n",
       "       [ 1.49097876e-04,  7.26271029e-05,  6.83419233e-04, ...,\n",
       "         1.76560721e-04, -6.28917901e-04,  6.08809325e-04],\n",
       "       [-1.29142891e-04, -4.61976869e-04,  1.09154540e-03, ...,\n",
       "        -4.91235853e-04, -4.44436629e-04, -1.02008974e-03],\n",
       "       [-6.89514695e-05, -6.19619120e-04,  5.39175210e-04, ...,\n",
       "        -3.25640802e-04,  8.10100592e-05, -1.33791114e-04]])\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
       "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
       "Attributes:\n",
       "    name:           dmm_v2\n",
       "    paramtype:      numeric\n",
       "    label:          Gate v2\n",
       "    unit:           V\n",
       "    inferred_from:  []\n",
       "    depends_on:     [&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]\n",
       "    units:          V\n",
       "    long_name:      Gate v2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'dmm_v2'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>dac_ch1</span>: 200</li><li><span class='xr-has-index'>dac_ch2</span>: 201</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1ac6f6a3-4856-40ef-986f-fd898e0843ed' class='xr-array-in' type='checkbox' checked><label for='section-1ac6f6a3-4856-40ef-986f-fd898e0843ed' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0002199 4.968e-05 8.903e-05 ... -0.0003256 8.101e-05 -0.0001338</span></div><div class='xr-array-data'><pre>array([[ 2.19904729e-04,  4.96785813e-05,  8.90286736e-05, ...,\n",
       "         1.25409489e-03,  5.85288737e-04, -4.15646598e-04],\n",
       "       [-2.02935808e-05, -9.16893860e-05, -9.86480204e-04, ...,\n",
       "         3.41653433e-04,  9.54207179e-05, -7.88635203e-06],\n",
       "       [ 5.41070373e-04,  4.33830815e-04,  2.15425823e-04, ...,\n",
       "         4.05248243e-04, -4.77157801e-04, -1.05358649e-04],\n",
       "       ...,\n",
       "       [ 1.49097876e-04,  7.26271029e-05,  6.83419233e-04, ...,\n",
       "         1.76560721e-04, -6.28917901e-04,  6.08809325e-04],\n",
       "       [-1.29142891e-04, -4.61976869e-04,  1.09154540e-03, ...,\n",
       "        -4.91235853e-04, -4.44436629e-04, -1.02008974e-03],\n",
       "       [-6.89514695e-05, -6.19619120e-04,  5.39175210e-04, ...,\n",
       "        -3.25640802e-04,  8.10100592e-05, -1.33791114e-04]])</pre></div></div></li><li class='xr-section-item'><input id='section-726420f9-f4a8-44b9-80b2-04fbcd68c2cc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-726420f9-f4a8-44b9-80b2-04fbcd68c2cc' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch1</span></div><div class='xr-var-dims'>(dac_ch1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.9899 -0.9799 ... 0.9899 1.0</div><input id='attrs-56649983-5ed6-49a8-85b2-63fa2dd74484' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56649983-5ed6-49a8-85b2-63fa2dd74484' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94bc158f-eb37-4e40-80ef-feca22443044' class='xr-var-data-in' type='checkbox'><label for='data-94bc158f-eb37-4e40-80ef-feca22443044' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dac_ch1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate ch1</dd></dl></div><div class='xr-var-data'><pre>array([-1.      , -0.98995 , -0.979899, -0.969849, -0.959799, -0.949749,\n",
       "       -0.939698, -0.929648, -0.919598, -0.909548, -0.899497, -0.889447,\n",
       "       -0.879397, -0.869347, -0.859296, -0.849246, -0.839196, -0.829146,\n",
       "       -0.819095, -0.809045, -0.798995, -0.788945, -0.778894, -0.768844,\n",
       "       -0.758794, -0.748744, -0.738693, -0.728643, -0.718593, -0.708543,\n",
       "       -0.698492, -0.688442, -0.678392, -0.668342, -0.658291, -0.648241,\n",
       "       -0.638191, -0.628141, -0.61809 , -0.60804 , -0.59799 , -0.58794 ,\n",
       "       -0.577889, -0.567839, -0.557789, -0.547739, -0.537688, -0.527638,\n",
       "       -0.517588, -0.507538, -0.497487, -0.487437, -0.477387, -0.467337,\n",
       "       -0.457286, -0.447236, -0.437186, -0.427136, -0.417085, -0.407035,\n",
       "       -0.396985, -0.386935, -0.376884, -0.366834, -0.356784, -0.346734,\n",
       "       -0.336683, -0.326633, -0.316583, -0.306533, -0.296482, -0.286432,\n",
       "       -0.276382, -0.266332, -0.256281, -0.246231, -0.236181, -0.226131,\n",
       "       -0.21608 , -0.20603 , -0.19598 , -0.18593 , -0.175879, -0.165829,\n",
       "       -0.155779, -0.145729, -0.135678, -0.125628, -0.115578, -0.105528,\n",
       "       -0.095477, -0.085427, -0.075377, -0.065327, -0.055276, -0.045226,\n",
       "       -0.035176, -0.025126, -0.015075, -0.005025,  0.005025,  0.015075,\n",
       "        0.025126,  0.035176,  0.045226,  0.055276,  0.065327,  0.075377,\n",
       "        0.085427,  0.095477,  0.105528,  0.115578,  0.125628,  0.135678,\n",
       "        0.145729,  0.155779,  0.165829,  0.175879,  0.18593 ,  0.19598 ,\n",
       "        0.20603 ,  0.21608 ,  0.226131,  0.236181,  0.246231,  0.256281,\n",
       "        0.266332,  0.276382,  0.286432,  0.296482,  0.306533,  0.316583,\n",
       "        0.326633,  0.336683,  0.346734,  0.356784,  0.366834,  0.376884,\n",
       "        0.386935,  0.396985,  0.407035,  0.417085,  0.427136,  0.437186,\n",
       "        0.447236,  0.457286,  0.467337,  0.477387,  0.487437,  0.497487,\n",
       "        0.507538,  0.517588,  0.527638,  0.537688,  0.547739,  0.557789,\n",
       "        0.567839,  0.577889,  0.58794 ,  0.59799 ,  0.60804 ,  0.61809 ,\n",
       "        0.628141,  0.638191,  0.648241,  0.658291,  0.668342,  0.678392,\n",
       "        0.688442,  0.698492,  0.708543,  0.718593,  0.728643,  0.738693,\n",
       "        0.748744,  0.758794,  0.768844,  0.778894,  0.788945,  0.798995,\n",
       "        0.809045,  0.819095,  0.829146,  0.839196,  0.849246,  0.859296,\n",
       "        0.869347,  0.879397,  0.889447,  0.899497,  0.909548,  0.919598,\n",
       "        0.929648,  0.939698,  0.949749,  0.959799,  0.969849,  0.979899,\n",
       "        0.98995 ,  1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch2</span></div><div class='xr-var-dims'>(dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.99 -0.98 ... 0.98 0.99 1.0</div><input id='attrs-cc37c9a5-f554-4c9f-9bda-91c08c959518' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc37c9a5-f554-4c9f-9bda-91c08c959518' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a389cd9-a6ae-4ac2-84a1-6c9d1e5d4292' class='xr-var-data-in' type='checkbox'><label for='data-2a389cd9-a6ae-4ac2-84a1-6c9d1e5d4292' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dac_ch2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate ch2</dd></dl></div><div class='xr-var-data'><pre>array([-1.  , -0.99, -0.98, ...,  0.98,  0.99,  1.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b66d27b8-1979-4207-96e2-04d72a546c4f' class='xr-section-summary-in' type='checkbox'  ><label for='section-b66d27b8-1979-4207-96e2-04d72a546c4f' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>dac_ch1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6ea304aa-0662-4d5d-b0bc-ff9b0acbe325' class='xr-index-data-in' type='checkbox'/><label for='index-6ea304aa-0662-4d5d-b0bc-ff9b0acbe325' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              -1.0, -0.989949748743719, -0.979899497487437,\n",
       "       -0.969849246231156, -0.959798994974874, -0.949748743718593,\n",
       "       -0.939698492462312,  -0.92964824120603, -0.919597989949749,\n",
       "       -0.909547738693467,\n",
       "       ...\n",
       "        0.909547738693467,  0.919597989949749,   0.92964824120603,\n",
       "        0.939698492462312,  0.949748743718593,  0.959798994974874,\n",
       "        0.969849246231156,  0.979899497487437,  0.989949748743719,\n",
       "                      1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;dac_ch1&#x27;, length=200))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dac_ch2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-77892c9a-26e5-4ea4-b6db-674783f37816' class='xr-index-data-in' type='checkbox'/><label for='index-77892c9a-26e5-4ea4-b6db-674783f37816' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -1.0, -0.99, -0.98, -0.97, -0.96, -0.95, -0.94, -0.93, -0.92, -0.91,\n",
       "       ...\n",
       "        0.91,  0.92,  0.93,  0.94,  0.95,  0.96,  0.97,  0.98,  0.99,   1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;dac_ch2&#x27;, length=201))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2486e272-f78b-45ca-9da0-9c4401651cd5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2486e272-f78b-45ca-9da0-9c4401651cd5' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dmm_v2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate v2</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'dmm_v2' (dac_ch1: 200, dac_ch2: 201)>\n",
       "array([[ 2.19904729e-04,  4.96785813e-05,  8.90286736e-05, ...,\n",
       "         1.25409489e-03,  5.85288737e-04, -4.15646598e-04],\n",
       "       [-2.02935808e-05, -9.16893860e-05, -9.86480204e-04, ...,\n",
       "         3.41653433e-04,  9.54207179e-05, -7.88635203e-06],\n",
       "       [ 5.41070373e-04,  4.33830815e-04,  2.15425823e-04, ...,\n",
       "         4.05248243e-04, -4.77157801e-04, -1.05358649e-04],\n",
       "       ...,\n",
       "       [ 1.49097876e-04,  7.26271029e-05,  6.83419233e-04, ...,\n",
       "         1.76560721e-04, -6.28917901e-04,  6.08809325e-04],\n",
       "       [-1.29142891e-04, -4.61976869e-04,  1.09154540e-03, ...,\n",
       "        -4.91235853e-04, -4.44436629e-04, -1.02008974e-03],\n",
       "       [-6.89514695e-05, -6.19619120e-04,  5.39175210e-04, ...,\n",
       "        -3.25640802e-04,  8.10100592e-05, -1.33791114e-04]])\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
       "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
       "Attributes:\n",
       "    name:           dmm_v2\n",
       "    paramtype:      numeric\n",
       "    label:          Gate v2\n",
       "    unit:           V\n",
       "    inferred_from:  []\n",
       "    depends_on:     ['dac_ch1', 'dac_ch2']\n",
       "    units:          V\n",
       "    long_name:      Gate v2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xaDataArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2)\n",
    "xaDataSet.dmm_v2.plot(ax=ax[0,0])\n",
    "xaDataSet.dmm_v1.plot(ax=ax[1,1])\n",
    "xaDataSet.dmm_v2.mean(dim='dac_ch1').plot(ax=ax[1,0])\n",
    "xaDataSet.dmm_v1.mean(dim='dac_ch2').plot(ax=ax[0,1])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we demonstrated a few ways to index the data from a DataArray. For instance the DataArray can be directly plotted, the extracted mean or a specific row/column can also be plotted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with XArray on non gridded data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes your data does not fit well on a regular grid. Perhaps you are sweeping 2 parameters at the same time or you are messuring at random points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 4. \n"
     ]
    }
   ],
   "source": [
    "# run a 2D sweep\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "\n",
    "    for v1, v2 in zip(np.linspace(-1, 1, 200), np.linspace(-1, 1, 201)):\n",
    "\n",
    "        dac.ch1(v1)\n",
    "        dac.ch2(v2)\n",
    "        val1 = dmm.v1.get()\n",
    "        val2 = dmm.v2.get()\n",
    "        datasaver.add_result((dac.ch1, v1),\n",
    "                             (dac.ch2, v2),\n",
    "                             (dmm.v1, val1),\n",
    "                             (dmm.v2, val2))\n",
    "\n",
    "dataset4 = datasaver.dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "xaDataSet = dataset4.to_xarray_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this is the case QCoDeS will export the data using a XArray [MultiIndex](https://docs.xarray.dev/en/stable/user-guide/indexing.html#multi-level-indexing). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (multi_index: 200)\n",
       "Coordinates:\n",
       "  * multi_index  (multi_index) object MultiIndex\n",
       "  * dac_ch1      (multi_index) float64 -1.0 -0.9899 -0.9799 ... 0.9899 1.0\n",
       "  * dac_ch2      (multi_index) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99\n",
       "Data variables:\n",
       "    dmm_v1       (multi_index) float64 6.076 6.029 6.155 ... 4.011 4.044 4.156\n",
       "    dmm_v2       (multi_index) float64 -0.0005091 0.0006515 ... 0.0005385\n",
       "Attributes: (12/14)\n",
       "    ds_name:                  results\n",
       "    sample_name:              no sample\n",
       "    exp_name:                 working_with_pandas\n",
       "    snapshot:                 {&quot;station&quot;: {&quot;instruments&quot;: {&quot;dmm&quot;: {&quot;functions...\n",
       "    guid:                     fb8b1a30-0000-0000-0000-018d7de56ab5\n",
       "    run_timestamp:            2024-02-06 11:10:03\n",
       "    ...                       ...\n",
       "    captured_counter:         4\n",
       "    run_id:                   4\n",
       "    run_description:          {&quot;version&quot;: 3, &quot;interdependencies&quot;: {&quot;paramspec...\n",
       "    parent_dataset_links:     []\n",
       "    run_timestamp_raw:        1707214203.6188297\n",
       "    completed_timestamp_raw:  1707214203.871863</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-509a7d3c-972c-40f1-84ad-69b0b8c1835e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-509a7d3c-972c-40f1-84ad-69b0b8c1835e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>multi_index</span>: 200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2642c6ea-bba5-4d02-abab-633343ffed35' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2642c6ea-bba5-4d02-abab-633343ffed35' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>multi_index</span></div><div class='xr-var-dims'>(multi_index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-3f367ac7-9df2-4e64-a0ab-371fa7a18638' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f367ac7-9df2-4e64-a0ab-371fa7a18638' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12261180-bf97-4be7-943d-e31f44411051' class='xr-var-data-in' type='checkbox'><label for='data-12261180-bf97-4be7-943d-e31f44411051' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([(-1.0, -1.0), (-0.989949748743719, -0.99), (-0.979899497487437, -0.98),\n",
       "       (-0.969849246231156, -0.97), (-0.959798994974874, -0.96),\n",
       "       (-0.949748743718593, -0.95), (-0.939698492462312, -0.94),\n",
       "       (-0.92964824120603, -0.93), (-0.919597989949749, -0.92),\n",
       "       (-0.909547738693467, -0.91), (-0.899497487437186, -0.9),\n",
       "       (-0.889447236180905, -0.89), (-0.879396984924623, -0.88),\n",
       "       (-0.869346733668342, -0.87), (-0.85929648241206, -0.86),\n",
       "       (-0.849246231155779, -0.85), (-0.839195979899498, -0.84),\n",
       "       (-0.829145728643216, -0.83), (-0.819095477386935, -0.82),\n",
       "       (-0.809045226130653, -0.81), (-0.798994974874372, -0.8),\n",
       "       (-0.788944723618091, -0.79), (-0.778894472361809, -0.78),\n",
       "       (-0.768844221105528, -0.77), (-0.758793969849246, -0.76),\n",
       "       (-0.748743718592965, -0.75), (-0.738693467336683, -0.74),\n",
       "       (-0.728643216080402, -0.73), (-0.718592964824121, -0.72),\n",
       "       (-0.708542713567839, -0.71), (-0.698492462311558, -0.7),\n",
       "       (-0.688442211055276, -0.69), (-0.678391959798995, -0.68),\n",
       "       (-0.668341708542714, -0.67), (-0.658291457286432, -0.66),\n",
       "       (-0.648241206030151, -0.65), (-0.638190954773869, -0.64),\n",
       "       (-0.628140703517588, -0.63), (-0.618090452261307, -0.62),\n",
       "       (-0.608040201005025, -0.61), (-0.597989949748744, -0.6),\n",
       "       (-0.587939698492462, -0.59), (-0.577889447236181, -0.58),\n",
       "       (-0.5678391959799, -0.57), (-0.557788944723618, -0.56),\n",
       "       (-0.547738693467337, -0.55), (-0.537688442211055, -0.54),\n",
       "       (-0.527638190954774, -0.53), (-0.517587939698493, -0.52),\n",
       "       (-0.507537688442211, -0.51), (-0.49748743718593, -0.5),\n",
       "       (-0.487437185929648, -0.49), (-0.477386934673367, -0.48),\n",
       "       (-0.467336683417086, -0.47), (-0.457286432160804, -0.46),\n",
       "       (-0.447236180904523, -0.45), (-0.437185929648241, -0.44),\n",
       "       (-0.42713567839196, -0.43), (-0.417085427135678, -0.42),\n",
       "       (-0.407035175879397, -0.41), (-0.396984924623116, -0.4),\n",
       "       (-0.386934673366834, -0.39), (-0.376884422110553, -0.38),\n",
       "       (-0.366834170854271, -0.37), (-0.35678391959799, -0.36),\n",
       "       (-0.346733668341709, -0.35), (-0.336683417085427, -0.34),\n",
       "       (-0.326633165829146, -0.33), (-0.316582914572864, -0.32),\n",
       "       (-0.306532663316583, -0.31), (-0.296482412060301, -0.3),\n",
       "       (-0.28643216080402, -0.29), (-0.276381909547739, -0.28),\n",
       "       (-0.266331658291457, -0.27), (-0.256281407035176, -0.26),\n",
       "       (-0.246231155778894, -0.25), (-0.236180904522613, -0.24),\n",
       "       (-0.226130653266332, -0.23), (-0.21608040201005, -0.22),\n",
       "       (-0.206030150753769, -0.21), (-0.195979899497487, -0.2),\n",
       "       (-0.185929648241206, -0.19), (-0.175879396984925, -0.18),\n",
       "       (-0.165829145728643, -0.17), (-0.155778894472362, -0.16),\n",
       "       (-0.14572864321608, -0.15), (-0.135678391959799, -0.14),\n",
       "       (-0.125628140703518, -0.13), (-0.115577889447236, -0.12),\n",
       "       (-0.105527638190955, -0.11), (-0.0954773869346733, -0.1),\n",
       "       (-0.085427135678392, -0.09), (-0.0753768844221105, -0.08),\n",
       "       (-0.0653266331658291, -0.07), (-0.0552763819095478, -0.06),\n",
       "       (-0.0452261306532663, -0.0499999999999999), (-0.035175879396985, -0.04),\n",
       "       (-0.0251256281407035, -0.03), (-0.0150753768844221, -0.02),\n",
       "       (-0.00502512562814073, -0.01), (0.00502512562814061, 0.0),\n",
       "       (0.0150753768844221, 0.01), (0.0251256281407035, 0.02),\n",
       "       (0.035175879396985, 0.03), (0.0452261306532664, 0.04),\n",
       "       (0.0552763819095476, 0.05), (0.0653266331658291, 0.0600000000000001),\n",
       "       (0.0753768844221105, 0.0700000000000001),\n",
       "       (0.085427135678392, 0.0800000000000001),\n",
       "       (0.0954773869346735, 0.0900000000000001), (0.105527638190955, 0.1),\n",
       "       (0.115577889447236, 0.11), (0.125628140703518, 0.12),\n",
       "       (0.135678391959799, 0.13), (0.14572864321608, 0.14),\n",
       "       (0.155778894472362, 0.15), (0.165829145728643, 0.16),\n",
       "       (0.175879396984925, 0.17), (0.185929648241206, 0.18),\n",
       "       (0.195979899497488, 0.19), (0.206030150753769, 0.2),\n",
       "       (0.21608040201005, 0.21), (0.226130653266332, 0.22),\n",
       "       (0.236180904522613, 0.23), (0.246231155778895, 0.24),\n",
       "       (0.256281407035176, 0.25), (0.266331658291457, 0.26),\n",
       "       (0.276381909547739, 0.27), (0.28643216080402, 0.28),\n",
       "       (0.296482412060302, 0.29), (0.306532663316583, 0.3),\n",
       "       (0.316582914572864, 0.31), (0.326633165829146, 0.32),\n",
       "       (0.336683417085427, 0.33), (0.346733668341709, 0.34),\n",
       "       (0.35678391959799, 0.35), (0.366834170854271, 0.36),\n",
       "       (0.376884422110553, 0.37), (0.386934673366834, 0.38),\n",
       "       (0.396984924623116, 0.39), (0.407035175879397, 0.4),\n",
       "       (0.417085427135678, 0.41), (0.42713567839196, 0.42),\n",
       "       (0.437185929648241, 0.43), (0.447236180904523, 0.44),\n",
       "       (0.457286432160804, 0.45), (0.467336683417085, 0.46),\n",
       "       (0.477386934673367, 0.47), (0.487437185929648, 0.48),\n",
       "       (0.49748743718593, 0.49), (0.507537688442211, 0.5),\n",
       "       (0.517587939698493, 0.51), (0.527638190954774, 0.52),\n",
       "       (0.537688442211055, 0.53), (0.547738693467337, 0.54),\n",
       "       (0.557788944723618, 0.55), (0.5678391959799, 0.56),\n",
       "       (0.577889447236181, 0.57), (0.587939698492462, 0.58),\n",
       "       (0.597989949748744, 0.59), (0.608040201005025, 0.6),\n",
       "       (0.618090452261307, 0.61), (0.628140703517588, 0.62),\n",
       "       (0.638190954773869, 0.63), (0.648241206030151, 0.64),\n",
       "       (0.658291457286432, 0.65), (0.668341708542714, 0.66),\n",
       "       (0.678391959798995, 0.67), (0.688442211055276, 0.68),\n",
       "       (0.698492462311558, 0.69), (0.708542713567839, 0.7),\n",
       "       (0.718592964824121, 0.71), (0.728643216080402, 0.72),\n",
       "       (0.738693467336683, 0.73), (0.748743718592965, 0.74),\n",
       "       (0.758793969849246, 0.75), (0.768844221105528, 0.76),\n",
       "       (0.778894472361809, 0.77), (0.788944723618091, 0.78),\n",
       "       (0.798994974874372, 0.79), (0.809045226130653, 0.8),\n",
       "       (0.819095477386935, 0.81), (0.829145728643216, 0.82),\n",
       "       (0.839195979899498, 0.83), (0.849246231155779, 0.84),\n",
       "       (0.85929648241206, 0.85), (0.869346733668342, 0.86),\n",
       "       (0.879396984924623, 0.87), (0.889447236180905, 0.88),\n",
       "       (0.899497487437186, 0.89), (0.909547738693467, 0.9),\n",
       "       (0.919597989949749, 0.91), (0.92964824120603, 0.92),\n",
       "       (0.939698492462312, 0.93), (0.949748743718593, 0.94),\n",
       "       (0.959798994974874, 0.95), (0.969849246231156, 0.96),\n",
       "       (0.979899497487437, 0.97), (0.989949748743719, 0.98), (1.0, 0.99)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch1</span></div><div class='xr-var-dims'>(multi_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.9899 -0.9799 ... 0.9899 1.0</div><input id='attrs-d60709bb-3b48-468c-a019-a2569aad18cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d60709bb-3b48-468c-a019-a2569aad18cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc9717d6-7605-43ed-854e-2428a8c3da75' class='xr-var-data-in' type='checkbox'><label for='data-dc9717d6-7605-43ed-854e-2428a8c3da75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dac_ch1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate ch1</dd></dl></div><div class='xr-var-data'><pre>array([-1.      , -0.98995 , -0.979899, -0.969849, -0.959799, -0.949749,\n",
       "       -0.939698, -0.929648, -0.919598, -0.909548, -0.899497, -0.889447,\n",
       "       -0.879397, -0.869347, -0.859296, -0.849246, -0.839196, -0.829146,\n",
       "       -0.819095, -0.809045, -0.798995, -0.788945, -0.778894, -0.768844,\n",
       "       -0.758794, -0.748744, -0.738693, -0.728643, -0.718593, -0.708543,\n",
       "       -0.698492, -0.688442, -0.678392, -0.668342, -0.658291, -0.648241,\n",
       "       -0.638191, -0.628141, -0.61809 , -0.60804 , -0.59799 , -0.58794 ,\n",
       "       -0.577889, -0.567839, -0.557789, -0.547739, -0.537688, -0.527638,\n",
       "       -0.517588, -0.507538, -0.497487, -0.487437, -0.477387, -0.467337,\n",
       "       -0.457286, -0.447236, -0.437186, -0.427136, -0.417085, -0.407035,\n",
       "       -0.396985, -0.386935, -0.376884, -0.366834, -0.356784, -0.346734,\n",
       "       -0.336683, -0.326633, -0.316583, -0.306533, -0.296482, -0.286432,\n",
       "       -0.276382, -0.266332, -0.256281, -0.246231, -0.236181, -0.226131,\n",
       "       -0.21608 , -0.20603 , -0.19598 , -0.18593 , -0.175879, -0.165829,\n",
       "       -0.155779, -0.145729, -0.135678, -0.125628, -0.115578, -0.105528,\n",
       "       -0.095477, -0.085427, -0.075377, -0.065327, -0.055276, -0.045226,\n",
       "       -0.035176, -0.025126, -0.015075, -0.005025,  0.005025,  0.015075,\n",
       "        0.025126,  0.035176,  0.045226,  0.055276,  0.065327,  0.075377,\n",
       "        0.085427,  0.095477,  0.105528,  0.115578,  0.125628,  0.135678,\n",
       "        0.145729,  0.155779,  0.165829,  0.175879,  0.18593 ,  0.19598 ,\n",
       "        0.20603 ,  0.21608 ,  0.226131,  0.236181,  0.246231,  0.256281,\n",
       "        0.266332,  0.276382,  0.286432,  0.296482,  0.306533,  0.316583,\n",
       "        0.326633,  0.336683,  0.346734,  0.356784,  0.366834,  0.376884,\n",
       "        0.386935,  0.396985,  0.407035,  0.417085,  0.427136,  0.437186,\n",
       "        0.447236,  0.457286,  0.467337,  0.477387,  0.487437,  0.497487,\n",
       "        0.507538,  0.517588,  0.527638,  0.537688,  0.547739,  0.557789,\n",
       "        0.567839,  0.577889,  0.58794 ,  0.59799 ,  0.60804 ,  0.61809 ,\n",
       "        0.628141,  0.638191,  0.648241,  0.658291,  0.668342,  0.678392,\n",
       "        0.688442,  0.698492,  0.708543,  0.718593,  0.728643,  0.738693,\n",
       "        0.748744,  0.758794,  0.768844,  0.778894,  0.788945,  0.798995,\n",
       "        0.809045,  0.819095,  0.829146,  0.839196,  0.849246,  0.859296,\n",
       "        0.869347,  0.879397,  0.889447,  0.899497,  0.909548,  0.919598,\n",
       "        0.929648,  0.939698,  0.949749,  0.959799,  0.969849,  0.979899,\n",
       "        0.98995 ,  1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch2</span></div><div class='xr-var-dims'>(multi_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.99 -0.98 ... 0.97 0.98 0.99</div><input id='attrs-30681063-023f-4ad5-bc4d-afac2527924c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30681063-023f-4ad5-bc4d-afac2527924c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1509c4fa-ff41-4ae1-a77c-8a0ef89ba55b' class='xr-var-data-in' type='checkbox'><label for='data-1509c4fa-ff41-4ae1-a77c-8a0ef89ba55b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dac_ch2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate ch2</dd></dl></div><div class='xr-var-data'><pre>array([-1.  , -0.99, -0.98, -0.97, -0.96, -0.95, -0.94, -0.93, -0.92, -0.91,\n",
       "       -0.9 , -0.89, -0.88, -0.87, -0.86, -0.85, -0.84, -0.83, -0.82, -0.81,\n",
       "       -0.8 , -0.79, -0.78, -0.77, -0.76, -0.75, -0.74, -0.73, -0.72, -0.71,\n",
       "       -0.7 , -0.69, -0.68, -0.67, -0.66, -0.65, -0.64, -0.63, -0.62, -0.61,\n",
       "       -0.6 , -0.59, -0.58, -0.57, -0.56, -0.55, -0.54, -0.53, -0.52, -0.51,\n",
       "       -0.5 , -0.49, -0.48, -0.47, -0.46, -0.45, -0.44, -0.43, -0.42, -0.41,\n",
       "       -0.4 , -0.39, -0.38, -0.37, -0.36, -0.35, -0.34, -0.33, -0.32, -0.31,\n",
       "       -0.3 , -0.29, -0.28, -0.27, -0.26, -0.25, -0.24, -0.23, -0.22, -0.21,\n",
       "       -0.2 , -0.19, -0.18, -0.17, -0.16, -0.15, -0.14, -0.13, -0.12, -0.11,\n",
       "       -0.1 , -0.09, -0.08, -0.07, -0.06, -0.05, -0.04, -0.03, -0.02, -0.01,\n",
       "        0.  ,  0.01,  0.02,  0.03,  0.04,  0.05,  0.06,  0.07,  0.08,  0.09,\n",
       "        0.1 ,  0.11,  0.12,  0.13,  0.14,  0.15,  0.16,  0.17,  0.18,  0.19,\n",
       "        0.2 ,  0.21,  0.22,  0.23,  0.24,  0.25,  0.26,  0.27,  0.28,  0.29,\n",
       "        0.3 ,  0.31,  0.32,  0.33,  0.34,  0.35,  0.36,  0.37,  0.38,  0.39,\n",
       "        0.4 ,  0.41,  0.42,  0.43,  0.44,  0.45,  0.46,  0.47,  0.48,  0.49,\n",
       "        0.5 ,  0.51,  0.52,  0.53,  0.54,  0.55,  0.56,  0.57,  0.58,  0.59,\n",
       "        0.6 ,  0.61,  0.62,  0.63,  0.64,  0.65,  0.66,  0.67,  0.68,  0.69,\n",
       "        0.7 ,  0.71,  0.72,  0.73,  0.74,  0.75,  0.76,  0.77,  0.78,  0.79,\n",
       "        0.8 ,  0.81,  0.82,  0.83,  0.84,  0.85,  0.86,  0.87,  0.88,  0.89,\n",
       "        0.9 ,  0.91,  0.92,  0.93,  0.94,  0.95,  0.96,  0.97,  0.98,  0.99])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4b75caa-bc64-4465-8e49-e3127e0933e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f4b75caa-bc64-4465-8e49-e3127e0933e3' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dmm_v1</span></div><div class='xr-var-dims'>(multi_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.076 6.029 6.155 ... 4.044 4.156</div><input id='attrs-bbd5262f-1f94-4e29-bcb4-322522dff0bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbd5262f-1f94-4e29-bcb4-322522dff0bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f68a690-3e99-4ffc-9e46-6b321c5523ca' class='xr-var-data-in' type='checkbox'><label for='data-7f68a690-3e99-4ffc-9e46-6b321c5523ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dmm_v1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate v1</dd></dl></div><div class='xr-var-data'><pre>array([6.07631879, 6.02917498, 6.15490858, 6.11849925, 6.04678186,\n",
       "       6.12375546, 6.08625745, 6.16261188, 6.05081379, 5.88366774,\n",
       "       6.07338364, 6.01226441, 5.92561159, 5.85270669, 5.87184158,\n",
       "       5.8777255 , 5.97664988, 5.96327949, 6.05616552, 5.80156641,\n",
       "       5.89235933, 5.94989447, 5.91869903, 5.76009059, 5.65961228,\n",
       "       5.76471596, 5.79016132, 5.86464866, 5.74585398, 5.66223949,\n",
       "       5.71616115, 5.86863826, 5.73508621, 5.48323795, 5.60886835,\n",
       "       5.61042352, 5.82795213, 5.49572062, 5.48257891, 5.80519732,\n",
       "       5.6770444 , 5.54896036, 5.61940738, 5.54733046, 5.72026031,\n",
       "       5.55533323, 5.49934671, 5.67434886, 5.471335  , 5.47727452,\n",
       "       5.47233934, 5.54776648, 5.49377296, 5.59132285, 5.40289244,\n",
       "       5.66088929, 5.26974208, 5.55323088, 5.54165263, 5.40236623,\n",
       "       5.15355292, 5.26173958, 5.49590004, 5.17444998, 5.51900895,\n",
       "       5.37171219, 5.40781407, 5.56340314, 5.47366172, 5.26395718,\n",
       "       5.35295337, 5.32507971, 4.99247468, 5.29156317, 5.29476048,\n",
       "       5.29094862, 5.33123314, 5.14261465, 5.31374378, 5.16293867,\n",
       "       5.255587  , 5.02667407, 5.26285888, 5.33552897, 5.10233602,\n",
       "       4.9818201 , 5.03296071, 5.09418872, 4.97147489, 4.86984177,\n",
       "       5.14743393, 5.14072344, 5.11448142, 5.06144863, 5.11384539,\n",
       "       4.97870331, 5.01355491, 4.92872541, 5.13443322, 4.87518731,\n",
       "       5.11119929, 4.98147287, 4.92945099, 5.11257587, 5.06818913,\n",
       "       4.91641625, 4.94576564, 4.88496349, 4.83648983, 5.05473432,\n",
       "       4.72768033, 4.62429408, 4.70136029, 4.96313252, 4.98981993,\n",
       "       4.67247918, 4.69828083, 5.02232252, 4.76738799, 4.69228525,\n",
       "       4.70945961, 4.96595501, 4.80142225, 4.63558476, 4.81510061,\n",
       "       4.58667354, 4.75515587, 4.78579783, 4.56419993, 4.74365108,\n",
       "       4.8488705 , 4.69415247, 4.79501493, 4.79738972, 4.6541709 ,\n",
       "       4.56942477, 4.75703495, 4.52634182, 4.64608724, 4.69892458,\n",
       "       4.59706932, 4.53232422, 4.77074117, 4.55768531, 4.43686677,\n",
       "       4.49771964, 4.57623002, 4.56145117, 4.52296724, 4.57019213,\n",
       "       4.70974354, 4.57407248, 4.58099892, 4.69938061, 4.46271432,\n",
       "       4.30871799, 4.47865775, 4.60691586, 4.25667411, 4.48140392,\n",
       "       4.43044763, 4.43701537, 4.33746029, 4.47573155, 4.35722306,\n",
       "       4.24925887, 4.20719749, 4.49760573, 4.470795  , 4.09778364,\n",
       "       4.28961626, 4.20483287, 4.42086181, 4.39725946, 4.40382307,\n",
       "       4.4378223 , 4.28840592, 4.1248542 , 4.43756304, 4.09240686,\n",
       "       4.30534662, 4.35091817, 4.39075277, 4.24799535, 4.14572659,\n",
       "       4.35941528, 4.07015969, 4.17702562, 3.99923977, 4.17361692,\n",
       "       4.27235282, 4.15362672, 4.10965295, 4.16323191, 4.12762101,\n",
       "       4.07264551, 4.1617071 , 4.01059089, 4.04442729, 4.15579677])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dmm_v2</span></div><div class='xr-var-dims'>(multi_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0005091 0.0006515 ... 0.0005385</div><input id='attrs-d3d78d46-f8c3-4c55-880b-d9ae2550db61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3d78d46-f8c3-4c55-880b-d9ae2550db61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37ffdb5d-b78f-400b-8711-7d4cb7628d0e' class='xr-var-data-in' type='checkbox'><label for='data-37ffdb5d-b78f-400b-8711-7d4cb7628d0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dmm_v2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate v2</dd></dl></div><div class='xr-var-data'><pre>array([-5.09066795e-04,  6.51540652e-04, -6.21491250e-04, -3.84035329e-04,\n",
       "       -4.12759774e-04,  1.43388929e-04, -2.44222273e-04,  1.66883921e-05,\n",
       "       -1.45021412e-04, -3.89155606e-04,  1.36486407e-03, -1.25628671e-04,\n",
       "        9.58405326e-04, -4.24902309e-04,  4.15497653e-05, -1.53063946e-04,\n",
       "        2.55521798e-04,  1.15935445e-05, -9.50840264e-05, -8.59610266e-04,\n",
       "       -5.45861432e-05,  4.81817238e-04,  1.10640653e-03, -6.78050682e-04,\n",
       "       -1.68567145e-04,  4.09188570e-05, -1.15456625e-05, -4.15909130e-04,\n",
       "       -3.65976377e-04, -7.10257018e-04,  4.06799929e-04,  3.14829773e-04,\n",
       "        5.22406827e-04, -2.38460938e-04,  2.89502340e-04,  2.65974752e-05,\n",
       "       -7.08330220e-05,  9.27276045e-05, -1.87147661e-04,  4.92315106e-04,\n",
       "        5.56792314e-04, -2.61323708e-05,  2.66782713e-04,  5.50932996e-04,\n",
       "        4.80920807e-04, -1.93199666e-04,  8.73975206e-04,  5.54239032e-04,\n",
       "        6.30980001e-04,  1.57007941e-03,  1.96860154e-03,  1.02466997e-03,\n",
       "        1.43701745e-03,  2.08626528e-03,  3.40596611e-03,  3.84604370e-03,\n",
       "        5.05877801e-03,  4.87057084e-03,  6.27186293e-03,  6.89340973e-03,\n",
       "        8.30174259e-03,  1.05391787e-02,  1.25009813e-02,  1.43360716e-02,\n",
       "        1.81387768e-02,  1.97614726e-02,  2.41582820e-02,  2.86150398e-02,\n",
       "        3.19880564e-02,  3.77355633e-02,  4.36077945e-02,  5.04391424e-02,\n",
       "        5.78217867e-02,  6.64524231e-02,  7.52734450e-02,  8.66832148e-02,\n",
       "        9.81658911e-02,  1.09189804e-01,  1.24747229e-01,  1.38342912e-01,\n",
       "...\n",
       "        1.03525628e+00,  1.01600397e+00,  9.94728160e-01,  9.69810775e-01,\n",
       "        9.43317767e-01,  9.13863126e-01,  8.81930975e-01,  8.49281994e-01,\n",
       "        8.16070996e-01,  7.79348910e-01,  7.44583757e-01,  7.07435918e-01,\n",
       "        6.69223148e-01,  6.32642067e-01,  5.95159125e-01,  5.58432600e-01,\n",
       "        5.22506750e-01,  4.87043123e-01,  4.52732987e-01,  4.19540293e-01,\n",
       "        3.85755255e-01,  3.57160290e-01,  3.26140303e-01,  2.98947375e-01,\n",
       "        2.72398263e-01,  2.47746591e-01,  2.24127340e-01,  2.02341211e-01,\n",
       "        1.80967518e-01,  1.64320234e-01,  1.45509161e-01,  1.30471538e-01,\n",
       "        1.15722125e-01,  1.02345465e-01,  9.05638259e-02,  8.02681259e-02,\n",
       "        6.95724743e-02,  6.07358225e-02,  5.48715791e-02,  4.67264654e-02,\n",
       "        4.02354419e-02,  3.50915657e-02,  2.93487582e-02,  2.51388241e-02,\n",
       "        2.22177454e-02,  1.88223058e-02,  1.51757753e-02,  1.30594203e-02,\n",
       "        1.13433965e-02,  9.21111091e-03,  7.55647981e-03,  7.30881607e-03,\n",
       "        4.69014810e-03,  4.12857655e-03,  3.68110912e-03,  2.62961188e-03,\n",
       "        2.47516226e-03,  2.39521080e-03,  2.32371199e-03,  6.81308665e-04,\n",
       "        1.18215198e-03,  1.55734664e-03,  1.62304671e-05,  9.79222196e-04,\n",
       "        3.87959879e-04, -5.00713629e-04,  1.12373087e-04, -1.94695439e-04,\n",
       "        6.07236893e-04,  3.08554870e-04, -5.63106156e-04,  2.09059618e-04,\n",
       "        7.46011324e-04, -3.32447028e-04,  4.60141311e-06,  3.40751040e-04,\n",
       "       -3.94652688e-04, -7.64127147e-04, -4.16083401e-04,  5.38521982e-04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec91f40b-b247-4f48-9731-048893de918b' class='xr-section-summary-in' type='checkbox'  ><label for='section-ec91f40b-b247-4f48-9731-048893de918b' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>multi_index<br>dac_ch1<br>dac_ch2</div></div><div class='xr-index-preview'>PandasMultiIndex</div><div></div><input id='index-5f26f19b-61e8-4756-98ef-0bc4664a31c4' class='xr-index-data-in' type='checkbox'/><label for='index-5f26f19b-61e8-4756-98ef-0bc4664a31c4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(MultiIndex([(              -1.0,  -1.0),\n",
       "            (-0.989949748743719, -0.99),\n",
       "            (-0.979899497487437, -0.98),\n",
       "            (-0.969849246231156, -0.97),\n",
       "            (-0.959798994974874, -0.96),\n",
       "            (-0.949748743718593, -0.95),\n",
       "            (-0.939698492462312, -0.94),\n",
       "            ( -0.92964824120603, -0.93),\n",
       "            (-0.919597989949749, -0.92),\n",
       "            (-0.909547738693467, -0.91),\n",
       "            ...\n",
       "            ( 0.909547738693467,   0.9),\n",
       "            ( 0.919597989949749,  0.91),\n",
       "            (  0.92964824120603,  0.92),\n",
       "            ( 0.939698492462312,  0.93),\n",
       "            ( 0.949748743718593,  0.94),\n",
       "            ( 0.959798994974874,  0.95),\n",
       "            ( 0.969849246231156,  0.96),\n",
       "            ( 0.979899497487437,  0.97),\n",
       "            ( 0.989949748743719,  0.98),\n",
       "            (               1.0,  0.99)],\n",
       "           name=&#x27;multi_index&#x27;, length=200))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b0cae920-f676-47df-96cf-be5f538e3ff1' class='xr-section-summary-in' type='checkbox'  ><label for='section-b0cae920-f676-47df-96cf-be5f538e3ff1' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ds_name :</span></dt><dd>results</dd><dt><span>sample_name :</span></dt><dd>no sample</dd><dt><span>exp_name :</span></dt><dd>working_with_pandas</dd><dt><span>snapshot :</span></dt><dd>{&quot;station&quot;: {&quot;instruments&quot;: {&quot;dmm&quot;: {&quot;functions&quot;: {}, &quot;submodules&quot;: {}, &quot;parameters&quot;: {&quot;IDN&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dmm_IDN&quot;, &quot;value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;raw_value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;ts&quot;: &quot;2024-02-06 11:09:01&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;unit&quot;: &quot;&quot;, &quot;vals&quot;: &quot;&lt;Anything&gt;&quot;, &quot;inter_delay&quot;: 0, &quot;label&quot;: &quot;IDN&quot;, &quot;post_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Anything&gt;&quot;], &quot;name&quot;: &quot;IDN&quot;}, &quot;v1&quot;: {&quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DmmExponentialParameter&quot;, &quot;full_name&quot;: &quot;dmm_v1&quot;, &quot;value&quot;: 4.037768729370727, &quot;raw_value&quot;: 4.037768729370727, &quot;ts&quot;: &quot;2024-02-06 11:10:00&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;inter_delay&quot;: 0, &quot;label&quot;: &quot;Gate v1&quot;, &quot;post_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;name&quot;: &quot;v1&quot;}, &quot;v2&quot;: {&quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DmmGaussParameter&quot;, &quot;full_name&quot;: &quot;dmm_v2&quot;, &quot;value&quot;: -0.00013379111441398194, &quot;raw_value&quot;: -0.00013379111441398194, &quot;ts&quot;: &quot;2024-02-06 11:10:00&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;inter_delay&quot;: 0, &quot;label&quot;: &quot;Gate v2&quot;, &quot;post_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;name&quot;: &quot;v2&quot;}}, &quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;label&quot;: &quot;dmm&quot;, &quot;name&quot;: &quot;dmm&quot;}, &quot;dac&quot;: {&quot;functions&quot;: {}, &quot;submodules&quot;: {}, &quot;parameters&quot;: {&quot;IDN&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_IDN&quot;, &quot;value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrument&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;raw_value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrument&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;ts&quot;: &quot;2024-02-06 11:09:01&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;unit&quot;: &quot;&quot;, &quot;vals&quot;: &quot;&lt;Anything&gt;&quot;, &quot;inter_delay&quot;: 0, &quot;label&quot;: &quot;IDN&quot;, &quot;post_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Anything&gt;&quot;], &quot;name&quot;: &quot;IDN&quot;}, &quot;ch1&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_ch1&quot;, &quot;value&quot;: 1.0, &quot;raw_value&quot;: 1.0, &quot;ts&quot;: &quot;2024-02-06 11:10:00&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;inter_delay&quot;: 0, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;post_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;name&quot;: &quot;ch1&quot;}, &quot;ch2&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_ch2&quot;, &quot;value&quot;: 1.0, &quot;raw_value&quot;: 1.0, &quot;ts&quot;: &quot;2024-02-06 11:10:00&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;inter_delay&quot;: 0, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;post_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;name&quot;: &quot;ch2&quot;}}, &quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;label&quot;: &quot;dac&quot;, &quot;name&quot;: &quot;dac&quot;}}, &quot;parameters&quot;: {}, &quot;components&quot;: {}, &quot;config&quot;: null}}</dd><dt><span>guid :</span></dt><dd>fb8b1a30-0000-0000-0000-018d7de56ab5</dd><dt><span>run_timestamp :</span></dt><dd>2024-02-06 11:10:03</dd><dt><span>completed_timestamp :</span></dt><dd>2024-02-06 11:10:03</dd><dt><span>captured_run_id :</span></dt><dd>4</dd><dt><span>captured_counter :</span></dt><dd>4</dd><dt><span>run_id :</span></dt><dd>4</dd><dt><span>run_description :</span></dt><dd>{&quot;version&quot;: 3, &quot;interdependencies&quot;: {&quot;paramspecs&quot;: [{&quot;name&quot;: &quot;dac_ch1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: []}, {&quot;name&quot;: &quot;dac_ch2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: []}, {&quot;name&quot;: &quot;dmm_v2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;]}, {&quot;name&quot;: &quot;dmm_v1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;]}]}, &quot;interdependencies_&quot;: {&quot;parameters&quot;: {&quot;dmm_v2&quot;: {&quot;name&quot;: &quot;dmm_v2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dac_ch1&quot;: {&quot;name&quot;: &quot;dac_ch1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dac_ch2&quot;: {&quot;name&quot;: &quot;dac_ch2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dmm_v1&quot;: {&quot;name&quot;: &quot;dmm_v1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;unit&quot;: &quot;V&quot;}}, &quot;dependencies&quot;: {&quot;dmm_v2&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;], &quot;dmm_v1&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;]}, &quot;inferences&quot;: {}, &quot;standalones&quot;: []}, &quot;shapes&quot;: null}</dd><dt><span>parent_dataset_links :</span></dt><dd>[]</dd><dt><span>run_timestamp_raw :</span></dt><dd>1707214203.6188297</dd><dt><span>completed_timestamp_raw :</span></dt><dd>1707214203.871863</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (multi_index: 200)\n",
       "Coordinates:\n",
       "  * multi_index  (multi_index) object MultiIndex\n",
       "  * dac_ch1      (multi_index) float64 -1.0 -0.9899 -0.9799 ... 0.9899 1.0\n",
       "  * dac_ch2      (multi_index) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99\n",
       "Data variables:\n",
       "    dmm_v1       (multi_index) float64 6.076 6.029 6.155 ... 4.011 4.044 4.156\n",
       "    dmm_v2       (multi_index) float64 -0.0005091 0.0006515 ... 0.0005385\n",
       "Attributes: (12/14)\n",
       "    ds_name:                  results\n",
       "    sample_name:              no sample\n",
       "    exp_name:                 working_with_pandas\n",
       "    snapshot:                 {\"station\": {\"instruments\": {\"dmm\": {\"functions...\n",
       "    guid:                     fb8b1a30-0000-0000-0000-018d7de56ab5\n",
       "    run_timestamp:            2024-02-06 11:10:03\n",
       "    ...                       ...\n",
       "    captured_counter:         4\n",
       "    run_id:                   4\n",
       "    run_description:          {\"version\": 3, \"interdependencies\": {\"paramspec...\n",
       "    parent_dataset_links:     []\n",
       "    run_timestamp_raw:        1707214203.6188297\n",
       "    completed_timestamp_raw:  1707214203.871863"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xaDataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how the expected coordinates can be seen above along with a coordinate called `multi_index`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QCoDeS has build in support for exporting such datasets to NetCDF files using [cf_xarray](https://cf-xarray.readthedocs.io/en/latest/coding.html) to compress and decompress the data. Note however, that if you manually export or import such XArray datasets to / from NetCDF you will be responsible for compressing / decompressing as needed. "
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