01 Getting Started with PCSE.ipynb

{
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   "cell_type": "markdown",
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    "<img style=\"float: right;\" src=\"data:image/png;base64,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\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started with PCSE/WOFOST\n",
    "\n",
    "This Jupyter notebook will introduce PCSE and explain the basics of running models with PCSE, taking WOFOST as an example.\n",
    "\n",
    "Allard de Wit, March 2018\n",
    "\n",
    "**Prerequisites for running this notebook**\n",
    "\n",
    "Several packages need to be installed for running PCSE/WOFOST:\n",
    "\n",
    "  1. `PCSE` and its dependencies. See the [PCSE user guide](http://pcse.readthedocs.io/en/stable/installing.html) for more information;\n",
    "  2. `pandas` for processing and storing WOFOST output;\n",
    "  3. `matplotlib` for generating charts\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the relevant modules\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was built with:\n",
      "python version: 3.10.13 | packaged by conda-forge | (main, Oct 26 2023, 18:01:37) [MSC v.1935 64 bit (AMD64)]\n",
      "PCSE version: 6.0.6\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import sys\n",
    "import pcse\n",
    "import pandas\n",
    "import matplotlib\n",
    "matplotlib.style.use(\"ggplot\")\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"This notebook was built with:\")\n",
    "print(f\"python version: {sys.version}\")\n",
    "print(f\"PCSE version: {pcse.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Starting from the internal demo database\n",
    "For demonstration purposes, we can start WOFOST with a single function call. This function reads all relevant data from the internal demo databases. In the next notebook we will demonstrate how to read data from external sources.\n",
    "\n",
    "The command below starts WOFOST in potential production mode for winter-wheat for a location in Southern Spain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "wofostPP = pcse.start_wofost(mode=\"pp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have just successfully initialized a PCSE/WOFOST object in the Python interpreter, which is in its initial state and waiting to do some simulation. We can now advance the model state for example with 1 day:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "wofostPP.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Advancing the crop simulation with only 1 day, is often not so useful so the number of days to simulate can be specified as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "wofostPP.run(days=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting information about state and rate variables\n",
    "Retrieving information about the calculated model states or rates can be done with the `get_variable()` method on a PCSE object. For example, to retrieve the leaf area index value in the current model state you can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2870809817505803"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wofostPP.get_variable(\"LAI\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5281221557442748"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wofostPP.run(days=25)\n",
    "wofostPP.get_variable(\"LAI\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing that after 11 days the LAI value is 0.287. When we increase time with another 25 days, the LAI increases to 1.528. The `get_variable()` method can retrieve any state or rate variable that is defined somewhere in the model. \n",
    "\n",
    "Finally, we can finish the crop season by letting it run until the model terminates because the crop reaches maturity or the harvest date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "wofostPP.run_till_terminate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that before or after the crop cycle, the object representing the crop does not exist and therefore retrieving a crop related variable results in a `None` value. Of course the simulation results are stored and can be obtained, see next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(wofostPP.get_variable(\"LAI\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving and displaying WOFOST output\n",
    "We can retrieve the results of the simulation at each time step using  `get_output()`. In python terms this returns a list of dictionaries, one dictionary for each time step of the the simulation results. Each dictionary contains the key:value pairs of the state or rate variables that were stored at that time step.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = wofostPP.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most convenient way to handle the output from WOFOST is to used the `pandas` module to convert it into a dataframe. Pandas DataFrames can be converted to a variety of formats including Excel, CSV or database tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>DVS</th>\n",
       "      <th>LAI</th>\n",
       "      <th>TAGP</th>\n",
       "      <th>TWSO</th>\n",
       "      <th>TWLV</th>\n",
       "      <th>TWST</th>\n",
       "      <th>TWRT</th>\n",
       "      <th>TRA</th>\n",
       "      <th>RD</th>\n",
       "      <th>SM</th>\n",
       "      <th>WWLOW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-05-27</th>\n",
       "      <td>1.934169</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18091.006102</td>\n",
       "      <td>8729.399813</td>\n",
       "      <td>3126.21567</td>\n",
       "      <td>6235.390619</td>\n",
       "      <td>1613.465879</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.3175</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-28</th>\n",
       "      <td>1.953874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18091.006102</td>\n",
       "      <td>8729.399813</td>\n",
       "      <td>3126.21567</td>\n",
       "      <td>6235.390619</td>\n",
       "      <td>1613.465879</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.3175</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-29</th>\n",
       "      <td>1.974056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18091.006102</td>\n",
       "      <td>8729.399813</td>\n",
       "      <td>3126.21567</td>\n",
       "      <td>6235.390619</td>\n",
       "      <td>1613.465879</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.3175</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-30</th>\n",
       "      <td>1.995758</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18091.006102</td>\n",
       "      <td>8729.399813</td>\n",
       "      <td>3126.21567</td>\n",
       "      <td>6235.390619</td>\n",
       "      <td>1613.465879</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.3175</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-31</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18091.006102</td>\n",
       "      <td>8729.399813</td>\n",
       "      <td>3126.21567</td>\n",
       "      <td>6235.390619</td>\n",
       "      <td>1613.465879</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.3175</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DVS  LAI          TAGP         TWSO        TWLV         TWST  \\\n",
       "day                                                                             \n",
       "2000-05-27  1.934169  0.0  18091.006102  8729.399813  3126.21567  6235.390619   \n",
       "2000-05-28  1.953874  0.0  18091.006102  8729.399813  3126.21567  6235.390619   \n",
       "2000-05-29  1.974056  0.0  18091.006102  8729.399813  3126.21567  6235.390619   \n",
       "2000-05-30  1.995758  0.0  18091.006102  8729.399813  3126.21567  6235.390619   \n",
       "2000-05-31  2.000000  0.0  18091.006102  8729.399813  3126.21567  6235.390619   \n",
       "\n",
       "                   TWRT  TRA    RD      SM WWLOW  \n",
       "day                                               \n",
       "2000-05-27  1613.465879  0.0  60.0  0.3175  None  \n",
       "2000-05-28  1613.465879  0.0  60.0  0.3175  None  \n",
       "2000-05-29  1613.465879  0.0  60.0  0.3175  None  \n",
       "2000-05-30  1613.465879  0.0  60.0  0.3175  None  \n",
       "2000-05-31  1613.465879  0.0  60.0  0.3175  None  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfPP = pandas.DataFrame(output).set_index(\"day\")\n",
    "dfPP.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides the output at each time step, WOFOST also provides summary output which summarizes the crop cycle and provides you the total crop biomass, total yield, maximum LAI and other variables. In case of crop rotations, the summary output will consist of several sets of variables, one for each crop cycle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reached maturity at 2000-05-31 with total biomass 18091.0 kg/ha, a yield of 8729.4 kg/ha with a maximum LAI of 6.23.\n"
     ]
    }
   ],
   "source": [
    "summary_output = wofostPP.get_summary_output()\n",
    "msg = \"Reached maturity at {DOM} with total biomass {TAGP:.1f} kg/ha, \" \\\n",
    "      \"a yield of {TWSO:.1f} kg/ha with a maximum LAI of {LAIMAX:.2f}.\"\n",
    "for crop_cycle in summary_output:\n",
    "    print(msg.format(**crop_cycle))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing output\n",
    "The pandas module is also very useful for generating charts from simulation results. In this case we generate graphs of leaf area index and crop biomass including total biomass and grain yield."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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37rz33nuu72/Xrl1dK1x/8803jBo1CrhSyHznnXdo3bp1hvcsIllDxUKRXCr1TUL16tVdvdX+yddff83evXt59dVX0/VQi4mJYeTIkZmSLS4ujmnTpgFXhlD06tUrw+O+/PLLDIuFlmWl25Z6v//617/44IMPrivHm2++yaVLl1i0aFG6T6Xfeecdpk+ffl3nyUyp93Hs2LEM9x89evSqz9mwYQN33HHHdV/rxIkT9O/f3/VJ8L/+9S+aN29ORETEjcYWERERyVCjRo1YuHAhCxYsoH///jf8/Ize98GV9z+nTp0iKSkpXa+85ORkTpw44VoI5e/+6T1WRoW2qzlx4gQpKSnpCobXe67U/Rm9v4O/hnDfSKYbsXr1aqZOnUqrVq2YM2dOmkUPnU4n7777brrnBAQE8Nprr/Haa69x4MABli5dyrfffsuPP/5IbGwsv//+u+vY9u3b0759ey5cuMCKFSuYNWsWn3/+OR06dGDdunXpps8RkayjOQtFcqng4GAqVarE5s2bOXXq1HU9Z9euXQB069Yt3b4lS5ZkWrbvvvuOxMREatasSf/+/TP8FxERwW+//cbevXuv65x16tTB4XCkeYPyT3bt2kV4eHiGw1cy835vROqKxRldPyUlhaioqHTb69WrB3BD926M4cEHH+TQoUOMHDmSkSNHcvjwYR544AGMMTeZXkRERCStvn374uPjw+TJk9PMn5yRGxnRUb16dZxOJ0uXLk23b+nSpaSkpLjeV/3d/v37iY2NTbd98eLFrvNer+TkZKKjo2/6XKn7o6KiSE5OTrd/0aJFABneR2ZIfe/fqVOnNIVCuDKn9qVLl675/GLFitGnTx/mzZtH2bJliYqKco14+bugoCBatGjBBx98wEsvvURiYiJz5szJvBsRkRumYqFILvb000+TmJhIv379Mhy+cPr06TS9DkuWLAn89QYn1bp163jnnXcyLVfqcNnPPvuMr7/+OsN/jz76qGsRlOtRoEAB+vTpw+rVq3njjTfSDO1NtXv37jTFx5IlS3Lq1Ck2btyY5rjRo0dn2kIuN6pBgwZUqFCBpUuXpuvZ+Mknn6SbrxCuvAkPCwtj2LBhaRZLSeV0OtO16QcffMDs2bPp0aMHDz/8MA8//DA9evRg7ty5jBgxIlPvSURERHKvkiVL8tprr5GYmEj79u1ZvXp1hsfNnTuXu+6667rP269fPwBefPFFLl686Np+8eJFXnjhBYAMezKmpKTw73//O828fHv37uWjjz7C29ub++6777ozpF7/70XOU6dO8eabbwJX3qNdS9GiRWndujWxsbHp5hhfsWIFY8eOJW/evHTp0uWGMl2vq733P378OI8//ni64+Pi4ti0aVO67RcuXOD8+fN4e3u7hoUvXbo0wwJoas/O/53jUESyloYhi+Rg/zvf39999tln9OvXjzVr1vDZZ59RpkwZ2rRpQ/HixTl16hR79+5l6dKl9O3bly+++AK4skraiBEjeOqpp1i0aBHlypVj586dzJo1i65duzJ+/Phbzrx48WJ27NhBlSpVMlyoJFX//v156623GDNmDMOGDUv3aWdGPvnkE3bu3Mkrr7zCDz/8QKNGjShYsCCHDx9m69atrFq1inHjxrkmtX7qqaeYN28ejRo14t577yVPnjysXr2aqKgounfvzqRJk275fm+UZVmMHj2a1q1b061bN7p27UrZsmVZv349CxYsoG3btsydOzfNc/Lly8ekSZPo0qUL9erVo2XLllSqVAnLsjhw4AAxMTGcPHnStXjLqlWrePHFFylVqpRr/hi4Mux71apVDBkyhCZNmrh6LIqIiIjcipdeeonk5GSGDRtG7dq1adCgAbVq1SI4OJhjx46xdOlSdu7cmeGiHVfTu3dvpk+fzoQJE6hUqRJ33303lmUxbdo09u7dS48ePdKtCAxwxx13sGLFCmrWrMmdd95JfHw8EyZMID4+nnfffZcyZcpcd4ZChQpx+fJlKleuTKdOnUhKSmLSpEkcOXKEgQMH0qRJk388xxdffEHDhg157rnn+PXXX6lVqxYHDhxg4sSJOBwOxowZQ0hIyHVnuhG1a9emYcOGTJkyhQYNGtCoUSOOHTvGnDlzqFChgmvBlFSHDh2ievXqVKlShTvuuINixYpx9uxZZs2axdGjRxk0aJAr66BBgzh06BANGzakZMmS+Pr6smbNGhYuXEiJEiXo2bOnW+5JRK6TEZEcB/jHf6dPn3YdP3PmTNO+fXsTERFhfHx8TMGCBU3t2rXNkCFDzNatW9Oce/PmzaZjx44mIiLCBAYGmho1apivvvrK7N271wDmwQcfTHP8okWLDGBeffXV68reu3dvA5iRI0f+47GtW7c2gJkyZYoxxphXX33VAGbRokVXfc7ly5fNxx9/bOrXr29CQ0ONr6+vKVasmGnRooX573//a06cOJHm+JkzZ5q6deua4OBgkydPHtO6dWuzZMkSM2bMGAOYMWPGXNd9pR7/v9+fa2W+2vfUGGNWr15t2rRpY4KDg01wcLBp2bKliY6O/sfzPf7446Zs2bLGz8/PhISEmAoVKpj77rvPTJ061RhjTHx8vClVqpTx8fExK1asSHeOVatWGV9fX1OyZMk0P0MiIiIit2rLli3miSeeMJUqVTIhISHGx8fHREZGmrZt25qvv/7aJCQkuI691vukVCkpKebTTz81NWvWNAEBASYgIMDUqFHDfPLJJyYlJSXd8YBp2rSpOXTokOnTp4+JiIgwfn5+pnr16uann366oXspUaKEKVGihImPjzcDBw40hQsXNr6+vqZixYpm5MiRxul0pjn+Wvdz8OBB89hjj5nixYsbHx8fky9fPtO5c2ezcuXKdMde6z3qtd6XX+36J0+eNP/3f/9nSpQoYfz8/Ezp0qXNiy++aC5cuOC6x1SnT582w4YNM82bN3fdb2RkpGnatKkZO3ZsmnseP3686dmzpylbtqwJCgoyISEhplKlSuall14yx48fv+b3VkTczzJGk0+JiIiIiIhI7mZZFk2bNk037FZEJLfRnIUiIiIiIiIiIiICqFgoIiIiIiIiIiIif1KxUERERERERERERACthiwiIiIiIiKCpvMXEblCPQtFREREREREREQEULFQRERERERERERE/qRioYiIiIiIiIiIiAAqFoqIiIiIiIiIiMifss0CJ6dPnyY5Odmt14iIiCAuLs6t15BbozbyDGqH7EHt5HnUJtmDO9rJ29ubvHnzZuo5xR7ufk+q/094PrWR51BbeD61kWdSu3g+u9+PZptiYXJyMklJSW47v2VZrutoFSzPpDbyDGqH7EHt5HnUJtmD2kn+iTvfk+rnz/OpjTyH2sLzqY08k9rF83lCG2kYsoiIiIiIiIiIiAAqFoqIiIiIiIiIiMifVCwUERERERERERERQMVCERERERERERER+VO2WeBERERERMRTJScnc/HixVs+z6VLl0hMTMyEROIumd1GgYGBeHvrzzIREfEc+q0kIiIiInILkpOTuXDhAiEhITgctzZwx8fHx22rLUvmyMw2cjqdnDt3jqCgIBUMRUTEY2gYsoiIiIjILbh48WKmFAol93E4HISEhGRKr1QREZHMonc0IiIiIiK3SIVCuVn62REREU+j30wiIiIiIiIiIiICqFgoIiIiIiIiIiIif1KxUEREREREskSRIkWYO3fuVfd3796dV1555ZrnqFu3Ll999VVmRxMREZE/acktEREREZFcpkiRItfc//TTT/PMM89kuO/AgQPUq1ePefPmUblyZXfEu6bZs2cTGBiY5dcVERHJLVQsFBERERHJZdatW+f6esaMGbz33nssXbrUtS0oKMiOWNclX758dkcQERHJ0TQMWUREREQkExljMJcT7PlnzHVlLFCggOtfSEgIlmW5HufPn58vv/ySmjVrUqpUKVq3bs2iRYtcz61Xrx4Abdq0oUiRInTv3h2A9evX07NnTypXrkzFihXp1q0bmzZtuuHvX0pKCkOGDKFixYpUrlyZd999N819/e8w5EOHDtG3b1/KlStHhQoVePTRR4mLi3Ptf//992ndujU///wztWvXply5crz44oukpKTw2WefUa1aNe644w5GjhyZJseoUaNo2bIlZcuWpVatWrz44otcuHDBtf/gwYM8+OCD3H777ZQtW5bmzZuzYMECAOLj43niiSeoUqUKZcqUoWHDhowfP/6GvxciIiJ2UM9CEREREZHMlHgZ5xP33tRTL9/ipR2fTAA//1s6x9dff82oUaMYPnw4lSpVYvz48fTt25eFCxdSunRpfvnlF9q3b8/PP/9MhQoV8PHxAeD8+fPcc889vPnmmxhjGDVqFPfffz9RUVEEBwdf9/UnTpxIz549mTVrFhs3buT555+nSJEi9OnTJ92xTqeTvn37EhQUxOTJk0lOTmbIkCH83//9H5MmTXIdt2/fPhYuXMhPP/1EbGwsjz76KPv376d06dJMmjSJNWvW8PTTT9O4cWNq1Khx5XvpcPD6669TvHhx9u3bx0svvcSbb77Je++9B8BLL71EUlISkydPJjAwkB07drh6ZI4YMYIdO3bw448/Eh4ezt69e0lISLjpNhEREclKKhaKiIiIiIjLqFGjGDhwIJ07dwZgyJAhREdH8/XXX/P222+7hgHnzZuXAgUKuJ7XqFGjNOd59913ue2224iJiaF169bXff3ChQszbNgwLMuibNmybNu2ja+++irDYmFUVBTbtm0jJibGNQ/jyJEjad68OevXr6datWrAlaLiBx98QHBwMOXLl6dBgwbs3r2bH374AYfDQdmyZfn000+Jjo52FQsHDBjguk6xYsV4/vnneeGFF1zFwsOHD9OuXTtuu+02AEqUKOE6/tChQ1SuXJmqVau6ni8iIpJdqFgoIiIiIpKZfP2u9PC7CT4+PiQlJd3StW/FuXPnOHr0KLVr106zvVatWmzZsuWaz42Li+Pdd98lOjqakydPkpKSwqVLlzh06NANZahRowaWZbke16xZk1GjRpGSkoKXl1eaY3fu3EnhwoXTLNhSvnx58uTJw86dO13FwmLFiqXp3Zg/f34cDgcOx1+zMkVERHDixAnX46VLl/LJJ5+we/duzp07R0pKCgkJCVy8eBEfHx/69evHiy++yJIlS2jcuDHt2rXj9ttvB+CBBx5gwIABbNq0iaZNm9KmTZt031MRERFPpTkLRUREREQykWVZWH7+9vz7W5Etqz311FNs3ryZ119/nenTp/Prr7+SN2/eWyt+ZhJv77R9JCzLcg2f/vs2p9MJXFnx+aGHHuK2227jyy+/ZM6cObz11lsArvvp3bs30dHRdOvWjW3bttGuXTu++eYbAFq0aMHKlSsZMGAAx44do2fPnrz++uvuvk0REZFMoWKhiIiIiIgAEBISQmRkJKtWrUqzffXq1ZQvXx7AVWRLLaylWrVqFf369aNly5ZUqFABX19fTp06dcMZ/r5SM8DatWspVapUul6FAOXKlePw4cNpei/u2LGDM2fOuPLejI0bN+J0Onn11VepWbMmZcqU4ejRo+mOK1KkCA888ABff/01jz76KGPHjnXty5cvH/feey8ff/wxr732Gj/99NNN5xEREclKGoYsIiIiIiIujz32GO+//z4lSpSgUqVKTJgwgc2bN/Pxxx8DV4bw+vv7s2jRIgoVKoSfnx+hoaGUKlWKyZMnU7VqVc6dO8ebb76Jv/+NL7Zy6NAhXnvtNe677z7++OMPvvnmG1555ZUMj23cuDEVK1bkySefZNiwYSQnJ/PSSy9Rv35913yBN6NkyZIkJSXxzTff0Lp1a1atWsUPP/yQ5phXXnmFFi1aULp0ac6cOcOyZcsoW7YscGWBkzvuuIPy5cuTmJjIb7/9Rrly5W46j4iISFZSsVBE5H8YY4iLi2P//v2cOnWKEydOcPbsWddE7hEREZQqVcq14qGIiEhO0r9/f86dO8frr7/OyZMnKVeuHGPGjKF06dLAlSG9b7zxBv/973957733qFu3LpMmTeL999/n+eefp23bthQqVIgXXniBN95444av3717dxISEujQoQNeXl7079+f++67L8NjLctizJgxDB06lK5du+JwOGjWrBlvvvnmLX0PKlWqxKuvvspnn33GO++8Q7169XjxxRcZPHiw6xin08mQIUM4cuQIwcHBNGvWjNdeew240vvynXfe4cCBA/j7+1O3bl0+++yzW8okIpJTmORkOHoAzsRjzp+F8+cg8bLdsWxnFSuJVbmm3TEAsIwxxu4Q1yMuLs6t851YlkWhQoU4cuQI2eRbkuuojTxDTmwHYwzr169n9uzZbNy4ka1bt3Ly5MlrPsfLy4s77riDOnXq0KBBA5o0aYKvr28WJf5nObGdsju1Sfbgrnby8fEhIiIi084n9snoPenZs2cJDQ3NlPPf8gIn4nbuaKPM/BnKLfR71fOpjTyTHe1inE7YuRmzcTVmz3bYvwsSE7Pk2tmJ1aQNjvsf94j3o+pZKCK51uHDhxk/fjyTJ09m7969afY5HA4KFy5MREQE4eHhhIaGEh8fz7Fjxzh27BgnT55k3bp1rFu3jlGjRhEeHk6XLl245557qFy5sq0TzIuIiIiIiNjNnIrDRC/ERC+AuP+Z9zUgCPJFQHAoVlAI+PlDbv8TqkxFuxO4qFgoIrnO3r17+eyzz5g4caKrZ4C/vz9t2rShcePG3H777ZQvX56AgICrnuPgwYOsWLGCFStW8Ntvv3Hs2DFGjx7N6NGjady4MW+++aZr3iIREREREZHcwhzYi5k7BbP6d0hdDMs/AKtGAyhfGat0BShYGMuhNXc9lYqFIpJrnDhxgjfeeIMpU6a4VnCsV68evXr1om3btgQHB1/3uYoWLUrRokXp1q0bycnJLF26lIkTJzJ37lx+//13WrVqxaOPPsrgwYMJDAx01y2JiIiIiIjYzhgD2zbinDcFNv9tVfvylbAa3YlVoz6W340veiX2ULFQRHKFGTNmMGTIEE6dOgVAixYtGDRoELVr177lc3t7e9OiRQtatGhBbGwsL7/8MgsXLuSTTz5h6tSpvP7667Rp00ZDk0VEREREJEcxKSmYtTGYeVNg364rGy0HVq2GWG26YpUoY29AuSkqFopIjnbq1CleeOEFfvnlFwBuu+02RowYQfXq1d1yvZIlS/L999/z66+/8sorr3Dw4EH69+9PixYteOONNyhZsqRbrisiIiIiIpKVzK6tOH/4FA7vv7LB1xerYSus1ndjRUTaG05uiYqFIpJjbd++nYceeoj9+/fj7e3Nk08+yaBBg9y+arFlWbRp04YmTZowcuRIvvjiCxYuXMiyZcsYPHgwAwcOxMfHx60ZRERERERE3MFcuoiZ+j1m8RwwBoJCsFq0x2reHiskj93xJBNoNkkRyZHmz59Pp06d2L9/PyVKlOCXX37h2WefdXuh8O8CAgJ44YUXWLBgAU2aNOHy5cu8++67tGvXjo0bN2ZZDhERERERkcxgNq/D+eoTmEWzwRisBi1xvPk5jk69VSjMQVQsFJEc56uvvqJv376cP3+e+vXrM2vWLCpXrmxbnjJlyjB27Fg+/vhj8ubNy5YtW+jQoQNvvfUWly5dsi2XiIiIiIjI9TCXE3D+9AXOD1+F0ycgIhLH02/g6DsYKzjU7niSyVQsFJEcZdSoUbz22msYY+jTpw9jx44lPDzc7lhYlkXXrl1ZvHgxnTt3JiUlhc8++4zWrVsTExNjdzwREREREZEMmd3bcL4+GLN4NgBW8/Y4Xv0Y67aqNicTd1GxUERyjDFjxvD6668D8OyzzzJ8+PAsHXZ8PfLnz89nn33GmDFjiIyMZO/evXTv3p1///vfxMfH2x1PRETEpW7dunz11VfXffz7779P69atr3nMU089Rb9+/W41moiIZAGTnIRz6g84h78Ax49A3vw4/vU6jt6PYvn52R1P3EjFQhHJEX788UeGDh0KwKBBg/jXv/6FZVk2p7q6O++8k0WLFnHfffcBV/I3adKE8ePH43Q6bU4nIiI5nTGGHj160Lt373T7vv32W2677TamTJni+j0lIiK5izkYi/OtZzGzJ4JxYtVrjuO1j7Bur2Z3NMkCKhaKSLY3fvx4/v3vfwPw2GOP8fzzz9uc6PqEhoYyfPhwJk6cSLly5Th58iRPP/00nTt3ZtOmTXbHExGRHMyyLD744APWrVvHDz/84Nq+f/9+3nrrLd544w2KFClCQECAjSlFRMQOzuiFON96Bg7uheAQHI+9gKP/v7ACg+2OJlnE2+4AIiK3YurUqTzzzDMA9O/fn6FDh3p0j8KMNGjQgPnz5zN69Gg++OAD1q5dy1133cX999/P888/T968ee2OKCJyU7Zs2cKMGTPYu3cvp0+f5tlnn6VOnTqu/ffee2+Gz7vvvvvo1KkTAI8//jhxcXFp9vfu3Zu7777b9Xjfvn2MHj2a3bt3ExoaStu2bencuXOa58TExDB+/Hji4uKIjIykT58+1KhRI5PuNC1jDJdTzE09NwUnSck338Pcz8u67t+DRYoUYdiwYQwdOpSmTZtSrFgxnnnmGZo2bUr37t2pW7cuDz/8MAMGDADgzJkzvPHGG8ybN4/ExETuuOMOXnvtNSpVqpTxvaSk8MYbbzB+/HgcDge9evXCmJv7voiIiPuZ5GTMpDGYBTOvbKhcE8dDg7Dy6O+R3EbFQhHJtmbNmsXgwYMxxnDfffcxbNiwbFcoTOXj48Njjz3G3XffzRtvvMG0adP4/vvvmTVrFi+88AI9e/bEy8vL7pgiIjfk8uXLlCxZkhYtWvDee++l2//ll1+mebxu3Tq++OIL6tatm2b7vffeS6tWrVyP/f39XV9fvHiRN998kypVqjBgwAD279/P559/TlBQkOs527dvZ+TIkfTu3ZsaNWoQFRXFiBEjGD58OMWLF8/MWwbgcoqhx/gdmX7e6zG+R3n8va//d+G9997L3Llzefrpp2nXrh3bt29n0aJFGR776KOP4u/vz48//khISAg//vgjPXr04Pfff8/wg61Ro0YxceJE3n//fcqVK8eoUaOYO3cuDRs2vOn7ExER9zDnz+L8YjhsvzLCyerQE6tjTyyHBqTmRllSLDx16hQ//vgj69ev5/Lly0RGRjJw4EDKlCmTFZcXkRxo/vz5PP7446SkpNCjRw/eeeedbFso/LvIyEg+/fRT+vTpw5AhQ9ixYwfPP/88Y8aMYejQoTRr1szuiCIi16169epUr179qvvDwsLSPF61ahWVKlWiYMGCabYHBASkOzZVVFQUycnJDBw4EG9vb4oVK0ZsbCyzZs1yFQtnz55NtWrVXL0Ve/bsyaZNm5g7dy6PPPLIzd9gDvHuu+/SvHlzVqxYwVdffUW+fPnSHbNy5UrWr1/Phg0b8PtzUvtXXnmFefPm8csvv2Q4t+HXX3/NE088Qbt27QD4z3/+w+LFi916LyIicuPMxfM4//sq7N8NfgFXhhxXr2d3LLGR24uF58+f5+WXX6ZSpUq89NJLhIaGcuTIEYKCgtx9aRHJobZv387AgQNJTk6ma9eujBgxAkcO+8SrQYMG/Prrr4wZM4YPP/yQrVu30qdPH5o2bcrQoUO5/fbb7Y4oIpKp4uPjWbduHY8//ni6fdOmTWPy5Mnkz5+fRo0a0b59e1dv6x07dnDbbbfh7f3X29qqVasyffp0zp8/T3BwMDt27KBDhw5pzlm1alVWrVp11TxJSUkkJSW5HluW5Zq/758+nPLzshjfo/w/33QGfLx9SEpO+ucDr3HtG5U/f37uu+8+5s2bR9u2bTM8ZsuWLVy4cIHKlSun2Z6QkMC+ffvSHX/27FmOHTuWpljs7e1N1apVNRT5KnLCh55ZKfX7pe+b51Ibeab/bReTcAnnR69fKRSG5MHr2bewipSwM2Ku5wmvHbcXC6dPn06+fPkYOHCga1uBAgXcfVkRyaHi4+Pp168fFy9epGHDhvz3v//NscNzfXx8eOSRR7jnnnv46KOPGDNmDEuWLGHp0qX06NGDZ599lkKFCtkdU0QkUyxZsgR/f/80cxoC3HXXXZQqVYrg4GC2b9/OuHHjOH36NA8++CBw5ffC/763TO2FGB8fT3BwMPHx8eTJkyfNMXny5CE+Pv6qeaZOncqkSZNcj0uVKsXw4cOJiIhId+ylS5fw8fFJs833H+/46vx9/G7h2TfH19cXb2/vNPdhWRZeXl74+Phw6dIlChYsyNSpU9M9N0+ePPj4+OBwOLAsCx8fH9d5MjonkO77ld1kdn5fX1/9Tr9JkZGRdkeQf6A28kyRkZE4Lydw4rVhpOzehhUUQoG3P8e39M192CWZz87XjtuLhatXr6Zq1ap88MEHbNmyhfDwcO68884088783a18insrPKFyK9emNvIMdrZDSkoKTz75JLGxsRQtWpQvvvgi2/+xcT3Cw8N57bXXeOihh3jnnXeYOXMmP//8M9OmTeORRx7hscceSzc8T68Xz6M2yR7UTvZZtGgRjRs3xtc3bZnt7z0CS5Qogbe3N1999RW9e/d26++ALl26pLl26s9EXFwcycnJaY5NTExM8/71Vvj4+GTauW6E0+nEGJPm2sYYUlJSSEpKolKlShw/fhyAYsWKpXt+UlJSmnMEBARQsGBBVq1aRa1atQBITk5mw4YNVKlSxZZ7zCzuaKPExESOHDmSqefM6SzLIjIykqNHj6q3qodSG3mm1HY5cugQKZ++hdm4+srQ48GvcjIgBPT/Itu567Xj7e2d4YeeGR6baVe9iuPHjzN//nzat29Ply5d2L17N2PGjMHb2zvDubdu5FNcd9CnHp5PbeQZ7GiHIUOGsHDhQgICApgxY0a6oVA5XaFChZgxYwYxMTE8++yzREdH89FHH/Hdd9/xzDPPMHjwYEJDQ9M8R68Xz6M2yR7UTllr69atHD58mKeeeuofjy1XrhwpKSnExcVRuHBhwsLC0vUQTH2c+kFKWFgYZ86cSXPMmTNnrjoPIpCmd9z/yo1/9DZu3JiaNWvSr18/hg4dSunSpTl69CgLFizgrrvuomrVqume079/fz755BNKlSpF2bJl+fLLLzl79qwN6bOH3PhzlRmMMfreeTi1kecxxpAy7kvMhpXg44tj0MtQqrzaycPY+dpxe7HQ6XRSpkwZevfuDVwp/u3fv5/58+dnWCy8kU9xM5M+9fB8aiPPYFc7LFmyhLfffhuAESNGXPk0LJd+6lWyZEkmTpzI3LlzGTFiBNu2beOVV17hv//9LwMHDqRv374EBQXp9eJh9P+w7METPsnNjRYuXEjp0qUpWbLkPx4bGxuLZVmuD0fKly/PuHHjSE5Ods1buHHjRgoXLkxwcLDrmE2bNtG+fXvXeTZu3Ei5cuUy/2ZyKMuy+OGHHxg+fDhPP/00J0+eJCIignr16pE/f/4Mn/Poo49y7NgxnnrqKRwOBz169KBt27acO3cui9OLiMjfnZ82FrPoF7AsHP2fxiqfuzphyD9ze7Ewb968FC1aNM22okWLsmLFigyPt/tTXH3q4fnURp4hK9shPj6ef/3rXwA8+OCDdOnSRT8DQNu2bbnzzjuZOXMm77//Prt37+att95i1KhRPPnkkzz33HN6vXggtUn2oHbKHAkJCRw9etT1+Pjx48TGxhIcHOwqMF28eJHly5dz//33p3v+jh072LlzJ5UqVSIgIIAdO3bw3Xff0bhxY1chsFGjRkycOJEvvviCzp07c+DAAebMmeOa0xCgXbt2vPbaa8ycOZMaNWqwbNkydu/erZWQ/+aZZ57hmWeeSbPtf9+vBwcH88Ybb/DGG29c1zm8vb15/fXXef311zM/sIiI3BTn2hjiR38IgNXtIayaDewNJB7J7cXCChUqcPjw4TTbDh8+rE/XReS6vfzyyxw9epRSpUoxdOhQu+N4FIfDQefOnWnfvj1Tpkzhv//9L/v37+fVV1/ls88+4+GHH+b+++8nJCTE7qgikgvt3r2bYcOGuR5///33ADRt2tS16nF0dDTGGBo1apTu+d7e3kRHRzNx4kSSkpIoUKAA7du3TzMKJTAwkKFDhzJ69GheeOEFQkJC6NatW5r5sStUqMCgQYP4+eefGTduHIUKFeK5556jePHi7rp1ERERj2O2rMP59XtgDFbTu7DuvNvuSOKhLOPmj8137drFyy+/zD333EODBg3YtWsXo0aN4pFHHqFx48bXfZ64uDi3ToRsWRaFChXiyJEj6kngodRGniGr22HWrFk8+uijOBwOpk2bRs2aNd1+zewsKSmJCRMmMHLkSA4dOgRcWaXyoYce4uGHHyY8PNzmhLmX/h+WPbirnXx8fPRBaQ6R0XvSs2fPppsz9mbZtcCJXD93tFFm/gzlFvq96vnURp7FuXIp5psPISUZ/1oNSRrwHDgcdseSDHjC+1G3/2SULVuWZ599lmXLlvHMM88wefJkHnzwwRsqFIpI7nT8+HFeeOEFAJ544gkVCq+Dj48Pffr0ITo6mm+//ZayZcty5swZRo4cSZ06dXj11VfT9fYWEREREZGcy/nbDMxX70FKMlatRuQfOgLLy8vuWOLB3D4MGaBmzZr6I19Ebtgrr7zC6dOnqVSpkmvOQrk+Pj4+PPjgg7Rq1Yo5c+bw8ccfs3HjRr7++mu+++47OnXqxCOPPJLrVpQWEREREcktjDGYaT9hZk8AwGrRAUfPAVg+vjYnE0+nPqci4pGWLl3KzJkzcTgcfPDBB/j66hfazXA4HLRr147Zs2czduxY6tevT1JSEpMnT6ZNmzZ0796defPmkZKSYndUERERERHJJMYYzITRfxUK774Pq+cALA09luugnxIR8TiXL19myJAhADz00EPq/ZYJLMuiadOmTJo0iV9++YW7774bb29vYmJi6NevH02aNGHMmDFcuHDB7qgiItmS0+m0O4JkU/rZEZHMZpxOzI+fY36bAYDV+zEc7e/Fsiybk0l2oWKhiHicL7/8kj179hAREcFzzz1nd5wcp1q1anz66afExMTw+OOPExYWRmxsLEOHDqVWrVq8/PLL7Ny50+6YIiLZRmBgIOfOnVPRR26Y0+nk3LlzBAYG2h1FRHIIYwzmu48xS+eC5cB6aBCO5u3sjiXZTJbMWSgicr0OHjzIhx9+CMDLL7+slQHdqHDhwrz00ks89dRTTJgwga+//pq9e/fyzTff8M0331C/fn3uv/9+7rrrLg0DFxG5Bm9vb4KCgjh//vwtn8vX15fExMRMSCXuktltFBQUhLe3/iwTkcxhVv2OiV4ADgdW/6dx1GlidyTJhvRbSUQ8ymuvvUZCQgL16tWja9eudsfJFQIDA3nooYd44IEH+P333/n+++/59ddfiYmJISYmhoiICHr27Ml9991H0aJF7Y4rIuKRvL29b/kDLsuyKFSoEEeOHMEYk0nJJDOpjUTEk5mLFzATRgNgdeypQqHcNA1DFhGPsWrVKubMmYPD4eCtt97SnBpZzOFw0LRpU0aPHs3y5ct56qmnKFCgAHFxcXz88cfUq1ePXr16MX36dBISEuyOKyIiIiIif2Om/QhnTkPBIlhtutkdR7IxFQtFxCMYY3j77bcB6NmzJxUrVrQ5Ue5WpEgRnnvuOVauXMmoUaNo2LAhxhiWLl3KwIEDqVGjBkOGDGHTpk12RxURERERyfVM7E7M4tkAOPo8huXjY3Miyc5ULBQRj7BgwQJWrlyJv78///rXv+yOI3/y8fGhQ4cOTJgwgZiYGP71r39RuHBhzpw5w7fffkvbtm1p3bo1q1evtjuqiIiIiEiuZJwpOH/8HIzBqtMU67aqdkeSbE7FQhGxndPp5D//+Q8Affv2pXDhwjYnkowUL16cZ599luXLlzN27Fg6deqEr68vW7ZsYfDgwZq7SURERETEBmbBLNi3CwKCsO7tZ3ccyQFULBQR202dOpWtW7cSGhrK448/bncc+QdeXl40bdqUzz//nJUrV+Lr60tsbCy7d++2O5qIiIiISK5iDu3DTPkeAKvbg1h58tqcSHICFQtFxFaJiYmMGDECgIEDB5I3r365ZScRERHUr18fgN9++83mNCIiIiIiuYdJSsL59QeQnARVamE1aWN3JMkhVCwUEVuNHz+eAwcOUKBAAfr37293HLkJrVq1AlQsFBERERHJSmb6T3BwLwSH4njwSSzLsjuS5BAqFoqIbZKSkvj0008BePzxxwkMDLQ5kdyM1GLhypUriY+PtzeMiIiIiEguYHb8gfl1KgCO+x/X8GPJVCoWiohtpkyZwoEDB8ifPz99+vSxO47cpOLFi1OhQgVSUlJYvHix3XFERERERHI0k5SI89uPrqx+3LAlVo36dkeSHEbFQhGxRXJyMh999BEAjz32GAEBATYnkluhocgiIiIiIlnDzJsKcUchLByrxwC740gOpGKhiNhixowZxMbGkjdvXh544AG748gtSi0WLlq0iOTkZJvTiIiIiIjkTObkccyciQBY3ftiBWgqJ8l8KhaKSJZzOp2uXoUDBgwgKCjI5kRyq2rWrElYWBjx8fGsXr3a7jgiIiIiIjmSc+I3kJgI5Sth1WlidxzJoVQsFJEsN3v2bHbu3EmePHno27ev3XEkE3h5edGiRQtAQ5FFRERERNzBbN0Aa6LBcuDo9YhWPxa3UbFQRLKUMYbPPvsMgH79+hEaGmpzIsksqUOR58+fb3MSEREREZGcxSQn4xz3JQBW83ZYRUvZnEhyMhULRSRLrVy5kg0bNuDv70+/fv3sjiOZqFmzZnh7e7Nr1y42b95sdxwRERERkRzDLJwFRw5AcChWp952x5EcTsVCEclSX331FQDdunUjPDzc5jSSmfLkycNdd90FwIgRI2xOIyIiIiKSM5gzpzEzxwFgdX0AKyjY5kSS06lYKCJZZt++fcydOxeAhx9+2OY04g7PPvssXl5ezJ8/n1WrVtkdR0REREQk2zOTv4OES1CyHFbDVnbHkVxAxUIRyTKjR4/GGEOzZs0oX7683XHEDcqWLUuPHj0AeOeddzDG2JxIRERERCT7Mru2YmIWAuDo/SiWQ2UccT/9lIlIljh79iw///wzAI888ojNacSd/vWvf+Hn58eKFStYtGiR3XFERERERLIl40z5a1GThq2wSqnDhWQNFQtFJEuMHTuWCxcuUKFCBZo0aWJ3HHGjwoUL07dvX+BK70Kn02lzIhERERGR7Mf8Ph/274aAIKyuD9gdR3IRFQtFxO2Sk5MZM2YMcGWuQsuybE4k7vb4448TEhLCli1bmDFjht1xRERERESyFXP+LGbqDwBYnXtjhYbZG0hyFRULRcTtFi5cyMGDB8mbNy9dunSxO45kgfDwcB577DEA3nvvPZKTk21OJCIiIiKSfZjpP8GFc1CkBFazdnbHkVxGxUIRcbsff/wRgB49ehAQEGBzGskqDz/8MHnz5mXv3r1MnjzZ7jgiIiIiItmC2b8Hs2QeAI5ej2B5edmcSHIbFQtFxK0OHjzIwoVXVu/q06ePzWkkKwUHB/P4448D8OGHH5KUlGRzIhERERERz2aMwTluFBgnVu3GWBWq2B1JciEVC0XErcaOHYsxhoYNG1K6dGm740gWe+ihh4iIiGD//v2MHz/e7jgiIiIiIh7NrFgMu7aCrx9W9752x5FcSsVCEXGbpKQkfv75ZwDuu+8+m9OIHQICAnjiiScAGDlyJJcvX7Y5kYiIiIiIZzKXEzCTvgPAan8vVnh+mxNJbqVioYi4zW+//caxY8fInz8/bdu2tTuO2OS+++4jMjKSw4cPM3bsWLvjiIiIiIh4JLPoFzhzCvIXxGp9t91xJBdTsVBE3ObvC5v4+vranEbs4u/vz6BBgwD4+OOPSUhIsDmRiIiIiIhnMZcuYuZOAcDq2AvLx8fmRJKbqVgoIm6xb98+lixZAkDv3r1tTiN269WrF4UKFeLYsWNMmjTJ7jgiIiIiIh7F/DYDLpyDyCJY9ZraHUdyORULRcQtxo8fjzGGJk2aULJkSbvjiM18fX157LHHAPjss89ITk62OZGIiIiIiGcwF85h5k8DwOrUG8vhZW8gyfVULBSRTOd0Opk8eTIAPXv2tDmNeIrevXsTHh7Ovn37mDVrlt1xREREREQ8gpk3FS5dhCIlsGo2tDuOiIqFIpL5VqxYwcGDBwkJCeHOO++0O454iMDAQPr37w9cmbvQ6XTanEhERERExF7mbDxmwUwAHHf3wXKoTCP200+hiGS6iRMnAtCxY0cCAgJsTiOe5KGHHiI4OJht27bx22+/2R1HRERERMQ2xhicP34GiZehRFmoWtfuSCKAioUikskuXbrkGmLavXt3m9OIpwkLC+OBBx4ArvQuNMbYnEhERERExB5mwQxYtxy8vHHc939YlmV3JBFAxUIRyWRz587lwoULFC9enNq1a9sdRzzQgAED8PPzY+3atSxfvtzuOCIiIiIiWc7s2Y6Z9C0A1r39sEqWszeQyN+oWCgimWrSpEkAdOvWDYfm25AMFChQgHvvvReAL7/80uY0IiIiIiJZy1w4h3PUu5CSglWzIVbz9nZHEklDf8mLSKY5evQoS5cuBTQEWa7t4YcfBmD+/Pns3bvX5jQiIiIiIlnH+d3HcCoOChTCeuAJDT8Wj6NioYhkmmnTpuF0OqlduzYlS5a0O454sLJly9KiRQuMMXzzzTd2xxERERERyRJm46o/5yn0wvHo81iBQXZHEklHxUIRyTSpQ5DVq1Cux4ABAwD4+eefOXPmjM1pRERERETcyyQl4vz5KwCslp2wipexOZFIxlQsFJFMsW3bNrZu3YqPjw8dOnSwO45kA40bN6ZChQpcvHiRcePG2R1HRERERMStzLypEHcUwsKxOvawO47IValYKCKZYtq0aQC0aNGCsLAwW7NI9mBZlmvuwjFjxpCcnGxzIhERERER9zAnj2PmTATA6t4Xyz/Q5kQiV6dioYjcMmMM06dPB6Bz5842p5HspEuXLoSHh3Pw4EHmzp1rdxwREREREbdwjv8aEhOhfGWsOk3sjiNyTSoWisgtW7t2Lfv37ycwMJA777zT7jiSjQQEBHD//fcDMHr0aJvTiIiIiIhkPrN1w5VFTRwOHL0f1erH4vFULBSRW5Y6BLlt27YEBATYG0aynQceeABvb29WrlzJli1b7I4jIiIiIpJpjNOJc/J3AFjN2mEVKWFvIJHroGKhiNyS5ORkZs6cCcDdd99tbxjJliIjI2nbti0A3377rb1hREREREQykVkdBft2gX8AVgctaiLZg4qFInJLoqOjiYuLI2/evDRpork35OY89NBDAEyZMoUzZ87YG0ZEREREJBOY5CTMtB8BsNp0xQrJY3MikeujYqGI3JLUIcgdOnTAx8fH3jCSbdWrV48KFSpw6dIlJk6caHccEREREZFbZpbMhbijkCcvVmstBCnZh4qFInLTEhISmD17NqAhyHJrLMviwQcfBOC7777D6XTanEhERERE5OaZSxcxs8YDYHXsheXnb3MikeunYqGI3LTFixdz7tw5ChUqRJ06deyOI9lct27dCA4OZs+ePURFRdkdR0RERETkppl5U+D8WYgsgtWotd1xRG6IioUictNmzZoFXBmC7HDofydya4KDg7nnnnsALXQiIiIiItmXiT+FmT8dAEeXB7C8vGxOJHJjvO0OICLZ06VLl/j1118B6Nixo81pJKd48MEHGTNmDPPnz+fQoUMUKVLE7kgicgu2bNnCjBkz2Lt3L6dPn+bZZ59N0xP9008/ZcmSJWmeU7VqVYYMGeJ6fP78eb755hvWrFmDZVnUrVuXvn374u//13Cuffv2MXr0aHbv3k1oaCht27alc+e0c0PFxMQwfvx44uLiiIyMpE+fPtSoUcNNdy4iIrmZmTkOEi9DmYpQvZ7dcURumLoCichNWbx4MRcuXKBw4cL6Y0syTbly5ahfvz5Op5MJEybYHUdEbtHly5cpWbIk/fv3v+ox1apV48svv3T9Gzx4cJr9H330EQcOHGDo0KG88MILbN26lVGjRrn2X7x4kTfffJP8+fPzn//8h/vuu4+JEyfy22+/uY7Zvn07I0eOpEWLFgwfPpzatWszYsQI9u/fn/k3LSIiuZo5chATNR8AR7eHsCzL5kQiN07FQhG5KalDkNu3b69fgJKpevfuDcC4ceNISUmxOY2I3Irq1avTs2fPa85r6+3tTVhYmOtfcHCwa9/BgwdZv349jz32GOXKlaNixYr069eP6OhoTp06BUBUVBTJyckMHDiQYsWK0bBhQ+666y7X7ymA2bNnU61aNTp16kTRokXp2bMnpUuXZu7cue67eRERyZWcU78HpxOq1sEqd7vdcURuioYhi8gN+/sQ5A4dOticRnKau+66izx58nDo0CGioqJo2rSp3ZFExI22bNnCww8/TFBQEJUrV6Znz56EhIQAsGPHDoKCgihTpozr+CpVqmBZFrt27aJOnTrs2LGD2267DW/vv97WVq1alenTp3P+/HmCg4PZsWNHut9XVatWZdWqVVfNlZSURFJSkuuxZVkEBAS4vnaH1PPqQzjPpTbyHGoLz5cb28js2grrloPlwKvbgx5577mxXbIbT2gjFQtF5IbNmzdPQ5DFbQICAujatStjxoxh7NixKhaK5GDVqlWjbt26FChQgKNHjzJu3Djefvtt3nrrLRwOB/Hx8YSGhqZ5jpeXF8HBwcTHxwMQHx9PgQIF0hwTFhbm2pd6bJ48edIckydPHtc5MjJ16lQmTZrkelyqVCmGDx9ORETEzd/wdYqMjHT7NeTWqI08h9rC8+WWNjLGcPy/L5MCBLXuSHjNunZHuqbc0i7ZmZ1tpGKhiNywiRMnAleGIGsVZHGHXr16MWbMGObNm8fJkyfJly+f3ZFExA0aNmzo+rp48eKUKFGCJ598ks2bN1OlShUbk0GXLl3S9EZM/XQ/Li6O5ORkt1zTsiwiIyM5evQoxhi3XENujdrIc6gtPF9uayPn5nU4N68HH18SWnfhyJEjdkfKUG5rl+zIXW3k7e193R96qlgoIjfk0qVLzJgxA9AQZHGfSpUqUbVqVTZs2MCkSZN49NFH7Y4kIlmgYMGChISEcPToUapUqUJYWBhnz55Nc0xKSgrnz5939R4MCwtL10Mw9fHfjzlz5kyaY86cOePanxEfHx98fHwy3OfuP66MMfoDzsOpjTyH2sLz5YY2MsbgnDkOAKtJGwgL9/h7zg3tkt3Z2UbqEiQiN2Tx4sWcP3+eQoUKaQiyuFWvXr0AGDt2rN7IiOQSJ0+e5Pz58+TNmxeA8uXLc+HCBfbs2eM65o8//sAYQ9myZV3HbN26NU1vv40bN1K4cGHXYinly5dn06ZNaa61ceNGypUr5+5bEhGR3GDbRti1Fbx9sNp2tTuNyC1TsVBEbkjq6pIdO3bUEGRxq7vvvpuAgAB27drF6tWr7Y4jIjchISGB2NhYYmNjATh+/DixsbGcOHGChIQEfvjhB3bs2MHx48fZtGkT7777LpGRkVStWhWAokWLUq1aNUaNGsWuXbvYtm0b33zzDQ0aNCA8PByARo0a4e3tzRdffMGBAweIjo5mzpw5aXq/t2vXjg0bNjBz5kwOHTrEhAkT2L17N23bts3y74mIiOQ8zlk/A2A1vhMrTNPnSPanYcgict0SEhKYP38+oCHI4n4hISF07NiRCRMmMH78eGrXrm13JBG5Qbt372bYsGGux99//z0ATZs2ZcCAAezfv58lS5Zw4cIFwsPDueOOO+jRo0ea4b+DBg1i9OjRvP7661iWRd26denXr59rf2BgIEOHDmX06NG88MILhISE0K1bN1q1auU6pkKFCgwaNIiff/6ZcePGUahQIZ577jmKFy+eBd8FERHJycz2P2DHZvD2xmrbze44IplCxUIRuW5Llizh/PnzFC1aVEOQJUvcc889TJgwgVmzZvHGG28QEBBgdyQRuQGVKlViwoQJV90/ZMiQfzxHcHAwgwcPvuYxJUqU4PXXX7/mMfXr16d+/fr/eD0REZEb4ZqrsFFrrPD8NqcRyRwaQygi123mzJkAdO/eXUOQJUvUq1ePIkWKcO7cOVevVhERERERT2B2bIbtm8DLG6ttd7vjiGQa/bUvItfl70OQ7733XpvTSG7hcDjo2vXKJNGTJ0+2OY2IiIiIyF9ccxU2bImVL8LmNCKZx+3DkCdMmMCkSZPSbCtcuDAffvihuy8tIpkodQhyoUKFqFu3LseOHbM7kuQS3bt35+OPP2bx4sWcOHGC/Pk1vENERERE7GV2bYWtG8DLC+su9SqUnCVL5iwsVqwYL7/8suuxhi+KZD+pqyB36NBBr2HJUmXLlqVq1aps2LCB6dOn079/f7sjiYiIiEgu5+pVWL8FVv6CNqcRyVxZ8he/w+EgLCzM9S80NDQrLisimSQhIYFff/0V0CrIYo9u3a6sLKehyCIiIiJiN7NnO2xeBw4HVrt77I4jkumypGfh0aNHefTRR/Hx8aF8+fL07t37qsPIkpKSSEpKcj22LMu1+qVlWW7LmHpud15Dbo3ayD5/H4Jcq1YtQO3g6XLa6+Xuu+9m2LBhbNiwgV27dlGuXDm7I92wnNYmOZXaSURERP6Jc9Z4AKx6zbEiIm1OI5L53F4sLFeuHAMHDqRw4cKcPn2aSZMm8corr/D++++7ioB/N3Xq1DRzHJYqVYrhw4cTEZE1k4VGRuqF7unURllvwYIFAPTo0YPChQsDaofsIqe0U6FChWjbti2//PIL8+bNo0mTJnZHumk5pU1yOrWTiIiIZMTs3QmbVl/pVdhevQolZ3J7sbB69equr0uUKOEqHsbExNCiRYt0x3fp0iXNMMfUT/bj4uJITk52W07LsoiMjOTo0aMYY9x2Hbl5aiN7JCQkMH36dACaN2/O0aNH1Q7ZQE58vXTs2JFffvmFn376iSeeeMLuODcsJ7ZJTuSudvL29s6yDz5FRETEfZwzxwFg1W2KVaCwzWlE3CNLhiH/XVBQEIULF+bo0aMZ7vfx8cHHxyfDfVnxx5UxRn/EeTi1UdaKiori/PnzREZGUqNGDdf3Xu2QPeSkdmratCkA+/btIz4+njx58tic6ObkpDbJydROIiIi8r/Mri1/9Spsd6/dcUTcJsuXNE1ISODo0aOEhYVl9aVF5CbMmzcPgDvvvFOrIIutQkNDKVSoEAA7duywOY2IiIiI5CbGGJxTvgfAatgKK7KIzYlE3Mftf/l///33bNmyhePHj7N9+3ZGjBiBw+GgUaNG7r60iNyilJQUV7HwrrvusjmNCFSoUAGA7du325xERERERHKVP9bCzi3g44vVoafdaUTcyu3DkE+dOsXIkSM5d+4coaGhVKxYkbfeeovQ0FB3X1pEbtHatWs5ceIEoaGh1KtXz+44IpQvX57FixerZ6GIiIiIZBnjdP7Vq7B5e6zw/DYnEnEvtxcLn3rqKXdfQkTcZO7cuQC0atUKX19fm9OIqGehiIiIiGQ9s+p3OLgXAgKx7upmdxwRt9MEZCKSIWOMq1jYpk0bm9OIXFG+fHlAcxaKiIiISNYwyUmY6T8BYLXpihWsUZKS86lYKCIZ2r59O7Gxsfj5+dG8eXO744gAfxULjx8/zunTp21OIyIiIiI5nVk4C+KOQkgerJYd7Y4jkiVULBSRDKX2KmzcuDFBQUE2pxG5Ijg4mCJFrqw8p96FIiIiIuJOJv4UZsbPAFjdHsTyD7A5kUjWULFQRDKUugpy27ZtbU4ikpbmLRQRERGRrGAmjYHLl6BUeaz6LeyOI5JlVCwUkXQOHTrExo0bcTgctG7d2u44ImmoWCgiIiIi7mZ2/IFZsQQsC0efx7AcKp9I7qGfdhFJJ7VXYa1atcifP7/NaUTSSp23UMVCEREREXEHk5KCc9yXAFiN22CVKGtzIpGspWKhiKTz22+/AVoFWTxTas9CzVkoIiIiIu5gFs+Bg7EQFILV5T6744hkORULRSSN8+fPExMTA0CrVq1sTiOSXrly5QA4efIkJ0+etDmNiIiIiOQkJv4kZtoPAFh334cVHGpzIpGsp2KhiKSxdOlSEhMTKVmyJGXKlLE7jkg6gYGBFC9eHNBQZBERERHJXGb8aEj4c1GTJhppJbmTioUiksb8+fMBaN26NZZl2ZxGJGOp8xZqKLKIiIiIZBbzxxrM6iiwHDjuG6hFTSTX0k++iLikpKSwYMECQEOQxbNVrFgRUM9CEREREckcJvEyzrGjALBadsQqXtrmRCL2UbFQRFzWr1/PyZMnCQkJoW7dunbHEbkq9SwUERERkcxkZk+EuKMQlg+rcy+744jYSsVCEXFJHYLcrFkzfHx8bE4jcnWpKyJv27YNY4zNaUREREQkOzMnjmHmTQHA0WsAln+gzYlE7KVioYi4/Pbbb8CV+QpFPFmZMmVwOBzEx8cTFxdndxwRERERycbM9LGQnAwV74Dq9e2OI2I7FQtFBICDBw+ydetWHA4HzZs3tzuOyDUFBAS4VkTetm2bzWlEREREJLsyB/ZiViwGwNHtQS3yKIKKhSLyp9RehbVq1SI8PNzmNCL/rGbNmgAsWbLE5iQiIiIikl05p3wHxmDVboxVspzdcUQ8goqFIgJoCLJkP23atAFg7ty5mrdQRERERG6Y2boB/lgLXl5Yd/exO46Ix1CxUES4cOECy5YtA6BVq1Y2pxG5Ps2aNcPPz4/Y2FitiiwiIiIiN8QYg3PydwBYTdpgFShscyIRz6FioYjw+++/k5iYSIkSJShXTl3vJXsICgqicePGwJXehSIiIiIi18usjoJ9u8DPH6tDD7vjiHgUFQtFxDUEuVWrVprQV7KVtm3bAjBv3jybk4iIiIhIdmGSEjGpvQrv7IIVmtfmRCKeRcVCkVzO6XSmKRaKZCetW7fGsiw2bNjAoUOH7I4jIiIiItmAWTATTh6HsHCsNl3sjiPicVQsFMnlNmzYQFxcHMHBwdSrV8/uOCI3JH/+/NSuXRuA+fPn25xGRERERDydORuPmT0RAKvLA1h+/jYnEvE8KhaK5HKpvQqbNm2Kr6+vzWlEbtzfV0UWEREREbkWM3McXLoIxctg1WtmdxwRj6RioUgul9obq3Xr1jYnEbk5qcXCmJgY4uPj7Q0jIiIiIh7LHNqPWXJlrmvHvf2xHCqJiGRErwyRXOzw4cNs3rwZy7Jo0aKF3XFEbkqpUqWoUKECycnJLFy40O44IiIiIuKhnFO/B+OE6vWwKlS2O46Ix1KxUCQXSx2CXLNmTfLly2dzGpGbp6HIIiIiInItZv9u2LASLAeOrg/aHUfEo6lYKJKLaRVkySnatm0LwKJFi0hISLA5jYiIiIh4Gmfqoia1G2FFFrE5jYhnU7FQJJe6dOkSy5YtAzRfoWR/d9xxB5GRkVy8eJGoqCi744iIiIiIBzFHDsDaGACsdvfYnEbE86lYKJJLRUVFkZCQQNGiRalQoYLdcURuiWVZrt6F8+bNszmNiIiIiHgSM2cSGAPV6mIVKWF3HBGPp2KhSC61aNEiAFq0aIFlWTanEbl1qfMW/vrrr6SkpNicRkREREQ8gYk7ilmxBABHu3ttTiOSPahYKJILGWNcxcLmzZvbnEYkc9SvX5/Q0FBOnDjB2rVr7Y4jIiIiIh7AzJsCTifcXg2rVDm744hkCyoWiuRCe/bsYf/+/fj6+tKwYUO744hkCh8fH9diPRqKLCIiIiLm9EnMsiuLOqpXocj1U7FQJBdK7VVYp04dgoKCbE4jknnuvPNOAObMmYMxxuY0IiIiImInM3cyJCdD2duhfCW744hkGyoWiuRCixcvBjQEWXKe5s2b4+vrS2xsLDt37rQ7joiIiIjYxMSfxCy9MtrE0amX5mkXuQEqForkMpcuXSImJgZQsVBynuDgYBo1agTA3LlzbU4jIiIiInYxc6dActKVXoUV77A7jki2omKhSC6zfPlyEhISKFy4MOXLl7c7jkima9u2LaB5C0VERERyKxN/ErPkygfH6lUocuNULBTJZf6+CrJ+aUpOdOedd2JZFuvXr+fIkSN2xxERERGRLPZXr8Lb1KtQ5CaoWCiSyyxcuBDQEGTJuSIiIqhWrRrwV3FcRERERHIHE3/qr7kKO6pXocjNULFQJBeJjY1l7969eHt7u+Z1E8mJWrZsCcCCBQtsTiIiIiIiWcnM+hmSEq/0Krytqt1xRLIlFQtFcpHUVZBr165NSEiIvWFE3Ci1WPj7779z+fJlm9OIiIiISFYw+3b/1auwy/3qVShyk1QsFMlFNARZcovKlSsTERHBhQsXWLlypd1xRERERMTNjDE4x40CY7DqNMEqX9nuSCLZloqFIrlEQkIC0dHRADRr1szeMCJu5nA4aNGiBaChyCIiIiK5gVm+GHZvAz9/rO597Y4jkq2pWCiSS6xcuZJLly4RGRnJ7bffbnccEbdLLRam9qgVERERkZzJXLqImTQGAKtDD6y8+WxOJJK9qVgokkukFkyaNWumuTskV2jSpAne3t7s3r2b2NhYu+OIiIiIiJuYmePgbDwULILVqpPdcUSyPW+7A4hI1khd3ERDkCW3CA0NpXbt2sTExLBw4UL69etndySRXGfLli3MmDGDvXv3cvr0aZ599lnq1KkDQHJyMj///DPr1q3j+PHjBAYGUqVKFXr37k14eLjrHI8//jhxcXFpztu7d2/uvvtu1+N9+/YxevRodu/eTWhoKG3btqVz585pnhMTE8P48eOJi4sjMjKSPn36UKNGDffdvIiIZAlzeD9m4SwAHD0HYHn72JxIJPtTsVAkFzh48CA7d+7Ey8uLJk2a2B1HJMu0bNmSmJgYFixYoGKhiA0uX75MyZIladGiBe+9916afYmJiezdu5du3bpRsmRJzp8/z7fffsu7777Lf/7znzTH3nvvvbRq1cr12N/f3/X1xYsXefPNN6lSpQoDBgxg//79fP755wQFBbmes337dkaOHEnv3r2pUaMGUVFRjBgxguHDh1O8eHE3fgdERMSdjDE4f/4KUlKgWj2syvoQSCQzaBiySC6QOgS5Zs2a5MmTx+Y0IlmnZcuWwJUeRRcvXrQ5jUjuU716dXr27OnqTfh3gYGBvPzyyzRo0IDChQtTvnx5+vXrx549ezhx4kSaYwMCAggLC3P9+3uxMCoqiuTkZAYOHEixYsVo2LAhd911F7NmzXIdM3v2bKpVq0anTp0oWrQoPXv2pHTp0sydO9d9Ny8iIu63Nhq2bgAfXxz36oNhkcyinoUiuYCGIEtuVa5cOYoWLcrBgweJiorizjvvtDuSiFzDxYsXsSyLwMDANNunTZvG5MmTyZ8/P40aNaJ9+/Z4eXkBsGPHDm677Ta8vf96W1u1alWmT5/O+fPnCQ4OZseOHXTo0CHNOatWrcqqVauumiUpKYmkpCTXY8uyCAgIcH3tDqnn1dzCnktt5DnUFp7P3W1kLifgnDD6yjXadsNRoJBbrpPT6LXj+TyhjVQsFMnhEhMTiYqKAv5aHVYkt7Asi5YtW/Ldd9+xcOFCFQtFPFhiYiI//fQTDRs2TFMsvOuuuyhVqhTBwcFs376dcePGcfr0aR588EEA4uPjKVCgQJpzhYWFufYFBwcTHx+frmd9njx5iI+Pv2qeqVOnMmnSJNfjUqVKMXz4cCIiIm7xTv9ZZGSk268ht0Zt5DnUFp7PXW105ofPOXvqBF4FCxPZ93Ecfv7//CRx0WvH89nZRioWiuRwK1eu5MKFC0RERFCpUiW744hkuRYtWvDdd9+xYMECjDH6FFXEAyUnJ/Pf//4XgIcffjjNvr/3CCxRogTe3t589dVX9O7dGx8f901i36VLlzTXTv1/R1xcHMnJyW65pmVZREZGcvToUYwxbrmG3Bq1kedQW3g+d7aRiTtKyqTvrnzd7SGOnTqdqefPyfTa8XzuaiNvb+/r/tBTxUKRHC51CHLTpk1xODRNqeQ+DRs2xN/fn8OHD7N9+3YqVqxodyQR+ZvUQuGJEyd45ZVX0g1B/l/lypUjJSWFuLg4ChcuTFhYWLoegqmPU3sYhoWFcebMmTTHnDlzxrU/Iz4+PlctRrr7jytjjP6A83BqI8+htvB87mgj55TvITkZbq8G1erqZ+Am6LXj+exsI1UORHK4RYsWARqCLLlXQEAADRo0AP5a7EdEPENqofDo0aO8/PLLhISE/ONzYmNjsSyL0NBQAMqXL8/WrVvT9PbbuHEjhQsXJjg42HXMpk2b0pxn48aNlCtXLhPvRkREsoLZuwOz6newLBz39NWoERE3ULFQJAc7evQo27Ztw7IsGjdubHccEdukroq8YMECm5OI5C4JCQnExsYSGxsLwPHjx4mNjeXEiRMkJyfzwQcfsGfPHp588kmcTifx8fHEx8e7Cn87duzgl19+ITY2lmPHjvH777/z3Xff0bhxY1chsFGjRnh7e/PFF19w4MABoqOjmTNnTpohxO3atWPDhg3MnDmTQ4cOMWHCBHbv3k3btm2z/HsiIiI3zxiDc9K3AFj1mmMVLWVvIJEcSsOQRXKw33//HYA77riD8PBwm9OI2Ce1Z+2qVas4c+ZMuoUORMQ9du/ezbBhw1yPv//+e+DK1Bj33HMPq1evBuD5559P87xXX32VSpUq4e3tTXR0NBMnTiQpKYkCBQrQvn37NIXAwMBAhg4dyujRo3nhhRcICQmhW7dutGrVynVMhQoVGDRoED///DPjxo2jUKFCPPfccxQvXtydty8iIplt02rY8Qd4+2Dd3cfuNCI5loqFIjlYarFQvQoltytevDjlypVj586dLFmyhE6dOtkdSSRXqFSpEhMmTLjq/mvtAyhdujRvvfXWP16nRIkSvP7669c8pn79+tSvX/8fzyUiIp7JOFNwTr6yqInVsiNWuPtXpxfJrTQMWSSHMsYQFRUFqFgoAn/1LtS8hSIiIiLZj4leCIf3Q1AIVrvudscRydFULBTJoXbs2MGxY8fw9/enVq1adscRsV1qsXDRokU4nU6b04iIiIjI9TLJyZiZPwNgtbsHKzDY5kQiOZuKhSI5VOoQ5Lp16+Lv729zGhH71alTh+DgYE6cOMHGjRvtjiMiIiIi18ksXwSn4iBPXqxmd9kdRyTHU7FQJIfSfIUiafn6+tKkSRNAqyKLiIiIZBcmJQUzZxIA1p13Y/n62ZxIJOdTsVAkB0pKSiImJgZQsVDk71q2bAmoWCgiIiKSXZjVUXD8CASHYDVpa3cckVxBxUKRHGjdunVcuHCB8PBwbr/9drvjiHiMZs2aAbBx40ZOnTplbxgRERERuSbjdGJ+mQCA1aozln+AzYlEcgcVC0VyoNQhyI0aNcLh0MtcJFVkZCQVK1bEGON6nYiIiIiIh1q3HI4cgIAgrObt7U4jkmuoiiCSA2m+QpGrS523UMVCEREREc9ljMH5y3gArJYdsAKDbE4kknuoWCiSw5w7d461a9cCfxVFROQvTZs2BWDJkiUYY2xOIyIiIiIZ+mMtHNgLfv5YLTvanUYkV1GxUCSHiYmJISUlhZIlS1K0aFG744h4nLp16+Ln58fhw4fZvXu33XFEREREJAPOeVMAsJq0wQoOtTmNSO6iYqFIDhMVFQVoCLLI1QQEBFC7dm3gSu9CEREREfEsZu8O2L4JvLywWnWyO45IrqNioUgOs3TpUkBDkEWuJXUocurrRUREREQ8h3Pun70K6zTFCo+wOY1I7qNioUgOcuTIEXbu3IllWTRo0MDuOCIeK7WYHh0dTWJios1pRERERCSVOXoI1sUAYLXpanMakdxJxUKRHCR1CHLVqlUJCwuzN4yIB7v99tvJnz8/Fy9eZM2aNXbHEREREZE/mfnTwBi4ozZWkeJ2xxHJlbK0WDht2jTuvfdevv3226y8rEiukTqkslGjRjYnEfFsDofDNa+n5i0UERER8QzmzGlM9EIAHOpVKGKbLCsW7tq1i/nz51OiRImsuqRIrmKMcfUs1HyFIv8s9XWieQtFREREPIOZPx2Sk6BMRSh3u91xRHKtLCkWJiQk8PHHH/Poo48SFBSUFZcUyXV27NjB8ePH8ff3p2bNmnbHEfF4qcXCjRs3curUKZvTiIiIiORu5twZzOLZADju6o5lWTYnEsm9vLPiIl9//TXVq1fnjjvuYMqUKdc8NikpiaSkJNdjy7IICAhwfe0uqefW/5A8l9ro2n7//XcA6tat63rNuIPaIXtQO/2zQoUKUbFiRbZt28ayZcvo1KmTW6+nNske1E4iIiL2ML9Og8sJULwM3FHb7jgiuZrbi4XLli1j7969vPPOO9d1/NSpU5k0aZLrcalSpRg+fDgREVmzXHpkZGSWXEduntooYytXrgSgQ4cOFCpUyO3XUztkD2qna2vXrh3btm1j1apVPProo1lyTbVJ9qB2EhERyTrm3FnMol8AcHTqpQ/tRGzm1mLhiRMn+Pbbbxk6dCi+vr7X9ZwuXbrQoUMH1+PU/0nExcWRnJzslpyp14mMjOTo0aMYY9x2Hbl5aqOrS0pKYtGiRcCVlZCPHDnitmupHbIHtdP1SR2yP2fOHA4fPuz2HuxqE8/nrnby9vbOsg8+RUREshszf6p6FYp4ELcWC/fs2cOZM2f497//7drmdDrZunUrc+fOZezYsTgcaadN9PHxwcfHJ8PzZcUfV8YY/RHn4dRG6a1du5aLFy+SL18+brvtNr1WxEXtdG1169bF19eXw4cPs2vXLsqWLev2a6pNsge1k4iISNYw585iFv7Zq7BjT/UqFPEAbi0WVqlShffeey/Nts8//5zChQvTuXPndIVCEbk5qau5NmrUSK8rkRsQEBBAnTp1iIqKYunSpVlSLBQRERGRv5j50/7sVVgaqtaxO46I4ObVkAMCAihevHiaf35+foSEhFC8eHF3XlokV0ld3KRRo0Y2JxHJfpo2bQrAkiVLbE4iIiIikruoV6GIZ1IXJJFs7sKFC6xfvx5QsVDkZjRp0gSA6OhoEhMTbU4jIiIikntc6VV4CYqVgqp17Y4jIn9y+2rI/+u1117L6kuK5GgrV64kOTmZYsWKqceuyE24/fbbyZcvHydPnmTt2rXUq1fP7kgiIiIiOZ45/7dehVoBWcSjqGehSDYXHR0NQIMGDWxOIpI9ORwOV+9CDUUWERERyRpm/nT1KhTxUCoWimRzKhaK3LrUYmHqYkEiIiIi4j7m/FnMglkAODqqV6GIp1GxUCQbO3v2LBs3bgRULBS5FanFwg0bNnDq1Cmb04iIiIjkbGb+jL96FVZTr0IRT6NioUg2tnz5cpxOJ6VKlaJw4cJ2xxHJtiIjI6lQoQLGGKKiouyOIyIiIpJjXVkBeSYAjg5aAVnEE6lYKJKNaQiySObRUGQRERER9zJOJ84xH0LCJSiqXoUinkrFQpFsLLVY2LBhQ5uTiGR/qcXC33//HWOMzWlEREREch4zdzJsWg0+vjj6DsZyqCQh4on0yhTJpk6fPs2WLVsAqF+/vs1pRLK/evXq4ePjw8GDB9m3b5/dcURERERyFLNtI2baTwBYvR/FKl7a5kQicjUqFopkU8uXL8cYQ7ly5ShQoIDdcUSyvcDAQGrVqgVoKLKIiIhIZjLxJ3F+OQKME6thSxyNWtsdSUSuQcVCkWxq2bJlgIYgi2SmRo0aAVeGIouIiIhI5nD++DmcOwNFS2L1eszuOCLyD1QsFMmmtLiJSOZr3LgxcOX1lZKSYnMaERERkezP7N0BG1aC5cAx4FksPz+7I4nIP1CxUCQbOnHiBNu3bwc0X6FIZqpatSohISHEx8fzxx9/2B1HREREJNtzTh8LgFWvKVbh4janEZHroWKhSDaU2qvwtttuIzw83OY0IjmHt7e3q7euhiKLiIiI3JrL2zZh/lgDDgdWhx52xxGR66RioUg2lDpfoYYgi2S+1KHIKhaKiIiI3JqzP40CwKrfAqtAYZvTiMj1UrFQJBtK7VmYuhiDiGSe1GLhqlWruHTpks1pRERERLIns3MzCWuXg5cXVvt77Y4jIjdAxUKRbObo0aPs2bMHh8NB3bp17Y4jkuOUKVOGyMhILl++zKpVq+yOIyIiIpItueYqbNgKKyLS5jQiciNULBTJZlJ7FVauXJk8efLYnEYk57Esy9W7MCoqyuY0IiIiItmP2f4HZttG8PbGoV6FItmOioUi2YzmKxRxP81bKCIiInJzjDE4Z/wEQNCdd2PlK2BzIhG5USoWimQzqT0LVSwUcZ/U+UA3bdrEqVOnbE4jIiIiko1s2wg7NoO3N6E9+tqdRkRugoqFItnIwYMH2b9/P15eXpqvUMSNChYsSIUKFTDGuAr0IiIiInJtV3oV/jlXYZO2eOcvaHMiEbkZKhaKZCOpQ5CrVq1KcHCwzWlEcrbU3oUaiiwiIiJynbauh11bwdsHR7vudqcRkZukYqFINqIhyCJZR4uciIiIiFw/Y8xfKyA3uwsrLJ+9gUTkpqlYKJJNGGNcPQsbNmxocxqRnK9+/fp4eXkRGxvL/v377Y4jIiIi4tn+WAt7toOvL1bbbnanEZFboGKhSDYRGxvLkSNH8PHxoXbt2nbHEcnxgoODqVGjBqDehSIiIiLXYpxOnFO/B8Bq1g4rT157A4nILVGxUCSbSB2CXKNGDQICAmxOI5I7pA5F1ryFIiIiIldn1iyDA3vBPwCrreYqFMnuVCwUySY0X6FI1vv7vIVOp9PmNCIiIiKexyQnY6b9BIB1ZxeskFCbE4nIrVKxUCQb+Pt8hSoWimSd6tWrExQUxKlTp9iyZYvdcUREREQ8jon+DY4fhpA8WK072R1HRDKBioUi2cCuXbuIi4vDz8/PNYeaiLifj48P9erVAzRvoYiIiMj/MomXMTN/BsBqdw+Wf6DNiUQkM6hYKJINpPYqrFWrFv7+/janEcldNG+hiIiISMbMol8g/hSER2A1vcvuOCKSSVQsFMkGNF+hiH1Si4XLly/n8uXLNqcRERER8Qwm4RJm7mQArE69sXx8bE4kIplFxUIRD+d0Ol3FwoYNG9qcRiT3qVChAhERESQkJLBmzRq744iIiIh4BLNkLpw/BwUKYdVrZnccEclEKhaKeLht27Zx+vRpAgICqFq1qt1xRHIdy7JchfrVq1fbnEZERETEfibxMubXqcCfcxV6edmcSEQyk4qFIh4utVdhnTp18PX1tTmNSO5UtmxZAPbv329zEhERERH7maj5cDYe8hXAqtvM7jgikslULBTxcBqCLGK/YsWKASoWioiIiJikJMzcKQBYbbtheXvbnEhEMpte1SIeLCUlheXLlwNa3ETETsWLFwfgwIEDNicRyV62bNnCjBkz2Lt3L6dPn+bZZ5+lTp06rv3GGCZMmMCCBQu4cOECFStW5OGHH6ZQoUKuY86fP88333zDmjVrsCyLunXr0rdvX/z9/V3H7Nu3j9GjR7N7925CQ0Np27YtnTt3TpMlJiaG8ePHExcXR2RkJH369KFGjRru/yaIiOQwJmYhnD4BYeFYDVvaHUdE3EA9C0U82ObNmzlz5gzBwcFUqVLF7jgiuVZqz8JDhw6RnJxscxqR7OPy5cuULFmS/v37Z7h/+vTpzJkzhwEDBvD222/j5+fHW2+9RWJiouuYjz76iAMHDjB06FBeeOEFtm7dyqhRo1z7L168yJtvvkn+/Pn5z3/+w3333cfEiRP57bffXMds376dkSNH0qJFC4YPH07t2rUZMWKEeguLiNwgk5KCmTMJAKtNVywfTZMkkhOpWCjiwVKHINetWxdvde8XsU3BggXx8/MjJSWFI0eO2B1HJNuoXr06PXv2TNObMJUxhtmzZ9O1a1dq165NiRIleOKJJzh9+jSrVq0C4ODBg6xfv57HHnuMcuXKUbFiRfr160d0dDSnTp0CICoqiuTkZAYOHEixYsVo2LAhd911F7NmzXJda/bs2VSrVo1OnTpRtGhRevbsSenSpZk7d27WfCNERHIIE70AThyDkDxYjdvYHUdE3ETVBxEPtmzZMkBDkEXs5nA4KFKkCHv27GH//v2unoYicvOOHz9OfHw8d9xxh2tbYGAgZcuWZceOHTRs2JAdO3YQFBREmTJlXMdUqVIFy7LYtWsXderUYceOHdx2221pPlSrWrUq06dP5/z58wQHB7Njxw46dOiQ5vpVq1Z1FSUzkpSURFJSkuuxZVkEBAS4vnaH1PO66/xy69RGnkNtkfVMUiJm5s8AOO7qjuNv00FkRG3kmdQuns8T2kjFQhEPlZSUxIoVKwBo1KiRzWlEpHjx4uzZs0fzFopkkvj4eADy5MmTZnuePHlc++Lj4wkNDU2z38vLi+Dg4DTHFChQIM0xYWFhrn2px17rOhmZOnUqkyZNcj0uVaoUw4cPJyIi4jrv8OZFRka6/Rpya9RGnkNtkXXOTf2J+NMn8MpfkEK9+mH5+l3X89RGnknt4vnsbCMVC0U81MaNG7lw4QJhYWHcfvvtdscRyfVSexPu27fP5iQikhW6dOmSpjdi6qf7cXFxbpu71LIsIiMjOXr0KMYYt1xDbo3ayHOoLbKWSbhIys+jr3zd7h6Onjz1j89RG3kmtYvnc1cbeXt7X/eHnioWinio1PkK69Wrh8Oh6UVF7KYVkUUyV2rvvzNnzpA3b17X9jNnzlCyZEnXMWfPnk3zvJSUFM6fP+96flhYWLoegqmP/37MmTNn0hxz5swZ1/6M+Pj44OPjk+E+d/9xZYzRH3AeTm3kOdQWWcM5bxqcPwsFi0CDljf0PVcbeSa1i+ezs41UgRDxUKnFQs1XKOIZUnsWavVUkcxRoEABwsLC2LRpk2vbxYsX2bVrF+XLlwegfPnyXLhwgT179riO+eOPPzDGULZsWdcxW7duTdPbb+PGjRQuXJjg4GDXMX+/Tuox5cqVc9v9iYjkFObcWcz8aQBYnftgeXnZG0hE3E7FQhEPlJiYyMqVKwFo2LChzWlEBNSzUORmJCQkEBsbS2xsLHBlUZPY2FhOnDiBZVm0a9eOKVOmsHr1avbv388nn3xC3rx5qV27NgBFixalWrVqjBo1il27drFt2za++eYbGjRoQHh4OHBlXl9vb2+++OILDhw4QHR0NHPmzEkzhLhdu3Zs2LCBmTNncujQISZMmMDu3btp27Ztln9PRESyGzNnIiRcguKlsWqqI4NIbqBhyCIeaN26dSQkJJAvXz4qVKhgdxwR4a+ehcePH+fSpUuuVVFF5Op2797NsGHDXI+///57AJo2bcrjjz9O586duXz5MqNGjeLixYtUrFiRl156CV9fX9dzBg0axOjRo3n99dexLIu6devSr18/1/7AwECGDh3K6NGjeeGFFwgJCaFbt260atXKdUyFChUYNGgQP//8M+PGjaNQoUI899xzrg8BREQkY+ZkHGbRLwA4utyPpemRRHIFFQtFPFDqEOT69etrSXsRD5E3b15CQkI4d+4cBw8e1PBFketQqVIlJkyYcNX9lmXRo0cPevTocdVjgoODGTx48DWvU6JECV5//fVrHlO/fn3q169/7cAiIpKGmTEWkpOhQhWoVMPuOCKSRfSxgIgHWrZsGaD5CkU8iWVZmrdQREREcg1zaD8mZhEAjq4PqBODSC6iYqGIh0lISGDt2rWA5isU8TSat1BERERyC+fU78E4oUZ9rNKaGkkkN1GxUMTDrFmzhsuXL1OwYEHKlCljdxwR+Rv1LBQREZHcwOzaAhtWguXAcff9dscRkSymYqGIh/n7EGR19RfxLOpZKCIiIjmdMQbn5CsLUlmNWmEVKmpzIhHJaioWiniY1MVNNF+hiOdRz0IRERHJ8Tauhl1bwMcXq2Mvu9OIiA1ULBTxIBcvXmT9+vWAioUinkg9C0VERCQnM86UK3MVAlaLDlh589mcSETsoGKhiAdZtWoVSUlJFC5cmBIlStgdR0T+R2rPwjNnzhAfH29vGBEREZFMZpYvgUP7IDAI667udscREZuoWCjiQaKiooArqyBrvkIRzxMYGEj+/PkB9S4UERGRnMUkJWFmjAXAatsdKyjY5kQiYhcVC0U8yO+//w5A48aNbU4iIlejeQtFREQkJzJLZsPJ4xAWjtWig91xRMRGKhaKeIhTp07xxx9/ANCoUSOb04jI1WjeQhEREclpzKWLmF8mAmB17IXl52dzIhGxk4qFIh5i2bJlGGOoUKECBQsWtDuOiFyFehaKiIhITmN+nQrnz0JkEayGreyOIyI2U7FQxEOkzleoXoUink09C0VERCQnMWdPY+ZPB8Bx9/1YXl42JxIRu6lYKOIhUouFmq9QxLOlFgv37NljcxIRERGRW2dmjYfLCVCqPNSob3ccEfEAKhaKeID9+/cTGxuLl5cX9erVszuOiFxD5cqVsSyL2NhYjh8/bnccERERkZtmjh/BLJ0HgKPrA1iWZXMiEfEEKhaKeIDUXoXVq1cnJCTE5jQici158+bl9ttvByAmJsbmNCIiIiI3z0z/CVJSoFJ1rIp32B1HRDyEioUiHuD3338HNARZJLto0KABcGVhIhEREZHsyOzfjVm5FLjSq1BEJJWKhSI2czqdroKDioUi2UPDhg0BFQtFREQk+3JO+R4Aq05TrOJlbE4jIp5ExUIRm23dupWTJ08SGBhI9erV7Y4jItehbt26OBwOYmNjOXz4sN1xRERERG6I+WMNbF4HXt5Yd/exO46IeBgVC0VsljoEuV69evj6+tqcRkSuR2hoKHfccWVen+joaJvTiIiIiFw/k5KCc+IYAKyWHbAiIm1OJCKeRsVCEZulLm7SqFEjm5OIyI3QUGQRERHJjkzUfDi8H4JDsNrfa3ccEfFAKhaK2CgxMZHly5cDmq9QJLtJXeREPQtFREQkuzAXL1xZARmwOvbCCgy2OZGIeCIVC0VstHbtWi5dukT+/PmpWLGi3XFE5AbUrl0bb29vDh48yP79++2OIyIiIvKPzJxJcO4MRBbBatLW7jgi4qFULBSxUep8hY0aNcLh0MtRJDsJCgqiWrVqgHoXioiIiOczcUcxv00HwNG9H5a3t82JRMRTqTohYqPUYqGGIItkT5q3UERERLILM/UHSE6G26rCHbXsjiMiHsztHyX8+uuv/Prrr8TFxQFQtGhRunfvTvXq1d19aRGPdvbsWdavXw9ocROR7KpBgwaMHDmS6OhojDFYlmV3JBEREZF0zO5tmFW/g2XhuKef3rOIyDW5vVgYHh5O7969KVSoEMYYlixZwrvvvsu7775LsWLF3H15EY+1fPlyUlJSKFmyJEWLFrU7jojchJo1a+Lr68vRo0fZs2cPZcqUsTuSiIiISBrGGJzjvwbAatgKq1gpmxOJiKdze7GwVq203Zt79erFr7/+ys6dO1UslFwtKioK0BBkkewsICCAmjVrEhMTQ3R0tIqFIiIi4nHMqt9h7w7w88fq3MfuOCKcu5zC7lMJxCckc/ZyCucup3A52Wl3LNtVyB9AwxKhdscAsqBY+HdOp5OYmBguX75M+fLlMzwmKSmJpKQk12PLsggICHB97S6p51Z3bM+V09oodb7CJk2aZKt7ymntkFOpnbJOw4YNiYmJYdmyZTzwwANXPU5tkj2onUREJCcxiZcxk78DwGrbDSss3OZEkhslpThZefA8qw6dZ/uJBA6fS7Q7kkdqU9bkrmLh/v37GTJkCElJSfj7+/Pss89eddjl1KlTmTRpkutxqVKlGD58OBEREVkRlcjIyCy5jty8nNBGhw8fZseOHViWRdeuXQkPz36/tHNCO+QGaif369SpE++99x4rVqwgMjLyH4tMapPsQe0kIiLZnXE6MZO+hVNxkDc/Vuu77Y4kuUzs6QR+3X2GpXvPcC4xbc/BQiE+FAjyIdTPixA/L/y9tf5u+XwBdkdwyZJiYeHChRkxYgQXL15k+fLlfPrppwwbNizDgmGXLl3o0KGD63HqH11xcXEkJye7LaNlWURGRnL06FGMMW67jty8nNRGkydPBqBKlSpcvnyZI0eO2Jzo+uWkdsjJ1E5Zp3jx4vj7+3P8+HGWLFlChQoVMjxObZI9uKudvL29s+yDTxEREZOUhBnz4ZUhyIB1Tz8sPz+bU0luYIxhw9GLTN1ykvVHL7q25wvwpmmpUCoXCKRc/gBC/bxsTCn/JEuKhd7e3q5P6EuXLs3u3buZPXs2jzzySLpjfXx88PHxyfA8WfHHlTFGf8R5uJzQRqlDkBs3bpxt7yUntENuoHZyPx8fH+rUqcPSpUuJioq66jQbqdQm2YPaSUREsitz4TzOz96CHZvBywvrgSdw1G5kdyzJ4VKchmX7zzF1y0n2nL4MgMOCesVCaF0mD1Ujg/ByaJqX7CJL5yxM5XQ608xLKJKbGGNcxcJGjfRLWyQnaNCgAUuXLiU6Opp+/frZHUdERERyKZOUhPO9IXBwLwQE4vi/F7Fuq2p3LMnBLic7+W33GaZtPcXxC1fqPH5eFq3LhtGpYl4KBvvanFBuhtuLhWPHjqVatWrkz5+fhIQEoqKi2LJlC0OGDHH3pUU80u7duzl69Ch+fn7Url3b7jgikgkaNGgAQExMDE6nE4dDc66IiIhI1jO/Tb9SKAzJg+PpN7CKlrQ7kuRgKw6eY9SqY5y8eGXKuFA/LzpUyMtd5fNqmHE25/Zi4ZkzZ/j00085ffo0gYGBlChRgiFDhnDHHXe4+9IiHim1V2Ht2rVdK32LSPZWtWpVgoODiY+PZ8uWLVSuXNnuSCIiIpLLmFMnMLPGA2Dd20+FQnGb05eS+Wr1MZbtPwdAgSBvutyej5al8+CnhUpyBLcXC//v//7P3ZcQyVY0BFkk5/H29qZOnTosXLiQZcuWqVgoIiIiWc5MGgOJl6Hs7Vh1m9kdR3IgYwxLYs/y5epjXEh04rDg7tvC6Vklv4qEOYxaUyQLJScnExMTA1xZ3EREco6GDRsCEB0dbXMSERERyW3M9k1XVj62HDh6PYJlaSEJyVxnE5J5N+ow/40+woVEJ2XC/Xi/bUkerF5AhcIcyJYFTkRyq40bN3L27Fny5MlDlSpV7I4jIpkotVi4YsUKkpOT8fbWr1gRERFxP5OSgnPclwBYTdtiFS9tcyLJaVYdPM8nK44Qn5CClwU9quSne6V8Wt04B9NfMiJZKHUIcsOGDfHy0oSvIjnJ7bffTp48eThz5gx//PEH1apVszuSiIiI5AJm8Ww4tA+CQ7Du7mN3HMlBLial8M2a48zffQaAYnl8+VeDwpQJ97c5mbib+oqKZCHNVyiSc3l5eVGvXj0Ali1bZnMaERERyQ3M2XjM9LEAWF3uxwoKsTmR5BSbj19k8C+xzN99BosrcxN+cFdJFQpzCRULRbLIpUuXWLNmDaD5CkVyKs1bKCIiIlnJTPkeLl2A4mWwGrW2O47kAMYYpm45ydDf9nP8QhIFgnx4s1Vx+tYogK+XSki5hYYhi2SRlStXkpiYSOHChSlVqpTdcUTEDRo0aAD89Xr39fW1OZGIiIjkVGbPdsyy3wBw9H4Uy6FpjuTWXE528snyoyzddxaA5qVCeaR2QQJ99LOV26gsLJJFUocgN27cWKuTieRQFSpUIF++fFy8eJENGzbYHUdERERyKON04hw7CgCrQUusMhVtTiTZ3YmLSfz7130s3XcWLwseqVWQwfULqVCYS6lYKJJF/l4sFJGcyeFwUL9+fUDzFoqIiIj7mGW/wb5dEBCI1e0Bu+NINnf6UjIv/7afvacvk8fPizdaFqd9hbzq5JKLqVgokgVOnTrFH3/8AWhxE5GcLnUosoqFIiIi4g7mwvkrcxUCVqdeWKF5bU4k2dnZyym8uuAAh88lUSDIm/falqRSwUC7Y4nNVCwUyQJRUVEA3HbbbURERNicRkTcKXWRkzVr1pCQkGBzGhEREclpzPSf4PxZKFQMq1l7u+NINnYhMYXXFh5g35nL5A3w5vWWxSkQ7GN3LPEAKhaKZIHUYmFqEUFEcq4yZcpQsGBBLl++7FoBXURERCQzmIN7MYvnAODo9QiWt9YslZuTmOLkzcUH2X0qgVA/L15vWYxCIVqcT65QsVDEzYwxLFmyBICmTZvanEZE3M2yLNcHAxqKLCIiIpnFGHNlURPjxKrZEOu2qnZHkmzKaQwfxRxhS9wlAn0cDGtRjOJ5/OyOJR5ExUIRN9uzZw8HDx7E19eXevXq2R1HRLJA6ryF0dHRNicRERGRnMKsXAo7t4CvH9Y9/eyOI9nYqKg9LI29surxC02KUDrc3+5I4mFULBRxs9RVkGvVqkVgoCaKFckNUnsWrlu3jgsXLticRkRERLI7k3AJM2kMAFa7e7DyaR50uTnzd8XzzfJ9ADxeN5KqkUE2JxJPpGKhiJtpCLJI7lO8eHGKFi1KcnIyq1atsjuOiIiIZHNmzmSIPwURkVh33m13HMmmovad5bMVRwC4t3I+WpYJszeQeCwVC0XcKCkpyTVnmYqFIrmL5i0UERGRzGBOxmHmTwPAcU8/LB8tQiE3btb2U7wXdZgUA+1uj6RPVfVOlatTsfD/27vv8KjqtI3j3zPpAUIqhN5DlyBVVpr0LtJEEazI2nDdVXEtK5a1r2VxLRSpFkSKUgQRkCJICRA60gkQQkISIAlpc94/QkZ4bZRMzpnJ/bkuLslkmLnHZyb5zTO/IuJGcXFxZGRkEB4eTsOGDa2OIyLFSPsWioiISFEwZ0+B3Byo2xhiW1kdRzyMaZpM23KK8RuTMIGeMWE816M+hmFYHU1sTOesi7hR4RLktm3b4nCoNy9SkhQ2C+Pj40lPTyc0NNTaQCIiIuJxzP27Cw42MQwcg+9Rg0euiNM0+XD9SRbvSwPg9usiGdw4Eh+Hnkfyx9S9EHGjlStXAlqCLFISVaxYkVq1auF0Olm7dq3VcURERMTDmE4nzi8mAGD8pTNG1ZoWJxJPku80effHEyzel4bDKDjMZHDjSDWc5bKoWSjiJqmpqWzduhUomFkoIiVP4Wu/8IMDERERkctlrl8JB/dCQBDGzcOsjiMeJDff5I3Vx1lx6Aw+BjzWpiJda4daHUs8iJqFIm6yZs0anE4nMTExVKxY0eo4ImKBdu3aAbBq1SqLk4iIiIgnMTMzMGd9AoDRYwBG2TCLE4mncJomr68+xtqjZ/F1GDzZrhJtq4dYHUs8jJqFIm5SOJNIswpFSq4bbrgBh8PBgQMHSEhIsDqOiIiIeAhz3gxIT4XylTC69rc6jniQ7/alsz7hHP4+Bs90qEyrymWsjiQeSAeciLiBaZquw020X6FIyRUSEkJsbCxxcXGsXr2aFi1aWB1JxFYefPBBTp069avLu3btyr333svzzz/Pzp07L/le586dGTlypOvr5ORkxo8fz44dOwgMDKR9+/bcdttt+Pj4uK6zY8cOpk6dytGjR4mIiGDAgAF06NDBbY9LRORamIf3Yy5fCIDj9lEYfn4WJxJPkXY+j6lbkgAYHhtF0wqlLE4knkrNQhE3OHjwIAkJCfj5+XHDDTdYHUdELNS2bVvi4uJYuXIlf/vb36yOI2Irr7zyCk6n0/X1kSNHeOmlly753dmpUyeGDBni+trf39/1d6fTySuvvEJoaCgvvfQSqampjBs3Dh8fH2677TYAkpKSePXVV+nSpQsPP/ww27dv58MPPyQ0NJTY2Fj3P0gRkStgOvNxTv8fmE6Mlu0w6jexOpJ4kMlxSZzLcVIjLICeMVq6LldPy5BF3KBwCXKLFi0IDg62OI2IWOnifQsvboqISMHs29DQUNefuLg4ypcvT4MGDVzXCQgIuOQ6F/9e3bp1KwkJCTz88MNUr16dpk2bMmTIEBYvXkxeXh4AS5YsoVy5cgwfPpzKlSvTvXt3WrduzYIFC4r98YqI/Bnzh8Vw6GcICsYYfI/VccSDbDuZwfKDZzCAv7aMxsehU4/l6mlmoYgbaAmyiBS6/vrrCQoKIiUlhe3btxMVFWV1JBFbysvLY9WqVfTq1QvD+OUNzqpVq1i1ahWhoaE0a9aMAQMGEBAQAMDevXupWrUqoaGhruvHxsYyYcIEjh49So0aNfj5559p3LjxJffVpEkTJk+e/Id5cnNzyc3NdX1tGAZBQUGuv7tD4e266/bl2qlG9uGNtTDTUzHnTAPA0f8OHKHhFie6Nt5YI7vKzTf5aMNJALrVCaVe1O9PWFFd7M8ONVKzUKSI5ebmsmbNGuCXGUUiUnL5+/tzww03sGzZMpYuXcrQoUOtjiRiS+vXrycjI+OSvQRvvPFGIiMjCQ8P5/Dhw8yYMYPjx4/zj3/8A4C0tLRLGoUAZcuWdX2v8L+Fl118naysLHJyci5Z1nyxOXPmMGvWLNfXNWrU4LXXXiuWhn90dLTb70OujWpkH95Ui5Tp/yMzKwO/2vUpf+vdGBftverJvKlGdjV+zUGOpucQHuzH490bExL45/tcqi72Z2WN1CwUKWJxcXFkZGQQFhZGo0aNrI4jIjbQtm1bli1bxnfffadmocjvWL58ObGxsYSH/zKTpnPnzq6/V61albCwMF544QUSExPdPoDu378/vXv3dn1d+On+qVOnXEuci5phGERHR5OYmIhpmm65D7k2qpF9eFstnLu24lyxCAwD5633kZiUZHWka+ZtNbKrPclZTFh7CIC7mkaRkZpMxh9cX3WxP3fVyNfX97I/9FSzUKSIFe5X2K5dOxwObQsqIgXNQij4+ZCdnf27M5lESqpTp04RHx/vmjH4e2rXrg3gahaGhoayb9++S66Tnp4O4JpxGBoa6rrs4usEBQX94WvRz88Pv985gdTdb65M09QbOJtTjezDG2ph5ubinPEBAEb7HlCttsc/pot5Q43s6nyek7fXHMNpQttqZWhXPeSy/1+rLvZnZY3UyRApYtqvUET+v3r16hEZGUlmZiZxcXFWxxGxneXLl1O2bFmuv/76P7zeoUOHAAgLKzjhMSYmhiNHjlzSDIyPjycoKIjKlSsDUKdOHbZt23bJ7cTHxxMTE1OEj0BE5OqZS+ZA4jEICcXoP8zqOOJBJm1K4vjZXCKCfRnVQsuKpeioWShShFJTU9m6dSvwy0wiERHDMC6ZXSgiv3A6naxYsYL27dvjc9H+XImJicyaNYsDBw6QlJTExo0bef/996lfvz7VqlUDCg4qqVy5MuPGjePQoUNs2bKFzz//nG7durlmBXbt2pWkpCSmT5/OsWPHWLx4MWvXrqVXr16WPF4RkYuZpxIxF8wEwBh0N0ZwaYsTiafYkHCOxfvSABh9QwVKB3jHHpdiD1qGLFKE1qxZg9PppE6dOlSsWNHqOCJiI+3atWPOnDmsWrWKJ554wuo4Iraxbds2kpOT6dix4yWX+/r6sm3bNhYuXEh2djYRERG0atWKW265xXUdh8PBmDFjmDBhAs888wwBAQG0b9+eIUOGuK5Trlw5xowZw5QpU1i4cCERERGMGjWK2NjY4nqIIiK/yTRNnJ99DLk5UO86jFZamSSXJzM3n/fXJwLQt14YTaJLWZxIvI2ahSJF6OL9CkVELnbjjTcCsGXLFtLT0391OqtISdWkSRNmzpz5q8sjIyMZO3bsn/77qKgonnrqqT+8TsOGDXn99devOqOIiFtsXgvbNoKPL47bRrkOUhL5MzO3pZCalUd0aT/uiL28AytEroSWIYsUEdM0tV+hiPyuSpUqUbduXZxOJ2vXrrU6joiIiFjIPJ+F8/MJABjdbsGoUNniROIpEs5k882e0wDc26w8/j5q60jR07NKpIgcPHiQhIQE/Pz8uOGGG6yOIyI21KVLF0D7FoqIiJR05jefQWoyRJbH6DXI6jjiIUzTZPzGJPKc0LxiKVpU1h6X4h5qFooUkRUrVgDQvHlzgoODrQ0jIrbUuXNnAFatWmVxEhEREbGKmXAQc+nXADhuux/DP8DiROIpfko4x5YTGfg6DO5tXt7qOOLF1CwUKSLff/89AJ06dbI4iYjYVYcOHXA4HBw4cIBjx45ZHUdERESKmel04pz+ATidcP0NGI2bWx1JPER2npOJm5IAuLl+OBXK+FucSLyZmoUiRSAzM9O1B9lNN91kcRoRsauyZcvStGlTAFavXm1xGhERESlu5pqlsH83BATiGHKv1XHEg8zZeZqkjFwign0Z1CjC6jji5dQsFCkCa9asITs7m8qVKxMTE2N1HBGxsbZt2wLat1BERKSkMc+ewfxqCgBG36EY4TrFVi7PyXM5fLUzBYC7ry9HoK9aOeJeeoaJFIHCJcg33XQThmFYnEZE7Kxdu3ZAwb6FTqfT4jQiIiJSXMyvJkPGWahUDeOmPlbHEQ/ySVwSOfkmjcsH85eqZayOIyWAmoUi18g0TZYtWwZov0IR+XPXX389wcHBpKSksHv3bqvjiIiISDEw9+0sWIIMOIb9FcPX1+JE4im2nMhg7dFzOAy4r3l5TU6RYqFmocg12rNnD8eOHSMwMJC//OUvVscREZvz9/endevWgE5FFhERKQnMvLyCQ00A48YuGLUbWJxIPEVuvsn4jScB6BUTRrVQnZwtxUPNQpFrVDirsE2bNgQFBVmcRkQ8QeG+hWoWioiIeD/z+2/g2GEoXQZjwAir44gHmb/nNAlncigb4MOt10VaHUdKEDULRa7RxfsViohcjsJ9C9euXUtWVpbFaURERMRdzNOnML/5DABjwJ0YpUMsTiSe4nRWHp9vKzjUZHjTKEr7+1icSEoSNQtFrkF6ejobNmwA1CwUkctXt25doqOjOX/+PD/99JPVcURERMRNnJ+Ph+zzULs+Rhvtby6Xb0pcEufznMREBHJTzbJWx5ESRs1CkWvwww8/kJ+fT+3atalWrZrVcUTEQxiG4fqAoXArAxEREfEuZvwG2LwOHA4ct/8Vw6G333J5diVlsuLQGQxgZIvyOHSoiRQz/bQSuQY6BVlErlbHjh0BWL58ucVJREREpKiZ2dk4P/0IAKNzP4zK1a0NJB4j32ny0YVDTTrXKkudCO2LL8VPzUKRq+R0Ol1v8rUEWUSu1I033oivry8HDhzg8OHDVscRERGRImQunAkpSRAeidHnVqvjiAdZsi+Ng6nZlPJ3cEdslNVxpIRSs1DkKsXHx5OcnEzp0qVp2bKl1XFExMOEhITQvHlzQLMLRUREvIl54ijm4jkAOIbchxGomWFyec7l5DNj6ykAbr8uirKBvhYnkpJKzUKRq1S4BLldu3b4+/tbnEZEPJGWIouIiHgX0zRxzvgQ8vOgcXNo2trqSOJBvt59mrM5TiqH+NO9TqjVcaQEU7NQ5Cp9//33gPYrFJGr16FDBwDWrFlDdna2tWFERETkmpk/rYA928DfH8fQkRg6mEIu05nzeXy9KxWA25tE4uPQc0eso2ahyFU4deoUW7ZsAX6ZGSQicqUaNmxI+fLlycrK4qeffrI6joiIiFwDM+Mc5sxJABg9B2NERVucSDzJnF2nycpzUjMsgNZVylgdR0o4NQtFrkLhksHGjRtTvnx5i9OIiKcyDMM1u1BLkUVERDybOXcanE2H6MoY3fpbHUc8SGpWHvP3FM4qjMKhGaliMTULRa5C4X6FOgVZRK5VYbNwxYoVluYQERGRq2fu2Y65YhEAjttHYfj6WZxIPMmsHSnk5JvUjQykWcVSVscRUbNQ5Erl5ubyww8/AGoWisi1a9euHQ6Hg71793Ls2DGr44iIiMgVMs9n4Zz8LgBG264Y9a6zOJF4klMZuXz7cxoAt10XpX0uxRbULBS5Qps2beLMmTOEhYXRtGlTq+OIiIcLDQ2lWbNmwC+zlkVERMRzmLM+geSTEB6FMehuq+OIB8l3mry39gR5TpNG5YJoEh1sdSQRQM1CkStW+Ga+Y8eO+Pj4WJxGRLxB4UFJ2rdQRETEs5g7N2P+8C0AjjsfwQhSs0cu38ztycSfzCTAx2BUy2jNKhTbULNQ5Ap9//33AHTq1MniJCLiLQqbhatXryYnJ8fiNCIiInI5zKxMnFP+C4DRsSdG/SYWJxJPsuVEBl9sSwHggVbRVCkbYHEikV+oWShyBRISEti9ezcOh4N27dpZHUdEvESjRo2IjIwkIyODDRs2WB1HRERELoM5cyKcToaoaIwBd1odRzxISmYu/1lzHBPoWrssHWqUtTqSyCXULBS5AkuWLAGgRYsWhIeHW5xGRLyFw+FwnYqspcgiIiL2Z27fhLn6OzAMHHeOxggItDqSeAjTNHln7QnSs/OpERbAvc3KWx1J5FfULBS5AosXLwaga9euFicREW9TuBR5xYoV1gYRERGRP2RmZuCc+j4Axk29MWIaWpxIPMmqw2eJT8zE38fg8RsrEeCrtozYj56VIpcpPT2ddevWAWoWikjRa9euHQ6Hg127dnH8+HGr44iIiMjvMGdOhNRkKFcBo/9wq+OIB8nMzeeTuCQABjSMoFKIv8WJRH6bmoUil2n58uXk5eVRp04datasaXUcEfEy4eHhxMbGAppdKCIiYlfmtk2Ya5ZetPxYh1LI5Zu5LYXTWXlEl/bjlgba1krsS81CkctUuAS5W7duFicREW9VuBRZ+xaKiIjYj5l5DufUcQAYnfpg1GlgcSLxJAnp2Xy9+zQA9zYrj7+P2jFiX3p2ilyGnJwc15t3LUEWEXcpbBauWrWK3Nxci9OIiIjIxcyZEyEtBcpVxLj5DqvjiAcxTZOPN54k34QWlUrRonJpqyOJ/CE1C0Uuw7p16zh79ixRUVE0bdrU6jgi4qWaNGlCeHg4Z8+eZdOmTVbHERERkQvMbRsx13xfsPz4rke0/FiuyPpj59iamImfw9Dpx+IR1CwUuQyFS5C7dOmCw6GXjYi4h8PhoH379oCWIouIiNjFpcuP+2LU1vJjuXz5TpOpm08B0K9+ONFldKiJ2J+6HiJ/wjRNlixZAmgJsoi430033QTA999/b3ESERERATC/mAhppy8sPx5mdRzxMN8fSCfhTA5lAnx0qIl4DF9338GcOXNYv349x44dw9/fn5iYGIYNG0bFihXdfdciRWL79u0cP36coKAgbrzxRqvjiIiX69ixIw6Hg127dpGQkEDlypWtjiQiIlJimfEbMH/U8mO5Otl5Tj6LTwZgcKMISvn7WJxI5PK4fWbhzp076datGy+//DLPPPMM+fn5vPTSS5w/f97ddy1SJAqXIHfo0IGgoCCL04iItwsLC6NFixYALF261OI0IiIiJZeZcQ7ntPcBMDpr+bFcuW92p3I6K49ypfzoUSfU6jgil83tzcKnn36aDh06UKVKFapXr86DDz5IcnIyBw4ccPddixSJRYsWAVqCLCLFp0uXLgB89913FicREREpucwvJhQsPy5fScuP5YqdOZ/HVztTALi9SSR+PtoFTjyH25ch/3+ZmZkAlC7920eF5+bmkpub6/raMAzXbC7DMNyWq/C23Xkfcm2sqNH+/fvZvXs3vr6+dOvWTc8P9FrxFKqT/VxJTbp06cJLL73Ejz/+SGZmJqVKlXJ3PLlArx0REQEwt67HXLusYPnxnY9g+Gv5sVyZmTtSyMx1UjMsgHbVQ6yOI3JFirVZ6HQ6mTx5MnXr1qVq1aq/eZ05c+Ywa9Ys19c1atTgtddeIyoqqlgyRkdHF8v9yNUrzhpNnToVKDhwoH79+sV2v55ArxXPoDrZz+XUJDo6mlq1arF//362bdtG//79iyGZXEyvHRGRkqtg+fH/ADC69MOorfcBcmVOnsth0d5UAEY0LYdDH0KKhynWZuHEiRM5evQoL7zwwu9ep3///vTu3dv1deEn+6dOnSIvL89t2QzDIDo6msTEREzTdNv9yNWzokZffPEFAJ06deLEiRPFcp92p9eKZ1Cd7OdKa9KxY0f279/PF198QevWrYshoYD7Xju+vr7F9sGniIhcG/Pz8ZB+GqIrYfS73eo44oGmb00mzwlNooOJraAVIuJ5iq1ZOHHiROLi4hg7diwRERG/ez0/Pz/8/Px+83vF8YbXNE29sba54qrRsWPH2Lx5M4Zh0LVrVz0v/h+9VjyD6mQ/l1uTzp07M2HCBL7//nvy8/NxOLTPTXHSa0dEpGQyt67HXLccDAeOO0dr+bFcsQOnz7Py0BmgYFahiCdy+zsP0zSZOHEi69ev57nnnqNcOb1YxDN8++23ALRo0ULPWxEpdq1ataJMmTIkJyezZcsWq+OIiIh4PTPj7C+nH3fth1GrnsWJxBNN2ZwEQLvqIdQKD7Q4jcjVcXuzcOLEiaxatYrRo0cTFBREWloaaWlp5OTkuPuuRa5J4SnIPXv2tDiJiJRE/v7+tG/fHoClS5danEZERMT7FSw/ToXoylp+LFdly4kMtiRm4uuAYU0irY4jctXcvgx5yZIlADz//POXXP7AAw/QoUMHd9+9yFVJTk7mp59+AqBHjx4WpxGRkqpz587Mnz+f7777jieeeMLqOCIiIl7L3PIT5roVBcuP7xqN4edvdSTxME7TdM0q7FEnjPKl9RwSz+X2ZuHMmTPdfRciRW7JkiU4nU6uu+46KleubHUcESmhOnXqhGEY7Ny5k2PHjlGpUiWrI4mIiHgdM+MszukXTj/uejNGzboWJxJPtD7hHAdSswnydTC40e+f0yDiCbRbushvKFyCrFmFImKl8PBwmjVrBmgpsoiIiLuYn31csPy4QhWMfrdZHUc8kGmafLEtGYBedcMICSy2s2RF3ELNQpH/Jz09nVWrVgHar1BErNelSxdAzUIRERF3MLesw/zpBy0/lmuy4VjBrMJAX4N+9cKsjiNyzdQsFPl/vv/+e3Jzc4mJiaF27dpWxxGREq5z584ArFmzhszMTIvTiIiIeA/z3Bmc0y4sP+7WH6NGjMWJxBOZpsnn21IA6BmjWYXiHdQsFPl/tARZROykbt26VKlShezsbFavXm11HBEREa9hfjYezqQVLD/uO9TqOOKhNh3PYP/p8wT4GNxcP9zqOCJFQs1CkYtkZWWxbNkyQEuQRcQeDMNwzS787rvvLE4jIiLiHcy4tZjrtfxYrk3BrMKCvQp7xIRRVrMKxUuoWShykRUrVnD+/HmqVKlCw4YNrY4jIgL8sm/h999/j9PptDiNiIiIZzPPnvnl9OPuWn4sV2/ziQx+TjmPv49Bf80qFC+iZqHIRRYuXAgUzCo0DMPiNCIiBVq3bk2pUqU4efIk27ZtszqOiIiIRzM/+wjOphcsP+6j04/l6ly8V2H3OqGEBmlWoXgPNQtFLsjJyXEt8dN+hSJiJwEBAbRv3x7QqcgiIiLXwoz7EXPDKnA4cNz1KIafn9WRxENtTcxkT3JWwazCBhFWxxEpUmoWilywZs0azp49S7ly5WjWrJnVcURELqF9C0VERK5NwfLjDwAwut2CUaOOxYnEU5mmyRcX9irsWjuUcM0qFC+jZqHIBYWnIHfv3h2HQy8NEbGXTp06YRgG27Zt48SJE1bHERER8Tiu5ccVq2L00enHcvW2ncxk56ks/BwGtzTQXoXifdQREQHy8/P59ttvAZ2CLCL2FBkZSdOmTQFYuXKlxWlEREQ8i7npouXHd2v5sVybwlmFXWqXJSJYzyXxPporKwKsX7+elJQUQkNDad26tdVxRER+U9OmTYmLi2P37t1WRxEpEjNnzmTWrFmXXFaxYkXeeecdoGA/4alTp/Ljjz+Sm5tLkyZNuPfeewkNDXVdPzk5mfHjx7Njxw4CAwNp3749t912Gz4+Pq7r7Nixg6lTp3L06FEiIiIYMGAAHTp0KIZHKCJ2YJ5NxznjwvLj7gMxqtW2OJF4su0nM9melIWvw2BAQ+1VKN5JzUIRYO7cuUDBEmQ/fcooIjZVu3bBm5t9+/ZZnESk6FSpUoVnn33W9fXFW4FMmTKFuLg4HnvsMYKDg5k4cSJvvfUWL774IgBOp5NXXnmF0NBQXnrpJVJTUxk3bhw+Pj7cdlvBCadJSUm8+uqrdOnShYcffpjt27fz4YcfEhoaSmxsbLE+VhGxhvnpheXHlaph9B5idRzxYKZp8tmFWYWda5UlUrMKxUtpGbKUeDk5OcyfPx+Am2++2dowIiJ/oE6dgo3Yf/75Z4uTiBQdh8NBaGio609ISAgAmZmZLFu2jBEjRtCoUSNq1qzJAw88wJ49e9i7dy8AW7duJSEhgYcffpjq1avTtGlThgwZwuLFi8nLywNgyZIllCtXjuHDh1O5cmW6d+9O69atWbBggWWPWUSKj7lpDebG1RdOPx6t5cdyTbYkZrL9ZCa+DoOBmlUoXkwzC6XEW7lyJWlpaZQrV442bdpYHUdE5HfFxMQAkJCQQFZWFkFBQRYnErl2iYmJ3H///fj5+RETE8Ntt91GZGQkBw4cID8/n8aNG7uuW6lSJSIjI9m7dy8xMTHs3buXqlWrXrIsOTY2lgkTJnD06FFq1KjBzz//fMltADRp0oTJkyf/Ya7c3Fxyc3NdXxuG4XrNGYZx7Q/8NxTerrtuX66damQfl1OLguXHHxZcr+cgHNV1+nFx8rbXi9M0mb7lFAA9Y8IoV9rf4kRXx9vq4o3sUCM1C6XEK1yC3KdPn0v2NxIRsZuIiAjCwsJITU1l//79NGrUyOpIItekTp06PPDAA1SsWJHU1FRmzZrFc889x1tvvUVaWhq+vr6UKlXqkn9TtmxZ0tLSAEhLS7ukUVj4/cLvFf638LKLr5OVlUVOTg7+/r/9Zm/OnDmX7KdYo0YNXnvtNaKioq7hEV+e6Ohot9+HXBvVyD7+qBbJk98l62w6ftVrU/5eHWpiFW95vSzdk8S+0+cJ9vPhoU4NCAv2zGZhIW+pizezskZqFkqJlpmZyeLFiwEtQRYRz1CnTh3Wr1/Pzz//rGaheLzCE74BqlWr5moerl279nebeMWlf//+9O7d2/V14af7p06dci1xLmqGYRAdHU1iYiKmabrlPuTaqEb28We1cG5cjXP1UnA4cN7xIInJyRakLNm86fWS5zT57/IDAPSrH8b59BROpFsc6ip5U128lbtq5Ovre9kfeqpZKCXad999R2ZmJtWqVbvkDYuIiF1d3CwU8TalSpWiYsWKJCYmct1115GXl0dGRsYlswvT09NdswlDQ0N/deBPenq663uF/y287OLrBAUF/WFD0s/P73cPPXP3myvTNPUGzuZUI/v4rVqYZ9JwTr9w+nHPQVC1luplIW94vXy/P43jZ3MICfChb70wj3884B118XZW1kgHnEiJVrgEuV+/ftqzQUQ8QuGJyGoWijc6f/48iYmJhIaGUrNmTXx8fNi2bZvr+8ePHyc5Odm1f2dMTAxHjhy5pBkYHx9PUFAQlStXBgoa7BffRuF1Cm9DRLyLaZoF+xSeOwOVq2P0Gmx1JPFw2XlOPo8vmJk6uFEEwX7aukq8n5qFUmKlpqayfPlyoGCpkYiIJyg8Efn/z6YS8URTp05l586dJCUlsWfPHt544w0cDgc33ngjwcHB3HTTTUydOpXt27dz4MAB/ve//xETE+Nq9DVp0oTKlSszbtw4Dh06xJYtW/j888/p1q2ba1Zg165dSUpKYvr06Rw7dozFixezdu1aevXqZeVDFxE3MTeuhrgfwcen4PRjX+1TKNdm9s4UUrLyiAr2pXudUKvjiBQLLUOWEmvhwoXk5uZSv359zS4QEY9R2Cw8ePAgubm5v7tMUsQTnD59mnfffZezZ88SEhJCvXr1ePnllwkJCQFgxIgRGIbBW2+9RV5eHk2aNOHee+91/XuHw8GYMWOYMGECzzzzDAEBAbRv354hQ4a4rlOuXDnGjBnDlClTWLhwIREREYwaNYrY2Njifrgi4mbmmVTMTy+cftxjEEbVWhYnEk+XeDaHr3acBuCuZuXw89F8KykZ1CyUEmvOnDmADjYREc9SsWJFgoKCyMrK4vDhw65lySKe6NFHH/3D7/v7+3Pvvfde0iD8/6Kionjqqaf+8HYaNmzI66+/fjURRcRD/LL8+CxUroHRa5DVkcQLTIxLItdpcl10MG2qlLE6jkixUVtcSqSEhATWrl0LaAmyiHgWh8OhfQtFRET+H3PDKohbq+XHUmQ2HjvH+oRz+Bgwsnl57XEvJYqahVIizZ49G4A2bdpQqVIli9OIiFyZwqXIahaKiIhcWH782UdAwenHRtWaFicST5eb72TCppMA9KkXTpWyARYnEileahZKiWOaJl999RUAAwcOtDiNiMiVU7NQRESkgGmaOKd/ULD8uEoNjJ5afizXbu6u05w4m0tYkC9DGkdYHUek2KlZKCVOfHw8+/btIzAwkJ49e1odR0TkiulEZBERkQLmj8tg8zrw8cVx16NafizXLPFsDjO3pwBwZ9Mogv18LE4kUvzULJQSp3BWYbdu3ShTRpvUiojnubhZ6HQ6LU4jIiJijbyTx3EWLj/uOxSjSg2LE4mnM02TjzacJCe/4FCT9tVDrI4kYgk1C6VEyc3NZe7cuQAMGDDA2jAiIlepWrVq+Pr6kpmZyYkTJ6yOIyIiUuxMZz6n//M8nM+CWvUwut9idSTxAj8eOUvciQx8HQajWkTrUBMpsdQslBLlhx9+ICUlhcjISNq3b291HBGRq+Ln50eNGgWzJ7RvoYiIlETmd/PI3h4HAUE47nkMw6GlonJtMnPzGb8pCYCBDcOpFOJvcSIR66hZKCVK4RLkfv364evra3EaEZGrp0NORESkpDITDuKcMw0Ax5B7MKKiLU4k3mDG1mRSs/KoUMaPAQ11qImUbGoWSolx5swZlixZAugUZBHxfLVr1wbULBQRkZLFzM3FOeE/kJdHYMu2GG27Wh1JvEBCejYL96YCMKpFNP4+apVIyaZXgJQYc+bM4fz589StW5fGjRtbHUdE5JrExMQAsHPnTouTiIiIFB9z3gw4dhjKlCX8kWe0p5wUiWlbT+E0oVXl0sRWKGV1HBHLqVkoJcZnn30GwNChQzWoEBGP17JlSwC2bNlCYmKixWlERETcz9y7HXPJHAAcwx/CJ0xLReXa7T6Vxbqj53AYMCw2yuo4IragZqGUCNu2bWPbtm34+/vrFGQR8QqVKlWiWbNmmKbJggULrI4jIiLiVmZWJs5J74BpYvylM46mra2OJF7ANE2mbC441OSmmmWpWjbA4kQi9qBmoZQIhbMKe/ToQXh4uMVpRESKRt++fQH45ptvLE4iIiLiXuYX4yElCSLLY9x6r9VxxEtsOHaOnaey8PcxGHpdpNVxRGxDzULxellZWcyZU7BcYejQoRanEREpOr169QJgw4YNHD9+3OI0IiIi7mHGrcVc8z0YBo67HsUIDLY6kniBfKfJ1C2nAOhdN4zIYD+LE4nYh5qF4vXmz5/PmTNnqFq1Kn/5y1+sjiMiUmQqVKjg2rtw/vz5FqcREREpemZ6Ks5p7wNgdLsFI6ahxYnEWyw7kM7R9BxK+zsY0ED7X4pcTM1C8XqFS5BvvfVWHA495UXEu2gpsoiIeCvTNHFO+S+cOwOVa2D0vc3qSOIlMnPzmbG1YFbhoEYRlA7wsTiRiL2ocyJebd++ffz00084HA4GDx5sdRwRkSLXs2dPDMMgLi6OhIQEq+OIiIgUGXPVEti2EXx9cdz7GIaflolK0Ziz8zSp5/OJLu1Hr5gwq+OI2I6aheLVZsyYAUDHjh2pUKGCxWlERIpe+fLlad264ERILUUWERFvYSYdx5w5EQCj/x0YlapZnEi8xamMXObuOg3AnU3L4eejtojI/6dXhXitrKwsvvjiCwDuuOMOi9OIiLhPnz59APj6668tTiIiInLtzPx8nJPegezzULcxRud+VkcSLzJ9yyly8k0aRAXRukppq+OI2JKaheK15s2bR3p6OlWqVOGmm26yOo6IiNv07NkTh8PB1q1bOXTokNVxREREron57VewfzcEBeO4azSG9h2XIvJzShYrDp0B4O5m5TAMw+JEIvakn7rilUzTZPLkyQAMHz4cHx9tWCsi3isqKsp12vu8efMsTiMiInL1zMP7Mb8pOKDQuHUkRkQ5ixOJN5myueBQk441QqgTEWRxGhH7UrNQvFJcXBzbtm0jICCAW2+91eo4IiJud/PNNwMwd+5cTNO0NoyIiMhVMHOycU78D+Tnw/VtMG7oaHUk8SK7TmWy7WQmvg64vUmU1XFEbE3NQvFKhbMK+/btS3h4uLVhRESKQY8ePfD392fv3r3s2rXL6jgiIiJXzPxyEpw4CmXDcAx7QEtEpUh9uT0FgA41yhJVSidri/wRNQvF6yQnJ7tOBL3zzjutDSMiUkzKli3r2p9VS5FFRMTTODesxlyxCADHnaMxyoRYnEi8yYHT59l0PAOHAQMbRlgdR8T21CwUr/PZZ5+Rk5NDbGwssbGxVscRESk2WoosIiKeyEw6gTn1vwAYPQZgNLre4kTibb7cUTCr8MZqIVQo429xGhH7U7NQvEpeXh5Tp04FYMSIERanEREpXp07d6ZUqVIkJCSwceNGq+OIiIj8KTM3F+dHr8P5LKhdH6PfMKsjiZc5mp7N2iNnAc0qFLlcahaKV1m4cCHHjx8nIiKCvn37Wh1HRKRYBQUF0b17d6BgdqGIiIjdmV9OgiP7oXQZHPc9juHjY3Uk8TKzdqRgAq0ql6ZaaIDVcUQ8gpqF4lUmTJgAwPDhwwkMDLQ4jYhI8evfvz8A33zzDXl5eRanERER+X3mph8xly8AwHH33zDCIy1OJN4m8WwOKw+dAWBQI80qFLlcahaK14iLi2PTpk34+/szfPhwq+OIiFjixhtvJDw8nJSUFFavXm11HBERkd9knkrEOeXCPoXdbsFo3NziROKNvtyRgtOEphVKUSciyOo4Ih5DzULxGuPHjwegX79+lCtXzuI0IiLW8PPzo0+fPgDMnj3b4jQiIiK/Zubl4vz4DcjKgFr1MG7WPoVS9BLP5rD8QDoAtzbWrFWRK6FmoXiFY8eOsWBBwRKGe++91+I0IiLWuuWWWwBYtGgRmZmZFqcRERG5lPnVFDj0MwSXLtin0NfX6kjihb7ckUL+hVmF9aI0q1DkSqhZKF5h8uTJ5Ofnc8MNN9CoUSOr44iIWKpZs2ZUr16dzMxMFi1aZHUcERERF3PzOsylXwPguPtRjIgoixOJNzp5TrMKRa6FmoXi8TIzM5kxYwYAI0eOtDiNiIj1DMNg4MCBAHz11VcWpxERESlgJp/EOfldAIyuN2M0aWlxIvFWX24vmFUYq1mFIldFzULxeJ9++inp6elUr16dTp06WR1HRMQWCpcir1q1isTERIvTiIhISefapzAzA2rEYPTXgYTiHifP5bDMNatQJyCLXA01C8Wj5eTk8OGHHwIwatQofHx8LE4kImIP1apVo0WLFjidTubOnWt1HBERKeHMOdPg4F4ILoXj/ie0T6G4zYytya5ZhfWjgq2OI+KR1CwUjzZ79mxOnDhB+fLlGTRokNVxRERspXAp8qxZsyxOIiIiJZm5dT3mkrkAOO4ajRFRztpA4rV2JGXyw6EzGMAdTbQfpsjVUrNQPFZ+fj7vv/8+ULBXYWBgoMWJRETspXfv3vj7+7Nr1y527NhhdRwRESmBzJRTOCe9A4DRuS9GbGtrA4nXyneajN94EoCutUOpHaH3hyJXS81C8VgLFy7kwIEDhIaGMmzYMKvjiIjYTmhoKJ07dwY0u1BERIqfmZeHc/wbkHkOqtfBGDDC6kjixRbvS+Ngajal/R0Ma6ITkEWuhZqF4pFM02TcuHEA3H333ZQuXdriRCIi9lS4RcPcuXPJy8uzOI2IiJQk5tzpsH83BJXCMfJxDF8/qyOJl0o/n8f0racAGNYkipBA7Ykpci3ULBSPtGLFCrZv305wcDB33XWX1XFERGyrQ4cOREREkJSUxLJly6yOIyIiJYQZvwFz8WwAHHc+ghEVbXEi8WbTt54iI8dJjbAAutYOtTqOiMdTs1A8jmmavP322wDcfvvthIeHW5xIRMS+/P39XQedfP755xanERGRksBMOYXzk3cAMDr1wbj+BmsDiVf7OSWL7/alA3B/8/L4OAyLE4l4PjULxeMsX76cTZs2ERgYyF//+ler44iI2N7QoUMBWLp0KUlJSRanERERb2bm5uL86DU4dxaq1cYYcKfVkcSLOU2TjzecxAQ61AihfrlgqyOJeAU1C8WjmKbJG2+8AcCIESMoX768xYlEROyvTp06NGvWjPz8fB10IiIibmXOnAgH90KpMjhGPYnhp30KxX2WHUhnb8p5gnwdjGhazuo4Il5DzULxKF9//TVbt24lODiYBx980Oo4IiIeo3B24WeffYZpmhanERERb+RctxxzxUIwDBz3PoYRqQ/2xX3O5eQzdXPBoSa3XhdBeJAONREpKmoWisdwOp0899xzQMEJyBERERYnEhHxHH369CE4OJgDBw6wfv16q+OIiIiXMY8dxpz2PgBGryEYjZpZnEi83efxyaRn51M5xJ/edbWPvUhRUrNQPMbChQuJj4+nTJkyjBo1yuo4IiIepXTp0vTt2xcomF0oIiJSVMysTJwfvAo5OdCgKUafIVZHEi93JC2bBXtTAbiveXl8daiJSJFSs1A8Qn5+Pm+++SYA9913H2FhYRYnEhHxPLfeeisA8+fP5+zZsxanERERb2CaJs7J78HJYxAeiePev2M4fKyOJV5uyuYknCa0rlKa2AqlrI4j4nXc3izcuXMnr776Kvfffz+DBw/W0ie5KrNnz2bv3r2EhYUxcuRIq+OIiHik5s2bU7t2bbKyspgzZ47VcURExAuY382DuB/BxxfHqDEYZUKsjiReLj4xg43HM/AxYESsDjURcQe3Nwuzs7OpXr0699xzj7vvSrxUdna2a1bhmDFjCAnRAERE5GoYhsHtt98OwLRp03TQiYiIXBNz7w7MryYDYAy5F6NGjLWBxOs5TZMpFw416VYnlIoh/hYnEvFObm8WNm3alFtvvZWWLVu6+67ES02fPp2EhASio6N56KGHrI4jIuLRBg0aRGBgIDt37mTTpk1WxxEREQ9lpqfi/Ph1cDoxWrXH6NDD6khSAqw5fJZ9p88T6OtgSONIq+OIeC3bnS2em5tLbm6u62vDMAgKCnL93V0Kb9ud9yFX7ty5c7z77rsAPPbYYwQHB3PmzBmLU5Vseq14BtXJfuxSk/DwcPr27cvMmTOZNm0aLVq0sDSP3dilTiIidmbm5+P8+A1IT4WKVTHueFA/N8XtcvNNpm8tmFV4S4NwQgNt184Q8Rq2e3XNmTOHWbNmub6uUaMGr732GlFRUcVy/9HR0cVyP3J5XnzxRVJSUqhTpw6PPvoooBrZhergGVQn+7FDTR577DFmzpzJN998w4cffkhERITVkWzHDnUSEbErc8402LsdAoNw/HUMRkCg1ZGkBPj251QSz+USFuhDv/rhVscR8Wq2axb279+f3r17u74u/ITq1KlT5OXlue1+DcMgOjqaxMRE7eFkEykpKbz++usA/P3vfyclJUU1sgG9VjyD6mQ/dqpJlSpVaNSoEdu3b+e9995j1KhRluaxE3fVydfXt9g++BQRcSczbi3m4tkAOO58BCO6ssWJpCTIznMya0cKAEOviyLQ1+07qomUaLZrFvr5+eHn5/eb3yuON1emaVr+Jk4KvP3225w7d45GjRrRq1cvV11UI3tQHTyD6mQ/dqnJ8OHDeeKJJ5g6dSr33XcfDocG3RezS51EROzETEzAOblgiyCjSz+MZn+xOJGUFEv3p5N2Pp9ypXzpVKus1XFEvJ7eGYgt/fzzz0yZMgWAZ555Rm9iRUSK2M0330zp0qU5dOgQq1evtjqOiIjYnJl5Due4lyErE2o3wLhlhNWRpITIzXfy1c6CWYW3NIjA16H9MUXcze0dmPPnz3Po0CEOHToEQFJSEocOHSI5Odnddy0e7IUXXiA/P59u3brRtm1bq+OIiHidUqVKMXDgQADXhzMiIiK/xXTm4xz/Jpw8BuGROP76JIav7RapiZdafvAMKZl5hAdpVqFIcXH7T/j9+/czduxY19dTp04FoH379jz44IPuvnvxQMuWLWPZsmX4+fnxzDPPWB1HRMRrjRgxgsmTJ7NkyRKOHj1KlSpVrI4kIiI2ZM6eCtvjwN8fxwNPY4SEWR1JSog8p+naq7B/g3D8fbTiTKQ4uL1Z2LBhQ2bOnOnuuxEvkZub62ou33PPPdSsWdPiRCIi3ismJoa2bduyatUqJk+ezLPPPmt1JBERsRnnuuWYi+cAYNw5GqNaLYsTSUmy8tAZTp7LpWyAD91qh1odR6TE0NxxsZVp06axb98+IiIiGD16tNVxRES83j333MOqVav47LPP+Pvf/05wcLDVkaQEmTNnDuvXr+fYsWP4+/sTExPDsGHDqFixous6zz//PDt37rzk33Xu3JmRI0e6vk5OTmb8+PHs2LGDwMBA2rdvz2233YaPj4/rOjt27GDq1KkcPXqUiIgIBgwYQIcOHdz+GEU8mXnwZ8wp4wAwegzE0ULbA0nxyb9oVmG/+uEE6ARkkWKjZqHYRlJSEm+++SYAjz/+OCEhIRYnEhHxfp06daJ69eocOnSIL7/8khEjtGG9FJ+dO3fSrVs3atWqRX5+Pp999hkvvfQS//nPfwgMDHRdr1OnTgwZMsT1tb+/v+vvTqeTV155hdDQUF566SVSU1MZN24cPj4+3HbbbUDBGOPVV1+lS5cuPPzww2zfvp0PP/yQ0NBQYmNji+3xingSM+00zv+9DHm5cF0LjJuHWR1JSpifEs5y7EwOpf0d9IgJtTqOSImi1rzYxtixY0lPT+e6665zDe5FRMS9HA4Hd911FwCTJk3C6XRanEhKkqeffpoOHTpQpUoVqlevzoMPPkhycjIHDhy45HoBAQGEhoa6/lw8A3br1q0kJCTw8MMPU716dZo2bcqQIUNYvHgxeXl5ACxZsoRy5coxfPhwKleuTPfu3WndujULFiwo1scr4inM3BycH7wCaaehQhUc9/4dw6G3jlJ8TNNk9s7TAPSMCSPYz+dP/oWIFCXNLBRbWLFiBXPnzsXhcPD6669fsmxIRETca8iQIbzxxhvs27ePlStXammmWCYzMxOA0qVLX3L5qlWrWLVqFaGhoTRr1owBAwYQEBAAwN69e6latSqhoaGu68fGxjJhwgSOHj1KjRo1+Pnnn2ncuPElt9mkSRMmT578u1lyc3PJzc11fW0YBkFBQa6/u0Ph7brr9uXalYQamaaJOf0DOLAHgkvh89AzGMGlrI71KyWhFp7uWmq0/WQmP6ecx9/HoHe9cNW5COm1Y392qJGahWK5rKwsnnrqKQDuvvvuXw3mRUTEvcqUKcOQIUOYOHEiEydOVLNQLOF0Opk8eTJ169alatWqrstvvPFGIiMjCQ8P5/Dhw8yYMYPjx4/zj3/8A4C0tLRLGoUAZcuWdX2v8L+Fl118naysLHJyci5Z1lxozpw5zJo1y/V1jRo1eO2114iKiiqKh/uHoqOj3X4fcm28uUbpn03gzI/fg8NB1FOvERjbzOpIf8iba+EtrqZGr6zZAkCfxhWpX6NKEScS0GvHE1hZIzULxXLvvPMOR44coUKFCjz++ONWxxERKZHuuusuJk2axLJly9i3bx+1a9e2OpKUMBMnTuTo0aO88MILl1zeuXNn19+rVq1KWFgYL7zwAomJiW4dRPfv35/evXu7vi78dP/UqVOu5c1FzTAMoqOjSUxMxDRNt9yHXBtvr5Fzzfc4p38IgGPo/aRWqAYnTlic6rd5ey28wdXW6GDqedYePI3DgK7VAjlh0+egp9Jrx/7cVSNfX9/L/tBTzUKx1I4dO/jww4IByUsvvfSrZUciIlI8atSoQZcuXViyZAkff/wxr7/+utWRpASZOHEicXFxjB07loiIiD+8bmEju7BZGBoayr59+y65Tnp6OoBrxmFoaKjrsouvExQU9JuzCgH8/Pzw8/P7ze+5+82VaZp6A2dz3lgjc+cWnFP/C4DRfQBGhx4e8Ri9sRbe5kprNPvCCcg3VClDdGk/1ddN9NqxPytrpF1qxTJZWVk89NBD5OXl0aNHD7p37251JBGREm3UqFEAzJo1i+TkZIvTSElgmiYTJ05k/fr1PPfcc5QrV+5P/82hQ4cACAsLAyAmJoYjR45c0gyMj48nKCiIypUrA1CnTh22bdt2ye3Ex8cTExNTRI9ExLOZCQcLDjTJz8do0Raj/x1WR5ISKulcLqsOnwHglgZ//OGRiLiPmoVimX//+9/s3buXcuXK8dprr1kdR0SkxGvZsiVNmzYlOzv7Dw9+ECkqEydOZNWqVYwePZqgoCDS0tJIS0sjJycHKJg9OGvWLA4cOEBSUhIbN27k/fffp379+lSrVg0oOKikcuXKjBs3jkOHDrFlyxY+//xzunXr5poZ2LVrV5KSkpg+fTrHjh1j8eLFrF27ll69eln22EXswjydjPPdF+B8FsQ0wrjrUZ18LJaZsysFpwnXRQdTOyLQ6jgiJZaWIYslli9fzqRJkwD4z3/+86dLjkRExP0Mw+D+++9n1KhRTJ48mQcffNB1+quIOyxZsgSA559//pLLH3jgATp06ICvry/btm1j4cKFZGdnExERQatWrbjllltc13U4HIwZM4YJEybwzDPPEBAQQPv27RkyZIjrOuXKlWPMmDFMmTKFhQsXEhERwahRo4iNjS2OhyliW2ZmBs73xkJaClSoguOBf2L8zvJ7EXdLycxlyb6CWeKDGur9oYiV1CyUYpeSksJjjz0GFGyo37FjR4sTiYhIoR49elC1alWOHDnCzJkzGTFihNWRxIvNnDnzD78fGRnJ2LFj//R2oqKieOqpp/7wOg0bNtRenCIXMfNycX74Khw7DGXDcIz+F0Yp7R8u1vlqRwp5TpMGUUE0Lh9sdRyREk3zy6VYOZ1OHnvsMZKSkoiJieHpp5+2OpKIiFzE19eX++67D4CPP/6Y/Px8ixOJiEhRM51OzCnjYNdWCAjE8fBzGBF/vmeoiLtcPKtw6HWRrhPoRcQaahZKsfroo49YunQpAQEBjBs3TsvbRERsaMiQIYSGhnLo0CEWL15sdRwRESlCpmlifvkJ5rrl4HDguP9JjGq1rI4lJdxXO0+Tq1mFIrahZqEUmw0bNvDKK68AMHbsWBo2bGhxIhER+S2lSpVi+PDhAPz3v//FNE2LE4mISFExF83CXDoPAOPO0RiNm1mcSEq6lMxclvycBsCtmlUoYgtqFkqxOH36NH/961/Jz8/n5ptvZtiwYVZHEhGRP3DvvfcSFBREfHw8y5cvtzqOiIgUAefKxZhzpgFgDL4Hxw3aO1ysN/uiWYXXaVahiC2oWShul5eXxyOPPMKJEyeoWbMmr732mj4tEhGxuYiICNfswnfeeUezC0VEPJwZ9yPm9A8AMHoMxNGln8WJRApmFS6+MKtwSGPNKhSxCzULxa1M0+Txxx9n+fLlBAYG8tFHH1G6tE5ZExHxBPfffz8BAQFs2rSJ1atXWx1HRESukrlrK87xb4LpxGjbFaP/HVZHEgF+mVVYPyqIJtGaVShiF2oWituYpsmLL77IzJkz8fHx4X//+x8NGjSwOpaIiFym8uXLc9tttwHw7rvvWpxGRESuhnl4H873/w15eXD9DRjD/qrZW2ILp7PyWLIvDYBbNatQxFbULBS3GTduHB999BEAb775Jt26dbM4kYiIXKm//vWv+Pn5sXbtWn766Ser44iIyBUwExNwvjsWsrOgbmMc9/4dw+FjdSwRAGbvSCEn36RepGYVitiNmoVS5JxOJ2+++SavvvoqAP/6178YPHiwxalERORqVKpUyfUz/J133rE2jIiIXDYz6QTOt56Fs+lQtRaOB5/G8PO3OpYIUDCrcPGFWYVDdQKyiO2oWShF6ty5c9x77728/fbbAPztb39j5MiRFqcSEZFr8dBDD+Hr68vKlStZt26d1XFERORPmKcScb71NKSlQIUqOB59HiNIM7fEPmbv1KxCETtTs1CKzMGDB+nTpw+LFy8mICCAt99+m3/84x9WxxIRkWtUtWpV196Fr776qk5GFhGxMTPlFM63noHTyRBdCcffX8IoU9bqWCIuyRedgHyrZhWK2JKahVIkVqxYQa9evdi7dy/R0dF89dVXWnosIuJFRo8eTWBgIBs2bGDZsmVWxxERkd/gmlGYkgTlKhQ0CsuGWR1LxCXPafLW6uOuWYWxmlUoYktqFso1MU2TDz/8kDvuuIP09HSuv/56Fi5cSNOmTa2OJiIiRSg6Opq77roLgNdeew2n02lxIhERuZh5IgHn60/BqUSIii5oFIZGWB1L5BLTt5xi56ksgv0cjL6hgmYVitiUmoVy1bKysnjkkUd48cUXcTqdDB06lFmzZlG+fHmro4mIiBs88MADlC5dmh07djB//nyr44iIyAXmkf04Xx/zyx6FT7yCER5ldSyRS6w7epY5u04D8EjrClQM0YE7InalZqFclWPHjnHLLbcwe/ZsfHx8eOmll3jjjTcICAiwOpqIiLhJeHg4o0aNAuCNN94gLy/P4kQiImLu343zzWfg3JmCU48ff0UzCsV2TpzN4b21JwDoVy+MG6qWsTiRiPwRNQvliq1fv56ePXsSHx9PeHg4n3/+OXfddZemkIuIlAD33Xcf4eHhHDhwgMWLF1sdR0SkRDN3bcX59nOQlQG16184zCTE6lgiv/K/nxLJyHVSLzKI4U3LWR1HRP6EmoVyRaZNm8bgwYNJTk6mQYMGLFy4kDZt2lgdS0REiknp0qUZNGgQAEuWLLE4jYhIyWVu+Qnne2Mh+zw0aIrj0bEYwaWsjiXyK9sSM4g/mYmvAx77SwV8HZpkImJ3ahbKZcnJyWHMmDGMGTOG3Nxc+vTpw7x586hSpYrV0UREpJh16dIFgGXLlpGfn29xGhGRkse5bgXOD16BvDxo2hrHQ89gBARaHUvkV0zT5NP4ZAC61AqlfGntUyjiCdQslD916tQphgwZwrRp0zAMgzFjxvDBBx8QHKxj7kVESqLmzZtTtmxZTp8+TVxcnNVxRERKDNM0cS6ahTnxP+B0YrTugOP+JzH8/KyOJvKbNh5JZUdSJr4Og4GNtJemiKdQs1D+UHx8PD169GD9+vWUKVOGyZMn8/DDD2t/QhGREszPz48OHToAsHTpUmvDiIiUEGZ+Pub0DzBnTwXA6NwP465HMXx8LE4m8ttM0+SjNQcB6FYnlMhgNbVFPIWahfK7Zs+eTf/+/Tlx4gS1atVi/vz5dO7c2epYIiJiA4W/D77//nuLk4iIeD8zKxPn+y9jrvwWDAPj1vtwDLkHw6G3c2JfW05ksPVYOv4+BgMahFsdR0SugK/VAcR+8vLyeOWVV/jwww8B6NSpE+PGjSMkRCeriYhIgY4dO+JwONi1axcJCQlUrlzZ6kgiIl7JTDqOc9zLcOIo+PvjuPcfGE1bWx1L5A+ZpsmM+FMAdK8TRoRmFYp4FH0UJZc4ceIEQ4YMcTUKH374YT755BM1CkVE5BJhYWG0aNEC0FJkERF3MXduwfnyPwoahaHhOP7xihqF4hHijmewN/k8Ab4OBjTUXoUinkbNQnFZvnw5Xbt2Zd26dZQqVYoPPviAMWPG4KN9UERE5DcULkVWs1BEpGiZpolz6dc433keMs9Bzbo4nv4PRo06VkcT+VMXn4A8qGllwoK0oFHE06hZKJw/f56XXnqJYcOGcfr0aRo2bMi3335L3759rY4mIiI2VtgsXLNmDRkZGRanERHxDmZuLuaU9zC/mACmE6NNJxz/eBkjVHu+iWfYcOwc+06fJ8DH4I4WVa2OIyJXQc3CEm779u307NmTDz74AIARI0bw9ddfU7NmTYuTiYiI3dWpU4eqVauSk5PD6tWrrY4jIuLxzPRUnG89jbnmezAcGIPvwbjzEQw/f6ujiVwW0zT57MKswt71wgkvpeeuiCdSs7CEys3N5Z133qFXr17s2bOHyMhIJk2axL///W8CAwOtjiciIh7AMAy6dOkCwIIFCyxOIyLi2cydm3G++Cjs3w3BpXCM/heOLv0wDMPqaCKX7aeEcxxIzSbQ10H/+poNK+Kp1CwsgbZs2UKPHj144403yMvLo2fPnixbtoxu3bpZHU1ERDxMnz59AJg9ezZbtmyxNoyIiAcyc3NxfjkJ59v/gvRUqFAFxz/fwmjY1OpoIlfEedGswj51wwgJ1F6FIp5KzcISJCMjg7Fjx9KnTx927dpFaGgo7733Hh9//DERETqhSkRErlyLFi3o378/pmny5JNPkpeXZ3UkERGPYSYm4Hz1ccwlcwEwOvQoOMikfEVLc4lcjbVHznIoLZtgPwf9NKtQxKOpWVgCmKbJvHnzaNeuHR9//DFOp5P+/fvzww8/MGDAAC1tEBGRa/Kvf/2LsmXLsn37dj755BOr44iI2J5pmjhXLcH54t/gyAEoXQbHg//EcftfMQICrI4ncsXynSYzCmcV1gujTICPxYlE5FqoWejltm3bxqBBg3jggQdITEykSpUqTJkyhXHjxhEZGWl1PBER8QJRUVE8/fTTALz++uscO3bM4kQiIvZlZpzF+eFrmFPHQU421G+C41/vYcS2tjqayFVbdiCdY2dyKBPgw82aVSji8bSJgBfKyMhg3rx5fPrpp2zevBmAwMBAHnroIUaNGkVQUJDFCUVExNsMHTqUL7/8kg0bNvDss88yadIkqyOJiNiKaZqYG9dgfvYRnE0HH1+M/sMwutyM4dAcDvFcOflOPttWMKtwUMMIgv00q1DE06lZ6CVM02T9+vXMnDmTb775hoyMDAB8fX3p06cPY8aMoXLlyhanFBERb+VwOHj11Vfp1q0bixcvZt68efTr18/qWCIitmCeTsb56YewdX3BBRWq4LjnbxjValsbTKQILNqbRkpmHhHBvvSICbU6jogUATULPdyxY8f48ssv+fLLLzl06JDr8urVq3P77bczaNAgoqKirAsoIiIlRr169XjkkUf4z3/+wz//+U9atWpFdHS01bFERCxj5uZgfjcPc+EsyM4qmE3YcyBGj0EYfn5WxxO5Zpm5+Xy5IwWAoY0j8ffRLFkRb6BmoQfKyspi4cKFzJw5kzVr1mCaJgDBwcH06dOHwYMH07JlSxxaziAiIsXskUceYenSpcTHx/P4448zdepUHaQlIiWOaZqwaQ3OWZMhJangwlr1cNzxEEalqpZmEylKc3ed5mx2PpVC/LmpZlmr44hIEVGz0EOYpsnGjRv54osv+Oabbzh37pzre23atGHw4MH07NmTUqVKWZhSRERKOj8/P9599126d+/OsmXL+PTTT7n99tutjiUiUmzMw/twfj4B9u0suCA0AmPACIyW7bQ3oXiVc9n5fL0rFYDbr4vEx6EPB0W8hZqFNpeVlcWcOXOYNGkSu3btcl1etWpVBg8ezMCBA6lSpYqFCUVERC4VExPDE088wYsvvsjYsWNp27YtVatqJo2IeDczLQVzznTMtcvANMHfH6PbAIxu/TECAq2OJ1Lk5u9JJSvPSbXQAG6oWsbqOCJShNQstKkdO3bw1Vdf8cUXX5CWlgZAUFAQffv21TJjERGxvfvuu48lS5bw008/8e677/LWW29ZHUlExC3M9FTMb7/C/OFbyM0BwGjdAaP/cIzwSIvTibhHZm4+3+w5DcDgRhE4tOWIiFdRs9BGEhMTmTt3LrNmzbpkFmGVKlW48847ufXWWwkNDbUuoIiIyGXy8fHhySef5JZbbmHevHk8//zzlCmjWQci4j3M06cwv/sa84dFriYhtevjGHQ3Rs261oYTcbNFe9M4l+OkUog/N1TR73cRb6NmocUyMjJYtGgRX331FatXr8bpdALg7+9P586dGTRoEJ06dcLHx8fipCIiIlemZcuW1KlTh59//pk5c+YwfPhwqyOJiFyznH27yf90PObG1ZCfX3Bhzbo4+t4GDWJ1qJN4vew8J/N2FcwqHNQwQnsVinghNQstkJ+fz5o1a5g1axaLFi0iMzPT9b0WLVowYMAA+vTpo1mEIiLi0QzD4Pbbb+f5559n+vTp3HHHHXoTLSIeyXQ6YdsmnN/N5eSebb98o25jHN1vgYbX6+eblBiL96WRnp1P+dJ+tKseYnUcEXEDNQuL0a5du5g1axZz584lMTHRdXn16tUZMGAAt9xyC9WrV7cuoIiISBEbMGAAr7zyCjt27GDr1q3ExsZaHUlE5LKZWZmY61diLv0aEhMKLnT4YLS4EaPLzRjValkbUKSY5eY7mbOzYFbhQM0qFPFaaha62f79+1mwYAHffPMNO3fudF0eGhpK3759GTBgAM2aNdMnkSIi4pXCw8Pp1asXs2fPZsaMGWoWiojtmaYJB/ZgrlqMuWE15GQXfCMoGKNdN6KH3sOpPLPgeiIlzLc/p3E6K4+IYF861ihrdRwRcRM1C4uYaZrs2bOHhQsXsmDBAnbv3u36np+fH507d2bAgAHcdNNNBAQEWJhURESkeNx+++3Mnj2buXPn8txzz+mgExGxJfPsGcx1yzFXLYETR3/5RnRljPbdMP7SBUdwKXyjouHECeuCilgkK9fJl9tTABjSKBI/H014EfFWahYWgZycHDZu3MgPP/zAokWL2L9/v+t7vr6+3HjjjfTs2ZOePXsSFhZmYVIREZHi16pVK2rVqsX+/fuZO3cud9xxh9WRREQAMDMzMOM3YMb9CNs2Ql5ewTf8/TGa3YjRrivUqq9VQCLAN7tPk56dT4UyfnSqpVmFIt5MzcKrUDh7cOXKlaxatYq1a9eSlZXl+r6/vz/t2rWjV69edO3aVQeViIhIiVZ40MkLL7zAxx9/TL9+/QgJ0YboImIN8+wZzC3rMDevg51bID/vl29Wq41xYxeMlu0wgktZllHEbs5k5zPnwgnIt10Xha/2KhTxamoWXob8/Hx27drFxo0b2bhxIz/++CMnT5685DqRkZG0bduWTp060blzZy2xEhERucjgwYP53//+x4EDB7jzzjuZMWMGQUFBVscSkRLAdObD0UOYe+Ixt8fBnm3gdP5yhejKGNe3wWj+F4wqNawLKmJjs3ekkJnrpEZYADdW03tdEW+nZuFvOHPmDHFxca7mYFxcHBkZGZdcJzAwkNatW9O2bVvatm1L/fr1cTgcFiUWERGxt7CwMGbMmMHAgQP56aefGDlyJBMnTsTf39/qaCLiRUzThLTTcHgf5pH9mIf3w75dkHnu0itWqVHQIGzWBqNCFWvCiniIlMxcFuxNBWBYkygcWpYv4vXULAROnTrFypUr2bFjBytXrmT37t2/Ot2sdOnSNGvWjObNm9OyZUuaN29OYGCgRYlFREQ8T6NGjZg6dSpDhw5l2bJljB49mnHjxuHj42N1NBHxQKZpQvJJOHIA88gBzCP74PB+OJv+6ysHBkGdhhj1rsOIbYVRrkLxBxbxUJ/GJ5OTb1I/KohmFbU8X6QkULMQ2Lx5M4888sgll1WrVo1mzZrRokULmjdvTt26dfVmRkRE5Bq1bNmSCRMmcNddd/H111+Tl5fHf//7X30AJyJ/yMzLg8SjmEcOFDQHjx6AowchK/PXV3Y4oEIVjKq1oFotjOp1oHodDI3lRa7YjpOZLN1f0IAfERulw35ESgg1C4FmzZrRrFkzOnToQIMGDbj++uspV66c1bFERES8UseOHfnggw944IEHWLhwIampqUyaNEmHnogIAOb5zII9Bo8WNgYPwvHDv5xUfDFfX6hUHaNqTahas6BBWLk6hn9A8QcX8TK5+U7eX58IQLfaodQvF2xxIhEpLmoWAhEREXzzzTdUqFCBEydO/GoJsoiIiBStHj16MGPGDO6++27Wrl3LgAEDmD59OuXLl7c6mogUEzPjLJxIwExMgMQEzMRjcPwInEr87X8QFFyw12CVwsZgTYiuguGrtzQi7jBrRwrHzuQQFujD8KZRVscRkWKk36wiIiJiiTZt2jBr1iyGDRvGzp07ueeee5g7dy6+euMv4hVMpxPOnYH0VEhNLmgGJhY2B4/99t6ChUIjChqCVWoUNAWr1ITI8loCKVJMjqZnM2tHCgD3Ni9PaX8t4xcpSTQaFxEREcs0atSIuXPn0qNHDzZv3sz777/P6NGjrY4lIn/AzM0paABe+GOmp8KZi/6engrpp+FMGjidf3xj4ZEQXRkjuvKF/1YqWEZcpmyxPBYR+bU8p8n7PyWS54TmFUvxl6plrI4kIsVMzUIRERGxVPXq1XnxxRcZPXo0b7/9Np06daJRo0ZWxxIpUUzThMyMgqZf2ulLGoCkpWKe+aU5SOa5y79hw4DSIVA2HKN8RahQ+ZfmYPmKGIFB7ntQInLFTNPkg/WJ7DqVRaCvwf0tojWjV6QEUrNQRERELDdgwAAWL17MwoULGT16NAsXLiQgQAcUiFwLMye7YKnvhT/mmXQ4m/bL12fT4cwv3ycv9/Jv3NcPyoYV/AkJwwi96O9lwyG04O+UKas9BUU8yBfbU1i6Px2HAX//S0XKlfazOpKIWEC/uUVERMRyhmHw6quvsn79enbv3s0rr7zCv/71L81mkBLNdDrhfCZkZRbM+svMgKwMzMyMgsuyzl24LPPCZReuk3muoAmYnXXldxpcCsqGQ9kwjJCwX5p+ZcMwCpuDZcMhuJRenyJe5vv9aXwWnwzAyOblaVlZy49FSqpiaxZ+++23fPPNN6SlpVGtWjXuvvtuateuXVx3LyIiIjYXERHB66+/zt1338348eNZs2YNjz76KD169MDhcFgdT7yA3cej+e+9QOLZNPLS0wqageczwTSv7UZ9faFMKJQpCyFlMUoX/JcyZaFMKEaZkAvfDyloCPr5F8EjERFPYpomi/el8fGGkwDc0iCcHjFhFqcSESsVS7Pwxx9/ZOrUqdx3333UqVOHBQsW8PLLL/POO+9Qtqw2LxYREZEC3bp149lnn+Xtt99m586djBw5krp16zJ06FD69u1L+fLlrY4oHsoTxqPm8cPkJif9+ht+/hAUXDDrL6jgjxFc6tLLLvzXCCoFpUpBSCiULgtBwZoBKCK/KyUzl3HrEok7kQFAu+oh3BEbZXEqEbFasTQL58+fT6dOnejYsSMA9913H3FxcSxfvpybb765OCKIiIiIhxg1ahRDhgxh4sSJTJw4kT179vD888/zwgsv0KZNG1q1akVUVJTrT7ly5YiMjCQoSAclyO/zhPGo444HiYiI4HRmNuZFjUDDT3uGiUjRSTufx/6U8+xNyWL+nlTO5TjxcxjcERtFn3phOPQBg0iJ5/ZmYV5eHgcOHLhkEOZwOGjcuDF79+791fVzc3PJzf1lc2XDMFyDf3d+Klp42/rk1b5UI3tQHTyD6mQ/qsmVCQ8P5/HHH2fkyJHMmjWLuXPnsmnTJlavXs3q1at/89+ULl0av6tsqkydOpVmzZqpTl7qSsejYM2YdPShUFJ35+J0AmRe+HPKLfclV8/h2IezoEhiMdXiyjlNk4ycS/+f1Q4P5NE2FakaWrQHi+l3qj2pLvZnhxq5vVl45swZnE4noaGhl1weGhrK8ePHf3X9OXPmMGvWLNfXNWrU4LXXXiMqqnimQkdHRxfL/cjVU43sQXXwDKqT/agmV6ZChQo888wzPPPMMxw4cICvvvqKffv2cfLkSRITE11/srOzOXfu3FXfT2hoKBUqVHB9rTp5lysdj4I1Y9KsvAOkZ13BicRikXyrA4iLanE1DKBaeDD1o8vQtHIofRpVwNfHfXsD63eqPaku9mdljWx3GnL//v3p3bu36+vCTuqpU6fIy8tz2/0ahkF0dDSJiYmY17qRtLiFamQPqoNnUJ3sRzW5dkFBQQwbNuxXl5umydmzZzl16hT5+Vf3xrFChQqcOHHCbXXy9fUttg8+pWhYMSYde1NlwsIjSU5O1s8JmzIMg8hI1cgOVIurFxnsS7C/j+vrU0kn3XI/GvvYk+pif3YYj7q9WRgSEoLD4SAtLe2Sy9PS0n716S6An5/f7y4hKo4nsmmaesHYnGpkD6qDZ1Cd7Ec1cY8yZcpQpkyZa7qNi+uiOnmXKx2PgjVj0opl/KkQWYqg3DN6/tmUYRiqkU2oFtemOP+f6XeqPaku9mdljdw31/gCX19fatasyfbt212XOZ1Otm/fTkxMjLvvXkRERERKOI1HRURERC5fsSxD7t27N++//z41a9akdu3aLFy4kOzsbDp06FAcdy8iIiIiJZzGoyIiIiKXp1iahW3atOHMmTPMnDmTtLQ0qlevzj//+c/fXfYhIiIiIlKUNB4VERERuTzFdsBJ9+7d6d69e3HdnYiIiIjIJTQeFREREflzbt+zUERERERERERERDyDmoUiIiIiIiIiIiICqFkoIiIiIiIiIiIiF6hZKCIiIiIiIiIiIoCahSIiIiIiIiIiInKBmoUiIiIiIiIiIiICqFkoIiIiIiIiIiIiF6hZKCIiIiIiIiIiIoCahSIiIiIiIiIiInKBmoUiIiIiIiIiIiICqFkoIiIiIiIiIiIiF6hZKCIiIiIiIiIiIoCahSIiIiIiIiIiInKBmoUiIiIiIiIiIiICqFkoIiIiIiIiIiIiF6hZKCIiIiIiIiIiIoCahSIiIiIiIiIiInKBr9UBLpevb/FELa77kaunGtmD6uAZVCf7UU08Q1HXSXX3HsVRSz1f7E81sg/Vwv5UI3tSXezPyvGoYZqmWaT3LiIiIiIiIiIiIh5Jy5AvyMrK4sknnyQrK8vqKPI7VCN7UB08g+pkP6qJZ1CdxEp6/tmfamQfqoX9qUb2pLrYnx1qpGbhBaZpcvDgQTTR0r5UI3tQHTyD6mQ/qolnUJ3ESnr+2Z9qZB+qhf2pRvakutifHWqkZqGIiIiIiIiIiIgAahaKiIiIiIiIiIjIBWoWXuDn58fAgQPx8/OzOor8DtXIHlQHz6A62Y9q4hlUJ7GSnn/2pxrZh2phf6qRPaku9meHGuk0ZBEREREREREREQE0s1BEREREREREREQuULNQREREREREREREADULRURERERERERE5AI1C0VERERERERERARQs1BsRGftiIiIiIiVNB4VEREpAc1Cp9N5yX/FnrKyssjPz3d9rYGaNc6dO0daWhp5eXmAXjd2lJyczJYtW6yOIf9PYmIiM2fOJDEx0eoo8juSkpIYP368Xj9iGY1J7U/jUfvQmNT+NCa1J41J7c9TxqS+VgdwpylTppCWlsbo0aNxOLy+L+qRTNNkypQp7Nu3j4CAAGJiYujfvz/+/v6YpolhGFZHLBFM0+STTz5h69atlCpVisDAQP7+979TqlQp1cFGTpw4waOPPoqfnx9vvPEGFSpUsDpSiWeaJhMmTGDp0qV06tSJ8PBwqyPJb/j0009ZsGABzZo1IycnRz/XpNhpTGpvGo/ah8aknkFjUvvRmNQzeNKY1CubhQcPHmT69OkcPnyYs2fP0r59e2JjY3E6nRqg2cjOnTuZMGECQUFB9O3bly1btrB+/XqysrK48847rY5XYsTFxTFt2jRKlSrFPffcQ3JyMvPnz2fSpEk8/PDDtv3hVRLl5+fTpEkTjh49yldffcVDDz1kdaQSbfXq1UyaNImoqCj+/e9/U6tWLdf37PyLv6TZvn07u3bt4vHHHyc2NtbqOFLCaExqfxqP2ofGpJ5DY1J70ZjUM3jamNQrm4X79+8nPDycXr16sWbNGqZNm0ZsbCwOh0MvFpvIzs5m3bp1xMTEcPfdd+Pv70+LFi345ptv2LJlC5mZmQQHB1sds0TYsWMH119/PUOHDsXXt+BHwsGDBy9ZhiP2cOjQIXx9ffnb3/7Gs88+S8eOHWnYsKHVsUqsH374gaCgIMaMGUNYWBhHjhwhNTWV6OhoQkNDCQgI0O8cG1ixYgXly5cnNjaWvXv3EhcXR/ny5alXr55mQojbaUxqbxqP2ovGpJ5DY1J70ZjUM3jamNQrm4XNmzcnJiaGqlWrEhAQwHvvvcf8+fPp3bu3XiQ2kZ+fT+3atalYsSL+/v6uT9jz8/PJysoiKChItSom/fr14/z5865BWVpaGvv27aNFixbs3buXmJgYixOWbBfPPvH19SUyMpKYmBgaN27MzJkzGTt2LOfPnycwMNDipCXPsGHDePPNN1m8eDHHjh3jwIEDBAYGcu7cORo2bMgjjzyin2EWcjqd5ObmkpqaSvv27Zk/fz7z5s2jTp06rFmzhry8PEaMGEHr1q2tjipeTGNSe9N41F40JrU3jUntS2NSe/PUMalhevjOvXPmzCE9PZ1KlSrRsWNH1y+XQhkZGcybN4/ly5fz3nvvERQUpKUfFvizOhXWZPr06SQlJfHYY49ZlNS7/Vkdli1bxvjx46lZsyb+/v4cOXKEjh07MnjwYPz9/S1KXbL8UY2++OILkpOTefDBBzlz5gz3338/jRs35syZM4waNYrq1atbF9zL/V5dJk2axNKlS2nVqhW9e/fGx8eH48eP88EHH3DzzTczYMAAvdEsJr9XoxdffBGn00lkZCTt27enfv36+Pj48Prrr5Ofn8/QoUP12pEioTGp/Wk8ah8ak9qfxqT2pDGp/XnLmNRjZxYeP36cN998E4fDQeXKlfnhhx/48ccfue2226hTp47rhVCqVCnatGnDpk2bmDZtGiNHjrQ6eonye3UaOnQoMTExvxok79+/n2bNmgHaX6Eo/dnrpZCvry9PP/00DRs2xDAMNm7cyFtvvUWHDh2oXLmyhY/A+11OjVJSUmjRogUA8fHx+Pn5ER8fzwMPPGCrXyze5PfqMmTIEOrVq8ett95KUFAQnTp1oly5cgBUr16d5ORk5syZw80334yPj4/Fj8K7/dnvmZtuuolx48YRGhrK0KFDXfUYOHAgr732GmfPnrX4EYin05jU/jQetQ+NSe1PY1J70pjU/rxtTOqxH2XGxcURHBzMa6+9xqOPPsrbb7/NuXPnmD9/PomJiRiG4drfolKlSnTp0oU1a9aQkJCAw+Fg586dnDt3zuJH4f1+r04LFiwgMTERh8PhGqClpaVx9OhRGjRoAIBhGJw8eRIo+KRXrt6fvV4KtW3blkaNGrkGxdWqVcMwDI4fP25V9BLjj2pU+P/fx8eHdevW8a9//YuJEyfSp08fypQp43qdSNH7vbosWrSI48ePExwcTL9+/VyDskLh4eH4+vpy7Ngxi5KXHH/02klOTqZRo0Y0bNgQHx8f1+8S0zSpUaMGubm5JCcnW/wIxNNpTGp/Go/ah8ak9qcxqT1pTGp/3jYm9chmYX5+PkePHiUkJMT1KWBoaCi33HILycnJLFu2DCj4IWaaJn5+flx//fXUq1eP9957j2effZZXXnmFM2fOWPkwvN7l1qnwe9u2baNMmTLUrFmThIQEnn/+ef7xj3+Qk5OjJTrX4HLrAPzqk/MNGzYQExNDo0aNijVzSfNnNVq5ciVQsBF7XFwcFSpU4PXXX2fQoEEMHDiQWbNmaQDgBn9WlxUrVgD85ub3e/fupU6dOlStWrU4I5c4f1SjlJQUli5dStmyZenduzfp6eksWrSI5ORkDMNg8+bNREdH07hxY4sfhXgyjUntT+NR+9CY1P40JrUnjUntzxvHpB65DNnHx4fc3Fxyc3MxTRPTNHE4HNxwww3s3LmTffv2cfDgQWrUqOFaOpCfn8+5c+c4fPgwHTt25J///CdBQUFWPxSvdiV1AkhISKBChQp8+umnfP3117Rp04YnnnhC+5JcoyutQ3JyMk6nk/nz57Nu3ToGDhxIcHCwluG40Z/VaNeuXSQlJTFw4ED69u3r+nQdoEuXLmRnZ1O+fHmLH4X3udrXzjfffMPGjRsZMWIEoCVs7vRnNdqzZw+HDx8mNjaWu+66iy+++IINGzZQrVo1tm7dSo8ePQgPD7f6YYgH05jU/jQetQ+NSe1PY1J70pjU/rxxTOpxH48VTtfs1KkT8fHxHDlyxHVqGcANN9xAcnKyaxq7w+Fg//79vPrqq+Tm5vLWW28xatQoDcrc7ErrBLBx40Y2bdrErl27+Pe//80jjzzym5+OyOW70jokJiayZMkSnn/+eQ4ePMjTTz9N165dgV9/witF43JqdPr0aQ4cOEDFihWpXr36JbVwOBz07dv3VxuDy7W50tfOiRMnWLhwIc888wwHDx7kqaeecp1opteOe1xujQpnONx000088cQT9O3bl+joaF588UWGDh2qmUJy1TQmtT+NR+1DY1L705jUnjQmtT9vHZPa8pWck5Pzu5/eFR7eXKdOHerXr8+0adN49tlnXf9jGzRogGmal0x/LleuHH/961+pV6+e+8OXIEVZp6ysLDp06EDFihVdG0rL5SnKOoSHh9OsWTNiY2Nde/XItbvWGkHBTIfC6+sXfdEoytdOREQEzZo14/rrr9cSqSJUFK+di/e3qlWrFrVq1XJzavEmGpPan8aj9qExqf1pTGpPGpPaX0kck9qqdZmXl8f48eN58803GTduHHv37nX9j8/LywNwbQaZmZnJ4MGD2blzJ0uWLHFd79y5cwQGBlK6dGmgoHBlypTRoKwIuaNOQUFB9OnTRwOzK+COOvj7+1O3bl0NyoqIO2qkQdm1c9drp2HDhhqUFRF31EjkSmhMan8aj9qHxqT2pzGpPWlMan8leUxqm2ZhWloaTz/9NEeOHKFZs2YcPnyY8ePHM2/ePADXdOaFCxcybNgwtmzZQoMGDRg0aBBffvklH3/8Mbt27eKrr74iKyvLtTmkfogVLXfVSa6M6mB/qpE9qS72pxqJ1TQmtT/9nLAP1cL+VCN7Ul3sr6TXyDAL250WW7duHV9++SVPP/004eHhZGZmsmDBAubOncurr75KlSpVeOedd9i1axe33XYb7dq1cw26Fi1axLp168jIyMAwDO6//35q165t8SPyTqqTPagO9qca2ZPqYn+qkVhNz0H7U43sQ7WwP9XInlQX+yvpNbK8Weh0OnE4HCxZsoSvvvqKjz76yPW9tLQ0/vvf/5KXl8fYsWP5+eefqVSpkmuT4cJ/W/j35ORkypUrZ8nj8Haqkz2oDvanGtmT6mJ/qpFYTc9B+1ON7EO1sD/VyJ5UF/tTjQpYsgx56dKlrF69msTERNf/SIfDQWhoKLt27XJdLzQ0lJtvvpm9e/eydetW6tSpQ2BgoOv7F58W43A4PLYIdqU62YPqYH+qkT2pLvanGonV9By0P9XIPlQL+1ON7El1sT/V6NeK9TTkLVu2MG7cOMLDw8nIyMDX15fOnTvTp08fYmJiWLBgAXv27KFOnTqu9d9VqlShadOmrFq1iiZNmtjuOGlvpDrZg+pgf6qRPaku9qcaidX0HLQ/1cg+VAv7U43sSXWxP9Xo9xVrs3DZsmW0bNmSkSNHcuLECTZs2MD06dOpUKECzZs3p2HDhvz000/UqVOHhg0bAgWdWx8fH68tgB2pTvagOtifamRPqov9qUZiNT0H7U81sg/Vwv5UI3tSXexPNfp9bn90hVsiJiUlsW3bNlq2bAlAhQoV6Nu3L3/5y1+YNm0aZ86cYdCgQTidTpYuXcrp06ddt5GTk0OpUqXcHbVEU53sQXWwP9XInlQX+1ONxGp6DtqfamQfqoX9qUb2pLrYn2p0edzWLDxx4gSmabpOgwkNDcXX15fk5GQA8vLyALj33ns5ffo0y5Yto2zZsvTr14/Tp0/z7LPPMn/+fMaNG8eBAwdo1aqVu6KWaKqTPagO9qca2ZPqYn+qkVhNz0H7U43sQ7WwP9XInlQX+1ONrkyRn4b8448/MmPGDPz8/AgODqZz587cdNNNnD9/ngkTJpCens6TTz6Jr68veXl5+Pr68umnn7J69Wr+97//AXD69GlmzZpFeno6eXl5jBgxgooVKxZlzBJPdbIH1cH+VCN7Ul3sTzUSq+k5aH+qkX2oFvanGtmT6mJ/qtHVKdI9C+Pj45kxYwZ9+/alfPnyxMfHM378eJxOJ507d6ZRo0YsWrSIFStW0LlzZ1dHt1WrVixbtoz9+/dTq1YtwsPDGTlyJDk5Ofj7+xdlREF1sgvVwf5UI3tSXexPNRKr6Tlof6qRfagW9qca2ZPqYn+q0dUrkmZh4VTOvXv3UqZMGTp16oSvry+xsbHk5OTw3XffERUVRatWrYiPj+eHH37guuuucx0jffLkSXx8fChTpswlt1tSilBcVCd7UB3sTzWyJ9XF/lQjsZqeg/anGtmHamF/qpE9qS72pxpduyLZs7Cw+5qQkED58uVd0zcBbr31VgICAli1ahUOh4Pu3btjGAbvvvsue/bsITk5mc2bN1OzZk1CQ0OLIo78DtXJHlQH+1ON7El1sT/VSKym56D9qUb2oVrYn2pkT6qL/alG1+6qmoXx8fFMmjSJBQsWsG/fPtfljRo1YsuWLTidTlcxSpcuTbt27di7dy8HDx4kJiaGUaNGkZ+fzwcffMBTTz3FkSNHuOOOO0pUl7Y4qE72oDrYn2pkT6qL/alGYjU9B+1PNbIP1cL+VCN7Ul3sTzUqeld0wElqaiofffQR+/fvp3Hjxhw5coTk5GSeeeYZateuzfHjx3nhhRfo2LEjQ4YMcW0OCTBy5Ej69u1L7969ATh//jxnzpwhPT2dOnXquOfRlVCqkz2oDvanGtmT6mJ/qpFYTc9B+1ON7EO1sD/VyJ5UF/tTjdznsvcszM7O5tNPPyUwMJCXX37ZtZb7n//8J0uWLKF27dqEhYXRtWtXZs+eTadOnYiMjHStFa9YsSIJCQmu2wsICKBcuXKu25GioTrZg+pgf6qRPaku9qcaidX0HLQ/1cg+VAv7U43sSXWxP9XIvS57GXJAQAB+fn506NCBcuXKkZ+fD0DTpk05duwYpmkSFBTEjTfeSI0aNXj77bc5deoUhmGQnJxMeno6LVu2dN1e4RpyKVqqkz2oDvanGtmT6mJ/qpFYTc9B+1ON7EO1sD/VyJ5UF/tTjdzripYhXzxl0+l04nA4eO+99wgICOD+++93Xe/06dM8//zz5OfnU6tWLfbs2UOlSpV45JFHSvQGkcVFdbIH1cH+VCN7Ul3sTzUSq+k5aH+qkX2oFvanGtmT6mJ/qpH7XFGz8Lc8++yzdOrUiQ4dOuB0OgFwOBwkJiZy4MABfv75Z6pVq0aHDh2KIq9cJdXJHlQH+1ON7El1sT/VSKym56D9qUb2oVrYn2pkT6qL/alGReOy9yz8LSdPniQxMZGqVasCBQXIy8vD4XAQHR1NdHQ0bdq0KZKgcvVUJ3tQHexPNbIn1cX+VCOxmp6D9qca2YdqYX+qkT2pLvanGhWdy96z8GKFkxF3795NYGAgNWvWBODLL7/kk08+IT09vegSylVTnexBdbA/1cieVBf7U43EanoO2p9qZB+qhf2pRvakutifalT0rmpmYeHGj/v27aNVq1bEx8fz0UcfkZOTw0MPPUTZsmWLNKRcHdXJHlQH+1ON7El1sT/VSKym56D9qUb2oVrYn2pkT6qL/alGRe+qlyHn5OSwdetWTp48yaJFixg0aBA333xzEUaToqA62YPqYH+qkT2pLvanGonV9By0P9XIPlQL+1ON7El1sT/VqGhddbPQ39+fqKgorrvuOoYPH46/v39R5pIiojrZg+pgf6qRPaku9qcaidX0HLQ/1cg+VAv7U43sSXWxP9WoaF3TaciFR1OLvalO9qA62J9qZE+qi/2pRmI1PQftTzWyD9XC/lQje1Jd7E81KjrX1CwUERERERERERER76GWq4iIiIiIiIiIiABqFoqIiIiIiIiIiMgFahaKiIiIiIiIiIgIoGahiIiIiIiIiIiIXKBmoYiIiIiIiIiIiABqFoqIiIiIiIiIiMgFahaKSIkxc+ZMBg8ebHUMERERESmhNB4VEU+gZqGIiIiIiIiIiIgAahaKiIiIiIiIiIjIBWoWioiIiIiIiIiICAC+VgcQEXGH3bt3M2XKFI4cOUJ4eDh9+/b91XWWL1/OypUrOXr0KJmZmZQvX54ePXrQtWtX13XGjRvH5s2b+eijj/D1vfRH5ksvvcSpU6d499133f54RERERMSzaDwqIp5KzUIR8TpHjhzhpZdeIiQkhEGDBpGfn8/MmTMJDQ295HpLliyhSpUqNG/eHB8fHzZt2sSECRNwOp10794dgHbt2rFy5Uq2bt1Ks2bNXP82LS2N7du3M3DgwOJ8aCIiIiLiATQeFRFPpmahiHidL774AtM0eeGFF4iMjASgVatW/OMf/7jkemPHjsXf39/1dffu3Xn55ZdZsGCBa3DWqFEjIiIiWLVq1SWDs9WrV2OaJu3atSuGRyQiIiIinkTjURHxZNqzUES8itPpZOvWrbRo0cI1MAOoXLkyTZo0ueS6Fw/MMjMzOXPmDA0aNODkyZNkZmYC4HA4uPHGG9m4cSNZWVmu669evZqYmBjKlSvn5kckIiIiIp5E41ER8XSaWSgiXuXMmTPk5ORQoUKFX32vYsWKbN682fX17t27+fLLL9m7dy/Z2dmXXDczM5Pg4GAA2rdvz7x581i/fj3t27fn+PHjHDhwgPvuu8+9D0ZEREREPI7GoyLi6dQsFJESKTExkRdffJGKFSsyfPhwIiIi8PX1ZfPmzSxYsACn0+m6buXKlalZsyarVq2iffv2rFy5El9fX2644QYLH4GIiIiIeDKNR0XErtQsFBGvEhISgr+/PydOnPjV944fP+76+6ZNm8jNzeXJJ5+8ZHnIjh07fvN227Vrx9SpU0lNTWXNmjVcf/31lC5duugfgIiIiIh4NI1HRcTTac9CEfEqDoeDJk2asGHDBpKTk12XJyQksHXr1kuuB2CapuuyzMxMVqxY8Zu3e+ONN2IYBp988gknT56kbdu27nkAIiIiIuLRNB4VEU+nmYUi4nUGDx7Mli1beO655+jatStOp5NFixZRpUoVDh8+DECTJk3w9fXltddeo3Pnzpw/f57vv/+ekJAQUlNTf3WbISEhNGnShHXr1lGqVCmuv/764n5YIiIiIuIhNB4VEU+mmYUi4nWqVavG008/TUhICDNnzmT58uUMHjyYFi1auK5TsWJFHnvsMQzDYNq0aXz33Xd07tyZnj17/u7ttm/fHoAbbrgBPz8/tz8OEREREfFMGo+KiCczzIvnPIuIyO/asGEDb7zxBmPHjqV+/fpWxxERERGREkbjUREpDppZKCJymb7//nvKly9PvXr1rI4iIiIiIiWQxqMiUhy0Z6GIyJ9Ys2YNhw8fJi4ujjvvvBPDMKyOJCIiIiIliMajIlKc1CwUEfkT7777LoGBgdx0001069bN6jgiIiIiUsJoPCoixUl7FoqIiIiIiIiIiAigPQtFRERERERERETkAjULRUREREREREREBFCzUERERERERERERC5Qs1BEREREREREREQANQtFRERERERERETkAjULRUREREREREREBFCzUERERERERERERC5Qs1BEREREREREREQANQtFRERERERERETkgv8DwHyix3pNv3UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axis1, axis2) = plt.subplots(nrows=1, ncols=2, figsize=(16,8), sharex=True)\n",
    "dfPP[\"LAI\"].plot(ax=axis1, label=\"LAI\", color='k')\n",
    "dfPP[\"TAGP\"].plot(ax=axis2, label=\"Total biomass\")\n",
    "dfPP[\"TWSO\"].plot(ax=axis2, label=\"Yield\")\n",
    "axis1.set_title(\"Leaf Area Index\")\n",
    "axis2.set_title(\"Crop biomass\")\n",
    "r = axis2.legend()\n",
    "fig.autofmt_xdate()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}