docs/model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](https://github.com/eWaterCycle/ewatercycle/raw/main/docs/examples/logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running Hype model using eWaterCycle package\n",
    "\n",
    "This notebook shows how to run [Hype](http://www.smhi.net/hype/wiki/doku.php?id=start) model using a demo use-case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import ewatercycle.models\n",
    "import ewatercycle.parameter_sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load parameter set\n",
    "\n",
    "The parameter set `demo.zip` should be downloaded from https://sourceforge.net/projects/hype/files/release_hype_5_6_2/ and unzipped.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_set_dir = \"<path where demo.zip was extracted to>\"\n",
    "parameter_set = ewatercycle.parameter_sets.ParameterSet(\n",
    "    name=\"hype_demo\",\n",
    "    directory=parameter_set_dir,\n",
    "    config=\"info.txt\",\n",
    "    target_model=\"hype\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the model\n",
    "\n",
    "Note that the model version and the parameterset versions should be compatible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('feb2021',)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ewatercycle.models.Hype.available_versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ewatercycle.models.Hype(version=\"feb2021\", parameter_set=parameter_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "eWaterCycle exposes a selected set of configurable parameters. These can be modified in the `setup()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('start_time', '1961-01-01T00:00:00Z'),\n",
       " ('end_time', '1963-12-31T00:00:00Z'),\n",
       " ('crit_time', '1962-01-01T00:00:00Z')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling `setup()` will start up a container. Be careful with calling it multiple times!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('/tmp/hype_20220607_121055/info.txt', '/tmp/hype_20220607_121055')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfg_file, cfg_dir = model.setup(end_time=\"1962-06-30T00:00:00Z\")\n",
    "cfg_file, cfg_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('start_time', '1961-01-01T00:00:00Z'),\n",
       " ('end_time', '1962-06-30T00:00:00Z'),\n",
       " ('crit_time', '1962-01-01T00:00:00Z')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the parameters have been changed. A new config file which incorporates these updated parameters has been generated as well. If you want to see or modify any additional model settings, you can acces this file directly. When you're ready, pass the path to the config file to `initialize()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.initialize(cfg_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the model\n",
    "\n",
    "Simply running the model from start to end is straightforward. At each time step we can retrieve information from the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1962-06-30T00:00:00Z\r"
     ]
    }
   ],
   "source": [
    "discharge = []\n",
    "time_range = []\n",
    "end_time = model.end_time\n",
    "\n",
    "while model.time < end_time:\n",
    "    model.update()\n",
    "    # The demo parameter set has a single sub catchment so store first value of array\n",
    "    discharge.append(model.get_value(\"comp outflow olake\")[0])\n",
    "    time_range.append(model.time_as_datetime.date())\n",
    "    print(model.time_as_isostr, end=\"\\r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interacting with the model\n",
    "\n",
    "Hype model exposes many variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('comp outflow olake',\n",
       " 'rec outflow olake',\n",
       " 'air temperature',\n",
       " 'corrected air temper',\n",
       " 'precipitation',\n",
       " 'corr precipitation',\n",
       " 'subbasin evaporation',\n",
       " 'computed runoff',\n",
       " 'computed soil water')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.output_var_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hype is a lumped sub-basin model so there are values per subbasin. The outflow of all sub-basins can be requested with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.71861417e-05])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da = model.get_value(\"comp outflow olake\")\n",
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has some info about the sub-basins:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 'unstructured', (1,), array([0.]), array([0.])]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\n",
    "    model.bmi.get_grid_size(1),\n",
    "    model.bmi.get_grid_rank(1),\n",
    "    model.bmi.get_grid_type(1),\n",
    "    model.bmi.get_grid_shape(1),\n",
    "    model.bmi.get_grid_x(1),\n",
    "    model.bmi.get_grid_y(1),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulated_discharge = pd.DataFrame(\n",
    "    {\"simulation\": discharge}, index=pd.to_datetime(time_range)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AANA0VpKJMmFIJiIiCgilFESsvmSGZKLJMSQTEREFhKnsSjJ7kokyYkgmIiIKCAVl9STrrCQTZcKQTEREFBDKbkrWRJBkSCaaFEMyERFRQFgT4AQhTWAyJBNNiiGZiIgoIJyFe7rGSjJRJgzJREREAeGMgNM1sTYWoaJ48dAZfPBb65E0zHIfCk0BQzIREVFAKHszEVaSi+uFQ7347Y6TODuSLPeh0BQwJBMREQWEQqrdgj3JxaPsKn3SZCW5kjEkExERBYRSsEKyCANcETnj9ZIGX4hUMoZkIiKigLCym9VuwXbZ4nGK9JxFXdkYkomIiAJDQbPbLQxWkovGdNstGJIrGUMyERFRQLjtFpqAnQDF4/R784VIZWNIJiIiCghrBJywklxkTgGZleTKxpBMREQUEKZnMxH2yxaP227Bcn1FY0gmIiIKCGtbamu6BUNy8bAneXpgSCYiIgoIa1tqQUhnSC4mJySzpaWyZRWSReRaEdkpIntE5NPjfP89IrLZ/ve0iKzO9rJERERUGgrWwj2NleSicnuS2W5R0TKGZBHRAdwB4A0AlgG4SUSWpZ1tP4CrlFKrAPwjgLtyuCwRERGVgrIW7oU0gaEY4IolNd2C93Ely6aSvBbAHqXUPqVUHMD3AdzgPYNS6mml1Bn7y2cBdGZ7WSIiIioNZ+GepgmrnEXEnuTpIZuQPBvAYc/XXfZpE/kQgIdyvayI3CoiG0Rkw6lTp7I4LCIiIsqFAqAJENLEDXJUeNxxb3rIJiTLOKeN+6iLyDWwQvKncr2sUuoupdQapdSatra2LA6LiIiIcmFtJiJWJZkBrmiccJzg3t8VLZTFeboAzPF83QngaPqZRGQVgLsBvEEp1ZPLZYmIiKj4FBQEdiWZIblolGJP8nSQTSV5PYAlIrJARCIA3gXgAe8ZRGQugJ8AeJ9SalculyUiIqLSMK0t96ALK8nFxB33poeMlWSlVFJEbgfwCAAdwL1KqW0icpv9/TsB/C2AFgBfFREASNqtE+Netkg/CxEREU3Gnm6hsZJcVAYrydNCNu0WUEo9CODBtNPu9Hx+C4Bbsr0sERERlZ6CchfuscpZPIrTLaYF7rhHREQUENbCPWsEHKdbFI+z0R533KtsDMlEREQBYSrlbibCKmfxGKwkTwsMyURERAHBbalLw91MhBu2VDSGZCIiooBw5iSHNIbkYlKcbjEtMCQTEREFiADQGZKLyrlv2ZNc2RiSiYiIAsCZuCDCkFxsJnuSpwWGZCIiogBw8ppArJDM6RZF49y1BnuSKxpDMhERUQCkV5KVAjcUKRKnSs9KcmVjSCYiIgoAJ65p9rbUAFhNLpJUuwV7kisZQzIREVEAOHlYRKDrdkhmpbMoTE63mBYYkomIiALAu8OeW0lmiCsK575mT3JlY0gmIiIKEKcnGWC7RbFwusX0wJBMREQUAE4e1kRSIZmVzqJwsjEr9ZWNIZmIiCgAlL10TwCEWEkuKpPTLaYFhmQiIqIASC3cAzSNPcnF5LZbGJxuUckYkomIiALACW4CSVWSGZKLwl24x/u3ojEkExERBYAT10SsvmSAIa5YnPHIbLeobAzJREREAeCdkxzinOSiYiV5emBIJiIiCgInJCNVSWalszi44970wJBMREQUAG5PsgAhTRtzGhWWwRFw0wJDMhERUQC4PckAdPvZP8k5yUWh7BcfCd6/FY0hmYiIKACc4KZpAp2V5KJiT/L0wJBMREQUAONWkhniisJwp1uwJ7mSMSQTEREFgOnZTcSpJLPSWRyKleRpgSGZiIgoCDzTLXTOSS6q1HQL3r+VjCGZiIgoAJy4polA5457ReXcr7x/KxtDMhERUQB4ui0YkovMua853aKyMSQTEREFgLJrydbCPTskc7pFUaSmW3DhXiVjSCYiIgoAc9xKMkNcMRjsSZ4WGJKJiIgCwJm4IBCE3JBcziOavpzXHmxnqWwMyURERAHg7UnWhJXkYnJekHBHw8rGkExERBQgIoKQzkpyMRmckzwtMCQTEREFgKlSC/ecSjJ3hCsOJxuzJ7myMSQTEREFgLfdwulJNjndoijcdgu+CKloDMlEREQBMN5mIuyZLQ53MxHevxWNIZmIiCgA3OkWnhFwrCQXB9stpgeGZCIiogDw5jW3kswQVxQmF+5NCwzJREREgeBUklPtFiZDXFE49yt7kisbQzIREVEAuAv3AOjCSnIxOXerqfhCpJIxJBMREQXAmIV77pxkBrhi8PZ684VI5WJIJiIiCgDvCDhdGJKLyVQKdkcL7+MKxpBMREQUAN7NRJyeZIPTLYrCVEAkZEUs9iVXLoZkIiKiABhTSXZCMuf4FoWpFMK6FbFYSa5cDMlEREQBoLzTLYSV5GJRSkEpIKI7lWTex5WKIZmIiCgAvNMtNE0gwipnMTh3qdNuwfu4cjEkExERBUCq3cKqIoc0YYArAqf322m3SBjsSa5UDMlEREQB4LZb2F9rwpBcDM59ykpy5WNIJiIiCgDvwj2AleRice5n9iRXPoZkIiKiAPBuJgJYfckMcIXntluwklzxGJKJiIgCQHlX7sGqJJucblFwzsSQiL2rYZJj9ioWQzIREVEAmGMzMnRWkotC2ev02JNc+RiSiYiIAiE1JxmwQrLJAFdw50y34I57FYshmYiIKACczgp7sz3owkpyoZimwucfehknzo542i1YSa50DMlEREQB4EQ1sRsudJ2V5ELpOjOMrz++D7/efsKtJDvtFuxJrlwMyURERAHgBGJhJbngnJaKvuHEOSPgWEmuXFmFZBG5VkR2isgeEfn0ON8/X0SeEZFREflE2vcOiMgWEdkkIhsKdeBERESUvVQl2aJr4rYG0NQ4QfjscML9POzOSWZPcqUKZTqDiOgA7gDwWgBdANaLyANKqe2es50G8BcA3jLB1VyjlOqe4rESERFRns7dllpDklsmF4TTUtE3nGC7xTSSTSV5LYA9Sql9Sqk4gO8DuMF7BqXUSaXUegCJIhwjERERTZG7LbVdSg6HBAkGuIJwqsfedotoiJXkSpdNSJ4N4LDn6y77tGwpAL8SkY0icutEZxKRW0Vkg4hsOHXqVA5XT0RERJmk7SWCiK4hnmSAK4SkpyfZCczRsBWx4nwhUrGyCckyzmm5POLrlFIXA3gDgA+LyJXjnUkpdZdSao1Sak1bW1sOV09ERESZpLdbREIMyYXirSQ77RbRkA4ASPA+rljZhOQuAHM8X3cCOJrtDSiljtofTwL4Kaz2DSIiIiqh9HaLSEjHKHuSCyIxpifZOs1pt0jwPq5Y2YTk9QCWiMgCEYkAeBeAB7K5chGpEZE653MArwOwNd+DJSIiovykbybCdovCGb+S7Oy4x3aLSpVxuoVSKikitwN4BIAO4F6l1DYRuc3+/p0iMgvABgD1AEwR+SiAZQBaAfzUfmsnBOD/lFIPF+UnISIiogmlopqVkqMhDfGkUa7DmVacnuT+kaRbOY6F2W5R6TKGZABQSj0I4MG00+70fH4cVhtGurMAVk/lAImIiGjqnApnqt1CQ5ytAAXh3TCkb9ga9OUs3GO7ReXijntERERBwOkWRePdubB3yA7JzsI9huSKxZBMREQUAM7CPY3TLQrOW0k+PRgHkNqWmiPgKhdDMhERUQCkRsBZHxmSC2dsJdkKybomCOvCXQ0rGEMyERFRAJhuu4WnkswAVxDeIHzGbrcQAcK6xnaLCsaQTEREFAAqfeGeriFhKJgcUTZl3krymUFvJVnj1t8VjCGZiIgoANKjWiTk9Myy0jlV3p7kM3a7hSZWuwXv38rFkExERBQAqc1EUnOSAYbkQhhTSU5vt2Dfd8ViSCYiIgqAc9otnJDMEDdlhv1CI6xLauGeWO0WSbazVCyGZCIiogBwopq3JxlgSC4EJwhXR0IYilu7GGoa2y0qHUMyERFRAKhxplsADMmFkArJOkYSdkhmu0XFY0gmIiIKgNRmItbXXLhXOM7CvaqIjpGEdX9qIhwBV+EYkomIiALgnM1E2G5RMEl7zFtNJOS+6HCmW3AEXOViSCYiIgoAU7ldyQBSleRRhuQpM0zrPqwK6+5prCRXPoZkIiKiACnFdIuegVHcv+lIwa/Xr5KmQlgX9z4FAE2z7mOG5MrFkExERBQAqYV7lmLOSb5/01F85Pub3HFo051hKuhaWkgW7rhX6RiSiYiIAiC1cM9ut9Ct1oBiVJKdFo7+kWTBr9uPEoZCSNPcPm/Aup9DmrCSXMEYkomIiALAbpstSbuF06M7MBqMkGyY5jmVZF0DwiGN00MqGEMyERFRAKSW7aXNSTaMgt+W02IwGJCQnDQVQmkhWUQQ0TV38gVVHoZkIiKiACjlttTO3ODgVJIn6klmu0UlY0gmIiIKgFJuS52w2y0GRwtfpfYja7pFek8yOAKuwjEkExERBYG7mUjx5yQbAWu3cCrJ0XGmW3CzlsrFkExERBQAzmYipRgBl7TbLfoDEpIThnlOT7Kmcce9SseQTEREFAClbLdIuu0WwQjJbk/yOO0Wzn1BlYchmYiIKABSm4lYKVnTrDm+RQnJAWu3SI6zcE/3bCaiFKvJlYghmYiIKABSm4mkTouEitMzmwzgdIuQPrYnWSQVmtlyUZkYkomIiALATA1KdkWKtNlF0ghWu4U1J1lDJKS7p2kChOxXJJxwUZkYkomIiILAXbiXSsmRIk1fSFWSgzECzjDPXbina1a7BcCQXKkYkomIiAIgfeEeUMR2i6D1JBvn9iSLCMJst6hoDMlEREQB4Kwd0zwpORLSMFrEEXCD8YCEZLsnOX26RURnu0UlY0gmIiIKgPQ5yUAx2y2s6wzKwj1ruoU2ZuEe2y0qH0MyERFRALgj4DwpOVqkdgvDDFa7xXg9yc6OewBDcqViSCYiIgqA1HCLse0WxQjJCXe6RTAW7iUNdU5IFgHCdrtFPMme5ErEkExERFRGSqmSVBpVajcRV7FGwBmenuQgbKRhjNOTrLOSXPEYkomIiMroxy8cwZLPPITDp4dKcntjNhMpUk+yM81BKWAoPv2ryYbdkzxRuwW3pq5MDMlERERl9Mi24wCA7cfOFvV23IV7Uvx2C8NMVY+D0JecGKcn2Wq3sL5mu0VlYkgmIiIqo5qItUtbscPkON0WiIT0orRbeNsL+gMQkg1nTrIdijVxtqXmCLhKxpBMRERURjXREIAShGT7o5Sg3cIwlRsYg1BJtralFncEnDOLOqSxJ7mSMSQTERGVUa0dkou9hfOEm4kUaVvq+qowgGDMSnYW7kVD1rsCzn3MhXuVjSGZiIh848++uxHfe/5QuQ+jpEpVSTbHmTJhzUkufDhPmiYaqqyfaygAY+CsSnJq4Z5dQHbbLe7fdBS/2Hy0XIdHeWJIJiIi33h232ls7uor92GUVLXdk1yqiquUYARc0lBu+C/G9fuNNd0itXAvvZL80NbjuPv3+8t2fJQfhmQiIvINUymYZrAmATiBqvgL95xtqT3tFkXbllq54X+0CJVqv0kY1nQL3f6XHpIBYDgAo/CmG4ZkIiLyDdNUMAKw+YSX0wYxGC/NdAstrZJsKiBZ4Gpv0jDdXutihHC/cSrJgPXCw7mPQ3rqzh5KTP/e7OmGIZmIiHxDKQSukuyE5GIv3HPu1vQ5yUDhWyKsSrIVkouxMNBPlFLudAvAuk81T2B2sJJceRiSiYjINwwVxEqy9bH4I+CcdouUiLvZRaEryQo1UbvdIjG9Q7Lz+IXs+zIS0thuMU0wJBMRkW+YSo3ZrS0InJ+3ZJuJpLVbAIUPycaYSvL0DofOltNj2y3ODclDCcPtC6fKwJBMRES+YapUmAsK5bZblGozkXPbLQrdEpEwTVSFdYhM/55k50WO024RDaV6ksOenmSlpn/ryXTDkExERL6hAllJtj4WfWc6pcZUkQG4O8QVsifZNBWUgr25RnE2K/GTpP3/Vff2JNt3tKTd4UNsuagoDMlEROQbpkIAe5KddoviL9xLy8hF6UlO2O0HYV1DRA9ASDbGVpIjIc0NzADw7+9YjduuWgQAGCryBBMqLIZkIiLyjSDOSXbaLeKGiZFE8YKygjqnslmMnmTDU1mNhvXg9CQ7C/d0bUzF/q0Xd2JZRz0ALt6rNAzJRETkC0pZb9MHrZLs/XnPjiSKdjtJzyxfRzRkTaAoZLtFwlNZDUK7hfOiIDxOu4WjOmzdz8NFfBFEhceQTEREvuBkxaD1JHt/3P6R4r0dbxjKDXKOYlaSgxKSnXYL3bNwL/3FiLP7IHuSKwtDMhER+YLTm2sGrJLsbS85O1zaSnIxQrKze19I1xAJ6dN+TrL7okBPVZLTF0jG7JDMdovKEir3ARAREQGptoPgVZJTP29RK8mmcje8cDgL9wpZ7U2mVZILvZuf36SmW1j35asvmImlM+vGnIeV5MrEkExERL7gZEVzemeqc3gzZKl7kouxLbU77UHXrHaLad6H6yzcc6ZbvGPNnHPOUx224hanW1QWtlsQEZEvOBXVoC3c81aSi1lpNEzTDXKOaDHaLTyhMRLAnuTxVEW4cK8SMSQTEZEvmIFduJf6eRNFbE0oWU+yp0c3GtKnfUh2p1voE4dktltUpqxCsohcKyI7RWSPiHx6nO+fLyLPiMioiHwil8sSEREBqbCoAlhJdhZ6JYoYKA1TnVNJTm0mUrjw5t1cIxrWCnrdfpTekzyeqjAX7lWijCFZRHQAdwB4A4BlAG4SkWVpZzsN4C8A/GselyUiIoKy82HQ2i0ME4jZ84qdGcPFULKeZLfdQgvECDjvyLuJaJogFtbYblFhsqkkrwWwRym1TykVB/B9ADd4z6CUOqmUWg8gfcVBxssSEREBnp7k6Z2pzqGUQixc+LCazjAUQmnVzmK2W+h6QOYkOzvuTRKSAauazIV7lSWbkDwbwGHP1132adnI+rIicquIbBCRDadOncry6omIaLpw5yQHrCfZMJW7812pe5JDmkCk0HOSnR3oNKsneZpXT73tJZOpjoTYk1xhsgnJ4z3q2f4Fy/qySqm7lFJrlFJr2trasrx6IiKaLozATrewqpAhTYoakg3TdDe8cIgIIrqG0SK0W+gBmZNsmJmnWwDWhAv2JFeWbEJyFwDv0L9OAEezvP6pXJaIiAIkNSc5aCFZQdOAsK6VvCcZsFouilJJ9rRbTOfFmEl3usXkkao6orOSXGGyCcnrASwRkQUiEgHwLgAPZHn9U7ksEREFSJDnJOsiCOtS0LCabrzpFoA1K7mQt5vapllDJKRBqeIuSCw3I4eeZC7cqywZd9xTSiVF5HYAjwDQAdyrlNomIrfZ379TRGYB2ACgHoApIh8FsEwpdXa8yxbpZyEiogoW1DnJhqmgibXxRjFbEyasJOuFDclOy4i1LbXVaz2aNNxFgtNNMovpFoBVSe4eiJfikKhAstqWWin1IIAH00670/P5cVitFFldloiIKJ3TZhG0dgulrBFhYV0r+pzkiD2v16vQ4dzwbiYSLvz0DL9xfl4tq4V7Q6U4JCqQ6fmyjoiIKo7TZRHEdgtNnJ7kIlaSDbMkPckJT2XV2fZ6Oo+Bc362SIae5FiYC/cqDUMyERH5QlDnJDvtFmFdir5wb7yWgEKHZMOzmUjEE5If3HIM7/7GswW7Hb9w7rtohnaS6oiOIfYkVxSGZCIi8oXgbksNuydZL+5mIpP1JBfwdp2gr3t6kuNJExsPnsHTe3umXTXVCcmZeq453aLyMCQTEZEvmAFut9A1QUQv7pzkpKnGHVMWmeKueP0jCXz3uYPuixvDMxIt1W5hYHDU2m3uzND0WrzmvMDIHJJDiCfNoj7GVFgMyURE5AtmWsgKilL1JE9YSQ7pU2q3eGTbCXzmp1uxv3sQgNX7DIytJI8mTfTbIbl3KJH3bflRPMue5LqYNSthYIRbU1cKhmQiIvKFIG9LnZpuUcyeZHP8nuQpjoDrH0nYH63wl/BsJuL2JCdMNxz2TrNK8mjSgCbWXOjJuCF5lCG5UjAkExGRL9jrvQLXbqHsnuRwqLDbQ6czjPEryVPdOtppo3DCn3ebZqfdIm542y2mXyXZqZhPxgnJZ0em188/nTEkExGRL6QqyWU+kBIzTGvHvYguRZ2TnDQVQvr40y1Gk/kvKBu0F6O5lWT7AQzrmjsneTRhuiG6dzhVST7eN4JNh3vzvm0/iCfNrDZKqYuFAbDdopIwJBMRkS8EeU6ylLMneYrtFudUkseZbjGaNN0Q7e1JvuN3e3DLt9fnfdt+EDeyDclWJbmfIbliMCQTEZEvBHvhnhR/MxFTIaSNP91iKiHZCcdOWPZuJhLxTreI2+0Wg6lK8unBOLoH4hU98WE0aWZctAcAtVH2JFcahmQiIvIF01NBDtLiPVNZVddISCvqZiITT7cocCXZtHb2Exm74567cG84VUl2+nMreSyc1ZOcfbtFP3uSKwZDMhER+YI3Fwep5cIwU+0WxdxMZMLpFlNcuDeU1pOcNFI7+znh8exwAkn7AfZOt3Au0zNQ2SE5l3aLs2y3qBgMyURE5AvenfbMAIVkVaLNRCbrSU4YKu/q/YBbSbYqpN7tr52e5B5Pi4V3uoVTVT09WLkheTTLkBwNaQjrwnaLCsKQTEREvuDtRQ7ShAvD25M8TtvD1x/fi21H+6Z8O97w6hVxx7Tld6e77RZuJdl0ZwaH7Wka3hA8XiW5e2A0r9v2g2zbLUQEdbEw2y0qCEMyERH5QlDbLUwzNSc5vSfZNBU+/9AO/OzFI1O8DQWlAH2chXvRKYdkq93CqZB6w7iIoD4WwpEzwwCAtrromOkWTkiu5EpyttMtAGvxHkfAVQ6GZCIi8gVvu0WQJlx4t6WOG+aY+2Eo4QTQ/OcYA3D7gSeakwwg78V7ztQKNyQbY+cxtzdUYdeJfgBAZ1MVeocTUEohYZgYtn++iu9JzmK6BWD1JXMEXOVgSCYiIl/w5uJgTbdI9SQDGFNNHrKD51TfovfugpfOCXh5h+S06Rbpo+baG2PuYrXOpmoYpkL/aHJMRbVnsLLbLbKtJNfFQuhnT3LFYEgmIiJf8C7WC1S7hbMttR1WvYv3nN3sprrYy9kFb7ye5FjYWlw3ksi9Wj2aNNxQ7/Ykm+Y5lWRHZ5P1ee9gYkxFtaIryYaJSBbbUgNAbTTMSnIFYUgmIiJfCOycZFNB0yYIyWmL4vLl3QUvXX1V/qPJhjxtIN5Ksvd2Ohpi7udzmqoBWJXjs57qeE8l9yTn0G5RHwtx4V4FYUgmIiJfUEFduGf3JI83ZWKoQJXkpGcXvHQNVREAY6dOZMs5rpaaiFshHYkbiHkqq+2NqUryso56AMCBnkH3/C01kYpeuJftCDgAqI2FOAKugvg2JG872ocXDp0p92EQEVGJmAFduGcoBV3ErUZ6e5KdRXFTfYvecBfunfu031ht7QTXN5x7hdM5vhn1MYwmTSQME33DCTRUhd3zOJVkEeCC9jrommDPyQG3ojq/taaiR8CNJo2sRsABqYV7KkAvAiuZb0Py9V9+Em/96tPlPgwiIiqRoM5JNk1rVFo4ZC/c8yygGxqduJL8ke+/iC89vCOr20jad+h47RZOoM0rJNvHNbM+6n6dHpKdSnJtNIRoSMf8lmo7JFuXnddSjf6R5JS2xi6nbOckA9bW1IapMJKozJ81aHwbkh2jyamNvSEiosoQ2DnJSkHXMMHCvdTkiPTq40uHe/HbHSezug1j0nYLK9B65xdny5mRPKveqhb3jyRxdiTh9jkDQLtdSa6NWqctnlGL3d5KcksNgMqccKGUynlOMjD1aSVUGr4Pyfu7B8t9CEREVAJB3Zba9Oy4B6T1JNuVWsNU7kxhx3DCwL5Tg0hmsQlIcpIRcGFdQ200lGdITrVbAFaYT68kx8I6mmsiY0LywZ4htw/5gnarT/lgz1DOt19uSXuTllzmJAPgGLgK4duQ7Lwq231ioMxHQkREpRDUOcmGCWiajDuv2BkBB5w74WIkYSJumDiQRbhMVZLHf9pvqArn1W4xkNZucXowjpGEOSYkA1Y1ucYOyUtm1MEwFbYc6UMkpGG5vZhvt73hSCVxHqtsK8nO/XLodOW9IAgi34bkuc3WmJhK/KUhIqLcBXVOsvLsuAekLdzzVBzTq49OZXlXFs+TyUlGwAFOSM59woRzfE67xZHeYff6vG67ahE+dMUCAFYlGQBePNyL+lgI7Q0x1EVD2FWBRbFcQ/LaBc1ob4jhXx7eGajFqZXKtyFZF+sXuRJ/aYiIKHdBn24RdnfcO3cEHDC2kmyYyg1oO49nDsmT9SQD1oSLvNot7OOb6YTkM1ZIrk8LyW9a3YE3re4AACxqq0U0pKF3KIG6WBgigiUza7GzAotiTmtMtiG5OhLCZ69fhu3HzuKBl44U89CoAHwbkp3dgXadrLxfGiIiyp0a025RvuMoNdNU9nSLc3uSvZVk74QL76L23Vk8T7rTLfSJQ3K+0y1CmriL8/bZ64jSK8leVREd169sB5AK7efNqsPuE/0VNxpt1J5SkW1PMgBct3IWYmEN24+eLdZhUYH4NyTbfyQO9gxV7FgYIiLKXlDbLUxltUG4c5KT41eSvRMRhj2n7yhAJbmhKozePENydcRamBcNadhxzAp+6ZXkdDe9Yi4AYPdJ693iJTPqcGYoge4K2546bliPQzSc3bbUgDXur6Oxym1NIf/ybUh2+qcMU+F430iZj4aIiIptzAi4ALVbpO+4l76ZSJO92Yd3Q5ERO0jXx0I42jucsQI72XQLwNp1r28okXMldzBuoDYagohVTc6mkgwAa+Y1AQBec8EMAFYlGciuv9pPRpO5V5IBYHZjlduaQv7l25CcMBTmt1iL97p6uQqUiGi68060CNIIOMNU0DQZd07y0Kjh9vt62y2cSvKc5mqMJMwxUzAmug1g4ukWjdVhxA3znDFzmQyOJt2pFe0NVe7tZArJIoId/3gt7nzvJQBSIXnb0b6cbr/cnHe6s91MxNHZxEpyJfBtSE6aJubZA8a7+GqLiGjaC+rCPaVgz0m2qrzxtM1E2uqs8WrehXsjdpid02QVk7r7J9+II3MlOb9d9wa8Ibkxds71TSYW1t1tsltro5jTXIUXD/XmdPvllut0C0dHQxW6B+Lu40j+5N+QbCjMba6GCPiWBBFRAAR2TrLTbjHOnOShuIHGaqvf11tJdkNys7Xl86mByUOyYS/cm3C6RZ677lmVZKsfd7a9/XR1RHer4rm4aE5T5YXkHKdbOGY3WfcVq8n+5tuQHDdMVEd1zKyLsZJMRBQAwV2454yAG2db6tEkaiI66mLhMXOShxOpdgsgi0pypjnJ1fmF5KG4gZpIqt0CyK6KPJ6L5zbi+NkRHOurnOf8+BR6kgEWAf3OtyE5aZgIa5rdt8OeZCKi6W7sttRlPJASUsra1tg7Ai59TnJ1JIS6WGjswj179JjTbpGpkuy0W4QmGgFXFQGQX7tFbVq7RX0sv5B80VxrMV8lVZNH82y3cCrJR1lJ9jXfhmRTWb/Ms5uqWEkmIgqAILZbOD+mrnk3E7FOVEphMG61M9RGQxjwjoCzK8kdjVXQJPue5InaLZy+51xDm3fhXscUK8kXtNcjEtLwwsEzeV2+HPJduDerPgZdE7Zb+JwvQ7JTTAjrViX5eN8IkgZnJRMRTWdBXLjn/Mzj9SSPJEwoZe3SVl8VGjPHeMSeZlET1dFcE826J1mfYLpFW10UrbVRbD+W2wYXg6MGqu2eZLeSnGdIjoQ0rJzdgBcP9+Z1+XLId+FeSNcwqz7Gdguf82dIRuoV7+zGaiRNhRMZXiUTEVFlGzMnOSA9yc6LAU0Tq+VCF7fdYjButVfURHXMqIvh5NnU8+CIveNeLKyjtTaCU/2Tb8Lh9CRPVEkGgGUd9TntAhdPmogbJmrtnuT6WBh10RAaq/MLyYDVl7zlSF/FbCI2mufCPcBquTh0mu2kfubPkOypJLsrQPlqi4hoWhszJzkglWTn+U4TK7yGdc0NyUOjVhCujoQwoz6Kk/0jbt+2Mye5KqyjrS6K7oyV5Ml7kgFgeUc9dp/szzqgDrkhPuSe9q/vWI0/ftXCrC4/novmNiGeNPFyjhXtcnHbLfTsd9xzzG+pxkGGZF/zZ0i2P4Z1wSx7iPopVpKJiKa1IE63cH5OfUxItk5zK8kRHbPqY0gYCmfs6RPOwr1YWEdbbTTjc2SmOckAsKy9HglDYY+9VXQmzki6Wk9Ifv3yWe7GIPm4aG4jAOCFQ5XRl5xvuwUAzGupwan+UQx6ppaQv/gzJCvnFa+G1lprxe2pfm5NTUQ0nQVxW2rnhYGdkRHWNYzarRROpbYqoru77p04az0XDicMRHQNuiZuJXmyLaUz7bgHWO0WALLuSx50Kt3R3KuoE2lvqMKs+ljFTLiYWki2JpOw5cK/fBqSrY8hTdBUHYGuScZFCUREVNnMMSPgAhKS0yq8NVHdbaVwqsVVYR0z663pE05IHkkYiIVTu9WNJs0xc5TTZVNJnt9Sg6qwjq1HstsaenCcdotCuHheI9YfOD1p6PeLuGFA12TS+3Ui85qtXYUP9jAk+5U/Q7L9Maxr0DSxFyUwJBMRTWdqzHSLMh5ICZlpPcnVkRAG3ZCcWpw3o86qJDuL96yQbFVwW+usd1x7BiZevJdpxz3ACtCr5zRgw8HTWR374DjtFoVwxeI2HOsbwd5T2bV9lNNowsx5/Jtjrl1JPtgzWMhDogLyZ0h2Ksn2AoO2usz9VkREVNmCOCfZO90CsPqPnfA57A3JaZXk4YSBqogVkp25xGcn2Qgkm0oyALxiQQu2Hz2LsyOZNxVxjtPZca9QrlzaCgB4bOepgl5vMcQNM69WC8B63Jqqw1y852P+DMl2LdnZorOtNvMMSCIiqmxBXLinPHOSAaA66q0kp9otoiEdTdVhnOhPtVtU2ZVkZ4e7yYKtkcUIOAB4xYJmmArYeCDzwjmnJ7nQleTOpmosbKvBE7u7C3q9xZAwTDer5GNuSw0ryT7mz5DsjoBjJZmIKCjGVJIDEpLTp1vURHQM2RXaVLuF9VQ9sz6GE3a7xXDCRNQJyW4leWo9yYA1gi2sC57bn7nlwulJLuTCPcdVS9vw3L4e9z7wq4ShEM6jH9kxv6UaB7pZSfYrX4ZkYOwqXGvlbjwwb78REQWRtyc5KH/vx+tJHkrrSXbC8Iz6GE6e9VaSrefIrCrJpoJub1gymaqIjlWdjXhuf0/GYx9vBFyhXLm0DaNJE89nEdbLKWmYCE2hkry4rRZHeofRn0V7C5WeL0PyOT3JtVEYpsKZocl3FCIiosrlHfsWmBFw6T3JUd2t0Doh2WmrmFkXdSvJ3oV7dTErpGbqSc52AsMrFjRjS1efO4JuIoOjSeia5L1wbTKXLWhBJKThiV3+7ktOmMp91zsfzti9ncf7C3VIVED+DMn2R7cn2V7Vy75kIqLpa+y21OU7jlIy03uSIyF3p72RhAlN0loPB0ZhmgrD8VRPcnVEh65JhkqymbEf2bF2QTOSpsILB3snPd/gqIGaiJ6xOp2PqoiOtfOb8cRun4fk5NR6ki9ot0JypewwGDT+DMkqbeFenbWql33JRETTVxDbLYz0OckRHXHDRDxpYtiuFjshtKk6AsNUGIgnMZJMhWQRQX0slLEnOdtK8iXzmqAJ8PwELRemqfCFh3Zg0+Hegs9I9rpyaSt2nRjAsb7hot3GVCVNNelW35m0N8TQWB3OegMXKi2fhmTro/OqlyGZiGj6C+J0C+e1gBOEq+3QORw3xkywAFKj3vqGEhiOpxbuAdbivUw9ydlWkutiYayY3YBnJ+gHPnR6CHc+vrfoIfma82YAAB59+WTRbmOqEoY56S6GmYgILphVj+3H2G7hR/4MyfbH9ErySYZkIvK5//7Nbjy1x/+jq/zIVKnWgqD0JKtxplsA1uSIkYTp9h0DQEO1HZKHExhNC9D1sfCkPckJQ0HPIcytnd+MTYd7x50usfNEKtBlG7zzsXhGLRa11eDBzceKdhtTlTSm1pMMWH3JO4+fDcz/+Uriz5CctnCvNhpCXTSE430jZTwqIqLM7n5yP365xb9P6n5mKuVW5QLTbjHOnGQAGIonMZIwEA2nnqbdSvJwwm7FSH2vviqEsyMTt1vk0pMMAOsWtyI+wXSJ3Z6QvKOIC85EBNevbMdz+3vQ7dM1SVOdkwwAy9rrMZIwsa8CdhgMGn+GZGczEc+r3o7GKhzp9W9fEhERYFVAjaCsOisw5a0kB6Xdwt5+22m3qLVnDg+Onttu0WhXkrsHRpE0VU6V5Fx6kgHgFQubJ5wusfPEANobrAX1dUVstwCA61a1w1TAw1uPF/V28pUw1ZRGwAHAys4GAMCWI32FOCQqIH+GZGczkVDqF7q9Mebr5n0iIsCqLCWc5EM5MVUqcASlkuz0YTsBttre4nnQXpwXG6cn2XlX1dmWGrBDcqae5BzaAqojIayd34zHxwnJu0/044L2evziz6/Az//8iqyvMx/nzazDwrYaPOjTd2eShjmlzUQAYFFbLaojOjZ3MST7TVYhWUSuFZGdIrJHRD49zvdFRL5sf3+ziFzs+d4BEdkiIptEZEM2t+f8aQylVZKP9rLdgoj8zTAVkqwk58VUCppYgTEgGfmcEXA1dkgeGjUwHB/bUuGE5D0nrbflW2uj7vfqqwo33cJx1dI27D45gKOed3EThom9pwawdGYdVsxuwPzWmpyuM1dOy8Wz+/zZcpE0pjbdArD+v6/oaMBLXb2FOSgqmIwhWUR0AHcAeAOAZQBuEpFlaWd7A4Al9r9bAXwt7fvXKKUuVEqtyeagUiPgUv/xZjdW4fRgHMNxf29RSUTBpZRC0lRIspKcF8O0QpEuEph2CyNtMxFni2dn4Z63paIqrCOia9hl9wTPrI+536uPhTGcMBBPjv9/zzDUmBbGbFx9XhsA4Dc7UtMlDvYMImEoLJ1Zm9N1TcV1K62Wi0e2+a/lImFOvScZsFouth89i4TBvx1+ks0juxbAHqXUPqVUHMD3AdyQdp4bAHxHWZ4F0Cgi7fkelFtJ9vzHc/qfjrLlgoh8ygk8CVaS86LsSrJIkNotrI+aO93CWbhnYCRpjBnzJiKorwpj1wmrkjyz3ltJtqrME21vnE8lefGMWixsq8HDW1OtDk4Ve/GM0oXk82fVYWGrP1suCrFwDwBWdTZgNGm6L4DIH7J5ZGcDOOz5uss+LdvzKAC/EpGNInLrRDciIreKyAYR2TA4MAhg7GiZjsYqAMAxtlwQkU8l7cSTZDUoL1a7hUDXJDDjsMy0EXBuJXk0iZG4gVhIH3P+xmqrYgykVZKr7K2pJ5hwYZhmzm0BIoI3rJiFZ/edxunBOADg8GmrUDWvubhtFunHcd3Kdjyztwc9Pmu5SBrZz5+ezKrORgDAFvYl+0o2IXm8Rz/9r9dk51mnlLoYVkvGh0XkyvFuRCl1l1JqjVJqTVV1NQCMeXU22w7JRznhgoh8yg3JAQl4hWYqq6IapHYLp2LujoALe6ZbJE1URcY+TTt9yXXR0JiNPOpj1ukTTbjIp5IMAG9Y0Q7DVPj1dqvV4dDpIdTHQu7M5lK5fpXTcnGipLebScKY+nQLAJjfUo36WAgvMST7SjaPbBeAOZ6vOwEczfY8Sinn40kAP4XVvpH5wARjfqFn1scgAo6BIyLfcirI7CvMj6kURKz+3KC0W7hzku3nu5CuIRrS3DnJ51SS7ZA8w9NqAaTaLSaacJHLjnteyzvqMae5Cg/ZI9gOnxnCnObqnK9nqvzacpE0zSlvJgJY1fJVnY3YcqR36gdFBZNNSF4PYImILBCRCIB3AXgg7TwPAHi/PeXiMgB9SqljIlIjInUAICI1AF4HYGumG1QK57wyi4Q0tNVGWUkmIt9KtVsEI+AVmnIqyVpwKskqrScZAGqiIQyMJu0NQ8aGZKeSPKshNuZ0p5LcN0EluW84kdcW0lbLRTue2tONvuEEDp0ewtwyhGQRwRtXtePpvd1uX7QfJJKF6UkGrMV7O471j7vLIZVHxkdWKZUEcDuARwC8DOAHSqltInKbiNxmn+1BAPsA7AHwDQB/Zp8+E8CTIvISgOcB/FIp9XDG2wTGnTs4p7kaB3uGMv5QRETl4ITjRECqoIVmKqslQBNBUIrxTu+1N2dVR3T0DiegFMaMgANSW1PPrBsbkmfZ/cldZ84tJCmlcKhnCPPyDLfXrpiFhKHw6PYT6DozXJZKMgC8//L5iIV1/Oeju8py++NJ5Dh/ejKrOxuQNBVePna2INdHU5fVy0ql1IOwgrD3tDs9nysAHx7ncvsArM71oJQav8dnRUc9frixC0aevVVERMXkjH7jwr38mMqabKFrQZpuYf2c4q0kR0I4PWAtlJuokjwzrZLcUB1Ge0MMO8YJWKcH4+gfTWJuS36L7S7sbER7QwzfevoA4kmzbCG5tTaKD65bgK/8bg/edWk3rljSWpbj8LI2EylUJbkRgLXz3kVzmwpynTQ1/txxDxi3x2dlZyOG4gYe3noc7/7Gs+gdipf+4IiIJuBUktlukR/TVMFbuJc23QKwJlw40yQmDMl1Y3uSAeCC9nrsOH7uCLGDp613YOe35BduNU3wlotmu9smz2mqyut6CuFPrlqI82bW4bb/3Yid4/yspWSaCqZCwSrJHQ0xtNREOOHCR/wZkhXG7fFZbe9v/rkHtuLpvT3jbpdJRFQuTk8yt6XOj7PjXpAW7jn/VbS0SnLPoDXqLD0kN1aP35MMWIvb9pwcwGhybE/rwR5rrOq8PEMyALz3snnuO7jl6El21MXC+NYHL0VIF3zp4R1lOw4g9XteqJ5kEcF5s+o4K9lH/BmSMX6Pz0J7f/Nu+22op/Z0l/rQiIgmlGq3CEbAKzTvnGQzIJXk1HSL1Gm10ZBbSa5KC8mdTVZAHW876PPb65E0FfaeHBxz+sGeIYikLpuP2Y1VeP3ymdA1wewyVpIBoL2hCh9ctwC/2XES24+Wr3/X2TSoENMtHEtn1mH3yYHAvEj0O3+GZIVxe3x0TbBitlVNbq2N4Kk9Pe4W1kRE5ZZqt2AlOR9WT7LTblHuoykN5znMW0me11Lt7sSXvnBvzbwmPP7Jq3H+rPpzrmtZex0AYMfxscHxUM8Q2utj51Slc/V3b16Oe25eg2hoatdTCDe/cj5qIjq+9vjesh2D83seKlBPMmCF5KG4wXG3PuHbkDxRj88Vi1sxu7EKf3r1YhzpHcah05x2QUT+YHAzkSlRQWy3sH9M72L0JTPr3M/Tg62IYN4EC/Dmt9QgEtKw8eCZMacf6BnE3Cm0Wjhm1MVw9Xkzpnw9hdBQHcZ7XzkPv9x8FPu7BzNfoAiKU0m2tvtmy4U/+DIkA2rCV2a3X7MYv/3EVbhqaRsA4Om9PaU8MCKiCbntFgEJeIU2Zse9gNyHRtqOewCwZEat+3ku1d+QruGG1R34/vrD2Hoktfjr0OkhzM9zsoWffeiKBQjpGr5epmpyssA9yUDqBdKuE/6ZBR1kvgzJpgLCofEPTdME0ZCORW01mFEXdfuSB0aT+MYT+/Bn392IrjOsLhNR6blzktlukRfvwr2gTbfwtlssHhOSc3ua/uz1y9BSE8EnfvgS4kkTA6NJdA/EC1JJ9psZdTG8c80c/PiFLhzrK317QiJpPXaF2Jba0VAVxqz6GCvJPuHLkAyMv5mIl4hg3eJWPLO3Bz0Do7jprmfxTw++jAe3HMcPN3SV6CiJiFK4497UmMoKyEGck5y+416nvTgu1z7ihuow/unGldhxvB9ffWxParJF8/SrJAPArVcuhKmAbzyxv+S3nZpuUdh9G5ZywoVv+DIkT9aT7PXKRS3oGYzjD+98BrtO9OPeD6zBxXMb8dsdJ0twlJb/enQ37t90pGS3R0T+5YZkjoDLi7KnW2hBmpNs/1dJ3yDLablIn26Rjdcum4k3r+7AHb/b47ZdTGX8m5/Naa7GDRd24P+eP4gDJe5Ndl4MF3LhHgAsbK3Bge5BDibwAX+GZKisenzWLbZ229nXPYj/95YV+IPzZ+IPzp+BLUf6cPLsSLEPEwDwP88ewD/98mW+vUpE7mr3hKH4BJcHw7TbLYLUk+zuuDf29KV2b2q+EyneevFsJAyFh7YeBzB9QzIAfOw1S1EV1vH+e59H98BoyW7Xed4v1GYijnkt1RiMG+64Wyoff4ZkBYSy2HZ6dmMVlnfU47qVs/D2SzoBANecb628fWxn8TcaMUyF04NxnOwfxaPbTxT99ojI37wL9oIS8grJVMoaARegOcnjjYADgDdf2IGb1s5Bo73DXq6Wd1jjUp/c3Y3mmgjqYvldTyWY01yNez9wKY6fHcH/+8X2kt2u8/seKWBPMgB3keWh0+WZ2kEpvg3J2a4W/dmH1+G/b7rY3fd+WXs9ZtXHStJy0TsUd8f3fPe5Q0W/PSLyN28vMidc5M6aboGATbewPqa3WyzvaMDn37oKWhYFo/G01UUxsz6KpKmmdRXZcdHcJtz6qoX42aajWH/gNPqGE/j19hNFfUenmJVkADjQzSEE5ebPkJxluwVghWnvHxcRwTXnz8CTe7oRTxauBWJwNInB0eSY03rsHZEWtdXgqb3dONVfurd5iMh/vL3IbMHKnduTrKV6dac7c4J2i0JwqsnzyriNdCn92TWL0NEQw9/evw2f/vFm/PF3NuALD+8oWlBOFGEzEcDaGVGT1HbiVD7+DMlZLtybyB+cPwMDo0msP3C6IMdjmArrvvhbLP/cI/jMT7e4pzu9T++9bB6UAn61/XhBbo+IKtOYSjInXOTMnZMcoHYL5+fUi5CSV3RYu/JNtPnIdFMdCeGvr78ALx87i4e2HsfC1hp8/fF9+OHG4ky8ShZhMxEAiIQ0zG6qwkFullZ2/gzJmNors3WLWxAJaQVruRhOGOgdSqAqrOOHG7vQN5QAAPTYTfXrFrdifks1Ht7KkEwUZN4WgURQSqEFZPUkI2DTLcbvSS6EZU4lOQDtFo7rV7bjyqVtWNBag1/8xRVYO78Z//iL7ThRhMX8xdhMxDGvuQYHehiSy82fIVmpKb0yq46E8MqFLXj05cL0Iw3HDQDAm1d3IJ408cstxwAAPXYlubU2ijesbMfTe3uw5yRnGxIFlTcYs5KcO1NZvbl6gLaldv6b5Nt7PJkrlrTiprVz3B1qg0BEcO/Na/DgX7wK1ZEQvvj2VYgnTXzmp1sL3nbhbEtd6J5kwHphw3aL8vNnSMbUX5ldu2IWDvYMYdvRs1M+npGEFZLXzG/Ckhm1+MkL1ls3PYNxaAI0VoVx8yvno6k6jA99ewP6hhNTvk0iqjzeSjJDcu6cnmQ9QJXk1HSLwl93bTSEz791FVpqo4W/ch8L6RqqItbovAWtNfj/XrcUj758Aj/ffKygt+P0JBejkrygtQa9QwmcGeQYuHLyZ0ieYk8yALx++SzomuChrVP/pXBCcnUkhLde3IkNB8/gYM8gugfiaK6JQtMEsxpi+Np7L8HBniH8YP3hKd8mEVUebzBmu0Xu3DnJmiAo6x6dF1bp0y2ocD50xUKsntOIv/nZVhztLdz21anNRAr/2F3QbvWTbz3aV/Drpuz5NCQrREP5DVB3NNdE8MqFLXhwy/Epv8UybIfkWFjDWy7qgAjwkxeOoGdgFK21Efd8l85vxvKO+oIEcyKqPEm2W0yJqay3y2Nh3S1OTHfOmw/F6Ekmi64J/uudFyJpmPj4DzYV7HqLWUleYfeTb+5iSC4nf4Zk5LcVZ7o3rmrH/u7BKf8nc3qSq8I62huqsG5RK37yYhe6B0bR4gnJAHDdyna8cKgXx/oK92qViCqDdzYyt6bOndVuAbTURNw1H9OdOcFmIlRY81tr8JHXLMGz+07jUIEWxDm/78UIyQ3VYcxvqXa3Ff/OMwdwz5P7C347NDlfhmQAqI5MPSRft6odkZCGH78wtfEvbiXZPqa3Xjwbh08PY3NXH1pqxvZ6XbtiFgBw0kWA7Tzezz6ygOIIuKkx7Z7k5poIzo4kCzrr3q9S0y3KfCAB8Af2jrxP7+0uyPUVazMRx4rZDdjc1QelFP77t3vw5d/sDswmO37h25BcVYCQXB8L43XLZuKBl45iNJn/W3cjCesXIWa3gFy3sh3NNREkTXVOJXlRWy3Om1mHhxiSA+t99zyHOx/fW+7DoDJgJXlqnDnJzt/VM0PT/8Wm81+GPcnFt6itFjPqonhqb09Brs+ZbhEu8GYijlWdDTjSO4zn95/Gqf5R9A0n3MoylYZ/Q3IB2i0A4O2XdKJ3yNqeMl9Ob5wT3GNhHe+6dA4Aa/xbujesnIX1B07jZH/h5zJWkl9sPoqr/+V3gXvl2zecCMSTO50raXh33AvW//tCcOYkO+/QdQeg5cJwd9xjSC42EcHli1rwzN7ugoyDSxa5krxydiMA4N9+vcs97fe7TxXltmh8vg3JhWi3AIBXLWlDZ1MV/ueZg3lfh9Nu4Q3u73vlPNTHQlg6s+6c81+3sh1KAY9syz+YTwe7jvfjQM8QBtK2857OlFIYTZruuw8ULBwBNzUqrZLsbNg0nSmlWEUuocsXt6J7II4dx6e+p0Exe5IB4NL5TTh/Vh2e338anU1VWN5Rjyd2F6ZVhLLj25AcK1BI1jXBe14xD8/tP41dJ/L7pRjxTLdwtDdU4YW/eS1eu2zmOedfMqMWi9pq8EjAWy5G7H7CoKxSB4DRAP7MlJLgCLgpMe3A2FJjheTTJertH44b+NmLR6bUlpcvZ+wdlcbVS9sggim9u+xITbcozgMY0jX8ww0rAACvXNiCK5e24YWDZ3CyCLsH0vh8G5KrC9RuAQDvvHQOIiEN33wqv5WhqRFwY48pNMGrRxHBusWt2HS4NzC7Ro3HmQoyFA9OYBy1K8jDDMmBZHAE3JSUq93ie88fwkfv24R3fv3Zki+6dfqwqTRm1MdwydymgiyuTxgmdE2K2iqzdkEzvv6+S/CR1yzBO9bMgaEUvvvcoaLdHo3l25BciIV7juaaCN5+SSd+vPFIXq/ARuIGRIBoKPu7a+XsBgyMJrE/wNtKOtXUoXhw2i1G7ErUKNstAikxpt2C/wdyZZpWYKyvCiGkCXpKFFhfPNyLulgIm7t68y6m5MuZ6EGlc+2KWdh+7OyUR8ElDVWUjUTSvX75LHQ2VWNBaw2uOW8GvvvcwbK86xFEvg3JhepJdtz6qoVImibuyeMP4EjSRCyk5/RqcVVnIwBgS4AHgTvtFsOsJFNAGGPaLVhJzpVpz0kWuy/5dIl6kjcdPoMrFrdi3eJW/PiFIyV9B9A02ZNcaq9fbo1qvfvJfVO6noShEClSP/JEbnnVAnQPxPGlh3eW9HaDyrchOb21Yarmt9bgjas68J2nD+Y8dWI4buRc2V7UVoOqsB7o3XKccBykwOhUktmTHEyJMe0WrCTnyltVba6Jomew+O0W3QOjOHx6GBfNbcTbL+m0Rm4dOF3023UYdosJlc6c5mp84PL5+M4zB/GzF4/kfT1J0yzaZIuJXL6oFR+4fD7ueXI/HtzC3X2LzbchuToSKvh1fuy1SxE3TNzx2z05XW44YeQ8ki6ka1jWUY8tR3pzutx04rwdFMSe5BG+FRZIhqncRTzsSc6dsy01ALTWRtBdgkrypkO9AIAL5zThdctmoTYawo83Tm0DqlwoxRnJ5fDX112AtQua8YkfvpR3f3LCMCdcm1RMf33dBbhobiP+8kebse/UQMlvP0h8G5ILNSfZa0FrDd556Rx897lD2HMy+/9YIwkD0XDud9XK2Q3YeuRs4OYEO9xKcoBCshOOh+OsIgZR0lTupkOcbpE7Z1tqwFpLUorpFpsO90LXBCtnN6AqouO6lbPw4JZjJVtLYU23YEgutUhIwz03r8Gqzgbc/n8v5BWUE4ZCuAwvcCIhDXe8+2JEQhr+6FvrcbyP0y6KxbchOZZHKM3Gx1+7FFURHX//821ZDxMfyaOSDADLO+oxnDBwMKCL99zAGKDWA6eSPBqgn5lSkobpjq9kJTl33kkPLTVR9JRgusWmw704b2ad21L3tos7MRg38KsSzbnnwr3yqYuF8e0PrsVKOyj/KMd3EJJlqiQDQEdjFe65eQ16BuJ49zee5Vi4IvFlSNakeCNVWmuj+NhrluL3u7vxs03j9yIppcYE6HzaLQDg/Fn1AFCQoeWVKIgj4Jxe5CC9MKAUw1TuC/wEe5JzZno21mipjWAwbqBvOFG82zMVXjrciwvnNrqnXTq/GZ1NVfjhxsNFu12v0aSJSIn7WimlLhbGdz64Fq9YaLVe/Oeju7IuoCU87VXlcNHcJnzrjy7F8bMjePfdz+FU//TfobLUfBqSi3v9N18+H2vmNeFvf7YNXWfOHQHzuv94Av/t6VseSZh5LSRcMrMWmgQ3JDu7zg0HaAScs5lI0lQMSQGUMJT7gjoZ0DarqTDN1CK2Vy5qAQA8VMTFSfu6B9A/msSFcxrd0zRN8K5L5+CpPT3Yc7L4f7t7hxJorI4U/XZoYnWxML75gbV4+yWd+M9Hd+Oj923Kqk0waZhF220vW2vmN+ObH7gUR84M4713P8eKcoH5MiQXew97XRP8xzsvhALw8R+8NKZneHA0id0nB/CdZw64IWc4buQVkmNhHQtaa7Dj2NlCHXpFCWJV1TvVghMugseqJOvu55Qb5Wm3uGhOIxbPqMUPNhSvovuivWjvYk8lGQBuWjvX3oDqQNFu29E3HEdDVbjot0OTi4Q0/MvbV+GTrz8PD7x0FG/+ypPYdLh30sskDFXy6RbjecXCFtxz8xocPD2Ia//r9/jZi6UdYzid+TIkl6IPfk5zNT73pmV4fv9pfO2xVNX4WN8wAKB7II7fvHwSgN2TnOfc5vNn1WNnntthV7rUZiLBCYtOJRlIVdIpOBKGmVq4x3cScmZ4Fu6JCN6xphMvHOrNaaF1LjbZm4gsbK0dc3pLbRRvubADP36hK+fFgwnDzPrtesCpJDMk+4GI4MPXLMa3/2gtBkaTeOtXn8I/P/jyhAWPhGEipPkjRl2+uBW/+PMrMLuxCh+9bxNu/OpTJXknZLrzx6ObplSLGN5+SSfevLoD//qrXe68waO9I/YxwG3iH0kYiOWw257X+bPqcLBnCIOjwWk5AKy+bqeCHKjpFqwkB5phKoRDAl0TLtzLQ/oitrdcNBu6JkXrD950uBerOxuhjVOZufXKhRhNmrgnxw0n3vjlJ/HR+zZlHZT7hhmS/ebKpW145GNX4p2XzsVdT+zDtf/5BJ7b13PO+ZJl2ExkMotn1OH+D6/Df7xzNQ6fGcaNdzyNsyPF6+kPAv88uh6lCskigi+9fRUumdeEj923CS8cOuNWktcuaMbmrl4A9sK9fCvJ7dbivZcD1nKRMBScd3uC1G4xtpIcnJ+bLAlTQdc0hDThCLg8eOckA8CMuhiuOW8GfvLCkYJvzjIcN7DjeP+YfmSvxTPqcN2Kdnz76YN4eOsxxJOZb793KI6dJ/px/6aj+N9nD2Y8v1IKvcMJNFSxJ9lv6mNhfP6tK/F/t7wChlJ4513P4q9+snlMz285NhPJRNMEN17UiX9/x2r0jyYDvetvIfg0JJfutmJhHd94/xrMaojhj7+9AXtPDUIEWLeoFSf7R3F2JJH3dAsAuGReE0SAJ/d0F/jI/c0bjIPUbjG2klyckLTn5ABe9x+Pl2SGLOXGME2ENUFY11hJzoN3TrLjHWs6cap/FI/tPFXQ29pypA+GqSYMyQDwkdcsgSbAbf/7Aj75o5cyXqezSLu9IYZ/fnCHW3SZyEjCRDxpspLsY5cvbsUjH70St1yxAD/c0IWr/uUx/PuvdmJgNIm4oco2Ai6T1Z2NABDoXX8LwZeP7nhvfRVTc00Ef/em5egZjOPBLcfQVhvFebPqAAB7Tw5gJGEimmdIbq6JYFVnY8H/wPudd05wkNotvJXkYlXQtxzpxa4TA9gd0F53P0sa1gizkC7cljoP3jnJjmvOn4G2uij+97nMldlcbDp8BgDGjH9Lt3RmHdZ/9jX4k6sW4v5NR/Hk7smLHc4i7a+8+yKYSuHzD+6Y9Py9w9YLXS7c87fqSAiffeMyPPrxq/AHF8zAl3+7B1d96XfYd2qgLJuJZKOpJoK5zdXuO+KUH3+G5DIMVr94bhMAoOvMMNobq7BohrWQY7v9R28qOwBevbQNL3X1BqryN7aSHJx+7FL0JJ8dtu7PM0O595ptP3oW//PMgQIfETmSpkJY1xDSNCS4ujxn5jiV5LCu4T2vmIvHdp7C3ilswWuaY+ffbzrciznNVWitjU56uWhIx8desxTzWqrxt/dvxegkW87vPNGPpuowLp7bhD+5ahEeeOkont9/esLz99q/w40MyRVhfmsN7nj3xbj/w+tw8bwmVIV1rOxsKPdhTWhVZwMryVPky5Bcjs2HGqrDWNRWAwCY3RjD3OZqhDTB1iNOSM7/rrrm/BlQCvj97uBUk72tBsMBmvJQip7ks/bmCr1Dub/oum/9IXzugW0cD1QkScOErgnCrCTnzNrEafx3Et/zinmI6Bq+NYWRbB/7wSas+8Jv3dn4mw714sI5TVldNhbW8Q83rMC+7kF8/fGJF/LtON6P82bVQUTwp1ctQkdDDJ97YNuE4wCdkNzAdouKsnpOI77x/jV4/jOvwUdfs7TchzOhVZ0NONI7jO4S7Fw5XfkyJJdri86L7Gpye0MVwrqGeS3V2H7UehWWz5xkx6rZDWiri+KhLbnvDV+pnIBYFw0FazMRb5tJsUKyvVo5n0py73ACpgJXPBdJ0rTmpnK6Re6cIu94f//b6qJ4y0Ud+MGGw3lvlvDQ1uM42jeCG7/6NO5bfwhH+0Zw6fzsQjIAXLW0DdevasdXfrcHO8fZIMo0FXYd73d3Wq2K6PjM9cvw8rGzuPv34wfrPrvdopEL96gInBeB2SwipfH5NCSX53YvsnvT2htiAIBFbbXYetSuJOc53QKwKiNvWtWB3+44WdQtVv3ECYhNNZFALdwbTZqotv+vjBapgu60W+RTSXYqV0Fq/Sklw1QI2Qv3Kr3dYvvRs/jc/VtL9q6DYafkif7+f/iaxUiaCl9/IreRbI6m6jBWz2nEcNzAp368BUtm1OIda+bkdB1/96blqIuG8NH7Np3TdrHx0BkMxg1c0F7nnnbdyll4/fKZ+Ldf7cJjO0+eMxbOeT7gwj0qhjXzmnDDhR34z0d34+8e2BaY/FFIPg3J5UnJly1sga6Ju2hv0Yxa922yqVSSAeCGCzsQN0w8sjUY1eQRT0gO0gi4kYTh9hcWv5KcR0gezv+ylFnCXu0e0iq/3eKRbcfx7WcOluytWtMOkBPtuDqvpQZvuXA2vvvcQZzqz+2YlFI4M5TAZQua8V/vuhDzWqrxb+9YnfPf9ba6KL74tlV4+dhZfOanW93QmzBM/M3PtqK9IYbrV3W45xcR/PONK9FWF8UHvrkeH/jm+jGj5NyeZIZkKgJNE/zbH67Gza+ch28/cwDX/Otj+P7zh3La7CboGJI9FrXV4tm/ejWuWNwKAHjXpakqQzTPzUQcqzobML+lGve/dGRK11MpnJDcXB0O3HSLejskF60neQrtFn12OD49yIpCMRimiZAmCOkaEhXebtEzaAXRkzkG0nxN1m7huP0PFiOeNHHXE3tzuu7hhGGPWovg1RfMxOOfvAar7BFZuXrNspn4yKuX4Ecbu/ANu43ihxu6sON4P/7uzctRGw2NOX9LbRSPfvwq/PV15+PxXafwyR+95Fbne4cTCOsypYXhRJMJ6Rr+/oYV+PntV2BRWw0+/ZMteP+9z+Mff7EdGw5MvKiULKHMZym9cu7y2FaXWuk8r6UG//Ohtfj7n2/HorbaSS6VmYjgzRfOxn//djdOnh3BjPrYVA/V15yFe001ESRNhXjSRGSKLzQqwUjCcKtCRaskT6HdwgnWZ9huURRJQyGkadbCvQrfTKRnwPo/cqrEleTJ2u0WtNbghgtn43+ePYibL5+PzqbqrK7b+X/fVKCK7UdevQS7T/bj8w/twMLWWnz76QNY3lGP1y2bOe75qyI6br1yERKGwr88shOz6mP4q+suQO+QtZHIRNVzokJZMbsB9936Snzz6QP479/uxnP7T+OeJ/fj+lXtOG9mHR7fdQoNVWG89eLZuG5Fe8lH8fqVL1NLuSrJ43nVkjY8+vGrMKc5uz/Gk7nhwg4oBTzw0tECHJm/DbuV5MiYr6c7qyc5hIiuFW0zkXwryYap3MuezhCwE4aZ1Q5jNJazcC80DRbu9dgvpHJtbciXmUUlGQA++polCOsa3n/v81n31jsvChurC7NAznob+0Ks6GjAn/zvRuw80Y+bL5+fMez+2dWL8L7L5uHrT+zDvzyyA71DcbZaUMlomuBDVyzApr99HTb97Wtx+zWL8diOk/j3X+9C0lTYc3IAt//fi7jhjqcCNY1rMr4MyVNtbfCrRW21WDm7IRAh2duTDARnQ5GRhIFYWEMsrPluBFz/SMJ9SztTJfmzP92KD317fV7HF2TJMe0Wlf0io8euIJcuJDs9yZOfb15LDe65+VIcOTOMP/rm8xgczTw9x+nBb64p3BSJqoiO73xwLZbOrENrbRRvXt2R8TIigr9783LctHYO7vjdXjz68gnOSKayqI6E8InXn4en/+rV+P1fXoP7P7wOv/vE1fi3P1yN04NxvO+e5/Geu5/FS4d7y32oZeXLNDrVRXJ+dsOFHdjc1Yd9UxiKXwncSrL9pBSUDUVGkyaiIR2xsF6UkKyUwtkRp90ikdMCjF5P5TlTBW7zkT5sOtzLBR45SrrTLWTC2biVotSVZGW/ptCzeJt37YJmfOXdF2Pr0bP47M+2Zjx/odstHE01Efzsw5fjkY++KuvnLV2zFvP96dVW+wUryVRODVVh951yXRO87ZJO/PYTV+Fv37gMLx/rxw13PIU/++7GKW3kU8l8GZKnszeu6oDI9G+5cHuSi9yf6zdOJbkqUpyQPBQ3YJgKrbVRJE2F/iyqaI5ez/ifTNMtus4MoX8kOSZY0+QM09oMIzQNdtxLGKb72Je6kpxtu91rl83ELa9agJ9tOoI9Jyd/AnfedSlUu4VXNKSjJcOufelEBJ+69nz8+ztW40+uWlTwYyKaimhIxwevWIAn/vIafOTVS/D4zlN43X88gU/+8CU8su047nx8Lx7fdari3y3Lhi8X7k1nsxpieOXCFty/6Sg+8uol03bBhhMWqyPWf7GgtFu4leSQXpQXBk5P8byWanQPjKJ3MIH6WHaVKCco1ET0SSvJfcMJ9NvV6gM9g27LDE3OWajn7LiXqOCebm87TulDcvaX+eNXLcS3nz6Ar/x2N/7zXRdNeL4zg/4ctfbWizvLfQhEE6qNhvCx1y7F+145D3f8bg/+77lD+OHGLvf7nU1VeO9l87CsvR6mUuhorMLittppteiPIbkMbrxoNj75o8244ou/w5r5TTh/Vj0WttWgtTaKttooWmojqIlW9kNjhWQdHY1VAIBNh3uxZn5zmY+q+EYSBqJuT3LhQ5Iz2WJeczU2HjyD3uE45iK7RaVOZXBBW82ki/6cbXsB4NDpIXcnSpqcs1AvrAtm1sfw3P7TUEpV5Athp9UiEtJKNt3CyDAneTyttVH80boF+Npje7FucSv+cILNQc4MxVEXDSGs881Toly11kbxuTctx4evWYw9JwewZEYtNhw8gzsf34svPLRjzHk7GmJ40+oOXLm0DfNaqrOeQFNKSil86+kDeM0FMzMOZajsJFah3n5JJyIhDb/cfAwbDpzB/ZvObb2oCutorYugpSaK1tooWmsj7seZ9THMbanGvJaac2Zy+sVw3EBVWMfiGbW4eG4jvvvcIXzoigUVGRiypZQqek+yU0me22L9Yucy4cKpJC9orcXjO09OeL6uM8Pu5wd7hiY8H42VtNsrdE3DitkN+O5zh3D49LD7WFUSZ/zbkhm1Jfs/kM2c5PF8/LVLsbmrF5/+yRY8s7cHf3XdBWNGeQLW//3GGn9VkYkqjZVBrN+t1y+fhdcvn4XjfSM4dHoIIsD+7kE8vPU47nlyv7sz5vKOerxpdQfWLWpFZ1MVaqKhso+DPdgzhL//+Xbs7x7EP9ywYtLz+jNhTXMighsunI0bLpwNAOgbSuDQ6SF0D46iu38UPYPx1MeBURzpHcZLXb04PRg/ZzHQ7MYqXNBejwva63BBez3On1WHeS01WS1+KaaRpOkOyH/vZfPw8R+8hC8+vBOrOxswu6kKHY1VaKqOlP04C2nUfnvd6UnefWIA3QOj7h+VTYd7EdIEK2Y35H0bfUOpdgsgtwkXTk/ygpZq/HwkiYRhjltZO3zaCkW10RAO9AzmfaxB4+ywF9YFyzvqAQBbj/ZVZki2NxI5f1Y9th09i6F40m2dKpZ82i0AIKxruPO9l+A/H92N/332IDYeOoP//dArxlSITg8l3HGURFQ4sxpimNVg7ftw6fxmvGPNHJwZjOPlY2ex/dhZ/HzzsTHV5rAuWDm7AZfOb8aa+c1YM6+p5C196+1NVJ7a053xvFn91RORawH8FwAdwN1KqS+kfV/s718HYAjAB5RSL2RzWQIaqsNYWZ05OJmmQu9wAkd7h3Ho9BD2dw9ix/F+7Dh2Fr/bedIN0FVhHUtm1mLpzDp0NFYhnjRRXxVCS00ETdURtNZF0d4QQ1ttFN0DcQyMJnGyfwT7TlmBSBNBQ1UYsxqimFEXw4z6KKKh1MptpRSO9o3AMBREYP8TxJMmjpwZxsBoAtuO9iFqh+TrVrbjnif3487Hx+6SJQLUx8Joqg6jvaEKyzvqsayjHvNaqtHRWIUZdbGKCtFOSI6GdNy0di7+/Hsv4k3//SQe/uiVqArruOXbGxDWBY9/8pq8X0k7leRFbbUQAXYe78/6sr1DCdTFQm6VrXcocU7FDbAqyTURHcs76nGIleSsGW4lWbB0Zh1CmmDrkT5ct7K9zEeWu267knxBe531dX8cc1uKHZKtj/nMya+LhfE3b1yG61a244PfWo+3fe1pfOdDa3H+LOvFSu9QHE0MyUQl0VQTweWLW3H54lbc8qqF6DozhJcO9+Fk/wiOnx3BxgNn8M2nDrjV5iUzarFmfjMund+ES+c3o7OpqqjvOjshee+pQRzvG5n0vBn/6omIDuAOAK8F0AVgvYg8oJTa7jnbGwAssf+9AsDXALwiy8tSljRN0FwTQXNN5Jxq5EjCwJ6TA9h+7CxePnYWu08M4PFdp3Cqf9Ta2GCKK+2bayKYUWf1Sx/rHcG+7skrjPWxED557fkArJF+v/yLV6FvOIGuM0M4cmYYR3uHcXoogd6hOM7YlfT/efagGzQBK2zMqo+hozGGjsYqtNVG0VwbQUuN1YbifN5cE0FtNFT2Vo5Ru70iFtbw+uWz8L0/vgxvv/NpfPWxPVg5uwHddm/n/ZuOTNg7mYkzI7mzqRqvPn8G7lt/GH/x6iVZjZ/qG06gsTrsvmrfe2pgwpDc2VSNeS3V+O0Ofw6UN02Fn206gssXtbpVjHJzfsfCmoaY3Wq07ejZMh9VfnoGrL8bzk6jXb1DRa+IO1s1T+XX+JJ5Tfjhba/E++55Du+48xnc+4FLsWZ+M84MxbGwtaZAR0pEuehsOrc3eSRhYHNXH9YfOI0NB07jF5uP4nvPHwIAzG2uxtoFzehorEK7Xalub4ihoSqMqrCOhqrwlJ7vNxw4gznNVTh8ehhP7528mpxNaWAtgD1KqX0AICLfB3ADAG/QvQHAd5Q1VPVZEWkUkXYA87O4LBVALKxjxeyGc8KzYSrommAonsTpwThOD8Zxqn8Ux/pGcLJ/FG21ETRUR1AfC+G8WXXQNYFpWgtdTpwdsf+Nup+fGUpgdlMVbr58PmqjIZhKQcGqLoc0DbObqtxKdvrbsw1VYTRUNWB5x/hV86RhYn/3ILp6rRB9tHcYx3pHcKR3GC8cOoPu/viEEyMiIc0NzM01EdRErL4n95+uYTRpQCnrvtI1wdnhBM7Ys4ZjER1VYR3RkIbhuIH+0SSUUqiOhFAT1VETCaE6GkJYEzgvN8K6c92CSEhzJ0I4VfdL5jXhxotm45tPHUBHQwydTVWoi4XxpUd2Ys+pAbTURFAVCaE6rCMS0saEA8HYPwDO91441AsAqIuF8MF1C/Dul5/DFx7agUvnN2cMF3tODqCpOoJ1i1oxsz6Kv/zRZnz8tUuhaQJNrAqeANh1oh9L7XcifrChC//4i+1YPafRPSIR6/isj1MLNfn6xeZj+MXmY2iri+L2axajLhZCVVgvy7E4TtnVV+fdjxWzG/DoyyfwwEtHEdEr5x0RANhypM99MV4XC+Gvf7IFH3vtUmgi1v8TcdoiCvdzOS8ip7rj6tKZdfjRbZfj/fc+j3d8/RlcNLcJJ/pGizL+jYjyEwvrWLugGWsXWAv6DVNh14l+PL//NB7fdQpP7DqFUwOjGG9Uf1VYTxXP6qJoqo6gLhaCQKCgoIu1oVNYF4R1DWFdc9u4EoaJfd2D+NS15+PrT+zFXXY1eyLZhOTZAA57vu6CVS3OdJ7ZWV4WACAitwK4FQDmzp2bxWFRNpwn7OpICNWRUNYrTWc1xHBBe30xD+0cIV3Dkpl1WDKzbsLzDMcN9AyOomfACvw9g3H0DIye8/nxvhF3a+W4/TEa1iGwXsEmTYWGqjAaqyPQxJrjPBI33F7qulgImgiGEwYGRpMYHE1iKMsxdjM81dlPXXs+jvYO48VDvfjM9Rdg5ewG/NMvX8bdv9+f92YTM+qiCOsaXrmoBZcvasG3nj6Abz19IKvLvmHFLDTVRPDV91yCd3/jWXz0vk3jnu/6Ve1472XzsPfUIO55cn9ex1lst1yxAI9sP47PPbCt3IcyhrOBzquWtOJHG7vwF997scxHlJ8185rQVhfFt/5oLd5/z3P4yPc3leR2mwqwwG5OczV+/KeX49tPH8Dvd5/C3JZqXLG4tQBHR0TFoGtir6+qx82XzwdgBdqT/aM43jeMo70j7nPx8b4RHO0bxpEzw9h3ahC9Q3EM5jBmVgS4amkb2hti+NLDOyY/b6YdtUTkDwG8Xil1i/31+wCsVUr9uec8vwTweaXUk/bXvwHwlwAWZrrseNasWaM2bNiQ4cckKi3DVDBM5XlFqlIh3A7iugjmNJ/bT5U+Csw0FYYTBgbjSQzHDcQ9bSbpv5Hpv6JtdVE3iJmmwqmB0aw2/VBQmNdcg6qIVenuHbJeaJjKWjTlbgsMwcK2GndR39HeYQzFk+5xKPuYFNS4r/JLoS5mveBLGiZOD8YxFDd8sWFNNKRhQWuN+1ifHUngeN9IRe6+N7upyp3B3TeUsKs61jtHplIwizAGOhrWsNBz/xERZcP5Gyuw/j4lDIWEaSKRNJEwFJTnmTUW0t22Q8NUCOnaRqXUmvGuN5tKchcAbwNlJ4D0mWUTnSeSxWWJKoKuyZiFhCEdbuDMJP1JX9MENdHQlOdha5o1k3dmfe59uY3VkazegnZmXftRSNcwI4+fvVTqY+GsN3vxs4bqMBp8thEHEZHD+9ysQaznZ2R+fs40HCCbJfbrASwRkQUiEgHwLgAPpJ3nAQDvF8tlAPqUUseyvCwRERERka9kLGMppZIicjuAR2CNcbtXKbVNRG6zv38ngAdhjX/bA2sE3B9Ndtmi/CRERERERAWSsSe5HNiTTERERETFJiIT9iRzI3siIiIiojQMyUREREREaRiSiYiIiIjSMCQTEREREaVhSCYiIiIiSsOQTERERESUhiGZiIiIiCgNQzIRERERURqGZCIiIiKiNAzJRERERERpGJKJiIiIiNIwJBMRERERpWFIJiIiIiJKw5BMRERERJRGlFLlPoZziEg/gJ3lPo40DQD6yn0Q8M9xpGsF0F3ug0jj1/vKj8flx8cP8Od9BfjzuPz6GAL+u7/8djxefn0c/XKf+eU40vnxcfPrfeW34zpPKVU33jdCpT6SLO1USq0p90F4ichdSqlbeRzjE5ENfMyy48fj8uPjB/jzvgL8eVx+fQwB/91ffjseL78+jn65z/xyHOn8+Lj5+L7y1XGJyIaJvsd2i+z9vNwHYPPLcVQCv95Xfj0uP/LrfeXX4/Irv91ffjueSuCX+8wvx1EJ/Hpf+fW4zuHXdgvfvSKjyfExq2x8/CofH8PpgY9jZeLjVrkme+z8Wkm+q9wHQDnjY1bZ+PhVPj6G0wMfx8rEx61yTfjY+bKSTERERERUTn6tJBMRERERlQ1DMhERERFRGoZkyomI3CgiSkTOL/exUP5EZCDD9x8TES5C8RkR6RSR+0Vkt4jsFZH/EpHIJOf/qIhUl/IYKbNMv3/kX3wODBaGZMrVTQCeBPCuXC4kInpxDocoGEREAPwEwM+UUksALAVQC+CfJrnYRwEwJBMVDp8DA4QhmbImIrUA1gH4EOw/ECJytYg8ISI/FZHtInKniGj29wZE5B9E5DkAryzfkdN47MfuF56vvyIiHyjjIdHk/gDAiFLqmwCglDIAfAzAB0WkRkT+VUS2iMhmEflzEfkLAB0AficivyvjcdM4RKRWRH4jIi/Yj9sN9unzReRlEfmGiGwTkV+JSFW5j5f4HBhEDMmUi7cAeFgptQvAaRG52D59LYD/D8BKAIsAvNU+vQbAVqXUK5RST5b6YImmmeUANnpPUEqdBXAIwC0AFgC4SCm1CsB3lVJfBnAUwDVKqWtKfbCU0QiAG5VSFwO4BsC/2e8WAMASAHcopZYD6AXwtvIcIqV5C/gcGCgMyZSLmwB83/78+/bXAPC8UmqfXdn6HoAr7NMNAD8u7SESTVsCYLyZnQLgSgB3KqWSAKCUOl3KA6O8CIB/FpHNAB4FMBvATPt7+5VSm+zPNwKYX/Kjo/HwOTBgQuU+AKoMItIC6+3eFSKiAOiwnrAfxLlP3M7XI/YfDfKnJMa+UI6V60AoK9uQVlEUkXoAcwDsw/gBmvzrPQDaAFyilEqIyAGkfgdHPeczALDdosz4HBhMrCRTtt4O4DtKqXlKqflKqTkA9sN6xbxWRBbYfVjvhLWogfzvIIBlIhIVkQYAry73AdGkfgOgWkTeD7gLgf4NwLcA/ArAbSISsr/XbF+mH0Bd6Q+VstAA4KQdkK8BMK/cB0ST4nNgADEkU7ZuAvDTtNN+DODdAJ4B8AUAW2H90Ug/H/mIHaRGlVKHAfwAwGYA3wXwYlkPjCalrO1RbwTwhyKyG8AuWH2tfw3gbli9yZtF5CVYv5eAtd3qQ1y45x/O7x+s37k1IrIBVlV5R1kPjDLhc2AAcVtqmhIRuRrAJ5RSbyzzoVCWRGQ1gG8opdaW+1iIgoa/f9MLnwOnN1aSiQJERG6DtbDks+U+FqKg4e8fUWVhJZmIiIiIKA0ryUREREREaRiSiYiIikRE7hWRkyKy1XPaahF5xt5p7+f2KD/ne6vs722zvx8TkWoR+aWI7LBP/0J5fhqiYGFIJiIiKp5vAbg27bS7AXxaKbUS1iSETwLu5Iv/BXCbvdve1QAS9mX+VSl1PoCLAKwTkTcU/9CJgo0hmYiIqEiUUk8ASN8B8TwAT9if/xqpTWJeB2CzUuol+7I9SilDKTWklPqdfVocwAsAOot+8EQBx5BMRERUWlsBvNn+/A9h7ZoIAEsBKBF5REReEJG/TL+giDQCeBOszWWIqIgYkomIiErrgwA+LCIbYe2IGLdPD8Hawe099scbRcTdCdNux/gegC8rpfaV9pCJgidU7gMgIiIKEqXUDlitFRCRpQCut7/VBeBxpVS3/b0HAVyMVNX4LgC7lVL/WdIDJgooVpKJiIhKSERm2B81WBuL3Gl/6xEAq+xpFiEAVwHYbp/3/wFoAPDRkh8wUUAxJBMRERWJiHwPwDMAzhORLhH5EICbRGQXgB0AjgL4JgAopc4A+HcA6wFsAvCCUuqXItIJ4DMAlgF4QUQ2icgtpf9piIKFO+4REREREaVhJZmIiIiIKA1DMhERERFRGoZkIiIiIqI0DMlERERERGkYkomIiIiI0jAkExERERGlYUgmIiIiIkrz/wNVKaSAUWKb6wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "simulated_discharge.plot(figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning up\n",
    "\n",
    "Models usually perform some \"wrap up tasks\" at the end of a model run, such as writing the last outputs to disk and releasing memory. In the case of eWaterCycle, another important teardown task is destroying the container in which the model was running. This can free up a lot of resources on your system. Therefore it is good practice to always call `finalize()` when you're done with an experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.finalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "01ca17e9ced11a2af392a5b687ce703e247d5832196a25892ccc3acd549c16f5"
  },
  "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}