{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "ZS2siGtqyq-f"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import os\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paths for input and output\n",
    "path_in = \"./../data/\"\n",
    "path_out = \"./../data/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "6SyijUrB4Y8t"
   },
   "outputs": [],
   "source": [
    "# Create dictionary for mapping of VehId to Vehicle_Type\n",
    "df_IceHev = pd.read_excel (path_in+'/VED_Static_Data_ICE&HEV.xlsx')\n",
    "df_IceHev.rename(columns={'Vehicle Type': 'Vehicle_Type'}, inplace=True)\n",
    "df_PhevEv = pd.read_excel (path_in+'/VED_Static_Data_PHEV&EV.xlsx')\n",
    "df_PhevEv.rename(columns={'EngineType': 'Vehicle_Type'}, inplace=True)\n",
    "df_VehId = df_IceHev.append(df_PhevEv, ignore_index = True)\n",
    "df_VehId.sort_values(by='VehId', inplace=True)\n",
    "df_VehId = df_VehId.set_index('VehId')\n",
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
    "dictVehId = df_VehId.to_dict('index')\n",
    "#dictVehId[12]['Vehicle_Type']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7KE0rghhp9YV"
   },
   "source": [
    "# Energy Consumption Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "LgwTfDyYarGs",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def calculateEnergyConsumption(df):\n",
    "    df_org = df.copy()\n",
    "    column_name = 'Energy_Consumption'\n",
    "    if column_name not in df.columns:\n",
    "        #print(\"Create column\", column_name)\n",
    "        df[column_name] = \"\"\n",
    "\n",
    "    arr_VehId = df[[\"VehId\" , \"Vehicle Speed[km/h]\", \"MAF[g/sec]\", \"Short Term Fuel Trim Bank 1[%]\", \"Long Term Fuel Trim Bank 1[%]\", \"HV Battery Current[A]\", \"HV Battery Voltage[V]\", column_name]].to_numpy()\n",
    "    list_Energy_Consumption = []\n",
    "    for row in arr_VehId:\n",
    "        vehicle_type = dictVehId[row[0]]['Vehicle_Type']\n",
    "        if vehicle_type == \"ICE\" or vehicle_type == \"HEV\":\n",
    "            '''\n",
    "            The formula to calculate the energy consumption is as following:\n",
    "\n",
    "            Energy consumption=DrivingLength/100*MassAirFlow*(1+ShortTermFuelTrimBank1/100+LongTermFuelTrimBank1/100)/AirFuelRation\n",
    "\n",
    "            where the Mass Air Flow is measured by gallon per second, Driving Length is measured by km, and the Air Fuel \n",
    "            Ration is provided with the value of 14.08 according to a literature:\n",
    "\n",
    "            G. Oh, D. J. Leblanc and H. Peng, \"Vehicle Energy Dataset (VED), A Large-Scale Dataset for Vehicle Energy \n",
    "            Consumption Research,\" in IEEE Transactions on Intelligent Transportation Systems, \n",
    "            doi: 10.1109/TITS.2020.3035596.\n",
    "            '''\n",
    "            #print(\"ICE or HEV found\")\n",
    "            # calculate energy consumption\n",
    "                # /3600 as sampling rate is 1 sec ; value from \"Vehicle Speed[km/h]\"\n",
    "                # The reason is as following:\n",
    "                # row[1] is measured by km/h, which is treated to be the constant value during the sampling time of one second.\n",
    "                # So, if we need to calculate the length during each sampling time, we need to multiple the speed (measured by km/h) by the duration (measured by h), which lead to:\n",
    "                # row[1]*1/60/60=row[1]/3600\n",
    "            DrivingLength = row[1] / 3600        # row[1] = Vehicle Speed[km/h]\n",
    "            MassAirFlow =   row[2] * 3.78541     # recalculation from gallons to liters; value from \"MAF[g/sec]\"\n",
    "            ShortTermFuelTrimBank1 = row[3]      # value from \"Short Term Fuel Trim Bank 1[%]\"\n",
    "            LongTermFuelTrimBank1 = row[4]       # value from \"Long Term Fuel Trim Bank 1[%]\"\n",
    "            AirFuelRation = 14.08\n",
    "            #print(\"Length=\", DrivingLength, \"MassAirFlow=\", MassAirFlow, \"ShortTermFuelTrimBank1=\", ShortTermFuelTrimBank1, \"LongTermFuelTrimBank1=\", LongTermFuelTrimBank1)\n",
    "            in_liter_per_hour=DrivingLength/100*MassAirFlow*(1+ShortTermFuelTrimBank1/100+LongTermFuelTrimBank1/100)/AirFuelRation\n",
    "            # recalcualte energy consuption into kWh ; 1 kw*h = 0.1123 liter\n",
    "            in_kilo_watts_perHour = in_liter_per_hour / 0.1123\n",
    "            #if not math.isnan(in_kilo_watts_perHour):\n",
    "            #  print(\"EC =\", in_kilo_watts_perHour, \"kWh (\", in_liter_per_hour, \")\" )\n",
    "            list_Energy_Consumption.append(in_kilo_watts_perHour)\n",
    "\n",
    "        elif vehicle_type == \"PHEV\" or vehicle_type == \"EV\":\n",
    "            '''\n",
    "            The energy consumption for EV can be calculated by integrating the voltage and current of the EV battery over \n",
    "            time, and this energy consumption can be measured by kw*h.\n",
    "\n",
    "            The convert between EV energy consumption (kw*h) and gas consumption (measured by liter) can be achieved as \n",
    "            following:\n",
    "\n",
    "            1 kw*h = 0.1123 liter\n",
    "\n",
    "            The EV energy consumption calculation can be referred from: \n",
    "            https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiY3Jzc3K70AhVpo4sKHTLUA9gQFnoECAYQAQ&url=https%3A%2F%2Fwww.mdpi.com%2F1996-1073%2F8%2F8%2F8573%2Fpdf&usg=AOvVaw3LyboWSueHI9UM0mJe5ypq\n",
    "\n",
    "            The convert from kw*h to liter can be referred from: \n",
    "            https://en.wikipedia.org/wiki/Miles_per_gallon_gasoline_equivalent#Description\n",
    "            '''\n",
    "            #print(\"PHEV or EV found\")\n",
    "            # calculate energy consumption\n",
    "            in_watts = row[5] * row[6] * (-1) # = \"HV Battery Current[A]\" * \"HV Battery Voltage[V]\", *(-1) EV formula needs to interpreted in that way that the consumed energy is negative while the produced one is positive\n",
    "            in_kilo_watts = in_watts / 1000\n",
    "            # recalcualte energy consuption into kWh\n",
    "            # measurement of A in 1 sec ; V in 5 sec => Therefore one line covers the smaller unit 1 second\n",
    "            #print(\"HV Battery Current[A]\", row[5], \"HV Battery Voltage[V]\", row[6])\n",
    "            in_kilo_watts_perHour = in_kilo_watts / 3600\n",
    "            #print(\"EC =\", in_kilo_watts_perHour, \"kWh (\", in_kilo_watts, in_watts, \")\" )\n",
    "            list_Energy_Consumption.append(in_kilo_watts_perHour)\n",
    "\n",
    "        else:\n",
    "            print(\"Unknown Vehicle_Type\", row[0], \"found\")\n",
    "\n",
    "    #df['Energy_Consumption'] = list_Energy_Consumption\n",
    "    df_org['Energy_Consumption'] = list_Energy_Consumption\n",
    "    return df_org"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j0syPBlrjmrd"
   },
   "source": [
    "# Reworking of all original files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_files = []    \n",
    "for file in os.listdir(path_in):\n",
    "    if file.endswith(\"_week.csv\"):\n",
    "        list_files.append(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time =  VED_171101_week.csv : 9.888816118240356\n",
      "Execution time =  VED_171108_week.csv : 9.541045904159546\n",
      "Execution time =  VED_171115_week.csv : 10.846029043197632\n",
      "Execution time =  VED_171122_week.csv : 8.577940940856934\n",
      "Execution time =  VED_171129_week.csv : 9.552509069442749\n",
      "Execution time =  VED_171206_week.csv : 10.801214933395386\n",
      "Execution time =  VED_171213_week.csv : 12.398997068405151\n",
      "Execution time =  VED_171220_week.csv : 9.741503953933716\n",
      "Execution time =  VED_171227_week.csv : 5.659049987792969\n",
      "Execution time =  VED_180103_week.csv : 7.128296136856079\n",
      "Execution time =  VED_180110_week.csv : 10.233113050460815\n",
      "Execution time =  VED_180117_week.csv : 9.690773963928223\n",
      "Execution time =  VED_180124_week.csv : 8.43485403060913\n",
      "Execution time =  VED_180131_week.csv : 8.949444055557251\n",
      "Execution time =  VED_180207_week.csv : 7.7250587940216064\n",
      "Execution time =  VED_180214_week.csv : 7.392001152038574\n",
      "Execution time =  VED_180221_week.csv : 5.865344047546387\n",
      "Execution time =  VED_180228_week.csv : 7.966830015182495\n",
      "Execution time =  VED_180307_week.csv : 7.3129050731658936\n",
      "Execution time =  VED_180314_week.csv : 7.454961061477661\n",
      "Execution time =  VED_180321_week.csv : 7.334964990615845\n",
      "Execution time =  VED_180328_week.csv : 6.493051052093506\n",
      "Execution time =  VED_180404_week.csv : 8.315284013748169\n",
      "Execution time =  VED_180411_week.csv : 7.657871961593628\n",
      "Execution time =  VED_180418_week.csv : 8.63765001296997\n",
      "Execution time =  VED_180425_week.csv : 7.897945880889893\n",
      "Execution time =  VED_180502_week.csv : 7.535109996795654\n",
      "Execution time =  VED_180509_week.csv : 7.370058059692383\n",
      "Execution time =  VED_180516_week.csv : 8.234884977340698\n",
      "Execution time =  VED_180523_week.csv : 7.743973970413208\n",
      "Execution time =  VED_180530_week.csv : 7.505343914031982\n",
      "Execution time =  VED_180606_week.csv : 7.749966144561768\n",
      "Execution time =  VED_180613_week.csv : 6.840182065963745\n",
      "Execution time =  VED_180620_week.csv : 9.27295184135437\n",
      "Execution time =  VED_180627_week.csv : 10.812565088272095\n",
      "Execution time =  VED_180704_week.csv : 6.055830001831055\n",
      "Execution time =  VED_180711_week.csv : 7.213330984115601\n",
      "Execution time =  VED_180718_week.csv : 7.271965980529785\n",
      "Execution time =  VED_180725_week.csv : 5.97207498550415\n",
      "Execution time =  VED_180801_week.csv : 6.417394161224365\n",
      "Execution time =  VED_180808_week.csv : 6.360198974609375\n",
      "Execution time =  VED_180815_week.csv : 5.3010618686676025\n",
      "Execution time =  VED_180822_week.csv : 5.262967109680176\n",
      "Execution time =  VED_180829_week.csv : 6.512995958328247\n",
      "Execution time =  VED_180905_week.csv : 9.022238969802856\n",
      "Execution time =  VED_180912_week.csv : 7.027529001235962\n",
      "Execution time =  VED_180919_week.csv : 6.726388931274414\n",
      "Execution time =  VED_180926_week.csv : 6.687731027603149\n",
      "Execution time =  VED_181003_week.csv : 6.175661087036133\n",
      "Execution time =  VED_181010_week.csv : 5.615024089813232\n",
      "Execution time =  VED_181017_week.csv : 7.1177568435668945\n",
      "Execution time =  VED_181024_week.csv : 7.9306700229644775\n",
      "Execution time =  VED_181031_week.csv : 6.453009128570557\n",
      "Execution time =  VED_181107_week.csv : 2.064950942993164\n",
      "Execution time overall =  415.75856614112854\n"
     ]
    }
   ],
   "source": [
    "time_overall_start = datetime.datetime.now().timestamp()\n",
    "for file in list_files:\n",
    "    time_start = datetime.datetime.now().timestamp()\n",
    "    df = pd.read_csv (path_in + file)\n",
    "    #print(df.info())\n",
    "    df = calculateEnergyConsumption(df)\n",
    "    #print(df.Energy_Consumption.describe())\n",
    "    #print(df.info())\n",
    "    df.to_csv(path_out + file, index=False, na_rep='nan')\n",
    "    time_end = datetime.datetime.now().timestamp()\n",
    "    print(\"Execution time = \", file, \":\", (time_end-time_start))\n",
    "    \n",
    "    #break\n",
    "    \n",
    "time_overall_end = datetime.datetime.now().timestamp()\n",
    "print(\"Execution time overall = \", (time_overall_end-time_overall_start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "M9tmmfISv6tM"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of generated Energy Consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def boxplot_EC_perVehicleType(df, surpress=False):\n",
    "    arr_VehId = df[[\"VehId\" , \"Energy_Consumption\"]].to_numpy()\n",
    "    list_EC_ICE = []\n",
    "    list_EC_EV = []\n",
    "    for row in arr_VehId:\n",
    "        vehicle_type = dictVehId[row[0]]['Vehicle_Type']\n",
    "        if vehicle_type == \"ICE\" or vehicle_type == \"HEV\":\n",
    "            if not math.isnan(row[1]):\n",
    "                #print(vehicle_type, row[1])\n",
    "                list_EC_ICE.append(row[1])\n",
    "        elif vehicle_type == \"PHEV\" or vehicle_type == \"EV\":\n",
    "            if not math.isnan(row[1]):\n",
    "                #print(vehicle_type, row[1])\n",
    "                list_EC_EV.append(row[1])\n",
    "        else:\n",
    "            print(\"Unknown Vehicle_Type\", row[0], \"found\")\n",
    "            \n",
    "    # Creating plot\n",
    "    if not surpress:\n",
    "        fig = plt.figure(figsize =(10, 7))\n",
    "        plt.subplot(1, 2, 1)\n",
    "        plt.title('EC for ICE/HEV vehicles')\n",
    "        plt.boxplot(list_EC_ICE)\n",
    "\n",
    "        plt.subplot(1, 2, 2)\n",
    "        plt.title('EC for PHEV/EV vehicles')\n",
    "        plt.boxplot(list_EC_EV)\n",
    "\n",
    "        # show plot\n",
    "        plt.show()\n",
    "    \n",
    "    avg_ICE = (sum(list_EC_ICE) / len(list_EC_ICE))\n",
    "    avg_EC  = (sum(list_EC_EV) / len(list_EC_EV))\n",
    "    if not surpress:\n",
    "        print(\"Average EC ICE:\", avg_ICE)\n",
    "        print(\"Average EC EV:\", avg_EC)\n",
    "    \n",
    "    return [avg_ICE, avg_EC]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average EC ICE: 0.0032574152263156668\n",
      "Average EC EV: 0.0011408219467592066\n"
     ]
    }
   ],
   "source": [
    "list_values = []\n",
    "df_EC171101 = pd.read_csv (path_out+'VED_171101_week.csv')\n",
    "list_values.append(boxplot_EC_perVehicleType(df_EC171101))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for file in os.listdir(path_out):\n",
    "    if file.endswith(\"_week.csv\"):\n",
    "        df_EC171115 = pd.read_csv (path_out+file)\n",
    "        list_values.append(boxplot_EC_perVehicleType(df_EC171115, True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aeXPLES4Y3Z++UWEADI6NIEjgUACDPvZb4cTOZVliI8NI79+bjcZZ36EYodINOFxSxYzffcJv39sd0POsswTBxKRYAJLjo5ps4b5QUFZNTlElV03Wuac9zQRWUFZNVkEFM4fFM35wDIXlNcZZb7FybyHFlbVcZ5m+AMJCgkiMieBgADWVrPwKBsWE07tXczdxRmpfNh880aYHo+5McHBws/yTRx55hF/84hc89NBDzdpt2bKF0aNH+/XcRqh0cRoaFQ+8soWiilqeXp3DS+sPBuxca7OLyEjtS1iI/rdIabKF+/aEuf6AfjK8ISOJ8NAg9vQwofJFjvYXzRzWj/GDYwDYlme0FYDXNuXRr3cY545IaLbfofUGin0F5Qx3Mn05yEiJo6y6vsk8dqYQERHRrKbXgw8+yMKFC3nllVeatVu6dCk33nijX89thEoXZ/GqbDIPnuAP10/kvBEJ/OzNnXyeVeT38xSW17Avv6LJnwKQatnCffWrrMsppnevEMYPjmHEgGj25vu3SmtnszarmD7hIYwZ1Icxg/oQJMZZD3CispZP9uRz1aTBhAY3v7WkxEdyKECO+sZGRZZTOLEzGanar2JMYDBy5EhiY2NZv359075ly5axYMECv57HLNLVhdlxpJQ/Ld/HpRMSuXbKYC4aO4Br/7GWb/1nE/+7ZxZDE/xX1ntd09N3fNO+QbHhhAQJB32MAFt/oISM1L6EBAcxckA0K/YW+G2eXYG1OUWcPSye4CAhMiyE4f17s8MIFd7aepS6BtUU9eXMkLhIiitrqaipb2Wiai9HTp6iuq7RpVBJjoukX+9ebDp4gq/PSPHreW3x/oNw3M91tgaOh/mPeGxy6tSpZiXtH3roIb761a+ycOFCli5dyvTp01m3bh3x8fGkp6f7dXpGU+miVNc18J1XttCvdy8evmocIkKf8FCevWUqIcFB3P5CJier/FdOfG12MdHhIYwd1KdpX0hwEIP7RvikqRRV1JBVUMH0NC2cRg6MpqiilqKK7lGD6ejJUx4/18MlVRwuOdVMoxs3OIbtR0rP+EWhXtucx9hBfRid2KfVsZQ4KwIsANqKw0mfPqC1UBERpqb2PeM0lZbmr69+9asALFiwgFdffZXGxkaWLl3KwoUL/X5uo6l0UR55fw9ZBRX85/bpxEaGNe0fEhfJU984i689s567X9rMC7dNa2VqaAtfZBcxPS2OkBZjJcf5Zgtfb4USTx+qy7yMGqhvMHuPl9NveK92zzOQKKVY8PQ6BvYJ55W7ZmDVl2uGo9ims0Y3YXAMr28+Qn5ZDQNjzswkyMMlVWzLK+Unl7h2+qbEa//coZJKxgxqLXTagyO6cHhCa58K6NDi93ccJ7+sOmDJl27xolF0NEOGDCE1NZVVq1bx2muv8cUX/l8ZxGgqXZDV+wp5fm0ut85K5RynDGEHU1Pj+O0141mbXczP3vRYvcYWR0+eIre4irOHtT5XSrxvuSrrDxQTGRbc5MAeOVD/0LuDsz67sJJDJVVsyC1hzX7Xfqu12cX0692rWejq+CR9rf70q9TUd68KQ45yPO6qJwQyAXJ/QYUuXBkZ6vL41FT9gJOZa/JVABYuXMgDDzzAsGHDSEpqbapsL0aodDFOVNby/f9uJb1/b340b5TbdtedlcSts1J5ecMhjp5sX1lxR1Vi56dvBylxUZSeqqPUKmHujfU5JZyV0rdJe0qI7kV8VBh7j3d9Z/2a/XophLioMB5fvq+VOUspxdrsYmYOi2+mxYxJjPGrs37HkVIyfvMx3//vVhq6SSjshzuPM2pgNMmWRtKSmIhQYiNDAxIBtr+gwqXpy8GYQX2ICA0+o0xgDp+K4/Xggw82Hbv++uvZuXOn3x30Doz5qxNQSvHKxsM8uSqbypp6ausbqWtQ1DU0Ut+oCA0Wnr1lKuGhnusVzRnZn+c+zyXvxCkGxUa0eT5rs4uJiwpjpIuQTMdN4mBJJRMiYz2OU1JZy978cq6Y1Hy1yJEDo7tFrsrqfYWk9YviznOH8tDr21mxt4CvjBrQdDy7sILC8ppWwjciLJj0/tFs90M5kCMnT3Hr8xtB6fpZSsGj100gOMjVckFdg8LyGjIPnuC+r3h2+Kb4aEq1g1KKrPxyrs8Y4rZNaHAQE4fEnFGZ9Q0N7jXdhIQE6ursPSS2BSNUOpj8smoefG0bK/YWclZKX2YN70dYcBBhIUGEBguhwUFMS41jnGU+8sSgWG0fPlbadk1FKcUX2UXMGBpHkIsblyOsOLe4iglWUqQ7Nlj1vmZY/hQHIwdGs3TDYRoblctzdAVq6htYl1PCDRlJXHdWEv9YmcXjy/cxZ2T/Jq1kbfbp/JSWjBscw6p9hSilXPpi7FB6qo5bn9tAdV0Dr909k/e3H+dPH+9DBH5/bdcVLB/vzkcp96YvB0PiIv2ez3OstJrK2oZm5khXZKTE8eSqbKpq64kMM7e9QGI+3Q5CKcXb247x0//toKa+gV9eMZZvzEhp1012YIzWTo6Vtj2b+1BJFUdLq/mWixslaEc92CuxsS6nhPDQIMYPjm22f9TAaE7VNXCopIrULrrg16aDJzhV18Ds9ARCg4O47yvp/ODVbXy0K7/pZrk2q5jBsREMiWutFY4f3IfXNue12VlfW9/It/6ziQNFlbxw6zRGDIhmhKU5/unjfUDXFSwf7jzOkLgIRie6dpQ7SImP5P0dx6lvaGwVENJWmmp+eREqk4bE0tCo2Hm0rMnHYggMxqfSAZRU1nLvki+57+UvGZoQxXv3zebmmantfmrv3SuE6PAQjrXDp+JwSLvyp4A27fSP7mXLwbr+gPanODLyHYy0IsC6srN+9b4iQoKEGdbncPXkwaT1i+JPy/fR2KhobFR8kVPMrOHxLjURh7N+WxtMYEopHnx9G2uzi3nkmgnMHH5awN8/N50H5o7g1U15PPjati5XbqS8uo61WcVcPGagVw0tJS6KhkbF0ZP+K2mzP98RTuxZoE0Yor+frYdP+u3cnugp4eVtuQ4jVAKMUorrF6/lo13H+eG8kfz3rrP9mrSYGBPeZk1FKcW/vzjIyAHRDPWgQaTER3qt23SyqpY9x8uYkdZaOI0Y0BuRrl0DbM1+bY50JOaFBAdx/wXp7Dlezvs7jrPrWBmlp+pcmr7gtLO+LUmQf/lkP69vPsIDc0e4TBy8f24635mbzn835fGDV7dRXdd1IsNW7i2ktqGRi20s2dsUAebHpYWzCiqIjwojLirMY7v+0eEkxoR3SDmd8PBwiouLu71gUUpRXFxMeLhvmrcxfwWYvfnlZBdW8vDV4/jadP9n9CbGRLRZqKzYW8De/HIev2Gix6fM5Lgor6VhNhzQFY6nD20tVCLDQkiOi+yUci17j5fzf//bzo/mjSLDjdmjsLyGnUfL+MHFI5vtv3ziIP62Ios/f7yv6WZ/tgeNLr1/tM8RYMs2HubPH+/nurOSuO+C4W7bfWfuCAThTx/vY9PBEn515bhW9bU6gw93Hic+KowpyX29tj2dq+I/Z/3+ggqv/hQHE5JiOqScTlJSErmHDpF96CjR4SFt9rF1BcLDw30OOzZCJcB8ZpmXzh/ZPyDjJ8aEN61A6CtPrsxmcGwEl08c5LFdSnwkr22uprquwW1E2voDJfQK0VE2rhg5ILrDzV87jpTyjX+t50RVHY9+sJdli8522c4hMM9Nb36TDg4SvjM3nXuXfMk/VmQxLCHKY/KcdtYX2HbWL9t4mB+9vo3Z6f347dXjvfa5f246Z6X05Wdv7uCmZzdw6YREfnrpmE5LuKypb2Dl3kIum5Boy9czsE84YcFBfsuqV0qx30W0oTsmJMXy4c58Sk/VERPhOqfFH4SGhrKxKJQfv3GYe+YM4wcXu08N6IkY81eA+TyriKH9ohjcjpBfTyTGRFBUUeNzslxmbgkbc09wx+w0rxn5jifMwx6eMNflFDMluS+9QlwLnVEDo8ktquww083mQydY+Mw6IsNCuG1WGhtyS9yGlK7eX0jfyNBmJWocXDIukVEDoymrrndr+nIwISmGoopajtsog79k/SF++No2Zqcn8MxNGa38UO44J70f739nNt+9cATLd+Uz9/FV/OuzAx2y3k5L1mYVU1FTb3u56KAgISnOt7I/nigor6Gsur4poMEbExxJqh1gAsu0cmKeWX3A5yrf3R0jVAJIbX0j6w+UMGu455tRe0i0nlILynyrrbV4VTZ9I0O5Yar7+H4HKU5hxa4oPVXHrmNlTaVZXDFyYB8albaBB5r1OcV845/riYsK45W7ZvD9i0cQGxnK4lXZrdoqpVizv4hz0hNcBk4EBQnfvXAEgFdzkyMM3NtN68UvcvnxG9v5yqj+PP2Ns7zmI7WkV0gw912QzvIHzuWslL78+p1dXPn3z9l5tGOLWn648zi9e4U0LepmB3/mqjSVZ7Fr/rKiEjtieeHMgyfISOlLaLDw63cCuw5SVyOgQkVE5onIXhHJEpEHXRwXEfmrdXybiEzx1ldEfm213SIiH4nIIGt/qoicsvZvEZHFgbw2O3x56ARVtQ0uS634i0QrV8WXrPq9x8v5eHcBt8xMsxWz721dlcxcy5/iwknvoKPKtXy2v4ibn9vAwJhwlt11Nkl9I4kMC+Gms1NZviufrILm599zvJzC8hrO9fAdXTR2IB9+51zmjvZswhyT2Mers/7Zzw7wszd3Mnf0AJ78+hSfBYozKfFRPH/rVP7xtSnkl9Vw5d8+548f7fWotdY1NJJbVEl+WTUVNfVtjiZraFQs35XP+SMT3GqnrnDUkvOHE7upkGR/e5pKTGQoqfGRbYrQ84WCsmoOlVQxb9xA7rsgnY9357Oyh1Xq9kTAfCoiEgz8HbgQyAM2ishbSqldTs3mA+nWazrwJDDdS9/HlFI/tc5xH/AzYJE1XrZSalKgrslXPs8qIkhghgvntb9waCp2TC4OnlqVTWRYMDedbS9wIDYylOjwELdPmKv3FRIWHMTk5Fi3Y6TGRxIWEhTQci2f7sln0X82M7RfFP/55nT69T5dwPKWmak8vTqbp1bl8Nj1E5v2O0qzzE73rIU4hKInIsKCGTEgmm1uhMozq3N4+L3dzBs7kL8unGzb5OUJEeGS8YnMHBbPr97ZxROfZvHBjuM8et0EJlvO85r6Bj7PKuK97cdZvkv7FJyJCgsmslcIi84bxu3npNk676aDJyiurLVt+nKQHB9FRU09JZW1xPduX4HR/QUVxEaG0q+358gvZyYkxQa8XEumZWbNSI1jTGIfXtl4mF+9vYuZw/r55Tt3x4q9BZw9NL5dDyr+IJCO+mlAllIqB0BElgJXAs5C5UrgRaUfW9aJSKyIJAKp7voqpZzvSlFAl43bW5NVxISk2IA6BROtBEi7sf95J6p4a+tRbjo7tWkNcW+IiNvCkqWn6nh1Ux7zxw/0+M8cEhxEev/ePmkqSimeXp3DWSl93UZuOVi9r5BF/97MqMRoXrxtWrPKzqDreX01YwhLNhziuxeNaPrcVu8rYuSAaL85u8cNjmHl3ubO+kPFVfzu/d28v+M4l45P5M8LJvmlsrQzsZFhPH7DJC6fOIgfv76da55cy9emJ1NRXc8nuwsor6knOjyEC0cPYMbQeGobGqmsqaeytoHKmnqW78rntU15toXKhzuPExYcxPkjfYtAc2i9h0qq2i1Udh0tI71/b5+iqyYkxfDW1qMUlFfTPzowAQ4bc3US8NhBfQgNDuKnl4/h1uc28vzaA9x57rCAnHPn0VJufW4jP5w3krvPdx9F2BEEUqgMBg47beehtRFvbQZ76ysiDwM3AaXAHKd2aSLyJVAG/J9Sak3LSYnIncCdAMnJyb5dkQ+UVdex9fBJ7pkT2C84qlcIfcJDOG6zVMs/1xwA4Juz7d08HKTERbHrWGst46X1B6msbeDOc4d6HWPkwOimaDg7HC2t5nfv7yEsOIg/L5jEJeMTXbZbl1PMnf/OZHj/3vz7tuluq9V+c/ZQ/rP+EP9ac4D/u2wMp2ob2JBbwk1+XLxp/OAYXt2Ux/GyaqJ6hfC3T7N4/vNcgi3fzN3nD/NbNrkr5ozsz0cPnMsj7+/hP+sOERsZyvzxA5k/LpGZw+PdmqqiwoL524osW2VMlFJ8uPM4s4bHEx3u2wNTslNY8WQbYcjuKKmsZWveSa/1xloycUgsANsOlzJ3TGCEyqaDJ5g0JLbpwWHOyP5cMKo/f/l4P1dNGkz/AJTfX7lXa9wf7czvdKESSJ+Kq8eHllqFuzYe+yqlfqKUGgK8BNxr7T4GJCulJgPfBZaISKtwHqXU00qpDKVURkJC4OL812UX06gIqJPeQWJMBEdt5KqUVNaydOMhrpo82OcClMnxkeSdqGpWNbemvoHnPs/lnOH9GDvIe62yUQOjKSiv4USlvcXFdluh0gNienHPks28sDa3VZvNh05w+/MbSeobyb9vn+ZWoIBOvrt8QiIvbzhEaVUd6w8UU1vfyGw/5ns4Musf+2Av5z+2kmfW5HDFpEGs/MH53HdBekAFioPo8FAevno8G38yl40/mcuj101kzqj+Hn0fk5P70qiwlRy461gZeSdO+Wz6AueyP+1z1q/ZX4hSMGeUb6H6Y63lnwPlV6msqWfn0TIyUppr1j+9bAx1DYpHPtgTkPOu2qeFypbDJynwwRQeCAL5H54HOIcWJQFHbbax0xdgCXAtgFKqRilVbL3fBGQDI9ox/3bxeVYREaHBHv0M/iIxNpzjNoTKC2tzqa5rZNF53rWKlqTERVLXoJoFBLz55VEKy2tsaSnge7mWXcfKEIH/3T2LuaMH8PO3dvLYh3uanLw7jpRyy7Mb6BfdiyXfnG7LnHLXecOorG3g3+tyWbO/iLCQIKan+a8W1JjEPgQHCa9/eYT0/r15+95z+MP1Ezt+cSj0sgN2zWyTrCf4Lw+d9Nr2o535iMAFowd4bduS8NBgBvTp5bVCgzdW7CkgPiqMCTYKrzoTGRbi0e/VXrYcPklDoyIjtbkWltovim/OTuP1zUf8Xi25rLqOzQdPNAWSfLy7c4MCAilUNgLpIpImImHAAuCtFm3eAm6yosBmAKVKqWOe+oqIs757BbDH2p9gOfgRkaFo539O4C7PM2uyipg+NM6nyJi2oku1eDZ/Vdc18MIXuVw4ZgDDbUbLOOMIK3Y46xsbFU+vyWF0Yh9m24xuG2U5u+0663cdLSM1Por43r148mtTWDgtmb+vyOaHr25j19Eybnp2A9Hhobz0zem2TQqjE/tw/sgEnvs8l0/3FDA9Lc6vjs3w0GAeu24CT3/jLJbeOcNWtemuQN+oMNL6RfHlIe83vM+yipgwOIaE6Lb5RJLjItulqTQ0KlbtK+S8ka7DwL0xISmGbXmBWf45M/cEIjAlpbVp7545wxnQpxeP+llbWZtVTH2j4o7ZQxkSF8HyXcf9Or6vBEyoKKXq0aapD4HdwDKl1E4RWSQijmit99A3/izgGeBuT32tPo+IyA4R2QZcBNxv7T8X2CYiW4FXgUVKqU5ZlefoyVPkFFZyTgeYvsCRAFnrMZR097EyTlbVce2Utq305kiAzLXCilfsLSCroII7z02z7SjtH92L2MhQ9ubb01R2Hy9rqnwbEhzEb68ex/0X6BpYlz2xhpAg4aVvTiepr+uFodyx6LxhFFfWcqCoslUWvT+4ZkoSF431XmCxqzF5SCxfHj7p8WZbUVPP1sMnmxW99JXkuKh25apsOXySE1V1zGljlYoJSbGUVNaSd6J9i9u5IvNgCSMHRNPHha8pqlcIV04azJeHT/o1WXXVvkKie4UwJaUvF40ZyOdWUmpnEVADr1LqPaXUCKXUMKXUw9a+xUqpxdZ7pZS6xzo+XimV6amvtf9apdQ4pdQEpdTlSqkj1v7XlFJjlVITlVJTlFJvB/LaPOEo+9ER/hSgKXIpv9R9AqSjRPgoG6GxLs/RJ5ywkNMlNp5ancOgmHAum2CvRAboKDK75VrKq+s4WFzFmMTTbjER4YELR/Dbq8czZlAfXvrm9DaV0p+eFtdk7pk9omO+o+7A5ORYCstrOOIh52ljbgn1jcptVWs7pMRHcrysus3VFVbuLSBIWpfVscuEporS/jWB1Tc0svngiVamL2dGDYymtr6x6eGsvSilWL2vkFnD+xEaHMSFYwZQ29DIasvH0hmYjPoA8FlWEf16h7X5Bu4rgxxhxR5MYPvzy+kVEtRUKdZXgoKEIX11iY0th0+y4UAJt53jvcRLS0YNjGbf8XKvSXcOwTPGRemUG6cn8863Z3std+4OEeHnl4/hlpmpLle7PFNxRGN58qt8kV1MWHBQK0e0Lzic9Z7K/nhixd4Czkrp6zEowxOjBvYhLDjIrbO+vLqO3723m5NV9gJKHOw5Xk5lbYPH9VpGWX7F3cf8kwScXVjBkZOnOM8K7c5I6UtsZCjLd+X7Zfy2YISKn1FK8XlWEbOG9+sw84dDU/HkrN9fUMGwhN7tWuQpJT6KgyVVPL06m+jwEBZM8z0ke+TAPlTWNng1Pey2wpdHJ7YWKv5gcnJffnHF2G5nogokIwdGEx4a5FGorM0uYnJyLBFhbfdDJbejWnFBWTU7jpS1q0BrWEgQoxOj3ZZr+fPH+3lqdQ5vbzvm07gOB/xZLvwpDob1jyIkSNjjpyRgRyixo4RQSHAQF4wawCe786nrhHpwYISK39mbX05RRW2Hmb7gdFa9Z02lgvQB7VvHJTkukuyCCj7YcZyvTU9pWnvEFxymh81eHMK7jpbRNzKUgZ0QNXWmEhocxITBsXx52PV3c7Kqlp1Hy7wW1vTG6bI/vguVlZZZp63+FAcTkmLZcaSslcacVVDRFLq+8YBvLtmNuSUkxoR7LB7bKySYoQlR7PGTprJqXyHp/Xs3O+eFYwZQVl3v8/z9hREqfsaR3NdRTnpwToB0ralU1tRz5OQp29Vc3ZESH0ltQyPBQcKts1LbNMboxD70CQ/hC2u9d3fsPlbGmEF9jCbRwUxOjmXnkTKXQR/rcnSNN18KSLoiLiqMqLDgVppKTX0DWQXlHp+wV+wpYGCfcK9LF3tjQlIMFTX15BSdLnCqlOLX7+wiIiyYmcPirTWC7EWIKaXIzD3BWSl9vf7PjhrYxy818E7VNrD+QAnntcizOndEP3qFBPFRJ5nAjFDxM59lFTE0Icrn5ML2Mig2wm2pFkdlYLvVXN2RaoUVXzVpcJvzLoKDhOlD41l3wL1QqW9oZM/xckYPDIzpy+Ceycmx1DY0ssvFGj1fZOvcq4lJse06h4iQHB9F5sESnvhkP/cu2cyFj69i7M8+ZO7jq/n5Wztd9qtraGTN/iLmjEpo98OGI7N+6+HTzvpP9xSwal8h91+QzrxxAzleVm07QuzIyVMcL6v26E9xMCoxmiMnT1FWXee1rSfW5ejk3fNalMqJDAvhnOH9WL4rv1NWnzRCxY/U1jeyPqekQ7UUBwNjwjle5voHsM8K4W2vpjIlpS8Xjx3At30sjdGSGUPjOVhc5bay8oGiSmrqG1066Q2BxZOz/vPsYqamxfmlKOKIAb3ZcaSMPy7fx9a8k6TER7HovGFcOWkQS9Yfair06Uxm7gkqaur9suDdsITeRIYFN60EWVPfwK/f2cWwhChunpnaJBw22DQh2fGnOHA8LLV3ee1V+woJDw1yKcguGjuAIydPuSytFGjMyo9+ZPOhE5yqa+gUoZIYE+F2HY+sggrCQoIY0rd92lNMRChPfSOjXWMAzLDWXVmXU8w1LvJmHD8EI1Q6ngF99FruXx4+2Wx/QVk1WQUVXH9W2/KcWvLLK8Zy66w0hvfv3cw3V13XwPYjpTz42nY++M7sZrXFVu4tIDRY/OKvDA4Sxg2OaXLWP/95LrnFVbxw2zRCg4OsXJMQNhwoaVpK2hMbc0vo3SvEVsTnKMt0t+dYmS3Nxh2r9hW6rUr8lVEDENnO8l35tkoo+RMjVNrAjiOl3Pzshlb7q+sadKn7dsTwt5VBMeEUV9a6XPJ3X345Q/tFdUjdKTuMHtiHmIhQvsh2L1TCgoMYltA+c52hbUxOjm2VWf9FjjZXttdJ7yA2MoxJka2rZIeHBvOH6ydy3ZNr+e17e/jdNeObjq3YW8C0tLg2BYi4YmJSDC9+cZCjJ0/xxKdZXDCqf5N/IihImJoaZ7tMfmbuCSYnx9r6jQ3sE05MRCi726GpHCyu5EBRJTe7Wb4iIboXU5L7snxXPt+Z27HVqmx9O1b5kwHO7ZVShwI1qa5OTISu/OoK7YgOXKl7dzQlQJZVN5VUcbC/oIIp7agI62+CgoTpaXFu/Sq7jpaRPqC338vDG+wxeUhf3tt+vFl5+LVZxfQJD+kQ7XFKcl/umD2Up1bncMn4gcxOTyDvRBX78iu4IcP7SqV2GZ8US039Ab710mZq6hv4v8vGNDs+LS2OT/YUeC2TX3qqjr355cwf57qKdktEhFEDo9nTDtOUI7nxPA+mwIvGDOB37+8h70SVz1Un2oNXoSIi3wZ+DuQDjrAMBUwI4Ly6NEPiIvnNVeO9N+xAHIEBx0qbC5XKmnryTpziq378MfqDs4fF89GufJf/8LuPlTPHx3U6DP7DUQR1y6GTXGRVIl6bU8SMofHtynPyhQcuHMHy3flNZrAVVj6Gr1WJPTHRCm/fevgkd507lLQW1RmmWoVGM3NPuF12AbTZWyk8ZtK3ZNTAaF7bfITGRtWm+mWr9hWSHBdJarx7YXGhJVQ+3pXPLbN8W+qiPdh5FLwfGGmVQBlvvc5YgdJVcWgqLQtLZhfqyK+2Zp8HCsdqmOtympsXCsqrKaqoCVjSo8E74wbHEBIkTX6VwyVVHC451a7SLL7iMIMdKz3Fb9/bw8o9BSTHRTK0DWV53JEcF2mtHNmLe7/Seg2ScYNiiAgN9uqs35R7guAgaSr9Y4dRiX2osEL9faWmvoG12cWcN8JzFNzQhN4MS4hi+e6ODS22I1QOoxfDMnRhmhIgW4QV78t3CJWu5Z8YOSCavpGhrMtpbgJzhLIaJ33nER4azJhBfZr8Ko6covYUkWwLDjPYyxsOsWpfIXNGtj+U2BkR4ZFrJvDk16e4XGwsLEQvke1NqGzMLWHsoD5E+eDrcTj0d7fBBLYp9wRVtQ22Vt28cMxA1ueUtDt82RfsCJUcYKWIPCQi33W8Aj0xg29EhoUQExHaKgFyf0E5YcFBTVnMXQXtV4lvlQTpqIlkNJXOZfKQWLbllVLf0Mja7CL69e5FejvznNrCAxeOYFhCFPWNivP9aPpyMG/cQI8RWNPS4th9vIzSU65vygXl1Xx56KTPa/KMGBCNiP21hZxZta+QsOCgJm3fE+ePTKC+UbHOS7KxP7EjVA4By4EwINrpZehiuFpXZX9+BUMTuk7klzMzhsZx5OSpZoUFdx0rI6lvBDERHR/sYDjN5OS+VNU2sDe/nLXZxcwcFt8p1Q3CQ4P5y4LJXDslqUPNbw6mpcahFGx2s7DW85/nUtfYyI3TfVuSOqpXCClxkT7XAKuua+DNLUeZPjTOlmY0JbkvkWHBrPFhGe/24nVWSqlfAohItN5UFV66GDoJLVRaayqThnSdyC9nzrbCU7/IKW6qnrzraKnRUroADmf9q5vyKCiv6ZQbuoNxg2P44w0TO+Xck5P7EhIkbMgtaRUkUFFTz7/XHWT+uIGtnPx2GDWwj881wF5af4jjZdU8/lV7n0dYSBBnD413mUwaKLw+vorIOBH5EtgB7BSRTSIyNvBTM/hKYmxEM6FSVasjvzrDbGGH9P69iYsKa/KrnKpt4EBRZbM1VAydQ3JcJHFRYby8QWcO+Cs/pbsRERbM+KQYl36VpRsOUV5dz13nDmvT2CMHRpNbXMmpWnvrylTW1POPFVnMGh7v0/dxTno/cour2rXapi/YsYk8DXxXKZWilEoBvodepdHQxUjsE06JlQAJkF1QiVJ0WaHSlK+SXYxSir355TQq46TvCogIk4fEUl3XyODYCIbEdWwtu67EtNQ4tuWdbLaoWG19I/9cc4AZQ+Oa6oj5yujEaBqVtibY4fm1uRRX1vK9i0b6dJ7Z1mJma7I6RluxI1SilFIrHBtKqZWA/+L6DH4j0cpVcTjrHf+sXS2c2Jmzh8VztLSawyWnmiJhjKbSNXCYwGYN7xx/SldhWlocdQ2qWT20t7Ye5XhZNYvOa5uWAqcX7LJjAis9VcdTq7K5YFR/nxOZhyVEMSgmvKmCeqCxFf0lIj8VkVTr9X/AgUBPzOA7g5pyVbRQ2ZdfQWiwNK0v3xU5na9SzK6jZUT3CiGpnTXKDP5hilUcsSPXBuqKZKTEIUJTyZbGRsXTq7MZNTC6Vdl5X0iOiyQiNJjdNpz1/1qTQ1l1Pd+9yPeSKyLC7PQEPs8qor4DFu6yI1RuAxKA14E3rPe32hlcROaJyF4RyRKRB10cFxH5q3V8m4hM8dZXRH5ttd0iIh+JyCCnYw9Z7feKyMV25tiTaJkAmVVQztB+XbvcSXr/3sRHhfFFTjG7jpUx2qyh0mU4e2g8/7o5g8smDPLeuAcTExnKyAHRTX6VlfsK2JdfwV3nDW3X/2pQkDByYLRXTaW4ooZ/fXaAS8cntrk45OwR/SirrmfbkcCnHHq92yilTiil7lNKTVFKTVZK3a+U8rxsH031wv4OzAfGAAtFZEyLZvOBdOt1J/Ckjb6PKaUmKKUmAe8AP7P6jAEWAGOBecA/rHHOGBJjTpdqAa2pDO9iSY8tERFmDNX5KnuOlRnTVxdCRLhg9IAOK83SlZmWFsfmQyeoa2hk8cocBsdG+EXYjk6MZs/xMo/rnjy1OodTdQ08cGHbl5yYNawfIrBmX+BNYG6Fioj82fr7toi81fJlY+xpQJZSKkcpVQssBa5s0eZK4EWlWQfEikiip75KKWddMQpdh8wx1lKlVI1S6gCQZY1zxhARFkxsZCjHSk9xqraBwyeqGNG/6/pTHMwYFs/xsmoqaxuMUDF0SaalxVFV28B/1h1kQ24Jt5+T5hcLwKiBfThRVUdheY3L4wVl1bywNperJg1meDt+y32jwhg/OKZDQos95an82/r7hzaOPRhd4sVBHjDdRpvB3vqKyMPATejyMXOcxlrnYqxmiMidaK2I5ORk2xfTXUiMieB4aTXZhRU68quLayoAZw89nY1sIr8MXZFpVtb9I+/vITYylAXT/FOgdaSjXMvxcvq7WE31byuyaGhU3D+3fQvjAcxO78fiVTmUV9e5LEvjL9yKWqXUJuvtJKXUKucXMMnG2K505pY6nrs2HvsqpX6ilBoCvATc68P5UEo9rZTKUEplJCT0vEq4iTHhHD1Z3RT5NaIbCJVhCb3p17sXwUHS7iWPDYZA0L9POCnxkdTUN3LTjBQiw/yzpoujBpirMvgHiyt5ecMhrs8Y0mo5i7YwOz2BhkbVqjSSv7Gjv93sYt8tNvrlAc7iPAk4arONnb4AS4BrfThfj8dRquV05FfXj/4WES4cM4ApybEuV7EzGLoCepXFIG6ameq3MWMjw0iMCW9VA2xffjkLnl5Hr5Bgvu2ignJb6KiSLW7FrYgsBG4E0lr4UKIBO6JuI5AuImnAEbQT/cYWbd4C7hWRpWjzVqlS6piIFLrrKyLpSqn9Vv8rgD1OYy0RkceBQWjnf+vlGXs4iTHhnKiqY8eRUtL6RXXpyC9nfn3l2NZqpcHQhfjRvFHcdk4a/Xr38uu4owZGN6tW/EV2MXf+O5Pw0GBeuWtG01pJ7SUsRBehDLRfxZMOtxY4BvQD/ui0vxzY5m1gpVS9iNwLfAgEA88qpXaKyCLr+GLgPeAStFO9CitU2V1fa+hHRGQkesGwg4BjvJ0isgzYBdQD9yil7NU/6EE4IsA2HChh7ugBnTwb+3TFgpcGgzN9o8LoG9V6CeT2MiqxD59lFVHX0Mj7O47z/WVbSY6P5Plbp/p9xcbZ6f34dE8Bh0uqmurt+Ru3QkUpdRB90z5bRAaiI6kUsFcpVW9ncKXUe2jB4bxvsdN7Bdxjt6+1/1oXzR3HHgYetjO3nopjXZWa+kbjnzAYugGjBkZT16D42Zs7eXnDIaalxvH0TWcRG+l/AdZUsmV/ETdOD0ygkp2CkrejzUjXANcB60TktoDMxtBuEp1U5RFduDyLwWDQOMq1vLzhEJeOT+TF26cFRKDA6ZItgTSB2Qlh+CEwWSlVDCAi8WjT2LMBm5WhzQx0CkvsDuHEBsOZztCEKCYOiWVGWhw/mjeqTWvW20VEOCe9Hx/sOE59Q2NAzM52RsxD+1EclNM8h8TQhYgIC6ZvZCghQUJqN4j8MhjOdEKDg3jznlk8dMnogAoUB7PTEwJassWOpnIEWC8ib6J9KlcCGxxLCiulHg/IzAxtJjEmgvjejYSFGOe3wWBozqzhp0u2+Frx2A52hEq29XLwpvXXGOy7KHecm9bZUzAYDF2UOKtky6GSwCzaJZ4KmfV0MjIyVGZmZmdPw2AwGDqU6rqGdiUai8gmpVSGq2NeNRURyQB+AqQ4t1dKTWjzjAwGg8HQaQSycoUd89dLwA+A7eiEQ4PBYDAYXGJHqBQqpeyUujcYDAbDGY4dofJzEfkn8AnQVPRfKfV6wGZlMBgMhm6JHaFyKzAKCOW0+Uuhlxc2GAwGg6EJO0JlolJqfMBnYjAYDIZuj53suHUu1pY3GAwGg6EVdjSVc4CbReQA2qci6ALDJqTYYDAYDM2wI1TmBXwWBoPBYOgR2BEqZ27KvcFgMBh8wo5QeRctWAQIB9KAvcDYAM7LYDAYDN0Qr0KlZeSXiEwB7grYjAwGg8HQbfG5NrpSajMwNQBzMRgMBkM3x05Bye86bQYBZwGBW4vSYDAYDN0WO5pKtNOrF/AOeqEur4jIPBHZKyJZIvKgi+MiIn+1jm+zTGse+4rIYyKyx2r/hojEWvtTReSUiGyxXovtzNFgMBgM/sOOT+WXjvciEgT0VkpVe+snIsHA34EL0UsSbxSRt5RSu5yazQfSrdd04Elgupe+y4GHlFL1IvJ74CHgR9Z42UqpSd7mZjAYDIbA4FVTEZElItJHRKKAXcBeEfmBjbGnAVlKqRylVC2wlNYazpXAi0qzDogVkURPfZVSHyml6q3+64AkG3MxGAwGQwdgx/w1RilVBlwFvAckA9+w0W8wcNhpO8/aZ6eNnb4AtwHvO22niciXIrJKRGa7mpSI3CkimSKSWVhoXEMGg8HgT+wIlVARCUULlTeVUnXYS4gUF/ta9nPXxmtfEfkJUI9eRAzgGJCslJoMfBdYIiJ9Wg2i1NNKqQylVEZCQoKXSzAYDAaDL9gRKk8BuUAUsFpEUoAyG/3ygCFO20nAUZttPPYVkZuBy4CvKaUUgFKqRilVbL3fBGQDI2zM02AwGAx+wqtQUUr9VSk1WCl1ieX7OAjMsTH2RiBdRNJEJAxYALRcQfIt4CYrCmwGUKqUOuapr4jMQzvmr1BKVTkGEpEEy8GPiAxFO/9zbMzTYDAYDH7CTp5KL+BaILVF+1956mdFZ90LfAgEA88qpXaKyCLr+GK0j+YSIAuoQi8I5ravNfTf0KHNy0UEYJ1SahFwLvArEakHGoBFSqkSr5+AwWAwGPyGWNYj9w1EPgBKgU3omzUASqk/BnZqgScjI0NlZmZ29jQMBoOhWyEim5RSGa6O2SkomaSUMuXvDQaDweAVO476tSJilhM2GAwGg1fsrvx4i1n50WAwGAzesCNU5gd8FgaDwWDoEdgJKT4IxAKXW69Ya5/BYDAYDM2wU/vrfnTWen/r9R8R+XagJ2YwGAyG7ocd89ftwHSlVCWAVRn4C+CJQE7MYDAYDN0PO9FfglN+ivXeVW0ug8FgMJzh2NFUngPWi8gb1vZVwL8CNiODwWAwdFvsLNL1uIisRIcWC3CrUurLQE/MYDAYDN0Pt0JFRKYC/ZRS7yulNgObrf1XiEiQVQnYYDAYDIYmPPlUHgN2u9i/yzpmMBgMBkMzPAmVeKVUbsudSqksID5gMzIYDAZDt8WTUInwcCzK3xMxGAwGQ/fHk1D5WEQeFmvREgci8kvg08BOy2AwGAzdEU/RX98D/glkicgWa99EIBP4ZoDnZTAYDIZuiFuhYmXQL7SW5h1r7d6plDJL9BoMBoPBJXbyVHIwa70bDAaDwQZ2yrQYDAaDwWCLgAoVEZknIntFJEtEHnRxXETkr9bxbSIyxVtfEXlMRPZY7d8QkVinYw9Z7feKyMWBvDaDwWAwtMZO6fs/iMhYb+1c9AsG/o5e5GsM2j8zpkWz+UC69boTeNJG3+XAOGvlyX3AQ1afMcACtP9nHvAPaxyDwWAwdBB2NJU9wNMisl5EFolIjM2xpwFZSqkcpVQtsBS4skWbK4EXlWYdECsiiZ76KqU+UkrVW/3XAUlOYy1VStUopQ4AWdY4BoPBYOgg7Kz8+E+l1CzgJiAV2CYiS0Rkjpeug4HDTtt51j47bez0BbgNeN+H8yEid4pIpohkFhYWerkEg8FgMPiCLZ+KZUYaZb2KgK3Ad0VkqaduLvYpm2289hWRnwD16FUp7Z4PpdTTSqkMpVRGQkKCiy4Gg8FgaCteQ4pF5HHgCuAT4LdKqQ3Wod+LyF4PXfOAIU7bScBRm23CPPUVkZuBy4ALlFIOwWHnfAaDwWAIIHY0lR3ABKXUXU4CxYEnn8VGIF1E0kQkDO1Ef6tFm7eAm6wosBlAqVLqmKe+IjIP+BFwhVKqqsVYC0Skl4ikoZ3/LedrMBgMhgBiZ+XHLcCoFiXASoGDSqlSd52UUvUici/wIRAMPKuU2ikii6zji4H3gEvQTvUq4FZPfa2h/wb0ApZbc1qnlFpkjb0MXZq/HrhHKeW8DLLBYDAYAoycth65aSCyDpgCbEP7LcZZ7+OBRUqpjwI9yUCRkZGhMjMzO3saBoPB0K0QkU1KqQxXx+yYv3KByZZz+yxgMtokNhd41G+zNBgMBkO3x45QGeVkekIptQstZEw9MIPBYDA0w45PZZ+IPIlOQAT4qrWvF1AXsJkZDAaDITAs+Sr0S4eLfuP3oe1oKjejHenfAR5AVyy+BS1QvCVAGgwGg6GrkbcRasoDMrRHTcVKenxbKTUX+KOLJhUBmZXBYDAYAkNtFVQVQ0yS97ZtwKOmYoXkVvlQ78tgMBgMXZkyKye8T2CEih2fSjWwXUSWA5WOnUqp+wIyI4PBYDAEjrI8/TdAmoodofKu9TIYDAZDd6fUIVRc1ehtP3aWE35BRCKAZKWUp1pfBoPBYOjqlB7Rf/sERqjYWaTrcnSplg+s7Uki0rKGl8FgMBi6A6WHIao/hPQKyPB2Qop/gS4ceRJAKbUFSAvIbAwGg8EQWMqOBMz0BfaESr2LwpGeC4YZDAaDoWtSeiRgTnqwWfpeRG4EgkUkXUSeANYGbEYGg8FgCAxKaUd9gMKJwZ5Q+TYwFqgBXgbK0Nn1BoPBYOhOVJ+EusqAmr/sRH9VAT+xXgaDwWDorpQGNkcF7C0nPAL4PpDq3F4p9ZWAzcpgMBgM/qcpnLgThQrwX2Ax8E/ArKRoMBgM3ZUAZ9ODPaFSr5R6MmAzMBgMBk801MPJgxA/rLNn0v0pzYOgEOjdP2CnsOOof1tE7haRRBGJc7wCNiODwWBwZvt/4YkpsPWVzp5J96f0CEQPgqDggJ3CjqZys/X3B077FDDU/9MxGAyGFhzfrv++eQ/0GQRpszt3Pt2Z0ryAmr7AhqailEpz8bIlUERknojsFZEsEXnQxXERkb9ax7eJyBRvfUXkehHZKSKNIpLhtD9VRE6JyBbrtdjOHA0GQxeneD/EDdXmr1e+BoWmBGGbKcsLaDgxeBAqIvJDp/fXtzj2W28DWwt8/R2YD4wBForImBbN5gPp1utO4EkbfXcA1wCrXZw2Wyk1yXot8jZHg8HQDSjaD4kT4cZlENwL/nMdlOd39qy6H40NUHasUzWVBU7vH2pxbJ6NsacBWUqpHKVULXqN+ytbtLkSeFFp1gGxIpLoqa9SareplmwwnCHU11hO+nTomwI3vgJVRbDkBqit9N7fcJqKAmisC1h1YgeehIq4ee9q2xWDgcNO23nWPjtt7PR1RZqIfCkiq0TEpeFVRO4UkUwRySwsLLQxpMFg6DRKDoBqhH7penvwFLjuWTi+DV69XT99G+xRZuWodKKmoty8d7XtCleCp2U/d23s9G3JMfSaL5OB7wJLRKRPq0GUeloplaGUykhISPAypMFg6FSK9um/8cNP7xs5H+Y/Cvveh09/3Tnz6o50QDY9eBYqE0WkTETKgQnWe8f2eBtj5wFDnLaTgKM229jp2wylVI1Sqth6vwnIBkbYmKfBYOiqFO/Xf52FCsC0O2DMlbDpeZ3HYvCOQ6h0lvlLKRWslOqjlIpWSoVY7x3boTbG3giki0iaiIShfTQtF/d6C7jJigKbAZQqpY7Z7NsMEUmwHPyIyFC08z/HxjwNBv9RXQYvXA6b/93ZM+kZFGVBdCKEtzI6wNir4dQJyNvQ8fPqjpQdgdAoiOgb0NPYSX5sE0qpeuBe4ENgN7BMKbVTRBaJiCMy6z30jT8LeAa421NfABG5WkTygLOBd0XkQ2usc4FtIrIVeBVYpJQqCdT1GQytaGyA126HA6vhSyNU/ELx/tZaioNhF+js8L3vd+ycuiulh3U4sdhxibcdO8mPbUYp9R5acDjvW+z0XgH32O1r7X8DeMPF/teA19o5ZYOh7Sz/Gez/CPqPhbxMrbW4esI22EMp7VMZd63r4+F9IGUW7PsALjK+Fa+UHgm46QsCqKkYDGcUm16AL/4G0xfB/N+DaoDczzp7Vt2byiKoLtXhxO4YOV8LnuLsjptXd6UssCs+OjBC5UxnyxJ4cpbOBzC0jdzP4N3vanPMRQ/DkGkQGgk5Kzp7Zt0bh5O+n4d4mxFWyty+D923Mejfd0W+ESqGAFNfC5/+BvJ3wIE1nT0b++SshMWzdQ5DZ1OSA698Q5cRuf45CA6BkF6QMhOyjVBpF0UOoeLGpwIQlwYJo3R4scE9ZVbwrBEqhoCybalWiSUI9rzT2bOxh1Lwya+s5LfbtGD0Jy9cDh/82F7b6lJYsgBQsHAphMecPjZ0jn7SdoRxGnyneL8uyxIzxHO7EfPg4Fr9fRhc00HhxGCEyplLYwN89idInASjL4e970FjY9vGKsqCPe/6dXpuOfQFHNkEIy+Fo5vhk1/6b+yacq2x2X3qXfUolGTDDf9uvdbHsDn6b85K/83vTKNov/5cvZVpHzkfGush6+OOmVd3pIOy6cEIlTOXnW9o083s78Goy7W99Uhm28b67E+w7KaOqcW09gmIiINr/wlTv6md457s6ZXF2sRnpwDh0S2A0p9LZZH39rlrtJnLVSn2/mMgqr8xgbWHov2ny7N4ImkqRMbD3g8CP6fuitFUDAFFKVjzOPQbCaMugxEXQVAo7H67beMV7dNPinltFEq2z7Nfa1TT7oCwSO0UHzAe3lh0eu1tZw5vhKdmw+rHdOa1N5yFqrdrqamA4ztgyHTXx0Vg6PlaU2mrBngmU18LJ3I9R345CAqG9It0OPeZkF2//il4+34o2G2/T2mefhgLiwzcvCyMUDkT2fcBFOyE2d+FoCDtC0ibrf0qyk5ZtxYUZ+m/B9f6d54tWfsEhITD1Dv0dmi4do7X18Drd5y+oSgF6xbDc/N1clxMsjabeSMvUz/JSbD3LO0jmTpseMgM922GzdEVdfN32Ls+Z07kntkReSdy9edrR1MB7VepPgmH1wdyVp1P3Sn45Nf6IekfM/QyADmrvP9uOyicGIxQOfNQClb/AWKTmyeVjbpMm30K9/g2XlUJnLIKFxz83H/zbElFAWxdChMXQm+nQqD90uHSP+pzr35UJxz+9xb44EcwfC7ctUprYnkbvT/FHtmszVkDx+n2nji0HhAYMtV9m6Hn67+++lVyP4O/ToHP/uxbv55EUyFJm0Jl2Fe0tt3To8D2L4facrj2XzDn/+DYVnjxCnjqXNj2X/dacQes+OjACJWuRsGewIb35q7RT9mz7odgpxJuIy/Rf32NAnNoKX3T9JO+v6OxHGx4Bhpq4ex7Wx+btFALm1WPwuJZ2ow395ewYImuc5R8NtRWeNYYyo5C+VEYnAFJ07SA8VRW/fB67TdxjvhqSZ9B2sToS75K2VEtFFWD9nudqRTbCCd2JrwPpJ7T8/0qO16DqAQYcxWc9wP4zna44glLW/8mZP7Ldb8OyqYHI1S6Hu99H/5zzel1uf3N6j9A7wEw6evN9/dJ1A5PX6O4HEJlyk1QfwqObfHLNJtRWwkbn4FRl7q/yVzyB6211NfAzW/BOd/Rpj3QQgU8m8AcPpSkDP051FZAwS7XbRsbtCYzZJr3uQ+bo82CddXe29bXaoFSWwUZt0Ph7tO5GmcaRVn6/9ST0G7JyPlaGPXU7Pqacm26Hnu1zocCbQKechPcvU7/365f3FpbqS6DmlKjqZyR1FbCoXX6ify1O+zdiHwhLxMOrNJP+6HhrY+PuhSOfulbbkVxlvZbTFyotwNhAtuyRFejnflt92169YY7VsB9X+onVmdiBnv3qxzZpM0nA8adNmm5M4EV7IaaMkj24E9xMHQO1FfD4XXe2370f1oDuvIJHZUHsNtjce6eS/F++6YvByMu1n97aoHJve/r/yVXtdCCgrSvsTgLDqxsfqwDw4nBCJWuxcG1ernPGffop9RPfuXf8dc8DuGxkHGb6+OjLtN/97Sq4+me4izom6o1nX4j/O+sb2zQYcNJU91HWjno1RvColwfS56hBbY7h+aRTTBwvBa2fdMgsp+OHnOFQ0B4mw9A6iwtdL35Vbb9FzY8pb/7cddqQTg4o+0Red2dov32TV8O+qZCwmj9NN8T2fEa9EnS5llXjL1K/99u+Gfz/aVGqHR9Kou109jV6+iXbR83Z6XOIL7gpzoHY93f/Zc8V5oHe9+F6Xfpm68r+qVrH8AeH25kRVmnS5OnzNQObH8u8br7bR0JNPO+9pXsTjlb5+KccFHapbFBf2+Dz9LbIlqIuYsAO7xBm2b6pno/b69oPZanfJX8nfD2fZA8Ey50SuYcfbme18nD7vv2RCqLdfCHr5oKwEgru/7UCf/PqzOpKoGsT2Dc1afNui0J6aVNYfveb/4/U2q9Nz6VLszJXHjjLtevZy7QIX5tIXsFJE+H0Ai48Nf6R/XGt/zzA3GEWjoc8u4YdSnkfq7/ib3R2Kgzyh1CJXmmtt3m72zfXB3UnYLP/6zrao26tH1jOfwqB12YwAr3ah9KUsbpfUOmai3M1edwaJ32p9gVckPn6CgdV2NVl8IrX4defeD655sHT4y+XP/tLiV0/IWdQpLuGDFfBzns6mFmw91vayvGuOs8t3NYITKfPb3PUYopOjFw83PCCJW20H+stt23fN2zUf8Q/nuzDs/1hfJ8nTsy1CrvERYJ1zwNlQXw7vfaP+e8TAiJgAFjPbcbfZn+Ue7/yPuYZUe0jddZUwH/mMCObIanztNP6uf9yHupDm/0G6lNf678Ko6kx8FOQsVhYmiZBFl+HE4e9Jyf0pJhcwDVWuvMWQnPXQInD2mBEj2g+fH4Yfp/7UwzgdkpJOmOpKkwaAqs/J1OUO0p7HgN4oZB4kTP7WKHaMG6+cXTeU6lR7RAcTj3A4wRKm0hNFw/Pbd8JYyAhS/rNi8v1FEXdjlgaTeOmlEAg6fAeQ/qf6ht/23fnPM2wqDJzZ+EXZE4GaIH2buRtVw/PHaI5RBvh1BpqIMVv4N/ztXRLl9/HSYuaPt4DoKCTvtVWpKXqaOM4oae3jdosn66a+msd/S346RvGmuK1kQcQqVgN7x0Pbx4pf4fueHf2jznijFXaCFdUWD/fN2d4v0QHAaxKb73DQqCeY9A+TGt5XYWBbt1cdKjW9o/Vnm+TgUYd6097XjaN3XS7c7/6e3Swx1m+gIjVPxPXBrc8KJ+2nr9Dvv+hewVOqdi4ITm+895QD81v/u9ttvW62u0+cXZvOOOoCBtasr6RIe2esIRuum83GvKTH0TbEtmfsEeLUxWPQLjr4O718LwC3wfxx3JZ+sbVsu6Xkc2a3+Ks626V2+t1bX0qxzeoLP6W35PnggOgdTZ+jN96z540vI9XfhruHcjjPJgkhx9OaB0eZozhaL9WsC3VTtNnq7NRGuf0FpgZ/DRT/Wy0i9e2T4/K8CuN0E1ul8BsyVp5+vf5MZn9HYHZtODESqBIe1cvfrfvg/gUxvLnCrLNJJ2XusfUnAIXPOUNjN9/pe2zef4dh2mnOQh+9uZUZfqnBNvSXvFWRDWG6IHnt6XcjZUFp7OX/FG2VGtib37PZ0VXHpYP7lf87QWsv7EVb5KbaU2Ow52IXCTpkHepuYPBofXac0jJMy3cw+bA2V5Ojx6+iK4fwvMus91aLcz/cfoG2xP8xF4osjDuvR2ufCXgOglnjuaQ+sgazlMu0snZb54pX5wcUdlkS694srfB7DjVR3q3n+UvfMHBelAn7yNVorAER1N2EEEVKiIyDwR2SsiWSLyoIvjIiJ/tY5vE5Ep3vqKyPUislNEGkUko8V4D1nt94rIxYG8Nq9Mu0M7zT77E2xb5rlt0T6dze1s+nImbqhO7Nr5ujYP+YrDhGNXqKSeo81B3kKLi7O03d9ZJU+Zpf+686s01Omld1+7A/40Hh4frddF2bJEC7O712mTTyAYNElH1zmbwI5u0U+BjsgvZ4ZM0yUxCvfq7doqrfEl2wglbsnEBbqsxr0bYN7vIDLOXj8Rra0cWAWnTvp+3u5GQ52O0GuLk96ZmCRdNWLnG/4Lc1//tF6QzVNNNqV0Veyo/jD353DLu9qX9+JVOmzdmYZ6PeYTU2DNH+D5S2DVY80fYk4e0kE2467xba4TF+rVR1f+HhpqdChyBxEwoSIiwcDfgfnAGGChiIxp0Ww+kG697gSetNF3B3ANsLrF+cYAC4CxwDzgH9Y4ncf8RyHlHHjzXs9PKo5wU0etKFdMuAGqittWSj1vo/6n6mMz+iM4VJtrcr2Ui3H1RBk/XJeRcPdD/vgXOnz2wCoYPBku/p1OWnzwkC4O2bu/vTm2hZBeWng4ayqOH7or06BDCDtMYEc362rMvjjpHfSK1mU1nP02dhl9hT6vP5bMPbwB3vtB4MrptJcTB/W12i0k6YlZ92tfwgcPtr9S9J534f0f6GRUT9aHnJX6d3Pu93XOVGyyFiyRfbVgcQR+HPwCnj5fj5k4Ce74VJu3VvxGV9Rw+NAcpXrG+ihUImL1PcNRC62HmL+mAVlKqRylVC2wFLiyRZsrgReVZh0QKyKJnvoqpXYrpfa6ON+VwFKlVI1S6gCQZY3TeQSHav9KVIK+kbrzr+Ss1Al3nvIehl+on3i2e9F6XJG30Z4/xZnUc3SUk7vs+voa/RTVUqiInPartCT3M/ji73DWrfC9vfqzOftuHZDgLYDAXyTP0NqGY+2XI5naIRzVr3XbuKF6nQ5HEqRDw7FTnsWfDJpiBU+00wTWUA9v3gMbnoY1f/Te/tg2Xdjy9Tu1P8if+UfuaAr+8INQCYvUNeCObYUtL7V9nPydWrMeNAUmf137alw93Dm0lD5JcNYtp/fHDrEESzz8+2q99tBz83SqwA0vwk1v6oeda57RdbwOrYMnZ+n7wvZX9bG4NN/n7ajmDT3G/DUYcPYs51n77LSx07ct50NE7hSRTBHJLCws9DKkH4iK1/bd49t1cmRLGur0zdad6ctBSJjOmN3zrm+hkuX5+uZv1/TloCk82I2dt+QAoFz/+JNnQumh5oEFNeXwv29pwXnRb9qXyNgeUmY2X/slb5Nr0xc4JUFaQuXwBm2WsWu68hdBQTrUO+uT9i2E9uW/tam1/xhtbjm2zX3bmgp49VadhLj3A/30/PgY+PAngatLB6erE7clnNgV46/TvrFPfuVbNKaDyiJ4eYH2jSxYAvMf0+HpbyzSSZrO7PtAP6Sc90OtFTsTk6QFS1Q/XW5l9ve1KXTMlad/CyI6efEOK2jnxav0stneclPcMXDcaT+ityWZ/UgghYqru0bLkCB3bez0bcv5UEo9rZTKUEplJCQkuOgSAMZdq29On/yq9U3hyCZtt/dk+nIw/gaoq/KttpEjB8NXoTJgnA6DdVfLq+mJcljrYw6B5Gxm+vDHWshcvdh9Rn9HkDQVEP00WH5cO889aXFJGVC0VycuHl5vrzRLIBh9uQ6eyPqkbf1rK2HlI3r+t7yrF2z6393ufXTvfV/nWt3wb/j+Prj+Ba1Rrl8Mi8/Rzuf2LIhVV+16Tfmi/Vqz91eQhgjMf0Tne614WD9k2Z13fa3WKioKYMFL2nwcFqlXHT1VAm99+3SUY2Oj1lLihsKkG12PFzMY7lylKwtf8FP3JYUGjIE7V+hxIvrqApJtZe4vtG83Mr7tY/hIILNh8gBn8ZgEHLXZJsxG37acr3MQ0asUPnuRVp3Pd4pZyF6h8yHSzvU+TvLZWrXevgwmXG/v3HkbdaHERB9CYEFHoSXPcO8bcUR3uYrSGTAWesVogTThBu0L2PwizPqOb/kdgSAiVs/v0Bf6SQ5cR345cCRBbn1ZLwLVWfNPnqlvDLvf1jXaivdrbevIJu3rSZoK837vvoTHun9AxXG44QWtaV3+Z1h6o64Hd/6Pmrfd8rK+3vMePL1U8tir9KuyWJdXX/GwDlmd8S3frqPsmO6X+awWKkNm6NIqIy/RfpTiLP+YvpwZfBZMvFELxPWLAdGfZVSCXptnwHhdjDJl5mkzrFLa33Hwc7jmn8212cQJcMHP4aOfwKbn9E171//00grXPOPZlBveR7+8ERYFV/1DCyt336kdkmd0+P9sIIXKRiBdRNKAI2gneksR/hZwr4gsBaYDpUqpYyJSaKNvS94ClojI48AgtPPfy/J9HUjydL0Gwud/gSk3n3aa56zQiXZ2nsyCgrQ6v/YJrZa78gO0JC/TKpQY4fucU2bqzPqKwuYLY4H+8fce4PoHEhSsr/fgWv2E/9a3dWb4nB/7PodAkDxDmyITJ+hij54E7uApWuive1Jvd5amEhyib7zbXtFCusZ6yg+L1qaiDU9b5X1cFCGtLILP/gIjLz19gxl1KYy/Xi9sNuoS/T8Cupbbu9/TUXzn/qD1WFHxev/hDfDpw/op2jmk3B3HtsIX/9Dh4431+vwJo2D/hzrsd/nPdMZ4+TH9P+5vLv+Lvs7y4zrkvaJA/y0/roXcur9rzXzYV/QqkhX5enXFc77r+gFuxt2Q/Ql88GMtGFf8VheztJtLYpf2CJROImBCRSlVLyL3Ah8CwcCzSqmdIrLIOr4YeA+4BO1UrwJu9dQXQESuBp4AEoB3RWSLUupia+xlwC6gHrhHKdUBnkUfmPsLncT26W/gqr9rG29epl77wy7jr9eZwjvf0GHLnmio11Fnk7/uuZ07HOHBh9Zq268zxdmecwkcAum127Vg+dqrre3MnUXy2bDxn/qJfMBYzwK3V7T2QeTv0E+37c2faA9Tb9efe8JIbZYbfJb28UiQFgSf/0UHfGTc2rzf6j9AXaUOcXVm/qO6Tt3/vqXt+I0N8Oot+nu65hn3ZT1EdB7WP2boJL9rn3E/5xO5Ovoxd43OaZp6uy5q6oiCu+Cn2iy67wNt1i07cvr/zp+EhJ2updaSmgodjbjvA9j3kdY6QAvxr/zUdZ+gILjqSZ3I+tw8rXV99T/tLyfUAwhoMRil1HtoweG8b7HTewXcY7evtf8NwOWSeEqph4GH2zHlwBKXBtPu1BFQ0+/SkVWq4XS9LzsMHKdvctuWeRcqhbv1zcRXf4qDxEk61v2gC6FStN9zJrjjxpD9KVzwM9/Nb4HE4bysLHB/o3EmaaoWKkOmd16AAWiN9jY3/rT5j+qAjHe/p6ONhs/V+0sOaAE6+RtaGDkTGQeX/Qle+ZrOp6os0k74ha94jxaKH6bNmasfhbNubr2GDWht4MWrtP/hot/oOUTEtm4XO0T/L0+7Q5udOvoz7tVba06jLtXmpuNbtVlxwgLPmkL0QLjib7B0of6tOJaOOMPpfrpVd+fcH2hT10c/0aav0EjfQ1THX69zJ0pclHF3pinp0cdwYgchYfqG2tJZf+qEri3k6ak9cZJ+Mk2aCjPvb9v5A4Vj0S5wH/nljOP76SzTlx2CQ3SeT//RsOyW05WiP/2NNvGd/5DrfqMv09FFKx+x1nO5W/s47HDOAzoP493vt3b4V5da+Rb5Wkud+W3XAqUlnSm0QQuRQZN1RrqdgJJRl+hghuuf6/y5dxGMUOloImK1o/7AavjyJW0m8tUs5LA573jVc7u8TL1oj511P9yRMguO72iezV1sVWD25FANCYPbPoAbl3VYdVSfcPgW7AjcYRfoG01XfxLtFa0/71694SUrQGLHqzoXyFPi6/xHtX9u0GRtorVLWKQODijcbTnALepO6YKqBbv1Dbej83o6mjFXtC2ptYdihEpnkHGbfsqvq/TN9OUgNllHA237r+fCjXkbtabQrsWtZgKqeWkTT5Ffzgwc3/E5HXaZcpN+QrcTaRQ9AO5c6b/ciUASMxhufEVrk0tu0KHDs7xoilHxujzOrR/4/oAz6hLt2F75iK7j1lCvy+4cXAtXPwXpc9t+LYZuiREqnUFwqC7PHRqla3q1hQnX6/yJ424S2E6d0IlkbTV9OUjK0GXInU1gxfu1c7g9GlBnkzYbrvtXt4yu8UriRG2OCQrREXfhMd77RMZ5L27pjnmPaPPXhz/WlSP2vgeXPBaYKC5Dl6cL2iXOENIvhIfy2n5TG3MVvPdD7bB3tXBPU02rNjrpHYRGaL+Dc75KcZYubeJrpV5DxzHiYvhRrjaJBZq4NJj9Xb0wFmj/jbcgEkOPpQc+pnUj2vOUHBmnBdOO11xXTc3LBETnWbSXlJlwbMvp8jDFWf4p+GcILB0hUBzMul/na8y6X6/UaThjMUKlO5Nxm04We+m61nWN8jbq0GN/3Fia6mVt1CGX3nJUDGceoRFw+4c6+dJEQZ3RGKHSnUm/EK5aDLmfwwuXnS6X3dioNZX2+lMcDJmufSgHP9dCrK7Kdc0vg8FwxmOESndn0kJYuBQK98G/LtK5KyXZuk5Ve/0pDnpFa7/NwbVOkV/G/GUwGFpjhEpPYMRFcPPbWpD86yLIfE7v95dQAZ2vkpd5OqnOmL8MBoMLjFDpKQyZCrd9qMOVHcXx2rskqzMps/SypNte0VUAom2uImkwGM4ojFDpSSSMhNs/skp5z/NvDoYjA/3YFl1NtifmdxgMhnZj8lR6GjFJ8K3P2r8md0si43T5+oKd3SOz3GAwdArmcbOnEghNwrGio/GnGAwGNxihYrCPESoGg8ELRqgY7DPiYjj7Xv3XYDAYXGB8Kgb7hEXBxV13DTSDwdD5GE3FYDAYDH7DCBWDwWAw+A0jVAwGg8HgNwIqVERknojsFZEsEXnQxXERkb9ax7eJyBRvfUUkTkSWi8h+629fa3+qiJwSkS3Wa3HL8xkMBoMhsARMqIhIMPB3YD4wBlgoImNaNJsPpFuvO4EnbfR9EPhEKZUOfGJtO8hWSk2yXosCc2UGg8FgcEcgNZVpQJZSKkcpVQssBa5s0eZK4EWlWQfEikiil75XAi9Y718ArgrgNRgMBoPBBwIpVAYDh52286x9dtp46jtAKXUMwPrb36ldmoh8KSKrRGS2q0mJyJ0ikikimYWFhb5ek8FgMBg8EEih4mr5N2WzjZ2+LTkGJCulJgPfBZaISJ9Wgyj1tFIqQymVkZCQ4GVIg8FgMPhCIJMf84AhTttJwFGbbcI89M0XkUSl1DHLVFYAoJSqAWqs95tEJBsYAWS6m+CmTZuKROSgjWvpBxTZaNdd6enXBz3/Gs31dX+60zWmuDsQSKGyEUgXkTTgCLAAuLFFm7eAe0VkKTAdKLWERaGHvm8BNwOPWH/fBBCRBKBEKdUgIkPRzv8cTxNUStlSVUQkUynlp7V5ux49/fqg51+jub7uT0+5xoAJFaVUvYjcC3wIBAPPKqV2isgi6/hi4D3gEiALqAJu9dTXGvoRYJmI3A4cAq639p8L/EpE6oEGYJFSqiRQ12cwGAyG1ohS3lwVhp7yBOGOnn590POv0Vxf96enXKPJqLfH0509gQDT068Pev41muvr/vSIazSaisFgMBj8htFUDAaDweA3jFAxGAwGg98wQsUD3gpidkdE5FkRKRCRHU77XBbp7I6IyBARWSEiu0Vkp4jcb+3vEdcoIuEiskFEtlrX90trf4+4PgciEmxVx3jH2u5p15crItut4reZ1r4ecY1GqLjBZkHM7sjzwLwW+zwV6exu1APfU0qNBmYA91jfW0+5xhrgK0qpicAkYJ6IzKDnXJ+D+4HdTts97foA5ljFbx0RXz3iGo1QcY+dgpjdDqXUaqBl/k6PKdKplDqmlNpsvS9H35gG00Ou0Sq+WmFthlovRQ+5PgARSQIuBf7ptLvHXJ8HesQ1GqHiHjsFMXsKnop0dltEJBWYDKynB12jZRragi5RtFwp1aOuD/gz8EOg0WlfT7o+0A8CH4nIJhG509rXI64xkGVaujttKWpp6CKISG/gNeA7SqkyEVdfZ/dEKdUATBKRWOANERnXyVPyGyJyGVBg1e87v5OnE0hmKaWOikh/YLmI7OnsCfkLo6m4x05BzJ5CvlWcE+cind0VEQlFC5SXlFKvW7t71DUCKKVOAivRPrKecn2zgCtEJBdtcv6KiPyHnnN9ACiljlp/C4A30Ob2HnGNRqi4p6kgpoiEoYtavtXJcwoUjiKd4FSkszsiWiX5F7BbKfW406EecY0ikmBpKIhIBDAX2EMPuT6l1ENKqSSlVCr6N/epUurr9JDrAxCRKBGJdrwHLgJ20EOu0WTUe0BELkHbdx1FLR/u3Bm1HxF5GTgfXWY7H/g58D9gGZCMVaSzuxbjFJFzgDXAdk7b5H+M9qt0+2sUkQloJ24w+qFwmVLqVyISTw+4Pmcs89f3lVKX9aTrs6qov2FthgBLlFIP95RrNELFYDAYDH7DmL8MBoPB4DeMUDEYDAaD3zBCxWAwGAx+wwgVg8FgMPgNI1QMBoPB4DeMUDGcEYiIEpE/Om1/X0R+4aexnxeR6/wxlpfzXG9VX17RYn+qiNzood8gEXm1recSkQwR+au1/xYR+VvbrsBwJmCEiuFMoQa4RkT6dfZEnLGqYdvlduBupdScFvtTAZdCRURClFJHlVK+Cr2mcymlMpVS9/nY33CGYoSK4UyhHr0G+AMtD7TUNESkwvp7voisEpFlIrJPRB4Rka9Z65lsF5FhTsPMFZE1VrvLrP7BIvKYiGwUkW0icpfTuCtEZAk6SbPlfBZa4+8Qkd9b+34GnAMsFpHHWnR5BJhtrc3xgKVN/FdE3kYXLUwVa/0c69ibIvKB6LWCfu7i/M3OZc33HRftEkTkNev6NorILA+fv+EMwRSUNJxJ/B3YJiKP+tBnIjAavVxADvBPpdQ00Yt/fRv4jtUuFTgPGAasEJHhwE1AqVJqqoj0Aj4XkY+s9tOAcUqpA84nE5FBwO+Bs4ATaKFwlZU1/xV0hnlmizk+aO13CLNbgLOBCUqpEtHVmp2ZBowDqoCNIvKu85gtz+WhsONfgD8ppT4TkWTgQ+uzMpzBGKFiOGOwqhW/CNwHnLLZbaOjHLmIZAMOobAdcDZDLVNKNQL7RSQHGIWu6TTBSQuKAdKBWmBDS4FiMRVYqZQqtM75EnAuupSOLyz3UOJjuVKq2Br/dbRW0lJQ2WEuMEZOV4DuIyLR1jo2hjMUI1QMZxp/BjYDzzntq8cyBVsFKcOcjtU4vW902m6k+e+nZb0jhV4+4dtKqQ+dD1hP/pVu5uevGv3uxgfXc20LQcDZSim7AtpwBmB8KoYzCuvpfRnaEe0gF21uAr36Xmgbhr5eRIIsP8tQYC/aHPQt0aX4EZERVlVaT6wHzhORfpYTfyGwykufciDah7leKHo99Aj06oKf+9DXmY+Aex0bIjKpjeMYehBGqBjORP6IrtLs4Bn0jXwDMB3PT/nu2Iu++b8PLFJKVaOXw90FbLYc5U/hxTpgmdoeAlYAW4HNSilvJdC3AfUislVEWgUiuOAz4N/AFuA1Fz4au9wHZFhBCLuARW0cx9CDMFWKDYYzCMuJn6GUutdbW4OhLRhNxWAwGAx+w2gqBoPBYPAbRlMxGAwGg98wQsVgMBgMfsMIFYPBYDD4DSNUDAaDweA3jFAxGAwGg9/4f9+3o0wF5r90AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# line 1 points\n",
    "x1 = list(range(1, len(list_values)+1))\n",
    "y1 = [row[0] for row in list_values]\n",
    "# plotting the line 1 points \n",
    "plt.plot(x1, y1, label = \"ICE\")\n",
    "# line 2 points\n",
    "x2 = list(range(1, len(list_values)+1))\n",
    "y2 = [row[1] for row in list_values]\n",
    "# plotting the line 2 points \n",
    "plt.plot(x2, y2, label = \"EV\")\n",
    "plt.xlabel('Number of trip file')\n",
    "# Set the y axis label of the current axis.\n",
    "plt.ylabel('Energy Consumption')\n",
    "# Set a title of the current axes.\n",
    "plt.title('Average EC over all trip files')\n",
    "# show a legend on the plot\n",
    "plt.legend()\n",
    "# Display a figure.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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