Example_1_Web_Scraping_Public_Dataset - US Labor Statistics.ipynb

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        "<a href=\"https://colab.research.google.com/github/tonyhollaar/projects/blob/main/Example%201%3A%20Web%20Scraping%20Public%20Dataset%20-%20US%20Labor%20Statistics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Example Webscraping with API -  U.S. Bureau of Labor Statistics\n",
        "* Link public datasets Bureau Labor Statistics: https://www.bls.gov/\n",
        "* source: https://www.bls.gov/developers/api_python.htm#python2\n",
        "* source2: https://github.com/jasonwashburn/bls_api_to_pandas/blob/main/bls_api_to_pandas.ipynb\n",
        "\n"
      ],
      "metadata": {
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      "source": [
        "# API Version 2.0 Python Example Code\n",
        "Multiple Series and Multiple Years\n",
        "\n",
        "Note: Use this code to retrieve data for more than one timeseries and more than one year."
      ],
      "metadata": {
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        "- Series Title \t: \tGasoline, unleaded regular, per gallon/3.785 liters in U.S. city average, average price, not seasonally adjusted\n",
        "- Series ID \t: \tAPU000074714\n",
        "- Survey Name \t: \tCPI Average Price Data, U.S. city average (AP)\n",
        "- Measure Data Type \t: \tGasoline, unleaded regular, per gallon/3.785 liters\n",
        "- Area \t: \tU.S. city average\n",
        "- Item \t: \tGasoline, unleaded regular, per gallon/3.785 liters"
      ],
      "metadata": {
        "id": "VIg1utNdYL4f"
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      "source": [
        "# Data Packages\n",
        "import pandas as pd\n",
        "from pandas.core.dtypes.dtypes import dtypes\n",
        "import math\n",
        "import numpy as np\n",
        "import json\n",
        "import requests\n",
        "import re\n",
        "\n",
        "# Visualization Packages\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.dates as mdates\n",
        "from matplotlib import pyplot\n",
        "import plotly.express as px\n",
        "import seaborn as sns"
      ],
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        "id": "nK7pCyeapH58"
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        "# create empty list to save data to\n",
        "parsed_data = []\n",
        "\n",
        "# source: https://www.bls.gov/developers/api_python.htm#python2\n",
        "headers = {'Content-type': 'application/json'}\n",
        "\n",
        "# set the variable to retrieve from public dataset -> APU000074714\n",
        "# set the daterange with startyear and endyear \n",
        "data = json.dumps({\"seriesid\": ['APU000074714'],\"startyear\":\"2018\", \"endyear\":\"2023\"})\n",
        "p = requests.post('https://api.bls.gov/publicAPI/v2/timeseries/data/', data=data, headers=headers)\n",
        "json_data = json.loads(p.text)\n",
        "\n",
        "# iterate over the json file\n",
        "for series in json_data['Results']['series']:\n",
        "    seriesId = series['seriesID']\n",
        "    # iterate over the list of lists that contains the data\n",
        "    for item in series['data']:\n",
        "        # within each list retrieve the year, period, value and footnotes\n",
        "        year = item['year']\n",
        "        period = item['period']\n",
        "        value = item['value']\n",
        "        footnotes=\"\"\n",
        "        for footnote in item['footnotes']:\n",
        "            if footnote:\n",
        "                footnotes = footnotes + footnote['text'] + ','\n",
        "            parsed_data.append([seriesId,year,period,value,footnotes[0:-1]])\n",
        "\n",
        "# convert json data into pandas dataframe\n",
        "df = pd.DataFrame(parsed_data, columns=['seriesID', 'year', 'period', 'value', 'footnotes'])\n",
        "\n",
        "# change datatype for column value to numeric\n",
        "df['value'] = pd.to_numeric(df['value'])\n",
        "\n",
        "# change period to integer value as numeric datatype\n",
        "df['month'] = pd.to_numeric(df['period'].replace({'M':''}, regex=True))\n",
        "\n",
        "# add date column\n",
        "df['date'] = df['month'].map(str)+ '-' +df['year'].map(str)\n",
        "df['date'] = pd.to_datetime(pd.to_datetime(df['date'], format='%m-%Y').dt.strftime('%m-%Y'))\n",
        "\n",
        "# add percent change column e.g. 1 equals 1%\n",
        "df['perct_change_value'] = df['value'].pct_change()*100\n",
        "\n",
        "# sort dataframe from oldest to newest value\n",
        "df = df.sort_values(by= ['year', 'month'], ascending=True)"
      ],
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        "# get last x rows from pandas dataframe\n",
        "df.head(5)"
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            "text/plain": [
              "        seriesID  year period  value footnotes  month       date  \\\n",
              "58  APU000074714  2018    M01  2.539                1 2018-01-01   \n",
              "57  APU000074714  2018    M02  2.575                2 2018-02-01   \n",
              "56  APU000074714  2018    M03  2.572                3 2018-03-01   \n",
              "55  APU000074714  2018    M04  2.737                4 2018-04-01   \n",
              "54  APU000074714  2018    M05  2.907                5 2018-05-01   \n",
              "\n",
              "    perct_change_value  \n",
              "58           -1.398058  \n",
              "57            0.116641  \n",
              "56           -6.028498  \n",
              "55           -5.847953  \n",
              "54           -0.240220  "
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              "           value      month  perct_change_value\n",
              "count  59.000000  59.000000           58.000000\n",
              "mean    2.930949   6.406780           -0.527564\n",
              "std     0.721453   3.434704            6.236835\n",
              "min     1.876000   1.000000          -16.697588\n",
              "25%     2.480500   3.500000           -4.255772\n",
              "50%     2.776000   6.000000           -0.433904\n",
              "75%     3.260000   9.000000            1.910876\n",
              "max     5.058000  12.000000           20.842217"
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              "      <th>mean</th>\n",
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              "      <th>std</th>\n",
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              "      <th>25%</th>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check dataframe datatypes\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fii8TDx6tSCH",
        "outputId": "b5a8d233-ef8a-4aba-ddda-2ef7b7985f13"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "seriesID                      object\n",
              "year                          object\n",
              "period                        object\n",
              "value                        float64\n",
              "footnotes                     object\n",
              "month                          int64\n",
              "date                  datetime64[ns]\n",
              "perct_change_value           float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualize (with Pandas)"
      ],
      "metadata": {
        "id": "SWG1Pu2rpaQO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the graph\n",
        "df.plot(x='date', y='value', title='U.S. Monthly Gas Price Per Gallon ($)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "z9f_27BIoONW",
        "outputId": "861898a3-4257-4e1c-d4ed-4297ce3625b7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5df9a50340>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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1jE+Jbnb79MGxbM4qprKmzsOR/ZcldGOMccFm55S5E1NOPEMHx3j0mjplY2axJ8P6Fkvoxhjjgi3ZJYSHBDIkrvlZFacNikUEr45Ht4RujDEu2JRVzLjkKAIDmp/3PCo8mJEJkV6d18USujHGtKGmrp7tOUebvSHa2LTUGNYfKKLWSwtHW0I3xpg27Mkro6q2nvEt9J83mJ4ay7HqOnYeKvVQZN9mCd0YY9qw2Tll7sQWRrg0mOFc8GKNl/rRLaEbY0wbNmeX0CcsiEF9w1stlxTVi+ToXqw76J0HjCyhG2NMGzZnFR9f0KIto5P6kJ5X1ma5rmAJ3RhjWlFZU8euQ6Vt9p83GJ4Qwb7DZV65MWoJ3RhjWrHrUCk1ddrmCJcGw+MjqKlTDhSWd3FkJ7KEbowxrVh3wNEfPnFA6zdEGwyPd8zEuCfP8yNdLKEbY0wrlu8pYEi/3vSPdm290KHxvQHHUEdPs4RujDEtqKyp45t9RzhtRJzLnwkPCSIlphd78i2hG2OMz1ibUUhlTT2ntyOhg6Mf3acTuogEisgGEVnazLZ5IlIgIhudX7e5N0xjjPG85bsLCAkM4KQhse363PCESPYWlFFX79n1kduzBN09wA6gTwvbF6nqXZ0PyRhjfMOXuwuYPjiG8JD2rdY5LD6C6tp6MgvLSe3Xu4uiO5FLZ+gikgJcDLzYteEYY4xvyC2pYHdeWbu7W8DR5QJ4vNvF1S6XPwM/A1obKf8dEdksIotFZEBzBUTkdhFJE5G0goKC9sZqjDEe89XuwwDtuiHaYNjxhO7ZoYttJnQRuQTIV9V1rRRbAqSq6gTgE2BBc4VU9QVVnaaq0+Li2t9IxhjjKV/uLiChTygjEyLb/dnIsGCSosI8PgWAK2fopwJzRCQDWAicJSKvNS6gqkdUtcr57YvAVLdGaYwxHlRbV8+K9MOcNjzOpflbmjPMCyNd2kzoqvo/qpqiqqnAtcDnqnpD4zIiktTo2zk4bp4aY0y3tCmrhJKKmg51tzQYHh9Jen4Z9R4c6dLhcegi8qiIzHF+e7eIbBORTcDdwDx3BGeMMd6wfHcBAQKzhvXrcB3DEyKoqKkju7jCjZG1rl1jcVR1GbDM+fpXjd7/H+B/3BmYMcZ4y5e7C5iQEk1M75AO19Ew0iU9v4wBsa3Po+4u9qSoMcY0UnSsms1ZxR0artiYN0a6WEI3xphGVqQfpl47NlyxsejwEOIiQz06SZcldGOMaWT57gL6hAUx0cUFLVrj6TldLKEbY4yTqrJ8TwGzh8cRFNj59Dg8PoL0/DJUPTPSxRK6MabHOVZVS3XtiQ++78orJe9oFaeN6PjolsaGJURSVlXLoaOVbqmvLe2bccYYY/zARU99xbGqOm48eRDXnzyQfhGhAHy5yzElSWf7zxscn9Mlr4ykKNcWyOgMO0M3xvQopZU1HDhSTkig8OSnu5n5/z7nZ4s3setQKcv3FDAiIcJtybchoe/20HJ0doZujOlRMgsdD/o8dPEYRiZG8o+V+3lzfRZvpGUhArfNGuy2ffWNCCW2dwjpHroxagndGNOjZBaVAzAgthfD4iP47eXjeeC8kfxzzUH+s/UQl09Ocev+PDmniyV0Y0yPklnoTOgx/316M6Z3CHeeOYw7zxzm9v0Nj49gyaYcVLXDE325yvrQjTE9SmZhORGhQUSHB3tkf8PjIzhaWUtBaVXbhTvJEroxpkfJLKogJaZXl58tNxjunE/dE90ultCNMT1KZmG5xybLgsZDF7t+pIsldGNMj6GqZBVVfKv/vKvFRYbSJyzIztCNMcadDpdVU1FTx4DYrn/Ip4GIMDwh0hK6Mca40/Ehix48Q4f/zunS1SyhG2N6jIYhiwP7ejahD4uPoPBYNUfKunakiyV0Y0yPkVXkeEo0JcZzXS7guZEultCNMT1GZmE5/SJCCA/x7DOVDSNdtmSVdOl+LKEbY3qMzKJyUjzcfw6QFBXGhJQoFqVldunc6JbQjTE9RmZhhUfHoDcQEW46JZX0/DK+3nuky/ZjCd0Y0yPU1Ss5xRUM8HD/eYNLJiQREx7Mgq8zumwfLid0EQkUkQ0isrSZbaEiskhE0kVktYikujNIY4zprNySCmrr1Stn6ABhwYFcO2Mgn2zPI8s5fNLd2nOGfg+wo4VttwJFqjoMeBL4fWcDM8YYd2qYB93TY9Abu/6kgQC8vvpgq+W+2JXPL97ZQl19+/rbXUroIpICXAy82EKRucAC5+vFwNniqZlvjDHGBY3nQfeWlJhwzh2TwMI1B6msqWu2TE5xBXf/awOvfXOQf6dltqt+V8/Q/wz8DDhxVVWHZCATQFVrgRKgb9NCInK7iKSJSFpBQUG7AjXGmM7IKiwnQKB/tPcSOsDNp6RSVF7D0s25J2yrr1d+ungTdfXKmKQ+/PHjXZRW1rhcd5sJXUQuAfJVdV27om6Gqr6gqtNUdVpcnHsWYTXGGFdkFlWQFNWL4EDvjgU5ZWhfhsVHsGBVxglDGF/95gAr04/wi4vH8PgV4zlcVs1fl+11uW5XjuxUYI6IZAALgbNE5LUmZbKBAQAiEgREAV03NscYY9ops7Dc40+INkdEuPmUQWzJLmFjZvHx9/cWlPH4hzs4c2Qc350xgIkDorliSjIvfbX/+JQFbWkzoavq/6hqiqqmAtcCn6vqDU2KvQfc7Hx9pbNM142eN8aYdsos8uw86K25fEoKEaFBvPL1AQBq6+q5b9FGwoID+f13JhxffONn548iMEB4/MOWxqN8W4evPUTkURGZ4/z2JaCviKQD9wEPdrReY4xxt8qaOvKOVnl1hEtjEaFBXDk1hfc351JQWsVfl+1lU1YJv71sPPF9wo6XS4wK44dnDOWDLYdYva/tTo92JXRVXaaqlzhf/0pV33O+rlTVq1R1mKrOUNV97Ts8Y4zpOg2TcnlzhEtTN54yiOq6eh5eso2nPtvD3En9uXhC0gnlvj97CP2jwnh06fY2hzHak6LGGL/33yGLvnGGDjA0LoLZw/vx/uZc+kaE8Oiccc2W6xUSyM8vHMW2nKO8uT6r1TotoRtj/F5WoXcWtmjLD04bSkRoEH+4ciJR4cEtlpszsT+TB0bzh492tVqfJXRjjN/LLKogJCiA+MhQb4fyLbOG92P9L8/ltBGtD+MWEX51yRgKSltfIMMSujHG72UWlpMS3YuAAN97gD0kyLU0PHlgDJdN6t9qGUvoxhi/l1lUTooP9Z931B+umtjqdkvoxhi/l1novWlz3amtp1wtoRtj/NrRyhpKKmp8aoRLV7GEbozxa5k+OsKlK1hCN8b4tYZ50AfaGboxxnRvWT4wD7qnWEI3xvi1zMJyIkODiOrV8oM7/sISujHGr2UWVZASG05PWETNEroxxq9lFpb7xZBFV1hCN8b4LVUlq6iiRwxZBEvoxhg/drismoqaOjtDN8aY7s4Xp83tSpbQjTF+6/hDRZbQjTGme2tI6L6wOLQnWEI3xvitzMIK+kWEEB4S5O1QPMISujHGL1XV1rEi/TAjEiK9HYrHWEI3xvilRWszyS6u4AenD/V2KB5jCd0Y43cqqut4+vN0ZqTGctrwft4Ox2PaTOgiEiYia0Rkk4hsE5FHmikzT0QKRGSj8+u2rgnXGGPa9uo3GRSUVnH/eSN6xCP/DVy5U1AFnKWqZSISDKwQkQ9V9Zsm5Rap6l3uD9EYY1xXWlnDc8v2Mnt4P04a0tfb4XhUmwldVRUoc34b7PzSrgzKGGM6av6KDIrKa3jgvJHeDsXjXOpDF5FAEdkI5AOfqOrqZop9R0Q2i8hiERnQQj23i0iaiKQVFBR0ImxjjD+rravnr8vS2XCwqF2fKy6v5sWv9nHemAQmDojuouh8l0sJXVXrVHUSkALMEJFxTYosAVJVdQLwCbCghXpeUNVpqjotLi6uM3EbY/xUXb3ywL838cR/dnH1377m9dUHcHQUtO35L/dRVl3L/T3w7BzaOcpFVYuBL4ALmrx/RFWrnN++CEx1T3jGmJ5EVXno7S28szGHH581jFOH9eOht7fyP29toaq2rtXP5pdW8vKq/cyZ2J+RiT1n7Hljbfahi0gcUKOqxSLSCzgX+H2TMkmqmuv8dg6ww+2RGmP8mqryyJLtLFybyV1nDuP+80ZSV688+clunvkinZ2HSnn+hqkkRoU1+/m/frGXmjrlJ+eM8HDkvsOVUS5JwAIRCcRxRv+Gqi4VkUeBNFV9D7hbROYAtUAhMK+rAjbG+Kc/fLSLl1dl8L1TB3P/eY6kHBggPHD+SMYl9+H+NzZxydMreOa6yYxO6kNZVS1llbWUVdVyuKyKf64+yFVTU0jt19vLR+I94mrflLtNmzZN09LSvLJvY4xveebzPfzx4918d8ZAfnf5uGbHju/JK+UHr65j3+FjzdbROySQj+87neRo/56IS0TWqeq05rb1jBlrjDE+6x8r9/PHj3dzxeRkfntZ88kcYHhCJO/cdSpvrM0EIDIsiIjQYHqHBhIZFsTA2N7ERYZ6MnSfYwndGOM1q9IP85ul2zlvTAJPXDmBgIDWn+rsExbMbbOHeCi67sfmcjHGeEVuSQU//tcGhsRF8OQ1kwgKtHTUWdaCxhiPq66t587X11NZU8fzN0yld6h1FriDtaIxxuN+98EO1h8s5tnrpjAsPsLb4fgNO0M3xnjUe5tyeHlVBrfOGszFE5K8HY5fsYRujPGYPXmlPPjmZqanxvDghaO8HY7fsYRujPGIwmPV/OC1dYSHBPHMdVMItpugbmd96MYYtyk6Vs3ajEK2ZJeQU1xJ3tFKcksqyDtaRVlVLYEBwuu3nURCn+Yf3zedYwndGNNhuSUVfLPvCGv2F7E2o5D0fMfSCQECCX3CSOgTxoiESE4bEUdinzCmpcYwdVCsl6P2X5bQjTEd8vrqA/zq3W3U1SuRoUFMTY3h8snJTE+NZUJKFGHBgd4OscexhG6MaZe6euXxD3bw4or9nDkyjp+eP4qRiZEEtvGUp+l6ltCNMS47VlXLPQs38umOPObNTOUXF4+2Jzx9iCV0Y4xLcksquPXlNHYeOsojc8Zy88xUb4dkmrCEboxp09bsEm5dsJZjVXW8NG86Z46M93ZIphmW0I0xrSqrquXm+WsICw5k8Q9nMCqxj7dDMi2whG6MadWCVRkcOVbN2z+aacncx9ndDGNMi45W1vDC8n2cNSqeyQNjvB2OaYMldGNMi+av2E9JRQ33ndtzF17uTiyhG2OaVVxezUtf7ef8sQmMS47ydjjGBZbQjTHNevGr/ZRW1XLvOXZ23l1YQjfGnKDwWDX/WLmfiyckMTrJboR2F20mdBEJE5E1IrJJRLaJyCPNlAkVkUUiki4iq0UktSuCNcZ4xt++3Et5TR0/OWe4t0Mx7eDKGXoVcJaqTgQmAReIyMlNytwKFKnqMOBJ4PfuDdMY4ykFpVUs+DqDuRP7Myw+0tvhmHZoM6GrQ5nz22DnlzYpNhdY4Hy9GDhbRGymHmO6oeeW7aWmTrnH+s67HZf60EUkUEQ2AvnAJ6q6ukmRZCATQFVrgRKgbzP13C4iaSKSVlBQ0LnIjTFud6ikktdWH+CKyckM7tfb2+GYdnIpoatqnapOAlKAGSIyriM7U9UXVHWaqk6Li4vrSBXGmC705Ce7qa9X7j7b+s67o3aNclHVYuAL4IImm7KBAQAiEgREAUfcEaAxxjM+35nHorRMbp01mAGx4d4Ox3SAK6Nc4kQk2vm6F3AusLNJsfeAm52vrwQ+V9Wm/ezGGB91pKyKny3ewqjESO47z/rOuytXJudKAhaISCCOPwBvqOpSEXkUSFPV94CXgFdFJB0oBK7tsohdpKqUVdVSXF5DUXk1ReU1xEeG2phaY5pQVR58awtHK2p47bYZhAbZ0nHdVZsJXVU3A5Obef9XjV5XAle5N7SO+XxnHg+9vZXDZVXU1J14kTBjcCy3zhrMOaMTesySWUcra3h3Yw6XTepPZFiwt8MxPuaNtEw+2Z7HQxeNttkUuzm/mj63sqaOX7y9lbDgQG6bPYSY8GCiw0OIDQ8hOjyYjZnF/GNlBj94dR0DY8O55dRUrpo2gIjQ9jWDqlJQWsXegmPsP3yMfQVlJMf04lv0zrkAABsfSURBVJZTB3fRkXXc2oxC7l24keziCrbnlPD4FRO8HZLxIQeOHOORJds5ZUhfbp3lez+/pn38KqEvWJVBTkkl//z+Scwc2u+E7dNSY5k3M5WPtuXx0op9PLJkO3/6eDczBseSHNOL/tGOr+ToXsRHhlJcXkN2cQU5xRXH/80qqmD/4WOUVdUerzdAoF7hjJHxPjPUq6aunqc+28OzX6STEhPOxROS+NeaTK6YksL01Fhvh2d8QG1dPfe9sYnAAOH/rp5IQA+5YvVnfpPQi45V88wX6Zw5Mq7ZZN4gKDCAiyckcfGEJDYcLOLVrw+wPfcoazMKOVpZ2+LnegUHkhzjSPZTB8UwJK43Q/pFMDiuN0EBwqzff84rX2fw60vHdsHRtU/G4WPcs2gjmzKLuXJqCg/PGUuAwMaDxfzvW1t4/+7ZhATZND493fNf7mXdgSL+cu0k+kf38nY4xg38JqE/+0U6x6pqefDC0S5/ZvLAmG9N2l9aWUNuSSXZRRXkHa0kpncIyc4z9ujwYFp7+PWSCf35d1oW9583st1dOO70zoZs/vftLQQFCM9eN4WLJyQd3/bo3LHcuiCNv3+1jzvPHOa1GI131dcrS7fk8udP93DpxP7MnZTs7ZCMm/hFQs8sLOeVrw9w5dQURiZ2fO6JyLBgIsOCGZHQ/jrmzUzl7Q3ZvLkuy2uroR8pq+KBf29i0oBonvru5BPOus4encCF4xJ56rM9XDIhiUF9faN7yHiGqvLFrnz+7+PdbMs5yqjESB6b26FnBI2P8ovr7j9+vIuAAPiJF1dVmTggmskDo1mwKoP6eu8Mwf9w6yFq65XfXDauxUvoX186luDAAH7xzlbsUYGeY2X6Ya54bhXfezmNsqpanrxmIu/fPZuocBv15E+6fULfklXCuxtzuHXWYJKivNsPOG9mKvsOH+PLPd6Zp2bp5hyGxvVmVCtXKYlRYTxw3gi+2nOYJZtzPRid8YZjVbXc8OJqrn9xNYdKKnn8ivF8et/pXD45pccM2+1JunVCV1Ue/3AHsb1D+MHpQ70dDheOSyI+MpSXV2Z4fN95RytZvb+QSyf2b7WvH+DGU1KZkBLFo0u2U1JR86063tmQzc8Wb+L3/9lpZ/B+4MOth1iRfpifXzCKLx44g+/OGEhwYLf+tTet6NZ96F/uLmDV3iM8fOkY+vjAAzMhQQHccPIg/vTJbvYWlDE0LsJj+35/cy6qjpuzbQkMEH53+XjmPLOCn/57Ewl9wli19zB7C44BEB4SSHl1HQmRoczzwbH1xnUfbTtE/6gw7jh9SJt/6E33123/VNfVK//vw50M6hvOdScN8nY4x313xkBCAgN4ZVWGR/e7ZHMOY5L6MCzetT8i45Kj+N6pg/l4ex5vrc9iYGw4D100mqU/nsXmX5/HOaMT+O0HO1h/sKiLIzddpby6luW7CzhvbKIl8x6i256hv7kui52HSnn2uik+NaY6LjKUSyYmsXhdFvefP9IjVw6ZheVsOFjMzy8Y1a7PPXjhKC6fksyIhMgTLsP/76qJXPLMV9z5+nqW/ngWfSNC3Rmy8YDluw9TVVvPeWMSvB2K8RDfyYTtcKyqlj98vIspA6O5aHyit8M5wS0zB3Osuo7FaVke2d/7Wxw3Ny9pNObcFUGBAYztH9Vsn2pUeDDPXT+VI8equXfRRuq8NHLHdNzH2w4R1SuYGYPtyeCeolsm9Oe/3EtBaRW/vGSMT15Kjk+JYtqgGBZ87ZkhjEs25TBpQLTb57AelxzFI3PG8tWewzz12R631m26Vk1dPZ/uyOPs0fEE2U3QHqPb/U/nFFfwwvJ9zJ3U/1tPefqaeaemcuBIOV/syu/S/ewtKGNbzlEundj2zdCOuHb6AL4zJYWnPt/DsnYcy9HKGr7Zd4Tauvouicu0bs1+x1QW54/1vStY03W6XUL/w0e7APhZO/uLPe38sYkk9gnj1W8OdOl+lm7KRQQuHt++7hZXiQiPXTaOkQmR3LtoI/sPH2u1fN7RSh7/YAczH/+ca1/4hgv/8hXLd9v6sZ720bZDhAUHcNpwW+qxJ+lWCX1TZjFvb8jmttmDSfbxyYSCAwO4YkoyX+05TEFpVZfsQ1VZsjmH6amxJEaFdck+AHqFBPLX66dQW6ec+cdlXPDn5Ty2dDvLduVTUV0HQHp+GT9fvJnZv/+Cv3+1jzNGxvH774ynuq6em+av4XsvryU9v6zLYjT/pap8vC2P04bH0SvEFqvoSbrNKBdV5bH3t9MvIoQfntE9Jpa6fHIyf122lyWbcvheF8w1vSuvlPT8Mn5zWdfPxzEkLoKlP57FB1tzWbHnMK98fYAXV+wnJDCAYfER7Dh0lJDAAK6ZPoDvzx7CwL6O/vzLJiezYFUGT3+WzgV/Xs6NpwzinrOHEx0e0uUx91Sbs0o4dLSSn44d6e1QjId1m4T+n62HWJtRxONXjPfqbIbtMTwhknHJfXhnY3aXJPQlm3IIDBAuHOeZftLUfr350RnD+NEZw6iormNtRiEr0g+z4WARPz5zGDfNTKVfk+GNoUGB3H7aUK6YksKfPtnNglUZLFqbyXljEpg7OZnZw/rZTTs3+2jbIQIDhLNHx3s7FONh3SIzVtXW8fiHOxmVGMnV0wZ4O5x2uWxSMo+9v4P0/DKXH/pxhaqyZFMuM4f2PSGJekKvkEBOGxHHaSNc66PtFxHK7y4fz02nDGLBqgze35zLOxtz6Ns7hEsmJDF3cjKTB0T75Kil7ubj7XmcNDjWroJ6IJ9K6JmF5eQUVxAaHEhoUAChQQGEBQfy1vosDhaW8+qtM7rdhEJzJvXndx/s4J0N2TxwvvsugTdnlXCwsJy7utm85qMS+/D4FRN4eM5Ylu0q4N2N2fxrbSYLvj5Av4gQJg2IZvLAGCYNiGZCSpStgdpOewvKSM8v48aTfefpaeM5Xk/olTV1fLg1l0VrM/lmX2GL5c4aFc/sbnjHPj4yjFnD43h7Qzb3nTvCbct8Ld2cQ3CgdNthaaFBgZw/NpHzxyZytLKGj7Ye4ut9R9iYWcynOxzDI0VgeHwEP79gFGePtqcdXfHRtkMAnGtPh/ZIXkvoFTV1/PKdrbyzMZvSyloGxobzwHkjmDwwhuraeipr6qiqraeqto7qOuX8sd33B/SKycncu2gjaQeK3PLUXmVNHW+uz+bMkfF+MZ91n7Bgrpo2gKuc3Wkl5TVszCpm48Fi3t+Sww9fW8+C783glKF9vRyp7/t4Wx4TUqJsSbkeqs2ELiIDgFeABECBF1T1L03KnAG8C+x3vvWWqj7aWr3p+WW8kZbJheMSuXr6AE4e3NdvF6k9b2wC4SGBvL0hyy0J/Z0N2RQeq+YWP50JMSo8mNNHxHH6iDhuOmUQV/3ta25/JY2FPziZsf2jvB2ezzpUUsnGzGIeOM97C70Y73JleEEtcL+qjgFOBu4UkTHNlPtKVSc5v1pN5gD9o3ux5qFz+PO1k5k5tJ/fJnOA8JAgLhibyNLNuVTW1HWqLlVl/sr9jE7qw8lD/H+OjpjeIbzyvRlEhgVx8/y1HDjS+oNNPdknO/IAum03nOm8NhO6quaq6nrn61JgB9DpVWX79g4hqlf37y5w1eVTkimtrOWLnZ2bCuCrPYfZnVfGrbMG95gRIf2je/HKrTOora/nxpfWkF9a6e2QfNLH2w4xpF9vt46mMt1LuwYAi0gqMBlY3czmU0Rkk4h8KCJjW/j87SKSJiJpBQU963HwmUP7ER8ZylsbsjtVz/yV++kXEcqlE7vmUX9fNSw+kn/Mm05BaRXz5q/laGVN2x/qQfYWlLFq7xEuGGdzn/dkLid0EYkA3gTuVdWjTTavBwap6kTgaeCd5upQ1RdUdZqqTouL634jVjojMECYO6k/y3blU3SsukN1pOeXsmxXATedMojQoJ73SPfkgTE8d8MUdueVcvsraVTVdq77yp888Z+dhAUFdMkDbKb7cCmhi0gwjmT+uqq+1XS7qh5V1TLn6w+AYBHp59ZI/cBlk5OpqVOWbunY4szzV2YQEhTAdScNdHNk3ccZI+N54soJfLOvkIVrMr0djk9Iyyjko2153HH6UK88ZGZ8R5sJXRzXby8BO1T1Ty2USXSWQ0RmOOs94s5A/cGYpD6MTIjknQ50uxQdq+at9VlcPim5x//SXj45mamDYnhh+T5qevj0vKrK7z7YQXxkKLfOtrPzns6VM/RTgRuBs0Rko/PrIhG5Q0TucJa5EtgqIpuAp4Br1ZaMP4GIcPmUZNYdKGr3aI1/rjlIZU29XVLjaMcfnj6U7OIK3t/csasdf/HRtjzWHyzmvnNHEB7i9ecEjZe5MsplhaqKqk5oNCzxA1V9XlWfd5Z5RlXHqupEVT1ZVVd1fejd09xJ/QkQePKT3bj6N6+6tp5Xvs5g1rB+jEyM7NoAu4mzRsUzIiGC55btdbkd/U1NXT1P/Gcnw+IjuHJqirfDMT7AprnzsKSoXtxz9gje2ZjD4nWurTn6wZZc8o5WcaudnR8XECDccfpQduWVdvmqUL5q4dpM9h0+xoMXjLIZKw1gCd0r7jprGKcM6cuv3t3GnrzSVsuqKi+t2M+QuN6c7uLMhj3FpRP7kxzdi+eW7fV2KB5XVlXLXz7dzYzBsTZNrjnOEroXBAYIf7l2Er1DA7nzn+uPr/rTnFV7j7Alu4TvnTrYr5+m7YjgwABuP20IazOKWJvR8sRu/uiF5fs4XFbN/1402sadm+MsoXtJfJ8wnrxmEnvyy3j4vW0nbK+vV+av2M/3Xl5LQp9QrpjS6Ydz/dLV0wYQ2zukR52l5x+t5O/L93HxhCQmDYj2djjGh1hC96LZw+P40RlDWZSW+a2hjNnFFdzw0moeXbqdmUP7suSuWTaCoQW9QgK5ZWYqn+/MZ0du0+fd/NOTn+6mtr6en7lxfn3jHyyhe9lPzhnB9NQYHnp7C/sKynhrfRYXPLmcjZnFPH7FeObPm058n65bANof3HRKKr1DAvnbl/5/lr41u4SFazO58eRUBvXt7e1wjI+xhO5lQYEBPPXdyYQEBTDnmZXc98YmRiVF8p97TuO7MwZa/6gLosKDue6kgSzZnEtmYbm3w+kyqsqv39tGbHgI95wz3NvhGB9kCd0HJEX14k9XTyI0KIAHLxzFwttPYWDfcG+H1a3cOmsIAQJ//2qft0PpMu9szGbdgSJ+fsGoHjVTqXGdJXQfceaoeNb98lzuOH1ot1s31RckRoVxxeQUFq3NpLy61tvhuF1ZVS2Pf7CTiSlR9hCRaZEldOM3LhifSFVtPRsOFns7FLd7+vM95JdW8fCcsTZ81bTIErrxG1MHxSCC341J31tQxvwV+7lyagqTB8Z4OxzjwyyhG7/RJyyY0Yl9/CqhqyqPLtlOWFAgP79glLfDMT7OBjcbvzJjcCyL1mZSU1dPsIfnN1FVsooq2J1Xyu68MhKjQjlvTCK9Qzv+a/bZjny+3F3ALy4eTVxkz5422bTNErrxK9NSY3h5VQbbco669SnKomPVvLk+i4rqOupUqa9X6hXqVCk6Vs2uvFJ2HyrlWJNpHMJDtnLB2ESumJLCKUP7tuuGd2VNHY8u3c6w+AhunpnqtmMx/ssSuvErM1JjAVi7v9BtCf1oZQ03vLSabTnffhI1MEAIEEdXz4iESK6aNoARCZGMTIxgWHwku/NKeWt9Fks35/LWhmwS+4Qxd3J/bps1pM2z7Zq6en717lYOFpbz6q0zPH61YbonS+jGr8T3CWNQ33DWZBTy/dOGdLq+ypo6bluQxq5DpcyfN43Zw+MIFHFppMn01Fimp8by60vH8tmOfN5an8WLX+3nzXXZ/PmaScwa3vwqjSXlNfzon+tYmX6Eu84cxuzhNsumcY392Td+Z3pqLGkZhdTXd27hi5q6eu58fT1rMwr50zWTOGtUAsGBAe0eNhgWHMjFE5J4ad50Prh7NtHhwdw4fzX/9/Euapssobf/8DEu/+tK1uwv5A9XTuABm6/FtIMldON3ZqTGUlRew77DZR2uo75e+em/N/HZznx+M3cccyb2d0tsIxMjee+uU7lySgpPf57OdX9fTW5JBQCr9h7msmdXUlRezeu3ncxV0wa4ZZ+m57AuF+N3pg929KOv2V/EsPj2L9mnqjyyZBvvbMzhp+eP5IaTB7k1vvCQIP5w1URmDuvLQ29v5aK/fMXV0wbw0or9DO7Xm5dunm5TP5gOsTN043dS+4bTLyKkw+PRn/x0Dwu+PsD3Zw/mR2cMdXN0/3X55BSW/ngWiVG9+NvyfZw6rB9v/mimJXPTYXaGbvyOiDA9NZY1+9uf0NdmFPLUZ3u4amqKR1YDGhIXwds/msnX+44we1g/WxvUdIr99Bi/ND01luziCnKKK9r1ub9+kU5s7xAenTvOY1MXhwUHcubIeEvmptPa/AkSkQEi8oWIbBeRbSJyTzNlRESeEpF0EdksIlO6JlxjXDPD2Y/enm6X7TlH+WJXAbfMTKVXSGBXhWZMl3HllKAWuF9VxwAnA3eKyJgmZS4Ehju/bgeec2uUxrTT6KQ+RIQGtavb5bkv99I7JJCbTkntusCM6UJtJnRVzVXV9c7XpcAOoOmKxXOBV9ThGyBaRJLcHq0xLgoMEKYMiiEto8il8hmHj/H+5hxuOHkQUeG2eITpntrVaSciqcBkYHWTTclAZqPvszgx6SMit4tImoikFRQUtC9SY9pp+qAYduWVUlxe3WbZF77aR1BAAN+bNdgDkRnTNVxO6CISAbwJ3KuqHVpeXVVfUNVpqjotLs4eZzZdq2E8eltn6flHK1mclsV3pqaQYAtym27MpYQuIsE4kvnrqvpWM0WygcaPtaU43zPGayYNiCY4UNq8MfrSiv3U1tdzx+mdn/vFGG9yZZSLAC8BO1T1Ty0Uew+4yTna5WSgRFVz3RinMe0WFhzIhJRo1rSS0EvKa3jtmwNcPKE/g/r29mB0xrifKw8WnQrcCGwRkY3O9/4XGAigqs8DHwAXAelAOXCL+0M1pv2mp8by4lf7qKiua3Yo4qvfZHCsus7Ozo1faDOhq+oKoNUnLFRVgTvdFZQx7jJjcAzPf6lsyCxi5tBvT1dbUV3H/JUZnDEyjrH9o7wUoTHuY4+mGb82dWAsIs3fGF209iCFx6r50RnDvBCZMe5nc7kYvxYVHszIhEj+9uVeFq45SFVtPVW19VTW1FFbr0wbFHP8qVJjujtL6Mbv3XvOCN7fkktYUAChwQGEBgUSGhRwfOEJY/yFJXTj9y4Yl8gF4xK9HYYxXc760I0xxk9YQjfGGD9hCd0YY/yEJXRjjPETltCNMcZPWEI3xhg/YQndGGP8hCV0Y4zxE+KYV8sLOxYpBXa5oaoooMRP6ugHHO5kHe6KxVfqAPe0iy8dj6/8rPjS8fhKm7grlq6sY5CqNr9CkKp65QtIc1M9L/hRHdYmXdQuPnY8PvGz4mPH4xNt4mPH0+46/KHLZYkf1eEuvnI81iZdW09n+dLx+EqbgO8cT7vr8GaXS5qqTvPKzn2UtUnzrF1OZG1yImsT794UfcGL+/ZV1ibNs3Y5kbXJiXp8m3jtDN0YY4x7+UMfujHGGCyhG2OM33BbQheRASLyhYhsF5FtInKP8/1YEflERPY4/41xvj9KRL4WkSoReaBJXT9x1rFVRP4lImHuitPT3Nwu9zjbZJuI3OuN43GHDrTJ9SKyWUS2iMgqEZnYqK4LRGSXiKSLyIPeOqbOcnObzBeRfBHZ6q3jcQd3tUlL9fgld4zbdPbDJwFTnK8jgd3AGOAJ4EHn+w8Cv3e+jgemA78FHmhUTzKwH+jl/P4NYJ674vT0lxvbZRywFQjHsdLUp8Awbx+fh9pkJhDjfH0hsNr5OhDYCwwBQoBNwBhvH58328T5/WnAFGCrt4/LF9qkpXq8fXxd8eW2M3RVzVXV9c7XpcAOHMl5LrDAWWwBcJmzTL6qrgVqmqkuCOglIkE4EliOu+L0NDe2y2gcP6DlqloLfAlc4YFDcLsOtMkqVS1yvv8NkOJ8PQNIV9V9qloNLHTW0e24sU1Q1eVAoYdC7zLuapNW6vE7XdKHLiKpwGRgNZCgqrnOTYeAhNY+q6rZwB+Bg0AuUKKqH3dFnJ7WmXbBcXY+W0T6ikg4cBEwoItC9ZgOtMmtwIfO18lAZqNtWfjBL2on28QvuatNmtTjd9y+SLSIRABvAveq6lEROb5NVVVEWh0n6ewPmwsMBoqBf4vIDar6mrtj9aTOtouq7hCR3wMfA8eAjUBdF4bc5drbJiJyJo5f1FkeDdSDrE1O5K42aVpPlwfuBW49QxeRYBwN9rqqvuV8O09Ekpzbk4D8Nqo5B9ivqgWqWgO8haNvrNtyU7ugqi+p6lRVPQ0owtEX2C21t01EZALwIjBXVY84387m21cpKc73uiU3tYlfcVebtFCP33HnKBcBXgJ2qOqfGm16D7jZ+fpm4N02qjoInCwi4c46z8bR59UtubFdEJF4578DcfSf/9O90XpGe9vEebxvATeqauM/YmuB4SIyWERCgGuddXQ7bmwTv+GuNmmlHv/jrrurOC5vFNiMoztgI45+3r7AZ8AeHCMzYp3lE3H0eR7F0bWSBfRxbnsE2Imj3/hVINRTd4nd/eXmdvkK2I5jNMfZ3j42D7bJiziuSBrKpjWq6yIcVyp7gYe8fWw+0ib/wnH/qcb583Ort4/Pm23SUj3ePr6u+LJH/40xxk/Yk6LGGOMnLKEbY4yfsIRujDF+whK6Mcb4CUvoxhjjJyyhmx5LRB6WJjNaNtl+mYiM8WRMxnSGJXRjWnYZjtn9jOkWbBy66VFE5CEcTxfm45jYax1QAtyOYwredOBGYBKw1LmtBPiOs4pngTigHPi+qu70ZPzGtMYSuukxRGQq8DJwEo6J6dYDzwP/UOe8HyLyGJCnqk+LyMvAUlVd7Nz2GXCHqu4RkZOAx1X1LM8fiTHNc/tsi8b4sNnA26paDiAiDfO+jHMm8mggAvio6QedM/XNxDH7Z8PboV0esTHtYAndGMdZ+2WquklE5gFnNFMmAChW1UkejMuYdrGboqYnWQ5cJiK9RCQSuNT5fiSQ65xi9fpG5Uud21DH/Nn7ReQqcMzg13gdT2N8gSV002OoYxmyRThmq/wQx/S7AL/EsYLNShyzfDZYCPxURDaIyFAcyf5WEdkEbKObLndn/JfdFDXGGD9hZ+jGGOMnLKEbY4yfsIRujDF+whK6Mcb4CUvoxhjjJyyhG2OMn7CEbowxfuL/AzAdM9hKDgiFAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualize (with Matplotlib)"
      ],
      "metadata": {
        "id": "iFKRQO89QwNU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#############################################\n",
        "# create a graph with seaborn and matplotlib\n",
        "#############################################\n",
        "# set the figure size \n",
        "my_figsize = (15,10)\n",
        "fig, ax = pyplot.subplots(figsize=my_figsize)\n",
        "# Set the x-axis label\n",
        "x_axis_label = \"Date\"\n",
        "# Set the y-axis label\n",
        "y_axis_label = \"Gas Price\"\n",
        "# Set the x-axis label\n",
        "plt.xlabel(x_axis_label)\n",
        "# Set the y-axis label\n",
        "plt.ylabel(y_axis_label)\n",
        "\n",
        "# set the style of the plot\n",
        "sns.set_style(\"whitegrid\")\n",
        "sns.lineplot(x=\"date\", y=\"value\", data=df)\n",
        "\n",
        "months = mdates.MonthLocator()\n",
        "monthsFmt = mdates.DateFormatter('%b') \n",
        "years = mdates.YearLocator()\n",
        "yearsFmt = mdates.DateFormatter('\\n\\n%Y')  # add some space for the year label\n",
        "\n",
        "ax.xaxis.set_minor_locator(months)\n",
        "ax.xaxis.set_minor_formatter(monthsFmt)\n",
        "plt.setp(ax.xaxis.get_minorticklabels(), rotation=90)\n",
        "ax.xaxis.set_major_locator(years)\n",
        "ax.xaxis.set_major_formatter(yearsFmt)\n",
        "\n",
        "for xmaj in ax.xaxis.get_majorticklocs():\n",
        "  ax.axvline(color='grey', x=xmaj, ls='--', linewidth=0.5)\n",
        "\n",
        "for xmin in ax.xaxis.get_minorticklocs():\n",
        "  ax.axvline(color='grey', x=xmin, ls='-', linewidth=0.1)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "lykM0ZWontrG",
        "outputId": "2d609cc7-65b0-476a-894e-260f022b6b27"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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m9wgICFqARAcw6AAWLcAKYwvNXUNpbXB+pILcqFZUFbMX3uu47IAFnkfKy8v9HgEBQQuQ6AAGHcCiBVhhayGVSmlXZ1yNaW6RcKSVtSXcovk6LjtggeeRtrY2v0dAQNACJDqAQQewaAFW2FroGRpT/8j4nBZ4q2pL1HpoREOJCQ8nCzeXHbDA80htba3fIyAgaAESHcCgA1i0ACtsLTTP4Qma1sraUknSq4dfC247YIHnkTA+8hZu0AIkOoBBB7BoAVbYWmjuMtsbzGYPPGvV4SdpvnqQBZ7FNgkhMDTE3h4waAESHcCgA1i0ACtsLTR3xRXLi6p+4ewfCrK4LKbi/KhePciDViyXHbDA80gY9zSBG7QAiQ5g0AEsWoAVthZ2dca1orpYOTmRWb9GTk5EK2tLtJMreFPYBy8EwrinCdygBUh0AIMOYNECrLC10Nw1tydoWitrS/VqZ1ypVMqDqcKPffBCoLh49vclI7PQAiQ6gEEHsGgBVphaGBlL6kDfiCcLvFW1JeofGVfnYMKDycLPZQcs8DxSUlLi9wgICFqARAcw6AAWLcAKUwt7uoeUSkmNs9zk/EgrDz9o5ZWD4XrIjCsuO2CB55GOjg6/R0BA0AIkOoBBB7BoAVaYWtjVZT4zd+octkiweJLm0Vx2wALPI/X19X6PgICgBUh0AIMOYNECrDC10NwZVyQinVI59yt4ZUX5WlRSwBW8w1x2wALPI4cOHfJ7BAQELUCiAxh0AIsWYIWpheauuJaUF6kwL+rJ6522qESvsNm5JLcdsMDzyMjIiN8jICBoARIdwKADWLQAK0wtNHcNzWmD89c7bdEC7eka0uQkT9J02QELPI+EbU8TuEMLkOgABh3AogVYYWlhcjKl3R5tkWA1lBdpLDnJkzTFPnihELY9TeAOLUCiAxh0AIsWYIWlhfb+USUmJj15wIpVX1YoSWo9NOzZa4YV++CFQJgeeQu3aAESHcCgA1i0ACssLTR3m8/KNXq4wGsoK5IktR4Kz22qrrBNQgjEYjG/R0BA0AIkOoBBB7BoAVZYWtjTNSRJnt6iyRW817jsgAWeRzo7O/0eAQFBC5DoAAYdwKIFWGFpYU/3kMqL8lRRnO/ZaxbmRVVZnK/9vVzBc9kBCzyPNDQ0+D0CAoIWINEBDDqARQuwwtLCnq4hTz9/Zy0ui6m1jyt4LjtggeeRrq4uv0dAQNACJDqAQQewaAFWWFrY3T3k6e2ZVkNFEZ/Bk9sOWOB5JJHgca8waAESHcCgA1i0ACsMLfQNj6l3eMzJAq++rFBtfSNKZvleeC47YIHnkbDsaQL3aAESHcCgA1i0ACsMLeztOfyAlRrvNjm3FpfFNJ5M6eDAqOevHSbsgxcCYdnTBO7RAiQ6gEEHsGgBVhha2Nlhtkg4rcb7R/nXl5mnR2b7bZrsgxcCZWVlfo+AgKAFSHQAgw5g0QKsMLTw0oE+lcfy1FDu/aP8G6YWeNn9oBWXHbDA80hubq7fIyAgaAESHcCgA1i0ACsMLbzUNqDVixcqEol4/tq1C+1eeNl9Bc9lByzwPNLd3e33CAgIWoBEBzDoABYtwAp6C8NjE9rVGdeZi0udvH5hXlQ1JQXa35vdV/BcdsACzyNLly71ewQEBC1AogMYdACLFmAFvYVtbQOaTEmr6xc6e4+G8ljWX8Fz2QELPI+0t7f7PQICghYg0QEMOoBFC7CC3sKW/X2SpDUNbq7gSdKSiqKs3+zcZQcs8DySTCb9HgEBQQuQ6AAGHcCiBVhBb2Fra79qSwtUvaDQ2Xs0lMfU3jeqieSks/cIOpcdsMDzyLJly/weAQFBC5DoAAYdwKIFWEFvYWtrn9Ysdnd7piQ1lBdpYjKljizeC89lByzwPLJnzx6/R0BA0AIkOoBBB7BoAVaQW+gfHtfenmGnn7+TNLX9QjZ/Ds9lByzwPFJRUeH3CAgIWoBEBzDoABYtwApyC1sPmM/fnen4Ct6S8iJJ2b3Ac9kBCzwAAAAA2traL0laXe/uASuSVFdWqEiEzc5dYYHnkd7eXr9HQEDQAiQ6gEEHsGgBVpBb2LK/T8urilUay3P6PgW5US0qKdT+3uy9gueyAxZ4Hlm+fLnfIyAgaAESHcCgA1i0ACvILWxp7dPaBre3Z1pmL7zsvYLnsgPnC7xkMqnLL79cH//4x4/5vfvuu0/nn3++LrvsMl122WW69957XY/jzL59+/weAQFBC5DoAAYdwKIFWEFt4eDAqA4OJLS2oWxe3m9JRVFWfwbPZQe5zl75sDvvvFONjY2Kx+PH/f13vetduuWWW1yP4Vw0GvV7BAQELUCiAxh0AIsWYAW1BbvB+VnzeAXvgS1mL7zcaPbdVOiyA6c/zY6ODj3xxBO68sorXb5NINTV1fk9AgKCFiDRAQw6gEULsILawtbWfkVzIs63SLAaymNKTqbU3p+de+G57MDpFbwvf/nL+sxnPqOhoaETHvPwww/r2Wef1fLly/W5z31u2m82kUho+/btU1+Pj49LkvLypv8wqKtjJWn37t0qLZ3+iUMzfd0gfF8cm/6xUna34Pf7B+VYKbs7yORj/e4gbMem+/PyewbOCRyb7rHpdiAFt4Xf72zXsoV52tv8yrz8XJOD5vbMpzbv0Fl1Mc9eNwzHSm7+98FytsB7/PHHVVFRoTVr1uiZZ5457jEXXXSRLr30UuXn52vjxo367Gc/qzvvvPOkr1tQUKCmpqaprxOJxNSvT8fVsZK5WnnkXHN93SB8Xxyb/rFSdrfg9/sH5VgpuzvI5GP97iBsx6b78/J7Bs4JHJvusel2IAWzhVQqpd337tc7VteqqalpXn6uxTXD0sPtyimtVlPTEs9eNwzHSnPv4MgLXq/n7BbN559/Xps2bdLFF1+sT33qU/rDH/6gT3/600cdU15ervz8fEnSVVddpW3btrkax7mJiQm/R0BA0AIkOoBBB7BoAVYQW2jpHVbf8Pi8PWBFkmoXFionkr2bnbvswNkVvJtuukk33XSTJOmZZ57RD37wA912221HHdPZ2amamhpJ0qZNm9TY2OhqHOf6+vr8HgEBQQuQ6AAGHcCiBVhBbGHL4Q3O52uLBEnKz81RbWmhWnuzc6sElx04f4rm691+++1as2aN1q9fr7vuukubNm1SNBrVwoUL9ZWvfGW+x/FMmBen8BYtQKIDGHQAixZgBbGFrfv7VJCbo5W1JfP6vg3l2btVgssO5uWZpOedd56+973vSZJuuOEGrV+/XpK5yverX/1KDzzwgO66665ABj9Tzc3Nfo+AgKAFSHQAgw5g0QKsILawtbVfZ9SXKm+etytoqMjezc5ddpB9m044ks6Ha5HZaAESHcCgA1i0ACtoLSQnU3qprV9nzePn76yG8iJ1DIxqbGJy3t/bby47YIHnkerqar9HQEDQAiQ6gEEHsGgBVtBa2NUZ1/BYcl4/f2c1lMc0mZLa+7PvNk2XHbDA80hra6vfIyAgaAESHcCgA1i0ACtoLWxpNQ/7mM8naFoN5Wb/u2z8HJ7LDljgecQ+DRSgBUh0AIMOYNECrKC1sLW1TyUFuVpRVTzv772kvEiSsvJzeC47YIHnkZGR7PubBxwfLUCiAxh0AIsWYAWtha2t/VqzeKFyciLz/t51CwsVzYlk5RU8lx2wwPPI4OCg3yMgIGgBEh3AoANYtAArSC0kJpLa3j6gtUvm//N3kpQbNXvh7c/CvfBcdsACzyNh3uIB3qIFSHQAgw5g0QKsILWwo31Q48mUL0/QtBrKY1l5BS/0++BlgyDuaQJ/0AIkOoBBB7BoAVaQWtg69YAVf67gSdKSiuzc7Jx98EIgFov5PQICghYg0QEMOoBFC7CC1MKW1n5VFudrcZl/MzWUx3RwcFSJiaRvM/jBZQcs8DxSXl7u9wgICFqARAcw6AAWLcAKUgtbW/u0tmGhIpH5f8CK1VBepFRKausb9W0GP7jsgAWeR9ra2vweAQFBC5DoAAYdwKIFWEFpYSgxoV2dcV/2vzvSa3vhZdeDVlx2wALPI7W1tX6PgICgBUh0AIMOYNECrKC08NKBfk2mpLN8eoKmtaTC7oWXXZ/Dc9kBCzyPBOmRt/AXLUCiAxh0AIsWYAWlha2t/ZLk+xW8RSUFys2JZN0VPLZJCIGhoSG/R0BA0AIkOoBBB7BoAVZQWtjS2qfFZTFVLSjwdY7caI7qygq1vze7ruC57IAFnkeCtKcJ/EULkOgABh3AogVYQWlha2u/r9sjHKmhrCjrruCxD14IBGlPE/iLFiDRAQw6gEULsILQwqGhMbX0Dvt+e6aVjZudsw9eCBQXF/s9AgKCFiDRAQw6gEULsILQwtYD5vN3ZwXkCt6SiiJ1DiY0Op49e+G57IAFnkdKSkr8HgEBQQuQ6AAGHcCiBVhBaGHr/j5J0pqALPDsVgkH+rLnKp7LDljgeaSjo8PvERAQtACJDmDQASxagBWEFra09mtFdbFKC/P8HkWS2excyq6tElx2wALPI/X19X6PgICgBUh0AIMOYNECrCC0sLW1T+sC8vk7KTs3O3fZAQs8jxw6dMjvERAQtACJDmDQASxagOV3CwcHRtU5mAjMEzQlaVFpofKikay6gueyAxZ4HhkZyZ4gcXK0AIkOYNABLFqA5XcLLx1+wMraJcG5ghfNiai+LKb9vdlzBc9lByzwPBKUPU3gP1qARAcw6AAWLcDyuoX+kTFNTqZmfPyLB/qVmxPRGXWlns4xV9m2VQL74IVAEPY0QTDQAiQ6gEEHsGgBlpct9A6N6aLbfqOrv/8H/Wlv74z+zEsHBrSytkSFeVHP5vCC2ew8exZ47IMXAkF45C2CgRYg0QEMOoBFC7C8bGF/77ASE5N69WBcV/4/T+sT97xw0q0GUqmUtrX1B2aD8yMtqYipO549e+GxTUIIxGIxv0dAQNACJDqAQQewaAGWly10xxOSpH+/9g36+4tP1cPbOrT+357QNx95RSNjxy6UWnpHNDA6EZgNzo/02lYJ2fE5PJfnBBZ4Huns7PR7BAQELUCiAxh0AIsWYHnZgl3gNZQX6VNvX6nHbnqb/qJpkW5/7FVd/G9P6P7NB5RKvfb5vJcOmA3Og3gFz26VsD9LbtN0eU5ggeeRhoYGv0dAQNACJDqAQQewaAGWly10x8ckSZXF+ea1y4v0nWveoJ98/AJVLsjXDRs364p//7227DcLuxcPDKgwL0enL1rg2QxeybbNzl2eE3KdvXKW6erq8nsEBAQtQKIDGHQAixZgedlCdzyhkoLcYx6Y8qblFbr/+j/Tz55r1a0P7dRl331KV7yhQTvbetVUW6rcaPCu8dSUFCg/mpM1t2i6PCewwPNIIpHwewQEBC1AogMYdACLFmB52UJ3fEyVC/KP+3vRnIg+cO4SvfPMWn338Wb94Hd7NJkc1/91/lLP3t9LOTkRLS6PqbU3O67guTwnsMDzCPvbwKIFSHQAgw5g0QIsL1voHkxM3Z55IiWFefrHd67S1W9aojuefFUfeOMSz97fa2YvvOy4gsc+eCHA/jawaAESHcCgA1i0AMvLFrrjCVUUF8zo2GWVxfrcu5q0ojp4n7+zsmmzc/bBC4GysuA9jQj+oAVIdACDDmDRAiwvW+iOJ1S5IM+z1/NbQ3mReobGNDw24fcozrk8J7DA80huLne7wqAFSHQAgw5g0QIsr1oYT07q0PC4Kmd4BS8M7FYJ2XAVz+U5gQWeR7q7u/0eAQFBC5DoAAYdwKIFWF610Dt0eIuEBZm0wMuezc5dnhNY4Hlk6dJgPpEI848WINEBDDqARQuwvGqha9A8hXG6h6yEyZIsuoLn8pzAAs8j7e3tfo+AgKAFSHQAgw5g0QIsr1rojpsFXlVJ5izwqksKVJCbkxULPJfnBBZ4Hkkmk36PgICgBUh0AIMOYNECLK9a6I4fvkUzgz6DF4mYvfD292b+LZouzwks8DyybNkyv0dAQNACJDqAQQewaAGWVy3YK3iZdIumZD6Hlw1X8FyeE1jgeWTPnj1+j4CAoAVIdACDDmDRAiyvWuiJJxTLi6qoILOe0Jotm527PCewwPNIRUWF3yMgIGgBEh3AoANYtADLqxa642MZ9fk7a0l5kQ4Nj2sokfSFDQMAACAASURBVNl74bk8J7DAAwAAAEKmO57IqM/fWXYvvLa+UZ8nCS8WeB7p7e31ewQEBC1AogMYdACLFmB51ULXYEJVGbQHnvXaZueZfZumy3MCCzyPLF++3O8REBC0AIkOYNABLFqA5VUL3fExVWfgLZp2s/O2vsx+0IrLcwILPI/s27fP7xEQELQAiQ5g0AEsWoDlRQvJyZR6hzLzCl7VgnwV5uXoQIYv8FyeE1jgeSQajfo9AgKCFiDRAQw6gEULsLxo4dDwmCZTysgFXiQSUUN5UcZ/Bs/lOYEFnkfq6ur8HgEBQQuQ6AAGHcCiBVhetGD3wMvEBZ50eKuEvsz+DJ7LcwILPI+0tLT4PQICghYg0QEMOoBFC7C8aKF7cEySuZ0xEzWUxzL+Cp7LcwILPI9UVVX5PQICghYg0QEMOoBFC7C8aGHqCl5JZl7BW1JepP6RcQ2Ojvs9ijMuzwks8DwyMZHZmzFi5mgBEh3AoANYtADLixYy/xZN+yTNzL2K5/KcwALPI319fX6PgICgBUh0AIMOYNECLC9a6I6PKT+ao9LCXA8mCh67F96BDN4Lz+U5gQWeRxobG/0eAQFBC5DoAAYdwKIFWF600B1PqHJBviKRiAcTBc/UAi+Dr+C5PCewwPNIc3Oz3yMgIGgBEh3AoANYtADLixa645m5B55VUZyvovxoRu+F5/KcwALPIwUFmfsvGdJDC5DoAAYdwKIFWF60YBZ4mfkETcnshbe4rFBtGbzAc3lOYIHnkerqar9HQEDQAiQ6gEEHsGgBlhctdA+OZfQVPEmqLytSawYv8FyeE1jgeaS1tdXvERAQtACJDmDQASxagDXXFlKplHqGEhm7RYKV6VfwXJ4TWOB5pKamxu8REBC0AIkOYNABLFqANdcW+kfGNZ5MZfwVvFMqizU4OqGWnsx8kqbLcwILPI+MjGTu3zAgPbQAiQ5g0AEsWoA11xZe2wMvcz+DJ0l/vtIsgH69rd3nSdxweU5ggeeRwcFBv0dAQNACJDqAQQewaAHWXFvoGhyTJFVn+BW8xeUxNdWV6H9e6vB7FCdcnhNY4HmE/W1g0QIkOoBBB7BoAdZcW5i6gpfhn8GTpLc3LdILLX1q78+8K+DsgxcC7G8DixYg0QEMOoBFC7Dm2sJrt2hm/gLvL86olSQ9lIFX8dgHLwRisZjfIyAgaAESHcCgA1i0AGuuLfTExxTNiagslufRRMG1orpYp9UsyMjbNF2eE1jgeaS8vNzvERAQtACJDmDQASxagDXXFrrjCVUU5ysnJ+LRRMH2zjW1enZv79SVy0zh8pzAAs8jbW1tfo+AgKAFSHQAgw5g0QKsubbQHU9kxe2Z1jvW1GkyJT287aDfo3jK5TmBBZ5Hamtr/R4BAUELkOgABh3AogVYc22hKz6W8VskHKmprkTLKov0Py9l1nYJLs8JLPA8wuOPYdECJDqAQQewaAHWXFvoHkxk/BYJR4pEInrHmlo93dyj/uFxv8fxDNskhMDQ0JDfIyAgaAESHcCgA1i0AGsuLaRSKXOLZhZskXCkd66p08RkSo9sz5zbNF2eE1jgeYT9bWDRAiQ6gEEHsGgB1lxaiCcmlJiYzKpbNCXprIaFql9YqF9n0G2aod4HL5lM6vLLL9fHP/7xY35vbGxMN954ozZs2KCrrrpKra2trsdxhv1tYNECJDqAQQewaAHWXFrojo9Jyo498I4UiUR0yZpaPflqt+KJiTm91nBiQn3DYx5NNnuh3gfvzjvvPOEK9d5771VpaakeeeQRffjDH9Ztt93mehxniouL/R4BAUELkOgABh3AogVYc2khmzY5f713rqnT2MSkNu3onNPrfGLjC7rmP/6g5GTKo8lmx+U5wekCr6OjQ0888YSuvPLK4/7+pk2b9L73vU+SdMkll+jpp59WKuXvD3u2SkpK/B4BAUELkOgABh3AogVYc2mhezB7F3jnLCtX1YKCOd2m+ftd3frD7l7t6x3RIy/7+3k+l+cEpwu8L3/5y/rMZz6jnJzjv83BgwdVV1cnScrNzVVJSYkOHTrkciRnOjo6/B4BAUELkOgABh3AogVYc2nhtSt42fUZPEmK5kR0yepFenxHl0bGkmn/+VQqpX975BUtKilQ/cJC/eCpPQ6mnDmX54RcVy/8+OOPq6KiQmvWrNEzzzzj2esmEglt37596uvxcfO41Ly8vGn/rKtjJamqquqoueb6ukH4vjg2/WOl7G7B7/cPyrFSdneQycf63UHYjk335+X3DJwTODbdY9PtQJpbCzv29ioiqXP/bvXkRNKeIex9rS4d093jSd296QW9ZVnxSY99vecODOu5fYd0/bllSkxM6vsv9OrB323WqZUnvhoapnPCkZwt8J5//nlt2rRJTz75pBKJhOLxuD796U8f9Tm7RYsWqb29XbW1tZqYmNDg4KDKy8tP+roFBQVqamqa+jqRSEz9+nRcHSuZD0oeOddcXzcI3xfHpn+slN0t+P3+QTlWyu4OMvlYvzsI27Hp/rz8noFzAseme2y6HUhzbGHHiyovHtaa1WfMaoaw93Xq6ZO69alH9VJfVB97x8zXA6lUSv/42FNaXBbT37zjDUpMJHXPtt/p8TbpPX924v8ugnxOONni0NktmjfddJOefPJJbdq0Sd/4xjd0/vnnH/MQlYsvvlg///nPJUkPPfSQzj//fEUiEVcjOTUyMuL3CAgIWoBEBzDoABYtwJpLC93xRFbenmnlRXO0oWmRHtveqcTEzG/TfGx7p7a09uvv15+q/NwclRTm6ao3LtGDW9rUOTjqcOITc3lOmPd98G6//XY99thjkqQrr7xSfX192rBhg+644w59+tOfnu9xPMP+NrBoARIdwKADWLQAay4tdMfHsvIBK0d655m1GkxM6Pe7emZ0/ORkSt945BUtqyzS+9/QMPXrH3rzKZqYTOk//9DiatSTCvU+eJJ03nnn6Xvf+54k6YYbbtD69eslmUuN3/rWt/TII4/opz/9qZYsWTIf4zjB/jawaAESHcCgA1i0AGtu++Alsn6B95ZTq1RSkKv/meHTNB/a1qGX2wd0w/rTlBd9bemzvKpY61fV6O4/7NPoePoPbZmrUO+Dly14/DEsWoBEBzDoABYtwJrrNgnZvsAryI3q4qYaPfLyQU0kJ096bHIypW8++ooaq4t12brFx/z+R96yXD1DY3pgS5urcU8otNskZJNYLOb3CAgIWoBEBzDoABYtwJptCyNjSQ2NJVVVkr2fwbPeuaZWh4bH9cye3pMe98utbXrlYFw3/sXpiuYc+5yPCxortaq2RD/43Z5534vb5TmBBZ5HOjs7/R4BAUELkOgABh3AogVYs23htT3wsvsKniS97fQaxfKiJ71NcyI5qdsffVUrF5Xo3WfWHfeYSCSij7xluXZ0DOrp3TP7TJ9XXJ4TWOB5pKGhYfqDkBVoARIdwKADWLQAa7YtdB1e4FWzwFMsP6o/X1mth7Yd1OTk8a+83b+5Tbu7h/TJDacp5zhX76z3rqtXRXG+fvC7vY6mPT6X5wQWeB7p6uryewQEBC1AogMYdACLFmDNtoXuQbPAq8zibRKO9I41teoaTOi5lkPH/N54clK3P/aqVteX6pLVtSd9ncK8qK49b6ke23FQe7uHXI17DJfnBBZ4HrGbEAK0AIkOYNABLFqANdsWeobGJHGLpnXxqhrlR3P0Py92HPN7P3uuVS29w/rUhtNntMf2tecvU25ORD/8/V4Hkx6fy3MCCzyPsL8NLFqARAcw6AAWLcCabQtcwTtaSWGe3npalR7a1nHUA1ISE0l9e9MurVtSpotX1czotWpKC/WetfW690/7NTA67mrko4R+H7xswP42sGgBEh3AoANYtABrti10xxMqLcxVQW7U44nC6x1ranWgb0QvtQ1M/dpPnt2vA30jM756Z133luUaGkvqJ8/udzHqMdgHLwTKysr8HgEBQQuQ6AAGHcCiBVizbaE7PqaqEm7PPNKGMxYpNyeiR14+KEkaHU/qO4/v0rmnlOutp1Wl9VpnNizUm06p0A9/v1fJEzy4xUsuzwks8DySm5vr9wgICFqARAcw6AAWLcCabQtdcTY5f72yonxd0FipR182t2ne/UyLDg4k9KkNK9O6emd95M9OUeuhkakFo0suzwks8DzS3d3t9wgICFqARAcw6AAWLcCabQvd8QRbJBzHO9bUal/viLYe6Ne/P7FLF6yo1AWNlbN6rQ1n1KqhPKYfPLXH4ymP5fKcwALPI0uXLvV7BAQELUCiAxh0AIsWYM22he7BhKp4wMox3n5GrSIR6dM/2aLu+Jhuevvps36taE5EH37zKfrjnl69dKDfwymP5fKcwALPI+3t7X6PgICgBUh0AIMOYNECrNm0kJhIamB0gls0j6O6pEDnLC1XW/+oLjy9Wm88pWJOr/eBc5eoOD/q/Cqey3MCCzyPJJNJv0dAQNACJDqAQQewaAHWbFroiR/eA4+HrBzXO8+sUyQifWrD7K/eWaWFebrqjUv04JY2dcVHPZju+FyeE1jgeWTZsmV+j4CAoAVIdACDDmDRAqzZtNAdP7wHXjG3aB7PVec06FefeKvWLfHmyZQfevMpmphM6cfPtnryesfj8pzAAs8je/a4/zAmwoEWINEBDDqARQuwZtOCXeBxBe/4ojkRLass8uz1llcVa/2qGv34j/s1Ou7mSpvLcwILPI9UVMztfl9kDlqARAcw6AAWLcCaTQvdh2/R5Cma8+cjb1mu3uEx/fdLHU5e3+U5gQUeAAAAEGBTV/BY4M2bCxordfqiBfr5c+5u03SFXTc90tvb6/cICAhagEQHMOgAFi3Amk0L3YNjKs6PKpYfdTARjicSiehLl5+p3d1xJ6/v8pzAAs8jy5cv93sEBAQtQKIDGHQAixZgzaaF7niCz9/54Iz6Up1RX+rktV2eE7hF0yP79u3zewQEBC1AogMYdACLFmDNpoXueILbMzOMy3MCCzyPRKNcModBC5DoAAYdwKIFWLNpwSzw2CIhk7g8J7DA80hdXZ3fIyAgaAESHcCgA1i0AGs2LXTHx7iCl2FcnhNY4HmkpaXF7xEQELQAiQ5g0AEsWoCVbgsTyUkdGmaBl2lcnhNY4HmkqqrK7xEQELQAiQ5g0AEsWoCVbgu9Q2NKpdjkPNO4PCewwPPIxMSE3yMgIGgBEh3AoANYtAAr3Ra67B54xXwGL5O4PCewwPNIX1+f3yMgIGgBEh3AoANYtAAr3Ra642OSuIKXaVyeE1jgeaSxsdHvERAQtACJDmDQASxagJVuCz32Ch6fwcsoLs8JLPA80tzc7PcICAhagEQHMOgAFi3ASreF7qkFHrdoZhKX5wQWeB4pKOBvVWDQAiQ6gEEHsGgBVrotdMfHVJCbowUFuY4mgh9cnhNY4Hmkurra7xEQELQAiQ5g0AEsWoCVbgvdgwlVLShQJBJxNBH84PKcwALPI62trX6PgICgBUh0AIMOYNECrHRb6IoneMBKBnJ5TmCB55Gamhq/R0BA0AIkOoBBB7BoAVa6LXTHx1TN5+8yjstzAgs8j4yMjPg9AgKCFiDRAQw6gEULsNJtoTue4AmaGcjlOYEFnkcGBwf9HgEBQQuQ6AAGHcCiBVjptDA5mVLv0BgLvAzk8pzAAs8j7G8DixYg0QEMOoBFC7DSaaFvZEzJyZQquUUz47APXgiwvw0sWoBEBzDoABYtwEqnhZ74uCQ2Oc9E7IMXArFYzO8REBC0AIkOYNABLFqAlU4LPUN2k3MWeJnG5TmBBZ5HysvL/R4BAUELkOgABh3AogVY6bTQEzcLvOoSbtHMNC7PCSzwPNLW1ub3CAgIWoBEBzDoABYtwEqnhZ4hbtHMVC7PCSzwPFJbW+v3CAgIWoBEBzDoABYtwEqnhZ6hhPKiES2M5TmcCH5weU5ggecRHn8MixYg0QEMOoBFC7DSaaFnMKHK4gJFIhGHE8EPbJMQAkNDQ36PgICgBUh0AIMOYNECrHRa6BkaUxWfv8tILs8JLPA8wv42sGgBEh3AoANYtAArnRZ62OQ8Y7EPXgiwvw0sWoBEBzDoABYtwEpvH7wEC7wMxT54IVBcXOz3CAgIWoBEBzDoABYtwJppC6lUSr1D46pcwC2amcjlOYEFnkdKSkr8HgEBQQuQ6AAGHcCiBVgzbWFwdEJjyUlVcwUvI7k8J7DA80hHR4ffIyAgaAESHcCgA1i0AGumLXTHxySxB16mcnlOYIHnkfr6er9HQEDQAiQ6gEEHsGgB1kxb6BlKSGKBl6lcnhNY4Hnk0KFDfo+AgKAFSHQAgw5g0QKsmbbQa6/gsU1CRnJ5TmCB55GRkRG/R0BA0AIkOoBBB7BoAdZMW+gZ4hbNTObynMACzyPsbwOLFiDRAQw6gEULsGbaQs9QQjkRqbyIK3iZiH3wQoD9bWDRAiQ6gEEHsGgB1kxb6IknVF6Ur2hOxPFE8AP74IUAjz+GRQuQ6AAGHcCiBVgzbaE7zh54mYxtEkIgFov5PQICghYg0QEMOoBFC7Bm2kJPPMHn7zKYy3PCjBZ4o6Oj2r17t7MhMkFnZ6ffIyAgaAESHcCgA1i0AGumLfQMJVRRzBW8TOXynDDtAm/Tpk267LLL9LGPfUyStH37dv3N3/yNs4HCqqGhwe8REBC0AIkOYNABLFqANZMWUqmUeobGVMkCL2O5PCdMu8D7zne+o5/+9KcqLS2VJDU1NenAgQPOBgqrrq4uv0dAQNACJDqAQQewaAHWTFoYGktqdHxSldyimbFcnhOmXeDl5ubyweAZSCQSfo+AgKAFSHQAgw5g0QKsmbSwvX1AklS3sND1OPCJy3NC7nQHnHrqqXrwwQeVTCa1d+9e3XXXXTr77LOdDRRW7G8DixYg0QEMOoBFC7Bm0sLGP+5XcX5Uf3569TxMBD/4ug/eP//zP2vXrl3Kz8/XTTfdpAULFuif/umfnA0UVuxvA4sWINEBDDqARQuwpmuhf3hcv9zapnevrVNRwbTXYhBSLs8J01YTi8X0yU9+Up/85CedDZEJysrK/B4BAUELkOgABh3AogVY07Xwi80HlJiY1FXnLJmnieAHl+eEaa/gXXfddRoYGJj6ur+/Xx/96EedDRRWubn8DQsMWoBEBzDoABYtwDpZC6lUSvf8sUVnLl6oM+pL53EqzDeX54RpF3iHDh2aeoKmJC1cuFA9PT3OBgqr7u5uv0dAQNACJDqAQQewaAHWyVp4YX+fdnQM6uo3LZ3HieAHl+eEaRd4OTk5amtrm/r6wIEDikQizgYKq6VL+RcRBi1AogMYdACLFmCdrIV7nmlRUX5U711XP48TwQ8uzwnTXhu88cYbdc011+jcc89VKpXSc889py984QvOBgqr9vZ2v0dAQNACJDqAQQewaAHWiVoYGB3Xg1vb9L6zF2tBQa4SieQ8T4b55PKcMO0C78ILL9R9992nLVu2SJJuvvlmVVRUOBsorJJJ/iWEQQuQ6AAGHcCiBVgnauH+Fw5odHyS2zOzhMtzwglv0bSP7ty2bZva29tVU1Ojmpoatbe3a9u2bc4GCqtly5b5PQICghYg0QEMOoBFC7CO10IqldLdz7RodX2pzly80IepMN9cnhNOuMD74Q9/KEn66le/esx/vva1rzkbKKz27Nnj9wgICFqARAcw6AAWLcA6XgtbWvunHq7Csy6yg8tzwglv0fziF7+oyclJ3XjjjTrnnHPSfuFEIqEPfvCDGhsbUzKZ1CWXXKK///u/P+qY++67T7feeqsWLVokSbr22mt11VVXpf1eQcBtq7BoARIdwKADWLQA63gt3PNMi2J5UV3Gw1Wyhstzwkk/g5eTk6MvfvGL+sUvfpH2C+fn5+tHP/qRiouLNT4+rmuuuUYXXnih1q1bd9Rx73rXu3TLLbek/foAAABA2A2OjuuBLW16z1l1KinM83scZIBpt0m44IIL9NBDDymVSqX1wpFIRMXFxZKkiYkJTUxMZPQl597eXr9HQEDQAiQ6gEEHsGgB1utbuH9zm0bGkzxcJcu4PCdM+xTNjRs36o477lBubq7y8/OVSqUUiUT0/PPPT/viyWRS73//+9XS0qJrrrlGZ5111jHHPPzww3r22We1fPlyfe5zn1NdXd3svhOfLV++3O8REBC0AIkOYNABLFqAdWQLqVRK//VMi1bVlmjdkjIfp8J8c3lOmHaB98ILL8z6xaPRqO6//34NDAzo+uuv1yuvvKLTTz996vcvuugiXXrppcrPz9fGjRv12c9+VnfeeedJXzORSGj79u1TX4+Pj0uS8vKmv6Tt6ljJPHXUXrH04nWD8H1xbPrHStndgt/vH5RjpezuIJOP9buDsB2b7s/L7xk4J3Bsusem24F0dAuvdCf0cvuA/va8Su3YscP5DPQVjGMlN//7YJ1wgbd371597Wtf0/79+7Vy5Ur9wz/8w9TDUNJVWlqq8847T7/97W+PWuCVl5dP/fNVV12lr3/969O+VkFBgZqamqa+TiQSU78+HVfHStLOnTuPmmuurxuE74tj0z9Wyu4W/H7/oBwrZXcHmXys3x2E7dh0f15+z8A5gWOtXe2HVF1SoIULijx7TevIFu68b6sK83L08Xeco4Wxo/8PvJ8tBOG/g0w+Vpr7OeHIC16vd8LP4N1888266KKL9K1vfUtNTU364he/OKNhrd7eXg0MDEiSRkdH9fvf/14rVqw46pjOzs6pf960aZMaGxvTeo8gCeutpfAeLUCiAxh0AIsWwuOFlkO69Nu/00d++KyGxyY8f33bQjwxofs3t+nStfXHLO6Q+VyeE064wBsaGtIHPvABrVixQh/72Md04MCBtF64s7NTf/3Xf633vOc9uvLKK/XmN79ZF110kW6//XY99thjkqS77rpL7373u/Xe975Xd955p77yla/M7bvxUUtLi98jICBoARIdwKADWLQQDv0j4/rEPS9oYSxPLx7o18fvek6JiaSn72FbeGBzm4bHeLhKtnJ5TjjhLZqJREIvv/zy1NMzR0dHj/p69erVJ33hVatWHXd7hRtuuGHqn2+66SbddNNNsxo8aKqqqvweAQFBC5DoAAYdwKKF4EulUrr5vhfV3j+q//rIOdrdPaTP/WK7bty4Wd+++mzlRqd9+PyM2Bbu+WOLVi4q0RuW8nCVbOTynHDCBV51dfVRV9Sqqqqmvo5EItM+DCXbTEx4fwkf4UQLkOgABh3AooXg2/jsfv3qxXZ99h2rtG5JmdYtKdPQuPSvv9qum3/+or52xVpPtvyamJjQSwf69eKBfn3+PWdk9DZiODGX54QTLvDuuusuZ2+aifr6+vweAQFBC5DoAAYdwKKFYHvl4KA+/8A2vfW0Kn38whUaHx+TJH3srSs0MDKub23apYWxPN38rqY5L8j6+vr0xB9bVJCbo/ed3eDF+Aghl+eEabdJwMyE+QEx8BYtQKIDGHQAixaCa2Qsqevvfl4lhXn6xgfWKSfn6AXcJzecroHRCf3Hb/doYSxPf3fxaXN6v8VLl+tf/rtN715bp4VFPFwlW7k8J3hzMzHU3Nzs9wgICFqARAcw6AAWLQTXF365Ta92xvXNvzxL1SXHPuI+EonolkvP0PvPXqzbHn5Fdz29d07v9+TmHYonJnQND1fJai7PCVzB80g6+58gs9ECJDqAQQewaCGYHtzSpnv+uF//95836q2nVZ/wuJyciL525VoNjE7olge2qTSWp8vWLZ7Ve27vndRpNQt0zrLy6Q9GxnJ5Tpj2Ct5zzz2n4eFhSdL999+vr3zlK2lvmZANqqtPfFJAdqEFSHQAgw5g0ULwtPQM6+b7XtQblpbpUxtOn/b4vGiOvnPN2TpveYU+9ZMtemz7wbTfc1tbv7YOxHT1m5bycJUs5/KcMO0C7/Of/7xisZh27NihO+64Q0uXLtVnP/tZZwOFVWtrq98jICBoARIdwKADWLQQLGMTk/rEPc8rEpFu/6uzlTfDLRAK86L6/ofO1er6Uv3t3c/r2b29ab3vxj/u14rcPr3/DbO7+ofM4fKcMG3Nubm5ikQievTRR/XBD35QH/zgBzU0NORsoLCqqanxewQEBC1AogMYdACLFoLltod3aktrv752xVotqShK688uKMjVD697k5ZWFOnv/usFbTswMPV7ycmU+obHtLd7SJv39+mJnZ26f/MB/ej3e3X7o6/qFy8cUE1NtcqK8r3+lhAyLs8J034Gr7i4WN/73vf04IMP6j//8z81OTnJXi7HMTIy4vcICAhagEQHMOgAFi0Ex+M7O/X/Prlb156/VO88s25Wr1FRnK+7Pnqe/vLfn9RHfvSsFi6IqW94XAOj40qlTv7nzqvlERhwe06YtrBvfvOb+uUvf6kvfelLqq6uVltbmz760Y86GyisBgcH/R4BAUELkOgABh3AooVg6Bwc1U0/2aJVtSX6X+8+Y06vVbuwUP/fh87VtzbtUk40V2VF+VoYy1NZ0eH/xPK1sChPZbE8lRXlq7QwV7nRHN17770efTcIM5fnhGkXeNXV1bruuuumvq6vr9fll1/ubKCwYn8bWLQAiQ5g0AEsWvBfcjKlf/zZixoZS+o715ytwrzonF9zSUWRvn7l2rSeiEgLkHzeB2/z5s264oordPbZZ2vNmjVqamrSOeec42ygsGJ/G1i0AIkOYNABLFrw3/ef3K1n9vTqX967WqfWlPg2By1ActvBtAu8L3zhC/rGN76hZcuWacuWLfrXf/1XXXPNNc4GCqtYLOb3CAgIWoBEBzDoABYt+GtHx4C++5tmvevMOl31xgZfZ6EFSG47mNEzYZctW6ZkMqloNKorrrhCv/3tb50NFFbl5WxWCYMWINEBDDqARQv+SaVS+uIvX9aCglz907tW+b7/HC1ActvBtJ/Bi8ViGhsbU1NTk2699VbV1NRocnLS/FUsfQAAIABJREFU2UBh1dbW5vcICAhagEQHMOgAFi3455GXD+qpXT265Z2nBWJ7AlqA5LaDaa/g3XrrrUqlUrrllltUVFSk9vZ2ffvb33Y2UFjV1tb6PQICghYg0QEMOoBFC/5ITCT1pf/erlNrFugD5y7xexxJtADDZQfTXsFbvHixJKmgoEB/93d/52yQsOPxx7BoARIdwKADWLTgjzue2qt9PcP60UfepLzojD6Z5BwtQHLbwQlLf/TRR3X33XdPfX3VVVdp/fr1Wr9+vX796187GyishoaG/B4BAUELkOgABh3AooX51zWY0Hc27dL6VTV62+nVfo8zhRYgue3ghAu873//+7r44ounvh4bG9NPf/pT3XXXXbrnnnucDRRW7GkCixYg0QEMOoBFC/Pvtod2KjGR1D+9u8nvUY5CC5B82gdvfHxcdXV1U1+fc845Ki8vV319vUZGRpwNFFbsaQKLFiDRAQw6gEUL8+ulA/36yXP79aELTtGK6gV+j3MUWoDk0z54AwMDR319yy23TP1zb2+vs4HCqri42O8REBC0AIkOYNABLFqYP6lUSv/y4DZVFOXrE+tP83ucY9ACJLcdnHCBt3btWv3kJz855tc3btyotWvXOhsorEpKSvweAQFBC5DoAAYdwKKF+fOrF9v17N5DuuntK7Uwluf3OMegBUhuOzjhUzRvvvlmXX/99XrwwQe1evVqSdK2bds0Njam7373u84GCquOjg6/R0BA0AIkOoBBB7BoYX6Mjif1lf/eoaa6Uv1lQLZFeD1agOS2gxMu8CorK7Vx40Y9/fTT2rVrlyTpbW97my644AJnw4RZfX293yMgIGgBEh3AoANYtDA//uPJ3TrQN6LbrjpL0ZyI3+McFy1ActvBtPvgXXDBBSzqZuDQoUN+j4CAoAVIdACDDmDRgnsd/aP6P080651ranVBY6Xf45wQLUBy20EwdnzMADxZFBYtQKIDGHQAixbc+9qvdyiZSunmdwVrW4TXowVIbjtggecR9jSBRQuQ6AAGHcCiBbeebzmkn79wQB/7s+VaUlHk9zgnRQuQfNoHD+lhTxNYtACJDmDQASxacGdyMqUvPPiyqksK9LcXner3ONOiBUg+7YOH9PDIW1i0AIkOYNABLFpw51cvtWnz/j79wyUrtaBg2sdL+I4WILntgAWeR2KxmN8jICBoARIdwKADWLTgxnBiQt94+BWtbVioK97Q4Pc4M0ILkNx2EPy/5giJzs5Ov0dAQNACJDqAQQewaMEbqVRKnYMJ7e0e0r6eYT3+cps6B8f07WvPUE5At0V4PVqA5LYDFngeaWgIx98awT1agEQHMOgAFi3MXHIypY6BUbUPDmpvz5Baeoa1t8cs6Pb1DGtkPDl1bGHOpP76gmU6Z1mFjxOnhxYgue2ABZ5Hurq6/B4BAUELkOgABh3AooWTG0pM6MlXuvTIywf15I529Y2Ma0JRSVJ+NEdLK4t0SmWR3nJqlU6pLNKyymKdUlmsylhEudFwfeKIFiC57YAFnkcSiYTfIyAgaAESHcCgA1i0cKzOgVE9ur1Tj7zcoaeaezQ2MamFsTytP61KZy8rV+OiMi2rKlZtaaGiJ7j9Mow/1zDODO+57IAFnkfY0wQWLUCiAxh0AIsWzOfnXj04qIdfPqhHXj6ozfv7JElLKmK69rxl2nDGIp17SrmSE+OSpIKCAj/HdYYWILEPXiiwpwksWoBEBzDoAFY2t5BKpfQfv92td3/rt9rwzSf19Yd2ajKV0k0bTtevb3yrnvzMRbrlPWfogsbK0N1uORvZ3AJe47IDruB5pKyszO8REBC0AIkOYNABrGxu4dubdulbj76q806p0IcvPF1/0VSjuoXZu1VANreA17jsgAWeR3Jz+VHCoAVIdACDDmBlaws/e65V33jkFb1/bZ2+/P4zVVhY6PdIvsvWFnA0lx1k/nXwedLd3e33CAgIWoBEBzDoAFY2tvDUrm599mdb9ebGSv3LZWsUiYRjnzrXsrEFHMtlB/wVgkeWLl3q9wgICFqARAcw6ABWtrWws2NQf3PXc1pRXax/v/Yc5edM+j1SYGRbCzg+lx1wBc8j7e3tfo+AgKAFSHQAgw5gZVMLBwdGdd0df1QsP6o7rnuTFsby/B4pULKpBZyYyw5Y4HkkmUz6PQICghYg0QEMOoCVLS3EExO67o5n1T8yrh98+FwtLsveh6mcSLa0gJNz2QG3aHpk2bJlfo+AgKAFSHQAgw5gZUMLE8lJXX/389p5cFDf/9AbtWbxQr9HCqRsaAHTc9kBV/A8smfPHr9HQEDQAiQ6gEEHsDK9hVQqpf/1i5f0m1e69K+Xr9FFK2v8HimwMr0FzIzLDljgeaSiosLvERAQtACJDmDQAaxMb+H/PNGsjc/u1/UXNerqN/EQkZPJ9BYwMy47YIEHAACAWfvFCwf09Yd26rJ19fr021f6PQ6Q9VjgeaS3t9fvERAQtACJDmDQAaxMbeGPe3r1mZ9u0fkrKnTrlWvZ624GMrUFpMdlByzwPLJ8+XK/R0BA0AIkOoBBB7AysYXdXXHdsPEFnVJZrO9d+0YV5Eb9HikUMrEFpM9lByzwPLJv3z6/R0BA0AIkOoBBB7AysYUv/fd25ebk6I7rztXCIva6m6lMbAHpc9kBCzyPRKP8rRUMWoBEBzDoAFamtdDSM6w/7O7VtectVUN5kd/jhEqmtYDZcdkBCzyP1NXV+T0CAoIWINEBDDqAlWkt/PhPLcqJSJe9od7vUUIn01rA7LjsgAWeR1paWvweAQFBC5DoAAYdwMqkFiaSk7r3T6268LRq1ZbG/B4ndDKpBcyeyw5Y4HmkqqrK7xEQELQAiQ5g0AGsTGrhiZ1d6hxM6Io3LPZ7lFDKpBYwey47YIHnkYmJCb9HQEDQAiQ6gEEHsDKphY3Ptqi6pEBvPb3a71FCKZNawOy57IAFnkf6+vr8HgEBQQuQ6AAGHcDKlBY6+ke1aUenrjqnQXlR/m/kbGRKC5gblx3wb6ZHGhsb/R4BAUELkOgABh3AypQWfvZ8qyZT0gfeuMTvUUIrU1rA3LjsgAWeR5qbm/0eAQFBC5DoAAYdwMqEFiYnU9r4bIsuWFGpU6qK/R4ntDKhBcydyw5Y4HmkoKDA7xEQELQAiQ5g0AGsTGjh6d092t87or96E1fv5iITWsDcueyABZ5Hqqv5oDEMWoBEBzDoAFYmtLDx2f1aGMvTJatr/R4l1DKhBcydyw5Y4HmktbXV7xEQELQAiQ5g0AGssLfQOzSmh17q0PvOXqzCvKjf44Ra2FuAN1x2wALPIzU1NX6PgICgBUh0AIMOYIW9hZ+/cEBjyUluz/RA2FuAN1x2wALPIyMjI36PgICgBUh0AIMOYIW5hVQqpR8/26J1S8q0qrbU73FCL8wtwDsuO2CB55HBwUG/R0BA0AIkOoBBB7DC3MLzLX165WBcf3UuV++8EOYW4B2XHbDA8wh7msCiBUh0AIMOYIW5hR8/26Ki/KguPave71EyQphbgHfYBy8E2NMEFi1AogMYdAArrC0Mjo7rwS3teu9Z9VpQkOv3OBkhrC3AW+yDFwKxWMzvERAQtACJDmDQAaywtvDLre0aGU/qL7k90zNhbQHectkBCzyPlJeX+z0CAoIWINEBDDqAFdYWNv6xRSsXlWjdkjK/R8kYYW0B3nLZAQu8/5+9+45vqz70//+W5W3Le8l7ZTiOs3cCIRNICEnYe7S0tE0bWlr640J7Ke0tl35vC7e9bSmllAYKAcJIGIGEMLIHSexMZ3jvvZdsSef3hziBQBJb9vnocyS9n49HHyXJ8dEn5sVH+lhH56ORmpoa2UMgnWALBLADcmAHpHLHFk7WdOBIVTtumZECg8Egezgewx1bIO2J7IALPI0kJCTIHgLpBFsggB2QAzsglTu28PrBSvj7+mD15CTZQ/Eo7tgCaU9kB8I+LWuxWHD77bejv78fNpsNV155JdauXXveMf39/fj5z3+OEydOICIiAk8//TSSk5NFDUko3vKWVGyBAHZADuyAVO7WQt+ADW8drsJVuQmICPaXPRyP4m4tkBhuuU2Cv78/1q1bh3feeQcbN27Ezp07UVBQcN4xGzZsQFhYGD766CPcc889+P3vfy9qOMJ1d3fLHgLpBFsggB2QAzsglbu18OHxOnT0Wbn3nQDu1gKJIbIDYQs8g8GAkJAQAIDVaoXVav3G9duffPIJVq9eDQC48sorsXfvXiiKImpIQnFPE1KxBQLYATmwA1K5Wwuvfl6BtOhgzMqMlj0Uj+NuLZAYIjsQuqGJzWbDddddh4qKCtx2222YOHHieX9eX18Ps9nsGIivL0wmE1pbWxEVFXXRc1osFhQWFp779cDAAADAz89v0PGIOhYATp8+PaTbnQ71vHr4e/FY548FvLsF2Y+vl2MB7+7Ak4+V3YG7Hevs90v2GDgnOI6t6RzAvpIW3DMlEqdPn5IyBnc51tkOAPdoQfb31dOPBcQ8P6iELvCMRiM2bdqEjo4OrFmzBmfOnMHo0aNHdM6AgADk5OSc+7XFYjn3+4MRdSwAlJaWnjeukZ5XD38vHuv8sYB3tyD78fVyLODdHXjysbI7cLdjnf1+yR4D5wTHse9/dAZGHwN+cNUUxIUFShmDuxzrbAeAe7Qg+/vq6ccCI+/gq294fZ1L7qIZFhaGmTNnYufOnef9fnx8PGprawE4LuPs7Ox0271BTCaT7CGQTrAFAtgBObADUrlLCwM2OzbmV2Ph2LhLLu5o+NylBRJLZAfCFngtLS3o6OgAAPT19WHPnj3IzMw875iFCxfi7bffBgBs2bIFs2bNctt9Vurq6mQPgXSCLRDADsiBHZDKXVrYfroRzd39vLmKQO7SAoklsgNhl2g2NDTg4Ycfhs1mg6IouOqqq7BgwQL88Y9/xPjx47Fo0SLccMMNeOihh7BkyRKEh4fj6aefFjUc4RITE2UPgXSCLRDADsiBHZDKXVpY/3kF4kz+mD86VvZQPJa7tEBiiexA2AJv7Nix2Lhx4zd+/4EHHjj3zwEBAfjTn/4kaggu1draKnsIpBNsgQB2QA7sgFTu0MK+kmbsK2nBz5aOhq/RJZ/i8Uru0AKJJ7ID/terkd7eXtlDIJ1gCwSwA3JgB6TSewuKouAPW08jNtQft85IlT0cj6b3Fsg1RHbABZ5GuKcJqdgCAeyAHNgBqfTewvYzjfi8rBXfm5+FQD+j7OF4NL23QK4hsgMu8DRSXFwsewikE2yBAHZADuyAVHpuwfHu3RkkRwbhuinJsofj8fTcArmOyA64wNMIb3lLKrZAADsgB3ZAKj23sOVEHY5Vt+OBRaPg78uXhqLpuQVyHbfcJsHbDGUnevIObIEAdkAO7IBUem3BZlfw1EdnkBkbgtWTk2QPxyvotQVyLZEdcIGnkYaGBtlDIJ1gCwSwA3JgB6TSawvvHqnBmfouPLiEd850Fb22QK4lsgP+l6yR5GRes04OntaCoijoG7DJHobb8bQOaHjYAan02MKAzY6nt51BjjkMy8abZQ/Ha+ixBXI9kR0I2wfP2zQ2NsoeAumEu7Rgtyto7e1HW7MFDZ19aOi0oLHTgoYOxz/Xd3z5ezbrAJZPMOORa/IQHxYoe+huwV06ILHYAan02MIbh6pQ3tyDf9w1DT4+BtnD8Rp6bIFcT2QHXOBpxGKxyB4C6YQ7tHCqrgM3/XUnevptsOL822GHBfoiLiwQcaYATEuLRFxYIPotFrx+qArbTjXjgcWjcM+cDH4QfxDu0AHgeIeWxHGXDkg8vbXQN2DDnz4+i0kpEViUEyd7OF5Fby2QHCI74AJPI9zThFTu0MK/dpdBUYD/uHoszFEmxJkCEGcKRFxYwAX3P7JYLLhjVhqe3FqEJzafwmufV+JX1+bislGxEkbvHtyhg8qWHnznhb2YlRmNX62eJHs4HskdOiDX0FsLr+yvQG17H35/40QYDHz3zpX01gLJwX3w3AD3NCGV3lvo6BvApoIaLBufgDtmpWFZnhnT0qOQGh18yc1tU6KC8Y+7p+Of90yD1a7gzucP4Pv/PoTqtl4Xjt596L2DksYu3PzsXhQ1dmP955WobOmRPSSPpPcOyHX01EJPvxV//awIszOjMTc7RvZwvI6eWiB5uA+eG4iIiJA9BNIJvbewMb8avQM23DQ9ZVhfv3BsPLb8+HI8dOUYfHq6AYv+8Bn+/MlZaTdisdsVlDR2YVNBNX7z3knc/fwBPPTGUSlj+So9d3C6rhM3PbsPFqsdf719MnwMwLM7+IJDBD13QK6lpxb+tacMTV39+NmVY2QPxSvpqQWSR2QHvERTI76+/FaSg55bUBQFr+yvQF5SOMYnhQ/7PIF+RqxZkI1Vk5Pw2/dP4vdbz2DDoSo8elU25o8W91kORVFQ2dKLo9VtOFbVjqNV7The3Y5OixUAEODrA7PJiIMVrVjb0InsOHmbyeq1g2NV7bjrn/vh7+uDl++bhZRwf6yalIjXD1Zh7cJRiONNdDSl1w7I9fTSQnvvAJ7dXoKFY+MwNS1S9nC8kl5aILlEdsDCNNLU1CR7CKQTem7hcEUrTtV14snr8jQ5X1JEEP56+1TsOtuEx945jh+8nI8Z6ZGYlB6LzNhQZMWGIDM2FOFBfk6dV1EUNHRaUNzQheKmbhTXtqK4sRtHa7vR3jsAAPA3+iDHbMLKyYmYkBSBvORwZMeForalEwv/sB2bj9Vh7SJ5Czw9dnCwrAX3vvA5woL88Mp3ZiItOgQWiwXfmpuJ1w7X4R+7SvHIshzZw/QoeuyA5NBLC8/vLEF77wAeXDJa9lC8ll5aILlEdsAFnkZSU1NlD4F0Qs8tvLyvAqYAX6yYmAhAu0sq542KwQcPXI4XdpzFm4cr8fcdJbDav7w7Y0xoADJjQ5AVG4Ks2FBkxoYgNdwfMaH+KG21oLihGyWNXShu7EJJUzdKGrvR9cW7cgAQ5g9kxIRgWZ4ZeUnhmJAcjtHxpgveyTM+LBCTUyKw+Vgt1i4apdnf0Vl662BPURO+ve4gEsID8fJ9M5EYEXTuz1Kjg7FiYiL+va8cP7giCxHB/hJH6ln01gHJo4cWmrsseH5XKZblJYzoKg4aGT20QPKJ7IALPI3U1tbKHgLphF5baO3ux3vHanHL9BSEBPjCYtH2M3P+vj64Z2467pmbDh9fP1S29KC48SsLt8ZufHi8Dq09jnfgfL9YYH51m4akiCBkxobghqnJXywIHYvByAADDAYDAgIChjSWpbkJ+O2HZ1HS2IXM2FBN/55DpacOPjlVj+/9+zAyokPw0n0zEGf65mWYP7giG5sKavDC7jL8hD/Z14yeOiC59NDCsztK0Dtg47t3kumhBZJPZAdc4GnEZpNzgwnSH7228ObhKvRb7bhtpvifHPoZfZAZG/rF4ir+vD9r6e5HSWMXzta2oanLgrS4cGTFhiAjJgTB/heekpzdK2bJuDj89sOz+OB4HdYsyB7uX2NE9NLB5mO1eODVfIxNCMOL35qByJALvzs3JsGEJePi8a89ZfjO5ZkIDeDTgxb00gHJJ7uFhs4+rNtThlWTk6R+Ppnkt0D6ILIDPoNrJC0tTfYQSCf02IKiKHh5fwWmpkVibEKY1LFEhfgjKiQKeeYQABjyu3LOMIcHYXJqBN4/WittgaeHDt46XIWfbTiCyamReOHe6QgLvPRnIdcsyMZHJ3fj5X3luH8+92nSgh46IH2Q3cLft5fAZlfw40V890422S2QPojsgNskaKS0tFT2EEgn9NjC3uJmlDZ143YXvHunF8vzzDhZ24Gypm4pjy+7g9cPVuKnG45gdlY0Xvr2jEEXdwAwKSUC87Jj8NzOUmnbXnga2R2QfshsoaK5B28crsLN01OQGh0sbRzkwHmBALEdcIGnkaioKNlDIJ3QYwsv769ARLAfluWZZQ/FZa4anwAA2HxczmcdZHbw/tEaPP7uSSwYE4fn755+0UtfL2TNgmw0dVmw4WClwBF6Dz3OBySHjBYURcHG/Grc8tw++Bt98KOF8m48RV/ivECA2A64wCPycA2dfdhyog43TElGoJ9x8C/wEMmRwZiYEoEPjtXJHorLvXGoClmxIfjbHVOd/nc+KzMKU1Ij8LftJRiw2QWNkIhEa+y04Hv/PoQfv1aAjOhgvH7/bCSEc59LIm/ABZ5GWlpaZA+BdEJvLWw4WAWrXcGtXnR5pmrZ+AQcq25HRXOPyx9bVge9/TYUVLbj8tGxF9xGYjAGgwE/XJiN6rZebCqoETBC76K3+YDkcWUL7x2twdKnt+PT0414ZNlYvPjtmUiPCXHZ49OlcV4gQGwHXOBpJCMjQ/YQSCf01ILNruCV/RWYkxWNLEnbBcikXpL6gYTLNGV1cLC8Bf02O2ZmDP/SjwVj4pBjDsNfPyuC7Sv7GZLz9DQfkFyuaKGlux9rXjmMH76Sj9SoYLz/o3n47uVZMPoYhD82DR3nBQLEdsAFnkbKy8tlD4F0Qk8t7DjTiOq2Xtw+0zvv2JUSFYy8pHBsPub6BZ6sDnYVNcHPaMC0tMhhn8NgMGDNgiyUNHZjywnvu8RVS3qaD0gu0S18eLwOS5/ejq0n6vDQlWPw5vfnYFQ8t0PQI84LBIjtgAs8jRiN3vPZJro0PbXw8v5yxIQGYMm4+MEP9lDL8sw4UtWOqlbXXqYpq4M9Rc2YmByBICdurHIhV483IzMmBH/5tAiKwnfxhktP8wHJJaqFtp5+/PjVfHzv34cQHxaId380D2sWZMPXyJd4esV5gQCxHfC/fo2Yzd5zd0K6NL20UNPWi09ONeDm6cnD+iyWp1iW57ibpqtvtiKjg7aefhyvacesjOgRn8voY8D3rsjCiZoOfHamUYPReSe9zAckn4gWPjvdgCVP78B7R2vx48WjsHHNXOl7ndLgOC8QILYD733Vp7GKigrZQyCd0EsLr35eCQXALdO97+YqX5UWHYLcxDCXb5cgo4O9xc1QFGB2tja3Xl41KQmJ4YH466dFmpzPG+llPiD5tG5hU0E11rySj+gQf2xcMxc/XjwafnzXzi1wXiBAbAecCTQSExMjewikE3poYcBmx6sHKjB/dCxSorip7bI8M/Ir2lDT1uuyx5TRwe7iJoT4G5GbGK7J+fx9fXD//Cx8XtaK/SXNmpzT2+hhPiB90LqFjwsbkBwZiE0/nIvxSdr8N0+uwXmBALEdcIGnEavVKnsIpBN6aOHjwgY0dFq89uYqX3f1F5uef3DcdZdpyuhgT1EzZmZGa/pT/JunpyAm1B9/+ax4SMdbbXbsPNuIJz84hZLGbs3G4a70MB+QPmjZgqIoOFLVhikpkQjw5ee53A3nBQLEdjCyT+HTOW1tbbKHQDqhhxZe3l8Oc3ggFoyJlT0UXciMDcXYBBM2H6vFt+e55vbUru6gpq0XJU3duH2Wtov6QD8jvj0vE7/78BSOV7df8J0CRVFwuKIV7xTU4P1jtWjq6ocvbDha1YYNP7gcPl58i3Y9zAekD1q2UN3Wi6aufkxMidDsnOQ6nBcIENsB38HTSFZWluwhkE7IbqGypQc7zzbhlumpvIvaVyzPM+NQeSvq2vtc8niu7mB3URMAYG72yG+w8nV3zEpFWKAvnttZcu73FEXByZoOPPnBKcz73ae4/pm9WP95JaanR+GZ26fgsRXjcKSqHW/lV2s+Hnciez4g/dCyhfwKxwvDCclc4LkjzgsEiO2Ar/40Ulw8tMuXyPPJbuH1Q5Uw+hhw8/QUqePQm6tdvOm5qzvYU9yMmFB/jBGw75Up0A/3zEnHtsIGbD/TgD99fBZLnt6BZX/aied2liA7LhR/uHEiDv1iMZ65YyquzjPjhinJmJgcjic/OIWOvgHNx+QuZM8HpB9atpBf0YYAXx+Mig/V7JzkOpwXCBDbARd4GgkICJA9BNIJmS1YrDZsPFyDxTlxSAgPlDYOPcqOC8WYeJPLtktwZQeKomBXURPmZMXAYBBzOeQ9czMQ5GfED17Ox1MfnUFUiD9+s2o8DjyyCOu+NQPXT02GKdDv3PE+PgY8siwHzd0W/GnbWSFjcgd8biCVli3kV7ZifGIY75rppjgvECC2A34GTyOxsfysEznIbGHbyXq09PTz5ioXcXVeAv748Vk0dPQhLkzsAtiVHRQ1dKGx0yLk8kxVVIg/nrwuD9VtvbhmcioSI4IG/ZrxSeG4eVoK/rWnDLfMSEF2nPbvLuodnxtIpVULFqsNJ6o7cPesJE3OR67HeYEAsR3wRz8aqaqqkj0E0gmZLbx+sAopkUGYl81bMF/I8jwzFAX48IT4d/Fc2cGuLz5/NydL7L/3xePicfec9CEt7lQPXTkGwf5G/Oqdk1AUReDo9InPDaTSqoWTNR3ot9kxkVsjuC3OCwSI7YALPI3ExcXJHgLphKwWCms7cLC8FTdOS/HquxZeyqh4E7LjQvH+UfGfw3NlB7uLmpEWHazLPQ+jQwPw4JLR2FXUhC0uWFjrDZ8bSKVVC+dusMI7aLotzgsEiO2ACzyN9Pa6bgNl0jdZLfxrdxkC/Xxww1RetnMpy/LMOFDWgsZOi9DHcVUHVpsd+0uahb97NxJ3zErDmHgTfvNeIfoGbLKH41J8biCVVi0UVLbBHB6IeMGXmZM4nBcIENsBF3ga6ezslD0E0gkZLbR092NjQTVWTEhEeJC/yx/fnbjqMk1XdXC0uh2dFqvQz9+NlK/RB7+6NhfVbb3423bvunscnxtIpVUL+ZWtmJzKd+/cGecFAsR2wAWeRrinCalktLD+QAUsVjtvrjIEo+NDkRkbgg+Oib1M01Ud7HHR5+9GanZWNJZPMOOZz4pR2dIjezguw+cGUmnRQmOnBZUtvZicEqnBiEgWzgsEcB88t8A9TUjl6hYGbHa8tLccc7OjuSfSEBgMBizPM2NfSTOau8VdpumqDnYz2bWjAAAgAElEQVQXNWOcOQxRIfp/5/bRZTnwMRjw2/cLZQ/FZfjcQCotWiiodHz+ju/guTfOCwRwHzy3EBQ09LvKkWdzdQtbTtShrqMP987JcOnjurOrx5thV4BPChuFPYYrOujtt+FQeSvmjdL3u3eqxIggrFmQhQ9P1GHX2SbZw3EJPjeQSosW8ita4etjwHjeQdOtcV4gQGwHXOBpJDKSl0uQg6tbeGF3GVKjgrFgLO/KNVQ5ZhMyYkKE3tXRFR0cLG9Bv82OOVn6/fzd1913WSZSo4Lxq3dPYMBmlz0c4fjcQCotWsivaEOOOQyBfkYNRkSycF4gQGwHXOBppKamRvYQSCdc2cLRqjYcKm/F3XPSYeTWCENmMBhw9fgEHChrQWtPv5DHcEUHu4ua4Wc0YEZGlPDH0kqgnxH/ec04FDV0Yd2eMtnDEY7PDaQaaQs2u4IjVW28PNMDcF4gQGwHXOBpJCEhQfYQSCdc2cK/dpchxN+IG6clu+wxPcWyPDNsdgWfFDYIOb8rOthd1ITJqZEI9vcV/lhaWpQThyvGxOKP284K365CNj43kGqkLZyp70RPv40LPA/AeYEAsR1wgacR3vKWVK5qoaGzD+8ercENU5MRFujnksf0JLmJYUiJDMKWk/VCzi+6g7aefhyvacdcnd8980IMBgP+85px6LPa8LsPTwl5jJLGLjz5wSkUN3QJOf9Q8bmBVCNtQd3gnHfQdH+cFwjgNgluobu7W/YQSCdc1cIr+yswYFNw95x0lzyepzEYDFiSG4/9Jc3oslg1P7/oDvaVNENRoOv97y4lMzYU35qXgTcOVeFIVZtm57VYbfjfbWdw1f/uxEv7ynHz3/dhw8FKzc7vLD43kGqkLRRUtiIy2A9p0cEajYhk4bxAgNgOuMDTCPc0IZUrWrBYbfj3vgpcMSYWmbHcGmG45mXFwGpXsK+4WfNzi+5gV1ETQvyNmJjivpdr/WjhKMSZAvDE5kLY7cqIz7e3uBlX/3En/nfbWVw5PgEb7p+NCUnheOiNo3jwtQJ0C1jID4bPDaQaaQv5FW2YnBoJg4Gft3Z3nBcI4D54boF7mpDKFS1sPlaLpi4L7p3LrRFGYlJqBAL9fLCrSPtb9ovuYE9RM2ZmRsPP6L7TeGiALx5ZloPj1R346YYj2H6mEdZh3FmzpbsfP339CG59bh+sNgXrvjUD/3frZIxLDMNzd0/DTxaPxsaCaqz48y4U1nYI+JtcHJ8bSDWSFtp7B3C2oQuT3fgHOvQlzgsEiO3AvT6Zr2MhISGyh0A6IboFRVHwwu4yZMWG4HI32f9MrwJ8jZiaFiVkgSeyg5q2XpQ0deO2manCHsNVVk5KxPHKJrx5uBqbTzYhOsQfyyeYsXJSIqYM8m6FoijYcKgKT2wuRFefFWsWZOFHC0eddwt5o48BDywehRkZUXjg1Xys/MtuPLZiHG6bkeqSd0L43ECqkbRwtErd4Jyfv/MEnBcIENsBF3gaMZlMsodAOiG6hcMVrTha1Y7frMzlpToamJMZjf/eWoTa9l6Yw7XbdFRkB7u/WJC6ywbnl2IwGPDQlWOxdtEo7C3rwDsFNXjt80q8uLccyZFBWDExESsnJWJsQth5X1fU0IlH3j6OA6UtmJ4eid+uzsPo+It/z2dnRWPzA5fhwdeP4NG3j2NvcTP++7o8mATfoIjPDaQaSQv5FW0wGIAJKdzg3BNwXiBAbAdc4Gmkrk7chsnkXkS38MLuMpgCfXHdFG6NoIVZmdEAirDrbBNunJai2XlFdrCnuBkxof4Yc4kFjbsJ8DXiytwEXJmbgM6+AWw9UY93jtTg7ztK8MxnxRgTb8K1kxKxcFQU3j9Wg2d3VSDY3xe/uz4PN05Ngc8Q9oGMCQ3Av+6Zjr/tKMYftp7Bsep2/PnWKchLFveimc8NpBpJC/kVrciODeUdkz0E5wUCxHbABZ5GEhMTZQ+BdEJkC7XtvfjgeB3unZOOkAD+56uF0fGhiAn1x+4ibRd4ojpQFAW7ipowOyvGY9/BNQX64fqpybh+ajKauizYfKwWmwpq8D9bTuPpLTYAwLWTU/HI8hzEhAY4dW4fHwN+cEU2ZqRH4Ufr83HdM7vxyLIc3DrVLOT7yecGUg23BUVRkF/ZhqXj4jUeEcnCeYEAsR3wFaJGWltbZQ+BdEJkCy/tLYeicGsELfn4GDA3Owa7ipqhKIpmL/JFdVDU0IXGTgvmuen2CM6KCQ3AXbPTcdfsdFS29GDrsSqMSTBh3hjziM47LT0Km9dehofeOILH3z2JfWfr8YtrcpAS4NyCcTB8biDVcFsoa+5BW88AP3/nQTgvECC2A/e9/ZrO9Pb2yh4C6YSoFvoGbFh/oAKLc+KREsV9kLQ0LzsGTV0WnKrTbtNRUR2on7+b44YbnI9USlQw7piVhunpUZqcLzLEH8/dNQ2/vGYcdpxtxJVP78Qjbx9DRXOPJucH+NxAXxpuC/kVjheBk1N5B01PwXmBALEdcIGnEe5pQipRLWwqqEZrzwDumZsu5PzeTL1Zya6z2t1NU1QHu4qakRoVzEW+RgwGA749LwPv/fAyrJqciDcOVmHBHz7DT14rwNn6kS/4+dxAquG2kF/RhhB/I0bFec5nbr0d5wUCuA+eW+CeJqQS0YK6NcLYBBNmZ3rHpXmuZA4PQlZsiKbbJYjowGqzY39JM+Zme9+7d6IlRwXhsRW52Pn/LcC9c9Lx4fE6LHl6B+5/6eC5W9QPB58bSDXcFgoq2zAxJQLGIdxIiNwD5wUCxHbABZ5GeMtbUolo4fOyVpyq68S9c9M99sYasl02Khb7S5thsdo0OZ+IDk7WdqDTYsVcL/n8nQzxYYH4xTXjsOfhhY6tG4qbce2fd+PO5/djX4njc5rO4HMDqYbTQm+/DYW1Hbw808NwXiBAbAdc4GkkKEi7/bPIvYlo4d/7yhEZ7IeVk5I0Pzc5zMuOQd+AHYfKtfnQs4gO9hU3A/DOz9+5WmSIPx5cMhq7H16Ih68ei8LaTtzy93246/kD+PRUA/oGhvaDAD43kGo4LRyvaYfVrmBSCm+w4kk4LxAgtgPeRVMjDQ0NsodAOqF1C9Wtvfj0dAPumz8agX5GTc9NX5qZGQWjjwG7zjZpsoASMSfsK2nBOHMYokL8NT83XZgp0A/fm5+Fe+ak4/WDlfjHZ2fww/X58PU7jtmZ0Zg/Ohbzx8QhPTr4gu+u87mBVMNpQb3ByqQUvoPnSTgvECC2Ay7wNJKczE2nyWEoLfRb7fjgWC16BmwwBQchJMCIYH9fhPj7IjjAeO7/g/2MeOVAOQwGA+6YleaC0XsvU6AfJqdEnLtL5UhpPSf09tuQX9mG2+dkanpeGppAPyPump2O6ybGY09xE/aUdmD7mUZ8+u5J4N2TSIkKciz2RsdhdlY0Qr/Yp5LPDaQaTgv5FW1IiQpCrEnb7TtILs4LBIjtgAs8jTQ2NsoeAunEpVoYsNnxxqEq/PmTItS3dQEArLj0u3K+sOGq8QlIjOAlHaLNGxWDP358Fm09/YgIHtm7ZFrPCfmVrei32XmDFcn8jD6YPzoOS/NSAADlzd3YcaYR28804a3D1fj3vgr4GQ2YmhaJ+aPjEFRfi9mzJQ+adGE4c0J+RRtmZGizLQjpB18zEiC2Ay7wNGKxWGQPgXTiQi0M2Ox463AV/u+TIlS19mJSSgQev2Y0suJCYVWM6O63ocdidfx/vxXdFsf/9/Tb0NvXhxunpkj4m3ifedkx+N9tZ7GnuBnL8ka2kbbWc8K+khb4GQ18saczadEhuHN2CO6cnY5+qx0Hy1uw40wTtp9pxO8+PIXJvk1Im9SABWPiZA+VJHN2Tqht70VdRx9vsOKB+JqRALEdcIGnEe5pQqqvtmC12fF2fjX+75MiVLT0YGJyOH6zajyuGB2L/v5+AEBAwKUvveETgetMTIlAaIAvdp5tGvECT+s5YefZRkxOiUCwP6dtvfL39cGcrBjMyYrBw1ePRX1HH+7868f4+RtHseXHl/Ozk17O2Tkhv8KxPcfkVN5gxdPwNSMB3AfPLXBPE1IVFxfDarPjzUNVWPzUdjz0xlGEBfni+bunYeOauVgwJo5bHeiUn9EHszKjNfkcnpZzQmVLD87Ud+EKvgvkVuLDAnFjYifaewbwH28ddXqLBfIszs4J+RWt8Pf1wThzmKARkSx8zUiA2A74o2CNRETwEgoCbHYFlX0BWPL0DpQ2dWOcOQzP3TUNi3O4qHMXl42KwbbCelQ09yA1OnjY59FyTvjoZD0AYOFYLvDcTbo5Gj9Lz8QTm09hw6Eq3DSNl1t7K2fnhILKNoxPDIO/L38W72n4mpEAsR1w1tCIry/Xyt6u32rHqr/sxhuFXQjw9cHf7piK99fOw5Jx8VzcuRH1JiY7i0b24Wct54RthfUYFReClKjhLzhJDl9fX9w3LxOzMqPw+DsnUNHcI3tIJIkzc8KAzY6jVe28PNND8TUjAWI74AJPI01N2txandyXn9GAy0fH4PpMBZvXXoarxidwYeeGsmJDYA4PxK6zI/tvWqs5ob1nAPtLW3h5pptqamqCj48Bv79xInwMBjz4egFsdl6q6Y2cmRPO1HXBYrVz/zsPxdeMBIjtgAs8jaSmpsoeAklmMBjw0JVjMS8vGz4+XNi5K4PBgHnZMdhT3DyiF+JazQmfnWmAza5gAS/PdEtqB8mRwfj1qlwcLG/F37bz8zfeyJk54Wi1eoMVLvA8EV8zEiC2A2ELvNraWtx5551YtmwZli9fjnXr1n3jmP3792Pq1KlYuXIlVq5ciT//+c+ihiNcbW2t7CGQTrAF9zdvVAzaewdwvLp92OfQqoNthQ2ICfVHXmK4Jucj1/pqB6smJWH5BDOe/ujMiNoi9+TMnHCksg2xpgAkcf9Tj8TXCQSI7UDYxZ9GoxEPP/wwcnNz0dXVheuvvx5z585Fdnb2ecdNmzYNzz77rKhhuIzNZpM9BNIJtuD+1M/h7SpqwsRhXiKlRQf9Vjs+O92AZePNfFfYTX21A4PBgN+uGo9DZa348WsFeO9H8xDoZ5Q4OnIlZ+aEo1VtmJwSycv8PRRfJxAgtgNh7+DFxcUhNzcXABAaGorMzEzU19eLejjp0tLSZA+BdIItuL+Y0ADkmMOw8+zwb7SiRQcHSlvQ2WfFknHxIz4XyfH1DiKC/fH7GyeiqKELT35wStKoSIahzgmtPf0ob+nlDVY8GF8nECC2A5d8Bq+qqgqFhYWYOHHiN/6soKAA1157Le677z6cPXvWFcMRorS0VPYQSCfYgme4bFQMDpe3oaffOqyv16KDbYX1CPTzOfeOIrmfC3Uwb1QM7p2bjn/tKcOOMyO7Wyu5j6HOCUer+Pk7T8fXCQSI7UD4fVq7u7uxdu1aPPLIIwgNDT3vz3Jzc/HJJ58gJCQE27dvx5o1a7B169ZLns9isaCwsPDcrwcGBgAAfn5+g45F1LGA413Kr45rpOfVw9+Lxzp/LODdLch+fC2PTfXvQb/Njjd3HMG0pGCXd6AoCjYfqcKkhECUFZ/R/ffLW47VqoOV6cDHx/3w4/WH8My1yQgy2jUfqx6Odfb7JXsMenhu+PBQPXwMgH9XHQoLG1w+Vh7r3LHOdgC4x+sE2d9XTz8W0L6DrxK6wBsYGMDatWuxYsUKLF269Bt//tUF3/z58/H444+jpaUFUVFRFz1nQEAAcnJyzv3aYrGc+/3BiDoWAGpqas4b10jPq4e/F491/ljAu1uQ/fhaHpueZcOvP92K8r4g3JmT4/IOCms70NBdip9elYOcnFTdf7+85VgtO3gmOhmr/rIb605a8NT1uTAYDG7xPXDmWGe/X7LHIPu5oW/Ahh2vl2FxTjymTMiVMlYe69yxznYAuMfrBNnfV08/FtDgdcIlFofCLtFUFAWPPvooMjMzce+9917wmMbGRiiK4zbkR48ehd1uR2Ske15z3tLSInsIpBNswTME+RsxLT0Su4qGt0/NSDvYdrIeBgOwcCw/f+fOLtVBbmI4HlwyBpuP1eGdIzUuHBXJMJQ54d0jNWjrHcAtM1JcMCKSha8TCBDbgbB38A4dOoRNmzZh9OjRWLlyJQDgwQcfRE2N40ns1ltvxZYtW7B+/XoYjUYEBgbiqaeects7RmVkZMgeAukEW/Ac80bF4P99eBoNnX0I93dubhppB9sK6zEpJQKxpqH/VJj0Z7AOvnt5Jj491YAnNp/CtLQoZCbw37enGqwFRVHw4t5yZMeGYEb6xa9kIvfH1wkEiO1A2Dt406ZNw+nTp/Huu+9i06ZN2LRpE+bPn49bb70Vt956KwDgjjvuwPvvv4933nkHr7/+OqZMmSJqOMKVl5fLHgLpBFvwHPO+uLnJnqJmp792JB3Ud/ThSFU7Fufw3Tt3N1gHRh8D/nCT4wZkj7x1DDa74ophkQSDtVBQ2YZj1e24dUaq2/6wm4aGrxMIENuBS+6i6Q2MRu5lRA5swXPkJoYjItgPO886f5nmSDrYVujYUmYpt0dwe0PpICUqGP/f1WNxsKL13L978jyDtfDS3nKEBvhixcREF42IZOHrBALEdsAFnkbMZrPsIZBOsAXPYfQxYG5WDHYVffl54aEaSQfbTtYjLToY2XGhgx9MujbUDlZMMCMyyA/vH60VPCKS5VItNHdZ8N7RWlw3JQkhAcJvcE6S8XUCAWI74AJPIxUVFbKHQDrBFjzLvFExqO+woLSp26mvG24H3RYrdhc3Y3FOPC/T8gBD7cDP6IPF4+KxrbAefQM2waMiGS7VwqufV6LfZsdds7kBtjfg6wQCxHbABZ5GYmK4ETE5sAXPcu5zeMXOfQ5vuB3sPNuEfqudn7/zEM50cNX4BPT02/DZ6YvvfUbu62ItWG12vLK/AnOyopEdZ3LxqEgGvk4gQGwHXOBpxGq1yh4C6QRb8CwpUcFIiw7GXicXeMPtYFthPcKD/DAt3T23jKHzOdPBtLRIRIf4411epumRLtbCx6caUN3Wi7tmp7t2QCQNXycQILYDLvA00tbWJnsIpBNswfPMy47B52UtGLDZh/w1w+nAZlfwyakGLBgTCz8jp2dP4EwHvkYfXDU+AZ8UNqCnny8APc3FWnhpbzkSwwOxOCfOxSMiWfg6gQCxHfAVhEaysrJkD4F0gi14nstGxaC734Zj1e1D/prhdHC4ohUt3f1YMi7B6a8lfXK2g+UTzOgdsOHTU42CRkSyXKiFooYu7Cpqwm0zU+HLH+p4Db5OIEBsB5xNNFJcXCx7CKQTbMHzzM6MgY/Buc/hDaeDbSfr4Wc04PLR/HyGp3C2g5kZ0YgJDcD7x2oEjYhkuVAL/95XDn+jD26ZkSphRCQLXycQILYDLvA0EhAQIHsIpBNswfOEB/thckoENuVXD/kOh8Pp4KPCeszKjIYp0M/pryV9crYDo48By/IS8MmpBnRbeJmmJ/l6C90WK948VIVleQmICeXzhjfh6wQCxHbABZ5GYmNjZQ+BdIIteKY1C7NR096H53eVDul4ZzsobuxCSWM3lnBzc48ynPlgeZ4ZfQN2fHyKd9P0JF9v4e38anRarLiTN1fxOnydQIDYDrjA00hVVZXsIZBOsAXPNDMjGotz4vCXT4vQ0NE36PHOdvBxYT0AYBG3R/Aow5kPpqVHIc4UgPeP8jJNT/LVFhRFwYt7yzA+KQxTUiPkDYqk4OsEAsR2wAWeRuLiePcrcmALnutnS8dgwGbH/2w5Peixznaw7WQDxpnDkBQRNNzhkQ4NZz5wXKZpxqenG9HFyzQ9xldb2F/agjP1XbhrVjoMBoPEUZEMfJ1AgNgOuMDTSG9vr+whkE6wBc+VEhWMb83NwBuHq3Cs6tJ31HSmg9aefhwsb8FiXp7pcYY7H1wzwYx+qx3bTtZrPCKS5astvLi3DOFBflgxMVHegEgavk4gQGwHXOBppLOzU/YQSCfYgmdbszAbUcH++M17J6EoykWPc6aD7WcaYVeApVzgeZzhzgdTUiOREBaI97jpucdQW6hr78OWE/W4eXoKgvyNkkdFMvB1AgFiO+ACTyPc04RUbMGzhQX64adLx+BAWQs2H6u76HHOdPDZqUYkhAUiNzFMiyGSjgx3PvD54jLNHWca0dE3oPGoSAa1hVcOVMCuKLhjZprkEZEsfJ1AAPfBcwvc04RUbMHz3Tw9BWMTTHhic+FFt00Yagd9AzbsLm7C4nFx/CyOBxrJfLB8ghn9Nl6m6SmKi4vRb7Xjlf0VWDAmDqnRwbKHRJLwdQIB3AfPLQQF8cYI5MAWPJ/Rx4D/vGYcqtt6L7ptwlA7OFDWgp5+Gxbz7pkeaSTzwZTUCCRFBOF9XqbpEYKCgvDhiTo0dVlw52y+e+fN+DqBALEdcIGnkcjISNlDIJ1gC95hTnYMlo6Lx18vsm3CUDqwWG14eV8FQvyNmJ0VLWKYJNlI5gODwbHp+Y6zjWjv4WWa7i4yMhIv7S1DWnQw5o/iPmjejK8TCBDbARd4Gqmp4X5F5MAWvMcjy3LQb7Pj91u/uW3CYB109g3g3hc+x66iJjywaBQCfHmzBU800vlg+YREDNgUbD158c97kns4UVyJz8taceesNPj48HJsb8bXCQSI7YALPI0kJCTIHgLpBFvwHukxIbh3bgY2HKrC8erzt024VAcNHX246dl9OFDagievH4/bZ/FyLU810vlgYnK44zLNY7xM090d6wpBoJ8PbpyaInsoJBlfJxAgtgMu8DTCW96Sii14lx9+sW3Cr7+2bcLFOihu7MLqv+5BeXM3/nnPdKyYkOSqoZIEI50PDAYDrplgxq6zTWjr6ddoVORq7T0DOFvbipUTkxAe7Cd7OCQZXycQwG0S3EJ3d7fsIZBOsAXvEhbohweXjsaB0hZ8ePzLy+gu1EF+RStueGYP+gZsePW7s3D5aH4Ox9NpMR9cMyERVruCLSd4maa72nCoEoFKH2+uQgD4OoEcRHbABZ5GuKcJqdiC97l52hfbJnzw5bYJX+/gk1P1uPW5fQgL8sOb35+DCckRMoZKLqbFfDA+KQypUcHc9NxNWW12vLC7DIhIxvikcNnDIR3g6wQCuA+eW+CeJqRiC97H1+iDX14zDpUtvY4Xcji/g9c/r8R3XjyEUXEmvPn9OUiPCZE0UnI1LeYDg8GA5RPM2FPcjJZuXqbpbj48UYfqtl5cHtEmeyikE3ydQAD3wXMLISF8wUYObME7zc2OweKcePzl0yI0dPYhJCQEiqLg/z4+i5+/eRRzsqLx6ndnISY0QPZQyYW0mg+W55lh42WabkdRFDy3owQZMSEYbea79uTA1wkEiO2ACzyNmEwm2UMgnWAL3uvR5TmwWG14ausZhISE4pebjuMPH53B6slJeP7u6QgJ8JU9RHIxreaD3MQwZMSE4L2jvL26O/m8rBVHqtrxrXkZCA/jcwM58HUCAWI74AJPI3V1/KkqObAF75URE4K7Z6fjtYOVWL+3CP/eV4H752fiDzdOhL8vp1tvpNV8YDAYsDzPjL3FzWjutmhyThLv7ztKEBnshxumJPO5gc5hCwSI7YCvODSSmJgoewikE2zBu/1o0ShEBPnh8/ZQ/PKacfiPq3O4qbEX03I+WD7BDLsCbDvZoNk5SZzixi58fKoed85OR5C/kc8NdA5bIEBsB1zgaaS1tVX2EEgn2IJ3Cw/ywwv3zsB9433x7XkZsodDkmk5H4xNMCEzNgRbjvOn/+7g+V2l8DP64K4vtkbgcwOp2AIBYjvgAk8jvb29sodAOsEWaFJKBGL8bbKHQTqg5XxgMBhwTZ4Zn5e3oLGrT7Pzkvaauyx481AVrp+SdO7GSnxuIBVbIEBsB1zgaYR7mpCKLRDADshB6w6umZgIuwJ8dIKXaerZS/vKYbHa8e15med+j3MCqdgCAdwHzy1wTxNSsQUC2AE5aN3B6HgTUiKDcKC0RdPzknb6Bmx4cW85Fo2NQ3Zc6Lnf55xAKrZAAPfBcwu85S2p2AIB7IAcRHSQYw7DmfoOzc9L2njrcDVauvtx32WZ5/0+5wRSsQUCuE2CWwgKCpI9BNIJtkAAOyAHER2MjjOhorUX3Rar5uemkbHbFfxjZwnyksIxKzPqvD/jnEAqtkCA2A64wNNIQwM/D0EObIEAdkAOIjoYkxAKRQFO1XVqfm4amU9ONaCkqRv3XZYBg+H87VE4J5CKLRAgtgMu8DSSnJwsewikE2yBAHZADiI6GJMQBgA4VcfLNPXm7ztLkBQRhGV55m/8GecEUrEFAsR2wAWeRhobG2UPgXSCLRDADshBRAeJEYEwBfiisJYLPD05UtmGA6UtuHduOvyM33x5xTmBVGyBALEdcIGnEYvFInsIpBNsgQB2QA4iOjAYDBgdH4rCWl6iqSfP7SyBKcAXN09PueCfc04gFVsgQGwHXOBphHuakIotEMAOyEFUB2MSTDhV2wG7XRFyfnJOZUsPPjheh1tnpsIU6HfBYzgnkIotEMB98NwC9zQhFVsggB2Qg6gORseHobvfhqrWXiHnJ+e8sLsMBgD3zEm/6DGcE0jFFgjgPnhuISIiQvYQSCfYAgHsgBxEdTAmwbGB9kl+Dk+69t4BvPZ5Ba6ZYEZixMVve845gVRsgQCxHXCBpxFfX1/ZQyCdYAsEsANyENXBqLhQGAzw6hut9A3YZA8BALD+QAW6+23f2Nj86zgnkIotECC2AxamkaamJtlDIJ1gCwSwA3IQ1UGQvy8yokO8ZquEAZsdp2o7cbiiFYfKW3G4ohV1rV2YnRmNtUtzMD09avCTCNBvteNfu8swNzsa45PCL3ks5wRSsQUCxHbABZ5GUlNTZQ+BdIItEMAOyEFkB2PNJhyv9swFXkt3Pw5/sZA7VN6Ko1Xt6P3iHbv4sABMSY3E1eNi8E5BDW78217HQm/RKMzOinbpOD88UYu6jj789/V5gx7LOb6MqMIAACAASURBVIFUbIEAsR1wgaeR2tpa2UMgnWALBLADchDZQU5CGDYfq0Nn38BF79woW2+/De8cqUGXFbDbFVjtCmxf+9+531MUdPf24UR1O4qaHbcP9/UxIDcxDDdPT8GUtEhMTYtEYnggDAYDLBYL1lyRjTeP1ONv24tx63P7MCMjCg8sGoU5WdEwGAxC/26KouCF3WUYFReKK0bHDno85wRSsQUCxHbABZ5GbDZ9fBaA5GMLBLADchDZQY45DABwpr4TU9PkXKI4mN++fxJvF9TACuO53zMYHAs3H4MBvj4GGM/9zwfBRjvGmsNw/fQMTE2LRF5SOIL8jRc9f5C/Ed+el4HbZ6bi1QMVeGZ7MW7/x35MTYvE2kWjcPmoGGF/t30lLThT34Unrp88pMUk5wRSsQUCxHbABZ5G0tLSZA+BdIItEMAOyEFkB2PNJgDAyVp9LvC2nqjD2wU1+M5lGXhg6TjHIs5ggI/PxRdD6sa/AQEBTj1WoJ8R98zNwC0zUrHhUBWe+bQId//zACamRGDN5am4fNTg77A5o6NvAL/78BRiQv2xcnLikL6GcwKp2AIBYjvgXTQ1UlpaKnsIpBNsgQB2QA4iO0iKCEJYoK8u76TZ1GXBf7x1DDlmE35wRTYC/YzwM/pccnGnhUA/I+6clYbPHlqA/74uD81dFvzg5Xzc/o/9aOjs0+Qx+q123P/iIZQ2dePJ6yYgwPfi7zB+FecEUrEFAsR2wAWeRqKi9PfTU5KDLRDADshBZAcGgwFjzWE4pbMFnqIo+I+3jqHTYsWT102Av6/rX2r4+/rg1hmp+PRnV+DXK3NxtqELtzy7D7XtI9sY3m5X8NAbR7C3pBm/WZXr1E1dOCeQii0QILYDLvCIiIjcVE6CCafqOmG3K7KHcs6GQ1X46GQ9fn7lGGTHhUodi5/RB9dPScZzd01FY6cFNz27F5UtPcM+3//bchqbCmrw0JVjcO3EJA1HSkSkHS7wNNLS0iJ7CKQTbIEAdkAOojvIMYehp9+GihEsWrRU2dKDX797EjMzovCtuRmyh3POpJRIvPydmejoteLmZ/eitKnb6XO8uLcMf9tejNtnpuIHV2Q5/fWcE0jFFggQ2wEXeBrJyNDPExnJxRYIYAfkILoD9U6aetjw3GZX8NMNRwAAf7hpovDP2zlrQnIE1n9nFvqsdtz87F4UNXQO+Ws/PF6Hx945gcU58fj1yvHD2oKBcwKp2AIBYjvgAk8j5eXlsodAOsEWCGAH5CC6g9HxJvgYHHfSlO2fu0pxoLQFj60Yh+TIYNnDuaBxiWF47buzoAC4+dl9Q7pBzaHyFjzwaj4mJkfg/26dDOMwF66cE0jFFggQ2wEXeBoxGod2Fy3yfGyBAHZADqI7CPI3Ij0mRPqdNE/XdeJ/tpzGknHxuGFqstSxDGZUvAmvfXeW40Ysz+3Dsar2ix5b3NiFb687CHN4IJ6/e9ol9+QbDOcEUrEFAsR2wAWeRsxms+whkE6wBQLYATm4ooMcc5jUBV6/1Y4fv1aAsCBf/Pd1ecO6fNHVMmND8fr9sxEa4IvbntuHQ+Wt3zimsdOCe144AKPBgHXfmoHoUOf25vs6zgmkYgsEiO2ACzyNVFRUyB4C6QRbIIAdkIMrOhhnDkNVay86+gaEP9aF/O+2Myis7cATq/MQM8JFkCulRAXjtftnIzrUH3c9vx/7S5rP/Vm3xYpv/etzNHX245/3TEdadMiIH49zAqnYAgFiO+ACTyMxMTGyh0A6wRYIYAfk4IoOxiaYADguk3S1Q+Ut+Nv2Ytw4NRlLcxNc/vgjlRQRhNfun42E8EDc/cIB7ClugtVmx5pXDuNETTv+fNtkTEyJ0OSxOCeQii0QILYDLvA0YrVaZQ+BdIItEMAOyMEVHah30nT1ZZo9FisefP0IEiOC8J8rxrn0sbUUHxaI1+6fjfToEKx5JR/ff/kwPjvdiP9alYdFOfGaPQ7nBFKxBQLEdsAFnkba2tpkD4F0gi0QwA7IwRUdmMMDER7kh0IX30nzf7aeRkVLD35/40SYAv1c+thaiwkNwPrvzMKouFDsKW7GjxZm47aZqZo+BucEUrEFAsR24CvszF4mK8v5TU/JM7EFAtgBObiiA4PBgLEJJpe+g7fjbCNeP1iF+y7LxqzMaJc9rkiRIf74593TcLiiDYvHJ2l+fs4JpGILBIjtgO/gaaS4uFj2EEgn2AIB7IAcXNVBjjkMp+s6YbMrwh+rb8CG/9x4HKPiQvDTpWOEP54rhQb64fLRsULuBMo5gVRsgQCxHXCBp5GAAPe5cxiJxRYIYAfk4KoOxpnD0DtgQ0VLj/DH+uRUAxq7+vGzK8ci0I/7eQ0V5wRSsQUCxHbABZ5GYmNjZQ+BdIItEMAOyMFVHYw1O+6k6YrLNN/Or0ZsqD9me8ilma7COYFUbIEAsR1wgaeRqqoq2UMgnWALBLADcnBVB6PjTfAxiF/gtXb347PTDVg+wQyjj/43NNcTzgmkYgsEiO2ACzyNxMXFyR4C6QRbIIAdkIOrOgj0MyIzNlT4nTTfO1qDAZuCayYkCn0cT8Q5gVRsgQCxHXCBp5He3l7ZQyCdYAsEsANycGUHrriT5tv51RgTbzq3uToNHecEUrEFAsR2wAWeRjo7Xbv/EOkXWyCAHZCDKzvIMYehuq0X7b0DQs5f1tSNwxVtWD0lSchdJj0d5wRSsQUCxHbABZ5GuKcJqdgCAeyAHFzZwThzGADgdJ2YFw0bC6phMAArJ/HyzOHgnEAqtkAA98FzC9zThFRsgQB2QA6u7CDniwWeiMs0FUXB2/nVmJ0ZDXN4kObn9wacE0jFFghw033wamtrceedd2LZsmVYvnw51q1b941jFEXBf/3Xf2HJkiVYsWIFTpw4IWo4wgUF8QmPHNgCAeyAHFzZQXxYACKC/YQs8A5XtKG8uQerJydpfm5vwTmBVGyBALEd+Io6sdFoxMMPP4zc3Fx0dXXh+uuvx9y5c5GdnX3umB07dqCsrAxbt27FkSNH8Ktf/QobNmwQNSShIiMjZQ+BdIItEMAOyMGVHRgMBuQkhKFQwCWaG/OrEeDrg6vGJ2h+bm/BOYFUbIEAsR0IewcvLi4Oubm5AIDQ0FBkZmaivr7+vGM+/vhjrFq1CgaDAZMmTUJHRwcaGhpEDUmompoa2UMgnWALBLADcnB1BznmMJyu64DNrmh2zn6rHe8ercHS3ASYAv00O6+34ZxAKrZAgNgOXPIZvKqqKhQWFmLixInn/X59fT0SEr78aWBCQsI3FoHu4qt/D/JubIEAdkAOru5grNmEvgE7ypq7NTvnZ6cb0NYzgOt4eeaIcE4gFVsgQGwHwi7RVHV3d2Pt2rV45JFHEBoaOuLzWSwWFBYWnvv1wIDjdtB+foP/VFHUsQDQ0tJy3rhGel49/L14rPPHAt7dguzH18uxgHd34MnHyu5gsGOD+iwAgG0HC9GfHqrJeV/cUY/wQB/E2JpQWNjs1Hid/X7J/nfGOYHHOnussx0A7tGC7O+rpx8LiHl+UAld4A0MDGDt2rVYsWIFli5d+o0/j4+PR11d3blf19XVIT4+/pLnDAgIQE5OzrlfWyyWc78/GFHHAsDx48fPG9dIz6uHvxePdf5YwLtbkP34ejkW8O4OPPlY2R0MdmzGgA3GzTXo9AlDTs6YEZ+3vXcAB6rLcNuMVOTljnN6vM5+v2T/O+OcwGOdPdbZDgD3aEH299XTjwVG3sGlFofCLtFUFAWPPvooMjMzce+9917wmIULF2Ljxo1QFAUFBQUwmUyIi4sTNSShuKcJqdgCAeyAHFzdQaCfEZkxIZrdSXPzsVr0W+24bgovzxwpzgmkYgsEuOk+eIcOHcKmTZuwb98+rFy5EitXrsT27duxfv16rF+/HgAwf/58pKSkYMmSJfjlL3+Jxx57TNRwhOOeJqRiCwSwA3KQ0UGOOUyzBd7b+dXIjA1BXlK4JufzZpwTSMUWCBDbgbBLNKdNm4bTp09f8hiDweDWi7qvCgkJkT0E0gm2QAA7IAcZHeSYw/DOkRq09wwg0Dj881S29OBAaQt+tnQ0DAaDdgP0UpwTSMUWCBDbgUvuoukNTCaT7CGQTrAFAtgBOcjoIMfseMzCupG9i7epoBoAsHISL8/UAucEUrEFAsR2wAWeRr56sxjybmyBAHZADjI6yDGHAcCILtNUFAVv51djRnoUUqKCtRqaV+OcQCq2QIDYDrjA00hiYqLsIZBOsAUC2AE5yOggzhSAqBB/nKrtHPY5jlW3o7ixG6t5cxXNcE4gFVsgQGwHXOBppLW1VfYQSCfYAgHsgBxkdGAwGJBjNo3oEs23DlfD3+iDZXlmDUfm3TgnkIotECC2Ay7wNNLb2yt7CKQTbIEAdkAOsjoYmxCG03WdsNrsTn/tgM2Od4/UYFFOHMKDhr6xLl0a5wRSsQUCxHbABZ5GuKcJqdgCAeyAHGR1kGMOg8VqR0WL8y8gdp1tQnN3P1ZP5uWZWuKcQCq2QICb7oPnbbinCanYAgHsgBxkdaDeSfP0MC7TfDu/GhHBfrhiTJzWw/JqnBNIxRYIENsBF3ga4S1vScUWCGAH5CCrg+y4UPj6GHC63rkbrXRbrNh6sg7XTDDD35cvEbTEOYFUbIEAbpPgFoKCgmQPgXSCLRDADshBVgcBvkZkxYbiTH2XU1+39WQ9+gbsWD05WdDIvBfnBFKxBQLEdsAFnkYaGhpkD4F0gi0QwA7IQWYHOWaT05dovne0BmnRwZiSGiFoVN6LcwKp2AIBYjvgAk8jycn8aSc5sAUC2AE5yOxgrDkMdR0WtPf2D+n4uo5e7C9twapJSTAYDIJH5304J5CKLRAgtgMu8DTS2NgoewikE2yBAHZADjI7yDGHAQBO1Q3tc3jvH62DooB3zxSEcwKp2AIBYjvwFXZmL2OxWGQPgXSCLRDADshBZgfqnTTP1HXh8rGO3xuw2dHTb0Nvvw09/VbHPw/Y0NNvw6aCakxKDkd6TIi0MXsyzgmkYgsEiO2ACzyNcE8TUrEFAtgBOcjsIM4UiOgQf/zpk7P402el6B2wYcCmXPR4X9jw+LW5Lhyhd+GcQCq2QIDYDrjA00hxcTGmTJkiexikA2yBAHZADrI7+MmS0ThU1oLgoEAE+fsi2N/4xf8c/xx07tdGBPooyOS7d8LIboH0gy0QILYDLvA0EhHBO46RA1sggB2Qg+wOVk9OwurJSQgICBj0WF42JpbsFkg/2AIBYjvgTVY04uvLtTI5sAUC2AE5sANSsQVSsQUCxHbABZ5GmpqaZA+BdIItEMAOyIEdkIotkIotECC2Ay7wNJKamip7CKQTbIEAdkAO7IBUbIFUbIEAsR1wgaeR2tpa2UMgnWALBLADcmAHpGILpGILBIjtgAs8jdhsNtlDIJ1gCwSwA3JgB6RiC6RiCwSI7YALPI2kpaXJHgLpBFsggB2QAzsgFVsgFVsgQGwHXOBppLS0VPYQSCfYAgHsgBzYAanYAqnYAgFiO+ACTyNRUVGyh0A6wRYIYAfkwA5IxRZIxRYIENsBF3hEREREREQeggs8jbS0tMgeAukEWyCAHZADOyAVWyAVWyBAbAdc4GkkIyND9hBIJ9gCAeyAHNgBqdgCqdgCAWI74AJPI+Xl5bKHQDrBFghgB+TADkjFFkjFFggQ2wEXeBoxGo2yh0A6wRYIYAfkwA5IxRZIxRYIENsBF3gaMZvNsodAOsEWCGAH5MAOSMUWSMUWCBDbARd4GqmoqJA9BNIJtkAAOyAHdkAqtkAqtkCA2A64wNNITEyM7CGQTrAFAtgBObADUrEFUrEFAsR2wAWeRqxWq+whkE6wBQLYATmwA1KxBVKxBQLEdsAFnkba2tpkD4F0gi0QwA7IgR2Qii2Qii0QILYDLvA0kpWVJXsIpBNsgQB2QA7sgFRsgVRsgQCxHXCBp5Hi4mLZQyCdYAsEsANyYAekYgukYgsEiO2ACzyNBAQEyB4C6QRbIIAdkAM7IBVbIBVbIEBsB1zgaSQ2Nlb2EEgn2AIB7IAc2AGp2AKp2AIBYjvgAk8jVVVVsodAOsEWCGAH5MAOSMUWSMUWCBDbgUFRFEXY2QUoKCjgW9tEREREROS1LBYLJk2adME/c7sFHhEREREREV0YL9EkIiIiIiLyEFzgEREREREReQgu8IiIiIiIiDwEF3hEREREREQewlf2AMh5drsdBQUFmDJlissf+8SJE5f889zcXBeNhAC2QA7sgFRsgQB2QF9iC97Jbe+ieffdd2PdunWD/h4A2Gw2GI1GVw3tkrq6ugAAoaGh3/gzm82Ge+65By+99NKg51m1ahU2btw45Mc9ffo0xowZM+hxpaWleP7551FTUwOr1Xru91988UUAwJ133nnRrzUYDOeO08qlvl+Acx0AbAEYWguDdQC4toXBOgA4J7ADB3edEwb7u7GF83FO4OsEFV8naNuCVh0AnBO0MpS/29e53Tt4FosFvb29aG1tRXt7O9T1aVdXF+rr6y/4NYsWLcJll12GZcuWYdasWTAYDBc9/1NPPYX77rsPYWFhAID29nb885//xE9+8pMRjfvo0aN49NFH0d3dDUVRYDKZ8MQTT2D8+PHnjjEajfDx8UFnZydMJtMlzzd79mxs2bIFS5cuveTfR/X444+jv78fq1evxrXXXnvR8z/wwAO45ZZbcNNNN8HH55tX8A5lIrmQ733ve9/4PZPJhPHjx+OWW275xt6Gg32/htMBwBaAobUwWAfA8FrQugOAc4LKnToAnGtBVAfA0FuQ2QHAFlScE/g6QSX7dYKoDobydwPEtaBVBwBfJ7jq+eFC3O4dvHXr1mHdunVoaGhAfHz8uX9JoaGhuOmmm3DHHXd842t6e3vx6aefYvPmzTh58iSuuOIKLFu2DNOmTfvGsRf6Kcfq1avx9ttvj2jcK1aswGOPPXbuMQ8ePIjHH38c77777nnHff/730dhYSHmzJmD4ODgc7//i1/84rzjJk+ejN7eXhiNRgQEBEBRFBgMhv+/vbuPiqrOHzj+HiDWp9bUjHzK1M3waJQedVd86IgKyoAoorLhQ7b+sychNdfN1Ohs2R5sDydda7VTaZa1usKKAYUru6nrw+FkmGtJqYkpKqYC8jzM8P39wZn5MTAj98IMw4yf1zmeI8zlzr3De77cO3PnXr7++muny1BYWEhaWhpffPEFISEhxMbGMn78eLtpYmNjSU9Pb3F9nL0SNGvWLIfff/311ykpKcFoNAKQnZ1Nt27dMBgMVFRU8Oabb9pN39Lj1ZoOQFqwaqkFrR2AvhZc3QHImGDlTR2Avhbc1QFob8HTHYC0ADImgGwnWHl6O8FdHWhZNyt3teDKDkC2E1pLz9+HprzuHbzFixezePFiPvroo7u+9dtY586diYyMJDIykrKyMjZs2MDChQs5e/Zss2ktFgsmk4nAwEAAampqMJlMbV5uf39/uyhGjx5NQEDzhz88PJzw8PAW55efn697GR599FGWL1/OiBEjeP311/nuu+9QSrFy5UrbfU6ePJldu3Yxbdo022MA8MADD9jN63//+5/t/7W1tRw/fpzhw4c7Hbjz8/NJS0uzfR0WFsacOXNIS0uzPYkba+nxak0HIC1YtdSC1g5AXwuu7gBkTLDypg6sy6u1BXd1ANpb8HQHIC2AjAkg2wlWnt5OcFcH4PkWXNkByHZCa+n5+9CU1+3gWfn5+XHnzh27t0MzMzNJSEhwOH1eXh7Z2dkcOXKEESNG8NZbbzmcLjo6msWLFxMbGwtAenq608FIC+sHTMeMGcMrr7yC0WjEYDCQnZ3N2LFjm00/e/ZszfM+cOAAJ0+exGAwMHr0aKZOnep02oKCAtLT0zl06BChoaFs3bqV4cOHU1xcTHx8vO0Ja3214f3337f9rMFgIDc3125+69evt/v6zp07d30ruqqqiqtXr9K3b18Arl69SlVVFQD33Xdfs+mdPV7Wx9P6wVy9HYC0oKUFrR2Avhbc1QHImADe0wHoa8GdHYC2FjzdAUgLIGMCyHaClae3E1zdAXSMFlzdAch2gl6t+fvQlNcdomkVExNDRkaG3fecfYg0LCyMYcOGMWPGDMLCwuzexnbk8OHDHD9+HIDQ0FAmTpzY6uXU+wHTwsJCUlNTOX/+PLW1tbbvN33CvPrqq/z00092b2E/8sgjJCcnO7yvBQsWEBcXx/Tp0+nUqZPdbfv27WvzoFRXV0dUVBQ5OTkObz906BDJyckMGDAAgCtXrpCcnMzYsWPZs2cPzz77rN30Wh83PR2AtACebcFdHYCMCd7UAehrwV0dgL4WPNkBSAsgYwJIB1YdYTvBlR1Ax2jB3R2AbCe0xBUnp/HaHbzo6Gj2799v+9CjxWJh5syZZGVlNZu2oqJC15lnioqKuHTpEqGhoVRXV2OxWHT9fFv89re/JSkpiTfeeIOtW7eSnp5OfX09L7zwgt1006dP5/PPP7etf319PUajkc8//7xN96/1OOnGH4Ktr6/n/PnzzJgxgz/84Q9O520ymfjxxx8BGDRoULMPyraGng5AWtBKz/HyeltwRwcgY4K3dQDeNyZ4sgOQFvSSMUG2E0DGBPC+MUG2E9rOaw/RnDBhAsuXLyc+Ph6ATz/91OneckVFBX/84x9tHyYdPXo0a9eu5eGHH2427Z49e9i9ezdlZWUcPHiQ4uJikpOTnZ5OV6ubN2+SmprKjRs3eO+99zh//jz5+fnMnTvXbrra2lrGjRsHQL9+/UhMTCQ2NrbZk3XgwIFcvXqVfv36AXDt2jUGDhzo9P61vsqj9Tjp5557zvYE8ff3p1+/fgQFBTm9/6YDQUFBAeD8w9ZaHy89HYC0ANpa0HO8vJ4W3NUByJjgTR2Avhbc1QFob8HTHYC0ADImgGwnWHl6O8FdHehZN3e04OoOQLYTWkvPujXltTt4L7zwAnv27OGTTz4BGn5pcXFxDqdds2YNUVFRbNq0CYD9+/ezZs0atm/f3mzaXbt28Y9//IN58+YBDR80vX37dpuX96WXXiI2NpatW7fa5rtixYpmv6TAwEDq6+sZOHAgH3/8MUFBQVRWVjabX2VlJZGRkYSEhAANT7QnnnjC9iqJ9X6s1qxZY3uVZ+fOnbZXeZpq6TjpkSNHYjAYaPrGr8FgIDAwkEceeYQVK1bYBhwrvR+21vp46enA+jhICy23oOV4+da04K4OQMYEb+rAunxWLbXgrg6sj4OWFjzdAUgLeh8vGRNkOwG8b0zQs27uaMFVHYBsJ7SVnnVryut28MxmM6mpqaSnp9OnTx+g4QOY/fv3p76+3uFFCW/fvs2cOXNsX8fGxjrdqw4MDLQ7G1Djize2RUlJCZGRkbz77rsABAQEOLxuyMsvv0x1dTXr1q1j06ZNnDhxgpSUlGbTJSUl2f6vlOKrr74iOzubxMREh/ev9VWepjp37syVK1dsX9/tTEwWi4Vz586xatUqMjMz7W7T+2Hrlh6v1nQA0gK0roWmHUDrWnB1ByBjgpU3dQD6WnBXB6C9BU93ANICyJgAHasDuHe3E9zVAXi2BVd1ALKd0FZ6/j405XU7eBs3bqSyspKDBw/ajm2tqKggJSWFlJSUZtf+gIZTtmZkZBAVFQVAZmamw9O4QsMZa7Zu3UpNTQ1Hjx7lk08+ISwsrM3L3aVLF0pKSmxvUZ86dcrhxSOtr6507dqVP//5z07nN3bsWL777js+++wzcnJy6NevH/Hx8U7PrqP1VR5nx0lr4e/vT3BwsNNryzTmaCBorKXHqzUdgLQA2lpoSwegvYW2dgAyJlh5cwdw9xbc1QFob8HTHYC0ADImgGwnWHl6O8FdHWhZNyt3tNAeHYBsJ2ih5+9DU153kpXw8HBycnKaXVHeYrEwY8YMDhw40OxnioqKeO211zh16hQGg4GRI0eybt062ylYG6uvr2fv3r3897//BRrevp07d+5dr2Cvxbfffstrr73GuXPneOyxxygpKWHTpk0EBwcD9k8UR6xvz168eJGsrCwyMzPp0aMHkZGRfPDBB/znP/+568+fPn2aIUOGUF5ezqZNmygvL2fp0qU89dRTdtPl5eXpOmZeq6YDwYULF5gxYwarVq1yOH1Lj1drOgBpAbS14C0dgIwJ3tgB6GvBXR2A9hY81QFIC43JmOCcbCe073aCuzrQsm7ubMHXOgDfHRPuxut28CIiIpyeYvdut+lhPW62Z8+ebZ5X42t5mM1mLl68iFKKQYMG2V3P4ze/+Q19+vTBaDTy5JNPNjte2foqS3BwMKNHj2bDhg22D8hOmTLF4XVH9GjtMfNa5eXl2f7v7+9PfX092dnZzU7Re/r0afr06UPv3r0xm83s3r2bnJwcfvWrX5GUlGR79aQ9OoB7rwVv6wBkTPDGDkBbC9JBA2lBWgDZTrDqSNsJruwAvKMFb+sAvK8FPX8fnPG6QzSHDBni8DocGRkZDBo0yO57W7ZscTofg8HA888/b/taKcWWLVv4+OOPbdH6+fmxYMECli1b1urlff75520Xg1yxYgV//etfHU539OhRjh49anul5emnnyYqKorHHnus2TplZWWxaNEiJk6ciNFobPYka0zrqzytPWZeK0eHB0RERDSbLjk52fYB1vz8fP72t7+xfv16zp49yyuvvMLmzZsBfR2AtADaWvC2DkDGBG/sALS14K4OQHsLnu4ApAWQMQFkO8HK09sJ7uoAPNuCr3YAvjsm3I3X7eAlJyezbNky0tLSbFenP3PmDDU1Nbz99tt20zq6KGFVVRVpaWmUlpba/ZJ27NjB119/zd69e20XV7x8+TKvvvoqO3bsaHZhRa0aP5EuX77sdDp/f38mTZrEpEmTMJlMZGZmsnDhQpYtW2Z3fPLUqVOZOnUqVVVV5Obm8uGHH3L79m2Sk5OZNm0aEyZMsJvvqVOn8b2ctwAADA9JREFU7voqjxZ6jplvytHhAUopPvroI4fTWywW26su2dnZzJ8/n4iICCIiIoiJibFNp6cDkBag7S10xA5AxgRv6gD0teCuDkB7C57uAKQFkDEBOm4H1mXraH8fvG1MAM+24KsdgO+OCS3NxCsdO3ZM7dy5U+3cuVMdO3asxenLy8vV22+/rSZPnqw2btyobt68aXd7TEyMunXrVrOfu3XrloqJiWn1cs6aNcvh/x2pra1VOTk5KjExUcXGxqotW7ao69evt3gfpaWl6u9//7tatGhRs9vMZrM6dOiQWr16tYqJiVGpqanqhx9+0L8irfT444+rhIQEVVhYaPteWFiY0+mNRqOqq6tTSikVERGh8vLy7G5rSm8HSkkLnmjB3R0oJWNCYx21A6X0tdAeHSh19xY6QgdKSQsyJjS41ztQyvPbCe7qQCnPtuDrHSjlm2OCM173Dp7VuHHjNB3fW1payvbt2/nss8+YPXs2//znP+nevXuz6cxms8PjZnv27Nmm050WFBQwatQolFLU1tYyatQooGHv3GAw2C6guHr1as6dO8ekSZNYtmwZQ4cO1Xwf3bt3Z/78+cyfP7/ZbVpf5XEXvYeKGI1GFixYQI8ePejUqROjR48G4NKlS7YzHzWmtQOQFjzZgrs7ABkTGuuoHYC+FtzZAWhrwdMdgLQAMiaAdGDl6e0Ed3UAnm3B1zsA3xsT7sbrTrKiR0pKCv/617+YN28eCQkJdO3a1em01l+g3ttcJTg4mM6dOwPYnXVHzy/TGZPJxJdffklmZiZFRUWEhYURFxfnsjOgaWE9PCArK4sTJ04QExPj8FARaDhM4Oeff2b8+PG2t8ovXrxIVVWV7a11vaSFBp5uQTrQzpc7AO0tuKMD0N6CpzsAacFKxgTpwMqTLXi6A3BfC9KBd40Jd+PTO3jBwcEEBgbi7+/f4hNg2LBhtidLY0opTCYT3377bbsss6s1fpXHaDTqepXHXcrKyvjiiy/Izs52etFIV5MWOl4L0oFndLQOoGO34KsdgLQAMiaAdGB1r48J0kEDXxkTfHoHT7j3FT/hXaQFAdKB+H/SggDpQDSQDnyL7OAJIYQQQgghhI/w8/QCCCGEEEIIIYRwDdnBE0IIIYQQQggfITt4Tly7do2FCxcSGRmJ0Wi0fbiztLSUJUuWEB4ezpIlSygrKwPgwoULzJ8/nxEjRvD+++/b5vPjjz8SExNj+zdq1Ch27NjhiVUSreSqFqDhophGo5GoqChWrlxJbW1tu6+PaB1XdvDhhx8SFRWF0WiU8cAL6W1h//79REdHEx0dTXx8PAUFBbZ5HT58mIiICKZNm8a7777rkfURrePKDtasWcO4ceOIioryyLqItnFVC87mI7yDqzqora0lLi6OmTNnYjQa2bx5s/6FadXV8+4BxcXF6syZM0qphgsdhoeHq3PnzqmUlBS1bds2pZRS27ZtUxs3blRKKXXz5k31zTffqNTUVPXee+85nKfZbFahoaHqypUr7bMSwiVc1cL169fV5MmTVXV1tVJKqaSkJJWWltbOayNay1UdfP/998poNKqqqipVV1enFi9ebHdhV9Hx6W3h5MmTqrS0VCml1Jdffqni4uKUUg1/E6ZMmaJ++uknVVtbq6Kjo9W5c+c8sEaiNVzVgVJK5eXlqTNnzji9QLPo2FzVgrP5CO/gqg7q6+tVRUWFUkopk8mk4uLiVH5+vq5lkXfwnHjooYds19Do1q0bgwcPpri4mNzcXGbNmgXArFmzOHjwIAC9evUiJCSEgADn144/fvw4AwYMoF+/fu5fAeEyrmzBYrFQU1OD2WympqaGhx56qP1WRLSJqzq4cOECISEhdO7cmYCAAMaMGcOBAwfad2VEm+htYdSoUbaL5D711FNcv34dgNOnTzNw4EAGDBhAYGAgRqOR3NxcD6yRaA1XdQAwZswYhxdSFt7BVS04m4/wDq7qwGAw2K6/ZzabMZvNdmc21UJ28DS4cuUKZ8+e5cknn+TWrVu2jfLevXtz69YtzfPJysqSwy+8XFtaCAoK4rnnnmPy5MlMmDCBbt26Obxwp+j42tLB0KFDOXnyJCUlJVRXV3P48GG7DT3hXfS2sHfvXiZNmgRAcXExDz/8sO22oKAg2ZjzUm3pQPgWV7XQeD7C+7S1A4vFQkxMDKGhoYSGhuruQHbwWlBZWUlSUhIvv/wy3bp1s7vNYDBo3qM2mUz8+9//Zvr06e5YTNEO2tpCWVkZubm55ObmcuTIEaqrq8nIyHDnIgs3aGsHQ4YMYenSpfzud79j6dKlBAcH4+cnQ7E30tvCiRMn2Lt3L6tWrWrPxRRuJh0IK1e1cLf5iI7PFR34+/uTkZHBoUOHOH36ND/88IOuZZCtiruoq6sjKSmJ6OhowsPDgYbDrm7cuAHAjRs36Nmzp6Z5HT58mOHDh/Pggw+6bXmF+7iihWPHjtG/f3969uzJfffdR3h4OPn5+W5fduE6rhoT5s6dS3p6Ort27aJ79+48+uij7lxs4QZ6WygoKGDdunW888479OjRA2h4x67xu7fFxcUEBQW141qItnJFB8I3uKoFR/MR3sPVY8Ivf/lLfv3rX3PkyBFdyyE7eE4opVi7di2DBw9myZIltu+HhYWxb98+APbt28eUKVM0zS8rKwuj0eiWZRXu5aoW+vbtyzfffEN1dTVKKY4fP86QIUPcuuzCdVw5JlgPz7h69SoHDhwgOjraPQst3EJvC1evXiUxMZGNGzcyaNAg2/RPPPEEhYWFXL58GZPJRFZWFmFhYe27MqLVXNWB8H6uasHZfIR3cFUHt2/f5s6dOwDU1NRw7NgxBg8erGtZDEop1dYV8kVfffUVCQkJDB061Hb41MqVKwkJCWH58uVcu3aNvn378tZbb/HAAw/w888/M2fOHCoqKvDz86NLly5kZ2fTrVs3qqqqmDx5MgcPHuT+++/38JoJvVzZwubNm8nOziYgIIBhw4axYcMGAgMDPbyGQgtXdvDMM89QWlpKQECA7fTownvobWHt2rUcOHCAvn37Ag2H3qSnpwNw6NAh3njjDSwWC3PmzOH3v/+9x9ZL6OPKDlauXEleXh4lJSX06tWLxMRE5s6d67F1E/q4qgVn83n66ac9tm5CO1d1UFBQwEsvvYTFYkEpxfTp01m2bJmuZZEdPCGEEEIIIYTwEXKIphBCCCGEEEL4CNnBE0IIIYQQQggfITt4QgghhBBCCOEjZAdPCCGEEEIIIXyE7OAJIYQQQgghhI8I8PQCCCGEEB3BsGHDGDp0KGazGX9/f2bNmsWzzz5rO921I1euXCE/P1+uZSiEEKLDkB08IYQQAujUqRMZGRlAw8XoX3zxRSoqKkhKSnL6M0VFRWRmZsoOnhBCiA5DroMnhBBCACNHjiQ/P9/29eXLl4mLi+PEiRMUFRWxevVqqqurAVi/fj2jRo1i3rx5XLhwgf79+zN79mwWLlzIX/7yF/Ly8jCZTCQkJBAfH++pVRJCCHEPknfwhBBCCAcGDBiAxWLh1q1b9OrVi+3bt/OLX/yCwsJCVq5cSXp6Oi+++CIffPAB27ZtA2D37t3cf//9pKWlYTKZiI+PZ/z48QwYMMDDayOEEOJeITt4QgghRAvMZjN/+tOfKCgowM/Pj8LCQofTHT16lO+//56cnBwAysvLuXTpkuzgCSGEaDeygyeEEEI4cPnyZfz9/enVqxdbtmzhwQcfJCMjg/r6ekJCQhz+jFKKdevWMXHixHZeWiGEEKKBXCZBCCGEaOL27dskJyeTkJCAwWCgvLyc3r174+fnR0ZGBhaLBYCuXbtSWVlp+7kJEybw6aefUldXB8DFixepqqryyDoIIYS4N8k7eEIIIQRQU1NDTEyM7TIJMTExLFmyBIBnnnmGxMRE9u3bx8SJE+nSpQsAjz/+OH5+fsycOZPY2FgWLVpEUVERsbGxKKXo0aMH77zzjidXSwghxD1GzqIphBBCCCGEED5CDtEUQgghhBBCCB8hO3hCCCGEEEII4SNkB08IIYQQQgghfITs4AkhhBBCCCGEj5AdPCGEEEIIIYTwEbKDJ4QQQgghhBA+QnbwhBBCCCGEEMJHyA6eEEIIIYQQQviI/wMgi6N3icBVHwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualize (with Plotly Package)\n",
        "- source: https://plotly.com/python/"
      ],
      "metadata": {
        "id": "OQdol5Gw1tLt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Absolute Values"
      ],
      "metadata": {
        "id": "yZd3oAaj95d7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create plotly linegraph object \n",
        "fig = px.line(data_frame=df, x=\"date\", y=\"value\")\n",
        "     \n",
        "# set labels for x and y axis\n",
        "fig.update_layout(title='U.S. Monthly Gas Price Per Gallon', # set title \n",
        "                  title_x=0.5, # center align title\n",
        "                  xaxis_title=\"Date\", # set x label\n",
        "                  yaxis_title=\"Gas Price per gallon ($)\", # set y label\n",
        "                  width=800, \n",
        "                  height=500,\n",
        "                  template='plotly',\n",
        "                  plot_bgcolor = 'white',\n",
        "                  # x-axis setttings in dictionary defined\n",
        "                  xaxis = dict(showline = True,\n",
        "                               showgrid = True,\n",
        "                               linecolor = 'black',\n",
        "                               linewidth=0.5,\n",
        "                               tickfont = dict(size=14, family = 'Arial'),\n",
        "                               ticks = \"outside\",\n",
        "                               ticklabelmode= \"period\", \n",
        "                               tickcolor= \"black\",\n",
        "                               rangemode=\"tozero\",\n",
        "                               gridcolor='whitesmoke'),\n",
        "                  yaxis = dict(ticks= \"outside\", \n",
        "                               ticklabelmode= \"period\", \n",
        "                               linecolor='black', \n",
        "                               linewidth=0.5,\n",
        "                               tickcolor= \"black\",\n",
        "                               rangemode=\"tozero\",\n",
        "                               gridcolor='whitesmoke',\n",
        "                              )\n",
        "                  )\n",
        "\n",
        "# set interval 3 months with year and do not rotate text 90 degrees e.g. set 0\n",
        "fig.update_xaxes(dtick=\"M3\", tickformat=\"%b\\n%Y\", tickangle=0)\n",
        "\n",
        "# set the line color\n",
        "fig.update_traces(line_color='cornflowerblue', showlegend=True, name=\"Gas Price ($)\" )     \n",
        "\n",
        "# show graph\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "txHFVJTpikTX",
        "outputId": "f0eff2aa-33ab-4e9a-e246-a9e3557207f1"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"0c19a16f-98b3-4d7d-903c-b898edeb9db2\" class=\"plotly-graph-div\" style=\"height:500px; width:800px;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"0c19a16f-98b3-4d7d-903c-b898edeb9db2\")) {                    Plotly.newPlot(                        \"0c19a16f-98b3-4d7d-903c-b898edeb9db2\",                        [{\"hovertemplate\":\"date=%{x}<br>value=%{y}<extra></extra>\",\"legendgroup\":\"\",\"line\":{\"color\":\"cornflowerblue\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"lines\",\"name\":\"Gas Price 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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## % Change Graph"
      ],
      "metadata": {
        "id": "dzDZNtYqJRyo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create plotly linegraph object \n",
        "fig = px.line(data_frame=df, x=\"date\", y=\"perct_change_value\")\n",
        "     \n",
        "# set labels for x and y axis\n",
        "fig.update_layout(title='Month-over-Month <br> Percentage Change <br> U.S. Gas Price Per Gallon', # set title \n",
        "                  title_x=0.5, # center align title\n",
        "                  xaxis_title=\"Date\", # set x label\n",
        "                  title_font = dict(size=15), \n",
        "                  yaxis_title=\"Gas Price per gallon ($)\", # set y label\n",
        "                  width=800, \n",
        "                  height=500,\n",
        "                  template='plotly',\n",
        "                  plot_bgcolor = 'white',\n",
        "                  # x-axis setttings in dictionary defined\n",
        "                  xaxis = dict(showline = True,\n",
        "                               showgrid = True,\n",
        "                               linecolor = 'black',\n",
        "                               linewidth=0.5,\n",
        "                               tickfont = dict(size=14, family = 'Arial'),\n",
        "                               ticks = \"outside\",\n",
        "                               ticklabelmode= \"period\", \n",
        "                               tickcolor= \"black\",\n",
        "                               rangemode=\"tozero\",\n",
        "                               gridcolor='whitesmoke'),\n",
        "                  yaxis = dict(ticks= \"outside\", \n",
        "                               ticklabelmode= \"period\", \n",
        "                               linecolor='black', \n",
        "                               linewidth=0.5,\n",
        "                               tickcolor= \"black\",\n",
        "                               rangemode=\"tozero\",\n",
        "                               gridcolor='whitesmoke',\n",
        "                              )\n",
        "                  )\n",
        "\n",
        "# set interval 3 months with year and do not rotate text 90 degrees e.g. set 0\n",
        "fig.update_xaxes(dtick=\"M3\", tickformat=\"%b\\n%Y\", tickangle=0)\n",
        "\n",
        "# set the line color\n",
        "fig.update_traces(line_color='cornflowerblue', showlegend=True, name=\"% Change <br> Month-over-Month <br> Gas Price ($)\" )     \n",
        "\n",
        "# show graph\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "QACr0BAo4h_2",
        "outputId": "25b8ae9a-a9f8-4886-d328-88b8f9cd4c24"
      },
      "execution_count": 49,
      "outputs": [
        {
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