LST_from_MODIS.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/gunjan2014/Colab-work/blob/main/LST_from_MODIS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "To authorize access needed by Earth Engine, open the following URL in a web browser and follow the instructions. If the web browser does not start automatically, please manually browse the URL below.\n",
            "\n",
            "    https://code.earthengine.google.com/client-auth?scopes=https%3A//www.googleapis.com/auth/earthengine%20https%3A//www.googleapis.com/auth/devstorage.full_control&request_id=8iJbLSuLOP6X0Ye7jAQRH_-irvWWqJ7h681HaSke7Aw&tc=d4NqY8IyEwBMNzIlrc8MgTP4T04I0_HrFgCryFzjGEs&cc=yGsLnvhYW-x_U1zv2oJc-aH8cu9Ef9frNup0aT1V5WE\n",
            "\n",
            "The authorization workflow will generate a code, which you should paste in the box below. \n",
            "Enter verification code: 4/1AX4XfWil-H4FxbN9g5QBONVYdwj0yfWan32z7u3WLkYZ2QRoMFnPvFHWOm8\n",
            "\n",
            "Successfully saved authorization token.\n"
          ]
        }
      ],
      "source": [
        "#load/import library\n",
        "import ee\n",
        "\n",
        "# Trigger the authentication flow.\n",
        "ee.Authenticate()\n",
        "\n",
        "# Initialize the library.\n",
        "ee.Initialize()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# the name of the sensor/satellite to download data\n",
        "sensor='MYD11A1' # MOD11A1 or MYD11A1\n",
        "\n",
        "# initial date of interest (inclusive).\n",
        "i_date = '2003-01-01'\n",
        "\n",
        "# Final date of interest (exclusive).\n",
        "f_date = '2020-12-31'\n",
        "\n",
        "# define variable of interest\n",
        "my_variable='LST_Night_1km' # 'LST_Day_1km' or 'LST_Night_1km'\n",
        "\n",
        "# Define the location of your interest as point geometry\n",
        "my_lon = 86\n",
        "my_lat = 28.5\n",
        "\n",
        "# spatial resolution of the product\n",
        "scale = 1000"
      ],
      "metadata": {
        "id": "tQxNOLJ-fyJs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_point = ee.Geometry.Point(my_lon, my_lat)"
      ],
      "metadata": {
        "id": "WfenF_EOf9c9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the MODIS land surface temperature collection.\n",
        "lst = ee.ImageCollection('MODIS/006/'+sensor)"
      ],
      "metadata": {
        "id": "A4kW9BB0gKOK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the data for the pixel intersecting our point.\n",
        "lst_my_point = lst.getRegion(my_point, scale).getInfo()"
      ],
      "metadata": {
        "id": "elVam5g9gPQL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preview first 6 rows of the result.\n",
        "lst_my_point[:6]"
      ],
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              "[['id',\n",
              "  'longitude',\n",
              "  'latitude',\n",
              "  'time',\n",
              "  'LST_Day_1km',\n",
              "  'QC_Day',\n",
              "  'Day_view_time',\n",
              "  'Day_view_angle',\n",
              "  'LST_Night_1km',\n",
              "  'QC_Night',\n",
              "  'Night_view_time',\n",
              "  'Night_view_angle',\n",
              "  'Emis_31',\n",
              "  'Emis_32',\n",
              "  'Clear_day_cov',\n",
              "  'Clear_night_cov'],\n",
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "def ee_array_to_df(arr, list_of_bands):\n",
        "    \"\"\"Transforms client-side ee.Image.getRegion array to pandas.DataFrame.\"\"\"\n",
        "    df = pd.DataFrame(arr)\n",
        "\n",
        "    # Rearrange the header.\n",
        "    headers = df.iloc[0]\n",
        "    df = pd.DataFrame(df.values[1:], columns=headers)\n",
        "\n",
        "    # Remove rows without data inside.\n",
        "    df = df[['longitude', 'latitude', 'time', *list_of_bands]].dropna()\n",
        "\n",
        "    # Convert the data to numeric values.\n",
        "    for band in list_of_bands:\n",
        "        df[band] = pd.to_numeric(df[band], errors='coerce')\n",
        "\n",
        "    # Convert the time field into a datetime.\n",
        "    df['datetime'] = pd.to_datetime(df['time'], unit='ms')\n",
        "\n",
        "    # Keep the columns of interest.\n",
        "    df = df[['time','datetime',  *list_of_bands]]\n",
        "\n",
        "    return df"
      ],
      "metadata": {
        "id": "_Fuof9P_ghEK"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# apply function to convert array to dataframe\n",
        "lst_df_my_point = ee_array_to_df(lst_my_point,[my_variable])\n",
        "lst_df_my_point"
      ],
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              "0              time   datetime  LST_Night_1km\n",
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      "cell_type": "code",
      "source": [
        "def t_modis_to_celsius(t_modis):\n",
        "    \"\"\"Converts MODIS LST units to degrees Celsius.\"\"\"\n",
        "    t_celsius =  0.02*t_modis - 273.15\n",
        "    return t_celsius\n",
        "\n",
        "# Apply the function to get temperature in celsius.\n",
        "lst_df_my_point[my_variable] = lst_df_my_point[my_variable].apply(t_modis_to_celsius)"
      ],
      "metadata": {
        "id": "FE6_O_RAg4Ph"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize head of the output\n",
        "lst_df_my_point.head()"
      ],
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        "colab": {
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              "0           time   datetime  LST_Night_1km\n",
              "1  1025827200000 2002-07-05          -4.03\n",
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              "4  1026086400000 2002-07-08          -7.19\n",
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              "                                                     [key], {});\n",
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              "\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",
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          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# delete first column as we have already converted time to date \n",
        "del lst_df_my_point[\"time\"]\n",
        "lst_df_my_point.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QaCycxVeiwfk",
        "outputId": "743409c8-ac16-4415-9a78-92a506a1bf09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   datetime  LST_Night_1km\n",
              "1 2002-07-05          -4.03\n",
              "2 2002-07-06          -3.35\n",
              "4 2002-07-08          -7.19\n",
              "7 2002-07-11          -4.91\n",
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              "      <th>datetime</th>\n",
              "      <th>LST_Night_1km</th>\n",
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              "      <th>1</th>\n",
              "      <td>2002-07-05</td>\n",
              "      <td>-4.03</td>\n",
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              "    <tr>\n",
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              "      <td>2002-07-06</td>\n",
              "      <td>-3.35</td>\n",
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              "      <th>4</th>\n",
              "      <td>2002-07-08</td>\n",
              "      <td>-7.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2002-07-11</td>\n",
              "      <td>-4.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2002-07-12</td>\n",
              "      <td>-3.51</td>\n",
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              "  </style>\n",
              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-a9eaccd6-3b58-486d-8a8b-4ca9176a03b7');\n",
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              "                                                     [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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load required packages\n",
        "from plotnine import* # asterisk (*) means load all functions from plotnine\n",
        "from mizani.breaks import date_breaks\n",
        "from mizani.formatters import date_format"
      ],
      "metadata": {
        "id": "uWqdAL3di10D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_daily_LST=(\n",
        "    ggplot(lst_df_my_point)  # What data to use\n",
        "    + geom_line(aes(x=\"datetime\", y=\"LST_Night_1km\"),colour='red')\n",
        "    +ylab('LST (degree C)')+xlab('Year')\n",
        "    + scale_x_datetime(breaks=date_breaks('2 years'), labels=date_format('%Y-%m'))\n",
        "    +ggtitle(sensor+' '+my_variable+' at lon= '+str(my_lon)+' lat ='+str(my_lat))\n",
        "    +theme(figure_size=(10, 5)) #x,y)\n",
        ")\n",
        "\n",
        "print(plot_daily_LST)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "UT_mxoiSi6jq",
        "outputId": "deeca9af-0fdc-460d-90e8-979da751a38a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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FNm/eHLt3wQUXYO7cuZg8eTJycnJskUeOhg0b4rHHHsNjjz2GkpISrF27FosWLcKMGTMwaNAgtGjRIsFzoZVMnDgRZ555JsaOHYu5c+dqfm7r1q0oKipCgwYNEu6dfPLJigqUnJLWoEEDQ2aEZrN371707t0bxx9/PD777DPVciL3HgUFBbL3hOta33H69Ol48cUX8dNPPyEUCsXds8Kb4NatWwEA7du3T7gnXNuyZUvCPa3fsk2bNmjTpo0ZosZ4+eWXMXLkSCxZsgS9e/eOXe/fvz9at26NESNGxDxqCkpFkyZNEhTYoUOH4tFHH1U96iAZVn8vM/PvrrvuwvTp0zFgwABMmjQp4f7YsWMxadIkbNy4Ee3atYtd79WrF0477TSMGTMG77zzjqG0O3TogH79+mHu3LnYvHnzYXmsAEEYgRQognARZ599Ntq1a4fnnnsO3333HYYPHx43W2o1lZWV+PXXX9GkSRPbVp8WLlwY2wvQqlUr2TBvvfUWhg8fbos8ycjPz0evXr3Qq1cvdOjQATfffDNeffVVWxWo008/HQMGDMCbb76Ju+++2/L0PB6P7HXmgk3mhYWFOPPMM7Fo0SLMnDkTd9xxh2JYpfdQu6flHZ999lnccccd6NGjB1588UU0adIEgUAAoVAIF198sSmOD8xC63sePHhQ854/j8ejaa/bU089hZycnDjlCYg4YunSpQs+/PBDlJeXIysrK+ZMpHHjxgnxCNeMKjp2fC+z8m/06NF47rnncN1112HWrFkJ36+2thb//e9/0bp16zjlCUDs2IlPP/3U2EtEadmyJQDgr7/+IgWKIKKQAkUQLuPGG2+MmRMJ5zXZxaxZs1BTU4PLL7/ctjSnT5+O3NzcBA+EAsLsq1sUKDHCap+wudtOHnvsMbz33nv417/+pfmMl+OOOw6//vor/v7774RVKDPMc6z2TiaHz+fDvHnzcP311+POO+9EeXm57Dk2VvLaa6+hRYsWWLZsWZy3TCtNngTnJj///DMuu+yyuHs//fRTXBgjjB49Gq+99pqmsM2bN9fk1GH37t3w+XxgjCWUFWEVSPi/ffv2yMzMxK5duxLiEQ4PlzpT0Iod3yvV/GOMYdSoUXjxxRdxww03YMaMGbKeWIuLi1FTU4O6ujrZuEOhUMIKm14E8067PI4SRDpAChRBuIxBgwahpKQE+fn5aNu2rW3pLl26FGPGjEFeXh7Gjh1rS5pbt27FypUrMXDgQFx55ZWyYb7++mtMmjQJ33//PTp06GCLXNL0TzrppJhJl5j58+cDgOmmTloQ3C5PmjQJVVVVmp7p27cvlixZggkTJuCpp56KXd+wYQOWL1+eskzZ2dmOHH7r9XrxxhtvIBgM4v7770d5ebmhvTFGEVYFwuFwbJDLGMPDDz8sG96MfOrZsyeys7PxwgsvYPjw4bHyWVdXh8ceewwAcMUVVxiO34o9UG3btsXGjRvx7rvv4p///Gfs+rZt2/DZZ5/hpJNOipkOB4NB/POf/8SsWbMwb968uPZhypQpAJCgOGrFju+VSv4xxjBy5EhMnToVI0aMwEsvvaQ4OdGoUSMUFRXh119/xddff41OnTrF7n311VfYtGkTevbsGffMjh07UFFRgeOPPz52PIXcpAoAfP7551i0aBHat2+va78lQRzukAJFEC6jsLBQk+MIo2zZsiV2Pk5lZSV27tyJlStXYs2aNTj22GPx9ttvJ2w4/uyzz/DZZ5/F/gYiLtZXrVoFALj++uvjnpk9ezZ+//13AMDvv/8OxhgeffTR2P37778fQMQNM2NMdcP2VVddhUmTJmH69Ol44YUXAETOQXn//fcBABs3bgQALFq0KDaL+49//AOnnHKKpvxYvXq14r377rsPb7/9NqZNm4bevXvHzo05cOAAVq1ahQ8//BDHHHOMLWZ0SvK98sorWL9+vabwN9xwA2bOnImJEydi+/btuOCCC7Bz5068+OKLOOOMM7B+/fqUVpE6deqEmTNnYty4cTj55JPB8zwuv/xyZGVlaY7jhRdeQElJCYCIGRTP87Gyk5+fj1GjRsk+x/M8Zs6ciczMTDzxxBMoLy/H5MmTbVkVu+qqq3DvvfeiV69euPLKK1FRUYEFCxagpqZGNnynTp2wYsUKTJgwAc2aNQPHcbEzfrSSl5eHyZMnY9iwYTjjjDMwdOhQZGVlYd68efjyyy8xbNiwBAcSerBiD9RDDz2Evn374rrrrsOqVatw6qmnYteuXXjppZdQVVWFCRMmxIV//PHHsWLFCgwcOBBr1qzBiSeeiFWrVmHu3Lk49dRTcdtttxmSw47vlUr+3XPPPZg6dSpOOukkdO7cGW+88Ubc/UaNGsWUIp7n8dBDD+HWW29Fz549cfPNN6NVq1b47bff8NJLL8Hv9ycohoMGDcLq1auxbds2tGjRAkCkzRbauZYtW4LneWzYsAGzZ89GZmamooUAQRyxOOX+jyAI7a60GUvuDjyZy2zBlbPwj+M4lpWVxVq0aMH+8Y9/sKlTpyZ1Xa70T+wiWJBVLTxjEde7TZo0YTk5OayqqkrxvcLhMGvWrBnLz89nFRUVjLF618RK/7S4DRfyXu1fbW0t+/nnn9kDDzzAzjvvPNakSRPm8/lYVlYWa9euHRszZgzbu3evYhpWuDGXMmnSJNnvIOfGnDHGDhw4wEaOHMmOOuooFggE2Gmnncbmz5/P7rrrLgYg7n0EV8xKMkldlu/du5f179+fFRQUyLpF1/quSt9D6u5Z7MZczD333MMAsJtuuonV1dXFyv6DDz6YEFbpHdWekVJXV8cmTJjAWrVqxQKBAGvSpAkbOXIk279/v6wL7E2bNrGePXuynJycuDqhhpKL+I8++oh17dqVZWdns0AgwE455RT2/PPPs3A4rOk91eK2gtWrV7PLLruMFRUVMY/HwwoKCtjFF1/MVq1aJRt+165dbOjQoaxRo0bM5/Ox5s2bs7vuuosdPHhQU3pybszt+F6pkKz9lPtWCxYsYN27d2f5+fnM4/Gwo446il155ZXsu+++U4xfXDe/+OIL1qdPH9a8eXOWmZnJ/H4/a9myJRs+fDjbunWrhW9LEOkJx5gLdgETBEEQjnLppZdi9erVKC0tld1rQRAEQRBEBOolCYIgjiAqKioSrn3zzTdYunQpevToQcoTQRAEQSSBVqAIgiAc4M8//0waJjs723R38oMHD8aBAwdw3nnnIS8vDz/99FPsIOOvvvpK9lyhVCgrK0NZWVnScOThiyAIgkgXSIEiCIJwAC3ODR588EHTHYq88cYbmDJlCn799VeUlpaisLAQ559/Ph588MGEc2TMYPz48XjooYeShqOuiCAIgkgXSIEiCIJwgBUrViQNc9xxx6W96+CtW7di69atScP16NHDBmkIgiAIInVIgSIIgiAIgiAIgtAI7RYmCIIgCIIgCILQCClQBEEQBEEQBEEQGiEFiiAIgiAIgiAIQiOkQBEEQRAEQRAEQWiEFCiCIAiCIAiCIAiNkAJFEARBEARBEAShEa/TAjhNcXFxSs9zHIdgMIjKykpXHATp9/tRU1PjtBgAKG+UcFu+AJQ3SrglXwDKGyXcli8A5Y0SlC/KUN7I45Z8AShvlHBbvgCp501RUVHSMLQClSI8zyMzMxM8746sDAQCTosQg/JGHrflC0B5o4Rb8gWgvFHCbfkCUN4oQfmiDOWNPG7JF4DyRgm35QtgT964520JgiAIgiAIgiBcDilQBEEQBEEQBEEQGiEFiiAIgiAIgiAIQiOkQBEEQRAEQRAEQWiEFCiCIAiCIAiCIAiNkAJFEARBEARBEAShEVKgCIIgCIIgCIIgNEIKFEEQBEEQBEEQhEZIgSIIgiAIgiAIgtAIKVAEQRAEQRAEQRAaIQWKIAiCIAiCIAhCI6RAEQRBEARBEARBaIQUKIIgCIIgCIIgCI2QAkUQBEEQBEEQBKERUqAIgiAIgiAI4kiFMSAUclqKtIIUKIIgCIIgCII4Qsl45RUUNW7stBhpBSlQBEEQNuLdsAGe//3PaTEIgiAIAgDgXb/eaRHSDq/TAhAEQRxJ5PfuDQAo3rfPYUkIgiAIAkAg4LQEaQetQBEEQRAEQRCETXD79oH780+nxYjBMjIif4TDzgqSRpACRRAEQRBE2hCcPBne775zWgyCMExhu3Zo0L6902LEiClQVVXOCpJGkAJFEDrht22LeKwhCIIgbCfrsceQffvtTotBEIbh3LbSEzXh4yorHRYkfSAFiiD0UFeHwrPOgn/RIqclIQiCOHKhSSyCMA+PJ/J/XZ2zcqQRpEARhB6is0aeXbscFoRQgyspAWgmjSAIghCorIz0DYQyNDGhGVKgCEIPwuyM25bfiTgatGqFvH79nBaDIIjDHN+KFShs3dppMQgN5A4YgAatWjkthqvgt2yJv0AKlGZIgSIIPQiKEylQrse3YYPTIiSQdd99TotAEIcHLhnoBRYsAP/3306LQWjA9803TovgKrwbN6KwUyfwO3bErnEOypNukAJFKMKVlAAVFU6L4So4UqCIFAhOm+a0CARBmAjLzHRaBEIjzOdzWgRXwUXPIuQqKgAuqjq5ZGIiHSAFilCkQatWyOvf32kx3AUpUIQBfKtWkXtYgjgc4Vw6Z0/tTSJ0WGwcnKAs8TwpTgYgBYpQxY1mUI4SVZw48lRD6CDvqquQOXmy02KkDb4vvoD/gw+cFoMgdMPt2wd+925HZeB37EBR06aRIzeIGG5cgeIOHEDeRRcB5eX2Jx5Vmgo6d0bgvffirhHJIQWKIPRATiS0U1sL79q1TkvhGrhDh5wWIQb3559AKOS0GPCuXQvPDz8kXM/r1w+5Q4c6IJH74MrKEHj7bafFAADwu3ejQbNm7vBw6ZaBnkSOgjPPROGppzokTAT+r78i/+/Z46gcrsPvd1qCBDxbt8K3cSMatG6NgjPPtDdxUdn1CMq2W+pVGkAKFEHowUUmfNzffyNj1iygrAz8H384LU4CwRdfRP5llzktBhHFu24dfF98ATCGBu3bIzhlitMiIf+yy1Bw4YXxF6urnRHGpQSffx45t93mtBgAImWIq6xExhtvOC2K+4gOPHknVhKkRM0KORoMx8FcqECxjAwAAFdVBc/27fYmLjeOoTKjmcNKgSorK8OECRNw9dVXY8iQIVhEh52aQkHHjk6L4B5ctAKV+eSTyB4zBrnXXYfCDh2cFicB8kyVCBMOK3SA/EsvRV6/fuD//BNApMN2I0XHHuu0CK4gZ/hw+BcurG9r3DCwiQ7Ms8eOdVgQF+KG7yPAR4Z2eX37OiyIy3ChAuVouSEFKiUOKwVq6tSpqK2txauvvorx48dj3rx52EB7eAzh++ST2N90aGw9sRk9FyhQwmDG+/33DgsSwbt2LYITJ9ZfcEMeuQmOqz/t3UE8mzcDAOpatHBWEDfCmGsGEIEFC5D14IOAsG+jpsZZgQBXlN8YLvlOMTncIg/gXscWDuPGPVCO4qYym4YcNgpUVVUVvvzyS1x//fXIzMxEixYtcNFFF2H58uVOi5Z+VFQg95prnJbCnbjJiYRgpuESRSVn5EhkPfVU/QWXyCXgX7gQnt9+c1YINwxAhb1P/GHT/JtGYZs2yL36aqfFqIex2Kqlf8kSZN1zj7PyuKHMRMuvN1qXPZs2OSmNK2FuVqCcPBqFVqDioRWolHBBa2gOu3fvBmMMzZs3j11r2bIldogOCEsnPN9/D/+HH8K/bJm9CZeVoah5c7KdVkLGhC/71lsRmDvXflmEwUx0QJF1773I797dfjkEojPkgTlzkD16tOsa4txhw5B71VXOCcBx4Nyw+d5lMBe5FuaLi+H/9FPkXnUVfKtWIfuuuxCYM8c5gUR1KPtf/0Lw1VedkwVwlQIFAPyWLSjo3FnWEYltuHEFyqXw27ejqHlzx7wDxvZAuWEC1GG4sjIEPvww8QaVY814nRbALKqqqpApOdAuKysLlZIBS3FxMYqLi2O/eZ5Hw4YNDafric4OekyeWS7o0SP294H9+zU/x3FcSrJwCrNDHp7XbRZgVd4YJdW8AQBetDlXiCvjnXcQWLIEoWuv1RSHKflSW4uM11+P/B3tDIKvvGI4XjPyhjKeOfYAACAASURBVKutBQDk3HknAKDqppt0y5NS3ggNv6icSuPhdMZtRr4IM64eyaBBb7xm1Sdv1MUyn2pbYUbeIHIQqRBPQHLQsJb4rWhn/KtWgWMMvtWrEXjzTYQGD9b1vFl5w3Ecsp58MvIjWr5tq09iBAcJIhMoJ9oZrqQEiLYzAOCLlmXvvn26VnfNypeCwkLURSdtPTwfJ4PeuM0qMwWFhai8917DcljZb3uiB7d6i4tRd8IJmp4xK18AgIsqUJ5QKLIaFQoha9gwVDzzDFhBQdLnzcqbzNGj6+OUTErY1T8FH30UARk/AYVnnIGKJ56A7/33UbZ4saa43DbWA8wtN0ocNgpURkZGgrJUUVGBYDAYd+29997D9OnTY7+HDBmCUaNGpZx+bm5uynHEkLgXLtBQscX4U1mmLi2VvVxw5pnAli2GojQ1b8Tk5QGvvAJccYXmR1LKGwCIKt8ZPh8yCgqAqGtqzu/X/Z1Sypd582IrPlJzQr1yCBjOm3AY+OCDuIENAGRE4zMij6G8uftu4Jln4mbQpGnzHo+99am4GOjcORKPZDW5oKAgcvZHVpauKFOtT1mffRb5PzMTWQbLikDK9QkAn5lZ/03+/e+4e3q+ldntjC8nB0Ckfhkpw6bkjcgNtTDMsq0+iRk4EHjzTUA0oCooKIh4TPR4AK/2oURK+VJYCOTnx37mRE0ac8rLASfyBYDn998BAAX5+XEmYgXTpwM6TS7NKDMAEPzqq3o5DNZxS/rtaJy5ubm6vlfK+bJkCbBwIZCdDQAoyMyMpL9nD7BoEfz9+wODBmmOLuW8mT1bMS7b+icVb5GZUScxto5pLMCs+qTEYaNAHXPMMQCAHTt2oFmzZgCAbdu2xf4WuOKKK9C1a9fYb57nceDAAcPpejwe5ObmorS0FHVmLQuXl0NcbPXIl5WVhfIU3KjyxcXIk7uxdavufDIlbxiD59tvUde2bWSAHh3UAEBBaSlqZsxAuUaztVTzBgD4AweQB6CqshKVBw4g8OyzyAQQBnBQY/6YkS++khJkK9wzUp5TyRvf4sXIvv76hOvVlZUI6JQnlbzJXbAAnmh6Qv0Rp10AoI4xlNpYnzLvvhuB6ABLyoHff0dBixYoXboUdWedlTSuVMtNrE1ZsAAAUF5ejpoU2j4z6lMBAOzeHftO0u5aS9kxsw0Wp1/j8UDofqtuuQWVjz2mOR7T8kYEC4fBwb76JCZ/7lxwAMr++ivW7hw4cAD5DRogdPbZKPvoI03xpJovBQBQUhL7XVdTAw8A3HgjDvTrpzkeU/IlHE7sp/3++mv33osDw4Zpjs6MMgNE8qi2thbCWqEj/bZS3KWlyAUicWuUy4x8yb3lFni2b0fN5ZfDD6Bk715gzx7we/YgF9rbQrPyRlxuSg8ehFjtsGu8l//uu0hmU1Ty448IvPYaqu67TzWclWXGKCm3NRqUx8NGgcrIyEDnzp0xe/Zs3Hnnndi3bx8+/vhjjBYtlQJAUVERioqKYr+Li4tN+eB1dXWmFRzpPgk98TLGUpND5QwWo/Gmkje+Tz6J29RdHDUBENDzvinnDRBbHWShEOrKy+H55pvI9dpa3XGnki8eFTtlI3GmkjdehVXLsBDfDz+A//tv1J5/vuY4DeVNdF+a+LmEOHje1vrEVBxpMGH1cssW1J1+uuY4zWprwpJ4/AsXInT22Qg3bqzpeVPqUxSlePTEb2YbDAAekQfXjJdeQtnDD2t+1nDehMMRE1QZc2murAyAsfqdct4I7stvuKE+ztraiJnj11/b2waL8OzcWS+P3fki8YrItm9H+PjjE+LXiql5I2p3nOi3lRAcHknbHjVMyZdo+Q1HTVDDlZUoPO202O1wOOxYWyONx64yE27aNOm5UxlPPIGMt95CucQyQAkryoxRzG5r5HDBjlDzGDFiBDweD4YMGYIHHngAV1xxBU7XMTBxmuzRo5Fzww0JSozn//7PPiEkZlhOwykMzgUCdjvZiFZILhxG5sSJCERNWji7881NGz0lJqcCQmdZcMEFyNNhZmkYLXlit3cqN8okIJEtd9gw5PXp44wsLsSJ4xsKTzrJWUcnSsiUUUcd1rgByeCssFMnhwRRx/fJJyhq2FCxnT4iEMqvYNIlPRLATf2pTYTzZG2N4gi8954NkqQvh80KFABkZ2fj3xo1ZTeS8eabAIDy8ePjrhd06YKSDz5AyIYGmnPDWSNi3OSOta6u/nDYujrw4qV2lymetqI0y2N3pySTnu+zz3StfJlOmnXMUkcXhL3wJSXwr17ttBiJyLTD3p9/dkAQ9yAcSO0W+N9/B5Ps+QaAvKgFB3foEMDzYBoGzpYhnF24caMt4xkBFnXUkPH22xExJJPUObfdhmqbjm4Rn7HpJFqOP3HdeNBlHFYrUIcLcoWWF9l9W4qKCd+RTuZjjyFPsLMPh+PO2uCO4Nk9xTOx7D4HSkZZSVj5cuMKlFO4WTbCXbhpIssFcGVlKNSwb9FOCs84Aw3atgUA8CLTRoGcG29EA42e76wm+4EHwCvsDbUESfl1SjHw/PBDTKF1HJed05iOkALlRuSUGJsGO8kaFn7XLni//dYWWQC4quP2is8aYcxVsjlF8IUXEHzuOdl7nAkbonXhRnM5NZmcVmAcTJ/fswecdLP0kbyKS6RMcNIk+xKzu23TieePPxKu+b7+2gFJlLFVHun5ZQ5NFBdceGHCNTpzM30hBcqFyK422VXJkjQs+T16IL9XL3tkAdylpIi/QV2do7K5pdHNeuihuI3cYgLvv2+vMBryhLlJgRJwUxm3icJTTkGDE0+MuyY4SXAK36pVjqbvatKgjMbOy7IBLeZPrsNt39DOPky6AuUmSxuJMs7ZZRrqkjFEOkMKlAvJk3PH6oIVqIyXX67fA2QXbmn0q6vhj56fA0Q7ULfIRkRQqiPiw6HdpEA53YE5nb4E2RVLG2W0vW1TIXfgQPUAlZW27MEJzJkTcUCQpN7YuvqjQs7QoShq2BBewTuqVWhVoKqqrJVDDy7orzLeeiv2d/jYY+1LWHpY7dat9qUdJWHFPYrUpI8/eNAOcVzX/qcjpEClC3YVdhUFyr90qT0yuJAEb4CSfT/VF19sozSELAp1JM7ZB8dFNlO7oPPIiR7cyJWVkT06nFeg3IT/449V7+eMGIHC9u2tl+PTTyN/JBl827n6o0bggw8AAH6N51IZRqN7ZM5FCpQbVl0y5syJ/c0yM+1L2AXKo0+oSxISvovU3NAqjtC21UxIgUoX7FqBUmlkHTFbsKsxSYY0/yUmfFwohOAzz9giR2DOHBpwyxH9RlnRU9QFOPHKAsehwXHHISCaCbVDJjl8338PAMgeM0ZxH5mluK0DlWt7qJzLelbzRsuO5bil/dWL12IHw1rL5RHoxcy7di0yXn4ZAJB/zjnwiPcOi7Gxbid4jHRCmdSoxHGSsy4tQ0/777a+wiWkaet4BOKCFSjfV1/F/uZ37LAk+cBbb4ErLo6ksWVL0oPebEOa/6FQXIPoX74cWU88Ac5ib4meH39Ezp13wrdmjaXppCOCgh+cMSPuOr9/f+xv76ZNkf/tGoBqrLfe776zWJBE/KtWaZ5Jtxp+5055T5YuUaBsnS2XIveN7OoPom0cSzNFikUPTLUMrStQlZXWyuFCcq+/HtnjxgHhMLybNyOwaJF8QAfrthtW45TI79MHuf37W5+QnvwPh+Fdvx5elzkicZr0ahWPZFywAiUmV3QivZnk3H47ssaNAxA5mDDrwQctSSdVuNpa2RklO8xqAJDHMh1wIgUqhl1u5108cxd4/30EoueiOE3Gm2/Kl2mXKFCOIldWbSpXgQULIn+4wARKFxavQImtMdQc0xSecYalcggIk45uQnAKw7Ky5AM4qUC5yLRSDv/nn1ufiI7853fsQP4llyD/8sstFCj9IAUqXbBLgdI6MLdSHjcOmmRWoLzr1ycEs7xhFmaCXTwwdwylPVAyzgE0l/NU0fqdHBqgcmIHGw4jtwJl22Gl4TCy77jDnrQU4BQ2j8uZTqelFzgT0OxF0++3VhBx/rtAueQVHBQ4QrTNE8qzGxUoRyYgXVBOxOjx5OtftswaIRhL61UtUqCIeDSuQFlq0pGsoQmHgejsVrioCNzBg7bPwHG1tbE9LLbi8UT+d4nplS6sVvqUFCgHV6A0d1JOda5uMs2SGdRkvPqqLUnzO3Y4Piutqw2zeQJF8bBsG+G3b9dcn5jVe6DE+UGTWbII5uxKClR+nz7g9+yxU6R6XFCeHUdHuc2OWgWZjXf9euRffjk8mzdbEr/VuKj3JFRxgRvzOKwceCV516wHHkBRy5YRMYqLUXDuuWhw8snWyQMgYXhrlwmYlGi+qw5oXNqhB59+2toElN5bZh+C7H4bKyAFShscJ6tAaW6PUqG8HIVnnml9Oir4ly5Fzi23OCqDlDyxuY4LVrx0fSMr90AxhoKuXWM/3XImn9vgBc+1gYBiGI/UuYNN2Nb+xyXqrhUop8cJmQ89hOCUKZEfLj+YWgmX9J6Ea9C6udLBgZfYmQUA8H/9ZX2ick4knEBYgVJLnzHHG0c5AvPnW5uAnncmEz5n05VBdlBjw3dS3ORuI1njxsH37bfaH7ChfvvEpjUuUKD0YKkTCZ1lsrBNG3h++cUiYaK4sL2PmaSqTfYJ/ZndOOCwxk3m0m4g84UXEIgeN5CukxCkQKULR9IKVBLk3Ppan2h8/jsyg4WIGQsA1cY+Z9gwFLZpY49AOvBavUyvVEfkrtv1/bR2yg7VJzlTXGHzt+3IfBM7vGU50p5I0WtSZLdCk2YKlKVOJHTmBb9vn+XnUnm2bbM0fiPkDh4c+UOtrXWTAmWxWV/O7bdrDlt7+ukWShLFTUqLm2TRASlQaYJtGrpWBcrCmetk72q5fbtsokz9tw1wf/6JvAEDIj9UGvvA+++Dd6FXJsvR8U3sciKhVQHQvDnebKLpit3vZ+vo6M2CcZzsoMbymXtAU1tm+eyxjsGbd82atB1w2IaVfYSRgbbF3yv3uussjV8r/iVLwEuO8lA1N3dKgZKTySmrEhnqolsULMVF75t2EzRRSIEiYnD79iGoddO2k3snRJ1jrVN7F9Q6RIsG5+JBvxs2dbsOhW8iq5Bb3XlUVsL77beAVscEDilQ/pUrkX/hhciYOTN2LfDBB47IIqfUOuKoRQkrB8E66nN+nz72K1AuMvV0miPVA6IWZI8dUWlrnTpfTNaCxE19qg3125b9pRrJmDUrLSeFSIFKF+yweddjg++kAiU+g0PJRarZSE34VA7M9X/8sTUyiO363dTYuwU9AxsL65Pn55+RM2wY8nv10r5y4dAANbB4Mbw//OBI2nEoOJGwLW0tWOilT++g3E2DH1dipZLjwhUo1yBXl9Qmq6y2Jqmuhu+TTxKvy5kLu6hPdZMsdpDx9tvgd+92WgzdkAKVLtjQAOsxA7B05ijJu/J799b/sGvgKZGJ37dPOWhOjjUiiPJcS2Nji/mTm9CzB8rC+lRwwQUIRM/NcLsCFcPpAR5jju0r1Prulp61o1eBstqk0AUmyynhNgXqCEHOFNlJE77g888j7+qrE2/IlQ83fVerHVrs26c6hnEEt3iE1UH6SXyk4rYOzMEBX12rVvbLIWnQ1GaALVsVE5UB76ZNSYPnjBhhjRwAcm68EfmdO1sWvxGU9s7x27ejqGHD+Is2meGQAqUDB1aguL17NZ//VNihg3WCuGnwBsC3alXcb8eU2ygevaacpEA5g84VKGaxAqWkJMjugT2C9gR5N260NH5DON0HGoAUqDQh8M47TosQj4WzBclMimrPPrv+h0MrUKpY1cHqHOR6f/nFslWowPvvw7tpE0LHH29J/HrxbN6sqKzIHdJnl1MWj+A10eW4wo2sAwOYBu3aIfvee7U/YFXddtmgnLNytc0ABT166HvAwgGokT1QrqhfTuGgYqI4OSJnwmdhmfH89pu+B47A8pJ9++3g/v7baTF0QQpUmuBfvdq2tEqnTUseyEIFyrN1q2VxG0ZP42rRYMiIqpjfq5fpcojxbtliafxaKTjnHF3hfWvWWCSJQZyefZMMKLjiYvhWrLBVBLs8Iyakq8cczqJBVjrueWAqB6Q6jkscfghkTpxogSDuQ/aoCpU6Y7liqeQF1UYvfJ5ff0XBuefqeygcBr9jR8TjpgX4NmywJN5U8K9alXZnZZEC5RZcNOOgyQTNwQGfI7N5elxkWzWTZeC9ucpKCwRJM1xUt5TImDsXBXac/aGAdKY2Z/Toepf5duGUEwk9WFWWjMZrUf3W0sayjIz4Z1Qc69iNpZ7y0lDZdRJV808L22buwAF4v/tO/p6NXvgMHSkSDiPvsssiHjctIPOZZyyJN2WcnkjUCSlQLiG3f3+nRahHyynuLtnw51+50pZ0+EOHtAe2qCH2L15sSbyEO/Ds2OFc4tKZPwdmAp3eZ6MJqwZ8BtsMy87/MaBAWeZ91AhWKlDp7sa8vNzeSSWHFKjcIUOULSTkzpzbtMmaVSgD3ju5cBhcaan5srgdUqAII/i/+MJpEerx+5MGsXrzJwCElbzZObCikDVunPbAFilQ2WPHWhLv4c4Rvf9AIwkrlTZ3ZBxj7trEbTcG2wzf11+bLIgOgkHn0k7GEbACFdsvorN9K2rRAhmvv26BRAqo5ZeF34n/80/lmzIy5V17LYKTJ5sviJHyEg6nnTJhCmn2zqRAEQkwLWcz2LECpWUlTBzcwj0b3MGD2gO7pIM9YiAFKWW48vL431FzOq6szD4h0kGBsqisGd0DZdl5UAZWoFyFlU4k3NDelJejQevW8H32maEy6bXxgGphZVnWxNPCvJRzqS6gVN8827aZLoeh8pLuq5xGIQWKSHu0rC7ZoUDpbHgyZs+2SBCd3tSO1MbPKZKVEzcMeFyOdAWKjw4krNrELCuDi/ZAVf3zn06LAMBBRw1a6owGSwXHcOMKlIntkLDHy7N5s6F3tfQcRylRBUr27EIrv5PaYFxpssaKvkLjO4aPOir2N1dWZpkyoWsy2G5IgSLSEnGDoqVxtaOg6zkY1WLUnDHUNW0aH5ZWoOyFFFZN1J55puI9Re9HNg20MidOdJUTiXDz5vI3bG57OCUvYlaj4T0Tjpuwqk8wsh/Pyu9ksL3xv/++eTIIeV1dbexd7VSghP5QTmmx8jsZUaCs6Lu1vqMonG/9en37rnXg/fbbxKRdsqc93aBcIwAgzv++pv1NdlS4NBkYs+zs+AvpYIp0OEErTNpQG1BIJghiJn12zgi6qN4omqdRWVPGorzxrV2r+xk3euHj//rLPBmiec1VVxvrJy3awyxdMWU8HzHhKy9H8NVXEx9waAVKyWGNFZOfuYMHawtoV9sily8uWflJt9aVFCiXUDl8uKPp82L7ZC2VyQIFSnqImqJXLrcNYqR5YUEjXNCxo+lxGsW/ZInTIsSTrBN2W3lxCpV8SDDhE/Y+mdyxZo8ciRyFtk7JhC/z0UdNlUELSqZz+T17WuY63FW4qM7kGTGnPNy98IkVKBetQFVdfXX8Bb8fCIUQnDEDGW+8kfiAUytQSn20k9/WLssVuXxxywqUSxQ5rbgk1wgtDQln5DwBrYgbDi2zUxYU9Jybb46/4KIZaTUSnG5Y0Ah7du0yPU6j5A4a5LQI8bhosOdqdChQ9TfMrecZ8+YhsGCB/E2F+p757LOmyiAHy8yM/63gYc67aRM8v/9uuTyOk6ROHZw3zyZBDGKlEwmjcZvZTglxVVUZe1eLBswZ77wT95t5vRHFQGnFxxIphMgNmPA5qUDZlbbct3eL4uIWOTRCCpRbkFSeULt2CUEanHyyPelraFytsJmVbm5U9DDltgGzROF02x6oooYNI2dcHK4Y7Hg80j0chztqCpTSPhMbOzQnnUgkvL+KgwTeyfO67CJJG1vbtWviRTcNfhxcgQoXFlqXtgSuqspQf2jJMSShUMKB3PD51CdCrewr1cYoSjLZNGkbzstLvOjkuEGUV7XnnOOcHG5qQzRACpRbkDTKtaefbm/64kZYg3LkW7/eUhlsdZ+cKtLOyGUKFAD4Vq1yWgTrMKhQBz780GRBXEw4DN+GDYq3FVeg7DTtcJETCTUX3cGZM22UxCGM1CkXTWz51q2zLnIXeOETm/AZcpNtQb0uatw48aLPp3pAdq6V3i51uDEve+KJyHW7yrBM/lu6b0+M3DuK5OH37LFHDjlIgSIMIS3UNp+xEWeWomF2ymPFLKwoDxq0bCmf7k8/mZ9uqni9KJ02rf63CxUoV9jtW0TSTk/hvppnxcONZGekuWEFyk0mu3UnnKB80+K6FGrb1tL4NZHm7YXfwjMBDe+5tMKEz+geKJv6KObzRdJSaoMtkCMwezaKGjZUDyRJN9ykielyqCLTrtbZLYMI8ZlZms4BtQpSoAhDSBplJRt8q8gdMqQ+bYcKsZYZmIw33nDVTCcQaXBq+vSpv5Dmg4+0w+CAht+5E0inlU6dFDVsiGB0/5DhmVU7Tficctktg93tb1zaIgcWjg2qjJQXC8qK14AHPkupq1NdUbEN4fuEQuAMePezzVw2mQmfBQQ++AAA4P35Z+VA0vcXyq7ZYwul+GTqyqHXXjM3bRn8CxYg74or1OWxyEOjJkiBIowgHeA4esq7xuV93+efm5uuhsYr8O67iXbWVpNslszjiav4btsD5SYC77yD7FGjzI3UqAnf4sXI69/fXFlchuAxkbn50NMo3IEDtp5H4vn1V5WbKoMIq+u3uDxH25Xyf//b2jQluKUNy7/sMqdFiKPwlFOQ17eveiALV6C4/fsRnDw5FlfG/PnIu/Za/RHZpNQwrzdSluyc9NQwCBeXb8bz1ilQOiZTZfdFmYzvyy/lb4jbXVqB0gwpUG5BugIl8QplKxpnIPzLlpmbrpbGizH4Fy+WvWXZDHay2TqJAmXqeR8m4eSsqfig4Zxbb0XG3LnmJpCCG3Pvd9+ZK4vbiOaNkbN0AJiuLKhtXuf37VN13mA2qt4k1RQ5qweDMgqU7avuLlGg3IbTbXtwyhRkPfZYXHnwbN2qPyK76pkDK1CaBuFimcThTaxnXHExCpVMgZ1SFJTSFV93i0vzNMBBVdMd+P1+BBTO/NACFy14WVlZYClUPq9kYBHIz5cNl5OTox6P15s0TDKyND7vCwRU09KbN7yGRoUNGADv1Kmy97zl5aryGM6bJLJ7MjLi4s2cOBGeRx5RDG9WmVGibuhQeF55Je5aAIDXirzRACdTv5TSMpQ3St4ao6iWqyTvbWW+AEBowgR477039tvM+gQAHp5HTk4OApMmGZIvGAggQ0EmXXkTCkU66aOOAhQ2KfPFxZG9nzIrzGbnC6A+e5iVm6t4z8txlrbDHvGG7mi/EFAZ8FqRN54kA2y5NDMyMuC3oX/SihX5oiVNpdYmw++HL8W2Rvgu2VlZRkWMpHX66ea2wQrwGRngAfCS8sSysmKHdZtdlzw+X9IwcSusHIdgdMLa6/GYVm64r76qP09Pek9mIik7O1s2bO66dWAXXih7T2/eeBXqtVge6beSIpeeWWUmOycHMKl9sKOtOeIVqJqaGtQkGYCp4fF44Pf7UV5ejroUZu2yq6shrlJVAOSagUOHDqnGk5OTkzSMHOIhbnlVFbSolLU1NShXSUtv3uTX1SVdEj108cXIV1Cg6nhe9d2N5g23f79sfjCPB1xdHUKIfBdxGLV0jJQZcdx1jRvDo+Ipp7xzZ+RKFKjq6mpUmpQ3eqcbmMw7KqVlJG+40lJVmcKMQWndg6utNb3M6Mmfso4dIZ4qMbPcBADUhUIJZVMPlWVlqFWQSU/eFLZtm3T2nquuRlhhAGp2fQKAIpWZ+/LKSsU8q0tSZgD95UacVjgUirWDYQAeANWhkGJnfWjPHkBh8GU0bzIqKiAfYzRNmTJVVVWFagvzRS9WlJlk8hw6dAiFjMmW4eqqqpTb4MyaGngBlKVQpwGgqrJS8VuZmTd1PA9UV6O2ujqu/DKfL5ZHZtel3Lo6xfY+hmQFqrKyEj4AoSR1W0/e+MrKoKSKMJkyUlZWJpuHnltuwYFvvpGNR2/eZNXWQm53J0P9mVx1HKc6DpNLL9UxjUBZeblpTiyMjvcEtCys0FqdW1DZA1Vz/vmWJp19xx3xaWtdXjZ7GVqDvTBTmRm2yswlT8nVqjBTbOOS96FnnkGdzBlhYsJHH5140UnHGxabcGSprPYBcJ3TETFW703S6xq3pnv3+AsmOUTRavrklr1aanuxarp1szhxfSZ8RQoeS02TgdCHnV74DJL10EMmCZIEj8edJnySFSgrHGeptb260jNTNi0mfOREQjOkQLkFaYMo9gJl8QA944034i9oTM/0RkfLYE1ted6iTt+rdOCq8P5Wb7oU27tv3qzYyDCfD5WDBiHUoYNqHLaTwgqvFjLefFM9gIsHg+GiIosTCCOQLH8QURjqGjVC+QMPxF23y5lAzOudBvObVOB37wa0uK9XaQOZgnm1aUTLq1OOhAKzZyNbUg40kWaDH73wu3enFoGL2iF+/35zI1R6N56Xd2NuYV74P/44eSCxUieq6/5PPzVPEDXFUa6uqOWhWZACZSqkQLkFqRMJ8UDC7kKltQKZLZeGRpWpDbDsdh8efX/ZjfFmyiItG0oKVCCA8kmTAJmlZ9sO6ZOBs1iBSoYn1YGPhbBGjSxOgMG3Zk3SYFw4HFm5lHbWNilQggcq1fptAoWnnoqcW29NuJ5Qhz0e1PTsiZoePRIjscsLH8c54kQi87nn4n7X9OxpW9puJl+rJYiVK1BCHC5SxgCoDv65UMh18nJ1dfX9qFXjK7V2QiZN1qCBbFBTPZNqUKDsOAeK27s3qRzpAClQLiFhgCuuNHZ7RdGansmFXdMgX61yO9VIyyhQwcmTzYtf/F48r/x91N7fybOpHFagjmS8//ufdq+HXKZvMgAAIABJREFUHJdYtmwqN6ygIPKHDSZ8nl9/TSyT0kkHnkfpm2+i9qyzEiMwMU+CkyYhe/Ro+ZscZ6tb9xhSc3In3RqngH/pUvB//GFafHxpafJAkkGzVWcqum6YqdD3MGEFyo0IdUs8UWEmetsJnw+lc+YkXrdDgbIqPQWU3Km7S81ODilQbkFNgbJ7SdUpN5ZaFCCLZ6gNITPA8H77rXnxS/dEKDSCfNSrEQD8/csvynGYiJYBnm2HNhKp49AKlGAWZ/UKlEDgnXfi05dZgYrckKk3JtalrCefTDRBddqNuTStNFWgcq+/HjlDh9qaJldSEpd/lbfdZk1CLlvRUT0wVs6UzQ3yi/tRuxUohX6z5qKLEJY6hLFjVUacBp0DpRlSoNyCigJl+yykm0341Cq3UyZ8MjJJD0ZOCXFcHo82kzi7BsJaFCgXrUBVDRjgtAgxypI5v7AbmRUPu0w/wzauQEUSlLyXpC1TWzmwfF+YjAIl9x0qb7459rfnxx+tSR9QHFDFvpmLsexsQI1U3H9/7O+kzm704AYFRIza/h0XmvABqO+/rVol1LsHKnr9wLp18ddsGP+J80DtrD7LIQWKMISog9y/caOjJnxWNSjJE9a/AlXXpIm+561A7vuYOciSmPApniYuRvoNTcobTupNLc0avLpmzZwWIUaVaAAshdu7N/kBzlYg/Z527YE66qjIH3bMfsqVWWkdVhtEWK1UalyB8n/4Yexvn3TQZVb6UF4VZEE5h8hHOFKHCVa1j06aZMuhsgJllyMaVTlkiFlGuMWELwpr2DD+gkXjv0rx6qzNTiRMnWB2EFKgXEj42GPjlRgHV6BsnbE3okAdd1z9Dws6Fc/PP2sIZLETCekeKC1xS8uMSQ0WLz1/Kt1OLU8Tha9Bu3aGXQ0XNWyILNHhvFrx/t//OWLCVzpjBupatQJgnxvzzGeeib8gfe/o79rOnRMfdokCZYWC7Vu5Ep5duyQXXWg2LUPxvn1Oi4AGbdvGmVJbRhopULIrUBYNoDkj3gWdcCKhp9+0SL7qK6+sT0J8eLkdK1Bq5SWNSLPRz2GMtEEUF2IbC1VN165xlTtcWKgc2A0mfOJ8sqBR9vz2m/JNNTfmFilQmjflSr+NVR2uSxWo2IpGGuP53/8MP+v/6CPdz3CVlfaVGxGhM8+sdw1ux2Cd4xI9MyqsQIXOPhvFO3bE37PLhI8x1TbWipn9vGuuSRRHYVXQY6KDBkInbnPMIO6jxH2ym034BHjemjGWDi98taeeqhjULJO6jJdfRnD6dPk0ol5QATi7B8ptEwNJcOfo50hEzYmETQpUxb/+hdJ3341Lu+7EExXDm+nhCICxc6BEslqxLJw7bJjyTZU9UJauQGnpPO3ay+LCGaO6445DhdLmbRfKq0gq5dmoYuuEFz6OQ1hwImHHCpRcGZBeU2t/rR4MivNckMOmFSg5VA8vJxJIcAJgAZzGVa4ara7XU0VcPiX9oayib1UdMtK+W2TCpzbBEZaa6amkzx88aIo82ePGKd4LiRQ4W/ZAKb1vmqx2C5AC5RKEAW5dixaRC+IOXFwRLRzQML8/UrBFFahaxYQvY948eDduNFEADY2qkrcsOLh3y85zoDhOk6KYEMKicuNYnidBUWG0WF4uhc6urmnT+AupDDKMdoJSxdsuL3zCDKhNK1BJr6kpUHbNktbV1actl6Z4k7qFSp3swdxy2LTKUH3ZZbakY5SK8eMtTyPrwQc1hTs0Y4bFkkSRODqKoTThJw5vZrlRad8VD6a2aoJEpZ1ghYXxJqdq/ZLdfawNViXhnJyEa9WXXWafEyGTIAXKLUQr24H16yO/xYVY1FF6pO6pzUQYvOhw72nI5lgJIw2XQ8424hq/aIchbhTMXPGJ+wJa39GqlQS1gaZLYByn/L4Wd0bBl14y/rCZA3MFBaritttw6MUXlZ9zwo05x9W7MZfpQJnHY+5EjQwVo0Yp35SUGbu88HE1Nap7oFS9fFlIpZJrcBeYae3/5hunRbBlUol32+HgYhM+0QoUE0z4JMT1j2bWJ5X+6NCUKfI3pCtQZpVjlfdKKCMqclt2DpvVbtxVYHJbQ1w6GauG+0Y/RyrSSisqTOIOO5UZ7qQiGKmoZs4YGxhAJthb24jQCAr5VvLllyh78snITatM+Iy6mD+CTPigpkBZVEayR45E5oQJ8gqA1lk1M7+RwnepeOABVF91Vdy1yiFDYn8ndOw2KFAMQFhYgZI7EqCuDvkXXQSuuNgyGapGjlS+afMKlLDCHGrTRjFM6JRT5M/XsYGaCy+Ml+X44yN/2LUyp1KHw82b2yODGja0iUaOhgiddBJqevSwQBrE9VFV111Xf53nI4q+RMGK8+xoYrnxL1mifFNpfGNV/daxB8qV/aiFeH/6yWkRTIEUKLcgqbRxAxlRR5nfp491MhhQhsycHTGyhynOC59TjVC0Qw83boy61q0jl7ZvNy9+6R4oHTLJxmEmLm34w40ayd+wSN6MefOQ+fTTQCAgI4zGDlkSLpU9fXrOjqsTBsCAM3ug/H6woiJUX3op6lQGwFxFhTnp6S0DdudJ9LuXLF0quwJVdc01KFm82Bbzyprzz0/edgSDqGvZ0jYFyi5PjXrYv3Zt/Q87FCit51uJZCn54guUvvWWNfKI/q4V77viuATX7n/v2RNfp0zsm3KMHFwsdSJhUjnWcg7UfjOPH0gFm/txWRNUl44l1CAFyiWobiq0a2bPiDJkZmdmoCGt+M9/6n+YVAGDL76IrLFjE67XNW4s/4DYZCEqg2fvXlNkiUQq8cKnBbs3vjuEVzxwEeA4VP/zn/IPWNxIp+JQJEFhcoETCTsO0mUFBYDXi0OzZoE1aKAc0IlzsQD790AJ3z0YlK3vLDsbUNrPYTKl772nKRzjefvaGJmJvvJ//9uetBVgYq+fdgwE3eaFT6wsiN/f602+UmrX+EbrPiOzyrFaexVNL9yypTlpGUF8eK7dnvfSzNueEqRAuYTyp55CybJl9RfEJnw2mWoYqURKhywaE8BAwyVS4GTPbDFA1oMPIiiz+bamd+/4C3JuzK0wETNyMKNVzgAk6ZvlIcgo+XIbytW8Kllt5imTrmYlxMwBqElOJOzaAyX7tzRYigpUwiHQmh90yImEOG0ZL2d2r8QoTt4wBr60FDl33GGLWaHce1cNHy4b1tQ9uiowu/fiRuvloRdesD4tDWQ+/nj9j+j713TujKrBgyN9j1rbZla7V1lp7DmL6rdqe6XHhM8OhdzGY3NyBg+OP3cqjSEFyiWw7GyETjut/oK4ENs122REGTJz5iLFhpRlZZkkiEaEPVBW78MyYsLn5KDPadQ8MVndGcl8n9BJJyVcqz3nnMRnzfxGRg9qtECB8nz/fcpxAEi5fWjQtq2xB6VOJKyuSxKvm9JrQntzcNEic9NNZhYmKC4ydYiPOtUx/WgLNTk0YMdZVaFWreL7QYU2xlTvgdF6WdOtm3lxpoD322/rf0TbkKpBg2J9csIksAVe+HJvuinud3W/fvEBFL4Lk064mVW/zVKgLFrZDZ1ySv0PG1egAkpnFJIJH2Ea4oGMTYNf1UNjRdQ1aVL/w0yFIdWGwikzNTcqUE7sZZGhZOFC/P3LL6i+9FLb05YdsFjdSMvEHzuaAEDJBx/g0AsvyHvCM9OEz+h7WqBABdW8/kkQFNyKMWNSTleRVE0B7axLcvVd8PopNftOkUIVpxWANgsF0/apqcmRRIGquvJK09LixN5WFShZs0bbDL6J/VPMosAl5+aIFaQ4L3zC3yoKlFnnN3q//DI+Ca1KinSCxAR5+D//hGfLFuUAbnAiISo7zOPBwYULUfL++5Yk5dm8Gdl33QXvhg2WxO8UpEC5FSf2QElQ2vNTJfLaZSqpvqfJClTO9derB7DLhE/uYM1kuGAPVNlTTyF07rlgRUU4NGuWfQlH3/3Qq68q3rM6bSVCnTqh+uqrET722MSb0vKv45vxW7eiqGFDQPDOZZYJnxnlxsBqWHWvXqmnq5REEg9mxckGzTa5MQcQy4+4QZ2gQIknskyALy1Vl0dlBSqGHX2VnNJgwR4W36pVaNCmDaDl0Fq7TPiEd9OqQNkxMA+H69sdIL7tEf6WKFBxhzOb1TdJFWs9faXJ463C9u0R+PBD9TSjhE45BZV6vIBagceD2s6dERJZRlTceqtp0eeffz4yZs9GQG3VnFagCNNQ8MJnKhUV8bOxksY4dPrp8s9ZVdCTNKQsMzOl5/USWLpUUzhm5x4onseBNWv0x2HVOVAqVN1wgzONopP25KnEn8I38kVnX7noYE/uNPk6Jc+EIqSztqbsnXPbWWEG2tOyiRPrf9jlRAJQ3QNl++qD0gqUzZMzdu398vz+OwCNdUDDPr7A4sXG9+lICEedVqS0+d+k79bg6KPhFVuvCDJxXKzuS/cDHRSvdJgkB1dWFvebSQ5rVTTftsqETw2xd8SVK1FzySXWp6lClXiyWNiaYOIKt/D9+V27TIvTDbisZyNiiAYdVrmrLWreHLniM2Ckgy6lhs0KjzVA8oYrmemN1R250rK7KN8s2WMjGVApegPUGkcKeMx0z344koqykErHLXxfofzJyBHnsVKM2uDPjLZHT52wYy+AgXwOtWtnvhxKyMUv9sQpt7pogxLD5A5adwK7nGcI5UTn+6p5SlXc/6GT6r59EW7QwBWTE1KTNya3H0xS58JNm4p+pK6weDduTFDSKm++WdvDMubugblz41fVzMZuixCV9ELt26NO3L7JTdqYhOoeSafbFQM4X/sIeWyaEfF//HH9D2nHrCVdMyuZJK7969fH/U7qhcumRqlY2gjYuAcqYcOrVkwqQ7k33GBKPJailj8WDziUFOj969Zh/3ffJXnYhD1QKgqUrucF7F6BEqV/cO5chMWOYRxUoOLSttOET8g7scxGzTNTRWnFywKHAFIOrFhRn0QyBcosGaLx6J4Us9KJjagc1J1wgmKwmgsuSB6XVf2lXH+oVudMqE+y5y7KuPoPHX88arp0ib/IcRDnBFdejpxRoxB4++2U5VKC2XQMQYxU27wUEeosv3u3aXG6AVKg3IrVXvjkPC5JzQG0rECZiSS9sGjzvRbM2oyaPCEu/n8NHphSQmrSY2BwbIbnsOBzz6Uch6ko1QsHFSiltMMtWyJ8zDHqj6agQJlW9qVtgBmKtxEFiuNQ2707SlavNlcWwNDAICw658c0L3xKciQ7tsDuM1uiyK58AfHymjnZJ4q3rkOH+usyCpQlLb/BFShVUowrVs/D4UTTMxEhcX4pYUd/qbACJSbw7ruppyP3LjLOIUq+/hqHXn5ZNZxg4mvX8TFJMaP8mfytPd9/j1w9zlqibRZv5vmYLoAUKJfCZBQo2dkTg3CHDiWmKemYFW1gLTLhS3kQaJcJn6RTiDPZsECBSojRiBJghgIl7XgcJs9CRwOGcWgPVOxZYc+BHm9oYpl9PlQOHWqOTAJGXapLnzVrr4QRBap5c9EPixUoOTfmyUz4LODgm2/GXxDSVSnjef/4h3kCKEyQVF17rfpzgYA56Rv9zjyP0Mkny99LtX+IloPMKVMiJu0+Hw6+/Xbi/mBpX2UjcceJCOmrTAJL9y4ZQW6bQ8Jqn9TMOfZw/G/f11/LhzOBUPQoBT0eNOuSeMfUhFpZlppgCvVHZYInOG0a/OLJrSQI5r9qba/lR4xYAClQbkVUmISZkNA558R7r0kFudkVkQJ1YMUKlD3ySPJ4zBrU/P136i5w7V6BEhptq2eERY1f+LjjHDPh4zW49LUTn4bzhQ6sXBl/QZJ3NT16mClSap1uKuVX8qz355+NRyUyLzFj/6XanpAEbFCgUo7HLIsALXVSbj+CTQpUqGPH+LQ1tHO8CYPhGEr5HAyqPlautNdPL8L3kSkvqgfIc5yyApXqCrhIFl/UJXTthRcmKtVa2iE7TPgEL5I6BvCGkItfyaNoEgUq5/bbU5dHQmziO5o3Ff/+t+Znwxqc/yRFRx5X3H8/SqdOVV8h1/vNXOJu32xIgXIrokpdd+KJAMz1PiQ3MKrp2bM+zQ4dAKWDaS0wg2rQunXqkdjsRIJzQIGq7dzZkRWogvbtU3reKerEhwWKYJmZ8Z6HTCLuQEm9SL6RT7IHUJVo2ef279efrto5KCbugaru3Vu7LDIz6KaZKRqMp3Tq1MgMssVyiN+TOahAJbQzwm+7jkhIUvbiziMUycSOPtoaecSofQOeV1ZgTFqBSopDs/lx7rg5Tr78CmHN3E8r078x6fhFySTTqm8lovbcc+Mv6NkDZZWCqRA/y8tDTf/+mlbIPT/+qEk+1QkHAVqBIkxDVJhqO3VC8e7dEdMEszormRUo2bNpABxcuFBRNscOrxVRe/bZkT/sXoGK5iHTcohiKkgbPx1p1J55ZuSPFPPG8+efKT1vJ1pWXw58+WXEi5XJDlqCr79u+FmuthYeDatqskS/b3DKFMPPxhDniYle+MLNmmkOKxDX8TrpRAJATf/+kfpkVplRikerG3OrkSpMShM3FrW7aqsWxX/+iQOpTFboSF+2tVWbxFJz9GOwf/D88EPknLeqKm0POLQCVXnDDfLxyrQj1QMGRP6wao9PVhYOfPZZYhgHFKiqm2/GwQULjD1stQKlN91wOHavoHt3+LSY8tEKFGErUtMVvz8ym2NWoyc3MFLoFBTNEUzC98knKT1/8MMPUdO9u+0KVGyTqdUzwtLGT+MKVPG+fSh/4AH5OI5UxJ2omfXJJALz5xt70KrjBMzcA6XnPB25s0gcdCIRg+PMO1ZCjwmfCFmTSCvKsZBO1PLBrvOXYqjls8djX7sr43BJdb+GBU5svNGJFUUzdyWlwMpZ/fJy8Hv2aJNDrqyrmEjqRqkuyU3ASCdpFPLIzD05LDsbteedZyxOE/LHVAdbkrzmlA7gjguk4b1pBYowDYvdmMsOApQKsFqjb4Ljh7yrr04tDiAio93nQAlY7IUv47XXtMkhhxYb9BSp6dEDVcJsYrogeDN0mQJluPwI75GK63MBUdtgSrnRsJFcURYxDq9AAYiUGTtXoIS218EVqJrevVE6Y0a9F0m7BjpWu4tPRvT7NGjdOnGSz+NBhdJeGTUTPhP3QKVMinHlDhmCQqmJtB4Fyu53cXAFKiXMyCe1OLR4AxUjWoECoK09EibE7HbfbjGkQLkVOQXKrBnz6mrkDB8ef6lPH22ywGRvKWYNRjjO+lUWqa1wdHbLNMceCmSkYBYm2IFzJSVmiZNA6VtvocwFLs4VBzRKuHAFKmUFyow6YNEeKE1ugdVmzt2wAuXxmCaH4qxwMhM+m/ZAxVL0eFDTp482N+ZmoiefrRjsit7Lv2RJ/D2eR8X99yeeCSjI4vAeKPEqZcmSJSh5//3U0pVB1lRa/H7ifFBZgfKoHa6qEU0TPXoVKAswtBJkRnuTYhzM60XVNddEf7CEe0mJ5nFdkqM80g1SoNyKjPcpvrQUgaVLU47a+7//wfvTT/EX1QY30lkzUYPDa1m+VcPEDdlJD9o1mbqo44uYtyrAksY4lfMohBPGfevWwbtmjVkiuY7KoUNRe955yQOKOlHmxhUoowhOJDR2lH9v2VL/Q80pgBle+ISBt4a4VCdn3OCFr7YWgQ8+sHZWOMlsvaYBixlI232JG3MmtzpmJi5ZgQIg7x2S42T3dqiV4VQnHxXrt4pSEDrjDITOOSfhkeDzz6cki2xfJ52UEiwgZL6lcFZVxpw5qckBaCsrDjqR0Mv+b79FyfLlqO7Xzx17oPx+VN58M4CoEqh3Qieal15xvwOg/N57E8KkE6RAuRWZFaiM2bPNiVuuMqk0QGqNfl7//qnJoqNxOPDFF4r3/CtWWH9OkVODbZMGEnxxsSnxuBa9po12rFrahVA2NSrb4lXTmosvjrsXN9ixew9UTAgLV6BSiCfw4YcAAM6MAyF17IGKm7m2+jBopXSkvy3eGG6l2bEm5FYCBdQGjUb3R+mVSQ0N6QQ++CA1UZQUKLnfct/SrPO6lOKHDhldNngPN22K0KmnmmftY4YbeaH+h8PxdVPDhI7ie9jVlllEWkj/ww8/4L777sM111yDQYMGJdwvKyvDhAkTcPXVV2PIkCFYtGiRA1KajKjAmd6R6I0vyYyN78svbZGl7qSTjKdjBmmuQJnaYaWKwys/DDDNhM+Q63AlUpXHQFsRFruDlsZhohc+t+yB4svLjT8syGCGGV2a7IGKoXSQ7uG6AqWmtNrsREJWJi2oyJLKWXGKcSuNFWw86iPu8nHH1btLVzLhU/omblCqzDIzV8ifULt2qLzlFvlnxOkypqwMa2kLFfK4esAA1J51VuSHG/JbJ2mhQGVkZKBHjx4YOnSo7P2pU6eitrYWr776KsaPH4958+ZhQ/SQuXQlTmOXVqBUTdV0eOADkFSByuvb17gs6WRCpUVWKxoBkwYStnvRUoMxoLLSvPi0ziJKZpVjkxMpyJIr2IY7SGyFwuTO1kwnEsLKVjgnB6XJVtNlvqWpnqRSxYyZU6NuzJ06B0rlsNY6hSMwUsJpBUr8fSR5UTV4sPJzevpSvTidJ2KSKVCiNjmcnw8ACLVqZY0sKqaNlSNGRP5Uaj9caMIXJ0MK7V7OddfBs2mTYv6UfPopqpX6L2m64skctdVZOaLPHnrppbjL4caNUSp1kpVGpIUCdeKJJ6Jbt25o3Lhxwr2qqip8+eWXuP7665GZmYkWLVrgoosuwvLlyx2Q1ETEhVLSaBaccUZqcctVJivNDvTKkoSSZcssEMQAMvkSLiw0P5kkDWjpK69oi8hGBSp81FGq9/O7d0dRs2bGJwOkz4lnyLQgdO6MATU1KGrWDL7PPzckimf3bvWk9HSARjtLoR6ZMcCSrEBlPvkkCoTzxIwgdLzCuWkNGqBG6VBdKz1+ylB77rkIFxTof9CMNtGgAqXozMFspM6DVBSoml69zEu2tBT+ZcscVxbiJg8keVFxzz0qD8ZP5oTatDFPKK39pR2Df7k0pINrqfmuVXKptQ2SOiQNWXPhherPOUmK+3QDy5YhY8YMQ3Ek9FsiEz79kUWdSMiM4V2RzwZJCwVKjd27d4MxhubNm8eutWzZEjt27HBQKhOQ88IXJVWvNbIDOj2dspkF3kDFDp12mnnpp4KM7KxhQ5S+8oolipQSNZdfrimcnStQJUns62PmIwYdZOSIT7wX0OuJSeicosqYZ9MmQ7KkOqCtuOMOVNx1V0pxxJxImDHoFJfrcBgZL78Mz/btqcerBbW2xYI9MQcXLMD+//s/7Q+o7enQix4vfGKcWoGSHqwbhWMM5Q8/bFqyWePGIfe661y1B8qvZ1uA2mp4qu+k9XmDK6SZjz2GzNGjtQVONg4Q5QNnsQKlWlakZrAiGYr//BMV48YpROqOgX3K9YDjUl+9Z6zeKkrixlxTzMKzcm2X3Cp7mmCTMbV1VFVVITMzM+5aVlYWKhVMcoqLi1Es2kzP8zwaig9r1IknWiA8JndqnMhmmOe4hPiV0uNkwkrhZQoq5/EoPyexX+Zlwsk9qylvVBp6Le+sNV8AbXmjBhc9w8Aj2QsgjZMPBADGFNNKpcwkywO1OHmvN6Vyo0cmPjMTnIb4PF5vXKOqNW98X30V95vjefCijkbpeT5a3jxeLziPB5zoO8nVMy35otTNHti1CwXHHgskiaP6gQfgXb0aeOYZ8Dyv+D3V8oaTczYgQRq3UhriOLhwOPZ+nlAobh+d1jLDCW7M6x9UfEfh+/AyB6UGVq5EuFu3yI+aGnhXr0aoZ0/D9an000/hkXGEoCUeD8epKs6ayo1g3lRYiEPLliEvuson5L/H46l3AS96jvf7E9ucJN/WUFsjafeFPimWlmiA6QkGE9KUQ1P/FD0sVtwzJGvjPCoH6xrNF3Gee/78M2mcAvz/s/flcVIUZ//f7jl2dmdPWASPoIiKRyQS8QIVD+IBRiUQxTNKPGKIRl+jwZ/i8aJR8mpiVIhR1KhR0Sh4m4AnISooeN8Iiiew7DV7zN2/P6Z7tqenuruqu6q7d5nv58OHnT6qnq7jqXrqucLhYt/S9hNg3TbavDDOb+NaZHxelmXTdlEikbL6am6+ufDHfffZjxmCgCHLcpHvy7Jc3CtokWQlwvf3XnSR6zFD4sFFvq7O8SJ/143rkHqoSFzDCHsip/NJe75n3jyEn3+e+n3Jpr6ytunsRN3Pf47EokWAlsIkFCJqSljbVOOVIUkqnRvhcJEXmtKpRZI28NtQKASo+3c5k+G6j3a736OB7wLUDTfcgFctwis/aZO/IBaLlQlLPT09qNYxdD0ee+wx3HnnncXfZ5xxBn7zm98wUExGPe9cQLrNYCwaRcxgZtJkYXYStdM0qBOr5J2jjkLUrEwD0zYKrHb0WLaNxQA3lkmqg+YZPWzbxgzJJGJXXVVah8qIy+qsqwMUxZYWJ2OmWOYttwA33gisX19Sj2U/1NUBbsaNHU06NA4bZllXybuE5Hq2bWMQvGNVVYjp5rxZO9SozzQ2NhbmQSiEJlVbWFNTgxrCe7bt8t13xMtNgwcX3o9EzOeWBnVOxqqqSuY66TuIbaO2YdTCWTsejyNuM4eamppKNhhRXTs3bb11GS+gGjOq0KXRFgqFzMdsXR0AoKGhoWz8xObPR2zevMKPG28ELrmkhB7W+VR/4IFE53bLeatuGhvr623Ht23baILCm2+iYcSIvirUb2pqaiq2XZWurLqmprK6zcauBle8RoM6Ruu0b9eEBEmi5kEARbuom6yG2lpimabrAGFdsqPHsl0s6LQqs66+vjgfm5qaSta4upoaZzxYbfuoge8V6TAIM9r+p2nQIEDXjnpINmuU7ZghrN2NjY1AOg0AqK2tLX5rVNVAhcLh0joPPhjVgwej2u2YIawhxXrUVCvx6uoC/9M9W3xG5Tt6mPFLgH0+Fes58EASPCc7AAAgAElEQVTgwANBHqkEVFUBsowq2jGzejXwxhto2rAB2HtvAIU9coxAr9081beTBKBB9WNrrK8vicBZV1cHqOWbtos6PusbGsppUK9Fczn7dZIRjvd7lPBdgJo1a5ar97dVE3OtX78ew4cPBwCsW7eu+LcRU6dOxYQJE4q/ZVlGW1ub4/pDoRDq6+vR2dmJHEebbamjA43q38meHvS2tUE/tMxojsfj6LaJMhXu6ICeXSRnzkTvtGmARTs01tZC6uoCAPT09pYxABI9VG3T2QmzKdNm8c1NhmvG3yTQtI2+rBJaenpQnUwipqujLpdDmFBnpLsbNfk8OkxooWoXzZZclsltcOqpqFuwAOH160vaifT92r3Ojg7kHI4bK7Zm7AMAaOvtLRtPxHZtbQUMp9c086kBpSfUyVQK2fZ2aFsF0lgBCocrNQDaOzoQ7e1FNJ1GQm2/nu5upAw004wZ0/Hb3o4mAOlMBt02PCbc2Yk6FDTqvSb9adU2sZ4eVANIJ5MwWzK6u7uRNplD+r/jvb3FMjKpFMLoO41sa2sDslk0brstMqtXo5siMWKst7dAWyaDKIBcPo9Ok2+MdHejFkBHZyfypHGlXott2oRq9TftmDH2U1tnZ8nGk4aHNCgKZADtra1QCJs2DTTjRmptRSOAjkQCeV17KCi0d1tbG+LZLKIAUskkNN1foqcHWUPb9PT0lI1dgH4+hV5/Hcatj7EdqrNZxAAkEglk29rQqNJp1Z9G0LRLPJVCFEBnW1uRJjMeV7zW2gqkUmXXzeixa5eGHXYwzXHYsWJFcWzqodWZ6O5GeNCg4vjU1gkA6OrsRMYBD452dyMOIJNMQn+Gr32bNi419KRSqNHuG/xFi22TzZa1jb7d7MZMPYAQCqbhkio0tbe3Q0qn0YBCdOSsuo/JdHcjAiCbzyOhq7NWUZD75hv0rl9PFGIAujET6+qC8chc+zapsxONALq7ugr8L5ksG0vhRALG2rt7ekr4JUA/n8p4jcM9Zo3arj2UY0b7js7OTuTUOZNMpZBqa0OD4V07mrQ1BSjwpM5EAg0A2tvaUJNOF9eIRFcXlM5Oy3apz2YRUsvQ8xn93iGTSKDLxV7cCNr9nhlsBUwEQICiQT6fRzabRVZVA6fTaUiShEgkglgshvHjx+P+++/HRRddhE2bNmHJkiX4rYkdb3NzM5qbm4u/W1pauAg+uVyOrwCl00Ap+XxZ2WZ1KYpiS4ds8DvJyzJyNna2ik5tmyeYCVnVadU2Vg74NN9M2y4AXduQkDr+eORyOeTVNiqWobaDsUxZtRG2q8uqXapvvBHxuXPRsmlT8VrbCy/YtoFVnfls1tW4MQORpkiEygk8l80Sn7ObT8YRqOTzyKuLjRlNAPr6MJ9HXpIK361eyxPqdNUu2pximJPGuW7W38bree23hU9ZnsBHjHXkcjkout9KNlui5cnlcpC6uyFlMpBeegm5k06y/C7tm/T/G9tD/3dIrcuOVoUw91h5sBnPoykjn832tTkBLHw4pyilZem+TWt5RUdrTpKI/e+UBwNAvcEKo+f888vr0EwO1b7RBD2r/jSCpl20b82ZzGfTcWFSrpN2sUoQn9lxR0velsvnkfqf/0Fy0qRC2Toh3SkPzhvnkI5+AEhPnIjYo4+WlFW8b/P9Vvcshe4vvyzQtu22yG2/PaIvv1ygU4u2qeOt+iin+jIVSULsr39F5Ikn0PbOO8R6aMZMnsD3tHdk3TO5XK7Eukd7huQ7mreol5nXOFxDFAAw4YVAedvIuvWtyCsB5AjBmmjnoVoRchpvzmZL+ZFuzTBrF+15/d4xX1dX+mxvL9c9tJu1mxb9IojEBx98gGnTpuGqq65Ce3s7pk2bhvN0TuTnnnsuQqEQzjjjDFx55ZWYOnUq9lbVl/0WelU970Hg0ikxu8cenAgBokuWWN7Pq2ZQfqLnoovYXuCQuyH6wgsAAFlnHpYbPdpRWbkddyz84aWTJm3eD6c0kRx8aeaJ0UE/lxPXLixOyLzyP/FwvNeXwSMBo05wsoXWZgFx4Dai6PfEY8yY5aWxK1uEXb+uzszee5Md643+NoIjqjE5zwdpvEgSEI2S+bXbcWPC47puu62cBo8QWreudI4TgqBImnbQJAeT28BYxQh7BNeNsiSutG0ThDEly2ID1rDSApTTQ9FOxbmse7btzTdLH0om3VDnC/qFBmrPPfe09IWqra11bQoYOOgGWubAA/kW7XJC5inMdnghu9deRWHCN6iMIzljBuQNG+yf55H8TrMZPuUUd+UAfbQ47XfdSbCGjgceQMMppyDfYDQMYARHAUpiiein5oCKvPNOIAQoRTVfsTILowKHxTY7ejSqnnmm8IPD4U3xdJdjHrXipsjryE2a7T9PQdUYSIcUPltgGPPI888XNsEqciNH+hv9r0gIw9gzGQfpI47gREwBXTTRBq0i4LkcN6ZRNo194/HmXzLLo6fRoa0hRro4jSltT6OEw+UBJYxzqB8JUEo4bN62ti/rvtcJn+QZxpwUsdgQqVjqhwJUv9BAbZFQJ2/XDTcgffTRfMt2cIIgDIa6M2PG+ESIBVTGkd9uO3RpTuwAkiedBEUXlawIHmFDNaiO5lzgkKb4ZZeVXcvusw9aNm1C65o1rkhyPPJImxQWDZQkQVYja0Xefrv0ng/I7rcfAED+6itnBbgVknXovfDC4t88wqJXq8kTJZoNDOvmxutQ15pmlaMAVXZCToLARLr1xqSwJt+mCW5FSkQJsVp5PCwvONNmmnRUD0N/5vTJYx2Om9g//6kWxtgmhLHV8dBDjmgoKbajg6l+bXOsmIXGdwsrLbdFGPPAIxrt096xQve9jg7NDW1Z/GUIY86cf9HsVkWAqoAXRJ6wasEg+i5QTAD9MwLzQEXeeotf2bxgwuSTZ52FzV9/Tf28I1i0NdXGS1+Uw7EUfv99QmGcxgBPEz5GDVRKPZiQTaLouYaDNtL7MTCBowBVMn4J5TnmSCwaKNpx4bHQ261G4uRqVmPkF6Rv0tdHax7LSocKU6HZSKfopKgBFKCovtnQTolbbinNoeMAxbQNtN9jQWd+m20c0VACo1WCUUjRaNDoMDHh46ZNZckDRQnW9VUElKqqYoAOZujN5twKUGpAq+LfrAKUzWFRdvfd0Xvuuew0+oyKABVUCBSgyk65GRhF6vjjkR861DUN4ddeQ/OQId6fIDsAMyN1yrCMZej/5wGnY0nkJpWXAKUozNqSrKrtzJOyo/OAhwuwxHPTCdBt+Fj7juJ5u4MjaeNGhPQCvccCVGbffbnVa6qRI+XpE6iBKoNZn5vkHBKlgeKmxecJizmdPugg8jPV1ei65RZX1aYPOQQAEDH6jZiAZc1qHjIE0X//m4meMnNps0NDTQNlYsLn2DzNCG2sWGigLDXgtNe8RjRKNKGngl5oUf9OHXWUc1ocCqI077S/8gqSZ5/tgCh/URGgggqBk7fMj4dBA5W4804uJ6CR1asLf9gJGkFYRB0IUFIyiZBJVCEhdVrBanFheV8PAn2bP//ctqi8MU8ETw0UIdKQ5fvahpBFc8UCLxdgtR1DvLRp2mLJM4ANS1+bPNswZQqaDj2U3YSP10GNthkT6QNl0068faDKYCdAqej+4x/F1M/xMCD6/POQEgnX5RRhMaeTM2aYPpOaPh257bZzPG6yY8eyvcBoJhs2JCa3Q73RlNGsPu26Zp5lFKB0Yabjs2ejyqkG3goOfaBCa9fyp4URSiTCVQPVNX8+Q+VkHyjJiQlfPzgod4KKABV0iDDhM240WU34eEALs7rrrlyKS//kJ1zKIYLVJE9tq6aJE53XKUID5ZSJUY5BxSa5YPu//oXOhx92RoMBWgjdEthsuJInnFC6iGrZ0z/8sHDND3MfXtAEKF6Lvto2PHygiuBgwqf5rTGVCfATBCkcqaXWVkiffmpfFosApShITZlS+NsnAcoouKWmTlVvFOhNH3IIsj/8oevqHWlTIxHTW5JFSHJmWM1pM5NM/buCeTCxTpprjOWHNZ6pIj1hAjL7719unWKjgdL/rr79dtSddx6qb76ZiZaS4hQFqeOOQ68mzALlc5aSL8dvuMExHdxQVeVaA6X/27FZot6Ez4kPvfYMT/eGAGBgfc1AgsgoUwEIIqGdPEU4RdjL7Ldfn3kNbzgUoFxBRJ84HEtEUxoH9GX33rt8geU1viXJNgqfPgAIJKnYr7VXXsmHhoEEKw2Um0XYDgS+13n33eV0sZQJ8NMyUghQDVOmIEqTcoBRgCqmj+DtA2VGlxEanSYmfNk99kBuu+3c188gQBXTXETN0kcXIplxg5vDRll2zu8CfoKvDBqEjqeeKu0HHY8tHtoaxzrhMCB+3XWuaEksWIDuuXNL6QD6ZRAJhUcQCf3fLHsZ41jVa/0ZNVCZceMAADkOByxBQkWACipETnI3Jwi8oEaXCxnMvrpuvNFZeRzoq3rgAQzeaiv3ZfMUoHhELNPg9eknCU43wHZQFKQmT+ZTVgXmDsMOUPQN0XK1MI7p9E9/2vfD6IdDSRs3TRqFH0Doiy/oyjIRoIhmXorSJwQGxISvCEHWCVT8imZDyJF/WZZkN7691EBZtYsIfx8dfRLD5jry+uvu6jXUIxGi1vqW8kBFjyFJNROiUcjr1tFHPdShxOfLiQBlhI73sfonRl5/Hakjj+xXwisNKgJUUKGaJGQOPph/2cbNhB8aKHUzEP3Pf0quZx0miwXgmkHG/vEPMmMQpIEKrVyJagqb5Mz++6Pnd78ru5486yzkdtiBni6ep59Ox4woAQpAfsQI22csHYmD4G+nA5OvC2fak9OmFf5wKXhIiUTZHLd+wWazY5UzyQpeBtegLkzdcNM+q5nh8BagjG1tprEhBJHovOsudGghtvUbNQ70hA1mkEpNDVLHH1/6LAUf0vP06JNPFoIXOQWNCZ+VBkqwAFXUyHm9puvoK86RUKh8bPuxgfZRgMrX16NHi9zpAEooBLmrCw3HHOPgZZ1Qa6btpnwfMPA+Rg1UaP16VKmBSlrffhst69fT0xFgVASooEKW0bJpE3K77Va8pFiYKdCi8aCD+nJKaOX6oIEyy3dTlieCQ9hWalgtfAygtTOuPfNMxE2Ya0kZikLMN5U68US0vfEGBUEMJ7qCUdY2Pgkt+lrzbjZUAxTdN92ExJ/+ZC14UPRdjd6UxqUPVHbkyD7+oJ0425eovuydCR81WDY1+iiTgjVQXf/3f2QSCPWmjz0W+ZEji7+5RM5Ty4hfc03J5c1fflkIYqR/lIbX6gUoxmhzZXCzViaT5T58tKBo15ZNm9CrRTKzel7w4VF+xx2R+NOfkJ440dLnCQCSp57KrV5TeClAKQpkXVJq13xC5Q3hjz92REsZHU4FKKMPFGsQCR3y224LVFczvRNUVASofgSlpsZ1GY4mIgFuM7xXPfccACBfV1d6Q5KQHzIE2T33dFU+TzgKY+4WehM+PfPyAzwXHmPbsETOs0B+++0dv9t7zjlcaBhwCIXcm77pHaBdCFA9M2cWEpIa/XC8DiLBcTNWFDZozdC0byBpiJzQk8mUJepOH3ggFLMDBTs6eR2ysXyL1xHAXASRCH37LeLXX++sXp5BJEQLEpKE1GmnFQR9GwEq/O67YmnR1+nBAWL08ccxSOeL7TZap7x5s/OXXQpQZYchmj+b06AWAxAVAao/QRADoDo1NDC+zgce4FJ3SZZ2AKGvv0bru++ifelStoJEaqBECVA0p4SaAOXm+9R6HJ8Omzi1O4KBgcc4ROVTqqvR+6tfmd7vufjiviiNBjOb7C679GkzHH5TbvhwR+/ZQqNrwwY+mx5WzUUoVH7ayJot3qBJpX6e5MCsM2Nj1arSCIJUByVehDE3g/oNZVp6h6g/4QQ0b7892dmcBLM8UHpw1EBRgaYteK6bVnPI4yAFbYTQ46np08maHx2EJQ4nQUdHbvhwdBsC9ngiQGljRHTwFRSE5BK4nA/U/pQkkBLpMozN3jPOKL2gvlt9++2uNFADCRUBqh+h++qrxRTsp++HMfxsJlNgdE7MVER9h59R+HhqoJxuJHhuQIzfweE0Kzd8uOV46Zk1C50PPlj4Ydzk5POuzbtSJ57o6n1TqLQO/uEPUfWPf1g/azPmEjffjNTPflZyrXX1arS+9Zb5S5oApUNYn8TWCoqC2O23lybKdDM/JQlSPo+QmsNO3rSJrUyaPmYQoLia8FGaoRWjTHLaCEaXLy9UrxMuLQ8RaHiyAB6cPOEE85uMJnxu6Ms3N1tG+yvCI2uB/KBB5de23RadDz3Ud4H0vTxTE9hB1z+JW25Bdp99Sm53z5olnoaqKiRuuQXJ004TX5dxPLrkE47DjgN9fa8okLq7oVRXM+1N8jvuWFqWOvbTIvzy+ykqAlQ/QkoUA6CZ5KJOGYwbG6cMh4cDMycfKO7asHyeT5lBCKFrbEu/Tq/UesNr1gQj34cNjNEqy2DTjqlTTik7rMj/4AfIq2Gnszo/Fg2KLJdvtkIhqnEkdXSgdvZsxOw2c0ZoGh4zDVSReIMmyg40m0avBSieJnyc0PuLX5jftNNACTLhK0k/4KROzkmUTeGxBspxPbEYXzoA83mop5Hgx5ujCPzDA6mTTiLWH3jwEKAARF57DYrRXcIBLblhwwr5HlkOJQIWnIknKgJUBY4FKFcR84qFlApQjssMUBAJEWF9XZntuAwiweoHY0mrSwGq/uSTEbv9dkOFnBi0PmpRVxdCtOYlvBcIB+W5NevqWLoUmw3JMUEQoKjrIYw1ywiIxYdsTPgo6iEiiAKURhOtFsUqiAQnrUdOyzVFIsErDRRvEz5O85N67AdcgNJy8gCALDoamo5GYhCsgJt/Vd90E9PzZRojTkEk6AkojPXoiy8W/w599hlqbr4Z8saNzunQ5lA4XNgPsMypigBVis2bN+O5557D3XffjXnz5mHhwoV46623oAzghgoaQp99xq8wh5O8a84c11VL2Syyu+/eR4rRnyQIE9XvIBK8TlAdtk9o7VqmstJHHmlemIUA1XD00aiyCeseXboU8f/9X2paymC1ic/nUbVwIZqHDEHthRei6fDDqcqz1Q6xwux70mnzsWAy5hTKaEdKXV158IBQCFI+D0nv90Th0yR1dUE2+gLQglKAit1zjyUNZcUS8sM4AkeHdCmdhhKJ0AsBViZ/vMzGrHgXjQ+UapIrf/658zWKdxAJwml5eMkSRqJg28ZMQUF4wGk9ujYbtPfenIixr4uoAfIxOBKNQMxsncBbIHRYXvwPf+jzfe7q4kdPOFw49A7CviwAoLYF6OjowL333ot7770Xb7/9dpmwJEkSamtrMWXKFJx99tkYP348d2Ir6IPU0gIYAjA4htMBbvRfYkB2110LEQGzWeR22w1h7fTb5JTTLsGuK1thDbzCmLtoFxIkH32gpLY2pudbvvzSclyU9ZPud+TNNwvCyOWXM9VpGjXMCoS+lr/4AiE1SiWtAFC1aBGqFi9mr98KpMzzioKmYcOA3/++8M8IMwEqHKYP9U0os+hrpIFiHNafcAIipPD6FHym+ISNACUxmvBFX3rJ/iFKHqLIMp9NQTIJhdKcStL7QJHgpQBlgppbbin+PWj//QEUQmszg4MAlR09uhiggOTXVTd9OrDPPkBTE31dlCZ8XNYiGriop/POO1GvhTwXCb0GirQu+KmB0venSkfX3LmoJfFXv+DG+sVB4Ag7KKFQuduF3XwNQPoUUaDqnT/84Q8YMWIE/vKXv+AnP/kJFi9ejHXr1iGRSCCdTmPjxo1YsWIF5s6di7a2Nhx++OGYOHEiPjSahFTADzyleqcD3IUtfvqwwwp/ZLOlJ0EmDCNJsM3PGUNXByWIBG2+LpoofICvPlBSZyfbCzU11oI1Zx+o7C67oFPTRrhE9X33MdMjf/01l7oBoOvaawEADcZkoXq88AL5OoHu5GmnITdqlHOCSCHmQyFbgcw00ARj0IQSmJnwUY5pKvMV2r6XJPNgC9kstbZL6u1ly4diZYYYAAGKGzgIUCWBJ9RxV3f66Yg9+mjfdcYUCiG7uR4kH6igmMYFWANFdEv44Q/51uF2T+LQhI9L3SSEw9YHOSQMYAGKagf88ssvY9GiRTjkkEOI95ubm9Hc3IyxY8fiV7/6Fdra2nDbbbfh5Zdfxu4686wK3CM5bVphEeA4OahC8hKYjeJCgNI2IFIu52hh3rxuXangZaCv6r77kJ40CUpzMwNRJqf4jIsRj4THZaZSPHygnIwZs2/nFMY8v9VW7OXqnsmOGQOFEI3K9l27PqU1DevooK/bBpr5amT1avaXCd+TPuggVLs5xCK1EcVcLYm8p4fbMOYk0C7OLHXbQZZN661hMPmRkklqE8sSHyhiYXw2zFa8zpjEWBSY0gnQBpFQlGLuQeHwSHhxpekyvOs6ATJFEAniushJgAqtWcP+kgjhzdgnogJb0bzqruZS6M1TWb9pSxegljDaCzc1NWH27NmOCKrABuoGhuvkcBrW1I2pmjYJMxlHjEyprTUvE0DdxRcjuWoVuv7yF/oyzehgZWKco/1IbW3uzDUWLkTTgQfyFaCcwihAOUmAq2fITunjJUCZCQtO4KatSWNXUZA87TTnuURIZequud50kcAYRIKmxeSvv0b1HXe4p61YoLkAFaYVWBUFdb/5jf0mWO9zZbER4ZUbyhIB1EBR8WxF8Sb3UZA0UF6DJgofQYBycwirIfTOO6h66in2F/uDAOXGhM/NwakVPcbcgJUofPZIJBJIWiRRTCaTSCQSXIiqwALaIuaxCR9xoXfD/LQIMd98g8iyZc7LoaiDGmYCISsT48GYtTDbH3xQSM7nYrHMjRqF3HbbidnwsoLDAqPXmCqNjXzoUFFM7EtLF4U5A7VZiJtAByRzlH33ReqUU9Cq+nUxg7BhVsxM6WjgQgNl6ndEUWb8mmtoqOOigaI+bVWfS0+aZP2cbhPEbDrjBG6DSPCACA0UBU+uvukm1M6cSV+3EQxBJKREAlVuk4hz1ECVYe1ahFmT2dvUQ9JAZTRTfhJSKdSddRZgEwRBZjUzJ9AmDC7XXOaDEX19IjQ/ToJaDWANFFXvvPjiixg0aBBWrFhh+syKFSswePBgLBO1Ga6gAG1CBcAHytXpkY7+kHo66Nr0zS2zMtMc+ShAcSvTgvGF3nsP+PJLOjpUUJseGWH8Drd9xprbgrY+Wg2UjR/F5k8+Qc+ll1KVVZbFngWGfmp9441ijifH4H1C69aEjzR+aXgX5wVc6u0tC64RfeYZxK+8knl89dJu2D0y4RsoPlDG8mjM3eI33IDYI4/Q1+2QprrTT0fdWWeh7je/oS+TNIZd9Ifc2lryu6x9Dj8cdSeeWKjX4uCcBsWySeu7xR4i9NlnqHriCUivvWZdgUM+JURry9s00s285qgRLYkwadRA0b47AEE1gubNm4cTTzwREyZMMH1mwoQJOOmkk3CLLhJPBfyhiNBAOS2LkwAFAL3nnIPEbbc5L4/k2O2TBopLzxiYnuvITha2y02HHYboj35ERUcRTs0UjW3pdnPrdOxyMuGzc0RXBg2i3ui4OpAwtKvS0OC8LJMy3SJz6KH2D1kIUJIaIrsENP1EO8YY5lidQfCpnTkT1X/9K7uATvJdO+II4mGOPn9PGXgJNzQ+UCbIDRvGhwaWOU0zRnkF4bED5Ya16rnnCnl6nJStB40JvUlbRl5+ueR3WbRN1TQ5fvnlaP7BD2goNIckFfYtTseozXhwvDYy8rfo4sXkCKl6+B3GXLAJn+JAgNriNVD//e9/MXXqVNvnpkyZguXLl7smqgILiNBAOZz0vDRQANB93XVIT5nivDzSNzC2kakGirV9RGigeJRnwcgks1NG0QuCKOdlu+d5+UAxRvKyBEeBRWEJz2xWBscAIj2XXIKeSy4p/LBo++yeeyJ19NHIEgT6yMqVzmihpdfDSJfFXGaEOjv/8Q9s1hKc6gTK5IwZaPn+e3K1lOHQbeHChK9n1izkdtwRoXfecUcDjzDmP/5x8e/qO+7wJtobgwkfK4in+G6i8BnWbtmYrkJdKyKvvEJDnjUkydmBG4tJrRMwvld/zjmoWrDA+iG/BSiSbxJPmiSpYEJf8YECQClAtbW1YQhFrpXm5ma0MeaNqYARIjRQNBOM9IzuBExizffB2ZSK+Cztuz09hYzsZiaEbkz4rGhguMfFFCAIQSSMcKs1FKSBom5vngKU8UDCzdjnAUIb0bRL6thjy64pskw3lqqrkbjvvkI4fBtaCgXb01P1zDP29fICZT9U33574Q/dd/XMnInuyy8vXDMelClK4brZKT6v/ndjwqfymFovA0iZ8Obs2LFoUcOOVz3xhLcBFxjqij73HEJmYf/1YD3Ft9PaGPtSURDVzxOem29JcmWeb8tznGq2TALvWNJC0oKXPMB3nKVOOsn5ywLyQEXeegvxq66CZKeJI9ExAEG1M2xubsbnn39u+9zatWvRzBI2ugJ26AUoRUH4rbfcl+lUgNJt+AazhqvnveFzwSTqZs7EoL33NmfybtToDGgeMgSR//yn8MPIdHgIUEFkZH5poOxA21Y8HfvdmGF5JEBRnTx6Ed1Kg49jWm8GJekFHRboBairr0bvhReSn/PqFNdNEAntkMbtho2zMCglk960n1oHi0lZ/emno4nGtNVAvytzd6CM14TffBP1Z5zRd4F1823VvpLkLGKvAw1UZuxY+vKdRP+ljZrJCXlWU2yCCV9U21NwBDFRuhmCuO/gBKoRNGHCBMybNw9Zi81CNpvFvHnzcCgNM6jAMbSTo+iSJYguXozGI45w7eTp2AmTowkfD4Tfew/VN93E/F4xhwSB+SUpTFfL4GIDWdyUudXMGKCPnlY7c2ZB40YDgSe3+Xic6bsiJL8Bn32gsnvtVXYt39SEPIXGvqxKHrm+eEJVMp4AACAASURBVMJMgLKrS8SY4WhOaAZWX4owyVTNhQBled/uZNwDDZTt+OTV7ywbLiuaaC0BeEGEyZSxbO2nUUNrhB0NBgGqLB0Dz02vJJmbx/OART5IS7A865cZGg8TPtGomPBZ47LLLsN7772HyZMn40NCnouPPvoIxxxzDN59913MmjWLO5EV6KAyi+oFC/oi6TjN46SBgwmf71DpizMksjS+yw1uFm6zDZPLBS38+ecFUxYAsUceQezuu+leFMn8GLViEUI0Ju6Z41lBmAOZcePQ6iSBrXEz6PcCz9l2nuf76QMPLPxB+O7IkiX2zt48QDpAYvW75NXGXprw2WiguH0TBZInn2zu/0VyqPcCIr6f9ym+UdttPBznLECZmsfTwM4c0enBk5cHPX4gKILLANZAUakQ9txzTyxcuBC/+MUvsOeee2KbbbbB8OHDIUkS1q9fj2+++QZ1dXV4+OGH8UO/NzQDHaTTFrcTxSkD4sQUy+yxdchvtZWrsqlg8f3KoEHs9YtwRufADKP6yEtaeTY5NoQLUC6+s/XNN9kT8fL+HpIPlNP+twiwIdmdbovoJwc+AoDJhsZtwkwz4ZJAT8Mpp6B71iz0XnwxWx2M/aYQhGeJdbNAqwkNwmkypQ+UUBoMSJ51FpJnnUW+6ZcGSoQJK6tgXl9f+MNkfBnHbtm45ek/49QHinZ/41ADxSR4eRmIxk15uueDEj48KHSIAPUIOu644/DJJ5/g6quvxq677oq2tja0trZi1KhR+N///V988sknOJbgPFwBZxCYhdspS3ViyJsx6CeVhSar+49/RPuSJfbl6enTNHK0E9eEkSb++ld0O3GIZl1A83nU/eIXpdc4a6CKMGgra/70J9NHw6+/jkFm4c15gHFMxe67r+S3Eo8zV5ndb7+S36mjjyY/SNneEkn7KzJfjtmYDroJn9s2MREuzRZnqbvbfR12IJkl8Tbhoy2Xst7YvfdaJ2y20tzZ9aFFqgRaNBx7LCI8/HoB7zVQLkz4YnfdRVc2JVJTp6Lj8cfLg7FosFujbASorNHn2c4Hym2ORys4NeGjfE9eu5a6/Gq3vmluoeuHhmOO8ZEQHQawBopppzd06FDMnj0bS5cuxYcffogPP/wQS5cuxeWXX46hQ4eKorECPfQT2G8NlBvoNVAWp9NKbS2yY8awla2ZI+RyBZ8oG3Oe4kmUgTlm9tkHcJAwltWERerqQtWzzxoK4a+BAoBmQ64W0/DlACKrVnGp0xQSIXeXBYzJH52M28z48WjRRYzMjB9PftCMrmy2mCOlUABBA+WHSUmANFCk7yjRMjv5TsM7xRNzH043c8OHF6ombQp5a6C0QzKTcplO9hUFtb/7HaotzHetomtpfWjK3zgEqiGZ6fKAm+O/jsWL2V4wmf/pQw4xfaXWzvXBgVWFKW8D7DXCdmZzxuAGNgKUUB8op6Dk04P224+6/UNffOGCIAJY9xK6tUnevJkvLWao+EBV0G/gQoAK//e/9mXS1MsB4Y8+0v1wad4DEDVQoc8/R/yGGxB96SXrd80YqdNTM9qTT6t7vKPwBREMJj8yKQooB8GfNcR13VlnoVndQAMgbhitTFJdw+9wzDROyhYaqM477kDn/fe7p8VHASp92GGFP0j9zFsDpZaX3WMP4u3W994rBBSgqZfCHDA3YoT5+zbzrRioJkh+IBpcjJPszjuz1WGmtWE9CCSVzQm2AaDsBGFWetxE4RO1Qffj4JgVjHOp7txzhZDRZreHskJFA1VBIMEoQDUefzx3ErrmzHH0XkkYTB7BKPS2v9qE1ZyLjRGGjDA4R6eOPBIdjz+O/NZbO6OFlTHbbVRJv12iZv58RJ980tG72T335EMEg8lPiJRAlMMCaGoHb0JX9IUX7J/zWaPLDTw3wuocS0+ZgrzVJt0EZVoP7XvdRiDVg9WcTnte74AvyISvW0u8ayRl0CDkm5vZBCirOq007nabbl4+UAJQP3265f3a3/7W/KYLPxQ90pMmsZWjL5L3JtSlCR8LFFl2lQfKvgLdmHMahW+AaFEkK/NcF8i5iW1QEaAqCAx0k15hFKBMcwrQnJhTOK9LdgEJTEByxnYFbUOjLhK2Sd+0b9P9b2n+IBKayY6hT0U4Ytb/8pfM77SsW4f2557jQwCDyQ/RbIiHoGI2rm3oqjNzXAf6x8kmBYht7tQHyq2W2VimqmWuXrDAXblWdZhBr8lJp9G89dbONy60YcyteCQt3W43xTTrREA3mmGbqJixBx80v0k5n+14dG6HHajKKZaXSPT98LpdeQeREGnCx0OACir6A412Y1MdS508eXVAMDBW+i0IJcIGowBVZrcMoOv669F7/vn2L5tMZP3J2GAHJ8sAuGugyoJI0G5SzH6zQr9ZdLrwiQoi4RbxONl53gkIJ9ammxCSXw2PxYVRA6Vdr3riCciff15qimpXJg+88Qakb781pYsnsqNHO6vHzgeKA7Q8OJl99oG8Zg3Q0sK1fCrk8+7DP3PaoJbNo9ZWxC++mM7kkhJUeaCCasLHgEG77FJ6gTZNiN16w9AuUns7Bu+4I8KaHyrv+U256bV7P/nzn9vXJUnemfCxROHT8SQtIJFipoENwMGAtGEDYnffjcjy5aU3AkCbJVT6Mgcc4DMh/MHB+aR/IxqNosrFhlBSJ2w8HofiwUAO1dUV/46pG/X6995DfvJkAEA4HEad7hk9pMZGwJBANXLRRaBhbbL6ncayQwbV/KDjjkNGTXhK2zZSNGpKMy1kXS6QWvXvkLrgx6qrEa2rM22bkMrco52dAKzbkBV1dXVlp++SJAGSVDy9qK2tLd6LVlUhVFcH2RC+vSoaRZiCJla6I7r+K+tbwryo08LjcoAky6iOxVClr9dkzEiEiHt1DQ2mJke07RA20Yxo491qLAzaf3/i9UhVlaPxIxvz2ShKsRx9P9W+/TZy06aVPBoybFC4jF9CGfGaGijqeJUliVhPmGCuE4vHETU8m7n7bkCWqWgNGfpZ67fYVlshsu++wM47I/7++8UxE41ESnilhvw++0BWzYeN9Uoqv7Cjp1h3NIqoYT6EdEIGzXfFa2uhWDyn1WVVlixJiMViJe0buucehO+7D6H58wv1xOPFDWNVLIZwXR0UQhAXS5pVU2gzmuWaGsiSBEm3MTWW54S38uLFZdi4EfERI8rWJrmtreR3rY4XW/aDyi9r6+qIc8dOECtpGzWKZHz9euQPOQRhwwFEdXV1Kd9khJFflEFtE9lkToRUeiJqObFYDBGzfYcsIxyPM/VjXV0dJI3P2PAISccbQgzjS9Y/O24c0kuXosbE8qROpSWq8jaW/Z6r8asbM4NVMzpFkpBW52J4+XI0T5yI1MaNAMXarKeFtT/MUFNTUzhYBbldJPWwq7a+njwvBIHnPs60DqcvfvTRR3jzzTfx1VdfYcaMGRg2bBjWrFmDoUOHCieaJ9LpNNLptOP3Q6EQotEouru7kXOb0JYCsXwe2nZbefRRAEB6xQr0HnwwgMJAT+hV/zo0xOPFTXvH448jO3IkFJNnjWgCEALKyq7u7S0ZRPKrrxafsWqb8A9+gNBXXwEA8qGQKc20qEqni4Jgd2cnqgDkslnIAJLJJFKJRFnbSO3tCL/1FiQAUQChRx4BAGRzOff0qP8nEokyASoky9BvMbu6uorPZ1pa0J1IIL7VVtBvGVPJJHotaCqpz+K+EZl0uth/ZX2bTJYxCLftoqdFURT09vQgrZZZVbgIAH1jJp9H6OOPISWTMG7LE11dZRoAu3Ywoj6fLysXALBhAwAgm82WlDWY4oQznc+j20E7VSWTJYcZkqKg++OPUYXSfkomk0gayq9JpUr6ikc/AeXjpru7G/lEAlUA8vk8sZ7abBZGfVMym0XK+OxPf6oRa0tHdWdnKZ9RD2mSHR2FNluzpjhmqlDg6z36cQWg9/TT0X3TTWgeMkSttrTexiFDEG5rs2272kwGIQDJnh6k1LbQoPEcUvl6aO909/QgZ/FcnVqXVVlNioJkb29J+8ZSKdSi0F/ReLzQNiqfSabTSCYShbFs2OxY1SP19qJKLZNEczSZRG0uh6xuThnLs1qfADKfcjOWq1AIwBH+4IPymwcfjO7W1uKYMUNCkpC65x7km5qQtaAlps7fru5uonZc6u62rEfPa7S2TrW3I5lIoDadLplTvb29Rb7pBDU6fgIA+aamMsERKMxxGeV90KCO80wuV5gLBJ6kYRCAjCyji2FtSiQSCHV3Iwogb7Meh9XnACBrwpNIdeZh+K699gIM81lD14YNBb6SySAMmO73eI9fSbc3KF5TlGKZjao2SjrxRHQ+8ojl+NJo6XrvvYJZJWU7ae/pf+vR09MDpbvbdK8XSiQQBdDV00O93+QBO15jBxrFCrOtSU9PD04++WTsueeemDFjBmbPno1vVZOSyy67DHMcBhWogA4pXSCI6LJlAABZ1ZzYIa87ocgNGwbFENLaEZxq3fQbX95R+LTQ0jYmFbWXXoqGE07wxGdF6urCoJEjIW3aZBlhr+hLITiIRBCgUASRiN17L5omTEDVE0+U3xTYb6ZziqIfePrPxS+/nO5Br8aHXyZ8ZoKrxkcc0qVHx6JFaHv+efty1LoiK1aUb8x5B5HgaQ7nIk8RgLJgO2UwuR766CNIHR3O6nSJ3hkzkKONomeB9DHHIGs3r534BppBWyMERZs0ah7z6qFC+YNkmsPvvGN531hG4INI2GAwhzHkCJRROkMkU3KzV4YNg8Iz7RCtOWg/N+0lgXkH8rvf/Q4vvvginn32WXR2dpao6yZNmoR//etfXAmsoBQkoaf69tvp3tWreBkHc27kSJNCnTF2SSdAcQkiof8erWwbO+5i0AvjRlzApkVevx5yZ2dh4bEKUa4FvPDIB0r+5hsh5VJBklD1z39C1vv0GL5TVqPvRUnJlEUGkXCAnksuQcumTUhPmcKflqBEiTLUE169GpIx3wipX9zOcbO+NtP6k9pDbd/u//f/kDz11PJXhg5FjiZxtDpGYwsXotGQiJk1WpqtXxENJAnxa69FxC7huLFNGMe+QiNAEXygmg4+uDxRuFcIheh9mNxCbV/TmehEgKIIPe8ItOuLSdsVAzPRpkBxIkCxBnVheQfoH8F+aL8nyMKJ1j/9ob0ZwfxFjz76KObOnYsjjjiiaA+qYYcddsAXvBOJVcANpqdMFEjcdRdaV6wov+GUsesZM2cNlGQUoOxOTL2e2BYCVDHQh0cClESpvRRTuYSqf/8btRdc0HdJtf03gqjBCJAAldlrL/RceimXskpAO7+81FDq6mo88kjUn3Za6W1Sm7o8gTYLGGIpsChKqeZDLaP3oovQ9ec/u6LHqk6eyJj42Rkhb9yIuvPOs6SjqHVwOuZra9F5553I7bYb+T7BJDCihv0POTyoaSdpnlkQCnkWgCe7666FPzhoWySj4CRYgDKbR8QgOXrrEVoNlIU51Ob33rN+X1AQCatnFaM/alDhdk57AW1sVQSogr/G1ib5cbpNNj8VeABFQdXDDyOi+RWQ4GKSKbW1yO+4Y/kNp4uTngnz0EDpJydBgGoeMgTynXeWvKJtymwT7TpAftAgi5vmAlRxw+TVhtiKqYmmgSWKJIlOHosGr28UtTgETIAiRUksy3hP6Jd8U5MYgiy0C9HHH8fgnXbiX6dVW3M24UuefTZaSFEXacsgReEzpm1gQPr4483DmRM0UA02+ZfskB03ztX7ipcC1PjxaPnmG1MBimlkCNZA9c6YUXrBpvyqRx4pBraQ2tv7btDwcLsofFbjiYI2x21jwrMTt92G1jfftKbJK/QHDRSlCR+XqLkBA/OqP3r0aDz22GPEe8888wzGjh3rmqgK2FF3+umo+cMfIC9dav6QovT5QVklTGRAxsECF3777RIfEy4hjgkmfMZTtdDixX1/f/ghqv79b/uy3NJDYi7GTZ/eB8pssRe1CfCTqbFoAEknmEESoDiH6S6Ckr7Q2rVi6rcCZcj59qeeQnbffd3VZdbXFiZ8oc8/Ly3CLh8cD4jwgbI5YKLOB2ijkc9tv731+3YgaKB8hyxD8sqED7DWPrkx4ePM/8sOQ236rW7mTNTccgsAQG5t7bvBQQPlmnc6HXMmtKdOPJGvj5BI9AfzuAHsA8VsOzV79mwcd9xx6Onpwc9//nNIkoSVK1fioYcewt13341nn31WBJ0V2KDqX/9CbpttLJ+RFAWZ8ePRc+mlyJtoEVmRPeAAtC9disaf/IT6nbJneWw+dQxEMgSRIG0wRGidqKAo1j5QJgumiES6hYItmJpohkfD9FUa5KCbBvMUuvWw8OXREH71VXKQDRFQFGZn+SylGZojWG2ODZvZ0Gef8anSab47EkTPMas8UIa6XSc7tRKgaL5ThKAjy67K5eKj5gQGAUpSFKQnTkT4jTcgiwjIQdNG6gFEic+j2q+265OVYMlTgGLJA+VB37o9HKZe9YMsnPQHM0OHYB5BkydPxsKFC7F8+XIcf/zxUBQFv/71r/Hwww/jgQcewOGHHy6CzgrcIp1G1f33A5KEnJpPgBecMglNsDFNXscCvdnI1KlqBRbsx+pET/BEL1tsSJscr6Lw+WjCp4Wxp4nQJWsBP3iDV18LWozNfMJKqt60SUjdRBDGhCJJqLn+esRF+IDZwMoHSlTkr16dz155pZw1UCxl0Jo6mdXpdr671EDF7r7bXf0kuA0iwXMtcKKB0qAoyG2/PdqtLEzcgKKNQl9+CXn9eki60NBUZlmSZB0oirR/yOXoNaucTfh4Irf77u4K6AdCh51mv8ijRVlp+AhHI2jatGlYt24dPv74Yyxfvhwffvgh1q9fj2mG5I4VBAdVixZB7uqCrLdf5gWnE0Nlql1/+pN7GtTEcgCKocCLIYZpT/a9gmFxrHrmmbJ7lkIWTwRA9R9+551ibh6vkT7sMKbnTRcLQWZCUWPWeRACf6hjI7vLLkJoINVlRM2f/oTqe+4p/PBy0WfQQHGDBb8Lf/ghW1kiwpSbXTOa0nDuJ8WlACVv3MiRGhVufaB48kfa9s5mEb/uusLfeosEkfOKoo2qnn4ag/beuySCLg1NiiSRTbC1+4T5FHn5Zdty+wpwGETCiw296H2GFsZ8/Xqx9ajIE5L11p15pvVLGo+uCFCl2GWXXTBu3DjsqkWfqSC4MCQc5QrjxNByGdlBjb6nNDe7JiH86adsL9g5vbqF8fRMX6YxVPeXX5bTxTkKX9bsJCwAJ1ysoZ8BIMPJ1zJz6KFcyom88Yb7Qij7In7RRaUXtLHCI5qlHaxMwgDI69ahWoQmwQwmY0dSlHINVADGOgBU6w+MRJl+kuDBoZHc2Ykoy+ZXDwH0KS41UF033siPGMp+iqxYUTTJLYnGJ3D8MvFgggCV3WMP8+dl2VobTNhYk5L6mkJR+rRVnKLwBQZ2NHp8ENx1661l12ytQyoaqFJ88MEHmD59OkaOHImqqiqsXr0aAHD55Zfjueee40pgBQ5gMaCFREIxTIzaWbOoXuOS/0kri8YM0GbzxxVW7Wy1oJskTjTNw0UJs6iAlnbgOhq6f/97tL71lisaeCJ50kl+k1CC1OTJ/lWuE9JTRx+N3PDh3tSnQTfWay+7TEydTszOjKfeHm+YamfOLLsmf/UV4tdf33eBowAlKQqQzSL297+Xh8HW/22X1sElHbzK7qZcRyzhMohE+phj3NPACn2f6RNFi7QWYBGgdO2Z+tnP0PLdd8hMmGD+vCwz+0DZheQvgV64FC1Asb4TtKAqLkFzcBl5/nk0HnRQ3wVtvPQHgZURzDNy6dKlGDNmDL788kuccsopyGgO+wAikQjmz5/PlcAK6KH5kjRZMTMBMKrgZdW3hYRq/fjgaGKj1Naa3yRNXB99oCzrJmx8cjvuiNQJJ4ilyQbJc89FfrvtfKWhBF4y495eRGyCjijxuEfElENvY5647z60rVolrjK7oBYehYwuwmKDonihkbNA7JFHSn6HV6/GoB//uPQhzuM48uKLqL3kEoTWrCm/aRcNi4cPlAmcHNylDUmKHaG/+kCpyA8eDKBgPhf65BN+tJRVRD9vNRO+7A9/iOwBB9hqvrv/8AekrXzjXQqGElBs29Sxx1K/l+oPLic+Cx2t775benBK0VfVd92F8Mcf913I5/lEWg4gmEfuZZddhunTp+O1117DlVdeWXJvzJgxeCtAp9RbGmTV4dzSHtYDDZSVOUD8qquKf3Pd4NAwYRMNVFKEcGIw4ZM3bOi7Z7WgE6Lw5QcPdt9vJpsjYdH9eMHkuyMrV/Ipn+L7a/7yFzT4LMBaQos26YU/myiHbocI6RdqOzp83oyESP5RopJBazyGIQ+U6+BCFu0bNoSUd1seNdz6QPkgQMnr1hX/rrvwQkSffBKAzqdXBFiETEZ3gPSkSVCs8iK6NVNTNVAtmzYhdfLJVDS1bNqE5BlnUD1bgoDkgYq88goAIMTDf9wC+a23Lj04ZYicW/yZzwfC11oEmL/q/fffx2lq5nnJ0FCNjY1oaWnhQ1kF3BBZuhR1mt+EBwIUNbw6IbYLImET3tcJSk5cFQXVetthmkScXm1EaWgBfNl8hletQo2ZD4JInz4jdAFKRCLf2Mj+UiaDul//uvC3VwKUgPniFLFFi8g3AihAEevnHURCK08VGqoeeKCvKhMTvsT8+dj83ntIqHl+HMPuW3TWKkSI4MOyjIgLjawfx0t1//M/Jb+jzz6L7B57oOfii7nWU2LW7dQHSiRYfPv8ntseo2HaNDRMmgTJo7WpCAcCVOjTT/tSywwwMK+4gwYNwrcmmdE//fRTbM0pv1AF7lD9l78UT5VKnLpFMBrjpKLd/PMUoKxOP995p/yiBY2kSDNu0LT//oguW1b8bWWTL5F8oEQKU5Rl+7GRsDRZ4SQs0PjhFbVdgs0+Mz/5CbKjRpFvmmkQ9WHOgxBVKsgazQBusri0lv67tHmhjtWa3/9eV5khL572vyxDGTYMiMX40UH7SmsrGiZNKuQYMo4dHnPcbRk+m/AB6nqRTnMPy59vatL9oBegagiBBIRgIAhQAvkha+Aixe38Bqjmk9Fcl9Ynvj+Cmbscf/zxuOqqq/CJbnMjSRK+//573HjjjZiq5eCpwFfEr70WYZI5pQBGYzQdItrfE9A9axaTzbJTaAxffuUVNI4fD8Bguqb7O7Pffui55hqu9YfWri29QGPCZ3eNF2ijEfqxQIkKQ61DfuRIdP7975bPRN58s/CHVb4LTsEAsqzRBf0UWDwcqxFCOPcBAd4aKIMAVXafd5162JVLuB9ZvhyRN96A/O235TTzMuFzgyAIUD09kNJpofxQpsg3pyGkmRgGwEoi9O67hbyPQRWg3IKjT6mUTLovxIEGaiCDWYC6/vrrMWTIEIwePRr77bcfAGDGjBkYNWoUGhoacPXVV/OmsQKnICWiEyFADR1a8ps2sWd2332RuOsuPkRQflcx3LmJAJWeMME6IAUrPSTmb2CKkj5qIsmET6RjfoBN+Czr5GiulqaMoOeJGQLrpkT/fBBM+ARtqsJmvk5mINApJAIpC0SZ8OnL0AQGEs8wEVC4tQtjOTXXXYfIa68VfpDG7kAToBxCkWUhGqh+E1bagqfEFi4EANtkrlyxahVCr79O92yQNfIOQOVnG4A54xWYV9yGhga8+uqruP3227HLLrtg4sSJGDVqFG666SYsX74ctTw2nxVsGfDLsbCnp3QzoWNy3IIqMIQxLyYgBQp0pVKI/fOfRPqcInvAAeQb+rKtBAQvmaLqbG+5sfNj7IjWQLmFFzTYjUV1XiX9skQQnd+NN3gKUDoNFDGQj1nb+CVA3XwzqhcsKL5rpJmHYOc6+lcANFCQJDEaKIvktv0BUkcHYvfdJ6x8ok+qJAFjx6J+0iS6Qtyu3TU1wUrZofKXtMeRnoMKpl1IMpnEsccei9deew1nnnkmHnzwQSxZsgQLFy7EWWedhagHJjcVWCRFNSKIjtR6+JHlHUDz9tuX+I7I7e386KCBTRjzamMqAA4CVM8ll6Bj8WJifUVwzuPiGHvsUXBUDgo9WpXptMVNf+aV/M033lZoo1E1C1TgNcLvvIP6c88tvRgk3qeBM01FocNKA+UkZw4N3JQnSWJM+NyuMT6tUWXvpdNc8yZygc/alepbbxWqeWpbuZJPLjKXSE+c6DcJfVDnQ3bMGPNngshnBYGJO8RiMbzyyivIucmrUIFrJP72N8fvRm3y2XgJX01qdJvh6Isv8i/fzIRPUayzvufzkBKJ0qJ4LFSybJtsOGJsBz9N+Oy+uaKBAgA0TJniKQ0SUNY34c8+6/vhtwCl1h9ZsaL8nt8Lu2gTPkVBTIu6R1qj/faBstMOiojuOFA0UKkUf41RfzEv84lOpakJ+a228qXuElAqJgZ7Qau27vY3Tb8gMO9CjjjiCCxZskQELRXQgnaAWvnhBAEcN8HchDEvTPjsEukaNz8iadKVHfriC7Z3RcImspInOY8MCKIPlKwmzwYQjIUrqLwmqOAcRKLqmWcs7xPr9MmErwSy7J8AZRWWOwBzquqZZyCl08gPGeI3KaUQPdeDEPHTpv/DK1dC/v578wc85Iee5HQcoPmcnII5jvSZZ56Jc889F4lEApMmTcLQoUPL8kH92JhtvQKukL/7zm8S+GAgT0YGHyg9wm+/DaW5ufQiryASNgJUkE6VQqtXB86EL4gaKM9hN15EaTl0dSmRiDNh1oc+qpsxA+kjj0TqxBOF1yVlMtZ+jIIFKFeHWCQTPh7rA4UAVUUybdYQBA2UCiVoPkt+H5b4ITAY+rBx8mRkR45Eu0lQie5rrxVFmT/QHcq3vfACmg4/3F96fAazAHXMMccAAObPn4/58+eXCE+KokCSpIqJn2hY+WLo4TeDs0MQ7Mv9gIVAFHvsMfSedZaYevuRAFU/aRI6rSI0ZZ2tagAAIABJREFU+tDffmroQp9/TryuyHLRJNQTk1hFgVUtRNM53ohE0PLtt2gmncgHaAwDQNVTTyHy6qtInXgi6i64QEgd4Q8+sH5gzRrI7e3IE6ISSoB3Gii7vhHgA0UzJ+QNG8xvejRmqPxcQqHgr+kcEVm9uvCHn99MM35Mog6nDzkEmUMO4UyQz9AJULnRo62f2QLALEC9FCAfmi0VGTWXUZAhf/st8IMf2DzkowZKMFNWmpqAr78m12N3wODAhK9j4UJnDrU+CFCZMWMQ+vxzyJ2d1g8GjBFHfTRdjj7/vG91K7FYXw6RAGzgHAuKAaC9DF6M8Z13RgOAtldfFVunTbkNkyYhbyJgE/uUA51hipyEcau8f0HiQaGQtbmh1xA8n0LvvSe0fCrYaKAAQApSn/gE+ZNPgP33L/wI0pwRDGYBakIlfKH/CLN1m5AgCTaIX3YZev7xD+uH/MxDYcb8OS0KXdddh0azJME2JnllUQEpaMpQqNJJm5SoLkGppQ01R6bY8eyzgKKgeZttrB8MmAmflYAaCB88AfNp80cfAbKMwaNGFS7Q0hfARTT6yit+k+AvzKLweaSBirzzDkxnkKAgEq7npUfjOPr880h98QUweLD5Q+EwXwEqiAcKOhQ1O4LXakvQ9L+Z2WzA2je7yy78CjN8W+3JJwNr1xZ+BJD3i8IAdkIZwKDV3Pg5kGn8dkQmiLWBWSQ8ef16LuVbJeO1jMIHgk2+R4w4ftVVCH3wQcE0Kp8XV284DLgNyevV2KZNaOzXXNPXy3iwQgOluRnKoEHF33VnnKG76aG5HO1YDNimJUiQv/yy8EcQg0iI8oEKmJ9t13XXmd6T9NEsCfAjcI4lBM+1klyIPtQPgG5MZ7NAJoPIsmWl1wcyLyJEFy6iIkBZvCDLCIVCxH/hcBiDBw/G4YcfjqeeekoEvRUA9IuCjxPYTkgAfHaKNaHPlmmzwokJnxEig0gY0KTZbBtPOv1gikHwZ9H3lQcCVGaffZy/LECAMkLu6vKHrwRs86iw5DwMyIai4ZRTCn/4pIGye7dszQhCHigvYfe9HszvfoWARJ2TFAXVd9yBBlHJw72IDMyAzF57lQfFGcjCogWYucv//d//YbvttsNOO+2Eiy66CNdffz0uvPBCjBw5Ettssw1mzpyJTCaD448/HgsXLhRBcwX9YVGg2fQHUIDiBqdhzEnwg4GKCCnMioAJUMb8XCXgRE/qtNPIZJj5E+qj03m0wWoaO9b+Ic790z17trCynSA/dCifgvz4Fq1O3jzQzbcoStl858L1giZ0WLWR3boetG/xCn5uzg1m2+H//If4WFG7q8cAFSo6li5FbrfdSi9uoRoo5hnZ2tqKsWPH4tFHHy2JwHfjjTdi6tSp6O3txbJlyzB9+nTMnTsX06dP50pwBegfA9Rr8zzWNvGRucVuvZXtBb9o9XsBsKrfK9p0ApSVD01+66351GcyjvPNzQh99VX54/p55pFPoUQTBZQzj8pb+YboYTEues8+mxM1/Rxq38jd3YXfToLPWJRrCZP+CX39dV8SYJby7GAzJyKLFrmvgwVWfMtOgPLTZ5gEr3iwST1e5D2KvPFGye/oc8/RvxwwzZFQbKECFLMq46677sLZZ59dlvtJkiScc845uPfeewEAJ598Mj7++GM+VFbADXkL3xy+Ffnn30QFH+mLvPAC2wtB0ED5gSBooCictrvmzEHv+ecLJYNKaAnSCTXv/tGX57Ds/LbbciKGEVYJoaurPSRErdNAT/jTT/mUSzP+TPhu4xFHlF/kYGlh5zdUKyplhBPY0KrwFqBctm/PVVdxIqQPveeeS/+wF2sUZcqY6nvuEUeDg+/MjBsngBAL6C0hKgKUOXp6erDexNH+yy+/RFINdxuPxxFlsROvYGCBtFD29oqrj3XSeiRAhb79tuR3MbcFA7idtLG0URAEYKvv9kpYoGiH3O67i6eHYiH3yoRPQ+jGGz2trwiHC3Ru5Eh+NPCak4LMmMNvvimkXEvQbPBZ2o3HRqw/bebsaOUsQHW63PSnjzySEyVAZt99ATD6FnogQDlKDRIAdFglhxYASVGAZBJSW1v/mnMuwSxAHXvssZg1axYefPBBJFSfgEQigX/84x+YNWsWjj/+eADAe++9h5122okvtRW4h1f+U4SNp2QW7tMPeOQDFfrkk5LLNTfdxF7WlqqBskC+ocGbiigCfij19cLJoMo14rEAJbW1WdzkbMJH63NkMW7Thx3GiZrgI+RHcliayJosfJcHnYY5kfjrX6lf9fpAwusgEvntt+danht0PPooNpvl7FLntPztt6aHsNk99hBDmJs9i59rqNd+8qkU8NOfopHnIVU/AHMrz58/H4cccghOPfVUNDY2IhaLobGxEaeffjoOO+ww3HbbbQCA4cOH4/rrr+dOcAUu4dHpAE0UPp7QzHN6f/ELque9ok8y5nRyAh8EKC/sy21pYI1W6AItmzYhqUUp09Owbp3le11z5yK7116iyOoDTVsMYBO+/MiR2KzlGQkAes85x3UZ3VdeyYGS4IBK4GDhKzw2gQatDUuUy665c93Xb0BEl3evTKC3+97+EDzKKaqroTQ0WI6PQT/6EerOO6/vgu7Z9pdfFkKWGw1UENZQryAlEoAWYGML0kAxr7j19fVYtGgRPvroI6xcuRLff/89tt56a4wdOxa777578bmf/exnXAmtgB2NRx+NlFkyV9EgCSgCGUp60iS0rlyJ6jvvpHvBIwGq7n/+x5N6uCMIzN+BANX++ONATY2z+ggblOiBB1q+kv3hD53VxQoKDZTnJ+YeQ6mro3jIG7+55HnnIfz++4g98gjdCwS6UqLCHtvB2A685roLHygieCTSNc5pnzd34Q8/LP6dWLAAg3fckeq9gT63aRH+6KO+Hx6sUZlx4xBl9VneEhEK9a1RFQHKHrvttht2M4YyFIRFixbhpZdewsaNGxGPx3HooYfi5JNPRkg9Xerq6sK8efOwevVqVFdXY8qUKTjuuOM8oc0vtD/9NBqPOcb2uaonn0TuBz8gRvASCfn770t+h9asQe1FF4mrUJKQHzGC/nk/w5izwi8fKL+FKAcCVHb8eMfVOXJ+9UqjS2NKEqAoXdFnnhFSbn7IECSdRnbl3VcsGgHSXApC8mWAn2aDxoTPax8ow7f5EbTDDGXCndWatCULUPoABR5r4VI//znic+Y4e9nv9dMAoabmstw3frV5qygDXphyNCszmQzuuusuvPHGG/jqq68wb9487Lzzznj44YcxevRo7oKVoii44IILMGLECLS2tuLaa69FTU0NpqoneH/729+QyWRwzz33YOPGjZg9eza222477L333lzpCBLyW21F/azU1aX74c2ADn3xRcnv2IIFiLz+uviKab8vYMzNEj77QHGP/kQLKwFKxDgOsABF5YsVoM2h3NoqpNxW3Qm+EdKmTUA8bv4y577KUWoPgg5em1IqLUl7O6KvvkoXgEBAGHOFIQqtcBMsI1+1muOCBIe2V15B3S9/ibCZ/5HXsGtzfTt4sYa7zW0WEOTjcXT+/e9iKzF+7xYgQDHPyrVr12LUqFG49NJLsWbNGrzwwgvFYBLLli3DH//4R+5ETp06FTvvvDPC4TC22morTJgwAR+qC2kymcR///tfnHbaaaipqcEOO+yAI444AkuXLuVOR6DAMDBlnbM3i+DFAttTYY8nUsdDD1k/EIQoc7TwWYBKH3UUn/oZYemntqUJUAaEPvoINYYwwiybw4GIhrFjPd20UOeVkqQtQwNFIUCFL7sM9aeeSlceB7pyw4eXXghSZGCjAGXB74rCqdYmnDRSud13R27nnbmUxQWkeULIMVR9002oXrBAPDnCa7AHlemyDbIHHABF0N4PQCmPE5WoO4Bg5lAXXHABhgwZgrVr1+KFF16AohvcEyZMwLJly7gSSMIHH3yA4Spj/Oabb6AoCrbXRZQZMWKEaaj1AQOHi0tZBmlOyP7oR0LKdQw7rUl/mty8aGU04Yv++9986nUKD4NIAAiMAKXIMtIHH1xyzSjE1k+fjpr580tf3JLNfKA6Mls+wLmvWNqbsDH0bXNmFOg8FKBg10c68GifMpNen81cu/SBtVg0UOqz+eHDkbjlFqRJebOcwsGhQ4cx6bFgxGfPLvyhjtWqxx7ztH4/kRk/Hh2PP+43GZZQSIdEAdLAiQLzivvyyy/joYceQnNzM3KGCT9s2DB899133Igj4emnn8YXX3yBCy+8EEBBA1VjcBqPx+PoNQl32dLSgpaWluJvWZYxZMgQx/Roflghjxmz7HCzJEmSEFplwuYkFAoB336LcGcnJMIiLYIOLcGzbDN5zcwzlHicC10yx2+TFIUPTQwbpehnnyHy1luF+gWNGTvfPKs+lGWZO02kMWoHORTiTkd7SwvCS5ciqjuMkgx1GPOLAQX6veZDNPCSJqu6QuEwXyGKxucHACQJYYJJYygU8sU8VpYkhHTtwG0MU6xJmlaZpr5QOMxd4GH5ThHzKX/UUej9/e9RPXcuQobxI+XziHzzDfnFcLhIS/bUU8GTKkczYocdPNtHSJKE6ttv134gFAqV0SyKx0gWY5pqDHOiK7/ffkzPG+vNjxollA9LOgFKW0dDslwyf71em0TtW/Rg3oWHw+ESrZMeGzZsQC2jGckNN9yAV1991fT+k08+Wfz7pZdewj//+U9cd911qFcd4mKxWJmw1NPTg2oTf4DHHnsMd+oitZ1xxhn4zW9+w0QzCfUe5IIpAcNJnh7RaBTRpibOxADYc8+yS/X19UBDA+oA4IILyu43iaBD7fc6m41S1GSzLD34IB+6OOYpkiWJD02NjdSP1ukSfAobMzZ9VGORZLS6pgbVvGlykNS0vqEBENE2BrONKorNek08jhoRtLiEkHluVpfZGF+xAk2DBvGtjFJDKksSGgkboMamJjFjxwa1tbUl/Kk6HuczlyhMjTSuSzMmmgYPLvJzLvjxj5nGYrymBnER/TNxIjB3bhktkVAIDSa+YXI4LG4eOTiMbWhsFDN2CTw4rNsEhyORQjsYNsbC2sYigbldnRGefUaRSF0PY72xP/8ZMdoDHweQdGt5VSxWoKGhAVD/JtHkBaKCTXaZZ86ECRNw00034eijjy6eaEuSBEVRcMcdd+Dwww9nKm/WrFlUz7388su45557MGfOHGy33XbF69uq+X/Wr19fNOtbt25d8W8jpk6digkTJhR/y7KMNquEkDYIhUKor69HZ2dnmUZOJKSODtBvh/uQzmTQ7eJ7TXHAAajbZx+E33ijeKmzsxOaWJlMpRAzvOKm3c1Q3duLGICu3l5YifKZVAokdtJ20EEAB7rkjg7wEqHy+Tw6eNDU2UlNU6KrC9p2KJ1OCxkztTvvjIiFqW1PIgFjQPLE00+j7phj0NvbiyRnmqozmbIxaofO7m7kBLRNpKOjZPymenthJ9719PYiJWJuA3Cz9ImY5wCZpra2NuL19kGDoPCmQ1Go2iWvKJD1gXw0mjo6oDgQ2o1g7ZuuRAKZzZuL7/Ukk/zGzfr1aDJZewEgn8lAhnk/6dHW3g4kk65Jqh03DpFXX0XbokUAod7smDEIq9p2Pbp7epAWMXb33htobS2jJZNMItzWRtYIffedsHkUz2TAus3s6OxEXuD6rUc2kyluVLOKgkRbG+pzuRItnKi2sdpn2Y3hbCaDBC+60mmmeW6krY3Af3hAq0NRlOK4TaVSqALQ1toKVFcXnxHVR2aIx+Po7u52/D6NwMcsQM2dOxfjxo3D7rvvjmOPPRaSJGHevHl4//338dlnn2HlypWOiLXCK6+8ggULFuCaa64p8XUCChqo8ePH4/7778dFF12ETZs2YcmSJfjtb39LLKu5uRnNzc3F3y0tLVwEn1wu56kA5dRqPdfYKIzO3ODBJQNKXw9JZymCDk07mrcx4TPTonKjiaP9r5LPc6FLYfClyuueVSCmrzoWLECzReh5hZD7KDVmDOpU+njTlDr6aMRo84ipyCmKkLYxzm+FJgqfIFrcwkuaYldcQaYhn6dqQyGw4DV+0JTP5ZDThcVXJIlfH6kao94zzkA1KeqXyldo6svl81z8IFNHHYXIq68iryjE9s5ttRVxI8SL71Ijny/xd1VqaiD19BR/C6PFgY9tXhCvIa3Lxmu5XK5sPyGqbaySudvVyXX8MPaRsV7R41hBnymo1je5bLZk/nq9NnmxHjLvw3fddVesWrUK48aNw0MPPYRQKISnn34aO+20E1auXImRI0dyJ/L+++9Hd3c3LrvsMpxwwgk44YQTcPXVVxfvn3vuuQiFQjjjjDNw5ZVXYurUqQM6hDkAx7b83bp2443kL36BvM6EU/r6a2F1uUYAg0i0vfYa8Tq3cLosY8aLCGF25r4ehzHP2CTNJUJQeOHsj39ceoFmvA7wkLE0qL7nHvINQW3TsmGDbXhg0/xiPvVX7aWXloynHEv+PAq0bNiA7htuIN/0OJGuHmZcVGh0MgZIa9ZAokiYzR1O1kIPx25EF+3SUa4+N6jwVHboo/AJ0nwFBY4iEYwYMQL33nsvb1pMscAmXGVtbS21KeBAgeMtdY3RKIofMhMnom3VKgweNQoA0Dh6dN9NjxhRz/nnQ4lRGGIF8aR+p53IN/yIZhOEhYPURyLpclK2KCdVhhDHGjzfXPQniGobWUb66KORHzIE8qZNYurgDLmtreRQxhjx0X0FFocKLHyX1+GE1vcm5eUHD+ZTj0uEL7us5LciSc4CPLDCgQAljNdQ5IEKr1iB8GefianfiAHAUzMeKBPkjo6+H2qbSQDil14qvG4/4W1a5wr4IagT2+NM4UYow4ahRwt5agHLHEM+Is8x+EQZnI4Zv8YaqY+CNu5FjXeDABXU8dpvIJIvyTKSP/85+3t+jmX9ePKSDh81UKZjYEufW04O6HwUoGr+/GcxdZMQlPVGR0fqmGOoXum69lp0zZmDjn/9SxRVZGi0KgpCQbZC4gAqDdRhhx3GVOiLL77oiJgKGOCzoGKKoNJlhGitjkPGm7jnHjT87GelFweqCZ8dCR6b8AFA++OPo/qee1D1xBNUzyuCxrux3CqaPCAB6LPAQnDbWJrZBsyED8CWKUCZlCcxRjgbcOhP+XpkGfmhQ/2mgg6i2pXS6iF57rli6tchO3Ikwp9/XnKtqJ3M54FUSjgNfoJq9a+vr0dDQ0Px36effoply5aho6MDsVgMHR0dWLZsGT777DM0MoRKrsAFgrpZMttQBo3eoJ46ktppCxWgPDfhQyHxJktunvw224ghxIlgFoQ+CyoqbVMKPU8JoACVGzHCO7p8XAt6Tz/dt7qLUL+/9Z130MMhpYsrUKx1iuDQ1CVwMAY7Fi8WS4dhbUhNnsy/PlqQ2kcnQEkVAQp4/PHHsXjxYixevBhTpkxBfX09PvnkE6xatQrPPvssVq1ahY8//hh1dXU47rjjRNNcARDYDYGZbXTg/DOCeuqma6eMljvGDwHKrw2WHlYClECamIJ28MxTo0cAE+L2ZwSO/wBbpgaK0geqbeVKbnRlNV9csznlowDVfdNNJYGXfIH6/flttrHVxnUb/LS4k2LnjyZJ3o5XB3XltDQ7gvYYvWedVfI7u+++Quqhgl37cEhDEGQwH3POmTMHc+bMKYu2t9NOO+Gaa67BnDlzuBFXgQWCuCEAgqOBsqtP9KLp8Hv1G72iqYIfC7xHdaaOOsoZDSLHUxC0k04EqKDyhCBAdNsEyY+EAiUmqgHUQPFEdtw4tGzaZJ4w1ueAQqmTTyZel3Sh5kVCf2Ak2eTNye28s/qgmDHTe/75aH/6afMHvHYRcPKdOh8gEcjtumvJb1Fm5G4hb9xYZt430MDc8l9//XVJ1mE9JEnCN99845qoCigQ0EkTWLqMCMImmYSgmPB51D6J++83FaKIgRPUb8gPGyaOKJv27rr+enF1a6iY8NkiecIJ9A+L5ktB1WibIKaPojvABSg7hN95x9f6u6+7DllCBFbPfLN0fZI56CBv6jRDNIqsZnlBgiwHn8+J4DUWJnzG9ui84w7+9TtA9Jln/CZBOJh7et9998UVV1yBtWvXllxfu3YtZs+ejf2sBn8F/KBOmqwaMjww6CcClGWAAj8RFAFKv3gLXrBS06aRb5iY8LW99hpSU6eKI8hmk0cVJp8Duq69lu2FoG8sOKNr3jykDzmE7mEf+VLo++/JN3zsL6m93R86Ash3I6tX+02Crya73VdcgeT06b7VzwKFYMKXMebM44kAaqDKzJENvC0zbpyQeomwaB9FYMqcoIB5Vfnb3/6GRCKBUaNGYcyYMTjyyCMxZswYjBo1ColEAn/9619F0FmBEdpJvGZvGxQExYTPBuF33/WbBDICIkCVhIoV3HcZsyifJput3E47iaWJ0N6JW2/t++HRWGaOohSgOdbx4IMAACUSEVsR7TdXNFCl0M8tD8eN9MUXntXVr+CjAJXdbz906flbkEHQQCnNzeLqC4oApafDOFaMNAbcR2wggXlV2XXXXbFmzRrceuutGDNmDGRZxpgxY3DrrbdizZo12G233UTQWYERNskBfUPQ6PELThkLqf18EKBkL01xzejyy9yH0N6KPrpoUBeNgNGVHTUKvb/+tdA6uq+4gu7BIPIlPzVQHgSRyO6+e3m9XV2W7yixGLquu04IPYFGUOZu0A8CCAJU4HIniuY1xvLtfvuFoIxpgaDKA2VEVVUVfvWrX+FXv/oVb3oqoISmxlVERQFzCpNJU3PbbR4T0k8hUgPFgNB33/X9CFIUPi9Aam99G2wBCwMPtC9fLryO/Lbb0j3ow6ZCiUSsAwEEYBz1nH++sLbpueQS1J95JtM7UjKJ5DnnCKEnyAh/8IHfJPQLyG1tiN55Z+lFgeujo5JFz2s7bWVFgPIMVC2dzWYdFe70vQoooAlQW4Cd6RYFPdNRFwamsNq0ZZtAMYtUJRBmX+eXn1p2r73Krik+CVBdN9xA//AWsGCVISibBcIcze6zjw+E0CGlJuvu3QKFFTukpkwp/BF0bcwWCKLpfcD6SRFtwmfkecZcS16uA1VV5dc8SDUSFFCtPiNGjMDNN9+MzZs3UxW6fPlyTJs2DTewLP4VsKEiQFnD78nrtH6RdFOUnQuSCa5PAlTvhRdiszH8qk8CVPKXv0S+vp7uYb/HvB5e0RIUAYoEuw2Uj/1VPCgJcvv5hGKQmAGew2aggBit1SPk9abdGtR5ze3g06R8x/c5Ij1hgmd1BRFUx8233347rrjiClx66aWYMGECxo8fj9GjR2PIkCGoqqpCe3s71q1bh1WrVuHf//43Nm3ahPPOO69i4icSQTXh64do2bQJzUOG+E1GAfoNDeeTLJpSSDklhCchJZSvSJJ/PlCyDMUotFidAAYFQRKgvEJAvjkzbhyqjaZFduM3CD5QQR3LPkJRE9va5USqICDwUwNlZ+7tAcoESA/bI3XiiagxBiDR6g8IbxYJKgFq8uTJmDx5Ml566SXcd999uOuuu4r5niRJgqIoiEaj2HvvvXHhhRfitNNOQ7PIyCgV9C18lQVwQIEorHA24csNH47MAQcg9vDDps/4BUWSCid3shyskMckwdYrUAqSwgVdFmxhGqj0MceUX/RoI7P5s88wWEtwSouKAGUK7VDSLuDFgETAzOH8hi0XI/FmbU551ZaVPvMNTA4Phx56KA499FAAwPfff4/vvvsOyWQSgwYNwg477IAqkj1kBWJgY2eaHzIE8qZNHhIUXHTNnYva3//ebzKcg/Mmp+fii5GaMoUsQPm9oQqFgGwWkOUyH6jciBE+EQVfg0j4aaISdAR662CzseFFu9LYiHw8DplFY+KFABUkgZ4F2kHTHnv4TEiwQDRXCwKCqoHyii7jQaOX7WF14Ntf5z8DHHuMDxs2DMOGDeNJSwUssBGgcttvXxGgVKSmTvVcgHKsDTC81/HAA8hvvz0HilDK2M02TqTNupcmfHrNqmFhaHvtNbF0WEFHI8nMUShog/F4sGBld9kF4U8/FV7PgICXPlCsZVU0UJZo+fZbwIeAOoFGUMdKUAUor2BYsxWRYd0pIG1BAlRAZ0QFttDlgUr99Kfm97dUqN/f8dBDUBoa0LFokeXjmf32Q2ryZC8os4Yxq/gRRyA3ahSfsk20KL26hK2+azv0+c2MtPiYbFLfXp6fTPvdJzrkRo6ke9Aj/hNULqcMHertxo61Lm1MbenrBAmKAkQilbYxIHn66X6TQIbIMOY2Y4AYKMJjQTO3ww6lF/xcJ4EtSgNVEaD6K7RJKklI3H03kqeeSr6/hUPT3uR23NHyuY6nn0bi73/nVq9kDC1K/WJ5GHNucKqBEg1KDZSv0LVXbpddvK3bph0UG200T3QFLJ9bUIPo5H7960AHkdAQKL+5CvyHxZrTc/nlHhISEMTj7O94bMJHnQtPBAKSt9IvVHbZ/RV2mya/TyEskPPS9FObzF6fCg0f7uxFr8KYm7WH3yYJunHtVx4oEnK8zCgdwDYcru4wRTQUJxsKkQiF0GWMfhcEhMOBGr9lqPhAVeAAmYMPRvrww4Vv2tPjx9M/7NOGPbxqlafrZffllyM9caJn9VVgj4oA1V+hN3UiwHM/DR1Sxx1neb/jqafE+7MYmYrXTCaIeaDM6tH/7femTx23SsA0UPnhw5E+7DC/ySBDOyzxYuyY8JWsUSu3pS/qNON3Cwlj3n3VVcLrGIhIkSI7+gyloQGdCxcC0ajQepi0/D4JUI1HHUWsW5RWt/fCC9H50ENCyuaJ2AMPFP7YAtYAKu45Y8YMrFu3TjQtFTiA6WQlLIxltrKC0DtzpuV9ZfBg5HbayRNainV6PZmdbky8OBFWFFMBKjljhrj6zUDqG5IPlMfo+uMfSy/4tCBkbUJUK15GPzVrA6PDvR9ayyBBkgItQBVp80CAovZdNK2VAAAgAElEQVSbq6AE+cGD/SbBP/QXFwSroEteCXZB5H9AcOniCKpR+ve//x2bKhHdggntJMg4WQkMKHXssR4QRKDFCD8mltcMmYcGinc7UZSXPPNMR+9xhyQh+vLL3terA7Et/IDdfNIEKD8XLD83PEFcqGUZ8tdfe1ZdnjXEf8WErwISguK/wuKCENQofFuCAGVRt9Te7iEh/qCfiPkVkJC4/faSCGp26Pl//08gNfTwxXE5aAKUmQ25Vz5Q/7+9O4+Ookz3B/7tdNKdpJOQYAirCSogKMPqhqgBUcZxVAZRcIPBEcWF5TCce8Wf1wFGh+vhOg4OwoXBMaMwXmRz8Dp32BSviiLcQR1QRBgSohyWhASydae3+v2RdKe705WuJF31vtX9/ZzjMd1pqp489dZb9XS99VYbRA7/DKw7pbpaWAxauO67D56hQ41ZWaznCaWlNf0g8ECqSDJDojRSUpAS60GscYw71iyjrRh5b2iUv9P23/+t/3o7yDt4sOgQADRNDnBh82bRYYhh5gLKgH3q3Lff6r6OznK88ILoEHSneUtbZDxIJbnGSZOA9PSmFxquQEkzsYQRN7tnZjb9P3ByaXRREGt9zfG1ImA/a1XQinyOhcq6PSNGGBSMNnXLl+PCrl2GrCvmJBKBq9Ai+2gWUDi/Y0fwZ01fQsQxbiUvr33/wIBpzAOFiL+goNXvckQMFdagsrQU7okTRYcBAHDOni3fpC0GiTVrbhjZCigDrkApF12E+kWLwtdHhtP8pLj58+cjV8OTqC0WC7Zu3dqpoCgORA6pkWAIn+eGG3Bh06aWoS2yXYFSeUhjaDGj2xj4yO1jscDz1lto/Oc/g68NFZqL9HTgwoVWH/HFuA/IEKIOVBJcgapfvLjtmSUjCygjT2okOYHwXXxxywvZ7+Hw+5v6Gh1z57/4YlRWVMB67Jhu64i7rCzREbRITZVnSJ3BXPffj6x/+RdtH9Y5R1Wff46u116rfd0G9UeKDF+cJTnNBVR9fT2sslzBoNZiXIFqePJJcbFEMmKHt1jgKS5ueS3bFSi1fSl0QofHHotjQFDNu79nT/jvvhuu2tqmNyJj13t72WyobL7H0vZ//4ec06eByPuPkvkgIcE05s4Y/Ufa/v3hbxg5g6IsbaO99y+KjLutZ8HFe1WybB8yjzbajGfUKKSFzOIb8wp9J/nbuBoW9cHzsn1Zq6Nk37c1F1D/+Z//iWuuuUbPWCiOIu9JcN92m6BIohA0KYFU64vYPg1z5rT6d3rdixR6wPFccw1cjz6KtLAPiOsUfddeC+TltS6gyFyMnEFRloO4luesqX3eYNbSUvmvkpHxV6BkueLVxr6RpvcjUDrL6EkkSBj2oIkq8uBo5NVDGa5ARTD8m5L2FlDPPaft33VGlI7d179/67bCE6uonA8/DK+IKZlj7U+CHhbdlqjfzOq2MgkLKMmlffGFVO2Fwl14+23RIZiHbIWKUQVU8/KT/SqQSOxBE0WU+1rCGHmwlLCAku5kIaSA8oRe2dVzGnOt2CFH5Rk/Huf37jV+xRoPxFIdSJOxgAol+RUoAIDHY8hqLA0NhqwnkRj1zEYtfGozxuqpPfuGrAUUJTxNZ5XFxcXIycnROxaKp8gDuGydjNFkK6CGDQv+qITOyCdBnFKdiJN0X0hEG1rq79o1/A0D74GyHjpk2Lra1N4hfIIZdpVQ8AOxzeD8zp3hbwTaj8Djtldt4gQjJEIBJVtcFHeaevndu3dj4MCBUX936NAhbNy4ER9//DEUNhh5RJ7AGHmSFasdqMxApyvZTmj69kV1VVXr9w1+DpS/Z09jY6D209qvGrTdzn/8cav3fP36hb8eMMCQWAAY+sDaNkkyiUTD7Nm6LLej9L7JPxH48/PD35Bgxk/3T34iJgbA3AVUQDI8SFej6vffFx2CLjSdVb711luYNm1aq/d//vOfY+jQobjvvvswZswYXHfddaipqYl7kKRBxM5q2707/PeSFFA1f/gDh/ABmmbhUyIPqvHSvH2qd+9Gwy9/GfvzMnTQgraff/x4NP7sZ0LWHSTZCULU4ihkOFjV55/Dd/nlxgUkQ/sEpJlEwv3Tn+qyXNKRyCH3agL9jiz7l4q6f/930SFEZ9A9ULJvH6Cdz/UyEU17aUlJCWyBOeebrV27FmvXrsWPf/xjbN26FUuXLsWhQ4fwu9/9TpdAKQaZhvm0FYvaA2T1JsMBKVRzAVW7fDnqli1reb95O9X+7ndQsrPju86IoQW+wYOBtLTWHzNyCmqN3LfcImS9nnffRe2aNULWHSRZARVJsdngvvPOljckmbAl+KBJEXGInMlSovtnSCOjHx2hhcgT9Hass61pxpOCyLaidd0ytGcdaBpL9fXXX+PhiGmF161bh9zcXGzatAmZmZm44447UFdXh02bNmHhwoW6BEvaKVYrRDVZf1GR6u8ip1c3jGw7cPMBs/G++6K+L/Sk2e0Wt+4oKs+elW/7GSlWWwjsU3a7/rFEce7kSVi/+gqOwBtGbyuVL0eE9TWAsCF8vt69oej1AO6OkvwLABm0uu9Uti/8jJbM/X0iStD2rKmAqq6uRp8+fYKvfT4fPvnkE9x+++3IDLmiMHr0aLz88svxj1JHNpsN9k6ceFiad3SHwyH0HrCUG28ENmwIvrZE3GeU6XDE/4qGmssvh5KVBUtdXatfpd90E9KNiiOC/5lnkBLlcn+2AfE0njoFe+j9RlZr9DbTnLP09HTYdIrLnp6OtIhlp6amBvMQeQUqzWYzJEdAy/4UKlvgBDaheRElJcbJhKWoCJ4FC2C/5x7YBRQN2dnZsDiC5RMcWVmAgTlLjXIVFWjahwKM3oZWlZhC6RFTisXSruUakRdLO0cdiNzfhK07YqbCwD5kycgIvmdEbCkh+4ytuQ0H+h/R/aAa2eIKxNPefbG9rM3nrY6srKjrN0TEutVkd+li+Bd8Rhy7NRVQPXv2RHl5efD1/v374XQ6ceONN4Z9LiUlBSkmqzTdbjfcnfjG3Wq1wmazob6+Hj6RQ5+mTEHm0aPI/P3vATR9oxV62tXQ0ABvba1h4VykMvNSrc0GGBhHqOyFC2GPUkDV6hRPaHdRm5oa9hpWa9Q2Y6mvhx2Ay+VCY7zjqquDHUCj0wlXxLKzs7ODeYjs5jweD+oM2maB/SmUXttHi9C8iJLn86Gtssjn86Hm9ttbnYTpKaxt19bC6nQisNXqnU74DcxZlkp+XC4XAod3Q7Zh8/4FAD6/P+b4+HjHZAfgVxTU1ta22oeNiiGa1IYG2GJ/LEjvmNrKjah9PdDvB9Q1NECprQ3LnRGx2Z3O4EPVPY2NSAXgB2A1aP1hsWj8nCF5acdnA/uf4vfrGlt6c/9WH7F/GbmdUkL6vLbU1tUZPrKls8duLRdWNFU7Y8aMwdKlS3Hy5Em43W689NJLsFqtmDBhQtjnvvjiCxQWFnYsWuociyW8wo8oZL2DBhkcENWuXKn+Sw2TSMRdc2GSqDd0JqqYs5jJMNxF5P0/Mvz9kWSMyQTqk3X4f4x7oM6/954hYXhuuKHlBYdedk4y5E9rP2eyCytaafqrfv3rX+PcuXMoLCyEw+HAli1bMH/+fBRF3Ouydu1ajBkzRo84qb0ir4aFXJonCah1KHp2NDYbzh07Bs/Yse37dzwZFCvGgViK53aJLKBkPDhLElPNhx+KDqFdJ5KGDTOXTWR7EXQvrL+wEN7Ag91NNMublDiNeQtJ+sN40zSEr0+fPvjmm2+wceNGnD9/HiNHjsTYiJOwyspKTJ06FT/lFKpS4NPf5aFEK15FXIECoHTpouvySQAZDqChB0hegZImJt+QIaJDaF8BJUneDBf5d8d6rafA9mIBZQ5m2D5miLEDND/RtEuXLpgxY4bq7/Pz8zF58mR8/PHHqg/dJQPx6e/S8IVMwBKk1qEkaEdDnRBxAurPzUXK+fMtb8jQZtr7DCS91i0LUTHJmItkGMrUWZJcgQpbpwm2G29NgNh9XrKHvBstrke6ffv2YerUqfFcJLVHaGNmAaVJ41136b+S5oNh9Z49Le+ZqYCSMaZkErkvRx60ZNg+ISeAhp92yfD3A+GPjZAkJimY4ERcOLUCSgQTbS9F0KMbiIA4F1AkERZQMXn79zfmIanNB0PfgAH6r4sSDyeRaJuM4+tljEkUE52Qi9Jq6CKH8GnD/cwcV6ASFFtfohLd6Zlhx0pLM6YDjrYOM12BIrFkvOIUibPwhX8bLklMUpD1WCDTaBm19iIydyyggjp0bx4nkUh4LKASlL9HD7EByHrQDGVUxxOyHiXwgGMzdXpmijURRdmX3KNHCwhEnSKwgFJycw1dn6rQh+dyn2kh67Hgj38UHUGLyEJA5Jcmsm6vaGTez/TOo0m2U/3/+3+iQ9ANC6gEooQewEU+1NcsjLr8356TS5N0imSgKG2i5i9/aXkhw0mEwEkkXI89Zuj6NOHQohbs02KLbC8ih+BHDOFL2pkRNVLND69AAQCc8+aJDkE3mmbhy87OhkXDRvJ6vZ0OiDrO+cQTsOfkIPWZZ2CR5R6om28GPvhAdBTRieh4zDAsguRihnugRE5jLtmN5Epqaswc1K5aZVA0EpC1gJJhvwkI2X9qf/97KF27Nr3gED45WCzq2yI9HXA6jY0HLQ9YZ4ErjqYCav78+ZoKKBIsIwO+Bx9E6jPPiJ9EItDZFBS0vCVZG9I9nkAOQk8uYx2UJMsRADljSiYqB+7KsjLk9+0rdPsEhxJyEokWihI1B/68PKRUVwMAGidNMjoqYZS8PNEhyC+kvTTef3+bv9edJNOYV1ZUIL9bt7Y/JEFx7isqQuq33wIAGp58suUXBsbWOHEi7O+8Y9j6qImmAmrRokU6h0FxE3hAqyxD+FavBtavBwDUyLaDiywMVNat2GwAAP/FFxsZDclM7UDscAAA3DffbGAwLZSUlJahhCILqJCHUp//n/9B7u23G7v+SIoStajzXXIJfIWFSPvqKwFBiePjs3piU/kSQMgRKvLLV36B1nYOQvvn0M8ZWEDV/uEPLKAE0PwgXTKJwMmELFegcnKCb/kuu0xQMCr0PjAElt+ejjQzE+e++QZKrG/eKGlY2mg/lRUVBkbShtATQIEP0vVecYWx647gmjwZ3muugT1K35J24AC8Q4YIiIqkF+tYxOHmYsmcA4GxtXVsSgaSjX2gTmsuoLxDhwoOpDXpdjW9T/SiHYA0HJSkK55kPngkgcBVSamJvAKlFocAdStWwPXznwuPgzTgNorOTM+BIm4fgVhAJZrmAsp9663w9eolOBjJGdTxhN5rFZzcg50eaXThb3+D+9ZbRYfRNpGTSISSZL+S7X5PMikR3/CLHr2SKJL86kwyYAGVaJoLKIvfL/bm6midh2wnFTzRIxPw9esH97hxLW/I2HZCn3UmMAxR9yC0GQe16fy2baJDkJ/IIXwSa7znHmNWJOP+zCuEwvEeqEQTKJr8fkDktPIm6HzrlyzRdfnusWMBAMpFF+m6Hr1c2LgRXe69V+i36b7u3YWtWyqh+5OE+1ZYRDygMwft4B050tD1nd+xA9bqamSbYRsJ2NcD97VYTHCC7nrkEdEhqDPqQbrN2+fC+vVQunTRd51qMSQpFlCJJjCJhKLAIstMfAGSdcT+Hj10XX7gXqbQBxz7u3ZFSlUVEJj+WWKeMWNEh4ALf/2r6BBIC5GTSISSpY8R/XBNDWr++EfRIQjhHT4cSsjMjaZgZLvmLHytyTwLX/M6PaGjFMgQHMKXaJpPXjyjRom9AmUCIk5llPT0ph+KigSs3XyU5qm6KYSMJzScRCKcbM+misJ9112iQyAJeYqLm36Q5HlQpsW8JTz5e3lqt8qKCnivvlrss6DMcA8UacPtJhcZD8yyTCIhC1E5MHvujSw8zZArAfu6c8kSwOmUs5+h1kS24yRvIyygEhi/vY9BQMejZGcbvk4zU/LyRIcgB9kPVLwCFU6WOEymcfJk0SFIydD7UFNSgPR0+fscI8m8P8scW4JjAZXALrz3nlw34cu2owuIp2bDBtRu3Wr4ek3LbPcqGEG2/QgIj4n3QKnngCelqtxjxgBmeOaZAEIeWMohfO0X+sgS5g3eAQNEh6ArFlAJzF9YCO+oUWJWbobOw6iTrZD1+Hv1gvfGG41Zb7zIclJK8pJoCJ/r/vuFrh+A8ByQ+fjuuAPVH38c9p7n+utR/6tfwTtkiPEBmeEYLoMoefIOHoyG2bONWa/EfU3Nm2+KDkFXLKASncQ7l3DMjSZ6z1ZIGoQcpP2XXiowEBUSDeFrmDdP3PpD4pBNzdq1okNom9E5k2wbeTdtgm/gwPA3U1PhnD1bzFVdXoFq0c62cn73brgef1ynYCJIfA+U/7LLDApEDE5jTvowwyQSssUjoerdu+G7/HLRYVDAl1+izuhnfWjRfILXMHcuh/AB8sQRwi/TcO5oTDBzISU37+DBSD10KPjac/XVsNTUCIwIUvY1yYI9VoIT+RBU6QkYwmc2vsGDgZDnWJFg/foZ/7BEDQJflwif9MNikWN/YzHQfjJsN2rBK0+t1H70Uav3atauRc0f/mB8MBJuH9dTT7X8/OCDAiMxBnv5RCfTQUmmWAD54iGKRdY2K2tcopjgQbrSEVR0usePF7Je6XEIX4s2+jf/JZfAPXGigcFEkKjv9Ywd2/JCorj0wiF8pAtTzECTBDs4JQjZbxgOnPyK3u9luQIlQwyRRG+bGESNlvD17InGO+4A/H4h65ee5O3GCEpKCqK2TpG5aV630K3T1t8vYx8YZyygEp1MjTg1SZubTNuAzE3WtiRhXEK/xOEQvvYT2IZqS0qErVtaLJyCLmzfDmtpaetfyJAjmfperzf4YzLcPsJePsG5pk6Ft18/0WEAaPoWRypJsIMT6UUZNKjlhSxXoAA59mvBMVR98QWqDhwQGkOoqoMH4b3yyrY/JNvxgaiZ7/LL4b7tNtFhSM8SUkCJ7gONkKSXBJKH9/rrcf6zz0SH0YQPRSXqGMmG8FX9/e9w9OrV8oYkcUkjSj4aJ0yA9dgxQ1bv79MHAKDW4xr9ZZa/R4/YbYRtSCpKdnbzDxJ8KWIWItqwRNOYK6ETTiXB/syvfMg4shVQPDCQ2UhyUPIXFgKhM+5JEhcAOWKJUqD4Rc+eGJqX9HRxcciGx4GoXPPn48Lmzea4nzmZydDfNfONHNnyQqK49MICiozDAoooMck0hE8G0U4eJDqhUEQUULwCZS7p6fDcdJPoKAAADfPniw6hxapVLT+zvwuXZMNwTTGEb9u2bdiyZQtqamqQlpaGkSNH4rHHHkNmZiYAoK6uDitWrMCBAweQkZGBiRMnYsKECYKjplZk27k46xJRfMh08itDLNFiMOJkS+PfrthsOgfSAaK2mwzthaJqvO022LdtQ8OCBcj87W9Fh9NElvYi2bBuIGJosERx6cUUBdSwYcMwevRoZGdno6GhAStXrsQbb7yBJ554AgCwevVqeDwelJSU4OzZs3juuefQp08fjAy9nEjiybZDGXVFTLa/myjemtu4e9w4wYFIQvZ9XsYhfLJ9wUZNBF5lqX3jDSArC2hoEBaDtGQooCLbRmgssveBcWCKHqtHjx7IDtzQCMBiseDUqVMAAJfLhT179mDq1KnIzMxE3759MX78eOzcuVNUuGQSSm6u6BCI2kfig1JlRQV8sWZaM4IMOVIrBiQZ8iNkCF8sMmw3ak304wBkG/ofwnPddaJDkGu/4RUoOe3fvx+//e1v0dDQALvdjgULFgAATp48CUVRUFRUFPzsJZdcgs9kmXmOiKiTGh96CFanExnJ+iw1s5Hp5GHlSuDJJ8Peqv/Vr4yPI0ZOGidONCgQos6r+uIL+Hv2DH9Tpv1eBBZQcrr66quxfv16VFRUYNu2bejRoweApitQgXuhAhwOB5xOZ9TlVFZWorKyMvg6JSUF3bp163Bc1uZvR6ySfEtisVikiSWS6LgsETu0UfH4f/SjsHXJ1maA6O1GRHwyxBBKmv2pSxd4nn4aGbLEA3lyE7lvWZpfh+7vhscZ5QpUSsh7esbTqq8ZOLDV+v0332x8TlROqKxWK6qrqpp+NiiU0L89RZJ2LNv+FIylebuJiq2tvBgdk9VqDeYjJTsbloh7CS0pKYbFFNiframpUARtG2tEP2cN+YLPyFxEY8T+JLyAevHFF/Hpp5+q/v7dd98Ne92tWzeMGDEC//Ef/4Fly5YhPT29VbHU0NCAjIyMqMvbvHkz1qxZE3w9ffp0zJo1qxN/QZOcnJxOLyNebDLeIAwgL3TaYwkYEk9FBTLy8pARZUeWqc0ArduNDNtLhhhk259kajey5SYvLw+orweAsC/WDG9HZ860estutweHI+kVjzUlRXXZoe0mr2tXIPSZLUZQOZkRvY/bbTbYJehnALn2p2B7aS4YRG4ntbyIjCk3Nzf8UQ4AMtLTkWFUTM3nuLl5ea3iMEzEsSgn5LaI9MxMpAver/Ten4QXUIGheO3h8/lw+vRpAEDv3r0BAOXl5SgsLAQAlJaWBn+ONGnSJBQXFwdfp6SkoLq6ut0xBFitVuTk5KCmpgY+n6/Dy4kXh8OB+uYTCJGi7TadyXM8OBwOhO5OhsRjtQI1NRFvydVmgPB2E9h2IrZXIDcBMrQZGfYnQL52I0turFYrAi2muroalvPnkYumL9IyQ943ksPjQeShu7GxEVafD6k6xZMHwOf3oyZk2aG5qampCcuT0QVUdvPfHkl0P9PY2IgGwf0MINn+FNLP5Pj9sEJcXxzIS+C4VLdmDbIefRQQEFPo/nT+/PmwWefyADgbG+EyKKZ0pxMZgTgMWWNr1pA+BQBq6uqCr12NjXAK3K86uz9pKc6FF1Ba7Ny5EyNHjkTXrl1x+vRprFu3DkOHDgUApKenY/To0Vi7di3mzZuHiooK7NixA3Pnzo26rPz8fOTn5wdfV1ZWxuVkxOfzSXFSoyiKFHFEIzouJeJmWNHxyNJmgOjtRobYRMcg4/4kS7uRNTcpzft56P5ueJxRhqv5m09E9Y5Hbdn+kMc2+Hw+w2e9UzvJE92G/BLEAMi3P0X2M6Jii8yL62c/CxZQIvPl83qhRB4z8/IMi8nfvB6f398qDsNE/v0hfa7o9mzE+k1RQB09ehTr1q1DQ0MDsrOzcdVVV2HatGnB38+cOROvvvoqpk+fjoyMDEyaNIlTmBMRkRCKgFn46p5/Hl6ts4IlwQ3emkkyM6K0JMxP1VdfiVu5yr5zfscOeIcMMS4OyacxV+0DE4gpCqgnn3wST0bMIhQqKyurQ0MBiYgowchaHOgcl+vxx6P/YuRIeIcPh7d/f13XH5Os24VMx9+rl+gQWvEOHy5mxTLtVxYLfN27w3rmjFxx6STxS0QiajfFbhcdAlHnKAoaZs+G64EHREciVk4Oat9/v+mBpNRaEpzodYqEV6BIDr7+/dF4++1h71UfOtT0QxLsV6a4AkVEBuNBk0wqtOU2iHjeEaB+8sD9isyGbTZcYN+WJS8iCxW7HbVvvAF7Jx4FZGa8AkVERBRPKo/RkIaIky5Zv5GW5USYqANkbb3uH/9YdAi64xUoIiJKHDKcqEv0rC4iiqOf/Qzud9+FIsmzw2TgLyhAytmzwdeVFRUCozEOr0ARERHFk8OBc0eOiI6CtJCh4JYZr9CFu+gi1P/pT6oPhjacDO1XhhgE4BUoImqNB00yK0kO5krXrqJDIC3Y17VNkvzUrF6N1MOHRYchHxn6OxliEIAFFBERmZ4/5AHpJKEkPcmi+HDffTfcooOQEfcrYTiEj4hak+RbR6J2k/mEgvuVfGRuL0RqJOpLPLfcIjoEIVhAEVEr3quvFh0CUftIdEIhK1/v3k0/yHL/hgzYbtrG/MhNgi8AGpYtAxobRYdhOA7hI0MoaWmiQyCNzh05AkXwNMz1L78MH2c5IoovyQonDrs0ARZQcpOggEJKCmCzAfX1oiMxFAsoIgojw83v7unT4fP5RIdBZhI4kZDhhEJWInMTsW7P9dfjwvr1goIhIuocDuEjIiJKBhIVl4rdLv8Dh4lkFbgyKNE+nWxYQBERkfmZ4YRC9HAoia5AkUmIbrPUNu5XwrCAIiIiSgJKCg/5RAmFBZQw7E3JGPwWi4iMwBMKdcxNa8wJEXUAJ5EgIqLEI+OXNoJjqlu5Emkffihm5c2FSmVFBfK7dRMTQzQythOZMD9SsphhyHKCYwFFxmAnTERG4AmFKu/w4fAOHy46DDIRC4/dcmN/JwyH8BEREREREWnEAoqIiBIHv5ElihvX1KnwZ2eLDoMicQifcCygyBgcBkBESc5zyy2iQyBql4YFC1B1/LjoMEgNCyhhWEAREZH5yf6NrMWC+sWLUXnqlOhIiIiok1hAERERGcFiAVI5dxMRkdmxJyfduR5/HJ5hw0SHEcbXp4/oEIgongJXnmS9AkVEFC+8LUI4FlCkO+eSJfD5fKLDCHP+o49Eh0BEySTZT3giC9tkzwcRmRqH8FFSUjirEFFi4Qk5EREZhAUUERElDlmH8Mkal0EUmy38jSTPB1Gn8Asj4TiEj4iIiHRVt3Ilsg8ebHmDJ4BEZGIsoIiIyPw4iYTU/D17wj9gAFBbKzoUIqJO4xA+IiIyP8mvaCi5uaJDIKIE4b71VtEhJD1egSIiooQhYxl1/r334JXsUQ7C8UohUYd5r7oKjS4Xr+gKxAKKiIgSj0RXpLzXXis6BPlItH2IiNqLQ/iIiChx8MoGERHpjAUUERERERGRRiygiIgocfAKFBER6Szp74Gy2Wyw2+0d/veW5oO1w+GAIsGY7tTUVGRnZ4sOI4xMuQkQmSPZ2gwgT7uRLTey5AVgbtQE8mKxWJrisVoBAHa7HamC4ovMjag8ydxmRLYfS0iRbUtLQ4oE7Vi2/UnGNiMacxOdbHkBjMlN0hdQbrcbbre7w525pAUAABXNSURBVP/earXCZrOhvr4ePp8vjpF1THZ2NmolmJUltCSVKTfW5p9F5ki2NgPI025ky40seQGYGzVWqxU2AIqiNMXjdMIOoLGxEU5B8QVyE+gHReVJ1jZjB+D1eoXnBQDcHg/qJWjHUu1PErYZGTA30cmWF6DzudFyYYVD+IiIiIiIiDRiAUW68A4cKDoEIkomgaEjvAeKiIh0lvRD+Egf57dtQ6qiIFd0IERERFG4ZsyAc+ZM0WEQkQmxgCJ9OBxQrNbYnyMiigdeeTIXCbaXc+lS+CW5Z4OIzIUFFBERJQ4JTsypbbUrVsBz442iwyAi6jAWUEREZH6STJ9LsTVOniw6BCKiTuEkEkRElDh4BYqIiHTGAoqIiIiIiEgjFlBERGR+gStPvAJFREQ6YwFFRETmF3kPFO+JIiIinbCAIiKixMErUEREpDMWUERERERERBpxGnMiIkocEl2Bcj34IFIqKkSHQUREccYCioiIzM1igfvee0VH0UrdsmWiQyAiIh2wgCIiInPz++GsrgZ8PqmuQBERUWLiPVBEREREREQasYAiIiIiIiLSiAUUERElDg7hIyIinbGAIiIiIiIi0ogFFBERJQ5egSIiIp2xgCIiooTjuf560SEQEVGC4jTmRESUUCr58FoiItIRr0ARERERERFpxAKKiIiIiIhIIxZQREREREREGrGAIiIiIiIi0ogFFBERERERkUYsoIiIiIiIiDRiAUVERERERKQRCygiIiIiIiKNWEARERERERFpxAKKiIiIiIhIo1TRAbTXs88+i4MHD2LTpk2w2WwAgLq6OqxYsQIHDhxARkYGJk6ciAkTJgiOlIiIiIiIEo2pCqj3338fPp+v1furV6+Gx+NBSUkJzp49i+eeew59+vTByJEjBURJRERERESJyjRD+GpqarBhwwY8/PDDYe+7XC7s2bMHU6dORWZmJvr27Yvx48dj586dgiIlIiIiIqJEZZoCqqSkBHfddRdyc3PD3j958iQURUFRUVHwvUsuuQTl5eVGh0hERERERAnOFEP4Dh06hBMnTmD27NmoqKgI+53L5UJmZmbYew6HA06nM+qyKisrUVlZGXydkpKCbt26dTg2q9Ua9n/RLBaLNLHImJsAkTHJlhdAnnYjW25kyQvA3KiRLS8Ac6OGeVHH3EQnS14A5kaNbHkBjMmN8ALqxRdfxKeffqr6+y1btmDVqlWYM2cOUlJaXzBLT09vVSw1NDQgIyMj6vI2b96MNWvWBF9Pnz4ds2bN6mD0LXJycjq9jHgJTK4hC5lyE5CXlyc6BOnyIlO7kSk3MuUFYG7UyJQXgLlRw7yoY26ikykvAHOjRqa8APrnRngBtWDBgjZ/f+bMGfzwww944YUXAAB+vx8AMGPGDMydOxdXXnklAKC8vByFhYUAgNLS0uDPkSZNmoTi4uLg65SUFFRXV3c4fqvVipycHNTU1ESd4MJoDocD9fX1osMAIGduArtTZ7Z5Z8mWF0CediNbbmTJC8DcqJEtLwBzo4Z5UcfcRCdLXgDmRo1seQE6nxstX7ILL6Biyc/PR0lJSfB1ZWUl5s+fj5deegl5eXlIS0vD6NGjsXbtWsybNw8VFRXYsWMH5s6dq7q8/Pz8sOXFY4P7fD4pGo6iKFLEEUqm3ATIEI8seQHkazey5Ea2vADMjRpZ8gIwN2qYF3XMTXSy5QVgbtTIkhfAmNxIX0BZrdawStDtdgMAcnNzkZaWBgCYOXMmXn31VUyfPh0ZGRmYNGkSpzAnIiIiIqK4k76AitS9e3e8++67Ye9lZWXFHApIRERERETUWaaZxpyIiIiIiEg0FlBEREREREQasYAiIiIiIiLSiAUUERERERGRRiygiIiIiIiINGIBRUREREREpBELKCIiIiIiIo1YQBEREREREWnEAoqIiIiIiEgjFlBEREREREQasYCipOIZNQpKWproMIiIiIjIpFJFB0BkpAvvvAP4/aLDICIiIiKTYgFFycVqbfqPiIiIiKgDOISPiIiIiIhIIxZQREREREREGrGAIiIiIiIi0ogFFBERERERkUYsoIiIiIiIiDRiAUVERERERKQRCygiIiIiIiKNWEARERERERFpxAKKiIiIiIhIIxZQREREREREGrGAIiIiIiIi0ogFFBERERERkUYsoIiIiIiIiDRiAUVERERERKQRCygiIiIiIiKNLIqiKKKDMLPKykps3rwZkyZNQn5+vuhwpMLcRMe8qGNu1DE30TEv6pib6JgXdcyNOuYmumTNC69AdVJlZSXWrFmDyspK0aFIh7mJjnlRx9yoY26iY17UMTfRMS/qmBt1zE10yZoXFlBEREREREQasYAiIiIiIiLSyLpo0aJFooMwu4yMDFx11VXIzMwUHYp0mJvomBd1zI065iY65kUdcxMd86KOuVHH3ESXjHnhJBJEREREREQacQgfERERERGRRiygiIiIiIiINEoVHYDePB4PVq1aha+++gq1tbXIz8/H5MmTUVxcDAA4ceIEli9fjrKyMnTv3h2PPfYYhg4dGvz3e/bswRtvvIGqqioMHDgQc+bMQUFBAQBgy5Yt2L17N86ePQuHw4GxY8figQcegNVqVY2nreX94x//wNtvv41//vOfsNlsePPNN3XMjL65CfXss8/i4MGD2LRpE2w2m2o8yZSb0tJSvPbaazh69ChsNhvGjx+PadOmqcYjS270zIvH48GaNWuwd+9eeDweXHbZZZg5cyYuvvhi1XgOHTqEVatW4fTp0ygsLMTs2bNxySWXBGN5/fXXcezYMdTW1sZsfyJzU1VVhZUrV+LYsWPBn/v06RO2/HXr1mHbtm3wer0YPXo0Hn/8caSlpanGI0ubAfTNjZn7Yb3bTECy9cFacpOMfXCsvCRzH7x//35s3rwZJ06cQGpqKoYMGYIZM2YgLy8vuPxk7YNj5cbMfXCnKAnO6XQq69atU06dOqX4/X7l66+/VqZMmaIcPnxY8Xg8yiOPPKK8/fbbitvtVj766CNlypQpSnV1taIoilJeXq7ce++9yoEDBxSXy6WsWbNGmT9/fnDZmzZtUr777jvF4/EoZ86cUWbPnq1s2rRJNZZYyzty5IjywQcfKNu3b1emTp2qX1Ka6ZmbgF27dilPP/20cueddyqNjY2qsSRTbmpqapSHHnpI2bVrl9LY2Ki4XC7l+PHjqrHIlBs98/L2228rs2bNUs6dO6d4PB7l9ddfV+bMmaMay4ULF5T77rtPef/99xW326288847yi9+8QvF7XYriqIo33//vbJ9+3Zl3759Mduf6NxUVVUpf/3rX5UjR44od955p/L999+HLXv79u3KjBkzlFOnTikXLlxQ/vVf/1UpKSlRjUWmNqMo+ubGzP2wnnkJSMY+OFZukrUPjpWXZO6DP/zwQ2Xfvn2K0+lUGhoalJdeeklZuHBhcNnJ3AfHyo2Z++DOSPghfOnp6XjwwQfRo0cPWCwWXHHFFRg0aBAOHz6MgwcPorGxEffccw/S0tJw4403orCwEHv27AEAfPjhhxgxYgSGDx8Ou92OBx54AKWlpSgvLwcATJo0Cf3790dqaioKCgpQXFyMb775RjWWWMsbMGAAxo4di549e+qfGOibGwCoqanBhg0b8PDDD8eMJZlys3XrVgwbNgzjxo2DzWaD3W4PfmMXjUy50TMvZ8+exYgRI9C1a1ekpqZi3LhxKC8vh6Iyz81nn32Gnj174uabb0ZaWhomTJgARVHw5ZdfAgD69OmD8ePHo7CwUPe8dDY3eXl5uP322zFgwICoy961axcmTJiAHj16ICcnB/fddx/ef/991VhkajOAvrkxcz+sZ16A5O2DY+UmWfvgWHlJ5j64uLgYV199NdLT05GRkYE777wThw8fDi47mfvgWLkxcx/cGQlfQEVyuVw4duwYioqKUF5ejr59+yIlpSUNl156KU6cOAGg6ZJmaKeamZmJHj16BH8f6euvv26zo2jv8owW79yUlJTgrrvuQm5ubsx1J1Nuvv32W+Tk5ODpp5/Ggw8+iOeeew7ff/+96rplzk0883Lrrbfim2++QUVFBTweD3bu3Inhw4fDYrFEXXd5eXnY8iwWC/r27RtWxIvUntzEUl5ejksvvTTs3164cAHV1dVRPy9zmwHim5tIZu6H452XZO2DY0nWPjgW9sEtDh06FNaPsA9uEZmbSGbug9sjqQoov9+PZcuWoX///hg+fDicTiccDkfYZxwOB5xOJ4CmBtbW70O99957KCsrw8SJE1XX357lGS3euTl06BBOnDiBn/zkJ5rWn0y5qaysxK5du/Dwww/jT3/6EwYNGoTf/OY38Pl8Udcva27inZfevXujoKAAjzzyCO69917s3bsXjz32mOr6Y61PpPbmJpbI3AV+Vvv3srYZIP65CWXmfjjeeUnmPjiWZO2DY2Ef3OS7775rdeWWfXCTaLkJZeY+uL0SfhKJAEVRsHLlSlRVVWHx4sWwWCzIyMhAfX192Ofq6+uRkZEBoOmSZ0NDQ9jvGxoagr8P2L17NzZu3Ijf/OY3yMnJAdB0iXLlypUAgG7dumHFihWal2e0eOfG6/Vi1apVmDNnTtg3GgHJnBsAsNvtuPbaazFw4EAAwJQpU7B582acPHkSx48fN0Vu9MjLqlWr4HQ6sXbtWjgcDuzatQv/9m//hhUrVuDYsWNYvHhx8N9t2LABGRkZrZYXuj5ROpKbWNLT08P+feDvzsjISPj9SSsz98Pxzkuy98GxJGsfHAv74KbJRZ5//nnMmjULV1xxRfB99sHquQkwcx/cEUlRQCmKglWrVgU3fmAjFRYWYvPmzfD7/cGDTGlpKW666SYAQFFREY4fPx5cjtPpxOnTp1FUVBR878MPP0RJSQmef/75sNlsxowZgzFjxoTFoWV5RtMjN+fOncMPP/yAF154AUDTtx0AMGPGDMydOzepcwMAffv2VV2nGXKjV17Kyspw//33o0uXLgCA2267Da+//jrKy8tx5ZVXYsOGDWFxFBYWYvv27WFxlZWVaf7GXQ8dzU0shYWFKC0tDR60jh8/ji5duiAvL88UbQbQLzeAufthPfKS7H1wLMnaB8eS7H1wWVkZFi5ciEceeQQ33HBD2HKTvQ9uKzeAufvgjkqKIXyrV6/GkSNHsHjxYmRmZgbf/9GPfgSbzYYtW7bA4/Hgk08+wYkTJzB69GgATRv+wIED+PLLL+F2u/HWW2+hb9++wbGd//u//4vXXnsNCxcu1LThYy3P7/fD7XbD6/UCANxuNzweT7zTEUaP3OTn56OkpASvvPIKXnnlFSxcuBAA8NJLL2HIkCFR40iW3ABN48w///xzHD16FD6fDxs3bkRBQQF69+4dNQ7ZcqNXXgYMGIDdu3ejtrYWPp8PO3bsAADVG0lHjRqFU6dOYffu3fB4PHj33XcBAMOGDQPQdLAIzYXH44Hb7dYnKc06mhugabsF4gvEGrh5e9y4cdi6dStOnz6N2tparF+/HuPGjVONQ7Y2A+iXG7P3w3rkJdn74EB8am0mWfvgQGxqeUnmPri8vBy/+tWvMG3atFYn/EBy98GxcmP2PrijLIra9CoJ4uzZs5gxYwbS0tLC5qS/5557MHnyZJSVleHVV19FWVkZCgoKMHPmzLDn1nzyySd44403UF1djcsvvxxz584NzlU/Y8YMnDt3Luw5AFdccQUWLVqkGk9byzt48CCeffbZsM8XFBTgtddei0cqWtEzN6HOnDmDRx99NOYzIJIpNzt27MD69evR0NCAfv364fHHH1d9hkus5RmZGz3zUltbi9deew0HDhyAx+NBr169MHXqVAwfPlw1noMHD2L16tXBZ5DMmjUreKNvoN1FChzk462zubnrrrtaLXPNmjXo3r07FEXBn//8Z/ztb3+Dz+fD9ddfjyeeeKLNZ5DI0mYAfXNj5n5Yz7yESsY+OFZukrUPbisvydwHv/LKK/jggw9gt9vDlhm46pbMfXCs3Ji5D+6MhC+giIiIiIiI4iUphvARERERERHFAwsoIiIiIiIijVhAERERERERacQCioiIiIiISCMWUERERERERBqxgCIiIiIiItKIBRQREREREZFGLKCIiIiIiIg0YgFFRERERESkEQsoIiKSztixYzFo0CC43e5Wv7v33ntx8cUXo66uTkBkRESU7FhAERGRdFatWoXS0lIsXbo07P1t27Zh06ZNWL58ObKysgRFR0REycyiKIoiOggiIqJIixcvxosvvoivv/4al156KVwuFwYPHozBgwfjL3/5i27rdTqdyMjI0G35RERkbrwCRUREUnrmmWdQVFSEp556CgCwZMkSnDlzBq+++ip++OEHPPTQQ8jPz0dGRgZuuukm/P3vfw/792+++SZuuOEGdO3aFXl5eRgzZgz27dsX9plFixYhKysL+/btw6hRo5Ceno4VK1YY9jcSEZH5pIoOgIiIKBqbzYbVq1djzJgxeOGFF7B06VK8+OKLcDgcGD58OLKysrB8+XJ06dIFy5cvx80334yjR4+ioKAAAFBWVoZp06bhsssug9vtxn/913/hpptuwj/+8Q8MGDAguB63240HHngA8+bNw5IlS3DRRReJ+pOJiMgEOISPiIik9otf/AIlJSUYMWIE9u3bh1//+td45ZVX8N133wWLpcbGRgwYMABTpkxpdd8UAPj9fvj9fgwePBh33303lixZAqDpCtTixYuxfv16TJkyxdC/i4iIzIlD+IiISGoLFiwAAMyfPx9WqxU7duzA2LFj0bVrV3i9Xni9XlitVhQXF2P//v3Bf3f48GFMnDgR3bt3h9VqRVpaGo4cOYLvvvuu1Tp++tOfGvb3EBGRuXEIHxERSc1ms4X9v7KyEnv37kVaWlqrz1522WUAgNraWowfPx7dunXDyy+/jKKiIqSnp2PGjBlwuVxh/yYzM5Mz+hERkWYsoIiIyFS6du2K2267Dc8//3yr39ntdgDAZ599hh9++AHvvfcehg4dGvz9hQsX0KdPn7B/Y7FY9A2YiIgSCgsoIiIylVtuuQXr1q3DoEGD4HA4on7G6XQCaLlqBQCffvopysrKcOWVVxoSJxERJSYWUEREZCq//OUv8ec//xnFxcWYO3cuCgsLUVFRgc8//xy9evXCvHnzcN111yErKwtPPfUUFixYgJMnT2LhwoXo3bu36PCJiMjkOIkEERGZykUXXYS9e/di2LBhePrppzF+/HjMmzcPZWVluPbaawEA3bt3x8aNG3H27FlMmDABy5Ytw+rVq9GvXz/B0RMRkdlxGnMiIiIiIiKNeAWKiIiIiIhIIxZQREREREREGrGAIiIiIiIi0ogFFBERERERkUYsoIiIiIiIiDRiAUVERERERKQRCygiIiIiIiKNWEARERERERFpxAKKiIiIiIhIIxZQREREREREGrGAIiIiIiIi0uj/AxyFZfGLIWKQAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<ggplot: (8760104934749)>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert date colum to index\n",
        "df_lst_my_point = lst_df_my_point.set_index('datetime') \n",
        "\n",
        "# aggregate daily to annual mean values\n",
        "df_annual_LST=df_lst_my_point.resample('Y').mean()\n",
        "\n",
        "# visualize first five rows to check output dataframe\n",
        "df_annual_LST.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "x86E3Ic7jeO1",
        "outputId": "9df418b2-8c8c-46ef-864c-ad5fdaf84927"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0           LST_Night_1km\n",
              "datetime                 \n",
              "2002-12-31     -12.718906\n",
              "2003-12-31     -13.727614\n",
              "2004-12-31     -13.756071\n",
              "2005-12-31     -14.380824\n",
              "2006-12-31     -12.888936"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1086652b-89d1-426d-9fae-4d604248bbe9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LST_Night_1km</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2002-12-31</th>\n",
              "      <td>-12.718906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-12-31</th>\n",
              "      <td>-13.727614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-12-31</th>\n",
              "      <td>-13.756071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-31</th>\n",
              "      <td>-14.380824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-31</th>\n",
              "      <td>-12.888936</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1086652b-89d1-426d-9fae-4d604248bbe9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-1086652b-89d1-426d-9fae-4d604248bbe9');\n",
              "          const dataTable =\n",
              "            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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define a column name for row  <if you rerun this line you may get error, in that case run from immediate step>\n",
        "df_annual_LST.index.name = 'Year'\n",
        "\n",
        "# reset index as a column\n",
        "df_annual_LST.reset_index(inplace=True)\n",
        "\n",
        "# visualize  first five rows\n",
        "df_annual_LST.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cGWigNemjjSi",
        "outputId": "8f1ae3bc-c39e-49d8-b158-0c2ff650c29e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       Year  LST_Night_1km\n",
              "0 2002-12-31     -12.718906\n",
              "1 2003-12-31     -13.727614\n",
              "2 2004-12-31     -13.756071\n",
              "3 2005-12-31     -14.380824\n",
              "4 2006-12-31     -12.888936"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>LST_Night_1km</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2002-12-31</td>\n",
              "      <td>-12.718906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2003-12-31</td>\n",
              "      <td>-13.727614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2004-12-31</td>\n",
              "      <td>-13.756071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2005-12-31</td>\n",
              "      <td>-14.380824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2006-12-31</td>\n",
              "      <td>-12.888936</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a10ffbe1-289a-4378-84f0-8770371b42fa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a10ffbe1-289a-4378-84f0-8770371b42fa');\n",
              "          const dataTable =\n",
              "            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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_MAAT=(\n",
        "    ggplot(df_annual_LST)  # What data to use\n",
        "    + geom_line(aes(x=\"Year\", y=\"LST_Night_1km\"),colour='red')\n",
        "    + geom_point(aes(x=\"Year\", y=\"LST_Night_1km\"),colour='blue')\n",
        "    +ylab('MAST (degree C)')+xlab('Year')\n",
        "    + scale_x_datetime(breaks=date_breaks('1 years'), labels=date_format('%Y'))\n",
        "    +ggtitle('MAST from '+sensor+' '+my_variable+' at lon= '+str(my_lon)+' lat ='+str(my_lat))\n",
        "    +theme(figure_size=(10, 5)) #x,y)\n",
        ")\n",
        "\n",
        "print(plot_MAAT)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "e3TewwaljqhJ",
        "outputId": "ea29fa4d-994e-4ce9-822b-8301a8b9aa95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<ggplot: (8760086351997)>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load necessary files\n",
        "from google.colab import files\n",
        "\n",
        "# define output filename\n",
        "my_filename=sensor+'_'+my_variable+'_lon_'+str(my_lon)+'_lat_'+str(my_lat)+'_'+i_date+'_'+f_date+'.csv'\n",
        "print('my file name is: ',my_filename)\n",
        "\n",
        "# Convert dataframe to csv\n",
        "lst_df_my_point.to_csv(my_filename, encoding = 'utf-8-sig') \n",
        "\n",
        "# download csv\n",
        "files.download(my_filename)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "w556RajujxVR",
        "outputId": "0678a337-eabb-4ef7-dcf4-7d13d75e3d62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my file name is:  MYD11A1_LST_Night_1km_lon_86_lat_28.5_2003-01-01_2020-12-31.csv\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_cb1696f4-ecaa-4ccf-8b2d-ae652e5fed9d\", \"MYD11A1_LST_Night_1km_lon_86_lat_28.5_2003-01-01_2020-12-31.csv\", 200870)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}