Matplotlib_PPT.ipynb

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      "source": [
        "# <center><font color='#333333'>Use Python to Automate the PowerPoint Update</font></center>\n",
        "### <center><font color='#3b5998'>Created by cyda - Yeung Wong & Carrie Lo</font></center>"
      ]
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      "source": [
        "--------------------------------------------------------------------------------------\n",
        "![logo](https://4.bp.blogspot.com/-LAXjdvVCYCU/WxeQFKQ-1wI/AAAAAAAAACs/o8IJ1eLLAEwQYv2Az7EqQi9jODTqRx7wACK4BGAYYCw/s1000/tight%2Bbanner_with_description.png)"
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      },
      "source": [
        "--------------------------------------------------------------------------------------\n",
        "Please acknowledge <b>team cyda - Yeung Wong & Carrie Lo</b> when using the code\n",
        "\n",
        "<b><font color='#3b5998'>If you find this script is helpful, please feel free to endorse us through Linkedin!</font></b>\n",
        "\n",
        "<b>Linkedin:</b>\n",
        "\n",
        "Yeung Wong - https://www.linkedin.com/in/yeungwong/\n",
        "\n",
        "Carrie Lo - https://www.linkedin.com/in/carrielsc/\n",
        "\n",
        "--------------\n",
        "\n",
        "Use Python to Automate the PowerPoint Update - https://towardsdatascience.com/use-python-to-automate-the-powerpoint-update-4a385acf1243\n",
        "\n",
        "--------------------------------------------------------------------------------------"
      ]
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      "source": [
        "# Step 1 - Data Preprocessing"
      ]
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            "text/plain": [
              "      Symbol     Name  Last Price  Change % Change  52 Week Range  Day Chart  \\\n",
              "0   EURUSD=X  EUR/USD      1.0880 -0.0001   -0.01%            NaN        NaN   \n",
              "1      JPY=X  USD/JPY    135.8550 -0.2100   -0.15%            NaN        NaN   \n",
              "2   GBPUSD=X  GBP/USD      1.2483 -0.0042   -0.34%            NaN        NaN   \n",
              "3   AUDUSD=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "4   NZDUSD=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "5   EURJPY=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "6   GBPJPY=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "7   EURGBP=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "8   EURCAD=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "9   EURSEK=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "10  EURCHF=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "11  EURHUF=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "13     CNY=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "14     HKD=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "15     SGD=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "16     INR=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "17     MXN=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "18     PHP=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "19     IDR=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "20     THB=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "21     MYR=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "22     ZAR=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "23     RUB=X      NaN         NaN     NaN      NaN            NaN        NaN   \n",
              "\n",
              "    pct_change  \n",
              "0        -0.01  \n",
              "1        -0.15  \n",
              "2        -0.34  \n",
              "3          NaN  \n",
              "4          NaN  \n",
              "5          NaN  \n",
              "6          NaN  \n",
              "7          NaN  \n",
              "8          NaN  \n",
              "9          NaN  \n",
              "10         NaN  \n",
              "11         NaN  \n",
              "13         NaN  \n",
              "14         NaN  \n",
              "15         NaN  \n",
              "16         NaN  \n",
              "17         NaN  \n",
              "18         NaN  \n",
              "19         NaN  \n",
              "20         NaN  \n",
              "21         NaN  \n",
              "22         NaN  \n",
              "23         NaN  "
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Symbol</th>\n",
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              "      <th>Last Price</th>\n",
              "      <th>Change</th>\n",
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              "      <th>0</th>\n",
              "      <td>EURUSD=X</td>\n",
              "      <td>EUR/USD</td>\n",
              "      <td>1.0880</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>-0.01%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.01</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>JPY=X</td>\n",
              "      <td>USD/JPY</td>\n",
              "      <td>135.8550</td>\n",
              "      <td>-0.2100</td>\n",
              "      <td>-0.15%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.15</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GBPUSD=X</td>\n",
              "      <td>GBP/USD</td>\n",
              "      <td>1.2483</td>\n",
              "      <td>-0.0042</td>\n",
              "      <td>-0.34%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.34</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AUDUSD=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NZDUSD=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>EURJPY=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>GBPJPY=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>EURGBP=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>EURCAD=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>EURSEK=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>EURCHF=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>EURHUF=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>CNY=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>HKD=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>SGD=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>INR=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>MXN=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>PHP=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>IDR=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>THB=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>MYR=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>ZAR=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>RUB=X</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import requests\n",
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "\n",
        "datetime_now = datetime.now()\n",
        "full_list_url='https://finance.yahoo.com/currencies'\n",
        "full_list_page = requests.get(full_list_url)\n",
        "df = pd.read_html(full_list_page.text)[0].drop_duplicates()\n",
        "df['pct_change'] = df['% Change'].str.slice(stop=-1).astype(float)\n",
        "df"
      ]
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Name  Last Price  Change % Change\n",
              "0  EUR/USD      1.0880 -0.0001   -0.01%\n",
              "1  USD/JPY    135.8550 -0.2100   -0.15%\n",
              "2  GBP/USD      1.2483 -0.0042   -0.34%\n",
              "3      NaN         NaN     NaN      NaN\n",
              "4      NaN         NaN     NaN      NaN"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Last Price</th>\n",
              "      <th>Change</th>\n",
              "      <th>% Change</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>EUR/USD</td>\n",
              "      <td>1.0880</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>-0.01%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>USD/JPY</td>\n",
              "      <td>135.8550</td>\n",
              "      <td>-0.2100</td>\n",
              "      <td>-0.15%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GBP/USD</td>\n",
              "      <td>1.2483</td>\n",
              "      <td>-0.0042</td>\n",
              "      <td>-0.34%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a24bc60-dfeb-4cdd-8e57-1860ba5995b2')\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [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",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "top_df = df.sort_values(['pct_change'], ascending=False).reset_index(drop=True)[:5]\n",
        "top_df = top_df[['Name', 'Last Price', 'Change', '% Change']]\n",
        "top_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "6d14bde1-f903-4084-8c2a-3c4ac15bc7cc",
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        "outputId": "900acdd1-dbe3-4461-94cc-a95a3c9001ca"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Name  Last Price  Change % Change\n",
              "0  GBP/USD      1.2483 -0.0042   -0.34%\n",
              "1  USD/JPY    135.8550 -0.2100   -0.15%\n",
              "2  EUR/USD      1.0880 -0.0001   -0.01%\n",
              "3      NaN         NaN     NaN      NaN\n",
              "4      NaN         NaN     NaN      NaN"
            ],
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              "      <th>Last Price</th>\n",
              "      <th>Change</th>\n",
              "      <th>% Change</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>GBP/USD</td>\n",
              "      <td>1.2483</td>\n",
              "      <td>-0.0042</td>\n",
              "      <td>-0.34%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>USD/JPY</td>\n",
              "      <td>135.8550</td>\n",
              "      <td>-0.2100</td>\n",
              "      <td>-0.15%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>EUR/USD</td>\n",
              "      <td>1.0880</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>-0.01%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-85c18e86-2044-46a3-8e70-4da63eac2eb4 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85c18e86-2044-46a3-8e70-4da63eac2eb4');\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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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "bottom_df = df.sort_values(['pct_change'], ascending=True).reset_index(drop=True)[:5]\n",
        "bottom_df = bottom_df[['Name', 'Last Price', 'Change', '% Change']]\n",
        "bottom_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "3518fc9c-d5fc-408d-b099-be252c70e10e",
      "metadata": {
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          "height": 691
        },
        "id": "3518fc9c-d5fc-408d-b099-be252c70e10e",
        "outputId": "763db585-3f88-4593-9381-ee79c07d8650"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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/aGBCFg809wHbNRJSOMJ54Fmk88BctjBtEg/oECKEkOqD4gEhhCwhTkjiQTCe3iqt3tCdB0g5DxJKaXc0R0JzabuohNQi5+YCaQ6Bg1NjAACvvVDxIOlYMLdqnInReUAIIdUOxQNCCFlCnJyZ0n+eXwLigdi9TDoPkk6LeAl3NKejYbztqR/jzY9/v2SvQUg52DsxjPc88zP8r91P6re9MHYRTw6fAwB4nYUGJlp3WzALbqJTCiGEkOqB4gEhhCwhJiKpnIOl5Dxw2OypzIMSdls4MzudfF2ohuR4QmqNxy6dAQDsmxzRb/vxueP6z69Ytrag5/U5tMyDuFk8YGAiIYRUO+y2QAghSwh5d28+Hq3gSMqDAiEepDIPSuk8sNtSORJTkRB6fI0ley1CSslAQ7P+81QkBBXAS+OXAABfveV1WNbQVNDzypkHqqrCpn1n0pwHFA8IIaTqoPOAEEKWEHJd8VIoW5C7LThspRcP5PZzE5FwliMJqW5kIezEzCQuzM9ABbDM31SwcAAAXmdSPDC7cwLMPCCEkKqH4gEhhCwRFFXFTDTlNlhq4oHLXvqyBfk9nVgCrTBJ/RKRMgmOz0xiMhICAHR4fIt6Xq/U4lHOPZhJa9XIbguEEFJtUDwghJAlwmwsqtv4gaWReZCwch6USTwQiy1CapGI1EpxNBTERLg44oHDZofbnhQQDOJBjGULhBBS7VA8IISQJYJ5Z2+pOQ9E5kEp+8cHDWULFA9I7WIoKYiG9c9zh3dx4gGQyj0ISaGJs7GkK8qliXwsWyCEkOqD4gEhhCwRAjFjDf5Sch44YNO7LcTK5jxg5gGpXWTnQSAW0Z007Yt0HgCp0gXZeSACXJvdHgB0HhBCSDVC8YAQQpYIIs1c1P4viW4LmsvAYbeXpdtCkGULpE6IKsY8gokiZR4AgM+ptWvUxIOYkkBYEytaNPGAzgNCCKk+KB4QQsgSQdQU9/uTSelLwXmgly3ABqeWHl/KHU1ZkLkYnC3Z6xBSasKS82A6GilaYCIAeEyZB3NayYINQJNLOA8YmEgIIdUGxQNCCFkiCOdBv68RQHJxIMIDnxm5gC8e2YVECS39lcAq86C0ZQup3dqLwVl9UURIrRGVxIPZWARjWveQYpQteLSyhaj2XRR5B36nSxf56DwghJDqg+IBIYQsEY4FJgAAKxtb9B7uwor813ufxg/OHcOTw+cqNr5SkIAkHmiZB6UUSMxujpMzUyV7LUJKSUQKTFQBhDSXQIfXv+jnFt0WRCijENmaXG793ETxgBBCqg+KB4QQsgSYioTw3OggAOCu/tVY1dgCADg5MwlVmqSPa7uL9YLsPHCUMPNgcH4GXz2xH2PheQApW/aJmcmivxYh5SAihRkKmlxuNGh5BYvBrXVbEKGMs1q5T6PLrbdUldvKEkIIqQ4oHhBCyBJg5+hFJFQVm1o6sLqpFeub2wEkF7dzUp2+WGDXC0I8cNhsegu4eAmcB/97z1P4+qkDuBScAwBc3tYNADg/Hyj6axFSDuRWjYI+reRpsbi180ya88CZch4kFIoHhBBSbdTXLJEQQoglx2eSJQvb23sAQBcPjs9MGloKhuPpu421TMIi86AUzoPz8zOG37t9SWt32GL3lpBqI6EoeGTwFEZC8/ptcqtGQZ+/WOJBqmwhqiTwg3PHACSdB7p4QOcBIYRUHRQPCCFkCXA8kLTPb2hp1/7fAQA4PDWOIW23HEimqtcTsvNA2KFL2apRIELlShnOSEix+PaZw/iHg8/jw7se128TZQtdUsZBsZwHemBiIoGHLpzU81gamXlACCFVDcUDQgipc+KKgjOz0wBSjoONLe3o8voxF4/io3ue1I8NxMKIKgm8NHapLnbN5cwDl734ZQtHAxO4FJw1LKq6vH7996jF7i0h1cZPL5wAYHTQiMDEW3pW6Lf1Fq1sIeU82DMxrN8eUxJw6C1VKbwRQki1QfGAEELqnLNz04ipChqdbn1R67DZcd+ytWnHzkQj+NapQ/jw7ifwb4dfKvdQi45YgNhhTzkPiiQejIWD+OOdj+B3nv6JvuABgHv6V+uLo5hF3Tgh1caYRVCqEL7uG1ij39blW3ynBSAlHkQSCXRIrR+bXB79u0TnASGEVB8UDwghpM4Rif/rm9tgkxa5b1y1EWub2gzHTkcjePDUQQDAoxdPl2+QJUIxZB6IHc3iLErOz6XCEAeDswCSmRJvW3s53A5jK7qJcEgPhSOkmrBqXaqqqu48aHN78b+vuAVvWX0ZrunsK8pryt8P+XvxW2u2wK5NTSkeEEJI9UHxgBBC6hwhHqzTShYETS4P/v3GVxpum4lF0Ohy67/XunVY5Bs4bDY4NedBsXIIZmLp+RB/tuV6eBxOKU1ewUhoHr/99I/wwRd+WZTXJaSYCOELSIpsqqoaviNuhxO39q7A7228SnfvLBbRyjSaSGBWEw8e2LoDbR4fMw8IIaSKcRbyoKlIGMcC4zgamMCxwASOBSb1SdQr+tfggW07ijK4hKLg9Nw0jk1P4NjMBI5OT+DcfEC/oHzj1vvRu4Dk30A0jB+eO4ZnRwYxEkoGhPX4GnFTzwDesHITWtyejI8dDs7ht5/+cV6vU8z3gBBCFos5LFHGbrPh01ffgf97dDfOz88gEI2g0ZXq435xfhYrGlvKNtZiI2q4e30NCGjXqbiqIJyIw+so6BKoI3epEPidyeeUa7p/eO4oYoqCM3PTiCTi8CzydQkpJpORkP6zoqqYjIT1fBAgtdAvJvL3Y15rFStEy1TmAcUDK4LxGLwOpy6yEEJIOSloBvPmJ75f7HFY8uDpg/jayQNFea4j0+P4+N6n0iZ7Z+amcWZuGg8PnsInrrwVm1o7i/J6hBBSDSRUBadNYYlmruvqx9a2Lrz2se8gqiQwGUnV6Z+YmaxZ8UBVVZzQhJP1LR14WQtm+9mFE3jowgn81bYbcVf/6oKffyKSXifucyaFF7e0s/rMyAXpMSH0+5sKfk1Cis2sqZzmrU/+AB+74mYAmmPHXnyTqlsT0KJKynnQ5EyKB3QeZObC3Aze8+zPcGffKvzVthsrPRxCyBJk0dsf3V4/lje0YPfEUDHGY0C+brjtDqxtakMgFsYlqa1YPoyG5vGxPU9iOhqBw2bDb6y6DDd0LQMAPD92Ed87ewQTkRA+uucp/PuNrzS0JbLiXeu348bugYz3N0mWX0IIKQeqqhryDASjoSCiSgIumz3rotXrcMLrcCBs6g4wU8N1+hOREKaiYdhhw5qmVhycGtXvUwF88/ShRYkH8o4tANhh03dpXdr/h0JzaY+heECqCassji8e2Q2gNK4DAHpZTySRyjwQzgM7uy1k5L9P7oOiqnjs0hmKB4SQilCQePD2tVuxsaUdG1s60ObxLcjSvxA2t3biA5uvw8bWDqxpbIXDbsdn9u9csHjwXyf26b3LP7L9JtzWu1K/b2t7N9Y3t+NT+57BdDSM/z6+L2fJQafHh9VNrQv+ewghpBT8evg8/uHg8/irbTdihyRsfvvMYXz52F4AQK+/MavN1Wazod3jSzu/WoWp1Qoi62FFYzO8DqeeeSAwh0UulAmTk83rdOoCjgiES39MyPJ2QirFbDxdPBAdSdwlKrFJBSbG9ddv0ssWGJiYCTmklRBCKkFBXrR3rN+GG7oH0Ca11ykF13b14zUr1mN9czscBdrmJiMhPH7pLADgms4+g3AguL1vpZ4g/NilM2m7SYQQUs184uVfYz4ew8f2PGW4XQgHAPQWjdnosDinx2t4929IE0JE2YX5OmIWExbKhKm9nfx8rgzXrMkwry+kurByHoh8kGUlcskIR0MgGtFFAmYe5OYsxQNCSIWp+24Lz40OQkHyAnTvsjUZjxP3KVDx3OhgWcZGCCGl4FJwFr/7zM8Mt/XlES7bXgfiwcODp/DeZx7CcHBO3+Xv1P4ul0ksiKmJtMcvBHOGTigR0392Z7B703lAqg1z5oHMKwfWluQ1RVmP+D64bHZdUGDmQTo/PHsU7/71Tys9DEIIqX/x4ODUmP7z9vaejMdta0vdd0h6DCGEVDvmYoQvHdubtkPVW6DzIKHU1gT+Hw8+jzNz0/j3o7v1hYkQRczBb4tp2RiMxzBnsnsb2tuZxIMrtOsPxQNSbcxnEA+cNrulW7MYeLSyhfl4UnBrdLn1kh9dPEBtnXtKyRe0bjiCTM4mQggpNXXfL0rUhzU4XZa7aoIOrw9+pwvBeMxwgrbiR+eP48HTBzEWDsJld6DL68fWti68emA91lu0QiOEkFLid7r0SXhCUSxLr7wZavBlZPFgwN+EweAsYsriducrxUQkBH8iDiD1dzlMmQ+L+duGtSDEZpdHb1Us4zKJBysbW/Dy5Ijh3yYUj+Pv9j+Lm3uW4xVZnHGElBKrzAMAWNXUAp+zRJkHpu+HHDTtoPMgJzFFgaq9P/986EW0uD1494YrKjsoQsiSoO6lyzGtJjVXBwUg2Tki+Zj5rMedmJnEpeAcYoqCYDyGc3MB/OzCSbxv58P450MvIFqjk21CSG3ikOz4o+EgrObc13T253weWWBd3tAMoHbrjmOSiCL+LvOCfjHOA5GnkKkcxG3aGRQdFkR4LwB87+wRPDc6iM8c2FnwOAhZLCLzYLOpVbU4B5QCjymIscGZEg/YbcGImuEcHFMUDIfm8dDgSXzz9CHMRNNFTEIIKTZ17zwQNajePBKDxTGheNzy/kanGzf1DGB7ew8G/E1wORyYjISwe3wIDw+eQigRx88unEQwHsdHtt+U9bWiSgIxRaEtjxCyKBKqYgg8GwrNYd60k/i1W1+XV+aBbOtf1tAEjNVO5sFMNIIvH08FRJ6andJ/LoXzQIgHvb4GHAtMpN3vsNtht9n03VMxhnAidX1hCQOpBkTmwXs2XIFmlwfvffYhAECnJ/emS6GYbfcNLpf+s72CgYlzsSi+fGwv7u5fja3t3WV/fStCCes5aVRJYFZyPZ2YmcTVWvg3IYSUiroXD6KJ5MQ3n/owcUzEYkLZ4fXhf+54g6UIcX3XMrxuxQY88NKvMBoO4vGhs7i9byVulFqmmfnWqUP4+qkD+f4ZhBBiyVwsZhAhz80FMBwyuqf680xMX9+cKrvyOZKT+XiNtGr88vG9eHjwlOV9pcg8GNLKFvp8jXjr6s349pnDuK13heEY2Xbd4U0XD2rlvSX1jRAfm1xurG5qRYvLg0AsgltMn+di4rEb51J+pyQeoHJlC/99Yh8eGjyJhwZP4rH73lb217cikMFREEkkDPdRPCCElIO6Fw/cDjvCiURek0RxjMciJdtld8CVdmuKgYZmfHjbTfjgi78EAPzo3LGs4sFvrt2C31h9GR546VeWu1aEEJIPgagx8f/FsYsFl04NNDTjX69/Bdo9Pvxq6CyA2nEenJqZynifqKc2t2ZciHigqip+eO4Yzs4F4HE48PLkCACgz9+EVyxbjSvae3B5W+adylaXF4BxFzG+yG4PhCwWVVX1zIMmlwcA8O83vhIjofm0MoZi4naYnAeSeCBaqpbaeRBVEvjumSO4rXcFBrQSjYvB2ZK+ZiEELDJVgOT4AybnASGElJq6Fw98DhfCiYRhtycT4phCA4K2tndjZWMLzs0FcHBqDIqq6vY7M267A267Q1fYCSGkEMwTy5fGhwy/m3fDc7G5rQsA4BTW4RrrtmCm1e3VU9zNQshCyhZOzU7hi0d3p93e52+Ey+7AtV3ZMyXEdSWciENVVdhstkU5HwgpBuFEQt/hb9QW8N2+BnT7Gkr6uubARIN4UCbnwVND5/DfJ/bh1yPn8X9vfBWAdIGxGpgxCcSCqGJ0HpwzddghhJBSUPfiQZfXj6loWA9OzMaoHq5Y+EVzZUNSPIgqCczEImh1ewt+LkIIyYUIyerw+Aw19Nvbe3BX3yrcWqD1WOz+1YrzwIpVjS346Pab9d/nYzHD/TFFwcsTw4irKq7JYfc9qjnEXHa7YdG/tqktr7GIkjdFVXFmbhonZ6YoHpCKE1VSGyvuPDqyFIt08SA9MLHU4oEo7zo5M4VfXDyNBqcrrbSpGpjOULYQTSQMzrNMeV2EEFJM6l48WNHYguMzk5iPxzAZCWVs1zgRDiGotTpbsZiEYRoJCCFlREws1zW3IRFQ9N+v7OjBq5avK/h5xQ5crYgHVgvx391wJVY1teq/m8//wXgMf/HSrwAAP7rrzWiU2sWZORFIWoJv612Bxy6dBZB0NbS4PXmNT87L+b1nf57XYwgpNZFE0n3jtNkNXVtKjc1mg9vu0Eus5MyDcrVqnIikNpVEx5NFzf9KRKYuChElbmgTmylYkRBCikn1SaxF5nLNggsA+7QaVSv2T6Xu2yI9ZqGc12xjLrsdza78JpWEEFIocjvCW3pSLoMNUvhhIQjxIFEju+NTJmvv+zZdjetNpQTb27vxh5uuxh9ddg0AY8mH3LECAPZPjuAXF09jSnt/RT3xDV2pLBtnhrI0K5x2O1xVaIkm9U1CVfDi2MWMC1CxeC+n60DQKglvctlCuVo1TkTSywEuzM/oP1e6VeSpmSlcnJ/Vz1PL/E34wObrsKqxBQAQVRSDK0Euzz0xM8kyBkJISaj7mcyN3QN6rsCjF09nPE7cZ4cta9BhNg5OjeKsdrK+vK07Y94BIYQUC7F71uHxGZwG65s7FvW8tVS2EFcUTEvigd1mw5tWbdKzDgQ2mw1vXLUJ11qUKMxK7S1Pzkziz198DJ85sBOf2vcsYkoCZ2anAQAbWtp1YeWKjp4FjdNbYJ4OIYXyi4tn8JHdT+Jje560vF8XDyyCoktNh9QK0tBtQft+ldx5YFHOKr9iJcsApiIhvP/5R/Enzz+KS1qI4z3LVuM1K9bDo7mYoom4QRSKKgkkVAWBaATve+5hvOeZn0GtQMcKQkh9UzHx4NHBU7j7kQdx9yMP4qsn9pfsddo9PtzZvwoAsGt8CE8Pn0875qnhc9ilhYzd3b/asrTh2ZELWU/CF+dn8el9z+q/v275+kWOnBBCciN2zzo8Pqxvbscfbroa7998Ldo8i8tbEa1r41pgoqqqODcXqMr2gmbXQa5Fh1XrXtl58LMLJ/VFxL7JEfx6+AJiqoJGpxt9vkZ8Ycd9eN2K9fjjy67Na3xCwrBq9UtIKXlEa196aHrc8v6oVrZg1WWq1Ij2pUAG5wFKu/AVrq33bbra8v75eNTy9nJweHpc76bw6+ELAIAVDUnHgVs7f0UVJS0wN5JIYFByT8RqQPwlhNQWBc1kDkyN4tJ8qp2NfPK6GJzFo6Ze2/cOrC1ocKF4LG2xL7fReXrkPFqk0oC1zW1YZ2HVfff67dg1fgnT0Qj+dt8zOBa4DDd0LwMAPD96Ed87ewRA0kL3rg3bLcfy8b1PY5m/CTf3LMfGlg50ef1w2e2YjISwa3wIDw+e0uvNbutdUdL+yIQQIpDLFgDgjas2FeV5HSbr8DdOHcRXT+7H7228Em9Zvbkor1Es5KBIAOjNkRTvslgozWrigaKqeFxrU+l3OBFMxPHFo7sAAOub22Cz2bC2uQ1/svm6vMcnXo/iASk3Da7UojwQDaPFFOJcybKFdkngbChz5kFCVTCpiY639a5As8uNv9dyDwTz8ZjVQ8uC3HZR0USU9dr8VrhEIom4odsCkCxdkHMQYopSEVcJIaR+KWgm8/CFU/jFJesSgEPTYzg0PWa4rVDxIBCN4LMHn894/5eO7TX8/va1Wy3Fg25fAz551e34+N6nMBkJ49tnDuPbZw4bjmn3ePGJK29Dl9ef9njBxeBs2uPMvHb5erzvMmsVmxBCio2w3nZkOXcVgrDmxxQFiqriqyeTDrFvnDxQdeKBnDh+XWc/fnfjFVmPz+Y8mI1F9fDc39t4Ff758ItSKGVhORLi9Xx1IB4kW00mF51NLjfL86oc2dZ+YmYqrauICEysdNmC3G0hJVyWTjyYiUagqCpsANrcXvT5G9OOqRbxAACaXG5dFHVr55FIImEQCoBkqYWhlCGRMAgzhBCyWGp/JpMnl7V24ss3vRo/OHcMz45cwIjWoqfX14gbewbwxpWbsqZmf/Kq23B4ehxHp8cxEp5HIBpBOBGH3+lCn68RW9u6cd/AWqyWkr0JIaSUKKqq7551ZOgkUyiiZVlCVQyC8OXt3UV9nWIgdt+u7ujDp6+5I+fxTpuF80CzKAshosHpwmWtnYZjNrQUJh6If5tadx6oqoo/fO5hnNds0a9Zvg4f2HJ9hUdFsiHaEQLJBalZPKhk5oHsPPBblC2U0nkg3Eotbi8cdjt6feniQbBC4kFCVXBM6+7S52vEUGgO65vb9QwXUbYwEQnp71GTy43ZWBThRFy/JgBATPv3JYSQYlHQTOaBbTvwwLYdi3rhewfW5nQk9Pob8dh9b1vU68i0uL141/rteNd669KEbOzoHsCOAoMUCSGkFASiYcPuWTFxSK0aR6UFSInLkAtCiAf5tk10Z3EeyM+1Uks1F2xvX1hA4ievug1fObEfH9Kul7UuHihQdeEASGZDUDyoXkLxmCFIdCQ0D0VVEYzH0OB0wWaz6eKBpwJlC7JgIJdXpMSD0tXrizIlcc6wyoiZj1VGPNg9PoTpaBhNLjc+esXN+JdDL+L1Kzfq93vsyfPIuBaW63e6dPEgqLUlF1i1sCWEkMVQ2zMZQghZwsi7Z06LBfFicOqBiQrCidTuVShROStvJoRboDVP8cBht8MOm15LDCQXE98+fQhfPv4yAKDZ5TG8py0uj2WYbjbMonOtd1tIKFWoHJGMDIXmDL+Ph4P4w50P4+TMFO7sW4WPbL9JD0yshPPA70gJBnJgo6MM3RZESYKw9DtsdjQ4XYZShUo5Dx7WcsPu6V+NjS0d+OKNrzTc73Yk35/RUFI8aHV7dGHygy/+0vBeRuk8IIQUmbpv1UgIIfWK2GEqdskCADiluuOI1D9cFhKqBRHa25yneACk5x5MhIO6cAAArZqT4wNbrkOHx5dXOUQusmUelLotXTGodN97sjBEe1HBqdkpnJyZApDsMhVTEohUMDBxe0cP1jS14taeFYa2qvYyZB4IYUB2PzS53IZjKtFtQVVV7J8cBQDc3rvS8hi35jwYCycdYc0uj+HcEpEEA5YtEEKKTW1vgxBCyBJmoqTigew8kMWDyvU+z4ReauDKXzxw2O2ANLHeOXbRcL+wM79m+Xq8pkitd7OVLSiqWvXhg1aLuVoY91LjR+eO4YfnjukZTJtbO3F4ehxjWrgqkPy3fOUv/kf/vRLOA7fdgf+48VUG4QAA7Ch95sG8VrYghwk2utyAVKJVicDEsXAQgVgEdq2rixWi7Gpc+/dscXsyttBl2QIhpNhQPCCEkBplwtSmsZg47KnMA3knK1TBBPJMpHIK8s99iOZwUDQvQIjIl2x15bXqPAjGY8lFF6kaPn8k2VpUtLa+oWsZDk+PZ31MJTIPAKQJB0B5ui3MJyycB07j5zgYL79QKrosrGpsgSeD2ChuD2pCbqvbq2e2mKHzgBBSbFi2QAghNcpEWHMeeEvnPEioStWXLYh2ZfkGJgJJUSQb+eYnLIRLwbmM99VCSUBcyjwQO9WzGRYtpHq4tqsfubwhlXAeZMIhdXopFSIMsVESDMwiWCXyXYR4sD5LW1hz4Guzy5PR1cTMA0JIsaF4QAghNcpkCZ0HqcBE1SAYVHPZQmsRO040OIu/m37fsjUAgNev2GhoUweUdpe1WIjFnNNm1+vDM+14kurAY3dgTWOr4bvhsNjtrybxwFnGzIO0sgWJUAWcBxe0bibZ2n6bhYIWtweZKodYtkAIKTYUDwghpEYRZQudpShb0GajcdWYeRBXlaqywiZUBbMiMLHAUgOrhVNYKf7C4fquZfjmba/HH112NVY3thruK8ZCKaEq+LfDL+FXl84s+rmsnz85RqfdposHsxUIlSP5s6apDQ673SAw3tg9gG/e9nq8fe1W/bZKBCZmwiHlrRSbXeND+PS+ZzGsZRvIZQvmRXmwAs4DcU7v8vozHiOPGUiWa81LQsf/3P4Gva0snQeEkGJD8YAQQmqUUmYe6GULirFsAagu90EwHtMbLprT0vPBBuAvt94AALizb5X+HFdok+9iYrPZ0O1rgM1mw8rGFsN9xcg82Dc5ih+fP45/OvhCSRwBwnlgt9l1ZwadB9WF+bu6viUZutcplTY1utzo9jUYynyqyXlQyrKFv9r1OB4fOovdE0MAjM4DcweWSuS75OMmM4sHzS63oa1kp9ev/y10HhBCig0DEwkhpAZRVRVTkTCAEpctqAoippyDcCKBJpfVo8pPNCEWtDZ9zAvB53Thjr5VWOZvQr+/CXFVwWhoHuuy1BwXgxUlEA8uaQF5ESWBXw2dxf0rNiz6OWWE88Bhk5wHFA+qCvO/R4+vEQDQq/0fSIlsstjmqSbxQHM9jYaD+I+je/CW1ZehrQTnOMC4EDcLKOUOTFRVNZVjk+XvNZdUNZnEAwBwaX8LnQeEkGJD5wEhhNQg09Ew4qoCG5BWP18MnPrun5rmNKimjgticlzozqnYedzQ0oFGlxutbi82tHQUbXyZuLK91/B7MXZZh6RAxp9fOAm1yDXjYowOm12vD5/TSkZIdWB2glylfc76/CnxQIQEyjX+rioqWxCuJwD47tkj+D/7nyvK85pdGQDQIL0H5nNIqMwOq/l4TO9sk00QbjA5DxpdbtzSsxxAsksDkApVpPOAEFJsKB4QQkgNMqTV7CYtqsWf+MsTeHO/82oqW1iseGC2AJeLZQ1N+Mfr7tZ/L4bzYCiUEg9OzU7hmJbcXiwSWrcFp+Q8mKuQkHRuLoAfnj1akrr4WkbOoPj01XdgfUvSQdNn5TxwVrfzQPDy5EhRnlfkHMhkcx4UKpIOBefwvbNHFhS4qKoqvnbyAICkOJCpewIANLiM56wmlwdvXn0ZPnbFzfgH7ZwirgnVlE9DCKkPWLZACCE1yJBmUZcXBcXEYRAPjLuZ1SkeLEwLb3C6MB+P4YauZaUYVl5sb++B3+FEMBEvSmDisOY8aHF5EIhFsHN0EJuK6KKQnQctWjilqNEuN+955mcAgKiq4K2rN1dkDNWIcB5saG7HdV39+u0G54Er3XlQTZkH5vKjYnVdGLJoldooiQdrmtsM9xUamPhHOx/BTCyC4eA8/njzNXk95pmRC/jBuaMAcpehmQXPRqcbLrsDt/Wu1G9j5gEhpFTQeUAIITWI2GWWFwXFxGlP7f6ZnQfltvNmI6blMSw0Lf7fb3wl/nDT1XjHum2lGFbe2DWRppjOg23t3QCAQDS86OeUiQvxwG7DKq2V3HOjg3hp7FJRX2chHJgcrdhrVyMi88AcHiqLjHZtZ18+xhwWWEmsWkkWA9mZI5AX4ld19OLPL78Bn776dgDJhXchzpYZrZRHhDLmw8GpMf1n0a4xE2bxwGtx7nMz84AQUiKq52pBCCEkb8QuWqmcB3LZgtjNFLXS1eg8WGjpRr+/CW9ctaniLerEQknB4sSD+XhMXziu18Ieix1mmApMtBte48O7n8CJIpdI5Att2Ub076pJPPBJC07R0lSu94+XoLNBociup2IyYiEemPMDXjmwFld19Om/hxbRrnEhEsj5+YD+87a27qzHml0iNguxxc2yBUJIiaB4QAghNYio3y2V88Bms+k7lGJZ26q1dguXOYU8G1FtZ7CabNcLQbzHhQQmnp6d0jtujIeDAJKLIdEjvthtFOVuC92mPvTn5wJWDyk5tGUbyeQ8AIBPXXUb3rl+m96GVP7OmDuqVJJSiQfTJifOysYWeCyyBZx2u+7EEB0XYkoC+yZHSrKTr6qqLr5d2d6DP7v8+kU/J8sWCCGlguIBIYTUIKXOPAAAh2nvrFXr6mBuC1ZJYgVmHlQLKfFgYc6DA5Oj+L1nf46/eflpAKnsgQ6Pr2RtFBNKKvPAZrMZ2skVqy59oXBxZETkk5jb+QHADd0D+O21Wy13qs078JWkVGULgaixM8h9y9ZmPNbnSL4fIjTxv47vw5+/+Bi+emJ/0cc1GQljOhqB3WbDp66+HQMNzYt+Tr1VYxWJQoSQ+qA2Z1uEELKEiSkJjGk7zaVyHgBAzLQbvszfBCDZf71aWGy3hUrjKFA8+OrJ5CLmwNQY5mJRHJkeB5AUD/Q2ivFiOw+EeJAc88evvFW/z5yLUS5i6tJdHKmqiuHgnKElp8gjybeLyIe27sBrlq/DjT0DJRljIZgDE4uFEA/u6F2J31i1CW9YuTHjsX5n0pEg3s/vnj0CAPj2mcN5v56VSGM5rljSEdHs8lg6IQqBrRoJIaWC3RYIIaTGGAnNQ0UyKKvV7S3b665qbAUADFvUDlcKsbNWTX3qF0IhgYmheByHplMBa3+08xFc1JwoHV4fmrSa9lJmHgDA5tZOvHJgLR4ePFUxN8pSXhz994l9+ObpQ/jItptwZ/8qAKnyA0+e34d7lq3BPcvWlGqIBVFq58EbVm3C5tbOrMcK54H5c72QYMl8/4r5WPI1iun+YKtGQkipoPOAEEJqiFA8pocl9voa897dKgbLG5LOA6vU8kpRL84DJc/Mg2A8hpMzk4ZFsxAOgGSbNxFsOR+LFaWLg0CIB3InDv21iuxyyJdC0vDrgaiSwDdPHwIAvcUfkAoz9dprd2/IaZF5YA5pTajKgoNbA1oXBJHdkg3hPJiJRXBUc/UkH5tdrE0U8HkUrp1CxAN7BolCzzyooiBMQkh9ULtXF0IIWWIcmBrFB1/4JVq0neVS5h1Y0aeVLVzSrNLlFC4yIQITq6nV3EJYSNnCT84fx78dfglb2royHtPh8euZBwpUBOOxtOT9QkmVLaTea2GPF7un5UC26S/VVnQvjF7Uf5bPAxEhHjhrd3pntziv/PZTP8LXb309fNrf9Zcv/gpn5wL4+m3357XojiTiutgguk1kw6s5D/5237OG2+dzuHkKaWObyqlYuHjQkkEIYeYBIaRU1OZsixBCliBfOLILQGoHrZR5B2buX7EBPb4GAMnd72Jb4gslVuPOA7vuPMgtHjw7MggVqZ7wt/WuSOt60O7xwu1w6O9HMTsuxBVj5gGQWvCUs2xBbiu4VJ0HB6ZSZStytkVYlC3U6PcBMHZ6EUxHIzgzOwUAmIlGsH9qFDOxCE4EJvJ6TlGy4LTZ81qkr21u1X+2IzWeYCKuhyhaIX8PhCCo5vhui8fkm1MBAJ+86jYs8zfhb666zfJ+tmokhJQKigeEEFIjmC2q5XIevHHlJrx/87XwOpx6wn61lC7UftlC8jKcy3kgt3MT3NO/Bt+8/Q342T1v1W8T74PecaGI5QTmzANAch6UUTyISoLBUs08ODGTWjTLXQQiSnLnu1jBe5XCqnTBp33W5O9Bvs4TIbi2uD15OaZeuWyd/vObV1+GR1/xm/Bq7+mE1tnECtl5IErM3vzE97N2aUiVLeTvENrRPYCv3vo6XJYhu0E4scR35Z8OvoD3PPMzBEztKgkhZKFQPCCEkBrB3P+8W3MClIr7V2xAq9uL31yzWb9NiAdTWSbQ5UTYcmtVPLDnmXkwGp7HTMzYam51UwsAwOtw4vbelejy+nFFRy8ApDouFNF5oIsHUolIQyXEA8mKvRTLFhRVxcmZKf13g3igvTfeGg0QFViFJoqyGVk8MLdfzIQ4Lp+SBQBY3tiMu/tXocvrxxtWbjS0Jp3Mcu6TnQehRBzfO3sE09EIvn7qQMbHzBfgPMiFOTDx54MncW4ugP86vq9or0EIWZpQPCCEkBrBbOUVk9lS8f7N1+I7d7wRbdLr6DvasSiOBSbwnmd+huel+utyUy+ZB6JsYS4WxR8+9zD+RwvDExwPGF0HTpsdnVLJwkevuBkP3vZ6fTEvggwzlZcMB+fw3mcewiODp/Ieq7lVIyA7D8pXxiILBlElseRKFwbnZww73KLVH5AKFqx554HF9zmufUfk70L+4kHyPcqUEWDFX227Cd+6/Q3690ycb/N2HiTiBsE3U2mSEBwaXMUUD4TzIGEom3hi6GzOMgpCCMlGbc62CCFkCWLejesw1buXArNgIe9o//Xep3FuLoCP7nmy5OPIhF62UKM7rXZTYOKPzh3D8ZlJ/Ofxlw3HmUsWenwNaU4U+d+qzZNMhc+00PnCkV04MzeNfzj4fN5jTVhlHrhEt4XyOQ/MddzlfO1qQJQMibKlcCKhiwZ6t4UaFw+snAeKoiAYj+HF8Uv6bYFYfuKB6EjStYhzphARRsPBjMfIeQiKqho6k2RyLAjhrZjOA9GqM6okDF0pgok4xrKMnxBCckHxgBBCqhxVVfG5gy9g/9So4fa2HG3DSoFcSz8dqXz9bL0EJgrxIJMN/7hJPGjK0UFBLCyHg9bZFIUsuK0yDxorEJhofo8q1SayUojd9n5/k54NMKPdVj9lC1bOAwVPDJ0zLIan86zhP6G5FTY0txc8JhFQO5ThOzUXi+Lje5823CYLBplyYubjyb+nkG4LmRDOk0gikfZdNwuRhBCyECgeEEJIlTMRCeGhwZNpt1tZe0uNsMPPxaJQUHn7a/0EJiZ39a3a1Kmqqi9+BLls6b1ioZNhwVLIv50Yo9MiMDEYj+XVMaIYmNvPzVRJ549yIcSDVrdHt+EHohEoqqp/H2q9bME680DF0cA4AOjtamfyLFsQ4tu6lkWIB77s4sEvLp5Ou200lNrlz/Q40f6xmOKB155ZPDALkYQQshBq++pCCCF1zu7xIXztZOawrXIjZx5UunR29/gQnhg6B6B2xQNzq0Yr8WA0HEQgFoHdZtOPc+cQjnItdAqpe04FJqa3alSRtMwX03qdCXOHhWwBdvXGzy+cxH8c2wMgWb/f7PJgIhJCIBbRXQcA4LHX9vTOynmQUFX9b1zW0ITAdATTOcSDhwdP4fGhs5iIhGADsK6preAxCefBcAZBLm4ReiqXCJi/i0cDE/jGyQO6o6yo4oEz+e8fTsTTnQcBigeEkMKh84AQQqqYD+16HIemx3IfWCYatR2/anAefGjX4/rP9RKYKLfjFDv9L4wlAynXNrXptutXDqxDNvol54GVUKAU8E8XtwhMdNsd+qLneGDC8nHFxly2MBleOuLBPx16Qf+5xe1Fty9Zh39kelxv0wikat5rFfkzJnIK4oqi/9uL28wdSGQSqoIvHtmFvRPDAIANLR16u8dCEILcSGhe/27KWHU2GZfEg9HwvOG+7585gufHUmGzC2nVmAuPJqbGVSXNnXFhfqZor0MIWXrUtjRNCCGkrMiZB9VEvQQmys6DcCIBv8OGhy4kS1bu7l+Ne/pX4+zcNLa2dWd93m5fA2xI7jwGYhG0mvIx1ILKFtIzD2w2G27vXYmHBk/iocGTeqvIUmIWD7Kl39cT5lyJFpcHd/atwgtjl/DVk/v1f1OP3WHpYKkl5PGLhXBCVfSSlW5vsk1ttsyDwflZhBJxeB0O/NmWG7C9vWdRY+rw+uCy2RFTFYyHQ+gxtcoV5SRXd/RiODSPi8FZxCSRwbyIH4sYgwuL6dqRAzOnosnvR5PLjdlYdMkFjBJCikttbtUQQsgS557+1RV5XbnbQjVRq2ULDhidB7KbIxSP4djMJE7NTsFlt+Pu/tVodnuwrb0HthyLQ7fdobeWGw7Np91fSD5BqtuCcepw78BaAMALY5fSHlMKlqp4YC7PaHZ7cEvPCl3QE+VNtZ53AMAgbYm/JyFlOojOB3OxaMbPsggGXNvUjjv7V6HDu7jWtg6bHV2a02MsnP6dEp0fbuxZbtkS0lxiYf73bC9i612XPeVhEt8PIbgE4zG2aySEFAzFA0IIqTE+vO1G/Mnmayvy2k3OVOZBNeGqVfHAbgxMlMMAw4k4fq65Dm7tWbGgHvUA0Kq1a5yx2J0tLDBROA+MwsWqxhYAyUXJ4PwMfnj2aFqoYTExP/dEZGm0nhs3lWe0uj1wOxz47LV3GW6v9ZIFM6JzREJV9MyDdu2zrSJzp4/josPCIkISzfgcSXdA2OLzHdC+Z61uD3wWAo4osVBUFQ8PnsQlLQPhE1feis9df8+Cv9/ZsNlsuugypXXF6RblH6qSsasLIYTkovblaUIIWWLcVSHXAZDZeRBTEhVdwNeq88Buch7IYYDhRBw7RwcBAPdpu/sLocWVXGBZhcoVsvGYSTzwO13wOpwIJ+L40+d/gUAsgtFwEL+/6aqFv0geRJVUZwpFVTFZBS1Dy4FZJGnRSlHWNbfjxu4BPKd9Vrz14DyQPqBiESxnHjQ63XDa7IirCoLxmH5ekjkzOwVgcSGJZoSQEZHaRQpE2UKLy2OZrSBKLL539gi+dGyvfvs1nX0lcYuI76RwHnR4fbAhKbjMx2N14VAhhJQfOg8IIaQKmItF8aSph7kVn7/h3jKNyBphkY6Ydq6C8ezjLgVyOrlV0nktYA5MlHcEQ1JSughAXAhyG7+do4OYkmzSBZUtiMBEi3BKUSIhrNvfPXtEL3MoNjHtPRIBdkvFeWAWSZpdqZ1qEaQJ1IfzQP50unR3jtyK0qFnBGSq4R/XPu/mbILFIBbcVudpXTxwey0FnPl4DHFFwXfOHNZv8zocJVvEC6FDlEc0Ot26qJHJrUEIIbmgeEAIIVXAJ/Y+jU/tewb/9+iejMe8ZfVl2NTaWcZRpeN3uix7sIcT5Z+Myk4HT606D0yBiXK7vVA8Ji2WFr7AEOLBfxzbg4/teQr/dmSXfl9hgYlJMcBp0Uavw6Je+4DWgq7YiPdELAqnIuGCxJBawyySNEu77bI1v1ZdOJkQGRtxKTBR7vKRaSGs77h7/EUbS0o8MIqnCVXRS7laTGULfodTdxgdC0wYnEBW5Q/FHqsQD/xOl/6ezVE8IIQUCMUDQgipAvZOjgAAfnHxdMZjitnKq1DsNhuu7uhLu70SzgOxA313/2pc3tZV9tcvBrp4gHTngbzIKMSKbq6hfnr4vP5zYc4D67IFwDrsbXB+dsGvkQ+ydR1I7lIvDfEguQjs9TXgga07DA4QuZPA4enxso+tlDgkgU04ntwG50F6/kooHtdFhfZFBiXKeO3WZQtzsZieI9Ls8sDnTH1fm90e3bH14KmDRRtLLoSgKj43DS53TsGFEEJyQfGAEEKqCKe0MDMnYhezlddiePXydWm3hSowGRWLyHet356z+0C1InZVhcXfKB6kbOqF7Ca3uIzigQhMA1CA7wBIKJnFA9l5sKapFQAwFJor4FVyI3af5QVaokbLVhbCRU2Mec+GK/CKZWsM93kcTqxubAWQFBfqCeF0UaTMA9l5YFW2IHbbvQ4n/EUsC8hUtiDaMDY4XXDa7XqwIpDMiRFC3ovjxo4k5haqxUQIjiJHpVFyHsxXWeAtIaR2oHhACCFVhFNaJMZMC6KGKhEPru9alnZbMEdWQ7FRVVWfFLstavBrBeE8ELuWcicBUUPtsTsMfe/zpcW0MJGfQ96pz3fhLXIlzK0aAcBhTz33LT0rAJROPBD/7rIbo96dB+fmAjg+Mwm7zYatbd2Wx3z2urtw37K1+Mi2m8o8uuIjl9WIz1ZcVfXvh8fhRIO2m28lHogSjw6Pr6jCovjMmTNf5rWyLSHwymULTU532nfhbWsvx+29K/G3V99etLFlGisA2ABc3talj4/OA0JIoTBqlRBCqgintAgzt6SrlhR1p92Oqzt6sXtiWL+t3M4DOSCxVts0AumBiREL50GhgWrmsgV5kSUvzuKKAocjtwCjByZaLMY89tQY1zYn0+2Hg6URDyJKUqiSvw+JOhcPHrt0BgBwQ9cydHqta/hb3V78xdYbyjmskiH/cwqxKqYk9O+9225Hg+Y8sRYPRN5B8UoWgFQYZdhUpiV+F6KBV3LFNLrcaHS6MSU5iW7pWY51zcVrIZltrABwbVc/enyNeulbppBJQgjJRXXMRAkhhAAwhtHFTLtbhYTclYqPbL8J/3DwBb2VYKjMzoOolORfywFx5sBEWTBKiQeF/X3msgWR9g4Yd+pjigJPHi+hZx5YOD1ev3IjDk2P4e7+1XoXhFI5D4Qjo01yVtR72cKlYLJkQc42qGfkM50QD+RzjNvu1BfCk+EQVFU1OAzOzE4DsM7iWAwp54FJPEgYBS3ZedDocuPD22/Cd84cRoPThQanG2uK2D4yE7LoeK2WU8PMA0LIYqF4QAghVYRTWphFTeKBlV28UrS4vfjkVbfhb/b+Gk+PnC/7ZFQWVpw1XLaQrVWjWCT7iuQ8UFQVf7XrcZyYmdRFBCD/hXcqMDH9/W5xe/CZa+8CkAyrA4DZWBTT0XDR67rFrnKn16/3ra/3sgVRU99q+jetX1L/nsKNJZ9j3A67bsH//rmj8DtdeMf6bQCAfZMj+ObpQwCKLx7ozgOTKyyklS2IVog+qcSsyeXGVR29uKqjt6hjyYVXEh37/E0AkLO9JSGE5KJ2Z1yEEFKHOG12qKpqqOkHgC2tnbiuq7+CI7OmUas7nitzAJd4b5w2e0F5ANWCWIhbiQeLLVtodXuxo3sAN/csh3iHXp4cwXw8ZiiPkD9n2RChjlZlCzI+pxNrtZ3VJ4bOLXzgOZiULOlm50a9EoglxYNm11IRD1LozgNNlHLa7HDY7IYMmK+fOqCfg16eGNFvv6V3eVHHIr6L5m4LQkwQC3aD86BCXXLkUqI+f9IN1OCieEAIWRwUDwghpMKYuyr8wXMP44GXfqUvJNvcXvzLDfdWpT1f7ITKbQXLgbD3u2rYdQDIZQvJhbm8KJmKLK5swWaz4ZNX3Ya/vvLWrJ064vmKB1kyD8y8amAtAODnF07m9dz5oqoqJsKaeOD1pbpV1HnZQkB3HpQunb+aMGYeaM4DbXdfBKSaA2Rf/6vv4vTslF4u8+712zOGSxaKTxcPjM6DbGULok1juZGFSNGBI5V5wG4LhJDCqO1ZFyGE1AHyLvD5+Rmcmp3C3skR3aZbjaKBoFkTDwKxcI4ji0tMatlWy9jTyhZSi2DxuZB3EAslW6eO+ALLFvIpE7m9bxUA4MzctG65Lwaya6Ld49MXlvXsPFBVVRcPmpdM2UKKlPNAOx+KBbrFZ/oHZ49hSAvqFLvtxSRVtmB0HoixiRaN8tgaKyQeyAGNwjEhxF5R+kMIIQuF4gEhhFSYTJ0KZjQbrrvAnedyIEL5AmV2Hgirfa07D8yL36hF8KS3CP/+DVms0wt3HuR+z1vcHj048cTMpOUx3zp9CK977Dv44Au/0EsiciEWPQ1OF7wOZ5r4Uo8EE3Fd4DHnWNQrhlaNwnmglS0I58GsVCr199fcCQB45OIpHJoeA5Cq8y8mQsgziwdm54HX4DyozL/ZdDRd0BXvyVCJOqEQQuqf2p51EUJIHZCpU4FYkFfz7nqLZqMuv3ggyhaq973Jh7RuCxaL6GK06MxWaRDL03kQV0RgYn4ZE+tbkq3oMokHPz53DMF4DAemxjCodRPIxaSpBV+qbKF+xYOAtgj0OhxV0661nAiniwglFLvoN3UPwOtw4NbeFbiqoxeXtXQaHifEq2IihLyIYi5byJJ5UCHnwXvWXwE7bPitNVv028R7MhEJpeU2EEJIPlA8IISQCpOpU8GMFpJWzbvrYidUjLVciHreanZl5IMcmPi1k/v1HeZVjS36MYUGJspkKx3Id9d/IZkHALBB62N/PJAuHkxFQhiXrNOzeX5+JtLEA2NmRD0ihLkW19LIOwAytGrUnQfJ73y3rwHfveNN+Nj2m2Gz2fDP19+Dbq9ff1xzCRbt4rt4bi6Ab546qN+e3m1Bch5UKDBxa3s3fnz3m/HuDVfotzW73Hr+yXBoviLjIoTUNtU7IyWEkCWCmBSbmakB50EqMDGcFvxYSqJ1UrYgnAeheAxfO3lAv/2y1tQuajHKFgJZFuf5Og+ytWq0YktrFwBg1/iltM/4cZMbId9uHXvGhwEAyxqaAaQ7N+oR4TxYKiULZpxpgYmp74PP6YJNu99ht+NvrroNbrsDO7qW6bcXEzm89L9O7NN/NpctuOwOLPM3ocnlRqe3uO0iF4I5F8Jms+nuA5YuEEIKYen53wghpMoQk2IzYje/mnfXm7Xd0JiiIJyIW4aYlQK9bMFWve9NPojFr9nav7LIzoNs7RgX7DzIU7DZ0taFfn8jLgXn8OTwObxS68AAACdMboTZPMSDuVgUTw4nWz++on91cix1LB6oqoovHduL58cuAli64kGa8yDL+XBdczu+fuv9aHSV5jyUqWwkVbaQuv8LO+5DQlWK8v0tJn3+RpySulIQQshCqO0tG0IIqQMyOQ/Egqqa6/p9Tic82vjK2a4xqtRHq0aXtjCSa/57fQ0YkMLeCm3VKPPW1ZsBAK9dvj7tvmzCgowIVnTmuaNrt9nwiv41AIA940OG+87MTRt+z8d5sHdiGFElgeUNzbozQy77qDdGQvP47tkjuDA/AwBY0dCS4xH1g+xiEmKVKOnx5Dgfdnh9JVuwZxQP9G4LxqyDlipsrSnaNo5QPCCEFEB1yaGEELIECWVyHuhlC9W9QG52ezAWDiIQi6APxQ8ps0IseKvZlZEP5sXIXX2r8N6NV2JeysHwFqFV47vWb8eO7mXY2NKBw9PjODU7pd+Xb6tGERK3kNC+VU2tAJC2yyks0+0eLyYjYczm0XdeuDMub+vSLen2Os48GI8EASTzHT645Tpc2dFb4RGVD2PmgVGsquR3PpMoIUJvvc7qn1Z3eJK5EGzXSAgphOqekRJCyBIgnMF5IOrUqznzAACatVZk2UL5ik2qVWN1vze5MC/Eb+pZjk6vHz3a7iCQclksBqfdjsvbuuGyO/DuDdsN9+XbqlHUdS9kVzdTfbUQE9ZroYr5OA9EToJ4DCCVLSj15zwQi7teXyNu6B6oOvt7KblbK0vZ2NKRlrFRyfNhpvwRc+ZBNSPCRicj6a0cCSEkFxQPCCGkwmQKrKuFwEQglSxu7n1eSmJ1UrZg3qls0hLi5UXIlEW/9sVwfdcyfO76e3T7cr7Og9QCKf/PY68/KR4EYhHdTTEXi+olORuaOwDkzjxQVVXPSdggiQfCeaCg/sSDybDWWaKCgXuV4p3rt+Gvr7wV/+eaO9LKZBrKlKtihcNmxyeuvBVAquQISH03fLUgHmifp4lwsMIjIYTUIrU96yKEkDog085voAYCE4HUQrec4oHeqrHKhZVcmBcbVoGTa5vaiv66W9u6MaB1LMgn8yChKvpxC9kBb3C60KI5U4Y194FwHbS6vejUWuvlch5MR8MIxCKwAVgjvR9iVzrf0MdaYtzUlnIp4bI7cHPPcjS5PGkBnf4KigcAsKklmbeRkASr2nQesGyBELJwKB4QQkiFybTzK0Lgqn2BLBbAobI6D+qjVaN5sSGLCV+55bX44JbrcY9m4S42Yuf0kcFTupMjE9FE6n7PAjMY+jT3gRANRAlDn69Rd1rkyjwQ7otml8cgptVzt4XJJSweyJgzDyrpPAAkt4uq6sGOteQ8aNc+T8FEHMG4dd4OIYRkorZnXYQQUgfkqjmvdvGgEs6DWJ04D8zigbyrOtDQjFcvX5d3a8SFIp53/9QofnL+RNZjw7J4sEAnjBAPBrWuAcOaiNDrb0CjEA9yOA8CWgmPuV1hPZctiMyD9iUvHhg//w3aZ6ZSyGKGAhXT0bD+/ai0KyIf/E6XLnLQfUAIWSgUDwghpMLkqjlvqvBkORe6eJAh+LEU1EurxrSyhTLuXMo12wemRrMeGxFhiXaHvmDPF1Fm8PTweZycmcS4Vsvf5fWjyZn8bAeiYbwwdtHQZUImk3hQL2ULg/MzODI9briNzoMkTtN3vFqcB0DS8fLoxdMAkuGOzabPZ7UiPlPsuEAIWSi1PesihJA6IJ4jKd68YKo2KhOYWCfdFpzmzIPyiQeyo6Hb25DlyMI6LQhEwOHxmUn80c5HcFLrmtDu8enC2HQ0gv+1+0n848HnLZ8joJUttLi9xr+hDsoWVFXFO3/9U7z/+UcxoQkrqqpiLJxq1biUqbayBYPzQFXx1NA5AMCrB9ZVakgLpkPLGhljaCIhZIFQPCCEkAqTq0e9ecFUbXgrkHlQSwFl2TCP32zRLiUzsVRrzQ5P5s+Yoqo4OD0GYOElCwCwTuqOkFBV7NdcDh0eH7q8fkP3hqeHz1s+hwgPFeGLAodUf16rXJLaWF4MitKOeczHY3DZ7OhvaKrU0KoC83ei0qUB8ngUVdW/R6ubWis0ooWzsjEZlnpqZkq/LZKI48zsdIVGRAipFSgeEEJIhcmVdl/tzoNU5kH5wrciWo1xIYvZasJTQefEuLzrmKUU4VunD+GfD70IoDCxJtPnt8Pjg8Nuz6ubRObMA61sIc92k9XI8ZkJ/Wfxd57Q3Bmrm1prPtdjsVRTq0bAXLag1OS5aL3WIvW49jkDgM8c2In3PvsQdo8PVWpYhJAagOIBIYRUmJzOA1d1iwc+XTzInthfTEQNvneByf/Vhm2B+QHFRLYsZ8sM+PrJA/rPhTo9/tf2m9Ju6/AkrdPLG1r02/q1cEUzKfHAVLZgr33nwfFAagEnatCPB5KCwnrJtbFUSQtMdFY2A8ZuKlsQ56JaEnk2tiQ/VydmJqGoKg5MjeIpzfXz8OCpSg6NEFLlUDwghJAKY3YemJeTteI8CJUxMDFcg7t9ubCn/cuXFlkIyBbaKdvEC32/7+hbhX+94V7DbaKLQLfPr9/mc1jvKovMg1ZzYCJqP/PgpLT7K0IShfNgfQvFA3O3kUqXLdhtNv2bmlBVRLTw1loqoVrZ0AK33YFgPIYfnTuGD77wS/2+ae27RgghVlA8IISQCmNeuC1vaNZ/dtntBYXUlZNKlC0sJsCvaimzCeFjV9yi/5xt8S3bxD2LcHqsbzaWJ4hwyDeu3GRo96moKsbDQd2Ro6oqzswFAADN5swDe+2XLQwGZ/WfJyIhqKqq28k30HlQdYGJQMp9EE0k9O9OLTkPHHY71mgZDf914mXDfSdmJqHWsBhHCCktFA8IIaTCmC3jslU5Vx5CNeDTJvNlLVtQ6iMwUabcBQybWzvxppWbAADxLJ8zeafXuwinh8vuwPb2nrTbG11u/PP19wAAQokY/mrX4/j/nvwh/kzbDf3Ckd36bmirqWzBXuOBidFEwpA9MREJYTg0j9lYFE6bHatqKISvVDilsgWP3ZHWurESiFIKOSS21lxQGzRXSziRgA3AV255LVx2O+bjMVyYn6ns4AghVUtBs66pSBjHAuM4GpjAscAEjgUm9bTZV/SvwQPbdhRlcAlFwem5aRybnsCxmQkcnZ7AufmAPkn4xq33ozdDfaQVgWgYPzx3DM+ODGIklEw37vE14qaeAbxh5aa8rcFnZqfxo3PHsGdiGBOREHwOJ5Y3NuOuvlV41cC6NIsdIYRkI6btmq5qbEEkkcB7NlyBXw2dreygFoBYUJazVaMIKVvMYrbasJVdPoC+EMu2c99gEA8WJ9Z8aOsOfHTPk7izb5Xhdrn0Zc/EMADg0PQ44oqCX4+c149Z2dhieFytly2MhOYhj3wiHNIDFBmWmER2HjS4Ku86AFKilSwe1Nq/VTI08QQA4OrOPgw0NGNbWzd2TwzjV0Nn8a712ys7QEJIVVLQLODNT3y/2OOw5MHTB/E1KahpMRyZHsfH9z6FyYixluvM3DTOzE3j4cFT+MSVt2JTa2fW53nowkl8/vBL+mQfAKJKAoGpMRycGsOjF0/jb6++vepbqxFCqgfhPPj/1mzB3f2rAQCdHh/GtfrnaifVqpFlC4uhEtmJYmGWb9nCYun2NeBLN7067XaRdWBu9zk4P4OJSAg2AN+9441wm8QiEZhYq+LBUGjW8PuZuWl88uVnALBkQSA7DSodligQ35tQPHnO89gdFQ0/LQT58/XqgXUAgFctX4fdE8N4ZPAU3rFumyEckhBCgCKULXR7/bi6o68YY0lDngu47Q5c1tKZMYk5G6OheXxsz5OYjIThsNnw1tWb8bnr7sHnrrsHb129GQ6bDRORED665ylD+rSZF8Yu4l8OvYiYqqDN7cUfXXYNPn/Dvfj01Xfg5p7lAICjgQl8fO/TNV1/SQgpL3HtZCfbcz92xS1w2x34g01XVWpYeSMWfuGyBibWX9lCuQMTgZT9OlvZgkvaURXhcMXG67T+d3x5cgRAMgfEZyFiiPErNXrNHQomXZBXdvSm/euLecVSR17ADvibKjiSFGJMQU08MItatcCqxhZsaG7HhuZ27OgeAADc2D0AO5Jz4gCDEwkhFhQ063r72q3Y2NKOjS0daPP4MBycw28//eNijw2bWzvxgc3XYWNrB9Y0tsJht+Mz+3fiknaxzZf/OrEP01qbp49svwm39a7U79va3o31ze341L5nMB0N47+P77Msu4grCj5/eBcUqPA7XfiXG16Bfukidl1XP/718Iv4yfkTODg1hscunsG9A2sL/MsJIUuJuLYgk3fYtrR14ad3v6UmyqBSzoM4VFUtyw5cXZYtVGCTL1W2kHnnXg70LFVHjUz/jkI82NDSYXm/PQ/nRLVyIjCJfzuyCwCwtqkV4+GgXmv+p5uvxbVd/ZUcXtUgt2q8r0rmVebMA0+NlSwAydDEL974SsM522V3oNXjwWQkjPFICG1aR5Slwv7JEfzb4V34ky3XYmtbd6WHQ0hVUtCs9B3rt+GG7oGSn1Su7erHa1asx/rm9oIn0JOREB6/dBYAcE1nn0E4ENzetxLXdCbdE49dOqO3SpJ5ZuQChrSchN9cs8UgHAh+b+NVaHIlLXXfOXukoPESQpYeKeeBcfVYC8IBkNo1VpEs4yo1iqrqr1MPZQvv1mqL//zyG8r+2qmyhcw797IrQQRVFn8cdsua8VMzUwCAFVIHEuPjalc8+OWlM/rP29p7sK4p1Y2CC5cUbR4vurx+rGhoxg1dyyo9HAAWZQs1fB4yi70dnmTr1IlwbZTNFZM/e/ExnJmbNrSuJIQYqd2zXZ48NzoIRYsjunfZmozH3btsDXaND0GBiudGB/Ga5esN9z87esFwrBVehxO39a7Azy6cxLm5AAbnZzCQYcJDCCECsThz1uDuFWDcNQ4n4iWfSEekrg6LaR1YLfzW2svxmuXr0ZxnaG8xceZRtiBn/ERK2FHD63CmiU+ilLDBZV3rnipbqB3x4L9P7DO4DN69fjtu7B7A86MX9WOWN3LuIHDbHfj6rfcjoSpVI6jqZQt69kptnrut6PD4cAKw3EgjhJDqOAuXkINTY/rPVi2iBNvaUvcdkh5jfp7lDc1oz+K4kJ/noMXzEEKIGWELNzsPagWHzQ6XNqkvR8eFcA23R8tEJYQDIOVuyVq2IC3o37L6spKNxWchOonvRmOG0EZ7Hs6JauLcXAAPnjqIRy+exuHpcQDQ683fsHIj7DYbbu5ZbrDqk2R5TTXt7pudB7XWaSEbYo47QfGAEGJB9ZyJS8T5uQCAZFp0tkV/h9cHv9OFYDyG86b+tqF4TN/9WJ7DSSDvFpyfDxQ6bELIEkLs+lbLrlohuO0OxBQFsSw72MUiookHbruDaeCLRCyC4lkW3+Lf9Pc3XoU7TC0Wi0mm0EQgc8eHWitbeOjCScPvXodDL8lY3dSKB297PRqrpKMAyYy5VWM9Bbd26OJB5gBxQsjSpXZnqnkiFv1dXn/OY7u1Y8bC85bPkc/zdEv3j4V44iWE5EaIB64a3m0UO2/lyDwQif/1NGGvFKJsIZGtbEG7b1VjS0nHku3f059RPKidsoVIIo5fXjptuG1La7dBNOzy+uHLIqKQ6kB87mq520ImdPFgCWYeEEJyU/dXKNF3PJ9Jpp4YbkqTDkoW2VzPI99v7lctE1USiCmKnsdACFm6iF3fWnceAEC0hDXxgnAd1hlXCnO3hbFwEN87cwT3r9ygBwOLz6erxJ9PuWzB73Aarr0NGXbjK1G2sGt8CPsnR/CO9dsWVF7w65ELmI1F0eNtwMevvAVDoTlckaWcklQvuvMgXrvdFjLR4V1Y2cJYOIjvnjmM+1dsxLKG6milWSg2gLNyQnJQ9+JBNJH/pEccY+5jHZMmw7meJ99+2N86dQhfP3Ug55gIIfVPXTgPHOVzHoTr0CpcKcxlC5/Y+zSOBibw/NhFfPXW1yXv0wM9S/v5lOvGVzS24GhgQv+9msoW/mrX4wCAVY2tuLN/Vd6P2z0+BAC4e9lqbGjpyNh+klQ/dojARNFtoX7Egx5fA4Bk6W0wHsvo+hF87uALeHH8El4Yu6SfM2oVp81uCIglhKRTuzPVPHE7kn9iPnW44hizguySLgq5niemyCngmS8mv7l2C35891uwkZMHQuqKE4FJfO/MkQXthOqBifXgPChj5kE9TdgrhcPUbUEs2C8GZ/VjxHWt1N1ADkyN6j/fqIUICnKVLVQi82BsgTXhwo3YUeI216T0OOymVo110PVFsLqxFcsbmhFOJPD40Nmcx++ZGAZgPGfUKrXs/iOkXNT9t8TnSE448kkAF8eY6w390u5WrueR77dKjha47Q40OF26ek0IqQ/et/Nh/N9je/Dk0Lm8HyOEhlpOWBeurFhZnAfMPCgW5rIFK/SyhRJ/Pm/pWQEAuLqjF61ur+G+TM6DcpctyC0tF2pVF66cekrmX6qIuVuoDoVMm82G+7SW5HL70Ew0Sm1UI2XotlNKnDV8DSakXNT9t6RLD0HMvUMwqocrNhhu75RDEHM8z6gcrujLHdK4VDgWmMDO0cFKD4OQkiKLh6dmp/J+nHA0lbqmvJSUNTBRTNjraLevUjjyWHzHylS28N6NV+ADm6/DJ6+6HQ2ulFjgdTgz7giK8ZcrMHE2FtF/Xuj7IfJA6mmhuVRJBSamOr/UE31a3slsLJr1OFVVDYLxqdnpUg6r5Djt3NAjJBe1O1PNkxVaOvR8PIbJLOEvE+GQnpq7wtSO0e906SLEBVMbRzMX5lL3r2gobTJ1LfFHOx/Bx/Y8hZMzk5UeCiEl4/RMSjAQeSu5UFVV3/WtbedB+QITU+3R6mvCXgn0soUs4kG8TOJWm8eH16xYD7fDYWhXmMl1AMjOg/KIB9PRlHgQW+Bnnc6D+iEVmJh/KHctIcqEROh4JmZjUczHU8ecrXHxoJavwYSUi7r/llze1qX/vG9yJONx+6dS922RHmN+ngvzM1lFCPl5Lrd4nqWIbPPcNzma5UhCapvjkjg2HJrL6zHyoqemnQeO8mUezGi7v81uT8lfq97RyxaULGULwnlQxon1SqktpHAXWJHKPFj8524kNI/9WeYJABCQxINsHZWsiCQoHtQL4jMpAhPrqVUjkCrXDcbjGAnNGb4XM9EIfnHxNPZODGPIdJ2ThYRapBTnuFA8jl3jQzXRTpaQfKjdmWqe3Ng9oNemPXrxdMbjxH122NKCmgDgpu7laceaCSfieGr4PIDkxGfA5GBYqsg2z6FgfgsqQmoROfDtUp7hUXIIay3veri1RWg5yhbEAq7FRfFgseQqW1BVVU8fL6e4JYcKjmYpFyxmt4W3PfUj/NmLj+Hw1FjGYwKxsP5zPllKMsLeXW8LzaWI+NxlCtqudXzCeRCP4W1P/Rh/9uJjOBFIiuNfPLoLnzmwE3/50q/w+KWzhsct9DtRbZSiNOsDL/wCf7XrcTwzcqHoz01IJajYTPXRwVO4+5EHcfcjD+KrJ/aX7HXaPT69ldKu8SE8rS3uZZ4aPoddooVS/2q0WyQh39yzHH2+RgDAt04fslwYfOnYHr0+7C2rLivWn1CzKKqKY4EJQ07EyVmWLZD6ZNf4kC4eAsD5+RmM55G1Ii/aarnespyZB7p4YArVIwvHmaNsQV6Ul7MbiC2L20DGXoLMg5e0+YAVsvNgoQsl4Tyot4XmUsRu+nzKoYH1gE93HqScBIemk6LafslB+v1zRw2Py1XmUO0U+xw3Hg7q+UfM/SL1QkFFWgemRnFpPrV4Dkg7yxeDs3h08JTh+HsH1hY0uFA8lrbYl1vBPD1y3rDztLa5Deua29Oe593rt2PX+CVMRyP4233P4FjgMtzQvQxAMkn2e2ePAABa3R68a8N2y7E47Xb88eZr8LHdTyEYj+FPn/8F3rb2cmxq6cBsLIqfD57ErzVV8fK2Lty9bHVBf3M98cjgKfzToRfQIwVQnprJP0SOkFrhaGBC7/2+pqkVZ+cCUFQVf/Dcz/HdO96UdSEkOw9qOelZiAcLrQMvhEA0ufvbwrKFRePIUbZg/HyWd9F7WUsnjgTGs/YkKkWrxmwhcYspW4jSeVA3mF1idSceaM6DmCQq2gBMR8NpTiC7zYb7lq3FzwdP1rzzIFuJVCE8OZzqusQWraReKEg8ePjCKfzikrV1/9D0mK5OCgoVDwLRCD578PmM93/p2F7D729fu9VSPOj2NeCTV92Oj+99CpORML595jC+feaw4Zh2jxefuPI2PRjRiuu7luFPt1yHzx9+CVPRMD5/ZFfaMZtaOvCJK2+taftxsfjayaSjZCQ8r98WSsSRUBT20iV1xfHAhP7zH266BgenR/GVE/sxHY1gKDSHfi252oqpaDJDxWGz5b3bWo2kMg/KIB7EhPOA4sFiEZPl+XgMcxaL5ria+vcsdybHR6+4GZ8/sgu/sWpTxmPy6RaxUObikbTbpqNh+B0uo/MgvkDnAQMT6waz86DJWV/igd+Zvjw4NTsNWFyjNrV0YFlD8hoXWuB3otootoA/EkrNf4M17sogRFBf8bBZuKy1E1++6dX4wbljeHbkgv6F7vU14saeAbxx5aa8JqKvXr4Om1s78cNzx7B3YhgTkRC8DidWNDbjrr5VeNXAOi6McxBVFPj4HpE6QmR5vGnlJlzR0YMrOnrw3Mggjs9M4sTMZEbxIJyI4/ee/TmA2nYdACxbqFXE524uHsVvPflDw30JRTEE3hZ7Vy4XPb4GfPKq27IeU6yyBVV6vNl5MBEO4a1P/gArGpr1Dk5AAZkHeqvGJTP1qlvM34V6cx647A64bHaD8+Dngyctj93Q3KF3m6h154FctqCoappItFBksXE+RvGA1AcFXcEe2LYDD2zbsagXvndgbU5HQq+/EY/d97ZFvY5Mi9uLd63fjnetty5NyJfVTa34s8uvL9Ko6pdMU7mokoBv6ehWpI6ZCIfgdTr1xOk+f6N+34aWdhyfmcTxwCRu611p+Xh5VyJShkV3KSmXeKCqKgMTi4gsWgVNE/9QIq6XLbhs9qp0xhQrMFHOfJiLRRFVEvpn+qXxSwCSOSbyd3YhZQsJVdEXYm6K5zVPmvOgzsQDAPA6nYiZhDS33YFeXwPuXbYGXz7+MoDktU5Q8+KBdD6MK8qiS4xEiR1Q+50oCBFwBUdKgqqqhqAdmViNL5IIAYDvnDmMLx3bC5fdDrFukcWD9c0dAE7ixEzmkFC1jlo3CUt7rMStGsOJuC5QsGxh8TiyhHSGE3F9UV3OsMSFIEoEF+s8iEhZHYemx/H6x76LN63ahPdsuMLw3LLIt5CFkvy9cNs59ap16j0wEQD8DleaC+eVA2vx/s3XYioS1sWDtU1teh7ZQnNAqg3ZORxVEosXD6RMuExzYkJqjeqcDZCaJxCLZLyIlMPWTEghLGQx/7LW9zqmKPoCS3RkAYBlmpCQreNCuI6+C7rzoMSBicJ14LLb9URwUjjZ8nlC8ZTzoFrFA3uRMg/M16WoksC3Th8CkN6JYoOWrbSQhZIsTrgd1flekvyRvzdehwOuOsyx8FnkHqzXPvutbg9u6h7A1R19WNXUUjdlC3YpnnU+Hl20wG8oW6B4QOoEXsFISRA14FaUemeSkEIYnJ/Bmx7/Pr5x8kBex0+EQ2m39UrigdiJmotnTm5faOBaNeMqU2CiHpbo8lSljb7WyJZj8K5nforJSPJzXu6wxHzRyxYydIvIl0yil6qqaUGS4nu+kO+vcNw5bXYGKtcBsvOgsc7CEgV+reOCzNrmNgDJVqqfuOo2/P21d8JhSwm5tR6YKBfcvu2pH+ODL/6yYAFBLrEDkmIEIfUAr2CkJIxJu612m81QD1jqnUlCCuEbpw5gJhbBV7QuIbkQiyoxwbqha5nB4ig+89navkWUWp9opShX5kGqTSPDEotBLkfBc1pvcleZ2zTmi162kDFlJz8yZY4EYhFMR43dF3p8yfbDC9llFc4D5h3UB7LoVo8lCwAMzq5VjS3Y0NyONY2tlsd6nfXhPDBvbR2cGkvLgsmXcCJhuB7SeUDqBXo+SUmY0MSDW3tW4K+23Qi7zYb3PPMzXAzOsmyBVCUL2VyIKwqmtUXsV295LbwOp27bFDRqYX4xRUEkEbdMWI/UkZAmFkXREjuLUp0WmHdQDHJ1+RALiKVSttDh8eErt7wW737mZxgLBzEUnMNMLGw4tlcTDxZStiCe381Sm7pAdh7UY1giAPg0YbzV7cGXbno1bEBGt5evTsoWrLJTkq6hdBdGLuSwRCApHqiqSsccqXmqczZAqp5c4VTj2q5sh9cHt8MBp92u214pHpBqRHYNJHIsgKeiYahI7j61uL3wOV1pEwK/w6lPMDO5D+SJ1j39qwsceXVQ7swDigfFQd5BddntaUFwYoG8VMoWPA4HfE6Xnl8yFJyzcB4k74sqibxFC+FsoPOgPpBLTxrrtOuL35FcMPf6GmG32bIueoV4HkrEazoI2GrshZbaihI7IS4pqopwHW0YkKULJXCyIJ4fvYgvHduD8UgIqxuTLStXSn2vBcLS3eHx6beVsw88IQtF3oGdiobR6fVnPHZC+3y3e3wZ+0DbbDY0Ot2YiUUwG4sanm/vxDC+cmI/1ja3AgCu6ezDA1sX1/620gjxpdTdVFKZByxbKAZyuvjWtm584spb8drHvqPfJhLCq9V5UKyyBd0ZoF2n+vyN2D81ikuhWcyYxAPhPACS1uQGZ+73JqaLE5x21QOy6NZUp5kHIjCx39+U81ghHiiqipiqwF2lZU65sDqPLFQQ/96ZI/jJheP6pkG3twHzsRgUJLuQWQVRElJLVOdsgFQtHocD5+dnEIzHcGh6DP9xbI/lcfLiSuAq084kIYUg1yOKz28mRFmOLI5ZIXYczIFrf/nSr3Boegw/OX8CQNIWWutWxnKJg6JcpJXOg6Igi2Yuux0+pwu/sWqTfpsuHlRpyJ9XE60W2wYtlUmQfL7VWm33i2OX9M+coMvXoGeyR/K0aUdM4gSpbQyBia6FW9prgdVNrQCALa2dOY+V8xFqOQjY2nmQ/zVNVVU8ePogLgXndPGg0+vTs5EYmkjqgeqcDZCqRbTpEWSyik5YOQ/KlMZOSCHIC/zJHOKBlbPGChGkNZtjwuCpg77v5RIPxC5wM8WDoiAvgsS/4Xs3Xol1Wqq6WJRXa9mCEKinIuGc5XTZSGUSJN+DO/tXwWGz4fD0OEZN7Vb9Dqf+XuW7sDA7G0htIzsPfBZdCeqBVw+swzduvR+vW7Eh57EOux0uTWAUpU57J4bxwRd+gU++/Oua2TRKLKBsYff4ED6+5ynDfGE4NJ9WprihuQMNunjA0ERS+1TnbIBULY0ut2ESuayh0fI467KF8gSqEVII8gXfqg2jjKiBbs2R+J/JeWCmHqzM4rxQ6las+b73ZOF4tIWzw2bH8oZmAKnvRbX2sW/zJD8HcVXBTCyS4+jMiMW9R/s72z0+XNe1zPJYm80mOenyzDzQFlRytgqpXWTRzV8H528rbDYbev2NebvizB0XvnX6EA5MjeGp4fPYMzFcsnEWEyv5MZMg/qFdj+PZ0UH886EX9duOz0ykHbe+pV13HizWIUVINUDxgCwKK+NBJBHXJ5wd3vTMg1LXRBNSCHOSOyBX2YKwHuZq0SVqYT9zYCemIuGMx3nrYEEhvt9j4SA+d/AFnJ6dKsnriAVic52GlFUSWSAQ/57iu1Ct7ehcdodewpLLMZSNaCLdGXB5Fru227GwAGAhqnmqVIQhC0MOTKxX58FCMYcmnpiZ1O8bCs5WalgLopCyhedGB/Gx3U/iY7ufxFdPpLd63tDcjgYXnQekfqB4QBbMO9dv13+2qvccDs0DSF5IGqUgIQYmkmpGdh4EYpkX+kBqAtCQY9IoL7j+68TLGY8zt3msRWT3xEODJ/Gnz/+iJK/DzIPSIS+chZAgFuTNVSoeAECHJxlGOp7DMZSNiKlsAQA2tHSkHderdVpY6PWMzoP6wuA8oHgAIOUGOzM7haHQnOGaOhSaq9SwFoRlYGIebrqdYxexc+wizs/PAADevnYrAMBls6PD45PKFph5QGqf2p+xkrLz5lWX4dDUGJ4bHbTs6Xs8kLRtrW1qM9jd9MwDqfbt4cFT8Doc2NzaiZ8PnsL9KzYYQhbrmaHgHB4ePIk3rNykW2+XCjtHBzEcmscbVm6s9FAAJHcbZiXLcybrvaqq+PngKTw1dA5A7kmjvBP68OApXNbSiVctX5d2XD2ULZhbJ4ZK0O975+igPiFlq8bi43GkOw/EVLqxihPl2z1enJpdpPNAL1tIfRfljJ/fWrMFW9u6sb4leduCxQNmHtQVdkiZB3Vw/i4Gt/euwImZSTx04SR8DuO1cShYI+KB5jz4jVWbcHBqDEcDE5bOA/P3fmVjC960Mhk02+Ry48aeAezoXoZmVzIM2c/MA1JH8IxHFozdZsNN3QN4bnRQT6iWEVa1DS3GcMVU2UJyYTYUnMM/HnweANDl9WMsHMTJmUn87dV3lHL4VcOn9z2LI4FxHJoexz9ed3elh1NWPrbnKQDAxuZ2bG7rqvBokjWaclBSpuC1Z0Yu4HOHXtB/z+U8uKqjF8+ODuq//9OhF7DF4u/11MFupM/hhMtmRyzPvveF8JkDO/WfWbZQfIxlC0ZjYlMNOA9ylRtlQy9bcKT+btk5dFVHH67o6NF/X2j3IFHrXA8uIwI47HQemHnFsrX4z+Mv42hgQs846PU1YDg0XzvOA+3af33XMpyYSZbeWQmEU6ZzzbvWb8fNPcsNt8nOpQZNfJ2PUTwgtQ/LFkhBiJ1S4TyIJOL44dmjmAiHcFyIB6bODC67sUb0klQDN6alWR+aGi/twHOQUBT85PxxnJsLlPy1jgSSf+u+yZGSv1Y1IdcUnp6brtxAJJ4cPmf4PW5yHoyE5vHDs0fx3bNHDLc35FhQvXL5Onxwy/WG2w5MjaYdVw87VzabraRugNlYRHcd/J9r7oSjStP/axl5V9xsr2+sYrGmXXNuTUSCOY7MjF5WYOp88t83vxafuup2g3CQPG5hzoMRrZyvx9dQ8BhJ9UDnQTptHq9euiCuc9d09gNIbhZZ5QlUG2KENtiyhgBPSBlGn7zqtjThwIzYaPjpheM4MJk+ByCkluDsixSECHgTVsz/d3wfvnB0Nz68+3EcDwjngbFe1DzZstolqnQd88ODp/Cvh1/Ce575WclfS97Jq4WLarGISJPtQLTwdPRikVAU/OPBFwy3xU2753/+4mP4wtHdODxtFLf8juw7Tm67A69evs7QdeTliXSxqB7KFgCgpYQdEIaDycVXm9uLazr7SvY6S5m1Wl93IN1eX93Og1S7xkIRdc1mx8Xyxmbc0J3edWGhrYeFWN7ns+5QRGoLWbxkYGIKEZJ9Qav9397eDRuS1/3pKrje50LMxey27ALhhLbhtbm1Ezu6B3I+rxAPpqMRfPDFXy6qrSwhlYbiASkIr+Q8iCkJ/ODcUQDA6dlpRJUEVjW2YIXW6kvglmye4+EgfnjuWNrzlnLxkQ9HA+ltdkqFvKAUIZNLgZBU87eYyX6xkGsQb9F2D8y9noczWC5FgnIu/u6aO7BVK1d4eTK9ZVW9JLCX0nkgbK/9fi6+is2/3nAv/nTztbheak1oFg+qtdsCkFq8LSZnI6okH5uvkOdeYGtSUfPNz2994ADLFqzoMGVWrWho0c8di2mlWgzGw0E8PXweiSyldeLan2zHms15kNz8yjejy1zieKlGuk8QYgXFA1IQYoIVScRxaGos7f5XL1+f1hvYJe3UfPbATkMbH0GlnQflrD0PS7WypWprV40E46kJ/lCo8hdQIR647Q5cq1ksE3kuCHJlHgjWNLXp5QtWuy+iP3at0yJZ22VbbzHQd265+Co6m1s78doVG4wBt2bnQRUHJnql61GhRBPWzoNMLKRsIZpIYFxbbPDzWx/IC1CWLaQwL6Z7/Y162OpcrLKdBt7965/ib17+NR4ePJXxGFUrXLDDpueaWAUmilJbs1iSCbPAJBy6hNQiPOORghCL7HAigSm9dZoXN3QtQ5PLjVcPpCfKy5Ot3RPpu68A0FThulo5zCqhKoZezsVGvpAGl1ACbzCR+lurIYFZtE5qcLrg0BZPctlCtpKSfMUDINXizQqvvT5OxfKubb5CXCAaxu7x4eSOrM2GFpcHl4Kz6PE1YD4eQ1RJoN/XpH9W+nxNJRk7MWLOPKjmsgXxWbMK8M2XiCJaKebrPNAWFnm8pnDN+B1OBn3WCbL476sT8bcYdGrhpUBSTG5wutDkcmMoZGyHXAmCmri4a3wIr1m+3vIYRbvc2222rALhSW3za7VU6pUNcz7SiZlJ3Nm/ChfnZ3EkMI7LW7vQS2GR1Ag845GCkHd6xM7tZa2d+IutN2R8TOpEXLo09sUi77bNxqJ6+E+xUVTV0O/XquVlvSKXLQyH5qGqappLpZwI4abB6YJT23WUyxaytVZaiF3V7XCgyeW2nEDVQ7eFQvnsgefx/NjFrMf0+Rr1HVvu3JYHl2kHvprLFkR7xcWcR0XJQ74lRAvJPBjRxINef2NFz3WkeMj/7qXcZKg15J14ca4W5465eGXFA4E9y3dQzaNsQVXVjMHgmTBvNJyYmYSqqvjj5x/BbCyKPl8jvn7b/Xn/DYRUEp7xSEGkxIOEvrhqyKG+CzvoTDRznXu2WrRyIO84FyvM7/xcwLBgBoBTM1OQ97MjiQRUVcXp2am8A7hqDfH3yYvnqJIw7OBUAvH59Ttd+iRQ7raQrf2ba4FZBZksjvW44xBREhldGyOheeweH8J8PKbv4GRjKDSHS7rzoP7eq2pEFlLtsFV1Xbco+1mMeHBmdhoAMNCQn7Ml1T0o9zVLnOMW4lQi1c1SEvwXQrs3dY0TZYDCtVRO50EkEceFuRnL+xxZxANFL1uQ24sb5yjDoXnMxqJw2uxYlafzwHz+vBScxUQkpL8nQ6Ha6EZBCEDnASkQYU9WoCKgiQENOWpixU6NbuF0unBz93L84tJp/Rhzyn25kScEyb+rZVHPdywwgT/a+QjWNrXhP256FYDkJPV9Ox82vq4SxxND5/Dp/c/inv7V+NC2Gxf1utXITy+cwL8efint9rl4tKK2T138crn1SYUsYi2md7yZDo8PZy3agJrry2uVTmniqKgq4qoCl834twXjMfzuMz9DKBHH9vYevRY8FyK0ks6D8iALYw0uV9bdukrjtS+ubGEqEsZYOAgbgHV57iQuJPNALD6cdfI9J/l32VhqtEtuzVcOrAWQEg/KmXnw0d1PYu/kCD6/4z5sMnX+yioeWDgPRB6KQC5ZyPfabRYOx8JBnJ83ihsxRUkrFyOkGqHzgBSEbO2cCCcn/7l2VcRJdlJL2O/w+NLUWHPKfbmRxYPpaASheAyheOE7DL+6dBYAcEoKRPz54Mm04yKJBL52cj8A4JeXzhT8etXMd88csbx9tsIJzPNZyhZmopGi5jJYJTMP+Ounhv8tqzfj+q5+/Xer3bnR0LxuEd83mWxb6XU4cf+KDTmf32135J1uTRaHPCluq3AXnFzoAb5K7nP1VCSMqJIwdHo5MZPssrO8oTlvh4VL6h6UC+FkctHeXjcsJl+jntnQ0oHXLF+HP9h0Fbp9DQCgByaWy3mwe3wIe7Vrywuj6SVx2cpMRGCiQ8o8MDsPprUNs26vH/li3lxTAbxsyv6S86AIqWboPCAF4bTb4bTZEVcVTOrOg+yTLrPFu8PjS2t1l2/KfamQJwSTkRD+4LmHEU7E8bVbX5d3C69MKKoKu81muVheChZIcw21oNIJzPMxOTBRK1tQFQSiYbzp8e8X9bWsyhY2tXYW9TUqid/pwt9efQfuffSbSKgqIokEmkynhVmLutdl/ia8f/O1ODcXwMvapM+KXl9DVe+A1xPyDljHAibJlcArBfhmy1DZPT6ED+96AgpU2GHD311zB67u7MOJmaS4uz5P1wGwQOeB5mRy5tnJgVQ/SzmnJht2mw0f0DoLCVJlC6XfKHh+9CI+uudJ/XdRhiC7CbNdQ0Rgog2ZMw9SOUn558BYzY/3mMWDeKxkOVuEFBNeyUjBiNyDSc12nGvHxnyx7fD40k6olXYeyK2+Hh48hYtaXdpIaL6g51Okv0eUd4iJqoxZPDBnJNQDmSbOlU5glp0HercFRcHeCeMi1mW3Y2tbF7526+uwpbVTb724EJqkVqRvW3s5Nrd24vc3XrWI0VcnQmiTP9fhRByqqupOJRlRivDnl2cOXJWPI6VHblnY4anuCa3cJSeSZTH/0d1P6osJBSq+ezbphpqIJNuuLeTztZDAROE8oHhQP7xz3XZsae3EA1t3VHooVU+j1mGkHBsFL41fMvwu5qfyxlBW54E2Z5O7LZgFbzknKV+cdjvuX7EBt/WuwI7uAQDA0cCE4ZilsIlE6gM6D0jBeBwOzMVTJ+dcKqy5F3K7RdlCNWUeyKUGhYYnBqRwyIlICC67wzLEJ5JIICCp8kOhOaxpaivoNasVZ4YLdqXFA3kXQUzuFVVNC+9874Yr8cZVmwAA/3LDvYt+3Xeu24Z3rd++6OepRrwOB4LxmD5h2zU+hI/sfsIgpsmIEMQ+fyM+c+1deOClXwEAVjW24Pa+lfjKif3acfVT4lHtyGULHZ7qdh7ILolIIm4QEwQjoTndASDYPT6EoeCcfg5aSEeJhTgPWLZQf3R4fUW5DiwFdOdBGbotHA8k8wiu7OjF3olhXazOt/xUiIs2pNyyO0cH8ZUT+/BO7XqdyklaWADq+zdfCwD49yO7Le9fSi27SW3DKxkpGDFBExOvXCdS84Su2Z3e77rSzgM5+V+22QcWYLeTE3PlsL2JSAiPXjytX5x6vA24vK1Lv09eRBezzr6cqKqKiLbDbKZqyxasnAeqgilTV5AO7+Jr7e9dtgYrGprx9rVb67plm9fkPPjysb1pwkG/vxFtbi9a3V7c3LNcv10WFP1OF27sHkCHx4dWtwe39C4HKQ9G8aC6cyYcNrt+fjF3bxHnooNTY2mPUwE8PHhSPwc1LcCG7M5gabaCZQtkKdNYpsDEhKLomz47upYBSM3B5I0hc4aBjCI5D+Q5yzdOHdR/Fm22C+2esrnNWKooxJXgIvK1CCkndB6QgjGXIeQ6kfocxvv9DmeaTavimQda4NbfXX0Hru3qx8d2P4mdYxf1gJxcHJgaxcd2P4X3brwSz48OYv/UqH7fp/c9qy9U37fparxp1SY8dukMDk6N6W3CBIPB2eL8QWXmr/c+jWdHB3FT9wA+cdVthvsyJY2XYzciG2Ii4He6dHfEpeAc/u/RPYbjirGAanV78V+3vHbRz1PteOxG8cBqR/eqjj58YMt1abc3SueRBqcLa5ra8O073liikZJMyLv5tRBS6XU4EVOihtKzp4fP4x8OPo+PbLtR35E088jFU/rfV3LnAcUDsgRpKlNg4pm5aUSVBPxOF7a2dwOwFg+yfWcNZQsZci3mtUV+oe1rb9TKFoCk667T68OBqTGEGJhIagReyUjBNJp2aXKdSL2mdnw+pwt39a1Gm9urL8zMVvFyE9YuCmLntEULr8m3bOE/ju7BXDyKzx16ATvHjCm/QjhocrnximWrAaS6VsyYnA2/Hj5f4F9QWZ4dHTT8X8ZcttCu1VFX2nkQlCyIjiyT+1pYQFULQlgU9edNFosyq9sAowhZ6OSMLB7ZedBq4RKrNsxuFwD4m5d/jWA8hr8/sBPHtY4Kt/augN/pwieuvBVtbi8mI2Gc1HJoMn0mrRALi1geqft6q0YbQ/bI0kO4Uktty3986CwAYFtbNzq1UqvpaBhxRTEszLO5hcQ9ycDEDOJBbHHOA5fdgT/adDV8Dice2LoDPqd4f+g8ILUBxQNSMGYbd67MA69JxfU7XWjzePHtO96ID2o7kPFKByZqkzyx+GnRJs0vjl3EO57+CZ4duZD18bk6MqxsbMH37nwTmrQAIfPxd/WtgtNmx9HABE5ZBCtWM3HTBdksBJl33bq9yTZOYjdCUVV86KXH8bf7nkl77nNzAbzj6Z/gYYs2l4tlXso8yNb/udqt29WEvpDTJkNWO06Zdnnl84gd9VvaUe3I31dxvqpmhBBr1UJPVaELBG9fuxU/ufstuKlnOe7T+tALCnEe7J0cwQ/PHs16rMjyofOALEV8krBnVdK4WH549ije88zP8B2tHfSrlq9Di9ujX88nIyFDOVO29qqpsoXM39f5ArotmHnDqk346T1vxdb2bvg1V249BmWT+oRXMlIwnaYQrVwqrMNmN+xmiQuK3WbT028r7jzQdq08uvMgOWk+ND2Oi8FZfHzv01kf35Jhki0WQb+99nJD0q85RPK6rn5c2dEDADg8nV6jW82YS1DM6r653rdLa/8m2jedmZ3G7okhPDF0Lq0m8cWxS7gYnMXPLhRfPJiTdhEyhToCuYUhkkKIB6IMKGBR9tOcYaHGBVZ14LY7sLqxFR0eH1Y0Nld6ODkxd/gwl5qFEnHYYcOKhtTfckffSsMxCxFJ5F3JLxy1DkATsGyBLGXE9UBF9m4ohfKFo7txbi4AAOj0+HB9Zz/sNps+xxgNzxsW5vmVLdgzdmUISjlJxcCnuXJD7LZAagTOhknBmG3cPmfuj5PX4dRP3LIlWdjFzbvX5UbsWomL3UJ77sr1+2uaWvH5HffBZbNjJhaFoqpoM7U8M+dGrG9ux7HABF4aH8JQqPpDE2eiEfz13qdxZ/8q3KAFFAliSgJPD5/H40Nn8aGtN6YljfdqCftiV3pOeu/m4zG0uh1QVRX/Z/9z+JVmRzw9O4W4ohQ1eEwPS3O5s/Z/JvkjPtdit0eU/bxv09X4d22hZS57EtRzkGQtYbPZ8MUb7wOAjPbdasKrf+biCCfiePtTP9bvE+eWNo/XUJo04DeKIpk+k1a4Lc5BCVXBX+95Gmua2wydVGJs1UiWMLLwHs7QDaVQzE6GewfW6t/xPl8jhkPzaQHU2cQDuduCWYD88xcfwwNbbzCELBcD4TxgtwVSK/BKRgpGtnG3uj1Ze+cK5J12+WentmDI1MqtHCRURb+oiIlo8wLtuvIO61tXb4bb7oDNZkOL25MmHADGi6rb7sBAQ7Petq4WOi788Nwx7J8axT8fejHNLhxNKPjMgZ3YNT6Ev9v/bNrEWexmDofmoaqq4UItLqIXg7O6cAAkJ+Fn56aLNv6EoiAohfplKlu4tWdF0V5zKSBEt9HQPBRV1buVbGhp149ZSH05qQwuu6MmhANAdrsksH9yxHIXz1x6ZA5EM5fWZcNt8b4cnBrDzrGLePDUQcOiRpQtZHM2EVKv2G02/buVb8vEfJmMpOYNXocDrx5Yp//e69fmUqE5w/kgu/Mg+X+7zYYr2nsM9+2bHMGHXno81W2hSNewlPOA4gGpDXglIwUjZx6sa27PcmQKOTTRJzsPtElVvIJlC984mWrF49GdB/mJBz84exRfOLJLv5B96qrbcFf/6pyPkxX4Pn8j7DYb+rQL3iVJPHh+dBAf3/NU3sGN5UIuVTCXLcgX6D0Tw2m7+ltau2C32TAdDWM8EtL7MQPAfCx5ER0NB9Ne88RMKjX99OwUPrb7Sd2yuFBkt0Oj050mgP2/m1+D/7n9Dfhf228q6PmXKuua2wAk/63mNNcNAKxubK3gqEg9I5ctCOHVLBZY5ZbIDriFuF7ka5XYgZTPcXLOR0wvW6gNIYaQYmMVaJqJS8FZfHT3kzggdavKxFAo2ZmqxeXBg7e9Ht2+Bv0+sRFzZHoc/3r4Jf32TJkHqqpKzgMbOr1+fNfU6WcwOKu3FC9WoC8DE0mtQfGAFIw8EduQp3gg77z4JSFB7PgmlMo4DxKqgq+fOqD/LnaVzG6BTFPLLx7djR+eO6bvnq9pasvrdeWdrh7totfnbwKQVMvF7tVH9zyFZ0cH8eVje/N63nIhXzzNoXhRJYE2qexjODRvuL/d48OqxhYAwPHAhN5SCUi1T7RyXxyTWq79wbMPY+fYRfzLoRcLGr8Ys9/hhNNuT3NHtHu86PT6s3ZhIOlsaO4AkBQPxHfC73AaAulWZhESxPnkFcvWlG6QpK7QO3wkEnrJ1x19Kw2lZ+aQXyDVRm6hLJeyE6zsy5PS+SzOsgWyxBHiQT51/Z879CKeH7uID77wy5zHik2WNU2tencsQb82l3ppfMhwezRDeaw8+7Rrk702jw9+izILG9IzqwpFPD8DE0mtwCsZKRhZPBhoWHiglqjzAqTMgwo5D8xWOrGD1OnxG6zsPotJopWK3ZKnY0EuWxDvZ68mIgTjMcyYFuSXgrN5PW+5kN8bc0ZDVEkY/j3NF0af06kvMo/PTGIiknIZiJrCIYu/VzgPBudn9F2Cs4U6D7T3VyxqzWULVtZkkpuVjS1w2e2Yj8dwNJBskdesTey+d+eb8LVbX2dZxiP4x+vuwZduehWu7eovy3hJ7SMWJ/9+dLfeqabP12i4TnWYQn4BoMldmHjQ5vHh41fcAiC1GJGvBbIYqndbYNkCWaIsxHmQb8nmZCSEzxzYCSBVoiDTZ3EbgLRAZoFcamTPMe/zO11Fy0gSmzBBBiaSGoFXMlIw8gl1e3t3Xo+RMw3k3VzdeVChzAP5gibvKDnsdnR5UzY4qx2m+Xh6G7p8k/nlxanYpfc4nHpK8Pl546I4W61eJZDft4vzM4b7YomEQZQxpyw7bHbd3n56dhoTUu2iEA8uWYRGnp6dQkxJ4OWJEf22YDyWtf1SJmb1sMSk2GOuSabNuDCcdrvuHvjvE/sAAN3aZ7rV7dV3hDLhczrzdu8QAgA90nl672Ty3NDnb9TFWMC6bOH+FRsAAJe1dC74NVc3tQJILUbk87MsHojb6TwgSxXvAnbX/XmEbwPALy+e1n+2Kp0VZQtmMs2jFMl7YMvRJriYmT3m1saEVDu8kpFF8dVbXocv7rgPPRlO0mZUWIsDzgq3apRTbv/+mjsN98kt5axsavOLsJrJyrXcvUJcCE9IFn2g+sQDuUbvkmm3YD4eMzgPrC6My7RF5M7RQeydGDY8FgCGTc/Z6HQjpij40rG9+MG5VG/1uKrg9Nw0QvEYvnxsb9r7lolZs/PANLln94XCuVvL/BjTcivu7FtVwdGQeufNqy8ztGEEkrblO6TPnZUj7N5la/H319yJT19z+4JfU4iLItMgksl5wFaNZIkjNpvycR74HHI5pHXO0w/OHsXXTyVzqtY2teFVA2vTjml2ewwdVK7s6AWQzXmQ+lm+9lvNu3rznPPmg9g8UzLMjwmpNnglI4tiWUMTNrR05H18plOjCKpLVKhVo6jD6/L6DYE7gNFhYeWMMIsHhexgAcDWtpR7Q+zaHp8xLoLNHQ0qjZwOfClkLDGYMV30rSYNmWyFQswx5yRs1NL6f3juGM6bnA67xi7hP4+/jG+fOYz37Xw4r/GL1ppNGcoWSOHc1b9ar+X0O124s39VZQdE6hqPw4l3Su0RnTY7un0NuKlnQL9tpZaxImO32XB1Z5/uPloIol1jVElAVVWj8yDMzANCBKmyhdxzGHmuYFXCcCk4iy8e3a0f96ZVmzK6BOU5xiuXJQWGaEJJa/EIGOd3svPASmzINHcpBHsVdBsjZCEUr9kqIXlgdcIGAKeWThOv0MlT2OutnAVyKKDVRUSIB6saW/DejVfqIYD58qWbXoWJcAhrm1M27fVCPNDqxQX5Og+eHbmAhKri1t7SthiUHRuX5s3igbGcI6Kkiwc9JqHG63AgnEhgPh5FVEnoAsRfXn4DtrR14dDUGHZLDgUg+V6dmJnEzwdPLrjMQM88cArxgJP7YuF3uvDZ6+7GgclRbG3vLloyNSGZ2Ci1Ar2uq18vC/vKLa/FaDhYUDZPNuTzTVxVTJkHqQwX4Uxgq0ayVNFbNebRjlDeePjXwy/hX254BRw2O8bDQTx26YyhtBQA+rMs5Pt8jXpOkljwK1CRUFW9RbhAdsbKGwnRRPqmVqaSiEJwVNh5S8hCoXhAqoJKnzzFBc0qGOe6zj7sHB0EkJoEysxrC9AGpwvXdy1b8GuvaWpLq+8WWQAX5mf0XSsgc4shmVA8jo/vfRoA8OO732KZ01As5LIFc9jPTNTsPEgfu3mx/9bVW/DVk/sxH4thUtu5c9nteMWyNbDZkq2TPnvwecNjXrN8Pf7z+F7Lto65mIsZnQekuGxs6cDGBTiTCFkM3VLuwauXp/q9DzQ0F104AIyZNVFFMeS6nJqdgqqqsNlsqcBEOg/IEkWUIuQqW1BVVe/QAwBHAxPYOzGCazr78Kl9z+Dg1JihSxUA9PkyZ+jIwbxyqUFUSaQ5geSdf1lXsConyJXbsxDoPCC1Bq9kpKxkOjmmxAM1ozuhlGRzHrxq+Tq8ceUmAKmd/+dHB3F0ehxAynnQUGDLLyvaPT7YYYMKICAtws2hg1bMxFIXXhFOdGZ2Gk8OnSva+PTnz7KLYC5byAchdDw0eFLfLejw+PX+616HE/92w72Gx3R5/binv7CWfrMUDwipG2w2Gz5/w7342BU3FyTkLhR58RFTEgZn2KXgHF7WghtjemAiA1jJ0iQVmJhdPAgn4mmbNMOhOYyE5nFwakw7xjgPas/SuUcW+OTMEysXp+w8sOcITGTZAlnKUDwgZWWr1pXBvAMjW8QqcQIVmQdWzgOHzY43rdoIIOk8GArO4aN7nsIfP/8oVFXVrfvFtGXbbTY0ay3EpqLpqd3ZkMUGMbb3PvsQPrXvGUOHgmIQzBIWGYjmJx6Inenru/oNLolPvvwMgPSE9MtaO3F770r99xa3x7DLKMhHhBKtL5st6p0ZlkhI7bGptRO3SeeHUmK32fRShJiipDnDnhm5AEAKTGTZAlmi+Jz5tWqc1uYNHrsDr9Gu6xPhEJ7TvktW2LJcqzdIpUx2m03/Dlq5OI3Og9RzXt7WBQCGQNZeU8nlYqh0tzFCFgrLFkhZ+f2NV6HL68cdvasMt8s7OAlVRbn3Z8QOvZXzAEip1zElYahl/fngKT1NvtjlAc0uD6ajEYxLdnxFc2Zku1hOS4v2kOlCfWp2Cld09BRtjNl2ETKlJANGVf9j22/GwxdP4fUrNuLA1Kh+u7AKWrVXkztTtLo96PE1YltbN/ZLjw8m4ln/TQbnZ7B/ahQ2ANd29WcdIyGEWOG22xFPJIUDIe7aYYMCVS/dEmULDEwkS5VUYGJqznAsMIFGpxvLGlIlAAGtZKHZ7dGv8xORkKXosKmlA7+/6aqsr3tb70oMh+axpTUZZO1yOBCLK5YbMUqGbgsf3X4zHrpwEq9evg77JkegqCpa3JndDgvFLjlvCakFKB6QstLocuN31m1Lu112HsRVBe4yywdikZ2pv7CozVdh7K7wuUMv6D83uIorHrS6vTg/P4MRUy1/OJHQVXwr5HKBUDxmCHks9uTVLE4YxxHNeJ9Q8gGg19+Id2kp6VbujQ5vunggiwLiIv7q5esM4sFcLJJVPHh6+DwA4NrO/rTgRoDOA0JIblx2B5CII6Yk9G44LW4PpqJh/fwYY7cFssTRyxa078TRwAT+eOcj6PT48D93vFE/TuQdtLq96PD4ASTDR0OJ5LX8zasuw/fPHYWiqnhg6w6syBFQbbfZ8Jtrtui/u+0OBBGz7FwlyhbMGwedXj/esT45b71La0FcTFJlCwxMJLUBr2SkKpBTqCsRmhjUnQfWi025zGIyErY8pthp8s1afd6YqV3hXDzzohyAIWwomIhjJpo6vthf+FDWsgXr96nF5cFHtt9ked9VHb3YKgkLgLXzwGFPXdzFpOT23pX4nXVb9dv3mLoymBHv05qmVsv7KR4QQnIhrg0xJbWb2aoJmuK6IsoW3Mw8IEsUn8l58J0zhwEA45GQoVxAtGfu8TXo1/6JSEhvfbqhpR0f2roD791wRU7hwIpmLd/IyhkpxlHuSz/LFkitQfGAVAXyQi2uVC7zwJuhbEHuCjAZCVkeU8zARCBpxwegl0UIzGKCGTlrIBSPIRAzignFIqYkELMQekTbw0zOgz/dch06vX7L++w2G/7wsmsMt7VbiAdyqrrAYbfjd9Zt0+sS//HgC3hi6GzG8YtOEZlEH54cCSG5cGvJ71ElgajWjrZVC3DTnQcqWzWSpY24zk5pmy+7xi7p981LGyJDwTkAyVaIwnU4GQlhQpt3tXt8uKt/Nd4quQkWgtiUCWQRD8pdsuhgYCKpMXglI1WBzWaT1NfyOw/Cieyhh3IwVmbxoLjOA2HHN4sHv7x0JuvjzIGJVgGKxSCYIe9AdC7IFIzkyCHrm9/HVovawjv7VuG+ZWvxoa070u5rlDon/PDcsYyvk609J5CqQySEkEy4pDwcYYVu1QJYzc4Dli2Qpcrm1k7YbTacmJnE2dlpw0bGrLTRcCmkiQf+Rt15MBkJ46IWbmzlRFwIYj4xbRHorJctlNl6IMQKigekVuCVjFQNqXaNlShbyNyqUSDsqWUTD7QJ6GjY6DT41dDZrO9RwFS2IJcxzMeKJx5kKlnI1fYwt3hgfHyrO70TgtNux19svQH3LEtv0RiX2jwdnh7H6dkpy9cRYY/+DP/mLFsghORCXBeictmC7jwwigfmLkOELBU6vX7coLVPfXjwlOG+uZjsPEiKBH2+RrS4PWnzhcWKB2JeZVVWKUyv2QKpS4GDgYmkxuCVjFQNzgrWfaVaNWYWD0S9aibxoCODFb9QRE/iUc150K/1FQ7GY4YcAzOyHW8kNGdQ2Odz5CVYkVAVPTVcZiZDN4W2LD2XgdyLcrMIs9BU4yFt50Lw0IWTlscFczoPFvSyhJAliNyJJyoFJgJJUTqhKnrnGJYtkKXMFi3P6JLpGi2cB6qqYlhyHjhsdvzWmssNx2a6XueL+G5atZJOBSaWFwYmklqDVzJSNTi0XRl557hc6K0as1yYUs6DpGL9R5uuNtzf72ss6pjEollY2XwOl76rnymMMHlf6qL4k/Mn8IUju/TfCylb+Nt9z+JNj38Pg/Mzlq/TY8ofsMookHHkmECbrb0tFs6DbJjdI7+8dMbyMxXKlXlA5wEhJAeibCGZeSDKFpLn7nAijmhCkY7llIssXUSmlDmsUIgHU9EwwokEbIDeAekd67dhh+ZYKAZiXmUlHqQCE8tctiDEA9B5QGoDXslI1VBJ65buPMhatpCcJE5Fk86DZtOiNpddf6E0mha1HocjZbnLsOsPZHZGAMY2k/ny9PB5qAB+fP64fpuqqhiPJB0R5jaHZlvhvabSgoUuyjOFWGbir7bdiNWNrfjstXfBbrMhGI8ZSjdUVUVCVXQhJWPZQplDkwghtYdb6rYQUYzOA8DYHYeZB2QpI+ZXZiejKFsQ+U7tHp8hpPrPLr8e29q68X5TmHIhpJwHVmULlck80PO+KhAWTkghLGxWTkgJESfQeAWsW/k4D0SqdlizpprFgmKr1eaxuO0OtLi9GAzOWqrmQHI3PZtAsFDxQA7wiSYS2D0+hA/vegKqbvBLFwvapN+/cstrsWt8CI9ePK3fVuoL87b2Hnz55lcDAJpdHkxHwwhEI+j0+qGoKv545yOIKAldUMj0b17u3QdCSO1hcB7o1wYP7LBBgWpYKLFsgSxlxEaAuRPTrCawiY4KVnOKf7r+nqKMIdsGjJjTlHvjQMyJEnQekBqB4gGpGirZ6zY/54Fx4tfoWpidfqGY7fRuh0O/bTpD2UI21wGQLFtQVdWwMDb/LiOr86FEHJ/a90yatc6cSZCQSgR6fA2GHQQgd2BiMWl1J8UD8X4Nzs/g+Myk4ZhM/+blHCchpDZxSc4DUbbgcTjgczoxH4/p2TBOm52CJFnS6M6DmLXzYEJzHhQ7P0qmtQrLFuRWjdnmY4RUC5TBSdUgLJ2JimQe5CEe2IyL4CapK0BLCYQE81iSzoPk61gFGALARCRoebvgYnAWb3r8+/jW6UMAko6Ld/76p/j0vmctjx+XxIjR8LyhpZLAnEkgH+OyO9JEl3K2QGzW/l3EZMUsHADZAhN5ASeEZMdt0arRIwm9YpHCkgWy1PFqgdTmloQzsQi+fGwv/uXwSwAW31EhG81SYKJqGkeqbKFkL2+JPCdi7gGpBXg1I1VDpVo1xpSEXiqRKTwPSJUtCBpdLnxwy/XwOpz42BW3FH1c5s4PHrsja49iIGX7y8ZMLIL/d/xlAMCeiWFcDM7i8aGzuoBieL5w6vnOzQUsn88sHrxq+To0udx448pNAFKTa0E5F+Xm9+t4IF088GfosGFj5gEhJAdWgYluu0MXf4U9mmGJZKmTKb9oLhbFt88c1n8vpXgg5itxVUkr4xQL93Jf+2WXo1lYIaQa4dWMVA2pzIPynjyD0qJ5IWULDU43Xr18HX5895txRUdP0cflsNnhlQQLtyEw0bpsIR/xQEauPXztY9/GztFB/fej0+P46J4n9d+tXAdAaoEu6PL68b0734Q/vCzZjcL8vpWzHEB2ajw6eAo/OHc07RhzWYWAZQuEkFwYyhYSKfFALzHTuvMsNPiVkHoj0/zq1yMXDL+XUjzwOpz6vOrJoXN459M/wb7JEQDQnQjldh3aKR6QGoPiAakadOdBmcsWwomk+uy2O/R2kVbIi0yXza47EXK1HlwMPkfKCSGXLWQKTBROgVf0r0GDyUVh5aoYCs4afv/Ynqf0C+i/H92T1xibLUo25PekkMyDD2y5DgDwv7bflNcYMiHer+loGI8PncvrMb+1ZgsA4I+KkOxMCKlvhLNqJDSHmKrADhuaXB69HErkrZgdWIQsNcwCWqZORx3e0okHANCitVJ98PRBDAZn8V/H9wEAxMyz3IGJ8pyoEplfhCwUigekatAzD8pcthDMI+8ASLXkAoAGV+byhmIiL/g9DkfWHsVAynmwuqkV/3P7Gw333da7wvB7XFEwFJxLe45D02MAMocyvmb5Onxbem6vZPu3UuzTyhbyuDC/Zvl6/OTut+COvlU5j82G/H6J1owf2roja3nKuzdcgZ/e/RZc3dm3qNcmhNQ/wnlwaGocALC8sRk+p1NfGE1p51GPg+IBWdqY84VWN7XhA5uvSzvOakOimIhNBdEa8tD0GM7OTusbJ+UOLKTzgNQaFA9I1VCpbgt6p4UMte8CWTXPtvgsJvKY/A6XHvZjTisWTGniQbvHm/b3/OaaLQaBJBCN4FIoXTz42YWTiCoJjITmU68t/b19/ia0e7xoc3vhtNmxsqFZv89t4dww35avU6MY73GrO9WWaV5rB9Xl9eu3ZyJby05CCBGIc/KQdi7d0NwOINlpBgB2jV8CAHjsLFsgSxuvSUBzOxxY2dhiuK3B6Uq7rdiYc5qA5LyncoGJdB6Q2oLiAakaRA/seJmdByFtR1ouEbBCvuA0SJ0WSolfGlOH14dmV/J1M+UPiHAuc/tEAOj3N+Ebt92viyDfPH0QxwITAIC/u/oOfE7ro/zU8DkcmBxFXFXQ6HTjf25/A167fH3qeXyNsNls+Nqtr8O373ijYaFtlR9gvq2c9YQp50FYD0fyO126bZEQQhbDuqZ2w+8bWjoAAPf0rwEAhKUODIQsZRw2u8GJ6LLbDSUKXV4/vnbr/SXfnJHnR2Le+ctLp/WNpHKXLcivp5R5/ktIIVA8IFWDUwqeKif5Og/kC445T6BUyCUB7R4fGjXRIpyII27xPolyhkytI1vcXnRrPZR/fP64fvvW9m5c3tqFZf4mxBQFP71wAgCwsaUdnV4/+nyN+rF9/uTPPqdLF1ReoU2Uf2ft1rTXNHepKKd40CSJLUI8aHC68f+t2QwAuK6rv2xjIYTUH+tbjOLBZa2d+u0rZFcWxQNCDA5Ot92BdikcsdHptnQFFBt5fnRTzwBa3V7Mx2M4pbVyLndgos1m01+TzgNSC9BHR6oGoTYHTe1zSk2+zoNWl+w8KFPmgew88PgMWQuzsSjaPClBQ1XVlHiQ5QLc6vbi/PyM/vtXbnmtfkHv9jXgYnBWt9qKXbRW6XX6/E1pz/lnl1+P31i9CasbW9Puq2S3BY+2yxGKxxDWRKIGlws39SzHl256FQb8zdkeTgghWTFfCzY2p8QE+VzrZdkCIfA5nHrZpctuN4gJ5cq7kudHG1s6MBScw3Q0rLd0LnfmAQA4YIMClZkHpCag84BUDaIeXzgBykUwb+dB6oJTrswD2era4fHDYbPrk9VZU+7BfDyml3yIsb5x5UYAwCsH1urHNUt/x7rmNgxIu2NCIBFW2/XaRHi9NCG2Ek6cdjvWNLVZXnTTAhPLKR5on6mg9JkS/3Zrmtq4G0gIWTSvW5Es63rfpqsN50D5msJzDSFGN6V5blCuklW5vfSG5g59vjQVTWZGVaJJs8NO5wGpHSiFk6pBLOpCZXYehHXnwULKFsqTeSALKeIC1+RyYz4ew5wp90C4DrwOp75o/t2NV2JH9wC2tHbpx8lhgRuaOwzP0WxyLGxsSYV//fuOV+plAAvBPEEoq/PANGF32e1smUYIKSq/u+FK3NG7Cpe3dRlul51jHp53CDE4DcyuRKtSzFLQYtpAEXO7yUiyM0op229nQuQe0HlAaoGCxIOpSBjHAuM4GpjAscAEjgUmdRvSK/rX4IFtO4o6SAB4/NJZPHrxFE7PTmMuHkWb24utbd24f8UGbDZdsDPx/OggHr14GkemxxGIRuBzutDvb8StvSvw2uUbcu483/3Ig3m9zra2bvyTFj5H8kcs3kXrxHIhFui53ASGwMQytWqUhRSxY9/ocgOheczGTeKB9h2UxQG33YErO3oNx8llB+bJrqzIN7nc6PY26L+ba3vzxbyAt5fxwmyesJdL9CGELB38The2tnen3e4ztNrlXg0hPke682BLaycOTY/j3mVrMz2sqIjcpjVNrWh0ufUMBNGeugJVC/q8iOIBqQUKupq9+YnvF3scGYkk4vjEy7/Gi2OXDLePhoP41dBZPDF0Dr+97nL8zrptGZ8jGI/h7/Y9i51jFw23x2IRzAQiOBqYwEMXTuJvrrqt5C1iSGbERCuUKK/zQIgV3gU4D5xlWgBblXA0aRe6dOdB8sKXq0fy65ZvQIvLA6fdjtt7Vxruk50Hy/xNRan9q6TzwGwVLldWBSGE+KUNCXZbIMTsPEh+Jz551e14eXIEN3QvK8sY1jS14W+vvl3PPNLLFjTnQbm7LQByq3J2WyDVz6Kl8G6vH8sbWrB7YqgY40njHw48rwsHV7T34A0rN6LD68eZ2Wl86/RBXArO4WsnD6Dd48NrpHZyAlVV8cmXf42XxpPj29Dcjjet2oTlDc0IxuN4YewifnT+GC4GZ/GRXU/gizfeZ9nmTua1y9fjdSs2ZLw/1yKUWOPXnQdlDkzUxIpczhO/9O8aUxIlHZPgmo4+HJwaM7gJmpzW7RpF2UJrjs+vz+nEfQPWCr/8OkKdXyw2mw02AEJPL2fmgcNmh8tmR0y7IFM8IISUCx/LFggxIM+zRNlCs9uDW3tXlHUc13elhAoxZ5rSnQflFw/EvEgBnQek+ilolfv2tVuxsaUdG1s60ObxYTg4h99++sfFHhv2TgzjieFzAIAdXcvw11fdqtcibWrpwI3dy/C+5x7GaDiI/zz2Mm7rXaHvygp+PXJBFw6u7ujFp66+3dB3/oqOHlzT2YcP734CI+F5fO3kAbx/87VZx9Xq9mJ1U2sR/1ICSM6DMpcthLXX8+fotiBfUMoVavOW1ZvR5vHhms4+/bZGLXcgk/NgMa2OWlwp4aHfoqtCoTjtdr0FZzmdB0DSLhzTSjwoHhBCyoWfZQuEGGh3p1ozVkv+kHnOVAnnAVs1klqiIO/1O9Zvww3dA2iT+rOWgu+eOQIgudj4ky3XpYWYtLi9eO/GKwEAc/Eofj54Ku05Hr2Yuu39m681CAeCqzv7cIdm337owknMRCNpx5DSo7dqLHfZQp7OA5lVZSpvcTscePXydejxpbIHRGjhjFk80DIPcpUtZEO+iPb5iuM8AACX9N0tdw9l2S7cUEDgIyGEFAK7LRBipMObWjdYzccrQZp4UIHMAwfFA1JDVG2rxmA8hr0TwwCAqzp60eX1Wx53c89yfdH57MiFtPuPByYBJOu35ZZ0Zq7t6geQbBWzc3RwUWMnhaG3aix3YKL2er4czgMA+MKO+/AHG6/Cnf2rSjyqzGRyHohyj8WEOcplC7JgsVic0iSh3OKBvLtB5wEhpFyw2wIhRjo8snhQHUuQ9FLPSjgPGJhIaofq+OZacCwwodcpb2vvyXicy+7AZS2d+mPMrV5mYvnVgbdJ9++fGi1ozGRx+CsUmChCCfNxHmxs6cBvrL6sIq18BCLzYC5uFg/y6xqRDdm1UEzxQJ4klNsSKDsPFvPeEELIQmBgIiFGOjypjcBqKVswrw/KnbsFpJwHCgMTSQ1QtUV45+YC+s8rsjgGAGBFYzN2Twwhoaq4GJw1dEzwOVyYi0cxb1pomZmXThbya1vx9Mh5PDV8DiOhedhtNrR7fNjc2ol7l63BFaa2eCR/xOK97K0atX97X43UpAr7a9QU2igueLmyG7LhsNvxoa07MBePFTfzQBJbyh1GJNcaN7JVIyGkTLBVIyFGOr3V5zxodLnxwNYd+MyBnQCAqWio7GNg2QKpJarjm2vBWDio/9yZoWRBIJc0jIbmDfetaEwKD+fnZ/Qerlbsn0y5DeTXtuLcXADn52cQURIIJeK4GJzFLy+dwV+89Ct8fM9TaXZykh9i0Rsqe7cF4TyojV1pccGNmVw2uniwyL/jnmVr8IaVGxf1HGacFZwkyHbhZmYeEELKBMsWCDHSLpUtxKtoofyKZWv0n6crkHumd1uooveEkExUrRQuW9dz7QjLrRHFQlCwo3sAh6fHoagq/vv4Pnzw8uvTHj84P2MIVsxkWfI6HNjRNYArO3qxvLEZPocTgWgE+yZH8LMLJzETi+DZ0UHM7nkKn7n2rqwLpqiSQExR2JZFQizeI0oCCUWBo0wLzppzHmiTUHO7SBH86F9A8GO5qOQOg2wXbqR4QAgpEz6WLRBiQN7cmI0xnFxA8YDUEtW3ytCIJlK7qrl2LeXEVrOV+7XL1+PH545hPBLCQ4MnEVHieMvqzVje0IxgPI4Xxy7iy8f3IpyIw2mzI64qiJieQ/A/t7/RcvFxdWcf3rByIz68+wmcnJnC/qlR/PT8cbxh1aaMY/7WqUP4+qkDWf+upYa86A0l4mi0l36hp6qqLjjVSj18JudBqAiZB6Wios4DSRRqonhACCkTcghvtdR3E1ItVJsbxw5bxTb0RI5WgpkHpAao2rIFtyM1NHMIohl5B9Z8gW50ufE3V92mB6I8duksfu/Zn+OVv/gfvOnx7+HvD+zEZCSMd2+4Ao1aSn2mmvFsu5ZtHh/+9xW36LXdPzp/POuYf3PtFvz47rdgY0tH1uOWEi67Q3//yhVYc35+Rr9U1IrzwJXBeTBfpLKFUuCsYMCkfE6g84AQUi6q0QVGSKX58LYbcXvvStw3sLbSQzHQ5skerF5K6DwgtUTVigeyYm8uRTATlu63WgBuaOnAf9z4Kty/YoOhqwKQTM//1FW34zfXbNGD+gpdYPT7m3BVZzIw8WJwFuNZshPcdgcanK6yJ89XO8LmmevfvBgcmBrFe575mf57rQRaOTM6DxYfmFgqqqVsocnlyXIkIYQUD7mkspIdegipJu7qX42PXnFz1c25Vklh6+VGrAUYmEhqger65krIIYjj4WDWHXo54LA7Q3u5Dq8P7998Ld6/+VpMRkIIxmNodXt1oWAsHNRLHhZzAlnZ0IIXxy4lxx0J5Qx7JEZ8DidmY1GDIFQqfnTumOF17WXuAlAownkgl+gklFS5TTUGPzoraE+UnQcsWyCElAubzYa3rt6M8XAQa5paKz0cQkgW/vzyG/B/9j+HN2YpOS4VDjvFA1I7VK14ILdbPD8/g5uyHHt+bgZAstXJsjzay7V7fIbEVwA4HpjQf960iFKCGll/Vi1ip6Yc4oG8qPTVkL3UbeE8CEoBo9Vola2k80Aue2LZAiGknLx345WVHgIhJA+6fQ34p+vvqchrC+cByxZILVC1PrqNLR1waTa//ZMjGY+LKQkcCYzrjyk0mO3p4fP6z7f3rSzoOYBkG0dBp0mgILkR4kGkDOKBbGevJUupy5aeeSAyItx2hyFAtFqoZOaBLLIwtIwQQggh1YTDJpwHDEwk1U/Vrpj8Theu7EjmB+yZGDaUJsg8M3JBXzjd1LO8oNc6NxfAk8PnAABXdfRioKG5oOcZCs5hz/gwAKDf38iShQIQC/pwwrrjRakoh9OhWFh1WwjqnRaqz3UAVLbbgrkDCyGEEEJItWDXNljoPCC1QMVm9I8OnsLdjzyIux95EF89sd/ymDevvgxAsgboXw+/lKbIBaJhfPnYXgBAo9ONV2VIbs0WXDgamsf/3vMUEqoKl92OP77sGsvjdo4OIpGl68NUJIRPvPw0YtoYX7d8Q8ZjSWY8JS5bUKUT81wsZfUvV3eHYiCcBXGDeFC9nRaAVKlFJaB4QAghhJBqxcFuC6SGKGib8sDUKC7Nz+q/B2IR/eeLwVk8OnjKcPy9BbZjubKjF3f0rsQTw+ewc3QQH3rpcbxx1SZ0eHw4MzuNb54+iFFNGPjdjVdkTFL/50MvYjoaxi29K7CxuR0NLjcC0Qj2TgzjpxdOIBiPwQ4bPrjleqzIEJb4+cO7EFcV3NKzAptbO9Hra4Db4UAgGsG+yRE8dOGk/j5c3taF162keFAIpSxb2DU+hL/d9ww+sOU63Na7ErOxqH5fLYXUCOeBAhUJRYHDbtfFA18VdloAAKetcuUCvBgTQgghpFqx2xiYSGqHgsSDhy+cwi8unba879D0GA5NjxluK1Q8AIC/2HoD5hMxvDh2CS9PjuBlU/6BHTa8be3leM3y9RmfQ4WKo4EJHJVCEWWaXG78yeZrcUffqqxjmYiE8KPzx/Cj88cyHnNLz3L8+eU3sLa6QLxa2UKkBGUL/3TweczGovjky8/gtvtWYi4ezf2gKkTONIgqCnx2ux6Y2FClzoNKli28Z8MV2D81ireu3lyxMRBCCCGEWEHnAaklqrNAWsLjcOLTV9+BX106g19cPI1Ts9OYj0XR6vFia1s3Xr9iAza3dWV9jt9cswXLG5pxYGoMY+F5zESjaHS50Odrwo09A3jVwFq0uL1Zn+OBbTuwf3IUh6fHMBScQyAWQTAeg8/hQpfXjy1tnXhF/5qcYyHZ8dhLV7Zg7ik8Kzlmagm5BCCmJOCDU888qNauEZXstrC6qRU/vOs3aioUkxBCCCFLA915AIoHpPopaKXxwLYdeGDbjkW98L0DaxfkSLirfzXu6l9d0Gtd3taNy9u6C3qsYHt7D7a39yzqOUhuStmqsc/fiAvzybaek5GQoWzhg1uuL/rrlQqH3Q47bFCg6qGJ1Z558PoVG/HDc8dwe2/hnUwWA4UDQgghhFQjQjxQsmSrEVItVOc2JVmyiG4LkRKE3Ml2sA+88AtdPPjKLa8tuMNGpXDa7YgqCb1dY0iIB1WaebCsoQk/vfutelkKIYQQQghJbXDQeUBqAW7HkaqilM6Deam7wqXgnP5zt7eh6K9VasztGuer3HkAJEsqbJq6TgghhBBCJOcBMw9IDUDxgFQVJRUPMgQkumtwN1yEJsZUzXmQqH7xgBBCCCGEGHGw2wKpISgekKrCo7dqLH7Zgtidl9nc2ln01ykHwnkQrSHnASGEEEIIMULnAaklKB6QqkLUxJfCeRA0iQfb23vw6avvKPrrlAPRCjSuZx4k3y9/lXZbIIQQQggh6bBVI6klKB6QqsKrOw+KKx4kVAUh03Nuae1Eo8td1NcpF+bMg2rvtkAIIYQQQtLRWzWq7LZAqh+KB6Sq8OiZB8UrW1BVFf/v+Mtptzvttfvx1zMPNOdBUMs88FVptwVCCCGEEJKO6LZA5wGpBWp39UTqEk8JyhZeGh/Cd84cAQC4bHbd8n9VR2/RXqPcZHIeNNB5QAghhBBSM9jBwERSO7BAmlQVpShbODs3rf8cUxU8eOv9GJyfxeVt3UV7jXIjnAdR4TzQMg98zDwghBBCCKkZcgUm/nr4PCYjIdy/cmM5h0WIJVxpkKrCay9+2cKpmSnD7z2+RvT4Gov2/P9/e3ceH/dd33n8PfeMNNLotmRdtmxLvuM4p5OQC0gCDWdJu7S0PJaFbWk5WhbShXbZQil0z/ZRjm2X7nZLH5TNg6WEQpo4JDHNndhJHDs+5Fu2LMm6R8do7tk/Zn4/zSXZlkfSSPN6/pPf/K75SpH10O8zn2M5kHkAAACw8l1uVONXDj4nSdrkq12xU8KwehA8QFFx2wufeXBiYrRg9yoWRubB/zj+mmaiETMDgZ4HAAAAK8d8mQfpTRQPDPfp8Nig7m5sW/EfgmHlIniAouIyGgEm4orG49fc1DASj+nC9IT5usrpuqb7FQtHqrnOZCSsbx47YO5n2gIAAMDKYTRMzDdtIZSWifu9U4clJcsYvrXngaVZHJCF4AGKSvrD73Q0It81PuxPRcLm9ic336Cb69Ze0/2KRb6gitNqW9ETJAAAAEqNLS3z4Lh/RNORsG6oa5IkzURzM3GP+0eWdH1AOoIHKCp2q1Vum03BWEyBAgQPJlLBgwqHU7+8bnMhllgUhoLTOfvIOgAAAFhZHKlJYz3Tfn3qpSckSf9w1/vV4CnPO32szMbjG5YPH1Oi6JTbnZKk6Wh43vNOTYzq1GX6GRiZB97UPVeLs5P+nH1lTFoAAABYUW5KZRm8NTZk7jvmH5YkzcQiOee3eX1Lsq6+wKQOjV5akvfCykHwAEXH+AR9Opr7C9MwHAzot198XL/z4hPzNlecTMs8WE06Kqpy9pF5AAAAsLJ0VFTnTFE44U9+OJYv88Aoc1hM0Xhcv/nsP+lzrz6l3rTeYQDBAxSd8isIHjzRe1qSFFdCF6Yn9MbIgGLx3EYzk5GQJMm7yoIHn99xq97f1qXNvlpzXxmTFgAAAFacz2y9SW9vWid3qiThkbNHNRaaMXsetJVXqrOyRpIUzvP3bqG9PHTR3D47Oa5YIq43Ry+Z071QuggeoOiYwYPI3GULT/efM7e/efSAvrD/af3tyTdzzptapZkHa8sq9KmtN2qdt8rcR+YBAADAyrOxskZfvO52/fkt7zT3ffn1Z83MgwqHS/+2a7ekwo4zn8szfefMbX8kpL87eUj/7tWn9NPzJxf9vVHcCB6g6Mz2PMifeRCNx3UxMGm+PjKerBF7rPeUwrHMiOhkdHUGDwzutKY5HnoeAAAArFgbK6r13rZNkpJ9Dw6NDUpK/r3nTDVWzPfpfzAWVc9Ubj+shbqU1pi7PzCln15IBg0uzUwV7D2wMhE8QNExPkEPzBE8GAxOK55I5OyfjIT17KXzGftWa8NEQ3rAoJzMAwAAgBXLYrHoM1tv1j1N7ZKkn6U+6ffY7XJZ5w4efPblJ/Vvnv+ZTl6mkfiV8odD5vbei6fNHmKRJSiZQHEjeICic7meB32BuaOeRjrV4My0wvHY7KjGaxz5WKw86ZkH9DwAAABY8XZWN0iSIonkw7rbZpcrlXkQysqyPT/l1+nJMUmZExuuhT8cNLfH0wIJ0QTBg1JHnjOKTrnDCB7k73nQnypZcNtsCqZ+gTZ6vBoKTuvI+JAe7enWt48d0C+1bprtebBKMw/SyxbIPAAAAFj5fE53xmuPzS6HkXmQFTx4qu+sue1OBRiuRTgW08wcfRWiZB6UPDIPUHTMngeR+TMP3rG2QzfXr1W716ePd+7Stqp6SdJfd7+uhKRXhi6aaVarbdqCIb1swUPwAAAAYMXzZWXMpmceRBLxjPLdoWDA3A5Gr72Z4nha1oHBquR4SDIPQOYBik7ZZcoW+lPNWtq9Pv3etpvN/cdTjWWMeqyhYMC8Ryk0TGTaAgAAwMpXlZ15kNbzQEr2PTD+BgymZQkECzBK0Z8ac26RZIQobm1o1ouDvWQegOABis9cPQ/iiYQSSmg4FWFtcJdlHO+srM25VyAakd1iVUdF1eIsdpl5MoIH/HMGAABY6fJlHjjTggeh2GzwIL0HQvAqxziGYzFzioPBaJa4zlulTl+NIvG4dlY3JIMHeRqWo7TwtIGiMxs8mO15MBEO6WPP/1Q7a9ZoJDQjSarNDh74asztWpfHPO9ta1pzasdWi4zMAxomAgAArHgVDmfGJ/8em102q1U2i0WxREKReP6AQegqggfHxof1uVd+ro9s3KFf37Dd3G80S6xyuvSFHXskSXt7T0uSogXIbMDKRs8DFB0j/X4mrW7rxMSoxsMhPTtw3qztqnN5Mq5bW1ah2xpadGNdkz63/RZ57U5VOV361Y6tS7f4JeZOK1WgbAEAAGDls1msqnDMZh8YHxYZpQtzZRsEY1f+cP+tYwcUScT1tyffVCgWVSzVz8CYrlCZlv1gtyYfGck8AJkHKDqePDVc8axfVhZJ1VnZBFaLRV/dfZf5+tF3PLR4iywSHnoeAAAArDpVTpcmUv0HjKbYTptNgVhUoXhatsEcWQiXY0nbfvDnj6jeXab/efsvaSyVuZved8EMHpB5UPLIPEDRMaKr6WNiwlm/rKpdbtms/Pi66XkAAACw6sTSPjhrLa+UJLPvQTitcWH6hIWrKVuwWWbDBwlJg8GATk2M6cXBXknSem+VedxuMYIHZB6UOp6+UHSM8YPheMxMocr+Zei1r87pCVfLkRZASQ8kAAAAYOW6NDNtbrd7fZIkV+pvvXB6n4N4etnClQcPrBZLzr5fDPTo/PSE3Da77lm7ztw/W7bAtIVSR/AARSf9Idio3Qpl1XAZ9Vilrsrp1jqvT+u8vozaOAAAAKxcn9pyoxwWq75+w93mvryZB2l/I2f/vTwfqyX3MfBnF05Kku5tWmc2MJfSMw8IHpQ6PqpE0XFabbLKorgSmolGVG535GQeTEXCc1xdWqwWi/76tneb2wAAAFj5HmzbpHe1bMgo03WaDROTfxcnEomMv5Hnyjw4P+XXVw8+p1/r2K57UxkF4XkCDQ+2bsx4bWQexMg8KHlkHqDoWCwWuVMzZ41fgqGsngef33Hrkq+rWNmsVvo/AAAArDLZf9+5bEbmQcz8b3oXgrmCB0/3n9O5Kb+e7Dtj7puM5M/iXef1qdNXm7HPyDyIkHlQ8njiQFEyusoaTRONNKz3tG7Sj+79kO5r7li2tQEAAABLzSxbSP1dnD2a0fh7+eDIgD778l6dmhiVJJ3wj0iSRlOTFCRpKpo/i3dLVV3OPnoewEDwAEXJnTWu0WgG47bZ5XNS2w8AAIDS4kxlHhgZudmZBsbrz+9/WkfGh/VfDr+sRCKhE/5kEGEkmAweJBIJTc5RAtxVWZuzj54HMNDzAEXJHNcYzcw8MH5pAgAAAKUkO/MguydY+uQFKTmxYTA4LX+qRMEfCSWnmcXjGaMg023y1eTsI/MABjIPUJSMcY3BrLIFl5XgAQAAAEpPds8D4+9kW6ppdjAW01haaUK53aHuVNaBYSw0o4msrIMPd2yTVcl7rK+oynlfMg9gIPMARWmusgWXjR9ZAAAAlB6HdbZx4d90v6Ef9RyXlBzdPZIKGhweGzLP90dC6k71OzB88cA+fXbbzZIkn8Ol/3TTvVpfUaX3tG6S1WIxsxvSkXkAA09iKEoeo2whO/OAsgUAAACUIOPBvjcwoX39PeZ+n9OVFjwYNPcHY1HtH+7LuMf56Ql98cA+SVKDp1wbK2vM7bmQeQADZQsoSmbmQTQiibIFAAAAlDZH6u/g4WAgY38kHjezEg6NDmYcOzM5nnMfo+zh7U3rruh9jcyDhKQY2QcljeABitJsz4NUQxjKFgAAAFDCjMyD0VAwY/+F6QmVp8acn54ck5T5gZvLatOHO7ZlXOO22fTOKxx9bgQPJLIPSh1PYihKRubB358+rPe2bTK7ylK2AAAAgFJkZBeMpjVFNDR6vBoPJ6cqWGXRJ7qu17eOHZAk3dnYpl/r2K7t1fVq9Hh1eHRQXb7aKx5/bk81ZJSkaCIhhqaXLjIPUJQ8Noe5/Z8Pv2Q2TqRsAQAAAKXIKFuYyRrReH1to5o8XvN1m7dSD7RsUFnqw7j3tnXKY7frlvpmtXt9erBtU96RjHPJzDyIXcuXgBWOzAMUpeno7AiZ/cP95jZlCwAAAChFTmvm5753N7br5vq1uqlurX6cmrwgSZ2VtXLb7PqLW+/TSHBGW6rqrul9bZbkIMeEpG8ePaA7G9t0Z2PbNd0TKxOZByhK7d6qvPspWwAAAEApcmRl4Na43LqvuUPVLreayirM/Z2prIKOimrdVL+2IO9tZB/8YqBHXz34nAKppuYoLXyMi6L0zrXrFYvH1VJeqc/vf8rc77LyIwsAAIDS48wKHlQ4ZrsPpJctdPpqC/7edotVEc02SxwLBVVmd8xzBVYjMg9QlOxWqx5s26RdtWv0Gxt2mPvJPAAAAEApcmSVLXgdTnN7bdls8GBDRVXB39ua1jRRkvyRUMHfA8WPj3FR9DrSfgESPAAAAEApctqyMw9mgwcNnnI9vGOPyu2ORekRFsxq0jgcDOgX/T26uX4tGQglhOABil5nWjdYGiYCAACgFGX3PEgPHkjSfc0di/besUQi4/VXDz4nSfq1jm36WOeuRXtfFBeexFD01ni8+ty2W2SxWHJqvQAAAIBSkD1tocLunOPMpfN472mCByWE4AFWhHe3blzuJQAAAADLJjvzwOtY/uBBR0X1ci8BS4iGiQAAAABQ5OZrmLhcJmmcWFIIHgAAAABAkcsu3y0vgkaF4+Hgci8BS4jgAQAAAAAUufSyBbvFWhS9wPxhMg9KCcEDAAAAAChy6cECj90ui8WyjKtJCsVjmolGL38iVgWCBwAAAABQ5NJ7HriWKeugrbxS9zS263c332Cux0/pQskgeAAAAAAARc5ps+XdXkpryyr0h7vu0AfWbVaV0y1JGqdpYskgeAAAAAAARc5hmX10W+p+B1/e9TbtrG7QZ7fdbO6rdLgkSRNkHpQM+3IvAAAAAAAwv/QeB44lDh7c2dimOxvbMvZVOZPBg3GaJpYMMg8AAAAAYAVxWpf/Ma4sNSpyJhpZ5pVgqSz/Tx0AAAAA4IotdeZBPq5U34VwPL7MK8FSIXgAAAAAACuIowgyD4wARijOqMZSsfw/dQAAAACAK1YUmQfWZPu8cCy2zCvBUiF4AAAAAAAryFJPW8hntmyB4EGpWNC0hbFQUN3+YR33j6jbP6Ju/6gmUvM971vboYd37inoIiXpmb5z2nvxtM5MjmsqGla1060d1Q16X1untlbXX9E9Xh7s1d6LZ3RsfFj+cEgeu0Nry7y6s7FN72ntlMd+Zd+OSzNT+nFPt14Z6tNQcFoOq01NHq/uamrX+9o65bYxxAIAAADA4iiG4IGxhhCZByVjQU+5D+37UaHXMadQLKqvHHxOrw71ZewfDAb0dP857evv0Uc2btdvbtw55z0C0Yi+8eYLemnoYsb+SCSkCX9Ix/0jeuzCKX11911q9/rmXc9Lg736xqEXFUjrKhqMxTQZGdWJiVE9fuGU/vSGe9RcXrGArxYAAAAA8nto3RY91ntKH9m4fbmXIieZByXnmj8ib3CXqbXcp9dG+guxnhz/9fDLZuBgV80afaC9S7XuMp2dHNcPzrylvsCUvnfqsGpcHj3Yuinn+kQioT85+Jz2DyfX11lZo19et1mt5ZUKRKN6ZeiiHj3frYuBSX3pwD5957YH5HO6867l5MSovnbweYXiMXlsdn24Y5t21axRKB7Tvv4e/XPvKfUGJvWHr+3Td257lzm+BAAAAACu1W9t3q2Pd+6SrQgaJrrIPCg5Cwoe/MaGHery1ajLV6tql0cDgSl95NmfFHptemNkQPsGeiRJe+qb9ce775TNkvyHstlXq9samvXJFx/XYDCgv+k+qLsa21ThcGXc47lLF8zAwQ21jfraDXdnNBjZVbtGN9Y16Yuv7dOl4LS+d+qwPr31przr+c6xAwrFY7JZLPpPN96bUS5xfW2jmssr9N3uN9QbmNQPzx7TRzfNnQ0BAAAAAFerGAIHEpkHpWhBP3kf3bRTtza0qNrlKfR6Mvzw7DFJks1i0We23WwGDgw+p1uf6LpekjQVDeufe0/n3GPvxdl9n956U97OpDfUNemexnZJ0mMXTmkiHMo55/j4sA6PDUmS3tWyIW+fhYfWbVFbeaUk6cc93Yoy8xQAAADAKkTPg9JTHGGrPALRiN4YGZAk7a5tVL27LO95d6xpNcsDXrh0Ief4Cf+oJKm5rEItqQf7fG6qXytJiibiemmwN+f4C2n77m/ekPceVotF72zukJQMZhwcvTTn+wEAAADASjU7bSG6zCvBUina4EG3f0SRRPKT+501a+Y8z2G1aYuvzrwm+9N+YwpE1Rx9DAzVaccPjQ3mHH8rtc9ts6uzsmbO+1xX3WBuH0llKgAAAADAamL0PAiTbV0yijZ40DPlN7fb5skYkKQ2b/J4LJHQxcBkxjGPLZmVMB0Nz3uP6bTpCenvbTg/NSEpmcEwX51Rq3d2rT3TufcBAAAAgJXOYZYtkHlQKoo2eDAUDJjbdXOULBjSSxoGZ6YzjhmBhfPTExoPB+e8x6HR2WyD9PeWpHAsJn8qg6HOPX+fhwqHS25bsg/l0Exg3nMBAAAAYCVy0TCx5BRt8GAmNpsJ4LHNPxTCnXZ8JivytaehRZIUTyT0tyfezHt97/RERmPFQFoWgiQFMtZy+fGLxnrSv4Zs4XhM09GI4kpc9n4AAAAAUExc1uQzT5iGiSVjQaMal0I4Nls7Y7/MOJL0CQrZka/3tG7ST3q6NRya0WO9pxSKR/Ur67eqtbxSgWhUrw5d1HdPvKFgLCq7xapoIq5Q1j3S/0Fcbi3J9VjzriXdD04f0d+fPnzZewEAAABAsTFGNWY/O2H1KtrggdM2+5B+uZGHkbQfWGfWKEavw6mv7r5LX3rtFxoPB/VU3zk91Xcu5x7/pnOXfnTumMbDIZVlZRcY/zCuZC3J9cTzriXdhzds04fWb9HD+59Wt3/ksvcEAAAAgGJhPOuQeVA6irZsIb08ILsUIVsw7Xi+EodOX63++rZ3631tnRlTFSSpy1err+2+Wx/u2KZANHkfr8OZcU5ZxlrmLkXIXs98JQ5Oq03ldoesslz2fgAAAABQTNJ7HiQSlGKXgqLNPEhvgjgcDKjLVzvnuekNDhs85XnPqXV79OmtN+nTW2/SaGhGgWhEVU63GSgYCgbMMoN1Xl/GtU6bTZUOlyYiIQ0HZ+Zd92QkZAYP6j3zN3oEAAAAgJXIyDxISIok4nJa5s66xupQtJkH7WkP8OenJ+Y91xijaLNY1FxWcdl717g8aimvzMgwOJFWOrA5T6CiPTW14WJgUrF5ShcuTM2utb3cN+d5AAAAALBSpZd2U7pQGoo2eNDlq5XDklzeodFLc54Xicd0zD9sXnMlDQ3zeXbgvLl9d1N7zvHt1Q2SkiUJJyZG57zPm2OzIx+3VdcvaC0AAAAAUMwcltkCbMY1loaiDR6U2R26vrZRkvT6yEBGaUK65y9dMEcr3r6mdUHv1TPl1y8GeiRJu2sb1VJemXPO7amRj5IyxjqmiycS+vnFM5Ikr92pXTVrFrQeAAAAAChmFovF7HsQIvOgJCxb8GBv72m944nv6x1PfF9/d/JQ3nMeWr9FkhRLJPSXR/crlsgsF/CHg/pu9xuSkg/r727ZkPc+w3MEHiRpcGZaX379XxRLJOSwWvWpLTfmPW9zVZ12pDIJHu89raNjQznn/PDcMbPE4gPtXQvOggAAAACAYuewzjZNxOq3oIaJh8cG1Tc9ab72R0Lm9sXApPb2Zn4yf/8cD/WXc31to+5pbNe+gR69NNirP9j/jD64brNqXR6dnRzXP5x5S4OpwMDHu3apwuHKe5+/OPKqxsNBva2xTV2VNSp3OOUPh/TGyIB+euGkAtGIrLLo97fdojbv3H0KfmfLjfq9l59UKB7THxx4Rh/u2KZdtY0Kx6La19+jx3pPSZJayirMwAcAAAAArEYuq02Tkp7uO6uPde5a7uVgkS0oePD4hdN6su9M3mNHxod0ZDzzU/mFBg8k6fM7btV0LKJXh/p0cPSSDmb1P7DKol/fsF0Ptm6a8x4JJXTcP6LjaU0R01U4nPrM1pt0T9O6edeyqbJGf7TrDn3j0IsKRCP63yfflE6+mXFOS1mF/vSGe1Rmn3tMIwAAAACsdD6nW8OhGf3DmSP64LrNqnK6l3tJWERFO6rR4LLZ9fUb7tHTfWf15MUzOj05rulIWFUut3ZUN+j9bZ3aepnGhB/u2KbW8kodHhvSUHBaE+GwvA6HmjwVum1Ni97dskG+K/xB39PQou/e/m7947luvTJ0UcOhgOwWm9aWeXVXY5ve194lt63ov60AAAAAcE1+f9vN+tTLeyUlp9d1+mr1yuBFvWPtetko4V51LIlEIrHciyhln35przktwrDFV6dv7rl/mVYEAAAAAFfmzw69qKf6zuqjG3fqkbNHFYxF9fCOPbqvuWO5l4Z5LOQ5lHAQAAAAAGBBuiprJCX7HgRjUUnSCf/co+2xchE8AAAAAAAsSJevVpLUG5htqO910P9tNSJ4AAAAAABYkC5frSqzpt4FotFlWg0WE8EDAAAAAMCC2KxW3de8PmPfTCyyTKvBYmIsAAAAAABgwX5z407FEgkNBwN67tIFBaIED1YjMg8AAAAAAAtWZnfod7fcqFvqmyVJM5QtrEoEDwAAAAAA16zMnmyUGCihsoVQLKqT/lElEonlXsqiI3gAAAAAALhmHluyKr6UMg++9uYL+uRLj2tff89yL2XRETwAAAAAAFwzM/OghHoevDTYK0n6f+eOLfNKFh/BAwAAAADANfPYk5kHpRQ8MFgsluVewqIjeAAAAAAAuGZlttLqeTAcDJjbM9GIpiLhZVzN4iN4AAAAAAC4ZkbZQiQeVzQeX+bVLK4jY0P6V7/4sfn6/PSEPvb8TxWOx5ZxVYuL4AEAAAAA4JoZwQNpdZYuxBMJTae+rkd7unOOj4aC6pueXOplLRmCBwAAAACAa2a3WuWwJh8xZ1Zh6cJfH39dH3jqhzo6PqwGT3nec/pnppZ4VUuH4AEAAAAAoCDMvgerbFyjPxzSj3qOK66EHu3pVjyRyHtef4DgAQAAAAAA8/KkShdmVlnZwjP958ztsXBQE5FQ3vP6CB4AAAAAADA/t80mSQoVSePA6WhEk5GQEnkyBfLtm8uFab+5fdI/qsm0yQrrvD5zu29m9fY8sC/3AgAAAAAAq4PDmgweRIogePDd7jf0yNmjkqQd1fX67ze/UxaLRbFEXJ99+UnZLBb995vfKZv18p+ppwcLpqJhHfcPS5L+6Lo7dHdTu14fGdDD+5+mbAEAAAAAgMsxGiZGimBUY3qpweGxITMbYiwU1HH/iI6MD+vloYtXdK/04IGUnKwgSRUOpySp1uWRJI2Hg9e67KJF5gEAAAAAoCCMzIPwMmQeRONx/btXf66eqQlNRZMP+xZJRnHCZCQst81ujluUpJ9dOKnb17TmvV8sEdcXD+yTRbPBg3esXa+n+s6a5xjBg/JUr4fpaESJREIWi6WwX1wRIPMAAAAAAFAQy5l5cH7aryPjw2bgQJKayryqcrokSZOpJofTaVkEh8cGFZtjrRemJvT6yIBeGxnQ2clxSdIDzR3y2GY/g/eawYPkf+OJhIKx1TVpwkDwAAAAAABQELM9D5Y+eJCv34DTapM39WA/lQoaTKVlHgRjMZ2fnsh7vxMTo+Z2JJH8eqqcbrWnNUg0Mg/cNpusqWyDH/Uc14M/f0QP7P2BDo8OXsuXVFQIHgAAAAAACmI282DpyxaygwcWSR/vvF4VDiPzIBk8mI5m9i844R/Je7/uPPsrHC41l1WYr42MA4vFYpYu7O09o2AsqmgiroSufKJDsaPnAQAAAACgIJzLWLZgjEl8aN0WfaLreiUSCdmsVv3T+ROS0oMHkYzr/stbL+sfe7pVbnfoq7vv0tHxIf23t17J2/ywwuHU2rTggTWtt0G53anJSFhDwYAk6bc379bmqrrCfpHLiMwDAAAAAEBBLOeoxr5U5kFreaWsFos5gtEoLTCCB4FU8KDeXWZee3pyTIfGBvV472n9n5OHNBKaUSyRmTXgstrktNn0YOsmeWx23VTXlHHcyDyIpkoc2sor5Ux9P1YDggcAAAAAgIJYyoaJ/nBQD+9/Wj9JZRYYZQtry7wZ53nN4EGqYWIqeHBbQ4v+6rZ3ZZz7gzNHdGJiVA6LVd/Z84B+/PYPqSwVFDDuU+v26JF7Pqiv7b4741ojeGDwZL1e6QgeAAAAAAAKwmFZulGN3zp6QK+PDOibR/crFo9rYCYZPGhKKyuQpMrsngeRZPCg3O7QxsoaPdC8wTx3IhVguLOxTZ2+WlU4XOqsrJE0m8EgSWV2h5nZYChPOy5JZTaCBwAAAAAA5FiqzINYPK59Az3m6/6ZKcUSCTksVtW5PRnnGhkDU5Gwnu47q0fPd0uafdj/vW0364f3fFAPtm40r3lP2yZzu9OXGzzIJzvzoIzMAwAAAAAAci1Vz4Mzk+MZrw+OXJIkNZZ5ZbNkPuaaPQ+iYX3j0IvmfuNh3261qtrl0fvbuuS02rS1qk7bqurN826qWytJWuetmndNucGD1TWfYHV9NQAAAACAZbNUmQfdE5ljFA+M9EuSmrL6HUi5DRMN2Q/76yqq9L0736tyu0OWtCkK19c26nt3vldr3OXzrmm19zwgeAAAAAAAKAjnEmUenPCPZrx+bTgZPFjrqcg51yhb6AtMZuzPftiXpLq0CQzp1pbl3jdbes8Du8W6qiYtSJQtAAAAAAAKZMkyD/zJzAMjG2AmFpWUO2lBym2YaLBaCvs4nB6M8KyykgWJ4AEAAAAAoECWoudBf2BKZybHJCWnIqSrz5M54LXnb3TYVl5Z0HUZQQpp9U1akChbAAAAAAAUiD2VeRBexMyDxy6cVELSDbWNas0KAOSbcJA9JeGG2ib91ubr1eCZv4fB1WryzGY9rMbMg9X3FQEAAAAAlsVs2cLiZR68NjIgSXqgZYMSicxjblvuI67TZpPLalMotaatVXXqqKgu+LrSmzVaZJnnzJWJsgUAAAAAQEHMNkxcvMyD4WBAktRaXimXLbMpYfZrgzct+yBfaUMhpL+HPxJclPdYTgQPAAAAAAAFYWYeJBYn8yAaj2s8nHwwr3WV5WQa5Ms8kDJLF2rdnkVZW7rxUGjR32OpUbYAAAAAACgIxyJmHkxFwvrWsf1KSLJZLPI5XQsLHrgWP3gQV+LyJ60wZB4AAAAAAApiMXse/Ombz+upvnOSpBqXR1aLJadMYa7ggSetkWKta3HKFiTp3qZ1kqS7sqZArAZkHgAAAAAACmKxMg9mohHtH+43XxvZA9nBgrl6HqSvx+d05T2nEH5/2826pX6tbqlvXrT3WC4EDwAAAAAABbFYmQdvjl7KeG2MhEwPHtgsFjN4kS0ci5rbVsviTULw2B16+9r1i3b/5UTZAgAAAACgIIxpC+ECZx70BiYzXo8EZyRlBg/slrkfb0OLODqyVBA8AAAAAAAUhJF5EC1w8KA/MJXxek9DsixgrjKFbDfVrZUkVTndBV1XKaFsAQAAAABQELM9D2JKJBKyFKhEoC+VefDRjTtVbnfo/pYNGe93Ob++YbtqXR7dugp7ESwVggcAAAAAgIIwMg8SkmKJhOwFCx4kMw92VNdrV23jVV/vttn1/vaugqylVFG2AAAAAAAoiPRMgIU2TXx58KKOjw+br2OJuC7NTEuSmsoqrm2BWDCCBwAAAACAgnBYrTJyDaajkau+vnd6Qn/0+i/0qZf3mvvOTIwrmojLYbWqzu0p0EpxtQgeAAAAAAAKwmaxqrW8UpJ0enLsqq9PvyYcS2YuPNZ7SpK0p6FFtnkmKmBx8Z0HAAAAABRMp69WktTtH7nqa8fDobTtoGKJuJ7pPydJek/rpoKsDwtD8AAAAAAAUDBdlTWSpBP+0au+diBtJGNfYEo/PtetQDQit82unTUNBVsjrh7TFgAAAAAABWNkHpyauPrggTGSUZK+dWy/zk35JUmbKqspWVhmfPcBAAAAAAWztswrSRoJzSgWj1/VtX1pmQdG4ECSNqayGbB8CB4AAAAAAArG53TLarEoIWksHLyqawdmpvLuX+etmvMaRyojYUNl9VW9F64OwQMAAAAAQMFYLRbVupIjFYeDgSu+LhyLaSYWzdnfWVmj+5rXz3ndt297QPc3d+gPr7vj6heLK0bPAwAAAABAQdW6PBoKBjQSmrniayYj4bz7v3Td7XJYbXNe11FRrS/s2HPVa8TVIfMAAAAAAFBQde4ySdLwVQUPQnn3VzndBVkTrg3BAwAAAABAQRllCyNXUbYwGc3NPLBbrCq3Owq2LiwcwQMAAAAAQEGZwYPQjGaiUQ3OTF/2GqNswW2bra6vcDhlsVgWZ5G4KgQPAAAAAAAFZZQtDAYD+vqhF/SRf/mJjo4Pz3vNVCp4sLWqztw3MUcpA5YewQMAAAAAQEG1lVdKks5MjOmlwV7FldBfHHll3muMQIHP6TL3xRKJxVskrgrBAwAAAABAQXVUVMtmscifljlwZnJcM9HcUYwGI/OgwuFUk8crSaJgoXgQPAAAAAAAFJTTZtP6iqqc/YPB/L0PJsIhM/OgwuHSV3ffpS2+On3jxnsXc5m4CvbLnwIAAAAAwNXpqqzVqYmxjH3DwYDavb6MfS8O9urLr/+L+brC7tT6iip9c8/9S7JOXBkyDwAAAAAABdfpq83ZdzEwqVginrHvawefz3hd4XAu6rqwMAvKPBgLBdXtH9Zx/4i6/SPq9o+aKSb3re3Qwzv3FHSRkvRM3zntvXhaZybHNRUNq9rp1o7qBr2vrVNbq+uv6B6vDvXpyYtndNw/orHQjOJKqMrp1sbKGt3b1K67GttlnWMMyEBgSh959idX9D6L9T0AAAAAgJWiy1eTs+8vj+7XqYkxfW77Lea+cDyWcY6X4EFRWlDw4KF9Pyr0OuYUikX1lYPP6dWhvoz9g8GAnu4/p339PfrIxu36zY0757xHOB7TN958Qc9dupBzbCgY0FAwoJcGe/VP50/oT3bfzQ8rAAAAAFyjdd4qOa22nODAP/ee0gfau7S+okqBaCTnuo2V1Uu1RFyFa+550OAuU2u5T6+N9BdiPTn+6+GXzcDBrpo1+kB7l2rdZTo7Oa4fnHlLfYEpfe/UYdW4PHqwdVPee3z76AEzcFDldOtX12/Vpspq2axWnZ0c1yNnjupScFqHx4b0tTef159dpinHv950nW5raJnzOGk2AAAAAEqd3WrVhopqHfMPq95dpqFgwDz2iRce05/ecLfctsxHUo/NrjWpSQsoLgsKHvzGhh3q8tWoy1erapfnqlL6r8YbIwPaN9AjSdpT36w/3n2nbJZkm4bNvlrd1tCsT774uAaDAf1N90Hd1dimCocr4x5joRk93ntaUvKh/n/c9i7Vu8vM4zuqG/T2tev1Wy88poGZaR0Y7le3f0RdeepzDHUuT97OoQAAAACAWe9u3ajR0Izua+7Q358+nHHskTNHdWtDs/l6bZlXX7rujqVeIq7QghomfnTTTt3a0KJql6fQ68nww7PHJEk2i0Wf2XazGTgw+JxufaLreknSVDSsf04FCdId848oroQk6f7mjozAgaHc7tAvt282Xx8dHy7Y1wAAAAAApepdLRv0/bvfrx01Dea+/3XHg7LKokNjg/r5xbOSpI9tuk7fu/N92jzPh7hYXkU7bSEQjeiNkQFJ0u7axrwP/ZJ0x5pWldkdkqQX8vQ0iMZnO3k2zZP+0lRWkfcaAAAAAMC12VZVp3avT/c2rVO716c9qYyDs1PjkjRv5jeKwzX3PFgs3f4RRVIjPHbWrJnzPIfVpi2+Or02kiw3iMbjsltnYyIt5bNBgf6ZqTnv0x+YNLdb064BAAAAAFwbl82uv7n9l2RJTbd7T1unXhjsNY935pnMgOJStMGDnim/ud1WXjnvuW3eSr020q9YIqGLgUm1e33msY6Kam2rqtOR8WHtvXhGH1q3RXVZWQyBaEQ/6umWlMxOuKGuad73e/T8CX3/zFsaCgbksNpU7y7Tjup6/VLLJm3ihx4AAAAAchiBAymZXb69ul5vjQ1pT31zTu86FJ+iDR6kd+LMftjPll7SMDgznRE8kKQv7Nijf39gnwZmpvTJFx/Xr67fqo2VNbJZLTo3Oa5Hzh7TwMyUfA6Xvnjd7XJYbfO+38mJUXM7Eo+rZ8qvnim/fnbhlB5s3ajf2XKjnJe5BwAAAACUKqvFoj+/+Z0KxmJy23h2WgmKNngwE5ud9+mxzb/M9PEeM7FozvGW8kp9e88D+umFE3rkzFH9VffrGcftFqseWrdFH1y3ec7eCpLktTt1+5oWXVezRi1lFXLYbBoNzei14X493ntaM7GofnbhlALRqL503e3zrjkcjykSj5vNHAEAAACglFgsFnnsRftIiixF+38qHJttWpjewyCf9EyBcDyW95yXB3v1TN+5vMGFaCKufxnoUZXTpV9ZvzUjncZQ6/bo/97zgZw5pJJ0S32z3tvWqYf3P63BYEDP9J/T3U3tuq2hZc41/+D0kZxRJQAAAAAAFKOiDR44bbMBg8tNP4ikBQzylQv81fHX9P/OHZck3d7Qol9Zv1UdFdWyWSzqmfbr0Z5u7b14Rt89cVDH/CP6D7vuyBkL6bDa5JhnDS3llfriztv1+6/+XJL0aE/3vMGDD2/Ypg+t36KH9z+tbv/IvF8fAAAAAADLqWhHNXpss4/q+bIF0gXTjmeXOLw8eNEMHNzf3KGv7L5L26rr5bHb5bTZtKmyRl/YsUcf2bBdkvT8pQv6p/MnF7TmHTUNZr+Ft8aGFE/MXZLgtNpUbnfIqtwsBwAAAAAAiknRBg/Sew8MpzVPzCe9uWKDpzzj2OO9pyRJFkn/etN1c97j1zq2m4GHJ3pPX+1yTe3lyeBBOB7TRCS04PsAAAAAAFAsijZ4kD4x4fz0xLznnp9KHrdZLGouq8g8lrq2yumed2qD02Yz3/PCZd5vXiQSAAAAAABWmaINHnT5auVI9R04NHppzvMi8ZiO+YfNa7KbK9pSzQ9j85QQGIxzbHkaJl6p81N+SZLDalUls0oBAAAAAKtA0QYPyuwOXV/bKEl6fWQgozQh3fOXLigQTY51vH1Na87xRo9XkjQRCakn9WCfz0Q4pHOT4xnXXK23xgZ1LvUe26sbZL2GIAQAAAAAAMVi2YIHe3tP6x1PfF/veOL7+ruTh/Ke89D6LZKSGQF/eXS/YonMqQv+cFDf7X5DkuS1O/Xulg0597i1odnc/s6x1zImMxjiiYS+feyAIqn7p19jeOHSBSXmyV64OD2pr7/5gvn6va2b5jwXAAAAAICVZEGjGg+PDapvetJ87U9rDHgxMKm9WQ0H78/zUH8lrq9t1D2N7do30KOXBnv1B/uf0QfXbVaty6Ozk+P6hzNvaTCVkfDxrl2qyFMmcH9zh/7x3HGdn57QayP9+p0Xn9D727u0oaJKVotFPVN+/fTCSR0dT5Y+VDvd+tC6zTn3+Y9vPKvmsgrdsaZVXb5a1bvL5LBaNRqa0YHhfj3ee9qcCnFXY5ve1ti2oK8ZAAAAAIBis6DgweMXTuvJvjN5jx0ZH9KR8aGMfQsNHkjS53fcqulYRK8O9eng6CUdzOp/YJVFv75hux6c45N+h9Wmb9x4j778+rM6PTmms1Pj+vMjr+Q9t9Hj1R9f/zb5nO68xy8GJvXI2aPzrvc9rZv0yS03XMFXBgAAAADAyrCg4MFSctns+voN9+jpvrN68uIZnZ4c13QkrCqXWzuqG/T+tk5tra6f9x5rPF59e88D2td/Ts9euqBTE6MaDwelhFThdGq9t1q3r2nRO9d2yGPP/y35k91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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "top_name = top_df['Name'][0].replace('/', '')\n",
        "bottom_name = bottom_df['Name'][0].replace('/', '')\n",
        "\n",
        "for idx in range(2):\n",
        "    \n",
        "    name = [top_name, bottom_name][idx]\n",
        "    file_path = ['static/top.png', 'static/bottom.png']\n",
        "    \n",
        "    url = 'https://query1.finance.yahoo.com/v8/finance/chart/' + name + '=X?region=US&lang=en-US&includePrePost=false&interval=30m&useYfid=true&range=1mo&corsDomain=finance.yahoo.com&.tsrc=finance'\n",
        "    header = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\n",
        "    page = requests.get(url, headers=header)\n",
        "    temp_json = json.loads(page.text)\n",
        "    price_list = temp_json['chart']['result'][0]['indicators']['quote'][0]['close']\n",
        "    price_list = [price for price in price_list if price != None]\n",
        "    fig, ax = plt.subplots(figsize=(12, 6))\n",
        "    ax.plot(price_list, color='#43B7A4')\n",
        "    ax.set_xticks([])\n",
        "    ax.tick_params(axis='y', colors='#43B7A4', labelsize=20)\n",
        "    for axis in ['top','bottom','left','right']:\n",
        "        ax.spines[axis].set_color('#43B7A4')\n",
        "        ax.spines[axis].set_linewidth(4)\n",
        "    plt.savefig(file_path[idx], transparent=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3ca4ce29-3790-4226-841a-ad2e44b0d0a6",
      "metadata": {
        "id": "3ca4ce29-3790-4226-841a-ad2e44b0d0a6"
      },
      "source": [
        "# Step 2 - PowerPoint Updating"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install python-pptx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lyrdO70BWbaj",
        "outputId": "fe3a86f9-42bd-444d-b5d5-ba8a56d89b3b"
      },
      "id": "lyrdO70BWbaj",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting python-pptx\n",
            "  Downloading python-pptx-0.6.21.tar.gz (10.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.1/10.1 MB\u001b[0m \u001b[31m79.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: lxml>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from python-pptx) (4.9.2)\n",
            "Requirement already satisfied: Pillow>=3.3.2 in /usr/local/lib/python3.10/dist-packages (from python-pptx) (8.4.0)\n",
            "Collecting XlsxWriter>=0.5.7 (from python-pptx)\n",
            "  Downloading XlsxWriter-3.1.0-py3-none-any.whl (152 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m152.7/152.7 kB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hBuilding wheels for collected packages: python-pptx\n",
            "  Building wheel for python-pptx (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-pptx: filename=python_pptx-0.6.21-py3-none-any.whl size=470935 sha256=5f03c3bbf698fa9fcb2c95f2899ac5762052c518990268cf1f0bb10f8b728746\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/dd/74/01b3ec7256a0800b99384e9a0f7620e358afc3a51a59bf9b49\n",
            "Successfully built python-pptx\n",
            "Installing collected packages: XlsxWriter, python-pptx\n",
            "Successfully installed XlsxWriter-3.1.0 python-pptx-0.6.21\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "54a8a21c-419f-4384-b36b-59ce112294be",
      "metadata": {
        "id": "54a8a21c-419f-4384-b36b-59ce112294be"
      },
      "outputs": [],
      "source": [
        "from pptx import Presentation\n",
        "from pptx.util import Inches\n",
        "\n",
        "# Open the PPT\n",
        "currencies_ppt = Presentation('static/Currencies.pptx')\n",
        "\n",
        "# Select the slide to be editted\n",
        "slide = currencies_ppt.slides[0]\n",
        "\n",
        "# Remove the old figures\n",
        "shapes = slide.shapes\n",
        "for shape in shapes:\n",
        "    #print(shape.shape_type)\n",
        "    if shape.shape_type == 13: # 13 = PICTURE\n",
        "        shapes.element.remove(shape.element)\n",
        "\n",
        "# Add the new figures\n",
        "top_img_path = 'static/top.png'\n",
        "bottom_img_path = 'static/bottom.png'\n",
        "top_pic = slide.shapes.add_picture(top_img_path, Inches(0.40), Inches(4.85), width=Inches(5.30))\n",
        "bottom_pic = slide.shapes.add_picture(bottom_img_path, Inches(5.25), Inches(4.85), width=Inches(5.30))\n",
        "\n",
        "# Send the figures to the back\n",
        "ref_element = slide.shapes[0]._element\n",
        "ref_element.addprevious(top_pic._element)\n",
        "ref_element.addprevious(bottom_pic._element)\n",
        "\n",
        "# Separate text box and table\n",
        "shapes = slide.shapes\n",
        "text_box_list = []\n",
        "auto_shape_list = []\n",
        "table_list = []\n",
        "for shape_idx in range(len(shapes)):\n",
        "    shape = shapes[shape_idx]\n",
        "    if shape.shape_type == 17: # TEXT_BOX\n",
        "        text_box_list.append(shape_idx)\n",
        "    if shape.shape_type == 1: # AUTO_SHAPE\n",
        "        auto_shape_list.append(shape_idx)\n",
        "    if shape.shape_type == 19: # TABLE\n",
        "        table_list.append(shape_idx)\n",
        "\n",
        "# Last update date shape index\n",
        "last_update_date_textbox_height = max([shapes[shape_idx].height for shape_idx in text_box_list])\n",
        "last_update_date_idx = [shape_idx for shape_idx in text_box_list if shapes[shape_idx].height == last_update_date_textbox_height][0]\n",
        "\n",
        "# Top 5 figure label shape index\n",
        "top_label_left = min([shapes[shape_idx].left for shape_idx in auto_shape_list])\n",
        "top_label_idx = [shape_idx for shape_idx in auto_shape_list if shapes[shape_idx].left == top_label_left][0]\n",
        "auto_shape_list.remove(top_label_idx)\n",
        "\n",
        "# Bottom 5 figure label shape index\n",
        "bottom_label_idx = auto_shape_list[0]\n",
        "\n",
        "# Top 5 table shape index\n",
        "top_table_left = min([shapes[shape_idx].left for shape_idx in table_list])\n",
        "top_table_idx = [shape_idx for shape_idx in table_list if shapes[shape_idx].left == top_table_left][0]\n",
        "table_list.remove(top_table_idx)\n",
        "\n",
        "# Bottom 5 table shape index\n",
        "bottom_table_idx = table_list[0]\n",
        "\n",
        "# Update last update date\n",
        "paragraph = shapes[last_update_date_idx].text_frame.paragraphs[0]\n",
        "paragraph.runs[4].text = datetime_now.strftime(\"%#d %b %Y %H:%M\")\n",
        "\n",
        "# Update top 5 figure label\n",
        "paragraph = shapes[top_label_idx].text_frame.paragraphs[0]\n",
        "paragraph.runs[0].text = top_df['Name'][0].replace('/', ' / ')\n",
        "\n",
        "# Update bottom 5 figure label\n",
        "paragraph = shapes[bottom_label_idx].text_frame.paragraphs[0]\n",
        "paragraph.runs[0].text = bottom_df['Name'][0].replace('/', ' / ')\n",
        "\n",
        "# Update top table\n",
        "top_table = shapes[top_table_idx].table\n",
        "for i in range(5):\n",
        "    for j in range(4):\n",
        "        cell = top_table.cell(i+1, j)\n",
        "        paragraph = cell.text_frame.paragraphs[0]\n",
        "        run = paragraph.runs[0]\n",
        "        run.text = str(top_df.iloc[i, j])\n",
        "\n",
        "# Update bottom table\n",
        "bottom_table = shapes[bottom_table_idx].table\n",
        "for i in range(5):\n",
        "    for j in range(4):\n",
        "        cell = bottom_table.cell(i+1, j)\n",
        "        paragraph = cell.text_frame.paragraphs[0]\n",
        "        run = paragraph.runs[0]\n",
        "        run.text = str(bottom_df.iloc[i, j])\n",
        "        \n",
        "# Save the PPT\n",
        "currencies_ppt.save('static/New_Currencies.pptx')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "47616b5b-7c8d-49d3-8fd7-44742bd18df2",
      "metadata": {
        "id": "47616b5b-7c8d-49d3-8fd7-44742bd18df2"
      },
      "source": [
        "# Step 3 - Export to Different Formats"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pywin32"
      ],
      "metadata": {
        "id": "z-EOZni6XMIX"
      },
      "id": "z-EOZni6XMIX",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "f99c1125-2c34-426a-8279-ecc5b80e5348",
      "metadata": {
        "id": "f99c1125-2c34-426a-8279-ecc5b80e5348"
      },
      "outputs": [],
      "source": [
        "import win32com.client\n",
        "import os\n",
        "\n",
        "#Open the PPT\n",
        "ppt_file_path = os.getcwd() + '\\\\New_Currencies.pptx'\n",
        "powerpoint = win32com.client.Dispatch('Powerpoint.Application')\n",
        "deck = powerpoint.Presentations.Open(ppt_file_path)\n",
        "\n",
        "# Save the PNG\n",
        "img_file_path = os.getcwd() + '\\\\Currencies.png'\n",
        "powerpoint.ActivePresentation.Slides[0].Export(img_file_path, 'PNG')\n",
        "\n",
        "# Save the PDF\n",
        "pdf_file_path = os.getcwd() + '\\\\Currencies.pdf'\n",
        "deck.SaveAs(pdf_file_path, 32)\n",
        "\n",
        "# Quit the PPT\n",
        "deck.Close()\n",
        "powerpoint.Quit()\n",
        "os.system('taskkill /F /IM POWERPNT.EXE')"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bizP-JNvW_AP"
      },
      "id": "bizP-JNvW_AP",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.4"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}