Sales Analytics .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cace7376",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4aba260a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Row ID         Order ID Order Date  Ship Date     Ship Mode Customer ID  \\\n",
      "0   32298   CA-2012-124891 2012-07-31 2012-07-31      Same Day    RH-19495   \n",
      "1   26341    IN-2013-77878 2013-02-05 2013-02-07  Second Class    JR-16210   \n",
      "2   25330    IN-2013-71249 2013-10-17 2013-10-18   First Class    CR-12730   \n",
      "3   13524  ES-2013-1579342 2013-01-28 2013-01-30   First Class    KM-16375   \n",
      "4   47221     SG-2013-4320 2013-11-05 2013-11-06      Same Day     RH-9495   \n",
      "\n",
      "      Customer Name      Segment           City            State  ...  \\\n",
      "0       Rick Hansen     Consumer  New York City         New York  ...   \n",
      "1     Justin Ritter    Corporate     Wollongong  New South Wales  ...   \n",
      "2      Craig Reiter     Consumer       Brisbane       Queensland  ...   \n",
      "3  Katherine Murray  Home Office         Berlin           Berlin  ...   \n",
      "4       Rick Hansen     Consumer          Dakar            Dakar  ...   \n",
      "\n",
      "         Product ID    Category Sub-Category  \\\n",
      "0   TEC-AC-10003033  Technology  Accessories   \n",
      "1   FUR-CH-10003950   Furniture       Chairs   \n",
      "2   TEC-PH-10004664  Technology       Phones   \n",
      "3   TEC-PH-10004583  Technology       Phones   \n",
      "4  TEC-SHA-10000501  Technology      Copiers   \n",
      "\n",
      "                                        Product Name     Sales Quantity  \\\n",
      "0  Plantronics CS510 - Over-the-Head monaural Wir...  2309.650        7   \n",
      "1          Novimex Executive Leather Armchair, Black  3709.395        9   \n",
      "2                  Nokia Smart Phone, with Caller ID  5175.171        9   \n",
      "3                     Motorola Smart Phone, Cordless  2892.510        5   \n",
      "4                     Sharp Wireless Fax, High-Speed  2832.960        8   \n",
      "\n",
      "  Discount    Profit  Shipping Cost  Order Priority  \n",
      "0      0.0  762.1845         933.57        Critical  \n",
      "1      0.1 -288.7650         923.63        Critical  \n",
      "2      0.1  919.9710         915.49          Medium  \n",
      "3      0.1  -96.5400         910.16          Medium  \n",
      "4      0.0  311.5200         903.04        Critical  \n",
      "\n",
      "[5 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "# Load the Excel file\n",
    "file_path = 'ECOMM DATA.xlsx'\n",
    "orders_df = pd.read_excel(file_path, sheet_name='Orders')\n",
    "\n",
    "# Display the first few rows of the dataframe\n",
    "print(orders_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "db5b126d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Sales: $12642501.91\n"
     ]
    }
   ],
   "source": [
    "# Calculate total sales\n",
    "total_sales = orders_df['Sales'].sum()\n",
    "print(f\"Total Sales: ${total_sales:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6075e9fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Year  Month         Sales\n",
      "0   2011      1   98898.48886\n",
      "1   2011      2   91152.15698\n",
      "2   2011      3  145729.36736\n",
      "3   2011      4  116915.76418\n",
      "4   2011      5  146747.83610\n",
      "5   2011      6  215207.38022\n",
      "6   2011      7  115510.41912\n",
      "7   2011      8  207581.49122\n",
      "8   2011      9  290214.45534\n",
      "9   2011     10  199071.26404\n",
      "10  2011     11  298496.53752\n",
      "11  2011     12  333925.73460\n",
      "12  2012      1  135780.72024\n",
      "13  2012      2  100510.21698\n",
      "14  2012      3  163076.77116\n",
      "15  2012      4  161052.26952\n",
      "16  2012      5  208364.89124\n",
      "17  2012      6  256175.69842\n",
      "18  2012      7  145236.78512\n",
      "19  2012      8  303142.94238\n",
      "20  2012      9  289389.16564\n",
      "21  2012     10  252939.85020\n",
      "22  2012     11  323512.41690\n",
      "23  2012     12  338256.96660\n",
      "24  2013      1  199185.90738\n",
      "25  2013      2  167239.65040\n",
      "26  2013      3  198594.03012\n",
      "27  2013      4  177821.31684\n",
      "28  2013      5  260498.56470\n",
      "29  2013      6  396519.61190\n",
      "30  2013      7  229928.95200\n",
      "31  2013      8  326488.78936\n",
      "32  2013      9  376619.24568\n",
      "33  2013     10  293406.64288\n",
      "34  2013     11  373989.36010\n",
      "35  2013     12  405454.37802\n",
      "36  2014      1  241268.55566\n",
      "37  2014      2  184837.35556\n",
      "38  2014      3  263100.77262\n",
      "39  2014      4  242771.86130\n",
      "40  2014      5  288401.04614\n",
      "41  2014      6  401814.06310\n",
      "42  2014      7  258705.68048\n",
      "43  2014      8  456619.94236\n",
      "44  2014      9  481157.24370\n",
      "45  2014     10  422766.62916\n",
      "46  2014     11  555279.02700\n",
      "47  2014     12  503143.69348\n"
     ]
    }
   ],
   "source": [
    "# Convert 'Order Date' to datetime format\n",
    "orders_df['Order Date'] = pd.to_datetime(orders_df['Order Date'])\n",
    "\n",
    "# Extract year and month from the 'Order Date'\n",
    "orders_df['Year'] = orders_df['Order Date'].dt.year\n",
    "orders_df['Month'] = orders_df['Order Date'].dt.month\n",
    "\n",
    "# Group by year and month and sum the sales\n",
    "sales_trends = orders_df.groupby(['Year', 'Month'])['Sales'].sum().reset_index()\n",
    "\n",
    "# Display the sales trends\n",
    "print(sales_trends)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "04e7fd96",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>98898.48886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>91152.15698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>145729.36736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>116915.76418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>5</td>\n",
       "      <td>146747.83610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month         Sales\n",
       "0  2011      1   98898.48886\n",
       "1  2011      2   91152.15698\n",
       "2  2011      3  145729.36736\n",
       "3  2011      4  116915.76418\n",
       "4  2011      5  146747.83610"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert 'Order Date' to datetime format\n",
    "orders_df['Order Date'] = pd.to_datetime(orders_df['Order Date'])\n",
    "\n",
    "# Extract year and month from the 'Order Date'\n",
    "orders_df['Year'] = orders_df['Order Date'].dt.year\n",
    "orders_df['Month'] = orders_df['Order Date'].dt.month\n",
    "\n",
    "# Group by year and month and sum the sales\n",
    "sales_trends = orders_df.groupby(['Year', 'Month'])['Sales'].sum().reset_index()\n",
    "\n",
    "# Display the sales trends\n",
    "sales_trends.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b7f5bfb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Apple Smart Phone, Full Size</td>\n",
       "      <td>86935.7786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>Cisco Smart Phone, Full Size</td>\n",
       "      <td>76441.5306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>Motorola Smart Phone, Full Size</td>\n",
       "      <td>73156.3030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2501</th>\n",
       "      <td>Nokia Smart Phone, Full Size</td>\n",
       "      <td>71904.5555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>Canon imageCLASS 2200 Advanced Copier</td>\n",
       "      <td>61599.8240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112</th>\n",
       "      <td>KitchenAid Stove, Black</td>\n",
       "      <td>19549.8842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>Hon Swivel Stool, Black</td>\n",
       "      <td>19536.6396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>Ikea Library with Doors, Mobile</td>\n",
       "      <td>19259.0630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>Eldon Lockers, Blue</td>\n",
       "      <td>19255.3236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>GBC Ibimaster 500 Manual ProClick Binding System</td>\n",
       "      <td>19024.5000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Product Name       Sales\n",
       "310                       Apple Smart Phone, Full Size  86935.7786\n",
       "970                       Cisco Smart Phone, Full Size  76441.5306\n",
       "2415                   Motorola Smart Phone, Full Size  73156.3030\n",
       "2501                      Nokia Smart Phone, Full Size  71904.5555\n",
       "866              Canon imageCLASS 2200 Advanced Copier  61599.8240\n",
       "...                                                ...         ...\n",
       "2112                           KitchenAid Stove, Black  19549.8842\n",
       "1885                           Hon Swivel Stool, Black  19536.6396\n",
       "2012                   Ikea Library with Doors, Mobile  19259.0630\n",
       "1231                               Eldon Lockers, Blue  19255.3236\n",
       "1498  GBC Ibimaster 500 Manual ProClick Binding System  19024.5000\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by product and sum the sales\n",
    "best_selling_products = orders_df.groupby('Product Name')['Sales'].sum().reset_index()\n",
    "\n",
    "# Sort the products by sales in descending order\n",
    "best_selling_products = best_selling_products.sort_values(by='Sales', ascending=False)\n",
    "\n",
    "# Display the top 10 best-selling products\n",
    "best_selling_products.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a3c40804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G5rtNCwsL/ve//3Hx4kUOHTrEjz/+yKVLl5g2bRpt27bF39+ftm3bMm3aNACsra2ZPHnyI12z9HTr1o0jR47k2yaFQpF3wleuIiU8HLe33jLpdSxcXCi3cAElB71K2NJfuT38dZJ1D6IFhXIwRmL9yUAmrD1LYEQcEgiMiGPC2rP5djIeHh7Ur18fAAcHB6pVq0ZQUBAbNmxg8ODBAAwePJj169cDmkZZ8+bNM+iWpdGkSRM8ssg5oVAoTE9qfDyhCxdi26QJtvXrmfx6wtKSMh9/jMe0qcSdOMHNPn2Jv3DB5NdNQ+3kN5AvNp3nQtDDLI+fvB1BYkpqhrK4pBQ+XH2GP47c1lunuqcjn3erYbANN2/e5OTJkzRs2JB79+49chYeHh6EhIQY3I5CoSgcIv5cTcqDB7h9+78Cva5zz56UqFSZgHff5eYrA/D46iucunYx+XWVgzESmZ1LTuW5JTo6mj59+jB79mwcHR2N0qZCoSg4UhMTCV2wAJuGDbBr1KjAr29TqyYVVv9JwOjRBH3wAfHnz1OiShXuf/cd7kFB+Ht64j5mNE7duhntmsrBGEhOI41m03YTGBH3WLmXsw0rRzyXr2snJSXRp08fBgwYQO/evYmKiqJ06dIEBwfj4eFBcHAw7sUkP4RC8bQSuXYtyffu4Tn160KzwcLNjfKLF3Nv2nTCFi8GMzNITUUAyUFBBH/6GYDRnIxJ12CEEDeFEGeFEKeEEMd0ZS5CiB1CCH/dz5Lpzp8ghLgqhLgshOiYrryBrp2rQojvhBBCV15CCLFSV35YCOGTrs5g3TX8hRCDTdlPgHEdq2BjmTHdqY2lOeM6VsmihmFIKRk2bBjVqlVj7Nixj8q7d+/O0qVLAVi6dCk9evTI13UUCoXpkImJPJg/H5s6dbB9Ln8PnPlFWFpS5tOJmDk7Q2rGGRYZH0/IrNlGu1ZBLPK3llLWlVI21H0eD+ySUvoCu3SfEUJUB14GagCdgLlCiLQ79jzgDcBX9+qkKx8GhEspKwOzgOm6tlyAz4HGQCPg8/SOzBT0rOfF1N618HK2QaCNXKb2rkXPel75avfAgQMsW7aM3bt3U7duXerWrcu2bdsYP348O3bswNfXlx07djB+/PhHdXx8fBg7dixLlizB29ubC7pFvQ8//BBvb29iY2Px9vbOoOasUChMR+TGjSQHBeP29lvono8LndTISL3lycHBRrtGYUyR9QBa6d4vBfyAj3TlK6SUCcANIcRVoJEQ4ibgKKU8CCCE+BXoCfytqzNJ19Zq4Afd6KYjsENKGaarswPNKf1hyo71rOeVb4eSmebNmz9KRJZGVFQUDg4O7Nq1S2+dmzdv6i2fMWMGM2bMMKp9CoUie2RyMg9+no91zZrYtWhR2OY8wsLDg+SgIL3lRruG0VrSjwS2CyEk8LOUcj5QWkoZDCClDBZCpC0eeAGH0tUN0JUl6d5nLk+rc0fXVrIQIhJwTV+up84jhBBvoI2MKF26NH5+fhmOOzk5ERUVlcsum56UlBST2hUfH//Yd1GQREdHF+r1TYnqW/Elr/2zPnQIpzt3uN+1C7f++cf4huUR644dcfj9d8zSJSxLtbIitGNHAo30ezS1g2kmpQzSOZEdQohL2Zyrb9wosynPa53/CjSHNx+gYcOGslWrVhmOX7x4EQcHh6wtLiTSRjCmwtramnr1TB+jnxV+fn5k/l08Kai+FV/y0j+ZksL1adMRVavS5L33isz0GACtWhFZvRohs2aTFBSEZXGLIpNSBul+hggh1qGth9wTQnjoRi8eQNoGjgCgbLrq3kCQrtxbT3n6OgFCCAvACQjTlbfKVMfPeD1TKBSKnHn491YSb97Ea86couVcdDh164ZTt24mezgw2SK/EMJOCOGQ9h7oAJwDNgJpUV2DgTQlto3Ay7rIsApoi/lHdNNpUUKIJrr1lUGZ6qS11RfYLbUFi21AByFESd3ifgddmUKhUBQIMjWVBz/No4RvZRzatytscwoFU45gSgPrdF7bAlgupdwqhDgKrBJCDANuAy8CSCnPCyFWAReAZOBtKWWKrq03gSWADdri/t+68oXAMl1AQBhaFBpSyjAhxGTgqO68L9MW/BUKhaIgiNq+g8Sr1/D83zcIs6dTlctkDkZKeR2oo6c8FGibRZ0pwBQ95ceAmnrK49E5KD3HFgGLcme1QqEo6kRu2kTIrNkkBwdj4eFh9HUDYyBTU3kwbx5WFSrg2KlTzhWeUJ5Ot1qMMJZcf2xsLF26dKFq1arUqFEjw74ZhaK4ELlpE8GffqaF10r5aPd55KZNhW1aBqL37CHh8mXcRo5AmJvnXOEJRTkYY3JmFcyqCZOctZ9nVuW7SWPK9X/wwQdcunSJkydPcuDAAf7+++/HzlEoijIh//sWGR+foczYu8/zi5SSBz/OxbJcORy7mF5QsiijHIyxOLMKNr0HkXcAqf3c9F6+nYyx5PptbW1p3bo1AFZWVtSvX5+AgAAUiqKOTE4meu9eAkaPIfnuXb3nGHP3eX6J2buX+AsXcBvxBsLi6ZZ7fLp7nxv+Hg93z2Z9POAopCRkLEuKgw3vwPGl+uuUqQWdpxlsgrHk+iMiIti0aROjRo0yuI5CUdAkXL9B5Lq1RG7YSHJICObOzpjZ2ZEaE/PYuRalShWChY8jpeT+3LlYenri1L17YZtT6KgRjLHI7FxyKs8lxpLrT05Opn///rz33ntUrFjRKLYpFMYiJSqK8JWruPlyf66/8AKhixZjXb06XnPmUHnvP5SZ9DlCTzK9lKgo4k6fLgSLMxLz77/Enz6D6xtvICwtC9ucQkeNYAwlp5HGrJq66bFMOJWFoZvzdWljyvW/8cYb+Pr6Mnr06HzZpFAYC5maSuzhw0SsXUfUjh3I+HisKlXCfdwHOHbrhmW6v+20aLH0UWQurw4k/I8V3Bo8BK/Zs3AoJDUBKSUP5s7DokwZnHr3KhQbihrKwRiLtp9pay5J6XLCWNpo5fkgJ7n+8ePHGyzXP3HiRCIjI1mwYEG+bFIojEHinTtErltHxPr1JAcFY+boiFOvnjj36oV1rVpZ7nxP232euezOiJEEvP0OHl9+iXOf3gXRhQzEHjlK3PHjlJ44ETMrqwK/flFEORhjUbuf9nPXlxAZAE7emnNJK88jaXL9tWrVom7duoDmKMaPH0+/fv1YuHAh5cqV488//3xUx8fHh4cPH5KYmMj69evZvn07jo6OTJkyhapVqz4KGnjnnXcYPnx4vuxTKHJDakwMD7dtJ3LtWmKPHQMhsGvWDPf338ehXTvMSpTIU7sWbm6UW7qUwFGjCP7kE5Lv38d1xBsFKs/yYO5czEu54dy3T4Fds6ijHIwxqd0v3w4lM8aU68/cjkJREEgpiTt2jIi163i4bRsyNhar8uUpNWYMTj26Y1mmjFGuY25vR9l5cwn6ZCL3Z88mOSSE0p98XCD7UGKPHyf28GHcx3+EmZ41oqcV5WAUCkW+SNtZnzmve1JQEJEbNhCxbj1Jt29jZmeHU5cXcOrVC5t69UwyuhBWVnhOn4aFeynCFi4i+cEDPGfOyPPIyFAezJ2HuYsLJV96yaTXKW4oB6NQKPJM2s56GR//X173jz/hwfz5JF69BlJi26QJpd5+C4f27TGztTW5TcLMjNLjxmFRqhQh06ZzJywM77k/Yp6P6MvsiDt9mpgDB3D/4H3MbGxMco3iinIwCoUiz4TMmv34zvqkJBKv38Dt7bdx6tkTK2/jZnk1FNchQ7BwK0XQhAncGvgqZX+Zj2Xp0ka/zoO58zB3csL55f5Gb7u4o/bBKBSKPJPlDvrUVEq983ahOZc0nLp2odzPP5EUEMDN/v1JuHbNqO3HnT9P9D//4DJ0COb2dkZt+0lAORiFQpFnssrfbsy87vnFrmlTyv+2DJmYxK1XBhB78qTR2n4wbx5mjo6UHDDAaG0+SSgHo1Ao8oz7mNGITAvowtoa9zGjC8egLLCuXh2fP5Zj7uzM7aGvEbV7T77bjL98meidu3B59VXMi2Bq9aKAcjBFHGPJ9QN06tSJOnXqUKNGDUaOHElKSspj11MocoNTt27YtWwJgAQsPD3xmPxlkcvPAmBVtizl/1hOCV9fAt55h/B0e8fywoN5P2FmZ4fLoFeNZOGTh3IwRmTz9c10WN2B2ktr02F1BzZfz59EDBhXrn/VqlWcPn2ac+fOcf/+/QybMxWKvJJ08wa2zz5LyE/z8N29q0g6lzQsXFwov2Qxds2acffTz7g/d26e9oclXL1K1LZtlBw4EHMnJxNY+mSgHIyR2Hx9M5P+nURwTDASSXBMMJP+nZRvJ2MsuX7gkUhmcnIyiYmJBbrLWfFkknDtGgn+V3Ho2LGwTTEYMzs7ys79EacePXjw3ffc/eILZC5H8w9++hlhY4PLkMEmsvLJQIUpG8j0I9O5FHYpy+Nn7p8hMTUxQ1l8SjyfHfiM1VdW661T1aUqHzX6yGAbjCHX37FjR44cOULnzp3p27evwddWKPQRtX07AA7t28PFC4VsjeEIS0s8pk3Fwt2d0F9+ISU0FM+ZMw3ahZ9w4wYPt2zBZegQLEqWLABriy9qBGMkMjuXnMpzi7Hk+rdt20ZwcDAJCQns3r3bKLYpnl4ebtuOTf36WJY2TM27KCGEwP39sZT++GOidu7i9rDhpERG5lgv9Of5CCsrXIcOLQArizdqBGMgOY00OqzuQHDM43sCPOw8WNxpcb6ubUy5ftDWabp3786GDRto3759vmxTPL0k3rxJwqVLlJ4wvrBNyRcug17FopQbQR9+xK2BAyk7fz6WWYRZJ965Q+SmTbgMHICFm1sBW1r8UCMYIzGq/iiszTMOr63NrRlVP39ZI3OS6wcMkuuPjo4mWLcpLjk5mS1btlC1atV82aZ4unm4Ld30WDHHsXNnyv7yC0nBd7nZ/xUS/P31nhc6fz7C3ByX14YVsIXFEzWCMRJdKnYBYM6JOdyNuUsZuzKMqj/qUXleMZZcv6urK927dychIYGUlBTatGnDyJEj82Wb4ukmats2rOvUxtLTs7BNMQp2TRpT/rdl3H79dW4OGEjZeXOxbdDg0XGz0FAi1m+g5IsvFsspwcJAORgj0qVil3w7lMwYU67/6NGjRrVN8fSSeOcO8Rcu4P7hh4VtilGxrloVnz9WcGf4cG6/Ngyv/32DQ7t2ANjpRmyur6scSoaipsgUCkWueRQ91qFDIVtifKy8vbQNmVWrEPDeKAI//gT/51ths3cvwtJSS5SmMAjlYBQKRa55uG071jVrFrqYpamwKFmS8osXY/WMLw/XriX53j0EIGNjCf70MyI3bSpsE4sFysEoFIpckRQYSPyZMzh0fPJGL+kxs7UlNfLhY+UyPp6QWbML3qBiiHIwCoUiVzzcsQMAxydweiwzyXfv6i/PKk2BIgPKwSgUilwRtW07JapVw6p8+cI2xeQUh3QERRnlYBQKhcEk3btH3MmTOD7h02NpuI8ZjcgkH1MU0xEUVZSDKeIYU64/je7du1OzZs0C64PiySFquzY95tCh+Ihb5genbt3wmPwlFp6eRT4dQVFEORgjErlpE/5t2nKxWnX827Q1SqSJMeX6AdauXYu9vX2+7VI8nURt20YJX19KVKxQ2KYUGE7duuG7e1exSEeQW9JSjLx7612jpRhJj3IwRiJy0yaCP/2M5KAgkJLkoCCjhDMaU64/Ojqab7/9lokTJ+bLJsXTSfL9+8QeP16spPkVWZM+xQhgtBQj6TH5Tn4hhDlwDAiUUnYVQrgAKwEf4CbQT0oZrjt3AjAMSAHek1Ju05U3AJYANsAWYJSUUgohSgC/Ag2AUOAlKeVNXZ3BQNqd9Csp5dL89OPu11+TcDFruf6406eRiRmVk2V8PMGfTCRilf7EXiWqVaXMxx8bbEN+5fo//fRT3n//fWxtbQ2+psI4RG7aRMis2bgHBeHv6Yn7mNHF7kk4audOkPKpWX950plzYg7xKfEZyuJT4plzYo7RFEkKYgQzCriY7vN4YJeU0hfYpfuMEKI68DJQA+gEzNU5J4B5wBuAr+7VSVc+DAiXUlYGZgHTdW25AJ8DjYFGwOdCCJMmbsjsXHIqzy35les/deoUV69epVevXkaxR2E46Ue3Aow2ui1oHm7bjlXFilhVrlzYpijyyb2Ye3rV3wHuxugPzc4LJh3BCCG8gS7AFCBNCrgH0Er3fingB3ykK18hpUwAbgghrgKNhBA3AUcp5UFdm78CPYG/dXUm6dpaDfwgtDSNHYEdUsowXZ0daE7pj7z2JaeRhn+bttr0WCYsPD0pv+zXvF4WMI5c/8GDBzl+/Dg+Pj4kJycTEhJCq1at8PPzy5dtipwJmTUbGZ/xSTFts15xGcUkh4URe+QIriPeUJlQizGB0YEsOruIdVfXZXlOGbsyRrueqafIZgMfAg7pykpLKYMBpJTBQoi0O6MXcCjdeQG6siTd+8zlaXXu6NpKFkJEAq7py/XUeYQQ4g20kRGlS5d+7Gbr5OREVFSUQR11GDmSiK+/znAjEdbWOIwcaXAb+pBSMmLECCpVqsTrr79OVFQUKSkpdOrUifnz5zN27Fjmz59P586dM1wnPj6exMTER2UDBw5k4MCBANy6dYt+/fqxadMmvbbFx8cXquOJjo5+ohyfu27kkpmkoKBi00+bfftxTE3liqsrF7Kw+Un7vWWmOPcvJCmE7ZHbORpzFIGgiX0TSluUZlPkJpJk0qPzLIUl7a3bG62fJnMwQoiuQIiU8rgQopUhVfSUyWzK81rnvwIp5wPzARo2bChbtcpo5sWLF3FwcMhcTS8O/V7ExsaakFmzSQ4OxsLDwyjz7Pv372fFihXUqlWLFi1aAJpc/2effUa/fv347bffHsn1p9maXq5/y5YtbN++nerVqz9q097eHjMzsyz7Zm1tTb169fJld37w8/Mj8++iOPOvkzklIx/P+R7uZF5s+nl72W8kli9HswEDshzBPGm/t8wUx/5dCb/CgjML2Ba8DUszS/pX68+QGkMejVIaXW/EnBNzCI4JxsPOwygpRtJjyhFMM6C7EOIFwBpwFEL8BtwTQnjoRi8eQNrqdABQNl19byBIV+6tpzx9nQAhhAXgBITpyltlquNnvK7px6lbN6NPeRhTrj8NHx8fzp07ZywTFTlwxT2Fxpky8SZawG/PS5oVjkm5Ijk8nJhDh3B97TU1PVZMOB96nvmn57P7zm5sLWwZXGMwg6oPws0mYxbOtBQjpnKeJlvkl1JOkFJ6Syl90Bbvd0spBwIbgcG60wYDG3TvNwIvCyFKCCEqoC3mH9FNp0UJIZro1lcGZaqT1lZf3TUksA3oIIQoqVvc76ArUygKlKTAQOrdEFzyhPuO2jBaAlc84Fqj4qFEHL17D6SkqPDkYsDJkJOM3DmSl/96maP3jjKyzki2993O2AZjH3MuBUFhJBybBqwSQgwDbgMvAkgpzwshVgEXgGTgbSll2rzCm/wXpvy37gWwEFimCwgIQ3NkSCnDhBCTgbQMW1+mLfgrFAXJvW++wcLMnDm9JKGO2tP/8K0ptD4jGesztJCtM4yH27dh6eWFdY3qOZ+sKHCklBy5e4Sfz/zM0btHKVmiJKPqj+KlKi/hYGXYFL+pKBAHI6X0QzdFJaUMBdpmcd4UtIizzOXHgMe0TaSU8egclJ5ji4BFebU5XTtP1bRA5uk4Rd6JPX6cqL+34t+zLmGO53G3KUVIXAh/NTKj/akU6u+/Bw0L28rsSXn4kJh/D+Ly6qtP1f9BcUBKyb7Afcw/M5/T909TyqYU4xqOo+8zfbG1LBp73dRO/mywtrYmNDT0qbnpSikJDQ3VqwKgyB0yNZV7X0/FzL0U31S5SrdK3djVbxfflP2GqNL23KnrSfgfK0iNjS1sU7Mles8eSEpSmyuLEKkylZ23dvLSXy/x9q63uR97n4mNJ/J3n78ZVGNQkXEuUDhTZMUGb29vAgICuH//fmGbkoH4+HiTOQFra2u8vb1zPlGRLZHrNxB//jzn3m7HQ7N/GFZzGAAlzErwQoUXWFJ7PZ+djCNi7TpcBg4oZGuz5uG27Vh4eGBdu3Zhm/KIzdc3M+fEHO7G3KWMXRmjRz4VVZJTk9l6cysLzizgWuQ1yjmU48umX9K1UlcszSwL2zy9KAeTDZaWllSoUPRE/fz8/Ao1jFiRPSnRMYTM+harWjWZ7XKMdl7tqOhc8dHxPr59ePnKn8RU8SZsyRJK9n8ZYW6eTYuFQ0p0NDH792v2FZHpsTT9rDSJkzT9LOCJcDL6nGeH8h346/pfLDi7gNtRt6nsXJnpLabT0acj5mZF7+8mPWqKTKEwMqHz55Ny/wFH+9fmYXI0w2oNy3C8umt1qpSswqbGZiQFBBClyxBZ1Ij2+weZmFikosey088q7qQXn5RIgmOCmXhgIq1Xteazfz/DztKO2a1ms6b7Gl6o+EKRdy6gHIxCYVQSAwIIW7IEu64vMC9pJ808m1HDtUaGc4QQ9PbtzTqPQPD2IHThoiK5zhe1bRsWpUphU7duYZvyiKx0soypn1VY6HOeyanJxCbHMrftXFZ2XUnb8m0xE8Xntl18LFUoigEhM78Bc3MO9/AlLD6M4bWG6z2vS8UuWFqU4GSbssSfPUvc8eMFbGn2pMbEEL13Lw4dOiDMisZtIjoxmhLmJfQeM6Z+VmGRlZNMTk2mhXeLIjNNmRuKxl+OQvEEEHPkCFHbtlHytaHMv7eG+u71aVhGfxyyUwkn2pVvx3yvK5g5OxO6aHEBW5s90Xv3IhMScCgi0WNXw6/Sf3N/ElISsBAZl47NhBnv1XuvkCwzHlk5yeLsPJWDUSiMgExJ4d60aVh4eHCwlTt3Y+5mOXpJo49vH0KJ5kHnhkTv3k3C9RsFZG3OPNy2HXNXV2wbNChsU/jr+l+8suUVopOiWdRxEV81/woPOw8EAgcrB1JlKmHxxX8fdecKnR8rsza3ZlT9UYVgjXFQUWQKhRGIXLeOhAsXKTNzBguvzqeaSzWaezXPtk7DMg0p61CW5dXDedfKirDFi/GY/GUBWZw1qXFxRP/zD049uhdqdFtiSiIzjs5g5eWV1HevzzfPf0Mp21LAfxFjUkrG+o3l2+PfUt21epYjxqLOnag7rL6yGncbd8yEGfdi7z0RIdhqBKNQ5JOU6GhCZs3Gpl49jtS05ObDmwyvNTzHOXMzYUZv397sjT2NeZe2RG7YQPKDBwVkddZE79uHjIvDsRCjx4KjgxmydQgrL69kSI0hLOi44JFzSY8QgsnNJlPWoSwf/PMBIbE5Z3YtasQmxfLebm2Kb0mnJex4cQdnBp9he9/txdq5gHIwCkW+Cf35Z1JCQ3GfMJ5fzi7Ax9GHtuX0qiE9RvdK3TEX5uxp6ohMSiJ8+XITW5szUdu2Y+7sjO2zzxbK9Q8EHqDfX/24EXmD2a1m837D97PdSGhvZc+sVrOITY7lfb/3SUpJyvLcooaUkokHJnI98jozW86krGPZnCsVI5SDUSjyQeLt24QtWYpTjx4cc4ngcvhlhtUaZvAeBXdbd1p4t+CPuH+wa92K8N+XkxoXZ1qjsyE1IYHoPXtwaN8OYVGwM+ipMpV5p+fx5s43KWVbihVdV9C2vGGOunLJynzZ9EtO3T/F/47/z8SWGo8FZxew49YOxtQfQ1OvpoVtjtFRDkahyAchM78BCwvcxo7hlzO/4GHnketpjd6Ve/Mg7gG3utYlJTKSiHVZp7M1NTEHDpAaG4tDh4KdHouIj+CtXW8x99RculXqxu8v/E55x/K5aqNThU68Wv1Vfr/4O1uubzGRpcZjb8Bevj/5PS9UeIHBNQbnXKEYohyMQpFHYg4fIWrHDtzeeJ0z8g6n7p9iaM2hudaFauHdglI2pVhpdRrrOrUJW7IUmfJ4BsyCIGrbNsycnLBr0rjArnnuwTn6/dWPI8FH+Oy5z/iq2VfYWNjkqa0xDcZQ370+kw5Owj/c38iWGo8bkTf4aO9HVHWpyqSmk4rlHhdDUA5GocgDMiWFe1OnYunpicvQofxy9hdcrF3oVblXrtuyMLOgR+Ue7Avaj8WAPiTdvk3UTv3ZSk1JamIiUbv34NCmDcLS9OKJUkpWXlrJoL8HIRAs67yMF595MV83W0szS/7X6n/YW9ozxm8MUYlRRrTYOEQlRvHe7vewMrdiTus5eXamxQHlYBSKPBCxZg0Jly7hPu4DLkZf49+gfxlUfRDWFnlTue5VuRepMpW/y4VhWbYsoYsWFrh8TOzBg6RGRRXI5srYpFg+3v8xXx3+isYejVnZdSU13GrkXNEA3Gzc+Ob5bwiMCuST/Z+QKlON0q4xSJWpfLzvY+5E3eGb57/Bw96jsE0yKbl2MEIIMyGEoymMUSiKAylRUdyfPQebBg1w6NSJBWcX4GDlwEtVXspzm+Ucy9GoTCPWXdtAycGDiD99hriTJ41odc483LYdM3t77JqadrH5ZuRNBmwZwObrm3m77tv82PZHnK2djXqN+qXr837D99lzZw+LzuU776DRmHtqLn4Bfnz47Ic8W6ZwovQKEoMcjBBiuRDCUQhhh5bS+LIQYpxpTVMoiiYP5v1ESng4pSdM4HrkdXbe3skrVV/B3so+X+329u1NQHQAV5uWw9zJidBFBXdjlElJRO3ahX2b1phZWZnsOjtu7eDlzS/zIO4BP7X/iZF1RppMvHFAtQF09unM9ye/52DQQZNcIzfsvLWTn8/8TK/KvehftX9hm1MgGPqbrS6lfAj0BLYA5YBXTWWUQlFUSbx1i7Bly3Dq1QubmjVYeHYhNhY2DKiW/6Rhbcu1xcHKgTUBf+H8Sn+id+0m4UbByMfEHD5CamSkyTZXJqUmMfPoTMb6jaWSUyVWdV1FU0/TjpSEEExqOomKThX5aO9HBEcHm/R62eEf7s/H+z+mtlttPmnySdFZ1D+zCmbV5Hm/njCrpvbZiBjqYCyFEJZoDmaDlDIJKHr64gqFibk3YyZmlpaUGj2KgKgAttzYQt9n+lLSumS+27a2sKZrxa7svLUT877dEJaWhC1ZagSrcyZq2zbMbG2xa9bM6G2HxIYwfNtwfr3wK/2r9mdJpyUFtvZga2nLrFazSExNZKzfWBJTEgvkuumJTIjkvd3vYWdpx6zWs7JUhC5wzqyCTe9B5B0EEiLvaJ+N6GQMdTA/AzcBO2CvEKI88NBoVigUxYCYgweJ3rUL1xEjsHR3Z8n5JZgJMwZXN94ehj6+fUhKTWLrw4M49ehB5Pr1JIeGGq19fcjkZKJ27sS+VSvMjJyK++jdo7y46UUuhl1keovpfNz4YyzNCza9r4+TD1OaTeFc6DmmHZlWoNdOTk3mw70fcjf2LrNazcLd1r1Ar6+X2DC4vBU2j4WkTJt6k+Jgl/H08AxyMFLK76SUXlLKF6TGLaC10axQKIo4MjmZe19PxdLLC5chg7kfe591/uvoUbkHpe1KG+06VVyqUMO1Bmv811ByyGBkQgLhy/8wWvv6iD12jJTwcKNmrpRSsvDsQoZvH46jlSPLX1jOCxVfMFr7uaVt+bYMqzmMP6/8yfqr6wvsut+d+I5/g/5lYuOJ1HWvW2DXfURqKty/DCd+hQ1vww/PwowK8MdLyAT9IdwyMsBolzdIC0IIURr4GvCUUnYWQlQHngMWGs0ShaIIE7F6NQn+/njNno1ZiRL8euxXkmUyr9V4zejX6u3bm8mHJnPVKQ6n1q0JX74c1+HDMLMxzX6Jh9u2IWxssG/ZwjjtJT5k4v6J7Lmzh44+Hfmi6RfYWdoZpe388E69dzj34BxfHfqKKiWrUM21mkmvt+X6FhafX8xLVV6izzN9THqtRyTGQOBxuHMY7hzRXvER2jEbFyjbGOr0h7KNubdkEGV4XFz1Hm4YKwONoWJDS4DFwCe6z1eAlSgHo3gKSHn4kPtzvsO2YUMcOnYgMiGSlZdX0smnk0nECV+o8ALfHPuGNf5rGPfaUG69OojI9esp2d/4kUcyJYWoHTuxb9kyzw5s8/XNzDkxh+CYYNxWuZGamsrDxId89OxHDKg2oMgsaFuYWTC95XRe+uslxviNYWXXlTiVcDLJtS6GXuTzfz+nvnt9Pnr2I5NcAykhMiCdMzkEd8+B1KlAlKoK1btD2SaaY3GtBEKQlJLKiiO3OZrYj2mWC7AV/61LxUorpia9yBwjmWiog3GTUq4SQkzQ+iWThRCFo2WhUBQwD+bOIyUigtIfT0AIwfKLy4lLjssxoVhesbeyp3359my5voUPXvwA69q1CV2yBOd+/YyenyXuxAlSHjzAsVPepsc2X9/MpH8nPcol/yBOeyIeWXskA6sPNJqdxsLVxpVvW33L4K2DGb9vPD+2/dHoYdJh8WGM2jMKpxJO/K/V/3K35nRmlbYGEhkATt7Q9jOo3U87lpwId8/qHIrOqUQFaccs7cC7AbQYqzkT74ZgkzHwRErJ1rPBzNh2mRsPYrAybwlJ8KHFKjxFKEHSlRnJ/Tju2N5I34ThDiZGCOGKLnJMCNEEiDSaFQpFESXhxg3CfvsNpz69sa5enZikGH67+Buty7bGt6Svya7bx7cPG69tZPut7bR9bSiBo8cQtXs3ju2N988P2uZKUaIE9i1b5qn+nBNzHjmX9Gy4toG3672dX/NMQu1StZnQaAKTD03m59M/82bdN43WdlJqEh/88wFh8WEs7bwUNxs3wyunRXWlLbxH3tHWTc6ugYSHEHQCknXftVM58GmmOZOyjcC9BphnfTs/ejOMqVsucuJ2BL7u9iwc3JCHcUl8vE6wMfG/xHg2luZM7VglL13Xi6EOZiywEagkhDgAlAL6Gs0KhaKIEjJjJmYlSuA+ejQAf17+k4eJD002ekmjnns9fBx9WOu/lp4dFmPp7U3YosVGdTAyNZWo7duxb9kCM7u8rZHcjbmbq/KiwovPvMjp+6eZd3oeNdxq0NI7bw42M98c/Yajd4/ydfOvqeGaS+mbXV8+HtWVkgj+W8GrITQcpjmTso3A0dOgJq+GRDN96yV2XLhHaccSTO9Tiz71vbEw10ZtQghmbrtMYEQcXs42jOtYhZ71vHJndzYY5GCklCeEEM8DVQABXNbthVEonliiDxwges8e3D94Hws3NxJSElh6YSmNPRpTu1Rtk15bCEEf3z787/j/uBF9G5fBg7k3ZQqxJ05iW7+eUa4Rd+o0ySEh+ZLmL2NXhuCYxzcwlrEz1jKxaRBC8GmTT7kSfoXx+8azsutKyjrkbz1tnf86ll9azqDqg+hWqVvuG8gyekvA67kTPw15GM+snf6sOnYHG0tzxnWswmvNKmBjlXGKtWc9L3rW88LPz49WrVrl3uYcyHbyUQjRO+0FdEdzMM8A3XRlCsUTiUxOJmTaNCzLlqXkoEEArPdfz4O4B7xR640CsaFbpW5YCAvW+q/FuU9vzJycCFtsPPmYqG3bEJaW2Ldulec26rk/7uysza0ZVX9U3g0rIKwtrPm21bcAjPUbS3zy41N9hnLm/hkmH5pMY4/GjGkwJm+N2GcR7u7kbXAT0QnJfLvjCs/P9OPPY3d4tUl5/hnXirdbV37MuRQEOY1gsnPDElhrRFsUiiJD+KpVJPhfxev77zCzsiIpNYnF5xdTu1TtAhMpdLVxpVXZVmy8tpFR9UdRsv/LhP48n8SbN7Hy8clX21JKHm7fjl3z5pjb501D7XrkdXbe2kk1l2pEJEQQHBOMh50Ho+qPKja55Ms6lGVai2m8vettvjr0FZObTc511Nv92PuM2TMGd1t3vmn5DRZmecgEGnHnv/WV9FjaaAv9OZAWGTZnlz8PohPpUtuDcR2q4ONWuOHh2X4TUsqhBWWIQlFUSImM5MF332PbqBEO7doBsPXGVgKjAxnfaHyBht329u3Nzts72XNnD20GDCBs4SJCly7F4/PP89Vu/NmzJAcH4zDqvTzVT05NZuL+idhY2jC33VzcbNxMNs1ialp6t2RknZH8dPon6rjX4cVnXjS4bmJKopZ3JimKZe2W5U0VOuYBLOulhR23/QyOLdYfRaYHKSVbz919FBnWuIILCwZXo27ZPNhhAgx2tUKILkAN4JGWhJTSeJoCCkUR4cHcuaQ8fPgoLDlVprLg7AJ8S/oabTHYUJp6NqW0bWnW+q+lQ/sOOPboTuTadZR6910sXFzy3O7DbdvA0hKH1nkT5Fh6filnH5xleovpuYuUKqKMrD2Ssw/OMvXwVKqWrEqtUrVyrCOl5OvDX3P6/mm+ef4bqrjkIfoqIQp+66NFjL26Hso/By3eN6jq0ZthfL3lIifTRYa1qepeZPYdgeFy/T8BLwHvoi3yvwjkLmG2QlEMSLh+nbDfl+Pcty/WVasCsPv2bq5HXuf1Wq+bTFo+K8zNzOnl24t/g/4lKDoI16FDNfmYP/IuHyOlJGrbduyea4K5U+43GvqH+/PjqR9pV64dnSt0zrMdRQlzM3Omt5iOu607Y/8ZS1h8WI51/rzyJ2v81/B6rdfp6JOHQImkeFjxira3pd+vmnMxgKshUbz+6zFe/OkgQRFxTO9Ti79HtaBttdJFyrmA4WKXTaWUg4BwKeUXaDIxxt/CrFAUMvemT8fM2ppSuqkjKSW/nP2Fcg7l6FDe9Jke9dGzck8A1l9dT4lKlbBv1Yrw35eTGp+3Ren4CxdICgjIkzR/UmoSEw9MxN7SnolNJmo3NBNLvhcUTiWc+LbVt4TFhfHh3g9JSc16L/nxe8eZengqLbxa8HbdPOz3SU2BtcPhxl7oOReeyfl3EfIwnglrz9Jh1l4OXgtlXMcq+H3QmpeeLfco7LioYahVacHZsUIITyAZqJBdBSGEtRDiiBDitBDivBDiC125ixBihxDCX/ezZLo6E4QQV4UQl4UQHdOVNxBCnNUd+07o3LQQooQQYqWu/LAQwiddncG6a/gLIYwnd6t4Yonet4+Yf/bi9uabWLi6AnAw6CAXQi/wWs3XMDcr+CgcAC97L57zfI71V9eTkpqCy2tDSQkLI3L9hjy1F7V1G5ibY9+mTa7rLjq7iAuhF/ikySe42rgWiOR7QVLdtToTm0zkcPBhfjj1g95z7sbcZazfWLwdvJnWclru/y6khL9Gw8VN0Gka1Hk529OjE5L5dvtlnp/px+rjdxj0nE+hRoblBkPXYP4SQjgDM4DjurIFOdRJANpIKaN1uWT2CyH+BnoDu6SU04QQ44HxwEc6Ac2X0dZ5PIGdQohnpJQpwDzgDeAQWsKzTsDfwDC0UVVlIcTLwHTgJSGEC/A50BAt2u24EGKjlDLcwP4qnjJkUhL3pk3Hsnw5XF79T+Jk/tn5lLYtTfdK3QvROujl24tx/4zjUPAhmj7bFOuaNQlbvBjnfi8izAx/etWix7Zh17gxFiVzl8PmcthlfjrzE518Ov03JaRvc2BSHGz5AOzdwbMeWJtG78tU9PLtxen7p1lwdgE13WrStlzbR8fik+MZtWcUCSkJzGk9B0erPGSP3/WFpm7c4gNoklFFYP3JQGZuu0xQRBweztY8V9EVv8v3CY1JpGttD8Z1rEJ518IXDjWUnPbBPCuEKCOlnCyljADsgbPAn8Cs7OrqZP2jdR8tdS8J9ADSsigtRUtihq58hZQyQUp5A7gKNBJCeACOUsqDUkoJ/JqpTlpbq4G2utFNR2CHlDJM51R2oDklhUIv4StWknjtGqU//BChSxl84t4Jjt87zpAaQwo8h0lm2pRtg3MJZ9b4r0EIgetrQ0m8dYvoPXty1U7C5csk3bqda2n+pBRtaszRypGPG3/834GsNgfGR8KvPWBaOU0ift2bcOQXCDyhaWoVcSY0nkAN1xpM3D+Rm5E3Ac05Tz40mQuhF5jafCoVnSvmvuF/v4f9s6DBUGgzMcOh9ScDmbD2LIERcUggKCKeNScCcbaxZP3bzfjhlfrFyrlAziOYn4F2AEKIlsA0tIX+usB8cpCLEUKYo414KgM/SikPCyFKSymDAaSUwUKItAw8XmgjlDQCdGVJuveZy9Pq3NG1lSyEiARc05frqZPevjfQRkaULl0aPz+/7LpTZIiOji42tuaFgu6fiInBbdYskqpW4biZGeiuPS9kHvZm9pS6Wwq/EOPYk5++1bOqx+5bu9m0axMOJWxxc3Xl+qxZhOdCANNu40bshOCMjTUyF3ZsjtjMpchLvF7qdU4fOv2ovEkJN6wT7j92fnwJNy5XeQfHh/44RF3B8cJmrE4vByBVWBBtX5GHjs/w0NGXKIdniLPxgCK2QN3Puh8zwmcwcONAhBBEpEQAUNemLuK6wO+6X67aKxO8i6qXvyOkVDMu2HeDf/7JcHyyXyxxSY8nCo6IjiXi2in8ruW1Jzljqv+5nByMuZQyLZziJWC+lHINsEYIcSqnxnXTW3V102vrhBA1szld31+XzKY8r3XS2zcfzVHSsGFDWVxi+IvrfgNDKej+3f1qCuHx8VSdPgPrKs8AcCnsEhc2XeDdeu/SsbbxEnHlp2/e4d7s2biHsDJhdKvRjbDAIO59/TWNnZ2xqVs3x/pSSq5Pn4FFo0ZU7274lN+F0Avs2LyDrhW78l6LTPtmXL6GdSP/k4gHsLTBustU6qTfv5EmLR94DLPA4zgGnsAxaDcE/qUdt3YGr/qa5pZXA+1lX0qvPUc3/kzZEzNxl/cJEaW4U38cz3YfYXB/csOt47dYdC6jesKlxEvElIvJ3WbSS1vgnx+hYivcX1mFu8XjaZPDtm7WWzUsXpr8/8FU/3M5OhghhIWUMhloi+5p38C6j5BSRggh/NCmqe4JITx0oxcPIER3WgAZI9O8gSBdubee8vR1AoQQFoATEKYrb5Wpjp+h9iqefCI3bSJk1mySg4NBSmyaNHnkXAB+OfML9pb2vFw1+wXYgqRyycrUKVWHNf5rGFR9EM59enP/hx8IXbQY7+9yzuCRePUqiTduUPJVw2X0E1MS+WT/J7hYuzC+0fjHT6ikCxSwskcmxiCy2hwoBDiX1V41emllKcnw4LKWICvgmDZ9tu9//zkrp3Ka0/HWOR2POhzd+hs1j0/ERiSCgDLcx+n4RI6CSZzM3zf+fqwsPiWeOSfmGO5gbu6HP4eAZ1146XfQ41wAPJytCYp4PDLQ09k0ieYKgpxWB/8A/hFCbECLJNsHIISoTA5y/UKIUrqRC0IIG7SptktoqsxpUV2DgbRQmI3Ay7rIsAqAL3BEN50WJYRooltfGZSpTlpbfYHdunWabUAHIURJXZRaB12ZQkHkpk0Ef/oZyUFB2pM1EH/yJJGbNgFwI/IGO27t4OWqL+dtEdeE9PHtw43IG5y+fxozOztKvvwyUTt2kHj7do51H27bDkI8UicwhJ9O/8TViKt8/tzn+pNznVymOYThu/in1XoYcy7bnecZMLeA0jWg/iDo/h28uR8m3IGhW6HDFM2xBJ2A7RNhcWeY6k3dExM055IOG5FI2RMzDe5Tbsi3WnTwafijP5T0gVf+hBJZy/I0qfD4xtk0ocriSrYORko5BXgfLaNlc93NO63euzm07QHsEUKcAY6iLbr/hbaO014I4Q+0131GSnkeWAVcALYCb+um2ADeRItauwpcQ4sgAy2jpqsQ4ipaSoHxurbCgMm66x4Fvkw31ad4ygmZNRuZaQ+JTEggZNZsABadW4SVuRUDqxW9hFkdfTpia2HLGv81AJQcOABhYUHYkqU51NTELW0a1MfS3T3HcwHOPTjHwnML6VGpB8+Xff7xE1JTNFkTnxbgXjVX/cgSKzttw2HTd+DFxTD6LHxwFV5ZBS3HYSH1701xl4+n/jUGWalCG6QWHXpN26VfwhFeXQt2rlmeeicslq3n71G1jANeztYIwMvZhqm9axlVPr+gyXGaS0p5SE/ZFQPqnQEek1qVUoaiTbfpqzMFmKKn/Bjw2PqNlDIeTVVAX1uLAONJzyqeGJKDH5eXTysPjg7mr2t/0a9KP22fRxHD1tKWzhU6s+XGFj569iPs3d1x7N6NiLVrcXv3nSxDjxOuXyfB35/SH3+s9/hj56ck8Mn+TyhlU4oPG32o/6SruyDiFrSblMfeGIh9KW0j4jMdufvPIjx4PKggWtjhmJIERo72G1V/VIaMnWCgWvTDYFjWE2QqvLouW0VkKSUT1p5FAAuHPItXMZ4Sy0zR3P6pUJiIhzt2ZHnMwsODxecXAzC0ZtHVee3t25u45Dj+vqkN5F2HDEHGx2crHxO1fTsADh0MS1j248kfuR55nS+afpH1NOGxhWDnDlW75q4DeSQxOZVfLAcQK60ylKdIgSPRMLeJtpguH4/EyitdKnZhUtNJeNh5AOBh58GkppOyX3+JDYPfems/B6yGUs9kfS6w6tgd9l99wIQXqj1RzgWUg1E8JaTGxBA0cSKB776HhYcHokTGhVZhbY3N28NY67+WbpW6FemEWbXcalHZuTJrr2jZMkr4+mL3fEvCf/ud1IQEvXUebtuOTd26WJbJuV+nQk6x5PwS+vj2oZlXM/0nhd+CK9ugwWCwsNJ/jpH5avMFFkU1YovPBO5SilQpCMKNMUlvcui5eSDMYEV/WNpNW/swEl0qdmF73+18X/57tvfdnr1zSYyB5S9B6FV4ebkWpJAN9x7G89XmizSu4MIrjcoZzeaignIwiiee2JMnud6rN5Fr1uI6YgSVt/6Nx1eTsfD0BCGw8PTEY/KXrPYJITElkddqvlbYJmdLWrbLc6HnuBx2GQDXoa9p8jEbHpePSbx1i4SLFw3aXBmXHMfEAxMpY1eGDxp+kPWJx5dokWENhuSxF7ljzfEAfj14i9dbVKDv0LGUmXQVsy8icP/UnwDvrrx+0JWAl3fCC9/AvfPw8/Ow/m1tqqqgSE6EVYMg8Bj0WQgV9axbpUNKySfrzpKUksr0PrUxMyta+4CMgXIwiicWmZTE/e++59aAgZCcTPllv+I+ZjTCygqnbt3w3b2Lahcv4Lt7F3RoyYrLK+jg0wEfJ5/CNj1HulbsiqWZJeuurgPAtnEjrKtXJ2zxEmRqaoZzH+qmxxwNmB777sR33Hp4iy+bfYm9VRYRT8mJWvTYM51ylW0xr5wLjOTjdWdpUtGFjzplDCawMDdj9kv1kBLGrr5ASsPh8N5JaPounF0F39cHv2nayMKUpKbC+jfh6k7oOhuq57zPaOPpIHZeDOGDIpAYzFQoB6N4Ikm8eZObAwbyYO5cnLp1o8KG9dg2bJjl+SsurSAmKYbXa71egFbmHWdrZ9qWa8uma5tISElACIHLsNdIvHGD6Ew7sqO2bce6Vi0svbKPRjp29xi/X/ydl6q8RBOPJlmfeHEjxNyHhsOM0JPsiYhN5M3fj1PS1oofXqmvVzW4nKstX/aowZGbYczzuwo2ztBhMrx9BHw7gN9U+L4BnPxdcwTGRkrY+hGcWw1tP9emDXMgNDqBLzZdoG5ZZ4Y2y1Y3uFijHIziiUJKSfiqVVzv1ZvEW7fwmvUtntOnYe7gkGWd2KRYfrv4Gy29W+YtaVQh0du3Nw8TH7Lr1i4AHDt2xMLTg9BF/wVPJgYEEn/uHI4ds081EJsUy6cHPsXT3pOxDcZmf+GjC7V9HWmbLE1ESqrkvRWnuBeZwLyB9XGz179BEaBXPS+61/Fk1k5/Tt2J0ApdKkC/pfDadnD0gg1vwfznNYl8Y/LPDDgyH557B5qPMajKpE0XiIpPYkbf2pg/gVNjaSgHo3hiSA4NJeDtd7j72efY1qtLxY0bcOycc0KsNf5riEiIKDajlzQaezTGy96Ltf7aYr+wsMB18GDijh0n7rS2yP1f9Fj2Dmb2idkERAcwudlkbC1tsz7x3gW4/S80fA1yoeKcF2bvvMLeK/f5vHt16pXLXvlZCMHknjUp42jNqBUniU5I/u9gucYwfKe2LhIXrgUB/NEfHvjn38gjv4Df11DnFejwlUF6atvP32XT6SDebePLM6WzfvB5ElAORpElkZs24d+mLRerVce/TdtHO92LIlF+flzv3oOY/fspPWE8ZRcswLJ06RzrJaYksuTcEp4t8yx13eua3lAjYibM6FW5F4fvHubOQ03b1alPX8wcHAhdvATQNleWqF4Nq3JZRygdCT7CH5f+YGC1gTxb5tnsL3psEZiXgLqm3YS648I9vt99lRcbeBscXeVkY8msl+pyJyyWLzaez3hQCKjVF945qk1j3dinhTVv+VALJ84LZ1fDlnFQ5QXo/r1BziUyLomJ689RzcORN1tVytt1ixHKwSj0kllOJTkoiOBPPytyTiY1Lo7gL74gYKSWJMznzz9xGTw4xxwpm69vpsPqDjT4rQEhcSHUcss5B3tRpEflHpgJs0eL/eb2OvmY7duJPXqUuNOnceyQdfRYTFIMn/37GeUcyvFe/feyPA+AhGg4vULTEstmV3p+ufEghrErT1HTy5HJPWvmKg1wowouvN26Mn8eD2DzGT0RZJY20GKsFghQfxAc/QW+q6vJ6CfrD/HWi/9OWDcCyjeFvos02RsDmLL5AqExiczsWxvLIpqF0pg8+T1U5Am9cirx8YR8+20hWfQ4cWfPcaN3HyL+WIHLa6/hs/rPDIKVWbH5+mYm/TuJ4Jj/bkDLLy5n83X9arZFmTJ2ZWju1ZwNVzeQnKpNC5UcqI0ubg0eAkD48uVZPhj879j/CIoO4qvmX2FjkcMmv7OrIDEKnjXd4n5MQjIjlx3Hwlzw08AGWFvmPmPje219qVvWmQlrzxAUEaf/JPtS0HUWvPkveDfS9M5+bAQXNuS8UfPOEVj1KrhXg/5/aE7LAPb532fVsQDeaFmRml7FKwlbXlEORqGXrOVU7nLnzbeIWLOG5LDCkXeTKSk8+OknbvbvT2pcHOWWLKb0h+MwszJsw9+cE3MySH/Afwq5JsOEeet7V+5NSFwIBwIPABB75LA2XaOLmEoOCdE7+vw38F/+vPIng6oPop77Y6pOGZFSW9wvXQu8c5hGyyNSSj5acwb/kCi+618P75LZrAVlg6W5GXNerktKqmTMylOkpGbjMNyrwcDVMHANWNpq+1gWd4aA4/rPD7kIv78IDmVg4FqDs3XGJCQzfs1ZKpayY1Rb3zz0qniiHIxCL2b2+vdAmNnZkXD5MsGfTMS/eQtuDhxI6OIlJN65o/d8Y5MYEMCtVwdxf/YcHDu0p+KG9dg1ySakVg/5VsjNLSbOW9+ybEtcrF0eCWCGzJoNKRlFIWV8/CMxT4CoxCg+P/g5FZwq8E69d3K+yJ0jcO+cNnoxUWKwhftv8NeZYN7vUIUWvvpzwRhKeVc7JnWvweEbYfy814BMXZXbwYh90G2Otgt/QRtYMxwi7qR7OOgB85r+py9mb5hoKMCMrZcIioxjZt/aeRqVFVcMzumieHp4uGMHqVFRYG6e4UYlrK0pM+lzHLt2JeHSJaJ27iJq505Cpk8nZPp0SjzzDA7t2mLfti3W1avnau48J6SURK7fwL2vtEgdz5kzcOzaNU/XcLd1517svcfKTSYPk1Xe+l1fGi5tnw2WZpb0qNyDX8//yv3Y+9mKeaYx8+hMQmJDWNZ5GdYW1jlf5NhCsHKAWnq1ZfPNoeuhTP37Eh1rlOYtIy1+923gjd+V+3y7/QrNKrlRp6xz9hXMLTRlgpp9tLTGB3+Ec+u09IWpyVoWQ5kKKYmawy3pY5AdR2+GsfTgLYY09aFB+ccl+Z9k1AhGkYH4ixcJ+vAjrGvXpszkx+VUnLp1QwiBdbVqlHr3HSpuWE+lHdtxH/8R5o6OPPjpZ2726cvVtm25O+VrYg4dRiYn53zhbEgODydw9BiCJ0zAulo1Km5Y/8iO3JKSmoK95eOjM4MUcvNCaqo2YtFHVvns80Dvyr1JkSlsuLYBCw8Pveekle8N2Mu6q+sYWmMotUvVzrnxmFA4vw7qvJxtPpO8EhwZxzvLT1De1ZZvXqxjtAcTIQRf96yFu0MJRq88RUyCgX+HJRy0pGnvHNN01lIz1UuO1x4ODCA+KYWPVp/Bu6RNsc7rklfUCEbxiOQHD7jz1tuYOznh/cP3WLq7U7J3rxzrWZUti+uQIbgOGUJyWBjRe/yI2rWLiFWrCF+2DHMnJ+xbtcK+XVvsmzXDzNbwufXo/QcInjCB5IgI3D94H5ehQxG5yEGfme9Pfs+1yGv09u3NwaCD3I25Sxm7MoyqPyp3KXANIeaBlk44K4wos+Lj5EOD0g1Y57+OvqNHc/ezzzIEaQhra9zHjCYyIZIv/v2Cys6VeavuW4Y1fnKZ9tRugsX9hOQU3vr9BLGJKfzxehMcrI0rt+9ka8m3L9Wl/y+H+HLTBab3NcChpuFc9vGRZxoGPhzM3unP9Qcx/D68MXYlnr7b7dPXY4VeUhMSCHjnXVLCwyn/+28GJ6XKjIWLC859euPcpzepsbFEHzhA9M5dRPn5EblhA6JECeyaNcOhbVvs27TOkL8kLY2xe1AQ/h4eWFWqSOz+A1hVqkSFn3/Cunr1fPVx161dLDy3kD6+fZjUdFK+2sqRG3thzevaxr46/eHC+ow3KzML7SnZiPTx7cPH+z/Gv2MZnpn85aOU0BYeHriPGY1Tt258sv8TQuND+a7td1iZGxAUkZoKxxdD+WbagriRmfzXBU7ejuDHV+rja6JNh00quvLm85WY63eNVlVK0bmW/hGeXpy89Y9ADXg4OBMQwfy913j52bI0q+yWC4ufHJSDySfpc7un/0cuTkgpufvZ58SdOoXX7NnY1KhhlHbNbG1xbN8ex/btkUlJxB4/rq3b7NpF9O7dYGaGbYMGOLRriwTu60KjBdp6QXJwMLbNmlH2xx8wszZgnSAbrkde55MDn1DTtSYfNzYs6VaeSEmGf6bD3pngWlmLUCpTS5NV2fUlMjIAYWULibFauRFpV74dXx/+mrX+a5nabepjf4d7bu9h47WNjKg9ghquBv6Or+2G8JvQ5lOj2grw57E7/HboNiNaVqRL7Vzc9PPAmPbPsP/qA8avPUvdcs54OBmYd6XtZ1pARvqHA0ubHB8OEpNT+XD1GUo5lODjLsZ3zMUFtQaTD4rLZsScCFu4kMgNG3B79x0cO+Us6Z4XhKUldk2aUGbiJ1TevQufNatxGzmClMhI7k2dRsjUaY/tuwFIvHEj384lJimGMXvGUMK8BLNazzLsyT0vRAZoMiR7Z0DdV2DEP/85kdr9YMw5LW/9qLNaeOvfHxk1OZaNhQ1dKnZhx60dRCZEZjgWER/BFwe/4JmSzzCi9gjDGz22EOxKQbWc1YFzw7nASD5Zf47nKroWyNqEFrpcj6SUVMauPE1qdqHL6andD7p9B05lkQhwKqt9ziE4Y57fNS7djWJKz1o4GnnarzihHEw+yHIzYrpw0KJO1O49hPzvWxw6d8LtLQPn5POJEAKbGjUo9d57VNy4gUrbt2V5blYRUYYipeTTA59y8+FNZrScYbpIsUtb4KfmcPcM9JoPPedq+eX1YecKbSbCjX/gonEfRnr79iYhJYEtN7ZkKP/6yNdEJkQypfkULA1NKxxxB65s1Xa8GzGpWHhMIiOWHcfVzorvX6mnVyHZFFRws2NStxocvB7K/H3XDa+Y/uFgzLkcncvlu1H8sMefHnU9aVc9Z7miJxnlYPKBIeGgRZn4y1cI+uADrKtXx/Prr40aVpwbrMqV06LV9JBVRJShLDm/hB23djC6/mgaezTOV1t6SU7QRiIr+mtPt2/8A3Veyrleg6HgXgO2fZL1QnIeqO5anWou1VhzZQ1SNzracWsHf9/4mxF1RlDVpWoOLaTj+BJthGXEpGKaQvJJ7kclMG9gg2wVkk3Biw296VyzDP/bfpmzAZE5V8glySmpfLj6NI7WlnzezThTzcUZ5WDyQZbhoAakpS1sksPCCHjrLczs7PCe+yNmNoWbC9x9zGhEpqmwtMinvHI4+DCzT8ymffn2DKkxJH8G6iP0GixoB4d/gsYjNcVet8qG1TW3gM7TIPI2HPjOqGb19u3N5fDLXAi7QFh8GF8d+opqLtUYVisXUWDJiXDiVy2pmLPxUvnO2nGFff4P+KJHDermtC/FBAghmNq7Fq52JRi14iSxifkLoc/MogM3OB0QyaTuNXCxK5hU0kUZ5WDygb6bIoC5qwupccZ7KjU2MjGRgPfeI/nBA7zn/miQ6rCpcerWDY/JX2Lh6YmEDPtu8sLdmLuM+2ccPo4+TG422fijszOr4OeWWoTRy39A5+lgkcun8QotoXpP2P8tRNw2mmkvVHwBc2HOa1tf4/mVzxMWH0ZHn45YmuViLeDSJogJMWpo8vbzd/lhz1VealiW/oWYf97Z1opvX6rDjdAYJv910Wjt3ngQw/+2X6FD9dJ0NXHQQnFBOZh8kP6mmLYZ0bF7dxLOX+DWkCEkh4YWtomPIaUk+IsviDt2HI+vp2BTq+ioCKelMQ75aR6+u3fl2bkkpiQy1m8siamJzGo9CztLI6ajTYyB9W/B2te1BfyR+6HqC3lvr8NkQMB240Vp7QvYB0Bscuyjsp9O/5Q7Mc+ji8C5PFRqaxSbrt+P5v1Vp6nt7cQXPQp/6qhpJTfeaFmRP47cZuu5/EsEpaZKPlp9hhIWZnyVSwXoJxnlYPJJ5tzuXjOm4/XdHBIuX+HmSy+TcP1GYZuYgbClS4lcsxbXN0fi1MXIGwuLCFOPTOXsg7NMaTaFik4Vjdfw3bPw8/Nwajm0/BAG/5X/zZLO5bQsiBfWazlKjMCcE3NIkRm1yHIl5hlyCW7th4ZDjZJULCYhmRHLjmNpYca8PCokm4L321ehppcj49ee4d7DxyMYc8Pvh29x5GYYE7tWx90xf1GPTxLKwZgAx/btKb90Camxsdzs35/Yo0cL2yQAovfuJWTGTBzat6fUu+8WtjkmYZ3/OlZfWc2wmsNoW944T99IqWUu/KUtJETB4I3Q5hODc4DkSLP3wKmcFiyQkv81gXyLeR5bCOZWUO/VfNsipeTDNWe4dj+a7/vXw8u5cNf60mNloYUuJySlMnbVKcNDlzMREB7LtL8v0cLXjRcbGE+d4UlAORgTYVOnDj4rV2Dh4sLt14YR+Vfh5hpJuHaNwLHvU6JKFTynT8sxIVdx5PyD83x16CuaeDTh3XpGcqBx4Vrujy0fQIUW2pRYhZbGaTsNSxvoOAVCzmu75vNJVqHYBoVopyUVq94T7PK/+3zBvhtsPhPMuI5Vi+Ru9kql7PmsW3UOXA1l4f7czzZIKfl43TkApvaupabGMvHk3WWKEFZly+Lzx3Js6tQh6IMPePDz/EehowVJcng4d958C2Ftre2Kz4UWWHEhPD6cMX5jcLVxZUbLGZibGWEa5s4R+KkFXP4b2k+GV/7UElWZgmrdoMLzsPsrTVwyH4yqPwpr84zTNAaLeZ5bDQkPjbK4f/BaKNO2XqJTjTKMfN6IU5VG5uVny9KxRmlmbLvEucDchS6vPh7A3iv3+ahz1Tznr3mSUQ7GxJg7O1N20UIcu3bl/qxZmghhUlKBXV8mJRE4ajTJwcF4f/8dllnsNynOpKSm8OHeDwmNC2VWq1mUtC6Zc6XsSE2Ffd/Cok4gzOC17do0Vj5GfetPBtJs2m6GbI2h2bTdrD8ZmPEEIbRItIQo2PNVvszvUrELk5pOwsPOA4HAw86DSU0n5SzmKSUcXQCla0LZ/O0ZSlNI9nG1ZeaLtYv0k70Qgmm9a+NiZ8WoFSeJS0zJuRIQ8jCeyX9doJGPCwMblzexlcUTpUVWAJhZWeE5cwaWZb0JnfcTScF38Zo9C/MsknoZCykld7+aQuyRI3hOn4ZtvRyyFhZTvj/5PYeCD/FF0y+o4ZbPCKXoEFj7Blzfo00Tdf/O4KyFWbH+ZCAT1p4lLkm7cQVGxDFh7VkAetbz+u9E92rQ6A1tX02DIeBRJ8/X7FKxS+7VoQOOaYEMXb7NV1KxhOQURv52gvikFH5+9TmjKySbgpJ2VvzvxboMXHiYrzZfYEqv7KMrpZR8uuEcCcmpTOtTCzOzoutACxM1gikghBC4jxqFx1eTiTl4kFsDBpJ010QZFHWE/76ciJUrcX19OE49epj0WoVFmkJy32f60tu3d/4au7Yb5jWD2we1zIYvLsm3cwGYue3yI+eSRlxSCjO3XX785FbjwdbF6DplBnFsIVjZ5zsJ2hebLnD6TgTfvFiHyu6mfYgyJs19tdDl3w/fZseFxxPSpWfL2btsO3+Pse2foWKp4tPHgkaNYAoY5759sSjjQeCoUdzs9xJlf/4J62rGV1uNPnCAe1OnYt+6NaXGjDF6+0WBNIXkWm61mNBoQu4qn1mlJY2KDAAnL21a6Mo2KFUFBm2A0vlLDZCeoAj9m271lts4Q9vPNQXfc2ugVl+j2ZEtsWFwbi3Uf1VLuJUL1p8MZOa2ywRGxOG8dzsRsUmMeL5i7mTxiwjvd3iG/f4P+GjNGep4t9AbchwWk8hnG85R29uJYc0rFIKVxQc1gikE7Js3o/zy38HMjFsDBhK9b79R20+4foPA0WMoUakSnjNnPpERY+kVkr9t9W3uFJLPrNJu4JF3AKk5mStbtZwnr+8xqnMBLemVPjyzCtmtN1CbHtv+qRbVVRCc/A1SEqBh7hb306b/AnXOMiI2CTMBVYrRyCU9JSzM+a5/XWITk3n/T/2qy19uOs/D+CRm9K1dYEKdxRX17RQS1lWq4LNyBZbly3Nn5EjCV60ySrspkZEEvPUWwsIC77lzMbc34i72IkK+FZJ3falfYDLiFlgZNxJo85ngRzfd9FiYiaxl6s3MofMMiArSZGRMTWoqHFsE5Z7LtXPVN/2XKuF/O/yNaWGBUtndgYldqrPP/wGL/72Z4djuS/dYfyqIt1tXpmoZx8IxsBhhMgcjhCgrhNgjhLgohDgvhBilK3cRQuwQQvjrfpZMV2eCEOKqEOKyEKJjuvIGQoizumPfCV1IihCihBBipa78sBDCJ12dwbpr+AshBpuqn/nBsnRpyi9bhl3Tptz97HNCvp2FTE3Nc3syOZnAMWNIDAzE+4fvsfL2yrlSMSTfCslZpbs1MA2uoey5FMKoFSd51qck03rXerTJ0MbSnFQpqe6ZzQ2qXBOo/RL8+z2E5UJaPi9c3w3hN+DZ4bmumqvpv2LEgMblaFetNNP/vsSFoIcAxCZJPl57jqplHHirlYGipk85phzBJAPvSymrAU2At4UQ1YHxwC4ppS+wS/cZ3bGXgRpAJ2CuECJtM8M84A3AV/fqpCsfBoRLKSsDs4DpurZcgM+BxkAj4PP0jsyYbL6+mQ6rO1B7aW06rO6QO70nwNzejrLz5uLcrx+h8+cT9ME4UhMS8mTLvanTiPn3IB6TPse2QYM8tZGe/PbNFORbIfneBW2EoI/8yr6k4+C1UEb+dpxqHo4sHPIs/Z4tx4HxbVjSyY79H7XGycaSD1efISW73ePtvgAzS9g20Wh26eXoIrB10/bi5JKspvmynP4rJgghmN6nFk62lgxdfISmU3fx1q5Y7j6M54VaZbCyUJM/hmCyb0lKGSylPKF7HwVcBLyAHsBS3WlLgZ669z2AFVLKBCnlDeAq0EgI4QE4SikPSm2X4q+Z6qS1tRpoqxvddAR2SCnDpJThwA7+c0pGY/P1zUz6dxLBMcFIJMExwUz6d1Kub8TCwoIyX0zC/YP3ebhlC7dfG0ZyeHiu2ghfsYLw33/HZcgQnPv0yVVdfRirb8YkXwrJUsLRhfBLa7CwAfNMyscGpME1lJO3wxm+9CjlXGxZ+lqjxzIautqXYFL3Gpy6E8HiA9nsHnf0gOfHweXNcHWnUWx7jMgAuPK3trifWzVoYEhTn8fKbCzNCyRLpalxtS9Bn/pe3ItKICjyP62yeX7XH9/HpNBLgbhh3dRVPeAwUFpKGQyaEwLcdad5AXfSVQvQlXnp3mcuz1BHSpkMRAKu2bRlVOacmEN8SkaRvFyJCqZDCIHr8OF4ffs/4s+e5Vb/V0i8bZiEe8yhw9z9agp2LVvgPu6DXF9bH8bsmzFIr5A8u/Xs3Ckkx4Zpci+bx2oL+e+dgB4/aAnCcpEG1xAuBD1k8KIjuDmU4PfhjbPMCdK9jidtq7rzzfbL3AqNybrBJm+BS0X4e7yWo8XYPEoqNjTXVZNSUvnrTBA2lmaUcdSck5ezDVN718q4v6cYs+n048kDswwxVzyGycOUhRD2wBpgtJTyYTZPnfoOyGzK81onvW1voE29Ubp0afz8/LKyTS/BMfozVwbHBOe6rUfY2mL53rs4z/sJ/959iHjrTZIqZpTZiI6OftS+eUgILtOmk+rmxo1evbi+L/+KvHGpcdn2bdm2ZXhbeZtsd3b6/qWxInQFZ6PPMrzUcG6dvMUtbhnUllPEeapd/BarxAiuVxpKgHd3OHYBcId6P/x3YhiQ19+ZjuDoVKYeicPCTPBuTcmFE4e4kOmc9H3rWiaVf6+mMnLhXj581jrL79PFawC1z07m6h8fEVDWePuZRGoyTQ4tINqlAWdP3wByp8W11j+R0wFJvF23BM+WsSA6Oh57ezOI9MfPr/gu8qcnMIu1pMCIuLz/jxdB9P3PGQOTOhghhCWac/ldSrlWV3xPCOEhpQzWTX+F6MoDgLLpqnsDQbpybz3l6esECCEsACe0W0UA0CpTHb/M9kkp5wPzARo2bChbtWqV+ZRs8VjtkeWN+IjdEYbWGEop2zxoV7VqRULbttwZMRLXOd/hOXMGjh06PDrs5+dHq1atSImK4ubL/UmxtMTn16VYlctfEqeI+Ah+u/gbyy8tz/a8GXdn4GnnSZtybWhXvh11S9U1jvaXjrT+pbHWfy0Hbh1gWM1hjGpggJ4WQGoK7J0Jp6dreU0G/Ullr/qYamn2TlgsE34+iJWVFatGPJfl5rvMfYt3uc2EtWcJtq3EK42z+v21gvjDVL71J5V7jgcHIyWIO78OEsMp0elDWj3TKsfT03PsZhh/bTtIn/rejOunKQ5k7tuTgNeh3XqdjJezzRPVV1P97kwZRSaAhcBFKWX6WMuNQFpU12BgQ7ryl3WRYRXQFvOP6KbRooQQTXRtDspUJ62tvsBu3TrNNqCDEKKkbnG/g67MqOgTFSxhXoL67vVZfnE5ndZ0YurhqYbLpKdvp0IFfFb8gXW1agSOGk3okiUZhDJlSgqBY98n8dYtvL77Ll/O5UHcA7499i0d1nTg5zM/06hMI96p+45ewcRPm3zKl02/xLekLysvr2TI1iG0+bMNXxz8ggOBB0hKMa7O2vkH55lyaEruFJIjA2BpN/CbCrVehJH7wKu+Ue1KT8jDeAYuPExsYgrLhjXO1c7ul58ty3MVXZm65SLBkdlEXnWcCsnxWoi1sTi6UEsTULldrqpFxScxeuUpvEraMKm7cfcMFTXGdayCTab8NU/KGlNBYMoRTDPgVeCsEOKUruxjYBqwSggxDLgNvAggpTwvhFgFXECLQHtbykdZk94ElgA2wN+6F2gObJkQ4irayOVlXVthQojJQFoili+llGHG7mCa1tOcE3O4G3OXMnZlGFV/FF0qduFO1B0Wnl3Iqsur+PPKn/T27c1rNV/D095wsUkLFxfKLVlM0IcfETJtOtF795F44wbuwcFcsbMjNSaGMl98gV3jRnmyPzg6mMXnF7PWfy1JqUl08unE8FrD8S3pC4C3g7fevgH08u1FTFIM+wL2sfP2TrZc38LqK6txsHSgZdmWtCvXjqaeTbG1zPu+kjwpJF/8Cza8DSlJ0OtnqPNynq9vCGExiQxYcJgHUQn8/noTqnnkbm+EEIJpfWrRcfZeJq47x4LBDfVPlblVhufeggNzoOFr4J3PKMH7l+HmPk01IJejz0kbLxAUEcefI4uHzlh+SFtLSlMq8HK2YVzHKk/MGpOpEYUhH18UadiwoTx27JjR2w2KDmLh2YWsvboWJPSo3INhtYZR1qFszpV1yNRU7rz5FjH//JPxgLk5ntOm5jq18O2Ht1l4biEbr24EoHvl7gyrOYxyjnkfBSWkJHAo6BA7b+9kz509RCZEYm1uTTOvZrQt15bnyz6Po5VhN18/Pz9atGzByJ0jOXHvBL92/jVnEcukONg+UVMD9qgDfReDa6U898cQHsYn8covh/C/F82SoY14rpJrjnWymopYsO86X22+yJyX69KjbhY3r4Qo+L6BFk49bGf+sk3+/ZE2ghl7MVcpCDafCebt5Sd4r01lxnbI+BT/JE6RpedJ7l9++iaEOC6lbKjvmNIiMzGe9p58+tynvF77dRadW8SaK2tYf3U9XSt25fXar1PeMWeZb2FmRoK/nkXTlBRCZs022MFcDb/KL2d/YevNrVgIC16s8iJDawzFwz7/mlElzEvwfNnneb7s8ySnJnP83nF23trJ7tu72XV7FxbCgkYejWhbri1tyrXBzSb75FNpCslfNv0yZ+cScglWv6Yl7HruHe2p3CIX0jF5IDYxmdcWH+Xy3Sjmv9rQIOeSHUObVeCvM8FM2nieZpXdcLPXEzJcwgHafwnrRsDpP6DegLxdLDFGS/tcvUeunEtwZBwfrztLnbLOvNvWN2/XVjxVKAdTQJSxK8PHjT9meK3hLD63mNVXVrPp+iZeqPACr9d+Pcfc8cnB+oMJsipPz/nQ8/xy5hd23d6FjYUNg6sPZlCNQTne5POKhZkFjT0a09ijMRMaT+Dsg7Psur2Lnbd2MvnQZL469BX13OvRtlxb2pZvi5e99sS++fpm5pyY8yhwolGZRvTy7ZX1haTUwmy3TgArOxiwGnzbm6RP6YlPSmHEsuOcuB3OD6/Up3VV95wr5YC5mWBG39p0+W4fkzae54dXslgzqtVPG3nsnATVuuZN7flsWlIxw3fup6ZK3l91mqSUVGa/VBdLpcGlMADlYAoYd1t3Pmr0EcNqDePX87+y4vIKNl/fTEefjrxR+41H6x+ZsfDwIDkoSG95VpwMOcnPZ37mQOABHCwdGFF7BAOrDcTZ2tlY3ckRM2FGnVJ1qFOqDmPqj8E/wp9dt3ax8/ZOZh6bycxjM6nmUg1ve2/2Bu4lIeU/FYMz98+w+fpm/XlN4sJh0yi4sAEqtoJe840XXZUNSSmpvPvHSfb5P+CbF+vwghEVg58p7cC7bXz5dscVute5S4caejTWzMy0xGS/tIF/ZmiplnNDWlIx9+qaHI2BLNx/g3+vhTKtdy0quD15+nYK06AeQwoJNxs3xjYcy9Y+WxlWaxh7A/bSe2NvxvqN5XLY45u43MeMRlhnjOoS1ta4jxmdoUxKycGggwzdOpRBfw/iwoMLjKo/im19t/FOvXcK1LlkRgjBMyWf4c26b7Km+xq29NrC+w3ex8rcih23d2RwLpDNxs7bh7RUxpc2a3IqA9cViHNJSZV88Odpdly4x5c9atC3gfGkZdJ4s1UlqpZxYOL6c0TGZRGR51VfU1w+/BPcv5K7CwQeh7tntEABA/cxXQh6yMxtl+lQvTQvPWv42qFCoRxMIeNi7cKo+qPY3nc7I2qP4GDQQfpu6st7u9/jfOj5R+c5deuGx+QvsfD0RAIWnp54TP7y0fqLlBK/O34M2DKAN3a8we2Ht/nw2Q/Z2mcrw2sNx8Eqdzk+CoKyjmUZUnMIv73wG0Lv3lgyhninpsA/M2HxC7pUxtug+ej8LXYbiJSSievPsuFUEB92qsKg53xMch1LczNm9q1DaEwiX2++mPWJbT8HS1vYOj53icmOpiUVe8mg0+OTUhi98iROtpZM61O0Ux8rih5qiqyI4FTCiXfqvcOgGoP4/eLvLLuwjD1/7aGld0tG1B5B7VK1cerWDadu3TJEfKSkprDj9g5+OfMLV8Kv4GXvxadNPqVn5Z65y5FSyJSxK6N30+ojKf6HQVoq45v7oGZf6PqtUbJNGoKUkimbL/LHkTu83bqSyZV0a3k78XqLivz0zzW61fGkua+etTL7UtBqAmyboOWyqdI554Zjw+D8Wqj7ClgbFtE37e9LXLkXzdLXGmUpe6NQZIVyMEUMRytH3qzzJq9We5UVl1ew9PxSBmwZQFPPptR2q82GaxsIjgnGY7UHzTybcezeMW4+vImPow9Tmk+hc4XOWJoVv70Jo+qPYtK/kzLon1mbWzOq/ii4tAU2vAXJCdBjrnaDLMAn6dk7/Vmw/wZDmvrwQYeC2WA3up0v287fZfzaM2wb3RK7Enr+VRu9rgtyGA8VW4Pl49kXM3Dqd22zpoFJxf65cp8l/95kSFMfnn8mD4oUiqceNUVWRLG3smd4reFs67ONsQ3GcjrkND+d+enRU35wTDCr/VcTnxzPzOdnsr7HerpX6m5c53JmFcyqCZOctZ9njJMUTR9dKnZhUtNJeNhpi+Yedh5MavQxXS75wYr+2t6PEXu10NwCdC6/7L3OnF3+vNjAm8+6Vi+wKSJrS3Om96lNQHhc1sKK5pbQeRqE34RDP2bfYFpSsbJNoEzNHK8fFpPIB3+e5pnS9ozvXDX3HVAoUA6myGNracvQmkOzXEMRQtDJp5NRtcAAPWmF72ifTelkomPYfieIMzfusP3mLbr8NRGOzIfGb8LwXeBWsHsvfj98iylbLtKltgfT+tTGLHNaShPTqIILg54rz9KDNzl2MwshikptoGpX2PsNRGYjIX/DT0tc9mzOoxcpJePXnCEyNonZL9XD2tLIf1uKpwblYIoJ92Lv6S3Pi86ZQehLK5wUp+0AP78eLm+Fa3vg1kEIPKEl8gq9pt3kYkK1XPIpyYZfL51DE0iIvgsPA7SNk52n5SlXSX5YdzKAievP0aaqO7P61cW8gJ1LGh92qoqnkw0frTlDfKbUxI/oOEULgNj5edYNHV0Itq7a5socWHXsDtsv3GNcxyrZZ91UKHJArcEUE3JcBDc2WaUPjguDP3ORgVqYa87BogRYWGf988Y+SNYj9nhhQ+73euSTrefu8sGfZ2hSwZW5A+oXavZC+xIWfN27FoMXHeH73f6M66hnuqqkDzQbBXtnaOsr5Z/LeDwyEC5vgabv5eiobzyI4YtNF2hayZVhzSsYryOKpxLlYIoJ2S6CmwKbkpozyYyDBwxcqy0WJycY+FNPWUrif58TovQ7F8ja0ZmIvVfu894fJ6nt7cSCwQ2LxPTQ88+Uok99b3765zqda3pQ00tP9FzzMZr8y9/j4I1/MgpYnliqhTI3zD6pWFJKKqNXnsLS3Iz/9atT4FOCiicP5WCKCemVm4NjgvGw88igbmxUji/VnIswA5n6X7mljaaFVdoEEu2zaurWezLhZPzNjFlx5EYYbyw7RmV3e5YMaaQ/cquQ+LRrNf65cp8PV59hwzvNHpdqsbKFDpNh9VDNoTR8TStPSdJ+n5XbaSOdbPh+91VO34ngx1fq4+FkY5qOKJ4q1BpMMaJLxS5s77ud78t/z/a+203jXA7P19ZCKreD7t+bJK2wXtp+pjmw9FjaaOUFwJmACF5bchQvZxt+HdYIJ9uiFertbGvFVz1rcCH4IfP3Xtd/Uo1eUL457JqsSemApnYQfTdH3bHjt8L4Ybc/fep706W28eRvFE83RecRTVH4HJgDOz6DKl3gxcXafH29gQVz7TTHtetLZGQAwslbcy4mcmjrTwYyc9tlgiLiKOVQgqj4JFztS/Db8Mb6lYyLAJ1qevBCrTLM2elPxxplqOyeKbGZEJpO2c8tYM9UeGEGHNMlFctGBDQ6IfmpSSCmKFjUCEahzc/7TdOcS43e0G8pWJRg/clAmk3bTYXxm2k2bTfrT2YTBmsMaveDMef4p9V6GHPOpM5lwtqzBEbEIYGQqATiklIZ0tSnyE8NfdG9JjZW5ny05gwpqXokYsrU1KbHjsyHGRXhxl6Ij4Rza7Jsc9LG8wSGxzGrX90nPoGYomBRDuZpR0pN+t1vKtQdAH0WgLnlYzfhwIg4Jqw9a3onUwDM3HaZOD0hv4sP3Cx4Y3JJKYcSfNa1OsdvhfPrwZv6TypdA5AQG6p9TojMcg/TlrPBrD4ewDutK9PQx8VkdiueTpSDeZpJTdX2tRyYrYW3dv/hUfSRvptwXFJK1rvKiwlSSgIj9EesBWVRXtToXd+L558pxYytl7kTFvv4Cfu+fbwsKU7b25SOu5HxTFirEogpTIdyME8rqSnw1yg48jM0eRu6/C+DKnFWN9vAiDj870UVlJVGIykllbUnAug8Z1+W53g6F+3psTSEEHzduxZmAiasPctjac+zCu1OV56aKnn/z1MqgZjCpKi/qvxSgHpdRiMlGda/CSd+hRbvaxsZ02lsrTkeQHYC8O1n7WXQoiPsvXL/8ZtbESM6IZkF+67TcsYexq46jZTwSqOyWFtm/NO3sTRnXMeCEbI0Bl7ONox/oRr7rz5g1bFM4d1ZhXanK1904AYHrobyWdfqKoGYwmSoKLL8kCZvkiapkqbXBaYL580vyYmwZhhc3AhtJkLLcY8OpaZKZm6/zDy/a/i623EnPI74pP/2wdhYmvNJl2qExySy9OAtBi06wjOl7RnWvAI96noViU2JaYQ8jGfxvzf57dAtouKTaVLRha9716LVM6UQQtCoguujKDJPZxvGdaxCz3pehW12rhjQqBybTgfx1eaLtKriTmlHnZpy288y/l1ChpDvi8EPmbH1Mu1VAjGFiVEOJj9kpde168ui6WCS4jWZlytbocMUaPrOo0OxicmMWXmKbefv0b9ROb7sUYPNZ4KzvAm/8XxFNp0OZsG+63y05iwztl5mYJPyvPpc+UIN870aEs0ve6+z7mQgyampdK7pwRstK1KnrHOG83rW8yp2DiUzZmaC6X1q02n2XiauP8f8Vxtoas/pQr6JDNBGLrqQ7/ikFEavOIWTrSXTVQIxhYlRDiY/GDDXXWRIjIUVr8D1Pdp6S7qNd8GRcQxfeoyLwQ/5tGt1XmvmgxAi25twCQtz+jbwpk99Lw5eC2XB/hvM2eXPvH+u0auuF681r0CVMgWXRfPYzTB++uc6Oy/ew9rSjJeeLcvwFhUo7/pkT/9UcLNjbPtnmPr3Jf46E0y3Op7agdr99D7kzNh6mcv3olQCMUWBoBxMfnDy1i9vYmauiTRW616guUuyJCEKlr8Et/6FHj9m2Dx5+k4Er/96jNjEFBYMbkibqrnLbS+EoGllN5pWduPa/WgW7b/BmhMBrDx2hxa+bgxrXoHnddNSxiY1VbLj4j1+/ucaJ25HUNLWklFtfRn0XHlci+hmSVMwrHkFNp8NZtLG8zSr7Jal49h75T6LDtxQCcQUBYZa5M8P+uRNzK3ArhSsGgQL22s39cIkLgJ+7Qm3D2l7XNI5l81ngun380Eszc1Y82bTXDuXzFQqZc+UXrU4OL4t4zpW4fLdKIYsPkqHWXv548jtrOXmc0l8Ugp/HLlNu2//YcSy49yPTuDLHjX4d3xbxrR/5qlyLgAW5mbM6Fubh/FJfLHpvN5z0hKI+bqrBGKKgkONYPJDVnPdNXrD6eWw52tY3Bme6QztJoF7Af9jx4TCsp4QclHbnV+tG6DtBflh91X+t+MKDcqX5OdXGxh13aSknRVvt67M6y0q8teZIBbsu8GEtWeZuU23TtOkPKUccn+9yNgkfjt8i8UHbvIgOoFaXk788Eo9OtUog8VTHmZbtYwjb7WqzJxd/nSv40nbav89LEgp+XjtWcJjE1k89NkiFYyheLJRDia/ZDHXTf1BULMvHJ4H+2fDvOe0nfKtPwZHT9PbFXUPfu0B4Teg/x+PtKjik1L4aM0ZNpwKolc9L6b2rmWyG46VhRm963vTq54Xh66HsXD/db7f7c9PftfoUdeTYS0qULVMzgmtAiPiWLjvBiuO3iY2MYXnnynFiOcr8lxFV7VInY63W1fm73PBfLLuHM9WcMFRJ/vy57EAtp6/y8cvVKWGpx6pf4XCRCgHY0qsbLV9JvWHwL5v4MgvcHY1NHkTmo8GaxP9s0cGwNLuEBUMr6yCis8DcD8qgTeWHePk7QjGdazCW60qFcgNWgjBc5Vcea6SK9fvR7P4wE1WHw/gz+MBNK/83zrNxtNBzNx2mcCIOLwO7eaVxuXwvxfFpjPBCKB7HU9eb1mRah4qy6I+rCzMmNG3Dr3nHmDqlktM7V2LW6ExTNp0nucqujK8ecXCNlHxlKEcTEFg5wqdpkLjEbD7K9j/LRxfou1BeXaYcdMBh9+Epd0gNhxeXQflmgBw6e5Dhi05RmhMAvMG1KdzrcKRZK9Yyp7JPWvyfodnWH7kNkv/vcnQJUdxd7AiPDaJpBRt42ZgRBwzt13GylwwtKkPrzWvUGx22hcmdcs6M6x5BX7Zd4Pt5+8SGpOIADrUKK0SiCkKnKd74rqgKemjLbS/8Q941IZtE+CHhnDmT00XLL88uAqLX4D4hzB4wyPnsuviPfrM/Zfk1FT+HNG00JxLepxtrXirVWX2fdiG2S/VzeBc0uNqX4KJXasr55ILfN0dEEBoTCIAEi08+UkQKlUUL5SDKQw868KgDVrqYWsnWDsc5j8P1/bkvc17F7SAguR4GPIXeDVASsmCfdcZ/usxKpSyY8PbzanlXbTm4K0szOhZz4tkPc4FNEFGRe6Ys8v/MamfJ0GoVFH8UA6mMKncFt7YC73ma+HEy3rCsl4QfCZ37QSfhiVdtBTHQ7ZAmVokJqcyYe1Zvtp8kY7Vy7BqxHOUcbI2RS+MQlYjFDVyyT1ZCZUWF7VoxZODcjCFjZkZ1HkJ3j2mybcEndQyEq59A8Jv5Vz/zlFY0g2s7GDoFnCvSkRsIoMWHWbF0Tu83boScwfUx9aqaC+3jetYBZtM0WzFTYCyqKCctaKooBxMUcGihKYN9t4paDZaUwL4oSFs+wRiw/TXuXlAG/XYltSci2slrt2PpuePBzhxK4Jv+9VhXMeqxWJxt6cuZNpLdxP0crZhau9axV4vrDBQzlpRVDDZY60QYhHQFQiRUtbUlbkAKwEf4CbQT0oZrjs2ARgGpADvSSm36cobAEsAG2ALMEpKKYUQJYBfgQZAKPCSlPKmrs5gYKLOlK+klEtN1U+jY+MM7b+ARm9oGzUPzYUTy6DFGE0hwG8az0cGwFE3iI0AFx8YvBEcPTlw9QFv/nYcS3Mzlr/euNhlKEzTPvPz86NVq1aFbU6xJc0pF3e1aEXxx5TzJkuAH9CcQBrjgV1SymlCiPG6zx8JIaoDLwM1AE9gpxDiGSllCjAPeAM4hOZgOgF/ozmjcCllZSHEy8B04CWdE/scaIgWQHNcCLExzZEZm/UnA03zj+zkBT1/hOfehl1faGmNEYBEAMTc1z43HgmOnvx++BafbThPpVJ2LBz8LGVdbPNvg6LY8iSoRSuKPyabIpNS7gUyz+30ANJGE0uBnunKV0gpE6SUN4CrQCMhhAfgKKU8KLXMVr9mqpPW1mqgrdB2DXYEdkgpw3ROZQeaUzI6Wt76M6bNW1+6OryyUhu9PBYbJJEHZvHFpvN8su4cLX3dWPNmU+VcFApFkaCgV35LSymDAaSUwUIId125F9oIJY0AXVmS7n3m8rQ6d3RtJQshIgHX9OV66mRACPEG2uiI0qVL4+fnl6vOTPaLJS4p400/LimFcX+eYvOhc7ham+FqI3C1EbhZm+FgRZ53zj8f8wB9NWVkIIsP3KRDeQte9onh+KEDeWq/KBEdHZ3r30VxQfWt+PIk989UfSsqoUV6753ZlOe1TsZCKecD8wEaNmwoczvvH7Z1s97ypFT4N1gSk5iYobyEhRlezjZ4Otv897OkDZ7O1ng721LGyRoriywGlSf1pwYIlq5M6VWTAY3L58r2osyTvAaj+lZ8eZL7Z6q+FbSDuSeE8NCNXjyAEF15AJA+d6s3EKQr99ZTnr5OgBDCAnBCm5ILAFplquNn3G5oeDrbEKhnb4GXsw37P2rNw7hkAiJiCYqIJzA8lqDIeALD4wiMiGP35RDuRyVkqCcEuDuUeOSAvNIckJMNqR4jaBHxJTbiP6cVK604UfndJ8q5KBSKJ4eCdjAbgcHANN3PDenKlwshvkVb5PcFjkgpU4QQUUKIJsBhYBDwfaa2DgJ9gd266LJtwNdCiJK68zoAE0zRmXEdqzBh7Vni0uU5SQsHFULgZGuJk61Tlgq2CckpBEfEExQRR0BEHEERcQSGxxEUGce5wEi2n79HYkqahExFupsN50OLVXiKUIKkKzOS+3E8sDbdTNE5hUKhyCemDFP+A20k4SaECECL7JoGrBJCDANuAy8CSCnPCyFWAReAZOBtXQQZwJv8F6b8t+4FsBBYJoS4ijZyeVnXVpgQYjJwVHfel1LKLDaS5I/8hoOWsDDHx80OHzf9aX1TUyUPYhIIioin548H2JjanI2JzTOcI9TubIVCUUQxmYORUvbP4lDbLM6fAkzRU34MqKmnPB6dg9JzbBGwyGBj84Epw0HNzATuDta4O1jjlcV0nNqdrVAoiipqJ38xQe3OVigUxY2iEkWmyIH003GBEXF4qd3ZCoWiiKMcTDFCSakoFIrihJoiUygUCoVJUA5GoVAoFCZBORiFQqFQmATlYBQKhUJhEpSDUSgUCoVJEJoKvkIIcR8wIEdxkcANeFDYRpiQJ7l/qm/Flye5f/npW3kpZSl9B5SDKYYIIY5JKRsWth2m4knun+pb8eVJ7p+p+qamyBQKhUJhEpSDUSgUCoVJUA6meDK/sA0wMU9y/1Tfii9Pcv9M0je1BqNQKBQKk6BGMAqFQqEwCcrBKBQKhcIkKAdTjBBClBVC7BFCXBRCnBdCjCpsm4yNEMJcCHFSCPFXYdtibIQQzkKI1UKIS7rf4XOFbZOxEEKM0f1NnhNC/CGEsC5sm/KKEGKRECJECHEuXZmLEGKHEMJf97Nkdm0UZbLo30zd3+UZIcQ6IYSzMa6lHEzxIhl4X0pZDWgCvC2EqF7INhmbUcDFwjbCRMwBtkopqwJ1eEL6KYTwAt4DGkopawLm6FKYF1OWAJ0ylY0HdkkpfYFdus/FlSU83r8dQE0pZW3gCjDBGBdSDqYYIaUMllKe0L2PQrtBPTEZx4QQ3kAXYEFh22JshBCOQEtgIYCUMlFKGVGoRhkXC8BGCGEB2AJBhWxPnpFS7gXCMhX3AJbq3i8FehakTcZEX/+klNullMm6j4cAb2NcSzmYYooQwgeoBxwuZFOMyWzgQyC1kO0wBRWB+8Bi3RTgAiGEXWEbZQyklIHAN8BtIBiIlFJuL1yrjE5pKWUwaA96gHsh22NKXgP+NkZDysEUQ4QQ9sAaYLSU8mFh22MMhBBdgRAp5fHCtsVEWAD1gXlSynpADMV7muURuvWIHkAFwBOwE0IMLFyrFHlBCPEJ2lT878ZoTzmYYoYQwhLNufwupVxb2PYYkWZAdyHETWAF0EYI8VvhmmRUAoAAKWXaiHM1msN5EmgH3JBS3pdSJgFrgaaFbJOxuSeE8ADQ/QwpZHuMjhBiMNAVGCCNtEFSOZhihBBCoM3hX5RSflvY9hgTKeUEKaW3lNIHbYF4t5TyiXkKllLeBe4IIaroitoCFwrRJGNyG2gihLDV/Y225QkJYEjHRmCw7v1gYEMh2mJ0hBCdgI+A7lLKWGO1qxxM8aIZ8Cra0/0p3euFwjZKYTDvAr8LIc4AdYGvC9cc46Abla0GTgBn0e4rxVZWRQjxB3AQqCKECBBCDAOmAe2FEP5Ae93nYkkW/fsBcAB26O4rPxnlWkoqRqFQKBSmQI1gFAqFQmESlINRKBQKhUlQDkahUCgUJkE5GIVCoVCYBOVgFAqFQmESlINRKAoIIYQUQixL99lCCHE/r8rROnXmt9J9bvUkqlArii/KwSgUBUcMUFMIYaP73B4IzEd7zsBbOZ2kUBQWysEoFAXL32iK0QD9gT/SDuhyjqzX5eQ4JISorSufpMvh4SeEuC6EeE9XZRpQSbcxbqauzD5dzpnfdTvrFYpCQTkYhaJgWQG8rEvIVZuMathfACd1OTk+Bn5Nd6wq0BFoBHyu06QbD1yTUtaVUo7TnVcPGA1UR1NwbmbCvigU2aIcjEJRgEgpzwA+aKOXLZkONweW6c7bDbgKIZx0xzZLKROklA/QhBZLZ3GJI1LKACllKnBKdy2FolCwKGwDFIqnkI1o+VNaAa7pyvVNZ6VpOSWkK0sh6/9dQ89TKEyOGsEoFAXPIuBLKeXZTOV7gQGgRYQBD3LI9xOFJlCoUBRJ1NONQlHASCkDgDl6Dk1Cy3h5BojlP3n4rNoJFUIcEEKcQwse2GxsWxWK/KDUlBUKhUJhEtQUmUKhUChMgnIwCoVCoTAJysEoFAqFwiQoB6NQKBQKk6AcjEKhUChMgnIwCoVCoTAJysEoFAqFwiT8HxWCKl6ANDJ3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a pivot table for easier plotting\n",
    "sales_trends_pivot = sales_trends.pivot(index='Month', columns='Year', values='Sales')\n",
    "\n",
    "# Plot the sales trends\n",
    "sales_trends_pivot.plot(kind='line', marker='o')\n",
    "plt.title('Monthly Sales Trends Over Years')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Sales')\n",
    "plt.legend(title='Year')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d9dde36",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}