{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7b75e6ad",
   "metadata": {},
   "source": [
    "# Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4f98ef84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:56.445044Z",
     "iopub.status.busy": "2026-10-23T03:48:56.444965Z",
     "iopub.status.idle": "2026-10-23T03:48:57.733938Z",
     "shell.execute_reply": "2026-10-23T03:48:57.733605Z"
    },
    "tags": [
     "hide-input",
     "thebe-init",
     "runnable"
    ]
   },
   "outputs": [],
   "source": [
    "path_data = '../../data/'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4356805c",
   "metadata": {},
   "source": [
    "```{contents}\n",
    ":local:\n",
    ":depth: 2\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67beff6c",
   "metadata": {},
   "source": [
    "```{note}\n",
    "**Optional section.** This section goes beyond the core clustering material in this chapter. The lab and homework do not use it. Read it if you want a tool for looking at customer data with many columns at once.\n",
    "```\n",
    "\n",
    "The customer data in the previous section had only two features, `annual_spend` and `orders_per_year`, so we could see every customer on one scatter plot. Real customer and product tables usually have many more columns. **Principal component analysis (PCA)** is a way to summarize many correlated columns with a few new ones, so that we can plot the data, see its main patterns, and check what a clustering found."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec3a73d7",
   "metadata": {},
   "source": [
    "## Too Many Columns\n",
    "\n",
    "```{index} dimensionality reduction\n",
    "```\n",
    "\n",
    "```{index} dimensionality\n",
    "```\n",
    "\n",
    "The online store from the previous section now keeps a fuller profile of each customer. The file `customer_profiles.csv` has 300 customers and seven features:\n",
    "\n",
    "| Feature | Meaning |\n",
    "|---|---|\n",
    "| `annual_spend` | Dollars spent in the last year |\n",
    "| `orders_per_year` | Number of orders in the last year |\n",
    "| `avg_order_value` | Average dollars per order |\n",
    "| `days_since_last` | Days since the most recent purchase |\n",
    "| `return_rate` | Share of ordered items that were returned |\n",
    "| `web_visits_per_month` | Average visits to the store's website per month |\n",
    "| `discount_share` | Share of orders that used a discount code |\n",
    "\n",
    "```{note}\n",
    "`customer_profiles.csv` is a **synthetic** data set generated for this book from four built-in customer types, so the patterns are cleaner than in real data.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "798c8212",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.735590Z",
     "iopub.status.busy": "2026-10-23T03:48:57.735479Z",
     "iopub.status.idle": "2026-10-23T03:48:57.742725Z",
     "shell.execute_reply": "2026-10-23T03:48:57.742392Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>annual_spend</th>\n",
       "      <th>orders_per_year</th>\n",
       "      <th>avg_order_value</th>\n",
       "      <th>days_since_last</th>\n",
       "      <th>return_rate</th>\n",
       "      <th>web_visits_per_month</th>\n",
       "      <th>discount_share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P001</td>\n",
       "      <td>236</td>\n",
       "      <td>4</td>\n",
       "      <td>58.88</td>\n",
       "      <td>216</td>\n",
       "      <td>0.065</td>\n",
       "      <td>3</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P002</td>\n",
       "      <td>2132</td>\n",
       "      <td>5</td>\n",
       "      <td>426.40</td>\n",
       "      <td>73</td>\n",
       "      <td>0.097</td>\n",
       "      <td>3</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P003</td>\n",
       "      <td>134</td>\n",
       "      <td>2</td>\n",
       "      <td>66.95</td>\n",
       "      <td>193</td>\n",
       "      <td>0.072</td>\n",
       "      <td>2</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P004</td>\n",
       "      <td>639</td>\n",
       "      <td>18</td>\n",
       "      <td>35.49</td>\n",
       "      <td>24</td>\n",
       "      <td>0.105</td>\n",
       "      <td>23</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P005</td>\n",
       "      <td>2103</td>\n",
       "      <td>4</td>\n",
       "      <td>525.65</td>\n",
       "      <td>97</td>\n",
       "      <td>0.106</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id  annual_spend  orders_per_year  avg_order_value  \\\n",
       "0        P001           236                4            58.88   \n",
       "1        P002          2132                5           426.40   \n",
       "2        P003           134                2            66.95   \n",
       "3        P004           639               18            35.49   \n",
       "4        P005          2103                4           525.65   \n",
       "\n",
       "   days_since_last  return_rate  web_visits_per_month  discount_share  \n",
       "0              216        0.065                     3            0.32  \n",
       "1               73        0.097                     3            0.04  \n",
       "2              193        0.072                     2            0.38  \n",
       "3               24        0.105                    23            0.76  \n",
       "4               97        0.106                     2            0.20  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles = pd.read_csv(path_data + 'customer_profiles.csv')\n",
    "profiles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89f67b6",
   "metadata": {},
   "source": [
    "Each customer is now a point in seven dimensions. The number of features is the data's **dimensionality**. We cannot draw a seven-dimensional scatter plot. We could draw a scatter plot for every pair of features, but seven features give 21 pairs, and none of them shows the whole picture.\n",
    "\n",
    "The features also overlap. Customers who order often tend to visit the website often, and customers who use many discount codes tend to buy cheaper items. The correlation table (correlation was introduced in Chapter 12) shows how strongly each pair of features moves together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eefe855f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.743742Z",
     "iopub.status.busy": "2026-10-23T03:48:57.743683Z",
     "iopub.status.idle": "2026-10-23T03:48:57.854561Z",
     "shell.execute_reply": "2026-10-23T03:48:57.854258Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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I/HrM0kApqj2l1lBUz0nBEtUr0uBBqkWVR1kkOp0vHaKmw7G5++3Kox9O+lGnFmAUIFLfaKrLpB9lOoRcnLMR0mtFWUnKjFENLwUO1DeZngPtnBSGaj6lulLKpFELNpqfTi8t9SAu6utZ2GtC2WcK5mgZUnkAZeLolODUs1u+ZKMoj12SbV5U1A6N3quU9aSBbPRYtA0oiKSyH3rO8ifJoSCParkpS0uBGH0eaD3ls8NF2Z75vcal8R7LT1m+3srQzgm9xpRtp/cMfe/Qc71//75oI0jZcDpCMGfOHCxcuFD0oaejBJS9pfeflJWW0LKobIg+49Tqj46OSX2eaX7qT01BO302aGeadvyoXj/34M780LamHQrasaLXi1rU0faiEiaqD6d2eFKSgQJtWnfarrQzRSU8tP1UKYdjTB20qOWGWpbEGCvSYX8KlqkfMwVl9CNAWSr5frQSCjjoh4wCBwoaKFCQPwUvjTqnw/l0CFO+9yqhoJsCW/mOHbnR4Wr6QaJ1oo4SdNhdPuNHPaLpR58Ce3kUyNEPMZ12mbKw9Dy2bNkiggQKTCS0bBq1T9kiCvjpLIoFLZfW49SpUyLbROtBP8IUgMmjkfn0Ay7f8YBQsEI9bOUDM6m3NZUJUDBKRwDk1y8/dLIZ+Z7GVC5ANbV0aL+gw/uFvZ4FvSbSgC6qb6YAlAIICQV4FIjQ65Xf4FBVHluV6Yry+haGAkV6rpTppe1NQRK913Oj9zcFwtRhhLY5PUcpOy1/8g1Vt2dBr3FJ3mNUPuPm5iZKaeTRESUK1Cl4VNfrXdB7k3YcVOltTMEndQGh14HWm8oactdg07rRhdaJPssURFOJGa2n/EA8aSc/Pj5eTEulaBK6jYJf2o70GBTIKnu/UNcMOnJGPemVoe1LgTxtXwqe6X1AO47ytcz0XqLvH9qZpWRDhw4dxM4BY2WFg2fGGGOMMcZUxDXPjDHGGGOMqYiDZ8YYY4wxxlTEwTNjjDHGGCtVERERopMQDX6nmvnc7Q2VoY4t1HGKxkpQVxUa6Jq7g4wq06gbB8+MMcYYY6xUzZkzR3RHoc43NDBYlVaGFBR/++232LRpkzhBEg0cpYG1ycnJRZpG3XjAIGOMMcYYKxNnzpwRLSmpU5SyM0pKqDsNZZLpJDhz586VtQ+lrj3Ui5xaS6oyTWngzDNjjDHGGKtQPD09xQmF5M+oSqeVp37w1M5Q1WlKA58khTHGGGOMFVlKSkqeM0LSmR7VcbZHqSY69wnB6Lp0tktVpikNHDyzSmualuJJDaqKOW95oCpy7d0GVdHMrNI7C11F1s09+5TLVc2ga9+jKhqR1h9V0cnZnSr1b2a1ZW+JM4TKozPoSmefVAf5E+RI1+lkOkWdRp04eGaMMcYY03A6Wupf5uLFi8UAQHnqyDrLZ5PpLJV0dlcJXZfOsqrKNKWBa54ZY4wxxliRGRgYiNOty1/UFTx7eHjA2NgY586dk90WFxcnTiUvnTZelWlKAwfPjDHGGGMaTkdLS+0Xdevbty/Gjh0r/qegeMqUKVi9ejUePHggguIPPvgAlpaWRZqmNHDZBmOMMcYYK1WfffYZ1qxZg4yMDHGdyiq0tLSwefNmjB49WtyWlJSkkLmm6WlAYtu2bUXfZuqiceTIEZHhLso06sZ9nlmlxQMGqxYeMFi18IDBqoUHDJa+D3Rrqn2Z36T7qjwt9WSmS26GhobQ1dWVBc8UUNNtudEAQGm6/KgyjTpw5pkxxhhjTMOVRplFUejr64tLQYyMjPK9T5WguCwCZ8I1z4wxxhhjjKmIM8+MMcYYYxquNFrVVVWceWaMMcYYY0xFnHlmjDHGGNNw5V3zrEk4eGaMMcYY03BctqE+XLbBGGOMMcaYijjzzBhjjDGm4bhsQ30488wYY4wxxpiKOPPMGGOMMabhOFuqPhw8M8YYY4xpOC7bUB/eEWGMMcYYY0xFnHlmjDHGGNNw3KpOfTjzzBhjjDHGmIo488wYY4wxpuG45ll9OHhmjDHGGNNwXLahPly2UckFBwcjPDwclV1ISAjCwsLKezUYY4wxxgrEmedKbtq0aXBxccHGjRtRmc2dOxeGhob49ddfy/yxzR3s4N69PUKf+cH/hqdK89jXrQkXj/qIDY2A98UbyMrMLNY05UnfwQkG1WsgPTYGSc8eAVkqrJ+ODoxq14eOiSmSvB4hIy5WPcstQ/7hsXgWEgUrE0M0c7WDjrZqOYTMzCwcf+gPPR1tdG/gqrblljU9bS3UszeFoa4OvCMSEJ2UVuD0jaqZwdxQT+G28IQUPAtLULjN2lgPrlbGSErLgG9EIlIzKtZ2T09NxfPHnkhNToJznQYws7YtdJ7EuBgEeT8BtAAH19owtbJROl1MeAiCfZ/B1NIaTnUaQEtLC+XteVQcvMJjYWVkAA8nG+hoq7ZOmVlZOPn0pXifd63jVOxpypuejhY8nC1gpKeDx8FxCE9IVWk+e1MD1LU3RWRiKh4Fx6ltuRUBl22oDwfPrMqycLTHqG+Xomb7FjAwMcb1HftUCp6HfLEQXWe9Aa/z1+HYsA7iw6PwXa8JSIyKKdI05cluxBuw7tEfiU8fwsCpOtLjY/H8q6XITIjPdx7DGnXgPGMhslJTkBL4AvYj30TY7t8Rd/NyiZZbltYfv4W/rz1Bczd7+ITFwNLYAD9O7AkLY4NC59164T5+OesJS2PDPMFzSZZblhzMDDC/ex2kpGUiJjkNU2zdsONmAM56R+Q7z+DGjiJQeBGdJLvNOzxeFjyb6Otgcjs3VLc0EtNYG+vDykgPP1/2wwMlwUd5iAgKwF9fLIC+gRFMLK3x349r0HPidDTv3j/feU7/9QseXj4Du+o1kZaSjJdej9B+0Bh0GjZRNg3tEB///QfcPXMEbg2bigAdWloYNX8ldPX0UV6+v3Afu+76oKmTLfwiY2FhZID1wzrCwrDwddp+/Sm2XH0MCyP9fANjVaYpTy6WRvhySGOxIxeZmIZFvc3wwzkfHHoQnO88tGsxo3Mt9G1YDXdfRosdA/LRfw+QlpFV7OUyzcTBsxwqG4iJyQ5ubGxsYGVllecFCwwMhIGBgbg/Li4OmZmZsLCwKPI0L168gJmZGSwtLRVKF7S1tWFnZ6fy+hQVrQs9jq2tLfT09JSuMz1mRkYGrK2tlS4jOTlZlIo4OjpCR0enyM9dEhoaChMTE3EpDxQw39p9BJvHv49553eqNI97t/bovWAqvnptBHwu34KBqQkWX98vguUd05aoPE15Mm7gAZu+w+D/xSIkeT2GtqERaixdB/sRbyB42w9K59E2MUX195ci9sZFhPzxk7hNS18fRrXcS7TcsnTNJwjbLj7Alrf7oKmrPRJS0jDx50PYcPI2Ph7YrsB5774Iw7+3nmFka3ccf+CvtuWWtbfbuiIwJhnfnPEGhQNdattgQqvquB8Uh4jE/DNoNwOisfdekNL79HW0cc47Ande5uwYjmvpgukda+K9PZ7Iyo47ytWhX9bB1tkNo+evgpa2Nm6fOohjv21ErSatYGHnoHQeh5p10WXU29B+9R338MoZ7F2/Eo079oCVQ3bAeHHfDty/cBKTVv4AW+fsHarnjzyRmZEBKCbry8yNF2H448Yz/DSqs8g4J6Sm4e2/zuDHCw+wqGfzAue9FxiBfff9MMyjJk4+e1nsacrb3B514R+ZiCX7H4j3eb9G1fBe19q48TwKoXEpSucZ17o6eta3x/S/b+NFVPaOYhMnc5Gxl4Ln4iy3IuGaZ/WpmMcVy8mGDRvw+uuvi0uNGjXg7u6OixcvKkzzxhtvYOrUqWjfvj3q1q0Le3t79OvXD0lJSUWaZuDAgXlKFObMmYPFixcXaX2K4pdffhFBc8uWLcXfKVOmIDExUbbOdJ3uq1Onjljn4cOHy+4nKSkpmDFjhpi3TZs2IihetGiRCJCL8twp8O7WrRucnZ3h5uaG7t27IyhI+Q9zaQr18sPNfw4gMz1d5XnajB8C/+ueIigmKfEJuPjr/9B67EDZoVpVpilPFu26INn3mQhwSWZyEqLPHYN5284ia6aMZade0DYwQOg/v8luy0pNReLjeyVablk6fM8XjZxtRIBLTAz0MKRFHRy95ydKMvITl5SKj3dfwNJB7UWmTV3LLWtUVuFub4bjT8LEDz+54BOBlPRMtHa1LHTe1tUtUdfORATL8qKS0hQCZ3I7IBqmBroiA13eYiNC8eLxPbTuM1QEzsSjcx/oGRji0dWz+c7XsF1XWeBMrOyzA2bKQpOM9DRcO7gLrfoMkQXOxLWBB/QNjVBejj5+gQYOViJwJib6ehjUuAaOPQ0Q5Rb5iUtOxbIjN/BRrxYwzydDrco05c3OVF+UVfx7N1D2Pj/6KATJaRnoUkd5qY6uthZGNncW80iBM7kXGIvktMxiL7cilm2o+1JVcfAsZ8WKFfDy8hKXqKgoTJo0CWPGjBGZVnkHDx7El19+KQbr+fn54fbt29i0aVORp1HX+qiC5qf66D/++ENkhyMiItCuXTv4++dk0f7991989NFHIuPt4+Mj1nnlypWy+z/44AM8fPhQ3EfLePDgAXbu3Invv/++SM+dlhMfHy8y4BRIDx48GKdOnUJl4NzEHYH3nyjc9vLeExiZm8HazVnlacqTgYsbUl4+V7gtJcAfOkbG0LPJDgBzM3ZvhESvxyL4MG3WBiaNmkHHxKzEyy1LXiHRqG2vGCTWcbASmbmgmPzLSj777zK6N3RFm1qOal1uWaNDzuRlTE5wQAm14Lhk2X35aeBghvY1rDG5rRvWDGwo6qAL0riaOeJT0hGVWHA9dVkIfe4r/tpVryG7TUdXFzaOLgh7kX1ffsJf+uP+hRO4enCXKPVo238k7F1ryZabnBiPWh6tEOz3DI+vnRN/y5t3eCxq25or3FbbxhyJqekIjs1JhuT2xcnb6FbXCa2q25VomvJW0yb7SKZvRE5NfkZmFgKiklDL1iTfecwM9XDDPwp17EzQqbaN+FvS5TLNxcGzErGxsSLoGzJkiMiIUsAojzKynTp1Ev9T6UKfPn1w69atIk+jrvVRhVRCQYMLia6uLt5++200aNBANk3Xrl0xbNgw8b+rqysWLFggC3ppfsqUz5w5U2SjfX19RWkHTb9r1y6VnzstZ8eOHfj0009lZSGzZ88W2e7KwNDCLE/dckJktPhrZGGu8jTlSdvIGBmJikFdRkKc7D5ldM0sRBlGzeXfwLJLb9gOHovaa3+BWeuOJVpuWYpPSYO5oWINspRJjk9WHuTtuv4UzyPiMKt7M7UutzxQ3TJJSM1QuD0hJQPGr+5Thso15u9/gPXnfbD4wEPcCojBtA41Ra2zMvXtTdGnvj123nkpy9CVp5Sk7GDHMNfOnqGpGVISFQc95hYdGgzvO9fw7NYlpCYnwsYpJ8OcGJf9mb56aBf2/7BGlG/8b/Vi/Llyniw7XR7iU9NgZqCY8ZeyxPReVWaPpy9eRCVgWodG+S5XlWkqAhOD7GrUuBTFI4qxyen5vmctjbNfr5EtXLCot7so31g9uDG+GtoEBrraxV5uRSzbUPelquKaZzmU/Zw1a5YIVKnumGqCKeAMCAhAixYtZNNJAajE1NRUTCNPlWnUtT6qoGCYlkXZ5l69eomyiREjRiisZ+PGjRXmadKkichYR0ZGimxzWlqaCKhz1zlXr15d5edOy6HnIP9YVMqQ+7Fzo5IRusjLQBZ0xDCPspOWlCJqmOUZmmVfT0tKVnma8pSVlgptA0OF27QNjWX3KZOZlgrjeg3ht2Iukv19xG22g8fA6e3ZeOp5E1kpycVablky0NVBYqpi8ED1yeI+JcEjHaL++ugNjG3XAMde1Tk/DYlCclo6Dt71EYMDnSxNi7zcslDTxhjVzHK2BdVkpr3qfmGoqy1KNSSGetoFZogfhuQM+qNgeN/9IPSoZ4c6tia4G6jYbaWmtTFmd66No09CCxyEWFoCvR4jMjjne7Z+m87Q1cvesaEuG/LlFHTdzKrgw+11mrcVF+Jz9zr+t2YxbJyqo7p745wBgVlZeHfNL+J7LCE2Gr8seAeX//sbnUe8ifJgoKODpDTFAC/x1XV6r+ZGAfV35+5hTPPaOPE0+7XzCo9BSloGDj96jmbOtiIYL2waR/Oy30F2dzAVA1UlZ73CkfrqvU07i1LJBTHW10F4vPISPWke8s6f2YkeCyM9bB7fAqNbumD71efFWi7TXJx5foWyqBRMTpgwQWR6qZzh8ePHIlCUr+lVF2W1r+lytbelsT5UQ/306VMMHToU58+fR7169XDhwgXZ/bnLQaTr1EJOGly4Z88eWSmJdDl9+rTK62BkZFTgY+Xniy++EDXW8pfbKPvOFeE+z/OUXli7uSAjPR0R/i9VnqY8pYaF5Cmj0LOxQ1ZGBtLCQ5XOkxYahLSIcFngTOJuXRHZaINqzsVebllytjJFUIxippGuU92eo4Wp0nm6NXBFcEwCLnkFigtloVMzMsT/4XFJxV5uaXO2MEQTR3PZRVdHC6Hx2TswNiaKtao2xvoIe3WfKqTAm2qa5dWwNsa87nVw1jscO+8EojyEBfjB++512SU9PRVWDo6ydnLyYsNDYWmvvBRHmVpNW8PI1AwBTx+I61YO2e/7ui07yL7PTcwt4VKvkWhbV16cLIwRFJtTmkOoXIPej9XyCXCpY0ZwXBKu+IeKC2WYqdUg/R+RkKzyNGWthrUJWrtZyy5Ujx/0qizJQW7nkdibGSAoVvl6SqVMl31ydvhiktLwIChWtHUkxVluRcOZZ/XhzPMrVA5BWdZRo0aJkgZy7tw5hYBWnWjQHdUESyggvnfvnqzcQd3rQ/PRcigr/NZbb4lL586dRTD82muviWkooM7KypL9EJw9e1YM+jM2NhblHdTtY/fu3WjevLnSZauCBj5S4EvPhYJ3QmUg169fFwMI80MDKT/88EOF2+ZaNEFpM69mh/o9OsJz/wkkx8Xj3sFTGLNxBczsbBAXlv1F22rMQDw5dQnprzLjqkxTnuLv3kC1CVOgY2aBjLjsHRDztp2Q8MgTWenZGUhdCysYN2yK+NtXxcC/uNvXYN62i+i6IbWdM3ByFa260iLDVF5ueepUzxlrDl1HZHwSrE2zd+KO3vdD65rVZBnisLhEXPMJRhd3F5gZ6mPV8OzPhuTXs57YdeOZwu2qLLesXfCJFBd5AdFJoj9zG1cr+ERk177WtjWBrakB7gbm7Ig2djQTATK1ojPS0waNeZTPVNPAQSItg7hZGYkWeNR14+/b5beD2LTr6+Iiz6C6Ccxt7fHoylnR35kEPH0ogmkpqyxllmkQYfX6TZCcEC/qoum6JCokEEkJ8TB/tYNobmMHhxp1RF20hD4PEUEvUKNhwV0tSlPHmtWw7vRdRCamwPpVq8TjTwLQsrqdLPMcnpCM689D0bmWI0wN9PDp660UlkFt6P6956twuyrTlDUasEcXefS+DIlNRte6tnj86qhJw2pmqGZuiCu+OZ+JVq6WIoN8PygW4fGpeBYaDzfrnJ0L+hWsbmWM2y+ii7TciqwqD/BTNw6eX3FychLlB1SLO2/ePFFeQAPbSqs7Qt++fUU2leqMqS74xx9/hLe3tyx4Vvf63Lx5UwwGnD59uujaQVlsT09P0T1D8uzZMzGokLpu3LlzB+vWrcMPP2S3GNPX18eaNWtE6Qe1ouvfv78I7g8dOiTuo+eiCpp2/vz5IhimIJq6baxevRrR0dlfUPmhx6SLvJKWbNBIegpqiamtFezr1RSdMpKiY0UATFyaNsTbf3yLZfV7IPlJPK5s34PX3h2LOcf/wKUt/6BG22ao3bEl1nUaJVuuKtOUp5hLp2DVpTdc561A9IUTMKpVD8Z1GsDvi5xOLwbVa8J5yofwXjwdqcEvEX/nGhIe3YXb/M8Qfe64GCxo3XsQIo78KztRiirLLU/9m9bGnptemP77SQxuXhv3A8Jx93kYNr/dRzbN0+AofPLvReyeOQimKnYTUGW5FQX1dJ7xWi3R/IRKNXrXt8dlv0h4hedkzoc2cZKdBIUO18/pUgu3A2IQFp8iDpFTZ4EDD4Jl2TbKXM/vXlf0vX0elSQGFkruBcUgPkWxxrqs0Xdmr4kz8e/6FcjKyhSlGteP7EGjDt1Fllhybvc2WNhWE8FzQkwUdn/7Keq1aA9LB0fERoTh7pnDcGvYDA2oe8wrvd+Yib/XfiQ69lA5x9Obl5AUF4t2A8vvs96vgatoJTd7zwUMbOSGB8FR8AyKwKaROev9LCwGnx69if+90VN0h9E035/zwdK+9cW2D49PwfDmzjjxOBQP5fqOv9nODSGxKSJ4JhvPeuPzwY3EUZrnkUnoUMsG5oa6+PtmQJGWy6oGDp5fof7K1CXi448/xrhx40RAu379enzyySeiZldC2VHKGsujemQqsyjKNO+9954YPEfBMfU5plIKCmSlx1J1feh2qS90Qdq2bSuCZ+qMQaUbDg4OooyDunfIrxOVVdBfyiZ//fXXePPNnLq9d999V9ROU0D9v//9TwT4AwYMEAF5UZ47Bc70/D7//HPR65oCcQqipZKOskLBc6PXu4j/fa/cFn/petSLIFnwHBMYgqt//IukmOwvWPqR/KbbGBEcuzRtgEj/l1jVrB/CvHOyT6pMU64yMuC/5iMx8M+QTgAREQafZe+L0gxJenQkYi6fQWZSTnYxYP3nsOjYDUZ16ovbX25ai4QHd4q03PJEJz34+a1e+PfmM1G77Ghpgr+m9YerTc4gTjszY/RtUhOmuc6oJ99Fo3uD6kVebkVBg/1WHXuCdjWs4GhuiF13A3E5V9bsXlAs4l4NdAyNT8HqE8/Qsaa1KMuISU7H58efwjcy532hr6sNz1e1z1QiIs8rPL7cg2fi3roj3lj+HR5cOoXIoBfoOuptNH6tp8I0tTxaw9g8O6tOgfD4j7/C/fMnRB21kZk5+k5+H7WbtVVIYFCg/daKjbh3/hiCfJ7CrUFTDJqxCEam5bftdXW08f2ITth3z0/UJVczM8b28d1R3TLnd8PWxBB96lcXWWdl6tiaF3ryE1WmKS8XfSIwe+dddHe3R3UrI2y+5CeCXHnX/aNEaYaEguiZf99B7wYOqOdgKk6UsvrYE4UBgqostyKrygP81E0ri47TsyqvZ8+eaNWqlcgCVxbTtHJaT1Ulc97yQFXk2rsNqqKZWf1QFXVzr7jt0ErToGuKrT+rihFp+Z/tUZOdnJ19tLks/G6b011LXSaGP0JVxJlnDUGdMOR7NudGmV35MwoyxhhjrOrgmmf14eBZQ9AAQzoTYUFt76jkIj/Kyi0YY4wxphm4bEN9OHjWEBQYU9u44tq2bZta14cxxhhjTBNx8MwYY4wxpuG4bEN9OHhmjDHGGNNwXLahPnyGQcYYY4wxxlTEmWfGGGOMMQ3HZRvqw5lnxhhjjDHGVMSZZ8YYY4wxDactd3ZMVjIcPDPGGGOMaTgtHjGoNly2wRhjjDHGmIo488wYY4wxpuG0OfOsNhw8M8YYY4yxCic9PV3p7To6OtB6VcOdmZkpLvK0tbXFpbRw2QZjjDHGmIbT0tFW+6WogfDcuXNha2sLfX19dOnSBQ8ePMh3+pCQEBgaGua56OnpYcOGDbLpevfuLZYnP83IkSNRmjh4ZowxxhirAgMG1X0piiVLlmDHjh04cuQIgoKC4ObmJgLf+Ph4pdM7ODiIgFv+8v3334us87BhwxSmXbBggcJ0u3fvRmni4JkxxhhjjJWa5ORk/PDDDyKAbtWqFWxsbET2ODw8HP/73/9UXs7mzZvRt29fuLi4lOvW4uCZMcYYY6wKDBhU90VVd+/eRUJCgijVkFhYWKBFixa4dOmSSsu4d+8erl+/jnfffTfPfZSR1tXVhbOzMyZPnozQ0FCUJg6eGWOMMcZYkaWkpCA2NlbhQrcpq18mdnZ2Crfb29vL7lMl6+zk5IT+/fsr3N6+fXscPnxYlH8cOHBABOr9+vXLd7ChOnDwzBhjjDGm4bS0tdV++eKLL0QGWf5Ct6mKumRIXTMKQgH5H3/8gUmTJomaZ3mfffYZXnvtNTFQsHnz5ti+fTtu3ryJs2fPorRw8MwYY4wxpuFKo2xj8eLFiImJUbjQbbk5OjqKv7nLKcLCwlCtWrVC133v3r2IjIwUJRmFqV+/vgiwvby8UFo4eGaMMcYYY0VmYGAAc3NzhQvdlpuHhwdMTU1x+vRp2W3R0dG4desWOnToILstIyMjT89mqWSjZ8+eqFmzZqHrRO3vaDlU/1xaOHhmjDHGGNNw5dmqzsDAALNnzxYlHRcvXkRgYCCmTp0qMtJjxoyRTdejR488bej8/f1x8uRJpQMFHz58KLLRt2/fFvXWV65cwcSJE9GoUSP06dMHpYXPMMgYY4wxxkrVihUrkJWVhVGjRolAlwb6HT9+HMbGxrJpqNwid00z1TDTwMLBgwfnWWaDBg1EwD19+nQ8evRIlIBQK7ulS5eKk6mUFq0seiaMVULTtGqgKprzlgeqItfebVAVzczqh6qom7viqPyqYtC171EVjUhT7KBQVZyc3anMHutMq/ZqX2bXG5dRFXHmmTHGGGNMwxWlLzMrGAfPrNKqqhnY737zRFXUavoGVEW39zxAVTS/Wx1URedXHEBVdOTutPJeBcZUxsEzY4wxxpiG09LmzLO6cLcNxhhjjDHGVMSZZ8YYY4wxDaetw/lSdeHgmTHGGGNMwxWlLzMrGO+GMMYYY4wxpiLOPDPGGGOMaTjOPKsPZ54ZY4wxxhhTEWeeGWOMMcY0HA8YVB8OnhljjDHGNByXbagPl20wxhhjjDGmIs48M8YYY4xpOG0+w6DacOaZMcYYY4wxFXHmmTHGGGNMw2nxGQbVhoNnxhhjjDENp81nGFQbLttgjDHGGGNMRZx5ZowxxhjTcNyqTn0488wYY4wxxpiKOPPMGGOMMabheMCg+nDwzBhjjDGm4XjAoPpw2QZjjDHGGGMq4swzY4wxxpiG0+IzDKoNB8+MMcYYYxpOm0+SojZctpGPH3/8EZs3b1bfK80YY4wxxio9zjzn4+LFizA0NMTkyZPLdouwMqVn5wC7YRNg4FIDGXExiDpxAHG3rhQ8k5YWLDv1hHm7LtAxNkXCgzsI2/cXslJTSrbcMuTYsC46TxuPhn0649qf+3BwxXeFzmNbszoGrZwHZ4/6iAuNwJkN23Bn79EiT1NR3D6xHw8vnkJqShJc6jXGa8PfhJGZeb7Tx0dH4Mbh3Qh4fE+8B6rVckeb/iNhbmMv7o+LDMf/Vs1TOm+HYRPQqGNPVASj2rmif3NnGOvr4JZfFH44/hQxiWlKp7U3N8DmKe2U3rfpxDMcvBMo/q/vZI4x7d3E3/jkdJx/Eoq/LvkjNT0TFcXhvTtx7vhhJCcloYFHM4ydNBVmFpb5Tp+akoKLZ07g5MF9iAwPw5c/bYNprvdHbHQ0dv+5FY/veyI9PQ3N27THiAlvw9DICOXJ2NUF9Re+B/OG9ZASHgnfLTsQfPhkgfM49OwCt4kjYVzdCWmxcQg9fRHem35DZkpqiZZbll4EBmHDL7/hqbcvrK0sMW74YPTs3LHAebx8/fDP3oO4eO0m+vfujhmTJqhluRUR93lWH848sypL29gEbotXQ0tbG0FbN4jg1nnGQpg2b1vgfI5vzYTtkHGIOnMEL39eh7SocNj2H1Hi5ZaVWu1b4N1/vkeYlz/SklNgZmdd6DzGluaYd34ntHV08Pvkhbi79xje3fk9mg7qVaRpKoqrB/7Gub+3oEXvwej15iyEPvfGrrVLkJmZke88/238XATKPd96D13GvIOo4AD8tXIu0l7tNJlYWGHEglUKl8adeiE6LAjOdRuhIpjUpRbmvO6Ovy764fN9D+DuaIYfJrVGfqWQEfGpmLH1usJl/60AuFgb487zKDFNbXtTLB3WBLf9orB89z38fsEXo9u5YeWopqgo9uzYhj9+/h79h43Bu+8vgK/XU6xYMBsZGflv73UrlsDzxlU0bdUWwYEByMxQ3BFISUnGJ+9Pgc/Tx3hrxvt4Z/Z8PLnvia+WL0J50rMwR8e926Clo4M7Hy5F8JFTaPXzOlTr0y3feey6dkCb39Yj4spN3JyxEF7fb0GNiaPQ5POPS7TcshQTF4c3Z85FRmYmViz8EN07dcDcpStx6vylfOe5fe8B5i5dheouTtDX10dUdLRalss0n0ZlngMDA/Hrr7/Cz88PLi4uImvs5uYmu3/fvn24ffs2hg0bhu3btyM0NFT8JWfOnME///wDMzMz9O3bV+nyfX19sW3bNgQEBKBWrVqYNGkSHB0dZfevXr0aNWvWhLGxMQ4fPizWYcmSJYiOjsbWrVvx+PFjODk5Yfz48ahTp06hz0d+eadPnxZf9G+88QZatmyplvXKz6FDh3DixAl8/fXXCrfTc6DX+KOPPlLpcWn6kyezsxI2NjZo164dxowZAy0trWKvmzpZdX0dWnp6ePnzN0BGOpJ9nsLQtRbsBo9F/O2rSucxbuABy8694bdqIZK8HonbUgNf0EiMEi23LPld98SKxr3F/63GDlJpnk5Tx0PX0ABbJ36AjLQ0+F27g+rNG6H/8vdxd/9xlaepCNLTUnH1v/+hw9AJaNA++4d/wPRF+PnDN+B9+yrqtuygdL7Ri78UOwYSI1NzbF08BeEvfOFYu764z8rBWWEe33s34dawGSztcz4X5UVPRxtvd62Nn0564YhnkLhtyd93cXhhN3RuYI8zD0PzzJORmYUXEYkKt3Woa4er3hF4GZkkrvuFJ2Dcxouy+x8HxkJXRxtfT2iBVXvv55vVLitpqanY8+dvGPXmO3itR/b7/oOPPsO0sYNx8/IFtHmti9L55i1fDT09Pdy6qjxIenDnJl74+eLnfzbA1t5B3Dbno08xZdRAPPK8I7Lb5YGyxzoGBrj13iJkpaUj+vY9WDSuD/e5MxB89LTSeew6tUecly+8f9wqrsc9fgb/xvXhOn5EiZZblnbuO4SU1FSs/niB2G4ejerj8VMv/LD1DxHwKtO4gTv2/f6L+P/wiTNqW25FxX2e1UdjMs/+/v5o2rQprl69ilatWuHBgwfw8PDAo0fZAQ65d+8evv32W4wePVoEsb17Z3+R7tq1C7169YKRkZG4febMmTh6VPFQ87lz58RyIyMj0aZNGxE4NmrUCM+ePZNNQwHn7NmzRdBJj926dWtkZmaic+fOOHjwoAh6dXR0MHLkSHh5eRX6nKTlrVq1SgSniYmJ6NChAy5cuFDi9SqIu7u7eJ3u3r0ru40C908++QSmpqYqPy5df/3118XF1dUVS5cuzVMGU9R1Uyfj+k2Q+OieCHAl8Z43YehWC9pGJkrnMW/TCSmBL2SBs0xWZomWW5Yy03PWS1X1urbD09OXRVAsuX/oNFybN4KRhbnK01QEof7eSElMQI0mOTuhFnbVYOPshhePct7zuckHzlmZmXh24yKMzSxg7Vhd6fSRQQF4+fQ+PLr1Q0VAJRXmRnq49DRMdltgVBK8Q+LQqpaNSstwszVBi5rW2HPtuUKAnZu0e5yZlfe+subz7AkSE+LRrHVO+Ym9oxOq16iJ+3du5jsfBUoFSUrM3qkwMTWT3WZimv0+v3f7OsqLbYfWCL90TQS4kpCT52HRpAF0zXPWVV7E5RswdnGCWf264rqOsRFsO7VDxMVrJVpuWbp++y7aNG+qsN06tW+Dx8+8ERsXr3QePV3dUlluRUVHQ9V9qao0JvO8bNky1K1bFwcOHBCZzRkzZojgePHixdi7d69suoSEBBHIUjBKKLhdsGCBCAwpuCOjRo0S2VB577zzDpYvX4733ntPXJ8yZQrS09PFbX/++adsOktLSxEQUpBMvL29RdAeEhICe/vs2si5c+ciNTWnjqwwlHWmwF4KYhcuXChqskuyXgWpXbs2unXrhi1btuC777JrYWlnIiwsDBMmTFD5cSmopouEMv607E8//RTVq1cv1rqpk561LRKoflVOekz2oWhdK2ukJiXkmUe/mjOSn/vCus8QWLTvgsy0VBEoRxzajczkpGIvt6Kzqu4oAmN5scHZQZilswOSYmJVmqYiiIvIXicTS8VyFSq7oLrlgnieOYyr//2NpPgYGBqbYtTiL2FgrHyH6N7ZIzAys8g3k13Wqlkair/hcTm1+dJ1B/Ps+woztJULIuNTcOphSIEZ7ne718blp2GISyr6jpq6RYRlr6uVta3C7ZbWNogIy5ttV1X9xk1hYGgoap7HvzMDWVlZ4n8SHpr/61PaDJ2qKQS9JCUs+31tVM0ecbFxeeYJOXEW95asQqdDO5AWFQM9KwuEHDuLO3OXlWi5ZSk4NAxtWiiWCtlaW4m/oeHhMDczrVDLZZWbxgTPlAmlQE6+JIAyzBQYy6NyCSlwJi9evBBZUwrsJFR6QBleCWVT6UIlCDdv3hRfknShMgzKBsvr3r27QhBIy7K1tcWiRYvE+lF2lQJhKRguDJWQyE87YsQIDBo0CGl0WNzPr9jrVRgKjml9165dK2rBKJCmx6XyC1VfDwr0d+/ejStXriA8PFzsqGhra+Pp06cKwXNR101ttLSQlSsLm/UqW6ylrXx9tHR1YUa1y5kZCNq6ETomZnAY9w6MatXD86+WFnu5FR1lGDLkMk4kPTVNISOryjQVQdarowQ6Oopffzq6urL78lOvdSdUr++BuKhwXNn/F/ZvXIkJy9fDINcRhcyMDDy4cAKNXusFHd2CM5hlRfvVd2N6hmI2OC0jEzoq9H+laQa0cMZ/t17mWYaEHmLVKA9Ymejjvd9uoCKg7x1p+8rT1dUrsMa9MDZ29pi77Av88u2XOLpvt7itRdsOcKtdF+WZcKfPYe6jS5mvPodUr6yMdbuWaLJqCZ6u+xGhpy7A2NUZjT9bhPoLZuHR598We7lliY5y6Ob6TOu9+uxl5KpXrwjLLQ/cqk59NCZ4puDM2loxk0SBXlRUlMiI6r764rSwsFCYJiIiQvy1ssrek5TIL4tKEwiVX0jZY0KZbamMQZJ7+VTLe/78eaxbtw5Dhw5FXFwcxo0bhy+//FKlAFrZelFQSs+rJOtVGNqZmDVrlqgTpyz0f//9J8vgq/q4tPPi6emJN998E02aNBGHvaiunF6Doq5bSkqKuMhLzciAfgm+tDPiY6Ejd8iV6L467Er3KZ0nLgZZ6WkI3LKevjnFbcE7foHb/M9Eh420sJBiLbeiiw+PhImNYmcCU1sr2X2qTlPWLuzehkeXcuoxxy//TmSDSVJ8rELWOCkuFvZutQtcnqGJqbhYVXOGYy13bJg+HI8vn0HT7v0VpvO+cxUJMZHw6Po6ysOMXnXRt6mT7PrEHy4hKiH7aJeFiR7iU3KCIEtjfVGnXJhO9e1gZ26IPddf5Bs4fzbSA81rWGPyz1cQGqv4eS0Lf23ZhPMnckruVv+wFeavOmrExUbDRO77KTYmGrXqupfo8Vq1f01cqOsGnYDCzNwCbw7qCdtO5TeILjUyCvpWip9D/VeZ0pTI7CNgudWbMwVhF66KgYIk9tFT6BgZosXG1eK2tJjYYi23LFlZmCM6VvF9HBUTk32fpUWFWy6r3DQmeKaaWsogy6Przs7OssA5v/mkmmmaVkJZ3WbNsgd8SFlSylpT9rWo6tevj19+yR6UQCUcffr0EQPjqPyiMLRe8mi9qIWenZ2d2CkoyXoVxMDAABMnThQZ55cvX4ogWaoRV+X1oACbss40QFN6HWmAJmXMi+OLL74Q5R7yZjSth1nNi//jl+T7DGbNcspKiFG9hkiLikB6tPJgL4kG/1GAJTdKPzMxuwxD28Cw2Mut6Pyve8JjYA+F2+q81hrRL4MRExSq8jRlrUWvwWj0Wk6LOAp8KUCmLFrgs4eygXypSYkIC/CFR1flg4WV0dU3gI6OHlJflevkLtlwcW8MG6fs75eyRm3iKEMsiU1KEwFyekYmmrpayQb7Ubu6utXM8g2I5Q1tVR03fSPhF5agNHD+dIQH2taxxTs/X8XzXIMMy0q/YaPRtXfOjoyJmRlq1nWHtrYOnjy4h2pOLuL2pMQEPPfxRq8BQ9TyuOaWlrJBhHGxMaJlXXmJvnMfDr26Ktxm07YFkoJCkBKSU+8uT8fQAMm57kuNiRUZZW1DAyCmeMstS43ru+PMJcV2oLc8H8De1kZcKtpyywMPGFQfjan2piwndXegDDSJiYnBpk2bRP1yQaikokuXLvjmm29kh/eoxpjKESQ0iLBnz56inpcyvpLg4OA8Awtz8/HxEZlnSePGjcXyYnPtyRbU+YLKIQhlXjds2CBKN6g8pSTrpWrpxvHjx7F+/XqRPZZKK1R5XCrPINL2IJ999lmx14Vq12mbyl+meBTesaQg0eeOQ8/GHpZd+8jqmS0790L06SOyaYzqNkTt1T+JrDKJuXASOsYmsOjYXVbGYdN3KFJDgsRAQlWXW9FR0Lvi2RnRt5lc/PV/sKnhgk5TxonrDvVqoeM7o3FuU05dvSrTlDVjc0vRAUO6UBBFXTLqt+0iOm4kxsWIQ/eUodY3MIJ725zOC/vWf4ZTf/wo/g974Ytbx/bK2tJRx47zO7eKv7VztSCkftA+d6/Bo2v5DRSkLDN1yZAuNKaPAuhj94IxuWttWBrrifZ0M3vXQ2JqBo696r5B1o5rjvkDGigsz9bMAK+52ykMFJS3fHgTtH8VOPuHl19Nv4WlFRxdqssu9J1F2eCO3XqKjhuUIaYjdzs2bxK9mDt2zWmjuHbZImzesK5Ij/ffzh2IjsreIaZ2dj9/uwbtu/RAvYaNUV78d+wWvZqpOwYxqV0DruOGwf/3f2TTWLdtgR6XDom+zSTs3BU4vt4d5o2ykxG6piaoM/UtxD7xkgXGqiy3PA0f8DoCg0Lwz76D4rrv8xfYc+AIRg3O2Zm6efce+o55S/RtVudyK1PwrO5LVaUxmWcahEft5ig4bdu2rQh+KUMqDQIsCAWHFAxStw6a5+HDh6I2WR61tBs+fDjq1auH9u3bi6AxKChIdKUoCJVmfPzxxyITS10sqN43OTkZ06dPV+l5UdaWOoHQXwqi6Yv/f//7X4nXSxX0WlJHjWvXrok2dPIKe1waBDhnzhxRqkKvLR0FoNppymgXB82Xe96SlGxILeaoT3O1CVNhP/wNaBkYIubiSYQf3CmbhrLJ+g6O0HpV80aZ4xfrV8Hx7fdgP3oStPX0kRzghxfrV1JxpcrLLU96Rob4xDM7kLdycUS1+rXR8PUuCH3qi439s7ezvokx7OvUgI6+vrge9MgLW8a/j9EbP8WQLxZA38QIl3/bjSNf/CBbrirTVBS93pqNg5vW4Mf3xkJXX1+Ucgz54FORmZbERYbJdgKpG8fDS6ewafY4kXFOjo+FrUsNDJ+3Mk+3jfvnjkHf0Bj12nRCRbPq3/tYNbopTizpgeT0DEQnpOL97TcRl5yuMLAwd5eMQS1dRJB9/F5wnmU2r2GFIa2qi2VtfKuVwn3z/ryFJ0HlO5CMTPlgEb77fCneGdEX+gYG4uQoi1atE5lpCQ3yk7Y3oWD7xMF9op8zWTj9TWhpaeOdOfNFbTNxdHHFgqlvIDMzC4nxcejSpx8mzfgA5Sn+qQ9uzVyEJp8vQYPF74vOGS/+2Ytn63+VTUO3mdR0hbZ+du3usw2/wsDOBp3++1OUaFBP52jPB7jx7odFWm55ql3TDauXLsTn33yPb3/agqTkFAzp2wvvTBgjmyYpORkvXgbJjoDS9WFvTRP/h4SFi8D4wtUbqFHdBT+uXanyclnVo5VFI700CAV6Up9nCurkBxDev39flCBQ2URulMmk4Jv6PFPASNPSFyn1JpZHZQjUZo6yr9R6jkooJDSAjuqspTIFebQ8CpwdHBzEMlUZIEdBJ63LvHnzcOvWLVGmQfXHymqli7tehXn33XfF4EB6bZQp6HHl75cGYe7cuRMdO3YU26ek6/ZokppKVbS0RReMjIR4ZL36oZTdpa8PXUsbpEWEKpRqEF1La2QkJeaZR5XllsR3v3mWeBl2tXP6n0vSU1IRFRAkC7AtnRwQ4f9SYZAQlTvQ7YlRMUhJUH5oXpVpiqPV1bNQt+SEeKSlJMPUykbhu0IWPOvoii4cEvq6TIiOhKGpGXT1sncscoun0pysLLFMddiw5wHUzcxQF0b6Okrrku3NDZGemYnI+JyOQDam+mLAYViuTh3EQFcb9hbKu3UERyeLAYnFsWPOa1C3hLg4JCcnwdrWLs/2puCZSvyoCweJi4lGfK7xGcTKxlbhDIL0nqAzEFJttd6rnc2S8G6hpu4s2towrGaPtOgYZCQqlhZRPbOhgx0SA4IUBzdracHA3lYE0JnJKUVebkn0vXtALcuhBFNoeAQszMxgbKz4W0nBclh4BByrOYg2dbTtXrzMPkumPBqf4+hgr/JyS0LPvgbKis8H2UcF1anWNztQFWlc8KxJpOCZTiRSHujkLnSSGarXLqz8pTyoLXiuZNQRPFdGpRE8VwalETxXBqURPFcGagueKxl1Bc+VDQfPlZPGlG1UNtT3mUpN8kPdOcrzsenELNR7uUGDBqI8gzHGGGOVV0VoKagpOHguJyYmJuLMewXdT4PkqKShPB6bykOo1pou5dKDmTHGGGNqU5UH+KkbB8/lhPohS2fry0+PHj3K7bFpoB9jjDHGGFPEwTNjjDHGmIaT7ybDSoaDZ8YYY4wxViYyMjJEy14qES0MnRMjNTWn8w+htrXUGa0kyy0p3g1hjDHGGNNw5X2SlOTkZEyePFkEt1ZWVuLcGjdu3ChwnmHDhokzQdOZmqXLzJkzS7zckuLgmTHGGGNMw5V38Dxv3jxxbgc67wVllF977TX07dtXtMUtyOzZs8XZiqULnaRNHcstCQ6eGWOMMcZYqUlISMCWLVuwaNEi1KlTR5xQbc2aNYiPj8dff/1V6Pw0nbLTkpR0ucXFwTNjjDHGmIajs7+q+6IqT09PJCUliaywfOevFi1a4OrVqwXO+/XXX8POzk7UOdN5J54/f66W5ZYEB8+MMcYYY6zIUlJSRKmE/IVuyy0sLEz8pSBYHl2X7svvTMu3bt0SAfK9e/fEtP369ZM9RnGXW1IcPDPGGGOMabjSqHn+4osvYGFhoXCh2/I8tpaWrCOGvPT09AJb6FE5RuPGjcX/NWvWFCUaDx48wNmzZ0u03JLiVnWMMcYYYxquNM4wuHj+Ynz44Yd5Wsnl5uTkJP6GhITI/peuN2/eXOXHq1WrljjrsY+Pj1qXW1SceWaMMcYYY0VmYGAAc3NzhYuy4LlJkyawtLTEiRMnZLdR54zbt2+jU6dOsttiYmIQFxcn/lc2QJBKOCjLXKNGjSItV90488wYY4wxpuG0SyHzrCp9fX3Mnz8fn3/+ORo2bChKMOg6ZZJHjRolm27w4MEiGN67dy8ePXqETz75BLNmzULt2rXx8OFDzJkzB61atUKvXr2KtFx14+CZMcYYY4yVqsWLF0NXVxcLFiwQAwvbt28vMsbymWqqmabsNaFgmE5+Qq3nKJCuVq0axo4dK4JjKt0oynLVTStLWV6csUrg0aRBqIq++80TVVGrq9kDRKqaDXseoCraMSen9VRV4t2iA6qivncPoCrSs88uPygLoV/NUfsy7ed9h6qIM8+MMcYYYxquNAYMVlX8SjLGGGOMMaYizjwzxhhjjGk4zjyrD2eeGWOMMcYYUxFnnhljjDHGNJxWKZ5xr6rh4JkxxhhjTMNpy7V3YyXDuyGMMcYYY4ypiDPPjDHGGGMajgcMqg9nnhljjDHGGFMRZ55ZpeXauw2qolbTN6AqutG2C6qiy4mPURXpJkWiKgo6dxpVUa8/H6IqOvNB2Z1hkDPP6sPBM2OMMcaYhuNuG+rDZRuMMcYYY4ypiDPPjDHGGGMajss21Iczz4wxxhhjjKmIM8+MMcYYYxqOM8/qw8EzY4wxxpiG4wGD6sNlG4wxxhhjjKmIM8+MMcYYYxpOS1unvFdBY3DwzBhjjDGm6Th4Vhsu22CMMcYYY0xFnHlmjDHGGNN02pwvVRd+JRljjDHGGFMRZ54ZY4wxxjSclg4PGFQXDp4ZY4wxxjQdDxhUGy7bYIwxxhhjTEWceWaMMcYY03SceVYbzjwzxhhjjDGmIs48M8YYY4xpOC1uVac2HDwzxhhjjGk6LttQGy7bYIwxxhhjTEWceWaMMcYY03SceVYbzjxXQCEhIQgLC0NlEBwcjPDw8PJeDcYYY4yxMsGZ5wpo7ty5MDQ0xK+//oqKbtq0aXBxccHGjRtRWfmHx+JZSBSsTAzRzNUOOioOqsjMzMLxh/7Q09FG9waualtuWUtPTcWLJ55IS06GY+36MLO2LXSepLhYBPk8hpaWFuxca8HU0kZ2X2pyEp7dvKh0vmo168HGKe9rVZbMHezg3r09Qp/5wf+Gp0rz2NetCReP+ogNjYD3xRvIysws1jQVQUpKCm7dvInEpEQ0adwE9g4OapknKysL169fEzv+vXr1hr6+PioCv+cv8NTLB9ZWlmju0Rg6KpxlTZV5Hjx+gsCgYJiZmsKjUUMYGxuhIkpLTYHX/btISU5CjXoNYWlrV+g8kaHBCPD1gpGxCVzr1oeBYc5zo+XcvXxO6XyudeqjWnU3VAT6OlrwcLaEkb4OHgXFIjwhVaX57M0MUM/eFJEJqXgYHKf0/jp2pkhISceTkDgkp1fMz7kyPGBQfTh4ZlXa+uO38Pe1J2juZg+fsBhYGhvgx4k9YWFsUOi8Wy/cxy9nPWFpbJgneC7JcstSZFAAdq5ZBD1DI5hYWOHQpjXoNmE6mnbrl+88Z//ejMdXzsDOpQbSUpIR6P0YbQeMRoehE8T96Wmp8PW8oTBPfGQ4Xjz2xOA5S8steLZwtMeob5eiZvsWMDAxxvUd+1QKnod8sRBdZ70Br/PX4diwDuLDo/BdrwlIjIop0jQVgb+/n9jhNTYygq2tLT7+6CPMn78Aw4YPL9E8e/f+iy2bt0BHRxt+fn7o0KFjhQiev/nhZ/y161+0aOoBHz9/WFqY45fvvoKFhXmx54mJjcO09xcgJCwMTRs3xMugYLwMDMbalUvRoU0rVCQhL59jw8cfwMDICOaWNtj25DOMeHc2Or4+SOn08bEx+OO7LxDo5w2nGrURHR6K6IgwvDl3KRo0by2mSUtJwcObVxXmo+me3b+Dd5esqhDBs4ulEb4a7oGktAwRBC95vT42nvHCwfvB+c6jBWBW19ro18gRdwKioa+rjawsYPG+e0jLyIK5oS4W9HZHbVsTeIcnwN7UALamBlh15DFuPo9CpcBlG2pT6YPn+Ph4UTpATExM4OjoqHB/YmIiAgMDUadOHYXbk5OTERAQgJo1a8qyCrGxsUhLS4ONjY34Pzo6Gq6uqv/QU3aGSi7s7OxgZKSYhYiKikJCQoLI0tKyqdShVq1asvtDQ0PF+tMlP7TONB89x9yZEHqOBgYGYt1pWenp6XBycsp3WREREWJ5zs7OedYzLi5OPG/KIMXEZP/403KtrKwKfQ1evHgBMzMzWFpaym6j10RbW1u8Lqo+n7JwzScI2y4+wJa3+6Cpqz0SUtIw8edD2HDyNj4e2K7Aee++CMO/t55hZGt3HH/gr7bllrWjm7+GjbMrhs9dKbISd08fwsnt36NGk5awsFWekXSoUQedRrwF7Vfb7PHVs/hv4yo06NAdVg5OMDazwIDpixTmOfn7D4gIfI7azcrv+VPAfGv3EWwe/z7mnd+p0jzu3dqj94Kp+Oq1EfC5fAsGpiZYfH2/CJZ3TFui8jQVxaeffiq+dzZs2Cg+k3t278bq1V+gXfv2+X5fqDKPjo4uNn7/vfj8z5o5AxXB1Ru3sPWP/2H7pvVo5tEYCQmJGPP2NHz74y9Ytmhusef5Z88+BAQG4tCuP0XWmSxcthJfrf8Re/7YjIrkz/Vr4OhaA9OXrRXb7uKR/fhn0zeo36INbOyrKc1Sd+g9AB5tX5PdtvOnb7H1y2VY/cd/4jNvamGJt+YtVZiPpgkO8EeTNh1REczvVQ/+EYlYtPcesgD0b1wNc7rVxQ3/KITEpSidZ0IbV/Rq4ICpO27ieVSSuM3D2QK62loieDbQ1cah+8G45BMhm4eC7aX9GmDoT5eQSQ/EqoyKeRy5CM6ePYvXX39dXJo1awZra2v88ssvsvsjIyPh7u6Oy5cvK8z3ww8/oFu3buKwc0ZGBqZOnSoCxAYNGohA+4MPPkD37t1VWgc6XPnRRx+J4LJVq1bi7/vvvy+WK/n+++/FOo4dOxZubm7o3bu3uJ2CR1oPCmLpdnrMoKCgPEH5jBkzRNanTZs2sLCwwKJFi5Apd1j4jTfewJQpU8Tje3h4YP78+QWu89GjR9GwYUMRwMp78803sXjxYvH/hg0bZK9tjRo1xOt48aLyw/GSgQMH5ik3mTNnjmyZqj6fsnD4ni8aOduIAJeYGOhhSIs6OHrPT5Rk5CcuKRUf776ApYPaw8JIX23LLWuxEaEIeHIfLXoPkR3Oa9ypN/QMDPHkqvLDsqR+2y6ywJlY2mfvsKanKv9Rokz0w0snxbJ1dMtvfz3Uyw83/zmAzPR0ledpM34I/K97iqCYpMQn4OKv/0PrsQPFd4eq01QElGS4fesWxo4dJwIpMnDQILGjf+L48RLNQ5/7oiQaysLBoyfQuIG7CIKJiYkxhg/qj8PHT+X7XaPKPOkZGTA1NZUFzsTRwUHh+74iiAoLgfeDu+g6cIRs27Xr2Q/6hoa4feGU0nmsbO0VAmfi0a4TEuJiRQZambS0VFw/cwztevQt18+3xM7UAE1dLLH7zksROJMjD0OQnJ6BrvWUl6xQgDyqpQv23H4pC5yJ58sYJKVlb/ew+FSFwJlc9I6AhZGeyEBXmsyzui9VVKUPnvv37w8vLy9xoQznrl27RLDm6Zl9OJYyvV26dMGff/6pMB9dHzcu+weBgr2dO3fi5s2bImv7xx9/4O+//1Z5HbZv3y4CzdOnT4v5r169it9//10EzPIePHggAlYK6Gl9CQXplD2ndadAevDgwTh1SvGLjaZ5+PAhfHx8RIaZlkPrm3v5+/fvx5o1a8QPXu7nm9uQIUPEj8GBAwcUstFHjhzBhAnZh99XrFghe20pIz1p0iSMGTMmT8BdVKo+n9LmFRKN2vY5GXJSx8EKCalpCIqJz3e+z/67jO4NXdGmlqNal1vWwl/4ib+2LjVkt9GPn5WjC8IDfAucN+Llczy4eALXD+/CoZ/WolXfEbCrXlPptF43LyE5IR4eXfuisnFu4o7A+08Ubnt57wmMzM1g7eas8jQVwbNnz8Rf+aNwenp6cKtRQ3afOuapKJ55+6JOLcX3ZN3aNZFARyODQ4o9z9gRQ2BvZ4u5Hy3H7v0HseGnLThy8jSWzJ2NiuSln4/46+RWS+Hz7eDsisBX96ni0e1rMDY1g6VtdjIgN8/L55EYH4cOvQeiIqhlm3301jc8QXZbRmYWXkQmoear+5TNY2aoh+v+UaKeuXMdW/G3MK3drBCbnIawfLLZTHNV+uBZPptJtXaU/WjSpAlOnDghu2/8+PEiGKZSBvL48WPcunVLFiRSpvrdd98VGVvSrl07EVirirLYb7/9Ntq2bSuuN23aVGRWcweDlGldsmSJLBtF5RE7duwQh0UpY05mz56t8ENF01BwP3PmTFGC4uvrKzIcw4YNEzsK8ij706NHD5XW2djYWATQtKMg+eeff0TWvFevXgrTUpkJvbY0PWXFKfAtrqI8n9IWn5IGc0PFjIGUSY5PTlM6z67rT/E8Ig6zujdT63LLQ0pS9o+LoYmZwu1GJmZIScz54VEmJiwYvnevw/vWFaQmJ8LGqXq+03qeOQzX+h6wqlZxAklVGVqY5albToiMFn+NXtXAqjJNRUA76cTcXHGdLMwtZPepY56KIj4hAeZmiu9ti1fPIz4+odjzGBkaoUG9Orj/8DHOX7qKcxcvw9HBHna2hQ+0LUvJidnbx8hU8fkYm5kjqZDPt+Sp5y2c2vs3Br81TZa9zu3Ssf9Qt0lz2Dvn/x1QlkwMdGTfw/IoyDXVV54ZtzTSE38p+/zR6/VF+cbaYU3w9XAPUa6hTDMXC4xs4YKfL/jKMtwVnZaOjtovVVX5H2MpoefPn2Py5MmifIOCUwoKKcDr2DGn9mrEiBGYNWuWKFWgTDUFjBQoU5BNvL29RdAqr1GjRnkywPmhDAyVTMijEhLKAlNgKNXzUumDfG0vZV4p+9u4cfYhQkKBtfz1J0+eiDrsBQsW5KkLrl5d8cuqdu3aKAraeRg0aJDIKlPQTNlqyizrvjr0Rs+fXjcKnKlemTJOtL5UK96iRYsiPVZxnk/unSO6yEtPS4eBXvHfwga6OkhMVfyCpfpkcZ9e3i+FuORUfH30Bsa2a4Bjr+qcn4ZEITktHQfv+ojBgU6WpkVeblkI8n6MyOAA2XX31p2hq6cv646hLzeanoJhU6uCA4FazdqIC6HBgbvWLoG1kytc6jVSmC4mPAT+D+/kqYGuLNKSUkQNszxDs+zraUnJKk9T1u7fuwf/5zm1+D179oKhQfYOHe200vekJCExAfb2yrOKxZmnojAw0EdiUs4heEIZZOm+4s6z4afNOHP+Enb/sRmmr8aorFz7LWbOW4wDf28vl/Ebfk8eIjTwhex6845doadvIOuOYWiUs+2SkxJhaVN4oO//9BF+WrkY3QePwmuvD1Y6TURoMJ7cvZmnBrqs1HcwQ3WrnO+us8/CkPqq+4WRno6s5IIY6+sgPF55x43UjJzpJv2ePdiZyjF+e6MVxraqjt+uKI5rcXcww8pBjbHzVgAO3FMss6zQKmjHp8qo0r+SFNzRIDsqeaASACoxaN26tUJNG9XUUtAslTJQtlfKOhNqC5c7MCtKaQLV/+WePykpSYw2l/8izf2lKg0qzP1Y8tcpYCV79uyRlVBIFyoTkVfUL+2ePXuKoJlKJihAvnTpkux1oaCfdjroOmWe/f39RcaeHqOg2mRlNZ5Sxr+oz0feF198Ibaj/OWrffnX5arC2coUQTGKGRi6rqOlBUcL5YfsujVwRXBMAi55BYoLZaFTMzLE/+FxScVebmkLf+kvglzpkpGeBotXtcqx4YqHsGMjwmR1zKqo6dEKhqZmePn0QZ777p09CkMTU9RtVTEGEhVVuM/zPKUX1m4uyEhPR4T/S5WnKWuUELh08aLskpqaCmcXF3FfUFCgwrTBQcFwcc6+L7fizFNRuDg55SnPoOvUEcSpWrViz3PrrifatW4pC5xJz66dEPAyEMGh5dOfP+i5r+iAIV3S09JgW81J1nYudy20dF9+/J89xoZPPkDHPgMxZFL+A0AvHz8oSjqaduiC8lDDxhhtaljLLtQ2NDAm+/fTwdxQYVoHM0MExijuGEleRmffLl/THJOUhvuBsajnoJi5d3cwxVfDmuDgvSCRdWZVU6XPPFMJAQ02kw4rUhb1zp07IvMrj4JAKt84duyYCATlyzKozOLcuXOidENy4cIFldeBHuvMmTOiVENCgSAttyCUiaYgkB67Xr16sgzP9evXZV0waAAjBbi7d+9G8+bN8wSlUpa4OCgQpkwz7VTQzgetA+14EMre02s5atQo2WPQesoHwspQ9l/qfkIo0L537x46depUoudDAw4//PBDxen3fo2S6FTPGWsOXUdkfBKsTbN3ZI7e90PrmtVkGeKwuERc8wlGF3cXmBnqY9VwxcE0v571xK4bzxRuV2W5Za1J5z7iIo9qlM1t7PHk2jk41Wkgbnv57KEIpms1zc4qEwq2aRChi3tjUbtMdZN0XRIVEihuN7dRHIxDfY4fnD+GRh17yrLcFZ15NTvU79ERnvtPIDkuHvcOnsKYjStgZmeDuLDsH9ZWYwbiyalLSH+1w6zKNGVt8JAh4iKPBrlVc3QU34FNmmSXqN29e1cExq+9+nySS5cuih375s1boG7duirNUxF17tAWn69bj4jIKNhYZ3cKOnLiFNq0bCHLIoeFR+DKjVvo1qmDCIZVmaeagz28fbPHC0i8fHyhq6MD21fld2Wtfa/+4iKPWs1Z2Tng1vlTqOmefUTI59F9EUw3bt1BNh0F2zSIsE6j7N+r516PseHj90XXjWGTZ+X7mPTdfuXEIbTp/jr0yunzTQMB6SLPJzwBwbHJYnDgo1d9mhs6mqOahSGu+EYq1Csnp2XgXmCsGAz4NCQObtY5GXpKA1FW+/aL7BIsQv2fvxrmIbpu/Hhe9brxCqMKD/BTt0ofPFOdMdUc00A8ypZ+8sknoq42t379+olWbhQgd+3aVaFFGwXf1P2CgmDqdkGD6GjgHAW3qli6dKkIDleuXCky3CdPnhQDBg8dOlTgfJSZpq4YFBhSEE3dNlavXi1a5MlPQ+UflGGn9aflU1BLy6b7KCNbErRDsX79elFCIl96Qi2oqIyC6rHnzZsn7qeBfoV1D+jbt69YJ3qNqQXdjz/+KLJgUvBc3OdD09JFXkIJSjZI/6a1seemF6b/fhKDm9fG/YBw3H0ehs1v5wSZT4Oj8Mm/F7F75iCYGuqrbbkVAW3L7hOmY//GlSLQpVKNm0f/RYP23eAsV35xcc920baOgufE2Cjs+24FardoL7LTcZFh8DxzBK4NmsK9TWeF5fvdvyU6elSUgYLUIYSCWmJqawX7ejVFp4yk6FgRABOXpg3x9h/fYln9Hkh+Eo8r2/fgtXfHYs7xP3Bpyz+o0bYZandsiXWdRsmWq8o0FWV7L5i/AAsWzEdWZpYou/jzzz/Qt28/hWTDph9/hKOTkwieVZ3n0aNH8PHxxrOn2YMIT5w4Lso8WrRomad9aFkZ2K+PGNA3Zc48DB3QF54PHuG2531s+3G9bJonz7yx5NPPse+vbSJ4VmWeyRPH4c1ps/H+oqXo2K616PG8Y+ceTH5jXL7lIOWBtt3IKXPw6+pPREcoKtU4vW8nWnXphVoNsksWycEdm2Fj7yiCZyrD2PjJh6Lrhkuturh2+qhsuoYt2oo2dZLHt6+LLDYF2RUN9XRe3r8htLW0EB6fghHNXXD8UQgeBMXKppnUvoYIsil4JhvOeGH1kCbQ1dbG86hEdKxlI0o3/rqRXQ7jYGaAtcM8EBafAq+wePSqn1O2dM0vEjHJqnfxqepiY2NFnEZxhqodiah1LiUAcrcBJlR1QInHPMmCfI4wqYNWFn2qKjHqXEHBJ7Wio/INGnhG5QXUl5Tax8lbuHChyHhSkEsZV3l0+zfffCNKJiggpwwqlRZQ1lQVlGleu3atKH+gDh/Uqo4CdgkF+DQNlUjk3nunYJIG61F/ZAomKXNLb5DPP/9cNh3Va9MyqLyB3nADBgzA9OnTZScioBZzVMNNgW5RUas86s96/Phx0fdaQs/9448/Fo9JP4BUF047J+vWrRMlH4TWgd6gy5YtE9epnpnWm3ZAaHsMHTpUBM/0Ri7K81FFwl8rUVJUr/zvzWeidtnaxBCDm9eBq425QvBMPZvf790CdmY5WQnJmccvRF/nBf3aFGm5JfFX7YlQpyCfJ3h06ZQ44Ymze2M06tBD4UxUFDwbmVmiRa/sEyskxkaLThuRgS9gZGou5qFMde4vwXvnjoquHF3H5hzRKYkbbUt2aFhXXx8TN6/Jc3vUiyDsXfKl+N+5SX3Rs3n3/M8RG5x9CF7P0EAExy5NGyAuNAIXN/+NMG/FGkhVpimurxMfQ50e3L+Pw4cPix+b5i1aiO8c+cFgFDzT0aHRct+Rhc1D9104n7eMaty48WgkN4ajKHSTcrKExZWcnILd+w+IINnaygrDBvaDa/WcxMkTL2/R13nurGmws7VRaR4SHBKK/YePITA4WAwwpJOjUCmHOpyLyC5tUxeqXb5+9jhSkpJQp5EHWnfro7DtDv65WQTFXQYMR/ALPxz5e7vS5fQfPxl2js4KJRvBL/wx9G319PVeeaD4A9GVobrknvXtRe0ztZyj4Fk+2HmrnRuik9Kw925OSZKrlRH6NKwGcyNdBEQliQxzXEp2UOxqbYyJbZS3Y9x62U9WLlJUZz4ou5KXtKt71b5MvbaKR7gKQoOMKVb577//xM41nQ9i27ZtoiOaMlQOS8k9utD/ND8dHf/pp59Qv3592XQUj1C3NGo3LKHGB5S8Ky2VPnhWB+klkP/xp+CaAlsKalnFpI7guTJSd/BcWZQ0eK6s1B08VxbqCJ4rI3UHz5WFuoPnyqJMg+fr+9W+TL3Wys9WqQw1d6CSWCoBpSYE1H1s06ZNIolGCcvcKBlJSTZqP0zVAhQ809Fyam/79OlT2U4gBc90jgs6cl9WKn3ZhjrQ4D6q7aXMKu25UL9kaptGmVhpA+ZX60uHMXO3caoIKANMtd35oRIRafAeY4wxxlhpiYuLE53OKBh2cMg+ey0dsd64caNo4pC74xmh0tkvv8w+KkjoCDYdXe/cubMoJZVv6yt1AqOgPHeJZ2ng4PlVz+P33ntPnOiEWt9RyzdqfSe1u5s4cWKes/5JqEZ35MiRqGhofenMgPmhNnQV7YxgjDHGGCsdWuU4YNDT01N0/Wnfvr3stuyByc1x40Z2e0BVSF2/ctczf/XVV+KEdVTKS2OsqGRDasRQGjh4fqVPnz7iosz58+dR2VBgLJ3FkDHGGGNM3VKUnINB2QB/6uhF5OuSCZVrSPcVhjLLNA5r6tSpIgstoRO4/fbbb2K8GS2LuqvRuAzqDCTfn16dKn2fZ8YYY4wxVgjKPKv58oWSczAo65ol1SdTSak8ykarco4KCorpaDp1VqOmBfKoexcFzlIwTgMKKXlIFQSlhTPPjDHGGGOarhTOMLhYyTkYlNUcS2cQpm5iUqArXW/TRrFbVW4RERFiUCBlramTF53YriC0fArICxr3VVKceWaMMcYYY0VmYGAgmibIX5QFz40bNxbBL7WplR+bRSe1o/NCSF6+fKlwojWqYabAmTLadD4IaoErj87vkduVK1fE7fIDCtWNM8+MMcYYYxpOS4XyiNKiq6srzhNB59+gLhp0TglqVdeoUSNxfg4JtaKj/s979+4VJ1Ohfs10/g2qaZZv3ECt62jAIZVnzJw5U1yo2QOddZrO6UEdOXr06FF6z6fUlswYY4wxxhgg+jVTwEvt6igwps4bdNI6+ba5FBRL7X+pHV1MTIz4f/jw4Qqv4Z9//ilOaOfu7o4VK1aIE6nQmU6pCwedDXnGjBkqn72wODh4ZowxxhjTdOXYqk4yZcoUcckPBcWSZs2aqdQ1rEOHDuJSljh4ZowxxhjTdBUgeNYUPGCQMcYYY4wxFXHmmTHGGGNMw2mVQqu6qopfScYYY4wxxlTEmWfGGGOMMU3HNc9qw8EzY4wxxpim0+JiA3XhV5IxxhhjjDEVceaZMcYYY0zTceZZbTh4ZowxxhjTcFkcPKsNl20wxhhjjDGmIs48M8YYY4xpOs48qw1nnhljjDHGGFMRZ54ZY4wxxjSdllZ5r4HG4OCZMcYYY0zT8em51YbLNhhjjDHGGFMRZ54ZY4wxxjQct6pTHw6eWaU1M6sfqqLbex6gKrqc+BhV0YfG9VEVLY+umu/zD345g6ro2JKu5b0KjKmMg2fGGGOMMU3HrerUhoNnxhhjjDFNx8Gz2vCAQcYYY4wxxlTEmWfGGGOMMU3HmWe14cwzY4wxxhhjKuLMM2OMMcaYhuNWderDwTNjjDHGmKbjsg214bINxhhjjDHGVMSZZ8YYY4wxTaelVd5roDE488wYY4wxxpiKOPPMGGOMMabpuOZZbTh4ZowxxhjTcNxtQ324bIMxxhhjjDEVceaZMcYYY0zTaXO+VF34lWSMMcYYY0xFnHlmjDHGGNN0PGBQbTh4ZowxxhjTdBw8qw2XbTDGGGOMMaYizjwzxhhjjGk6zjyrDWeeGWOMMcYYUxFnnhljjDHGNByfJEV9OHhmjDHGGNN0XLahNlW2bOPHH3/E5s2bUdF9++232L59OyqSirhOjDHGGGNlocpmni9evAhDQ0NMnjwZFdmZM2fg4uKCN954A5q6TuvWrYODgwMmTJiA8tS9ri3a17CGoa4OnobFY49nIBJSM/KdfnrHGqhhbaxw292XsdhxK0B23dXKCD3r2cHVyhhJaRnwDIzBiSdhSMvMQkUxqp0r+jd3hrG+Dm75ReGH408Rk5imdFp7cwNsntJO6X2bTjzDwTuB4v/6TuYY095N/I1PTsf5J6H465I/UtMzUVH88/ffOHjwIJKSEtG8eQtMnzEDlpaWJZonJSUFx44dw7//7kF4WBj++HMHzM3NUd4cG9ZF52nj0bBPZ1z7cx8Orviu0Hlsa1bHoJXz4OxRH3GhETizYRvu7D1a5Gkqin93/YPjhw+JbefRrDnenjIdFoVs78LmiY6Owl/bf4PnnTtISU5GdbcaGD1+Aho2boKKYnR7Nwygz7eBDm75RmLjsYI/31untVd634/Hn+HA7Zfif1szA7zRuSZa1rRGVhbwICAGW854IyQmGRXFXtp2R15tu6bNMWnqdFhYWJZoHtref/62Bfc97yI9PR1t2rXHhEnvwMjICJWCllZ5r4HGqLKZ58rigw8+wJtvvglNdvr0ady4caNc16FfAweMbOaME0/D8PuN5yLondutToHfNVZG+vAMjMW6096yy4GHwbL7nSwMMamNqwjEt1z1x9HHoehe1w7vtq+BimJSl1qY87o7/rroh8/3PYC7oxl+mNQa2vk874j4VMzYel3hsv9WAFysjXHneZSYpra9KZYOa4LbflFYvvsefr/gi9Ht3LByVFNUFFu3bsH69d9h7LixWLR4CZ48eYKZM2YgIyOjRPMsXLgAV69cRvv27fHixYsCl1dWarVvgXf/+R5hXv5IS06BmZ11ofMYW5pj3vmd0NbRwe+TF+Lu3mN4d+f3aDqoV5GmqSh2bPsNP3+/AcNHj8X78xfB6+lTzJ8zq8Dto8o8S+Z+gNs3bmDKzPewZPlnsLSyxPvTp+K5vx8qgre71sL7fd3x50VfrPr3PtydzLFpcpsCP9/TNl9TuOy78erz7R8pm27t+OYIjkoWy/zm0GO42Zpg2/T2MNCtGCHFju3Z227YqLF4f94ieD17igWFbe9C5qGdo/envYunTx5jxpwPMXvuAty/54nlixeU4TPTDAEBAXjw4AHS0tLUOk9xlltcVSbzTNnSf/75B2ZmZujbt2+e+7du3YqTJ0+K/21sbNCuXTuMGTMGWq+ip0OHDuHEiRP4+uuv88wXGBiIjz76CNHR0eL648eP4eTkhPHjx6NOnTqFrltB892+fRvW1tZo3bq1uL569WrUrFkTFhYWYn3ogz1q1CjxYy3Pz89PlFbQD3iTJk0wZcoUkWmX+Pr6Ytu2beLNVqtWLUyaNAmOjo7Fem0Le+0Keo4///wz7ty5A29vb1nmee3atcVel+LQ1dbCgEYO2HcvCFf9swPAny754avBjdHMyQK3X8bkOy9lpkPjU5TeFxybjE+PPpFdfx6VJB5rVqda2Hb9eYFZ7bKgp6ONt7vWxk8nvXDEM0jctuTvuzi8sBs6N7DHmYeheebJyMzCi4hEhds61LXDVe8IvIxMEtf9whMwbuNF2f2PA2Ohq6ONrye0wKq99/PNepWV1NRUbNm8BVOmTsXrr2d/F3z++efo378fzp07h27duhV7nrVrv4Kenp44slVR+F33xIrGvcX/rcYOUmmeTlPHQ9fQAFsnfoCMtDT4XbuD6s0bof/y93F3/3GVp6kIaNv9uW0L3npnCnr07iNu+3jFKowZMgCXL5zHa126FmuepKQkPKDg6fM1aN6ylZhmzryF+O/ff3Hv7h24utUo98/35G518OOJZzhyN/vzveivOzi6uDu6NHDA6Ychqn2+3e1w1SscAa8+3+Sdn6+KaSXRBx5i79wuqOtohvsv8v++LAvStntTbtt99OkqjB1a+PYuaJ47t27Cz9cH/3x3GPYODtnTLP8Mowb1g+ed2+LIRIVXzjXPUVFRGDFiBK5cuSLimuTkZPz222/o379/ieYpznJLqmLsJpayXbt2oVevXuLQCgVuM2fOxNGjiocWGzVqhNdff11cXF1dsXTpUoWSDnd3d1Hre/fuXdltFLh+8sknMDU1RWZmJjp37iwO6bZs2RI6OjoYOXIkvLy8Cly3wuajoP/atWuy6SlgnjNnjgjia9euLdahU6dOuH79ukImt3Hjxrh37x6aN28uAlNapoR+7Fu1aoXIyEi0adNGBNL0/J89e1as17ew166g50iBvZ2dHdzc3GTLMDExQVlyszKCsb4u7gXFym4LT0jFy+gk1HcwLXDeDjWtsapfA8zvXge93e0VMjoFVWbQoc7yRiUV5kZ6uPQ0THZbYFQSvEPi0KqWjUrLoIxTi5rW2HPtuew2+R9VifSyZFaAJ047cPHxcejQoYPsNidnZ7ETeTOfIyCqzkOBc0WTmZ5e5HnqdW2Hp6cvi6BYcv/Qabg2bwQjC3OVp6kInj2hbReP1u1yEgyOTk6oUbMW7ty8Uex56PfEvUFDXL18URzCJ5cvnoeOjjYae5T/UZYGzq8+308UP99ewXFoXbvwow/S55tKM3Zfe6Fwu/xnnHIkPRpVQ0R8CvzCElDeaNsl5Lftbt0o9jyJidnPzdTMTDaNqVn2+/z2jZzf34rebUPdl6Kg2Cs8PBxBQUEisUdH1kePHi2ul2Se4iy3pDQ+80yB24IFC0SQS0EdoUwtZW/lURBJF8mwYcNEcPrpp5+ievXq4n/KLm3ZsgXffZddL0gBeFhYmMiYUgBKwWpISAjs7e3F/XPnzhV7tAUpznyUlT1y5Ai0tbPfuA8fPsTvv/8ustNZWVkicJ04caIYFCmh5UveeecdLF++HO+99564Tllp+vKn2/78888ivLqqvXYFPUfKPjs7O4u/5VXzbG2sL/7GJCtmROm6dJ8yYQkpOOcTjueRSXC2NMLIZk6oZ2eCjRd8lU5PWeeBjarhflAsEtPK/3B+NcvsIxHhcYqZc7ruYJ5zlKIgQ1u5IDI+BaeUZLHkM2Dvdq+Ny0/DEJdU9EBO3UKCs0trbGxsFW63tbVV+JyUdJ7KzKq6owiM5cUGZwdhls4OSIqJVWmaiiD01faxtlHcIaTroaGhJZpn7XcbsWTeBxjYqxsMDY2QnpaGNd9ugFsNxd+X8lDNooDPt4VqNbrDWlcXn++TD3LK0SRDW1fHO91qw9JEH7FJaXjnpytifEN5CwvNf9uF5bO9VZmnsUczcfT2z982453ps8RvLf1PQl/Nz/IXExODnTt3ikYN0jgQigPWrFmDv/76Cx9++GGx5inOctVB44Nn2guh4I0COvngUz6DRCiDu3v3bpH2pz0YCropOH369KkIAKWgkwJOKivQ19cXgfSgQYNEqQJlIeiHdNGiRWIaDw8PcVthAwloXYo6X5cuXWSBM6lXrx5evsweyEHrS8/37bffVpiHBuQRyi7Thcosbt68Kb4A6EKZtcRExcN1qirstSvOcyxLUnlJ7oxpemaWQulJbr9e8ZdlkJ9HJyE6KQ0Le9SFm1Uw/KOSFB8DELXOZoa6+OasNyoC7VfPLT1D8XmnZWRCJ7+iSDk0zYAWzvjv1ss8y5DQQ6wa5QErE32891v51rVLMrOyBy3q6ip+/enq6SEzM0Nt81RmWtrayEhTDITSU7N3LqnGWdVpKoKsYmw7VedZ/dlypKWmYuWar2Bmbo5D/+3HyqUf4ftftsLR2RnlSevVZ5i+x+SlZ2TKPvuFfb4HtnTG/pvKP98n7gXhpm8E7M0N8W73Olg3oaUo10pIKd8Amn5/ivz5VmEeO3t7LFu1Bt9+tRr79uwSt7Vt3xG169QVv6GVQjmWbXh6eooknVSCSgwMDNC0aVPcunWr2PMUZ7nqoPHBc0REhPhrZWWlcDvVxcijFD9tBBqcR6UEdPiVaqTj4uJk01AAPmvWLOzbt09kof/77z/s3btX3GdsbIzz58+LzhFDhw4V840bNw5ffvllgUFiceaTr10mFKhKgxqotphQKYQyVKpBqIxCygKT3r17i/KT4ijstSvuayOPuhjQRV5GWip09PLPDCsztIkj2tXIeS98duwJ4l592Zvq6yIpLSfjb2qgK+qU85P7+9IrLF78dbYwUgie6WfqnfZuIiu9+uQzEWSXtRm96qJvUyfZ9Yk/XEJUQvZztTDRQ7zcD56lsb6oUy5Mp/p2sDM3xJ7riod0JfT7/NlIDzSvYY3JP19BaKzy2vDS9MMP3+PI4cOy69u2/w5Ly+ztTxkLGgMhiY6KRv369ZUupzjzVGbx4ZEwsVHsTGBqayW7T9Vpytrmn37EyaM52/uHLdtk3RJiY2Jgapqz7WKio1HX3V3pclSZhzouXDp/Dr/8vgP13LPfA/XqN8CNq1ew+++/MOvDeSgrM3vXQ79mOZ/v8RsvIvrV59vSWE8hI0yZYlU+353r22d/vuVKsuTFJaeLy/PwRNx/cQMXlvdG32aO2HVV+fdBadjy0484cUxue2/eJuuGonTb1ctne6s4T/vXOokLdd3Q1tKGuYUFBvXujk6vklNVUYqS32YKXumiLBajZKM8SqxJ9+WmyjzFWa46aHzwTDW4xN/fX5QHyA+oa9asmSygpMwpDc6TbqNDc7lHbNKbgcohKONMmV4KPinolNCP6C+//CL+pzKFPn36iJZuCxcuLHAdizufMlKWnLLLNWrUyPd+KpOgrHlJqfraFfQcC8ruSr744gtRBiKv6bApaDZ8apHWl7ppXPTN+WGnQXvPoxJF1rmOnQnCXv3gGOpqo7qlEc56hau8bBuT7EBeviSDntnkdm5o6GAmAueQXIdQywq1iaMMsYQOs9IPKGWhmrpayQb7Ubu6utXM8g2I5Q1tVR03fSOV1jnSJv10hAfa1rEVg4ue5xqEVFbGjBmLAQMGyq7TYT16L1Ldvefdu+I9SBISEuDl9UzhCJW84sxTmflf94THwB4Kt9V5rTWiXwYjJihU5WnK2vBRY9CnX84gITMzc9R1z952NLjPyTl72yUmJMDX2wsDhgxVuhxV5kl9FTDQY8gzMzdDUnL+O92lYcdFP+y/GaDw+X708tXn281KNtiP2tXR53t3PgGxvKFtquOmTwR8VahjTk7LFEesjA3KNqQYNmoMeufe3vXqiyMfubedD227wfls7yLOI+1M0yDC2JhotGmneCS7osoqhVZ1q5X8Ni9btkyUgcqTxoPkDrRp4G1+Y0VUmac4y1UHjR8wSHsfVObwzTffyA7N0IA6KlmQSCUQVHIg+eyzz5Quj0o3jh8/jvXr14tMK33BEh8fH5FdldCAPRqcGBtb8B5+cefLD83bvXt3rFy5Uvy4S6hbiHR/z549xRubRqhKgoOD8wyiVIUqr11hz5H2GAvbQ1y8eLHI+slfmgyaVOT1pSwzdceQLpQ9pgD62vMo9G9YDaYGOiLwG+bhJH4Qrj/PzuSTma/VxLgW2V+sjuaGYoCgvk72l5GZgS7eaF0dEQmpeBics+0mtXVFo2rlGzgTyjLTKHrpQkdy6Qf22L1gTO5aW2Sn6CgvZbASUzNw7FX3DbJ2XHPMH9BAYXnU5/U1d7t8s1LLhzdB+1eBs394+Q0ioiNMtAMtXejzSp1qevXqjS1bNovPAB21+eH778UREvmd4fnz5mHtl1+K/1Wdp7KioHfFszOibzO5+Ov/YFPDBZ2mjBPXHerVQsd3RuPcppwxEapMU9YsrazgUt1VdqHtTdnBrj164c/ftoqMIW27zT/9ACMjY3TrmbPtli5agA1frxX/qzJP3foNYG5ugT9+24z09OxkAWWiHz98iFZtlPdCL/PPt2eQ6Lghfb5n9XYXn++jr7pvkHUTWmDBwIZ5Pt+d3O2wK9dAQVLHwRTjOtSQtaWjMQ2zX3eHga4Ozj0KrRDbuxttu215t11Xue29bLHi9lZlnp1//YGoV0dvA18G4Nsvv0CX7j0rVF/vgtDvnbovi5X8NtNt+SUyqTuZPLou3VeceYqzXHXQ+MwzoUCXAkaqgaHMKw2wo7pbCZ3kgDpYUEkBTUc1w1TTnPuwgxT4UacK6oBB7d0kVH7w8ccfi0wsdeagel9qlzJ9+vQC16248xWEWtANHDhQ1ELTulIWmrLM/fr1E/dTC7vhw4eL+6nFHQUDNCqVuokUlSqvXWHPkTJ31NqOyjmodERZqzplh4GKWrJRkO3Xn2NK+xr4dkgTpGZkiTKG7855K2SRafCg1C0iIiEF5oa6+HpIE5FxoRKPB8FxWHPymZifUJlG59q2Ylkfdq2t8Hjfn/cVddLljfq0rhrdFCeW9EByeoY41Pv+9pvicKz8wMLcXTIGtXQRP8LH7+UdSNS8hhWGtKoulrXxrewWXpJ5f97Ck6CcUqjysuSjj/Dxxx+hd6+eogyK3sfffPOtqFuVhIQEQ1tHu0jzUHC9999/kZScfbKIiRPGix3MhYsWo2PHjigPekaG+MTziPjfysUR1erXRsPXuyD0qS829s/+DtM3MYZ9nRrQ0c/+TAU98sKW8e9j9MZPMeSLBdA3McLl33bjyBc/yJaryjQVxYeLFmPVsk8wvF8fGBgYisP0q776WqEEJyw0WHTKUHUe+rvyy6/wzZerMaBHVxgaGYn657enTke3nhWj1/Vn/97H52Oa4dTHPZGcloHoxFTM/u2GwufbwcIwz3iPwa1ckJiSgeNyO9ESOkpl38IAJz/ugeTUDFiY6IsOHjO2XKsQ3TbIBwsX4/Pln2BEf7ltt1Zxe4fS51tu7JAq87hUd8PUtyaIRBx13+nTb4Do+VyVGSj5bc6vK1e1atVE1622bdvKKgLoSLR8lpr651PtOTUdUGUeVZerblpZlabSvWRob4javtEHgQLK+/fviw8O9SSWUOkBtU+TBhTSCE76wZMO00reffddEZDS8nKj5VJwSAP0aNlSZrow+c139uxZkd2SiuFpoB9laqUSCUJt6miwH2XYJfThvnr1qtj7oh2FunXr5nlM6flSFphayOWupc5P7nVS9bUr6LWhdnpUN03Zcgr0VTkr21s71D8YwFhPB/q62krrkq2M9JCRlYXYXCPKLQx1RUY7d4c2PR0tcSIVZSITU/MM5FHVbc+8AWtJ0UBGI30dpXXJNCAoPTMTkfE59eA2pvpi0FGYkmw6ZaTsX430zy04OlnsbBTH5WXdoW5xsbHi8B4NBspdPkRdNOhLPHctXUHz0JgDZUeN7OxsRRarOD40LnlNtV1ttzy3paekIiogSBZgWzo5IML/pUJrOxoUSLcnRsUgJUF56Y0q0xTH8ugHUDfaQU9OSoKtnV2ebRf6anvn7rhQ0DwSanNGCQGaV5UytIL0WpX3d6U0P98UPFN5B50gRWJjagCKK8MKGadgZ26A6IS0Yn+m5R1bkrf/ckXc3hQy0dlDKbimRFFJOVqWXWvW+ET1J2xMjVUf+P/rr7+KcWM0/ok6nlF5B8UB1Bdfige6du0qEhPSeDJV5lFlGnWrMsGzutCPI/UkpvpdannHyk9pBM+VQWkEz5VBaQTPlYE6gufKqDSC58qgNILnyqA0gufKoCyD57hSCJ7NihA8E2omQEfHKclAR76XLFkigmXJ1KlTRZLzq6++UnkeVadRJw6ei2D27NniJCWUFVV1j4ayV9RzMD+0pyS1kasIKtP6cvBctXDwXLVw8Fy1cPBcNYJnTVElap7VhdrT0ZkK6aLqoQA6Wx6dNa+g+yuSyra+jDHGGCtcMSsFmRIcPBcBDYorKhoAV15nziuOyra+jDHGGGNliYNnxhhjjDENx0Pc1IeDZ8YYY4wxDcdlG+qj8SdJYYwxxhhjTF0488wYY4wxpuF4vKD6cOaZMcYYY4wxFXHmmTHGGGNMw3HNs/pw8MwYY4wxpuG424b6cNkGY4wxxhhjKuLMM2OMMcaYhsss7xXQIJx5ZowxxhhjTEWceWaMMcYY03BZ3KtObTh4ZowxxhjTcNxtQ324bIMxxhhjjDEVceaZMcYYY0zDcas69eHMM2OMMcYYYyrizDNjjDHGmIbjVnXqw8EzY4wxxpiG424b6sNlG4wxxhhjjKmIM8+MMcYYYxouk1PPasOZZ8YYY4wxxlTEmWfGGGOMMQ3HJxhUHw6eGWOMMcY0HJ9hUH04eGaVVjd3u/JehXIxv1sdVEW6SZGoipZHP0BVtNyyEaqiUzEPURXp7/wcVdLkleW9BqwYOHhmjDHGGNNwPF5QfXjAIGOMMcYYYyrizDNjjDHGmIbL5CGDasPBM2OMMcaYhuOyDfXhsg3GGGOMMcZUxJlnxhhjjDENx63q1Iczz4wxxhhjjKmIM8+MMcYYYxqOa57Vh4NnxhhjjDENx9021IfLNhhjjDHGGFMRZ54ZY4wxxjQcl22oD2eeGWOMMcYYUxFnnhljjDHGNFwmp57VhoNnxhhjjDENl5FZ3mugObhsgzHGGGOMMRVx5pkxxhhjTMNx2Yb6cPDMGGOMMcYqpPv37yMmJgYeHh4wMzMrdPqEhAQ8fPhQTFunTh3o6iqGurdv30ZERITCbfb29mL5quLgmTHGGGNMw2VUsgGDISEh6N+/P54/fw4HBwf4+fnh119/xejRo5VOHx8fj4ULF+Lvv/9GrVq1EBoaKgLn7du3o0OHDrLp5s+fDy8vLxFYSzp27MjBM2OMMcYYq7xlG9OmTYO2tjb8/f1hZGSE9evX48033xSBcPXq1fNMT9npRo0a4eXLlzAwMEBmZiamTp2KoUOHitvkM9BjxozB6tWri71uPGCQMcYYY4xVGJGRkfjvv//wwQcfiMCZTJ8+HcbGxvjrr7+UzuPs7IwZM2aIwJlQ4P3WW2+JDLSPj4/CtFFRUbhw4QKePXuGjIyMIq8fl20wxhhjjGm40mhVl5KSIi7yKHiVAtjiunfvnghqW7RoIbtNT08PTZo0wZ07d1RezpUrV8S6uLq6KtxOAfjdu3dFUG1paYnffvtNobSjMBw8M8YYY4xpuNIo2/jiiy/w6aefKty2bNkyLF++PM+0N27cQHR0dIHL69mzpywzTKytrRXut7Gxkd1XmAcPHoj1WLx4MQwNDRXKQSirTRnt1NRUvPPOOxg2bBgePXoEKysrlZbNwXMFRBvQ19cX/fr1Q2V35swZMeK1ZcuWqOjSU1Px/LEnUpOT4FynAcysbQudJzEuBkHeTwAtwMG1NkytbJROFxMegmDfZzC1tIZTnQbQ0tJCRZGakoKHnreRnJSIug0aw8bOvtB5srKycP/2DUSGh6FD157Q09fPM010ZAR8nj1BenoaGjRpBjNzC1QEfs9f4KmXD6ytLNHcozF0dHTUMs+Dx08QGBQMM1NTeDRqCGPj7EONFQ1liTxv30JSUiIaNGoCO3t7tcwTHR2Fp48eITk5Ga5uNVCjVi1UBOYOdnDv3h6hz/zgf8NTpXns69aEi0d9xIZGwPviDWRlZhZrmoqAtt1d2naJiWjYWPXtnd88SUlJOHf6lNL56jdsCLcaNVGe/CPj4BUWAytjAzR1toWOtpbKgeXJxwHQ1dFGt3rOeb7vnoZGIzAmAXamRmjoaA3tCvQdXp4WL16MDz/8UOG2/LLOf/75p8goqxI867/6TaH3m7zExESVstqUUe7Tpw8GDhyITz75ROG+ESNGyP6nx1m3bp3otnHu3DkMHjwYquDguQI6ePAg/vjjD40Inr/66isxorWiB88RQQH464sF0DcwgomlNf77cQ16TpyO5t375zvP6b9+wcPLZ2BXvSbSUpLx0usR2g8ag07DJsqmoR/U47//gLtnjsCtYVMRoENLC6Pmr4SuXt6As6wFvvDH8nmzxB64pbUNvvt8Gd6e9SF6DRia7zwnD+3Hnj9/E/VkL1/4o3mbDnmC54N7/sYfP2+Ee6Ps1j/rP1+G9xYtR9tOXVGevvnhZ/y161+0aOoBHz9/WFqY45fvvoKFhXmx54mJjcO09xcgJCwMTRs3xMugYLwMDMbalUvRoU0rVCQv/P0x973pMDIyhrWNDVYt+wSzPpyHgUOGlWieA3v/xYav16J+w0YwNTPH7RvX0aJ1ayz/fDV0dfVQHiwc7THq26Wo2b4FDEyMcX3HPpWC5yFfLETXWW/A6/x1ODasg/jwKHzXawISo2KKNE1F8NzfHx/Mmia2nY2NLT5b9jFmfzgfg4YOK/Y8KSnJuHr5ksI8oaEhuHPrJlZ9ua5cg+eNZz2x85YXmrnYwTciFpZG+tgwqgssjAr/rt125TE2X3oISyMDheD5aUg0Vh65LoJlB3NjPAqOgoGuDr4b2QlOFiao6t02DIpQovHNN9+ovNwaNWqIvwEBAQolF3S9R48ehQbOXbt2FR00fv/9d/FbVRAq26CESFBQkMrrx8FzKaFRn3RYgPZw6NABtUWhDeni4iLuf/LkiehdSHs7rVq1khXE00a/deuWOCxBATRp27YtYmNjERcXJ94QEip0p16G0p4SLY9GlHbp0kUUwlOR/Lhx43Dy5ElxqMPNzQ03b94UdURU26NKv0QizU/rfunSJVGwT3uH165dw9OnT8U0dH+zZs3g6Ogom+/y5ctifdLT02XPZdCgQTA3NxejYGl++iBQS5nmzZuXazb20C/rYOvshtHzV0FLWxu3Tx3Esd82olaTVrCwc1A6j0PNuugy6m1ov8pCPrxyBnvXr0Tjjj1g5eAkbru4bwfuXziJSSt/gK1z9hfA80eeyKQBCuUTUyj4Ye0qVHeriY9Wfyu+YI4f+Be/fLcWTVu1g321nG0pj57vx2u+Q9DLF1i5cE6e+18+98PWjV9jzkcr0KlHH3HbuRNHsGH1cjRsuq/cMtBXb9zC1j/+h+2b1qOZR2MkJCRizNvT8O2Pv2DZornFnuefPfsQEBiIQ7v+FFlnsnDZSny1/kfs+WMzKpIvV62AW81aWPPNerG9/9u7B9+tXYPWbduhmqNTseahzzcFzhPeehsT335HFnBPGDkUl86fQ+duBf/QlRYKmG/tPoLN49/HvPM7VZrHvVt79F4wFV+9NgI+l2/BwNQEi6/vF8HyjmlLVJ6moliz6lPUqFkLa7/dILbd/n/34Ju1q9GmXf7bu7B5LC2tsPSzVQrzfPvVl/D380XHTp1QXq77h+L3q0/wy/hu8HC2RUJKGt76/SR+OHcPi/sUnLzxfBmBvZ4+GN68tsg+y0vLzMSqQe1R3Sr7s52ekYk3fz+Bn87fx6cD2pbqc6rKGjRoIDpq7N27V1aLTDEPxVNr1qyRTUcxDdVCSz2aqZ1dt27d0L59e5Hpzn2UkDLZ1HWD5pEcO3ZMxEVNmzZVef2420YpefHiBSZOnChapLz99tvYt2+f6FlIPzTUaqVz585ij2ju3Llo2LCh7FAGBZsUWFOgfOTIEXGhZdG0lMXNHdTS/BJ6k1FbljZt2uDLL7/EoUOHZDVJNAKVbt+wYYMYvdq4cWMEBwer9Fxofhrl2rp1a2zZsgVXr14Vt9ObWFrHr7/+GnXr1sXmzTnBAj2nsLAw0WZGmo6al9PeHa0LjYLdsWMHRo4cie7du4vnXB5iI0Lx4vE9tO4zVATOxKNzH+gZGOLR1bP5ztewXVdZ4Eys7LN/jCgLTTLS03Dt4C606jNEFjgT1wYe0Dcs/0P64aHBolyj//DRsj3zbq8PhKGhES6dOZHvfN369IejS942QRKvJ4/EzlG7Tt1kt9H/iQkJuHYh/9eztB08egKNG7iLIJiYmBhj+KD+OHz8lFjf4s6TnpEBU1NTWeBMHB0cijWCuzSFhgTD885tDB89Vra9+w4YCEMjI5w5eaLY89DrQBc7h2qy+Wzt7aGjo4v09PJ7DUK9/HDznwPITE9XeZ4244fA/7qnCIpJSnwCLv76P7QeO1C2c6/KNBVBSHAw7t6+jRFy267fwOxtd+rECbXNQzWjx44cQt8Bg8rtKAM58tBflFNQ4ExMDPQw2KMmjj16XmCtb1xyKpYeuIqPX28Nc8O8GepGjtaywJlQWYeDmTGSy/G9XVyZWeq/lBb6LFGQTNnqVatWiViB4ikKjOWPylMMtHTpUvE/xRt0PyX4KO6istETJ06Ii1QnTfEHJSxpufv37xft6iZMmICxY8eKgFtVnHkuZZStPXz4sOw6vRlohKe3t7f4wSWLFi0SBesUlHbq1ElsRMrUStlaQhtZFRSoUsZ7wIABCrdTRpselxqNUwBPWeIffvgBK1asULkOmzLbUuacTJo0SVwkR48exfDhw8WFDoNMmTJFrDeVbXz77bey6fr27SsyzT///LP4gKSlpYnDMCtXrlTYoywroc99xV+76tmHiYiOri5sHF0Q9iL7vvyEv/QXtcwJMdG4e+Yw2vYfCXvXWrLlJifGo5ZHKwT7PUN0aBAs7R1RrUZdVAT+Pt7ir2vN2rLbaI/cqbobnvtm31ccNrbZ9ZEB/r6oWdc9+//nfuKvn/czlJdn3r6oW1vxkDJdT0hMRGBwCFycHIs1z9gRQ3Dlxi3M/Wg5OrRtjcCgEBw5eRqfLp6HisTHy0v8rVVbfnvrobqrm+y+4sxDNYPvz1+E37f8isiIcPG9dvLYUXTp3h2du3VHZeLcxB0v7z1RuI2uG5mbwdrNGRF+ASpNUxH4vPqs1apdR2Hbubq5ye5Txzznz5xGXGwsBg4egvLkHRaD2naKR7XoekJqOoJiEuFsqbzEYtXRm+hezxmt3ezh+TI83+Uff/QCSWnpeBAUKeqqvxyqemeGiiKjNKPdUkCxEB3V3rZtm+iaQUfS33//fYWdVCoJlWKp8PBw1H71XbV27VqFZVFdMw0GpCPdlGj86aefRHBdrVo1bN26FUOGFO39y8FzKaOMrzxqh0KtVw4cOCAGIdCFNvz169dFVtbEpGQ1VFQblDtwJlTaQYGzFCDRHpZUcqEKml8+cJZQaQi1jaE3LWXaaLAQlZLk1/KFsu+UgaZAmc4CJL0GdHjm9OnTKA8pSQnir6GJYhmLoakZUhKz78tPdGgwvO9cQ1xUOFKTE2HjlJNhTozLHlV89dAuRAS+gHU1ZwQ8fQA7lxqi5pky2+UpMSFe/DUxVaz3NTU3R2J89n3F0bBpc3i0aI3VH8/HgBFjxPY9cXAfzC2tZI9ZHuITEmCeq1TJwjz7ucfHJxR7HiNDIzSoVwenz11ERkYmXgYGwdHBHna2hQ84LUt09i1CNcnyzC0skJDP9lZ1HicXF1H3SGUaZubmCA4KRJv2HVQajFmRGFqY5albTojM/hwbvapxV2WaikDadrQ95JmbF769izLPf/v+RfOWrcQOVXmKT0nLkzm2eHWdSjiU2X3bGy+i4vCZCuUXV/1CEJeSKoLnBtWsZMtmpat3797ikh8KiuVLPSjLXJiaNWuW6AQphIPnUkZ7NfKoHodar1DwLI/2qKgWp6TBc+7Hk+Ruv0I/dHSIoyTLpRIQGmlLWWQnJydRQ0R7hAUtl56/VKdE2WwJzUc14UXpJZmWmgI9/aL1kgz0eozI4JzMUP02naGrl70M6rIhX05B182sCg6A6jRvKy7E5+51/G/NYtg4VUd198Y5AwKzsvDuml/Ec0yIjcYvC97B5f/+RucRb6KsPHv0QAwOlLTv0gP6r1476rJhZGwsuy85MRHWtnbFfiw63PvJl+tx9vhheD95BG0dbXzwyUp8veIjGBrlPE5ZMzDQR2KukduUQZbuK+48G37ajDPnL2H3H5th+urzu3Ltt5g5bzEO/L29XALIRw/u48XznO3dpXtP2aAe6qBAhzUlVE5DZRbKqDJPVGQkFn0wG+/OfE8c7ieBLwPw9rjRsLK2Rr+Bqo1erwjSklJEDbM8Q7Ps62lJySpPU9Ye0vb2z9neXXvQ9jZUvu0SE2CbT0edos5DO0k3r1/LUwNdHgz0dJCYqliiQ1lncZ+ujtJg+9vTdzG2VV2cePxC3PYsLAbJ6ek4/MAfzVxs4Sg3IPDjvtmDf9MyMvHeP+ew/OA1bBzdBZVJZTvDYEXGwXMpy10DZ2FhIep1PvrooyIth36Ac9dl5m7houzx1CX3cinDTO1pdu3aJRuwSC1kqC6JMo35oecv1SkVFCyr0kty8LvvY+hUxRY5hQkL8IPfg9uy63VatIOVg6OsnRy1kpPEhoeiunsTlZddq2lrGJmaiewyBc9WDtkjtuu27CB7/UzMLeFSr5Eo9ShLz/28ce/mddn1Vh06w8Epe/3CQoJhZZOzk0DXG3g0K9HjUdlL974DxYXExcYgKOAFhozJ6URS1lycnESphTy6rqOjDad8djpVmefWXU+0a91SFjiTnl074e89+xAcGgZnR+XLLk2+Pt64df2a7HqHTp3h5Jy9vUOCg2AjlxWnumaPZs2VLkeVeZ48zm5P10VuYKCTswvq1HPH3Vu3KlXwHO7zXJReyLN2c0FGejoi/F+qPE1Z8/X2FkGspGPnnO0dnGvbUV2zR7OcE0/IK+o8B/fTAGBzhW1fXpwtTBEcm71jKwmOTYCOlhaqWSjfaaeuGjSPNB9loVMzMnHZNxguVqYKwbNE71Uru41nC265xjQbB89ljOp9qb6GAk+pwwZ5/vy5rB0LlXHQj1Hu005S/TAFplIgduqU8l6bZYGK76l2muqZJRRI5w6ccz8Xd3d3UXNE9da5g2caGKnsfPX59ZL850Fokde7adfXxUWeQXUTmNva49GVs6K/Mwl4+lAE01JWWcosU6lF9fpNkJwQLwJE+dKLqJBAJCXEw9wmO0NjbmMHhxp1RF20fOu6iKAXqNFQebBSWnr0HSQu8oxN6sDWoRounjmOeg2zB8Q9eeCJsJAgtGyXs21uX7ssBhEWJaCmHs/U+k7y3z87YGJqivZdyq8GtnOHtvh83XpEREbBxjr7SMyRE6fQpmULWRY5LDxC1C9369RBBMOqzFPNwR7evtlHVCRePr7Q1dGBba4G/2WFAtbcQatJHVM4VKuG0yeOi9695L7nXQQHBaH9azldEq5dviQGiFFwXKtO3ULnsX9VDubn4y3rB0xHiSj7TIfzKzLzanao36MjPPefQHJcPO4dPIUxG1fAzM4GcWERYppWYwbiyalLSH915EuVacpa/0GDxUVe7Vfb+9TxY2iUa9t16PiabLqrr7Z302bNUfvV9i5sHkLJnEMH9uP1fgNkPXnLU8fajvjqxC1EJiTD2iT7e/nYoxdo5WYvyzyHxyeJrhyd6jjB1EAPK3KVa1Crun/v+CjcHhqXBHszxQHeD4OjUM28/I6iFVcGJ57VhoPnMkYZVGolR50rqNsElTpcvHhRZJFpoB+h+6glC01LwSS1qqOOFNTo+4033sBrr70mAmeqk5YK5csataSj9aLOIe+++64YkEg7BVRPLY+6alBNEg1QpHWlVnXUsYMal/fv319cKBCnziBUsD9//nyVe0nq6aunpyrtjPSaOBP/rl+BrKxMUapx/cgeNOrQXWSJJed2b4OFbTURPCfERGH3t5+iXov2sHRwRGxEmBgw6NawGRq07Sybp/cbM/H32o/EiH8q53h68xKS4mLRbuAolDd63pNnzcVXyxchKzNLlGoc2PU/dOrZB/Ub57Ts+d/Wn0XbOil49n76GAF+PvD3yR4wdvnsSfHj27BpC1nHhe0/bRB9YmvUqYfH9+7g8tlTWPDZlzA2KZ/3KxnYrw927z+IKXPmYeiAvvB88Ai3Pe9j24/rZdM8eeaNJZ9+jn1/bRPBsyrzTJ44Dm9Om433Fy1Fx3atRY/nHTv3YPIb4/ItBymv7f3eh/OxbPFCsRNHZRe7/rcDPfv0RWOPnO299ZdNoiUZBc+qzEODy7r26IVVyz8RXTnMzMxx4uhhEVwNGTGy3J4vdcKhoJaY2lrBvl5N0SkjKTpWBMDEpWlDvP3Ht1hWvweSn8TjyvY9eO3dsZhz/A9c2vIParRthtodW2Jdp5zPqyrTVAS07ebMXYBPFi0QSQ0qu9j5vz/Rq09fNGmasyO8+edN4vucgmdV5yHXr14RGekBg/PvCV+W+jdyw767Ppj1zzkMalIT94MixADAn8fldP15FhqDZQev4Z/JfUTwrAoKqGOSUtDUxVZknW/4h+KiTxC+GFz5Bgxy2Yb6aGUVdIydFRv1L6YuGt9//72sVEFCAwOp/yCdqpICSgqGqQWLfGkE1URT9wo6lSV1tKBWblQjTAEqZXJpHhqFSp08pBZ21A7v9u3beU6LSYXxVCA/evRo2W3bt29HZGSkGLlaGGXzS72sKYNMPazpy5fWU2prJ50UhdoYUbBM60XPm0bA0rT0+lD7PZqX6qVpkCMF40Wx7WZ2nZq6BHo/xoNLp5CWnCTKNRq/1lPWuo6c27UNxuaWaNU7O8ND9cv3z59AROBzGJmZi1KN2s3a5ilxCX/5HPfOHxNBMw0abNqtL4xyDdIrilZO6u2T/OzxA5w/cVTUPjfwaI4uvfoqNJWn4NnC0hJ9h2YHB+dPHsXNyxfyLKf/iDGoWz97Z4MOYZ868p+otba1d0DXPgPy7RutqroGJR9smJycgt37D4gg2drKCsMG9oNr9ZxD8E+8vEVf57mzpsHO1kaleUhwSCj2Hz6GwOBgMcCQTo5CpRzqEKmj3u396OEDnDxyWNRyU4Dcu28/he299edNsLSywtCRo1Weh35Gzp46KbKUKcnJcHF1Q7+Bg/IMPCuK5ZY5O67Foauvj4mb83bviXoRhL1LvhT/OzepL3o2757/OWKDs8dq6BkaiODYpWkDxIVG4OLmvxHmnXP0SNVpiuuzmIdQp0cPHuD40cOijtmjeXP06dtfYdtR8Ey9m4ePGq3yPFLJBvV2njG78N8QVejv/LzEy0hOy8A+Tx9xNkBrY0MM8qip0GbuWWg0fr/2BLO7esDWNG+70LPPXorM9LyeikcGqYzjml8IUtIz4Gxhgj4N3WBrqp4B3xaTV6Ks7H+oWnvaohjUsOzL0ioCDp5ZpaXu4LmyUHfwXFmoI3iujNQdPFcWJQ2eKyt1B8+VhTqC58qoLIPnf++rfgY9VQ1tXLKkSGXFZRtVHLUmop6H+aFSivIqDWGMMcYYq2g4eK7iqJSC+i7np1evXhw8M8YYY5Uc1zyrDwfPVRydOEX+TIaMMcYY0zzcbUN9FEcAMMYYY4wxxvLFmWfGGGOMMQ3HZRvqw5lnxhhjjDHGVMSZZ8YYY4wxDZeZyaf1UBcOnhljjDHGNBwPGFQfLttgjDHGGGNMRZx5ZowxxhjTcDxgUH0488wYY4wxxpiKOPPMGGOMMabhMrJ4wKC6cPDMGGOMMabhuNuG+nDZBmOMMcYYYyrizDNjjDHGmIbjVnXqw5lnxhhjjDHGVMSZZ8YYY4wxDcet6tSHg2fGGGOMMQ3H3TbUh8s2GGOMMcYYUxFnnhljjDHGNFxGJvd5VhfOPDPGGGOMMaYizjwzxhhjjGk4zjyrDwfPjDHGGGMajoNn9eGyDcYYY4wxxlTEmWfGGGOMMQ3HmWf14eCZVVqDrn2Pquj8igOoioLOnUZV9MEvZ1AVnYp5iKroE4uGqIo2vjhY3qvAmMo4eGaMMcYY03CceVYfDp4ZY4wxxjQcB8/qwwMGGWOMMcYYUxFnnhljjDHGNBxnntWHg2fGGGOMMQ3HwbP6cNkGY4wxxhhjKuLMM2OMMcaYhuPMs/pw5pkxxhhjjDEVceaZMcYYY0zDceZZfTh4ZowxxhjTcOmZWahs0tPTceXKFcTExKBVq1ZwcHAocPrz588jJCRE4TZnZ2e0b9++RMvNjYNnxhhjjDFWoTx//hy9e/dGamqqCIBv3bqFb7/9Fu+++26+83z66acICAhA48aNZbe1adNGIXguznJz4+CZMcYYY0zDVbayjWnTpsHe3h4nT56Enp4etmzZIm7r3r07ateune98Q4YMwerVq9W+XHk8YJAxxhhjjFUYYWFhOHLkCGbPni0CXPLmm2/CwsICf//9d4HzBgcH48CBA7h+/TqSk5PVtlx5nHlmjDHGGNNwpZF5TklJERd5BgYG4lIS9+/fR1ZWFjw8PGS36ejooFGjRvD09Cxw3qNHj4oA+unTp6I0Y+vWrejVq1eJlyuPg2fGGGOMMQ2XkaX+4PmLL74Qdcbyli1bhuXLl+eZ9syZMwgPDy9weSNGjBB/aSAfsbKyUrjfxsYG0dHR+c4/d+5cESjr6uoiIyNDZJjHjBmDJ0+ewNbWttjLzY2DZ8YYY4wxVmSLFy/Ghx9+qHBbfllnqjF+9OiRSsGztIzExESF++Pj42FkZJTv/H379lXIKH/22Wf44YcfRBeOoUOHFnu5uXHwzBhjjDGm4UqjbMOgCCUaFMiqqlatWrLOGG5ubrLb6bp8gFwYU1NTEUSHhoaqdbk8YJAxxhhjjFUY7u7uItDdtWuX7LZ79+7h8ePHCkHu2bNncfnyZVn2OHdG+b///hPlG9TLuSjLLQxnnhljjDHGNFxla1X3zTffYPjw4dDX10fNmjXx9ddfY8CAAbLBf1J9taWlJfbu3YuIiAgRAI8cOVK0nHv48CE2bNgg2tC1bNmySMstDGeeGWOMMcaqQPCs7ktpGjRokKhVpmwyZZeptnrPnj0K03Tp0gUdOnQQ/1MZBg1KpNplqq+mrhoHDx7Ejz/+WOTlFoYzz4wxxhhjrMJp166duOQnd6cPOvnJokWLSrzcwnDwzBhjjDGm4TIyM8t7FTRGuZZt0OkTi3JGl6Kic5Vv375d7dOyir3dGWOMMcZKS7lmnk+cOCFGQI4ePbpUlk+1Ly4uLnjjjTeKPO26devg4OCACRMmlMq6VRXKXsfS3u5F8TImAT9degiv8BhYGRlgZLPa6FrHSaV5/SPjsOC/K7A0MsBPozoXe5ryYOzqgvoL34N5w3pICY+E75YdCD58ssB5HHp2gdvEkTCu7oS02DiEnr4I702/ITMltUTLLS/nDu7BtdPHkJKchDqNPNB//DswNbfId/oX3k9x9sBuBPg8g6GxCRq1ao+uA4dDTz+7TVN0eBi+WTxL6bz9x72NNt36oCIY3d4NA5o7w9hAB7d8I7Hx2FPEJKYpndbe3ABbp7VXet+Px5/hwO2X4n9bMwO80bkmWta0Bp2H4UFADLac8UZIjOKpccvTnp3/4Njhg0hKSoJHs+Z4Z+p0WFhaFnuesNBQzJr6jtL53n53Kvr064/y4tiwLjpPG4+GfTrj2p/7cHDFd4XOY1uzOgatnAdnj/qIC43AmQ3bcGfv0SJPU55eBAZj/da/8NTHD9aWFhg/tB96vlbwoXkvv+f4+7+juHj9Dgb07IwZbyj+Lt1+8BiLV6/PM98va5aiulM1VCaVbcBgRabRAwY/+OADcc7y4kx7+vRp3LhxoxTXrmqoyK9jbHIqpv1zTnyhLOnZAp1rO+Gjg9dwzjuw0HlT0zPwyeHrMNHXQ2BsQrGnKQ96FubouHcbtHR0cOfDpQg+cgqtfl6Han265TuPXdcOaPPbekRcuYmbMxbC6/stqDFxFJp8/nGJllteju36A/t+24SuA0dg9LQPEeDjhe+XzUVmRobS6QP9fbBjwxrUbuiB8bMXofuQ0Th/6F9s+3qlbBozKyvM/HSdwqVdj76ICA5ErQaNURG83bUW3u/rjj8v+mLVv/fh7mSOTZPbQFtL+fQR8amYtvmawmXfjQC4WBvjjn+kbLq145sjOCpZLPObQ4/hZmuCbdPbw0C3YvzE/LFtKzZ9vx4jxozFhwsWwevpE8ydPVO0sCruPFbW1li3fqPCpe+AgQh8GYDGHk1RXmq1b4F3//keYV7+SEtOgZmddaHzGFuaY975ndDW0cHvkxfi7t5jeHfn92g6qFeRpilPMXHxeOP9j5GZmYHP5s1Ej45t8eGnX+HUxWv5zkOB8Ycr1sHVyREG+vqIjI7NM01ycooIyn9a/YnCpZq9LSqbyjZgUCMyz9evX8eWLVtkoxbpNIazZs1Cv379MG7cOFk/vVu3bonWIcTX1xfbtm1DQECA6Ks3adIkODo65lk2zUdBFn0pUeZXvqVIfg4cOCDmocymPHq8Fy9e4OOPP8bt27dhbW2N1q1by9aZznFO/fycnJwwfvx41KlTR9wnP+3PP/+MO3fuwNvbW5YxXbt2rRjBmd/8hZUpUDsUY2PjAp9nYa+X/HIOHz4sMuVLlixR6bGpJcuFCxfEed7psel5Up9DygJbWFhgypQporWLvJs3b+J///sfIiMjxXngJ0+eLBqO5142zU/Loec1atQotG+fnanK73WUHDlyROl8ZWXvPV+kZGRg+eutoKejjcaO1ngaFo1frzwWgXRB1p+/j/oOlqhmZox/7/kWe5ryQNljHQMD3HpvEbLS0hF9+x4sGteH+9wZCD56Wuk8dp3aI87LF94/bhXX4x4/g3/j+nAdP6JEyy0PaWmpOLbzD/QdOwmtuvQUt701bymWTh6Je9cvoWm7TnnmcXBxxcJvN8uuV69dDxnp6fj1i48RHxsjMtY6Orqwd3JRmO/RrWtwb9oSttWcUd7oPT65Wx38eOIZjtwNErct+usOji7uji4NHHD6YUieeejH8UWEYt/UDu52uOoVjoDIJNlt7/x8VeGHNPrAQ+yd2wV1Hc1w/0X26XDLC33n/fHbFkx6Zwp69n5d3Lb0s88xcnB/XLpwDp26dCvWPHT6X5fqrgrzXbtyGS1bt4Gzi+L7oCz5XffEisa9xf+txg5SaZ5OU8dD19AAWyd+gIy0NPhdu4PqzRuh//L3cXf/cZWnKU//HDiGlNRUfLFoDvT19ODRoB4eefng++1/o3vHNkrnaexeB/u3ZGflD50+X+Dy3Zzzxi6s6lI5LUAjGDdt2iQCPHLu3Dn89ddf+Omnn2TTUGBJffak++nQPAVebdq0EfM1atQIz549U1guzbNq1SoRLFLbEGo5QkFeYWrUqCF689H5yiXUloTOp66npycrxbh2LXuvMzMzE507dxZtSyhopTPOUC9ALy+vPNM2adIEdnZ2ou3J66+/Li4UOBc0f0EoQKTzqxf0PFV5vaTl0POmYFbaKSjssadPn45ffvkFdevWRVRUFDp27Ihhw4aJGu9mzZrBx8dHjDqNjc3Z696/f78IZulwJU1DQT2tc0pKisKy58yZI9aHAm8Kgjt16iR2tPJ7HU1MTMR9v/32W77zlZVbAeFo6WInggpJhxrV8DQsBnHJOaUIuVFm+qp/CD7o4lGiacqLbYfWCL90TQS4kpCT52HRpAF0zc2UzhNx+QaMXZxgVr+uuK5jbATbTu0QIZfVKc5yy0OA91MkJcSjYYu2sttsHBxRzbUGnt27rXQeCoxzo+8bop1P2jbk5XN4P/REhz4DURH8v737AG+q/P4AfpiltJQCBcreW0D2EGQvAUFRxO1PARVxIKCAoKAi4y8qCIiKiCyRJbL3kC2y996rUKAtXbSU//N9yw1Jmja3pZL2vd/P8+QpzSLJTZpzzz3nvBUK+YmfdxbZfOSq7byLNyLl+OUwqVXKfXYSkFFGacbcf845nG8fOGfIINKsUqAE34qW01c9f8Tl6JHDavGEOnXjx1lBgYIFpXiJkrJrx45Uu83ZM2dk7+5d0r7j0+JJcbH3P39mlW1cV46u3aKCYsP+JWulaLVK4p3Tz/R1PGn7nv1S+9FHVOBseLxODTl8/JSE3nL9PsyS2Xzl6gs9+8kzb/aWfsNGq1KP9Cg27m6qn6zK9DsHARBOyJwi24hgEzWrmI2HACtbtmxqbp4RTHft2lUFsu+++676HZnN2NhYdd706dMd7hv3aawpjkDq448/lk2bNiX5eB555BGpWrWquq/PP/9cnbd582Y5c+aMLRNuD8EoVpG5cuWK2hGA3r17qwyDMwSNhQoVUlllI2OK7KnZ2ycmqedp9vXCMHAErQjezcKOBoL+DBkyqPvF47h06ZJtVR5klFGXjGw2timCgg8++EA9v2HDhqnroKQF2x1HHnCZAZlxZJAzZowPQDGUfOrUqSqwd/U6mrndw3IlLFJqFMnrcF4en/j61avhUZIjW9YEtwkKi5Thq3fL10/WE+8srj8+Zq7jSdkKBjoEvRB99Zr66R2YT8JCwxLc5sqq9bJvwFBpuGSGxNwIkSy5csqVFetld+/PHuh+PeHGtfhlWv1yOQaMfv555Oa9y8xkr5fPmiLlq9WS7L6uA4ctKxaJr5+/VK2bNmrdA3NmUz+vhd3fATZ+z58z/u+SO0/XKiLXb0XL6gOXE1z2VK0i0rVJKfH3ySqhkTHS9cetcisq+YFcagu6Ep9Rzx2Qx+H8PHkCJCjoSqrdZtGC+erv8+ON016Zkju5ihRQgbG90MvxO1n+hfJLZEioqet40uWgYBU82wvIFV+fHnQtWPx84xM3yYXvzWfatpD2zR9X37tzl6ySTm/2kd+/HyYVyzoerSXrSNY3e+PGjVXQ/Prrr6ufmK+HMg0EYQEBASrrjOwssqU4YUg1Dv0jGMMJ5Q7OSydiNRgjoIRnnnlGDbCOiYmxZZATg7IJZMON4BlBJgZmFylSJMF1EazhMWL+HwJUZG7x/9r/30l50Nsn9TxPnz5t+vVq2rRpsgJnwDbBHwBAsIqdIJxnwOuMEpCLF+NrfVH2gp0NlFIY/Pz8VIkOdpDsg2e83kYADGXLlpULF+KbiJKS0tulpri7dyWzU9Yw873H5KqWC+d9umy7aiqsGJjL5X2auY6nZciYMUF2Ku52fDYJ9cqu5K5bQyoPHSBHR/0gQWs2SvaiheSRL/pJ+Y96yqGvvkvx/XrC3XvbNpNT1ilzlszqCJU7uM6Ub76UWyE35e1PR7q8zp07sbJtzXKp06y1ZHbzd+xhyXDvve6cLYq9EycZ7/19SEqmjBmkfY1CsmDHBYm9k/DzsWrfJdlxKljy+WWTbk1Ly6iXasgLYzdJeLRnA+i797YpyiwSbO87calyGyQ6li9eJK3btnf7vZUW4bN7x+6IEcTe++yixtnsdTzp7t04yZzZ8XFkuZe8uJPIdjajVtVKUrf6/SOI1SqVl7MXLqtykHFfJl02mdZYuUbZ48HzoEGDVO3w/v37VQCG85DJxOF5ZIMRYCJLC7jcyNJCy5YtHWpmIVcuxwADdcfIyqK8wP62riDDjGB269atqpRi1qxZMmLECJfXRZ0wAj/USD/11FMSFhambj9y5EhTAfCD3j6p54lSDbOvF+qLkwtHBewhaHV1ntEIc+3aNdtjtJcnTx6V2Xd330k14aT0digXsS8ZUefFxIrXA2R2MQEjxKk8w/jd3zth1vlmZLTsvhCsMsuLD8S/DqHRt1Vw8MyvK6RX4ypSPp+/2+s8VsKzHdq3r9+QrPcyMoasuePfn9HXb7i8Tdn3u8vVjdtUoyCEHjoqmbyzSfWxw9V5MSGhKbrf/9qiaRNl+/r79Zh9v/5RfO5N1AgPCxFvn/ufL9QuFykZX5aSVOA89duhcuLAXvlg2PfiH+B45MKwf/sWCb0RLPVbeqZk452WZeWJR+/X7b84dpPcDL/33s6exSEjjEzx4Yvus4aPl88nef2yybx/XB+yDouKVaez1yJk/7l/ZePgltLm0QIyZ5tjicd/aeKE8bJy+TLb7z/++pttOkbIzRDx9b1fPhRy86aUKVfe5f0k9zZbNm2Q4OBr0r7jU5Ie3bp2XXzyOH52fQNy2S4zex1P8vfzkxCno1s37mXDcz1AWYmrZFW1R8rL8nVJHx1Pixg8p55kRR5NmjRRzWyoU0bmFYEcgudx48ap4Bn/BiPzi8P1yK4mxTkYQxYWgRXuzx2UBCCDiYwzAr7w8HCV0U1M+fLlVe0voASjVatWKuOK8glnRqY2pbdPzvNE1sLs6/UwFC0a3wSD7DOy1Abn381w9TqmBMpHnFcS+uiJx6RfuwYpvs8K+f1l4ynHw897LgRLXp9sktfX22WwPfs1x87yuXtOysoj5+Xbp+pLnuzZxCtzJrfX8bSbu/dL/hbxn1VDnjrVJfLSFYm+cr8e1l6mbF4S5XTZ7ZBQlVHOmM1LJCRl9/tfa9S+k9RuGt/wBdl9c6hmv4wZM8nJQwdsjXxRERFy8fRJeSyJ+mQEztNGD5Mje/6V94d9L/kKJTzCZV+yUapSVQkskrzPS2qZsem0LNhx3vY7yigOXQhVWeaqxXLZmv0wrq5MYA6Zm0hAbO+p2kVkx8lgOWWijjkqJk5i7sRJdq+HW7bU6bku0rptO9vvOXL4Sdly5VUAdGDfXlsjX0R4uJw8cTzR+uTk3mbRX/OlarVqUqx4CUmPzmzfK1XaN3M4r3SDWnLzwmUJuRRk+jqehOa/dVsdJzvt3H9Y8uXJLfkCzNX0m3XhcpDkSGEZCOkhWXOEjLpnZHcRSAMCZjTaoYzDCJ4xiaJ58+aqXheZVcPly5dl+XLHmZBLlixR5QmAzOL333+vAmCzQRdqabHgBhra2rVrl2hmFk1xyBwbkCXH47RvknPOshrNjym5vbOknmdyXq+HAUcPUB6Chj4jG7x3715VE21fymGG8+uYUv3795eQkBCHU69WKV9aEzo8UlwuhUbIn3tP2WYyLzhwWp6qcv8LcPeFaypjjHnQOGxdxN/X4eSXLatqGMO/s2fNbOo6nnZmxlw1qxnTMcCnVHEp+sLTcmbqLNt1ctepLs02L1Fzm+Hq31ulQOum4lepnPo9s6+PlH7zNQk9ctwWGJu534ctR85cagKGccLhZZ8cflK9YRNZMXuqKr3AeLpF034WL29vqd6gqe22mKQx56f4TnyUUU0fM1wO79quAuf8hRynLNgLuX5NDvy7NclA/L92I/y2mpJhnHC0FgH0ir2X1MQNZJ9RxdGzZTmJuH1Hlt+bvgGjXqouH7Wv6HB/mOPcsFxemePUKAil8/vKC/WL28bSoQH3vdbl1I7k34ceblCVK1duNQHDOCEA9suZU5o0byFTJ0+SmzdvqL9pP08YL97e2aVpi/ipFDCwX18ZPSp+GpDZ28C1a1dl6+ZNHm8UTA4EvZ8fW6fmNsOmiTMlT/HC0rB7fL9Q/rIl5bGuz8nfE+7325i5jid1eqK5XLwcJLMWxn9nnjp3QdUnd25/f3vt2HtQWr/cQ42eM2vSH/Pl8In705KWrt0oK9Zvlo6t7/+tSC84qi71JPubHAEyAlUjUA4MDFSNZEePHnWoo8Ukh06dOqlaVjSOIShEkxpW8rOHSQ4tWrRQPxFc4o8UxqOZhf/jnXfeUWPX5s+fn+j1UFqB8XUokShXrpx6vFFRUWoShSuYRtGlSxdVnoHSiT59+iTr9s7cPU+zr9fDMn78eJVZx04CJmKsX79eNRa2b5+8gMD5dbQfVZccXl5e6mTvzgM245XI4ydDWteUUWv3yA+bDkhk7B1pW6GovFKrrO06kTF35HxIuMqi6eLW0ZOy851+UvmrAVKh/wdqcsa5WfPl2JiJtuvgPJ8SRSVj1vj6zWPfTxSvvHmk4cLpqkQDM51v7j0g/3b7MFn3m1Z06dFHfhv1hQx4taNa5ASj5t4cOExlpu0bC5GhBpRpbF21RAXe4wf3dbivrv2/dCj32LpqqQrEqz2W9hrHvvhzv3zV5VFZM7C5RMXckZsRt+W9yf+qcgtD/pzZEhze7VCzsERE35GVe+8H2YYL1yMlX3UvWT2wmUTdviM5fbKqCR49Jv2TJqZtQJ9+A+SLTwdKxzYtxcsrmyrLGDbqW8mRI4dDk2Amuz4MM7eBpQsXiHf27NKkafzYQ0/L4p1NBu2NL13JVbiABJYvJRVbN5Kgo6dkbNv/qfOz+mSXfKWLS6as8eVplw4dl0kvfiDPjR0iHYd9JFl9vGXL5LmybNh42/2auY4nlS5eREYM+ECGfv+zfPvLdImMipKOLZtItxc62a4TERWlAueYe0d7I6Oi5aluvdS/r1wNllNnL8jG7bukeOGCMmFY/Az7qhXLyeff/iinzl9UR5+yeWWVj995XV7s+ISHnimlBRnuGvOWTDp06JBqakPdrzF2DBnZoKAgFfw5w/xkjHNDdhV1yfa1rmiQQ2YSpQ9oPET5AjLaZpvwDJjagLINTIqwb9ZAwIdaZfsJDqjVRuCL6RIYz2bUM7m6Lmq3kXFFOQjKKdA0l9jtk4KsMsbQIQB39zzNvF4IwM1ydZsVK1aonR6U3thnxrETVKFCBdt5yJBj2yKQx9g5lK24u2+Mm0OTI8ppEnsdcR0zt3Pnxg/9JDUgULgWHik5vLImyAxHxcSqyRsFcmSXzHYj7ewXWrkVHSMFcyZ+CM/MdZJjw+eLUuV+BLXvgfkk5maI3Im4P7MXUM+cLX9eiTh/Se7aNwFmyCBe+QJUAB0XFZ3s+30Q2f9O/VnREbfC5HZUlOTME5DgaBeCZ4yow1SO29HRcjPYdelJrrz5JEuW+zXyITeCVVOif57UWUSh18+JL/KQUjmyZRbvrJkkKDThNkTwjPIOLJBiyOPrhc0qV11c315ePy+5GR6TKjubawam/s4HduKjIiMlIG/eBNsbwTMaBHPnyWP6NhB87ZpqVgvIm3SPjlmDcjpm/VMib6mE5UKx0bflxvlLtgDbv2B+CT5zwaHJF02BOD/iRohEhzs2qyfnOikx9tziVLkfJKaCgm+IXw4f8XH6jkWwHBR8XQrmz6vG1CH8OesiC41xdwWcFkEJj4iU2Dt3JGcOxz6kB5W5cCV5WFqNT/067eU9HhMrSnbwTMlnBM9YVIRST2oFz+lNqgXP6cx/ETynB/9F8Jwe/BfBc3qQGsFzepRawXN68zCD5+Zj3a+hkVyreqa87yg983wBZiIwTxlzhhODqRfI/qaHx2n114iIiIhIF2k2eEZJCFakS+ry9PI40eyGMgVP/N9ERERExox70jh4RnOZ86p06fVxNmvWzGP/NxERERFZIHgmIiIiotQRx8xzqmHwTERERKQ5zofw0CIpRERERERWxswzERERkebYMJh6mHkmIiIiIjKJmWciIiIizbFhMPUweCYiIiLS3N04Tz8CfbBsg4iIiIjIJGaeiYiIiDTHUXWph8EzERERkeZY85x6WLZBRERERGQSM89EREREmuOc59TDzDMRERERkUnMPBMRERFpjpnn1MPgmYiIiEhzcXfvevohaINlG0REREREJjHzTERERKQ5lm2kHmaeiYiIiIhMYuaZiIiISHPMPKceBs9EREREmuMKg6mHZRtERERERCYx80xERESkubscVZdqGDxTuvVMTFuxomV73hIrajH9oFjRigGNxYqyzv5KrGjsucViRT2LWPPv+YS7pz39ECgFGDwTERERae5unKcfgT4YPBMRERFpLj02DEZERMiaNWskJCRE6tSpI6VLl070upGRkTJ9+nSXl9WuXVuqVKmi/r106VK5cOGCw+XFixeX5s2bm35cDJ6JiIiIKE05duyYNGvWTHLlyiVFixaV7t27y+effy69e/d2ef2YmBjZunWrw3kIkpctWyZ//PGHLXgeNWqUXL16VWrVqmW7XnR0NINnIiIiIkq/c57ffPNNqVChgsoUZ8yYUWbOnCkvvviitG3bVsqXL5/g+n5+fjJx4kSH8/r06SPbt2+XDh06OJzfpk0bGT58eIofG0fVEREREVGacfnyZVm7dq306NFDBc7QuXNnyZcvn8yaNcvUfSATPXXqVHn11VfFy8vL4bLTp0/LtGnTZOXKlXLz5s1kPz6WbRARERFp7r/IPEdHR6uTPQSqzsFqch04cED9rFixou08BNHIOO/fv9/UfSxYsECCgoKka9euCS7buXOn+nn48GE5d+6c/PLLL9KxY0fTj4/BMxEREZHm4v6DOc/Dhg2TIUOGOJz32WefyeDBgxNcd9GiRSqjnBQj0A0NDVU/Ue9sL3fu3LbL3EFA3KBBA1X64fz4cH6GDBnU7/369ZNXXnlFjh49KoGBgabum8EzERERESVb//795cMPP3Q4L7GsM7LJaAI0Ezx7e3urn2FhYRIQEGC7HL/7+Pi4fVxoFFyxYoVMmjQpwWUNGzZ0+H3AgAEyYsQI2bhxozzzzDNiBoNnIiIiIs39F2UbXsko0fj4449N368xkg61ySVKlLCdj9+dm/9c+fXXX8XX11eeffZZt9fF40dJyPXr100/PjYMEhEREVGaUbp0aVXf/Pvvv9vOw9QMZK7bt29vO2/x4sWyatWqBMuQI3h+6aWXbBlsA0o+bty44XDe7NmzJS4uTurWrWv68THzTERERKS59Daq7vvvv5cnnnhCBcPIPo8fP16ef/55efzxx23X+b//+z/x9/d3mNGMKR0nT56Ubt26JbhPBM8tWrSQli1bSqlSpeTgwYMyefJkVfdszIE2g8EzERERkebS2wqDzZs3l127dsmMGTPk0qVLMnLkSOnSpYvDdTDzOXv27A7nYcIGguGqVasmuM/ChQvLtm3b1H0eOnRIihUrJps3b5bq1asn67ExeCYiIiKiNKdSpUoydOjQRC/v27dvgvMQYDsH2c6Lqbz11lsP9LgYPBMRERFpDuUPlDrYMEhEREREZBIzz0RERESaS28Ng2kZg2ciIiIizaW3hsG0jGUbREREREQmMfNMREREpLm7cXc8/RC0wcwzEREREZFJzDwTERERaY6Z59Rj+cxzeHi4nDp1yvaChIWFyenTp8XKQkJC5OzZs55+GERERJSKwXNqn6zK8pnnpUuXqpVmrl27pl6QuXPnysCBA+X8+fOSnmC99hs3bqilJh/U1KlTZezYsXL48GGxkiyZMkiVQjnFO0smOXw5TK6F3zZ1u3y+XlImn69cj7gthy6Hpdr9/tdOnz0vR0+ckty5/KVa5YqSKVMmt7e5Fnxdtu3cLUULF5LKFcql2v16Qla1XfzFO2smOXQp1Pz2zuElZbG9w2/LQRfbG5eXzusr4dGxcuRKmETFxklaEh0dLXt37ZTIyAipUKmy5M2XL1Vucz04WI4dPSyxMbFSueqj4pczp6QFZ66HyfGrIZIru5dULRQgmTJmMHW7uLt3ZfXh85I5U0ZpUrZQgsUmjgbdlIsh4ZLX11sqFsgtGTOYu9+H5fS5i3Lk5GnJkyunVKtU3tTn8Or1G7Jt1z4pVqiAVC5fJsFlW3fsTXCbJvVria+P4/LInuSXP6+Ua1pPgo6dljP/Jny8ruQrU0IKVykvoUHBcmLTv3I3Li5F1yHrsHzw7GrZxhIlSkh6M2vWLPnyyy8tnzVPqcL+3jKy4yMSGXNHrkfESL+WOWT83ydlyYHLid4GX5U9Hi8pbSoGyp4LNyVLpvgDOZ8sPCAxd+6m+H4fhm8n/CIz5i2QGlUekROnz0qunH7y87fDJKefn8vrB10LlhFjfpDdBw5JZGSUtG3RxGXwnNz79RRsl687VYnfLuG3ZUDr8jJ23XFZvD/p7d2zcSl5olIB2X3+pmTNnFGwYFf/v/ap7e2XLbN81LKclArwkRPXwtVOVYCvlwxddlh2nL0hacG5s2ekz7tvi7d3dsmdJ498NXiQ9OzVR9p1fPqBbjNv1kz5adwYqVSlqvod1+n32RBp2KiJeNLY9Xtl9s7j8mjhvHIqOFT8vbPK950bSU7vrG5v+9vWw/LL5oPi7+3lEDwfvXJTvly2XQXL+f2yy6HLN8QrcyYZ/WxDKZjTR9KCb36eKjPmL5UalSvIiTPn4j+HIz8Tf78cLq8fdO26DB8/SfYcPCIRUdHStmnDBMHz8VNnpd/w0dKu2eMO59etXjlNBM85C+STzt99KiXqVRcvn+yyfcZfpoLnjsM+lsY9X5HjG7ZLgYql5da1GzK6xUsScSMkWddJD+7esW6mOLVZMni+deuWREZGSt68eRNc1qJFC6levXqC869fv6723HO6yKYgC3Hp0iXJlSuXeHt7J7j89u3bcvnyZQkICJDs2bMnuF88lkKFCjk8vqtXr9qCeJRR4FS0aFGJjY2V4OBgyZ8/v0PpCa6Py44fP67Ow//l7+/v9rVI6nmBq/8PIiIi5OLFi+rfeM4FCxaUDE6ZF/vHjecUFBQkxYsXl4wZ44PMqKgolfEvUKCAx7OTvZuVkTPXI2TAggOCsPeJSoHybuNS8u/ZGxIUFu3yNi/UKiLNy+eTt//YJeduRKrzKhf0U5ktI3hOyf3+17bt2C2TZsyWKeNGSbXKlSQ8IkK6dHtXvvvxV/ms7/sub4OAuXmjBjL8037y6ju9U+1+PaVvi7JyJjhC+s3fp7ZL20cC5f0mZeTfMzfkSiLb5aXaRaVFhfzy5owdcvbe9sYRhcz3trdX5oyyZP9l2Xwy2HYbBNufPlFBnvpxs6SFEav/N/RzKVaipAz/Zoz6HC6aP09Gfz1CatapK4EFCqboNmfPnJax334tnwz+Upq1aq1us2r5Uhk+5DOp+md1j2Wgt58JkqnbjsjPLzaRKoUCJDw6Rl6bulrG/71P+reqkeRt914Ilvl7T0qnaqVU9tleTFycDH2ynhTJ5at+j70TJ69OXSU/btgvQ9rVEU/bumufTPpjvkwdPVRlnMMjIuW5Hh/JdxOnyeAP33Z5m8ioKGnRsK6MGPCBvPLBJ0neP66TFiFg3jl3mfzy4gfSZ8NsU7cp16SetPzoTfm6wTNycstO8fL1kf7bF6hgecZbA0xfh6zHcjXP/fr1k9y5c0ulSpVUULd69WqHy1G20aBBA9vve/bsUdctWbKklC5dWurXr+9QzjBlyhQpXLiwVKhQQQWB77zzjgqWDV999ZXkyZNHatasqYLrbt26qUOghjFjxshzzz3n8BgWLVoktWrVciijaNSokbz55psSGBgo5cqVU4H17t271eVbtmyRb7/9Vq5cuSKtW7dWpz/++CPJ18Hd88JjTOz/gx07dtj+Lzw3BN/ffPONw/9hPO433nhD7Ry0bNlSBfq47x49eqgAv3bt2uq22C5xHjoMltc3qwqC/txzUQVSsPzQFYmKuSONSge4vA0CpmerFVK3MQJn2HcxVKJi4lJ8vw/DopVr5JHyZVWACz7Zs8vT7VrLklXrEt0GxYoUkjbNGkuWzJlT9X49Ia+vl1Qt7C9zd1+wbZdlB69IVOwdaVw24Q61sb071ygs83ZdsAXOsPdCiETe295Xb912CJxh04lgyemdRWWgPS3oymXZu3uXdOr8vG0HtnW79pLN21vWrV6V4tscOXRQbd+GTZrabtewcVMJD78lG9evFU9ZdvCMKqdA4Aw+XlmkQ5USsuLQWVWSkZiwqNvy6aJtMrB1LfHLljBDXalAblvgDCjryJ8ju3r/pAWLV/0tj5QrrQJn8MnuLZ2eaCZL1m5M/PNduKC0adIgyc+3YeP2XbJm8z9y5sIlSUuCjp+WHbMWSVxsrOnb1H6xo5zZvlcFxRB9K1w2TZwptZ5vb0sGmblOesGa59RjqeAZgTFqedevX6+yoAsWLJCZM2cmeZsPPvhAGjZsqDK0yO5+9913snNn/Ido8eLF8vrrr8v//d//yc2bN9XlCEgRxMJff/0ln3/+ufp/8P/t27dPlixZIiNGjEj2Y0cTI7K/+D+QCa5Ro4a8/358Nq958+YqSEeAiswzTgh8U/q83P1/gNsa/xey7itXrpTBgwfLxo0bEzxu7DSgHhvXzZEjh/Tq1UsOHjwoJ0+eVNnrAwcOyOzZs2XcuHHiCSXyxB9qPRUcbjvvTtxdOX8jUkoG+CR6mxzZsqhMZem8PtKwVB7180Hv92E4dvKUlC5Z3OG8siVLqEzxxctBae5+U5vx2p+65rhdzl2PlBKJbBfcBtt7u9revvJ46QD1051axXJJaFSMXPXQUQZ7J+8dlSpRqpTtvMyZs0iRosXk1InjKb5NQN74+uczp+83Xp+99+8Tx4+Jp5y4GiIlAxzLhUrlzSnht2PlUkhEorcbunyHNC1bSGoVS7oWfOWhc7Jg7ykZtnyHqqvu3iB+p9HTjp46I2WKF3U4r0yJYioDffHK1Qe676xZssgvM/+U3+cvlae69pK+Q7+V2zExkl4VqlxOLu4/4nDehX1HxNsvh+QuVsj0ddILBs+px1JlGz/++KO88MILUq9ePfX7o48+qrKikydPTvQ2KDtASYKRdUGmFCdAtveZZ55R9wlZsmRRGVXD+PHjpUuXLtKkSXzdX9myZeXDDz+UkSNHyqeffprsWmwEp9jTRYnDSy+9JC+//HIKXgX3zys5/59RkoLsOm6/YsUKh8w9SjqGDh1q+38wzWTixIkyffp0VfqBSScoe3n66adlzpw58u6778rD5uMV/zEIi3bMWIRGxYpPVtflJP7Zs6ifz1YvLEVyecuFm5FSqYCfnA6OUDXP0bFxKbrfh+HWrQjxy+EY+Pndq4W8FR6e5u43tfl4xb/2t6Idv/QR5Ppmdf0n0d87fnsj+1w0V3Y5fzNSHinopwLw/n/tV9vb2aOFc6r3x+i1x20Zbk8Kv3VL/fTN4RhQ+vnlVGVVKb1N1WrVpXqt2jLwow/lmeeeV3Xgi//6U/xz5bLd3hOwfZ0zxznv/Y4SDlfm7joh526EyRcmyi+2nb4iYdG35cCl61IhMJftvj3tVjg+h447gTnvfS7DwhPfaXCnYGA+WfjrGClcIL6E79S5C9LlnY9l4u/zpMcrjkdP04tsOXMkqFsOv35T/fTO6Wf6OmQ9lgqejx07Ju3atXM4r3Llykne5rPPPlNBI7LMyPA++eSTUqdO/B/WQ4cOqSxqUv8fShXsIWBHFhrTMRCgmoWSECMABV9f30S/8MxI6nmZ+f/wHJB1R7CMwNnHx0dl3FEKYw/ZcC+v+4esjxw5IjExMfLRRx8lqHMuUqRIoo8XpR725S4QF3tbMmZO3hdWufy+UsT/fl36+uPX5Pa9wAfTMIySC8ieNZNcu+X6EKBxG+g6PT5jj8Pzv7xYXZ6rUVimbDubovt9GLy8skpE5P3SA4iIiLRdltbu90GUz59D7dwY1h+76rBdjJKL+9vF9cSN23fuX+9/U/+1be/Jr9SU52sWkclbzzhcv1z+HPLlk4/I7J3nZdG+h394+9CB/arRz9CoaXPJmi3+c4iJGfa9FxER4bbssTMzt8HfiZHfjZWVy5aoEo5MGTPJoC+GyeeD+om3U4/Hw+SVJZNE3Hb8nCHrrC7LnMllsP3d2j3yfM0ysurwOXXesashEhUbK0sPnJFHCwdIAbuGwIFtaqqfMXfi5N1Zf8vgxf/I2OcaiafFfw6jHM4Lv/d7tgf4HGICh70SRQqpxsJ1W/9Nt8FzTGS0qmG2l+3ejkfMvdfMzHXSCyuPlkttlirbQBYUTWr2nH931qFDB1WWMGjQIFWvi2DYyBpnzZpVNfsl9f85B3y4Pr5sjIDSVc0UmvT+a0k9LzP69OmjnguyzrgflGQ0a9YsQU2dc4CM7DzMmzfPVvZhnNauTbw+ctiwYao22v50euW0ZD/v4rl9pFax3LZT1kwZ5VJI/DbMnyNbgpFjl0Jdvz8u3LvNFrsa15DIGDlwKVSNMYOU3O/DULhgAbnkVEZx8fIVyZQpoxR0agxNC/f7IIrnyS61i+e2nTAR5WJI/Guf389xu2A7Xby3zZzhyAJsdtre+y+GStn8ORLsoH39dGVZvO+S/LTxfinDw3T65An5Z+tm2ykm5rYULBh/iPnKJcdg/sqVy1LQrmHZntnbZM6cWdq0e1I+6NtP3u3dV/Llzy/nz52TMmVdjzN8GArl9JXLoY6Z1suh4ZIpQwYJzOk6qMdUDdxmy6nL6oQsNHac8O9r4a4/s3hP4XZ7LjjWu3tKkQL5E5RnXLoSJJky4nPouqY/pbyzecnNkITjGtOLayfPJii9yF2ssNxBo/yZC6avQ9ZjqeAZWd+///7b4TzUPycFgSyyqshYoy4YGdtp0+KDNjTZLVu2LMFt7twbB4P/b926dQ6XIUAsX768LXhG0xwCUOdmvuTC/SUn6E7qeZmBmuU2bdqo5ktACca2bdvc3g6NlaiBRv25q8eUmP79+9umdxin4i1ekuRCw96wFUdsp/Dbd+RkcIRcCY2SxmXuN/FVDMwhgX7ZZOup67bzahb1l0cKxB8tQIbyWNAtKZb7/pcwdoOK5MouV0Ljd5jM3u/D9ni92rJ9114JvhF/6BGWrl4vtas/assQX70WLAuXr05WuYWZ+33Y0AiIUXHGSW3va+FyGdvFrjmwYgE/CczpuF1Qr4zpKUYz4NErYS62t7faxgbsOH39dBU1deOHDSfFU9q07yADhwy1nXx9c0jJ0mUkf2CgrFu90na9A/v2qMC47mMNbech2EaTIJi9DWY825v1+zT1fyLj7SmPlSogO88FyXW7oHfFoXNSs1g+W+b52q1IlVVG1tnXK4t83q6Ow6lp2cLi55VV/btywTzqNkFhCXewDl6+IYF+nh/XBo/XqSHb9xxw+BwuWbtJ6lSrLF5Z732+g6/LwpXrVYmHWVeuOm7j27djZP3WHVLFaaRdWuYXmFc1AGa7V8ayb/EaKdu4ruTIG79toWaX9nJkzWaJvZf4MnOd9II1z6nHUmUbKBVAXS5qeVGmgCY3BHFoYksM6ndfeeUVVdKATCvqco3aYGRt8e/XXntNunfvrrLYo0ePVieMZPvkk0/U2DsEfs8++6yaioGmONT72o/GQ/Pe8OHD5YknnpBNmzbJhAkTHEodzMBEDAThCOYxPcPdqLqknpcZuN0vv/yiboOsE+qaUcrhDrL1aJjs2bOneo5t27ZVzYRopMRlyDC7gus6vybJLdlIyri/T8qnbcqrIwHXbkVLp2qFZNXhIIdFMF6tW0wFxvsvharfx64/IV91qCSZM2WQs9cjpX7JPGrW7x87zifrfh+2J1s3l7kLl0q3Xv3kqbatZN/Bw7Jr/wGZMnaU7TpHTpyU/l+OlAXTfhZfHx+Jjb0jS1fH7wjeCAmR0+cuqOA6h6+PNH6srun7TSsw03lw24pqVi+2yzPVCsvKQ1fUkQPD/+oVV0E2JqjA9+uOy/COlSVzxoxy9kaEPFYyjyrd+P3f+EP8+XN4yf89XUWu3oqW41dvSYvy90sh/jl9XUKiPFeqA3gP9vywrwzu/7E6QoSyizkzZ0jzVm3kkXvzmeHXnyaoEXRVHq1m+jY/jh2tSjRKly0n+/bslvVrVskXw78WH1/3TZX/lbaVislfe05Kz1l/y5OVS8j+S8Gy98I1+emF+7OnjwWFyGeL/5FZb7RSwbMZmP0cEhktVQsHqKzzv2eCZNPJSzKsQ31JC55s2VjmLFkpXT8aIk+3biZ7Dx+TXfsPy9TvvrRd5/CJ02pmM2qYMaM59s4dWbomvtn7RkiYnDl/UQXX6vNdL7485acZc1XQXbNqJVXX/teKtRIZHS3vvh7f8+NpGTNlUkEt+AbkknxlS6hAOfJmqAqAoXDVivL6tO/ks/LNJOrILdk6ZZ406Pa8vL9ymmyeNEuK13lUSj1WQ0Y17Gy7XzPXSS9YtpF6MtxFt5aFLF++XAVoqDnGBAlMjcAEjn/++cdWToBGwA0bNqjfsUw1GvwQ+CK4a9q0qRqrZgTcGO+GwBFj3NCAh4Y3+7pq3A7/H8oSMPYNDYVoMrS3cOFCNeYNzXQISnGyf0zICGPsGx67YfPmzSr4NeY6A/6f+fPnqwkaKKtIauJGUs/LzP+Hx4qdA2TuEdTiOWMqR7Zs2WzTRFzdj/12QEMl7g+vG27/9ttvq8diVrMx8dsotZTL5ytNy+UT7ywZVcCEINf+w/FKnaLqUP1fe+8fwkbmsWWF/CpoxqH9pQfQRBSbrPtNrmVdEq8NNysqOlrmLFwqR46flNz+/tKpXSu1aqAB52Nmc58eXSVvQB7VGDpo+LcJ7icwX17p9dbrpu/3QbSY7lhX/KBQl4w53ah9xsg5BM/22+W1usXkZmSMzN8TP88ciubyllYVA8XPO7OamoIMs7G9i+bOLi/Xdqz5N/y65bStXCS5fv9ffPCSWg4fPKDmMGOnGQFyi9ZPOPQ3/PrzBPH3zyVPPfuc6dvgqNGyRQvk0MEDki9ffmnVtl2ic6PNyj7X9Y50cmAs5F97T6rVAHNnzyZPVinhMGbuWNBNmfrPEXmvcRUJ8E04o3/9sQtqXnSf5tUczkcZxz+nr0h07B0plNNHWlUsJgG+jmVAKeXT6vkHvg/1OVy8So6cOC25c+WUp9s0c6hZxvmYBd3nzVckb57cKos86OuE044C8wVIr64vOYyp27Jjj8TExkrpYkWkfYvGqnQjNfQs0vaBbp85a1Z5+ZeEk6xunLsk8weMVP8uVLm8mtk8t+9XEno5vrQlSzYvFRwXrlpBwoKCZdMvf8jVE45/a8xcJ6Um3D0tD0tgp9Gpfp+X56atGf4Pi+WCZ9JHagfP6UVqBM/pUWoHz+lFagfP6UVqBM/pUWoEz+nRgwbP6dXDDJ7zPeW4FkNqCPrzQ7EiS5VtWMm5c+cSNCsazK4+SERERHpg2UbqYfCsKZRsHD161OVlqMHGfGsiIiIiSh4Gz5pCAx4RERERMPOceiw1qo6IiIiI6EEw80xERESkubv31qCgB8fgmYiIiEhzLNtIPSzbICIiIiIyiZlnIiIiIs0x85x6mHkmIiIiIjKJmWciIiIizTHznHoYPBMRERFp7m5cnKcfgjZYtkFEREREZBIzz0RERESaY9lG6mHmmYiIiIjIJGaeiYiIiDTHzHPqYfBMREREpLm4OC7PnVpYtkFEREREZBIzz0RERESau3uHmefUwswzEREREZFJzDwTERERaY4Ng6mHwTMRERGR5tJj8BwdHS1//vmnnD9/Xt566y3x9fV1e5vr16/LkiVLJCQkROrVqyfVq1dP0XWSwrINIiIiIkpTxowZIyVLllQ/+/btKzdv3nR7m3379km5cuXkp59+kq1bt0qjRo3ks88+S/Z13GHmmYiIiEhz6S3zXLp0admzZ4/s379fmjRpYuo2b775ptSvX1/mz58vGTJkkIULF0qHDh2kU6dOUqVKFdPXcYeZZyIiIiJKU5544gkJCAgwff0LFy7Ili1bpHv37ioohnbt2knBggVlzpw5pq9jBjPPRERERJr7LzLP0dHR6mTPy8tLnR62Q4cOqZ8oyTAgQMbvxmVmrmMGg2dKt1a/19Aj/y/+UAwbNkz69+/vkT8QnuLp572uV3Gx4vP2FI8/7ze+tObz9hBPP+8Jd0+LFZ/3w3R716RUv8/BgwfLkCFDHM5D/TDOdzZz5kzV+JeUPn36pPixhIWFqZ/+/v4O5+N34zIz1zGDZRtEKfhjiz8WznvbuuPz5va2Ar7P+T4n8/r3768mVtifcJ4rwcHBcvny5SRPDyJ79uzqZ2hoqMP5eEw+Pj6mr2MGM89ERERElGxeySjReOedd/7TV9goxThx4oSa0mE4efKkdO7c2fR1zGDmmYiIiIjSnT/++EP++usv9e/ixYtL1apVZcqUKbbL169fL6dOnZKOHTuavo4ZzDwTERERUZqyadMmNRkDWWL48ccfJWfOnNK2bVupUKGCOu+HH35Q9coYNQfjxo2Tli1bSlRUlJQoUUJ+/fVXNZqubt26tvs1cx13GDwTJRMOUaEhQvfmEmd83tzeVsD3Od/nlDaEhYWpOmjUIvfu3VsiIyNtJ8Nzzz0n3t7ett8fe+wxOXDggMyePVvVNf/2229q5J09M9dxJ8Pdu3fvpsJzJCIiIiLSHmueiYiIiIhMYvBMRERERGQSg2ciIpLr16/L1atXk30ZEZHVMHgmIpdmzZqlOpnJGiZNmiQjRoxI9mVERFbDaRtELqxYsUIFj2a0atVKnn32We1ex5s3b8qePXvEiq5du6aWl928ebM888wzMmDAANWdvXHjRjXSyGpu3bolvr6+oqtDhw6pVUP37dsnX3/9tbRp00YWLVokWbJkUZ9vXWFhCIz/wozb27dvO1yGmbevvfaa6Iifb3pQDJ6JXIiNjVUzIA3//POPHD9+XOrUqSOFCxeWK1euqPmTBQoUkGbNmmn5GjZt2lRGjRqlli3FbE2riImJkSZNmkiRIkXUtkbJgrEyFcYiYT4oZoPqYu3atWqRgZ07d0p4eLh88MEHDpfjvHnz5qlZqDo6e/as1KtXT+0AZ82aVT1fwEIKLVq0UAE1gmjdXLp0SWrUqCGlS5eW6tWrJ3iOmJ2rI6t9vum/weCZyAXMfDTmPp4/f17q168vO3bskGrVqtmug2Aaf2gfeeQRLV9DfLkimMCXCobS582b1+Hyxx9/PNmzMdPLUQfMDV28eLF888036nWAzJkzS+PGjVUgiZmjOh1hwHsZ2bjo6Gj1b3t+fn7y1VdfSfv27UVHP//8s7zyyisyZswYhyNICK6QbceOM+bC6vg+xw7CunXrxEqs9vmm/waDZyI3/v77b2nevLlD4AzI2CBTsWrVKqlcubJ2ryOG0+fPn1+dzpw5o072SpYsKTrCIexatWpJhgwZ1Mlerly5bJkqXTz11FPqtGzZMnWUAe9pK8H2NkozrLC9DRkzZpTy5cuL1Vjt803/DQbPRCZWOXIOHO0P+dqvbqQTZOF0rOV2p1ChQjJnzhz1b/sv17i4OFm+fLm8/fbboqPWrVvLhQsX1E5TYGCgYP2sX375RQ4fPiwvvfSSPProo6Lr9kY9u/P2xnSRf//9V+0k66hhw4YybNgwVaaCFdyswqqfb0pdXGGQyA2UbSBD06VLF9UsVrBgQfXFOm3aNHWoF7WiupZuWBGWfq1YsaLKwKL2HTtP2Pao/0YwdfToUVXKoBsEUaj9XbhwoRQrVkwFzr169VJHXHbt2qXKOfLlyye6wc4ByjJQmjJ//nxp166d+ox/9tlnEhAQIGvWrBFdbNiwQZUrGJYsWaLe3yi/ypEjhyXKsqz6+abUxeCZyARMXXjvvfdU3bMBf4C//fZbVfesM9TCYhoBDmciG2koU6aMVKpUSXR07NgxNWkA292AHaSpU6dqm4FF4Dh9+nSZPXu2+h31n2+88Ya8/PLL8sILL0iDBg2kR48eoqOVK1eqHWMc0jdg4sZvv/2WoNY/PcO2xXQNMzp37izdu3cXHVnx802pi8EzUTLgkDYObeOwNrJTzjVzuhk5cqQMGjRI7ty5o2ok0akO6Mzv27evDB06VHQ/6oDtnSdPHm0P3xvGjx8v+/fvVz8jIiJU1hXPP3fu3PLFF1+obf/555+Lbi5evKiyrmgORFCFBko0C2KSjnGZc1ZWB9gZxufa1c5BUpeld2iKRRIgW7Zslvp8U+riIilEyYCgGeOdUDene+CMOlAEx2iYHD58uMq84xDn6NGj1Zfqhx9+KLrDKCuMJ7TCFyumqqBpEPX9EyZMUJk4BM6AQ9m4XEd4X6PWFZ/nsmXLSu3atVXgbH+Zjqy6KM64ceNk4MCBlvt8U+piwyCRCVZcTAAjujp06KC+XLA4CDKPyM4hoEBtIBaR0bG5xt0CObouioO53ijDKV68uMrKYWSXcbRl69atpg/360T3xWGs+LyRALEvvyNKCQbPRG5YdTGB4OBgW4MYDuEjmDaUKlVKHe7UEXaOEDw4N9OhPhJTCbDAgo6QeUWzIDLPWBTHeF/j/KVLl0r27NlFJ5MnT5bdu3erVTSxIBJ2EO2hKRgNdhMnThSdWH1RHDSEogwJR1kwYYYoJRg8E7lh1cUE7GEu6vvvv68mD2AWKiaNfPLJJ6LrlytOrnYmMIEAr4XOMGnDnjHrWzfIqGOCCHaU8G972GHADuPcuXPVoX2dWH1RHGxT7BihIRRlSc513a+//rp89NFHHnt8lD4weCZyw6qLCaDrHItmGJNF3nnnHbVcMeahonQBExisBE1FWEwEWVjUxurKKtNV+vXrZ6uBRWlSzZo1xQqsvigORi8OHjw4ycuJ3OG0DSI3Tp8+readbt++3VKLCbiCQ7rI1OmYiTSja9euUqJECW2z7lafrkJEZAYzz0RunDt3TjJnzqymD1hpMQFXsPOg+w4EmiGdS3QQTO7bt081EqJ5TvfpKqj1RSkDMnTG5AXdp6ug1huj6pzr3TF9A2MpdWTFRmii1MDgmcgNBBFonMPpyJEjCS4vWbKk1ofwEUChWe6ZZ56RAQMGqCALzVVYVEJHBw8eVDXd9rDzVLRoUbU6G5pGdWTV6SooQ8JCMDNmzJBMmTI5ZNy9vLxk5syZKpDUjVUboQErC/7++++qYdS5PAm10FYrZaHkY/BM5AbGkuk4mswdBBCYLIEFI9A0hS8ZwLxffLlgZUWUMOjmlVdeUSersep0FawiiCDq7NmzajlyLNWM1RSxPDdqv3UMnK3cCI1AuXnz5uqIIo4iIuOOIws42oL3Pfo5iNzhIilElOiXq7e3t8q2NmrUyCELi6WbjTnApB9MFMHiIJiusmvXLpWJ13UhCZThoBkWO4mYsoGdRswCxkIxQUFB6vnryKqN0KtXr1Y7glhN86WXXlIld6tWrVK/ozwL5XlE7jDzTGSCFcsXUAeJIAoBhfNqihhXZ2SidflC/fPPP01dF1krHbORVp2u4pxxN8bW4T2PIysItHScwNCwYUMZNmyYagLWvY/BHkrv8L5GYgClKpGRkbZpMjjihGRB5cqVPf0wKY1j8EzkhlXLF7AE+Zw5c9S/7YNnBFPISupU/4omsfPnz5uek6sj7BTYQ/MgdhKtNF0FzYHffPONei0QNK9fv179riOrNkIjWDYW/EG5xqJFixz+1mPBHCJ3GDwTJaN8AV+kaLRxLl/o3bu3dq8jVt/ChAXMw0WDTVhYmFqdbNSoUao+tFOnTqILNMrhRNabroKdX9R0A7LrWESjSpUqaocRi2VUqFBBdGTlRmhDs2bN1KIoaIrF0bSffvpJrbJJ5A6DZyI3rFS+YA87DNhxwLgqlKsAvlyQqcICC1iJjNI3lquIdO/e3fZ6YLoGMpEo5cDOMZYp15VVG6FRdoeVFQErDGJ7Y0XF0NBQlRho2rSppx8ipQMMnoncsFL5gjPUAW7atEmVNOAwNlbZ07VxzIor7bFcRVQvA8qy7Jti8T4nPRUvXtzhd2x3+21PZAZXGCQyUSOHBirUNxvlCxhnhSwF5t8ePXpUyyzs9OnT1eHc//3vf1rWdCeFK+0lXfONqQS6BJg4bI/6XisuCIKdQsyxRqkCdpALFCig6r3xmmDmNRG5xlF1RCbLFzAHFAEzShdwaA8rkulcvoCMO+o/UQ+K2kAE00Znus7sV9obPny4qofEDtPo0aMlb9682q+0587EiRPVlAZdYFEMLACDI0lWg74FBMqYdYwFUzC+Dv0byMQjUaCrX375RTV8Z82a1VaOZ5z69Onj6YdH6QDLNohMsGL5ApohEUhiDi6WaO7Ro4caX/b888/LG2+8ITVr1hQdWXWlPatC4Lht2zZVy4/3vPPOMGYB6zj7F5NE8DcNn3H75kDMtq5Xr57acdZxpb2dO3eqv2Wffvqpy5UVixUr5rHHRukHg2eiZMBoI/yxxTxYq6hbt646fffdd+oL9fvvv1cLSGAWKmZco6zDGP2kA6uutGdVJ06csM1xRgmWs7Zt24qO9u7dK+3bt08wVQPvfZSl4XIdg2d8njt37iyffPKJpx8KpWMMnolMQO3vxx9/LEuXLlWZKgTQyFKhNhaLS1ihNnLLli2qQRIrcZUtW1YtsoAu9a+//lplsYoWLSq6wZSV999/X620h8kqWGmPX7p6QQbS7Gg3zELWZXQf3s+udhaMv3fIPusIdd1ED4o1z0Qmpi40aNBA1fvOmDFDlTFg+gYO7yKAxCg7XaGue8iQISo7hSwVagKxA4Ev13Hjxsnp06fVTNwpU6aILrAzZAQO9ivt4RAvMs+6rrRHSevZs6d67+sCC6CgZOPll19WoyjxWUdWFiUNmGmv0xx3e+jfwJLrKMkiSilmnoncWLBggQqa8MWJhhrDk08+qQJKdKv3799fu9dx8uTJqpmoatWqqokItZ/+/v4O10EGHl/CV69eFV1wpT2yAsw4RiM0guXHHnvMdj7GMKIR2nmkW3r266+/yogRIxwSIkh8oAzPeWVF/M3D4jhESWHwTOQGFktAfa994GxAYKnrSKfy5curRiqULiQFdZEYXaYLTBXZt2+fQ6OYFVbaI+vBhA18xq9cuWIbVYeAUjeoaceRA7PXJXKHc56J3Dh+/LjKRqIzHePbDDdu3JD69eurDC0mM1gVap5RD4qfOkD99gcffCC7d+9WO0cIolGqoWNQkRK6zXlOzsp0aKTDTyKyNmaeidy4dOmSOrSHuaDovEcQhTIF1AWi6ebPP/9UJ8BiCyhjoPQLq42hJvLgwYMqC43abjSLYvYtAmnUgjof6tUFDmdjxT3UwCJIHDBggKqLRX0oJquAc+kOpV8Yw4gSDfRtoBHaHo446bry3rx581QfB/obkARB3ffhw4flrbfe4pxnMoXBM5EbyKrmz59fnTDGDCfjkCdg9q/BeewTpV9oFsRiKV9++aUKJjFpAwEkpo3okmV3DqSwg1CkSBEpXLiwWpYcsNOI0pyWLVtabqVJnWFpdhw5w6g+1Dc7zztGOZqOwTMaIwcOHKh2kAG10ChbQWPw559/rj4Dxt92osQweCZy49lnn1UnsiZMFsGIPjRXYdU1Xcs38PywmiaOqHzzzTfqiItR84+xjMjWoXGU9GmEBmxnXVdJTawsCzsNXl5e6ncsTY6jS3iPnzt3TlavXs3gmdxi8ExkAhbGQCYmMDBQzTzG8q44zIfD+FaY82w1CCgwRQVlGzt27FDbGKsK6lz7jEP3OFRvLFNsD+VJRibaqsaOHatVuU5oaKiasmGlwBlQnhIdHa3+ffHiRTVuE8E0oCnYuXyFyBUGz0RuhIeHS5s2bVSGArBU9Ycffqi6sn/66SfVUGisSEfpHxpAsfw4yhcQLGOGNUo4dIdmWMwvB/vgOS4uTmXedV6SHM3A2FnCTjKerz3Uu2PuN3acdYJM65gxY1QgaWRhrQA7DL169ZLRo0erxY/wtz1r1qzqMszw79evn6cfIqUDDJ6J3Fi5cqWq+yxWrJj6ferUqeowH5pMEFwh4MCsVNIDtvW6devUwjjOGVidp060bt1a7RQieEB5SlhYmKxdu1ZGjRolZ8+e1XbRDDxHTNPBCfPcnUdSopRF11GUqGXHRBk0Qjtn1ZGNRZ27bipUqKDe0z/88IPqY8EOBGC6Dj7vWESFyB2OqiNyY/z48apJDD8jIiIkICBAzUTFIgNffPGFarRCo4luZs2apZoj3WUcdQsirTqiD44dOyavvfaaapA0YNY1dhh1LU/C+ztnzpwyfPhwsRI0yWHb4rNbpkyZBA2Dzz//vGqisyo0guOoo45Nk/TgmHkmMpGJRICELu25c+eqLxwEznD06FGVsdMRguI9e/a4vR5Hl+kDQRRKGLBziBIG7BCVLl1adIZMM7KwVoMytKJFi6rtnS1bNk8/nDQHR5+wc8zgmVxh8EzkRtOmTdWStRjnhC8ZTB0A/GFFjdyPP/6o7fPG4c2QkBCVmSPrwKg6nKygRYsWavnmV199NckyHd1gigoaRBk4EyUfg2ciN/CFiiwNMs8IIo1MK85funSpZM+eXcvyBUycQCONsThM3rx5HS7ngjD66dq1q3Tr1s1hxUyUcnzyySeqoc7VEvXpHQ7NI/tau3ZtVefu3Dxnv0y7TtAE+dVXX6nnz6XniZKHwTORSUbDoMFYOMUwceJErWpg7ReHwY4DTva4IIxe/v77bzU5xnmpeZRyYEcRR1x0XJoai4QY9dz79u1LcDl2HHUUFBSktit2DLAqqnPDIHeOiRLH4JmIXOLiMNayd+9edZTBFZyPy3UMnj/99FOxIuwcYxyjsRCQM+4cEyWOwTMREalAasKECWpMHephDVgUCKuyYfqCjjp37qxOOu4YpMbOsW7laESpQb8CNiJKNdeuXZOePXtK9erVVX0kHDhwQNsmSWNEH2bAmqkP7t+/v+iiVatWqv61Y8eOKljGymsYWYdZ5hjVqOsS9ajlRwkDuYZytGHDhmnz8vzzzz9qtre7yzCStECBAg/50VF6wcwzEbmE+dVNmjRRGUlMXjCWZ8YhfCyugAUUSpQood2rZ9URfZi6sGzZMrX4D1afM2B1RawwqGvm8X//+5+8/vrr0r59e1sZA+ld24+SFfxtc3UZ5l/jMsw7J0oMM89E5NKKFSvU6mqLFy92mHWKQ/oIroyRfbrBiD5knzCiz2qwY4TsG7LOGMOIBkIcaahZs6boCu9jPF+sLogdhbp16zqcEstSkn4wWQYZZyJ3mHkmIpdOnTql5sCiI995/m2uXLlsmWjdcERf/GQZ5+kyuqpRo4Z89NFHiV7OQ/d6+Pnnn2Xo0KESGhqqarjnzJnjcDlKlnAZVhYkcofBM1EqQQ0s/ijrolChQrYvGPvgOS4uTh3Gd7dsd3pl5RF9J0+eVPXs2HG6ffu2w2WohdbxUHanTp08/RDoIcBRhIEDB6ojajiq5FzD7+fnp2Zfs3SHzMhwF63URORg9erV8ueff5p6VZo3b64CC91ERkaqw9iob8YEhrCwMOnSpYtadRDZGSxNji8c0ifjju2N5bjRIJolSxZLvM8paZhbr9P8epQiRUdHq1VjiVKKmWciF27duiXnz5833WCmI9Q7I0uDbCOmLsBPP/2kFlVAYxkDZ71gW1etWlXWrVsnVvLuu++qlUITM3bsWGnduvVDfUz038HOIdGDYuaZiNzCjsSFCxfUxAUrfPlgRN/gwYPVTgPm/w4YMEA1zm3cuFHefPNN0dHUqVPVMtWY9WwlK1euVKsM2gsODpYpU6ZI8eLF1dhCHUt1MJIRz9Nd+ZWOc55/+eUXGTlypCpPwlQhe71799Ymy07/HQbPROQW6pyvXr2qOtEzZcqk9SuGL1OULaD2EZNFypYtq75MUbqCZZwXLlyo5Yg+TJzAMs3bt28XHx8fsbobN26o98GOHTskd+7cohscRdq5c6fldpbwnFHbjJUlXZUnoVEWS9ITJYVlG0TJyEYeOnRITZmwbxXAH1pd6+ewbO/HH3+sDmujgQxfNBhTh6wNAkndR/R98803qhbYeUQfslO6OXfunHqOKMtBEJ0jRw6Hyx9//HF1vlVgokz58uVl9+7danyhbvCc0L+A5rmcOXOKVWAUI1aU/OSTTzz9UCgdY/BMZAKCxUGDBqnDlxkzZrQd6kMw2bdvXzUCScedhQYNGqjszIwZM9RCKVhAAIezGzZsKHv37tUyA2vVEX1oCsuXL586YafJmY6lC+7KFfbt26d2pHRk1ZGMHD1IqYHBM5EbqHVFcIzVpzZs2KCCDNTDTpo0SUaMGCEffvihlq/hggUL1MIRyDpjh8Hw5JNPqtXYZs6cqdXy1FYf0YfRXbouwZ2U4cOHq1p2e5jGgMP7eP9jR0pHVh3J2KxZM5V1xjZHcoAoJRg8E5k4zNehQwepU6eO+oOLrLOvr6+89957amQbGm90DKhwCL9y5coOgbMBUxl0rX3GZAXsEPXr1882og+rzOEQ99mzZzkXWDOBgYEJmmCxVPmLL74ozz//vPoc6MiqO0uzZ89WOw44elawYMEE5UlYqj2pRXOIQM+/CkSpCB3pOJQNaJhDMG1AZgpTKHRUv3591VSD54dsrH0j1dy5c2Xy5MmiIyuN6DPmmWOGM4KIpGab6zrnGe9tlCi4yrRiEgc+47pmYa2oWrVq6shhUpcTucPgmSgZcAj3/ffflzVr1qj612nTpmnbeIKaSARURk0ksjSYuIFGOjx3BFpGsKVbfSSaQDG2TfcRfcY8c9T3okQlqdnmus4zx4qKWPzHVYCMy5B91jV4tuJIRjQ669rsTA8Pg2ciN/CHFh3pgBXY3nnnHWnRooWqgW3VqpW88MIL2tdEIvuOE9SoUUP9RMmKQdfgAk2SOOkK5Ug42f9O92FnQscxdYDysyZNmqiRjHiPG42w2FnGqqItW7bUsiGYKDVwzjNRCoSHh6usHQJLq9NhEQWUaaB23QzsMOlaK4osO2rZUQeMcYxYTOLw4cPy0ksvaZetw5QclGVgvjWOpDiPa0NWFo2DWEBFp1IdA44gDRkyRLZt22YbyWgsDtKzZ08VOOs4knH8+PGqHC0xSI7gdSFKCjPPRCmARSS4kES8iRMnqix1el6VC42BUVFRtt9R1378+HHVJGqM6NuyZYsac4VufV13CNu0aaMWgQFMk0HjJGpAUfON18Oo/dcBSo2w84vRi3iOaI41oIQF/Q1oHtUxcLbySEZ8fp2PJuC9j/f9wYMH5amnnvLYY6P0g8Ez0QNmJXXORFoF6rWNmm0cqkezJFaWs28eQvCIQ9loHNQRsrA4ZI8V1ozluseNGycvv/yyKk3C+L4ePXqILjBuEaeaNWuqLKvxvK3CqiMZ8R7Hydkbb7yhyvFwxIHInYQzqIjIAVbWQ4mG/QmZSDTLIbDG5aQPzPPGZAnnrns0DKIWdNWqVaKjixcv2hbKiIiIUNl3NIpChQoV1NEFHWHVSCNwRkMsnrsVIKuO2c4YyYhyHWMkI+a4W3UkI+Y+b9261dMPg9IBZp6J3GjXrp06OUMDHQ796rqIglUhiHBeMMKAoELXFeeQjUPpDZ47RhEiw24c3j569KgKtnSF0g3M9sVOMeYAY/IEzkMwrWPdr9VGMpqB+nZMUbLiTgMlH4NnohRCgxzq47ACX9myZfk6agLZVgRMXbt2VeO6jBF9GEuIwAorz+moadOmUqlSJSlevLhaJGTevHnqfGSckY3D2DYdIXDs1auXjBkzRn777Tfb+Qigy5cvL926ddM2kLTKSEZ7WBl17NixDueh4fnYsWPqfa/r9CRKXQyeiR4AAgtdM5FWhQZBZOSwgmTt2rVt52NM4aJFi7SteUbdK5qmkHnG5Al/f3/b+dhBzJ49uzbTVeyh7rdPnz5qRcH58+fbzsfzxQhG7Dig1l1n2EHMkiWLapLUXdGiRVVZlj2sIonzkQxxXnGQyBUGz0RuYJ7xunXrHM5D8LBv3z7VSMgaOf2gYRDbHTtHyMhhdBsCDOepBDpybpwzZn3rNF3FXlBQkFpuHpy3L2Yh47OuqyNHjsjHH3+sdo7Qu4EAGjXgI0eO1G40of1nGyeiB8HgmcgNjC/CIXtXmQrMSq1evbq2r6GZub8ob9ApwMDMWzTPITjE88aJ9IUjCWgCffXVVx2CZ5Tn4KRrEImpEmiQw9+vGTNm2EYyota7YcOGsnfvXksskoKRfBjNZ4UdY0o9DJ6J3HjllVfUyWrMzv01Du/rAhMndu/e7emHQQ9J9+7dVYCMZbjxnkbT2IYNG1SGHePLMNtbRwsWLJBSpUqprHPGjPcHb2HaBkb4oTa4f//+oiOUJqFBFGVYmK6CWmfU/GOHGZNliNzhqDoiStbc3/Xr16vpI8aMWB0bBrHqGhZFIf1hogi2NQIorLI3efJkNW3is88+U02EusLRMywMYx84G1DGgiNOuiYFGjVqpI6qoQkWJXnY5jh6hkw8yniI3GHmmciEkydPqj+0WJXLea5zx44d1bgnK8z9NSYw6Dz3F4ETZnmjLhINY8ZrYEBj2bvvvuuxx0epa/v27WrBkF9//dVSLy3e31imGkEknr/hxo0balQhAkododQOZRqYaY0ab0Pnzp1VY+gff/zBzze5xeCZyA1ko2rUqKFGOKE+0P4PLuhWtmD1ub8ImJNaXQ1TN0i0qnF/+umnLbdKKP6uYbIEPuc42mKMZDSCSywChRNgnr2xAmd6h51+zOZ3/juOmud69eqp14XIHQbPRG5gbBkOYzpP3NCdVef+1q1bV53IGrBTfODAAcsFz/gcG5NUsOATToBEAWDajP0OpU7b+7vvvlMjF+0TH5GRkaq/g0eVyIwMd9FCT0SJQq0vFhKYMGGCJV8l57m/6MjHKny6L6ZgiI2NVfWhVmBmuopuc55PnDghTZo0kQEDBqgdRucFUZCF9fLyEqvSbXsbtc3nzp1TC6KgZAV/01CugWw0JqwYM82JEsPgmciN06dPq0OWqI308fGxzOuFGdbIRiVVwqArfJmiHhSZKGToUPeMhRW++uqrBHOQdWqkwmFrPGc8RwTOWHkP01V27dplm66iG6wkiLKkxBjLdVsVSrd0muttZJm//fZb9V7H6oqYqNKiRQs181rX1SQpdVkjnUL0AJChQOYRNb8Iop1XoNKpHtA547Rnzx6xmujoaLVNUaqCbGSRIkVU8IClm1HOgbnfyEZaZbrKyy+/rDJ0mK7So0cP0Q2CqMGDByd6Oea5k16wKiw+2zgRpQSDZyI3EDgh44YTVuRyplM9oD0cwh41apSEhISosg2rQMMUdpYwrg4BtAGLwWD1td9//13LINKq01Wwc4QTWQcWRkGDoP1OcGhoqNpxdp6uQ+QKg2ciN9BIZLVmIkDXedasWW3d+M5fKrpm3HEYFzWR9oEzoBYYtbG4XEdWna5C1oIdQyQGcCTFPnhGKQdKNzDCTpf6bvrvMHgmIrfd+AiocLJCxh3TRVCugBpg+xr3mJgYWb58uVp1TkdWna5C1rJkyRIpW7ZsgoZn/J3D4imocX/rrbc89vgofWDwTGSCFRdJsWrGHUuSo1kQI7uwLLvRjT9jxgxVB45lnHWEw9hooHKeroLzsYQzJxCQDrCCoPNRJQPOx8QZIne4PDeRyUVS1qxZow7zFS5c2OGk6yIpVoVxVViC/Mknn1RNcz179pSJEyfKY489puqAnRtGdTF9+nS10xAXF+fwnkZGzipjCUl/WOgKfQ3OQTJWVsSEIUyXIXKHo+qI3MCUBSzda7VFUuDatWtqEsHmzZvVuC50p2NBiY0bN8qbb74pVrZv3z5VJ1m7dm3RAd7f77zzjhw6dEjVdr/++utq5T1MJiA9WXGuN3To0EH9DcORJUyXQbMsdpRLlCghGzZsUK8JUVKYeSZyI2PGjFK+fHnLvU6o8UUQhZIVZNjRoW40ln3//feqhMXKUP+MTJUuMEkEO0bYUSpVqpSaKIL5t5jzbb/aHOkBNf0oUcKECZg0aZJ8+OGHap49moFR3gA4CqFT4Ayoa+7du7faYRwxYoQqS8LOIsY1MnAmMxg8E7nRsGFD+fvvv9WXjdWWJUfWEYc40UhjwBg3BFpGQxnpBbOsf/rpJ1WuhJ2kHTt2SK1ataRKlSqqkRLTCkjfud4oWWrXrp2aRqErTBHCUTQs/oP3OY4iDR061FKLYNGDYcMgkRtWXSQFmWUETWgYw8kear+NTDTpB4fwt2zZorLr+/fvV9MJsBOJFRYxzg4BFhcPSd+sOtcb8DwxLQilKah1xkJAKFfBlI0+ffp4+uFROsDMM5HJRVJwKBuLpOAQtv1J17m/mDKBw/hgHzyjoQxBFZvI9INJG0OGDFGBRfv27W2TNvC+R1YSS9UjAz1lyhRPP1R6QMg6L1u2TG3zCRMmJJjrjct1hOc7cOBAtYMAKNvANB3U+yP7jCMtRO4w80zkhlVHtmFRDNRA9uvXT2JjYyUsLEwtIIBVB8+ePSudOnXy9EOkVDR58mRV91m1alVVD4qmMedJMphEgqMsV69e5Wufzll1rjeOmtSvX1+8vLzU7xjPiB1DlKLhKOPq1avVdCWipDB4JiKXUO+MumfMsEYTGaAWFhkqZKz8/Pz4ymkETbFYkhylOkmx+pQVXaBcAU2CKNlwnuuNJejR46HjbG/M6TeaJFG6gqMpCKYBNc/Oc/yJXGHwTJSMsW0Y44VaX9SEGsqUKaMyODrCc9u0aZMqTcFYK3Tds1wjHmrfo6KiRKdGQbIOBM7IMqOG3R7meqN50NVlOsC89l69esno0aPV7H5MHEEDISDjjiNtRO4weCYyYeTIkTJo0CA17xSj6zDGzTiM3bdvX1UrpzNjQRjSPwNrxZ1EcnTr1i3x9fXV8mVBrTNKz3744Qe1ozBmzBh1/u7du1XWvVmzZp5+iJQOcJEUIjfQNIfDeihhwAB9ZGSwcAgyN2g22bt3rzZzUPEczc4ubtWqlZa14FgoYtiwYS4vw44TDnHXq1dPPv74Y9VUqROr7yRaAfoW/vrrL9m5c6cqzcAUFXs4D/XPWBgKq2wSUUKctkHkBkY4YUWqOnXqqMwEAgpkZd577z2VpdBpoQw0BqIUwThhvjV2ErADERISorrwseIigmxcV0dYMRD13Jgq8txzz6lDvGiew6FdBJVPPfWUKmVBg5FOM4+xjREcY5sPHz5cvb/RJIrD2xhphuZRSv+wYuDx48fVEQbj3/YnBM8YSYhpK0TkGss2iNwIDg5Wo+ogICBABdMGjK9DLbAuMEnBmFmNOmdk3DG6qVq1arbr4Au2ZcuWqnFQRyhVQFCMIwr2h66xqAKydGioQ9YZP5GhQ2Ct204ili6230nESEbsJGK1QUrfsPOHE5p+sUOMHUTdYXLIF198oeY4Y0cQ/04MroNRdkRJYfBMlAwImN5//33VaIKFQqZNmyaffPKJlq8hMpDNmzd3CJwBDYP4wl21apVUrlxZdIMgEisqOtd8IvOMUhVc3qJFC2nbtq3akdCFlXYSKX4UpVU0aNBAvvzyS7XDjylC+HdidE0KUOpi8EzkBlahQoYGKlasqIbpI3jCYX0EUy+88IKWryEO2WNBAVcw5xlfQjrCzFuUZWCclTELFlCysW7dOpWdhaCgIFX7rCMr7SRaCWYY//nnn2qnGNNi8O/E4DodO3YUHaDR1b7ZlY2v9KDYMEiUAqgLREc6urV1hbINzP7t0qWLmixRsGBBtTgGAil0qKPhSMcsDbYrFgpB5hnL9uJ5I1DG7FvMhN2zZ48KpDHiCg2kaCDUAY4kYCfRWPwGwTJqn42dxAULFthGelH6hEZBNAIiKMZOEf6dGFwHM96JKCEGz0SUKCyOgppX+yVrkX3/9ttvVd2zrhAsY+LGkiVL1EIK2EnCimyYRFGkSBFVD4zaaN2DSSvsJJL+xo8fL59++qmp6+LIIpaoJ0oKg2cicgvj+VDzGhgYqDKxmDqiKwTGGM1GpDt8pjNlyqQ+19gZxJjGw4cPqyZYlKvpAlOC0ABswI5xZGSkvPrqq2p+/ZUrV2TGjBlq+giagKtUqeLRx0tpH4NnInLpm2++UVlXHVcZSwoWUJg9e7YKIFCyguY5XXGut3XhqAJq9hcuXCjFihVTgTPGMqJBeNeuXaoZ1mgg1QkmxyBoxk/7vg2M3sT4yf79+6tmYKKkcM4zEbmEkU4oX7AajPFCUIGZxwUKFJB27drJzJkzVaZKN5zrbV0rV66UcuXKqcAZsCT3uHHjZP369eo9P2fOHNERejWwRLdzw3PmzJlVaRYuJ3KHwTMRuYTsy7Zt22TLli2WeoVKliyparrRMImaZ2Seu3fvrup+0UCFZXx1gZneaADFCc2BWBgH9e3Y5si+Y1whlupGYKFjc6iV4agSdpABc80xmtDIuGIJa5Rq6QgLIGGVRUwTsnf79m01+1qXBmD6b3FUHRG5hC8SNIthoRQElMYXreHFF1+Ud999V9tXD7WgGEmI0w8//CBjx45ViycgmNapHtTqc72tCllnlGRhHOXcuXPVzlHu3LltNcK6zoHGFBGsoIi6ZowZNaYIYREg7Dxiwg6ROwyeicglBMxJrSiHqRu6u379usrATp8+Xa26h4ADqwzqyKpzva0KJQqYd1y8eHE12xyNcoCM89atW9WqfDrPccey84sWLbI1QiOo7tOnjxrhR+QOGwaJiOygthlNVAiYly5dKnny5FGNg8i016xZU9vXyqpzva0OO0woVfD391e/Y/IEdqRwxIGIXGPwTESmm8tQ+6o7HMrGnFc0DmLiRrNmzVQJhxVYda43EVFyMHgmokQhC4XFBZCJxeFc1D2jLhY1g0aXvo4ZWGSbrVymYKW53lZi1eW5iVKb/mkkIkqR6Ohoefzxx1WN4IABA9TKegiqfvvtN6lbt64cPHhQy/pALJpgdQiacSK9oAEYO4dYDAQ7RPh3YnAdInKNmWcicgkNRFiOGofwEUAb7ty5oxYTeP7556VHjx5avnpYbQ3NRBhLh6ZB/G7faPXkk0969PEREZHnMPNMRC4hK9WgQQOHwBlQ/9ukSZMks1bpXYcOHdTcWzRSodYbGXasuoYV17h0L+kADbH79u1Tdf1sBCVKHi6SQkQuYYTVunXr1DK+9mJiYmT58uXqch3hOe/du1fNuu3WrZtqHMRSvjghkMbqZEQ6lCfhc4zZ3ZhbjkZZLJxCRO4xeCYil9q0aaOa5mrUqKEaBFHrPHLkSKlVq5ZqJETZho5Qy40FIrASGaaLYOEEwOIhWEBhwYIFnn6IRA+sUaNG6mjKgQMH1MqCWJobfQ1oFJw8eXKCFfiI6D4Gz0TkUpYsWWT9+vWqvnfq1KnSs2dPmThxosq8oqQB3fo6wlLFvr6+6t9omjtx4oTtMgTTaLoi0gVGEQ4dOlROnjypVpksU6aMmvONcY1E5BprnokoUaj5RbYZJytCc2DXrl3VuD7UPWPVtZkzZ3r6YRGluiNHjqgyjhUrVqjyJIwoJCLXOG2DiMgpiMAqg6gDBUzd+OKLLyQ0NFReffVV6devH18v0sKlS5fUziCaBzFVB+95rKT5wgsvMHgmSgKDZyIiIotBXfMbb7yh6pwRLGPqBko4iMg9Bs9EREQWs2XLFlWegXGUSa0gicVSMNsdq24SUTwGz0REROQSRthhZVH8JKJ4nLZBRERERGQSg2ciIiIiIpMYPBMRERERmcTgmYiIiIjIJAbPREREREQmMXgmIiIiIjKJo+qIiKO7z6YAAAQWSURBVIgsLC4uTq5evSoBAQGSKVMmh8s455koIWaeiYiILLoUfceOHcXb21sCAwPVz5YtW8ru3btt1/H39+cCKUROGDwTERFZzLVr19TqgpGRkTJjxgzZunWrzJkzR/z8/KRhw4Zy6tQpTz9EojSLZRtEREQWM2nSJPnpp59k8+bNkjGjYx6tffv2Ur9+fenfv7/HHh9RWsbMMxERkcVkzpxZKleunCBwhqpVqyaofSai+xg8ExERWQwyyytXrpQLFy44nH/jxg2ZO3euNGrUyGOPjSity+zpB0BERET/vQ0bNsjixYttv+fIkUPKlSsnbdu2lYIFC6qJG7g8V65cEhwczE1ClAgGz0RERBZw+fJl+ffff22/58+fX50QKBvBco0aNdTP8+fPe+xxEqV1bBgkIiIiIjKJNc9ERERERCYxeCYiIrLorOeePXtK9erV5auvvlLnHThwQH788UdPPzSiNI3BMxERkcXExMRIkyZN5OTJk1K4cGG5fv26Oh8NhN9//z0XSSFKAoNnIiIii1mxYoVajhvTNezH0mH+c+PGjWXevHkefXxEaRmDZyIiIovB8tu1atWSDBkyqJM9jKozMtFElBCDZyIiIospVKiQqm8G++A5Li5Oli9fLqVLl/bgoyNK2ziqjoiIyGIiIyOlYsWK8txzz0lsbKyEhYVJly5dZNSoUWoW9NGjR8XPz8/TD5MoTWLwTEREZEHHjh2T1157TTZv3mw775FHHpGpU6fKo48+6tHHRpSWMXgmIiKyMKwmeOHCBcmTJw/LNYhMYPBMRERkMdOmTZM1a9aoyRo4FS1a1NMPiSjdYMMgERGRxZQvX17VOg8aNEiKFSsmJUuWlNdff12mTJkiZ8+e9fTDI0rTmHkmIiKyMCyUsm7dOnVau3atKuP49NNPZciQIZ5+aERpEjPPREREFnXnzh21TPfVq1fV6ebNm2rFweLFi3v6oRGlWcw8ExERWczGjRtl2LBhsmHDBsmZM6daZdCof+aMZ6KkZXZzOREREWlm69atsmTJEmnSpIl069ZNBc0FChTw9MMiSheYeSYiIrKYqKgoNd/ZqHX+559/VKkGgmkE0s2aNZOAgABPP0yiNInBMxERkcUhmJ43b5588cUXcvjwYendu7d8/fXXnn5YRGkSyzaIiIgsKCgoSNavX68mbOCEoDlv3rzSqVMneeKJJzz98IjSLGaeiYiILGbChAny9ttvq2D58ccftzULVqpUSTJkyODph0eUpjF4JiIishhkmbFICoNlouRj8ExEREREZBIXSSEiIiIiMonBMxERERGRSQyeiYiIiIhMYvBMRERERGQSg2ciIiIiIpMYPBMRERERmcTgmYiIiIjIJAbPREREREQmMXgmIiIiIhJz/h/0+uEn7vBw4QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['annual_spend', 'orders_per_year', 'avg_order_value', 'days_since_last',\n",
    "            'return_rate', 'web_visits_per_month', 'discount_share']\n",
    "X = profiles[features]\n",
    "\n",
    "plt.figure(figsize=(7, 5.5))\n",
    "sns.heatmap(X.corr(), annot=True, fmt='.2f', cmap='RdBu_r', vmin=-1, vmax=1)\n",
    "plt.title('Correlations Between Customer Features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40b16b8c",
   "metadata": {},
   "source": [
    "Orders and web visits have a correlation of 0.80, so they carry much of the same information: knowing one tells us a lot about the other. Average order value and discount share have a correlation of -0.62. When columns overlap like this, the data have fewer independent patterns than they have columns.\n",
    "\n",
    "**Dimensionality reduction** means replacing many features with a few new ones that keep most of the information. PCA is the most common method for numeric data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "308d1e43",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.855676Z",
     "iopub.status.busy": "2026-10-23T03:48:57.855594Z",
     "iopub.status.idle": "2026-10-23T03:48:57.858063Z",
     "shell.execute_reply": "2026-10-23T03:48:57.857658Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Find the Most Correlated Pair\n",
    "# Use the customer profiles data.\n",
    "#   1. Compute the correlation table of the seven features.\n",
    "#   2. Find the correlation between annual_spend and orders_per_year.\n",
    "#   3. Find the correlation between annual_spend and discount_share.\n",
    "# Print each correlation rounded to 2 decimal places, for example:\n",
    "# spend vs orders: 0.6\n",
    "# spend vs discount: -0.6\n",
    "import pandas as pd\n",
    "\n",
    "profiles = pd.read_csv(path_data + 'customer_profiles.csv')\n",
    "features = ['annual_spend', 'orders_per_year', 'avg_order_value', 'days_since_last',\n",
    "            'return_rate', 'web_visits_per_month', 'discount_share']\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc378e62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.858864Z",
     "iopub.status.busy": "2026-10-23T03:48:57.858801Z",
     "iopub.status.idle": "2026-10-23T03:48:57.861349Z",
     "shell.execute_reply": "2026-10-23T03:48:57.861086Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spend vs orders: 0.6\n",
      "spend vs discount: -0.6\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import pandas as pd\n",
    "\n",
    "profiles = pd.read_csv(path_data + 'customer_profiles.csv')\n",
    "features = ['annual_spend', 'orders_per_year', 'avg_order_value', 'days_since_last',\n",
    "            'return_rate', 'web_visits_per_month', 'discount_share']\n",
    "\n",
    "corr = profiles[features].corr()\n",
    "print('spend vs orders:', round(corr.loc['annual_spend', 'orders_per_year'], 2))\n",
    "print('spend vs discount:', round(corr.loc['annual_spend', 'discount_share'], 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1f11a32",
   "metadata": {},
   "source": [
    "## Principal Components as New Axes\n",
    "\n",
    "```{index} principal component\n",
    "```\n",
    "\n",
    "```{index} principal component analysis\n",
    "```\n",
    "\n",
    "Start with just two features that move together, orders and web visits. We put both in standard units first (Chapter 12), so that each is measured in standard deviations from its mean, as we did for clustering in the previous section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f9f3d866",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.862285Z",
     "iopub.status.busy": "2026-10-23T03:48:57.862225Z",
     "iopub.status.idle": "2026-10-23T03:48:57.873543Z",
     "shell.execute_reply": "2026-10-23T03:48:57.873213Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.70710678, 0.70710678])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two = profiles[['orders_per_year', 'web_visits_per_month']]\n",
    "two_su = (two - two.mean()) / two.std(ddof=0)      # standard units\n",
    "\n",
    "pca_two = PCA(n_components=2)\n",
    "pca_two.fit(two_su)\n",
    "\n",
    "direction = pca_two.components_[0]     # direction of the first component\n",
    "direction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af271a8f",
   "metadata": {},
   "source": [
    "A **principal component** is a new axis through the data. The first principal component, PC1, points in the direction in which the data are most spread out. `components_[0]` holds that direction: it moves about 0.71 standard units in orders for every 0.71 standard units in visits, so it runs diagonally through the cloud of points. The second component, PC2, is at a right angle to PC1 and picks up the spread that is left over.\n",
    "\n",
    "The plot on the left draws both axes on the scatter plot. The plot on the right shows the same customers measured on the new axes: PCA has simply **rotated the axes** so that the long direction of the cloud lies along the horizontal axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "954b2cce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.874437Z",
     "iopub.status.busy": "2026-10-23T03:48:57.874372Z",
     "iopub.status.idle": "2026-10-23T03:48:57.938328Z",
     "shell.execute_reply": "2026-10-23T03:48:57.937963Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores_two = pca_two.transform(two_su)     # each customer's position on PC1 and PC2\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4.5))\n",
    "\n",
    "axes[0].scatter(two_su['orders_per_year'], two_su['web_visits_per_month'], alpha=0.5)\n",
    "for i, name in enumerate(['PC1', 'PC2']):\n",
    "    d = pca_two.components_[i] * 1.6\n",
    "    axes[0].annotate('', xy=d, xytext=(0, 0),\n",
    "                     arrowprops=dict(arrowstyle='->', color='crimson', lw=2))\n",
    "    axes[0].text(d[0] * 1.1, d[1] * 1.1, name, color='crimson', fontsize=12)\n",
    "axes[0].set_xlabel('Orders per year (standard units)')\n",
    "axes[0].set_ylabel('Web visits per month (standard units)')\n",
    "axes[0].set_title('Original axes')\n",
    "axes[0].set_xlim(-1.8, 3.3)\n",
    "axes[0].set_ylim(-1.8, 3.3)\n",
    "axes[0].set_aspect('equal')\n",
    "\n",
    "axes[1].scatter(scores_two[:, 0], scores_two[:, 1], alpha=0.5)\n",
    "axes[1].set_xlabel('PC1')\n",
    "axes[1].set_ylabel('PC2')\n",
    "axes[1].set_title('Rotated axes')\n",
    "axes[1].set_aspect('equal')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5583ee07",
   "metadata": {},
   "source": [
    "Most of the spread now runs along PC1, and the points vary only a little along PC2. The model reports how much of the total spread each component captures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "70c91bf3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.939400Z",
     "iopub.status.busy": "2026-10-23T03:48:57.939339Z",
     "iopub.status.idle": "2026-10-23T03:48:57.941130Z",
     "shell.execute_reply": "2026-10-23T03:48:57.940876Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.89894022, 0.10105978])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_two.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9455b17",
   "metadata": {},
   "source": [
    "PC1 captures about 90% of the variation in the two features, and PC2 only about 10%. One number per customer, the PC1 score, which we could call \"activity\", keeps almost all of what orders and visits tell us. That is the idea of PCA: when features overlap, a few well-chosen directions can replace many columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3c50d52b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.942005Z",
     "iopub.status.busy": "2026-10-23T03:48:57.941953Z",
     "iopub.status.idle": "2026-10-23T03:48:57.944313Z",
     "shell.execute_reply": "2026-10-23T03:48:57.944014Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: PCA on Two Features\n",
    "# A manager wants one \"basket size\" measure built from annual_spend and avg_order_value.\n",
    "#   1. Put the two columns in standard units.\n",
    "#   2. Fit PCA with 2 components.\n",
    "#   3. Print the share of variation captured by PC1, rounded to 2 decimal places.\n",
    "# Print exactly:\n",
    "# PC1 share: 0.7\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "profiles = pd.read_csv(path_data + 'customer_profiles.csv')\n",
    "pair = profiles[['annual_spend', 'avg_order_value']]\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3ffef2de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.945304Z",
     "iopub.status.busy": "2026-10-23T03:48:57.945238Z",
     "iopub.status.idle": "2026-10-23T03:48:57.948687Z",
     "shell.execute_reply": "2026-10-23T03:48:57.948354Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PC1 share: 0.7\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "profiles = pd.read_csv(path_data + 'customer_profiles.csv')\n",
    "pair = profiles[['annual_spend', 'avg_order_value']]\n",
    "\n",
    "pair_su = (pair - pair.mean()) / pair.std(ddof=0)\n",
    "pca_pair = PCA(n_components=2).fit(pair_su)\n",
    "print('PC1 share:', round(pca_pair.explained_variance_ratio_[0], 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c30f289",
   "metadata": {},
   "source": [
    "## Standardize, Then Fit PCA\n",
    "\n",
    "```{index} explained variance ratio\n",
    "```\n",
    "\n",
    "```{index} scree plot\n",
    "```\n",
    "\n",
    "PCA looks for the directions with the most spread, so, like k-means, it depends on the units of the features. Spend is measured in dollars and has a standard deviation of about \\$1,000, while the return rate is a share with a standard deviation of about 0.04. Here is PCA on the raw features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd27c0c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.949588Z",
     "iopub.status.busy": "2026-10-23T03:48:57.949540Z",
     "iopub.status.idle": "2026-10-23T03:48:57.952086Z",
     "shell.execute_reply": "2026-10-23T03:48:57.951790Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "annual_spend            0.997\n",
       "orders_per_year         0.006\n",
       "avg_order_value         0.062\n",
       "days_since_last        -0.044\n",
       "return_rate            -0.000\n",
       "web_visits_per_month    0.004\n",
       "discount_share         -0.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_raw = PCA().fit(X)\n",
    "pd.Series(pca_raw.components_[0], index=features).round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0a8c50ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.952969Z",
     "iopub.status.busy": "2026-10-23T03:48:57.952914Z",
     "iopub.status.idle": "2026-10-23T03:48:57.954568Z",
     "shell.execute_reply": "2026-10-23T03:48:57.954274Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9755988446977628)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_raw.explained_variance_ratio_[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99690a1f",
   "metadata": {},
   "source": [
    "On the raw features, PC1 is almost exactly `annual_spend` (weight about 1.0, the others near 0) and claims about 98% of the variation, only because dollars are big numbers. As with k-means, put the features in standard units first so that each feature starts with the same spread:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6a217a7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.955399Z",
     "iopub.status.busy": "2026-10-23T03:48:57.955353Z",
     "iopub.status.idle": "2026-10-23T03:48:57.957888Z",
     "shell.execute_reply": "2026-10-23T03:48:57.957641Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.425, 0.312, 0.162, 0.041, 0.033, 0.021, 0.005])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled = (X - X.mean()) / X.std(ddof=0)      # standard units\n",
    "\n",
    "pca = PCA()            # keep all 7 components for now\n",
    "pca.fit(X_scaled)\n",
    "\n",
    "pca.explained_variance_ratio_.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "155baadc",
   "metadata": {},
   "source": [
    "The **explained variance ratio** of a component is the share of the total variation in the scaled data that the component captures. The ratios are in order from largest to smallest and add up to 1. A **scree plot** shows them as bars, often with the running (cumulative) total as a line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bfa238c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.958804Z",
     "iopub.status.busy": "2026-10-23T03:48:57.958759Z",
     "iopub.status.idle": "2026-10-23T03:48:57.994736Z",
     "shell.execute_reply": "2026-10-23T03:48:57.994381Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratios = pca.explained_variance_ratio_\n",
    "pcs = [f'PC{i}' for i in range(1, len(ratios) + 1)]\n",
    "\n",
    "plt.figure(figsize=(7, 4))\n",
    "plt.bar(pcs, ratios, color='steelblue', label='Each component')\n",
    "plt.plot(pcs, ratios.cumsum(), color='crimson', marker='o', label='Cumulative')\n",
    "plt.axhline(0.9, color='gray', linestyle='--', linewidth=1)\n",
    "plt.ylim(0, 1.05)\n",
    "plt.ylabel('Explained variance ratio')\n",
    "plt.title('Scree Plot for Customer Profiles')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "03a72e2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.995831Z",
     "iopub.status.busy": "2026-10-23T03:48:57.995760Z",
     "iopub.status.idle": "2026-10-23T03:48:57.998609Z",
     "shell.execute_reply": "2026-10-23T03:48:57.998273Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>component</th>\n",
       "      <th>explained</th>\n",
       "      <th>cumulative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PC1</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PC2</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PC3</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PC4</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PC5</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PC6</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PC7</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  component  explained  cumulative\n",
       "0       PC1      0.425       0.425\n",
       "1       PC2      0.312       0.738\n",
       "2       PC3      0.162       0.900\n",
       "3       PC4      0.041       0.940\n",
       "4       PC5      0.033       0.974\n",
       "5       PC6      0.021       0.995\n",
       "6       PC7      0.005       1.000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'component': pcs,\n",
    "              'explained': ratios.round(3),\n",
    "              'cumulative': ratios.cumsum().round(3)})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d473ec1",
   "metadata": {},
   "source": [
    "**Choosing how many components to keep.** PC1 captures about 43% of the variation, PC2 about 31%, and PC3 about 16%. After PC3 the bars are small: PC4 to PC7 together hold about 10%. Three common rules of thumb:\n",
    "\n",
    "- Keep the components before the scree plot levels off (the \"elbow\", as with choosing k in the previous section). Here, that is three.\n",
    "- Keep enough components to reach a target share, such as 80% or 90%. Here, three components reach about 90%.\n",
    "- For a picture, keep two. PC1 and PC2 together hold about 74% of the variation, enough to show the main patterns on one scatter plot.\n",
    "\n",
    "As with choosing k, these are guides. The right number depends on what the components will be used for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "65eeb0cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:57.999578Z",
     "iopub.status.busy": "2026-10-23T03:48:57.999523Z",
     "iopub.status.idle": "2026-10-23T03:48:58.002271Z",
     "shell.execute_reply": "2026-10-23T03:48:58.001955Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: How Many Components?\n",
    "# Use X_scaled, the seven customer features in standard units.\n",
    "#   1. Fit PCA with all components.\n",
    "#   2. Compute the cumulative explained variance ratio.\n",
    "#   3. Find the smallest number of components whose cumulative share is at least 0.85.\n",
    "# Print exactly:\n",
    "# Components needed: 3\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "profiles = pd.read_csv(path_data + 'customer_profiles.csv')\n",
    "X = profiles.drop(columns='customer_id')\n",
    "X_scaled = (X - X.mean()) / X.std(ddof=0)\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6be6d484",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:58.003046Z",
     "iopub.status.busy": "2026-10-23T03:48:58.002999Z",
     "iopub.status.idle": "2026-10-23T03:48:58.006293Z",
     "shell.execute_reply": "2026-10-23T03:48:58.005955Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Components needed: 3\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "profiles = pd.read_csv(path_data + 'customer_profiles.csv')\n",
    "X = profiles.drop(columns='customer_id')\n",
    "X_scaled = (X - X.mean()) / X.std(ddof=0)\n",
    "\n",
    "cumulative = PCA().fit(X_scaled).explained_variance_ratio_.cumsum()\n",
    "needed = int(np.argmax(cumulative >= 0.85)) + 1     # first position that reaches 0.85\n",
    "print('Components needed:', needed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c1e9b47",
   "metadata": {},
   "source": [
    "## Reading the Loadings\n",
    "\n",
    "```{index} loading\n",
    "```\n",
    "\n",
    "A component is a weighted combination of the original features. The weights are called **loadings**, and they are stored in `components_`, one row per component. A large positive loading means the feature rises with the component; a large negative loading means it falls; a loading near 0 means the feature has little to do with that component."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "79d49150",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:58.007095Z",
     "iopub.status.busy": "2026-10-23T03:48:58.007045Z",
     "iopub.status.idle": "2026-10-23T03:48:58.009658Z",
     "shell.execute_reply": "2026-10-23T03:48:58.009398Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>annual_spend</th>\n",
       "      <td>0.43</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orders_per_year</th>\n",
       "      <td>0.54</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_order_value</th>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_since_last</th>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>return_rate</th>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>web_visits_per_month</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discount_share</th>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       PC1   PC2   PC3\n",
       "annual_spend          0.43 -0.41  0.05\n",
       "orders_per_year       0.54  0.15 -0.12\n",
       "avg_order_value      -0.07 -0.54  0.51\n",
       "days_since_last      -0.49 -0.09 -0.35\n",
       "return_rate          -0.14  0.29  0.77\n",
       "web_visits_per_month  0.51  0.19  0.04\n",
       "discount_share       -0.06  0.63  0.08"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loadings = pd.DataFrame(pca.components_[:3].T, index=features,\n",
    "                        columns=['PC1', 'PC2', 'PC3'])\n",
    "loadings.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "98d48205",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:58.010447Z",
     "iopub.status.busy": "2026-10-23T03:48:58.010404Z",
     "iopub.status.idle": "2026-10-23T03:48:58.078365Z",
     "shell.execute_reply": "2026-10-23T03:48:58.077998Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4), sharey=True)\n",
    "for ax, pc in zip(axes, ['PC1', 'PC2']):\n",
    "    sns.barplot(x=loadings[pc], y=loadings.index, ax=ax, color='steelblue')\n",
    "    ax.axvline(0, color='black', linewidth=0.8)\n",
    "    ax.set_title(f'Loadings on {pc}')\n",
    "    ax.set_xlabel('Loading')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb0a25f",
   "metadata": {},
   "source": [
    "Read each component by looking for the features with the largest loadings, positive and negative:\n",
    "\n",
    "- **PC1**: orders (0.54), web visits (0.51), and spend (0.43) are high, and days since the last purchase is strongly negative (-0.49). A customer scores high on PC1 by buying often, visiting often, and having bought recently. We might name PC1 **overall engagement**.\n",
    "- **PC2**: discount share is high (0.63) and return rate is positive (0.29), while average order value (-0.54) and spend (-0.41) are negative. A customer scores high on PC2 by using discount codes on small orders. We might name PC2 **price sensitivity**.\n",
    "- **PC3** is mostly return rate (0.77), with some average order value: a **returns** dimension that the first two components do not cover.\n",
    "\n",
    "```{caution}\n",
    "The names are our interpretation, not something PCA computes. Two analysts can read the same loadings and choose different names. The sign of a component is also arbitrary: another version of scikit-learn could flip PC1 so that all its loadings change sign, which would turn \"engagement\" into \"disengagement\" without changing anything about the data. Always check the loadings before you name a component, and say which features drive it when you present it.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c0b211c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:58.079498Z",
     "iopub.status.busy": "2026-10-23T03:48:58.079431Z",
     "iopub.status.idle": "2026-10-23T03:48:58.082357Z",
     "shell.execute_reply": "2026-10-23T03:48:58.082066Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Read the Loadings\n",
    "# Use the loadings table for the seven scaled customer features.\n",
    "#   1. Fit PCA on X_scaled and build a table of loadings for PC1 and PC2 (features as rows).\n",
    "#   2. Find the feature with the largest absolute loading on PC2.\n",
    "# Print exactly:\n",
    "# Largest PC2 loading: discount_share\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "profiles = pd.read_csv(path_data + 'customer_profiles.csv')\n",
    "X = profiles.drop(columns='customer_id')\n",
    "X_scaled = (X - X.mean()) / X.std(ddof=0)\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f4df237e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:58.083313Z",
     "iopub.status.busy": "2026-10-23T03:48:58.083257Z",
     "iopub.status.idle": "2026-10-23T03:48:58.086657Z",
     "shell.execute_reply": "2026-10-23T03:48:58.086346Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Largest PC2 loading: discount_share\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "profiles = pd.read_csv(path_data + 'customer_profiles.csv')\n",
    "X = profiles.drop(columns='customer_id')\n",
    "X_scaled = (X - X.mean()) / X.std(ddof=0)\n",
    "\n",
    "pca_fit = PCA(n_components=2).fit(X_scaled)\n",
    "loadings = pd.DataFrame(pca_fit.components_.T, index=X.columns, columns=['PC1', 'PC2'])\n",
    "print('Largest PC2 loading:', loadings['PC2'].abs().idxmax())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8583134",
   "metadata": {},
   "source": [
    "## PCA with Clustering\n",
    "\n",
    "```{index} PCA; with clustering\n",
    "```\n",
    "\n",
    "PCA and k-means work well together. We cluster the customers on all seven scaled features, exactly as in the previous section, and then use PCA to draw the result. First, a quick silhouette check for the number of clusters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "81e8bd33",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:58.087478Z",
     "iopub.status.busy": "2026-10-23T03:48:58.087418Z",
     "iopub.status.idle": "2026-10-23T03:48:58.154409Z",
     "shell.execute_reply": "2026-10-23T03:48:58.154057Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 0.383\n",
      "3 0.467\n",
      "4 0.582\n",
      "5 0.49\n",
      "6 0.404\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 0.296\n"
     ]
    }
   ],
   "source": [
    "for k in range(2, 8):\n",
    "    labels = KMeans(n_clusters=k, n_init=10, random_state=101).fit_predict(X_scaled)\n",
    "    print(k, round(silhouette_score(X_scaled, labels), 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e61c40a",
   "metadata": {},
   "source": [
    "The silhouette score is highest at k = 4 (about 0.58). We fit k-means with 4 clusters and plot each customer at its PC1 and PC2 scores, colored by cluster:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ec5e48a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:58.155750Z",
     "iopub.status.busy": "2026-10-23T03:48:58.155688Z",
     "iopub.status.idle": "2026-10-23T03:48:58.208131Z",
     "shell.execute_reply": "2026-10-23T03:48:58.207817Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4, n_init=10, random_state=101)\n",
    "profiles['cluster'] = kmeans.fit_predict(X_scaled)\n",
    "\n",
    "scores = pca.transform(X_scaled)          # PC scores for every customer\n",
    "profiles['PC1'] = scores[:, 0]\n",
    "profiles['PC2'] = scores[:, 1]\n",
    "\n",
    "sns.scatterplot(data=profiles, x='PC1', y='PC2', hue='cluster', palette='tab10')\n",
    "plt.xlabel('PC1: overall engagement')\n",
    "plt.ylabel('PC2: price sensitivity')\n",
    "plt.title('Customer Segments on the First Two Components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6485ecf5",
   "metadata": {},
   "source": [
    "Seven features are now one picture, and the four segments are clearly separated. The axis names come from the loadings, so the picture already suggests what each segment is: high or low engagement, high or low price sensitivity. To describe the segments, though, go back to the original units, as in the previous section. A manager can act on dollars and days, not on PC scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c8e42924",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T03:48:58.209476Z",
     "iopub.status.busy": "2026-10-23T03:48:58.209391Z",
     "iopub.status.idle": "2026-10-23T03:48:58.215267Z",
     "shell.execute_reply": "2026-10-23T03:48:58.214933Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>annual_spend</th>\n",
       "      <th>orders_per_year</th>\n",
       "      <th>avg_order_value</th>\n",
       "      <th>days_since_last</th>\n",
       "      <th>return_rate</th>\n",
       "      <th>web_visits_per_month</th>\n",
       "      <th>discount_share</th>\n",
       "      <th>customers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>167.32</td>\n",
       "      <td>2.88</td>\n",
       "      <td>57.91</td>\n",
       "      <td>201.09</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.94</td>\n",
       "      <td>0.39</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>622.63</td>\n",
       "      <td>17.31</td>\n",
       "      <td>36.14</td>\n",
       "      <td>20.38</td>\n",
       "      <td>0.12</td>\n",
       "      <td>17.99</td>\n",
       "      <td>0.70</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2560.13</td>\n",
       "      <td>25.80</td>\n",
       "      <td>99.72</td>\n",
       "      <td>11.81</td>\n",
       "      <td>0.05</td>\n",
       "      <td>21.22</td>\n",
       "      <td>0.16</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1682.42</td>\n",
       "      <td>3.85</td>\n",
       "      <td>436.75</td>\n",
       "      <td>80.03</td>\n",
       "      <td>0.10</td>\n",
       "      <td>6.23</td>\n",
       "      <td>0.10</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         annual_spend  orders_per_year  avg_order_value  days_since_last  \\\n",
       "cluster                                                                    \n",
       "0              167.32             2.88            57.91           201.09   \n",
       "1              622.63            17.31            36.14            20.38   \n",
       "2             2560.13            25.80            99.72            11.81   \n",
       "3             1682.42             3.85           436.75            80.03   \n",
       "\n",
       "         return_rate  web_visits_per_month  discount_share  customers  \n",
       "cluster                                                                \n",
       "0               0.08                  1.94            0.39         80  \n",
       "1               0.12                 17.99            0.70         91  \n",
       "2               0.05                 21.22            0.16         69  \n",
       "3               0.10                  6.23            0.10         60  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile = profiles.groupby('cluster')[features].mean().round(2)\n",
    "profile['customers'] = profiles['cluster'].value_counts().sort_index()\n",
    "profile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af9fec66",
   "metadata": {},
   "source": [
    "The profiles match the picture. One cluster buys often, spends the most, and rarely uses discounts (loyal high-value, high on PC1). One orders often but in small, discounted baskets (bargain hunters, high on PC2). One places a few large orders at full price (big-ticket buyers, low on PC2). One has barely bought in months (lapsed customers, low on PC1). As before, the cluster numbers themselves are arbitrary.\n",
    "\n",
    "**PCA for visualization vs. PCA before clustering.** Above, k-means used all seven features, and PCA only drew the result. Another option is to reduce the data first and cluster the PC scores. This can help when there are many noisy or overlapping features, and it makes the clusters easier to plot. But the clusters then ignore whatever the dropped components held; here, clustering only PC1 and PC2 would ignore most of the return-rate information in PC3. Let's compare:"
   ]
  },
  {
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>first 2 PCs</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "      <td>80</td>\n",
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       "first 2 PCs      0   1   2   3\n",
       "all 7 features                \n",
       "0               80   0   0   0\n",
       "1                0  91   0   0\n",
       "2                0   0  69   0\n",
       "3                0   0   0  60"
      ]
     },
     "execution_count": 25,
     "metadata": {},
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    }
   ],
   "source": [
    "labels_2pc = KMeans(n_clusters=4, n_init=10, random_state=101).fit_predict(scores[:, :2])\n",
    "pd.crosstab(profiles['cluster'], labels_2pc, rownames=['all 7 features'], colnames=['first 2 PCs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c57f989",
   "metadata": {},
   "source": [
    "Each row has one nonzero count, so the two approaches put every customer in the same group (the cluster numbers differ, but the groups are the same). That is because these synthetic segments differ mostly in engagement and price sensitivity. In real data the two approaches often disagree, so report which one you used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
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     "thebe-interactive"
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   "source": [
    "### Exercise: Cluster the PC Scores\n",
    "# Use the PC scores of the seven scaled customer features.\n",
    "#   1. Fit PCA on X_scaled and keep the first 3 components' scores.\n",
    "#   2. Fit KMeans with 4 clusters (n_init=10, random_state=101) on those scores.\n",
    "#   3. Print the cluster sizes sorted from smallest to largest, as a list.\n",
    "# Print exactly:\n",
    "# Cluster sizes: [60, 69, 80, 91]\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "profiles = pd.read_csv(path_data + 'customer_profiles.csv')\n",
    "X = profiles.drop(columns='customer_id')\n",
    "X_scaled = (X - X.mean()) / X.std(ddof=0)\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
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     "output_type": "stream",
     "text": [
      "Cluster sizes: [60, 69, 80, 91]\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "profiles = pd.read_csv(path_data + 'customer_profiles.csv')\n",
    "X = profiles.drop(columns='customer_id')\n",
    "X_scaled = (X - X.mean()) / X.std(ddof=0)\n",
    "\n",
    "scores3 = PCA(n_components=3).fit_transform(X_scaled)\n",
    "labels = KMeans(n_clusters=4, n_init=10, random_state=101).fit_predict(scores3)\n",
    "print('Cluster sizes:', sorted(np.bincount(labels).tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def9a5b9",
   "metadata": {},
   "source": [
    "## Limits of PCA\n",
    "\n",
    "```{index} PCA; limits\n",
    "```\n",
    "\n",
    "PCA is a useful first look at data with many columns, but keep its limits in mind:\n",
    "\n",
    "- **Linear only.** Each component is a straight-line direction, a weighted sum of the features. Curved or more complex patterns may not show up in the first few components.\n",
    "- **Sensitive to scaling.** As we saw, PCA on raw features is dominated by whichever feature has the biggest numbers. Standardize first unless the features already share a unit.\n",
    "- **Harder to explain.** \"PC1 score of 1.8\" means nothing to a manager until you translate it into the features that drive it. Original columns such as spend and orders are easier to explain, so present results in the original units and use components as a summary or a map.\n",
    "- **Spread is not importance.** PCA keeps the directions with the most variation, which are not always the ones that matter for a decision. A small component can still hold the signal that predicts churn or profit.\n",
    "- **Numeric features.** PCA works with numeric columns. Categories such as region or product line need other methods."
   ]
  }
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