phase3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "## Advance operation with Business examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==== Zomato sales analysis ==== \n",
      "\n",
      " Sales data shape (5, 5)\n",
      "\n",
      " Sample data for 1st 3 restau:  [[     1 150000 180000 220000 250000]\n",
      " [     2 120000 140000 160000 190000]\n",
      " [     3 200000 230000 260000 300000]]\n"
     ]
    }
   ],
   "source": [
    "# Data structure: [restaurant_id, 2021, 2022, 2023, 2024]\n",
    "sales_data = np.array([\n",
    "    [1, 150000, 180000, 220000, 250000],  # Paradise Biryani\n",
    "    [2, 120000, 140000, 160000, 190000],  # Beijing Bites\n",
    "    [3, 200000, 230000, 260000, 300000],  # Pizza Hub\n",
    "    [4, 180000, 210000, 240000, 270000],  # Burger Point\n",
    "    [5, 160000, 185000, 205000, 230000]   # Chai Point\n",
    "])\n",
    "\n",
    "print(\"==== Zomato sales analysis ==== \")\n",
    "print(\"\\n Sales data shape\", sales_data.shape)\n",
    "print(\"\\n Sample data for 1st 3 restau: \", sales_data[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[     15  810000  945000 1085000 1240000]\n",
      "[ 810000  945000 1085000 1240000]\n",
      "[150000 120000 200000 180000 160000]\n",
      "[200000 230000 260000 300000]\n",
      "[200000. 152500. 247500. 225000. 195000.]\n"
     ]
    }
   ],
   "source": [
    "# total sales per year\n",
    "print(np.sum(sales_data, axis=0))\n",
    "yeary_total = np.sum(sales_data[:, 1:], axis=0)\n",
    "print(yeary_total)\n",
    "\n",
    "# Minimum sales per restaurant\n",
    "min_sales = np.min(sales_data[:, 1:], axis=1)\n",
    "print(min_sales)\n",
    "\n",
    "# Maximum sales per year\n",
    "max_sales = np.max(sales_data[:, 1:], axis=0)\n",
    "print(max_sales)\n",
    "\n",
    "# Average sales per restautant\n",
    "avg_sales = np.mean(sales_data[:, 1:], axis=1)\n",
    "print(avg_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[150000 330000 550000 800000]\n",
      " [120000 260000 420000 610000]\n",
      " [200000 430000 690000 990000]\n",
      " [180000 390000 630000 900000]\n",
      " [160000 345000 550000 780000]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Aw05Os5FUO0YsqPanK/j70djxdirPg/HPEmMXG+22Y9JGBy2ItlE0++XERkUt9cbvROkfp9o2q//vf//bXWz7dlxY4GiB07FktW12rFpgaaOfJ9v/yeocj71uzKpVq9LSrPzsOv/t2Xms9jzb5dlnn3VfdO2EYPtS7v9yk5mliFgqhb3HpJ9v3F7LZ5r/ubZ+zspzbe95lvpiF/tFwk4otMfpD6KP9zqxLxc2E4s9xvR17ETG0/V69rOTt+3EbRv5Tn8CcvqZa7LrdK90ityHdA7gBGyk0N7gr7rqKjetXeaLnZFuuYr+n+ht9MOut0DMRuwsvy/9SKM/kLLRj/fff/+Y+8vKnKY2SmJv4OlHYuyM/8wze1g+s7HR6vTsjPTM27OfnC04P1bgYT+hn4iNJNljtxGzzCM66UeI7cPZ8inTsxSS0zVll58FOfaYMu8rcz8ci38EKn277afezF9QjI1kZf5gPxE7FixAtw9yy1e3YyHz82WBi806cqz82ZM9D5mngrPRQkvd8acOHOuxmawshJHVtlm+bua5zO15ty9pJ0phyGrb7Diz481eY5a7nJn//nY825dV+5J7vDrHYzm59mXBvhynb7P9wmSzTvhnt8nKY7UvB5n35/+imd3+sC+cWTmG/ymbkcMeh81KkT6HP/Nzba/bzGkQ1m82JV/6x2avk2OlZxzrMVp+9ezZs0/b6/lE+7L/25faU+Wf9SM77wHIWxiJBk7AgmMLki114lhsZMm/8Io/WLa/FqTaaIqlbdhJhOnZVHP2k76dtGKjm+edd577MLIThex6G10+1glT6dmHuKUa2M/clhpgoz82Um4BU/ocWPswtGDCAhELsPxT3NmITOaRlOeff961x3K9bbopy2+2nz7t52Ob3sn+fzy2Xxtxt6my7Kddm+7JRshtCkD7QLWfZ42NutnjtjbZT6sW5NjjTT/d2OlgOaSWa2zPgz1GCwgsh/xkOcXGUnMsfcN+ord5qS2IsC88FhxkHjG3/rU89Geeecb1gdXJPHKZno3Q+fvKgozMX7AsSLXt2TFidS09yI4vy+W0PFE7VoYNG3bc7dtzZvPyWrtsRNqCTP/0Y/7t+/N2LRC2kxrtJ/isjG5mtW12bNmvLPYFwdpjaR4WyNr5A/5pHo+3/ay2zQJ5u83SHfzTRNpzYyft2vRqlsdu6QH22O04sPQJ6xM7hu01bcFx+hSrzCw1ylISbDTf9mEnU/qnuLNfD2xuYZOVx2pfmCzYs9xmOw7t/cSOJ3u8No/x8dgJbzYCa+lVNle6Hcf2xfxkXwBOB/uiYqlANpJsUz1aP9jzYV/+7T3C2m5fYuyxWO6xDRxYf9qXNnuvsNe9jcT7Wd/bLy82VZ9NGWj17PVlgxM2SGF9Y+9p9lzbc2N9mT54/yevZz9L37D72TR+9jjsMdigwT/JcbZfH62t9tjsPAR7zVkeflZz8ZEHeD09CBDIrr76al9kZKTv8OHDx61j08WFhYWlTQ1nU0JVqlTJTX30zDPPHPM+NoXWCy+84KZIi4iI8BUrVsxNnTR48GBfbGxsWj3bxvGmj/rwww/dNFR2/zp16rhpno41XZu13bZRvHhxX6FChXwdO3b0rVq1ytV7/vnnM9TduXOnq2vtt8dUtmxZ32WXXeZ77733stRfH330kZsGyv+YbPo3/7SAxqa/e/jhh30lS5Z003TZFFpr16497hR3macF8z8+m1oqPbtvwYIFM1xndWxKOduPtcWmBlu2bFmWprj7/vvvfY0aNXLPfdWqVd1zZY8t/ZRv/undbPquwoULu9v8090db0ou89hjj7nbatasedx+tPtZ39j0YdaGGjVquOPMplo8ETveWrZs6aaiK1CggDsunn32WXe8+W3ZssVNRWh1bPs33HCDb9u2bX+bqivzFHdZbZu9DuwYsn3bc2L1WrVq5abzO5msts3YlGQ21V2pUqXc8WbTtNl+00+ZtnfvXl/v3r3d9ILh4eFuKkQ7VvyvVf/zNHr06GO2Z+TIkWnHs71+brnlFtdGv6w81gULFvi6du3qq1y5stuOTZ131VVXnfS5NDbd5TnnnOOey/Lly/v69++fNpVm+mPrn05xd7zHv3DhQl+nTp18JUqUcG23fdx4442+qVOnututr/v16+emVrTXgPWB/f/tt9/OsJ1Dhw75br75Zve82v78bbX3yueee86VbfvW1zaN4LEeT1Zfz8d6L/Bbvny5r02bNu590N6DbFrGxYsXZ3kbx3qvmDVrlnvvtuOL6e7ynyD7x+tAHkDOsims7KQny9n0r+YFAACyjpxoII/LvKStsfQO+8k2/QlLAAAg68iJBvI4yzGdP3++m+facjbt5Ci7WC5p5un0AABA1pDOAeRxNoWTTRtm08/ZyTo2bZudHGYnt51s3mEAAHBsBNEAAABANpETDQAAAGQTQTQAAACQTSRE5iBbyW3btm1uNSuWCwUAAAg8lulsiwnZYmE2k9XxEETnIAugmQ0BAAAg8G3evNmtynk8BNE5yEag/U+KLTl6ptnSubY0ri1jbMvYImfQ796g371Bv3uDfvcG/Z4/+j0uLs4NevrjtuMhiM5B/hQOC6BzKoiOiopy++LFnnPod2/Q796g371Bv3uDfs9f/R50ktRbTiwEAAAAsokgGgAAAMgmgmgAAAAgmwiiAQAAgGwiiAYAAACyiSAaAAAAyCaCaAAAACCbCKIBAACAbCKIBgAAALKJIBoAAADIJoJoAAAAIJsIogEAAIBsIogGAAAAsokgGgAAAMgmgmgAAAAgmwiiAQAAgGwKze4dAAAAgJzg8/k0YvZGBccr4BBEAwAAIOAkJqfq8bFLNWreFpUpEKIbElMUFhamQEEQDQAAgIBy4Eii7vtsgWb/uVfBQdL5ZVJVIDxEgYQgGgAAAAFj/Z7D6jFirv7cc1iFIkL12o0NdWTtXAUaTiwEAABAQPj9z7267u2ZLoCuULSAvr6vtS6uVUqBiJFoAAAAeG70vM16dMxSJaX41KRSUb13e3OVLhyppKQkBSKCaAAAAHgmNdWnlyav0vDp61y5Q6NyeuWGxooMC6wc6MwIogEAAOCJo4kp6jtqkX5ctsOV77+0ph5qU0vBdjZhgPM0JzolJUVPPPGEqlWrpgIFCqhGjRp6+umn3ZyAfvb/gQMHqly5cq5OmzZttGbNmgzb2bdvn2655RZFR0eraNGi6tGjhw4dOpShzpIlS3TBBRcoMjJSlSpV0osvvvi39owePVp16tRxdRo2bKgJEyZkuD0rbQEAAMDJ7YqLV5f3ZrsAOjwkWK/e2Fj/blc7VwTQngfRL7zwgoYPH65hw4ZpxYoVrmzB7ZtvvplWx8pvvPGG3nnnHf3xxx8qWLCg2rdvr/j4/826bQF0TEyMpkyZonHjxumXX37RPffck3Z7XFyc2rVrpypVqmj+/Pl66aWX9OSTT+q9995LqzNr1ix17drVBeALFy5Ux44d3WXZsmXZagsAAABObPm2OF371kwt2RKrYlFh+vzuVurUrKJyFZ+HOnTo4OvevXuG6zp16uS75ZZb3P9TU1N9ZcuW9b300ktptx84cMAXERHh+/LLL115+fLlNmztmzt3blqdH3/80RcUFOTbunWrK7/99tu+YsWK+RISEtLqPPzww77atWunlW+88UbXnvRatWrlu/fee7PclpOJjY11bbW/OSExMdE3duxY9xc5h373Bv3uDfrdG/S7N+j302NKzA5f3Sd+9FV5eJzv0pd/9m3Ycyig+j2r8ZqnOdHnnnuuGw1evXq1atWqpcWLF+u3337Tq6++6m5fv369duzY4dIm/IoUKaJWrVpp9uzZuummm9xfS+Fo0aJFWh2rHxwc7EaLr7vuOlfnwgsvVHh4eFodG0G2ke/9+/erWLFirk7fvn0ztM/qjB07NsttySwhIcFd0o+IGzvLNCfONPXvI1DPas2r6Hdv0O/eoN+9Qb97g34/HUt4b9KQiatkmbvnVi+uN29qrOgCYSfs05zu96zux9Mg+pFHHnGBpeUhh4SEuBzpZ5991qVnGAtaTZkyZTLcz8r+2+xv6dKlM9weGhqq4sWLZ6hjedeZt+G/zYJo+3uy/ZysLZkNGTJEgwcP/tv1kydPVlRUlHKKpbkg59Hv3qDfvUG/e4N+9wb9nn0pqdLXG4I1a+dfmcTnlk7V9aV26befpwRcvx85ciTwg+hRo0bp888/1xdffKH69etr0aJF6tOnj8qXL69u3boptxswYECG0W37wmAnNVp+tp0EmRPfpOyAa9u2bUCtNZ/X0e/eoN+9Qb97g373Bv1+auKOJumBkUs0a+deBQVJj7SvpTvPraIgKwRgv/szBwI6iO7Xr58bjfanQtiMGBs3bnQjuBZEly1b1l2/c+dONyOGn5WbNGni/m91du3alWG7ycnJbsYO//3tr90nPX/5ZHXS336ytmQWERHhLpnZAZCTL76c3h/+Qr97g373Bv3uDfrdG/R71m3ae0TdP5mrtbsOKSo8RENvaqq29TL+qh9o/Z7VfXg6O4cNl1vucnqW1pGamur+bykYFrxOnTo1w7cDy3Vu3bq1K9vfAwcOuFk3/KZNm+a2YfnK/jo2Y0f6HBf7RlO7dm2XyuGvk34//jr+/WSlLQAAAPjLvA371PHtmS6ALhsdqdE9W59yAB2IPA2ir776apcDPX78eG3YsEFjxoxxJxXayYDGhvktveOZZ57R999/r6VLl+r222936R42/ZypW7euLr/8ct19992aM2eOZs6cqd69e7vRbatnbr75ZndSoU1fZ1PhjRw5UkOHDs2QavHggw9q4sSJeuWVV7Ry5Uo3Bd68efPctrLaFgAAAEhjF27Vze//oX2HE9WwQhF91/s81S9fRHmJp+kcNh+0Lbbyr3/9y6VkWEB67733ugVN/Pr376/Dhw+7eZ9txPn88893wa4tiOJnedUW7F522WVuZLtz585uPuf0s2jYyXy9evVS8+bNVbJkSbeP9HNJ20whlpv9+OOP69FHH9VZZ53lZuZo0KBBttoCAACQn2fgeG3Kar0xba0rX16/rF7t0lhR4XlvkWxPH1HhwoX1+uuvu8vx2AjwU0895S7HYzNxWAB8Io0aNdKvv/56wjo33HCDu/yTtgAAAORH8Ukp+s/oxRq3ZLsr97yohvq3zz0rEGZX3vtaAAAAgBy1+2CC7vl0nhZuOqDQ4CA916mhbmxRSXkZQTQAAABO2aodB9V9xFxtPXBURQqE6Z1bm6t1jRLK6wiiAQAAcEqmr9ql3l8s1KGEZFUrWVAfdmuh6qUKKT8giAYAAEC2fTJrgwb/EKNUn9SqWnG9e1tzFY0KV35BEA0AAIAsS05J1dPjluuT2Rtd+YbmFfXsdQ0VHurpzMk5jiAaAAAAWXIwPkn3f7lQ01ftduWHL6+jnhdVz/IS3nkJQTQAAABOasv+I+oxYp5W7TyoyLBgvd6liS5vUE75FUE0AAAATmjBpv2657/ztOdQokoXjtAH3VqoUcWiys8IogEAAHBcPyzepn+PXqzE5FTVLRftZuAoX7SA8juCaAAAABxzCe83p63Vq1NWu/JldUrrja5NVTCC8NHQCwAAAMggITlFj3yzVGMWbnXlHudX06NX1lVIHl3C+1QQRAMAACDN3kMJuvfT+Zq3cb8Lmp+6tr5uaVXF62YFHIJoAAAAOGt32RLe87Rp3xEVjgzV27c00wVnlfK6WQGJIBoAAAD6bc0e3ff5fB2MT1al4gX08R1nq2bpwl43K2ARRAMAAORzn/+xUQO/i1FKqk8tqhRzS3iXKBThdbMCGkE0AABAPmVB83MTVujD39a78nVNK+j5zg0VERriddMCHkE0AABAPnQ4IVkPfrVQP63Y5cp929bS/ZfWzJdLeJ8KgmgAAIB8ZtuBo+rxyTyt2B6n8NBgvXJDY13duLzXzcpVCKIBAADykSVbDuiuT+Zp18EElSwUrvdub6FmlYt53axchyAaAAAgn5i4bLv6jFyk+KRU1S5TWB90a6FKxaO8blauRBANAACQD5bwHj5jnV6cuMqVL6pVSsNubqrCkWFeNy3XIogGAADIwxKTU/XYmKUaPX+LK3drXUVPXFVPoSHBXjctVyOIBgAAyKP2H05Uz8/m64/1+xQcJA26ur66nVvV62blCQTRAAAAedD6PYfVfcRc97dQRKjevLmpLqld2utm5RkE0QAAAHnM7HV73Qh07NEkVShaQB/dcbZql2UJ79OJIBoAACAPGTVvs8uBTkrxqUmlonr/9hYqVZglvE83gmgAAIA8IDXVpxcnrdI7M9a58lWNyunlGxorMowlvM8EgmgAAIBc7mhiih4auUgTY3a48gOX1lSfNrUUbGcT4owgiAYAAMjFdsbFuxUIl26NVXhIsF64vqGua1rR62bleQTRAAAAuVTMtlgXQG+PjVfxguF697bmOrtqca+blS8QRAMAAORCPy3fqQe+WqgjiSmqUaqgPr6jpSqXYAnvnEIQDQAAkMuW8P7wt/V6dsIK+XzS+TVL6q1bmqlIAZbwzkkE0QAAALlEUkqqBn4Xoy/nbHLlm1tV1uBr6iuMJbxzHEE0AABALmALp/T6fIF+W7tHQUHSY1fWVY/zqynICshxBNEAAAABbuPev5bwXrf7sKLCQ/TGTU3Vpl4Zr5uVrxFEAwAABLC5G/bpnv/O0/4jSSpXJFIfdGuh+uWLeN2sfI8gGgAAIECNWbhFD3+9VIkpqWpUsYg+uL2FSkdHet0sEEQDAAAE5hLer/20Wm9OW+vKl9cvq9e6NFGBcJbwDhQE0QAAAAEkPilF/x69WOOXbHfl+y6uoX7tarOEd4AhiAYAAAgQuw8m6O7/ztOizQcUFhKkZ69rqBtbVPK6WTgGgmgAAIAAsHJHnHqMmKetB46qaFSY3rm1uc6pXsLrZuE4CKIBAAA89vOqXbr/i4U6lJCsaiUL6qM7znZ/EbgIogEAADw0YuZ6PTVuuVJ90jnVi7sR6KJR4V43CydBEA0AAOCB5JRUFzz/d/ZGV76xRUU907GhwkNZwjs3IIgGAADIYQfjk9T7i4WasXq3W8L74cvr6N4Lq7OEdy5CEA0AAJCDNu87oh6fzNXqnYcUGRas17s01eUNynrdLGQTQTQAAEAOWbBpv1vCe8+hRJUuHKEPu52thhVZwjs3IogGAADIAd8v3qb/jF6sxORU1SsXrQ/vaKFyRQp43SycIoJoAACAM8jn8+mNqWvdMt6mTd0yGnpTExWMIAzLzXj2AAAAzuAS3o98s0RjF21z5bsvqKZHrqirEJbwzvUIogEAAM6AvYcSdO+n8zVv436FBgfp6Y4N1LVlZa+bhdOEIBoAAOA0W7PzoLp/Mleb9x1V4chQt4DKeTVLet0snEYE0QAAAKfRr2t261+fL9DB+GRVLh7llvCuWbqQ183CaebpkjhVq1Z1k4pnvvTq1cvdHh8f7/5fokQJFSpUSJ07d9bOnTszbGPTpk3q0KGDoqKiVLp0afXr10/JyckZ6kyfPl3NmjVTRESEatasqREjRvytLW+99ZZrT2RkpFq1aqU5c+ZkuD0rbQEAAPnbZ79v1B0fz3UB9NlVi2lsr/MIoPMoT4PouXPnavv27WmXKVOmuOtvuOEG9/ehhx7SDz/8oNGjR2vGjBnatm2bOnXqlHb/lJQUF0AnJiZq1qxZ+uSTT1yAPHDgwLQ669evd3UuueQSLVq0SH369NFdd92lSZMmpdUZOXKk+vbtq0GDBmnBggVq3Lix2rdvr127dqXVOVlbAABA/pWS6tNTPyzX42OXuf93alpBn93VSsULhnvdNJwpvgDy4IMP+mrUqOFLTU31HThwwBcWFuYbPXp02u0rVqzwWZNnz57tyhMmTPAFBwf7duzYkVZn+PDhvujoaF9CQoIr9+/f31e/fv0M++nSpYuvffv2aeWWLVv6evXqlVZOSUnxlS9f3jdkyBBXzkpbsiI2Ntbdx/7mhMTERN/YsWPdX+Qc+t0b9Ls36Hdv0O+B1e8H45N83T+e46vy8Dh3eXPqahfLIHce71mN1wImJ9pGkz/77DM3ImwpHfPnz1dSUpLatGmTVqdOnTqqXLmyZs+erXPOOcf9bdiwocqUKZNWx0aQ77vvPsXExKhp06auTvpt+OvYiLR/v7avAQMGpN0eHBzs7mP3NVlpy7EkJCS4i19cXJz7a9uyy5nm30dO7Av/Q797g373Bv3uDfo9cPp9e2y87vlsoVbuOKiI0GC92KmBrmxY9m+ppcg9x3tW9xMwQfTYsWN14MAB3XHHHa68Y8cOhYeHq2jRohnqWcBst/nrpA+g/bf7bztRHQtojx49qv3797u0kGPVWblyZZbbcixDhgzR4MGD/3b95MmTXQ53TvGnySBn0e/eoN+9Qb97g373tt83HpI+WBmiuKQgFQ7z6a7aidLmBZqw2esW5k1Tcuh4P3LkSO4Koj/88ENdccUVKl++vPIKG922kXU/C9wrVaqkdu3aKTo6Oke+SdkB17ZtW4WFhZ3x/eEv9Ls36Hdv0O/eoN+97/epq/fp7W+WKj4pVbXLFNK7tzZVhaIs4Z0Xjnd/5kCuCKI3btyon376Sd9++23adWXLlnWpFjY6nX4E2GbEsNv8dTLPouGfMSN9ncyzaFjZgtgCBQooJCTEXY5VJ/02TtaWY7HZQOySmR0AOfmml9P7w1/od2/Q796g371Bv+c8n0/6cNZmvfLTWle+pHYpvdG1qQpH8jzkleM9q/vwdHYOv48//thNT2ezaPg1b97cPYipU6emXbdq1So3pV3r1q1d2f4uXbo0wywa9k3FAuR69eql1Um/DX8d/zYsTcP2lb5OamqqK/vrZKUtAAAgb0tMTtUX64LTAug7zq2q929vQQCdT3k+Em0BqwXR3bp1U2jo/5pTpEgR9ejRw6VDFC9e3AXG999/vwta/SfyWVqEBcu33XabXnzxRZef/Pjjj7v5nP0jwD179tSwYcPUv39/de/eXdOmTdOoUaM0fvz4tH3ZPmz/LVq0UMuWLfX666/r8OHDuvPOO7PcFgAAkHftP5yoez6dr7m7gxUSHKQnr66n21pX9bpZyM9BtKVx2IiuBbiZvfbaa26mDFvYxGa5sFk13n777bTbLQ1j3LhxbjYOC2gLFizoguGnnnoqrU61atVcwGzzPA8dOlQVK1bUBx984Lbl16VLF+3evdvNL22BeJMmTTRx4sQMJxuerC0AACBv+nP3IXUfMVcb9h5RZIhPb9/STJfWK+d1s5Dfg2gbTfZZgtEx2OqBtpKgXY6nSpUqmjBhwgn3cfHFF2vhwoUnrNO7d293OZ6stAUAAOQts9bt0X2fLVDs0SRVLBqpW6sc0gVnlfS6WQgAAZETDQAAEGhGzd2s2z+c4wLoZpWL6ut7W6lczs1QiwDn+Ug0AABAIElN9emFSSv17ow/XfmaxuX14vWNFKJUr5uGAEIQDQAA8P+OJCbroZGLNCnmr6lvH7zsLPVpc5ZbTTkpiSAa/0MQDQAAYOs/xMWrxydztWxrnMJDgvXSDY10bZMKXjcLAYogGgAA5HvLtsbqrk/maUdcvEoUDNd7tzdX8yrFvW4WAhhBNAAAyNemLN+pB75cqKNJKTqrdCF9dMfZqlScMwhxYgTRAAAgX7Ipdj/4db2e+3GFW87bpq5765ZmimYFQmQBQTQAAMh3klJSNfC7ZfpyzmZXvvWcynry6voKDWH2X2QNQTQAAMhXYo8k6V9fzNfMtXsVHCQ93qGe7jyvqpuBA8gqgmgAAJBvbNx7WHeOmKs/dx9WwfAQvXlzU11ap4zXzUIuRBANAADyhTnr9+neT+dp/5EklS8SqQ/vOFt1y0V73SzkUgTRAAAgz/tm/hY98u0SJaX41LhiEb1/ewuVjo70ulnIxQiiAQBAnl7C+9UpqzXs57WufGXDsnrlhiYqEB7iddOQyxFEAwCAPCk+KUX/HrVY45dud+Vel9TQv9vWVrCdTQj8QwTRAAAgz9l1MF53/3e+Fm8+oLCQIA3p1EjXN6/odbOQhxBEAwCAPGXF9ji3hPfWA0dVNCpM797aXK2ql/C6WchjCKIBAECe8fPKXer9xQIdTkxR9ZIF3RLeVUsW9LpZyIMIogEAQJ5YwnvErA16etxypfqkc2uU0PBbmqtIFEt448wgiAYAALlackqqBv+wXJ/+vtGVbzq7kp7u2EBhLOGNM4ggGgAA5Fpx8Unq/cVC/bJ6t2zV7gFX1NHdF1RnCW+ccQTRAAAgV9q874i6j5irNbsOqUBYiF6/qYna1y/rdbOQTxBEAwCAXGf+xv2657/ztPdwospER+jDbmerQYUiXjcL+QhBNAAAyFW+W7RV/b5eosTkVNUvH+0C6LJFWMIbOYsgGgAA5JoZOIZOXaPXf1rjyu3qlXEpHFHhhDPIeRx1AAAgVyzh/fA3S/Tdom2ufO+F1fXw5XVYwhueIYgGAAABbc+hBN376XyXBx0aHKRnOjbQTS0re90s5HME0QAAIGCt2XlQd46Yqy37jyo6MlTv3Npc59Ys6XWzAIJoAAAQmGzu516fL9DBhGRVKRHllvCuUaqQ180CHIJoAAAQcGz1wSe/j1FKqk8tqxXXu7c2V7GC4V43C0hDEA0AAAKGBc3PjF+uj2ducOXOzSrquU4NFBEa4nXTgAwIogEAQEA4lJCsB75cqGkrd7lyv/a19a+La7CENwISQTQAAPDc1gNH1WPEXK3ccVARocF6rUsTXdmwnNfNAo6LIBoAAHhq0eYDuuuTeW4qu1KFI/TB7S3UuFJRr5sFnBBBNAAA8MyEpdv10MhFSkhOVZ2yhfXhHWerQtECXjcLOCmCaAAA4MkS3m9PX6eXJq1y5UvrlNYbXZuqUAShCXIHjlQAAJCjEpJT9Oi3y/TNgi2u3P28anqsQ12FsIQ3chGCaAAAkGP2HU5Uz0/na86GfS5oHnxNfd16ThWvmwVkG0E0AADIEet2H1L3EXO1ce8RFY4I1Vu3NNOFtUp53SzglBBEAwCAM27W2j3q+dl8xcUnq1LxAvqo29k6q0xhr5sFnDKCaAAAcEZ9NWeTHh+7TMmpPjWvUkzv3dZcJQpFeN0s4B8hiAYAAGdsCe8XJq7Ue7/86crXNimvFzo3UmQYS3gj9yOIBgAAp92RxGQ9+NUiTVm+05UfalNLD1xWkyW8kWcQRAMAgNNqR2y8enwyVzHb4hQeGqyXb2isaxqX97pZwGlFEA0AAE6bZVtjXQC9My5BJQqG673bW7g8aCCvIYgGAACnxaSYHerz1SIdTUpRrTKF9GG3s1WpeJTXzQLOCIJoAADwj5fwtpMHn5+4Uj6f3NzPw25uqujIMK+bBpwxBNEAAOCUJSan6omxyzRy3mZXvr11FQ28qp5CQ4K9bhpwRhFEAwCAUxJ7JMktoDL7z70KDpILnu84r5rXzQJyBEE0AADItg17DrslvP/cc1gFw0M07OZmuqROaa+bBeQYgmgAAJAtf/y5V/d+Nl8HjiSpQtEC+vCOFqpTNtrrZgE5iiAaAABk2dfzt2jAt0uUlOJT40pF9f7tzVW6cKTXzQJyHEE0AAA4qdRUn16evEpvT1/nyh0aldMrNzRmCW/kWwTRAADghI4mpujfoxdpwtIdrnz/pTXdMt7BdjYhkE8RRAMAgOPaFRevu/87T4u3xCo8JFjPd26oTs0qet0swHOeT+K4detW3XrrrSpRooQKFCighg0bat68eRkmcB84cKDKlSvnbm/Tpo3WrFmTYRv79u3TLbfcoujoaBUtWlQ9evTQoUOHMtRZsmSJLrjgAkVGRqpSpUp68cUX/9aW0aNHq06dOq6OtWPChAkZbs9KWwAAyCuWb4tTx7dmugC6WFSYPrurFQE0EAhB9P79+3XeeecpLCxMP/74o5YvX65XXnlFxYoVS6tjwe4bb7yhd955R3/88YcKFiyo9u3bKz4+Pq2OBdAxMTGaMmWKxo0bp19++UX33HNP2u1xcXFq166dqlSpovnz5+ull17Sk08+qffeey+tzqxZs9S1a1cXgC9cuFAdO3Z0l2XLlmWrLQAA5AVTV+zUDe/M0rbYeNUoVVBje52nltWKe90sIGB4ms7xwgsvuFHhjz/+OO26atWqZRj5ff311/X444/r2muvddf997//VZkyZTR27FjddNNNWrFihSZOnKi5c+eqRYsWrs6bb76pK6+8Ui+//LLKly+vzz//XImJifroo48UHh6u+vXra9GiRXr11VfTgu2hQ4fq8ssvV79+/Vz56aefdkH5sGHDXNCclbZklpCQ4C7pg3mTlJTkLmeafx85sS/8D/3uDfrdG/R73ut3+7wbMXuTnp+4Sqk+6dzqxfXGTY1VpEBYvn+eOd7zR78nZXE/QT57tXikXr16biR3y5YtmjFjhipUqKB//etfuvvuu93tf/75p2rUqOFGhps0aZJ2v4suusiVLfC1wPjf//63G9X2S05OdikZlp5x3XXX6fbbb3cBrAW7fj///LMuvfRSlwpiI9+VK1dW37591adPn7Q6gwYNcvdZvHhxltqSmY12Dx48+G/Xf/HFF4qKijpNvQgAwOmRkip9syFYM3f+9UP1uaVTdX21VLGCN/KTI0eO6Oabb1ZsbKxLFQ7IkWgLTIcPH+6C10cffdSNJj/wwANutLhbt27aseOvs4BttDc9K/tvs7+lS2dcISk0NFTFixfPUCf9CHf6bdptFkTb35Pt52RtyWzAgAHusflZIG8j75ZacqIn5XR+k7LR9LZt27qUGeQM+t0b9Ls36Pe80+9xR5P0wMglmrlzr4KCpEfa19Kd51ZRkBXgcLznj36P+//MgZPxNIhOTU11KRjPPfecKzdt2tTlIFv6hAXRuV1ERIS7ZGYHQE6++HJ6f/gL/e4N+t0b9Hvu7vdNe4+o+ydztXbXIUWFh2joTU3Vtl7GQSP8D8e7N3Kq37O6D09/oLFZLiylI726detq06ZN7v9ly5Z1f3fu3JmhjpX9t9nfXbt2Zbjd0jksTSN9nWNtI/0+jlcn/e0nawsAALnNvA371PHtmS6ALhsdqVH3tiaABgI9iLaZOVatWpXhutWrV7tZNIylYFiAOnXq1AxD7DYzRuvWrV3Z/h44cMDNuuE3bdo0N8rdqlWrtDo2Y0f6RHH7WaB27dppM4FYnfT78dfx7ycrbQEAIDcZu3Crbn7/D+07nKiGFYrou97nqUGFIl43C8gVPA2iH3roIf3+++8unWPt2rXuhDubdq5Xr17udsvDshP9nnnmGX3//fdaunSpO0nQZtyw6ef8I9c2q4adjDhnzhzNnDlTvXv3drNlWD1jyeGWZ23T19lUeCNHjnQnAqbPV37wwQfdLB82xd7KlSvdSYE2X7VtK6ttAQAgN7A5BV6dslp9Ri5SYkqq2tcvo5H3nqMy0ZFeNw3INTzNiT777LM1ZswYdwLeU0895UZ7bRo5m/fZr3///jp8+LCbis5GnM8//3wX7NrsG342hZ0Fu5dddpmCg4PVuXNnN5+zX5EiRTR58mQXnDdv3lwlS5Z0i6akn0v63HPPdUG8TWFnJzmeddZZbmaOBg0aZKstAAAEsvikFPX7eol+WLzNlXteVEP929dmCW8gty37fdVVV7nL8dgIsAXYdjkem4nDAuATadSokX799dcT1rnhhhvc5Z+0BQCAQLX7YILu+XSeFm46oNDgID13XUPdeHYlr5sF5EqeB9EAAODMW7XjoLqPmKutB466hVPeubW5Wtco4XWzgFyLIBoAgDxu+qpd6v3FQh1KSFbVElH66I6zVb1UIa+bBeRqBNEAAORh/529QU9+H+OW8G5VrbgbgS5WMNzrZgG5HkE0AAB5UHJKqp4Zv0IjZm1w5eubV3Q50OGhrOENnA4E0QAA5DEH45N0/5cLNX3Vblfuf3lt3XdRDZbwBk4jgmgAAPKQLfuPqMeIeVq186Aiw4L12o1NdEXDcl43C8hzCKIBAMgjFm7ar7v/O197DiWoVOEIfdithRpVLOp1s4A8iSAaAIA8YNySbfr3qMVKSE5V3XLRLoAuX7SA180C8iyCaAAAcvkS3sOmrdUrU1a78mV1SuuNrk1VMIKPeOBM4hUGAEAuZaPOD3+7WN8u3OrKPc6vpkevrKsQlvAGzjiCaAAAcqFDSdIdI+Zp3sYDLmh+6tr6uqVVFa+bBeQbBNEAAOQy63Yf1qtLQ7Q34YAKR4bq7Vua6YKzSnndLCBfIYgGACAXmbhsh/p9vVgHE4JUsVgBfXzH2TqrTGGvmwXkOwTRAADkAvFJKRoyYYU+mb3RlasV9unLe1upbNGCXjcNyJcIogEACHDr9xxW7y8WKGZbnCvffX5V1U1aqxIFw71uGpBvBXvdAAAAcHzfLdqqq9741QXQxQuG6+M7z1b/9rUUwic44ClGogEACEBHE1P05PcxGjlvsyu3rFZcb9zUVGWLRCopKcnr5gH5HkE0AAABZvXOgy59Y/XOQwoKku6/9Cw9cGlNhTL8DAQMgmgAAAJo9cHR87Zo4PfLFJ+UqlKFIzS0SxOdW7Ok100DkAlBNAAAAeBQQrIeH7NUYxdtc+ULziqp17o0UclCEV43DcAxEEQDAOCxmG2x6v3FQjcLh60++O92tdTzwhoKZvluIGARRAMA4GH6xqe/b9Qz41coMTlV5YtE6o2uTdWianGvmwbgJAiiAQDwQOzRJD389RJNjNnhym3qltZL1zdWMeZ+BnIFgmgAAHLYwk37df+XC7Vl/1GFhQTpkSvqqvt5VRVkU3EAyBUIogEAyCGpqT59+Nt6vTBxpZJTfapcPErDbm6qRhWLet00ANlEEA0AQA7YdzhR/xm9WNNW7nLlDg3LaUjnhoqODPO6aQBOAUE0AABn2Jz1+/TAlwu1Iy5e4aHBGnR1Pd3csjLpG0AuRhANAMAZkpLq0/Dpa/XqlNVK9UnVSxXUWzc3U91y0V43DcA/RBANAMAZsOtgvPqOXKzf1u5x5U7NKujpaxuoYAQfvUBewCsZAIDT7Lc1e9Rn5CLtOZSgAmEherpjA13fvKLXzQJwGhFEAwBwmiSnpOr1n9borelr5fNJdcoWdrNv1Cxd2OumATjNCKIBADgNtscedScPzt2w35VvblVZA6+qp8iwEK+bBuAMIIgGAOAfmrZyp/49arH2H0lSoYhQDenUUFc3Lu91swCcQQTRAACcosTkVL00aaXe/3W9KzesUERvdm2qqiULet00AGcYQTQAAKdg874j6v3lQi3efMCV7zyvqh65oo4iQknfAPIDgmgAALLpx6Xb1f+bJToYn6wiBcL00vWN1K5+Wa+bBSAHEUQDAJBF8Ukpenb8Cn36+0ZXbla5qN68uZkqFC3gddMA5DCCaAAAsuDP3YfU64uFWrE9zpXvu7iG+ratpbCQYK+bBsADBNEAAJzE2IVb9eiYpTqSmKISBcP1yo2NdXHt0l43C4CHCKIBADiOI4nJevL7GI2at8WVz6leXENvaqoy0ZFeNw2AxwiiAQA4htU7D6rX5wu0ZtchBQVJD152lu6/9CyFBAd53TQAAYAgGgCAdHw+n0bN26xB38coPilVpQtH6PWbmujcGiW9bhqAAEIQDQDA/zuUkKzHxizVd4u2ufKFtUrp1Rsbq2ShCK+bBiDAEEQDACBp2dZY9f5igTbsPeJSNvq1r617LqiuYNI3ABwDQTQAQPk9feO/sze6+Z8TU1LdnM9vdG2i5lWKe900AAGMIBoAkG/FHklS/28Wa1LMTlduW6+MW32waFS4100DEOAIogEA+dKCTft1/xcLtfXAUYWFBOnRK+vqjnOrKsim4gCAkyCIBgDkK6mpPn3w2596ceIqJaf6VLl4lIbd3FSNKhb1umkA8lsQnZKSoqVLl6pKlSoqVqzY6dgkAACn3b7Difr3qEX6edVuV+7QqJyGdGqo6Mgwr5sGIJcJPpU79enTRx9++GFaAH3RRRepWbNmqlSpkqZPn3662wgAwD/2x597dcXQX1wAHREarOeua6hhXZsSQAPIuSD666+/VuPGjd3/f/jhB61fv14rV67UQw89pMcee+zUWgIAwBmQkurTm1PXqOv7v2tnXIJqlCqo73qfp5tbVSb/GUDOBtF79uxR2bJl3f8nTJigG264QbVq1VL37t1dWgcAAIFgV1y8bvvwD70yZbVSfdL1zSvqh/vPV52y0V43DUB+zIkuU6aMli9frnLlymnixIkaPny4u/7IkSMKCQk53W0EACDbfl2zWw+NXKQ9hxIVFR6ip69toM7NK3rdLAD5eST6zjvv1I033qgGDRq4n8LatGnjrv/jjz9Up06dLG/nySefdPdPf0l///j4ePXq1UslSpRQoUKF1LlzZ+3c+ddcnn6bNm1Shw4dFBUVpdKlS6tfv35KTk7OUMfytC1nOyIiQjVr1tSIESP+1pa33npLVatWVWRkpFq1aqU5c+ZkuD0rbQEAeC85JVUvTVqp2z+a4wLoOmUL6/ve5xNAA/A+iLbg94MPPtA999yjmTNnuuDU2Cj0I488kq1t1a9fX9u3b0+7/Pbbb2m3WY615VyPHj1aM2bM0LZt29SpU6e02+2kRgugExMTNWvWLH3yyScuQB44cGBaHcvXtjqXXHKJFi1a5E6KvOuuuzRp0qS0OiNHjlTfvn01aNAgLViwwOV7t2/fXrt27cpyWwAA3tt24Khueu93vfXzOvl80i2tKmtsr/NUs3Qhr5sGIK/x/UNHjx495fsOGjTI17hx42PeduDAAV9YWJhv9OjRadetWLHCZ02ePXu2K0+YMMEXHBzs27FjR1qd4cOH+6Kjo30JCQmu3L9/f1/9+vUzbLtLly6+9u3bp5Vbtmzp69WrV1o5JSXFV758ed+QIUOy3JasiI2NdfexvzkhMTHRN3bsWPcXOYd+9wb97o1A6vcpMTt8jQdP8lV5eJyvwcCJvh8Wb/XlVYHU7/kJ/Z4/+j02i/HaKeVE2wjwc889p3feecelNKxevVrVq1fXE0884VIievTokeVtrVmzRuXLl3dpFK1bt9aQIUNUuXJlzZ8/X0lJSWmpIsZSPey22bNn65xzznF/GzZs6HK0/WwE+b777lNMTIyaNm3q6qTfhr+OjUgbG8W2fQ0YMCDt9uDgYHcfu6/JSluOJSEhwV384uLi3F/bll3ONP8+cmJf+B/63Rv0e/7t98TkVL08ZY0+nrXRlRuUj9brXRqpSvGoPHs8BEK/50f0e/7o96Qs7ueUguhnn33WpU68+OKLuvvuu9Outxzp119/PctBtOUeW/pF7dq1XSrH4MGDdcEFF2jZsmXasWOHwsPDVbRoxhWkLGC224z9TR9A+2/333aiOhbQHj16VPv373dfCo5Vx6bt82/jZG05FvtCYI8ps8mTJ7sc7pwyZcqUHNsX/od+9wb9nr/6fU+89MnqEG06/NdUdReVS9U1lfYp5vfpilHex/HuDfrdGznV7zZRxhkLov/73//qvffe02WXXaaePXumXW+5xP7AMyuuuOKKtP83atTIBdW26uGoUaNUoEAB5XY2um251n4WuNuCNO3atVN0dHSOfJOyA65t27YKC2MxgZxCv3uDfs9//f7jsh16bexyHUpIVpECoXrhuga6rG5p5Qcc796g3/NHv8f9f+bAGQmit27d6ma5yCw1NfUfDbXbSK/NN7127VrXUZZqceDAgQwjwJY+4p+j2v5mnkXDP2NG+jqZZ9GwsgWxFqjbyZB2OVad9Ns4WVuOxU649J90mZ4dADn54svp/eEv9Ls36Pe83+/xSSl6Zvxyffb7JlduXqWY3ujaVBWK5v7Bl+ziePcG/e6NnOr3rO7jlGbnqFevnn799ddjrmRoecin6tChQ1q3bp2bf7p58+buQUydOjXt9lWrVrkp7Sx32thfW9wl/Swa9k3FAmRro79O+m346/i3YWkatq/0dezLgJX9dbLSFgDAmbdu9yF1fGtmWgD9r4tr6Kt7zsmXATQAb53SSLRNIdetWzc3Im0B57fffuuCSkvzGDduXJa385///EdXX321S+GwKeNsijkbFe7atauKFCnicqstHaJ48eIuML7//vtd0Oo/kc/SIixYvu2221x+tuUnP/74424+Z/8IsKWbDBs2TP3793crKk6bNs2li4wfPz6tHbYPezwtWrRQy5YtXV734cOH3XzYJittAQCcWd8u2KLHxy7TkcQUlSgYrle7NNFFtUp53SwA+dQpBdHXXnutmzP5qaeeUsGCBV1QbYuZ2HWWhpFVW7ZscQHz3r17VapUKZ1//vn6/fff3f/Na6+95mbKsIVNbJYLm1Xj7bffTru/BdwWtNtsHBbQWlssGLZ2+VWrVs0FzDbP89ChQ1WxYkU3x7Vty69Lly7avXu3exwWiDdp0sStxJj+ZMOTtQUAcGYcSUzWwO9i9PX8La7cunoJDb2piUpHR3rdNAD52CkF0cZm0finZ0l+9dVXJ7zdpr2zlQTtcjw2ij1hwoQTbufiiy/WwoULT1ind+/e7vJP2gIAOL1W7ohT7y8Wau2uQwoOkh68rJZ6X1pTIVYAgNwYRAMAcKb4fD59NXeznvw+RgnJqSpdOEJDb2qq1jVKeN00AMheEF2sWDEFBWXtm/++ffuyulkAADI4GJ+kR8cs0w+Lt7my5T2/emNjlSj099mOACDgg2g72Q4AgDNp6ZZY9f5ygTbuPaLQ4CD1a19bd19QXcGkbwDIrUG0nbAHAMCZSt8YMWuDnpuwQkkpPjdlnc39bHNAA0CezImOj493C5GklxOr8QEA8oYDRxLV/+slmrz8r0Wv2tUro5eub6wiUSxmASCPBdE2h/LDDz/s5lu26ekyS0lJOR1tAwDkcfM37tcDXy7U1gNHFR4SrEevrKNu51bN8jk4AOCVU1qx0BYusUVLhg8f7hY1sXmXBw8erPLly7sFVwAAOJHUVJ/embFON7472wXQVUpE6Zv7ztUd51UjgAaQd0eibVEVC5Zt/mVb1c/mjK5Zs6abs/nzzz/XLbfccvpbCgDIE/YeSlDfUYs1Y/VuV766cXk9d10DFY4kfQNAHg+ibQq76tWrp+U/+6e0sxUHbfVAAACO5fc/9+rBrxZqZ1yCIkKDNfia+upydiVGnwHkj3QOC6DXr1/v/l+nTh2XG+0foS5atOjpbSEAINdLSfXp9Z9W6+b3f3cBdM3ShfRd7/N0U8vKBNAA8s9ItKVwLF68WBdddJEeeeQRXX311Ro2bJiSkpL06quvnv5WAgByrZ1x8erz1SLN/vOvE9FvaF5Rg6+tr6hwFs0FkHud0jvYQw89lPb/Nm3aaOXKlZo/f77Li27UqNHpbB8AIBezvOe+Ixdp7+FERYWH6NnrGui6phW9bhYA5Gw6x+zZszVu3LgM1/lPMOzZs6cbjU5ISPjnrQIA5GpJKal6YeJKdftojgug65aL1g/3n08ADSB/BtFPPfWUYmJi0spLly5Vjx493Gj0gAEDXE70kCFDzkQ7AQC5hE1Zd9N7v2v49HWufNs5VTTmX+eqRqlCXjcNALxJ51i0aJGefvrptPJXX32lVq1a6f3333flihUratCgQXryySdPXwsBALnGlOU79Z/RixV7NEmFI0L1wvWNdGXDcl43CwC8DaL379+vMmXKpJVnzJihK664Iq189tlna/Pmzae3hQCAgJeYnKohE5fro5l/zdzUuGIRvdm1mSqXiPK6aQDgfTqHBdD+qe0SExO1YMECnXPOOWm3Hzx4UGFhTJYPAPnJnnjppg/mpAXQd51fTaN7nksADSBPy9ZI9JVXXummtHvhhRc0duxYRUVFudUK/ZYsWaIaNWqciXYCAALQhKU79OKSECWkxKloVJhevr6x2tT73y+WAJBXZSuItnzoTp06ufmhCxUqpE8++UTh4eFpt3/00Udq167dmWgnACCAxCel6Klxy/XFH5skBal55aJ68+ZmKl+0gNdNA4DAC6JLliypX375RbGxsS6IDgkJyXD76NGj3fUAgLxr7a5D6v3FAq3ccVC22GDb8qka2r2FCkRGeN00AAjsxVaKFClyzOuLFy/+T9sDAAhg38zfosfHLtPRpBSVLBSulzo3VNzqPxQakq1TbAAg12PNVQDASR1OSNbA72L0zYItrnxujRJ6/aYmKhYZogmrvW4dAOQ8gmgAwAmt2B7n0jfW7T6s4CDpoTa19K9LaiokOEhJSUleNw8APEEQDQA4Jp/Ppy/nbNbgH2KUkJyqMtEReuOmpmpVvYTXTQMAzxFEAwD+Ji4+SQO+XarxS7a78sW1S+mVGxqrRCFOHgQAQxANAMhgyZYD6v3FQm3ad0ShwUHqf3lt3XV+dQVbLgcAwCGIBgCkpW98PHODhvy4QkkpPlUoWkBv3txUzSoX87ppABBwCKIBADpwJFH/Gb1EP63Y6cqX1y+rFzo3UpGoMK+bBgABiSAaAPK5+Rv36f4vFmpbbLzCQ4L1+FV1dds5VRRkK6kAAI6JIBoA8qnUVJ/e/eVPvTx5lVJSfapaIkrDbm6mBhWOvaAWAOB/CKIBIB/acyhBfUct1i+rd7vyNY3L67lODVUogo8FAMgK3i0BIJ+ZtW6P+ny1SLsOJigyLFiDr6mvG1tUIn0DALKBIBoA8glL2Xhj6hq9MW2NfD7prNKFXPpG7bKFvW4aAOQ6BNEAkA/sjIvXg18t1O9/7nPlG1tU1JPX1FdUOB8DAHAqePcEgDxu+qpdLv953+FEFQwP0bPXNVTHphW8bhYA5GoE0QCQRyWlpOqVyav1zox1rlyvXLSG3dxU1UsV8rppAJDrEUQDQB60Zf8RPfDlQi3YdMCVb29dRY9eWVeRYSFeNw0A8gSCaADIYybH7FC/r5co9miSCkeG6sXOjXRFw3JeNwsA8hSCaADIIxKSU/T8jyv18cwNrty4UlEN69pUlYpHed00AMhzCKIBIA/YsOewen+5QMu2xrny3RdUU7/2dRQeGux10wAgTyKIBoBc7vvF2/Tot0t1KCFZRaPC9MoNjXVZ3TJeNwsA8jSCaADIpeKTUjT4h+X6cs4mVz67ajG90bWpyhUp4HXTACDPI4gGgFxo7a6D6vX5Qq3aeVC2WnfvS2rqwcvOUmgI6RsAkBMIogEgl/l6/hY9MXaZjialqGShCL3epYnOP6uk180CgHyFIBoAconDCckueP524VZXPq9mCb3WpYlKF470umkAkO8QRANALrB8W5ybfePP3YcVHCT1bVtL911cUyFWAADkOIJoAAhgPp9Pn/+xSU+NW67E5FSVjY50Jw+2rFbc66YBQL5GEA0AASouPkkDvlmq8Uu3u/KldUrr5Rsaq3jBcK+bBgD5HkE0AASgxZsPuPSNzfuOKjQ4SA9fXkc9zq+mYNI3ACAgEEQDQIClb3z423q9MHGlklJ8qlisgN7s2lRNKxfzumkAgHQIogEgQOw/nKh+Xy/WTyt2ufIVDcrq+c6NVKRAmNdNAwBkQhANAAFg3oZ9uv/LhdoeG6/wkGA9cVVd3XpOFQXZSioAgIBDEA0AHkpN9Wn4jHV6dcpqpaT6VK1kQQ27uanqly/iddMAACcQMOvDPv/8827EpU+fPmnXxcfHq1evXipRooQKFSqkzp07a+fOnRnut2nTJnXo0EFRUVEqXbq0+vXrp+Tk5Ax1pk+frmbNmikiIkI1a9bUiBEj/rb/t956S1WrVlVkZKRatWqlOXPmZLg9K20BgOzYfTBB3T6eo5cmrXIBdMcm5fXD/ecTQANALhAQQfTcuXP17rvvqlGjRhmuf+ihh/TDDz9o9OjRmjFjhrZt26ZOnTql3Z6SkuIC6MTERM2aNUuffPKJC5AHDhyYVmf9+vWuziWXXKJFixa5IP2uu+7SpEmT0uqMHDlSffv21aBBg7RgwQI1btxY7du3165du7LcFgDIjplr9+jKN37Vr2v2KDIsWC9e38itPlgogh8IASA38DyIPnTokG655Ra9//77Klbsf2efx8bG6sMPP9Srr76qSy+9VM2bN9fHH3/sguXff//d1Zk8ebKWL1+uzz77TE2aNNEVV1yhp59+2o0qW2Bt3nnnHVWrVk2vvPKK6tatq969e+v666/Xa6+9lrYv28fdd9+tO++8U/Xq1XP3sZHtjz76KMttAYCsSE5J1auTV+nWD/9wI9G1yhTS973P140tKpH/DAC5iOdDHpYiYSPFbdq00TPPPJN2/fz585WUlOSu96tTp44qV66s2bNn65xzznF/GzZsqDJlyqTVsRHk++67TzExMWratKmrk34b/jr+tBELtm1fAwYMSLs9ODjY3cfum9W2HEtCQoK7+MXFxbm/ti27nGn+feTEvvA/9Ls3ckO/74iLV9/RSzV3w35XvrF5BT1+ZR0VCA8J6Hbn9n7Pi+h3b9Dv+aPfk7K4H0+D6K+++sqlT1g6R2Y7duxQeHi4ihYtmuF6C5jtNn+d9AG0/3b/bSeqYwHt0aNHtX//fpcWcqw6K1euzHJbjmXIkCEaPHjw3663EXQb6c4pU6ZMybF94X/od28Ear/H7A/S52uDdTg5SBHBPt1YPVUtwjfq5582Ki8I1H7P6+h3b9Dv3sipfj9y5EhgB9GbN2/Wgw8+6DrETubLi2x023Kt/Sxwr1Spktq1a6fo6Ogc+SZl/du2bVuFhTHPbE6h370RqP2eZOkbP63VBys3uHK9coU1tEsjVS1RUHlBoPZ7Xke/e4N+zx/9Hvf/mQMBG0RbioSduGezZvjZiPAvv/yiYcOGuRP/LNXiwIEDGUaAbUaMsmXLuv/b38yzaPhnzEhfJ/MsGla2ILZAgQIKCQlxl2PVSb+Nk7XlWGw2ELtkZgdATr74cnp/+Av97o1A6vfN+47oga8WauGmA658x7lVNeDKOooIDVFeE0j9np/Q796g372RU/2e1X14dmLhZZddpqVLl7oZM/yXFi1auJMM/f+3BzF16tS0+6xatcpNade6dWtXtr+2jfSzaNg3FQuQ7QRBf5302/DX8W/D0jTsRMH0dVJTU13ZX8duP1lbACC9ict2qMMbv7oAOjoyVO/c2kxPXlM/TwbQAJAfeTYSXbhwYTVo0CDDdQULFnTzMPuv79Gjh0uHKF68uAuM77//fhe0+k/ks7QIC5Zvu+02vfjiiy4/+fHHH3cnK/pHgHv27OlGtvv376/u3btr2rRpGjVqlMaPH5+2X9tHt27dXODesmVLvf766zp8+LCbrcMUKVLkpG0BABOflKIhE1bok9l/5To3qVRUb3ZtqkrFc+48CABAPpid40RsGjqbKcMWNrFZLmxWjbfffjvtdkvDGDdunJuNwwJaC8ItGH7qqafS6tj0dhYw2zzPQ4cOVcWKFfXBBx+4bfl16dJFu3fvdvNLWyBu0+VNnDgxw8mGJ2sLAKzfc1i9v1igmG1/5dPde2F1/ad9bYWFeD6bKAAgLwfRtrJgenbCoc35bJfjqVKliiZMmHDC7V588cVauHDhCevY/NF2OZ6stAVA/vXdoq169NulOpyYomJRYXr1xia6pE5pr5sFAMgPQTQA5DZHE1M0+IcYfTV3syu3rFpcQ7s2UbkiBbxuGgDgDCKIBoBTtGbnQfX6YoFW7zwkW2zw/ktq6oHLzlIo6RsAkOcRRANANvl8Po2ev0UDv1um+KRUlSocode7NNF5NUt63TQAQA4hiAaAbDiUkKwnxi7TmIVbXfmCs0q6/GcLpAEA+QdBNABkUcy2WN3/xUL9ueewQoKD1LdtLd13UQ0FBwd53TQAQA4jiAaALKRvfPb7Rj09foUSk1NVrkik3ujaVGdXLe510wAAHiGIBoATiD2apEe+WaIfl+1w5cvqlNbLNzRWsYLhXjcNAOAhgmgAOI5Fmw+4xVO27D+qsJAgPXx5HfU4v5qCbCoOAEC+RhANAMdI3/jwt/V6/seVSk71qVLxAnqzazO3hDcAAIYgGgDS2X84Uf8ZvVhTV+5y5SsbltWQTo1UpECY100DAAQQgmgA+H9zN+zTA18u1PbYeIWHBmvgVfV0S6vKpG8AAP6GIBpAvpea6tPwGev06pTVSkn1qXrJghp2czPVKx/tddMAAAGKIBpAvrbrYLz6jlys39buceXrmlbQMx0bqGAEb48AgOPjUwJAvvXbmj3qM3KR9hxKUIGwED11bX1d37wi6RsAgJMiiAaQ7ySnpGro1DUa9vNa+XxS7TKFNezmpjqrTGGvmwYAyCUIogHkK9tjj+rBLxdpzoZ9rty1ZSUNvKq+CoSHeN00AEAuQhANIN+YtnKn/j1qsfYfSVLB8BA916mhrm1SwetmAQByIYJoAHleYnKqXp68Su/98qcrN6gQrWFdm6lqyYJeNw0AkEsRRAPI0zbvO6L7v1zolvA2d5xbVQOurKOIUNI3AACnjiAaQJ7149Lt6v/NEh2MT1Z0ZKheuqGx2tcv63WzAAB5AEE0gDwnPilFz01Yof/O3ujKTSsX1Ztdm6pisSivmwYAyCMIogHkKev3HFafUUu1fHucK997UXX9p11thYUEe900AEAeQhANIM+YtztIjw7/XYcTU1S8YLheubGxLqld2utmAQDyIIJoALleXHySBn+/TN+stZMFU9SyWnG9cVNTlS0S6XXTAAB5FEE0gFxtcswOPfHdMu2MS1CQfOp1cQ31aVtboaRvAADOIIJoALnS7oMJevKHGI1fst2VqxSP0jXl4vTAZTUJoAEAZxxBNIBcxefz6dsFW/XUuOWKPZqkkOAg3X1BdfW6qKqmTZnkdfMAAPkEQTSAXLVwyqNjlurXNXtcuV65aL14fSM1qFBESUlJXjcPAJCPEEQDCHgpqT59MmuDW7r7SGKKwkOD1afNWW4EmqnrAABeIIgGENDW7DzoVh1cuOmvZbtbVi2uIZ0bqkapQl43DQCQjxFEAwhIicmpGj59nYb9vEZJKT4VigjVI1fU0c0tKys4OMjr5gEA8jmCaAABZ9HmA3r46yVatfOgK19Wp7Seua6ByhUp4HXTAABwCKIBBIwjicl6ZfJqfTxzvVJ9cqsOPnlNfV3dqJyCghh9BgAEDoJoAAHhtzV7NGDMEm3ed9SVr2taQU9cVc8F0gAABBqCaACeij2SpGfGL9fo+VtcuXyRSD3bqaEuqV3a66YBAHBcBNEAPPPj0u0a+H2MW33QsjVuP6eK+l1ex51ECABAIOOTCkCO2xUXrye+W6ZJMTtduUapgnqhcyO1qFrc66YBAJAlBNEAcnTJ7lHzNuuZ8St0MD5ZocFB6nlRDfW+tKYiw0K8bh4AAFlGEA0gR2zce1gDvl2qWev2unKjikX0fKdGqlc+2uumAQCQbQTRAM74kt0f/bZer0xZpfikVEWGBevfbWvrzvOqKpQluwEAuRRBNIAzZsX2OD3yzRIt3hLryq2rl9DznRuqSomCXjcNAIB/hCAawGmXkJyiYdPWumW7k1N9KhwZqseurKsuZ1di0RQAQJ5AEA3gtJq/cZ8e/map1u465Mrt6pXR0x0bqEx0pNdNAwDgtCGIBnBaHE5I1kuTVumT2Rvk80klC0XoqWvr64oGZRl9BgDkOQTRAP6x6at26bExy7T1wF9Ldl/fvKIe71BXRaNYshsAkDcRRAM4ZfsPJ+rpccv17cKtrlyxWAEN6dRQF5xVyuumAQBwRhFEAzilRVPGLdmuJ7+P0d7DiW7J7jvPrab/tK+lqHDeVgAAeR+fdgCyZXvsUT0xdpl+WrHLlWuVKaTnOzdSs8rFvG4aAAA5hiAaQJakpvr05dxNen7CSh1MSFZYSJB6XVJT/7q4psJDWTQFAJC/EEQDOKk/dx/SI98u1Zz1+1y5SaWievH6RqpVprDXTQMAwBME0QCOKzklVe//ul6v/bRaicmpKhAWon7ta6vbuVUVEsy0dQCA/IsgGsAxLdsaq4e/WaKYbXGufMFZJfXcdQ1VqXiU100DAMBzBNEAMohPStHQqWv03i9/KiXVpyIFwvTEVfXUuVkFFk0BAOD/eXo20PDhw9WoUSNFR0e7S+vWrfXjjz+m3R4fH69evXqpRIkSKlSokDp37qydO3dm2MamTZvUoUMHRUVFqXTp0urXr5+Sk5Mz1Jk+fbqaNWumiIgI1axZUyNGjPhbW9566y1VrVpVkZGRatWqlebMmZPh9qy0Bcjt/vhzr64Y+quGT1/nAugODctpSt8L3eIpBNAAAARIEF2xYkU9//zzmj9/vubNm6dLL71U1157rWJiYtztDz30kH744QeNHj1aM2bM0LZt29SpU6e0+6ekpLgAOjExUbNmzdInn3ziAuSBAwem1Vm/fr2rc8kll2jRokXq06eP7rrrLk2aNCmtzsiRI9W3b18NGjRICxYsUOPGjdW+fXvt2vXXFF5ZaQuQmx2MT9JjY5aqy3u/a/2ewypdOELv3tZcb93STKULR3rdPAAAAo8vwBQrVsz3wQcf+A4cOOALCwvzjR49Ou22FStW+KzJs2fPduUJEyb4goODfTt27EirM3z4cF90dLQvISHBlfv37++rX79+hn106dLF1759+7Ryy5Ytfb169Uorp6Sk+MqXL+8bMmSIK2elLVkRGxvr7mN/c0JiYqJv7Nix7i9yTm7r95+W7/Cd89xPvioPj3OXR75Z7DtwJHe0PTf3e15Bv3uDfvcG/Z4/+j02i/FawORE26iyjfIePnzYpXXY6HRSUpLatGmTVqdOnTqqXLmyZs+erXPOOcf9bdiwocqUKZNWx0aQ77vvPjea3bRpU1cn/Tb8dWxE2tgotu1rwIABabcHBwe7+9h9TVbaciwJCQnu4hcX99cJWrYtu5xp/n3kxL6Q+/p976EEPT1hlcYv3eHKlYsX0LPX1tc51Yvnivbn1n7Pa+h3b9Dv3qDf80e/J2VxP54H0UuXLnVBs+UcW67xmDFjVK9ePZd6ER4erqJFi2aobwHzjh1/fejb3/QBtP92/20nqmMB7dGjR7V//34XwB+rzsqVK9O2cbK2HMuQIUM0ePDgv10/efJkl8OdU6ZMmZJj+0Lg97vPJ83bE6QxG4J1ODlIQfLpkvI+XVHxoPat/F0T/jrsc61A7fe8jn73Bv3uDfrdGznV70eOHMkdQXTt2rVdwBwbG6uvv/5a3bp1cznHeYGNbluutZ8F7pUqVVK7du3ciZQ58U3KDri2bdsqLCzsjO8Pgd/v2w4c1cDvV2jG2j2uXKdMIT13XX01rFBEuV0g93teRr97g373Bv2eP/o97v8zBwI+iLYRXpsxwzRv3lxz587V0KFD1aVLF5dqceDAgQwjwDYjRtmyZd3/7W/mWTT8M2akr5N5Fg0rWxBboEABhYSEuMux6qTfxsnaciw2G4hdMrMDICdffDm9PwRev9uS3Z/+vlEvTlypw4kpCg8J1gOX1dS9F9VQWEjeWrI7kPo9P6HfvUG/e4N+90ZO9XtW9xFwn56pqakuj9gCansQU6dOTbtt1apVbko7S/8w9tfSQdLPomHfVCxAtpQQf5302/DX8W/DgnjbV/o61gYr++tkpS1AoFq766BueHe2Bn0f4wLoFlWKacKDF6j3pWfluQAaAICcEup1usMVV1zhTtA7ePCgvvjiCzens00/V6RIEfXo0cOlQxQvXtwFxvfff78LWv0n8llahAXLt912m1588UWXn/z444+7+Zz9I8A9e/bUsGHD1L9/f3Xv3l3Tpk3TqFGjNH78+LR22D4sjaRFixZq2bKlXn/9dXeC45133uluz0pbgECTlJKqd6av05vT1ioxJVUFw0P0yBV1dEurKgpmyW4AAHJvEG0jyLfffru2b9/uAlVbeMUCaMt5Ma+99pqbKcMWNrHRaZtV4+233067v6VhjBs3zs3GYQFtwYIFXTD81FNPpdWpVq2aC5htnmdLE7G5qT/44AO3LT9LHdm9e7ebX9oC8SZNmmjixIkZTjY8WVuAQLJkywH1/3qJVu446MoX1y6lZ69rqApFC3jdNAAA8gRPg+gPP/zwhLfb6oG2kqBdjqdKlSqaMGHCCbdz8cUXa+HChSes07t3b3f5J20BvHY0MUWvTlmlD39br1SfVCwqTIOurq9rm5RnxUEAAE4jz08sBHB6zFq7R498u1Sb9v01Nc81jctr0NX1VKLQ309uBQAA/wxBNJDLxR5N0pAJK/TV3M2uXK5IpJ69roEurZNx7nMAAHD6EEQDudikmB16Yuwy7Tr418qYt55TWQ9fXkeFI5l6CQCAM4kgGsiFdh2M15Pfx2jC/y/ZXb1kQQ3p1FCtqpfwumkAAOQLBNFALuLz+fT1/C16ZvwKl8YREhykey+srgcuO0uRYSFeNw8AgHyDIBrIJTbvO6JHxyzVr2v+WrK7QYVovdC5keqXz/1LdgMAkNsQRAMBLiXVpxGzNujlSat0NClFEaHBeqhtLd11fjWFsuIgAACeIIgGAtiqHQf18DdLtGjzAVduVa24nu/cSNVKFvS6aQAA5GsE0UAASkhO0ds/r9Pb09cqKcWnwhGhGnBlXd10diWW7AYAIAAQRAMBZsGm/Xr46yVas+uQK7epW0bPdGygskUivW4aAAD4fwTRQIA4nJCslyevcvnPPp9UomC4Bl9bXx0almPJbgAAAgxBNBAAflm92828sWX/UVfu1KyCnuhQT8UKhnvdNAAAcAwE0YCHDhxJ1NPjVuibBVtcuULRAnquU0NdVKuU100DAAAnQBANeLRoiq02OOj7ZdpzKFGWrdGtdVX1a19bBSN4WQIAEOj4tAZy2M64eD0xdpkmL9/pyjVLF3KLpjSvUszrpgEAgCwiiAZycPT5q7mb9dyEFToYn6zQ4CD96+Ia6nVpTUWEsmQ3AAC5CUE0kAM27DmsAd8u1ew/97py44pF9ML1jVSnbLTXTQMAAKeAIBo4g5JTUvXRzPV6ZfJqJSSnKjIsWP9pV1t3nldNISyaAgBArkUQDZwhK7Yf1GPfLdfSrbGufG6NEnq+UyNVLhHlddMAAMA/RBANnGYJSSkatylYP//xu5JTfYqODNXjHerphhYVWTQFAIA8giAaOI3mbtinh79erD/3BNuphLqiQVkNvqa+SkezZDcAAHkJQTRwGhxKSNaLE1fqv7M3unJ0mE/PdW6iq5pU9LppAADgDCCIBv6hn1fu0mNjlmpbbLwrX9+sgpqHbFT7+mW8bhoAADhDCKKBU7TvcKKe+iFGYxdtc+XKxaM0pFNDtaxSRBMm/DUiDQAA8iaCaOAUFk35fvE2Df5huQukbaa67udVU992tRQVHqqkpCSvmwgAAM4wgmggG7YdOKrHxy7TtJW7XLl2mcJu0ZQmlYp63TQAAJCDCKKBLEhN9enzOZv0wo8r3UmE4SHB6n1pTfW8qIbCQ20mDgAAkJ8QRAMnsW73IQ34ZqnmbNjnys0qF9ULnRvprDKFvW4aAADwCEE0cBxJKal675c/NXTqGiUmpyoqPET929fWba2rsmQ3AAD5HEE0cAzLtsaq/9dLtHx7nCtfWKuUnruugSoWY8luAABAEA1kEJ+Uotd+Wq0Pfl2vlFSfikaFaeBV9XRd0wos2Q0AANIQRAP/b/a6vRrw7RJt2HvEla9qVE5PXlNfJQtFeN00AAAQYAiike/FxSdpyISV+nLOJlcuEx2hZzo2VNt6rDgIAACOjSAa+dqU5Tv1+Nil2hmX4Mo3t6qsR66oo+jIMK+bBgAAAhhBNPKl3QcT9OQPMRq/ZLsrVy0Rpec7N9I51Ut43TQAAJALEEQj3y3Z/e2CrXp6/HIdOJLkpqq7+4Lq6tPmLEWGhXjdPAAAkEsQRCPf2LL/iB4ds0y/rN7tyvXKRevF6xupQYUiXjcNAADkMgTRyPNsqrpPZ2/Qi5NW6Uhiilum+8HLztI9F1ZXWAhLdgMAgOwjiEaetmbnQT38zRIt2HTAlVtWLa4hnRuqRqlCXjcNAADkYgTRyJNsme7h09fprZ/XKjElVYUiQt2sGze3rKxgluwGAAD/EEE08pxFmw/o4a+XaNXOg658aZ3SeqZjA5UvWsDrpgEAgDyCIBp5xpHEZL06ebU+mrleqT6peMFwDbq6nq5pXJ4luwEAwGlFEI08YebaPXrk2yXavO+oK1/XtIKeuKqeC6QBAABON4Jo5GqxR5L07ITlGjVviyuXLxKpZzs11CW1S3vdNAAAkIcRRCPXmrhsu574LsatPmhub11F/S+v404iBAAAOJOINpDr7IqL18DvYjQxZocr1yhVUC90bqQWVYt73TQAAJBPEEQjVy3ZPXreFj0zfrni4pMVGhyknhfVUO9La7JkNwAAyFEE0cgVNu09ogFjlmjm2r2u3LBCETf6XK98tNdNAwAA+RBBNAJ+ye6PZ67Xy5NXKT4pVRGhwfp3u1rqfl41hbJkNwAA8AhBNALWyh1xevibpVq8+a8lu1tXL6EhnRqqasmCXjcNAADkcwTRCDgJySl6a9pavT19nZJTfSocGarHrqyrLmdXYtEUAAAQEAiiEVDmb9znRp/X7jrkyu3qldHTHRuoTHSk100DAABIQxCNgHA4IVkvTVqlT2ZvkM8nlSwUrqeubaArGpRl9BkAAAQcT8/MGjJkiM4++2wVLlxYpUuXVseOHbVq1aoMdeLj49WrVy+VKFFChQoVUufOnbVz584MdTZt2qQOHTooKirKbadfv35KTk7OUGf69Olq1qyZIiIiVLNmTY0YMeJv7XnrrbdUtWpVRUZGqlWrVpozZ06224Lsm7F6t9q99otGzPorgL6+eUX91PciXdmwHAE0AAAISJ4G0TNmzHBB6e+//64pU6YoKSlJ7dq10+HDh9PqPPTQQ/rhhx80evRoV3/btm3q1KlT2u0pKSkugE5MTNSsWbP0ySefuAB54MCBaXXWr1/v6lxyySVatGiR+vTpo7vuukuTJk1KqzNy5Ej17dtXgwYN0oIFC9S4cWO1b99eu3btynJbkD37Dyeq78hF6vbRHG09cFQVixXQf7u31Ms3NFbRqHCvmwcAAHB8vgCya9cunzVpxowZrnzgwAFfWFiYb/To0Wl1VqxY4erMnj3blSdMmOALDg727dixI63O8OHDfdHR0b6EhARX7t+/v69+/foZ9tWlSxdf+/bt08otW7b09erVK62ckpLiK1++vG/IkCFZbsvJxMbGuvr2NyckJib6xo4d6/4GktTUVN/3i7b6mj012Vfl4XG+qo+M8w3+PsZ3KD7JlxcEar/ndfS7N+h3b9Dv3qDf80e/x2YxXguonOjY2Fj3t3jxv5Zvnj9/vhudbtOmTVqdOnXqqHLlypo9e7bOOecc97dhw4YqU6ZMWh0bQb7vvvsUExOjpk2bujrpt+GvYyPSxkaxbV8DBgxIuz04ONjdx+6b1bZklpCQ4C5+cXFx7q9txy5nmn8fObGvrNoRF68nf1ihqSt3u/JZpQvq2Y711bRSUTteA6qteanf8wP63Rv0uzfod2/Q7/mj35OyuJ+ACaJTU1NdUHveeeepQYMG7rodO3YoPDxcRYtagPU/FjDbbf466QNo/+3+205Ux4Lao0ePav/+/S4t5Fh1Vq5cmeW2HCvne/DgwX+7fvLkyS5/O6dYqozXUn3S7F1B+n5jsOJTghQS5FPbCqlqWyFW25fO0valynMCod/zI/rdG/S7N+h3b9Dv3sipfj9y5EjuCqItN3rZsmX67bfflFfYyLblWftZ0F6pUiWX9x0dHZ0j36TsgGvbtq3CwsLklQ17D+uxscs1Z8N+V25csYiGdKyvs8oUUl4UKP2e39Dv3qDfvUG/e4N+zx/9Hvf/mQO5Ioju3bu3xo0bp19++UUVK1ZMu75s2bIu1eLAgQMZRoBtRgy7zV8n8ywa/hkz0tfJPIuGlS2QLVCggEJCQtzlWHXSb+NkbcnMZgKxS2Z2AOTkiy+n9+eXnJKqD35br9emrFZCcqoKhIXoP+1r645zqyokOO/PuuFVv+d39Ls36Hdv0O/eoN+9kVP9ntV9eDo7h8/ncwH0mDFjNG3aNFWrVi3D7c2bN3cPZOrUqWnX2RR4NqVd69atXdn+Ll26NMMsGvZtxQLkevXqpdVJvw1/Hf82LE3D9pW+jqWXWNlfJyttwf/EbItVx7dn6vkfV7oA+oKzSmryQxeqx/nV8kUADQAA8rZQr1M4vvjiC3333Xdurmh/bnGRIkXcCLH97dGjh0uJsJMNLTC+//77XdDqP5HPUiMsWL7tttv04osvum08/vjjbtv+UeCePXtq2LBh6t+/v7p37+4C9lGjRmn8+PFpbbF9dOvWTS1atFDLli31+uuvu6n27rzzzrQ2nawtkOKTUvTG1DV695c/lZLqU5ECYXriqnrq3KwCcz4DAIA8w9Mgevjw4e7vxRdfnOH6jz/+WHfccYf7/2uvveZmyrCFTWymC5tV4+23306ra2kYlgpis3FYQFuwYEEXDD/11FNpdWyE2wJmm+d56NChLmXkgw8+cNvy69Kli3bv3u3ml7ZAvEmTJpo4cWKGkw1P1pb8bs76fXrkmyX6c89f83x3aFhOg66pp9KFWbIbAADkLaFep3OcjK0eaCsJ2uV4qlSpogkTJpxwOxaoL1y48IR1LLXELv+kLfnRwfgkvTBxpT77fZMrly4coac7NlD7+sfOFQcAAMjtAuLEQuRe01bu1GNjlml7bLwr33R2JQ24sq5L4wAAAMirCKJxSvYeStDgH5br+8XbXLlKiSgN6dRQ59Yo6XXTAAAAzjiCaGQ7Bee7Rds0+IcY7T+SJJto464LquuhNrVUIDzE6+YBAADkCIJoZNnWA0f12Jilmr7qryW765QtrBevb6RGFTOu4ggAAJDXEUTjpFJTffrsj4164ceVOpyYovCQYD1wWU3de1ENhYV4OtU4AACAJwiicUJrdx1y09bN2/jXkt0tqhTT850bqWbpvLlkNwAAQFYQROOYklJS9e6MdXpj6lolpqSqYHiIHr6ijm5tVUXBrDgIAADyOYJo/M2SLQfU/+slWrnjoCtfXLuUnr2uoSoULeB10wAAAAICQTTSHE1M0es/rdb7v/6pVJ9ULCpMg66ur2ublGfJbgAAgHQIouHMWrdHA75dqo17j7jyNY3La9DV9VSiUITXTQMAAAg4BNH5XOzRJA2ZsEJfzd3symWjI/XsdQ10Wd0yXjcNAAAgYBFE52OTYnboibHLtOtggivfek5lPXx5HRWOZMluAACAEyGIzod2H0zQk9/HaPzS7a5cvWRBt2R3q+olvG4aAABArkAQnc+W7P56/hY9M36FS+MICQ7SvRdW1wOXnaXIMJbsBgAAyCqC6Hxi874jenTMUv26Zo8r1y8frRc6N1KDCkW8bhoAAECuQxCdx9lUdSNmb9SrU9bqaFKKIkKD1adNLd19QTWFsmQ3AADAKSGIzsPW7Dyk15eFaOOhVa7cqlpxt2R3tZIFvW4aAABArkYQnUdt2HNY1w6fraSUIBWKCNWAK+uo69mVWbIbAADgNCCIzqOqliyotnVLa9PW7Xq7x7mqXLKw100CAADIM0iKzcNe7NRAd9VOVbkikV43BQAAIE8hiM7DIsJCFET2BgAAwGlHEA0AAABkE0E0AAAAkE0E0QAAAEA2EUQDAAAA2UQQDQAAAGQTQTQAAACQTQTRAAAAQDYRRAMAAADZRBANAAAAZBNBNAAAAJBNBNEAAABANhFEAwAAANlEEA0AAABkE0E0AAAAkE0E0QAAAEA2EUQDAAAA2UQQDQAAAGRTaHbvgFPn8/nc37i4uBzZX1JSko4cOeL2FxYWliP7BP3uFfrdG/S7N+h3b9Dv+aPf4/4/TvPHbcdDEJ2DDh486P5WqlTJ66YAAADgJHFbkSJFjnt7kO9kYTZOm9TUVG3btk2FCxdWUFBQjnyTsoB98+bNio6OPuP7w1/od2/Q796g371Bv3uDfs8f/e7z+VwAXb58eQUHHz/zmZHoHGRPRMWKFXN8v3bA8WLPefS7N+h3b9Dv3qDfvUG/eyMn+/1EI9B+nFgIAAAAZBNBNAAAAJBNBNF5WEREhAYNGuT+IufQ796g371Bv3uDfvcG/e6NiADtd04sBAAAALKJkWgAAAAgmwiiAQAAgGwiiAYAAACyiSAaAAAAyCaC6FzurbfeUtWqVRUZGalWrVppzpw5J6w/evRo1alTx9Vv2LChJkyYkGNtza/9PmLECLdCZfqL3Q/Z88svv+jqq692K0hZH44dO/ak95k+fbqaNWvmzuiuWbOmey5w5vrc+jvzsW6XHTt25Fib84IhQ4bo7LPPdqvbli5dWh07dtSqVatOej/e33O+33l//+eGDx+uRo0apS2k0rp1a/3444+54lgniM7FRo4cqb59+7ppXxYsWKDGjRurffv22rVr1zHrz5o1S127dlWPHj20cOFC9wZhl2XLluV42/NTvxt7Y9i+fXvaZePGjTna5rzg8OHDrq/tC0xWrF+/Xh06dNAll1yiRYsWqU+fPrrrrrs0adKkM97W/NrnfhZ4pD/eLSBB1s2YMUO9evXS77//rilTpigpKUnt2rVzz8fx8P7uTb8b3t//GVvJ+fnnn9f8+fM1b948XXrppbr22msVExMT+Me6TXGH3Klly5a+Xr16pZVTUlJ85cuX9w0ZMuSY9W+88UZfhw4dMlzXqlUr37333nvG25qf+/3jjz/2FSlSJAdbmPfZW9eYMWNOWKd///6++vXrZ7iuS5cuvvbt25/h1uXfPv/5559dvf379+dYu/KDXbt2uX6dMWPGcevw/u5Nv/P+fmYUK1bM98EHHwT8sc5IdC6VmJjovrW1adMm7brg4GBXnj179jHvY9enr29sBPV49XF6+t0cOnRIVapUUaVKlU74DRunD8e7d5o0aaJy5cqpbdu2mjlzptfNyfViY2Pd3+LFix+3Dse7N/1ueH8/fVJSUvTVV1+50X9L6wj0Y50gOpfas2ePO9jKlCmT4XorHy//0K7PTn2cnn6vXbu2PvroI3333Xf67LPPlJqaqnPPPVdbtmzJoVbnT8c73uPi4nT06FHP2pWXWeD8zjvv6JtvvnEXCyouvvhil/aEU2PvF5aKdN5556lBgwbHrcf7uzf9zvv76bF06VIVKlTInb/Ss2dPjRkzRvXq1Qv4Yz00x/cI5DP2bTr9N2p7g61bt67effddPf300562DTidLKCwS/pjfd26dXrttdf06aefetq23MpydC3X87fffvO6KflKVvud9/fTw9437NwVG/3/+uuv1a1bN5ejfrxAOlAwEp1LlSxZUiEhIdq5c2eG661ctmzZY97Hrs9OfZyefs8sLCxMTZs21dq1a89QK3Gi491OAipQoIBn7cpvWrZsybF+inr37q1x48bp559/didfnQjv7970e2a8v5+a8PBwN4NS8+bN3SwpdkLz0KFDA/5YJ4jOxQecHWxTp05Nu85+RrLy8fKI7Pr09Y2dgXy8+jg9/Z6ZpYPYT1f20zfOHI73wGCjSxzr2WPncVogZz9pT5s2TdWqVTvpfTjeven3zHh/Pz3sczUhISHwj/UcP5URp81XX33li4iI8I0YMcK3fPly3z333OMrWrSob8eOHe722267zffII4+k1Z85c6YvNDTU9/LLL/tWrFjhGzRokC8sLMy3dOlSDx9F3u/3wYMH+yZNmuRbt26db/78+b6bbrrJFxkZ6YuJifHwUeQ+Bw8e9C1cuNBd7K3r1Vdfdf/fuHGju9363Pre788///RFRUX5+vXr5473t956yxcSEuKbOHGih48ib/f5a6+95hs7dqxvzZo17n3lwQcf9AUHB/t++uknDx9F7nPfffe5GR+mT5/u2759e9rlyJEjaXV4fw+Mfuf9/Z+z/rQZUNavX+9bsmSJKwcFBfkmT54c8Mc6QXQu9+abb/oqV67sCw8Pd1Ov/f7772m3XXTRRb5u3bplqD9q1ChfrVq1XH2b/mv8+PEetDp/9XufPn3S6pYpU8Z35ZVX+hYsWOBRy3Mv//RpmS/+vra/1veZ79OkSRPX99WrV3fTUeHM9fkLL7zgq1Gjhgsiihcv7rv44ot906ZN8/AR5E7H6nO7pD9+eX8PjH7n/f2f6969u69KlSquD0uVKuW77LLL0gLoQD/Wg+yfnB//BgAAAHIvcqIBAACAbCKIBgAAALKJIBoAAADIJoJoAAAAIJsIogEAAIBsIogGAAAAsokgGgAAAMgmgmgAAAAgmwiiAQAAgGwiiAaAfMoWrG3Tpo3at2//t9vefvttFS1aVFu2bPGkbQAQ6AiiASCfCgoK0scff6w//vhD7777btr169evV//+/fXmm2+qYsWKp3WfSUlJp3V7AOAVgmgAyMcqVaqkoUOH6j//+Y8Lnm10ukePHmrXrp2aNm2qK664QoUKFVKZMmV02223ac+ePWn3nThxos4//3w3Yl2iRAldddVVWrduXdrtGzZscIH6yJEjddFFFykyMlKff/65Nm7cqKuvvlrFihVTwYIFVb9+fU2YMMGjHgCAUxPks3dMAEC+1rFjR8XGxqpTp056+umnFRMT44Lbu+66S7fffruOHj2qhx9+WMnJyZo2bZq7zzfffOOC5EaNGunQoUMaOHCgC5wXLVqk4OBg9/9q1aqpatWqeuWVV1xQboH03XffrcTERHedBdHLly9XdHS0LrzwQq+7AQCyjCAaAKBdu3a5oHnfvn0uOF62bJl+/fVXTZo0Ka2O5UfbyPWqVatUq1atv23DRqlLlSqlpUuXqkGDBmlB9Ouvv64HH3wwrZ4F3Z07d9agQYNy7PEBwOlGOgcAQKVLl9a9996runXrulHpxYsX6+eff3apHP5LnTp1XF1/ysaaNWvUtWtXVa9e3Y0k24iz2bRpU4Ztt2jRIkP5gQce0DPPPKPzzjvPBdJLlizJsccJAKcLQTQAwAkNDXUXY+kZlrdsqRnpLxY4+9Mu7HYbuX7//ffdyYl2MZaqkZ6lbKRnKSJ//vmny7G2UWsLsu0kRgDITf56twQAIJ1mzZq5tA4bXfYH1unt3bvXpXVYAH3BBRe463777bcsb9/SQnr27OkuAwYMcNu5//77T+tjAIAziZFoAMDf9OrVy40yW7rG3LlzXQqH5UffeeedSklJcTNr2Iwc7733ntauXetONuzbt2+Wtt2nTx+3LZsNZMGCBS5txNJIACA3IYgGAPxN+fLlNXPmTBcw23R3DRs2dMGvTWdnM2/Y5auvvtL8+fPdSYQPPfSQXnrppSxt27ZpQboFzpdffrk7SdEWdwGA3ITZOQAAAIBsYiQaAAAAyCaCaAAAACCbCKIBAACAbCKIBgAAALKJIBoAAADIJoJoAAAAIJsIogEAAIBsIogGAAAAsokgGgAAAMgmgmgAAAAgmwiiAQAAAGXP/wEHr4esoF8EWAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumsum = np.cumsum(sales_data[:, 1:], axis=1)\n",
    "print(cumsum)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(np.mean(cumsum, axis=0))\n",
    "plt.title(\"Average cumulative sales accross all restaurant \")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Sales\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vector addition:  [ 7  9 11 13 15]\n",
      "\n",
      " Multiplication vector [ 6 14 24 36 50]\n",
      "\n",
      "Dot Product 130\n",
      "0.26554161733900966\n"
     ]
    }
   ],
   "source": [
    "vector1 = np.array([1, 2, 3, 4, 5])\n",
    "vector2 = np.array([6, 7, 8, 9, 10])\n",
    "\n",
    "print(\"Vector addition: \", vector1 + vector2)\n",
    "\n",
    "print(\"\\n Multiplication vector\", vector1 * vector2)\n",
    "\n",
    "print(\"\\nDot Product\", np.dot(vector1, vector2))\n",
    "\n",
    "angle = np.arccos(np.dot(vector1, vector2) / (np.linalg.norm(vector1) * np.linalg.norm(vector2)))\n",
    "print(angle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorized Upper ['BIRYANI' 'CHINESE' 'PIZZA' 'BURGER' 'CAFE']\n"
     ]
    }
   ],
   "source": [
    "restaurant_types = np.array(['biryani', 'chinese', 'pizza', 'burger', 'cafe'])\n",
    "vectorized_upper = np.vectorize(str.upper)\n",
    "print(\"Vectorized Upper\", vectorized_upper(restaurant_types))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12500.         15000.         18333.33333333 20833.33333333]\n",
      " [10000.         11666.66666667 13333.33333333 15833.33333333]\n",
      " [16666.66666667 19166.66666667 21666.66666667 25000.        ]\n",
      " [15000.         17500.         20000.         22500.        ]\n",
      " [13333.33333333 15416.66666667 17083.33333333 19166.66666667]]\n"
     ]
    }
   ],
   "source": [
    "monthly_avg = sales_data[:, 1:] / 12\n",
    "\n",
    "print(monthly_avg)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}