module-1/data_presentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Presentation\n",
    "\n",
    "## Case Study\n",
    "\n",
    "Diberikan data mentah yang diperoleh dari suatu survei penelitian berbentuk data interval.\n",
    "\n",
    "|  66  |  76  |  79  |  86  |  89  |\n",
    "|------|------|------|------|------|\n",
    "|  97  | 125  | 110  | 105  | 111  |\n",
    "| 111  | 102  | 101  | 121  | 117  |\n",
    "| 122  | 120  | 101  | 115  | 100  |\n",
    "| 115  |  95  | 122  | 139  | 144  |\n",
    "| 137  | 128  | 138  | 136  | 136  |\n",
    "|  92  |  97  | 106  |  81  | 112  |\n",
    "| 107  | 106  | 133  | 133  | 103  |\n",
    "\n",
    "Bagaimana cara menyajikan data tersebut supaya lebih informatif?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Table of Frequency Distribution\n",
    "\n",
    "Menyajikan data dengan lebih informatif dalam bentuk tabel.\n",
    "\n",
    "Langkah-Langkah:\n",
    "1. Menentukan banyak data `n = 40`\n",
    "2. Menentukan data minimum dan maksimum `Dmin = 66, Dmax = 144`\n",
    "3. Menghitung rentang data `R = Dmax – Dmin = 144 – 66 = 78`\n",
    "4. Menentukan banyak kelas dengan menggunakan **kaidah empiris Sturgess** `k = 1 + 3,3 log(n)` -> `k = 1 + 3,3 log(40) = 1 + 5,29 = 6,29` dibulatkan menjadi `7` (kelas harus bilangan bulat, sehingga selalu dibulatkan ke atas)\n",
    "5. Menentukan panjang kelas interval `I = R / k -> I = 78 / 7 = 11,2` dibulatkan ke atas menjadi `12`\n",
    "6. Menentukan tepi kelas (class boundaries) dengan rumus `BBK – 0,5` dan `BAK + 0,5 `\n",
    "7. Menghitung frekuensi tiap kelas menggunakan `Tally`.\n",
    "8. Menghitung frekuensi absolut `fi`  dan frekuensi relatif tiap kelas `fr = fi / n * 100%`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. n = 40\n",
      "2. Dmin = 66 , Dmax = 144\n",
      "3. R = 78\n",
      "4. Banyak kelas = 7\n",
      "5. Panjang interval kelas = 12\n",
      "6. Kelas = [1, 2, 3, 4, 5, 6, 7]\n",
      "6. Interval kelas = ['66-78', '78-90', '90-102', '102-114', '114-126', '126-138', '138-150']\n",
      "6. Tepi kelas = [65.5, 77.5, 89.5, 101.5, 113.5, 125.5, 137.5, 150.5]\n",
      "\n",
      "Table of Frequency Distribution\n",
      "   Kelas    Range  Frequency  Relative Frequency (%)\n",
      "0      1    66-78          2                     5.0\n",
      "1      2    78-90          4                    10.0\n",
      "2      3   90-102          7                    17.5\n",
      "3      4  102-114         10                    25.0\n",
      "4      5  114-126          8                    20.0\n",
      "5      6  126-138          6                    15.0\n",
      "6      7  138-150          3                     7.5\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import math\n",
    "\n",
    "# Sample data\n",
    "data = [\n",
    "    66, 76, 79, 86, 89, 97, 125, 110, 105, 111, 111, 102, 101, 121, 117, 122, 120, 101, 115, 100, 115, 95, 122, 139, 144, 137, 128, 138, 136, 136, 92, 97, 106, 81, 112, 107, 106, 133, 133, 103,\n",
    "]\n",
    "\n",
    "# 1. Menentukan banyak data\n",
    "n = len(data)\n",
    "print(\"1. n =\", n)\n",
    "\n",
    "# 2. Menentukan data minimum dan maksimum\n",
    "d_min = min(data)\n",
    "d_max = max(data)\n",
    "print(\"2. Dmin =\", d_min, \", Dmax =\", d_max)\n",
    "\n",
    "# 3. Menghitung rentang data\n",
    "R = d_max - d_min\n",
    "print(\"3. R =\", R)\n",
    "\n",
    "# 4. Menentukan banyak kelas\n",
    "k = math.ceil(1 + 3.3 * math.log(n, 10))\n",
    "print(\"4. Banyak kelas =\", k)\n",
    "\n",
    "# 5. Menghitung panjang interval kelas\n",
    "I = math.ceil(R / k)\n",
    "print(\"5. Panjang interval kelas =\", I)\n",
    "\n",
    "# 6. Menghitung interval kelas dan tepi kelas\n",
    "kelas = []\n",
    "interval_kelas = []\n",
    "tepi_kelas = [] # hanya ambil BAK - 0,5\n",
    "for i in range(k):\n",
    "    BAK = d_min + i * I\n",
    "    BBK = BAK + I\n",
    "    tepi = BAK - 0.5\n",
    "    kelas.append(i + 1)\n",
    "    interval_kelas.append(str(BAK) + \"-\" + str(BBK))\n",
    "    tepi_kelas.append(tepi)\n",
    "tepi_kelas.append(BBK + 0.5)\n",
    "print(\"6. Kelas =\", kelas)\n",
    "print(\"6. Interval kelas =\", interval_kelas)\n",
    "print(\"6. Tepi kelas =\", tepi_kelas)\n",
    "\n",
    "\n",
    "# 7. Menghitung frekuensi tiap kelas\n",
    "\n",
    "# Convert data into a pandas DataFrame\n",
    "df = pd.DataFrame(data, columns=[\"Value\"])\n",
    "\n",
    "# Create frequency distribution using pd.cut and value_counts\n",
    "df[\"Range\"] = pd.cut(df[\"Value\"], bins=tepi_kelas, labels=interval_kelas, include_lowest=True)\n",
    "frequency_table = df[\"Range\"].value_counts().sort_index()\n",
    "relative_frequency = frequency_table / frequency_table.sum() * 100\n",
    "\n",
    "# Convert to DataFrame for a cleaner look\n",
    "frequency_distribution = pd.DataFrame(\n",
    "    {\n",
    "        \"Kelas\": kelas,\n",
    "        \"Range\": frequency_table.index,\n",
    "        \"Frequency\": frequency_table.values,\n",
    "        \"Relative Frequency (%)\": relative_frequency,\n",
    "    }\n",
    ")\n",
    "frequency_distribution.reset_index(drop=True, inplace=True)\n",
    "\n",
    "\n",
    "# Display the frequency distribution table\n",
    "print(\"\\nTable of Frequency Distribution\")\n",
    "print(frequency_distribution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Histogram\n",
    "\n",
    "Grafik yang menampilkan distribusi frekuensi dari suatu variabel numerik.\n",
    "\n",
    "Langkah-Langkah:\n",
    "1. Gunakan tabel distribusi frekuensi\n",
    "2. Gunakan tepi kelas untuk titik absis pada sumbu x dan frekuensi absolut untuk titik absis pada sumbu y\n",
    "3. Gambarlah balok dengan tinggi sesuai frekuensi absolut dan lebar sesuai dengan tepi kelas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting the histogram\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(\n",
    "    frequency_distribution[\"Range\"],\n",
    "    frequency_distribution[\"Frequency\"],\n",
    "    width=1,\n",
    "    edgecolor=\"black\",\n",
    "    color=\"skyblue\",\n",
    ")\n",
    "plt.title(\"Histogram of Frequency Distribution\")\n",
    "plt.xlabel(\"Range\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis=\"y\")\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Polygon of Frequency\n",
    "\n",
    "Grafik yang dibuat dengan menghubungkan titik-titik tengah tiap interval kelas secara berturut-turut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//kHv3r2Ngrl+ssiH30sbN26M856pXbs29u3bF2cm9qpVq+Ds7JysJXKI0gJ77IjSQOPGjVG8eHH4+voalm2YM2cOcufObZjpV6JECQDA3Llz0alTJ9jZ2aFQoUIoVKgQevTogZ9++gk2NjaoX7++YVasj48PBgwYAEAOfQ4cOBCTJ09GpkyZ0KxZM9y/fx+BgYHw9PQ0uh7pYxo1aoTx48djzJgxqFmzJkJCQjBu3DjkzZs3zYOQm5sbatSogenTp8PDwwN58uRBcHAwfvnllxQv2lykSBG0b98ec+bMgZ2dHerUqYOLFy9ixowZyV5EOrnGjRuHPXv2oEqVKujbty8KFSqEd+/e4fbt29i+fTsWLlwIb29vdOzYEbNnz0bHjh0xceJEFChQANu3b8euXbviPGeHDh2waNEitG/fHt27d8fz588xbdq0OJ9L69atsWbNGjRo0AD9+vVDhQoVYGdnh/v37yMoKAhNmjRBs2bNkv357NixAzVq1MCIESNQokQJvHr1Cjt37sTAgQNRuHBhw7nt27fH8OHDceDAAYwaNSrBIeHksrGxwbRp09CuXTs0atQIPXv2RFRUFKZPn45Xr15hypQpiT6HWq1Gnz598P3338PFxSXO9avFihVDmzZtMHPmTKjVatSqVQuXLl3CzJkz4e7ubvS9NGbMGMO1lD/88AMyZ86MNWvWYNu2bXFmBhMpQuHJG0QmRz+z7uTJk/E+3rBhw0Rnxc6cOVNUqVJFeHh4CHt7e5ErVy7RtWtXcfv2baOPGz58uPDy8hI2NjYCgAgKChJCCKHVasXUqVNFwYIFhZ2dnfDw8BDt27cX9+7dM/p4nU4nJkyYILy9vYW9vb0oWbKk2Lp1qyhVqpTRjFb9DL3ff/89zucTFRUlBg8eLHLmzCkcHR1F2bJlxebNm+PMutTPip0+fbrRxyf03Im1o979+/fFV199JTJlyiRcXV3FF198IS5evBinTRN6Pv3r69tO/zkNGjRIZMuWTTg6OopKlSqJo0ePxnnOhAAQffr0SfDx3Llzi4YNG8b72NOnT0Xfvn1F3rx5hZ2dncicObMoV66cGDlypAgPD4/zeWfIkEG4urqKr776Shw5ciTOrFghhFi5cqUoUqSIcHR0FEWLFhW//fZbnK+PEEJoNBoxY8YMUapUKeHo6CgyZMggChcuLHr27CmuX7+eaP3xzcC9d++eCAgIEDly5BB2dnbCy8tLtGzZUjx+/DjOx3fu3FnY2tqK+/fvJ9h2H0qsrfU2b94sKlasKBwdHYWLi4uoXbu2OHz4sNE58c2K1bt9+7YAIHr16hXv8797904MHDgwznvG3d09zgzrCxcuiMaNGwt3d3dhb28vSpUqFedrltD3hf776MPziVKLSogkLMpFRGbj1q1bKFy4MMaMGYMRI0YoXQ4lw+3bt5E3b14sX7482fceVlp0dDTy5MmDatWqYcOGDUqXE8dPP/2Evn374uLFiyhWrFiSPubIkSOoWrUq1qxZ80kzlonSE4diiczYuXPnsG7dOlSpUgVubm4ICQkxDNN17dpV6fLICjx9+hQhISFYvnw5Hj9+bDQJxhScOXMGt27dwrhx49CkSZMEQ92ePXtw9OhRlCtXDk5OTjh37hymTJmCAgUKoHnz5ulcNVHKMdgRmTEXFxecOnUKv/zyC169egV3d3f4+flh4sSJCS55QpSatm3bhi5dusDT0xM///xzkpc4SS/NmjXDo0ePUL16dSxcuDDB89zc3LB7927MmTMHb968gYeHB+rXr4/Jkycb7k5BZA44FEtERERkIbjcCREREZGFYLAjIiIishAMdkREREQWwuInT+h0Ojx8+BCurq7x3gKIiIiIyJQJIfDmzRt4eXkluvi8xQe7hw8fwsfHR+kyiIiIiD7JvXv34txD/EMWH+z0N76+d+9emt9OiIiIiCi1hYWFwcfHx5BpPsbig51++NXNzY3BjoiIiMxWUi4p4+QJIiIiIgvBYEdERERkIRjsiIiIiCyExV9jR0RElk2r1UKj0ShdBlGK2dnZQa1Wp8pzMdgREZFZEkLg0aNHePXqldKlEH2yjBkzIkeOHJ+85i6DHRERmSV9qMuWLRucnZ25CD2ZJSEEIiMj8eTJEwCAp6fnJz0fgx0REZkdrVZrCHVZsmRRuhyiT+Lk5AQAePLkCbJly/ZJw7KcPEFERGZHf02ds7OzwpUQpQ79e/lTrxdlsCMiIrPF4VeyFKn1XuZQLBFRCmi1wMGDQGgo4OkJVK8OpNKkNiKiFFO0x+7AgQNo3LgxvLy8oFKpsHnzZqPHhRAYO3YsvLy84OTkBD8/P1y6dEmZYomI/rNpE5AnD+DvD7RtK//Pk0ceJyLTcvv2bahUKpw9e1bpUtKFosEuIiICpUqVwrx58+J9fNq0aZg1axbmzZuHkydPIkeOHKhbty7evHmTzpUSEUmbNgEtWgD37xsff/BAHme4Mz9aLbB/P7Bunfxfq03b1+vcuTNUKlWcfzdu3EjbFzZTY8eONbSRWq2Gj48PunXrhqdPnypdmklSdCi2fv36qF+/fryPCSEwZ84cjBw5Es2bNwcArFy5EtmzZ8fatWvRs2fP9CyViAhaLdCvHyBE3MeEAFQqoH9/oEkTDsuai02b5Nf0/aDu7Q3MnQv896snTXzxxRdYvny50bGsWbPGOS86Ohr29vZpV4iZKFasGP755x9otVqcOXMGXbt2xYMHD7Bjxw6lSzM5JnuN3a1bt/Do0SPUq1fPcMzBwQE1a9bEkSNHEgx2UVFRiIqKMuyHhYUBkLNMuDI5EX2K4GAV7t9P+MemEMC9e0BQUAxq1own/VGq0Wg0EEJAp9NBp9Ol6Dk2bQJatlT9F9RjL1x/8ECgRQtgwwaRJuFOCAF7e3tky5YtzmN+fn4oVqwY7O3t8euvv6JYsWIICgrC5cuXMWTIEBw8eBAuLi6oW7cuZs2aBQ8PDwByBKx37974888/4erqikGDBmHr1q0oVaoUZs+eDQBQq9X4448/0LRpU8PrZc6cGbNmzULnzp3/+9wfYNCgQdizZw9sbGxQtWpVzJkzB3ny5AEAdOnSBa9evUK1atUwa9YsREdHo1WrVpg9ezbs7OwAyN/DP/zwA9avX48nT54gV65cGDp0KAICAlCoUCH07NkTgwYNMtRw8eJFlC5dGiEhIciXL1+87WVra2toL09PT3z33XcYM2YMIiIi4ODggIkTJ2LJkiV4+vQpihQpgkmTJuGLL74AAMP7Q6fTQavVJqmGq1evokePHjh16hQ+++wzzJkzB59//rlR+124cAEDBgzA0aNH4ezsjObNm2PmzJnIkCFDktvqfTqdDkIIaDSaOMudJCe/mGywe/ToEQAge/bsRsezZ8+OO3fuJPhxkydPRmBgYJzju3fv5rR4IvokBw7kBOCb6Hk7dpxFRMSDtC/Iitna2iJHjhwIDw9HdHR0sj9e9r66xQl1ACCECiqVQP/+gL9/WKr3vmo0GsTExBg6Ht4XExODVatWoUuXLtixYweEELh27Rr8/PzQsWNHBAYG4t27dxg7dixatGiBLVu2AAAGDRqEffv24ddff0W2bNkwfvx4/O9//0ORIkWMXuft27dG+0IIvHv3DmFhYYiMjIS/vz8qV66MrVu3wtbWFjNmzMAXX3yBQ4cOwd7eHhqNBkFBQciSJQv++usv3Lx5E127dkWhQoXQqVMnAEBAQABOnDiBKVOmoHjx4rhz5w6eP3+ON2/eoE2bNli2bBm6d+9uqGHRokWoXLkysmbNGm+bREVFQavVGj2mUqmg0+nw4sUL/Prrr5g5cyZmz56NkiVLYvXq1WjatCmOHj2KfPnyITw8HIAMv0mp4dWrV2jatCm8vb2xZ88ehIeHY8SIEUbtFxkZifr168PX1xd79+7Fs2fP0LdvX/Tq1Qs///yz4eucWFu9Lzo6Gm/fvsWBAwcQExNj9FhkZGRCb6c4TDbY6X04/VcI8dEpwcOHD8fAgQMN+2FhYfDx8UG9evXg5uaWZnUSkeVzcVFh1qzEz6tfvzRq1iyV9gVZsXfv3uHevXvIkCEDHB0dDccrVFDhv36Bj4qKAp49S/h3iRAqPHigQuHC7nBwSPz5cuQATpxIWi+tnZ0ddu3aBW9vb8OxL774Ahs2bICtrS3y58+POXPmGB4bM2YMypYtixkzZhiOrVixArlz58ajR4/g5eWF1atXY8WKFWjSpAkAYPXq1ciVKxfs7e2Nfvc5OTkZ7atUKjg6OsLNzQ0bN26Era0tVqxYYfg9++uvvyJz5sw4ffo06tWrBzs7O2TOnBmLFi2CWq2Gr68v/vjjDxw5cgTfffcdrl27hj///BO7du1CnTp1AAAlS5Y0vF6vXr0wefJkXL16FRUqVIBGo8Hvv/+OqVOnJvg72sHBAWq12vD41atXsWLFClSoUAE5c+bE/Pnz8f3336NLly4AgHLlyuHo0aP45ZdfMG/ePEMPmouLC9zc3BKtYefOnbh16xb279+PHDlyAJC9nZ9//rmh/X777Te8e/cOa9asgYuLCwDAxsYGTZo0wcyZM5E9e/ZE2+pD7969g5OTE2rUqGH0ngYQb+BNiMkGO31jPnr0yOj2Gk+ePInTi/c+BwcHOMTzXWhnZxdv1ycRUVL5+wPu7sDr1wmf4+kJ+Pvb8hq7NKbVaqFSqWBjYwMbm9h5gI8eyYksqeVj4e9DNjZJO1elUsHf3x8LFiwwHHNxcTF8Hr6+vkaf0+nTp7F///54g8+tW7cQFRWF6OhoVK1a1fBxHh4eKFSokKGNYms0bq/3j505cwY3btyAu7u70ePv3r3DrVu3YGNjA5VKhWLFihn9PvXy8sKFCxdgY2OD8+fPQ61Ww9/fP87rAEDOnDnRsGFDrFixApUqVcL27dvx7t07tGrVKt7z9e114cIFuLm5QavVIioqCn5+fli8eDHCw8Px8OFDVKtWzejjq1atinPnzhl9vvrtxGq4fv06fHx84OXlZXi+SpUqGT1HSEgISpUqBVdXV8M51atXh06nw/Xr1+Hp6ZloW31I377x5ZXk5BeTDXZ58+ZFjhw5sGfPHpQpUwaA7KYMDg7G1KlTFa6OiKzRjRtAYiMiMTHyOrv/LkmidPZfn0CiZI9d4ud5eCDJPXbJ4eLigvz58yf42Pt0Oh0aN24c7+8+T09PXL9+PUmvqVKpID6Y+fP+tVs6nQ7lypXDmjVr4nzs+xM7PgwZ+mFRIPbWWB/TrVs3dOjQAbNnz8by5cvRqlWrRC+VKlSoELZs2QK1Wg0vLy9DB46+Jyu5o3sfqyGxj03snPePf6yt0oqiwS48PNxoevetW7dw9uxZZM6cGbly5UL//v0xadIkFChQAAUKFMCkSZPg7OyMtm3bKlg1EVkjrRbo0gXQ/x7MkAH479IdAICtrQx1T58CNWvKZTPy5lWkVKt26lTSztNqZfh+8CD+Wc4qlZwde+uW8jOcy5Ytiz/++AN58uSBrW3cX9v58+eHnZ0djh07hly5cgEAXr58iWvXrqFmzZqG87JmzYrQ0FDD/vXr142u3Spbtix+++03ZMuWLcWXLpUoUQI6nQ7BwcGGodgPNWjQAC4uLliwYAF27NiBAwcOJPq89vb28QZhNzc3eHl54dChQ6hRo4bh+JEjR1ChQoUEn+9jNRQuXBh3797F48ePDSOEJ0+eNPr4okWLYuXKlYiIiDAE8cOHD8PGxgYFCxZM9PNJS4quY3fq1CmUKVPG0CM3cOBAlClTBj/88AMAYOjQoejfvz969+4NX19fPHjwALt37zbq+iQiSg9z5gBHj8rt/PmBhw+BoCBg7Vr5/+3bQOHC8vG7dwE/PxkKyDSp1XJJE0CGuPfp9+fMUT7UAUCfPn3w4sULtGnTBidOnMDNmzexe/duBAQEQKvVIkOGDOjatSuGDBmCvXv34uLFi+jcuXOc4b5atWph3rx5OH36NE6dOoVevXoZ9Si1a9cOHh4eaNKkCQ4ePIhbt24hODgY/fr1w/0PF25MQJ48edCpUycEBARg8+bNhmvVNmzYYDhHrVajc+fOGD58OPLnz4/KlSt/UvsMGTIEU6dOxW+//YaQkBAMGzYMZ8+eRb9+/RL8mI/VULduXeTLlw+dOnXC+fPncfjwYYwcORJAbG9cu3bt4OjoiE6dOuHixYsICgrCd999hw4dOnz0crH0oGiw8/PzgxAizr8VK1YAkA04duxYhIaG4t27dwgODkbx4sWVLJmIrNDVq8CoUXJbpQKWLwdcXWV4a9NG/p8zpwx4RYrI8/Th7uZNhYqmRDVvDmzcKL927/P2lsfTch275PDy8sLhw4eh1Wrx+eefo3jx4ujXrx/c3d0N4W369OmoUaMGvvzyS9SpUwfVqlVDuXLljJ5n5syZ8PHxQY0aNdC2bVsMHjzYaAjU2dkZBw4cQK5cudC8eXMUKVIEAQEBePv2bbJ68BYsWIAWLVqgd+/eKFy4MLp3746IiAijc7p27Yro6GgEBAR8QstIffv2xaBBgzBo0CCUKFECO3fuxJYtW1CgQIGPflxCNajVamzevBnh4eEoX748unXrhlH//QDQT2pwdnbGrl278OLFC5QvXx4tWrRA7dq1E7zhQnpSiQ8H3C1MWFgY3N3d8fr1a86KJaJk02qBatWAY8fk/oAB+OjM2MeP5SSLK1fkvo+PHJb97LM0L9Wq6C/oz5s3b5wZhMllqff99fPzQ+nSpY1m2JqKw4cPw8/PD/fv31eshys5NRw+fBjVqlXDjRs34l1rLzV87D2dnCxjspMniIhMwaxZsaGuQAFgwoSPn589u+y504e7e/dkz11QEJBGvw/oE6nV8mtEaS8qKgr37t3D6NGj0bJlS0VCXVJq+PPPP5EhQwYUKFAAN27cQL9+/VC1atU0C3WpSdGhWCIiU3blCjB6tNzWD8EmZZ1zfbgrWlTu37sng96//6ZdrUTmYN26dShUqBBev36NadOmmWwNb968MQwld+7cGeXLl8dff/2VzpWmDIdiiYjiodUCVasCx4/L/YEDgZkzk/ccjx8DtWoBly/LfW9vOSxrBn/0m7zUHIolMgWpNRTLHjsionjMnBkb6goWTHwINj76nrtixeT+/ftyyO+9VZ6IiFIVgx0R0QeuXAH+W3XJMASbhHVX45UtG7Bvn3G48/dnuCOitMFgR0T0npgYoHNneWcCQA7BVqnyac+pD3f61ZrYc5d60noVf6L0klrvZc6KJSJ6z4wZwIkTcrtQIWD8+NR5Xn24q1ULuHhR3vFAP1s2keW2KB729vawsbHBw4cPkTVrVtjb2yd6GygiUySEQHR0NJ4+fQobGxvY29t/0vNx8gQR0X8uXQLKlgWiowEbG+DwYeC/e3+nmqdPY8MdAHh5yQkVDHfJFx0djdDQUKPbYhGZK2dnZ3h6esYb7LiOHRFRMumHYKOj5f6gQakf6gAga1bZc1e7NnDhgrw1mZ8fw11K2NvbI1euXIiJiYFWq1W6HKIUU6vVsLW1TZVeZwY7IiIA06fH3kC+cGFg3Li0e62sWYG9e+OGu6AgOQOXkk6lUsHOzs7onqdE1oyTJ4jI6l28CIwdK7dtbIAVK4C0XhpN33NXsqTc14e7a9fS9nWJyLIx2BGRVdNojIdgBw8GKlZMn9f28JA9d/pwFxoqw11ISPq8PhFZHgY7IrJq06YB//uf3C5SBAgMTN/X14e7UqXkfmioXOeO4Y6IUoLBjois1oULsUEuvYZg48NwR0SphcGOiKySfghWo5H7Q4cCFSooV0+WLDLclS4t9/XDslevKlcTEZkfBjsiskpTpwKnT8vtokVjJ08oKUsW4J9/YsPdo0ey547hjoiSisGOiKzO+fOxy5mo1XII1sFB0ZIM9OGuTBm5z3BHRMnBYEdEViW+Idjy5RUtKY74wp2fH3DliqJlEZEZYLAjIqsyeTJw5ozcLlYMGDNG2XoSkjmzDHdly8r9x49lz93ly8rWRUSmjcGOiKzGuXPA+PFyW60GVq40nSHY+GTODOzZYxzuatViuCOihDHYEZFV0A/BxsTI/WHDgHLlFC0pSeILd+y5I6KEMNgRkVWYNAk4e1ZuFy8OjB6taDnJoh+W1QfRJ09kuLt0Sdm6iMj0MNgRkcU7exaYMEFum9os2KTKlEn23DHcEdHHMNgRkUWLjjYegh0+3DyGYOOjD3e+vnL/6VOGOyIyxmBHRBZt0iQ5aQIASpQwryHY+CQU7i5eVLYuIjINDHZEZLHOnAEmTpTbtrZyCNbeXtGSUkXGjDLc6dffe/pUzpZluCMiBjsiskgfDsGOGBE7s9QSZMwI7N5tHO7Yc0dEDHZEZJEmTJC3DgOAUqWAkSOVrSct6MNdhQpy/9kzGe4uXFC0LCJSEIMdEVmc06fltXWAZQ3Bxie+cFerVmyoJSLrwmBHRBZFPwSr1cr9kSOB0qWVrCjtubvLcFexotxnuCOyXiYf7N68eYP+/fsjd+7ccHJyQpUqVXDy5EmlyyIiEzV+fOxQZKlS8to6a+DuDuzaFRvunj9nuCOyRiYf7Lp164Y9e/bg119/xYULF1CvXj3UqVMHDx48ULo0IjIxp04BkyfLbVtbeS9YSx2CjY8+3FWqJPf14U6/3AsRWT6TDnZv377FH3/8gWnTpqFGjRrInz8/xo4di7x582LBggVKl0dEJiQqyngIdvRo2WNnbeILd7VrM9wRWQtbpQv4mJiYGGi1Wjg6Ohodd3JywqFDh+L9mKioKERFRRn2w8LCAAAajQYajSbtiiUiRY0ZY4NLl9QAgNKlBQYPjoG1fss7OQFbtwKNGqlx7JjNfz13Ajt3xlj89YZElig5+cWkg52rqysqV66M8ePHo0iRIsiePTvWrVuH48ePo0CBAvF+zOTJkxEYGBjn+O7du+Hs7JzWJRORAm7cyIjp06sDAGxtdejcORh79oQpXJXy+va1xcuXlRESkhkvXqhQu7ZAYOBhfPYZ24bInERGRib5XJUQQqRhLZ/s33//RUBAAA4cOAC1Wo2yZcuiYMGCOH36NC5fvhzn/Ph67Hx8fPDs2TO4ubmlZ+lElA6iooCKFW1x+bIKADBmjBYjR+oUrsp0hIUBjRurcfSovPImc2aBHTtiUKaMwoURUZKFhYXBw8MDr1+/TjTLmHyw04uIiEBYWBg8PT3RqlUrhIeHY9u2bYl+XFhYGNzd3ZPUGERkfkaMiJ0wUbYscOwYYGenbE2m5s0b4IsvgCNH5H6mTMDevWC4IzITyckyJj154n0uLi7w9PTEy5cvsWvXLjRp0kTpkohIYSdOAFOnym07O7kQMUNdXK6uwM6dQJUqcv/lSzmh4swZZesiotRn8sFu165d2LlzJ27duoU9e/bA398fhQoVQpcuXZQujYgU9O6dnAWr+2/UdcwYoEQJRUsyafpwV7Wq3NeHu9Onla2LiFKXyQe7169fo0+fPihcuDA6duyIatWqYffu3bDjn+VEVm3sWODKFbldrhzw/feKlmMWXF2BHTuAatXk/suXQJ06DHdElsRsrrFLKV5jR2R5jh+Xw4o6nVyA+H//A4oXV7oq8/HmDdCgAaBfNSpjRuCff2RAJiLTY5HX2BERAfEPwTLUJc+HPXevXsmeu//9T9GyiCgVMNgRkVkZMwa4elVu+/oCQ4cqW4+5ypBBhrvqcvk/Q7g7dUrRsojoEzHYEZHZOHYMmDFDbtvby1mwtia9zLppy5AB2L4dqFFD7r96BdSty3BHZM4Y7IjILLx9azwEGxgIFCumaEkWIUMGYNs243BXpw5w8qSiZRFRCjHYEZFZ+OEHICREbleoAAwerGw9luTDnrvXr2XPHcMdkflhsCMik3f0KDBzpty2tweWL+cQbGpzcZHhrmZNua8PdydOKFsXESUPgx0RmTT9EKx+YaZx44CiRRUtyWK5uMhhWT8/uc9wR2R+GOyIyKSNHg1cuya3K1QABg1Sth5L5+ICbN0K+PvL/bAwhjsic8JgR0Qm68gRYNYsue3gwFmw6cXFBfj777jh7vhxZesiosQx2BGRSYqMjDsEW6SIoiVZlfh67urVk0vOEJHpYrAjIpM0ahRw/brcrlSJQ7BKcHaW4a5WLbnPcEdk+hjsiMjkHDoEzJkjtx0c5CxYtVrRkqyWs7McltWHuzdvGO6ITBmDHRGZlMhIoEuX2CHYCROAwoWVrcna6cNd7dpyXx/ujh5Vti4iiovBjohMysiRwI0bcrtyZWDAAGXrIcnZGdiyRd6VApDh7vPP5QQXIjIdDHZEZDIOHgTmzpXbjo4cgjU1DHdEpo/BjohMQkQEEBBgPARbqJCyNVFcTk7G4S48nOGOyJQw2BGRSRgxInYItkoVoH9/Rcuhj9CHu7p15b4+3B0+rGxdRMRgR0Qm4MAB4Mcf5TaHYM2DkxPw119yEgUgw90XX8gZzUSkHAY7IlJURIScBas3aRJQsKBy9VDSOTkBmzfL3jpAhrv69RnuiJTEYEdEiho+HLh5U25XrQr07atsPZQ88YU79twRKYfBjogUExwM/PST3HZy4hCsuXJ0NA53EREy3B08qGhZRFaJwY6IFBEeLmfB6k2aBBQooFw99Gn04e6LL+R+RIQclj1wQNGyiKwOgx0RKWLYsNgh2GrVOARrCRwdgT//lIEOkOGuQQOGO6L0xGBHROkuKAiYP19u64dgbfjTyCI4OgKbNjHcESmFP0qJKF19OAQ7ZQqQP79y9VDq04e7Bg3kvn5YNjhY2bqIrAGDHRGlq++/B27flts1agDffqtoOZRGPgx3kZFye/9+RcsisngMdkSUbvbtA37+WW47OwPLlnEI1pI5OMQNdw0bMtwRpSX+SCWidPHmDdC1a+z+lClAvnzK1UPpQx/uGjaU+wx3RGmLwY6I0sXQobFDsDVrAn36KFoOpSMHB+CPP4BGjeS+flg2KEjZuogsEYMdEaW5vXuBhQvltosLh2CtkYMDsHEj0Lix3H/7VvbcMdwRpS7+aCWiNPXhEOzUqcBnnylXDynHwQH4/fe44W7fPmXrIrIkJh3sYmJiMGrUKOTNmxdOTk747LPPMG7cOOh0OqVLI6IkGjIEuHNHbvv5Ad98o2g5pLD4wl2jRsDu3fK6u3Xr5P9arZJVEpkvW6UL+JipU6di4cKFWLlyJYoVK4ZTp06hS5cucHd3R79+/ZQuj4gS8c8/wKJFcptDsKSnH5b9+mtgyxYZ7vT3mdXz9gbmzgWaN1emRiJzZdI/Yo8ePYomTZqgYcOGyJMnD1q0aIF69erh1KlTSpdGRIkICzMegp02DcibV7l6yLTY28ueu/Ll43/8wQOgRQs5o5aIks6kg121atWwd+9eXLt2DQBw7tw5HDp0CA30iyIRkckaMgS4e1du+/sDvXopWw+ZHrUaePgw/seEkP/3789hWaLkMOmh2O+//x6vX79G4cKFoVarodVqMXHiRLRp0ybBj4mKikJUVJRhPywsDACg0Wig0WjSvGYiAvbsUWHxYvnjxcVFYOHCGGi1/AVNxoKDVXjwIOFfQ0IA9+4BQUExqFlTpGNlRKYlOfnFpIPdb7/9htWrV2Pt2rUoVqwYzp49i/79+8PLywudOnWK92MmT56MwMDAOMd3794NZ2fntC6ZyOpFRNiiXz9/6H+8dOhwHleu3MaVK8rWRabnwIGcAHwTPW/HjrOIiHiQ9gURmajIyMgkn6sSQpjsn0E+Pj4YNmwY+ry3kumECROwevVqXL16Nd6Pia/HzsfHB8+ePYObm1ua10xk7Xr1UmPZMnmVR61aOuzYoYVKpXBRZJKCg1WoWzfx/oU9e9hjR9YtLCwMHh4eeP36daJZxqR77CIjI2HzwRQ6tVr90eVOHBwc4ODgEOe4nZ0d7OzsUr1GIoq1a5ec+QoAGTIAy5bZwN7epC/lJQX5+8vZrw8exF5TFx+Nxhb88U3WLDn5xaR/4jZu3BgTJ07Etm3bcPv2bfz555+YNWsWmjVrpnRpRPSB16+Bbt1i92fMAHLnVq4eMn1qtVzSBMBHe3WbNZN/NBBR4kx6KPbNmzcYPXo0/vzzTzx58gReXl5o06YNfvjhB9jb2yfpOcLCwuDu7p6k7ksiSrlu3YBffpHbderIBWc5BEtJsWkT0K8fcP9+7DFvb/nv2DG57+AA/PVX3PXuiKxBcrKMSQe71MBgR5T2duyQN3UHAFdX4MIF9tZR8mi1wMGDQGgo4OkJVK8O6HRAmzbAH3/IcxwcgM2bgS++ULRUonTHYPceBjuitPXqFVC8uLxOCgAWLwa6d1e0JLIgGg3Qtq28UwXAcEfWKTlZxqSvsSMi0zdwYGyoq1fP+Do7ok9lZwesXStvPwYAUVFAkyayl5iI4mKwI6IU274dWL5cbru6AkuW8Lo6Sn12dsCaNbHhLjoaaNqU4Y4oPgx2RJQir14ZD7nOmgXkyqVYOWThPuy504e77dsVLYvI5DDYEVGKDBgQe5/Pzz8HunZVth6yfLa2Mty1bCn3o6PlUijbtilbF5EpYbAjomTbtg1YsUJuu7lxCJbSj62tHJZt1UruR0cDzZsDW7cqWxeRqWCwI6JkefnSeAh29mzAx0e5esj62NoCq1cDrVvL/eho4KuvGO6IAAY7Ikqm/v3lWmMAUL8+0KWLouWQlbK1BX791TjcseeOiMGOiJLh77+BVavktru7XLOOQ7CkFH24a9NG7ms0Mtz9/beydREpicGOiJLk5UugZ8/Y/dmz5S2fiJRkayv/2GjbVu5rNHJYdssWZesiUgqDHRElSb9+sUOwDRoAnTsrWg6Rga0tsHKlcbhr0YLhjqwTgx0RJWrLFjnkBXAIlkxTfD13LVoAf/2lbF1E6Y3Bjog+6sUL4yHYOXOAnDkVK4coQWq1DHft2sl9jUYuaMxwR9aEwY6IPqpvX+DRI7ndsCHQqZOy9RB9jFoth2Xbt5f7+p67zZsVLYso3TDYEVGC/vpLLgYLABkzcgiWzINaLRfQ1oe7mBjZc8dwR9aAwY6I4vX8ufEQ7Ny5gJeXcvUQJYc+3HXoIPf14e7PPxUtiyjNMdgRUbz69gUeP5bbjRrF/oIkMhdqNbB8OdCxo9yPiZH3md20Sdm6iNISgx0RxfHnn/Jm64Acgl20iEOwZJ7UamDZsthrQ2Ni5H1mGe7IUjHYEZGRZ8+AXr1i93/8kUOwZN7UauCXX+KGuz/+ULYuorTAYEdERr77DnjyRG5/+WXsBehE5kwf7vQLazPckaVisCMig02bgPXr5XamTMDChRyCJcuhVgNLlwJdush9rVaGu40bla2LKDUx2BERADkE+803sfs//QR4eipXD1FaiC/ctW7NcEeWg8GOiAAA334bOwTbpEnsrZmILI2NTfzh7vffla2LKDUw2BERNm4EfvtNbmfOzCFYsnz6cBcQIPe1WqBNG2DDBmXrIvpUDHZEVu7pU6B379j9n34CcuRQrh6i9GJjAyxZAnTtKve1WtlTrf8jh8gcMdgRWbk+fWS4A4CmTWWvBZG1sLGRt8rr1k3ua7VAu3YMd2S+GOyIrNjvv8deV5QlC4dgyTrZ2MhFuBnuyBIw2BFZqSdPjIdg580DsmdXrh4iJenDXffucl8/LKtf/ofIXDDYEVmpPn3kEicA0Ly5XM+LyJrZ2Mhe6x495L5OJ3vu1q1Tti6i5GCwI7JCGzbErtuVJQvw888cgiUCZLhbsMA43LVvz3BH5oPBjsjKPH5sPAQ7fz6HYInepw93PXvKfX24W7tW2bqIksLkg12ePHmgUqni/OvTp4/SpRGZDa0W2L9f/mL6+mvg+XN5/KuvgJYtFS2NyCTZ2Mie7F695L5OB3ToAKxZo2xdRImxVbqAxJw8eRJardawf/HiRdStWxdff/21glURmY9Nm4B+/YD7942Pu7pyCJboY2xsZI+2SiV78HQ6oGNHQAjZg0dkiky+xy5r1qzIkSOH4d/WrVuRL18+1KxZU+nSiEzepk1AixZxQx0AvHkDHDqU/jURmRMbGzljXH8fZZ0O6NQJWL1a2bqIEmLywe590dHRWL16NQICAqBiNwPRR2m1sqdOiPgfV6mA/v3leUSUMH3PHcMdmQOTH4p93+bNm/Hq1St07tw5wXOioqIQFRVl2A8LCwMAaDQaaDSatC6RyGQEB6tw/37C3+JCAPfuAUFBMahZM4H0R0QGc+YAQthg4UL1f8OyAhqNFu3b8/uH0lZy8otZBbtffvkF9evXh5eXV4LnTJ48GYGBgXGO7969G87OzmlZHpFJOXAgJwDfRM/bseMsIiIepH1BRBbg88+Bu3dLYPv2zyCECl27qnHu3Bn4+99TujSyYJGRkUk+VyVEQgM1puXOnTv47LPPsGnTJjRp0iTB8+LrsfPx8cGzZ8/g5uaWHqUSmYTgYBXq1k38b7c9e9hjR5QcQgD9+9tgwQI1AEClEli6VIsOHfh9RGkjLCwMHh4eeP36daJZxmx67JYvX45s2bKhYcOGHz3PwcEBDg4OcY7b2dnBzs4urcojMjn+/kDmzMCLF/E/rlIB3t6Av78t1Or0rY3I3M2fD6jVcmKFECp06ya/jzp1UroyskTJyS9mMXlCp9Nh+fLl6NSpE2xtzSaLEinqyRPgvc5rI/q5R3PmgKGOKAVUKuDHH4HvvpP7QgBdugArVihaFpF5BLt//vkHd+/eRUBAgNKlEJkFIeTCqhERct/Jyfhxb295S7HmzdO/NiJLoVIBc+cCffvKfSGAgABg+XJl6yLrZhbdX/Xq1YOZXApIZBLWrAG2bJHb2bIB588DV64AoaGApydQvTp76ohSg0ole74B2YMnBNC1q9zv0kWxssiKmUWwI6KkCw2N7UEAgIUL5b1geT9YorShD3f6Hjx9uNP34BGlJ7MYiiWipBFC3rj85Uu536YN0KyZsjURWQOVCpg9Wy76DcjvxW7dgGXLFC2LrBCDHZEF+fVX4O+/5Xb27MBPPylbD5E1UamAWbOAAQPkvr7n7pdflK2LrEuKgt2tW7dSuw4i+kQPH8pbiOktWgRkyaJcPUTWSKUCZs6MDXeA7LljuKP0kqJglz9/fvj7+2P16tV49+5datdERMkkBNCjB/Dqldxv1w74yDreRJSG9OFu4MDYY926AUuXKlcTWY8UBbtz586hTJkyGDRoEHLkyIGePXvixIkTqV0bESXRqlXAtm1yO0cOOTuPiJSjUgEzZgCDBsUe694dWLJEuZrIOqQo2BUvXhyzZs3CgwcPsHz5cjx69AjVqlVDsWLFMGvWLDx9+jS16ySiBDx4EHcINnNm5eohIkmlAqZPNw53PXoAixcrVxNZvk+aPGFra4tmzZphw4YNmDp1Kv79918MHjwY3t7e6NixI0JDQ1OrTiKKh34I9vVrud++PfDll8rWRESx9OFu8ODYYz17MtxR2vmkYHfq1Cn07t0bnp6emDVrFgYPHox///0X+/btw4MHD9CEF/kQpakVK4Dt2+V2jhxyDS0iMi0qFTBtGjBkSOyxnj1l7zpRakvRAsWzZs3C8uXLERISggYNGmDVqlVo0KABbGxkTsybNy8WLVqEwoULp2qxRBTr/v3YNbMA2QPAIVgi06RSAVOnxoY8QN72T3/7P6LUkqJgt2DBAgQEBKBLly7IkSNHvOfkypULv3B+N1GaEEJeiB0WJvc7dgQaN1a2JiL6OJUKmDJFbuvD3TffyO/nb75Rri6yLCph4TdhDQsLg7u7O16/fg03NzelyyFKFcuWxd6P0ssLuHgRyJRJ2ZqIKGmEAIYPlz14ej//zHBHCUtOlknRNXbLly/H77//Huf477//jpUrV6bkKYkoie7dM178dPFihjoic6JSAZMnA8OGxR7r3VuGO6JPlaJgN2XKFHh4eMQ5ni1bNkyaNOmTiyKi+H04BNupE9CwobI1EVHyqVTApEmy506vTx9g/nzlaiLLkKJgd+fOHeTNmzfO8dy5c+Pu3bufXBQRxe+XX4Bdu+S2lxcwZ46i5RDRJ1CpgIkTjcPdt98y3NGnSVGwy5YtG86fPx/n+Llz55CFN6ckShN37xrfomjJEiBjRsXKIaJUoA93I0bEHmO4o0+RomDXunVr9O3bF0FBQdBqtdBqtdi3bx/69euH1q1bp3aNRFZPCHmvyTdv5H6XLkCDBsrWRESpQ6UCJkwARo6MPfbtt8C8ecrVROYrRcudTJgwAXfu3EHt2rVhayufQqfToWPHjrzGjigNLF0K7Nkjt3PmBGbNUrYeIkpdKhUwfnxsyAOA776Tf9R9952ytZF5+aTlTq5du4Zz587ByckJJUqUQO7cuVOztlTB5U7I3N29CxQvHttbt307UL++sjURUdoQAvjhh9hwBwA//shwZ+2Sk2VS1GOnV7BgQRQsWPBTnoKIPuLDIdiAAIY6IkumUgHjxsX24AFA377yZ0HfvsrWRuYhRcFOq9VixYoV2Lt3L548eQKdTmf0+L59+1KlOCJrt2RJ7BCstzeHYImsgUoFBAbGhjwA6NdPhrt+/ZStjUxfioJdv379sGLFCjRs2BDFixeHSqVK7bqIrN7t28CgQbH7S5cC7u6KlUNE6UilAsaOldv6cNe/vwx3798jmuhDKQp269evx4YNG9CA0/KI0oR+CDY8XO537Qp8/rmyNRFR+nq/5y4wUB7T33WG4Y4SkqLlTuzt7ZE/f/7UroWI/rNoEbB3r9z28QFmzlS2HiJSztixwJgxsfsDBgCzZytWDpm4FAW7QYMGYe7cufiECbVElIDbt4EhQ2L3OQRLRGPHxg7NAnKxcl5zS/FJ0VDsoUOHEBQUhB07dqBYsWKws7MzenzTpk2pUhyRtdHp5LCrfgi2e3egXj1layIi06DvtdMHvEGD5GUb71+LS5SiYJcxY0Y0a9YstWshsnqLFgH6SeW5cgEzZihbDxGZljFj5DV3+pA3eLD8n+GO9D5pgWJzwAWKyVzcugWUKAFERMj93buBunWVrYmITNP48XIhY73p02NDHlme5GSZFF1jBwAxMTH4559/sGjRIrz5b/XUhw8fIlw/hkRESabTycWH9aGuRw+GOiJK2OjRsQsYA/K63OnTlauHTEeKhmLv3LmDL774Anfv3kVUVBTq1q0LV1dXTJs2De/evcPChQtTu04ii7ZgAbB/v9zOnZtDsESUuFGj5P+jR8v/hw6V19wNHapcTaS8FPXY9evXD76+vnj58iWcnJwMx5s1a4a9+jUaiChJbt40/kH8yy+Aq6ty9RCR+Rg1yvi+st9/D0ybplw9pLwUz4o9fPgw7O3tjY7nzp0bDx48SJXCiKyBfgg2MlLu9+oF1K6tbE1EZF5GjpQTKkaOlPvffy977gYPBg4eBEJDAU9PoHp1QK1WtlZKeynqsdPpdNBqtXGO379/H66p3NXw4MEDtG/fHlmyZIGzszNKly6N//3vf6n6GkRK+flnIDhYbufOzb+0iShlRowAJk2K3R82DMiSBfD3B9q2lf/nyQNwNTLLl6JgV7duXcyZM8ewr1KpEB4ejjFjxqTqbcZevnyJqlWrws7ODjt27MDly5cxc+ZMZMyYMdVeg0gp//4r/7LWW7aMQ7BElHLDhxuHu9evjR9/8ABo0YLhztKlaLmThw8fwt/fH2q1GtevX4evry+uX78ODw8PHDhwANmyZUuV4oYNG4bDhw/j4MGDKX4OLndCpkink39BHzgg97/5RvbeERF9Cq1W9tR9GOr0VCrA21sur8RhWfORnCyT4nXs3r59i3Xr1uH06dPQ6XQoW7Ys2rVrZzSZ4lMVLVoUn3/+Oe7fv4/g4GDkzJkTvXv3Rvfu3RP8mKioKERFRRn2w8LC4OPjg2fPnjHYkcmYN88GAwfKn6p58gicPh2DDBkULoqIzF5wsAp16yZ++fyePTGoWdOil7G1KGFhYfDw8EhSsEvR5AkAcHJyQkBAAAICAlL6FIm6efMmFixYgIEDB2LEiBE4ceIE+vbtCwcHB3Ts2DHej5k8eTICAwPjHN+9ezecnZ3TrFaipAoNdcGwYX6G/a5dj+DAgWfKFUREFuPAgZwAfBM9b8eOs4iI4GRHcxGpn2GXBCnqsVu1atVHH08odCWXvb09fH19ceTIEcOxvn374uTJkzh69Gi8H8MeOzJlOh1Qp44ahw7Jy1u/+UaLuXN1CldFRJaCPXaWKc177Pr162e0r9FoEBkZCXt7ezg7O6dasPP09ETRokWNjhUpUgR//PFHgh/j4OAABweHOMft7OxgZ2eXKnURpdTcucChQ3I7b15g2jQ17Ox4oQsRpQ5/f3kN3YMHcsmT+Pj4AP7+trzGzowkJ7+kaFbsy5cvjf6Fh4cjJCQE1apVw7p161LylPGqWrUqQkJCjI5du3YNuXPnTrXXIEov16/LWWt6y5aB19URUapSq+UfkICcKBGfb7/lxAlLluJ7xX6oQIECmDJlSpzevE8xYMAAHDt2DJMmTcKNGzewdu1aLF68GH369Em11yBKD1ot0KUL8Pat3P/2W8DPT9GSiMhCNW8ObNwI5MwZ/+PLlsUuik6WJ9WCHQCo1Wo8fPgw1Z6vfPny+PPPP7Fu3ToUL14c48ePx5w5c9CuXbtUew2i9PDjj8Dhw3L7s8+AKVOUrYeILFvz5sDt20BQELB2LbBnD1CunHwsJMR4DU2yLCmaPLFlyxajfSEEQkNDMW/ePPj4+GDHjh2pVuCn4jp2pLRr14BSpYB37+R+cDBQo4ayNRGR9QkJAcqUiR052LkT+PxzZWuipElOlknR5ImmTZsa7atUKmTNmhW1atXCzJkzU/KURBZJPwSrD3V9+zLUEZEyChUCZswA9FczdekCXLggFzQmy5GiYKfTcXkGoqSYOxfQr9aTL5/x7X6IiNLbN98AW7YAu3YBoaFAr17Ahg0JT7Qg85Oq19gRUayQEGDkSLmtUgHLlwMuLsrWRETWTaWSkycyZ5b7GzcCq1crWxOlrhT12A0cODDJ586aNSslL0Fk1uIbgq1eXdmaiIgAwMsLWLwYaNFC7n/7rbxEhCuJWYYUBbszZ87g9OnTiImJQaFChQDI9eXUajXKli1rOE/Fvl2yUrNnA/qbo+TPzyFYIjItX30FdOwIrFoFhIUBnToB+/YBNhzHM3spCnaNGzeGq6srVq5ciUyZMgGQixZ36dIF1atXx6BBg1K1SCJzcvUqMGqU3NYPwfI2xURkan78Edi/H7h7V87Wnz0b4K9v85ei5U5y5syJ3bt3o1ixYkbHL168iHr16qXqWnafisudUHrSaoFq1YBjx+T+gAEAr0YgIlMVHCxvQyYEYG8PnDwJlCypdFX0oeRkmRR1uoaFheHx48dxjj958gRv3rxJyVMSWYRZs2JDXYECwIQJytZDRPQxNWsCgwfL7ehooH17ICpK2Zro06Qo2DVr1gxdunTBxo0bcf/+fdy/fx8bN25E165d0bx589SukcgsXLkCjB4ttzkES0TmYvx4oEQJuX3hQuzPMTJPKRqKjYyMxODBg7Fs2TJoNBoAgK2tLbp27Yrp06fDxYTWdOBQLKWHmBigalXgxAm5P3AgwLW6ichcXLgA+PrKXjuVSt6KrGZNpasiveRkmRQFO72IiAj8+++/EEIgf/78JhXo9BjsKD1MnQoMGya3CxYEzp4FnJwULYmIKFlmzowdls2VCzh/HnB3V7YmktL8Gju90NBQhIaGomDBgnBxccEnZEQis3X5MvDDD3JbPwTLUEdE5mbAAMDPT27fvSvX3yTzk6Jg9/z5c9SuXRsFCxZEgwYNEBoaCgDo1q0blzohqxITA3TuLIcvADkEW6WKoiUREaWIjQ2wYgWg7xBatUremYLMS4qC3YABA2BnZ4e7d+/C+b2rw1u1aoWdO3emWnFEpm7GDLk8ACBvsD1+vLL1EBF9ity5gXnzYvd79gRMaAUzSoIUBbvdu3dj6tSp8Pb2NjpeoEAB3LlzJ1UKIzJ1ly4BY8bIbf1fuhyCJSJz17498PXXcvvFCyAgQK5zR+YhRcEuIiLCqKdO79mzZ3BwcPjkoohM3YdDsIMGAZUqKVoSEVGqUKmABQsAT0+5v2uX3CfzkKJgV6NGDaxatcqwr1KpoNPpMH36dPj7+6dacUSmavp04NQpuV24MDBunLL1EBGlpixZ5EQwvcGDgZAQ5eqhpEvRcieXL1+Gn58fypUrh3379uHLL7/EpUuX8OLFCxw+fBj58uVLi1pThMudUGq7eBEoWxbQaOQQ7JEjQMWKSldFRJT6vvsu9po7X1/5887OTtmarFGaL3dStGhRnD9/HhUqVEDdunURERGB5s2b48yZMyYV6ohSm0Yjh2D/W5cbgwcz1BGR5Zo6VU4MA+QoBW+TaPqS3WOn0WhQr149LFq0CAULFkyrulINe+woNU2cCIwaJbeLFAFOnwYcHZWtiYgoLZ06BVSuLK8tVquBQ4d4TXF6S9MeOzs7O1y8eBEqlSrFBRKZowsXgMBAua2fBctQR0SWztc3dhF2rRbo0AEID1e2JkpYioZiO3bsiF9++SW1ayEyWR8OwQ4dClSooGhJRETpZvjw2F66Gzdibz1Gpsc2JR8UHR2NpUuXYs+ePfD19Y1zj9hZs2alSnFEpmLqVDnsCgBFiwJjxypaDhFRurK1BX79FShVCoiMBBYtAho3Bho2VLoy+lCyrrG7efMm8uTJg9q1ayf8hCoV9u3blyrFpQZeY0ef6vx5ORSh0cjrS44eBcqXV7oqIqL0t3ixvBsFAGTPLi9RyZpV2ZqsQXKyTLKCnVqtRmhoKLJlywZA3kLsxx9/RPbs2T+t4jTEYEefQqORs17PnJH7w4cDkyYpWxMRkVKEAL78Eti6Ve43bQps2iQXNaa0k2aTJz7MgDt27EBERETyKyQyE5Mnx4a6YsVibyFGRGSNVCpg6VLAw0Pub94sJ5KR6UjR5Am9FKxtTGQ2zp0Dxo+X22q1/OHFO+YRkbXLnh1YsiR2v29f4NYt5eohY8kKdiqVKs4yJ1z2hCyRfhZsTIzcHzZMXmdHRERyCDYgQG6Hh8slULRaRUui/yRrVqwQAp07d4bDf90W7969Q69eveLMit20aVPqVUikgEmTgLNn5Xbx4sDo0YqWQ0RkcubMAYKCZG/d4cPyHtrDhildFSVr8kSXLl2SdN7y9+8crDBOnqDkOntWznrVr7J+/DhQrpzSVRERmZ7Dh4EaNQCdTt5D9vhxoEwZpauyPGk2K9YcMdhRckRHy1B3/rzcHzUq9jo7IiKKa8QIOdEMkOt8njoFODkpW5OlSdNbiqWnsWPHGq7r0//LkSOH0mWRBZs4MTbUlSjBIVgiosSMHRvbS3f5sgx6pByTDnYAUKxYMYSGhhr+XbhwQemSyEKdORO7Rp1+Fqy9vaIlERGZPHt7YPXq2FUD5swB9u5VtCSrZvLBztbWFjly5DD8y8olrikNREcbz4IdORIoW1bRkoiIzEbRovLWi3qdOwMvXypWjlVL0b1i09P169fh5eUFBwcHVKxYEZMmTcJnn32W4PlRUVGIiooy7IeFhQEANBoNNPo7uBN9IDDQBufPqwEAJUsKDB0aA75diIiSrlcvYMsWNfbts8H9+8A33+jw669cAyU1JCe/mHSwq1ixIlatWoWCBQvi8ePHmDBhAqpUqYJLly4hS5Ys8X7M5MmTERgYGOf47t274ezsnNYlkxn69193TJlSAwCgVuvQuXMw/vknTOGqiIjMT9u2jjh+3B8REfb47Tcb5Mx5GjVqPFC6LLMXGRmZ5HPNalZsREQE8uXLh6FDh2LgwIHxnhNfj52Pjw+ePXvGWbEUR1QUUKmSLS5dkgttjxqlxQ8/6BSuiojIfP32mwodOsh+o4wZBU6fjoG3t8JFmbmwsDB4eHgkaVasSffYfcjFxQUlSpTA9evXEzzHwcHBsIDy++zs7GBnZ5eW5ZEZCgwELl2S26VKAaNHq2Fnp1a2KCIiM9a+PbB9O7BuHfDqlQrdu9th927AxuSv6jddyckvZtXMUVFRuHLlCjw9PZUuhSzAqVPAlCly29aWs2CJiFLL/Pkw9NLt3Qv89JOy9VgTkw52gwcPRnBwMG7duoXjx4+jRYsWCAsLQ6dOnZQujcxcVJSctaW/t+GoUUDp0kpWRERkOTJlkn8s633/vVzjjtKeSQe7+/fvo02bNihUqBCaN28Oe3t7HDt2DLlz51a6NDJz48bFDsGWLs0FNYmIUlvt2kD//nI7KkoO0UZHK1qSVTCryRMpwVuK0YdOngQqV5a9dXZ2cki2ZEmlqyIisjxv3wK+vrG9dcOHxy4ET0lnMbcUI0pt794ZD8GOHs1QR0SUVpyc5F0p9Nf+T50KHDqkbE2WjsGOrEpgYOxfjmXLAsOGKVsPEZGlK1NGXv4CADod0LEj8OaNsjVZMgY7shonTgDTpsltOzt5YS9XwCEiSntDhgBVq8rtW7dir72j1MdgR1ZBPwSr+2/t4R9+AEqUULQkIiKroVYDv/4KZMgg95ctAzZvVrQki8VgR1Zh7FjgyhW5XbasnHpPRETpJ29e4McfY/e7dwceP1auHkvFYEcW7/hxYPp0uW1nB6xcySFYIiIldO4MNG0qt589A7p1Ayx7bY70x2BHFu3DIdixY4HixZWsiIjIeqlUwOLFQPbscn/rVmDJEmVrsjQMdmTRfvgBuHpVbvv6AkOHKlsPEZG1y5oV+OWX2P0BA4AbN5Srx9Iw2JHFOnYMmDlTbtvby1mwtraKlkRERAAaNgR69pTbkZFAhw5ATIyyNVkKBjuySG/fxh2CLVZMyYqIiOh9M2YA+fPL7WPHgMmTla3HUjDYkUX64QcgJERuly8v11AiIiLTkSGDXAJFrZb7gYHylo/0aRjsyOIcPcohWCIic1CpEjBypNzWauWQbGSksjWZOwY7sij6IVj99Plx44CiRRUtiYiIPmLUKDm5DZAjLVxn9NMw2JFFGTUKuHZNbleoAAwapGw9RET0cXZ2wOrVgJOT3J83D9i1S9mazBmDHVmMw4eB2bPltoMDh2CJiMxFoUKxC8kDQJcuwPPnytVjzhjsyCJERsofBO8PwRYpomxNRESUdL17A59/LrdDQ4FevXhXipRgsCOLMGoUcP263K5YkUOwRETmRqUCli0DMmeW+xs3yiFaSh4GOzJ7hw4Bc+bIbf0QrH76PBERmQ8vL2DRotj9b78F7txRrh5zxGBHZu3DIdjx44HChZWtiYiIUq5FC6BjR7kdFgZ06hS72DwljsGOzNrIkbH3GKxUCRg4UNl6iIjo0/34I5Arl9wODo6dGEeJY7Ajs3XwIDB3rtx2dOQQLBGRpXB3B1atktfdAcCIEcD588rWZC4Y7MgsRUQYD8FOmCCnyxMRkWWoWTN2Ilx0NNC+PRAVpWxN5oDBjszSiBHAv//K7SpVgP79FS2HiIjSwIQJQIkScvvCBWD0aGXrMQcMdmR2DhyQ118Acgh2+XIOwRIRWSIHB7nkib293J8xQ15zRwljsCOzoh+C1Zs4EShYULl6iIgobZUsKX/WA/Lym44dgdevla3JlDHYkVkZPhy4eVNuV60K9OunbD1ERJT2Bg4E/Pzk9t27QN++ipZj0hjsyGzs3w/89JPcdnLiECwRkbWwsZErH7i5yf1Vq+SdKSguBjsyC+HhQEBA7P6kSUCBAsrVQ0RE6St3bmDevNj9nj2Bhw+Vq8dUMdiRWRg2DLh1S25Xq8ZueCIia9S+vbwzBQC8eCH/4Ncve0USgx2ZvKAgYP58ua0fgrXhO5eIyOqoVMDChYCnp9zftQtYsEDZmkwNfz2SSftwCHbyZCB/fuXqISIiZWXJIv/A1xs8GAgJUa4eU2NWwW7y5MlQqVToz9VoLZpWKydKrFsnu91v35bHq1cHvvtOycqIiMgUfP450KeP3H77Vv6u0GiUrclUmE2wO3nyJBYvXoySJUsqXQqloU2bgDx5AH9/oG1b4K+/5HF7e2DZMg7BEhGRNG1a7K0kT52Sd6kgMwl24eHhaNeuHZYsWYJMmTIpXQ6lkU2b5EWx9+/HfSw6mjeAJiKiWM7O8q4UtrZyf+JE4NgxZWsyBWYR7Pr06YOGDRuiTp06SpdCaUSrlYsNJzS7SaWS94PVatO1LCIiMmG+vsAPP8htrRbo0EFem23NbJUuIDHr16/H6dOncfLkySSdHxUVhaioKMN+WFgYAECj0UDDAXiTFRyswv37Cb8dhQDu3QOCgmJQsybnthMRkTR4MLBtmxrHj9vgxg1g4EAt5s/XKV1WqkpOfjHpYHfv3j3069cPu3fvhqOjY5I+ZvLkyQgMDIxzfPfu3XB2dk7tEimVHDiQE4Bvouft2HEWEREP0r4gIiIyG507u+DsWT9ERdliyRI1smc/CV/fx0qXlWoiIyOTfK5KCNNd2m/z5s1o1qwZ1O/dN0qr1UKlUsHGxgZRUVFGjwHx99j5+Pjg2bNncNPfi4RMzv79KtSrl/jfGXv2sMeOiIjiWrpUhd695e+R7NkFTp+OQdasCheVSsLCwuDh4YHXr18nmmVMuseudu3auHDhgtGxLl26oHDhwvj+++/jhDoAcHBwgIODQ5zjdnZ2sLOzS7NaKeWEAPbt+/g5KhXg7Q34+9vy/rBERBRHr17Atm3y3+PHKvTpY4dNm+TvD3OXnPxi0sHO1dUVxYsXNzrm4uKCLFmyxDlO5kkIYNQoYMqUhM/Rf1POmQOGOiIiipdKBSxdCpQoATx7BmzeDKxYAXTponRl6cssZsWSZRICGDkSmDQp9li3brJn7n3e3sDGjUDz5ulbHxERmZccOYAlS2L3+/YFbt5Urh4lmPQ1dqkhLCwM7u7uSRqXpvQjBDBihHFP3bx5ciVxrRY4eBAIDZX3A6xenT11RESUdF27ykXtAaBqVSA42Lx/jyQnyzDYUboTAhg+HJg6NfbY/PlA797K1URERJbjzRugVCng1i25P3kyMGyYsjV9iuRkGQ7FUrqKL9T9/DNDHRERpR5XV+DXX2NvQ/nDD8CZM8rWlF4Y7CjdCCH/Yno/1C1YAHzzjXI1ERGRZapaFfj+e7mt0QDt2wNv3ypbU3pgsKN0IYT8Bps2LfbYggVyejoREVFaGDsWKF1abl++LK/ttnQMdpTmhACGDgWmT489tnAhQx0REaUte3tg9WpAv7ztnDnA3r2KlpTmGOwoTelD3YwZsccWLQJ69lSuJiIish7FihlfAtSpE/DypXL1pDUGO0ozQgBDhsQNdT16KFcTERFZn+++A2rXltsPHsiltSwVgx2lCSGAwYOBmTNjjy1ezFBHRETpz8ZG3oUiY0a5v26d/GeJGOwo1elD3axZsccWLwa6d1euJiIism7e3nLSnl7v3sD9+8rVk1YY7ChVCQEMGmQc6pYsYagjIiLltW4NtGkjt1+9Ajp3BnQ6JStKfQx2lGqEAAYOBGbPlvv6GzJ366ZsXURERHrz5wM5c8rtvXuBn35Stp7UxmBHqUIIYMAAOZUciA11XbsqWhYREZGRTJmAlStj97//Xq5xZykY7OiT6UPd3LlyXx/qAgKUrYuIiCg+tWsD/fvL7agooF07IDpa0ZJSDYMdfZL4Qt0vvzDUERGRaZs0CShaVG6fPSvvUmEJGOwoxYSQf/G8H+qWLQO6dFG0LCIiokQ5Ocm7UtjZyf2pU4FDh5StKTUw2FGKCAH06wf8+KPc14e6zp0VLYuIiCjJypQBxo2T2zod0LEj8OaNsjV9KgY7SjYhgL59Y2cSqVTA8uUMdUREZH6GDAGqVpXbt27FXntnrhjsKFn0oW7ePLmvD3WdOilbFxERUUqo1cCqVUCGDHJ/2TJg82ZFS/okDHaUZELI++29H+pWrGCoIyIi8/bZZ7HXiwNyUf3Hj5Wr51Mw2FGSCAF8+61c2BGIDXUdOypaFhERUaro0gVo2lRuP3sm12EVQtGSUoTBjhIlBNCnD/Dzz3JfpZKLOzLUERGRpVCp5H3Ns2WT+9u2yVtimhsGO/oonU6GOv2Nk21s5LUIHTooWxcREVFqy5pVXmOnN2AAcOOGcvWkBIMdJSi+ULdyJdC+vbJ1ERERpZWGDYGePeV2ZKTsyIiJUbam5GCwo3jpdEDv3sDChXJf31PHUEdERJZuxgwgf365fewYMHmysvUkB4MdxaEPdYsWyX0bG+DXX+W99IiIiCxdhgzy957NfykpMBA4eVLZmpKKwY6M6HTAN9/EDXVt2ypbFxERUXqqVAkYOVJua7VySDYyUtmakoLBjgx0OqBXLzkrCJChbvVqhjoiIrJOo0cDvr5yOyQEGDpU2XqSgsGOAMhQ17Nn7NRufahr00bZuoiIiJRiZyd/Fzo5yf3584GdO5WtKTEMdmQIdUuXyn0bG2DNGoY6IiKiQoWA6dNj9wMCgOfPlasnMQx2Vu7DUKdWA2vXAq1bK1sXERGRqejdG/j8c7kdGiovWzLVu1Iw2FkxnQ7o0cM41K1ZA7RqpWxdREREpkSlkgsXZ84s9zdulEO0psikg92CBQtQsmRJuLm5wc3NDZUrV8aOHTuULssi6HTyJse//CL39T11DHVERERxeXnFrhgByAX8168H1q0D9u+XM2dNgUkHO29vb0yZMgWnTp3CqVOnUKtWLTRp0gSXLl1SujSzpg91+tumqNXyjdmypbJ1ERERmbIWLWJvqfnmjbwWvW1bwN8fyJMH2LRJ0fIAACohTHWUOH6ZM2fG9OnT0bVr1ySdHxYWBnd3d7x+/Rpubm5pXJ3p0+mAbt2A5cvlvj7Uff21snURERGZg19/BTp2jHtcpZL/b9wING+euq+ZnCxj0j1279NqtVi/fj0iIiJQuXJlpcsxS1ot0LWrcahbv56hjoiIKCm0WmDEiPgf03eT9e+v7LCsrXIvnTQXLlxA5cqV8e7dO2TIkAF//vknihYtmuD5UVFRiIqKMuyHhYUBADQaDTQaTZrXa6q0WqBnTzVWrZJZXq0WWL1aiyZNBKy4WYiIiJIsOFiF+/cTjk5CAPfuAUFBMahZM/UGRJOTX0w+2BUqVAhnz57Fq1ev8Mcff6BTp04IDg5OMNxNnjwZgYGBcY7v3r0bzs7OaV2uSdJqgXnzyiAoKBcAQK3WYdCgU3ByCsX27QoXR0REZCYOHMgJwDfR83bsOIuIiAep9rqRybiXmdldY1enTh3ky5cPi96fmvKe+HrsfHx88OzZM6u8xk6rBbp3V2P1atlTZ2sre+qaNzerLzsREZHigoNVqFs38T6xPXtSt8cuLCwMHh4eSbrGzuR77D4khDAKbh9ycHCAg4NDnON2dnaws7NLy9JMjv6aOv1aO7a2wG+/qdC8udl92YmIiBTn7w94ewMPHsS/QLFKJR/397eFWp16r5uc/GLSv+FHjBiB+vXrw8fHB2/evMH69euxf/9+7DT1G7WZAK0W6NJFzt4BZKjbsAFo1kzZuoiIiMyVWg3MnSuXPVGpjMOdflbsnDlI1VCXXCY9K/bx48fo0KEDChUqhNq1a+P48ePYuXMn6tatq3RpJo2hjoiIKG00by6XNMmZ0/i4t3faLHWSXGZ3jV1yWds6dlot0Lmz8fDr778DTZsqWRUREZFl0WqBgwflvWM9PYHq1dOupy45Wcakh2IpebRaoFMneb9XgKGOiIgorajVgJ+f0lXExWBnIbRauRL22rVy385OhromTZSti4iIiNKPSV9jR0kTExM31G3cyFBHRERkbdhjZ+b0oW7dOrmvD3VffqlsXURERJT+GOzMWEwM0KGDvN8rIEPdH38AjRsrWxcREREpg0OxZoqhjoiIiD7EYGeGPgx19vbApk0MdURERNaOQ7FmJiYGaN8e+O03uW9vL3vqGjVSti4iIiJSHoOdGYmJAdq1k3eRAGJ76ho2VLYuIiIiMg0MdmYivlD3559AgwbK1kVERESmg9fYmQGNBmjblqGOiIiIPo49diZOH+o2bpT79vbA5s1A/fqKlkVEREQmiD12JuzDUOfgwFBHRERECWOPnYnSaIA2beSMVyA21H3xhaJlERERkQljj50JYqgjIiKilGCPnYnRaIDWreUyJoAMdX/9BXz+ubJ1ERERkeljsDMhGg3QqpWc8Qow1BEREVHycCjWRERHG4c6R0dgyxaGOiIiIko69tiZAH2o27xZ7js6yp66evUULYuIiIjMDIOdwqKjgZYtZZADYnvq6tZVti4iIiIyPwx2Coov1P39N1CnjrJ1ERERkXniNXYKYagjIiKi1MYeOwVERwNffy2HXAEZ6rZuBWrXVrYuIiIiMm/ssUtnUVFAixaxoc7JiaGOiIiIUgd77NJRVJTsqfv7b7mvD3W1ailbFxEREVkGBrt0ou+p27pV7jPUERERUWpjsEsHUVHAV18B27bJfScnue3vr2xdREREZFl4jV0aY6gjIiKi9MIeuzQUFQU0bw5s3y73nZ1lqPPzU7QsIiIislDssUsj797FDXXbtzPUERERUdphj10a0Ie6HTvkvj7U1aypbF1ERERk2RjsUoFWCxw8CISGAlmyALNnAzt3yscY6oiIiCi9mPRQ7OTJk1G+fHm4uroiW7ZsaNq0KUJCQpQuy8imTUCePHIyRNu2wOefx4Y6FxfZa8dQR0REROnBpINdcHAw+vTpg2PHjmHPnj2IiYlBvXr1EBERoXRpAGSoa9ECuH8//seHDQNq1EjfmoiIiMh6qYQQQukikurp06fIli0bgoODUSOJiSksLAzu7u54/fo13NzcUq0WrVb21CUU6gDAxwe4dQtQq1PtZYmIiMjKJCfLmNU1dq9fvwYAZM6cOcFzoqKiEBUVZdgPCwsDAGg0Gmg0mlSrJThYhfv3P9589+4BQUExqFnTbLIzERERmZjk5BezCXZCCAwcOBDVqlVD8eLFEzxv8uTJCAwMjHN89+7dcHZ2TrV6DhzICcA30fN27DiLiIgHqfa6REREZF0iIyOTfK7ZDMX26dMH27Ztw6FDh+Dt7Z3gefH12Pn4+ODZs2epOhQbHKxC3bqJ5+I9e9hjR0RERCkXFhYGDw8PyxmK/e6777BlyxYcOHDgo6EOABwcHODg4BDnuJ2dHezs7FKtJn9/wNsbePAAiC8aq1TycX9/W15jR0RERCmWnPxi0rNihRD49ttvsWnTJuzbtw958+ZVuiQDtRqYO1duq1TGj+n358zhxAkiIiJKPyYd7Pr06YPVq1dj7dq1cHV1xaNHj/Do0SO8fftW6dIAyLtLbNwI5MxpfNzbWx5v3lyZuoiIiMg6mfQ1dqoPu8L+s3z5cnTu3DlJz5FWy5287/07T3h6AtWrs6eOiIiIUofFLHdiwpnTiFoN+PkpXQURERFZO5MeiiUiIiKipGOwIyIiIrIQDHZEREREFoLBjoiIiMhCMNgRERERWQgGOyIiIiILwWBHREREZCEY7IiIiIgsBIMdERERkYUw6TtPpAb93SvCwsIUroSIiIgo+fQZJil35LL4YPfmzRsAgI+Pj8KVEBEREaXcmzdv4O7u/tFzVMJcbsiaQjqdDg8fPoSrqytUKlWyPjYsLAw+Pj64d+9eojfdtTZsm/ixXRLGtokf2yVhbJuEsW3iZ6ntIoTAmzdv4OXlBRubj19FZ/E9djY2NvD29v6k53Bzc7OoN0hqYtvEj+2SMLZN/NguCWPbJIxtEz9LbJfEeur0OHmCiIiIyEIw2BERERFZCAa7j3BwcMCYMWPg4OCgdCkmh20TP7ZLwtg28WO7JIxtkzC2TfzYLlYweYKIiIjIWrDHjoiIiMhCMNgRERERWQgGOyIiIiILwWBHREREZCEY7IiIiIgsBIMdESlKp9MpXYJJYrskjG2TMLZN/KypXSz+lmKpTQiR7HvOWpq7d+/iwoULCA0NRcOGDeHm5gYXFxelyzIJbJukiYiIgL29PWJiYuDk5KR0OSaD7ZIwtk3C2Dbxs9Z24Tp2CQgJCcGiRYvw8OFDlC5dGvXq1UPZsmUBWHe4O3/+POrVqwcvLy/cunULrq6uaNWqFXr37o28efMqXZ6i2DZJc/HiRfTp0wdv377Fs2fPMHjwYNSrVw/58+dXujRFsV0SxrZJGNsmftbcLgx28bh8+TKqVKmC6tWrI2PGjPjnn39QoEABNG3aFAMHDgRgneHu1atXqFOnDmrVqoXhw4cjU6ZMGDduHP755x9kypQJM2fOtIpvmviwbZLm1q1bKFeuHNq1awdfX1+EhIRg1apVqFmzJnr16oXq1asrXaIi2C4JY9skjG0TP6tvF0FGoqKiRMeOHUXXrl0Nx+7cuSN69eolypYtKyZMmGA4rtPplChRMXfu3BG5c+cWu3btMjq+cuVKUaNGDdG2bVvx8OFDhapTFtsmaWbNmiWqV69udGzTpk2iatWqonnz5uL48eMKVaYstkvC2DYJY9vEz9rbhZMnPmBvb4+HDx9C/NeRKYRArly58MMPP6BGjRrYunUr1qxZAwBW12NnY2MDJycnPHz4EAAQExMDAOjYsSPatWuHixcvYs+ePQBgaD9roVKp4OjoyLZJhE6nw6tXr/DmzRvDxczNmjXD8OHDcefOHaxevRqRkZFW10Zsl4SxbRLGtomf1beLYpHSxNy7d0+cPHlSCCFEly5dRLNmzcTbt2+FTqcTWq1WCCF7ZerXry++/PJLJUtVVKNGjUTp0qXFy5cvhRBCaDQaw2MtWrQQlStXVqiy9KfVag3vDSGEaNmypShevDjb5iPWr18vHB0dxalTp4QQsodcb+XKlcLe3t7wmDX57bff2C4JYNskjN9P8bP29wyDnRDi4sWLwsfHRwwcOFAIIcQ///wjbGxsxNy5cw3n6H+BnzhxQqhUKnHmzBklSk1X9+7dE+vXrxcbN24Up0+fFkII8fTpU5E3b15Rt25do28WIYRYsmSJqFSpUpzjlujSpUuiQ4cOolatWqJLly5i+/bt4smTJ6JUqVLCz8/PqtsmMV9++aXw8fERjx8/FkII8e7dO8NjRYsWFVOnTlWqtHT3/uUczZo1Y7skgG0j6XQ6ERMTY3SsadOmVt82z58/F0+ePDE6Zs3vGasfij137hwqVKgAW1tbrFmzBqGhoahduzamTp2KAQMGYOnSpQDkMCQAZMiQAUWLFoWzs7OSZae5CxcuoFq1apgxYwb69OmDsWPH4vr16/Dw8MDatWtx5coV1KtXDyEhIXj37h0A4MSJE3B1dbXc7u3/XL16FdWqVYO9vT0aNmyIe/fuoXfv3pg4cSJ+/vlnPH36FLVq1bLKtnlfSEgIBg4ciNatW2PKlCk4deoUAGD27Nnw8vJCpUqVcO/ePTg4OAAA3r17BxcXF3h4eChZdpp78uQJXr16BUAO4euHisaPH49cuXJZbbsA8qL32bNnY9CgQfjtt98MxwMDA62+ba5du4YBAwagSZMmGDduHJ4+fQqA75ubN2+ifPny+OmnnwyXwgDAuHHjrLddlE6WSjp79qxwcnISI0aMEE+fPhXFihUT48ePFzqdToSHh4vAwEChUqnEyJEjxalTp8TTp0/FsGHDxGeffSYePXqkdPlp5vbt2yJnzpxi2LBhIjw8XGzfvl3kyJFDnDhxwnDOxYsXRdGiRUW+fPmEr6+vaNy4sXB1dRVnz55VsPK09+7dO9GuXTvRt29fw7HIyEhRsmRJoVKpRJs2bcT58+dFxYoVRd68ea2qbd536dIl4e7uLho1aiTat28vcuTIIapVqybmzJkjhJDvn+rVqwt3d3fx888/i9WrV4vvv/9eZM6cWdy4cUPh6tPO5cuXhb29vWjRooV4/fp1nMdPnDgh/P39ra5dhBDi/PnzwtvbW9SpU0dUqVJF2NjYGHpVdDqd1bdNtmzZRIsWLUTPnj2Fvb29GDNmjOHx48ePi5o1a1pl2yxYsECoVCpRpkwZMXHiRMMkNZ1OJ44dOyZq1Khhde1itcHu3LlzwsHBQYwYMUIIIYdaW7RoIXx9fQ3naLVasWrVKpEjRw7h5eUlChcuLHLmzGkYlrRUCxcuFH5+fkbDRA0aNBCLFi0SK1asEEFBQYbjP/74oxg2bJgIDAwUV69eVaDa9Fe7dm0xduxYIYQQb9++FUIIMXToUNG8eXNRrlw5MX/+fCGEEPPmzbO6thFCiOjo6ARnlpcuXVpMmTJFCCFERESE6N+/vyhcuLAoVKiQqFy5skV/bz169EhUrVpV1K5dW3h4eIivv/463nD34sULMXDgQKtpFyHkH5P58+cXQ4cONQw1/vLLLyJHjhwiJCTEcJ41ts3NmzdFnjx5xPDhww3Hxo4dK3r37m00vPjmzRvRr18/q2obIeTv8k6dOokJEyYILy8vMX78ePH8+XPD42/fvhUDBgywqnax2mB34sQJMXr0aCFE7PVzV69eFe7u7oZfzHq3bt0SwcHBYvfu3eL+/fvpXmt6W7Bggfjss88Mb/4JEyYIlUol6tSpI3x9fUW2bNnE4sWLFa4y/el0OhERESGqV68uOnToYJgccf/+fZE7d26xbNky0b59+zjT7K1R3bp1RUBAgBAi9jqyhw8fiv79+4sKFSqINWvWGM598OCBePnypXj16pUitaaXHTt2iHbt2okTJ06I48ePi8yZMycY7oSQ7ytraBetViumTJkivvjiC6PP9cKFC8LHxyfeP4qspW1iYmLE9OnTxTfffGP0PunWrZuoXLmyKF++vOjZs6f4+++/DY9Zy/eT3tmzZ0WBAgWETqcTgYGBwsfHR8yZM0c0bdrU0HEjhHW1i9UGuw/pdDrx6tUr0bRpU9GyZUuh0WhETEyM0axHa3Hz5k1RpUoVkT9/fvHVV18JlUolNm/eLHQ6nXj8+LHo27ev8PPzE0+fPjW0jzWt6Xfo0CFhY2MjatSoITp06CBcXFxEt27dhBDyl1GGDBnElStXDD0P1tQ2MTExIjo6Olkzy62lfZ48eWLU23306FFDuHv/l837s6mtRXBwsBg2bJjRMa1WK/LmzWvUZtbo3r174ujRo4b98ePHC7VaLUaOHCl+/PFHUb58eVGrVi1Dp4O1fD+9r169euLWrVtCCCGmTZsmXFxchLu7u9G6otb0u9zqJ0/oqVQquLu7o0OHDvj9999x/PhxqNVqw6QJa5I3b16sWbMGkydPRokSJfDVV1+hSZMmUKlUyJYtG7y8vPDy5UtkyJDB0D7WtKZf1apVcezYMeTKlQsODg6YNm0alixZAkBeyOvj4wNPT0+o1WoA1tE2Wq0WAKBWq2FnZ4dOnTphy5YtWLx4MVQqFWxsbKDT6ZArVy4EBgbi77//xtmzZwFYdvvo2wUAsmbNCj8/PwByna1KlSph+/bt2Lt3L7p3746wsDBoNBosWbLEsOahJXu/bWrUqIHJkycDiF3nUaVSQaVSQaPRGM7bu3evYdKAJXu/bby9vVGpUiUAwPPnz/H8+XNs3boVEyZMwHfffYeVK1ciKCgIFy5cAGA930/vi46OxoEDBwDISVtqtRpOTk44d+6cYUKFNf0ut57PNIkaNWqEunXrYsGCBXj79q3S5SgmT548aNGiBXLmzIm3b98iOjra8Njjx4+RJ0+eBL/JrEH58uWxatUqLFmyBL179zYcP3jwILJnz27RP1w/dO3aNcyZMwehoaGGYzVr1rT6meXxtYuevi0qVqyIHTt2GMJdz5490a9fP3z22WfpXW66iq9t3g90MTExiIyMhI2NDdzc3AAAI0aMQN26dY2CniX62PsmS5YsmDhxIr744gsIIaDT6aDT6VC2bFnkzJlTgWrTT3zton8vVKxYETY2Nujbty927NiBs2fPom/fvhg7dizWr19vfb+rFO4xNEmTJ08Wbm5uIjQ0VOlSFKef3Tht2jSxatUqMXToUJExY0Zx/vx5pUszKefPnxe9e/cWbm5uVjX79fr16yJz5sxCpVKJ4cOHi6dPnxoei4iIsNqZ5R9rl/gcOnRIqFQqkTlzZvG///0vnapURlLaRqvVirdv34p8+fKJU6dOiXHjxgkXFxejmfmW6GNtox9i/XCodeTIkaJixYpx1nGzJIm9Z5YtWyZUKpXw9PQ03GhACCGmTp0qrl27lt7lKo7B7j36b5gXL16IcuXKGcbsrd2+fftEvnz5RIECBYSfn584d+6c0iWZlHfv3olNmzaJ1q1bW1XbhIeHi4CAANG5c2cxb948oVKpxJAhQ4x+wVjjzPKE2iWhcBcVFSV69eolXF1dxaVLl9K52vSV3LYpU6aMKF++vLC3tzf6hW2Jkts2ly5dEqNGjRJubm4W/XMnKe0SEhIiRo0aZbhxgDVdTxcfW6V7DE2JfvgsY8aMCA4OhouLi8IVmQZ/f3+cOHECGo0GDg4OyJgxo9IlmRQHBwc0aNAA9erVs6r3jI2NDcqVK4csWbKgVatWyJo1K1q3bg0AGDJkCLJmzQobGxt06NAB1atXx927d/H27VsUL17cooeNPtYuQ4cOjbMw6rlz53Dw4EHs3bsXRYsWVaLkdJPUttFqtXj9+jVu3ryJ8PBwnDlzBiVKlFCy9DSXnPfN3bt3MWrUKFy9ehUHDhxAyZIllSo7zSWlXQoWLIjhw4cbLu+wpkth4qV0siQi8xUeHm60v379eqFSqcTgwYMNf1FrNBpx584dJcpTzMfa5dmzZ0II2atw9+5dIYQcJbAWSWkbjUYjnj17Jnbu3CkuXryoRJmKSErbxMTEiMePH4t79+6Je/fuKVFmuvtYu+hHCLRarbh586YS5Zkc9tgRUYrpeyi1Wi1sbGzQqlUrCCHQtm1bqFQq9O/fHzNmzMCdO3ewatUqODs7W8Vf00ltl1u3bmHt2rXIlCmTwhWnn6S2ze3bt7F69WqLn2TzvuS8b9atWwdHR0eFK04fyfk58+uvv1rVeyZeisZKIrIY769Xt379emFnZycKFSokbG1tDde+WKOPtYslX2uYFAm1jVqttur3jBB83ySEP2cSpxLCiu5KTkRpSry3ZEXt2rVx9uxZ7N+/3+Kvj0oM2yVhbJuEsW3ix3b5OA7FElGqUalU0Gq1GDJkCIKCgnD27Fn+sAXb5WPYNglj28SP7fJxXKCYiFJdsWLFcPr0aYuerZcSbJeEsW0SxraJH9slfhyKJaJUJ4SwikkSycV2SRjbJmFsm/ixXeLHYEdERERkITgUS0RERGQhGOyIiIiILASDHREREZGFYLAjIiIishAMdkREREQWgsGOiIiIyEIw2BERERFZCAY7IrI6nTt3hkqlgkqlgq2tLXLlyoVvvvkGL1++VLo0IqJPwmBHRFbpiy++QGhoKG7fvo2lS5fi77//Ru/evZUui4jokzDYEZFVcnBwQI4cOeDt7Y169eqhVatW2L17NwBAq9Wia9euyJs3L5ycnFCoUCHMnTvX6OM7d+6Mpk2bYsaMGfD09ESWLFnQp08faDQawzmhoaFo2LAhnJyckDdvXqxduxZ58uTBnDlzDOe8fv0aPXr0QLZs2eDm5oZatWrh3Llz6dIGRGR5bJUugIhIaTdv3sTOnTthZ2cHANDpdPD29saGDRvg4eGBI0eOoEePHvD09ETLli0NHxcUFARPT08EBQXhxo0baNWqFUqXLo3u3bsDADp27Ihnz55h//79sLOzw8CBA/HkyRPDxwsh0LBhQ2TOnBnbt2+Hu7s7Fi1ahNq1a+PatWvInDlz+jYEEZk9Bjsiskpbt25FhgwZoNVq8e7dOwDArFmzAAB2dnYIDAw0nJs3b14cOXIEGzZsMAp2mTJlwrx586BWq1G4cGE0bNgQe/fuRffu3XH16lX8888/OHnyJHx9fQEAS5cuRYECBQwfHxQUhAsXLuDJkydwcHAAAMyYMQObN2/Gxo0b0aNHjzRvByKyLAx2RGSV/P39sWDBAkRGRmLp0qW4du0avvvuO8PjCxcuxNKlS3Hnzh28ffsW0dHRKF26tNFzFCtWDGq12rDv6emJCxcuAABCQkJga2uLsmXLGh7Pnz8/MmXKZNj/3//+h/DwcGTJksXoed++fYt///03NT9dIrISDHZEZJVcXFyQP39+AMCPP/4If39/BAYGYvz48diwYQMGDBiAmTNnonLlynB1dcX06dNx/Phxo+fQD93qqVQq6HQ6AHKYNT7vH9fpdPD09MT+/fvjnJcxY8ZP+OyIyFox2BERARgzZgzq16+Pb775BgcPHkSVKlWMZskmtwetcOHCiImJwZkzZ1CuXDkAwI0bN/Dq1SvDOWXLlsWjR49ga2uLPHnypManQURWjrNiiYgA+Pn5oVixYpg0aRLy58+PU6dOYdeuXbh27RpGjx6NkydPJuv5ChcujDp16qBHjx44ceIEzpw5gx49esDJyQkqlQoAUKdOHVSuXBlNmzbFrl27cPv2bRw5cgSjRo3CqVOn0uLTJCILx2BHRPSfgQMHYsmSJWjatCmaN2+OVq1aoWLFinj+/HmK1rhbtWoVsmfPjho1aqBZs2bo3r07XF1d4ejoCEAO3W7fvh01atRAQEAAChYsiNatW+P27dvInj17an96RGQFVCKhC0GIiChV3b9/Hz4+Pvjnn39Qu3ZtpcshIgvEYEdElEb27duH8PBwlChRAqGhoRg6dCgePHiAa9euxZl4QUSUGjh5gogojWg0GowYMQI3b96Eq6srqlSpgjVr1jDUEVGaYY8dERERkYXg5AkiIiIiC8FgR0RERGQhGOyIiIiILASDHREREZGFYLAjIiIishAMdkREREQWgsGOiIiIyEIw2BERERFZCAY7IiIiIgvxfyIDz/8JypZ8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Buat poligon frekuensi\n",
    "midpoints = [\n",
    "    (tepi_kelas[i] + tepi_kelas[i + 1]) / 2 for i in range(len(tepi_kelas) - 1)\n",
    "]\n",
    "plt.plot(\n",
    "    midpoints,\n",
    "    frequency_table.values,\n",
    "    marker=\"o\",\n",
    "    color=\"blue\",\n",
    "    linestyle=\"-\",\n",
    "    linewidth=2,\n",
    "    label=\"Frequency Polygon\",\n",
    ")\n",
    "\n",
    "# Penambahan label dan grid\n",
    "plt.title(\"Histogram and Frequency Polygon\")\n",
    "plt.xlabel(\"Range\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis=\"y\")\n",
    "plt.legend()\n",
    "\n",
    "# Tampilkan plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}