Exercise5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba907cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "00f903b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    \"month\": [1, 2, 3, 4, 5],\n",
    "    \"weather\": [\"rainy\", \"sunny\", \"cloudy\", \"windy\", \"cloudy\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3871c44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'month': [1, 2, 3, 4, 5], 'weather': ['rainy', 'sunny', 'cloudy', 'windy', 'cloudy']}\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6944d214",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ca1a9b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "0      1   rainy\n",
      "1      2   sunny\n",
      "2      3  cloudy\n",
      "3      4   windy\n",
      "4      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "12f486be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "m = list(df['month'])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32c2b38a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rainy', 'sunny', 'cloudy', 'windy', 'cloudy']\n"
     ]
    }
   ],
   "source": [
    "m = list(df['weather'])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71b260a8",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'other'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'other'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mother\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3806\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m   3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3809\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'other'"
     ]
    }
   ],
   "source": [
    "m = list(df['other'])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8d60abf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 'rainy']\n"
     ]
    }
   ],
   "source": [
    "m = list(df.loc[0])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8352d903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 'sunny']\n"
     ]
    }
   ],
   "source": [
    "m = list(df.loc[1])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4221c0ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 'windy']\n"
     ]
    }
   ],
   "source": [
    "m = list(df.loc[3])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bb145af0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 'cloudy']\n"
     ]
    }
   ],
   "source": [
    "m = list(df.loc[4])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cc62a56e",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "5",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\range.py:391\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m    390\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 391\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[0;32m    392\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "\u001b[1;31mValueError\u001b[0m: 5 is not in range",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[14], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(df\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;241m5\u001b[39m])\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:1073\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1070\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m   1072\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m-> 1073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_axis(maybe_callable, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:1312\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1310\u001b[0m \u001b[38;5;66;03m# fall thru to straight lookup\u001b[39;00m\n\u001b[0;32m   1311\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m-> 1312\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_label(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:1260\u001b[0m, in \u001b[0;36m_LocIndexer._get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m   1258\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_label\u001b[39m(\u001b[38;5;28mself\u001b[39m, label, axis: \u001b[38;5;28mint\u001b[39m):\n\u001b[0;32m   1259\u001b[0m     \u001b[38;5;66;03m# GH#5567 this will fail if the label is not present in the axis.\u001b[39;00m\n\u001b[1;32m-> 1260\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39mxs(label, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4056\u001b[0m, in \u001b[0;36mNDFrame.xs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m   4054\u001b[0m             new_index \u001b[38;5;241m=\u001b[39m index[loc]\n\u001b[0;32m   4055\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4056\u001b[0m     loc \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m   4058\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[0;32m   4059\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m loc\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mbool_:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\range.py:393\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m    391\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[0;32m    392\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m--> 393\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m    394\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n\u001b[0;32m    395\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 5"
     ]
    }
   ],
   "source": [
    "m = list(df.loc[5])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8e86c3ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month          1\n",
      "weather    rainy\n",
      "Name: 0, dtype: object\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[0]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e695c921",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month           5\n",
      "weather    cloudy\n",
      "Name: 4, dtype: object\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[4]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "903156fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "1      2   sunny\n",
      "2      3  cloudy\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[1:2]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "20fb133d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "0      1   rainy\n",
      "1      2   sunny\n",
      "2      3  cloudy\n",
      "3      4   windy\n",
      "4      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[0:4]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "533d48e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "3      4   windy\n",
      "4      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[3:20]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "396dfca9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "0      1   rainy\n",
      "1      2   sunny\n",
      "2      3  cloudy\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[-1:2]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8c7a3dae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "a      1   rainy\n",
      "b      2   sunny\n",
      "c      3  cloudy\n",
      "d      4   windy\n",
      "e      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "idx = ['a', 'b', 'c', 'd', 'e']\n",
    "df = pd.DataFrame(data, index=idx)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e9e878bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month          1\n",
      "weather    rainy\n",
      "Name: a, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.loc['a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c9cac6f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month           5\n",
      "weather    cloudy\n",
      "Name: e, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.loc['e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "79fbaf83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "a      1   rainy\n",
      "b      2   sunny\n",
      "c      3  cloudy\n"
     ]
    }
   ],
   "source": [
    "print(df.loc['a':'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2a824ea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['a']['month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5a46d507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'windy'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['d']['weather']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "76328c78",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_no_idx_sep_comma.csv\", sep=\",\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c6e1d8fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_def_option.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e6af27d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_sep_semicolon.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "de368268",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_sep_tab.csv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "234988d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_sep_space.csv\", sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f7fbcf13",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel(\"data_no_idx.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "180abfce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "0      1   rainy\n",
      "1      2   sunny\n",
      "2      3  cloudy\n",
      "3      4   windy\n",
      "4      5  cloudy\n",
      "5      6   snowy\n",
      "6      7   snowy\n",
      "7      8   windy\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.read_excel(\"data_no_idx.xlsx\")\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "6e78dae4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "0      1   rainy\n",
      "1      2   sunny\n",
      "2      3  cloudy\n",
      "3      4   windy\n",
      "4      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "df3 = pd.read_csv(\"data_no_idx_sep_comma.csv\")\n",
    "print(df3)\n",
    "\n",
    "\n",
    "\n",
    "# Question\n",
    "# 1. What would be the best separation characters for CSV file?\n",
    "# 2. Explain why you choose that answer ?\n",
    "\n",
    "# Answer\n",
    "# 1. Comma (,): Comma is the traditional and most widely used separator for CSV files. It's supported by \n",
    "# most software and programming languages, making it a safe choice in many cases. However, \n",
    "# if your data contains commas within fields, it can lead to parsing issues\n",
    "# 2. In the realm of CSV (Comma-Separated Values) format, the comma is chosen for its simplicity and broad \n",
    "# support across various systems. As a common separator, the comma facilitates data interpretation and \n",
    "# ensures compatibility across platforms. This makes it a popular and reliable choice for data exchange."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1338f9f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       t         x         y\n",
      "0    0.0  2.000000  0.000000\n",
      "1    0.2  1.984229  0.250666\n",
      "2    0.4  1.937166  0.497380\n",
      "3    0.6  1.859553  0.736249\n",
      "4    0.8  1.752613  0.963507\n",
      "..   ...       ...       ...\n",
      "95  19.0  1.618034 -1.175571\n",
      "96  19.2  1.752613 -0.963507\n",
      "97  19.4  1.859553 -0.736249\n",
      "98  19.6  1.937166 -0.497380\n",
      "99  19.8  1.984229 -0.250666\n",
      "\n",
      "[100 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "ljc = pd.read_excel(\"D:/Users/DELL/Downloads/lissajous_circle.xlsx\")\n",
    "print(ljc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f4e41ea1",
   "metadata": {},
   "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>t</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.984229</td>\n",
       "      <td>0.250666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4</td>\n",
       "      <td>1.937166</td>\n",
       "      <td>0.497380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>1.859553</td>\n",
       "      <td>0.736249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.8</td>\n",
       "      <td>1.752613</td>\n",
       "      <td>0.963507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     t         x         y\n",
       "0  0.0  2.000000  0.000000\n",
       "1  0.2  1.984229  0.250666\n",
       "2  0.4  1.937166  0.497380\n",
       "3  0.6  1.859553  0.736249\n",
       "4  0.8  1.752613  0.963507"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ljc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0e594067",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1b994702",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6)) \n",
    "\n",
    "plt.scatter(ljc[\"x\"], ljc[\"y\"], s=5, alpha=0.8, label=\"Circle\") \n",
    "\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.title(\"Lissajous Circle (Scatter Plot)\")\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8a7c7fc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       t         x         y\n",
      "0    0.0  2.000000  0.000000\n",
      "1    0.2  1.996053  0.250666\n",
      "2    0.4  1.984229  0.497380\n",
      "3    0.6  1.964575  0.736249\n",
      "4    0.8  1.937166  0.963507\n",
      "..   ...       ...       ...\n",
      "95  19.0  1.902113 -1.175571\n",
      "96  19.2  1.937166 -0.963507\n",
      "97  19.4  1.964575 -0.736249\n",
      "98  19.6  1.984229 -0.497380\n",
      "99  19.8  1.996053 -0.250666\n",
      "\n",
      "[100 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "ljb = pd.read_excel(\"D:/Users/DELL/Downloads/lissajous_butterfly.xlsx\")\n",
    "print(ljb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "48082ed8",
   "metadata": {},
   "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>t</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.996053</td>\n",
       "      <td>0.250666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4</td>\n",
       "      <td>1.984229</td>\n",
       "      <td>0.497380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>1.964575</td>\n",
       "      <td>0.736249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.8</td>\n",
       "      <td>1.937166</td>\n",
       "      <td>0.963507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     t         x         y\n",
       "0  0.0  2.000000  0.000000\n",
       "1  0.2  1.996053  0.250666\n",
       "2  0.4  1.984229  0.497380\n",
       "3  0.6  1.964575  0.736249\n",
       "4  0.8  1.937166  0.963507"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ljb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6d7fafac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6)) \n",
    "\n",
    "plt.scatter(ljb[\"x\"], ljb[\"y\"], s=5, alpha=0.8, label=\"Circle\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.title(\"Lissajous Butterfly (Scatter Plot)\")\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d93e44ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "dea9a0e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          x\n",
      "0        36\n",
      "1        31\n",
      "2        34\n",
      "3        29\n",
      "4         5\n",
      "...     ...\n",
      "249995  997\n",
      "249996  997\n",
      "249997  998\n",
      "249998  998\n",
      "249999  999\n",
      "\n",
      "[250000 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "distri = pd.read_excel(\"D:/Users/DELL/Downloads/distribution.xlsx\")\n",
    "print(distri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "db8a5caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x='x', data=distri, color='red')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Count Plot of x Values')\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "dcc1e806",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        fruit  amount\n",
      "0      tomato      18\n",
      "1      orange      12\n",
      "2       apple      13\n",
      "3      banana      20\n",
      "4        kiwi      14\n",
      "5      durian      15\n",
      "6      manggo       5\n",
      "7  strawberry      15\n"
     ]
    }
   ],
   "source": [
    "fruitpie = pd.read_excel(\"D:/Users/DELL/Downloads/fruit_pie_chart.xlsx\")\n",
    "print(fruitpie)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ceda44e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BnJeXFxcTE8MtXryYKywsbPG8F154gYuIiOAEAgEHgDtw4ECn70V7s4y//vpr7m9/+xsXHBzMSSQS7vrrr2/z+3bdunXcoEGDOKlUyiUmJnLff/99mz8Pv/76Kzd8+HBOIpFwAKz3bOs95jiO++KLL7jk5GTOy8uL8/Pz42bPns1duHChxTkLFizg5HJ5q0yvvvpqp38vhHgqhuO6MJWNEEIIIYS4LRpDSAghhBDi4aggJIQQQgjxcFQQEkIIIYR4OCoICSGEEEI8HBWEhBBCCCEejgpCQgghhBAPRwUhIYQQQoiHo4KQEEIIIcTDUUFICCGEEOLhqCAkhBBCCPFwVBASQgghhHg4KggJIYQQQjwcFYSEEEIIIR6OCkJCCCGEEA9HBSEhhBBCiIejgpAQQgghxMNRQUgIIYQQ4uGoICSEEEII8XBUEBJCCCGEeDgqCAkhhBBCPBwVhIQQQgghHo4KQkIIIYQQD0cFISGEEEKIh6OCkBBCCCHEw1FBSAghhBDi4aggJIQQQgjxcFQQEkIIIYR4OCoICSGEEEI8HBWEhBBCCCEejgpCQgghhBAPRwUhIYQQQoiHo4KQEEIIIcTDUUFICCGEEOLhqCAkhBBCCPFwVBASQgghhHg4KggJIYQQQjwcFYSEEEIIIR6OCkJCCCGEEA9HBSEhhBBCiIejgpAQQgghxMNRQUgIIYQQ4uGoICSEEEII8XBUEBJCiBNJSUnBY489hscffxxKpRKhoaFYvXo1dDodFi1aBB8fH8TFxeGXX34BALAsi8WLF6Nv376QyWRISEjABx980OKaCxcuxJw5c/B///d/CA8PR2BgIP7yl7/AZDJZz1GpVLjlllsgk8nQt29fbNy4EX369MGqVaus51y+fBnXXXcdpFIpEhMT8euvv4JhGGzbts16zrlz5zBlyhTIZDIEBgZi2bJl0Gq1dn3PCCG9RwUhIYQ4mXXr1iEoKAgnTpzAY489hkcffRTz58/HhAkTcObMGcyYMQMPPPAA9Ho9LBYLoqKisGnTJly8eBGvvPIKXnzxRWzatKnFNQ8cOIC8vDwcOHAA69atw9q1a7F27Vrr4w8++CDKy8tx8OBBbN68GatXr4ZarbY+brFYMGfOHHh7e+P48eNYvXo1/vGPf7S4h16vx0033QSlUomTJ0/ihx9+wK+//oq//vWvdn2/CCE2wBFCCHEakydP5q677jrr12azmZPL5dwDDzxgPaZSqTgA3NGjR9u8xvLly7k77rjD+vWCBQu42NhYzmw2W4/Nnz+fu+uuuziO47hLly5xALiTJ09aH8/JyeEAcO+//z7HcRz3yy+/cCKRiFOpVNZz9u3bxwHgtm7dynEcx61evZpTKpWcVqu1nrNr1y5OIBBwFRUVPXg3CCGOQi2EhBDiZJKTk63/LxQKERgYiKSkJOux0NBQALC24H366acYNWoUgoODoVAo8Pnnn6O4uLjFNQcPHgyhUGj9Ojw83Pr8rKwsiEQijBgxwvp4fHw8lEql9eusrCxER0cjLCzMemzMmDEt7nHp0iUMHToUcrncemzixImwWCzIysrq/htBCHEYKggJIcTJiMXiFl8zDNPiGMMwAK50427atAlPPPEEHnroIezduxdnz57FokWLYDQaO72mxWIBAHAc12aOa49zHGe9b3s6Oqez5xJC+EUFISGEuLC0tDRMmDABy5cvx/DhwxEfH4+8vLxuXWPgwIEwm81IT0+3HsvNzUVdXV2Lc4qLi1FZWWk9dvLkyRbXSUxMxNmzZ6HT6azHDh8+DIFAgAEDBnTzlRFCHIkKQkIIcWHx8fE4deoU9uzZg+zsbLz88sutCrXODBw4EDfeeCOWLVuGEydOID09HcuWLYNMJrO27E2bNg1xcXFYsGABMjMzcfjwYeukkqvn3HfffZBKpViwYAHOnz+PAwcO4LHHHsMDDzxg7eYmhDgnKggJIcSFPfLII7j99ttx1113YezYsaipqcHy5cu7fZ3169cjNDQUkyZNwty5c7F06VL4+PhAKpUCuDKWcdu2bdBqtRg9ejSWLFmCl156CQCs53h7e2PPnj3QaDQYPXo05s2bh6lTp+Kjjz6y3QsmhNgFw7U3eIQQQojHKi0tRXR0NH799VdMnTq1zXMOHz6M6667Drm5uYiLi3NwQkKILVFBSAghBL/99hu0Wi2SkpKgUqnw7LPPoqysDNnZ2dYJKVu3boVCoUD//v2Rm5uLv//971Aqlfj99995Tk8I6S0R3wEIIYTwz2Qy4cUXX0R+fj58fHwwYcIEfPPNNy1mJzc2NuLZZ59FSUkJgoKCcOONN+K9997jMTUhxFaohZAQQgghxMPRpBJCCCGEEA9HBSEhhBBCiIejgpAQQgghxMNRQUgIIYQQ4uGoICSEEEII8XBUEBJCCCGEeDgqCAkhhBBCPBwVhIQQQgghHo4KQkIIIYQQD0cFISGEEEKIh6OCkBBCCCHEw1FBSFzG2rVr4e/vz3cMQgghxO1QQUi6ZeHChZgzZw7fMQghhBBiQ1QQErswmUx8R+iytrK6Un5CCCGkt6ggJG368ccfkZSUBJlMhsDAQNx444145plnsG7dOmzfvh0Mw4BhGBw8eBCFhYVgGAabNm1CSkoKpFIpNmzYgJqaGtxzzz2IioqCt7c3kpKS8O2331rvsWPHDvj7+8NisQAAzp49C4Zh8Mwzz1jPefjhh3HPPfe0yLZt2zYMGDAAUqkU06ZNQ0lJSYvHd+zYgZEjR0IqlaJfv35YuXIlzGaz9XGGYfDpp59i9uzZkMvleOONN7BixQoMGzYMX331Ffr16weJRIJ169YhMDAQBoOhxfXvuOMOPPjggzZ7r4n9GI1G6HQ61NbWQq1Wo6ysDIWFhajOU6HpYg2azldDn1EFXboaulMV0B5XQXu0HI2/l6HxcBm0J1TQp6vRdL4azdm1MBTUw1jaCFOlDmZNM1itERYDC87C8f1SCSGkV0R8ByDOR6VS4Z577sE777yDuXPnorGxEWlpaXjwwQdRXFyMhoYGrFmzBgAQEBCA8vJyAMBzzz2H9957D2vWrIFEIkFzczNGjhyJ5557Dr6+vti1axceeOAB9OvXD2PHjsWkSZPQ2NiI9PR0jBw5EqmpqQgKCkJqaqo1y8GDB/HEE09Yv9br9XjzzTexbt06eHl5Yfny5bj77rtx+PBhAMCePXtw//3348MPP8T111+PvLw8LFu2DADw6quvWq/z6quv4q233sL7778PoVCINWvWIDc3F5s2bcLmzZshFArRv39//P3vf8dPP/2E+fPnAwCqq6uxc+dO7N69275/CaRNzc3N0Ol01j96vb7dr/V6vfXDxp9NjB6BQTlK24YTMWDEQgjEAjBeQjBiARiJEEIfLwh9vCDw9bL+v9DXCwIfLwjlYttmIISQHqKCkLSiUqlgNptx++23IzY2FgCQlJQEAJDJZDAYDAgLC2v1vMcffxy33357i2NPP/209f8fe+wx7N69Gz/88APGjh0LPz8/DBs2DAcPHsTIkSOtxd/KlSvR2NgInU6H7OxspKSkWK9hMpnw0UcfYezYsQCAdevWYdCgQThx4gTGjBmDN998E88//zwWLFgAAOjXrx9ef/11PPvssy0KwnvvvRcPPfRQi6xGoxFff/01goODW5y3Zs0aa0H4zTffICoqqkUmYjtGoxG1tbXQaDTQaDTW/6+trUVDQwNYlrXJfVi0XSj2ipkDZzaDberGc4QMhH4SiPwlEPpLIFRKr/y/UgKRvxRCfwkYEXXkEELsjwpC0srQoUMxdepUJCUlYcaMGZg+fTrmzZsHpbLjFpVRo0a1+JplWfzrX//C999/j7KyMhgMBhgMBsjlcus5KSkpOHjwIJ588kmkpaXhjTfewObNm/H777+jrq4OoaGhGDhwoPV8kUjU4j4DBw6Ev78/Ll26hDFjxuD06dM4efIk3nzzzRY5mpubodfr4e3t3WZWAIiNjW1RDALA0qVLMXr0aJSVlSEyMhJr1qzBwoULwTBMF95J0haDwYCqqqoWBd/V/9dqtQ7JYJeCsCdYDqymGaymue3HGUDo4wVRmBziMDm8wuUQh8shCpaBEVKhSAixHSoISStCoRD79u3DkSNHsHfvXvznP//BP/7xDxw/frzD511b6AHAe++9h/fffx+rVq1CUlIS5HI5Hn/8cRiNRus5KSkp+PLLL5GRkQGBQIDExERMnjwZqampqK2txeTJk1vdp61i7Ooxi8WClStXtmqpBACpVNpu1vaODR8+HEOHDsX69esxY8YMnDt3Djt27OjgXSDX0ul0UKlUqKiogEqlgkqlgkaj4TuW8xSEneEAtsEItsEIQ3btH8eFDMQh3hCHXSkQr/4RKrz4y0oIcWlUEJI2MQyDiRMnYuLEiXjllVcQGxuLrVu3wsvLq8vddmlpaZg9ezbuv/9+AFeKtZycHAwaNMh6ztVxhKtWrcLkyZPBMAwmT56Mt956C7W1tfj73//e4ppmsxmnTp3CmDFjAABZWVmoq6uztiKOGDECWVlZiI+Pt8XbAABYsmQJ3n//fZSVleHGG29EdHS0za7tTmpra62F39X/NjY28h2rTSxs0/XMG5aDSaWDSaUD0v84LFCIrxSHVwvFMDnEId7U7UwI6RQVhKSV48ePY//+/Zg+fTpCQkJw/PhxVFVVYdCgQWhubsaePXuQlZWFwMBA+Pn5tXud+Ph4bN68GUeOHIFSqcS///1vVFRUtCgIr44j3LBhAz744AMAV4rE+fPnw2QytRqrJxaL8dhjj+HDDz+EWCzGX//6V4wbN85aIL7yyiu49dZbER0djfnz50MgECAzMxPnzp3DG2+80aP347777sPTTz+Nzz//HOvXr+/RNdyNyWRCaWkpioqKUFRUBJVKhebmdro9nRDLuUgLYTdZtCYYcupgyKn74+D/WhO9Yn0h6esHST8/CH2oJZEQ0hIVhKQVX19fHDp0CKtWrUJDQwNiY2Px3nvv4eabb8aoUaNw8OBBjBo1ClqtFgcOHECfPn3avM7LL7+MgoICzJgxA97e3li2bBnmzJmD+vr6FufdcMMNOHPmjLX4UyqVSExMRHl5eYviEQC8vb3x3HPP4d5770VpaSmuu+46fPXVV9bHZ8yYgZ07d+K1117DO++8A7FYjIEDB2LJkiW9ej/uuOMO7Nq1y2MX5TYajSgpKUFRUREKCwtRVlZmswkefDBzrpu9265pTdQdUwEARIFSePX1u1Ig9vWDKEDayUUIIe6O4TiOFtAipBPTpk3DoEGD8OGHH/IdxSEMBgOKi4utBWB5eXm7S7i4ov4R/TA5vy/fMZyGMEqGs8whxAwZitikYVAEBPIdiRDiYNRCSEgHNBoN9u7di99++w0fffQR33HshuM4lJaWIjs7G3l5eVCpVHDnz4oe1ULYBTquHhfTfsPFQ78BAAIio63FYcyQZHjJvHlOSAixNyoICenAiBEjUFtbi7fffhsJCQl8x7Epo9GI/Px8ZGVlITs7Gzqdju9IDsNaqCC8lkpb0OJrTVkJNGUlOLtnJ4QiEaIHJyN+9DjEjRxLrYeEuCnqMibEgzQ0NCA7OxtZWVkoKChosaWfJ4kIDsfMkkS+YziNPbXrUFdX0fmJDIOwuP6IHzUO8aPHIzCKZtwT4i6oICTEzalUKmRlZSErKwsqlYrvOE4hNDAEs8qS+I7hFJgAMb473bMZ+MrwSMSNGov40eMR0T8BjICWtyHEVVFBSIgbqqqqQkZGBs6fP4+6ujq+4zidQP8AzK0YzncMp9AUacBPv6/q9XW8/fwRN3IM+o+ZgNjk4RAIhb0PRwhxGCoICXETjY2NOH/+PDIzM6klsBN+vn6Yr269faEnyvO5gFOZO216TW8/fwy6bjISJ01FSJ9+Nr02IcQ+qCAkxIUZjUZcunQJmZmZyM/Pd+uZwbakkCtwd81YvmPwjwF+rvoSjY3VdrtFcGxfJE6agkHXpUDu3/F+6IQQ/lBBSIiLsVgsyM/PR2ZmJi5fvtxib2jSNVKpFPfXTeQ7Bu+YQDG+O9Wz8YPdJRAK0WfoCCROmoq4UWMhEosdcl9CSNfQsjOEuIiamhqcOnUK586dg1ar5TuOS/PU2dV/ppU2OOxeFpZF/pmTyD9zElK5AgPGX4fBk6ciYsCgzp9MCLE7aiEkxIlZLBbk5OTgxIkTyMvL4zuO22AYBoubpvAdg3fZikykn/uF1wzK8EgMnTYTQ26YBok3LYBNCF+oICTECTU1NeHMmTM4deoUamtr+Y7jlhaZpkDIMnzH4I8A2FmxGjqdc3x/eclkSJw0FSNungVleCTfcQjxOFQQEuJEVCoVTpw4gXPnzlG3pp0t4KZAbPDggjBIhO9Pvsl3itYYBn2HjsCIm29D7NARYBgP/jsixIFoDCEhPGNZFhcvXsSJEydQUlLCdxyPYRFyADy32Gj0quc7Qts4DgVnT6Pg7GkERERh+E2zMHjyVIilUr6TEeLWqIWQEJ7o9XqcOHECp06dokkiPLjX6wZ4N3juzhqXvdORcWEv3zG6ROItx5AbbsTwm2bBLySM7ziEuCUqCAlxsIaGBhw5cgSnT5+GyWTiO47Huts7BQqNh+6mIWCwvfxjNDc5bpaxLTCMAHGjxmDs3LsQFtef7ziEuBXqMibEQWpqanD48GFkZGSAZVm+43i8K13GHipIiOY81yoGAYDjLMg9eQy5J4+h7/BRGD/vHoTHJ/AdixC3QAUhIXamVqtx6NAhXLhwgXYScSJmgef+XTQInWNmcW8UpJ9CQfop9Bk6AuPn3UPrGRLSS1QQEmInlZWVSE1NxcWLF/mOQtrgyS2EJbWX+I5gM4UZZ1CYcQaxycMx/o57EDkwke9IhLgkKggJsTEqBF0D66kFoZBBdt5xvlPYXFFmOooy0xEzJBnj77gXUYlD+I5EiEuhgpAQG9FoNNi/fz8uXLjAdxTSBRbGQwvCICGMuXq+U9hN8flMFJ/PRHRiEsbPuwfRg5P5juRQKSkpGDZsGFatWtXja6xduxaPP/446urqbJaLOD8qCAnpJb1ej9TUVJw6dYomi7gQVmDhOwIv6gU1fEdwiJKL51Dy2jnEDBmKyQ8sRkiffnxHchl33XUXZs6cyXcM4mBUEBLSQ2azGcePH0daWhqam5v5jkO6ifXQFsLiGs8aylB8PgMbnn8cg1OmYuJdD0ChDOA7klMzmUyQyWSQyWR8RyEO5rmrshLSQxzHITMzEx999BH27dtHxaCL8sgWQrEAOfkn+E7hcBxnwfkD+/DV35fh6I/fwmRwj59ZnU6HBx98EAqFAuHh4XjvvfdaPM4wDLZt29bimL+/P9auXQsAKCwsBMMw2LRpE1JSUiCVSrFhwwasXbsW/v7+1ufk5eVh9uzZCA0NhUKhwOjRo/Hrr7+2uG6fPn3wz3/+Ew899BB8fHwQExOD1atX2+NlEzuhgpCQbigsLMTnn3+OLVu20PgaF8cynlcQcoEMTCb3KIZ6wmRoxpEfvsFXTzyCC6n7XX4ZqGeeeQYHDhzA1q1bsXfvXhw8eBCnT5/u9nWee+45/O1vf8OlS5cwY8aMVo9rtVrMnDkTv/76K9LT0zFjxgzMmjULxcXFLc577733MGrUKKSnp2P58uV49NFHcfny5R6/PuJY1GVMSBdUVVVh3759yM7O5jsKsRFP7DKuQzXfEZyCtqYauz9+H+m7dyDlgSUuOSNZq9Xiyy+/xPr16zFt2jQAwLp16xAVFdXtaz3++OO4/fbb23186NChGDp0qPXrN954A1u3bsVPP/2Ev/71r9bjM2fOxPLlywFcKTLff/99HDx4EAMHDux2JuJ4VBAS0oGmpib89ttvOH36NCwWz2tRcmcsPG8CUFHVeb4jOJXK/Fx8v/J5xI8ej0n3L4IyLILvSF2Wl5cHo9GI8ePHW48FBAQgIaH7O7eMGjWqw8d1Oh1WrlyJnTt3ory8HGazGU1NTa1aCJOT/5jRzTAMwsLCoFaru52H8IMKQkLakZmZiT179kCn0/EdhdgBC88q8BmxALl5p/iO4ZRyTx5FQfpJDLtpFibOvw9iqZTvSJ3qSnc3wzCtzmtr/3S5XN7hdZ555hns2bMH//d//4f4+HjIZDLMmzcPRqOxxXlisbjV/emDtOugMYSE/ElNTQ3Wr1+PLVu2UDHoxjytIGQDAZY1dn6ih2LNZpzeuRVrn16OgnTnL5zj4+MhFotx7Ngx67Ha2toWw1qCg4OhUqmsX+fk5ECv7/4alGlpaVi4cCHmzp2LpKQkhIWFobCwsFf5ifOhFkJC/sdsNuPw4cNIS0uD2WzmOw6xM7OHFYS1oK67rmioUmPLv1YgYfz1uGHhMsj9lXxHapNCocDixYvxzDPPIDAwEKGhofjHP/4BgeCPdp4pU6bgo48+wrhx42CxWPDcc8+1asXrivj4eGzZsgWzZs0CwzB4+eWXqeXPDVFBSAiuzB7euXMnqqtp0L2nYDnPGkNYVHmO7wguJetoGooy0zHp/oeQNGU633Ha9O6770Kr1eK2226Dj48PnnrqKdTX11sff++997Bo0SJMmjQJERER+OCDD3o0C/n999/HQw89hAkTJiAoKAjPPfccGhoabPlSiBNgOFefd09IL+j1euzduxdnz57lOwpxsEFR/TExN4bvGA7BSATYlP0OLBbPKoJtJToxCTcu/SsCIiL5jkKI3VALIfFY6enp2LdvX4/G1BDXx3pQcWQOABWDvVBy8RzWP/tXjJ17J8bMng+hiH51EvdD39XE49TV1WH79u0oKCjgOwrhkdmDuoxrLarOTyIdYk0mHNn0DbKOpGH6w48hYsAgviMRYlPUZUw8SkZGBn7++WcYDAa+oxCexYRGYXpR99dsc0UnuL0oKEznO4b7YBgMn3Errr9vIcReEr7TEGIT1EJIPIJer8fOnTtx8eJFvqMQJ+EpXcaMTIiCS2f5juFeOA7pu3eg+HwGZj72NEL69OM7ESG9RusQEreXl5eHTz75hIpB0oLZQwpCs5IFqCPILmpKi7HxH0/i1I4tLr8vMiHUQkjclslkwq+//orjx4/zHYU4IU9pIawx0/hBe2LNZqRu+AoFGWdw8/InoAgI5DsSIT1CLYTELalUKqxevZqKQdIuM+sZi4/nl5/lO4JHKD53FuuefQw5x4/wHYWQHqFJJcStWCwWHD58GAcOHKCV9EmHFHIF7q4Zy3cMu2K8Rfju4j+py9jBhtwwHVMWLnOJPZEJuYq6jInbqKurw5YtW1BcXMx3FOICPKGF0ORvpmKQB+cP7EXZ5fOY+denERY/gO84hHQJdRkTt5CTk4PPPvuMikHSZZ6wX3W1qZTvCB6rVlWOb195Bse2fA+OeiuIC6AWQuLSOI5DamoqUlNTaZYf6RaWdf9JJbllZ/iO4NEsLIvD33+N0kvnccvfnoHMx5fvSIS0i1oIictqamrCxo0bcfDgQSoGSbdxHAdW6L7fN4xChLKyS3zHIACKMtPx9fN/R0VuNt9RCGkXFYTEJalUKnz22WfIycnhOwpxYRY37iMx+hr5jkCu0Vhdhe9WPIeMfb/wHYWQNrnxP4fEXaWnp2PXrl0eMQaM2JdFyAFg+I5hF2pDCd8RyJ+wJhN+/eK/UOVcxtQly2nbO+JUqCAkLsNsNuOXX37B6dOn+Y5C3ATrxv8C5pae4jsCaceF1P2oKi7E7Kdfgm9QMN9xCAFAXcbERdTV1eGrr76iYpDYlEXknmMIGR8RKipy+Y5BOqAuyMM3Lz6B0kvn+Y5CCAAqCIkLyM/Px+rVq1FeXs53FOJmLG46qcTga+A7AukCfX0dfnj9JaTv2cl3FEKoICTO7cyZM9iwYQP0ej3fUYgbMgvcsyBUN9F6nK7Cwprx21efYs+nH4I1m/iOQzyYG4+gIa6M4zj89ttvSEtL4zsKcWPu2kKYXXyC7wikm84f2Iu6ynLMfvolSOUKvuMQD0QthMTpmM1mbNmyhYpBYnfuuA4h4ydGVVUh3zFID5RePI/vXnkWDdVqvqMQD0QFIXEqer0eX3/9Nc6dO8d3FOIBLIz7FYRNiia+I5BeqCktxrcvPQ11YT7fUYiHoYKQOA2NRoMvv/wSRUVFfEchHoIVuN8es5W6Ar4jkF7S1mrw/YrnUJiZzncU4kGoICROobS0FF988QVqamr4jkI8COuGLYTZRcf5jkBswNjUhK3/WokLqfv5jkI8BBWEhHcXL17E2rVraSYxcTh3ayFk/EXQaMr4jkFsxMKasfvj93F087d8RyEegGYZE14dOXIE+/btA8e5X0sNcX4s414FoV5OH6rc0ZFN36Cxpho3Ll4OgVDIdxzipqggJLzZs2cPjh49yncM4sHcrcu4QksTEdzVuf17oNXUYNbjz0MslfIdh7gh6jImDsdxHHbt2kXFIOEdC5bvCLbDAFkFx/hOQeyoIP0Uvl/5AvQN9XxHIW6ICkLiUBaLBdu3b8fJkyf5jkIIWLhPlzGjFKO+vpLvGMTOKvNz8MNrL0JfX8d3FOJmqCAkDsOyLLZu3YqzZ8/yHYUQAO5VEOpkjXxHIA5SXVKE71e+AF1dLd9RiBuhgpA4BMuy+PHHH2nBaeJUzG5UEJY35PIdgTiQpqwE3698AVoNLdVFbIMKQmJ3ZrMZ3333HS5dusR3FEJaYDk3GUPIANn5NH7Q09SWl+L7lc+jobqK7yjEDVBBSOzKaDRi48aNyMnJ4TsKIa24TZdxoBiNWmop8kR1FSpsWvk8Gqpo/2PSO1QQErsxGAzYsGED8vNpKQzinFiLe7QQ6rwa+I5AeFSvrsR3K55DXWUF31GIC6OCkNhFU1MT1q9fj+LiYr6jENIuM+ceLYRl9dl8RyA8a6yuwvcrn0etinaqIT1DBSGxuebmZnz99dcoK6N/mIhzYzkz3xF6TwBk5dOangTQ1lRj08oXoCkv5TsKcUFUEBKbMplM+Pbbb1FeXs53FEI6ZXaHLuNAEfR6WqiYXKGt1eD7Fc+jpqyE7yjExVBBSGyGZVn88MMPKCoq4jsKIV3Csq5fEDaKqRgkLenr67D5zVdo9jHpFioIiU1wHIdt27YhO5vGMhHX4Q4thGV1WXxHIE6osaYKm998mba5I11GBSGxiZ9//pkWnSYux+zqLYRCBll5NH6QtE1TXoqt/1oBY3MT31GIC6CCkPTa/v37aW9i4pJY1sUnlQQK0dys5TsFcWIVeTnY/u4bYM0mvqMQJ0cFIemVI0eOIC0tje8YhPSIq7cQNgg1fEcgLqD4fAZ+/vD/wFncY5klYh9UEJIeO3PmDPbu3ct3DEJ6zOziLYQltbQdJOma7OOH8euXH/MdgzgxKghJj1y8eBE7duzgOwYhvWI2u3BBKGKQnXuc7xTEhWT+uhuHv/+a7xjESVFBSLotLy8PmzdvBsdxfEchpFc4joNF4Jrfx1ygAEYTTRYg3XNsy/c488tPfMcgTogKQtItlZWV2LRpk1us30YIALAivhP0TL2ghu8IxEUdWPc5LqUd4DsGcTJUEJIu02q12LhxIwwGA99RCLEZi8g1WwiLqy/yHYG4Ko7D7k8+QPH5TL6TECdCBSHpkqtb0tXX0yKnxL24ZEEoFiAnn8YPkp6zsGbsWPUv1FVW8B2FOAkqCEmnOI7Dli1bUFZWxncUQmyOFfKdoPu4QAZms5HvGMTFNTc2YNs7r8HYpOc7CnECVBCSTu1LO45Ll2h5C+KeWKHrtRDWMbRHLbGNmtJi7PrwXVqjkFBBSDq2Nb0Uy3fXwCd6EN9RCLELiwsWhIXq83xHIG4k/8xJpH27ju8YhGdUEJJ2nS2pw/Obz8FsAf6TowAbORwCAX3LEPfiai2EjJcAeQW0VSSxrZM/bcbFQ7/xHYPwiH67kzZVNjRj2fpTMJj/6Eb4Ok+E4oCRkEilPCYjxLZcbR1CNtAN9mAmTmnv6v+gPPsy3zEIT6ggJK00m1gsW38K6sbWy8v8Wgr8LkiGr5+Sh2SE2B7rYgVhraWS7wjETbEmE35670001lTzHYXwgApC0srzmzORUdr+8jIXNBy+bYiHMjTSgakIsQ9XKwgLK8/xHYG4MV1dLbb/3xswGWm9WU9DBSFp4bPUPGw7W97peVVNwH9Lw+EXPcABqQixH1bQ8ezKj45uQPTbk7Di1w/bPedocTqi357U6k9uTZH1nEMFJzFp9b1IfP9mPLHrTRhZk/WxBoMWk1bfi7KGjlv/GIkQ+UVnuvjKCOmZyvxc7P54Fd8xiIO56KZNxB5OFWrwzp6sLp9vZBl8kOOHRfHDgLIM2tuYuCQL0/737VnVJWzM+AmDguO6dK3Upd9A4eVt/TrQ2//KPTgL/rbzdSwfex8m9x2DR7a9jI0ZO7BwxO0AgLcOfor7h81GpG9oh9c3B1hgsdC2kcT+so+m4WS/eIy+7Q6+oxAHoRZCAgCo15vwt2/TwVq6X9StyRWjIngkvLy87JCMEPsyM20XWDqjHn/b8TrevulZ+El9unStQG9/hCgCrX+EgiurXmv09ajR1+HBEXOQENwX0/pPRE51IQDgZOk5ZFRkYfGoeZ1eX2OhXSWI4/z+3dc0ycSDUEFIAABP/5iB8vrmHj//l2IGJ7yGQeHrZ8NUhNgfy7TdZfzSvvcxJW48ru8zqsvXunntYoz8aA7u/u5xHLmma/dqoXio4CSaTAacKMnEoJA4GFkTXtz7Ht6a/pS1eOxIgepsl7MQ0lsW1oxdH76DZp2W7yjEAaggJFh7uAD7LvZ+5uLZag6btQlQhoTbIBUhjsGidav49ov7ca4iG89PXtala4TIA/H2jGfw2ZzXsXruG+gXEIO7v3sCx0rOAgAYhsEns1figyPrMfXLBzA4tD/uSroFHx/7BhNjR0IqkmDuhuWY/Pl9WHt6c5v3YGRCFBRn9Ph1EtITDVVq7PnkA75jEAegMYQe7kJ5Pf75i+26BFR6Dp+UR+HRPnLUluba7LqE2AuLll3G5Q2VWLH/Q3xz13uQiiRdukZcYAziAmOsX4+MHAJVgxqfnfgO46KHAQDGRCVj14LV1nPyNSXYfGEPdi/8EvM2PobFo+Yjpe9Y3PjVAoyNHoZBIS3HLZqVLEDjdAkPck8eRfruHRh+0yy+oxA7ohZCD6YzmPHYxnQYzbbdw7LZDLyfq4Q4Osmm1yXEHli0/P7PrMhGtb4WM9cuRZ93bkCfd27AsZKz+Or0ZvR55wawXZzUMTwiEYWa0jYf4zgOz+1+Fy/f8BdYOA7nK3NwS0IKguRKjIseZm1ZvFa1SdXt10aIraRu+AqVBXl8xyB2RC2EHuzlbeeRX62z2/U/z5FiduwohGgyYDKZOn8CITz4c0F4XexI7HtobYtjT/38L8QHxuDRsfd2aawfAFyozEGIIrDNx77L3AWlzBfT+1+HuuZGAIDJYobsf/9tq+jML6flZgh/WJMJuz54G/e/tQpeMu/On0BcDrUQeqjNp0uxJb3M7vfZXsQgw3sY5IquzdIkxNFYrmVBqJB4Y2BwvxZ/vMVSKKW+GBjcDwDwr9TP8PjON63P+eLkJuzOTkOBpgRZVQX4V+pn+Dk71bqszLWqdbX48Mh6rLzx7wAAf6kP+gfG4suTP+B02XkcLjqNkZFDWjyHkYtQXHre1i+dkG6pVZVj3+f/5TsGsRNqIfRA+VVavLzdcb9cTlQCKsUg3BFUhLpq2naLOBczur+uX6W2psUi0iaLGW8c+BgV2ipIRRIMCOqLdfPexpS48a2e++r+D/HwmLsQ7hNsPfbezBfw5K5/4qvTm/HImHswPCKxxXOMftTCTpzD5cOpiB6cjOSpM/iOQmyM4Wg1YY9iMLOY898juKRqcPi95WJgWUwNasvyHX5vQtozIKIfJuX35TtGh1RBJTh0ciPfMQgBAIi8JLjvn/9GUHQs31GIDVGXsYd5c9clXopBANCZgFX5AZBGD+n8ZEIcxMzZdlKVPeSV0fhB4jzMRgN2rnobZqOR7yjEhqgg9CC7z1dg/dGizk+0I45j8GmODNrwkRCJaMQC4R/LmfmO0CFGIUJZOe0WQZxLTWkxjvzwDd8xiA1RQeghqhoNeH5LJt8xrH4sEOCSz3DIvOV8RyEezuzkewMb/KgVhjinUzu3QpWbxXcMYiPUROMhVvx0AXV65xqYflgFVPgNxq0B+ajXVPMdx2UVFRXhyJEjKC8vh1arxV133YWBAwdaHz948CDOnz+PhoYGCIVChIeHY8qUKYiKiurS9c+fP4/NmzcjISEBd999t/V4ZmYm9u/fD6PRiOHDh2P69OnWx+rq6vD1119j2bJlkEi6trgzX1jWuQvCquYSviMQ0ibOYsGeTz7A/f/6ACKxmO84pJeohdAD7L1QgV3nnHNR27x6Dmuq+yIgggYn95TRaERoaChmzpzZ5uOBgYGYOXMmHn30USxatAj+/v7YsGEDdLrO16Csq6vD3r17ERMT0+K4Xq/Hjh07MG3aNNx///3IyMhAdna29fFdu3bhxhtvdPpiEHD+FsLckpN8RyCkXTWlxTj6I014cgdUELq5hmaTQ5eY6YkGI7CqIBjymMTOTyat9O/fH1OmTMGgQYPafDwpKQn9+vWDUqlESEgIZsyYAYPBgMrKjpcAslgs2LJlC1JSUqBUKls8VltbC4lEgiFDhiAyMhJ9+/ZFVVUVAODcuXMQCoXt5nE2ZiduIWR8RaiopN0hiHM7+dNmVOTl8B2D9BIVhG7urZ8vo7LBwHeMTlk4Bv/NlsMQORwCAX1b2gvLsjh9+jQkEgnCwsI6PDc1NRVyuRwjRoxo9VhAQABMJhNUKhWamppQVlaG0NBQNDU14cCBA7j55pvt9RJsjmWdd1JJs4/z/+wScqXreBVYs3MNSyLdQ2MI3dix/Bp8d7KY7xjd8m2eCDdEjERC03k0NzXxHcdtZGdn48cff4TJZIKPjw8eeOABeHu3v/1UcXEx0tPT8cgjj7T5uEwmw5w5c7Bt2zaYTCYMHToU8fHx2L59O8aMGYO6ujp89913YFkWKSkpSEx03tZfZ24hVOsL+Y5ASJdUlxTh2ObvMPGuB/iOQnqICkI31Wxi8cKWc3DFZccPlAPlyiTcpMxFfa2G7zhuoU+fPnjkkUeg1+tx+vRp/Pjjj1iyZAnk8tazvA0GA7Zu3YpZs2Z1WDQOGjSoRbdwYWEh1Go1Zs6ciQ8//BB33HEHFAoFvvjiC8TGxrZ5L2dgduIWwuziE3xHIKTLTmz/EfFjJiC0bxzfUUgPUN+cm/pgfw4KqjufNOCssmo5rK+NQ0BYNN9R3IKXlxcCAgIQFRWF2bNnQyAQ4MyZthc7rq2tRV1dHb799lu89tpreO2115CRkYGsrCy89tpr0GhaF+lmsxm7du3CrbfeCo1GA4vFgj59+iAoKAiBgYEoLS2190vsMbPZOQtCxl+M6mrXauEnns3Cstjz8ftgnfRninSMWgjd0Pmyenx+yPW3h6ttBj4sDsPyODkaSmhhXlviOK7d5VaCgoLw6KOPtjj222+/wWg04qabboKfn1+r5xw6dAjx8fEIDw+HSqWCxfLH7h8sy8KZd8jkOA4WAQeBheE7SgtNcj3fEQjptqriQhzf+j0mzL+P7yikm6ggdDOshcPzWzJhtjjvL+DuMFuAD3N88GDcMIhUmS0KDXKF0Whs0WpXW1uLiooKyGQyyGQypKWlISEhAQqFAk1NTTh58iQaGhpajOvbunUrfHx8cOONN0IkEiEkJKTFPaRSKQC0Og4AarUaFy5cwMMPPwzgSkHJMAzOnDkDhUKB6upqRERE2OOl2wwrAgROtv5zpa6Q7wiE9MjxrT9gwLjraK9jF0MFoZv5Ii0f58v42avYntbniTE9eiT6NmTCYKCZl9cqLy/HunXrrF/v3bsXADB06FDceuutqK6uRkZGBvR6PWQyGSIjI7Fo0aIWxV19fT0YpvstZBzHYefOnZgxYwa8vLwAAGKxGHPmzMHPP/8Ms9mMmTNnwtfXt5ev0r4sIg4wOlcLYXbhMb4jENIjFtaMA2s/w/yX/8l3FNINDOfMfTmkWwqrdbjpg0NoNrlvK9qQAOAGcQ4a6+v4jkLcyH1eKZA1CPmOYcUoxfjuzBt8xyCkV2Y98TwGjLuO7xiki2hSiRt5Ycs5ty4GAeC8Bvi+oT+UoZF8RyFuhHWeWhAAoPd23QlhhFx18OsvYTI08x2DdBEVhG5iy5lSHM2v4TuGQ6ibgP+WhsM/agDfUYibYIXO1VFS0Ui7kxDX11hdhRPbf+Q7BukiKgjdQLOJxbt7sviO4VBGlsGqXD8Io4f2aOwbIdeyOFNByABZBTR+kLiHUz9tQb26gu8YpAuoIHQDnx/Kh6reM5vlv8zxQlXISIj/N6GBkJ5wphZCRilGfYOa7xiE2ITZZMSBdV/wHYN0ARWELq6q0YBPUz27e2lnEYPTkmFQ+Dj3TFbivCwC5ykIdbJGviMQYlN5p46h8OxpvmOQTlBB6OL+vS8bOqPz7sXqKGeqOGzRD4QyOIzvKMQFsU5UEJY35PIdgRCb+23d57SDiZOjdQhdWHZlIzadKuE7htMo13H4xBCNR/vIUVvq2a2mpHucpiBkgKz8I3yn6FBeVQ0OXs5HWW09GpoNWDhxJIZE/vFBbM/5bJwtKUedvhkiAYMopR9uSkpAbKCyS9dPLy7HN8fSMTgiFIuuG2U9fqaoDLsyL8PIshjTNxqzhv6xj7ZGp8fq1BN4fNpESMVi271YYjO15aU488tPGD3rdr6jkHZQC6ELe3PXJbBusiOJrTSbgfdzA+AVncR3FOJCWIGTLNcUKIJWW8t3ig4ZzSwi/H0xd8TgNh8P9pFj7ogheHrGJPxlygQo5d74/NAJaJs7X1Beo9NjZ8Yl9A0KaHFcZzBi06lMzBo6CEsnjcGpwlJcLK+0Pr759HnckpxAxaCTO7b5W2hrW++FTpwDFYQuKi2nCqnZVXzHcFqrc6SoDxsFkYgawUnnLIxzfLDSeTn/LkODwkNwc1ICkqLC23x8RGwkBoQGIVDhjTA/H9w2bBCaTWao6jseG2mxcNh4/CymD+6PQIV3i8dqtHrIxGIMi4lATIA/4kMCUdmgBXCl5VAkELSbhzgPY1MT0jau5TsGaQcVhC7IYuHw5q5LfMdwelsLGZyXj4BcruA7CnFyZsY5xuGW1mfzHcGmzKwFx/KKIRWLEOHf8aSvfRdzoJB4YWy/mFaPBfnIYTSzKKuth95gRImmDhH+vtAbjNhzIbvd1krifC6lHURVcSHfMUgbqPnEBf1wugSXK2gmYlccq+Sg8knE3KBC1FXTUh6kbSzjBF3GAgbZ+Uf5TmETF8srseFYOkxmFj4yCZZNHgu5pP2loQqqNThRUIInp1/f5uPeXmLcPWYovj2RARPLYmRsFBLCgvH9iQxcF98HNTo9vvr9FFiLBdMHD8DQaGotdFYcZ8Hv367D3Ode5TsK+RMqCF2M3mjGe3vdqxXB3ooaOXzRHItlMXJoygr4jkOcEAsn6DIOFEKf5/xdxl0RFxKIJ6ddD53RiOP5xfj66Bn8bepE+Eglrc5tNpmx8fhZzBuV1GHRmBQVhqSoPyav5KproKpvxNwRQ/Cvnw/gvnHD4SOV4MP9h9EvOKDNexHnkH/mJMouX0TkwES+o5BrUJexi/ksNR/qxs4HZ5OWtCbg/fxAeMcM4TsKcUIW8N9C2Ciu4zuCzUhEIgT5yBEbqMSdo4dCyDA4UdD2igg1Wh1qdU1Y8/spPPvDz3j2h59x+n+TRp794WdUa1vv62xmWWw5cx7zRiWhWqsDy3GICwlEiK8CQQo5ijV1dn6FpLfSvl3LdwTyJ9RC6EIqG5qx+lA+3zFcFscx+Dhbhrv6jYCiMgMs6xzjxgj/zE5QEJbWuu/2kxyujCdsS4ivAk/NmNTi2O5zWTCYzZg9fDD8ZbJWz9l3MRcDw4IRpfRDWW09LNwfLbwWjgPHOUGLL+lQ2eWLyD9zEv1GjOY7CvkfKghdyL/3ZqPJREVMb32fL8Sk8BEY3HwBTU16vuMQJ8CC558rIYMsFxk/aDCZW7TaabR6lNXWw9vLC94SMfZfzMXgyFD4SCXQG004kluEen1zi3F93x4/Cz+ZFDOTB0IsFCLcz6fFPWReV5aP+fNxAKiob0RGSTme+N94wxAfBRgAx/OL4SOVQN2gRbTS3/YvnNiUf1gULh2rQt/hHO1H7ySoIHQRxTV6/HimlO8YbuOQCij3G4JbAvJRr6nmOw7hGcvx3EIYKIQht3XXqDMqqa3HpwePWb/+KePKigej+kThjpFDoG7U4tSRUugMJsi9xIgO8MfyKeMRdk1xV6tv6lERwHEcfjx1DrcNS4Tkf0tKiUVC3D1mKLacuQDWYsHcEYPh5y3t5ask9uIbFAq/8BtQVRqOwkwGeWeqED8yhO9YBADDUdu6S3hhyzl8e6KY7xhux1/C4KHISmjKi/iOQng0KKo/Jua2Xu7EUeojGrD78Ce83Z8Qe5MrAxEYfQOqSqPBcX98GAiIkOPul8aAEVArId9oUokLqGxoxmZqHbSLOgOHVQXBUEQP6vxk4rb4biEsqaF1RYl7kvn4IiZ5NjjRA1CXxLQoBgFAU65DzunKdp5NHIkKQhfw+aF8GM38D3p3VxaOwUc5Cpgih0MgoB8JT8RaeBxDKGKQk3ecv/sTYgcSbzlikm+BULYI6pI4WMzt/9t6cmchLLQNK+/ot5+Tq9UZsZG6ih3imzwRCpUjIZW2ntVI3JuZ468g5AIFMJqaeLs/IbYklkgRkzwdEr/FUJckwGwSdvqcuko9ck5UOCAd6QgVhE5uzeEC6I00s9hRfisDDgmS4OsfwHcU4kB8thDWC2p4uzchtiIUixE95AZ4By2BumQIjM3dm7N6Zm8xLRfEMyoInZjWYMa6ozTZwdEuajh8UxeHgLAovqMQBzHzWBAWV1/g7d6E9JZAKERU4nXwDV+GqrLhMOjb322mI5pyHYrO04cjPlFB6MS+PlqE+iYT3zE8Uk0z8J/icPhGJ/AdhTgAb4uUiwXIyT/Bz70J6QWGESBy4Fgoo5ehWjUGTY293yowfS8Nj+ITFYROqtnE4svfad9dPpkswIc5vkDUMFo41c2ZLWZe7msJZGA2G3m5NyE9FT5gBIL6LUVN5UTo6m035ro8pw4VBfU2ux7pHioIndSmUyWo1tKexc5gba4YqqBR8JL0/hMwcU5mnloI61DFy30J6YnQfkMQOmAJaqtS0KiR2+UeZ6mVkDe0U4kTMrEWfJZKexY7k90lQHLgUEyWZKGxgT7Buhszy08LYZH6HC/3JaQ7gmMHQCSbiNpKpd3vlX+2CnVqPfxDvO1+L9IStRA6oW3pZSiro2UonE1mDYcftAlQhkTwHYXYmNns+IKQ8RIgr/CUw+9LSFcFRPZB5OAH0dhwq0OKQQDgOODsPmol5AMVhE7GYuHwSWoe3zFIOyr0HD4pi4R/VH++oxAb4qMgZAMBlqeWSUI64hcaiagh90Knn4ua8iCH3//ysQroG2hsraNRQehkdl+oQH6Va2xy76maWWBVrj+E0ck02cRNcBwHi8CxuwFpONquizgXn8AQxCTdCYPpTlSXhYEBP/++sSYLzh2k7VodjQpCJ7P2SCHfEUgXfZkjQXXISIjFYr6jEBuwOHhEdWFFpmNvSEg7vP2UiEm+HWbcC3VpFMDx/0H3XGopTAbalMGRqCB0IrnqRpwo0PAdg3TDjiIG6dLhkPv48B2F9JKl8x22bIaRCpFfeMZxNySkDVKFD2KSbwPj9SDUJX3AWZynJDDozLh0pJzvGB7Fef72CTYeL+E7AumBU1UctjcNgjI4lO8opBdYkeO2zTIrLeA4x3ZRE3KVl8wbMck3QyR/COqSeLBmB34a6obzqWV8R/AoVBA6iWYTi81naMyEqyrVcvhUFQNlZD++o5AeYh3YZaxhVY67GSH/I/KSICZpGiTKxVCXDILZ6JyF4FW1FXqU59TxHcNjUEHoJHZlqmibOhfXZAbezwuEJDqJ7yikByxCx7UQ5qvOOuxehAhFIkQPSYEiZCnUpUkwNbnOuOcLadRK6ChUEDqJjSdo3SV38VmOFA3hIyES0brvroR1UEHIyIQoLKEJJcT+GIEAUYkT4BfxMKrKRqBZ58V3pG7LO1OFZi01ljgCFYROIKuiEaeLavmOQWxoS4EAFxQj4C23z/ZOxPYsAscUhCYle2X1XULshWEQkTAagbEPo1o1DvpG1912kzVbcPkYDbFwBCoIncDG40V8RyB2cLSCwy+mwfAPDOY7CukCR7UQVpto5iSxn7D4oQiJXwqN+npo62R8x7GJi7/Tz4wjUEHIsyYjiy3pNEbCXRU0cPiyqg8CIvrwHYV0gnVQC2F+GS03Q2wvpO8ghCUsRl3NVDRUK/iOY1O1FXqUZVMvmr1RQcizHZnlaGym7avcWaMRWFUQBHnMYL6jkA44YqcSRi5CSdkFu9+HeI7A6DhEJC5EQ93NqFP78R3Hbi6kUSuhvVFByLONx2kyiSewcAz+m+2N5ogREAqde6kHT8Uy9m8hNPrR4HhiG8rwGEQOfgA67WxoVAF8x7G7/PQqNGlpf2N7ooKQRxfLG3C2pI7vGMSBvssXItdvBKQy9xjb407MjP1bCKuNtNYo6R2/kHBEJ90DffMdqCn3nPHJrNmCy0cr+I7h1qgg5NHGEzSZxBOllgP7uSHwUwbyHYVcwxGzjHNLT9n9HsQ9KQKCEJM8Hwbz3agqDQcD/vcbdjSaXGJfVBDyRG80Y3s6fXN7qpw6YL2mHwLCY/iOQv6HhX1bCBkfEcpV2Xa9B3E/Ml9/xAydC1ZwP9Ql0QDneYXgVXWVelTk1/Mdw21RQciTX85VoNFAk0k8Wa0B+LAoFD7Rg/iOQgCwdu4yNvjS+CfSdRK5AjHJt0IgXQB1cV9wLP26BoCcU5V8R3Bb9B3Gk5/P0UKbBDBbgP/kKMBGDodAQD+OfLJ3C6G6mSaQkc6JpVLEJN8EL5/FUJcMAGuiSWjXyj2tBmehhd3tgX4D8aCh2YS0nGq+YxAn8nWeCCUBIyGRSPmO4rHMdi4Ic4tp/CBpn1DsheghUyELWAp1SSJMBioE26KvN6Isp47vGG6JCkIe/HqxEkbW/jMaiWvZVwr8LkqGj58/31E8EgvWbtdmfEWoVOfZ7frEdQmEIkQPngSfsGWoKhsKY5OY70hOL+ckdRvbAxWEPKDuYtKeCzUcvmvoD2VoJN9RPA7L2e9DWrNPs92uTVwTwwgQOWgclNFLUVU+Cs1aL74juYy8dDVYalSxOSoIHayx2YRD1F1MOlDVBPy3NBx+0QP4juJRzHZsIazU0xJT5H8YBhEDRiKw3zLUVEyArp7WJO0ug86MkosavmO4HSoIHWz/JTWMZvpkQzpmZBl8kOMHJmooGMZzl5lwJHu2EGYXHbfbtYnrCI1LQmj8EmiqJkOr8eY7jkuj2ca2J+I7gKfZRd3FpBvW5HphZsxIRNVlwmikZUvsibXYp4WQ8RejpqDELtcmriG4TwJE0omorfTnO4rbKMiohtnIQuRFk29shVoIHUhrMONQdhXfMYiL+bmYwQmvYVD4uu/G9c7AzNmnIGyS6+1yXeL8AqP6ISJxARrrb6Fi0MZMzSwKz9XwHcOtUEHoQPsvVcJA3cWkB85Wc9isTYAyJJzvKG7LXi2EFdp8u1yXOC//sChEDbkfOt0caFS0RaW9ULexbVFB6EC7Mqm7mPScSs/hk/IoKKPi+Y7ilsx2KgizCmn8oKfwDQpFdPJdaDbMR3VZCN9x3F7R+RqYjPabDOZpqCB0EJ3BjFTqLia91GwG3s9VQhydxHcUt8Oytv/FwgSIUVdHHwTdnVwZiJjkeTBZ7kVVSSQAmgjmCKzJgrLLtXzHcBtUEDrI/stq6i4mNvN5jhS1oaMgFtMitrZitth+b3G9TGvzaxLnIfPxRUzybHCiB6AuiQHHUSHoaIXnaRyhrVBB6CA/U3cxsbHtRQwyvIdBrvDhO4pbMNuhhVDVSLuTuCOJtxwxybdAKFsEdUkcLGb6VcqXovO0rq+t0HexA+iNZhzMVvMdg7ihE5XADsNA+AeF8h3F5ZlZG7cQMsDlgmO2vSbhlVgiRUzydEj8FkNdkgCziZY84ZtWY0BNGbXE2wIVhA5wJLcGzSbqLib2UdwIfF4ZA2VkP76juDSz2bYFIRMgRmMDjRt2B0KxGNFDboB38FKoS4bA2ExL+DqTIuo2tgkqCB3gcB41aRP70pmAVfkBkEUP4TuKy7J1QaiVNNr0esTxBEIhohKvg2/4MlSVDYdBR2N2nREVhLZBBaEDHMmlb1ZifxzH4JMcGXThIyAUUldWd3EcB4vAdi355Y05NrsWcSyGESBy4Fgoo5ehWjUGTY0SviORDlTk1cOgN/Edw+VRQWhnVY0GZFVSSwFxnB8KhLjsOwIybznfUVyOxVY9gQyQnX/URhcjjhTefziC45agpnIidPUyvuOQLrBYOBRf1PAdw+VRQWhnR6i7mPDgsArYyw6GX0AQ31FcisVGDatMoBhaLa2P5kpC+w1B6IAlqK2+AQ01Cr7jkG6ibuPeo5GxdnY4lwpCwo+8eg5rmvpiSZQcmvIivuO4BFbE2eQ6jV51NrkOsb+gmP7wkl8HTYWS7yikF4ov1IDjODAMrQXZU9RCaGeHafwg4VGDEVhVEAx5TCLfUVwCa6OPyGV12ba5ELGbgMg+iBz8ILSNs6gYdANNjSaoC2l4Vm9QQWhHRTU6lNU18R2DeDgLx+C/2XIYIodDIKAf+Y5YhDZoIRQwuJxP6w86K7/QCEQNuRc6/VzUlNOQCndSmkXjCHuDfjvYEbUOEmfybZ4IBf4jIZXRQPn2sLYoCAOFaG5q6P11iE0pAoMRk3QnDKa7UF0WBob2G3Y7qtx6viO4NCoI7YjGDxJnc6AcOIAk+PoH8B3FKVkEvS8IG8V1vQ9CbMbbT4mY5NvBMvdBXRoF0H7DbkuVVw/OYptxwJ6ICkI74TgOR/OphZA4n6xaDt/UxSEgLJrvKE7HFi2EJZrLNkhCekuq8EFM8m1gvB6EuqQPOJZ+3bk7Y5MZ1bSNXY/RT4idXFQ1QKMz8h2DkDbVNAP/KQ6Db/RAvqM4Fba3LYRCBll5tP4gn7xk3ohJvhki+UNQl8SDNdMi7Z5ElVvHdwSX1a2CMCUlBY8//ridonTPwoULMWfOHL5jtIt2JyHOzmQBPszxgSVqGC3V8D+97jIOFMJo1NsmDOkWkZcXYpKmQaJcDHXJIJiNVAh6ovIcGkfYU9RCaCe0fzFxFetzxSgLGgmJhLbnYpnebV1XL6RZjo4mFIkQPWQyFCHLoC5NgqmJ9hv2ZNRC2HNdLggXLlyI1NRUfPDBB2AYBgzDoLCwEKmpqRgzZgwkEgnCw8Px/PPPt9gkPiUlBY899hgef/xxKJVKhIaGYvXq1dDpdFi0aBF8fHwQFxeHX375xfoclmWxePFi9O3bFzKZDAkJCfjggw+sj69YsQLr1q3D9u3brVkOHjwIADh37hymTJkCmUyGwMBALFu2DFqtY8cUmFgLThTQLwbiOvaWMDgiGgofP3++o/CKZXrXQlhSc9FGSUhnGIEAUYkT4BfxMKrKRqJZ58V3JOIE9A1G1FVSK31PdLkg/OCDDzB+/HgsXboUKpUKKpUKYrEYM2fOxOjRo5GRkYFPPvkEX375Jd54440Wz123bh2CgoJw4sQJPPbYY3j00Ucxf/58TJgwAWfOnMGMGTPwwAMPQK+/8pdosVgQFRWFTZs24eLFi3jllVfw4osvYtOmTQCAp59+GnfeeSduuukma5YJEyZAr9fjpptuglKpxMmTJ/HDDz/g119/xV//+lcbvmWdu1DeAL2Rdeg9CemtczUcvm/oD2VoJN9ReNOrFkIRg+z847YLQ9rGMIhIGI3A2IdRrRoHfSO1bJOWyqmVsEcYjuO6/JE4JSUFw4YNw6pVqwAA//jHP7B582ZcunTJOgbp448/xnPPPYf6+noIBAKkpKSAZVmkpaUBuNL65+fnh9tvvx3r168HAFRUVCA8PBxHjx7FuHHj2rz3X/7yF1RWVuLHH38EcKXFsq6uDtu2bbOe8/nnn+O5555DSUkJ5HI5AODnn3/GrFmzUF5ejtDQ0O69Oz20/mghXtl+wSH3IsTWvIQclvdtQF2p5+22cX3USCTk+vfouVyoEJuO/dO2gUgLYfFDYWHGoqGa9hom7Rs4PgxTF9DuTN3VqzGEly5dwvjx41sMSJ84cSK0Wi1KS0utx5KTk63/LxQKERgYiKSkJOuxq4WaWq22Hvv0008xatQoBAcHQ6FQ4PPPP0dxcXGneYYOHWotBq/msVgsyMrK6vkL7aaMEhrUSlyXkWWwKtcPwuihHjfZpDcthPUCGjdsLyF9ByEsYTHqaqZSMUg6VU4LVPdIrwrCtjaSvtrgeO1xsbjlIF+GYVocu3quxXLlH+NNmzbhiSeewEMPPYS9e/fi7NmzWLRoEYzGjpdx6Whja0f+YsssrXPYvQixly9zvFAVMhJiL88Zm8Wi5wVhURX1CthaYHQcIhIXoqHuZtSp/fiOQ1xEQ1UTdPUGvmO4nG4VhF5eXmDZP8bGJSYm4siRI7i21/nIkSPw8fFBZGTPxyGlpaVhwoQJWL58OYYPH474+Hjk5eV1mOVqnrNnz0Kn01mPHT58GAKBAAMGDOhxnu7QGczIq6KFMYl72FnE4LRkGBQ+vnxHcYieFoSMWICc/JM2TuO5lOExiBz8AHTa2dCoaFcd0n1VxY18R3A53SoI+/Tpg+PHj6OwsBDV1dVYvnw5SkpK8Nhjj+Hy5cvYvn07Xn31VTz55JMQCHre+BgfH49Tp05hz549yM7Oxssvv4yTJ1v+Y9unTx9kZmYiKysL1dXVMJlMuO+++yCVSrFgwQKcP38eBw4cwGOPPYYHHnjAYeMHz5fVg3bOIe7kTBWHLfqBUAaH8R3F7lj0bDIYG8iAZWkh+t7yDQ5HdNI90DffgZryYL7jEBdWXUINM93Vrart6aefhlAoRGJiIoKDg2EymfDzzz/jxIkTGDp0KB555BEsXrwYL730Uq9CPfLII7j99ttx1113YezYsaipqcHy5ctbnLN06VIkJCRYxxkePnwY3t7e2LNnDzQaDUaPHo158+Zh6tSp+Oijj3qVpzsyS2nsAnE/5ToOn6qioYyK4zuKXZl72EJYC3XnJ5F2KQKCEJM8Hyb2blSVhoOBZ41dJbZXXUothN3VrVnGpHN/3XgGOzNVfMcgxG4e7t8MQ8k5vmPYRWLUAEzI7f4ez2cEB5GTR0vOdJfM1x/BfW9AdVksLLTXMLEhv2AZ7n99PN8xXAr9BNrYxfIGviMQYlef5UhRHzYKIpGI7yg2x3Ld7zJmJALkFZyyQxr3JZErEJN8KwTSBVAX96VikNhcfXUTjM3mzk8kVvRTaENNRhaFNbrOTyTExW0tZHBBPhzecvdaAsRs6X5ByAYAlh48zxOJpVLEJN8EL5/FUJcMAGui/YaJnXBAdSmNI+wOKght6HJFA00oIR7jaCWwy5gI/6AQvqPYTE9aCDWWSjskcS9CsRdikqbCO3Ap1CWJMBmoECT2RxNLusf9+nx4dFFF3cXEsxQ1cviiORbLYuTQlBXwHafXetJCWFCRYYck7kEgFCFi4ARoG5KhLvWc9SyJc6CJJd1DLYQ2dIkKQuKBtCbg/fxAeMcM4TtKr7HdLAgZqRAFRel2SuO6GEaAyEHjoIxeiuryUWjWUjFIHI9aCLuHWght6JKKPo0Qz8RxDD7OluGufiOgqMxotWi8q+huC6E5wAKO6/nuJm6HYRDRfwSM7GjUVHjznYZ4OI1KBwtrgUBIbV9dQQWhjXAch8vUQkg83Pf5QkwKH4HBzRfQ1KTnO063mbtZyNaYaYmpq0LjkgDBOGiqfPiOQggAgDVZUFuhR2Cke01+sxcqCG2ktLYJOqNrtooQYkuHVEC53xDcEpCPek0133G6xcx2b5mKfNVZ+wRxIcGxCRDJJqK20p/vKIS0UlOmpYKwi6ggtJFijeu1hhBiL7n1HNY198NDEd7QqIr5jtNlbDcKQkYmRNGlTDumcW6BUf0g9b0eNapAgDpHiJOqr2riO4LLoI51G6GCkJCW6gwcPiwKhSJ6EN9Ruqw7XcYmJQt44EZP/mFRiBpyP3S6OVeKQUKcWL2aCsKuooLQRkqoICSkFbMF+ChHAVPkcAgEzv/Pjdnc9RbCalOZHZM4H9+gUEQn34Vmw3xUl7nP2pPEvVELYdc5/7/QLqKklr7pCGnPN3kiFCpHQiKV8h2lQxaLBRZB11r98srO2DmNc5ArAxGTPA8my72oKokEwPAdiZAuq6+m381dRQWhjVCXMSEd+60MSBMkw9dfyXeUDlm6sIkGoxChtOyi/cPwSObji5jk2eBED0BdEgOOo0KQuJ6mBiPtadxFNKnERkqpIOyx5pLzaDi+GcbKPLBaDYLn/gPeA8a3OMdUXYLa1DVoLj4PgIM4MAbBc56DyLftritjVRHqf/8GhopcsA1qKKcshe/o2S3O0V44gLrUdeBMzVAkT4fyhoesj5nrK1H5/csIX7AKAgmtp2YrFzUc1NL+eDCsHJqKUr7jtMki4gBTx8WP0c/koDSOJ/GWIzQ+BZqKeKhLaIs54voaqpsRFEUzjTtDLYQ2oDOYUaMz8h3DZXHGZohD+iHgxkfafNxUq0LFN89CHBCFsHvfQvii/8Bv4t1ghO3vfsCZDRD5h0E5eQGE8tYtUqy+Hprd/4HyhocQcudr0J7fD33eSevjNXs+hnLyQioG7aC6mcN/isPhG53Ad5Q2sV34mFzVXGL/IA4mlkgRkzwdEr/FUJckwGyiYpC4hwYaR9gl1EJoAyW11DrYG7K4UZDFjWr38bpD6yGLG9WiBU/sH9bhNSXhAyAJHwAAqE1d1+pxc10FGIk35IMmAQCkMckwVRcDcaOhu3gQjFAE74QJPXk5pAtMFuDDHF8sjB8GpiwDnBPN1mWFnWfJLT3lgCSOIRSLEZFwHRrqhkBdIuY7DiE2RxNLuoZaCG2gREPfbPbCcRY05Z+CSBmByu9fRsl/7oNq/ZPQZx/t1XVFAZHgTIYr3dRNjTCqsuEV3AdsUyPq0r5BwLS2WyuJba3NFUMVNApeEgnfUaw6G0PI+IigqshxTBg7EgiFiEq8Dr7hy1BVNhwGHRWDxD3RxJKuoYLQBmhCif1YdPXgjE1oOP4jZP1GIvTO1+E9YDyqtv4TzcXnenxdoVSBoFueQPXOf6Ni/ZOQD5kCWb+RqD3wJXxG3gpzfSXK1/wN5V8uh+7y7zZ8ReTPdpcAx0RD4ePrx3cUAP8bQ9gBg6/BQUnshGEQOXAMlDHLUK0ag6ZG5ynGCbGHhirH/Y5OSUnB448/3uZjCxcuxJw5c3p03bVr18Lf37/HubqCuoxtgNYgtB+OswAAZPHj4Dt6DgDAK7QfDGWX0Hj2F0hjknp8be8BE+A94I9u4ebiTJiqihAw7RGUr16GoFnPQChXQrX+SUijh0Ao9+/NSyEdyKzhoPZOwD0hpahVl/Oahe1k2Rl1k+uOHwzvPxwsNxo1lTTAnngOZ+ky/uCDD3o8POauu+7CzJkzbZyoJSoIbYAKQvsRevsCAiHEQdEtjosDo2Eotd2yH5zZBM3eTxB461Mw16rAWVhrsSkOiIRBlQXv+LE2ux9prULP4ZOySDzSV466Uv66ZFmBpcPHc0pOOCiJ7YT2GwKIxqG2ypfvKIQ4XKPGAIuFg0DA79JJfn497wWRyWSQyWQ2TNMadRnbAE0qsR9GKIYkrD/Mmpa7Qpg0ZRC2s+RMT9Qd+Q7SfiMhCYsHOAtg+WMLM85iBiwdFwnENppZYFWuP0TRQ3nL0NHC1IyvCGp1gQPT9E5QTH9EDFqE+trpqKdikHgozsKhqZGflUB2794NPz8/rF+/vkWX8Y4dO+Dv7w/L/363nD17FgzD4JlnnrE+9+GHH8Y999wDwDFdxlQQ2kAp7VLSKxZjE4yV+TBW5gO4sgagsTIf5gY1AMB37O3QXUpD49ndMNWWo+H0DjTlnoDPiD+az6t3vofa1LXWrznW9Mc1LWaw2hoYK/Nhqm3dHWmsKoL+8iH4X3c/AEAUEAUwAjRm7IU+7yRMNaXwCu9vx3eA/NkXOV7QhI6CWOz4iQ4ddRk3+zQ7MEnPKSNiETn4AWgbZ0FT4dwLgRPiCPp6xxeE3333He68806sX78eDz74YIvHJk2ahMbGRqSnpwMAUlNTERQUhNTUVOs5Bw8exOTJkx2Wl7qMe6laa4DeyHZ+ImmXsSIHld++aP269rcvAADyIVMRdMsT8B4wAYEzlqP+2A+o3b8aooBIBM99EdKowdbnmBuqAOaPzzesVgPV2r9Zv244sQUNJ7ZAEj0EYff+y3qc4zho9nwE5ZSlEHhd2VZNIJYgcObj0Oz7BBxrQsC0RyDyCbLb6ydt+6mIwajg4RgruQSdttFh9+2oy7hSX+iwHD3hFxoBn+AUVJWFoqmJdhYh5CpdvQHB8HHY/T7++GO8+OKL2L59O2644YZWj/v5+WHYsGE4ePAgRo4ciYMHD+KJJ57AypUr0djYCJ1Oh+zsbKSkpDgsMxWEvVStdfEZh05AGpOM2Od2dniOInk6FMnT23382iIPAER+oZ1eEwAYhkHY/e+2Ou4dPwbe8WM6fT6xr1NVHCoVgzAvqAi11ZUOuSfLtN9CmF143CEZuksRGIyAiBugLouEoYyh3YYJ+RN9g+NaCDdv3ozKykr8/vvvGDOm/d8jKSkpOHjwIJ588kmkpaXhjTfewObNm/H777+jrq4OoaGhGDhwoMNyU5dxL9Xr3XcLK0KcQYmWw+rKGCgj+znkfizTdgsh4y9Gjca5ttvz9lMiJvl2sMx9UJdGAbTfMCFtcmSX8bBhwxAcHIw1a9Z0OKs4JSUFaWlpyMjIgEAgQGJiIiZPnozU1FSHdxcDVBD2Wn0TFYSE2JvOBLyfFwhJdM+XGeqq9grCJrnO7vfuKqnCBzHJt4HxehDqkj7gWPqnnJCOOLKFMC4uDgcOHMD27dvx2GOPtXve1XGEq1atwuTJk8EwDCZPnoyDBw9SQeiKqCAkxHE+y5GiIXwkRCL7jXZpryBUafmfXewl80ZM8s0QyxdBXRIP1kz7DRPSFU1ax04qGTBgAA4cOIDNmze3u1D11XGEGzZssI4VnDRpEs6cOePw8YMAjSHsNSoICXGsLQUCjA8bgeGmC9DrbN9qx6LtgjC78JjN79VVIi8vhCdMQn11ItQl9M82Id3V1Oj439UJCQn47bffkJKSAqGw7Q9vN9xwA86cOWMt/pRKJRITE1FeXo5BgwY5MC3AcM60q7wLem9vFv7zWy7fMQjxOH19Gcz2KUBdTZVNrzsqOgnDclqucckEiPHd6Tdsep+uEIpEiBg4EY11SWjWeTn8/p4otzwTv2Z8j+LqHDToa7B0+koM7XsdAIBlzdhx8itcKDmBmgYVpF5yDIwcgdvGLoG/vP2VCI5l7caGg60nr72/+BeIRVf+Xk/m/Irtx7+A0dyM8Qk3Y+74h63n1TRW4KNdz+LZ2z+BzEtu41fsGQIjFbj7ZZoo2BH6qNlL1EJICD8KGjh82dwHS6Pk0JQX2uy6LFovI6WXaW12/a5gBAJEJIxDk244qspor2FHMpibEBkYh3EJN+GLfStaPGY0N6OkOgc3j7gfkYFx0BsasfnIx/hs98t47o5POryu1EuOV+5a2+LY1WJQ21SPjanv4f6UZxHkG45PfvkH+kcMxZDYcQCA79NWYfaYpVQM9kKzg7uMXREVhL1EBSEh/Gk0AqsKgvBofzl0xRdsck1zG13G5Q0O6gVgGEQMGAWTaSRqKrwdc0/SwuCYsRgc0/Y2lTKJAo/d2rKlb/7Ev+LdrX+BprESAT6h7V6XAeDrHdDmY9WNV1obR8ZfWa9uQMQwVNQWYUjsOJzM2Q+hQIxh/a7v2QsiAIAmHf2u7gwVhL1EBSEh/LJwDP6b7Y27+42AvDIDLNu7heJZ7k8FIQNkFdh//GBY/FBYmLHQqBV2vxexnSajDgwYyCQd/70ZTE14+Zt7wHEWRAbG4dbRixAddGUHpBC/SJjMBpRU5yBAEYqiqiyMG3gTdM0N2HVqLf4+6z1HvBS3ZjFzMDab4SWlsqc99M70EhWEhDiH7/KFSIkYiYFN59Dc1PPtJFmuZUHJBIjRmF/d23jtCuk7CAKvCahT93zje8IPk9mI7ce/wKj4KR1254b6x+D+lGcREdgPzUYdDp7bgn9v/ztemLcaIX5R8Jb44IEbnsP6A2/DZDZgzIBpSIwejQ0H38XkIXNQ01CBz3a/DNZixsxRD2J4P8cuR+IuTAaWCsIO0DvTS1QQEuI8DpZzKPdPws3KPNTX1vToGmZLy4JQK22wRbRWAqPjIFFcD01F292IxLmxrBlr9r8ODhbcef3fOzy3b2gi+oYmWr/uFzYEb29+BKnnt2H+xL8CAIb2vc46eQUAssvPolxTgDsnPoYV3z2IRVP/AV/vALy79S+ID0+Gj4z2qO4us7H9bSkJFYS9RjuVEOJcsus4VDX1w6IIOTSq4m4//88thGX1ObaKBgBQhsfAO2AyasqDoXPsXBViIyxrxpe/voaahgo8Nuv/uj3ZQ8AIEBucgKr6tne+MbFGbEr7AAumvICqhjJYLCz6RwwFAIT4RaGw8hKS+kzo9evwNGZj74aTuDtamLqXqIWQEOdTawA+LAqFT3T39wFt0UIoALLzbTN+0Dc4HNFD7oa++Q7UlAfb5JrE8a4Wg1X1Zfjrre9CIe1+Vz/HcSityYOvd2Cbj+8+vQGJMWMQHTwAFs4CyzUfUliLGZY/j3MlXUIthB2jFsJe0BnMMFtoGUdCnJHZAvwnxwcPxA2HWJUBi6VrvwzYawvCABF0ebW9yqEICEJA1A2oKo1CVRkD2m3YuRlMTaiqL7N+XdNYgdLqXHhLfOAnD8IX+1aipDoHj9z8JjjOgga9BgDgLfGBSCgGAKz/7V/wkwdh9tglAICfT61Hn9BBCPGLRLNRj4Pnt6K0Jhd3Xve3VvdXaQpxJu8gnp/3GYAr4w8ZhsGRyz/DVxaAyrpixIYk2PttcEtmE7UQdoQKwl6g1kFCnN/XeSJMixqJfo3nYDA0d3r+tS2EjV71Pb6vzNcfwX1vQHVZLNQl1BnjKoqqsvDhjqesX285emV9wbEDpmPmqAU4V3QEAPCvH5e1eN7fZr2HARHDAAAarRoM80fp32TU4ttD/0ajvhZSLzmiguLx+Kz30SekZQs2x3H49tC/cfuERyERywAAXiIJ7k95Fpt+/xBm1oQ7Jz4Gfzm1MPcEtRB2jHYq6YWcykZMe/8Q3zEIIV0wOJDBFFE2GuvrOjxP6afEHZUjAACXvdORcWFvt+4jkSsQGpeCmoo4sCbaa5gQZ3HTsiGIGxHS+YkeiloIe4F6iwlxHRdqOKhl/XF/aDlqK8vaPc/Mmq/8j4BBVjfGD4qlUoQPmIxa9UCoS6gQJMTZ0KSSjlE/Ri9woIqQEFdS1QT8tzQcftED2j2HvVoQBgnR3NT5kjNCsRdikqbCO3Ap1CWDYTJQMUiIMzKbqMu4I1QQ9gJ1thPieowsgw9y/MBEDW0xzusq8/92OmkQdjyZRCAUIWrwJPiELYO6dCgMerFd8hJCbIPGEHaMuox7gQpCQlzXmlwvzIwZiai6TBiNf2x8bzZfaSEsqb3U5vMYRoCIgWPQ3DQc1eUyh2QlhPSeibqMO0QFYS9QlzEhru3nYgbDg4dhouQytI1XuoctFgs4MYPsvOOtzg8fMBJmdhRqKrq3EDEhhH8sdRl3iArCXqAWQkJcX3oVh0p5Au4KKUWtWgUAMAdzMGbrreeExiUBgnGorfLhKyYhhNgVFYSEEI9XrgM+MUTh0T5y1JbmopqpAgAExyZAJJuI2kp/fgMSQnqtjSHD5Bo0qaQXqIWQEPfRbAbez1VCHJ2EKlM1IhMXoLHhFioGCXETjIAqwo5QC2Ev0BhCQtxHSkAtFgeeA+ddAcVxBXwCRkIcFoJKNQvaOpYQ10cFYceoIOwFaiEkxLXdGlyFB/0zMVR7CJLaHBiMUswfOByvcEYEbXsXQQASgiLROHYO1H6JUFUJaGA6IS6Kuow7RgVhL1A9SIhrYRgOd4VV4B7FWSTWH4K4oQho/OPx/yZNRUHdOWiCQnF1HrGwugz+u/4LfwDxCj/oxs1BVchwlNdKYWyiZSwIcRXUQtgxKgh7gbaBJsT5iQUcFoSXYp73GfTXpEJYWwG0seZ0ZtRQrK+/CAAo9WcR3ca1BNp6+Py6Dj5Yhz5eEjSPvgU1seNR1ugLfaPZvi+EENIrAioIO0QFYS9QOUiIc5ILLVgSUYjZktPoU3MIgpoaoKb9841CCV5WysFqr1SKOQodxndyD4HRAO/DW+B9eAuiGAaG4VOh6Z8ClSkE9RqT7V4MIcQm2tqZiPyBCsJeoAZCQpyHUmzGo5H5mCk6iciqQ2CqGjt/0v/8N+lG5Nefs359TlrdrXszHAfpmV8RceZXRAAwJoxGXdIMVAiiUa2mlkNCnAF1GXeMCkJCiMsKlxqxPDwb05iTCFX/DqaiqdvXOB+ZhHUNF1scKxLVgVHIwWl1PcrllXUSIVknEQLAHNUf9SNvg9o7HpWVHCwW+iRJCB8EtNBeh6gg7AWpmL67CHG0ft7N+Ev4ZaRYjiFAfQyMytj5k9phFErwcoAvWG19q8cskaFgsvJ7ExUAICrNQWDpewgEkBAQdmXGsnIIVFVCmGnGMiGOQ13GHaKCsBd8pWK+IxDiEZJ8dHg09AImmo7BV30STJltZvd+knQjcq/pKr6WLswPiiyb3MZKoKmA3y+fwg9AnNwXurG3oTpsJMprZTDQjGVC7IomlXSMCsJe8JHS20eIvYxX1mNp0AWMbf4d3lUZYEpt29V6ITIJa/7UVXytmiAxFDa9Y0sCXQN8ftsAH2xArMgLzWNmoiZ2Asp1ftA10LhDQmxNLBHyHcGpUUXTCz7UQkiITU0L0mCRMhMjdGmQai4BJfa5j0nohZfa6Sq+qsSPRax9bt+KwGyE95Ft8D6y7cqM5aEpqE2YApU5FHU1NGOZEFvw8qaSpyP07vSCUMBA7iWEzkhdPYT01O2hatznexZJjWnwqssDtPa/56dJ09rtKr4qR6HFdfaP0grDcZCePYDwswcQDsDYfwTqk29GhTAGVVVmWu+KkB6SyKjk6Qi9O73kIxVTQUhINwgZC+4NV+Eu+VkMrDsIUX0Z0H5Dnc1dCk/EVw2XOj0vU1rlgDSd88o5g+CcMwgGYI6MR/3IWVDL+6NSDVhYqg4J6SovKgg7RO9OL/lIRaho4DsFIc5NIrDgocgSzJWeQZwmFUKNGtA4PodJIMZLQQEwa4s7PbdM2ADG1xdcg/P8gIvKchFY9j4CAQxQhkA7dg6qApJQXi2C2UgzlgnpCLUQdozenV6iiSWEtM1HZMayiELM8jqNmOpUCKrq+I6E1cnTkd1JV/G1LJEhYJyoILyWsFYNv92r4Qegn0wB/bjZqA4fhfI6GZr11GtByJ9RC2HH6N3pJZpYQsgfgr1MeCQyDzcJTiCiKg2MumcLO9vD5fBEfNHYeVfxtbRhPvDp3lN4IWjSQnHgGyjwDWKFIjSNvhmavtehXO8PbT3NWCZEIGBolnEnqCDsJV8ZFYTEs0VJDVgekY0bcQLB6sNgVM18R2rFJBDjpeAAmBs77yq+VnWgGD52ymQvDGuG97Ed8D62A1EADMmTUTtwClRsGGprqDgknolaBztH71AvUZcx8UQD5E1YHnYJk9mj8FcfB1Pu3IXGF8nTkFV/vtvPK/Yzo68d8jiSJDMVYZmpCANgihuGuqE3o0Iciyo1SzOWicegJWc6R+9QL1FBSDzFCD8tHg6+gAnGw1BUnQFT5hqTGLLCBmF14+UePTdHrsVkG+fhkzjvLILzziIYABveF/WjZkOtGIAKmrFM3BxNKOkcvUO9RNvXEXc2KaAOiwPPYXTTYXhXZwKlfCfqHrNAhJdDgmBuLOrR88/KKm2cyHkIVQUI2LEKAQAG+AWhcdxcVAcmobxGDJPBNYp9QrrKS0bjBztDBWEvUQshcTc3B1djof85DNMegqQ2C9DznajnvkyajksN3e8qvkot0IFR+oOrrbNdKCckqK+G357P4Qegr1QO/dhZqIkcjbJ6BZp1zj0cgJCukNB4/05RNdNLVBASV8cwHOaFVuI+n7MY3JAGcX0B0Mh3qt7LCU3AZ9qsXl+HjQiGwM0LwmsJmnVQpH4HBb5DjFCE5pEzUNPvOpQ3BUJbT9voEdfk7evFdwSnR9VMLym96ZuMuB6xgMP9YaWYLz+LhNpUCOvKgTq+U9mOWSDCS6GhMDUW9vpa2lAf+F7ofSZXxLBmyE7sQtSJXVdmLCddj9qBU1HBhUNTTS2HxHXIlRK+Izg9Kgh7KdJfxncEQrpEJmSxOKIEc6Wn0bc6FQJNNS+7hTjCmqRpuNhgmyquKlAEX5tcyfVJzqUh7FzalRnL/ZJRP2wmKsR9UKVmwdGcFOLEFFQQdooKwl6KVFJBSJyXn9iMRyIKcIv4JKKrDoGpcs5dN2wpNzQBn2izbXa9Ej8z4mx2Nfchzs9EUH4mggCYQ2PROHo21D4JUKkZmrFMnI7CnwrCzlBB2EveXiIEyL2g0Rn5jkIIACBMYsSjEbmYITiBUPXvYCpdeFZIN7GMEC+HhcHUUGCza2YpGpBis6u5J1FlEZQ7P4QSQH+fAGjHz0FV0DCasUychkIp5TuC06OC0AYi/WVUEBJe9ZE1Y3lEFqZYjiFQfQyMysB3JF6sSZqO8zbqKr4qQ1Jl0+u5O0GjBr57v4IvgH5eUujG3YaaqLEor1egiWYsE57QGMLOUUFoA1FKGc6V1fMdg3iYwT46PBJyCdezR+FXeQJMGct3JF7lh/THJ7ocm1+3WqADE6AEp6m1+bXdHWNshuLQJiiwCTECIZpHToMmbhLKm4PQWEczloljSLxFEHvROoSdoYLQBmhiCXGUMf4NWBZ8AeOaD0NelQ6mjMZqAf/rKg6PhLEh3z7XjwyGgArCXmEsLGQndyPy5G5EAjAmjkft4OlQIQKaKmo5JPZDE0q6hgpCG4iiiSXEjqYE1uKhgEyM1B+GrOY8UMJ3IuezLmk6Mm3cVXytxlAf+J2z2+U9ktfFowi9eBShAEx9BqN++C2olPSDupJmLBPbkvvT+MGuoILQBiKV3nxHIG5mdqgaD/hmILkxDV51uYCO70TOqyA4Dh/rc+16D3WAEH52vYNnExdeQFDhBQQBGBgchYYxs1HllwiVmgFrpuqQ9A61EHYNFYQ2QC2EpLcYhsM9YSrc43MWg+pSIaovAWhYaqcsjAAvR8TA0JBn1/sU+5nQ3653IFcJq0qh3PVfKAHEK/yhHT8X1cHDUK6RwNjs2eNkSc9QQdg1VBDaAK1FSHpCIrBgQUQp7pCdQbwmFcLaSoCGqXXL10nTkdFw0e73yZI3YKrd70L+TKCtg+++NfAF0NdLCv3YW1ETNQ5ljT5o0tK4Q9I1VBB2DRWENuArFcNXKkJDM/0DRTomF7FYFlGE2ySnEFt9CIJqN90qxAEKg+Pwkd6+LYNXZUjUDrkPaR9jbIY87UfI8SOiGQaGEdOg6T8Z5YZgNNTSjGXSPr8QGtbVFVQQ2kik0hsNKvffBYJ0X6CXCY9E5ONm4UlEVqeBUTfyHcnlWRgBXomMRXO9fccOXlUraIIgKBCW6hqH3I90jOE4SE/vRcTpvYgAYBw0FrVDpqMCUaihGcvkT5RhVBB2BRWENhKllOESFYTkfyKlBiyPyMGNOIEQ9WEwFU18R3IrG4ZMR3q9/buKr2WKDIKQCkKn5HXpOEIvHb8yYzkmAQ0jbkOlNA6VahYcbZTi0aQKMWQKL75juAQqCG2E1iIkcd5N+Ev4ZaRYjkFZeQxMOXVj2UNxUF/8p8kxXcXXagxRwN/hdyXdJS7OQmDxuwgEkBAYjsaxc6H2HwxVlQCsiapDT0Otg11HBaGNRAfQN50nSvbV4tGQi5hoOgIf9WmP3y3E3iyMAC9H9XVYV/G1KgOFVBC6GGGNCv4/fwx/APEKP+jGzkZ16AiU1UphbKKfVU+gDJfzHcFlUEFoIwNCFXxHIA4yUVmPJUHnMKb5CLyrMsCU0jppjrJxyDScqb/Ey72LfI1I4OXOxBYE2nr47F8PH6xHrJcEzaNnoiZmPMq0ftA30rhDd6UMpcaarqKC0EYSw335jkDsaEZQDRYqz2G47hCkmsu0WwgPSgL74MOmAt7uf1lej+m83Z3YksBogPfhrfA+vBVRDAPDsKmoHZCCclMI6jU01MOdUAth11FBaCOBCglCfSWobDDwHYXYyB2hlbjP9yySGtIgrs8HtHwn8lwcGLwSHYem+hzeMmR4qQGGAe2r5l4YjoM0/VeEp/+KcADGAaNQl3wTKgTRqK4yA/TX7dJoDGHXUUFoQ4nhvqhsqOI7BukhIWPB/eEq3ClPR0JdKkT1ZbRbiJP4dsg0nKq/zGuGRoEBTEgQuEr6GXdnXtmnEJJ9CiEAzFH9UT9yFtTe/VFZycFioerQlYi8BPAJoH2Mu4oKQhsaFO6LA1n0y8KVyIQsFkaU4HbpGcTVpEKgqQJorWinUhoQg1WGIr5jAABMEUEQUUHoMUSlOQgs/feVGcsBYWgcOwdq5RCoqoUwG2nGsrPzD/UGwzB8x3AZVBDaUGIEjSN0BT4iMx6OLMQs8SlEVx+CoKqO70ikHRwYvBozAE312XxHAQA0BHsjgO8QhBcCTQX8fvkUfgDivH2gGzcb1WEjUV4ng0FPM5adkTKMxg92BxWENkQTS5xXiMSER8NzMUN4EuHqNDCVOr4jkS74fsg0nOC5q/ha6gAhFYQEAn0jfH7bAB9sQKzIC81jZqImdgLKdX7QNdCMZWcREE7jB7uDCkIb6hMoh9xLCJ2RPi06gxhZM5aH52AqdwxB6iNgKmjCjyspC4jB+4ZivmO0UOhrxEC+QxCnIjAb4X1kG7yPbEM0gOZhN6A2YQpU5lDU1dCMZT4FRfvwHcGlUEFoQwIBg4QwH5wpruM7iscaqNDj0dBLmMQeg7/6OJhy+rTuiq52FeudpKv4qsvyetzEdwji1KRnDyD87IErM5b7j0B98k2oEMaiimYsO1xoX+q16w4qCG0sMcKXCkIHG+HXiEeCL2C88QgUVWfAlNFgb1f3w+Abcbw+i+8YrWRKKmnpGdJlXjlnEJxzBsEAzBH90DBqNirl/VGpBiwsfQ/Zk2+QlPYw7iYqCG0sMdyP7wgeYXJgLRYHnMeopt/hXX0OKOU7EbGVcmUM/m10zpW/tYwRTGgwuAo131GIixGV5yPgp/cRAGCAMgTasXNQFZCE8moRzVi2g5BYah3sLioIbYxmGtvPLcHVeNA/E8O0hyCpzQZoXohberVPAnR1ztc6eJUpIggiKghJLwhr1fDbvRp+APpJ5dCPm43qiFEor/NGM81YtomQPvS7uLuoILSxgWE+EAoYsLSAaa8xDIe7wipwj89ZJNYdgrihCGjkOxWxpx8HT8MxJy4GAaA+xBuBfIcgbkPQrIPi4EYosBGxQhGaRt0MTb+JKNcroa2nMdA9FUoFYbdRQWhjUrEQfYPkyFXTPmc9IRZweDC8FPO9z6B/7SEIa1VALd+piCNU+EfhPZPz9/1XBAioICR2wbBmeB/fAe/jOxAFwJA8CZqBU1HBhqG2horDrmIEDIJjaYZxd1FBaAeDI3ypIOwGudCCxZGFmCM5gz7VqRDU1AA1fKcijraibyK0dc6z5mB7Cn0NGMx3COIRJJmHEJ556MqM5bhk1A+9BZXiWKjVLM1Y7kBAuBxiLyHfMVwOFYR2MDzaH9vPlvMdw6kpxWY8HJGPW8QnEVWVBkbdwHckwqMtiVNx2AWKQQC45F2HW/gOQTyOV14mgvMyr8xYDu+DhlFzoFYMQAXNWG4ltA+1DvYEFYR2MD4uiO8ITilMYsTyiBxMF5xAqPowmEo935GIE6jwj8T/mVV8x+iyTK9KQCgEWBr8T/ghUhUiYMeqKzOW/YKgHTcHVYHJKK8Rw2SgGcs0oaRnqCC0gwGhCgTKvVCjM/IdhXf9vJuxPCwLN3DHEKA+CkZF7wlpaWXfwWh0kdZBAGhmzFeWnimv4DsKIRDUV8N3zxfwBdBPIrsyYzlyNMrqFWjWeea4QyoIe4YKQjtgGAZj+wXg53Oe+QsjyUeHR0Iv4jrTEfiqT4Epp5YU0ratiVPxuwsVg1cZIwIhpoKQOBnG0AR56neQ4zvECIRoHjUDmn7Xo6wpENp6z9hGTyQRIjBCzncMl0QFoZ2M7xfoUQXhWP8GLAs+j7HNhyGvOgumlMa0kI5V+kXgXRfqKr5WXYg3gvkOQUgHGAsL2YmfEXniZ0QCMAy5DrWDbkQFFw5Ntfu2HEbE+0EgFPAdwyVRQWgn4+Pcf2GKGwM1WBRwDiP0aZDVXAScc3MJ4qRW9hviUl3F16pQMlQQEpciOf87ws7/jjAApr5DUD/sFlR69YVazbrVToxRCQF8R3BZVBDaSXyID4J9JKhqNPAdxabmhKrxgG8GkhoPwasuj3YLIT2yfdBUpLloMQgABb7NSOI7BCE9JC44j6CC8wgCkBAai8bRt0HtOxAVagas2bWrw6iBSr4juCwqCO1oXL9A7Mhw7eVnhIwF94SpcJfiLAbVpUJUXwrU852KuDK1XzjeZl2zq/iqS961uI3vEITYgKiyCMqd/4ESQH+fAGjHz0FV0FCoNBIYm11r/LdULkZQtILvGC6LCkI7Gu+iBaFEYMGiiBLMlZ1BvCYVwlo17RZCbOb1uGQ01l7iO0avnBOrAZEIMLvvWCzieQSNGvju/erKjGUvKXTjbkNN5FiUNyrQpHX+7/XIBH8wDMN3DJdFBaEdudI4QrmIxSORhZglPoWY6kMQVFMFSGxvx8AbcNDFi0EAMDIsmPAQcCWu94GPkK5gjM1QHNoEBTZdmbE8cho0/SZBZQxCQ61zzliOGkjjB3uDCkI76hskR5ivFBUNzXxHaVOwlwmPROThJuFJRFSlgamk7faI/VT7hOJtrorvGDZjCA+AFxWExAMwFhayk7sReXI3IgEYE8ejdvB0qLgIp5qxHJVA4wd7gwpCOxvXLwDbnGgbu0ipAX+JyMaNOI5g9REwFc5ZrBL381r/4aivvch3DJupC5YhhO8QhPDA6+JRhF48ilAA5thE1I+4FRWSvlCrLeB42ihFoZTAP9Sbn5u7CSoI7Wx8XCDvBeEAeRMeDbuEyewxKNXHwZQ7Z3M/cV+7BqbggBsVgwCgUoIKQuLxREUXEVh0EYEABgZHoWHMbFT5JUJVJQBrclx1SLOLe48KQjsb34+ffY2H+WrxSMgFTDAegU/VaTBltL8l4Ue1IgT/4mr4jmFzBb7NGMp3CEKciLCqFMpd/4USQLzCH7pxc1AVMhzlDpixTOMHe48KQjuLCfRGpL8MZXVNdr/XdQH1WBJ4DmOaDsO7OgMotfstCenUGwNGoM7NWgcB4KKsFnP4DkGIkxJo6+Dz61r4YC36eEnQNOZW1MSMQ3mjL/SNth93SC2EvUcFoQNMGhCEb0/YZxuPm4OrscD/HIbpDkGqyQL0drkNIT3yS0IK9rthMQgA52npGUK6RGA0QP77ZsixGdEMA8PwG6EZkIJyQ7BNZiwHRMgh95PYIKlno4LQAaYlhtqsIGQYDvNCK3GvbwaG1B+CuL4AaLTJpQmxqRpFMN6C+3UVX2VmLGAiQsEVl/EdhRCXwXAcpGf2IeLMPkQAMAwcg/ohM6BiolBT1bMPV32T+Rma5W6oIHSAifFBkHsJoTP2bAyFkLHggfBy3ClPR0JtKoR15UCdbTMSYmtvDhiF2toLfMewq+bwAEioICSkxySXTyDk8gmEADBHJ6B+5CxUSuNQ2Y0Zy32GUkFoC1QQOoBEJMSkAcH45XxFl58jE7J4KKIEc6Wn0a8mFQJNNaCxY0hCbGhPwmTsc/NiEADqgqUI5TsEIW5CVJKFwJIsBAJICAxH45g5qFIOhqpKCHM7M5a9/bwQ2sfXsUHdFBWEDjJ9cGinBaGf2IyHIwpwq/gUoqsOgamiTYOJ66mVB+KfjGfsdFOuBBWEhNiBsEYF/18+gT+AOLkvdOPmoDp0BMprpTA0/dHb1icpiLarsxEqCB1kSkIoRAIGZgvX4niIxITlEbmYITiBMPXvYCp1PCUkxDbeTBgDjQe0DgJAvk8ThvMdghA3J9A1wGf/evhgPWJFXmgeewtqYsajTOtH4wdtiOE4juv8NGIL935+DEfyatBH1ozl4VmYwh1HoPooGNbAdzRCbGLfgEl40lTIdwyHGWIKxSv/R2MICeEDo1BgwOHDEEi8+I7iFqiF0IGeG84ilv0IfuoTYMppqQriXuq8A/CGwLOGOVwSq8F4eYEzGvmOQojH8Zk0iYpBGxLwHcCTDB3QF/4VR8FYqBgk7uefA8dBY/CMsYNXseCACBpFSAgffGbM4DuCW6GC0JF8I4DoMXynIMTm9ve/Hr/Unuc7Bi+awmmHBEIcjfH2hmLyJL5juBUqCB0tcQ7fCQixqXpvJV4XNvAdgze1wVK+IxDicRTXXw+BlH72bIkKQkdLvA0ATZEn7uOfA8ejxsO6iq9V7k/z8ghxNN8Z0/mO4HaoIHQ0vyjqNiZu47f+1+NnD+0qvirPhzYQJ8SRGKkUismT+Y7hdqgg5EPynXwnIKTX6mX+eF1IG2mfl7nvfs2EOCP5dRMhkMv5juF2qCDkw5A7AKGE7xSE9Mrbgyag2kD7KWaJqsHQWCZCHMZv5ky+I7glKgj5IFMCA2i6PHFdqfETscPDu4qv4hiAo6VnCHEIob8/FDfeyHcMt0QLU/Nl2L3ApZ/4TuFxDhWZ8e4RI06Xs1BpOWy9S4Y5A8XWxzmOw8pUA1afNqG2mcPYSCH+O1OKwSHCdq+ZslaH1CK21fGZ/UXYda83AOCbTBOe398MnZHD4uFeeHf6Hy1KhXUWTP9aj1PL5PCVOP+EowaZH14T64HWL9ljNYX7Q5ZfxHcMQtye3+zZEHjRYtT2QC2EfImfBnjTHoyOpjNyGBoqwEcz2+7ie+ewEf8+asRHM6U4uVSOMAWDaV/r0Whofybplru8oXpKYf1z/lE5hAwwP/HK561qvQVLdjTh/6ZJsed+OdZlmLAr22R9/qO7mvCvGyUuUQwCwNuDJkLdTOPmrqUJoiEghDiC/53z+Y7gtqgg5ItQBCTRN7aj3dxfjDemSHH7IHGrxziOw6rjRvzjegluHyTGkBAh1s2RQW/isPGcqY2rXREgYxCmEFj/7Ms3w1sMzE+8co/8Wg5+EgZ3DRFjdKQQN/QV4mKVBQCw8ZwJXkKmzTzO6FDcBPxEXcWtlPtb+I5AiNuTjRgBSVwc3zHcFhWEfBp6N98JyDUK6jhUaDlMj/tjJIVExGByHxGOlHa9f/TLdBPuHiKG3OtKi1//AAH0Jg7pKhaaJg4ny1gkhwqhaeLwyoFmfHSza0xIaJT6YaVXM98xnBItPUOI/fnPp0YUe6KCkE8Rw4CQRL5TkP+p0F5p5QlVtOy6DZUz1sc6c6KMxXm1BUtG/DHGRSljsG6ODA9ua8KYz7V4cKgYM+JFeHpvMx4b44WCOguGf6bFkI+1+PFi+y2RfHsn8Tqom6v5juGUztHSM4TYlcDHB7430WRMe6JJJXwbejew7xW+U5Br/HkkH8d1fW+ZL88YMSREgDGRLSehzB0kxtxruoUPFppxTs3io5lSxH+oxbd3yBCmYDDmCx0mxQoRIneuz2q/x43HttpzfMdwWrnCGjAyKbgmakElxB78Zt0KgUzGdwy35ly/dTzR0HsAgWuMH3N3YYorPw4V2pYTSNR6DqGKzn9U9CYO310wYcnwjv8+DWYOy3c147NbZcjVWGC2AJP7iJAQJMSAQAGO/3979x0eVZWwAfy902t67wmhk4AhINJZSgSpFiwfsICCohRxF9ClKBZERVHUVUHWWAGxILKuCLi4rKxKSQAhRAiE0NIL6ZMp3x+RaAwlgSRnZu77e548Jpk7d94BhDfn3HtOE6anW0OZzgNPaKtFx3BqDglwhAaJjkHktjhd3PJYCEUzBfy6vzGJFu0lIcgkYdsJa933LDYHvsu0onfY5ZeduejjwzWotgIT4q9cCJ/6TzWGx6qQEKyEzQ5Y7b8V0BobYHOyrXFXdOqHnEpOFV9NRZCn6AhEbknXpQt0HTuKjuH2OGXsDHpMA37+VHQKWSizOHC88LfrAU8W2ZGabYOPXkKEpwIP36jBsl3VaOujQFtfBZbtqoZBLeGeuN9K3qTPKxFqlvDskPo3g6xNqcHYDir4Gi7/c9bhXBs2HLYi9f7abZc6+CmgkCSs3W9BkEnC0Xw7eoRcvXy2lt3RN+JTThU3SqGfFgbRIYjcEEcHWwcLoTOIvAkI7ALkcDmPlrb3nA2D3v3tjtBHvqkGUI0/d1Ujeawe8/toUGl14MGvqlBU6cCNYUp8M9EA8+/WCMwqsUMh1S99vxTY8N8sG76ZcPlK4HA4MH1LFVYmaevuQNarJSSP1eGhr6pQbQVeG6FDqIdzDNyXa814Qm8FKkUncQ1nvewIEx2CyM1IBgM8brlFdAxZkBwOh5NNUMnU3neALQ+LTkFUZ2nCLfiEo4ONNrq0LSa8liY6BpFb8bz9NoQ8/bToGLLgHEMRBMSPB7S8Bomcww/RPVkGm+hnPa+zJGpu3pwubjUshM5CY6zd35hIsAqtCY/rufNGU51QFUEyGkXHIHIb2nbtoO/aVXQM2WAhdCY97kPjV7wjahkvdh6Ac5W5omO4JEdogOgIRG6DN5O0LhZCZ+IXC8QMFJ2CZOzH6B7YyL2Kr1k5l54hahaSwQDP0aNEx5AVFkJn03Oa6AQkUxVaEx43AA7wPrNrVeCnufpBRHRV3uPHQ+nJH7BaEwuhs2k3HPCNFZ2CZOilzgNwtiJHdAyXdsbTuXaZIXJFkkYDn6lTRMeQHRZCZ6NQAL1ni05BMrMnqgc+5lTxdTtmKhcdgcjleY4bB3UAr8dtbSyEzqjr3YA5WHQKkolKjQFLjBKnipvBIS49Q3R9VCr4TrtPdApZYiF0RioN0OtB0SlIJl7uPAhnKrJFx3ALWcpiSGaT6BhELstjxHBowrjnjwgshM4qcQqg8xKdgtzc3sjuWFfMqeLmZA8NFB2ByDVJEvymTxedQrZYCJ2V1vzruoRELaNSY8DjJhWniptZeaCH6AhELsk8ZDC0sbypUhQWQmfWawag0otOQW5qVedByKo4LzqG28n3U4uOQOSSfO9/QHQEWVOJDkBXYPQDbpgA7FkjOgm5mf0RCfio5LDoGG7ptJcNUaJDtKK9FRX4R2EBDldVI89mxaqQUAwxm+se75R+9JLP+4u/P+718b3kYzUOB9YUFOCLCyXIsVoRrdHgEX9/9DP+dn3mlxdKsDIvDxV2O27z9MK8392VerbGgvtOn8bGyCiYlMpmeqfUkox9+kDfpbPoGLLGEUJn13sWoGBvp+ZTpdZjiYcGdgf3K24Jx41loiO0qgq7He21OiwKvPS1k9+1ia338XRQECQAw0zmSx4PAKvy8/BxSTH+FhCIL6OicaenF2afPYsjVVUAgCKrFUuyszHPPwBrwsLxxYUSfFf226/70pwcPOIfwDLoQnzv57WDorEQOjvvSCD+TtEpyI2s6vInnCo/JzqG2zqoyxMdoVX1N5kwx98fQ82XLnj+KlW9j2/LytDTYEC45vK7umwuuYDpPr4YYDIhXKPBXd7e6GM0IrmwEABwuqYGJoUCwz08EKfXo6fBgOOWagDAlgslUEvSZfOQ89EnJMDYs6foGLLHQugKBiwAlNwSi65favgN+JBTxS3qrPICJA/eWHIp+VYr/lNWhtuusiWZxWGHVpLqfU8nSdhfWQEAiNRoUOVw4EhVFYptNvxcVYX2Wi2KbTa8mp+PRQG809uV+HF00CmwELoC70ggYZLoFOTiqlU6LPbUcqq4FdhDucvCpXxRUgKDQoGhV5guBoC+RhOSiwqRabHA7nBgd3k5vi0rQ56tdmtAT6USzwYF47Hz53HnqUyM9vBAX6MJL+TmYoK3N87W1ODWzJMYffIEtpZeaI23RtdI26kjTAMGiI5B4E0lrqP/PCDlQ8BaKToJuajX4oYgs/ig6BiyUBZkhjlNdArn89mFEoz08IBWceWxiMcCArAkJxsjT56ABCBcrcE4T098XlJSd8wQs7nezSs/VZTjmKUaiwIDcfOJE1gREgI/lRJ3njqFRL0Bvir+c+eMuO6g8+AIoaswBwE9p4lOQS7qQHhXvFfCBahbS74vl575o70VFThpseB2T6+rHuujUuG10DDsa9sO22Pa4J/R0TAoFAhVX/rX1WK348mcHDwRGIQsiwU2ONDDYEC0RosojQYHq/iDtDPSxMTAPGyY6Bj0KxZCV9J3LqDltUnUNLVTxQZOFbeiLE+r6AhO57OSYnTW6tBBp2v0c7QKBQLValgBfFNaij9dZqr5jYIC9DMa0Umngw2A1fHbYus1DgdsXHvdKfnPngXpKqPF1Hr4O+FKDD7c45ia7PW4wThZflZ0DFk5JqOlZ8rtdqRVVSHt1yVhztbUIK2qCudqauqOKbPZsLW0FLd5XfpmkkfPn8NLebl1Xx+orMS20lKctliwt6IC08+chgPAvT4+DZ57rLoa/yq9gFl+/gCAGI0GCknCp8XF+K6sDCctFsQ1oYRS69B37w6Pm28WHYN+hxdVuJqbHgJ+eguoLBKdhFzAobB4vFdyRHQM2UnV54iO0GoOV1Vi8unTdV8/92uxG+vhgWXBIQCAr0pL4QBwi/nSMxzna2rqjU5YHA68kp+HMzU1MCgU6G804rngYHj8YV1Bh8OBJ7Kz8WhAIAy/jjTpFAosCwrGUznZsDgcWBQQiMDLTDWTIJKEwEcfFZ2C/kByOBwcTHc1/30Z2P646BTk5CxKLcZ36o6MsjOio8jSxjdNcBQVi45B5HQ8x4xByHPLRcegP+CUsSu68X7AI1R0CnJyf48fwjIokC3EX3QEIqcj6fXwf2Su6Bh0CSyErkitB4Y8IToFObHDoXFI5lSxUGVB3CmD6I98770X6stsc0hisRC6qrg7gDBu9UMN1Sg1WOTjAZvDJjqKrOX58BJtot9TBQbC996pomPQZbAQuipJAoYvByBd9VCSlzfihuJ42emrH0gtikvPENUX8MhcKPR60THoMlgIXVlod6DrXaJTkBM5EtIZ71zgFhnOIN3ILdOILtLFxcFj9GjRMegKWAhd3eDHAbVRdApyAjUKNRb5esPq4MiUMzigy736QUQyEfjYo5Akzmg5MxZCV+cRDPTjHVsEvBU/DMfKskTHoF8VKCog+TZcSJlIbszDb4YhIUF0DLoKFkJ3cNMswCtCdAoSKC24E9ZyqtjpWLn0DMmcpNEg4C9/FR2DGoGF0B2odcDQp0SnIEFqFGos9vflVLETKg00iY5AJJTPn/8MTRjXzXUFLITuovNYILq/6BQkwJq4oUgvPSU6Bl1Cnq/y6gcRuSmlnx98779fdAxqJBZCdzLyZUDFTdzlJD2oE9aUHRUdgy7jlIdFdAQiYfznzIbSxJseXQULoTvxbQP057UacmFVqLAowBdWO6eKnVW6sVR0BCIh9DfcAK/bbhMdg5qAhdDd9HkYCOgkOgW1gjVxw3CUU8VOLVWbIzoCUauTtFoEL3sGkoIVw5Xwd8vdKNXAqFWAxN9ad5Ye1BGrOVXs9EoUVVD4+YqOQdSq/GfPhjY6WnQMaiK2BncU3gNI5H6R7sqqUGFxgD+nil1ETaif6AhErUbfrRt8pkwWHYOuAQuhuxr8OGAOEZ2CWsA/4oYhrTRTdAxqpNIALj1D8sCpYtfG3zV3pfMARjwvOgU1s+OB7fFmWbroGNQEOVx6hmTCf9ZMaGNiRMega8RC6M46jgI6jBSdgpqJTVJiUVAQauw1oqNQE3DpGZIDXdd4+EyZIjoGXQcWQnd3y4uAnvupuoN34ofh8IWTomNQEx01loiOQNSiJI0GIcuWQVJyNNyVsRC6O3MQMPIl0SnoOmUEtMMbZcdEx6BrcECTC0iS6BhELcZv5kxo27QRHYOuEwuhHHQeB8SNF52CrpFNUmJxcAgsdk49uqJSRTWkAN5pTO5JFxcH33u5qoU7UIkOQK3klhXAqd3AhTOik1ATJccNw6ELh0XHuKzy9HLkf5WPylOVsBZbETErAh7dPeoeL9lbgqKdRajMrIStzIY2S9tAH6m/6nlt5TbkfJqDC/suwFZug8Zfg6C7gmDuagYAFO8uRvYn2XBUO+DdzxtBdwXVPdeSZ0Hmiky0eaINlHrx01g1IX5Q5eSJjkHUrGqnip/hVLGb4AihXOg8gbF/B8CpK1dyIqAt/l7u3FPF9mo7dBE6BE8IvuzjhrYGBN4R2PhzWu3IXJEJS74F4TPD0XZ5W4RMCYHKu/ZnWGupFWffOYvgO4MR+ZdIFH1fhNLU37aJO/feOQTeEegUZRAALgRwP1dyP34PPght27aiY1Az4QihnMQMAHrNAH74u+gk1Ai1U8WhsFw4ITrKFZnjzTDH147ancbpBo979/EGUDtq11jF/ymGtcyKmIUxkFS1P8Ro/DR1j1vyLFDqlfC80RMAYOxoRNW5Kpi7mVH8v2JIKgmeiZ7X/J6aW46PAry1i9yJrksX+E67T3QMakYcIZSbwY8D/h1Ep6BGeD9uGA46eRlsKRdSL8AQa8C5988hbXYaji08htwvc+GwOwAA2kAt7BZ77TR1mRWVJyuhC9fBWmZF7ue5lx2tFCXTo1p0BKJmI6nVtQtQc6rYrXCEUG7UOuDW1cCawQDXs3NaJ/3b4LWK46JjCGPJtaAmvwaeN3ki6pEoVGdX4/z75wE7EDAmAEqjEmHTwnBmzRk4LA549faCOc6MM2vPwGeID2rya5D1ShYcNgcCxgbAs4fY0cKjhhIMF5qAqPn4zZ4FXbt2omNQM2MhlKPgrsCgx4AdT4pOQpdglxRYEhKB6gsZoqOI4wBUHiqETgmFpJCgj9LDWmxF/r/yETAmAADg0d2j3s0rZWllqD5TjZAJIfhlwS8IfyAcKk8VMp7MgLG9ESoPcX/dHdDkAAoFYLcLy0DUHEyDBsH3Pk4VuyNOGctVn7lAzEDRKegS3u8yDKlyLoMAVF4qaII0kBS/3QSlDdHCWmKF3dqwVNlr7Dj//nmE/DkEllwLHDYHjB2M0AZroQ3SoiKjojXjN1ChqIEU6C80A9H1UoeFIeS55ZC4rqZbYiGUK4UCuPVtwNT4Oz+p5Z3yi8FrlfIugwBgaGuAJcdSd80gAFRnV0PlpYJC1fCvrbzNeTDFmaCP0tc+53ed0WGt/7UolhBf0RGIrpmk0SD0lZeh9PC4+sHkklgI5czkD9y6BpD4x8AZ2CUFloRFocrmWjcg2KpsqDxVicpTlQAAS74FlacqYSmovavYWmZF5alKVJ+rfV+W7NrHa4p/u4b1zOozyN6YXfe1zyAf2MptOP/heVRnV6M0tRR5W/Lg86eG9+pWna1CyU8lCLy19ocbbbAWkIDC7wpRmlqK6vPV0Mdcfd3DllbibxAdgeiaBS5aCH3nzqJjUAviNYRyFzMA6D8f+G656CSy92GXYdhfckR0jCarPFmJzOcy677OXldb7Lz6eCFsWhhKU0pxdu3ZusdPv1G7NI3/GH8EjqstcZYCS70lMjW+GkT9NQrnPzqP44uOQ+Wtgu9QX/jfUn/a1eFw4Nw75xB0dxAU2tofbBQaBULvC8X598/DUeNA8MRgqL3VLfHWmyTHRwHuV0KuyHPsWHiP525X7k5yOByOqx9Gbs1uBz64FTjxb9FJZCvLLxq3e6lRaasSHYVayKSizhj55gHRMYiaRNu+PaI2rIdCpxMdhVoY5wqp9nrC29YCHqGik8iSAxIWh0WzDLq5NEOR6AhETaIwmRD2ysssgzLBQki1jL7AHe8CCvFTa3LzUZdh2F8i3zUH5eKgJgfgQr7kQoKfXQZNVJToGNRKWAjpN+E9gGFPi04hK6d9I/FK1UnRMagVVEs2Lj1DLsNnyhR4DB0qOga1IhZCqq/XA0DXu0WnkAUHJDweHsupYhnh0jPkCvSJ3RHwl0dEx6BWxkJIDY16BQjrKTqF21vfZSj2lBwTHYNaUXEAl54h56b080PoSy9BUnERErlhIaSGVFrgrg8BjzDRSdzWGZ8IrKw+JToGtbJsb+7wQE5MqUToiy9CHRAgOgkJwEJIl2YKAO7+CFBzRKO5OSDh8Yh2qLRWio5CreykBy8PIOflP2cOjDdydkiuWAjp8oK7AmPfQL0Vg+m6fdxlKH4q+UV0DBKAS8+Qs/IYPQq+0+4THYMEYiGkK+s8FhiwQHQKt3HOOwIvVWeJjkGCHFLnArw2i5yMsfdNCHnmGUgSf/iXMxbCRnI4HLBaraJjiDHwUaDTGNEp3MKSqHaosFaIjkGCWCQbpGBen0XOQ9uhA0JXrYKk5hq0cifrQlhdXY3Zs2cjICAAOp0Offv2xZ49ewAAO3fuhCRJ2Lp1KxITE6HVarFr1y5kZGRgzJgxCAwMhMlkQo8ePbB9+/Z6542KisKyZcswdepUmM1mREREYPXq1fWO2b17N7p16wadTofExERs2rQJkiQhNTW17pgjR45gxIgRMJlMCAwMxMSJE5Gfn9/ivy4NSBIw9s3aKWS6Zh93HoofizlVLHfVwT6iIxABAFQhwQhf/RaUJpPoKOQEZF0I58+fj08//RTvvvsu9u/fj9jYWCQlJaGwsLDeMc8++yzS0tIQHx+PsrIyjBgxAtu3b0dKSgqSkpIwatQoZGXVnwZ88cUXkZiYiJSUFDz44IOYMWMGjh49CgAoLS3FqFGjEBcXh/379+Opp57CggX1p2XPnz+PAQMGoFu3bti7dy++/vpr5OTkYLyoDcY1BuCejwHPCDGv7+LOe4fjJctp0THICRT760VHIILC0xMRa9bwjmKqIzkcDofoECKUl5fD29sbycnJuOeeewAANTU1iIqKwsMPP4wePXpg0KBB2LRpE8aMufJ0aefOnTFjxgzMnDkTQO0IYb9+/fD+++8DqJ1uDgoKwtKlS/HAAw/gzTffxKJFi3DmzBnoft0j8u2338a0adOQkpKCbt26YcmSJfjxxx+xdevWutc5c+YMwsPDkZ6ejnbt2rXEL8vV5R8D1g4DKguvfizVmX7DUPyvOF10DHICC0/fgK4f7BEdg2RM0mgQ8Y+1MCQmio5CTkS2I4QZGRmoqalBnz596r6nVqvRs2dPpKWl1X0v8Q//w5SXl2P+/Pno1KkTvLy8YDKZcPTo0QYjhPHx8XWfS5KEoKAg5ObmAgDS09MRHx9fVwYBoGfP+rf679u3D//+979hMpnqPjp06FCXXRi/trUjhVyOptE+7TyEZZDqnODSMySSQoGQ559nGaQGZHu728WB0T/eVeVwOOp9z2g01nt83rx52Lp1K1asWIHY2Fjo9XrcfvvtsFgs9Y5T/+ECXUmSYLfbL/kav89zkd1ux6hRo/Dcc881yB4cHNyYt9hywnsAdyQD6+8B7DK90aaRsr3CsKLmrOgY5EQO6wsxTnQIkq3ARxfA4+Yk0THICcl2hDA2NhYajQb//e9/675XU1ODvXv3omPHjpd93q5duzB58mSMGzcOcXFxCAoKQmZmZpNeu0OHDjh48CCqq6vrvrd37956xyQkJODw4cOIiopCbGxsvY8/llQh2iUBI18WncLpPRHdCWU15aJjkBM5os7j0jMkhM+UKfCZNEl0DHJSsi2ERqMRM2bMwLx58/D111/jyJEjmDZtGioqKnDvvfde9nmxsbH47LPPkJqaigMHDuCee+6pG/lrrIvPmT59OtLS0upGHIHfRiwfeughFBYW4u6778ZPP/2EEydO4JtvvsHUqVNhs9mu/Y03p4SJwJ8WiU7htD7vNATfFx8VHYOcjFWyQwoNEh2DZMZjxAgEzJ8nOgY5MdkWQgBYvnw5brvtNkycOBEJCQk4fvw4tm7dCm9v78s+Z+XKlfD29kbv3r0xatQoJCUlISEhoUmv6+HhgS+//BKpqano1q0bFi5ciCVLlgBA3XWFISEh+P7772Gz2ZCUlIQuXbpgzpw58PT0hELhRL9t/ecBPaaJTuF0cjxD8IL1nOgY5KSqgi//dwxRczPceCNClj/LhafpimR7l7Gz+fDDDzFlyhSUlJRAr3exZSnsduCTKcCRTaKTOI0Hb0jCruK0qx9IsrTq0A0I2sI7janladu1Q+SHH0BpNouOQk6OF7II8t577yEmJgahoaE4cOAAFixYgPHjx7teGQQAhQK47W3AZgHSvxKdRrhNHQezDNIVnfcBOGlMLU0VFFS78DTLIDWCE809ykt2djYmTJiAjh07Yu7cubjjjjsa7GbiUpRq4I53gbbyvnst1zMYz9vOi45BTi7DXCk6Ark5VVAQIpPfgTqIP3pQ43DKmJqXtRpYdxeQ8a3oJELMvCEJ33F0kK6isyUAj7/Ia0ypZahDQhDxbjI04eGio5AL4QghNS+VFrjrIyC6v+gkrW5zxz+xDFKjHNXkQdJoRMcgN6QOC0Pk+++xDFKTsRBS81Prgbs3AJF9rn6sm8jzCMJz9hzRMchF2OCAIyRQdAxyM+rICES+/x7UoaGio5ALYiGklqEx1G5xF95LdJJW8WSbbrhgKRUdg1wIl56h5qSJjkbke+9DLXonK3JZLITUcrQmYMInQKh775m5pcMg7Cw+IjoGuZgif93VDyJqBE1sG0S+9y7UgQGio5ALYyGklqU1AxM/ByJ6i07SIvJNAVjuyBMdg1zQOS/ez0fXT9u+PSLfew8qf3/RUcjFsRBSy9N5ABM/A2KHik7S7J5ql4ASywXRMcgFZZgrREcgF6ft1BERye9A5eMjOgq5ARZCah1qPXD3OqDTWNFJms1XHQbi2yJOFdO1+VlfIDoCuTBdly6IfOcdqK6w1SpRU7AQUutRqoHb3wFumCg6yXUrMPnjWQf/Qadrl67Kh6TjdYTUdPquXRHxzj+g9PQUHYXcCAshtS6FAhj9KtDrIdFJrsvT7RJRbCkRHYNcmEMCl56hJtMnJCB87VpuR0fNjoWQWp8kATcvAwb+TXSSa/J1+wHYXnRYdAxyA5XBXqIjkAsx9OiBiLfXQGkyio5CboiFkMQZuAC4eTkASXSSRis0+mEZCkXHIDdR6KcVHYFchGnIYISvWQ2FwSA6CrkpFkISq9cM4NY1gNI1tvF6pn0PFHGqmJrJOW+76AjkAnz+PAlhq1ZBwWtOqQWxEJJ48XcAk74A9M59t9w37fvjG04VUzM6buLSM3QFCgUC//Y3BD72GCQF/7mmlsU/YeQcInsD924HvKNFJ7mkIqMvnpE4MkjN62cd71SnS5P0eoS9ugo+k1x/VQZyDSyE5Dz8YoH7dgDhN4pO0sCy9j1RWF0kOga5mWPqAkh6vegY5GSUvr6IfDcZ5sGDRUchGWEhJOdi9AUmbQY6jxOdpM72dv3wNaeKqYU4wrj0DP1GExODqA3roY+PFx2FZIaFkJyPWle7gHXfuaKToNjgg6cV3JqOWk5FoJfoCOQkjH37ImrDemjCwkRHIRliISTnJEnAkCeAUasAhUpYjGUdeqGAU8XUggr8XeMOe2pZ3pMmIvytN7ngNAnDQkjOrfufa6eQjQGt/tI72vbDv4p+bvXXJXk568WlZ2RNrUbQk0sR9Le/QVIqRachGWMhJOcX1Qe4/zsgNLHVXrLE4I2nlaWt9nokX8dN5aIjkCBKLy9ErH0b3uPHi45CxEJILsIjBJjyLyDhz63ycs92uAn51dyRhFreQV2e6AgkgLZtLKI+2Qhjz56ioxABYCEkV6LSAKNXAaNeadGdTXbG9sU/OVVMrSRTVQzJyL1p5cSclITIdbx5hJwLCyG5nu6Ta0cLzSHNfuoSvReeVJc1+3mJrsTOpWdkQdLpEPTEEwh75WUoTfwhgJwLCyG5prDE2usKI/s062mf79gbeVWcKqbWVRHoIToCtTBNbBtEfbwB3nfdKToK0SWxEJLrMgXU3oHcexYA6bpP9582vbGZU8UkQIEfl55xZ56334bojRuha9dOdBSiy2IhJNemVAHDngYmfHpdS9Nc0HtiqaayGYMRNd4ZT5voCNQCFCYTQl5cgZCnn4aCWxSSk2MhJPcQOxiY8T0QO+Sanv58xz7IrSpo5lBEjXOMS8+4HV1cHKI//wyet9wiOgpRo7AQkvswBQD/90ntiGET7kLe1eYmfMGpYhLokD5fdARqLpIEn8mTEfXRh9CEh4tOQ9RoLITkXiSp9prCe78BfNpc9fBSnSeWaqtbIRjR5WUpiyGZTaJj0HVS+vgg/M03EPjoAkhqtdAsAwcOxMMPPyw0A7kWFkJyTyE3APf/B+h69xUPe6FTX+RUcnSGxLOHcukZV2a48UZEf/45TAMGiI5CdE1YCMl9aU3AuDeB29YCOq8GD38f0wufFx1q/VxEl1DOpWdck1IJv9mzEPHOP6AObP0914maCwshub+424GHfgTaJtV9q0zngSd0NQJDEdWX7yd2ipGaTh0ejsh3k+H/4IOQFM73z6nVasXMmTPh5eUFX19fLFq0CA6HAwDwwQcfIDExEWazGUFBQbjnnnuQm5tb99ydO3dCkiTs2LEDiYmJMBgM6N27N9LT0+uOycjIwJgxYxAYGAiTyYQePXpg+/bt9TJERUVh2bJlmDp1KsxmMyIiIrB69ep6xyxYsADt2rWDwWBATEwMFi9ejJoa/v3c2pzvTzBRSzAHAf/3MTDmdUDriRWd+iG7knvIkvM47cWlZ1yGSgXf++5FzJebYUhMFJ3mst59912oVCr8+OOPWLVqFVauXIm3334bAGCxWPDUU0/hwIED2LRpE06ePInJkyc3OMfChQvx4osvYu/evVCpVJg6dWrdY2VlZRgxYgS2b9+OlJQUJCUlYdSoUcjKyqp3jhdffBGJiYlISUnBgw8+iBkzZuDo0aN1j5vNZiQnJ+PIkSN45ZVXsGbNGqxcubJlflHosiTHxR8XiGTCWnIOs/c8jV1nd4mOQlTn5vI2mLoq/eoHklC6uDgEP/UkdB06iI5yRQMHDkRubi4OHz4MSapduP/RRx/F5s2bceTIkQbH79mzBz179kRpaSlMJhN27tyJQYMGYfv27Rg8eDAA4KuvvsItt9yCyspK6HS6S75u586dMWPGDMycORNA7Qhhv3798P777wMAHA4HgoKCsHTpUjzwwAOXPMcLL7yADRs2YO/evdf960CNxxFCkh2VZwj+PuTvWNZ3GTy1nqLjEAEADuhyr34QCaMwGBD4t8cQtWG905fBi3r16lVXBgHgpptuwrFjx2Cz2ZCSkoIxY8YgMjISZrMZAwcOBIAGo3vx8fF1nwcHBwNA3dRyeXk55s+fj06dOsHLywsmkwlHjx694jkkSUJQUFC96elPPvkEffv2RVBQEEwmExYvXtzgHNTyWAhJtka1GYVNYzZhaORQ0VGIcF5ZCsmTN5Y4I9OgQYj55xb4TJrklNcKNlVVVRWGDRsGk8mEDz74AHv27MHnn38OoHYq+ffUv1s+52K5tNvtAIB58+bh008/xTPPPINdu3YhNTUVcXFxVzzHxfNcPMcPP/yAu+66C8OHD8eWLVuQkpKChQsXNjgHtTyV6ABEIvnp/fDSwJew49QOLN+zHNnl2aIjkYzZQgOgKLkgOgb9SuXvj8CFC+Fxc9LVD3ZCP/zwQ4Ov27Zti6NHjyI/Px/Lly9H+K+LZ1/L9OyuXbswefJkjBs3DkDtNYWZmZlNOsf333+PyMhILFy4sO57p06danIWun6u/6MOUTMYHDkYm8duxn1x90Gt4N2eJEYZl55xDpIErzvvRMxX/3TZMggAp0+fxiOPPIL09HSsW7cOr776KubMmYOIiAhoNBq8+uqrOHHiBDZv3oynnnqqyeePjY3FZ599htTUVBw4cAD33HNP3chfU86RlZWF9evXIyMjA6tWraobraTWxUJI9Cu9So85CXPw2ejP0Dukt+g4JEP5fpy0EU0T2waRH36A4KVPQGk2i45zXSZNmoTKykr07NkTDz30EGbNmoXp06fD398fycnJ2LhxIzp16oTly5djxYoVTT7/ypUr4e3tjd69e2PUqFFISkpCQkJCk84xZswYzJ07FzNnzkS3bt2we/duLF68uMlZ6PrxLmOiy9h2ahue3/M8p5Gp1TyYF4+Bb+8XHUOWJI0GvvdPh9+0aZA0jd8LnchdcISQ6DKGRg7lNDK1ql+MpaIjyJKhZ09Eb9oE/4ceYhkk2eIIIVEjZJZk4tmfnsXuc7tFRyE3FmAz4bXni0XHkA1t27bwf2QuzIMGiY5CJBwLIVET/Dvr33h5/8s4UXJCdBRyUxvfNMFRVCw6hltTBQfDf9YseI4d4xbLyBA1BxZCoiay2W34IuMLvJ76OnIruJgwNa/1X8ZA8fMvomO4JaWnJ3ynT4f3hP+DQqsVHYfIqbAQEl2jKmsVPkz7EGt/XotSC6/9ouax5qd4eO7gjSXNSdLp4DNxInynT3P5O4eJWgoLIdF1KqkuwZqDa7Du6DpY7Fxdn67PsuMJiN34k+gY7kGphNet4+A3cxbUgQGi0xA5NRZComZyvuw8Xkt9DVtObIHd0bTFWYkumpEXj0Fceua6mYcOgf/cudDGxIiOQuQSWAiJmtmxomN4PfV1fJv1LRzg/17UNH+qiMIDrxwXHcNl6RO7I/Cvf4W+WzfRUYhcCgshUQs5XnQcb//8Nr4++TVsDpvoOOQifO0GvPEc9zNuKi4hQ3R9WAiJWtjpC6ex9ue12JyxGTX2GtFxyAVsXO0BR0Gh6BguwZCYCJ+pU2AaNAiSJImOQ+SyWAiJWkl2eTbe+fkdfHbsM1TZqkTHISe2bksbKA+li47hvJRKeCQNg8+UKdDHxYlOQ+QWWAiJWllBZQHeO/IeNqRvQHlNueg45IRW7+kKr+37RMdwOgqDAV533A7viZOgCQsVHYfIrbAQEglSUl2CDekbsCF9Axe4pnqeOZGAthu49MxFqsBA+EycAK/x46H08BAdh8gtsRASCWa1W7E9azvWpa3D/lwuN0LA9PwuGLImVXQM4bQdOsB3ymR4jBgBSa0WHYfIrbEQEjmRtII0fHT0I/zr5L9QbasWHYcEGVAZiYdezhAdQxhj377wnToFxt69RUchkg0WQiInVFRVhE+PfYoN6RuQXZ4tOg61Mk+7DmueKxMdo1VJajU8Ro6Ez5TJ0LVrJzoOkeywEBI5MZvdhm9Pf4t1R9dhb/ZeLnQtIxvf9oIjL190jBan7dQRXmPHwmPkSKh8fETHIZItFkIiF3G69DQ2Z2zGlxlf4mzZWdFxqIWt+yoWygNHRcdoEUp/P3iOGg3PMWOga8/RQCJnwEJI5GIcDgf25uzFpuObsO3UNlRaK0VHohbw1r6u8P7GfZaekbRamAcPhufYMTD26QNJqRQdiYh+h4WQyIVV1FRg26lt+CLjC04pu5knTyagw3rXX3pG3707PMeMhsfw4VCazaLjENFlsBASuYmzZWex+fhmfHXyK2ReyBQdh67TvYVdkPRWqugY10QdFgbP0aPhOXYMNBERouMQUSOwEBK5oeNFx7Etaxt2nNqB9CJugeaK+lSFY87Kk6JjNJrCZIL55iR4jRkDfWIi9xUmcjEshERu7vSF09iWtQ3bT23Hz/k/c1rZRZjtWqx9vgJw4r+i1eHhMA0YANOA/jD07AmFVis6EhFdIxZCIhnJLs/Gjqwd2HZqG1JyU2B32EVHoivY+A8fOHKcaFtDtRqG7t3rSqA2JkZ0IiJqJiyERDJVUFmA3ed243/n/of/nf8f8ivdf807V/PR122hSkkTmkHp7wdT//4w9R8AY5/eUJpMQvMQUctgISQiOBwO/FL0C3af243d53YjJTeFW+c5gTf2d4Pv1r2t+6IKBXRxXWpHAfsPgK5zJ14PSCQDLIRE1ECVtQr7cvbVFcTjxcdFR5KlpScT0LEVlp5RennB2PsmmAYMgLFfP+4YQiRDLIREdFX5lfnYl7MPqbmp2J+7H+mF6bA5bKJjub0phZ0x/K0DzXtStRq69u2hj4+HvltX6Lt2hSYysnlfg4hcDgshETVZRU0FDuYfRGpuKg7mHcSh/EMori4WHcvt3FQVhrkrM6/rHKqQYOjja4ufvmtX6Dp34t3ARNQACyERNYusC1k4mH8Qh/IOIb0oHceKjuGC5YLoWC7NYFcj+YVqwN64u8ElgwH6zp3rRv508fFQBwS0cEoicgcshETUYrLLs3Gs6Bh+KfoFx4pr/3uy5CSsdqvoaC5jY7IvHOdzGnxf6ecHbXQ0NDEx0HXsCH23rtC2bcs9gonomrAQElGrqrHXILMks7YkFh3D6dLTOFt2FmfKzqCkukR0PKeiVqjx7oFe8KpUQBsTDU10TO1/Y2Kg9PAQHY+I3AgLIRE5jTJLGc6UncHZ0tqCeKb0TF1ZPFd2zu2WwjGoDAgwBMBP7wd/vT9CTCEIN4fXfQQaA6GQFKJjEpEMsBASkcsoqS5BYVUhiqqKUFhViMKqQhRUFdT7+uLnpZZS1NhrWi2bQlLAqDLCoDbAqDbCpDbBqDbCT+8HP0Nt4fPX+9eWP0Pt5wa1odXyERFdCQshEbktm92GKlsVKq2VqLJW1X78/mtb7fdq7DWQIEEhKSBJ0m+f/+57F79WSkoY1UYYNUYYVcbaz9VGljsicmkshEREREQyx4tTiIiIiGSOhZCIiIhI5lgIiYiIiGSOhZCIiIhI5lgIiYiIiGSOhZCIiIhI5lgIiYiIiGSOhZCIiIhI5lgIiYiIiGSOhZCIiIhI5lgIiWQqOTkZXl5eomMQEZETYCEkIiIikjkWQiIiIiKZYyEkckJff/01+vbtCy8vL/j6+mLkyJHIyMgAAGRmZkKSJKxfvx69e/eGTqdD586dsXPnzrrn79y5E5Ik4Z///Ce6du0KnU6HG2+8EYcOHbri63755Zfo3r07dDodYmJisHTpUlit1pZ8q0RE5ARYCImcUHl5OR555BHs2bMHO3bsgEKhwLhx42C32+uOmTdvHv7yl78gJSUFvXv3xujRo1FQUFDvPPPmzcOKFSuwZ88eBAQEYPTo0aipqbnka27duhUTJkzA7NmzceTIEbz11ltITk7GM88806LvlYiIxJMcDodDdAgiurK8vDwEBATg0KFDMJlMiI6OxvLly7FgwQIAgNVqRXR0NGbNmoX58+dj586dGDRoENavX48777wTAFBYWIiwsDAkJydj/PjxSE5OxsMPP4zi4mIAQP/+/TF8+HA89thjda/7wQcfYP78+Th37lyrv2ciImo9KtEBiKihjIwMLF68GD/88APy8/PrRgazsrLQqVMnAMBNN91Ud7xKpUJiYiLS0tLqnef3x/j4+KB9+/YNjrlo37592LNnT70RQZvNhqqqKlRUVMBgMDTb+yMiIufCQkjkhEaNGoXw8HCsWbMGISEhsNvt6NKlCywWyxWfJ0nSVc99uWPsdjuWLl2KW2+9tcFjOp2uccGJiMglsRASOZmCggKkpaXhrbfeQr9+/QAA//3vfxsc98MPP6B///4AaqeM9+3bh5kzZzY4JiIiAgBQVFSEX375BR06dLjk6yYkJCA9PR2xsbHN+XaIiMgFsBASORlvb2/4+vpi9erVCA4ORlZWFh599NEGx73++uto27YtOnbsiJUrV6KoqAhTp06td8yTTz4JX19fBAYGYuHChfDz88PYsWMv+bpLlizByJEjER4ejjvuuAMKhQIHDx7EoUOH8PTTT7fEWyUiIifBu4yJnIxCocD69euxb98+dOnSBXPnzsULL7zQ4Ljly5fjueeeQ9euXbFr1y588cUX8PPza3DMnDlz0L17d5w/fx6bN2+GRqO55OsmJSVhy5Yt2LZtG3r06IFevXrhpZdeQmRkZIu8TyIich68y5jIxWRmZiI6OhopKSno1q3bJY+5eJdxUVERt6cjIqKr4gghERERkcyxEBIRERHJHKeMiYiIiGSOI4REREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMsdCSERERCRzLIREREREMvf/3xsL8p9ZfvcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(fruit['amount'], labels=fruit['fruit'], autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Pie Chart of Fruit Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ffb030ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       x    y          z\n",
      "0      0    0  82.462113\n",
      "1      5    0  77.620873\n",
      "2     10    0  72.801099\n",
      "3     15    0  68.007353\n",
      "4     20    0  63.245553\n",
      "..   ...  ...        ...\n",
      "436   80  100  80.000000\n",
      "437   85  100  80.156098\n",
      "438   90  100  80.622577\n",
      "439   95  100  81.394103\n",
      "440  100  100  82.462113\n",
      "\n",
      "[441 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "contour = pd.read_excel(\"D:/Users/DELL/Downloads/contour.xlsx\")\n",
    "print(contour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e99af966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.tricontourf(contour['x'], contour['y'], contour['z'], cmap='twilight_shifted')\n",
    "plt.colorbar(label='z')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Contour Plot')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5215cabb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}