PYTHON_BASICS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "905724b7-6ffc-44ae-bdc2-263ff06d0e89",
   "metadata": {},
   "source": [
    "# <strong> PHYTON BASICS </strong>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97310354-38d0-48ac-af9f-a70d8163913d",
   "metadata": {},
   "source": [
    "### DATA TYPES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "48e7c31d-62a8-41b0-bc1e-12459236d978",
   "metadata": {},
   "outputs": [],
   "source": [
    "int = 12;\n",
    "float = 27.123;\n",
    "string = \"HELLO WORLD\";"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b083499f-4dbf-4e0c-8c84-4761480318de",
   "metadata": {},
   "source": [
    "### EXPRESSIONS AND VARIABLES "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac61f95-53b8-445b-a308-eb9e8a502822",
   "metadata": {},
   "source": [
    "### STRING OPERATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3637a557-a5c3-4d78-8ddb-d87ef3e22701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m\n"
     ]
    }
   ],
   "source": [
    "name =\"mike\";\n",
    "print(name[0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f1c5199-e32d-44dd-924b-d7a8daa03e07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mi\n"
     ]
    }
   ],
   "source": [
    "#length of a string \n",
    "len(\"mike\");\n",
    "#slicing\n",
    "name[0:2];\n",
    "#stride\n",
    "#name[::2]\n",
    "print(name[0:2])\n",
    "#NEW LINE =\\N\n",
    "#TAB \\t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd15b540-c351-4d76-bfb1-464f2b18b2ec",
   "metadata": {},
   "source": [
    "### STRING OPERATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "126267cd-a3cd-44ff-a64e-b4f9e3a811da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MIKE\n",
      "mike\n"
     ]
    }
   ],
   "source": [
    "name =\"mike\";\n",
    "b=name.upper();\n",
    "print(b);\n",
    "\n",
    "name =\"mike\";\n",
    "c=name.lower();\n",
    "print(c);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15b71bc-fe5f-4541-a07a-4dc1bbd6420e",
   "metadata": {},
   "source": [
    "### COMPOUND DATA TYPES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e813029c-27fd-4b1f-87e1-2e602fb41be8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "#TYPLES = ()\n",
    "TUPLE = (\"disco\",10,1.2);\n",
    "print(TUPLE[1]);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "513ec7e8-40a2-4afb-b48d-94c398b67a8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.1\n"
     ]
    }
   ],
   "source": [
    "#list = []\n",
    "list = [\"mike\",10.1,1982];\n",
    "print(list[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "128aa56d-7ac1-4aa4-a6be-162451343b14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mike', 10.1, 1982, ['pop', 10]]\n"
     ]
    }
   ],
   "source": [
    "#LIST OPERATIONS\n",
    "list.append(['pop',10]);\n",
    "print(list);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7a7d68ff-10ea-4a74-9883-461c5565d562",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mike', 10.1, 1982, ['pop', 10], 'pop', 10]\n"
     ]
    }
   ],
   "source": [
    "#extend\n",
    "list.extend(['pop',10]);\n",
    "print(list);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9690de12-ad6b-4517-acfd-0798f8eb7e9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.1, 1982, ['pop', 10], 'pop', 10]\n"
     ]
    }
   ],
   "source": [
    "#delete\n",
    "del(list[0]);\n",
    "print(list);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73bc2d17-e01a-4f6b-8aef-0be890041f9d",
   "metadata": {},
   "source": [
    "### DICTIONARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d695b4f-4791-4614-9b49-0a179c697c19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ket1': 1, 'ket2': 2, 'ket3': [3, 3, 3], 'ket4': (4, 4, 4)}\n"
     ]
    }
   ],
   "source": [
    "dict ={\"ket1\":1,\"ket2\":2,\"ket3\":[3,3,3],\"ket4\":(4,4,4)};\n",
    "print(dict);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "179e2dff-1e53-4fa6-b653-ac89cb91795a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print(dict[\"ket1\"]) #access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea538c40-0ebd-4273-befb-7b4c22e961b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ket1', 'ket2', 'ket3', 'ket4'])\n"
     ]
    }
   ],
   "source": [
    "print(dict.keys());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9cc0f26f-b7ee-4dba-9293-c962990d8cd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values([1, 2, [3, 3, 3], (4, 4, 4)])\n"
     ]
    }
   ],
   "source": [
    "print(dict.values());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "326a703d-e995-494d-b97f-666ca676fdda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name of the first key: ket1\n"
     ]
    }
   ],
   "source": [
    "first_key = next(iter(dict.keys()))\n",
    "\n",
    "# Print the name of the first key\n",
    "print(\"Name of the first key:\", first_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5f7de273-9967-4ce5-a0b3-89c3b5816da9",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'list' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[44], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m keys_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(keys_list[\u001b[38;5;241m1\u001b[39m])\n",
      "\u001b[1;31mTypeError\u001b[0m: 'list' object is not callable"
     ]
    }
   ],
   "source": [
    "keys_list = list(dict.keys())\n",
    "print(keys_list[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f875322d-ae92-4931-a0da-abea780d614c",
   "metadata": {},
   "source": [
    "### SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a77669e-e559-4bb5-be61-43b476786ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'pop', 'r&b', 'rock', 'sell'}\n"
     ]
    }
   ],
   "source": [
    "set1={\"pop\",\"rock\",\"sell\",\"rock\",\"r&b\",\"rock\"};\n",
    "print(set1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ff6be24-fdea-4f2d-ae79-011f4b1695bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'pop', 'r&b', 'rock', 'sell'}\n"
     ]
    }
   ],
   "source": [
    "#convert list to set\n",
    "A = set(set1);\n",
    "print(A);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "90663be0-096e-4fc1-9998-7a44ca7d057b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sell', 'r&b', 'pop', 'rock', 'NSYNC'}\n"
     ]
    }
   ],
   "source": [
    "#add element\n",
    "A.add(\"NSYNC\");\n",
    "print(A);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ad9e6f9b-b996-4439-b7fa-829c3cfe1f8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sell', 'r&b', 'pop', 'rock'}\n"
     ]
    }
   ],
   "source": [
    "#removw\n",
    "A.remove(\"NSYNC\");\n",
    "print(A);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ea1f651b-9cfe-4f58-af37-9a7fd9ac0571",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'AC/DC', 'Thiller', 'Back in Black'} {'AC/DC', 'The dark side of the moon', 'Back in Black'}\n"
     ]
    }
   ],
   "source": [
    "#EXAMPLE SET\n",
    "albumset1=set([\"Thiller\",\"AC/DC\",\"Back in Black\"]);\n",
    "albumset2 = set([\"AC/DC\",\"Back in Black\",\"The dark side of the moon\"]);\n",
    "print(albumset1,albumset2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "30be36f3-626e-47a9-b088-6b7be26abf33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'AC/DC', 'Back in Black'} {'Thiller'}\n"
     ]
    }
   ],
   "source": [
    "#find the intersections and difference\n",
    "intersection = albumset1&albumset2;\n",
    "print(intersection,albumset1.difference(albumset2));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "673b3fd4-1967-4b48-89ab-973216512f5f",
   "metadata": {},
   "source": [
    "### CONDITION AND BRANCHING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e23377-4e53-4edd-b4f0-7995e0e29050",
   "metadata": {},
   "source": [
    "### LOOPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "517da1f8-7f73-4216-bced-31f6aa841b5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range(10, 15)\n"
     ]
    }
   ],
   "source": [
    "#range (N)\n",
    "print(range(10,15));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e740f2f4-5a27-489e-a584-24c9f0d2721e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1982\n",
      "1980\n",
      "1973\n"
     ]
    }
   ],
   "source": [
    "#for loop example\n",
    "dates=[1982,1980,1973];\n",
    "N=len(dates);\n",
    "for i in range(N):\n",
    "    print(dates[i])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9946dc5-7ede-496e-86a0-35c92be8595b",
   "metadata": {},
   "source": [
    "### WHILE LOOP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ddce6a5f-7a34-4048-a616-cc7fdc39afa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1982\n",
      "1980\n",
      "1973\n"
     ]
    }
   ],
   "source": [
    "#while loop example\n",
    "i=0;\n",
    "year = len(dates);\n",
    "while (i<year):\n",
    "    print(dates[i]);\n",
    "    i=i+1;\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e939a65-6a7a-45c2-9f30-db409b035d34",
   "metadata": {},
   "source": [
    "### FUNCTIONS"
   ]
  },
  {
   "cell_type": "raw",
   "id": "83f53a22-9136-4b7f-8390-d2fb92f4481b",
   "metadata": {},
   "source": [
    "#defining a function\n",
    "def name():\n",
    "    body\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b24c5b8c-a55b-40a6-8a22-9723d31b947e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Example\n",
    "def square(a):\n",
    "    #local variabl b\n",
    "    b=1;\n",
    "    c=(a*a)+b;\n",
    "    print(a,\"if you square +1\",c);\n",
    "    return (c);\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "76499e00-55b3-4e7d-bd2f-8cc2822a3cac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 if you square +1 5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "square(2) #calling the function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe8f7de-25dc-4ed3-a79a-a81691c26642",
   "metadata": {},
   "source": [
    "### GLOBAL VARIABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2f63f2e0-a5c5-41a8-98ed-aa65c11631b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Micheal Jackson is an artist\n"
     ]
    }
   ],
   "source": [
    "#Eample of a global variable in @ outside a function\n",
    "artist=\"Micheal Jackson\";\n",
    "def printer(artist):\n",
    "    global interval_var #internal variable\n",
    "    internal_var =\"Whithney Houston\";\n",
    "    print(artist,\"is an artist\");\n",
    "printer(artist); #calling the function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1875fdba-be8e-4101-a917-bc0473aab7a3",
   "metadata": {},
   "source": [
    "### COLLECTION AND FUNCTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e0114b5f-7e29-4d9e-a533-1757ecb06801",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Example\n",
    "def printall(*args):\n",
    "    print(\"No of argument:\",len(args))\n",
    "    for argument in args:\n",
    "        print(argument)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "762799a1-37bf-4adc-98d2-c3c319ff4cff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of argument: 3\n",
      "forefeather\n",
      "Adonis\n",
      "Bone\n"
     ]
    }
   ],
   "source": [
    "printall('forefeather','Adonis','Bone') #calling the function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fba48fe-8776-4f53-8239-9f3101967858",
   "metadata": {},
   "source": [
    "### OBJECT AND CLASS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e3c09e4f-38cb-4310-b36b-7be65f32a813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+o1sYpaWlwWQyYeLEiXodUheXL8sJDc2MdSLSkdksV4e9cEF1Jf6hyyl0x44dWLhwIW644QY9Dqerl18Gjh8PztEtRGRcbjdw+jQwc6bqSvxD8zA6f/48HnjgAbz99tu45pprtD6cro4dkx+EYOq3JaLA4XbLP4gzMlRXUnOah9HYsWPRr18/3HbbbVXu63Q64XA4Sm1GNmECh3ETkXrjxgX+H8WahtGSJUvwn//8B2lpaV7tn5aWBpvNVrQlJiZqWV6NfPUVsHIlBy0QkVqFhcD69cDy5aorqRnNwigrKwsTJkzAokWLEBkZ6dVzpkyZArvdXrRlZWVpVV6NFBQAY8dy0AIRGYNnMMPly6orqT7NTqe7du3C6dOnkZKSAqvVCqvVis2bN2PevHmwWq1wldO/FRERgZiYmFKbEb37LpCZyUELRGQMbjeQkwO88YbqSqrPJIQ2PY15eXn45ZdfSj328MMPo2XLlnjqqafQpk2bKn+Gw+GAzWaD3W43TDBduAAkJQFnzwZ+Hy0RBRebDfjlF/lfo/D2PG7VqoDo6OgygVO7dm3UrVvXqyAyqnnzGEREZEx5ecCcOYE53JtXPXzw22/Aiy8yiIjImNxu4JVXgFOnVFfiO81aRuXZtGmTnofzu5dekvPPEREZVUGBbBkF2vUjtoy8dOIE8Pe/874iIjI2lwtYsAA4fFh1Jb5hGHlp+nQGEREFBpMJePZZ1VX4hmHkhePHgffeYxgRUWAoLASWLgUOHVJdifcYRl545RXVFRAR+cZiAWbNUl2F9xhGVThzRva/slVERIGksBD44AN5vTsQMIyqMG8e558josAkBDB3ruoqvMMwqoTDwRF0RBS4XC5g/nx5o77RMYwqsWABcP686iqIiKovPx/4xz9UV1E1hlEFLl8GZs/mbAtEFNjcbuDVV43/hzXDqAKLFwO5uaqrICKqOYdD3p5iZAyjCsybx/WKiCh4vP66sXt6eLotx//9H7BnD9crIqLgIIS8AXbzZtWVVIxhVI433wSsuk4hS0SkLavV2JOnMoyucPYs8PHHvLeIiIJLYSGwYgWQna26kvIxjK6Qns77iogoOAkBvPOO6irKxzAqwe2WF/l4rYiIgpHbLbvqCgpUV1IWw6iEtWvlDN1ERMHq9Glg1SrVVZTFMCrhvfc4cIGIgpvFIi9HGA3D6Hd5ecBnn3HgAhEFN5cLWLPGePPVMYx+t2IF4HSqroKISHtuN/DJJ6qrKI1h9Lv335fNVyKiYGcyyXOekTCMAJw6BWzcyCHdRBQa3G5g+3YgM1N1JcUYRgCWLJF/KRARhQqLRd7gbxQMI8hRdEaeQJCIyN9cLtlVZ5RzX8iHUUYG8OOPxnlDiIj0cvgwsHu36iqkkA+jzz/nUhFEFJosFnkONIKQPw2vWMFWERGFJpdLngONIKTD6MwZ4PvvGUZEFLr27gWyslRXEeJh9OWXDCIiCm0mkzHmqgvpMPr8c97oSkShzWSSU6GpFrJhdPmynJ+JN7oSUShzu+VN/3l5auvQNIzS0tLQsWNHREdHIy4uDnfddRcyMjK0PKTXNm6UgUREFOoKC+USOippGkabN2/G2LFjsX37dqxbtw6FhYVITU3FhQsXtDysV1av5nIRRESAPBd+8YXaGkxC6HcJ/8yZM4iLi8PmzZtx6623Vrm/w+GAzWaD3W5HTEyMX2tp1kze8EVEREBCAnDypP9/rrfncV3bBna7HQBQp06dcr/vdDrhLLGOg8Ph0KSOX39lEBERlZSdDRw7BiQlqTm+bgMYhBCYNGkSunXrhjZt2pS7T1paGmw2W9GWmJioSS2bN2vyY4mIAtqmTeqOrVsYjRs3Dj/++CMWL15c4T5TpkyB3W4v2rI0uhNr40ZeLyIiKslqledGZcfX4yDjx4/HypUrsWXLFjRs2LDC/SIiIhAREaF5PevWcXlxIqKSCgvluVEINUvqaNoyEkJg3LhxWL58OTZs2IDk5GQtD+eVnBzgyBHVVRARGc+pU/K6kQqahtHYsWOxaNEifPzxx4iOjkZOTg5ycnJw6dIlLQ9bKZV9okRERqeqq07TMJo/fz7sdjt69OiB+Pj4om3p0qVaHrZSW7YAYWHKDk9EZFhWqzxHKjm2lj9cx1uYvLZ9O1BQoLoKIiLjKSwEtm1Tc+yQmpuuoADYt091FURExnX4MKBikpyQCqP9+9kqIiKqjNsN/PCD/scNqTDatUt1BURExmYyqTlXhlQY7dzJwQtERJWxWOS5Um8hFUbff89uOiKiyhQWyoFeeguZMOLgBSIi76gYxBAyYfTTT2wVERF5w+0GfvxR32OGTBgdPKi6AiKiwKH3otwhE0aHDnGmbiIib4SFyXOmnkIqjIiIqGouF8NIMwcOcNkIIiJvuN1ykgA9hUQYCcGWERGRLzIzZSjpJSTCKDcXyMtTXQURUeDIzwc0Wmy7XCERRmwVERH5Ts9zJ8OIiIjKMJkYRn534gSHdRMR+cJqledOvYREGJ06JVOeiIi8I4Q8d+olJMIoO5vDuomIfFFYyJaR3x0/LlOeiIi8xzDyMz2bmkREwYLddH7kdgNnzqiugogo8DgcgNOpz7GCPozOnpXzLBERke/0ah0FfRixi46IqPqys/U5TtCH0dmzqisgIgpcv/2mz3GCPow4Jx0RUfXpdQ4N+jA6f151BUREgUuvc2jQhxFbRkRE1WOxsGXkN3l58hdKRES+MZsZRn5z/rz8hRIRke/YTecn7KYjIqo+toz8hGFERFQ9bneQhdGbb76J5ORkREZGIiUlBd98840ehwUAXLyo7zruRETBwuWS51A9aB5GS5cuxcSJEzF16lTs3r0bt9xyC/r27Yvjx49rfWgAMog4YzcRUfXo9ce85mE0d+5cjBgxAiNHjsT111+P1157DYmJiZg/f77WhwbAVhERUU0ERRjl5+dj165dSE1NLfV4amoqtm7dWmZ/p9MJh8NRaqsptoyIiKpPr4mmNQ2j3NxcuFwu1K9fv9Tj9evXR05OTpn909LSYLPZirbExMQa18CWERFR9QVFy8jDZDKV+loIUeYxAJgyZQrsdnvRlpWVVeNj8x4jIqLq0+scatXyh8fGxsJisZRpBZ0+fbpMawkAIiIiEBER4dcazGbAZGJXHRFRdeg1g42mmRceHo6UlBSsW7eu1OPr1q1D165dtTx0EbaMiIiqLyhaRgAwadIkDB06FB06dECXLl2wcOFCHD9+HKNHj9b60ACKW0ZEROS7oAmj+++/H2fPnsWMGTNw6tQptGnTBl9++SUaN26s9aEBALVry18mlx4nIvKNxSLPoXrQPIwAYMyYMRgzZowehyrjqquUHJaIKOCZzfqdQ4P+ikp0tOoKiIgCl17n0JAII95rRERUPQwjP4mO5vUiIqLqcLsZRn7Da0ZERNXjcvGakd/wmhERUfWxZeQnDCMioupjGPlJ3bqqKyAiClx16uhznKAPo/h41RUQEQWuhAR9jhP0YVS3LmDV5dZeIqLg06CBPscJ+jAymYB69VRXQUQUeGw2wM8LKVQo6MMI0K+ZSUQUTPS8zBESYdSoEWfuJiLyVcOG+h0rJMIoPp7XjYiIfGG1Moz8Lj6eK70SEfnCZGI3nd8lJgKFhaqrICIKHIWF8typl5AIo+bNVVdARBRYhND33MkwIiKicjGM/KxuXSAmRnUVRESBIyICuPZa/Y4XEmEEsHVEROSLJk3ksuN6CZkwat2aw7uJiLxhNgNt2uh8TH0Pp07z5hzeTUTkDYtF/96kkAojLj9ORFS1ggKGkWZatlRdARFR4GjRQt/jhVQYhYerroKIyPjMZuCGG3Q+pr6HU8dq1f+XS0QUiJo3B6Ki9D1myIQRAHTqBISFqa6CiMi4rFagSxf9jxtSYZSSIi/MERFR+Vwuea7UW8iFERERVUwIhpHmWrXiIAYiosqYzcCNNyo4rv6HVIeDGIiIKqdi8AIQYmEEAJ07cxADEVF5VA1eADQMo2PHjmHEiBFITk5GVFQUmjZtimnTpiE/P1+rQ3rl1ls5iIGIqDyFhUD37mqOrdnUoQcPHoTb7caCBQtw3XXXYd++fRg1ahQuXLiAl19+WavDVknVL5qIKBD06KHmuCYh9Js+dM6cOZg/fz4yMzO92t/hcMBms8FutyPGjwsStWgBHDrktx9HRBQUGjYEsrL8+zO9PY/ruqiC3W5HnTp1Kvy+0+mE0+ks+trhcGhSx+23A0ePsruOiMjDapXnRlV0G8Bw5MgRvP766xg9enSF+6SlpcFmsxVtiYmJmtTSsyeDiIiopMJCeW5Uxecwmj59OkwmU6Xbzp07Sz0nOzsbf/zjHzFo0CCMHDmywp89ZcoU2O32oi3L3+3F3916qyY/logooKm8pu7zNaPc3Fzk5uZWuk9SUhIiIyMByCDq2bMnOnXqhPfeew9mH9ax1eqaESBn8c7I8OuPJCIKWImJwPHj/v+5ml0zio2NRWxsrFf7njx5Ej179kRKSgrS09N9CiKt9ekDHDkim6ZERKHMagVSU9XWoFk6ZGdno0ePHkhMTMTLL7+MM2fOICcnBzk5OVod0if9+zOIiIgAeS7s319tDZqNplu7di0OHz6Mw4cPo2HDhqW+p+No8gp17w7UqgVcvKi6EiIitcLC1I6kAzRsGQ0fPhxCiHI3IwgPB+64A7BYVFdCRKSOxQLcdhtQu7baOoxzEUeBgQPl2h1ERKHK7Qbuukt1FSEeRnfcIadLJyIKVUKov14EhHgY1akDdO0KmEyqKyEi0p/JBLRrByQkqK4kxMMIAO6+m2FERKHJbAbuuUd1FVLIh9HAgbLPlIgo1Lhc8hxoBCEfRk2byvXe2ToiolBiMsmZaNq2VV2JFPJhBADDhqmugIhIXyYTMHy4cf4QZxgBuP9+47whRER6cLuBIUNUV1GMYQQgLk7Oy8QbYIkoFJjNQLduQKNGqispxjD63dChvAGWiEKDEMa7PMEw+t3AgcDvq14QEQU1iwX4059UV1Eaw+h3tWvLN8eq60LsRET6slqBO+8Err5adSWlMYxKGD6cy0oQUXArLAQeflh1FWUxjEro1Uved8SRdUQUrBIS5LycRsMwKsFkAsaPV10FEZE2zGZg3DhjjhxmGF1h2DC51hERUbAxm4ERI1RXUT6G0RWuvhp48EEOZCCi4GK1AoMGyfsqjYhhVI4xYziQgYiCS2EhMHas6ioqxjAqx003AR07cuE9IgoOJhPQqpVcv82oeLqtwPjxXFqCiILHX/5i7JHCDKMK3H8/0KCB6iqIiGru6qvllGdGxjCqQHg48NRTxv5LgoioKmYz8Ne/ArVqqa6kcgyjSowaBdhsqqsgIqq+yEg5KMvoGEaVqF0beOwxDmQgosBkscjr30abh648PM1WYdw4ICJCdRVERL4zm4GJE1VX4R2GURXq1JFNXCNOn0FEVBGLRc62ECgDsRhGXpg0iQMZiCiwuN3AE0+orsJ7DCMvJCQA//u/bB0RUWCwWuW0Zk2aqK7EewwjLz3zDBAWproKIiLvzJihugLfMIy8FB8vu+s4so6IjMxikXPQJSWprsQ3PLX64Mkngeho1VUQEVUsIgKYOlV1Fb7TJYycTifatWsHk8mEPXv26HFITdhswLPPcjADERmT2SxnjqlXT3UlvtMljJ588kkkJCTocSjNjR0L1K/PQCIi47HZ5I36gUjzMFqzZg3Wrl2Ll19+WetD6SIyEnjhBUAI1ZUQERUzmYDnnw/cSwmahtGvv/6KUaNG4cMPP0QtL2bpczqdcDgcpTYjeughoEULDvUmImMwm4HERHkLSqDSLIyEEBg+fDhGjx6NDh06ePWctLQ02Gy2oi0xMVGr8mrEagXmzwdcLtWVEBHJG1zfeCOwpy7zOYymT58Ok8lU6bZz5068/vrrcDgcmDJlitc/e8qUKbDb7UVbVlaWr+XppmdP4L77ZDAREalitQJ33AH076+6kpoxCeHb1Y/c3Fzk5uZWuk9SUhIGDx6MVatWwVTiSr/L5YLFYsEDDzyA999/v8pjORwO2Gw22O12xMTE+FKmLk6cAJo1Ay5fVl0JEYWqsDDg4EHjzrbg7Xnc5zDy1vHjx0td88nOzkafPn2wbNkydOrUCQ0bNqzyZxg9jABg9mxg8mQOaCAi/ZlMwHPPAdOnq66kYt6exzXrZGrUqFGpr6+66ioAQNOmTb0KokAxcSLw9tvA0aO8hkRE+jGbgWuvlfcVBQPOwFBD4eEczEBE+nO7gX/8A4iKUl2Jf+gWRklJSRBCoF27dnodUje33Qb86U8c6k1E+vAMWrjzTtWV+A9bRn7yxhvyZjPOzEBEWouMBBYsCK7zDcPIT+LigLfe4kAGItLevHlAEF16B8Aw8qv77gPuvpvddUSkDasV6NMHGD5cdSX+xzDyI5NJto7YXUdEWoiMBN55JzjPLwwjP2N3HRFpJRi75zwYRhpgdx0R+VMwd895MIw04Omus9m4TDkR1YzJBNSqFbzdcx48VWokLg5YvFjemEZEVF1CAB9+GLzdcx4MIw2lpgJTpgT3XzNEpB2zGZgwARgwQHUl2mMYaWzGDKBTJy41QUS+sVqBtm2BWbNUV6IPhpHGrFZg6VLZ58sWEhF5w2SS814uWxbYC+b5gmGkg0aNZJ8vh3sTkTeEAN59F7juOtWV6IdhpJMBA+RyExxdR0SVMZuBkSOBwYNVV6Ivnhp1NGsW0LEjrx8RUfk814n+/nfVleiPYaSj8HDg88+B2FjeEEtEpVks8t7EVavkNeZQwzDSWf36wOrV8i8gDmggIg+TSQZRYqLqStRgGClw003A++9zQAMRFVu4EOjSRXUV6jCMFLn/fmDqVNVVEJFqJpMc3PTww6orUYthpNCMGXLZYF4/IgpNFgvQqxcwZ47qStRjGClkNgMffQQ0b84RdkShxmoFGjcGPvmE//4BhpFy0dHAunVyYlW2kIhCg8UCXHMN8O9/y/8Sw8gQrr1WfiijoxlIRMHObJYrtq5fDyQlqa7GOBhGBtGyJfDVV0BYGId8EwUrk0n+wbl6NXDDDaqrMRaGkYF06gQsX84pg4iC2ZIlQPfuqqswHp72DKZvX+Cf/1RdBRFpYf584J57VFdhTAwjA3roIQ71JAo2zz8P/PnPqqswLoaRQf31r/I+JCIKfE89BTz7rOoqjI1hZGDPPgs895zqKoioJh5/HEhL48CkqjCMDG76dODpp1VXQUTVMWGC7HJnEFWNYWRwJhMwcybwzDOqKyEiXzz2GPDqqwwibzGMAoDJBPztb7KVRETG98QTwCuvMIh8oXkYrV69Gp06dUJUVBRiY2NxD8c1Vtu0abKVRETG9fTTclVnBpFvNJ2e79NPP8WoUaPw4osvolevXhBCYO/evVoeMuhNnQrUrQuMGSO/5ppIRMYxd67sniPfmYTQ5nRWWFiIpKQkPP/88xgxYkS1fobD4YDNZoPdbkdMTIyfKwxsn34KDB4MuN1yIyI1zGbZCnr/feCBB1RXYzzensc166b7z3/+g5MnT8JsNqN9+/aIj49H3759sX///gqf43Q64XA4Sm1UvnvvlRMtRkVxclUiVSwWIDwc+PJLBlFNaRZGmZmZAIDp06fjmWeewRdffIFrrrkG3bt3x2+//Vbuc9LS0mCz2Yq2xFBdDN5L3bsD330np6DneihE+rJagZgYYMsWIDVVdTWBz+cwmj59OkwmU6Xbzp074f6972jq1Km49957kZKSgvT0dJhMJnzyySfl/uwpU6bAbrcXbVlZWTV7dSHgxhuB//s/IDGRgUSkF6sVaNAA2L4d6NhRdTXBwefT17hx4zB48OBK90lKSkJeXh4AoFWrVkWPR0REoEmTJjh+/Hi5z4uIiEBERISvJYW85GTg+++BAQNkMPEaEpF2zGa5/MMXXwDx8aqrCR4+h1FsbCxiY2Or3C8lJQURERHIyMhAt27dAAAFBQU4duwYGjdu7HulVKl69YDNm4FHH+Ws30RaGjIEePtteb2W/Eeza0YxMTEYPXo0pk2bhrVr1yIjIwOPPvooAGDQoEFaHTakhYcD77wDzJsn/3rjukhE/uEZMTd7NvDhhwwiLWh6lWHOnDmwWq0YOnQoLl26hE6dOmHDhg24hou+a8ZkAsaPB1q1kuumXLwIFBaqrooocFmtQEQE8K9/AXfcobqa4KXZfUb+wPuMaubIEfmP58gRwOVSXQ1R4LFYgEaN5NDtli1VVxOYlN9nROo1bQrs2AH066e6EqLA1Ls3sGsXg0gPDKMgFxMDfPaZvI5ktfIGWaKqWCxymz0bWLNG3sdH2mMYhQDPdaTt24GGDRlIRBWxWID69YFvvpEzb3MQkH74qw4hKSnAjz8Cf/qT6kqIjKl/f2DfPqBLF9WVhB6GUYiJiQEWL5b3SYSHs5VEZLHILuzXXwdWrGC3nCoMoxBkMgEjR8oLsy1acN0VCl1mM9CkiZzBZNw4/ltQiWEUwtq0AXbvBp59tviiLVEosFhk8DzxhOy6vukm1RURwyjEhYcDzz8vh4CzlUShwNMa2rYNeOklIDJSdUUEMIzod+3bs5VEwe3K1lCnTqoropIYRlSErSQKViYTW0NGxzCiMjytJM8/Wq6TRIHKM6/cjBlsDRkdw4jKFR4OPPkk8PPPwN13y8fYdUeBwnOz6h13AAcPAs88w9aQ0TGMqFING8rZitevl90c7LojozOZ5OSma9YAn38OJCWproi8wTAir/TuLe9Mnz1bruXCrjsyGk+X3MyZsjX0xz+qroh8wTAir4WHA3/9q+y6GzpU/gXKUCLVrFb5WRw8GDh0CHj6aRlKFFgYRuSza6+VS5vv21e8PAWvJ5HePJ+5228H9uyRK7A2aqS0JKoBhhFVW6tWcnmKbduKJ5ZkKJHWPIMTOnQAtmyRC9/dcIPamqjmGEZUY507y5PCmjXFi5Bx6n3yN89nqlkzOTBh2zbgllvU1kT+w1MG+YXJJC8Y//ijHH3XurV8nC0lqinPZ6hFC+Cjj4D9+4EBAziyM9gwjMivzGZg0CDghx+Ar76SrSaAAx3Id54Q6tABWLVKhtD//A//wAlWDCPShMkE9OkDfPst8N13QGqqfJyhRFXxfEZ69gQ2b5bdcf37syUU7BhGpLmuXYHVq2UX3n33yZMNrynRlSwW+bm4+2651ta6dcCttzKEQgVPCaSbtm1ln39WlpwrLD5ePs5ul9Dlee/j4uSM8cePy2uOXF8o9DCMSHcNGgBTp8oTz2efye4YgKEUSjxdcd26AcuWASdOANOmyXvYKDQxjEgZqxUYOFB2xxw6BEyYAMTEFH+Pgovnj42rrgIefRQ4cADYtAm4914gLExpaWQADCMyhGbNgFdeAU6fBlaskCHlOUHx+lLgMpuLp43q10+2gs6cAebNA66/XnV1ZCT8+5MMJSICuOsuuf33v8Dy5cD77wPffCNPam634gLJK2azfK86dwaGDQP+9CegTh3VVZGRmYQQQnURFXE4HLDZbLDb7Yjx9N9QSDpxAli8GPj4YzkPmcnEcDISsxkQQm5t28pJSx94AGjcWHVlpJq353GGEQWc7Gzgiy/k4If164GCAtkNVFiourLQ4vmdW61Ar16yNdu/P5CYqLoyMhKGEYWECxfkAIiVK2U4nTtXfI2JrSb/Kvl7tdnkdb0BA+QNzdHRamsj42IYUchxueSNtRs3Ahs2yLv3z59nOFWXZ/CBywXUqiVvQO3VSw7Fb9eOIx7JO4YIo0OHDuGJJ57Ad999h/z8fLRt2xYzZ85ET8+NJVVgGFFNuFzy+tKmTcC//y3D6eJF+b2wMNm9R8VK/k6iouSM2L17Az16yJtQGT5UHd6exzX9ePXr1w/NmzfHhg0bEBUVhddeew39+/fHkSNH0KBBAy0PTQSLBUhJkdvjj8vrGz/9JKea2bUL2L5dtqTy8+X+oRRQJV9rWJgcdNCpk5yUNCVFrlXFe39IT5q1jHJzc1GvXj1s2bIFt/y+6EheXh5iYmKwfv169O7du8qfwZYRae3KgNq9G8jIAHJzi/exWuUoMZdLXZ3VYbHIbraSAzvq1pVLMdx4I4OH9KG8ZVS3bl1cf/31+OCDD3DTTTchIiICCxYsQP369ZGSklLuc5xOJ5xOZ9HXDodDq/KIAMigadtWbsOHFz+elydnhSi57d8PZGbK75VkNssTvxD6jejzTCrqcpW9FnbVVUByslxTqnlzGT7Nm8sbi202feoj8pVmYWQymbBu3ToMHDgQ0dHRMJvNqF+/Pr766itcffXV5T4nLS0Nzz//vFYlEXktOrq4i+9Kly8Dp04Vb9nZxf/NzpY36zocMrQuXJDXqarb/RcWJgcP1K4ta4qJkYGSkCC3+Hi5lfz/qKgavXQiJXzupps+fXqVgbFjxw6kpKTgrrvuQkFBAaZOnYqoqCi88847WLlyJXbs2IF4z5TNJZTXMkpMTGQ3HQW8/Hw5si8vTwaT2128CVHc0jGbZWstOlpu4eGqKyeqGc1G0+Xm5iK3ZId6OZKSkvDdd98hNTUV586dK1VAs2bNMGLECEyePLnKY/GaERFRYNPsmlFsbCxiY2Or3O/i72NozVfMcmk2m+HmDR9ERFSCZvMhd+nSBddccw2GDRuGH374oeieo6NHj6Jfv35aHZaIiAKQZmEUGxuLr776CufPn0evXr3QoUMHfPvtt/j8889x4403anVYIiIKQJwOiIiINOPteZzLlhERkXIMIyIiUo5hREREyjGMiIhIOYYREREpxzAiIiLlGEZERKQcw4iIiJRjGBERkXKGXtXeMzkEF9kjIgpMnvN3VZP9GDqM8n5fUjMxMVFxJUREVBN5eXmwVbLUsKHnpnO73cjOzkZ0dDRMJpPmx/Ms5peVlRUUc+EF2+sBgu81BdvrAYLvNQXb6wH0fU1CCOTl5SEhIaHMkkIlGbplZDab0bBhQ92PGxMTEzQfOiD4Xg8QfK8p2F4PEHyvKdheD6Dfa6qsReTBAQxERKQcw4iIiJRjGJUQERGBadOmISIiQnUpfhFsrwcIvtcUbK8HCL7XFGyvBzDmazL0AAYiIgoNbBkREZFyDCMiIlKOYURERMoxjIiISLmQDaNNmzbBZDKVu+3YsaPC5w0fPrzM/p07d9ax8solJSWVqW/y5MmVPkcIgenTpyMhIQFRUVHo0aMH9u/fr1PFFTt27BhGjBiB5ORkREVFoWnTppg2bRry8/MrfZ7R3qM333wTycnJiIyMREpKCr755ptK99+8eTNSUlIQGRmJJk2a4K233tKp0sqlpaWhY8eOiI6ORlxcHO666y5kZGRU+pyK/p0dPHhQp6orN3369DK1NWjQoNLnGPX98SjvHGAymTB27Nhy9zfKe2ToGRi01LVrV5w6darUY88++yzWr1+PDh06VPrcP/7xj0hPTy/6Ojw8XJMaq2vGjBkYNWpU0ddXXXVVpfvPnj0bc+fOxXvvvYfmzZtj5syZuP3225GRkYHo6Gity63QwYMH4Xa7sWDBAlx33XXYt28fRo0ahQsXLuDll1+u9LlGeY+WLl2KiRMn4s0338TNN9+MBQsWoG/fvjhw4AAaNWpUZv+jR4/ijjvuwKhRo7Bo0SJ89913GDNmDOrVq4d7771XwSsotnnzZowdOxYdO3ZEYWEhpk6ditTUVBw4cAC1a9eu9LkZGRml7vSvV6+e1uV6rXXr1li/fn3R1xaLpcJ9jfz+eOzYsQMul6vo63379uH222/HoEGDKn2e8vdIkBBCiPz8fBEXFydmzJhR6X7Dhg0TAwcO1KeoamjcuLF49dVXvd7f7XaLBg0aiJdeeqnoscuXLwubzSbeeustDSqsmdmzZ4vk5ORK9zHSe/SHP/xBjB49utRjLVu2FJMnTy53/yeffFK0bNmy1GN//vOfRefOnTWrsbpOnz4tAIjNmzdXuM/GjRsFAHHu3Dn9CvPBtGnTxI033uj1/oH0/nhMmDBBNG3aVLjd7nK/b5T3KGS76a60cuVK5ObmYvjw4VXuu2nTJsTFxaF58+YYNWoUTp8+rX2BPpg1axbq1q2Ldu3a4YUXXqi0W+vo0aPIyclBampq0WMRERHo3r07tm7dqke5PrHb7ahTp06V+xnhPcrPz8euXbtK/W4BIDU1tcLf7bZt28rs36dPH+zcuRMFBQWa1VoddrsdALx6P9q3b4/4+Hj07t0bGzdu1Lo0n/z8889ISEhAcnIyBg8ejMzMzAr3DaT3B5CfwUWLFuGRRx6pcrJp1e8Rw+h37777Lvr06VPlchV9+/bFRx99hA0bNuCVV17Bjh070KtXLzidTp0qrdyECROwZMkSbNy4EePGjcNrr72GMWPGVLh/Tk4OAKB+/fqlHq9fv37R94ziyJEjeP311zF69OhK9zPKe5SbmwuXy+XT7zYnJ6fc/QsLC5Gbm6tZrb4SQmDSpEno1q0b2rRpU+F+8fHxWLhwIT799FMsX74cLVq0QO/evbFlyxYdq61Yp06d8MEHH+Drr7/G22+/jZycHHTt2hVnz54td/9AeX88PvvsM/z3v/+t9I9sw7xHSttlGpg2bZoAUOm2Y8eOUs/JysoSZrNZLFu2zOfjZWdni7CwMPHpp5/66yWUUZ3X5LFs2TIBQOTm5pb7/e+++04AENnZ2aUeHzlypOjTp4/fX4sQ1Xs9J0+eFNddd50YMWKEz8fT4z0qz8mTJwUAsXXr1lKPz5w5U7Ro0aLc5zRr1ky8+OKLpR779ttvBQBx6tQpzWr11ZgxY0Tjxo1FVlaWz8/t37+/uPPOOzWoqubOnz8v6tevL1555ZVyvx8o749Hamqq6N+/v8/PU/EeBd0AhnHjxmHw4MGV7pOUlFTq6/T0dNStWxcDBgzw+Xjx8fFo3Lgxfv75Z5+f663qvCYPzyiyw4cPo27dumW+7xk5lJOTg/j4+KLHT58+XeYvQH/x9fVkZ2ejZ8+e6NKlCxYuXOjz8fR4j8oTGxsLi8VSphVU2e+2QYMG5e5vtVrLff9UGD9+PFauXIktW7ZUa4mXzp07Y9GiRRpUVnO1a9dG27ZtK/ysBML74/HLL79g/fr1WL58uc/PVfEeBV0YxcbGIjY21uv9hRBIT0/HQw89hLCwMJ+Pd/bsWWRlZZU6kfubr6+ppN27dwNAhfUlJyejQYMGWLduHdq3bw9A9jNv3rwZs2bNql7BVfDl9Zw8eRI9e/ZESkoK0tPTK12cqyJ6vEflCQ8PR0pKCtatW4e777676PF169Zh4MCB5T6nS5cuWLVqVanH1q5diw4dOlTr8+lPQgiMHz8eK1aswKZNm5CcnFytn7N7927d3wtvOZ1O/PTTT7jlllvK/b6R358rpaenIy4uDv369fP5uUreI13bYQa0fv16AUAcOHCg3O+3aNFCLF++XAghRF5ennj88cfF1q1bxdGjR8XGjRtFly5dxLXXXiscDoeeZZdr69atYu7cuWL37t0iMzNTLF26VCQkJIgBAwaU2q/kaxJCiJdeeknYbDaxfPlysXfvXjFkyBARHx+v/DV5uuZ69eolTpw4IU6dOlW0lWTk92jJkiUiLCxMvPvuu+LAgQNi4sSJonbt2uLYsWNCCCEmT54shg4dWrR/ZmamqFWrlnjsscfEgQMHxLvvvivCwsKq1YXsb48++qiw2Wxi06ZNpd6LixcvFu1z5et59dVXxYoVK8ShQ4fEvn37xOTJkwUA3btMK/L444+LTZs2iczMTLF9+3bRv39/ER0dHZDvT0kul0s0atRIPPXUU2W+Z9T3KOTDaMiQIaJr164Vfh+ASE9PF0IIcfHiRZGamirq1asnwsLCRKNGjcSwYcPE8ePHdaq2crt27RKdOnUSNptNREZGihYtWohp06aJCxculNqv5GsSQg7vnjZtmmjQoIGIiIgQt956q9i7d6/O1ZeVnp5e4TWlkoz+Hr3xxhuicePGIjw8XNx0002lhkIPGzZMdO/evdT+mzZtEu3btxfh4eEiKSlJzJ8/X+eKy1fRe1Hys3Tl65k1a5Zo2rSpiIyMFNdcc43o1q2bWL16tf7FV+D+++8X8fHxIiwsTCQkJIh77rlH7N+/v+j7gfT+lPT1118LACIjI6PM94z6HnEJCSIiUo5Du4mISDmGERERKccwIiIi5RhGRESkHMOIiIiUYxgREZFyDCMiIlKOYURERMoxjIiISDmGERERKccwIiIi5RhGRESk3P8D6JGqwd8hlykAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EXAMPLE: creating an object the displays a circular shape.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class Circle(object):\n",
    "    # CONSTRUCTOR USED TO INITIALIZE THE OBJECT\n",
    "    def __init__(self, radius, color):\n",
    "        self.radius = radius  # SELF: CONTAINS ALL THE ATTRIBUTES IN THE SET\n",
    "        self.color = color\n",
    "\n",
    "    # METHOD TO ADD RADIUS\n",
    "    def add_radius(self, r):\n",
    "        self.radius += r\n",
    "        return self.radius\n",
    "\n",
    "    # METHOD TO DRAW CIRCLE\n",
    "    def draw_circle(self):\n",
    "        circle = plt.Circle((0, 0), self.radius, color=self.color)\n",
    "        plt.gca().add_patch(circle)\n",
    "        plt.axis('scaled')\n",
    "        plt.show()\n",
    "\n",
    "# Example Usage:\n",
    "# Create a Circle object\n",
    "my_circle = Circle(5, 'blue')\n",
    "\n",
    "# Draw the circle\n",
    "my_circle.draw_circle()\n",
    "\n",
    "# Add radius\n",
    "my_circle.add_radius(3)\n",
    "\n",
    "# Draw the circle again with updated radius\n",
    "my_circle.draw_circle()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "42d3b861-b655-4eb0-ad16-d17593ae3dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating the object red circle\n",
    "RedCircle = Circle(10,'red');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6d3c1248-e27a-4f5c-8973-64134dcb8f7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " 'add_radius',\n",
       " 'color',\n",
       " 'draw_circle',\n",
       " 'radius']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find the method that can be used on the object\n",
    "dir(RedCircle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "952a7f17-b638-4541-8c58-a3fff406a158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'red'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for the color of the circle\n",
    "RedCircle.color\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1b777d38-3551-4920-a100-8fb56665a4df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Set the object attribute radius\n",
    "RedCircle.radius =1\n",
    "RedCircle.radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "44085863-e9c1-4c18-9788-1e9369265500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#call the method draw circle\n",
    "RedCircle.draw_circle()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9a62428-1c19-48ea-8190-c9ba72060b80",
   "metadata": {},
   "source": [
    "### Excercise (TEST ANALYSIS)\n",
    "Create a class that format a text as follows. \n",
    "1) create a text with the punctuations replaced with an empty space. \n",
    "2) (method1 = freq.map) create a list that has the frquency of words in the text.\n",
    "3) (method2 =freqof)check the frequency of words in a text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "247cd1ff-45b2-4098-85a0-607a2b5354c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Excercise (TEST ANALYSIS)\n",
    "#Create a class that format a text as follows. \n",
    "#1) create a text with the punctuations replaced with an empty space. \n",
    "#2) (method1 = freq.map) create a list that has the frquency of words in the text.\n",
    "#3) (method2 =freqof)check the frequency of words in a text.\n",
    "\n",
    "class AnalysedText:\n",
    "    def __init__(self, text):\n",
    "        formattedText = text.replace('.', '').replace('!', '').replace(',', '').replace('?', '')\n",
    "        formattedText = formattedText.lower()  # make text lowercase\n",
    "        self.text = formattedText\n",
    "\n",
    "    def freq_map(self):\n",
    "        word_list = self.text.split()  # split text into words\n",
    "        freq_map = {} #empty set that has the frequency of words \n",
    "        for word in set(word_list):  # use set to remove duplicates\n",
    "            freq_map[word] = word_list.count(word) # The count() method returns the number of occurrences of a specified element in the list. \n",
    "        return freq_map\n",
    "\n",
    "    def freq_of(self, word):\n",
    "        freq_dict = self.freq_map()  # get frequency map\n",
    "        if word in freq_dict:\n",
    "            return freq_dict[word]\n",
    "        else:\n",
    "            return 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "20b2247a-30fb-41ca-8614-531cfe0e4deb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency Map: {'test': 2, 'for': 1, 'a': 1, 'purposes': 1, 'this': 2, 'testing': 1, 'is': 2}\n",
      "Frequency of 'test': 2\n"
     ]
    }
   ],
   "source": [
    "# Example usage:\n",
    "text = \"This is a test. This test is for testing purposes!\"\n",
    "analysed_text = AnalysedText(text)\n",
    "print(\"Frequency Map:\", analysed_text.freq_map())\n",
    "print(\"Frequency of 'test':\", analysed_text.freq_of('test'))"
   ]
  }
 ],
 "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}