data/Python_Basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "73791c49",
   "metadata": {},
   "source": [
    "# Python Basics\n",
    "In this tutorial, we will cover some python basic concepts like data types, conditional statements, loops, containers, functions, and plots.\n",
    "\n",
    "This notebook has been created with the help of the following resources:\n",
    "\n",
    "1. Python [Colab Notebook](https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-colab.ipynb#scrollTo=0vJLt3JRL9eR) from CS231n.\n",
    "2. Python Tutorial from [W3Schools](https://www.w3schools.com/python/default.asp).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c567ac3c",
   "metadata": {},
   "source": [
    "## Basic data types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18b2cc8c",
   "metadata": {},
   "source": [
    "### Numbers\n",
    "There are three numeric types in Python:\n",
    "\n",
    "* `int`\n",
    "* `float`\n",
    "* `complex`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3354787d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 <class 'int'>\n",
      "2.4 <class 'float'>\n",
      "(2+5j) <class 'complex'>\n"
     ]
    }
   ],
   "source": [
    "# Numbers\n",
    "x = 5\n",
    "y = 2.4\n",
    "z = 2 + 5j\n",
    "print(x, type(x))\n",
    "print(y, type(y))\n",
    "print(z, type(z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16cacc39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n",
      "1.6666666666666667\n",
      "1\n",
      "2\n",
      "6\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "# Math operators\n",
    "# We have +, -, *, /, **, //, %\n",
    "\n",
    "print(x ** 2)  # Exponentiation\n",
    "print(x / 3)   # Division\n",
    "print(x // 3)  # Floor Division\n",
    "print(x % 3)   # Modulus\n",
    "\n",
    "x += 1\n",
    "print(x)\n",
    "x *= 2\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8833ffaa",
   "metadata": {},
   "source": [
    "### Booleans\n",
    "Python implements all of the usual operators for Boolean logic, but uses English words rather than symbols (`&&`, `||`, etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7deaae2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True <class 'bool'>\n",
      "False <class 'bool'>\n"
     ]
    }
   ],
   "source": [
    "t, f = True, False\n",
    "print(t, type(t))\n",
    "print(f, type(f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b32dba98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Logical operators\n",
    "\n",
    "print(t and f) # Logical AND\n",
    "print(t or f)  # Logical OR\n",
    "print(not t)   # Logical NOT\n",
    "print(t != f)  # Logical XOR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b04f59f",
   "metadata": {},
   "source": [
    "### Strings\n",
    "Strings in python are surrounded by either single quotation marks, or double quotation marks.\n",
    "\n",
    "`'hello'` is the same as `\"hello\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c633f98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello <class 'str'> 5\n",
      "Hello World!\n"
     ]
    }
   ],
   "source": [
    "hello = 'Hello'\n",
    "print(hello, type(hello), len(hello))\n",
    "print(hello + ' World!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b31c9cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is your name? Ali\n",
      "How old are you? 29\n",
      "Next year Ali will be 30 year old\n",
      "Next year Ali will be 30 year old\n"
     ]
    }
   ],
   "source": [
    "# Input data\n",
    "Name = input('What is your name? ')\n",
    "Age = int(input('How old are you? ')) # casting\n",
    "\n",
    "# String format\n",
    "print(f'Next year {Name} will be {Age+1} year old')\n",
    "print('Next year {} will be {} year old'.format(Name, Age+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4693e040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Happy new year!\n",
      "Happy new year!\n",
      "HAPPY NEW YEAR!\n",
      "happy new year!\n",
      "1\n",
      "2\n",
      "Happyy new year!\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# String methods\n",
    "\n",
    "s = 'Happy new year!'\n",
    "print(s)\n",
    "print(s.capitalize())\n",
    "print(s.upper())\n",
    "print(s.lower())\n",
    "print(s.find('a'))\n",
    "print(s.count('a'))\n",
    "print(s.replace('Happy', 'Happyy'))\n",
    "print('new' in s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9e7496d",
   "metadata": {},
   "source": [
    "## Conditional statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e406aafc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# Comparison operators\n",
    "# ==, !=, <, >, <=, >=\n",
    "\n",
    "print(2 < 5)\n",
    "print(2 > 1 > 5)\n",
    "print(2 >= 2 and 5 < -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bf2d9dd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter integer number a 25\n",
      "Enter integer number b 12\n",
      "a is greater than b\n"
     ]
    }
   ],
   "source": [
    "a = int(input('Enter integer number a '))\n",
    "b = int(input('Enter integer number b '))\n",
    "if b > a:\n",
    "  print(\"b is greater than a\")\n",
    "elif a == b:\n",
    "  print(\"a and b are equal\")\n",
    "else:\n",
    "  print(\"a is greater than b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1dc4244",
   "metadata": {},
   "source": [
    "We can use short hand if ... else:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "dc1c85ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a = b\n"
     ]
    }
   ],
   "source": [
    "a = 5\n",
    "b = 5\n",
    "print('b > a') if b > a else print('a = b') if a == b else print('a > b')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88c1f57e",
   "metadata": {},
   "source": [
    "## Loop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d605ed",
   "metadata": {},
   "source": [
    "Python has two primitive loop commands:\n",
    "* `while` loops\n",
    "* `for` loops"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc1e9bf1",
   "metadata": {},
   "source": [
    "### While loop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c8b237",
   "metadata": {},
   "source": [
    "With the while loop we can execute a set of statements as long as a condition is true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "709a5abc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "i is no longer less than 6\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "while i < 6:\n",
    "  print(i)\n",
    "  i += 1\n",
    "else:\n",
    "  print(\"i is no longer less than 6\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "376cd364",
   "metadata": {},
   "source": [
    "With the `break` statement we can stop the loop even if the while condition is true.\n",
    "\n",
    "With the `continue` statement we can stop the current iteration, and continue with the next:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ab5b4705",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "while True:\n",
    "  print(i)\n",
    "  if i == 5:\n",
    "    break\n",
    "  i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b14012",
   "metadata": {},
   "source": [
    "### For loop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6735607",
   "metadata": {},
   "source": [
    "A for loop is used for iterating over a sequence (that is either a list, a tuple, a dictionary, a set, or a string).\n",
    "\n",
    "The `break` and `continue` works here as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c0a4b67d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "str1\n",
      "2\n",
      "48.4\n",
      "(7+1j)\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "l = ['str1', 2, 48.4, 7+1j, True, 2]\n",
    "for i in l:\n",
    "    print(i)\n",
    "    if type(i) == bool:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b232b6d",
   "metadata": {},
   "source": [
    "To loop through a set of code a specified number of times, we can use the `range()` function.\n",
    "    \n",
    "The `range()` function returns a sequence of numbers, starting from 0 by default, and increments by 1 (by default), and ends at a specified number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9e966b31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "20\n",
      "30\n",
      "40\n",
      "50\n",
      "60\n",
      "70\n",
      "80\n",
      "90\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 100):\n",
    "    if i % 10 == 0:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9996a037",
   "metadata": {},
   "source": [
    "## Containers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b91dfeba",
   "metadata": {},
   "source": [
    "Python includes several built-in container types: lists, dictionaries, sets, and tuples.\n",
    "\n",
    "* **List** is a collection which is ordered and changeable. Allows duplicate members.\n",
    "* **Dictionary** is a collection which is ordered and changeable. No duplicate members.\n",
    "* **Tuple** is a collection which is ordered and unchangeable. Allows duplicate members.\n",
    "* **Set** is a collection which is unordered, unchangeable, and unindexed. No duplicate members."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef50c966",
   "metadata": {},
   "source": [
    "### Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c953ade",
   "metadata": {},
   "source": [
    "A list is the Python equivalent of an array, but is resizeable and can contain elements of different types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f212226f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['str1', 2, 48.4, (7+1j), True, 2] <class 'list'> 6\n",
      "['Mohammad', 'str1', 2, 48.4, (7+1j), True, 2, 0]\n",
      "['str1', 2, 48.4, (7+1j), True, 2]\n"
     ]
    }
   ],
   "source": [
    "l1 = ['str1', 2, 48.4, 7+1j, True, 2]\n",
    "print(l1, type(l1), len(l1))\n",
    "\n",
    "# List methods\n",
    "l1.append(0)               # Add a new element to the end of the list\n",
    "l1.insert(0, 'Mohammad')   # Add a new element at the desired index (here 0)\n",
    "print(l1)\n",
    "l1.pop()                   # Remove and return the last element of the list\n",
    "l1.pop(0)                  # Removes a desired element of the list\n",
    "print(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cb60e2e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4]\n",
      "[1, 2, 3]\n",
      "[0, 1]\n",
      "[0, 1, 2, 3, 4]\n",
      "[0, 1, 2, 3, 4]\n",
      "[4, 3, 2, 1, 0]\n"
     ]
    }
   ],
   "source": [
    "# List slicing\n",
    "\n",
    "nums = list(range(0, 5, 1))\n",
    "print(nums)\n",
    "print(nums[1:4])\n",
    "print(nums[:2])\n",
    "print(nums[:])\n",
    "print(nums[::1])\n",
    "print(nums[::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "290033c2",
   "metadata": {},
   "source": [
    "*Note*: In python, the indices start from **0** (unlike MATLAB that start from 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "43d7d00e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#1: cat\n",
      "#2: dog\n",
      "#3: monkey\n"
     ]
    }
   ],
   "source": [
    "# Loop over a list\n",
    "animals = ['cat', 'dog', 'monkey']\n",
    "for idx, animal in enumerate(animals):\n",
    "    print('#{}: {}'.format(idx + 1, animal))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f33a883",
   "metadata": {},
   "source": [
    "**List comprehension** offers a shorter syntax when you want to create a new list based on the values of an existing list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a3273d82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 4, 16]\n"
     ]
    }
   ],
   "source": [
    "nums = [0, 1, 2, 3, 4]\n",
    "even_squares = [x ** 2 for x in nums if x % 2 == 0]\n",
    "print(even_squares)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52cc25e6",
   "metadata": {},
   "source": [
    "### Dictionaries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a37333c",
   "metadata": {},
   "source": [
    "A dictionary stores (key, value) pairs, similar to a `Map` in Java or an object in Javascript. You can use it like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2149975e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'person': 2, 'cat': 4, 'spider': 8, 'fish': 0} <class 'dict'>\n",
      "2\n",
      "dict_keys(['person', 'cat', 'spider', 'fish'])\n",
      "dict_values([2, 4, 8, 0])\n",
      "dict_items([('person', 2), ('cat', 4), ('spider', 8), ('fish', 0)])\n",
      "N/A\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# Dictionary\n",
    "d = {'person': 2,\n",
    "     'cat': 4,\n",
    "     'spider': 8}\n",
    "d['fish'] = 0\n",
    "\n",
    "# Dictionary methods\n",
    "print(d, type(d))\n",
    "print(d['person'])\n",
    "print(d.keys())\n",
    "print(d.values())\n",
    "print(d.items())\n",
    "print(d.get('dog', 'N/A'))\n",
    "print(d.get('cat', 'N/A'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5fb61cb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A person has 2 legs\n",
      "A cat has 4 legs\n",
      "A spider has 8 legs\n",
      "A fish has 0 legs\n"
     ]
    }
   ],
   "source": [
    "# Dictionary loop\n",
    "for animal, legs in d.items():\n",
    "  print(f'A {animal} has {legs} legs')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8655b07",
   "metadata": {},
   "source": [
    "### Tuples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7348d54",
   "metadata": {},
   "source": [
    "A tuple is an (immutable) ordered list of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c4bc342f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('apple', 12, False, 2.2) <class 'tuple'> 4\n"
     ]
    }
   ],
   "source": [
    "t = (\"apple\", 12, False, 2.2)\n",
    "print(t, type(t), len(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e8b4b06d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "banana\n",
      "cherry\n"
     ]
    }
   ],
   "source": [
    "# Tuple unpacking\n",
    "\n",
    "fruits = (\"apple\", \"banana\", \"cherry\")\n",
    "(green, yellow, red) = fruits\n",
    "print(green)\n",
    "print(yellow)\n",
    "print(red)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6507489f",
   "metadata": {},
   "source": [
    "### Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23fdb4b0",
   "metadata": {},
   "source": [
    "A set is an unordered collection of distinct elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "afe3257c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{False, 'apple', 'cherry', 'banana'} <class 'set'> 4\n",
      "True\n",
      "{False, 2, 'banana', 'cherry'}\n"
     ]
    }
   ],
   "source": [
    "# Set\n",
    "s = {\"apple\", \"banana\", \"cherry\", False, 0}\n",
    "print(s, type(s), len(s))\n",
    "print('apple' in s)\n",
    "s.remove('apple')   # Remove an element from a set\n",
    "s.add(2)            # Add an element to a set\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af1f586a",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f0bf736d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n",
      "zero\n",
      "positive\n"
     ]
    }
   ],
   "source": [
    "# function\n",
    "def sign(x):\n",
    "  \n",
    "  if x > 0:\n",
    "    return 'positive'\n",
    "  elif x < 0:\n",
    "    return 'negative'\n",
    "  else:\n",
    "    return 'zero'\n",
    "\n",
    "for x in [-1, 0, 2.5]:\n",
    "    print(sign(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c33c284c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 2, 3, 6, 8, 10]\n"
     ]
    }
   ],
   "source": [
    "def quicksort(arr):\n",
    "    if len(arr) <= 1:\n",
    "        return arr\n",
    "    pivot = arr[len(arr) // 2]\n",
    "    left = [x for x in arr if x < pivot]\n",
    "    middle = [x for x in arr if x == pivot]\n",
    "    right = [x for x in arr if x > pivot]\n",
    "    return quicksort(left) + middle + quicksort(right)\n",
    "\n",
    "print(quicksort([3,6,8,10,1,2,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c932696",
   "metadata": {},
   "source": [
    "### Arbitrary Arguments (*args)\n",
    "If you do not know how many arguments that will be passed into your function, add a `*` before the parameter name in the function definition.\n",
    "\n",
    "This way the function will receive a *tuple* of arguments, and can access the items accordingly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1631feef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The youngest child is Sara\n"
     ]
    }
   ],
   "source": [
    "def my_function(*kids):\n",
    "  print(\"The youngest child is \" + kids[2])\n",
    "\n",
    "my_function(\"Ali\", \"Mohammad\", \"Sara\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6ce6db2",
   "metadata": {},
   "source": [
    "### Keyword Arguments\n",
    "We can also send arguments with the key = value syntax.\n",
    "\n",
    "This way the order of the arguments does not matter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "04e29086",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The youngest child is Sara\n"
     ]
    }
   ],
   "source": [
    "def my_function(child3, child2, child1):\n",
    "  print(\"The youngest child is \" + child3)\n",
    "\n",
    "my_function(child1 = \"Ali\", child2 = \"Mohammad\", child3 = \"Sara\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "175c99ad",
   "metadata": {},
   "source": [
    "### Arbitrary Keyword Arguments (**kwargs)\n",
    "\n",
    "If you do not know how many keyword arguments that will be passed into your function, add two asterisk: `**` before the parameter name in the function definition.\n",
    "\n",
    "This way the function will receive a *dictionary* of arguments, and can access the items accordingly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "db87d744",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "His last name is Nasiri\n"
     ]
    }
   ],
   "source": [
    "def my_function(**kid):\n",
    "  print(\"His last name is \" + kid[\"lname\"])\n",
    "\n",
    "my_function(fname = \"Ali\", lname = \"Nasiri\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab67d2f3",
   "metadata": {},
   "source": [
    "### Default Parameter Value\n",
    "The following example shows how to use a default parameter value.\n",
    "\n",
    "If we call the function without argument, it uses the default value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "db41bf83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am from Iran\n",
      "I am from Brazil\n"
     ]
    }
   ],
   "source": [
    "def my_function(country = \"Iran\"):\n",
    "  print(\"I am from \" + country)\n",
    "\n",
    "my_function()\n",
    "my_function(\"Brazil\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ab56f4",
   "metadata": {},
   "source": [
    "## Classes\n",
    "Python is an object oriented programming language. Almost everything in Python is an object, with its properties and methods.\n",
    "\n",
    "A Class is like an object constructor, or a \"blueprint\" for creating objects.\n",
    "\n",
    "All classes have a function called \\_\\_init\\_\\_(), which is always executed when the class is being initiated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7d37c275",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mohammad\n",
      "Hello, Mohammad!\n",
      "HELLO, MOHAMMAD!\n"
     ]
    }
   ],
   "source": [
    "# Class\n",
    "class Greeter:\n",
    "\n",
    "    # Constructor\n",
    "    def __init__(self, name):\n",
    "        self.name = name  # Create an instance variable\n",
    "        # self.greet()\n",
    "\n",
    "    # Instance method\n",
    "    def greet(self, loud=False):\n",
    "        if loud:\n",
    "          print('HELLO, {}!'.format(self.name.upper()))\n",
    "        else:\n",
    "          print('Hello, {}!'.format(self.name))\n",
    "\n",
    "g = Greeter('Mohammad')  # Construct an instance of the Greeter class\n",
    "print(g.name)\n",
    "g.greet()            # Call an instance method; prints \"Hello, Mohammad!\"\n",
    "g.greet(loud=True)   # Call an instance method; prints \"HELLO, MOHAMMAD!\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41de8e26",
   "metadata": {},
   "source": [
    "### Inheritance\n",
    "Inheritance allows us to define a class that inherits all the methods and properties from another class.\n",
    "\n",
    "Parent class is the class being inherited from, also called base class.\n",
    "\n",
    "Child class is the class that inherits from another class, also called derived class.\n",
    "\n",
    "Python has a `super()` function that will make the child class inherit all the methods and properties from its parent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6131a1b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome Mohammad Rasouli to the class of 1400\n"
     ]
    }
   ],
   "source": [
    "class Person:\n",
    "  \n",
    "  def __init__(self, fname, lname):\n",
    "    self.firstname = fname\n",
    "    self.lastname = lname\n",
    "  \n",
    "  def printname(self):\n",
    "    print(self.firstname, self.lastname)\n",
    "\n",
    "class Student(Person):\n",
    "  \n",
    "  def __init__(self, fname, lname, year):\n",
    "    super().__init__(fname, lname)\n",
    "    self.graduationyear = year\n",
    "  \n",
    "  def welcome(self):\n",
    "    print(\"Welcome\", self.firstname, self.lastname, \"to the class of\", self.graduationyear)\n",
    "\n",
    "x = Student(\"Mohammad\", \"Rasouli\", 1400)\n",
    "x.welcome()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a15898",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda1fe55",
   "metadata": {},
   "source": [
    "Matplotlib is a plotting library. In this section give a brief introduction to the `matplotlib.pyplot` module, which provides a plotting system similar to that of MATLAB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "cb52b459",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install matplotlib\n",
    "# !pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2e621fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bff0c782",
   "metadata": {},
   "source": [
    "The most important function in `matplotlib` is plot, which allows you to plot 2D data. Here is a simple example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "566ddd46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A basic plot\n",
    "x_vals = [1, 2, 3, 4]\n",
    "y_vals = [0, 1, 2, 3]\n",
    "plt.plot(x_vals, y_vals, label= 'A Basic Plot')\n",
    "plt.xlabel('x-axis label')\n",
    "plt.ylabel('y-axix label')\n",
    "plt.legend()\n",
    "plt.title('A Basic Sample Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f4079e2",
   "metadata": {},
   "source": [
    "With just a little bit of extra work we can easily plot multiple lines at once, and add a title, legend, and axis labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7908c8d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute('tabindex', '0');\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;' +\n",
       "            'z-index: 2;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: relative;' +\n",
       "            'z-index: 0;'\n",
       "    );\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'left: 0;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: absolute;' +\n",
       "            'top: 0;' +\n",
       "            'z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            /* This rescales the canvas back to display pixels, so that it\n",
       "             * appears correct on HiDPI screens. */\n",
       "            canvas.style.width = width + 'px';\n",
       "            canvas.style.height = height + 'px';\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        /* User Agent sniffing is bad, but WebKit is busted:\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=144526\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=181818\n",
       "         * The worst that happens here is that they get an extra browser\n",
       "         * selection when dragging, if this check fails to catch them.\n",
       "         */\n",
       "        var UA = navigator.userAgent;\n",
       "        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n",
       "        if(isWebKit) {\n",
       "            return function (event) {\n",
       "                /* This prevents the web browser from automatically changing to\n",
       "                 * the text insertion cursor when the button is pressed. We\n",
       "                 * want to control all of the cursor setting manually through\n",
       "                 * the 'cursor' event from matplotlib */\n",
       "                event.preventDefault()\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        } else {\n",
       "            return function (event) {\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        }\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    canvas_div.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    canvas_div.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.canvas_div.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "function getModifiers(event) {\n",
       "    var mods = [];\n",
       "    if (event.ctrlKey) {\n",
       "        mods.push('ctrl');\n",
       "    }\n",
       "    if (event.altKey) {\n",
       "        mods.push('alt');\n",
       "    }\n",
       "    if (event.shiftKey) {\n",
       "        mods.push('shift');\n",
       "    }\n",
       "    if (event.metaKey) {\n",
       "        mods.push('meta');\n",
       "    }\n",
       "    return mods;\n",
       "}\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        modifiers: getModifiers(event),\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# magic command\n",
    "%matplotlib notebook \n",
    "\n",
    "x_values = np.arange(0, 2*np.pi, 0.05)\n",
    "y_sin = np.sin(x_values)\n",
    "y_cos = np.cos(x_values)\n",
    "\n",
    "# plt.figure(figsize= (5, 5), facecolor= 'w', edgecolor= 'k')\n",
    "plt.plot(x_values, y_sin, x_values, y_cos)\n",
    "plt.xlabel('x-axis label')\n",
    "plt.ylabel('y-axix label')\n",
    "plt.title('Sine and Cosine Plot')\n",
    "plt.legend(['sin(x)', 'cos(x)'])\n",
    "plt.axis([0, 2*np.pi, -1.5, 1.5])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b15037",
   "metadata": {},
   "source": [
    "We can also control line properties:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8d062a53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "(x1, y1) = ([1, 2, 3, 4], random.sample(range(0, 5), 4))\n",
    "(x2, y2) = ([1, 2, 3, 4], random.sample(range(0, 5), 4))\n",
    "(x3, y3) = ([1, 2, 3, 4], random.sample(range(0, 5), 4))\n",
    "\n",
    "#plt.figure(figsize= (5, 6))\n",
    "line1 = plt.plot(x1, y1)\n",
    "line2 = plt.plot(x2, y2)\n",
    "line3 = plt.plot(x3, y3)\n",
    "\n",
    "plt.setp(line1, color= 'r', linewidth= 1.5, linestyle= '--')\n",
    "plt.setp(line2, color= 'b', linewidth= 1.5, marker= 'x')\n",
    "plt.setp(line3, color= 'g', linewidth= 1.5, marker= 'D')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ee2230",
   "metadata": {},
   "source": [
    "We can plot different things in the same figure using the subplot function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1a59ba77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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c8PbbEi7+8Q+GC1fEYEF24fp12RTs73+XfT0AIDgYePVV4IknpJ0wEdm3Z5+VkYsXXpDRRg8PmVhNroXBgpQqKgL+8x/gb3+TnUcBoH59YOpU4KmnAC8vpeURkZnGjZPv60mTZPm3uzvw2muqqyJbYrAgJYqKpMnOzJnAsWPyWGCgdM8cM0a2aiYixzRxotwWmTxZ5kXp9fLLArkGBguyqeJiaQk8cyZw6JA8Vrs28PLLMoxatara+ojIMl58UX6BmDIFmDZNbotMnqy6KrIFBguyiZISYOlS6Ttx4IA8FhAAvPQSkJgI+PgoLY+IrOCVVyRcTJ8u3+t6vYxmkHNjsCCr0jRg+XIZDt27Vx6rXl1+mxk3DvDzU1kdEVnbq69KuJg5U+Zd6PXyvU/Oi8GCrELTgO+/l0CRmSmP+fnJD5YJEwB/f6XlEZENzZgB3LgBvPGGrBhxd5dlqeScGCzIojQNWLtWZoFnZMhjPj4SJiZNAmrUUFoeESmg08lS8qIiacufmCgjF2PGqK6MrIHBgixC04AffpBAsXWrPFa1qgx5vvgiUKuW2vqISC2dTlrzFxXJrsRjx0q4ePJJ1ZWRpTFYUKWlpkqg2LRJPvf2lt9IXnoJqFNHbW1EZD90OtmZuKhI9hV56im5LTJypOrKyJIYLKjCNm6Ue6epqfK5lxfwzDOydLRePbW1EZF90ulkB9SiItkRddQoCRfDh6uujCyFwYLMtmWLBIqUFPnc0xN4+mlZr96ggdraiMj+6XTAv/4lfW0++URGLPR64NFHVVdGlsBgQSbbvl0CxZo18rleL1slT50KNGyotjYicixubjJiUVQkGw8OHy4jF0OHqq6MKovBgu4pM1MCxcqV8rm7u2wM9uqrsvMoEVFFuLkB8+bJyMWCBcBjj8nPlyFDVFdGleGmugCyX3v2yDd4x44SKtzcZMjy0CFg/nyGCiKqPDc3+XkyfLgEjEceAb77TnVVVBkMFnSH/fuBhx8GwsKA5GS5H/rXvwK//iq/VTRtqrpCInIm7u6GEYuiIrkd8v33qquiimKwoJsOHZIA0b49sGSJPPbww8C+fcBXXwEtWqitj4icl7s78J//SKi4cQN48EFptkeOx6xgkZSUhE6dOsHX1xd16tRBQkICDpVuUUkO6+hR4PHHgTZtZCtzTZNbIHv2AIsXy+NERNam18svMUOGANevAwkJhtVn5DjMChbp6elITEzE1q1bkZKSgqKiIsTHx6OgoMBa9ZEVnTghqzpatQIWLpQdSAcNAnbulJ1I27dXXSERuRoPD/kFZ/BgoLBQfib99JPqqsgcOk3TtIq++Ny5c6hTpw7S09PRvXt3k16Tl5cHf39/5Obmwo9bWypx+jTwj38An30m9zMBoF8/2X0wIkJtbUREgIxYPPigzLWoWhVYvRro0UN1Va7N1PfvSs2xyM3NBQAEBASUe0xhYSHy8vKMPkiN336TVtvNmskSr6IiID5eGl6tWsVQQUT2w9MT+PZboG9f4MoVoH9/w7YBZN8qHCw0TcOkSZMQFRWFdu3alXtcUlIS/P39b34EBwdX9EtSBZ09C4wfL4Hio49kYlRMjLTkXrcO6NJFdYVERHfy8gKWLQN69QIKCiRkbNmiuiq6lwrfCklMTMSqVauwadMmBAUFlXtcYWEhCgsLb36el5eH4OBg3gqxgZwc4M03JUxcuyaPdesGzJoF9OyptDQiIpNduQIMHChzLXx9ZUJn586qq3I9Vr0VMm7cOKxYsQKpqal3DRUA4OXlBT8/P6MPsq7z52UjsJAQ2Z742jUgMlK+GdPTGSqIyLFUrQqsWCFzLPLzgd69gYwM1VVRecwKFpqm4fnnn8eyZcvw008/ISQkxFp1UQX8+ScwbZoEirfekpTfqZPs7bF5MxAXJ82uiIgcTbVqMpEzKgrIzZXbIzt3qq6KymJWsEhMTMSXX36J//73v/D19UV2djays7Nx9epVa9VHJrh0SfbyCAkB3ngDuHwZ6NBB2nBv2wb06cNAQUSOz8dHVodERsrPvV69gN27VVdFtzNrjoWunHenzz//HE888YRJfweXm1pOXh7w4YfAP/8p32QAEBoqy0YHD2aYICLnlJcnK9q2bQNq1gRSU9l3xxZMff82a3fTSrS8IAu6fBmYMwd4+225/QFId8yZM6VjnRsbtRORE/Pzk3bfvXrJXIvYWCAtjV2C7QXfghzIlSvAO+/ILY8pUyRUtGwpXer27AEeeoihgohcQ/XqwPr1ctv33DlZQn/woOqqCGCwcAhXrwLvvw80aQJMniyrPpo1kzbc+/cDjz4qG/gQEbmSGjVktVtYGPDHHxIuDh9WXRUxWNixwkK55dG0KTBxonzjNG4srbh//RUYPpyBgohcW82awA8/yByLs2eB6GjZWJHUYbCwQ9evAx9/LKMS48bJN0vDhtKG+/BhYNQo2QWQiIiAWrUkXLRpA5w5IyMXJ06orsp1MVjYkRs3gPnzgRYtgGeflb09GjSQzpmHDwNPPy07/xERkbE6daQzZ6tWQFaWjFycOqW6KtfEYGEHioqAL76Qb4inn5Zvhrp1ZSnp0aMSMry8VFdJRGTfAgMlXLRoIT9Ho6MlZJBtMVgoVFwMfPWVDN898QRw/Lik7nfflT+PGwd4e6uukojIcdSrJ+GiaVO5HRIdDfz+u+qqXAuDhQIlJcDixUC7djIB88gRmYD01lsSKCZOBKpUUV0lEZFjatBAmmaFhADHjkm4OHtWdVWug8HChkpKgKVLpTvmo4/KmusaNaQN94kTspS0WjXVVRIROb7gYAkXjRrJL28xMUB2tuqqXAODhQ1oGvDdd0DHjtLEav9+wN9fti8/cUKaXfn6qq6SiMi5NGok4SI4WH6Ri40FcnJUV+X8GCysSNOAVatkh9GEBNksx9cXmD5dAsX06RIwiIjIOkJCZM5FgwbAgQMSLs6fV12Vc2OwsAJNA9atA7p0AQYMAHbskFscU6ZIoJg1S26BEBGR9TVrJuGiXj1g3z4gLg64cEF1Vc6LwcKCNA348UcgKkq2Kv/lF5mEOXmyBIo33pBJmkREZFstWki4CAyU0eNevYCLF1VX5ZwYLCwkPR3o2VOS8M8/yzLRiRMlULz1FlC7tuoKiYhcW6tWEi5q1wYyM2Xr9UuXVFflfBgsKmnzZgkTPXsCGzYAnp7Sf+LYMelHERioukIiIirVpo2Ei1q1ZMv13r2B3FzVVTkXBosK2rZNbndERcntDw8P6ZB59Kh0zKxfX3WFRERUlnbtZG+RgAC5Zd23L5Cfr7oq58FgYaYdO2RCZpcuMkFTr5c23EeOyJ4ewcGqKyQionsJC5NwUb06sGUL0K8fcPmy6qqcA4OFiXbtkiWjERGyhNTNTdpwHzoku442aqS4QCIiMkuHDkBKiiz737RJfmksKFBdleNjsLiHffukqVWHDtLkys1N2nAfPAh8/jnQpInqComIqKIiIoD16wE/P5mEP2gQcOWK6qocG4NFOX79Vdpuh4ZKG26dTj7ftw9YuBBo3lx1hUREZAn33QesXQv4+MjEzoQE4No11VU5LgaL2xw+LCMSbdvKRmGaJiMWe/YAX38NtG6tukIiIrK0yEhgzRppZpiSAjzwAFBYqLoqx8Rg8T/HjwOjRklw+OorCRQJCTK3YskSmUVMRETOKyoKWL0aqFpVRjAefJDhoiJcPlicPCmrOlq0ABYskB1IBwyQ9c3JyTJzmIiIXEP37sD330vX5FWrgEceAa5fV12VY3HZYJGVJX0nWrQA5s8HioulL8W2bcDKlUB4uOoKiYhIhehoYMUK6aD83XfAY48BN26orspxuFywOHNGOmM2awZ8/LFcLHFx0kFzzRqZxENERK4tLg5Yvly6KS9bBgwbBhQVqa7KMbhMsMjOlr07mjYF5syRoa0ePWR5UUoK0LWr6gqJiMie9O4tocLDQ+bajRjBcGEKpw8W587J7qJNmgDvvy9LiLp2lTbcqalyP42IiKgs/fsD334rXZYXLZJJ/sXFqquyb2YHiw0bNmDgwIGoX78+dDodli9fboWyKu/CBWDKFCAkBHjnHeDqVaBzZ2nDvWkTEBMjvSmIiIjuZtAg4JtvAHd34MsvgdGjZaI/lc3sYFFQUICwsDDMmTPHGvVU2sWLwPTpEihmz5b2rOHhMrt3yxbZJpeBgoiIzPHAA9LLyN0d+OILYMwYhovy6M19Qd++fdG3b19r1FIpubnABx/IVuWlW+CGhQGzZgEDBzJMEBFR5QwdKrdBhg0DPv1UQsbcubLVAxmYHSzMVVhYiMJbOozk5eVZ/Gvk5ACtWsloBSDNrGbOlAZX/B9ORESW8uijEi5GjJANKPV6WRDAX14NrP62m5SUBH9//5sfwVbYV7xOHZmQ2bq1tOHevRsYMoShgoiILG/YMNmEUqcDPvpIVhxqmuqq7IdO0yp+OnQ6HZKTk5GQkFDuMWWNWAQHByM3Nxd+fn4V/dJ3OH8eqFFDhqaIiIis7bPPZCInAEyaJAsFnHnkIi8vD/7+/vd8/7b6rRAvLy94eXlZ+8ugVi2rfwkiIqKbnnxS+lqMHSvz+/R6WTTgzOHCFLxZQEREVEFjxsjtEAB46y3g1Vd5W8TsEYvLly/j6NGjNz8/ceIEdu3ahYCAADRs2NCixREREdm7Z5+VkYsXXgDeeEM6db7+uuqq1DE7WGRkZCA6Ovrm55MmTQIAjBw5EgsWLLBYYURERI5i3DgJF5MmyapEd3fpqeSKzA4WPXv2RCXmexIRETmliRNlKerkycBrr8mciylTVFdle5xjQUREZCEvvggkJcmfp04F3n5bbT0qMFgQERFZ0CuvAH/7m/z5pZeA995TW4+tMVgQERFZ2Kuvyu0QQOZd/OtfauuxJQYLIiIiK3j9dbkdAsiKkdJlqc6OwYKIiMgKdDrg73+X2yEAkJgo+4s4OwYLIiIiK9HppBvn/zozYOxYaQXuzBgsiIiIrEink31EXnhBPn/qKeCLL9TWZE0MFkRERFam0wHvvw8895y0/B41CvjqK9VVWQeDBRERkQ3odLI6ZOxYCRePPw4sXqy6KstjsCAiIrIRNzdZHTJ6NFBSAgwbBnz7reqqLIvBgoiIyIbc3GR1yBNPSAvwxx4DkpNVV2U5DBZEREQ25uYGzJ8PDB8um5c9/DCwYoXqqiyDwYKIiEgBd3dgwQIZsSgqAh56CFi1SnVVlcdgQUREpIi7O/Cf/wBDhwI3bgBDhgDr1qmuqnIYLIiIiBTS62Xp6ZAhwPXrwODBwA8/qK6q4hgsiIiIFPPwAL7+WkJFYSEwcCDw00+qq6oYBgsiIiI74OkJfPMNMGAAcO2ahIv0dNVVmY/BgoiIyE54ekpfiz59gCtXgP79gU2bVFdlHgYLIiIiO+LlBSxbBvTqBRQUAH37Alu2qK7KdAwWREREdqZKFWD5ciAmBrh8GejdG9i2TXVVpmGwICIiskNVq0rTrB49gPx8CRcZGaqrujcGCyIiIjtVrRrw/fdAVBSQmwvExwOZmaqrujsGCyIiIjvm4wOsXg1ERgIXLwJxccDu3aqrKh+DBRERkZ3z9QXWrgU6dwb+/FPCxb59qqsqG4MFERGRA/Dzk3AREQGcPy8TOw8cUF3VnRgsiIiIHET16sD69UCHDsC5cxIuDh5UXZUxBgsiIiIHUqMGkJIChIUBf/wh4eLIEdVVGVQoWHz00UcICQmBt7c3wsPDsXHjRkvXRUREROWoWVM2KmvfHjh7FoiOBo4dU12VMDtYLF68GBMmTMC0adOQmZmJbt26oW/fvjh9+rQ16iMiIqIy1Kol4aJNG+D33yVcnDihuipAp2maZs4LOnfujI4dO2Lu3Lk3H2vdujUSEhKQlJR0z9fn5eXB398fubm58PPzM79iIiIiuumPP4CePWWuRaNGsnFZo0aW/zqmvn+bNWJx/fp17NixA/Hx8UaPx8fH4+effy7zNYWFhcjLyzP6ICIiIssIDJQt1lu0AE6dkpGLrCx19ZgVLM6fP4/i4mIEBgYaPR4YGIjs7OwyX5OUlAR/f/+bH8HBwRWvloiIiO5Qr56Ei6ZNZVfU/Hx1tVRo8qZOpzP6XNO0Ox4rNWXKFOTm5t78yFIZo4iIiJxUgwZAaiqQlibzLlTRm3NwrVq14O7ufsfoRE5Ozh2jGKW8vLzg5eVV8QqJiIjIJPZwU8CsEQtPT0+Eh4cjJSXF6PGUlBR07drVooURERGR4zFrxAIAJk2ahBEjRiAiIgKRkZGYN28eTp8+jWeeecYa9REREZEDMTtYPPLII7hw4QJmzZqFs2fPol27dli9ejUaWWNtCxERETkUs/tYVBb7WBARETkeU9+/zR6xqKzSHMN+FkRERI6j9H37XuMRNg8W+f9bXMt+FkRERI4nPz8f/v7+5T5v81shJSUlOHPmDHx9fcvtfVEReXl5CA4ORlZWFm+x3APPlel4rszD82U6nivT8VyZzprnStM05Ofno379+nBzK39Rqc1HLNzc3BAUFGS1v9/Pz48Xnol4rkzHc2Ueni/T8VyZjufKdNY6V3cbqShVoc6bRERERGVhsCAiIiKLcZpg4eXlhRkzZrB9uAl4rkzHc2Ueni/T8VyZjufKdPZwrmw+eZOIiIicl9OMWBAREZF6DBZERERkMQwWREREZDEMFkRERGQxDhMsNmzYgIEDB6J+/frQ6XRYvnz5PV+Tnp6O8PBweHt7o0mTJvj444+tX6gdMPdcpaWlQafT3fFx8OBB2xSsSFJSEjp16gRfX1/UqVMHCQkJOHTo0D1f56rXVUXOl6teW3PnzkVoaOjNJkWRkZFYs2bNXV/jqteVuefKVa+psiQlJUGn02HChAl3Pc7W15bDBIuCggKEhYVhzpw5Jh1/4sQJ9OvXD926dUNmZiamTp2KF154AUuXLrVypeqZe65KHTp0CGfPnr350bx5cytVaB/S09ORmJiIrVu3IiUlBUVFRYiPj0dBQUG5r3Hl66oi56uUq11bQUFBmD17NjIyMpCRkYGYmBgMHjwY+/fvL/N4V76uzD1XpVztmrrd9u3bMW/ePISGht71OCXXluaAAGjJycl3Peall17SWrVqZfTY2LFjtS5dulixMvtjyrlKTU3VAGgXL160SU32KicnRwOgpaenl3sMrysDU84Xry2DGjVqaPPnzy/zOV5Xxu52rnhNaVp+fr7WvHlzLSUlRevRo4c2fvz4co9VcW05zIiFubZs2YL4+Hijx3r37o2MjAzcuHFDUVX2rUOHDqhXrx5iY2ORmpqquhyby83NBQAEBASUewyvKwNTzlcpV762iouLsWjRIhQUFCAyMrLMY3hdCVPOVSlXvqYSExPRv39/xMXF3fNYFdeWzTchs5Xs7GwEBgYaPRYYGIiioiKcP38e9erVU1SZ/alXrx7mzZuH8PBwFBYWYuHChYiNjUVaWhq6d++uujyb0DQNkyZNQlRUFNq1a1fucbyuhKnny5Wvrb179yIyMhLXrl2Dj48PkpOT0aZNmzKPdfXrypxz5crXFAAsWrQIO3fuxPbt2006XsW15bTBAsAd27Jr/2syasnt2p1By5Yt0bJly5ufR0ZGIisrC++8845LfKMCwPPPP489e/Zg06ZN9zyW15Xp58uVr62WLVti165duHTpEpYuXYqRI0ciPT293DdMV76uzDlXrnxNZWVlYfz48Vi/fj28vb1Nfp2try2nvRVSt25dZGdnGz2Wk5MDvV6PmjVrKqrKcXTp0gVHjhxRXYZNjBs3DitWrEBqaiqCgoLueiyvK/POV1lc5dry9PREs2bNEBERgaSkJISFheGDDz4o81hXv67MOVdlcZVraseOHcjJyUF4eDj0ej30ej3S09Px4YcfQq/Xo7i4+I7XqLi2nHbEIjIyEitXrjR6bP369YiIiICHh4eiqhxHZmam0w+/apqGcePGITk5GWlpaQgJCbnna1z5uqrI+SqLK1xbZdE0DYWFhWU+58rXVVnudq7K4irXVGxsLPbu3Wv02KhRo9CqVSu8/PLLcHd3v+M1Sq4tq00LtbD8/HwtMzNTy8zM1ABo7777rpaZmamdOnVK0zRNe+WVV7QRI0bcPP748eNa1apVtYkTJ2oHDhzQPv30U83Dw0P79ttvVf0TbMbcc/Xee+9pycnJ2uHDh7V9+/Zpr7zyigZAW7p0qap/gk08++yzmr+/v5aWlqadPXv25seVK1duHsPryqAi58tVr60pU6ZoGzZs0E6cOKHt2bNHmzp1qubm5qatX79e0zReV7cy91y56jVVnttXhdjDteUwwaJ0idHtHyNHjtQ0TdNGjhyp9ejRw+g1aWlpWocOHTRPT0+tcePG2ty5c21fuALmnqs333xTa9q0qebt7a3VqFFDi4qK0latWqWmeBsq6xwB0D7//PObx/C6MqjI+XLVa+vJJ5/UGjVqpHl6emq1a9fWYmNjb75Rahqvq1uZe65c9Zoqz+3Bwh6uLW6bTkRERBbjtJM3iYiIyPYYLIiIiMhiGCyIiIjIYhgsiIiIyGIYLIiIiMhiGCyIiIjIYhgsiIiIyGIYLIiIiMhiGCyIiIjIYhgsiIiIyGJsvrtpSUkJzpw5A19fX6vtBU9ERESWpWka8vPzUb9+fbi5lT8uYfNgcebMGQQHB9v6yxIREZEFZGVlISgoqNznbR4sfH19AUhhfn5+tv7yREREVAF5eXkIDg6++T5eHpsHi9LbH35+fgwWREREDuZe0xg4eZOIiIgsplLBIikpCTqdDhMmTLBQOUREROTIKhwstm/fjnnz5iE0NNSS9RAREZEDq1CwuHz5MoYNG4Z///vfqFGjhqVrqpicHGDzZtVVEBERubQKBYvExET0798fcXFx9zy2sLAQeXl5Rh8Wl5MDREcD8fHAhg2W//uJiIjIJGYHi0WLFmHnzp1ISkoy6fikpCT4+/vf/LBKDws/PyA4GLhyBejXjyMXREREipgVLLKysjB+/Hh8+eWX8Pb2Nuk1U6ZMQW5u7s2PrKysChV6V97eQHIyEBcHFBQAffoAW7ZY/usQERHRXek0TdNMPXj58uV44IEH4O7ufvOx4uJi6HQ6uLm5obCw0Oi5suTl5cHf3x+5ubmW72Nx5QowYACQmiqjGCkpwH33WfZrEBERuSBT37/NGrGIjY3F3r17sWvXrpsfERERGDZsGHbt2nXPUGF1VasCK1cC3bsDeXky52LHDrU1ERERuRCzOm/6+vqiXbt2Ro9Vq1YNNWvWvONxZapVA1atktshmzcDvXoBP/4IdOigujIiIiKn55ydN318gDVrgMhI4OJFmXuxe7fqqoiIiJyeWXMsLMGqcyxul5srt0N++QWoVUvmXtjLyAoREZEDscocC4fj7w+sWweEhwPnzwMxMcCBA6qrIiIiclrOHSwAoHp1YP16mWNx7pyEi4MHVVdFRETklJw/WABAQIAsPQ0NBf74Q8LFkSOqqyIiInI6rhEsAKBmTeCHH4C2bYGzZ6UF+LFjqqsiIiJyKq4TLACgdm1Zetq6NfD77xIuTpxQXRUREZHTcK1gAQCBgcBPPwEtWwJZWXJb5NQp1VURERE5BdcLFgBQt66Ei+bNgZMnJVxYYw8TIiIiF+OawQIA6teXcNGkCXD8uISL339XXRUREZFDc91gAQBBQdI0q3Fj4OhRCRdnz6quioiIyGG5drAAgIYNJVw0bAgcPizh4o8/VFdFRETkkBgsABmxSE2VEYyDByVc5OSoroqIiMjhMFiUatJEwkX9+tL2Oy5O2oATERGRyRgsbtWsmYSLunWBvXslXPz5p+qqiIiIHAaDxe1atJBwERgoW6336iVbrxMREdE9MViUpVUr6dBZuzawc6dsvX7pkuqqiIiI7B6DRXnatpVwUbMmkJEB9OkD5OWproqIiMiuMVjcTfv2Ei4CAoBt24C+fYH8fNVVERER2S0Gi3sJC5Mt16tXB37+GejfHygoUF0VERGRXWKwMEXHjhIu/P2BjRuBAQOAK1dUV0VERGR3GCxMFREBrFsH+PoCaWnAoEHA1auqqyIiIrIrDBbm6NwZWLsW8PGRuRcJCcC1a6qrIiIishsMFubq2hVYvRqoWhVYvx4YMgQoLFRdFRERkV1gsKiIbt2AVauAKlWANWuAhx4Crl9XXRUREZFyDBYV1bMnsHIl4O0NfP898PDDwI0bqqsiIiJSisGiMmJjge++A7y85L+PPcZwQURELo3BorLi44HkZMDTE1i6FBg+HCgqUl0VERGREgwWltC3r4QKDw/gm2+Axx8HiotVV0VERGRzZgWLuXPnIjQ0FH5+fvDz80NkZCTWrFljrdocy4ABwJIlgF4PfP01MGoUwwUREbkcs4JFUFAQZs+ejYyMDGRkZCAmJgaDBw/G/v37rVWfYxk8GFi8GHB3BxYuBJ56CigpUV0VERGRzeg0TdMq8xcEBATg7bffxujRo006Pi8vD/7+/sjNzYWfn19lvrT9WrJEJnIWF0u4+OQTwI13nYiIyAYuXZL9rSzM1PdvfUW/QHFxMZYsWYKCggJERkaWe1xhYSEKb2kglecKW48PHSoTOIcPB+bPlxGMuXMBnU51ZURE5GxycmSridRU+fjtN+DPP2VRgQJmB4u9e/ciMjIS165dg4+PD5KTk9GmTZtyj09KSsLMmTMrVaRDKh2xePxxGbHQ64F//YvhgoiIKufCBSA93RAkbp+OoNPJYx06KCnP7Fsh169fx+nTp3Hp0iUsXboU8+fPR3p6ernhoqwRi+DgYOe+FXKrL76QiZyaBowfD7z3HsMFERGZ7tIlYMMGQ5DYs0feU24VGgpER8tH9+5AjRoWL8PUWyGVnmMRFxeHpk2b4pNPPrFoYU7l009lrgUA/N//AW+/zXBBRERly88HNm40BInMzDsXArRpYwgSPXoAtWpZvSyrz7EopWma0YgElWH0aLktMnYs8M9/ym2RpCSGCyIiAgoKgM2bDUEiI+POdgUtWhiCRM+eQGCgklJNYVawmDp1Kvr27Yvg4GDk5+dj0aJFSEtLw9q1a61Vn/MYM0YmdCYmAm++KeHib39juCAicjXXrgFbtgA//SRB4pdf7twOokkT4yDRoIGSUivCrGDxxx9/YMSIETh79iz8/f0RGhqKtWvXolevXtaqz7k895yEi/HjgX/8Qzp1zpihuioiIrKm69eBbdsMIxJbtgC3j/QHB0uIiImRINGokZJSLcGsYPHpp59aqw7X8cILEi7+7/+A11+Xpaivvqq6KiIispQbN+R2RmmQ2LwZuHrV+Jh69QwjEtHRMkLhJCPYlZ5jQRUwaZKEi5dfBqZPl9sir7yiuioiIqqIoiKZYFkaJDZtAi5fNj6mTh0ZiSgNEi1aOE2QuB2DhSovvSQX47RpwJQpEi5efFF1VUREdC8lJcDu3YYgsWEDcHvzx4AA4yDRpo3TBonbMVioNHWqhIsZM4DJkyVcTJiguioiIrpVSYk0nCoNEunpwMWLxsf4+8uyz9Ig0b69y27lwGCh2muvSbj429+AiRMlXDz/vOqqiIhcl6YBBw8agkRaGnD+vPExPj7SiKo0SPzlLzJnjhgs7MLMmRIukpKAcePk4nz2WdVVERG5Bk0Djh0zBInUVCA72/iYqlWBqChDkAgPl18E6Q48K/ZAp5Plp0VF0pXzuefkgn36adWVERE5p5MnjYPEb78ZP+/tDXTtaggSnTop29TL0TBY2AudThpnFRXJfiJjxsjIxZNPqq6MiMjx/fabcZA4edL4eQ8PoEsXQ5Do0kXCBZmNwcKe6HTS8ruoSHZCfeopGbl4/HHVlREROZbsbOMgcfSo8fN6vYxClAaJrl3ldgdVGoOFvdHpgA8+kHAxdy7wxBMycjFsmOrKiIjs17lzMsmyNEgcPGj8vJubzIsoDRJRUTIBkyyOwcIe6XTAnDmyCc28eTJiodcDjzyiujIiIvvw55+y7LM0SOzbZ/y8TicrNUqDRLdusiSUrI7Bwl65ucmIRVER8NlnMmKh1wMPPqi6MiIi28vNla3ESzfu2r1bVnPcqn17Q5Do3l2aVJHNMVjYMzc34N//lpGLL74AHn0UWLIESEhQXRkRkXVdviytsUtHJHbskEZVt2rd2hAkevQAatdWUysZYbCwd25uwKefysjFV18BDz8MLF0KDByoujIiIsu5cgX4+WdDkNi+XX7u3ap5c+OtxOvWVVIq3R2DhSNwdwcWLJCRi0WLgIceApKTgX79VFdGRFQx164BW7cagsTWrbIr6K1CQoyDRFCQklLJPAwWjkKvBxYulHCxZAkwZAjw3XdA796qKyMiurfr14FffjEEiS1bJFzcKijIeCvxxo2VlEqVw2DhSPR6uR1SVCQjFgkJwMqVQFyc6sqIiIwVFQEZGYYgsXmz3O64Vd26xkGiaVOX2QHUmTFYOBoPD8PtkJUrgUGDgFWr5JuSiEiV4mIgM9MQJDZulAmYt6pd23gr8ZYtGSScEIOFI/L0NNwOWb0aGDAAWLNGllcREdlCSQmwd6/xVuK5ucbH1KhhHCTatmWQcAEMFo7Ky0tWhyQkAOvWyUTOdeuA++9XXRkROSNNAw4cMA4SFy4YH+PnJ8s+S4NEaKisbCOXwmDhyLy9Za7FoEHADz8AffoA69cDkZGqKyMiR6dpwOHDhiCRlgbk5Bgf4+MjHS1Lg0SHDrKKjVwag4Wjq1JFVocMGCDf/H36ACkpwH33qa6MiByJpgHHjxtv3HX2rPExVarIHhulQSI8XOZ9Ed2CwcIZVK0qEzn79QM2bADi44Eff5RveiKi8pw6ZRwksrKMn/fykhHQ6GggJkZ+YfH0VFMrOQwGC2dRrZqsDunTR5Z19eol4aJDB9WVEZG9+P134yBx4oTx8x4eQOfOhhGJyEi55UpkBgYLZ+LjI6tDeveW5jNxcbJhT1iY6sqISIU//jAOEkeOGD/v7g506mQIEl27yi8pRJXAYOFsfH0lXMTHS5e7uDj5gdKunerKiMjazp+XSZalQeLXX42fd3MDOnY0BImoKPmZQWRBDBbOyN9flp7GxcmOgLGx8sOmdWvVlRGRJV28KPOqSoPEnj3Gz+t0MmJZGiS6dQOqV1dSKrkOBgtnVb26LD2Ni5NueDExEi5atlRdGRFVVF6edLQsDRKZmbKa41bt2hlvJR4QoKZWcllmBYukpCQsW7YMBw8eRJUqVdC1a1e8+eabaMk3K/sUECBLT2Ni5DeZ6GhpatO8uerKiMgUBQXApk2GILFjh7TOvlWrVsZBok4dNbUS/Y9ZwSI9PR2JiYno1KkTioqKMG3aNMTHx+PAgQOoxgk/9qlmTWmeFRMD7NtnCBdNm6qujIhud/Uq8PPPhiDxyy+ymdetmjUz3kq8Xj0lpRKVR6dpt4+jme7cuXOoU6cO0tPT0d3EfSry8vLg7++P3Nxc+Pn5VfRLk7lycuQH0YEDQHCwhIuQENVVEbm2wkJg61ZDkNi6VbYXv1WjRsY7gAYHq6mVXJ6p79+VmmOR+78NZwLucg+vsLAQhYWFRoWRAnXqSF+Lnj2BQ4cMcy4aNVJdGZFr0TRZBv7OO/I9eO2a8fMNGhgHCf4CQA6mwiMWmqZh8ODBuHjxIjZu3Fjuca+//jpmzpx5x+McsVDkzBkJF0eOAE2ayA82/gZEZBvp6cBrr8lKjlKBgcZBolkz7gBKdsnUEYsKB4vExESsWrUKmzZtQlBQULnHlTViERwczGCh0m+/ySSv48flh1hamvyWRETWsXkzMGOGjBoC0hZ77FjgmWdkGTiDBDkAq94KGTduHFasWIENGzbcNVQAgJeXF7y8vCryZchagoLkfm6PHsDRo4bbIpwERmRZ27ZJoFi3Tj738ACeegqYOlW+D4mckJs5B2uahueffx7Lli3DTz/9hBDe+3NcDRtKuGjYULZGjomR9r9EVHk7dsiOw126SKjQ64Gnn5ZbkB99xFBBTs2sYJGYmIgvv/wS//3vf+Hr64vs7GxkZ2fj6tWr1qqPrKlxYwkXQUHAwYMSLnJyVFdF5Lh27QISEoCICNkU0N0dGDVKJkzPm8fJ0uQSzJpjoSvnPuDnn3+OJ554wqS/g8tN7dDRo3Jb5MwZoH17mbFeq5bqqogcx759wOuvA0uXyudubsCwYcD06WxIR07DKnMsKtHyguxZs2aGORd79xp2RWUrYKK7+/VXYOZM4JtvZBmpTgc8+qis/GjVSnV1REqYdSuEnFiLFhIuAgOB3buBXr1kgyMiutPhw8Dw4UDbtsDixRIqhg6VYP7f/zJUkEtjsCCDVq1kOVzt2sDOnUDv3sD/mqAREWSJ9qhRskT0q68kUCQkSBj/5hsJGkQujsGCjLVtK+GiZk1g+3agTx/ZUZHIlZ08Kas6WrQAFiwASkpk1UdGBpCcDISGqq6QyG4wWNCd2reXcBEQIHsX9O0L5OerrorI9rKygGeflUAxf77sLNqnj/SnWLkSCA9XXSGR3WGwoLKFhcmW69Wry26L/fvLFs5EruDMGWDcOJnY/PHHwI0bMql582ZgzRrgvvtUV0hktxgsqHwdO0q48PcHNm6Uod8rV1RXRWQ92dnAxImyj86cObLTaI8essdHSgrQtavqConsHoMF3V1EhHQO9PWVtt+DBgFsiEbO5tw5YPJkCRTvvy/bmd9/vyy7TksDundXXSGRw2CwoHvr3BlYuxbw8ZG5FwkJd271TOSILlwApkyRrcnfeUdCc+fOwPr1MkoXHa26QiKHw2BBpunaVe4tV6smP3SHDJHf6ogc0cWL0hUzJASYPVvmD0VEAKtXA1u2SB8X7jhKVCEMFmS6qCjZ/6BKFQkZDz0k96CJHEVuLjBrlgSKv/9dVjuFhQHffQf88ousgGKgIKoUBgsyT48ewPffA97e8t+HH5YZ80T2LD8feOMNCRQzZkjAaNdO9vbYuVPmDjFQEFkEgwWZLyYGWLEC8PKS3/Qee4zhguxTQQHw1lsSKKZNk1sgrVtLG+7du+WWnht/DBJZEr+jqGJ69ZKOg56e8lvf8OFAUZHqqojElSvAu+9KoHj5ZZmk2aKFtOHeu1dG2hgoiKyC31lUcX37Sqjw8JB9Eh5/XDoTEqly7Rrw4YdA06bA//2fLCNt0gT44gtg/37gr38F3N1VV0nk1BgsqHIGDACWLAH0euDrr2WDJoYLsrXCQuCjj6RT5vjx0uiqcWPg00+Bgwcl9Or1qqskcgkMFlR5gwfLPWt3d2DhQuCpp2STJiJru34dmDcPaN4cSEwEfv8dCA4GPvkEOHQIePJJGVEjIpthsCDLGDJERizc3WX3x7FjGS7IeoqKgM8+A1q2lGstKwuoX1/acB85AowZI/N/iMjmODZIljN0qPzAHz5cdoLU62V4msv4yFKKiiTAzpwJHDsmjwUGSvfMMWOkxwoRKcVgQZb12GMyx+Lxx2VXSL1eJtMxXFBlFBfLBOGZM+UWBwDUri0rPp59FqhaVW19RHQTgwVZ3vDh8kYwapQMTbu7A++9x3BB5ispkZVHr78OHDggjwUEAC+9JHMqfHyUlkdEd2KwIOsYOVKGrZ96CvjgAxm5ePtthgsyjaYBy5dLl8y9e+Wx6tWBF18Exo0D/PxUVkdEd8FgQdYzerSMXIwdC/zznxIukpIYLqh8miat4mfMADIz5TE/P2DSJGDCBMDfX2l5RHRvDBZkXWPGyMhFYiLw5psSLv72N4YLMqZpwNq1wGuvARkZ8piPj4SJSZOAGjWUlkdEpmOwIOt77jkJF+PHA//4h/QVmDFDdVVkDzQN+OEHCRRbt8pjVavK7Y4XXwRq1VJbHxGZjcGCbOOFF+S2yKRJMhHP3R149VXVVZFKqakSKDZtks+9vWVk66WXgDp11NZGRBXGYEG2M3GijFy89BIwfbrcFnnlFdVVka1t3CgjVqmp8rmXF/DMM7J0tF49tbURUaUxWJBtTZ4s4WLqVGlqpNfLkDc5vy1bJFCkpMjnnp7A00/LddCggdraiMhizG7pvWHDBgwcOBD169eHTqfD8uXLrVAWObUpU4BZs+TPkycD77+vtByysu3bgX79gK5dJVTo9bJS6MgR6XPCUEHkVMwOFgUFBQgLC8OcOXOsUQ+5iunT5QOQWyS8npxPZiYwaBBw333AmjUyr2b0aAkUH38MNGyoukIisgKzb4X07dsXffv2tUYt5GpmzpTbIklJsgrA3V3aM5Nj27NHJugmJ8vnbm7AiBESJJs2VVoaEVmf1edYFBYWorCw8ObneXl51v6S5Ch0Oll+WlQkXTmfe06GyZ9+WnVlVBH790tYXLJEPtfpgL/+VVZ+tGihtjYishmrb5uelJQEf3//mx/BwcHW/pLkSHQ6aZw1caJ8PnYs8Pnnamsi8xw6JAGifXtDqHj4YWDfPuDLLxkqiFyM1YPFlClTkJube/MjKyvL2l+SHI1OJy2/x42ThkmjRwMLF6quiu7l6FHZxbZNG9nKXNOAIUPkVsjixfI4Ebkcq98K8fLygpeXl7W/DDk6nU42KysqAubOBZ54QuZc/PWvqiuj2504Afz978AXX0jTM0Amab7+OtChg9LSiEg99rEg+6HTyeqQ4mJg3jyZ8OfuDjzyiOrKCABOn5Y5MZ99JgEQkGWkM2cCERFqayMiu2F2sLh8+TKOHj168/MTJ05g165dCAgIQEMuH6PKcnOTEYuiInkDGzZMJnQ++KDqylzXb7/Jyp1//xu4cUMei4+XQNGli9raiMju6DRN08x5QVpaGqKjo+94fOTIkViwYME9X5+Xlwd/f3/k5ubCz8/PnC9NrqSkBHjySRlu1+tlUmBCguqqXMvZs8Ds2cAnnwClK7tiYiRQREWprY2IbM7U92+zg0VlMViQyYqLgZEjga++kh1Rly4FBg5UXZXzy8mRlToffQRcuyaPdesm3VJ79lRaGhGpY+r7t9VXhRBVmLs7sGAB8OijMgT/0EPA6tWqq3Je58/LRmAhIcC770qoiIyUbc3T0xkqiMgkDBZk3/R6WXo6dChw/bosZ1y3TnVVzuXPP4Fp0yRQvPUWcOUK0KmTtOHevBmIjZWJtUREJmCwIPun18vtkAcekHv9CQnyWzRVzqVLsttoSAjwxhvA5cuyXHTlSmDbNqBPHwYKIjIbgwU5Bg8PYNEi6Zdw7Zr8NzVVdVWOKS9P+lCEhMi8ibw8IDRU9vbYsQMYMICBgogqjMGCHIenJ/DNN0D//sDVq/IGuGGD6qocx+XLssojJEQ2BLt0SbpjLlkiO5EmJDBQEFGlMViQY/HyAr79Vobpr1yRBk2bN6uuyr5duQK8844EiilTZE5Fy5bShnvPHpkU68YfBURkGfxpQo7H2xtYtgyIiwMKCiRkbNmiuir7c/Uq8P77QJMmwOTJsuqjWTOZDLt/v6y2cXdXXSURORkGC3JMVaoA330HREfLEH+fPsAvv6iuyj4UFkpr9KZNZdfYP/6Q0YrPPwd+/RUYPpyBgoishsGCHFfVqrKCoXt3mYDYu7dMPnRV168DH38soxLjxknnzIYNZd+VQ4dkYzc9twciIutisCDHVq0asGoVcP/9MhmxVy9g1y7VVdnWjRvA/PlAixbAs8/K3h4NGkjnzMOHgaefllU1REQ2wGBBjs/HR5o5RUYCFy/K3Is9e1RXZX1FRbKXSqtWEh5OnQLq1gU+/BA4elRChpeX6iqJyMUwWJBz8PWVcHHffcCFC9Itct8+1VVZR3GxNAxr00Zubxw/DtSpI224jx+X2yDe3qqrJCIXxWBBzsPfX9p9h4fLCojYWJms6CxKSoDFi4F27WQC5pEjQM2a0ob7+HGZqFmliuoqicjFMViQc6leHVi/XlpT5+TINt+HDqmuqnJKSmRn19BQWSJ68CBQo4a04T5xQpaSVqumukoiIgAMFuSMAgKAlBR5I87OliWpR46orsp8miZLajt2lCZW+/fLqMysWcDJk9LsytdXdZVEREYYLMg51awpG5W1ayfLLqOjgWPHVFdlGk2TlS6dOkmb7d27JUBMny4jFNOnA35+qqskIioTgwU5r9q1gR9/lEmOv/8u4eLECdVVlU/TZI5Ily6yD8qOHXKLY8oUqXvWLLkFQkRkxxgsyLnVqSPhomVLICtL5lycOqW6KmOaJjVGRRk6iFapInMnTpyQuRQ1a6qukojIJAwW5Pzq1gV++glo3lzmJsTESMiwB+npQM+e0nvj559lmejEiRIo3npLRl2IiBwIgwW5hvr1gdRU2T/j+HEJF7//rq6ezZslTPTsKVu/e3pK/4ljx6QfRWCgutqIiCqBwYJcR4MGEi5CQqQzZUyMTOy0pW3b5HZHVJTc/vDwkA6Zx45Jx8z69W1bDxGRhTFYkGsJDpZw0aiR7KMREyO7f1rbjh0yIbNLF5mgqddLG+4jR2RPj6Ag69dARGQDDBbkeho1kjkXwcHSbComBjh3zjpfa9cuWTIaESFLSN3dgVGjpGnXvHlSCxGRE2GwINfUpImEi/r1gQMHpP33+fOW+/v37ZOmVh06SJMrNzdpw/3rr8Bnn8nXJyJyQgwW5LqaNZPbInXrAnv3ypbrf/5Zub/z11+l7XZoqLTh1unk8337gIULZWUKEZETY7Ag19aihYSLwEC5bdGrl2y9bq7Dh2VEom1b2ShM02TEYs8e4OuvgdatLV46EZE9YrAgatVKVmjUrg3s3An07g3k5pr22uPHZc5E69aylbmmyZyKXbuAJUukpTgRkQupULD46KOPEBISAm9vb4SHh2Pjxo2WrovIttq2lXBRsyawfbssCc3LK//4kydlVUeLFsCCBbID6YABQEYGkJwMhIXZqnIiIrtidrBYvHgxJkyYgGnTpiEzMxPdunVD3759cfr0aWvUR2Q77dtLuAgIALZuBfr2BfLzjY/JypK+Ey1aAPPnA8XFEkK2bQNWrgTCw9XUTkRkJ3SapmnmvKBz587o2LEj5s6de/Ox1q1bIyEhAUlJSfd8fV5eHvz9/ZGbmws/7tBI9mjnTlklcukS0K0bsGaN3BpJSpIlotevy3FxccDMmUDXrkrLJSKyBVPfv80asbh+/Tp27NiB+Ph4o8fj4+Px888/V6xSInvTsSOQkgL4+wMbN0oPiqZNgTlzJFT06CF7fKSkMFQQEd1Gb87B58+fR3FxMQJv28cgMDAQ2dnZZb6msLAQhYWFNz/Pu9t9ayJ7EREhHTJ79ZImWgBw//2ydXl0tCwjJSKiO1Ro8qbuth+qmqbd8VippKQk+Pv73/wIDg6uyJcksr3OnaWJ1ujREjI2bpQunQwVRETlMitY1KpVC+7u7neMTuTk5NwxilFqypQpyM3NvfmRZS/bVROZIiJCJmnGxzNQEBGZwKxg4enpifDwcKSkpBg9npKSgq7l3Gv28vKCn5+f0QcRERE5J7PmWADApEmTMGLECERERCAyMhLz5s3D6dOn8cwzz1ijPiIiInIgZgeLRx55BBcuXMCsWbNw9uxZtGvXDqtXr0Yj7tJIRETk8szuY1FZ7GNBRETkeEx9/zZ7xKKySnMMl50SERE5jtL37XuNR9g8WOT/r0Uyl50SERE5nvz8fPj7+5f7vM1vhZSUlODMmTPw9fUtt/dFReTl5SE4OBhZWVm8xXIPPFem47kyD8+X6XiuTMdzZTprnitN05Cfn4/69evDza38RaU2H7Fwc3NDUFCQ1f5+Lmk1Hc+V6XiuzMPzZTqeK9PxXJnOWufqbiMVpSrUeZOIiIioLAwWREREZDFOEyy8vLwwY8YMeHl5qS7F7vFcmY7nyjw8X6bjuTIdz5Xp7OFc2XzyJhERETkvpxmxICIiIvUYLIiIiMhiGCyIiIjIYhgsiIiIyGIcJlhs2LABAwcORP369aHT6bB8+fJ7viY9PR3h4eHw9vZGkyZN8PHHH1u/UDtg7rlKS0uDTqe74+PgwYO2KViRpKQkdOrUCb6+vqhTpw4SEhJw6NChe77OVa+ripwvV7225s6di9DQ0JtNiiIjI7FmzZq7vsZVrytzz5WrXlNlSUpKgk6nw4QJE+56nK2vLYcJFgUFBQgLC8OcOXNMOv7EiRPo168funXrhszMTEydOhUvvPACli5dauVK1TP3XJU6dOgQzp49e/OjefPmVqrQPqSnpyMxMRFbt25FSkoKioqKEB8fj4KCgnJf48rXVUXOVylXu7aCgoIwe/ZsZGRkICMjAzExMRg8eDD2799f5vGufF2Ze65Kudo1dbvt27dj3rx5CA0NvetxSq4tzQEB0JKTk+96zEsvvaS1atXK6LGxY8dqXbp0sWJl9seUc5WamqoB0C5evGiTmuxVTk6OBkBLT08v9xheVwamnC9eWwY1atTQ5s+fX+ZzvK6M3e1c8ZrStPz8fK158+ZaSkqK1qNHD238+PHlHqvi2nKYEQtzbdmyBfHx8UaP9e7dGxkZGbhx44aiquxbhw4dUK9ePcTGxiI1NVV1OTaXm5sLAAgICCj3GF5XBqacr1KufG0VFxdj0aJFKCgoQGRkZJnH8LoSppyrUq58TSUmJqJ///6Ii4u757Eqri2bb0JmK9nZ2QgMDDR6LDAwEEVFRTh//jzq1aunqDL7U69ePcybNw/h4eEoLCzEwoULERsbi7S0NHTv3l11eTahaRomTZqEqKgotGvXrtzjeF0JU8+XK19be/fuRWRkJK5duwYfHx8kJyejTZs2ZR7r6teVOefKla8pAFi0aBF27tyJ7du3m3S8imvLaYMFgDu2Zdf+12TUktu1O4OWLVuiZcuWNz+PjIxEVlYW3nnnHZf4RgWA559/Hnv27MGmTZvueSyvK9PPlytfWy1btsSuXbtw6dIlLF26FCNHjkR6enq5b5iufF2Zc65c+ZrKysrC+PHjsX79enh7e5v8OltfW057K6Ru3brIzs42eiwnJwd6vR41a9ZUVJXj6NKlC44cOaK6DJsYN24cVqxYgdTUVAQFBd31WF5X5p2vsrjKteXp6YlmzZohIiICSUlJCAsLwwcffFDmsa5+XZlzrsriKtfUjh07kJOTg/DwcOj1euj1eqSnp+PDDz+EXq9HcXHxHa9RcW057YhFZGQkVq5cafTY+vXrERERAQ8PD0VVOY7MzEynH37VNA3jxo1DcnIy0tLSEBIScs/XuPJ1VZHzVRZXuLbKomkaCgsLy3zOla+rstztXJXFVa6p2NhY7N271+ixUaNGoVWrVnj55Zfh7u5+x2uUXFtWmxZqYfn5+VpmZqaWmZmpAdDeffddLTMzUzt16pSmaZr2yiuvaCNGjLh5/PHjx7WqVatqEydO1A4cOKB9+umnmoeHh/btt9+q+ifYjLnn6r333tOSk5O1w4cPa/v27dNeeeUVDYC2dOlSVf8Em3j22Wc1f39/LS0tTTt79uzNjytXrtw8hteVQUXOl6teW1OmTNE2bNignThxQtuzZ482depUzc3NTVu/fr2mabyubmXuuXLVa6o8t68KsYdry2GCRekSo9s/Ro4cqWmapo0cOVLr0aOH0WvS0tK0Dh06aJ6enlrjxo21uXPn2r5wBcw9V2+++abWtGlTzdvbW6tRo4YWFRWlrVq1Sk3xNlTWOQKgff755zeP4XVlUJHz5arX1pNPPqk1atRI8/T01GrXrq3FxsbefKPUNF5XtzL3XLnqNVWe24OFPVxb3DadiIiILMZpJ28SERGR7TFYEBERkcUwWBAREZHFMFgQERGRxTBYEBERkcUwWBAREZHFMFgQERGRxTBYEBERkcUwWBAREZHFMFgQERGRxTBYEBERkcUwWBAREZHF/H8f3/dtxq4VIQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Subplot\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(x1, y1, color= 'b')\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x2, y2, color= 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "982f2617",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot\n",
    "\n",
    "x = [\"A\", \"B\", \"C\", \"D\", \"E\"]\n",
    "y = [5, 3, 1, 4, 7]\n",
    "\n",
    "plt.bar(x,y)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}