notebooks/ObjectOrientation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Object Orientation in Python\n",
    "\n",
    "We will cover Object Orientation based on three main topics:\n",
    "* Object Orientation basics\n",
    "* Implementing OO in Python\n",
    "* Inheritance"
   ]
  },
  {
   "attachments": {
    "oop_1.png": {
     "image/png": 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"
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    "oop_2.png": {
     "image/png": 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eqO6/PhIKFouyQsMQafHANHqQqtlPPHc+nJfC8BulF2tsGB+KytO374ZdaVsrPrHFVagMnOrVcWfMru936+Q+nGpKxDQSRZHBoa6FM6fRe7M8hMUj7+w4SJ3t0KVhY7YDxVvV7Jyoe+LYtlaqWnwUjTnIzzAmcUVa9J+Kvix+C5OW8ehkvP7Fk5/vIYulbQzun34nWV1ZtXEPw6cx28GfmV6s/kwZhVmleWuWO0E/eJlIQWnt1Jgcd5dSZeFbHH9u2sY8LD0wPc/z5FqGDEMmgMmdUHNCiLtJ4x6GT/tsB82JDU5Me0BEhcq+U9laLJ58x8ZkeYSRSX8+b60xqwdmg/dmEk48bxImeWqTS0RDdUL7bAd0LLHBqWkPZKOsp1p9fRQDhFNj8jwnKwex1rFbW3hgFt6bsvvKLZDfIznx4bw09YivVSZ3xegmpyMnbLWQqSwMc405NibTw9hcZSfEB56tZe4C/SbLX75HgjZzz/O8OQXyb+4vXwKqVbXsYfgYf2ND7d02//Wvd9vKtKt5q3WfeaeQa8yxMY23AwOnh4QHne0wCgP2Bo02jr+clebR7OUzYDBgRurjr68XDLbOp/id7TASA/aEtMZIRcYOROYEDwtJQD9AZIAdiAywA5EBdiAywA5CRznm5ubG87xLn8VQkKm68AjDDOzgEAyXgB2IDLBTnZNhSiFB3k+HYOYB2MFwCdiByAA7EBlgByID7EBkgB2IDLADkQF2IDLADkQG2IHIADsQGWAHIgPsQGSAHYgMsAORAXYgMsAORAbYgcgAOxAZYAciA+xAZIAdiAywA5EBdiAywA5EBtiByAA7EBlgByID7EBkgB2IDLADkQF2ILLrQE1QfyqVXPfuGZLItB97jtVGgfy19UySRabo5h/PrgyHOBRZYTXVaBVb8JlmTEYnojyFohCCNq/lWe9XrxsSol0u1LHgTmQyZ7MQRJvPbkle/eXuUGZ7/hncLxZCScO8//pIKFgstDpF31b8ifarqUw+n4QT7Z/19VzvHZXmatWiT5i+fRsP5jDvsascrWkkiIKtlkc31bITiyDQvkbpYRsQiSgKsu9qRt7scxQo1YtdVr9UDqTsQSvRS10n+z0ReSKZxdQflBtRlik2Mdi2+GHZzwq2lXr5UapNlN1mTUWUVo4WaHnuz8CVyLZB9lsq2ZortjCbJv9eE1n2TWllFpllz/Vrw2PEThSXv1RZbkTlt1gzYdt/r/pFK1a+GnZb1lf/k05wNFzGnxsicTfJc6ar04xjiOjdNkJmm+Q+y0Gl7fnIrO3ZGZVjuNyRv1wPY2D2nxaCkvAtjj83RMEvNQexzISd55uXQ+QJyObty2m23gbF8ZyNl25EVr+mpyjCPdZrw2PEM8n+BfN5TWPk395TtWc5IRO2bN6+nIhm66JzO6WnaMSJyOLPDVWvKYPK8tT2x2m4NhxGPBepMqKaxohmvwKzLRt0UmsevsVE2Z0rBS9LnyZ3ZUcQvxV/wv1qKm8N2u29LS5EJvsx5YqmUfETKmd74skn4e/VPrvnykbj23siSr5T0qxT3bPl2jAZ8WxyldU0RjRbp5HI/79q/zv7E4na3WWN2TqNBG3m2b8/2Mo/m7/cbYOsW3i92+b3Tf7yffEx8TzPm28o2Dq70T97VleZgSpl6r0O6dNwMsw/6xN/ERU/X7kXLO4Ps836/LV+IK29Unrhu8sfBNLeAHaG9FoJXCkQGWAHIgPsQGSAHYgMsPP/UZqc9vdNwXoAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Object Orientation basics\n",
    "Object-Oriented (OO) is a programming paradigm in which different \"components\" of your software are modeled based on real-world objects. An object is anything that has some characteristics and can perform a function.\n",
    "\n",
    "OO basically relies in abstraction concept, by defining \"**classes**\" of **objects** in the software level, which can be represented by the object's data (attributes) and \"code\" (actions they can perform)\n",
    "\n",
    "A **class** can be defined as a \"blueprint\" of objects\n",
    "\n",
    "Used to make the code easier to maintain, by modularizing and creating better representations of real things.\n",
    "\n",
    "Dummy example used to explain OO:\n",
    "![oop_1.png](attachment:oop_1.png)  ![oop_2.png](attachment:oop_2.png)\n",
    "\n",
    "Other example of class would be **Person**, which would have different attributes, like: birth_date, gender, height, weight, city_of_birth, hair_color. a **Person** can also perform some actions like: exercise (which may change their weight), sleep, work, etc.\n",
    "\n",
    "Let's brainstorm a bit, and think about a real-world thing that we all may know...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OO in Python\n",
    "Now we will explore how to deal with Object Orientation in Python.\n",
    "\n",
    "As a first step, we will use a well-known example when learning OO:\n",
    "* designing a class to define a **point** in a plane\n",
    "* what are the clear attributes that a point would have?\n",
    "\n",
    "See below how to create a class that abstract points, with attributes representing its position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Point:\n",
    "    x = 0\n",
    "    y = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Great!** \n",
    "\n",
    "We've just created a class point! But this is just our \"blueprint\" or our \"template\". To actually create points we need to **instantiate** objects out of this **class**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class '__main__.Point'>\n"
     ]
    }
   ],
   "source": [
    "#let's pretend this is our main program\n",
    "point1 = Point()\n",
    "point2 = Point()\n",
    "\n",
    "print(type(point1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have TWO points, created based on our class Point. When we do ```point1 = Point()```, we are creating a variable called point1, which is a variable typed as a Point (as per the print statement).\n",
    "\n",
    "#### Attributes\n",
    "\n",
    "So far, our class template only provides attributes to the point. So, what we can do is change the attributes of each point... (although, I anticipate that this is not a good practice, because we are messing up with the **encapsulation** of the objects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point 1 position is: 10.40, 2.20. Point 2 position is: 0.00, -4.30\n"
     ]
    }
   ],
   "source": [
    "point1.x = 10.4\n",
    "point1.y = 2.2\n",
    "point2.x = 0\n",
    "point2.y = -4.3\n",
    "\n",
    "print(\"Point 1 position is: %0.2f, %0.2f. Point 2 position is: %0.2f, %0.2f\"%(point1.x, point1.y, point2.x, point2.y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Methods\n",
    "Methods are the actions that map the behavior of our objects. In Python, the way to declare these actions is through `def`, similarly to the way we define functions (methods are functions that apply to the object)\n",
    "\n",
    "See the example of adding a `translate` method to our Point class. This method translates our points by a given `x, y` units in the plane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Point:  \n",
    "    __x = 0\n",
    "    y = 0\n",
    "    def translate(self, dx, dy):\n",
    "        self.__x += dx # self.x = self.x + dx\n",
    "        self.y += dy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point 1 position is: 2.00, 0.00.\n",
      "Point 1 position is: 2.00, 2.00.\n"
     ]
    }
   ],
   "source": [
    "point1 = Point()\n",
    "point1.__x= 2\n",
    "print(\"Point 1 position is: %0.2f, %0.2f.\"%(point1.__x, point1.y))\n",
    "\n",
    "point1.translate(2,2)\n",
    "print(\"Point 1 position is: %0.2f, %0.2f.\"%(point1.__x, point1.y))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what we've done there:\n",
    "* Created our method, that receives *three* parameters: ``self``, ``dx``, and ``dy``. While `dx` and `dy` are the units that I want to translate the points, `self` is a *implicit parameter* representing the object we are dealing with. We do not need to provide any value to this parameter, it is used internally. **self must be the first parameter of any method**\n",
    "* then, we change `x` and `y` attributes that belong to the `self` object (which is a self reference, saying: \"I want to change MY values of x and y\"\n",
    "\n",
    "#### Let's code together\n",
    "1. We will write the method `set_location(self, x, y)`, which is reponsible for setting the position of the point without accessing the attributes directly\n",
    "2. You will code a method `distance_from_zero(self)`, which returns the distance from the point to the origin of the plane (position 0,0)\n",
    "3. You will code a method `distance (self, other)`, which calculates the distance from the object to `other` point received via parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "###space for our exercise\n",
    "import math\n",
    "\n",
    "class Point:  \n",
    "    x = 0\n",
    "    y = 0\n",
    "    def translate(self, dx, dy):\n",
    "        self.x += dx # self.x = self.x + dx\n",
    "        self.y += dy\n",
    "    def set_location(self, new_x, new_y):\n",
    "        self.x = new_x\n",
    "        self.y = new_y\n",
    "    def distance(self, pointB):\n",
    "        return math.sqrt((self.x-pointB.x)**2 + (self.y-pointB.y)**2)\n",
    "    def distance_from_zero(self):\n",
    "        return math.sqrt(self.x**2 + self.y**2)\n",
    "        \n",
    "\n",
    "point1 = Point()\n",
    "point1.set_location(3, 4)\n",
    "point2 = Point()\n",
    "point2.set_location(2, 4)\n",
    "print(point1.distance_from_zero())\n",
    "print(point1.distance(point1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummy master to test our methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n",
      "10.295630140987\n",
      "10.295630140987\n",
      "5.385164807134504\n"
     ]
    }
   ],
   "source": [
    "point1 = Point()\n",
    "point1.set_location(3, 4)\n",
    "print(point1.distance_from_zero())\n",
    "\n",
    "point2 = Point()\n",
    "point2.set_location(-2, -5)\n",
    "\n",
    "print(point1.distance(point2))\n",
    "print(point2.distance(point1))\n",
    "print(point2.distance_from_zero())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Constructors\n",
    "Nice concept in OO languages.\n",
    "\n",
    "When you build an object, a specific method called constructor is called.\n",
    "\n",
    "In previous examples, whenever we called `point1 = Point()`, a constructor with no parameters was called (which is a default, and do not change any attribute). We can define which kinds of constructors we want to make available, with or without parameters.\n",
    "\n",
    "In Python, this method is named `__init__`\n",
    "\n",
    "For example, if we want to instantiate our points providing its position, we would define a `__init__` method like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point 1 position is: 3.00, 4.00. \n"
     ]
    }
   ],
   "source": [
    "class Point:\n",
    "    def __init__(self, x=0, y=0):\n",
    "        self.x = x\n",
    "        self.y = y        \n",
    "        \n",
    "point1 = Point(3,4)\n",
    "#point1= Point()\n",
    "print(\"Point 1 position is: %0.2f, %0.2f. \"%(point1.x, point1.y))\n",
    "##And now I've create a point in position x=2, y=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can define different *signatures*  to different constructors. Meaning that, we can define many `__init__` methods if we change the parameters required. \n",
    "\n",
    "For example, let's create a constructor that does not require any parameter, and sets the position to 0,0.\n",
    "\n",
    "**Let's do together**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "###sandbox for the constructor\n",
    "class Point:\n",
    "    def __init__(self, x, y):\n",
    "        self.x = x\n",
    "        self.y = y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Solving the question about overriding a method**\n",
    "\n",
    "The way to enable calling a method using multiple number of parameter in Python is using defaults (even for the constructors).\n",
    "\n",
    "So, if we want to enable someone calling \n",
    "\n",
    "`point1 = Point()` AND `point2 = Point(2,3)` and both work is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "###sandbox for the constructor\n",
    "class Point:\n",
    "    # when you add an assignment statement in the parameters it means:\n",
    "    # assign this value if you do not receive anything\n",
    "    def __init__(self, x=0, y=0):\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        \n",
    "point1 = Point()\n",
    "point2 = Point (2,3)\n",
    "print(point2.x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining the way the object is print\n",
    "We can also define what would be an output when someone wants to print our object, like (`print(point1)`). What would happen now??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<__main__.Point object at 0x7fefe0541210>\n"
     ]
    }
   ],
   "source": [
    "point1 = Point(2,4)\n",
    "print(point1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Not a good expression of what this point actually is, right?*\n",
    "\n",
    "We can make it better. Defining a method called `__str__` is the way to go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 4)\n"
     ]
    }
   ],
   "source": [
    "class Point:\n",
    "    def __init__(self, x, y):\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        \n",
    "    # FOCUS HERE: \n",
    "    def __str__(self):\n",
    "        return(\"(\" + str(self.x) + \", \" + str(self.y) + \")\")\n",
    "    \n",
    "# let's see how it goes now\n",
    "point1 = Point(2,4)\n",
    "print(point1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Our complete code should come here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "###space for our exercise\n",
    "import math\n",
    "\n",
    "class Point: \n",
    "\n",
    "    def __init__(self, x, y):\n",
    "        self.x = x\n",
    "        self.y = y     \n",
    "\n",
    "    def __str__(self):\n",
    "        return(str(self.x) + \", \" + str(self.y))\n",
    "    \n",
    "    def translate(self, dx, dy):\n",
    "        self.x += dx # self.x = self.x + dx\n",
    "        self.y += dy\n",
    "    \n",
    "    def set_location(self, x, y):\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "    \n",
    "    def distance_from_zero(self):.\n",
    "        distance = math.sqrt(self.x**2 + self.y**2) \n",
    "        return distance\n",
    "    \n",
    "    def distance (self, other:\n",
    "        return math.sqrt((self.x-other.x)**2 + (self.y-other.y)**2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One more example for your delight\n",
    "\n",
    "In the following example, we have 2 classes:\n",
    "* `Contact`: an abstraction of the contacts in our Contact List. We represent this class of objects as something with the attributes `name` and `email_address`, because these are important in the context of our problem scope.\n",
    "* `Email`: representing the email itself, which contains recipients, sender, body, subject, a flag identifying whether the email was sent or not. It also maps a set of actions that we may use to interact with our email objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Contact:\n",
    "    def __init__(self, name=\"\", email_address=\"\"):\n",
    "        self.name = name\n",
    "        self.email_address = email_address\n",
    "    \n",
    "    def set_email_address(self, new_address):\n",
    "        self.email_address = new_address\n",
    "        \n",
    "    def set_name(self, new_name):\n",
    "        self.name = new_name\n",
    "    \n",
    "    def __str__ (self):\n",
    "        return (self.name + \" <\" + self.email_address + \">\")\n",
    "\n",
    "\n",
    "class Email:\n",
    "    def __init__(self):\n",
    "        self.is_sent = False\n",
    "        self.subject = \"\"\n",
    "        self.from_ = \"\"\n",
    "        self.to = []\n",
    "        self.cc = []\n",
    "        \n",
    "    def send_email(self):\n",
    "        if (not self.is_sent):\n",
    "            #do the magic of sending the email and:\n",
    "            self.is_sent = True\n",
    "        \n",
    "    def set_sender(self, from_):\n",
    "        self.from_ = from_\n",
    "    \n",
    "    def add_recipient(self, contact, where=\"to\"):\n",
    "        if (where == \"to\"):\n",
    "            self.to.append(contact.email_address)\n",
    "        elif (where == \"cc\"):\n",
    "            self.cc.append(contact.email_address)\n",
    "        else:\n",
    "            print (\"please provide a valid field (cc or to)\")\n",
    "    \n",
    "    def set_body (self, body):\n",
    "        self.body = body\n",
    "\n",
    "    def set_subject (self, subject):\n",
    "        self.subject = subject\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inheritance\n",
    "\n",
    "Inheritance enable us to define a class that takes all the characteristics and functions from a parent class. And we can extend the parent or change specific ways that an action is performed.\n",
    "\n",
    "Look at this simple example built upon our `Point` class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n"
     ]
    }
   ],
   "source": [
    "class Point3D(Point):\n",
    "    z = 0\n",
    "    def __init__(self, x, y, z):\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        self.z = z\n",
    "    def translate(self, dx, dy, dz):\n",
    "        Point.translate(self, dx, dy)\n",
    "        self.z += dz\n",
    "                \n",
    "point1 = Point3D(3,4,5)\n",
    "print(point1.distance_from_zero())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well... what we have is:\n",
    "    * we define the class `class Point3D (Point)`: this means that Point is the *parent* class for Point3D. Everything inside Point is part of Point3D\n",
    "    * so, we can change any attribute and call any function of Point when we create a Point3D object.\n",
    "    * we can call any parent function from the child class by mentioning the name of the parent class: `Point.translate(...)`\n",
    "    * Here we see the value of `self` attribute. We can pass the object of the child class to the parent\n",
    "    \n",
    "One issue to deal with:\n",
    "    * if there is a different behavior for some action (e.g.: `distance_from_zero(self)`), we need to **\"overwrite\"** the function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.0710678118654755\n"
     ]
    }
   ],
   "source": [
    "class Point3D(Point):\n",
    "    z = 0\n",
    "    def __init__(self, x, y, z):\n",
    "        Point.__init__(self, x, y)\n",
    "        self.z = z\n",
    "    def translate(self, dx, dy, dz):\n",
    "        Point.translate(self, dx, dy)\n",
    "        self.z += dz\n",
    "    def distance_from_zero(self):\n",
    "        distance = math.sqrt(self.x**2 + self.y**2 + self.z**2)\n",
    "        return distance\n",
    "    \n",
    "point1 = Point3D(3,4,5)\n",
    "print(point1.distance_from_zero())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another example (inheritance):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "class Polygon:\n",
    "    lengths_of_sides = list()\n",
    "\n",
    "    def __init__(self, sides):\n",
    "        self.sides = sides\n",
    "\n",
    "    def print_num_sides(self):\n",
    "        print('This polygon has %d sides'%(self.sides))\n",
    "    \n",
    "    def perimeter(self):\n",
    "        perimeter = sum(self.lengths_of_sides)\n",
    "        return perimeter\n",
    "\n",
    "#This is a Rectangle, child of Polygon!\n",
    "class Rectangle(Polygon):\n",
    "\n",
    "    #lengths of sides is a list of size 2\n",
    "    def __init__(self, lengths_of_sides):\n",
    "        Polygon.__init__(self, 4)\n",
    "        double_sides = []+lengths_of_sides\n",
    "        self.lengths_of_sides = lengths_of_sides+double_sides\n",
    "        print(self.lengths_of_sides)\n",
    "\n",
    "    def area(self):\n",
    "        #multiple assignment. \n",
    "        # side_1 receives lenghts_of_sides[0] \n",
    "        # side_2 receives lenghts_of_sides[1]\n",
    "        side_1 = self.lengths_of_sides[0]\n",
    "        side_2 = self.lengths_of_sides[1]\n",
    "        return side_1 * side_2\n",
    "    \n",
    "    \n",
    "#And class Polygon has another child!!!\n",
    "class Triangle(Polygon):\n",
    "    \n",
    "    #lengths of sides is a list of size 3\n",
    "    def __init__(self, lengths_of_sides):\n",
    "        Polygon.__init__(self, 3)\n",
    "        self.lengths_of_sides = lengths_of_sides  \n",
    "\n",
    "    def area(self):\n",
    "        a, b, c = self.lengths_of_sides\n",
    "\n",
    "        # calculate the semi-perimeter\n",
    "        semiperimeter = Polygon.perimeter(self) / 2\n",
    "        return math.sqrt((semiperimeter*(semiperimeter-a)*(semiperimeter-b)*(semiperimeter-c)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.0\n",
      "12\n",
      "[2, 4, 2, 4]\n",
      "8\n",
      "12\n",
      "<class '__main__.Rectangle'>\n"
     ]
    }
   ],
   "source": [
    "triangle = Triangle([3, 4, 5])\n",
    "print(triangle.area())\n",
    "print(triangle.perimeter())\n",
    "\n",
    "rectangle = Rectangle([2,4])\n",
    "print(rectangle.area())\n",
    "print(rectangle.perimeter())\n",
    "\n",
    "print(type(rectangle))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "list1 = [1,2,3]\n",
    "list2 = []+list1\n",
    "list1[1]=1000\n",
    "\n",
    "print(list2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hands on!\n",
    "\n",
    "1. Create a class Square (inheriting...)\n",
    "2. Create a way to print (`__str__`) the information about the Polygon that applies to all types of Polygons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c1\n"
     ]
    }
   ],
   "source": [
    "###CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In Class Assignment!!! ###\n",
    "\n",
    "**How to Turn In:** Create a Python Notebook on your repository named `InClassSept29.ipynb`\n",
    "\n",
    "**Deadline:** Oct-01\n",
    "\n",
    "We want to manage our movie collection. To do so, we need to write a program that helps us. You need to use OO design and follow the constraints below:\n",
    "\n",
    "1. A `Movie` needs to have a title, a genres (may be more than one), year, my review, a list of actors, and a watch counter (how many times I watched), borrowed (a flag - True/False - that says if this movie is currently with someone), borrower name.\n",
    "2. We can interact with a movie by: \n",
    "  - watching the movie (increase the counter), \n",
    "  - writing a review about the movie, \n",
    "  - set any of the fields (except flag, borrower, and counter, which are changed by different actions)\n",
    "  - borrowing the movie (set the borrower and change the flag)\n",
    "  - returning the moving (set borrower to \"\" and flag to False)\n",
    "  - list the details of the movie when printing it in the following format:\n",
    "  ```\n",
    "     Movie: The Godfather     Year: 1972\n",
    "     Genre: Crime, Drama\n",
    "     List of Actors:\n",
    "         Marlon Brando\n",
    "         Al Pacino\n",
    "         Robert Duvall\n",
    "  ```\n",
    "  \n",
    "3. The list of actors, need to have objects of type `Actor`, which are composed of name, date_of_birth, and nationality. You should be able to:\n",
    "    - set the fields name, date_of_birth, and nationality.\n",
    "\n",
    "\n",
    "***CHALLENGE:*** *change your classes to make it possible to list (from an object actor) all the movies that the actor participated.* \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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