{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# P3-M 4/25 Simulations\n",
    "> Creating simulations using pandas and python libraries\n",
    "- toc: true\n",
    "- badges: false\n",
    "- comments: true\n",
    "- author: Shruthi Malayath, Lina Awad, Lydia Cho, Sarah Liu, Jake Warren\n",
    "- categories: [student]\n",
    "- type: ap\n",
    "- week: 31"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "1. Understand how computers can be used to represent real-world phenomena or outcomes\n",
    "2. Compare simulations with real-world contexts.\n",
    "3. Implement code to mimic real world situations, problems, or phenomena."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are simulations by College Board definition?\n",
    "\n",
    "- Simulations are _____________ that mimic more complex objects or phenomena from the real world\n",
    "    - Purposes include drawing inferences without the __________ of the real world\n",
    "- Simulations use varying sets of values to reflect the ___________ state of a real phenomenon\n",
    "- Often, when developing a simulation, it is necessary to remove specific ________ or simplify aspects\n",
    "    - Simulations can often contain ____ based on which details or real-world elements were included/excluded\n",
    "- Simulations allow the formulation of ___________ under consideration\n",
    "- Variability and ___________ of the world is considered using random number generators\n",
    "- Examples: rolling dice, spinners, molecular models, analyze chemicals/reactions..."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://www.itgsnews.com/wp-content/uploads/2017/05/ecosystem-model-678x381.webp)\n",
    "![](https://images.computerhistory.org/makesoftware/mscw.car-crash-simulation.real-and-simulated-crash-test.simulation-on-cray-supercomputer.jpg)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing an Example: Air-Traffic Simulator\n",
    "- Say we want to find out what the optimal number of aircrafts that can be in the air in one area is.\n",
    "- A simulation allows us to explore this question without real world contraints of money, time, safety\n",
    "    - Unfortunately we can't just fly 67 planes all at once and see what happens\n",
    "- Since the simulation won't be able to take all variables into control, it may have a bias towards one answer\n",
    "- Will not always have the same result"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions we often need (python)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random # a module that defines a series of functions for generating or manipulating random integers\n",
    "random.choice() #returns a randomly selected element from the specified sequence\n",
    "random.choice(mylist) # returns random value from list\n",
    "random.randint(0,10) #randomly selects an integer from given range; range in this case is from 0 to 10\n",
    "random.random() #will generate a random float between 0.0 to 1."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions we often need (js)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "javascript"
    }
   },
   "outputs": [],
   "source": [
    "// Math.random(); returns a random number\n",
    "// Math.floor(Math.random() * 10); // Returns a random integer from 0 to 9:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## College Board Question 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: The following code simulates the feeding of 4 fish in an aquarium while the owner is on a 5-day trip:\n",
    "\n",
    "    numFish ← 4\n",
    "\n",
    "    foodPerDay ← 20\n",
    "\n",
    "    foodLeft ← 160\n",
    "\n",
    "    daysStarving ← 0\n",
    "\n",
    "        REPEAT 5 TIMES {\n",
    "\n",
    "        foodConsumed ← numFish * foodPerDay\n",
    "\n",
    "        foodLeft ← foodLeft - foodConsumed\n",
    "\n",
    "        IF (foodLeft < 0) {\n",
    "\n",
    "        daysStarving ← daysStarving + 1\n",
    "\n",
    "        }\n",
    "}\n",
    "\n",
    "- This simulation simplifies a real-world scenario into something that can be modeled in code and executed on a computer.\n",
    "- Summarize how the code works:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Card Flip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "cards = [\"Ace\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"Jack\", \"Queen\", \"King\"] \n",
    "suits = [\"Diamonds\", \"Hearts\", \"Spades\", \"Clubs\"]\n",
    "\n",
    "print(random.choice(cards) + \" of \" + random.choice(suits))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coin Flip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tails\n",
      "Tails\n",
      "Heads\n",
      "Tails\n",
      "Tails\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "def coinflip():         #def function \n",
    "    randomflip = random.randint(0, 1) #picks either 0 or 1 randomly \n",
    "    if randomflip == 0: #assigning 0 to be heads--> if 0 is chosen then it will print, \"Heads\"\n",
    "        print(\"Heads\")\n",
    "    else:\n",
    "        print(\"Tails\")\n",
    "\n",
    "#Tossing the coin 5 times:\n",
    "t1 = coinflip()\n",
    "t2 = coinflip()\n",
    "t3 = coinflip()\n",
    "t4 = coinflip()\n",
    "t5 = coinflip()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your turn: Change the code to make it simulate the flipping of a weighted coin."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding images (in Python)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Add a heads and tails images into your images directory with the correct names and run the code below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tails\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=130x130 at 0x7FB598D18CD0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "# importing Image class from PIL package\n",
    "from PIL import Image\n",
    " \n",
    "# creating a object\n",
    "head = Image.open(r\"images/head.jpeg\")\n",
    "tail = Image.open(r\"images/tail.jpeg\")\n",
    "\n",
    "i=random.randint(0,1)\n",
    "\n",
    "if i == 1:\n",
    "    print(\"heads\")\n",
    "    display(head)\n",
    "\n",
    "else:\n",
    "    print(\"tails\")\n",
    "    display(tail)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to display an image in python, we can use the PIL package we previously learned about."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spin the Wheel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spin the wheel!\n",
      "----------------------------------\n",
      "Number of red: 104\n",
      "Number of green: 98\n",
      "Number of blue: 98\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "print(\"Spin the wheel!\")\n",
    "print(\"----------------------------------\")\n",
    "\n",
    "n = 300\n",
    "blue = 0\n",
    "red = 0\n",
    "green = 0\n",
    "\n",
    "for i in range(n):\n",
    "    spin = random.randint(1,3)\n",
    "    if spin == 1: # blue\n",
    "        blue = blue + 1\n",
    "    elif spin == 2: # green\n",
    "        green = green + 1\n",
    "    else:         # red\n",
    "        red = red + 1\n",
    " \n",
    "print('Number of red:', red)\n",
    "print('Number of green:', green)\n",
    "print('Number of blue:', blue)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your turn: Add a visual to the simulation!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population Growth and Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50.14019267336515\n",
      "50.28077842644361\n",
      "50.421758361373264\n",
      "50.5631335833823\n",
      "50.704905200797775\n",
      "50.847074325054315\n",
      "50.98964207070282\n",
      "51.13260955541927\n",
      "51.27597790001347\n",
      "51.41974822843774\n",
      "51.563921667795874\n",
      "51.70849934835185\n",
      "51.85348240353876\n",
      "51.99887196996765\n",
      "52.14466918743647\n",
      "52.290875198939005\n",
      "52.43749115067384\n",
      "52.5845181920533\n",
      "52.731957475712534\n",
      "52.87981015751845\n",
      "53.028077396578915\n",
      "53.17676035525173\n",
      "53.325860199153716\n",
      "53.47537809717\n",
      "53.62531522146305\n",
      "53.775672747481956\n",
      "53.92645185397153\n",
      "54.0776537229816\n",
      "54.22927953987637\n",
      "54.38133049334356\n",
      "54.533807775403844\n",
      "54.68671258142016\n",
      "54.840046110106975\n",
      "54.993809563539834\n",
      "55.14800414716474\n",
      "55.302631069807575\n",
      "55.45769154368362\n",
      "55.613186784406984\n",
      "55.769118011000224\n",
      "55.92548644590379\n",
      "56.082293314985726\n",
      "56.239539847551235\n",
      "56.39722727635229\n",
      "56.55535683759732\n",
      "56.71392977096098\n",
      "56.87294731959368\n",
      "57.032410730131446\n",
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      "871681.0377565115\n",
      "874125.103656607\n",
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      "888934.0823004647\n",
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      "903993.9470561118\n",
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      "911619.2871721458\n",
      "914175.334071343\n",
      "916738.5477515013\n",
      "919308.9483072248\n",
      "921886.5558894586\n",
      "924471.390705649\n",
      "927063.4730199015\n",
      "929662.8231531383\n",
      "932269.4614832586\n",
      "934883.408445299\n",
      "937504.6845315925\n",
      "940133.3102919292\n",
      "942769.306333718\n",
      "945412.6933221479\n",
      "948063.4919803509\n",
      "950721.7230895628\n",
      "953387.4074892879\n",
      "956060.5660774591\n",
      "958741.2198106066\n",
      "961429.3897040186\n",
      "964125.096831907\n",
      "966828.3623275728\n",
      "969539.2073835713\n",
      "972257.6532518797\n",
      "974983.721244062\n",
      "977717.4327314354\n",
      "980458.8091452414\n",
      "983207.8719768105\n",
      "985964.6427777322\n",
      "988729.1431600227\n",
      "991501.3947962954\n",
      "994281.4194199304\n",
      "997069.2388252458\n",
      "999864.8748676664\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "totalPopulation = 50 \n",
    "growthFactor = 1.00005\n",
    "dayCount = 0 #Every 2 months the population is reported\n",
    "\n",
    "while totalPopulation < 1000000:\n",
    "    totalPopulation *= growthFactor\n",
    "    #Every 56th day, population is reported\n",
    "    dayCount += 1\n",
    "    if dayCount == 56: \n",
    "        dayCount = 0\n",
    "        print(totalPopulation)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we initialize the total population to be 50, then set the growth factor as 1.00005 (.005 percent change). It will print the population every 56th day until it reaches one million. It multiplies the current population by the growth factor in each iteration, and increments the day count. When the day count reaches 56, it prints the current population and resets the day count to 0.\n",
    "\n",
    "Note! This simulation assumes that the growth factor remains constant as time progresses, which may not be a realistic assumption in real-world scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the initial population and growth rate\n",
    "population = 100\n",
    "growth_rate = 0.05\n",
    "\n",
    "# Define the number of years to simulate\n",
    "num_years = 50\n",
    "\n",
    "# Create lists to store the population and year values\n",
    "populations = [population]\n",
    "years = [0]\n",
    "\n",
    "# Simulate population growth for the specified number of years\n",
    "for year in range(1, num_years+1):\n",
    "    # Calculate the new population size\n",
    "    new_population = population + (growth_rate * population)\n",
    "    # Update the population and year lists\n",
    "    populations.append(new_population)\n",
    "    years.append(year)\n",
    "    # Set the new population as the current population for the next iteration\n",
    "    population = new_population\n",
    "    \n",
    "# Plot the population growth over time\n",
    "plt.plot(years, populations)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Population')\n",
    "plt.title('Population Growth Simulation')\n",
    "plt.show()"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "If we create quantative data, we can plot it using the [Matplotlib library](https://matplotlib.org/)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example on how simplification can cause bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "When a fire hit tall birds died\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "beak =  [\"small-beak\", \"long-beak\", \"medium-beak\"],\n",
    "wing = [\"small-wings\", \"large-wings\", \"medium-wings\"],\n",
    "height = [\"short\", \"tall\",\"medium\"]\n",
    "\n",
    "\n",
    "naturaldisaster = [\"flood\", \"drought\", \"fire\", \"hurricane\", \"dustbowl\"]\n",
    "\n",
    "\n",
    "print(\"When a\" , random.choice(naturaldisaster) , \"hit\",  random.choice(height), \"birds died\") "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does this simulation have bias?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# JS examples"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Dice Roll](https://shruthim0.github.io/canada/diceroll/)\n",
    "[Binary Coin Flip](https://shruthim0.github.io/canada/2023/04/13/Coin-Flip.html)\n",
    "[Card Pull](https://shruthim0.github.io/canada/2023/04/16/Card-Pull.html)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hacks"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Answer all questions and prompts in the notes (0.2)\n",
    "- Create a simulation\n",
    "    1. Create a simulation that uses iteration and some form of data collection (list, dictionary...) (0.4)\n",
    "        - try creating quantative data and using the Matplotlib library to display said data \n",
    "        - Comment and describe function of each parts\n",
    "        - How does your simulation help solve/mimic a real world problem?\n",
    "        - Is there any bias in your simulation? Meaning, are there any discrepancies between your program and the real event?\n",
    "- Answer these [simulation questions](https://shruthim0.github.io/canada/2023/04/03/simulationsplanning2.html) (0.3)\n",
    "- Bonus: take a real world event and make a pseudocode representation or pseudocode on a flowchart of how you would make a simulation for it (up to +0.1 bonus)"
   ]
  }
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