W1T2 Two Lines_solutions.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
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        "name": "ipython",
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.9"
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    "colab": {
      "name": "W1T2 Two Lines.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/john-s-butler-dit/Basic-Introduction-to-Python/blob/master/W1T2%20Two%20Lines_solutions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlTL0CmO9ZSe"
      },
      "source": [
        "# Two lines Week 1, Tutorial 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEBTjrue9ZSp"
      },
      "source": [
        "In this notebook we will show some of the basics of plotting multiple lines."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffjzr2nJ9ZSq"
      },
      "source": [
        "### Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8z3T5SYD9ZSq"
      },
      "source": [
        "# LIBRARY\n",
        "\n",
        "import numpy as np # vector manipulation\n",
        "\n",
        "# THIS IS FOR PLOTTING\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt # side-stepping mpl backend\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3X3AEVMc9ZSr"
      },
      "source": [
        "## A plot with two lines\n",
        "\n",
        "The code below will plot two lines from 20 to 70 on the x axis.\n",
        "The first line will have a slope of $m=0.3$ and an intercept of $c=30$.\n",
        "The formula for the first line is\n",
        "$$ y1=0.3x+30.$$\n",
        "The second line will have a slope of $m=0.5$ and an intercept of $c=10$.\n",
        "The formula for the second line is\n",
        "$$ y2=0.5x+10.$$\n",
        "\n",
        "\n",
        "We first assign the values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5lUhCrMW9ZSr"
      },
      "source": [
        "m1=0.3 # slope for first line \n",
        "c1=30 # intercept for first line\n",
        "m2=0.5 # slope for second line\n",
        "c2=10 # intercept for second line"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDOXqVWK9ZSr"
      },
      "source": [
        "Now we define a range of x values starting at 20 and ending at 69 in unit steps. To do this we use the __numpy__ library function __arange__."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pteMyJUp9ZSs"
      },
      "source": [
        "x=np.arange(20,70,1)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jMMllwVk9ZSs"
      },
      "source": [
        "We now write the formula for the lines\n",
        "$$ y1=m1x+c1$$\n",
        "and\n",
        "$$ y2=m2x+c2.$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oVYlGsVq9ZSs",
        "outputId": "5f5554a7-9f60-40c0-c1be-57198258d28e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y1= m1*x+c1\n",
        "y2= m2*x+c2\n",
        "print(y1)\n",
        "print(y2)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[36.  36.3 36.6 36.9 37.2 37.5 37.8 38.1 38.4 38.7 39.  39.3 39.6 39.9\n",
            " 40.2 40.5 40.8 41.1 41.4 41.7 42.  42.3 42.6 42.9 43.2 43.5 43.8 44.1\n",
            " 44.4 44.7 45.  45.3 45.6 45.9 46.2 46.5 46.8 47.1 47.4 47.7 48.  48.3\n",
            " 48.6 48.9 49.2 49.5 49.8 50.1 50.4 50.7]\n",
            "[20.  20.5 21.  21.5 22.  22.5 23.  23.5 24.  24.5 25.  25.5 26.  26.5\n",
            " 27.  27.5 28.  28.5 29.  29.5 30.  30.5 31.  31.5 32.  32.5 33.  33.5\n",
            " 34.  34.5 35.  35.5 36.  36.5 37.  37.5 38.  38.5 39.  39.5 40.  40.5\n",
            " 41.  41.5 42.  42.5 43.  43.5 44.  44.5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGk-hiiI9ZSs"
      },
      "source": [
        "To plot the result we use the __matplotlib__ library function __plt__."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYA7668p9ZSt",
        "outputId": "de12f1e9-d59e-4005-a382-3ed950491a4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "fig = plt.figure(figsize=(6,6)) # This setups the size of the figure\n",
        "plt.plot(x,y1,'--',color='red')\n",
        "plt.plot(x,y2,'-.',color='blue')\n",
        "\n",
        "plt.show() # This plots the figure"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ylsFZPmK9ZSu"
      },
      "source": [
        "# Problems \n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Problem 1\n",
        "Re-do the plot with two new lines:\n",
        "1. the first with a slope of 0.05 and an intercept of 50.\n",
        "2. the second with a slope of 1 and an intercept of 5."
      ],
      "metadata": {
        "id": "yyqBf8c8DIzO"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJom6OfH9ZSu",
        "outputId": "4de5bc3e-4341-4b78-a0c0-d21292c8bef6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "##############################################################################\n",
        "## INSERT: Update the lines \n",
        "##############################################################################\n",
        "\n",
        "\n",
        "##############################################################################\n",
        "#######################ANSWER#################################\n",
        "m1=0.05\n",
        "m2=1\n",
        "c1=50\n",
        "c2=5\n",
        "\n",
        "y1= m1*x+c1\n",
        "y2= m2*x+c2\n",
        "##############################################################################\n",
        "\n",
        "fig = plt.figure(figsize=(6,6))\n",
        "plt.plot(x,y1,'-',color='red')\n",
        "plt.plot(x,y2,'-',color='blue')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8bMV5tT39ZSu"
      },
      "source": [
        "## Problem 2\n",
        "Add in the label age on the x axis and TDT(ms) on the y axis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFyaM7RE9ZSv",
        "outputId": "6e31c9f6-cd79-474f-81e5-b6a6a79a7df8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "##############################################################################\n",
        "## TO DO: change the labels\n",
        "##############################################################################\n",
        "fig = plt.figure(figsize=(6,6)) # This setups the size of the figure\n",
        "plt.plot(x,y1,'-',color='red',label=\"Female\")\n",
        "plt.plot(x,y2,'-',color='blue',label=\"Male\")\n",
        "\n",
        "#######################ANSWER#################################\n",
        "plt.legend()\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('TDT(ms)')\n",
        "#######################ANSWER#################################\n",
        "\n",
        "plt.show() # This plots the figure"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vTdPLOY59ZSv"
      },
      "source": [
        "## Problem 3\n",
        "What is the value of the 5th element of the y1 vector and the 10th element of the y2 vector."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tg2n3Ghy9ZSw",
        "outputId": "30c429bc-a483-41f6-9455-44560a5e49a0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#######################ANSWER#################################\n",
        "\n",
        "print(y1[4])\n",
        "print(y2[9])\n",
        "\n",
        "#######################ANSWER#################################"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "51.2\n",
            "34\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jyS4tdQQ9ZSw"
      },
      "source": [
        "## Problem 4\n",
        "Why does the code below not work?\n",
        "How would you fix it?\n",
        "\n",
        "### Solution\n",
        "Define m and c.\n",
        "Use m1 and c1 instead."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xW115Xn19ZSx",
        "outputId": "54cf0106-2502-4c69-9f8e-95a21c44780b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        }
      },
      "source": [
        "y= m*x+c\n",
        "print(y)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-18c9897835a4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0xYPdSvY9ZSx"
      },
      "source": [
        "---\n",
        "# Summary\n",
        "\n",
        "In this tutorial, we learned:\n",
        "\n",
        "* To plot multiple lines.\n",
        "* To add infromation to a plot.\n",
        "* To not worry if the code does not work."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uz6ISSJ59ZSx"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}