02_Matplotlib_Exercises.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iCvutCQk0A6"
      },
      "source": [
        "** * NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP. * **\n",
        "\n",
        "# Exercises\n",
        "\n",
        "Follow the instructions to recreate the plots using this data:\n",
        "\n",
        "## Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "RS8oZDfRk0A9"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "x = np.arange(0,100)\n",
        "y = x*2\n",
        "z = x**2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kYu6pJIpk0A-"
      },
      "source": [
        "** Import matplotlib.pyplot as plt and set %matplotlib inline if you are using the jupyter notebook. What command do you use if you aren't using the jupyter notebook?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "Qgf5nVcIk0A_"
      },
      "outputs": [],
      "source": [
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xFo6gO0qk0A_"
      },
      "source": [
        "## Exercise 1\n",
        "\n",
        "** Follow along with these steps: **\n",
        "* ** Create a figure object called fig using plt.figure() **\n",
        "* ** Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax. **\n",
        "* ** Plot (x,y) on that axes and set the labels and titles to match the plot below:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-s9yTmWjk0A_"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "fig=plt.figure()\n",
        "ax=fig.add_axes([0.0,0.0,1.0,1.0])\n",
        "x=np.linspace(0,100,6)\n",
        "y=x*(4/5)\n",
        "ax.plot(x,y,'b')\n",
        "ax.set_xlabel('x')\n",
        "ax.set_ylabel('y')\n",
        "ax.set_title('title')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "soKDDdd8k0BA"
      },
      "source": [
        "## Exercise 2\n",
        "** Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "7Mtkpml-k0BA",
        "outputId": "bd051fdf-1a4c-48d3-fdc1-e717363bf828"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "fig=plt.figure()\n",
        "ax1=fig.add_axes([0,0,1,1])\n",
        "ax2=fig.add_axes([0.2,0.5,.2,.2])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sP-adQM2k0BA"
      },
      "source": [
        "** Now plot (x,y) on both axes. And call your figure object to show it.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "UTFfTho2k0BB",
        "outputId": "811ec757-0383-47ed-f550-1d27a4db7c93"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "fig=plt.figure()\n",
        "ax1=fig.add_axes([0,0,1,1])\n",
        "ax2=fig.add_axes([0.2,0.5,.2,.2])\n",
        "x=np.linspace(0,100,6)\n",
        "#y=np.linspace(0,200,5)\n",
        "y=x*2\n",
        "x1=np.linspace(0,100,6)\n",
        "y1=x1*2\n",
        "ax1.plot(x,y,'r')\n",
        "ax1.set_xlabel('x')\n",
        "ax1.set_ylabel('y')\n",
        "ax2.set_xlabel('x')\n",
        "ax2.set_ylabel('y')\n",
        "ax2.plot(x1,y1,'r')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Of24KKkJk0BB"
      },
      "source": [
        "## Exercise 3\n",
        "\n",
        "** Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "y8Z-3OuQk0BB",
        "outputId": "d35145ab-5baf-4844-ceeb-360bfff4799c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "fig=plt.figure()\n",
        "axes1=fig.add_axes([0,0,1,1])\n",
        "axes2=fig.add_axes([0.2,0.5,.4,.4])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iKWMhMUgk0BB"
      },
      "source": [
        "** Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "58J1_e1kk0BB",
        "outputId": "bd9dbc81-f741-4625-fd16-c2ec8bf428a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb243add3c0>]"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "fig=plt.figure()\n",
        "axes1=fig.add_axes([0,0,1,1])\n",
        "axes2=fig.add_axes([0.2,0.5,.4,.4])\n",
        "axes2.set_xlabel('X')\n",
        "axes2.set_ylabel('Y')\n",
        "axes1.plot(x,z,'b')\n",
        "axes1.set_xlabel('X')\n",
        "axes1.set_ylabel('Z')\n",
        "axes2.set_title('zoom')\n",
        "axes2.set_xlim([20,22])\n",
        "axes2.set_ylim([30,50])\n",
        "axes2.plot(x,y,'b')\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GO9c1fsOk0BC"
      },
      "source": [
        "## Exercise 4\n",
        "\n",
        "** Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "ecjIzvjxk0BC",
        "outputId": "098c8ca1-12f0-4ca6-e46b-6f734623baa3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "fig=plt.figure()\n",
        "fig,axes=plt.subplots(1,2)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izOCVI8wk0BC"
      },
      "source": [
        "** Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "ZiSVPfi5k0BC",
        "outputId": "eac0cd09-2925-4828-ce7e-41117f9a62c1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "fig=plt.figure()\n",
        "fig,axes=plt.subplots(1,2)\n",
        "axes[0].plot(x,y,'b--',lw=3)\n",
        "axes[1].plot(x,z,'r',lw=3)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HxoCdD8Xk0BC"
      },
      "source": [
        "** See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "gvlHfFqZk0BC",
        "outputId": "47c6242c-afc0-4c83-eafb-f8e22587a5ff"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "fig=plt.figure()\n",
        "fig,axes=plt.subplots(1,2,figsize=(12,3))\n",
        "axes[0].plot(x,y,'b--',lw=3)\n",
        "axes[1].plot(x,z,'r',lw=3)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xcx5JbJHk0BD"
      },
      "source": [
        "# Great Job!"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.8.5"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}