220016_AasmeeShende02_Matplotlib_Exercises.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8Yq5Apxupdq"
      },
      "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": "P5_GB5wPupd2"
      },
      "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": "4NKxBQlKupd3"
      },
      "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": "rdmg3SRDupd4"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mNBe7jDHupd4"
      },
      "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": "woWVZTZ7upd4",
        "outputId": "352cb3d9-fb0a-453f-c0df-8eaa72ceac53"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.text.Text at 0x111534c50>"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x10f0b1710>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.set_xlabel('x')\n",
        "ax.set_ylabel('y')\n",
        "ax.set_title('title')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJ8oYhzZupd6"
      },
      "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": {
        "id": "gmdh5r-Cupd7",
        "outputId": "f7e00787-9b94-46b5-e7dc-9a77ed00e6cd"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x1144d8160>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure()\n",
        "ax1 = fig.add_axes([0,0,1,1])\n",
        "ax2 = fig.add_axes([0.2,0.5,.2,.2])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9aMfWX-hupd8"
      },
      "source": [
        "** Now plot (x,y) on both axes. And call your figure object to show it.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iZRKWt0uupd9",
        "outputId": "ebcce41e-94a3-4bfc-b1b4-59e6340672e2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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4ETqpKfvZ65dpwaplQTcJAEKDxI1Qafssm6INAKcicSMUSNkAkB4SNwJHygaA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\nraoRPgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1144d8160>"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fig = plt.figure()\n",
        "ax1 = fig.add_axes([0,0,1,1])\n",
        "ax2 = fig.add_axes([0.2,0.5,.2,.2])\n",
        "ax1.plot(x,y)\n",
        "ax2.plot(x,y)    \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_jFpMAPupd9"
      },
      "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": {
        "id": "6XX2yZtBupd-",
        "outputId": "77b6a2b7-c338-4076-8573-f0634ee02c8e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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FCzp9+nQUcdOWS7/IcyVLruSQcisLcKOgaCOSSqVUWFjYt11QUKBUKnXNMYlE4qoxAIDc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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x10f0b15c0>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig1 = plt.figure()\n",
        "ax1 = fig1.add_axes([0,0,1,1])\n",
        "ax2 = fig1.add_axes([0.2,0.5,.4,.4])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-2Cppa-Hupd-"
      },
      "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": {
        "id": "kcgFa7xcupd-",
        "outputId": "39f3cdd2-f83c-4484-a0c9-f7f6f65c0450"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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X9L49nClToFMnGDVKIS9hEvS5shI8J+ek8NasWePmzJnjnHNu69atrnbt2m7h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\nH51ua+eImT++AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x10c212cf8>"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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X9L49nClToFMnGDVKIS9hEvS5shI8J+ek8NasWePmzJnjnHNu69atrnbt2m7h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\nH51ua+eImT++AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x10c212cf8>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax1.plot(x,y)\n",
        "ax1.set_xlabel('x')\n",
        "ax1.set_ylabel('z')\n",
        "ax2.plot(x,y)\n",
        "ax2.set_xlabel('x')\n",
        "ax2.set_ylabel('y')\n",
        "ax2.set_xlim([20,22])\n",
        "ax2.set_ylim([30,50])\n",
        "fig1 \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tg3tIGHxupd-"
      },
      "source": [
        "## Exercise 4\n",
        "\n",
        "** Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qfv8BDlfupd-",
        "outputId": "e5492f6c-ff34-47e3-df26-f9826ba84175"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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o6c/8d4EXL/PcovLV+9xFZntRuV5ktlvMdS9huMTOrAAvTUL38OSx/cAfT615bPKH+QLw\ngXnMBf6WtSvh/w48B3x3nu95au3f9fhh6PJn/aes/RTCMeBT83rPwO8AT0zmHgPu72nul4HzwP8C\nP2TtyGrh+Rpq7iKzvahcLzLbreXaX4aSpIb5v1BKUsMseUlqmCUvSQ2z5CWpYZa8JDXMkpekhlny\nktQwS16SGvZ/GNp0aN6HtcEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x114be5e48>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = pltsubplots(nrows=1, ncols=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6bqQ3Cyupd-"
      },
      "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": {
        "id": "8cGrwSTNupd_",
        "outputId": "7bf98d45-2b67-4c5a-fe01-424f88a28b17"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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SCR/guuuii6WR1NKX2FJLX0LTvz+89ZY/btEiMYMxA9SnLyKSSdu3JxI+pI5zzgFK+nlq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\nyP8DG1xU47jRvuwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x114be5e48>"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "axes[0].plot(x,y,linestyle='--', color='blue')\n",
        "axes[1].plot(x,z,linestyle='-', color='red')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kWMCoRNupd_"
      },
      "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": {
        "id": "9pgMDrT9upd_",
        "outputId": "67fbf8ea-16c0-40c9-ee17-efeae7ad4832"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.text.Text at 0x1141b4ba8>"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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Yxm0iBUxpJHkknbLp7dvXlU3v31952CIikuf+7d+SB9X//GfY+a9jsUQArWxn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\nJEsUbIuIiIiIZMn/A0B/LV+00DLqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x11400a908>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12,2))\n",
        "axes[0].plot(x,y,linestyle='-', color='blue')\n",
        "axes[1].plot(x,z,linestyle='--', color='red')\n",
        "fig"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oNeaYAkoupd_"
      },
      "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
}