Untitled.ipynb

emphasizing in graphs.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pylab as plt\n",
        "%matplotlib inline"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Emphasizing points in a graph"
      ],
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's say we have some data and we want to emphasize several points according to a criterion. There are many ways to do that, we'll explore the simplest ones"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.linspace(0, 10, 50)\n",
        "y = np.sin(x) + np.random.randn(len(x))\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(x, y, 'o')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x10e016630>]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's say we want to emphasize the last point. One way to do that is to plot all the points in the set and then to plot the point of interest using a different marker"
      ],
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(x, y, '*')\n",
        "ax.plot(\n",
        "    x[-1], ## -1 means the last point\n",
        "    y[-1], \n",
        "    marker='o',\n",
        "    markersize=15,  # can also use ms\n",
        "    markeredgewidth=2,  # can also use mew\n",
        "    markeredgecolor='C3',\n",
        "    markerfacecolor='none',  # Note that 'none' is a string, not Python's None\n",
        ")\n",
        "ax.set_title('Take a look at the last point')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": [
              "Text(0.5,1,'Take a look at the last point')"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here's another way to emphasize points. In this case, we'll show all the points above 2"
      ],
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sel = y > 2  # Select all the points where y > 2. sel is an array of booleans\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(\n",
        "    x[~sel], y[~sel], # '~' is a logical NOT operation that negates the selection\n",
        "    'o', color='gray'\n",
        ")\n",
        "ax.plot(\n",
        "    x[sel], y[sel],\n",
        "    '*', color='C2', \n",
        "    markersize=15\n",
        ")\n",
        "ax.set_title(f'{sel.sum()} outliers in the data')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": [
              "Text(0.5,1,'3 outliers in the data')"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also use text labels and arrows as explained [here](https://matplotlib.org/gallery/text_labels_and_annotations/annotation_demo.html)."
      ],
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ix_min = np.argmin(y)  # index of the lowest point\n",
        "ix_max = np.argmax(y)\n",
        "ax.plot(x, y, 'o', color='gray')\n",
        "ax.text(\n",
        "    x[ix_max], y[ix_max], \n",
        "    'Highest point\\nin the data set',\n",
        "    horizontalalignment='center',\n",
        "    verticalalignment='top',\n",
        "    color='red', \n",
        "    fontsize='xx-large'\n",
        ")\n",
        "ax.annotate(\n",
        "    'Global minimum',\n",
        "    xy=(x[ix_min], y[ix_min]), \n",
        "    xycoords='data',\n",
        "    xytext=(-100, 150), textcoords='offset points',\n",
        "    size=20,\n",
        "            arrowprops=dict(arrowstyle=\"fancy\",\n",
        "                            fc=\"0.6\", ec=\"none\",\n",
        "                            connectionstyle=\"angle3,angleA=0,angleB=-90\")\n",
        ")\n"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": [
              "Text(-100,150,'Global minimum')"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 24,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also emphasize vertical and horizontal regions as follows"
      ],
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ix_min = np.argmin(y)  # index of the lowest point\n",
        "ix_max = np.argmax(y)\n",
        "ax.plot(x, y, 'o', color='gray')\n",
        "ax.axhspan(\n",
        "    ymin=-4, ymax=0,\n",
        "    color='C4',\n",
        "    alpha=0.5  # Alpha means transparency\n",
        ")\n",
        "ax.axvspan(xmin=8, xmax=10, color='C5', alpha=0.25)\n",
        "ax.text(0, -3, 'Negative values', \n",
        "        horizontalalignment='left',\n",
        "        fontsize='x-large'\n",
        "       )"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 34,
          "data": {
            "text/plain": [
              "Text(0,-3,'Negative values')"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 34,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "gist_id": "130cadf1cf8c19f657ecdfeea030e403"
  },
  "nbformat": 4,
  "nbformat_minor": 4
}