matplotlib_save_a_plot_as_vector.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Save A Matplotlib Plot As A Vector\n",
      "\n",
      "- **Author:** [Chris Albon](http://www.chrisalbon.com/), [@ChrisAlbon](https://twitter.com/chrisalbon)\n",
      "- **Date:** -\n",
      "- **Repo:** [Python 3 code snippets for data science](https://github.com/chrisalbon/code_py)\n",
      "- **Note:**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Import Numpy and matplotlib.pyplot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Create two new variables for mu and sigma"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mu, sigma = 100, 15"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Create some simulated data of 10000 values"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = mu + sigma * np.random.randn(10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Create a histogram of the simulated data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SqtoKfBS4cwZtC/hsVV3RfB6YxzFJks7TsCOFq4CJqpqsqtPAPcB1PXWuBe4GqKrDwPok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\nUstQkCS1DAVJUstQkCS1DAVJUuv/A4mwTD1m/JlAAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1085674d0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Set the x axis label."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.xlabel('Smarts')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.text.Text at 0x108709b90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x108567810>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Set the y axis label."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.ylabel('Probability')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.text.Text at 0x108790890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x108747110>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Set the plot title."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.title('Histogram of IQ')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.text.Text at 0x108937650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10879e690>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Add some text in the corner."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.text.Text at 0x1089b9390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1089318d0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Set the axes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.axis([40, 160, 0, 0.03])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "[40, 160, 0, 0.03]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1089d7790>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Display the grid."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Save the plot as an EPS vector file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.savefig(\"example_plot.eps\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x1089c0250>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    }
   ],
   "metadata": {}
  }
 ]
}