.ipynb_checkpoints/matplotlib_save_a_plot_as_vector-checkpoint.ipynb

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  "name": "",
  "signature": "sha256:70c1fa49dd6c51370bac0eb4f2ffb6a508d814baad3dd5ac2d03126190720725"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Save A Matplotlib Plot As A Vector\n",
      "\n",
      "This snippit was written by [Chris R. Albon](http://www.chrisralbon.com/) and is part of his collection of [well-documented Python snippits](https://github.com/chrisalbon/code_py). All code is written in Python 3 in iPython notebook and offered under the [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/\"><img alt=\"Creative Commons License)."
     ]
    },
    {
     "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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AUvyJ8iQmy7uRcPgIAGAQCgAAg1AAABiEAgDAmDAUAoGAcnJy5Ha71dDQELNNbW2t3G63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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1021aa4d0>"
       ]
      }
     ],
     "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 0x10a7438d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEPCAYAAAC5sYRSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFiLuAGAh4g4AFiLuAGCh/wOyNCfmhkJk1gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108b09a10>"
       ]
      }
     ],
     "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 0x1087e1810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10a6a3110>"
       ]
      }
     ],
     "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 0x10bc5e8d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1087d6690>"
       ]
      }
     ],
     "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 0x10bebaf10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x10be17ed0>"
       ]
      }
     ],
     "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 0x10a6eaf50>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Display the grid."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Save the plot as an EPS vector file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.savefig(\"example_plot.eps\", format=\"eps\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x108b0e850>"
       ]
      }
     ],
     "prompt_number": 23
    }
   ],
   "metadata": {}
  }
 ]
}