RinkeHoekstra · April 15, 2014 08:24
diff --git a/provomatic-matplotlib-fill b/provomatic-matplotlib-fill
 {
 "metadata": {
  "name": "",
  "signature": "sha256:570e83e113f52fe82864da1a63257d4f9eaa26f9a15ccd6e36727534f4abc725"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%reload_ext provomatic.extension"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Simple demo of the fill function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(0, 1)\n",
      "y = np.sin(4 * np.pi * x) * np.exp(-5 * x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.fill(x, y, 'r')\n",
      "plt.grid(True)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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m4UbDtN7CmhoO7tnDwT172BUYyLve3uRVV3NXYiJJP/859953HwkJCXjLyrfi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\nITzM/wdY8ba2kQuSagAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11289a610>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "view_prov()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe width='100%' height='450px' src='http://localhost:8000/fe0f1173f9c443c16d34c4f4daedf774_provoviz.html'></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<IPython.core.display.HTML at 0x1128bb090>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
 }
	{
	"metadata": {
	"name": "",
	"signature": "sha256:570e83e113f52fe82864da1a63257d4f9eaa26f9a15ccd6e36727534f4abc725"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"%reload_ext provomatic.extension"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 1
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"## Simple demo of the fill function."
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"import numpy as np\n",
	"import matplotlib.pyplot as plt"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 2
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"%matplotlib inline"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 3
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"x = np.linspace(0, 1)\n",
	"y = np.sin(4 * np.pi * x) * np.exp(-5 * x)"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 4
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"plt.fill(x, y, 'r')\n",
	"plt.grid(True)\n",
	"plt.show()"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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m4UbDtN7CmhoO7tnDwT172BUYyLve3uRVV3NXYiJJP/859953HwkJCXjLyrfi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\nITzM/wdY8ba2kQuSagAAAABJRU5ErkJggg==\n",
	"text": [
	"<matplotlib.figure.Figure at 0x11289a610>"
	]
	}
	],
	"prompt_number": 5
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"view_prov()"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"html": [
	"<iframe width='100%' height='450px' src='http://localhost:8000/fe0f1173f9c443c16d34c4f4daedf774_provoviz.html'></iframe>"
	],
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 7,
	"text": [
	"<IPython.core.display.HTML at 0x1128bb090>"
	]
	}
	],
	"prompt_number": 7
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 6
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [],
	"language": "python",
	"metadata": {},
	"outputs": []
	}
	],
	"metadata": {}
	}
	]
	}