bernatfp · February 9, 2014 00:02
diff --git a/HelloWorld.ipynb b/HelloWorld.ipynb
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  "name": ""
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Testing IPython Notebook\n",
      "One of the most exciting features that brings IPython is what's called notebooks. Notebooks allow to write Python code in the browser and evaluate it directly in the same page. This way we can show the results of running a piece of code and we can document the steps if we want with proper formatting (Markdown in this case), so we can get rid of writing comments besides the code. Oh, and it can even display plots!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Hello world!\"\n",
      "\n",
      "for x in range(10):\n",
      "    print \"number %d\" % x\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Hello world!\n",
        "number 0\n",
        "number 1\n",
        "number 2\n",
        "number 3\n",
        "number 4\n",
        "number 5\n",
        "number 6\n",
        "number 7\n",
        "number 8\n",
        "number 9\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So, the output above is the result of running the previous code."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let's try a dummy plot example.\n",
      "\n",
      "First of all, we must tell the backend to use the notebook as the frontend for the plots."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can import the libraries that are required to plot (namely `matplotlib` and `numpy`)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "from matplotlib import pyplot as plt\n",
      "\n",
      "x = np.arange(0, 5, 0.1);\n",
      "y = np.sin(x)\n",
      "plt.plot(x, y)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[<matplotlib.lines.Line2D at 0x104bd8f10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Zviiz0Pfs2ZP9JRz39uijj9K3b98z/nCPWw+AFpGQV7262VmbmGjaOocOwYYN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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104bb50d0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    }
   ],
   "metadata": {}
  }
 ]
 }
	{
	"metadata": {
	"name": ""
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# Testing IPython Notebook\n",
	"One of the most exciting features that brings IPython is what's called notebooks. Notebooks allow to write Python code in the browser and evaluate it directly in the same page. This way we can show the results of running a piece of code and we can document the steps if we want with proper formatting (Markdown in this case), so we can get rid of writing comments besides the code. Oh, and it can even display plots!"
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"print \"Hello world!\"\n",
	"\n",
	"for x in range(10):\n",
	" print \"number %d\" % x\n"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"Hello world!\n",
	"number 0\n",
	"number 1\n",
	"number 2\n",
	"number 3\n",
	"number 4\n",
	"number 5\n",
	"number 6\n",
	"number 7\n",
	"number 8\n",
	"number 9\n"
	]
	}
	],
	"prompt_number": 2
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"So, the output above is the result of running the previous code."
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"Now let's try a dummy plot example.\n",
	"\n",
	"First of all, we must tell the backend to use the notebook as the frontend for the plots."
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"%matplotlib inline"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 3
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"Now we can import the libraries that are required to plot (namely `matplotlib` and `numpy`)."
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"import numpy as np\n",
	"from matplotlib import pyplot as plt\n",
	"\n",
	"x = np.arange(0, 5, 0.1);\n",
	"y = np.sin(x)\n",
	"plt.plot(x, y)\n",
	"\n"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 4,
	"text": [
	"[<matplotlib.lines.Line2D at 0x104bd8f10>]"
	]
	},
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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	"text": [
	"<matplotlib.figure.Figure at 0x104bb50d0>"
	]
	}
	],
	"prompt_number": 4
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 4
	}
	],
	"metadata": {}
	}
	]
	}