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 "metadata": {
  "name": "",
  "signature": "sha256:74dbf5caa25c937be70dfe2ab509783a01f4a2044850d7044e729300a8c3644d"
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plotting with Matplotlib"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython works with the [Matplotlib](http://matplotlib.org/) plotting library, which integrates Matplotlib with IPython's display system and event loop handling."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "matplotlib mode"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To make plots using Matplotlib, you must first enable IPython's matplotlib mode.\n",
      "\n",
      "To do this, run the `%matplotlib` magic command to enable plotting in the current Notebook.\n",
      "\n",
      "This magic takes an optional argument that specifies which Matplotlib backend should be used.  Most of the time, in the Notebook, you will want to use the `inline` backend, which will embed plots inside the Notebook:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can also use Matplotlib GUI backends in the Notebook, such as the Qt backend (`%matplotlib qt`). This will use Matplotlib's interactive Qt UI in a floating window to the side of your browser. Of course, this only works if your browser is running on the same system as the Notebook Server.  You can always call the `display` function to paste figures into the Notebook document."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Making a simple plot"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With matplotlib enabled, plotting should just work."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(0, 3*np.pi, 500)\n",
      "plt.plot(x, np.sin(x**2))\n",
      "plt.title('A simple chirp');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GRkJ8jthyKEhHewcgpK/qQBOKsOwdPxsG0BfIFbF3VHn6UeXpu9k7LIodcK+9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\nCcy6efRRKX1ZCJP+d7/7XbzyyiuYO3c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nJ/H0L7ooujlR2FNK3303HMtp7lyy0Bw8SHYt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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1084796d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These images can be resized by dragging the handle in the lower right corner.  Double clicking will return them to their original size."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One thing to be aware of is that by default, the `Figure` object is cleared at the end of each cell, so you will need to issue all plotting commands for a single figure in a single cell."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Loading Matplotlib demos with %load"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython's `%load` magic can be used to load any Matplotlib demo by its URL:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load http://matplotlib.org/mpl_examples/showcase/integral_demo.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Plot demonstrating the integral as the area under a curve.\n",
      "\n",
      "Although this is a simple example, it demonstrates some important tweaks:\n",
      "\n",
      "    * A simple line plot with custom color and line width.\n",
      "    * A shaded region created using a Polygon patch.\n",
      "    * A text label with mathtext rendering.\n",
      "    * figtext calls to label the x- and y-axes.\n",
      "    * Use of axis spines to hide the top and right spines.\n",
      "    * Custom tick placement and labels.\n",
      "\"\"\"\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from matplotlib.patches import Polygon\n",
      "\n",
      "\n",
      "def func(x):\n",
      "    return (x - 3) * (x - 5) * (x - 7) + 85\n",
      "\n",
      "\n",
      "a, b = 2, 9 # integral limits\n",
      "x = np.linspace(0, 10)\n",
      "y = func(x)\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "plt.plot(x, y, 'r', linewidth=2)\n",
      "plt.ylim(ymin=0)\n",
      "\n",
      "# Make the shaded region\n",
      "ix = np.linspace(a, b)\n",
      "iy = func(ix)\n",
      "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n",
      "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n",
      "ax.add_patch(poly)\n",
      "\n",
      "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n",
      "         horizontalalignment='center', fontsize=20)\n",
      "\n",
      "plt.figtext(0.9, 0.05, '$x$')\n",
      "plt.figtext(0.1, 0.9, '$y$')\n",
      "\n",
      "ax.spines['right'].set_visible(False)\n",
      "ax.spines['top'].set_visible(False)\n",
      "ax.xaxis.set_ticks_position('bottom')\n",
      "\n",
      "ax.set_xticks((a, b))\n",
      "ax.set_xticklabels(('$a$', '$b$'))\n",
      "ax.set_yticks([])\n",
      "\n",
      "plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10848fb10>"
       ]
      }
     ],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}