emacs-ipython-notebook/tests/notebook/nbformat3/Animations Using clear_output.ipynb

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  "name": ""
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Simple animations Using clear_output"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Sometimes you want to clear the output area in the middle of a calculation.  This can be useful for doing simple animations. In terminals, there is the carriage-return (`'\\r'`) for overwriting a single line, but the notebook frontend can clear the whole output area, not just a single line.\n",
      "\n",
      "To clear output in the Notebook you can use the `clear_output()` function.  If you are clearing the output every frame of an animation, calling `clear_output()` will create noticeable flickering.  You can use `clear_output(wait=True)` to add the *clear_output* call to a queue.  When data becomes available to replace the existing output, the *clear_output* will be called immediately before the new data is added.  This avoids the flickering by not rendering the cleared output to the screen."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Simple example"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we show our progress iterating through a list:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "import sys\n",
      "import time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import display, clear_output\n",
      "for i in range(10):\n",
      "    time.sleep(0.25)\n",
      "    clear_output(wait=True)\n",
      "    print(i)\n",
      "    sys.stdout.flush()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "9\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "AsyncResult.wait_interactive"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The AsyncResult object has a special `wait_interactive()` method, which prints its progress interactively,\n",
      "so you can watch as your parallel computation completes.\n",
      "\n",
      "**This example assumes you have an IPython cluster running, which you can start from the [cluster panel](/#clusters)**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython import parallel\n",
      "rc = parallel.Client()\n",
      "view = rc.load_balanced_view()\n",
      "\n",
      "amr = view.map_async(time.sleep, [0.5]*100)\n",
      "\n",
      "amr.wait_interactive()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 100/100 tasks finished after    6 s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "done\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Matplotlib example"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can also use `clear_output()` to clear figures and plots."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.special import jn\n",
      "x = np.linspace(0,5)\n",
      "f, ax = plt.subplots()\n",
      "ax.set_title(\"Bessel functions\")\n",
      "\n",
      "for n in range(1,10):\n",
      "    time.sleep(1)\n",
      "    ax.plot(x, jn(x,n))\n",
      "    clear_output(wait=True)\n",
      "    display(f)\n",
      "\n",
      "# close the figure at the end, so we don't get a duplicate\n",
      "# of the last plot\n",
      "plt.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x10f01b310>"
       ]
      }
     ],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}