{
 "metadata": {
  "name": "",
  "signature": "sha256:8abfcc4f24abe7e7592c8c402c6784ee753805d0c9211245e4923be0209921fb"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Demonstrate simultaneous printing and capturing capabilities\n",
      "\n",
      "see [this branch](https://github.com/ihrke/ipycache/tree/capture_and_print)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%reload_ext ipycache\n",
      "import pylab as pl\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cache test.pkl var\n",
      "import time\n",
      "var=[i for i in range(100000)]\n",
      "\n",
      "print \"hallo\"\n",
      "for i in range(3):\n",
      "    time.sleep(1.0)\n",
      "    print i\n",
      "pl.plot(var)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[Skipped the cell's code and loaded variables var from file '/home/mittner/work/prog/stuff/ipycache/test.pkl'.]\n",
        "hallo\n",
        "0\n",
        "1\n",
        "2\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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c1BdrRdJ8LBedFWtF0kJYLlowa0XSQlkuOiNrRdLZslw0L2tFUj8sF83JWpG0GJaLXsNa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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe00f7e2e10>"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import shelve\n",
      "import ipycache "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a=ipycache.load_vars('./test.pkl', ['var'])\n",
      "print a.keys()\n",
      "print \"stdout>\", a['_captured_io']['stdout'].getvalue()\n",
      "print \"stderr>\", a['_captured_io']['stderr'].getvalue()\n",
      "print \"output>\",a['_captured_io']['outputs']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['var', '_captured_io']\n",
        "stdout> hallo\n",
        "0\n",
        "1\n",
        "2\n",
        "\n",
        "stderr> \n",
        "output> [('display', {u'image/png': 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