AsyncOutput.ipynb

{
 "metadata": {
  "name": "AsyncOutput"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython import parallel"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rc = parallel.Client()\n",
      "dview = rc[:]\n",
      "def ff(x):\n",
      "    print(x)\n",
      "    return x**2\n",
      "sync = dview.map_sync(ff,[1,2,3,4])\n",
      "print('sync res=%r' % sync)\n",
      "async = dview.map_async(ff,[1,2,3,4])\n",
      "print('async res=%r' % async)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "sync res=[1, 4, 9, 16]\n",
        "async res=<AsyncMapResult: ff>\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Wait for the asyncresult to finish"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "async.get()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[1, 4, 9, 16]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`AsyncResult.stdout` is a list of strings, where each string is a single engine's stdout"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "async.stdout"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "['1\\n2\\n', '3\\n4\\n']"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`display_outputs()` actually prints / displays the output of the engines.\n",
      "It does not return anything."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('async output:')\n",
      "async.display_outputs()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "async output:\n",
        "[stdout:0] \n",
        "1\n",
        "2\n",
        "[stdout:1] \n",
        "3\n",
        "4\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can even make a simple table, showing stdout of each engine side-by-side"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "split_output = [ stdout.splitlines() for stdout in async.stdout ]\n",
      "transposed = zip(*split_output)\n",
      "\n",
      "header = '\\t'.join(\"[%i]\" %i for i in dview.targets)\n",
      "print(header)\n",
      "print('-' * 8 * len(dview))\n",
      "\n",
      "for row in zip(*split_output):\n",
      "    print('\\t'.join(' %s ' % s for s in row))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0]\t[1]\n",
        "----------------\n",
        " 1 \t 3 \n",
        " 2 \t 4 \n"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}