mcsolve-parallel_map-tests.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from qutip import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from qutip.ipynbtools import parallel_map as ip_parallel_map"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N = 25\n",
      "a = destroy(N)\n",
      "H = a.dag() * a\n",
      "c_ops = [0.1 * a, 0.01 * a.dag()]\n",
      "times = np.linspace(0, 100, 100)\n",
      "psi0 = basis(N, N//2)\n",
      "e_ops = [a.dag() * a, a + a.dag()]\n",
      "ntraj = 500"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result_me1 = mesolve(H, psi0, times, c_ops, e_ops)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result_mc1 = mcsolve(H, psi0, times, c_ops, e_ops, ntraj=ntraj, options=Options(num_cpus=2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10.0%. Run time:   6.04s. Est. time left: 00:00:00:54\n",
        "20.0%. Run time:  11.55s. Est. time left: 00:00:00:46\n",
        "30.0%. Run time:  17.37s. Est. time left: 00:00:00:40\n",
        "40.0%. Run time:  23.40s. Est. time left: 00:00:00:35\n",
        "50.0%. Run time:  29.20s. Est. time left: 00:00:00:29\n",
        "60.0%. Run time:  34.86s. Est. time left: 00:00:00:23\n",
        "70.0%. Run time:  41.01s. Est. time left: 00:00:00:17\n",
        "80.0%. Run time:  47.11s. Est. time left: 00:00:00:11\n",
        "90.0%. Run time:  53.17s. Est. time left: 00:00:00:05\n",
        "100.0%. Run time:  58.97s. Est. time left: 00:00:00:00\n",
        "Total run time:  59.03s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result_mc2 = mcsolve(H, psi0, times, c_ops, e_ops, ntraj=ntraj, options=Options(num_cpus=4))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10.0%. Run time:   3.16s. Est. time left: 00:00:00:28\n",
        "20.0%. Run time:   6.13s. Est. time left: 00:00:00:24\n",
        "30.0%. Run time:   9.22s. Est. time left: 00:00:00:21\n",
        "40.0%. Run time:  12.29s. Est. time left: 00:00:00:18\n",
        "50.0%. Run time:  15.30s. Est. time left: 00:00:00:15\n",
        "60.0%. Run time:  18.40s. Est. time left: 00:00:00:12\n",
        "70.0%. Run time:  21.46s. Est. time left: 00:00:00:09\n",
        "80.0%. Run time:  24.53s. Est. time left: 00:00:00:06\n",
        "90.0%. Run time:  27.58s. Est. time left: 00:00:00:03\n",
        "100.0%. Run time:  30.62s. Est. time left: 00:00:00:00\n",
        "Total run time:  30.71s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result_mc3 = mcsolve(H, psi0, times, c_ops, e_ops, ntraj=ntraj,\n",
      "                     map_kwargs={'num_cpus': 8})  # alternative way of configuring num_cpus"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10.0%. Run time:   2.62s. Est. time left: 00:00:00:23\n",
        "20.0%. Run time:   4.86s. Est. time left: 00:00:00:19\n",
        "30.0%. Run time:   7.21s. Est. time left: 00:00:00:16\n",
        "40.0%. Run time:   9.48s. Est. time left: 00:00:00:14\n",
        "50.0%. Run time:  11.85s. Est. time left: 00:00:00:11\n",
        "60.0%. Run time:  14.11s. Est. time left: 00:00:00:09\n",
        "70.0%. Run time:  16.30s. Est. time left: 00:00:00:06\n",
        "80.0%. Run time:  18.40s. Est. time left: 00:00:00:04\n",
        "90.0%. Run time:  20.74s. Est. time left: 00:00:00:02\n",
        "100.0%. Run time:  22.82s. Est. time left: 00:00:00:00\n",
        "Total run time:  22.89s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result_mc4 = mcsolve(H, psi0, times, c_ops, e_ops, ntraj=ntraj)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10.0%. Run time:   2.59s. Est. time left: 00:00:00:23\n",
        "20.0%. Run time:   4.90s. Est. time left: 00:00:00:19\n",
        "30.0%. Run time:   7.10s. Est. time left: 00:00:00:16\n",
        "40.0%. Run time:   9.30s. Est. time left: 00:00:00:13\n",
        "50.0%. Run time:  11.41s. Est. time left: 00:00:00:11\n",
        "60.0%. Run time:  13.66s. Est. time left: 00:00:00:09\n",
        "70.0%. Run time:  15.84s. Est. time left: 00:00:00:06\n",
        "80.0%. Run time:  18.03s. Est. time left: 00:00:00:04\n",
        "90.0%. Run time:  20.35s. Est. time left: 00:00:00:02\n",
        "100.0%. Run time:  22.36s. Est. time left: 00:00:00:00\n",
        "Total run time:  22.40s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result_mc5 = mcsolve(H, psi0, times, c_ops, e_ops, ntraj=ntraj, map_func=ip_parallel_map)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "16.8%. Run time:   4.35s. Est. time left: 00:00:00:21\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "22.0%. Run time:   5.84s. Est. time left: 00:00:00:20\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "33.2%. Run time:   8.64s. Est. time left: 00:00:00:17\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "44.6%. Run time:  11.15s. Est. time left: 00:00:00:13\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "53.8%. Run time:  13.63s. Est. time left: 00:00:00:11\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "64.0%. Run time:  15.58s. Est. time left: 00:00:00:08\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "71.2%. Run time:  17.52s. Est. time left: 00:00:00:07\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "81.2%. Run time:  19.89s. Est. time left: 00:00:00:04\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "90.8%. Run time:  22.00s. Est. time left: 00:00:00:02\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100.0%. Run time:  23.99s. Est. time left: 00:00:00:00\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Total run time:  23.99s\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result_mc6 = mcsolve(H, psi0, times, c_ops, e_ops, ntraj=ntraj, map_func=serial_map)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10.0%. Run time:  11.61s. Est. time left: 00:00:01:44\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "20.0%. Run time:  23.38s. Est. time left: 00:00:01:33\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "30.0%. Run time:  35.06s. Est. time left: 00:00:01:21\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "40.0%. Run time:  46.74s. Est. time left: 00:00:01:10\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "50.0%. Run time:  58.34s. Est. time left: 00:00:00:58\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "60.0%. Run time:  69.83s. Est. time left: 00:00:00:46\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "70.0%. Run time:  81.54s. Est. time left: 00:00:00:34\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "80.0%. Run time:  92.80s. Est. time left: 00:00:00:23\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "90.0%. Run time: 104.15s. Est. time left: 00:00:00:11\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Total run time: 115.55s\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_expectation_values([result_me1, result_mc1, result_mc2, result_mc3, result_mc4, result_mc5, result_mc6]);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jYcFhC90tZpFZssEuInHAFVUtfJs6FuzGmHtOVXG7+5iYqPUFfg0TE9U4nddw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\ni7dvBv0MAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f068bb9e2e8>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Versions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from qutip.ipynbtools import version_table\n",
      "\n",
      "version_table(verbose=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Cython</td><td>0.21.1</td></tr><tr><td>IPython</td><td>2.3.0</td></tr><tr><td>QuTiP</td><td>3.1.0.dev-aabbc25</td></tr><tr><td>OS</td><td>posix [linux]</td></tr><tr><td>Numpy</td><td>1.9.1</td></tr><tr><td>matplotlib</td><td>1.4.2</td></tr><tr><td>Python</td><td>3.4.2 (default, Oct  8 2014, 13:08:17) \n",
        "[GCC 4.9.1]</td></tr><tr><td>SciPy</td><td>0.14.0</td></tr><tr><th colspan='2'>Additional information</th></tr><tr><td>Installation path</td><td>/home/rob/py-envs/py3-devel/lib/python3.4/site-packages/qutip</td></tr><tr><td>User</td><td>rob</td></tr><tr><td colspan='2'>Wed Dec 10 11:27:27 2014 JST</td></tr></table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<IPython.core.display.HTML at 0x7f068bb85e80>"
       ]
      }
     ],
     "prompt_number": 14
    }
   ],
   "metadata": {}
  }
 ]
}