{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "QuTiP mesolve: demonstate how to use the `e_ops` argument"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Copyright (C) 2011 and later, Paul D. Nation & Robert J. Johansson"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from qutip import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example: the Janyes-Cummings model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wc = 1.0  * 2 * pi  # cavity frequency\n",
      "wa = 1.0  * 2 * pi  # atom frequency\n",
      "g  = 0.05 * 2 * pi  # coupling strength\n",
      "kappa = 0.005       # cavity dissipation rate\n",
      "gamma = 0.05        # atom dissipation rate\n",
      "N = 15              # number of cavity fock states\n",
      "tlist = linspace(0,25,100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Operators, initial state, Hamiltonian and collapse operators"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a  = tensor(destroy(N), qeye(2))\n",
      "sm = tensor(qeye(N), destroy(2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "psi0 = tensor(basis(N,0), basis(2,1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "H = wc * a.dag() * a + wa * sm.dag() * sm + g * (a.dag() * sm + a * sm.dag())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c_ops = [sqrt(kappa) * a, sqrt(gamma) * sm]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Expectation value operators"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "e_ops = [a.dag() * a, sm.dag() * sm]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result = mesolve(H, psi0, tlist, c_ops, e_ops)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(result.expect)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "2"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result.expect[0].shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "(100,)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_expectation_values(result);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5e6VrpX5E5WmzqW23b9P6cVCQ4VTZMs2rU4eqWWfNoh9ycrd8ObBwIfX7tbMT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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fbb6f23f750>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "State-vectors"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "e_ops = []"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result = mesolve(H, psi0, tlist, c_ops, e_ops)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(result.states)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "100"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result.states[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$\\text{Quantum object: dims = [[15, 2], [15, 2]], shape = [30, 30], type = oper, isHerm = True}\\\\[1em]\\begin{pmatrix}0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\\\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\\\end{pmatrix}$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "Quantum object: dims = [[15, 2], [15, 2]], shape = [30, 30], type = oper, isherm = True\n",
        "Qobj data =\n",
        "[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# loop over states and calculate the atom entropy\n",
      "entropy_atom_vec = zeros(len(result.states))\n",
      "\n",
      "for idx, state in enumerate(result.states):\n",
      "    entropy_atom_vec[idx] = entropy_linear(ptrace(state, 0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1)\n",
      "\n",
      "ax.plot(tlist, entropy_atom_vec)\n",
      "\n",
      "ax.set_xlabel('time', fontsize=16)\n",
      "ax.set_ylabel(r'$E(\\rho_{\\rm atom})$', fontsize=16);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Qu7o3R3Ex91jU/BRkNgPffy/airYh4VCG0FD+1K5F9OxtABJ6HM888wwKCgrw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\nBEE4xf8HEDLGuocpY18AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fbb65478390>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Callback function"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "entropy_cavity_vec = zeros(len(tlist))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# calculate the entropy of the cavity in a callback function\n",
      "\n",
      "def e_ops_func(t, rho):\n",
      "    # rho is only the data, so do Qobj(rho) to obtain a Qobj representation\n",
      "    # of rho\n",
      "    entropy_cavity_vec[e_ops_func.idx] = entropy_linear(ptrace(Qobj(rho), 1))\n",
      "    e_ops_func.idx += 1\n",
      "        \n",
      "e_ops_func.idx = 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result = mesolve(H, psi0, tlist, c_ops, e_ops_func)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1)\n",
      "\n",
      "ax.plot(tlist, entropy_atom_vec, label='atom')\n",
      "ax.plot(tlist, entropy_cavity_vec, label='cavity')\n",
      "\n",
      "ax.legend()\n",
      "ax.set_xlabel('time', fontsize=16)\n",
      "ax.set_ylabel(r'$E$', fontsize=16);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WxeSrtq+m/p2ZabtD59D4EAl5OWRWi4EbbxSmvQIblJTQamFrmM1mEr0pmnQOdk77udFI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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fbb654f8ed0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Software versions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from qutip.ipynbtools import version_table\n",
      "version_table()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.3.1 (default, Apr 17 2013, 22:30:32) \n",
        "[GCC 4.7.3]</td></tr><tr><td>OS</td><td>posix [linux]</td></tr><tr><td>SciPy</td><td>0.14.0.dev-2a4ba40</td></tr><tr><td>QuTiP</td><td>2.3.0.dev-2283478</td></tr><tr><td>IPython</td><td>2.0.0-dev</td></tr><tr><td>Cython</td><td>0.19</td></tr><tr><td>matplotlib</td><td>1.4.x</td></tr><tr><td>Numpy</td><td>1.7.1</td></tr><tr><td colspan='2'>Sat Sep 28 11:41:47 2013 JST</td></tr></table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<IPython.core.display.HTML at 0x7fbb65411750>"
       ]
      }
     ],
     "prompt_number": 23
    }
   ],
   "metadata": {}
  }
 ]
}