MBEs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/IPython/core/magics/pylab.py:161: UserWarning:\n",
      "\n",
      "pylab import has clobbered these variables: ['Annotation', 'f', 'Figure', 'gamma']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import numpy as np\n",
    "from scipy import integrate\n",
    "from scipy.special import gamma\n",
    "from scipy.optimize import fsolve\n",
    "import matplotlib.pyplot as plt\n",
    "import qutip.ipynbtools\n",
    "import scipy.stats\n",
    "import plotly.plotly as py\n",
    "from plotly.graph_objs import *\n",
    "import plotly.tools as tls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "gcoll = 2*pi*8.31 #HWHM\n",
    "kappa = 4.4*pi*2 #HWHM\n",
    "q = 1.389\n",
    "delta = 0*2*pi\n",
    "deltac = 0*2*pi\n",
    "gperp = 2*pi*0.06 #HWHM\n",
    "gpar = 2*pi*0.00001 #HWHM\n",
    "gammaq = 2*pi*9.4 #FWHM\n",
    "nspins = 10000#5000#20\n",
    "g0 = gcoll/sqrt(nspins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def odeintz(func, z0, t, **kwargs):\n",
    "    \"\"\"An odeint-like function for complex valued differential equations.\"\"\"\n",
    "\n",
    "    # Disallow Jacobian-related arguments.\n",
    "    _unsupported_odeint_args = ['Dfun', 'col_deriv', 'ml', 'mu']\n",
    "    bad_args = [arg for arg in kwargs if arg in _unsupported_odeint_args]\n",
    "    if len(bad_args) > 0:\n",
    "        raise ValueError(\"The odeint argument %r is not supported by \"\n",
    "                         \"odeintz.\" % (bad_args[0],))\n",
    "\n",
    "    # Make sure z0 is a numpy array of type np.complex128.\n",
    "    z0 = np.array(z0, dtype=np.complex128, ndmin=1)\n",
    "\n",
    "    def realfunc(x, t, *args):\n",
    "        z = x.view(np.complex128)\n",
    "        dzdt = func(z, t, *args)\n",
    "        # func might return a python list, so convert its return\n",
    "        # value to an array with type np.complex128, and then return\n",
    "        # a np.float64 view of that array.\n",
    "        return np.asarray(dzdt, dtype=np.complex128).view(np.float64)\n",
    "\n",
    "    result = integrate.odeint(realfunc, z0.view(np.float64), t, **kwargs)\n",
    "\n",
    "    if kwargs.get('full_output', False):\n",
    "        z = result[0].view(np.complex128)\n",
    "        infodict = result[1]\n",
    "        return z, infodict\n",
    "    else:\n",
    "        z = result.view(np.complex128)\n",
    "        return z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tsallis(f, q, width):\n",
    "    delta = (width)/2*sqrt(2*q-2)/sqrt(2**q-2)/sqrt(2)\n",
    "    norm = sqrt(q-1)*gamma(1/(q-1))/(sqrt(2*pi)*delta*gamma((3-q)/(2*(q-1))))\n",
    "    val = (1+(q-1)*f**2/delta**2/2)**(1/(1-q))\n",
    "    return norm*val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_nearest(array,value):\n",
    "    idx = (np.abs(array-value)).argmin()\n",
    "    return idx, array[idx]\n",
    "\n",
    "def cauchy(x, pos, fwhm, ampl):\n",
    "    return 1/((x-pos)**2+0.25*fwhm**2)*0.25*fwhm**2*ampl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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5l8OvfxNu+hvY/m/w0kPw+P/2jxmLYckNsPidULNaJ3BjRIk+jZzzN4xu3uLL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2749c33470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "holes = True #set to true if you want to include holes in your simulation\n",
    "inhomo = True #do you want inhomogneous broadening\n",
    "\n",
    "f = np.linspace(-gammaq/2, gammaq/2, 10000 )\n",
    "pdf = tsallis(f, q, gammaq/2/pi)\n",
    "\n",
    "\n",
    "if holes:\n",
    "    frequency_exclude = gcoll/2/pi\n",
    "    exclude_width = 0.8 #FWHM\n",
    "\n",
    "    indf, freq = find_nearest(f, frequency_exclude)\n",
    "    scale = pdf[indf]\n",
    "\n",
    "    toex = cauchy(f, frequency_exclude, exclude_width, -scale)+cauchy(f, -frequency_exclude, exclude_width, -scale)\n",
    "    pdf = pdf+toex\n",
    "\n",
    "pdf = clip(pdf, 0, max(pdf) )   \n",
    "    \n",
    "spdf = sum(pdf)\n",
    "pdf = pdf/spdf    \n",
    "    \n",
    "spins = numpy.random.choice(f, p=pdf, size=nspins) #draw samples according to distribution\n",
    "if inhomo:\n",
    "    figure()\n",
    "    hist(spins, 200);\n",
    "    plot(f, pdf*nspins*spdf)\n",
    "else:\n",
    "    #print(spins)\n",
    "    spins = zeros(shape(spins))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(a, eta, deltdrive):\n",
    "    spins2 = 2*pi*(spins)+deltdrive\n",
    "    sn = gperp-1j*spins2 \n",
    "    sd = gpar*gperp**2+4*np.abs(a)**2*g0**2*gperp+gpar*spins2**2\n",
    "    s = sn/sd\n",
    "    \n",
    "    ret = 1j*a*deltdrive + eta + a*kappa + a*g0**2*gpar*np.sum(s)\n",
    "    return ret.real, ret.imag\n",
    "\n",
    "def a_steady(eta, deltadrive, init):\n",
    "\n",
    "    def equations(x):\n",
    "        ar, ai = x\n",
    "        return func(ar+1j*ai, eta, deltadrive)\n",
    "    ar, ai = fsolve(equations, init)\n",
    "\n",
    "    return ar, ai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-8.4192096])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z44+t3KnlCHKBBvcRak9LV8aDuzOX7Nz8WfUJRaLsa+se1vGQZN4/dwLbv3Ul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\nBnellMpDGtyVUioPaXBXSqk89P8B6uke4q02sJwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2746a7db38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "initsteady = (0.,0.1)\n",
    "fstart = 30\n",
    "deltaarr = linspace(-fstart*2*pi,fstart*2*pi, 2000)\n",
    "etasteady = 0.00001\n",
    "losasteady = np.zeros(shape(deltaarr))\n",
    "\n",
    "newinit = initsteady\n",
    "for i, delt in enumerate(deltaarr):\n",
    "    ar, ai = a_steady(etasteady, delt, newinit)\n",
    "    newinit = (ar, ai)\n",
    "    losasteady[i] = ai**2+ar**2\n",
    "\n",
    "fig = figure()\n",
    "plot(deltaarr/2/pi, losasteady)\n",
    "deltaarr[find(losasteady == max(losasteady))]/2/pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this is as optimized as possible using numpy slicing so calling this function SHOULD be very fast\n",
    "ret = np.zeros(nspins*2+1, dtype=complex)\n",
    "def mbes(Y, t, eta):\n",
    "    ret[0] = -kappa*Y[0]-1j*deltac*Y[0]+g0*np.sum(Y[1::2])+eta\n",
    "    ret[1::2] = -(gperp+1j*spins*2*pi)*Y[1::2]+g0*Y[2::2]*Y[0]\n",
    "    ret[2::2] = -gpar*(1+Y[2::2])-2*g0*(Y[1::2]*np.conj(Y[0])+np.conj(Y[1::2])*Y[0])\n",
    "    return ret\n",
    "\n",
    "#%%timeit gives\n",
    "#10000 loops, best of 3: 45 µs per loop\n",
    "#for 800 spins it becomes ~70µs. This is probably not the bottleneck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def do_calculation(drive, tlistpump, tlistafter, init):\n",
    "\n",
    "    #this should be very fast as well because scipy uses fortran \n",
    "    asolpump = odeintz(mbes, init, tlistpump, args=(drive,))\n",
    "    init2 = asolpump[-1,:]\n",
    "    asoldecay = odeintz(mbes, init2, tlistafter, args=(0,))\n",
    "\n",
    "    return asolpump, asoldecay\n",
    "    #solution is that the first entry is the cavity, and then the first spin (sigma-, sigmaz) then the second etc..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pumptime = 400e-3\n",
    "endtime = 1500e-3\n",
    "drive = 0.1\n",
    "\n",
    "numsteps = 1000\n",
    "\n",
    "tp = linspace(0,pumptime,numsteps)\n",
    "tdec = linspace(pumptime, endtime, numsteps)\n",
    "\n",
    "#starting with all spins down\n",
    "init = np.ones(nspins*2+1)*(-1)\n",
    "init[0] = 0\n",
    "init[1::2] = 0\n",
    "\n",
    "asolpump, asoldecay = do_calculation(drive, tp, tdec, init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/numpy/core/numeric.py:531: ComplexWarning:\n",
      "\n",
      "Casting complex values to real discards the imaginary part\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f274b8912b0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8EYknJbcYSozcck1LHnbYYZhZXsktn8sAUmOfccYZPPfcc3mNrNyde++9l6OP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\no0djst29O3qFK2JyMouSUaK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274b9070f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274b53b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274b7372e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = figure()\n",
    "plot(tp*1000, abs(asolpump[:,0])**2, 'k-')\n",
    "plot(tdec*1000, abs(asoldecay[:,0])**2, 'b-')\n",
    "xlabel('Time [ns]')\n",
    "ylabel('$|a|^2$')\n",
    "#py.iplot_mpl(fig1)\n",
    "fig2 = figure()\n",
    "plot(tp*1000, np.sum(asolpump[:, 2::2], axis=1)/nspins, 'k-')\n",
    "plot(tdec*1000, np.sum(asoldecay[:, 2::2], axis=1)/nspins, 'r-')\n",
    "xlabel('Time [ns]')\n",
    "ylabel('$S_z$')\n",
    "#py.iplot_mpl(fig2)\n",
    "fig3 = figure()\n",
    "plot(tp*1000, np.sum(asolpump[:, 1::2], axis=1)/nspins, 'k-')\n",
    "plot(tdec*1000, np.sum(asoldecay[:, 1::2], axis=1)/nspins, 'g-')\n",
    "xlabel('Time [ns]')\n",
    "ylabel('$S_-$')\n",
    "#py.iplot_mpl(fig3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"border: 2px solid grey; width: 600px\">\n",
       "  <div id=\"d03fd869-3e35-40fe-9ea7-bf7a1616eebc\" style=\"background-color: rgba(121,195,106,0.75); width:0%\">&nbsp;</div>\n",
       "</div>\n",
       "<p id=\"57611525-5bb5-4681-8e27-c8021ab46cbc\"></p>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('1%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  10.29s. Est. remaining time: 00:00:10:06.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('3%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  15.25s. Est. remaining time: 00:00:07:22.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('5%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  19.71s. Est. remaining time: 00:00:06:14.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('6%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  24.83s. Est. remaining time: 00:00:05:47.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('8%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  29.54s. Est. remaining time: 00:00:05:24.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('10%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  34.64s. Est. remaining time: 00:00:05:11.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('11%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  39.39s. Est. remaining time: 00:00:04:58.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('13%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  44.13s. Est. remaining time: 00:00:04:46.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('15%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  48.72s. Est. remaining time: 00:00:04:36.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('16%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  53.57s. Est. remaining time: 00:00:04:27.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('18%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  58.01s. Est. remaining time: 00:00:04:18.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('20%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  63.15s. Est. remaining time: 00:00:04:12.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('21%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  67.60s. Est. remaining time: 00:00:04:04.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('23%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  72.91s. Est. remaining time: 00:00:03:59.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('25%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  79.19s. Est. remaining time: 00:00:03:57.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('26%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  86.34s. Est. remaining time: 00:00:03:57.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('28%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  92.81s. Est. remaining time: 00:00:03:54.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('30%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time:  99.11s. Est. remaining time: 00:00:03:51.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('31%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 104.45s. Est. remaining time: 00:00:03:45.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('33%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 109.41s. Est. remaining time: 00:00:03:38.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('35%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 113.79s. Est. remaining time: 00:00:03:31.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('36%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 118.23s. Est. remaining time: 00:00:03:24.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('38%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 122.50s. Est. remaining time: 00:00:03:17.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('40%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 126.95s. Est. remaining time: 00:00:03:10.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('41%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 131.74s. Est. remaining time: 00:00:03:04.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('43%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 136.44s. Est. remaining time: 00:00:02:58.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('45%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 141.02s. Est. remaining time: 00:00:02:52.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('46%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 145.43s. Est. remaining time: 00:00:02:46.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('48%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 150.02s. Est. remaining time: 00:00:02:40.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('50%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 154.99s. Est. remaining time: 00:00:02:34.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('51%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 159.44s. Est. remaining time: 00:00:02:29.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('53%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 164.17s. Est. remaining time: 00:00:02:23.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('55%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 169.17s. Est. remaining time: 00:00:02:18.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('56%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 175.13s. Est. remaining time: 00:00:02:13.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('58%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 181.91s. Est. remaining time: 00:00:02:09.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('60%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 189.42s. Est. remaining time: 00:00:02:06.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('61%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 196.41s. Est. remaining time: 00:00:02:02.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('63%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 203.15s. Est. remaining time: 00:00:01:57.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('65%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 209.66s. Est. remaining time: 00:00:01:52.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('66%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 214.61s. Est. remaining time: 00:00:01:47.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('68%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 219.25s. Est. remaining time: 00:00:01:41.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('70%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 224.00s. Est. remaining time: 00:00:01:35.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('71%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 228.79s. Est. remaining time: 00:00:01:30.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('73%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 233.67s. Est. remaining time: 00:00:01:24.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('75%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 238.26s. Est. remaining time: 00:00:01:19.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('76%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 242.98s. Est. remaining time: 00:00:01:13.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('78%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 247.49s. Est. remaining time: 00:00:01:08.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('80%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 252.19s. Est. remaining time: 00:00:01:03.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('81%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 256.70s. Est. remaining time: 00:00:00:57.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('83%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 261.36s. Est. remaining time: 00:00:00:52.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div#d03fd869-3e35-40fe-9ea7-bf7a1616eebc').width('85%');$('p#57611525-5bb5-4681-8e27-c8021ab46cbc').text('Elapsed time: 265.74s. Est. remaining time: 00:00:00:46.');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
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     "metadata": {},
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   ],
   "source": [
    "#tlistpump, asolpump, tlistafter, asoldecay = do_calculation(pumptime, endtime, drive)\n",
    "drive = 3000\n",
    "pumptime = 100e-3\n",
    "numsteps = 1000\n",
    "\n",
    "\n",
    "tall = linspace(0, endtime, numsteps)\n",
    "\n",
    "\n",
    "drivelist = linspace(0, drive, 100)\n",
    "pumptimelist = linspace(40e-3, pumptime, 60)\n",
    "\n",
    "\n",
    "pulselength = True #change if you want to simulate different pulselengths / drive powers\n",
    "\n",
    "p = qutip.ipynbtools.HTMLProgressBar()\n",
    "if pulselength:\n",
    "    p.start(iterations=len(pumptimelist))\n",
    "    p.N = len(pumptimelist)\n",
    "    los = zeros((len(pumptimelist), numsteps, 3), dtype=complex128)\n",
    "    for i, pl in enumerate(pumptimelist):\n",
    "        tp = tall[:int(ceil(pl/endtime*numsteps))]\n",
    "        tdec = tall[int(ceil(pl/endtime*numsteps)):]\n",
    "        losp, losdec = do_calculation(drive, tp, tdec, init)\n",
    "        # i can for now only keep the collective operators otherwise I don't have enough RAM\n",
    "        los[i,:len(tp), 0] = losp[:,0]\n",
    "        los[i,len(tp):, 0] = losdec[:,0]\n",
    "        los[i,:len(tp),1] = mean(losp[:, 1::2], axis=1)\n",
    "        los[i,len(tp):,1] = mean(losdec[:, 1::2], axis=1)\n",
    "        los[i,:len(tp),2] = mean(losp[:, 2::2], axis=1)\n",
    "        los[i,len(tp):,2] = mean(losdec[:, 2::2], axis=1)\n",
    "        p.update(i)\n",
    "    p.finished()\n",
    "\n",
    "else:\n",
    "    p.start(iterations=len(drivelist))\n",
    "    p.N = len(drivelist)\n",
    "    los = zeros((len(drivelist), numsteps, 3), dtype=complex128)\n",
    "    for i, dri in enumerate(drivelist):\n",
    "        tp = tall[:int(ceil(pumptime/endtime*numsteps))]\n",
    "        tdec = tall[int(ceil(pumptime/endtime*numsteps)):]\n",
    "        losp, losdec = do_calculation(dri, tp, tdec, init)\n",
    "        # i can for now only keep the collective operators otherwise I don't have enough RAM\n",
    "        los[i,:len(tp), 0] = losp[:,0]\n",
    "        los[i,len(tp):, 0] = losdec[:,0]\n",
    "        los[i,:len(tp),1] = mean(losp[:, 1::2], axis=1)\n",
    "        los[i,len(tp):,1] = mean(losdec[:, 1::2], axis=1)\n",
    "        los[i,:len(tp),2] = mean(losp[:, 2::2], axis=1)\n",
    "        los[i,len(tp):,2] = mean(losdec[:, 2::2], axis=1)\n",
    "        p.update(i)\n",
    "    p.finished()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/numpy/core/numeric.py:531: ComplexWarning:\n",
      "\n",
      "Casting complex values to real discards the imaginary part\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2749ce7be0>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SG61yGzQrKFYVn2/tqJ628X0IM1um5rwHIelsSfcDD6b5MyT9ac8jMzOzgermJvUfAW8D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\nE4SZmZVygjAzs1JOEGZmVur/A6a0mRcjW+5yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2746da0c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qGfH99AdVvRF4Gbihm/ol6n2Dt/2A/ppU1QPAPhGZ4xV9ENjMMNmH\nxLuBzhGREd6/d6J+w2YfJkl3n70AXCYio70Wz2Ve2YARkSuId09eo6ptx9V9oTe7ajowG1jDIH7e\nVXWDqo5T1TLvM1NFfOLHAYbRPkzbUA9KDMSF+Ej9NuKzBu4ZwnqcT7zJvR5407tcRbwPeCWwHXgJ\nGONtL8APvHpvAMoHsa4Xc3R20AziH7BK4L+BXK88z7td6d0/Y5DqNg+o8Pbjr4jPshg2+xD4N2AL\nsBH4KfEZLEO6D4HlxMcowsS/rG7vyz4j3i9f6V1uG4Q6VhLvQ098Xh5O2v4er45bgSuTygfk895V\n/Y67fzdHB4aHZB9m4mJHDBtjjMOysTvIGGNMiiwEjDHGYRYCxhjjMAsBY4xxmIWAMcY4zELAGGMc\nZiFgjDEOsxAwxhiH/S/UZqjncszVwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2749dc3898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iMqhApGF7IkVVPMIwX1gsIlKwAhH++7tSVJToXv4iIv0CEf77ulJUKfxFRA4K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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2749dc6320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cavampl = abs(los[:, :, 0])**2\n",
    "\n",
    "if pulselength:\n",
    "    pcolor(tall*1000, pumptimelist*1000, cavampl)\n",
    "    ylabel('PulseLength [ns]')\n",
    "else:\n",
    "    pcolor(tall*1000, drivelist, cavampl)\n",
    "    ylabel('Drive Amplitude [a.u.]')\n",
    "xlabel('Time [ns]')\n",
    "#clim([0,200])\n",
    "figure()\n",
    "plot(tall*1000, cavampl[13,:])\n",
    "figure()\n",
    "plot(tall*1000, los[13, : ,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}