GRAPE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing Jupyter notebook from _create_pdf.ipynb\n",
      "Importing Jupyter notebook from _helper_functions.ipynb\n",
      "Importing Jupyter notebook from _steady_state.ipynb\n",
      "Importing Jupyter notebook from _mbes.ipynb\n",
      "Importing Jupyter notebook from _do_calculation.ipynb\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from scipy.stats import logistic\n",
    "from scipy.optimize import basinhopping\n",
    "from scipy.misc import derivative\n",
    "from scipy import interpolate\n",
    "from IPython.display import clear_output, Javascript, Markdown\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "import gc\n",
    "import datetime\n",
    "import time\n",
    "import random\n",
    "import matplotlib\n",
    "import nbimporter\n",
    "from _create_pdf import create_spins, create_spins_inhomo_g\n",
    "from _helper_functions import tsallis, cauchy, find_nearest, odeintz, find_nearest_sorted_left\n",
    "from _steady_state import a_steady\n",
    "from _mbes import mbes, mbes_soc\n",
    "from _do_calculation import solve_mbes\n",
    "#matplotlib.pyplot.switch_backend('nbAgg')\n",
    "#%qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.33619992429376\n"
     ]
    }
   ],
   "source": [
    "gcoll = 1.98596327e+01\n",
    "q = 2.448741\n",
    "quali = 914\n",
    "fr = 3.2981e3;\n",
    "kappa = np.pi*fr/quali #HWHM\n",
    "delta = 0*2*np.pi #detuning cavity to spin central frequency ws-wc\n",
    "deltac = 0*2*np.pi #detuning drive to cavity wc-wp\n",
    "gperp = 2*np.pi*0.09 #HWHM\n",
    "gpar = 2*np.pi*.001#0.001 #HWHM\n",
    "gammaq = 1.28912544e+01\n",
    "nbins = 401#701#5000#20\n",
    "g_number = 1 #how many bins for inhomogeneous coupling\n",
    "#g0 = gcoll/sqrt(nspins)\n",
    "print(kappa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pulse_rwa(t, args):\n",
    "    wgrund = args[0]\n",
    "    fcomps = args[1]\n",
    "    pulse = 0\n",
    "    for i in range(len(fcomps)):\n",
    "        pulse = pulse+(fcomps[i])*np.sin((i+1)*wgrund*t)\n",
    "    return pulse\n",
    "\n",
    "def pulse_rwa_gauss(t, args):\n",
    "    wgrund = args[0]\n",
    "    fcomps = args[1]\n",
    "    nfcomps = len(fcomps)\n",
    "    tend = np.pi/wgrund\n",
    "    \n",
    "    width = 2*tend/(nfcomps+5)\n",
    "    t0 = 3*width/2\n",
    "    pulse = 0\n",
    "    for i in range(len(fcomps)):\n",
    "        pulse += fcomps[i]*np.exp(-4*(t-t0)**2*np.log(2)/width**2)\n",
    "        t0 += 0.5*width\n",
    "    pulse *= logistic.cdf(t, loc=0.01, scale=.002)*(1-logistic.cdf(t, loc=tend-0.01, scale=.002))\n",
    "    pulse *= 0.46971947398573455\n",
    "    return pulse\n",
    "\n",
    "def pulse_rwa_gauss_const(t, args):\n",
    "    wgrund = args[0]\n",
    "    fcomps = args[1]\n",
    "    maxampl = args[2]\n",
    "    nfcomps = len(fcomps)\n",
    "    tend = np.pi/wgrund\n",
    "\n",
    "    #xint = np.linspace(0, tend, nfcomps)\n",
    "    \n",
    "    #spline = interpolate.BSpline(xint, fcomps, 2)\n",
    "    spline = 0\n",
    "    for i in range(len(fcomps)):\n",
    "        spline += (fcomps[i])*np.sin((i+1)*wgrund*t)\n",
    "\n",
    "    #spline = spline/nfcomps\n",
    "    pulsei = 0\n",
    "    pulseq = 0\n",
    "    pulse = 0\n",
    "    pulse = maxampl*logistic.cdf(t, loc=0.02, scale=0.004)*(1-logistic.cdf(t, loc=tend-0.02, scale=0.004))\n",
    "    \n",
    "    pulsei = pulse*np.cos(spline)\n",
    "    pulseq = pulse*np.sin(spline)\n",
    "\n",
    "    return pulsei, pulseq, spline\n",
    "\n",
    "def pulse_rwa_const_spline(t, args):\n",
    "    wgrund = args[0]\n",
    "    spline = args[1]\n",
    "    maxampl = args[2]\n",
    "    \n",
    "    tend = np.pi/wgrund\n",
    "    \n",
    "    #spline = spline/nfcomps\n",
    "    pulsei = 0\n",
    "    pulseq = 0\n",
    "    pulse = 0\n",
    "    pulse = maxampl*logistic.cdf(t, loc=0.02, scale=0.004)*(1-logistic.cdf(t, loc=tend-0.02, scale=0.004))\n",
    "    \n",
    "    pulsei = pulse*np.cos(spline)\n",
    "    pulseq = pulse*np.sin(spline)\n",
    "\n",
    "    return pulsei, pulseq\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def g_inhomo_function(x, sigma):\n",
    "    return np.exp(-x**2/sigma**2)\n",
    "\n",
    "x_start = 5\n",
    "x_stop = 20 #µm\n",
    "x = np.linspace(x_start, x_stop, g_number)\n",
    "sigma = 5\n",
    "g_temp = g_inhomo_function(x, sigma)\n",
    "g_list = g_temp/np.sum(g_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spins, gs, pdf = create_spins_inhomo_g(gcoll, g_list, q, delta, gammaq, nbins, holes=False, inhomo=True, hyperfine=True)\n",
    "plt.figure()\n",
    "plt.plot(spins[:len(pdf)], pdf/max(pdf))\n",
    "plt.xlabel('$\\Delta f$ (MHz)')\n",
    "plt.ylabel('weight');\n",
    "#%qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd66d5cf790>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "initsteady = (0.1,0.1)\n",
    "fstart = 50\n",
    "deltaarr = np.linspace(-fstart*2*np.pi,fstart*2*np.pi, 2000)\n",
    "etasteady = 0.00001\n",
    "losasteady = np.zeros(np.shape(deltaarr))\n",
    "losareal = np.zeros(np.shape(deltaarr))\n",
    "losaimag = np.zeros(np.shape(deltaarr))\n",
    "\n",
    "newinit = initsteady\n",
    "for i, delt in enumerate(deltaarr):\n",
    "    ar, ai = a_steady(etasteady, delt, gs, spins, gpar, gperp, kappa, newinit)\n",
    "    newinit = (ar, ai)\n",
    "    losasteady[i] = ai**2+ar**2\n",
    "    losareal[i] = ar\n",
    "    losaimag[i] = ai\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.xlabel('$\\Delta f$ (MHz)')\n",
    "plt.ylabel('|A|² (dB)');\n",
    "plt.plot(deltaarr/2/np.pi, 10*np.log10(losasteady/0.0000000001))\n",
    "#deltaarr[find(losasteady == max(losasteady))]/2/pi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# SOC pulses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pumptime = 50e-3#5*np.pi/gcoll\n",
    "decaytime = 500e-3\n",
    "dt = 4e-3 #The time resolution of the AWG. Don't change that because the solution of the MBEs are written in a way that use that as time resolution\n",
    "numsteps = int(pumptime/dt)\n",
    "tlist = np.arange(0,pumptime+dt,dt)\n",
    "tlistdecay = np.arange(0, decaytime+dt, dt)\n",
    "\n",
    "init = np.ones(nbins*g_number*2+1)*(-1)\n",
    "init[0] = 0\n",
    "init[1::2] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibrate power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pulsei = np.ones((len(tlist)))\n",
    "\n",
    "pin = np.arange(-15, 0,0.5)\n",
    "aref = 1250\n",
    "\n",
    "alos = np.zeros((len(pin), len(tlist)+len(tlistdecay)))\n",
    "szlos = np.zeros(np.shape(alos))\n",
    "szall = np.zeros((len(pin), len(tlist)+len(tlistdecay), nbins))\n",
    "small = np.zeros(np.shape(szall))\n",
    "smlos = np.zeros(np.shape(alos))\n",
    "\n",
    "for ctr, p in enumerate(pin):\n",
    "    dr = aref*10**(p/20.)\n",
    "    asolpump, asoldecay, infodict = solve_mbes(mbes, init, pdf, tlist, tlistdecay, dr, kappa, deltac, gs, gperp, spins, gpar)\n",
    "    alos[ctr, : ] = np.hstack((abs(asolpump[:,0])**2, abs(asoldecay[:,0])**2))\n",
    "    szlos[ctr, :] = np.hstack((np.real(np.sum(asolpump[:, 2::2], axis=1)), np.real(np.sum(asoldecay[:, 2::2], axis=1))))\n",
    "    szall[ctr, :, :] = np.vstack((np.real(asolpump[:,2::2]), np.real(asoldecay[:,2::2])))\n",
    "    small[ctr, :, :] = np.vstack((np.real(asolpump[:,1::2]), np.real(asoldecay[:,1::2])))\n",
    "    smlos[ctr, :] = np.hstack((np.real(np.sum(asolpump[:, 1::2], axis=1)), np.real(np.sum(asoldecay[:, 1::2], axis=1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tplot = np.linspace(0, 1000*(pumptime+decaytime), len(tlist)+len(tlistdecay))\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,5))\n",
    "\n",
    "ax1.pcolor(tplot, pin, alos, vmin=0, vmax=200)\n",
    "ax1.set_xlabel('Time (ns)')\n",
    "ax1.set_ylabel('Input power (dB)');\n",
    "\n",
    "ax2.pcolor(tplot, pin, szlos)\n",
    "ax2.set_xlabel('Time (ns)')\n",
    "ax2.set_ylabel('Input power (dB)');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Rabi oscillations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alos = np.zeros((len(tlist)+len(tlistdecay)))\n",
    "szlos = np.zeros(np.shape(alos))\n",
    "\n",
    "asolpump, asoldecay, infodict = solve_mbes(mbes, init, pdf, tlist, tlistdecay, 0.00001, kappa, deltac, gs, gperp, spins, gpar)\n",
    "alos = np.hstack((abs(asolpump[:,0])**2, abs(asoldecay[:,0])**2))\n",
    "szlos = np.hstack((np.real(np.sum(asolpump[:, 2::2], axis=1)), np.real(np.sum(asoldecay[:, 2::2], axis=1))))\n",
    "                  \n",
    "                  \n",
    "tplot = np.linspace(0, 1000*(pumptime+decaytime), len(tlist)+len(tlistdecay))\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,5))\n",
    "\n",
    "ax1.plot(tplot, alos)\n",
    "ax1.set_xlabel('Time (ns)')\n",
    "ax1.set_ylabel('Cavity population');\n",
    "\n",
    "ax2.plot(tplot, szlos)\n",
    "ax2.set_xlabel('Time (ns)')\n",
    "ax2.set_ylabel('Inversion');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "number_fcp = 6\n",
    "ttest = np.linspace(0,pumptime,numsteps)\n",
    "wgrund = np.pi/ttest[-1]\n",
    "sumsin = np.zeros((number_fcp, len(ttest)))\n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize=(8,5))\n",
    "\n",
    "for i in range(number_fcp):\n",
    "    sumsin[i, :] = np.sin(wgrund*ttest*(i+1))\n",
    "    ax.plot(ttest, sumsin[i,:])\n",
    "ax.plot(ttest, np.sum(sumsin, axis=0))\n",
    "maxfourier = max(np.sum(sumsin, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ampl = 739.99982973\n",
    "pumpt = 0.34\n",
    "\n",
    "class GRAPE:\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.tp = np.arange(0,pumpt+dt,dt)\n",
    "        self.td = np.arange(0, decaytime+dt, dt)\n",
    "        self.number_fcp = number_fcp\n",
    "        self.max_ampl = ampl\n",
    "        self.max_t_ampl = aref\n",
    "        self.target_time_decay = 0.1 #when after switching off the pulse the inversion should be maximum\n",
    "        self.wgrund = np.pi/self.tp[-1]\n",
    "        self.plot_handle = None\n",
    "        self.counter = 0\n",
    "        self.solution = None\n",
    "        self.fcomps = None\n",
    "        self.fitness = 0\n",
    "        self.vars = []\n",
    "        self.fig = None\n",
    "        self.disp1 = display(Markdown(\"\"), display_id='0')\n",
    "        self.disp2 = display(Markdown(\"\"), display_id='1')\n",
    "        self.best_fitness = 1.\n",
    "        self.epsilon = -0.1\n",
    "        self.target_fitness = 0.1\n",
    "        self.dummy_fitness = []\n",
    "        self.dummy_solution = []\n",
    "        self.grad_idx = -1\n",
    "        self.pulse = None\n",
    "        self.gradients = np.zeros(np.shape(self.tp))\n",
    "        \n",
    "    def fitness_func(self, x, *args):\n",
    "        tidx = args[0]\n",
    "        pulse = args[1][:]\n",
    "        ichan = args[2]\n",
    "        \n",
    "        init = np.ones(nbins*2+1)*(-1)\n",
    "        init[0] = 0\n",
    "        init[1::2] = 0\n",
    "        \n",
    "        \n",
    "        pi = pulse[:]\n",
    "        pi[tidx] = x\n",
    "\n",
    "        pulsei, pulseq = pulse_rwa_const_spline(self.tp, args=(self.wgrund, pulse, self.max_ampl))\n",
    "\n",
    "        fitness, solution = self.calculate_fitness(pulsei, pulseq)\n",
    "        \n",
    "        self.dummy_fitness.append(fitness)\n",
    "        self.dummy_solution.append(solution)\n",
    "        return fitness\n",
    "    \n",
    "        \n",
    "    def adjust_gradient(self, t, pulse, iquad=True):\n",
    "        self.gradients = np.zeros(np.shape(t))\n",
    "        pn = pulse[:]\n",
    "        tl = np.arange(len(t))\n",
    "        random.shuffle(tl)\n",
    "        ctr = 1\n",
    "        for i in tl:\n",
    "            k = pn[i].copy()\n",
    "            gra = scipy.misc.derivative(self.fitness_func, k, dx=abs(self.epsilon), args=((i, pn.copy(), True)))\n",
    "            self.gradients[i] = gra\n",
    "            \n",
    "            \n",
    "            self.grad_idx = i\n",
    "\n",
    "            self.plot_stuff(self.fitness, self.solution, self.pulse, self.gradients)\n",
    "            self.disp2.update(Markdown(\"### Current value: %i of %d; Current fitness: %f; Gradient: %f\" %(ctr, len(self.tp), 1-self.dummy_fitness[1], gra)))\n",
    "            self.dummy_fitness = []\n",
    "            self.dummy_solution = []\n",
    "            pn[i] = pulse[i] + self.epsilon*self.gradients[i]*40\n",
    "            ctr += 1\n",
    "        return pn.copy()\n",
    "        \n",
    "        \n",
    "        \n",
    "    def main(self, initial=None):\n",
    "        t = datetime.datetime.now()\n",
    "        self.filename_save = \"/home/andreas/Physik/nelder_mead/grape_%s\"%t.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "        filename_save = self.filename_save\n",
    "        if initial is None:\n",
    "            self.pulsei, self.pulseq, spline = pulse_rwa_gauss_const(self.tp, (self.wgrund, np.random.uniform(-0, np.pi*2/self.number_fcp, (self.number_fcp)), self.max_ampl))\n",
    "            init = spline[:]\n",
    "        else:\n",
    "            init = initial\n",
    "            self.pulsei, self.pulseq = pulse_rwa_const_spline(self.tp, args=(self.wgrund, init, self.max_ampl))\n",
    "        \n",
    "        self.pulse = init.copy()\n",
    "        while self.best_fitness > self.target_fitness:\n",
    "            self.fitness, self.solution = self.calculate_fitness(self.pulsei, self.pulseq)\n",
    "            self.grad_idx = -1\n",
    "            self.plot_stuff(self.fitness, self.solution, self.pulse, self.gradients)\n",
    "            self.pulse = self.adjust_gradient(self.tp, self.pulse)\n",
    "            self.pulsei, self.pulseq = pulse_rwa_const_spline(self.tp, args=(self.wgrund, self.pulse, self.max_ampl))\n",
    "            self.counter += 1\n",
    "            \n",
    "            if self.fitness < self.best_fitness:\n",
    "                savedict = {'wgrund': self.wgrund, 'time': self.tp, 'timedecay': self.td, 'pulse':np.vstack((self.pulsei, self.pulseq)), 'solution': self.solution, 'fitness': self.fitness, 'nbins':nbins, 'gammaq':gammaq,'gpar': gpar, 'gperp':gperp, 'deltac': deltac, 'delta':delta, 'kappa':kappa,'gs':gs, 'spline': self.pulse, 'gradients':self.gradients}\n",
    "            \n",
    "                pulseforsave = np.zeros((len(self.tp), 3))\n",
    "                pulseforsave[:,0] = self.tp\n",
    "                pulseforsave[:,1] = self.pulsei\n",
    "                pulseforsave[:,2] = self.pulseq\n",
    "                savedict2 = {'wgrund': self.wgrund, 'puls': pulseforsave}\n",
    "                if self.fitness < 0.5:\n",
    "                    scipy.io.savemat(self.filename_save, savedict)\n",
    "                    scipy.io.savemat(self.filename_save + '_simple', savedict2)\n",
    "                self.best_fitness = self.fitness\n",
    "            \n",
    "        \n",
    "    \n",
    "   \n",
    "    def plot_stuff(self, fitness, solution, pulse, gradients):\n",
    "        if not self.fig:\n",
    "            self.fig, ((ax1, ax0), (ax2, ax3)) = plt.subplots(2,2,figsize=(15,10))\n",
    "        else:\n",
    "            ax1, ax0, ax2, ax3 = self.fig.axes\n",
    "            ax0.clear()\n",
    "            ax1.clear()\n",
    "            ax2.clear()\n",
    "            ax3.clear()\n",
    "            gc.collect()\n",
    "        pos1 = ax1.get_position()\n",
    "        pos2 = ax2.get_position()\n",
    "        pos3 = ax3.get_position()\n",
    "        pos0 = ax0.get_position()\n",
    "        pos1n = [pos2.x0, pos1.y0,  pos1.width, pos1.height] \n",
    "        \n",
    "        \n",
    "        ax1.plot(self.tp*1000, self.pulsei, 'b-')\n",
    "        ax1.plot(self.tp*1000, self.pulseq, 'g-')\n",
    "        \n",
    "        ax0.plot(self.tp*1000, self.gradients, 'b-')\n",
    "        ax0.set_ylabel('Gradient')\n",
    "        ax0.set_xlabel(\"Time (ns)\")\n",
    "        \n",
    "        \n",
    "        szsol = np.real(np.sum(solution[:, 2::2], axis=1))\n",
    "        asol = solution[:,0]\n",
    "        \n",
    "        ax1.plot(self.tp*1000, np.sqrt(self.pulsei**2+self.pulseq**2), 'k--')\n",
    "        ax1.set_ylabel(\"Amplitude (a.u.)\")\n",
    "        ax1.set_xlabel(\"Time (ns)\")        \n",
    "        ax1.plot(self.tp*1000, self.max_ampl*pulse/max(abs(pulse)), 'r.')\n",
    "        ax1.plot(self.tp*1000, self.max_ampl*pulse/max(abs(pulse)), 'r-')\n",
    "        if self.grad_idx > -1:\n",
    "            ax1.plot(self.tp[self.grad_idx]*1000, self.max_ampl*pulse[self.grad_idx]/max(abs(pulse)), 'k.')\n",
    "        ax1.set_ylim([-self.max_t_ampl-50, self.max_t_ampl+50])\n",
    "        ax1.set_xlim([-20, 20+self.tp[-1]*1000])\n",
    "        ax2.plot(self.tp*1000, (szsol[:len(self.tp)]), 'r-')\n",
    "        ax2.plot(self.td*1000+self.tp[-1]*1000, szsol[len(self.tp):], 'k-')\n",
    "        ax2.set_ylabel(\"Inversion\")\n",
    "        ax2.set_xlabel(\"Time (ns)\")\n",
    "        ax2.set_ylim([-1,1])\n",
    "        ax3.plot(self.tp*1000, abs(asol[:len(self.tp)])**2, 'g-')\n",
    "        ax3.plot(self.td*1000+self.tp[-1]*1000, abs(asol[len(self.tp):])**2, 'm-')\n",
    "        ax3.set_ylabel(\"|A|²\")\n",
    "        ax3.set_xlabel(\"Time (ns)\")\n",
    "        ax1.set_position(pos1n)\n",
    "        plt.close('all')\n",
    "        if not self.plot_handle:\n",
    "            self.plot_handle = display(self.fig, display_id='plot')\n",
    "        else:\n",
    "            self.plot_handle.update(self.fig)\n",
    "        idx = np.abs(self.td - self.target_time_decay).argmin()\n",
    "        self.disp1.update(Markdown(\"### Generation %i; Inversion: %f; Fitness: %f\" %(self.counter, max(np.real(np.sum(solution[:, 2::2], axis=1))[len(self.tp)+idx:]), 1-fitness)))\n",
    "        \n",
    "\n",
    "\n",
    "            \n",
    "    def calculate_fitness(self, pulsei, pulseq):\n",
    "\n",
    "        init = np.ones(len(spins)*2+1)*(-1)\n",
    "        init[0] = 0\n",
    "        init[1::2] = 0\n",
    "        \n",
    "        solutionpump, solutiondecay, infodict = solve_mbes(mbes_soc, init, pdf, self.tp, self.td, pulsei, pulseq, self.tp, kappa, deltac, gs, gperp, spins, gpar)\n",
    "\n",
    "        max_p_ampl = max(np.sqrt(self.pulsei**2+self.pulseq**2))\n",
    "\n",
    "        solution = np.vstack((solutionpump, solutiondecay))\n",
    "        idx = np.abs(self.td - self.target_time_decay).argmin()\n",
    "        inversion = np.real(np.sum(solutiondecay[idx:, 2::2], axis=1))\n",
    "        maxinv = max(inversion)\n",
    "        idxinv = int(np.where(inversion==maxinv)[0])\n",
    "        maxcav = max(abs(solutiondecay[:,0])**2)\n",
    "        cavity_photons = (abs(solutiondecay[idxinv+idx,0])**2)/maxcav\n",
    "        fitness = (1-max(inversion)) # + cavity_photons\n",
    "        \n",
    "        #small numbers are better!!\n",
    "        \n",
    "        return fitness, solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Generation 0; Inversion: 0.775112; Fitness: 0.775112"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Current value: 13 of 86; Current fitness: 0.859556; Gradient: -0.008677"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-0691e19d7eb7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0mgg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGRAPE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minitial\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0minit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-28-47598c291519>\u001b[0m in \u001b[0;36mmain\u001b[0;34m(self, initial)\u001b[0m\n\u001b[1;32m     90\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_stuff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfitness\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpulse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradients\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpulse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madjust_gradient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpulse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpulsei\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpulseq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpulse_rwa_const_spline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwgrund\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpulse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_ampl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcounter\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-28-47598c291519>\u001b[0m in \u001b[0;36madjust_gradient\u001b[0;34m(self, t, pulse, iquad)\u001b[0m\n\u001b[1;32m     57\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtl\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m             \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m             \u001b[0mgra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmisc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mderivative\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfitness_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradients\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgra\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/scipy/misc/common.py\u001b[0m in \u001b[0;36mderivative\u001b[0;34m(func, x0, dx, n, args, order)\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[0mho\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0morder\u001b[0m \u001b[0;34m>>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m         \u001b[0mval\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mho\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mval\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mprod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-28-47598c291519>\u001b[0m in \u001b[0;36mfitness_func\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0mpulsei\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpulseq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpulse_rwa_const_spline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwgrund\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpulse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_ampl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m         \u001b[0mfitness\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculate_fitness\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpulsei\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpulseq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdummy_fitness\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfitness\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-28-47598c291519>\u001b[0m in \u001b[0;36mcalculate_fitness\u001b[0;34m(self, pulsei, pulseq)\u001b[0m\n\u001b[1;32m    174\u001b[0m         \u001b[0minit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 176\u001b[0;31m         \u001b[0msolutionpump\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolutiondecay\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfodict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolve_mbes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmbes_soc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpulsei\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpulseq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkappa\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeltac\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgperp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m         \u001b[0mmax_p_ampl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpulsei\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpulseq\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/Maxwell-Bloch-equations/_do_calculation.ipynb\u001b[0m in \u001b[0;36msolve_mbes\u001b[0;34m(mbes_func, init, pdf, tlistpump, tlistdecay, *args)\u001b[0m\n\u001b[1;32m      7\u001b[0m    \"outputs\": [\n\u001b[1;32m      8\u001b[0m     {\n\u001b[0;32m----> 9\u001b[0;31m      \u001b[0;34m\"name\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"stdout\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m      \u001b[0;34m\"output_type\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"stream\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m      \"text\": [\n",
      "\u001b[0;32m~/Diverses/Maxwell-Bloch-equations/_helper_functions.ipynb\u001b[0m in \u001b[0;36modeintz\u001b[0;34m(func, z0, t, **kwargs)\u001b[0m\n\u001b[1;32m     20\u001b[0m    \u001b[0;34m\"outputs\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m    \"source\": [\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0;34m\"def tsallis(f, q, width):\\n\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m     \u001b[0;34m\"    delta = (width)/2*np.sqrt(2*q-2)/np.sqrt(2**q-2)/np.sqrt(2)\\n\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;34m\"    norm = np.sqrt(q-1)*gamma(1/(q-1))/(np.sqrt(2*np.pi)*delta*gamma((3-q)/(2*(q-1))))\\n\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/scipy/integrate/odepack.py\u001b[0m in \u001b[0;36modeint\u001b[0;34m(func, y0, t, args, Dfun, col_deriv, full_output, ml, mu, rtol, atol, tcrit, h0, hmax, hmin, ixpr, mxstep, mxhnil, mxordn, mxords, printmessg, tfirst)\u001b[0m\n\u001b[1;32m    242\u001b[0m                              \u001b[0mfull_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0matol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtcrit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m                              \u001b[0mixpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmxstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmxhnil\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmxordn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmxords\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m                              int(bool(tfirst)))\n\u001b[0m\u001b[1;32m    245\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m         \u001b[0mwarning_msg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_msgs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" Run with full_output = 1 to get quantitative information.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/Maxwell-Bloch-equations/_helper_functions.ipynb\u001b[0m in \u001b[0;36mrealfunc\u001b[0;34m(x, t, *args)\u001b[0m\n\u001b[1;32m     14\u001b[0m    ]\n\u001b[1;32m     15\u001b[0m   },\n\u001b[0;32m---> 16\u001b[0;31m   {\n\u001b[0m\u001b[1;32m     17\u001b[0m    \u001b[0;34m\"cell_type\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"code\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m    \u001b[0;34m\"execution_count\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/Maxwell-Bloch-equations/_mbes.ipynb\u001b[0m in \u001b[0;36mmbes_soc\u001b[0;34m(Y, t, ilist, qlist, tlist, kappa, deltac, gs, gperp, spins, gpar)\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0;34m\"from _helper_functions import find_nearest_sorted, find_nearest_sorted\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m    ]\n\u001b[0;32m---> 22\u001b[0;31m   },\n\u001b[0m\u001b[1;32m     23\u001b[0m   {\n\u001b[1;32m     24\u001b[0m    \u001b[0;34m\"cell_type\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"code\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "init = array([ 2.76901073e-03, 1.64447337e-01, 3.30674462e-01, 4.94832097e-01,\n",
    "        6.26268959e-01, 7.24806906e-01, 8.67565407e-01, 1.19338327e+00,\n",
    "        1.84042070e+00, 2.71417066e+00, 3.46633834e+00, 3.91458982e+00,\n",
    "        4.17221743e+00, 4.37198256e+00, 4.52885319e+00, 4.66040290e+00,\n",
    "        4.82299927e+00, 5.00037313e+00, 5.21022119e+00, 5.47317643e+00,\n",
    "        5.81701932e+00, 6.23778302e+00, 6.70418409e+00, 7.13156167e+00,\n",
    "        7.49090992e+00, 7.77839522e+00, 7.98967977e+00, 8.18697325e+00,\n",
    "        8.36441428e+00, 8.55490042e+00, 8.71862010e+00, 8.81765013e+00,\n",
    "        8.80866621e+00, 8.66744945e+00, 8.39492444e+00, 7.96213627e+00,\n",
    "        7.45570228e+00, 6.90537100e+00, 6.21114504e+00, 5.65397321e+00,\n",
    "        5.19864511e+00, 4.83898909e+00, 4.60251169e+00, 4.46376912e+00,\n",
    "        4.39678609e+00, 4.39406066e+00, 4.41123460e+00, 4.44507513e+00,\n",
    "        4.48238726e+00, 4.51342359e+00, 4.55113791e+00, 4.58687937e+00,\n",
    "        4.61694849e+00, 4.65045723e+00, 4.67554793e+00, 4.68633827e+00,\n",
    "        4.66713529e+00, 4.54905316e+00, 4.15983543e+00, 3.55318111e+00,\n",
    "        1.76005151e+00, 1.35203200e+00, 1.31572455e+00, 1.32094485e+00,\n",
    "        1.33771726e+00, 1.35792756e+00, 1.37536560e+00, 1.39089123e+00,\n",
    "        1.40394874e+00, 1.41381692e+00, 1.42212454e+00, 1.42809149e+00,\n",
    "        1.43216330e+00, 1.43467457e+00, 1.43375123e+00, 1.42852460e+00,\n",
    "        1.41682849e+00, 1.39737770e+00, 1.36195213e+00, 1.29268383e+00,\n",
    "        1.14149613e+00, 8.80434519e-01, 5.98375768e-01, 3.67253700e-01,\n",
    "        1.76449095e-01, 1.23144311e-03])\n",
    "\n",
    "gg = GRAPE()\n",
    "gg.main(initial= init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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
}