BasinHoppingOptimizePhotons.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 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_pump_probe, solve_mbes\n",
    "#matplotlib.pyplot.switch_backend('nbAgg')\n",
    "#%qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.0948370707497\n"
     ]
    }
   ],
   "source": [
    "gcoll = 45.18409188666773\n",
    "q = 1.0058294512857613\n",
    "quali = 1200\n",
    "fr = 3092;\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 = 116.7755270154724\n",
    "nbins = 1701#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": 3,
   "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f102756fbe0>]"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pumptime = 200e-3#5*np.pi/gcoll\n",
    "decaytime = 500e-3\n",
    "intertime = 10000e-3\n",
    "probetime = 20e-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",
    "# test time it takes to calculate\n",
    "\n",
    "\n",
    "init = np.ones(len(spins)*2+1)*(-1)\n",
    "init[0] = 0\n",
    "init[1::2] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibrate power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pulsei = np.ones((len(tlist)))\n",
    "pin = np.arange(-15, 0,0.5)\n",
    "aref = 150\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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-0993edfcf8c6>:5: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  ax1.pcolor(tplot, pin, alos, vmin=0, vmax=60)\n",
      "<ipython-input-9-0993edfcf8c6>:9: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  ax2.pcolor(tplot, pin, szlos)\n"
     ]
    },
    {
     "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=60)\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)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Rabi oscillations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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 = 3\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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BasinHopping:\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.tp = np.arange(0,pumptime+dt,dt)\n",
    "        self.td = np.arange(0, decaytime+dt, dt)\n",
    "        self.ti = np.arange(0, intertime+dt, dt)\n",
    "        self.tpr = np.arange(0, probetime+dt, dt)\n",
    "        self.number_fcp = number_fcp\n",
    "        self.pulse_length = 0.3\n",
    "        self.min_pulse_length = 0.02\n",
    "        self.max_ampl = aref/2\n",
    "        self.max_t_ampl = aref\n",
    "        self.target_time_decay = 1.9 #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.max_time = .8 #maximum time pulse is allowed to last\n",
    "        self.counter = 0\n",
    "        self.solution = None\n",
    "        self.fcomps = None\n",
    "        self.idx = -1\n",
    "        self.fitness = 0\n",
    "        self.constant_on = True\n",
    "        self.maxcav = 0\n",
    "        self.ampl_probe = 1e-3\n",
    "        self.vars = []\n",
    "        self.fig = None\n",
    "        self.disp1 = display(Markdown(\"\"), display_id='0')\n",
    "        self.best_fitness = 1.\n",
    "        self.scale_factor = 2*np.pi if self.number_fcp == 0 else 2*np.pi/self.number_fcp\n",
    "        \n",
    "\n",
    "        \n",
    "    def main(self, initial=None):\n",
    "        self.counter = 0\n",
    "        t = datetime.datetime.now()\n",
    "        self.filename_save = \"/home/andreas/Physics/nelder_mead/basinhopping_photons_%s\"%t.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "        filename_save = self.filename_save\n",
    "        pumpt = pumptime\n",
    "        if initial is None:\n",
    "            if self.constant_on:\n",
    "                init = np.random.uniform(0, self.scale_factor, self.number_fcp +2)\n",
    "                init[-1] = 0.3*self.scale_factor\n",
    "                init[-2] = 0.8*self.scale_factor\n",
    "            else:\n",
    "                init = np.random.uniform(-self.max_ampl, self.max_ampl, (self.number_fcp*2))\n",
    "        else:\n",
    "            init = initial\n",
    "            init[-1] *= self.scale_factor\n",
    "            init[-2] *= self.scale_factor\n",
    "        self.res = basinhopping(self.calculate_fitness, init, niter_success=15, T=0.02, stepsize=np.pi/4, disp=True, minimizer_kwargs= {'callback':self.plot_stuff, 'method': 'Nelder-Mead'})\n",
    "        return self.res\n",
    "    \n",
    "   \n",
    "    def plot_stuff(self, fcomps):\n",
    "        if not self.fig:\n",
    "            self.fig, ((ax1, ax2), (ax3, ax0)) = plt.subplots(2,2,figsize=(15,10))\n",
    "        else:\n",
    "            ax1, ax2, ax3, ax0 = self.fig.axes\n",
    "            ax1.clear()\n",
    "            ax2.clear()\n",
    "            ax3.clear()\n",
    "            ax0.clear()\n",
    "            gc.collect()\n",
    "        pos0 = ax0.get_position()\n",
    "        pos1 = ax1.get_position()\n",
    "        pos2 = ax2.get_position()\n",
    "        pos3 = ax3.get_position()\n",
    "        pos1n = [(pos2.x0+pos3.x0)/2, 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",
    "        szsol = np.real(np.sum(self.solution[:, 2::2], axis=1))\n",
    "        asol = self.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.set_ylim([-self.max_t_ampl-50, self.max_t_ampl+50])\n",
    "        ax1.set_xlim([-20, 20+self.tp[-1]*1000])\n",
    "        \n",
    "        idxi = len(self.tp)\n",
    "        idxpr = idxi +len(self.ti)\n",
    "        idxdec = idxpr + len(self.tpr)\n",
    "        \n",
    "        tlp = self.tp*1000\n",
    "        tli = self.ti*1000+tlp[-1]\n",
    "        tlpr = self.tpr*1000+tli[-1]\n",
    "        tld = self.td*1000+tlpr[-1]\n",
    "        \n",
    "        \n",
    "        ax2.plot(tlp, szsol[:idxi], 'r-')\n",
    "        ax2.plot(tli, szsol[idxi:idxpr], 'g-')\n",
    "        ax2.plot(tlpr, szsol[idxpr:idxdec], 'm-')\n",
    "        ax2.plot(tld, szsol[idxdec:], 'k-')\n",
    "        ax2.set_ylabel(\"Inversion\")\n",
    "        ax2.set_xlabel(\"Time (ns)\")\n",
    "        ax2.set_ylim([-1,1])\n",
    "        ax3.semilogy(tlp, abs(asol[:idxi])**2, 'b-')\n",
    "        ax3.semilogy(tli, abs(asol[idxi:idxpr])**2, 'y-')\n",
    "        ax3.semilogy(tlpr, abs(asol[idxpr:idxdec])**2, 'k-')\n",
    "        ax3.semilogy(tld, abs(asol[idxdec:])**2, 'c-')\n",
    "        ax3.set_ylabel(\"|A|²\")\n",
    "        ax3.set_xlabel(\"Time (ns)\")\n",
    "        \n",
    "        ax0.plot(tlpr, abs(asol[idxpr:idxdec])**2, 'r-')\n",
    "        ax0.plot(tld, abs(asol[idxdec:])**2, 'b-')\n",
    "        ax0.set_ylabel(\"|A|²\")\n",
    "        ax0.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",
    "        self.counter += 1\n",
    "        self.disp1.update(Markdown(\"### Generation %i; Number of Photons: %f; Fitness: %f\" %(self.counter, self.maxcav, self.fitness)))\n",
    "        if self.fitness > self.best_fitness:\n",
    "            savedict = {'wgrund': self.wgrund, 'time': self.tp, 'timedecay': self.td, 'timeinter': self.ti, 'timeprobe': self.tpr, 'fourier': self.fcomps, '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}\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 >20:\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",
    "    def calculate_fitness(self, fourier):\n",
    "\n",
    "        init = np.ones(nbins*g_number*2+1)*(-1)\n",
    "        init[0] = 0\n",
    "        init[1::2] = 0\n",
    "        val_add = 0\n",
    "        if self.constant_on:\n",
    "            pt = round(fourier[-1]/self.scale_factor/dt)*dt\n",
    "            if pt <= self.min_pulse_length:\n",
    "                pt = self.min_pulse_length\n",
    "                val_add += 1e6/pt\n",
    "            ampl = aref*fourier[-2]/self.scale_factor\n",
    "            self.tp = np.arange(0,pt+dt,dt)\n",
    "            self.wgrund = np.pi/pt\n",
    "            self.fcomps = fourier[:-2]\n",
    "            self.pulsei, self.pulseq = pulse_rwa_gauss_const(self.tp, [self.wgrund, self.fcomps, ampl])\n",
    "        else:\n",
    "            self.fcomps = np.reshape(fourier, (self.number_fcp, 2))\n",
    "            self.pulsei = pulse_rwa_gauss(self.tp, [self.wgrund, self.fcomps[:,0]])\n",
    "            self.pulseq = pulse_rwa_gauss(self.tp, [self.wgrund, self.fcomps[:,1]])\n",
    "        solutionpump, solutioninter, solutionprobe, solutiondecay, infodict = solve_mbes_pump_probe(mbes_soc, init, pdf, self.tp, self.ti, self.tpr, self.td, self.ampl_probe, self.pulsei, self.pulseq, self.tp, kappa, deltac, gs, gperp, spins, gpar)\n",
    "\n",
    "        max_p_ampl = max(np.sqrt(self.pulsei**2+self.pulseq**2))\n",
    "        max_t = self.tp[-1]\n",
    "        if self.max_time < max_t:\n",
    "            val_add += max_t*1e6\n",
    "        if max_p_ampl > self.max_t_ampl:\n",
    "            val_add += max_p_ampl*1e6\n",
    "        self.solution = np.vstack((solutionpump, solutioninter, solutionprobe, solutiondecay))\n",
    "        self.maxcav = np.sum((abs(solutionprobe[:,0])**2))+np.sum((abs(solutiondecay[:,0])**2))\n",
    "        self.fitness = self.maxcav\n",
    "        return -self.maxcav+val_add\n",
    "\n",
    "def test_func(mbes_soc, init, pdf, x, x1, i, q, tp, kappa, deltac, gs, gperp, spins, gpar):\n",
    "    return np.ones((len(x), nbins*2+1)), np.ones((len(x1), nbins*2+1)), {}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Generation 7816; Number of Photons: 0.006427; Fitness: 0.006427"
      ],
      "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basinhopping step 0: f -0.0163573\n",
      "basinhopping step 1: f -0.0221223 trial_f -0.0221223 accepted 1  lowest_f -0.0221223\n",
      "found new global minimum on step 1 with function value -0.0221223\n",
      "basinhopping step 2: f -0.0221223 trial_f 5e+07 accepted 0  lowest_f -0.0221223\n",
      "basinhopping step 3: f -0.0221223 trial_f -0.000301119 accepted 0  lowest_f -0.0221223\n",
      "basinhopping step 4: f -0.0221223 trial_f -0.0192044 accepted 0  lowest_f -0.0221223\n",
      "basinhopping step 5: f -0.0221223 trial_f 5e+07 accepted 0  lowest_f -0.0221223\n",
      "basinhopping step 6: f -0.00201846 trial_f -0.00201846 accepted 1  lowest_f -0.0221223\n",
      "basinhopping step 7: f -0.0226297 trial_f -0.0226297 accepted 1  lowest_f -0.0226297\n",
      "found new global minimum on step 7 with function value -0.0226297\n",
      "basinhopping step 8: f -0.0223769 trial_f -0.0223769 accepted 1  lowest_f -0.0226297\n",
      "basinhopping step 9: f -0.0200937 trial_f -0.0200937 accepted 1  lowest_f -0.0226297\n",
      "basinhopping step 10: f -0.0227158 trial_f -0.0227158 accepted 1  lowest_f -0.0227158\n",
      "found new global minimum on step 10 with function value -0.0227158\n",
      "basinhopping step 11: f -0.0227158 trial_f -1.72448e-09 accepted 0  lowest_f -0.0227158\n",
      "basinhopping step 12: f -4.43971e-05 trial_f -4.43971e-05 accepted 1  lowest_f -0.0227158\n",
      "basinhopping step 13: f -0.000282906 trial_f -0.000282906 accepted 1  lowest_f -0.0227158\n",
      "basinhopping step 14: f -0.0121405 trial_f -0.0121405 accepted 1  lowest_f -0.0227158\n",
      "basinhopping step 15: f -0.0160495 trial_f -0.0160495 accepted 1  lowest_f -0.0227158\n",
      "basinhopping step 16: f -0.0173316 trial_f -0.0173316 accepted 1  lowest_f -0.0227158\n",
      "basinhopping step 17: f -0.00524065 trial_f -0.00524065 accepted 1  lowest_f -0.0227158\n",
      "basinhopping step 18: f -0.0149124 trial_f -0.0149124 accepted 1  lowest_f -0.0227158\n",
      "basinhopping step 19: f -0.00789767 trial_f -0.00789767 accepted 1  lowest_f -0.0227158\n",
      "basinhopping step 20: f -0.0235707 trial_f -0.0235707 accepted 1  lowest_f -0.0235707\n",
      "found new global minimum on step 20 with function value -0.0235707\n",
      "basinhopping step 21: f -0.0193882 trial_f -0.0193882 accepted 1  lowest_f -0.0235707\n",
      "basinhopping step 22: f -8.86353e-06 trial_f -8.86353e-06 accepted 1  lowest_f -0.0235707\n",
      "basinhopping step 23: f -8.86353e-06 trial_f 5e+07 accepted 0  lowest_f -0.0235707\n",
      "basinhopping step 24: f -8.86353e-06 trial_f 5e+07 accepted 0  lowest_f -0.0235707\n",
      "basinhopping step 25: f -0.0128395 trial_f -0.0128395 accepted 1  lowest_f -0.0235707\n",
      "basinhopping step 26: f -0.0182354 trial_f -0.0182354 accepted 1  lowest_f -0.0235707\n",
      "basinhopping step 27: f -0.0153227 trial_f -0.0153227 accepted 1  lowest_f -0.0235707\n",
      "basinhopping step 28: f -0.0202155 trial_f -0.0202155 accepted 1  lowest_f -0.0235707\n",
      "basinhopping step 29: f -0.0226072 trial_f -0.0226072 accepted 1  lowest_f -0.0235707\n",
      "basinhopping step 30: f -0.0207705 trial_f -0.0207705 accepted 1  lowest_f -0.0235707\n",
      "basinhopping step 31: f -0.0207705 trial_f -0.00137616 accepted 0  lowest_f -0.0235707\n",
      "basinhopping step 32: f -0.0225194 trial_f -0.0225194 accepted 1  lowest_f -0.0235707\n",
      "basinhopping step 33: f -0.0225194 trial_f -2.32037e-05 accepted 0  lowest_f -0.0235707\n",
      "basinhopping step 34: f -0.0206327 trial_f -0.0206327 accepted 1  lowest_f -0.0235707\n",
      "basinhopping step 35: f -0.0206327 trial_f 5e+07 accepted 0  lowest_f -0.0235707\n",
      "basinhopping step 36: f -0.0206327 trial_f 5e+07 accepted 0  lowest_f -0.0235707\n",
      "Finished 0 step\n",
      "basinhopping step 0: f -0.016405\n",
      "basinhopping step 1: f -0.016405 trial_f -0.0144775 accepted 0  lowest_f -0.016405\n",
      "basinhopping step 2: f -0.0224343 trial_f -0.0224343 accepted 1  lowest_f -0.0224343\n",
      "found new global minimum on step 2 with function value -0.0224343\n",
      "basinhopping step 3: f -0.0151682 trial_f -0.0151682 accepted 1  lowest_f -0.0224343\n",
      "basinhopping step 4: f -0.0107809 trial_f -0.0107809 accepted 1  lowest_f -0.0224343\n",
      "basinhopping step 5: f -0.0156625 trial_f -0.0156625 accepted 1  lowest_f -0.0224343\n",
      "basinhopping step 6: f -0.011926 trial_f -0.011926 accepted 1  lowest_f -0.0224343\n",
      "basinhopping step 7: f -0.00837664 trial_f -0.00837664 accepted 1  lowest_f -0.0224343\n",
      "basinhopping step 8: f -0.0106888 trial_f -0.0106888 accepted 1  lowest_f -0.0224343\n",
      "basinhopping step 9: f -0.129801 trial_f -0.129801 accepted 1  lowest_f -0.129801\n",
      "found new global minimum on step 9 with function value -0.129801\n",
      "basinhopping step 10: f -0.129801 trial_f -0.032558 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 11: f -0.129801 trial_f -0.0186283 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 12: f -0.129801 trial_f -0.0119702 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 13: f -0.129801 trial_f -0.00848043 accepted 0  lowest_f -0.129801\n",
      "warning: basinhopping: local minimization failure\n",
      "basinhopping step 14: f -0.129801 trial_f -0.00760768 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 15: f -0.129801 trial_f -0.0248964 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 16: f -0.129801 trial_f -0.0489708 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 17: f -0.129801 trial_f -0.00815359 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 18: f -0.129801 trial_f -0.0229509 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 19: f -0.129801 trial_f -0.0143834 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 20: f -0.129801 trial_f -0.0158032 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 21: f -0.129801 trial_f -0.00908107 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 22: f -0.129801 trial_f -0.0179278 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 23: f -0.129801 trial_f -0.0124894 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 24: f -0.129801 trial_f -0.0203398 accepted 0  lowest_f -0.129801\n",
      "basinhopping step 25: f -0.129801 trial_f -0.0296793 accepted 0  lowest_f -0.129801\n",
      "Finished 1 step\n",
      "basinhopping step 0: f -0.0265973\n",
      "basinhopping step 1: f -0.0308867 trial_f -0.0308867 accepted 1  lowest_f -0.0308867\n",
      "found new global minimum on step 1 with function value -0.0308867\n",
      "basinhopping step 2: f -0.0308867 trial_f -0.0228653 accepted 0  lowest_f -0.0308867\n",
      "basinhopping step 3: f -0.0308867 trial_f -0.0123346 accepted 0  lowest_f -0.0308867\n",
      "basinhopping step 4: f -0.0308867 trial_f -0.0116759 accepted 0  lowest_f -0.0308867\n",
      "basinhopping step 5: f -0.0174481 trial_f -0.0174481 accepted 1  lowest_f -0.0308867\n",
      "basinhopping step 6: f -0.0151821 trial_f -0.0151821 accepted 1  lowest_f -0.0308867\n",
      "basinhopping step 7: f -0.0102392 trial_f -0.0102392 accepted 1  lowest_f -0.0308867\n",
      "basinhopping step 8: f -0.0102392 trial_f 5e+07 accepted 0  lowest_f -0.0308867\n",
      "basinhopping step 9: f -0.0102392 trial_f 5e+07 accepted 0  lowest_f -0.0308867\n",
      "basinhopping step 10: f -0.0102392 trial_f 5e+07 accepted 0  lowest_f -0.0308867\n",
      "basinhopping step 11: f -0.0164079 trial_f -0.0164079 accepted 1  lowest_f -0.0308867\n",
      "basinhopping step 12: f -0.0210547 trial_f -0.0210547 accepted 1  lowest_f -0.0308867\n",
      "basinhopping step 13: f -0.0207032 trial_f -0.0207032 accepted 1  lowest_f -0.0308867\n",
      "basinhopping step 14: f -0.0202875 trial_f -0.0202875 accepted 1  lowest_f -0.0308867\n",
      "basinhopping step 15: f -0.0202875 trial_f 5e+07 accepted 0  lowest_f -0.0308867\n",
      "basinhopping step 16: f -0.0201018 trial_f -0.0201018 accepted 1  lowest_f -0.0308867\n",
      "basinhopping step 17: f -0.0201018 trial_f -0.0130585 accepted 0  lowest_f -0.0308867\n",
      "Finished 2 step\n",
      "basinhopping step 0: f -0.020239\n",
      "basinhopping step 1: f -0.020239 trial_f -0.000682572 accepted 0  lowest_f -0.020239\n",
      "basinhopping step 2: f -0.0212548 trial_f -0.0212548 accepted 1  lowest_f -0.0212548\n",
      "found new global minimum on step 2 with function value -0.0212548\n",
      "basinhopping step 3: f -0.0192698 trial_f -0.0192698 accepted 1  lowest_f -0.0212548\n",
      "basinhopping step 4: f -0.0192698 trial_f -0.0125474 accepted 0  lowest_f -0.0212548\n",
      "basinhopping step 5: f -0.0176443 trial_f -0.0176443 accepted 1  lowest_f -0.0212548\n",
      "basinhopping step 6: f -0.0210086 trial_f -0.0210086 accepted 1  lowest_f -0.0212548\n",
      "basinhopping step 7: f -0.0210086 trial_f 5e+07 accepted 0  lowest_f -0.0212548\n",
      "basinhopping step 8: f -0.0159352 trial_f -0.0159352 accepted 1  lowest_f -0.0212548\n",
      "basinhopping step 9: f -0.0215297 trial_f -0.0215297 accepted 1  lowest_f -0.0215297\n",
      "found new global minimum on step 9 with function value -0.0215297\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basinhopping step 10: f -0.0272175 trial_f -0.0272175 accepted 1  lowest_f -0.0272175\n",
      "found new global minimum on step 10 with function value -0.0272175\n",
      "basinhopping step 11: f -0.0272175 trial_f -0.0128109 accepted 0  lowest_f -0.0272175\n",
      "basinhopping step 12: f -0.0272175 trial_f -0.0178643 accepted 0  lowest_f -0.0272175\n",
      "basinhopping step 13: f -0.0272175 trial_f -0.0178329 accepted 0  lowest_f -0.0272175\n",
      "basinhopping step 14: f -0.0272175 trial_f -0.0150287 accepted 0  lowest_f -0.0272175\n",
      "basinhopping step 15: f -0.0232413 trial_f -0.0232413 accepted 1  lowest_f -0.0272175\n",
      "basinhopping step 16: f -0.0362426 trial_f -0.0362426 accepted 1  lowest_f -0.0362426\n",
      "found new global minimum on step 16 with function value -0.0362426\n",
      "basinhopping step 17: f -5.88826e-09 trial_f -5.88826e-09 accepted 1  lowest_f -0.0362426\n",
      "basinhopping step 18: f -0.0205478 trial_f -0.0205478 accepted 1  lowest_f -0.0362426\n",
      "basinhopping step 19: f -0.0231009 trial_f -0.0231009 accepted 1  lowest_f -0.0362426\n",
      "basinhopping step 20: f -0.0151344 trial_f -0.0151344 accepted 1  lowest_f -0.0362426\n",
      "basinhopping step 21: f -0.158647 trial_f -0.158647 accepted 1  lowest_f -0.158647\n",
      "found new global minimum on step 21 with function value -0.158647\n",
      "basinhopping step 22: f -0.158647 trial_f -0.0138579 accepted 0  lowest_f -0.158647\n",
      "basinhopping step 23: f -0.158647 trial_f -0.0326261 accepted 0  lowest_f -0.158647\n",
      "basinhopping step 24: f -0.158647 trial_f -0.00887964 accepted 0  lowest_f -0.158647\n",
      "basinhopping step 25: f -0.158647 trial_f -0.0128867 accepted 0  lowest_f -0.158647\n",
      "basinhopping step 26: f -0.158647 trial_f -0.0207784 accepted 0  lowest_f -0.158647\n"
     ]
    },
    {
     "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-15-b992a0151444>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0minit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnfcp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\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----> 5\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmain\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[1;32m      6\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Finished %s step\"\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0mnfcp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0minit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\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[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\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[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\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\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-14-5e190fed78c4>\u001b[0m in \u001b[0;36mmain\u001b[0;34m(self, initial)\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0minit\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[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale_factor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m             \u001b[0minit\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[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale_factor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbasinhopping\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[0minit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mniter_success\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.02\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstepsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminimizer_kwargs\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'callback'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_stuff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'method'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Nelder-Mead'\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     51\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/scipy/optimize/_basinhopping.py\u001b[0m in \u001b[0;36mbasinhopping\u001b[0;34m(func, x0, niter, T, stepsize, minimizer_kwargs, take_step, accept_test, callback, interval, disp, niter_success, seed)\u001b[0m\n\u001b[1;32m    684\u001b[0m                \" successfully\"]\n\u001b[1;32m    685\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mniter\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--> 686\u001b[0;31m         \u001b[0mnew_global_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mone_cycle\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    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\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/usr/local/lib/python3.8/dist-packages/scipy/optimize/_basinhopping.py\u001b[0m in \u001b[0;36mone_cycle\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0mnew_global_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m         \u001b[0maccept\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_monte_carlo_step\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    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maccept\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/python3.8/dist-packages/scipy/optimize/_basinhopping.py\u001b[0m in \u001b[0;36m_monte_carlo_step\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m         \u001b[0;31m# do a local minimization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m         \u001b[0mminres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminimizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_after_step\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    105\u001b[0m         \u001b[0mx_after_quench\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mminres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m         \u001b[0menergy_after_quench\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mminres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/scipy/optimize/_basinhopping.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x0)\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminimizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    283\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminimizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\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    285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/scipy/optimize/_minimize.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m    604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'nelder-mead'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 606\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_minimize_neldermead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\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    607\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'powell'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    608\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_minimize_powell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbounds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\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/python3.8/dist-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36m_minimize_neldermead\u001b[0;34m(func, x0, args, callback, maxiter, maxfev, disp, return_all, initial_simplex, xatol, fatol, adaptive, **unknown_options)\u001b[0m\n\u001b[1;32m    734\u001b[0m                     \u001b[0;31m# Perform an inside contraction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    735\u001b[0m                     \u001b[0mxcc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mpsi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mxbar\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mpsi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0msim\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[0m\n\u001b[0;32m--> 736\u001b[0;31m                     \u001b[0mfxcc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxcc\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    737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    738\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mfxcc\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mfsim\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\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[0;34m(*wrapper_args)\u001b[0m\n\u001b[1;32m    425\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwrapper_args\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    426\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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[0;32m--> 427\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper_args\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    428\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mncalls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-14-5e190fed78c4>\u001b[0m in \u001b[0;36mcalculate_fitness\u001b[0;34m(self, fourier)\u001b[0m\n\u001b[1;32m    153\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpulsei\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpulse_rwa_gauss\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[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[0mfcomps\u001b[0m\u001b[0;34m[\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpulseq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpulse_rwa_gauss\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[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[0mfcomps\u001b[0m\u001b[0;34m[\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\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m         \u001b[0msolutionpump\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolutioninter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolutionprobe\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_pump_probe\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[0mti\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\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[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mampl_probe\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[0mself\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    156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    157\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~/Dropbox/iPython-Notebooks/_do_calculation.ipynb\u001b[0m in \u001b[0;36msolve_mbes_pump_probe\u001b[0;34m(mbes_func, init, pdf, tlistpump, tlistinter, tlistprobe, tlistdecay, ampl_probe, *args)\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;34m\"        sold = 0\\n\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0;34m\"        sold_ret = np.empty((len(tlistdecay), len(pdf)*2+1), dtype=complex)\\n\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m     \u001b[0;34m\"        for k in range(reshape_idx):\\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     94\u001b[0m     \u001b[0;34m\"            sold += soldecay[:, (1+2*k*len(pdf)):(1+2*(k+1)*len(pdf))]\\n\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;34m\"        solp_ret[:,2::2] = solp[:,1::2]*pdf/reshape_idx\\n\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Dropbox/iPython-Notebooks/_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/python3.8/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    239\u001b[0m     \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0my0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m     output = _odepack.odeint(func, y0, t, args, Dfun, col_deriv, ml, mu,\n\u001b[0m\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~/Dropbox/iPython-Notebooks/_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~/Dropbox/iPython-Notebooks/_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     21\u001b[0m    ]\n\u001b[1;32m     22\u001b[0m   },\n\u001b[0;32m---> 23\u001b[0;31m   {\n\u001b[0m\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[1;32m     25\u001b[0m    \u001b[0;34m\"execution_count\"\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[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "gg = BasinHopping()\n",
    "gg.number_fcp = 1\n",
    "init = None\n",
    "for nfcp in range(6):\n",
    "    res = gg.main(init)\n",
    "    print(\"Finished %s step\"%nfcp)\n",
    "    init = [*res.x[:-2],0, res.x[-2], res.x[-1]]\n",
    "    gg.number_fcp += 1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%qtconsole"
   ]
  },
  {
   "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
}