BasinHopping2.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\n",
    "#matplotlib.pyplot.switch_backend('nbAgg')\n",
    "#%qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.892875266154258\n"
     ]
    }
   ],
   "source": [
    "gcoll = 58.49348910584943\n",
    "q = 1.39\n",
    "quali =1000\n",
    "fr = 3149;\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 = 125.66370614359172\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 0x7f0c25311b20>]"
      ]
     },
     "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 = 2000e-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(-30, 0,0.5)\n",
    "aref = 500\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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pumptime2 = 50e-3\n",
    "\n",
    "class BasinHopping:\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.tp = np.arange(0,pumptime2+dt,dt)\n",
    "        self.td = np.arange(0, decaytime+dt, dt)\n",
    "        self.number_fcp = number_fcp\n",
    "        self.min_pulse_length = 0.02\n",
    "        self.max_ampl = aref*0.8\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 = 2 #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.inversin_spectrum = None\n",
    "        self.fitness = 0\n",
    "        self.constant_on = True\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_%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] = 0.05*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+1))\n",
    "                self.scale_factor = 2*np.pi*self.max_ampl\n",
    "                init[-1] = pumptime2*self.scale_factor\n",
    "        else:\n",
    "            if self.constant_on:\n",
    "                init = initial\n",
    "                init[-1] *= self.scale_factor\n",
    "                init[-2] *= self.scale_factor\n",
    "            else:\n",
    "                init = initial\n",
    "                self.scale_factor = 2*np.pi*self.max_ampl\n",
    "                init[-1] *= 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'}, accept_test=self.accept_test)\n",
    "        return self.res\n",
    "    \n",
    "   \n",
    "    def plot_stuff(self, fcomps):\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",
    "            ax1.clear()\n",
    "            ax2.clear()\n",
    "            ax3.clear()\n",
    "            ax0.clear()\n",
    "            gc.collect()\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",
    "        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",
    "        \n",
    "        ax0.plot(spins, np.real(self.inversion_spectrum))\n",
    "        ax0.set_ylabel(\"Inversion\")\n",
    "        ax0.set_xlabel(\"Frequency (MHz)\")\n",
    "        \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; Inversion: %f; Fitness: %f\" %(self.counter, max(np.real(np.sum(self.solution[:, 2::2], axis=1))[len(self.tp)+self.idx:]), 1-self.fitness)))\n",
    "        if self.fitness < self.best_fitness:\n",
    "            savedict = {'wgrund': self.wgrund, 'time': self.tp, 'timedecay': self.td, '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 < 1.1:\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_add2 = 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_add2 += 100\n",
    "            if pt > self.max_time:\n",
    "                pt = self.max_time\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",
    "            pt = round(fourier[-1]/self.scale_factor/dt)*dt\n",
    "            if pt <= self.min_pulse_length:\n",
    "                pt = self.min_pulse_length\n",
    "                val_add2 += 100\n",
    "            self.tp = np.arange(0,pt+dt,dt)\n",
    "            self.wgrund = np.pi/pt\n",
    "            self.fcomps = np.reshape(fourier[:-1], (self.number_fcp, 2))\n",
    "            self.pulsei = pulse_rwa(self.tp, [self.wgrund, self.fcomps[:,0]])\n",
    "            self.pulseq = pulse_rwa(self.tp, [self.wgrund, self.fcomps[:,1]])\n",
    "        solutionpump, solutiondecay, infodict = solve_mbes(mbes_soc, init, pdf, self.tp, self.td, 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",
    "        val_add = 0\n",
    "        if self.max_time < max_t:\n",
    "            val_add += max_t\n",
    "        if max_p_ampl > self.max_t_ampl:\n",
    "            val_add += max_p_ampl\n",
    "        self.solution = np.vstack((solutionpump, solutiondecay))\n",
    "        self.idx = np.abs(self.td - self.target_time_decay).argmin()\n",
    "        inversion_pure = solutiondecay[self.idx:, 2::2]\n",
    "        middle_idx = int(nbins/2)\n",
    "        idx2 = 10\n",
    "        inversion = np.real(np.sum(inversion_pure[:, middle_idx-idx2:middle_idx+idx2]/pdf[middle_idx-idx2:middle_idx+idx2], axis=1))/idx2/2\n",
    "        #inversion = np.real(inversion_pure[:, middle_idx])/pdf[middle_idx]\n",
    "        maxinv = max(inversion)\n",
    "        idxinv = int(np.where(inversion==maxinv)[0])\n",
    "        self.inversion_spectrum = inversion_pure[idxinv, :]/pdf\n",
    "        maxcav = max(abs(solutiondecay[:,0])**2)\n",
    "        cavity_photons = (abs(solutiondecay[idxinv+self.idx,0])**2)/maxcav\n",
    "        self.fitness = (1-max(inversion)) + val_add# + cavity_photons\n",
    "        return self.fitness\n",
    "    \n",
    "    \n",
    "    def accept_test(self, f_new, x_new, f_old, x_old):\n",
    "        if round(x_new[-1]/self.scale_factor/dt)*dt > self.max_time:\n",
    "            return False\n",
    "        else:\n",
    "            return True\n",
    "\n",
    "def test_func(mbes_soc, init, pdf, x, x1, i, q, tp, kappa, dletac, gs, gperp, spins, gpar):\n",
    "    return np.ones((len(x), nbins*2+1)), np.ones((len(x1), nbins*2+1)), {}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Generation 1798; Inversion: -0.568881; Fitness: -0.172513"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAAJMCAYAAACigmhWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAADHHUlEQVR4nOzdd5xU1fnH8c+zvTdYegcBRQRhRQVRxN5ii7HGYFQSY2JMLImaojH+oqZYoxFL7FFjLBF7iQioKCAIKkgTpLddtrfZ8/vj3llmlwV2YXZnZ+f7fr3ua+499869z4yLM888555jzjlEREREREQk9sRFOgARERERERGJDCWEIiIiIiIiMUoJoYiIiIiISIxSQigiIiIiIhKjlBCKiIiIiIjEKCWEIiIiIiIiMSoh0gG0ts6dO7t+/fpFOgwREWllc+bM2eycy490HNFCn48iIrFjV5+RHT4h7NevH7Nnz450GCIi0srMbGWkY4gm+nwUEYkdu/qMVJdRERERERGRGKWEUEREREREJEYpIRQREYkSZjbJzD40s5lmNqrRvmvNbJa/7x4zs0jFKSIi0UMJoYiISBQws1zgCmACcAFwd6NDXnTOHeycGwd0BSa2bYQiIhKNlBCKiIhEhzHAdOdctXNuBZBpZsnBnc65JSHHVgG1bR2giIhEHyWEIiIi0aETUBiyXQTkNT7IzI4AugMftE1YIiISzZQQioiIRIetQE7IdrbfVs/MDgBuBc5xzrnGJzCzyWY228xmb9q0qTVjFRGRKKGEUEREJDrMAg4zs0Qz6wOUOueqgjvNbBDwCF4yuLmpEzjnpjjnCpxzBfn5Tc5PLCIiMUYJoYiISBRwzhUC9wHTgH8BV5rZSDO7xj/kTrwK4mNm9r6ZnRSRQEWiRHl1LSWVNSxaX0x1bV2kwxGJmIRIByAiIiLN45x7BK8KGGqev+/kNg9IJIod+qf32FZRA8AJ+3fj/gtGRzgikchQhVBEREREYk4wGQR4feH6CEYiEllKCEVERERERGKUEkIREREREZEYpYRQREREREQkRikhFBERERERiVFKCEVERERERGKUEkIREREREZEYpYRQREREREQkRikhFBERERERiVFKCEVERERERGKUEkIREREREZEYpYRQREREREQkRikhFBERERERiVFKCEVERERERGKUEkIREREREZEYpYRQREREREQkRikhFBEREZGY4pyLdAgi7YYSQhERERGJKXXKB0XqtZuE0MwGm1mNmR1mZilm9pSZTfcfU/xj+pnZe2Y208yuj3TMIiIiIhJ96lQhFKnXbhJC4LfANH99ErDIOTceWOxvA9wK/N45Nw6YaGZD2zpIEREREYluSghFtmsXCaGZHQysB1b7TUcAU/31V/xtgJHOuen++qsh7SIiIiIizaJ8UGS7dpEQAjfgVf+COgGF/noRkOevh8Yb2i4iIiIi0iyqEIpsF/GE0MxOAmY757aENG8Fcvz1bH8boC7kmND2xuecbGazzWz2pk2bwhyxiIiIiEQzDSojsl3EE0JgJDDBzN4AjgH+AnwFnOjvP5Ht9xbON7Ox/voJwAdNndA5N8U5V+CcK8jPz2+1wEVEREQkumwqqeLDpZsjHYZIu5EQ6QCcc7cAtwCY2aPAQ8Ac4BEzm453X+FF/uHXAQ+bWRLwunPuq7aPWERERESi1Sn3zGB9cWWkwxBpNyKeEIZyzk0K2Ty3if3LgSPbLCARERER6VCUDIo01B66jIqIiIiIiEgEKCEUERERERGJUUoIRURERCTm1WnoUYlRSghFREREJOYFNDehxCglhCIiIlHCzCaZ2YdmNtPMRjXal2JmT5nZdP8xJVJxikSjgCqEEqOUEIqIiEQBM8sFrgAmABcAdzc6ZBKwyDk3Hljsb4tIMykhlFilhFBERCQ6jAGmO+eqnXMrgEwzSw7ZfwQw1V9/xd8WkWZSl1GJVUoIRUREokMnoDBkuwjI28n+xvtEZDcCASWEEpuUEIqIiESHrUBOyHa239bU/sb7ADCzyWY228xmb9q0qZXCFIlOqhBKrFJCKCIiEh1mAYeZWaKZ9QFKnXNVIfunASf66yf62w0456Y45wqccwX5+fmtH7FIFNE9hBKrlBCKiIhEAedcIXAfXqL3L+BKMxtpZtf4hzwKDDez6cBwf1tEmkkJocSqhEgHICIiIs3jnHsEeKRR8zx/XwVwblvHJNJRKCGUWKUKoYiIiIjEPCWEEquUEIqIiIhIzNOgMhKrlBCKiIiISMxThVBilRJCEREREYl5SgglVikhFBEREZGYp4RQYpUSQhERERGJeW99uSHSIYhEhBJCEREREYl5d7+7hG+3lkc6DJE2p4RQRERERARYW1QR6RBE2pwSQhERERERoLK2LtIhiLQ5JYQiIiIiIkCgTgmhxB4lhCIiIiISE9xuJp+vDWikUYk9SghFREREJCbsbmqJWk09ITFICaGIiIiIxITA7iqESgglBikhFBEREZGYsLtbBGsDuodQYo8SQhERERGJCaoQiuxICaGIiIiIxITd3kOoQWUkBiVEOoCOorKykuTkZMyM1157jaeffprKykoqKyupqqoC4LHHHqNHjx4888wzPPjggzuc4/nnnyc3N5eHH36YJ598cof9b7zxBsnJydx77708//zzDfbFxcXx3nvvAXD77bfz+uuvN9ifnp7O1KlTqaqt4le/+RUfTPuA2rra+iU5O5kTrzuR0upSZjwyg41fb8Q5Rx11OOdI6ZTCfpP3wznHoicXUfptacPzd09n30n74nB8+fCXVGxsOLFrZt9MBp83GIAv7v+C6m3VmBmGYWZ0GdqFggsKSIxL5J1b36G2vJYESyA+Lp54i2fE2BF8//Lv0zW9K9dOuhYCEGfbf8/4zne+w89//nMCgQDHHXfcDu/d2WefzaWXXkppaSmnnXbaDvsnTZrEBRdcwMaNGznvvPN22H/ZZZdx5plnsnLlSi655JId9v/yl7/khBNO4KuvvuKKK67YYf9vfvMbjjjiCD777DOuvfbaHfbfcsstjBkzhpkzZ3LTTTftsP+vf/0rw4cP55133uH222/fYf/999/PwIED+e9//8u99967w/5HH32UHj168Nxzz/Hwww/vsP/ZZ58lJyeHxx57jKeffnqH/f/9739JTk7m/vvv56WXXmqwLy4urv7v7Y477uDNN9/EOa9bjnOQkpLOQw/9B+fgzjtvYdas6fXPdQ5yczvzt789iXPwt7/9loULZ2NG/dK7dx/uuusB4uPhN7+5mkWLvqjfBzBkyBDuvPNOAC6//HKWL1/eIL4RI0Zw6623AvDDH/6Q9evXN9h/yCGH8Lvf/Q6Ac889l23btjXYP3HiRK6++moATjvtNKqrqxvsP+mkk7j88ssJBAJ85zvf2eG9++53v8tFF11EaWkpZ5999g77L7jgAs4991w2bdrEpEmTdtg/efJkTj31VFatWsVPfvKTHfZfccUVHHvssSxevJirrrpqh/2/+tWvGD9+PPPmzeM3v/nNDvtvvPFGCgoK+Pjjj7nlllt22H/rrbcybNgw3nvvPe64444d9t91110MGDCAV199lQceeIC0tDSys7Pp06cPQ4cO5YQTTiAtLW2H54mIRMLuEkJNOyGxSAnhXqqtreX666/ngQce4O2332bMmDGUlpby0UcfkZKSQmpqKklJSZhZ/VDHgUBghy+VoZxz1DXxP6RdDZUcus85R0VNBaVVpZTXlFNeU05lXCVd/tyFTeWb4GNgXaMTVMDXs74mIymDqq1V1JbXegmbGXHEUVdTR1FlEfEWT3VNNbW1tQ2eXl1bTWl1af17EqgNNNhfU1tDeU05zjmqaquorq7G4XDO4XBUFVWxbfk2autq2Vy4mUB5gDq3/T1YvGAxz/3rOW9jGRCAhLgEEuMTSY5PpvLzSuo+qqN/dn+KSotISUhpcP1gvM45Kisrd3j/drc/EPBeT11dHeXl5Tt9/u72BwKBJvcHzx8IBCgtLd3p/tra2j3aH/x7qqqqori4eKf7y8ur2Ly5mJoa6pfaWrj5Zigrgw8/rGLp0mJqayEQ8JK+ujqjZ0+oqoKSknJqaopo+KdaS/fuwfVyoKjR1ZN4+eXgegmwtdH+LB5/PLhe3GC/Gbz7bjHPPAMpKbB16zYCga2YQVyctyxaVMI333j7p00rpKJiM/Hx3r74eO+aL74IGRmwfPlWyssL6/fFxUFxcUn99bZs2VL/A09Q6Pu9cePGHd7b4H7nXJP7y8rKAO+/wa7219bW7pDMAlRUeD++1NTUNLk/+PdcXV3d5P7g66moqGDt2rX17cGkfvnyaqqrYdasCr78ci2BAA2Wa66pwTn4+utyli//ltracgKBQurqNgHwy18u59JL+zN06A6XFhFpc7tLCGtUIZQYZLubjyXaFRQUuNmzZ7fKucvLyzn33HP573//yznnnMNvf/tb9ttvv1a51u6s2raKV79+lRnfzmDmqpms3Layfl/PzJ7sm78vA3MH0jOzJz2zetIjswedUjuRnZJNdnI22SnZOyRRkRaoC1BaXUppdSkl1SUUVRaxsWwj60vXs6F0A+tL17O+bD2rtq1iyZYlbKvaXtnJSs7igK4HMLLrSMb2HsthfQ6jd3bvCL6a9iEQgG++gWXLYPnyhsu338LmzTt/bmYmZGVBdvb2JSMDkpO9ZCs5ueESbEtK2p6chVb+mtoGLwkJBKhPOnf1WF0NlZU7X6qqGm5XVEB5OZSWQnP/1xcX573Oxkt6essf09O99yUpqeESrHbuKee896KqavtrDj6WlkJxsbeUlOy4XlIC27ZBUZG3BNeb+F1hB5mZ3t9BTs72x5wcSEuDrVvL+eKLr1i0aDQJCY6zzrqLe++9kLy8vL17sbtgZnOccwWtdoEOpjU/H0Xaqw3FlRz8f+/udP8NJ+7LpYcPaMOIRNrGrj4jVSHcC5dccgmvvPIK9957L5dffnmbX3/R5kU8/+XzvLjoReaumwtA94zujOszjisPuZKDex7Mfvn7kZ2S3eaxhUN8XLyXsDYjfuccWyu2smTrEhZsWMC89fOYv2E+/5z3T+791OtC2Te7L0f0O4ITBp3AsQOPJS+19b6YtgelpTB/fsNlwQIvIQpKSoL+/WHAABgzBrp1a7h07Qq5ud4Xf6+a1jE45yWHpaXbl5KShts7W0pKvGrppk1ecl1a6m2XlnpJ2Z5ITNyeHCYnb0+Og7E2tR4IbE8AGxUtmyUlxfvvmpm5PaEbPLhhchea5DVez8qChF1+gqQBo1m3Dk4//Uv+9a9f8fHHL7F8+fstD1ZEJEx2WyFUl1GJQUoI99CKFSt45plnuOaaa9o0GawJ1PDiohe579P7mLZyGgCH9jqU246+jVOHnMrgToOxvS03RCEzo1NaJzqldeKQXofUt9fW1fL5hs+ZsWoG01dN59WvX+Xx+Y8TZ3GM7T2WU4ecytnDzu4Q1cNNm2DaNJgxw1vmzfOSBvC+xI8YAZdcAgccAPvs4yWBPXo0TD5ihZlXxUpLgy5dwnfemhovOQwmiMHHxkljsJoXXA9tq6rasXoZ+k86uB4Xt2N1tqn1YNKXldXwMTExfK97V7p3h+nTh1FQcDuff34lL788k1NPHdc2FxcRaaSsqnaX+wPqMioxSF1G95BzjnfeeYfhw4fTrVu3sJ+/sbLqMu6edTf3fHIP60rX0T+nP5cVXMYFB1xA98zuuz+BAF431E/XfsprS15j6tdT+Wz9ZwAc1ucwzhl2DucOPzdqKoeBAHz0EbzxhrfMneslEqmpcMghMG6cV/UbMQJ69977bokie2PBgjIOOKAPgwaNZ8mSl1rlGuoy2jLqMiqx5t2vNnDxY7v+m7/iqH345TGD2ygikbajLqOtwMw45phjWv061YFqHpr7EH+Y9gc2lG3guIHH8eApD3L8oOOJj+tAffjaSHxcPIf0OoRDeh3CH478A8u2LuOZhc/wr4X/4qev/5Sr3rqKs4adxY9H/5ixvce2u2prbS28/z48/zy8+CJs3Oh15TzkEPjDH+CYY2DUqLar/og01/Dh6QwbdjlffPFHZs1axMEHa5QZEWlb878t2u0xGmVUYpESwj1w9913s2rVKm6//XbiWrG/3dSvp/LzN37O8sLljO8znhfOfoGxvce22vVi0cC8gdxw+A3ccPgNzF8/nylzpvDE50/w5OdPsn+X/fnFIb/g/OHnk5yQHNE4FyyAf/4TnnzS6xqang4nnwxnnAHHHut1CRVp7+6556dMnPgpTz5ZxcEHRzoaEYk5zfiRV/MQSiyKwbuH9t7UqVN55513+POf4+jXz7sPacwY+O9/mz9y4a5sKd/CBS9cwCn/OoXUhFReO+81pk2apmSwlY3oNoK/n/R31l61lgdPeZA4i+Pi/15M/7v6c/vM29lWuW33Jwmj8nJ48EE46CDvvr9774XDD4cXXvCSwmeege99T8mgRI8jj+zCoYe+zpw5IyIdiojEoF2lgyN6eQPY1e5m0BmRjijiCaGZHWhmM83sAzN7z8wGmFmKmT1lZtP9xxT/2H7+MTPN7PpIxFtXV8esWbPo2fNQfv1r6NcPfvQj2LoVTj0VjjvO68a3p1746gWG3TeMZ794lt8f8Xvm/mguJ+xzQrvrutiRZSRlcMmoS5j3o3m8ecGbDOsyjF+98yv63tmXm6fdTHHVjvP4hdOaNXD99d59f5Mne4OM3HknrF3rdRU9/XTvPkGRaDRuHMyevZpt23ac71NEpDXt6qvUyz89jOzURGoD6jIqsSfiCSHeFOnHO+cOB/4C3ARMAhY558YDi/1tgFuB3zvnxgETzazNb0JZtGgRxcXFTJ9+CMOHe4N53HEHLFoEf/87TJ8OI0fCxx+37Lw1gRqueP0KznzuTHpk9mD2pbO5ccKNJMUntcrrkN0zM44deCxvf/9t5kyew4R+E/jd+7+j/139uW3GbZRVl4X1ekuWwEUXeT8y3HYbTJjgjRo6fz78/OfQuXNYLycSERkZ/6OmpjePPTYj0qGISIyxXdYIITHeVCGUmBTxhNA5t945V+JvVgG1wBHAVL/tFX8bYKRzbrq//mpIe5v52M/0KisP4V//8oZ2B28+rp/8BGbN8rqQTpwIr7/evHNuKN3AUY8fxT2f3MNVh17FrEtmMaKbulS1J6O6j+Klc17i00s/5eCeB/Prd3/N4HsH8/j8x6lze/dr4qJF8P3vw9Ch8OyzcPnlsHQp/Oc/XhdRFYelIzn77FGA8cYbLfzVTERkLzl2nezFx5nuIZSYFPGEMMjM0oE/An8GOgGF/q4iIDgPQGi8oe1tJhAIkJw8kokT92HYsB33H3AAzJzpfbn/zne8QUB25ZM1nzB6ymhmr53NU2c8xV+O/QuJ8Roisr0q6FHAa+e/xoyLZtAzsyc/eOkHHPzQwcxcNbPF51qzBi6+GIYN8+4L/OUvYcUKr3to//7hj12kPRg6NJvExH357LOPIh2KiMSY3SV7CXFxqhBKTGoXCaGZJQLPArc5574EtgI5/u5sfxsgtBQT2t74fJPNbLaZzd60aVNYYz3xxEupqvqMo4/e+VvXtas3NcDhh3uVn7/9renjXlvyGkc8egSJ8Yl8ePGHnDf8vLDGKq1nXJ9xfHzJxzxx+hOsK1nHYf88jHOeP4eVRSt3+9ziYvjNb7zJ4Z94wusOumIF/PnP3t+OSEfXq9ehbNz4MR19HlwRaV9qdnN/YEK8UatpJyQGRTwhNLM44EngJefcS37zNOBEf/1EfxtgvpkFh9o8AfigqXM656Y45wqccwX5+flhi9U5x3vveetHH73rY7Oy4LXX4Lvfhauugj/9qeH+5754jlOfOZV9O+/LrEtmMbLbyLDFKW0jzuK44IALWPzTxfz+iN/z38X/Zejfh3LLB7dQHaje4Xjn4PHHvUTwllvgtNNg8WLvB4MuXdo+fpFIGTPmEOrqtjJjxtJIhxJVzCzPzKb6A67dY41GGzOzXmY2zd8/08yanIBYJFZV7y4hjNM9hBKbIp4QAmcAJwEXmNn7ZnYP8Cgw3MymA8P9bYDrgFvMbCYw3Tn3VVsG+t577/HjH/ciJ2c+Bxyw++OTk72pAS64wBs18rbbvPaH5z7Muf85l0N6HcL/fvA/uqQrG4hm6Unp3DjhRhb/dDEnDz6Z3/zvNxz4wIHMWLV90IwFC7yK8Q9+4HUH/eQTePppdQ2V2HT++ccBT7J8efh+sIsR1wLP+gOupQPHNdpfApzl778UuKON4xNp13ZXIUyMj6OmVhVCiT0Rn5jeOfc88HwTu85t4tjlwJGtHtROzJ37GeXlazjmmN40dz76+Hh49FEIBODXv4aP6+7lpeqfcdzA43jh7BdIS0xr1Zil7fTO7s2/z/o3U7+eyuWvXc74f47nhwdMJu3DW7n/jlxycuChh7yRRJv79yPSER13XG+Sks7nqzb9Sa9DOALwf1qsH3DtjeBO51zoZKnBQdpExFdTu+vqX2K87iGU2BTxhDCafPrpEqAzJ53UsrFs4uO9roLLM57kpeqfsX/Cqbx8zrMkJyS3TqASUScPPpkJ/SZw8RO/55F5dwIvM/Hyu3jud9+jUycNGSqSlAQ9esxj5swtwFGRDiea5OENqAa7GFjNzOKBu4Fb2iQqkSix+wqh7fYYkY6oRXUKM4vzJ5I/ycwmmllM9XX8/POlwCAmTmz5c99a8Rpzel9EfumRLLzxGZ58TMlgR1VSAtf8PIPnLvkrvV7/lMHdevFe3jlMfve7bCzbGOnwRNqFyso/8OmnP410GO2OmcWb2cdNLL/FG3072z90pwOrAQ8Arzvn3mni/K026JpIe7e7ewgT4+OoVpdRiUHNSgjNbKCZTQGW4k0Ofy7wE+Ad/4PqIn9wmA5t7dqlJCQMYsCAlj1v5qqZfPe573JA1wP48ncvcfzRKUye7M0zJx3LRx/ByJEwZYo3jcTi90fxxS8+5k9H/YmpX09lv7/vx3NfPBfpMEUirlevfaiqWk5tbSDSobQrzrmAc+6QJpab2fmAa/XM7C/AOufcvTs5f6sMuiYSDXY37URSQpwqhBKTmpvE/RFvJNCBzrnjnHMXOOe+65w7APgO3i+V32+tINsD5xy5uafSq9fxLZoo/MtNX3Lyv06md3ZvXj//dTpnZvH883DIIXDeefD2260Xs7Sd2lq48UYYPx7q6mDaNPjrXyEtDRLiEvj1Yb9m7uS59M/tz9nPn81Z/z5L1UKJaYMHDwKqmT3720iHEk1uB873B1yrAd4CMLM7zSzfH1X058B4f5C2f0cwVpF2p6lk79ABnbj+xKGAN8pojSamlxjUrHsInXM7DPASsm8jcGe4AmqvzAyzuxk7dvfHBhVWFHLqM6eSHJ/Mmxe8WT+aaHo6TJ0KEybA6afDO+94CaJEp2XLvJFkP/7Ym3fynnsgO3vH44Z1GcZHF3/En2f+mRun3cj737zPfSfex1nDzmr7oEUibOTIQTz9NMyYsYRDDukX6XCignNuC96o3I3br/RXNwGJbRmTSLSY920R81cX7dD+8KQC0pK8r8OJ8aoQSmza626eZtYtHIG0d1u2lLNyZQ1DhjTv+EBdgPNeOI+VRSv5z/f+Q7+cfg325+bCm29Ct25w4omwcGH4Y5bW99hjXhfRRYu8KUYef7zpZDAoIS6B68Zfx5zJc+ib3ZfvPf89vvfv77GpTPfySGw57LB9AJg3T3MRikjrO+3vM9lcuuMcwcb2bl+JCXG7vc9QpCMKx31/D4fhHO3e//3f/UAqvXsXN+v43/7vt7yx9A3uOeEexvUZ1+Qx3bp5XUZTUuDYY2H58jAGLK2qogIuvhgmTYKCAvj8czj77OY/f/8u+/PxJR9zy8RbeGnRSwy7bxgvfvViq8Ur0t4UFPQgIWEanTqdE+lQRCSGhd4GlBQft9v7DEU6or1OCJ1zO3Rf6Yg+/3wJkMOBB2bt9tjnvniOP834E5NHTeZHBT/a5bH9+3tJYVUVHHMMrFsXpoCl1axYAYcdBo88Ar/5jdflt3fvlp8nIS6B68dfz5zJc+iV1YsznjuDC164gK0VOxs4UKTjSEyMY9Cgw1m9OjfSoYhIDIsLyQg17YTEqpZOO9GnqaW1gmtPli/3ppwYPHjXxy3avIiLXr6Isb3HcvcJdzfr3MOGwWuvwYYNcNxxUFi49/FK63j9dRg92rtv8JVX4OabvXkm98bwrsOZdcksbppwE89+8SzD7hvG1K+nhidgkXasU6eZfPzx/ZEOQ0RiWFxIhVD3EEqsammF8FVgqv/4LrAceD3cQbVHGzYsJS1tEGlpOz+mqraK8/5zHqkJqfz7rH+3aOL5gw+Gl16CxYu9ewpLS/c+ZgmfujpvFNGTToI+fWDOHDj55PCdPzE+kd8d8Ts+ueQT8tPyOeVfp3DRyxdRVFkUvouItDOVlS+zdu2V1NRo6gkRiYyGFcI4KqoDBOrUbVRiS4sSQufccOfcAf7jPsAY4KPWCa39qKyspKxsFd267bPL42547wY+W/8Zj5z6CD0ye7T4OkcfDf/6F3zyCZxxhteNVCJv61Yv+bvpJrjwQvjwQxg4sHWudWD3A5k9eTY3jL+BJ+Y/wfD7h/Pm0jdb52IiETZkiDf1xGefrYl0KCLSge0qwWtwD2FCHGXVAU79+4w2iEqk/direwidc3OBg8MUS7tVU1NLUtKfGDXquJ0eM3PVTP720d+4rOAyvjPkO3t8rTPOgIcf9u4rPO88b347iZy5c70uou++C//4B/zzn+yyShwOSfFJ/HHiH/no4o/ITMrk+KeOZ/IrkympKmndC4u0sZEjvR/Zpk9fEuFIRKQjqwjphXDkkHzeuHJ8/bY1uocQYOGa5g0gKNJRtPQewl+GLFeb2dPA2laKrd0oKcmguvpXTJjQ9GSBlbWVXPLKJfTJ7sPtx9y+19ebNAnuuANeeAEmT/a6K0rbe+QRGDsWAgGYPh1+9KOGvyS2toN6HsTcH83l2rHX8vBnDzP8/uG8t+K9tgtApJWNGzcI0NQTItK6yqu3/7qemhTP0G5NDxCYEBeOwfdFok9L//IzQ5ZkvHsJTw13UO3NjBmrgW8ZPLjpLgd//OCPLNq8iCmnTCEjKSMs17zySvjd77yK1NVXg1N39jZTWekl4hdfDOPHe/cLjhkTmVhSElK47ZjbmHHRDJLikzjq8aP46Ws/pbRaN5lK9DvooJ5ACosXKyEUkdZTUb29Qlhdu/Nf2at2sU+kI0toycHOuZtaK5D27N57bwGeZejQHacDWFG4gj9/+GcuOOACjh14bFive+ONUFTkVQtzc+G3vw3r6aUJK1fCmWd6SeD118Mf/rD3o4iGw6G9D2Xej+dxw7s3cNesu3h96es8eMqDTOw/MdKhieyxxMQ4Bg5cSs+e3SIdioh0YOWhCeEu5hksrqxpi3BE2p29ro2b2eRwBNKeffPNUuLiBtGz5477rn/veuItnluPujXs1zXzksEf/MCrFt5zT9gvISHefBNGjYKlS+Hll+GWW9pHMhiUlpjGHcffwfuT3scwjnr8KH7w0g/YVLYp0qGJ7LH99uvJsmXt6B+aiHQ4oQmh20WXq545qW0Rjki7E47O0m14V1VkbNq0hOzsfWjctfzTNZ/yzMJnuOrQq+iZ1US2GAZxcfDQQ3DaaXDFFfD4461ymZhWV+fNJ3jCCdCzJ8yeDd/Z83GBWt3hfQ9nwWULuGH8DTy94Gn2/fu+PDrv0V1+yIm0Vykp0/jyy59Qq65aItJKQruMJifs/AeoCw7py8D8dOLjOvxXW5EG9johdM49EI5A2quqqioqK1fRo8egHfb95n+/oUt6F64dd22rxpCQ4E1HcdRRcNFF3rqER2Ghl/z97ndw/vnw8ccwaMf/1O1OamIqf5z4R+b9aB5DOw/lopcvYuLjE1m8eXGkQxNpkcTExQQC9zN37upIhyIiHVTooDKDuux8rIfs1ES+M6IngTpHneYilBjS4oTQzE4ys2vN7HfBpTUCay++/HI54Nhnn4ZZwsKNC3lr2VtcefCVZCZntnocKSleN8bx4+GCC+C551r9kh3evHlQUABvvQV//7tXfW3tKSXCbViXYXxw0QdMOXkK89bPY/j9w7n6ravZVrkt0qGJNMvIkd7/W2fM0MAyIhJ+b36xnslPzAHg1ycM5ZfHDN7l8UkJ3lfj6oB6LUjsaOm0E/8AzgZ+htdV9CygbyvE1W6Ul3cDnuKoo45o0H7nx3eSmpDKjwp+1GaxpKfD1Kkwbpw3R+Hzz7fZpTucRx+FQw+F6mr44AP4yU/adkqJcIqzOC4dfSmLLl/E9w/4Pn/76G/sc88+TJkzhUBdYPcnEImgsWO9hHDuXM1FKCLh99e3tvecOXVkj/qEb2eC+6tqlBBK7GhphXCsc+5CoNAfcfRQYNc/tUS5detygfMYN65PfdvGso08+fmT/GDED8hLzWvTeDIy4LXX4JBD4JxzlBS2VGWlN5/gRRd5cwzOmeO9lx1B14yuPHzqw8yePJshnYfwo6k/YtSUUfxvxf8iHZrITo0Z0wtI5uuvVSEUkfCLDxkAIi1x94PrJwcTwoB+UJXY0dKEsMJ/LDezHkAN0D28IbUvb789C/iUwSFp75Q5U6gKVHHlIVdGJKaMDHj9dS+ROftsr9olu7d0qVcVnDIFfv1rb1TRLl0iHVX4jeo+ig8mfcBz332ObZXbmPj4RE751ynMXz8/0qGJ7CAxMY7k5MFs3FgU6VDCyszOMLMlZrbNzIrNrMTMiiMdl0isSQ6pCKYm7X5E4/ouoxroSmJIi+YhBKaaWQ7wZ2Au4IAHwx1Ue/LKK78lMbGI9PRPAG+44ic/f5IJ/SYwpPOQiMWVmeklNKed5lW7SkrgZz+LWDjt3vPPww9/CImJ8OqrcOKJkY6odZkZZw07i5MHn8ydH9/J7R/ezsgHRnL2sLO5acJNEf3bjWUlVSV8W/wtq4tXs6lsE1sqtrClfAubyzezpWILxVXFVNZW1i9VgSoqayuJszgS4xJJik8iMT6RxLhEUhJSyEvNIy81j06pnerXe2T2oH9uf/pm9yU1MTqGUD/mmM9YubLDTT1xO3CKc+6rSAciEstCE8LQ7qI/PXIQ2yp2nHewvkKohFBiSEsnpr/ZX/2PmU0FUpxzHXr0iq1bl5KXt71P4bz181i8ZTG/PPSXEYzKk54Or7zidR294govKbz++khH1b5UV8M118Ddd8PBB3uD8fTps/vndRSpialcN/46LjvoMv7y4V+48+M7+feX/+Z7w77Hr8f9mhHdRkQ6xA4lUBfg2+Jv+XrL1yzevJivt3zNiqIVfFv8Lau2raKosmiH5xjmJXVpnchKziI1IZX0pHQ6pXUiOT6Z5IRk6lwdNYEaaupqqK2rpSZQQ3lNOV9t/oot5VvYWrGVmrodv9h0Te9K/9z+9M/pz/5d9ueArgdwQNcD6J3VG2tHN80OHhzPu+96U8A0nt4nim1QMigSeTv7X93VxzX9w2hSvCqEEnualRCa2WHOuRmhbc65KqDK358F9HHOLQx/iJFTVVVNVdVKevf+fn3b0wueJiEugTP3PTOCkW2XkgL//rdXJbzhBli/3pvMvj1NqB4py5Z5g+988glceSXcdhskJUU6qsjIScnhjxP/yBUHX8FfPvwL98++n2cWPsOJ+5zIr8b9ivF9xrerBKG9c86xtmQt8zfM5/MNnzN/w3wWblzIki1LqApU1R+XlZzFgNwB9Mvpx+F9Dqd3dm/6ZPehV1Yv8tPy6ZzWmZyUHOLj9u4frHOO0upStlRsYU3xGr4p+oYVRSv4pugbvin6hg+//ZB/Ldw+X012cjYHdD2AUd1HMa73OMb1GUePzB57FcPeMJtJRcX/MXfugxQURC6OMJttZs8CL+F/VgI4516IWEQiMSh0DsLmSE5UQiixp7kVwjPN7HbgDWAOsAlIAQYBR+KNNHpVq0QYQR9+uASoY9999wGgztXxzBfPcPyg4+mU1imywYVITPSmTOjaFf72N/jmG2+uwvT0SEcWGc5591VecYWXGP/nP3DGGZGOqn3okt6F24+5nesOu477Pr2PO2fdyRGPHsGIriP46Zifct7w80hLjLK5N1pZVW0VX276kvkb5jN//Xw+3/g589fPZ0vFlvpj+mb3ZXjX4Rw/8HgGdxrMkM5DGNJpCF3Su7RJom1mZCZnkpmcSb+cfozrM26HY4qrilm4cSGfb/i8PomdMmcKd826C4D+Of0Z12cch/U+jKMHHM3AvIGtHndQt25VwGu8887CjpQQZgHlwLEhbQ5QQijShspbmBAm+b+oa9oJiSXNSgidc78wszzgTLypJrrjDTDzFfBA4+phR/Hii7MAOO64gwCYsWoGq4tXc9vRt0UyrCbFxcFf/wr9+8PPfw5HHOFNUdGtW6Qja1ubN3ujiL7wAkyYAI89FltdRJsrNzWXGw6/gV8c+gue+vwp7vnkHi595VKufftaLhxxIReOuJADux0Yc1XDqtoqPt/wOXPWzWHO2jnMWTeHhRsX1nfHTE1IZf8u+3P60NMZ0W1EfRfMnJScyAbeDFnJWYztPZaxvcfWt9UEapi3fh4zVs1gxrczeHvZ2zz5+ZMADMobxAmDTuD4Qcczod+EVv2h4KyzRnPNNcY773zCr3997O6fEAWccxdFOgYR8RLCPnlp/P28Uc06XtNOSCxq9j2EzrmteAPIdOhBZELFx3+P+Pj+nHaaVyH89xf/JiUhhe8M+U6EI9u5n/4U+vb17is8+GCvOlZQEOmo2sabb3pdZzdvhj//GX75yw51P1KrSEtM49LRl3LJqEuYsWoG9356L/fPvp+7Zt3F/l3258IDLuSsYWfRL6dfpEMNu8raShZsWNAg+VuwcQG1dbUA5KbkMrrHaK469CoO7H4gI7qOYFDeoL3u3tmeJMYnclDPgzio50H84tBf4JxjydYlvLn0Td5Y9gYPzX2Iez65h+T4ZCb0m8CZ+57J6fueTue0zmGNo2/fbJKS9mXBgo/Det5IMrNewD1AsFw7Hfi5c2515KISiT3l1bWcfEAPhvfKbtbx2yem17QTEjtaOspoTJk9O4OCgiNJT/e6i7646EWOH3Q8GUkZkQ5tl045BaZPh9NP9yaxv+ceuPTS6J14fXdKSuC66+Dvf4dhw7x5GkeOjHRU0cXMGN93POP7jqewopDnvniOxz9/nGvfuZZr37mWkd1GctqQ0zh16KmM6Doi6iqHFTUVLNi4oD7xC1b+gslfXmoeo7uP5upDr2Z0j9GM7j6afjn9ou517i0zY3CnwQzuNJifHfwzKmsr+WDlB7yx9A1eXvwyk6dO5rJXL2NCvwl8d7/vcvrQ0+ma0TUs1+7b9xCWLn2ZujpHXFyHeN//CTyN16sG4AK/7ZiIRSQSg8qrA6Q1Y7qJoGRNOyExSAnhTmzZUsJHH/2ZCy+8ABjMJ2s+YU3JGv409E+RDq1ZRo3yJl0//3yvC+VHH8F990FqdIxC32yvvgqXXQarV3tdZf/0p473GttabmouPyr4ET8q+BHLti7jpUUv8dLil7hp2k3cOO1G8tPymdh/IhP7T+SIvkewT6d9iLP2UYoN1AVYVriMBRsWsHDjQhZsXMCCjQtYunUpdc77cO+U2onRPUZzzdhrGN19NKN7jKZvdt+YS/6aIyUhhWMHHsuxA4/lr8f+lXnr5/H8l8/z7y//zWWvXsblr13OxP4TmTRiEqfve/pedSsdP/5IlixZwbx52xg1Kid8LyJy8p1z/wzZftTMroxUMCKxKFDnqKqta9b8g0FJmnZCYpASwp145plPCQRupmfPccBgXvjqBRLiEjhlyCmRDq3ZOnf2qmU33QQ33wyffuoNPjOqed3o27UNG+AXv/AGz9lvP5g505t0XsJrYN5Arhp7FVeNvYoNpRt4fenrvLfiPd5d8S7PfvEs4I1YObrHaAq6F3BA1wMY0nkIgzsNJis5q1ViqnN1rC1Zy7Kty1hWuIylW5eyrHAZS7Ys4avNX1FZWwl40zkMyhvE/l3255xh5zCi2whGdx9Nn+w+Sv72gJlxYPcDObD7gfxx4h9ZuHEhz33xHE8ueJILXryAzFcz+d6w7zFp5CTG9R7X4vf4yisv4JFHLuDLLzvG/6OALWZ2ARAc3vVcYMsujheRMKuo8bp9tqRCGJx2QgmhxJIWJYRmloY3mmgf59ylZrYPMMQ5N7VVoougV1/9CIDzzjsY5xwvfPUCR/U/KioGjwgVHw9/+AOMHetNzH7wwfDb33pdLBMTIx1dy1VXe3MK/uEPUFXlJbu//nXsTifRlrpmdGXSyElMGjkJ5xyLtyzmw28/5NM1n/Lp2k+54+M7GsyF1y2jG32y+9Ajswc9MnrQLaMb2SnZZCVnkZmUSWpiKnEWR7zFY2ZUB6opqy6jrKas/rGkqoT1petZX7aedSXrWF+6ng1lG+q7egLEWzz9cvoxMG8gE/pNYHiX4QzvOpz98vfTiKmtxMwY3tV7n2868iY+WPkBj81/jGcWPsPDnz3MoLxBXFZwGT888IfN/n/mfvtBRgZ8+GGACy7oEPdp/hDvHsI78EYX/RDQQDMibeiBacsASE1q/tdddRmVWGTOueYf7M2pNAe40Dm3v58gfuicG9lK8e21goICN3v27BY/r0ePU9i8eRnV1V8yb/08DnzgQB44+QEmj57cClG2ja1b4Wc/g6efhtGj4aGHoudeO+e87qG//CUsWQInneSNqjqk6XllJQKqaqtYunUpi7cs9iZl3/o1a4rXsLZkLWtL1lJYWdjic8ZZHF3Su9AtoxvdMrrRPaM73TK60SurF4PyBjEobxB9svuQEKfODu1BaXUp//nyPzw490FmfjuT9MR0LhxxIVccfAVDOw/d7fN79bqMwsK5lJXN2qPrm9kc51yMDKO19/b081EkGhRX1nDAjW8B8NezRnDm6F7Net628hpG/OEtfnfyfvzwsP6tGaJIm9rVZ2RLv0UNdM6dbWbnAjjnyq0D9r2qrQ2wYcPH9OvndQ+dMmcKyfHJnLFvdE9ml5cHTz3lzcl32WVet6xLL4U//hHy8yMd3c7973/wu9/BjBleAvjaa3DCCZGOShpLTkhmWJdhDOsyrMn91YFqSqpKKK4qpqS6hIqaCupcHQEXoM7VkRSfRHpiOulJ6fWPaYlp7eb+RNm9jKQMfjDyB/xg5A+Yu24u93xyD4989gj/mP0Pztn/HG6acBP7dNpnp8/v168Ta9bMYf36Urp1a9+Dd+2MmV3rnLvdzO7Bqww24Jy7IgJhicScbeXbe6ykJ+seQpFdaek3rWozS8X/kDOzgUBV2KOKsIcf/pq6ugAnnXQS2yq38fj8xzl3+LlhH2o9Us48ExYv9gZheeQR2Gcfr9pWURHpyLZzDt5/35tLcOJEWL4c7r0XFixQMhitkuKT6JTWif65/Tmg6wEc3OtgDu19KIf1OYzD+x7OIb0OYXjX4QzIHUDXjK5kJGUoGYxio7qP4p+n/pNvf/Et1467lpcXv8z+9+/Pje/fWH+fZ2MXXngSEOCKKx5t01jD7Cv/cTZej5rGi4i0gW0V2xPCjOTm3yOTpC6jEoNa+m3r98AbQG8zewp4F7g27FFF2L/+tS89eqzitttO47H5j1FWU8blB10e6bDCKjcX7rjDS7DGjoWrr/bmL/y//4OiosjFVVkJjz7qdWk98kgvcb3rLli2DC6/PDrvexSJZfnp+dx69K0su2IZ393vu9w07SbGPjyWlUUrdzh28uRDycg4lBdf/BuVlbVNnK39c8694j8+FlyAJ4AX/fU9ZmZ5ZjbVzKab2T0766HjH7fVH9RGJCaFJoQD8tOb/bz4OCMhzjQPocSUFiWEzrm3gTOASXgjpxU4594Pf1iR8+abW5k2rY6rr84gOcW479P7OLjnwRT06Ji3pQwd6nXBnDbNm8D+hhu8xPCqq+Dzz9smBufgs8/g2muhd29vcvnqanjgAa8yeMUVkJLSNrGISOvoltGNp854iv+e81+WFy6n4MECpn0zbYfjfvKTa6itXcF1170QgSjDx8yeNrMsM0sHFgJfmtk1e3naa4FnnXPjgXTguJ0cdx3eIDYiMSuYEN597oH0yGnZfFRJCXFU1ahCKLGjWQmhmY0KLkBfYB2wFujjt7UpM5tkZh+a2cxwXr+yspbzzz+bhIQJXHKJ45YPbmHxlsX8/OCfh+sS7dbhh3uJ4WefeV0y774bRozwlr/8xUvMWjD+0G7V1nrTYPz+915SOmqUV7EcNw7efderXE6erDkFRTqaU4acwqxLZtE5rTNHP3E0f//k74QObnbzzd+hS5d/8M47J1BeHsFA995+zrli4DTgdaA/8P29POcRQHBU71f87QbMrA/QHa/LqkjMCiaEB/XLbfFzkxLiqA4oIZTY0dwK4V/95e/ALGAK8KC//vfWCa1pZpYLXAFMAC4A7g7HeTduLKNv39PYsuUdTj31bN5e/SK/e/93fP+A73PO/ueE4xJRYeRIeOYZWLsW7rnHS8iuuQYGDoR+/WDSJHjsMZg7F0pKmndO5+Dbb+Gtt7x7FU85BTp1gjFjvPkRe/WCKVNg/Xp46SXvnsGON1SRiAQN6TyEjy/+mOMHHc9PX/8ppz97OmuK1wCQlBTPfff9iC++yGTcuEUsWRK1U/clmlkiXkL4X+dcDU0MMtNCeUCRv17kbzf2e+CWvbyOSNQr9hPCrJSW32uSFB+newglpjRrlFHn3JEAZvYCMMo5t8Df3h+4sdWia9oYYLpzrhpYYWaZZpbsnNujwW0++aSWy35yDfPmPUddYD1HnPUzEr43ne/9+3kO7nkwU06ZEpOTWOfnw09/6i1LlnjJ3P/+B6+84iWEQd27Q//+kJkJaWmQnu4lgNu2eUtRkVddLCvb/px99oFzzvHuETzySOjatc1fnohEWHZKNi+f8zJ//fCv/O793zH070M5c98zOXnwyQw+bDAPPNmZyd8/iSFDitl3vzP5619u4/jjsyMddkv8A/gGmA98YGZ9geLdPcnM4oGZTex6FSgEsvGSwWxga6PnDgecc+6rnX1umdlkYDJAnz59mvdKRKJQcWUN8XHWoknpg5IT4zTKqMSUlk47MSSYDAI45xaa2b5hjml3OuF9KAYV4f1Kum5PTnbh85exeP1DMAg4GKYNuIfspdn88tBf8qtxvyIlQTev7bOPt1x+OdTVwVdfeYO9fP21t6xcCYWFsGbN9sQvOxtycmDAAC/p23ff7UuXLhF9OSLSTsRZHNeMu4Yz9j2Dmz+4mZcXv8xj80N+cfohuI/gy3UPcOHrq9h4/GuRC7YFzCwO2OCc6xnStgo4cnfPdc4FgEN2ct4M4ETgaf+x8Y2Wo4EhZvYG3qdamZl97Zz7JOT8U/B6+VBQUBDGGwFE2peSyloykhP26Ef9ODNe/GwNt515QP2ooyIdWUsTws/N7CHgSX/7fKCNhh6ptxXICdlu6lfSZv8C+uvTv8PiCb3plJNGXmoe/XP6U9CjgMzkzPBG3UHExcGwYd4iIhIOA/MG8uhpj1IdqGbe+nmsLFrJhrIN1ARqqP5BNSVlVXRJGhTpMJvNOVdnZtcCz4W0OWBvh069HXjczC7D++x9C8DM7gRucc49Cjzqt90ILA1NBkViSUllLZkpLf2a61m5xbuB+Y0v1vOdET3CGZZIu9TSfykXAZcBwVFWPgDuD2tEuzcL+KN/b0Z3oLRxd9GW/AI66dBTgFNaKVQREWmupPgkxvQcw5ieYyIdSji8Y2ZXA88C9Z3mnXNbd/6UXXPObQFOaqL9yibabtzT64h0BCWVNWTuwf2DAJ3Sk9hSVk1ReXWYoxJpn1qUEDrnKoE7/CUinHOFZnYfMA3vBv2OPwSoiIhEm7P9x9BJbB0wIAKxiMSUhWu28c5XGxnTr6lxl3bvvasnMOKmtygsq9n9wSIdQIsSQjNbQROjpDnn2vQDzjn3CPBIW15TRESkuZxz/SMdg0is+v7DswDYWFK5R8/PTk0kLSme0iolhBIbWtplNHR29hTgLJoe9lpERCRmmVka8Eugj3Nuspntgzcw29TdPFVE9lJ8nDcQTEpiy0cYDUpLSqCsOhCukETatRYNneSc2xKyrHHO3UkT9zOIiIjEuH8C1cBYf3sN8MfIhSMSOzqlJwHw4IUFuzly59KT4ymv2ttxoESiQ0u7jI4K2YzDqxju2RBOIiIiHddA59zZZnYugHOu3GJxUluRCCgsr+acg3rTOy9tj8+RmhivCqHEjJYmc38NWa8FVgDfC184IiIiHUK1maXi33dvZgOBql0/RUT2lnOOwvJqctKS9uo86ckJVCghlBjR0oTwYufc8tAGM9ON8yIiIg3dCLwB9Dazp4BxwKRIBiQSC8qqA9QEHLlpezblRJA3qIy6jEpsaGlC+Dwwqom20eEJR0REJPo5594ysznAIYABP3fObY5wWCIdXmGZN3dgbvpeVgiTEthYrKK+xIZmJYRmNhQYBmSb2Rkhu7LwRhsVERERn5m9AjwN/Nc5V7a740UkPKYv8X53yd3LLqNpSfGUVatCKLGhuRXCIcDJQA5wSkh7CXBpmGMSERGJdn/Bm5z+VjP7FHgGmOqc27OJ0URktzYWV3L9iwsAyEvfyy6jyfG6h1BiRrMSQufcy8DLZnaoc+6jVo5JREQkqjnnpgHTzCwemIj34+kjeD1rRCRMyqpqeevL9Zw2sidLN5XWt/fK3fMRRsHrMqoKocSK5nYZvdY5dztwXnAI7VDOuSvCHpmIiEgU80cZPQWvUjgKeCyyEYl0PH989Uv+9cm3bCmt5pstXu/sg/rlkp+RvFfnTUtKoLKmjkCdIz5OM8ZIx9bcLqNf+Y+zWysQERGRjsLMngPG4I00ei8wzTlXF9moRDqWSx77lHe+2gjAH1/1vqomJ8Tx7ORDidvLJC4tKR6A8upaMlP2rvupSHvX3C6jr/iP+nVTRERk9x4GznXO6SYkkVZQVlVbnwyGykpN3OtkELx7CAEqqgNKCKXDa26X0VfwJ9dtinPuO2GLSEREJMo55940s7Fm1o+Qz1rn3OORi0qk41hTVNFke2WYBoJJT/L+2ZZpYBmJAc3tMvqXVo1CRESkAzGzJ4CBwDwg+I3SAUoIRcJgdWE5AL84ejAJ8cY97y0hMS6O607cNyznD3YZLdPk9BIDmttldFpw3cySgKF4H2yLnXPVrRSbiIhItCoA9nPO7bR3jYjsuTWFXoXwnDG96ZqVwuVHDsI5h1l4BoBJ8yuE5aoQSgyIa8nBZnYSsAy4G+8m+aVmdkJrBCYiIhLFFgLdIh2ESEe1urCCpPi4BqOJhisZhO33ED43+9uwnVOkvWpul9GgvwJHOueWApjZQOBV4PVwByYiIhLFOgNfmtknQFWwUffci4TH6qIKeuamhmUAmaZ0Sk8C4Pk5q7n9zANa7Toi7UFLE8KSYDLoWw6UhDEeERGRjuDGSAcg0lEF6hxfrNlG77y9m3x+V/p2SuewQZ2ZsXQzm0ur6JKV0mrXEom0liaEs83sNeA5vHsIzwI+NbMzAJxzL4Q5PhERkagTeu+9iITXu19t4Jst5Vx93JBWvc73D+3LjKWb2ViihFA6tpYmhCnABuAIf3sTkAqcgpcgKiEUEZGYZWYlND1NkwHOOZfVxiGJdDhzVxWRGG8cN6x1b9PNTfO6jRaWa/xE6dhalBA65y5qrUBERESinXMuM9IxiHR0a4sq6J6dSmJ8i8ZGbLG8dG9C+sLymla9jkiktSghNLP+wM+AfjScaFc3yYuIiIhIq9tcWkXnjKRWv059hbBMFULp2FraZfQl4GHgFaAu7NGIiIiIiOzC5tIq+nVKb/XrZKd6FcKtSgilg2tpQljpnLu7VSIREREREdmNLaXVFPTLa/XrJMTHkZ2aSJHuIZQOrqUJ4V1m9nvgLRrOqzQ3rFGJiIiIiDRSG6hja3k1nUMmpG9NeelJrNtWiXMurBPfi7QnLU0IhwPfByayvcuo87dFRERERFrNlrJqnIMumW2TEBaVV/PWlxv458xv+OFh/dvkmiJtraXDM50FDHDOHeGcO9JflAyKiIiISKvbWOx1UGurhPAgv2vqK5+vbZPriURCSxPChUBOK8QhIiIiIrJLG4orAdpsovi/nT2Sod0y2VahqSek42ppl9EcYJGZfUrDewg17YSIiEgrMbM84HEgG5gHXOGcc42OGQDcBaQDq51zF7Z1nCKtbX0wIWyjCmFGcgITh3ZhygfLqa6tIymhdec+FImEliaEv2+VKERERGRXrgWedc49YWaPAMcBbzQ65l7gYufcujaPTqSNLFyzjezURLpnt02FEGBIt0xq6xxPfrxS9xFKh9Sinzmcc9NCFyAAfK91QhMRERHfEcBUf/0Vf7uemfUF0vBGA59mZme2cXwibeKzVUUc2CenTUf8HNItE4A/TP2SlVvK2uy6QW9+sZ6X561p8+tK7GhphRAzOxA4D2+AmRXAf8IdlIiIiDSQBxT560X+dqgewIHAfkAJ8KGZveecK2yrAEVaW2VNgK83lnDc/t3a9LpDumZy2sgevDRvLfO+LaJvp/Swnv+rdcV8vaGEzJQEJg7t2mBfTaCOHz0xB4DhPbMZkJ8R1muLQDMTQjMbDJzrL5uBZwFzzh3ZirGJiIjEDDOLB2Y2setVoBDv/sEi/3Fro2O2Agucc2v8c80D9gE+aXSNycBkgD59+oQveJE24M0HCH3z0tr0umbGn88awWsL1/Pl2mJOHdkzbOeurAlwwl3T67cX3nQcGcnbv57PWLK5fv22NxbxwPcLwnZtkaDmVggXAdOBk51zSwHM7BetFpWIiEiMcc4FgEOa2mdmGcCJwNP+4wuNDlkKpJlZJlCBVylc2cQ1pgBTAAoKClzj/SLt2bqiCgB65KS2+bUT4+MY0jWTL9cVh/W8s1Y0/G1n5tLNHDfMq4A65/jzm4vpkZ3CxH278Mwn31ITqCMxXgPbSHg19y/qDGAd8D8ze9DMjgLarvO2iIhIbLsdON/MpgM1wFsAZnanmeX7yeS1wOt4VcYHnXMbIhatSCtYU58Qtt2AMqH2657FwjXbKK+uDds5/7doI8kJcSy48VgyUxK4739LqawJALBicxlfrivmxxMGMqpPLrV1jm82t/09jNLxNSshdM695Jw7BxgK/A+4EuhiZveb2bGtGJ+IiEjMc85tcc6d5Jwb75y73DlX57df6Zzb5K+/45w7zDl3sHPu/shGLBJ+67Z5U050a8MRRkOdMLwb2ypquO31RWE5X2FZNc/PWc0Rg/PJTEnkpu8MY/7qbfxv0UYAPvGrh+MGdWZwV29gmyUbS8NybZFQLR1ltMw597Rz7hSgF/AZ8KtWiUxERERExLe2qILOGUkkJ8RH5PoThnThyCFdmL508+4PboZPv9lKaVUtk8b2A+CUET1ITYzn4+VbAJi9spC89CQGdE6nTyfvvslvt5aH5doiofa4E7JzrtA5N8U5d9TeBOB3d/nYX34d0n68mX3kL8eFtF9vZjPN7D0z67c31xYRERGR6LC6sIKeuW07oExjo/rmsnxTGdvKa/b6XPO+LSIhzhjVNxfw7lM8ZEAeL81by+eri3jri/UU9M3FzMhKSSQ7NZFvC5UQSvi1h7tS/+6cOwQYC5xqZgP9kdZuB07wl9vNLN7MhgITnXPjgBuBWyMVtIiIiIi0nW8Ly+md2/YDyoTav2c2QFgGl5m/uoih3TNJSdxe8fxeQW+2VdTwnXtnUlYd4MqjB9fv65WbyurCir2+rkhjEU8InXNL/Mc6oBZvsvtBwArnXJFzrgj4xm87Am/4bZxzHwAjIhCyiIiIiLSh2kAda4sq6N3GU040tl/3LADmrGw880vL1NU5Pv92GyN75zRoP2F4d351/FBvff9u7Ncjq35f79w0dRmVVtHiielbi5mdDyx3zn1jZmPx5lwKKsKbhLcTsDakPTKdyEVERESkzXy+Zhs1Ace+3bN2f3Arys9MZkz/PJ78eBUXju1HVkriHp3ny3XFlFTVcmDv3B32XTSuH6mJcTvMd9g7L5X/Ld6Icw4zDfYv4dMmFUK/u+fHTSy/9fcfDVwE/Nh/ylYgJ+QUwUl4G7cHdnK9yWY228xmb9q0KcyvRkRERETa0owlmzGDwwZ1jnQoXHfCUNYXV/LQB8v3+ByvfL6WpPg4jtq3yw77UhLjmTSuP7npSQ3ae+WmUVVbx6bSqj2+rkhT2iQhdM4FnHOHNLHcbGYHAzcD33XOBTtGLwH6m1mWmWUB/fEm3Z2Gd08hfhVx/k6uN8U5V+CcK8jPz2/11yciIiIireejZVvYt1sWeY2SpEg4sE8uJw7vxiMzv6GovBqAmkBdi87x2aoi9u+ZRU5a819P7zzv/slvt+o+Qgmv9tBl9GH/8SW//H2Vc26OmV0HvOnvu86fdPcrM5thZjOBauDitg9XRERERNpKZU2AOasK+f4hfSMdSr2fHzWY1xeuZ+Qf3gYgPs6459wDOXF4990+N1Dn+GLNNs4q6N2ia/byR1hdXVjO6L47djUV2VMRTwidc/vvpP014LUm2m/GqyiKiIiISAdU61fcEuLjmLuykOraOg4d0CnCUW03pFsm150wlNcXruezVUUE6hw/eWouFx/Wn9+evN8un7ticyll1QGG+yOWNlcvf4RVjTQq4RbxhFBEREREBKC0qpZHZqzg2U+/JS4O7jrnQJ7+ZBVJCXEcOrD9JIQAkw8fyOTDB7JqSznLNpdy0T8/5eEZKzh3TB8GdcnY6fM+X70NgAN6tSwhTEtKIDctkTVFSgglvCI+7YSIiIiICMA/Z6zgb29/zZqiCr7dWsEZ933I1M/XceqIHqQnt886Rp9OaRw5pAuf3nA0CXHGs5+u2uXxc1cVkpYUz4D8nSeNO9MlM4VNJRpURsKrff7LEhEREZGYsqmkiqc/WcXovrk8MukgVmwu4+sNJQzonL7DfH3tUX5mMsfs15UHp69g4tCuTVY0q2oDvPzZWiYO7UJ8XMunjuiSlayEUMJOFUIRERERibhfPDuPwvJqrj9xKNmpiYzsncP3CnpT0C+PhPjo+Mp643eGkZOWyEPTm56SYu7KIkqqajmt0RyDzZWfoYRQwi86/nWJiIiISIe1pqiCGUs385MJgxjdNy/S4eyxrlkpnH5gT6Yv3UxpVe0O+2cu3Ux8nHHwgD17jfmZXkLonNvbUEXqKSEUERERkYh66bM1AJw6skeEI9l7xw/rRnVtHY/MWLHDvhlLNzOiVzaZKYl7dO78zGSqA3UUV+yYbIrsKSWEIiIiIhIxqwvLmfLBcg4d0Im+ndIjHc5eO6hfHicN787f3v6aOSu31re/v3gj81cXMX6f/D0+d35mMgCbSiv3Ok6RICWEIiIiIhIxj334DeXVtdx65vBIhxIWcXHG7d89gNTEeO56dymVNQGcc9w89Ut65abyg7H99vjcwYRwo+4jlDDSKKMiIiIiEhF1dY5X5q/jiMH5HaI6GJSenMApI7rz3OzVHHbbe+SkJbFsUxm3f/cA8tKT9vi8XTJTADSwjISVKoQiIiIiEhFPf7KK9cWVnHbgno262Z793+nDufzIgWwurWbpxlJ656Vy8gHd9+qc9V1GlRBKGKlCKCIiIiJtrrImwJ3vLOHg/nmcuP/eJUrtUUJ8HFcfO4Q+eWmM7J3LwPz0vZ4+IyslgaSEOCWEElZKCEVERESkzf17zmo2l1Zx9zkjiduDSdqjgZlx9kF9wnq+/Ixk3UMoYaUuoyIiIiLSpuauKuSPU79kTL88Dh3YKdLhRJUuWZqcXsJLCaGIiIiItBnnHLe9vois1ETuv2AUZh2zOtha8jOUEEp4KSEUERERkTbhnOOKZ+Yxa8VWfnrkIDplJEc6pKiTn5nMplIlhBI+SghFREREpNUF6hz3vb+MV+av5ZLD+vP9Q/pGOqSo1CUzha1l1VTX1kU6FOkglBCKiIiISKt74INl/PnNxRw+OJ9fnTC0ww4k09qCU09sKVOVUMJDCaGIiIiItKpbX1/E7W8s5pj9uvL4D8eQuJfTL8QyzUUo4aZ/jSIiIiLSahau2cYDHyzj9AN7ctc5IyMdTtQLJoQbi5UQSngoIRQRERGRVlFRHeDSx2eTnZrI70/Zj7QkTYG9t7oEE0JVCCVM9K9SRERERFrFvz5ZxbptlTx1ycHkpCVFOpwOoUtmMnEG67dVRDoU6SBUIRQRERGRsKusCfDAB8s4uH8e4wZ1jnQ4HUZCfBxdMlNYU1QZ6VCkg1BCKCIi0s6ZWZ6ZTTWz6WZ2jzUxk7eZXWdmn5rZJ2b2y0jEKRLqP3NXs6G4ip9N3CfSoXQ4PXJSWKcKoYSJEkIREZH271rgWefceCAdOC50p5llAj8EDgHGAj82s/Q2j1IkxCvz1zK4awbjBnWKdCgdTvecVNZtU4VQwkMJoYiISPt3BDDVX3/F3w5VAawFUv2lAqhps+gkppRU1vD8nNVU1gR2esy2ihpmf1PIUft2pYmCtuylHtkprC2qwDkX6VCkA1BCKCIi0v7lAUX+epG/Xc85Vwu8BiwGvgamOOeqG5/EzCab2Wwzm71p06ZWDVg6pro6x8+fmcfV/57PMXdMY2Nx01WqN79YT22d45j9urZxhLGhR04qVbV1bC3b4Z+5SIspIRQREWkHzCzezD5uYvktUAhk+4dmA1sbPXcwcCYwwF9+YGY9G1/DOTfFOVfgnCvIz89v3RckHdL905bx3qKNHDE4n2+3VvDkxyubPO6lz9bQv3M6B/bOadsAY0T37FQA1mpgGQkDJYQiIiLtgHMu4Jw7pInlZmAacKJ/6In+digDSpxzVc65CqAKyGi76KUjatwdsbImwAPTlnHMfl159KKDOHxwPv+Zu4a6uobHFVfW8MmKrRy/fzd1F20lPXJSAFirgWUkDJQQioiItH+3A+eb2XS8ewPfAjCzO80s3zm3GPjEryjOAmb6bSIttrWsmpPuns7g37zOYx9+U98+9fN1FFfWctHYfpgZZxzYkzVFFXy8fEuD53/w9SZq6xwTh3Zp48hjR48cr0K4rkgJoew9TUwvIiLSzjnntgAnNdF+Zcj6dW0Zk3Rc/523hi/WFtMnL43b3ljEEYPz6ZSRxJ9e+4p9u2dx6EBv1NDj9+9G9n8TeW72t4wNmWfw9QXr6ZyRpO6irahTehJJCXGs1UijEgaqEIqIiIhIvXe+2sjA/HSeuHgMSQlxnPr3mVz4yCdsKavm1jOG13cDTUmM55j9uvLuoo1U19YBUF5dy7uLNnDC/t1JiNfXzNZiZnT3RxoV2Vv6lyoiIiIigHef4CcrtjJxaBf6dkrniR8eTEV1gM9WFXHRuH6MaFT1O25YN0oqa+u7jb771UYqa+o46YDuEYg+tvTI1lyEEh7qMioiIiIiAMxdWUh1oK6+W+jwXtn875oJpCXGk5uetMPx4/fpTFpSPC/NW8Phg/N5ed4a8jOTOahf3g7HSnh1z0nho2Vbdn+gyG6oQigiIiIiAHz6TSFmNEjoeuakNpkMgtdt9NwxfXjpszW88+UG3lu0kTNH9SI+TqOLtrYe2alsKK6kNlAX6VAkyikhFBEREREAvt5YQp+8NDJTEpv9nJ9NHERGcgKXPD6b1MR4LjikTytGKEE9clKpc7CxpCrSoUiUU5dREREREQFg6YZSBuW3bArLnLQkbv/uCN7+cgMXjetHr9y0VopOQnUPzkVYVFE/DYXInlBCKCIiIiLUBupYsbmMCUPzW/zc4/fvxvH7d2uFqGRnemR7SaCmnpC91W66jJrZY2b2Tsj28Wb2kb8cF9J+vZnNNLP3zKxfRIIVERER6WBWbS2nOlDX4gqhREbPXC8h/HZreYQjkWjXLiqEZnYAkBOyHQ/cDhzuN03zk8V9gInOuXFmdjhwK3BOG4crIiIi0uEs3VgKwD5dMyMciTRHRnICPXNS+XpDSaRDkSjXXiqEvwX+L2R7ELDCOVfknCsCvvHbjgBeBXDOfQCMaNswRURERDqmJX5CODA/PcKRSHPt0zWDrzeURjoMiXIRTwjNbALwNbAhpLkTUBiyXQTkNdEe37rRiYiIiMSGZRtL6ZaV0qIRRiWyBnfNZNmmUk09IXulTbqM+l1AZzax61VgHF63z5yQ9q2NtrP9tsbtgZ1cbzIwGaBPHw19LCIiIrI7SzaWsk9X3T8YTYb3zKa6to4v1hYzondOpMORKNUmFULnXMA5d0jjBbgT6AY8AzwGjDSzG4AlQH8zyzKzLKA/sBSYBpwAYGZjgfk7ud4U51yBc64gP7/lI2WJiIiIxJK6OseyTaUM1IAyUeXgAXkAzFqxJcKRSDSL6KAyzrkSYCSAP2LoQ865W/zt64A3/UOvc84FgK/MbIaZzQSqgYvbPGgRERGRDmZNUQXl1QEGdVFCGE26ZKYwoHM6Hy/fyuTDB0Y6HIlS7WKUUQDn3DfA0SHbrwGvNXHczcDNbReZiIiISMe2eL03UuW+3TXCaLQ5eEAeU+evozZQR0J8xIcHkSikvxoRERGRGLdofTHgDVIi0eXIIV0oqarlna82RjoUiVJKCEVERERi3FfrS+idl6oRRqPQxKFd6JGdwqMfroh0KBKllBCKiIiIxDDnHJ+tLOSAnjmRDkX2QEJ8HD8Y24+Pl29l4ZptkQ5HopASQhEREZEYtrqwgrXbKhnTPy/SocgeOmdMH9KT4nl4hqqE0nJKCEVERERi2CcrtgLbpzCQ6JOdmsj3DurNK/PXsn5bZaTDkSijhFBEREQkhk37ehN56UkM7qIBZaLZD8f1p845Hvvom0iHIlFGCaGIiIhIjNpYUslbX67n+P27ERdnkQ5H9kLvvDSO378bj3/4DSu3lDH7m6388rl5vLFwXaRDk3au3cxDKCIiIiJt669vfk1twDF5/IBIhyJhcMNJ+zFjyQcce8cHVNXWAfDC3DX83+nDOe/gPhGOTtorVQhFREREYtCzn67i2dnfcvFh/enXOT3S4UgY9MxJ5ZFJB3HowE5cc9wQ5v/uWA4d0IlbX/+KwrLqSIcn7ZQSQhEREZEY89YX6/nNSwsZv09nrjluSKTDkTAq6JfHoxeN4fIjB5GdlshNpw6jtKqWu95dEunQpJ1Sl1ERERGRGFAbqGPa15t45tNvefvLDQzvmc29540iIV71gY5scNdMzh3Thyc+Xsl3R/di/57ZkQ5J2hn9H0BERESkg1u4ZhvH3vEBFz82m89WFfHTIwfx7x8fSnZqYqRDkzZwzXFD6JSexPcfnsWrn6+jrs5FOiRpR1QhFBEREenAXvxsNde9sIC8tCTuO38Ux+zXlURVBWNKTloSz0w+hJ88NZfLn57LPl0yuOnUYYwd2DnSoUk7oIRQREREpIPZWlbNm1+s57/z1vLR8i2M6ZfHfReMonNGcqRDkwgZkJ/B1J8dxmsL1/PnNxdx3oOzOHrfrpw6sgcHD8ijS2ZKpEOUCFFCKCIiIhLl1m+rZN63Rcz7toi5qwqZs7KQQJ2jb6c0rjthKBcf1l/3CgoJ8XF8Z0QPjt2vK/e/v4ynZq3kna82ADCgczpjB3Vi7MDOjOmfpx8PYogSQhERkXbOzE4H/g/o75xr8md8Mzse+L2/eaNz7s22ik9aX2lVLRuKK+uXtUWVrNhcxvJNpazYXEZheQ0AifHGvt2z+PERAzhxeHf2656FmSacl4ZSEuP5xTGDueKofZi/uohPV2zl4+VbeHHuGp78eBUAOWmJ9O+cTv/O6QzMz6BvpzS6ZqXQJTOZLpkppCbFR/hVSLgoIRQREWn/PgAOBBY2tdPM4oHbgcP9pmlm9o5zLtBG8cU85xx1Dmrr6gjUufqlqraOypoAlTXBxwCVtXVU+Y+VNQGqagKUVNVSXFHLtooaiitrKK7wlm0VNWwuraa0qnaHa+ZnJjOgczrH79+dwV0zGNE7h/26Z5GSqC/q0jzxccaoPrmM6pPLj44YSE2gjs9XF/HZqiKWby5jxaYyPly6hRfmrtnhuZnJCeRnJpObnkRWSgJZqYlkpSSSlZpAVkoiackJpCbGk5IYR0pCPKlJ3npy/Xo8iXFGfJyREB9HQpyREG8kxMURH6cfMdqSEkIREZF2zjm3BdhVpWcQsMI5V+Qf943ftri1YtpWUcNVz80HnB9jcM1Ljrav+49+Ozu0hzw/pC30eTR17G6uFdxwu73WzuOqT+ycIxBw1NY56pz3GNwOuO3H7a2k+DjvS7X/hTonLYneeWnkZybTNSuFrlnJdM1MoUtWCt2yU8hI1tc4Ca/E+DhG981jdN+8Bu1lVbV8W1jOxuIqNpZUsbGkko3FVWwqqaKooprNpdUs31zm/ZBRWbvX/x7M8BLEuO2JYry/Hh9nmEGcGXHm/X/RDAyvLbiPRttx5p04rtGxZoa/C29tewxNru/kmIbxhxzT6HU13b7r4688enCrThei/5OIiIhEv05AYch2EZDX+CAzmwxMBujTp89eXbCuzrG2qMI/L/WPwS9LwS9ooQdY6LFs/xJk7Ph8/OcbYHHBdtvptUJeY7OvRROxbn+OER9vxJvVfwkNLglxRlxcsD2O+DgafFmtP8aMZL86khKslCRur5KErmemJKiyJ+1WenICQ7tlMbTb7o91zlFeHaC8OlBfFa/wq+QVNdvbKmsC1NY5av0fV2oDdfXbgbo6avwfWmoCdf6j115b5/0i5IA6vzLvnPN/KHLU1fmPLvjjj2vesXVQ/wMXO/5ItH3v9tfZdHvTx9Os45u+bmVN63b2UEIoIiLSDvjdPmc2setV59zNu3n6ViAnZDvbb2vAOTcFmAJQUFCwVz/h56Yn8drPx+/NKUSkAzIz0pMTSFcFO2rov5SIiEg74N/vd8gePn0J0N/Msvzt/sDSsAQmIiIdmsYfFhERaefMbLyZvQP0MLN3zOwMv/1OM8v3k8nrgDf95ToNKCMiIs2hCqGIiEg755ybDhzdRPuVIeuvAa+1YVgiItIBqEIoIiIiIiISo5QQioiIiIiIxCglhCIiIiIiIjFKCaGIiIiIiEiMUkIoIiIiIiISo5QQioiIiIiIxChzzkU6hlZlZpuAlbs5rDOwuQ3CaQ3RGnu0xg2KPRKiNW5Q7G2pr3MuP9JBRItmfj7urWj7G2qpjvz69Nqik15bdGqL17bTz8gOnxA2h5nNds4VRDqOPRGtsUdr3KDYIyFa4wbFLrGto/8NdeTXp9cWnfTaolOkX5u6jIqIiIiIiMQoJYQiIiIiIiIxSgmhZ0qkA9gL0Rp7tMYNij0SojVuUOwS2zr631BHfn16bdFJry06RfS16R5CERERERGRGKUKoYiIiIiISIyK+YTQzCaZ2YdmNtPMRkU6nsbM7E0z22Rmv/G3zczuMbPpZjbVzPL89jx/e7q/3yIc94H+e/qBmb1nZgPMLMXMnvJjfMrMUvxj+/nHzDSz6yMZtx9Plv838b6ZfWJmR0XL+x5kZoPNrMbMDoui973Cf8/fN7OLo+k9N7PRZvaWmf3PzG6PltjNbL+Q9/wjM9sSLbFL+2dm8Wb2FzN7x/8b289vH+X/f+dDM5sU4TD3ipk9ZmbvhGwf7/9b+sjMjotkbHvKzO40s4/95dch7R3htbXr73wtZS34rhWNmvNdJlo193tDm3HOxewC5AJzgSSgPzAj0jE1EWMvYBLwG3/7eOBhf/1C4FZ//Vbg+/76I8DxEY67G5Dpr58IPAH8GPit3/Y74Mf++jPAeH/9HWBohGOPAxL89QHAp9Hyvoe8hif89/KwKHrflzbajor33P//x9vBv/doir3R6/ge8I9ojF1L+1yAy4DJTbTP9D9zk/zP4NxIx7qHr+8A4GXgHX87HvgcyPGX+UB8pOPcg9e1j/8YB3wEDOwIr40o+M63B6+p2d+1onFpzneZaFxa8r2hrZZYrxCOAaY756qdcyuATDNLjnRQoZxzqxs1HQFM9ddf8bd31R4Rzrn1zrkSf7MKqGXnMY50zk33118l8rHXOedq/c0svA/BqHjfAczsYGA9EPzbiYr3HehmZtPM7AUz60f0vOeHAqXA0/4vtOOJnthDXQA8SXTGLu3TWUBf/xfwe80syf+MTXfOrXDOVQPT8T6Lo9Fvgf8L2R4ErHDOFTnnioBv/Lao4pxb4j/W4X12B+gYr63df+drqRZ+14oqLfguE41a8r2hTcR6QtgJKAzZLgLatkTbcqExF+H94gVe3EUh7e3idZhZOvBH4M/sGHswxtC/w9D2iDGznmY2A3gLeJHoet9vwKvmBEXL+97POXcE8ADwMNHznvcARgDnA98HHgQ6Ex2xA2BmnYCheJWbaHnfpf3rCaxzzh0JVAI/xPv7Kgo5pogo/FsyswnA18CGkOZo/E6xU2Z2PrDcOfcNHeO1dYTX0KRmfteKNs39LhONWvK9oU0ktOXF2qGteF0fgrL9tvYsNOZstv/xFPrbRbST12FmicCzwG3OuS/NrHHswRjrQp7WLmJ3zq0BDvMrVe/jda/M8Xe32/fdzE4CZjvntoTc4hUV77tzbrP/+KaZ/R2vq26Ov7vdvuf+9T90zhUDxWa2Ga97VY6/vz3HHnQ28G/nnGvi32l7j10iyMzi8X5IaOxVvL+RN/ztN4AzgEeJks/d3by2ccA5NHwtUfOdYlevzTl3s5kdDVwEnOK3R81r24WO8Bp20ILvWlGjhd9lolFLvje0iVivEM7C+9KfaGZ9gFLnXFWkg9qNaXj9xPEfp+2mPSLMLA6v+9lLzrmX/OadxTjfzMb66ycAH7RVnE1p1IWkGCghSt53YCQwwczeAI4B/gJ8RTt/380sw/+CgpkdAGwmet7zWcBgM0sws0ygC/AfoiP2oPPx/r1C9Lzv0g445wLOuUOaWG7G+zGtwD+0AO8+4UqgzMz6+F9kDwM+iUz0u7az1wbciXfv1jPAY8BIM7sBWAL0N29gsiy8+9SWRir+XdnVfze/q97NwHedcxX+U6Lmte1CNH7n26UWfteKJiNp/neZaNSS7w1tIubnITSzHwKXAA74uXNudoRDasDMHgTGAsnAQrxfWO/Bu5m9GLjQ/wWlE/A42+95+5nf/z8izOy7eL8EB9/PBcC1eINR9MLrE36Rc67SzAbgdRFMAl53zv2x7SPezsxGA3fg3TeRANwI/I8oeN9DmdmjwEPAHNr5+25mY/C6ipbg/Vu8Au9vJireczP7PvAjIBGvi8vLRE/sA4DnnHMF/nYcURK7tG9mlgv8E+9X7614gxKVmVkBcBdgwEPOuUciF+Xe8XuRPOScO9rfPhHv3kKAm51zr0Uqtj1lZgv91c3+41XOuTkd5LW16+98LdWS71oRCTAMdvddJoKh7ZXmfm9os3hiPSEUERERERGJVbHeZVRERERERCRmKSEUERERERGJUUoIRUREREREYpQSQhERERERkRilhFBERERERCRGKSEUERERkbAws4CZzQtZ+kU6pnAxswPN7GF/fZKZOTM7OmT/aX7bd/3t9/1pVoL7+4VM69HU+fP9ufdE2pQSQpEIMbNOIR+Y681sjb9eamb3tdI1rzSzC/fgeUlm9oGZJbRGXCIi0mFUOOdGhizfBHeYJ5q/e14P3B2yvQA4J2T7XGD+np7cObcJWGdm4/b0HCJ7Ipr/UYpENefcluAHJvAP4A5/O8M595NwX89P5n4IPN3S5zrnqoF3gbPDHZeIiHRcflVssZk9DiwEepvZNWb2qZl9bmY3hRx7g5l9bWYzzOxfZna1315faTOzzmb2jb8eb2Z/DjnXj/z2Cf5znjezRWb2lJmZv+8gM/vQzOab2Sdmlun/4DkyJI4ZZjai0evIBA5wzoUmfNOBMWaWaGYZwCBgXjPfl4dCfhTeZGa/93e9BJzfzLdXJCyUEIq0M/4H2VR//UYze8zMppvZSjM7w8xuN7MFZvaGmSX6x402s2lmNsfM3jSz7k2ceiIw1zlX6z/nfTO7zf9A/NrMxvvtw/y2ef4H7D7+819CH1IiIrJrqSGJzot+2z7Afc65YcAQf3sMMBIYbWaHm9lovGrbSOBE4KBmXOtiYJtz7iD/+EvNrL+/70DgSmA/YAAwzsySgGeBnzvnRgBHAxXAw8AkADMbDKQ0SvwACvAS2lAOeAc4DjgV+G8TMT4VfD+A1+qf6Nwl/g/CpwKbgUf9XbOB8c147SJho4RQpP0biJfMfQd4Evifc2443ofYSX5SeA/wXefcaOAR4JYmzjMOmNOoLcE5NwbvQzP46+SPgbv8D6oCYLXfvpDmfUCLiEjsCu0yerrfttI597G/fqy/fAbMBYbiJYjjgRedc+XOuWKaTq4aOxa40E+2ZgGd/HMBfOKcW+2cq8Or2vXDS0bXOec+BXDOFfs/kv4bONn/PP0h25OzUN2BTU20P4OXyJ4D/KuJ/eeH9AY6MXSHmaX41/6Zc26l37wR6LG7Fy4STrofSKT9e905V2NmC4B4IHjD+QK2f8DtD7zt94iJB9Y1cZ7uwFeN2l7wH+f45wL4CLjBzHoBLzjnlgA45wJmVm1mmc65knC8MBERiQllIesG/Mk590DoAWZ25S6eX8v2IkZKo3P9zDn3ZqNzTQCqQpoC7OI7r3Ou3MzexqvWfQ8Y3cRhFY2uHXzuJ2Y2HCh3zn3tfw431z/wPmffCWlL8a8l0mZUIRRp/6oA/F85a5xzzm+vw/uAM+CLkF9khzvnjm3iPE19mAU/MOs/LJ1zT+NVIyuA18xsYsjxyUBlGF6TiIjEpjeBH/r33GFmPc2sC/ABcJqZpfr3650S8pxv2J6kfbfRuS4LuX1isJml7+Lai4HuZnaQf3xmyGBpD+ENGPOpc66wied+hXePYFN+jTfgTLOZ2eVApnPu1ka7BrNj11SRVqUKoUj0Wwzkm9mhzrmP/A/Gwc65Lxodt6sPs3pmNgBY7py728z6AAcA75lZJ2Czc64m3C9ARERig3PuLTPbF/jIr6aVAhc45+aa2bN4o3RuBD4NedpfgOfMbDLwakj7Q3i9W+b6g8ZsAk7bxbWrzexs4B4zS8X74fNooNQ5N8fMioF/7uS5i8wsu6leMs6515v/DtS7Gqjxu7sC/MM59w/gyEavUaTV2fZig4hEipndiPeB9Be/q8vVzrmTQ9v940qdcxlNPGck3i+b2Xg/9NzpnHuw0TX6Ak845w73t9/3rzPbzDoDs51z/czs18D3gRpgPXCec26refMqHeqcu6oV3woREREaf/61wfV6AO8DQ/0eOU0d8wugxDn3UCvG8QFw6k6qlCKtQgmhSAzxR3y7NnhfYAuf+wLwa+fc1+GPTEREZLu2TAjNm5/3FuCXzrl/7+K4FOAs59wTrRRHPjDOOfdSa5xfZGeUEIrEEDMbAnR1zn3QwuclAec45x5vnchEREREJBKUEIqIiIiIiMQojTIqIiIiIiISo5QQioiIiIiIxCglhCIiIiIiIjFKCaGIiIiIiEiMUkIoIiIiIiISo5QQioiIiIiIxCglhCIiIiIiIjFKCaGIiIiIiEiMUkIoIiIiIiISo5QQioiIiIiIxCglhCIiIiIiIjFKCaGIiIiIiEiMUkIoIiIiIiISo5QQioiIiIiIxKiIJYRm9qaZbTKz3zSxL8XMnjKz6f5jit/ez8zeM7OZZnZ920ctIiIiIiLScUSyQngxcM1O9k0CFjnnxgOL/W2AW4HfO+fGARPNbGhrBykiIiIiItJRRSwhdM6t3sXuI4Cp/vor/jbASOfcdH/91ZB2ERERERERaaH2eg9hJ6DQXy8C8vz10HhD20VERERERKSFEiIdwE5sBXL89Wx/G6Au5JjQ9gbMbDIwGSA9PX300KHqWSoi0tHNmTNns3MuP9JxRIvOnTu7fv36RToMERFpA7v6jGyvCeE04ERgnv84zW+fb2ZjnXMfAicAVzb1ZOfcFGAKQEFBgZs9e3ZrxysiIhFmZisjHUM06devH/p8FBGJDbv6jIxYQmhmDwJjgWQzKwBuBI5xzv0ZeBR4xMymA6uBi/ynXQc8bGZJwOvOua/aPHAREREREZEOImIJoXPu0iaa5/n7KoBzm3jOcuDI1o1MREREREQkNrTXQWVERERERESklSkhFBERERERiVFKCEVERERERGKUEkIREREREZEYpYRQREREREQkRikhFBERERERiVFKCEVERETauXnr53HGs2dQWVsZ6VBEpINRQigiItKOmdloM3vLzP5nZreb5x4zm25mU80szz8uz9+e7u+3SMcu4XPy0yfz4qIXmbV6VqRDEZEORgmhiIhIO2VmScCtwJnOuSOdc9cCxwFpzrnxwHPAtf7h1wLP+u3p/nHSQawtWQvA3HVzIxyJiHQ0SghFRETar0OBUuBpM3vPzMYDRwBT/f2v+Nvsol2iXE2gpn59wcYFEYxERDqihEgHICIiIjvVAxgBjAQygXeB6UChv78IyPXX8/ztYHte24QorW196XocDoANZRsiHI2IdDSqEIqIiLRfW4EPnXPFzrk1wGYgHsjx92ezPTks9LeD7Vsbn8zMJpvZbDObvWnTplYNXPbclvIt/GHaH/h8w+fA9u6iABvLNkYqLBHpoJQQioiItF+zgMFmlmBmmUAX4D/Aif7+E4Fp/vq0nbTXc85Ncc4VOOcK8vPzWzdy2WNPL3ia37//e65927s9dE3JGgBGdB2hhFBEwk4JoYiISDvlnCsC7gHeB94BfgW8DtSY2XTgfODP/uG3A+f77TXAW20dr+yZTWWbKK0urd/+ctOXACzduhTYXiEc2W0kG8s24pxr+yBFpMPSPYQiIiLtmHPuCeCJRs2XN3HcFuCkNglKwqbO1dHlL104tNehfHjxhwB8tfkrAJYXLqesuoy1JWtJiEtgv/z9qKytpKS6hKzkrEiGLSIdiCqEIiIiIhEye+1sAD5a/VF925KtS8hMysThWLJ1CWtK1tA9oztd0rsAsLl8c0RiFZGOSQmhiIiISIR8tu6z+vUt5VsI1AXYULqBw/ocBsCKwhWsLVlLz6ye5KV6A8cWVhQ2eS4RkT2hhFBEREQkQkKnkVhWuIzN5ZsJuABje48FYEWRlxD2yOxBboo3w8jWih0GkBUR2WNKCEVEREQiZEPp9oRwbcla1pWuA2C//P3ISs5iReEKvt32LT0ze5Kb6iWEhZWqEIpI+CghFBEREWkDlbWVPP/l8w1GCd1QtqG+8remeA3rSryEsHtGd/rn9GfehnmUVJfQJ7tP/XHqMioi4aSEUERERKQN3PnxnZz177N47ovn6ts2lG1g/y77kxCXwJqSNfUVwu6Z3RmQO4AZq2YAeAmhKoQi0gqUEIqIiIi0gZKqEgDeWrZ9isiNZRvpntmd7hndvYTQrxB2y+hG/5z+9cf1ye5DakIqyfHJqhCKSFhpHkIRERGRNrCpfBMAq0tW17cVVhSSl5JHz6yerCleQ2ZSJrkpuaQkpNA/d3tCOCB3AGZGbmquBpURkbCKWIXQzCaZ2YdmNtPMRjXa90sze99fVpjZX/32R83sM7/935GJXERERKTlVm1bBcCmMi8xdM5RVFlETkoOPTN71ncZ7Z7ZHYBjBhxT/9zgHIS5KbnqMioiYRWRCqGZ5QJXAIcAPYEngMOC+51zfwP+5h/7GhCa/P3MOTej7aIVERER2Xuri73KYLBSWFFbQU1dTX1C+Nayt8hNyaV7hpcQDuk8hKsPvZpR3bf/bp6bqoRQRMIrUl1GxwDTnXPVwAozyzSzZOdcVehBZtYF6O+c+zik+W9mVgXc65x7tg1jFhEREdljxVXFgFchDFYHAXJScjAzSqpLWLxlMSftc1L9c/587J8bnCM3JZe1JWvbLGYR6fgilRB2AkJ/3ioC8oB1jY47B3guZPtq59xmM8sD3jWzT51zy1s1UhEREZEwKK0uBaAqUEVJdUmDhDArOQvwJp0flDdop+fIS83ji01ftHqsIhI7InUP4VYgJ2Q7229r7HzgyeCGc26z/7gVeBsY0dTJzWyymc02s9mbNm0KV8wiIiIie8Q5R0l1CT0yewBelTA0Idy/y/71xw7uNHin58lN0aAyIhJekUoIZwGHmVmimfUBSpvoLjoYcM65JSFtOf5jEjAO+LqpkzvnpjjnCpxzBfn5+a31GkRERESapTpQTW1dLX2z+wKwpWJLg4RwSOch9ccO6TSkqVMA3j2ExVXFBOoCrRqviMSOiHQZdc4Vmtl9wDTAAT83s5HAMc65YGf5C4CnGj31WTPLABKBJ51z6jMhIiIi7V5JtTcHYd+cvny0+iO2VmxtkBAmxScxedRk6lwdI7o12QEK8CqEAEWVRXRK69TqcYtIxxexeQidc48AjzRqnhey/3dNPOe4Vg5LREREJOyC9w8GK4SNE0KAB055YLfnyU31EsLCykIlhCISFpqYXkRERKSVNU4It5Rvqa8aZqdkN/s8eal5gDehvYhIOERsYnoRERGRjuq37/2Wf8z+R/12SZWX/PXO7g1srxCmJKSQkpDS7PMGu4xqLkIRCRdVCEVERETCyDnHH6f/EYAfF/wY2F4hzEnJITs5m60VW6morajvLtpcwS6jGmlURMJFCaGIiIhIGK0vXb9DWzAhzEzKJC81jy0VW6gOVLc8IQxWCNVlVETCRF1GRURERMLoy01f1q9vq9wGbB9lNCMpg7zUvPouo3taIVSXUREJFyWEIiIiImG0oWxD/fo3Rd8A2yuEGUkZdErrtMcJYfCeQ1UIRSRclBCKiIiIhFFxVXH9enBqifouo8nbu4wWVha2OCEEb6RRVQhFJFx0D6GIiIhIGAW7iQJsq/K7jFaVYBipCankpXhdRmsCNXRJ69Li8+em5GpQGREJGyWEIiIiImG0swphRlIGZlbfZRSgW0a3Fp8/NzVXFUIRCRt1GRUREREJo9CEMHRQmczkTGD75PIAXTO6tvj8uSm5uodQRMJGCaGIiIhIGBVXF9MjswewvctosEII0Cm1U/2xXdP3ICFUhVBEwkgJoYiIiEgYFVcV0ym1E2mJaQ26jGYmeRXCQXmD6o/dkwphXkqeKoQiEjZKCEVERETCqLiqmKzkLLKTsxt0GQ1WCId1GVZ/7D55+7T4/LmpuZRUl1ATqAlPwCIS05QQioiItGNmVmFm7/vLxea5x8ymm9lUM8vzj8vzt6f7+y3SsceqYEKYk5JDUVUR0LDLaFZyFgDpielkp2S3+Py5Kd7k9MHqo4jI3lBCKCIi0r6tcc5N8JeHgeOANOfceOA54Fr/uGuBZ/32dP84iYDS6lLSk7xkL1ghLK0urR9UBmD5FctZ8fMVe3T+3FQvIdR9hCISDkoIRURE2rduZjbNzF4ws37AEcBUf98r/ja7aJc2Vl5T7lX/krMbzEOYkZhRf0z/3P7kp+fv0fmDFULdRygi4aB5CEVERNq3fs65zWZ2HPAwsAwIZgJFQK6/nudvB9u3z20gbaq8ppy0xDRyUnJYUeRVAUO7jO6t4LQVqhCKSDioQigiItKOOec2+49vAn2BrUCOvzub7clhob8dbN/a+FxmNtnMZpvZ7E2bNrVm2DEtmBAGB5Vxzu3QZXRvBLuMBie3FxHZG0oIRURE2ikzyzCzeH/9AGAzMA040T/kRH+bXbTXc85Ncc4VOOcK8vP3rLui7JpzbnuX0ZRsiiqLKK8px+HCViFUl1ERCSd1GRUREWm/9gMeMLMSwAE/AhYAJ5vZdKAYuNA/9nbgcTO7DPgceCsC8cacad9MIz0pnYIeBQBU1lYCkJaYRnJCMlWBKjaXbwaon4dwb2lQGREJJyWEIiIi7ZRz7hPgwCZ2Xd7EsVuAk1o9KGlgwmMTAHC/dwCU1ZQBXkIYZ15HrDUlawDCViFMik8iLTFNFUIRCQslhCIiIiJ7oLautn7dOYeZUV5TDngJYUpCCgBrisObEII3sMyWii1hO5+IxC7dQygiIiKyB1ZtW1W/vrFsI0CDhDA46fzq4tUAYRtUBiA/Lb++K6qIyN6IWEJoZpPM7EMzm2lmo5rYt8LM3veXnn57PzN7z3/O9ZGJXERERASWbV1Wv/5N0TdAo4QwuWFCGM4KYX56fn0SKiKyNyKSEJpZLnAFMAG4ALi7icMeds5N8Jc1ftutwO+dc+OAiWY2tE0CFhEREWkktMtmUWUR0DAhzEnJAeDb4m+B8CaEXdK7sKlcU4eIyN6LVIVwDDDdOVftnFsBZJpZcqNjLjSzGWZ2s5kF4xzpnJvur78KHNFWAYuIiIiEKq4qrl9vKiEMdhkNTk4fnFA+HPLT8tlUpoRQRPZepBLCTmyfSBegCAj9v+TLwL54CV9f4Hy/PW4Xz6mniXdFRESktZVUldSvN5kQ+l1GVxS2TkJYVlNGRU1F2M4pIrEpUgnhViAnZDvbbwPAOVfonAs45wLAM0CBv6tuZ88JpYl3RUREpLXtrkKYmZyJYWyp2NJg1NFwyE/3vt+o26iI7K1IJYSzgMPMLNHM+gClzrmq4E4zywk5diKw2F+fb2Zj/fUTgA/aIlgRERGRxoqrislIyiApPqnJhDDO4shKzgKgU2qnsF47P81LCDWwjIjsrYgkhM65QuA+YBrwL+BKMxtpZtf4h1xjZrPMbCZet9AH/fbrgFv89unOua/aOnYRERFp/+atn4fdZMxfP7/VrlFcVUxWchY5KTk7JITpSekA9QPLhLO7KHiDygC6j1BE9lrEJqZ3zj0CPNKoeZ6/7wbghiaesxw4stWDExERkaj2pxl/AuD1pa8zotuIVrlGSXUJWclZ1Lk6iqqKgIYVQoDOaZ1ZuW1l2BNCdRkVkXDRxPQiIiLS4awtWQs0vM8v3IIVwuzk7AYVQsNIjvcGTx/SeQiwvaIXLsEuo6oQisjeiliFUERERKS1bCn35ghcXri81a5RXFVMZlImsH3E0bLqMtIS0zAzANITva6jI7qGt0qZlZxFYlyiKoQistdUIRQREZEOJzjYysptK1vtGqXVpWQkZZCVnFVfiSyvKa/vLgowrvc4AI4acFRYr21m5KdrLkIR2XuqEIqIiEiHUltXy5YKr0JYWFG4m6P3XHlNOelJ6STEJWxPCGsbJoQXjriQif0n0ju7d9ivn5uSW3/voojInlKFUERERDqUzeWb69eD9/YF1dbV8sDsB6gOVO/1dcpryklLSCMraecVQjNrlWQQvBFMWzPhFZHYoIRQREREOpRgd9EBuQN2SAif+vwpfvzqj/nLh39p9vmcc3y+4XOccw3ag8lfsMuoc26HhLA15abm7vD6RERaSgmhiIiIRK3K2kp+895v2Fqxtb4tuD4gdwBVgSoqayvr9wWrhws2Lmj2Nd5e/jYj/jGCv3701wbtoQlhwAWorK1s04QwJyWHwkpVCEVk7yghFBERkaj1wlcvcMv0W7jh3e3TFwerZv1z+gOwrXJb/b4VRSsAWF28utnXWLVtFQD/mP2P+raaQA01dTX1CSF4o462aYUwRRVCEdl7SghFREQkai3Y4FX6Fm5aWN8WTJL65fRrsA2wrHAZsH1aiubYULoBgPi4+Pq2itoKANKT0slM9qaeKK4qrp92oi3kpuSyrXIbda6uTa4nIh2TEkIRERGJWvM3zAcaVvyCFcG+2X2BhglhMBFsSVfL9aXrAW+aiaCy6jKAHSqEJdUl9Qlia+szuw9/efQvbF2/dfcHi4jshBJCERERiVrBZG1tydr6QV+CCWCf7D4NtgG2VXnJYktG51xXug7w7j8MXqO8phxomBCWVJdQUlVSP1l9a+te3Z1R34yisqhy9weLiOyEEkIRERGJWhvKvO6c1YHq+sFkiiqLyEzKJCclB/AStaBgclgVqKKipqLBuf40/U/cOuPWXV4jWCVsKiHcVrnNqxC2UUJYMKgAgFyX2ybXE5GOSQmhiIiIRKU6V8fGso0MzB0IeFVC8KqA2SnZ9V03S6q2J4TbKreRnZwN7Nht9Pr3rue6d6/bYXqJ4ByDsH2U0tCEMJgAbi7fTG1dbZt1GY3P8O5pDJQF2uR6ItIxKSEUERGRqFRYUUhtXS0Hdj8Q2J4QFlUWkZOSU5+oBSuElbWVVAWq6gebCe02GqjbnlQt3rK4wXVKq0vrq41NJYTBCuGakjUAbVYhjEv3vsYFSpUQisieU0IoIiIiUSk4Af3++fsDsKl8ExCSEIaM/gnbB5vpn+tNRxFaIQwmkwDfFH3T4Dql1aX0zOzpncO/BzGYEKYnptcnhMFzqEIoItFECaGIiIhEpWC1bt/8fRtsBxPCpPgkkuKT6ruMBpO53lm9gYZdSUOTwMYDzpRWl9IjswewPbksq9k+ymhKQgoJcQltXiGMT/cTQlUIRWQvKCEUERGRqBSs8PXP6U+8xbOpzKsQbqvafp9gZlJmfZfR4IAyvbJ6AQ2nkQitFgYHpwGvK2l5TTk9s7wKYTAhDO0yamZkJmWypthLCDOSMsL7QncimBDWlWkeQhHZc0oIRUREJCoFK3l5qXl0Suu0Q4UQvO6bwYQw2GU02P0zdPTR0IFjQhPCYOLXPaN7g+NCE0KArOQsdRkVkaikhFBERESiUrCql5uaS+e0zmyu8OYJbJAQJmXWdw3dVYUwNCEMrRYGjwkmhMGksqmEMDg9RfDarU1dRkUkHJQQioiItHNmNtjMaszsMDNLMbOnzGy6/5jiH9PPzN4zs5lmdn2kY24LwQphdnI2+Wn5bC7fTGl1KXWurukKoX8P4a4Sws5pnRtUCIPH5KbmkpqQukOFMDUxFYCuGV3rn9Mto1t4X+hOWJJhCaYKoYjsFSWEIiIi7d9vgWn++iRgkXNuPLDY3wa4Ffi9c24cMNHMhrZ1kG2tsLKQ7ORs4uPi6ZzWmU1lm+qTvgb3EFY17DKan55PYlxig4SwpKqEeIunZ2bPBglhcPCYjKQMspKzGiSESfFJJMQlANsriIlxieSmtM1E8WZGXHqcKoQisleUEIqIiLRjZnYwsB5Y7TcdAUz111/xtwFGOuem++uvhrR3WIWVheSmeslX57TObC7fXN8ttKkKYVFlEYaRkZRBRlLGDhXCrOQsclNz688B2yuE9Qlh9faEMD0xvf644CikXTO6Ymat8nqbEp8RrwqhiOwVJYQiIiLt2w141b+gTkDwJrciIM9fD/1MD22vZ2aTzWy2mc3etGlT+CNtY4UVhfXVuM5pndlSsYUt5VsA6hPFBhXCqm1kp2QTZ3FkJGU0HFSm2ksIQ0clhSYSwuC0E9Vl9fcPwvaEMFgxbCvx6fGqEIrIXolYQmhmk8zsQ/9eh1GN9l1rZrP8ffeY/1Obmb1vZh/5j/dEJnIREZG2YWYnAbOdc1tCmrcCOf56tr8NEDr3QGh7PefcFOdcgXOuID8/vxUibltbK7bSKa0TAPlp+dS5Or7e8nX9NjScdiJ0OorGFcKSqhIykzO9imJV0wlhdkr29i6jteUNEsK+2X0B6JPdp1Ve687Ep8dr2gkR2Stt+zOWz8xygSuAQ4CewBPAYSGHvPj/7d15mFx1ne/x97eq931N0iEJYYksshMRQYSgjiwCjhs6KG5XdBwXnlHw6nVG5wHvqOOIoyOjOKKC6EUdhnUUhkUIAWGIbMEECElIOmsv6X3v/t0/zjldp6qruqu7q7qquz+v56mnqn51qup3sp18+vtbnHPf8o/9NXAu8ID/2nucc82IiIgsfCcB55jZGcDxwNHA74ALgGf8+2Bu4bNmdoZz7jHgfODKue7sXGvvb2dltbfJfENZAwCbWzcD3jxB8IaM9gz1TFx91G8PBENGp6oQbju4DfCGjIYD4QVrLuC37/ktp684PUtnm1y0QhVCEZmdnARC4DRgvXNuCNhuZpVmVuycGwRwzr0cOnYQGPEfO+D/mdkQcK1z7sE57bWIiMgccs59Hfg6gJn9DPh3YCNwo5mtx5tX+BH/8C8BPzGzIuB3zrnNc9/judXe305diTcyNjEQBs8riyoZc2P0DffROeANGYWJFcKuwS7qy+rjhphCLBCWF5ZTVVwVt+1EOBAWRgt517HvytapphQtjzLcNjzn3ysiC0euAmF4/gPE5jrsDR9kZmcDTcAjftN7nHOtZrYSuN/M1jrnuklgZlcAVwCsWjW3QzdERESywTn34dDT9yd5fRuwbs46lGPOOS8QlnqBcEn5EgCe3/881cXVFEWLgNgm8d1D3XQMdIwP6awoqmBn587xz+sa7OKw2sOoLK6kf6SfkbERCiIF8RXCovhVRsOBMFeiFVEGXh3IdTdEZB7L1RzC8PwHSDLXwcxOwJtE/z7nnANwzrX697uAZ4Ejk334QpsjISIiIvG6h7oZdaPjgfDQGm8O3+7u3ePDRcGrEII3R7BzsHN8yOiEOYRD3VQWVVJRVAHEKoO9Q71ELEJJQcn4ojLOOXqGeigviq0ymiuR8ohWGRWRWclVIHwCeKOZFZrZKqAnGC4KYGZHAjfihcFWv83MrMp/XIk3l+LVue+6iIiI5FqwV2AQCGtLasfDX7DiJ8RXCDsHQovKFFbEDQ0NzyEExl/rGeqhoqgCM6OquIpRN0r/SD89Qz3jx+aSVhkVkdnKSSB0zh0ErsebCP8r4EozO8nMrvIP+S5eBfHn/oqiF+INb33IzB4F7ge+5pybsIKaiIiILHyJgdDMxquExzUeN35cENq6BrvGt52A+EVlxtwYPUM9XiAMBUiIBUKAquKq8c/qHuweb88l7UMoIrOVqzmEOOduxKsChj3jv/b2FG87NZt9EhERkfkhMRAC1Jd6W1Ac1XDUeFsQAHd37WbMjcUNGe0d7h0Pg+CFxwkVwuHkgTCfKoRu0DE2MkakQNtLi8j06V8OERERmXeSBcIbL7mRdavXcdFrLhpvC14PtosI70MI3uIwwUIxU1UIg3DZ3t9O/0j/+LG5FK2IAmgvQhGZsZxVCEVERERmKlkgPLz2cB78UPyOVEHVcHvHdoC4bSfAC3xxgTDFHMLgdfCqjeHPyKVouRcIR3tGKajWf+tEZPpUIRQREZF5p62vDYDa0tpJj6sqriJqUV45+ApA3JBR8IJfEP7SnUO4p3sPQF4MGY2Uef+VG+3XPEIRmRkFQhEREZl32vvbKSsso6SgZNLjzIza0lo2t3gb1i8tXwokrxBWFieZQzjUQ3mht73EeIWwe/f48bkWKfX+KzfWryGjIjIzCoQiIiIyL3QMdNDS2wJA+0B73HDRydSX1tPS572vqbIJiFX3JgwZTagQ9g71pqwQ5sWQ0TJ/DmGfAqGIzIwCoYiIiOS9lt4War9Zy9k/OxvnHK19rWkHwuC4qEXH5xSmmkNYWlBKxCJJ5xAGIXK8QphPQ0b7NGRURGYmrUBoZu80s5fNrNPMusys28y6st05EREREYANuzYAsLl1M8/tf47mrmZWVK1I673BRvVLypcQjXgVtXAgDKqBVcVVmBmVRZVJ5xAWFxRTHC1mZ+fO8eNzLVrqVwg1ZFREZijdCuG3gIudc9XOuSrnXKVzLvf/CoqIiMii8Nz+58YfP7D9AXZ17mJV1aq03nts47FAbEEZCC0qM9Qdm0PoV/wqi71AODQ6xPDYcNzQ0OqSara2bwW8gJlrqhCKyGyluz7xfufc5qz2RERERCSFTQc2cWTdkTjnuPeVe2nrb2NVdXqBcHXNagCWVSwbb0scMlpSUEJhtBDwgmH3YPf4hvXhQFhVXMWB3gMANJQ1zPq8ZkuLyojIbKUbCJ8ys1uB24HBoNE5d1s2OiUiIiIS1tzVzKrqVaysWsnPn/05ACurV6b13gvWXMAZK8/gR2//0XhbsHhMz1AP3YPdccM/gwphqkAI3gb3xQXFszupDNCiMiIyW+kOGa0C+oC/AC7yb2/PVqdEREREwvb27KWpook3rnrjeNupTaem9d5lFcvY8NENrKlfM95WFC2iMFLoVQiHuuIWiJmsQriyyguhjeWNszqfTNGQURGZrbQqhM65j2S7IyIiIiLJOOfY2+0Fwrce/lYAiqPFHN1w9Kw+t6KoYnzIaGKFcH/vfnqHegHG9yEEOKL2CCA/houCFpURkdlLKxCa2Qrg+8CZftN64HPOueZsdUxEREQEvP0HB0cHaaps4tCaQ/nzp/7MyNgIZjarz60oqhhfVCYuECZUCMuLYoEwWEgmH1YYBbAig4gqhCIyc+kOGf0pcCew3L/d5beJiIiIZNW+nn1AbFGYYxqP4filx8/6c4MKYXt//Cb3wbYTnYOdgDdfMLDusHUAfOWsr8z6+zPBzIiWRTWHUERmLN1FZRqdc+EA+DMzuzIL/RERERGJ09bfBkBjWWbn7VUWVyYPhMVehbBjoAOI367itENOY/jvhimIpPtfqOyLlEY0ZFREZizdCmGbmX3AzKL+7QNAWzY7JiIiIgLQ3t8OQG1pbUY/t7Koks6BTtr62uICYUVRBcNjw+zv2Q/EB0Igr8IgeAvLaMioiMxUuoHwo8B7gX3AXuDdgBaaERERkawLAmE4tGXCkvIlvNr5KoOjg9SX1o+3ByuO7uraBeTPfMFUNGRURGYj3VVGXwUuznJfRERERCY42H8QyE4g3NO9Z8JnB3sUNnc1U1VcRTQSzej3ZlqkNMJovyqEIjIzkwZCM7vaOfctM/s+4BJfd859Nms9ExEREcGrEEYskvFKXbBiKDBhURnwKoThBWXylSqEIjIbU1UIN/v3T2W7IyIiIiLJtPe3U1tSS8TSnemSnnAgDO8rGATPnZ07aapoyuh3ZkOkLMJI50iuuyEi89SkgdA5d5d///OgzcwiQIVzrivLfRMREVlQzCzinFMpZ5raB9ozPlwU4gPhUQ1HjT9eWrEUgNa+Vo5pOCbj35tpkdIIY/v0x0pEZiatH7WZ2S/NrMrMyoFNwJ/N7Krsdk1ERGT+M7OfmNldZnYo8MNc92c+au9vz/gKo0Bc2FtavnT88fLK5bH2iqXkOw0ZFZHZSHfsxbF+RfAdwO+Aw4APZqtTIiIiC4jDW6n774D6KY6VJA72H8xKhXBN/RoAaktqMbPx9oayhvGtJVZXr87492aatp0QkdlINxAWmlkhXiC80zk3TJJFZqbDzD5sZo+Z2QYzOyXhtRIzu8XM1vv3JX77ajN70H/Pl2fz/SIiInOk2TnXD1wFHJrrzsxHiRvHZ1LLVS289JmX4toiFhmfr3hoTf7/lkVLo9qYXkRmLN1A+ENgB1AOPOIPe5nxHEIzqwU+C5wDfAD4XsIhHwa2OOfOAl70nwN8A/iqc+5M4FwzO3qmfRAREZkLzrmv+fcHnXNrc9ydeam9v526kuwEwoayhrgFZQIVRRUAHFqd/4FQFUIRmY0p9yH0F5HZ75w7JNS2E1g3i+89DVjvnBsCtptZpZkVO+cG/dfPBr7lP74LuBovlJ7knFvvt9/jH7dlFv0QERHJKjO7PM1Dn3HOPZfw3irg98AQUAZ8CXgQ7wepJwGdwOXOuXYzqwNuAqqBZ4DPOudmNZonH4yOjdIx0JG1CmEqD33oIb6+/uucuerMOf3emYiWRXGDDjfqsKhN/QYRkZApK4T+amhXJ7Q559xs1jeuBw6GnncAdSleD78W7m/ie8aZ2RVm9pSZPdXS0jKLboqIiMzaYWneapK8twd4k3PuHOB9eCNl3gaU+aNofk3sGn01cKvfXu4fN+91DnbicFlZVGYyJyw9gVvffeucB9GZiJR6/z0aG9CwURGZvikrhL77zewLwK1Ab9DonGuf4fe2E3/hq/bbkr0efm1skveMc87dANwAsHbt2nn/01EREZm/nHP/kOo1Myv05+Wneu8YsWtfFfAc3uiYu/22u4C/9h+fDXwz1H42XnVxXjvY7/18eD4Es1yJlHmBcLRvlGh5NMe9EZH5Jt05hJcCfwM8Amz0b7PZrP4J4I1mVmhmq4Ce0HBRgIeBC/zHF/jPAZ41szP8x+f7/REREZk3zPNmM/sJ0JzG8YeY2aPAfcB/MnEUTVA6q/OfB+0TEtR8HEHT1t8GKBBOJlrqhUAtLCMiM5FWhdA5d1gmv9Q5d9DMrscLeg74nJmdBLzVOfdPwM+AG81sPd7F8iP+W78E/MTMioDfOec2Z7JfIiIi2WJmpwN/hbdidx3eD1q/MNX7nHO78X6Iuhr4A/D/iB9FE4TDg/7zDlKMopmPI2ja+rxAWF+qHTtSCVcIRUSmK92N6cvM7CtmdoP/fI2ZvX02X+ycu9E5d4Zz7kzn3FPOuWf8MIhzrt85937n3Fn+/YDfvs05t85/z7Wz+X6ZwoMPghk0J/zweu9e+Nu/heGUI5xERCTEzP6vmb0MfB1vyOfJQItz7ufOuYNTvLc49LQL6Cb1KJpU7fNae7+Xa+vLFAhTiZb5FUJtTi8iM5DukNGf4q1wFgzX3A0okC0EmzbB9u0T2//lX7z7xx+Pb//85+G66+Dee7PfNxGRheF/AfuBfwNuds61kf5evseZ2SNm9hBwB3AlcC8w7I+iuQz4J//YbwGX+e3DeENM5z0NGZ3a+KIyGjIqIjOQ7qIyRzjnLjWz9wM45/rMTOsaLwTHH+/dJ65MPuZfVHp64ttH/MVl22e6npCIyKLTBLwVeD/wXT/clZpZwVQrdjvnNgJvSvLS3yQ5tg24MAP9zSttfW0YRm3J3K4yOp9oyKiIzEa6FcIhMyvF/4mmmR0BDE7+FskbQ0Pwy1/GQl4yfX3xz4Pgt3NnfHtZmXf/6quZ65+IyALmnBt1zv3eOfch4AjgdmADsNvMfpnTzs0D7f3t1JTUEI1o9cxUNGRURGYj3UD4Nbylq1ea2S3AAyTsTSh57Gc/g8sug+uvj28PzwPcsiX+tX37vPvE4NfR4d2/8kp8e2sr/OlPs+2piMiCY2arghuwFPgf4G+BtwCbQ69X5bSjeaqtv03zB6cQDBkd7VeFUESmL91VRu8zs43A6YABn3POtWa1Z5I5Q0Pe/d13w6c/HWs/cCD2uKsr/j3d3d79wYT1DoKgmHj8YYd5w0t374bly2ffZxGRhePneCNswlMtgnH6BpzrP/8ZcNOc9mweaOtv0/zBKahCKCKzkVYgNLO7gF8Cdzrneqc6XnLEOXjhBTjuuPj2INT1JvzW7d8fe5wY8AYGkr8n2LcqsT2Ya7h/vwKhiEiIc25drvswn7X3t9NY1pjrbuQ1zSEUkdlId8jot4GzgD+b2W/N7N1mVpLFfslMXHedt0jMY4/FtwfBr78/eTukHwiDuYaJ7YHOzvT7KyIiMoW2Pg0ZnYpWGRWR2Uh3yOjDwMNmFsUb2vJx4EZA8x3yyXXXeffhoAexoaGJoS8c3tINhEGoTFyEJtlnioiIzFJbfxt1JRoyOploqYaMisjMpbvtBP4qoxcBlwKn4M2JkHwShLe2tvj2IBAmhrVw2AvmDAaCQJgY/JJVCEdDQ1QUCEVEJEOGR4fpGuxShXAKFjWs2DRkVERmJK0ho2b2a2AzXnXwX/H2JfxMNjsmM1Dg5/vWhPV+gpVBE6uA4bAXfm14OBbywsFveDi2Mmm4PTwUNRwIH3oIqqomVixFRETScHDAmwNfX6pAOJVoaVQVQhGZkXTnEP4ELwR+0jn3kHNO/+Lko2A10cRAGIS3gYHYMeH24uL4QBhUB8PHQHzwSycQ/vjHXuXx1lvTPwcRERFfW5834kUVwqlFK6KM9qpCKCLTl1YgdM7dC7zezP7KzC4Pblnum6SyYwf86EexzePB23Q+CHXBSqCBcHgLB7/eXjCDJUvih4wGgbCyMr6KGDyuqopvTxUIV6707p99Nq3TivP88/CpT6VevEZERBa8fT3eVkdLy5fmuCf5L1oZZbRbgVBEpi/dIaM34600+kbgdf5tbRb7JZM55xz45CfhkUdibT093rYTMLFC2NcH1dXe43Ag7OuDsjIv4CWrENbXxw8TDYJfY6NXaQwCaTgQBsNTITaXMdyWrptvhn/7N/jmN6f/XhERWRD2dO8BYHmltjOaSrQyykjXyNQHiogkSHdRmbXAsc4FiUNy6tVXvftw8AuHrmBPwEBvLxx5pFe9C1fwenu9QFhaGj9MNBwId+zwjqupiVUFGxrglVe89urq+EAYrhwG/ZvJQjODg979rl3Tf6+IiCwIe3v2AtBU2ZTjnuS/gqoCVQhFZEbSnUO4CViWzY7IDIRXEw2HrnAoGx72KnmN/qa+ifMAy8uhpCR5IKyrix0X/tzgs8JzEwPhzw8CYbj62NUFN94Ie/bE2h56CE44If58gmGv7e2IiMjitLd7L2WFZVQWVea6K3lPFUIRmal0A2ED3qb095rZncEtmx2TFMJF2mSBMHF+XxDagnAXVN7AO6683FtUJtweBLyGhvjPCFcIw8/DITAcDpNVCG+5BT72Mfjyl2NtN93kzRn813+NtQWBMHELDRERWTR2d++mqaIJM8t1V/Ke5hCKyEylO2T0a9nshExDePGXcFgK2pcsSb4aaBAIE1cQLSvzKoQHD8bag2Nqa+OfBwEw8bOC7yspST6fMBwIgy0owpW/sjLvfvv2WJsqhCIii972ju0cVntYrrsxL2jIqIjMVFqB0Dn3cLY7ImkKB6Tw48R5f4FUIS54LVmFMAh1wUI0wWtBe2K1MWivrY0PhMHjcCAMqobhOY9BSAyHUlUIRUQWva3tW3nvse/NdTfmhWDIqHNOFVURmZZJh4yaWbeZdSW5dZtZ12TvlQz4znfgM5+JbRIP8QEp/DgIX/X1yYeM1vt7OKWqECabQ5gYCIPPramJPy4cFBMDZ3BcsP9h0Odw35MFwuBxe3v8MFkREVkUDvYfpL2/nSPrjsx1V+aFaGUURmFsQFtFi8j0TBoInXOVzrmqJLdK51zVXHVy0fr85715dQ+HCrRBZS0aja+8hUNZX18sRCVWCBMrgWVlqecQJga/4JggKCYGwnCFMHExm2BhmSAIhldITRxGOjrqfU5FhfcZ4WGyIiKyKGw6sAmAoxqOynFP5oeCKm/Ql4aNish0pbuojORSeEXO8Eqf4UpgeMjo6Ghs78DJ5hAODnrVwXQrhFMFwrq62OPgfskS7z7YCiNcIUzcNzGoCgbn1dQU/15gbGyM008/nW9/+9uIiMjC9Xjz4wCcdshpOe7J/BCtjAJopVERmTYFwnw1EvoHPZhPB/ErfYYDYXjIaPi4cPUOJga/4uKZVwiTzS1MDImJK5IGgXB01KtwOherAAYVwiDEJoZJYNOmTTzxxBNcddVV9AbHiYjIgnPfK/expm4NS8qX5Lor80IQCFUhFJHpUiDMVwcOxB6nEwgT9w4Mz9+DWLhLDH4zqRBWVcUfF4S/mpqJFcLEgNrZCaWlscdDQ174rajwPm9gIBYIly717kOB8OHQ8NmXXnoJERFZeB7Y9gAPbH+AD534oVx3Zd4YHzLapUAoItOTk0BoZnVmdreZrTez71vCclhmtsLMHvZf32Bma/32c8xsr5n9wb+dmov+z4nwMNFwOJysQhiNxsJa8FqqYZ7BayUlXoVweBjGxuKPmU4gLC725iMGQTD4/sRA2NsLy5Z5j3t6YmHvkEO8+87OWCAMH+fbtm3b+OPNmzcjIiILy97uvbzr1+/itY2v5TOv/0yuuzNvjA8Z7daQURGZnlxVCK8GbnXOnQWUA29LeL0beI//+seB60Kv3eOcO8e/bZyb7uZAeFuGdIeMlpTE9vRLrBCWlkJhYfIhoyUl3vMg8E02ZDQa9baqCLcPDHifX1rqDQUdGUk+ZHR42LuFK3/BcNEg/PX2xgJgkgphc3Mzq1evJhKJsGXLlgm/bCIiMr99/8nv0z3UzW2X3kZVsdavS5eGjIrITOUqEJ4N3O0/vst/Ps451+mcC8pig0D4x11vC1UWS7Pf1Sxrb4eLL4bE4Y9BUKqri9+iIVx5C68mGoSyVIEwcWjoyIgX3oIKIcTPCSwqig3tDFcIkwXI/v5YIAyeh4Nr0J9kQ0GDsBdeQCbxuNBcwebmZo444giWLVtGc3MzIiKysNzz8j2sW72O19S/JtddmVc0ZFREZipXgbAO6PAfd/jPJzCzKPA94Ot+00ZgjV857AK+kOJ9V5jZU2b2VEu4upaP7r8f7roLLrwwvj0clEIVMvr6vFBWWek9Dw/bLC2NhbXEhWCCQJg4/DNoD78nPLcw8bOCRWgSvzt8fH9/8grhZIEwPDx0kiGjzc3NrFixgqamJvbu3YuIiCwcB/sP8vz+5zln9Tm57sq8oyGjIjJTBdn6YD/MbUjy0j3AQaAaLwxWA+0pPuZHwO+cc/cDOOfCG9LdAvxjsjc5524AbgBYu3Ztfu9qvsnbZyku9IWfL10KO3fG2vv6vCpguBJYWhoLZYnVviC0BUEuHPrC7YnvSfZZQ0OpA2G4QjgwkHwOYbJAGAw/TTavMGHIqHOOffv20dTURFtbG7t27UJERBaO5w88j8OxdvnaXHdl3olWaMioiMxM1gKhc24UOD3Za2ZWAVwA/NK/vy3JMd8G9jrn/jXUVu2cC3ZjPxd4MdP9nnNBIGxr8xZ1ifhF22DI6NKlEF48JVkgrK+PDRlNVu2LRKCgIH7IaOJQ0sT3lJTE3pc4ZNQsfquKZENGJ6sQBuGvuzt2HskqhAmBsKenh5GREerq6mhqauLJJ59M4xdYRETmi5favOkTR9VrM/rpsogRrYhqyKiITFuuhox+C7jMzNYDw8B9AGb2XTNr9FcV/Rxwlr+a6G/8913mDwV9BC8Q/t9cdD6jgtVEh4fjVxPt6fGCV2Nj3By6CYEweC0IZYlVvXCISxUIU1UIg9cTh4xCfLUxcbhqOBCGK4TJhocmtvX2xs6prs4LpP4xB/2N62tra2lqaqKlpYWREQ2NERFZKF5qe4niaDGrqlfluivzUkFNAcMHh3PdDRGZZ7JWIZyMc64NuDBJ+5X+wxagMMnr1wPXZ7Vzc+3AAW8bh64uaG6Or55VVHi33l5v8Riz5BVCSD3vLzHcJc4hDC8Sk+w94UpgOBCGg2J/vzenMdmQ0epqb2XScNALbzg/2ZDR4Pz95x3+yqs1NTUMDg7inKOtrY2lQSVRRETmta3tWzm89nCikWiuuzIvFTYUMtyqQCgi06ON6XPtwAE46STvcXiRlJ4eLwyVl3urgQ4Nee2pAuFkFcJwuMtUhTAxEKYaMhr0NTxktLLSa5tsUZniYi9IhgJhuELY4A9FbW1tnepXWERk3jKzk/39eB8xswfN7HAzKzGzW/wVt28xsxL/2NX+MRvM7Mu57vtMNHc1s7J6Za67MW8VNioQisj0KRDmUlA1O8qfK+EHHiA+EAbHQiwQhsNXcJ+qQpgsxKUzhxAmrxAmziEMDxkNgmqwFUY4EJaXx4Jed7dX+Wxo8O6DQFhR4R2rQCgii9te4Dzn3JuAbwP/AHwY2OKvuP2i/xzgG8BXnXNnAuea2dFz393Z2d29mxWVK3LdjXmrsKGQ4RYFQhGZHgXCXArmDCYLhN3dXiUtVSBMFuKSVQinGjKaah/CqQJhsjmE4SGj/f3eMZHI5IEwGDYajcaqhr29sfNWIBSRRcw5ty+0wnawL2+qvXxPcs6t9x/fQ8Iev/luZGyEfT37OKTqkFx3Zd7SkFERmQkFwlwK9kg88kjvfrYVwlT7ECYb/hnediJVuEx8T6oho0HoTNyYPnieKhB2d8fOE2LzJcNzC6cIhG1tbSl/eUVEFgozKweuBf4JqMfbvgni9/INX9PD7eHPydt9evf17GPMjbGiShXCmSpsLGS0a5SxobFcd0VE5hEFwlzyF0mhvt5bWGY2gTAIZQUF3tDLcIUwWVUvnX0Ig9fTWVQm2SqjwTzHqSqElZVee9A2SYXQzKiqqqLeX71UFUIRWejMrBC4Ffimc+7PeHv31vgvh/fyDaeApHv8OuducM6tdc6tbWxszF6nZ2B/z34AlpZrobCZKlpSBMDQ/qEc90RE5hMFwlzq9LdUrKmB2tpYQITpDxkNQlmy7SVSLRADqfcuTGdRmVT7EAZDRpNVCIPFYiorY3MIE+cLTjKHsKamhkgkQklJCRUVFQqEIrKgmVkE+AVwu3Pudr/5Ybw9fPHvH/YfP2tmZ/iPzwcemat+ZkJrn/fveWN5fgXV+aR4pXeNHmwezHFPRGQ+USDMpSAQVld7gXCmFULn0pv3Fw5x6a4ymvhZRUWx9oEBb//E0dGJq4ymGjKaWPkLDxktL4+fVxg+Di8Q1tbWjv8SNTQ0KBCKyEL3Trxtmj7g78v7feBnwPH+Xr7H+88BvgR83cw2AOudc5tz0N8ZCwJhQ1lDjnsyf40Hwp0KhCKSvpzsQ7ioPPqoF4hOOWXia9MNhGNjXghLrBAOD3uhMNm8v4EBCPbpSzVkdDb7EAZDVtMdMpoY9MrKYvsSVlR4vyaTDBmdTiA8cOAA119/PUcddRTve9/7MLOUx4qI5CPn3G+B3yZ56f1Jjt0GrMt6p7JEgXD2SlZ51+GBXQM57omIzCcKhNn00ENw7rleqNm1yxsaGtbR4Q3xrKjwQuG2bV67c7G5deFAGN7bLzF8QXwgTLYPYaptJ2azD2H4u6NRKCyMbUyfToWwrAwOPzzWtmdP8iGjzk0rEI6MjHDeeefx9NNPA7B9+3a+/OV5uS2XiMii0NrXSsQi1JTU5Lor81ZBdQHRqiiDr6pCKCLp05DRbPrBD7z7nh64/faJr3d2eovJRCLefVeX197f71UDEyuEwd5+ZWXxcwXD4Q4mVgLDIW5kxBvimWzbiYEBL4wmbmafatuJwcGJYbS0NP0KYXd38jmEiUNG/cpoR0dH2oHwN7/5DU8//TS/+tWvuPTSS/nqV7/KK6+8kvRYERHJvda+VupL64mY/msyG2XHlNH7Qm+uuyEi84j+1c2W0VF44AH46Ee9yuDjj088prMzVjUMB0J/iOSkgRC80DZVhTCx2gfea+Eho2be3MBwe7IK4dDQ5BXCcJ9SVQjD4W94GFpb41cZ7e72fh2qq2Nt/q/JdCqEN910E4cddhjvfe97+c53vkNBQQHXXntt0mNFRCT3WvtbNVw0AypOqKDnuR6cc7nuiojMEwqE2fLcc96Q0De/GU47DZ58cuIxnZ2x4BMEQue8UAReUArCX7JAGK7GQfIKYWK1D2JVxcJCrzoZvDdVtXFw0KvSjYykDoTB8aWlk68yGgTcIAQODkKdv1VWeTm0tXnfFfy6+Me77u6kgbC7u5vBwfihMT09Pdx///28613vIhKJsHz5cj7xiU9w8803s2fPnom/DyIiknOtfQqEmVBxUgUjbSMMbNc8QhFJjwJhtjz7rHe/di0ccwy8/LIX9sLCgbCy0gtcAwPxFcJIxAtWPT3JK4ThEJeqQhgOcUFbuHIIseCX+FlBe/B5iSuWpjtkdGwM2ttjgTAU7MYDYVANhAkVwv7WVoaGhiYEQpi4Of1jjz3GyMgIb33rW8fbPv3pTzM6OspPf/pTREQk/ygQZkbtW73rZNs9bVMcKSLiUSDMlmef9cLREUfAmjVedWz//vhjOjriK4TgVQmDCmF4O4Z0KoThEBfMB0w1ZDRcOQxeS1YhDNoTA2FxsTcsNgivUw0ZBThwIBYIG0IX/TQC4cF9+wCSBsLEYaMbNmwgEolwxhlnjLcdeeSRrFu3jn//939nbGwMERHJLwqEmVG2pozy48vZe8NeDRsVkbQoEGbLs8/Cccd5K28eeaTX9vLL8cckziEELxAGISsYVjlZIJwsxI2MeKFwJhXC8JDRsbHYPoiJnxVslRGuEAZDRoN+BkGvtzd2To2hjYeDkFdfH2sLfl1mEAg3bdrEkUceSUU4YAIf+chH2LFjB0888QQiIpI/nHMKhBm08gsr6d3US/t/tee6KyIyDygQZoNz3hzCE0/0nq9e7d3v3Bl/XOIcQvCqg+Eho5A6EKZaVGaycAfe8eGhpMExqcIlxBa8SQyEHR3x3x0Mbx0airWFt9sIAl2yCmGwHyFMrBC2tPhvjwXCej9AJguExx13HIkuueQSiouL+fWvfz3hNRERyZ2uwS5GxkYUCDNkyfuXULyymJ3f2Dn1wSKy6CkQZsPevd7iKCec4D1vaoq1B5xLHginO2Q02cIuicM8U60ymm6FMOhX+HmqCmFJiTdXMNzP8HzB4HG4QhgEwqVLY22JgdAPfVNVCAcGBti6dSuvfe1rSVRVVcV5553Hb37zGw0bFRHJI9qUPrMihRFWXLmCzkc76XupL9fdEZE8p0CYDZs3e/fHHuvdB6uFhgNhX583By9ZIEx3yGhQIUy1EEx4a4nw/XTmEAbv6exM/lnJhowGgTBZhTB4HMwlhFgQnKxC6H/mVBXCF198kbGxsaSBEODSSy9l9+7dPPbYY0lfFxGRuadAmHkN7/B+Ldt/r2GjIjI5BcJs2LrVu1+zxrs386qE4UAYDLUMrzIK8YEwvGffdBaVmWr452RzCJNVG4N+BceF25MFwsS2ZBVCM7jySvj5z2NhOFw1DMJhEAj9zwwHwsLCQqqrq+NWGX3hhRcAUgbCiy66iJKSEn77298mfV1EROaeAmHmlR5eSvGhxXRu6Mx1V0QkzykQZsPWrV5wWrEi1rZsWXwgDCpuyRaV6e72FqMJwtd0F5VJ3CoinSGjwTYS6VYIE+cQhr9jZCS+n8kCIcB118Hll8eeB5996qne+QOutJTHgUe3bqWgoIDqIED7Ejenf+GFFygoKOA1r3kNyVRUVPDmN7+ZO++8U6uviYjkCQXC7Kg8tZKep3ty3Q0RyXMKhNnw8svedhOR0C9vYoUwCFiphoxWVnpVNPACYbAPoVl8KEunQpg4zDN4LbyoTLBVxVSLyhQVxbcH1cDwxvSB4HE4xIWHjyazZw+sXx97HonwFuA/Nm9m6dKlRCLxf2QTA+GmTZtYs2YNRUE/k7j44ovZvn07f/7znyfvi4iIzAkFwuyoOLmC/pf7GekeyXVXRCSPKRBmw9atseGigcRAGASpoGJWUgIFBbFAGN4yITxktKwsFhRTLSoT7BEYbBWRbMhosjmE06kQhucQlpTE9ykQVAjDIS68tUQyTU1xn2FmrFy50n+pacLhySqEyVYYDXv7298OwJ133jl5X0REZE609rVSGCmksqgy111ZUMqO8q7DA9sGctwTEclnCoSZNjYGr7wS23sw0NTkhb1g2Gcw1DKomJl5VcLubu8WDoThIaNByIL4RWUKC8eHWY6HuSDEJYa7VKuMhiuE4QVqYPI5hOEQGP7McPsPfgDf+x4sX850rfCHfy5btmzCa+FA2NfXx7Zt21LOHwwsX76ctWvXctddd027LyIiknlt/W00lDVgwQ8XJSNKj/Cuw/3b+nPcExHJZzkJhGZWZ2Z3m9l6M/u+JbkCmNl2M/uDf/s/ofYvm9kGM3vQzFbPacfTsXu3F6qSVQgB/A3WxyuE4SGUVVXJK4Tl5d68vI6O+EBYWurt99fbGx++ppr3N52N6dPZhzDZMFGI7+unPgWf+UyskjgNK/y5mFMFwi1btuCcmzIQgjds9I9//CP79++fdn9ERCSztCl9dpQc7l2r+19RIBSR1HJVIbwauNU5dxZQDrwtyTGjzrlz/NvXAczsaOBc59yZwNeAb8xVh9O2fbt3f/jh8e2JexEmVgghFgg7O+Pbg3B44MDEQBh8VuLwT5i42mdihTBxY/p0howmtk8WCMOPZ2F0dBSAU045ZcJrjY2N9PX10dPTM+UKo2EXXXQRzjnuueeejPRRRERmToEwOwprCimoLdCQURGZVK4C4dnA3f7ju/zniczMHjKz35vZSaH33QPgnHsEODHbHZ22HTu8+8MOi29PDITBUMtwKKus9AJhR0d8IAz27GtpmThkNPxZgVR7BE42hzA8ZNTMG4Iafk8QYBM/y7n0hozOwuc//3n++q//mo997GMTXlu1ahUAO3fuZNOmTRQWFnJk4nDdJE488URWrlypYaMiInlAgTB7So8o1ZBREZlUrgJhHdDhP+7wnyd6vXNuHfBF4Bd+Wz1wMHRMNNmHm9kVZvaUmT3V0tKSkQ6nbccOL1D5C6GMCzZfD4YodnTEb8EA3mqcHR0TA2FQIWxpSV6BCxZ2CSSGuCBEFhR48wwn23airy9+kZipwmW4DeLPKbyv4CycdNJJXH/99UlXDg0HwhdeeIGjjjqKwiDMTsLMuPjii7nvvvvo79eFUkQklxQIs6fk8BIGXlGFUERSy1ogNLOomf0xye3v8EJdsBdBNdCe+H7nXKt//yzQZ2a1/nE1ocNGk323c+4G59xa59zaxgyFkrTt2OEtnBKu/IG3uqZZfCBM3IKhoQHa2rzwlaxCuG9ffHsQytrb06sQBq/19HhzEhMrhOBVKJO1p6oQJj4OLxoT9DuLDj30UABeffXVtFYYDbv44ovp6+vjgQceyFb3RERkCmNubHxRGcm80sNLGdgxgBvV3rsiklzWAqFzbtQ5d3qS2zXAw8AF/qEX+M/HmVmxmZX4jw/BC4Ed/nHn++1nAM9mq/8ztmMH+CElTkGBF/iCQHjw4MQKYX19bFGaZBVCiG8PVwiTDdtsb48/LngtWCAmWajr7Jx8PmLiHMLEzz/kEOZSU1MThYWFbNy4kR07dnD88cen/d5zzjmHyspK7rjjjiz2UEREJtMx0MGYG1MgzJKSw0pwI47B3YO57oqI5KlcDRn9FnCZma0HhoH7AMzsu2bWCCwBHvNf/w3wCefZDDxqZhuArwNfzk33J7FjB6xenfy1pUu9hWEgdYVwxN88NhwWUz1ONWQ0GCLa1ubdJ1b8ElcfDT9ODISp9htMFQiT7BWYTdFolBNOOIEf//jHALzhDW9I+71FRUWcf/753HXXXYyNjWWriyIiMomWXm9qR33pFPvUyowUr/Ku14O7FAhFJLmcBELnXJtz7kLn3FnOub9xzo357Vc651qcc7ucc6f4r5/hnHso9N5rnHNnOufWOee25aL/KY2MwK5dkwfCcIUwMRCGN20Pv5aqPQhig4PeCqWBYKhma6t3THirh5KS2PDPVPMOk7UnLh4TiUAwpy/VojJz5HWve13Sx+m45JJL2L9/P08++WSmuyUiImlo6fMC4ZLyJTnuycJUssq7Lg/s1DxCEUmuINcdWFD27PFC4WSB8I9/9B4nW1SmITRcJp1AGA6BkwXCsHCFMFklsLMzfiXT8AIt4fbg/UND3uqoYbffPnGV1Sy6/PLL+eEPf8gVV1xBRXh4bRrOP/98CgoKuOOOOzj99NPTek9XVxd9fX00NjYSjU5c16ivr4/W1tbxW0tLC+3t7TjnKCgooKioiLKyMsrLyykvL6esrIyysrLxzzKz8c2Zp3ocjUaJRCLTuo9Go9r8WUTyxoFeb+RMY/kcz/lfJIpXqkIoIpNTIMykYMuJqSqEY2PJh4yGg184UIWDWDhEVlfHHicLhG1tE+f0pVMhrAst+moW27Q+WbhM7DfAJZcwl97whjfQ3NzM0mAl12mora3lTW96E3feeSf/+I//OOmxTz75JFdeeSWPP/44AJFIhCVLltDY2MjIyAjd3d20tbXNm1VLZxIms3WfD33Ih74oqMtiFAwZVYUwOwoqCyioKVCFUERSUiDMpHQCYW+vt1qocxMrhMceG3t8xBHJPyMcIsOBMPw42eb1gfJyePVV73F4HmDwnvb2+H5AbEuKxM8K5jvWJds1ZG4dMovFbC655BI+97nPsXXr1pR7GD700ENceOGF1NfXc80111BXV8fevXvZt28fLS0tFBYWUllZSV1dHQ0NDTQ2NtLQ0DB+q6urIxKJMDo6yuDgIH19ffT29tLb20tfXx99fX2MjY3hnMM5byW4yR4DjI2NMTY2xujoaNr30zk2E/fDw8MMDg5mtQ8LTapwGo1GKSgoSHorLCxM+Vo6r2fiM2b7HYWFhWltGSMLT1Ah1KIy2VO8qpjBnaoQikhyCoSZFARCf2+8CYIK1osveveJFcIloZ+OJtlzD4iv+E1VIYSJwzwrKiauGBq0gzcENHEeYBAcEwOhH0zyIRDORhAIb7vtNq6++uoJr3d1dfHBD36QQw89lIcffpglS/RT7Hwyk2Ccq4A8mz6NjIwwOjrKyMjI+G14eDjuefjW19c36euTfUYunHvuudoCZpFq6WuhpqSGomiK657MWsmqEg0ZFZGUFAgzKdUehIEgSGzZ4t0nBkKA//xPb9uJRF/5CjQ3Q3jRlPD3hMNhsBpo4kIwEB8WkwXCxPbw88TPqqycOMR0Hjr00EN5wxvewM0338xVV101Ydjel770Jfbs2cMf//hHhcE8FIlEiEQiFBTon7NMcM6NB9CpQmc6r6f7GStXrsz1qUuOHOg9QGOZ5g9mU/HKYjof68x1N0QkT+l/UJm0cydM9p+aYEuGjRvjn4e94x3J33vNNZN/d7hCaOYFv56eiSEuVfALB8XERWJSBcLqam9V1XkeCAE+9KEP8clPfpI//elPnHrqqePtjz76KNdffz1XXnklp512Wg57KDI3gsWKotEoxal+uCVzxszuBU4B/sU5d615P7H6HnAS0Alc7pxrN7M64CagGngG+KwLxpfnuZa+Fs0fzLLiVcWMtI8w0jNCQYX+6yci8XK1D+HCtGvX5IEw2LB+/XrvfsWKzH13uEIIsaGi4aAH8YEwHPDC7eFwCbG5jomBMAiCkfn/x+jSSy+ltLSU6667brxtYGCAj3/846xevZprr702h70TkUXsY8BVoedvA8qcc2cBvwaCce5XA7f67eX+cfPCgd4DWmE0y4KtJzRsVESSmf//k88XznlDOicLhLW1XnB76SUvRGViE/dg2Ony5fHtQRBsaEjeHn4vxAfCxHAZfEZiUPzpT+GDH4QFUDmrqanh05/+NL/85S/ZvHkzAF/5ylfYsmULP/rRjyhPDNYiInPAOdec0HQ2cLf/+C7/+WTtea+lt4UlZaoQZpM2pxeRySgQZkpHB/T1TV71M4ttJ9HUBJmY8/Tb38LNN8PJJ8e3pwqEqYJfOPAkBr9gW4nE0Hn44XDTTannTM4zV111FZWVlVx22WX8/d//Pf/8z//Mpz71Kf7iL/4i110TEQnUA/7KYHQAwXLVdf7zoH1ejOUfc2O09rWqQphlJSu1Ob2IpKaB5Jmya5d3P9XCCMceC888k3pbiel685uTtwdhc7JAGJ4rGA6niYEwWNp/Fls7zAeNjY386le/4j3veQ9PP/00F110UdwQUhGRPNAO1PiPq4mFw4P+8w7/vj3Zm83sCuAKgFWpVsSeQwf7DzLqRrWoTJYVLS+CCNp6QkSSUoUwU5r9UT1TzQs8/3zv/uwsj+YJqn+TDRlNNfcvMRAO+heQxH0TF6ALLriAXbt2sXnzZu644w6KUm3/ISKSGw8DF/iPL/CfT9Yexzl3g3NurXNubWNj7kPY/t79gDalz7ZIYYTi5cWqEIpIUqoQZkpQIZwqEL7//TA6Cpdemt3+BEEwcQuJcIUwlcRAGISiRTKPrq6ujroFsHKqiMx/ZvZj4Ayg2MzWAu8E3m5m64Eu4HL/0G8BN5nZXwPPAfflor/TtbtrNwArqjK4yJokVbyqWHMIRSQpBcJMaW5Ob6GYaBQ+9KHs9+fUU+E//mNiAEwMe8kkLirz7W9DY2OsuikiInPCOffxJM1/k+S4NuDC7Pcos5q7vNE1h1Qt7CkJ+aB4ZTE9G3ty3Q0RyUMaMpopzc2ZWygmE66+2guEl1wS3/7616d+z1/9lXcfXn0UYNkyuO66WKVQREQkA3Z3exXC5ZXLpzhSZqtkVQkDuwaYJ9tTisgcUiDMlKn2IJxr0Si8853eyqZhS5Z4+yG+//0T3/Ozn8G998Jxx81JF0VEZHHb3bWbhrIGSgpKpj5YZqV4VTFu0DHcMpzrrohInsmTctYC0NwMxx+f616kZ/v2iUERoLAQtMWCiIjMkebuZg6p1HDRuRDeeqJoiUb8iEiMKoSZkM6m9PkkWRgUERGZY7u7dmtBmTkyvjm9tp4QkQQKhJnQ0QG9vVOvMCoiIiLjmrtUIZwrJau0Ob2IJKdAmAnp7kEoIiIiAAyODNLS16IK4RwpqCsgUh5hYIcCoYjEUyDMhGAPwvkyZFRERCTHghVGteXE3DAzyo4uo29zX667IiJ5RoEwE1QhFBERmZZX2l8B4IjaI3Lck8Wj/Lhyejf15robIpJnFAgzId1N6UVERASAl9peAmBN/Zoc92TxKD+unKE9Qwy3a+sJEYnJSSA0szozu9vM1pvZ983il730X/9D6DZsZrVmttrMDobaL8xF/yfYtSu/NqUXERHJcy+3v0x5YTlNFfph6lwpP64cgN4XVCUUkZhcVQivBm51zp0FlANvC7/onGt3zp3jnDvHP/YB59xB/+WNwWvOuXvmtNepzKctJ0RERPLAS20vsaZ+DaatkObMeCDUsFERCclVIDwbuNt/fJf/PJUPAL8IPT/RryzeZGb12ergtOzapfmDIiIi0/By+8usqdNw0blUfEgxBbUFdG/sznVXRCSP5CoQ1gEd/uMO//kEZlYAvB243W/aCxzuVxYfBb6VzU6mJdiUXoFQREQkLcOjw2w/uF2BcI6ZGdVvqqbjDx257oqI5JGsTXozsyiwIclL9wAHgWq8MFgNtKf4mL8AHnfO9QA45waBQf+1XwB/k+K7rwCuAFi1atXMTiBdnZ3epvQaMioiIpKWre1bGXWjvKb+NbnuyqJTu66WtjvaGNg5ML5ZvYgsblkLhM65UeD0ZK+ZWQVwAfBL//62FB/zAeDm0PuqnXOd/tNzgRdTfPcNwA0Aa9eudTPpf9qCPQhVIRQREUnLU3ueAuDU5afmuCeLT826GgA6Hupg2YeW5bYzIpIXcjVk9FvAZWa2HhgG7gMws++aWaP/uAIvUP536H3rzOxPZvYw8BngC3Pb7SSCPQhVIRQREUnL/+z5H8oKyzim4Zhcd2XRKT+unKJlRbTc1pLrrohInsjJPgnOuTZgwpYRzrkrQ497gMMTXr+d2HzC/KBN6UVERKblf/b8D6c0nUI0Es11VxYdixjLPrKMnd/cyUDzACUrNGxUZLHTxvSztWuXNqUXERFJ0/DoMM/se4bXLX9drruyaDV9vAkc7P7e7lx3RUTygALhbDU3a1N6ERGRND215ykGRgY4fUXSZQZkDpQeVsqyDy2j+bpmup/WFhQii50C4Wzt2qX5gyIiImm65+V7iFqUtxz+llx3ZVE74p+PoLCxkOcvfJ7eF7RRvchipkA4Wzt3KhCKiIik6e6X7ubMVWdSV5p0C2KZI4V1hZz43yfiRh0b125k13d2MTY0lutuiUgOKBDOhnNehTDbex2KiIgsANsObuPZ/c9y4ZoJ68pJDpS/tpy1z66l9i21vPL5V3hizRPs+uddDO4dnPrNIrJgaOLbbLS3Q3+/KoQiIiJp+MGTPyBqUS47/rJcd0V8xcuKOe7O4zh430F2XLODV77wCq984RVKjiih5NASopVRoqVRChsKqX1bLfUX1mNmue62iGSQAuFs7Nzp3SsQioiITKpjoIOfPP0T3n3suzmk6pBcd0dCzIy6t9VR97Y6erf00nZnG11PdjG0b4jh1mHGBsYY2jPE7n/dTfXZ1bz2t6+lqKEo190WkQxRIJyNXbu8ew0ZFRERmdTn7/083UPdfPHML+a6KzKJ8qPLKT+6fEL72PAY+366j5c/+zLPrnuWkzecTEGV/hspshDob/JsBIFQFUIREZFxHQMdbNi5gWgkSmVRJb/f+ntufOZGvnjmFzm56eRcd09mIFIYYfkVyyk5rITnzn+OLR/dwmt/81oNHxVZABQIZ2PnTigqgsbGXPdEREQkL7xw4AXW/XwdLX0tce0fPOGDXLPumhz1SjKl7q11HP6Nw9l21Taar2tm5d/qh+Ii850C4WwEexBGtFiriIiIc44r7r4CM+O/P/jflBeW0zXYRWN5I6c0nZLr7kmGrPz8Sroe62LbF7dRdUYV1adX57pLIjILCoSzoU3pRURExt2/7X4e2/UYP7jgB9p4fgEzM4668Sg2nryRP1/6Z9Y+vZbCusJcd0tEZkilrdnQpvQiIpJHzOzDZvaYmW0wszktyTnn+IeH/4FDKg/hYyd/bC6/WnKgsKaQY399LEN7h3j+wucZ6RzJdZdEZIYUCGdqdBR279YKoyIikhfMrBb4LHAO8AHge3P5/Q9uf5ANuzbwpTd+ieKC4rn8asmRqtdVceytx9L9VDdPnfQU+2/Zz+DeQZxzue6aiEyDhozO1L59XihUhVBERPLDacB659wQsN3MKs2s2Dk3mI0vGx4dZnPrZoZHh+kZ6uHTv/s0K6pW8LFTVB1cTBr/spGTHj6JFz/2Ips/sBmASGmEwsZCCusLKWzwb/WFFNQVUFBTQEFtAYW1hUQro1ihYYVGpDACBjiv2kyQKR2xx2GW5HG+Lniab/lY/ZlSvv1Qo+zoMgprsjcsW4FwprZu9e4POyy3/RAREfHUAwdDzzuAOmBvNr6svb+dE3944vjz8sJy7nr/XZQUlGTj6ySPVZ9RzeteeB2dj3XS80wPAzsGGGkbYbh1mOHWYQa2DzDcOuwNK82v/2eLzAvH/+546s+rz9rnKxDO1JYt3v3RR+e2HyIiIp52oCb0vNpvG2dmVwBXAKya5ZSHmpIa/uO9/0FBpICiaBGnNJ3CkvIls/pMmb8sYtS8sYaaN9akPMaNOka6RhjpGGHk4Aij3aOMDY/hhh1uOJQUzbuN73HoP4990MTHcRUdR95VC/Nuv8Y8607e9Qfyqk+Vp1Zm9fMVCGfqxRehtBRWrMh1T0RERACeAK41s0KgCehJHC7qnLsBuAFg7dq1s6rVFBcU885j3jmbj5BFxqJGYW0hhbWFoAFWInlDgXCmXnwRjjpKexCKiEhecM4dNLPrgYfxaiSfy3GXRERkHlAgnKktW+B1r8t1L0RERMY5524Ebsx1P0REZP5QeWsmBgdhxw6vQigiIiIiIjJPKRDOxJYtMDYGxxyT656IiIiIiIjMmALhTDz+uHf/+tfnth8iIiIiIiKzkJNAaGZ/aWabzWxgkmPOM7PH/dvbQu1fNrMNZvagma2ekw4nWr8eli6F1bn5ehERERERkUzI1aIyjwAnA5uSvWhmUeBbwJv8pofN7H5gDXCuc+5MM3sT8A3gfXPQ35jBQbjnHvjLv4R821NGRERERERkGnJSIXTOtTnnUlYHgSOB7c65DudcB7DDbzsbuMf/jEeAE7Pc1Xijo/Dtb0NnJ3zgA3P61SIiIiIiIpmWr9tO1AMHQ887gDq/fU+oPZr1nlxxBfziFzA05AVCgHe8A849N+tfLSIiIiIikk1ZC4T+sM8NSV66xzl3zRRvbwdqQs+r/bbE9tEU330FcIX/tMfMXkyjy5NpAFrHn91++2LZkD7+vBeXxXruOu/FZyGd+6G57sB8snHjxlYzezUDH7WQ/gxNh8578Vms567zXhhSXiOzFgidc6PA6TN8+8vAYWZW5T8/DNiKN8T1u8B3zewM4NkU330DcMMMv3sCM3vKObc2U583XyzW84bFe+4678VnMZ/7Yueca8zE5yzWP0M678VnsZ67znvhy9Uqo2f5i8QsN7P7zeydfvt3zazRD5NfAu71b19yzo065zYDj5rZBuDrwJdz0X8REREREZGFICdzCJ1z64G3JGm/MvT4v4D/SnLMNcBUQ05FRERERERkCotiIlwGZGz46TyzWM8bFu+567wXn8V87pIZi/XPkM578Vms567zXuDMOZfrPoiIiIiIiEgOqEIoIiIiIiKySCkQTsHMPmxmj5nZBjM7Jdf9yQYz6zezP/i3j5nn+2a23szuNrM6/7g6//l6/3XLdd+nw8zuNbMWM/uK/3xa52lmp/h/Dh4zsw/n8FSmLcm5n2Nme0O/76f67Qvq3M3sZL/fj5jZg2Z2uJmVmNkt/jneYmYl/rGr/WM2mNmXQ59xnpk97t/elruzSV+K8/6wmW0P/Z4f4h+7YM5b5paujwvn+giL9xqp66Ouj7o+As453VLcgFrgT0AR3tYXj+a6T1k6z60Jz88DfuI/vhz4hv/4G8AH/cc3Aufluu/TPM8VwIeBr8zkPPH21TzM//PwJ6A21+c0i3M/B/j3JMctqHMHlgGV/uMLgJuBTwJ/57f9PfBJ//H/A87yH98PHA1Egefw9j+twdvqJprr85rheY///iccu2DOW7e5u+n6uLCuj36/F+U1UtdHXR91fXSqEE7hNGC9c27IObcdqDSz4lx3KguWmdnDZnabma0Gzgbu9l+7y3/OJO3zgnOuOaEp7fP0f9/LnXPbnXNDwHq8Px/zQpJzB3hb6CedpX7bgjp359w+51y3/3QQGCH17/tJzlsBGeAev/1IYLtzrsM51wHs8NvyWorzBrjczB41s2vMLPj3f8Gct8wpXR8X0PURFu81UtdHQNdHWOTXRwXCydUDB0PPO4C63HQlq1Y7584GfgT8hPjz7sD7STB4594Rap/vvxbTOc/6UFu4fb7aCKxxzp0FdAFf8NsX5LmbWTlwLfBPTPx9D84l/O9h0D6v/w1IOO87gGPwLmiHApf5hy2485Y5sVj+jCzW6yMs3mukro+eDnR9XHDnnYoC4eTa8crBgWq/bUFxzrX69/fi/UUIn3c1sT/4B/3nQft8/7WYznkuqD8Lzrlu59yA//QWYK3/eMGdu5kVArcC33TO/ZmJv+/BuYyF3jbvzz3xvJ1zB51zo865UbxhMMHv+YI6b5kzi+LPyCK+PsIivUbq+qjrI4vw+qhAOLkngDeaWaGZrQJ6nHODue5UJplZhZlF/ccnAK3Aw3jjqvHvH/Yfp2qfr9I+T//i0Gtmq/x/SN4IPDmXnc0kM6sOPT0XeNF/vKDO3R/28Qvgdufc7X5zqt/3Z83sDP/x+cAjwMvAYWZWZWZVePNEts5F32cj2XmbWU3okPDv+YI5b5lTuj4u7OsjLNJrpK6Puj6yCK+P2odwCmb2UeB/AQ74nHPuqRx3KaPM7DS8oTDdeOf4WeB54PvACXjDJS53zrWZWT1wE1CFN6H2M865saQfnIfM7MfAGUAxsAl4J9M4TzNbC/wLYHgTzm/MwWnMSJJzvw/4KNCH95+cjzrnOhbauZvZu4GfAcHf2+eBq/EWBFgBNAMfcc4NmNnheEPCioDfOeeu9T/jAuDv/Pdf45z7r7k7g5lJcd5dwFvw5ku8CHzCOTe8kM5b5paujwvn+giL9xqp66Ouj+j6qEAoIiIiIiKyWGnIqIiIiIiIyCKlQCgiIiIiIrJIKRCKiIiIiIgsUgqEIiIiIiIii5QCoYiIiIiIyCKlQCiSI2ZWb2bP+Ld9Zrbbf9xjZtdn6TuvNLPLZ/C+IjN7xMwKstEvERGRgK6PInNL206I5AEz+xrexs7fzuJ3FAB/Ak5xzo3M4P1fBbY6527JeOdERESS0PVRJPtUIRTJM2Z2jpnd7T/+mpn93MzWm9mrZvZOM/uWmT1vZr83s0L/uFPN7GEz22hm95pZU5KPPhf4U3CxM7M/mNk3zexJM3vJzM7y21/rtz1jZs+Z2Rr//bcDl2X9F0BERCQJXR9FskOBUCT/HYF3sboY+AXwkHPueKAfuNC/6H0feLdz7lTgRuDrST7nTGBjQluBc+404Ergq37bJ4F/cc6dBKwFmv32TcDrMnROIiIis6Xro0gGaLyzSP77nXNu2MyeB6LA7/3254HVwFHAccB/mxn+MXuTfE4TsDmh7Tb/fqP/WQCPA//HzFYAtznnXgZwzo2a2ZCZVTrnujNxYiIiIrOg66NIBigQiuS/QQDn3JiZDbvYxN8xvL/DBrzgnHvDFJ/TD5Qk+2xg1P8snHO/NLMngAuB/zKzTzjnHvSPKwYGZnU2IiIimaHro0gGaMioyPz3ItBoZm8AMLNCM3ttkuM2A0dO9WFmdjiwzTn3PeAO4AS/vR5odc4NZ6znIiIi2aPro0gaFAhF5jnn3BDwbuCbZvYs8AxwRpJDfwe8KY2PfC+wycyewRtqc5Pfvg64Z7b9FRERmQu6PoqkR9tOiCwiZvafwNXBvIdpvvc24H87517KfM9ERERyR9dHWcxUIRRZXP433uT5aTGzIuB2XexERGSB0vVRFi1VCEVERERERBYpVQhFREREREQWKQVCERERERGRRUqBUEREREREZJFSIBQREREREVmkFAhFREREREQWKQVCERERERGRRer/AyIxpml7sY4sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basinhopping step 0: f 1.09064\n",
      "basinhopping step 1: f 1.09064 trial_f 1.13532 accepted 0  lowest_f 1.09064\n",
      "basinhopping step 2: f 1.09064 trial_f 1.19196 accepted 0  lowest_f 1.09064\n",
      "basinhopping step 3: f 1.09064 trial_f 1.76003 accepted 0  lowest_f 1.09064\n",
      "basinhopping step 4: f 1.08155 trial_f 1.08155 accepted 1  lowest_f 1.08155\n",
      "found new global minimum on step 4 with function value 1.08155\n",
      "basinhopping step 5: f 1.08155 trial_f 1.2453 accepted 0  lowest_f 1.08155\n",
      "basinhopping step 6: f 1.08155 trial_f 1.25491 accepted 0  lowest_f 1.08155\n",
      "basinhopping step 7: f 1.08155 trial_f 1.73007 accepted 0  lowest_f 1.08155\n",
      "basinhopping step 8: f 0.834689 trial_f 0.834689 accepted 1  lowest_f 0.834689\n",
      "found new global minimum on step 8 with function value 0.834689\n",
      "basinhopping step 9: f 0.834689 trial_f 1.03427 accepted 0  lowest_f 0.834689\n",
      "basinhopping step 10: f 0.834689 trial_f 1.09257 accepted 0  lowest_f 0.834689\n",
      "basinhopping step 11: f 0.834689 trial_f 1.19891 accepted 0  lowest_f 0.834689\n",
      "basinhopping step 12: f 0.834689 trial_f 0.910056 accepted 0  lowest_f 0.834689\n",
      "basinhopping step 13: f 0.834689 trial_f 0.886205 accepted 0  lowest_f 0.834689\n",
      "basinhopping step 14: f 0.834689 trial_f 0.98884 accepted 0  lowest_f 0.834689\n",
      "basinhopping step 15: f 0.868607 trial_f 0.868607 accepted 1  lowest_f 0.834689\n",
      "basinhopping step 16: f 0.868607 trial_f 0.876566 accepted 0  lowest_f 0.834689\n",
      "basinhopping step 17: f 0.851629 trial_f 0.851629 accepted 1  lowest_f 0.834689\n",
      "basinhopping step 18: f 0.851629 trial_f 0.85833 accepted 0  lowest_f 0.834689\n",
      "basinhopping step 19: f 0.851629 trial_f 1.06029 accepted 0  lowest_f 0.834689\n",
      "basinhopping step 20: f 0.851629 trial_f 1.04025 accepted 0  lowest_f 0.834689\n",
      "basinhopping step 21: f 0.848709 trial_f 0.848709 accepted 1  lowest_f 0.834689\n",
      "basinhopping step 22: f 0.848709 trial_f 0.880158 accepted 0  lowest_f 0.834689\n",
      "basinhopping step 23: f 0.848709 trial_f 1.17736 accepted 0  lowest_f 0.834689\n",
      "basinhopping step 24: f 0.832979 trial_f 0.832979 accepted 1  lowest_f 0.832979\n",
      "found new global minimum on step 24 with function value 0.832979\n",
      "basinhopping step 25: f 0.832979 trial_f 0.902814 accepted 0  lowest_f 0.832979\n",
      "basinhopping step 26: f 0.855648 trial_f 0.855648 accepted 1  lowest_f 0.832979\n",
      "basinhopping step 27: f 0.855648 trial_f 0.951661 accepted 0  lowest_f 0.832979\n",
      "basinhopping step 28: f 0.860996 trial_f 0.860996 accepted 1  lowest_f 0.832979\n",
      "basinhopping step 29: f 0.860996 trial_f 1.13607 accepted 0  lowest_f 0.832979\n",
      "basinhopping step 30: f 0.860996 trial_f 0.877646 accepted 0  lowest_f 0.832979\n",
      "basinhopping step 31: f 0.860996 trial_f 0.887368 accepted 0  lowest_f 0.832979\n",
      "basinhopping step 32: f 0.860996 trial_f 0.976111 accepted 0  lowest_f 0.832979\n",
      "basinhopping step 33: f 0.860996 trial_f 0.995045 accepted 0  lowest_f 0.832979\n",
      "basinhopping step 34: f 0.860996 trial_f 1.09227 accepted 0  lowest_f 0.832979\n",
      "basinhopping step 35: f 0.860996 trial_f 0.87768 accepted 0  lowest_f 0.832979\n",
      "basinhopping step 36: f 0.882021 trial_f 0.882021 accepted 1  lowest_f 0.832979\n",
      "basinhopping step 37: f 0.882021 trial_f 0.9914 accepted 0  lowest_f 0.832979\n",
      "basinhopping step 38: f 0.882021 trial_f 1.14106 accepted 0  lowest_f 0.832979\n",
      "basinhopping step 39: f 0.882021 trial_f 1.13743 accepted 0  lowest_f 0.832979\n",
      "basinhopping step 40: f 0.881975 trial_f 0.881975 accepted 1  lowest_f 0.832979\n",
      "Finished 0 step\n",
      "[-0.00320813  1.33504492  0.88376935]\n",
      "basinhopping step 0: f 0.897541\n",
      "basinhopping step 1: f 0.883975 trial_f 0.883975 accepted 1  lowest_f 0.883975\n",
      "found new global minimum on step 1 with function value 0.883975\n",
      "basinhopping step 2: f 0.883975 trial_f 1.13018 accepted 0  lowest_f 0.883975\n",
      "warning: basinhopping: local minimization failure\n",
      "basinhopping step 3: f 0.883975 trial_f 0.969203 accepted 0  lowest_f 0.883975\n",
      "basinhopping step 4: f 0.900049 trial_f 0.900049 accepted 1  lowest_f 0.883975\n"
     ]
    }
   ],
   "source": [
    "gg = BasinHopping()\n",
    "gg.number_fcp = 1\n",
    "#init = array([ 142.49161423, -108.42653027,   0, 0, 63.06377972])\n",
    "#init = array([  1.68325148,  77.52548987,  -0.22800116,  18.95515465, -73.91778102,\n",
    "# -40.24173738,  47.46180413,  66.71024046, 206.9681236 ])\n",
    "init = [ -8.1301843 , -87.71575796 ,152.61138485]\n",
    "init = None\n",
    "for nfcp in range(6):\n",
    "    res = gg.main(init)\n",
    "    print(\"Finished %s step\"%nfcp)\n",
    "    print(res.x)\n",
    "    init = [*res.x[:-1],0, res.x[-2]/gg.scale_factor, res.x[-1]/gg.scale_factor]\n",
    "    gg.number_fcp += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%qtconsole\n"
   ]
  },
  {
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}