Model coupling pulse.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import numpy as np\n",
    "from scipy.stats import logistic\n",
    "from scipy.optimize import minimize\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 mat73\n",
    "import hdf5storage\n",
    "import random\n",
    "import matplotlib\n",
    "import _spin_params as spin_pars\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, find_nearest_sorted\n",
    "from _steady_state import a_steady, a_steady_with_sz\n",
    "from _mbes import mbes, mbes_soc\n",
    "from _do_calculation import solve_mbes, solve_mbes_without_cavity, get_pulses_from_cavity_field, solve_mbes_wait, solve_mbes_only_decay\n",
    "# matplotlib.pyplot.switch_backend('nbagg')\n",
    "# %qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.629990368543535\n"
     ]
    }
   ],
   "source": [
    "gcoll = spin_pars.gcoll\n",
    "q = spin_pars.q\n",
    "quali = spin_pars.quali\n",
    "fr = spin_pars.fr\n",
    "kappa = np.pi*fr/quali  # HWHM\n",
    "delta = spin_pars.delta  # detuning cavity to spin central frequency ws-wc\n",
    "deltac = spin_pars.deltac  # detuning drive to cavity wc-wp\n",
    "gperp = spin_pars.gperp  # HWHM\n",
    "gpar = spin_pars.gpar  # 0.001 #HWHM\n",
    "gammaq = spin_pars.gammaq\n",
    "nbins = spin_pars.nbins  # 701#5000#20\n",
    "g_number = spin_pars.g_number  # how many bins for inhomogeneous coupling\n",
    "#g0 = gcoll/sqrt(nspins)\n",
    "print(kappa)"
   ]
  },
  {
   "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",
    "\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": {
      "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 = (0.001,0.001)\n",
    "fstart = 20\n",
    "deltaarr = np.linspace(-fstart*2*np.pi,fstart*2*np.pi, 2000)\n",
    "etasteady = 0.0001\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\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit to data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "/home/andreas/Physics/reference_traces/referenceTrace_24mK_loop1A_2subs_4.mat is not a MATLAB 7.3 file. Load with scipy.io.loadmat() instead.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/mat73/__init__.py\u001b[0m in \u001b[0;36mloadmat\u001b[0;34m(filename, use_attrdict, verbose)\u001b[0m\n\u001b[1;32m    207\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mhdf5\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m             \u001b[0mdictionary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdecoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmat2dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhdf5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/h5py/_debian_h5py_serial/_hl/files.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, **kwds)\u001b[0m\n\u001b[1;32m    386\u001b[0m                 \u001b[0mfapl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_fapl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdriver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrdcc_nslots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrdcc_nbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrdcc_w0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 387\u001b[0;31m                 fid = make_fid(name, mode, userblock_size,\n\u001b[0m\u001b[1;32m    388\u001b[0m                                \u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmake_fcpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/h5py/_debian_h5py_serial/_hl/files.py\u001b[0m in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0mflags\u001b[0m \u001b[0;34m|=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_SWMR_READ\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'r+'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mh5py/_debian_h5py_serial/_objects.pyx\u001b[0m in \u001b[0;36mh5py._debian_h5py_serial._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/_debian_h5py_serial/_objects.pyx\u001b[0m in \u001b[0;36mh5py._debian_h5py_serial._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/_debian_h5py_serial/h5f.pyx\u001b[0m in \u001b[0;36mh5py._debian_h5py_serial.h5f.open\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: Unable to open file (file signature not found)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-079d53083404>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfile_powerscan\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/home/andreas/Physics/reference_traces/referenceTrace_24mK_loop1A_2subs_4.mat\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata_ps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmat73\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadmat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_powerscan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0miq_data_ps_raw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_ps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'IQdata_scan'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miq_data_ps_raw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0miq_data_ps_raw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/mat73/__init__.py\u001b[0m in \u001b[0;36mloadmat\u001b[0;34m(filename, use_attrdict, verbose)\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdictionary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m         raise TypeError('{} is not a MATLAB 7.3 file. '\\\n\u001b[0m\u001b[1;32m    213\u001b[0m                         'Load with scipy.io.loadmat() instead.'.format(filename))\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: /home/andreas/Physics/reference_traces/referenceTrace_24mK_loop1A_2subs_4.mat is not a MATLAB 7.3 file. Load with scipy.io.loadmat() instead."
     ]
    }
   ],
   "source": [
    "file_powerscan = \"/home/andreas/Physics/reference_traces/referenceTrace_24mK_loop1A_2subs_4.mat\"\n",
    "data_ps = mat73.loadmat(file_powerscan);\n",
    "iq_data_ps_raw = np.asarray(data_ps['IQdata_scan'])\n",
    "for i, val in enumerate(iq_data_ps_raw):\n",
    "    iq_data_ps_raw[i] = val-np.mean(val[-1000:,:], axis=0)\n",
    "if len(iq_data_ps_raw) > 1:\n",
    "    iq_data_ps = np.mean(iq_data_ps_raw, axis=0)\n",
    "else:\n",
    "    iq_data_ps = np.asarray(data_ps['IQdata_scan'])[0]\n",
    "pl_idx = 5000\n",
    "ps_idx = 0\n",
    "\n",
    "hori = float(data_ps['pars']['acqiris']['acqirisStruct']['horizontal']['sampInterval'])*1e9\n",
    "aplotr = iq_data_ps[ps_idx:pl_idx, 0]\n",
    "aploti = iq_data_ps[ps_idx:pl_idx, 1]\n",
    "a2plot = (iq_data_ps[ps_idx:pl_idx, 0]**2+iq_data_ps[ps_idx:pl_idx, 1]**2)\n",
    "tdata = np.arange(0, len(a2plot)*hori, hori)*1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tdata' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-e9507a56017c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpumptime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m400e-3\u001b[0m\u001b[0;31m#5*np.pi/gcoll\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mbeforetime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1075e-3\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdecaytime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mpumptime\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mbeforetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0005\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mnumsteps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpumptime\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'tdata' is not defined"
     ]
    }
   ],
   "source": [
    "pumptime = 400e-3#5*np.pi/gcoll\n",
    "beforetime = 1075e-3;\n",
    "decaytime = tdata[-1]-pumptime-beforetime\n",
    "dt = 0.0005\n",
    "numsteps = int(pumptime/dt)\n",
    "tlist = np.arange(0,pumptime,dt)\n",
    "tlistdecay = np.arange(0, decaytime, dt)\n",
    "initeta = 28\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\n",
    "\n",
    "\n",
    "initphase = 5*np.pi/4*1.02\n",
    "\n",
    "solpump, soldecay, infodict = solve_mbes(mbes, init, pdf, tlist, tlistdecay, initeta*np.exp(1j*initphase), kappa, deltac, gs, gperp, spins, gpar)\n",
    "asol2 = np.hstack((np.zeros(int(beforetime/dt)), abs(solpump[:,0])**2, abs(soldecay[:,0])**2))\n",
    "asolr = np.hstack((np.zeros(int(beforetime/dt)), solpump[:,0].real, soldecay[:,0].real))\n",
    "asoli = np.hstack((np.zeros(int(beforetime/dt)), solpump[:,0].imag, soldecay[:,0].imag))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Q')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vb/P+hsz5umt3eNn3Ndu9f9NP2mN+8NjRSsblW2o46faX2by7IRFw7vAbzry9bhejb3iaS++fx/w1O3lrbUv5adQx3P7ssozPFTd/7c6sjXJWbMn8vT7l5690aZ5kTyqWJlwjRCIlAFSWhtlJOf2pJiMy76CZ72/mo531NPj/T7vqmxk5fSbPfvAxtU0xwMvaJftwU3Vivmm8tBi8stCon5nMlrl2XMM3H3qbsT94lg27Ui9qnHj7y4z/8fP87JklGGO4/C8L+OOc1SzfUsOSzdWJUmqg3aWy/3o7eyOl6x97n7Xb67I+1lW9GSwOAJJ/g6uA/jkeT34seYzx7bleazNwgDHmROA1vOxjBhG5XEQWiMiCbdu6p22y6mVONKkbaoRimrvUZUypvcXcldv55D2vJf74qT1bCU1pDSTK6a+ZRRUAy7fU4rqGRR9VUeUHQ4+9k1oCWtMY69BrXvant7jwt6/juIa6phhH3Pwcv3h+Gf9csMF/vcyT/fTSwucXf5y4bYxhwTrvdNO0EcDMfC91uYczfvUq63fWc9zPZrHZD0I+/4c3E/u6piVguf25pa2+9q/a0bznnXVVGdtcA/NXd3weZG8ooZkGIkTCLdm47aaCiDhU0LVMMsCSzS3Huficziv+9nZi2+JNqcfB+Wt3JuabXvfv91Ie+/Pra3FckxJExo258RmeW+yVIl+dlPGLcw38fvZqNu1u5PkPt3DrzCWc8atXOfvXc1L2+/Mb69p1HiutpLDj5bbdXX3cm8HiTqBv0v1Kf1u2x5Mfc7M8J+trGWOajDHxSyt/AyZlG4gx5j5jzCRjzKRBgwZ1+IOoAEhucOPPWezuGm2l9kTX/fs9Fm3YnbVjX9C9vHQrI6fP7JF1pPZUpdJEvUkKFimnH7Xg6sW19li5tbbLJZAqu3+/vYEZzy7t1jPbjVUNACzaUMXKrbVUN8a4e9bKxONzVm5n5PSZnHvXnFwvwaINu2mMOnz292+klHlOf/R9Yo7Lmu11vL5qe6fGV90Y4621O9nkjzPug41dv4CTK4PoBPTcKF71EJ+XCnDRSRMA6C/VxDp4QTN9GYutSZnDG//zQeJ2/FxxY9r3IK6+OfMCxa0zva68q7e1nrl7d31VYtwrt6Zmej/enf39ko374bOJ299ICmyTtVZh2lPf6t4MFucBJ4hIWERGALXGpKykPhs4x799jn8fYJGITPFvnw28ijdncZSIVIhIBTAKWCkilUmvNw1IzeOrPYfTnGhw00iYUmliSwGe/CrV2yz/qJ9+VbUQJBpNbM4st1q8aTdn/3pOosRIeUpopjGtNb0lBhp0rcX2OO2XsznpjpfzPYw91t/nr2f+mu7PfF3429f53H1vZmz/3Sve2n5tHf8c1zBvzU5+/VJLsLixqoH3Nu7mlJ+/wuf/MK/V58eXMMjm0/e+wbNJ2ctcRk6fyX/Tmu3E/eSpDzO2vZ6jYVB3rAXYE0qkmUZTRMRuCUUqB+4LwACq+f2rq9t8jcfe2cDJd7xMY9TJ+J7+4L8fZH1OrI1yz/E/fr7N980l5hp+/KT3vTntl6lzWq9Ny1a25ZkPUn9GRk6f2Wapavxb3d2NjXotWDTG7AJ+ixcE/h24SkQmiMi1/i4PAoeLyBzgcP8+wPXAbSIyF2/O4xJjjONvf87/ut7fdoqIvCMis4HvAN/vnU+nep0TJWriS2d4J0Jn/eKFfI5IqYIQL7m64m9v8+wHm9vY25ujkXzis6mqgZHTZ/Lm6uwnJj0pXgaW7Q/h7c8uY8nm6pS5P3u7NdvrKKaJBr+5DXjBIgB1ncuMKNWdahpjKdm77tTQzqkp2Y4nh970XNZ9ZzzTerlo3Jf+1PX198Dr5vmP+et5e92ed3EnfmxKbsay79DhAAyQ6nbNv7vmn4tYu6OecT98lvPubt+81raC5/QGRx311zfXpTRpikvuvtpZh970HHNW5D52x/9GOq7hNy+vzOjm2lm91g0VwBjzAPBA2uaF/mMNwMVZnrMaOCXL9qeBp9O2/Rf4b7cMVgVbWhkqQBHa5EaptthJf5iXflzDWYcNbXX/7/z9XV5dvo35N57K4PJiFvod5B6cu5ZjD+jZHmKua/i4upF9+5akbM82ZyPbn/fd9VEqS7N3odsbrN9ZT5k002BagsUdlHs36jVYVD2vORb8udHtDTKAHsmCtmX6Y+938RWCuX6GN2exKHVj2UAA+ksNDW2UocYrTTrqu/9Y2KnndcSryzP7oWzuhuq3ti6AxAPdd9ZXccdzy7jjuWWJLqpd0avrLCrVbbIEi7rWotobzVmxjV++kNr8YNnHNYycPpPFm7zOfzHHTSxWnbzelJvSTnw7q7fVZrx+fFtTNPUPd2tlLmu31/HRzq7P87rxvx8wZcYsRk6fycjpM1m1tf2d3j7YuJvxP3mexxdmL+PK5XP3vcHPnl6SsX1HbVOX1nDLh5AllNJEA8WJbfHM4oMvLMjXsPKmpjHKzU8s1mZoveik27WEV2U3sMihuKRP6sZSL1gcQHWrFxoamh1uyVKKqzzJXVm7owxZg0VVmJwozfjrLBovc1Aiutai2vu8sWoHv3vFa+Bw6R/ncfUjC3nOnw/zrD/n4ZI/zmPsD56lpjHKhl0tk+yTpz9c8sd5TPvF7MT9//33e4ycPjOxf3oDqR11zWyv9X7n0hsrnPzzVzjx9pd59oPN/OTJD3nRX7S6NZt3N2QEY3+fvz7l/sfV3pXZbIFq+qYP/fkrrZXspFu1rZY3V+9MzJWpa4r57dY3c9StL3LULYVV6i6QUYa6ww8WV65dm59BJXn2g82tXlTYVddMfXOMHbVN7QrwahqjGU0x6ptjfLDRu2jym5dX8eDra3lo3vpsT89QVa/VKl0V/51VKl2RaSRqpWUWw8XUmBIGSHWrHbvb6ky7t0teb7OpG7L7GiyqwpS0dEa8jKFYy1DVXsgSSSw4/NrK7fwnS0OEeX7p1M60+QsiZD0J37y7gUcWpK7lFA8s4zHj/DU7mXTri7y3oYojbn4+azbuir+9wwNz1/DVv6RmsRqjDr96YXki2wlw3M9mcdovZ6e/RFaCFxgkZw3TTx3iAWVHuiSnN8RY53fBjM+paqsxQuCI10SiwRSx+qde/7hdfhlqR9ZavPWpD5mctDh0d7nib+/kLAG8beaHHHnLC5x15xyOuvXFds0BO/zm5/lG0iLX4K09dt7dr7GzrjkRSDpu+06erk8q/9tU1ZD4udXMZPtkW6ZCqbiwaSZmRTK2x5f3aS3ICWjPnkDqjnU2NVhUhSm5wU2iDFWDRbX3sSxp92LR6X9g7561knE/fDaReQF4ackWjvvZrIznCvCL55cxZ0XqXIx5/hpev391Na8u38bWdmQS7n9tDb9+aQV/fn1tyvb0RZJz+XBzNdc/9j7f/cfCRKlt8jihpZFPR04q0hsQxAPObK+xq645a9lukFgi/jqLkcT6YFFCVJvSDq21+MfX1qS0oe9Ouxui3Dt7VcbyFH+Y481HWu9nHt9oZ0OlF9Ky2AvWeo1B6trokuu4hleWbU0p2Uo+ybrn5ZX8ae5afv3SipSr9vkiIpeJyOsiMldEJqY9ViwiD4nIHP/fYn/7SBGZ5T/nhqT9zxKRN/yvM7trjPEF6JXKKtbER9WZF152UMEAWs8sBnU5kCBq57WxVmmwqApT0pzFJrwyVJ2zqPZG8TWCk+cf1ufouvbS0uzt3BckdRBdtGF31n0MXnD5j7dSM463JWUUv/DAfCb/9KU2xxyfi1LblH2cf31zHSOnz8z5/FtnLuEpfwHsXJ81vkRIa/M1Zi/flljvrL45lrLsRkOzw2V/8jrarcnSle/IW15IKdsNIgE/WPSqL56/+iTAW/h6QAeCxWxeWbY148JBe1zzyELufmlFys/rjGeW8oUHWl+KoLPi3/+7Z61odZ7tH+es5rI/vZUSbM5d2RKgGtOShY83mZi3egfXPLKw15cmEJF+wJXAycClwF1pu1wGLDXGnIi3hNhl/vYZwE3GmOOBaSIyTkRs4Ha8pcnOBm73tynVoyLEaCazAVmd3ZcBUtN6ZjH4fZMCozvWIO/VbqhKdRfjr7N40VHDWf2O19yjSPQqptr7xLubJv9BuHf2qqz7bsmR9WtIal5z10s908Y+WUvWzxvz9Y+1rD918xOLmZUjqM3mo531HD2yf8b2tdu9jNSHm6tZ9nENY/cp5+PdjZQV2ZQXhzHG8MUH5lNeHOL9m89k0UepQfL/PbuULdVeNi25KdDvZ6/i61NHt3t8+STGoUhiNPjLCx00xCtB3UlFh8pQs7nMLwvtaKe9x/wy6cZYapBf14G28jtqm9he20xpxGa//qWt7hvPQPxzwQa+duKojMd310f54eMfJH5/cmVQjTEtwaYxzF6+jS/67fFnfOoIIqFe7Tg5GW8psWZgjYiUi0hR0trVU/ECQIAngeuAe4EJxpj4avQz/f0MsMYYUwUgImuBMXTDOtV2czXTQw939WVUF1XsDAP75HsYGSISJRwuztgeLe5P//pVrTa4yXdm0cKlmGZKaaJYmiihmRL8f6WJYpqJEMXCIP6XJd6/3vOTtifd9r681xdI2x7fN8d2iT/mprwGtYdA38xjX0dosKgKUrS5iSghlmyuBi1DVXuxeHlhtj+er6/awWn+UhcAD+do7NEUa/tE/cM2FrFuy4sfbuHUgwenrKl196yVfOuUMfx9fku28sHX1zKwT+Y8llyu+eciLpw4PGXbqm21icW0l2+p5cw7X2XtjHM59mde1vOByyYlMkg1jV428d9vb0h5jVzNTX72zFIuP+mAdo8vn2zXuziQ3OAGvCY3+8sWPwBqPchJzppNuvUFXrn2FErCXU88pS843R5rttexf/9Sjrq1Zf5kW8FqG933+f2rq3hi0abE/fiFjOfTFk0XkcSSLQb4S1IJdXdcue+gAUDywntVQH9gc5bH449BajVZFV4Ekeu1UojI5cDlACNGjGjXIIeXOXzR7vwC56rrSqSZzcsdOOmMfA8lhTGGCDH2G9wv47HdVl/6U01zjrnBryzbysgBZSnbDpQN7Cs7+NAdwTYyXzNdKY1Ms95lorWCA2UDA6SGPtTjYuFgESWEQbBxsHGxcSmSaCIgLJLgVrK5xgsX46HpjrrvAhosqr2QuF4Z6saqBgZoGarai8VPbk++45WMx95et4tP/mZu4n5tjnlb7VkU+1sPv9PmPq356l8WcO+lEzPWdRz3w2cz9t3ehblO76zbRVGo9WDmyw+mNtxxXMOj76QGi/9duIlcnn6/JZD49L2v868rpnRipD3Pjnkltulrme0wFUy0lmNM60uggNf1Nm57bTPLPq7h4j+82coz2mf1tszS3pjjcteslXw1SwYQ4JSfv8LK285O2Xb7s0u57qxxKduWb6nhgIFlhGyr1UDu588t4531qYudx/8/bn5iccr2v89fz5eOHwl4JcrJJd1O7zc+2gn0Tbpf6W/L9njyY26W57T1WgAYY+4D7gOYNGlSuz6wVA7n4KYH27Or6iHPRP6Xfm7r83XzwTUQIYqbpcFNtVVJRBx2V+3AcQ221XKQ2ljVwGV/eovx+/UFIESMGeE/cpH9KgCOEZ5zj+b+2Nm8bQ4iuU92Ec1MtRbxCfsNTrXeoUSaqTdFLDfD2WgGUkMJgiGMQwgHC5cYdiKAbHAjNFBEIxEaTBEN5LhvItRTRJRQImhzEzlDwTUtgZyL5ecaJWNfA37OkLTtyV8trxF//XSvDDi4y98vDRZV4TEG28SS5ix6BxstQ1V7o/jf0e5Y8Lenxcs6e9Jds1byzwUbMrZ/vpUA5+L7Ohb8rNvZEui8tXZXuzJ0+WA7frBoUoPF7X4ZquvEsKzMOUPJrIzPZVLKw/751kdc9+h7zL/xVAaXeyVlq7fVUl4cJua6DCkvTmS/2/L8h1u466UVrTZJWpUWZP72lVVcd9a4lA6lZ/zqVU4YM5C/ffWYlEAuvZvtPS+vzHj96x97n2MPGMCmLL9P8cY2f0xbDDwPXXLnAbeKSBgYCtQmlaACzAbOARb6/8Yn1y4SkSnGmNfx5ideBawARolIhb/PKCDzP0YVJAcbywQvWHRcL7No7OzBIkBpbBf/9+xSbjinJdhpaPY+y8otXhn9d0L/4SL7VX4T+wSznfGcYi/kYnsW5xTN5z13FG+4h9BIEaNlIydZ71EhDWw3FfzLmcqTznG8bQ7C3cPbt+icRbV3ch0sDM0mRFV9lFA8WNTMotoL2e08EQ+CqOPyqd+9ntF1tKtmL9+WUi6ZbW2311fl7qY5f21GIqVV6X9756zYzkkHDerQa/QGK5FZTC9DrcQWw0vvreDUow5p/TXSfrzSP3s8I7tyS20iWExv/HPbBYdx8dEjMpojJatviiUCu4VJpdPpzrzz1Yxt63bUJeZQxr22cjtX/ePdlLU748FeY9Rla03ugPRHj3+Q87Fs7nxxOTedf2iHntMVxphdIvJbvCDQAN8VkQnA6caYO4AHgQdEZA6wAfiS/9TrgftFJAI8Y4xZAiAi1wPPxfcxxujaIHuIGBYSwG+n4ziUiJM1s3jc4WPhNRhANfe9ujolWEw83xj2ZTvftJ/gUecE7oh9DoD5sYO5K3YBn7LncJE9my/ZzxIRh4/cQTzrTOYp91jmuofhsPf0cNJgUe2dHC+DGPN/2RsTZaiaWVR7nzdaCYKC5taZmWsxdoebn1ictWNpT0n/P//CA/NZ9dNzAhe4W1EvWGzMMmcRYMa/57QZLEad1BON9BLh+BqerZ2O3PifD5i7cntK+W66umaHtf73cOnHHWu+MzVLCTbkLiX+5QvL+eULy3O+3pwV2zv0/n+au5YfnntIuzOo3cEY8wDwQNrmhf5jDcDFWZ6zGjgly/angae7f5R7pjs/O4GrHlmY72Fk9c+vH8dnfv9G4r6DjbjBCxYXrdnCsUB1NDOrN+mQg7xgUarBeBcZw3Z8P7+hmwtfDj0DwC+in0l5fgPF/M05nYbxX+LRdz5CMJhezB6OGljWq3+P2tIdhQ97du5V7Zn8+vuY/+Or6yyqruqNNctE5AZ/31kiMrK7xj57eceXL9jT9PYf5tdWZgYTrXXuyxfjX1hrSmtPvwMvWBwo2ZdJSXbJH1NLdK/429udGktrgWLcL1oJ4IKuNwPFvcmJBw7s8muMHlTW9k7tNLSyGNPqpZGecUA7P8PEEX1T7sewkQCWof5rvtexe/n2LFMTyrzveX/xLholr8ebWPvWaebT9myecSeziew/I7/4zHjwe4W2pb3/v+1x8eT9cj7W2s/zEcMrOe+IoTkf76zuyCxqsKgKj19SES8jiBEiZiyKdc6i6oTeWLNMRMYB0/x9b/af2y3y3UJceYIYLO7c7XWwbTapweJ2480JGkDbHW6Xb6nt/oEp1U79StvfGTmX//vUEd0wEjjpoEG8cf2pGaXYXTFqYGaQUl4c4tozx6Zss0Q4Mi0QLC/yigP7FIU4ZpTXwDa9usExwSxDNTEvSNxUm+W4WeoFVPHjU7wiZVddM9V+WfkJ1vtUSj3/dY7v8liG9S3h7ouPzNj+1RM610HUmJbvTbLjxwzgZxcenrj/4JeOTnn8x584lDs/O6FT75nLfv1LCHXDhSwNFlXh8UsqYkk1502ENbOoOiuxZpkxZg1QLiLJHUGmAk/5t5/070P2NcvG4K9Z5q9bttbfNtXfB2PMq8D47hq8HcDGKnuj9iw/0tsemO0tlZeRWfTLUAdI15ZDySaI/w+q8Izw18/81iljuOn8llLpolDu09Z7L52YsW3tjHOZlGUd1lyGVmau+3fRUd7SPPFz7lzB4rh9yjO2ff+Mg7Lue8dFR/CzCw/n5e+fzFmHpq6B+P7NZ/KtU8akbDvnsH345Ph9AbjkmBGcdvBg7vr8kYlx3X/Z0Tx/9UkpjbYOG1bhZRYD2A11v3Lv+1hSkmWd1HAxVaaMwZLaqfjIW17ggt++DsB59pvsNqXMcdt3IeD0Q4Zwz+eP5JSxmXPLSyJ21ikEPziv9RL9XEYP6pN1+0NfPZbh/Vo+b3yOd9x+/UuzNBTzfONkb23fbOO88tQDU+4/e9WJidtzrpvGmMGZP5cdpXMWVeHxD3zJHawaiWiDG9VZvbFm2QAgeQJVt82u/2HZf/l083+76+VUJ9RRTHTnC1Axtu2de1EE71jZnBYsVtEHxwgD2lGG2l5NMYf/uX9e1vl+falhP9lGpdRRRLNXDYLlzafCYPnrmIVwKKY5ZWHrYv92Cc0U00Rx0lpnJdJECCe5KX1iweqWha5zbc+xCLa0b3Hs5McWuqOB1td7VB3zswsP55anPmTUwDLG7lPO8H6lfO0vC1KWevnUxOEpS96kL8vTGff9zyTOv+e1lG3fP2Ms/357A5+f7K0vmR4r/v1rxzJnxTa+f8ZYxv3wWZqTFvc8fsxAfv58Znn1pye1lCre+z9HMXL6zJxjWvSjMygvDvHnN9YCELYt/vjFo9ld33LO06coxEFDUoMC2/KWfCCAmcVxg7zrsZefkn1Zh82mP0OlpfFYSikqLidbC3nJnZjoip9L/7IIO+ua+cMXJgFw3hH78ssXlnPWofvwrYffYc32Om77f4cxdkjXA6rLpozk4skjGLtPebtKP5MDv+lnj2Ngn6KURm1vXD+N4342C4Dxw/sCcP4RQzlon3Juf3ZZYr+rTzuQP722hhp/aaxx+1Tw2DenUFnSeqfrjtBgURWetDmL4AWLmllUndQba5alb8/617szC19POfks/vqMlgrmy76yg/PtN9m0ay2MDFqw6J1MNqf9qXex2Ek5A9tRhuoxfNKay/n2GzQT5iVnIk+5xyaWLYLUtSstXI6xlnCe9San2O+yr3Ss22y6ZmPTiL+OmfHWNYt/1Zlif4Sp65C1hHip65C1uXaZEYxJfyz3cwA+MoM5qUufcM83sE8R22ubuOn8Q/jxkx+mPLZ2xrkZwdLxYwby7FUt/6vHHOBdo0vOvHx72piM9VHPPXwoc1Zso7qx/dm0GRcezvTH3geyrzu6T2Uxa2e0XAwwaYHAwD6RxFqf8fmMJWGbhqjDkSNSF4gfXF7EH784KeM97rjoCK7993sp2246/xAmj+pPZal30n/8GK8885zDvaC4vDjE547ej0uP3T/leQ9/9Rg+/8d5TBheSexjG3EDeG7kl6GGIkVZH/7Y9GeotDQSuylp3dODZT0DpIY5zuHZngrArO95BUCvXndKxhSBa073sr2HDatkzfY6hvUrQUQ4dN8KFm9q/Zh476UTueJv2dcctkQY62eXb73gMP7vmWWJztxlkczrw8nL+sQrhCTxLwytLOHFa06iKeYydkg5V0wdzddPOoB+ZRFKwzY3+79HIsJzV5/ElBmziPiNgCam/dx1lQaLqvD4ZaiOafnlazQRnbOoOqs31iyzgDuBO0VkCrAo20A6s/C1ddAZ/OzJ7H9wVc87SpZxvv0mxgleZUORxIPFlivMN59/CDc/+SE7TGW7y1CvDj3Kd0OPsdYdQkgczrHnc715mIec0/iXcxIbzCCKiHKErOYcex7n2vMYLFXUmyJedsfzJ3cMa80+7DJ9aCJCyF/0OiQOrrFwEFwsYtg0+EFhoymikTCNRIgF/FQlbAs/yvcgAu6io4Zz7+xVXDx5BGu31/HnN9YB8L9+kNWWbMV52eb7/eYSrxS1tUxdsimjB/C5ySNoirnc9MRi9u1b0uZz0g/MxeGWc5F4HPni96Ymtg0uL2Jrjfcn5ZJj9ucIP0uU7NOT9ssIFr90fOqcuYOGlKcErZYlzMgyH3PKmIE8890TKQnbLF0QzG6oSzZs52xAwtn/dm02/TnMWpP1seMtb2mbue5hAHzhuP0ZUFbEuKHlfP2vXgOuA/xS0D5FIcjx5/H2Tx3BxZP3S5SG/ulLR7Ng7S6++VBLMPj81SfxyXvm0hB1OHhoRUr2+ofnHcJZh+3D8TO87F9yhegFRw7ngiOH85uXVxJ1XK46raUc+TOThmesBZx8keLGcw7mBL8RTnIJ6fSzW35XvjhlZCJYBK98+pJjRnDhxOHZP2wXBfsIrFQ2WTKLzYQp1jJU1Qm9tGbZEhF5TUTmAs3AV7pr/GFLp57nU7zRlnGCNy8oXobaZFr+1I8b6l3H2GEq2gwWjTEcJcv4jv0f/u2cxLXRyzEIx1sfcJn9HN+x/8N3Q4/RZEKEcLDF0GjCzHKP5CnnWGa5R9KY60wNWl9vo4B8/aTR+R5CYJ0ydhDvb9zNdWeO5cpTx1ActvnmKWP48xvrGFRelJiLddrBg3lxyVYA7vl8ZrOReBYm3qzj8GGVrb7v/BtOpT5pPddffmY81/xzUWJMLy/zukjf55cnfnHKSL44ZSTgnay/v3E3TyzKvvRK/Of27MP24cKJw9mvf2n6Q/QrDVMa8X7vXvvfaTiu4W9vruMLU/Ynl/HDK1m0oXtKww8eWsFHO+u986QAdkOdu2wT1xSBFcqcIwrwsRnAIKkmQjSjjP54azEr3GFsxcue/eSTh3VqDCURmymjW7qTDi4vTmRt4w4aUs7Zh+3DY+9u5I6LUgPzr5wwisZoy89YtvmE6XNPAW6/aDy3XzSeJZtbjr9nJs1b/dpJB7Q5dklLgYsIt12QO9PaVRosqsLjXyVzsfjTl47mqn8spNGJUKRlqKqTemPNMmPMLcAt3TDcFLatDW7yKeoHi24QM4v+BbTrz2vppxRfr2wHFRzOakZOn8ms701NXIkHeHvdLu57dRV3XzyRq0P/ZjuV/DB6WaIF/Vz3cOa6hzNCtnCS9R7DZTtNhPnQ3Z/X3MOoo+3szJ5Ee0zl9qcvTU7cjgdP8blU157RUrZ91mFDE8GiZMkjVpaE+fRRw/n8MSOIhKxENujhrx3Dz59blhEwDK5IDUIunDicMw/dh7KiEHVNMf44Zw0XT97PyzyliZ+sf++Mg2jK0uU43oG6ojjM6YcMSXksXqKaXC4b8ZvytBUE/PsbU1JKE7vKtiSw6yxGxAtgrVyZRb81wGDZxQYzuOV5RJlsLeUR5+QeG9vZh+3DMx+0LPVzy/87jDMOHcJhWS5QFIdt/vaVY7j0/nkctX/HSj8HlHll/F8+flTKBYf2OvPQIXxi/LAOP68zNFhUhSeRWbSZuF8/zjtiKI3vRBIlV0rtTcK6vltetWQWA3hC5geLA/tVJLbF12HbYSoYYHlXthes3ZUSLH7zobfZUt3EX//7FF+xF/Oz6MU0kJkBWG+G8Dfn9B78BIVBfwM7pjhsp5RTQuo8wGxr3okId3w6s4n0lNEDeeyb7VuLscwPDMuKQnz3tAPb2Bv2H5B97b2TDvI6av7PcbmzhLm6WrYmbFuEu631mZeFjWEFcp3F+IUsO2cZ6gAAhrKTDbQEi0fKSkqlKVGCmu4PX5hEcbhr1Ta/vWRiSsfbsqJQq82TTjhwIPNvPDWju2lbBlcU8+q1p7Bv3449L+73/5M597WnaLCoCk9inUULxDsoNxKmPzV5HphSvS9b6YvqPfFyeBPA9vTxMtSy0paTXhGhX2mY7U2VVEhDRkXGayu2U9fkHWOjCx8hats9ehV/T5BeEqY678QDB3Lw0Iq2d8yjYX1LMoLduHiMEYTDsmUJjrGxAtgNNX4hywpnD5Q2Gy+zuK9sTylXn2IvxjHCPDd7F9X0TG9niEiHqwU6GijGjRjQ8YxiPuhkF1V4kjKLlngny03aDVXtpUK2HsbzyQlwGeoRQ0uIGYujRqVmXg4eWsEOvBPygezmuke9xho765q59P551DbFEFzOs9/kVfcIquh6W/k9mcaKXRdvEpNrjbpCMcrPRnYms9jdbPEzi0EsQ/UvZOXKLF5/8Zk4RjjA+jhl+7HWh7xvRlGDF2QN7BPJ9vQe9fi3juf16dN6/X3zSc8yVOFJmrNo+VeAGglrsKj2SqEgXMIOoBGdmAPSGUHOLBZLjKhkrrV19ekH8bF/5X6fpGUtoknrw42X1QyX7TzpHNfzAy1wdgACg0J3zuFDue6ssVx7ZrCWn+mof3z9WB64bBJWAI7LVnzOYgDLUCOJMtTsGbnTjtifhrLhHCAtTYaKaeJIWcGb7qGJbXPzELSN369vu7rm7kk0WFSFJyWz6B2QG403Z3FnnQaMau8S0gY3WcWbSvS0+BI+QeyGartNGZ0EAY4e2Z/Tj/WWGNg3aS2z5JjnROs9XCO84mbOE1OpOtrYQmWyLeGbJ49JzCssVIPLi5k2ruulkN3BtoQYNk3NwTsvije4kVDubsm1fUYxWjYn7k+ylhMRhzfcQxLbikLdOMlT5aTBoio8bsucRUnMWfTKUC++7808D06p3hVqZemM48cM6MWRBEt3XNj/7KT92twnFuClM2zTnDVYBNhhe00j4gtfO67h9ZUtgeMJ9gd8YEYWfAnqt7O0ru9OV592EFPGtK/BilK9yRbBwcIms6NrvsUzi7QWLJaPYpRsRvzxH2ctJmps3nILO/tciDRYVIUnnlk0NiLegqzxYHHZFm1yo/YurTW4eeirx3b59R/+2jEsv/Xsdu37uaPbDq4ALj12ROL2qeMG59zvB+cezP8cm7vjIMA7P8zsxrl2xrlZ2+93VDwOb63U14n/GQ1gGartRonmCBZ3ucXUmBKG+mWoNzz2Plc9shCAMhqYKCt4ze25dbs64vf/c1TGthPaGaB1JVM1ZnDb8+euPLVng1GlOsuyvItZIYI7ZxE795zDmvIxlEgzo8Sbt3iytYiFZjT1fmfmy9uxHqHqHhosqsLjn5Q5/pzF/QeU0USYYomyx6zyrFQPa8/coFPHDWbK6IHtLun86QWHJxbZBpgyOntm87IpoxK3f3dpZiAQ99UTD+CTE/YFvCUfsgWO/ctSTzbicV22aWS//Mx4/nH5sZx3xNDE/fb48Sdb5sikr8uWyCwGMlhszjpnEaCh2eFj0z8RLD6y4KPEY8daHxIWhzlJweJnJ+3Hqp+ew9JbzurZQWeRvGD1o9+YwrNXncjvLp3Yo+95zKj+XDF1dJv7aSdUFVQhy/Izi8ELFoeX++WjrWQWqwYcCcBR1nKGyzYOtdbxgtPy9+KGc7J3RFXdT4NFVXiSylDjcxabjHfCGF+7R6m9SXeXm57ln5yffXjutaXSHTy0AssS/vescYkA7qKjhicef/n7Jyduj05aRy1b0u7Xn5vAw187Bmi5/CMijN+vb6tjuPfSibx385k5H79w4nCOPWBA4rhhifDbS3IHHTHHe/fkzOK/rkht+JLILAawG+rWXdXUO9nn9NQ3O2w2/RNlqMlOsD6gwUR4x21Zi+7b08ZgW0KkE913sy183l4XHpm66PRR+/dj3D4ViYx6a5n1C44c1ulOpSJ7dxm3KnyWxDOLwStDDRl/HmUrmcXa8gPYafpwnPUh51lvAPC8660t+MVW1rhU3a+wZxKrvZO/ZlB86QyARuLBYvAmcivV00wnEuom7Ul3X3wkb6/bxaXHjmDM4HIamh1KIu1vHjD97HGJ2yceOJDHF25CBMqLQ9Q0xhiQ1OI8ORuTLTPzyQnDMra155w/eeHk+Ove+v8OY3B5UUpQcdAQr7xw374lTB7Vn/0HlLJuR33G63196gG8vW4Xpx+yD//76PuAFxS/Pn0aU2bMAoI9ZzFCjKYcf+brmx02mQEcbK3PeOwE6wPecsfSRPL3LPXfjrjoqOGs2lbLnBXb27X/Lf/vMH743w8Y2CfC/110RNZ9ikI2+/Uv4ZrTD+LqRxZlPP781SdxwMAy/jBnDQBfOWEU088ex676Zn78xIc0xRxeXLI143lfOG5//vLGOo4ZNYChlXtXx0O1ZxHx11kUA67bUlcfACG8EvlwKweU/QaU8ZxzNJ+2Z9NgFfG6cwhrjXeM71+WOyOpup8Gi6rwJMpQ7cQJYTxYLO6hzOLW6kYGV3h18v99dyMhWzjviH175L2U6qiGaO4yo4U/Op0JP3khY3t6gHn++H05f3zLz3R6oHjhxGFMGT2Q7/8r88QcUoO5YX5b8X6lEd668TQAYm72iDb5eZccM4JD9k1dkDt5nMkB7sNfO4an399MWybs15fDhlWmbLti6miOGz2Ao/b3lo/4+9eO5e11u/jO398F4P4vTmLkwDJGD+rDLD8jeuW0MTz27kbACzK/efJoxu5Tzvf+scAfXPBKvYqIpgR8yRqiMdabwQyS3ZTRQB3e92wQuzjQ2si/oyel7B8/1uYquxw7pDznnHFLhCH+8TMejOXSrzTMcQd435fKkjBhP5P5/NUnUdfUEpDbljDnOq9tfnKwWFkSZndDlH6lEUK2xbB+3ucaNbCMsG0xuLyY31wykdnLt6UEi6cfMoR7Lz0KS+DSY/dnTI71/ob1LWFjVUPO8SsVJLHkOdVW769JmEvYRIlZ4Rwzqj0T9uvL95xz+IT9OqV2jDuaP5t4zOiUo16lwaIqOMaJISSVfwFNxjvkFEv3Zxbnr9nJZ37/BndffCTnj9830QRCg0UVFBdPHsG766uyPta3NMKI/qWs31nPRUcN599vbwAyg8G2/PIzEwByBovxk3KAq047iCOGVzL1oEGJ4KK6MfuFnOTY47YLMhuq9C/zfrcPTQsip4weyJTRuZucHDCojCWbq6ksyTwdCdlWIlAEL/jbt29JIlg89eDM1vfXnDGWa85omed53VleJvWqf7wDBDSzKFHqTfYr8CcfNJgFq71j2CjZzAfGaxZxnPUhAHOT1jKDtjO7Ywb3yRksnnnoEA4bVslxBwzgwonDcgaL7998BrYlbKluAqAi6Xt30JCOdWWN/1ydf8RQ+pdGMkpK3bSrJTecc3Ai+5z+Xl8+fhTHHtCf5xZv4bjRA3L+DigVNI5f+eBdZA9OsGibGG47QpC68tGcUv1LfnHR4bz77w29MDKVjQaLquAYNzNYbMksdn+w+OGm3QAsWLszJfOiVFB8ZtJ+fGbSftz0+Af8OcuJ+KvXnULUcQlZkggWv3DcSBqaHX7xwvJuGcPopExMJGSllIQClIZt9h9QynVnjkvZ3laDkDGDy3n0G1M4fFgljy/cmHWfl743NSNTevunjuDio0ewX//SDnwK+ObJbTc1SWawcIwgAWtwY4whQpQqsmfIvnriKB55xjuejZZNiWBxirWY3aaUD83IbhnH2hnnJm5/KmkOazblxV5wOHKAzQ/POyTRiKgt3z31QH790oqUbVZSJvSEAzMvKkzcrx/9SsPsqvcuYqSXZWcb/xn+XN6zDtuHw256rl1jUyqfYgHs1hxzXOobGmiO2JS1se+zV53I7oYoI/qX8it7AEs/ruH3s1d3auqF6rzgFDAr1U6ufwU/PlcIoNEvZujJBjd6bFJBF8+iXX3aQSknuQBh20oJzCIhi++ceiAd9cLVJzHzyhNStlUUt33dMWRbzL72FM7NEgDsU1FMaSuZzqP270ckZOX8HRw9qE/GMgdlRaGsQUJr1s44N5ExbK8/f3kyMWycWLAa3LjGm7PYnOOasIiw3gwhZixGW5sS26dYi3nTPQQ37fQgOaZ/4eqWEtXrzhrL2hnncuCQzKD0y8ePytjWHiLCV04YlShdbcvVpx+U+RptPKeyNMy7PzqD/Qd07GIC0KkmP0rlwz59/d/LAAWLC9btIoxDvdP271Hf0gj7DyhDRLjgyOEUh/w54j09SJVCM4uq4MTLvRzTO5nF+Am2XslSCg7sYDlgLt87/SDum7MagLnTp+XM7KQI4O/gIUMrcLADt3RGzHWJEKW5lVlBlxx/IOveGsI48ZbNGCWbGWFt44/Rc1p97eSfgYuP9tbM/M60A7nzxdTs3hVT27cO2mPfnMLmqsZ27duW+M9RexvxxDvehjsQAMZfu7VOrEoFQSjs//67wZlTbQyExCFmOjYVApJ+r/WErFfp5TFVcNykdRbjKsu9k5eemLOoy2ipQpGvP599Szs+F+Y7px7I+/4yF7YlhNpxsh7Epga2JcSwkACdjIHX/LBIojSb3NeETz94CO+aAznKWg4YzrTeAuBFJ3Pty8HlqVm+C48cxj2fP5J+/jIp2QKntoKpf379OL5ywigmjuiXNePcET887xDu/OyExDlke9c//Pa0MQAMKm9/d0Xbf+1s634qFSRGkucsBkcYJ6U6rL3ErxkI3l+CPVuvZhZF5DLgcrzv83eMMe8kPVYM3A+MANYDXzHGNIrISOABoAiYaYz5qb//WcBN/tNvNsY8l/RapwCzgP2MMTojdg9j/PXMnKQDzRdPHAcv9kxmMfG+enhSBaIjFzgunjwiZ/OZ9vjBuQenLJrek+INVQ8Y1NZMl95jiX8sCtjJmGOMv3RGa/0GYb47lovsVzlQNnKu/SbvuaPYxEBGDijlF58ZT1PM5dhRA7DSAr9ffnZCxmv99ILDueE/3hIj5x0xlH5tXESYPKo/k0f1b3Wf9vrKCV7J66srtvHYOxspCbfvRPTiySO4ePKIDr2XZQnLbz07Zf1NpYLIFf80P2DHp3ArJfKtif9t08Ri7+q1YFFE+gFXAscCw4C/AskTXy4DlhpjLhGRH/n37wVmADcZY+aIyIsi8hiwArgdiE+cmC0iLxpjHPEuJ14DLOiFj6XywDjeArOx5MR4xLvq3ZNzFl9euo2YE7zFbZVK19of0sqSMGcc0tLt82cXZnYg7Yivnti+UsPuEO9gecyo4CyWbllCExZignUytn5HPcPbKEMFmO2MxwkJd4Xv4WBrPT+IfgmAx791ApWlrT833TH+khejBpZxz+cntrrvkSP6dui122vGhUcw/exxREI9WzjV06+vVHdwA5pZDHUysxj/G6DXaXpXb2YWJwNzjDHNwBoRKReRImNMk//4VLwAEOBJ4Dq8YHGCMWaOv32mv58B1hhjqgBEZC0wBlgGfBp4Drioxz+RygvjZmYWXdsLFnuiDDVuY1UDd81a2WOvr1RvWHTTGfkeQqfFg+AgZXQsET+zGKwy1Mv+NJ85xNhvUN9W99tCfx53j+dC+zXWuYN51DkR6Nz63fHvS7blSpLNu+FUKoo7Foi2VyRkZZTMKrW3ailDDdbxKUyMWCdCkHjFQEUbxxjVvXozWBwA7Eq6XwX0BzZneTz+GKTOq6wC9sn1WiISBr4KnEcrwaKIXI5XDsuIER0rP1H5l2hwk/SjYeLBYg+UoSZnaT7aWd/tr69Udzl//L7cPWsl5x7RO2Whx3RTCWF7XXTUcBZvquZ7Z2R2v8wXS7zOzEHLLO6qbyYcilFWWtLmvtdHv8rLzgTmuQfTgHcstToxWXv/AWXcdP4hnHN46/MP29vlVCnVNUHOLEY7kVn80vGjsET44pSR3T8olVNvBos7gb5J9yv9bdkeT37MzfKcXK91OfA3Y0xza5PbjTH3AfcBTJo0SSufC0w8WDxvQkug79pec4KiHgkW9UdEFYaDhpRnLJnRUxb+6HRKWlnuoicUh+0ul812N0sEx1g0NTW1vXMvMq6DJQZj5b4CH5+H2ESEJ90pKY91ttPnlzq5XIZSqvvVx2fmBCxYDEuMZtPx7GAkZPG1k3pv6oPy9GbR/TzgBBEJi8gIoDapBBVgNhDv132Ofx9gkYjE/4qdDbyKN2dxlIhUiEgFMApYCRwGXCoizwJHAH/1G+eoPYgxXjnFkSNb5i25oXhmMYrr9lxw9593sy8KrtTepm9phKJQ7waLQWSJEMNm/fbqfA8lhR3PdFq5rwlPHpk7M6xdoJUqfEu2Nng3AhQsGkynu6Gq/Oi1zKIxZpeI/BYvCDTAd0VkAnC6MeYO4EHgARGZA2wAvuQ/9XrgfhGJAM8YY5YAiMj1eHMTAa43XgTxjfj7icgrwP8YY7pn8SYVGPHMolgtBxpjRXCNUCTNOMZgtbkkcwfer9teSSm1p/GWzrCxCVbzqxDeRTVj5e5Imt7hNJmt0aJSBS/RCDBgcxY7W4aq8qNXl84wxjyAtwxGsoX+Yw3AxVmesxo4Jcv2p4GnW3mvk7swVBVgxo3hGsFKChYRoZEIxURxXEM7u6YrpVSXxJfOiAdnQRHGu6hmWskstkYXnFeq8MUbARo32o2X0LvO64baqyGI6gLt/awKjxMjhpVxMtNImGKaiXWxDPVnzyxh5PSZAGytbmTl1touvZ4KNhHpLyJPicgcEblbskx4FpGzROQN/+vMpO03iMhcEZnlrwmLiFwnIvP87YnXE5FX/Oe/IiJ399oHVD1KRLzjUcAyi2E/eHU6GSy2d1F7pVRwxUs9XSc4ZajgXczSzGLh0LBeFRzjOjjYKd36BK9JQzHNOF0MFn8/ezVAImBUe7zrgEeMMX8VkQeAM4Fn4w+KiE2WdV2BA4FpxpjjReQkvDVhPwf8xxhzu//cfwLTgJf8537aGLOhNz6U6j1BzCzGx+OKtphXam/lGC8n5MQCFJqZeLCoIUih0MyiKjjGjRHDzphv02jCFEm0y8Gill/tdaYCT/m3n/TvJxuDv66rv7brWn/bVLy1XzHGvAqM92+vSHpuExC/pGuAf/hZyGnd/zFUvniZxWAFi2Hxfuxc0RMypfZW8cyiCVBm0QAhcYiZwISvqg36V0QVHieGi2Q0YGjsYmZx5dYajPEaOzhttLU5xXoXNg2DfSd06r1UoPTHW6sVUtd4jcu1RuwAYFPS9pS/fCIyFRiK18EZvKzidhHZD3hRRCYZY2q6YfwqzxxsQhK0MlQ/WOxkGapSqvDF16N2nGgbe/aemKvdUAuN/hVRBSeeWbST8uJFIavLZain/dI7py8OW7SWJCijgd+E74LX18BF93fqvVTv8ktJ52Z5aCZeIFiJFwSmr/8Kudd1Td+e+KkRkSPwylLPN/5CncaY7f6/H4nIIrzs5Ltp47wcb71YRowYgSoMMWNRJME5GYOkMtRW1llUSu3ZLj72AHgXTCw4mUXHdQnhcOK4ffM9FNVOGiyqwuPPWUxuwDB5VH/mE6ZYmok6XbvCH7IsaKVZxdn2fEqlCdxgnRyq3PyldY7N9piI9MFb2/Vh/9/H0nZJrOvq34+v62oBdwJ3+mvBLvJfbwxe1+dPxQNEv8lNuTGmWkTKgcOBdVnGeR9wH8CkSZN01ZYCUVpSTEXQylD9zKKjf+aV2muVFhcB4AZoncWYYwgTo6RYl0EvFDpnURUe1++GmtzgRoRGE6GIZq7429sdfsmZ721O3K5tav2g+ilrjnfDBKvsTHXa7cAl/hqvUeB5ABG5U0QG+YFmfF3X5/DXdfXXfH1NROYCtwE3+K93J17G8c9+59Nz8S7MvSwirwEvAjcbY9IzmKpAudhYJmjBYnydxdaDxctPOqA3hqOUygfb+/13A1SG6vhlqBLSqodCoZccVeExDm7WpTMiDCbK4k3VHX7Jbz38Trv2G8Y2jrM/9MehiZ89gTFmB3Bulu1XJd3Ouq6rMeYW4Ja0befleKujujRQFViu2NgBunIPyd1QW/8zf8M5B3Pfq6tTtn3txFE9Ni6lVO+x/ItFQWpwE3UNIWKIrcFiodDMoio4xokRMzbpy4DFG9z0pAvs1wDYbio0s6iUAvzMYtDKUP1uqLF2LJ3RvyyScv/iyTpfVqk9gQQws1jX0IQtBrEjbe+sAkGDRVV4jIOTLbNoIhT3aJMJw4X2HN50D2aTGaDBolIKgF1Nhubm4JyMQUsZanFRUZv7PnvViYnba2ecywGD+vTYuJRSvSiRWQzOxayb/7sQ0E7NhUSDRVV44t1Q01KLTYQ7lVncVNXQrv0mygoOsD7mUedEXATc4Bx8lVL50+BIouwzKI7dvxyAMw4f3ua+g8u10YRSeyLbL/UMUhlqKDGfWstQC4UGi6rwuDFcrJRuqOCVoRbR8av7X37wrXbt9yl7Dg0mwtPOMbhYmllUSgHgGAs7oOsshsJtZxaVUnuokF+GGqDu7fFgUecsFg7NAauCI67jdUPNaHATzyy2v/HMRzvrWfpx2+uiF9PE+fbrPO1Opo4SDRaVUgkx7MBlFsX4mYR2npDNvPIENlU19uCIlFK9LYgNbuIl8mgZasHQ75QqPP46i3ZaXrzRRLDEEKHtg+KW6kZ+98oqHnx9bbve8hxrHhXSwD9jp3hDQDRYVEoBeHOogxYsxruztvOE7NB9Kzl038oeHJFSqrdJAMtQh1XY0AwVZaX5HopqJw0WVeEx3pxFK60MtaysDJpp17zF6Y++x8vLtrX7LT8beoU17hDmmXHeEDRYVEr5vMxisI4HVrzsTDsOKrXXsuzgZRbPGjcA3oNwREvkC4XOWVSFx/W7oaYFi18++WAApo4ub/XpI6fP7FCgOFI2c4y1lH85JwPee7pGy1CVUh4HO3CZRcvtWBmqKhwi0l9EnhKROSJyt6RP4Pf2OUtE3vC/zkzafoOIzBWRWSIy0t92mYisEZFX/K9hvfhxVA+y48FigNaBNfELWdrgpmBosKgKjrgxr6FE2pzFkhKv3fuASPvnLLbH5+1ZxIzFv52TEtsczSwqpXwxrMBlFhNzFvWEbE90HfCIMeZEoAw4M/lBEbGB24Gz/a/bRcQWkXHANGPM8cDNwIykp91vjDnZ/9rYGx9C9TwJ+b//AQoWSVQ9aHFjodBgURUe18lahkrYa//+2tKPuu2t+lDP5+xZPO0ew1b6tQxBG9wopXyBzCya+AmZBot7oKnAU/7tJ/37ycYAa4wxVcaYKmCtv20qMBPAGPMqMD7pOV8QkddE5BYR0XPDPYRt2bhGAlWGajlaIl9o9ICgCo4YBxcLK/2nN1QCtG/OYnt9xp5NhTRwf+zslO1G11lUSvliWC0d/gJCy1D3aP2BKv92lX8/2QBgV9L9+D7p223/38eBg/GCyf2BS7pzsCp/bEuIYQWqDLWl+ZYemwqF5oBV4TExYhRlzFkk5E2W7q5gsYwGvhF6gjfdg1lkxqQ85iLEHEd/gZRSTBo1COsjA65L5lWs/LB0XlBB80tJ52Z5aCZewFeJFwRWAjvT9tkJ9E26H98nfbsDYIxJBJAi8g+8sta/po3ncuBygBEjRnTsw6i8sS3BwQYnOOssahlq4dHvlCo44i+dYVnpZaheZrFIuueg+K3Q4wyS3VzefE3GYy4WxtUyVKUUuOL/KXVjYAWjtMo2MVwEy7Lb3lkFjjHGAY7N9piI9AHOAR72/30sbZcVwCgRqfDvjwJW4lWT3QncKSJTgEX+6/X1y1UBpgHLsoznPuA+gEmTJnVvYwDVY7zMoh2oOYvbd9d6N/RCVsEIxiVQpTpATIwYVuacxZA3Z7GYZuavSb/Qmt2X7Wd4KfI9Hg7fyqnW24D3N/Bk612usJ/kn7GpvGsOzHiei4XROYtKKcBIvONgcK7eb9td650kZjbKVIXvduASEZkDRIHnAUTkThEZ5Aea1wPP+V/XG2McY8wS4DURmQvcBtzgv961IjLP394f+EMvfx7VQ7zMYrDKUJdt8s/PdM5iwdDMoio8roubZemMeGaxmCi3zfyQx799Qqsv8wnrdX4U/isL3IMYJtu5P/ILlrr7scoM5QzrbZaYEdwc+2L2ISBIwLofKqXyw/Wzd8aJEZTQLIRD1Njo6diexxizAzg3y/arkm4/DTydZZ9bgFvStt0I3NjtA1V5Z0s8sxicOdWh+PxuLUMtGPqdUgXHyyzaWRrctMxZXLRhd6uvYeFydehfvO+O5LPNP0QwfNJ6nUtCLzLeWs2/nKnMiF1MPcVZn+9qgxullK8lsxicY0IIr2u0UmrvFc8sBqkMNdEMTMtQC4YGi6rgWMafs5hRhupnFiV3gxtjvDLTY6wljLK28O3m73iTv4FH3ZN4tPmknM9N5uo6i0qpOIlnFoNThhohhhuQ+ZNKqfwI2X5mMUBLZ7RkFjVYLBQ6Z1EVHHFjxIyFndHgxssCFpH7hM2PFTndeptGE+Yl98hOjcFbZ1Hn+CulwPhlqG5ATsiMMYRwCIU1WFRqb2ZbgmOCllmML+ujx6dCocGiKjzGm7OYM7PYytIZ8fDuFOtd5rqH0ZCjzLQtLhbonEWlFMllqME4IYu5hpDEWrq0KqX2SiFLiBICE4xjE0BY4uss6vGpUGiwqAqOZby5OOmJRUJFuEYolqacz3173S76U80oawvz3HGdHoOLIAGan6SUyp94ZjEopV4xxxAhhtE5QUrt1VrmLAbnfEXLUAuPBouq4IiJ4ZClDFWEeoooJXew+Jnfv8EEayUAC90xOfcbP7wycftbp4xOeezGcw7WOYtKqYREZjEowaLrEsLB1Sv3Su3VQpbl9WUISNUDJAWLejGrYGiwqAqOxDOLGalFqKOYMhpbff4EayUxY/G+GZX18XsvPYpffXYCACMHlPL1qanBYr+yCAZLg0WlFNCSWQzKOosxx5uzaDRYVGqvZltCDAsJULAY1sxiwdG/JKrgWMb1MotZFpuuM8WUSevB4hGyhhVmeMZ8xU9NHM6xB/TnrMP2YdW2WgAky3sIEDMaLCqlPEaCVYYadV0tQ1VKEbLEzywGpwy1xNJgsdBoZlEVnHgZakaDG4BInzYziyPlY1aafTO2jx5cxqcn7Qe0NDoVIWORbcvSpTOUUi3qY95RYs3W1td37S0tmUU9GVNqb2b7S2dIgBrc2FqGWnA0WFQFp6UMNfOxIYMG0EcaABg5fSbba1PnL4aIMVy2sdbsk/Hckw8anLg9tNLLOn77lDEp2cU7LjqCpqiLQXCc4FypU50nIv1F5CkRmSMid0uWdLKInCUib/hfZyZtv0FE5orILBEZ6W+7TETWiMgr/tcwf/tIf7+5InJDr31A1eM+3OIdcx5+c3WeR+JxXENItAxVqb1dKIANbmy0G2qh0WBRFRbXxcLgGDtrGSqRPpQmZRaXb6lJeXhf2UFIXNaZISnb1844l0P2rUjcLysKsXbGuVw4cXgisxgJWXx60n7MX7sTF6GuMfcSHaqgXAc8Yow5ESgDzkx+UERs4HbgbP/rdhGxRWQcMM0YczxwMzAj6Wn3G2NO9r82+ttmADf5+0/zn6/2AI5fhmqZYJyQRR2XMDGMpeuYKbU3sy0hZuxAzVm0jYOLTdYr/iqQ9DulCot/MtbuMlST+vBI+RiAda4XLB40pA+PfuO4Vt8y/jbJ7+ZgIekvrgrVVOAp//aT/v1kY4A1xpgqY0wVsNbfNhWYCWCMeRUYn/ScL4jIayJyi4jEj7MTjDFz/Nszs7yPKlAuwQoWY67xFr629cq9UnszrxuqhQTk2GSMwcbBjc/zVgVBg0VVWPyrYw5W1m6oJlKWs8GNMYb9ZQsAa/3M4lmH7sNR+/dv9S3j8xfjS3VYIrhY2OicxT1Ef6DKv13l3082ANiVdD++T/r2+F+/x4GD8YLB/YFL/O3Jx9ts76MKVPzExyIYJ2RRx1s6Q+cEKbV3s4RAzVl0Ddi4GiwWmF4NFv25PK/7c3Ympj1WLCIP+fOGHhKRYn971nk+2eYQichB/uu/4u8/HrVniQeLuQ40kfLUzGJSPPnsBx+zv2yh3hSxjb4AXHnqgW2/pR8txjOZAhgE0WCxYPhlo29m+fohXsAXX1izEtiZ9vSd4P/ApO6Tvt0BMMbsMsY4xhgH+AcwyX/czfIa6eO8XEQWiMiCbdu2deajqjxw/D+ldkCCRcc1hHEw2m1Qqb2aiOCKjQRkzuJT720ihEOjk6UyTAVWrwWLItIPuBI4GbgUuCttl8uApf68oWX+fcgyzyfXHCJgNXC8MeZk4IfAD3rwI6l88IPFytKS7I9HyiilKWsg942H3mF/2cI6M5h4FBmy2/4ViPc7Gd6v5T1dBEvLUAuGH7wdm+XrFmA2cI6/6zn+/WQrgFEiUiEiFcAoYKW/39kAIjIFWOTf7pv03Gl4xzOARf5++M97Ncs47zPGTDLGTBo0aFAXP7XqLfEyVDsgpV5RxytDFc0sKrXXc7ADU4a6q64ZG4eYFjYWlN78bk0G5hhjmo0xa4ByESlKejzXvKFs83yyziEyxsSMiRcNUgG812OfRuWH6wWBJtfE6KI+WGIowW8+kzFncQvrsnRCbU2fohD3fP5I/vKVyYA3h9HVOYt7ktuBS0RkDhAFngcQkTtFZJCfIbweeM7/ut4PPpcAr4nIXOA2IF75cK2IzPO39wf+4G+/HrjN3z7Hf77aAyQa3ASk2iDmuITE0XXMlFIYK1hlqCFcb+1HVTB6c/Z7rnk/m7M8Hn8MMuf57NPKayEiRwH3ACOAC7MNREQuBy4HGDFiRIc/iMojP7NoJPuPrhSVA1BGA/UUpz6GywjZykvukR1+2/OOaFmX0StAtQJzYqi6xhizAzg3y/arkm4/DTydZZ9bgFvStt0I3Jhl39XAKV0fsQqa+PyboGQW42WoGiwqpUyAylBdv8FNTIPFgtKbmcVc836yPZ78WLZ5PjlfyxjztjHmOOAC4O5sA9FSrwKWCBazH2ikqAwg0eRmwTrvmsL3/7WIfdhFkURZn7ZsRkd5mUXRYFEpBZC4Sh6UbqhR1xDSbqhKKcCVEFZAMos76poJi5OY560KQ29+t+YBJ4hIWERGALXGmOQV03PNG8o2zyfrHKJ4UxxfFVDfMx9F5U08WLRyBYvxzKIXLP7yheVsrWnk329vYKTlLZuxtovBIuicRaVUi3ilQ4hgnJDFHJcQrs5ZVEqBBGfO4vjhfbFxCIX02FRI2rzsKCKnA58BfmOMWSgilxtj7uvoGxljdonIb/GCQAN8V0QmAKcbY+4AHgQe8OcNbQC+5D/1euB+EYkAz8Tn+YhIfA4R+HOI/A6p/wuJlnRXdXScKuD8A966nU1ZH7YifQBSOqJ+9vdvAjBCtgJ0ObMIXrCocxaVUhDAzKLjlXqJnpAptdfb3WRwQsG4kFUSsYnh0rdPjiaFKpDaU6PyZeAbwA9EpD8wobNvZox5AHggbfNC/7EG4OIsz8k6zyfbHCJjzEz8RbLVHsqvu881OVqK/WBRGhPNbdZsrwNgpHxMs7HZZAYA8MkJ+2Z9jXYNQ9dZVEr54g1ugrR0RggXS8tQA6G+vp6VK1fG7+qaAapXBWqdRde7kGW06qGgtOcvSY3fcfT7IjIDOLpnh6RUK/wy1Fxtl61ib7m88iwVyPvLFj4yg3GxKAnb/OLTnVuGUwRc47+/60KuzqxKqb2Cm+iGGoxgMea6XmbR0mAxn6LRKNdeey1/+ctfGDVqFH6z9sNFZLoxZoaITDDGLMzzMNUezsEiFJBjU/xCFjmmEqlgas9fkkSmzhgzXUS+04PjUap1frCYK7Nol3lNdPtKbcZj3hqLXglqn+JQu9ZYzMYYrwzVu+PSu1N/lVJBkwgWg1KGGnMJiYul3VDz6nvf+x719fWsW7eO8nJvPr2IfAAcICK/A87C67mgVI/p16eUUGMwKqEc43dq1gtZBaXN75Yx5vH4bREZADgi8iVgMfC+Xz6qVO9IlKFmD9CkpC8AldSlPWLYX7Yw3x3XPcNICRaVUnuz+MWrwCydEfPWmRUtQ82rp59+mhUrViCSUnnq4k3t2Y7XtE+pHlVSXBScYNE1lOCAlqEWlI6mRP4DDAR+CtwB7BaRpd0+KqVycJ3Wy1Cxw9SYEvpKarA4kGr6SCNrzT5A1yeNmPj7a7Co1F4vESwGpNQrfpzUOYv5ZVlWeqAIgDHGAbYZY97s/VGpvY4VCsyxyXGN1+8hx1rZKpg6GiyWG2N+AmwxxkzFa0jzz+4fllLZxfwr5gcP65dzn+ZweUYZ6gjZAsA6Mxjw5h121kFDynESmcVgHID3BuXl5VRUVGR8xbcrlS/xpXy2VQdjtSYnFgXACkXyPJK92yGHHMJf/vKXjO0icimwpPdHpPZKlu3NWTT57+DuGkNIHLB1zmIh6WhoH1+PoElESowxj4rItcCPunlcSmXlxLwr5ocM659znzqrgoq0MtSR4q2xuC6RWex8tPjpScP59TOaWextNTU1+R6CUlm5/p9S4wSk46BmFgPhN7/5DRdeeCEPPPAARx11VHzzWOBK4IL8jUztTRKNrowLkt8gLZFZ1DmLBaWj362f+8tnPIK3JuLrQN9uH5VSOcSDRbuVk6B6q09GZnF/awuOETaYQV0eg4hgdM6iUspnxLt4FJJgVBrEj5Pa4Ca/hg0bxrx585g1axaLFy+Ob95kjJmcz3GpvUti7rIby3sXUq8bqja4KTQd+m4ZYx71b/5SRP4HOBy4sNtHpVQOsXh5VSvBYp1dTiXbUrbtL1vYZAYS9X/ku1KGKmiDG6VUEhFiJjhrrxrHO07aIT0hC4Jp06Yxbdo0AK688kotkVC9y0oKFinK61DiwaIu61NYOv3dMsb8tTsHolR7uE58Lk7uK+Z1Us5+kl6GuoW1/rIZ0LUGNyJJ3VgDMAdAKZV/DnZg1jJLHCe1DFUpFc8muvkvk3eM0TVgC5AuEKcKSnvKq+qscn/pjJZALnmNRSBrh7r2EgQ3/qvjBuPkUCmVPyJCLEALX8eDRdEyVKX2eiaRWcz/8WnRR1WEcEEvZBUUDRZVQYk58TmLuevua6xyiiRKmd+PqZJa+kltSrB41mH7dHoMIuicRaVUwueO3i9QmUXjX1TTtcyUUpJShppf/1ywwcss6oWsgqLBoioobmLOYu4DzUFjDgRgsFQBcIBsBmC1GZrY5/KTDuj0GER0zqJSqsXJYwcRxQ7MnEU3flKY52YWSqkAsOLdmqN5HohHG9wUHg0WVUGJt4RvrXHDhEMOBmAf2QnAKD9YXJMULHZFShmqBotK7fUECVRmsbquwbuhJ2RKKf844AZkaR9bXJ1PXWA0WFQFpbbeKy11pZUDTbkXFA5mFwCjrI+JGYuPzODELlZX5iymZBaDcXKolMofEYgRnG6ory7z1pXVYFEpFZ8fGO/5kG+aWSw8GiyqghKfs+i09qNb4QWL+4gfLMpmPjKDEstmANhWVxrcgGs0s6iU8oiAY+zArLMYjmc49YRMqb2eJDKLwShDtdHMYqHRYFEVlLB4wdnAitLcOxWVU2NKGOIHi6NlM2tNS0ObYX1LqCju/IFKRHTOolIqQRA/sxiMYPH4UX29GzpnUam9XjxYdGLBCBZDOIgGiwVFg0VVUMRv3NDWgaZs0H4Mk+1EiDJaNrLUjEg8Nnf6NEJ253/0heQyVF1nUam9nbf2qu21hA8AwS83046DSin/fMkNSLBo4yLaqbmgaLCoCks8WGyjvMoaeBBjZCMHygYi4vC+O6rbhuDNWdR1FpVSHkuEGHZgMotitAxVKeVLZBab8zwQOPeIoYTF0aqHAqPBoiooxvWu3FttnQQNPpj9ZQuTrOUALDYju20MXhmqzllUSnkEbx51UDKLVmLpDA0Wldrbxdc0DMKcRWOMNrgpQBosqsJi4uVVbVyVGnIoIXG5IvQkm01/1pkh3ToMnbOolIrzuqEGMbOoV++V2tvFg0Xj5D+z6DoaLBYiDRZVQUnMWWyr3n3UVACGyk5mOUcCne9+mo0RDRb3FCLSX0SeEpE5InK3SOa6KiJyloi84X+dmbT9BhGZKyKzRGSkv+0aEXnF/1ojIr/wtz8oIu/62//Vax9Q9TgR8TOLwQgWLaOZRaWUzz9fcqL5zyy6WiJfkDRYVAXFOO1rcENpf+6MXchKd1+e7ftZ7v/iJMDrhNot49B1Fvck1wGPGGNOBMqAM5MfFBEbuB042/+6XURsERkHTDPGHA/cDMwAMMb80hhzsjHmZGAJkBwYfsd/7NM9/JlUL4thByZY1DmLSqk4CcUzi/kPFhMXsrQbakHRYFEVFv8kyG6rDBX4tXMRpzX/nEMPHc+pBw/hT186mse+OaVbhuHiv79mFvcEU4Gn/NtP+veTjQHWGGOqjDFVwFp/21RgJoAx5lVgfPKTRGQwMMoY82bS5l/6GczPdveHUPkVMza2BON40JJZ1I6DSu3tjBUBoKGxIc8joaUpoF7IKigaLKrCkihDbTtY/P4ZY1PunzJ2MEMqirtnHFqGuifpD1T5t6v8+8kGALuS7sf3Sd+e/kP5OeCfSfe/b4yZDHwSmC4iB3Rl0CpYYtiE40tW5Jm4OmdRKeVZ/HE9AE++uz7PI9Gqh0Kl3y1VUMR1iBoby2r/dY7MGWhdZxLdUHWdxULgl5LOzfLQTLyArxIvCKwEdqbtsxPom3Q/vk/69vQaxEuAS+N3jDHb/X93isgLeJnI1WnjvBy4HGDEiBGowqFlqEqpIBpQUQYQiItZopnFgqTfLVVYTAwHC6sdAaDjeoFcxO7+BLorunRGITHGOMCx2R4TkT7AOcDD/r+Ppe2yAhglIhX+/VHASrzKjDuBO0VkCrAo6TUP8t7WrEja1tcYUyUiEeB44M9ZxnkfcB/ApEmT9EpEAfEyi8EIFrXBjVIq7tTDhsFbMKg0/5UG81dthWK06qHA6F8SVVDEdf1gse1o8UvHj2Tz7ga+dlL3V/s1OXhFh24wTg5Vl9wO/EVEvgG8BzwPICJ3ArcZY7aJyPXAc/7+1/vB5xIReU1E5gLNwFeSXvNS4KG093nED0zDwN+MMYt77BOpXhf1M4vGGLI01O1VlmYWlVI+y186Y96qLXwqz2NJLC+kx6aCot8tVVhMDAe7XaWl5cVhfnbhET0yDBfNLO4pjDE7gHOzbL8q6fbTwNNZ9rkFuCXL9h9l2XZm+ja154gSIkwMxzWE7HwHi5pZ3JOJSH/gL3gl8QuBK41JnRMhImcBN/l3bzbGPCci5XgXww4Gvm2M+Zu/bzFwPzACWA98xRjT2BufRfU8CXkNboJQ+RDSYLEgaYMbVVjcGLF2ZhZ7UsvSGRosKqUgRoiQOLgBKB5OzFnU9vR7qk4t9wM0ABfglc8nuwxY6r/eMv++2kPYiWAx/3MWEx2jNVgsKBosqoIirtPuMtSetE9lqXdDg0WlFBA1XjdUNwBNr7QMdY/XqeV+jDExY8zHnXg9VcDE9oLFIDTgSgSsemwqKBosqsJiHBxsOtAMtWeGkcgs5v/gq5TKv6LiIn/OYr5HosHiXqCzy/3kkrx/1n1F5HIRWSAiC7Zt29bhAav8sULecSAIZag28cyiNrgpJPqXRBUUcWPEsCnOdxmq+Ac6zSwqpYAD9+lH+KNgZBa37a7z2ihpsFiwemi5n1yS98+6r3ZqLlx2OJ5ZzH8Zqs5ZLEz63VIFRYyDY/JfhoroOotKqRbGChPGIRaAY0JI/BMy0eKhQtVDy/3kMtt/nYX+v7M7PXAVOJYfmCWOC3nUklkM53cgqkP0L4kqKC1zFvM9EG1wo5Rq4VohQgSjwU0Ih6ixaVfbaFWIbgcuEZE5QJSk5X5EZJAfaMaX+3mOluV+EJEngS8A00XkXv/1HgQO91/vcP++2kPYtkWzCcY6sCW2f4DUzGJB0e+WKiz+nMV8r2NGvAxV11lUSgFREyIsDjtrm6gsye9VcxuXGDZ67X7P1MXlfs7Psq0BuLh7R6mCwhahwV/aJ99a1lnUOYuFpFcziyJymYi8LiJzRWRi2mPFIvKQiMzx/y32t48UkVn+c25I2v8sEXnD/zrT33amiLwpIrNF5GkRGdCbn0/1PMs4/tIZ+R2HsXSdRaVUi6VbGwD4xbMf5HkkXmYxhp6MKaXAsoQYdiC6oWrzrcLUa8GiiPQDrgROBi4F7krb5TKyr/MzA7jJGHM8ME1ExrWyhtASYKoxJt4G+qoe/EgqH9yY1w01z5lFEQ0WlVItmv3gzI1F8zoOYww2Lo7OMlFK+aIBySxaRpfOKES9+ddkMjDHGNNsjFkDlItIUdLjudb5mWCMmePfnulvz7WG0HpjTJO/bxME4DdDdSsx3pxFO8+pRZMIFvN/pU4plX8xv+jTNvn9s+MazSwqpVI52JSG8j+h2ko0uNFgsZD0ZrDY1ro/udb5SR5jfHurryUiQ4BvA7/LNhBdL6hwxYPFfE9ZTHQZ1DmLSinA8ecx26btzGLMcfnxk4vZVtPU5r4d5RpDCIdIWGcsKqU8oXCEPnkOFr2qB52zWIh6M1hsa92fXOv8uFmek/O1/FbR/wauMMZszTYQY8x9xphJxphJgwYN6sRHUfkixrtinu8yVCP+VTEtQ1VKAY5/TLDaUW0wa+lW/jR3LTc90f3zG+PBoth65V4p5XEk1FICmifG6DqLhao3g8V5wAkiEhaREUBtUskotKzzA6nr/CwSkSn+7bOBV0laQ8gPDkcBK0WkBPgPcJsxZl4Pfx6VB5br4JggBIvxzKJWOiulwCEeLLadWXT9tRhjTvdf6Y86BltcXNGTMaWUx5UwoTzPzIpfyAI0WCwwvRYsGmN2Ab/FCwL/DlwlIhNE5Fp/lwfJvs7P9cBtIjIXb87jklbWEPoWMB5v/aBXROTG3vl0qrdsrqrFQfLfDVWXzlBKJYlnFtszZ9GPFXuknP7+OWsI4VDT3P2vrZQqTK4VCsh8ap2zWIh69btljHkAeCBt80L/sazr/BhjVgOnZNmesYaQMebnwM+7abgqgEI4uBTlfZ3FD7fUe5datMGNUoqkMtQOVBsI3X8c27y7gTE4NLv5ntitlAoKNwhlqOicxUKlvbVVQbFwifbuNY6s6pr91ICWoSqlSJ6z2I7MYg+PJYSLo91QlVI+V0LYeb64bQyExB+DrQ24CokGi6qghAKyflhiDK42uFFKwUnjhgJw4MCiNvbs2TLUeBOJeHdWpZRyrRB2AOYs2lqGWpDyf9atVAfYAVk/LIaus6iUajFh5GAABhS3HQEaP7coAq+t2M6KLTXdNg6D10RCM4tKqThXQoTyfL7iuNrgplDpd0sVFO8kKP/XOFy0G6pSqkV8qQrpwDHh6fc/5un3PwZg7Yxzu2UcrgnORTWlVDC4VpgQbXdq7klNMVcziwUq/2fdSnWAjRuIk6CWMlTNLCqlwApFvBtufk/IXGMIic5ZVEq1MFaoJauXJ80xNymzqMenQqLBoiooIXFasnp5lDgR0zJUpRQgth8sOm0HiyZLh5vttU2ZGzvB+JlFV+csKqV8RsIdXjrDdQ0xx+XWpz5kRzccn7zMopahFiL9bqmCYuMSM/k/CdLMolIqWTyzKJ3MLJ5yxyu8/+MzuzwOb+Frl6gGi0opn7FChHFwXYPV2kLVrst791/B2vXrmR79GqdPOIDHF25iS00Td198ZJfG0BRzdJ3FAqXfLVVQ7IB0Q/361ANhHhosKqUAsEJ+K/h2zFnMtnRGTVP3zH/2mkjEaNQyVKWUrzYqDMah2XEpbq0EdMkTHLHxEY6wYaMZyP8t9JY/jzld7/zeFNXMYqHK/1m3Uh0QlMYNlmXhGNEyVKUUAJa/blh7MosmWx1qN3nqvc2EcCmKRHrsPZRShWXljiZC4vDBxt2t7/j+v9hGP55xjuZz9suEunG5jV31zS3rLIqGH4VEv1uqoASlG6qIX4qq3VALnoj0F5GnRGSOiNwtkrn6nYicJSJv+F9n+tvK/ftVInJp0r7FIvKQ/3oPiUixv32kiMwSkbkickPvfULVGyRehtqOOYurttX16FhsHA7at1+PvodSqnBEsQkTw3FbuVBlDKyby0ux8fzHOYF+UsvR1jKge9aE/eZD7/hrwIZ6ZpFZ1WPyf9atVAcEpRuqiHiNdrQMdU9wHfCIMeZEoAxImTgmIjZwO3C2/3W7v60BuAC4M+31LgOW+q+3zL8PMAO4yRhzPDBNRMb1xIdReWK1L7PYGHW466UVPToUGxfRMi+llG/0Pv0IE2NAn1YqDqrWQcMu3jOjmeMeTqMJc6r1DgBC14O7+mYHGzcQTQpVx+h3TBWUEA4DKkrzPQwsgRg2RjOLe4KpwFP+7Sf9+8nGAGuMMVXGmCpgLTDGGBMzxnzcgdebYIyZ49+emeV9VCGLl6Ga1oPFZz7Y3ONDCWk3VKVUksF9+xDCydqJOWHTuwC8746igWLecQ/kGGsJAMu31PDvtzd0eRwhHIx/YU0VDg0WVUEJiUvfspJ8DwPByywazSzuCfoDVf7tKv9+sgHArqT72fbJtX/yvsnH26yvISKXi8gCEVmwbdu2NgeuAiQeLDqtX0B6eN56Kqjlu/ajjJeVKY+9u34XK7fWdn0o4mJEM4tKKY/YYUI4RJ1WosUd3vFohRkGwFtmHIfIOvpQz4qttXz/X4u6NIY+RSGv6sHWY1Oh0WBRFQ5jvKtSATgJshJzFjVYLAQiYovIm1m+fogX2FX6u1YCO9OevhPom3Q/2z659k/eN7mdXNbXMMbcZ4yZZIyZNGjQoHZ8MhUY7SxDfWvtLn4c/jNXhx/lL5EZVNISHF7w29c57ZezuzyUMDEi2uBGKRVnhQnjtDpncemS99lq+tJIEQDz3bHYYjjK6p6y+a+cMIoQDqGQZhYLjQaLqnAY71zbBKCLlgjEsLQMtUAYYxxjzLFZvm4BZgPn+Lue499PtgIYJSIVIlIBjAJWkluu11skIlP822cDr3bxY6kg8TOLzc25F69+cO4ayqnnHGs+b7oHUyn1fNZ+uVuH8YXj9sfGpbhIg0WllEdCISwxRGO5L2ZVbVzOOjM4cf8d90CixmayX4raVQav+ZYum1F48n/WrVR7+Vm8Rif/XbTiDW60DHWPcDtwiYjMAaLA8wAicqeIDDLGOMD1wHP+1/X+NkTkSeALwHQRudd/vQeBw/3XO9y/j/8at4nIXGCOMaZ7/gKrYPAzi8s37cq5y81PfshkawlFEuVX0YtY6I7mHHteh9+qMepQ05j7pC8sTmI8SikllnfxaP7KbNPsPfvJVtYnBYsNFLPY7M8ka3li29/eXEe0k2suOq5LWLT5ViHS75gqGA1NTZQA72+u47w8j8US0TLUPYQxZgdwbpbtVyXdfhp4Oss+52fZ1gBcnGX7auCULg5XBZVlETMWYWm92uAIazWOEd4zo3jemcR14UcYwk62tDoNNtW0n7/Cpt2NrJ2R8WOLMV43VL16r5RK8Jf2uefFJVxx2mGZj8eaGMpOPkoKFgEWuGO51H6RMDGihPjBfz9g3Y46bjz3kA4PwXEhLC5Y2nyr0GhmURUMx1+/LBaAH1sRcIyt6ywqpRJi2IRo/QLSeFnNcrMfDRQz2x0PkOg42F6bdjfmfMxg/FIvPSFTSnksv6lMzuNT9UYsMWw0A1M2v+WOpViiHCZrEtsWflTVqTG4ft8JvZBVePJ/1q1UO7l+l0EnAOssbqluxMEiFtNgUSnliRIi3EawOMbayFKzHwBLzAiqTSnHpgWLtz71YafHYAyENLOolEoi/pzqnMen2q0AbDH9Uja/7Y4FYJK1rOW1OrnmouMaQqLHpkKkwaIqGMafmH3gPn3zOxDgoXnrcbDYXFWX76EopQLCyyxmXkByXMO81TsIE2MoO1hvhgDgYvGWO5bJ1tKU/f/42pqM18ilOeamzF80xhAmpidkSqkEyy9DzZlZrPHmMm4zfVM2b6eSNe4Qjk4KFuev3YlpdcHG7BzX+POp9dhUaDRYVAXD8TOLo4ZUtrFnz2uOuTpnUSmVwsssZgaL985exWfve5Phsg1bDOvclnlB891xjLE2MZDdnXrPy/40n8Nvfr5lg981Wk/IlFJxdTHvdD8iORpj+ZnFrWnBInjzFo+yluP1M/W8tTZ3I69cHNf4VQ/5rw5THaPBoioYrj9nMSgnQQ4WYjRYVEp5otiEcTK6Ba7YUgPA/rIFgHV+ZhG8OUGQWubVEa+v2pFy3zJ+sKoLXyulfAP7VQAQyXIxC4Daj4kZi52UA9C/rGXpnbfMWAZIDQfI5sS2uuaOT8FxjCGkmcWCpMGiKhjGnx8oEoyrUg42lulcC2ml1J4nZmxC4rB0c03Wx0f4weL6pGDxfXMADSaSUubVmt31uZfMAFouYOkJmVLKZ4WLAIiQK7O4he1UYvyw4NXrWhp3L8hyQeu9jzpeCeHGM4u2LutTaDRYVAUj3g1VAnDF/LqzxuJgUabHPKWUL16GGrKzN4AYJttpNGG2UZnynIXuGI5Om7eYbU7Q6m213PDf91sdg6XBolIqTWWfPgAU5QoWa7aklKCGrJZjWOnQseww5RwtLcHir15cztWPLGTJ5up2j6GlDFWPTYVGg0VVMFwnOCdBR4/sj4uF1UbnQ6XU3qNfeSlhHMqLsx+jBkuVf0KWGkzON2M5VNZSRkNi21PvbU4JGOubY0z7xWxmvtdSCpbc2CbOcoNznFRKBYOE/MxijnVg63duyggWzz18KHdffCS3XnAEb7sHZVQ//OfdjZz96zlU1Te3awwtZajBqA5T7afBoioY8aUzxM7/gcYS8dZ7dLUMVSnlsUIRQjg5DwuDqWIr/TK2v+WOwxbDRGtFYtt3/v4uf5q7FoA12+t4eN76jOclN7b55QvLefaDzYiJz+3O/3FSKRUQdutlqKZ2S0onVNsSfnPJRM4fvy8hS5jjHs5IawujZWPGc7fWNFHb1PYcRs0sFi4NFlXBqK73rrobyf+BxhKv7T2urrOolPK4VoQIUdwcbeVbMoup3nXH4BjJKEX97Ssr2V0f5RN3v8atM5dkPC/ZXS+t4Iq/vaNlqEqpTP7SGVkb3BhDUdNOdlCR2CSSWv3wvDMJgDOtBRlPP+NXr3LYTc+1OQTHNdhog5tCpMGiKhi/f3k5AO9vyv/ahrYlOEa7oSqlWrh2hIjEaIylHhfioeNg2ZU1WKyjhMVmJJPTyry21zbzqXtfp6YdV+3jWhrc6IRqpZSvtcxiUzUhcdllvHmNv7tkYurDMYct9Odddwzn2PNIXkKjI1xjvHUeNVgsOBosqoLR3OzVxTsB+LH1ylDtljXNlFJ7vZqoRRFRbnnqw4zHimimUuqzBovglaJOkJUZ6zSu3FrboTFIfOkMPSFTSsXF5ywSw3XTgr0Gb83E3XjBYp+0Odfx3R91TuQway0TZQXtsWJLDSOnz+TxhRvZXtvkZxZ1ncVClP+zbqXaKX7F3ATgJMgS0TJUpVSKJhMiQowPN2V2CBws3gnZNvqmbF9x29mM26ec+e5YiiXK4bK6S2N4afEm74aekCml4my/DFWixNKDxfqdAInM4gljBqY8fNSIfpwydhCPOiey25Ty3dBj5Mou3vHcUn73yiruemkFLyzxlgr67j8WMunWF3FctAy1QOl3TBUM2w8WXcn/NQ7bEhyslmYSSqm9nmtFKKYZJ+1kzBivuQ3AVpPa4CZsW0RCVmIts2OspbzjHNTpMdj41Q56QqaUikvKLKYfn+KZxSo/WEyfr2hZwjWnj+X8Zdu4M/Ypbgr/lSvd//B75zyGyg7GyyomWKvgsSeoebuIPzsnUktpxhAc19Uy1AKl3zFVMPatDMF2GNa/PN9Dwbb8clgtQ1VK+WJWxCvzynLRfbBUAeQsQ91BJR+4IzndXsDvnE90egxhtMGNUiqNn1ksohknvQFXPFj0y1CziTft+rNzJhOsVVwT/jfXhP+deLzeFNG8sj8/CW/mm6EnuDr6Td5wD015jZeXbcMu1WCxEOU/RaNUOx0zsi8AJ43dJ78DwStDdbAQLUNVSvlcK0JEoplX7oEB4pWm7jAtHQcf++YUAM4+bCgAzziTmWitZB92dHoMtgaLSql0fmaxiBiO03pmMZv4M1wsrop+ky81X8vPo5/muujXOLvpZxze9Ed+N/FJLmy6mRpTyoPh/+P0LJ1TvcyilsgXGg0WVeHwF5sWO/9d/uJlqJpZVErFRSVMhBi2lVrGZYBKvC7OyVfvJ47wSlKvmHoAAM+4kwE4z36z02MIxYNFW4NFpZQv3g1VosTSF4L15yy2lllsjrU8x2Dxsnsk9zgX8E/nFJaY/XGw+dWLy3nHHMSnmm/iQzOSu8N3M15WpryOGEc7NRcgDRZV4fCzeGLn/6qUl1m0dekMpVTCgMpyIkQ59oD+GY/1kxrqTBEPXXFSxmPxOUKrzb7Md8dyWei5lgxhB+mcRaVUBsvCEa8BV3IZalV9M28uXkm1KcEh97nV4cMq2/1W1fThK83fZ6vpyx8jv2AQu1oedGN6bCpAGiyqwuF4waIVgMyiZWkZqlIq1YjBfSkiytEjswWLteyinNGD+lAWsbnlk4dmeQX4Q+xchst2Pm+/1KkxhETLUJVSmVwrQoTUMvkZzyxl08ebEiWoT33nhKzPLYnYXDx5RLvfaycVfCV6LX1o4Ffh32L5F7FCuHpsKkC9GiyKyGUi8rqIzBWRiWmPFYvIQyIyx/+32N8+UkRm+c+5IWn/s0TkDf/rTH9buX+/SkQu7c3PpnqBH5hZofwHi7a/dIZoGapSKi5UTEhcjJuaFTTG0JdaqkwfbBEW/+Qs/ue4kSn7/PdbxwPwgnsUs50juDH0EJfaL3CctZgv2c9wZ/geXohcy38iP+IK+wlKaMw6BM0sKqWycf0GXLGkOYsx19CPmkQJ6v4DMruYxk0e5ZXN33HREe16vxVmOD+KXcYJ9mK+YT8BxJfOyH91mOqYXgsWRaQfcCVwMnApcFfaLpcBS40xJwLL/PsAM4CbjDHHA9NEZJyI2MDtwNn+1+3+tgbgAuDOnvwsKj/iJZ8SgJMgy4KYsUDLUJVSPvGbSIjTlPFYP6lhl+lDrpV/JuzXN/4qXBX9Jh+YUdwa/hN/j9zGTeG/coy1lLVmCC7C9PA/eCZyPWNkQ8brhPCrHQJwnFRKBYdre5lFN6UMNUpfqUtkFsN27rDggiOH88b10zjrsPY3GfyXM5X/OlO4JvQvjpalmlksUL35HZsMzDHGNANr/CxgkTEm/ld1Kl4ACPAkcB1wLzDBGDPH3z7T388Aa4wxVQAishYYY4xZBnycvkaM2kPEM4sBmLMYzyxaGiwqpXyJYDHWnPFYX2rZxECsdvx92kUFFzXfxOGyhj7SwCp3X7bSsj7jMbKEuyN388/IT/h88w9YalrKw0KJzGL+j5NKqeDwujXHiKV0azb0pYaPGARAcbj148bQyhIamjty3iPcGP0K4yOruCtyD2F0zmIh6s0y1AGQPMuVKqB/jseTH0seY3x7W6/VKhG5XEQWiMiCbdu2tfdpKt8SDW7yf6CxLSGqDW6UUkniwSJuZmaxr9Syy/TBave1TOF9cwBvuIemBIoA88zBXNR8E01EuD9yR0oDiZalM/Jfrq96hoj0F5Gn/Gk7d0uWK+QdmarjTxFaIyKv+F/DevPzqN4RzyymLu0j9JU6drWybEa6juZj6ijh29Er6U81lhgNFgtQbwaLO4G+Sfcr/W3ZHk9+zM3ynLZeq1XGmPuMMZOMMZMGDRrU3qepPIvPD7QCcBJkWUKMEJYbzfdQlFIBEQ8WbScts2hcKqljF33alVlsj/VmCF9p/j59qeX3kV95V+xJzizqCdke7DrgEX/aThlwZvKDnZyqc78x5mT/a2NPDl7lh7EiFKXNWbRwqKSu1WUz0hWFOh46LDaj+Fns896dAFzwVx3Tm8HiPOAEEQmLyAigNqkEFWA2cI5/+xz/PsAiEZni3z4beBVYAYwSkQoRqQBGAamLuag9T6IMNf8HGluEKCHthroH6IGr9NeJyDy/KVfi9fwr9m/4/97de59Q9RYJFXv/uqnBYolTiy2GKlNOpJU5QR31oRnJ96NXMNFaybWhR4DkzGL+j5Oqx0wFnvJvP+nfTzYGf6qOP11nLd5UnZgx5uMcr/kFEXlNRG4RyTWzVhUyk2XOYqlbhyUmMWexPUSEm88/pMPv/6BzJjdGvwyHf6bDz1X51WsHBGPMLuC3eEHg34GrRGSCiFzr7/IgcLiIzAEO9+8DXA/cJiJz8eY8LjHGOP725/yv6/1tiMiTwBeA6SJyb698ONU73OAsnREvQ7XczLlJquB091X6/xhjjvGbcg0BpiU99mn/yv13euSTqPwKRQBY83FqoUupsxvAK0Ntfx1quzzjHsODsTO4PDST06y3CSWCRZ2zuAfrjzf9BrJPw+noVJ3HgYPxgs79gUu6YYwqYLxgMXXOYplbDUCV6cN3po1p92t1rjeI8JBzGvTbvxPPVfnUq5cejTEPAA+kbV7oP9YAXJzlOauBU7Jsfxp4Osv287tjrCp4Xlu+hZPDYIXyf8XctoSoCWGLAVdbQRe4qcD/+bfjV+mfTXo8cZUe2m6oZYxZkXS3CeLtKTHAP0SkGbjVGDOrez+GyjvbK0NdtHZryubiqBcsVkt5q0+//aIjmL9mJ185YRRn/3pOq/sm+2nsEo6ylvOL8O940PGvdWhmsaD5F6TmZnloJl4gWIkXBGabhtOhqTr+xfz4+/4D74LZX9PGczlwOcCIEe1fb08Fh7GLKJIqHLdldldprAaAXfTh+AFl7X6tqQfpFK69iZYaqIIRClB5Vdi2iOEHiI7OWyxw3X2VHgARmQoMxSudBy+reALwReB3IpmRgzbfKnB+ZrGI1GPC1i2bAeg7YHCrT//MpP34+afHc/DQCk4/ZEi737aZMN+KfhfB8E1/PbMgHCdV5xljHGPMsVm+biH3tJ24Dk3VEZG+SXen4S1flj4e7fVQ4OKZRSepE0gf17uQtbtDzbdg5MAybvnkoVkf+79PHU6kE/MaVXDpd1MVjKAtNl1W4s1PQpvcBJ6I2CLyZpavH9JylR664Sq9/35H4K0R+zljvAkixpjt/r8fAYvwMpYp9ISswPlzFiOSdkxo8K411NuV6c/I6defm9Cht15vhnBd9OuEJTgX1VSPuR24xJ+2EwWeBxCRO0VkUCem6lwbn2eNdyHsD737cVRviM9Z/Mzv30hsK3VaMot2B0vkL5w4nE8fNTxj+2ePHsF7N51BZUn+pwyp7qF/TVTBSGQWJRgln068K6tmFgPPP1E6NttjItIH7+r8w/6/j6XtkrhK799v6yr9GLxy+0/FA0S/yU25MabazygeDqzr/CdSgeSXoUZIbXzVT2oBaAy3P1gsjYRYO+NcTv3FK6zaVteu5zzrTuaB2Fl83n6J4kj7S8pUYTHG7ADOzbL9qqTb7Z6qY4y5Ebixe0epAidUnHFsKol5mcUq04fjxwzs0MuVFYW449Pj+dfbGzIeKw7b/M+x+3PPy96fylPGDuLlZVotU6g0WFQFIyQOjhFsKxgJcRO/cq/BYqG7HfiLiHwDeI+kq/TAbcaYbSISv0oPmVfpDwXqReQEY8wVeA1v+gJ/9ucz3uG/5ssi0gCEgZuNMe1e7kcVCL8MNUKUj3bWs1//UgD6Sg2OEZrs1ucsZtPRRhI/if0Pd8UuYGGktMPvpZTacxk7QlFS1cPiTbvZsX0Lri1UU8bAPkWdet0nvn08A/oUcfyM1Gn415x+EG+s3sHb63bxhSkjOe2QIYwf3rcrH0HliQaLqmCEcYgRIhh5RXAlnlnUjqiFrAeu0p+X462O6vwoVUHwM4tFxHhvw27261/KJ+95jYuoZTdl2KGOH7063nNQOGT0yA4/Sym1hwuXUEzL+co763bRlxp2U4bbhVlpR/gB4GVTRjJ/Tcs1UMsSyoq8MEOAS47RLqiFSoNFVTBCxGgmROeufXU/E1/CQ+csKqWgpcGNNBNPCC7asJuvhWvZZcoJd+Mai6357NH79cr7KKUKhwmVUkJTyra+Usdu0z0l6zd/IrPhzacmDuPV5ds4aEjHqypUcGiwqApGmFhLB9IAaMksarColKKlwQ2xlIxgX2qo6kQDCYDkKtSDhvRh+Zbadjyne9dyVErtASKlFEsUi5Z2qP38Y1NP+eSEYXxywrAee33VO4Ix+UupdhhQYmGHAtRdy9Y5i0qpJHbLnMXkeK2v1FFl+lDUiXbyffwyrn9+/Tge/9YJ2pJeKdUpJlQCQDHNOK4BoFLq2GV6LlhUewb9q6MKhm2ixCQ4waKxvBNDLUNVSgEQis9ZjALCrKVbAOgrtVTRh0uP7ficnd9dehT/e9Y4jh7Zj5KIzUvXTE089vDXjsn6HM0rKqXSuX7lQwlNvLRkCwboS22PZhbVnkGDRVUwbBPDleBUTms3VKVUCv9kzAsWDcs+9kpG+1HDLtOHYw8Y0OGXHFJRzDdOHp0oLY13WAWYMrpjre6VUnsvx/aOHSXSTMzPLPaVWqo0s6jaoMGiKhi2cXACFCyi6ywqpZJZNo0mTIk04bjefMMIUcqkiV1GGzwopfLHsb2LWcU04RqDZRwqpZ7d6JqsqnUaLKqCYZtooDKLb33kN5rQpTOUUj6JeB0Hb3piMQJU4h0nuvOEbNb3pjL/hlNTtq2dcS7nHj60295DKbVnKSrxMoilNPHo2xuwm3YDaGZRtSk4Z95KtcE2MVwrOD+y0fivjxvL70CUUoEhkVJKGprZXtvEz55ZykHiBYvdmVk8YFDrJ3faDFUplW7IwP4AlNDMy8u2sXb5Zi4uQhvcqDZpZlEVDJsYrhWcBjcjB1d6NzSzqJTyhYv7UCota5n18zOLu3qhicQh+1YAMLSypMffSylVYMLxOYve8alvoupBg0XVuuCkaZRqg21iGInkexgJh+43EHajcxaVUi3CJRQnLXzd188s9kap19dPOoApowdw5Ih+Pf5eSqkCE25ZOgOgMunY9McvTMrbsFTwabCoCkbIOIHKLIqtDW6UUqkkUkYpuxP3ezpYfPn7JyfWXgzZlgaKSqns/GCxxL+YFa96qKKM0w4ZkrdhqeDTYFEVjBDRluUqAiAeLBqnWdc1U0p5wiWUyLbE3Xip1/9eeFyPvN2ogdrJUCnVDmHvWBEvk+/NqgdV2HTOoioIjmsI4QQqWMT2SmKNZhaVUnHh0pQy1H5SS5MJM7CfZvyUUnmUKEONB4t1uEao1qUzVBs0WFQFIeq4hInhWsGZsyghL7PoxLTBjVLKFy6lNClYrKSWKsqwbf1zq5TKo0QZqj9nkVqqKcXVUEC1QX9CVEGI+ZlFApRZlHhmUYNFpVRcuIQSaTkm9JNadplyLF3PQimVT3aYRhOmjzQA3rFJS1BVe2iwqApCzHEJ42Ds4DS4IeQFi260Mc8DUUoFRrg00UACvHlBuynDcU0eB6WUUlBDKRXUA9CPGqoo4zvTxuR5VCroNFhUBSHqGMISgwB1Q7UjXkmH21yf55EopQIjEg8WveCwL15mUYNFpVS+9akcQIV45ywDpJodppLvnTE2z6NSQafBoioIMdf1ylADlFl8/sNtNJgIKzduz/dQlFJBES4hJF4lBEA/qWGX6UPMdfM8MKXU3q64vD8V1AFesLjdVOZ5RKoQaLCoCkLMMUQIVmaxujFKAxEa6mvyPRSlVFD47elLaAQMfaljN31wjWYWlVL5JcWVVEg9gssAqtlORb6HpAqABouqIESd4GUWf3jeITRQxDCdH66UiouUAlBGEyU0USRRdpk+HDZMr+ArpfKsuJJy6qmkjrA47NDMomqH4LSWVKoVMcelmGZMqDjfQ0moLAnTaCIUxxryPRSlVFAUeydfFVIXn7bI9RceB+XBOXYppfZSxZVUSB0DZTeAlqGqdtHMoioIsWgTlhgIULAYsoRGIlixlm6oSzZXU9sUy+OolFJ5FQ8WqWeAf0JG2aA8DkgppXzFlVRQzyD/2LQNDRZV2zRYVAXBafazd+HgBIth26KBIsTPLDqu4exfz+Frf16Q55EppfKmuC/gZRbjJ2T0GZy/8SilVFxxJUUSY7hsA8CUDszzgFQh0DJUVRDiwaIEKLMYCVk0mAi2n1mMOl63w/lrd+ZzWEqpfErKLIbE64iqmUWlVCD0GQLABFkFwNWfOiWfo1EFQjOLqiCs37oLABMqyfNIWkRsi0aKsBwvkDWrXubRyE0czeI8j0x1hIj0F5GnRGSOiNwtIpJln7NE5A3/60x/W7l/v0pELk3a9zIRWSMir/hfw/ztI0VklojMFZEbeu8Tql6VyCzWMwjNLCqlAqRiXwAmWcvYZfowYIBeyFJt02BRFYRfP/seAFvrg9N+PhKyaCRMXa23dIb94o84ylrBj0MP8sj8dXkeneqA64BHjDEnAmXAmckPiogN3A6c7X/d7m9rAC4A7szymvcbY072vzb622YANxljjgemici4Hvk0Kr+KvVb0FXhNJKpNCYSDc5FLKbUX84PFsdYGNpiBjBms7dxV2zRYVAXhgL5exfSAvsFZE8grQy0i5DZC9SYi2xezwh3GWGsDf/7PU/kenmq/qUD8G/akfz/ZGGCNMabKGFMFrAXGGGNixpiPc7zmF0TkNRG5RUTix9kJxpg5/u2ZWd5H7QnsMDG7lEq/46B2G1RKBYYfLAI0le+fx4GoQqLBoioI08Z4QeJBw4JTMhGyhGpKqaSOr8+4D4A7Yp/BNcKp1jtZn/P3+etZsHZnYn6jCoT+QJV/u8q/n2wAsCvpfrZ9kj0OHIwXDO4PXOJvTz7eZn0NEblcRBaIyIJt27a1a/AqeJxIOZXUMYjdbNdug0qpoCgqpxav0mF90YF5HowqFNrgRhUEy2kCwI4Ep8GNiLDTlFMmTUyylhE1NrPd8SwyoznNfoeo4xK2W+KDkdNnJm4fPqySq047kCNH9KN/WSQfw9+r+GWjc7M8NBMvEKzEC+AqgfQORTuBvkn3s+2TYIxJBJYi8g+8sta/AslXCLK+hjHmPuA+gEmTJgWn5lp1SKTvUIbU7WYY23jH6AmZUio4doy+gJKVf+e90mO5MN+DUQVBM4uqIEhzrXejqDy/A0lThTeeqdYilpvhNBHhBecoxlurWbdmhbdPfTMn3PYMX7Of4kehvzBeVvL+xt185c8L+PpfdZmN3mCMcYwxx2b5ugWYDZzj73qOfz/ZCmCUiFSISAUwCliZ671EpG/S3WnAMv/2IhGZ4t8+G3i1a59KBZVUDmdcSRX7yg7GHHRovoejlFIJS8ffyNTmO9kUGZXvoagC0avBot8l8HW/G+DEtMeKReQhvyPhQyJS7G/P2kEwW3dCf/sN/r6zRGRkr3041aPWbNjk3SgKzpxFgCPHHgDAQdZGFrne7efcSQA8+KffAfDs+5uY3ngnN4Yf5hL7Rf4TuYmrQ//CwmX1tjqaYg71zbH8fAAFXvOaS0RkDhAFngcQkTtFZJAxxgGuB57zv673tyEiTwJfAKaLyL3+610rIvNEZC5eqekf/O3XA7f52+cYY5b00udTva1iXwY3rSMkLnWlw/I9GqWUSjCWzQYTnCk9Kvh6LVgUkX7AlcDJwKXAXWm7XAYs9TsSLvPvQ5YOgrm6E/rdBaf5+97sP7fbPTRvHf/vN3N5d/0uXlqyJePx+Wt2cuLts3jxwy38ae4a/vnWR9w280PeWb+Lj3bWs6O2ifU76qlvjrG1upHGqMOuumYamh2q6pt5f8NuHNcwb/UOjvnpiyzfUsPjCzdmGYlnd0OU5VtqEvebYk7i9rKPa9jdEAWgodmhtimGMZnVbcu31PDEok2s2lbLPxd8xOm/nJ14Xty81TvYUt3If9/dmPEaxhiijosxhsaoQ11TDMc1OG7me22raeLlpVszthtjeGnJlsRzXNfw1zfX8ZuXV1Jf41f2+W3pg2JDU2ni9kIzhqGVxawy+7LKHco51jzumbWC2ienc579JrdFP8/Ept/zqHMi3w39h7+Ff4pdt4WxP3iWQ370HNtrmxKvZYzhT3PX8Jc31rK7PsoLH25hU1UDu+uj3PzEYq76x7s8uWgTb63dSUOzw4ebqlm5tZYlm6tpjrlU1Tdzz6wVOK7h8YUbWeH/DO1Ieo/d9VHqmmIsWLuT7bVNOK5hyeZqGqMO6ZpjLm+s2sEvX1hOXVOMt9bu5KOd9YnvVWPUoSnm8K2H3+H9Dd5yATWNUT7aWQ/Aqm21bK1pzPp/+NHOer750NvUNEaJOm5iPuf7G3azeNPuLn6H2maM2WGMOdcYc6Ix5lvGGNfffpUxZpt/+2ljzHH+19NJzz3fGHOAMeYwY8wV/rYbjTHHGGOON8Z82RgT9bevNsac4m+/tcc/mMqfipYA0anUq/dKKaUKl2QLHHrkjbzs3znGmO/69xcBk40xTf79vwO3G2PeFZEjgeuMMReLyFJjzDh/n6uBeuAVf99P+tsfx2t/fzJQaoz5lb99iTHm4NbGNWnSJLNgQeulgM0xl8t/9FPCZJ5EC7n//zIWa0vI/pzc++d+n868f+vP6fjYcn+e7hvbFaEnGWtt+P/t3XuMXOV9xvHvM9e92GZ9xWsb0xqCm6ZJk2BXDW2ok5qGEClSW6tVRYOcUIU2bUiktIiqiQoytKJEagIKf6SK46YhSqSIkpZbhJQayEXU2DKYhgAVhApqgo0X413vZWbn1z/mbBl2xjvrjXfmnPHzkVY7c94zM8+8u/tI786ZM/C5o5Avzpmmk/Y8uI+dP9gOwO9M3sIDN1/Dc0fH+PYXP8P1xW9y//RWPpjfx1erH+DG6lUsLRc5MVllR/4hdhW+CsADta0cqm3i1VhKJXkbcSQzFG1mvr5PK823a7Vfq/ufz2N2w9994krWbLhgXvtK2h8RWxY50qKaTzdZSj37INy5A4DadS+QGxjqbh5LDXeTddu9Txzmz79xgCvevpY7rry423EsRU7VT508wc2pzih4uMX4zBg0n0Fw7Rz3tRL434bt+VZBJH0c+DjAxo0b2wYvFXLcVvwSy3Sy7b62eA73XcBwihaKADsv28pPHjmPAtP841/8IbmcuHDNEj5y7U38+Es/4oP5fXx7+lJ2VT/CkzdezpJy/U9u79NbuXzPZv4s/2/8dv4Av5tvde4Va/TYgVWs2fCJbscwa2/jr0P/Chh+hxeKZpYqm1YPArDtojVdTmJZ0cnFYrszCjaON461OoPgqe5r9vbmlwJZ2BkH8x+7j2dGxpio1Bgo1degk9UayweKvDI6xarBEuOVGrUIVg6WCYL/OXaSc/pLIMhJFHL1V2xyghOT00xWaizrLzIyNsWSvgInp6YpFXIMlArkczBRqVHMixMTVQr5HH2lPGMT0wwP9TEyVmH5QJFnXxmlv1SgmBelfI7RqSqDpQJ9xTw/e32CfC6HBH3FPFPVGmuWlnl1bAopx3StfuhoPicmK9OUi3lOTlWRxAVrlnD0xCTPHR1j/VA/r41XyEss7Suxfnkfo5PTVKo1jk9UKEgcPj7B8FA/pUIOCAr5HBFQLuQZm6oyNFCiUq1x+PgE/cUcCAZLJV4bn2LdUD/Hx6foL+UZLBc5cmKKgVI93wvHTrLunH6Gh/oYXr1hPj+qjlv3mYeRxNJly/9/2/pzV7P2c/s48vJP+f31b2FH7s1HfG/bvIaH/v5q4GqI4PWRVxh/7RVGRscByAvGJirUApYPFpmq1lhSLjBYzjNycopCPkcxl2O8UuXIiUk2LO+nkMtxdHSCZX0FysU8zx8ZY9XSMsW8OH6yQrVWY+OKAcanpslJlAvi+ESFajUIgvHKNMv7i0xWZ/7kgsPHJ1i1pEx1OlizrMzoZJVjY1OsXlJmOoLXJyqMTlT5xVWDVKaDYiHHSyPjDPUXmaxOs26on9HJKuNT0wz1F6lB8ndRpL+Y5+joJCsGSywpF3jmZycYLBeo1eqH4ZYKOfpLeXIStQi2bPaJQiwjykvhk/uhONB+XzOzDnrr8DL2f3Y7K5eUux3FMqKTi8VHgZskFYFhYHTmENTEzBkJD/LmMxI+LumSiPgh9fcnfpqGsxMm+8ycnTAHfAH4QnLWwcfPVPjB89/FRaf4/NJ1rTezmG8fXjXzfY6j8k71munwPB9jDfDLc4wtxKZTbD+v4fLahssXLfBxOmnZOa0/ci9fLLP6vM3t70Bi2YpzWbbiXM6dx+MNzbre+I6oxp/L6gvfuNw4v43m+rBAgIWc9H/2Y83+O2hc8jf+Pqz9pQU8mFlaDbT76zIz6w4vFO10dGyxGBEjku6gvggM4FOS3glcFhG3AnuA3ckZCV8EPprc9K+Br0gqAffPnEFQ0szZCeGNsxM+Jen7ydkGp4CrO/PszMzMzMzMeksnX1kkInYDu2dtPpiMjQN/1OI2zwHva7H9PuC+Ftt3AbvOQFwzMzMzM7OzVkc/Z9HMzMzMzMyywYtFMzMzMzMza+LFopmZmZmZmTXxYtHMzMzMzMyaeLFoZmZmZmZmTbxYNDMzMzMzsyZeLJqZmZmZmVkTLxbNzMzMzMysiSKi2xm6StIR4IV57r4KOLqIcRZD1jJnLS84c6ecTubzI2L1YoZZbO6mVHLmxZe1vOBuaidrP9Os5QVn7pSsZT7dvC376axfLJ4OSY9FxJZu5zgdWcuctbzgzJ2SxcydksW5cebOyFrmrOWFbGbupKzNT9bygjN3StYyn6m8PgzVzMzMzMzMmnixaGZmZmZmZk28WDw9X+52gAXIWuas5QVn7pQsZu6ULM6NM3dG1jJnLS9kM3MnZW1+spYXnLlTspb5jOT1exbNzMzMzMysiV9ZNDMzMzMzsyZeLLYgaaekH0r6gaR3zxrrk3SnpEeS733dytmoTeadkp6XtDf5Wt+tnA2ZvivpiKTPthhL6xzPlTmNc/yu5PfhYUnfk7Rp1vgKSfck83y7JHUra0OmdplvkPRUwzznu5W1W7LWT1nrJsheP2Wtm8D91Iuy1k2QvX7KWjdB9vrJ3dTMi8VZJC0HrgW2AX8M3DZrl53ATyLivcDTyfWumkdmgK9ExLbk66VO5juFq4G/OsXYTlI2x4m5MkP65vgwcHlEXAp8Hrhx1vh1wLeSeR4EPtDhfK20ywxwc8M8T3c2XndlrZ8y2k2QvX7KWjeB+6mnZK2bILP9lLVuguz1k7tpFi8Wm/0a8EhETEXE88BSSeWG8d8C7kku/3tyvdvaZQa4StL3Je2S1PWfe0S8OMdwGue4XWZI3xy/HBEnkquTQHXWLqmb53lkBrgumedrOxgtLbLWT5nrJsheP2Wtm8D91IOy1k2QwX7KWjdB9vrJ3dSs6z+UFFoJjDRcfw1YcYrx2WPd0i7zd4C3Uv+FPh+4smPJFiaNc9xOaudY0iBwE3DrrKEV1OcXUjbPc2S+HfhV4DLgw5Le2+lsXZa1fuq1boL0zXE7qZ5j91PPyFo3Qe/1UxrnuJ3UzrG76Q1eLDY7Bgw1XD8n2dZqfPZYt8yZOSJGImI6edn5m8CWzsY7bWmc4zmldY4lFYFvAbdExI9nDY9Qn19I0TzPlTkiXo26ceAuUjLPHZS1fuq1boL0zfGc0jzH7qeekrVugt7rpzTO8ZzSOsfupjfzYrHZo8BvSipK2giMRsRkw/hDwBXJ5SuS6902Z2ZJQw37vp/6sexplsY5nlMa5zg5nOPrwN0RcXeLXVI3z+0yz8xz8obybaRgnjssa/3Ua90E6ZvjOaV1jt1PPSdr3QS9109pnOM5pXGO3U0t7j/8OYtNJH0M+BMggE9RP/b3soi4VVI/sBvYALwIfDQiJroWNtEm883A9mTb08A1EVHpWlhA0j8BlwBl4EngBtI/x3NlTuMc7wD2AI8lmw4B9wKrI+JfJK0EvgYsA54APhkRtW5knTGPzHuAzYCAvRFxfTdydlPW+ilr3QTZ66esdRO4n3pR1roJstdPWesmyF4/uZta3L8Xi2ZmZmZmZjabD0M1MzMzMzOzJl4smpmZmZmZWRMvFs3MzMzMzKyJF4tmZmZmZmbWxItFMzMzMzMza+LFoqWGpJWSDiZfL0t6Kbk8KumORXrMT0u6agG3K0l6WFJhMXKZWbq4n8wsjdxNttj80RmWSpJuoP4BuZ9fxMcoAAeAd0dEdQG3/1vgvyPizjMezsxSy/1kZmnkbrLF4FcWLfUkbZN0T3L5Bkn/LOkRSS9I+j1J/yDpkKQHJBWT/S6W9JCk/ZK+K2m4xV2/HzgwU3aS9kraklxeJemnyeW3SfrP5D91T0h6S3L7u4ErF/fZm1mauZ/MLI3cTXameLFoWXQB9bL6MPB14D8i4u3AOPChpPRuB3ZExMXAbuDmFvfzG8D+eTzenwJfjIh3AluAF5PtTwJbf47nYWa9x/1kZmnkbrIF8THDlkX3R0RF0iEgDzyQbD8E/AKwGfgV4EFJJPscbnE/w8BT83i8HwF/I2kDcFdEPAsQEdOSpiQtjYgTP88TMrOe4X4yszRyN9mCeLFoWTQJEBE1SZV44423Neq/0wL+KyLe0+Z+xoG+WduUfC/ObIiIb0h6FPgQcJ+kayLie8lwGZhY+FMxsx7jfjKzNHI32YL4MFTrRU8DqyW9B0BSUdLbWuz3FHDhrG0zh0Zso/5fNSRtAp6LiNuA7wDvSLavBI5GROWMPwMz61XuJzNLI3eTteTFovWciJgCdgC3SHocOAhc0mLX+4FLZ23bLmkfsB04Jula4A+AJyUdpH6IxteSfd8H3HvGn4CZ9Sz3k5mlkbvJTsUfnWFnNUn/ClwXEc9K2gv8ZUQ8Ns/b3gVcHxHPLGZGMzs7uZ/MLI3cTWcXv7JoZ7vrqb9Z+7RIKgF3u+zMbBG5n8wsjdxNZxG/smhmZmZmZmZN/MqimZmZmZmZNfFi0czMzMzMzJp4sWhmZmZmZmZNvFg0MzMzMzOzJl4smpmZmZmZWRMvFs3MzMzMzKzJ/wHJm2Vwow1o+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "tplot = np.linspace(0, pumptime + decaytime +beforetime, len(asol2))\n",
    "ratio = 6800*1e-7\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(15,5))\n",
    "ax1.plot(tdata, a2plot)\n",
    "ax1.plot(tplot, ratio*asol2)\n",
    "ax1.set_xlabel(\"Time (µs)\")\n",
    "ax1.set_ylabel(\"$a^2$\")\n",
    "\n",
    "ax2.plot(tdata, aplotr)\n",
    "ax2.plot(tplot, np.sqrt(ratio)*asolr)\n",
    "ax2.set_xlabel(\"Time (µs)\")\n",
    "ax2.set_ylabel(\"I\")\n",
    "\n",
    "ax3.plot(tdata, aploti)\n",
    "ax3.plot(tplot, np.sqrt(ratio)*asoli)\n",
    "ax3.set_xlabel(\"Time (µs)\")\n",
    "ax3.set_ylabel(\"Q\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def minimize_func(x):\n",
    "    gcoll_min = gcoll*x[0]\n",
    "    qval_min = q*x[1]\n",
    "    gammaq_min = gammaq*x[2]\n",
    "    ampl_min = ratio*x[3]\n",
    "    phase = initphase*x[4]\n",
    "    \n",
    "    spins_min, gs_min, pdf_min = create_spins_inhomo_g(gcoll_min, g_list, qval_min, delta, gammaq_min, nbins, holes=False, inhomo=True, hyperfine=True)\n",
    "    solpump, soldecay, infodict = solve_mbes(mbes, init, pdf_min, tlist, tlistdecay, etasteady*np.exp(1j*phase), kappa, deltac, gs_min, gperp, spins_min, gpar)\n",
    "    asolr = np.sqrt(ampl_min)*np.hstack((np.zeros(int(beforetime/dt)), solpump[:,0].real, soldecay[:,0].real))\n",
    "    asoli = np.sqrt(ampl_min)*np.hstack((np.zeros(int(beforetime/dt)), solpump[:,0].imag, soldecay[:,0].imag))\n",
    "    \n",
    "    res = 0\n",
    "    for i, t in enumerate(tdata):\n",
    "        idx = find_nearest_sorted(tplot, t)\n",
    "        res += abs(aplotr[i]-asolr[idx])**2\n",
    "        res += abs(aploti[i]-asoli[idx])**2\n",
    "    return res\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter\t X1 \t X2 \t X3   \tX4  \tX5  \tf(X)\n",
      "   1\t 31.174\t 1.8049\t 48.181\t 2500000000\t 0.742201\t 0.084812\n",
      "   2\t 31.174\t 1.8049\t 48.181\t 2500000000\t 0.742201\t 0.084812\n",
      "   3\t 31.174\t 1.8049\t 48.181\t 2500000000\t 0.742201\t 0.084812\n",
      "   4\t 31.174\t 1.8049\t 48.181\t 2500000000\t 0.742201\t 0.084812\n",
      "   5\t 31.174\t 1.8049\t 48.181\t 2500000000\t 0.742201\t 0.084812\n",
      "   6\t 31.174\t 1.8049\t 48.181\t 2500000000\t 0.742201\t 0.084812\n",
      "   7\t 31.217\t 1.7770\t 45.787\t 2608315200\t 0.756484\t 0.082800\n",
      "   8\t 31.217\t 1.7770\t 45.787\t 2608315200\t 0.756484\t 0.082800\n",
      "   9\t 31.217\t 1.7770\t 45.787\t 2608315200\t 0.756484\t 0.082800\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[0;32m~/Dropbox/iPython-Notebooks/_helper_functions.py\u001b[0m in \u001b[0;36modeintz\u001b[0;34m(func, z0, t, **kwargs)\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdzdt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomplex128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mintegrate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0modeint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrealfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'full_output'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/scipy/integrate/odepack.py\u001b[0m in \u001b[0;36modeint\u001b[0;34m(func, y0, t, args, Dfun, col_deriv, full_output, ml, mu, rtol, atol, tcrit, h0, hmax, hmin, ixpr, mxstep, mxhnil, mxordn, mxords, printmessg, tfirst)\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0my0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m     output = _odepack.odeint(func, y0, t, args, Dfun, col_deriv, ml, mu,\n\u001b[0m\u001b[1;32m    242\u001b[0m                              \u001b[0mfull_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0matol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtcrit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m                              \u001b[0mixpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmxstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmxhnil\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmxordn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmxords\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Dropbox/iPython-Notebooks/_helper_functions.py\u001b[0m in \u001b[0;36mrealfunc\u001b[0;34m(x, t, *args)\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrealfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m         \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomplex128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m         \u001b[0mdzdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m         \u001b[0;31m# func might return a python list, so convert its return\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;31m# value to an array with type np.complex128, and then return\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Dropbox/iPython-Notebooks/_mbes.py\u001b[0m in \u001b[0;36mmbes\u001b[0;34m(Y, t, eta, kappa, deltac, gs, gperp, spins, gpar)\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mret\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mkappa\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1j\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdeltac\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0meta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0mret\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgperp\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mgpar\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1j\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspins\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdeltac\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m     \u001b[0mret\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mgpar\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "def callbackF(Xi):\n",
    "    global Nfeval\n",
    "    global minimize_func\n",
    "    print('{0:4d}\\t{1: 3.3f}\\t{2: 3.4f}\\t{3: 3.3f}\\t{4: 3.0f}\\t{5: 3.6f}\\t{6: 3.6f}'.format(Nfeval, Xi[0]*gcoll, Xi[1]*q, Xi[2]*gammaq, Xi[3]*ratio, Xi[4]*initphase, minimize_func(Xi)))\n",
    "    Nfeval += 1\n",
    "print('{0:4s}\\t{1:4s}\\t{2:4s}\\t{3:6s}\\t{4:4s}\\t{5:4s}\\t{6:4s}'.format('Iter', ' X1', ' X2', ' X3', 'X4','X5', 'f(X)'))\n",
    "Nfeval = 1\n",
    "p0 = [1, 1, 1, 1, 1]\n",
    "bnds = ((0, np.inf),(1,2), (0, np.inf), (0, np.inf))\n",
    "res = minimize(minimize_func, x0=p0, method='Nelder-Mead', options={\"disp\":True}, callback=callbackF)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "gcoll_optim = gcoll*res.x[0]\n",
    "q_optim = q*res.x[1]\n",
    "gammaq_optim = gammaq*res.x[2]\n",
    "ampl = ratio*res.x[3]\n",
    "phase = initphase*res.x[4]\n",
    "\n",
    "spins_optim, gs_optim, pdf_optim = create_spins_inhomo_g(gcoll_optim, g_list, q_optim, delta, gammaq_optim, nbins, holes=False, inhomo=True, hyperfine=True)\n",
    "solpump, soldecay, infodict = solve_mbes(mbes, init, pdf_optim, tlist, tlistdecay, np.exp(1j*phase)*etasteady, kappa, deltac, gs_optim, gperp, spins_optim, gpar)\n",
    "\n",
    "asol2_optim = np.hstack((np.zeros(int(beforetime/dt)), abs(solpump[:,0])**2, abs(soldecay[:,0])**2))\n",
    "asolr_optim = np.hstack((np.zeros(int(beforetime/dt)), solpump[:,0].real, soldecay[:,0].real))\n",
    "asoli_optim = np.hstack((np.zeros(int(beforetime/dt)), solpump[:,0].imag, soldecay[:,0].imag))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31.174474083809773 1.8048699796219885 48.18060262895924\n",
      "31.99831409585 1.976180662170014 46.85515520402958\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Q')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(gcoll, q, gammaq)\n",
    "print(gcoll_optim, q_optim, gammaq_optim)\n",
    "\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(15,5))\n",
    "ax1.plot(tdata, a2plot)\n",
    "ax1.plot(tplot, ampl*asol2)\n",
    "ax1.set_xlabel(\"Time (µs)\")\n",
    "ax1.set_ylabel(\"$a^2$\")\n",
    "\n",
    "ax2.plot(tdata, aplotr)\n",
    "ax2.plot(tplot, np.sqrt(ampl)*asolr_optim)\n",
    "ax2.set_xlabel(\"Time (µs)\")\n",
    "ax2.set_ylabel(\"I\")\n",
    "\n",
    "ax3.plot(tdata, aploti)\n",
    "ax3.plot(tplot, np.sqrt(ampl)*asoli_optim)\n",
    "ax3.set_xlabel(\"Time (µs)\")\n",
    "ax3.set_ylabel(\"Q\")"
   ]
  },
  {
   "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": 4
}