{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Online Convolutional Dictionary Learning\n",
    "========================================\n",
    "\n",
    "This example demonstrates the use of [dictlrn.onlinecdl.OnlineConvBPDNDictLearn](http://sporco.rtfd.org/en/latest/modules/sporco.dictlrn.onlinecdl.html#sporco.dictlrn.onlinecdl.OnlineConvBPDNDictLearn) for learning a convolutional dictionary from a set of training images. The dictionary is learned using the online dictionary learning algorithm proposed in [[33]](http://sporco.rtfd.org/en/latest/zreferences.html#id33)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "from builtins import input\n",
    "\n",
    "import pyfftw   # See https://github.com/pyFFTW/pyFFTW/issues/40\n",
    "import numpy as np\n",
    "\n",
    "from sporco.dictlrn import onlinecdl\n",
    "from sporco import util\n",
    "from sporco import signal\n",
    "from sporco import plot\n",
    "plot.config_notebook_plotting()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load training images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "exim = util.ExampleImages(scaled=True, zoom=0.25)\n",
    "S1 = exim.image('barbara.png', idxexp=np.s_[10:522, 100:612])\n",
    "S2 = exim.image('kodim23.png', idxexp=np.s_[:, 60:572])\n",
    "S3 = exim.image('monarch.png', idxexp=np.s_[:, 160:672])\n",
    "S4 = exim.image('sail.png', idxexp=np.s_[:, 210:722])\n",
    "S5 = exim.image('tulips.png', idxexp=np.s_[:, 30:542])\n",
    "S = np.stack((S1, S2, S3, S4, S5), axis=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Highpass filter training images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "npd = 16\n",
    "fltlmbd = 5\n",
    "sl, sh = signal.tikhonov_filter(S, fltlmbd, npd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct initial dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(12345)\n",
    "D0 = np.random.randn(8, 8, 3, 64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set regularization parameter and options for dictionary learning solver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lmbda = 0.2\n",
    "opt = onlinecdl.OnlineConvBPDNDictLearn.Options({\n",
    "                'Verbose': True, 'ZeroMean': False, 'eta_a': 10.0,\n",
    "                'eta_b': 20.0, 'DataType': np.float32,\n",
    "                'CBPDN': {'rho': 5.0, 'AutoRho': {'Enabled': True},\n",
    "                    'RelaxParam': 1.8, 'RelStopTol': 1e-7, 'MaxMainIter': 50,\n",
    "                    'FastSolve': False, 'DataType': np.float32}})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create solver object and solve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Itn   X r       X s       X ρ       D cnstr   D dlt     D η     \n",
      "----------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  9.81e-04  1.58e-03  5.00e+00  8.03e+01  6.07e+00  5.00e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1  1.82e-03  1.59e-03  5.00e+00  7.30e+01  4.64e+00  4.76e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   2  3.25e-03  2.01e-03  5.00e+00  2.38e+01  2.63e+00  4.55e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   3  1.91e-03  1.91e-03  5.00e+00  4.86e+01  2.31e+00  4.35e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   4  2.86e-03  1.80e-03  5.00e+00  2.00e+01  1.69e+00  4.17e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   5  1.87e-03  1.53e-03  5.00e+00  3.53e+01  1.98e+00  4.00e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   6  2.35e-03  3.19e-03  5.00e+00  3.60e+01  2.23e+00  3.85e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   7  1.69e-03  1.87e-03  5.00e+00  4.22e+01  2.15e+00  3.70e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   8  1.73e-03  1.51e-03  5.00e+00  3.19e+01  1.74e+00  3.57e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   9  2.01e-03  2.87e-03  5.00e+00  3.23e+01  1.86e+00  3.45e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  10  2.31e-03  1.91e-03  5.00e+00  1.58e+01  1.47e+00  3.33e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  11  1.90e-03  2.79e-03  5.00e+00  2.98e+01  1.57e+00  3.23e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  12  2.17e-03  1.87e-03  5.00e+00  2.49e+01  1.85e+00  3.12e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  13  2.61e-03  2.11e-03  5.00e+00  1.38e+01  1.17e+00  3.03e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  14  1.96e-03  2.23e-03  5.00e+00  3.45e+01  1.92e+00  2.94e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  15  2.37e-03  1.98e-03  5.00e+00  1.33e+01  1.03e+00  2.86e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  16  2.35e-03  2.16e-03  5.00e+00  2.16e+01  1.46e+00  2.78e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  17  2.27e-03  3.43e-03  5.00e+00  2.64e+01  1.82e+00  2.70e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  18  2.29e-03  2.04e-03  5.00e+00  1.25e+01  1.02e+00  2.63e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  19  1.92e-03  2.32e-03  5.00e+00  2.98e+01  1.54e+00  2.56e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  20  2.23e-03  2.11e-03  5.00e+00  1.95e+01  1.40e+00  2.50e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  21  2.18e-03  1.90e-03  5.00e+00  2.13e+01  1.24e+00  2.44e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  22  2.14e-03  3.30e-03  5.00e+00  2.33e+01  1.49e+00  2.38e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  23  1.85e-03  2.24e-03  5.00e+00  2.75e+01  1.41e+00  2.33e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  24  2.13e-03  1.98e-03  5.00e+00  1.78e+01  1.18e+00  2.27e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  25  2.10e-03  1.89e-03  5.00e+00  1.99e+01  1.14e+00  2.22e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  26  1.83e-03  2.28e-03  5.00e+00  2.58e+01  1.26e+00  2.17e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  27  1.70e-03  2.18e-03  5.00e+00  2.48e+01  8.44e-01  2.13e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  28  1.68e-03  2.22e-03  5.00e+00  2.44e+01  6.93e-01  2.08e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  29  2.16e-03  1.96e-03  5.00e+00  1.59e+01  1.21e+00  2.04e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  30  1.77e-03  2.36e-03  5.00e+00  2.40e+01  8.80e-01  2.00e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  31  1.92e-03  1.67e-03  5.00e+00  1.75e+01  1.15e+00  1.96e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  32  1.75e-03  1.60e-03  5.00e+00  1.76e+01  8.28e-01  1.92e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  33  2.10e-03  1.94e-03  5.00e+00  1.48e+01  1.00e+00  1.89e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  34  1.74e-03  2.23e-03  5.00e+00  2.26e+01  1.18e+00  1.85e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  35  2.12e-03  2.02e-03  5.00e+00  1.43e+01  8.84e-01  1.82e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  36  1.72e-03  2.25e-03  5.00e+00  2.16e+01  8.68e-01  1.79e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  37  2.15e-03  2.10e-03  5.00e+00  1.39e+01  7.84e-01  1.75e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  38  2.04e-03  1.82e-03  5.00e+00  1.57e+01  9.79e-01  1.72e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  39  1.70e-03  2.23e-03  5.00e+00  2.08e+01  1.00e+00  1.69e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  40  2.27e-03  3.35e-03  5.00e+00  1.62e+01  1.47e+00  1.67e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  41  2.03e-03  3.22e-03  5.00e+00  1.58e+01  1.04e+00  1.64e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  42  1.85e-03  3.02e-03  5.00e+00  1.58e+01  9.14e-01  1.61e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  43  1.68e-03  2.84e-03  5.00e+00  1.58e+01  9.14e-01  1.59e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  44  1.97e-03  1.74e-03  5.00e+00  1.38e+01  9.08e-01  1.56e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  45  2.12e-03  1.96e-03  5.00e+00  1.21e+01  9.53e-01  1.54e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  46  1.86e-03  3.08e-03  5.00e+00  1.57e+01  1.02e+00  1.52e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  47  1.82e-03  2.28e-03  5.00e+00  1.83e+01  1.24e+00  1.49e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  48  1.60e-03  2.72e-03  5.00e+00  1.52e+01  9.08e-01  1.47e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  49  2.05e-03  1.96e-03  5.00e+00  1.14e+01  8.44e-01  1.45e-01\n",
      "----------------------------------------------------------------\n",
      "OnlineConvBPDNDictLearn solve time: 231.13s\n"
     ]
    }
   ],
   "source": [
    "d = onlinecdl.OnlineConvBPDNDictLearn(D0, lmbda, opt)\n",
    "\n",
    "iter = 50\n",
    "d.display_start()\n",
    "for it in range(iter):\n",
    "    img_index = np.random.randint(0, sh.shape[-1])\n",
    "    d.solve(sh[..., [img_index]])\n",
    "\n",
    "d.display_end()\n",
    "D1 = d.getdict()\n",
    "print(\"OnlineConvBPDNDictLearn solve time: %.2fs\" % d.timer.elapsed('solve'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display initial and final dictionaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "D1 = D1.squeeze()\n",
    "fig = plot.figure(figsize=(14, 7))\n",
    "plot.subplot(1, 2, 1)\n",
    "plot.imview(util.tiledict(D0), title='D0', fig=fig)\n",
    "plot.subplot(1, 2, 2)\n",
    "plot.imview(util.tiledict(D1), title='D1', fig=fig)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get iterations statistics from solver object and plot functional value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UiEjVIiOIuDRHRKRaqg8iM4sViIhUTfVBxO3CiYjUTf1BZNSj02qHze4I9VCIiGgc1B9EbPNDRKRq6g8iNj4lIlI11QeR2ejck4gFC0REqqT6IBrYLpwzIiIiNVJ/EHFpjohI1dQfRNFcmiMiUrMICCLOiIiI1Ez1QRRr0EEIbo5HRKRWqg8ijUYgNooduImI1Er1QQSw8SkRkZpFRhBF61msQESkUpERREYdZ0RERCoVEUFk5uZ4RESqFRFBFBetY9NTIiKViowgYrECEZFqRUYQRevR0WuDwyFDPRQiIvJRZASRUQcpgfZeLs8REalNhASRs80PCxaIiFQnMoKIu7QSEalWZAQRt4IgIlKtyAiiaC7NERGpVWQEUf+MiEtzRERqExFBZDa6NsfjjIiISG0iKohYrEBEpD4REUQ6rQYmg5bFCkREKhQRQQS4toJgEBERqU3EBJGZW0EQEalSxARRnJGb4xERqVHkBFG0Hu29nBEREalN5ASRUccZERGRCkVOEEVzTyIiIjWKmCAyG3Vo6+6DlNyTiIhITSImiOKMejgk0Gm1h3ooRETkA49BJIQwCiF2CyEOCCGOCCF+eS4G5itX49N2Ls8REamKNzOiXgBrpJQLACwEcIkQ4rzgDst3A5vjsWCBiEhNdJ6eIJWDLh3OH/XOS9gdiHFtjseCBSIidfHqGJEQQiuE2A+gHsBHUspdbp5zqxCiSAhR1NDQEOhxemTmduFERKrkVRBJKe1SyoUAMgEsE0LMdfOcjVLKAillgcViCfQ4PYozckZERKRGPlXNSSlbAHwM4JKgjMYPA8UKPEZERKQm3lTNWYQQCc7b0QDWATgW7IH5ipvjERGpk8diBQDpAJ4XQmihBNerUsp3gjss30XptDDqNdwunIhIZbypmjsIYNE5GIvfzEbuSUREpDYR01kBcDY+ZbECEZGqRFYQRetZrEBEpDKRFURcmiMiUp3ICqJoPYsViIhUJqKCyLUVBBERqUdEBVGcUdkcj3sSERGpR2QFUbQOfXaJXpsj1EMhIiIvRVYQsfEpEZHqRFQQmdn4lIhIdSIqiFyNT1u5OR4RkWpEVhC5luY4IyIiUo2ICqJ45y6t7K5ARKQeERVELFYgIlKfiAoiM5fmiIhUJ6KCyKjXQK8VaGOxAhGRakRUEAkhkDkpBgerW0I9FCIi8lJEBREAXLVwMj470YSq5q5QD4WIiLwQcUF0/ZJMCAG8tqc61EMhIiIvRFwQZU6KwQUzkvHanmrYHWx+SkQU7iIuiADgKwVTcLqlG5+daAz1UIiIyIOIDKKL81IRH63Hq0VcniMiCncRGURGvRZXL5yMD46cQUuXNdTDISKiMURkEAHAV5ZOgdXmwFv7a0I9FCIiGkPEBlH+5HjkT47Dq0VVoR4KERGNIWKDCFCKFo7UtOHw6dZQD4WIiEYR0UF01cLJMOg0+AdnRUREYSuigyghxoAN+Wl4c38NevrsoR4OERG5EdFBBABfLZiC1u4+fHi0LtRDISIiNyI+iJbnJCEjIZrLc0REYSrig0ijEbh+SSZ2lDWi+iwboRIRhZuIDyIAuKEgEwAboRIRhaMJEUSZk2KwIicZ/yiqhoONUImIwsqECCJA6bRwuqUbn5c3hXooREQ0yIQJovV5qYgz6vBKIYsWiIjCyYQJIqNei4vz0rCzrBFScnmOiChcTJggAoAFU+LR1GlFTWtPqIdCREROEyqI5mXEAwAOVbP3HBFRuJhQQZSbHgetRrAJKhFRGJlQQWTUazEzJRaHAhhEfXYHbvi/z3Dni3tR18YlPyIiX02oIAKU5bnDp1sDVrDwxt7TKKw8i/cOn8G6P36CFz6vhJ3nKhEReW3iBVGmUrBQG4CCBZvdgSe2lWFuRhy2/GAlFk5NwM/eOoLrnvwMR2vaAjBaIqLIN+GCaK6rYCEAy3Nv7a/BqeYu3L1mJrKTTfjrvy/DY19biKrmLlzxxA7817vF6LLa/P4cIqJINuGCKC9ABQt2h8QT28qQmx6Hi/NSAQBCCFy1MANbfrgSNyzJxJ+3l+PiR7bj8xPs5kBENJoJF0SBKlh452ANKho7cfeaGRBCDHksIcaAh6+bj1e/ez50WoHvv7KfJ9ESEY1iwgURoCzP+VOwYHdI/GlrGWanmrEhP23U5y2blog7Vs/AmbYeHOExIyIityZkEM3LiEdjhxVnxllu/d7hWpTVd+CutTOg0Ygxn7tmTgqEALYU14/rs4iIIt2EDKK5fnRYcDgkHt9Sihkpsbh0brrH5yfHRmHhlARsPcatyomI3JmQQZSXHgeNwLgKFj44cgbH6zpw15oZ0HqYDbmsnZOCA9WtqOcJr0REI0zIIIo2aDEzxYyDPgaRwyHx2JZSTE824fL5k71+3dpcpapuWwmX54iIhvMYREKIKUKIbUKIYiHEESHEPediYME2L9P3goXNxXU4dqYdd6z2fjYEAHPSzJgcb8RmHiciIhrBmxmRDcAPpZS5AM4DcIcQIi+4wwo+XwsWpJR4fGspspJicNVC72dDgHJ+0drcVOwobURPn308wyUiilgeg0hKWSul3Ou83Q6gGEBGsAcWbL4WLGwrqcfh0224Y/UM6LS+r2iuyU1Bd5+dW5UTEQ3j019UIUQ2gEUAdrl57FYhRJEQoqihoSEwowsiXwoWpJR4bEsZMidF45pF48vg86cnIVqvxVYuzxERDeF1EAkhYgFsAnCvlHLE2ZlSyo1SygIpZYHFYgnkGIPCVbDgTYeFz0804UBVC25fNQP6ccyGAKWjwwUzk7GluI5dFoiIBvHqr6oQQg8lhP4upXw9uEM6d+ZmxOPQ6TaPwfDsZ5VINBlw7WL/ViTX5aagprUHx860+/U+RESRxJuqOQHgLwCKpZSPBH9I5868jDg0dvSirq131OdUNXdhc3Edblw2BUa91q/PWz07BQCwpZgntxIRuXgzI1oB4BsA1ggh9jsvXw7yuM6JeZmet4R44YuT0AiBm87L8vvzUuKMWJAZjy3H1Hmc6I8flqCosjnUwyCiCONN1dwOKaWQUs6XUi50Xt49F4MLtrz0eGjE6EHUZbXh5d2ncEl+GtLjowPymWvmpGJ/VQsaO0afhYWj1u4+/GlrGV7bUx3qoRBRhJmQnRVcog1azEiJHbVy7o19p9HWY8O3VmQH7DPX5qZASmCbymZFZfXKca3Kps4Qj4SIIs2EDiLAVbAwMoiklHj+s0rkT45DQdakgH1e/uQ4pMUZPXbjbu3qw/9sK0NbT1/APtsfpXUdAICTTV0hHgkRRZoJH0TzMuLR0N6LumEdFj4/0YTjdR24eXn2iI3v/CGEwJrcFHxa2oBem/suC209ffjmM7vw+w9K8GphVcA+GwA+LW3ALc8VwmZ3+PS60noliGpbe9gdgogCikHk7LBwcFiHBVfJ9pULfGvn4411uSnotNqxq3zkgf+OXhu+9cxuHK1tg8UchQ+OnAnY50op8fB7x7DlWL3PS2zH6wZKzk81B3ZW5HDwvCqiiWzCB1He5LgRBQtVzV3YEqCSbXeW5yTDqNdg67DjRF1WG7797G4cqG7Fn25cjK8vm4qik2cDVtjwRXlz/06xR2t9O5eprL4D0y0mAEBlY+COExXXtiHvF+/jSI1/W7cTkXpN+CCKMehGFCy88MVJiACVbLtj1GtxwYxkbB7UZaHbasctzxVhz8mzeOxrC3HJ3DRsyE+DlMDmo4E57+gvO8qRaDJArxU4Vuv91uVtPX2obe3BxXnKdhaBPE60aU81evoc2HuqJWDvSUTqMuGDCBhasBCMkm131sxJRfXZbpTWd6Cnz45bXyjCFxVNeOQrC/v3OspNNyNzUjQ+DEAQnWjowObietx0XhZyLLEo9iGIypzHhwqyEpEQo8fJ5sDMiBwOiXcO1irjc34GEU08DCIMLVh4c18N2npsuHl5dlA/c80cpcvCe4fO4Ht/24NPSxvxu+vm4+pBTVWFENiQn4YdpY3o6LX59XnP7KiAQafBN87LQm56nE9thkqdx4dmpsQiKzEmYDOiPafO4kxbD4RQgpKIJiYGEYYWLDz3WQXy0uOwNDtwJdvupMUbMTcjDo9uOY5tJQ34zTXzcEPBlBHPW5+XCqvdgU9Kxt/RvLnTik17q3HNwgxYzFHITTejtrUHLV1Wr15fWteBKJ0GUxJjkJVkCti5RG8fqIFRr8G63FSUN/D8JKKJikGEgYKFpz4tx/G6DnxrRWBLtkezIU85BvSrq/Lx9S9NdfucguxEJJoMflXPvbjrJHr6HLjlwmkAgDlpcQCAo14uz5XWdyDHEgutRiA7KQanz3bDavOt/Hs4m92Bdw/VYu2cVMzLiMfplm50Wf2b9RGROjGIoBQs5FhisbuiGZNi9EEp2XbntlU5+ODei/DN87NHfY5WI7AuNwXbjtWP649/r82O5z8/iYtmWTAr1QwAyE1XguiYl5VzpXXtmJUaCwDISjLBIYHqs/4tz+2qaEZjhxWXz09HjkV574oAVuMRkXowiJxcy3M3LpsalJJtd/RaDWanmT0+b0N+Gtp7bePa3fWf+2vQ0N6L/+ecDQGAxRyF5FiDVwUL7T19qGntwUxniGUnxwDwv3LunYM1MBm0WD0nBTkpSln4CS7PEU1IDCKn85w7qAarZNsfK2YkI8agxYc+Ls9JKfGXHRWYnWrGBTOShzzmbcGCq2JuZsrAjAjwr+ec1ebAe4fP4OK8VBj1WmQnmZSCBVbOEU1IDCKn65dk4osH12JyQvBKtsfLqNdi1WwLPjpa51MXgp1lTTh2ph23XDhtxDGv3PQ4lNS1e2z142rt45oRJZkMiI3S+TUj2lnWiJauPlzhXAI16rWYMimGlXNEExSDyEmjEYiP1od6GKNan5eG+vZe7K/2/sTPpz4tR3JsFK5aOPKY15w0M6w2h8fjMqV17TDoNJiaqCzJCSEwNTEGJ/2YEb19sAZxRh0unDmwpXyOxcSlOaIJikGkEqvnpECnEV5Xzx2va8cnxxtw8/lZiNKNPOblKlgo9rA8N7hiziU7efznEvX02fHhkTpcMjcNBt3AP78cSyzKGzrYd45oAmIQqUR8tB7n5yThwyMDbYHG8syOCkTpNPi3UY555VhiodcKjwULpXUd/ceHXLKSTKg62+VzB28A+OR4Azp6bf3dI/rHkxKLXpsDp1u6fX5PIlI3BpGKrM9PQ0Vjp8djKQ3tvXh932lctyQTiSaD2+cYdBrkWGLH7DnX2WvD6Zbu/tJtl+ykGPTZJWpbe0Z55ejePlCDRJMBy3OShtw/PVkpgihnCTfRhMMgUpGLc5Wmox8cGbv33FOflsNqc+CWC6aN+bzc9DgUj3EukatibkbK0BLz8VbOdVlt2FJcjy/PS4NOO/SfXo5z1sXKOaKJh0GkImnxRiyckjBqGbfN7sAv3z6CjdvLce2ijP4TRUeTm27GmbYenO103+rHtQfRyBmRK4h8O060pbge3X32EctygFKNFx+tZ+Uc0QTEIFKZ9fmpOFDditrWocdS2nr68J2/FuHZnZX49xXT8PsbFnh8L1ern+Iz7pfnyuo7YNAOVMy5pJijYNRrcMrHGdHbB2qQGheFpdmJIx4TQjgr57wPon8drMWLu075NAYiCj8MIpXZkJ8GAPho0NYQp5q6cN3/foYdpY34zTXz8PMr8oZUuY2mv3JulOW543XtmG4xjVhG02iUEm5fZkRtPX34+HgDvjwvfdSx5VhifSrh/v0Hx/DgG4fwSiHDiEjNGEQqk2OJRY7F1F/GvbuiGVf/707Ut/fir7csG7V5qjtKq5+oUQsWSus7+k9kHS4ryeTTuUQfHamD1eboP4nVnZyUWDS096K1u8/j+9W39aCyqQtmow4PvnEY20rqPb6GiMITg0iFNuSn4YvyZjyzowL/9vQXSIjW4807VmB5TrLnFw+Tm252uzTXZbWh+mw3ZqW4P86UnaScS+TteT/vHKxBRkI0Fk1JGPU5rmNa5V4sz+2ubAYA/PmmJZidasYdf9+LQ9X+bzcupcSjm4/j7QM1XpXJE5H/GEQqtD4/DXaHxK/eOYovTUvCG7evwDRn+bOvctPjcLyuY8Q5Qf095lLdB1FWkgm9Ngfq2pTjnNIAACAASURBVD2XcJ/ttOLT0kZcviB9zO01plucJdxeLM/trmhGjEGLZdMS8dy3l2JSjAHffq4QVc3+NWPdVlKPRzeX4q6X9uHrT+3q3xSQJp4vypvwyIcloR7GhMAgUqH5GfG4cGYy/n3FNDz77aWIjxl/a6LRWv2U1rkv3Xbpr5xr9PyH/8OjZ2BzSFzhplpusKmJMdBphFcFC7srmrEkaxJ0Wg1S4ox4/t+Xwmqz4+Znd3u94d9wUko88tFxTE2Mwa+vysfR2jZc+tin+M27xX7vkEvqs3F7OR7fWoYanmQddAwiFdJoBF645Uv4+RV50Gv9+59wtFY/x+vbodcqG+G5k5Xk2g7C8+zlo6P1yEiIRv7kuDGfp9dqkJXkuflpa1cfSurah1TfzUgx4+mbl6K6uRvfeb4IPX12j+MaOc46HD7dhrvXzsQ3zs/G1h+uxHWLM7FxeznW/vFjLtdNIH12B3Y5t1352I/dkck7DKIJbrRWP2V1HZieHDuiYs5lckI09FqBkx6Wwnptduwsa8SaOSle7XrrTeVc0clmSAksmza0DHzZtEQ88tUFKDp5Fj94db9PfescDon/3lyKackmXO1sEpsUG4XfXj8fr9++HMmxUbjrpX34t6d3eXUMi9TtQFULOq3Kf8x8cpyFMMHGIJrgXK1+hgeRUjE3+gmxWo3AlEmeu3DvKm9Gd58da+akeDWenJRYnGzqRN8Yfex2VzTDoNVgoZvCh8vnT8Z/XJaLdw+dwUPvFnv1mQDwwZEzKK5tw91rZ4wI38VTJ+Gfd16AX189F4dPt+Jbzxb6vVU6hbcdZY0QAvjyvDTsLGvi/95BxiAiZZO8QecSdVvtqDrbhZmjHB9yyUqK8XiMaFtJPaJ0Gpw3PWnM57nkWGLRZ5djFh3srmzG/Mz4UXfSveWCafjW8mz8ZUcFXi2s8viZymzoOKZbTLhyQYbb52g1At84LwuP37gIp5q78NJunrsUyT4ra8LcyfG4amEGOnpt2HPybKiHFNEYRDSi1c+Jhg5IObK1z3Cuc4nGOm6y7Vg9luckIdrg3fbrOZaxtw3vstpwqLp1xLLcYEII/MdlubhgRjL+483D2Hdq7D8i/zpUi+N1Hbh33SyPJwKvnGXB+dOT8PiWUhYwRKjOXhv2njqLFTOSsTwnCTqNwMdcngsqBhENKlhQludcPebGWpoDlHOJOq12NHa4r1KraOxEZVMXVnu5LAcA0z2cS7TvVAtsDomlYwQRAOi0GvzpxkVIjY/C9/62F/WjlJnbHcp5Q7NSY3HZvHSP4xNC4P5L56Cp04qntpd7fD6pz+7KZtgcEitmJMFs1KMgexI+YcFCUDGIaKDnnHN5rrS+A3qt6O+yPZos57lLox0n2npM+a/I1bO9D6L4aD2SY6NGrZzbXdEMjQCWZE3y+F6TTAb8+aYCtHRbccff97pd53/7QA1ONHR6NRtyWTAlAZfNS8dTn5ajob3Xq9eQeuwsbYRBp+mvylw1OwXHzrTjzDi2PSHvMIhoRKuf0rp2TEs2eSwN99SF++OSesxIicWURPcl4KMZa9vw3RXNyE2PQ5zRu3On8ibH4XfXL0Bh5Vn8+p2jQx6z2R14fEsp5qSZcYmzh5+3frh+FnptDjyxtdSn11H423miCUumTuo/BrlqtrKlPavngodBRACGtvopre/wWKgAABkJ0dBqhNsu3J29Nuwqb/a6Wm6wnJRYlNV3jDj2ZLU5sK/q7JjHh9y5csFk3HrRdLzwxckhxQtv7a9BeWMnvn/xLGi8nA25TLfE4mtLp+Dvu0751HOPwltjRy+Ka9twwcyBdlmzU81IizPyfKIgYhARgIFWPx29Npxq7vJ4fAhQSr8nJxjdzoh2ljXCanf0/9ekL3IssWjt7kPzsH2SDp1uRU+fA1/yMYgA4L4Ns/uLF/ZXtaDP7sDjW0uRPzkO6/NSfX4/ALhn7UzotRr84cPj43o9hZ/PTygnsQ7eQVgIgVWzLdhR2jjmaQU0fgwiAjDQ6mfz0TpICa9mRICyPOduRrCtpB6xUTq3ew95MlrlXKGz0WnBON7TVbyQEheF217Yg43by3GyqQs/uHiWVyfaupMSZ8R3LpyGtw/U+NVw1e6QeOHzSr/75JH/dpY1wmzUYV5G/JD7V86yoL3Xhn2nWkI0ssjGICIAA5Vz/zxQA8Bz6bZLVtLIfYmklNh2rAEXzkweVwsiVxfu4QULuyuakWMxITk2yuf3BJzFC99YgpZuK37/QQkWZMaPa+lwsFsvmo5JMXr89v1j43q93SHx438cwM/eOoLHtvB4U6jtPNGI86YnjTipecXMZKWMm9uNBAWDiAAMtPrZfrwBOo3nijmX7CQTWrv7hjQaLa5tx5m2Hp/KtgfLSIhGlE4zpITb7pAorGz2+fjQcPmT4/Hb6+bDqNfgxxvmjHs25GI26nHnmpnYUdaIT0t9O4Zgd0jc99pBvL7vNFLMUdh2rN6ntkQUWKeaulDV3I0LZozcTiXOqMfirEmqPE4kpcRtL+zBHz4I307iDCICMNDqx+aQmJZsgkHn3T+NLDeVc65N6sZzfAhQmrpOSx5aOVdyph3tPTa/gwgArlqYgQO/WD/kgLQ/bjpvKjISovHb9495HSQOh8T9mw5i095qfH/dLPz0slw0dVpxoDpwSz+tXX34xVuHvdpokJTZEACsmOG+C8iq2RYcrW1DfZu6yrjfO3wG7x85g2d3VqDLGp4nYTOIqF+ec3nOm0IFl2w3Xbi3HavHvIx4pJiN4x5LTkrskKW53RXKQeTxHHNyJ0rnXacHb9/rRxtm4fDpNrxzqNbj8x0OiQdeP4R/7KnGPWtn4p51M7FylgUaoXx3gfLu4Vo8//lJ/KPIc5sjUvrLpcZF9S8ND7dqljLD//i4emZFvTY7/uu9YiSZDOi02vHuoTOhHpJbDCLqNyddKVAYbQ8id6YkxkAI4KRzRtTSZcXeU2exepyzIZccSyyqmrv6t3MorDyLjIRoZE7y7Zykc+WqBRmYk2bGr985iqe2l4968qPDIfHgG4fwSlEV7l4zA/eumwkASIgxoCArEVsCGESFFUpxx2t7qgP2nv5o6ujFVf+zE+8fDr8/hg6HxOcnmrAiJ3nU5drcdDNSzFH4REVB9Pxnlahq7sZ/f3UhspNiwvY/ShhE1C8vXakUmp3qfRAZ9VqkxRlR6ZwRfXK8AQ6JcR8fcsmxmOCQSsBJKbGrohlLsz13UwgVjUbg99cvwOR4Ix56txjnP7wFN278Aq8UnupfGnM4JH765iG8XFiFO1fPwPeHVeytnpOCIzVtATuDf3dlM6J0Ghw7044jNf5vo+4PKZVZ4IGqFjy6+XjY7etUfKYNzZ1WrHBzfMhFCIGVsyz49HjDiB2Nw1FTRy/+tKUMa+ak4KJZFly/JBO7KppxapQT0EOJQUT9luck4ffXz8fFPp5Xk5UU0z8j+rikAYkmA+ZnjtyiwReDK+cqm7rQ2NGLZdO86+AdKvMy4/HWnRdg6w9X4u41M1Hb2o2fbDqEpQ9txm0v7ME9r+zHS7urcPuqHPxw/ciy8bW5SnhvDcCsqLa1G9Vnu/Hdi6ZDrxXYtOe0T6/fuP0E/vp5pd/jcHltTzU+PFqHRVMTcOxMO4rCrJv1Z2XK0u9YQQQo7X7aemzYXxX+ZdyPbi5FV58dD355DgDg2sWZEAJ4bW94zJAHYxBRP41G4IaCKV4XKri4ziWyOyQ+LqnHqlkWr/u2jWa661yi+o7+40OBKFQ4F6ZbYvH9i2dh249W4c07VuDry6ai6GQz3j5Qg9tW5uDHG2a7Xf6ZmRKLzEnRAQmi3c5luYvz0rB2Tire2n/a65MxT7d043fvl+B375cE5OB2VXMXfvn2USyblogXbvkSzFE6vPD5Sb/fN5B2lDUix2JCWvzYxzUvmJkMrUaEffVcaV07Xtx9Cv/2pan9S+2TE6JxwYxkbNpTHXbVmQwi8ltWkgmNHVbsLGvE2a4+rPJzWQ4AYgw6ZCREo7yxE7srziLJZOg/0VUthBBYOCUB/3llPr54YC02/2AlfnKJ+xByPX/NnBTsLGsc11bngxVWNsNk0CI33YzrlmSiqdPqdQfpv3xaAZtDoqPXhn8d9Fx8MRaHQ+JH/zgAKSX+eMMCxEbpcN2STLx3uDZsGsZabQ7srmj2OBsClKa8i6cmhP22EL95txgxBi3uXTdryP3XL8nE6ZZufO7cBj1cMIjIb67Kuec/q4RGACtn+leo4DLdYsKJhg7srmzC0uxEv8/5CSWdVoMZKbEef4c1c1LQ3Wf3+w9FYcVZLM6aBJ1Wg1WzLUgyGbDJiyWZli4rXi48hasXTsb0ZBNe9fPg9jM7K7Crohm/uCK/v/ntTedloc8u/X7vQNl36iy6++xeBRGgdFk4fLotbIJ0uO3HG7CtpAF3r5mJRJNhyGMb8tNgNurCrmiBQUR+c51LtLWkHkuyJiE+xrvO2J64tjCvau72uP9QpDhvehKi9Vq/yrhbuqwoqWvHMmepu16rwZULJ2NLcf2QE4/d+evnJ9FlteO2VTn4ytIpKKw8i7J691tyeHK8rh2/+6AE63JTcUNBZv/9M1JisTwnCS/uOgV7GCwR7TzRBI2A17sIr3Jua7I9DKvn7A6Jh/5VjKmJMfjm8qwRjxv1Wly5YDLeP3IGbT3hc34Zg4j8luWcEckAVMsNlmMxoc+u/KEaT6NTNTLqtVgxIxlbiuvHXVlWVKkUAgwO7+uXZMJqd+BtZwsnd7qtdjz3WSVWz7ZgTlocrl2cAa1GjOu/nq02B+59eT/MUTo8fN28ETPBb5yXhdMt3QE9b2q8dpY1Yl5mAuKjvdxaJD0OybFRYXk+0atFVSipa8cDl84Z9Vy5GwqmoKfP4feyayB5DCIhxDNCiHohxOFzMSBSH1OUrr//my+b4HniqpyLjdL198KbCNbmpuB0SzeO141vJlJY2Qy9Vjk+5ZI/OR5z0sx4be/o1XOv7alCc6cVt63MAQCkmI1YOycFm/ZW+9x1+rEtx3G0tg3/de08t70B1+WlIjUuCi98EdqihfaePuyvasEFo3RTcEejcZZxlzaExYzOpb2nD3/8sARLsyfhkrmj76+1IDMeM1Niw2p5zpsZ0XMALgnyOEjlpiXHID3eiDlp3p+D5ElOihJES7Im+V2FpyauMB9v9VxhZTPmZyb0b+zmcv2STByoanG71GazO7Dx03IsnJIwpDrxq0unoLHDii3F3o9lz8lmPPnxCdywJBPrR9lwUK/V4MZlU/HJ8YaQ7ue0u6IZdofEihzf2j2tzU1BS1cfHgujc6Ke/PgEGjus+I/L8sY8FimEwPVLMrH3lPt/C6HgMYiklNsBNJ+DsZCK/ezyPDx+46KAFhSkmKOwJGsSrlwwOWDvqQZp8UbkT47D1mN1Pr+2p8+OQ6db3bZCumqhstTmrmjh3cNnUNXcjdtW5gz533DlLAtS46LwSuEprz6/s9eGH7x6AOnx0fj5FXljPvfGZVOh1Qi8uMu79w60Xpsdm4vrEaXTYLEXW88PtiE/DTcsycTjW8vwuw9KQhpGNS3d+Nmbh7FxezmuXZSBBVM8n8N3jXPZNVy6buhCPQCKDP6ewOqOEAKbvrc84O+rBmvnpOCJbWU422nFpGGVT2PZd6oFfXaJZdNG/mG1mKOwcpYFb+w9jR+tn90/y5RS4v8+PoHpFtOITQJ1Wg2uX5KJJz8+gTOtPR7Ps/n//lWMU81deOn/nQezh+3cU+OM2JCfileKqvD9i2eNmMEFipQSp1u6UXKmHcecl5IzbShv6ITNIbFylsXnz9ZqBH573XzotBo8+fEJ9Nkc+Olluee0srOmpRtPfnwCrxRWQULiK0un4Ccb5nj12hSzEatmWfDGvmr8eMPskK84BCyIhBC3ArgVAKZOnRqotyWakNbkpuLxrWXYXtqAqxZmeP26wspmCAEsyXJf3HHd4kxsPbYXn51oxIXOMvtPSxtxtLYNv71untst079SMAX/s+0EXttThTvXzBz1szcfrcNLu0/huxdN97oC7aYvZeHdQ2fwr4O1uG5JpucX+KipoxfXPfnZkO7wGQnRmJNmxsV5qZidFud22wdvaDQCv7lmLgxagad3VKDP7sB/Xpkf9DCqaenG/35chlcLqyEhcUPBFNy+KsfnPow3FGRiy9/qsb20IaDHdscjYEEkpdwIYCMAFBQUhMeiKZFKzc+IR3KsAVuK630Ootmp5lErwNbmpiDOqMOmPdX9QfR/n5xAalwUrl7k/nOykkw4f3oSXimqwu2rZrgNq4b2Xvxk00HkpsfhB+tnuXkX987PScJ0iwkvfHEyKEH063eO4nRLN355ZT7mZsRhVqrZ40zNF0II/OeV+dBrNXh6RwWsdomHrp7r9jvy17EzbXjh85P951+NN4Bc1sxJxaQYPV4rqo6cICKiwNFoBFbNTsGHR87AZneM2DHUHZvdgb0nz475B92o1+KKBZOxaW812nv6UNHYic9ONI1Z7gsoRQv3vrIfX5Q3YfmwGYSUyt5K7b02vPjVhT5tsSGEwDfOy8Iv3z6KQ9WtmJcZ7/lFXvrkeAPe3F+De9bOxM3LswP2vsMJIfDTy3Jh0Gnwvx+fQJ/dgd9eNz8gy1317T345/4avL73NI7WtkGvFX4HkItBp8FVCzPw4q5TaOmyIiHG+yXgQPMYREKIlwCsApAshKgG8Asp5V+CPTCiiW7tnBS8tqcae0+1eNVn72htGzqtdo97Nl23JBN/33UK7x6qxfbjjTAbdfj6l8ZeTr9kbhri3tLh5cKqEUH00u4qbDlWj59dnofZ46iavHZxJn73fgn+9sVJ/Pb6+T6/3p0uqw0/feMQciwm3L46JyDvORYhBH68YTb0Wg0e21IKm92B21fPgKuGQUK54fpZr9UgNkoHU5QWMQbdkNDqttrx4dEzeH3vaXxaqnSzX5AZj/+8Ig9XLJiMJDfl8ON1Q0EmnvusEm/trwlqWHviMYiklDeei4EQ0VAXzEyGXiuw5VidV0HkanTq6bmLpiRgerIJf95ejsrGTnx3ZY7H5SqjXourF2Xg5cIqtHb19XfPqGjsxK/fOYoVM5Lw7XH+IYuP1uPqRZPxxr7TePCyXK9PLB3Lf390HNVnu/Hqd88P6CaIYxFC4PsXz4JBp8HvPyjBm/tHP3l4uGi9FqYoHWKjtGjssKKj14bJ8UZ8b1UOrlmUiRkp3m9W6Yv8yfHIS4/DszsrsHjqpIDOSH3BpTmiMGU26rFsWiK2FtfjgUtzPT6/sLIZUxNjkBo3dmWbEALXLcnE7z8ogUGr8TpAvrp0Cv76+Um8uf80bl6eDZvdge+/sh96rcAfbljg13GRm87Lwku7q/DHD0tw5YLJmJoYA4s5alwH/g9Vt+IvOypw47KpIenYfsfqGVg2LRF1zi3FBQRcv4YAIATQa3Ogs9eOzl4bOq02dPba0OH8OdaowxXzJ+NL0xKDcqxpuPsumY17X9mPK57YgYvzUvGDi2ed8xPIGUREYWzNnFT8+p2jqGru6m8a6o6UEkWVZ7HSy51xr1mUgUc+Oo7rlmQixUNwueRPjsfcjDi8XFiFb56fhSe2lWF/VQue+PoipMdHe/UeY7336tkW/PXzk/irc4sIo16DKZNikJUUgymJMVg4JQFXzJ885h9nm92B+18/iOTYKNx/qXelzMEQqC3tz4VVs1Pw6X2r8cyOSjy9oxyXPvYpLpuXjnvXzcRMHzbJ9AeDiCiMrZ2Tgl+/cxRbj9WPuYZ/oqETTZ3W/kannkxOiMabt69ATopvW2t8delU/OzNw/jbFyfxp61luGZRBi6fH5gTjp/6ZgFONXfhVHMXqpzXJ5uU689PNOHZnZV4eXcVfnf9/FFD+S87KnCkpg1P/tvigCzxTRRmox73rJuJby3PxtM7yvHMjgq8e7gWVy2YjHvWzcK05OBuwSKCcUZwQUGBLCoqCvj7Ek1Ea/74MYw6LV6/ffmoJ16+tPsUHnj9ELb8cGV/j75gaO3uw7KHNqPX5kBGQjTeu/dCxAWwHHo0UirbRvz6nWJIKfHTy/Jw47IpQ5buTjV1Yf2jn+CCGRY89c0lqt42JNSaO6348/YTeP6zSvTZJf5x2/lYPNW37hPDCSH2SCkL3D3G7ttEYe4nl8zB0do2PPj6oVFbyRRWNCM51oDpQf4v1/hoPS6blw4hgD9+ZcE5CSFAOa711aVT8f69F2LBlAQ8+MYh3PxsIWpbuwEoQfXTNw9BKwR+fXXwTyqNdIkmAx64NBef3rcG3183EwuC0DllMAYRUZjbkJ+GH1w8C6/vO42N28vdPmd3ZTMKss7N5oG/unou3rx9hdfdEwIpc1IM/nbLl/Crq/JRWNGM9f+9HZv2VOONfafxaWkj7rtkjt/Hq2iAxRyFO9fMDHoLIB4jIlKBu9bMQMmZdjz8/jHMSjUP2feptrUb1We78e0V087JWGKjdF411gwWjUbgm+dn46KZFvz4tQP44T8OQKsRWDQ1ATedN3IzOAp/nBERqYAQSol0Xnoc7n5pH8rq2/sf6z9/SEWVWoGQnWzCy7eej/+4LBfTkk0B62ZA5x6DiEglog1aPPXNAkTptbjl+aL+bb8LK5thMmiRm35uSm3DiVYj8J0Lp2PzD1Zi1jkqNabAYxARqcjkhGj8+RtLUNvSgzte3Is+uwOFFWexOGuSV/3oiMIR/+USqcySrEn4zbXzsLOsCT/ZdBAlde0TblmOIguLFYhU6PolmThW24and1QAAJaGoJUNUaBwRkSkUg98ORcrZ1lgMmixMIRVbET+4oyISKW0GoGnvlmA+vaeoG2zTXQucEZEpGIGncbvDdKIQo1BREREIcUgIiKikGIQERFRSDGIiIgopBhEREQUUgwiIiIKKQYRERGFVHgGka0XcNhDPQoiIjoHwrOzwsFXgPd+AqTOBdLnA2nzleuUPEAXFerRERFRAIVnEKXkAYtvBmoPAAdeAQqfVu7X6ABL7kAopeYBKflAbArAPeqJiFQpPIMos0C5AIDDAZytAM4cBGoPKuFU+hGw/+8Dz49OBFLzlXBKyVUuybOAGHYkJiIKd+EZRINpNEBSjnLJv2bg/s5GoO4IUF8M1B8B6o4C+/4G9HUOPCcmGbDMVkLJMhtInqncjstU3peIiEIu/INoNKZkYPpK5eLicACtp4CGEqDx+MD1kTeAnpaB52mjgMTpSri5rpNmAIk5QGwqQ4qI6BxSbxC5o9EAk7KVy6wNA/dLqcygGkuAxlKg+QTQdEK5XfohYLcOPFdnHHiPSdMGbidOA+KnAAZ2OiYiCqTICqLRCAHEWpRL9gVDH3PYgdYqJZiay4GzlcqluQKo+HToUh8AmCxAwlQllBKmOi9ZQHwmEJ8BGOPP1W9FRBQRJkYQjUWjHZj1YO3Qx1wzqbOVSsFEy6mBS91hoOQ9wN479DVRcUBcxkAwxTmvzelA3GTlEmU+N78bEZEKMIjGMngmNWXpyMcdDqCzXgmm1iqg9TTQWg20nVZ+rtkHdDWOfJ3BDMSlD4STOQ2ITVOuzWnKcSpzGqCPDv7vSEQUYgwif2g0A+ExZZn75/R1A201QHutcj34dnutsvzXUQc4+ka+1hivhFJsqnKulClFue6/z6IsFcYkAzpDcH9XIqIgYRAFmz56oPx8NA4H0N0MtJ9RLh1nlJBqr1NCqqNemV111APWDvfvYYxXQsmUolQUmpKVgDIlAzFJysV1X0wSg4uIwgaDKBxoNAPhkTZ37OdaO5VA6qgDOhucl8ahtxtKgJM7ga5mANL9+xjMygm/MYlKMEU7r2MSgehJymXw7ehE5dgWO1gQUYAxiNTGYFJKyROneX6uww50tyjHqTobB103KSHV3Txwu7FUuba2j/5+Gh1gTACiE5RwcnfbGK/cNsYrF9d9BjPPzyIitxhEkUyjBUxJysUy27vX2KzKyb9dzUD3WefFeburWXms++xAwDWVKrd7WjHq7AsAIJSKQmM8YHReD/45Ks55bR64P8r5s+t+Q6zyOxFRRGEQ0VA6g7MgIsW31zkcQG+bElQ9rcrFFVD997Up173O69ZqpU1TbyvQ2w5Ih+fPMcQ6w8o8EE7917ED11Fxym2DyXm/eejtqFhAa+BSI1EYYBBRYGg0zmW6hPG9Xkrl+FdvmxJYve1KQPW0KQUave2DLs7HXY91NipLir3tQG+H+wpEt2PWKeGkNynXBtNAeBlihj0Wozymd90fo9yndz7Wf3+0cpszNyKvMYgoPAjhnMnEKudW+cPWqwRSb5sSbtZOJaisncr9rmDr63I+1jHoeZ1KxWJfF2B1Pt7XCThsvo1BZ3SGkiucoocGleu+/tsxg17jvOgG3zY6n2t03u+81vL/wqR+/FdMkUcXpVxMSYF7T5t1ILBcAdbXpZwn5u4+V5C5brse721Xqh4HP6+ve2i/Q19odEODSW90/v7D7xt0vy7KGW5Rg+43Drvt4VobxeITChgGEZE3dAZAlxi8Pa7sNsDW7Qwn56X/5y6grwew9TjvH3btuj3i8V7lWJytd+Bnm+u6x/8xa/RKMGkNzpByXmsNzvujBv6joP++wY8ZRn+O6zL4Z53r/ihAqx/5XK2B4ahSDCKicKDVAVrzuetDKKUzvHoHgmnIdbcyC3QFnN3q5jnOi911n3XQc52P9bQo99sHP9868Bxvj+d5S6NzhpLeGViu24ZhtwcFWf9rhj0++HVDnuO8rdEPfZ5GN+i23v3z3N1mwQyDiGhCEmLg+FMoORzOYOodCCy7dVB4WYc9bh0aZPa+gZBz2Ibdbx14vP9n68D93V3Kezpcj9mGPu66PeZpCQEgtIOCSTcQUK5gG3G/62fd0Mf6n68b5bZeKaLpv3/Y+2h0Ix/XaJXbGYuVY5xBwiAiotDRaACNUTmOFa4c9kHhcUz9PAAAB4RJREFU5AwoR9+gEOsbuB5+2251BuTg29ZBz7MNeo/Bj9kGnuOwjXyetUu57n+P0W4Pei9/AvX2L4CU3IB9pcMxiIiIxqLRApowmD36y+EYGmauS3/g2YfdZx8ItYSpQR0ag4iIaCLQaACNszAkzLDEhIiIQopBREREIcUgIiKikPIqiIQQlwghSoQQZUKI+4M9KCIimjg8BpEQQgvgfwBcCiAPwI1CiLxgD4yIiCYGb2ZEywCUSSnLpZRWAC8DuCq4wyIioonCmyDKAFA16Odq531DCCFuFUIUCSGKGhoaAjU+IiKKcN4EkbtGSCNO0ZVSbpRSFkgpCywWi/8jIyKiCcGbIKoGMGXQz5kAaoIzHCIimmi8CaJCADOFENOEEAYAXwPwz+AOi4iIJgqPLX6klDYhxJ0APgCgBfCMlPJI0EdGREQTgle95qSU7wJ4N8hjISKiCYidFYiIKKQYREREFFIMIiIiCikhZeC3wRVCNAA46efbJANoDMBwIhm/o7Hx+/GM39HY+P145u13lCWldHuSaVCCKBCEEEVSyoJQjyOc8TsaG78fz/gdjY3fj2eB+I64NEdERCHFICIiopAK5yDaGOoBqAC/o7Hx+/GM39HY+P145vd3FLbHiIiIaGII5xkRERFNAAwiIiIKqbAMIiHEJUKIEiFEmRDi/lCPJxwIIZ4RQtQLIQ4Pui9RCPGREKLUeT0plGMMJSHEFCHENiFEsRDiiBDiHuf9/I4ACCGMQojdQogDzu/nl877+f0MIoTQCiH2CSHecf7M72cQIUSlEOKQEGK/EKLIeZ/f31HYBZEQQgvgfwBcCiAPwI1CiLzQjiosPAfgkmH33Q9gi5RyJoAtzp8nKhuAH0opcwGcB+AO578bfkeKXgBrpJQLACwEcIkQ4jzw+xnuHgDFg37m9zPSainlwkHnDvn9HYVdEAFYBqBMSlkupbQCeBnAVSEeU8hJKbcDaB5291UAnnfefh7A1ed0UGFESlkrpdzrvN0O5Y9JBvgdAQCkosP5o955keD3008IkQngMgBPD7qb349nfn9H4RhEGQCqBv1c7byPRkqVUtYCyh9iACkhHk9YEEJkA1gEYBf4HfVzLjvtB1AP4CMpJb+foR4FcB8Ax6D7+P0MJQF8KITYI4S41Xmf39+RV/sRnWPCzX2sMSevCCFiAWwCcK+Usk0Id/+cJiYppR3AQiFEAoA3hBBzQz2mcCGEuBxAvZRyjxBiVajHE8ZWSClrhBApAD4SQhwLxJuG44yoGsCUQT9nAqgJ0VjCXZ0QIh0AnNf1IR5PSAkh9FBC6O9Sytedd/M7GkZK2QLgYyjHHPn9KFYAuFIIUQnlcMAaIcTfwO9nCClljfO6HsAbUA6l+P0dhWMQFQKYKYSYJoQwAPgagH+GeEzh6p8AbnbevhnAWyEcS0gJZerzFwDFUspHBj3E7wiAEMLinAlBCBENYB2AY+D3AwCQUj4gpcyUUmZD+ZuzVUp5E/j99BNCmIQQZtdtAOsBHEYAvqOw7KwghPgylPVaLYBnpJQPhXhIISeEeAnAKigt1+sA/ALAmwBeBTAVwCkAN0gphxc0TAhCiAsAfArgEAbW+B+Ecpxown9HQoj5UA4ka6H8B+irUspfCSGSwO9nCOfS3I+klJfz+xkghJgOZRYEKId1XpRSPhSI7ygsg4iIiCaOcFyaIyKiCYRBREREIcUgIiKikGIQERFRSDGIiIgopBhENOEJITqc19lCiK8H+L0fHPbzZ4F8f6JIwCAiGpANwKcgcnaLH8uQIJJSLvdxTEQRj0FENOBhABc691r5vrNJ6O+FEIVCiINCiO8CygmPzr2PXoRyAi2EEG86G0EecTWDFEI8DCDa+X5/d97nmn0J53sfdu7v8tVB7/2xEOI1IcQxIcTfnV0jIIR4WAhx1DmWP5zzb4coSMKx6SlRqNwP5xn1AOAMlFYp5VIhRBSAnUKID53PXQZgrpSywvnzv0spm53tcwqFEJuklPcLIe6UUi5081nXQtkXaAGUbhmFQojtzscWAciH0mNxJ4AVQoijAK4BMEdKKV3teogiAWdERKNbD+Cbzq0TdgFIAjDT+djuQSEEAHcLIQ4A+AJK096ZGNsFAF6SUtqllHUAPgGwdNB7V0spHQD2Q1kybAPQA+BpIcS1ALr8/u2IwgSDiGh0AsBdzt0oF0opp0kpXTOizv4nKb3J1gE437kD6j4ARi/eezS9g27bAeiklDYos7BNUDYee9+n34QojDGIiAa0AzAP+vkDAN9zbi8BIcQsZ9fh4eIBnJVSdgkh5kDZqtylz/X6YbYD+KrzOJQFwEUAdo82MOc+S/FSyncB3AtlWY8oIvAYEdGAgwBsziW25wA8hv+/vTu0QSiGwjD6X4dhIGZhASyGYViC4JjiJTzHBiQMUUQfCeIpTBHnyLqqL71p2j4Wm5YLA6+sf4N8S3KoqjnJI30893FOMlfV1Frbf61fkuyS3NM/fjy11p5LyNZsk1yrapN+mjr+tkX4P17fBmAoozkAhhIiAIYSIgCGEiIAhhIiAIYSIgCGEiIAhnoDX37IQN2/K7IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "its = d.getitstat()\n",
    "fig = plot.figure(figsize=(7, 7))\n",
    "plot.plot(np.vstack((its.DeltaD, its.Eta)).T, xlbl='Iterations',\n",
    "          lgnd=('Delta D', 'Eta'), fig=fig)\n",
    "fig.show()"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}