{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convolutional Dictionary Learning\n",
    "=================================\n",
    "\n",
    "This example demonstrating the use of [dictlrn.DictLearn](http://sporco.rtfd.org/en/latest/modules/sporco.dictlrn.dictlrn.html#sporco.dictlrn.dictlrn.DictLearn) to construct a dictionary learning algorithm with the flexibility of choosing the sparse coding and dictionary update classes. In this case they are [cbpdn.ConvBPDNGradReg](http://sporco.rtfd.org/en/latest/modules/sporco.admm.cbpdn.html#sporco.admm.cbpdn.ConvBPDNGradReg) and [admm.ccmod.ConvCnstrMOD](http://sporco.rtfd.org/en/latest/modules/sporco.admm.ccmod.html#sporco.admm.ccmod.ConvCnstrMOD) respectively, so the resulting dictionary learning algorithm is not equivalent to [dictlrn.cbpdndl.ConvBPDNDictLearn](http://sporco.rtfd.org/en/latest/modules/sporco.dictlrn.cbpdndl.html#sporco.dictlrn.cbpdndl.ConvBPDNDictLearn). Sparse coding with a CBPDN variant that includes a gradient regularization term on one of the coefficient maps [[52]](http://sporco.rtfd.org/en/latest/zreferences.html#id55) enables CDL without the need for the usual lowpass/highpass filtering as a pre-processing of the training images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-10-20T19:54:36.996904Z",
     "iopub.status.busy": "2020-10-20T19:54:36.996149Z",
     "iopub.status.idle": "2020-10-20T19:54:42.647467Z",
     "shell.execute_reply": "2020-10-20T19:54:42.648711Z"
    }
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "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.admm import cbpdn\n",
    "from sporco.admm import ccmod\n",
    "from sporco.dictlrn import dictlrn\n",
    "from sporco import cnvrep\n",
    "from sporco import util\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,
    "execution": {
     "iopub.execute_input": "2020-10-20T19:54:42.657472Z",
     "iopub.status.busy": "2020-10-20T19:54:42.656038Z",
     "iopub.status.idle": "2020-10-20T19:54:43.396521Z",
     "shell.execute_reply": "2020-10-20T19:54:43.395204Z"
    }
   },
   "outputs": [],
   "source": [
    "exim = util.ExampleImages(scaled=True, zoom=0.5, gray=True)\n",
    "img1 = exim.image('barbara.png', idxexp=np.s_[10:522, 100:612])\n",
    "img2 = exim.image('kodim23.png', idxexp=np.s_[:, 60:572])\n",
    "img3 = exim.image('monarch.png', idxexp=np.s_[:, 160:672])\n",
    "S = np.stack((img1, img2, img3), axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct initial dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-10-20T19:54:43.402374Z",
     "iopub.status.busy": "2020-10-20T19:54:43.401001Z",
     "iopub.status.idle": "2020-10-20T19:54:43.403693Z",
     "shell.execute_reply": "2020-10-20T19:54:43.405088Z"
    }
   },
   "outputs": [],
   "source": [
    "np.random.seed(12345)\n",
    "D0 = np.random.randn(8, 8, 64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct object representing problem dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-10-20T19:54:43.409436Z",
     "iopub.status.busy": "2020-10-20T19:54:43.408900Z",
     "iopub.status.idle": "2020-10-20T19:54:43.410767Z",
     "shell.execute_reply": "2020-10-20T19:54:43.410219Z"
    }
   },
   "outputs": [],
   "source": [
    "cri = cnvrep.CDU_ConvRepIndexing(D0.shape, S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up weights for the $\\ell_1$ norm to disable regularization of the coefficient map corresponding to the impulse filter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-10-20T19:54:43.414925Z",
     "iopub.status.busy": "2020-10-20T19:54:43.414385Z",
     "iopub.status.idle": "2020-10-20T19:54:43.416109Z",
     "shell.execute_reply": "2020-10-20T19:54:43.416644Z"
    }
   },
   "outputs": [],
   "source": [
    "wl1 = np.ones((1,)*4 + (D0.shape[2:]), dtype=np.float32)\n",
    "wl1[..., 0] = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set of weights for the $\\ell_2$ norm of the gradient to disable regularization of all coefficient maps except for the one corresponding to the impulse filter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-10-20T19:54:43.420715Z",
     "iopub.status.busy": "2020-10-20T19:54:43.420175Z",
     "iopub.status.idle": "2020-10-20T19:54:43.422392Z",
     "shell.execute_reply": "2020-10-20T19:54:43.421841Z"
    }
   },
   "outputs": [],
   "source": [
    "wgr = np.zeros((D0.shape[2]), dtype=np.float32)\n",
    "wgr[0] = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define X and D update options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-10-20T19:54:43.429172Z",
     "iopub.status.busy": "2020-10-20T19:54:43.428497Z",
     "iopub.status.idle": "2020-10-20T19:54:43.429800Z",
     "shell.execute_reply": "2020-10-20T19:54:43.430457Z"
    }
   },
   "outputs": [],
   "source": [
    "lmbda = 0.1\n",
    "mu = 0.5\n",
    "optx = cbpdn.ConvBPDNGradReg.Options({'Verbose': False, 'MaxMainIter': 1,\n",
    "            'rho': 20.0*lmbda + 0.5, 'AutoRho': {'Period': 10,\n",
    "            'AutoScaling': False, 'RsdlRatio': 10.0, 'Scaling': 2.0,\n",
    "            'RsdlTarget': 1.0}, 'HighMemSolve': True, 'AuxVarObj': False,\n",
    "            'L1Weight': wl1, 'GradWeight': wgr})\n",
    "optd = ccmod.ConvCnstrMODOptions({'Verbose': False, 'MaxMainIter': 1,\n",
    "            'rho': 5.0*cri.K, 'AutoRho': {'Period': 10, 'AutoScaling': False,\n",
    "            'RsdlRatio': 10.0, 'Scaling': 2.0, 'RsdlTarget': 1.0}},\n",
    "            method='cns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalise dictionary according to dictionary Y update options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-10-20T19:54:43.434915Z",
     "iopub.status.busy": "2020-10-20T19:54:43.434253Z",
     "iopub.status.idle": "2020-10-20T19:54:43.436487Z",
     "shell.execute_reply": "2020-10-20T19:54:43.435812Z"
    }
   },
   "outputs": [],
   "source": [
    "D0n = cnvrep.Pcn(D0, D0.shape, cri.Nv, dimN=2, dimC=0, crp=True,\n",
    "                 zm=optd['ZeroMean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Update D update options to include initial values for Y and U."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-10-20T19:54:43.440472Z",
     "iopub.status.busy": "2020-10-20T19:54:43.439907Z",
     "iopub.status.idle": "2020-10-20T19:54:43.441805Z",
     "shell.execute_reply": "2020-10-20T19:54:43.441258Z"
    }
   },
   "outputs": [],
   "source": [
    "optd.update({'Y0': cnvrep.zpad(cnvrep.stdformD(D0n, cri.Cd, cri.M), cri.Nv),\n",
    "             'U0': np.zeros(cri.shpD + (cri.K,))})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create X update object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-10-20T19:54:43.445395Z",
     "iopub.status.busy": "2020-10-20T19:54:43.444866Z",
     "iopub.status.idle": "2020-10-20T19:54:49.003160Z",
     "shell.execute_reply": "2020-10-20T19:54:49.002382Z"
    }
   },
   "outputs": [],
   "source": [
    "xstep = cbpdn.ConvBPDNGradReg(D0n, S, lmbda, mu, optx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create D update object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-10-20T19:54:49.009407Z",
     "iopub.status.busy": "2020-10-20T19:54:49.008370Z",
     "iopub.status.idle": "2020-10-20T19:54:49.072531Z",
     "shell.execute_reply": "2020-10-20T19:54:49.071339Z"
    }
   },
   "outputs": [],
   "source": [
    "dstep = ccmod.ConvCnstrMOD(None, S, D0.shape, optd, method='cns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create DictLearn object and solve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-10-20T19:54:49.078772Z",
     "iopub.status.busy": "2020-10-20T19:54:49.078216Z",
     "iopub.status.idle": "2020-10-20T20:00:02.850577Z",
     "shell.execute_reply": "2020-10-20T20:00:02.849383Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Itn   FncX      r_X       s_X       ρ_X       FncD      r_D       s_D       ρ_D     \n",
      "------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  7.30e+03  9.76e-01  1.03e-01  2.50e+00  1.64e+04  3.28e-01  2.20e-01  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1  4.09e+03  4.54e-01  1.19e+00  2.50e+00  3.64e+04  3.86e-01  1.21e-01  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   2  5.30e+03  4.28e-01  8.99e-01  2.50e+00  6.12e+03  3.89e-01  1.71e-01  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   3  3.28e+03  3.40e-01  4.64e-01  2.50e+00  3.14e+03  3.67e-01  8.04e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   4  2.01e+03  3.82e-01  6.02e-01  2.50e+00  8.88e+02  3.30e-01  8.14e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   5  3.21e+03  3.96e-01  3.82e-01  2.50e+00  3.12e+03  2.64e-01  6.81e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   6  2.44e+03  2.80e-01  3.85e-01  2.50e+00  5.56e+02  2.20e-01  6.41e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   7  1.58e+03  1.95e-01  4.71e-01  2.50e+00  1.53e+03  1.81e-01  5.54e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   8  2.52e+03  1.91e-01  1.82e-01  2.50e+00  1.22e+03  1.53e-01  5.40e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   9  1.02e+03  1.78e-01  4.42e-01  2.50e+00  3.93e+02  1.29e-01  5.43e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  10  1.90e+03  1.96e-01  1.88e-01  2.50e+00  1.12e+03  1.08e-01  5.47e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  11  1.22e+03  1.37e-01  2.97e-01  2.50e+00  1.35e+02  9.16e-02  5.27e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  12  1.39e+03  1.17e-01  2.27e-01  2.50e+00  7.79e+02  7.76e-02  5.40e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  13  1.23e+03  1.10e-01  1.93e-01  2.50e+00  1.42e+02  6.67e-02  5.32e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  14  9.07e+02  1.05e-01  2.29e-01  2.50e+00  4.18e+02  5.72e-02  5.05e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  15  1.13e+03  9.59e-02  1.15e-01  2.50e+00  1.35e+02  5.12e-02  4.79e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  16  6.80e+02  7.06e-02  1.87e-01  2.50e+00  2.43e+02  4.53e-02  4.47e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  17  9.34e+02  7.06e-02  8.33e-02  2.50e+00  1.12e+02  4.06e-02  4.21e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  18  5.22e+02  5.91e-02  1.50e-01  2.50e+00  1.52e+02  3.71e-02  4.21e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  19  7.61e+02  5.80e-02  6.45e-02  2.50e+00  8.39e+01  3.41e-02  4.20e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  20  4.60e+02  4.38e-02  1.14e-01  2.50e+00  1.06e+02  3.17e-02  4.10e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  21  6.14e+02  4.26e-02  5.37e-02  2.50e+00  6.32e+01  2.94e-02  4.07e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  22  4.28e+02  3.60e-02  8.19e-02  2.50e+00  8.21e+01  2.74e-02  4.03e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  23  4.92e+02  3.35e-02  5.20e-02  2.50e+00  5.06e+01  2.57e-02  3.93e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  24  4.16e+02  2.86e-02  5.83e-02  2.50e+00  6.41e+01  2.40e-02  3.78e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  25  3.95e+02  2.55e-02  4.80e-02  2.50e+00  4.52e+01  2.23e-02  3.61e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  26  3.94e+02  2.41e-02  3.89e-02  2.50e+00  5.30e+01  2.11e-02  3.46e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  27  3.22e+02  2.03e-02  4.41e-02  2.50e+00  4.40e+01  1.99e-02  3.32e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  28  3.58e+02  1.95e-02  2.84e-02  2.50e+00  4.60e+01  1.87e-02  3.20e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  29  2.82e+02  1.65e-02  3.44e-02  2.50e+00  4.52e+01  1.77e-02  3.14e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  30  3.18e+02  1.63e-02  2.52e-02  2.50e+00  4.41e+01  1.69e-02  3.08e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  31  2.76e+02  1.48e-02  2.95e-02  2.50e+00  4.38e+01  1.61e-02  3.01e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  32  2.82e+02  1.36e-02  2.27e-02  2.50e+00  4.24e+01  1.51e-02  2.93e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  33  2.74e+02  1.29e-02  2.16e-02  2.50e+00  4.26e+01  1.44e-02  2.84e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  34  2.58e+02  1.23e-02  2.33e-02  2.50e+00  4.12e+01  1.38e-02  2.74e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  35  2.64e+02  1.17e-02  1.96e-02  2.50e+00  4.09e+01  1.32e-02  2.65e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  36  2.48e+02  1.10e-02  1.93e-02  2.50e+00  4.09e+01  1.25e-02  2.56e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  37  2.49e+02  1.07e-02  1.88e-02  2.50e+00  4.05e+01  1.20e-02  2.48e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  38  2.43e+02  1.02e-02  1.82e-02  2.50e+00  4.05e+01  1.15e-02  2.41e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  39  2.38e+02  9.67e-03  1.68e-02  2.50e+00  4.05e+01  1.10e-02  2.35e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  40  2.37e+02  9.42e-03  1.64e-02  2.50e+00  4.04e+01  1.06e-02  2.30e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  41  2.32e+02  9.15e-03  1.64e-02  2.50e+00  4.01e+01  1.03e-02  2.25e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  42  2.31e+02  8.79e-03  1.51e-02  2.50e+00  3.99e+01  9.93e-03  2.20e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  43  2.29e+02  8.54e-03  1.47e-02  2.50e+00  3.98e+01  9.46e-03  2.14e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  44  2.26e+02  8.31e-03  1.46e-02  2.50e+00  3.95e+01  9.12e-03  2.09e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  45  2.24e+02  8.02e-03  1.41e-02  2.50e+00  3.94e+01  8.92e-03  2.03e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  46  2.22e+02  7.77e-03  1.36e-02  2.50e+00  3.94e+01  8.70e-03  1.98e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  47  2.20e+02  7.59e-03  1.34e-02  2.50e+00  3.93e+01  8.37e-03  1.94e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  48  2.18e+02  7.37e-03  1.30e-02  2.50e+00  3.92e+01  8.08e-03  1.90e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  49  2.16e+02  7.17e-03  1.26e-02  2.50e+00  3.92e+01  7.90e-03  1.87e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  50  2.15e+02  7.01e-03  1.25e-02  2.50e+00  3.91e+01  7.73e-03  1.84e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  51  2.14e+02  6.86e-03  1.22e-02  2.50e+00  3.90e+01  7.52e-03  1.81e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  52  2.12e+02  6.68e-03  1.19e-02  2.50e+00  3.89e+01  7.32e-03  1.78e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  53  2.11e+02  6.54e-03  1.17e-02  2.50e+00  3.88e+01  7.25e-03  1.74e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  54  2.10e+02  6.39e-03  1.15e-02  2.50e+00  3.87e+01  7.00e-03  1.71e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  55  2.09e+02  6.26e-03  1.12e-02  2.50e+00  3.86e+01  6.84e-03  1.68e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  56  2.08e+02  6.13e-03  1.09e-02  2.50e+00  3.86e+01  6.68e-03  1.65e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  57  2.07e+02  6.01e-03  1.08e-02  2.50e+00  3.85e+01  6.58e-03  1.63e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  58  2.05e+02  5.90e-03  1.06e-02  2.50e+00  3.85e+01  6.41e-03  1.60e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  59  2.05e+02  5.77e-03  1.04e-02  2.50e+00  3.84e+01  6.30e-03  1.58e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  60  2.04e+02  5.67e-03  1.02e-02  2.50e+00  3.84e+01  6.15e-03  1.56e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  61  2.03e+02  5.57e-03  1.01e-02  2.50e+00  3.83e+01  6.09e-03  1.54e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  62  2.02e+02  5.47e-03  9.91e-03  2.50e+00  3.83e+01  5.99e-03  1.52e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  63  2.01e+02  5.38e-03  9.81e-03  2.50e+00  3.82e+01  5.89e-03  1.50e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  64  2.00e+02  5.27e-03  9.59e-03  2.50e+00  3.81e+01  5.71e-03  1.48e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  65  2.00e+02  5.19e-03  9.45e-03  2.50e+00  3.81e+01  5.66e-03  1.46e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  66  1.99e+02  5.12e-03  9.39e-03  2.50e+00  3.81e+01  5.53e-03  1.45e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  67  1.98e+02  5.03e-03  9.16e-03  2.50e+00  3.80e+01  5.49e-03  1.43e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  68  1.98e+02  4.96e-03  9.09e-03  2.50e+00  3.80e+01  5.37e-03  1.42e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  69  1.97e+02  4.89e-03  8.97e-03  2.50e+00  3.79e+01  5.29e-03  1.40e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  70  1.96e+02  4.81e-03  8.81e-03  2.50e+00  3.79e+01  5.29e-03  1.39e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  71  1.96e+02  4.75e-03  8.75e-03  2.50e+00  3.78e+01  5.16e-03  1.37e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  72  1.95e+02  4.69e-03  8.69e-03  2.50e+00  3.78e+01  5.11e-03  1.36e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  73  1.94e+02  4.62e-03  8.54e-03  2.50e+00  3.78e+01  5.02e-03  1.35e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  74  1.94e+02  4.56e-03  8.41e-03  2.50e+00  3.77e+01  4.97e-03  1.34e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  75  1.93e+02  4.51e-03  8.37e-03  2.50e+00  3.77e+01  4.92e-03  1.32e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  76  1.93e+02  4.45e-03  8.26e-03  2.50e+00  3.76e+01  4.89e-03  1.31e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  77  1.92e+02  4.40e-03  8.19e-03  2.50e+00  3.76e+01  4.80e-03  1.30e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  78  1.92e+02  4.35e-03  8.10e-03  2.50e+00  3.76e+01  4.79e-03  1.29e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  79  1.91e+02  4.30e-03  8.01e-03  2.50e+00  3.76e+01  4.72e-03  1.28e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  80  1.91e+02  4.26e-03  7.97e-03  2.50e+00  3.75e+01  4.66e-03  1.27e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  81  1.91e+02  4.22e-03  7.89e-03  2.50e+00  3.75e+01  4.64e-03  1.26e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  82  1.90e+02  4.18e-03  7.82e-03  2.50e+00  3.75e+01  4.61e-03  1.25e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  83  1.90e+02  4.15e-03  7.80e-03  2.50e+00  3.74e+01  4.56e-03  1.25e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  84  1.89e+02  4.11e-03  7.75e-03  2.50e+00  3.74e+01  4.52e-03  1.24e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  85  1.89e+02  4.07e-03  7.66e-03  2.50e+00  3.74e+01  4.50e-03  1.23e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  86  1.89e+02  4.04e-03  7.65e-03  2.50e+00  3.73e+01  4.45e-03  1.22e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  87  1.88e+02  4.01e-03  7.59e-03  2.50e+00  3.73e+01  4.44e-03  1.21e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  88  1.88e+02  3.97e-03  7.50e-03  2.50e+00  3.73e+01  4.41e-03  1.20e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  89  1.88e+02  3.94e-03  7.45e-03  2.50e+00  3.72e+01  4.36e-03  1.19e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  90  1.87e+02  3.90e-03  7.37e-03  2.50e+00  3.72e+01  4.35e-03  1.18e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  91  1.87e+02  3.87e-03  7.33e-03  2.50e+00  3.72e+01  4.38e-03  1.17e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  92  1.86e+02  3.84e-03  7.35e-03  2.50e+00  3.72e+01  4.34e-03  1.17e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  93  1.86e+02  3.82e-03  7.34e-03  2.50e+00  3.72e+01  4.25e-03  1.16e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  94  1.86e+02  3.78e-03  7.26e-03  2.50e+00  3.71e+01  4.19e-03  1.15e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  95  1.86e+02  3.74e-03  7.17e-03  2.50e+00  3.71e+01  4.22e-03  1.14e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  96  1.85e+02  3.71e-03  7.16e-03  2.50e+00  3.71e+01  4.16e-03  1.13e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  97  1.85e+02  3.69e-03  7.17e-03  2.50e+00  3.70e+01  4.17e-03  1.12e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  98  1.85e+02  3.66e-03  7.11e-03  2.50e+00  3.70e+01  4.09e-03  1.11e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  99  1.84e+02  3.63e-03  7.06e-03  2.50e+00  3.70e+01  4.13e-03  1.10e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 100  1.84e+02  3.60e-03  7.04e-03  2.50e+00  3.70e+01  4.12e-03  1.10e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 101  1.84e+02  3.57e-03  7.04e-03  2.50e+00  3.70e+01  4.03e-03  1.09e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 102  1.83e+02  3.53e-03  6.93e-03  2.50e+00  3.69e+01  4.01e-03  1.08e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 103  1.83e+02  3.50e-03  6.85e-03  2.50e+00  3.69e+01  3.96e-03  1.07e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 104  1.83e+02  3.47e-03  6.86e-03  2.50e+00  3.69e+01  4.01e-03  1.06e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 105  1.82e+02  3.44e-03  6.78e-03  2.50e+00  3.69e+01  3.91e-03  1.06e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 106  1.82e+02  3.41e-03  6.75e-03  2.50e+00  3.69e+01  3.92e-03  1.05e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 107  1.82e+02  3.39e-03  6.76e-03  2.50e+00  3.69e+01  3.86e-03  1.04e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 108  1.82e+02  3.36e-03  6.69e-03  2.50e+00  3.68e+01  3.83e-03  1.04e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 109  1.81e+02  3.33e-03  6.65e-03  2.50e+00  3.68e+01  3.85e-03  1.03e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 110  1.81e+02  3.32e-03  6.69e-03  2.50e+00  3.68e+01  3.77e-03  1.03e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 111  1.81e+02  3.28e-03  6.58e-03  2.50e+00  3.68e+01  3.78e-03  1.02e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 112  1.80e+02  3.25e-03  6.47e-03  2.50e+00  3.68e+01  3.76e-03  1.01e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 113  1.80e+02  3.23e-03  6.50e-03  2.50e+00  3.68e+01  3.76e-03  1.01e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 114  1.80e+02  3.21e-03  6.51e-03  2.50e+00  3.67e+01  3.79e-03  1.00e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 115  1.80e+02  3.19e-03  6.45e-03  2.50e+00  3.67e+01  3.74e-03  9.97e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 116  1.79e+02  3.17e-03  6.46e-03  2.50e+00  3.67e+01  3.70e-03  9.91e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 117  1.79e+02  3.15e-03  6.42e-03  2.50e+00  3.67e+01  3.69e-03  9.86e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 118  1.79e+02  3.11e-03  6.31e-03  2.50e+00  3.67e+01  3.71e-03  9.81e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 119  1.79e+02  3.09e-03  6.30e-03  2.50e+00  3.67e+01  3.67e-03  9.76e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 120  1.79e+02  3.09e-03  6.36e-03  2.50e+00  3.67e+01  3.70e-03  9.71e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 121  1.78e+02  3.06e-03  6.29e-03  2.50e+00  3.67e+01  3.58e-03  9.67e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 122  1.78e+02  3.03e-03  6.19e-03  2.50e+00  3.66e+01  3.62e-03  9.62e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 123  1.78e+02  3.03e-03  6.26e-03  2.50e+00  3.66e+01  3.56e-03  9.58e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 124  1.78e+02  3.00e-03  6.18e-03  2.50e+00  3.66e+01  3.76e-03  9.53e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 125  1.78e+02  2.98e-03  6.11e-03  2.50e+00  3.66e+01  3.72e-03  9.48e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 126  1.77e+02  2.97e-03  6.16e-03  2.50e+00  3.66e+01  3.79e-03  9.43e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 127  1.77e+02  2.95e-03  6.13e-03  2.50e+00  3.66e+01  3.87e-03  9.37e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 128  1.77e+02  2.92e-03  6.02e-03  2.50e+00  3.66e+01  3.75e-03  9.32e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 129  1.77e+02  2.90e-03  6.00e-03  2.50e+00  3.66e+01  3.76e-03  9.26e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 130  1.77e+02  2.90e-03  6.10e-03  2.50e+00  3.66e+01  3.66e-03  9.22e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 131  1.76e+02  2.89e-03  6.11e-03  2.50e+00  3.65e+01  3.58e-03  9.18e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 132  1.76e+02  2.86e-03  6.02e-03  2.50e+00  3.65e+01  3.38e-03  9.14e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 133  1.76e+02  2.84e-03  5.95e-03  2.50e+00  3.65e+01  3.37e-03  9.10e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 134  1.76e+02  2.83e-03  5.98e-03  2.50e+00  3.65e+01  3.41e-03  9.06e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 135  1.76e+02  2.82e-03  6.00e-03  2.50e+00  3.65e+01  3.41e-03  9.02e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 136  1.75e+02  2.78e-03  5.82e-03  2.50e+00  3.65e+01  3.33e-03  8.99e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 137  1.75e+02  2.76e-03  5.77e-03  2.50e+00  3.65e+01  3.34e-03  8.95e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 138  1.75e+02  2.77e-03  5.88e-03  2.50e+00  3.65e+01  3.32e-03  8.92e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 139  1.75e+02  2.75e-03  5.79e-03  2.50e+00  3.64e+01  3.45e-03  8.88e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 140  1.75e+02  2.73e-03  5.75e-03  2.50e+00  3.64e+01  3.42e-03  8.84e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 141  1.75e+02  2.73e-03  5.82e-03  2.50e+00  3.64e+01  3.27e-03  8.79e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 142  1.74e+02  2.70e-03  5.68e-03  2.50e+00  3.64e+01  3.26e-03  8.74e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 143  1.74e+02  2.67e-03  5.60e-03  2.50e+00  3.64e+01  3.32e-03  8.69e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 144  1.74e+02  2.67e-03  5.65e-03  2.50e+00  3.64e+01  3.24e-03  8.63e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 145  1.74e+02  2.65e-03  5.62e-03  2.50e+00  3.63e+01  3.39e-03  8.57e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 146  1.74e+02  2.63e-03  5.54e-03  2.50e+00  3.63e+01  3.23e-03  8.51e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 147  1.74e+02  2.61e-03  5.53e-03  2.50e+00  3.63e+01  3.16e-03  8.46e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 148  1.73e+02  2.59e-03  5.50e-03  2.50e+00  3.63e+01  3.21e-03  8.40e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 149  1.73e+02  2.59e-03  5.55e-03  2.50e+00  3.63e+01  3.20e-03  8.35e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 150  1.73e+02  2.59e-03  5.66e-03  2.50e+00  3.63e+01  3.17e-03  8.30e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 151  1.73e+02  2.56e-03  5.55e-03  2.50e+00  3.63e+01  3.23e-03  8.25e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 152  1.73e+02  2.54e-03  5.47e-03  2.50e+00  3.63e+01  3.24e-03  8.20e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 153  1.73e+02  2.55e-03  5.61e-03  2.50e+00  3.63e+01  3.23e-03  8.16e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 154  1.72e+02  2.54e-03  5.66e-03  2.50e+00  3.62e+01  3.29e-03  8.12e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 155  1.72e+02  2.51e-03  5.52e-03  2.50e+00  3.62e+01  3.15e-03  8.08e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 156  1.72e+02  2.50e-03  5.48e-03  2.50e+00  3.62e+01  3.16e-03  8.04e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 157  1.72e+02  2.50e-03  5.60e-03  2.50e+00  3.62e+01  3.23e-03  8.01e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 158  1.72e+02  2.50e-03  5.68e-03  2.50e+00  3.62e+01  3.35e-03  7.98e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 159  1.72e+02  2.49e-03  5.65e-03  2.50e+00  3.62e+01  3.24e-03  7.95e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 160  1.72e+02  2.49e-03  5.69e-03  2.50e+00  3.62e+01  3.27e-03  7.91e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 161  1.71e+02  2.48e-03  5.73e-03  2.50e+00  3.62e+01  3.34e-03  7.87e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 162  1.71e+02  2.47e-03  5.74e-03  2.50e+00  3.62e+01  3.14e-03  7.83e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 163  1.71e+02  2.45e-03  5.67e-03  2.50e+00  3.61e+01  3.38e-03  7.79e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 164  1.71e+02  2.42e-03  5.55e-03  2.50e+00  3.61e+01  3.30e-03  7.75e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 165  1.71e+02  2.42e-03  5.57e-03  2.50e+00  3.61e+01  3.15e-03  7.72e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 166  1.71e+02  2.41e-03  5.57e-03  2.50e+00  3.61e+01  3.18e-03  7.69e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 167  1.71e+02  2.40e-03  5.57e-03  2.50e+00  3.61e+01  3.22e-03  7.66e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 168  1.71e+02  2.38e-03  5.47e-03  2.50e+00  3.61e+01  3.23e-03  7.62e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 169  1.71e+02  2.38e-03  5.52e-03  2.50e+00  3.61e+01  3.17e-03  7.59e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 170  1.70e+02  2.39e-03  5.70e-03  2.50e+00  3.61e+01  3.17e-03  7.55e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 171  1.70e+02  2.37e-03  5.56e-03  2.50e+00  3.61e+01  3.01e-03  7.51e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 172  1.70e+02  2.35e-03  5.51e-03  2.50e+00  3.61e+01  3.15e-03  7.48e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 173  1.70e+02  2.36e-03  5.67e-03  2.50e+00  3.60e+01  3.23e-03  7.45e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 174  1.70e+02  2.34e-03  5.54e-03  2.50e+00  3.60e+01  3.12e-03  7.41e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 175  1.70e+02  2.32e-03  5.46e-03  2.50e+00  3.60e+01  3.18e-03  7.39e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 176  1.70e+02  2.35e-03  5.71e-03  2.50e+00  3.60e+01  3.13e-03  7.36e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 177  1.70e+02  2.33e-03  5.62e-03  2.50e+00  3.60e+01  3.16e-03  7.33e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 178  1.70e+02  2.29e-03  5.40e-03  2.50e+00  3.60e+01  3.19e-03  7.30e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 179  1.69e+02  2.29e-03  5.41e-03  2.50e+00  3.60e+01  3.16e-03  7.28e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 180  1.69e+02  2.29e-03  5.45e-03  2.50e+00  3.60e+01  3.32e-03  7.25e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 181  1.69e+02  2.29e-03  5.50e-03  2.50e+00  3.60e+01  3.35e-03  7.22e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 182  1.69e+02  2.28e-03  5.47e-03  2.50e+00  3.59e+01  3.29e-03  7.19e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 183  1.69e+02  2.27e-03  5.42e-03  2.50e+00  3.59e+01  3.28e-03  7.16e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 184  1.69e+02  2.28e-03  5.59e-03  2.50e+00  3.59e+01  3.35e-03  7.13e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 185  1.69e+02  2.30e-03  5.74e-03  2.50e+00  3.59e+01  3.18e-03  7.11e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 186  1.69e+02  2.28e-03  5.60e-03  2.50e+00  3.59e+01  3.29e-03  7.09e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 187  1.69e+02  2.27e-03  5.56e-03  2.50e+00  3.59e+01  3.23e-03  7.07e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 188  1.69e+02  2.29e-03  5.76e-03  2.50e+00  3.59e+01  3.09e-03  7.05e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 189  1.69e+02  2.28e-03  5.76e-03  2.50e+00  3.59e+01  2.97e-03  7.02e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 190  1.68e+02  2.26e-03  5.60e-03  2.50e+00  3.59e+01  3.07e-03  7.00e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 191  1.68e+02  2.29e-03  5.82e-03  2.50e+00  3.58e+01  3.00e-03  6.99e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 192  1.68e+02  2.31e-03  6.02e-03  2.50e+00  3.58e+01  3.18e-03  6.97e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 193  1.68e+02  2.29e-03  5.91e-03  2.50e+00  3.58e+01  3.09e-03  6.96e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 194  1.68e+02  2.30e-03  6.00e-03  2.50e+00  3.58e+01  3.20e-03  6.95e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 195  1.68e+02  2.32e-03  6.13e-03  2.50e+00  3.58e+01  3.44e-03  6.94e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 196  1.68e+02  2.32e-03  6.16e-03  2.50e+00  3.58e+01  3.16e-03  6.92e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 197  1.68e+02  2.31e-03  6.15e-03  2.50e+00  3.58e+01  3.12e-03  6.91e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 198  1.68e+02  2.31e-03  6.18e-03  2.50e+00  3.58e+01  3.29e-03  6.90e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 199  1.68e+02  2.33e-03  6.31e-03  2.50e+00  3.58e+01  3.68e-03  6.90e-03  1.50e+01\n",
      "------------------------------------------------------------------------------------\n",
      "DictLearn solve time: 313.77s \n",
      "\n"
     ]
    }
   ],
   "source": [
    "opt = dictlrn.DictLearn.Options({'Verbose': True, 'MaxMainIter': 200})\n",
    "d = dictlrn.DictLearn(xstep, dstep, opt)\n",
    "D1 = d.solve()\n",
    "print(\"DictLearn solve time: %.2fs\" % d.timer.elapsed('solve'), \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display dictionaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-10-20T20:00:02.866569Z",
     "iopub.status.busy": "2020-10-20T20:00:02.863818Z",
     "iopub.status.idle": "2020-10-20T20:00:03.070118Z",
     "shell.execute_reply": "2020-10-20T20:00:03.071184Z"
    }
   },
   "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": [
    "Plot functional value and residuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-10-20T20:00:03.213830Z",
     "iopub.status.busy": "2020-10-20T20:00:03.124281Z",
     "iopub.status.idle": "2020-10-20T20:00:03.889435Z",
     "shell.execute_reply": "2020-10-20T20:00:03.890296Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJgAAAE9CAYAAABHvdhKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAACf5klEQVR4nOzdeXyU1fXH8c+ZJXsISSAQCEvYdwKExR13XHCr1qXuVhSrtlrbauuvaq2ttda622JV1LrUaq1ixQ3FFUV2WWWHsIUdsmeS+/vjmYQkhBAgyQT4vl+v5zUzd+7zzJnYMsmZc8815xwiIiIiIiIiIiL7yxfpAERERERERERE5OCmBJOIiIiIiIiIiBwQJZhEREREREREROSAKMEkIiIiIiIiIiIHRAkmERERERERERE5IEowiYiIiIiIiIjIAQlEOoDG0qpVK9e5c+dIhyEi0uxMnz59k3OudaTjiDR9ToiI1E6fE/qMEBGpy54+Jw7ZBFPnzp2ZNm1apMMQEWl2zGxlpGNoDvQ5ISJSO31O6DNCRKQue/qc0BI5ERERERERERE5IEowiYiIiIiIiIjIAVGCSUREREREREREDsgh24NJRERERA4PpaWl5OTkUFRUFOlQDioxMTFkZGQQDAYjHYqIiBwClGASERERkYNaTk4OiYmJdO7cGTOLdDgHBeccmzdvJicnh8zMzEiHIyIihwAtkRMRERGRg1pRURGpqalKLu0DMyM1NVVVXyIi0mCUYBIRERGRg56SS/tOPzMREWlISjCJiIiIiByA1atXk5mZyZYtWwDYunUrmZmZrFy5cre5fr+frKws+vXrxwUXXEBBQUGt1zzyyCMbJLbx48dz4403Nsi1RERE6qIEk4iIiIjIAejQoQNjx47l9ttvB+D2229nzJgxdOrUabe5sbGxzJo1i7lz5xIVFcXf/va3as+XlZUB8NVXXzV+4CIiIg1ITb6r2FlUyoTZ6xjRJYUurRMiHY6IiDRXm5d6t6ldIxuHiDQbt9xyC0OGDOHhhx/miy++4LHHHtvrOccccwxz5sxh8uTJ3HPPPaSnpzNr1izmz59PQkICeXl5TJ48mbvuuos2bdowa9YszjvvPPr3788jjzxCYWEh//3vf+natSsTJkzg97//PSUlJaSmpvLSSy/Rpk2bJnjnUs3E22H9d5GOQkRk79r2h9Pub9BLqoKpiq35pfz6ze+YsWpbpEMREZHm7N3b4K2fRDoKEWlGgsEgf/7zn7nlllt4+OGHiYqKqnN+KBRi4sSJ9O/fH4CpU6dy3333MX/+/N3mzp49m0ceeYTvvvuOF198ke+//56pU6fy4x//uDKRdfTRR/P1118zc+ZMLrroIh544IGGf5MiIiJ1UAVTFX6/1+iwvNxFOBIREWnWSvJ3VTGJSLNyz4R5zF+7o0Gv2addC+4a3Xev8yZOnEh6ejpz587l5JNPrnVOYWEhWVlZgFfBdM011/DVV18xbNgwMjMzaz1n6NChpKenA9C1a1dOOeUUAPr3788nn3wCQE5ODhdeeCHr1q2jpKRkj9eSRtbA1QAiIgcTJZiq8Id30ggpwSQiInUpD0HBJijeCdGJkY5GRJqBWbNm8eGHH/L1119z9NFHc9FFF1Umhaqq6MFUU3x8/B6vHR0dXXnf5/NVPvb5fIRCIQBuuukmbr31Vs466ywmT57M3XfffWBvSEREZB8pwVSF3+clmMqcEkwiIgcTM4sHngRKgMnOuZca9QXLvT/o2LIc0gc06kuJyL6pT6VRQ3POMXbsWB5++GE6duzIL37xC2677TZeeqlx/ymqavv27bRv3x6A559/vsleV0REpIJ6MFVRmWAqK49wJCIiYmbPmlmumc2tMT7KzBaZ2RIzuz08fB7wunPuWuCsRg+u3Nvlia0rGv2lRKT5e/rpp+nYsWPlsrgbbriBhQsX8umnnzZZDHfffTcXXHABxxxzDK1atWqy1xUREalg7hCt1snOznbTpk3bp3O2F5Yy8J4P+L8z+3DN0Vq3LiKHJjOb7pzLjnQce2NmxwJ5wAvOuX7hMT/wPXAykAN8C1wMnA1MdM7NMrOXnXOX7O36+/M5UemJ4bBxIZz8Ozjqp/t3DRFpMAsWLKB3796RDuOgVNvP7mD5nGhMB/QZISJyiNvT54QqmKqorGAqVwWTiEikOec+A7bUGB4GLHHOLXPOlQCv4iWXcoCM8JzG/2yrukROREREREQa75dwM+tpZrOqHDvM7GdmlmJmH5rZ4vBtcpVz7ggveVhkZqdWGR9iZt+Fn3vULNyNu4EFKhNMjXF1ERFpAO2B1VUe54TH/gP8wMyeAibs6WQzG2Nm08xs2saNG/f5xZdtzOPut+dRWlrqDWxVgklEREREBBoxweScW+Scy3LOZQFDgALgTeB2YJJzrjswKfwYM+sDXAT0BUYBT4aXQgA8BYwBuoePUY0Rs89UwSQi0szV9gWDc87lO+eucs6NravBt3NunHMu2zmX3bp1631+8U15JYz/agVloYoE04p9voaIiIiIyKGoqZbInQgsdc6txFvKULG1xfPAOeH7ZwOvOueKnXPLgSXAMDNLB1o456Y4r2HUC1XOaVB+VTCJiDR3OUCHKo8zgLVN9eLx0eHvPSqWyG1bDWWlTfXyIiIiIiLNVlMlmC4CXgnfb+OcWwcQvk0Lj+9p2UP78P2a4w0unF+i7BBtfC4icgj4FuhuZplmFoX3+fJ2U714QnTAu1NeBv5ocGWwfXXdJ4mIiIiIHAYaPcEU/gPgLODfe5tay5irY7y21zqg3hpmht9nWiInItIMmNkrwBSgp5nlmNk1zrkQcCPwPrAAeM05N28frzvazMZt3759n2OKi/ISTOZCkNzZG9yes+cTREREREQOE01RwXQaMMM5tyH8eEN42Rvh29zw+J6WPVTdGajq+G4OtLcGEE4w7depIiLSgJxzFzvn0p1zQedchnPumfD4u865Hs65rs65+/bjuhOcc2OSkpL2OaZdFUwhiG/l3S/a90SViBxaVq9eTWZmJlu2eBtfbt26lczMTFauXLnbXL/fT1ZWFn379mXgwIE89NBDlB/Al5sjR45k2rRp+32+iIhIQ2mKBNPF7FoeB95ShivC968A3qoyfpGZRZtZJl4z76nhZXQ7zWxEePe4y6uc0+D8pgomERGpXUzQh8/A58ogNrwJatGOyAYlIhHXoUMHxo4dy+233w7A7bffzpgxY+jUqdNuc2NjY5k1axbz5s3jww8/5N133+Wee+5p6pBFREQaXKMmmMwsDjgZb/voCvcDJ5vZ4vBz9wOElzi8BswH3gN+4pwrC58zFvgHXuPvpcDExoo5oAomERHZAzMjPjqAuTKIS/EGi3dGNigRaRZuueUWvv76ax5++GG++OILfv7zn+/1nLS0NMaNG8fjjz+Oc47x48dz4403Vj5/5plnMnnyZADGjh1LdnY2ffv25a677mqstyEiIrLfAo15cedcAZBaY2wz3q5ytc2/D9htuYNzbhrQrzFirMmnHkwiIlKH+KgAvpIqFUzFqmASEQgGg/z5z39m1KhRfPDBB0RFRdXrvC5dulBeXk5ubm6d8+677z5SUlIoKyvjxBNPZM6cOQwYMKAhQhcREWkQjZpgOhgFfKZd5EREDmFmNhoY3a1bt/06PyHK8JWUQyDWO9SDSaR5mXg7rP+uYa/Ztj+cdv/eX3riRNLT05k7dy4nn3xyvS/v6vG752uvvca4ceMIhUKsW7eO+fPnK8EkIiLNSlP0YDqoeBVMSjCJiByqDqTJN0CL6PBHpy8AMS20RE5EAJg1axYffvghX3/9NX/9619Zt25dvc5btmwZfr+ftLQ0AoFAtYbfRUVFACxfvpwHH3yQSZMmMWfOHM4444zK50RERJoLVTDVEFCCSURE6pAYbd4dnx+iE7VETqS5qUelUUNzzjF27FgefvhhOnbsyC9+8Qtuu+02XnrppTrP27hxI9dffz033ngjZkbnzp158sknKS8vZ82aNUydOhWAHTt2EB8fT1JSEhs2bGDixImMHDmyCd6ZiIhI/SnBVIPPjJASTCIisgcJwYoEUwCiW2gXORHh6aefpmPHjpXL4m644QbGjx/Pp59+ynHHHVdtbmFhIVlZWZSWlhIIBLjsssu49dZbATjqqKPIzMykf//+9OvXj8GDBwMwcOBABg0aRN++fenSpQtHHXVU075BERGRelCCqQa/zyhXgklERPagRUXfXi2RE5GwMWPGMGbMmMrHfr+f6dOn1zq3rKys1nHwdqrcU9XT+PHjax2v2GVOREQk0tSDqQavyXekoxARkcZiZqPNbNz27fvXnDshqmoFk5bIiYiIiIiAEky78Zp8l+99ooiIHJQOtMn3riVyfohO0hI5ERERERGUYNqNmnyLiEhdEoLebZn5tURORERERCRMCaYafKYEk4iI7Fl8OMFUXO7zlsiV7ITyPfdUERERERE5HCjBVEPArwSTiIjsWUUFU3GZebvIgaqYREREROSwpwRTDT4zQkowiYjIHsSFE0xFZeYtkQMo2AxTnoBQSeQCExERERGJICWYagj4jHKnBJOIyKHqQHeRiw94nxFFVSuY5r0J7/8aVnzeUGGKyEHG7/eTlZVF3759GThwIA899BDltWwcs2LFCmJjY8nKyqJPnz5cf/31tc5bu3Yt559/foPEdvfdd/Pggw82yLVERET2RAmmGnw+I1SmBJOIyKHqQHeRiwt4t0Xl5vVgAlg707vN39QAEYrIwSg2NpZZs2Yxb948PvzwQ959913uueeeWud27dqVWbNmMWfOHObPn89///vfas+HQiHatWvH66+/3gSRi4iINAwlmGpQBZOIiNQlNlzBVBgyiAknqSoTTBsjFJWINCdpaWmMGzeOxx9/HFfH75WBQIAjjzySJUuWMH78eC644AJGjx7NKaecwooVK+jXrx8A48eP55xzzmH06NFkZmby+OOP89BDDzFo0CBGjBjBli1bAHj66acZOnQoAwcO5Ac/+AEFBQVN8n5FRERACabd+H3qwSQiIntWUcFUWMauJXI71ni3BapgEhFPly5dKC8vJzc3d49zCgoKmDRpEv379wdgypQpPP/883z88ce7zZ07dy4vv/wyU6dO5Te/+Q1xcXHMnDmTI444ghdeeAGA8847j2+//ZbZs2fTu3dvnnnmmcZ5cyIiIrUIRDqA5sbvM8qVYBIRkT2I9XufEQUh364lchW0RE4k4v409U8s3LKwQa/ZK6UXvxr2q30+b0/VS0uXLiUrKwsz4+yzz+a0005j/PjxnHzyyaSkpNR6zvHHH09iYiKJiYkkJSUxevRoAPr378+cOXMALwl15513sm3bNvLy8jj11FP3OWYREZH9pQRTDX4zyrRETkRE9mDXEjl27SJXQQkmEQlbtmwZfr+ftLS03Z6r6MFUU3x8/B6vFx0dXXnf5/NVPvb5fIRCIQCuvPJK/vvf/zJw4EDGjx/P5MmTD+xNiIiI7AMlmGpQk28REalLtK+iggkIxoH5wZV5T2qJnEjE7U+lUUPbuHEj119/PTfeeCNm1mSvu3PnTtLT0yktLeWll16iffv2TfbaIiIiSjDVoCbfIiJSFyv3kkl5pYCFd5Ir2gateqjJt8hhrLCwkKysLEpLSwkEAlx22WXceuutTRrDvffey/Dhw+nUqRP9+/dn586dTfr6IiJyeFOCqQafmnyLiBzSzGw0MLpbt277d4FybylKQShclRDTwkswdRgG895qkBhF5OBTVlZWr3mdO3dm7ty5u41feeWVXHnllbXOq/ncihUraj1v7NixjB07drdr33333fWKTURE5EBoF7kaAmryLSJySHPOTXDOjUlKStq/C4QTTPml4cfRSZDQBpI7Q8lOKC3yxiffD5/9+YDjFRERERE5GCjBVIPfVMEkIiJ1qEwwhT8rktpD2/4Q39p7XNGHacYLsPjDCAQoIiIiItL0tESuBr8qmEREpC7hBNOCDQVszS8h+ZynvPGVX3m3+ZsgEAs71kBseLvxbasAg5Ydmj5eEREREZEmoAqmGvzqwSQiInUJN/neVlTOve/Mh7gU76ioYMrfBOvnePdLC7zbCT+Fd37W9LGKiIiIiDQRJZhq8GsXORERqUu4gumiEZn8Z+Ya5q7Z7o3Ht/JuCzbB+u+8+6WF3m3+Ju8QERERETlENWqCycxamtnrZrbQzBaY2RFmlmJmH5rZ4vBtcpX5d5jZEjNbZGanVhkfYmbfhZ971MyssWL2+4wyVTCJiMiehBNMZwz0lrvNyQknmOJSvdv8qgmm/PBtwa5qJhERERGRQ1BjVzA9ArznnOsFDAQWALcDk5xz3YFJ4ceYWR/gIqAvMAp40sz84es8BYwBuoePUY0VsJbIiYhIncIJprYtE4gN+lmcu9Mbj0nC+YK4vI1VlsiFK5hKCqAkPwLBikhT8fv9ZGVl0bdvXwYOHMhDDz1EeXn5bvNWrFhBbGwsgwYNonfv3gwbNoznn3/+gF67c+fObNqkKkkREYmsRmvybWYtgGOBKwGccyVAiZmdDYwMT3semAz8CjgbeNU5VwwsN7MlwDAzWwG0cM5NCV/3BeAcYGJjxO03NfkWETmUmdloYHS3bt327wLlZZQDKwvX0b7dKr7fkATApvwSzCWyYu53DNn5PfijoKwEykLhSqZGK74VkWYgNjaWWbNmAZCbm8sll1zC9u3bueeee3ab27VrV2bOnAnAsmXLOO+88ygvL+eqq65qypBFREQaVGNWMHUBNgLPmdlMM/uHmcUDbZxz6wDCt2nh+e2B1VXOzwmPtQ/frzneKPx+VTCJiBzKnHMTnHNjkpKS9u8C5SFub53KWe9fwYa4x1hQ8A7bCkr44d+msL4skT47vgBXDu0GefNDhapgEjnMpKWlMW7cOB5//HHcXnp7dunShYceeohHH30UgLvvvpsHH3yw8vl+/fqxYsUKAM455xyGDBlC3759GTduXKPFLyIisj8aM8EUAAYDTznnBgH5hJfD7UFtX+26OsZ3v4DZGDObZmbTNm7cuK/xAuEKJjX5FhGRPSgrK+GzuFhGtjuajrGDKUmcyBOff8OyTfnMTxjBStcWhl8Pvc70TijaDuWl3hEqiWzwItJkunTpQnl5Obm5uXudO3jwYBYuXLjXec8++yzTp09n2rRpPProo2zevLkhQhUREWkQjbZEDq/SKMc590348et4CaYNZpbunFtnZulAbpX5HaqcnwGsDY9n1DK+G+fcOGAcQHZ29n5lidSDSURE6rK4aCP5Ph+ndjqF4rTu3DX9Sl5d9iTd0q5ny5BfMWriQr47/hQSF/7bO6Hq7nGl+RCIikzgIoeJ9X/4A8UL9p6s2RfRvXvR9te/3ufz9la9tK/zHn30Ud58800AVq9ezeLFi0lNTd3nuERERBpDo1UwOefWA6vNrGd46ERgPvA2cEV47ArgrfD9t4GLzCzazDLxmnlPDS+j22lmI8K7x11e5ZwG5/cZzqE+TCIiUquZhesBGNxmMMM6dKFk6whc7HyO7x1L26QYANZvL4JgnHdCQZUEU4l2khM5XCxbtgy/309aWtpe586cOZPevXsDEAgEqjUHLyoqAmDy5Ml89NFHTJkyhdmzZzNo0KDK50RERJqDxqxgArgJeMnMooBlwFV4Sa3XzOwaYBVwAYBzbp6ZvYaXhAoBP3HOlYWvMxYYD8TiNfdulAbf4C2RAyhzDp8asoqISA0zizaQFgqRnpCBMx/+gmys1afEJM+lbYuuAKzfUUT3igRTfpUlLKVKMIk0tv2pNGpoGzdu5Prrr+fGG2/ErO7fJ1esWMFtt93GTTfdBHg7wr3zzjsAzJgxg+XLlwOwfft2kpOTiYuLY+HChXz99deN+yZERET2UaMmmJxzs4DsWp46cQ/z7wPuq2V8GtCvQYPbA78/nGAqdwT9TfGKIiJyMJlZvJFBxSWY348B3ZO7saq0HTM2T+L87hcC4Qqm1FjvhGoVTHlNH7CINInCwkKysrIoLS0lEAhw2WWXceutt9Y6d+nSpZUVSImJidx0002VO8j94Ac/4IUXXiArK4uhQ4fSo0cPAEaNGsXf/vY3BgwYQM+ePRkxYkSTvTcREZH6aOwKpoNOZQWTlsiJiEgN6/LWsb6sgCuLQ5Vjfzi3PxNWnMPLS56k1Oclk9ZvL4K2FRVMVTad0BI5kUNWWVnZ3ifhVSgVFhbu8fnY2Fg++OCDWp+bOLH2Iv6KXeZEREQiqTF3kTso+X27lsiJiIhUlZOXQ6IFGFS66w/Jfu2TOL/PSQAs2jqX5Lgg63cUQVRFgqlqBVN+U4YrIiIiItJklGCqoSLBpCbfIiJS09C2Q/mi9an0KqveU6Vzi84EfAGWbFtC26TYcJPviiVyVXswKcEkIhIJZtbFzJ4xs9cjHYuIyKFKCaYaAuEEU0gJJhERqYXPleHzVW/SF/QH6dyiM0u2LqFti2ivgimoCiYRkcZkZs+aWa6Zza0xPsrMFpnZEjO7HcA5t8w5d01kIhUROTwowVSDTxVMIiJSl/IQ+HZvYditZTcWb1tM26QYNlRLMNXowTT/be8QkQbl1N5gnx0CP7PxwKiqA2bmB54ATgP6ABebWZ+mD01E5PCjBFMNqmASETm0mdloMxu3ffv2/btAHQmmNXlrSE2ATXklFFuU90TVJXIlefDFQ/DZA/v32iJSq5iYGDZv3nwoJEyajHOOzZs3ExMTE+lQ9ptz7jNgS43hYcCScMVSCfAqcHaTBycichjSLnI1+LSLnIjIIc05NwGYkJ2dfe1+XaC8rPYEU3I3AHwxGwDIzQvRwR8NxTvCMwxKCyB/MxRuAefAbLfriMi+y8jIICcnh40bN+59slSKiYkhIyMj0mE0tPbA6iqPc4DhZpYK3AcMMrM7nHN/rHmimY0BxgB07NixKWIVETmkKMFUQ8CvBJOIiNShPAQ1ejABdG/ZHYAS3zoglfU7iugQjIWyYvBHeUdJgbdkLlQIebmQ2KaJgxc5NAWDQTIzMyMdhjQPtWXunXNuM3B9XSc658YB4wCys7P1x4CIyD7SErkaKiuYVGItIiK12cMSufYJ7Yn2R7M9tArA20kuKt57Mire68mUn+sllwC2LGuqiEVEDic5QIcqjzOAtRGKRUTksKIEUw1+nyqYRESkDntIMPl9frokdWF94QqAcKPvWO/JYLyXZNq6ctcJSjCJiDSGb4HuZpZpZlHARYB2VhARaQJKMNUQUIJJRETqsoceTAB9Uvvw/bYFxAZ9rNteJcEUFeclmLat2jVZCSYRkQNiZq8AU4CeZpZjZtc450LAjcD7wALgNefcvEjGKSJyuFAPphrU5FtEROq0hx5MAP1a9eONxW/QOjmP9TuKvGVx4N0GomHD3F2TlWASETkgzrmL9zD+LvBuE4cjInLYUwVTDWryLSIiddrDEjmA/q36AxDfYq3Xg6kiwVTRg6lCajclmERERETkkKIEUw1q8i0iInWqI8HUtWVXYvwxWMzq6gmmYNyuht8AGcNgy3LQZ42IiIiIHCKUYKoh4PN+JKpgEhGRWtXRgyngC9A7tTdFvhXk7izC1ezBBBCIhbb9oHg7FGyB8nJY/JGSTSIiIiJyUFOCqYZwfkkJJhERqV0dPZjA68O0NbSc0rIQRUR7g8EqS+TiW0FKV+/+5sWw+AN46QewemojBy4iIiIi0niUYKpBFUwiIrInBdOn8/3jqylYW7rHOf1b9SfkSvBFryffRXmDVSuY4ltB+kDvfs40WDUlfPFNjRi5iIiIiEjj0i5yNfhVwSQiIntgwSBlBeWUFe95Tt/UvgD4Y9aysyxIK/CqlyqWy8W1ghbpkNzZSy4VbvXGi3c2ZugiIiIiIo1KFUw1+KtUMC3flE+5Ek0iIhLmS0wEoLx4z58NGYkZxAXi8cWsYXso6A1G1VgiB9DxCC/BtGaG91gJJhERERE5iCnBVIM/vIvc+h1FnPTQp3y0YEOEIxIRkebCn5QEQFkdCSaf+eid0gt/7Fq2loYLhYM1lsiBl2Aq2AyhQu9x8Y7GCltEREREpNEpwVRDRZPvTTuLKSt3bC0oiWxAIiLSbPgTEoC6K5gAeqf2xh+9jo3F4WbgUXG4cAWTi6uSYKqqSAkmERERETl4KcFUQ0WT77ySEAAlZVoiJyJyKDGz0WY2bvv27ft+blQUFoCy4vI65/VJ7QO+UqZs3ewNBOOZucFrDL6yKNyLqVV3iE2BhLberZbIiYhE3IF8RoiIHO6UYKqhosl3QXEZAKGyuv+IEBGRg4tzboJzbkxSeLnbvvJHQVlR3Z8NvVJ6AbAzvgiAb9cW8+ESL4G0ttSrgsIMhl8HQ6+BmBZegqm8HBZNBKcvN0REIuFAPyNERA5nSjDVUNHkOz9cwVSqBJOIiFThi4byorI652QmZRLtj6Z7T6/J9wvTNvJSThr/LTuSBYHeuyaOvB2O+yVEJ3o9mFZ+Aa9cBKu/acy3ICIiIiLS4JRgqqGiyXd+cUWCSd8ii4jILv4oR9leEkwBX4CeyT1ZULKZUDCBOYUp5PsSuZ2bWVkQtfsJ0UleBdPO8MYShdsaPnARERERkUakBFMNfn9Fgsn740EVTCIiUpU/qpzywtBe5/Vv3Z/5O5bh7ljB8CHZXDKsI51S4lm/vWj3yRUVTAWbvMcleQ0ctYiIiIhI42rUBJOZrTCz78xslplNC4+lmNmHZrY4fJtcZf4dZrbEzBaZ2alVxoeEr7PEzB41C5cZNYKKCqa8cAVTSBVMIiJShS/oKCsKUbpmDUtPO53StWtrnTew9UAKQ4V8v/V7Hjh/IPee0482STGs37GnBNNOKAg3BS/Jb8R3ICIiIiLS8Jqigul451yWcy47/Ph2YJJzrjswKfwYM+sDXAT0BUYBT5pZeH9nngLGAN3Dx6jGCtbv8xJMBerBJCIitfAHyykvLKVwzhxKli+nePHiWudltc4CYHbu7Mqx9BYxe65gKtqhBJOIiIiIHLQisUTubOD58P3ngXOqjL/qnCt2zi0HlgDDzCwdaOGcm+Kcc8ALVc5pcBUJpl1L5FTBJCIiu/iCZZQVllK6bj0A5QUFtc5rG9+WtNg0Zm/clWBqkxTDxrzi3b+8qNhFLr9iiZwSTCIiIiJycGnsBJMDPjCz6WY2JjzWxjm3DiB8mxYebw+srnJuTnisffh+zfFGUZFgyitWBZOIiOzOHwyBg5LlywEoz689GWRmDEwbWC3B1LZFDM7Bxp3F1SdHJ0J5Kexc5z1WDyYRaebMzG9m/4x0HCIi0nw0doLpKOfcYOA04Cdmdmwdc2vrq+TqGN/9AmZjzGyamU3buHHjvkdL1QqmcA+mciWYREQkzDn8Aa/CtWJp3J4STOD1YVqTt4ZNhV5lUnpSDADrai6Ti27h3W7xklaqYBKR5s45Vwa0NrNatsYUEZHDUaMmmJxza8O3ucCbwDBgQ3jZG+Hb3PD0HKBDldMzgLXh8Yxaxmt7vXHOuWznXHbr1q33K+ZAOMEUKvdyWCUhLZETEZEwV44vyvvioSLBVLaXBBPs6sPUpoWXYNpQs9F3RYKpoMoSucKt8J/roGh7Q0UvItLQVgBfmtn/mdmtFUekgxIRkchotASTmcWbWWLFfeAUYC7wNnBFeNoVwFvh+28DF5lZtJll4jXznhpeRrfTzEaEd4+7vMo5Dc5XY4M6VTCJiEil8jL8Qe9zoaJyqa4Kpj6pfQj6gpXL5NqGK5h2a/QdnVj9cUkerJ4Kc16FNTMaKHgRkQa3FngH72+KxCqHiIgchgKNeO02wJteTogA8LJz7j0z+xZ4zcyuAVYBFwA45+aZ2WvAfCAE/CRcegswFhgPxAITw0ejqFgiVyGkJt8iIlKhPIQvqvrnQl0Jpih/FL1TezNr4ywAkuOCRAV8rN+tgqlmgikfCrd590trbyIuIhJpzrl7wPsy2Tmntb0iIoe5RkswOeeWAQNrGd8MnLiHc+4D7qtlfBrQr6FjrE2N/BIlavItIiIVykP4o6p/LpTn150AGth6IP9a+C9Ky0oJ+oO0bRFTjwqm/F1L49SPSUSaKTM7AngGSAA6mtlA4Drn3A2RjUxERCKhsZt8H3TMrFoVU0gJJhERqVAeqlwiVzlURwUTQFbrLErKS1i4ZSEA7VvGsnJzjXNiWuy6H4gJJ5i2eY+VYBKR5uth4FRgM4BzbjZQ16Y+IiJyCFOCqRZVE0ylWiInIiIVysvwBXd9LlhsLOUFe69gAir7MGV1bMm8tTsoKAntmhRdJcHUsqPXg6liiZwSTCLSjDnnVtcYKqt1ooiIHPKUYKqF36ommFTBJCIiYeUhzAe+GG9X7ujMzL1WMLWJb0Pb+LaVfZiGdU4hVO6YtWrbrklVl8hVJJi0RE5Emr/VZnYk4MwsysxuAxZEOqgDYWajzWzc9u3awVNEZF8pwVSLgE8JJhERqUW5V3Xki4/BgkGCHTrsNcEEXhVTRQXT4E7JmMHUFVt2TQhEg99LWnkJpipL5EqVYBKRZut64CdAeyAHyAIO6v5LzrkJzrkxSUlJkQ5FROSgowRTLXxVezCVa4mciIiEhRNM/vhYAm3a4EuIr3eCaX3+ejbkbyApNkivti34tmqCCbxlcv5oiE/zdo4rCD+vCiYRab56Oud+5Jxr45xLc85dCvSOdFAiIhIZSjDVomoFU0lIFUwiIhJW7rUWCaS2IKpTJ3zx9U8wwa4+TMM6JzNj5bbqVbLRiRDfCqITvMc713q3JXX3eBIRiaDH6jkmIiKHASWYaqEKJhERqVW4gqndDT+g3f1/9BJMBQU4V/dnRe+U3kT5oioTTEMzUygsLWPK0s27JkUnQlwKRMV7j3dUJJjyGvxtiIgcCDM7wsx+DrQ2s1urHHcD/giHJyIiEaIEUy3Ug0lERGoVTjAFUpMJtG6NPz4eystxhYV1nhb0B+mT2qcywXRirza0bxnLPRPmURwKb7jUdgCkZ0FUuIKprMS7LS3wkk3/OBl2bmiMdyUisq+igAQgACRWOXYA50cwLhERiaBApANojnxVdpELlamCSUREwsIJJnzex6fFxXnD+fn4wvf3ZGDrgby88GVKykqIjYri9+f246rnvuXJT5Zyy8k94JwnvIkL/1f9xJJ8WDsTcqbChu8gsU2DviURkX3lnPsU+NTMxjvnVppZvHNODeNERA5zqmCqRcBfpQeTKphERJo9M+tiZs+Y2euN+kLhHkwVCSZ/vLecrbxg732SBqYNpLS8lAVbvB28j++ZxjlZ7Xjs48XVl8pVLJGrUJIPhdu8+8VaLicizUo7M5sPLAAws4Fm9mSEYxIRkQhRgqkW/moVTEowiYg0JjN71sxyzWxujfFRZrbIzJaY2e11XcM5t8w5d03jRkqVCiavxYivIsG0L42+c2dXjv3+3P50bhXPTa/MZEt+eElcxRK5CiX5ULQtfF8JJhFpVh4GTgU2AzjnZgPHRjIgERGJHCWYalHR5Ds26KdUS+RERBrbeGBU1QEz8wNPAKcBfYCLzayPmfU3s3dqHGlNFmmNJXL7kmBKi0sjPT6dOZvmVI4lRAd4/OLBbMor5p9fr/QGq1Ywxbf2ejCpgklEminn3OoaQ2URCURERCJOCaZaVDT5TogJqMm3iEgjc859BmypMTwMWBKuTCoBXgXOds5955w7s8aR22TBxqVC/wsgweuDVJFgKqtHggm8KqaKRt8V+rRrwbE9WvPSNyu9z5yqCaYW7WpUMO084LcgItKAVpvZkYAzsygzu43wcjkRETn8KMFUi4om34kxAULlqmASEYmA9kDVb8VzwmO1MrNUM/sbMMjM7qhj3hgzm2Zm0zZu3LjvUaX1gh/8w7tl3yqYwEswrc9fz4b86rvBXXFEJzbsKOaDeRuqL5Fr0T7cg2mr91gVTCLSvFwP/ATv3+ccICv8WEREDkNKMNWiosl3YnSAsnJHuZJMIiJNzWoZ2+M/xs65zc65651zXZ1zf6xj3jjnXLZzLrt169YHHOT+JJiA3aqYRvZMIyM5ltenr969gsmVQV44IaUeTCLSjDjnNjnnfuSca+OcS3POXeqc27z3M0VE5FAUiHQAzdGuCqYgAKXl5USHG7qKiEiTyAE6VHmcAayNUCx75IuLA6A8f++7yAH0SulFtD+a2Rtnc0rnUyrH/T4jq0NLvluzHfxRXo+n8pCXYALYvsa7VQWTiDQjZpYJ3AR0psrfFc65syIVk4iIRE6dCSYzm0Dd3xgfkh8elT2Yor0fT2mZI1qpOBGRpvQt0D38x8sa4CLgksiGtLvKBFNB/SqYgv4gfVP7MmvjrN2ey0iO4/156yl34IuK95JJ8eH+5TvCuTVVMIlI8/Jf4BlgAqDGpSIih7m9pU0ebJIompmKXeQSY7wfT0iNvkVEGo2ZvQKMBFqZWQ5wl3PuGTO7EXgf8APPOufmNdDrjQZGd+vW7cCvFQhgMTH1rmACGJQ2iOfnPU9hqJDYQGzleEZyLKVljtydxbSNSvCqmCqWy4UKvVslmESkeSlyzj0a6SBERKR5qDPB5Jz7tKkCaU4CvupL5EqUYBIRaTTOuYv3MP4u8G4jvN4EYEJ2dva1DXE9X3x8vXswAQxpM4Rn5j7DnI1zGJ4+vHI8I9lLNuVsLaBtVDwEoqv3YwItkROR5uYRM7sL+AAorhh0zs2IXEgiIhIp9Vr4ZWbdgT8CfYCYinHnXJdGiiui/BVL5CormNTkW0REarevCaastCwMY8aGGTUSTN5yu5ythWRHxQO2e4JJFUwi0rz0By4DTmDXEjkXfiwiIoeZ+nYWeg64C/grcDxwFbXv8HNIqEgwtYip6MGkCiYRkT0xs1vret4591BTxRIJvvh4ihYsYP0f/kDrm3+KPyG+zvmJUYn0TOnJ9A3Tq41XrWCiy/Hg80MwrvrJxXmwPQdeuxwuehkS2zboexER2UfnAl2ccyWRDqShNOQyahGRw42vnvNinXOTAHPOrXTO3c0h/M2E33Zv8i0iInuUuJfjkBZISaFk6VK2vvAieZ98Uq9zhrQZwpxNcygtL60ciwn6aZUQzZpthXDSXXDCnRCVsOukhLZQshNypsGa6bB2VgO/ExGRfTYbaBnpIBqSc26Cc25MUlJSpEMRETno1LeCqcjMfMDicNPVNUBa44UVWf4aPZhUwSQismfOuXsiHcO+aOhvp9N/fy+h3FxWXnkVhXPmkDT6zL2eMzhtMC8teIkFmxcwoPWAyvH2ybHkbC3cNTGqSgVTUgasnQn5G73HFbciIpHTBlhoZt9SvQfTIbnTtIgcHkpLS8nJyaGoqCjSoURcTEwMGRkZBIPBes2vb4LpZ0AccDNwL1710hX7E+DBwO8zovw+ogNegZd6MImI7J2ZxQDXAH2p3q/v6ogFVYuGbvIdTE8nmJ5OTN8+FM2ZU69zBrcZDMD0DdOrJZgykmOZv3bHrolVezAlZcCaabB9tfe4YNMBxy4icoDuinQAIiINLScnh8TERDp37ozZIdsZaK+cc2zevJmcnBwyMzPrdU69lsg55751zuU553Kcc1c5585zzn19QNE2Yz6fER30EfB7/2PSLnIiIvXyItAWOBX4FMgAdkY0oiYUO2AgRQsW4Er23oqkVWwrOrfozIwN1TdaykiOZc3WQsrLw19sBGskmAC2LPNu85VgEpHIcs59WtsR6bhERA5EUVERqamph3VyCcDMSE1N3adKrnolmMysh5k9bWYfmNnHFcd+R9rMBXxGTNBPlL+igkkJJhGReujmnPs/IN859zxwBt4OQ4eF2AH9cSUlFC1aVK/5g9sMZkbuDMrdrs+YjOQ4SsrKWbmlAOccBKLAFy5JTurg3W5Z7t0qwSQiEWZmI8zsWzPLM7MSMyszsx17P1NEpHk73JNLFfb151DfJt//BmYAdwK/qHLUJyC/mc00s3fCj1PM7EMzWxy+Ta4y9w4zW2Jmi8zs1CrjQ8zsu/Bzj1oj/9dunRBNu6QYAuEEk5p8i4jUS0XH6m1m1g9IAjpHLpymFTvAW+pWWM9lckPaDGFHyQ6WbFtSOdYxxeu5dPyDk7n1tdneYFQcYNAi3XtcUcGkJXIiEnmPAxcDi4FY4MfhMREROQzVN8EUcs495Zyb6pybXnHU89yfAguqPL4dmOSc6w5MCj/GzPoAF+H17hgFPGlm/vA5TwFjgO7hY1Q9X3u/3HZqT/754+GVS+RKy1XBJCJSD+PCXxr8H/A2MB94ILIh7c7MRpvZuO3btzfodQPp6fhbtaJw1ux6zR+c5vVhqrpM7siuqfzpB/0Z1jmFTxblelVMUQkQkwTR4Q35Sgu8WzX5FpFmwDm3BPA758qcc88BIyMckojIQc/v95OVlUW/fv0YPXo027ZtA2Dy5MmceebeN5SJlPommCaY2Q1mlh6uQEoxs5S9nWRmGXhLJP5RZfhs4Pnw/eeBc6qMv+qcK3bOLQeWAMPMLB1o4Zyb4pxzwAtVzmkUMUE/iTHBKkvkVMEkIrI3zrl/OOe2hntwdHHOpTnn/hbpuGpqrC2ozYyEY49l5/vvU7x8+V7nt09oT1pcGtM37Pq+Juj3ceHQjpw7uD3bCkpZsbnAa/Qd2xKiEqtfIH9zg8YvIrIfCswsCphlZg+Y2S1A/N5OEhGRusXGxjJr1izmzp1LSkoKTzzxRKRDqpf6JpiuwFsS9xUwPXxMq8d5DwO/BKqWALVxzq0DCN+mhcfbA6urzMsJj7UP36853ugqK5jUg0lEZK/M7Le1HZGOqym1/tlPseho1t/zO6/6qA5mRnabbKZtmLbb3KwOLQGYtXorBOMgpiVEJ+ya4At4S+T28hoiIo3sMry/J24E8oEOwA8iGpGIyCHmiCOOYM2aNZWP8/LyOP/88+nVqxc/+tGPKn+PnDRpEoMGDaJ///5cffXVFBcXN3msgfpMcs7Vb0+6KszsTCDXOTfdzEbW55TaXrqO8dpecwzeUjo6duxYv0DrEKzswaQEk4hIPeRXuR8DnEn1JdKHvGBaGq1v+RkbfncvBd98Q/yIEXXOH9p2KO8uf5cVO1aQmbTro7ZHm0TiovzMWrWNczOywfzeUrkKqd1h4wIoydu1dE5EpAmFW1nc55y7FCgC7olwSCIiDe6eCfOYv7Zh9y7o064Fd43uW6+5ZWVlTJo0iWuuuaZybObMmcybN4927dpx1FFH8eWXX5Kdnc2VV17JpEmT6NGjB5dffjlPPfUUP/vZzxo09r2p7y5yQTO72cxeDx83mllwL6cdBZxlZiuAV4ETzOyfwIbwsjfCt7nh+Tl433pUyADWhsczahnfjXNunHMu2zmX3bp16/q8tToFfWryLSJSX865v1Q57sPrw9EkFafNScuzz4ZAgPwvv9rr3KFthwLw7fpvq437fUb/9knMWr0NzvgLnP5A9QqmNn28W+0kJyIR4pwrA1qHl8iJiEgDKiwsJCsri9TUVLZs2cLJJ59c+dywYcPIyMjA5/ORlZXFihUrWLRoEZmZmfTo0QOAK664gs8++6zJ465XBRNek+0g8GT48WXhsR/v6QTn3B3AHQDhCqbbnHOXmtmf8Zbc3R++fSt8ytvAy2b2ENAOr5n3VOdcmZntNLMRwDfA5cBj9X2DByIY8IqnQqpgEhHZH3FAl0gH0dR88fHEDhhA/tdf73Vux8SOpMWmMW39NH7Y84fVnsvq2JJnv1hOUWkZMUF/9R5Mab292/xNkLLPRcYiIg1lBfClmb1NlSpW59xDEYtIRKQB1bfSqKFV9GDavn07Z555Jk888QQ333wzANHR0ZXz/H4/oVBor60Zmkp9ezANdc5d4Zz7OHxcBQzdz9e8HzjZzBYDJ4cf45ybB7yGt+vQe8BPwt+MAIzFaxS+BFgKTNzP194nAZ+WyImI1JeZfWdmc8LHPGAR8Eik46qpsXaRqyp+xHCK5s2jbEfdJdVmxpC2Q/h2w7e7/WIwuGMypWWOn782m9VbCsAfgECM92RauIJp5zqY/SqUlyEiEgFrgXfw/qZIrHKIiEgDSEpK4tFHH+XBBx+ktLR0j/N69erFihUrWLJkCQAvvvgixx13XFOFWam+FUxlZtbVObcUwMy6APX+bdY5NxmYHL6/GThxD/PuA+6rZXwa0K++r9dQovxaIicisg+q7pkaAjY450KRCmZPnHMTgAnZ2dnXNtZrxI0YAU8+RcG335J4Yq0feZWGth3KxOUTWb5jOV2SdhV8ndS7DTeM7MpzX65gR1EpL14z3OvDFCqC1r28SV8/CaumQGwy9Di1sd6OiEitnHPquyQi0sgGDRrEwIEDefXVV+nQoUOtc2JiYnjuuee44IILCIVCDB06lOuvv76JI61/gukXwCdmtgyv6XYn4KpGi6qZ0C5yIiJ7Z2Yp4bs7azzVwsxwzm1p6pgiLTYrC4uJIe/zz/eaYDqq3VEAfLzqY7r035Vg8vuMX47qxYYdxXy5JNxrKToBXDkktvUer5ri3a6dqQSTiDQ5M2uNt2N0X7zNHQBwzp0QsaBERA4BeXl51R5PmDCh8v7IkSMr7z/++OOV90888URmzpzZ6LHVpb67yE0ys+5AT7wE00LnXNPvedfEKnaRC5WrgklEpA7T2bXrZ0dga/h+S2AVcNg1CfJFRZFw/Ei2vfovyjZvAb+f4sWLKdu2jba//S3xI4azc9LHxA0ZTLtOnRjQagDvr3ifH/ffvbVhp9Q43phR5PViikr0lslFxUMwDkoLvEnrZjftGxQR8bwE/AuvgvV6vP6qGyMakYiIREydCSYzO8E597GZnVfjqa7hb6X/04ixRVwwXMFUElIFk4jInjjnMgHM7G/A2865d8OPTwNOimRskdTuT39iU+fObBn/PIHWrYnu3p3SdWtZc+ut+Fu2pGzzZgBa/vCHnPrDU/jz9AdZsX0FnZM6V7tOx5Q4AFZvKaB7Ugb4/N4T8a1g2ypvudzaWU34zkREKqU6554xs5865z4FPjWzTyMdlIiIRMbeKpiOAz4GRtfynAMO6QSTmRHwGaFyJZhEROphqHOucrG3c26imd0byYAiyRcVRdpPf0rrm2/GzPvCoiwvn5wbb6S8oID2f36AHR9+yLZXXuW4HrfxZ+C9Fe9x/cDq6+U7pnoJplVbCuh+3t93PRGf5t0Ougw++A3k5UJCWlO8NRGRChUdZ9eZ2Rl4Tb8zIhjPXplZPN7O2CXAZOfcSxEOSUTkkFFngsk5d1f47u+cc8urPmdmh8WSh4Df1ORbRKR+NpnZncA/8b6EuBTYHNmQIq8iuQTgT4in0/jnKh/HjRhByYoVFD74OCf/tDcfrPxgtwRTp3AF08rNBdC7za4nRv3Ruy0r8W7XzYbuJzfOmxARqd3vzSwJ+DnwGNACuKU+J5pZS7xdovvhfWZc7Zybsq8BmNmzeEv0cp1z/Wo8NwpvN1M/8A/n3P3AecDrzrkJZvYvvGV+IiLSAHz1nPdGLWOvN2QgzVXQ51OTbxGR+rkYaA28CfwXSAuPNStmNtrMxm3fvj3SoWA+H+3+9Cd8cXH86JX1rMj9npydOdXmpMRHkRAdYNWWguondxjmHW0HeI+1TE5EmoiZxZjZz4BRwEV4/VmPd84Ncc69Xc/LPAK855zrBQwEFtR4jTQzS6wx1q2W64wPx1EzRj/wBHAa0Ae42Mz64FVYrQ5Pq/eu2CIisnd1JpjMrJeZ/QBIMrPzqhxXUmWniENZMKAEk4hIfTjntjjnfuqcGxQ+ftocd5Bzzk1wzo1JSkqKdCgABNPSaPen+4lbtZEfTS7nk9WfVHvezOiYErd7gqlCTAtI7QbrZjV+sCIinueBbOA7vATOX/blZDNrARwLPAPgnCtxzm2rMe044C0ziwmfcy3waM1rOec+A2r7rBkGLHHOLXPOlQCvAmcDOexaxlffL9tFRKQe9vaPak+8ktOWeH2YKo7BwLWNGlkzEfAZIS2RExHZIzN7OHw7wczernlEOLyDQsIxx5B8ycWcOsMxa+r/dnu+Y0ocKzfn7/kCbfvDhrmNGKGISDV9nHOXOuf+DpwPHLOP53fB223uOTObaWb/CPdGquSc+zfwHvCqmf0IuBr44T68Rnt2VSqBl1hqj9dD9gdm9hQwoeZJzanKVUQOb/fddx99+/ZlwIABZGVl8c033/DjH/+Y+fPnRzq0PdpbD6a38L45OGJ/1kQfCoJ+n3owiYjU7cXw7YMRjeIg1+rGG8l98w2y3pjD1ou2khyTXPlcp9Q4Pl6US3m5w+ez3U9u0xfmvQnFOyE6cffnRUQaVkVzb5xzoaq95uopgPeF9U3OuW/M7BHgduD/qk5yzj1gZq8CTwFdnXN5+/AatQXlnHP5wFV7Osk5NwGYkJ2dfVh8mS4izdOUKVN45513mDFjBtHR0WzatImSkhL+8Y9/RDq0OtW3LPT6cCM+AMwsOdxQ75AX9JuWyImI1ME5Nz18+2nFAcwBtobvSz0EUlIIXH4B2YsdX094utpzHVPjKAmVs35HUe0ntwn3tc1dADnT1Y9JRBrbQDPbET52AgMq7pvZjnqcnwPkOOe+CT9+HS/hVI2ZHYPXBPxN4K6az9fjNTpUeZyBt8udiEizt27dOlq1akV0dDQArVq1ol27dowcOZJp06YB8Mwzz9CjRw9GjhzJtddey4033gjAlVdeydixYzn++OPp0qULn376KVdffTW9e/fmyiuvrHyNsWPHkp2dTd++fbnrrn39J7Z2dVYwVTGg6rpo59xWMxvUIBE0c0G/j1C5EkwiIntjZpOBs/A+W2YBG83sU+fcrZGM62DS8/qf89W//0WLx16h/Kyf4gv/UtGzjVeVdNfb83jwgoEkxQarn5jWx7td/x18+TAkdYSrdl9qJyLSEJxz/gM8f72ZrTazns65RcCJQLU1H+G/NZ4GzgCWA/80s9875+6s58t8C3QP73y9Bq8Z+SUHEreIHIYm3u79ftWQ2vaH0+6vc8opp5zC7373O3r06MFJJ53EhRdeyHHHHVf5/Nq1a7n33nuZMWMGiYmJnHDCCQwcOLDy+a1bt/Lxxx/z9ttvM3r0aL788kv+8Y9/MHToUGbNmkVWVhb33XcfKSkplJWVceKJJzJnzhwGDBhwQG+tvhVMPjOrrNU3sxTqn5w6qAX8PkpCWiInIlIPSc65HXhbQD/nnBsCnBThmA4q/thYcq47g5TcIpZe9iNWX3c9a391O12nf8Kdp/Xkk4W53Pbv2buf2LIjRCXC3P/AtlWQt77pgxcR2Tc3AS+Z2RwgC/hDjefjgAucc0udc+XAFcDKmhcxs1eAKUBPM8sxs2vAW7oH3Ai8j7dD3WvOuXmN9WZERBpSQkIC06dPZ9y4cbRu3ZoLL7yQ8ePHVz4/depUjjvuOFJSUggGg1xwwQXVzh89ejRmRv/+/WnTpg39+/fH5/PRt29fVqxYAcBrr73G4MGDGTRoEPPmzWuQ3k71TRL9BfjKzF4PP74AuO+AX/0gEOU3VTCJiNRPwMzS8Zqw/ibSwRysRpxzHS99+janr1qPP1RO0cKFbH/rLU4cOIC49H68OSWV0IUDCERH7TrJzOvDtPIL73H+xsgELyJST865WXg70e3p+S9rPC7Fq2iqOe/iOq7xLvDu/kcpIoe9vVQaNSa/38/IkSMZOXIk/fv35/nnn698zrm6i2Aqltb5fL7K+xWPQ6EQy5cv58EHH+Tbb78lOTmZK6+8kqKiPbRi2Af1qmByzr2At0PEBiAXOM8592LdZx0aAn6fejCJiNTP7/C+KV7qnPvWzLoAiyMc00EnMymTOef24w83tyXzjTfoNvkT0u//I2XbtjHovZf53SePsfioo9n66qvVf7lo03fX/aLtECpp+uBFRERE5IAtWrSIxYt3/Ro9a9YsOnXqVPl42LBhfPrpp2zdupVQKMQbb7yxT9ffsWMH8fHxJCUlsWHDBiZOnNggcdd3iRzAQrxtPd8C8sysY4NE0Mx5Tb61RE5EZG+cc/92zg1wzo0NP17mnPtBpOOq6WDYgvr8HuezaOsiZm+cjZnR8pxz6Pb++8ROnMS9w65gR6durL/7Htbc/FNcSTiRVJFgapHh3RZsikzwInLYMLMbq7bREBGRhpGXl8cVV1xBnz59GDBgAPPnz+fuu++ufL59+/b8+te/Zvjw4Zx00kn06dOHpKSkel9/4MCBDBo0iL59+3L11Vdz1FFHNUjc9VoiZ2Y34e3csAEow9v20wEH1gHqIBD0+8grDkU6DBGRZs/MeuBtJd3GOdfPzAYAZznnfh/h0Ko5GLagPrPLmfx1+l/516J/kZWWVTneqXM63/cYwr+6n8pv8meR+8AD5Nx6Kxl//SuWeRwkdYChP4aP7vKWybVoF7k3ISKHg7bAt2Y2A3gWeN/tbd2GiIjs1ZAhQ/jqq692G588eXLl/UsuuYQxY8YQCoU499xzOeWUUwCq9Wrq3Lkzc+fOrXxc9bmq9xtKfSuYfgr0dM71DX873d85d8gnlwACPiOkCiYRkfp4GrgDKAVwzs3B27VH9lFcMI6zup7F+yveZ3Ph5spxM2NQx2Rmrt5O6tVX0ebOO8n7aBLr7rkHl9oVbpkLHY/wJuepD5OINK7wjm7dgWeAK4HFZvYHM+sa0cBERA4Dd999N1lZWfTr14/MzEzOOeecSIdU7ybfq4Hmu5agEQXVg0lEpL7inHNTzazqmEpA99OFvS7k5YUv89qi1xibNbZyfEinZD6cv4HNecWkXvojyrZsZtOTTxHVvj2txo6F+FbeRDX6FpEm4JxzZrYeWI/3b34y8LqZfeic+2VkoxMROXQ9+OCDkQ5hN/WtYFoGTDazO8zs1oqjMQNrLpRgEhGpt03hb60dgJmdD6yLbEgHry5JXTgu4zheWfgKRaFdu3oc2TUVgDdm5ADQ6qabSDr7LDY+8ijb334bEtK8iUowiUgjM7ObzWw68ADwJdA/3IdvCNDsevCJiNSXVvt69vXnUN8E0yrgQyAKSKxyHPLU5FtEpN5+Avwd6GVma4CfAddHNKKD3JV9r2Rr8VbeXvp25diAjJYc16M1T3yylO0FpZgZ6ffeS9zw4az9zZ3kT5sDgRgvwTTnNZj3ZgTfgYgc4lrh7S59anijh4ol0uXAmZENTURk/8TExLB58+bDPsnknGPz5s3ExMTU+5x6LZFzzt2z31Ed5KIDfopDZZEOQ0Sk2XPOLQNOMrN4vC8wCoELgZURDewgNqTNEPql9uPZuc9ybrdzCfqDANx+Wi9Of/Rznvp0Kbef1guLiiLjsUdZeell5Nx0Mx1HtSI2fxN8fC/EpULfcyP8TkTkEJXpnKv2b7yZveicu8w5tyBSQYmIHIiMjAxycnLYuFHV4DExMWRkZNR7fn13kfuE8JKHqpxzJ9Q/tINTdNBHUam3RO7lb1Yxd+12/nBu/whHJSLSfJhZC7zqpfbAW8BH4ce3AbOBlyIX3cHNzLgh6wZumHQD/1n8Hy7sdSEAvdNbcGKvNN79bh23n9YLAH+LFnR4+mlWXnwxq/63jo4x3xFbvgpK8iP5FkTk0Na36gMz8+MtjxMROWgFg0EyMzMjHcZBqb5L5G4DfhE+/g+YBUxrpJialZjgrgqmL5du4oN5GyIckYhIs/Mi0BP4DrgW+AC4ADjHOXd2JAM7FBzd/mgGpQ1i3Jxx1XoxHdG1Fau2FLB++66xYJs0Or7wPP64ICtf28jmRfG4nZuVZBKRBhXuy7oTGGBmO8LHTiAX74sGERE5DNUrweScm17l+NI5dyswvJFjaxZiAl4Fk3OO4tIy8opLIx2SiEhz08U5d6Vz7u/AxUA2cKZzblZkwzo0mBk3DbqJ3MJc/rXoX5XjwzqnADB1xZZq86MyMug0dihxrUrInZnE0v+lseW5cbiSkiaNW0QOXc65PzrnEoE/O+dahI9E51yqc+6OSMcnIiKRUa8Ek5mlVDlamdmpQNtGjq1ZiA76ASgOlVNYWkZRabl2lRMRqa4y8+6cKwOWO+d2RjCeQ87QtkM5Iv0InvnuGfJLvWqk3umJxEf5mbp8827zg+nt6ThyCx2O20wgtpwNfx3HsrPPIf/rb5o6dBE5BJnZYDMbDPy74n7VI9LxiYhIZNR3idz0KscU4OfANXWdYGYxZjbVzGab2Twzuyc8nmJmH5rZ4vBtcpVz7jCzJWa2KJzEqhgfYmbfhZ971MxsX9/o/ooOeD+i4tLyyl5M+cWhpnp5EZGDwcAaSyQqlkzsNLMdkQ6uJjMbbWbjtm/fHulQ9slNg25ia/FWXpz/IgABv4/BnZL5dvnW3SfHtwYgYdggOp20iYyfn4sr3MGqq64i96G/4kL6HBORA/KXOo4HIxiXiIhEUJ0JJjPrCOCcy6xydHfOneKc+2Iv1y4GTnDODQSygFFmNgK4HZjknOsOTAo/xsz6ABfhNQscBTwZbhQI8BQwBugePkbt+1vdPzGVFUxlFJV6vZh2FukXcxGRCs45f40lEoEq91tEOr6anHMTnHNjkpKSIh3KPunfuj/Hdzie5+c9z/ZiLzk2PDOFRRt2sq2gxvK3cIKJHqdg/iCJnYN0OX0rSX2j2TxuHDk/uZHyfPVlEpH945w7vo7jkN8ESEREare3Cqb/Vtwxszf25cLOkxd+GAwfDjgbeD48/jxwTvj+2cCrzrli59xyYAkwzMzSgRbOuSnOOQe8UOWcRleRYCoqLVeCSUREIurGQTeSX5rPs3OfBWBEl1QAPl+8qfrElh292w4jICkDVn6JryCHdkM20fau35L3+eesuOwySjfkNmX4InIIMrN+ZvZDM7u84oh0TCIiEhl7SzBVXYrWZV8vbmZ+M5uFt6PEh865b4A2zrl1AOHbtPD09sDqKqfnhMfah+/XHG8SMUHvR1QUKqtcIpenJXIiIhIBPZJ7cFrmaby84GU2FmxkUMdkWiVE8d7c9dUndhwBYyZD56OgZQfI+dYbL95B8ugT6fDUk5SsWMmKCy+kePHiJn8fInJoMLO7gMfCx/HAA8BZEQ1KREQiZm8JJreH+/XinCtzzmUBGXjVSP3qmF5bXyVXx/juFzAbY2bTzGzaxo0b9zXcWsUEKiqYdi2R005yIiISKTdm3UiZK+PP0/6M32ec2rctnyzKpbCkbNckM2g3yLtfUc1UYfNSEo47js7/fBHKylhx6WUUzJzZdG9ARA4l5wMnAuudc1cBA4HoyIYkIiKRsrcE08Bamrbuc+NW59w2YDJe76QN4WVvhG8r6vNzgA5VTssA1obHM2oZr+11xjnnsp1z2a1bt65veHWKrqhg0hI5ERFpBjq06MCP+/+Yicsn8tXarzitXzoFJWV8+v0evlhJCieY0vp4t5uXABDTpw+dXnkZf8skVl11NXmfftoE0YvIIabQOVcOhMysBd7v9fu86kFERA4NdSaYqjRurdq0tV6NW82stZm1DN+PBU4CFgJvA1eEp10BvBW+/zZwkZlFm1kmXjPvqeFldDvNbER497jLq5zT6Ko1+Q55S+SUYBIRkUi6pv81dEzsyP1T72dI5xYkxwX578w1tU+uqGAacCH4grBlaeVTURkZdH75ZaK7dGH1DT9hy0sv4bU7FBGpl2nh3/efxtttegYwNaIRiYhIxOytgulApAOfmNkc4Fu8HkzvAPcDJ5vZYuDk8GOcc/OA14D5wHvAT5xzFfX+Y4F/4DX+XgpMbMS4q6lYIpdfHKKs3PulWz2YREQkkqL90dw65FaWb1/OxBXvcNmITrw3bz3vz1u/++T2QyCuFfQ6A5I7V1YwVQikptLxhedJOOYYNtz7e9b95k7Ki4ub5o2IyEHNOXeDc26bc+5veL/XXxFeKnfQMrPRZjZu+/btkQ5FROSgE2isCzvn5gCDahnfjLdWu7Zz7gPuq2V8GlBX/6ZGU9Hke2vBrr5LeapgEhGRCDuh4wkMaD2AJ2Y9wZujJ/Dxolzu+M93DOucQnJ81K6JrXvAL8NVS6ndYPOy3a7lT0gg48kn2PT4E2x68kmKv/+edvf/kehu3Zro3YjIwcrM2gOdCP9dYWbHOuc+i2xU+885NwGYkJ2dfW2kYxEROdg0ZgXTISE6XMG0rWqCSRVMIiISYWbGzwb/jNyCXN5Y8i9+d3Y/tuSX8NniOja5SO3qLZErL/eOlVMgvCTOfD5a33wTGU88TsmqVSw751xyH3yQ8vz8JnpHInKwMbM/AV8CdwK/CB+3RTQoERGJGCWY9qKigmlbYUnl2I4i7SInIiKRN7TtUI5ufzRPf/c0nVsZcVF+ZqzcuucTUrpAqAh2roUZ4+G5UbDi82pTEk88ka4T3yXprLPY/I9nWHr6GeyYOFG9mUSkNucAPZ1zpzvnRoePsyIdlIiIRIYSTHsRHW7yvV1L5EREpBn62eCfkVeSx/j5zzIwoyUzVm3b8+T0gd7tlCfh0z9791dO2W1aIDWVdn+4z9tlLiWFNbfcyqorr6J48eKGfwMicjBbBgQjHYSIiDQPSjDtRUUF0/ZCLZETEZHmp2dKT87udjYvzH+BTu22sGDdDgpLymqfnJENg6+Ar5/wqpiiEiHn2z1eO27QIDJf/zdtfvt/FC1YwLJzz2Pjo4/hSkr2eI6IHFYKgFlm9ncze7TiiHRQIiISGUow7UWU34cZbC3wfpkO+k0JJhERaVZuy76NlJgUZhb8nVB5KXNytu158ml/goyh0GMU9DvPSzCVl+9xuvn9pFxyCV3fm0iL009j05NPsvzCiyhatKjh34iIHGzeBu4FvgKmVzlEROQwpATTXpgZ0QFfZZPvVgnRWiInIiLNSlJ0Er894resLVxGVKtP6l4mF4yFqz+Ai172Ek1F22Dzkr2+RiAlhfYPPEDG448Rys1l+fkXsOlvf8OF9Jkocrhyzj0PvAZ87Zx7vuKIdFwiIhIZSjDVQ0zQX7lErnViNDuUYBIRkWZmZIeRnNnlTKJbfcKEhd9SWrbnqiR8PvD5vQQT1LlMrqbEk06iyzsTSDzpRDY+/Agrfnghhd/NPcDoReRgZGajgVnAe+HHWWb2dkSDEhGRiFGCqR5iArsSTK0Soskr1i5yIiIHKzMbbWbjtm/fHulQGtztw24nIZjECnuWX74+a+87v7XqAdFJ8P17ECqu9+sEkpPJ+Otfaf/ww4Q2bmTFD3/I+t/dS9mOHQf4DkTkIHM3MAzYBuCcmwVkRi4cERGJJCWY6iEm6KMg3DC1VUIURaXldX8zLCIizZZzboJzbkxSUlKkQ2lwSdFJ/O7o/8Mfs453Vr7KlGWb6z7B5/P6MC14Gx7Jgvlv7dPrtRh1Kl0mvkvypZey9dVXWXLSyWx89DFCW7fu/5sQkYNJyDlXM1u/l8y2iIgcqpRgqofogL/yfuvEaADy1ehbRESaoZM6nsQx7UcS3fojPlo8f+8nnPlXuPQ/EN8KXrsc3vgxlBbW+/X8CQm0/c2vyXzjdeKHD2PTk0+y5MST2PCnByhdv/4A3omIHATmmtklgN/MupvZY3gNv0VE5DCkBFM9xAR3/ZhaJ3gJpp3qwyQiIs2QmXHXEXfiMz8T1z2+92VyZtDtRLj2Ezj+N/Dd6zD+TCjeuU+vG9O7NxmPPUbm22+ReMIJbHnhBZacdDJrf/UrihZ9fwDvSESasZuAvkAx8DKwHfhZJAMSEZHIUYKpHqKDuyqYWiXuSjCVlpXzf/+dy9pt9f+mV0REpLG1iW9D/9iLyfMt4M3F9Vz25g/Acb+EC8bDmmnw9d/267VjevSg/YN/puv775N8ycXs+PAjlp99NquuHUPBdO1eLnIoMLMYM/sZ8ACwCjjCOTfUOXenc64ostGJiEikKMFUDzHhBFPAZ7SMjQIgrzjEys35vPj1Sr5YvCmS4YmIiOzm/B4XECrozH3f/J5ZubPqf2Lfc6DHaTDl8X2uYqoqKqM9bX/9a7p/PInWP/spRfPmsfJHl7Lyyqso+Lb+u9aJSLP0PJANfAecBjwY2XBERKQ5UIKpHqID3o8pJugnISYAQF5xaWXj72I1/BYRkWZmWGYrinIuJc6fwo0f38iqHavqf/Jxv4SibfDlowcch79lS1pdfz3dJn1E2q9+RfGSJay87HJWXn4FeV98iSvXZ6jIQaiPc+5S59zfgfOBYyMdkIiIRJ4STPVQUcEUE/STEO0lmHYWhcgv9hJMJSH9ciwiIs1L+5axpCe0IpB7HaWhcm6ZfAuFoXou6W4/GPqeC589AP8ZA188DDkHtrzNFxtL6lVX0u3DD2hzx+0UL1/G6h//mKWjTmPT008T2qRqYJGDSGnFHeecGpOKiAigBFO9xFRWMPkqE0x5xSEKSrzP0+JQWcRiExER2ZNfjupFUWFLNi0/n8VbF/OnqX+q/8nn/QOOvhW++zd8dBf861IIFR9wTL7YWFKuuIJukybR7sEHCaalsfEvD7H4uJGsuu46tv/vf5QXqrehSDM30Mx2hI+dwICK+2a2I9LBiYhIZCjBVA9VK5jio737+cUh8ktUwSQiIs3XOYPa8/kvjyfVN4C2nMobi99g9sbZ9TvZH4CT7oI71sDF/4Kda2H2qw0Wmy8qiqQzz6DTP1+kyzsTSL36KooXfc/an9/G4qOPYe0dvyZ/yhRcmb7EEWlunHN+51yL8JHonAtUud8i0vGJiEhkKMFUDzHBXRVM8VEVFUxlFBRXVDApwSQiIs1TwO/j3MHtWfr9kaTGtOKP3/yRcrcPn1tRcdDjVGg3CL74K5Q1/GqY6G7dSPv5z+n28SQ6jh9P4qhT2fnBB6y66mqWnHAiuQ8+SNGi7xv8dUVERESk4SjBVA/RgXAFU8CPz2fERflVwSQiIgeNC4ZkUFYWRXbi5czbPI/n5j63bxcwg2N/CVuXwysXwtYVUNrwO5Gbz0f8iOG0u+8+un/5Be0f+gsxvXuzefzzLD/7bJaeeSa5f/kLBTNmqLJJREREpJkJRDqAg0FFBVNslJdoio8OkF8cojDcg0kJJhERac66pSUyqGNLps2P4+QhJ/PYzMcY0mYIWWlZ9b9Ir9PhzIfh3V/AIwMhEAPnPQ19zmqUmH0xMbQ4/XRanH46oS1b2PHuRHZ+9BGbnxvP5qf/gT85mYRjjyX+6KOIP+IIAq1aNUocIiIiIlI/SjDVQ0UPpopKpoToAPklZZUVTGryLSIizd3VR2Vy0yszuSXlJ8zfMp9bJt/Ci6e9SEZiRv0vkn0VdBgGq76GWS/BG9dA9L+g6wmNFzgQSEkh5dIfkXLpjyjbsYP8L75g5yeTyZs8me1vvQVAdK9exB95JPFHHklc9hB8MTGNGpOIiIiIVKclcvUQXdnk2/txxUd7S+QqejCpgklERJq70/q1pX3LWP45ZQNPnPgEJWUlXPfhdWwu3LxvF2rTF4ZeAz96HVK7w0sXwDd/h/Km+bLF36IFLU4/nfZ/foDuX31J53+/Ruuf/Qx/ixZsefFFVv/4x3w/bDgrr7qKTU8/TeHceVpOJyIiItIElGCqh+hARZPv8BK5qAB5VXowqcm3iIg0dwG/j2uOzuTbFVtZsS6BJ058gtyCXG6YdAP5pfn7fsG4FLjqXeh2Ekz8JTzUB756HJxr+OD3wPx+Yvv3p9X119Hphefp+c3XdBj3d5IvvpiyTZvZ+JeHWHH++Xw/bDirrr6GjU88Qf6UKZTn78f7FREREZE6aYlcPVQklmKDu5bIrd9RRIF6MImIyEHkkuEd+ff0HG59bTbv3HQ0fxn5F27++GZ+9dmveOyExzCzfbtgbEu46BVY+A5MexY++A2smwVnPQbB2MZ4C3XyxcWRcOyxJBx7LAClubkUfPMNBTNmUDhjJpsef8JLgPn9xPTqRezgwcQOHEhM3z5EdeqE+fS9m4iIiMj+UoKpHmIqK5i827hwk++Cil3kypRgEhGR5i8m6Odvlw7mzMe+4Pp/TueNsUfxi6G/4P6p9/Paote4sNeF+35Rn89r9N17NHz+F/j4XtiyDH74IiS1b/g3sQ+CaWkkjR5N0ujRAJTt2EHh7Nlewmn6DLb9+99sffFFAHwJCcT07k1M377E9O1DTN++RHXurKSTiIiISD0pwVQPMZU9mCoqmPzkFZdRUBxeIleqBJOIiBwcOqXG8/CFWVzz/DR++9Zc/vSDS/g853MenPYgA9MG0iul1/5d2AyOvQ1a94T/XAePDIDup8LRt0CHoQ37JvaTv0ULEo45hoRjjgHAlZZSvHQpRfPmhY/5bH3lFVxxMeBVREX36kVMnz5e8qlPb6K7dsWioiL5NkTkAJhZPPAkUAJMds69FOGQREQOGY2WYDKzDsALQFugHBjnnHvEzFKAfwGdgRXAD51zW8Pn3AFcA5QBNzvn3g+PDwHGA7HAu8BPnWu6Jg81E0zxUV4FU354iVyxKphEROQgcmLvNtx4fDce/2QJ2Z1SuPeoe7nk3Uu47sPreOG0F+jUotP+X7z3aLi+D0x/Dma9As+cBH3PhdP/AvGpDfcmGoAFg8T06kVMr17wgx8A4EIhipcuo2j+fC/ptGAB2//zH7YWFFSeE929O9F9eoeTTn2I6dkTX1xcJN+KyEHJzPzANGCNc+7M/bzGs8CZQK5zrl+N50YBjwB+4B/OufuB84DXnXMTzOxfgBJMIiINpDErmELAz51zM8wsEZhuZh8CVwKTnHP3m9ntwO3Ar8ysD3AR0BdoB3xkZj2cc2XAU8AY4Gu8BNMoYGIjxl5NRZPvitv46ACFpWXsLAonmEq1O42IiBxcbjm5BzNXb+X/3prLm+2P4u8n/50rJ15ZmWRKi0vb/4undoVTfg/H3Q5THofPHoQVX3oVToMvj0h/pvqyQICYnj2I6dkDzj0HAFdeTsnKlRQvWOAlnuYvIG/Sx2x//Y3wSUZUZqaXbKo8euNv0SJyb0Tk4PBTYAGw2/9ZzCwNKHTO7awy1s05t6TG1PHA43hfbFc93w88AZwM5ADfmtnbQAbwXXiafokXEWlAjZZgcs6tA9aF7+80swVAe+BsYGR42vPAZOBX4fFXnXPFwHIzWwIMM7MVQAvn3BQAM3sBOIcmTDBVNvmO2tXkG2BTnldCrx5MIiJysPH7jEcuGsTpj3zOra/N4p2bjuapk57i6vev5roPr2P8qPEkRScd2ItEJ8DI26HXGfC/27zd5j77MxxxIwy/rlknmqoyn4/ozEyiMzNpcfrpADjnCK1fT9GCBRTN9xJPBdOmseOddyrPC3boUD3p1LcPgZSUSL0NkWbFzDKAM4D7gFtrmXIcMNbMTnfOFZnZtcC5wOlVJznnPjOzzrWcPwxY4pxbFn69V/H+3sjBSzLNQjtqi4g0qCbpwRT+R38Q8A3QJpx8wjm3LvztBHjJp6+rnJYTHisN36853mSS44P4fUZaYgzgVTABu5p8axc5ERE5CLVKiOaes/oy9qUZvPrtai4d0ZdHT3iUsR+N5YZJN/D0yU8TF2yApV9t+8PV78HKL71qpo/u8nadO+0B6DnqwK8fAWZGMD2dYHo6iSecUDke2rKlMuFUcex8//3K5wNt21Ymm7zEU18Caa33fQc/kYPfw8AvgcTannTO/dvMMoFXzezfwNV41Uj11R5YXeVxDjAceBR43MzOACbUPMnMRgOju3Xrtg8vJSIi0AQJJjNLAN4Afuac21HHL1C1PeHqGK/ttcbgLaWjY8eO+x7sHqQlxjD5tpG0b+l90xof7a/2fLESTCIicpAa1a8twzNT+MsHixjZszXD04fz52P/zK2f3sqtk2/lsRMeI+gPHvgLmUHno71j2afw7i/glQu9RuBH3QydjvLmHOQCKSkkHH0UCUcfVTlWtmPHbkmnvE8+gXA7SX+rVsT06V2l2qkvwfbtlHSSQ5aZVfRMmm5mI/c0zzn3QLjy6Cmgq3Mub19epvZLunzgqjpecwIwITs7+9p9eC0REaGRE0xmFsRLLr3knPtPeHiDmaWHq5fSgdzweA7QocrpGcBadpWx1hzfjXNuHDAOIDs7u0GbgHdI2fUNbsUSuQqqYBIRiSwzOwdvqUUa8IRz7oPIRnTwMDPuObsvFzw1hdMf+ZyHL8rixF4ncvcRd/Pbr37Lr7/4NX885o8EfA34K0OX4+D6L+Cbp7yKpsXvQ3ImDL4Mhl8PUfEN91rNgL9FC+JHDCd+xPDKsfL8fIoWLaJo3q6k0+Yvv4Iyrzran5RUpcrJO4IdO2I+reiRQ8JRwFlmdjoQA7Qws3865y6tOsnMjgH6AW8CdwE37sNr7OlvCxERaSSNuYucAc8AC5xzD1V56m3gCuD+8O1bVcZfNrOH8Jp8dwemOufKzGynmY3AW2J3OfBYY8VdH/FVEkzRAR/FoTKcc7wxYw2n9Wtb7XkREanbnnYA2sPuP7Vyzv0X+K+ZJQMPAkow7YNebVvwv5uP4YaXp3P9izN44ZphnNv9XLYVb+Oh6Q+xtWgrDxz3ACkxDdg/KBAFR/0Uhl4LCybAzBdh0u9gxgtw6h+gxyjw+fd+nYOULz6euMGDiRs8uHKsvKiI4sWLdyWd5s1jy/Mv4EpLvXPi4ojKzAwfnYmuuN+5M77Yg6OflQiAc+4O4A6AcAXTbbUklwYBT+N9ebAc+KeZ/d45d2c9X+ZboHt4md0avM2ELmmQNyAiIrVqzEzIUcBlwHdmNis89mu8xNJrZnYNsAq4AMA5N8/MXgPm4+1A95PwDnIAY/F2iIjFa+7dZA2+a1O1gik5LorcnUWs3FzAbf+ejXOOC7I71HG2iIjUMJ4aOwDVsfuPH/hjjfOvds5VVMPeGT5P9lHH1Dj+ec1wzv/bFMa8MI23bzyaq/pdRXJMMvdOuZdL372UZ055hvSE9IZ94ag4GHihd6z4Et6+EV69BFq0h0GXwqDLoOXh8bnqi4khtn9/Yvv3rxxzJSUUL13qJZwWLKRk+XIKZ8xgx//+V7nEDiCQnk50ZmeiOmdWJqGiMzsTSE9X1ZMcrOKAC5xzSwHM7Aq83airMbNX8DYQamVmOcBdzrlnnHMhM7sReB/vs+NZ59y8pgpeRORwZM416EqyZiM7O9tNmzatUa69fFM+xz84GYDe6S1YsG4Hb95wJOc++RV3ntGbHx/TpVFeV0SkIZjZdOdcdqTjqCq8GcQ7FRVMZnYEcLdz7tTw4zsAnHM1k0sV5xveFxgfOuc+qs9rNubnxMFs9ZYCznj0c7q0TuD1648g4Pcxe+Nsxn44lsSoRF447QXaxLdpvADKSmHRRJjxPCyZ5I11OxF6ng69R0NCWt3nHybKi4ooWbmSkuXLKVm+nOLlyylZvoKS5cspz9vVpsZiYojq1MlLOnXIIJjRgWBGe6I6dCCYno4FG6C/lhxymuPnRFPTZ4SIyJ7t6XNCa7n2Q9Um38lx3i9mWwtKANhRWBqRmEREDjF72v1nT24CTgKSzKybc+5vtU1qrM0gDiUdUuL4w3n9ufHlmdz37gLuPKMPA1sP5OlTn+bq967m1k9vZfyp4xum8Xdt/EHoc5Z3bF3pLZ2b8y9Y8hF8+Fs49jbIvhpikhrn9Q8SvpgYYnr2JKZnz2rjzjnKNm2qlnAqWb6c4gUL2DlpEpRW+T3F5yPYti3BjAyCHTK8pFNGBy8R1aED/uRkNRoXERGRelOCaT/ER1VfIgewJd/7hW1HUSgiMYmIHGLqvYMogHPuUbytp+vUmJtBHErOHNCOb5Zt4bkvV7Bw3U4eviiLvql9+d1Rv+O2T2/jd1//jnuOvAefNfLSq+ROcMKdcPxvIHeB16Ppo7th8v3Q+RhoPwSGXwdxDdgb6iBnZgRatybQujXxw4ZVe86VlRHKzaVk9WpKV+dQuiaHktU5lK5eTd5nn1G2cVO1+b64OIIdOnjJp46dvEqoTh2J6tiRQNu2WnonIiIi1SjBtB/iovyYea0PWlZUMOV7FUzbVcEkItIQtPtPhP3u7L70z0jirrfmcdojn/PwhVmc2uNUlmxbwt9m/41yV85vj/gt0f7oxg/GDNr0gUtehZzpMPsVWPkVLJ0E3/4DRoyFjGzoeKTXPFxqZX4/wfR0gunpUCP5BFBeUEDpmjVe0ilndWXyqWT5CvI//ayy2TiARUUR7NiBqI6dCGa0J5jezrt2O+/6/tRUJaBEREQOM0ow7QczIz4qQF5xaFcFk5bIiYg0JO3+E2Fmxg+zOzC4YzI3vjyDa57/lnGXZ3PDwBvwm58nZj3B9A3T+dXQX3F8x+ObLrCMId4BsGEevHMrfHyv9zimJQy4EI68EVpqCeS+8sXFEd29O9Hdu+/2nCsrI7RhAyWrVlGyYqV3u2olpStXkv/117iCgmrzLRgk0LZtZUIrEE48VT188fFN9dZERESkCSjBtJ/io/3kFYd2q2DaUaQEk4jIvtjTDkCNtfuPmY0GRnfr1q0hLnfI65aWwL/GHMEl//ia616czl8uGMj1A69nYOuBPPDtA9z8yc2clnkadx9xN3HBuKYNrk1fuOZ9KNgCq7+B716Hac/A1L9Dcia07QdtB3o71CnhdEDM7yfYrh3Bdu2IHzGi2nPOOcp37KB03TpK166jdN1aQuvXh++vI3/qVEK5uVBWVu08X1JSuOqpHdFduxLdw0tuRXXpgi9KlWgiIiIHGyWY9lN8dAAortKDqaKCST2YRET2hXPu4j2Mvwu82wivNwGYkJ2dfW1DX/tQlRQX5J/XDOfHL0zjpldmsmDdDn5+ygheG/0az3z3DH+b/TfW5q3lqZOeIjEqsekDjEuBnqd5x7a7Yc6rsP47r8JpwTsw+Q/Q6wwY+mNoOwBik71ld9IgzAx/UhL+pCRievWqdY4LhQht3Fg9CbVuvfd49SryPvsMQuHfofx+ojp1qqymiu7WjeiuXYjq1AlT4klERKTZUoJpPyVEez+65Pjqu8ipB5OIiByKkuOjePna4dz99jyenLyUBet28MjFg7h+4PV0a9mNX3z2C378wY/5+0l/p2VMy8gF2rIDHPuLXY+353h9mqaPhwUTvLHkTBjwQ+h6IrQbpL5NTcACgV39nwbv/rwrKaFk5UqKFy+maPFi73bhAnZ+8IHX9BK8xFOHDkR17Up0ly5Ede1CdJcu3o53LVtqxzsREZEIU4JpP8VHBYgJ+ogJ+oEqFUxaIiciIoeo6ICfP543gL7tkrj77Xmc8/iXjLs8m5M6ncQjxz/CLZ/cwtUfXM1fR/6VTi06RTpcT1IGnHQ3HPcrWPoJbFkKiz+ATx+AT/8EgVjoOBy6nQQdhkNKF4hvFemoDzsWFVVZsdSiynh5YSHFy5ZRsmwZxUuXUrJ0GcXLlpH36ae7Kp4I73jXvn31I6M9wXbtCbZvpwSUiIhIE1CCaT/FRweIiwoQHaieYCooKaO0rJygXzuniIjIoenSEZ3onpbADS/N4NwnvuSRi7M4odexPHHSE9z6ya2c99Z5XD/weq7seyVBfzDS4XqCsdDrdO/+kTdB/mZY+aV3LP8MPrhz19zU7tDlOOh0JCS0gRbtoGUn8PkjE/thzBcbS2zfvsT27Vtt3JWWUrJ6NSXLl1O6Zo23+12Od1swbRrleXnV5ltsbGUPqV1HeuX9QFoa5td/XxERkQOhBNN+SmsRTauEKKIDXiJpW5WlcTuLQqTEq9xeRKQ5UpPvhjG8Sypv33Q01704jWuen8ZlIzrxi1OH8NY5b3H/1Pt5dOajvLv8Xe4+8m4Gth4Y6XB3F58Kfc7yDoBtqyF3PmxcBCs+h1mveEvrKgTjodsJXtPwYCykD4T2QyCqiRubC+DtUhfdxVsiV5uy7dspycmhdO1aQmvXUlpxrFlL0XffUbZtW/UTAgGCbdp4y/jatyOQXpF8au8lotLT8cXGNv4bExEROYiZq1jXfojJzs5206ZNa7TrbysoIa84REFJGaf89bNqz31y20gyW2nrXRFpnsxsunMuO9JxRFpjf04cLgpLyvjTewt5fsoK2reM5YlLBjOwQ0smr57M77/+PbkFufyo94/42ZCfEe2PjnS49VdW6iWcCrbA9tWwdiYsmgg71+2a4wtA2/7QYQR0GAYdR3jVTtLslefnhxuOr/WajlckoMJjoQ0boLy82jn+lJTKXe8qqp8CbdMJpLUmmJZGoHXrQ6YJuT4n9BkhIlKXPX1OqIJpP7WMi6JlXBQrN+dXjpl5fSh3qNG3iIgcJmKj/Nx9Vl9GD2zHza/M5Py/fcWvT+/NlUcex9BzhvLw9If554J/MmXtFE7vcjqndDqFzkmdIx323vmDXpVShcGXwxkPQXkIirbDmumw6mtYPdVrIP7NU968pA5eD6fiPPBHQWpX6Hk69DjV2+1OmgVffLy3O90eKhldKERow4ZqSaeKRFTx0qXkffEFrrBwt/P8yckE0tIItEkjkJbmJZ4qjtbh8dRULccTEZFDkhJMBygqsKvXUuuEaHJ3FqvRt4iIHHaGdErmfzcfzW3/ns09E+bz2fcbuWt0X34z4jccm3EsD01/iMdmPsazc5/lsRMeY2jboZEOed+ZeYmn+FZewqjHqd54WSmsn+Mlm1Z/A8U7vZ5NZaWQ8y0seBvM5yWf/FFQWghxyV4CKz0LWvWAhDQwv1cBFZ0Q0bcp4V3vws3Ca+Oco2zbNkIbNhDKzaU0fBvK3Ri+zaV4wUJCmzbt2gWvgs9HoFWrcNKpNf6UZAIpKfiTU7wEVUoy/uRk/CkpBJKTsbg4NSgXEZGDghJMB6iiyTdAestYL8FUGKrjDBERkUNTy7gonr48m2e/XMFDHyzilL9+xo+PyeQnxx/Bm2e/ydq8tYz9aCzXf3g9N2TdwOV9Lm8+TcAPhD/o9WNqPwRGjK3+XHk5rAsvr9u2CspKvJ3r8jZ4YzP/WX1+IBZ6ngb9L4C2/bzEVIv2XnJLmg0zI5CcTCA5GXr12uM8FwoR2ry5MukUys2lNDeX0Ibw/bVrKZo7l9C2bVBa+xeUFgzii4+vfsTFVb8fF4dFRVU5vB5V8Ucc0Ug/ARERkd0pwXSAqlYwtUuKYfZq2K4lciIizZaafDcuM+OaozM5c0A6f5q4kCcnL+XNmWu4a3RfTu2bzvhR47n7q7t5eMbDvL30be4ccefBWc1UXz7fruRTTc7B9hzYuhzyN3rJqNVfw7w3Yd5/ds1LzoSOR0BiW6/6qUU76Hait9tdeSkUboWkjKZ7T1JvVtE8vE2bOuc55yjPy6Ns61bKtmwhtHUrZVu2UrZtK2Vbt1JeUEB5fj5l+fnebd5OSjespzzfGy8vKIBQ9S84W5x5phJMIiLSpNTk+wCVlpXT/TcTAbj6qEye/XI5vxzVk8ToAGdltScp9hD4ZlZEDilq3upRA9emMX3lFn7z5lwWrt/Jib3SuPusvnRIiePT1Z/yx6l/ZE3eGoa0GcKYAWM4st2RkQ63eSgrhWWfeg3FSwtgyUewfi7krYdgHJTkefPMBy7ciDpjGBx5I/Q6EzDvPBxEJ0bqXUgTc+XluJISXGkprqQE8/vxt2y5X9fS54Q+I0RE6qIm340k4LPK5t6tEqMI+IyPF+QybeVWdhSF+Mnx+oZcREQOX0M6pTDhpqMZ/+UK/vrR95z40Kdce0wmY0cexZtnv8lri17j5QUvc92H1/GD7j/gJ1k/oXVc60iHHVn+IHQ/adfj4dd5t855S+W2rYYVX8CWpV5PJ38UTH8OXrscYpOhpADKir1zUrtBYjqUl0HLDtD5GG/5XTDGS2TtWOP1i9ISvIOe+XxYTAzExHj/WylXywYREWlaSjAdIDMjyu+jOFROQnSApNgg01ZuBeCz7zcqwSQiIoe9oN/Htcd24YwB6Tzw3kKe+GQpr03L4Ren9OTSIZdzUa+LeGLWEzw/73neXvo2P+j+A346+KckRKnZdTUVSaCWHSDr4urPHXkTLPwfLHoX4ltDXKq3fG7NTCja5p27/HOY8y/44E6vr9O2VVCyE1pkQL/zvN5Pa2d6VVI9T4fEupd1STO1brb337jdYDj5nkhHIyIihxElmBpAdMBLMMVFBWgRG2RzfgkA01duJa84REK0fswiIiLtWsby8EWDuOLIzvzunfn88o05jP9qBT8/pQe3DL6F87ufz/h543nt+9f4aNVHHJdxHCM7jOTYjGPxmW/vL3A48/mhz1nesSfOwfJP4bvXoWAzdBgGab1h6Scw5Qn46tFdc9+5xXs+GOdVSiWmQ+axMOw6SGjtJTHmvQkl+V6VVN9zvZ3wpOnsXA+f/glypnn/Pc0HxTugaDvEpnj/TURERJqQMh8NICrgB0IkRPtpEeP9SId0Smb6yq1MWbqZk/voG0AREZEKgzom85+xRzJhzjoeeG8h1zw/jX7tW3DzCd35vxH/x7ndzmXcnHF8sPID3lj8Bp1bdOayPpdxVteziAnERDr8g5cZdBnpHVUNv85LVqz8CjKyoTgPFkzwqqFKCyBjKOxYC589CJ//xauOyt8IvgAE46F4O7x3u3fdxHZe0/KCzdDpSDjlPoiK816nYomf7DvnYMrj8OWj0O8HXnXa7Fe9XQkzj4O2A7x+XFHxkNwZBl0KsS0jHbWIiBxmlGBqANHhneTio70KJoCfntid616czmffb1SCSUSkGdEucs2DmXHWwHac1q8tb85Yw+OfLGHMi9Ppk96Cm0/sziPHP0o5ZXy08iOem/cc9359Ly/Mf4G/jvwr3ZO7Rzr8Q09iW2+ZXIU2fWDkr6rP2fg9fPdvr9l4ancYcgXEJEHuQvjuNZj7H9gwz6toSuoA056DlVNg4EWweiosfh+6ngi9ToeENt6SvdQuMOQqrwKrqZQWQeEWr+InsW3Tve6ehIq9ZF3Fz6AsBKumeEmk9CyIaQkTfwnfPg1t+nu3vgD0Hg0j74DUrpGMXkREpJISTA2gIsEUF+X1YIoJ+hjeJYUjuqby1dJNEY5ORESqcs5NACZkZ2dfG+lYxOvP9MOhHThvcHv+O2stj3+8mOv/OZ1ebRO5+cTujOp7Kqd2PpWv1n7FnV/eycX/u5hhbYdxepfTOT3zdC2da0qte8AJv9l9PK0XnPhb76jq+w/gg9/AR3d5iagBF8HSSV6iCbwkSXkIZr0CI8Z6zc1zF0BKV6/6Kan97q+1ZgYsmeQlwyoSK1tXekv/8jfC4CshPtUbz8v1EmIrv/ISScU7Yd0c2PQ9uDJvTtv+kH2NV/Hjr2Pn33lvwlePwzG3Qq8zvAbpXz3qNVUffCX4fLDgHXj/194yxSNurF/yauG7MOGn3s/njAehfTa8fvWun1FcK+9nseBtr8/WSb/zemr5AhDTYu/XFxERaULmnIt0DI2iKbcWHfXwZyxcv5P3f3YsBSUhVm8t5KyB7fjTewv5x+fLWHjvafh9KgkXkeZB2097tAV18xQqK2fCnLU89vESlm3Mp0ebBG46oTun909nS9Emxs0Zx5drv2T1ztX0Te3L8R2O5/iOx9MjuUekQ5c92bnBW7oVnQDl5bBtBWxfA+0He0mZSfd4u9lVY15iJTkT4lt5O+Wt/sZLJIGXYOl3vlct9fmDECryxuNbe0v+ysu85WSl+d6SsYKt3lK9tgMgfQC0aOctBZz7Bqyb5VVUmR9Chd5yNID0gTDgQu/5qeO8flSlBV7z7PIQrJ/jzWufDf3Ph0m/86qN8jZ4TdSved97nfJyL6m1+mvvfbXu6cXx8b3w9ZPQpp+X/Nq20nse4JR7vf5YH/wf5M6Ho34KJ93TZEsM9TmhzwgRkbrs6XNCCaYGcPbjXzA7Zzuf//J4OqTEVY6/MnUVd/znO7741fFkJMfVcQURkaajPxw8+uOheSsrd7wzZy2PTlrM0o35dEtL4KYTunHmgHaYOSYsncBzc59j6falGMa53c/lir5X0CWpS6RDl31VXg45UwHzKoq2LPV6QH3/vleFlL/RS+ik9YE+Z3s9iL59Gma+5O2C1/0Ur9dTWbHXnDznW++6Pc/wqqrSeu35tZ2DRRO93fWiEiAY6yVxysu88Z1rvcTTwIvgtD/B9PHebn071sJJd3mVTB//Hrav9pYFXvsxbM+B58/yeiC17AS586Bwa/XXNZ/XM2n4WDj5d977WzABNsyFzsdAj1O8eaWF3s5+HY9o0v5V+pzQZ4SISF2UYGpEF/ztK75dsZWZ/3cyyfFRleNfLtnEj/7xDS9fO5zCkjJWbi7g6qMzmyQmEZE90R8OHv3xcHAoK3e8+906Hvt4Md9vyKNL63guH9GJcwdlkBQXZFvRNp7+7mleXvgyofIQKTEpxAZiuW7AdZzT7RxMTaUPfs55fYqCNRq8F+fB5iVepVHV/85F270m4ykHmGwsK/Wqh1K7eRVYe1KRIEvK8A7wluV9dI+XSErpAp2OgA4jwB+A9XPDDdWHeMmyZkifE/qMEBGpy54+J9SDqQFEB7ymjPHR1X+cHcPVTKs2F/DfWWtYkpunBJOIiMg+8PuM0QPbcUb/dN6bt56/f7qUuyfM548TF3LmgHZcMrwDt2XfxlX9ruLtpW+TszOH77d+z2+/+i3j543nyHZHcmHPC+mc1DnSb0X2l9nuySXwlty1y9p9PCbJOw6UP+glr/bG54OOI6qPdTrSWyJXm+TO0PvMAw5PRESkuWm0BJOZPQucCeQ65/qFx1KAfwGdgRXAD51zW8PP3QFcA5QBNzvn3g+PDwHGA7HAu8BPXTMru4oK+Aj6jahA9Uaj6UkxBHzGyi0FzFu7g7ziEKGycgJ+NSQVERHZFz6fcXr/dE7vn87cNdt5Zeoq3pq1ljdm5NCzTSIXD+vAeYMupWVcFOWunP8u+S/vLX+P1xa9xssLX2ZY22EMThvMwLSBDGw9kPhgHRUpIiIiIrLPGrOCaTzwOPBClbHbgUnOufvN7Pbw41+ZWR/gIqAv0A74yMx6OOfKgKeAMcDXeAmmUcDERox7n0UHfLtVLwEE/D7aJ8fy1ZJN7CwKAbA5v4Q2LWr5Fk5ERJqEmY0GRnfr1i3Soch+6tc+ifvO7c+vT+/NO3PW8vI3q7h7wnz+8O5CTu7ThvOzMzi7+7mc1/08NhVu4uUFL/NZzmc8NfspHA6f+eiV0ouLel7ECR1PIC4QR7CuHcREREREZK8aLcHknPvMzDrXGD4bGBm+/zwwGfhVePxV51wxsNzMlgDDzGwF0MI5NwXAzF4AzqGZJZhio/wkxtT+o+yYEsfnizdVPt64s1gJJhGRCHLOTQAmZGdnXxvpWOTAxEcHuHBoRy4c2pH5a3fw7+mr+e/MNfzvu3W0aRHN2VntOa1fW24adBM3D76ZvJI85mycw8yNM/lk1Sf89qvf8tuvfovf/Bzd/mjO6noWx3U4jmh/dKTfmoiIiMhBp6l7MLVxzq0DcM6tM7O08Hh7vAqlCjnhsdLw/ZrjzcoNI7uSuyOj1uc6plTfPS53ZxHQAH0BREREpFKfdi24q11f7jitNx8v3MC/p+Xw3JfLGffZMtolxTCqXzqn9W/LiI5HcGT7I7lh4A1MWTuFJduWsKFgA+8tf49Pcz4lLhBH15Zd6ZDYgc5JnRmUNoiBrQcSG4iN9FsUkQZgZvHAk0AJMNk591KEQxIROWQ0lybftW3x4uoYr/0iZmPwltPRsWPHhomsHrqlJdItLbHW5yoSTEmxQbYXlrJxZ3GTxSUiInK4iQr4GNUvnVH90tleWMqkBRt497v1/POblTz75XLSEqMZ1a8tp/VLZ3iml2wCuHXIrXyz/hs+WfUJK3asYFbuLCYun4jDEfAF6Jval04tOtEntQ/HZRxHWlwaUf6ovUQjIrUxsxjgMyAa7++R151zd+3ntXbr+1rluVHAI4Af+Idz7n7gvPDrTTCzfwFKMImINJCmTjBtMLP0cPVSOpAbHs8BOlSZlwGsDY9n1DJeK+fcOGAceFuLNmTg+6siwXRM91a8M2cduTuKeW3aapbm5nHH6b0jHJ2IiMihKyk2yHmDMzhvcAY7i0r5eGEu781dz2vTVvPClJW0Soji5D5tOb1/W0Z0SeXIdkdyZLsjK8/PK8ljZu5Mpm2YxuyNs/l63de8vfRt7p96PwApMSn0SO5Bz+Se9EzpSfuE9qzcsZJofzTD0ofRKrZVpN66SHNXDJzgnMszsyDwhZlNdM5VrmgIr3QodM7trDLWzTm3pMa1xrN731fMzA88AZyM9zfFt2b2Nt7fE9+Fp5U17NsSETm8NXWC6W3gCuD+8O1bVcZfNrOH8Jp8dwemOufKzGynmY0AvgEuBx5r4pgPSMdUL8E0qGMyny/exMa8Yj5fsonvN+xUgklERKSJJMYEOTurPWdntaegJMTkRRt597t1vDVrDa9MXUXLuCDH9WjNsMwUhnVOoVtaAglRCRyTcQzHZBxTeZ3l25czdd1UtpdsZ/XO1SzasohXFr5CSXlJtdfzmY/TM0/nmn7X0C1ZDeVFqgrvCJ0XfhgMHzW/HD4OGGtmpzvniszsWuBc4PQa16qt7yvAMGCJc24ZgJm9itf3teIL7FmAtnYWEWlAjZZgMrNX8Bp6tzKzHOAuvMTSa2Z2DbAKuADAOTfPzF4D5gMh4CfhHeQAxuJ9MxGL19y7WTX43pvebVtw1+g+nDc4g1enriJ3RzHfb9jJtoJSikrLiAn6Ix2iiIjIYSUuKsDp/dM5vX86RaVlfPb9RibOXc8XSzbx1iyvUDolPoqhnZMZ2jmF4Zmp9E5PJOD3kZmUSWZSZrXrhcpDrNyxkjV5a+jU4v/bu/PoNss77ePfnyTL+27HcWLHzkIgCdlIwpKSllIKCWWf6UALFKYtFPpSBjhlCkPbgTOd0naGOe0UujK8dGEKLZ2ylbVMEngpZN8JSRxncTbHibd4t6X7/UOyYye248SJ9Di+Pufo6NGtZ7l0S/bPvvUsJTS0NfD69td59qNneaX8FUoySqhpqWFEygguLb0UwxifNZ6Liy+msaORxrZGMhMzSUlI6S2uyGkpuofRCmAC8IRzbkn3551zfzCzscCzZvYH4ItE9kYaqNFARbfHu4DzgP8EHjezzwAv95JLVxoVETlBFvkC4fQze/Zst3z58njH6OHzv/yAipomKqqbAVh8/0WU5KbGOZWIDDdmtsI5NzveOeLNi3VC4ss5x46DTSzdVs3S7dUs3VbNzuomANISA5xTks2sMdlMK8pkalEmeWn9X22uuqWaP5f/mSV7lzAiZQSbajaxtmpt1/NZiVnUttYC4Dc/N066kVun3Epech5mvZ2GUiQ2YlknzCwL+BPwNefc+l6ef5bIXkvjnXNVfayjFHil+zmYzOyzwGXOuS9HH98MnOuc+9pAcqlGiIj0ra864ZWTfA8LI9IT+evWg12P99W1aIBJRETEI8yM0rxUSvNS+bs5kVND7qtriQ42HWTptmp++HYVnd/NFeckM6c0ckjdnLE5jMtL7TEwlJOUw82Tb+bmyTd3tR1qO0TQH2RRxSLe3vk2E7ImkJuUy5qqNfz6w1/z6w9/TXIgmU8UfYK61jrWH1zP7VNv56bJN9ER7iApkBTLLhE55ZxztWa2CJgP9BhgMrN5wNlEBqD+GbjrOFbd1zleRUTkFNEAUwzlp/f8pnNffUuckoiIDF86/EGOx8jMJK6aPoqrpo8CoKG1gw2761i7q44VO2pYvKmK/1m5G4Dc1GB076Yspo7O5IwRaRTnpOD3HR50Sg9Grjp7WellXFZ6WVf730z8Gz531udYtX8VW2q38PaOt0kKJDEpZxKPrXiMx1Y8hmHMK5rHJWMuoSi9iNrWWsZnjmdc1rhT2gdtoTZeKHuBwtTCHuejEjlRZpYPtEcHl5KBS4DvHzHPTOCXwGeAbcBvzew7zrlvDnAzy4AzoofZ7QZuAD5/sl6DiIgcTQNMMTQiPfKtY2rQT2NbiMr6Fp5ZsoMtlQ08fNWUOKcTERkenHMvAy/Pnj37tnhnkaEnLTHAeeNyOW9cLrcROayu/EAjy7ZVs2x7Det317F48xbC0b2c0pMCkb2cxkZuU0dnkuDv/bzCk3InMSk3cgGQb5//bcwM5xyvb3+drbVbaQu18XL5y7yz652uZXzm47KSy/D7/CQHkinJKKEj3MHuht1UNlUyu2A2nxrzKcZkjBnwa2xsb+TlrS8zc8RMPjz4IT9d81P2Nu4F4Lapt1GSUcKk3ElMzJ54Yp0oAoXAr6LnYfIBv3fOvXLEPCnAZ51zWwHM7Bbg1iNX1Nt5X51z/+Wc6zCzu4A3AD/wlHNuw6l6QSIionMwxdQLq3Zzz3OrOX9cDmt31XHDnDGs2VXLxr31bHjkMp1vQURiQudgivBinZDTQ1NbBxv3HqJs/yFWV9SydFs1W6saAUhK8DF1dCbTi7KYXpzFOSXZjM5KHvC6Q+EQuxp2sadhDxmJGbyy9RVeKHuBjGAGDe0N1LfVA5ARzCAnKYft9dsBGJM+hqykLJram2hqb2L2yNkE/UEONB/gazO/1mOw6MF3H+SV8sP/65+dezZfnfFVXil/hVe3vQpEBrZunHQjd824Sycn96An1z3J6LTRLBi74ISWV51QjRAR6Y/OweQBI6KHyJ1ZkM7++lb21Tezad8hmtpC7D/USkGGzqsgIiIy1KUEA8wqyWZWSTbXz4nsOVR1qJXl2yMnD1+7q47ffLCDJ//fNgAKM5O6TiA+qySbyaMy+tzLye/zU5JRQklGCQBTcqfwjXO/AUT2pqpvqyfBl0ByIBkzY0/DHhZWLGTZvmU0tTeRm5RLwBdgYcVCcODz+bj51ZuZlj+NstoyZuTP4C87/8KtU26lNKOU3ORcPlH0CcyMC0dfyB3T7wDgmY3P8NsPf8ub29/kuxd+l3MLzz3V3SoD1NTexM/X/JyrJ1x9wgNMIiIiJ0IDTDE0MjMygHRWYQabKxtYsaOGhtYOALYfaNQAk4iIyGkqPz2RBVMLWTC1EID2UJiP9h5i5c4aVuyI3P68NnIYWlKCjzmlOcwszuLMkRmcU5JFYeax93IyMzITM3u0jUobxY2TbuTGSTf2aA+7MAD7m/bzwLsPsK9xH9PyprFo1yKm5k3l7nPuJsGXcNT6x2aOBeCb53+TK8Zdwbfe+xb3Lb6PF65+gbzkvGNmdM4RdmH8Pv8x55UTs7BiIS2hFg0uiYhIzGmAKYbG5afx5Bdmc+EZeSzdVs375YevKLf9YCPnjcuNYzoRERGJlQS/j6lFmUwtyuSWuaUA7K1rZuWOWpZtr+aD8oM8vrCs61xOo7OSmV2azZkj05lcmMHMMdlkJif0vYFj8FlkD6mRqSN5ev7TXe11rXUk+BKOGlzqzYwRM/jRJ3/EZ1/+LI+8/wh3zbiLjdUbWb5vOcmBZM4fdT4XF1/cdQqAqqYqvvKXr7Dr0C5m5M/gOxd+hxEpI45ab1lNGakJqRSmFZ7w6xuoztc7FA/zc85xx1/uYHLuZP7hnH/oan9t22sUpBQwc8TMOKYTEZHhSANMMXbJ5AIARmREDpczA78Z2w408eLq3ZRXNXLvp3XSTBERkeGmMDOZz0xL5jPTIgMrLe0hNlceYvn2GpbvqGbptmpeXB25yroZTByRzlmF6RRnpzB3Qi6zS3IIBno/tG6gjtwD6ljGZY3jrpl38R8r/oNFFYsAyEnKoT3UzrObnmV6/nQCvgDJgWQqDlWwv2k/V42/ipe2vsR9i+7jqcueIuzCvLbtNbKTsslJyuFLb3yJjMQMnrviuQHtFQWRc1Ptb9p/1KDU4orFPL3hae6ddS/T8qdRUV/Bj1f/mGl50/jsmZ/l+leup7mjmQfPfZD5Y+cf12sfqIPNB/npmp+ybN8yRqaO5KZJNzExeyKvlL/CO7ve4dF5j1KYWsjBloMDfr0A/7vzf/nrnr+ysnIlt0y+haykLOpa63hvz3vcNOmmrkFEERGRWNEAU5yMjB4OV5qbilnkELlFm/ZTXtXInReNJylBu46LiIgMZ0kJfqYVZTGtKIsvXhg5NK2+pZ31u+pYvqOG5dFD615Zu5fHF5aRlhjg/HG5TBiRRnFOMqW5qUwryiQ96cT3dBqIW6fcyqyCWext3EthaiFT86YSciGe2/Qcz29+noxgBlVNVXSEO/jJp37C7JGzmTNyDl9f/HUW/HEBbeE2altrAQj4AoxIHkF1SzX3LbqPH1/8Y5IDyWw4uIH0hHSKM4rxm583t79JWW0ZraFW/D4/C3cuZHv9dr574Xe5cvyVhF2Y3330O36w7AeRjK/fypTcKWw4uIH2cDtvbX+LstoydjfspjSjlPvfuZ/a1lpuOOsGwi7M2qq1bK7ZTHu4nSvGXXHUwJtzjjd2vMHIlJHMGDGjz75pD7dz36L7WHdgHecVnsdH1R/x1be/2vV8wALc/879jMscxwtlLzB31Fzum3UfZ+ac2W+fh12YJ9Y8QX5yPlXNVTy/5Xm+PPXLPL3haTrCHVw+9vITezNFREQGQVeRi5PX1u3lzmdWsuDskbR2hFm3u46qQ60A/OGOC5hTmhPnhCJyutLVgSK8XidEBqqxtYP3yg6waHMVH2w9SEVNE+2hyN93Poscoj+xII0zRqQzoziL88blkBKM/3eML5a9yPt738cwrplwDVtrt/LG9jd4eO7DbKrZxDfe+QbpwXT85qe6pRqAoC9IRmIGB5oP4Dc/QX+Q9lA7E3MmErAAG6s38oXJX2B55XLWVK1h3uh5fPP8b/LY8seobKpkRv4Mrhx/JV9680vUtdZxQeEF/OSSn3DPwnt4d/e7fLL4k6w/sJ7KpsqunOnBdKblTyPZn8xD5z9ESiCFb733Ld7c8SYBX4C7Z97NugPrSPAlMHfUXLbVbWNN1Rp2HtpJZmImW2q28Oi8R7li3BW0hlpZsncJFYcqmJg9kYMtB7l/8f0AzC+dz9J9S+kId/Cr+b9iQvaEHv218eBGnlj9BLdNu43FFYv55bpf8r153+OFshcory3ny9O+zKNLHuXaM67lkbmPDOq9UZ1QjRAR6U9fdUIDTHGycmcN1/3kr9z36YnUNrXz1Hvbup77xvyzuPOi8XFMJyKnM/3jEOH1OiFyokJhR2V9C2X7IxcU+XBvPVsqD7GjugnnwO8zSnJTGJmRRFZKQmTQaWwuU0ZlEOjj6nXxsLlmM4+vehzDuGL8FbSH2tlYvZGd9Tu5esLVfLL4k13nd4LI+ZRuff1WymrLKEwt5M7pd3LNhGt6zNPppa0v8a8f/CtPzX+KKblTaGpv4t5F97K9bjtT8qZw8ZiLmV0wm7rWOn625mfsa9zH5prNzBk5B4djyd4l3Dn9Tt7b8x6r9q8iOzGbMGHqWusIWICzcs6iOKOY8tpy5hXN63GOpCP97qPfkZ2Yzfyx89l1aBc3v3YzPvPx2wW/JTGQyNs73+bSkku55bVb2Fq3tWu5aydcy8NzH2Zl5Uru/MudtIRaKMko4fdX/H7Q55RSnVCNEBHpjwaYPKahtYObnlzCd645m1U7a/jWixsoyEgkJRhgXF4q91wykX31LXw6es4mEZGTRf84RHi9ToicbM1tIVbsqGHJtoNsqWzgQEMrVQ2t7DjYBEDQ7yM56GdUVjIzx2RxzphsxuWnkp+WyOisZHy+owdqvCbswrSH20n0Jx5z3rZQG0F/cMDr/v2m3/MvH/wLAI/MfYTrzriO5o5mlu5dynmF5+E3P+V15ZRklJAUOPErA2+q3sTfv/735CbnEnIhKg5VkBxIprmjmR98/Aes2r+K4vRibpp0U9fgWXNHM2uq1lCaUcrI1JEnvO1OqhOqESIi/dEAk4e9u6WKm/9rKX87qwifwevr9xHw+6hrbueNe+YxYUR6vCOKyGlkuP/jYGZXAldOmDDhti1btsQ7jkjc7a9vYen2atbtrqO5LcS2A42srqjlUEtH1zzJCX6mFmUyb0IeZxSkkZOaSFKCj8LMZPLSgr3uJXS6cc7xw5U/pCClgM9P+vwp3daKyhV85a2vkBJI4e5z7uaZjc8wq2AW3zz/m6d0u52Ge52AofW/hIhIrGmAycOqDrVy0b8t5Ikbz2F/fSv/+Me1BP0+EgM+ZpVm8/TfnxvviCJyGtE/DhFDqU6IxFo47Cg/0EBFdTOV9S1sqjzEkvJqPtxbf9S8eWmJXDA+l7NHZVCal0p+eiL5aYkUZiZ56pC7oWZr7VbSEtIoSI393uyqE6oRIiL96atOxP8Mj0J+eiLrHr4Mn8+oqG7C7zPuu3QiAZ/xnT9v5FsvrOf6OcVkpwYZnZUc77giIiJymvP5jAkj0o/ai7q+pZ0dB5qob2mnqS3E7pomVlXUsnRbNS+v2dNj3uyUBD41qYBpRZlkJkeuZDc2L5UJI9I8cZJxrxufpfNxiojI0KLq7hGd5zUozknh/QcvJj8tkfaQY2tVI/+9dCe/+WAHAN+9diqfP29MPKOKiIjIMJWRlMDUoswebbdG72sa29hd20xVQyv761t4f+tB3vqwkudX7OoxvxmMykwmJehnfH4al0wuYE5pNsXZKUPiPE8iIiLSOw0wedCI9MiJIYMB49HrpvLVi8azdlcdv/rrdh59bSN5aUF+/k45/3T5WcwqyYlzWhERERHITg2SnXr4pNnXzxmDc4599S00toYIO0d5VQOb9jWw42AjTW0hVlXU8PqGfUBk4CkjKYHSvFQm5KcxYcTh25icFPwafBIREfE0DTANAcU5KRTnpHDmyHQW/Ogdbv/NCgAe+tN6/uerc3lx9R4unVxAbtqxr5giIiIiEitmRmHm4cP7JxakM//sw8+Hw45NlYdYtbOWfXXNVDe1UV7VyLtbqvjjysN7PgX9PsbmpZKVkkAw4GNmcRbnj89lelEWDa0dOAeZyQkkB/2xfHkiIiLSjQaYhpAJI9J4cMEkVlXUcv64HB7603ou/vfF7Ktv4ZfvlvPD62fQ1BaiKDuZ0VnJw+KKLiIiIjJ0+XzGpMIMJhVmHPVcXXM7W6saKNvfwNb9kfuG1g5qmtp4fGEZ//m/ZT3XZXDu2Bw+Nj6PMbkphMKOlvYwYecozU1lZGYiqYkB0hIDpAYDOhxPRETkJNMA0xDzxQvHApFL5b64eg/rdtXx9Usn8rPF5Vz1+Htd843KTOKK6aPoCDmSgz4un1pITmqQ5AQ/mckJGnwSERERT8tMTuCcMdmcMyb7qOfqW9pZtq2ajXvryUwJ4jdjV00Tb31YyWNvbR7Q+rNSEphdksOFE3I5b1wuo7OTSU8M6G8kERGRE6QBpiHKzPi/t86hqS1Efnoil08tZMWOGgoykthxsJG3P9rPk++Wkxjw0x4K88TCrV3LpicGKMpJIS8tSILfR15akLy0yLd6qUF/5D56Swn6SU7wkxy9Twn6SUrwkxjw6Q8wERERiYuMpMgV6j41qaBH+z/OP4vG1g721DaT4PeRHPTjHGw70EhVQyuNrR00tHTQ0NpBZX0L75cf5C8bK7uWT0rwkZ+eSH5aIuPy05gyKoOUoJ/dtS3srW1m7oRcPjY+j/z0RCrrW0lLiuwRJSIiIhpgGtI6B4EAxuWnMS4/LfpMPjdfUEpLe4jEgI/qxjYWbqqiPRSmsbWDXTXN7KxuorqxjfZQmPW76zjY2EYo7Aa8bZ9BcoKfYMBHMOAjwR+5D3a772rr1p7gt+hjPwkBI+j34fcZAZ/h9/mi94dvnY8D/p7PH7739Tm/zzpvHJ720aPdLDJ/57TPiD42zI6eV0RERLwtNTHAGQXpPdpGZib1OX9FdRMrd9awv76VqoZWqg61UlnfwsKP9nddAc9nkJ6UwB+ij4N+H22hMAl+49yxOUwvymLbgUZW7qxhYkE6n55cwN/OKiIleOw/tVs7Ql1/z/SnpT3EzuomSnNTSfAbh1o72F/fQmV9K9WNbfh9RoLfR2tHiA/31FOal8rfzS4+5vZFREROFg0wncaSEiInusxNS+RvZxX1O69zjtaOME1tIRpbO2hs64jct4Zobg/R0h6iuS1EU1vkcXP0vq0jTFtHmPZQmNZQmPaOMG2hw21NTR20hRxtHSHaQmHaO1z0Pjp/KIwb+LhWXB0ecOKowSvrMTDV+0BV1z1At+nO9sg2OgezwIhMc8R8ndN2ZHt0me7P+XrM1zlPt/lPdJleMx2Rgci5NTpfb3Sq67nD05H5o7N1vd6+nu+atsPrizxn/a67xzJH5ei5bPc+777unssebrDe1t3Lsl3bGsDr65am5/LR+6yUIOeO1VUkRUQGq/NiKkdyznGwsY2W9hA5qUGSAn7W7KpldUUtu2uaGZObwu6aZt7dcoCfv1NOdkqQC8bnsmlfPd9+cQPffXUjaYkJJPgjX2aFwo7M5ARKclMoyU2ltqmNdbvr2Vx5iLTEAB+bkEtxTgoV1U2s211HUsDPpMIMphVlsnhzFUvKq2kLhQkGIl+4NbWF+nxNAZ9x/ZxiDTDFwSMvb+DDPfXxjiEickyTR2Xwz1dOOanr1ACTAJF/epMSIoe/5XS7xHAshMOOkHOEwo6OsCMUcnSEw4cfh12P6SOf6wgdbg+7yOOOsCPsHGEX+QMx7ByhMISdiz6OTIfD3aYd0ceH21wvy4Wi63Nd83cu7/pcTzjscBBZxkWm6doGODqzRp7ozO2ga5ruy3dfJgwhwkctQ9d0dP7oshyRw7nD2Vwvy3R/PtzLvJ390tt6erze6GvufBR5Dd1eG52vXwZidkk2z985N94xREROW2ZG3hFX6J05JpuZR5wT6kF67oXknGP5jhpeXbeXlvYwHaHI3y1+n1HT1MbWqkYWflRFaqKfaUVZXHxWPpX1rSzdVs1bH1aSl5bIrJJs2jrCvLulipfW7KE0N4Vb5pZw1sgMNlceoiPsKMhIpCAjiYKMJHJSg11/A5nB+Py0ri8aRUREYkUDTBJ3Pp/hw9DfQdKd6xpI6/a4azoyUNV9QMoNdPDqiGVdH+umx/Od071k6iXDkevuuZ4jM/ZcN93aOtu7r79Tii7FLSLiGYmBw7+TzYw5pTnMKe17L9Nw2HXtEdydc65HW0cozN66FoqydXXgoeJk7w0gIjKUDJkBJjObD/wI8ANPOue+F+dIInIKdR621q0lXlFEREROKl8f51s6chAp4Pf1evieiIiIF/niHWAgzMwPPAEsACYDnzOzyfFNJSIiIiIiIiIiMEQGmIBzgTLnXLlzrg14Frg6zplERERERERERIShM8A0Gqjo9nhXtE1EROS4mNmVZvaLurq6eEcRERERETltDJUBpt4OVD/qelNmdruZLTez5VVVVTGIJSIiQ41z7mXn3O2ZmZnxjiIiIiIictoYKgNMu4Dibo+LgD1HzuSc+4VzbrZzbnZ+fn7MwomIiIiIiIiIDGdDZYBpGXCGmY01syBwA/BSnDOJiIiIiIiIiAgQiHeAgXDOdZjZXcAbgB94yjm3Ic6xRERERERERESEITLABOCcexV4Nd45RERERERERESkp6FyiJyIiIiIiIiIiHiUBphERERERERERGRQzDkX7wynhJlVATtOcPE84MBJjHMyeDETeDOXFzOBN3N5MRN4M5cXM8GJ5Spxzg37S22qTsSEFzOBN3N5MRN4M5cXM4E3c51opmFfJ07DGgHezOXFTODNXF7MBN7M5cVM4M1cJ7VOnLYDTINhZsudc7PjnaM7L2YCb+byYibwZi4vZgJv5vJiJvBurtOdF/tdmQbOi7m8mAm8mcuLmcCbubyYaTjwar97MZcXM4E3c3kxE3gzlxczgTdznexMOkROREREREREREQGRQNMIiIiIiIiIiIyKBpg6t0v4h2gF17MBN7M5cVM4M1cXswE3szlxUzg3VynOy/2uzINnBdzeTETeDOXFzOBN3N5MdNw4NV+92IuL2YCb+byYibwZi4vZgJv5jqpmXQOJhERERERERERGRTtwSQiIiIiIiIiIoOiAaZuzGy+mW0yszIzeyBOGYrNbKGZbTSzDWb2D9H2h81st5mtjt4uj0O27Wa2Lrr95dG2HDN7y8y2RO+zY5zpzG59strM6s3snlj3l5k9ZWb7zWx9t7Y++8bMHox+zjaZ2WUxzvVvZvaRma01sz+ZWVa0vdTMmrv12c9imKnP9yvOffVct0zbzWx1tD1WfdXX74O4f7aGK9WJY2bzVJ3wSo2IZvFcnfBijegnV1zrhBdrRHRbqhMeozpxzGyqE31nUZ0YXC7ViaMzxb5GOOd0ixwm6Ae2AuOAILAGmByHHIXAOdHpdGAzMBl4GPh6nPtoO5B3RNsPgAei0w8A34/ze7gPKIl1fwEfB84B1h+rb6Lv5xogERgb/dz5Y5jrUiAQnf5+t1yl3eeLcV/1+n7Fu6+OeP4x4Nsx7qu+fh/E/bM1HG+qEwPK5tk6Ec8aEd2+5+qEF2tEP7niWie8WCOi21Kd8NBNdWJA2VQn+t6+6sTgcqlOHL3NmNcI7cF02LlAmXOu3DnXBjwLXB3rEM65vc65ldHpQ8BGYHSscxyHq4FfRad/BVwTvyh8CtjqnNsR6w07594Bqo9o7qtvrgaedc61Oue2AWVEPn8xyeWce9M51xF9+AFQdCq2fTyZ+hHXvupkZgb8HfC7U7HtfjL19fsg7p+tYUp14sR4pU7ErUaAN+uEF2tEX7n6Ebe+6hSvGgGqEx6kOnFiVCdQnRhsrn4M2zoRjxqhAabDRgMV3R7vIs6/iM2sFJgJLIk23RXdFfGpWO462o0D3jSzFWZ2e7StwDm3FyIfYGBEHHJ1uoGeP7Tx7q+++sZLn7UvAq91ezzWzFaZ2WIzmxfjLL29X17pq3lApXNuS7e2mPbVEb8PhsJn63Tkuf5VnTguXqsR4P2fZS/VCPBunYh7jQDVCY/wXP+qThwX1YnjpzoxMHGvE7GqERpgOsx6aYvbJfbMLA34I3CPc64e+CkwHpgB7CWyi12sfcw5dw6wAPg/ZvbxOGTolZkFgauAP0SbvNBfffHEZ83MHgI6gGeiTXuBMc65mcB9wH+bWUaM4vT1fnmir4DP0fMPjpj2VS+/D/qctZc2XSr05PFU/6pODNwQqxHggc+ax2oEeLtOxLVGgOqEh3iqf1UnBk514gQCqE4cj2Hzv4QGmA7bBRR3e1wE7IlHEDNLIPIBeMY59z8AzrlK51zIORcGfkkcdmd2zu2J3u8H/hTNUGlmhdHchcD+WOeKWgCsdM5VRjPGvb/ou2/i/lkzs1uAK4AbXfSA2+iukAej0yuIHHM7MRZ5+nm/vNBXAeA64LnOtlj2VW+/D/DwZ+s055n+VZ04bl6sEeDRn2Wv1YjoNj1ZJ+JdI6IZVCe8wzP9qzpx3FQnjoPqxMDFu07EukZogOmwZcAZZjY2OoJ9A/BSrENEj8/8L2Cjc+4/urUXdpvtWmD9kcue4lypZpbeOU3k5G7rifTRLdHZbgFejGWubnqMCse7v6L66puXgBvMLNHMxgJnAEtjFcrM5gPfAK5yzjV1a883M390elw0V3mMMvX1fsW1r6IuAT5yzu3qbIhVX/X1+wCPfraGAdWJ/nN5uU54sUaAB3+WvVgjotv0ap2IW42Irl91wltUJ/rPpTpx/Dz3s6w6cdyG1/8SLgZneR8qN+ByImdW3wo8FKcMFxLZDW0tsDp6uxz4DbAu2v4SUBjjXOOInFF+DbChs3+AXOBtYEv0PicOfZYCHAQyu7XFtL+IFKS9QDuRkd8v9dc3wEPRz9kmYEGMc5UROba28/P1s+i8fxN9b9cAK4ErY5ipz/crnn0VbX8auOOIeWPVV339Poj7Z2u43lQn+s3lyTrhhRoR3abn6oQXa0Q/ueJaJ7xYI6LbUp3w2E11ot9cqhP951CdGFwu1YmjM8W8Rlh0JSIiIiIiIiIiIidEh8iJiIiIiIiIiMigaIBJREREREREREQGRQNMIiIiIiIiIiIyKBpgEhERERERERGRQdEAk4iIiIiIiIiIDIoGmESizKwhel9qZp8/yev+pyMe//Vkrl9ERE491QkREemP6oQMdxpgEjlaKXBcBcHM/MeYpUdBcM7NPc5MIiLiHaWoToiISN9KUZ2QYUgDTCJH+x4wz8xWm9m9ZuY3s38zs2VmttbMvgJgZheZ2UIz+29gXbTtBTNbYWYbzOz2aNv3gOTo+p6JtnV+u2HRda83s3Vmdn23dS8ys+fN7CMze8bMrHN9ZvZhNMu/x7x3REREdUJERPqjOiHDUiDeAUQ86AHg6865KwCiv9jrnHNzzCwReM/M3ozOey5wtnNuW/TxF51z1WaWDCwzsz865x4ws7ucczN62dZ1wAxgOpAXXead6HMzgSnAHuA94GNm9iFwLXCWc86ZWdbJfekiIjIAqhMiItIf1QkZlrQHk8ixXQp8wcxWA0uAXOCM6HNLuxUDgLvNbA3wAVDcbb6+XAj8zjkXcs5VAouBOd3Wvcs5FwZWE9nVth5oAZ40s+uApkG+NhERGTzVCRER6Y/qhAwLGmASOTYDvuacmxG9jXXOdX7j0Ng1k9lFwCXABc656cAqIGkA6+5La7fpEBBwznUQ+Zbjj8A1wOvH8TpEROTUUJ0QEZH+qE7IsKABJpGjHQLSuz1+A7jTzBIAzGyimaX2slwmUOOcazKzs4Dzuz3X3rn8Ed4Bro8el50PfBxY2lcwM0sDMp1zrwL3ENkdVkREYkt1QkRE+qM6IcOSzsEkcrS1QEd019SngR8R2Z10ZfTEeFVERvuP9Dpwh5mtBTYR2a210y+AtWa20jl3Y7f2PwEXAGsAB/yjc25ftKD0Jh140cySiHxbce8JvUIRERkM1QkREemP6oQMS+aci3cGEREREREREREZwnSInIiIiIiIiIiIDIoGmEREREREREREZFA0wCQiIiIiIiIiIoOiASYRERERERERERkUDTCJiIiIiIiIiMigaIBJREREREREREQGRQNMIiIiIiIiIiIyKBpgEhERERERERGRQfn/BcTExl6kU3EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "itsx = xstep.getitstat()\n",
    "itsd = dstep.getitstat()\n",
    "fig = plot.figure(figsize=(20, 5))\n",
    "plot.subplot(1, 3, 1)\n",
    "plot.plot(itsx.ObjFun, xlbl='Iterations', ylbl='Functional', fig=fig)\n",
    "plot.subplot(1, 3, 2)\n",
    "plot.plot(np.vstack((itsx.PrimalRsdl, itsx.DualRsdl, itsd.PrimalRsdl,\n",
    "          itsd.DualRsdl)).T, ptyp='semilogy', xlbl='Iterations',\n",
    "          ylbl='Residual', lgnd=['X Primal', 'X Dual', 'D Primal', 'D Dual'],\n",
    "          fig=fig)\n",
    "plot.subplot(1, 3, 3)\n",
    "plot.plot(np.vstack((itsx.Rho, itsd.Rho)).T,  xlbl='Iterations',\n",
    "          ylbl='Penalty Parameter', ptyp='semilogy', lgnd=['Rho', 'Sigma'],\n",
    "          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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}