{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dimensional-accommodation",
   "metadata": {},
   "source": [
    "Online Convolutional Dictionary Learning (CuPy Version)\n",
    "=======================================================\n",
    "\n",
    "This example demonstrates the use of [onlinecdl.OnlineConvBPDNDictLearn](http://sporco.rtfd.org/en/latest/modules/sporco.dictlrn.onlinecdl.html#sporco.dictlrn.onlinecdl.OnlineConvBPDNDictLearn) for learning a convolutional dictionary from a set of training images. The dictionary is learned using the online dictionary learning algorithm proposed in [[33]](http://sporco.rtfd.org/en/latest/zreferences.html#id33). This variant of the example uses the GPU accelerated version of [onlinecdl](http://sporco.rtfd.org/en/latest/modules/sporco.dictlrn.onlinecdl.html#module-sporco.dictlrn.onlinecdl) within the [sporco.cupy](http://sporco.rtfd.org/en/latest/modules/sporco.cupy.html#module-sporco.cupy) subpackage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "viral-entry",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-18T00:58:57.252182Z",
     "iopub.status.busy": "2022-02-18T00:58:57.251385Z",
     "iopub.status.idle": "2022-02-18T00:59:04.120982Z",
     "shell.execute_reply": "2022-02-18T00:59:04.119303Z"
    }
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "from builtins import input\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from sporco import util\n",
    "from sporco import signal\n",
    "from sporco import plot\n",
    "plot.config_notebook_plotting()\n",
    "from sporco.cupy import (cupy_enabled, np2cp, cp2np, select_device_by_load,\n",
    "                         gpu_info)\n",
    "from sporco.cupy.dictlrn import onlinecdl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "equivalent-genesis",
   "metadata": {},
   "source": [
    "Load training images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cubic-entrance",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-18T00:59:04.128767Z",
     "iopub.status.busy": "2022-02-18T00:59:04.127390Z",
     "iopub.status.idle": "2022-02-18T00:59:04.513264Z",
     "shell.execute_reply": "2022-02-18T00:59:04.512050Z"
    }
   },
   "outputs": [],
   "source": [
    "exim = util.ExampleImages(scaled=True, zoom=0.25)\n",
    "S1 = exim.image('barbara.png', idxexp=np.s_[10:522, 100:612])\n",
    "S2 = exim.image('kodim23.png', idxexp=np.s_[:, 60:572])\n",
    "S3 = exim.image('monarch.png', idxexp=np.s_[:, 160:672])\n",
    "S4 = exim.image('sail.png', idxexp=np.s_[:, 210:722])\n",
    "S5 = exim.image('tulips.png', idxexp=np.s_[:, 30:542])\n",
    "S = np.stack((S1, S2, S3, S4, S5), axis=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "royal-found",
   "metadata": {},
   "source": [
    "Highpass filter training images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "caring-municipality",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-18T00:59:04.518761Z",
     "iopub.status.busy": "2022-02-18T00:59:04.517475Z",
     "iopub.status.idle": "2022-02-18T00:59:07.518900Z",
     "shell.execute_reply": "2022-02-18T00:59:07.517888Z"
    }
   },
   "outputs": [],
   "source": [
    "npd = 16\n",
    "fltlmbd = 5\n",
    "sl, sh = signal.tikhonov_filter(S, fltlmbd, npd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "approved-scout",
   "metadata": {},
   "source": [
    "Construct initial dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "adult-lounge",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-18T00:59:07.523923Z",
     "iopub.status.busy": "2022-02-18T00:59:07.523093Z",
     "iopub.status.idle": "2022-02-18T00:59:07.527793Z",
     "shell.execute_reply": "2022-02-18T00:59:07.527254Z"
    }
   },
   "outputs": [],
   "source": [
    "np.random.seed(12345)\n",
    "D0 = np.random.randn(8, 8, 3, 64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "regulated-direction",
   "metadata": {},
   "source": [
    "Set regularization parameter and options for dictionary learning solver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "floating-pension",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-18T00:59:07.534303Z",
     "iopub.status.busy": "2022-02-18T00:59:07.533710Z",
     "iopub.status.idle": "2022-02-18T00:59:07.536121Z",
     "shell.execute_reply": "2022-02-18T00:59:07.536633Z"
    }
   },
   "outputs": [],
   "source": [
    "lmbda = 0.2\n",
    "opt = onlinecdl.OnlineConvBPDNDictLearn.Options({\n",
    "                'Verbose': True, 'ZeroMean': False, 'eta_a': 10.0,\n",
    "                'eta_b': 20.0, 'DataType': np.float32,\n",
    "                'CBPDN': {'rho': 5.0, 'AutoRho': {'Enabled': True},\n",
    "                    'RelaxParam': 1.8, 'RelStopTol': 1e-7, 'MaxMainIter': 50,\n",
    "                    'FastSolve': False, 'DataType': np.float32}})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "limiting-coffee",
   "metadata": {},
   "source": [
    "Create solver object and solve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "simple-inclusion",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-18T00:59:07.549697Z",
     "iopub.status.busy": "2022-02-18T00:59:07.549037Z",
     "iopub.status.idle": "2022-02-18T00:59:20.544071Z",
     "shell.execute_reply": "2022-02-18T00:59:20.545395Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on GPU 0 (NVIDIA GeForce RTX 2080 Ti)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Itn   X r       X s       X ρ       D cnstr   D dlt     D η     \n",
      "----------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  9.81e-04  1.58e-03  5.00e+00  8.03e+01  6.07e+00  5.00e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1  1.82e-03  1.59e-03  5.00e+00  7.30e+01  4.64e+00  4.76e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   2  3.25e-03  2.01e-03  5.00e+00  2.38e+01  2.63e+00  4.55e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   3  1.91e-03  1.91e-03  5.00e+00  4.86e+01  2.31e+00  4.35e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   4  2.86e-03  1.80e-03  5.00e+00  2.00e+01  1.69e+00  4.17e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   5  1.87e-03  1.53e-03  5.00e+00  3.53e+01  1.98e+00  4.00e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   6  2.35e-03  3.19e-03  5.00e+00  3.60e+01  2.23e+00  3.85e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   7  1.69e-03  1.87e-03  5.00e+00  4.22e+01  2.15e+00  3.70e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   8  1.73e-03  1.51e-03  5.00e+00  3.19e+01  1.74e+00  3.57e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   9  2.01e-03  2.87e-03  5.00e+00  3.23e+01  1.86e+00  3.45e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  10  2.31e-03  1.91e-03  5.00e+00  1.58e+01  1.47e+00  3.33e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  11  1.90e-03  2.79e-03  5.00e+00  2.98e+01  1.57e+00  3.23e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  12  2.17e-03  1.87e-03  5.00e+00  2.49e+01  1.85e+00  3.12e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  13  2.61e-03  2.11e-03  5.00e+00  1.38e+01  1.17e+00  3.03e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  14  1.96e-03  2.23e-03  5.00e+00  3.45e+01  1.92e+00  2.94e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  15  2.37e-03  1.98e-03  5.00e+00  1.33e+01  1.03e+00  2.86e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  16  2.35e-03  2.16e-03  5.00e+00  2.16e+01  1.46e+00  2.78e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  17  2.27e-03  3.43e-03  5.00e+00  2.64e+01  1.82e+00  2.70e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  18  2.29e-03  2.04e-03  5.00e+00  1.25e+01  1.02e+00  2.63e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  19  1.92e-03  2.32e-03  5.00e+00  2.98e+01  1.54e+00  2.56e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  20  2.23e-03  2.11e-03  5.00e+00  1.95e+01  1.40e+00  2.50e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  21  2.18e-03  1.90e-03  5.00e+00  2.13e+01  1.24e+00  2.44e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  22  2.14e-03  3.30e-03  5.00e+00  2.33e+01  1.49e+00  2.38e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  23  1.85e-03  2.24e-03  5.00e+00  2.75e+01  1.41e+00  2.33e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  24  2.13e-03  1.98e-03  5.00e+00  1.78e+01  1.18e+00  2.27e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  25  2.10e-03  1.89e-03  5.00e+00  1.99e+01  1.14e+00  2.22e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  26  1.83e-03  2.28e-03  5.00e+00  2.58e+01  1.26e+00  2.17e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  27  1.70e-03  2.18e-03  5.00e+00  2.48e+01  8.44e-01  2.13e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  28  1.68e-03  2.22e-03  5.00e+00  2.44e+01  6.93e-01  2.08e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  29  2.16e-03  1.96e-03  5.00e+00  1.59e+01  1.21e+00  2.04e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  30  1.77e-03  2.36e-03  5.00e+00  2.40e+01  8.80e-01  2.00e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  31  1.92e-03  1.67e-03  5.00e+00  1.75e+01  1.15e+00  1.96e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  32  1.75e-03  1.60e-03  5.00e+00  1.76e+01  8.28e-01  1.92e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  33  2.10e-03  1.94e-03  5.00e+00  1.48e+01  1.00e+00  1.89e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  34  1.74e-03  2.23e-03  5.00e+00  2.26e+01  1.18e+00  1.85e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  35  2.12e-03  2.02e-03  5.00e+00  1.43e+01  8.84e-01  1.82e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  36  1.72e-03  2.25e-03  5.00e+00  2.16e+01  8.68e-01  1.79e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  37  2.15e-03  2.10e-03  5.00e+00  1.39e+01  7.84e-01  1.75e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  38  2.04e-03  1.82e-03  5.00e+00  1.57e+01  9.79e-01  1.72e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  39  1.70e-03  2.23e-03  5.00e+00  2.08e+01  1.00e+00  1.69e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  40  2.27e-03  3.35e-03  5.00e+00  1.62e+01  1.47e+00  1.67e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  41  2.03e-03  3.22e-03  5.00e+00  1.58e+01  1.04e+00  1.64e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  42  1.85e-03  3.02e-03  5.00e+00  1.58e+01  9.14e-01  1.61e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  43  1.68e-03  2.84e-03  5.00e+00  1.58e+01  9.14e-01  1.59e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  44  1.97e-03  1.74e-03  5.00e+00  1.38e+01  9.08e-01  1.56e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  45  2.12e-03  1.96e-03  5.00e+00  1.21e+01  9.53e-01  1.54e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  46  1.86e-03  3.08e-03  5.00e+00  1.57e+01  1.02e+00  1.52e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  47  1.82e-03  2.28e-03  5.00e+00  1.83e+01  1.24e+00  1.49e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  48  1.60e-03  2.72e-03  5.00e+00  1.52e+01  9.08e-01  1.47e-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  49  2.05e-03  1.96e-03  5.00e+00  1.14e+01  8.44e-01  1.45e-01\n",
      "----------------------------------------------------------------\n",
      "OnlineConvBPDNDictLearn solve time: 12.33s\n"
     ]
    }
   ],
   "source": [
    "if not cupy_enabled():\n",
    "    print('CuPy/GPU device not available: running without GPU acceleration\\n')\n",
    "else:\n",
    "    id = select_device_by_load()\n",
    "    info = gpu_info()\n",
    "    if info:\n",
    "        print('Running on GPU %d (%s)\\n' % (id, info[id].name))\n",
    "\n",
    "d = onlinecdl.OnlineConvBPDNDictLearn(np2cp(D0), lmbda, opt)\n",
    "\n",
    "iter = 50\n",
    "d.display_start()\n",
    "for it in range(iter):\n",
    "    img_index = np.random.randint(0, sh.shape[-1])\n",
    "    d.solve(np2cp(sh[..., [img_index]]))\n",
    "\n",
    "d.display_end()\n",
    "D1 = cp2np(d.getdict())\n",
    "print(\"OnlineConvBPDNDictLearn solve time: %.2fs\" % d.timer.elapsed('solve'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "analyzed-stevens",
   "metadata": {},
   "source": [
    "Display initial and final dictionaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "virtual-indiana",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-18T00:59:20.564302Z",
     "iopub.status.busy": "2022-02-18T00:59:20.560598Z",
     "iopub.status.idle": "2022-02-18T00:59:20.701609Z",
     "shell.execute_reply": "2022-02-18T00:59:20.702859Z"
    }
   },
   "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",
   "id": "double-purpose",
   "metadata": {},
   "source": [
    "Get iterations statistics from solver object and plot functional value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "printable-douglas",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-18T00:59:20.709106Z",
     "iopub.status.busy": "2022-02-18T00:59:20.707897Z",
     "iopub.status.idle": "2022-02-18T00:59:20.811213Z",
     "shell.execute_reply": "2022-02-18T00:59:20.812451Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+iMwsViAiUjXVBxG3CyciUjf1B5FRjw6rHTa7I9hDISKicVB/ELHNDxGRqqk/iNj4lIhI1VQfRGajc08iFiwQEamS6oOof7twzoiIiNRI/UHEpTkiIlVTfxBFcmmOiEjNwiCIOCMiIlIz1QdRtEEHIbg5HhGRWqk+iDQagegIduAmIlIr1QcRwManRERqFh5BFKlnsQIRkUqFRxAZdZwRERGpVFgEkZmb4xERqVZYBFFMpI5NT4mIVCo8gojFCkREqhUeQRSpR3uPDQ6HDPZQiIhojMIjiIw6SAm09XB5johIbcIkiJxtfliwQESkOuERRNyllYhItcIjiLgVBBGRaoVHEEVyaY6ISK3CI4j6ZkRcmiMiUpuwCCKz0bU5HmdERERqE1ZBxGIFIiL1CYsg0mk1MBm0LFYgIlKhsAgiwLUVBIOIiEhtwiaIzNwKgohIlcImiGKM3ByPiEiNwieIIvVo6+GMiIhIbcIniIw6zoiIiFQofIIoknsSERGpUdgEkdmoQ2tXL6TknkRERGoSNkEUY9TDIYEOqz3YQyEiojHwGERCCKMQYo8Q4qAQ4qgQ4pfnYmBj5Wp82sblOSIiVfFmRtQDYI2UcgGAhQAuFUKcF9BRjUP/5ngsWCAiUhOdpydI5aBLu/NHvfMScgdiXJvjsWCBiEhdvDpGJITQCiEOAKgD8LGUcreb59wmhCgQQhTU19f7eZiembldOBGRKnkVRFJKu5RyIYB0AMuEEHPdPGeDlDJPSplnsVj8PEzPYoycERERqdGYquaklM0AtgO4NBCD8UV/sQKPERERqYk3VXMWIUSc83YkgIsBHA/wuMaMm+MREamTx2IFAKkAXhRCaKEE12tSyncDO6yxi9BpYdRruF04EZHKeFM1dwjAonMwFp+ZjdyTiIhIbcKmswLgbHzKYgUiIlUJryCK1LNYgYhIZcIriLg0R0SkOuEVRJF6FisQEalMWAWRaysIIiJSj7AKohijsjke9yQiIlKP8AqiSB167RI9Nkewh0JERF4KryBi41MiItUJqyAys/EpEZHqhFUQuRqftnBzPCIi1QivIHItzXFGRESkGmEVRLHOXVrZXYGISD3CKohYrEBEpD5hFURmLs0REalOWAWRUa+BXivQymIFIiLVCKsgEkIgfVIUDlU2B3soRETkpbAKIgC4euFkfHayERVNncEeChEReSHsguiGJekQAnh9b2Wwh0JERF4IuyBKnxSFC2Yk4vW9lbA72PyUiCjUhV0QAcBX8qbgTHMXPjvZEOyhEBGRB2EZRJfkJCM2Uo/XCrg8R0QU6sIyiIx6La5ZOBkfHq1Bc6c12MMhIqJRhGUQAcBXlk6B1ebA2weqgj0UIiIaRdgGUe7kWOROjsFrBRXBHgoREY0ibIMIUIoWjla14siZlmAPhYiIRhDWQXT1wskw6DT4J2dFREQhK6yDKC7KgPW5KXjrQBW6e+3BHg4REbkR1kEEAF/Nm4KWrl58dKw22EMhIiI3wj6IlmclIC0ukstzREQhKuyDSKMRuGFJOnaWNKDyLBuhEhGFmrAPIgC4MS8dABuhEhGFogkRROmTorAiKxH/LKiEg41QiYhCyoQIIkDptHCmuQuflzYGeyhERDTAhAmidTnJiDHqsDGfRQtERKFkwgSRUa/FJTkp2FXSACm5PEdEFComTBABwIIpsWjssKKqpTvYQyEiIqcJFUTz0mIBAIcr2XuOiChUTKggyk6NgVYj2ASViCiETKggMuq1mJkUjcN+DKJeuwM3/t9nuOvlfaht5ZIfEdFYTaggApTluSNnWvxWsPDmvjPILz+L94/U4OI/foKXPi+HnecqERF5beIFUbpSsFDth4IFm92Bp7aVYG5aDLbcvxILp8bhZ28fxfVPf4ZjVa1+GC0RUfibcEE011Ww4IflubcPVOF0UyfuWTMTmYkm/O0/luGJry1ERVMnrnxqJ/77vUJ0Wm0+fw4RUTibcEGU46eCBbtD4qltJchOjcElOckAACEErl6Yhi0/XIkbl6TjLztKccljO/D5SXZzICIayYQLIn8VLLx7qAplDR24Z80MCCEGPRYXZcCj18/Ha987HzqtwA82HuBJtEREI5hwQQQoy3O+FCzYHRJ/2lqC2clmrM9NGfF5y6bF487VM1DT2o2jPGZEROTWhAyieWmxaGi3omac5dbvH6lGSV077l47AxqNGPW5a+YkQQhgS2HduD6LiCjcTcggmutDhwWHQ+LJLcWYkRSNy+amenx+YnQEFk6Jw9bj3KqciMidCRlEOakx0AiMq2Dhw6M1OFHbjrvXzIDWw2zIZe2cJBysbEEdT3glIhpmQgZRpEGLmUlmHBpjEDkcEk9sKcb0RBOumD/Z69etzVaq6rYVcXmOiGgoj0EkhJgihNgmhCgUQhwVQtx7LgYWaPPSx16wsLmwFsdr2nDnau9nQwAwJ8WMybFGbOZxIiKiYbyZEdkA/FBKmQ3gPAB3CiFyAjuswBtrwYKUEk9uLUZGQhSuXuj9bAhQzi9am52MncUN6O61j2e4RERhy2MQSSmrpZT7nLfbABQCSAv0wAJtrAUL24rqcORMK+5cPQM67dhXNNdkJ6Gr186tyomIhhjTX1QhRCaARQB2u3nsNiFEgRCioL6+3k/DC5yxFCxIKfHElhKkT4rEtYvGl8HnT09ApF6LrVyeIyIaxOsgEkJEA9gE4D4p5bCzM6WUG6SUeVLKPIvF4s8xBoSrYMGbDgufn2zEwYpm3LFqBvTjmA0BSkeHC2YmYkthLbssEBEN4NVfVSGEHkoI/UNK+UZgh3TuzE2LxeEzrR6D4fnPyhFvMuC6xb6tSF6cnYSqlm4cr2nz6X2IiMKJN1VzAsBfARRKKR8L/JDOnXlpMWho70Fta8+Iz6lo6sTmwlrctGwKjHqtT5+3enYSAGBLIU9uJSJy8WZGtALANwGsEUIccF6+HOBxnRPz0j1vCfHSF6egEQI3n5fh8+clxRixID0WW46r8zjRHz8qQkF5U7CHQURhxpuquZ1SSiGlnC+lXOi8vHcuBhdoOamx0IiRg6jTasOre07j0twUpMZG+uUz18xJxoGKZjS0jzwLC0UtXb3409YSvL63MthDIaIwMyE7K7hEGrSYkRQ9YuXcm/vPoLXbhm+vyPTbZ67NToKUwDaVzYpK6pTjWuWNHUEeCRGFmwkdRICrYGF4EEkp8eJn5cidHIO8jEl++7zcyTFIiTF67Mbd0tmLP28rQWt3r98+2xfFte0AgFONnUEeCRGFmwkfRPPSYlHf1oPaIR0WPj/ZiBO17bhleeawje98IYTAmuwkfFpcjx6b+y4Lrd29+NZzu/H7D4vwWn6F3z4bAD4trsetL+TDZneM6XXFdUoQVbd0szsEEfkVg8jZYeHQkA4LrpLtqxaMrZ2PNy7OTkKH1Y7dpcMP/Lf32PDt5/bgWHUrLOYIfHi0xm+fK6XEo+8fx5bjdWNeYjtR219yfrrJv7Mih4PnVRFNZBM+iHImxwwrWKho6sQWP5Vsu7M8KxFGvQZbhxwn6rTa8J3n9+BgZQv+dNNifH3ZVBScOuu3woYvSpv6doo9Vj22c5lK6tox3WICAJQ3+O84UWF1K3J+8QGOVvm2dTsRqdeED6Iog25YwcJLX5yC8FPJtjtGvRYXzEjE5gFdFrqsdtz6QgH2njqLJ762EJfOTcH63BRICWw+5p/zjv66sxTxJgP0WoHj1d5vXd7a3Yvqlm5ckqNsZ+HP40Sb9laiu9eBfaeb/faeRKQuEz6IgMEFC4Eo2XZnzZxkVJ7tQnFdO7p77bjtpQJ8UdaIx76ysG+vo+xUM9InReIjPwTRyfp2bC6sw83nZSDLEo3CMQRRifP4UF5GPOKi9DjV5J8ZkcMh8e6hamV8zs8goomHQYTBBQtv7a9Ca7cNtyzPDOhnrpmjdFl4/3ANvv/3vfi0uAG/u34+rhnQVFUIgfW5KdhZ3ID2HptPn/fczjIYdBp887wMZKfGjKnNULHz+NDMpGhkxEf5bUa09/RZ1LR2QwglKIloYmIQYXDBwguflSEnNQZLM/1Xsu1OSqwRc9Ni8PiWE9hWVI/fXDsPN+ZNGfa8dTnJsNod+KRo/B3Nmzqs2LSvEtcuTIPFHIHsVDOqW7rR3Gn16vXFte2I0GkwJT4KGQkmv51L9M7BKhj1GlycnYzSep6fRDRRMYjQX7DwzKelOFHbjm+v8G/J9kjW5yjHgH51dS6+/qWpbp+TlxmPeJPBp+q5l3efQnevA7deOA0AMCclBgBwzMvlueK6dmRZoqHVCGQmROHM2S5YbWMr/x7KZnfgvcPVWDsnGfPSYnGmuQudVt9mfUSkTgwiKAULWZZo7ClrwqQofUBKtt25fVUWPrzvInzr/MwRn6PVCFycnYRtx+vG9ce/x2bHi5+fwkWzLJiVbAYAZKcqQXTcy8q54to2zEqOBgBkJJjgkEDlWd+W53aXNaGh3Yor5qciy6K8d5kfq/GISD0YRE6u5bmblk0NSMm2O3qtBrNTzB6ftz43BW09tnHt7vqvA1Wob+vB/3POhgDAYo5AYrTBq4KFtu5eVLV0Y6YzxDITowD4Xjn37qEqmAxarJ6ThKwkpSz8JJfniCYkBpHTec4dVANVsu2LFTMSEWXQ4qMxLs9JKfHXnWWYnWzGBTMSBz3mbcGCq2JuZlL/jAjwreec1ebA+0dqcElOMox6LTITTErBAivniCYkBpHTDUvS8cXDazE5LnAl2+Nl1GuxarYFHx+rHVMXgl0ljThe04ZbL5w27JhXdmoMimrbPLb6cbX2cc2IEkwGREfofJoR7SppQHNnL650LoEa9VpMmRTFyjmiCYpB5KTRCMRG6oM9jBGty0lBXVsPDlQ2e/2aZz4tRWJ0BK5eOPyY15wUM6w2h8fjMsW1bTDoNJgaryzJCSEwNT4Kp3yYEb1zqAoxRh0unNm/pXyWxcSlOaIJikGkEqvnJEGnEV5Xz52obcMnJ+pxy/kZiNANP+blKlgo9LA8N7BiziUzcfznEnX32vHR0VpcOjcFBl3/P78sSzRK69vZd45oAmIQqURspB7nZyXgo6P9bYFG89zOMkToNPjGCMe8sizR0GuFx4KF4tr2vuNDLhkJJlSc7RxzB28A+OREPdp7bH3dI/rGkxSNHpsDZ5q7xvyeRKRuDCIVWZebgrKGDo/HUurbevDG/jO4fkk64k0Gt88x6DTIskSP2nOuo8eGM81dfaXbLpkJUei1S1S3dI/wypG9c7AK8SYDlmclDLp/eqJSBFHKEm6iCYdBpCKXZCtNRz88OnrvuWc+LYXV5sCtF0wb9XnZqTEoHOVcIlfF3IykwSXm462c67TasKWwDl+elwKddvA/vSznrIuVc0QTD4NIRVJijVg4JW7EMm6b3YFfvnMUG3aU4rpFaX0nio4kO9WMmtZunO1w3+rHtQfR8BmRK4jGdpxoS2Edunrtw5blAKUaLzZSz8o5ogmIQaQy63KTcbCyBdUtg4+ltHb34rt/K8Dzu8rxHyum4fc3LvD4Xq5WP4U17pfnSuraYdD2V8y5JJkjYNRrcHqMM6J3DlYhOSYCSzPjhz0mhHBWznkfRP8+VI2Xd58e0xiIKPQwiFRmfW4KAODjAVtDnG7sxPX/+xl2FjfgN9fOw8+vzBlU5TaSvsq5EZbnTtS2YbrFNGwZTaNRSrjHMiNq7e7F9hP1+PK81BHHlmWJHlMJ9+8/PI6H3zyMjfkMIyI1YxCpTJYlGlkWU18Z956yJlzzv7tQ19aDv926bMTmqe4orX4iRixYKK5r7zuRdaiMBNOYziX6+GgtrDZH30ms7mQlRaO+rQctXb0e36+utRvljZ0wG3V4+M0j2FZU5/E1RBSaGEQqtD43BV+UNuG5nWX4xrNfIC5Sj7fuXIHlWYmeXzxEdqrZ7dJcp9WGyrNdmJXk/jhTZoJyLpG35/28e6gKaXGRWDQlbsTnuI5plXqxPLenvAkA8Jebl2B2shl3/mMfDlf6vt24lBKPbz6Bdw5WeVUmT0S+YxCp0LrcFNgdEr969xi+NC0Bb96xAtOc5c9jlZ0agxO17cPOCerrMZfsPogyEkzosTlQ2+a5hPtshxWfFjfgigWpo26vMd3iLOH2YnluT1kTogxaLJsWjxe+sxSTogz4zgv5qGjyrRnrtqI6PL65GHe/sh9ff2Z336aANPF8UdqIxz4qCvYwJgQGkQrNT4vFhTMT8R8rpuH57yxFbNT4WxON1OqnuNZ96bZLX+Vcg+c//B8dq4HNIXGlm2q5gabGR0GnEV4VLOwpa8KSjEnQaTVIijHixf9YCqvNjlue3+P1hn9DSSnx2McnMDU+Cr++OhfHqltx2ROf4jfvFfq8Qy6pz4YdpXhyawmqeJJ1wDGIVEijEXjp1i/h51fmQK/17X/CkVr9nKhrg16rbITnTkaCazsIz7OXj4/VIS0uErmTY0Z9nl6rQUaC5+anLZ29KKptG1R9NyPJjGdvWYrKpi5898UCdPfaPY5r+DhrceRMK+5ZOxPfPD8TW3+4EtcvTseGHaVY+8ftXK6bQHrtDux2bruy3Yfdkck7DKIJbqRWPyW17ZieGD2sYs5lclwk9FqBUx6WwnpsduwqacCaOUle7XrrTeVcwakmSAksmza4DHzZtHg89tUFKDh1Fve/dmBMfescDon/2VyMaYkmXONsEpsQHYHf3jAfb9yxHInREbj7lf34xrO7vTqGRep2sKIZHVblP2Y+OcFCmEBjEE1wrlY/Q4NIqZgb+YRYrUZgyiTPXbh3lzahq9eONXOSvBpPVlI0TjV2oHeUPnZ7yppg0Gqw0E3hwxXzJ+M/L8/Ge4dr8Mh7hV59JgB8eLQGhdWtuGftjGHhu3jqJPzrrgvw62vm4siZFnz7+Xyft0qn0LazpAFCAF+el4JdJY383zvAGESkbJI34FyiLqsdFWc7MXOE40MuGQlRHo8RbSuqQ4ROg/OmJ4z6PJcsSzR67XLUooM95U2Ynx474k66t14wDd9enom/7izDa/kVHj9TmQ2dwHSLCVctSHP7HK1G4JvnZeDJmxbhdFMnXtnDc5fC2WcljZg7ORZXL0xDe48Ne0+dDfaQwhqDiIa1+jlZ3w4ph7f2Gcp1LtFox022Ha/D8qwERBq82349yzL6tuGdVhsOV7YMW5YbSAiB/7w8GxfMSMR/vnUE+0+P/kfk34ercaK2HfddPMvjicArZ1lw/vQEPLmlmAUMYaqjx4Z9p89ixYxELM9KgE4jsJ3LcwHFIKIBBQvK8pyrx9xoS3OAci5Rh9WOhnb3VWplDR0ob+zEai+X5QBguodzifafbobNIbF0lCACAJ1Wgz/dtAjJsRH4/t/3oW6EMnO7QzlvaFZyNC6fl+pxfEIIPHjZHDR2WPHMjlKPzyf12VPeBJtDYsWMBJiNeuRlTsInLFgIKAYR9feccy7PFde1Q68VfV22R5LhPHdppONEW48r/xW5erb3QRQbqUdidMSIlXN7ypqgEcCSjEke32uSyYC/3JyH5i4r7vzHPrfr/O8crMLJ+g6vZkMuC6bE4fJ5qXjm01LUt/V49RpSj13FDTDoNH1VmatmJ+F4TRtqxrHtCXmHQUTDWv0U17ZhWqLJY2m4py7c24vqMCMpGlPi3ZeAj2S0bcP3lDUhOzUGMUbvzp3KmRyD392wAPnlZ/Hrd48Nesxmd+DJLcWYk2LGpc4eft764bpZ6LE58NTW4jG9jkLfrpONWDJ1Ut8xyFWzlS3tWT0XOAwiAjC41U9xXbvHQgUASIuLhFYj3Hbh7uixYXdpk9fVcgNlJUWjpK592LEnq82B/RVnRz0+5M5VCybjtoum46UvTg0qXnj7QBVKGzrwg0tmQePlbMhluiUaX1s6Bf/YfXpMPfcotDW096CwuhUXzOxvlzU72YyUGCPPJwogBhEB6G/1095jw+mmTo/HhwCl9HtynNHtjGhXSQOsdkfff02ORZYlGi1dvWgask/S4TMt6O514EtjDCIAeGD97L7ihQMVzei1O/Dk1mLkTo7BupzkMb8fANy7dib0Wg3+8NGJcb2eQs/nJ5WTWAfuICyEwKrZFuwsbhj1tAIaPwYRAehv9bP5WC2khFczIkBZnnM3I9hWVIfoCJ3bvYc8GalyLt/Z6DRvHO/pKl5IionA7S/txYYdpTjV2In7L5nl1Ym27iTFGPHdC6fhnYNVPjVctTskXvq83Oc+eeS7XSUNMBt1mJcWO+j+lbMsaOuxYf/p5uAMLMwxiAhAf+Xcvw5WAfBcuu2SkTB8XyIpJbYdr8eFMxPH1YLI1YV7aMHCnrImZFlMSIyOGPN7As7ihW8uQXOXFb//sAgL0mPHtXQ40G0XTcekKD1++8Hxcb3e7pD48T8P4mdvH8UTW3i8Kdh2nWzAedMThp3UvGJmolLGze1GAoJBRAD6W/3sOFEPncZzxZxLZoIJLV29gxqNFla3oaa1e0xl2wOlxUUiQqcZVMJtd0jklzeN+fjQULmTY/Hb6+fDqNfgx+vnjHs25GI26nHXmpnYWdKAT4vHdgzB7pB44PVDeGP/GSSZI7DteN2Y2hKRf51u7ERFUxcumDF8O5UYox6LMyap8jiRlBK3v7QXf/gwdDuJM4gIQH+rH5tDYlqiCQadd/80MtxUzrk2qRvP8SFAaeo6LXFw5VxRTRvaum0+BxEAXL0wDQd/sW7QAWlf3HzeVKTFReK3Hxz3OkgcDokHNx3Cpn2V+MHFs/DTy7PR2GHFwcpmv4wJUJrD/uLtI15tNEjKbAgAVsxw3wVk1WwLjlW3oq5VXWXc7x+pwQdHa/D8rjJ0WkPzJGwGEfXJcS7PeVOo4JLppgv3tuN1mJcWiySzcdxjyUqKHrQ0t6dMOYg8nmNO7kTovOv04O17/Wj9LBw504p3D1d7fL7DIfHQG4fxz72VuHftTNx78UysnGWBRijfnb+8d6QaL35+Cv8s8NzmiJT+cskxEX1Lw0OtmqXM8LefUM+sqMdmx3+/X4gEkwEdVjveO1wT7CG5xSCiPnNSlQKFkfYgcmdKfBSEAE45Z0TNnVbsO30Wq8c5G3LJskSjoqmzbzuH/PKzSIuLRPqksZ2TdK5cvSANc1LM+PW7x/DMjtIRT350OCQefvMwNhZU4J41M3DfxTMBAHFRBuRlxGOLH4Mov0wp7nh9b6Xf3tMXje09uPrPu/DBkdD7Y+hwSHx+shErshJHXK7NTjUjyRyBT1QURC9+Vo6Kpi78z1cXIjMhKmT/o4RBRH1yUpVKodnJ3geRUa9FSowR5c4Z0Scn6uGQGPfxIZcsiwkOqQSclBK7y5qwNNNzN4Vg0WgEfn/DAkyONeKR9wpx/qNbcNOGL7Ax/3Tf0pjDIfHTtw7j1fwK3LV6Bn4wpGJv9ZwkHK1q9dsZ/HvKmxCh0+B4TRuOVvm+jbovpFRmgQcrmvH45hMht69TYU0rmjqsWOHm+JCLEAIrZ1nw6Yn6YTsah6LG9h78aUsJ1sxJwkWzLLhhSTp2lzXh9AgnoAcTg4j6LM9KwO9vmI9LxnheTUZCVN+MaHtRPeJNBsxPj/NpLAMr58obO9HQ3oNl07zr4B0s89Jj8fZdF2DrD1finjUzUd3ShZ9sOoylj2zG7S/txb0bD+CVPRW4Y1UWfrhueNn42mwlvLf6YVZU3dKFyrNd+N5F06HXCmzae2ZMr9+w4yT+9nm5z+NweX1vJT46VotFU+NwvKYNBSHWzfqzEmXpd7QgApR2P63dNhyoaD4Ho/LN45uL0dlrx8NfngMAuG5xOoQAXt8XGjPkgRhE1EejEbgxb4rXhQournOJ7A6J7UV1WDXL4nXftpFMd51LVNfed3zIH4UK58J0SzR+cMksbPvRKrx15wp8fdlUFJxqwjsHq3D7yiz8eP1st8s/M5OikT4p0i9BtMe5LHdJTgrWzknG2wfOeH0y5pnmLvzugyL87oMivxzcrmjqxC/fOYZl0+Lx0q1fgjlCh5c+P+Xz+/rTzpIGZFlMSIkd/bjmBTMTodWIkK+eK65tw8t7TuMbX5rat9Q+OS4SF8xIxKa9lSFXnckgIp9lJJjQ0G7FrpIGnO3sxSofl+UAIMqgQ1pcJEobOrCn7CwSTIa+E13VQgiBhVPi8F9X5eKLh9Zi8/0r8ZNL3YeQ6/lr5iRhV0nDuLY6Hyi/vAkmgxbZqWZcvyQdjR1WrztI//XTMtgcEu09Nvz7kOfii9E4HBI/+udBSCnxxxsXIDpCh+uXpOP9I9Uh0zDWanNgT1mTx9kQoDTlXTw1LuS3hfjNe4WIMmhx38WzBt1/w5J0nGnuwufObdBDBYOIfOaqnHvxs3JoBLBypm+FCi7TLSacrG/HnvJGLM2M9/mcn2DSaTWYkRTt8XdYMycJXb12n/9Q5JedxeKMSdBpNVg124IEkwGbvFiSae604tX807hm4WRMTzThNR8Pbj+3qwy7y5rwiytz+5rf3nxeBnrt0uf39pf9p8+iq9fuVRABSpeFI2daQyZIh9pxoh7biupxz5qZiDcZBj22PjcFZqMu5IoWGETkM9e5RFuL6rAkYxJio7zrjO2JawvziqYuj/sPhYvzpicgUq/1qYy7udOKoto2LHOWuuu1Gly1cDK2FNYNOvHYnb99fgqdVjtuX5WFryydgvzysyipc78lhycnatvwuw+LcHF2Mm7MS++7f0ZSNJZnJeDl3adhD4Elol0nG6ER8HoX4VXObU12hGD1nN0h8ci/CzE1PgrfWp4x7HGjXourFkzGB0dr0NodOueXMYjIZxnOGZH0Q7XcQFkWE3rtyh+q8TQ6VSOjXosVMxKxpbBu3JVlBeVKIcDA8L5hSTqsdgfecbZwcqfLascLn5Vj9WwL5qTE4LrFadBqxLj+69lqc+C+Vw/AHKHDo9fPGzYT/OZ5GTjT3OXX86bGa1dJA+alxyE20sutRVJjkBgdEZLnE71WUIGi2jY8dNmcEc+VuzFvCrp7HT4vu/qTxyASQjwnhKgTQhw5FwMi9TFF6Pr6v41lEzxPXJVz0RG6vl54E8Ha7CScae7CidrxzUTyy5ug1yrHp1xyJ8diTooZr+8buXru9b0VaOqw4vaVWQCAJLMRa+ckYdO+yjF3nX5iywkcq27Ff183z21vwItzkpEcE4GXvghu0UJbdy8OVDTjghG6Kbij0TjLuIvrQ2JG59LW3Ys/flSEpZmTcOnckffXWpAei5lJ0SG1POfNjOgFAJcGeBykctMSo5Aaa8ScFO/PQfIkK0kJoiUZk3yuwlMTV5iPt3ouv7wJ89Pj+jZ2c7lhSToOVjS7XWqz2R3Y8GkpFk6JG1Sd+NWlU9DQbsWWQu/HsvdUE57efhI3LknHuhE2HNRrNbhp2VR8cqI+qPs57Slrgt0hsSJrbO2e1mYnobmzF0+E0DlRT28/iYZ2K/7z8pxRj0UKIXDDknTsO+3+30IweAwiKeUOAE3nYCykYj+7IgdP3rTIrwUFSeYILMmYhKsWTPbbe6pBSqwRuZNjsPV47Zhf291rx+EzLW5bIV29UFlqc1e08N6RGlQ0deH2lVmD/jdcOcuC5JgIbMw/7dXnd/TYcP9rB5EaG4mfX5kz6nNvWjYVWo3Ay7u9e29/67HZsbmwDhE6DRZ7sfX8QOtzU3DjknQ8ubUEv/uwKKhhVNXchZ+9dQQbdpTiukVpWDBgJjySa53LrqHSdUMX7AFQePD1BFZ3hBDY9P3lfn9fNVg7JwlPbSvB2Q4rJg2pfBrN/tPN6LVLLJs2/A+rxRyBlbMseHPfGfxo3ey+WaaUEv+3/SSmW0zDNgnUaTW4YUk6nt5+EjUt3R7Ps/n//l2I002deOX/nQezh+3ck2OMWJ+bjI0FFfjBJbOGzeD8RUqJM81dKKppw3HnpaimFaX1HbA5JFbOsoz5s7Uagd9ePx86rQZPbz+JXpsDP708+5xWdlY1d+Hp7SexMb8CEhJfWToFP1k/x6vXJpmNWDXLgjf3V+LH62cHfcXBb0EkhLgNwG0AMHXqVH+9LdGEtCY7GU9uLcGO4npcvTDN69fllzdBCGBJhvvijusXp2Pr8X347GQDLnSW2X9a3IBj1a347fXz3G6Z/pW8KfjztpN4fW8F7lozc8TP3nysFq/sOY3vXTTd6wq0m7+UgfcO1+Dfh6px/ZJ0zy8Yo8b2Hlz/9GeDusOnxUViTooZl+QkY3ZKjNttH7yh0Qj85tq5MGgFnt1Zhl67A/91VW7Aw6iquQv/u70Er+VXQkLixrwpuGNV1pj7MN6Yl44tf6/DjuJ6vx7bHQ+/BZGUcgOADQCQl5cXGoumRCo1Py0WidEGbCmsG3MQzU42j1gBtjY7CTFGHTbtrewLov/75CSSYyJwzSL3n5ORYML50xOwsaACd6ya4Tas6tt68JNNh5CdGoP7181y8y7unZ+VgOkWE1764lRAgujX7x7DmeYu/PKqXMxNi8GsZLPHmdpYCCHwX1flQq/V4NmdZbDaJR65Zq7b78hXx2ta8dLnp/rOvxpvALmsmZOMSVF6vF5QGT5BRET+o9EIrJqdhI+O1sBmdwzbMdQdm92BfafOjvoH3ajX4soFk7FpXyXauntR1tCBz042jlruCyhFC/dtPIAvShuxfMgMQkplb6W2Hhte/urCMW2xIYTAN8/LwC/fOYbDlS2Ylx7r+UVe+uREPd46UIV7187ELcsz/fa+Qwkh8NPLs2HQafC/20+i1+7Ab6+f75flrrq2bvzrQBXe2HcGx6pbodcKnwPIxaDT4OqFaXh592k0d1oRF+X9ErC/eQwiIcQrAFYBSBRCVAL4hZTyr4EeGNFEt3ZOEl7fW4l9p5u96rN3rLoVHVa7xz2brl+Sjn/sPo33Dldjx4kGmI06fP1Loy+nXzo3BTFv6/BqfsWwIHplTwW2HK/Dz67IwexxVE1etzgdv/ugCH//4hR+e8P8Mb/enU6rDT998zCyLCbcsTrLL+85GiEEfrx+NvRaDZ7YUgyb3YE7Vs+Aq4ZBQrnh+lmv1SA6QgdThBZRBt2g0Oqy2vHRsRq8se8MPi1WutkvSI/Ff12ZgysXTEaCm3L48boxLx0vfFaOtw9UBTSsPfEYRFLKm87FQIhosAtmJkKvFdhyvNarIHI1OvX03EVT4jA90YS/7ChFeUMHvrcyy+NylVGvxTWL0vBqfgVaOnv7umeUNXTg1+8ew4oZCfjOOP+QxUbqcc2iyXhz/xk8fHm21yeWjuZ/Pj6ByrNdeO175/t1E8TRCCHwg0tmwaDT4PcfFuGtAyOfPDxUpF4LU4QO0RFaNLRb0d5jw+RYI76/KgvXLkrHjCTvN6sci9zJschJjcHzu8qweOokv85Ix4JLc0QhymzUY9m0eGwtrMNDl2V7fH5+eROmxkchOWb0yjYhBK5fko7ff1gEg1bjdYB8dekU/O3zU3jrwBncsjwTNrsDP9h4AHqtwB9uXODTcZGbz8vAK3sq8MePinDVgsmYGh8FizliXAf+D1e24K87y3DTsqlB6dh+5+oZWDYtHrXOLcUFBFy/hgAgBNBjc6Cjx46OHhs6rDZ09NjQ7vw52qjDlfMn40vT4gNyrGmoBy6djfs2HsCVT+3EJTnJuP+SWef8BHIGEVEIWzMnGb9+9xgqmjr7moa6I6VEQflZrPRyZ9xrF6XhsY9P4Pol6UjyEFwuuZNjMTctBq/mV+Bb52fgqW0lOFDRjKe+vgipsZFevcdo7716tgV/+/wU/ubcIsKo12DKpChkJERhSnwUFk6Jw5XzJ4/6x9lmd+DBNw4hMToCD17mXSlzIPhrS/tzYdXsJHz6wGo8v6scz3xaisue+BSXz0vFfRfPxMwxbJLpCwYRUQhbMycJv373GLYerxt1Df9kfQcaO6x9jU49mRwXibfuWIGspLFtrfHVpVPxs7eO4O9fnMKftpbg2kVpuGK+f044fuZbeTjd1InTTZ2ocF6falSuPz/ZiOd3lePVPRX43Q3zRwzlv+4sw9GqVjz9jcV+WeKbKMxGPe5ZOxO3nJ+JZ3eW4rmdZXjvSDWuXjAZ9148C9MSA7sFiwjEGcF5eXmyoKDA7+9LNBGt+cN2GPVavHHH8hFPvHxlz2k89MZhbPnhyr4efYHQ0tWLZY9sRo/NgbS4SLx/34WI8WM59EikVLaN+PW7hZBS4qeX5+CmZVMGLd2dbuzEusc/wQUzLHjmW0tUvW1IsDV1WLFhRyle/KwcVrsD/7z9fCyeOrbuE0MJIfZKKfPcPcbu20Qh7ieXzcGx6lY8/MbhEVvJ5Jc1ITHagOkB/i/X2Eg9Lp+XCiGAP35lwTkJIUA5rvXVpVPxwX0XYsGUODz85mHc8nw+qlu6AChB9dO3DkMrBH59TeBPKg138SYDHrxsDnY8sBr3XzILCwLQOWUgBhFRiFufm4L7L5mFN/afwYYdpW6fs6e8CXkZ52bzwF9dMxdv3bHC6+4J/pQ+KQp/v/VL+NXVucgva8K6/9mBTXsr8eb+M/i0uAEPXDrH5+NV1M9ijsCdq2cEvAUQjxERqcDda2agqKYNj35wHLOSzYP2fapu6ULl2S58Z8W0czKW6AidV401A0WjEfjW+Zm4aKYFP379IH74z4PQagQWTY3DzecN3wyOQh9nREQqIIRSIp2TGoN7XtmPkrq2vsf6zh9SUaWWP2QmmvDqbefjPy/PxrREk9+6GdC5xyAiUolIgxbPfCsPEXotbn2xoG/b7/zyJpgMWmSnnptS21Ci1Qh898Lp2Hz/Ssw6R6XG5H8MIiIVmRwXib98cwmqm7tx58v70Gt3IL/sLBZnTPKqHx1RKOK/XCKVWZIxCb+5bh52lTTiJ5sOoai2bcIty1F4YbECkQrdsCQdx6tb8ezOMgDA0iC0siHyF86IiFTqoS9nY+UsC0wGLRYGsYqNyFecERGplFYj8My38lDX1h2wbbaJzgXOiIhUzKDT+LxBGlGwMYiIiCioGERERBRUDCIiIgoqBhEREQUVg4iIiIKKQUREREHFICIioqAKzSCy9QAOe7BHQURE50BodlY4tBF4/ydA8lwgdT6QMl+5TsoBdBHBHh0REflRaAZRUg6w+Bag+iBwcCOQ/6xyv0YHWLL7Qyk5B0jKBaKTAO5RT0SkSqEZROl5ygUAHA7gbBlQcwioPqSEU/HHwIF/9D8/Mh5IzlXCKSlbuSTOAqLYkZiIKNSFZhANpNEACVnKJffa/vs7GoDao0BdIVB3FKg9Buz/O9Db0f+cqETAMlsJJctsIHGmcjsmXXlfIiIKutAPopGYEoHpK5WLi8MBtJwG6ouAhhP910ffBLqb+5+njQDipyvh5rpOmAHEZwHRyQwpIqJzSL1B5I5GA0zKVC6z1vffL6Uyg2ooAhqKgaaTQONJ5XbxR4Dd2v9cnbH/PSZN678dPw2InQIY2OmYiMifwiuIRiIEEG1RLpkXDH7MYQdaKpRgaioFzpYrl6YyoOzTwUt9AGCyAHFTlVCKm+q8ZACx6UBsGmCMPVe/FRFRWJgYQTQajbZ/1oO1gx9zzaTOlisFE82n+y+1R4Ci9wF7z+DXRMQAMWn9wRTjvDanAjGTlUuE+dz8bkREKsAgGs3AmdSUpcMfdziAjjolmFoqgJYzQEsl0HpG+blqP9DZMPx1BjMQk9ofTuYUIDpFuTanKMepzCmAPjLwvyMRUZAxiHyh0fSHx5Rl7p/T2wW0VgFt1cr1wNtt1cryX3st4Ogd/lpjrBJK0cnKuVKmJOW67z6LslQYlQjoDIH9XYmIAoRBFGj6yP7y85E4HEBXE9BWo1zaa5SQaqtVQqq9TpldtdcB1nb372GMVULJlKRUFJoSlYAyJQJRCcrFdV9UAoOLiEIGgygUaDT94ZEyd/TnWjuUQGqvBTrqnZeGwbfri4BTu4DOJgDS/fsYzMoJv1HxSjBFOq+j4oHIScpl4O3IeOXYFjtYEJGfMYjUxmBSSsnjp3l+rsMOdDUrx6k6GgZcNyoh1dXUf7uhWLm2to38fhodYIwDIuOUcHJ32xir3DbGKhfXfQYzz88iIrcYROFMowVMCcrFMtu719isysm/nU1A11nnxXm7s0l5rOtsf8A1Fiu3u1sw4uwLACCUikJjLGB0Xg/8OSLGeW3uvz/C+bPrfkO08jsRUVhhENFgOoOzICJpbK9zOICeViWouluUiyug+u5rVa57nNctlUqbpp4WoKcNkA7Pn2OIdoaVuT+c+q6j+68jYpTbBpPzfvPg2xHRgNbApUaiEMAgIv/QaJzLdHHje72UyvGvnlYlsHralIDqblUKNHraBlycj7se62hQlhR72oCedvcViG7HrFPCSW9Srg2m/vAyRA15LEp5TO+6P0q5T+98rO/+SOU2Z25EXmMQUWgQwjmTiVbOrfKFrUcJpJ5WJdysHUpQWTuU+13B1tvpfKx9wPM6lIrF3k7A6ny8twNw2MY2Bp3RGUqucIocHFSu+/puRw14jfOiG3jb6Hyu0Xm/81rL/wuT+vFfMYUfXYRyMSX47z1t1v7AcgVYb6dynpi7+1xB5rrterynTal6HPi83q7B/Q7HQqMbHEx6o/P3H3rfgPt1Ec5wixhwv3HIbQ/X2ggWn5DfMIiIvKEzALr4wO1xZbcBti5nODkvfT93Ar3dgK3bef+Qa9ftYY/3KMfibD39P9tc192+j1mjV4JJa3CGlPNaa3DeH9H/HwV99w18zDDyc1yXgT/rXPdHAFr98OdqDQxHlWIQEYUCrQ7Qms9dH0IpneHV0x9Mg667lFmgK+DsVjfPcV7srvusA57rfKy7WbnfPvD51v7neHs8z1sanTOU9M7Act02DLk9IMj6XjPk8YGvG/Qc522NfvDzNLoBt/Xun+fuNgtmGEREE5IQ/cefgsnhcAZTT39g2a0Dwss65HHr4CCz9/aHnMM25H5r/+N9P1v77+/qVN7T4XrMNvhx1+1RT0vwA6EdEEy6/oByBduw+10/6wY/1vd83Qi39UoRTd/9Q95Hoxv+uEar3E5brBzjDBAGEREFj0YDaIzKcaxQ5bAPCCdnQDl6B4RYb//10Nt2qzMgB962DniebcB7DHzM1v8ch23486ydynXfe4x0e8B7+RKod3wBJGX77SsdikFERDQajRbQhMDs0VcOx+Awc136As8+5D57f6jFTQ3o0BhEREQTgUYDaJyFISGGJSZERBRUDCIiIgoqBhEREQWVV0EkhLhUCFEkhCgRQjwY6EEREdHE4TGIhBBaAH8GcBmAHAA3CSFyAj0wIiKaGLyZES0DUCKlLJVSWgG8CuDqwA6LiIgmCm+CKA1AxYCfK533DSKEuE0IUSCEKKivr/fX+IiIKMx5E0TuGiENO0VXSrlBSpknpcyzWCy+j4yIiCYEb4KoEsCUAT+nA6gKzHCIiGii8SaI8gHMFEJME0IYAHwNwL8COywiIpooPLb4kVLahBB3AfgQgBbAc1LKowEfGRERTQhe9ZqTUr4H4L0Aj4WIiCYgdlYgIqKgYhAREVFQMYiIiCiohJT+3wZXCFEP4JSPb5MIoMEPwwln/I5Gx+/HM35Ho+P345m331GGlNLtSaYBCSJ/EEIUSCnzgj2OUMbvaHT8fjzjdzQ6fj+e+eM74tIcEREFFYOIiIiCKpSDaEOwB6AC/I5Gx+/HM35Ho+P345nP31HIHiMiIqKJIZRnRERENAEwiIiIKKhCMoiEEJcKIYqEECVCiAeDPZ5QIIR4TghRJ4Q4MuC+eCHEx0KIYuf1pGCOMZiEEFOEENuEEIVCiKNCiHud9/M7AiCEMAoh9gghDjq/n1867+f3M4AQQiuE2C+EeNf5M7+fAYQQ5UKIw0KIA0KIAud9Pn9HIRdEQggtgD8DuAxADoCbhBA5wR1VSHgBwKVD7nsQwBYp5UwAW5w/T1Q2AD+UUmYDOA/Anc5/N/yOFD0A1kgpFwBYCOBSIcR54Pcz1L0ACgf8zO9nuNVSyoUDzh3y+TsKuSACsAxAiZSyVEppBfAqgKuDPKagk1LuANA05O6rAbzovP0igGvO5ZhCiZSyWkq5z3m7DcofkzTwOwIASEW780e98yLB76ePECIdwOUAnh1wN78fz3z+jkIxiNIAVAz4udJ5Hw2XLKWsBpQ/xACSgjyekCCEyASwCMBu8Dvq41x2OgCgDsDHUkp+P4M9DuABAI4B9/H7GUwC+EgIsVcIcZvzPp+/I6/2IzrHhJv7WGNOXhFCRAPYBOA+KWWrEO7+OU1MUko7gIVCiDgAbwoh5gZ5SCFDCHEFgDop5V4hxKogDyeUrZBSVgkhkgB8LIQ47o83DcUZUSWAKQN+TgdQFaSxhLpaIUQqADiv64I8nqASQuihhNA/pJRvOO/mdzSElLIZwHYoxxz5/ShWALhKCFEO5XDAGiHE38HvZxApZZXzug7Am1AOpfj8HYViEOUDmCmEmCaEMAD4GoB/BXlMoepfAG5x3r4FwNtBHEtQCWXq81cAhVLKxwY8xO8IgBDC4pwJQQgRCeBiAMfB7wcAIKV8SEqZLqXMhPI3Z6uU8mbw++kjhDAJIcyu2wDWATgCP3xHIdlZQQjxZSjrtVoAz0kpHwnuiIJPCPEKgFVQWq7XAvgFgLcAvAZgKoDTAG6UUg4taJgQhBAXAPgUwGH0r/E/DOU40YT/joQQ86EcSNZC+Q/Q16SUvxJCJIDfzyDOpbkfSSmv4PfTTwgxHcosCFAO67wspXzEH99RSAYRERFNHKG4NEdERBMIg4iIiIKKQUREREHFICIioqBiEBERUVAxiGjCE0K0O68zhRBf9/N7Pzzk58/8+f5E4YBBRNQvE8CYgsjZLX40g4JISrl8jGMiCnsMIqJ+jwK40LnXyg+cTUJ/L4TIF0IcEkJ8D1BOeHTuffQylBNoIYR4y9kI8qirGaQQ4lEAkc73+4fzPtfsSzjf+4hzf5evDnjv7UKI14UQx4UQ/3B2jYAQ4lEhxDHnWP5wzr8dogAJxaanRMHyIJxn1AOAM1BapJRLhRARAHYJIT5yPncZgLlSyjLnz/8hpWxyts/JF0JsklI+KIS4S0q50M1nXQdlX6AFULpl5AshdjgfWwQgF0qPxV0AVgghjgG4FsAcKaV0teshCgecERGNbB2Abzm3TtgNIAHATOdjewaEEADcI4Q4COALKE17Z2J0FwB4RUppl1LWAvgEwNIB710ppXQAOABlybAVQDeAZ4UQ1wHo9PF3IwoZDCKikQkAdzt3o1wopZwmpXTNiDr6nqT0JrsYwPnOHVD3AzB68d4j6Rlw2w5AJ6W0QZmFbYKy8dgHY/g9iEIag4ioXxsA84CfPwTwfef2EhBCzHJ2HR4qFsBZKWWnEGIOlK3KXXpdrx9iB4CvOo9DWQBcBGDPSANz7rMUK6V8D8B9UJb1iMICjxER9TsEwOZcYnsBwBNQlsX2OQsG6uF+G+QPANwuhDgEoAjK8pzLBgCHhBD7pJTfGHD/mwDOB3AQysaPD0gpa5xB5o4ZwNtCCCOU2dQPxvUbEoUgdt8mIqKg4tIcEREFFYOIiIiCikFERERBxSAiIqKgYhAREVFQMYiIiCioGERERBRU/z/GYMg47QI8ZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "its = d.getitstat()\n",
    "DeltaD = [float(x) for x in its.DeltaD]\n",
    "fig = plot.figure(figsize=(7, 7))\n",
    "plot.plot(np.vstack((DeltaD, its.Eta)).T, xlbl='Iterations',\n",
    "          lgnd=('Delta D', 'Eta'), fig=fig)\n",
    "fig.show()"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}