Arrythmia.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "655d115b",
   "metadata": {},
   "source": [
    "#Link to dataset : https://www.physionet.org/content/mitdb/1.0.0/\n",
    "\n",
    "#Dataset Description: Participants: 47 Sample Rate : 360 samples per second Number of samples: 48 Bit depth : 11 bits \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e63a9e69",
   "metadata": {},
   "source": [
    "## Import and configure modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2d88dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the necessary modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "#waveform-database:A library of tools for reading, writing, and processing WFDB signals and annotations.\n",
    "import wfdb \n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib as mlp\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mlp.rc(\"xtick\",labelsize=12)\n",
    "mlp.rc(\"ytick\",labelsize=12)\n",
    "mlp.rc(\"axes\",labelsize=14)\n",
    "\n",
    "MAIN_PATH = \"/home/moon/gitWorkspace/MachineLearning/ECG-Arrythmia\"\n",
    "DATA_PATH = \"/home/moon/gitWorkspace/MachineLearning/ECG-Arrythmia/mit-database\"\n",
    "FIG_PATH=\"/home/moon/gitWorkspace/MachineLearning/ECG-Arrythmia\"\n",
    "FIGURE_PATH = os.path.join(FIG_PATH,\"saved_figures\")\n",
    "os.makedirs(\"saved_figures\",exist_ok=True)\n",
    "os.chdir(DATA_PATH)\n",
    "\n",
    "#a function to save plotted figures\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(FIGURE_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f11cdd3",
   "metadata": {},
   "source": [
    "## Visualizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efb3aaa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([[-0.145, -0.065],\n",
      "       [-0.145, -0.065],\n",
      "       [-0.145, -0.065],\n",
      "       ...,\n",
      "       [-0.675, -0.365],\n",
      "       [-0.765, -0.335],\n",
      "       [-1.28 ,  0.   ]]), {'fs': 360, 'sig_len': 650000, 'n_sig': 2, 'base_date': None, 'base_time': None, 'units': ['mV', 'mV'], 'sig_name': ['MLII', 'V5'], 'comments': ['69 M 1085 1629 x1', 'Aldomet, Inderal']})\n",
      "\n",
      "Shape of data:  (650000, 2)\n",
      "\n",
      "Data:  [[-0.145 -0.065]\n",
      " [-0.145 -0.065]\n",
      " [-0.145 -0.065]\n",
      " ...\n",
      " [-0.675 -0.365]\n",
      " [-0.765 -0.335]\n",
      " [-1.28   0.   ]]\n",
      "\n",
      "Type of data:  <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "sample = wfdb.io.rdsamp(\"100\")\n",
    "print(sample)\n",
    "print(\"\\nShape of data: \",sample[0].shape)\n",
    "print(\"\\nData: \",sample[0])\n",
    "print(\"\\nType of data: \",type(sample[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9124b4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure MLII and V5 100\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_MLII = sample[0][:,0]\n",
    "sample_V5 = sample[0][:,1]\n",
    "plt.plot(sample_MLII[:1000])\n",
    "plt.plot(sample_V5[:1000])\n",
    "plt.xlabel(\"Number of Samples\")\n",
    "plt.ylabel(\"Amplitude(in mV)\")\n",
    "plt.legend((\"MLII\",\"V5\"),loc=\"upper right\")\n",
    "save_fig(\"MLII and V5 100\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b32b88",
   "metadata": {},
   "source": [
    "## An example of a normal heart beat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "42a980a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'record_name': '100', 'extension': 'atr', 'sample': array([  18,   77,  370,  662,  946, 1231, 1515, 1809]), 'symbol': ['+', 'N', 'N', 'N', 'N', 'N', 'N', 'N'], 'subtype': array([0, 0, 0, 0, 0, 0, 0, 0]), 'chan': array([0, 0, 0, 0, 0, 0, 0, 0]), 'num': array([0, 0, 0, 0, 0, 0, 0, 0]), 'aux_note': ['(N\\x00', '', '', '', '', '', '', ''], 'fs': 360, 'label_store': None, 'description': None, 'custom_labels': None, 'contained_labels': None, 'ann_len': 8}\n"
     ]
    }
   ],
   "source": [
    "a = wfdb.rdann(\"100\",\"atr\",sampto=2000)\n",
    "r = wfdb.rdrecord(\"100\",sampto=2000)\n",
    "wfdb.plot_wfdb(record=r,annotation=a,time_units=\"seconds\")\n",
    "print(a.__dict__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c484c19b",
   "metadata": {},
   "source": [
    "## An example of a abnormal heart beat "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "afbb1de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'record_name': '200', 'extension': 'atr', 'sample': array([  67,  225,  487,  689,  964, 1147, 1433, 1610, 1882]), 'symbol': ['+', 'V', 'N', 'V', 'N', 'V', 'N', 'V', 'N'], 'subtype': array([0, 1, 0, 1, 0, 1, 0, 1, 0]), 'chan': array([0, 0, 0, 0, 0, 0, 0, 0, 0]), 'num': array([0, 0, 0, 0, 0, 0, 0, 0, 0]), 'aux_note': ['(B\\x00', '', '', '', '', '', '', '', ''], 'fs': 360, 'label_store': None, 'description': None, 'custom_labels': None, 'contained_labels': None, 'ann_len': 9}\n"
     ]
    }
   ],
   "source": [
    "a = wfdb.rdann(\"200\",\"atr\",sampto=2000)\n",
    "r = wfdb.rdrecord(\"200\",sampto=2000)\n",
    "wfdb.plot_wfdb(record=r,annotation=a,time_units=\"seconds\")\n",
    "print(a.__dict__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ef1904c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure 101-MLII.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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cz6SrwH4OHA6sAobTNIgjkwpsNlDiBlvMcfeNIb2CmQrxCo8zgUtERHxuxJ1xAkTylpgqJcVF1EdipsACJqpOBeziIgn1j7oQicZ8gwdr+6yTLzlX03UhXgVcoqq9VXUnVR3t23bOpECquhF4BrhWRDqKyATgWOCR5HNF5FgR6SYOewAX40QeArwDRIGLRaSdiFzk7n87k/IGTcz3Q7bfcbDEYlAkQrGIlVMJmJgqxTYHFhgJC8dD7LJNV4EV40QCBsWFQHtgJY478AJVnSki+4pIte+8U4C5OG7Bh4GbVPUhADdU/jjgp8A6nJRYx+VzCD04VoCNRHODMw8DIuGe2C5EVPEN3Kzts43fXR7m5k7XhfgAcBqZdRU2i6quwVE+yfvfxwnO8N6fmnxO0vlfALtlWr5cEov5EsqG+MEqRKIxpVhcF6I1fqBEY41BHNb02ceZqhAiMQ11e6erwDoA54jIYTih6MlBHBdnWjAjNd6D5b02gsEb9RcVuS5Ea/tAianSrthzIVrbZ5toPOI23HPt6SqwHYAv3Ncjk46F9+4KEH9GdPshB4dncRWJUFQkoXarFCIxNQssSGJK47KFED/saSkwVT0w24IY6ZFogeVYmK0Iz+IqLhKKxBYyB03M5sACJV+CxdpckdnILQnhxNaJBoaXmaBILIw+FzhzYK7nIceybA3ky0DZFFiekVAVOMQPVqHRaIE5SsysgGCJWfRtoESVvMg6Ywosz4j5orGsEw2OhDkwsSjEoFHfnIw1ffaJxfIjWMwUWJ4RdTNxgP2QgyQehRgPo8+xQFsZ0YQ5GXvws40/aCbMzW0KLM+IxtRXkyrET1aB4VlcxUVCUZF1okGTLx1qoZAwYAjxrGO6YfSISAec4pK9SVJ8qvpMZsUymiOe1BRTYEHizYEVFbkuRGv7QPEHFYS5Qy0U1D8HFmJvQ7oVmX+Ak9KpR4rDipNqygiAqNVFygnej7g4ngvRGj9IYnnSoRYKUd/C8TAPlNN1If4FeBkYoKpFSZsprwCJ5snkaqGREIVYJNb2AZO4kNnaPtvki8s2XRfiEOCHqro0i7IYreCt+2oMb82lNFsXsYQoRLMCgkRVE6IQ7bHPPrE8mQNL1wL7ANg+m4IYreONPEvNAgucmPoVmM2BBYk3UPOeewugyT4JLtsQN3e6Fti9wJ9EpB+pKzJ/nmnBjKb40xmB/ZCDxB+FWFwklgUlQBrbPvwdaqGQL1MV6Sqwp9y/96U4ZkEcAeG5rSwTR/DEfFGIxTYHFiheW+dDbr5CQRMyn+RYmBZIV4Ftm1UpjLSIJv2QrRMNjqgvClFEiFrTB0YsyfNgz332iSZUwA5ve6ebjX5RtgUxWsdzpVgmjuBpdGNBsVgi5SDxmtoGbsGRmP0/x8K0QLMKTEROAF5U1Qb3dbPYQuZg0CaulBA/WQWG12mKmAsxaJItMCP7xGJKcZ7PgT0F9AVW0jgHlgqbAwuIRgss/A9WoRHvRD0XollggdG4fMSe+6CIqT9lXY6FaYFmFZiqFqV6beSOaDyM3jISBE1CFKIIEWv8wPA60GLLxBEYCfXXQjxgCKViEpHuIvKsiGwUkUUiMrGZ8y4Vka9EZIOILBCRS5OOLxSRTSJS7W6vB3MH2cH74dpINHiaRiHmWKCtiGTPgzV99vHPgYW5n2lWgYnIPuleREQqRGR0ZkQC4C6gHugDnAbcIyKjUn018FOgG3A4cJGInJJ0zjGqWuFuh2ZQxsCxcOLckRiFiLkQA0QtCjFwYto4Bxbm5m7JAvs/EXlLRE4Vkc6pThCRnUXkZmAuMCYTAolIR+BE4CpVrVbVycALwOnJ56rqzar6uapGVPVb4HlgQibkCCNNoxBD/GQVGI0WGBbEETDJy0fMBMs+iWWbcixMC7SkwHYCngGuBtaIyLciMklEXhGRj0RkLfAx0B84SFUfzZBM2wERVZ3t2zcNSGWBxRERAfYFZiYdekxEKkXkdRFpVsmKyHkiMlVEplZWVm6u7FklboEVh//BKjT8uRAtG32wNM6BmQUWFE7ZpvAPlJtVYK5Vc5eqjgT2BO4BvgQWAW8C5wD9VfU0Vf06gzJVAOuT9lUBnVr53DU49/OAb99pOImIBwOTgNdEpGuqD6vqfao6TlXH9erVq+1SB4DXaZYWhf/BKjT8abxEbA4sSJpGIeZSmq2DqOZHBex0FzJPBaZmWRaPaiDZZdkZ2NDcB0TkIpy5sH1Vtc7br6of+E67QUTOwLHSXsycuMERX9BpSU0DJ+q3wIpsIXOQNK4Dc6PizIeYdfKlnEqbohBFZJyI/Nidp0JEOopI2lWd02Q2UCIiI3z7xtDUNejJdDZwOXCwqi5u5dqKE/iRlyQHcVgfGhz+xbTFRZaNPkiSs9Hbc59dvPI1+dDPpKXARKSPiHwEfAL8Eyc6EOA24NZMCqSqG3Hm3q51FeQE4FjgkRRynQb8EThEVecnHRskIhNEpExEyt0Q+544pWHyEgviyB3+KMQisSCOIPGvwQPzPGSbJuvuQtze6VpgfwZWAD2AGt/+fwPZCE2/EGiPkwXkceACVZ0pIvuKSLXvvOtdmT71rfW61z3WCWfebi2wBCfM/ghVXZ0FeQMhanMBOaMxlZTjRjQXYnA0TaGWS2kKH3/eTwj3gCFd99/BOC66tU6wX5x5wKBMC6Wqa4DjUux/HyfIw3vfbJZ8VZ0J7Jxp2XKJxl0p4V8hX2jEYuZCzBX5ZBEUAv5F+0US7oFyuhZYe5yFxcn0AmozJ47REskFLe2HHBz+tncssBwLtBVhnodgSa4+HuagmXQV2HvAmb73KiLFwG+AtzItlJGaeBi9TWYHjj8K0RmVWuMHRXI2evM8ZJe4xRuf782tPC2RrgvxMuBdEdkdaIcTuDEK6EIBZ74IG41RiOZKCZomUYhh/lUXGMkL+O2xzy7esy3ibGHuZ9KywNyFyqOBKcDrQDlOAMcuqjove+IZfuILOu2HHDgxfxSiJfMNFMvEESz+3JMi4e5n0l7DparLcdJKGTkimmyBWS8aGNGEKETrRIMkeQ7MWj67JLrLJdQu25YqMu+X7kVU9b3MiGO0RLycis2BBU5CFKLlQgyURovAXOdBEE2IQgy3t6ElC+wdEjNXeLeR/B6sInMgNBa0NFdK0CREIRbZOrAgiadQs3VggeC1b1Gez4H1Anq7f48GvsXJNzjc3X4KzAJ+mGUZDRd/eKvjmw7vg1VoxJLcKmH+URcalokjWOLtHXch5ligFmjWAvNnrBCR64D/VtU3fKfMF5GVwM3Ay9kT0fBo2onmWKCtCH8ggS1kDpbkTBz23GcX/0LmfLbA/OwIpEqUuwQYmTlxjJbwj0QtkCBYGie2sYXMAZO8gN8e++ziPdtFeWCBpavAZgJXi0h7b4f7+nc0kyXeyDxRXxi91aQKFv+otLjIBg9BEvEW8JdYEEcQNK55DH/Ebbph9BcALwFLRGS6u280EAWOyoZgRlMivnDiIpsDC5TkeQFzIQZHNJqczNfaPptEE+bawz1QTreg5aciMhSnwrHnMnwM+Kdb/sQIgEYXYpEFEgRMci5EVacjTUpubWSBxLa3dWDZxhsgiAhCuAcMbVnIvBG4L4uyGK2QaIGFe2RUaPgDaIp9wQTFpr+yTuNCZhu4BUFyPxPm5k5LgYnICS0dV9VnMiOO0RJRd3a1OA+igwoNfxSiq7+IxhrLrhvZIxJrnJORkJf3KATyKVgsXQvsqWb2e3dmC5kDIJ9GRoVGQhSi5eQLlJjPdS723Gcdf+qusM+BpZvMt8i/AWXAeOB9IO2UU8aWEYnmz8io0Iiputm5nVRS3j4j+/gHbmGfkykEIn4LrCjc7Z1uGH0CqhpR1U+B/wXuzqxIRnNEbC4gZ0RjGldcRe5fy4cYDH7XuT332cc/5yiEu703S4H5WAcMy4AcRhp4P2RbBxY8UdW46zDuQrTFzIEQSZqTCXF/WhAke3rC3NzpBnHsmrwL2AanIvMXmRbKSE3TH3KYH63CQpW4BeZFHtpasGDwVwKwgVv2SQziCHd7p2uBTQU+df96r1/ACd44N9NCiUh3EXlWRDaKyCIRmdjMeSIiN4nIane7SXwLc0RkrIh8JiI17t+xmZY1SPwLOi2dUbBEYxqPPrQgjmBJmAMT0FDbBPmPf91d2KOd041C3DbpfQyoVNXaDMvjcRdQD/QBxgIvi8g0VU1OW3UecBwwBsfSfQNYANwrImXA88DtOPN0PweeF5ERqlqfJbmzSrIFFuYHq9CIxnwuRC+II8xD0wIi2SKwxz67xKcqXIs3zJ6edC2wwcASVV3kbt+raq2IlLSl8GU6iEhH4ETgKlWtVtXJONbe6SlOPwO4VVUXq+oS4FbgTPfYATgK+nZVrVPVv+K4Pg/KpLxB4q07kjxI8VJoxLRxzZf311yIwRCJ5Y9FUAg0mQMLcXOnq8AmAd1T7O/iHssk2wERVZ3t2zcNGJXi3FHusVTnjQKma+LwYXoz18kLIr6Fs2EPby00/FGIjWH0uZRo68EssGDxJw0Pe9RnugpMSB2M0gPIdC7ECmB90r4qoFMz51YlnVfhzoMlH2vpOojIeSIyVUSmVlZWbpbg2SYai8UTmob9wSo0Yr4oRG+W1VyIwZCYSsossGwTyaOFzC3OgYnIC+5LBR4VkTrf4WJgJ2BKhmWqBjon7esMbEjj3M5AtaqqiLTlOqjqfbi5HseNGxfKf1mCBRbyB6vQSLDAPBei/QMCwetQnWa35z7bNGadCX+0c2sW2Gp3E2Ct7/1qnAKX9wI/ybBMs4ESERnh2zeG1HXHZrrHUp03E9jZH5UI7NzMdfKCaEzjFpjNBQRLJKqUFCcqMGv/YIjGYvG5X6fprd2zScJC5pDnnmzRAlPVswBEZCHwpyBKp6jqRhF5BrhWRM7BiUI8Ftg7xekPA78Skf/gPNWXAHe4x97BqVd2sYjcS2O4/9vZkz67OBaYM+awuYBgiSQMHkyBBUk0RqLnwZaPZJX4nGOxN+cY3uc83VyIvw+47teFQHtgJfA4cIGqzhSRfV3XoMffgBeBGcBXwMvuPtxQ+eOAn+JkDDkbOC5fQ+jBWQfWOAdmLqwgicRilBQ7PxfPlRi1jjQQ/HO/5nnIPgUxB+ZWXt5fVdeKyAxasNtVdedMCqWqa3CUT/L+93GCM7z3Clzmbqmu8wWwWyZlyyVN58BC/GQVGA2+wYOrx6z9AyKSlIfSWj27JOaeDPdz3pIL8WnAC9porpyKESDRWCw+DxP2kVGhEYnGKHU1l4gFcQRJLKYU+yqHhrlDLQSaZv/PrTwt0awCU9Xfp3pt5A6/BVZs68ACJRLzBXHYHFig+OcfnfWPORaowIlHIXrr7kJs825pNnojQPxRiOZCDJaGaIxSN4DGwuiDJZrkOreBW3bxF7QMe9BMS3NgLc57+cn0HJiRmoZoYxSiuRCDxRk8eG3v7LP2Dwb/HJhg7Z5t8il1V0tzYDbvFTISM3GE+8EqNBqiSnmprQPLBf45MAviyD6JmU8k1J6GtObAjHDgn4exdWDBEok1BnEUWxBHoESSrF8bOGQXf+aTsLd3uuVUABCRYcAO7ttvVHVe5kUymiNxDizcD1ahEfGvwTMLLFD8tdjCXt6jEPA8PU7mk3BbvGkFcYhIDxF5DpgDPOdus0XkeRHpkTXp8pj5ldVc8uQ06iLRjF3TH4UoFsQRKA2+MPrGemC5lGjrIRKLxS2wsJf3KAQiscTE1WHuZ9KNQvw/YDiwL1DubvvhFLr8e3ZEy29uenUWT3++mPdmr8rYNf2BBEUhz1FWaCSE0bu/GqsHFgxNUklZu2eVWJNo5xwL1ALpKrDDgHNV9QNVjbjbBzhVjg/Lnnj5S8d2jnd2XmV1wv5T7vuQP7327WZdMzkTh7lSgsNxISZZYNb+geBfwA82cMs2iZ6ecK83TXcOrJLUdb9qcDLTGy6n3vcR0Zgyf5WjuKYuXAv7O8dUlY/mr+Gj+Wv49WHbt/naTeuBZUxsoxUiSW0PVg8sKCIxjbe5BS9ln+T1pmFu73QV2LXA7SJyuqouARCR/sCt7jHDxRspjujdiTUbV/PmNyt4efoyOpWXENnCSZNINHFkZBZAcKQqp2JRiMEQ0+RMHNbu2SSx6kW4+5l0FdgvgSHAQhFZ4u7rD9QCvUXkYu/ErX1R8yM/Gx9//dWSKo6+YzK/+OfnTc5TVRJLlbVONCmM3vrP4EgZxGHtHwgJAzdsDizb+KtehD1hQroKzBY1bwaj+nVmr6E9+HB+Uy/rhroInctL23S9aPLIKMxPVoGRnI8Pwj0yLSSiMaVdqS8KMcfyFDqJc+3htnjTUmC2qHnzEBFuOGE0B/zpnSbH1m1saLMC83eixUXhXiFfaDguRFvInAsiMaW9560IuUVQCMQ0fyzeNifzFZFyEeng37IhWKEwuEeHuNLxs6am7XU1k5OahvnBKjQaYjFKPfetLWQOlIQ5sJBbBIVAPmX/T3ch82B30fJ6nGjEDUmb0QwiwqRfH8Afjt+JspIiKtzw+hXra9t8LX8kXEmxxFO+GM1T27DlC8ljMUU1cS0SmAILikjU7zoPd1RcIRCNxfImYUK6FtijQD/gv4CjgCOTNqMFBnbvwGnjBzPtd4cy+TcHAjB7edv1fhMLzBRYi8yvrGanq1/jgQ8WxPfNXVnNi9OWtuk6DW70aNNciBkS1GgR57l3XjvZ6O25zyb+oJmwDxjSDeLYBdhdVb/JpjCFTvuyYtqXFdOvSzkLVqVaVtcyftO+pEgsE0Qr/GfGMiIx5fY353DWhG0B+MFt7wJw9M7bpB0FGok21kcCXxCHDSBapC4SZfbyakYP6LJF10lMJRXuDrUQ8A+Uwz5gSNcCmwb0yqYgWxO9Opfz/dqaNn8u6nelFEm8YzVSM3PpegCqNjXwx/98Q9Wmhvix2ob0zae4AvMsMJsDS4vLn57BMXdOpnJDHQBrNtbzqye/bLP7POZz39r6x+yTMAcW8pR16Sqw84CrReRYERkmIoP8W6aEEZHuIvKsiGwUkUUiMrGFcy8Vka9EZIOILBCRS5OOLxSRTSJS7W6vZ0rOLeXb5ev5dOFaPlu0tk2fq482BhKUFIXbNx2LKY98tIiNdZGcfP/GughvfrOC/l3bA3Dfe/PZ56a348f9yqw1Gl2IiXNgZgG3zHNfOktGl1c5Cuued+byzOdL+Ncn37fpOv65X7FkvlnHH4XoZKMPb4Onq8CKgD7As8BsYIG7LXT/Zoq7gHr3u04D7hGRUc2cK8BPgW7A4cBFInJK0jnHqGqFux2aQTm3CG/0/+X369L+jKpSH41RVtJoBYQ5iGPStyu56rmvuPqFmcxdGXycz/L1tTRElZ/vP5TuHcvo2qGUDbWNyrQtCqzRhZicjT687R8GPEVzzJ2T+fMbs1ld7UTeemnW0iUabcyOHvYONUwsq9oUHzy0BX/eTxEJddWFdBXYQ8BK4BhgPLCHu+3u/t1iRKQjcCJwlapWq+pk4AXg9FTnq+rNqvq5m1j4W+B5YEImZMk2/7l4XwBWVdel/ZmIGwlX5nNjhbUDbYjGmOUGqTz12WJ+cNt7XPPCTGrqg7PGFlQ6c4zb9uzI51cdwke/PZhendrFR5br2rCMwUsB5l+DB+F2reSa79ckusj/8tYcFq/bBMDamvQHD+BYupYDtGVWbqjlsqemUe3zeOx1w9vsecNbbb5WNI8WMqerwEYC56vqy6o6VVU/828ZkmU7IKKqs337pgHNWWBxxJmN3xeYmXToMRGpFJHXRWRMhuTcYnbs15k9tu3OC18uZebSKt6dXdnqZxrckLe4BSbhtMBiMeXYOz/glqSM+w9OWciEG99u5lOZpbYhyjkPTwWgZ0U7AMpLi3nv0gN5+oK9gc20wIobf9RgC5lb4tWvlgNOsIzHJwvWALB2Y9vWQEbzqD5VrrjplW95cupiXnPbfUsGtxFf9n8pkDmwT3Bqf2WTCmB90r4qoFMan70G514e8O07DSd/42BgEvCaiHRt7gIicp6ITBWRqZWVrSuULeX4XfqzZN0mjvrrZM64/5NWO8P6SGIod1FILbD5q6r5epnzb+xQVkyn8sZA17U1DYy86hUOv/09XpmxjKP++j5rNtbz1ZKqjMrgH/0P6t64zr59WTHdO5QBbVRgngXma3uwjrQ5llfVctOrs9htcDfuOHUXpv0u0Xu/to2L+BuiSqlvXZI1e1MWrnY8Dp8sWMN3q2v4dkWj235TfdvWQkaTs/+H2GWbrgK7Bycb/TkiMl5EdvVv6VxARN4REW1mmwxUA52TPtaZVhZKi8hFOHNhR6lq3Cfn1i7bpKo1qnoDsA7HSkuJqt6nquNUdVyvXtkPuNxtcLeE99MXrwOcjjWVMvMUmGeBlYR0DmzR6kblcfkRI5lxzWEsvPEo/nXenoAz/zdr+QYueOxzZi5dz8S/f8TRd0zms0VriWRgYdWSdZs48Z4pADxz4d7xumweXTo46bvaFMThWmBeJ1psC5lb5Nf/nkYkphw52lmq0Ll94/9geO8KllXVsqxqU9rXi0RjjYOHkLu0ckFVTUN8EPjE1O/Z75ZJCWsd2xrx7I9CDHsy33QV2OPA9sB9wIfAVN/2aToXUNUDVFWa2fbBCQ4pEZERvo+NoalbMI6InA1cDhysqotbEwEn8CMUbNuzY8L7KfNWs7q6jvF/fJPT//ExU+atSrBM6pIUWFFI14HNd+ee3vzVfpy+5+D4/j2H9uCTKw7mhF36J5zvzZWdeM8Uhl/xSoIPvy288fUKFqzayAOTF7DeDdbYvk9T471TuxJENteFmBhG32DLGJqwqT7KJwvWcNioPpy59xDA6QS7ugOHm0/amWhMeX3mirSv2eCrwuCsS8q01PnLV0uqGHPt69RFYhw+qm98/93vzIu//m51GxVYtPDmwLZtYRuaCUFUdSPwDHCtiHQUkQnAscAjqc4XkdOAPwKHqOr8pGODRGSCiJS5uRsvBXoCH2RC1kxQWlzEv8/fi7tP25WB3dtzy2vfstv1b1LbEGPKvNVM/PvHHH3H5Pj53hxYO58FFjYX4vKqWv716XcM6dGB4b07NVko3LtTObf9eCz9upQ3e42npjoh1mfc/wnnPTw1Hqm5ckMtS9c1HbV/t7qGFetrOffhqRxzx2SmuZbs25fs38T6AkfxV7Qr4d535zU51hwNcReicz+eGzcf1+HNWbFhswcJ6fDxgtXUR2NMHD843gkC/POcPfnnuePZdVA3hveu4N+fpR9K3xCNUepfyBxil1bQPO8uVRjZtxM3nbgzQ30D46uP2RFwvBJtocEX7Rz2ObB0s9Evau6YiPwAaPZ4G7kQuB8n4nE1cIGqznS/Z1/gFVWtcM+9HugBfOrrKB9V1fNx5s3uAYbh1Cz7EjhCVUNVPXr3Id0BeG3mcr5fk/ohq6ppoEuHUuqjTdMZhc2F6EU8XdpKtenz9hvKNS9+zePn7smMJeuYu7KaJ6c6BvQ1L35NJKbxwJbXv17B3aftyoWPOTXVZl13OOWlxYAzMtzvlknx61bXRfj8u3VcdOBwhvaqoDnGDOjK5LmrWF+bXkUAz6Xrj0IsksZBRZioj8Soj8aYt7Ka179ezn8dNAJVZ/5v0eqNHPLn9zhl94Fce+xO8U4qU6xcX8uZDzgOmT3cZ9tjx36NswNHjt6GO96eQ21DNP6/bI6oG32bEFQQsmaPxZRV1XX06tSOukgsfk+fLVrD+3NW8csfbJe1737uy6UcPLI3/zhzdwDe/vUB8WPRmHLdS1+3KdoZnPWmZb7ad2F2laebSioBtxrzWcDZOEESLT+FaaKqa4Djmjn2Pk6gh/e+2aASV+nlTWHNG0/YmbMnbMuxdzU1ED9asJrDRvVtnAOLu7GcvzFfhFYytQ1Rnv9yCT/abWCz52SKV79aFn991oQhLZ57xt5DOHpMP3pWtGOvYT2IxZTLDh/J4be/x6rqeq5/OTFj2bUvfh1/PfKqV/n1odvxo3EDU4YIR2PKxPEtr60/e58hTJ67itnLNzAuqaNNhaeovLUx4AwkwqjAfvJ/HzN9ybr4WsO7Js2je8cyjh3bL56+7F+ffs+/Pv2eKZcfRD93oXcmeMGddxnYvT3ty5rvEob16oiqE2wzIoWb109D0sAtTMllq+siPPLhIl6avpSZS9ez+5BufLpwLU+dvxefLlzLTa/OAuCsCdvSpX3bSie1RkM0xh1vz6VyQx0ThvdMeU5xkdC9Y7s2KzC/BRb21F1pD8FEpFhEThCRl3EWMB8P3AsMz5JsWw3ty4oZM7ArR+28DceN7cfBI3vT2Y3e+8aN6EsO4vCSmzrrw5RLnpzGXZPmJlz3vvfm85unZ/Di9LYlr90cPl3oZBWZ9rtD6VDW8rhIROLh7eC49XpWtOP640bH9/l/8MuTUg/96fXZXPDoZ83+sFrrlEf2dayBD+et5u1ZK1rNFpLc9uAMJMI0B7ZyQy1zVmzgk4VrmqTJWrOxngc+WMg73yZG1/7ptW+Z9O3K+P1tKd6i2ZcvbjZWCmic/52fRj7QSJL1m+VxWJt4ZcYybnp1VjxlmfcbOOneD+PKC+DbzUjc3RpPf7aYv741B2gaEOanZ0UZj3/yfZsCpBqi6hswhHvBfqsKTES2F5FbgKXAn4Av3EOnu4uJM5mJY6vmrom7cvspu/CPM3dn+jWHsV2fCibNWgkQdyE2KjDnb10kysyl63n688Xc8tq38Qe1IRpjjbve5uEPEz281XWRhIfyw3mrufudROXXFmobony9dD2j+3eJR/ltDofv1Jdvrj2cn+83lMfOGc9zv5jAXRNTB7l+/t06xgzowr/P34uFNx7F7OuPAODYsf1a/Z5tupTTubyEW9+YzdkPTuXK574CnDa77qWvWbhqY4J15XXw7XwKrLQkXBbYCXdP4ZA/v9fqeTv1b3TlPfPFEs564FPufXce9ZFYfLL+w3mrueed9OcIPeav2sjIvp1adcsOcRVYOgmtvefZC6AJU4FFT3EB/O303Zo97+S/fdhkcLmleAPbm04czZiBXZs9b5jrSl/ThrV3DZFYXIGFPWl4i0NlEXkf2Al4GjhZVd919/8mANm2eo4d259bXvuWtRvrm7XADvvzeyz1pYsZfsUrDO3ZkTU19Qzs5qyBmrGkitqGKDOXVvGfGcv5x+QFjOhdwaWHbc+gHh049e8fAbByfR2/PXIktQ0xXvtqOT8aN6DVjO2rqus47M/vsXpjPYeN6rPF99y+rJjfHrlD/P3YgV35xT9Tn3vfT8fRp7MTEFJWUsQXVx2SMnAjGRFhWO8KvvhuHQCT564C4LKnpvPsF0v4x+QF9Kxox38u3ofencsbw+iLGxVYSZGERoFFY8ritY1zqBOG92DPbXtw6xuNOQGG967g7tN2ZVivCn7972mUlxbz+CffAfDe7Er+9u48Dh3Vl10Hd+MqV6HvsW03Zi3fwMnjBlJaXMTUhWtYtLqGfUf05NOFaxkzsAvTF1cxfXEVYwd2ZX5lNaP6tZ55vnN5KZ3alXDjK7M4e8K2Lc7FNbZ9uCqRv/H1Ch6cspC9h/Xgnp/sRpf2pXz1+8N4cdpSfvvMjCbn3/Latwzu0YHuHcrYuxmXX1tYVV3P0F4d+fHuLbvLjxy9DS/PWMbamgZ6d24+eMpPfTRGaUlj8dawzbX7ae3XvhdOfsL7vGAKIzhG93c6g2+Wr292DmxpilxnnmtmXU0V5aVF1DbEOOz292hfWhwPW5+zsprzHklMovLglIW8O7uSDbURVlXXceXzX7FdnwouOnAEj3y0kIfO2iM+EvZ4b3Ylq93RnWRplcL7lx3IpoYof3rtW7p3LKOmPkpJscSVl0e3jmVpX/OQHfuwaHUNYwd25e1ZK3lp+lKe/WJJ/Piq6jre/GYlE8cPoj7qLAT1d7SlxUVxqzjXzPYtWj1h1/7cdvJYAF6duZx9R/Ti8J36sk2X8nh7/fnHY9lYF+G5L5bQsV0xU92k0s9+sSShDU6850PAyWm4y6CuXPLvaQnr/CralTSJaDxh1wFpybzB/dzJf/uQ537RfAa4xjRevhRqOe5P566s5lw308tP9hwcd3dXtCvhlN0H0q9re0b27cSi1TV8NH81d02aS10kxkX/dJxX7192IAO7d2DlBue327tTasWydmM91XURBnZvWvS+srouwQ3fHN06OrK1yQLzBXGEMdrZT2sKbHfgHGCyiCwEHsZZE2YEwJAejqtl8ZpNdGjnTIo3ppJKPHfvYT2YMq8xyHLMwK5M+34d5+47lDvenpvQ8YCTCcTfWXn43Tr1kRhfLVnP+Y86im7W8g3s1D9xhL3Mp0DHDuraxjtMD+8HfN9Px2XsmhceMJwLDxjOKzOW8faslfHOxc+8SifpbMo5sJKi0ITRe66sty7ZP+4ygpbnojq2K+Gb6w5n+uJ1/PDOlleXeC7WZFKF4+8zIj3rYv/tevHu7Eq+/H4dVzw7g98euUO8Wrmf5DRezgL+3A4cPprv/M6OHN2Xg0b2TjgmIuy/nZMIoU/ncvbYtjs/3WswY699I37OAx8s5HfH7Mgef3CCkOb8wXF/exZ+bUOUpes2cdCtTu26hTceFf/s7BUbOO6uD6ipj3J80prKVHRzM8+kq8Ai0RgxDXe0s58W58BU9QtV/QWwDXAb8EPge/dzR4lI87OHxhbTu7MzwnIyqydbYI0arENZMVcfM4r3LzuQfUf05P3LDmTbHk6n37tTOz64/KAm177q6B259UeN6SEvP2IkR+zUt8l5frw1WZPnrIovjlxeVUuX9qVMvfIHnLNPtrONZZ4f7NiHfZvpdOd7CizJjeW9DosLcZm7zqf/ZkQU7jygK1/+7hD+cspYAIb26sjOA7rQPkV4+5gBXZoswP/5fkP55H8P5uRxAzh7wrbs0sJ8jJ+/nb4blx8xEoDHPv6OW193cmeqKs9+sTiuHFM999EcDxy+WlJF1w6l3DVx11aXAQBNIhDv/2ABO1/zWvz9iCteYcQVr8Tv9Qe3vRtXXgB/ePlr7ntvHte++DVn3P8JNW5qqAsOGNbqd3sBTV6qqdZIdpf7o53DSLrrwGpxFhQ/IiLDcayy/wGuF5G3VfWILMq41VJeWkyPjmUsq6qlj6vMkoM4AB4+ew+27+uEIz/ys/GAE7r73JdL2XdEL/p3bc/E8YN49avlvPRf+7Bw1Ua6dyzjuF368+/Pvme3wd04f3/nx3DFszOYV1nNz/cfxsBuHfjLW3P4ZMFqqjY1cOVzXyWMxvfYtjufLFjDjtt0TsudEUZKi4t45GfjueLZGTz28Xdcd+wobn1jNv26tGfSt5XMXrGhMYijuLGzKikKRxDHpvpofK4rnc40FV07lPHDMf3o1akduw/pTmlxEV98t5bj757C2IFd+fL7dVx+xEjO338YNfURXp+5gnU19TwwZSEXHTScTuWl3HxS23Jll5cWJyi7l6cv49Q9BvHd6hr+54lpnLDrKm47eWxjFKJvDizXFsFXS6vYqV+XtCt6iwj/Om9PSosl7pZdX9vUeh1xxSu0KymKZ93x+Pv7TePkHjp7D7ZrZQkCOMpzcI8OzFicXr7RpsFizv5ITCkLUwioS5vXganqXOByEbkCOBpnLZiRJfp2KWfF+lrqI87D6o8O8qgob/pvHDOwa4Lr4Y/Hj+YPx+2EiMRHZcVFwuPn7pnwQ/zD8aMTrvNXd2T+/JdL+eUTXyYc87KLh3Ns1jb+cPzo+L2fvtcQfvPUdL5etp43vl4RT2xalhSFWJ9kCag6i269NXf+shQt0dJavtZ45KOFm/W5ZESEvYc1WqK7DOrGwhuPYlV1HU9/tpgz3LRQHcpKOM51XZ05Ycss7mG9G92dKzfUcagvitILPU9eg5frII76SIxvl2/g7DZ6G/Yc2gOAJ87bk6+Xref3L35NWUkRw3tVxJNfA02UVypG9+8Sd1Omw+j+XXhp+jJufnUWlx62PSJCXSSKIE0CaBotXm/A4FpgbYxEdBICJ1bTzgabtZAZQFWjODW4ns+cOEYyfTuXs6yqNt5Zeg9cu9LGB69TGtkkgJQjxtZGkd7x43bpz/DeFQnprXbcpjPb9+0U79AKieuO24knpn7Pmo318bDwxHVgEg/x/mj+aqZ9v44Fqzbyr0+/Z8ENR/LB3NX85B8f89ov94tbx6mYsbiKY+6czL/P3yuemaUtfDB3NUN6dOD5i/Zp82fToWdFO36+f+uuqs2hR8cyTh43gN0Gd+M3TydG7nkdeSTJfZvrsO7ZKzbQEFV2SiPaMhXjh/Zg/NAe/GjcQGKqaMxJtuv/XQFMHD+IfYb3ZNzgbrw6czm/e34mew/rQafyEi4+eEQzV0/NzgMcBXb3O/MYP7QH+2/Xi4NvfZfy0mLe/NX+CecmLxz3W2DpEosp2135Ctv27MjcldVN5mYzyWYrMCMY+nYp561ZKxnjVjX21iL55yhSTX5ngx23aVxDdNvJY9hrWA+26ZK5TA5hoqykiGG9OrKsahPtS4spksSRpJeJ458ff8f/PpvY+c6r3Mgf/uNkE3ni0+85cnRfxg7sGo/gXLG+lplLq7j19dmscBdpP/bRIm5+dRZnTdiWI0dvQ7osr6plRJ9OGc/0EAQiEnc9vjdnFS9Pb8zmUrmhDlVtUsqmuKiIaFSp2tTAotUb2a5PJ5as25R2Bzl35Qa27VmR0ip49KNFPPvFEm7/8diUkX8AM5c6rrjR/TdPgXn4f7NdOnRh/Lbd2a5PJz7/bi17Du3BVUfvGD++6yAn1KCkuIi/nd72QCa/sj3j/k/o16U8Hr28eG0NJUVF9HXzkzZEUs+BzVmxgTUb67lr0lzumLgrNXURBvfoSFlJESvW13LJk9O49LDtGTOwKzOXricaU+audOaQ3/pmhSmwrZV9R/TksY+/44O5TuST92D5U/UEpcCKisT9cbdnt8Fttxbyjc7tS9lQG3FywyW5WkqLi/h+bU0T5QXOJLzH/R8s4P4PFnDc2H7cdvJYNjVEGf/HpimwnvvSyZYyc+l6Pp6/mt8euUNac1rLqjYxfmj+/y/umrgrf/lxjPs/WMDqjfX87d35rNxQ16SUTUmx0BCLMeb3ryd8/oYTRnP8Lv1TtlkkGuO2N2bz2szlzKvcyOl7Dua643YCHIsjpsr/vb8gXoT1mDsn82VSDTOAjXUR/vLmHLq0L02oM5cJnvj5Xs0eG9WvM1ccuQOHtxJk1Ry7b9udiw8ewftzKvniu3UJS2/2ucnJJepNN8RzrpYkTlUcf/eU+GfOfWgqXy9bz8i+nThg+96UlxYxee4qZiyp4vBRfVm0JjFgZNue2VFeYAos9Bw0sg9FAt+tqaG4SOIjR78Flk0fczKF6C5sjk7lpVRtaqA+0rguxqNDWXE8AfOlh21PXUPUUfBvzkl5ree+XMpzXy5lB58Vm4qa+igPfbiICcN7cuioljus1dV1rK+NxBes5zslxUWct98wpsxdxd/enc/sFRviawv9pWySU2UB/PaZGfz2mRlcceQODO7Rgf2268VL05dxwi79efOblQnlRR75aBG/PXIkHcpKOPbOD5i7sjphTd+6mgb++tYcLjpweMLc5NRFa1laVct/HTQ867lF/YgI5+63+UU/SouL+NUh23HqHgO5/OkZKSvAN0Sd7BuN600bFzIn483ZzVq+Ib6uFJwSRU+41ST6di5n+76duPmknZus18wkpsBCTllJEb06tWPF+rqETnRzI86M9OlcXsLiNTXURZpaYP58j+MGd2P80B6oaoICu/qYHfm9LxExNKYA8nPk6L78Z8byhH3nPfIZZ+49hGt+OKpZ+bxAh5HbpFO0PH/wEvzOXlHN0F5O2H48CtE3Z3vs2H48/2Vink/Pdest4H/1q2W8+c3KJt9x8eNfsKE2khBAAU7uwFXV9dz2xmz6dinn3dmVjOrXmQsPGM53bqXv08YPbnK9fGCbLu156Ow9uGvSXJ77YglzXBcfOImVh/aqaDIHVtIGRX33abvyzOeL+Xj+Gu46bdcWczRmClNgeUCn8lJHgfk60ZayfRuZoVN5KetrIwmZCTw6+Nq/R4WzWFREuOGE0VTXRjh3v6Goajxizp9h//lfTOCtWSvZY0h3Zi1fz8TxgxjWq4JhvSoSIj0fnLKQqk0N9OtaziWHbN9kNOzVecq0OyvX9Kwoo1uHUuas2BC/t1JfFKLHufsO5S+n7MLcldX8+c3ZCXNonpXmV149OpbFs8akUmoLbzyKTfVRdvjdq4CTWgycEP+ZS9fz8vRllJcW0btTfi4Z8fjFgcP5xYHDOf7uD+Lp1OZVbkypwJIHDOfsM5SPF6xmQLf2nP/o5wzt1ZExA7py4q4D2GdET44cvU3a0beZwBRYHuDNcSUoMLPAsk7n9iWsr21IbYG18ymwjo0d2ql7NOamExHO2ddx/Rwzph/dO5ZRtamBnhXt4glYvcwVlxy6ParKdn06URuJcu5DU1m9sT6eLeWInbZpkgVlVbXTGefrGrzmEBFG9OnE7BUb4uHi/kwcHl7gwfDeFdw1cVf2HLqIV2YsS8hI4zGwe/t4ZpLdr38zHuXYqbyEDbURerhpyNqXFfPx/x7M92tqOOneD+Of95Tj2IFdA3UfZpPHzhlP5YY69r/lHRasqgb6pFgH1nivFx04nBF9OjF6gPMcTr3yB/ToWNYkkjnIKQ1TYHlAJ3edl19peS7EbluQ/d1omX5d2lMfibFs3aamCqzUF0WWRgSgNw/QkrIRkXjhx8+uOoQ9/vAmKzc4tZx+dO+HXHrY9vH1R5FoLF6yI50ExvnG9n068dwXS2iIJSXz9WVD6d4hMffl6XsO5vQ9BzPk8pcBOG5sP1asr+OCA4Yxun+X+HKIJ3++FyvW11JdF2HPoT1Yvr6WbXxVwvt0dvJGvvLf+/Lrf0+Lp+q6a+KujOrX8hxmPtGhrIRB3YtpV1IUHww1zcTR2N7J0xZhGDgV3pNfgHgWmN9t1a1DKZcdvj3H7Nx6+RBj8/DcV3NWVjOgW+JygY6uBdaupChrI/KnL9ibJ6d+zz3vzGNTQ5RrX/qasyYMQZV4BetCZUC39myoi1BV43Ss3kJmzwLr3rGs2XZ/7hcT+GbZ+gRr2E9y+ZHm6sftsE1n7py4K+c89ClXHr0jB27fO+V5+YyI0L1jGWtd12pDk6ThiSnrwoYpsDwglQITES48wGqJZpP+rtKq2tQQDybw8II42rVQCmRLGdi9A5ccun1CBN3ht79PQ8yp9bbjNp15+Gd7ZO37c4k3uveSRZckZYZoyeodO7ArY9PMydga2/bsyFuXHJCRa4WVbh3KWFvjWWBeGH1j6i6P1grV5oLs/fqMjNHZ/bEWoqsozPhdJMlBHD0r0i/dsqVce+woBnZ3lOm3KzYwv3Ij62oaOHX8oFC4cbJBTzdQwqvy3BhU4BzP5sBha6O7L7ilvkkmjkYFFsY2D59ERhN6BNhZGo10bV8a/wEnz4F5AQQtFWPMFKeNH8x7lx7YZH+6md/zEW+AELfAirw5MDeVWgg703xlQLf2LFy1EVVtWnfQF6ARxuAVewryAG+Uvckto2AEQ1GRxKPTki2wvm5QxpgBXQORRUS4/8xxHDe2cc6zpRyL+U6visZSQtC4kNlTZO1Kwjcfk6+M7NuJtTUNVPozn3gKLLnwYMgInQITke4i8qyIbBSRRSIysYVzrxGRBhGp9m1DfcfHishnIlLj/h0byE1kGG80GsRo30jEGzwkt33vzuU8eNbu/NnN1h8EB43sw+2n7ML7lx3I4+fuGe9kCpHuHcsQcVJlgS8K0VNgpYV770HjBbH46w6Wplg4HkbC+BTcBdQDfYDTgHtEpPl0BPCEqlb4tvkAIlKGkyn/UaAb8BDwvLs/r9h9SHcmjh/Erw/bPteibHV4czGpBg8HbN87HpodJAO7d2CvYT0C/94gKSkuoluHsviC5OQoxGSL2Nh8vGd8dXV9YzmVpFyIYSVUT4GIdAROBK5S1WpVnQy8AJy+GZc7ACfK8nZVrVPVvwICNC1PHHI6lZfyx+NHx7NSG8HhWb+FbO2ElV6+ABXPIvD+D7URc6dnCq+dK6vr4gu8UwVxhJGw/Sq3AyKqOtu3bxrQkgV2jIisEZGZInKBb/8oYLpqQvGg6c1dS0TOE5GpIjK1srJpsktj68T7cVvmk+Dp2ckZPBQXSTzbw4ThTuaSk3YbkDO5Co1endrRvrSYKXNXUdcQRaQxSKZ3FhPxZoKwxWVXAMnZTquA5marnwTuA1YA44GnRWSdqj7uXiu5jnaz11LV+9xrMW7cuEIoMmxkgC5uppNym3MJHG/+0e/G6t6xjAU3HNlqIVYjfcpLizlsVB8+mr+G3p3LaVdSFG/f/s0s8g4Lgf4qReQdEdFmtslANZCcq6UzsKHp1UBVv1bVpaoaVdUpwF+Ak9zDbbqWYaSiyDrKnOEpsGT3rSmvzLNjv84sX1/L8qrahAjPspIiTtilP3ecuksOpWueQC0wVT2gpePuHFiJiIxQVa8uxRhgZrpfAXhP90zgEhERnxtxZ5wgEcNIi/IUFbCNYPAUmOmr7DOyrzPWn7Z4XRNvw20/HpsDidIjVH4RVd0IPANcKyIdRWQCcCzwSKrzReRYEekmDnsAF+NEHgK8A0SBi0WknYhc5O5/O6s3YRQUp+wxiJ/vN5Tz9h+Wa1G2OrwAGm9xrZE9tnNrsC1aXZNXtQZDpcBcLgTaAyuBx4ELVHUmgIjsKyLVvnNPAebiuAUfBm5S1YcAVLUeOA74KbAOOBs4zt1vGGlRXlrMb4/cIZ6P0ggOL7y7zhRY1unqq2qRT1lOQverVNU1OIon1bH3cYIzvPentnKtL4DdMimfYRjB0DfkEXCFRLuSIkqKhEhMzQIzDMPYUpIrABjZQ0TiycLNAjMMw9hC2pUUc8Ku/dl7WM9ci7JVUNGuhKpNDXllgZkCMwwjtNx28thci7DV4BVpDWPhyubIH1vRMAzDyBqeC7FjCAtXNocpMMMwDCMeaZtPhXNNgRmGYRhxBdahnbkQDcMwjDzCiz6sMBeiYRiGkU94OSY7mAvRMAzDyCdqG5waa13bB1+kdXMxBWYYhmGweO0mAAb16JBjSdLHFJhhGIbBlUftwOj+XRjdv0uuRUmb/HF2GoZhGFlj/NAevPhf++RajDZhFphhGIaRl5gCMwzDMPISU2CGYRhGXmIKzDAMw8hLTIEZhmEYeYkpMMMwDCMvMQVmGIZh5CWmwAzDMIy8RFQ11zKEDhGpBBZtwSV6AqsyJE6+srW3wdZ+/2BtANYGsOVtMFhVe6U6YAosC4jIVFUdl2s5csnW3gZb+/2DtQFYG0B228BciIZhGEZeYgrMMAzDyEtMgWWH+3ItQAjY2ttga79/sDYAawPIYhvYHJhhGIaRl5gFZhiGYeQlpsAMwzCMvMQUmGEYhpGXmALLICLSXUSeFZGNIrJIRCbmWqZMIiLtROQf7r1tEJEvReQI3/GDRWSWiNSIyCQRGZz02ftFZL2ILBeRX+XmLjKHiIwQkVoRedS3b6LbPhtF5DkR6e47VlDPh4icIiLfuPczT0T2dfdvFc+BiAwRkf+IyFr3Xu4UkRL32FgR+cxtg89EZKzvcyIiN4nIane7SUQkZzeSJiJykYhMFZE6EXkw6dhm/89b+myrqKptGdqAx4EngApgH6AKGJVruTJ4fx2Ba4AhOIOfo4EN7vue7v3+CCgHbgE+8n32BuB9oBuwA7AcODzX97SF7fG6e0+Puu9Hue2xn/sM/BP4VyE+H8AhONlq9nSfhf7uttU8B8B/gAfd++wLzAAuBsrctvkfoJ27bxFQ5n7u58C3wAC3zb4Gzs/1/aRxvycAxwH3AA/69m/2/7y1z7YqU64bpVA2t3OvB7bz7XsEuDHXsmX5vqcDJwLnAVOS2mMTMNJ9vxQ41Hf8On/nnm8bcArwJI5C9xTYH4F/+s4Z5j4TnQrt+QCmAD9LsX+reQ6Ab4Ajfe9vAf4GHAoswY3ydo995+u0pwDn+Y79rC2ddq434PokBbbZ//PWPtvaZi7EzLEdEFHV2b5903BG5QWJiPTBue+ZOPc5zTumqhuBecAoEekGbOM/Th63jYh0Bq4Fkt1fyW0wD1dpUUDPh4gUA+OAXiIyV0QWu+6z9mxFzwFwO3CKiHQQkf7AEcCrOPczXd0e2WU6jfeZ0EbkdxvAlv3Pm/1sOl9sCixzVADrk/ZV4Yy+Cw4RKQUeAx5S1Vk491+VdJp3/xW+98nH8pHrgH+o6uKk/a21QaE8H32AUuAkYF9gLLALcCVb13PwHk5Hux5YDEwFnqPlNiDF8SqgIh/mwZphS/7nrbVVi5gCyxzVQOekfZ1x5kQKChEpwnF/1QMXubtbuv9q3/vkY3mFOxn/A+DPKQ631gaF8nxscv/eoarLVHUVcBtwJFvPc1CEY209g+P26okzx3MTrf+vk493BqqTLLZ8Ykv+51v0uzAFljlmAyUiMsK3bwyOe61gcEeJ/8AZhZ+oqg3uoZk49+ud1xFnDmimqq4FlvmPk79tcwBO0Mp3IrIc+DVwooh8TtM2GIoziT+bAno+3P/nYsDf4Xqvt5bnoDswCLhTVetUdTXwAI4SnwnsnGRR7UzjfSa0EfnbBh5b8j9v9rNpfXOuJwQLaQP+hRNp1hGYQB5HmbVwj/cCHwEVSft7ufd7Ik400U0kRiLdCLyLM0od6T7UeRd9BnTAiTjztj8BT7n377mT9nWfgUdJjEIsmOcDZw7wU6C3+z99H8e1ulU8B+69zAcuB0qArsCzOJGnXhTif+MMYC4iMQrxfJwAkP5AP7ezzocoxBL3f3oDjgem3N232f/z1j7bqky5bpRC2nBGZc8BG3GijibmWqYM399gnJF2LY7p722nucd/AMzCcTG9AwzxfbYdcL/bwa8AfpXr+8lQm1yDG4Xovp/o/u83As8D3Qvx+cCZA7sbWIcTFv1XoHxreg5w5v7eAdbiFGx8EujjHtsF+Mxtg8+BXXyfE+BmYI273YwvYjGsm/usa9J2zZb+z1v6bGubJfM1DMMw8hKbAzMMwzDyElNghmEYRl5iCswwDMPIS0yBGYZhGHmJKTDDMAwjLzEFZhiGYeQlpsAMI0OIyIMi8lKu5fAjIseKyBwRiSTXcMoXROQdEbkz13IY4cMUmFEQuMpDReSqpP0HuPt75kq2HPMP4GmcRej/neoEEdlWRB51s8rXichSEXlZRHYJVFLDaCOmwIxCoha4VER65VqQTOJm/t+cz3UFegCvqeoSVU3O+u1d+w2clD4n45R9ORH4BCdziGGEFlNgRiExCVgIXNXcCaksMrc0vIrIuKRzjnDLwW8SkfdFZICI7C8i00SkWkReEpEeKb7jShFZ4Z7zgFsnyzsmInKZiMxzrztDRH6SQpZTReRtEdmEU8E31b10E5GHxClpv0lE3hSRUd494KQ4AnjbveYBKS4zCid56i9UdYqqLlLVD1X196r6lu+7fiUi00Vko4gsEZH/cxWkd/xM936P8JWHf0FEuojISa4bs0pEHklqj3dE5F4R+Yt7H2tF5BY323tz/8MyEbnJtRhrRORTETnMd7xURP7qWpJ1IvK9iNzY3PWM/MUUmFFIxHCSq54vIsMycL3fA78ExuMkIn0C+B1OFdkDcDr/a5I+sz9Odu2DcSyZQ3ESlHpcj1OB9xfAjjiJUf8mIkclXecGnFyDO+LkT0zFg65sxwJ7ADXAq66CmEJjUcATcYoKTklxjUqcdjtRREqa+R7cc37pXnOi+313JJ3TDrgEOA3n/sfhuC/PcGU4DjgauDDpc6fh9EV74Sjr89zvao4HcNp5IrAT8BDwooh4Wc0vBo7HqZo9Avgx8G0L1zPylVwniLTNtkxsOJ35S+7rSTSWLD8AJ+loz1Tv3X1D3H3jks45zHfORe6+XX37rgG+SpJhHb5M/cBPgDqcDPReufR9k2S/HfhPkiyXtHK/I9zz9vPt64KT2fsc931P95wDWrnWL3ASDFfjZA2/jlay5AOHu/dV5L4/0/2u7X3n/AmIJrV1/P/kvn8Hp9SM+PZdCSxOOudO9/UwHGU6KEme54C73dd/Bd4iDxLk2rZlm1lgRiHyG+BHIrLbFl5nuu/1CvfvjKR9vZM/o6rVvvcf4pTXGIZjTZXjWEnV3gZc4B73M7UV2XbA6cg/9HaoM8c1w/2etFHVu3BKw0wEJuNYdF+KyOneOSJykIi84brtNuAUcixzP+dRp6p+S2cFsFydgpf+fclt9pG6msflQ6C/iCQXOgTYFSeb+9dJbXgUjW34IE6m+NkicpeIHNWSS9LIX1pyGRhGXqKqn4jI0zhlKq5LOhxz//qLDTYXJNHge63utZP3taVj9M49BqecSnPfBY5FtLm0ucSEqm4AXgBeEJErgddw2u4RERkMvAz8HceFuhpHkTyOo8Q8IinkSL6vtrZZMkXuNXZPce1N7r18LiJDgMNwXJkPAdNE5BBVjWEUDKbAjELlf4GvcVxdfirdv9v4Xo/N4PeOFpGOquopoD2BemAeTudbBwxW1be38Hu+oXHe6D0A12IZjTNHtNmoqorILBwlBc5cVhnwP6oadb/r6C35jiTGi4j4rLA9gaWquj7FuV/gDD76quqk5i7oKuSngKfEWf/2ETAcx11pFAimwIyCRFXnish9NF37NBf4HrhGRC7HmXO6MoNfXQLcLyLX4lTbvRH4u6fQRORPwJ9ERHAUTwVOhx1T1fvS/RJVnSMiz+MEgJyHM/f2B5yigf9M9zoiMhYnWOURHIVfjxMgcTaOhQUwB0dZ/lJEnnHl/WW635EG/YDbReRuHAV8KU6wSxNUdbaIPAY8KCKX4BSL7I4zbzlfVZ8RkV/hVP39EsdKm4jTLoszKLMRAkyBGYXMtTgRcHFUtUFETsGJ8JuG08n9L5CpDBrv4pSInwR0wInCu8x3/CqceaBfA/fgdKxf4rg728pZOAEgL+DMrX2AU6p9UxuusRiYj+MaHIKjqL7DCcC4EUBVp4vIf+PMLV6PE834a5yozEzwGFAMfIzjHvwH8OcWzj8LuAKnzQbgVDX+BKfNATbgKEEv0OUL4AhVrcmQvEZIsIrMhmHkDBF5ByeS86Jcy2LkHxaZYxiGYeQlpsAMwzCMvMRciIZhGEZeYhaYYRiGkZeYAjMMwzDyElNghmEYRl5iCswwDMPIS0yBGYZhGHnJ/wN6NKxEa7xzcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sample_MLII[:1000])\n",
    "plt.xlabel(\"Number of Samples\")\n",
    "plt.ylabel(\"Amplitude(in mV)\")\n",
    "plt.title(\"100 MLII\")\n",
    "\n",
    "save_fig(\"101-MLII.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "790a7f3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure 101-V5.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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oq3iDe6EuaNunm/iwfj8/a1RB+QgnSKIwP8/Xo5pcJeheqKtBKp7h+ESiUWXa9mkl3CJIIpPStI0qKB/RbOJTH1Qm0BlUZgjFmfi07dOL4+uOBkn4uLlVQfkIp+MU5gd0oWgG0IXSmcFpazXxeUP8Oig/t3eyYeaISClWbaZEFXWnpVasrkm042jSzIwQ0lRTGSEUv3A0k8J0AVr4oDIpTDskW1H3WKy6TL0SHDZYqZCUXaTZB6WlrzOBRvFlhrArOAi07dNNSx+Uf9s7WRPfn4BXgcHGmEDcS5VTinAvWPRzp8lVgmGD7QbxteM41wjFLRzVrp9eWsxYfdzeyZr4hgPfNMasS6MsXZ6gawGdPiC9JxwxFOYHaAhGdIDgIc0jev/7RHKBROVN/EqyM6gPgDHpFERxhdvmB3zdaXKVUCTiGlVq+3tFyBW9Cv4e0ecCzow1G8LMk51B3Q/cISIDSVxR99NUC9YVcacg0Sg+7wmGDUUFedAQIqxrcTwjPhefzqDSS8sgCf+2d7IK6t/23wcTHNMgiRQRiriDJDIsTBckFI5QXmT9JPQh6R0tMxtkUprcx/2cgdyYQY1IqxQK0Dz1djqOlr72lmDEUKRrcTxHo/i8Jb69/WzOTjab+ap0C6K0jK7R0tfeEgpHXIODDAvThdAZlLdEfVAB/7d3qwpKRM4AXjbGBO3/W0UX6qYGZx2Oe0V9HqqhvCASMUSM2+zh419tjhFfn0jbPr2EI/GWmkxK0zZtzaD+DfQHNtHsg0pEyn1QIlIJPAJMAbYAvzDGPJ3gvGuA84Fh9nn3GmNuT6UsXtJyBuXjnpNjBFvUyMmkNF2LUIuFo5mUJvfJptyHrSooY0wg0f8ecQ/QBPTDSq/0qojMNcYsjDtPgO9hlaEfCfxXRFYbY571UthUEXLl4gN/j2xyjXj/n59/tLlGOGLICwgB8b9PJBdoUcE4k8K0g++SxYpIGTAVuMEYU2uMeR94CTgv/lxjzG3GmE+NMSFjzJfAi8Bh3kqcOuJnUBpq7h2OgooGSWjbe0YoXkFlWJ5cJ5jAleBXWlVQInJ4shcRkXIR2Ts1IrE7EDLGLHHtmwuMbUcGASYB8bMs5/jFIjJbRGZv3rw5RaKmlmD8Q9LHHSfXiJr4siD0NtcIRyLkB8SVZkobP52EWyyM9m97tzWDelhE3haR74pIRaITRGQfEbkN+AoYnyKZyoHquH07gG7tvO9GrPt5LNFBY8yDxpiJxpiJffr02WUh00FTyHpIFhdYLj19SHpHyFUsEvQh6SXODMpZUqHFItNLNkVNthUkMQ74EfBr4EkRWQasAxqAnlipj4qBacDRxpgvUiRTLRCvECuAmtbeICKXY/miJhljGlMkh+c0hcPkBSTqLPbzyCbXcMweOnv1nnDEkB8QRGdQntAy92EmpWmbtoIkQljBCveIyETgcKxouRJgDnA78K4xpirFMi0B8kVktDFmqb1vPK2b7i4ErgOOMMasSbEsnhIMGwrymm3xfu44uUZjdPbq/1FlrmHNoALRfq+kl/govqxPdWSMmQ3MTrMszmfVicg04CYRuQgriu9U4ND4c0XkHOD3wFHGmOVeyJdOmkJWslK1xXtP1LyqJj7PCYeN+qA8pIXPz8cm1Q5F8YnIRBH5jh1ph4iUiUjSVXk7wGVYM7VNWIUSLzXGLBSRSSJS6zrvZqwiip+ISK39uj8N8nhCYyhCYX5esy1ef6ie0RRW/1+miI/i07ZPL6FwrM8v62dQItIPK4T7QKwo0NHAcuCPWD6pK1IplG02PC3B/plYQRTOdk7lCAyGIxTlN5s6/DyyyTXUB5U5wpGI5XfVGZQnhByfn73t5+ZOdgZ1F7ARa7ZS79r/L6xsD0oKaApFKMgTbN+l/lA9xDHxlRTaMygdxntGi3VQ2u/TSjhiyM8LuNo7wwK1QbLmuWOAY4wx2+Kyay8DhqZcqi5KU8hKVqomPu9xFFSRmvg8Jxwx5EmzT0S7fXoJOT6oLBgIJzuDKsFKPRRPHywTn5ICmuxs2tkwssk1olF8auLzHPVBeYuTWsox8vm5ryeroGYAF7i2jYjkAT8H3k61UF2VYDhCgUbxZYSoD8qeQamZyTssk5Oug/KKUDh23ZmfWztZE9+1wHsicgBQBNyJlXqoO1mc+85vNEbDzHUk6TVNLWZQmZSma+Gsg8qGEX0uEIoY8vJcUXw+bu+kZlB2loi9gQ+B/2JlkPgXsK8xZln6xOtaNPugrG1NFusdLcPMte29IhKJXQfl6yF9DhAMRygIBLLC55f0GiZjzAastEdKmmgKxYaZ+3lkk2toHsTMEYpELB9UQC0HXhAK2yZV/N/ebVXUPSLZixhjZqRGnK6N44PK0x+q5zSGwgCUFGiYudeEI0Z9rx7SnFrK2vbzQLitGdR0rMl2/MQ70UQ8pRV1uyrNUXzWtv5QvWNnkzWDKi1SE5/XhCKG4gIB9UF5QihirbdsXhidWXnaoi0fVB+gr/33FOBLrIzho+zX94DFwDfTLGOXwcnFp+ugvGdnMExhXkBLvmeAcJwPSps+vYSiuQ+zONWRMWar87+I/Ba4whjzpuuU5SKyCbgNeDV9InYdnCAJXQflPQ3BMMUFAQ11zgBWbjj1vXpFMBzJmkwSya6D2gtIVMpiLbBH6sTp2jTpOqiMsbMpTGlhvj4kM0DzDEpzUHpBKGKV9cmGwViyCmoh8GsRKXF22P//ilbqNCkdJz6KT8PMvWNnMExJYZ6uQcsAoUjEXpdjbfv5gZkLhMIR8nMszPxS4BVgrYjMs/ftDYSBk9MhWFfDGNMcJKFRfJ6zMximuCBPZ68ZIL6irrZ8enEKo2ZDUEqyBQs/EZHdgHNoNun9HXjaGFOXLuG6EuGIwRhiChaqmck7GoJhSgrcASoZFqgLodnMvcVKFhvIiqCUjizUrQMeTKMsXRonk0FBvqY6ygQNwTBF+a4ZlDa+Z7TwQWnTp5XoQt0sGBAkW7DwjLaOG2OmpUacrouTycAKM7f2+XnqnWs0hQ0lhe5F0tr2XhHNxaf93hNC8WH9Pm7uZGdQ/25lv3NrulB3F0k8g/Jxz8kxQuEIha5RpY7ivaPFOiht+7QSssPMsyHVUbLJYgPuF1AIHATMBJJOiaS0TjBs9ZLCPMmK9Qm5RrBFZJM2vleEwlYuPl2g7g3BuDBzP/f1ZMPMYzDGhIwxnwC/BO5NrUhdk6Bt4nOvg9Iwc+9w7PI6e/WeeB+UNn16ccLMm02qmZWnLTqloFxsB0amQI4uT8henZif5w4z93HPyTGawlqLK1NE6xPZ29rv00v8YMzPM6hkgyT2i98FDMCqqPtZqoXqijgmvgIdSWaE5sgma1sfkt6hUXzeEow4GWucXHz+JdkgidnEZjZ3mAVcmFKJuighR0FpqqOMEAzH/mg1zNwbjDEtovj8PKLPBeJLvvv5OZOsiW8EsJv9dwQwDCg1xhxqjFmcaqFEpFJEnheROhFZJSJnt3KeiMitIrLVft0qjqc1y3Ci+GL9IJmUqGuhtbgyg9PO+a6ChT5+XmY9zoAgPy+3Uh0NAz40xoTcO0UkHzg0DQUL7wGagH7ABOBVEZlrjInP+3cxcBowHmuG9yawArg/xfKknVC4OUgiG0Y2uYaT/kVnr97i+F7zAtr2XhCKNLsSsiHVUbIzqHeBygT7u9vHUoaIlAFTgRuMMbXGmPeBl4DzEpx+PnCnMWaNMWYtcCdwQSrl8Ypox1EzU0YIRey1ITp79RQnUjU/kB0lyLMdp73zXIMxP5OsghIS+9J6AanOxbc7EDLGLHHtmwuMTXDuWPtYe+chIheLyGwRmb158+aUCZsq1MSXOYwx9gzK+jkERP0gXuEMzNwzKD8X0Mt2go6lJpAdhVHbNPGJyEv2vwZ4SkQaXYfzgHHAhymWqRyojtu3A+jWyrk74s4rFxExcU8YY8yD2LkEJ06c6LtvJBokEQhgPyd93XFyiVizBwREtO09IhxuVlA6e00/znMmPy87Mne054NyquoKsA3Y6TrWBLwPPJRimWqBirh9FUBNEudWALXxyikbiPqg8nVFvddEBwf5zgxK9CHpESGXiU+zeKSfoGu9ZTaYVNtUUMaY7wOIyErgDo9KaywB8kVktDFmqb1vPIkLIy60j33cznm+J2riC2RHKeZcorntrXYXUf+fV0R9Im6Tk7Z92gi51ltmQzBWsrn4fuNV3Sf7c6YBN4lImYgcBpwKPJng9L8BV4nIIBEZCFwNPO6FnKmmeR2URjN5jTuCEtTE5yXRDCoxPiglXTSb+Joz1vi5wVudQdmVc480xmwTkfm0cRvGmH1SLNdlwKPAJiwz46XGmIUiMgl43RhTbp/3ANb6rPn29sP2Ps+JRAyfrd7GXgO6U1LY8eTuzg+1IC8QHVXqQNIbgq5F0mD5Q7TtvcHu9uqD8ohg9DmTHaml2jLxPQc4QRGtldtIC8aYKqz1TfH7Z2IFRjjbBrjWfmWUNxdt5EdPzuGSI0dy3Yl7tP+GOJpczkunw6ipwxuCrghKsE18Pv7R5hLNOSjVB+UF0RlUIMtTHRljfpPofyUxf/yvFRX/1abaTr0/5Ar/DIkzg/Jz18kdHAVV6DLxadN7Q9gVZq7BQenHPRjLGR+U0jZLN9bw5UYryHDt9p3tnJ0YdySZptvxlmgkWZ4TZu7vH20ukTiKL4MC5TgxC6OzoL3b8kG16XdykwYflG+pqmuiqs6yfBYX5DG4ZymzllvR+CeO68/0LzcTiZhmB2SSuCPJsmFkk0s0hTRIIlO4o/h0gXr6CSUIM/ezSbUtH5Snfqds4ZmPv+b2/3zZvP3Dg/nH7NVUlhVy2KjevL5gA//9YiMnjOvfoevGZjP3f8fJJZrTTDk+KA2S8IpQghG9Dg7SR2xZH2ufn1s7KR+U0syUvfoxtLKU+qYQP39uPnf890sWrK3m7IOGslvvMgAueWoOr/zkcMYN6p70dUORCAFxUr7oSNJLgi3CzDVAxSvCrmSx2TCiz3bcYebZsO6sQz4oERkpIqfYry5ZSXd0v258Y/xAvj1xCIX5Aeas2kZZYR43fmMsI/qURc97aObyDl23KWwlKwV0HZTHBF2LpMEJM9e294LmqDL1QXlBMFHUZAblaY+kFJSI9BKRF4ClwAv2a4mIvCgivdImnY8REQ4aYSV4329YTwrzA/SvKI4e/2RFVYeuFwqbaC44Z2QT9vHIJpdwHpKF+e5cfJmUqOsQjkkWq5aDdOPO+ZkN686SnUE9DIwCJgHF9usIrOKFqc7FlzXce85+fO+QYVx57GjAUiyPf/8AJgzpwbodDZzyl5kEwxE2Vje0e61QOBLNBedE8elI0hviZ1C6Dso73BGU6oNKP6EEYeZ+Nqkmq6COB35ojPnAGBOyXx8AP7KPdUm6FRdw06nj2H9Yc6msyWP6ctd3JgCwYG01V/7jcw76/dvUN4VauYpFU9hEH5Bq4vOW+EwSug7KOxLl4vPzAzPbCboCgrIh52eyCmozies+1dOc8VyxGdG7jGtPGAPAq/PWA7BofXwFkVhC4Ug0ikxNHd7SHCSh66C8xh3FB07bZ1Ki3MaZQeUFAlmR6ihZBXUTcLeIDHJ22P/faR9T4jhidJ+Y7S83tJ1hIhRpLpinpg5vca8NAfVBeYk7ig/s2auv3fbZTezCaP8PhJNVUFcCBwArRWSlXX5jJXAQ8FMRmee80iJlFjKwR0nM9p/eXsKBv3uLLzdYGSfuefcrzn/04+jxYDjiymSgpg4vCYbi10H5O/Q2l2g5g9LBQTqJWW+ZBYVR2ytY6KCLdjtIz9ICigsCNAStEeLGaiv7xGvz11NcEIgu9m0IhikuyCMYjsTkggOwZ+NKmglGNJNEpnBH8YEGqKQbd3LevCyIFk5KQemi3Y4jIvTpVsTqqtjcfH96eyl/entpdHvxhhomDOlBKGxicsGB/lC9IhiX6kjXQXmHO7s2WApKmz59BF1h5s6gwM8KqsPJYkWkWERK3a90CJYLlBXG6v8epQUtzpm/dgdgRdc0/0jF/qH6t+PkEvHJYjXVkXdEZ1Au87b2+/QRG2Yuvp+xJrtQd5i9KLcaK5qvJu6lJMD54s8/ZBjfHD+Q8YN7tDjn661WcGQw1GziA//b4sMRw0+e+YyZSzdnWpRdpqlFuQ0dHHSUzvrs1AfVMWobQ5z90CxWbOlcgfOWmfv9bS1I1gf1FNbi3J8AG/F3dgzfMKhHCUs21nLxkSMZ1KOEOau2AfDekuaH+rb6IGDZhgtiFJSl4LbWNiIiVJYVeit8G5xw9wyK8gPMXbODWcu38t41k6MPlYBAqT1z/HprPf26F1GU3/EKw17iTrcD+pDsKH/875c8MGM5b1x5BH26FVFelOxjpWUUn99H9JnmncWb+HDZVm7/z2J+e+o4epUXdej9QVfdOYA8EV/7upPtSfsCBxhjFqVTmFzjzm9P4OMVWxlkR/TtP6wnT1x4IKff+wGffb0dgG11TYBlGy4pbFZQIkLYGA6/9V3KivKZff2xgFVvqiEYZmSfcjLBzqYwizc0T5o31zQy8ea3qG8KAzBuUAWv/GQSSzbWMOWuGUzdbzB3fnt8RmRNllA4gog71Fkfkh3h0Q9W0hiKcNQd0wFY+YeTk35v/AxKUB9UWzjtNGt5Ffvf/BYPnrc/U8YmXzkhFDYEhGg5oEDA3309WR/UXKBPu2cpMVSWFXLCuAEt9j9y/gE888ODOXxUb6rqHQUVoTCvuYZUQGDd9gZ2BsNsqW3kjQXWgt/D/vAOx9z5njc3kID1O1oWZHSUE1jZM/76zlKem7MGgPeWbPJMts7SFDYxucnUB9UxmuKG4Dtd/aE94qP4AgH1QbWFo9Cr7IHtjA6a2IORSNTXDc4Myr/tnayCuhj4tYicamc0H+p+pVPAXKSyrJBDRvaiR2kB2x0TnyvVEVhmpmWu8vGXPPUpv5gWu8zsi3XV3PjSQk872Pod7ecVvOO/S3hghpPNvWOFGzOBO4sHaLmNjhKMU1DLNre9KN1N8wxKF0knw46dwZhtZxlLsoRd0cJgDQj8rKCSNfEFgH7A88T6n8Te9reTwad0Ky6gpsHK0ReMRGI7jghbahtjzn/m49XR/6sbgtzy+iJmLt3CtE/XMLRXKVceszs3v/oFlxw5krMOTM+4YZ1d0n76/02mID/AYX94p83zdzaFMMZEZyep5sdPf8qgHiXMWLKZe8/Zj97diigtyItmhUiGoKvUCWRHmPlDM5azaEM1d5w5noZQOOr385Kn//c1by/a2MIkt2ZbPWMHViT1nTsPR2ds5nfzajr7cjJUxymo2oa2c3zGE4qYqJkQ/N/Xk+3VTwCbgJ+jQRIpo6I4n9pGq8O5F+qC9UPdVNPY2lvZ58b/Rv+vbgixYG01F/1tNgDXTZvPGws3cNvUfUCgb7fi1i7TYZwZVP/uxdEH0/Bepfzm1HEsWl/NH15fHHN+XVOY6oYQFcX5rNxaz4jeZfGX7DQ7m8LRXIcAR9umz0mje/PkDw5K+jpBV5opcEx8/u7iv3vNcgeHI4bX5q/n9Ssm0ausiJ4eBtP88vn5Cff//rXFXPLUp8z99RS6l7RcWuEmfh0U+HcGNWfVNqbe9yEv/vgwuhXns1sG/MCb454Ja7bXd+j9wXBsMJbfTXzJKqg9gAnGmCXpFKarUV6UT0MwQjAciVmoC81OzM4y/cvNHPj7twGYf+MUPllZRe/yItZt35nQL5Ysq6vq6V1eSHGBNWn++0UHMW5Qd7qXFHDk7n0Y078bL362lhc+Xxd9z7RP17Bu+04emrkiWmm4uiHIp6u2MXlM307LsnxLYlPS53YASrIEQwlMfD79za6uqufDZVui2y/a7XzsH2cA8Nylh7CltokeJQUs21zH1P0HeRJF+dvTxjF+cHfOe+Rjvq6yHprrtu9sV0E5UXxOd7f++rPxX5tvDYZ+/tw8Fm+o4dELJtIUMhw/tl/aZ1XGGN78YiMrt8aGl6/cUs/r89dz/Nj+ST0zEj1n/DwYS1ZBfYxV+ymtCkpEKoFHgCnAFuAXxpinWzn3GuB8YJh97r3GmNvTKV+q6VZsNX9tQ4hg2MSYmQKuDv/qTw9nR32Qsx/+H6WFeTFBCfsO7RGNCGyNM+/7iC83Nkfezbn+2A6HpwJ89vU2/jVnDZNG947uO2xU75hzjhrTl0NH9uKFz9cxum85SzfV8puXv4ge32ybLX/1wgJe+Hwd7/7f5E7NquqbQvz475/G7OtZWsC2+iA1jSHqGkOUJRnuHIqYFuZV58HpN34xbT7vf7Wl1eNT7/soZvve6V/xl+/uy4J11Rw4vJIx/bulRI74QIaDR1Qyul83ivKb+/Dmmkb2bGcs5JicnAd8QAS/Nf3OpjD3Tf8qat5ebSvgCx+3LBaXTR7JtSfskVYZ3v1yExc/OQeAIZUlrK7ayYHDK/l4ZRWX/v1Trj1hDJdNHtXudXI1SOI+rGzmF4nIQSKyn/uVQnnuAZqw/F3nAPeJyNhWzhXge0BP4ATgchE5K4WypJ3yYmt0uWNnsIWJz+GnR49i7MDuHDqqN1/cdDxvX31kzPHvHjiUA4b3ZOp+g1v9HLdyAjq9yO8W23z3jfED2zyvKD+PlX84mZd/cniLY599vZ3h170anWE98N6ypD77sQ9W8MSHK6PbZz/0P1ZujTVv/O+Xx/KnsyYAsHpb8qaPpnD8GjT/mpncIf7JsGbbTk6/90NueGEB3/jr+zz50cpomzcEw1z+9Kes7GB/qKpr4uyH/hezb4C9lOL8Q4dTWmjN2G58aSFPzVrV5rXCxkQj+MCfPqhHP1jBn9/5itcXbACgIRSrQd/8YmNaP/+LddVRZQgwcVglK245iQsPHx7d97cPV3HpU3OobWzbJxUKmxbWglxYB/WM/ffBBMdSEiQhImXAVGCcMaYWeF9EXgLOA65r8aHG3Oba/FJEXgQOA57dVVm8YuzACsAaHYXCkRjnpVPgsFtxs4mktDCfkoI8LjlyJBOGdOeTlds4fd9BfHviEN5ZvJHnPl3DyfsMiPHLJOLM+z9i937lvHHFER0yJS5eX835hwzj2xOHJHV+cUEe154whlfnrWfhOqse1p9deQgBvrIjFc95eBZ7D+rBdSdaI9FgOEJVXRP9Kiz/mTMLO//Q4THvA0uJlxTmU5gfiM7GTrh7JrOvP5bJt0/noe9N5JCRvVqVMxSORBcugn8Xi9Y0BKOBM98cP5CyojwaQxEOGF7Ju4s30b97MeGI4e//+zrh+5tCEW54cSEAU/cfzNzV23ll3no21zTyjx8dQiRiOOuhWUwe04e/z/qaSaN789HyrVw0aTdue2MxxQV5FBcEOGJ0Hz5aHlsGzlmc++OjRnHhYSPY81dvsHxLHde/sICCPOHpj1fTvaSAvQZU8NNjRlGUn0deQKyoMlcf9FOI/8bqBk75y/st/D7xM46lm2qZePOb3P2dfTl8dKxFYVepbghy0p9nxuzrV1GMiDC4Z3OWuQ3VDby+YANH7dG3zd9nKBIbEOT3sP5kFdSItEphsTsQivNzzQWObOX8KGLZByYBD7RxzsVY4fIMHeqPyPg9B1Sw96DuvDx3nZWLz9VxnPBRxwzoICLRh7jbl3TE6D789tSxTN1/MFceM5rfvbaI6V+2vkZiycZa1lc3RBcRt0cwHKG6IURlWcdMg5dNHsWm6saogopn3podrNu+kw++2soHX23lR0fsRlV9Ew/PXMEzH3/NKz85nCGuH2JDMEzEGGobQ0wc1pObTx/HHv0roseHVTabC6/+51xqG0M8MGNZmwoqGDYU5Mea+PwWZm6M4alZluJ57IIDOGqPWN/dd11Rm60pKDcTb34rqhgaQhFmLNnMtvomPl5RxccrqgB49hMravSGFxYARCNO3df/zTfHMqQytg+VFMaOV29+ZRE19sh+xpLN3P/eMr4zcQi3nrkPoUjsDEoE39SDetVW3smwpbaJHz05m7/94CC6lxSwsynM3oO7d+pzl26soW9FMd1LCnhnUew6wjP3H8xlR40EYGivlmlQ11TV887ijRyyW+8W3wNYfT0+ii+c7QrKGNPqPF1EjgXanscnRzkQ/xTbASRjNL8Ry1z5WGsnGGMexJ4BTpw40TffyJ4DujH9y80tFuo6lBcnN4bIzwtw3iHDARjdrxuPnn8An63eztT7Pow9LyDRtSfLNtUmraC22QuKK8vadnon4rBRvXncZZ7rXV4UnQk0hSMc6gpVv+SpOfzPfkACnHHfh0zdL1onk/vfW8Yp+1gmxnMPHhajnAC6lxbw7MUHc9aDs6Ippdyz0EQEw5G4NWj+C5KYvWobt75hmVjHDqpo52yLmdceRX1TmOPvnpHwuNMP5q7ezvdctcmSYdLo3nzvkOEcvUffGAWTiJoEZqd/zF7NrWfuQzjS0vfql+flhuqWa/7O2G8Q0z5dy7TLDmVrbRNVdY38/DkrmrGuKRzze3vn6iNZVVXPkJ4lbNjRGJ1dfb56O+GIYf9hPQErLH/B2mpOGNefbXVNHHfXDKbs1Y9vjB/IZ19vi17vx0eN5Jrjm31dFcUFlBflx5j1/vzOVwBccuRIxvQv57QJg2ICOEI5GsUXg11N9/vAhVhBCu2a+ERkOq3Phj7AyvMX/8uroJ1ktCJyOZYvapIxJrnhjo/oV1EcDSdPtHanI3nN3AQCwv7DenLOQUOZv3YH0y49lLrGMOc8MosFa61xwLLNtRyxe3IJQrbVWeHwnQljPm6vfsz91RS++9AsvlhfzRXHjOKed5fRvaSghX/MrZzAMku513/d/dZS3rB9AQO6Jw6fP3i3Xhw4ojI6E6iqs9q3tjHE7W8s5qopY2Kiy+L9f35cG+KE95cW5rW7bOCGU/bir+8sZXDPkpRHl/XtVsSmmkb2Gdyd4/bq1+p5J+8zgGAown9b8c8U2sEU8TMoP/mgquqa6NOtKDqLmvurKXQvLeCGk/eK/g6MMRw6sjcn/WlmC0V8dFzGlxW3nISIcNo9HwBw57fGU1aUz+9fW8TXVfXMv3EK/1thmU6nf7k52nYBgU9vOC5hROT7Pz+KOau28YMnZsfsv9/2M94/fTk/O253ThhnpUNqERBk9/UXP19LYV6AE/fufIRvOkj66SciecCpwA+wouzmAfcD/0rm/caYye1cvwzIF5HRxhjHUTEeWNjGey7E8k8dYYxZk4wcfqNvt2aTWX6CGVR7o//2+N3pe0f/714aiFF4bj9Oezijyc6uqepeWsCI3mV8sb6a3fqUM+uXxzB7ZRXXTZvfqhzxEYvjBlWwYG11NFAgvmqxm+tO3IMz7rVGswvWVvPtBz5iZJ8ynvl4Na/OX0+fbsX87cID6dOtiFDYRB+Y4C8/iMMmu/0/+sUx7Z77g8NH8IPDm63yfz17Xx59fwWfJhF+f9xe/WKc/qfvO4jnP1sb3b7ljL155uPVfGv/tv2Q95xtxU7d/dYSepUXsWJzHf9bsTVq6g2GI2zY0UA4EvGtD2pbXRN9yov47oFDGdG7lO52uRz3IE1EGFJZyuDKUhatT2zGdlhdtZP+rkHV1f+aG3N8b9faRnf6qIiBHqWJB4Y9Sgs5akxfzthvEPPW7GjxW/pyYw2XPDWH5b8/iUBAbGuBy8Rnz6CuePZzoGN5FL2gXQUlImOAi7BmKXXA01gK6jxjzBdtvbcjGGPqRGQacJOIXARMwFKIh7Yi1znA74GjjDHLE52TDbg7e6Iovngf1K5y82l78+YXG3l38Sa+aOcH5cZZ2zK0svPlv246dSwj+5Rx0IhKACYOr+Stq45k0fpqXp+/PmqecJh57VE88eFKmsIGg2HpxlrcVmAngCIR4wf34GfH7s6qqjqmfbo2xreypbaJLbVNzFq+lW+MH0gwYiiLWyTtN8fx+h0NlBbmUdGJ/nDKPgOZNLoP43/zXw4Y3pOJwyvZo3833lq0iR4lBUwc3pNX5q3nzS82csTo3jEK6s5vjWdU33JenruOxRtq2HNABQ+fPzHpz77y2N1jtp+ctYr6xhC3vL6YJRtrEvqg/DKD2lrXRGVZIVcdt3u75z543v68PG8dyzfX8e85icfK331oFmu3t8xl2R7tfeeBgPDHb09g8YZqTrh7ZsJzvq6qZ3jvMjuKLzZIwkm3Blauzf52EIYfaPPORWQmMA54Dvi2MeY9e//P0yTPZcCjWFkrtgKXGmMW2p85CXjdGOMs374Z6AV84mrMp4wxl6RJtrTgnrbnJ7Dlp1pBjepbzqi+5WyuaeTRD1bwz9mrOWPfQW2mBtpe38S0T9dQmB+ImfF1lF7lRVw1ZUyL/XsOqGDPARUcMrI3q7fVM7SylII8aXH+bW8s5p3Fm/jB4SM4YHhlzKwnnryAcMWxo/nHJ18z7dO1Cc9ZuK7aUlAtFur6z8S3obphlx4c3UsKePIHB7LngAp622vgTp3Q7Ns7ZZ+BvDJvHSftPYCRfcsJiFCUHyAQEH581CjOPXgYc1ZVtTlrTYbzDh7G6qp6bnl9Met37LR8UG4FhRWc8PPj6xMGAXhFTUOQz1dv5ztJRqwOqSzlssmjiEQMo/uWR5dkuGlPOQ3sXszvTt+b+6Yv4+OV1mDqT2dNYEiSg8I9+lfw8Pcm8tqC9Uz7dC0XHT6Ch99fAcAX66stBRUxlBS6gyRis1Mccss7XH/ynhy8Wy96lRcyoPuufd+7SntPv0Ow1iY96CiKdGKMqQJOa+XYTKxACmfbi8jCtONWUE52Bjed9UG1x+h+VlNe+28rAW1boanfuv8jlm6qpV9F0S5nuGiLQ0b24hBaj7a74tjRfHviEIZ3YGGvOwIQiHEqO+t/Wtbi8o+ZyWHDjoYY81BnmDS6dX9jXkCiCuvQkS1DpbuXFHD0Hq37nDqCFSZtZeuPn0E5a/SOuP1dLj5iN44a05cZSzfz06NHJ4xKSxdO5GJ8Itz2CASECw4bnlBBJeKSI0dyxn6DKC/Kp7w4n4riAkoL8/jOg7OA2EFEMhy7Vz8mj+nDZZNHUpAXiCooxwKSqK87FRUcbn61uaqSY/KbvbKKrXVNbNjRwNF79E1aae4q7T39DsAy770vIiuBv9G8JkpJAe0pqPiy8ani6D36skf/bizeUMOHX21pU0Ette3amS48WJSf1yHlBLDXwOa4m3k3TqGsMJ8z7/+Qz77eHk0bE5/Fw09mJocNOxo4aLfKTIuREgrzA/QuL7JmUHFZ/N0DgwdnLOdBOyv+oB4lnHvwMM9kdGY75x7S8c90/06m7jeY/63YyhXHjOaR91dQ0xCKmUkdt1dfdu8XG6h80G69+PLmE2LapSPk5wUY1de65rLfn8TEm99sVlBxYeYBiTXxxfPy3HUs21zL3W81r1+8+60lHDWmLxdN2i3m95UO2mwBY8xnxpgfAwOAPwLfBFbb7ztZRHqmVbouQKyCav46/n3JIVx+1Ki0zVj6VRTzxpVHcPBule1mJ3BE+MWJ6U3nkg56lBbyr0sO4YeTRlBRXEBeQHj+ssP4weEjWLm1jkjE2Ak0U7cO6i9vL+WHf5vd/olJ0hgKs872DeQKA7sXs35HyxlUa3y+ejtVdU18srKKw299h1fmrWPKXe+xdGMNU+56LxqO/fDM5Xz7/o/auVrb7GwKM3/tDs45aCj7De3cI+5nx+7O+YcM445v7cP7Pz+ab00cwhtXHsHTPzyIsw9qXrPWWhCUs5B5V8kLCP0qiqNmvPhkse35Wn/yzGcxygmsKuDTPlvLSX+eyZbaxrSGqSe7DqoBeBJ4UkRGYc2qfgbcLCLvGGNOTJuEOY47X1yRawY1cXglE4enf8Q8vFcZzy5fzUtz1/HNBCmMmkIRIgauPm5334WgJssBwys5IK4th/cqpSEYYVNNo/WjDcSHmbd+vU3VDazf0cCeAypYtbWO0iIrw8eSjTVUlhVy55vWWvPlm2sZ0L1kl01TE37zJsa0HlafjQzoXsJXm2sZ0rMk4YP4e4cMY/GGmmhgy7/nrIkJPrj86c8AOO4ua43Xza8u4uopu0fNUw3BcEKLRDKs3V5PQzDCgSM6//u74tjRCfcP61XG70/fm+fmrKExFEk6X+Su0LO0kO22GS8+zDzevNdRJt78VnTRdTrocOsYY74CrhOR/wecgrUWSukk7tFMSSd/ULvCpZNH8uwnq/n9q4sSKiinY3tZxsELHFPhii11LTI8t2biq28K8eYXG3lo5nIWrK1m/JAezF29vdXPOPrO9/jW/oO5/VudL3lf3xRiZ9AKtR/R2/vyDumiX0URHyzbQv+K4oTBLldPGcPqqnpO+cv7SV1vzqptMfkBH3hvOQcM78mho1r609pjU3WjLWP6BgTnHDSMRz9Y0W6291TQo7QgGn4eXxjVqSd1/iHDOO+QYRTkBTjrwVlJFSZ1aCt58a7SafVtjAkDL9ovJQV0dsS3KwzrVca5Bw/lqVlfJxx1zluzA4DBPTMbzZNqhveyFNTKrXUJk8XG66dNNQ0c+Lu3Y/a1pZwc/jVnDWFjGFpZyvrtDVx+9KgOOZjfX2r9+K8/eU8OG9V6AEm20beimJqGEDUNwZg+d92Je/CH1xfTrSifcYO6U1aYx7hB3Zmzals080Uy3PWWNYt9/PsHUN8U5qQOzP431jhr/jofsdoe/+/kPbnsqJFpC4Jy06O0gO07m+vOuc3ZTsDQlLH9o36rl39yOMFwhIv/NofGUJimUISi/DzWbt/J/03ZnRtf/oILDxvBox+siLlGOvC+DKfSKm4flJdMHFbJU7O+ZtXW+hblGD5ZWUVhfqBFWY1sZ2CPEgrzAqy0Z1At60E1Pwx/MW1eh0aU8bjD3PtVJA61b405X2+jIE+44NDhvlmbkgqch//a7TvZc0Czo/2SI0dyyZEjo9sLbzoBsBK0PjxzecLouCN37xNNaxXPBY99AnRsAeqGHemfQeUFJBrun2562CY+Y0wLE1/QLhjpnsk5cr3448NiruP4wy84bAQNwTBLN9XQt1sxC9bu2CWTaluogvIRxRmKktt/WE9E4NX56xnUsyRmVPfZ19sZ0rMkZoaRC+QFhCGVJazcWtfCcexeB3XoLW+zrg3lNKpvOcfu2Y+hlaUMrSzl3EcsM9Nj3z+Aa/41ly21sTb++Wt3UFXXRM/SgqQUzubqRvp2K+5QCftsoK/98N9S25RU38oLCKeMH8iXG2qYZme2uOGUvVi4bgdXTxnDoB4lPDRjebTS8K6wdns9PUoLPPEPeUGPkgKCYUN9U7hF3skJQ3rw+ertCU2NbQVoFRfkdahqdWfJjW8gRyj1cJ2HmyGVpYzqU86f317Kn99eGh1tbqpu4OOVVRycI+HN8YzoXc6yzXX2qLJlqqPaxlCbygng/nP3Z1TfZt/Qg+ftT0CEo8b05b1rjuK1+evp060oOpJ/98vN7PfbN7n9zH34VhKLQDfVNNInjaamTOE2nxUkSPGViEE9SvjjdyZQUVLA4x+u5IRx/WNSOp1/6HC6Fefz3Kdr+GTltpj3dmSEv2578ln+s4Gedpqk7TuDLawFf/vBgby9aKNn65o6Sm4Ny7KcTD6Ixg1qLg3g1KJyHKvuUg65xF4DukXvsTDexBcxLN/cnNfsmR8ezMxrj2LS6N6UF+UzfkgPepcXxSgnsGz5x9pJVMuK8vnWxCFMGNKjxWfPWJqcY3lLbW4qKLf5rKOz81+etCevXzGphRIpzA9w1oFDuf/c/fnjt2MDU1or95KIddt37nLGDD/h5BDcVtfUoh5URXEBp+/berHTTKMzKB9wwaHDWbNtZ0Z9DL8/fe9oUtBlm+rYe3D3aIkMpyxArjHatUAyvuRDxJio8vrHxQdz0G5WgMJjFxwQXbuT7GLeHqWFfHbDcXywbEs0PLqojTRNbmoaQlTsYsJgP9KztCBa1iRRDsq2KMwPxPit4ulVXsRpEwbx8MwVfF1VT21jiOlfbkq6H6/dtpODd8udgJQetvlue33Qqn2WxmwwqUYVlA+48ZutVbX3jpLCPF776SRO+vNMvv/4J/TvXsTabTspyg8wMMP5uNJFj9LmB3/LchtWOZL8gLCf68GWnxegM67CnmWFMbOGUJIpdGoaginPx+gHRISyonxqGkJp8W8GAsJrV0wC4IS7Z/CXd77ijQUbeO2KSW1+XnVDkJrGUE6Z+HqVWya+TXZ0Yl4nM1RkguyRVEk7TmXULbWNLFhbzbb6INccPyat+fcyidsx7Pb/OeugttZa2axT9QDt44raem/J5nazVRi7crAXociZwLkvdzXjdHCOnblh6abadivkrrPTEOWSic8JSHHuLVFZH7+iCkqJ0q24gEfOn8h3D2x23rt9U7mGW0G5sz0466BqGkMpnb0M61XKn7+7L5PH9GFbfZBfPj+/zZlUfVOYiEl9Rnu/EFVQaY5QPO+Q4Zx1gNWnq+razpzQrKByJ2tHt6J8igsCrN1uzaCSDUrxA6qglBiO2bMf/+/kvaLbY9OcDDKTtJao11kHVdMQojyF/h8R4ZvjB3LbVCstzLOfrObleetaPd9ZAFmeowrKCePuqA+qM0zd3woE2NZOap8VW6ykqn6NausMIkLfbsWs32HPoLLIxJebPV/ZJcqL8qOLJXe1oq+fcd9bSUHsDMpSUMFOFQhsj74uX1RtY7jV83bYq/9zMUgCvJtBQXOodXszqAVrd9CvosizRbRe0beb5VMGnUEpOcB1J+7BdVmYvbwjuJOUlsT4oOx1UA3p8//88iSrbdvyiTjZK3rlWB5EB6fNvVBQg3qUEBD4Yl11mzWeFqzdwbiBuWfW7ldR7PJBZc9jP3skVZQ0UhJv4otYAQrpyiZw8REj6dutKFqgLxHnP/oxkHuJeh2cJLHpDpIASxmO6lvOAzOWt1oKpSEYZtnm2pw0a/fpVkRdkzVbT1S526+oglIUYmdQeQEhbKzUMGVpzO4xeUwfXp67jncXb2pxzB084Zincg2nFpFXZmQn79z0LxPn7dtc00jEwOCeueN/cqh0DXKyKW1Z9kiqKGmkd1mzzyEvYEXx1TeF0pphfo/+1kj9+49/wpbaWFPfV64sFs46llzDKXSXDj9fIq45vjlJb6LoyY3Vlkm1T0Vu+Z/AWhjtoGHmipIl/O70cVxxzOhoOhhoHmEGwyatCsodKeYU5nNYsNZKzfPWVUdm1Yi3IzgKqsijJMkn7T2AO+zaXMsTmFY32f7Aft1yJ8TcoYdrFp5NUXzZI6mipIFzDhrGz47bPWaf20afTgW139Ae0f8XrtsRc2x1VT0i1tqpXMVRUKkobZ4sI/tYdcCm3DWDW9+ILd2xyZ5B9c3JGZTbxKczKEXJWvJjqhyn7yfSq7yIpy+yShas2x6bNX1TTSO9UpjFwo9MHG5lyXcymHiBe+H5U7NWxRzbVNNIfkCozEGfX48YE1/29CnfSSoilSLyvIjUicgqETk7ifcUisgiEVnjhYxKbuPObF6S5hIoh47qzb5De7B0Uw33Tv+KppDlG9lc00CfHDQ1ubn0yJG8ddWRUV+cF8Qo/LhMUxurrczxuZjayx0Jmk3JYn2noIB7gCagH3AOcJ+ItJdN9RogcWiOonQQ9wgznSY+h37dilmwtprb3viSFz9fSzAc4dOvt7co5ZFrBAKSkXu80/ZD1QfD3PGfL5m51Hp0bKppSGuZ90ziDpIoTDKTvh/wlaQiUgZMBW4wxtQaY94HXgLOa+M9I4BzgVu8kVLJdbzyQTnsPbjZ7PTJyipWbqmjqq6Jybv3Sftnd0Wm7j+Y287ch3DE8Nd3v+K8Rz4mHDEs21Sbs7NW9zq/bKoU7CsFBewOhIwxS1z75gJtzaD+AvwS2JlOwZSuQ4HHM6ip+zUXjJuxZAvH3TUDgOG9czdAItMMiVvrdM2/5rJuRwPVDcEMSZRe3LXmygpVQXWWciC+9OUOoFuCcxGR04E8Y8zz7V1YRC4WkdkiMnvzZrUGKq3jXidS4oGC6t+9mOcuPZTvHjiUDdXNwRLDe5Wl/bO7KkPjoiOn2cU6c7U4p5uyIm/C+lOBpwpKRKaLiGnl9T5QC8R7TCuAmgTXKgNuA36azGcbYx40xkw0xkzs00dNJ0rruNeJeKGgwHowHjG6d3T7+pP3pFeOJSz1E/0rWpryHv/+AVwVt+QgF8kmE5+nkhpjJrd13FY6+SIy2hiz1N49HliY4PTRwHBgpj19LQS6i8gG4GBjzMoUia10MdzrRIrTGGYejxMCPbxXKRdN2s2zz+2K5AWE8w4eRt9uRdz5puVRmDS6j6drsjJFURYFSfhKlRpj6kRkGnCTiFwETABOBQ5NcPoCYIhr+1Dgr8B+aESfsgt4HcXnMLhnCVceO5qT9h7g2Wd2ZX572jhC4UhUQeW6crLqnMX6o/yOrxSUzWXAo8AmYCtwqTFmIYCITAJeN8aUG2NCwAbnTSJSBUSMMRsSXFNRkqaglTIc6UZEuPLY3Dcx+Yn8vAAPfW8iY/oldHPnFO9dcxQrt7aePd+P+E5BGWOqgNNaOTYTK5Ai0bHpwOBExxSlI2RqBqVkhuP26pdpETxhSGVp1lUKzh5jpKJ4hDuKrziL7PWKkmvor09R4ihwRfFlU94yRck19NenKHF0L/GmgJ6iKG2jCkpR4sjFcguKko2oglKUODQwQlH8ge+i+BTFD5w6YSB7u2oHKYriPaqgFCUBfzpr30yLoChdHjXxKYqiKL5EFZSiKIriS1RBKYqiKL5EFZSiKIriS1RBKYqiKL5EFZSiKIriS1RBKYqiKL5EFZSiKIriS8QYk2kZPEdENgOrduESvYEtKRInW9E20DYAbQPQNoBda4Nhxpg+iQ50SQW1q4jIbGPMxEzLkUm0DbQNQNsAtA0gfW2gJj5FURTFl6iCUhRFUXyJKqjO8WCmBfAB2gbaBqBtANoGkKY2UB+UoiiK4kt0BqUoiqL4ElVQiqIoii9RBaUoiqL4ElVQHUBEKkXkeRGpE5FVInJ2pmVKNSJSJCKP2PdXIyKfi8iJruPHiMhiEakXkXdFZFjcex8VkWoR2SAiV2XmLlKDiIwWkQYRecq172y7bepE5AURqXQdy6n+ISJnicgi+36Wicgke3+X6AMiMlxEXhORbfa9/FVE8u1jE0Rkjt0Gc0Rkgut9IiK3ishW+3WriEjGbqQDiMjlIjJbRBpF5PG4Y53+3tt6b5sYY/SV5At4BvgHUA4cDuwAxmZarhTfYxlwIzAcawBzClBjb/e27/lbQDFwOzDL9d5bgJlAT2BPYANwQqbvaRfa4r/2/Txlb4+12+IIuw88DTybi/0DOA4r28rBdj8YZL+6TB8AXgMet++zPzAf+ClQaLfNz4Aie98qoNB+34+AL4HBdpt9AVyS6ftJ8p7PAE4D7gMed+3v9Pfe3nvblCfTDZItL/vB3QTs7tr3JPCHTMvmwb3PA6YCFwMfxrXJTmAPe3sdMMV1/LfuB3g2vYCzgH9iKWtHQf0eeNp1zki7T3TLtf4BfAj8IMH+rtQHFgEnubZvBx4ApgBrsaOg7WNfux7IHwIXu479INkHsl9ewM1xCqrT33t7723rpSa+5NkdCBljlrj2zcUaVecsItIP694XYt3rXOeYMaYOWAaMFZGewAD3cbK0fUSkArgJiDdPxd//MmylRA71DxHJAyYCfUTkKxFZY5u3SugifcDmbuAsESkVkUHAicAbWPczz9hPW5t5NN9nTBuR3W3gsCvfe6vvbe9DVUElTzlQHbdvB9boOScRkQLg78ATxpjFWG2wI+40pw3KXdvxx7KN3wKPGGPWxO1v7/5zpX/0AwqAM4FJwARgX+B6uk4fAJiB9RCtBtYAs4EXaLsNSHB8B1CeLX6oVtiV77299moVVVDJUwtUxO2rwPJJ5BwiEsAyUTUBl9u722qDWtd2/LGswXZ2HwvcleBwe/efK/1jp/33L8aY9caYLcAfgZPoAn0Aov3/DWAalkmqN5Z/5Vba/67jj1cAtXEzrmxjV773Tv82VEElzxIgX0RGu/aNxzJ95RT2SO8RrJH0VGNM0D60EOuenfPKsPwwC40x24D17uNkZ/tMxgoI+VpENgD/B0wVkU9pef+7YTnJl5BD/cP+LtcA7geq839X6AMAlcBQ4K/GmEZjzFbgMSwlvRDYJ25GtA/N9xnTRmRvG7jZle+91fe2+6mZdsZl0wt4FitSqww4jCyO0mrnPu8HZgHlcfv72Pc8FSsa51ZiI3n+ALyHNdLcw+60WRXBBZRiRWw5rzuAf9v37ph7Jtl94Clio/hypn9g+eA+Afra3+dMLNNnzvcB170sB64D8oEewPNYkZtOFN8VWAOUy4mN4rsEK8BiEDDQfhBnSxRfvv293oJlQSm293X6e2/vvW3Kk+kGyaYX1qjqBaAOK2rn7EzLlIZ7HIY1Wm7Ampo7r3Ps48cCi7HMQNOB4a73FgGP2g/xjcBVmb6fFLTHjdhRfPb22fZ3Xwe8CFTmYv/A8kHdC2zHChn+M1DclfoAlu9tOrANqxjfP4F+9rF9gTl2G3wK7Ot6nwC3AVX26zZcEX9+ftn93cS9btzV772t97b10mSxiqIoii9RH5SiKIriS1RBKYqiKL5EFZSiKIriS1RBKYqiKL5EFZSiKIriS1RBKYqiKL5EFZSidAAReVxEXsm0HG5E5FQRWSoiofgaPtmCiEwXkb9mWg7FX6iCUrIGWzkYEbkhbv9ke3/vTMmWYR4BnsNaZH1FohNEZISIPGVnJm8UkXUi8qqI7OuppIrSAVRBKdlGA3CNiPTJtCCpxM4c35n39QB6Af8xxqw1xsRnjXau/SZWyplvY5UGmQp8jJX9QlF8iSooJdt4F1gJ3NDaCYlmVHb5biMiE+POOdEu2b1TRGaKyGAROVJE5opIrYi8IiK9EnzG9SKy0T7nMbtWknNMRORascqk7xSR+SJybgJZvisi74jITqwqrInupaeIPCFW2fGdIvKWiIx17gErDQ/AO/Y1Jye4zFis5Jw/NsZ8aIxZZYz5yBjzG2PM267PukpE5olV4n2tiDxsK0Dn+AX2/Z7oKt/9koh0F5EzbTPjDhF5Mq49povI/SLyJ/s+tonI7XbG8Na+w0KxSqWvsT/nExE53nW8QET+bM8EG0VktYj8obXrKdmJKigl24hgJfC8RERGpuB6vwGuBA7CSnT5D+BXWFVAJ2M93G+Me8+RWNmZj8GaiUzBSoDpcDNWFdUfA3thJd58QEROjrvOLVj57vbCyuGXiMdt2U4FDgTqgTdsBfAhzUXfpmIVjfswwTU2Y7XbVBHJb+VzsM+50r7m2fbn/SXunCLgauAcrPufiGVePN+W4TTgFOCyuPedg/W8OQRLGV9sf1ZrPIbVzmcD44AngJdFxMmK/VPgdKzKx6OB72CVWVdyiUwnJ9SXvpJ9YT2sX7H/f5fmktKTsZJa9k60be8bbu+bGHfO8a5zLrf37efadyOwIE6G7bgyvQPnAo1YWcydctaT4mS/G3gtTpar27nf0fZ5R7j2dcfKDH2Rvd3bPmdyO9f6MVYS21qsrNO/pZ1M68AJ9n0F7O0L7M8a4zrnDiAc19bR78neno5VjsRdIv16YE3cOX+1/x+JpSyHxsnzAnCv/f+fgbfJkiSs+urcS2dQSrbyc+BbIrL/Ll5nnuv/jfbf+XH7+sa/xxhT69r+CKsEw0is2VAx1iyn1nkBl9rH3cxuR7Y9sR7UHzk7jOVjmm9/TtIYY+7BKh9yNvA+1ozscxE5zzlHRI4WkTdts1oNVrG+Qvt9Do3GGPdMZSOwwVhFDd374ttslrE1i81HwCARiS9kB7AfVkbwL+La8GSa2/BxrGzjS0TkHhE5uS2ToZKdtDXdVxTfYoz5WESewypl8Nu4wxH7r7ugXGtBCEHX/8a+dvy+jjz4nHO/gVVyo7XPAmtG01k6XIbAGFMDvAS8JCLXA//BarsnRWQY8CrwEJaJcyuWongGS0k5hBLIEX9fHW2zeAL2NQ5IcO2d9r18KiLDgeOxTI1PAHNF5DhjTAQlJ1AFpWQzvwS+wDJFudls/x3g+n9CCj93bxEpM8Y4CuZgoAlYhvVwbQSGGWPe2cXPWUSz32YGgD3j2BvLR9NpjDFGRBZjKSGwfEmFwM+MMWH7s07Zlc+I4yAREdcs6mBgnTGmOsG5n2ENLvobY95t7YK2wv038G+x1n/NAkZhmROVHEAVlJK1GGO+EpEHabn25ytgNXCjiFyH5fO5PoUfnQ88KiI3YVVM/QPwkKOwROQO4A4RESzFUo71QI4YYx5M9kOMMUtF5EWsAIuLsXxfv8MqCvd0stcRkQlYwSBPYin0JqwAhAuxZkgAS7GU4ZUiMs2W98pkPyMJBgJ3i8i9WAr2GqxgkhYYY5aIyN+Bx0XkaqyCgJVYfsPlxphpInIVVtXWz7FmWWdjtcuaFMqsZBhVUEq2cxNWBFkUY0xQRM7CipCbi/UQ+yWQqgwQ72GV8X4Xq0T8c8C1ruM3YPlh/g+4D+vB+TmWObKjfB8rwOIlLN/WB1iltHd24BprsMqX/wpLWQewzI93YClXjDHzROQKLN/ezVjRgP+HFdWYCv4O5AH/wzLfPQLc1cb53wf+H1abDcaqTPsxVpsD1GApOSeQ5DPgRGNMfYrkVXyAVtRVFCWtiMh0rEjIyzMti5JdaNSLoiiK4ktUQSmKoii+RE18iqIoii/RGZSiKIriS1RBKYqiKL5EFZSiKIriS1RBKYqiKL5EFZSiKIriS/4/sWBz6LNnGQMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sample_V5[:1000])\n",
    "plt.xlabel(\"Number of Samples\")\n",
    "plt.ylabel(\"Amplitude(in mV)\")\n",
    "plt.title(\"100 V5\")\n",
    "\n",
    "save_fig(\"101-V5.png\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be98cb70",
   "metadata": {},
   "source": [
    " ## Importing and Preparing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c0c4944",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['100' '101' '102' '103' '104' '105' '106' '107' '108' '109' '111' '112'\n",
      " '113' '114' '115' '116' '117' '118' '119' '121' '122' '123' '124' '200'\n",
      " '201' '202' '203' '205' '207' '208' '209' '210' '212' '213' '214' '215'\n",
      " '217' '219' '220' '221' '222' '223' '228' '230' '231' '232' '233' '234']\n",
      "48\n"
     ]
    }
   ],
   "source": [
    "# RECORDS file contains all the record names of the corresponding patients\n",
    "records = np.loadtxt(os.path.join(DATA_PATH,\"RECORDS\"), dtype=str)\n",
    "print(records)\n",
    "print(len(records))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "00b2ddf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data():\n",
    "    dataset = []\n",
    "    for record in records:\n",
    "        temp = wfdb.rdrecord(record)\n",
    "        dataset.append(temp.p_signal)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6997e7d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure 234-MLII.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = read_data()\n",
    "dataset[47]\n",
    "\n",
    "#Plotting the last record MLII data\n",
    "plt.plot(dataset[47][:1000,0])\n",
    "\n",
    "plt.xlabel(\"Number of Samples\")\n",
    "plt.ylabel(\"Amplitude(in mV)\")\n",
    "plt.title(\"234 MLII\")\n",
    "\n",
    "save_fig(\"234-MLII.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9075ab02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure 234-V5.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#PLotting the last record V5 data\n",
    "plt.plot(dataset[47][:1000,1])\n",
    "plt.xlabel(\"Number of Samples\")\n",
    "plt.ylabel(\"Amplitude(in mV)\")\n",
    "plt.title(\"234 V5\")\n",
    "\n",
    "save_fig(\"234-V5.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "676c9d98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    label_store symbol                                    description\n",
      "0             0                              Not an actual annotation\n",
      "1             1      N                                    Normal beat\n",
      "2             2      L                  Left bundle branch block beat\n",
      "3             3      R                 Right bundle branch block beat\n",
      "4             4      a                Aberrated atrial premature beat\n",
      "5             5      V              Premature ventricular contraction\n",
      "6             6      F          Fusion of ventricular and normal beat\n",
      "7             7      J              Nodal (junctional) premature beat\n",
      "8             8      A                   Atrial premature contraction\n",
      "9             9      S     Premature or ectopic supraventricular beat\n",
      "10           10      E                        Ventricular escape beat\n",
      "11           11      j                 Nodal (junctional) escape beat\n",
      "12           12      /                                     Paced beat\n",
      "13           13      Q                            Unclassifiable beat\n",
      "14           14      ~                          Signal quality change\n",
      "16           16      |                     Isolated QRS-like artifact\n",
      "18           18      s                                      ST change\n",
      "19           19      T                                  T-wave change\n",
      "20           20      *                                        Systole\n",
      "21           21      D                                       Diastole\n",
      "22           22      \"                             Comment annotation\n",
      "23           23      =                         Measurement annotation\n",
      "24           24      p                                    P-wave peak\n",
      "25           25      B              Left or right bundle branch block\n",
      "26           26      ^                      Non-conducted pacer spike\n",
      "27           27      t                                    T-wave peak\n",
      "28           28      +                                  Rhythm change\n",
      "29           29      u                                    U-wave peak\n",
      "30           30      ?                                       Learning\n",
      "31           31      !                       Ventricular flutter wave\n",
      "32           32      [      Start of ventricular flutter/fibrillation\n",
      "33           33      ]        End of ventricular flutter/fibrillation\n",
      "34           34      e                             Atrial escape beat\n",
      "35           35      n                   Supraventricular escape beat\n",
      "36           36      @  Link to external data (aux_note contains URL)\n",
      "37           37      x             Non-conducted P-wave (blocked APB)\n",
      "38           38      f                Fusion of paced and normal beat\n",
      "39           39      (                                 Waveform onset\n",
      "40           40      )                                   Waveform end\n",
      "41           41      r       R-on-T premature ventricular contraction\n"
     ]
    }
   ],
   "source": [
    "#Showing all the labels present in our data\n",
    "wfdb.io.show_ann_labels()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a17a7ba3",
   "metadata": {},
   "source": [
    "--> Among all these labels we select the labels needed for our project and save it as the label_beats and other as the invalid_beats\n",
    "\n",
    "A - Atrial Premature Contraction (APC)\n",
    "L - Left Bundle branch Block Beat (LBB)\n",
    "/ - Paced Beat\n",
    "V - Premature Ventricular Contraction (PVC)\n",
    "R - Right Bundle Branch Block Beat (RBB)\n",
    "E - Ventricular Escape Beat (VEB)\n",
    "N - Normal Beat\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "203aaaeb",
   "metadata": {},
   "source": [
    "## Valid and Invalid beats "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "225a2b55",
   "metadata": {},
   "outputs": [],
   "source": [
    "invalid_beat = [\n",
    "    \"[\", \"!\", \"]\", \"x\", \"(\", \")\", \"p\", \"t\", \n",
    "    \"u\", \"`\", \"'\", \"^\", \"|\", \"~\", \"+\", \"s\", \n",
    "    \"T\", \"*\", \"D\", \"=\",'\"', \"@\", \"B\", \"a\", \"J\", \"S\",\n",
    "    \"r\", \"F\", \"e\", \"j\", \"n\", \"f\", \"Q\", \"?\"\n",
    "]\n",
    "\n",
    "label_beats=[\n",
    "    \"A\", \"L\", \"/\", \"V\", \"R\", \"E\", \"N\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "847b63dc",
   "metadata": {},
   "source": [
    "## Beat Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "231b5d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify(symbol):\n",
    "    if symbol in label_beats:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def segment(signal_MLII, beat_loc):\n",
    "    window=180\n",
    "    count=1\n",
    "    x=beat_loc-window\n",
    "    y=beat_loc+window\n",
    "    samp=signal_MLII[x:y]\n",
    "    return samp\n",
    "\n",
    "all_signals=[]\n",
    "all_labels=[]\n",
    "for record in records:\n",
    "    temp=wfdb.rdrecord(record)\n",
    "    annot_temp=wfdb.rdann(record, 'atr')\n",
    "    ann_sample=annot_temp.sample\n",
    "    ann_symbol=annot_temp.symbol\n",
    "    fs=temp.fs\n",
    "    signal=temp.p_signal\n",
    "    signal_MLII=signal[:,0]\n",
    "    for i, i_sample in enumerate(ann_sample):\n",
    "        label_dec=classify(ann_symbol[i])\n",
    "        segmentation=segment(signal_MLII, i_sample)\n",
    "        if label_dec==1 and len(segmentation)==360:\n",
    "            all_signals.append(segmentation)\n",
    "            all_labels.append(ann_symbol[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e909853f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vstack Stacks arrays in sequence vertically (row wise).\n",
    "all_signals_fin=np.vstack(all_signals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b3d5572",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(107141, 360)\n"
     ]
    }
   ],
   "source": [
    "print(all_signals_fin.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0efd812",
   "metadata": {},
   "source": [
    "## Visualizing Segmentated Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "21e6804d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#PLotting\n",
    "plt.plot(all_signals_fin[187])\n",
    "print(all_labels[187])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "865d1c68",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_fin=pd.Series(all_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0b7d54e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E\n",
      "60641\n"
     ]
    }
   ],
   "source": [
    "for i, value in enumerate(labels_fin):\n",
    "    if value=='E':\n",
    "        print(value)\n",
    "        print(i)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da2ef9c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure Ventricular Escape Beat.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(all_signals_fin[60641])\n",
    "plt.xlabel('Samples')\n",
    "plt.ylabel('Amplitude(in mV)')\n",
    "plt.title('Ventricular Escape Beat')\n",
    "save_fig(\"Ventricular Escape Beat.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3f2230c",
   "metadata": {},
   "source": [
    "## Check distribution of types of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b412d551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N    75011\n",
       "L     8071\n",
       "R     7255\n",
       "V     7129\n",
       "/     7023\n",
       "A     2546\n",
       "E      106\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_fin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "152e3768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure Pie chart before resampling\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "my_circle=plt.Circle( (0,0), 0.7, color='white')\n",
    "plt.pie([70.01, 7.33, 6.77, 6.65, 6.56, 2.38, 0.10], labels=['N','L','R','V','/', 'A', 'E'], colors=['red','green','blue','skyblue','orange', 'purple', 'pink'],autopct='%1.1f%%')\n",
    "p=plt.gcf()\n",
    "p.gca().add_artist(my_circle)\n",
    "plt.title(\"Pie Chart of the 7 classes\")\n",
    "save_fig(\"Pie chart before resampling\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed0ac88",
   "metadata": {},
   "source": [
    "## Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9ea51794",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(all_signals_fin)\n",
    "labels_array=np.array(all_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4f5d7334",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7023\n",
      "['/' '/' '/' ... '/' '/' '/']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "7023"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_0=all_signals_fin[labels_fin=='/']\n",
    "df_1=all_signals_fin[labels_fin=='A']\n",
    "df_2=all_signals_fin[labels_fin=='E']\n",
    "df_3=all_signals_fin[labels_fin=='L']\n",
    "df_4=all_signals_fin[labels_fin=='N']\n",
    "df_5=all_signals_fin[labels_fin=='R']\n",
    "df_6=all_signals_fin[labels_fin=='V']\n",
    "y_df0=labels_array[labels_fin=='/']\n",
    "print(len(df_0))\n",
    "print(y_df0)\n",
    "len(y_df0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1ccc2673",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dd6c824e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df0_sampled=resample(df_0, replace=True, n_samples=20000, random_state=42)\n",
    "df1_sampled=resample(df_1, replace=True, n_samples=20000, random_state=42)\n",
    "df2_sampled=resample(df_2, replace=True, n_samples=20000, random_state=42)\n",
    "df3_sampled=resample(df_3, replace=True, n_samples=20000, random_state=42)\n",
    "df4_sampled=resample(df_4, replace=True, n_samples=20000, random_state=42)\n",
    "df5_sampled=resample(df_5, replace=True, n_samples=20000, random_state=42)\n",
    "df6_sampled=resample(df_6, replace=True, n_samples=20000, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b4a2bf3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20000\n",
      "20000\n",
      "20000\n",
      "20000\n",
      "20000\n",
      "20000\n",
      "20000\n"
     ]
    }
   ],
   "source": [
    "print(len(df0_sampled))\n",
    "print(len(df1_sampled))\n",
    "print(len(df2_sampled))\n",
    "print(len(df3_sampled))\n",
    "print(len(df4_sampled))\n",
    "print(len(df5_sampled))\n",
    "print(len(df6_sampled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fe60a220",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_0=['/']*len(df0_sampled)\n",
    "y_1=['A']*len(df1_sampled)\n",
    "y_2=['E']*len(df2_sampled)\n",
    "y_3=['L']*len(df3_sampled)\n",
    "y_4=['N']*len(df4_sampled)\n",
    "y_5=['R']*len(df5_sampled)\n",
    "y_6=['V']*len(df6_sampled)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7727b198",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_final=[]\n",
    "X_final.extend(df0_sampled)\n",
    "X_final.extend(df1_sampled)\n",
    "X_final.extend(df2_sampled)\n",
    "X_final.extend(df3_sampled)\n",
    "X_final.extend(df4_sampled)\n",
    "X_final.extend(df5_sampled)\n",
    "X_final.extend(df6_sampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "affe5c70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "140000\n"
     ]
    }
   ],
   "source": [
    "print(len(X_final))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "26422851",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_final=[]\n",
    "y_final.extend(y_0)\n",
    "y_final.extend(y_1)\n",
    "y_final.extend(y_2)\n",
    "y_final.extend(y_3)\n",
    "y_final.extend(y_4)\n",
    "y_final.extend(y_5)\n",
    "y_final.extend(y_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "75722f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fc8a1773",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure Pie chart afetr resampling\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "my_circle=plt.Circle( (0,0), 0.7, color='white')\n",
    "plt.pie([14.29, 14.29, 14.29, 14.29, 14.29, 14.29, 14.29], labels=['N','L','R','V','/', 'A', 'E'], colors=['red','green','blue','skyblue','orange', 'purple', 'pink'],autopct='%1.1f%%')\n",
    "p=plt.gcf()\n",
    "p.gca().add_artist(my_circle)\n",
    "plt.title(\"Pie Chart of the 7 classes after resampling\")\n",
    "save_fig(\"Pie chart afetr resampling\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e77b8ef7",
   "metadata": {},
   "source": [
    "## Standarization of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c68fa330",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3600ed59",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled=scaler.fit_transform(X_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "212e0a6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_strat(y):\n",
    "    dummy = pd.DataFrame(y,columns=[\"Labels\"])\n",
    "    print(\"Value distribution:\\n\")\n",
    "    count = dummy[\"Labels\"].value_counts()\n",
    "    percent = dummy[\"Labels\"].value_counts(normalize=True).mul(100).round(2)\n",
    "    print(pd.concat([count,percent],axis=1,keys=[\"Counts\",\"Percent\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2cfab2b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value distribution:\n",
      "\n",
      "   Counts  Percent\n",
      "V   20000    14.29\n",
      "/   20000    14.29\n",
      "A   20000    14.29\n",
      "N   20000    14.29\n",
      "E   20000    14.29\n",
      "L   20000    14.29\n",
      "R   20000    14.29\n"
     ]
    }
   ],
   "source": [
    "check_strat(y_final)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52444d91",
   "metadata": {},
   "source": [
    "## Splitting Data into Train and Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6ef17a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "strad=StratifiedShuffleSplit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7baf952c",
   "metadata": {},
   "outputs": [],
   "source": [
    "assin_strad=strad.split(scaled, y_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1a030a6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_index, test_index=next(assin_strad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9bb5f863",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_scaled=scaled[train_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c6fc760e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "lab=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7828e06e",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_final=lab.fit_transform(y_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1cb7daa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['/', 'A', 'E', 'L', 'N', 'R', 'V'], dtype='<U1')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lab.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "05c1cf12",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_label=labels_final[train_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6141db95",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_scaled=scaled[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f7aa83b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_labels=labels_final[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c976aefe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ddf91a0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value distribution:\n",
      "\n",
      "   Counts  Percent\n",
      "0    2000    14.29\n",
      "1    2000    14.29\n",
      "2    2000    14.29\n",
      "3    2000    14.29\n",
      "4    2000    14.29\n",
      "5    2000    14.29\n",
      "6    2000    14.29\n"
     ]
    }
   ],
   "source": [
    "check_strat(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "48d83487",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value distribution:\n",
      "\n",
      "   Counts  Percent\n",
      "0   18000    14.29\n",
      "1   18000    14.29\n",
      "2   18000    14.29\n",
      "3   18000    14.29\n",
      "4   18000    14.29\n",
      "5   18000    14.29\n",
      "6   18000    14.29\n"
     ]
    }
   ],
   "source": [
    "check_strat(train_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "581347b9",
   "metadata": {},
   "source": [
    "## Training Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a88f07c",
   "metadata": {},
   "source": [
    "### Training a SVM Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e6b9e6fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]*.\n",
      "Warning: using -h 0 may be faster\n",
      "*\n",
      "optimization finished, #iter = 1018\n",
      "obj = -203.143391, rho = 0.112482\n",
      "nSV = 497, nBSV = 279\n",
      "*\n",
      "optimization finished, #iter = 346\n",
      "obj = -105.497810, rho = -1.567448\n",
      "nSV = 211, nBSV = 170\n",
      "*.\n",
      "Warning: using -h 0 may be faster\n",
      "*\n",
      "optimization finished, #iter = 1045\n",
      "obj = -203.554214, rho = 0.335205\n",
      "nSV = 494, nBSV = 264\n",
      "*.*\n",
      "optimization finished, #iter = 1377\n",
      "obj = -299.040087, rho = 0.765215\n",
      "nSV = 739, nBSV = 394\n",
      "*\n",
      "optimization finished, #iter = 661\n",
      "obj = -121.831473, rho = -0.309416\n",
      "nSV = 297, nBSV = 160\n",
      ".\n",
      "Warning: using -h 0 may be faster\n",
      "*\n",
      "optimization finished, #iter = 1698\n",
      "obj = -281.640332, rho = 0.881421\n",
      "nSV = 884, nBSV = 351\n",
      "*\n",
      "optimization finished, #iter = 409\n",
      "obj = -161.398148, rho = -3.280072\n",
      "nSV = 331, nBSV = 279\n",
      ".\n",
      "Warning: using -h 0 may be faster\n",
      "*.\n",
      "Warning: using -h 0 may be faster\n",
      "*\n",
      "optimization finished, #iter = 2189\n",
      "obj = -602.476214, rho = 0.061840\n",
      "nSV = 1301, nBSV = 860\n",
      ".....\n",
      "Warning: using -h 0 may be faster\n",
      "*..\n",
      "Warning: using -h 0 may be faster\n",
      "*\n",
      "optimization finished, #iter = 7802\n",
      "obj = -4853.849451, rho = 0.526012\n",
      "nSV = 6873, nBSV = 6047\n",
      ".\n",
      "Warning: using -h 0 may be faster\n",
      "*.\n",
      "Warning: using -h 0 may be faster\n",
      "*\n",
      "optimization finished, #iter = 2502\n",
      "obj = -1251.811243, rho = -1.545528\n",
      "nSV = 2133, nBSV = 1777\n",
      ".\n",
      "Warning: using -h 0 may be faster\n",
      "*.\n",
      "Warning: using -h 0 may be faster\n",
      "*\n",
      "optimization finished, #iter = 2519\n",
      "obj = -799.585190, rho = 1.036394\n",
      "nSV = 1610, nBSV = 1027\n",
      "*\n",
      "optimization finished, #iter = 568\n",
      "obj = -241.919569, rho = 3.796168\n",
      "nSV = 454, nBSV = 405\n",
      "*\n",
      "optimization finished, #iter = 736\n",
      "obj = -360.678186, rho = 4.523422\n",
      "nSV = 656, nBSV = 605\n",
      "*\n",
      "optimization finished, #iter = 429\n",
      "obj = -140.217110, rho = 1.140274\n",
      "nSV = 278, nBSV = 245\n",
      "*\n",
      "optimization finished, #iter = 405\n",
      "obj = -222.197817, rho = 6.500349\n",
      "nSV = 397, nBSV = 352\n",
      "..\n",
      "Warning: using -h 0 may be faster\n",
      "*.*\n",
      "optimization finished, #iter = 3682\n",
      "obj = -1317.755723, rho = 0.814505\n",
      "nSV = 2337, nBSV = 1873\n",
      ".\n",
      "Warning: using -h 0 may be faster\n",
      "*\n",
      "optimization finished, #iter = 1498\n",
      "obj = -322.762903, rho = -0.755483\n",
      "nSV = 723, nBSV = 461\n",
      ".\n",
      "Warning: using -h 0 may be faster\n",
      "*.\n",
      "Warning: using -h 0 may be faster\n",
      "*\n",
      "optimization finished, #iter = 2763\n",
      "obj = -815.275720, rho = 1.023374\n",
      "nSV = 1662, nBSV = 1093\n",
      "..*.*\n",
      "optimization finished, #iter = 3202\n",
      "obj = -1261.941935, rho = -2.051152\n",
      "nSV = 2105, nBSV = 1791\n",
      "..\n",
      "Warning: using -h 0 may be faster\n",
      "*..*\n",
      "optimization finished, #iter = 4227\n",
      "obj = -1313.729765, rho = 0.413398\n",
      "nSV = 2538, nBSV = 1557\n",
      ".\n",
      "Warning: using -h 0 may be faster\n",
      "*\n",
      "optimization finished, #iter = 1450\n",
      "obj = -445.080992, rho = 1.827176\n",
      "nSV = 889, nBSV = 614\n",
      "Total nSV = 16459\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(verbose=3)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "cls=SVC(verbose=3)\n",
    "cls.fit(train_data_scaled, train_label.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "22e6bc17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(train_label[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b148fbd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls.predict([train_data_scaled[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fb784cc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9856904761904762\n"
     ]
    }
   ],
   "source": [
    "#Accuracy of SVM Classifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(train_label, cls.predict(train_data_scaled))\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f23c4d8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9856904761904762\n"
     ]
    }
   ],
   "source": [
    "#Recall of SVM Classifier\n",
    "from sklearn.metrics import recall_score\n",
    "recall=recall_score(train_label, cls.predict(train_data_scaled), average='macro')\n",
    "print(recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d1252fd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9859438449326587\n"
     ]
    }
   ],
   "source": [
    "#Precision of SVM Classifier\n",
    "from sklearn.metrics import precision_score\n",
    "precision=precision_score(train_label, cls.predict(train_data_scaled), average='macro')\n",
    "print(precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ef6bd0da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9857054031572906\n"
     ]
    }
   ],
   "source": [
    "#F1-Score of SVM Classifier\n",
    "from sklearn.metrics import f1_score\n",
    "f1=f1_score(train_label, cls.predict(train_data_scaled), average='macro')\n",
    "print(f1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4822c0c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure ConfusionMatrixSVM.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5928/1930540345.py:28: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix SVM Classifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(train_label, cls.predict(train_data_scaled))\n",
    "\n",
    "plt.matshow(confusion_matrix(train_label,cls.predict(train_data_scaled),normalize=\"true\"),cmap=\"gray\")\n",
    "save_fig(\"ConfusionMatrixSVM.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "289a995a",
   "metadata": {},
   "source": [
    "### Training a Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ab0cb055",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "24053f7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =         2527     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  2.45185D+05    |proj g|=  2.29010D+04\n",
      "\n",
      "\n",
      "ITERATION     1\n",
      "\n",
      "---------------- CAUCHY entered-------------------\n",
      " There are            0   breakpoints \n",
      "\n",
      " GCP found in this segment\n",
      "Piece      1 --f1, f2 at start point  -1.3842D+11  1.3842D+11\n",
      "Distance to the stationary point =   1.0000D+00\n",
      "\n",
      "---------------- exit CAUCHY----------------------\n",
      "\n",
      "        2527  variables are free at GCP            1\n",
      " LINE SEARCH           1  times; norm of step =   0.27945375592070504     \n",
      "\n",
      "At iterate    1    f=  2.15965D+05    |proj g|=  2.05790D+04\n",
      "\n",
      "\n",
      "ITERATION     2\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.11457168920674976     \n",
      "\n",
      "At iterate    2    f=  1.99097D+05    |proj g|=  1.64184D+04\n",
      "\n",
      "\n",
      "ITERATION     3\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   5.17547328687666605E-002\n",
      "\n",
      "At iterate    3    f=  1.89612D+05    |proj g|=  1.08645D+04\n",
      "\n",
      "\n",
      "ITERATION     4\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.23401168056798247     \n",
      "\n",
      "At iterate    4    f=  1.75140D+05    |proj g|=  1.27674D+04\n",
      "\n",
      "\n",
      "ITERATION     5\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.12111223531433846     \n",
      "\n",
      "At iterate    5    f=  1.64359D+05    |proj g|=  8.17083D+03\n",
      "\n",
      "\n",
      "ITERATION     6\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.25611905526011985     \n",
      "\n",
      "At iterate    6    f=  1.52045D+05    |proj g|=  8.06722D+03\n",
      "\n",
      "\n",
      "ITERATION     7\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.48954852997762704     \n",
      "\n",
      "At iterate    7    f=  1.33186D+05    |proj g|=  5.70268D+03\n",
      "\n",
      "\n",
      "ITERATION     8\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.71115113181524803     \n",
      "\n",
      "At iterate    8    f=  1.20695D+05    |proj g|=  6.09915D+03\n",
      "\n",
      "\n",
      "ITERATION     9\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.35216664649356227     \n",
      "\n",
      "At iterate    9    f=  1.14273D+05    |proj g|=  4.77955D+03\n",
      "\n",
      "\n",
      "ITERATION    10\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.20481714361155848     \n",
      "\n",
      "At iterate   10    f=  1.11246D+05    |proj g|=  4.12428D+03\n",
      "\n",
      "\n",
      "ITERATION    11\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.30728877505801555     \n",
      "\n",
      "At iterate   11    f=  1.07414D+05    |proj g|=  3.07071D+03\n",
      "\n",
      "\n",
      "ITERATION    12\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.37885020261495322     \n",
      "\n",
      "At iterate   12    f=  1.03401D+05    |proj g|=  3.91764D+03\n",
      "\n",
      "\n",
      "ITERATION    13\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.28874581620600653     \n",
      "\n",
      "At iterate   13    f=  1.00105D+05    |proj g|=  2.66674D+03\n",
      "\n",
      "\n",
      "ITERATION    14\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.11658836308509560     \n",
      "\n",
      "At iterate   14    f=  9.82095D+04    |proj g|=  3.15806D+03\n",
      "\n",
      "\n",
      "ITERATION    15\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.30496288456449538     \n",
      "\n",
      "At iterate   15    f=  9.66773D+04    |proj g|=  3.97592D+03\n",
      "\n",
      "\n",
      "ITERATION    16\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   7.91210070464410109E-002\n",
      "\n",
      "At iterate   16    f=  9.45326D+04    |proj g|=  2.72972D+03\n",
      "\n",
      "\n",
      "ITERATION    17\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   9.98407441516647592E-002\n",
      "\n",
      "At iterate   17    f=  9.37101D+04    |proj g|=  2.36884D+03\n",
      "\n",
      "\n",
      "ITERATION    18\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.41506522842208793     \n",
      "\n",
      "At iterate   18    f=  9.11697D+04    |proj g|=  2.60919D+03\n",
      "\n",
      "\n",
      "ITERATION    19\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.43875471599138532     \n",
      "\n",
      "At iterate   19    f=  8.91999D+04    |proj g|=  2.18085D+03\n",
      "\n",
      "\n",
      "ITERATION    20\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.36688256991552526     \n",
      "\n",
      "At iterate   20    f=  8.75534D+04    |proj g|=  2.53333D+03\n",
      "\n",
      "\n",
      "ITERATION    21\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.19009448564640627     \n",
      "\n",
      "At iterate   21    f=  8.63177D+04    |proj g|=  2.54434D+03\n",
      "\n",
      "\n",
      "ITERATION    22\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.32380589837749463     \n",
      "\n",
      "At iterate   22    f=  8.44726D+04    |proj g|=  1.97364D+03\n",
      "\n",
      "\n",
      "ITERATION    23\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.34152889865052399     \n",
      "\n",
      "At iterate   23    f=  8.33444D+04    |proj g|=  2.28956D+03\n",
      "\n",
      "\n",
      "ITERATION    24\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.24578437429250713     \n",
      "\n",
      "At iterate   24    f=  8.20599D+04    |proj g|=  2.08589D+03\n",
      "\n",
      "\n",
      "ITERATION    25\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   7.35586269444434143E-002\n",
      "\n",
      "At iterate   25    f=  8.12717D+04    |proj g|=  2.07445D+03\n",
      "\n",
      "\n",
      "ITERATION    26\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.16351288832192554     \n",
      "\n",
      "At iterate   26    f=  8.02499D+04    |proj g|=  1.72421D+03\n",
      "\n",
      "\n",
      "ITERATION    27\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.23543329601163063     \n",
      "\n",
      "At iterate   27    f=  7.93483D+04    |proj g|=  1.39264D+03\n",
      "\n",
      "\n",
      "ITERATION    28\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.51329055061695983     \n",
      "\n",
      "At iterate   28    f=  7.84975D+04    |proj g|=  2.22915D+03\n",
      "\n",
      "\n",
      "ITERATION    29\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.12690351679779469     \n",
      "\n",
      "At iterate   29    f=  7.76289D+04    |proj g|=  1.46016D+03\n",
      "\n",
      "\n",
      "ITERATION    30\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.24732373549948014     \n",
      "\n",
      "At iterate   30    f=  7.68563D+04    |proj g|=  1.64866D+03\n",
      "\n",
      "\n",
      "ITERATION    31\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.21289948110228307     \n",
      "\n",
      "At iterate   31    f=  7.62574D+04    |proj g|=  1.46193D+03\n",
      "\n",
      "\n",
      "ITERATION    32\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.55543435001931651     \n",
      "\n",
      "At iterate   32    f=  7.49079D+04    |proj g|=  1.46548D+03\n",
      "\n",
      "\n",
      "ITERATION    33\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.83627189826411419     \n",
      "\n",
      "At iterate   33    f=  7.46811D+04    |proj g|=  1.99699D+03\n",
      "\n",
      "\n",
      "ITERATION    34\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.18590094887613126     \n",
      "\n",
      "At iterate   34    f=  7.33673D+04    |proj g|=  1.33493D+03\n",
      "\n",
      "\n",
      "ITERATION    35\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   6.42899491124350603E-002\n",
      "\n",
      "At iterate   35    f=  7.28043D+04    |proj g|=  1.14239D+03\n",
      "\n",
      "\n",
      "ITERATION    36\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.18122023511890542     \n",
      "\n",
      "At iterate   36    f=  7.21344D+04    |proj g|=  1.11691D+03\n",
      "\n",
      "\n",
      "ITERATION    37\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.36186248268098653     \n",
      "\n",
      "At iterate   37    f=  7.15716D+04    |proj g|=  1.46520D+03\n",
      "\n",
      "\n",
      "ITERATION    38\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.31745511471823240     \n",
      "\n",
      "At iterate   38    f=  7.10084D+04    |proj g|=  1.46912D+03\n",
      "\n",
      "\n",
      "ITERATION    39\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.24773094850484012     \n",
      "\n",
      "At iterate   39    f=  7.05716D+04    |proj g|=  1.03890D+03\n",
      "\n",
      "\n",
      "ITERATION    40\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.26298216228713867     \n",
      "\n",
      "At iterate   40    f=  7.00201D+04    |proj g|=  7.84776D+02\n",
      "\n",
      "\n",
      "ITERATION    41\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.30005680915099853     \n",
      "\n",
      "At iterate   41    f=  6.92753D+04    |proj g|=  1.24584D+03\n",
      "\n",
      "\n",
      "ITERATION    42\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.58699283468679764     \n",
      "\n",
      "At iterate   42    f=  6.89028D+04    |proj g|=  1.41075D+03\n",
      "\n",
      "\n",
      "ITERATION    43\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.21863174435769667     \n",
      "\n",
      "At iterate   43    f=  6.83203D+04    |proj g|=  8.89027D+02\n",
      "\n",
      "\n",
      "ITERATION    44\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   8.70132767046289252E-002\n",
      "\n",
      "At iterate   44    f=  6.80396D+04    |proj g|=  9.60072D+02\n",
      "\n",
      "\n",
      "ITERATION    45\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.10677881882599950     \n",
      "\n",
      "At iterate   45    f=  6.76506D+04    |proj g|=  1.01864D+03\n",
      "\n",
      "\n",
      "ITERATION    46\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.32087490521651441     \n",
      "\n",
      "At iterate   46    f=  6.69290D+04    |proj g|=  8.90277D+02\n",
      "\n",
      "\n",
      "ITERATION    47\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.56453002618100023     \n",
      "\n",
      "At iterate   47    f=  6.62383D+04    |proj g|=  1.01018D+03\n",
      "\n",
      "\n",
      "ITERATION    48\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.36705600529872934     \n",
      "\n",
      "At iterate   48    f=  6.56764D+04    |proj g|=  9.83382D+02\n",
      "\n",
      "\n",
      "ITERATION    49\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.10660879004093017     \n",
      "\n",
      "At iterate   49    f=  6.53840D+04    |proj g|=  1.11763D+03\n",
      "\n",
      "\n",
      "ITERATION    50\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   4.04622433333022974E-002\n",
      "\n",
      "At iterate   50    f=  6.51780D+04    |proj g|=  8.03714D+02\n",
      "\n",
      "\n",
      "ITERATION    51\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   5.94248469497902568E-002\n",
      "\n",
      "At iterate   51    f=  6.50071D+04    |proj g|=  7.48469D+02\n",
      "\n",
      "\n",
      "ITERATION    52\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.11461334258007864     \n",
      "\n",
      "At iterate   52    f=  6.47797D+04    |proj g|=  7.50591D+02\n",
      "\n",
      "\n",
      "ITERATION    53\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.17410635605696184     \n",
      "\n",
      "At iterate   53    f=  6.44670D+04    |proj g|=  6.65995D+02\n",
      "\n",
      "\n",
      "ITERATION    54\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.42977953376166289     \n",
      "\n",
      "At iterate   54    f=  6.43876D+04    |proj g|=  1.48777D+03\n",
      "\n",
      "\n",
      "ITERATION    55\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   6.38873148984290679E-002\n",
      "\n",
      "At iterate   55    f=  6.39917D+04    |proj g|=  6.92624D+02\n",
      "\n",
      "\n",
      "ITERATION    56\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   4.03169453411390266E-002\n",
      "\n",
      "At iterate   56    f=  6.38427D+04    |proj g|=  7.33021D+02\n",
      "\n",
      "\n",
      "ITERATION    57\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.16826943988877499     \n",
      "\n",
      "At iterate   57    f=  6.36096D+04    |proj g|=  8.49783D+02\n",
      "\n",
      "\n",
      "ITERATION    58\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.20859351980880061     \n",
      "\n",
      "At iterate   58    f=  6.33147D+04    |proj g|=  6.97566D+02\n",
      "\n",
      "\n",
      "ITERATION    59\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.47141991376704073     \n",
      "\n",
      "At iterate   59    f=  6.30730D+04    |proj g|=  1.32133D+03\n",
      "\n",
      "\n",
      "ITERATION    60\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   4.54802659047553182E-002\n",
      "\n",
      "At iterate   60    f=  6.27976D+04    |proj g|=  5.13722D+02\n",
      "\n",
      "\n",
      "ITERATION    61\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   3.53431769988296896E-002\n",
      "\n",
      "At iterate   61    f=  6.26839D+04    |proj g|=  4.80148D+02\n",
      "\n",
      "\n",
      "ITERATION    62\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   7.44258024350337027E-002\n",
      "\n",
      "At iterate   62    f=  6.25466D+04    |proj g|=  5.03027D+02\n",
      "\n",
      "\n",
      "ITERATION    63\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " LINE SEARCH           0  times; norm of step =   0.26273793941402701     \n",
      "\n",
      "At iterate   63    f=  6.24972D+04    |proj g|=  1.32963D+03\n",
      "\n",
      "\n",
      "ITERATION    64\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   4.51919631787340847E-002\n",
      "\n",
      "At iterate   64    f=  6.22395D+04    |proj g|=  6.04773D+02\n",
      "\n",
      "\n",
      "ITERATION    65\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   8.10888438645934323E-002\n",
      "\n",
      "At iterate   65    f=  6.21398D+04    |proj g|=  5.84723D+02\n",
      "\n",
      "\n",
      "ITERATION    66\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.14923931585824105     \n",
      "\n",
      "At iterate   66    f=  6.20098D+04    |proj g|=  5.54792D+02\n",
      "\n",
      "\n",
      "ITERATION    67\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.22468665103817950     \n",
      "\n",
      "At iterate   67    f=  6.17581D+04    |proj g|=  7.82694D+02\n",
      "\n",
      "\n",
      "ITERATION    68\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.50321318382840663     \n",
      "\n",
      "At iterate   68    f=  6.14426D+04    |proj g|=  1.04487D+03\n",
      "\n",
      "\n",
      "ITERATION    69\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.33278850504129964     \n",
      "\n",
      "At iterate   69    f=  6.11458D+04    |proj g|=  8.47451D+02\n",
      "\n",
      "\n",
      "ITERATION    70\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   7.15570141599028847E-002\n",
      "\n",
      "At iterate   70    f=  6.09934D+04    |proj g|=  7.84876D+02\n",
      "\n",
      "\n",
      "ITERATION    71\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   7.09080428922798517E-002\n",
      "\n",
      "At iterate   71    f=  6.07822D+04    |proj g|=  7.40094D+02\n",
      "\n",
      "\n",
      "ITERATION    72\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.14881763424336350     \n",
      "\n",
      "At iterate   72    f=  6.06063D+04    |proj g|=  8.14595D+02\n",
      "\n",
      "\n",
      "ITERATION    73\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.39994577906508050     \n",
      "\n",
      "At iterate   73    f=  6.03297D+04    |proj g|=  8.38658D+02\n",
      "\n",
      "\n",
      "ITERATION    74\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.33956405289336911     \n",
      "\n",
      "At iterate   74    f=  6.00795D+04    |proj g|=  6.49012D+02\n",
      "\n",
      "\n",
      "ITERATION    75\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.19652150875367191     \n",
      "\n",
      "At iterate   75    f=  5.98933D+04    |proj g|=  7.19147D+02\n",
      "\n",
      "\n",
      "ITERATION    76\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.33864725576878202     \n",
      "\n",
      "At iterate   76    f=  5.96392D+04    |proj g|=  9.93051D+02\n",
      "\n",
      "\n",
      "ITERATION    77\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.28521260090109296     \n",
      "\n",
      "At iterate   77    f=  5.95878D+04    |proj g|=  1.32518D+03\n",
      "\n",
      "\n",
      "ITERATION    78\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   5.24282534352843027E-002\n",
      "\n",
      "At iterate   78    f=  5.93280D+04    |proj g|=  1.01460D+03\n",
      "\n",
      "\n",
      "ITERATION    79\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   7.22437302559070099E-002\n",
      "\n",
      "At iterate   79    f=  5.91873D+04    |proj g|=  9.77484D+02\n",
      "\n",
      "\n",
      "ITERATION    80\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.11567152623196518     \n",
      "\n",
      "At iterate   80    f=  5.90817D+04    |proj g|=  8.95628D+02\n",
      "\n",
      "\n",
      "ITERATION    81\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.18559485491241542     \n",
      "\n",
      "At iterate   81    f=  5.89078D+04    |proj g|=  7.57290D+02\n",
      "\n",
      "\n",
      "ITERATION    82\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           1  times; norm of step =   0.25043195786817646     \n",
      "\n",
      "At iterate   82    f=  5.87478D+04    |proj g|=  9.71736D+02\n",
      "\n",
      "\n",
      "ITERATION    83\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.28332885544846964     \n",
      "\n",
      "At iterate   83    f=  5.84771D+04    |proj g|=  5.84910D+02\n",
      "\n",
      "\n",
      "ITERATION    84\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.18761707621419485     \n",
      "\n",
      "At iterate   84    f=  5.83355D+04    |proj g|=  5.67086D+02\n",
      "\n",
      "\n",
      "ITERATION    85\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.23697034371589895     \n",
      "\n",
      "At iterate   85    f=  5.81701D+04    |proj g|=  6.13696D+02\n",
      "\n",
      "\n",
      "ITERATION    86\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.32096811441893786     \n",
      "\n",
      "At iterate   86    f=  5.80507D+04    |proj g|=  6.42920D+02\n",
      "\n",
      "\n",
      "ITERATION    87\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.21665788320444682     \n",
      "\n",
      "At iterate   87    f=  5.79039D+04    |proj g|=  5.99884D+02\n",
      "\n",
      "\n",
      "ITERATION    88\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.17918655962117083     \n",
      "\n",
      "At iterate   88    f=  5.77759D+04    |proj g|=  6.01958D+02\n",
      "\n",
      "\n",
      "ITERATION    89\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.20804762785166289     \n",
      "\n",
      "At iterate   89    f=  5.76395D+04    |proj g|=  5.60511D+02\n",
      "\n",
      "\n",
      "ITERATION    90\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.29812319413422200     \n",
      "\n",
      "At iterate   90    f=  5.74818D+04    |proj g|=  5.97863D+02\n",
      "\n",
      "\n",
      "ITERATION    91\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.42035798285956999     \n",
      "\n",
      "At iterate   91    f=  5.74094D+04    |proj g|=  9.26004D+02\n",
      "\n",
      "\n",
      "ITERATION    92\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   2.80902267465786872E-002\n",
      "\n",
      "At iterate   92    f=  5.72331D+04    |proj g|=  5.13565D+02\n",
      "\n",
      "\n",
      "ITERATION    93\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   5.08216359861016487E-002\n",
      "\n",
      "At iterate   93    f=  5.71333D+04    |proj g|=  5.21359D+02\n",
      "\n",
      "\n",
      "ITERATION    94\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.16653397932359085     \n",
      "\n",
      "At iterate   94    f=  5.69955D+04    |proj g|=  5.36346D+02\n",
      "\n",
      "\n",
      "ITERATION    95\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.37696766551555755     \n",
      "\n",
      "At iterate   95    f=  5.67465D+04    |proj g|=  6.43199D+02\n",
      "\n",
      "\n",
      "ITERATION    96\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           1  times; norm of step =   0.19238549725194518     \n",
      "\n",
      "At iterate   96    f=  5.66520D+04    |proj g|=  6.08025D+02\n",
      "\n",
      "\n",
      "ITERATION    97\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.35727815449510081     \n",
      "\n",
      "At iterate   97    f=  5.64979D+04    |proj g|=  5.54471D+02\n",
      "\n",
      "\n",
      "ITERATION    98\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.20539218714481469     \n",
      "\n",
      "At iterate   98    f=  5.63856D+04    |proj g|=  4.70145D+02\n",
      "\n",
      "\n",
      "ITERATION    99\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.33527978950753640     \n",
      "\n",
      "At iterate   99    f=  5.62471D+04    |proj g|=  4.63792D+02\n",
      "\n",
      "\n",
      "ITERATION   100\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.27148482553035547     \n",
      "\n",
      "At iterate  100    f=  5.61994D+04    |proj g|=  5.75844D+02\n",
      "\n",
      "\n",
      "ITERATION   101\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   1.99544854637431907E-002\n",
      "\n",
      "At iterate  101    f=  5.60910D+04    |proj g|=  4.88191D+02\n",
      "\n",
      "\n",
      "ITERATION   102\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   5.09945098188312046E-002\n",
      "\n",
      "At iterate  102    f=  5.60106D+04    |proj g|=  3.81869D+02\n",
      "\n",
      "\n",
      "ITERATION   103\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.13095786913757987     \n",
      "\n",
      "At iterate  103    f=  5.59340D+04    |proj g|=  3.85962D+02\n",
      "\n",
      "\n",
      "ITERATION   104\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.30341544238386647     \n",
      "\n",
      "At iterate  104    f=  5.57948D+04    |proj g|=  3.77171D+02\n",
      "\n",
      "\n",
      "ITERATION   105\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.67488360507028256     \n",
      "\n",
      "At iterate  105    f=  5.56220D+04    |proj g|=  5.57764D+02\n",
      "\n",
      "\n",
      "ITERATION   106\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.33692844170115643     \n",
      "\n",
      "At iterate  106    f=  5.54471D+04    |proj g|=  4.06387D+02\n",
      "\n",
      "\n",
      "ITERATION   107\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.18951670285871039     \n",
      "\n",
      "At iterate  107    f=  5.53691D+04    |proj g|=  4.11270D+02\n",
      "\n",
      "\n",
      "ITERATION   108\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   3.53540963784442011E-002\n",
      "\n",
      "At iterate  108    f=  5.53165D+04    |proj g|=  4.30496D+02\n",
      "\n",
      "\n",
      "ITERATION   109\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   3.88038109244491711E-002\n",
      "\n",
      "At iterate  109    f=  5.52712D+04    |proj g|=  4.26529D+02\n",
      "\n",
      "\n",
      "ITERATION   110\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.19442593924118576     \n",
      "\n",
      "At iterate  110    f=  5.51369D+04    |proj g|=  4.08303D+02\n",
      "\n",
      "\n",
      "ITERATION   111\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.28190201846305130     \n",
      "\n",
      "At iterate  111    f=  5.50174D+04    |proj g|=  6.23784D+02\n",
      "\n",
      "\n",
      "ITERATION   112\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.40017392921487849     \n",
      "\n",
      "At iterate  112    f=  5.48897D+04    |proj g|=  4.17874D+02\n",
      "\n",
      "\n",
      "ITERATION   113\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   6.50792722529831141E-002\n",
      "\n",
      "At iterate  113    f=  5.48055D+04    |proj g|=  3.03568D+02\n",
      "\n",
      "\n",
      "ITERATION   114\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.11881492434315089     \n",
      "\n",
      "At iterate  114    f=  5.47157D+04    |proj g|=  3.29402D+02\n",
      "\n",
      "\n",
      "ITERATION   115\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.13150630215765768     \n",
      "\n",
      "At iterate  115    f=  5.46374D+04    |proj g|=  2.90996D+02\n",
      "\n",
      "\n",
      "ITERATION   116\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.39136149169046996     \n",
      "\n",
      "At iterate  116    f=  5.45293D+04    |proj g|=  6.60096D+02\n",
      "\n",
      "\n",
      "ITERATION   117\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.16440647102455500     \n",
      "\n",
      "At iterate  117    f=  5.44115D+04    |proj g|=  3.50187D+02\n",
      "\n",
      "\n",
      "ITERATION   118\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   8.26670995840526751E-002\n",
      "\n",
      "At iterate  118    f=  5.43454D+04    |proj g|=  3.23326D+02\n",
      "\n",
      "\n",
      "ITERATION   119\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.12256503956649085     \n",
      "\n",
      "At iterate  119    f=  5.42634D+04    |proj g|=  3.55955D+02\n",
      "\n",
      "\n",
      "ITERATION   120\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           1  times; norm of step =   8.60915646672716917E-002\n",
      "\n",
      "At iterate  120    f=  5.42186D+04    |proj g|=  3.52167D+02\n",
      "\n",
      "\n",
      "ITERATION   121\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.14341559081364644     \n",
      "\n",
      "At iterate  121    f=  5.41438D+04    |proj g|=  2.96542D+02\n",
      "\n",
      "\n",
      "ITERATION   122\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.11267470793257245     \n",
      "\n",
      "At iterate  122    f=  5.40785D+04    |proj g|=  2.71250D+02\n",
      "\n",
      "\n",
      "ITERATION   123\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.11027170533644906     \n",
      "\n",
      "At iterate  123    f=  5.40070D+04    |proj g|=  2.78310D+02\n",
      "\n",
      "\n",
      "ITERATION   124\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.14011233483201738     \n",
      "\n",
      "At iterate  124    f=  5.39801D+04    |proj g|=  4.44450D+02\n",
      "\n",
      "\n",
      "ITERATION   125\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   1.71447945118945490E-002\n",
      "\n",
      "At iterate  125    f=  5.39042D+04    |proj g|=  3.32221D+02\n",
      "\n",
      "\n",
      "ITERATION   126\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   2.43778028933920553E-002\n",
      "\n",
      "At iterate  126    f=  5.38586D+04    |proj g|=  3.08706D+02\n",
      "\n",
      "\n",
      "ITERATION   127\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " LINE SEARCH           0  times; norm of step =   9.04002593335423849E-002\n",
      "\n",
      "At iterate  127    f=  5.37896D+04    |proj g|=  3.20612D+02\n",
      "\n",
      "\n",
      "ITERATION   128\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.18730208663109649     \n",
      "\n",
      "At iterate  128    f=  5.36793D+04    |proj g|=  3.75502D+02\n",
      "\n",
      "\n",
      "ITERATION   129\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           1  times; norm of step =   0.28241324321307409     \n",
      "\n",
      "At iterate  129    f=  5.36066D+04    |proj g|=  4.09302D+02\n",
      "\n",
      "\n",
      "ITERATION   130\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.16029019222858151     \n",
      "\n",
      "At iterate  130    f=  5.35224D+04    |proj g|=  3.84354D+02\n",
      "\n",
      "\n",
      "ITERATION   131\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.10643069655901728     \n",
      "\n",
      "At iterate  131    f=  5.34687D+04    |proj g|=  3.04409D+02\n",
      "\n",
      "\n",
      "ITERATION   132\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.14603557128896980     \n",
      "\n",
      "At iterate  132    f=  5.34029D+04    |proj g|=  3.19332D+02\n",
      "\n",
      "\n",
      "ITERATION   133\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           1  times; norm of step =   9.06815694185272719E-002\n",
      "\n",
      "At iterate  133    f=  5.33660D+04    |proj g|=  3.86245D+02\n",
      "\n",
      "\n",
      "ITERATION   134\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.13730248327974862     \n",
      "\n",
      "At iterate  134    f=  5.33020D+04    |proj g|=  3.36452D+02\n",
      "\n",
      "\n",
      "ITERATION   135\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.10928771401260783     \n",
      "\n",
      "At iterate  135    f=  5.32464D+04    |proj g|=  5.11760D+02\n",
      "\n",
      "\n",
      "ITERATION   136\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.15304879225697912     \n",
      "\n",
      "At iterate  136    f=  5.31805D+04    |proj g|=  4.79275D+02\n",
      "\n",
      "\n",
      "ITERATION   137\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.15397442894784519     \n",
      "\n",
      "At iterate  137    f=  5.31213D+04    |proj g|=  4.08231D+02\n",
      "\n",
      "\n",
      "ITERATION   138\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.15335081899017342     \n",
      "\n",
      "At iterate  138    f=  5.30669D+04    |proj g|=  3.45787D+02\n",
      "\n",
      "\n",
      "ITERATION   139\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   7.17798839587793086E-002\n",
      "\n",
      "At iterate  139    f=  5.30059D+04    |proj g|=  3.56809D+02\n",
      "\n",
      "\n",
      "ITERATION   140\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.14286303086472313     \n",
      "\n",
      "At iterate  140    f=  5.29295D+04    |proj g|=  3.00692D+02\n",
      "\n",
      "\n",
      "ITERATION   141\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.24731126611023330     \n",
      "\n",
      "At iterate  141    f=  5.28596D+04    |proj g|=  3.45225D+02\n",
      "\n",
      "\n",
      "ITERATION   142\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.26022030734011475     \n",
      "\n",
      "At iterate  142    f=  5.27900D+04    |proj g|=  2.50355D+02\n",
      "\n",
      "\n",
      "ITERATION   143\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.13208449660712013     \n",
      "\n",
      "At iterate  143    f=  5.27368D+04    |proj g|=  2.97899D+02\n",
      "\n",
      "\n",
      "ITERATION   144\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.10752787668778521     \n",
      "\n",
      "At iterate  144    f=  5.26806D+04    |proj g|=  3.44302D+02\n",
      "\n",
      "\n",
      "ITERATION   145\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.13958008765809449     \n",
      "\n",
      "At iterate  145    f=  5.26413D+04    |proj g|=  3.25797D+02\n",
      "\n",
      "\n",
      "ITERATION   146\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   2.27888568458153688E-002\n",
      "\n",
      "At iterate  146    f=  5.26011D+04    |proj g|=  2.44056D+02\n",
      "\n",
      "\n",
      "ITERATION   147\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   6.56170161980521582E-002\n",
      "\n",
      "At iterate  147    f=  5.25396D+04    |proj g|=  2.77380D+02\n",
      "\n",
      "\n",
      "ITERATION   148\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   9.45969084526847998E-002\n",
      "\n",
      "At iterate  148    f=  5.24903D+04    |proj g|=  2.78358D+02\n",
      "\n",
      "\n",
      "ITERATION   149\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           1  times; norm of step =   9.53145449281079388E-002\n",
      "\n",
      "At iterate  149    f=  5.24519D+04    |proj g|=  3.88180D+02\n",
      "\n",
      "\n",
      "ITERATION   150\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.16865246182173990     \n",
      "\n",
      "At iterate  150    f=  5.23900D+04    |proj g|=  2.75492D+02\n",
      "\n",
      "\n",
      "ITERATION   151\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.11067184251695875     \n",
      "\n",
      "At iterate  151    f=  5.23468D+04    |proj g|=  2.75997D+02\n",
      "\n",
      "\n",
      "ITERATION   152\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.14242636794544028     \n",
      "\n",
      "At iterate  152    f=  5.22815D+04    |proj g|=  2.34746D+02\n",
      "\n",
      "\n",
      "ITERATION   153\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.17506538232456059     \n",
      "\n",
      "At iterate  153    f=  5.22748D+04    |proj g|=  4.47063D+02\n",
      "\n",
      "\n",
      "ITERATION   154\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   4.65588359354566633E-002\n",
      "\n",
      "At iterate  154    f=  5.22210D+04    |proj g|=  2.38417D+02\n",
      "\n",
      "\n",
      "ITERATION   155\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   2.02223190490263514E-002\n",
      "\n",
      "At iterate  155    f=  5.21979D+04    |proj g|=  2.10636D+02\n",
      "\n",
      "\n",
      "ITERATION   156\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   6.29360441770621531E-002\n",
      "\n",
      "At iterate  156    f=  5.21630D+04    |proj g|=  2.61435D+02\n",
      "\n",
      "\n",
      "ITERATION   157\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.16363335775502866     \n",
      "\n",
      "At iterate  157    f=  5.21126D+04    |proj g|=  2.54551D+02\n",
      "\n",
      "\n",
      "ITERATION   158\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           1  times; norm of step =   5.42034450380679753E-002\n",
      "\n",
      "At iterate  158    f=  5.20937D+04    |proj g|=  2.66543D+02\n",
      "\n",
      "\n",
      "ITERATION   159\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.24947474576087827     \n",
      "\n",
      "At iterate  159    f=  5.20415D+04    |proj g|=  2.90991D+02\n",
      "\n",
      "\n",
      "ITERATION   160\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.18083823435566887     \n",
      "\n",
      "At iterate  160    f=  5.19910D+04    |proj g|=  2.47325D+02\n",
      "\n",
      "\n",
      "ITERATION   161\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.32310781535292876     \n",
      "\n",
      "At iterate  161    f=  5.19550D+04    |proj g|=  2.91578D+02\n",
      "\n",
      "\n",
      "ITERATION   162\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   2.82600146912702493E-002\n",
      "\n",
      "At iterate  162    f=  5.19203D+04    |proj g|=  2.94453D+02\n",
      "\n",
      "\n",
      "ITERATION   163\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   2.89609653471664188E-002\n",
      "\n",
      "At iterate  163    f=  5.19011D+04    |proj g|=  2.71258D+02\n",
      "\n",
      "\n",
      "ITERATION   164\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   7.60205304475857768E-002\n",
      "\n",
      "At iterate  164    f=  5.18502D+04    |proj g|=  2.05754D+02\n",
      "\n",
      "\n",
      "ITERATION   165\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.12973808432720213     \n",
      "\n",
      "At iterate  165    f=  5.18036D+04    |proj g|=  2.67854D+02\n",
      "\n",
      "\n",
      "ITERATION   166\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.36226503995708992     \n",
      "\n",
      "At iterate  166    f=  5.17573D+04    |proj g|=  3.62949D+02\n",
      "\n",
      "\n",
      "ITERATION   167\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.11137982906304382     \n",
      "\n",
      "At iterate  167    f=  5.17158D+04    |proj g|=  2.58132D+02\n",
      "\n",
      "\n",
      "ITERATION   168\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.13985532158368852     \n",
      "\n",
      "At iterate  168    f=  5.16691D+04    |proj g|=  2.59247D+02\n",
      "\n",
      "\n",
      "ITERATION   169\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.13045899085073845     \n",
      "\n",
      "At iterate  169    f=  5.16209D+04    |proj g|=  2.75603D+02\n",
      "\n",
      "\n",
      "ITERATION   170\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.20511046841583019     \n",
      "\n",
      "At iterate  170    f=  5.15538D+04    |proj g|=  2.55859D+02\n",
      "\n",
      "\n",
      "ITERATION   171\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.13460466109188982     \n",
      "\n",
      "At iterate  171    f=  5.15106D+04    |proj g|=  3.82042D+02\n",
      "\n",
      "\n",
      "ITERATION   172\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   4.46196530719011886E-002\n",
      "\n",
      "At iterate  172    f=  5.14795D+04    |proj g|=  2.14023D+02\n",
      "\n",
      "\n",
      "ITERATION   173\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   1.94928634453709060E-002\n",
      "\n",
      "At iterate  173    f=  5.14610D+04    |proj g|=  2.08904D+02\n",
      "\n",
      "\n",
      "ITERATION   174\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   5.95058385904757500E-002\n",
      "\n",
      "At iterate  174    f=  5.14245D+04    |proj g|=  2.17047D+02\n",
      "\n",
      "\n",
      "ITERATION   175\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.21249972560670463     \n",
      "\n",
      "At iterate  175    f=  5.13906D+04    |proj g|=  2.69006D+02\n",
      "\n",
      "\n",
      "ITERATION   176\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   9.68641820894177996E-002\n",
      "\n",
      "At iterate  176    f=  5.13509D+04    |proj g|=  2.05139D+02\n",
      "\n",
      "\n",
      "ITERATION   177\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.24103513634582041     \n",
      "\n",
      "At iterate  177    f=  5.12995D+04    |proj g|=  2.74260D+02\n",
      "\n",
      "\n",
      "ITERATION   178\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.16107925306941556     \n",
      "\n",
      "At iterate  178    f=  5.12557D+04    |proj g|=  2.43785D+02\n",
      "\n",
      "\n",
      "ITERATION   179\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           1  times; norm of step =   0.23393291340106320     \n",
      "\n",
      "At iterate  179    f=  5.12203D+04    |proj g|=  3.79063D+02\n",
      "\n",
      "\n",
      "ITERATION   180\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.18680357023977989     \n",
      "\n",
      "At iterate  180    f=  5.11696D+04    |proj g|=  2.10877D+02\n",
      "\n",
      "\n",
      "ITERATION   181\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   1.70888220151803535E-002\n",
      "\n",
      "At iterate  181    f=  5.11527D+04    |proj g|=  1.58015D+02\n",
      "\n",
      "\n",
      "ITERATION   182\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.11410536470611825     \n",
      "\n",
      "At iterate  182    f=  5.11027D+04    |proj g|=  2.36016D+02\n",
      "\n",
      "\n",
      "ITERATION   183\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.22276321618571235     \n",
      "\n",
      "At iterate  183    f=  5.10524D+04    |proj g|=  3.05735D+02\n",
      "\n",
      "\n",
      "ITERATION   184\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.30690694880374375     \n",
      "\n",
      "At iterate  184    f=  5.10227D+04    |proj g|=  4.05846D+02\n",
      "\n",
      "\n",
      "ITERATION   185\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   1.66308052272717312E-002\n",
      "\n",
      "At iterate  185    f=  5.09854D+04    |proj g|=  2.40396D+02\n",
      "\n",
      "\n",
      "ITERATION   186\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   6.48148436546637924E-002\n",
      "\n",
      "At iterate  186    f=  5.09676D+04    |proj g|=  2.24833D+02\n",
      "\n",
      "\n",
      "ITERATION   187\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   6.73748013424260994E-002\n",
      "\n",
      "At iterate  187    f=  5.09512D+04    |proj g|=  2.04103D+02\n",
      "\n",
      "\n",
      "ITERATION   188\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.16090757196198802     \n",
      "\n",
      "At iterate  188    f=  5.09134D+04    |proj g|=  2.58437D+02\n",
      "\n",
      "\n",
      "ITERATION   189\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           1  times; norm of step =   0.15982724485728783     \n",
      "\n",
      "At iterate  189    f=  5.08887D+04    |proj g|=  2.49447D+02\n",
      "\n",
      "\n",
      "ITERATION   190\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   3.45822623937238269E-002\n",
      "\n",
      "At iterate  190    f=  5.08641D+04    |proj g|=  1.82546D+02\n",
      "\n",
      "\n",
      "ITERATION   191\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " LINE SEARCH           0  times; norm of step =   6.35486102323018637E-002\n",
      "\n",
      "At iterate  191    f=  5.08321D+04    |proj g|=  2.04324D+02\n",
      "\n",
      "\n",
      "ITERATION   192\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   5.10699914866076729E-002\n",
      "\n",
      "At iterate  192    f=  5.08132D+04    |proj g|=  1.90057D+02\n",
      "\n",
      "\n",
      "ITERATION   193\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.10290488044619464     \n",
      "\n",
      "At iterate  193    f=  5.07802D+04    |proj g|=  1.82155D+02\n",
      "\n",
      "\n",
      "ITERATION   194\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.17366009400456656     \n",
      "\n",
      "At iterate  194    f=  5.07439D+04    |proj g|=  2.24088D+02\n",
      "\n",
      "\n",
      "ITERATION   195\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.13269753550692012     \n",
      "\n",
      "At iterate  195    f=  5.07163D+04    |proj g|=  2.01958D+02\n",
      "\n",
      "\n",
      "ITERATION   196\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.12177209706888766     \n",
      "\n",
      "At iterate  196    f=  5.06955D+04    |proj g|=  2.83535D+02\n",
      "\n",
      "\n",
      "ITERATION   197\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   8.72752405766815398E-002\n",
      "\n",
      "At iterate  197    f=  5.06684D+04    |proj g|=  2.53395D+02\n",
      "\n",
      "\n",
      "ITERATION   198\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.12242250939105073     \n",
      "\n",
      "At iterate  198    f=  5.06297D+04    |proj g|=  2.69818D+02\n",
      "\n",
      "\n",
      "ITERATION   199\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.14978134623834635     \n",
      "\n",
      "At iterate  199    f=  5.05849D+04    |proj g|=  2.65996D+02\n",
      "\n",
      "\n",
      "ITERATION   200\n",
      "\n",
      "----------------SUBSM entered-----------------\n",
      "\n",
      "\n",
      "----------------exit SUBSM --------------------\n",
      "\n",
      " LINE SEARCH           0  times; norm of step =   0.27255096069062140     \n",
      "\n",
      "At iterate  200    f=  5.05298D+04    |proj g|=  2.65783D+02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      " 2527    200    211      1     0     0   2.658D+02   5.053D+04\n",
      "\n",
      " X =  3.4375D-01  1.9064D-01  8.8421D-02 -1.6543D-02 -1.6124D-02  1.5313D-02\n",
      "      9.5623D-03  3.4551D-03  4.7066D-02  1.4346D-01  2.1172D-01  1.4945D-01\n",
      "      1.0398D-01  3.5262D-02  2.0764D-02 -3.3641D-02 -1.2313D-02  2.5167D-02\n",
      "      9.5861D-02  1.8376D-01  1.6968D-01  6.7766D-02 -1.3514D-01 -1.4484D-01\n",
      "     -5.3148D-02  5.9884D-02  8.3022D-02  2.4758D-02 -3.4170D-02 -8.3817D-02\n",
      "     -6.6830D-02 -5.9412D-02 -7.8935D-02 -1.4695D-02  3.3821D-02  5.3651D-02\n",
      "      1.7444D-02 -3.9394D-02 -1.1137D-01 -7.6192D-02 -1.7673D-02  3.9812D-02\n",
      "      9.8478D-02  7.0075D-02  2.6091D-02  4.0864D-02  1.3053D-02  1.0257D-02\n",
      "     -1.9819D-02 -9.1134D-02 -1.4924D-01 -1.7232D-01 -1.2173D-01 -3.1646D-02\n",
      "      7.6484D-02  1.3883D-01  1.9438D-01  1.7289D-01  1.5208D-01  3.6355D-03\n",
      "     -9.0767D-02 -1.2342D-01 -8.9391D-02 -1.4223D-02  1.7837D-02  1.2974D-02\n",
      "      1.3325D-02  5.2646D-03  1.7026D-02  4.1295D-02  1.0034D-01  1.0834D-01\n",
      "      1.0050D-01  8.5174D-02  5.2744D-02 -6.3817D-02 -3.2307D-02 -6.8664D-02\n",
      "     -7.0574D-02 -4.6925D-02 -1.6895D-02  2.6456D-02 -8.2860D-03  5.4170D-02\n",
      "     -6.4327D-03  6.2262D-03 -1.3758D-02 -3.3436D-02  4.0830D-02  1.4215D-01\n",
      "      1.6956D-01  1.9806D-01  8.9056D-02 -3.4083D-02 -2.5658D-02 -2.4658D-02\n",
      "     -1.8757D-02 -4.4548D-02 -6.5276D-02 -3.8942D-02  4.3714D-02 -9.1035D-03\n",
      "     -1.3203D-01 -2.9441D-01 -2.3876D-01 -1.7184D-01 -9.8215D-02 -7.9154D-03\n",
      "     -2.5685D-02  6.1032D-03  3.2608D-02  4.0217D-02  7.9626D-02  8.0491D-02\n",
      "      1.4506D-02  4.5108D-02  3.9393D-02 -3.6926D-03 -4.5227D-02 -1.2795D-01\n",
      "     -1.2338D-01 -8.4888D-02 -7.2215D-02 -6.0842D-02 -1.0151D-01 -1.3555D-01\n",
      "     -1.7247D-01 -9.2619D-02 -4.8827D-02 -1.3939D-01 -1.1968D-01 -1.0120D-01\n",
      "     -1.3432D-01 -1.3713D-01 -1.0487D-01 -5.0671D-02  1.6942D-01  7.0093D-02\n",
      "     -5.7621D-02 -1.2244D-01 -1.6935D-01 -1.7351D-01 -1.4081D-01 -7.4935D-02\n",
      "      5.2353D-02  1.5696D-01  2.3848D-01  4.9417D-01  7.9160D-01  8.7696D-01\n",
      "      4.4211D-01 -1.1307D-01 -6.3543D-01 -7.6688D-01 -7.2345D-01 -5.9321D-01\n",
      "     -2.4326D-01  3.4039D-01  9.0774D-01  1.2396D+00  1.0866D+00  6.2593D-01\n",
      "      1.0209D-01 -3.5657D-01 -8.4329D-01 -1.3091D+00 -1.6340D+00 -1.4989D+00\n",
      "     -6.6766D-01  6.9150D-01  1.9733D+00  2.2529D+00  1.3400D+00 -1.9141D-01\n",
      "     -1.3277D+00 -1.8023D+00 -1.5196D+00  8.3364D-02  2.8744D+00  4.2852D+00\n",
      "      2.3608D+00 -1.3039D+00 -3.6327D+00 -3.5738D+00 -2.1297D+00 -6.6783D-01\n",
      "      2.1297D-01  5.7335D-01  7.5876D-01  8.0155D-01  7.6484D-01  6.9939D-01\n",
      "      6.4672D-01  5.3628D-01  3.6888D-01  1.5121D-01 -2.9866D-02 -1.0449D-01\n",
      "     -1.6738D-01 -1.8112D-01 -1.3332D-01  2.8950D-02  2.7213D-01  4.1202D-01\n",
      "      4.1929D-01  3.3816D-01  2.8907D-01  3.3499D-01  3.6784D-01  2.7139D-01\n",
      "      1.1489D-02 -3.2974D-01 -6.4026D-01 -8.5010D-01 -8.3241D-01 -8.8220D-01\n",
      "     -9.2032D-01 -1.0270D+00 -1.1709D+00 -1.2572D+00 -1.2556D+00 -1.1843D+00\n",
      "     -9.8519D-01 -6.9901D-01 -3.7102D-01  1.7031D-02  5.1248D-01  8.8523D-01\n",
      "      1.0398D+00  1.0545D+00  9.7835D-01  8.5026D-01  6.9665D-01  5.5012D-01\n",
      "      3.3280D-01  1.3243D-01 -7.7771D-02 -2.0877D-01 -3.1163D-01 -3.4889D-01\n",
      "     -3.7171D-01 -3.4870D-01 -2.5227D-01 -1.6800D-01 -7.5984D-02 -3.7589D-02\n",
      "      2.8239D-02  6.0804D-02  5.6705D-02  5.6176D-02  9.5450D-02  1.4171D-01\n",
      "      1.9518D-01  2.2155D-01  1.8501D-01  1.7475D-01  1.8574D-01  1.7329D-01\n",
      "      1.4107D-01  9.9653D-02  6.8905D-02  3.3537D-02  3.8755D-03  7.0238D-03\n",
      "     -3.1103D-02 -8.5385D-02 -8.7072D-02 -7.4019D-02 -6.4900D-02 -4.6354D-02\n",
      "     -2.9641D-02  9.3666D-03  8.4034D-03 -1.1531D-02 -6.6723D-02 -9.3357D-02\n",
      "     -2.2056D-01 -2.8099D-01 -3.4038D-01 -3.1800D-01 -1.5828D-01 -9.4747D-02\n",
      "     -8.3714D-02 -6.9446D-02 -3.4050D-02 -6.2285D-02 -5.4781D-02 -6.7022D-02\n",
      "     -2.2319D-02  8.1966D-03  6.0482D-02  1.3630D-01  1.7217D-01  2.3150D-01\n",
      "      2.6246D-01  2.6686D-01  2.8225D-01  2.5371D-01  2.7165D-01  3.2459D-01\n",
      "      3.4006D-01  3.0319D-01  2.6190D-01  2.6713D-01  2.7093D-01  2.2165D-01\n",
      "      1.3241D-01  8.1219D-02  5.3407D-02  1.2177D-02  3.2974D-02  6.8661D-02\n",
      "     -2.7965D-02 -9.4547D-02 -1.0993D-01 -8.3347D-02 -1.6836D-02  2.7350D-02\n",
      "     -8.4246D-03 -1.6768D-02  2.1082D-02  9.9453D-02  1.1335D-01  5.5230D-02\n",
      "     -2.0620D-02  8.7688D-03  9.5099D-02  5.3130D-02  5.4216D-02 -2.1121D-02\n",
      "     -6.9636D-02 -1.1791D-01 -3.8015D-02 -1.8781D-02  1.8662D-01  2.7290D-01\n",
      "      2.1715D-01  6.3558D-02 -1.4956D-01 -1.5495D-01 -1.0528D-01  2.0483D-02\n",
      "      1.9955D-02  6.7579D-02  5.1299D-03 -8.1074D-02 -9.4343D-02 -2.8478D-03\n",
      "     -3.5322D-02 -9.4630D-02 -8.8930D-02 -8.9588D-02 -3.0259D-02  4.2690D-03\n",
      "     -3.0710D-03 -4.1638D-02 -6.6545D-02 -7.8655D-02 -3.4778D-02 -8.7089D-02\n",
      "     -3.0600D+00 -1.9411D-02 -8.4537D-02 -4.7205D-02 -1.8338D-04 -4.0094D-02\n",
      "      1.0843D-01  1.1761D-01 -3.4982D-02 -1.1879D-01 -2.8959D-01  2.7462D-02\n",
      "      1.2443D-01  2.2142D-01  1.3062D-01 -9.2105D-02 -2.2373D-01 -8.9480D-02\n",
      "      3.9514D-02 -3.7512D-02 -8.3691D-03  1.8056D-01  1.3363D-01  6.8405D-02\n",
      "      1.5005D-02 -3.5257D-02 -1.9090D-01 -5.3958D-02  2.7456D-01  3.2655D-01\n",
      "      1.7790D-01  5.8240D-02 -1.3999D-01 -1.4251D-01 -1.2870D-01  1.1881D-01\n",
      "      1.1806D-01  1.1796D-01  3.2619D-02  2.6061D-02 -1.2500D-01  7.1023D-02\n",
      "      4.5309D-02 -1.0899D-01 -4.3841D-02  1.8477D-01  2.0854D-01 -1.4330D-01\n",
      "     -1.0787D-01  7.1560D-02  9.6911D-03  2.1922D-01 -7.6267D-02  1.2445D-01\n",
      "      1.1451D-02 -1.1474D-01 -3.7738D-03 -1.5948D-03 -1.6622D-01 -2.4264D-01\n",
      "      6.6822D-02  1.1399D-01  3.0703D-02 -1.6475D-01 -3.3741D-01 -2.7215D-01\n",
      "     -4.5009D-01 -2.2072D-01 -1.1235D-01  1.2482D-01  3.5195D-01  2.3193D-01\n",
      "      5.5464D-01  2.1224D-01  1.0224D-01 -2.4600D-01 -3.9501D-02  3.1934D-02\n",
      "      2.3573D-01  8.2842D-02  8.0371D-02  1.4097D-01  1.3986D-01  1.4226D-01\n",
      "     -2.5178D-02  4.5934D-02 -3.3637D-01 -4.3855D-01 -3.3579D-01 -2.0658D-01\n",
      "     -2.2808D-01  5.2895D-02 -1.8197D-03  1.0754D-01  1.9251D-01  4.0375D-01\n",
      "      2.7156D-01 -3.9970D-02 -4.2190D-03  4.0430D-02  5.8872D-02  1.4344D-01\n",
      "      3.5426D-01  1.1179D-01  2.2723D-01  2.6961D-01  3.2558D-01  1.2814D-01\n",
      "      7.1970D-02 -2.2920D-02 -2.4198D-02  2.1102D-01 -6.9818D-03  7.5871D-02\n",
      "     -2.5666D-01 -2.3457D-01 -1.7335D-01 -5.0011D-02  1.9959D-02  3.1479D-02\n",
      "      2.3557D-01  9.8326D-02 -1.4506D-01 -2.8448D-01 -3.7812D-01 -2.6472D-01\n",
      "     -1.2952D-02  9.8977D-02 -2.2525D-02 -6.3112D-02  2.1055D-01  1.8269D-01\n",
      "      7.3722D-02 -3.5705D-01 -5.3367D-01 -3.7592D-01 -3.0180D-01 -1.4440D-01\n",
      "      1.0159D-01  2.0290D-01  2.3659D-01  2.4735D-01  1.7544D-02  6.3947D-02\n",
      "     -1.4547D-01  1.0435D-01  2.0374D-01  1.7307D-01  1.4361D-01 -5.7368D-04\n",
      "     -1.1027D-01 -3.8267D-01 -2.3475D-01  2.8671D-02  2.9263D-01  6.0467D-01\n",
      "      5.3418D-01  2.5896D-01 -6.8440D-02 -3.1228D-01 -3.7085D-01 -1.4443D-01\n",
      "     -1.8246D-01 -3.0826D-02  2.4751D-01  2.5500D-01  4.1701D-02 -4.1288D-01\n",
      "     -5.6820D-01 -4.9862D-01 -8.0114D-02  3.4490D-01  1.3401D-01 -2.6462D-01\n",
      "     -3.7713D-01 -1.9057D-01  9.2939D-02  3.5705D-01  8.6525D-02 -7.0469D-01\n",
      "     -9.6271D-01 -6.0124D-02  1.3909D+00  1.7881D+00  1.0273D+00 -1.0428D-01\n",
      "     -6.6450D-01 -8.0643D-01 -5.0604D-01 -7.3145D-01 -7.1240D-01 -5.6275D-01\n",
      "     -7.8249D-01 -6.6051D-01 -2.0234D-01  6.6147D-01  1.3271D+00  1.1666D+00\n",
      "      5.2073D-01 -2.8555D-01 -8.7995D-01 -8.3244D-01 -4.1925D-01  7.3312D-02\n",
      "      4.6151D-01  5.8321D-01  3.6341D-01 -3.2337D-02 -3.5936D-01 -4.0073D-01\n",
      "     -1.3272D-01 -1.3842D-02 -7.4398D-02 -1.6542D-01 -4.2676D-01 -5.6240D-01\n",
      "     -2.7736D-01  1.3224D-01  6.6665D-01  1.1878D+00  1.4573D+00  1.5017D+00\n",
      "      1.1978D+00  7.9669D-01  2.2268D-01 -4.3902D-02 -1.7187D-01 -1.4237D-01\n",
      "     -5.5328D-02 -1.2090D-02 -1.5246D-02 -6.7287D-02 -2.4966D-01 -2.4998D-01\n",
      "     -4.0820D-01 -5.5158D-01 -2.9337D-01 -1.9953D-01 -3.7368D-02 -2.1969D-02\n",
      "     -7.1204D-02 -2.0112D-01 -1.2266D-01 -2.8869D-02  9.0083D-02 -9.4691D-02\n",
      "     -2.4593D-01 -2.5764D-01 -1.0496D-01  3.0381D-02  2.5571D-01  3.2914D-01\n",
      "      8.8852D-02 -8.3273D-02  6.7533D-03  6.4941D-02  9.4014D-02  1.5378D-01\n",
      "     -5.2827D-02 -6.1963D-02  2.0791D-02  1.2055D-01  2.3124D-01  2.4987D-01\n",
      "      1.8787D-02 -2.9545D-01 -4.7339D-01 -6.6140D-01 -5.5600D-01 -4.6922D-01\n",
      "     -3.5561D-01 -3.2210D-01 -2.0647D-01 -1.0945D-01  1.2228D-01  2.6737D-01\n",
      "      2.5331D-01  1.1008D-01  3.4325D-02  1.6784D-01  3.1545D-01  4.3318D-01\n",
      "      3.1129D-01  2.8862D-01  2.9424D-01  2.7967D-01  2.4906D-01  1.5103D-01\n",
      "      1.0200D-01 -8.8332D-03 -2.0748D-02 -3.2209D-03  4.9608D-02  7.8286D-02\n",
      "     -4.5622D-02 -2.2256D-01 -3.5728D-01 -2.4321D-01 -3.5402D-01 -3.3129D-01\n",
      "     -4.7394D-01 -4.9825D-01 -3.6360D-01 -2.9592D-01 -2.0684D-01  1.9111D-02\n",
      "     -2.1190D-02  7.0446D-02 -3.0987D-02  2.3146D-02 -2.8288D-02  4.4625D-02\n",
      "      5.0214D-02 -1.5709D-02  5.0860D-02  8.1736D-02 -5.3096D-02  4.0088D-02\n",
      "     -2.9825D-02  4.2988D-02  4.1556D-02  7.3104D-02  8.6249D-02  1.8422D-03\n",
      "      2.6455D-03  5.4073D-02  3.5123D-03 -1.5626D-02 -5.8389D-02  1.1270D-02\n",
      "      4.2450D-02  3.6353D-02 -9.0551D-02 -1.3075D-01  1.9677D-03  3.5837D-02\n",
      "     -3.1615D-02 -4.2122D-02 -2.9180D-02  1.8656D-02 -1.4640D-01  4.9998D-03\n",
      "      1.6082D-01  3.0706D-01  5.9805D-02  1.4815D-01 -1.2534D-01  4.6900D-02\n",
      "     -1.2360D-01 -1.8540D-01  6.9862D-02  1.3160D-01  1.3027D-01 -3.7646D-02\n",
      "     -2.8748D-01 -2.9320D-01 -6.7608D-02  2.8093D-01  2.9617D-01  4.6376D-02\n",
      "     -5.7460D-01  4.6051D-01 -7.4103D-02 -1.2897D-01 -3.4165D-02  1.3792D-01\n",
      "      5.4164D-02  7.6878D-02  1.3657D-02 -1.4497D-01 -8.5843D-02 -1.2702D-01\n",
      "     -2.9833D-01 -2.7191D-01 -2.8104D-01 -7.1596D-02 -1.3606D-02  1.9924D-01\n",
      "     -2.6478D-01 -9.0798D-02  7.4548D-02 -9.6112D-02 -1.8397D-01  1.8172D-02\n",
      "      8.9850D-02  1.3346D-01 -6.4013D-02 -2.4265D-01 -1.0162D-01  1.1523D-01\n",
      "      2.0034D-01  1.2648D-01 -1.4821D-01 -1.1537D-01 -4.4508D-02 -8.7627D-02\n",
      "     -4.0821D-02  4.8224D-02 -7.2643D-02 -2.8810D-01 -1.4188D-01  7.1218D-02\n",
      "      1.6498D-01  2.3042D-01  5.3303D-02  1.5655D-01 -8.0076D-02 -4.5555D-01\n",
      "     -2.5478D-01 -1.1517D-01  3.0105D-02  2.4055D-01  2.6410D-02  5.1758D-02\n",
      "     -1.3592D-01  1.5482D-01  4.1641D-01  3.4198D-01 -5.9775D-02 -4.0434D-01\n",
      "     -6.2116D-01  1.3743D-01  3.9331D-02  2.0195D-01 -1.9449D-02  2.4259D-01\n",
      "     -1.4240D-01 -2.8457D-01 -6.1814D-03 -7.2990D-02  3.2042D-01  4.6974D-01\n",
      "      1.0053D-01 -1.9264D-01 -2.7907D-01 -3.1509D-01  8.6334D-03  2.2188D-01\n",
      "      1.7993D-01 -1.3406D-01 -2.5458D-01 -4.3991D-01 -1.9834D-01 -3.5039D-01\n",
      "      2.7411D-01  8.6848D-02  1.7377D-01  3.0547D-01  6.7835D-01  6.6298D-01\n",
      "     -1.0033D-01 -1.4003D-01  2.6520D-01  4.0878D-01  1.4281D-01  5.8962D-03\n",
      "     -2.7712D-01  2.4734D-02  1.6817D-01 -2.9164D-01 -4.2880D-01 -4.2473D-01\n",
      "     -6.1535D-01 -3.7431D-01 -3.5653D-01 -2.8882D-01 -3.3676D-01 -6.6548D-01\n",
      "     -7.4140D-01 -6.9066D-01 -8.3045D-02  7.2396D-01  8.2266D-01  3.0881D-01\n",
      "     -2.4301D-01 -1.5729D-01  4.4519D-01 -1.7942D-01 -1.8220D-01  7.6215D-02\n",
      "      3.9487D-01  2.9941D-01  1.8295D-01  2.0222D-01  4.6443D-01  5.6964D-01\n",
      "      2.0093D-01 -2.5592D-01 -1.9618D-01 -1.8021D-01 -3.3372D-01 -3.3058D-01\n",
      "     -7.8329D-01 -1.0278D+00 -4.2195D-01 -1.9638D-01  3.4382D-02  6.1763D-01\n",
      "     -2.3953D-02  5.3346D-01  4.7634D-01  6.8705D-01  5.3502D-01  2.8738D-01\n",
      "      1.5409D-01  3.0031D-01  1.4002D-01 -4.9324D-01 -5.4812D-01 -3.1964D-01\n",
      "     -2.6129D-01 -2.1276D-01  1.1462D-01  3.9548D-01  5.4318D-01  7.8381D-01\n",
      "      3.3846D-01  5.5508D-02 -1.2010D-01 -1.8817D-01 -1.6178D-01 -2.7278D-01\n",
      "      1.6956D-01  7.5483D-02 -1.1382D-02 -1.0322D-01  1.9172D-01  4.2301D-01\n",
      "      4.3420D-01  1.8287D-01  1.2977D-01  4.7731D-02  3.8779D-01  3.3984D-01\n",
      "      1.2997D-01 -5.0838D-02 -4.8762D-01 -4.0054D-01 -1.7615D-02  3.5903D-01\n",
      "      3.3324D-01 -5.8697D-02 -5.9859D-01 -8.4322D-01 -1.2778D+00 -1.1825D+00\n",
      "     -5.6957D-01  8.2755D-01  2.0225D+00  2.1222D+00  1.3428D+00  6.5023D-01\n",
      "      1.0033D-01 -1.8063D-01 -3.8885D-01 -2.8216D-01 -6.8533D-02  1.7786D-01\n",
      "      2.4479D-01 -1.8788D-01 -4.4980D-01 -7.5109D-01 -6.7155D-01 -3.3453D-01\n",
      "      1.5183D-01  5.9988D-01  7.6354D-01  4.8124D-01 -2.4416D-01 -6.4988D-01\n",
      "     -6.7148D-01 -3.4040D-01 -1.7510D-01  5.3734D-02  4.9126D-01  8.8097D-01\n",
      "      1.2664D+00  1.4324D+00  1.3796D+00  1.2935D+00  1.1613D+00  7.2031D-01\n",
      "      3.2015D-01  1.7221D-02 -2.4425D-01 -8.2092D-01 -1.2213D+00 -1.3957D+00\n",
      "     -1.7247D+00 -1.3211D+00 -1.1717D+00 -9.3505D-01 -8.6871D-01 -7.5786D-01\n",
      "     -4.4132D-01 -2.0123D-01  1.5191D-01  1.5790D-01  2.5860D-01  4.8098D-01\n",
      "      6.3337D-01  1.0683D+00  9.9647D-01  7.5284D-01  5.9773D-01  4.7537D-01\n",
      "      2.9401D-01  4.7113D-01  4.5996D-01  7.6249D-02  4.1848D-02  5.9537D-02\n",
      "      9.8988D-02  2.7263D-02 -2.2475D-01 -4.1709D-01 -3.3264D-01 -3.3360D-01\n",
      "     -1.2436D-01 -1.2999D-01 -1.6633D-01 -3.6720D-01 -2.4295D-01 -1.8849D-01\n",
      "      7.7362D-02 -4.8843D-02 -1.1552D-01  1.1386D-01  4.3669D-02 -2.2263D-02\n",
      "      2.9453D-01  6.5146D-01  5.6273D-01  1.2136D-01 -1.5200D-01 -1.1849D-01\n",
      "     -7.5039D-02 -1.8366D-01  1.3808D-01  1.6202D-01  3.5909D-01  2.8064D-01\n",
      "      1.0036D-01  1.2490D-01  1.5808D-02 -1.9939D-01 -2.5988D-01 -2.2678D-01\n",
      "     -3.0267D-01  1.0441D-01  3.5124D-01  2.6968D-01 -5.9455D-02 -6.4424D-01\n",
      "     -3.9252D-01 -3.0633D-02  1.5833D-01  1.7311D-01  2.1171D-01 -1.0760D-01\n",
      "      2.2923D-02  1.0216D-02 -1.4401D-01  1.0337D-01  9.0482D-02  2.9624D-03\n",
      "     -1.8667D-01  5.5909D-02  1.3359D-01  1.8459D-01 -2.0719D-01 -2.4558D-01\n",
      "     -1.9891D-01 -2.6192D-01  7.5906D-02  1.7695D-01  2.9306D-01  3.7630D-01\n",
      "      1.6279D-01  1.9018D-01  1.4324D-01 -2.6377D-01 -1.2332D-01 -3.7830D-01\n",
      "     -6.4248D-02 -1.6653D-01  1.3185D-02  6.0459D-02 -9.6594D-03  2.4432D-03\n",
      "     -2.2344D-01 -1.9515D-01  2.2366D-01  1.4497D-01  2.1890D-01  3.8165D-01\n",
      "     -3.4902D-02 -2.6574D-01  1.0262D-01  3.6671D-01  1.8534D-01 -9.7494D-02\n",
      "     -2.0724D-01 -2.7951D-01  5.5801D-02  1.9790D-01 -3.1616D-01 -1.8201D-01\n",
      "     -3.8449D-01 -3.6922D-01  1.5047D-01  8.7290D-02  7.2422D-02  9.2590D-02\n",
      "      2.2039D-01  3.3355D-01 -1.7165D-02 -4.3355D-02 -2.8221D-01  6.2859D-02\n",
      "     -4.3583D-02  9.1586D-02 -2.3508D+00 -3.7392D-01  1.0922D-01  1.4079D-01\n",
      "      4.6087D-02 -4.1959D-02 -1.0041D-01 -1.4421D-01  1.6006D-02 -4.5422D-02\n",
      "      1.3267D-03  1.2201D-01  8.6799D-02 -6.2777D-02 -5.4740D-02 -2.4501D-02\n",
      "      6.0179D-02  1.4785D-01 -8.4045D-03 -8.4319D-02 -1.1233D-01 -6.2538D-02\n",
      "     -3.3975D-02 -7.6082D-02 -9.0875D-02 -2.9780D-03  1.7387D-01  1.3256D-01\n",
      "     -1.8892D-02 -2.0314D-01 -1.8170D-01  5.8631D-02  1.3512D-01  1.0723D-01\n",
      "      4.3977D-02 -2.5465D-01 -1.4220D-01 -1.2329D-02  1.3967D-01  1.3380D-01\n",
      "      1.3931D-01 -1.3364D-01 -2.5086D-01 -1.2092D-01 -7.2119D-02  9.6410D-03\n",
      "      1.7193D-01  1.0641D-01  4.4875D-02 -1.7835D-02 -9.0482D-02 -4.7846D-04\n",
      "      1.2325D-01 -1.6507D-02 -8.0924D-02 -8.0886D-02 -6.4088D-02  9.4722D-02\n",
      "      1.9552D-01  1.9344D-01 -9.5164D-02 -3.8703D-03 -2.7331D-02  9.0404D-02\n",
      "      5.8989D-02  1.4837D-01  2.2238D-01  6.7777D-02  5.4208D-02 -2.4757D-01\n",
      "     -2.6938D-01 -3.4105D-01 -3.1206D-01 -2.3872D-01 -2.6187D-01  4.2007D-03\n",
      "     -4.4583D-02 -1.5437D-01 -6.3909D-02  5.1230D-02  8.5241D-02  6.0092D-02\n",
      "      1.0975D-01 -9.3606D-02  1.4342D-01  4.0046D-02 -6.0475D-02 -4.3654D-02\n",
      "      2.0541D-01  5.8937D-01  6.6825D-01  3.2874D-01  1.4011D-01  2.0064D-01\n",
      "     -1.9017D-02 -9.9711D-02 -1.1092D-01  3.9469D-02  1.6265D-01  2.9007D-01\n",
      "      1.0163D-01 -1.3248D-01 -3.6023D-01 -1.4854D-01 -1.8118D-01 -3.4207D-01\n",
      "     -1.1778D-01 -1.9016D-01 -2.5534D-01 -2.9545D-01 -4.3616D-01 -3.4424D-01\n",
      "      9.6574D-03  2.5215D-01  1.7093D-01  1.7083D-01  3.5263D-01  3.0053D-01\n",
      "      3.5037D-02 -1.9205D-01 -1.3230D-01  1.4658D-01  4.5088D-01  4.0372D-01\n",
      "      3.5579D-01  2.1547D-01 -2.1174D-02 -1.6444D-01 -7.9396D-02  2.8377D-03\n",
      "     -1.2630D-01 -1.5830D-01  1.3080D-01 -1.4882D-01 -2.8287D-01 -2.2579D-01\n",
      "     -3.3913D-01 -2.6577D-02 -2.1501D-01 -2.9645D-01 -3.3118D-01 -2.2090D-01\n",
      "      8.5357D-02 -1.0233D-02 -4.8088D-02 -3.2424D-01  1.7474D-02  2.6699D-01\n",
      "      2.3212D-01  8.3082D-02 -1.7704D-01 -9.7897D-02  1.0291D-02 -7.0754D-02\n",
      "      2.9243D-02  5.1724D-02  3.0087D-01  2.3941D-01  2.8230D-01 -7.4940D-02\n",
      "     -1.0709D-01 -4.9453D-01 -6.5603D-01 -7.0588D-01 -7.0892D-01 -5.9191D-01\n",
      "      9.1684D-03  1.6604D+00  3.2245D+00  2.8298D+00  4.7006D-02 -3.0962D+00\n",
      "     -3.7540D+00 -1.7873D+00  7.4929D-01  2.5739D+00  2.7656D+00  1.0439D+00\n",
      "     -1.1578D+00 -2.1309D+00 -1.6381D+00 -9.6230D-01 -6.8575D-01  5.4205D-01\n",
      "      2.4211D+00  2.7122D+00  2.1168D-01 -1.7136D+00 -1.5865D+00 -3.5057D-01\n",
      "      5.5592D-01  1.1575D+00  1.2579D+00  9.9107D-01  1.1951D-01 -1.0623D+00\n",
      "     -1.6570D+00 -1.4075D+00 -4.2762D-01  4.7024D-01  1.1049D+00  1.2313D+00\n",
      "      9.6282D-01  2.6121D-01 -5.0212D-01 -1.1236D+00 -1.1844D+00 -6.2040D-01\n",
      "      8.6545D-02  6.9143D-01  1.0180D+00  1.1882D+00  9.4529D-01  3.8655D-01\n",
      "     -2.2516D-01 -6.6310D-01 -9.3383D-01 -8.3690D-01 -7.6765D-01 -6.3480D-01\n",
      "     -3.5251D-01 -1.0665D-01  1.8165D-01  4.7762D-01  8.1314D-01  8.2126D-01\n",
      "      6.6535D-01  3.8454D-01 -6.3909D-02 -7.7642D-02 -1.3114D-01 -2.8925D-02\n",
      "     -4.6110D-02 -1.2355D-01 -9.7545D-02 -1.9826D-01 -1.1168D-01 -1.4608D-01\n",
      "     -2.6596D-01 -3.4991D-01 -4.5013D-01 -1.8337D-01 -2.7432D-02  3.9283D-02\n",
      "      5.2822D-02  2.3018D-02 -6.9153D-03  1.6462D-01  3.3099D-01  4.2247D-01\n",
      "      2.1317D-01  3.7225D-02  1.0574D-01  2.6568D-01  2.7458D-01  2.2235D-01\n",
      "      1.0605D-01 -2.1551D-01 -2.5302D-01 -2.6323D-01 -9.6662D-02 -4.2837D-02\n",
      "     -1.6799D-01 -4.7462D-01 -4.6226D-01 -3.4719D-01 -2.8381D-01 -1.8279D-01\n",
      "     -1.1404D-01 -1.2583D-01 -1.2051D-01  9.9819D-03  2.4730D-01  4.6038D-01\n",
      "      4.8414D-01  4.8660D-01  6.4496D-01  5.7256D-01  5.2448D-01  2.5543D-01\n",
      "      8.5998D-02 -1.3126D-01 -3.7098D-01 -3.4979D-01 -2.0520D-01 -2.0670D-01\n",
      "     -3.2348D-01 -3.1108D-01 -4.0450D-01 -3.8739D-01 -1.6396D-01  4.5290D-02\n",
      "      3.1046D-01  1.6075D-01  1.4314D-01 -2.2223D-03  8.9546D-02  1.1003D-01\n",
      "      3.5626D-01  2.4266D-01  1.5100D-01  1.0668D-01 -1.2659D-01 -1.0333D-01\n",
      "     -8.4147D-02 -6.4706D-02 -1.3215D-01  5.2816D-02  1.9711D-02  2.0314D-01\n",
      "      1.0679D-01  1.2138D-02 -8.1952D-02 -1.4129D-01 -4.3592D-02  3.3093D-02\n",
      "      4.9674D-02  4.9556D-02  7.0859D-03 -2.7378D-02  1.8927D-01  7.9039D-02\n",
      "      1.4866D-01  5.0113D-02  6.8021D-02  6.5855D-02 -8.5653D-02 -1.2080D-01\n",
      "      4.6422D-02  2.2627D-01  1.7605D-01 -3.5791D-02 -9.7681D-03 -1.4011D-01\n",
      "     -2.4766D-01 -4.5881D-02  1.8173D-01  8.0439D-02 -3.6313D-02 -7.4234D-03\n",
      "      5.9172D-02  8.4320D-02  4.0125D-02 -9.4539D-02 -4.9079D-02 -3.1189D-02\n",
      "     -2.7819D-02  1.8093D-02  2.4278D-01 -5.9025D-02  1.9110D-01  1.1089D-01\n",
      "     -3.2412D-02 -4.1756D-02 -1.7873D-01  6.4573D-02 -9.1533D-03 -1.2652D-02\n",
      "     -6.7315D-02 -1.1088D-02  1.4540D-01  1.9994D+00 -2.3737D-01  1.4920D-02\n",
      "      9.2029D-03 -5.3653D-02 -4.8874D-02 -1.4012D-01 -6.2525D-02  4.8682D-02\n",
      "      1.1096D-02  1.0581D-01 -7.3901D-02 -8.8524D-02 -1.6604D-02 -4.4150D-03\n",
      "      2.3820D-02  7.6864D-02  2.7809D-02 -1.5676D-01 -6.6090D-02  2.9405D-02\n",
      "     -2.6190D-02  5.0306D-02  8.0972D-02  4.8522D-02 -9.8079D-02 -5.1920D-02\n",
      "     -2.0417D-01 -1.4901D-01 -6.3641D-02  2.7529D-02  4.7031D-02 -5.6530D-03\n",
      "      2.2759D-02  1.2690D-02 -6.0354D-02 -9.9626D-02 -1.5802D-02  4.2984D-02\n",
      "      7.2184D-03 -2.4765D-02 -1.5245D-01 -4.9837D-02  8.7898D-02  6.5993D-02\n",
      "     -1.1319D-01 -6.2011D-02  1.1108D-01  8.0936D-02  2.8036D-04  3.5098D-02\n",
      "      6.2326D-03  1.2098D-01 -2.4568D-02 -1.8042D-01 -9.0254D-02 -6.6228D-02\n",
      "      6.0513D-02  3.8911D-02  6.4512D-02 -2.9662D-02 -2.7597D-02  2.8443D-02\n",
      "      1.0487D-01  1.1420D-01  6.7282D-02  1.7466D-01  4.8627D-02  2.4323D-02\n",
      "      9.6514D-02 -1.2137D-01 -3.5313D-03 -1.2644D-01  2.1380D-01  1.3308D-01\n",
      "      1.1661D-01 -6.7937D-02 -2.4249D-03 -1.1700D-01 -8.5896D-02  1.3556D-02\n",
      "     -1.3713D-01  9.9123D-02  1.1923D-01  5.5681D-03 -1.9485D-01 -8.6787D-02\n",
      "      2.3126D-02  6.8175D-02  9.2616D-02 -3.7685D-02 -2.1663D-01 -2.1661D-01\n",
      "     -3.0996D-01 -1.7910D-01 -2.8179D-01 -1.4383D-01 -1.6177D-01  8.2958D-02\n",
      "      4.4904D-02  2.2609D-01  3.4344D-01  1.2487D-01  9.4055D-02  1.5176D-01\n",
      "      1.2882D-01  1.2872D-01  1.6490D-01  1.0630D-01  3.9123D-02 -9.6319D-02\n",
      "     -4.4145D-01 -1.9852D-01 -1.1201D-01  6.8089D-02 -3.9010D-02  3.2271D-02\n",
      "      1.5749D-01  1.3689D-01 -1.6912D-03 -2.1491D-01 -2.7023D-01 -2.7299D-01\n",
      "     -2.7508D-01 -3.5708D-01 -3.8930D-01 -1.4379D-01  2.9718D-01  4.1783D-01\n",
      "      3.9452D-01  1.1410D-01  2.5212D-01  1.8215D-01  2.9097D-01  2.1454D-01\n",
      "      3.2921D-02  7.6597D-03 -1.0850D-01 -2.0153D-01 -2.1439D-02 -1.8820D-01\n",
      "     -8.2942D-04  2.6192D-01  3.9503D-01  4.0538D-01  3.5928D-01  3.8746D-01\n",
      "      2.8946D-01 -9.0110D-02 -1.0543D-01 -2.7206D-01 -3.3462D-01 -6.1795D-01\n",
      "     -4.1256D-01 -1.5927D-01  1.0920D-01  5.9025D-01  5.7717D-01  2.9257D-01\n",
      "     -1.7396D-01 -3.1305D-01 -2.3989D-01 -2.6844D-01 -3.4725D-01 -4.4483D-01\n",
      "     -1.8782D-01  4.6249D-01  5.4011D-01 -6.7431D-02 -6.8828D-01 -6.8596D-01\n",
      "     -4.2755D-01 -2.0531D-01 -1.1775D-01 -5.7071D-02  4.2015D-02  1.4060D-01\n",
      "      7.5924D-01  9.6681D-01  1.5217D-01 -6.7072D-01 -1.7339D-01  9.4450D-01\n",
      "      1.0534D+00 -2.2836D-01 -9.6187D-01  1.0132D-01  8.5194D-01  2.2989D-01\n",
      "     -5.9601D-01 -1.4726D+00 -1.8302D+00 -1.2352D+00 -4.9092D-01  2.7289D-01\n",
      "      8.9428D-01  1.3560D+00  1.5087D+00  1.2023D+00  7.1123D-01  2.1709D-01\n",
      "     -4.2355D-01 -8.4543D-01 -1.0560D+00 -9.9755D-01 -7.5840D-01 -3.2645D-01\n",
      "      7.6339D-02  5.4755D-01  8.1111D-01  9.8443D-01  1.0729D+00  1.1477D+00\n",
      "      9.7932D-01  6.6973D-01  2.2187D-01 -3.4792D-01 -8.1534D-01 -1.0271D+00\n",
      "     -1.0924D+00 -9.9215D-01 -9.2287D-01 -6.7834D-01 -3.6382D-01  8.4444D-02\n",
      "      2.4006D-01  2.6503D-01  3.7414D-01  3.5378D-01  2.2909D-01  1.8413D-01\n",
      "      4.8602D-02  4.6643D-02  1.7666D-02  6.5116D-02  1.5416D-01  9.7151D-02\n",
      "      1.7693D-01  2.4108D-02 -2.5394D-03 -1.0406D-02 -8.1362D-02 -1.0139D-01\n",
      "     -1.1315D-01 -1.0892D-01  1.2802D-01  1.0077D-01 -2.0226D-02 -1.5400D-01\n",
      "     -3.2108D-01 -2.8688D-01 -2.3317D-01 -1.4766D-01 -1.0152D-01 -3.5933D-02\n",
      "     -4.6267D-02 -1.4520D-01 -1.2716D-01  1.3847D-01  2.5389D-01  2.7942D-01\n",
      "      1.2125D-01  1.5339D-02  1.6501D-02  3.6970D-02  5.5968D-02  6.0109D-02\n",
      "      3.2179D-01  4.7854D-01  3.4367D-01  1.1377D-01 -8.2235D-02 -1.8880D-01\n",
      "     -8.7555D-02 -3.6400D-02  7.8169D-02  8.2641D-02  9.9372D-02  1.1960D-01\n",
      "      1.3993D-03 -8.1877D-02 -1.7982D-01 -1.5579D-01 -1.3966D-01 -1.1681D-01\n",
      "      2.6992D-02  3.2092D-01  5.2714D-01  3.6727D-01  1.3500D-01 -2.0225D-02\n",
      "     -1.1896D-01 -2.4620D-03  2.2190D-02  4.4801D-02  7.5669D-02 -1.3470D-01\n",
      "     -2.5674D-01 -1.9808D-01 -1.9810D-01 -7.3497D-02 -7.4647D-02 -7.7096D-02\n",
      "     -1.2165D-01 -3.9892D-02 -2.5513D-02  1.0292D-01  7.1994D-02  9.5517D-02\n",
      "      5.5034D-02  5.1727D-02 -6.4964D-02 -1.4919D-01 -1.4793D-01 -9.7438D-02\n",
      "     -1.5103D-01 -7.0295D-02  9.3682D-02  2.5329D-02 -3.2514D-02 -1.4631D-01\n",
      "     -1.0704D-01 -1.1710D-02  3.2461D-02 -6.6536D-02  7.9921D-03  1.0361D-01\n",
      "      1.0254D-02 -5.3231D-02 -6.8554D-02 -6.5309D-02 -8.2481D-02 -1.0463D-02\n",
      "     -5.2168D-02 -1.0215D-01  3.4479D-03  4.5416D-02 -8.2128D-02 -1.1795D-01\n",
      "     -8.3821D-02  1.1303D-01  1.5556D-01  1.3778D-01 -2.0683D-01 -1.8013D-01\n",
      "     -4.5219D-02  2.7712D-01  3.7081D-01  3.0299D-01  1.6923D-01 -2.2726D-01\n",
      "     -3.5199D-01 -1.9120D-01 -9.8336D-02  3.0199D-02  7.3370D-02  1.0653D-01\n",
      "     -1.8048D-02 -8.3303D-02 -4.1078D-03  1.7991D-01  1.8472D+00 -2.7335D-01\n",
      "     -1.4100D-01 -5.0234D-02  1.0824D-02  6.1126D-02  2.4259D-03 -5.6143D-02\n",
      "     -1.3504D-02  3.6742D-02  5.4918D-02  4.1521D-02  4.8852D-02  1.2026D-02\n",
      "      2.1826D-02  3.7214D-02  6.4003D-03 -6.4602D-02 -8.0287D-02 -7.7408D-02\n",
      "     -3.4082D-02  5.9597D-02  1.0828D-01  5.9228D-02 -2.8435D-02  3.8996D-02\n",
      "      1.1219D-01  1.3347D-01  1.8421D-02 -8.3194D-02 -9.9196D-02 -1.7442D-02\n",
      "      9.8816D-02  1.3355D-01  1.6200D-01  1.5631D-01  1.2260D-01 -3.4337D-03\n",
      "     -4.4452D-02 -6.3482D-02 -6.5725D-02  5.3368D-02  9.5128D-02  1.6155D-01\n",
      "      1.3465D-01  8.7807D-02  3.0429D-02 -3.2640D-02 -2.1165D-01 -1.9101D-01\n",
      "      4.5370D-04  1.3800D-01  2.5624D-01  9.4514D-02 -1.9527D-02 -1.2575D-01\n",
      "     -2.9201D-01 -3.1085D-01 -3.4562D-02  2.0734D-01  1.7127D-01  1.4080D-01\n",
      "      3.0851D-02  9.0079D-02  7.7818D-02 -1.4621D-02  6.7962D-02  3.9100D-03\n",
      "      1.1899D-02 -2.2009D-02  5.3536D-02  1.6452D-01 -2.2181D-02 -6.6859D-02\n",
      "     -6.5336D-02  2.4862D-02  1.0533D-01  1.4971D-01  3.4064D-02 -8.9108D-02\n",
      "     -9.3464D-02 -1.1467D-01 -2.9339D-04  1.0049D-02  5.4962D-02  1.3087D-01\n",
      "      1.1352D-01 -3.5677D-02 -3.4286D-01 -5.2359D-01 -5.3752D-01 -5.5284D-01\n",
      "     -4.1007D-01 -2.5143D-01 -1.4446D-01 -1.0156D-01 -1.7315D-01 -1.2460D-01\n",
      "     -1.2846D-01 -1.1859D-01 -3.4306D-02  1.4613D-01  3.2358D-01  4.1184D-01\n",
      "      5.5884D-01  7.3508D-01  6.6215D-01  6.5046D-01  5.8463D-01  5.4536D-01\n",
      "      3.5068D-01  8.6003D-02 -1.2324D-01 -2.8318D-01 -3.3761D-01 -2.4352D-01\n",
      "     -1.1201D-01 -1.5243D-01 -5.8430D-02 -9.7371D-02 -8.2866D-02 -3.4756D-02\n",
      "     -2.6033D-02 -1.5151D-01 -8.2514D-02  4.6157D-02  2.0040D-01  1.6606D-01\n",
      "      2.6108D-01  3.3704D-01  5.3802D-01  6.1173D-01  6.3263D-01  6.5440D-01\n",
      "      5.4937D-01  2.8180D-01  1.0406D-01 -1.0390D-01 -2.5806D-01 -2.3514D-01\n",
      "     -1.7425D-01 -1.7301D-01 -3.8656D-01 -5.9648D-01 -6.2163D-01 -5.6215D-01\n",
      "     -5.1250D-01 -4.4452D-01 -2.1255D-01 -2.1885D-01 -1.7938D-01 -1.7868D-01\n",
      "     -1.9044D-01 -3.2609D-01 -2.5510D-01 -2.2287D-01 -2.0179D-01 -2.8649D-02\n",
      "     -7.3725D-02 -4.3856D-02 -4.9321D-02  2.7932D-02  4.7936D-01  7.3975D-01\n",
      "      8.8500D-01  7.0647D-01  1.0922D-01 -6.0595D-01 -1.2471D+00 -1.3762D+00\n",
      "     -9.8887D-01 -3.1684D-01  3.9132D-01  7.3971D-01  7.7361D-01  4.2858D-01\n",
      "     -1.9550D-02 -5.3983D-01 -7.8645D-01 -7.2317D-01 -2.6211D-01  5.4024D-01\n",
      "      1.2032D+00  1.4851D+00  1.2237D+00  2.1298D-01 -9.5765D-01 -1.1374D+00\n",
      "     -7.3139D-01 -4.6895D-02  5.5984D-01  7.3056D-01  4.2598D-01 -8.3181D-02\n",
      "     -4.2652D-01 -6.7217D-01 -9.4530D-01 -1.1915D+00 -1.2544D+00 -1.0725D+00\n",
      "     -6.7648D-01 -1.4202D-01  3.6603D-01  8.0378D-01  1.0242D+00  1.0192D+00\n",
      "      7.8725D-01  4.3361D-01 -1.6426D-01 -7.8730D-01 -1.3068D+00 -1.4259D+00\n",
      "     -1.1901D+00 -6.5009D-01 -8.2966D-02  5.0235D-01  9.1220D-01  1.1753D+00\n",
      "      1.2673D+00  1.1864D+00  1.0984D+00  9.5758D-01  8.1383D-01  6.0422D-01\n",
      "      5.0864D-01  4.7233D-01  3.9625D-01  2.5245D-01  1.0522D-01  8.8780D-02\n",
      "      3.4389D-02 -1.4454D-02  5.1267D-02 -1.0548D-02 -7.8319D-02 -5.3955D-02\n",
      "     -1.6064D-01 -1.8325D-01 -1.6475D-01 -1.4313D-01 -2.6287D-02  5.6053D-03\n",
      "     -1.4663D-01 -2.4929D-01 -3.6556D-01 -4.3529D-01 -4.2877D-01 -4.0363D-01\n",
      "     -2.9532D-01 -2.1768D-01 -2.2216D-01 -2.6383D-01 -2.6582D-01 -2.4058D-01\n",
      "     -2.4479D-01 -1.9036D-01  9.5579D-03  5.3956D-02  1.5150D-01  1.4214D-01\n",
      "      8.0991D-02  1.2457D-02 -5.7660D-02 -6.9423D-03  1.5484D-01  3.3384D-01\n",
      "      4.7204D-01  4.8836D-01  3.6842D-01  3.6080D-01  2.9662D-01  2.7299D-01\n",
      "      2.2760D-01  8.3471D-02 -1.3914D-02 -1.7765D-01 -3.2847D-01 -4.2621D-01\n",
      "     -4.2308D-01 -4.2715D-01 -4.7838D-01 -4.2243D-01 -3.6863D-01 -3.8517D-01\n",
      "     -3.3370D-01 -2.0221D-01 -1.3264D-01 -1.0891D-01 -1.3509D-01 -2.5365D-02\n",
      "      6.8137D-02  1.6426D-02  1.0470D-02  1.0442D-02  6.9633D-02  1.8566D-01\n",
      "      2.6067D-01  2.2988D-01  1.6501D-01  1.0115D-01  1.0078D-01  3.6236D-02\n",
      "     -4.8936D-02 -4.6043D-02 -1.5510D-02  4.3205D-02  8.9747D-02  8.0188D-02\n",
      "      1.5973D-01  1.6589D-01  1.0880D-01 -2.3624D-02 -7.7366D-02 -1.1596D-01\n",
      "     -1.0447D-01 -2.9339D-02  5.0093D-02 -1.2786D-02 -1.5040D-02 -8.5207D-02\n",
      "      5.1411D-02  5.6299D-02 -2.9193D-02 -2.5657D-03 -3.8174D-02 -9.4272D-02\n",
      "     -5.2680D-02  5.4322D-02  6.1386D-02  1.0306D-01  4.4303D-02  2.4140D-02\n",
      "      8.0645D-02  3.7119D-02  4.1966D-02  3.5686D-02 -1.2734D-01 -2.1231D-01\n",
      "     -2.3471D-01 -1.3637D-01 -2.6938D-02  2.0544D-02  6.9201D-02  1.2003D-01\n",
      "      8.6929D-02 -7.5926D-02  4.0252D-02  1.1470D-02  3.7519D-02  5.8537D-02\n",
      "     -9.5581D-02 -8.5038D-02 -9.0028D-02  6.0284D-02  1.5655D-01  1.6096D-01\n",
      "      5.1600D-02  1.6570D-02  1.6142D-02 -1.0795D-02 -1.3516D-02 -1.9571D+00\n",
      "      6.3440D-01  3.9725D-02 -1.0681D-01 -1.2445D-01  3.1761D-02  3.7486D-02\n",
      "      1.2205D-01  1.2531D-01  1.5515D-01  1.1110D-01 -3.0487D-02 -4.9096D-02\n",
      "      2.3001D-02 -5.6957D-02  4.8414D-02 -8.5306D-02  2.5551D-01  2.7156D-01\n",
      "      9.4919D-02  3.7733D-02 -1.3714D-01 -3.4418D-01 -8.7229D-02  6.7166D-02\n",
      "      2.1448D-01  1.3953D-01  1.0695D-02 -2.6508D-01 -1.4274D-01  3.2803D-02\n",
      "      6.8580D-02  8.6489D-02  2.4075D-03  1.2346D-02  4.6887D-02 -1.0071D-01\n",
      "     -3.1197D-02  1.5667D-01  1.4966D-01  8.1154D-02  1.4399D-02 -1.0997D-01\n",
      "     -1.7132D-01 -3.1131D-01 -1.1504D-01  6.5801D-02  2.0018D-01  2.9862D-01\n",
      "      1.2671D-01 -1.0418D-01 -2.4014D-01 -3.0365D-01  7.9766D-02  1.4625D-01\n",
      "     -8.1266D-02 -5.4706D-02  2.2612D-02  1.9780D-01  2.4642D-01 -2.5433D-01\n",
      "     -1.7188D-01 -1.4119D-01 -1.1756D-02 -1.4196D-01  1.9568D-01  2.5667D-01\n",
      "      9.3258D-02  8.9649D-02 -2.8920D-01 -5.2576D-01 -2.5275D-01 -9.6556D-03\n",
      "      5.8105D-02  3.2180D-01  3.8958D-02 -1.1137D-01 -1.7247D-01  1.1383D-01\n",
      "      3.6609D-01  4.0113D-01  2.6597D-01 -2.4499D-02 -4.4376D-01 -3.1979D-01\n",
      "     -1.8933D-01  5.8420D-02 -1.6985D-01 -2.2447D-01  1.0769D-01  1.3292D-01\n",
      "     -4.6929D-02 -1.1845D-01  2.1354D-02  1.7826D-01  3.8209D-01  1.5627D-01\n",
      "      1.3746D-01  2.2327D-01  2.3726D-01  1.1139D-01  7.1115D-02 -5.9064D-02\n",
      "      1.9420D-02 -1.7342D-01 -2.1592D-01 -1.6135D-01  8.6275D-02  1.9101D-01\n",
      "     -1.5739D-01 -5.2406D-01 -3.6660D-01 -2.9948D-02  2.3055D-01  4.3205D-01\n",
      "     -1.1342D-01  3.4760D-02 -1.1277D-01 -2.0598D-01 -9.0006D-02  2.3041D-02\n",
      "      5.0513D-04 -1.2413D-01 -8.4875D-02 -4.6870D-02  2.9296D-01  3.6899D-01\n",
      "     -2.9119D-02 -3.0416D-01 -2.8874D-01 -2.6639D-01  1.4724D-02  1.0968D-01\n",
      "      1.1677D-01  3.8613D-01  3.5748D-01 -3.7755D-02  2.3791D-01 -3.0545D-02\n",
      "     -6.8589D-02 -1.0756D-01 -2.1827D-01 -9.2137D-02  1.3446D-01  1.8443D-01\n",
      "      2.3039D-01  2.4011D-01  2.4639D-02 -2.4759D-01 -2.8855D-01  7.4551D-02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/moon/.local/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   27.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   27.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=200, random_state=42, verbose=3)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log=LogisticRegression(verbose=3, random_state=42, solver='lbfgs', max_iter=200)\n",
    "log.fit(train_data_scaled, train_label.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cb002455",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8905634920634921\n"
     ]
    }
   ],
   "source": [
    "#Accuracy of Log Reg\n",
    "log_acc=accuracy_score(train_label, log.predict(train_data_scaled))\n",
    "print(log_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6a724619",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8905634920634921\n"
     ]
    }
   ],
   "source": [
    "#Recall of Log Reg\n",
    "log_recall=recall_score(train_label, log.predict(train_data_scaled), average='macro')\n",
    "print(log_recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "583ba70c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8903275107769212\n"
     ]
    }
   ],
   "source": [
    "#Precision of Log Reg\n",
    "log_precision=precision_score(train_label, log.predict(train_data_scaled), average='macro')\n",
    "print(log_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c567ef04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8903496921399212\n"
     ]
    }
   ],
   "source": [
    "#F1-Score\n",
    "log_f1=f1_score(train_label, log.predict(train_data_scaled), average='macro')\n",
    "print(log_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6e2c70fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure CM-LOGREG.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5928/1930540345.py:28: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix\n",
    "confusion_matrix(train_label, log.predict(train_data_scaled))\n",
    "\n",
    "plt.matshow(confusion_matrix(train_label,cls.predict(train_data_scaled),normalize=\"true\"),cmap=\"gray\")\n",
    "save_fig(\"CM-LOGREG.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "36ea85bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.predict([train_data_scaled[556]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a1b71ad0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(train_label[556])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}