Data cleaning and labelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Dataset Cleaning and Relabelling</h1>\n",
    "<h3>Intended to curate the final labelled dataset for the CMB and Non-CMB patches in order to train the classifiers</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current Working Directory: /Users/shwetankpanwar/Documents/BTPData/python_scripts/cmb_segmentation\n",
      "The file structure creation failed. Structure already exists!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pfca import init_path\n",
    "from pfca.visuals.img2D import imgplot\n",
    "from pfca.core.preprocessing import *\n",
    "from pfca.core.processing import *\n",
    "from pfca import file_read as f\n",
    "import ants\n",
    "import skimage.feature as feature\n",
    "from time import time\n",
    "from pfca.exp.results import roi_snipping\n",
    "from pfca.exp.dataset import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_dir, nifti_dir = init_path()\n",
    "cur_path = os.getcwd()\n",
    "#patients_list = f.get_immediate_subdirectories(raw_dir)\n",
    "#Curating the patients list on my own since I don't have the raw data folders with me on the local machine\n",
    "patients_list = ['1','2','3','4','5','6','7','8','9','10','11','12','13','15','p1','p2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the dataframes with the RST outputs \n",
    "#pdata : Dataframe containing all the peaks in the positive data\n",
    "#ndata : Dataframe containing all the peaks in the negative data\n",
    "pdata = pd.read_pickle('RST_outputs_all/rst_output_all_subjects_2_2_0.1')\n",
    "ndata = pd.read_pickle('RST_outputs_all/rst_output_all_subjects_negative_2_2_0.1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of detections in the pdata list: 605\n",
      "No of detections in the ndata list: 1994\n"
     ]
    }
   ],
   "source": [
    "print(\"No of detections in the pdata list: \" + str(len(pdata)))\n",
    "print(\"No of detections in the ndata list: \" + str(len(ndata)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndataset = dataset_management()  #Adding the negative RST data to the dataset class\n",
    "pdataset = dataset_management()  #Adding the positive RST data to the dataset class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#store all the snippets for manual labelling in negative RST dataset\n",
    "#store_results(ndata, 5, 'rst_output_all_subjects_negative_2_2_0.1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelled_ng = load_labels(ndata, './visuals/RST_snips/rst_output_all_subjects_negative_2_2_0.1/labels.csv')\n",
    "labelled_ps = load_labels(pdata, './visuals/RST_snips/rst_output_all_subjects_2_2_0.1/labels.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no. of Cerebral Microbleeds in -ive RST data: 66\n",
      "Total no. of Cerebral Microbleeds in +ive RST data: 125\n"
     ]
    }
   ],
   "source": [
    "print(\"Total no. of Cerebral Microbleeds in -ive RST data: \" + str(len(labelled_ng.loc[\n",
    "    labelled_ng['label'] == 1\n",
    "])))\n",
    "print(\"Total no. of Cerebral Microbleeds in +ive RST data: \" + str(len(labelled_ps.loc[\n",
    "    labelled_ps['label'] == 1\n",
    "])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>47</th>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "          RST_peak                                        image_patch  label  \\\n",
       "47  [151, 102, 63]  [[[1945.9607, 1910.2585, 1863.1324, 1812.476, ...      1   \n",
       "52   [131, 85, 69]  [[[1325.6112, 1283.7367, 1238.4597, 1224.3926,...      1   \n",
       "56  [87, 135, 117]  [[[1801.9604, 1786.9603, 1756.9005, 1715.188, ...      1   \n",
       "58    [60, 77, 47]  [[[1199.5022, 1073.3145, 1000.4437, 938.7167, ...      1   \n",
       "\n",
       "   patient_name  \n",
       "47            1  \n",
       "52            1  \n",
       "56            1  \n",
       "58            1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Additional cleaning of positive RST dataset\n",
    "#Displaying all the CMB marked points \n",
    "pdata.loc[(pdata['label'] == 1) & (pdata['patient_name'] == '1')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdataset.dataset = pdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('1',58)  \n",
    "#This is not a CMB .... Need to remove it from the dataset labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the wrong labelled points to zero.....\n",
    "pdataset.dataset.loc[58, 'label'] = 0\n",
    "pdataset.dataset.loc[56, 'label'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>[151, 102, 63]</td>\n",
       "      <td>[[[1945.9607, 1910.2585, 1863.1324, 1812.476, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>[131, 85, 69]</td>\n",
       "      <td>[[[1325.6112, 1283.7367, 1238.4597, 1224.3926,...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RST_peak                                        image_patch  label  \\\n",
       "47  [151, 102, 63]  [[[1945.9607, 1910.2585, 1863.1324, 1812.476, ...      1   \n",
       "52   [131, 85, 69]  [[[1325.6112, 1283.7367, 1238.4597, 1224.3926,...      1   \n",
       "\n",
       "   patient_name  \n",
       "47            1  \n",
       "52            1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remaining labels in the patient 1 are now correct\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '1')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>[122, 33, 72]</td>\n",
       "      <td>[[[1774.0656, 1767.2006, 1729.4329, 1708.2155,...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RST_peak                                        image_patch  label  \\\n",
       "37  [122, 33, 72]  [[[1774.0656, 1767.2006, 1729.4329, 1708.2155,...      1   \n",
       "\n",
       "   patient_name  \n",
       "37            2  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient 2\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '2')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('2',37)\n",
    "#The labels in the patient 2 are completely correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>[77, 38, 98]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RST_peak                                        image_patch  label  \\\n",
       "227  [77, 38, 98]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...      1   \n",
       "\n",
       "    patient_name  \n",
       "227            3  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient 3\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '3')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('3',227)\n",
    "#The labels in the patient 3 are completely correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>[150, 100, 75]</td>\n",
       "      <td>[[[1993.5889, 2027.2733, 2019.5259, 2015.476, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>[60, 141, 51]</td>\n",
       "      <td>[[[1885.6003, 1853.675, 1800.5067, 1746.5947, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>[53, 49, 81]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RST_peak                                        image_patch  label  \\\n",
       "212  [150, 100, 75]  [[[1993.5889, 2027.2733, 2019.5259, 2015.476, ...      1   \n",
       "214   [60, 141, 51]  [[[1885.6003, 1853.675, 1800.5067, 1746.5947, ...      1   \n",
       "215    [53, 49, 81]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...      1   \n",
       "\n",
       "    patient_name  \n",
       "212            4  \n",
       "214            4  \n",
       "215            4  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient 4\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '4')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/av5cVqpIqzjw/pP9b21tZSKZ6XSaQg1ccPPoo49mYQw898svv5xlhLnTKBXHJUrfBe49BLMLBAKBwMojmF3gHWNzczNJw8ka1tfXU75DrtTV5+PZ9V2mrvuoz0ZFK8Cin8nZRYmxtVqtTDBSyiTifqbSNl5HBRfK7EoB4OyX+wQJDRcoZVXxNiuDdObYarWyrCqaZ9OL0Jb8kof5+JrNZvZdqWaft09zg7pYqFRAF0D2fKmgx60E3/d935eOf+GFF7J+vBvoOLslQfumeUk5ri6qCgHNvYVgdoFAIBBYeQSzCywF2RdX1NPpFE8++SSAuRJwY2MjrcJdXj6ZTDJfGn0w7XY7y+KvKcQ8lVir1coYSmkVr+zCZfWE+lhclajHsV9a6aBU/XsZO1R1ack/5T4u9Qm6orPT6WSMtiTx1+v4+Z1ll6CMW4Pw3bep52QFBA9eV5bkwf6agkxznrriVsfLfby0IDz88MPY3d0FAFy6dAnA4QxP/b/+fGoFCW7b2trKQi+UqXI/Xltr/WkqtMB7i5jsAhkee+wxAPMfO4usPvzww+mFqT9oDR0AFjOb8OXAEATuq6ZHfaF7sVMVRvDaXpRUc0rq5KWZT3Sbwichjefz4z05M8/pE5rmqSyV1+G53OzXbDbTi9wnPTXj+jk1n+dhWVx08l4mhNH4RH52u910rJcs0jybJWGPx/jpp5uJ67rOzLi6P03bvrgA5jk3+VxevXp1oR1+Lp+82IYHH3wwLe74nG1tbeHkyZMA5jGOnMSazWZ63pm0/OLFiwCAN998M/XHE2jH5Pe9R5gxA4FAILDyCGYXADA3Xz366KN46qmnAMzL82guRq5oyUDqul7IAALMmU2/30/nJbNTUxpl6MpivCwNV8L7+/uZvF5Nbi4EUYEK26yhAMvMXHVdZ8Hbh7ElZ4b6nYYLOFscjUYZG6vreiFH57JrOlMtmd4ajUbGIrQoq/dfzXl+H5VxOctVk6jnz1QTn37Htngflb27yVbvNa0DvK+bm5t49NFHASCZM1utVmJhWgkDmJlleY7HH388nQOYJUTw3Ktra2tpu5+r0WikcAm2naEb29vbSTjjZmll14HvDW57squq6mUANwFMAIzrun6mqqrjAH4FwGMAXgbwY3VdRxbXQCAQCLwnuFPM7jN1Xb8pf/8MgC/Vdf3zVVX9zK2//9odulbgDoArW660P/rRjwIAnn766SQ+4apai5iqwASYsT36SZTRETyXM5uSnL3RaGTMTv10PJarbA9m5jn46aEJ6rNzZqIMxVfcKmZwX5oKWghlM8tk7IPBoCiSUP+lHndYSIS2V9vn4gtlFx6WoH49F7CMx+MsHRtZk17f+6r3s5TqjefwihilPqrYxa+7t7eX6t1p2Msbb7yxsD+fpVOnTiX/2kMPPbTQrm63m6Vj6/V6yV+ozzb39yB8Pp/6vDC1GY/Xgrh3OyA+MMPd8tn9KIBfvPX/XwTwp+7SdQKBQCAQeFvcCWZXA/j3VVXVAP7vuq6/AOB0XdcXb22/BOD0HbhO4A7h2LFj+KEf+iEAwLlz5wAsJm/2FSc/2+12WuGrOs/9OboC9woF9NOtra1lLEn9bARZ5XA4TKxSA3p5/GFBzx7kq/s7s9Ng7xJTKdWBKyk62R9nI7wumbW2VX1jpSTJ7kMkNIxB2VxJ0clz+pjwvqo/TwPV3f+pad38XKX0ZyUWy3O99dZbAGaMyNWoyl6d7RGj0SgpIOmzGwwG6VzO4judTkoq7VXWtY/KmMnWnOGVnn9lhDwvFc5keMqMPWQncHdwJya7/7qu6wtVVZ0C8MWqqp7TjXVd17cmwgVUVfV5AJ+/A9cPvEMwg/wnPvGJVIXATYLT6XQhowmwaOrixKd5F/l/z8ahghBCxR88F/dTcw5fTHypTKfT1NZSrNsyYYu2Sz/dxKcvUDW16raSGEXb4y9hjRt0wUWn08lk/yoOIdRc6pMJ0Wq1ijFuy9o9mUwyE6i2wY9bW1tbWtlBQ0+8mO50Ol0aZ9lqtZLJkZPLhQsX0mKI8n+2bzAYLIiVgHm4wc2bN7PMK8PhMBsTLuharVYSX9EEWQp/0BJPuuDTz6qqshykOil7PtNPfepTAIDf+I3fAFEKoQncedy2GbOu6wu3Pi8D+BcAfhDAG1VVnQWAW5+XC8d9oa7rZ+q6fuZ22xAIBAKBwGG4LWZXVdUGgEZd1zdv/f+/AfA3APwrAH8BwM/f+vyXt9vQwHePs2fPAgA++9nPApg74zc2NrJAcGI8HmdmLGUUNBPR9KRsjCttmnHUjMdzcFVeMvGNRqPMHKmZKpxxlOTyek7NMKJ/67G+TYOknSXqNm1fiWnxGh5wrcyolPXEx14FHh60r2Y57/OyMQEWs5e4yVoFKtpWjlPJxOchFG7+BuYMXXNdnj49827wudzd3U3PlT9Lk8kknY8mwJ2dnXRuf56V2bJdZJKtVivtT7an/XJzuY6Fmxp1f34qw/VsPLzupz/9aXzpS19aaHOn08kEMIE7h9s1Y54G8C9u/cBaAP7fuq7/v6qqfgfAP6uq6icAvALgx27zOoFAIBAIvGvc1mRX1/V3AHy08P1bAD57O+cOvDtwlfxH/+gfTfJqrl75ubGxkfnJ+He/38+k0GQLe3t7eOWVVxaOG41GaTud/rzucDjMUjW5b0X/f3BwkFbTJcZFVsiVPcUCmoNSc1x60LL6RlzQoaEOywLG1Z+i+7uPTxnnskz4mv6rVPVAg8+BRb+Z+xnVB6c+u1J7eG3vG+/nYDDIArtV9k/moQHnXquQ7EWZjYYJAMCVK1fS/fzQhz4EYFbN/tq1WTguWRvTfik79SoTnU4n+d60z16Fg+3c2tpK1+Y+9AdrSIgydF6TY6P3ic+h5//sdDppLHg9Zew/8AM/AAD47d/+7TSW3D8Y3p1HZFBZEVDt9WM/NiPRzWYzvQA4+WhxVX8R8O+rV6+mFw5/2G++OQuhfOmll9KPUHNePvDAAwCQ8gfyx379+vVMCUioKU0nITffafJnmrbYVops1tbWMtNbSdl5WFaSEjxPoyoCtc0+oeuk5GZiNYe5qEbP60myB4NBptrjp4o3KPBQcZCPQ6PRyOLeeC80U4kuNFT4of0YjUZZInA+D5qg2vs/GAzSRHb+/HkAM5Mln1nPg7q/v58thLQclN//wWCQmXG1+Kub1bXNLgTSZ4TtKcVPull5a2srG1dVYbJEEeMBX3311SxjjBf9Dbx7RG7MQCAQCKw8gtmtCP7Mn/kzABZz/LFaAZ38pWwcBFe4N27cSMzuwoULAIDXXnsNwGy1TFMlr3PixImsIKuGMXAlq4VDgcU8iJoP0s1+Ktkn66FIplSCpdQ3N1keVv1As4v4uZSl6f5exsbZkh6rggVndqWsLyqI8D5qOz3/Z+nahIZesK28BzrOeh0NAdHj9Npkalqo10MOiM3NzXRNmiyHw2HWHs3As6zqw/b2dnoGOZZqEndGeOLEiYUQCO2PFsItsTdem+Nx5MiRTMiksZQa7qDtG4/H6Vyf+cxnAABf/OIXcfny5YV+B7O7c4jJLhAIfFf4sz/7s9i6pZq8HVzf3sbf+emfvgMtCgTeHjHZrQjItLgiPHPmTPqOK1plKlxVUjDADBSXLl1KK/QrV64s7Hvs2LEkF6dvRTOhECqqIGPU+l/ATFpO1kefotZNc5GEyt/ZnlJNPWVJzlDILrVunjOuZrNZzOhPeLiAStxLQdwlfxkwW9m7/0dZpRdaHY1GGXMo5Y1U4YgH+WtYh8vydWy9H1qNAQC23noL/+Dv//3UPmd26je8fv16NobA7B787M/9HHq9XhaWoP3nddXP5gKqzc3NJBJRMN8rfb0UV3U6nbQ/nxt+DofDLKtMq9VaqLAAIFk/dGy85l8pjEX9jZ4l5vu///szQQ7byWc98O4RPrtAIBAIrDyC2a0AHn300SxIWtNxeRqnyWSS5SUki+v3+4ntEVRsbm1tJZ8dV++q7PT6Yd1uN61MS3XQeJxXsdb9NOBWg3x1/8FgkKWG0tp4hCopXe1ZYknunypV8VbVZylTfylwHJjdJ2evJSapUnoPEynlBtVwAWe2Os7LqjFUVZWpPTUvpytPe71eYkVk/dpHjgWfM1WQAjPGoqza75kye/eNKjvlc69hAJ/85CcBzJkdrRdA7kNUpauzXa0Er0HuwGJ6PULvq/us9Xfgat9jx46lNGZkjqpODf/d7SEmuxXA6dOn00uIP2IttOqCifF4nGWh0Bc196NznXkKt7a20sSn1/OXKc+9traWXj6cHGnW0vgkfQm7Cc3FAro/XzxqetISNDyG+xEu2PBtPpmUXlDaf5+ES4IGn+z0GH05ekkcjynT/+vk6C9hnRxKE26pVJG3Tc2G3g9tly92aHI7e/ZsyuLDPvIlrnkuS2EcHm+meSa93NBgMEjPui602B4+czznZDLJfhMqpNEQHX76ODHM5ubNm1nMnoY1uDlaQxHYVp04n3lmlj3x13/916E4ceLEwmQd+O4RZsxAIBAIrDyC2b0PobknAeDxxx/PCq5qVg0tTAnMVqo3btwAMJfxk+Ht7++n/ci8yPA2NzcXzITAYhAu9+fx6+vraRVOdsh2Xrt2LcuzOB6Ps4zzyl48I4g6+90UqGYfL1Rbyp+pq/JlJXuqqkrtUul6qa3c3wOTtWyOmx6V2XguSv2OUGZQYqOah1LbNxgMMrGPimR8m5rjPLPL3t5eYl//6T/9JwBzc+GJEyfw5JNPAkB2PPu1t7eXxlKz1xBaUcHNxBp64Sb78XiMr33tawCQJSNQ06Ozcu3/YSZHzRbD35IGjPNvF9oQu7u7iRHSbaCJA/g7URHTqVOnACCFJwS+OwSzCwQCgcDKI5jd+whkTo888ggA4Cd/8icBzFaQXi1A/VIuRhgMBilQnFJnZRe8Dv1z6lvjyrOUeozgPioC8JXtdDpNachcEKHn0O+WBQKzHQpNF+XsRcMFCPXheD0/FdKwDbq/70eUBCpeucDPT3iuy5JUXc/luRSVVZL16Bi50EYDsH3s1b/kuSGVefr9vHnzJr71rW8BQJZ4gO29ePFi2tbr9bLUXir/9zYQyq71OzImXot+406nk87hBV7VZ81x0/RqnsZNGefrr7++sG06nWY+WJ77rbfeSoxT7x377zUiq6rKfM+B7w7B7AKBQCCw8ghm9z6BKhU/97nPAVgMiHXGoMzBV4mvv/56YnaltEz0r3ElTHQ6nRRU6yti/b+monJfldYRU78Nr+214TSAWP1KQDmLvzIhl/Yrm3Nmp+fkSptt5tgo69GVfenahLMxHSv3QSrj9OTKqq70IHFllMrQ2Ub6ZbXKtvugtA9eJV6l/dyPz4ayLCovn3322dROV5NSSs/vd3d3FyopOANSNa+HJWiKL/f1qVWBbeVnqT96n9gePp91XWfKYWXGvGdaOR2YjTOfHU2AwOP4He9Po9HIfOIKXvODH/wggHkavwhJeGeIye59grqu8Qu/8AsA5mIPThL6stN4Hv4IPIvF66+/nn5MzF7CH9loNEoTGsEXw+bmZnphqHiBLyh/sWuJH4dObGridKGBThLu7Fczm5tqu91uJlrRF03J5MhPF47oi9TPqfksiZIJ2SdxvWbJvKgv2mVt0AnK4wY196K3eTKZZOV4SrlFtQKD59JUMza/+9jHPrbQv5deeilNEvzk/eVztLa2tlASx2Mc1UTM54zn0smfE4yPI5BXSShNKmq6LVVQ4Fh7fF5d10mYwnvmIRJAXiR3Z2cnTXI6JiXxDT/9flI4RvNx4HCEGTMQCAQCK49gdu8TPPTQQ6l+G6HmP2dVo9EoC6pV5zqDYrlK9jpyQB6y0O12M7OPFqgk9Dqe65JtVrapzIbXcmZXVVVaOZPZaiCxC2A0G4cH+6qUvCRUoenJjxuNRhnDUWZD6OrcTW96vJshlY14/7V/hxV9VUawrJaeCprI7DW3pptJS5ltCK3ewHN9+tOfBgA89dRTWagCBSu8z5ubm+l6KlA57L64WGR3dzc9687YtR9Eq9XKQjx4vcFgkLErYPEZ0HOOx+MUeuCWCu2Pt2F9fT1ZUGg2PXnyZHq2aeL85je/mfWHfeR4aFmWd1MAACAASURBVD7XwHIEswsEAoHAyiOY3fsEf/Wv/tXsO/UpOLsYDoeJRWmGdmC2Al8mY242m+m8HlyuK2JdqS5LvaQiCX6qb4WMUP0SGkSs5+p0OtnKWVljKb2WswTuX0oXVqquwHOqvJ5wgYNeW+HilVJNvFL9PGdEGxsbS/NZAvPxUv+kp6gitIKC90OD0Uu5N7WqAs+lEn1g/rx84AMfWBCiAPMcrLzPR44cSSx/Op2m59JDPNbX19MxHBOec29vLwuJUQEQGZSGV7j/T0MEnGmrj89Z397e3oLARMe0VDdQ06FpKBDbye30aX7qU58CAHzta18rWl/YB/dZBnIEswsEAoHAyiOY3T0OhhkcP348k9Lzb1WLqRLSJdSaxd1XqBoI7itirn7VP1VSB3KFT3+D1uBy5lHK1A/kqa3U/0G/jIdSKBsl6rrOAqDVh7fMn6UqRkdJ4l/yyymj5H7u1/Rr6qeen2N47NixYmiD76+VATwcRceeDMqVjer/VSbsbFqfJVfQahC7V15gyite94EHHlhIe+a+RK8yDswrKLAt6+vrydel/jwew3AHDbdQhTHPwU+3AJQCzbnPzs5Ouib7pky35EvW8fZ20RrDT47TU089ha9//esL48xzHD16NLHLw5Kc3++Iye4eAx/8n/iJnwAAPP3002mbTm76OR6Ps+zq/X4/TQ6HFeh080+v10vf8UWgJhx3tKuggT9QOuz1x+4vYzUXqrmNLw6vWKBmV++Xmp405ogvnVIc3LKCq5oH0rNylEyWmkFf49h0H/ZX26zxeXrvXBSiE5abJTUswSf7ZrOZlcJRU+2ZM2cAzMU+Gs/IZ4ETrVY9mEwm2D91Cn/yR38Ut4sbx47hyJEjC1lD2Gb2XydVX7TpAqIkOFFhlY5Dq9VaeN71OPZRz6+5NLU0Ebd5piFmJdrZ2VmIBdQ2aOYhFcf4ZMfrNJvNVELpwoULC+dUMVbE3C1HTHaBQOC7wm/8o39UTLhNlALU+YLm38ePH0/+u0Dge4GY7O4hNJvN5JB+4oknAGChmoHL97nK1ABtmi5v3LiR5QTUVawXwqSpp91up5WwixeUjWlRSrICtstZIzBnNG42AhbruXktMV3hs4002ahwxM1lakIjNM+ii0J0m2dQUVbjZtySqdLl7DqGnrnE+6qCHB0nLYTqIhHdpmzBaxyyP5ubmymMxRmnmmW1uobn19Rx9swu3Lff7ydxFNmOFiNlu1gQWLOX+D1uNBrp/wym5nW1Jp6ycS3k6mPObV43cTweL5iT2S6/f1pRg/ed52cfdnZ2lhbobTabCzX92GcKTbzY67Fjx7LsMvossujrK6+8gkAZIVAJBAKBwMojmN17CK7MaPP/wR/8QfzQD/0QAGSrxUajkVaMZFWasd7l9f1+Px3rzE5FKM7iSqICriibzWY6v7ILl7Gr38VzKSqj44pWfV5sj4s3NHiZ19F6YGQMXlOPY6Hn0vpvzt5K/jxCA7VLFRR89a+sx5lkXdeZr286nWZhDiqXL7FC/u3B1MpQPP3V2tpaFhKg99XZ3vr6enZNzWe5TJi0u7ub/Lfso7KykkjGGRD7sLOzk1gPmSCfqVartcCAeTy/8xyv2ke/jrI4HUv3l+rvh//3e9dsNpO1w9m4HqdCMm+PPiP87qMf/SgA4Bvf+EbW5g9/+MMAgG9/+9sILCImu/cQTJz7mc98BgBw5syZzOSiLyrPRnJY+ZeSE56fCjf1TSaThfgibcNgMCiKI3xCU5OgZ2Hhvjdu3MiOKwlNVKDhLwCO1cHBQWojX3Bra2sLohNgsZSKZ47hi0qzUfi90IldJx7PvUlonNWy8jSKqqqyJMyqfiypMIFF82cpTyrPpe1jbJvHLqq5XMUVuhjid8Bs7JmbkSVuuBjTYqZcjLANBwcHC0Vbuc0XNDxHv99P/aCvj/fs+PHjxYTW/uzpIsafDRWOcH+dvPh/j6nrdDrZYkoXDj7JaWydm0vX19dTu/z51Fy3PAcz1fzWb/1W1v/HH38cL774IgJzhBkzEAgEAiuPYHbvIR566CEAc9PO+vp6WlW6M3oymSyV0k+n02yVuLGxkZn2eM79/f10LrK/UvkbXkdji1zirUIDv46urvlJU9TBwUEmIW82m5m5T4/3SgCUzXPsdP+qqhZk68B8de6FThWj0SgzWal50mMcm81mlglGBS3OnDU7h5vsRqNRxkJLUvWSjL1kSvUs/Goi3tnZATC/VyWmSgyHwwW2rmPTbDYTS6QYReMhub+bOAeDQXruNT8nr0PTPu/r5uZmep4JjdPkeGkGllLOUWD27JZCNTgObuJW5qzCFGJZ1QsN8SllF3Kmtr29nfrN/fX34ved2zS/qAvBAnMEswsEAoHAyiOY3XuI559/HgDwR/7IHwGwWC+OK246uA8ODrKVOlej+/v7aSWoQeIaTqDHqV/OP1Vw4SxLcwoS0+k0C8xVvwuvyZU3V6N1XRfDDLzYJ6HMznMw9nq9tLJVRqRZYXT/vb29xDA88Hg8Hicm65n0m81mxi41HMPHTfNTOsNTZsfP8XicMYCSeMOL1ypT0eu5YEJ9tn4dzWLj7EXDK/hJZtjv99MzyuNU7OH5H5X9qsiF13MWynvY6/WWFlDd3d3NxDvKdnmvVWjl4h1C75kmbfDfl1aQ4H7O1Hq9XuY31dyYZF9a/84rOvD30mq1kjCH5+Ln2bNncfHixYU++u80EMwuEAgEAvcBgtm9h+CKkD6L48ePp9Wer9RLikPN9ehpidbW1pZWEFdlH1eeysbcz6bVrLkyVZUgV9wlHxf386zsW1tbmTq0JMfnCvfg4CBLIaa+QWcVk8kkYxPqb/SgXa6S1f/pAcGNRiOrZ6ay/FLIgjMObYvLzIfDYboWmQ2Dv5WNc9yUcXtbNZ8lPzluymY8f+ZkMslCSbTfZElUXqrviWOibJTWBc8fqgpPrcvm7eF9mk6nmeWAOHr0aFa7UPdxn9pgMMgYurJrtxIMh8MFlgcshvj4fec4HD16NPsNaT5Lf8ZHo1Hme1WLgP/2eC9arRbOnTu30I/pdJpydV6+fBmBmOzeU9D8w4ngiSeeSD8ASpz15eVSai1L4hONxpkRal7yJMwlU6ULPHQ/zajhJX70by8cqxlUPJ+fSsI9K8doNEovDF8QsN26TRNhe4kbzRvpLw59MXryXy1ZpCYxf2HqOLiZTJNT8wWqJkG/H5qzUuPK9DpVVWVZbDRMYZm4RP+v/S69TPkdRSh8gWq8nE/GnU4ne1Y1dENNlBzL0phzm8fG6fFuSlaBEqELSM+cwutpSIDmwXTTod4fF23pIoEiKrZLz1MSYXnychXJ+L3V3wNznXLbCy+8kMQuMdnN8K4nu6qqPgzgV+SrDwL43wBsA/gfADDx3c/Vdf1v3nULA4FAIBC4Tbzrya6u628D+BgAVFXVBHABwL8A8JcA/O26rv/mHWnhCoOrN5VZe/5DFVxwZe8FTtX0pKtMN4mQLY7H46w4ppp/XNjBlbuKNzz4V89BHBwcJHbANmvguZt99BylYG8PWlczJduq2Sx4Dme5ymx83EqJjdW05uVs2u32AgPS/RuNRmKA7L8ez2BslqzR8kKaexQATp48mUyCzuI14FgZtzMNbV9JTMNPz/Go7WFbyfA6nU4KoVG2yz6X2D4/NaML2+lZeLhNhVPsq+5zWD5SZ8tajNXbpzis7JOzRu8Hx8HHnp8qqlETp2dVYR83NjYSe6MlgONw5syZlBuTCbfX19eToOVDH/oQAKQg8/u1DNCdEqh8FsCLdV1HFtJAIBAI3HO4Uz67Pwvgl+Tvv1JV1Z8H8FUAP13X9bU7dJ2VAldYXCV3u93kvyNbUDmzO/s1QzrPpSthQp3pwGKAsq88VRLvwhbdpitw35/Xu3HjRpZfU1f4JWe8ClK4H7AYcO/Mq9PpLIRhALOVMxkpg5dVqq5iHWAxLIF+xhJcJDAYDNJKW2XvHDe22YOFr1+/nkQevI/tdjsdS4b2xhtvAJixC/oj+alB4p5CTdlrKfTE01e93Wqffh9+OvMG5inBNMDZi52qz8tTo6kIxS0Bw+GwWLSXbXeBjjJHhz7HLjxRwYkmanDWTmiogvu4S1YPtaDwXmlNQU8vp4IzCk78vbG1tYUHHngAwPz5f+ihh1IfyQhfffXVdJ37EbfN7Kqq6gD4kwB+9dZXfw/A45iZOC8C+FtLjvt8VVVfrarqq7fbhkAgEAgEDsOdYHY/DOD36rp+AwD4CQBVVf0/AP516aC6rr8A4Au39rs/jci38KUvfQnALCE0V2aEKruc2ajk2wOtlSVxP01FxlWlpyfTlWopKbFL40v7q7rQfWkaVOzKSw0X8Bpxa2trmepRA2jZR60IwbEgw+OKttvtZj4bVYY6E9Kq1B5CMB6Pky+0tHrnsa56vHjxYqoIoJJ9jhfZG8fy6tWrOH/+PIB5qApX8zqGHK/d3d1MharhGe6rdX8rMH/2NjY28Nxzz6Xz6pheu3YtsT22x9On8Zo6DqpU5X3RtGTeLmVZ3L9U9V2ZmiZF9/09cFzTmDm70koYrhLWZ8Khvw3v//b2dlaNZDAYZExb/a4aogEsVnMohaxwnHgO9fkdZr1YVdyJye7HISbMqqrO1nV98daffxrAf7kD11hpfOc73wGwaJ4riQv8h1AKM9AXjWfcoLlEhRMe16ZO8mU/VP1/KeOEyvhdHKKycTeT9vv9dA6XpWtFAA254PH+Atze3k4ThgsUNFOHVxfQNnNMSrk0dWxohvR4vm63m+UxZDuvXbuWCRR00eIFV/v9fpoc+cmJcH19PZlq9b6WwjcIn0xKpYd0svBip3zRDgaDNAHyO35qKIXHtVVVVcwbqnGlOl4qKiF0kVSarF3speE2nuOU19P4RxVQ+W9J/3axij4vvkDT59OrkUyn0yxcRvtAsyXvJ8e5rusUlsBJTHO8EhoG4YWg7wfc1mRXVdUGgD8O4Cfl6/+jqqqPAagBvGzbAoFAIBD4nuO2Jru6rvcAPGDf/bnbatF9iKtXrwIAfvmXfxk/8iM/AmC+0laTnUvu1VHvWU80l6IH/Wp2DYeukLmCLJlsdMXthUC5vwYVez5HXdVzNbu3t1dkmhwHHqOMDljMDUqRRKfTScfqqp191IwZ2h89v4tXGo1GJsyo6zqd301dmvWGx6lJlGPD7/r9fmai4t9bW1upjWR2PO7o0aNpG+XmmqHEWUyJVStr5zYNYuaYeDWCM2fOpPM6IxqNRlmlCQ15KWUC0goQ2gatM0dorTgXkOi5nCVprs/Dsuso+3Wzrz5LzsJVLONCMBWxuKBFn0sPrr969Wqq41eqWOJFiDX3KJ9PPi/r6+sLgqz7BZEbMxAIBAIrj0gXdg/hypUrmfRe82FyBUj/T2nlWQoO9ppiyvo82FfZW2lFXPLxsM1exbnb7RarJPDT82aORqMs7yPHod1uZyt79mc4HGYB98Biaipt12AwyFa7xObmZhagrblBPW9io9FI/3e/lDIOb4MyYvXxcH8NzOb3nqqKbdnb28sYwfb2dpahX9mr+7jUv+M+2IODgyzdm4aUkKFxfxWcEOyPMiIPIC+1pyQE4hjq8+whIc1mMwtRKeVsdT+w+qD1XnnwvaZB84BxZ9S6Ta/DNqjAyZkt/bKXL1/O8ssqg3Srh6a2Y/iKBt5TTES2eD8gJrt7CF/60pfSC+1zn/scgPmPY319PStLopNZSb3GH4CbONXhTmiskBeC1ReUQ53q/kJstVpZDB6hLy/NiKE/VmD+cllbW0vfqXKOx3miXVXoubqu3+9nogWO8/b2droms4Xoedz8B+RmYn2B+gtQX2ZezqjT6SQz7DKFH8dC29xoNBYKpgKLJtSSSMivrbGUXhpIx4nnohBCBRq+SNB+l+LT3KRdmji4iNnb28vapZOZZ7bRl70/S/p/H2ed9FV5u6wQrF6zlAjcny+d7Nzsqf3xeFtV0LqATPug4+CTYyk36v2EMGMGAoFAYOURzO4ewmQyWZB0A4tMhat3Z14lsYma0Aj92018hGbx95Wk7qtCgFKuQm7T3Jba1n6/nwlH1tbWspW2/k0mSKe6Xs9l9pPJpFhok/CYJUJNr26eVXOeMmjP7E9odplS+SNf7fd6vdQuF1Co4MTFEtoPrux3dnZSPxiW4LlFdX8NESCb0FACz9CjJkTNJuPj4NvYpl6vl5kvG41Gxsa1nJMzoFL8p5qL3SRYyhziY1nKranw/UoSf4ULs0r3U83Mbgrm9TgO2mZ9RjxvqDJH3jM13d6P+TGD2QUCgUBg5RHM7h6DCye4stvZ2clW+yWbvbIyz/bBFbfm0lQ5OuEFMFVc4MIW9Y2RCWjOxhITAmY+n5JPyVfomgXex0Sv44Hfem2V9rOdy+qs6erfqz9oHTye88aNG2k/+tu4Otc6cxqOAcyYjfveDkscUFVVVoNPV+rcRgawv7+fQhQ0Ewj7z3a5tUAraGgWD89ZqYH3rN5w8eIslwRFFZubm1mIh2bs0Sw8hDMaZaHuqyI0QF2DvUvf8XrO+pzha7tUmOXWglKeUc1TWmLh3MezxWjxXs9Co1UfCLca6LlGo1GWOIJhIzdu3MjafBg7XRUEswsEAoHAyiOY3T2EZrOJl19+GQDwkY98JH0HzFbqvgrjyq2qqmL6J1eh6Sr7sIzw7htS352vCFX2rH4FXtd9cFTxXb9+PaUv0pWt90Oz5vM7qj01d6X7BAeDQcaqdHXuY6KB154JX9N6KTvidQhX7I3H48xvQvZ39OjRTKmnffTM++6nLY2RnkvbRqal6kRXUGqYSYnlsN8MWufn+vo6fv/3fx8A8M1vfhPAoo/Tc7Cqf8pzVyoTdsvDaDTK/F7adg8g1+2lsXFlszIot0KoGtOZ2nQ6LY4X4Wys5P9Wy4mHEHiSBWB+X7UKREnt6uE4qgT1sBoPwVlFxGR3D0Fjb37v934PAPCJT3wibXczob4kXFas2Rj4Q9Afif94Cf1hE/q3v4wPDg4yIQv7oLFUmhMSmL1IPEGvOs49zrDVaqXJkcd5yIPur2IUNxfVdZ0JZ/Rl6YsKjZvyjCCaccPLEgHze0YTEj+73W5mQtY4K2/7wcFBNjGVCptqRhCfDBlvtbu7m2VC0VAMLQUELGaoYSHQRx55JLXh0qVLAPISP6PRKC1uNP6R/fdkx/qceQJthT8jpcWbTpylLD5uJi0tJtSUXBpfoJyzUyd0FyHpp//OOp1OtsDSkA8+l1xM8Bns9/vZs6pZbzycp6qqZOJmXOv9MNmFGTMQCAQCK49gdvcQptNpMjl53sSDg4OFAHNgkRG4CU2/c+ahK9tSsVc3iei53bw0Ho8XsvbrtlarlRVV5fHr6+sLZljtj/5fM1bwOy8DpDkr1fTk5YvUjOsraF2Nsz++6h8OhynsgQG7GxsbxZIw/CTbYVkeNXm5yVal96UKD571g/1TlshxbjabmciBbb5+/XqWS5RQUYVmUHG2y/uipYRYQoZ48803MzaqLNkFVhqgTtbigeF6bW2fM7tSGIM+1z6+pVAcNVl6EL7eYzc56rNYql6ibdKxabVa6betvy+e202cHKPr169nz4YWgvXAfn2WKC66HxDMLhAIBAIrj2B29xi4+vaA5mazmZgZg4RVGl2qwUW4/FtXiSXf3WH+PK8Rpitbv3az2VwQmABzJtnr9TJfjQpgSkHfy9KllUIP2u12MY8nPw9Lx+W+FLJtbYOLK0qo63qhv8Bi/kwXNmibPNVbq9VaYHLalvF4XMxj6kHlWl3CfWElVs19rl+/nnxvLNRKn5+mKisVnlUfJbBoSXBxkKYQ80QJGtDvuUHH43HGhNQn5rUOS+xamVspXKAk1uLfbKvfM63U4FYM9lc/9bekITdsn6dQ41hOJpMshZ5exwsUlypI3A8IZhcIBAKBlUcwu3sUr732GoC5T2V9fT3zS5SSLB9Ws4tQBdlhsmlf9ep5NBi3VOMOmK2yyQi0Lh8wW+mXKlX7KlRl+dp+bY/K2HWVvSzZc6nfmrCZbSCr5nnUn0VlaK/XW6jMoFB/Jo8jY9VqCaUUVc6uJpNJVv+MGet3d3czNaH6bL2y+2g0ypSKGiTO/vDz9ddfT/09deoUgHmS7Ha7jZMnT6b+an8Gg0FiGjyOz/PNmzezzP7K7EqJA9hmV2pqmq3S86+JtoFFy4b7etvtdtYG9XEfxsZdqak+SLcWqHWhpHZ2lqjV1Z0Rrq+vZ6E0atkoJb3mNqprX3jhBaw6YrK7R0FzEZ3+DzzwQPrhe2HHkuN9Op1mogD+2EvFOxVuJtIfo29bW1tL1+HLm2asg4ODZJb1WJ/Sy1izURClXJf80apT3oU2b2eW9evoRMIXOcUoPLfK51kipdPppAldC5OyzTRf8l7x71LWD4ULgQaDQZooPTTg6tWrmZlY20Gzlz4Pvpjgvnt7e6k/nIQODg7w5JNPAphPsM8//zyA2aR/7tw5AHMRjmb9ocSdE+fZs2dT+zhpuYgFWHxWuc2rBGgojWdCKcU/aviATybLhCn89HEiNAdlaVFVqrjA8xw2qZby03r/VXDix+ki1CdJHWf+Vu8HhBkzEAgEAiuPYHb3KMgEvvGNbwAAPv7xj2diiFIwtUqqXaqseQfdka3My7N3qHPeQwiazWZmviFLunbtWhbsqmbGkil1WTZ2Fa8442y32xnj7HQ6mXDgMGgdOMqxeT0yo2azmdgVWYyahL1o53g8znJp6n1ydqGm2hJ7d7bHbVevXs0CzdUcx/OTea+vr2erfi0aTEar4Qxf+cpXAMyTHXDbk08+iYcffhgAEsNT0ztNwew/me329nbaTy0DLqEnI1Szpwd4A3kYx2g0ykIBCBV7OGNT4ZCaPz2rkJoSlWHppwqHHHpcSThzWFWCUuIBf9b19+Kislarlc7xxBNPAEDKgrPKCGYXCAQCgZVHMLt7FFzt0W/y9a9/HY899hiAuVO55M8oMS5fCasQZJlQg+fV40ejUWIHuiL2cAGuiPf29rLcfsr+XNAxmUyK2eG5bVl4xWQySe1SUYIHk2vIgvvx6Fu6cOFCOi8l+6VM+ryeVpco5U/0RADqU/Ks/+r/8TZXVZVJ7zXhgLOKRqORWJ6Hs7Rarcz/owyPzI6sTPOMsv1kcxsbG3j99dcBzEUr9Gc2Go3Ub/qg+Xns2LHMX6TVyNketmUwGBTzXxL+3TIxFcfS9y8lNtActO4nJUohLPp789/eYT51PZ/f/5J4R9kfj1O/sdcq1N8s762G1aw6gtkFAoFAYOURzO4ehQdJ37hxA7/zO78DYL5Ce/DBBwHMbPFecUB9dn5O3aY+DmDmU1m2GtWqBFxBahAuV6hcLe7t7SV2VGKQLv/WVbPvryt198VotWg9zitTl87B/bWOIGX2ZEZaK05TVAGLPh73QerK3v16ADJ1rTJ0HSdgkdnxk/fz2LFjmb+03W6ndntohMrYyZyUxfO8ZIStVgsPPfQQAKQwA/reNHm5M0KtRu5VD0ajUZbGS4OqvQah+r88gHwwGGQ1/lTZ6L8NbUeJJblPTMMe3Kqg9/MwlbQzwlK4if7fLRwl/6TeM2+zVoFXXx2P9/qE9wNisnufYDwepx/Ys88+C2BuEjp+/HgSB6iEXLNcAOWMK4S+eNwkqj9imp5KIQQEJzuV3hNqLvNcmpr1wyfC6XSamXhUXu6CA93PXw76sndT7cmTJ7OYOApUNHZLY/2WmbZKVQx0seCTo2bvV6EBsJj/0csGaa5PFUt4Dk2V6vvEpLlIOcnxvnQ6nSTI4f3nPlVVLWTf1zYDeUkgjiWAbPJSU2XJnOull1RUU5L2ez5Krcahz6+Omz5nPF4nO8+kopOqP0tqLi9NYv6dPl+lfXwRqm3hd/qbcIGKTvAf+MAHAACvvPIKAKTQkj/4gz/AqiLMmIFAIBBYeQSzex+BqzaaLy9cuAAAOH/+fGJQNDOpcKJkxqKZyIUjpWz0pcBYtkVXry4S0Rpcvrrs9/tLa3f5tfw6KrTgdXhttksD551B7e/vp3OQ4fD4Bx98MLFLjmmpUG1J1u6sxPsEzNmFhktoHzUXpp5TBQoubBiNRun/ZPMlhq7FbHn/2X+O1f7+fgoqJ+vb3t5OY0LWTqvCxsZG6pNnhOn1ekVxBPfxjCj9fr/I2r0/LlRRNubhMj6G2lf97rCCtSXGpVYDZ3sl0UppW8lcuozZaTHmUu06f/YODg6yzDHaV5rque37vu/7AASzCwQCgUDgfY1gdu8jPPfccwCAD3/4wwDmLO7g4AD/8T/+RwDAZz7zGQCztExchTNImkG86rPRnIjAbEXtvg6i1Wql1ThX3uobUsEAsOgb9DybGvTL1WW73c5k4v6p7VIBiTMC9yNqHzudTlYTTdkJ/VMuqphOp1k4g1b25rmU9Tr0XCVGoGwNwELVCPcvqa+LbSaz0xRiy/yGwNz3pn+z/adPnwYwu69sh0vV1f/laeP29/cTO9S8nGwz+6E+P3+GtJagh0uUAu6dSWr/S2nvnCUt8+d5QoOS2IlQ/7cHjpdqF5YETe+E9anlxZ8lTVDgbLfX66X7zn7/2q/9GlYdMdm9j8Af4UsvvQRgnoz44x//eBKtcNJ76qmnMlUYk70eOXIkvRz9RzwajRaUXMCi8owvJnX2+8SiBTfdJKrn5HU0WbJnySD0b8/GMh6Pi8q7ZSahtbW1pXk2+/1+VkBXY5dcVKFxhv6i1vyE/jLWcVNlLM/LSULHw1/QvBdra2tJ9arK2VJMJNugk4juMxgM0jbGzZ04cWIhUTIwf/a0xI8re/v9fraNY6OmZLZTE1qXlMDLBCo6qRBqjneUFkKlnJKEPscuKtF8rh4bp8mYvaROaSGkJtHDUDIJezzncDhMsaN8nvl3r9dLibld/bvKCDNmIBAIBFYewezeR+DqnZnnKSRosFZvvAAAIABJREFUNpv44Ac/CGC+GmdWeiBnKJcuXUomUI+3UfOKO/a73W6WeaWUg5GrzPF4nJllXJ4NLJqLfGWrq3iXhBMaT0dp+8HBQbZ/Sartbb548WIqr0QmwWwhk8lkIb8o4ebCktnToUyN7Gc0GqX+UrShYhzN38lzADPm5TGOw+FwqThEGWqJxXgcW6fTyUJOaBm4cuVK1h5lup6hRVkf92Nf+TzrtdVky2PZlpLYRcM43EqgTFpFVHo9hYq2lgmHNITGmaSGJZTytOrvxPcvlfhxQZd+lip70FXBz0uXLqXrkk1/+ctfzvq9qghmFwgEAoGVRzC79xHURwPMV5JvvvlmEq3w8+rVqwsOaQApkPS5557LimpytazZ0l1w0G63sxpsJYbG9g2HwyxIWlfqvhrXPvl1NONKKSTC2SXbq+1Rv4wLWvTcZBpvvPFGujYw81N5hnstEusyds364exqOp2mlTrZ2O7ubiaBV/8n/XhavQCYMTv346mgwwUko9Eo83+5KEPburW1la7FTx1TZ8lqGXC2x23KPDXMwDO7EBqgzzFiW44ePZq2cSx3dnay3JAlH55bHkrWCLV2eJuVvXNM+HvrdruZYErH6DBLiPsz+/3+Qj5WYJEtasgBMHsnMGcpx0SZM/13FK/dDwhmFwgEAoGVxztidlVV/UMAPwLgcl3XT9/67jiAXwHwGICXAfxYXdfXqtkS5v8C8CcA7AP4i3Vd/96db/r9B2cH9E998pOfTP48TY1Evxy30Rd17ty5tBL0vIFaecB9ESpnVpm9r3o1mNt9fKVq6ep78bAHlU17CjH1dXi6JA0Ad1ahqZe8P91uN0uvxRVyScXZbrezmnDqU1zGekqKyP39/YWK6cB8ZV9VVfLH0l/G5AKtViut0LX/HkxO7O/vZ0HbyuzIUJSVsl3uZxsOhxmj5TOlKdFcQavpv9Tn6b4zZSyeL5PPf1VViU15QoDSufQabqFYpuD0Y1U16uEL7HOv11tIw8a28rolxTGZGYP2ycZUvaohNMBs3DkmZMRvvfVWskw4e6/rOvn97ye8UzPmPwbwdwD8E/nuZwB8qa7rn6+q6mdu/f3XAPwwgCdu/fsUgL936zNwG9CXqhfCfOSRR9LDqz9kj9linN3m5mY6h5t4VHrNH4f+qPylrdk7SjkS/fwaU1eKPfIE1aV8ni4dV9OfvoQ0t6Pup7kxffLqdrtZf4iSxF1DKPii0va4QEGFIf7S0nhBj128ceNGumcf+9jHAMzl/xcuXMjK+JTi33TiYH9pvuazoX1WkyjPxYmTL2E1Vbt5rdvtLhSTVai5WU13br7UcXSzr5rn2H69ni++Sibaw0JCStd2kYgWQqVpkM/B8ePHM3GUuiA8P6uGC3Bh46E+vCb7CJTDJSaTSVpMqPmS2+6n0j7EOzJj1nX9HwBcta9/FMAv3vr/LwL4U/L9P6ln+M8AtquqOnsnGhsIBAKBwLvB7QhUTtd1ffHW/y8BOH3r/+cAvCb7nb/13UUE3jVU4k/mwCKuFy5cSCZKbtve3k6rUJpE1HHOlSBXkMxK3+l0FgqA8trAbEVJ8xrPVSpoquyy5JgHZitbz1mpIQGeB1NZopdX0WDsw2TcpWKXJRGLi1BY3kYDg52paZtVLOPMkVhbW8syofT7/cTQyOJ0HCgw+kN/6A8BmIeX7O/vp3Mpk3CToyYCYIFVnp+mUS35QkZwcHCQ2ArvMZnB+vp61g8NcXCmzvu7ubmZsr2UsrPwOzXjksmWGJ5Xbzhy5EiRTfNcnh2IY6ThNfrcl1g4t7mohu3a39/PzKscX828o2E5/I5jo8IgP4eanl3Ysre3l37bzKGrArfz58/jfsMdUWPWdV1XVVW//Z5zVFX1eQCfvxPXDwQCgUDgMNzOZPdGVVVn67q+eMtMefnW9xcAPCz7PXTruwXUdf0FAF8AgO92oryfoIVD3VZPB7T6jbhibzQauHp1ZnnWUABgtuKkaMXZWEmMoAyKUH+BpnTiObiP+0Y0ANlDCDTsoVT1wAPT1eHurFdX3L7q1XbxnHourqY5lo888shC27X/BwcH2XX42Wq1slpq6ht04cDa2lpiB2QavK9bW1t45plnACAlEPjKV76S+uB+VpX2q7+H1/HwFY7bmTNn0n5k+9/+9reT5cCP63a72XPCc6mgx/OZbm9vJ/ZCVra2tpb6y+80uN5FNURJOLW/v5+FnJQKtXKMyMY2NzeT/09ZuYeEKHvlefmb0jygXhGEn2pB0d+L/t6BuS91fX098/8dhuFwmMaXlgkev7W1hV/91V9923OsGm4n9OBfAfgLt/7/FwD8S/n+z1cz/FcAboi5MxAIBAKB7zneaejBLwH4YwBOVFV1HsBfB/DzAP5ZVVU/AeAVAD92a/d/g1nYwQuYhR78pTvc5vsK9BNpULWzkbW1tYXVNLCYXourdU16zJUjfQrKzpb57BqNRiaXV9Wj1mpj+5wlKpsprWy9GjlRqtSsK3Vnlepfc+m9toNQZRwZnftNVMWpsnnPKq/9KbEJ9kH9kcDsXngyab3/jz/+OIC55F7vmVfSVnglCQ0JIPvn55kzZxJDURZGeJUMZT3OflQl6GnQNOhdwyV4Xj6XpRqMPpZ7e3sLKe3YZzJx3jNN3eZqV1WnUqGqvxH3Pau61BMTqG/Rwz9U9enP+HQ6zdibWgvcglDy2TmT5nZgUY39Uz/1UwCAv/t3/y7uF7yjya6u6x9fsumzhX1rAD91O40KLEr0gdnD61lCzp07l/6mGUal4wxHoPiAP14t3siXipps3BynghDPl6mZUAgVnLgJVCdoz9pxWIaLwWCwNBuLwmX2Cp3EdbIG5mYzYP5S4HV0IuH/+SJVCTlftKVciT4haO5GrdjA+8FzajgDTYkuJW80Ggu5TYHZC42LnG9961sA5qa6qqoWwgr0Oq+88kqa7Di+b775ZurbiRMnAMxNdpcuXcrEHprpg/3lhECx1P7+fmqfx6IByPpzcHCQlWXSBZ4/l+vr65lJWBc0atID5vfu9OnTaTHBSe/o0aNpuy9eDg4O0kLBf58aLuP9mUwmmehHJ9BS2AufX3/OdNJUEY7HGvI3f/Xq1RS+cj8hMqgEAoFAYOURuTHvUXguQl31PfXUUwDmTOX69euJJZw9Owtp3NvbyyTKXAmeOHEirV5dOFCSi2sgLb/jtUsmNGWlvhJWsxxxGHtTM5gHCRPNZjNjB5qp3s2lyqrYdrLg7e3tTLKvDLRUoNal7coq1XSo+2gGDRXV8D6wj8qEfvM3f3NhG/vw0EMPJfZOhjqdTlOeVG772te+BmD2bJD1cLVPFnPt2rXEBNUsS/bC4yh66PV6KRBaRTvAzNxKE6gGTrPtHCd9Fn1MNG9oSarP63r4AzBnbWQ4akp14Qivt7e3l9Wg06wyHlzebDaze6bM3qs36LPrtfs6nc5CjUYdLzV7eqIC/U6zuXhBV7Zhd3cX165dAwD88A//MADg3/7bf4tVRzC7QCAQCKw8gtndY+Aq130Jk8kkrSC5CucKem1tLa20yTj29vbSio7+Ffpbjh07lvnLSn4zHq8Z3rmaZBtu3LiROdqJkvyfq2wVUmgYgNeEY5+73W7mZ/OcnLwmMFsZuw+wJCrhd1zhHxwcpFWvp7hSIQCPU3+lix0ajcbSenb6vTJQtp/PAcUyvV4vsSqyPbZhe3s7BZxzTHZ2dlL9MloHeM+63W4KK3C/z4ULF1I/6Bv84Ac/mCUO8GruQF5BXfOfqk+M/eOz4PXptB8q1HHxBsdDq5If9kywDRo4T/ZOXLt2LQX2M9/olStX0v3gtZWNe5v1eXampr7eUi5RZ4KKZQKtdrud1dnT2ot81tjX7e1tfPvb3wYAPProo+kcQB6mskqIye4eAycmvgD0weZk5S9EzSDCF83W1lYyX/Gc/FF1Op1MMKJmFs91WEqSy3Pt7e0tLdCp8CS5mhHFXyAKFXb4i0xfKn6OUhyfTpa+vwpDaNJkIm198fBlpBlbXByhgh5e0wUzy2IKXbWnAh+PjeQ+r776ajpe1YicFJ944gkA8+fl5s2bqf80M7L/GxsbqT8UkDz99NOpXTRZckHQ6/XS5MC4T75UG41GJr7QsfHsNZrP0SeOTqeTtvkCYn9/P90PFVr5fVFVppsCdVLye7C/v58le9ZPFyTpAs8nEY0z9X70+/3sOvqcullSr+e/Y+2Hm1A1STyzq/DZWuWSP2HGDAQCgcDKI5jdPQausD1upt1up9UYP1X8wRU6v9vf308rQGcoyipcVFGScat5yZ3kGxsbWSFQjWHykisluTRR13VmHlL24/kJdaXqq1dlDi7x1zgz9o3CnuvXryfzGD81tkwzYPDTzZel2Dpf2ZfGQsUuPj51Xad2sOAu/758+TK+853vAFiMjeN9IPPiPWAIAzCPr6N5cXd3NzNf93q9FOby4osvApgzgH6/n0noS1lsfEwmk0kaQzLO/f391Fa3LqytrWXFfjVsxkMoSrkn9Zz+TGiMqMcq9nq9TOSkrMzDSjQsQV0ACo3BIzT3rGdS0XZpmSSey5mz9s0xGAySkEljHFcdwewCgUAgsPIIZnePwVe0WqjVfWL065WEADs7O1kmCLXxewYJXdk66yk5/TU3Iq/pvpHBYJCJA/S6vhIuVULQlbGvqnXl6uIV7QehbNnrkqnMnGPhQclHjhzJAsi73W5iE85sdZx9vCeTyYK4g9dzNlryZ7rEXUMveK/7/X5WG46MsN/vZ8yZzPDKlSspWz77+uUvfxmf/OQnAcx9O/TdjUajrDoE0el0Mn+vtpnMTEVIhBd21QBtFxWVguoBZD4r9XWViqkCZcGJZgIqsaVS0WL2q+R75Pi59abX62VZeEpt9ixDnU4nE61ovlyOOcdyZ2cn3b9lzHMVEcwuEAgEAiuPYHb3EDqdTlq1+aoXmK986Z/jKns4HKZtutpVGb6i5EtSFZeHJWhOTWcazWYz+Vz8nFot2pmN+izUH+JsUtvs+TV1H/d/aIC6r5bVN0bwXEeOHEn7ke1Qgj8ejxeYE7BY9cB9l5ouzX0qqjxUH5kqExWdTiddk/4WMrdGo5GUugwSv3z5cuZ75H0pVUng+G1tbaW20n9248aNpNrjc8ltmrzgwQcfTG0FZoyYbfXcraPRKKtPePTo0fQsuerz4OAgjT2hAerOklTaT6jf0K0dnm8WWPSNucJYn61lacy0soFDU3kpo11WXV1r8LliU0NWNBetqza1/p/WKgTm473KiMnuHsLx48eznJV8cZ44cSKVduEkxwf7yJEj6YfPl1Cz2czCEfSH45NJKRsDoSIWTypcVVWW5YVt0UlQJdfAollGTUju7FfzkZuc9MXgk6SKA9ycU9d1ccLkPmw3Y9H4Ytjd3c3CGPb397OXqsY38X56G0oioWazmZlCNdML7yPNqqX4R51M+QxxwuE9OHr0aCZy4Fj2er3UZopSdnd3s6TKmoPUt+liyUtIsT/dbrcYGuDmWH2uPSSAY1rXdZrQS+IoXyRplqBSm7UUFPd3+EJQof0pCXPYdg890d9EKT9tqdixt0dNlp61hs/Nzs7OwmLlfkGYMQOBQCCw8ghmdw9hMBhkphr+/fDDDyfBiZfSUVOKmr88oFdZkpsjS45wF0lMp9PEEkpZIsgklEmqCVSPU2ajQeIuBNAVq6/GVbJfMr1yhe4Z5EtB9Sp+UPYBzEM3bt68meVG7Pf72f4qQilVlWB/nFVoVQVnsf1+P8tGw/E4ODjAxYuzkpE0ZzabzcRMnamcPHkyY2Oaw1PzfgIzIRSzsXA/ZnY5cuRIJmNXpsv+klUou+Z9oSCm3+9nuVPVVOcsRPPH0jyqxV79edGxXybL12eQ0ALF/ntRlvhOiqrqc+3MvtVqFUMUuL9vKyVVIOq6TveDn2RzN2/eTGPpv41VRjC7QCAQCKw8gtndQ9DVq+cSPHbs2EIhS2C+8u73+5lAQ0UYKlrhtsOKRBJaaJXwnJBVVWX5MukI11yXpYKTJVm+5gLVbSpQ8coLymZV9OL5HNl2PZbQ1FAa+A3M78GJEycSC+F9arVaidl4+waDQfL3kXnQH9ZoNIpiHw8v0b9LxXS5z/nz5xf6RbEIMPfZkY0dPXo0CwDncZubm2mc9D6y/TyOfdVgZw//0D7yeBXguJVAhSbus9NjD7NGEMqc/FnSe+++S2VqpbR3bnGo6zrzl+m2ZSEO0+k08/sp2/TfuO7vtfVarVZmOVDLhuf6DGYXCAQCgcCKIpjdPYStra0FPwkwVwR2u90slZbW5CoFaBOe/VxXvb4i1uNcSaYrT92Pq1C2mRUbdGXr56yqqpj42Zmgfmo2fQAL9frcl6aM05WNWhvNfTfKEryada/XS/8nk1IZeymBMH1Ir7/++kJbtre3s4z42ha2VdWY7mckNjY2kkSfbEnHl6EqZHZan80Vi41GI1OQDofDTJnqKk79TgOcnUH7s6v9HwwG2X3h2K6trS3I6oHFkAoPHdjc3MzGVa/p1gENwTnMJ+wqYZX4l1JuueWE0N9G6fiSz9rvv9aN9DCD8Xicfi+8Z/wcDofpOWH1g/sBMdndA6AA4siRI+mlwu/4Yx+NRksLgdZ1nQkbSpORvhCWhRdMp9NMtKDmllKGd/8RUpRxcHBQlFcDi9ni9aXiMYH6killzvD+6ATt/eALan9/P4s90wmUEwFDELQEC//Pa1+9ejW9OLiNbTh+/HhqK01IzEU5mUzSxKlhAx7/posYD6VQIRCfCX2xMXSA1+G5Dw4OMrOqLjJ4Lvbn5s2b6Zp8PjVmS83DwGJhU88EpPekFP7ABRMnaLZTM6gQuiDw34YW++V1NCuLZxzxv4FF4ZCbDlUktKwahy7mXOBSMr2rSbQUlrIs92opZGE4HKaFFmPo1NxOQdP9kBOTCDNmIBAIBFYewezuAWhBU67CueJWZ7kHXGvmEQ9H6Pf72QrzMLEH0el0MiaoJh83VU2n08xcxHbpit9X0KUwg1JWDaLZbGbmQjVPepb46XSaZTQpyevJjlSMoHX/2FaeU7N9sI+sG6dZMXicszcN5mX7ySDX1tayoGplPR5KoYVAPYRCg5Cd2ZSYijIvfsf+aKC1B8fr2Gl7eF3PCans34VNyipLrM9N9fx7a2srO7+yN7ZPTZae0cfPrdBnz2sJlo7RMdWsNdo+zS5UOo9bS1Ts46Ekk8kky4Q0mUxSW2ni5rPXarXuq5yYRDC7QCAQCKw8gtndA9AVnq/QCV2pewBpKRBW/WWaX9OP93N1Op2llcf1Opqqy4PQNYh9GYMs+Qo0HMF9G6PRKKtnpz4Yv44G6LpwpNVqJTahfixeVwUmus90Os3CHUajUVpBa65GHrcseHk4HGa5Kzc2NhLLY//ZBmUjXtdNg9c1R6an6mI/VOzEberrcYm7pjHzc7Xb7QW/mvZf/a9kuGTEx48fL7I4Z9wcb/098Dr06ylT4X3U+18ShxClagb8v4beeNiHWxc4TvqpbJzQ8S6lvSuFVXAft44oG+V3vBcHBwe4cuUKgHkFeY7pxsbGfRVyQASzCwQCgcDKI5jde4SjR48mKThXif1+P63k3W+k/hxX6qnPhuh2u1kAsPpZvOKAKr2WVV5W1VdpVek+u3a7na2A1afiak/dz9s5HA4zubwmTfYAWmWhnpV+OBxm1a6VxbANVK8pM2RbqUYcjUYZO+I2TeNGxuGp23heYMbinB2qT8kDjZUFHjt2DMA8efN0Os0UuqV0Wc4g1J/L8VK1q/dVE4HzvihjdSWshs/wfipbYh859np/eB33HypT0+eH+/E49SG7OlitJqWg8mXB4fq8eH1CreyhibNLbeW1fewJ9X/673IymWSs+tKlS3jllVcAzJ9H9vHKlSv3JbOLye57BD6oH/rQhwDMzDh88Clw6Pf7KUOHx9LoREiUyodophL/4WiBS5dJ+8Sg32khVY+vUvFCSV7O/6t5iXBHO5DHaqmZ1a+tk7I74/f391N/+cLUCcQz56tJzU12HHetpKA5GHlemuq05A/NpS4IabfbCwIQoFx6SBcEXrJJhSA0X5bCPjycQU3PLgTRyhMqVPL9NQMLJ10vrurjyj7qvgAWFjGe9Uaz2bg5Xp8H76Pmi3RB12AwSAsBH8tSuIROUF4SSYukehHj0sJBFxce9qJlnzyXaLfbzdwS+vvk4oChJ88//3wqy+Rt/+pXv4r7EWHGDAQCgcDKI5jdXQZXYXSmqxyaq/6zZ88CWKwtxZUdV7i9Xm+BTei5G41GJtrQ1S5XsWpC06wVwKIZz+XSPG59fX0ha4uD++s2D4DmSn1jY6NYecFrjxFaz65UscCZ2v7+flrl0rRJ5qXs0kUMygh8TPf394sSb56D52fbd3Z2UghJqZBuKVDZ7y2hx3koSa/Xy0Qig8EgBROXsqUcFkzsrFrNZDyX1g10NsbnbX19PTFNNcezPxxXmhlbrVZWG1GfRbcgKEt2NtbtdlM73Hqh7NXPqWOojNCDtlXs4c+ljrfeIx1bFTYRg8EgmRxdoKPt0VAd/s1nncHir732WvqtcUyZ2OB+RTC7QCAQCKw8gtndZThTUUbFFRfl2EeOHMl8CMrUPHhbV/ilCgLuTFdxAffj6o8rTw1x8HO2Wq2sZlu73c4k0SrZ5v7sB30LnU5nIVcnsCgq8XABDZzldzz3aDRK+2tgNyXXvKZWfeex7gfUOmiOkrBD/YW8Nu/rkSNHkj/LhUBaebskR/eQkOFwmDEUtRpwG9ty+fLlrHoDWWapIoD2yxm9skAXyWiws1cxOHXqVCbe0GB5f8Y1B6WmaOP+HiRPlO6XplDz35SKarymoDJITTe2TGhVCrhX9uvCEVpvNKcmt/X7/bRdw2o4HprIQPt1/fr1FGbw2muvAZhZFdxfTvZ3vyImu7sMPpCurtNErTRn6o/YTZaqXnRF32AwKOaB5A/SzSVaLsRjt1SN5uYyPU7LzJSyd/CTP2i+XGhau3HjRhaDpqo/N1VNJpNMqcgf8Xg8zsq3tNvtZDr2ibbb7WbiGx0HF1F4RhFgUQjkExnHY2trKzMvct+jR48ulFxhv3wsNRG0JjkG5nkq1eSpL0vvo5pnvW9qEteyOmy7q0r1efGFE1/KJ0+eTPebE4+KK1zsoQuNkirXFyZqZi3F1LnZTycsz3ajZmbup6ISf1Z9MaZ90+v6uHE8NJ5RJ7uSMIf7eE5MYn9/P+Vc5XtDM6i4Svh+RZgxA4FAILDyCGZ3l6EydGBR6l4yr3jZF82HWCo0yXO5U11Xo14lQHM8OpNQlqSiFfbF2WVJOKFxQC644CpWBSRcJa+trWWiDc3FyDH0wpPj8ThjsWpCpSyfZXbI8LRdyi68vI6arJxxrq2tZeEF3GdrayuJVjwH59raWhYSodfyeDZl0DxO4y49Xm5jYyNjGvrpDF3bXipxQ7iwqd1uZ4yeuHnz5tJKAOvr65lIRGM83QSvsZGlLC7OBNvtdlZ81fNnanvUquJiFA37INSk7ubrUpkdhsSQzWvcKDGZTDJBF69z/fr17HesIUFejmoymSQzvpdL8hCm+wXB7AKBQCCw8nhbZldV1T8E8CMALtd1/fSt7/5PAP8tgCGAFwH8pbqur1dV9RiAbwFgRcD/XNf1X74L7X7fQe3ywGwlzVWYZ2UH5oU26c/p9/tpBamrXZ7LV+G6SvacmpoJpFSg1QUtpWwpynCW1QZTsA0USahviJ9az8vZiF7T2ehwOCxm5ef+XrtNa31xfHWF76Ii9WG5cKLb7ab7otn7eT1mNuF9p8+m2WwuCEaAGWN1Rq++Ue+P+jedCZ88eTJ9Rxam92eZX7aU4WNZVhHu7yKM559/Pm1/5JFHFq7jhXe1Xe12O6uqoNaPZdl1SsxYz+tMcH19PY2Jt0ufwVLohbP+breb3Q+1QNBPRp8a/y5lHqqqKgma3Me/v7+/EMivbW61WukZpL90Mpks1CPkGN7PeCfM7h8D+Jx990UAT9d1/REAfwDgZ2Xbi3Vdf+zWv5joAoFAIPCe422ZXV3X/+EWY9Pv/r38+Z8B/Hd3tlmrB2djg8EgWy1PJpO0nwbaAjObPVmBBk4Di6qvUgVlgtdutVpZwK0GarvsXZVuvhrV3IAlX4f7cdS34Mo+HRMyJ+7T7/eLNcGAxfRfynCdrSgbc78f0Wq1MpWgBvurapF/8ztP47SxsZFW3GTxWuncg/Bv3ryZVv70Z2rlBV/RKxt1P6uOr7N+lb0TqvosKWL9OVFG6GpPMonJZJL5DbVyhbNRbb9bHhqNRuqPB/2rWrSUqsufz1arlSkV9TqeZq2U47Lk/3QV540bN9Jv1v2/qvpUS4U/2xpe42OiFiH3421vb6druyL8fsWdEKj89wB+Rf7+QFVVXwOwA+B/rev6y3fgGisDPnAaLlBKtMwHlKax0WiUXoD8kWuJEz+u2+1mLwV9cS7LrlLKrKHmj5L8eZkkWidbf/FoQUxib28vM7lpLBG30TSoLyU3vfb7/cz0pJMF+0+zUandPulNJpMsxksnIRcJ6b68B8yWo/dMX5K8x+fPnwcwz5uqpudSbBjBZ2J9fT0rwquT5LLExrqfPp/LCvTq/rwv7OPOzk4ae4qCeM/rus7GaW1tLXsxa/5Qjcfz/vtix8Mn9DjNxuPlkvQ3qPfdM/rQBK3PvCZ053H+vGiia43jAxbFYSq0AmbPmZvq+azoYk/NuKdOnQIAvPTSSwvXvl9xW5NdVVX/C4AxgH9666uLAB6p6/qtqqo+AeDXqqr6/rquswCPqqo+D+Dzt3P9QCAQCATeCd71ZFdV1V/ETLjy2frW8qau6wGAwa3//25VVS8CeBJAlma7rusvAPjCrXPVvn1VoVkjyLRoqhgOh2mVR5ONiiu42qOpqxTYrSvaZUHomjfTP/X/LinXHJy6kvbs8GrbRiW+AAAgAElEQVTyckagq2YvEqrlcsgE1NnvuRdVsOLlj9Ts46an8XicxpXMToUALhhSplMKel5W/mY6naZ7XMpK4sy+2+0mJu9FXEvB7touMiAyOxUoOEpZP9Q86Qyd4wnkeSa1UoFnLGm1WpnIic/1dDpN7eN3BwcHScjBPj722GPpXH5tDcVxRqjmyFIIhbNiHVu3Eijb8yQJ+/v7S5/xZrOZ5fhUoRqP0wBybqcpWM2eLgBjGMPu7m4aew2TIfO73xkd8a5CD6qq+hyA/xnAn6zrel++P1lVVfPW/z8I4AkA37kTDQ0EAoFA4N3inYQe/BKAPwbgRFVV5wH8dczUl10AX7y12mCIwacB/I2qqkYApgD+cl3XV+9S29/XGA6Hqd6U+iU8lyRZzMbGRkoP5cHVk8kkMQJNqeT+OIokdnd3s3yOJfFCyXdVkmW72KGUS9GDclVUo2mwyITIXrVIqgaRa780qJa+FBXOlGqJuS/FfTK6v7JZr9nnfVJoSASZKvu3LP2VBt3zHMDsXnvKLbK/brebGJ1WavD6ai5Y0X5rW5zZqZjC2WK73U7t4X3kc3njxo3McqA+Na8g0Gw2s4Kr6uNmG9lXvZ/eRw1VcN+lJm8o5Y9VsQ63eZ5Y3Z/td7/3aDRKIhG2S4PxCe2/9lePq6pqoWIEsJjzteTju9+rHDjeiRrzxwtf/4Ml+/5zAP/8dhsVCAQCgcCdRKQLe49Q13VaOZK9NBqNLHBUbf1ctfOT265du5YFXOsq2VN2ra+vp/RFXHmWat05w9MVPqFyafWXAYsswQNaSxXIdcXNtnJsSqtXVQQ6E97a2soqjWvwsjMB7bNL+1UOrj40RynIWWuO6d8l5a0qdEuMS2XlwGLaME/fNhqNMp8V7/Xm5mbRx8v+OxPSNFZki4Rem+1jerY333wzPWc8pyZSIAPUsAlngJcuXUrn9tp4+tw46zk4OCgyOp7LQ2J03Ly6xnA4zJIpcGzb7XZ6vjTROMHnkj44DWZ3RjydTjMrhI6zW0fOnDmTxsgrwZf8rvc7YrJ7D8EfFQsurq+vL7zcgcXSO/7ypRlLxS76nYcXaJkV/pCZP++tt94CsDgR+EtCpdQq1PD8h6VMLf5iVzOmmv+8CCszQmjGFTfx8AWqfR2Px2lMPFtGr9fLBAoap+T5GUuTnYpylpm41tfXizFbOh76nUrIvYqDmmXd9FZVVRY3pguNUokXZuhx6X2n0ymWuPHMMXrPfJx47jNnzqTQCU4EpVJCfOkfPXo0nZ9tZT7TEydOJBO1j5EuHFSE5EWIS0WCCZ3EXFTT6XQWsvzouKlJXMtxcTzoXvBJ7+bNm+m51d84x47vAa3iQfD8PNfBwUF2z7iwCcwRuTEDgUAgsPIIZncPgCvQN954I5mAuBrlCk1NGFwJa7FIN1mqmMJFBY1GI60qyX5oblpbW0tCGDeJqGnF2Y/upyZLz9pQCtTV8zpLVBbL79hWrmxLZrlr165l9e9U2OFiAmV2Lj4pFQ0tFc4thVe4KVjHxu+ZmnEJHre/v19kx37O0vjyHpP9adaYUv5Uz5LS6XQyk5u2z+872dm5c+fwwgsvAJgzNTKWXq+XWDufxVLlAR63tbW1MBbaPjXZ6vPoFRrUdMlrea5OFXb5vQPmDJUmWB2Pw0IdeH793XnNShX7uBBME0EQH/rQhwDMzJhuxWGfA3MEswsEAoHAyiOY3T0AXYGrWAWYr4Q3NjYyxzyZzXA4TOxNV5zus9AVOM9/8uTJdG1glp6Kfj+vsqDOew3wdck9of4PDTTm8e7j0hV6yQ/G69B3o/5NbqPvsdPpZGIXprNaW1vL2AtRyuepbfYAdQ3H8HqDOs7eH/ZXx0av5Wm5NjY2Mn+mrvqd5U0mk0zuXmLQh/kgnV2wv9pmvf+aqg2YsRj6rPipqcX4HY/v9/upXZ679Pr16zh9+jSA+bOqbMxTb2k1DrId7Y/mKNUxLYUsqIjHBSrqZy0xQmfTvO6pU6cyn7KmuOM5lIW7EExT3vFe850QyBGT3T2E8XicOfK1mCMnH88WMZlM0gTF/Xd3d9MPh+fk8ap680nv1VdfTT8YFyPUdZ39sNXsVyqXUsocwXNr5hRu8/2I4XCYmXHUtKYJo9kWmoxoluXLVUU4rmIrJdBWMYu/jLSUkKtkVUFZSpK9LAZNwW3Hjh3Lkv0elvVDFyYE+9/pdNI2N5uybbptOp2m/d1UV1LVakYYPpc0z6vZsPRs+PPPvu7t7aV77EnS9doq2vGJScfIhUJqGvbnUsexpAT1c+k5ufhyMVWj0ciSimuycy+wqnGJ3uZr164V72NgEWHGDAQCgcDKI5jdPQQ1iXBlp1kvuM1Zj67U1RzFY7kC5jnb7XaWs0+zrHA/fnIlXRJ2lAQBahojm2S7KDjY29srVhxwUQGPU6bCdlHgoJknVMbuWUUIdfZzFa8rdWd2JXNeqbBnicU5u1BmuIyp6f957bW1tYWx0DZr/GOpeoHH56mp1pmK5rNU86SbNkvsXUsoAbP7xP+TXfPZ3dvbSyxOGSrvO9tKk/X+/n4ybfI7ZVTOrpQ5Md+mCqJ4LrcWqPlTn3GPr+Qz2Ov1MhbqIh5tA9vX6XSyeFH9jdPkTmao94Dj/ZWvfCWdvxT3GVhEMLtAIBAIrDyC2d1DUOc4V4CU2dd1nVaH9L2QcbXb7bRS1Qz0XEW7o35/fz9zcvN6x44dSzn1+MlA1/X19QWfIM/lRUt1BeoZ+nn8zZs3s1yXy1bo/NsDZVWgwP3III4dO5ZVaNC8ix6sq8IRDUzW4zXzvgpOSvJ9ttkLbmomFfeD6f+dXdaFAqUqSvGMK8re2R7PQAIgy8WqAdeHiXC4TYVDZOr8+9q1a9l91OBqzxbU6/XSM03flhZ/pSCJ29SXymdbM73wvC4SUUGQh7jo+BKl0A493tluKc9oyWLhiQa0Xfps85z8jr+9wHeHYHaBQCAQWHkEs7vH4KmQNAO7+zgIrWKsvjeveqzScK+EUFJzOVus6zqrz9ZsNtOqusTwvAZdaZWswd/ue+Pq98aNG2kF7ZJ1VT2qT9ErGSg7cX+WsrHSCt3PoZ/OEtWn9k4k/svYnKLkG9RtzgSU2XnbtS6h+6JKdfo02N3ZorJEnosWiMuXL2fPkI4HmaBWb/CAbH6u/f/tnVuMXVd5x//f2HO1ndjFjnGCm0kjJyJEwqVJADWgNCoNoEopfaDhoUUtaqgU1FbqC/ACUoXUF4pUtVAFEQWkNoBaaFGFSisUioTScEkjiOPc7CT22I7HF3wbz5w5l9WHc741//PtdcaX8Xhmtv8/abTP7L3P3muvs89Z+/+t7zI2lo/lx3elCpTnsR3/3rClo1R7z4lqtNPpVNSbWxD4u+efu8+Vnz9/Pr/2tvo+nP6N+z6u47l4f+1V7MWlocFulREnmv1LWSqbw4mRo3v1+vXr8w+Mm3/8B/FNb3pT/uL4DwgPDFyYE+gvrunbOFeiO4pwWIG3IU7oczaLaL7h8AIOuYjniebCUv91Op38IxozoLBzQCyDMz8/P3DQabVaFWeSdevWVY5RMsuWMmLEQauUvSU6sfD1+L6jo6N5HZeQicfytoyOjlbuF3Z2KTnoLBZC4cdw5yPPt+r3FrDwebpzycTEROU+m5mZqQzoHOLAYQi8LCX9Xr9+fSXjCidEj4M9O9zEpOXr16+vPPj4Axevi1lZ5ubm8vV6aI+3+cyZM5XQkOHh4fz5HTt2rK/NQ0ND+Xvo32dxaciMKYQQovZI2a1SXKHwxLsrlWj+mpubq2TeYNd+X+dP3vxky0/fvs0VlC/dMeTcuXP5GJzFIrqce/tmZ2crZkx+UvdjsRLibBp8jdu2bSs6bTh+bjZnDarewNlVYlhCybw4qEwR0O+0UnKuKeWS9PNG5yBWwk5J2ZVUYjT/cdZ/dlry83BuT162Wq1Km9kcGk3D8/PzlfyqrCDjvcGhKFGpNZvNijMNu/9zGAKwcF9OTEz03XN+PdFUyZ9nNCs7pc+YM6FwMVWg+zn6PeRt5rb7dzA646xfv77iQNZqtSqVE1hRvv7665W2iYtHyk4IIUTtkbJbI5w4cSIHmO7cuRNAf8C1z8dxii+evwMW1NWxY8fyU67PvXGQsT+pxrp5p0+fzk/TrspSSvm1K0FOwVXKPejL6FTAlOr5DZrPYfd/Lmga92OVECsUcGBwLLi6WDqvkmNDaU4xzt2xomRnn0EhIeziHp0leH8OBI8p0eI9Urp+XsfXE+cjWUnFqgJ+bWZWub946e/jAq++f+x7dgSKc3EnT57sc+Ty9kWVxtc6aFspDKTRaOR5stL9wnligYXv1ObNmyvFgXne0PuC6zN6/b94/apisHSk7IQQQtQeKbs1yMGDBwEsPF1v2bIlP+W6SzQweM5qZmYmr/P93VuslNjZn1ivv/76PndvoD+NU5xvGhsby2mf/GnX1RxnZ2dPvzhfxtsGJSHmbVzjL87HlZRQrKk3PDxcSdDMQeZx22IVFEqqj5Vd/Hz4/TEkgBMbx33a7XbF47TVauU+5+QD3paoNHhuKM4XsfdmDLTnhOMlj824jVWZVyF3Zbd58+ZKtn/u71LFcaBr9YiVxDds2FBMw+bEpNKxv5mzZ8/m/TnI3YlelRyK4seL4TLnz5+vzEufOHGiMrfny0OHDlXaJS4NDXZrGHfx3rZtW6UkEBc09R85Nl/5lzCaZzqdTiWrCMeu+ZfVB7Fms5nXxVycwMKX1J0LSj/QbKKJuTr5xy7GP0WzFp97ZGSk8iPPpqE4aMX38/HZDBpNiGzii6bRkZGR3Id+LL6GQXFwvB8/qESzLA9U0Qll48aNlQcTjuHyPuewD6D7GUbHFg5jiDFlHI4RnV6Gh4cr1+0PZVNTU9ksHwub8nm47TEW0Puv0WhU8l9yuajovMMxq9EZh82/fp4zZ85UyiuxWT6aQLmvuDoIX2uz2ayE5Rw/fjwfw/vXHwjE0pEZUwghRO2RslvD8FO5Z0nnJ243ifg2VkvR2YEzXfATNsOOHX6siYmJShZ+f2JtNBr5iZaLqvr7OU+knzuaDrmdrlqiI8TIyEgxEDzm4+TwDN8/hkuwS3zpiX2xrP/RQaUUCM5qLKpLNrfF84yOjlYKtLJZz6/Dj7Vx48aKc0N04uDzOKxUOONOVIKcsSeaFVllRouDWyPOnDmTlecNN9yQrzUq4WiC5/bz+bx9rpLm5uby65hLkk2iJauHwyERpbAC74eo0NnUW1KVvvQ2P/fcc/m9se+jiVRcPlJ2Qgghao+UXQ14/vnncddddwFYmPdoNptZ2flTNQe/xlyHnA/SnUeiq30pp+TExEQOPYj5PJvNZq5QHQN8ec7C38e1wWL7SuqF1WWce+PAYYeVJNeJ4+vn/aLqYUXMxNCGxbLkL9Y+ni/lit68BKqOIBMTExVnHE7HFvuL3xv34WB0nouNrvCx+gFfN4cl+LF8Ts3/Hx8fx44dOwD0V3iP87E85zWoigFXuPfliRMn8n3M86tAV/15Gz1cJjreAAvhNZzH02HFFhM6OGwliHOQQL+Tlvebt8tDEMSVQ8pOCCFE7ZGyqwl79+4FsPDE7U+lwMI8gz/Nv/Wtb8Xtt98OoN9Vn9/P2zjxtM+DlEICHFYlMXyB69nFNE6dTqcyN+Jqjj37YvJeTuxcUmWxqnq73a5cB7uUl+aJnKhezawyj1VKfxaDxdm7kufeYgA411uLIQ7s9Rnn/Vg5R0rXxXOY0RN2eHg4e9NGBc2qyq/D+3ZmZiZv88/uxhtvBNBVfWyF8POUvF2dGHrgc16nTp3K5/Rr5nCB7du35+MD/SEk3s8c2O2fn8/Zbdu2rbLO38f3XlSHrN79+OyN6+m/eD5vsUQLYmlosKsJMccl4z8Ezv79+7Fr1y4A1dyQIyMj2bTjX2g3QXHJEjZnRpOj/+CMjo7mHzL/svOAxaY5oPsD4Os8bIAzqERnAh4IYgYRHhxiXJ6Z9bna8zZ2BIkOKhwHyFlDBhXv5JCAUoxcDCXgPiwNjvGhwt9XMjO22+0+pxtu19zcXF8OSb5GPncpl2gcCBuNRsWM69d1+vTp3M8ex+nOKIcPH66chweHGErBDlfxejjDicN94sWHnUajUQmJcJNio9GoOPSklPK5SzGLvr87wrDJMpqLvT+efPLJyv3VarUqpk1x5ZAZUwghRO2RsrsGOXXqFJ566ikAwPve9z4AyI4kwIJDS3xib7VaFZMg56yMrv68v8MB2NGEODo6WjFjsrLzJ/lS/sioYphoSmUlGIOleZvjT+MlJwMOiShlGYlOG/y+UjB6NJM6KaXc1lhBIl4bn4OXnF0/qgpWMTGpQKfTqZjoosLj126eHBsby/eSf45+b2zYsKESesFFeB1WfzG/qP/faDQqplS+9zyfq+9/9uzZ7HDi7XKlC6CSjWV2dravWgOwoOK4qK4fg0Np/LVfh2eL4ULF3mbfJpYHKTshhBC1R8ruGuXAgQMAFgJa3/nOdwLoPnHH2l2sAnybz1Ncd911FZfwWGUc6J8vAroqzp+g/al3fHy84jDDc4MxVIGf8GPIAjvORAUxPDyc9yspKCc6fXQ6nUowOtesi+3j2nhR2XH6Mz73YinEIuyoE0NCFgtG5kDw6LLP7eTrj8djJczt5zZwP7vK8rnljRs3VhRzqZYcX2uc/+RjxtRbs7OzFRXGc2ruwBUTFXDeUGdubi5fhx+/lBLM4fZ5nzz55JMA+q0m3qdelVwsLxrsrnGefvppAAtf+snJyYonIDs/+JfXfyzOnj2bnQ98APRjNRqNnADXv9huquEMH+zYMijPJFD1QmQzXnxfKWclb4tmT/agjLkR+YetlEvR9/Mfcn6fH7cUUxcdZ9hBJ3pelhyB2NmnlIWlNJj6/pwBhs/DWWLYESQ6n/ADUOxfHqD9XijltYz7L5a9hokDe6vVyv3sA9qpU6cq5kXPJLRhw4a8vztf+X3K1+rnYecv/3zYgzLmc2XHqx/96EcAFgY574eZmZlsXhVXB5kxhRBC1J4LKjszewzA7wKYTind2Vv3WQB/CsD196dTSt/tbfsUgI8BaAP485TS95ah3eIKs2fPHgBdM41XNPClOxxw+R/n5MmT2bTjT8f+RDw3N5eP4U/Svm9KKbtxs0MLOx0A/aa0qI74qT86L3DoQYzZYoXKZk+gX41F0yub8jjLzKDyMHw9UamyiYuzhgzKbLNu3bq+vKLxehz+fC6mQGmMWZyfn6+YXtlxJppV+f+YG7PUxlL5H24f53bl/dlBhc3E/r+v8yoB7XY795N/fsePHwfQ/ew8V6u3dXJyEkC/WZ4dtOJ96YqVt3lbXQm+/PLLmJqa6murn1fxdFefi1F2jwN4f2H9F1JKu3t/PtDdAeAhAG/rveeLZrau8F4hhBDiqnFBZZdS+qGZTV7k8R4E8PWUUgPAq2b2CoB7ADx12S0UVwV/4ux0OlnJuSrz7BmbNm2qVCMYHR2tBOH6/jx5H+f/ZmZm+hQg0J3r4KzwQP+8VFRh/NQfFUe73a7MCZbCEkrB2DEQ3p/UzSwfi4OyYyA0z0XFddxmDor3dsZ6aazsSvk/gf55w1J+zsXm1Er5HONnxseLyp7rxvm1umLn+c2YLYadffg6vM9LTh+xJp6r67m5uVzQuJQ1Jir08+fP52vztr700ksAgB07duS5PT/f+Ph4PqcrOs68EkMO3OmLKxtwRiOxMixlzu4TZvZzM3vMzLb01t0E4CDtM9VbJ4QQQqwYl+uN+SUAfw0g9ZafB/Anl3IAM3sYwMOXeX6xTPzyl7/MnmP+5Mwu/v5k//a3vx1ANyjXA3R9f64fxqoNWJjXO3bsWCUFE6uqUtXnQXXTuOo3ezHG+aiSp2IMnOc2cD0zv4YYoM3vjSEOXM+tNGe1WN27Uib9UnopX8bQgeHh4cqcIB8n1h4spbhiFRaD3VnNxZyV0aOSr4f7LAbJc27IONfJx/P7jIPFSwHZUeVyDsuoNP2zPnXqVE4v5ur6uuuuy+91q4Xf4/v376/McfJ1LZbGT1xdLmuwSykd9ddm9mUA/9H79xCAnbTrW3rrSsd4FMCjvWOU66CIFcG/rItldPAMLHfffXc2+3iWCf+hGhsbq+S/9Ewt09PTeVD1XJwAKkU4SybBOIhxfko2r7rJKf7QAtVimmyK9XP7kt8f482Ghob6MmZwG9gsGQdcdqBw2Dkkmgt5wIo5P1utVqW/uE/iNbfb7YrTTcnZg5elQrZ+nhiXxllZfL9YjLXRaFRi43jQLjmv+H5eANYdTqamphaN0YtZYrj9JTN7zMHJ2W5ixh2xdrgsM6aZ7aB/PwTAS+1+B8BDZjZqZrcA2AXgx0trohBCCLE0Lib04AkA9wHYamZTAD4D4D4z242uGfM1AB8HgJTSHjP7JoDnAbQAPJJSapeOK9Y2/qS7b98+3HrrrQAWlJ0rtU2bNvUpOd5nfHw8O8V4YC9n9oiMjo5WzGusNqJpc2RkpBKgXQpQjtUP2PTk69z0yqEEJbf8kmm0ZPYD+h0u2HU9mhedUj5Pvp4Y9G1mRfOt/+/timZc3r/UXyVTalS7XHA1qio2EccQgmaz2fea2zUzM5PvF3fn97JWvn4plMzSol5cjDfmRwqrv7LI/p8D8LmlNEoIIYS4kihdmFgSXIQ1ZosfHR3NYQw+/+dzY1u2bMnrXPXNzs5i587ulK+rQ56rKrn2+zK68Zfqv7EyLM1Lefu8jTHTf6lSQelYvq3RaORjxDCIUoovvtZS2+PxuQ2xll673c7roqMGK+HFHFt4WQo052MD5c8/Hovd/+MxZ2Zm+nJb+n5AV/27gnvhhRcA9DtCCXEhlC5MCCFE7ZGyE0tifn4ezzzzDADgnnvuAbDwtD83N5erK7iScLfxjRs35vk7zkrvT/SuCN3Vm9N/xYS7PJ/FIQjRQ4896Uru9b4tutKX5vxK81kx6XOiGnQOu8FHF31We6UA+HgsVnbeF66EONg7hg1wnb0438YenDyfN8jbkVV1VHZ8bu8bnht1Be1euSdPnszzpXwdQDck4NVXXwUAufOLy0KDnbhiuHnJB79Wq5WdO7Zv3w4A2L17N4Cuo4pXS/AfyQ0bNlTyTPqAyFnyY27IkZGRSiYUNr1FcyQPdtHhhH/UOXuLb4uDXCkvZWyLtye2JQ5onCUmxtlxXFqpECyXSfL9fTBx/H/eFmMEm81mxVRZum4mmmF5EPeBLJaLajQa2SzpZmzORhLDPw4dOtRXYFWIS0VmTCGEELVHyk5cMfwpnomFM11JvPvd786ZKvxpf+PGjVmZ+LFcCfm+QNVJguvMsUJzZ4joqs9OJTGDCJvlYlaWUqZ+phQQXnL28GOWqh7EY3HgfLxub9fIyEg+hmezaTabfZUZ+Jjz8/P5uv0Ybi7kmnKl3JgOB8Rz3TtecsC9KzRXlMePH8cbb7zRdz3Dw8P5c/CCph44vlgxWiEuBik7IYQQtUfKTlxVXnvtNQDduTtXBJxuLFZJd0W4devWSjoyTnUV58tKgdNx3gxApcpCp9Mpzj35sUspuEq5KoGuKouhBxw2wO7+TlSVrCRjuziPZlR7ZpaVIldoALrqajHnnRiMXgra5wD3uI0dVfy157H07P9HjhzJny2HSBw6dKhvv8WUtBCXggY7sSI888wzuPvuuwEsDA4bNmzoM+UBC4PdoUOHcMMNNwBY+FHlAa5UvNXx/ePgB1RNj61Wq2K+LGUscdi8FrOlcMwel/Hx/0seodEDlAfJ6LzCptt4HVxU1vuU2xfNl/y+eG52Dop90Gq1Kh6kft7Tp09n5xM3R7on5fHjx7Fv376+a9XAJpYTmTGFEELUHik7sSIcOHAA999/P4B+h5NYCcAV29mzZ7OTg5s93eTJyoNza0aToyshVmPRvMjrYmZ8bqvTaDQGlgtiM563yx1IhoeHi4oxtpnNkjGEghVqNP9yW6NSGxkZqWTtZ1XmSjBmpfHrjeeO2zhcwJ1QPFvOnj17APTHyknRiauBlJ0QQojaI2UnVoxvfetbAIAHHngAQNdBxdWBB6O7EkopZacFX+f16koqgzPus8OIL2NOSJ7zi5n62fEk1jrjTCVR4bEaK+WSdFhBDZqzm5uby8eKwe6jo6OVorKs8GIbhoeH835+LA5jiGEcZpbb7cqbs8y4enX15k4mU1NTOHz4MADg5ZdfBgAFhosVQ8pOCCFE7ZGyEyuGB47/4Ac/AADs3LkzVzu47bbbAAB33nkngP5gZ1cjPk/FrvTs4ehqJaYEa7fbeZ0rSVZsg5Rds9nM+/Oxo9s/Vy6PgdaugnjesFTDj/OL+nmipykrtej1OTY2VlGJnA8zzj3GvmU49MDh6/fgdfe4fOWVVwB0ld3rr79euV4hVgINdmLF8WwZ586dy4Odr/Mf1a1bt1Z+cLmwZ/yxL+Hv5wHKj+HmtXXr1lXixTgsoTTYxXaVirH6+TxDzPz8fMVUOTQ0NNAc2el0stnW28WDXskZJZb44TI7PvhEx575+flKCMHo6GjF+cQdTI4ePZqdUNxU+eKLLwLoxsppkBOrBZkxhRBC1B4pO7FqmJ2dzWY7Vw6uuHbt2oXJyUkAC04SnpXj7Nmz2STKzh7RrOgmupRSJRMI56KMJkc2/5VK9pQyjvgyBkxzYVeHi51GJcQqy/vG2+4ZZcbGxioB5/Pz8/m4bmb09zebzUULx/oxXEl2Op18DFdxBw8eBNBVc3Gdqz+FFIjVhJSdEEKI2iNlJ1YVUY34ct++fdi5cycA4N577wWw4KBy5syZnFaM57V8e6yDByyoPN/f9x0ZGak4pjicg9MZGhqqOMCUUpU5pZp0HNgeC6CyM050OCkF0PtxOVTBFZ0fc2xsrDJn6aEefCxXn0ePHsVLL70EYCG3qRdSnZqaygpbiNWMlJ0QQojaY6vBrm5mK46XM/wAAAhnSURBVN8IsebYunUrAGBycjKrF1coPP/nHp6u7IaHh7NXpCs6roge1Rsnkh7kss/4ecbGxrLKc5VVSnHGipPDCbjt4+PjeX+u7O7LzZs397Wn3W73VTnw9vu2qDC9XadOncoVxH0ubv/+/VnZnTx5EsCCt+xq+P0QgvhZSumu0gaZMcWaxeO6Tp8+nc2R7tDCA5abCX1Ae/Ob35wHSh/0ODYuhhLEAYvhgSqaGTm7Sozn44KrnNklhk64s0e73a44k3B2mWjaHBoayueKWWJmZ2ez6dGznri5+PDhwzkDig9o09PTleKrQqw1ZMYUQghRe2TGFNcM7JTiZjtXR27+nJiYwLZt2/reFzOxAP0ZR2IxVc4zybkteTkyMpLb40s2Vfr30o89Pj6eqz3ceOONfW1PKVXyZZ47dy4rM1dtboKcnp7G0aNH+9b5PjMzM9l8GZ1lhFgDDDRjStkJIYSoPZqzE9cMrlTm5+ezknH1w/g8nCspd/7wSunAgkNHp9PJ+7s7vs8Hbtq0Ke/vio5Td8X8l+fPn68Eu7MDjStHn5fk8IzohDI9PY2pqSkAyA4nXjVibm6u4rziSk+IuiJlJ4QQovZI2QkRYHUE9KcU83kyp91uY+/evQAWVNKBAwcAdOf4du/eDWBBVfq83/XXX59VHM/5sWcmt6XZbOb5NVeCnC7NX3vatHPnzmVPy1jhPIZPCHEtIAcVIS6SoaEh3HTTTQAWwhiOHDmSB5joXMLceuutAPorIrhTyc033wyg64QSywtx+IC7/7s50s87OzubB9pSeIQQ1xByUBFCCHHtImUnxFXEg983bdpUUW87duzIuSeFEJeFlJ0QQohrFyk7IYQQdUHKTgghxLXLBQc7M3vMzKbN7Dla9w0ze7b395qZPdtbP2lms7TtH5ez8UIIIcTFcDFxdo8D+HsAX/MVKaU/8Ndm9nkAXL1xX0pp95VqoBBCCLFULjjYpZR+aGaTpW3WDSz6MID7r2yzhBBCiCvHUufs3gPgaErpZVp3i5n9n5n9j5m9Z4nHF0IIIZbMUtOFfQTAE/T/EQC/mlI6YWa/AeDfzOxtKaUz8Y1m9jCAh5d4fiGEEOKCXLayM7P1AH4fwDd8XUqpkVI60Xv9MwD7ANxWen9K6dGU0l2D3ESFEEKIK8VSzJi/DeCFlNKUrzCzbWa2rvf61wDsArB/aU0UQgghlsbFhB48AeApALeb2ZSZfay36SH0mzAB4L0Aft4LRfgXAH+WUjp5JRsshBBCXCrKoCKEEKIuKIOKEEKIaxcNdkIIIWqPBjshhBC1R4OdEEKI2qPBTgghRO3RYCeEEKL2aLATQghRezTYCSGEqD0a7IQQQtQeDXZCCCFqjwY7IYQQtUeDnRBCiNqjwU4IIUTt0WAnhBCi9miwE0IIUXs02AkhhKg9GuyEEELUHg12Qgghao8GOyGEELVHg50QQojao8FOCCFE7dFgJ4QQovZosBNCCFF7NNgJIYSoPRrshBBC1B4NdkIIIWqPBjshhBC1R4OdEEKI2qPBTgghRO3RYCeEEKL2aLATQghRezTYCSGEqD3rV7oBPY4DmOktxZVnK9S3y4H6dXlQvy4fde/bmwdtsJTS1WzIQMzspymlu1a6HXVEfbs8qF+XB/Xr8nEt963MmEIIIWqPBjshhBC1ZzUNdo+udANqjPp2eVC/Lg/q1+Xjmu3bVTNnJ4QQQiwXq0nZCSGEEMvCqhjszOz9Zvaimb1iZp9c6fasZczsNTP7hZk9a2Y/7a37FTP7bzN7ubfcstLtXAuY2WNmNm1mz9G6Yl9al7/r3cM/N7N3rFzLVzcD+vWzZnaod98+a2YfpG2f6vXri2b2wMq0evVjZjvN7Ekze97M9pjZX/TW657FKhjszGwdgH8A8AEAdwD4iJndsbKtWvP8VkppN7kYfxLA91NKuwB8v/e/uDCPA3h/WDeoLz8AYFfv72EAX7pKbVyLPI5qvwLAF3r37e6U0ncBoPdb8BCAt/Xe88Xeb4ao0gLwVymlOwC8C8Ajvf7TPYtVMNgBuAfAKyml/SmleQBfB/DgCrepbjwI4Ku9118F8Hsr2JY1Q0rphwBOhtWD+vJBAF9LXf4XwGYz23F1Wrq2GNCvg3gQwNdTSo2U0qsAXkH3N0MEUkpHUkrP9F6fBbAXwE3QPQtgdQx2NwE4SP9P9daJyyMB+C8z+5mZPdxbtz2ldKT3+g0A21emabVgUF/qPl46n+iZ0x4jU7v69TIws0kAvw7gaeieBbA6BjtxZbk3pfQOdE0Uj5jZe3lj6rrfygX3CqC+vKJ8CcCtAHYDOALg8yvbnLWLmW0E8K8A/jKldIa3Xcv37GoY7A4B2En/v6W3TlwGKaVDveU0gG+ja/I56uaJ3nJ65Vq45hnUl7qPl0BK6WhKqZ1S6gD4MhZMlerXS8DMhtEd6P4ppfSt3mrds1gdg91PAOwys1vMbATdyejvrHCb1iRmtsHMNvlrAL8D4Dl0+/Ojvd0+CuDfV6aFtWBQX34HwB/1PNzeBeA0mY7EBQhzRR9C974Fuv36kJmNmtkt6DpT/Phqt28tYGYG4CsA9qaU/pY26Z7FKqh6kFJqmdknAHwPwDoAj6WU9qxws9Yq2wF8u3vPYz2Af04p/aeZ/QTAN83sYwBeB/DhFWzjmsHMngBwH4CtZjYF4DMA/gblvvwugA+i60BxHsAfX/UGrxEG9Ot9ZrYbXRPbawA+DgAppT1m9k0Az6PrbfhISqm9Eu1eA/wmgD8E8Asze7a37tPQPQtAGVSEEEJcA6wGM6YQQgixrGiwE0IIUXs02AkhhKg9GuyEEELUHg12Qgghao8GOyGEELVHg50QQojao8FOCCFE7fl/j9R/E8a4WAUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#All the labels are correct....As checked using MRICRON\n",
    "pdataset.disp_detection('4',214)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>[142, 133, 82]</td>\n",
       "      <td>[[[2551.3567, 2555.9075, 2553.1658, 2540.4075,...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>[139, 142, 65]</td>\n",
       "      <td>[[[2408.4695, 2475.584, 2553.3855, 2612.0293, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>[138, 142, 69]</td>\n",
       "      <td>[[[2688.436, 2751.2214, 2771.8093, 2783.0876, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>[117, 105, 61]</td>\n",
       "      <td>[[[1809.8258, 1731.849, 1562.9722, 1338.0624, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>[95, 149, 56]</td>\n",
       "      <td>[[[1974.1235, 1878.3071, 1924.5144, 2106.6187,...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RST_peak                                        image_patch  label  \\\n",
       "446  [142, 133, 82]  [[[2551.3567, 2555.9075, 2553.1658, 2540.4075,...      1   \n",
       "449  [139, 142, 65]  [[[2408.4695, 2475.584, 2553.3855, 2612.0293, ...      1   \n",
       "450  [138, 142, 69]  [[[2688.436, 2751.2214, 2771.8093, 2783.0876, ...      1   \n",
       "459  [117, 105, 61]  [[[1809.8258, 1731.849, 1562.9722, 1338.0624, ...      1   \n",
       "466   [95, 149, 56]  [[[1974.1235, 1878.3071, 1924.5144, 2106.6187,...      1   \n",
       "\n",
       "    patient_name  \n",
       "446            5  \n",
       "449            5  \n",
       "450            5  \n",
       "459            5  \n",
       "466            5  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient 5\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '5')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('5',459)\n",
    "#This is not a CMB.....Also confirmed using MRIcron and Dr. Hazarika"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the wrong labelled points to zero.....\n",
    "pdataset.dataset.loc[446, 'label'] = 0\n",
    "pdataset.dataset.loc[450, 'label'] = 0  #Repeating point with misplaced center\n",
    "pdataset.dataset.loc[459, 'label'] = 0\n",
    "pdataset.dataset.loc[466, 'label'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>[139, 142, 65]</td>\n",
       "      <td>[[[2408.4695, 2475.584, 2553.3855, 2612.0293, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RST_peak                                        image_patch  label  \\\n",
       "449  [139, 142, 65]  [[[2408.4695, 2475.584, 2553.3855, 2612.0293, ...      1   \n",
       "\n",
       "    patient_name  \n",
       "449            5  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remaining labels in the patient 5 are now correct\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '5')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
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       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>[134, 110, 37]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 196.0366, 143.74533, 250.047...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>[132, 149, 28]</td>\n",
       "      <td>[[[1450.9822, 1510.8649, 1566.1918, 1608.9363,...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>[124, 88, 75]</td>\n",
       "      <td>[[[2143.4924, 2137.8994, 2135.98, 2142.6934, 2...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>[119, 126, 135]</td>\n",
       "      <td>[[[2290.9626, 2310.4807, 2318.1333, 2322.1401,...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>[110, 139, 54]</td>\n",
       "      <td>[[[342.14502, 956.3019, 1422.5354, 1558.2509, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>[110, 103, 115]</td>\n",
       "      <td>[[[2215.8376, 2217.8628, 2196.3394, 2174.253, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>[85, 140, 54]</td>\n",
       "      <td>[[[1814.7252, 1721.9404, 1629.695, 1473.8016, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>[80, 139, 53]</td>\n",
       "      <td>[[[2202.0847, 2237.8328, 2240.1602, 2214.826, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>[77, 140, 44]</td>\n",
       "      <td>[[[2046.9435, 2091.3289, 2136.6199, 2180.5178,...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "      <th>199</th>\n",
       "      <td>[59, 144, 22]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 434.43127, 1138.0171, 1221.4...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>[57, 148, 39]</td>\n",
       "      <td>[[[1850.9685, 1877.6367, 1903.4276, 1878.0004,...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>[55, 109, 38]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 77.64828, 477...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RST_peak                                        image_patch  \\\n",
       "140   [134, 110, 37]  [[[0.0, 0.0, 0.0, 196.0366, 143.74533, 250.047...   \n",
       "141   [132, 149, 28]  [[[1450.9822, 1510.8649, 1566.1918, 1608.9363,...   \n",
       "147    [124, 88, 75]  [[[2143.4924, 2137.8994, 2135.98, 2142.6934, 2...   \n",
       "149  [119, 126, 135]  [[[2290.9626, 2310.4807, 2318.1333, 2322.1401,...   \n",
       "155   [110, 139, 54]  [[[342.14502, 956.3019, 1422.5354, 1558.2509, ...   \n",
       "156  [110, 103, 115]  [[[2215.8376, 2217.8628, 2196.3394, 2174.253, ...   \n",
       "183    [85, 140, 54]  [[[1814.7252, 1721.9404, 1629.695, 1473.8016, ...   \n",
       "186    [80, 139, 53]  [[[2202.0847, 2237.8328, 2240.1602, 2214.826, ...   \n",
       "188    [77, 140, 44]  [[[2046.9435, 2091.3289, 2136.6199, 2180.5178,...   \n",
       "199    [59, 144, 22]  [[[0.0, 0.0, 0.0, 434.43127, 1138.0171, 1221.4...   \n",
       "202    [57, 148, 39]  [[[1850.9685, 1877.6367, 1903.4276, 1878.0004,...   \n",
       "203    [55, 109, 38]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 77.64828, 477...   \n",
       "\n",
       "     label patient_name  \n",
       "140      1            6  \n",
       "141      1            6  \n",
       "147      1            6  \n",
       "149      1            6  \n",
       "155      1            6  \n",
       "156      1            6  \n",
       "183      1            6  \n",
       "186      1            6  \n",
       "188      1            6  \n",
       "199      1            6  \n",
       "202      1            6  \n",
       "203      1            6  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient 6\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '6')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('6',203)\n",
    "#Not a CMB....nothing seen on MRIcron as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the wrong labelled points to zero.....\n",
    "pdataset.dataset.loc[140, 'label'] = 0\n",
    "pdataset.dataset.loc[141, 'label'] = 0\n",
    "pdataset.dataset.loc[147, 'label'] = 0\n",
    "pdataset.dataset.loc[149, 'label'] = 0\n",
    "pdataset.dataset.loc[155, 'label'] = 0\n",
    "pdataset.dataset.loc[156, 'label'] = 0\n",
    "pdataset.dataset.loc[183, 'label'] = 0\n",
    "pdataset.dataset.loc[186, 'label'] = 0\n",
    "pdataset.dataset.loc[188, 'label'] = 0\n",
    "pdataset.dataset.loc[199, 'label'] = 0\n",
    "pdataset.dataset.loc[202, 'label'] = 0\n",
    "pdataset.dataset.loc[203, 'label'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [RST_peak, image_patch, label, patient_name]\n",
       "Index: []"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remaining labels in the patient 6 are now correct\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '6')]\n",
    "#No correct CMBs detected in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>[140, 154, 45]</td>\n",
       "      <td>[[[1688.382, 1597.4652, 1475.8611, 1365.1674, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>[131, 142, 52]</td>\n",
       "      <td>[[[1690.1099, 1668.2125, 1653.0569, 1564.124, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>[122, 90, 59]</td>\n",
       "      <td>[[[1432.6627, 1474.22, 1594.2905, 1735.7402, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>[116, 106, 62]</td>\n",
       "      <td>[[[1445.7876, 1448.7748, 1428.6733, 1410.3706,...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>[110, 141, 125]</td>\n",
       "      <td>[[[1772.6095, 1765.7646, 1773.8698, 1790.8541,...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>[73, 84, 51]</td>\n",
       "      <td>[[[1494.8796, 1504.3376, 1480.5326, 1433.6578,...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>[30, 103, 63]</td>\n",
       "      <td>[[[116.86486, 304.68124, 447.71936, 507.62054,...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RST_peak                                        image_patch  \\\n",
       "375   [140, 154, 45]  [[[1688.382, 1597.4652, 1475.8611, 1365.1674, ...   \n",
       "380   [131, 142, 52]  [[[1690.1099, 1668.2125, 1653.0569, 1564.124, ...   \n",
       "387    [122, 90, 59]  [[[1432.6627, 1474.22, 1594.2905, 1735.7402, 1...   \n",
       "389   [116, 106, 62]  [[[1445.7876, 1448.7748, 1428.6733, 1410.3706,...   \n",
       "391  [110, 141, 125]  [[[1772.6095, 1765.7646, 1773.8698, 1790.8541,...   \n",
       "418     [73, 84, 51]  [[[1494.8796, 1504.3376, 1480.5326, 1433.6578,...   \n",
       "435    [30, 103, 63]  [[[116.86486, 304.68124, 447.71936, 507.62054,...   \n",
       "\n",
       "     label patient_name  \n",
       "375      1            7  \n",
       "380      1            7  \n",
       "387      1            7  \n",
       "389      1            7  \n",
       "391      1            7  \n",
       "418      1            7  \n",
       "435      1            7  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient 7\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '7')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('7',435)\n",
    "#This is the edge of the cortex region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the wrong labelled points to zero.....\n",
    "pdataset.dataset.loc[375, 'label'] = 0\n",
    "pdataset.dataset.loc[380, 'label'] = 0   #Blood vessel end looking like microbleed\n",
    "pdataset.dataset.loc[389, 'label'] = 0 \n",
    "pdataset.dataset.loc[391, 'label'] = 0 \n",
    "pdataset.dataset.loc[418, 'label'] = 0 \n",
    "pdataset.dataset.loc[435, 'label'] = 0 #Sulcii and edges of the cortex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>[122, 90, 59]</td>\n",
       "      <td>[[[1432.6627, 1474.22, 1594.2905, 1735.7402, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RST_peak                                        image_patch  label  \\\n",
       "387  [122, 90, 59]  [[[1432.6627, 1474.22, 1594.2905, 1735.7402, 1...      1   \n",
       "\n",
       "    patient_name  \n",
       "387            7  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remaining labels in the patient 7 are now correct\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '7')]\n",
    "#No correct CMBs detected in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>[145, 97, 90]</td>\n",
       "      <td>[[[2576.3916, 2574.2893, 2554.3533, 2532.3188,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>[141, 71, 82]</td>\n",
       "      <td>[[[2134.3745, 2138.9934, 2172.1443, 2210.0732,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>[137, 167, 98]</td>\n",
       "      <td>[[[2111.8208, 2146.8962, 2175.7349, 2199.1118,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>[86, 182, 62]</td>\n",
       "      <td>[[[2203.7852, 2308.6045, 2387.8716, 2453.87, 2...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>[86, 181, 58]</td>\n",
       "      <td>[[[2039.4565, 2092.0078, 2142.0762, 2189.4058,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>[65, 145, 75]</td>\n",
       "      <td>[[[1850.9977, 1777.6261, 1713.4049, 1655.6232,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>[65, 145, 70]</td>\n",
       "      <td>[[[2052.042, 2071.882, 2063.082, 2001.6915, 19...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>[61, 110, 75]</td>\n",
       "      <td>[[[1180.2657, 1180.0599, 1237.1514, 1302.8916,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>[60, 153, 33]</td>\n",
       "      <td>[[[1767.4229, 1840.2651, 1912.6459, 1865.2168,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>[58, 80, 126]</td>\n",
       "      <td>[[[1480.1185, 1472.2357, 1457.7295, 1503.3276,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>[58, 66, 108]</td>\n",
       "      <td>[[[1779.4401, 1824.6875, 1869.6906, 1852.0815,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>[55, 97, 83]</td>\n",
       "      <td>[[[1332.5581, 1347.55, 1359.3192, 1353.7123, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>[52, 112, 83]</td>\n",
       "      <td>[[[319.9311, 472.5236, 694.97595, 1068.5575, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>[50, 68, 46]</td>\n",
       "      <td>[[[1820.1744, 1142.6737, 123.96429, 0.0, 0.0, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>[38, 113, 107]</td>\n",
       "      <td>[[[1639.0476, 1655.6733, 1673.211, 1670.772, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RST_peak                                        image_patch  label  \\\n",
       "100   [145, 97, 90]  [[[2576.3916, 2574.2893, 2554.3533, 2532.3188,...      1   \n",
       "102   [141, 71, 82]  [[[2134.3745, 2138.9934, 2172.1443, 2210.0732,...      1   \n",
       "104  [137, 167, 98]  [[[2111.8208, 2146.8962, 2175.7349, 2199.1118,...      1   \n",
       "110   [86, 182, 62]  [[[2203.7852, 2308.6045, 2387.8716, 2453.87, 2...      1   \n",
       "111   [86, 181, 58]  [[[2039.4565, 2092.0078, 2142.0762, 2189.4058,...      1   \n",
       "112   [65, 145, 75]  [[[1850.9977, 1777.6261, 1713.4049, 1655.6232,...      1   \n",
       "113   [65, 145, 70]  [[[2052.042, 2071.882, 2063.082, 2001.6915, 19...      1   \n",
       "114   [61, 110, 75]  [[[1180.2657, 1180.0599, 1237.1514, 1302.8916,...      1   \n",
       "115   [60, 153, 33]  [[[1767.4229, 1840.2651, 1912.6459, 1865.2168,...      1   \n",
       "116   [58, 80, 126]  [[[1480.1185, 1472.2357, 1457.7295, 1503.3276,...      1   \n",
       "117   [58, 66, 108]  [[[1779.4401, 1824.6875, 1869.6906, 1852.0815,...      1   \n",
       "118    [55, 97, 83]  [[[1332.5581, 1347.55, 1359.3192, 1353.7123, 1...      1   \n",
       "119   [52, 112, 83]  [[[319.9311, 472.5236, 694.97595, 1068.5575, 1...      1   \n",
       "120    [50, 68, 46]  [[[1820.1744, 1142.6737, 123.96429, 0.0, 0.0, ...      1   \n",
       "122  [38, 113, 107]  [[[1639.0476, 1655.6733, 1673.211, 1670.772, 1...      1   \n",
       "\n",
       "    patient_name  \n",
       "100            8  \n",
       "102            8  \n",
       "104            8  \n",
       "110            8  \n",
       "111            8  \n",
       "112            8  \n",
       "113            8  \n",
       "114            8  \n",
       "115            8  \n",
       "116            8  \n",
       "117            8  \n",
       "118            8  \n",
       "119            8  \n",
       "120            8  \n",
       "122            8  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient 8\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '8')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('8',122)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the wrong labelled points to zero.....\n",
    "pdataset.dataset.loc[100, 'label'] = 0\n",
    "pdataset.dataset.loc[110, 'label'] = 0  #Repeating microbleed\n",
    "pdataset.dataset.loc[115, 'label'] = 0\n",
    "pdataset.dataset.loc[120, 'label'] = 0\n",
    "pdataset.dataset.loc[122, 'label'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>[141, 71, 82]</td>\n",
       "      <td>[[[2134.3745, 2138.9934, 2172.1443, 2210.0732,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>[137, 167, 98]</td>\n",
       "      <td>[[[2111.8208, 2146.8962, 2175.7349, 2199.1118,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>[86, 181, 58]</td>\n",
       "      <td>[[[2039.4565, 2092.0078, 2142.0762, 2189.4058,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>[65, 145, 75]</td>\n",
       "      <td>[[[1850.9977, 1777.6261, 1713.4049, 1655.6232,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>[65, 145, 70]</td>\n",
       "      <td>[[[2052.042, 2071.882, 2063.082, 2001.6915, 19...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>[61, 110, 75]</td>\n",
       "      <td>[[[1180.2657, 1180.0599, 1237.1514, 1302.8916,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>[58, 80, 126]</td>\n",
       "      <td>[[[1480.1185, 1472.2357, 1457.7295, 1503.3276,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>[58, 66, 108]</td>\n",
       "      <td>[[[1779.4401, 1824.6875, 1869.6906, 1852.0815,...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>[55, 97, 83]</td>\n",
       "      <td>[[[1332.5581, 1347.55, 1359.3192, 1353.7123, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>[52, 112, 83]</td>\n",
       "      <td>[[[319.9311, 472.5236, 694.97595, 1068.5575, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RST_peak                                        image_patch  label  \\\n",
       "102   [141, 71, 82]  [[[2134.3745, 2138.9934, 2172.1443, 2210.0732,...      1   \n",
       "104  [137, 167, 98]  [[[2111.8208, 2146.8962, 2175.7349, 2199.1118,...      1   \n",
       "111   [86, 181, 58]  [[[2039.4565, 2092.0078, 2142.0762, 2189.4058,...      1   \n",
       "112   [65, 145, 75]  [[[1850.9977, 1777.6261, 1713.4049, 1655.6232,...      1   \n",
       "113   [65, 145, 70]  [[[2052.042, 2071.882, 2063.082, 2001.6915, 19...      1   \n",
       "114   [61, 110, 75]  [[[1180.2657, 1180.0599, 1237.1514, 1302.8916,...      1   \n",
       "116   [58, 80, 126]  [[[1480.1185, 1472.2357, 1457.7295, 1503.3276,...      1   \n",
       "117   [58, 66, 108]  [[[1779.4401, 1824.6875, 1869.6906, 1852.0815,...      1   \n",
       "118    [55, 97, 83]  [[[1332.5581, 1347.55, 1359.3192, 1353.7123, 1...      1   \n",
       "119   [52, 112, 83]  [[[319.9311, 472.5236, 694.97595, 1068.5575, 1...      1   \n",
       "\n",
       "    patient_name  \n",
       "102            8  \n",
       "104            8  \n",
       "111            8  \n",
       "112            8  \n",
       "113            8  \n",
       "114            8  \n",
       "116            8  \n",
       "117            8  \n",
       "118            8  \n",
       "119            8  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remaining labels in the patient 8 are now correct\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '8')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>[56, 119, 105]</td>\n",
       "      <td>[[[1758.0206, 1762.9435, 1770.1068, 1766.2421,...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>[48, 85, 103]</td>\n",
       "      <td>[[[1605.1948, 1597.0873, 1584.0826, 1564.188, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RST_peak                                        image_patch  label  \\\n",
       "440  [56, 119, 105]  [[[1758.0206, 1762.9435, 1770.1068, 1766.2421,...      1   \n",
       "441   [48, 85, 103]  [[[1605.1948, 1597.0873, 1584.0826, 1564.188, ...      1   \n",
       "\n",
       "    patient_name  \n",
       "440            9  \n",
       "441            9  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient 9\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '9')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('9',441)\n",
    "#No incorrect detections here....."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>[148, 90, 75]</td>\n",
       "      <td>[[[2318.3162, 2329.3535, 2327.0522, 2317.8542,...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>[108, 173, 95]</td>\n",
       "      <td>[[[2021.6532, 1919.8341, 1805.5599, 1663.6737,...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RST_peak                                        image_patch  label  \\\n",
       "436   [148, 90, 75]  [[[2318.3162, 2329.3535, 2327.0522, 2317.8542,...      1   \n",
       "437  [108, 173, 95]  [[[2021.6532, 1919.8341, 1805.5599, 1663.6737,...      1   \n",
       "\n",
       "    patient_name  \n",
       "436           10  \n",
       "437           10  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient 10\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '10')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('10',437)\n",
    "#No incorrect detections here...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>[158, 101, 84]</td>\n",
       "      <td>[[[1833.6031, 1757.3969, 1677.1187, 1583.645, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>[158, 90, 77]</td>\n",
       "      <td>[[[1694.1539, 1776.5427, 1857.2911, 1918.053, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>[155, 66, 92]</td>\n",
       "      <td>[[[1768.0466, 1748.4073, 1767.395, 1771.5117, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>[151, 78, 46]</td>\n",
       "      <td>[[[1671.243, 1683.8589, 1738.353, 1759.2461, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>[148, 66, 83]</td>\n",
       "      <td>[[[1595.6456, 1564.5089, 1524.9172, 1520.9941,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>[146, 71, 120]</td>\n",
       "      <td>[[[1926.1199, 1922.8368, 1939.3748, 1996.1311,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>[142, 107, 88]</td>\n",
       "      <td>[[[2039.5864, 1844.112, 1705.1454, 1607.4609, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>[142, 79, 120]</td>\n",
       "      <td>[[[2135.4863, 2072.649, 2010.3287, 1955.9777, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>[140, 60, 71]</td>\n",
       "      <td>[[[1848.844, 1871.6157, 1879.5189, 1888.7152, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>[124, 83, 56]</td>\n",
       "      <td>[[[1581.8629, 1619.6411, 1632.0082, 1628.4435,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>[102, 68, 104]</td>\n",
       "      <td>[[[1030.3358, 996.40894, 956.571, 926.6099, 90...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>[101, 66, 99]</td>\n",
       "      <td>[[[1447.9072, 1501.1353, 1555.6167, 1556.9982,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>[101, 57, 71]</td>\n",
       "      <td>[[[1256.8654, 1155.5258, 1027.8593, 897.02686,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>[87, 75, 111]</td>\n",
       "      <td>[[[1989.562, 2060.2195, 2144.162, 2216.1619, 2...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>[85, 73, 84]</td>\n",
       "      <td>[[[1778.518, 1839.9125, 1922.4174, 2000.3539, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>[84, 74, 89]</td>\n",
       "      <td>[[[2039.7502, 2064.2544, 2072.1475, 2075.4563,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>[80, 142, 132]</td>\n",
       "      <td>[[[1762.771, 1806.5059, 1850.5065, 1867.1599, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>[78, 62, 61]</td>\n",
       "      <td>[[[1402.7825, 1430.7272, 1490.0577, 1551.9355,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>[69, 141, 83]</td>\n",
       "      <td>[[[1232.1311, 1314.0474, 1433.2991, 1566.3601,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>[58, 52, 86]</td>\n",
       "      <td>[[[1433.638, 1434.7697, 1463.0463, 1527.761, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>[58, 50, 81]</td>\n",
       "      <td>[[[1108.4706, 1163.9814, 1192.8955, 1176.3685,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>[55, 111, 79]</td>\n",
       "      <td>[[[1702.3887, 1739.2933, 1761.8115, 1776.1716,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>[52, 71, 124]</td>\n",
       "      <td>[[[943.41815, 726.77374, 503.3569, 262.3121, 8...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>[51, 50, 66]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>[49, 153, 81]</td>\n",
       "      <td>[[[2011.198, 2011.3706, 1883.7678, 1661.8715, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RST_peak                                        image_patch  label  \\\n",
       "60  [158, 101, 84]  [[[1833.6031, 1757.3969, 1677.1187, 1583.645, ...      1   \n",
       "61   [158, 90, 77]  [[[1694.1539, 1776.5427, 1857.2911, 1918.053, ...      1   \n",
       "62   [155, 66, 92]  [[[1768.0466, 1748.4073, 1767.395, 1771.5117, ...      1   \n",
       "63   [151, 78, 46]  [[[1671.243, 1683.8589, 1738.353, 1759.2461, 1...      1   \n",
       "64   [148, 66, 83]  [[[1595.6456, 1564.5089, 1524.9172, 1520.9941,...      1   \n",
       "65  [146, 71, 120]  [[[1926.1199, 1922.8368, 1939.3748, 1996.1311,...      1   \n",
       "66  [142, 107, 88]  [[[2039.5864, 1844.112, 1705.1454, 1607.4609, ...      1   \n",
       "67  [142, 79, 120]  [[[2135.4863, 2072.649, 2010.3287, 1955.9777, ...      1   \n",
       "68   [140, 60, 71]  [[[1848.844, 1871.6157, 1879.5189, 1888.7152, ...      1   \n",
       "70   [124, 83, 56]  [[[1581.8629, 1619.6411, 1632.0082, 1628.4435,...      1   \n",
       "74  [102, 68, 104]  [[[1030.3358, 996.40894, 956.571, 926.6099, 90...      1   \n",
       "75   [101, 66, 99]  [[[1447.9072, 1501.1353, 1555.6167, 1556.9982,...      1   \n",
       "76   [101, 57, 71]  [[[1256.8654, 1155.5258, 1027.8593, 897.02686,...      1   \n",
       "85   [87, 75, 111]  [[[1989.562, 2060.2195, 2144.162, 2216.1619, 2...      1   \n",
       "87    [85, 73, 84]  [[[1778.518, 1839.9125, 1922.4174, 2000.3539, ...      1   \n",
       "88    [84, 74, 89]  [[[2039.7502, 2064.2544, 2072.1475, 2075.4563,...      1   \n",
       "89  [80, 142, 132]  [[[1762.771, 1806.5059, 1850.5065, 1867.1599, ...      1   \n",
       "90    [78, 62, 61]  [[[1402.7825, 1430.7272, 1490.0577, 1551.9355,...      1   \n",
       "91   [69, 141, 83]  [[[1232.1311, 1314.0474, 1433.2991, 1566.3601,...      1   \n",
       "93    [58, 52, 86]  [[[1433.638, 1434.7697, 1463.0463, 1527.761, 1...      1   \n",
       "94    [58, 50, 81]  [[[1108.4706, 1163.9814, 1192.8955, 1176.3685,...      1   \n",
       "96   [55, 111, 79]  [[[1702.3887, 1739.2933, 1761.8115, 1776.1716,...      1   \n",
       "97   [52, 71, 124]  [[[943.41815, 726.77374, 503.3569, 262.3121, 8...      1   \n",
       "98    [51, 50, 66]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...      1   \n",
       "99   [49, 153, 81]  [[[2011.198, 2011.3706, 1883.7678, 1661.8715, ...      1   \n",
       "\n",
       "   patient_name  \n",
       "60           11  \n",
       "61           11  \n",
       "62           11  \n",
       "63           11  \n",
       "64           11  \n",
       "65           11  \n",
       "66           11  \n",
       "67           11  \n",
       "68           11  \n",
       "70           11  \n",
       "74           11  \n",
       "75           11  \n",
       "76           11  \n",
       "85           11  \n",
       "87           11  \n",
       "88           11  \n",
       "89           11  \n",
       "90           11  \n",
       "91           11  \n",
       "93           11  \n",
       "94           11  \n",
       "96           11  \n",
       "97           11  \n",
       "98           11  \n",
       "99           11  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient 11\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '11')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('11',99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the wrong labelled points to zero.....\n",
    "pdataset.dataset.loc[60, 'label'] = 0    #Not dark enough to be qualified as microbleed\n",
    "pdataset.dataset.loc[61, 'label'] = 0    #Part of a blood vessel\n",
    "pdataset.dataset.loc[63, 'label'] = 0\n",
    "pdataset.dataset.loc[66, 'label'] = 0    #Not dark enough to be qualified as microbleed\n",
    "pdataset.dataset.loc[67, 'label'] = 0    #Not dark enough to be qualified as microbleed\n",
    "pdataset.dataset.loc[74, 'label'] = 0    #Repeating part of the earlier detected microbleed\n",
    "pdataset.dataset.loc[88, 'label'] = 0    #Repeating part of the earlier detected microbleed\n",
    "pdataset.dataset.loc[98, 'label'] = 0    #Part of a blood vessel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>[155, 66, 92]</td>\n",
       "      <td>[[[1768.0466, 1748.4073, 1767.395, 1771.5117, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>[148, 66, 83]</td>\n",
       "      <td>[[[1595.6456, 1564.5089, 1524.9172, 1520.9941,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>[146, 71, 120]</td>\n",
       "      <td>[[[1926.1199, 1922.8368, 1939.3748, 1996.1311,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>[140, 60, 71]</td>\n",
       "      <td>[[[1848.844, 1871.6157, 1879.5189, 1888.7152, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>[124, 83, 56]</td>\n",
       "      <td>[[[1581.8629, 1619.6411, 1632.0082, 1628.4435,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>[101, 66, 99]</td>\n",
       "      <td>[[[1447.9072, 1501.1353, 1555.6167, 1556.9982,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>[101, 57, 71]</td>\n",
       "      <td>[[[1256.8654, 1155.5258, 1027.8593, 897.02686,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>[87, 75, 111]</td>\n",
       "      <td>[[[1989.562, 2060.2195, 2144.162, 2216.1619, 2...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>[85, 73, 84]</td>\n",
       "      <td>[[[1778.518, 1839.9125, 1922.4174, 2000.3539, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>[80, 142, 132]</td>\n",
       "      <td>[[[1762.771, 1806.5059, 1850.5065, 1867.1599, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>[78, 62, 61]</td>\n",
       "      <td>[[[1402.7825, 1430.7272, 1490.0577, 1551.9355,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>[69, 141, 83]</td>\n",
       "      <td>[[[1232.1311, 1314.0474, 1433.2991, 1566.3601,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>[58, 52, 86]</td>\n",
       "      <td>[[[1433.638, 1434.7697, 1463.0463, 1527.761, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>[58, 50, 81]</td>\n",
       "      <td>[[[1108.4706, 1163.9814, 1192.8955, 1176.3685,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>[55, 111, 79]</td>\n",
       "      <td>[[[1702.3887, 1739.2933, 1761.8115, 1776.1716,...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>[52, 71, 124]</td>\n",
       "      <td>[[[943.41815, 726.77374, 503.3569, 262.3121, 8...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>[49, 153, 81]</td>\n",
       "      <td>[[[2011.198, 2011.3706, 1883.7678, 1661.8715, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RST_peak                                        image_patch  label  \\\n",
       "62   [155, 66, 92]  [[[1768.0466, 1748.4073, 1767.395, 1771.5117, ...      1   \n",
       "64   [148, 66, 83]  [[[1595.6456, 1564.5089, 1524.9172, 1520.9941,...      1   \n",
       "65  [146, 71, 120]  [[[1926.1199, 1922.8368, 1939.3748, 1996.1311,...      1   \n",
       "68   [140, 60, 71]  [[[1848.844, 1871.6157, 1879.5189, 1888.7152, ...      1   \n",
       "70   [124, 83, 56]  [[[1581.8629, 1619.6411, 1632.0082, 1628.4435,...      1   \n",
       "75   [101, 66, 99]  [[[1447.9072, 1501.1353, 1555.6167, 1556.9982,...      1   \n",
       "76   [101, 57, 71]  [[[1256.8654, 1155.5258, 1027.8593, 897.02686,...      1   \n",
       "85   [87, 75, 111]  [[[1989.562, 2060.2195, 2144.162, 2216.1619, 2...      1   \n",
       "87    [85, 73, 84]  [[[1778.518, 1839.9125, 1922.4174, 2000.3539, ...      1   \n",
       "89  [80, 142, 132]  [[[1762.771, 1806.5059, 1850.5065, 1867.1599, ...      1   \n",
       "90    [78, 62, 61]  [[[1402.7825, 1430.7272, 1490.0577, 1551.9355,...      1   \n",
       "91   [69, 141, 83]  [[[1232.1311, 1314.0474, 1433.2991, 1566.3601,...      1   \n",
       "93    [58, 52, 86]  [[[1433.638, 1434.7697, 1463.0463, 1527.761, 1...      1   \n",
       "94    [58, 50, 81]  [[[1108.4706, 1163.9814, 1192.8955, 1176.3685,...      1   \n",
       "96   [55, 111, 79]  [[[1702.3887, 1739.2933, 1761.8115, 1776.1716,...      1   \n",
       "97   [52, 71, 124]  [[[943.41815, 726.77374, 503.3569, 262.3121, 8...      1   \n",
       "99   [49, 153, 81]  [[[2011.198, 2011.3706, 1883.7678, 1661.8715, ...      1   \n",
       "\n",
       "   patient_name  \n",
       "62           11  \n",
       "64           11  \n",
       "65           11  \n",
       "68           11  \n",
       "70           11  \n",
       "75           11  \n",
       "76           11  \n",
       "85           11  \n",
       "87           11  \n",
       "89           11  \n",
       "90           11  \n",
       "91           11  \n",
       "93           11  \n",
       "94           11  \n",
       "96           11  \n",
       "97           11  \n",
       "99           11  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remaining labels in the patient 11 are now correct\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '11')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[138, 120, 27]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 168...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[126, 79, 71]</td>\n",
       "      <td>[[[1812.4193, 1821.4623, 1826.1653, 1805.4312,...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[125, 76, 75]</td>\n",
       "      <td>[[[1681.3533, 1696.9663, 1738.3156, 1780.3088,...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[122, 82, 80]</td>\n",
       "      <td>[[[1856.1481, 1847.7457, 1856.007, 1877.7863, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[119, 85, 80]</td>\n",
       "      <td>[[[1805.539, 1808.1421, 1817.4318, 1832.182, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[116, 96, 60]</td>\n",
       "      <td>[[[1979.7942, 1983.8441, 1967.6024, 1956.6797,...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>[75, 145, 46]</td>\n",
       "      <td>[[[1661.6884, 1629.5023, 1624.8228, 1642.1377,...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>[67, 81, 71]</td>\n",
       "      <td>[[[1713.5363, 1708.1868, 1707.8729, 1689.371, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>[53, 149, 28]</td>\n",
       "      <td>[[[0.0, 244.08865, 755.6409, 1002.22943, 971.1...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RST_peak                                        image_patch  label  \\\n",
       "2   [138, 120, 27]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 168...      1   \n",
       "4    [126, 79, 71]  [[[1812.4193, 1821.4623, 1826.1653, 1805.4312,...      1   \n",
       "5    [125, 76, 75]  [[[1681.3533, 1696.9663, 1738.3156, 1780.3088,...      1   \n",
       "6    [122, 82, 80]  [[[1856.1481, 1847.7457, 1856.007, 1877.7863, ...      1   \n",
       "9    [119, 85, 80]  [[[1805.539, 1808.1421, 1817.4318, 1832.182, 1...      1   \n",
       "11   [116, 96, 60]  [[[1979.7942, 1983.8441, 1967.6024, 1956.6797,...      1   \n",
       "29   [75, 145, 46]  [[[1661.6884, 1629.5023, 1624.8228, 1642.1377,...      1   \n",
       "33    [67, 81, 71]  [[[1713.5363, 1708.1868, 1707.8729, 1689.371, ...      1   \n",
       "35   [53, 149, 28]  [[[0.0, 244.08865, 755.6409, 1002.22943, 971.1...      1   \n",
       "\n",
       "   patient_name  \n",
       "2            12  \n",
       "4            12  \n",
       "5            12  \n",
       "6            12  \n",
       "9            12  \n",
       "11           12  \n",
       "29           12  \n",
       "33           12  \n",
       "35           12  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient 12\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '12')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('12',35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the wrong labelled points to zero.....\n",
    "pdataset.dataset.loc[2, 'label'] = 0  \n",
    "pdataset.dataset.loc[4, 'label'] = 0  \n",
    "pdataset.dataset.loc[5, 'label'] = 0\n",
    "pdataset.dataset.loc[6, 'label'] = 0\n",
    "pdataset.dataset.loc[9, 'label'] = 0 \n",
    "pdataset.dataset.loc[11, 'label'] = 0 \n",
    "pdataset.dataset.loc[29, 'label'] = 0  \n",
    "pdataset.dataset.loc[33, 'label'] = 0\n",
    "pdataset.dataset.loc[35, 'label'] = 0  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [RST_peak, image_patch, label, patient_name]\n",
       "Index: []"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#No CMBs in patient 12 are detected\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '12')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>[136, 162, 52]</td>\n",
       "      <td>[[[2103.1387, 2273.2432, 2328.389, 2322.6233, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>[136, 121, 27]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 190.88985, 49...</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>[92, 85, 73]</td>\n",
       "      <td>[[[2289.3008, 2342.893, 2366.1921, 2352.6963, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>[84, 72, 60]</td>\n",
       "      <td>[[[1787.4331, 1719.0642, 1665.594, 1617.5492, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>[77, 110, 50]</td>\n",
       "      <td>[[[1842.3402, 1835.4174, 1845.5071, 1918.7576,...</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>[70, 175, 49]</td>\n",
       "      <td>[[[0.0, 0.0, 18.706364, 522.05444, 1019.1927, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>[52, 71, 47]</td>\n",
       "      <td>[[[1598.3458, 1424.9714, 1198.909, 1333.42, 15...</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RST_peak                                        image_patch  label  \\\n",
       "497  [136, 162, 52]  [[[2103.1387, 2273.2432, 2328.389, 2322.6233, ...      1   \n",
       "498  [136, 121, 27]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 190.88985, 49...      1   \n",
       "560    [92, 85, 73]  [[[2289.3008, 2342.893, 2366.1921, 2352.6963, ...      1   \n",
       "566    [84, 72, 60]  [[[1787.4331, 1719.0642, 1665.594, 1617.5492, ...      1   \n",
       "572   [77, 110, 50]  [[[1842.3402, 1835.4174, 1845.5071, 1918.7576,...      1   \n",
       "579   [70, 175, 49]  [[[0.0, 0.0, 18.706364, 522.05444, 1019.1927, ...      1   \n",
       "592    [52, 71, 47]  [[[1598.3458, 1424.9714, 1198.909, 1333.42, 15...      1   \n",
       "\n",
       "    patient_name  \n",
       "497           13  \n",
       "498           13  \n",
       "560           13  \n",
       "566           13  \n",
       "572           13  \n",
       "579           13  \n",
       "592           13  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient 13\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '13')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('13',592)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the wrong labelled points to zero.....\n",
    "pdataset.dataset.loc[497, 'label'] = 0 \n",
    "pdataset.dataset.loc[498, 'label'] = 0 \n",
    "pdataset.dataset.loc[560, 'label'] = 0 \n",
    "pdataset.dataset.loc[566, 'label'] = 0 \n",
    "pdataset.dataset.loc[572, 'label'] = 0 \n",
    "pdataset.dataset.loc[579, 'label'] = 0 \n",
    "pdataset.dataset.loc[592, 'label'] = 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [RST_peak, image_patch, label, patient_name]\n",
       "Index: []"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#No CMBs in patient 13 are detected\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '13')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>[149, 116, 114]</td>\n",
       "      <td>[[[1970.627, 2125.6987, 2244.773, 2287.1013, 2...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>[148, 114, 110]</td>\n",
       "      <td>[[[2228.9395, 2242.6506, 2241.2651, 2235.7266,...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>[139, 62, 48]</td>\n",
       "      <td>[[[1777.3505, 1828.9514, 1857.3271, 1834.0022,...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>[138, 56, 51]</td>\n",
       "      <td>[[[1701.2329, 1447.7605, 1082.0846, 708.8056, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>[131, 124, 42]</td>\n",
       "      <td>[[[2286.1667, 2218.054, 2062.6357, 1953.812, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>[126, 106, 38]</td>\n",
       "      <td>[[[1343.2925, 1419.9238, 1295.873, 1257.4146, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>[124, 85, 76]</td>\n",
       "      <td>[[[2430.5933, 2347.05, 2167.163, 2024.4688, 19...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>[119, 81, 59]</td>\n",
       "      <td>[[[1834.5675, 1759.4454, 1673.7349, 1629.4539,...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>[91, 91, 72]</td>\n",
       "      <td>[[[1707.1428, 1710.1475, 1727.5896, 1801.3091,...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>[88, 51, 80]</td>\n",
       "      <td>[[[2065.0886, 2073.7832, 2063.2263, 2037.3219,...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>[84, 74, 91]</td>\n",
       "      <td>[[[1913.648, 1836.0333, 1762.9834, 1730.9915, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>[76, 192, 75]</td>\n",
       "      <td>[[[2145.7598, 2127.214, 2104.2258, 2109.0806, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>[72, 72, 101]</td>\n",
       "      <td>[[[1871.0984, 1865.0822, 1897.0979, 1946.5951,...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>[67, 187, 60]</td>\n",
       "      <td>[[[2004.2842, 2017.1635, 2034.8507, 2039.276, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>[67, 69, 96]</td>\n",
       "      <td>[[[2044.8667, 2047.6685, 2052.9065, 2082.3152,...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>[66, 189, 66]</td>\n",
       "      <td>[[[1931.5786, 1921.4572, 1944.4878, 2002.6146,...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>[66, 125, 39]</td>\n",
       "      <td>[[[2390.8262, 2292.2136, 2148.142, 1963.1372, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>[44, 95, 64]</td>\n",
       "      <td>[[[1983.1395, 1979.745, 1937.7911, 1905.968, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RST_peak                                        image_patch  \\\n",
       "255  [149, 116, 114]  [[[1970.627, 2125.6987, 2244.773, 2287.1013, 2...   \n",
       "257  [148, 114, 110]  [[[2228.9395, 2242.6506, 2241.2651, 2235.7266,...   \n",
       "263    [139, 62, 48]  [[[1777.3505, 1828.9514, 1857.3271, 1834.0022,...   \n",
       "266    [138, 56, 51]  [[[1701.2329, 1447.7605, 1082.0846, 708.8056, ...   \n",
       "271   [131, 124, 42]  [[[2286.1667, 2218.054, 2062.6357, 1953.812, 1...   \n",
       "273   [126, 106, 38]  [[[1343.2925, 1419.9238, 1295.873, 1257.4146, ...   \n",
       "274    [124, 85, 76]  [[[2430.5933, 2347.05, 2167.163, 2024.4688, 19...   \n",
       "281    [119, 81, 59]  [[[1834.5675, 1759.4454, 1673.7349, 1629.4539,...   \n",
       "322     [91, 91, 72]  [[[1707.1428, 1710.1475, 1727.5896, 1801.3091,...   \n",
       "328     [88, 51, 80]  [[[2065.0886, 2073.7832, 2063.2263, 2037.3219,...   \n",
       "334     [84, 74, 91]  [[[1913.648, 1836.0333, 1762.9834, 1730.9915, ...   \n",
       "340    [76, 192, 75]  [[[2145.7598, 2127.214, 2104.2258, 2109.0806, ...   \n",
       "342    [72, 72, 101]  [[[1871.0984, 1865.0822, 1897.0979, 1946.5951,...   \n",
       "344    [67, 187, 60]  [[[2004.2842, 2017.1635, 2034.8507, 2039.276, ...   \n",
       "345     [67, 69, 96]  [[[2044.8667, 2047.6685, 2052.9065, 2082.3152,...   \n",
       "346    [66, 189, 66]  [[[1931.5786, 1921.4572, 1944.4878, 2002.6146,...   \n",
       "347    [66, 125, 39]  [[[2390.8262, 2292.2136, 2148.142, 1963.1372, ...   \n",
       "361     [44, 95, 64]  [[[1983.1395, 1979.745, 1937.7911, 1905.968, 1...   \n",
       "\n",
       "     label patient_name  \n",
       "255      1           15  \n",
       "257      1           15  \n",
       "263      1           15  \n",
       "266      1           15  \n",
       "271      1           15  \n",
       "273      1           15  \n",
       "274      1           15  \n",
       "281      1           15  \n",
       "322      1           15  \n",
       "328      1           15  \n",
       "334      1           15  \n",
       "340      1           15  \n",
       "342      1           15  \n",
       "344      1           15  \n",
       "345      1           15  \n",
       "346      1           15  \n",
       "347      1           15  \n",
       "361      1           15  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient 15\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '15')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('15',361)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the wrong labelled points to zero.....\n",
    "pdataset.dataset.loc[255, 'label'] = 0 \n",
    "pdataset.dataset.loc[257, 'label'] = 0 \n",
    "pdataset.dataset.loc[263, 'label'] = 0 \n",
    "pdataset.dataset.loc[266, 'label'] = 0 \n",
    "pdataset.dataset.loc[271, 'label'] = 0 \n",
    "pdataset.dataset.loc[273, 'label'] = 0 \n",
    "pdataset.dataset.loc[274, 'label'] = 0 \n",
    "pdataset.dataset.loc[281, 'label'] = 0\n",
    "pdataset.dataset.loc[322, 'label'] = 0 \n",
    "pdataset.dataset.loc[334, 'label'] = 0 \n",
    "pdataset.dataset.loc[347, 'label'] = 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>[88, 51, 80]</td>\n",
       "      <td>[[[2065.0886, 2073.7832, 2063.2263, 2037.3219,...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>[76, 192, 75]</td>\n",
       "      <td>[[[2145.7598, 2127.214, 2104.2258, 2109.0806, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>[72, 72, 101]</td>\n",
       "      <td>[[[1871.0984, 1865.0822, 1897.0979, 1946.5951,...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>[67, 187, 60]</td>\n",
       "      <td>[[[2004.2842, 2017.1635, 2034.8507, 2039.276, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>[67, 69, 96]</td>\n",
       "      <td>[[[2044.8667, 2047.6685, 2052.9065, 2082.3152,...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>[66, 189, 66]</td>\n",
       "      <td>[[[1931.5786, 1921.4572, 1944.4878, 2002.6146,...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>[44, 95, 64]</td>\n",
       "      <td>[[[1983.1395, 1979.745, 1937.7911, 1905.968, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RST_peak                                        image_patch  label  \\\n",
       "328   [88, 51, 80]  [[[2065.0886, 2073.7832, 2063.2263, 2037.3219,...      1   \n",
       "340  [76, 192, 75]  [[[2145.7598, 2127.214, 2104.2258, 2109.0806, ...      1   \n",
       "342  [72, 72, 101]  [[[1871.0984, 1865.0822, 1897.0979, 1946.5951,...      1   \n",
       "344  [67, 187, 60]  [[[2004.2842, 2017.1635, 2034.8507, 2039.276, ...      1   \n",
       "345   [67, 69, 96]  [[[2044.8667, 2047.6685, 2052.9065, 2082.3152,...      1   \n",
       "346  [66, 189, 66]  [[[1931.5786, 1921.4572, 1944.4878, 2002.6146,...      1   \n",
       "361   [44, 95, 64]  [[[1983.1395, 1979.745, 1937.7911, 1905.968, 1...      1   \n",
       "\n",
       "    patient_name  \n",
       "328           15  \n",
       "340           15  \n",
       "342           15  \n",
       "344           15  \n",
       "345           15  \n",
       "346           15  \n",
       "361           15  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMBs in patient 15 are detected\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == '15')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>[138, 136, 62]</td>\n",
       "      <td>[[[1949.0409, 1838.6971, 1839.5374, 1852.4894,...</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>[135, 100, 91]</td>\n",
       "      <td>[[[1719.8491, 1681.455, 1653.2905, 1628.386, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>[133, 132, 75]</td>\n",
       "      <td>[[[1667.2189, 1613.0066, 1533.9448, 1476.0802,...</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>[129, 124, 81]</td>\n",
       "      <td>[[[1526.9496, 1497.1355, 1463.6461, 1456.7793,...</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>[128, 114, 84]</td>\n",
       "      <td>[[[1709.516, 1723.7808, 1710.9332, 1682.6996, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>[66, 96, 40]</td>\n",
       "      <td>[[[1516.832, 1572.2003, 1573.4683, 1546.348, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>[62, 105, 59]</td>\n",
       "      <td>[[[1476.1425, 1371.5486, 1263.5171, 1152.273, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>[51, 192, 66]</td>\n",
       "      <td>[[[1.0986899, 0.0, 24.371206, 42.21421, 65.379...</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RST_peak                                        image_patch  label  \\\n",
       "234  [138, 136, 62]  [[[1949.0409, 1838.6971, 1839.5374, 1852.4894,...      1   \n",
       "235  [135, 100, 91]  [[[1719.8491, 1681.455, 1653.2905, 1628.386, 1...      1   \n",
       "236  [133, 132, 75]  [[[1667.2189, 1613.0066, 1533.9448, 1476.0802,...      1   \n",
       "237  [129, 124, 81]  [[[1526.9496, 1497.1355, 1463.6461, 1456.7793,...      1   \n",
       "238  [128, 114, 84]  [[[1709.516, 1723.7808, 1710.9332, 1682.6996, ...      1   \n",
       "240    [66, 96, 40]  [[[1516.832, 1572.2003, 1573.4683, 1546.348, 1...      1   \n",
       "241   [62, 105, 59]  [[[1476.1425, 1371.5486, 1263.5171, 1152.273, ...      1   \n",
       "242   [51, 192, 66]  [[[1.0986899, 0.0, 24.371206, 42.21421, 65.379...      1   \n",
       "\n",
       "    patient_name  \n",
       "234           p1  \n",
       "235           p1  \n",
       "236           p1  \n",
       "237           p1  \n",
       "238           p1  \n",
       "240           p1  \n",
       "241           p1  \n",
       "242           p1  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient p1\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == 'p1')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('p1', 242)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the wrong labelled points to zero.....\n",
    "pdataset.dataset.loc[236, 'label'] = 0\n",
    "pdataset.dataset.loc[237, 'label'] = 0\n",
    "pdataset.dataset.loc[242, 'label'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>[138, 136, 62]</td>\n",
       "      <td>[[[1949.0409, 1838.6971, 1839.5374, 1852.4894,...</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>[135, 100, 91]</td>\n",
       "      <td>[[[1719.8491, 1681.455, 1653.2905, 1628.386, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>[128, 114, 84]</td>\n",
       "      <td>[[[1709.516, 1723.7808, 1710.9332, 1682.6996, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>[66, 96, 40]</td>\n",
       "      <td>[[[1516.832, 1572.2003, 1573.4683, 1546.348, 1...</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>[62, 105, 59]</td>\n",
       "      <td>[[[1476.1425, 1371.5486, 1263.5171, 1152.273, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RST_peak                                        image_patch  label  \\\n",
       "234  [138, 136, 62]  [[[1949.0409, 1838.6971, 1839.5374, 1852.4894,...      1   \n",
       "235  [135, 100, 91]  [[[1719.8491, 1681.455, 1653.2905, 1628.386, 1...      1   \n",
       "238  [128, 114, 84]  [[[1709.516, 1723.7808, 1710.9332, 1682.6996, ...      1   \n",
       "240    [66, 96, 40]  [[[1516.832, 1572.2003, 1573.4683, 1546.348, 1...      1   \n",
       "241   [62, 105, 59]  [[[1476.1425, 1371.5486, 1263.5171, 1152.273, ...      1   \n",
       "\n",
       "    patient_name  \n",
       "234           p1  \n",
       "235           p1  \n",
       "238           p1  \n",
       "240           p1  \n",
       "241           p1  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Final CMBs in patient p1 are detected\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == 'p1')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>[142, 81, 44]</td>\n",
       "      <td>[[[1559.1396, 1496.1488, 1412.9056, 1219.0812,...</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>[142, 58, 70]</td>\n",
       "      <td>[[[1602.9769, 1620.1228, 1629.8143, 1630.2369,...</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>[105, 176, 118]</td>\n",
       "      <td>[[[1300.7921, 1227.8966, 1195.182, 1173.0802, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>[84, 75, 87]</td>\n",
       "      <td>[[[1408.8854, 1312.9576, 1327.3866, 1405.3656,...</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>[84, 74, 83]</td>\n",
       "      <td>[[[1570.5077, 1598.8821, 1526.3732, 1481.858, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>[61, 136, 81]</td>\n",
       "      <td>[[[1335.9462, 1329.416, 1323.1154, 1319.3567, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RST_peak                                        image_patch  label  \\\n",
       "38    [142, 81, 44]  [[[1559.1396, 1496.1488, 1412.9056, 1219.0812,...      1   \n",
       "39    [142, 58, 70]  [[[1602.9769, 1620.1228, 1629.8143, 1630.2369,...      1   \n",
       "42  [105, 176, 118]  [[[1300.7921, 1227.8966, 1195.182, 1173.0802, ...      1   \n",
       "43     [84, 75, 87]  [[[1408.8854, 1312.9576, 1327.3866, 1405.3656,...      1   \n",
       "44     [84, 74, 83]  [[[1570.5077, 1598.8821, 1526.3732, 1481.858, ...      1   \n",
       "45    [61, 136, 81]  [[[1335.9462, 1329.416, 1323.1154, 1319.3567, ...      1   \n",
       "\n",
       "   patient_name  \n",
       "38           p2  \n",
       "39           p2  \n",
       "42           p2  \n",
       "43           p2  \n",
       "44           p2  \n",
       "45           p2  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CMB labels for patient p2\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == 'p2')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdataset.disp_detection('p2',45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the wrong labelled points to zero.....\n",
    "pdataset.dataset.loc[38, 'label'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>[142, 58, 70]</td>\n",
       "      <td>[[[1602.9769, 1620.1228, 1629.8143, 1630.2369,...</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>[105, 176, 118]</td>\n",
       "      <td>[[[1300.7921, 1227.8966, 1195.182, 1173.0802, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>[84, 75, 87]</td>\n",
       "      <td>[[[1408.8854, 1312.9576, 1327.3866, 1405.3656,...</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>[84, 74, 83]</td>\n",
       "      <td>[[[1570.5077, 1598.8821, 1526.3732, 1481.858, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>[61, 136, 81]</td>\n",
       "      <td>[[[1335.9462, 1329.416, 1323.1154, 1319.3567, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RST_peak                                        image_patch  label  \\\n",
       "39    [142, 58, 70]  [[[1602.9769, 1620.1228, 1629.8143, 1630.2369,...      1   \n",
       "42  [105, 176, 118]  [[[1300.7921, 1227.8966, 1195.182, 1173.0802, ...      1   \n",
       "43     [84, 75, 87]  [[[1408.8854, 1312.9576, 1327.3866, 1405.3656,...      1   \n",
       "44     [84, 74, 83]  [[[1570.5077, 1598.8821, 1526.3732, 1481.858, ...      1   \n",
       "45    [61, 136, 81]  [[[1335.9462, 1329.416, 1323.1154, 1319.3567, ...      1   \n",
       "\n",
       "   patient_name  \n",
       "39           p2  \n",
       "42           p2  \n",
       "43           p2  \n",
       "44           p2  \n",
       "45           p2  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Final CMBs in patient p2 are detected\n",
    "k = pdataset.dataset\n",
    "k.loc[(k['label'] == 1) & (k['patient_name'] == 'p2')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Finally, after all the cleaning we have the following results for our positive RST dataset</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final no. of Cerebral Microbleeds in +ive RST data: 57\n",
      "Total no. of Cerebral Microbleeds in -ive RST data: 66\n"
     ]
    }
   ],
   "source": [
    "l = pdataset.show()\n",
    "print(\"Final no. of Cerebral Microbleeds in +ive RST data: \" + str(len(l.loc[l['label'] == 1])))\n",
    "print(\"Total no. of Cerebral Microbleeds in -ive RST data: \" + str(len(labelled_ng.loc[\n",
    "    labelled_ng['label'] == 1\n",
    "])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>So, the total no of CMB patches, which is our target data is (57+66) = 123 </h3>\n",
    "<p>Saving all these datasets are dataframe in datasets folder</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdataset.dataset.to_pickle('./datasets/learning/rst_output_all_subjects_2_2_0.1') #Saving Postive RST Data\n",
    "labelled_ng.to_pickle('./datasets/learning/rst_output_all_subjects_negative_2_2_0.1') #Saving Negative RST Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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  "language_info": {
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   "file_extension": ".py",
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