1_Event_log_without_event_abstraction.ipynb

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  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Plot stats of Raw and Abstracted Event logs (Event log consist of only TP and TN patinets)",
   "id": "ab42f0a7be748b5a"
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 367
    },
    "id": "65ad26576e3c7415",
    "executionInfo": {
     "status": "error",
     "timestamp": 1714085293458,
     "user_tz": -600,
     "elapsed": 315,
     "user": {
      "displayName": "Prabodi Senevirathna",
      "userId": "09214050870882713824"
     }
    },
    "outputId": "f69b88fb-fe13-42e6-e1fe-21b6b7c95157",
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     "start_time": "2024-11-24T08:16:34.591816Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "#from pm4py.objects.log.util import get_log_representation as get_\n",
    "from Levenshtein import distance as levenshtein_distance\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "from scipy.spatial.distance import squareform\n",
    "from scipy.cluster.hierarchy import linkage, fcluster, dendrogram\n",
    "import seaborn as sns"
   ],
   "id": "65ad26576e3c7415",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:16:46.286021Z",
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   },
   "cell_type": "code",
   "source": [
    "# Train and Test set (To find only TP and TN patients)\n",
    "\n",
    "Train_set_ground_truth_and_predicted_label = pd.read_csv('/Users/psenevirathn/Desktop/PhD/Coding/Python/input_csv_files/label_train_actual_and_predicted_with_updated_ground_truth.csv')\n",
    "\n",
    "Test_set_ground_truth_and_predicted_label = pd.read_csv('/Users/psenevirathn/Desktop/PhD/Coding/Python/input_csv_files/label_test_actual_and_predicted_with_updated_ground_truth.csv')"
   ],
   "id": "3c2a23bb0156a0c1",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
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     "start_time": "2024-11-24T08:16:46.815219Z"
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   },
   "cell_type": "code",
   "source": [
    "def determine_class(row):\n",
    "    if row['HIT_label_actual'] == 1 and row['HIT_label_predicted'] == 1:\n",
    "        return 'TP'\n",
    "    elif row['HIT_label_actual'] == 1 and row['HIT_label_predicted'] == 0:\n",
    "        return 'FN'\n",
    "    elif row['HIT_label_actual'] == 0 and row['HIT_label_predicted'] == 1:\n",
    "        return 'FP'\n",
    "    elif row['HIT_label_actual'] == 0 and row['HIT_label_predicted'] == 0:\n",
    "        return 'TN'\n"
   ],
   "id": "1339ee7d6db7e80d",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:16:47.594234Z",
     "start_time": "2024-11-24T08:16:47.439580Z"
    }
   },
   "cell_type": "code",
   "source": [
    "Train_set_ground_truth_and_predicted_label['data_set'] = 'train'\n",
    "Test_set_ground_truth_and_predicted_label['data_set'] = 'test'\n",
    "\n",
    "both_train_test_labels = pd.concat([Train_set_ground_truth_and_predicted_label, Test_set_ground_truth_and_predicted_label], axis=0).drop('Unnamed: 0', axis=1)\n",
    "\n",
    " # Apply the function to each row in the DataFrame\n",
    "both_train_test_labels['class'] = both_train_test_labels.apply(determine_class, axis=1)"
   ],
   "id": "e32d0520217de258",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 1. Analysing Trajectory lengths Distribution WITHOUT Abstraction\n",
   "id": "f0c28a0f40069645"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:16:59.440755Z",
     "start_time": "2024-11-24T08:16:48.362021Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Import files - Raw event log (contain all the patients, not only TP and TN)\n",
    "\n",
    "events_WITHOUT_abstraction = pd.read_csv('/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Input/Events_WITHOUT_abstraction_From_BigQuery_V19_1.csv')\n",
    "\n",
    "# hadm_id count = 13415"
   ],
   "id": "98db8dae4e9b425f",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hd/ds7hts1d50j2wcpcm9y14v440000gn/T/ipykernel_59329/1098256528.py:3: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  events_WITHOUT_abstraction = pd.read_csv('/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Input/Events_WITHOUT_abstraction_From_BigQuery_V19_1.csv')\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
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    }
   },
   "cell_type": "code",
   "source": [
    "# save the formatted event log to check the process map in DISCO\n",
    "\n",
    "print(events_WITHOUT_abstraction)"
   ],
   "id": "c98a8055a76e6f51",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           hadm_id                             event  value     details  \\\n",
      "0         27675397                 bgART_pCO2_normal  50818       bgART   \n",
      "1         28259228  bgART_Potassium, Whole Blood_low  50822       bgART   \n",
      "2         28259228  bgART_Potassium, Whole Blood_low  50822       bgART   \n",
      "3         28259228              bgART_Lactate_normal  50813       bgART   \n",
      "4         29538539                    bgART_pCO2_low  50818       bgART   \n",
      "...            ...                               ...    ...         ...   \n",
      "14277936  25660703      temperature_vitalsign_normal    NaN  VitalSigns   \n",
      "14277937  29446555      temperature_vitalsign_normal    NaN  VitalSigns   \n",
      "14277938  29446555      temperature_vitalsign_normal    NaN  VitalSigns   \n",
      "14277939  29446555      temperature_vitalsign_normal    NaN  VitalSigns   \n",
      "14277940  20331048      temperature_vitalsign_normal    NaN  VitalSigns   \n",
      "\n",
      "                   event_time  \n",
      "0         2156-12-23 02:30:00  \n",
      "1         2136-10-02 18:06:00  \n",
      "2         2136-10-03 00:01:00  \n",
      "3         2136-10-02 05:53:00  \n",
      "4         2175-07-24 19:59:00  \n",
      "...                       ...  \n",
      "14277936  2137-12-27 22:00:00  \n",
      "14277937  2171-09-15 00:00:00  \n",
      "14277938  2171-09-13 22:00:00  \n",
      "14277939  2171-09-14 04:00:00  \n",
      "14277940  2190-05-29 00:00:00  \n",
      "\n",
      "[14277941 rows x 5 columns]\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:09.165046Z",
     "start_time": "2024-11-24T08:16:59.472329Z"
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   },
   "cell_type": "code",
   "source": [
    "full_raw_event_log_with_patient_class = pd.merge(events_WITHOUT_abstraction, both_train_test_labels, on='hadm_id', how='left')[\n",
    "        ['hadm_id', 'event', 'event_time', 'HIT_label_actual', 'HIT_label_predicted', 'class']]"
   ],
   "id": "921a4c1bde9fdf96",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:11.364621Z",
     "start_time": "2024-11-24T08:17:09.168373Z"
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   },
   "cell_type": "code",
   "source": [
    "raw_event_log_TP_and_TN_only = full_raw_event_log_with_patient_class[\n",
    "        (full_raw_event_log_with_patient_class['class'] == 'TP') | (full_raw_event_log_with_patient_class['class'] == 'TN')]\n",
    "# 12355 - hadm_ids"
   ],
   "id": "a54919e2746fb22b",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:13.959100Z",
     "start_time": "2024-11-24T08:17:11.365578Z"
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   },
   "cell_type": "code",
   "source": [
    "seq_length_raw_event_log = raw_event_log_TP_and_TN_only.groupby('hadm_id').agg(count=('hadm_id', lambda x: len(x)),\n",
    "                                                        sequence=('event', lambda x: x)).reset_index()\n",
    "\n",
    "seq_length_raw_event_log.sort_values(by='count', ascending=False, inplace=True)"
   ],
   "id": "b0fdc07ad131c799",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:13.969255Z",
     "start_time": "2024-11-24T08:17:13.963185Z"
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   },
   "cell_type": "code",
   "source": "patient_count = seq_length_raw_event_log['hadm_id'].nunique() # 12355",
   "id": "41df7aeed5cce6d8",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 1.1 Plot trace length distribution as a Histogram",
   "id": "6b5045be439f991"
  },
  {
   "metadata": {
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     "end_time": "2024-11-24T08:17:14.471312Z",
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   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10, 5))  # Create a new figure with specified size\n",
    "\n",
    "list_seq_lengths_raw_event_log = seq_length_raw_event_log['count'].tolist()\n",
    "\n",
    "# Custom bins\n",
    "\n",
    "bins_raw = list(range(0, max(list_seq_lengths_raw_event_log)+500, 500)) \n",
    "\n",
    "# Plot histogram\n",
    "hist_data_raw = plt.hist(list_seq_lengths_raw_event_log, bins=bins_raw, color='#4682B4') # edgecolor='black', \n",
    "#87CEEB - Light blue , Steel Blue: #4682B4 Light Green: #98FB98 , Cornflower Blue: #6495ED, Light Pink: #FFB6C1 , Light Gray: #D3D3D3 # FA8072 - Salmon pink\n",
    "\n",
    "# Set x ticks\n",
    "plt.xticks(bins_raw, rotation='vertical', fontsize=6)\n",
    "\n",
    "# Set y ticks\n",
    "\n",
    "# print(plt.hist(list_seq_lengths_of_each_hadm_id_when_HIT_positive, bins=bins)) # returns an array of two arrays, where first array contain frquency of each bin, and the second array contain bin edges values\n",
    "\n",
    "plt.yticks(range(0, int(max(hist_data_raw[0]) + 1), 200),fontsize=6) \n",
    "# Add axis labels and title\n",
    "plt.xlabel('Trace Length (Event Count)')\n",
    "plt.ylabel('Patient Count')\n",
    "plt.title('Trace Length Distribution - Raw Events Data (No Abstraction)')\n",
    "\n",
    "# Add grid\n",
    "plt.grid(True)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ],
   "id": "e5cee9c6c5e0280f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 1.2 Plot Cumulative trace length distribution as a Histogram",
   "id": "c187c53119e84c43"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:14.773286Z",
     "start_time": "2024-11-24T08:17:14.472528Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10, 5))  # Create a new figure with specified size\n",
    "\n",
    "# hist_data_raw[0] - array([5.479e+03, 3.430e+03, 1.307e+03, 6.870e+02, 4.440e+02, 2.530e+02,\n",
    "#        1.880e+02, 1.330e+02, 1.010e+02, 6.900e+01, 5.700e+01, 4.300e+01,\n",
    "#        2.900e+01, 2.800e+01, 1.800e+01, 1.600e+01, 1.200e+01, 9.000e+00,\n",
    "#        1.300e+01, 1.200e+01, 3.000e+00, 2.000e+00, 6.000e+00, 1.000e+00,\n",
    "#        2.000e+00, 2.000e+00, 1.000e+00, 3.000e+00, 0.000e+00, 1.000e+00,\n",
    "#        1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00,\n",
    "#        2.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
    "#        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.000e+00])\n",
    "\n",
    "# hist_data_raw[1] - array([    0,   500,  1000,  1500,  2000,  2500,  3000,  3500,  4000,\n",
    "#        4500,  5000,  5500,  6000,  6500,  7000,  7500,  8000,  8500,\n",
    "#        9000,  9500, 10000, 10500, 11000, 11500, 12000, 12500, 13000,\n",
    "#       13500, 14000, 14500, 15000, 15500, 16000, 16500, 17000, 17500,\n",
    "#       18000, 18500, 19000, 19500, 20000, 20500, 21000, 21500, 22000,\n",
    "#       22500, 23000, 23500])\n",
    "\n",
    "# Calculate cumulative distribution\n",
    "cumulative_count = np.cumsum(hist_data_raw[0])\n",
    "\n",
    "#print(cumulative_count)\n",
    "#[ 5479  8909 10216 10903 11347 11600 11788 11921 12022 12091 12148 12191\n",
    "# 12220 12248 12266 12282 12294 12303 12316 12328 12331 12333 12339 12340\n",
    "# 12342 12344 12345 12348 12348 12349 12350 12350 12351 12351 12352 12352\n",
    "# 12354 12354 12354 12354 12354 12354 12354 12354 12354 12354 12355]\n",
    "\n",
    "# Plot cumulative distribution\n",
    "# consider x-axis to be start from index 1. Index 0 is zero. Index 2 is 100, index 2 is 200 so on (when the step size is 100).\n",
    "\n",
    "plt.plot(hist_data_raw[1][1:], (cumulative_count / patient_count) * 100, linestyle='-', marker='o', color= '#4682B4')\n",
    "\n",
    "# Set x ticks\n",
    "plt.xticks(hist_data_raw[1][1:], rotation='vertical', fontsize=6)\n",
    "\n",
    "# Set y ticks\n",
    "plt.yticks(range(0, 105, 5), fontsize=6)\n",
    "\n",
    "# Labeling\n",
    "plt.xlabel('Trace Length (Event Count)')\n",
    "plt.ylabel('Cumulative Patient Percentage')\n",
    "plt.title('Cumulative Trace Length Distribution - Raw Events Data (No Abstraction)')\n",
    "\n",
    "# Add grid\n",
    "plt.grid(True)\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ],
   "id": "70d245242c82239f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 1.3 Analyse trace length Stats",
   "id": "aaab22528157bc8d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:15.423707Z",
     "start_time": "2024-11-24T08:17:14.774579Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Convert list to Pandas Series\n",
    "series_seq_lengths = pd.Series(list_seq_lengths_raw_event_log)\n",
    "\n",
    "# Get descriptive statistics\n",
    "stats_trace_length_row_data = series_seq_lengths.describe()\n",
    "\n",
    "print(stats_trace_length_row_data)\n",
    "\n",
    "Distinc_events_without_abstraction = raw_event_log_TP_and_TN_only['event'].nunique()\n",
    "print(Distinc_events_without_abstraction) # 13194"
   ],
   "id": "46fa81775950dd61",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    12355.000000\n",
      "mean       992.865156\n",
      "std       1316.541650\n",
      "min         21.000000\n",
      "25%        315.000000\n",
      "50%        565.000000\n",
      "75%       1097.500000\n",
      "max      23031.000000\n",
      "dtype: float64\n",
      "13194\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:15.491764Z",
     "start_time": "2024-11-24T08:17:15.427140Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#box plot for trace lengths (without abstration)\n",
    "\n",
    "# Create a box plot using seaborn\n",
    "plt.figure(figsize=(8, 6))  # Optional: adjust the size of the plot\n",
    "sns.boxplot(y=series_seq_lengths, color= '#4682B4')\n",
    "\n",
    "# Add labels and title (optional)\n",
    "#plt.title('Box Plot of Sequence Lengths (Abstracted)')\n",
    "plt.xlabel('Trajectory Length')\n",
    "plt.ylabel('Patient Count')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ],
   "id": "ee6e3ab2d6289e68",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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fbvB1ubm5GjdunIYOHao2bdpo6tSp6tKlizZv3hyYz5gxQz179lSHDh30xBNPaMuWLSopKdEXX3yhHTt2aM6cOYqOjlbv3r2VmZmpNWvWtPjnBYDWxuPxKC8vT5ZlSZIsy1Jubq48Ho/NyQAgdCLt+KY333yzxo8fr8jIyDpleP/+/aqpqdGqVav00UcfqWPHjpo4caKmT58up9Op4uJiTZw4sc579e/fX/v371d5ebmOHTumgQMHBmaxsbHq3LmzPvvsM0lSTEyMevToEZj369dPHo9H58+fV6dOnRqd3+fz/asfHQBsZ1mWVq9eHSjBX1+fP3++HA6HTekAoGmC6Wm2FOG4uLgG18vLyzVixAg98MAD+sUvfqFPP/1Us2bNktPp1PTp01VZWano6Og6r3G5XLpw4YIqKyslSe3atas3r519/bW1jy9cuBBUES4qKmr0cwGgtTl58mSd8ytq+f1+7d69W+++++43/pwGgHBiSxH+JqNGjdKoUaMCj4cMGaIpU6Zo8+bNmj59uqKjo+udzOH1etWlS5dAqa3dL/zVefv27WVZVr1Z7eP27dsHlXPw4MGKiIgI6jUA0FpYlqVt27Zpz5498vv9gXWn06nvfOc7uuOOOzgiDOCq5fP5Gn3QslUV4XfffVenTp3Sf/zHfwTWvvzyS7lcLknSgAEDdPDgwTqvKS4u1q233qrOnTurR48eKi4uDmyPOHnypM6ePauBAwfK7/fr7NmzOnXqlGJjYyVJhw4dUnx8vDp27BhUzoiICIowgKua2+3WzJkz661nZmYqMrJV/WoAgBbTqq4jbFmWli1bpg8//FCWZWn37t165ZVXAleNSE9P18aNG7V9+3ZVV1crJydHp0+f1pgxYyRJaWlpWr16tUpKSlRRUaGlS5dqxIgRuuaaa9SnTx8NHTpUS5cuVUVFhUpKSpSVlaX09HQ7PzIA2CIhIUHXX399nbUbbrhBPXv2tCkRAIReq/pr/5gxY/T444/r6aef1vHjxxUbG6sf//jHuueeeyRJN910k+bPnx+Y9+/fXy+++KJiYmIkSbNmzVJNTY0mT56syspKpaamauXKlYH3X7VqlRYuXKjbb79dTqdTEyZMUGZmpg2fFADs5fF4AicS19q/f788Ho8SEhJsSgUAoeWwvn7aML6Rz+dTYWGhkpKS2BoB4KplWZbmz5+vwsLCenuEk5KStGDBAvYIA7hqBdPXWtXWCABAyystLVVBQUGdEixdumpEQUGBSktLbUoGAKFFEQYAwyQmJiolJUVOZ91fAU6nUykpKUpMTLQpGQCEFkUYAAzjcDg0c+bMetsfHA6H3G432yIAGIMiDAAGSkhIUHp6eqD0OhwOZWRkcNUIAEahCAOAoTIyMtS1a1dJUrdu3bicJADjUIQBwFAul0uZmZnq3r273G534OZFAGCKVnUdYQBAaKWmpio1NdXuGABgC44IAwAAwEgUYQAAABiJIgwAAAAjUYQBwGD5+fmaNm2a8vPz7Y4CACFHEQYAQ3m9Xq1cuVInTpzQypUr5fV67Y4EACFFEQYAQ/3hD39QeXm5JKm8vFyvv/66zYkAILQowgBgII/HozfeeKPO2rp16+TxeGxKBAChRxEGAMNYlqWVK1fKsqxGrQNAuKIIA4BhSkpKtG/fvgZn+/btU0lJSYgTAYA9girCf/7znxtcX7t2bbOEAQAAAELlirdYrqqq0pkzZyRJP//5z5WUlFTnn83Ky8u1fPlyTZo0qeVSAgCaTWJiotq3b6/Kysp6s/bt2ysxMdGGVAAQelcswhUVFRo3blzgsjq33XZbYGZZlhwOh+64446WSwgAaFZHjx5tsARLUmVlpY4eParevXuHOBUAhN4Vi3BcXJzeffddVVVVafz48dq0aVOdeVRUlGJjY1ssIACgeSUmJiolJUW7d++u8y98DodDycnJHBEGYIwrFmFJ6tatmyTpo48+ktPJ+XUAcDVzOByaOXOm3G63fD5fYN3pdMrtdsvhcNiYDgBCp1FFuNbJkye1evVqHT58WH6/v87slVdeadZgAICWk5CQoPT0dP3xj38MbHPLyMhQz5497Y4GACETVBF+/PHHderUKX3ve99TmzZtWioTACAEMjIytHnzZpWXl6tDhw5KT0+3OxIAhFRQRbioqEhvv/22unbt2lJ5AAAAgJAIasNvx44d1bZt25bKAgAIodzcXFVUVEi6dIWgvLw8mxMBQGgFVYQzMzP1+OOP6+OPP5bH46nzBQC4eng8HuXl5QWuGmFZlnJzc/l5DsAoQW2NmDdvniTpL3/5S+Cs4tqTLD799NPmTwcAaHaWZSk7O7vOpdO+ur5gwQKuHAHACEEV4ffee6+lcgAAQqS0tFQFBQX11v1+vwoKClRaWsoNNQAYIagi3KtXr5bKAQAIkdobahQWFta5FKbT6VRSUhI31ABgjKCK8PXXX/+N/1zG1ggAuDp89YYaX1/nhhoATBJUEf76TTPKysr0v//7v7rnnnuaNRQAoGVxQw0ACLIIjxgxot7a0KFDNXXqVN17773NFgoA0PIyMjK0YcMGVVVVyeVycUMNAMYJ6vJpDenUqZOOHz/eHFkAACF08eJFeb1eSZLX69XFixdtTgQAoRXUEeE333yzzuPq6mq99957uuGGG5ozEwAgBJYsWVLnOsJLly7VihUrbE4FAKETVBFetWpVnccRERHq16+f5s+f36yhAAAtq7CwUPv27auztnfvXhUWFiopKcmeUAAQYkEV4ffff7+lcgAAQsTv93/jkd8VK1ZozZo1cjqbvHMOAFq9oIqwJH3yySfKy8vT0aNHFRcXp7S0NA0bNqwlsgEAWsCuXbtUXl7e4Ky8vFy7du1q8ORoAAg3Qf2V/4MPPtB9992ns2fP6rrrrlNFRYUefPBBvfvuuy2VDwDQzIYNG6aOHTs2OOvUqRMHNwAYI+g9witWrNBdd90VWPvzn/+srKws3XHHHc0eDgDQ/JxOp+bOnat58+bVm82dO5dtEQCMEdRPu88//1xjx46tszZ27FgdPny4OTMBAFpYUlKSYmNj66zFxcXpO9/5jk2JACD0girCMTExOnDgQJ21/fv3Ky4urllDAQBalsfj0ZkzZ+qslZWVyePx2JQIAEIvqCKckZEht9ut119/XR988IFee+01zZo1i7sRAcBVxLIsZWdny+/311n3+/3Kzs4OXFsYAMJdUHuEZ8yYoYsXL+rXv/61Tp06pV69eun+++/Xgw8+2FL5AADNrLS0VAUFBfXWLctSQUGBSktL1bt3bxuSAUBoBVWEHQ6HfvzjH+vHP/5xS+UBALSwxMREDRo0qN4NNSRp0KBBSkxMtCEVAIReo7dGfPLJJ3rjjTcCj6urqzV9+nQVFRW1SDAAQOg5HA67IwBAyDSqCO/bt08PPPCADh06FFirqqqSy+XSlClTtH///hYLCABoXqWlpQ0eDZYu3Wa5tLQ0xIkAwB6NKsK/+tWv9PDDD2vOnDmBtU6dOumFF17Q5MmT9cILL7RYQABA80pMTFRKSkqDs5SUFLZGADBGo4rwnj17NGXKlAZnP/rRj1RYWNicmQAALcjhcCgtLa3B2cSJE9keAcAYjSrCXq9X0dHRDc5iYmLk9XqbNRQAoOVYlqU33nijXuF1OBxat24dl08DYIxGFeGEhIRv3Af82WefqVu3bs0aCgDQcmovn/b1wvvVy6cBgAkaVYTHjRunJUuWqKqqqs76hQsXtHTpUt1xxx0tEg4A0Pxq9wg7nXV/BTidTvYIAzBKo64j/OCDD+q9997TmDFj9N3vflexsbE6efKk/va3vykuLk6zZs1q6ZwAgGbicDg0c+ZMud3ueutut5s9wgCM0agjwm3bttWrr76qqVOn6vPPP9fbb7+tkpISTZ8+XWvXrlW7du1aOicAoBklJCQoPT09UHodDocyMjLUs2dPm5MBQOg4LM6KaDSfz6fCwkIlJSUpIiLC7jgA0CRer1cPP/ywTp8+rdjYWGVnZ8vlctkdCwCaJJi+1ug7ywEAwovL5VJmZqa6d+8ut9tNCQZgHIowAAAAjEQRBgBDeb1eZWVl6cSJE8rKyuKa8ACME1QR3rNnT4PrW7ZsaZYwAIDQyc3N1enTpyVJp0+fVl5ens2JACC0girCDz74YL21iooK/fSnP222QACAlufxeLR27do6a6+//ro8Ho9NiQAg9K54HeEvvvhC48aNk8/nk2VZuuGGG+o9JyUlpUXCAQCan2VZys7ObnCWnZ2tBQsWcC1hAEa4YhH+t3/7N+Xm5ur8+fN66KGH9OKLL9aZR0VFaeDAgS0WEADQvGpvsdyQ2lss9+7dO8SpACD0GnVnudqjwJs2beKHIwBc5a500wxuqgHAFI0qwrW6du2qF198UYcPH5bf768zW7ZsWbMGAwC0jLfffvuK83HjxoUoDQDYJ6iT5R5//HG98sorunjxYkvlAQC0sDvvvFNOZ8M//p1Op+68884QJwIAewR1RDg/P195eXlsjwCAq5jT6VR8fHyDV4iIj4//xpIMAOEmqJ92UVFR6tGjR0tlAQCEQElJyTdeJs3j8aikpCTEiQDAHkEV4fvuu0/Lly9XWVlZS+UBAAAAQiKorRF//OMf5fF49Ic//KHe7NNPP222UACAltO7d2/1799fxcXF9Wb9+/dn+xsAYwRVhJcvX95SOQAAIdS2bdsG16OiokKcBADsE1QRHjFihCTp3LlzKikp0aBBg1RTU/ONP1ABAK1PaWmp9u3b1+Bs79693FADgDGC2iNcWVmpn/3sZ0pNTdX999+vw4cPa8yYMfrHP/7RUvkAAM0sMTFRKSkp9a4O4XQ6lZKSosTERJuSAUBoBVWEn3nmGV24cEF//vOf1aZNG/Xu3Vvf+973tGTJkpbKBwBoZg6HQzNnzpTD4ai37na7660DQLgKqgj/9a9/1fLly3XttdfK4XCoTZs2+u///m8VFRW1VD4AQAtISEjQD37wgzpr48eP5/bKAIwSVBH2+/2B/cCWZdVbAwBcPT777LPLPgaAcBdUEb7xxhu1cOFCVVVVBf7pbOXKlYGT6AAAV4fCwkLt37+/ztqnn36qwsJCewIBgA2CKsKPP/64Dh06pOHDh6u8vFzJycnauXOn5s6d21L5AADNzO/3a8WKFQ3OVqxYIb/fH+JEAGCPoC6f1q1bN61du1ZFRUU6evSo4uPjNWTIEEVERLRUPgBAM9u1a5fKy8sbnJWXl2vXrl38Sx8AIzSqCB87dkzx8fGBe9PHxsYqNjZWknT8+HFJl068AAC0fkOHDlVERIR8Pl+9WUREhIYOHWpDKgAIvUYV4e9///sqKCjQbbfdVu+yOpZlyeFwcItlALhKeDyeBkuwJPl8Pnk8Hm6oAcAIjSrCf/rTnyRJ7733XouGAQC0vF69eqljx44Nbo/o2LGjevXqZUMqAAi9Rp0sV3tdycWLF6tXr171vjhZDgCuHkePHr3sHuGjR4+GOBEA2OOKR4RLS0v15ptvSpI++OADvfDCC3XmFRUV//K1J8vKyjRp0iQtXrxYqampkqQ9e/Zo8eLFKi4uVpcuXeR2u5WRkRF4zfr165WVlaWTJ0+qb9++evLJJ5WcnCzp0j/pPffcc3rrrbdUVVWlG2+8UQsWLFD37t0lSadPn9aTTz6pHTt2KCIiQnfffbfmzp2ryMigzhkEgKta7S2Wd+/eHbgmvHTpznLJycncYhmAMa54RDghIUEHDx5Ufn6+fD6f8vPz63wVFxdr/vz5QX/jjz76SJMmTdKRI0cCa+fOndNDDz2kCRMmaOfOnVqyZImWLVumjz/+WJKUn5+vRYsWafny5dq5c6fuvvtuud1uVVVVSZJWr16tbdu2ad26ddq6datcLpfmzZsXeP9HHnlE7dq109atW5WXl6cPP/xQOTk5QWcHgKsZt1gGgEuueCjU6XTq+eeflyTNmzdPixcvbvI3Xb9+vVatWqU5c+bo0UcfDay/8847iomJ0eTJkyVJN910k8aPH681a9ZoyJAhys3N1bhx4wJnNE+dOlVr167V5s2bNXHiROXm5mr27NmBrRxPPPGEbr75ZpWUlMjv92vHjh3asmWLoqOj1bt3b2VmZurZZ5/V9OnTm/yZAOBqkpCQoIEDB9a5qcZ1113HLZYBGCWoPQGLFy9WVVWVzp07F7jgenV1tQ4cOKAxY8Y0+n1uvvlmjR8/XpGRkXWK8MGDBzVw4MA6z+3fv7/y8vIkScXFxZo4cWK9+f79+1VeXq5jx47VeX1sbKw6d+4c2LoRExOjHj16BOb9+vWTx+PR+fPn1alTp0bn/6azrQHgavHPf/6zwTvLlZaWUoYBXNWC6WlBFeE33nhDCxcu1MWLF+usd+vWLagiHBcX1+B6ZWWloqOj66y5XC5duHDhivPKykpJUrt27erNa2dff23t4wsXLgRVhIuKihr9XABobSzL0ssvv9zgbOnSpZo2bRrbIwAYIagivHr1aj3yyCNq3769du7cqSlTpujZZ5/VqFGjmiVMdHR0vTOZvV6v2rdvH5h7vd568y5dugRKbe1+4a+/3rKserPax7Xv31iDBw/mbnoArlpHjhypc37G12fdunXTNddcE+JUANA8fD5fow9aBlWET548qSlTpujo0aNat26dvvWtb2np0qWaOnWqZsyY8S+F/aqBAwdq27ZtddaKi4s1YMAASdKAAQN08ODBevNbb71VnTt3Vo8ePVRcXBzYHnHy5EmdPXtWAwcOlN/v19mzZ3Xq1KnAXfEOHTqk+Ph4dezYMaicERERFGEAVy2n8/LnSTudTn7GATBCo64jXKtbt26qrq5Wz5499fnnn0u6dMLF6dOnmyXMmDFjdOrUKeXk5Ki6ulrbt2/Xxo0bA/uC09PTtXHjRm3fvl3V1dXKycnR6dOnA9sy0tLStHr1apWUlKiiokJLly7ViBEjdM0116hPnz4aOnSoli5dqoqKCpWUlCgrK0vp6enNkh0ArhZfvWTavzIHgHARVBEeMmSInnrqKXm9XvXp00d/+MMftH79esXExDRLmC5duujll1/W//3f/yk1NVXz5s3TvHnzdOONN0q6dBWJ+fPn6+mnn9aIESP0pz/9SS+++GLg+8+aNUujR4/W5MmTNXr0aF28eFErV64MvP+qVatUU1Oj22+/Xffee69uueUWZWZmNkt2ALhaHDt2rElzAAgXDiuIv/ofP35cTz75pBYvXqwjR45o5syZ8nq9WrZsmcaPH9+SOVsFn8+nwsJCJSUl8c+GAK5ahw8f1n/913994/yFF15Qnz59QhcIAJpRMH0tqD3CPXr00G9+8xtJUvfu3QNbFL5+NQYAAACgtWv01ogXXnhBbrdba9asCaxFRkZSggHgKuPxeJo0B4Bw0agi/Mwzz+i1115TmzZttGrVqsBRYQDA1aesrKxJcwAIF40qwps2bdLvf/97rVq1SqtWrdLGjRtbOhcAoIXceeedTZoDQLhoVBEuLy8PXMt36NChOn78eIuGAgC0HLZGAMAljSrCX734emRkUOfXAQBaGb/f36Q5AISLRhVhLq4OAOGD6wgDwCWNOrxbU1OjN998M/C4urq6zmNJmjBhQjPGAgC0lPj4+CbNASBcNKoIx8bGatWqVYHHXbp0qfPY4XBQhAHgKsEtlgHgkkYV4ffff7+lcwAAQmTv3r1XnF977bUhSgMA9mn0DTUAAOGBy6cBwCUUYQAwTElJSZPmABAuKMIAYJgtW7Y0aQ4A4YIiDACGqb1B0r86B4BwQREGAMP06tWrSXMACBcUYQAwTO/evRUdHd3grF27durdu3eIEwGAPSjCAGCYo0ePqqqqqsHZhQsXdPTo0RAnAgB7UIQBwDC9evVS+/btG5y1b9+erREAjEERBgDDlJaWqrKyssFZZWWlSktLQ5wIAOxBEQYAAICRKMIAYJhevXrJ4XA0OHM4HGyNAGAMijAAGGbXrl2yLKvBmWVZ2rVrV4gTAYA9KMIAYJi4uLgmzQEgXFCEAcAw+/bta9IcAMIFRRgADHPXXXc1aQ4A4YIiDACGcTgcioyMbHAWGRn5jSfSAUC4oQgDgGHy8/NVU1PT4Kympkb5+fkhTgQA9qAIA4BhTp8+3aQ5AIQLijAAGObOO+9s0hwAwgVFGAAMc/To0SbNASBcUIQBwDDHjh1r0hwAwgVFGAAMM3z4cEVFRTU4i4qK0vDhw0OcCADsQREGAANd7hbLAGAKijAAGGbHjh368ssvG5x9+eWX2rFjR4gTAYA9KMIAYJgrHfXlqDAAU1CEAcAwFGEAuIQiDACGOXnyZJPmABAuKMIAYBiHw9GkOQCEC4owABgmLi6uSXMACBcUYQAwTHx8fJPmABAuKMIAYJjjx483aQ4A4YIiDACG4YgwAFxCEQYAAICRKMIAYJhjx441aQ4A4YIiDACGYWsEAFxCEQYAw3BnOQC4hCIMAIb5+OOPmzQHgHBBEQYAAICRKMIAYBi2RgDAJRRhADDM+fPnmzQHgHBBEQYAw4wcObJJcwAIFxRhADDMvn37mjQHgHBBEQYAw8TExDRpDgDhgiIMAIbZunVrk+YAEC4owgBgmF69ejVpDgDhgiIMAIYZNWpUk+YAEC4owgBgmA8++KBJcwAIFxRhAAAAGIkiDACG6dKlS5PmABAuKMIAYBiHw9GkOQCEC4owAAAAjEQRBgDDOJ2X/9F/pTkAhAt+2gGAYQYNGtSkOQCEC4owABhm3759TZoDQLigCAOAYQYOHNikOQCEC4owABjmlVdeadIcAMIFRRgADNO3b98mzQEgXFCEAcAw3FADAC6hCAOAYTweT5PmABAuKMIAYJixY8c2aQ4A4YIiDACG+eSTT5o0B4BwQREGAMOUlpY2aQ4A4YIiDACGGTJkSJPmABAuKMIAYJgdO3Y0aQ4A4YIiDACG4RbLAHAJRRgADBMbG9ukOQCEC4owABimU6dOTZoDQLigCAOAYW644YYmzQEgXFCEAcAwBw8ebNIcAMIFRRgADJOamtqkOQCEC4owABgmLy+vSXMACBcUYQAwzIkTJ5o0B4Bw0SqL8ObNmzVo0CAlJycHvubMmSNJ2rNnjzIyMpScnKzbbrtNubm5dV67fv16jRkzRklJSUpLS9Pu3bsDM5/PpxUrVmjkyJFKTk6W2+3mBz4A43Tp0qVJcwAIF62yCBcVFemee+7R7t27A1/PPvuszp07p4ceekgTJkzQzp07tWTJEi1btkwff/yxJCk/P1+LFi3S8uXLtXPnTt19991yu92qqqqSJK1evVrbtm3TunXrtHXrVrlcLs2bN8/OjwoAIde1a9cmzQEgXETaHaAhRUVFuuuuu+qtv/POO4qJidHkyZMlSTfddJPGjx+vNWvWaMiQIcrNzdW4ceM0dOhQSdLUqVO1du1abd68WRMnTlRubq5mz56tnj17SpKeeOIJ3XzzzSopKVHv3r0bnc/n8zXDpwQAe/Tv31979+697JyfcwCuVsH8/Gp1Rdjv92vv3r2Kjo7WSy+9JJ/Pp9GjR2v27Nk6ePCgBg4cWOf5/fv3D5zYUVxcrIkTJ9ab79+/X+Xl5Tp27Fid18fGxqpz58767LPPgirCRUVFTfiEAGCvU6dOXXFeWFgYmjAAYKNWV4TLyso0aNAgjR07VqtWrdKZM2c0d+5czZkzR3FxcYqOjq7zfJfLpQsXLkiSKisrv3FeWVkpSWrXrl29ee2ssQYPHqyIiIhgPxoAtAoVFRXatm3bN85HjhyppKSk0AUCgGbk8/kafdCy1RXh2NhYrVmzJvA4Ojpac+bM0b333qu0tDR5vd46z/d6vWrfvn3guQ3Nu3TpEijItfuFG3p9Y0VERFCEAVy1Dhw4cMX56NGjQ5QGAOzT6k6W279/v5577jlZlhVY+/LLL+V0OjVkyJB6dzwqLi7WgAEDJEkDBgz4xnnnzp3Vo0cPFRcXB2YnT57U2bNn6223AIBwdqVtD2yLAGCKVleEY2JitGbNGr300kuqqamRx+PRs88+qx/+8IcaO3asTp06pZycHFVXV2v79u3auHFjYF9wenq6Nm7cqO3bt6u6ulo5OTk6ffq0xowZI0lKS0vT6tWrVVJSooqKCi1dulQjRozQNddcY+dHBoCQKi8vb9IcAMJFq9saER8fr1//+tf6xS9+odWrVysqKkrjxo3TnDlzFBUVpZdffllLlizRqlWr1LVrV82bN0833nijpEtXkZg/f76efvppHT9+XP3799eLL76omJgYSdKsWbNUU1OjyZMnq7KyUqmpqVq5cqV9HxYAbNCxY0eVlZVddg4AJnBYX92DgMvy+XwqLCxUUlISe4QBXLVmzpyp0tLSb5wnJiYqOzs7hIkAoPkE09da3dYIAEDLOnfuXJPmABAuKMIAYJja7WL/6hwAwgVFGAAMwxFhALiEIgwAhqmoqGjSHADCBUUYAAzj9/ubNAeAcEERBgDDJCQkNGkOAOGCIgwAhomPj2/SHADCBUUYAAzDHmEAuIQiDACGOX78eJPmABAuKMIAYJhOnTo1aQ4A4YIiDACGKSkpadIcAMIFRRgADBMZGdmkOQCEC4owABimpqamSXMACBcUYQAwzLXXXtukOQCEC4owABiGPcIAcAlFGAAM43Re/kf/leYAEC74aQcAhvnyyy+bNAeAcEERBgAAgJEowgAAADASRRgAAABGoggDAADASBRhAAAAGIkiDAAAACNRhAEAAGAkijAAAACMRBEGAACAkSjCAAAAMBJFGAAAAEaiCAMAAMBIFGEAAAAYiSIMAAAAI1GEAQAAYCSKMAAAAIxEEQYAAICRKMIAAAAwEkUYAAAARqIIAwAAwEgUYQAAABiJIgwAAAAjUYQBAABgJIowAAAAjBRpdwAArYtlWbp48aLdMWAzr9drdwS0kKioKDkcDrtjAK0CRRhAgGVZeuyxx/Tpp5/aHQU2S09PtzsCWsigQYO0YsUKyjAgtkYA+Bp+OQIATMERYQABDodDK1asYGtEmDt8+LBmz579jfPnnntOffr0CV0ghBRbI4D/hyIMoA6HwyGXy2V3DLSg66+/vklzAAgXbI0AAANt2rQpqHUACEcUYQAw1NeP/A4aNMimJABgD4owABhq8eLFdR4/88wzNiUBAHtQhAEAysvLszsCAIQcRRgAAABGoggDAADASBRhAAAAGIkiDAAAACNRhAEAAGAkijAAAACMRBEGAACAkSjCAAAAMBJFGAAAAEaiCAMAAMBIFGEAAAAYiSIMAAAAI1GEAQAAYKRIuwOgdbMsSxcvXrQ7BoAW4PV6G/xvAOElKipKDofD7hitEkUYl3Xx4kWlp6fbHQNAC7v//vvtjgCgheTl5cnlctkdo1ViawQAAACMxBFhNJoreaLk5H8yQDixLEuS+GdTINz4a+Tdvc7uFK0erQaN54yUI4L/yQDhhPoLhCfL7gBXCbZGAAAAwEgUYQAAABiJIgwAAAAjUYQBAABgJM58QqNZvhq7IwAAgEbgd3bjUIRxWbWXVpKki4VchgUAgKvNV3+Xoy62RgAAAMBIHBHGZX31IvtRSRO5jjAAAFcBy1cT+JdcbpjzzWg1aDRHBDfUAAAA4YNWg8bz13CnGiDMcItlIEz5OVmuMSjCaDTuWQ4AAMIJJ8sBAADASBwRxmVFRUUpLy/P7hgAWoDX69X9998vSXr11VflcrlsTgSgJURFRdkdodUyrgifPn1aTz75pHbs2KGIiAjdfffdmjt3riIjjfujaBSHw8EvR8AALpeL/68DMI5x7e+RRx5Rjx49tHXrVp06dUput1s5OTmaPn263dGAVsGyLF28eNHuGAgBr9fb4H8jvEVFRXFyJPD/M6oIf/HFF9qxY4e2bNmi6Oho9e7dW5mZmXr22WcpwoAuleDHHntMn376qd1REGK1WyQQ/gYNGqQVK1ZQhgEZVoQPHjyomJgY9ejRI7DWr18/eTwenT9/Xp06dWrU+/h8vpaKCNiK23AC4c+yLPl8PoowwlYwPc2oIlxZWano6Og6a7WPL1y40OgiXFRU1OzZgNZi0qRJqq6utjsGQoTrCJunTZs22rNnj90xgFbBqCLcrl07VVVV1Vmrfdy+fftGv8/gwYMVERHRrNkAAADQdD6fr9EHLY0qwgMGDNDZs2d16tQpxcbGSpIOHTqk+Ph4dezYsdHvExERQREGAAC4yhl1Q40+ffpo6NChWrp0qSoqKlRSUqKsrCylp6fbHQ0AAAAhZlQRlqRVq1appqZGt99+u+69917dcsstyszMtDsWAAAAQsyorRGSFBsbq1WrVtkdAwAAADYz7ogwAAAAIFGEAQAAYCiKMAAAAIxEEQYAAICRKMIAAAAwEkUYAAAARqIIAwAAwEgUYQAAABiJIgwAAAAjUYQBAABgJIowAAAAjEQRBgAAgJEowgAAADBSpN0BriaWZUmSfD6fzUkAAADQkNqeVtvbLociHAS/3y9JKioqsjkJAAAALqe2t12Ow2pMXYakS3+gNTU1cjqdcjgcdscBAADA11iWJb/fr8jISDmdl98FTBEGAACAkThZDgAAAEaiCAMAAMBIFGEAAAAYiSIMAAAAI1GEAQAAYCSKMAAAAIxEEQYAAICRKMIAAAAwEkUYAAAARqIIAwAAwEgUYQAAABiJIgwAAAAjUYQBAABgJIowAIQBn8+nkpISu2O0OFM+J4DQoAgDQCM99dRTSk5OVnJysgYPHqzrr78+8Dg5OVm7du0K+j2nT5+u7OzsJmd79NFH9eabbzb5fRrjgQce0C9/+cuQfK+v++rnLC0t1XXXXafS0lJbsgC4+kXaHQAArhYLFy7UwoULJUlvvPGGXnjhBb3//vtNes+XXnqpOaLpzJkzzfI+rZ0pnxNAaHBEGACaSe0RyuXLl2v48OFasGCBvvzyS61YsUJ33XWXkpOTddNNN2nRokWyLEtS3aOrlmXplVde0dixYzVs2DDdd999+uSTTwLvX1ZWptmzZ2v48OFKTU3Vo48+qnPnzumJJ57Qrl279Otf/1ozZ86UJH322WeaMWOGRowYoVtvvVVPP/20ysvLJV0q8WlpaZo2bZqGDRum7Oxs3XDDDTp27FjgexUVFSkpKUkVFRVB/zns3btXDzzwgIYPH65///d/V05OTuDz/vKXv9RPfvITzZ49W8OGDdOtt96q//mf/wm81uv1av78+RoxYoRGjx6tlStX6rbbblN+fn6Dn1OSNm7cqLvuuktJSUmaOnWqjh8/HnRmAGaiCANAM6usrNS2bdv06KOP6ve//722bt2q3//+99q9e7eysrL0+uuva/v27fVe99prr+l3v/udnn/+eX344YdKS0vTgw8+qFOnTkmSfvrTn6qiokLvvPOO3nvvPZ0/f14LFizQkiVLNGzYMD388MPKzs7WmTNn9J//+Z/q37+/tmzZonXr1unzzz/XY489Fvhee/fu1fjx4/X3v/9dU6dOVd++fbVhw4bA/M0339TYsWPVoUOHoD778ePHNWXKFN155536+9//rqysLL322mtau3Zt4DnvvPOObr75ZuXn52vRokV68cUXVVhYKElaunSpioqK9NZbb2nz5s3yeDw6evSoJNX7nF/9LH/84x/1t7/9TefOndOvfvWroDIDMBdFGACa2YQJE9S2bVt16tRJ9957r3JychQXF6cTJ07I6/Wqffv2DR61XLNmjR5++GFdf/31atOmjdLT09WvXz9t2LBBR48e1Y4dOzR37lx16dJFHTp00PLly+V2u+u9z3vvvac2bdpo9uzZcrlciouL05NPPqn3339fJ0+elCS1adNG99xzj9q2bSuXy6W0tLRAEa6urtamTZs0ceLEoD/7hg0b1K9fP02ePFlt2rRR//799aMf/Uhr1qwJPKdPnz6aMGGCIiIiNHr0aMXFxenw4cOqrq7Whg0b9Oijj6pnz55q3769nnrqKUVERFz2e86cOVMdO3ZU586ddcstt+jIkSNB5wZgJvYIA0Az6969e+C/q6qqtHDhQu3cuVPx8fEaNGiQLMuS3++v97qjR49qxYoVeu655wJrNTU1+va3vx0osL169QrM4uLiFBcXV+99Tp8+rYSEhDoFMjExMfA9al/rdP6/YyH33HOPfvGLX2jfvn0qLS1Vx44dNXz48KA/+9GjR7V3714NGzYssOb3++tk+XrmNm3ayO/36+zZs6qqqqrzGTt06KAuXbpc9nvGxMTUeS+fzxd0bgBmoggDQDNzOByB/543b546d+6sDz74QFFRUfL7/d9YMOPj4/WTn/xE48aNC6wdOXJEMTExqqqqkiR5PB716dNHklRcXKxNmzbpkUceqfM+vXr1ksfjkc/nCxTQ2qOkcXFx+sc//lEnoyTFxsbq1ltv1Z/+9CeVlpYqLS2t3nMaIz4+Xqmpqfrtb38bWDtz5owqKyuv+Npu3brJ5XLJ4/Gob9++kqQLFy5wghyAFsPWCABoQRUVFYqKipLT6VRFRYWeeeYZVVRUqLq6ut5z7733Xq1evVqHDh2SJG3dulXjxo3Tzp071aNHD40aNUrPPPOMzp8/r4qKCj377LOBa+q2bds2cDLc6NGjJUnPPfecvF6vTp48qSVLlujGG2+sc7T16yZOnKi//OUv+vvf/64f/vCHV/xcx44dq/N14cIFjR8/XoWFhdqwYYNqamp04sQJzZw5U8uXL7/in5XT6VR6erp++ctf6vjx46qqqtKyZcvqHOH96ucEgKaiCANAC5o3b57279+vESNG6M4771RFRYVuueUWHThwoN5zp06dqgkTJigzM1PJyclasmSJnnrqKd1+++2SLhXbDh066K677tLtt9+url27asGCBZIu7Utet26d7rvvPnXs2FG/+93vdODAAY0ePVo/+MEP1KtXLz3//POXzfrd735XlZWVGjJkiHr27HnZ5+bk5Gj06NF1vt566y316tVLL730ktauXauRI0fqnnvuUd++fRtVhCXpZz/7mfr27avvf//7Gjt2rOLj4+V0OtWmTZt6nxMAmsph1V7TBgAQcpMnT9bIkSM1a9Ysu6NIkn74wx9qxowZ+v73v2/L99+5c6euu+46derUSdKlI89Dhw7V22+/HdgSAgDNhT3CAGADv9+vM2fO6J///KdiY2PtjqPPP/9c+fn5OnnypO644w7bcrz88svq1KmTFixYIIfDoVWrVunaa6+lBANoEWyNAAAbFBcX67bbblNcXJzGjBljdxw9+eSTev7557VkyRK1bdvWthy1N/4YPXq0Ro0apS+++EK/+c1vbMsDILyxNQIAAABG4ogwAAAAjEQRBgAAgJEowgAAADASRRgAAABGoggDAADASBRhAAAAGIkiDAAAACNRhAEAAGCk/w94PTi3v/7/GgAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Save raw event log of only 10% of TP/TN cases (randomly selected) to analyse in DISCO",
   "id": "f2c2c6fbceca3b2e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:15.991850Z",
     "start_time": "2024-11-24T08:17:15.492898Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Randomly select 1000 unique hadm_ids\n",
    "unique_hadm_ids = raw_event_log_TP_and_TN_only['hadm_id'].unique()\n",
    "sampled_hadm_ids = pd.Series(unique_hadm_ids).sample(n=1000, random_state=1).tolist()\n",
    "\n",
    "# Step 2: Filter the DataFrame to include only rows with the sampled hadm_ids\n",
    "filtered_df = raw_event_log_TP_and_TN_only[raw_event_log_TP_and_TN_only['hadm_id'].isin(sampled_hadm_ids)]"
   ],
   "id": "4288a3928f1be827",
   "outputs": [],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:15.996018Z",
     "start_time": "2024-11-24T08:17:15.993541Z"
    }
   },
   "cell_type": "code",
   "source": [
    "file_path = '/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Input/Event_of_only_10_percent_of_hadmids_from_only_TP_and_TN.csv'\n",
    "#filtered_df.to_csv(file_path, index=False)"
   ],
   "id": "c5bd12f32c28e463",
   "outputs": [],
   "execution_count": 16
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 2. Analysing Trajectory lengths Distribution WITH Abstraction",
   "id": "9b39e2773aeddd69"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:16.096888Z",
     "start_time": "2024-11-24T08:17:15.997524Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Import Abstracted Event log (but this log contain all the patients, not only TP and TN cases)\n",
    "# Below is the event log WITH admission and discharges.\n",
    "# However, the evnt log used by our final model is the log WITHOUT admission and discharge records, but still for this length analysis, we used the log WITH admission and discharge records (coz we considered those event when analysed trace length WITHOUT abstractions too)\n",
    "\n",
    "events_WITH_abstraction = pd.read_csv('/Users/psenevirathn/Desktop/PhD/Coding/Trajectories/Input/Input_for_clustering/May8_v1.csv') # WITH admission and discharge records\n",
    "# May15_v1.csv # WITH admission and discharge records\n",
    "# May8_v1.csv # WITHOUT admission and discharge records\n"
   ],
   "id": "5767ffa44c2440e2",
   "outputs": [],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:16.142035Z",
     "start_time": "2024-11-24T08:17:16.097867Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# join event log with patients class (TP/TN/FP/FN)\n",
    "full_abstracted_event_log_with_patient_class = pd.merge(events_WITH_abstraction, both_train_test_labels, on='hadm_id', how='left')[\n",
    "        ['hadm_id', 'event', 'event_time', 'HIT_label_actual', 'HIT_label_predicted', 'class']]\n",
    "\n",
    "# Only filter TP and TN patients\n",
    "abstracted_event_log_TP_and_TN_only = full_abstracted_event_log_with_patient_class[\n",
    "        (full_abstracted_event_log_with_patient_class['class'] == 'TP') | (full_abstracted_event_log_with_patient_class['class'] == 'TN')]\n",
    "# 12355 - hadm_ids"
   ],
   "id": "4d52b55dfed98cd5",
   "outputs": [],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:16.442503Z",
     "start_time": "2024-11-24T08:17:16.142948Z"
    }
   },
   "cell_type": "code",
   "source": [
    "seq_length_abstracted_event_log = abstracted_event_log_TP_and_TN_only.groupby('hadm_id').agg(count=('hadm_id', lambda x: len(x)),\n",
    "                                                        sequence=('event', lambda x: x)).reset_index()\n",
    "\n",
    "seq_length_abstracted_event_log.sort_values(by='count', ascending=False, inplace=True)"
   ],
   "id": "e592d6332eafedc6",
   "outputs": [],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 1.1 Plot trace length distribution as a Histogram",
   "id": "116227db3c510638"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:44.347590Z",
     "start_time": "2024-11-24T08:17:44.187561Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10, 5))  # Create a new figure with specified size\n",
    "\n",
    "list_seq_lengths_abstracted_event_log = seq_length_abstracted_event_log['count'].tolist()\n",
    "\n",
    "# Custom bins\n",
    "bins_abstracted = list(range(0, max(list_seq_lengths_abstracted_event_log)+5, 5)) # 73\n",
    "\n",
    "# Plot histogram\n",
    "hist_data_abstracted = plt.hist(list_seq_lengths_abstracted_event_log, bins=bins_abstracted, color='#4682B4') \n",
    "\n",
    "#print(hist_data_abstracted)\n",
    "#(array([7.600e+01, 7.655e+03, 3.403e+03, 8.290e+02, 2.450e+02, 7.400e+01, 4.400e+01, 7.000e+00, 1.000e+01, 7.000e+00, 1.000e+00, 0.000e+00, 1.000e+00, 1.000e+00, 2.000e+00]),\n",
    " #array([ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75]),\n",
    " \n",
    "# colours for the plot - edgecolor='black', colors - 87CEEB - Light blue , Steel Blue: #4682B4 Light Green: #98FB98 , Cornflower Blue: #6495ED, Light Pink: #FFB6C1 , Light Gray: #D3D3D3 # FA8072 - Salmon pink\n",
    "\n",
    "# Set x ticks\n",
    "plt.xticks(bins_abstracted, rotation='vertical', fontsize=6)\n",
    "\n",
    "# Set y ticks\n",
    "\n",
    "# print(plt.hist(list_seq_lengths_of_each_hadm_id_when_HIT_positive, bins=bins)) # returns an array of two arrays, where first array contain frquency of each bin, and the second array contain bin edges values\n",
    "\n",
    "plt.yticks(range(0, int(max(hist_data_abstracted[0]) + 500), 500),fontsize=6) # int(max(hist_data_abstracted[0]) - patient count of the highest bar\n",
    "\n",
    "# Add axis labels and title\n",
    "plt.xlabel('Trace Length (Event Count)')\n",
    "plt.ylabel('Patient Count')\n",
    "plt.title('Trace Length Distribution - With Abstraction')\n",
    "\n",
    "# Add grid\n",
    "plt.grid(False)\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ],
   "id": "ebbb354236d2a1d0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 1.2 Plot Cumulative trace length distribution as a Histogram",
   "id": "127eb96244d4cff8"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:16.610003Z",
     "start_time": "2024-11-24T08:17:16.606828Z"
    }
   },
   "cell_type": "code",
   "source": "patient_count = seq_length_abstracted_event_log['hadm_id'].nunique() # 12355",
   "id": "f24ad6234b61602d",
   "outputs": [],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:16.840611Z",
     "start_time": "2024-11-24T08:17:16.611257Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10, 5))  # Create a new figure with specified size\n",
    "\n",
    "# Calculate cumulative distribution\n",
    "cumulative_count_abstracted = np.cumsum(hist_data_abstracted[0])\n",
    "\n",
    "# hist_data_abstracted[0] - array([7.600e+01, 7.655e+03, 3.403e+03, 8.290e+02, 2.450e+02, 7.400e+01, 4.400e+01, 7.000e+00, 1.000e+01, 7.000e+00, 1.000e+00, 0.000e+00, 1.000e+00, 1.000e+00, 2.000e+00])\n",
    "# hist_data_abstracted[1] - hist_data_abstracted\n",
    "\n",
    "# Plot cumulative distribution\n",
    "# consider x-axis to be start from index 1. Index 0 is zero. Index 2 is 100, index 2 is 200 so on (when the step size is 100).\n",
    "\n",
    "plt.plot(hist_data_abstracted[1][1:], (cumulative_count_abstracted / patient_count) * 100, linestyle='-', marker='o', color= '#4682B4')\n",
    "\n",
    "# Set x ticks\n",
    "plt.xticks(hist_data_abstracted[1][1:], rotation='vertical', fontsize=6)\n",
    "\n",
    "# Set y ticks\n",
    "plt.yticks(range(0, 105, 5), fontsize=6)\n",
    "\n",
    "# Labeling\n",
    "plt.xlabel('Trace Length (Event Count)')\n",
    "plt.ylabel('Cumulative Patient Percentage')\n",
    "plt.title('Cumulative Trace Length Distribution - With Abstraction')\n",
    "\n",
    "# Add grid\n",
    "plt.grid(True)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ],
   "id": "94c6f027cdf0e720",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 1.3 Analyse trace length Stats",
   "id": "f71b16a441252cca"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:16.853580Z",
     "start_time": "2024-11-24T08:17:16.841755Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Convert list to Pandas Series\n",
    "series_seq_lengths_abstracted = pd.Series(list_seq_lengths_abstracted_event_log)\n",
    "\n",
    "# Get descriptive statistics\n",
    "stats_trace_length_abstracted = series_seq_lengths_abstracted.describe()\n",
    "\n",
    "print(stats_trace_length_abstracted)\n",
    "\n",
    "Distinc_events = abstracted_event_log_TP_and_TN_only['event'].nunique()\n",
    "print(Distinc_events) # 49"
   ],
   "id": "36a74237c4bb6208",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    12355.000000\n",
      "mean         7.684824\n",
      "std          4.373218\n",
      "min          2.000000\n",
      "25%          5.000000\n",
      "50%          7.000000\n",
      "75%          9.000000\n",
      "max         72.000000\n",
      "dtype: float64\n",
      "47\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:16.918224Z",
     "start_time": "2024-11-24T08:17:16.854659Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#box plot for trace lengths (with abstration)\n",
    "\n",
    "# Create a box plot using seaborn\n",
    "plt.figure(figsize=(8, 6))  # Optional: adjust the size of the plot\n",
    "sns.boxplot(y=series_seq_lengths_abstracted, color= '#4682B4')\n",
    "\n",
    "# Add labels and title (optional)\n",
    "#plt.title('Box Plot of Sequence Lengths (Abstracted)')\n",
    "plt.xlabel('Trajectory Length')\n",
    "plt.ylabel('Patient Count')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ],
   "id": "aa405098bb3fc2bf",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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Hx2vt2rVavny5Ghsb5Xa7tXjxYv3sZz/T6NGjdfjwYa1atUoNDQ16++23tWHDBmVlZVkdEwCMkJOTo4SEBElSYmKisrOzbU4EANaxvKj27NlTjz32mLZs2aKhQ4cqKytLKSkpuvfee9W1a1etWLFCmzZtUmZmpvLy8pSXl6dhw4ZZHRMAjOByuTRz5kz16NFDubm5crlcdkcCAMs4/N989skpzuv1qqysTKmpqdyjCgAAYKDW9jVeoQoAAAAjUVQBAABgJIoqAAAAjERRBQAAgJEoqgAAADASRRUAAABGoqgCgOFKSko0depUlZSU2B0FACxFUQUAg3k8HhUWFurgwYMqLCyUx+OxOxIAWIaiCgAGKyoqUlVVlSSpqqpKxcXFNicCAOtQVAHAUG63W8XFxTr+AkG/36+ioiK53W6bkwGANSiqAGAgv9+vZcuW6ZtvuT7ROACEIooqABiosrJSpaWl8vl8AeM+n0+lpaWqrKy0KRkAWIeiCgAGSk5OVnp6upzOwK9pp9Op9PR0JScn25QMAKxDUQUAAzkcDs2YMaPFudzcXDkcDosTAYD1KKoAYKikpCQNGDAgYGzgwIHq1auXTYkAwFoUVQAwlNvt1t69ewPG9uzZw6p/AGGDogoABmLVPwBQVAHASKz6BwCKKgAYiVX/AEBRBQAjHV/1/83V/Q6Hg1X/AMIGRRUADJWUlKTs7OymUupwOJSTk8OqfwBhg6IKAAbLyclRQkKCJCkxMVHZ2dk2JwIA61BUAcBgLpdLM2fOVI8ePZSbmyuXy2V3JACwTKTdAQAA3y4zM1OZmZl2xwAAy3FGFQAMt3r1al111VVavXq13VEAwFIUVQAwWHV1tZ599ln5fD49++yzqq6utjsSAFiGogoABsvPz296C5Xf79eCBQtsTgQA1qGoAoChysrKtHv37oCxXbt2qayszJ5AAGAxiioAGMjn86mgoKDFuYKCgmavVgWAUERRBQADbdu2TTU1NS3O1dTUaNu2bRYnAgDrBVVUX3755RbHn3nmmXYJAwD4SkZGhjp37tziXJcuXZSRkWFxIgCw3kmLan19vdxut9xut+666y598sknTZ/dbrf27t2rRYsWWZEVAMKG0+nUnDlzWpybM2eOnE4uiAEIfSd94H9tba2uvPJKeTweSdLIkSOb5vx+vxwOhy677LKOSwgAYSo1NVUOh6Np1b8kORwOnXfeeTamAgDrnLSodu/eXa+99prq6+s1ZswYbdy4MWA+JiZG3bp167CAABCu1q9fH1BSpa9OEKxfv15ZWVk2pQIA67TqFaqJiYmSpO3bt3O5CQAs0NjYqJUrV7Y4t3LlSo0dO1aRkbwFG0BoC+pb7tChQ3r00Uf1wQcfNHs0ylNPPdWuwQAgnK1bt+6k85MmTbIoDQDYI6iiOnfuXB0+fFg/+tGPFBUV1VGZACDsXXPNNd9aVq+55hoL0wCAPYIqquXl5XrllVeUkJDQUXkAAJIiIyM1ZcqUFi//T506lcv+AMJCUDecdu7cWdHR0R2VBQDwNVlZWc0KaWRkpMaPH29TIgCwVlBFdebMmZo7d6527twZ8CxVt9vdUfkAIGy53e5m6wH8fj/fuQDCRlDXjvLy8iRJr776qhwOh6T/Pkv1vffea/90ABCm/H6/li1bdsLx+fPnN30PA0CoCqqobtmypaNyAAC+prKyUqWlpc3GfT6fSktLVVlZqTPPPNOGZABgnaCKau/evTsqBwDga5KTk5Wenq6ysrKAy/9Op1OpqalKTk62MR0AWCOoojpgwIATXmri0j8AtB+Hw6EZM2ZoxowZzeZyc3O57A8gLARVVL/5UP+qqiqtXr1aY8eObddQAAApKSlJAwYM0O7du5vGBg4cqF69etmYCgCsE1RRHTp0aLOx888/X9dff70mTJjQbqEAAF+t+t+7d2/A2J49e+R2u5WUlGRTKgCwTlCPp2pJly5ddODAgfbIAgD4P8dX9/v9/laNA0AoCuqM6gsvvBDwuaGhQVu2bNHAgQPbMxMAhD1W/QNAkEV1yZIlAZ8jIiJ0zjnnaN68ee0aCgDCHav+ASDIovr66693VA4AwNccX/Wfm5vbbJxV/wDCRdD3qL777rv61a9+pRtvvFF33XWXtm3b9p0P7vV6NXnyZN15551NYzt27FBOTo7S0tI0cuRIFRUVfef9A8CpLCkpSdnZ2U2l1OFwKCcnh1X/AMJGUEX1jTfe0LXXXqsvvvhC/fv3V21traZMmaLXXnvtOx186dKlAUW3urpa06dP17hx47R161bl5+dr4cKF2rlz53faPwCc6nJycpSQkCBJSkxMVHZ2ts2JAMA6Qd+jWlBQoCuuuKJp7OWXX1ZhYaEuu+yyoA781ltvafPmzfrxj3/cNLZ582bFx8dr4sSJkqThw4drzJgxWrt2rQYPHhzU/r1eb1DbA4CJoqKiNGPGDD3++OOaPn26oqKi+H4DcMpr7fdYUEX1/fff1+jRowPGRo8erbvvvjuY3eizzz7T3XffrcLCQq1atappvKKiQv369QvYtk+fPiouLg5q/5JUXl4e9M8AgImio6P185//XJJUVlZmbxgAsFBQRTU+Pl7/+te/NGDAgKaxPXv2qHv37q3eh8/n06xZszRlypSA/UjSkSNHFBsbGzDmcrlUV1cXTExJUkpKiiIiIoL+OQAwzZo1a7R+/XplZWVp0qRJdscBgDbzer2tOqkYVFHNyclRbm6ubrrpJiUnJ2v//v164okndO2117Z6H4899piio6M1efLkZnOxsbGqqakJGPN4PIqLiwsmpqSvHp1FUQVwqquurlZxcbH8fr+Ki4s1btw4nX766XbHAgBLBFVUb7zxRh09elSPPfaYDh8+rN69e2vSpEmaMmVKq/fx4osv6uDBg8rIyJD0VRGVpNdee02zZ8/WP/7xj4Dt9+3bp759+wYTEwBCRn5+ftNbqPx+vxYsWKCCggKbUwGANYIqqg6HQ7/4xS/0i1/84jsfcNOmTQGfjz+aatGiRfr888+1ePFirVq1ShMnTtT27du1YcMGFRYWfufjAcCpqqysTLt37w4Y27Vrl8rKypSammpPKACwUKsfT/Xuu+/queeea/rc0NCgadOmteuipa5du2rFihXatGmTMjMzlZeXp7y8PA0bNqzdjgEApwKfz3fCM6cFBQUBb6sCgFDVqjOqu3fv1uTJkwPuRa2vr5fL5dJ1112np59+utnCqNZatGhRwOeUlBStW7fuO+0LAELFtm3bmt2zf1xNTY22bdumoUOHWpwKAKzVqjOqv//973XTTTdp1qxZTWNdunTR0qVLNXHiRC1durTDAgJAOMrIyFDnzp1bnOvSpUvTff4AEMpaVVR37Nih6667rsW5G264gef6AUA7czqdmjNnTotzc+bMkdMZ9BuwAeCU06pvOo/H0+z5psfFx8c3rdwHALSf1NRUORyOgDGHw6HzzjvPpkQAYK1WFdWkpCTt2bOnxbm9e/cqMTGxXUMBAKT169c3PZrqOL/fr/Xr19uUCACs1aqieuWVVyo/P1/19fUB43V1dVqwYIEuu+yyDgkHAOGqsbFRK1eubHFu5cqVamxstDgRAFivVav+p0yZoi1btmjUqFG65JJL1K1bNx06dEh//etf1b17d918880dnRMAwsrJnn6ybt06XqcKIOS16oxqdHS01qxZo+uvv17vv/++XnnlFX300UeaNm2annnmGZ122mkdnRMAwso111zTpnkACAWtfjNVdHS0pk2bpmnTpnVkHgCApMjISE2ZMqXFy/9Tp05VZGRQLxYEgFMSzzcBAENlZWU1K6SRkZEaP368TYkAwFoUVQAwlNvtbvaqVL/fL7fbbVMiALAWRRUADOT3+7Vs2bITjn/zsVUAEIqCKqo7duxocfxvf/tbu4QBAHylsrJSpaWlzc6o+nw+lZaWqrKy0qZkAGCdoIrqlClTmo3V1tbq1ltvbbdAAAApOTlZ6enpzV6V6nQ6lZ6eruTkZJuSAYB1Trps9MMPP9SVV14pr9crv9+vgQMHNtsmPT29Q8IBQLhyOByaMWOGbrrppoBxv9+v3NzcZq9WBYBQdNKi+j//8z8qKirSl19+qenTp+uJJ54ImI+JiVG/fv06LCAAhKukpCQlJibq8OHDTWPdunVTr169bEwFANZp1YP4jp9F3bhxo84888wODQQA+EpZWVlASZWkQ4cOqaysTKmpqfaEAgALBfXE6ISEBD3xxBP64IMPmt3gv3DhwnYNBgDhzOfzqaCgoMW5goICrV27ttn9qwAQaoL6lps7d66eeuopHT16tKPyAAAkbdu2TTU1NS3O1dTUaNu2bRYnAgDrBXVGtaSkRMXFxVz+B4AOlpGRoc6dO7dYVrt06aKMjAwbUgGAtYI6oxoTE6Mzzjijo7IAAP6P0+nUnDlzWpybM2cOl/0BhIWgvumuvfZaLVq0SFVVVR2VBwDwf1JTUzVo0KCAsXPPPVfnnXeeTYkAwFoOfxDv4Rs5cqTcbneLz+9777332jXYd+X1eptWxEZERNgdBwDapLq6WpMmTZLf75fT6dTq1at1+umn2x0LANqktX0tqHtUFy1a1OZgAIDWO/300zVhwgQVFRUpJyeHkgogrAR1RvW46upqffTRRxo0aJAaGxsVHR3dEdm+E86oAgAAmK21fS2oe1SPHDmiX/7yl8rMzNSkSZP0wQcfaNSoUfrPf/7T5sAAgJatXr1aV111lVavXm13FACwVFBF9cEHH1RdXZ1efvllRUVF6cwzz9SPfvQj5efnd1Q+AAhr1dXVevbZZ+Xz+fTss8+qurra7kgAYJmgiupf/vIXLVq0SN///vflcDgUFRWlO++8U+Xl5R2VDwDCWn5+vo7foeX3+7VgwQKbEwGAdYIqqj6fr+l+1ONfnF8fAwC0n7KyMu3evTtgbNeuXSorK7MnEABYLKiiOmzYMN13332qr69vekTVww8/rKFDh3ZIOAAIVz6fTwUFBS3OFRQUyOfzWZwIAKwXVFGdO3eu/v3vf2vIkCGqqalRWlqatm7desK3pwAAvptt27a1+PpUSaqpqdG2bdssTgQA1gvqOaqJiYl65plnVF5ero8//lg9e/bU4MGDeQwUALSzjIwMde7cucWy2qVLF2VkZNiQCgCs1aozqp9++qkkye1265NPPlG3bt103nnn6YwzztCBAwfkdrs7NCQAhBun03nCq1Vz5syR0xnUBTEAOCW16ozqT37yE5WWlmrkyJHNXp/q9/vlcDiMeYUqAISK1NRURUZGqrGxsWksMjJS5513no2pAMA6rSqqf/rTnyRJW7Zs6dAwAID/2rJlS0BJlaTGxkZt2bJFl156qU2pAMA6rbp21KtXL0nSAw88oN69ezf7h8VUANC+vF6vlixZ0uLckiVL5PV6LU4EANY76RnVyspKvfDCC5KkN954Q0uXLg2Yr62t1d69ezskHACEq02bNp2wjHq9Xm3atElXXnmlxakAwFonLapJSUmqqKhQVVWVvF6vSkpKAuZjYmI0b968DgsIAOHo8ssv1+OPP95iWY2IiNDll19uQyoAsNZJi6rT6dQjjzwiScrLy9MDDzzQ4aEAINxFRETolltu0W9/+9tmc7feeiuPBQQQFoJ6vskDDzyg+vp6ffrpp3K73XK73frwww/16quvdlQ+AAhbJ1owNXLkSIuTAIA9gnrg/3PPPaf77rtPR48eDRhPTEzUqFGj2jUYAIS7RYsWnXD8zjvvtDgNAFgvqKL66KOP6rbbblNcXJy2bt2q6667TosXL9aFF17YUfkAICwdPXpUb7zxRotzb7zxho4ePaqYmBiLUwGAtYK69H/o0CFdd911Gj58uPbv369zzz1XCxYsUFFRUUflA4CwtGDBgjbNA0AoCKqoJiYmqqGhQb169dL7778v6aunAnz22WcdEg4AwtVdd93VpnkACAVBFdXBgwfr3nvvlcfj0VlnnaU//vGPev755xUfH99B8QAgPMXExOiHP/xhi3MXX3wxl/0BhIWgiuqdd96pqqoqHTlyRLNmzdJDDz2ke+65R7fddlsHxQOA8HWiBVOzZ8+2OAkA2MPh9/v93/WHGxsb1dDQoNjY2PbM1CZer1dlZWVKTU3lOYMATmlz5szRrl27mo2fe+65KigosCERALSP1va1Vp9RXbp0qXJzc7V27dqmscjISKNKKgCEivr6+hZLqiTt2rVL9fX1FicCAOu1qqg++OCDevrppxUVFaUlS5bo8ccf7+hcABDWTvacVJ6jCiActKqobty4UX/4wx+0ZMkSLVmyRBs2bOjoXAAQ1k70sP/WzgNAKGhVUa2pqVHfvn0lSeeff74OHDjQoaEAINzFxsbq3HPPbXEuJSWF264AhIVWFVWn87+bRUYG9TKrFr311lvKyclRenq6LrzwQt1///3yeDySpB07dignJ0dpaWkaOXIkLxMAELZOtGBq4cKFFicBAHu0qqi24cEAzVRVVemmm27S//7v/2rbtm16/vnn9c9//lOPP/64qqurNX36dI0bN05bt25Vfn6+Fi5cqJ07d7bb8QHgVDFv3rygxgEg1LTq9GhjY6NeeOGFps8NDQ0BnyVp3LhxrTpgQkKC3nzzTXXq1El+v19ffPGFjh49qoSEBG3evFnx8fGaOHGiJGn48OEaM2aM1q5dq8GDB7dq/8d5vd6gtgcAk3g8Hm3fvr3Fue3bt+vIkSNyuVwWpwKA9tHantaqotqtWzctWbKk6XPXrl0DPjscjlYXVUnq1KmTJGnEiBE6cOCAMjIyNH78eD388MPq169fwLZ9+vRRcXFxq/d9XHl5edA/AwCmePLJJ791ftasWbrhhhssSgMA9mhVUX399dc75OCbN29WdXW17rjjDt1yyy0644wzmi0QcLlcqqurC3rfKSkpPPAfwClr8eLFuvrqq791njOqAE5VXq+3VScV274yqg1cLpdcLpdmzZqlnJwcTZ48WTU1NQHbeDwexcXFBb3viIgIiiqAU1ZcXJzOP//8Fi//Dxky5Dt9LwLAqabVb6ZqL6Wlpbr88st17NixprFjx44pKipKffr0UUVFRcD2+/bta3o0FgCEk/nz57c4zmIqAOHC8qLav39/eTwePfTQQzp27Jg+/vhjFRQUKDs7W6NHj9bhw4e1atUqNTQ06O2339aGDRuUlZVldUwAsN3MmTODGgeAUGN5UY2Li9Py5ctVUVGhCy+8UJMnT9YFF1ygu+66S127dtWKFSu0adMmZWZmKi8vT3l5eRo2bJjVMQHAVkeOHNH+/ftbnNu/f7+OHDlicSIAsJ7D354PSTWA1+tVWVmZUlNTuUcVwClr2rRp+vTTT08437NnTy1fvtzCRADQflrb1yw/owoAOLlHHnmkTfMAEAooqgBgoLi4OH3ve99rce6ss85i1T+AsEBRBQBDFRYWtji+dOlSi5MAgD0oqgBgKFb9Awh3FFUAMBCr/gGAogoARrr11lvbNA8AoYCiCgAGYtU/AFBUAcBIrPoHAIoqABiLVf8Awh1FFQAMddtttwU1DgChhqIKAAaqq6vTvn37Wpzbt2+f6urqLE4EANajqAKAgX75y1+2aR4AQgFFFQAM9NBDD7VpHgBCAUUVAAx02mmnqU+fPi3O9evXT6eddprFiQDAehRVADDUww8/3OL4b37zG2uDAIBNKKoAYKg5c+YENQ4AoYaiCgAGqq+v165du1qc27Vrl+rr6y1OBADWo6gCgIHuvPPONs0DQCigqAKAgRYtWtSmeQAIBRRVADBQbGyszj333BbnUlJSFBsba3EiALAeRRUADFVQUNDi+MKFCy1OAgD2oKgCgKGmTJkS1DgAhBqKKgAYqKamRocOHWpx7tChQ6qpqbE4EQBYj6IKAAbKzc1t0zwAhAKKKgAY6NFHH23TPACEAooqABioc+fO6t69e4tzZ5xxhjp37mxxIgCwHkUVAAy1cuXKFseffPJJi5MAgD0oqgBgqPHjxwc1DgChhqIKAAb67LPPdOzYsRbnjh07ps8++8ziRABgPYoqABjohhtuaNM8AIQCiioAGOhk96FynyqAcEBRBQADJSYmKjo6usW5mJgYJSYmWpwIAKxHUQUAQz333HMtjq9fv97iJABgD4oqABjqpz/9aVDjABBqKKoAYKBPPvmkTfMAEAooqgBgoBtvvLFN8wAQCiiqAGCgJ554ok3zABAKKKoAYKBevXqdcM7hcHzrPACECooqABhq48aNLY5v2LDB4iQAYA+KKgAYilX/AMIdRRUADLR///42zQNAKKCoAoCBZs6c2aZ5AAgFFFUAMFBhYWGb5gEgFFBUAcBA3/ve99o0DwChgKIKAIY60ar/E40DQKihqAKAoVj1DyDcUVQBwEDvvfdem+YBIBRQVAHAQLNmzWrTPACEAooqABho8eLFbZoHgFBAUQUAAw0cOLBN8wAQCmwpqnv27NGUKVM0dOhQXXjhhZo9e7aqqqokSTt27FBOTo7S0tI0cuRIFRUV2RERAGzHqn8A4c7yourxeDRt2jSlpaXpjTfe0MaNG/XFF1/orrvuUnV1taZPn65x48Zp69atys/P18KFC7Vz506rYwKA7Vj1DyDcRVp9QLfbrQEDBujmm29WRESEoqOjdfXVV2v27NnavHmz4uPjNXHiREnS8OHDNWbMGK1du1aDBw8O6jher7cj4gOAJXbv3v2t8+Xl5Ro0aJBFaQCgfbW2p1leVM8++2wtX748YOyVV17Rueeeq4qKCvXr1y9grk+fPiouLg76OOXl5W3KCQB2mjdv3rfOz507V/Pnz7coDQDYw/Ki+nV+v18PP/yw/vKXv2jNmjV66qmnFBsbG7CNy+VSXV1d0PtOSUlRREREe0UFAEstXLhQc+fO/dZ5zqgCOFV5vd5WnVS0rajW1tZq7ty52rVrl9asWaP+/fsrNjZWNTU1Adt5PB7FxcUFvf+IiAiKKoBTVkpKSpvmASAU2LLqf//+/crKylJtba2Ki4vVv39/SVK/fv1UUVERsO2+ffvUt29fO2ICgK1Y9Q8g3FleVKurq3XdddcpPT1dTz75pBISEprmRo0apcOHD2vVqlVqaGjQ22+/rQ0bNigrK8vqmABgO1b9Awh3ll/6f+655+R2u/Xyyy9r06ZNAXPvvPOOVqxYofz8fC1ZskQJCQnKy8vTsGHDrI4JALb661//etL5ESNGWJQGAOzh8Pv9frtDtCev16uysjKlpqZyjyqAU1ZrzppyCwCAU1Vr+xqvUAUAA82aNatN8wAQCiiqAGCgk13W57I/gHBAUQUAQ7HqH0C4o6gCgKFY9Q8g3FFUAcBAzz33XJvmASAUUFQBwEArVqxo0zwAhAKKKgAYaOrUqW2aB4BQQFEFAAONHz++TfMAEAooqgBgKFb9Awh3FFUAMBSr/gGEO4oqABhozZo1bZoHgFBAUQUAA61bt65N8wAQCiiqAGCga665pk3zABAKKKoAYKBJkya1aR4AQgFFFQAMxap/AOGOogoAhmLVP4BwR1EFAAPdc889bZoHgFBAUQUAA73zzjttmgeAUEBRBQADpaWltWkeAEIBRRUADHT//fe3aR4AQgFFFQAMxap/AOEu0u4AAILn9/t19OhRu2PAJh6Px+4I6GAxMTFyOBx2xwBsR1EFTjF+v1+zZ8/We++9Z3cU2CQ7O9vuCOhggwYNUkFBAWUVYY9L/8ApiL+8AADhgDOqwCnG4XCooKCAS/9hwuPxNL0udc2aNXK5XDYnghW49A98haIKnIIcDgeFJQy5XC7+uwMIK1z6BwAAgJEoqgAAADASRRUAAABGoqgCAADASBRVAAAAGImiCgAAACNRVAEAAGAkiioAAACMRFEFAACAkSiqAAAAMBJFFQAAAEaiqAIAAMBIFFUAAAAYiaIKAAAAI1FUAQAAYCSKKgAAAIxEUQUAAICRKKoAAAAwEkUVAAAARqKoAgAAwEgUVQAAABiJogoAAAAjUVQBAABgJIoqAAAAjGRrUa2qqtKoUaNUUlLSNLZjxw7l5OQoLS1NI0eOVFFRkY0JAQAAYBfbiur27dt19dVXa//+/U1j1dXVmj59usaNG6etW7cqPz9fCxcu1M6dO+2KCQAAAJtE2nHQ559/XkuWLNGsWbN0++23N41v3rxZ8fHxmjhxoiRp+PDhGjNmjNauXavBgwcHdQyv19uumQHADl//LvN6vXy3AQgJrf0us6Wo/vCHP9SYMWMUGRkZUFQrKirUr1+/gG379Omj4uLioI9RXl7e5pwAYLdjx441/Xnnzp2Kjo62MQ0AWMuWotq9e/cWx48cOaLY2NiAMZfLpbq6uqCPkZKSooiIiO+UDwBM4fF4mv48ePBguVwuG9MAQPvwer2tOqloS1E9kdjYWNXU1ASMeTwexcXFBb2viIgIiiqAU97Xv8f4XgMQbox6PFW/fv1UUVERMLZv3z717dvXpkQAAACwi1FnVEeNGqXFixdr1apVmjhxorZv364NGzaosLDQ7mhG8/v9Onr0qN0xAHSAr1/6//qfAYSWmJgYORwOu2MYx6ii2rVrV61YsUL5+flasmSJEhISlJeXp2HDhtkdzWhHjx5Vdna23TEAdLBJkybZHQFABykuLuYe9BbYXlT37t0b8DklJUXr1q2zKQ0AAABMYXtRRftypWVJTv6zAqHE7/dLEpcFgVDja5TnnfV2pzAajSbUOCPliOA/KxBKqKdAaPLbHeAUYNSqfwAAAOA4iioAAACMRFEFAACAkSiqAAAAMBJFFQAAAEaiqAIAAMBIFFUAAAAYiQduhhi/t9HuCAAAoBX4O/vkKKoh4PhbayTpaBlvuAAA4FTz9b/L8V9c+gcAAICROKMaAr7+/u+Y1CxeoQoAwCnA721suhL69b/L8V80mhDjiIikqAIAgJDApX8AAAAYiaIKAAAAI1FUAQAAYCSKKgAAAIxEUQUAAICRKKoAAAAwEkUVAAAARuKBm6HG1yhewgaEluOvVuSB4ECI8TXancB4FNUQ43lnvd0RAAAA2gWX/gEAAGAkzqiGgJiYGBUXF9sdA0AH8Hg8mjRpkiRpzZo1crlcNicC0BFiYmLsjmAkimoIcDgc/OUFhAGXy8X/1gGEFS79AwAAwEgUVQAAABiJogoAAAAjUVQBAABgJIoqAAAAjERRBQAAgJEoqgAAADASRRUAAABGoqgCAADASBRVAAAAGImiCgAAACNRVAEAAGAkiioAAACMFGl3AADB8/v9Onr0qN0xYAGPx9PinxHaYmJi5HA47I4B2I6iCpxi/H6/Zs+erffee8/uKLDYpEmT7I4AiwwaNEgFBQWUVYQ9Lv0DpyD+8gIAhAPOqAKnGIfDoYKCAi79hxG/3y+J/4MSTrj0D3yFogqcghwOh1wul90xAADoUFz6BwAAgJEoqgAAADASRRUAAABGoqgCAADASBRVAAAAGImiCgAAACMZWVQ/++wzzZw5UxkZGcrMzFR+fr4aGxvtjgUAAAALGVlUb7vtNp122mn6+9//ruLiYr311ltatWqV3bEAAABgIeMe+P/hhx/qn//8p/72t78pNjZWZ555pmbOnKnFixdr2rRprd6P1+vtwJQAAAD4rlrb04wrqhUVFYqPj9cZZ5zRNHbOOefI7Xbryy+/VJcuXVq1n/Ly8o6KCAAAAAsYV1SPHDmi2NjYgLHjn+vq6lpdVFNSUhQREdHu+QAAANA2Xq+3VScVjSuqp512murr6wPGjn+Oi4tr9X4iIiIoqgAAAKcw4xZT9e3bV1988YUOHz7cNPbvf/9bPXv2VOfOnW1MBgAAACsZV1TPOussnX/++VqwYIFqa2v10UcfqbCwUNnZ2XZHAwAAgIWMK6qStGTJEjU2NurSSy/VhAkTdNFFF2nmzJl2xwIAAICFjLtHVZK6deumJUuW2B0DAAAANjLyjCoAAABg5BnVtvD7/ZJ44D8AAICpjve0473tREKuqPp8Pkk88B8AAMB0x3vbiTj8J6uypxifz6fGxkY5nU45HA674wAAAOAb/H6/fD6fIiMj5XSe+E7UkCuqAAAACA0spgIAAICRKKoAAAAwEkUVAAAARqKoAgAAwEgUVQAAABiJogoAAAAjUVQBAABgJIoqAAAAjERRBQAAgJEoqgAAADASRRUAAABGoqgCAADASBRVAAAAGImiCgAW8Xq9+uijj+yO0eHC5fcE0PEoqgBCyr333qu0tDSlpaUpJSVFAwYMaPqclpambdu2Bb3PadOmadmyZW3Odvvtt+uFF15o835aY/Lkyfrd735nybG+6eu/Z2Vlpfr376/KykpbsgA4tUXaHQAA2tN9992n++67T5L03HPPaenSpXr99dfbtM/ly5e3RzR9/vnn7bIf04XL7wmg43FGFUBYOX6Gb9GiRRoyZIjmz5+vY8eOqaCgQFdccYXS0tI0fPhw3X///fL7/ZICz076/X499dRTGj16tDIyMnTttdfq3Xffbdp/VVWV7rjjDg0ZMkSZmZm6/fbbVV1drbvvvlvbtm3TY489phkzZkiS9u7dqxtvvFFDhw7VxRdfrF/96leqqamR9FXJHj9+vKZOnaqMjAwtW7ZMAwcO1Kefftp0rPLycqWmpqq2tjbofw+7du3S5MmTNWTIEP34xz/WqlWrmn7f3/3ud7rlllt0xx13KCMjQxdffLEeeuihpp/1eDyaN2+ehg4dqhEjRujhhx/WyJEjVVJS0uLvKUkbNmzQFVdcodTUVF1//fU6cOBA0JkBhB+KKoCwdOTIEf3jH//Q7bffrj/84Q/6+9//rj/84Q965513VFhYqHXr1untt99u9nNPP/20Vq5cqUceeURvvfWWxo8frylTpujw4cOSpFtvvVW1tbXavHmztmzZoi+//FLz589Xfn6+MjIydNNNN2nZsmX6/PPP9f/+3/9Tnz599Le//U3r16/X+++/r9mzZzcda9euXRozZozefPNNXX/99Tr77LP10ksvNc2/8MILGj16tDp16hTU737gwAFdd911uvzyy/Xmm2+qsLBQTz/9tJ555pmmbTZv3qwf/vCHKikp0f33368nnnhCZWVlkqQFCxaovLxcL774ov785z/L7Xbr448/lqRmv+fXf5dnn31Wf/3rX1VdXa3f//73QWUGEJ4oqgDC0rhx4xQdHa0uXbpowoQJWrVqlbp3766DBw/K4/EoLi6uxbN+a9eu1U033aQBAwYoKipK2dnZOuecc/TSSy/p448/1j//+U/NmTNHXbt2VadOnbRo0SLl5uY228+WLVsUFRWlO+64Qy6XS927d9c999yj119/XYcOHZIkRUVFaezYsYqOjpbL5dL48eObimpDQ4M2btyorKysoH/3l156Seecc44mTpyoqKgo9enTRzfccIPWrl3btM1ZZ52lcePGKSIiQiNGjFD37t31wQcfqKGhQS+99JJuv/129erVS3Fxcbr33nsVERHxrcecMWOGOnfurNNPP10XXXSR9u/fH3RuAOGHe1QBhKUePXo0/bm+vl733Xeftm7dqp49e2rQoEHy+/3y+XzNfu7jjz9WQUGBfv3rXzeNNTY26gc/+EFTwezdu3fTXPfu3dW9e/dm+/nss8+UlJQUUPCSk5ObjnH8Z53O/55PGDt2rH7zm99o9+7dqqysVOfOnTVkyJCgf/ePP/5Yu3btUkZGRtOYz+cLyPLNzFFRUfL5fPriiy9UX18f8Dt26tRJXbt2/dZjxsfHB+zL6/UGnRtA+KGoAghLDoej6c95eXk6/fTT9cYbbygmJkY+n++EBbBnz5665ZZbdOWVVzaN7d+/X/Hx8aqvr5ckud1unXXWWZKkffv2aePGjbrtttsC9tO7d2+53W55vd6mgnj8LGP37t31n//8JyCjJHXr1k0XX3yx/vSnP6myslLjx49vtk1r9OzZU5mZmXryySebxj7//HMdOXLkpD+bmJgol8slt9uts88+W5JUV1fHAioAHYJL/wDCXm1trWJiYuR0OlVbW6sHH3xQtbW1amhoaLbthAkT9Oijj+rf//63JOnvf/+7rrzySm3dulVnnHGGLrzwQj344IP68ssvVVtbq8WLFzc9UzQ6OrppsdSIESMkSb/+9a/l8Xh06NAh5efna9iwYQFnK78pKytLr776qt5880397Gc/O+nv9emnnwb8U1dXpzFjxqisrEwvvfSSGhsbdfDgQc2YMUOLFi066b8rp9Op7Oxs/e53v9OBAwdUX1+vhQsXBpwh/frvCQBtQVEFEPby8vK0Z88eDR06VJdffrlqa2t10UUX6V//+lezba+//nqNGzdOM2fOVFpamvLz83Xvvffq0ksvlfRV8ezUqZOuuOIKXXrppUpISND8+fMlfXVf7Pr163Xttdeqc+fOWrlypf71r39pxIgR+ulPf6revXvrkUce+dasl1xyiY4cOaLBgwerV69e37rtqlWrNGLEiIB/XnzxRfXu3VvLly/XM888owsuuEBjx47V2Wef3aqiKkm//OUvdfbZZ+snP/mJRo8erZ49e8rpdCoqKqrZ7wkAbeHwH38eCQCgRRMnTtQFF1ygm2++2e4okqSf/exnuvHGG/WTn/zEluNv3bpV/fv3V5cuXSR9deb2/PPP1yuvvNJ0ywMAtAfuUQWAE/D5fPr888/1ySefqFu3bnbH0fvvv6+SkhIdOnRIl112mW05VqxYoS5dumj+/PlyOBxasmSJvv/971NSAbQ7Lv0DwAns27dPI0eOVPfu3TVq1Ci74+iee+7RI488ovz8fEVHR9uW4/iLCUaMGKELL7xQH374oR5//HHb8gAIXVz6BwAAgJE4owoAAAAjUVQBAABgJIoqAAAAjERRBQAAgJEoqgAAADASRRUAAABGoqgCAADASBRVAAAAGOn/AwKU5tkpKHKbAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# HIT classifier - LIKELIHHOD RATIOS PLOT (T_PLOT)",
   "id": "b3baf3db485b997"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T08:17:17.161153Z",
     "start_time": "2024-11-24T08:17:16.919358Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 1. Import Libraries\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import interp1d\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# ------------------------------------\n",
    "# # 2. Likelihood ratios - leaf plot\n",
    "\n",
    "x_new = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18,\n",
    "         0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37,\n",
    "         0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56,\n",
    "         0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75,\n",
    "         0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94,\n",
    "         0.95, 0.96, 0.97, 0.98, 0.99, 1]\n",
    "\n",
    "y_positiveLR_original = [0.0792410714285714,\t0.148122392211405,\t0.20855091383812,\t0.261992619926199,\t0.309593023255814,\t0.35226019845645,\t0.390723270440252,\t0.425574425574426,\t0.457299618320611,\t0.486301369863014,\t0.512915936952715,\t0.537426408746846,\t0.560072815533981,\t0.581060015588465,\t0.600563909774436,\t0.618736383442266,\t0.635709269662921,\t0.651597552685248,\t0.666501976284585,\t0.680511182108626,\t0.69370347394541,\t0.706148282097649,\t0.717907385697538,\t0.729035938391329,\t0.739583333333333,\t0.749593936112615,\t0.759107708553326,\t0.768160741885626,\t0.776785714285714,\t0.785012285012285,\t0.792867435158501,\t0.80037576326914,\t0.807559742647059,\t0.814439946018893,\t0.821035242290749,\t0.827362969356927,\t0.833439086294416,\t0.839278307756118,\t0.844894222945484,\t0.850299401197605,\t0.855505485893417,\t0.860523278183917,\t0.865362811791383,\t0.87003341997772,\t0.874543795620438,\t0.878902045209903,\t0.883115737473536,\t0.887191947240542,\t0.891137295081967,\t0.894957983193277,\t0.898659827928524,\t0.902248289345064,\t0.905728498074454,\t0.90910527979766,\t0.912383177570094,\t0.915566472213694,\t0.918659200968523,\t0.921665174574754,\t0.924587992937022,\t0.927431059506531,\t0.930197594501718,\t0.932890647075445,\t0.935513106525376,\t0.938067712634187,\t0.940557065217391,\t0.942983632948752,\t0.945349761526232,\t0.947657681235279,\t0.949909513960703,\t0.952107279693487,\t0.954252902574457,\t0.956348216512846,\t0.958394970414201,\t0.960394833048989,\t0.962349397590361,\t0.964260185847034,\t0.966128652214892,\t0.967956187368912,\t0.969744121715076,\t0.971493728620296,\t0.973206227436823,\t0.974882786336236,\t0.976524524966858,\t0.978132516947299,\t0.979707792207792,\t0.981251339190058,\t0.982764106915571,\t0.984247006931317,\t0.985700915141431,\t0.987126673532441,\t0.988525091799266,\t0.989896948878561,\t0.991242994395516,\t0.992563950029744,\t0.993860510805501,\t0.995133346311077,\t0.996383101851852,\t0.997610399541197,\t0.998815839333081,\t1]\n",
    "\n",
    "y_negativeLR_original = [0.00741929015440144,\t0.0148773622838762,\t0.0223745212658738,\t0.0299110751818917,\t0.0374873353596758,\t0.045103616416091,\t0.0527602363006722,\t0.0604575163398693,\t0.0681957812819988,\t0.0759753593429158,\t0.0837965822524192,\t0.0916597853014038,\t0.0995653073897744,\t0.107513491075135,\t0.115504682622268,\t0.123539232053422,\t0.131617493199414,\t0.139739823751574,\t0.147906585314538,\t0.156118143459916,\t0.164374867780834,\t0.172677131947391,\t0.181025313763029,\t0.189419795221843,\t0.197860962566845,\t0.206349206349206,\t0.214884921488492,\t0.223468507333909,\t0.232100367726584,\t0.240780911062907,\t0.24951055035893,\t0.258289703315881,\t0.267118792386786,\t0.27599824484423,\t0.284928492849285,\t0.293909973521624,\t0.302943129010843,\t0.312028406569019,\t0.321166258624527,\t0.330357142857143,\t0.339601522274457,\t0.348899865289627,\t0.358252645800495,\t0.367660343270099,\t0.377123442808607,\t0.386642435256701,\t0.396217817270449,\t0.405850091407678,\t0.415539766215906,\t0.425287356321839,\t0.435093382522481,\t0.444958371877891,\t0.454882857805614,\t0.464867380176826,\t0.474912485414236,\t0.48501872659176,\t0.495186663536041,\t0.505416862929816,\t0.515709898417198,\t0.5260663507109,\t0.53648680770145,\t0.546971864568431,\t0.557522123893805,\t0.568138195777351,\t0.578820697954272,\t0.589570255915017,\t0.600387503027367,\t0.611273080660836,\t0.622227638313429,\t0.633251833740831,\t0.644346333088055,\t0.655511811023622,\t0.666748950876327,\t0.678058444774641,\t0.68944099378882,\t0.700897308075773,\t0.712428107026757,\t0.724034119417963,\t0.735716083564057,\t0.747474747474748,\t0.759310869014441,\t0.771225216065074,\t0.78321856669217,\t0.795291709314227,\t0.807445442875481,\t0.819680577022154,\t0.831997932282244,\t0.844398340248963,\t0.85688264376789,\t0.869451697127937,\t0.882106366256222,\t0.89484752891693,\t0.907676074914271,\t0.920592906299629,\t0.933598937583001,\t0.946695095948827,\t0.959882321476331,\t0.973161567364466,\t0.986533800161594,\t1]\n",
    "\n",
    "y_positiveLR_balanced = [0.0378073670910681,\t0.073548875023634,\t0.107389343885157,\t0.139476514879885,\t0.169943206640454,\t0.19890915288904,\t0.22648257506446,\t0.252761533463288,\t0.27783509245298,\t0.301784328937161,\t0.324683208134153,\t0.346599346599347,\t0.367594679072472,\t0.387726043001566,\t0.407045692361353,\t0.425601750547046,\t0.443438610608194,\t0.460597289830286,\t0.477115744625912,\t0.493029150823828,\t0.508370153712116,\t0.523169091575987,\t0.537454195951222,\t0.551251771374587,\t0.564586357039187,\t0.577480872444901,\t0.589956748862551,\t0.602034048198099,\t0.613731570643599,\t0.625066952329941,\t0.636056754048209,\t0.646716541978387,\t0.657060961253007,\t0.667103803086856,\t0.676858066119811,\t0.68633601254656,\t0.695549219542841,\t0.704508626441712,\t0.713224578064031,\t0.721706864564007,\t0.729964758112499,\t0.73800704670702,\t0.745842065367637,\t0.753477724951576,\t0.760921538795914,\t0.768180647377007,\t0.775261841156765,\t0.782171581769437,\t0.788916021687844,\t0.795501022494888,\t0.801932171874368,\t0.808214799424644,\t0.814353991389185,\t0.820354604389596,\t0.826221278239042,\t0.831958447907119,\t0.837570354701016,\t0.843061056722218,\t0.848434438652915,\t0.853694220921727,\t0.858843968294184,\t0.863887097929651,\t0.868826886942957,\t0.873666479505895,\t0.878408893520931,\t0.883057026896884,\t0.887613663454007,\t0.892081478483745,\t0.896463043986507,\t0.900760833608998,\t0.904977227301026,\t0.909114515710205,\t0.913174904331608,\t0.917160517428153,\t0.921073401736385,\t0.924915529971218,\t0.92868880414225,\t0.932395058693381,\t0.936036063476592,\t0.939613526570048,\t0.943129096949924,\t0.946584367024749,\t0.949980875040457,\t0.953320107363753,\t0.956603500650947,\t0.959832443908877,\t0.963008280454145,\t0.966132309776473,\t0.969205789311609,\t0.972229936128853,\t0.975205928537976,\t0.978134907619985,\t0.981017978685902,\t0.983856212667492,\t0.986650647443599,\t0.989402289105553,\t0.992112113164883,\t0.994781065706383,\t0.997410064489394,\t1]\n",
    "\n",
    "y_negativeLR_balanced = [0.0054249547920434,\t0.0109002825999193,\t0.0164266882985196,\t0.0220048899755501,\t0.0276356192425793,\t0.0333196215549157,\t0.0390576565406076,\t0.0448504983388704,\t0.0506989359482579,\t0.0566037735849057,\t0.0625658310511902,\t0.0685859441151566,\t0.0746649649010849,\t0.0808037622915776,\t0.0870032223415682,\t0.0932642487046632,\t0.0995877630722499,\t0.105974705625818,\t0.112426035502959,\t0.118942731277533,\t0.125525791454505,\t0.132176234979973,\t0.138895101766943,\t0.14568345323741,\t0.152542372881356,\t0.159472966833258,\t0.166476364466773,\t0.173553719008264,\t0.180706208169859,\t0.187935034802784,\t0.195241427571728,\t0.202626641651032,\t0.210091959443527,\t0.217638691322902,\t0.225268176400477,\t0.232981783317354,\t0.240780911062907,\t0.24866698982065,\t0.256641481842554,\t0.264705882352941,\t0.272861720483116,\t0.281110560237977,\t0.289454001495886,\t0.297893681043129,\t0.306431273644388,\t0.315068493150685,\t0.323807093646338,\t0.33264887063655,\t0.341595662277304,\t0.350649350649351,\t0.359811863078129,\t0.369085173501577,\t0.37847130388786,\t0.387972325705162,\t0.397590361445783,\t0.407327586206897,\t0.417186229330442,\t0.427168576104746,\t0.437276969530607,\t0.447513812154696,\t0.457881567973311,\t0.468382764409625,\t0.479019994367784,\t0.489795918367347,\t0.500713266761769,\t0.511774842044802,\t0.522983521248916,\t0.534342258440047,\t0.545854087313214,\t0.557522123893805,\t0.569349569349569,\t0.58133971291866,\t0.59349593495935,\t0.60582171012735,\t0.618320610687023,\t0.6309963099631,\t0.643852585939919,\t0.656893325015596,\t0.670122525918944,\t0.683544303797468,\t0.697162894485177,\t0.710982658959538,\t0.725008087997412,\t0.739243807040417,\t0.753694581280788,\t0.768365320979484,\t0.78326108702901,\t0.798387096774194,\t0.813748730104978,\t0.829351535836177,\t0.845201238390093,\t0.86130374479889,\t0.87766515204474,\t0.894291754756871,\t0.911190053285968,\t0.92836676217765,\t0.945828819068256,\t0.963583394027677,\t0.981637899375689,\t1]\n",
    "\n",
    "# Create a figure with subplots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))  # Adjust the figure size\n",
    "\n",
    "# Use seaborn style for improved aesthetics\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "# Plot for original data (left plot)\n",
    "f1 = interp1d(x_new, y_positiveLR_original, kind='quadratic')\n",
    "y1_smooth = f1(x_new)\n",
    "axes[0].plot(x_new, y1_smooth, color='r', label='Positive test (LR+ = 8.52)', linewidth=2)\n",
    "\n",
    "f2 = interp1d(x_new, y_negativeLR_original, kind='quadratic')\n",
    "y2_smooth = f2(x_new)\n",
    "axes[0].plot(x_new, y2_smooth, color='b', label='Negative test (LR- = 0.74)', linewidth=2)\n",
    "\n",
    "axes[0].plot(x_new, x_new, linestyle='--', color='k', label='Without test', linewidth=1)\n",
    "\n",
    "axes[0].set_xlabel('Pre-test probability', fontsize=14)\n",
    "axes[0].set_ylabel('Post-test probability', fontsize=14)\n",
    "axes[0].legend(loc=\"lower right\", fontsize=12)\n",
    "axes[0].set_title(\"Leaf plot - Original data\", fontsize=16)\n",
    "axes[0].tick_params(axis='both', labelsize=12)\n",
    "\n",
    "# Plot for balanced data (right plot)\n",
    "f3 = interp1d(x_new, y_positiveLR_balanced, kind='quadratic')\n",
    "y3_smooth = f3(x_new)\n",
    "axes[1].plot(x_new, y3_smooth, color='r', label='Positive test (LR+ = 3.89)', linewidth=2)\n",
    "\n",
    "f4 = interp1d(x_new, y_negativeLR_balanced, kind='quadratic')\n",
    "y4_smooth = f4(x_new)\n",
    "axes[1].plot(x_new, y4_smooth, color='b', label='Negative test (LR- = 0.54)', linewidth=2)\n",
    "\n",
    "axes[1].plot(x_new, x_new, linestyle='--', color='k', label='Without test', linewidth=1)\n",
    "\n",
    "axes[1].set_xlabel('Pre-test probability', fontsize=14)\n",
    "axes[1].set_ylabel('Post-test probability', fontsize=14)\n",
    "axes[1].legend(loc=\"lower right\", fontsize=12)\n",
    "axes[1].set_title(\"Leaf plot - Balanced data\", fontsize=16)\n",
    "axes[1].tick_params(axis='both', labelsize=12)\n",
    "\n",
    "# Adjust layout and spacing\n",
    "plt.subplots_adjust(wspace=0.3)  # Adjust the space between subplots\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n",
    "# python 8_Jan_3_LR_curve_and_Feature_Importance.py\n"
   ],
   "id": "45794599b2e16301",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 25
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "colab": {
   "provenance": []
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}